From df7a67de7d114654dff42a40e50a0d56c408ffc9 Mon Sep 17 00:00:00 2001 From: mo-gregmunday Date: Fri, 5 Jul 2024 19:41:54 +0100 Subject: [PATCH 001/276] Added emulation code --- montecarlo.py | 484 ++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 484 insertions(+) create mode 100644 montecarlo.py diff --git a/montecarlo.py b/montecarlo.py new file mode 100644 index 0000000..7286107 --- /dev/null +++ b/montecarlo.py @@ -0,0 +1,484 @@ +import os, os.path, fnmatch +from collections.abc import Sequence + +import numpy as np +from scipy.stats import norm +import pandas as pd +import dask.array as da +from config import settings +import matplotlib.pyplot as plt + +# First year of AR5 projections +endofhistory=2006 + +# Last year of AR5 projections +endofAR5=2100 +endyr = 2300 + +# Fraction of SLE from Greenland during 1996 to 2005 assumed to result from +# rapid dynamical change, with the remainder assumed to result from SMB change +fgreendyn=0.5 + +# m SLE from Greenland during 1996 to 2005 according to AR5 chapter 4 +dgreen=(3.21-0.30)*1e-3 + +# m SLE from Antarctica during 1996 to 2005 according to AR5 chapter 4 +dant=(2.37+0.13)*1e-3 + +# Conversion factor for Gt to m SLE +mSLEoGt=1e12/3.61e14*1e-3 + +exp_efficiency = 0.12e-24 + +def vlikely_range(data): + # Compute median and 5-95% range for the first (or only) axis of data. + # Return array (stat[,dom1,...]), where stat=0,1,2 for 50-,5-,95-percentile, + # and dom1,... are any remaining axes of data. + # NB model 5-95% range is judged to be "likely" for the AR5 projections + # data -- array-like + # numpy.array() to convert masked array into unmasked, because percentile() + # gives a warning if there are no non-masked elements + return np.percentile(np.array(data),[50,5,95],0) + + +def actual_range(data): + # Compute mean and actual range for the first (or only) axis of data + # Return array (stat[,dom1,...]), where stat=0,1,2 for mean,minimum,maximum + # of data, and dom1,... are any remaining axes of data. + # data -- array-like + return np.array([np.mean(data,0),np.amin(data,0),\ + np.amax(data,0)]) + + +def project(scenarios=None, **kwargs): + # input -- str, path to directory containing input files. The directory should + # contain files named SCENARIO_QUANTITY_STATISTIC.nc, where QUANTITY is + # temperature or expansion, and STATISTIC is mean, sd or models. Each file + # contains one field with a dimension of time. The mean and sd fields have + # no other dimension and are used if nt>0 (default), while the models fields + # have a model dimension and are used if nt==0. + # scenarios -- str or sequence of str, scenarios for which projections are to be + # made, by default all those represented in the input directory + # output, str, optional -- path to directory in which output files are to be + # written. It is created if it does not exist. No files are written if this + # argument is omitted. + # ensemble -- bool, optional, default False, write output files of the ensemble + # as well as the statistics. + # seed -- optional, for numpy.random, default zero + # nt -- int, optional, number of realisations of the input timeseries for each + # scenario, default 450, to be generated using the mean and sd files; specify + # 0 if the ensemble of individual models is to be used instead, which is read + # from the models files. + # nm -- int, optional, number of realisations of components and of the sum for + # each realisation of the input timeseries, default 1000, must be a multiple + # of the number of glacier methods. + # tcv -- float, optional, default 1.0, multiplier for the standard deviation + # in the input fields + # glaciermip -- optional, default False => AR5 parameters, 1 => GlacierMIP + # (Hock et al., 2019), 2 => GlacierMIP2 (Marzeion et al., 2020) + # levermann -- optional, default None, specifies that Antarctic dynamics should + # use the fit by Palmer et al. (2020) to the results of Levermann et al. + # (2014); if dict, must have an key for each scenario whose value names the + # Levermann RCP fit to be used; if str, identifies the single fit to be used + # for every scenario; otherwise it is treated as True or False; if True the + # scenarios must all be ones that Levermann provides. + # palmer -- bool, optional, default False, allow integration to end in any year + # up to 2300, with the contributions to GMLSR from ice-sheet dynamics, Green- + # land SMB and land water storage held at the 2100 rate beyond 2100. + + + if scenarios is None: + # Obtain list of scenarios from the input filenames + bname=fnmatch.filter(os.listdir(settings['test_input_dir']),'*_*.nc') + scenarios=[tname.split('_',1)[0] for tname in bname] + scenarios=sorted(set(scenarios)) + + for scenario in scenarios: + project_scenario(scenario,**kwargs) + + +def project_scenario(scenario,output=None,\ + seed=0,nt=450,nm=1000,tcv=1.0,\ + glaciermip=False,ensemble=False,levermann=False,palmer=False): + # Make GMSLR projection for the specified single scenario + # Arguments are all the same as project() except for: + # scenario -- str, name of the scenario + # levermann -- optional, treated as True/False to specify that Levermann + # should be used, if str it identifies the Leverman scenario fit to be used, + # otherwise assumed to be the scenario being simulated + + np.random.seed(seed) + + startyr=endofhistory # year when the timeseries for integration begin + + # Read the input fields for temperature and expansion into txin. txin has four + # elements if nt>0: temperature mean, temperature sd, expansion mean, expansion + # sd. txin has two elements if nt==0, for temperature and expansion, each + # having a model dimension. Check that each field is one-dimensional in time, + # that the mean and sd fields for each quantity have equal time axes, that + # temperature applies to calendar years (indicated by its time bounds), that + # expansion applies at the ends of the calendar years of temperature, that + # there is no missing data, and that the model axis (if present) is the same + # for the two quantities. + variable = ['temperature','ocean_heat_content_change'] # input quantities + + txin = [] + for v in variable: + file = os.path.join(settings['test_input_dir'], 'ssprcmip_' + scenario + '_'+ v + '.csv') + df = pd.read_csv(file) + df_2300 = df.loc[:, '2007':'2300'] + central_estimate = np.percentile(df_2300.to_numpy(), 0, axis=0) + std = np.std(df_2300.to_numpy(), axis=0) + + if v == 'ocean_heat_content_change': + central_estimate = central_estimate * exp_efficiency # convert to thermal expansion + std = np.std(df_2300.to_numpy() * exp_efficiency, axis=0) + + txin.extend([central_estimate, std]) + + nyr=txin[0].shape[0] # number of years in the timeseries + + # Integrate temperature to obtain K yr at ends of calendar years, replacing + # the time-axis of temperature (which applies at mid-year) with the time-axis + # of expansion (which applies at year-end) + + itin = [np.cumsum(txin[i]) for i in range(2)] + + # Generate a sample of perfectly correlated timeseries fields of temperature, + # time-integral temperature and expansion, each of them [realisation,time] + z = np.random.standard_normal(nt)*tcv + + # For each quantity, mean + standard deviation * normal random number + # reshape to [realisation,time] + + zt = z[:, np.newaxis] * txin[1] + txin[0] + zx = z[:, np.newaxis] * txin[3] + txin[2] + zit = z[:, np.newaxis] * itin[1] + itin[0] + zitmean = itin[0] + + # Create a cf.Field with the shape of the quantities to be calculated + # [component_realization,climate_realization,time] + template=np.full([nm, nt, nyr], np.nan) + + + # Obtain ensembles of projected components as cf.Field objects and add them up + # Report the range of the final year and write output files if requested + + expansion = np.tile(zx, (nm, 1)) + + + glacier = project_glacier(zitmean,zit,template,glaciermip) + + greensmb = project_greensmb(zt,template,palmer) + + fraction = np.random.rand(nm*nt) # correlation between antsmb and antdyn + antsmb = project_antsmb(zit,template,fraction) + + + greendyn = project_greendyn(scenario,template,palmer) + greennet = greensmb+greendyn + + antdyn = project_antdyn(template, fraction, output, palmer) + antnet = antsmb+antdyn + + landwater = project_landwater(template,palmer) + + # add expansion last because it has a lower dimensionality and we want it to + # be broadcast to the same shape as the others rather than messing up gmslr + gmslr=glacier+greennet+antnet+landwater+expansion + + sheetdyn=greendyn+antdyn + + components = [expansion, glacier, greensmb, greendyn, greennet, antsmb, antdyn, antnet, landwater, sheetdyn, gmslr] + component_names = ['exp', 'glacier', 'greensmb', 'greendyn', 'greennet', 'antsmb', 'antdyn', 'antnet', 'landwater', 'sheetdyn', 'gmslr'] + for i, component in enumerate(components): + np.save(f'/data/users/gmunday/slr/montecarlo_tests/{scenario}_{component_names[i]}.npy', component) + + # fig = plt.figure(figsize=(24, 5)) + + # time = np.arange(2007, 2301) + # for i, component in enumerate(components): + # ax = fig.add_subplot(1, 8, i+1) + # ax.plot(time, np.percentile(component, 5, axis=0), color='navy', lw=1) + # ax.plot(time, np.percentile(component, 50, axis=0), color='black', label='Central estimate') + # ax.plot(time, np.percentile(component, 95, axis=0), color='navy', lw=1) + # ax.fill_between(time, np.percentile(component, 5, axis=0), np.percentile(component, 95, axis=0), color='navy', alpha=0.2) + # ax.set_title(component_names[i]) + # ax.set_xlabel('Year') + # if i == 0: + # ax.set_ylabel('Sea level change (m)') + # ax.legend(loc='upper left', fancybox=True) + + # plt.tight_layout() + # plt.savefig('ssp126_components.png', dpi=300) + + +def project_glacier(it, zit, glacier, glaciermip): + # Return projection of glacier contribution as a cf.Field + # it -- cf.Field, time-integral of median temperature anomaly timeseries + # zit -- cf.Field, ensemble of time-integral temperature anomaly timeseries + # template -- cf.Field with the required shape of the output + # glaciermip -- False => AR5 parameters, 1 => fit to Hock et al. (2019), + # 2 => fit to Marzeion et al. (2020) + + startyr=endofhistory + + dmzdtref=0.95 # mm yr-1 in Marzeion's CMIP5 ensemble mean for AR5 ref period + dmz=dmzdtref*(startyr-1996)*1e-3 # m from glacier at start wrt AR5 ref period + glmass=412.0-96.3 # initial glacier mass, used to set a limit, from Tab 4.2 + glmass=1e-3*glmass # m SLE + + nr=glacier.shape[0] + if glaciermip: + if glaciermip==1: + glparm=[dict(name='SLA2012',factor=3.39,exponent=0.722,cvgl=0.15),\ + dict(name='MAR2012',factor=4.35,exponent=0.658,cvgl=0.13),\ + dict(name='GIE2013',factor=3.57,exponent=0.665,cvgl=0.13),\ + dict(name='RAD2014',factor=6.21,exponent=0.648,cvgl=0.17),\ + dict(name='GloGEM',factor=2.88,exponent=0.753,cvgl=0.13)] + cvgl=0.15 # unnecessary default + elif glaciermip==2: + glparm=[dict(name='GLIMB',factor=3.70,exponent=0.662,cvgl=0.206),\ + dict(name='GloGEM',factor=4.08,exponent=0.716,cvgl=0.161),\ + dict(name='JULES',factor=5.50,exponent=0.564,cvgl=0.188),\ + dict(name='Mar-12',factor=4.89,exponent=0.651,cvgl=0.141),\ + dict(name='OGGM',factor=4.26,exponent=0.715,cvgl=0.164),\ + dict(name='RAD2014',factor=5.18,exponent=0.709,cvgl=0.135),\ + dict(name='WAL2001',factor=2.66,exponent=0.730,cvgl=0.206)] + cvgl=0.20 # unnecessary default + else: raise KeyError('glaciermip must be 1 or 2') + else: + glparm=[dict(name='Marzeion',factor=4.96,exponent=0.685),\ + dict(name='Radic',factor=5.45,exponent=0.676),\ + dict(name='Slangen',factor=3.44,exponent=0.742),\ + dict(name='Giesen',factor=3.02,exponent=0.733)] + cvgl=0.20 # random methodological error + ngl=len(glparm) # number of glacier methods + if nr%ngl: + raise ValueError('number of realisations '+\ + 'must be a multiple of number of glacier methods') + nrpergl=int(nr/ngl) # number of realisations per glacier method + r = np.random.standard_normal(nr) + r = r[:, np.newaxis, np.newaxis] + + # Make an ensemble of projections for each method + for igl in range(ngl): + # glacier projection for this method using the median temperature timeseries + mgl = project_glacier1(it, glparm[igl]['factor'], glparm[igl]['exponent']) + + # glacier projections for this method with the ensemble of timeseries + zgl = project_glacier1(zit, glparm[igl]['factor'], glparm[igl]['exponent']) + + ifirst = igl * nrpergl + ilast = ifirst + nrpergl + if glaciermip: cvgl = glparm[igl]['cvgl'] + glacier[ifirst:ilast,...] = zgl + (mgl * r[ifirst:ilast] * cvgl) + + glacier += dmz + glacier = np.where(glacier > glmass, glmass, glacier) + + glacier = glacier.reshape(glacier.shape[0]*glacier.shape[1], glacier.shape[2]) + + return glacier + + +def project_glacier1(it,factor,exponent): + # Return projection of glacier contribution by one glacier method + scale=1e-3 # mm to m + return scale * factor * (np.where(it<0, 0, it)**exponent) + + +def project_greensmb(zt,template,palmer=False): + # Return projection of Greenland SMB contribution as a cf.Field + # zt -- cf.Field, ensemble of temperature anomaly timeseries + # template -- cf.Field with the required shape of the output + + dtgreen=-0.146 # Delta_T of Greenland ref period wrt AR5 ref period + fnlogsd=0.4 # random methodological error of the log factor + febound=[1,1.15] # bounds of uniform pdf of SMB elevation feedback factor + + nr = template.shape[0] + # random log-normal factor + fn = np.exp(np.random.standard_normal(nr)*fnlogsd) + # elevation feedback factor + fe = np.random.sample(nr)*(febound[1]-febound[0])+febound[0] + ff = fn * fe + + ztgreen = zt - dtgreen + greensmbrate = ff[:, np.newaxis, np.newaxis] * fettweis(ztgreen) + + if palmer and endyr > endofAR5: + greensmbrate[:, :, 95:] = greensmbrate[:, :, 94:95] + + greensmb = np.cumsum(greensmbrate, axis=-1) + + np.add(greensmb, (1 - fgreendyn) * dgreen, out=greensmb) + + greensmb = greensmb.reshape(greensmb.shape[0]*greensmb.shape[1], greensmb.shape[2]) + + return greensmb + + +def fettweis(ztgreen): + # Greenland SMB in m yr-1 SLE from global mean temperature anomaly + # using Eq 2 of Fettweis et al. (2013) + return (71.5*ztgreen+20.4*(ztgreen**2)+2.8*(ztgreen**3))*mSLEoGt + + +def project_antsmb(zit,template,fraction=None): + # Return projection of Antarctic SMB contribution as a cf.Field + # zit -- cf.Field, ensemble of time-integral temperature anomaly timeseries + # template -- cf.Field with the required shape of the output + # fraction -- array-like, random numbers for the SMB-dynamic feedback + + nr=template.shape[0] + nt=template.shape[1] + # antsmb=template.copy() + # nr,nt,nyr=antsmb.shape + + # The following are [mean,SD] + pcoK=[5.1,1.5] # % change in Ant SMB per K of warming from G&H06 + KoKg=[1.1,0.2] # ratio of Antarctic warming to global warming from G&H06 + + # Generate a distribution of products of the above two factors + pcoKg=(pcoK[0]+np.random.standard_normal([nr,nt])*pcoK[1])*\ + (KoKg[0]+np.random.standard_normal([nr,nt])*KoKg[1]) + meansmb=1923 # model-mean time-mean 1979-2010 Gt yr-1 from 13.3.3.2 + moaoKg=-pcoKg*1e-2*meansmb*mSLEoGt # m yr-1 of SLE per K of global warming + + if fraction is None: + fraction=np.random.rand(nr,nt) + elif fraction.size!=nr*nt: + raise ValueError('fraction is the wrong size') + else: + fraction.shape=(nr,nt) + + smax=0.35 # max value of S in 13.SM.1.5 + ainterfactor=1-fraction*smax + + z = moaoKg * ainterfactor + z = z[:, :, np.newaxis] + antsmb = z * zit + antsmb = antsmb.reshape(antsmb.shape[0]*antsmb.shape[1], antsmb.shape[2]) + + return antsmb + + +def project_greendyn(scenario,template,palmer=False): + # Return projection of Greenland rapid ice-sheet dynamics contribution + # as a cf.Field + # scenario -- str, name of scenario + # template -- cf.Field with the required shape of the output + + # For SMB+dyn during 2005-2010 Table 4.6 gives 0.63+-0.17 mm yr-1 (5-95% range) + # For dyn at 2100 Chapter 13 gives [20,85] mm for rcp85, [14,63] mm otherwise + + if scenario in ['rcp85','ssp585']: + finalrange=[0.020,0.085] + else: + finalrange=[0.014,0.063] + return time_projection(0.63*fgreendyn,\ + 0.17*fgreendyn,finalrange,template,palmer=palmer)+fgreendyn*dgreen + + +def project_antdyn(template,fraction=None,output=None, + palmer=False): + # Return projection of Antarctic rapid ice-sheet dynamics contribution + # as a cf.Field + # template -- cf.Field with the required shape of the output + # fraction -- array-like, random numbers for the dynamic contribution + # levermann -- optional, str, use Levermann fit for specified scenario + + final=[-0.020,0.185] + + # For SMB+dyn during 2005-2010 Table 4.6 gives 0.41+-0.24 mm yr-1 (5-95% range) + # For dyn at 2100 Chapter 13 gives [-20,185] mm for all scenarios + + return time_projection(0.41,0.20,final,template, + palmer=palmer,fraction=fraction)+dant + + +def project_landwater(template,palmer=False): + # Return projection of land water storage contribution as a cf.Field + + # The rate at start is the one for 1993-2010 from the budget table. + # The final amount is the mean for 2081-2100. + nyr=2100-2081+1 # number of years of the time-mean of the final amount + + return time_projection(0.38,0.49-0.38,[-0.01,0.09],template, + nfinal=nyr,palmer=palmer) + + +def time_projection(startratemean,startratepm,final,template, + nfinal=1,fraction=None,palmer=False): + # Return projection of a quantity which is a quadratic function of time + # in a cf.Field. + # startratemean, startratepm -- rate of GMSLR at the start in mm yr-1, whose + # likely range is startratemean +- startratepm + # final -- two-element list giving likely range in m for GMSLR at the endofAR5, + # or array-like, giving final values at that time, of the same shape as + # fraction and assumed corresponding elements + # template -- cf.Field with the required shape of the output + # nfinal -- int, optional, number of years at the end over which finalrange is + # a time-mean; by default 1 => finalrange is the value for the last year + # fraction -- array-like, optional, random numbers in the range 0 to 1, + # by default uniformly distributed + + # Create a field of elapsed time since start in years + timeendofAR5 = endofAR5 - endofhistory + 1 + time = np.arange(endyr - endofhistory) + 1 + + # more general than nr,nt,nyr=template.shape + nr, nt, nyr = template.shape + + if fraction is None: + fraction=np.random.rand(nr,nt) + elif fraction.size!=nr*nt: + raise ValueError('fraction is the wrong size') + fraction = fraction.reshape(nr,nt) + + # Convert inputs to startrate (m yr-1) and afinal (m), where both are + # arrays with the size of fraction + momm=1e-3 # convert mm yr-1 to m yr-1 + startrate=(startratemean+\ + startratepm*np.array([-1,1],dtype=float))*momm + finalisrange=isinstance(final,Sequence) + if finalisrange: + if len(final)!=2: + raise ValueError('final range is the wrong size') + afinal=(1-fraction)*final[0]+fraction*final[1] + else: + if final.shape!=fraction.shape: + raise ValueError('final array is the wrong shape') + afinal = final + startrate=(1-fraction)*startrate[0]+fraction*startrate[1] + + # For terms where the rate increases linearly in time t, we can write GMSLR as + # S(t) = a*t**2 + b*t + # where a is 0.5*acceleration and b is start rate. Hence + # a = S/t**2-b/t = (S-b*t)/t**2 + # If nfinal=1, the following two lines are equivalent to + # halfacc=(final-startyr*nyr)/nyr**2 + finalyr = np.arange(nfinal) - nfinal + 94 + 1 # last element ==nyr + halfacc=(afinal-startrate*finalyr.mean())/(finalyr**2).mean() + quadratic=halfacc[:, :, np.newaxis]*(time**2) + linear=startrate[:, :, np.newaxis]*time + + # If acceleration ceases for t>t0, the rate is 2*a*t0+b thereafter, so + # S(t) = a*t0**2 + b*t0 + (2*a*t0+b)*(t-t0) + # = a*t0*(2*t - t0) + b*t + # i.e. the quadratic term is replaced, the linear term unaffected + # The quadratic also = a*t**2-a*(t-t0)**2 + + if palmer: + y = halfacc[:, :, np.newaxis] * timeendofAR5 * ((2 * time) - timeendofAR5) + quadratic[:, :, 95:] = y[:, :, 95:] + + np.add(quadratic, linear, out=quadratic) + + quadratic = quadratic.reshape(quadratic.shape[0]*quadratic.shape[1], quadratic.shape[2]) + + return quadratic + +if __name__ == '__main__': + project(['ssp126'], palmer=True) From 95bf1865b4f8a1b182dee504035015a0c19839e2 Mon Sep 17 00:00:00 2001 From: mo-gregmunday Date: Sat, 6 Jul 2024 23:19:28 +0100 Subject: [PATCH 002/276] Refactoring, integrating into main code --- montecarlo.py | 872 +++++++++--------- slr_pkg/__init__.py | 3 + ...3_process_regional_sealevel_projections.py | 47 +- 3 files changed, 467 insertions(+), 455 deletions(-) diff --git a/montecarlo.py b/montecarlo.py index 7286107..9b484bd 100644 --- a/montecarlo.py +++ b/montecarlo.py @@ -1,4 +1,8 @@ -import os, os.path, fnmatch +# Calculate Monte Carlo projections of GMSL rise using methods +# from Jonathon Gregory and AR5. Staying close to JG's original +# code where possible. +import os, os.path +import glob from collections.abc import Sequence import numpy as np @@ -8,477 +12,451 @@ from config import settings import matplotlib.pyplot as plt -# First year of AR5 projections -endofhistory=2006 - -# Last year of AR5 projections -endofAR5=2100 -endyr = 2300 - -# Fraction of SLE from Greenland during 1996 to 2005 assumed to result from -# rapid dynamical change, with the remainder assumed to result from SMB change -fgreendyn=0.5 - -# m SLE from Greenland during 1996 to 2005 according to AR5 chapter 4 -dgreen=(3.21-0.30)*1e-3 +class GMSLREmulator: + + def __init__( + self, + scenarios: list, + data_dir: str, + output_dir: str, + end_yr: int, + seed: int=0, + nt: int=450, + nm: int=1000, + tcv: float=1.0, + glaciermip: bool=False, + ensemble: bool=False, + palmer_method: bool=False): + # input -- str, path to directory containing input files. The directory should + # contain files named SCENARIO_QUANTITY_STATISTIC.nc, where QUANTITY is + # temperature or expansion, and STATISTIC is mean, sd or models. Each file + # contains one field with a dimension of time. The mean and sd fields have + # no other dimension and are used if nt>0 (default), while the models fields + # have a model dimension and are used if nt==0. + # scenarios -- str or sequence of str, scenarios for which projections are to be + # made, by default all those represented in the input directory + # output, str, optional -- path to directory in which output files are to be + # written. It is created if it does not exist. No files are written if this + # argument is omitted. + # ensemble -- bool, optional, default False, write output files of the ensemble + # as well as the statistics. + # seed -- optional, for numpy.random, default zero + # nt -- int, optional, number of realisations of the input timeseries for each + # scenario, default 450, to be generated using the mean and sd files; specify + # 0 if the ensemble of individual models is to be used instead, which is read + # from the models files. + # nm -- int, optional, number of realisations of components and of the sum for + # each realisation of the input timeseries, default 1000, must be a multiple + # of the number of glacier methods. + # tcv -- float, optional, default 1.0, multiplier for the standard deviation + # in the input fields + # glaciermip -- optional, default False => AR5 parameters, 1 => GlacierMIP + # (Hock et al., 2019), 2 => GlacierMIP2 (Marzeion et al., 2020) + # levermann -- optional, default None, specifies that Antarctic dynamics should + # use the fit by Palmer et al. (2020) to the results of Levermann et al. + # (2014); if dict, must have an key for each scenario whose value names the + # Levermann RCP fit to be used; if str, identifies the single fit to be used + # for every scenario; otherwise it is treated as True or False; if True the + # scenarios must all be ones that Levermann provides. + # palmer_method -- bool, optional, default False, allow integration to end in any year + # up to 2300, with the contributions to GMLSR from ice-sheet dynamics, Green- + # land SMB and land water storage held at the 2100 rate beyond 2100. + + self.scenarios = scenarios + self.data_dir = data_dir + self.output_dir = output_dir + self.end_yr = end_yr + self.seed = seed + self.nt = nt + self.nm = nm + self.tcv = tcv + self.glaciermip = glaciermip + self.ensemble = ensemble + self.palmer_method = self.palmer_method + + # First year of AR5 projections + self.endofhistory=2006 -# m SLE from Antarctica during 1996 to 2005 according to AR5 chapter 4 -dant=(2.37+0.13)*1e-3 + # Last year of AR5 projections + self.endofAR5=2100 -# Conversion factor for Gt to m SLE -mSLEoGt=1e12/3.61e14*1e-3 + # Fraction of SLE from Greenland during 1996 to 2005 assumed to result from + # rapid dynamical change, with the remainder assumed to result from SMB change + self.fgreendyn=0.5 -exp_efficiency = 0.12e-24 + # m SLE from Greenland during 1996 to 2005 according to AR5 chapter 4 + self.dgreen=(3.21-0.30)*1e-3 -def vlikely_range(data): - # Compute median and 5-95% range for the first (or only) axis of data. - # Return array (stat[,dom1,...]), where stat=0,1,2 for 50-,5-,95-percentile, - # and dom1,... are any remaining axes of data. - # NB model 5-95% range is judged to be "likely" for the AR5 projections - # data -- array-like - # numpy.array() to convert masked array into unmasked, because percentile() - # gives a warning if there are no non-masked elements - return np.percentile(np.array(data),[50,5,95],0) + # m SLE from Antarctica during 1996 to 2005 according to AR5 chapter 4 + self.dant=(2.37+0.13)*1e-3 + # Conversion factor for Gt to m SLE + self.mSLEoGt=1e12/3.61e14*1e-3 -def actual_range(data): - # Compute mean and actual range for the first (or only) axis of data - # Return array (stat[,dom1,...]), where stat=0,1,2 for mean,minimum,maximum - # of data, and dom1,... are any remaining axes of data. - # data -- array-like - return np.array([np.mean(data,0),np.amin(data,0),\ - np.amax(data,0)]) + self.exp_efficiency = 0.12e-24 - -def project(scenarios=None, **kwargs): - # input -- str, path to directory containing input files. The directory should - # contain files named SCENARIO_QUANTITY_STATISTIC.nc, where QUANTITY is - # temperature or expansion, and STATISTIC is mean, sd or models. Each file - # contains one field with a dimension of time. The mean and sd fields have - # no other dimension and are used if nt>0 (default), while the models fields - # have a model dimension and are used if nt==0. - # scenarios -- str or sequence of str, scenarios for which projections are to be - # made, by default all those represented in the input directory - # output, str, optional -- path to directory in which output files are to be - # written. It is created if it does not exist. No files are written if this - # argument is omitted. - # ensemble -- bool, optional, default False, write output files of the ensemble - # as well as the statistics. - # seed -- optional, for numpy.random, default zero - # nt -- int, optional, number of realisations of the input timeseries for each - # scenario, default 450, to be generated using the mean and sd files; specify - # 0 if the ensemble of individual models is to be used instead, which is read - # from the models files. - # nm -- int, optional, number of realisations of components and of the sum for - # each realisation of the input timeseries, default 1000, must be a multiple - # of the number of glacier methods. - # tcv -- float, optional, default 1.0, multiplier for the standard deviation - # in the input fields - # glaciermip -- optional, default False => AR5 parameters, 1 => GlacierMIP - # (Hock et al., 2019), 2 => GlacierMIP2 (Marzeion et al., 2020) - # levermann -- optional, default None, specifies that Antarctic dynamics should - # use the fit by Palmer et al. (2020) to the results of Levermann et al. - # (2014); if dict, must have an key for each scenario whose value names the - # Levermann RCP fit to be used; if str, identifies the single fit to be used - # for every scenario; otherwise it is treated as True or False; if True the - # scenarios must all be ones that Levermann provides. - # palmer -- bool, optional, default False, allow integration to end in any year - # up to 2300, with the contributions to GMLSR from ice-sheet dynamics, Green- - # land SMB and land water storage held at the 2100 rate beyond 2100. - - - if scenarios is None: - # Obtain list of scenarios from the input filenames - bname=fnmatch.filter(os.listdir(settings['test_input_dir']),'*_*.nc') - scenarios=[tname.split('_',1)[0] for tname in bname] - scenarios=sorted(set(scenarios)) - - for scenario in scenarios: - project_scenario(scenario,**kwargs) - + def project(self): + for scenario in self.scenarios: + print(f'Projecting {scenario}...') + self.project_scenario(scenario) -def project_scenario(scenario,output=None,\ - seed=0,nt=450,nm=1000,tcv=1.0,\ - glaciermip=False,ensemble=False,levermann=False,palmer=False): - # Make GMSLR projection for the specified single scenario - # Arguments are all the same as project() except for: - # scenario -- str, name of the scenario - # levermann -- optional, treated as True/False to specify that Levermann - # should be used, if str it identifies the Leverman scenario fit to be used, - # otherwise assumed to be the scenario being simulated - - np.random.seed(seed) - - startyr=endofhistory # year when the timeseries for integration begin + def project_scenario(self, scenario): + + np.random.seed(self.seed) + + # Read the input fields for temperature and expansion into txin. txin has four + # elements if nt>0: temperature mean, temperature sd, expansion mean, expansion + # sd. txin has two elements if nt==0, for temperature and expansion, each + # having a model dimension. Check that each field is one-dimensional in time, + # that the mean and sd fields for each quantity have equal time axes, that + # temperature applies to calendar years (indicated by its time bounds), that + # expansion applies at the ends of the calendar years of temperature, that + # there is no missing data, and that the model axis (if present) is the same + # for the two quantities. + variable = ['temperature','ocean_heat_content_change'] # input quantities + + txin = [] + for v in variable: + file = glob.glob(os.path.join(self.data_dir, f'*{scenario}*_{v}*.csv')) + if file: + df = pd.read_csv(file[0]) + df_2300 = df.loc[:, str(self.endofhistory + 1):str(self.end_yr)] + central_estimate = np.percentile(df_2300.to_numpy(), 50, axis=0) + std = np.std(df_2300.to_numpy(), axis=0) + + if v == 'ocean_heat_content_change': + central_estimate = central_estimate * self.exp_efficiency # convert to thermal expansion + std = np.std(df_2300.to_numpy() * self.exp_efficiency, axis=0) + + txin.extend([central_estimate, std]) + else: + raise FileNotFoundError(f'Emulator input file {file} not found') + + nyr=txin[0].shape[0] # number of years in the timeseries + + # Integrate temperature to obtain K yr at ends of calendar years, replacing + # the time-axis of temperature (which applies at mid-year) with the time-axis + # of expansion (which applies at year-end) + + itin = [np.cumsum(txin[i]) for i in range(2)] - # Read the input fields for temperature and expansion into txin. txin has four - # elements if nt>0: temperature mean, temperature sd, expansion mean, expansion - # sd. txin has two elements if nt==0, for temperature and expansion, each - # having a model dimension. Check that each field is one-dimensional in time, - # that the mean and sd fields for each quantity have equal time axes, that - # temperature applies to calendar years (indicated by its time bounds), that - # expansion applies at the ends of the calendar years of temperature, that - # there is no missing data, and that the model axis (if present) is the same - # for the two quantities. - variable = ['temperature','ocean_heat_content_change'] # input quantities - - txin = [] - for v in variable: - file = os.path.join(settings['test_input_dir'], 'ssprcmip_' + scenario + '_'+ v + '.csv') - df = pd.read_csv(file) - df_2300 = df.loc[:, '2007':'2300'] - central_estimate = np.percentile(df_2300.to_numpy(), 0, axis=0) - std = np.std(df_2300.to_numpy(), axis=0) - - if v == 'ocean_heat_content_change': - central_estimate = central_estimate * exp_efficiency # convert to thermal expansion - std = np.std(df_2300.to_numpy() * exp_efficiency, axis=0) + # Generate a sample of perfectly correlated timeseries fields of temperature, + # time-integral temperature and expansion, each of them [realisation,time] + z = np.random.standard_normal(self.nt)*self.tcv + + # For each quantity, mean + standard deviation * normal random number + # reshape to [realisation,time] + zt = z[:, np.newaxis] * txin[1] + txin[0] + zx = z[:, np.newaxis] * txin[3] + txin[2] + zit = z[:, np.newaxis] * itin[1] + itin[0] + zitmean = itin[0] - txin.extend([central_estimate, std]) - - nyr=txin[0].shape[0] # number of years in the timeseries - - # Integrate temperature to obtain K yr at ends of calendar years, replacing - # the time-axis of temperature (which applies at mid-year) with the time-axis - # of expansion (which applies at year-end) - - itin = [np.cumsum(txin[i]) for i in range(2)] - - # Generate a sample of perfectly correlated timeseries fields of temperature, - # time-integral temperature and expansion, each of them [realisation,time] - z = np.random.standard_normal(nt)*tcv - - # For each quantity, mean + standard deviation * normal random number - # reshape to [realisation,time] - - zt = z[:, np.newaxis] * txin[1] + txin[0] - zx = z[:, np.newaxis] * txin[3] + txin[2] - zit = z[:, np.newaxis] * itin[1] + itin[0] - zitmean = itin[0] - - # Create a cf.Field with the shape of the quantities to be calculated - # [component_realization,climate_realization,time] - template=np.full([nm, nt, nyr], np.nan) - + # Create a cf.Field with the shape of the quantities to be calculated + # [component_realization,climate_realization,time] + template=np.full([self.nm, self.nt, nyr], np.nan) - # Obtain ensembles of projected components as cf.Field objects and add them up - # Report the range of the final year and write output files if requested - expansion = np.tile(zx, (nm, 1)) + # Obtain ensembles of projected components as cf.Field objects and add them up + # Report the range of the final year and write output files if requested + expansion = np.tile(zx, (self.nm, 1)) - glacier = project_glacier(zitmean,zit,template,glaciermip) + glacier = self.project_glacier(zitmean, zit, template) - greensmb = project_greensmb(zt,template,palmer) + greensmb = self.project_greensmb(zt,template) - fraction = np.random.rand(nm*nt) # correlation between antsmb and antdyn - antsmb = project_antsmb(zit,template,fraction) + fraction = np.random.rand(self.nm * self.nt) # correlation between antsmb and antdyn + antsmb = self.project_antsmb(zit,template,fraction) - greendyn = project_greendyn(scenario,template,palmer) - greennet = greensmb+greendyn + greendyn = self.project_greendyn(scenario,template) + greennet = greensmb+greendyn - antdyn = project_antdyn(template, fraction, output, palmer) - antnet = antsmb+antdyn + antdyn = self.project_antdyn(template, fraction) + antnet = antsmb+antdyn - landwater = project_landwater(template,palmer) + landwater = self.project_landwater(template) - # add expansion last because it has a lower dimensionality and we want it to - # be broadcast to the same shape as the others rather than messing up gmslr - gmslr=glacier+greennet+antnet+landwater+expansion + # add expansion last because it has a lower dimensionality and we want it to + # be broadcast to the same shape as the others rather than messing up gmslr + gmslr=glacier+greennet+antnet+landwater+expansion - sheetdyn=greendyn+antdyn - - components = [expansion, glacier, greensmb, greendyn, greennet, antsmb, antdyn, antnet, landwater, sheetdyn, gmslr] - component_names = ['exp', 'glacier', 'greensmb', 'greendyn', 'greennet', 'antsmb', 'antdyn', 'antnet', 'landwater', 'sheetdyn', 'gmslr'] - for i, component in enumerate(components): - np.save(f'/data/users/gmunday/slr/montecarlo_tests/{scenario}_{component_names[i]}.npy', component) - - # fig = plt.figure(figsize=(24, 5)) - - # time = np.arange(2007, 2301) - # for i, component in enumerate(components): - # ax = fig.add_subplot(1, 8, i+1) - # ax.plot(time, np.percentile(component, 5, axis=0), color='navy', lw=1) - # ax.plot(time, np.percentile(component, 50, axis=0), color='black', label='Central estimate') - # ax.plot(time, np.percentile(component, 95, axis=0), color='navy', lw=1) - # ax.fill_between(time, np.percentile(component, 5, axis=0), np.percentile(component, 95, axis=0), color='navy', alpha=0.2) - # ax.set_title(component_names[i]) - # ax.set_xlabel('Year') - # if i == 0: - # ax.set_ylabel('Sea level change (m)') - # ax.legend(loc='upper left', fancybox=True) + sheetdyn=greendyn+antdyn - # plt.tight_layout() - # plt.savefig('ssp126_components.png', dpi=300) - - -def project_glacier(it, zit, glacier, glaciermip): - # Return projection of glacier contribution as a cf.Field - # it -- cf.Field, time-integral of median temperature anomaly timeseries - # zit -- cf.Field, ensemble of time-integral temperature anomaly timeseries - # template -- cf.Field with the required shape of the output - # glaciermip -- False => AR5 parameters, 1 => fit to Hock et al. (2019), - # 2 => fit to Marzeion et al. (2020) - - startyr=endofhistory - - dmzdtref=0.95 # mm yr-1 in Marzeion's CMIP5 ensemble mean for AR5 ref period - dmz=dmzdtref*(startyr-1996)*1e-3 # m from glacier at start wrt AR5 ref period - glmass=412.0-96.3 # initial glacier mass, used to set a limit, from Tab 4.2 - glmass=1e-3*glmass # m SLE - - nr=glacier.shape[0] - if glaciermip: - if glaciermip==1: - glparm=[dict(name='SLA2012',factor=3.39,exponent=0.722,cvgl=0.15),\ - dict(name='MAR2012',factor=4.35,exponent=0.658,cvgl=0.13),\ - dict(name='GIE2013',factor=3.57,exponent=0.665,cvgl=0.13),\ - dict(name='RAD2014',factor=6.21,exponent=0.648,cvgl=0.17),\ - dict(name='GloGEM',factor=2.88,exponent=0.753,cvgl=0.13)] - cvgl=0.15 # unnecessary default - elif glaciermip==2: - glparm=[dict(name='GLIMB',factor=3.70,exponent=0.662,cvgl=0.206),\ - dict(name='GloGEM',factor=4.08,exponent=0.716,cvgl=0.161),\ - dict(name='JULES',factor=5.50,exponent=0.564,cvgl=0.188),\ - dict(name='Mar-12',factor=4.89,exponent=0.651,cvgl=0.141),\ - dict(name='OGGM',factor=4.26,exponent=0.715,cvgl=0.164),\ - dict(name='RAD2014',factor=5.18,exponent=0.709,cvgl=0.135),\ - dict(name='WAL2001',factor=2.66,exponent=0.730,cvgl=0.206)] - cvgl=0.20 # unnecessary default - else: raise KeyError('glaciermip must be 1 or 2') - else: - glparm=[dict(name='Marzeion',factor=4.96,exponent=0.685),\ - dict(name='Radic',factor=5.45,exponent=0.676),\ - dict(name='Slangen',factor=3.44,exponent=0.742),\ - dict(name='Giesen',factor=3.02,exponent=0.733)] - cvgl=0.20 # random methodological error - ngl=len(glparm) # number of glacier methods - if nr%ngl: - raise ValueError('number of realisations '+\ - 'must be a multiple of number of glacier methods') - nrpergl=int(nr/ngl) # number of realisations per glacier method - r = np.random.standard_normal(nr) - r = r[:, np.newaxis, np.newaxis] - - # Make an ensemble of projections for each method - for igl in range(ngl): - # glacier projection for this method using the median temperature timeseries - mgl = project_glacier1(it, glparm[igl]['factor'], glparm[igl]['exponent']) + components_dict = { + 'exp': expansion, + 'glacier': glacier, + 'greensmb': greensmb, + 'greendyn': greendyn, + 'greennet': greennet, + 'antsmb': antsmb, + 'antdyn': antdyn, + 'antnet': antnet, + 'landwater': landwater, + 'sheetdyn': sheetdyn, + 'gmslr': gmslr + } + + for name, component in components_dict.items(): + np.save( + os.path.join(self.output_dir, f'{scenario}_{name}.npy'), + component.T) + + def project_glacier(self, it, zit, glacier): + # Return projection of glacier contribution as a cf.Field + # it -- cf.Field, time-integral of median temperature anomaly timeseries + # zit -- cf.Field, ensemble of time-integral temperature anomaly timeseries + # template -- cf.Field with the required shape of the output + # glaciermip -- False => AR5 parameters, 1 => fit to Hock et al. (2019), + # 2 => fit to Marzeion et al. (2020) + dmzdtref=0.95 # mm yr-1 in Marzeion's CMIP5 ensemble mean for AR5 ref period + dmz=dmzdtref*(self.endofhistory-1996)*1e-3 # m from glacier at start wrt AR5 ref period + glmass=412.0-96.3 # initial glacier mass, used to set a limit, from Tab 4.2 + glmass=1e-3*glmass # m SLE + + nr=glacier.shape[0] + if self.glaciermip: + if self.glaciermip==1: + glparm=[dict(name='SLA2012',factor=3.39,exponent=0.722,cvgl=0.15),\ + dict(name='MAR2012',factor=4.35,exponent=0.658,cvgl=0.13),\ + dict(name='GIE2013',factor=3.57,exponent=0.665,cvgl=0.13),\ + dict(name='RAD2014',factor=6.21,exponent=0.648,cvgl=0.17),\ + dict(name='GloGEM',factor=2.88,exponent=0.753,cvgl=0.13)] + cvgl=0.15 # unnecessary default + elif self.glaciermip==2: + glparm=[dict(name='GLIMB',factor=3.70,exponent=0.662,cvgl=0.206),\ + dict(name='GloGEM',factor=4.08,exponent=0.716,cvgl=0.161),\ + dict(name='JULES',factor=5.50,exponent=0.564,cvgl=0.188),\ + dict(name='Mar-12',factor=4.89,exponent=0.651,cvgl=0.141),\ + dict(name='OGGM',factor=4.26,exponent=0.715,cvgl=0.164),\ + dict(name='RAD2014',factor=5.18,exponent=0.709,cvgl=0.135),\ + dict(name='WAL2001',factor=2.66,exponent=0.730,cvgl=0.206)] + cvgl=0.20 # unnecessary default + else: raise KeyError('glaciermip must be 1 or 2') + else: + glparm=[dict(name='Marzeion',factor=4.96,exponent=0.685),\ + dict(name='Radic',factor=5.45,exponent=0.676),\ + dict(name='Slangen',factor=3.44,exponent=0.742),\ + dict(name='Giesen',factor=3.02,exponent=0.733)] + cvgl=0.20 # random methodological error + + ngl=len(glparm) # number of glacier methods + if nr%ngl: + raise ValueError('number of realisations '+\ + 'must be a multiple of number of glacier methods') + + nrpergl=int(nr/ngl) # number of realisations per glacier method + r = np.random.standard_normal(nr) + r = r[:, np.newaxis, np.newaxis] + + # Make an ensemble of projections for each method + for igl in range(ngl): + # glacier projection for this method using the median temperature timeseries + mgl = self._project_glacier1(it, glparm[igl]['factor'], glparm[igl]['exponent']) + + # glacier projections for this method with the ensemble of timeseries + zgl = self._project_glacier1(zit, glparm[igl]['factor'], glparm[igl]['exponent']) + + ifirst = igl * nrpergl + ilast = ifirst + nrpergl + if self.glaciermip: cvgl = glparm[igl]['cvgl'] + glacier[ifirst:ilast,...] = zgl + (mgl * r[ifirst:ilast] * cvgl) + + glacier += dmz + glacier = np.where(glacier > glmass, glmass, glacier) - # glacier projections for this method with the ensemble of timeseries - zgl = project_glacier1(zit, glparm[igl]['factor'], glparm[igl]['exponent']) - - ifirst = igl * nrpergl - ilast = ifirst + nrpergl - if glaciermip: cvgl = glparm[igl]['cvgl'] - glacier[ifirst:ilast,...] = zgl + (mgl * r[ifirst:ilast] * cvgl) - - glacier += dmz - glacier = np.where(glacier > glmass, glmass, glacier) - - glacier = glacier.reshape(glacier.shape[0]*glacier.shape[1], glacier.shape[2]) - - return glacier - - -def project_glacier1(it,factor,exponent): - # Return projection of glacier contribution by one glacier method - scale=1e-3 # mm to m - return scale * factor * (np.where(it<0, 0, it)**exponent) - - -def project_greensmb(zt,template,palmer=False): - # Return projection of Greenland SMB contribution as a cf.Field - # zt -- cf.Field, ensemble of temperature anomaly timeseries - # template -- cf.Field with the required shape of the output - - dtgreen=-0.146 # Delta_T of Greenland ref period wrt AR5 ref period - fnlogsd=0.4 # random methodological error of the log factor - febound=[1,1.15] # bounds of uniform pdf of SMB elevation feedback factor - - nr = template.shape[0] - # random log-normal factor - fn = np.exp(np.random.standard_normal(nr)*fnlogsd) - # elevation feedback factor - fe = np.random.sample(nr)*(febound[1]-febound[0])+febound[0] - ff = fn * fe - - ztgreen = zt - dtgreen - greensmbrate = ff[:, np.newaxis, np.newaxis] * fettweis(ztgreen) - - if palmer and endyr > endofAR5: - greensmbrate[:, :, 95:] = greensmbrate[:, :, 94:95] - - greensmb = np.cumsum(greensmbrate, axis=-1) - - np.add(greensmb, (1 - fgreendyn) * dgreen, out=greensmb) - - greensmb = greensmb.reshape(greensmb.shape[0]*greensmb.shape[1], greensmb.shape[2]) - - return greensmb - - -def fettweis(ztgreen): - # Greenland SMB in m yr-1 SLE from global mean temperature anomaly - # using Eq 2 of Fettweis et al. (2013) - return (71.5*ztgreen+20.4*(ztgreen**2)+2.8*(ztgreen**3))*mSLEoGt - - -def project_antsmb(zit,template,fraction=None): - # Return projection of Antarctic SMB contribution as a cf.Field - # zit -- cf.Field, ensemble of time-integral temperature anomaly timeseries - # template -- cf.Field with the required shape of the output - # fraction -- array-like, random numbers for the SMB-dynamic feedback - - nr=template.shape[0] - nt=template.shape[1] - # antsmb=template.copy() - # nr,nt,nyr=antsmb.shape - - # The following are [mean,SD] - pcoK=[5.1,1.5] # % change in Ant SMB per K of warming from G&H06 - KoKg=[1.1,0.2] # ratio of Antarctic warming to global warming from G&H06 - - # Generate a distribution of products of the above two factors - pcoKg=(pcoK[0]+np.random.standard_normal([nr,nt])*pcoK[1])*\ - (KoKg[0]+np.random.standard_normal([nr,nt])*KoKg[1]) - meansmb=1923 # model-mean time-mean 1979-2010 Gt yr-1 from 13.3.3.2 - moaoKg=-pcoKg*1e-2*meansmb*mSLEoGt # m yr-1 of SLE per K of global warming - - if fraction is None: - fraction=np.random.rand(nr,nt) - elif fraction.size!=nr*nt: - raise ValueError('fraction is the wrong size') - else: - fraction.shape=(nr,nt) - - smax=0.35 # max value of S in 13.SM.1.5 - ainterfactor=1-fraction*smax - - z = moaoKg * ainterfactor - z = z[:, :, np.newaxis] - antsmb = z * zit - antsmb = antsmb.reshape(antsmb.shape[0]*antsmb.shape[1], antsmb.shape[2]) - - return antsmb - - -def project_greendyn(scenario,template,palmer=False): - # Return projection of Greenland rapid ice-sheet dynamics contribution - # as a cf.Field - # scenario -- str, name of scenario - # template -- cf.Field with the required shape of the output - - # For SMB+dyn during 2005-2010 Table 4.6 gives 0.63+-0.17 mm yr-1 (5-95% range) - # For dyn at 2100 Chapter 13 gives [20,85] mm for rcp85, [14,63] mm otherwise - - if scenario in ['rcp85','ssp585']: - finalrange=[0.020,0.085] - else: - finalrange=[0.014,0.063] - return time_projection(0.63*fgreendyn,\ - 0.17*fgreendyn,finalrange,template,palmer=palmer)+fgreendyn*dgreen - - -def project_antdyn(template,fraction=None,output=None, - palmer=False): - # Return projection of Antarctic rapid ice-sheet dynamics contribution - # as a cf.Field - # template -- cf.Field with the required shape of the output - # fraction -- array-like, random numbers for the dynamic contribution - # levermann -- optional, str, use Levermann fit for specified scenario - - final=[-0.020,0.185] - - # For SMB+dyn during 2005-2010 Table 4.6 gives 0.41+-0.24 mm yr-1 (5-95% range) - # For dyn at 2100 Chapter 13 gives [-20,185] mm for all scenarios - - return time_projection(0.41,0.20,final,template, - palmer=palmer,fraction=fraction)+dant - + glacier = glacier.reshape(glacier.shape[0]*glacier.shape[1], glacier.shape[2]) + + return glacier + + def _project_glacier1(self, it, factor, exponent): + # Return projection of glacier contribution by one glacier method + scale=1e-3 # mm to m + return scale * factor * (np.where(it<0, 0, it)**exponent) + + def project_greensmb(self, zt, template): + # Return projection of Greenland SMB contribution as a cf.Field + # zt -- cf.Field, ensemble of temperature anomaly timeseries + # template -- cf.Field with the required shape of the output + + dtgreen=-0.146 # Delta_T of Greenland ref period wrt AR5 ref period + fnlogsd=0.4 # random methodological error of the log factor + febound=[1,1.15] # bounds of uniform pdf of SMB elevation feedback factor + + nr = template.shape[0] + # random log-normal factor + fn = np.exp(np.random.standard_normal(nr)*fnlogsd) + # elevation feedback factor + fe = np.random.sample(nr)*(febound[1]-febound[0])+febound[0] + ff = fn * fe + + ztgreen = zt - dtgreen + greensmbrate = ff[:, np.newaxis, np.newaxis] * self._fettweis(ztgreen) + + if self.palmer_method and self.end_yr > self.endofAR5: + greensmbrate[:, :, 95:] = greensmbrate[:, :, 94:95] + + greensmb = np.cumsum(greensmbrate, axis=-1) + + np.add(greensmb, (1 - self.fgreendyn) * self.dgreen, out=greensmb) + + greensmb = greensmb.reshape(greensmb.shape[0]*greensmb.shape[1], greensmb.shape[2]) + + return greensmb + + def _fettweis(self, ztgreen): + # Greenland SMB in m yr-1 SLE from global mean temperature anomaly + # using Eq 2 of Fettweis et al. (2013) + return (71.5*ztgreen+20.4*(ztgreen**2)+2.8*(ztgreen**3))*self.mSLEoGt + + def project_antsmb(self, zit, template, fraction=None): + # Return projection of Antarctic SMB contribution as a cf.Field + # zit -- cf.Field, ensemble of time-integral temperature anomaly timeseries + # template -- cf.Field with the required shape of the output + # fraction -- array-like, random numbers for the SMB-dynamic feedback + + nr=template.shape[0] + nt=template.shape[1] + # antsmb=template.copy() + # nr,nt,nyr=antsmb.shape + + # The following are [mean,SD] + pcoK=[5.1,1.5] # % change in Ant SMB per K of warming from G&H06 + KoKg=[1.1,0.2] # ratio of Antarctic warming to global warming from G&H06 + + # Generate a distribution of products of the above two factors + pcoKg=(pcoK[0]+np.random.standard_normal([nr,nt])*pcoK[1])*\ + (KoKg[0]+np.random.standard_normal([nr,nt])*KoKg[1]) + meansmb=1923 # model-mean time-mean 1979-2010 Gt yr-1 from 13.3.3.2 + moaoKg=-pcoKg*1e-2*meansmb*self.mSLEoGt # m yr-1 of SLE per K of global warming + + if fraction is None: + fraction=np.random.rand(nr,nt) + elif fraction.size!=nr*nt: + raise ValueError('fraction is the wrong size') + else: + fraction.shape=(nr,nt) + + smax=0.35 # max value of S in 13.SM.1.5 + ainterfactor=1-fraction*smax + + z = moaoKg * ainterfactor + z = z[:, :, np.newaxis] + antsmb = z * zit + antsmb = antsmb.reshape(antsmb.shape[0]*antsmb.shape[1], antsmb.shape[2]) + + return antsmb + + def project_greendyn(self, scenario, template): + # Return projection of Greenland rapid ice-sheet dynamics contribution + # as a cf.Field + # scenario -- str, name of scenario + # template -- cf.Field with the required shape of the output + + # For SMB+dyn during 2005-2010 Table 4.6 gives 0.63+-0.17 mm yr-1 (5-95% range) + # For dyn at 2100 Chapter 13 gives [20,85] mm for rcp85, [14,63] mm otherwise + + if scenario in ['rcp85','ssp585']: + finalrange=[0.020,0.085] + else: + finalrange=[0.014,0.063] + return self.time_projection( + 0.63*self.fgreendyn, 0.17*self.fgreendyn, + finalrange, template) + self.fgreendyn*self.dgreen + + def project_antdyn(self, template, fraction=None): + # Return projection of Antarctic rapid ice-sheet dynamics contribution + # as a cf.Field + # template -- cf.Field with the required shape of the output + # fraction -- array-like, random numbers for the dynamic contribution + # levermann -- optional, str, use Levermann fit for specified scenario + + final=[-0.020,0.185] + + # For SMB+dyn during 2005-2010 Table 4.6 gives 0.41+-0.24 mm yr-1 (5-95% range) + # For dyn at 2100 Chapter 13 gives [-20,185] mm for all scenarios + + return self.time_projection( + 0.41, 0.20, final, template, fraction=fraction) + self.dant -def project_landwater(template,palmer=False): - # Return projection of land water storage contribution as a cf.Field + def project_landwater(self, template): + # Return projection of land water storage contribution as a cf.Field - # The rate at start is the one for 1993-2010 from the budget table. - # The final amount is the mean for 2081-2100. - nyr=2100-2081+1 # number of years of the time-mean of the final amount + # The rate at start is the one for 1993-2010 from the budget table. + # The final amount is the mean for 2081-2100. + nyr=2100-2081+1 # number of years of the time-mean of the final amount - return time_projection(0.38,0.49-0.38,[-0.01,0.09],template, - nfinal=nyr,palmer=palmer) + return self.time_projection(0.38, 0.49-0.38, [-0.01,0.09], + template, nfinal=nyr) - -def time_projection(startratemean,startratepm,final,template, - nfinal=1,fraction=None,palmer=False): - # Return projection of a quantity which is a quadratic function of time - # in a cf.Field. - # startratemean, startratepm -- rate of GMSLR at the start in mm yr-1, whose - # likely range is startratemean +- startratepm - # final -- two-element list giving likely range in m for GMSLR at the endofAR5, - # or array-like, giving final values at that time, of the same shape as - # fraction and assumed corresponding elements - # template -- cf.Field with the required shape of the output - # nfinal -- int, optional, number of years at the end over which finalrange is - # a time-mean; by default 1 => finalrange is the value for the last year - # fraction -- array-like, optional, random numbers in the range 0 to 1, - # by default uniformly distributed - - # Create a field of elapsed time since start in years - timeendofAR5 = endofAR5 - endofhistory + 1 - time = np.arange(endyr - endofhistory) + 1 - - # more general than nr,nt,nyr=template.shape - nr, nt, nyr = template.shape - - if fraction is None: - fraction=np.random.rand(nr,nt) - elif fraction.size!=nr*nt: - raise ValueError('fraction is the wrong size') - fraction = fraction.reshape(nr,nt) - - # Convert inputs to startrate (m yr-1) and afinal (m), where both are - # arrays with the size of fraction - momm=1e-3 # convert mm yr-1 to m yr-1 - startrate=(startratemean+\ - startratepm*np.array([-1,1],dtype=float))*momm - finalisrange=isinstance(final,Sequence) - if finalisrange: - if len(final)!=2: - raise ValueError('final range is the wrong size') - afinal=(1-fraction)*final[0]+fraction*final[1] - else: - if final.shape!=fraction.shape: - raise ValueError('final array is the wrong shape') - afinal = final - startrate=(1-fraction)*startrate[0]+fraction*startrate[1] - - # For terms where the rate increases linearly in time t, we can write GMSLR as - # S(t) = a*t**2 + b*t - # where a is 0.5*acceleration and b is start rate. Hence - # a = S/t**2-b/t = (S-b*t)/t**2 - # If nfinal=1, the following two lines are equivalent to - # halfacc=(final-startyr*nyr)/nyr**2 - finalyr = np.arange(nfinal) - nfinal + 94 + 1 # last element ==nyr - halfacc=(afinal-startrate*finalyr.mean())/(finalyr**2).mean() - quadratic=halfacc[:, :, np.newaxis]*(time**2) - linear=startrate[:, :, np.newaxis]*time - - # If acceleration ceases for t>t0, the rate is 2*a*t0+b thereafter, so - # S(t) = a*t0**2 + b*t0 + (2*a*t0+b)*(t-t0) - # = a*t0*(2*t - t0) + b*t - # i.e. the quadratic term is replaced, the linear term unaffected - # The quadratic also = a*t**2-a*(t-t0)**2 - - if palmer: - y = halfacc[:, :, np.newaxis] * timeendofAR5 * ((2 * time) - timeendofAR5) - quadratic[:, :, 95:] = y[:, :, 95:] - - np.add(quadratic, linear, out=quadratic) - - quadratic = quadratic.reshape(quadratic.shape[0]*quadratic.shape[1], quadratic.shape[2]) - - return quadratic - -if __name__ == '__main__': - project(['ssp126'], palmer=True) + def time_projection( + self, startratemean, startratepm, final, + template, nfinal=1, fraction=None): + # Return projection of a quantity which is a quadratic function of time + # in a cf.Field. + # startratemean, startratepm -- rate of GMSLR at the start in mm yr-1, whose + # likely range is startratemean +- startratepm + # final -- two-element list giving likely range in m for GMSLR at the endofAR5, + # or array-like, giving final values at that time, of the same shape as + # fraction and assumed corresponding elements + # template -- cf.Field with the required shape of the output + # nfinal -- int, optional, number of years at the end over which finalrange is + # a time-mean; by default 1 => finalrange is the value for the last year + # fraction -- array-like, optional, random numbers in the range 0 to 1, + # by default uniformly distributed + + # Create a field of elapsed time since start in years + timeendofAR5 = self.endofAR5 - self.endofhistory + 1 + time = np.arange(self.end_yr - self.endofhistory) + 1 + + # more general than nr,nt,nyr=template.shape + nr, nt, nyr = template.shape + + if fraction is None: + fraction=np.random.rand(nr,nt) + elif fraction.size!=nr*nt: + raise ValueError('fraction is the wrong size') + fraction = fraction.reshape(nr,nt) + + # Convert inputs to startrate (m yr-1) and afinal (m), where both are + # arrays with the size of fraction + momm=1e-3 # convert mm yr-1 to m yr-1 + startrate=(startratemean+\ + startratepm*np.array([-1,1],dtype=float))*momm + finalisrange=isinstance(final,Sequence) + if finalisrange: + if len(final)!=2: + raise ValueError('final range is the wrong size') + afinal=(1-fraction)*final[0]+fraction*final[1] + else: + if final.shape!=fraction.shape: + raise ValueError('final array is the wrong shape') + afinal = final + startrate=(1-fraction)*startrate[0]+fraction*startrate[1] + + # For terms where the rate increases linearly in time t, we can write GMSLR as + # S(t) = a*t**2 + b*t + # where a is 0.5*acceleration and b is start rate. Hence + # a = S/t**2-b/t = (S-b*t)/t**2 + # If nfinal=1, the following two lines are equivalent to + # halfacc=(final-startyr*nyr)/nyr**2 + finalyr = np.arange(nfinal) - nfinal + 94 + 1 # last element ==nyr + halfacc=(afinal-startrate*finalyr.mean())/(finalyr**2).mean() + quadratic=halfacc[:, :, np.newaxis]*(time**2) + linear=startrate[:, :, np.newaxis]*time + + # If acceleration ceases for t>t0, the rate is 2*a*t0+b thereafter, so + # S(t) = a*t0**2 + b*t0 + (2*a*t0+b)*(t-t0) + # = a*t0*(2*t - t0) + b*t + # i.e. the quadratic term is replaced, the linear term unaffected + # The quadratic also = a*t**2-a*(t-t0)**2 + + if self.palmer_method: + y = halfacc[:, :, np.newaxis] * timeendofAR5 * ((2 * time) - timeendofAR5) + quadratic[:, :, 95:] = y[:, :, 95:] + + np.add(quadratic, linear, out=quadratic) + + quadratic = quadratic.reshape(quadratic.shape[0]*quadratic.shape[1], quadratic.shape[2]) + + return quadratic diff --git a/slr_pkg/__init__.py b/slr_pkg/__init__.py index b1a85e8..bc7d495 100644 --- a/slr_pkg/__init__.py +++ b/slr_pkg/__init__.py @@ -324,6 +324,9 @@ def choose_montecarlo_dir(): """ Choose the Monte Carlo directory based on the projection end year. """ + if settings["emulator_settings"]["emulator_mode"]: + return settings["emulator_settings"]["emulator_input_dir"] + end_yr = settings["projection_end_year"] if (end_yr >= 2050) & (end_yr <= 2100): mcdir = settings["short_montecarlodir"] diff --git a/step3_process_regional_sealevel_projections.py b/step3_process_regional_sealevel_projections.py index 240d83b..40a7ae3 100644 --- a/step3_process_regional_sealevel_projections.py +++ b/step3_process_regional_sealevel_projections.py @@ -15,6 +15,7 @@ from tide_gauge_locations import extract_site_info from slr_pkg import abbreviate_location_name, choose_montecarlo_dir # found in __init.py__ from directories import read_dir, makefolder +from montecarlo import GMSLREmulator def calc_baseline_period(sci_method, yrs): @@ -203,16 +204,27 @@ def calculate_sl_components(mcdir, components, scenario, site_loc, loc_coords, offset = G_offset * offset_slopes[comp] - cube = iris.load_cube(os.path.join(mcdir, f'{scenario}_{comp}.nc')) - montecarlo_G[cc, :, :] = cube.data[:nyrs, resamples] + offset + if settings['emulator_mode']: + # Input timeseries provided as numpy objects + mc_timeseries = np.load(os.path.join(mcdir, f'{scenario}_{comp}.npy')) + montecarlo_G[cc, :, :] = mc_timeseries[:nyrs, resamples] + offset + else: + cube = iris.load_cube(os.path.join(mcdir, f'{scenario}_{comp}.nc')) + montecarlo_G[cc, :, :] = cube.data[:nyrs, resamples] + offset if comp == 'exp': if sci_method == 'global': - coeffs = load_CMIP5_slope_coeffs(site_loc, scenario) + if settings["emulator_settings"]["emulator_mode"]: + coeffs = load_CMIP5_slope_coeffs(site_loc, 'rcp85') + else: + coeffs = load_CMIP5_slope_coeffs(site_loc, scenario) rand_coeffs = np.random.choice(coeffs, size=nsmps, replace=True) elif sci_method == 'UK': - coeffs, weights = load_CMIP5_slope_coeffs_UK(scenario) + if settings["emulator_settings"]["emulator_mode"]: + coeffs, weights = load_CMIP5_slope_coeffs_UK('rcp85') + else: + coeffs, weights = load_CMIP5_slope_coeffs_UK(scenario) rand_coeffs = np.random.choice(coeffs, size=nsmps, replace=True, p=weights) montecarlo_R[cc, :, :] = montecarlo_G[cc, :, :] * rand_coeffs @@ -531,11 +543,30 @@ def main(): settings["siteinfo"]["sitename"], settings["siteinfo"]["sitelatlon"]) - scenarios = ['rcp26', 'rcp45', 'rcp85'] - for scenario in scenarios: + if settings["emulator_settings"]["emulator_mode"]: + print('Initiating ProFSea emulator...') + if settings["projection_end_year"] > 2100: + palmer_method = True + else: + palmer_method = False + + gmslr = GMSLREmulator( + settings["emulator_settings"]["emulator_scenarios"], + settings["emulator_settings"]["emulator_input_dir"], + settings["baseoutdir"], + settings["projection_end_year"], + palmer_method=palmer_method + ) + gmslr.project() # Get the metadata of either the site location or tide gauge location - for loc_name in df_site_data.index.values: - calc_future_sea_level_at_site(df_site_data, loc_name, scenario) + for scenario in settings["emulator_settings"]["emulator_scenarios"]: + for loc_name in df_site_data.index.values: + calc_future_sea_level_at_site(df_site_data, loc_name, scenario) + else: + scenarios = ['rcp26', 'rcp45', 'rcp85'] + for scenario in scenarios: + for loc_name in df_site_data.index.values: + calc_future_sea_level_at_site(df_site_data, loc_name, scenario) if __name__ == '__main__': From e222eed913977ca6214215ed0a24f3665a4f25c1 Mon Sep 17 00:00:00 2001 From: mo-gregmunday Date: Mon, 8 Jul 2024 10:35:05 +0100 Subject: [PATCH 003/276] Couple of bug fixes --- montecarlo.py | 2 +- slr_pkg/__init__.py | 2 +- step3_process_regional_sealevel_projections.py | 12 +++++++----- 3 files changed, 9 insertions(+), 7 deletions(-) diff --git a/montecarlo.py b/montecarlo.py index 9b484bd..a6ef895 100644 --- a/montecarlo.py +++ b/montecarlo.py @@ -72,7 +72,7 @@ def __init__( self.tcv = tcv self.glaciermip = glaciermip self.ensemble = ensemble - self.palmer_method = self.palmer_method + self.palmer_method = palmer_method # First year of AR5 projections self.endofhistory=2006 diff --git a/slr_pkg/__init__.py b/slr_pkg/__init__.py index bc7d495..2c8a674 100644 --- a/slr_pkg/__init__.py +++ b/slr_pkg/__init__.py @@ -325,7 +325,7 @@ def choose_montecarlo_dir(): Choose the Monte Carlo directory based on the projection end year. """ if settings["emulator_settings"]["emulator_mode"]: - return settings["emulator_settings"]["emulator_input_dir"] + return os.path.join(settings["baseoutdir"], 'emulator_output') end_yr = settings["projection_end_year"] if (end_yr >= 2050) & (end_yr <= 2100): diff --git a/step3_process_regional_sealevel_projections.py b/step3_process_regional_sealevel_projections.py index 40a7ae3..11d6f25 100644 --- a/step3_process_regional_sealevel_projections.py +++ b/step3_process_regional_sealevel_projections.py @@ -204,7 +204,7 @@ def calculate_sl_components(mcdir, components, scenario, site_loc, loc_coords, offset = G_offset * offset_slopes[comp] - if settings['emulator_mode']: + if settings["emulator_settings"]["emulator_mode"]: # Input timeseries provided as numpy objects mc_timeseries = np.load(os.path.join(mcdir, f'{scenario}_{comp}.npy')) montecarlo_G[cc, :, :] = mc_timeseries[:nyrs, resamples] + offset @@ -544,22 +544,24 @@ def main(): settings["siteinfo"]["sitelatlon"]) if settings["emulator_settings"]["emulator_mode"]: - print('Initiating ProFSea emulator...') + print('\nInitiating ProFSea emulator') if settings["projection_end_year"] > 2100: palmer_method = True else: palmer_method = False + makefolder(os.path.join(settings["baseoutdir"], 'emulator_output')) + gmslr = GMSLREmulator( - settings["emulator_settings"]["emulator_scenarios"], + settings["emulator_settings"]["emulator_scenario"], settings["emulator_settings"]["emulator_input_dir"], - settings["baseoutdir"], + os.path.join(settings["baseoutdir"], 'emulator_output'), settings["projection_end_year"], palmer_method=palmer_method ) gmslr.project() # Get the metadata of either the site location or tide gauge location - for scenario in settings["emulator_settings"]["emulator_scenarios"]: + for scenario in settings["emulator_settings"]["emulator_scenario"]: for loc_name in df_site_data.index.values: calc_future_sea_level_at_site(df_site_data, loc_name, scenario) else: From 8dd88a8a27aeb33e2722c1ac6caa05f1d65f733a Mon Sep 17 00:00:00 2001 From: mo-gregmunday Date: Mon, 8 Jul 2024 11:06:35 +0100 Subject: [PATCH 004/276] Doc change --- montecarlo.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/montecarlo.py b/montecarlo.py index a6ef895..2a7f3c2 100644 --- a/montecarlo.py +++ b/montecarlo.py @@ -1,4 +1,4 @@ -# Calculate Monte Carlo projections of GMSL rise using methods +# Calculate Monte Carlo projections of GMSLR using methods # from Jonathon Gregory and AR5. Staying close to JG's original # code where possible. import os, os.path From f511fc1ac0e8b1bd03df41409f7470366e3fe7a7 Mon Sep 17 00:00:00 2001 From: mo-gregmunday Date: Mon, 8 Jul 2024 11:10:54 +0100 Subject: [PATCH 005/276] Plotting for emulator_mode --- step4_plot_regional_sealevel.py | 34 +++++++++++++++++++++++++++++---- 1 file changed, 30 insertions(+), 4 deletions(-) diff --git a/step4_plot_regional_sealevel.py b/step4_plot_regional_sealevel.py index 1576849..b99cf52 100644 --- a/step4_plot_regional_sealevel.py +++ b/step4_plot_regional_sealevel.py @@ -755,10 +755,10 @@ def read_G_R_sl_projections(site_name, scenarios): G_df_list = [] R_df_list = [] for sce in scenarios: - G_filename = '{}{}_{}_projection_2100_global.csv'.format( - in_slddir, loc_abbrev, sce) - R_filename = '{}{}_{}_projection_2100_regional.csv'.format( - in_slddir, loc_abbrev, sce) + G_filename = '{}{}_{}_projection_{}_global.csv'.format( + in_slddir, loc_abbrev, sce, settings["projection_end_year"]) + R_filename = '{}{}_{}_projection_{}_regional.csv'.format( + in_slddir, loc_abbrev, sce, settings["projection_end_year"]) try: G_df = pd.read_csv(G_filename, header=0, index_col=['year', 'percentile']) @@ -899,6 +899,32 @@ def main(): # multiple functions sealev_fdir = read_dir()[5] makefolder(sealev_fdir) + + if settings["emulator_settings"]["emulator_mode"]: + emulator_scenarios = settings["emulator_settings"]["emulator_scenario"] + for df_loc in df_site_data.index.values: + # Read global and regional sea level projections calculated previously + g_df_list, r_df_list = read_G_R_sl_projections(df_loc, emulator_scenarios) + + # Read in the IPCC AR5 + Levermann global sea level projections + ar5_low, ar5_mid, ar5_upp = read_IPCC_AR5_Levermann_proj(rcp_scenarios) + + # Read annual mean sea level observations from PSMSL + tg_name, nflag, flag, tg_years, non_missing, tg_amsl = \ + read_PSMSL_tide_gauge_obs(settings["baseoutdir"], settings[ + "tidegaugeinfo"]["source"], settings["tidegaugeinfo"][ + "datafq"], settings["siteinfo"]["region"], df_site_data, + df_loc, sealev_fdir) + + plot_figure_two(r_df_list, tg_name, nflag, flag, tg_years, + non_missing, tg_amsl, df_loc, emulator_scenarios, + sealev_fdir) + + plot_figure_three(g_df_list, r_df_list, ar5_low, ar5_mid, ar5_upp, + df_loc, emulator_scenarios, sealev_fdir) + + plot_figure_seven(g_df_list, r_df_list, df_loc, emulator_scenarios, + sealev_fdir) rcp_scenarios = ['rcp26', 'rcp45', 'rcp85'] for df_loc in df_site_data.index.values: From 14537df8db4cc59b3eefc96926399859ef2a8539 Mon Sep 17 00:00:00 2001 From: mo-gregmunday Date: Mon, 8 Jul 2024 12:22:25 +0100 Subject: [PATCH 006/276] Refactor --- montecarlo.py => emulator/__init__.py | 0 emulator/plotting.py | 177 ++++++++++++++++++ ...3_process_regional_sealevel_projections.py | 3 +- step4_plot_regional_sealevel.py | 144 +++++++------- 4 files changed, 249 insertions(+), 75 deletions(-) rename montecarlo.py => emulator/__init__.py (100%) create mode 100644 emulator/plotting.py diff --git a/montecarlo.py b/emulator/__init__.py similarity index 100% rename from montecarlo.py rename to emulator/__init__.py diff --git a/emulator/plotting.py b/emulator/plotting.py new file mode 100644 index 0000000..cb1140c --- /dev/null +++ b/emulator/plotting.py @@ -0,0 +1,177 @@ +import os +import glob + +import numpy as np +import matplotlib.pyplot as plt +import matplotlib +import pandas as pd + +def plot_slr(r_df_list, tg_name, nflag, flag, tg_years, + non_missing, tg_amsl, site_name, scenarios, fig_dir): + """ + Local sea level projections for specified RCPs - sum total, and annual + mean tide gauge observations. + :param r_df_list: regional sea level projections + :param tg_name: tide gauge name + :param nflag: number of flagged years + :param flag: flagged data + :param tg_years: tide gauge years + :param non_missing: boolean to indicate NaN values + :param tg_amsl: annual mean sea level data + :param site_name: site location + :param scenarios: emissions scenario + :param fig_dir:figure directory + """ + # Get the values of the multi-index: years and percentile values + years, percentiles = multi_index_values(r_df_list) + + # UKCP18 colour scheme for RCP scenarios + rcp_colours = ukcp18_colours()[0] + + fig = plt.figure(figsize=(5, 4.5)) + matplotlib.rcParams['font.size'] = 10 + + # Draw regional sea level projections and uncertainties under each scenario + ax = fig.add_subplot(1, 1, 1) + + for rcp_count, rcp_str in enumerate(scenarios): + # Get the sums of all components of local sea level projections + r_df_rcp = r_df_list[rcp_count] + rlow, rmid, rupp = extract_comp_sl(r_df_rcp, percentiles, 'sum') + + label = scenario_string(scenarios, rcp_count) + ax.fill_between(years, rlow, rupp, alpha=0.3, + color=rcp_colours[rcp_str], linewidth=0) + ax.plot(years, rmid, color=rcp_colours[rcp_str], label=label) + + # Calculate sensible x-axis range and y-axis range based on tide gauge data + # and projections + xlim = calc_xlim('tide', tg_years, years) + df_tmp = pd.DataFrame([rlow, rmid, rupp]) + ylim = calc_ylim('tide', tg_amsl, df_tmp) + + plot_zeroline(ax, xlim) + + # Plot the annual mean sea levels from the tide gauge data + plot_tg_data(ax, nflag, flag, tg_years, non_missing, tg_amsl, tg_name) + + ax.set_xlim(xlim) + ax.set_ylim(ylim) + ax.yaxis.set_ticks_position('both') + ax.yaxis.set_label_position('left') + ax.set_title(f'Local sea level - {location_string(site_name)}') + ax.set_ylabel('Sea Level Change (m)') + ax.set_xlabel('Year') + ax.legend(loc='upper left', frameon=False) + + # Save the figure + fig.tight_layout() + ffile = f'{fig_dir}02_{site_name}.png' + plt.savefig(ffile, dpi=300, format='png') + plt.close() + +def plot_slr_components(): + """ + Subplot 1 - Global sea level projections for RCP8.5 - total and component + parts, including uncertainty range. + Subplot 2 - Local sea level projections for RCP8.5 - total and component + parts, including uncertainty range. + Same style as Figure 4 Howard and Palmer (2019). + :param g_df_list: global sea level projections + :param r_df_list: regional sea level projections + :param site_name: site location + :param scenarios: emission scenario + :param fig_dir: figure directory + """ + # Get the values of the multi-index: years and percentile values + _, percentiles = multi_index_values(r_df_list) + + # UKCP18 colour scheme for sea level components + comp_colours = ukcp18_colours()[1] + # UKCP18 labels for sea level components + comp_labels = ukcp18_labels() + + # Calculate sensible x-axis range and y-axis range + xlim = ([-0.4, 7.4]) + r_ylim = calc_ylim('proj', [], r_df_list) + g_ylim = calc_ylim('proj', [], g_df_list) + ylim = [min(r_ylim[0], g_ylim[0]), max(r_ylim[1], g_ylim[1])] + + # Show components for the RCP8.5 scenario (only plot the uncertainty for + # sum and thermal expansion) + for rcp_count, rcp_str in enumerate(scenarios): + if scenarios[rcp_count] == 'rcp85': + break + else: + raise ValueError('RCP8.5 scenario not in list') + r_df_rcp = r_df_list[rcp_count] + g_df_rcp = g_df_list[rcp_count] + + # ------------------------------------------------------------------------- + # First figure, global sea level projections and uncertainties under + # RCP 8.5 and component parts + fig = plt.figure(figsize=(7.68, 4.5)) + matplotlib.rcParams['font.size'] = 9 + + ax_labels = [] + ax = fig.add_subplot(1, 2, 1) + for cc, comp in enumerate(['sum', 'ocean', 'antnet', 'greennet', 'glacier', + 'landwater']): + # Get the components of regional sea level projections + clow_85_G, cmid_85_G, cupp_85_G = extract_comp_sl(g_df_rcp, + percentiles, comp) + + colour = comp_colours[comp] + label = comp_labels[comp] + ax_labels.append(label) + xpts = [cc - 0.4, cc + 0.4] + + ax.fill_between(xpts, clow_85_G[-1], cupp_85_G[-1], + facecolor=colour, alpha=0.35, label=label) + ax.plot(xpts, [cmid_85_G[-1], cmid_85_G[-1]], linewidth=2.0, + color=colour) + + plot_zeroline(ax, xlim) + ax.set_xticks([0, 1, 2, 3, 4, 5]) + ax.set_xticklabels(ax_labels, rotation=90) + ax.yaxis.set_ticks_position('both') + ax.yaxis.set_label_position('left') + plt.ylim(ylim) + plt.ylabel('Sea Level Change (m)') + plt.title(f'Global sea level - {scenario_string(rcp_str, -999)}') + + # ------------------------------------------------------------------------- + # Second figure, regional sea level projections and uncertainties under + # RCP 8.5 and component parts + bx = fig.add_subplot(1, 2, 2) + bx_labels = [] + for cc, comp in enumerate(['sum', 'ocean', 'antnet', 'greennet', 'glacier', + 'landwater', 'gia']): + # Get the components of local sea level projections + clow_85_R, cmid_85_R, cupp_85_R = extract_comp_sl(r_df_rcp, + percentiles, comp) + + colour = comp_colours[comp] + label = comp_labels[comp] + bx_labels.append(label) + xpts = [cc - 0.4, cc + 0.4] + + bx.fill_between(xpts, clow_85_R[-1], cupp_85_R[-1], facecolor=colour, + alpha=0.35, label=label) + bx.plot(xpts, [cmid_85_R[-1], cmid_85_R[-1]], linewidth=2.0, + color=colour) + + plot_zeroline(bx, xlim) + bx.set_xticks([0, 1, 2, 3, 4, 5, 6]) + bx.set_xticklabels(bx_labels, rotation=90) + bx.yaxis.set_ticks_position('both') + bx.yaxis.set_label_position('left') + plt.ylim(ylim) + plt.title(f'Local sea level - {location_string(site_name)} - ' + f'{scenario_string(scenarios[rcp_count], -999)}') + + # Save the figure + fig.tight_layout() + outfile = f'{fig_dir}07_{site_name}_rcp85.png' + plt.savefig(outfile, dpi=200, format='png') + plt.close() \ No newline at end of file diff --git a/step3_process_regional_sealevel_projections.py b/step3_process_regional_sealevel_projections.py index 11d6f25..db763e4 100644 --- a/step3_process_regional_sealevel_projections.py +++ b/step3_process_regional_sealevel_projections.py @@ -15,7 +15,7 @@ from tide_gauge_locations import extract_site_info from slr_pkg import abbreviate_location_name, choose_montecarlo_dir # found in __init.py__ from directories import read_dir, makefolder -from montecarlo import GMSLREmulator +from emulator import GMSLREmulator def calc_baseline_period(sci_method, yrs): @@ -43,6 +43,7 @@ def calc_baseline_period(sci_method, yrs): return yrs[0] - midyr, G_offset + def calc_future_sea_level_at_site(df, site_loc, scenario): """ Calculates future sea level at the given site and write to file. diff --git a/step4_plot_regional_sealevel.py b/step4_plot_regional_sealevel.py index b99cf52..2350774 100644 --- a/step4_plot_regional_sealevel.py +++ b/step4_plot_regional_sealevel.py @@ -17,6 +17,7 @@ from directories import read_dir, makefolder from plotting_libraries import location_string, scenario_string, \ ukcp18_colours, ukcp18_labels, calc_xlim, calc_ylim, plot_zeroline +from emulator.plotting import plot_slr, plot_slr_components def compute_uncertainties(df_r_list, scenarios, tg_years, tg_amsl): @@ -906,9 +907,6 @@ def main(): # Read global and regional sea level projections calculated previously g_df_list, r_df_list = read_G_R_sl_projections(df_loc, emulator_scenarios) - # Read in the IPCC AR5 + Levermann global sea level projections - ar5_low, ar5_mid, ar5_upp = read_IPCC_AR5_Levermann_proj(rcp_scenarios) - # Read annual mean sea level observations from PSMSL tg_name, nflag, flag, tg_years, non_missing, tg_amsl = \ read_PSMSL_tide_gauge_obs(settings["baseoutdir"], settings[ @@ -916,84 +914,82 @@ def main(): "datafq"], settings["siteinfo"]["region"], df_site_data, df_loc, sealev_fdir) - plot_figure_two(r_df_list, tg_name, nflag, flag, tg_years, + plot_slr(r_df_list, tg_name, nflag, flag, tg_years, non_missing, tg_amsl, df_loc, emulator_scenarios, sealev_fdir) - plot_figure_three(g_df_list, r_df_list, ar5_low, ar5_mid, ar5_upp, - df_loc, emulator_scenarios, sealev_fdir) - - plot_figure_seven(g_df_list, r_df_list, df_loc, emulator_scenarios, + plot_slr_components(g_df_list, r_df_list, df_loc, emulator_scenarios, sealev_fdir) + + else: + rcp_scenarios = ['rcp26', 'rcp45', 'rcp85'] + for df_loc in df_site_data.index.values: + # Read global and regional sea level projections calculated previously + g_df_list, r_df_list = read_G_R_sl_projections(df_loc, rcp_scenarios) + + # Read in the IPCC AR5 + Levermann global sea level projections + ar5_low, ar5_mid, ar5_upp = read_IPCC_AR5_Levermann_proj(rcp_scenarios) - rcp_scenarios = ['rcp26', 'rcp45', 'rcp85'] - for df_loc in df_site_data.index.values: - # Read global and regional sea level projections calculated previously - g_df_list, r_df_list = read_G_R_sl_projections(df_loc, rcp_scenarios) + # Read annual mean sea level observations from PSMSL + tg_name, nflag, flag, tg_years, non_missing, tg_amsl = \ + read_PSMSL_tide_gauge_obs(settings["baseoutdir"], settings[ + "tidegaugeinfo"]["source"], settings["tidegaugeinfo"][ + "datafq"], settings["siteinfo"]["region"], df_site_data, + df_loc, sealev_fdir) + # ------------------------------------------------------------------------- + # Plotting section - comment functions in and out depending on which graphs + # you wish to save + # Figure 1 + # Subplot 1 - Local sea level projections for all RCPs - sum total + # Subplot 2 - Local sea level projections for RCP8.5 - total and + # component parts + plot_figure_one(r_df_list, df_loc, rcp_scenarios, sealev_fdir) + + # Figure 2 + # Local sea level projections for specified RCPs - sum total, + # and annual mean tide gauge observations + plot_figure_two(r_df_list, tg_name, nflag, flag, tg_years, + non_missing, tg_amsl, df_loc, rcp_scenarios, + sealev_fdir) - # Read in the IPCC AR5 + Levermann global sea level projections - ar5_low, ar5_mid, ar5_upp = read_IPCC_AR5_Levermann_proj(rcp_scenarios) + # Figure 3 (one output per RCP) + # Subplot 1 - Comparison of IPCC AR5 + Levermann global projections + # and global projections developed here + # Subplot 2 - Comparison of global projections and local sea level + # projections + plot_figure_three(g_df_list, r_df_list, ar5_low, ar5_mid, ar5_upp, + df_loc, rcp_scenarios, sealev_fdir) + + # Figure 4 + # Local sea level projections for all RCPs - sum total + # Same style as Figure 3.1.4 of UKCP18 Marine Report (pg 16) + plot_figure_four(r_df_list, df_loc, rcp_scenarios, sealev_fdir) + + # Figure 5 + # Comparison of global mean sea level projections calculated by the + # tool and local sea level projections - sum total + # Also shown local sea level projections - component parts + # Subplot 1, 2, 3 - RCP2.6, RCP4.5, RCP8.5 respectively + plot_figure_five(g_df_list, r_df_list, df_loc, rcp_scenarios, + sealev_fdir) - # Read annual mean sea level observations from PSMSL - tg_name, nflag, flag, tg_years, non_missing, tg_amsl = \ - read_PSMSL_tide_gauge_obs(settings["baseoutdir"], settings[ - "tidegaugeinfo"]["source"], settings["tidegaugeinfo"][ - "datafq"], settings["siteinfo"]["region"], df_site_data, - df_loc, sealev_fdir) - # ------------------------------------------------------------------------- - # Plotting section - comment functions in and out depending on which graphs - # you wish to save - # Figure 1 - # Subplot 1 - Local sea level projections for all RCPs - sum total - # Subplot 2 - Local sea level projections for RCP8.5 - total and - # component parts - plot_figure_one(r_df_list, df_loc, rcp_scenarios, sealev_fdir) - - # Figure 2 - # Local sea level projections for specified RCPs - sum total, - # and annual mean tide gauge observations - plot_figure_two(r_df_list, tg_name, nflag, flag, tg_years, - non_missing, tg_amsl, df_loc, rcp_scenarios, - sealev_fdir) - - # Figure 3 (one output per RCP) - # Subplot 1 - Comparison of IPCC AR5 + Levermann global projections - # and global projections developed here - # Subplot 2 - Comparison of global projections and local sea level - # projections - plot_figure_three(g_df_list, r_df_list, ar5_low, ar5_mid, ar5_upp, - df_loc, rcp_scenarios, sealev_fdir) - - # Figure 4 - # Local sea level projections for all RCPs - sum total - # Same style as Figure 3.1.4 of UKCP18 Marine Report (pg 16) - plot_figure_four(r_df_list, df_loc, rcp_scenarios, sealev_fdir) - - # Figure 5 - # Comparison of global mean sea level projections calculated by the - # tool and local sea level projections - sum total - # Also shown local sea level projections - component parts - # Subplot 1, 2, 3 - RCP2.6, RCP4.5, RCP8.5 respectively - plot_figure_five(g_df_list, r_df_list, df_loc, rcp_scenarios, - sealev_fdir) - - # Figure 6 - # Comparison of IPCC AR5 + Levermann projections and local sea level - # projections - sum total - # Also shown local sea level projections - component parts - # Subplot 1, 2, 3 - RCP2.6, RCP4.5, RCP8.5 respectively - if settings["projection_end_year"] <= 2100: - plot_figure_six(r_df_list, ar5_low, ar5_mid, ar5_upp, df_loc, - rcp_scenarios, sealev_fdir) - - # Figure 7 - # Subplot 1 - Global sea level projections for RCP8.5 - total and - # component parts, including uncertainty range - # Subplot 2 - Local sea level projections for RCP8.5 - total and - # component parts, including uncertainty range - # Same style as Figure 4 Howard and Palmer (2019) - plot_figure_seven(g_df_list, r_df_list, df_loc, rcp_scenarios, - sealev_fdir) + # Figure 6 + # Comparison of IPCC AR5 + Levermann projections and local sea level + # projections - sum total + # Also shown local sea level projections - component parts + # Subplot 1, 2, 3 - RCP2.6, RCP4.5, RCP8.5 respectively + if settings["projection_end_year"] <= 2100: + plot_figure_six(r_df_list, ar5_low, ar5_mid, ar5_upp, df_loc, + rcp_scenarios, sealev_fdir) + + # Figure 7 + # Subplot 1 - Global sea level projections for RCP8.5 - total and + # component parts, including uncertainty range + # Subplot 2 - Local sea level projections for RCP8.5 - total and + # component parts, including uncertainty range + # Same style as Figure 4 Howard and Palmer (2019) + plot_figure_seven(g_df_list, r_df_list, df_loc, rcp_scenarios, + sealev_fdir) if __name__ == '__main__': From a71a9caca7573bcc59aa69fda9a5db90173a33c0 Mon Sep 17 00:00:00 2001 From: mo-gregmunday Date: Mon, 8 Jul 2024 15:51:43 +0100 Subject: [PATCH 007/276] Move emulator --- {emulator => profsea/emulator}/__init__.py | 0 {emulator => profsea/emulator}/plotting.py | 0 2 files changed, 0 insertions(+), 0 deletions(-) rename {emulator => profsea/emulator}/__init__.py (100%) rename {emulator => profsea/emulator}/plotting.py (100%) diff --git a/emulator/__init__.py b/profsea/emulator/__init__.py similarity index 100% rename from emulator/__init__.py rename to profsea/emulator/__init__.py diff --git a/emulator/plotting.py b/profsea/emulator/plotting.py similarity index 100% rename from emulator/plotting.py rename to profsea/emulator/plotting.py From e1fb3888206685db8e75e71d0c42bcb142c29a9e Mon Sep 17 00:00:00 2001 From: mo-gregmunday Date: Mon, 8 Jul 2024 21:36:52 +0100 Subject: [PATCH 008/276] Added plotting for emulator projection outputs --- profsea/emulator/__init__.py | 5 + profsea/emulator/plotting.py | 181 ++++++++++++------------ profsea/plotting_libraries.py | 87 ++++++++++++ profsea/step4_plot_regional_sealevel.py | 66 +-------- 4 files changed, 186 insertions(+), 153 deletions(-) diff --git a/profsea/emulator/__init__.py b/profsea/emulator/__init__.py index 2a7f3c2..65958c3 100644 --- a/profsea/emulator/__init__.py +++ b/profsea/emulator/__init__.py @@ -1,3 +1,8 @@ +""" +Copyright (c) 2023, Met Office +All rights reserved. +""" + # Calculate Monte Carlo projections of GMSLR using methods # from Jonathon Gregory and AR5. Staying close to JG's original # code where possible. diff --git a/profsea/emulator/plotting.py b/profsea/emulator/plotting.py index cb1140c..8a0ef12 100644 --- a/profsea/emulator/plotting.py +++ b/profsea/emulator/plotting.py @@ -1,11 +1,17 @@ -import os -import glob - -import numpy as np +""" +Copyright (c) 2023, Met Office +All rights reserved. +""" import matplotlib.pyplot as plt import matplotlib import pandas as pd +from profsea.plotting_libraries import ( + calc_xlim, calc_ylim, plot_zeroline, multi_index_values, + extract_comp_sl, plot_tg_data, location_string, ukcp18_labels, + get_emulator_colors) + + def plot_slr(r_df_list, tg_name, nflag, flag, tg_years, non_missing, tg_amsl, site_name, scenarios, fig_dir): """ @@ -23,26 +29,24 @@ def plot_slr(r_df_list, tg_name, nflag, flag, tg_years, :param fig_dir:figure directory """ # Get the values of the multi-index: years and percentile values - years, percentiles = multi_index_values(r_df_list) - - # UKCP18 colour scheme for RCP scenarios - rcp_colours = ukcp18_colours()[0] + years, percentiles = multi_index_values(r_df_list) fig = plt.figure(figsize=(5, 4.5)) matplotlib.rcParams['font.size'] = 10 + + scenario_colours = get_emulator_colors(len(scenarios)) # Draw regional sea level projections and uncertainties under each scenario ax = fig.add_subplot(1, 1, 1) - for rcp_count, rcp_str in enumerate(scenarios): + for i, scen in enumerate(scenarios): + scen_col = next(scenario_colours) # Get the sums of all components of local sea level projections - r_df_rcp = r_df_list[rcp_count] - rlow, rmid, rupp = extract_comp_sl(r_df_rcp, percentiles, 'sum') - - label = scenario_string(scenarios, rcp_count) + r_df = r_df_list[i] + rlow, rmid, rupp = extract_comp_sl(r_df, percentiles, 'sum') ax.fill_between(years, rlow, rupp, alpha=0.3, - color=rcp_colours[rcp_str], linewidth=0) - ax.plot(years, rmid, color=rcp_colours[rcp_str], label=label) + color=scen_col, linewidth=0) + ax.plot(years, rmid, color=scen_col, label=scen) # Calculate sensible x-axis range and y-axis range based on tide gauge data # and projections @@ -66,11 +70,12 @@ def plot_slr(r_df_list, tg_name, nflag, flag, tg_years, # Save the figure fig.tight_layout() - ffile = f'{fig_dir}02_{site_name}.png' + ffile = f'{fig_dir}_SLR_{site_name}.png' plt.savefig(ffile, dpi=300, format='png') plt.close() -def plot_slr_components(): + +def plot_slr_components(g_df_list, r_df_list, site_name, scenarios, fig_dir): """ Subplot 1 - Global sea level projections for RCP8.5 - total and component parts, including uncertainty range. @@ -86,92 +91,84 @@ def plot_slr_components(): # Get the values of the multi-index: years and percentile values _, percentiles = multi_index_values(r_df_list) - # UKCP18 colour scheme for sea level components - comp_colours = ukcp18_colours()[1] - # UKCP18 labels for sea level components - comp_labels = ukcp18_labels() - # Calculate sensible x-axis range and y-axis range xlim = ([-0.4, 7.4]) r_ylim = calc_ylim('proj', [], r_df_list) g_ylim = calc_ylim('proj', [], g_df_list) ylim = [min(r_ylim[0], g_ylim[0]), max(r_ylim[1], g_ylim[1])] - # Show components for the RCP8.5 scenario (only plot the uncertainty for - # sum and thermal expansion) - for rcp_count, rcp_str in enumerate(scenarios): - if scenarios[rcp_count] == 'rcp85': - break - else: - raise ValueError('RCP8.5 scenario not in list') - r_df_rcp = r_df_list[rcp_count] - g_df_rcp = g_df_list[rcp_count] - # ------------------------------------------------------------------------- - # First figure, global sea level projections and uncertainties under - # RCP 8.5 and component parts - fig = plt.figure(figsize=(7.68, 4.5)) + # Global sea level projections, uncertainties and component parts + fig = plt.figure(figsize=(7.68*len(scenarios), 4.5*len(scenarios))) matplotlib.rcParams['font.size'] = 9 - ax_labels = [] - ax = fig.add_subplot(1, 2, 1) - for cc, comp in enumerate(['sum', 'ocean', 'antnet', 'greennet', 'glacier', - 'landwater']): - # Get the components of regional sea level projections - clow_85_G, cmid_85_G, cupp_85_G = extract_comp_sl(g_df_rcp, - percentiles, comp) - - colour = comp_colours[comp] - label = comp_labels[comp] - ax_labels.append(label) - xpts = [cc - 0.4, cc + 0.4] - - ax.fill_between(xpts, clow_85_G[-1], cupp_85_G[-1], - facecolor=colour, alpha=0.35, label=label) - ax.plot(xpts, [cmid_85_G[-1], cmid_85_G[-1]], linewidth=2.0, - color=colour) - - plot_zeroline(ax, xlim) - ax.set_xticks([0, 1, 2, 3, 4, 5]) - ax.set_xticklabels(ax_labels, rotation=90) - ax.yaxis.set_ticks_position('both') - ax.yaxis.set_label_position('left') - plt.ylim(ylim) - plt.ylabel('Sea Level Change (m)') - plt.title(f'Global sea level - {scenario_string(rcp_str, -999)}') - - # ------------------------------------------------------------------------- - # Second figure, regional sea level projections and uncertainties under - # RCP 8.5 and component parts - bx = fig.add_subplot(1, 2, 2) - bx_labels = [] - for cc, comp in enumerate(['sum', 'ocean', 'antnet', 'greennet', 'glacier', - 'landwater', 'gia']): - # Get the components of local sea level projections - clow_85_R, cmid_85_R, cupp_85_R = extract_comp_sl(r_df_rcp, - percentiles, comp) - - colour = comp_colours[comp] - label = comp_labels[comp] - bx_labels.append(label) - xpts = [cc - 0.4, cc + 0.4] - - bx.fill_between(xpts, clow_85_R[-1], cupp_85_R[-1], facecolor=colour, - alpha=0.35, label=label) - bx.plot(xpts, [cmid_85_R[-1], cmid_85_R[-1]], linewidth=2.0, - color=colour) - - plot_zeroline(bx, xlim) - bx.set_xticks([0, 1, 2, 3, 4, 5, 6]) - bx.set_xticklabels(bx_labels, rotation=90) - bx.yaxis.set_ticks_position('both') - bx.yaxis.set_label_position('left') - plt.ylim(ylim) - plt.title(f'Local sea level - {location_string(site_name)} - ' - f'{scenario_string(scenarios[rcp_count], -999)}') + for i, scen in enumerate(scenarios): + scenario_colors = get_emulator_colors(len(scenarios), get_all=True) + r_df = r_df_list[i] + g_df = g_df_list[i] + + ax_labels = [] + ax = fig.add_subplot(len(scenarios), 2, (i*2)+1, label=f'Scenario_{i + 1}_Plot_1') + for cc, comp in enumerate(['sum', 'ocean', 'antnet', 'greennet', 'glacier', + 'landwater']): + # Get the components of regional sea level projections + clow_G, cmid_G, cupp_G = extract_comp_sl(g_df, + percentiles, comp) + + colour = next(scenario_colors) + label = ukcp18_labels()[comp] + + ax_labels.append(label) + xpts = [cc - 0.4, cc + 0.4] + + ax.fill_between(xpts, clow_G[-1], cupp_G[-1], + facecolor=colour, alpha=0.85, label=label) + ax.plot(xpts, [cmid_G[-1], cmid_G[-1]], linewidth=2.0, + color=colour) + + plot_zeroline(ax, xlim) + ax.set_xticks([0, 1, 2, 3, 4, 5]) + ax.set_xticklabels(ax_labels, rotation=90) + ax.yaxis.set_ticks_position('both') + ax.yaxis.set_label_position('left') + ax.set_ylim(ylim) + ax.set_ylabel('Sea Level Change (m)') + ax.set_title(f'Global sea level - {scen}') + + # ------------------------------------------------------------------------- + # Second figure, regional sea level projections and uncertainties under + # RCP 8.5 and component parts + + scenario_colors = get_emulator_colors(len(scenarios), get_all=True) + bx = fig.add_subplot(len(scenarios), 2, (i*2)+2, label=f'Scenario_{i + 1}_Plot_2') + bx_labels = [] + for cc, comp in enumerate(['sum', 'ocean', 'antnet', 'greennet', 'glacier', + 'landwater', 'gia']): + # Get the components of local sea level projections + clow_R, cmid_R, cupp_R = extract_comp_sl(r_df, + percentiles, comp) + + colour = next(scenario_colors) + label = ukcp18_labels()[comp] + + bx_labels.append(label) + xpts = [cc - 0.4, cc + 0.4] + + bx.fill_between(xpts, clow_R[-1], cupp_R[-1], facecolor=colour, + alpha=0.85, label=label) + bx.plot(xpts, [cmid_R[-1], cmid_R[-1]], linewidth=2.0, + color=colour) + + plot_zeroline(bx, xlim) + bx.set_xticks([0, 1, 2, 3, 4, 5, 6]) + bx.set_xticklabels(bx_labels, rotation=90) + bx.yaxis.set_ticks_position('both') + bx.yaxis.set_label_position('left') + bx.set_ylim(ylim) + bx.set_title(f'Local sea level - {location_string(site_name)} - {scen}') # Save the figure fig.tight_layout() - outfile = f'{fig_dir}07_{site_name}_rcp85.png' + outfile = f'{fig_dir}_components_{site_name}.png' plt.savefig(outfile, dpi=200, format='png') plt.close() \ No newline at end of file diff --git a/profsea/plotting_libraries.py b/profsea/plotting_libraries.py index 2db00e8..f996406 100644 --- a/profsea/plotting_libraries.py +++ b/profsea/plotting_libraries.py @@ -6,6 +6,7 @@ import numpy as np import pandas as pd import re +import itertools from config import settings @@ -126,6 +127,34 @@ def ukcp18_colours(): return rcp_colours, comp_parts_colours +def get_emulator_colors(num_scenarios, get_all=False): + colors = [ + '#031326', '#13385a', '#45587a', '#6a5e76', + '#8d616d', '#b86462', '#e37861', '#e8a077'] + + def get_equidistant_indices(num_colors, num_iterations): + if num_iterations >= num_colors: + return list(range(num_colors)) + + # Avoid first and last index if possible + if num_iterations > 1: + start = 1 + stop = num_colors - 2 + indices = np.linspace(start, stop, num=num_iterations, dtype=int) + else: + # If only one iteration, take the middle color + indices = [num_colors // 2] + + return indices + + # Return a cyclical color generator + if (num_scenarios >= len(colors)) or get_all: + return itertools.cycle(colors) + + color_idxs = get_equidistant_indices(len(colors), num_scenarios) + return itertools.cycle([colors[i] for i in color_idxs]) + + def ukcp18_labels(): """ Define sea level component labels as used in UKCP18 Marine Report @@ -140,3 +169,61 @@ def ukcp18_labels(): 'ocean': 'Ocean'} return comp_parts_labels + + +def multi_index_values(df_list): + """ + Get the values of the multi-index: years and percentile values. + :param df_list: DataFrame list of regional sea level projections + :return: years and percentile values + """ + df = df_list[0] + proj_years = np.sort(list(set(list(df.index.get_level_values('year'))))) + percentiles_all = np.sort( + [float(v) for v in list(set(list( + df.index.get_level_values('percentile'))))]) + + return proj_years, percentiles_all + + +def extract_comp_sl(df, percentiles, comp): + """ + Get the sums of all components of local sea level projections. + :param df: global or regional DataFrame of sea level projections + :param percentiles: specified percentiles + :param comp: components of sea level + :return: sum of sea level components at lower, middle and upper percentile + """ + # 5th percentile - based on UKCP18 percentile levels + rlow = df.xs(percentiles[0], level='percentile')[comp].to_numpy(copy=True) + # 50th percentile + rmid = df.xs(percentiles[4], level='percentile')[comp].to_numpy(copy=True) + # 95th percentile + rupp = df.xs(percentiles[8], level='percentile')[comp].to_numpy(copy=True) + + return rlow, rmid, rupp + + +def plot_tg_data(ax, nflag, flag, tg_years, non_missing, tg_amsl, tg_name): + """ + Plot the annual mean sea levels from the tide gauge data. + :param ax: subplot number + :param nflag: number of flagged years + :param flag: flagged data + :param tg_years: tide gauge years + :param non_missing: boolean to indicate NaN values + :param tg_amsl: annual mean sea level data + :param tg_name: tide gauge name + """ + if nflag > 0: + # There are some years with less than min_valid_fraction of flag data; + # plot these annual means as open symbols. + print(f'Tide gauge data has been flagged for attention - ' + + f'{tg_years[(flag & non_missing)]}') + ax.plot(tg_years[(flag & non_missing)], tg_amsl[(flag & non_missing)], + marker='o', mec='black', mfc='None', + markersize=3, linestyle='None', label='TG flagged') + if nflag < len(flag): + ax.plot(tg_years[(~flag & non_missing)], + tg_amsl[(~flag & non_missing)], 'ko', markersize=3, + label=f'{location_string(tg_name)} TG') diff --git a/profsea/step4_plot_regional_sealevel.py b/profsea/step4_plot_regional_sealevel.py index fc75bc1..d15112b 100644 --- a/profsea/step4_plot_regional_sealevel.py +++ b/profsea/step4_plot_regional_sealevel.py @@ -15,9 +15,11 @@ from profsea.slr_pkg import abbreviate_location_name, choose_montecarlo_dir # found in __init.py__ from profsea.surge import tide_gauge_library as tgl from profsea.directories import read_dir, makefolder -from profsea.plotting_libraries import location_string, scenario_string, \ - ukcp18_colours, ukcp18_labels, calc_xlim, calc_ylim, plot_zeroline -from emulator.plotting import plot_slr, plot_slr_components +from profsea.plotting_libraries import ( + location_string, scenario_string, ukcp18_colours, ukcp18_labels, + calc_xlim, calc_ylim, plot_zeroline, multi_index_values, extract_comp_sl, + plot_tg_data) +from profsea.emulator.plotting import plot_slr, plot_slr_components def compute_uncertainties(df_r_list, scenarios, tg_years, tg_amsl): @@ -77,39 +79,6 @@ def compute_variability(x, y, factor=1.645): return stdev * factor -def extract_comp_sl(df, percentiles, comp): - """ - Get the sums of all components of local sea level projections. - :param df: global or regional DataFrame of sea level projections - :param percentiles: specified percentiles - :param comp: components of sea level - :return: sum of sea level components at lower, middle and upper percentile - """ - # 5th percentile - based on UKCP18 percentile levels - rlow = df.xs(percentiles[0], level='percentile')[comp].to_numpy(copy=True) - # 50th percentile - rmid = df.xs(percentiles[4], level='percentile')[comp].to_numpy(copy=True) - # 95th percentile - rupp = df.xs(percentiles[8], level='percentile')[comp].to_numpy(copy=True) - - return rlow, rmid, rupp - - -def multi_index_values(df_list): - """ - Get the values of the multi-index: years and percentile values. - :param df_list: DataFrame list of regional sea level projections - :return: years and percentile values - """ - df = df_list[0] - proj_years = np.sort(list(set(list(df.index.get_level_values('year'))))) - percentiles_all = np.sort( - [float(v) for v in list(set(list( - df.index.get_level_values('percentile'))))]) - - return proj_years, percentiles_all - - def plot_figure_one(r_df_list, site_name, scenarios, fig_dir): """ Subplot 1 - Local sea level projections for all RCPs - sum total. @@ -713,31 +682,6 @@ def plot_figure_seven(g_df_list, r_df_list, site_name, scenarios, fig_dir): plt.close() -def plot_tg_data(ax, nflag, flag, tg_years, non_missing, tg_amsl, tg_name): - """ - Plot the annual mean sea levels from the tide gauge data. - :param ax: subplot number - :param nflag: number of flagged years - :param flag: flagged data - :param tg_years: tide gauge years - :param non_missing: boolean to indicate NaN values - :param tg_amsl: annual mean sea level data - :param tg_name: tide gauge name - """ - if nflag > 0: - # There are some years with less than min_valid_fraction of flag data; - # plot these annual means as open symbols. - print(f'Tide gauge data has been flagged for attention - ' + - f'{tg_years[(flag & non_missing)]}') - ax.plot(tg_years[(flag & non_missing)], tg_amsl[(flag & non_missing)], - marker='o', mec='black', mfc='None', - markersize=3, linestyle='None', label='TG flagged') - if nflag < len(flag): - ax.plot(tg_years[(~flag & non_missing)], - tg_amsl[(~flag & non_missing)], 'ko', markersize=3, - label=f'{location_string(tg_name)} TG') - - def read_G_R_sl_projections(site_name, scenarios): """ Reads in the global and regional sea level projections calculated from From 9ad790c8185131316a3714edaa9a2ecd1f165ebf Mon Sep 17 00:00:00 2001 From: mo-gregmunday Date: Mon, 8 Jul 2024 22:56:06 +0100 Subject: [PATCH 009/276] Adding ensemble input option --- profsea/emulator/__init__.py | 187 +++++++++++++++++++---------------- 1 file changed, 104 insertions(+), 83 deletions(-) diff --git a/profsea/emulator/__init__.py b/profsea/emulator/__init__.py index 65958c3..7310ed0 100644 --- a/profsea/emulator/__init__.py +++ b/profsea/emulator/__init__.py @@ -11,16 +11,14 @@ from collections.abc import Sequence import numpy as np -from scipy.stats import norm import pandas as pd -import dask.array as da -from config import settings -import matplotlib.pyplot as plt class GMSLREmulator: def __init__( self, + T_change: np.ndarray, + OHC_change: np.ndarray, scenarios: list, data_dir: str, output_dir: str, @@ -30,7 +28,7 @@ def __init__( nm: int=1000, tcv: float=1.0, glaciermip: bool=False, - ensemble: bool=False, + input_ensemble: bool=True, palmer_method: bool=False): # input -- str, path to directory containing input files. The directory should # contain files named SCENARIO_QUANTITY_STATISTIC.nc, where QUANTITY is @@ -43,8 +41,9 @@ def __init__( # output, str, optional -- path to directory in which output files are to be # written. It is created if it does not exist. No files are written if this # argument is omitted. - # ensemble -- bool, optional, default False, write output files of the ensemble - # as well as the statistics. + # ensemble -- bool, optional, default True, if provided use an input ensemble of + # temperature and ocean heat content change in place of Monte Carlo simulations + # for thermosteric sea level rise # seed -- optional, for numpy.random, default zero # nt -- int, optional, number of realisations of the input timeseries for each # scenario, default 450, to be generated using the mean and sd files; specify @@ -67,6 +66,8 @@ def __init__( # up to 2300, with the contributions to GMLSR from ice-sheet dynamics, Green- # land SMB and land water storage held at the 2100 rate beyond 2100. + self.T_change = T_change + self.OHC_change = OHC_change self.scenarios = scenarios self.data_dir = data_dir self.output_dir = output_dir @@ -76,28 +77,32 @@ def __init__( self.nm = nm self.tcv = tcv self.glaciermip = glaciermip - self.ensemble = ensemble + self.input_ensemble = input_ensemble self.palmer_method = palmer_method # First year of AR5 projections - self.endofhistory=2006 + self.endofhistory = 2006 # Last year of AR5 projections - self.endofAR5=2100 + self.endofAR5 = 2100 + + # Length of projections + self.nyr = self.end_yr - self.endofhistory # Fraction of SLE from Greenland during 1996 to 2005 assumed to result from # rapid dynamical change, with the remainder assumed to result from SMB change - self.fgreendyn=0.5 + self.fgreendyn = 0.5 # m SLE from Greenland during 1996 to 2005 according to AR5 chapter 4 - self.dgreen=(3.21-0.30)*1e-3 + self.dgreen = (3.21 - 0.30) * 1e-3 # m SLE from Antarctica during 1996 to 2005 according to AR5 chapter 4 - self.dant=(2.37+0.13)*1e-3 + self.dant = (2.37 + 0.13) * 1e-3 # Conversion factor for Gt to m SLE - self.mSLEoGt=1e12/3.61e14*1e-3 + self.mSLEoGt = 1e12 / 3.61e14 * 1e-3 + # Sensitivity of thermosteric SLR to ocean heat content change self.exp_efficiency = 0.12e-24 def project(self): @@ -106,87 +111,30 @@ def project(self): self.project_scenario(scenario) def project_scenario(self, scenario): - np.random.seed(self.seed) - - # Read the input fields for temperature and expansion into txin. txin has four - # elements if nt>0: temperature mean, temperature sd, expansion mean, expansion - # sd. txin has two elements if nt==0, for temperature and expansion, each - # having a model dimension. Check that each field is one-dimensional in time, - # that the mean and sd fields for each quantity have equal time axes, that - # temperature applies to calendar years (indicated by its time bounds), that - # expansion applies at the ends of the calendar years of temperature, that - # there is no missing data, and that the model axis (if present) is the same - # for the two quantities. - variable = ['temperature','ocean_heat_content_change'] # input quantities - - txin = [] - for v in variable: - file = glob.glob(os.path.join(self.data_dir, f'*{scenario}*_{v}*.csv')) - if file: - df = pd.read_csv(file[0]) - df_2300 = df.loc[:, str(self.endofhistory + 1):str(self.end_yr)] - central_estimate = np.percentile(df_2300.to_numpy(), 50, axis=0) - std = np.std(df_2300.to_numpy(), axis=0) - - if v == 'ocean_heat_content_change': - central_estimate = central_estimate * self.exp_efficiency # convert to thermal expansion - std = np.std(df_2300.to_numpy() * self.exp_efficiency, axis=0) - - txin.extend([central_estimate, std]) - else: - raise FileNotFoundError(f'Emulator input file {file} not found') - - nyr=txin[0].shape[0] # number of years in the timeseries - - # Integrate temperature to obtain K yr at ends of calendar years, replacing - # the time-axis of temperature (which applies at mid-year) with the time-axis - # of expansion (which applies at year-end) - - itin = [np.cumsum(txin[i]) for i in range(2)] - # Generate a sample of perfectly correlated timeseries fields of temperature, - # time-integral temperature and expansion, each of them [realisation,time] - z = np.random.standard_normal(self.nt)*self.tcv + tas, therm = self.read_input(scenario) - # For each quantity, mean + standard deviation * normal random number - # reshape to [realisation,time] - zt = z[:, np.newaxis] * txin[1] + txin[0] - zx = z[:, np.newaxis] * txin[3] + txin[2] - zit = z[:, np.newaxis] * itin[1] + itin[0] - zitmean = itin[0] + zt, zx, zit, zit_mean = self.calculate_drivers() - # Create a cf.Field with the shape of the quantities to be calculated + # Create a field with the shape of the quantities to be calculated # [component_realization,climate_realization,time] - template=np.full([self.nm, self.nt, nyr], np.nan) - + template=np.full([self.nm, self.nt, self.nyr], np.nan) - # Obtain ensembles of projected components as cf.Field objects and add them up - # Report the range of the final year and write output files if requested expansion = np.tile(zx, (self.nm, 1)) + fraction = np.random.rand(self.nm * self.nt) # correlation between antsmb and antdyn - - glacier = self.project_glacier(zitmean, zit, template) - + glacier = self.project_glacier(zit_mean, zit, template) greensmb = self.project_greensmb(zt,template) - - fraction = np.random.rand(self.nm * self.nt) # correlation between antsmb and antdyn antsmb = self.project_antsmb(zit,template,fraction) - - greendyn = self.project_greendyn(scenario,template) - greennet = greensmb+greendyn - antdyn = self.project_antdyn(template, fraction) - antnet = antsmb+antdyn - landwater = self.project_landwater(template) - - # add expansion last because it has a lower dimensionality and we want it to - # be broadcast to the same shape as the others rather than messing up gmslr - gmslr=glacier+greennet+antnet+landwater+expansion - - sheetdyn=greendyn+antdyn + + greennet = greensmb + greendyn + antnet = antsmb + antdyn + sheetdyn = greendyn + antdyn + gmslr = glacier + greennet + antnet + landwater + expansion components_dict = { 'exp': expansion, @@ -205,7 +153,80 @@ def project_scenario(self, scenario): for name, component in components_dict.items(): np.save( os.path.join(self.output_dir, f'{scenario}_{name}.npy'), - component.T) + component.T + ) + + def read_input(self, scenario: str): # TAKE THIS OUT AND INTO PROFSEA BEFORE CALL + # Read in the input fields + variable = ['temperature','ocean_heat_content_change'] # input quantities + txin = [] + tas = glob.glob(os.path.join(self.data_dir, f'*{scenario}*_temperature*.csv')) + ohc = glob.glob(os.path.join(self.data_dir, f'*{scenario}*_ocean_heat_content_change*.csv')) + if tas and ohc: + tas = pd.read_csv(tas[0]) + tas = tas.loc[:, str(self.endofhistory + 1):str(self.end_yr)] + ohc = pd.read_csv(ohc[0]) + ohc = ohc.loc[:, str(self.endofhistory + 1):str(self.end_yr)] + + if self.input_ensemble: + txin.extend([tas.to_numpy(), np.std(tas.to_numpy(), axis=0)]) + txin.extend([np.percentile(ohc.to_numpy(), 50, axis=0), np.std(ohc.to_numpy(), axis=0)]) + else: + raise FileNotFoundError(f'Emulator input file(s) {tas}, or {ohc} not found') + # for v in variable: + # file = glob.glob(os.path.join(self.data_dir, f'*{scenario}*_{v}*.csv')) + # if file: + # df = pd.read_csv(file[0]) + # df = df.loc[:, str(self.endofhistory + 1):str(self.end_yr)] + + # central_estimate = np.percentile(df.to_numpy(), 50, axis=0) + # std = np.std(df.to_numpy(), axis=0) + + # if v == 'ocean_heat_content_change': + # central_estimate = central_estimate * self.exp_efficiency # convert to thermal expansion + # std = np.std(df.to_numpy() * self.exp_efficiency, axis=0) + + # txin.extend([central_estimate, std]) + # else: + # raise FileNotFoundError(f'Emulator input file {file} not found') + + def calculate_drivers(self): + # Check if dimensions are the right way around + if self.T_change.shape[1] != self.nyr: + self.T_change = self.T_change.T + if self.OHC_change.shape[1] != self.nyr: + self.OHC_change = self.OHC_change.T + + if self.input_ensemble: + print(self.T_change.shape) + therm_ens = self.OHC_change * self.exp_efficiency + T_int_ens = np.cumsum(self.T_change, axis=1) + T_int_med = np.percentile(T_int_ens, 50, axis=1) # using median here instead of mean... CHECK THIS + + return self.T_change, therm_ens, T_int_ens, T_int_med + + T_med = np.percentile(self.T_change, 50, axis=0) + T_std = np.std(self.T_change, axis=0) + + therm_med = np.percentile(self.OHC_change, 50, axis=0) * self.exp_efficiency + therm_std = np.std(self.OHC_change * self.exp_efficiency, axis=0) + + # Time-integral of temperature anomaly + T_med_int = np.cumsum(T_med) + T_std_int = np.cumsum(T_std) + + # Generate a sample of perfectly correlated timeseries fields of temperature, + # time-integral temperature and expansion, each of them [realisation,time] + z = np.random.standard_normal(self.nt) * self.tcv + + # For each quantity, mean + standard deviation * normal random number + # reshape to [realisation,time] + T_ens = z[:, np.newaxis] * T_std + T_med + therm_ens = z[:, np.newaxis] * therm_std + therm_med + T_int_ens = z[:, np.newaxis] * T_std_int + T_med_int + + return T_ens, therm_ens, T_int_ens, T_med_int + def project_glacier(self, it, zit, glacier): # Return projection of glacier contribution as a cf.Field From 6fac8d6a1a58925d2ada61332d17c92a7e0bbf27 Mon Sep 17 00:00:00 2001 From: mo-gregmunday Date: Tue, 9 Jul 2024 17:16:18 +0100 Subject: [PATCH 010/276] Generalising and refactoring --- profsea/emulator/__init__.py | 126 ++++++------------ ...3_process_regional_sealevel_projections.py | 53 ++++++-- 2 files changed, 81 insertions(+), 98 deletions(-) diff --git a/profsea/emulator/__init__.py b/profsea/emulator/__init__.py index 7310ed0..c8486e7 100644 --- a/profsea/emulator/__init__.py +++ b/profsea/emulator/__init__.py @@ -19,8 +19,7 @@ def __init__( self, T_change: np.ndarray, OHC_change: np.ndarray, - scenarios: list, - data_dir: str, + scenario: str, output_dir: str, end_yr: int, seed: int=0, @@ -68,8 +67,7 @@ def __init__( self.T_change = T_change self.OHC_change = OHC_change - self.scenarios = scenarios - self.data_dir = data_dir + self.scenario = scenario self.output_dir = output_dir self.end_yr = end_yr self.seed = seed @@ -104,16 +102,12 @@ def __init__( # Sensitivity of thermosteric SLR to ocean heat content change self.exp_efficiency = 0.12e-24 - - def project(self): - for scenario in self.scenarios: - print(f'Projecting {scenario}...') - self.project_scenario(scenario) - def project_scenario(self, scenario): - np.random.seed(self.seed) + if input_ensemble: + self.nt = self.T_change.shape[0] - tas, therm = self.read_input(scenario) + def project(self): + np.random.seed(self.seed) zt, zx, zit, zit_mean = self.calculate_drivers() @@ -121,88 +115,53 @@ def project_scenario(self, scenario): # [component_realization,climate_realization,time] template=np.full([self.nm, self.nt, self.nyr], np.nan) - expansion = np.tile(zx, (self.nm, 1)) + self.expansion = np.tile(zx, (self.nm, 1)) fraction = np.random.rand(self.nm * self.nt) # correlation between antsmb and antdyn - glacier = self.project_glacier(zit_mean, zit, template) - greensmb = self.project_greensmb(zt,template) - antsmb = self.project_antsmb(zit,template,fraction) - greendyn = self.project_greendyn(scenario,template) - antdyn = self.project_antdyn(template, fraction) - landwater = self.project_landwater(template) + self.glacier = self.project_glacier(zit_mean, zit, template) + self.greensmb = self.project_greensmb(zt,template) + self.antsmb = self.project_antsmb(zit,template,fraction) + self.greendyn = self.project_greendyn(template) + self.antdyn = self.project_antdyn(template, fraction) + self.landwater = self.project_landwater(template) - greennet = greensmb + greendyn - antnet = antsmb + antdyn - sheetdyn = greendyn + antdyn - gmslr = glacier + greennet + antnet + landwater + expansion + self.greennet = self.greensmb + self.greendyn + self.antnet = self.antsmb + self.antdyn + self.sheetdyn = self.greendyn + self.antdyn + self.gmslr = self.glacier + self.greennet + self.antnet + self.landwater + self.expansion components_dict = { - 'exp': expansion, - 'glacier': glacier, - 'greensmb': greensmb, - 'greendyn': greendyn, - 'greennet': greennet, - 'antsmb': antsmb, - 'antdyn': antdyn, - 'antnet': antnet, - 'landwater': landwater, - 'sheetdyn': sheetdyn, - 'gmslr': gmslr + 'exp': self.expansion, + 'glacier': self.glacier, + 'greensmb': self.greensmb, + 'greendyn': self.greendyn, + 'greennet': self.greennet, + 'antsmb': self.antsmb, + 'antdyn': self.antdyn, + 'antnet': self.antnet, + 'landwater': self.landwater, + 'sheetdyn': self.sheetdyn, + 'gmslr': self.gmslr } for name, component in components_dict.items(): np.save( - os.path.join(self.output_dir, f'{scenario}_{name}.npy'), + os.path.join(self.output_dir, f'{self.scenario}_{name}.npy'), component.T ) - - def read_input(self, scenario: str): # TAKE THIS OUT AND INTO PROFSEA BEFORE CALL - # Read in the input fields - variable = ['temperature','ocean_heat_content_change'] # input quantities - txin = [] - tas = glob.glob(os.path.join(self.data_dir, f'*{scenario}*_temperature*.csv')) - ohc = glob.glob(os.path.join(self.data_dir, f'*{scenario}*_ocean_heat_content_change*.csv')) - if tas and ohc: - tas = pd.read_csv(tas[0]) - tas = tas.loc[:, str(self.endofhistory + 1):str(self.end_yr)] - ohc = pd.read_csv(ohc[0]) - ohc = ohc.loc[:, str(self.endofhistory + 1):str(self.end_yr)] - - if self.input_ensemble: - txin.extend([tas.to_numpy(), np.std(tas.to_numpy(), axis=0)]) - txin.extend([np.percentile(ohc.to_numpy(), 50, axis=0), np.std(ohc.to_numpy(), axis=0)]) - else: - raise FileNotFoundError(f'Emulator input file(s) {tas}, or {ohc} not found') - # for v in variable: - # file = glob.glob(os.path.join(self.data_dir, f'*{scenario}*_{v}*.csv')) - # if file: - # df = pd.read_csv(file[0]) - # df = df.loc[:, str(self.endofhistory + 1):str(self.end_yr)] - - # central_estimate = np.percentile(df.to_numpy(), 50, axis=0) - # std = np.std(df.to_numpy(), axis=0) - - # if v == 'ocean_heat_content_change': - # central_estimate = central_estimate * self.exp_efficiency # convert to thermal expansion - # std = np.std(df.to_numpy() * self.exp_efficiency, axis=0) - - # txin.extend([central_estimate, std]) - # else: - # raise FileNotFoundError(f'Emulator input file {file} not found') def calculate_drivers(self): - # Check if dimensions are the right way around - if self.T_change.shape[1] != self.nyr: - self.T_change = self.T_change.T - if self.OHC_change.shape[1] != self.nyr: - self.OHC_change = self.OHC_change.T - if self.input_ensemble: - print(self.T_change.shape) + # Check if dimensions are the right way around + if self.T_change.shape[1] != self.nyr: + self.T_change = self.T_change.T + if self.OHC_change.shape[1] != self.nyr: + self.OHC_change = self.OHC_change.T + therm_ens = self.OHC_change * self.exp_efficiency T_int_ens = np.cumsum(self.T_change, axis=1) - T_int_med = np.percentile(T_int_ens, 50, axis=1) # using median here instead of mean... CHECK THIS - + T_int_med = np.percentile(T_int_ens, 50, axis=0) # using median here instead of mean... CHECK THIS + return self.T_change, therm_ens, T_int_ens, T_int_med T_med = np.percentile(self.T_change, 50, axis=0) @@ -240,7 +199,6 @@ def project_glacier(self, it, zit, glacier): glmass=412.0-96.3 # initial glacier mass, used to set a limit, from Tab 4.2 glmass=1e-3*glmass # m SLE - nr=glacier.shape[0] if self.glaciermip: if self.glaciermip==1: glparm=[dict(name='SLA2012',factor=3.39,exponent=0.722,cvgl=0.15),\ @@ -267,12 +225,12 @@ def project_glacier(self, it, zit, glacier): cvgl=0.20 # random methodological error ngl=len(glparm) # number of glacier methods - if nr%ngl: + if self.nm%ngl: raise ValueError('number of realisations '+\ 'must be a multiple of number of glacier methods') - nrpergl=int(nr/ngl) # number of realisations per glacier method - r = np.random.standard_normal(nr) + nrpergl=int(self.nm/ngl) # number of realisations per glacier method + r = np.random.standard_normal(self.nm) r = r[:, np.newaxis, np.newaxis] # Make an ensemble of projections for each method @@ -373,7 +331,7 @@ def project_antsmb(self, zit, template, fraction=None): return antsmb - def project_greendyn(self, scenario, template): + def project_greendyn(self, template): # Return projection of Greenland rapid ice-sheet dynamics contribution # as a cf.Field # scenario -- str, name of scenario @@ -382,7 +340,7 @@ def project_greendyn(self, scenario, template): # For SMB+dyn during 2005-2010 Table 4.6 gives 0.63+-0.17 mm yr-1 (5-95% range) # For dyn at 2100 Chapter 13 gives [20,85] mm for rcp85, [14,63] mm otherwise - if scenario in ['rcp85','ssp585']: + if self.scenario in ['rcp85','ssp585']: finalrange=[0.020,0.085] else: finalrange=[0.014,0.063] diff --git a/profsea/step3_process_regional_sealevel_projections.py b/profsea/step3_process_regional_sealevel_projections.py index 15486b0..c9e0012 100644 --- a/profsea/step3_process_regional_sealevel_projections.py +++ b/profsea/step3_process_regional_sealevel_projections.py @@ -5,6 +5,7 @@ import os import iris +import glob import pickle import numpy as np import pandas as pd @@ -66,14 +67,20 @@ def calc_future_sea_level_at_site(df, site_loc, scenario): # calculated separately. components = ['exp', 'antdyn', 'antsmb', 'greendyn', 'greensmb', 'glacier', 'landwater'] - # nesm, nyrs, yrs = get_projection_info(mcdir, scenario) - nesm = 450000 - yrs = np.arange(2007, settings["projection_end_year"] + 1) - nyrs = yrs.size + + # Select dimensions from sample file, [time, realisation] + sample = np.load(os.path.join(mcdir, f'{scenario}_exp.npy')) + nesm = sample.shape[1] + nyrs = sample.shape[0] + yrs = np.arange(2007, 2007 + nyrs) # Determine the number of samples you wish to make CHOGOM # project = 100000, UKCP18 = 200000 - nsmps = 200000 + if nesm >= 200000: + nsmps = 200000 + else: + nsmps = nesm + array_dims = [nesm, nsmps, nyrs] # Get random samples of global and regional sea level components @@ -514,6 +521,18 @@ def setup_FP_interpolators(components, sci_method): return nFPs, FPlist +def read_csv_file(file_pattern: str, start_yr: int=2007, + end_yr: int=settings["projection_end_year"]): + file = glob.glob( + os.path.join( + settings["emulator_settings"]["emulator_input_dir"], + file_pattern)) + if not file: + raise FileNotFoundError(f'File {file_pattern} not found') + df = pd.read_csv(file[0]) + return df.loc[:, str(start_yr):str(end_yr)].to_numpy() + + def main(): """ Reads in and calculates global and local (regional) sea level change @@ -552,17 +571,23 @@ def main(): palmer_method = False makefolder(os.path.join(settings["baseoutdir"], 'emulator_output')) - - gmslr = GMSLREmulator( - settings["emulator_settings"]["emulator_scenario"], - settings["emulator_settings"]["emulator_input_dir"], - os.path.join(settings["baseoutdir"], 'emulator_output'), - settings["projection_end_year"], - palmer_method=palmer_method - ) - gmslr.project() + # Get the metadata of either the site location or tide gauge location for scenario in settings["emulator_settings"]["emulator_scenario"]: + print(f'Projecting {scenario}...') + T_change = read_csv_file(f'*{scenario}*_temperature*.csv') + OHC_change = read_csv_file(f'*{scenario}*_ocean_heat_content_change*.csv') + + gmslr = GMSLREmulator( + T_change, + OHC_change, + scenario, + os.path.join(settings["baseoutdir"], 'emulator_output'), + settings["projection_end_year"], + palmer_method=palmer_method, + input_ensemble=settings["emulator_settings"]["use_input_ensemble"] + ) + gmslr.project() for loc_name in df_site_data.index.values: calc_future_sea_level_at_site(df_site_data, loc_name, scenario) else: From 70836ceb88bd417443eae9d5352f0c8cbe3a9358 Mon Sep 17 00:00:00 2001 From: mo-gregmunday Date: Wed, 10 Jul 2024 12:18:15 +0100 Subject: [PATCH 011/276] Speeding it all up --- profsea/emulator/__init__.py | 204 +++++++++--------- ...3_process_regional_sealevel_projections.py | 10 + 2 files changed, 112 insertions(+), 102 deletions(-) diff --git a/profsea/emulator/__init__.py b/profsea/emulator/__init__.py index c8486e7..8504dbe 100644 --- a/profsea/emulator/__init__.py +++ b/profsea/emulator/__init__.py @@ -8,7 +8,9 @@ # code where possible. import os, os.path import glob +import time from collections.abc import Sequence +import concurrent import numpy as np import pandas as pd @@ -29,17 +31,6 @@ def __init__( glaciermip: bool=False, input_ensemble: bool=True, palmer_method: bool=False): - # input -- str, path to directory containing input files. The directory should - # contain files named SCENARIO_QUANTITY_STATISTIC.nc, where QUANTITY is - # temperature or expansion, and STATISTIC is mean, sd or models. Each file - # contains one field with a dimension of time. The mean and sd fields have - # no other dimension and are used if nt>0 (default), while the models fields - # have a model dimension and are used if nt==0. - # scenarios -- str or sequence of str, scenarios for which projections are to be - # made, by default all those represented in the input directory - # output, str, optional -- path to directory in which output files are to be - # written. It is created if it does not exist. No files are written if this - # argument is omitted. # ensemble -- bool, optional, default True, if provided use an input ensemble of # temperature and ocean heat content change in place of Monte Carlo simulations # for thermosteric sea level rise @@ -105,31 +96,8 @@ def __init__( if input_ensemble: self.nt = self.T_change.shape[0] - - def project(self): - np.random.seed(self.seed) - - zt, zx, zit, zit_mean = self.calculate_drivers() - - # Create a field with the shape of the quantities to be calculated - # [component_realization,climate_realization,time] - template=np.full([self.nm, self.nt, self.nyr], np.nan) - - self.expansion = np.tile(zx, (self.nm, 1)) - fraction = np.random.rand(self.nm * self.nt) # correlation between antsmb and antdyn - - self.glacier = self.project_glacier(zit_mean, zit, template) - self.greensmb = self.project_greensmb(zt,template) - self.antsmb = self.project_antsmb(zit,template,fraction) - self.greendyn = self.project_greendyn(template) - self.antdyn = self.project_antdyn(template, fraction) - self.landwater = self.project_landwater(template) - - self.greennet = self.greensmb + self.greendyn - self.antnet = self.antsmb + self.antdyn - self.sheetdyn = self.greendyn + self.antdyn - self.gmslr = self.glacier + self.greennet + self.antnet + self.landwater + self.expansion - + + def get_components(self): components_dict = { 'exp': self.expansion, 'glacier': self.glacier, @@ -143,12 +111,46 @@ def project(self): 'sheetdyn': self.sheetdyn, 'gmslr': self.gmslr } + return components_dict + + def project(self): + np.random.seed(self.seed) + zt, zx, zit, zit_mean = self.calculate_drivers() - for name, component in components_dict.items(): - np.save( - os.path.join(self.output_dir, f'{self.scenario}_{name}.npy'), - component.T - ) + self.expansion = np.tile(zx, (self.nm, 1)) + fraction = np.random.rand(self.nm * self.nt) # correlation between antsmb and antdyn + + self.run_parallel_projections(zit_mean, zit, zt, fraction) + + self.greennet = self.greensmb + self.greendyn + self.antnet = self.antsmb + self.antdyn + self.sheetdyn = self.greendyn + self.antdyn + self.gmslr = self.glacier + self.greennet + self.antnet + self.landwater + self.expansion + + def run_parallel_projections(self, zit_mean, zit, zt, fraction): + with concurrent.futures.ThreadPoolExecutor() as executor: + futures = { + executor.submit(self.project_glacier, zit_mean, zit): 'glacier', + executor.submit(self.project_greensmb, zt): 'greensmb', + executor.submit(self.project_antsmb, zit, fraction): 'antsmb', + executor.submit(self.project_greendyn): 'greendyn', + executor.submit(self.project_antdyn, fraction): 'antdyn', + executor.submit(self.project_landwater): 'landwater' + } + results = {} + for future in concurrent.futures.as_completed(futures): + key = futures[future] + try: + results[key] = future.result() + except Exception as e: + print(f"{key} generated an exception: {e}") + + self.glacier = results['glacier'] + self.greensmb = results['greensmb'] + self.antsmb = results['antsmb'] + self.greendyn = results['greendyn'] + self.antdyn = results['antdyn'] + self.landwater = results['landwater'] def calculate_drivers(self): if self.input_ensemble: @@ -187,7 +189,7 @@ def calculate_drivers(self): return T_ens, therm_ens, T_int_ens, T_med_int - def project_glacier(self, it, zit, glacier): + def project_glacier(self, it, zit): # Return projection of glacier contribution as a cf.Field # it -- cf.Field, time-integral of median temperature anomaly timeseries # zit -- cf.Field, ensemble of time-integral temperature anomaly timeseries @@ -198,6 +200,8 @@ def project_glacier(self, it, zit, glacier): dmz=dmzdtref*(self.endofhistory-1996)*1e-3 # m from glacier at start wrt AR5 ref period glmass=412.0-96.3 # initial glacier mass, used to set a limit, from Tab 4.2 glmass=1e-3*glmass # m SLE + + glacier = np.full((self.nm, self.nt, self.nyr), np.nan) if self.glaciermip: if self.glaciermip==1: @@ -228,26 +232,29 @@ def project_glacier(self, it, zit, glacier): if self.nm%ngl: raise ValueError('number of realisations '+\ 'must be a multiple of number of glacier methods') - - nrpergl=int(self.nm/ngl) # number of realisations per glacier method + + nrpergl = self.nm // ngl r = np.random.standard_normal(self.nm) r = r[:, np.newaxis, np.newaxis] + + # Precompute mgl and zgl for all glacier methods + mgl_all = np.array([self._project_glacier1(it, glparm[igl]['factor'], glparm[igl]['exponent']) for igl in range(ngl)]) + zgl_all = np.array([self._project_glacier1(zit, glparm[igl]['factor'], glparm[igl]['exponent']) for igl in range(ngl)]) + cvgl_all = np.array([glparm[igl]['cvgl'] if self.glaciermip else cvgl for igl in range(ngl)]) # Make an ensemble of projections for each method for igl in range(ngl): - # glacier projection for this method using the median temperature timeseries - mgl = self._project_glacier1(it, glparm[igl]['factor'], glparm[igl]['exponent']) + mgl = mgl_all[igl] + zgl = zgl_all[igl] + cvgl = cvgl_all[igl] - # glacier projections for this method with the ensemble of timeseries - zgl = self._project_glacier1(zit, glparm[igl]['factor'], glparm[igl]['exponent']) - ifirst = igl * nrpergl ilast = ifirst + nrpergl - if self.glaciermip: cvgl = glparm[igl]['cvgl'] - glacier[ifirst:ilast,...] = zgl + (mgl * r[ifirst:ilast] * cvgl) + + glacier[ifirst:ilast, ...] = zgl + (mgl * r[ifirst:ilast] * cvgl) glacier += dmz - glacier = np.where(glacier > glmass, glmass, glacier) + np.clip(glacier, None, glmass, out=glacier) glacier = glacier.reshape(glacier.shape[0]*glacier.shape[1], glacier.shape[2]) @@ -258,31 +265,31 @@ def _project_glacier1(self, it, factor, exponent): scale=1e-3 # mm to m return scale * factor * (np.where(it<0, 0, it)**exponent) - def project_greensmb(self, zt, template): + def project_greensmb(self, zt): # Return projection of Greenland SMB contribution as a cf.Field # zt -- cf.Field, ensemble of temperature anomaly timeseries # template -- cf.Field with the required shape of the output - dtgreen=-0.146 # Delta_T of Greenland ref period wrt AR5 ref period - fnlogsd=0.4 # random methodological error of the log factor - febound=[1,1.15] # bounds of uniform pdf of SMB elevation feedback factor + dtgreen = -0.146 # Delta_T of Greenland ref period wrt AR5 ref period + fnlogsd = 0.4 # random methodological error of the log factor + febound = [1, 1.15] # bounds of uniform pdf of SMB elevation feedback factor - nr = template.shape[0] # random log-normal factor - fn = np.exp(np.random.standard_normal(nr)*fnlogsd) + fn = np.exp(np.random.standard_normal(self.nm)*fnlogsd) # elevation feedback factor - fe = np.random.sample(nr)*(febound[1]-febound[0])+febound[0] + fe = np.random.sample(self.nm) * (febound[1] - febound[0]) + febound[0] ff = fn * fe ztgreen = zt - dtgreen - greensmbrate = ff[:, np.newaxis, np.newaxis] * self._fettweis(ztgreen) + + greensmb = ff[:, np.newaxis, np.newaxis] * self._fettweis(ztgreen) if self.palmer_method and self.end_yr > self.endofAR5: - greensmbrate[:, :, 95:] = greensmbrate[:, :, 94:95] + greensmb[:, :, 95:] = greensmb[:, :, 94:95] - greensmb = np.cumsum(greensmbrate, axis=-1) + greensmb = np.cumsum(greensmb, axis=-1) - np.add(greensmb, (1 - self.fgreendyn) * self.dgreen, out=greensmb) + greensmb += (1 - self.fgreendyn) * self.dgreen greensmb = greensmb.reshape(greensmb.shape[0]*greensmb.shape[1], greensmb.shape[2]) @@ -293,14 +300,12 @@ def _fettweis(self, ztgreen): # using Eq 2 of Fettweis et al. (2013) return (71.5*ztgreen+20.4*(ztgreen**2)+2.8*(ztgreen**3))*self.mSLEoGt - def project_antsmb(self, zit, template, fraction=None): + def project_antsmb(self, zit, fraction=None): # Return projection of Antarctic SMB contribution as a cf.Field # zit -- cf.Field, ensemble of time-integral temperature anomaly timeseries # template -- cf.Field with the required shape of the output # fraction -- array-like, random numbers for the SMB-dynamic feedback - nr=template.shape[0] - nt=template.shape[1] # antsmb=template.copy() # nr,nt,nyr=antsmb.shape @@ -309,29 +314,29 @@ def project_antsmb(self, zit, template, fraction=None): KoKg=[1.1,0.2] # ratio of Antarctic warming to global warming from G&H06 # Generate a distribution of products of the above two factors - pcoKg=(pcoK[0]+np.random.standard_normal([nr,nt])*pcoK[1])*\ - (KoKg[0]+np.random.standard_normal([nr,nt])*KoKg[1]) - meansmb=1923 # model-mean time-mean 1979-2010 Gt yr-1 from 13.3.3.2 - moaoKg=-pcoKg*1e-2*meansmb*self.mSLEoGt # m yr-1 of SLE per K of global warming + pcoKg = (pcoK[0] + np.random.standard_normal([self.nm, self.nt]) * pcoK[1]) * \ + (KoKg[0] + np.random.standard_normal([self.nm, self.nt]) * KoKg[1]) + meansmb = 1923 # model-mean time-mean 1979-2010 Gt yr-1 from 13.3.3.2 + moaoKg = -pcoKg * 1e-2 * meansmb * self.mSLEoGt # m yr-1 of SLE per K of global warming if fraction is None: - fraction=np.random.rand(nr,nt) - elif fraction.size!=nr*nt: + fraction = np.random.rand(self.nm, self.nt) + elif fraction.size != self.nm * self.nt: raise ValueError('fraction is the wrong size') else: - fraction.shape=(nr,nt) + fraction.shape = (self.nm, self.nt) - smax=0.35 # max value of S in 13.SM.1.5 - ainterfactor=1-fraction*smax + smax = 0.35 # max value of S in 13.SM.1.5 + ainterfactor = 1 - fraction * smax z = moaoKg * ainterfactor z = z[:, :, np.newaxis] antsmb = z * zit - antsmb = antsmb.reshape(antsmb.shape[0]*antsmb.shape[1], antsmb.shape[2]) + antsmb = antsmb.reshape(antsmb.shape[0] * antsmb.shape[1], antsmb.shape[2]) return antsmb - def project_greendyn(self, template): + def project_greendyn(self): # Return projection of Greenland rapid ice-sheet dynamics contribution # as a cf.Field # scenario -- str, name of scenario @@ -346,9 +351,9 @@ def project_greendyn(self, template): finalrange=[0.014,0.063] return self.time_projection( 0.63*self.fgreendyn, 0.17*self.fgreendyn, - finalrange, template) + self.fgreendyn*self.dgreen + finalrange) + self.fgreendyn*self.dgreen - def project_antdyn(self, template, fraction=None): + def project_antdyn(self, fraction=None): # Return projection of Antarctic rapid ice-sheet dynamics contribution # as a cf.Field # template -- cf.Field with the required shape of the output @@ -360,22 +365,20 @@ def project_antdyn(self, template, fraction=None): # For SMB+dyn during 2005-2010 Table 4.6 gives 0.41+-0.24 mm yr-1 (5-95% range) # For dyn at 2100 Chapter 13 gives [-20,185] mm for all scenarios - return self.time_projection( - 0.41, 0.20, final, template, fraction=fraction) + self.dant + return self.time_projection(0.41, 0.20, final, fraction=fraction) + self.dant - def project_landwater(self, template): + def project_landwater(self): # Return projection of land water storage contribution as a cf.Field # The rate at start is the one for 1993-2010 from the budget table. # The final amount is the mean for 2081-2100. nyr=2100-2081+1 # number of years of the time-mean of the final amount - return self.time_projection(0.38, 0.49-0.38, [-0.01,0.09], - template, nfinal=nyr) + return self.time_projection(0.38, 0.49-0.38, [-0.01,0.09], nfinal=nyr) def time_projection( self, startratemean, startratepm, final, - template, nfinal=1, fraction=None): + nfinal=1, fraction=None): # Return projection of a quantity which is a quadratic function of time # in a cf.Field. # startratemean, startratepm -- rate of GMSLR at the start in mm yr-1, whose @@ -392,31 +395,28 @@ def time_projection( # Create a field of elapsed time since start in years timeendofAR5 = self.endofAR5 - self.endofhistory + 1 time = np.arange(self.end_yr - self.endofhistory) + 1 - - # more general than nr,nt,nyr=template.shape - nr, nt, nyr = template.shape if fraction is None: - fraction=np.random.rand(nr,nt) - elif fraction.size!=nr*nt: + fraction = np.random.rand(self.nm, self.nt) + elif fraction.size != self.nm * self.nt: raise ValueError('fraction is the wrong size') - fraction = fraction.reshape(nr,nt) + fraction = fraction.reshape(self.nm, self.nt) # Convert inputs to startrate (m yr-1) and afinal (m), where both are # arrays with the size of fraction - momm=1e-3 # convert mm yr-1 to m yr-1 - startrate=(startratemean+\ - startratepm*np.array([-1,1],dtype=float))*momm - finalisrange=isinstance(final,Sequence) + momm = 1e-3 # convert mm yr-1 to m yr-1 + startrate = (startratemean + \ + startratepm * np.array([-1,1],dtype=float)) * momm + finalisrange = isinstance(final, Sequence) if finalisrange: - if len(final)!=2: + if len(final) != 2: raise ValueError('final range is the wrong size') - afinal=(1-fraction)*final[0]+fraction*final[1] + afinal = (1 - fraction) * final[0] + fraction * final[1] else: - if final.shape!=fraction.shape: + if final.shape != fraction.shape: raise ValueError('final array is the wrong shape') afinal = final - startrate=(1-fraction)*startrate[0]+fraction*startrate[1] + startrate = (1 - fraction) * startrate[0] + fraction * startrate[1] # For terms where the rate increases linearly in time t, we can write GMSLR as # S(t) = a*t**2 + b*t @@ -425,9 +425,9 @@ def time_projection( # If nfinal=1, the following two lines are equivalent to # halfacc=(final-startyr*nyr)/nyr**2 finalyr = np.arange(nfinal) - nfinal + 94 + 1 # last element ==nyr - halfacc=(afinal-startrate*finalyr.mean())/(finalyr**2).mean() - quadratic=halfacc[:, :, np.newaxis]*(time**2) - linear=startrate[:, :, np.newaxis]*time + halfacc = (afinal - startrate * finalyr.mean()) / (finalyr**2).mean() + quadratic = halfacc[:, :, np.newaxis] * (time**2) + linear = startrate[:, :, np.newaxis] * time # If acceleration ceases for t>t0, the rate is 2*a*t0+b thereafter, so # S(t) = a*t0**2 + b*t0 + (2*a*t0+b)*(t-t0) diff --git a/profsea/step3_process_regional_sealevel_projections.py b/profsea/step3_process_regional_sealevel_projections.py index c9e0012..e090657 100644 --- a/profsea/step3_process_regional_sealevel_projections.py +++ b/profsea/step3_process_regional_sealevel_projections.py @@ -588,6 +588,16 @@ def main(): input_ensemble=settings["emulator_settings"]["use_input_ensemble"] ) gmslr.project() + components_dict = gmslr.get_components() + print('Saving components...') + for name, component in components_dict.items(): + np.save( + os.path.join( + os.path.join(settings["baseoutdir"], 'emulator_output'), + f'{scenario}_{name}.npy'), + component.T # Transpose to match the shape of original Monte Carlo simulations + ) + print('Saved!\n') for loc_name in df_site_data.index.values: calc_future_sea_level_at_site(df_site_data, loc_name, scenario) else: From b54da56b188def23f5c497a16b81b636beb1976f Mon Sep 17 00:00:00 2001 From: mo-gregmunday Date: Wed, 10 Jul 2024 21:25:58 +0100 Subject: [PATCH 012/276] Tweaks --- profsea/emulator/__init__.py | 32 ++++++++++++++++++++++---------- profsea/emulator/plotting.py | 6 +++--- 2 files changed, 25 insertions(+), 13 deletions(-) diff --git a/profsea/emulator/__init__.py b/profsea/emulator/__init__.py index 8504dbe..f8377e5 100644 --- a/profsea/emulator/__init__.py +++ b/profsea/emulator/__init__.py @@ -6,14 +6,10 @@ # Calculate Monte Carlo projections of GMSLR using methods # from Jonathon Gregory and AR5. Staying close to JG's original # code where possible. -import os, os.path -import glob -import time from collections.abc import Sequence import concurrent import numpy as np -import pandas as pd class GMSLREmulator: @@ -360,7 +356,20 @@ def project_antdyn(self, fraction=None): # fraction -- array-like, random numbers for the dynamic contribution # levermann -- optional, str, use Levermann fit for specified scenario - final=[-0.020,0.185] + + # lcoeff=dict(rcp26=[-2.881, 0.923, 0.000],\ + # rcp45=[-2.676, 0.850, 0.000],\ + # rcp60=[-2.660, 0.870, 0.000],\ + # rcp85=[-2.399, 0.860, 0.000]) + # lcoeff = lcoeff['rcp85'] + + # from scipy.stats import norm + # ascale=norm.ppf(fraction) + # final=np.exp(lcoeff[2]*ascale**2+lcoeff[1]*ascale+lcoeff[0]) + # final = final.reshape(self.nm, self.nt) + + # final=[-0.020, 0.185] + final = [0.06, 0.49] # AR6, SSP2-4.5 # For SMB+dyn during 2005-2010 Table 4.6 gives 0.41+-0.24 mm yr-1 (5-95% range) # For dyn at 2100 Chapter 13 gives [-20,185] mm for all scenarios @@ -373,8 +382,9 @@ def project_landwater(self): # The rate at start is the one for 1993-2010 from the budget table. # The final amount is the mean for 2081-2100. nyr=2100-2081+1 # number of years of the time-mean of the final amount - - return self.time_projection(0.38, 0.49-0.38, [-0.01,0.09], nfinal=nyr) + # final = [-0.01,0.09] # AR5 + final = [0.01, 0.04] # AR6 + return self.time_projection(0.38, 0.49-0.38, final, nfinal=nyr) def time_projection( self, startratemean, startratepm, final, @@ -400,14 +410,15 @@ def time_projection( fraction = np.random.rand(self.nm, self.nt) elif fraction.size != self.nm * self.nt: raise ValueError('fraction is the wrong size') + fraction = fraction.reshape(self.nm, self.nt) # Convert inputs to startrate (m yr-1) and afinal (m), where both are # arrays with the size of fraction - momm = 1e-3 # convert mm yr-1 to m yr-1 startrate = (startratemean + \ - startratepm * np.array([-1,1],dtype=float)) * momm + startratepm * np.array([-1,1],dtype=float)) * 1e-3 # convert mm yr-1 to m yr-1 finalisrange = isinstance(final, Sequence) + if finalisrange: if len(final) != 2: raise ValueError('final range is the wrong size') @@ -416,6 +427,7 @@ def time_projection( if final.shape != fraction.shape: raise ValueError('final array is the wrong shape') afinal = final + startrate = (1 - fraction) * startrate[0] + fraction * startrate[1] # For terms where the rate increases linearly in time t, we can write GMSLR as @@ -439,7 +451,7 @@ def time_projection( y = halfacc[:, :, np.newaxis] * timeendofAR5 * ((2 * time) - timeendofAR5) quadratic[:, :, 95:] = y[:, :, 95:] - np.add(quadratic, linear, out=quadratic) + quadratic += linear quadratic = quadratic.reshape(quadratic.shape[0]*quadratic.shape[1], quadratic.shape[2]) diff --git a/profsea/emulator/plotting.py b/profsea/emulator/plotting.py index 8a0ef12..b1e6931 100644 --- a/profsea/emulator/plotting.py +++ b/profsea/emulator/plotting.py @@ -70,7 +70,7 @@ def plot_slr(r_df_list, tg_name, nflag, flag, tg_years, # Save the figure fig.tight_layout() - ffile = f'{fig_dir}_SLR_{site_name}.png' + ffile = f'{fig_dir}emulator_scenarios_SLR_{site_name}.png' plt.savefig(ffile, dpi=300, format='png') plt.close() @@ -169,6 +169,6 @@ def plot_slr_components(g_df_list, r_df_list, site_name, scenarios, fig_dir): # Save the figure fig.tight_layout() - outfile = f'{fig_dir}_components_{site_name}.png' - plt.savefig(outfile, dpi=200, format='png') + outfile = f'{fig_dir}emulator_components_{site_name}.png' + plt.savefig(outfile, dpi=300, format='png') plt.close() \ No newline at end of file From b88ed3df5ff3a76dc87f730f20b9b4281cb686ba Mon Sep 17 00:00:00 2001 From: mo-gregmunday Date: Thu, 11 Jul 2024 16:40:39 +0100 Subject: [PATCH 013/276] Emulator docstrings --- profsea/emulator/__init__.py | 259 +++++++++++++++++++++++++---------- 1 file changed, 189 insertions(+), 70 deletions(-) diff --git a/profsea/emulator/__init__.py b/profsea/emulator/__init__.py index f8377e5..187da54 100644 --- a/profsea/emulator/__init__.py +++ b/profsea/emulator/__init__.py @@ -6,8 +6,9 @@ # Calculate Monte Carlo projections of GMSLR using methods # from Jonathon Gregory and AR5. Staying close to JG's original # code where possible. -from collections.abc import Sequence +import os import concurrent +from collections.abc import Sequence import numpy as np @@ -108,27 +109,62 @@ def get_components(self): 'gmslr': self.gmslr } return components_dict + + def save_components(self, output_dir: str, scenario_name: str): + """Save all SLR components as .npy files to a directory. + + Parameters + ---------- + output_directory: str + Directory to save components to. + scenario_name: str + Name of the scenario you've run the emulator for. + + Returns + ------- + None + """ + for name, component in self.get_components().items(): + np.save( + os.path.join( + output_dir, + f'{scenario_name}_{name}.npy'), + component.T # Transpose to match the shape of original Monte Carlo simulations + ) def project(self): + """Run the emulator to project GMSLR components. + + Returns + ------- + None + """ np.random.seed(self.seed) - zt, zx, zit, zit_mean = self.calculate_drivers() + T_ens, Exp_ens, T_int_ens, T_int_med = self.calculate_drivers() - self.expansion = np.tile(zx, (self.nm, 1)) + self.expansion = np.tile(Exp_ens, (self.nm, 1)) fraction = np.random.rand(self.nm * self.nt) # correlation between antsmb and antdyn - self.run_parallel_projections(zit_mean, zit, zt, fraction) + self.run_parallel_projections(T_int_med, T_int_ens, T_ens, fraction) self.greennet = self.greensmb + self.greendyn self.antnet = self.antsmb + self.antdyn self.sheetdyn = self.greendyn + self.antdyn self.gmslr = self.glacier + self.greennet + self.antnet + self.landwater + self.expansion - def run_parallel_projections(self, zit_mean, zit, zt, fraction): + def run_parallel_projections(self, T_int_med: np.ndarray, T_int_ens: np.ndarray, + T_ens: np.ndarray, fraction: np.ndarray): + """Run components of the emulator in parallel. + + Returns + ------- + None + """ with concurrent.futures.ThreadPoolExecutor() as executor: futures = { - executor.submit(self.project_glacier, zit_mean, zit): 'glacier', - executor.submit(self.project_greensmb, zt): 'greensmb', - executor.submit(self.project_antsmb, zit, fraction): 'antsmb', + executor.submit(self.project_glacier, T_int_med, T_int_ens): 'glacier', + executor.submit(self.project_greensmb, T_ens): 'greensmb', + executor.submit(self.project_antsmb, T_int_ens, fraction): 'antsmb', executor.submit(self.project_greendyn): 'greendyn', executor.submit(self.project_antdyn, fraction): 'antdyn', executor.submit(self.project_landwater): 'landwater' @@ -149,6 +185,20 @@ def run_parallel_projections(self, zit_mean, zit, zt, fraction): self.landwater = results['landwater'] def calculate_drivers(self): + """Calculate the drivers of GMSLR: temperature change and + thermosteric sea level rise. + + Returns + ------- + T_ens: np.ndarray + Ensemble of temperature changes. + therm_ens: np.ndarray + Ensemble of thermosteric sea level rise. + T_int_ens: np.ndarray + Ensemble of time-integral temperature anomalies. + T_int_med: np.ndarray + Median of time-integral temperature anomalies. + """ if self.input_ensemble: # Check if dimensions are the right way around if self.T_change.shape[1] != self.nyr: @@ -169,8 +219,8 @@ def calculate_drivers(self): therm_std = np.std(self.OHC_change * self.exp_efficiency, axis=0) # Time-integral of temperature anomaly - T_med_int = np.cumsum(T_med) - T_std_int = np.cumsum(T_std) + T_int_med = np.cumsum(T_med) + T_int_std = np.cumsum(T_std) # Generate a sample of perfectly correlated timeseries fields of temperature, # time-integral temperature and expansion, each of them [realisation,time] @@ -180,16 +230,26 @@ def calculate_drivers(self): # reshape to [realisation,time] T_ens = z[:, np.newaxis] * T_std + T_med therm_ens = z[:, np.newaxis] * therm_std + therm_med - T_int_ens = z[:, np.newaxis] * T_std_int + T_med_int + T_int_ens = z[:, np.newaxis] * T_int_std + T_int_med - return T_ens, therm_ens, T_int_ens, T_med_int + return T_ens, therm_ens, T_int_ens, T_int_med - def project_glacier(self, it, zit): - # Return projection of glacier contribution as a cf.Field - # it -- cf.Field, time-integral of median temperature anomaly timeseries - # zit -- cf.Field, ensemble of time-integral temperature anomaly timeseries - # template -- cf.Field with the required shape of the output + def project_glacier(self, T_int_med: np.ndarray, T_int_ens: np.ndarray): + """Project glacier contribution to GMSLR. + + Parameters + ---------- + T_int_med: np.ndarray + Time-integral of median temperature anomaly timeseries. + T_int_ens: np.ndarray + Ensemble of time-integral temperature anomaly timeseries. + + Returns + ------- + glacier: np.ndarray + Glacier contribution to GMSLR. + """ # glaciermip -- False => AR5 parameters, 1 => fit to Hock et al. (2019), # 2 => fit to Marzeion et al. (2020) dmzdtref=0.95 # mm yr-1 in Marzeion's CMIP5 ensemble mean for AR5 ref period @@ -234,8 +294,8 @@ def project_glacier(self, it, zit): r = r[:, np.newaxis, np.newaxis] # Precompute mgl and zgl for all glacier methods - mgl_all = np.array([self._project_glacier1(it, glparm[igl]['factor'], glparm[igl]['exponent']) for igl in range(ngl)]) - zgl_all = np.array([self._project_glacier1(zit, glparm[igl]['factor'], glparm[igl]['exponent']) for igl in range(ngl)]) + mgl_all = np.array([self._project_glacier1(T_int_med, glparm[igl]['factor'], glparm[igl]['exponent']) for igl in range(ngl)]) + zgl_all = np.array([self._project_glacier1(T_int_ens, glparm[igl]['factor'], glparm[igl]['exponent']) for igl in range(ngl)]) cvgl_all = np.array([glparm[igl]['cvgl'] if self.glaciermip else cvgl for igl in range(ngl)]) # Make an ensemble of projections for each method @@ -256,16 +316,40 @@ def project_glacier(self, it, zit): return glacier - def _project_glacier1(self, it, factor, exponent): - # Return projection of glacier contribution by one glacier method + def _project_glacier1(self, T_int: np.ndarray, factor: float, exponent: float): + """Project glacier contribution by one glacier method. + + Parameters + ---------- + T_int: np.ndarray + Time-integral temperature anomaly timeseries. + factor: float + Factor for the glacier method. + exponent: float + Exponent for the glacier method. + + Returns + ------- + np.ndarray + Projection of glacier contribution. + """ scale=1e-3 # mm to m - return scale * factor * (np.where(it<0, 0, it)**exponent) - - def project_greensmb(self, zt): - # Return projection of Greenland SMB contribution as a cf.Field - # zt -- cf.Field, ensemble of temperature anomaly timeseries - # template -- cf.Field with the required shape of the output + return scale * factor * (np.where(T_int<0, 0, T_int)**exponent) + def project_greensmb(self, T_ens: np.ndarray): + """Project Greenland SMB contribution to GMSLR. + + Parameters + ---------- + T_ens: np.ndarray + Ensemble of temperature anomaly timeseries. + + Returns + ------- + greensmb: np.ndarray + Greenland SMB contribution to GMSLR. + + """ dtgreen = -0.146 # Delta_T of Greenland ref period wrt AR5 ref period fnlogsd = 0.4 # random methodological error of the log factor febound = [1, 1.15] # bounds of uniform pdf of SMB elevation feedback factor @@ -276,7 +360,7 @@ def project_greensmb(self, zt): fe = np.random.sample(self.nm) * (febound[1] - febound[0]) + febound[0] ff = fn * fe - ztgreen = zt - dtgreen + ztgreen = T_ens - dtgreen greensmb = ff[:, np.newaxis, np.newaxis] * self._fettweis(ztgreen) @@ -292,19 +376,36 @@ def project_greensmb(self, zt): return greensmb def _fettweis(self, ztgreen): - # Greenland SMB in m yr-1 SLE from global mean temperature anomaly - # using Eq 2 of Fettweis et al. (2013) + """Calculate Greenland SMB in m yr-1 SLE from global mean temperature + anomaly, using Eq 2 of Fettweis et al. (2013). + + Parameters + ---------- + ztgreen: np.ndarray + Global mean temperature anomaly. + + Returns + ------- + np.ndarray + Greenland SMB in m yr-1 SLE. + """ return (71.5*ztgreen+20.4*(ztgreen**2)+2.8*(ztgreen**3))*self.mSLEoGt - def project_antsmb(self, zit, fraction=None): - # Return projection of Antarctic SMB contribution as a cf.Field - # zit -- cf.Field, ensemble of time-integral temperature anomaly timeseries - # template -- cf.Field with the required shape of the output - # fraction -- array-like, random numbers for the SMB-dynamic feedback - - # antsmb=template.copy() - # nr,nt,nyr=antsmb.shape - + def project_antsmb(self, T_int_ens, fraction=None): + """Project Antarctic SMB contribution to GMSLR. + + Parameters + ---------- + T_int_ens: np.ndarray + Ensemble of time-integral temperature anomaly timeseries. + fraction: np.ndarray + Random numbers for the SMB-dynamic feedback. + + Returns + ------- + antsmb: np.ndarray + Antarctic SMB contribution to GMSLR. + """ # The following are [mean,SD] pcoK=[5.1,1.5] # % change in Ant SMB per K of warming from G&H06 KoKg=[1.1,0.2] # ratio of Antarctic warming to global warming from G&H06 @@ -327,20 +428,21 @@ def project_antsmb(self, zit, fraction=None): z = moaoKg * ainterfactor z = z[:, :, np.newaxis] - antsmb = z * zit + antsmb = z * T_int_ens antsmb = antsmb.reshape(antsmb.shape[0] * antsmb.shape[1], antsmb.shape[2]) return antsmb def project_greendyn(self): - # Return projection of Greenland rapid ice-sheet dynamics contribution - # as a cf.Field - # scenario -- str, name of scenario - # template -- cf.Field with the required shape of the output - + """Project Greenland rapid ice-sheet dynamics contribution to GMSLR. + + Returns + ------- + np.ndarray + Greenland rapid ice-sheet dynamics contribution to GMSLR. + """ # For SMB+dyn during 2005-2010 Table 4.6 gives 0.63+-0.17 mm yr-1 (5-95% range) # For dyn at 2100 Chapter 13 gives [20,85] mm for rcp85, [14,63] mm otherwise - if self.scenario in ['rcp85','ssp585']: finalrange=[0.020,0.085] else: @@ -350,13 +452,18 @@ def project_greendyn(self): finalrange) + self.fgreendyn*self.dgreen def project_antdyn(self, fraction=None): - # Return projection of Antarctic rapid ice-sheet dynamics contribution - # as a cf.Field - # template -- cf.Field with the required shape of the output - # fraction -- array-like, random numbers for the dynamic contribution - # levermann -- optional, str, use Levermann fit for specified scenario - - + """Project Antarctic rapid ice-sheet dynamics contribution to GMSLR. + + Parameters + ---------- + fraction: np.ndarray + Random numbers for the dynamic contribution. + + Returns + ------- + np.ndarray + Antarctic rapid ice-sheet dynamics contribution to GMSLR. + """ # lcoeff=dict(rcp26=[-2.881, 0.923, 0.000],\ # rcp45=[-2.676, 0.850, 0.000],\ # rcp60=[-2.660, 0.870, 0.000],\ @@ -377,8 +484,13 @@ def project_antdyn(self, fraction=None): return self.time_projection(0.41, 0.20, final, fraction=fraction) + self.dant def project_landwater(self): - # Return projection of land water storage contribution as a cf.Field - + """Project land water storage contribution to GMSLR. + + Returns + ------- + np.ndarray + Land water storage contribution to GMSLR. + """ # The rate at start is the one for 1993-2010 from the budget table. # The final amount is the mean for 2081-2100. nyr=2100-2081+1 # number of years of the time-mean of the final amount @@ -387,21 +499,28 @@ def project_landwater(self): return self.time_projection(0.38, 0.49-0.38, final, nfinal=nyr) def time_projection( - self, startratemean, startratepm, final, - nfinal=1, fraction=None): - # Return projection of a quantity which is a quadratic function of time - # in a cf.Field. - # startratemean, startratepm -- rate of GMSLR at the start in mm yr-1, whose - # likely range is startratemean +- startratepm - # final -- two-element list giving likely range in m for GMSLR at the endofAR5, - # or array-like, giving final values at that time, of the same shape as - # fraction and assumed corresponding elements - # template -- cf.Field with the required shape of the output - # nfinal -- int, optional, number of years at the end over which finalrange is - # a time-mean; by default 1 => finalrange is the value for the last year - # fraction -- array-like, optional, random numbers in the range 0 to 1, - # by default uniformly distributed - + self, startratemean: float, startratepm: float, final, + nfinal: int=1, fraction: np.ndarray=None): + """Project a quantity which is a quadratic function of time. + + Parameters + ---------- + startratemean: float + Rate of GMSLR at the start in mm yr-1. + startratepm: float + Start rate error in mm yr-1. + final: list | np.ndarray + Likely range in m for GMSLR at the end of AR5. + nfinal: int + Number of years at the end over which final is a time-mean. + fraction: np.ndarray + Random numbers in the range 0 to 1. + + Returns + ------- + np.ndarray + Projection of the quantity. + """ # Create a field of elapsed time since start in years timeendofAR5 = self.endofAR5 - self.endofhistory + 1 time = np.arange(self.end_yr - self.endofhistory) + 1 From 7918956341e6b85d69e8c7b721cca16f71b64b31 Mon Sep 17 00:00:00 2001 From: mo-gregmunday Date: Thu, 11 Jul 2024 16:41:18 +0100 Subject: [PATCH 014/276] Adding saving method --- .../step3_process_regional_sealevel_projections.py | 12 ++++-------- 1 file changed, 4 insertions(+), 8 deletions(-) diff --git a/profsea/step3_process_regional_sealevel_projections.py b/profsea/step3_process_regional_sealevel_projections.py index e090657..8c2d2d0 100644 --- a/profsea/step3_process_regional_sealevel_projections.py +++ b/profsea/step3_process_regional_sealevel_projections.py @@ -588,15 +588,11 @@ def main(): input_ensemble=settings["emulator_settings"]["use_input_ensemble"] ) gmslr.project() - components_dict = gmslr.get_components() print('Saving components...') - for name, component in components_dict.items(): - np.save( - os.path.join( - os.path.join(settings["baseoutdir"], 'emulator_output'), - f'{scenario}_{name}.npy'), - component.T # Transpose to match the shape of original Monte Carlo simulations - ) + gmslr.save_components( + os.path.join(settings["baseoutdir"], 'emulator_output'), + scenario + ) print('Saved!\n') for loc_name in df_site_data.index.values: calc_future_sea_level_at_site(df_site_data, loc_name, scenario) From 1f2a986b9d0499ecfde86a6b91fb34da8d7d4f08 Mon Sep 17 00:00:00 2001 From: mo-gregmunday Date: Thu, 11 Jul 2024 16:53:02 +0100 Subject: [PATCH 015/276] Added class docstring --- profsea/emulator/__init__.py | 79 +++++++++++++++++++++++++----------- 1 file changed, 55 insertions(+), 24 deletions(-) diff --git a/profsea/emulator/__init__.py b/profsea/emulator/__init__.py index 187da54..4c651fd 100644 --- a/profsea/emulator/__init__.py +++ b/profsea/emulator/__init__.py @@ -13,6 +13,61 @@ import numpy as np class GMSLREmulator: + """Emulator for global mean sea level rise components. + + Parameters + ---------- + T_change: np.ndarray + Array of temperature change values. + OHC_change: np.ndarray + Array of ocean heat content change values. + scenario: str + Name of the scenario. + output_dir: str + Directory to save the components to. + end_yr: int + End year of the projections. + seed: int + Seed for numpy.random. + nt: int + Number of realisations of the input timeseries + nm: int + Number of realisations of for each component. + Must be a multiple of the number of glacier methods. + tcv: float + Multiplier for the standard deviation in the input fields. + glaciermip: bool | int + If False, use AR5 parameters. If 1, use GlacierMIP + (Hock et al., 2019). If 2, use GlacierMIP2 (Marzeion et al., 2020). + input_ensemble: bool + If True, use an input ensemble of temperature and + ocean heat content change. + palmer_method: bool + If True, allow integration to end in any year up to 2300, + with the contributions to GMLSR from ice-sheet dynamics, + Greenland SMB and land water storage held at the 2100 rate + beyond 2100. + + Attributes + ---------- + endofhistory: int + First year of AR5 projections. + endofAR5: int + Last year of AR5 projections. + nyr: int + Length of projections. + fgreendyn: float + Fraction of SLE from Greenland during 1996 to 2005 assumed + to result from rapid dynamical change. + dgreen: float + m SLE from Greenland during 1996 to 2005 according to AR5 chapter 4. + dant: float + m SLE from Antarctica during 1996 to 2005 according to AR5 chapter 4. + mSLEoGt: float + Conversion factor for Gt to m SLE. + exp_efficiency: float + Sensitivity of thermosteric SLR to ocean heat content change. + """ def __init__( self, @@ -28,30 +83,6 @@ def __init__( glaciermip: bool=False, input_ensemble: bool=True, palmer_method: bool=False): - # ensemble -- bool, optional, default True, if provided use an input ensemble of - # temperature and ocean heat content change in place of Monte Carlo simulations - # for thermosteric sea level rise - # seed -- optional, for numpy.random, default zero - # nt -- int, optional, number of realisations of the input timeseries for each - # scenario, default 450, to be generated using the mean and sd files; specify - # 0 if the ensemble of individual models is to be used instead, which is read - # from the models files. - # nm -- int, optional, number of realisations of components and of the sum for - # each realisation of the input timeseries, default 1000, must be a multiple - # of the number of glacier methods. - # tcv -- float, optional, default 1.0, multiplier for the standard deviation - # in the input fields - # glaciermip -- optional, default False => AR5 parameters, 1 => GlacierMIP - # (Hock et al., 2019), 2 => GlacierMIP2 (Marzeion et al., 2020) - # levermann -- optional, default None, specifies that Antarctic dynamics should - # use the fit by Palmer et al. (2020) to the results of Levermann et al. - # (2014); if dict, must have an key for each scenario whose value names the - # Levermann RCP fit to be used; if str, identifies the single fit to be used - # for every scenario; otherwise it is treated as True or False; if True the - # scenarios must all be ones that Levermann provides. - # palmer_method -- bool, optional, default False, allow integration to end in any year - # up to 2300, with the contributions to GMLSR from ice-sheet dynamics, Green- - # land SMB and land water storage held at the 2100 rate beyond 2100. self.T_change = T_change self.OHC_change = OHC_change From 2d87aae82fdafbc561a006669025022e3bb6023d Mon Sep 17 00:00:00 2001 From: mo-gregmunday Date: Thu, 11 Jul 2024 16:55:46 +0100 Subject: [PATCH 016/276] Return to AR5 vals --- profsea/emulator/__init__.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/profsea/emulator/__init__.py b/profsea/emulator/__init__.py index 4c651fd..84df3d9 100644 --- a/profsea/emulator/__init__.py +++ b/profsea/emulator/__init__.py @@ -506,8 +506,8 @@ def project_antdyn(self, fraction=None): # final=np.exp(lcoeff[2]*ascale**2+lcoeff[1]*ascale+lcoeff[0]) # final = final.reshape(self.nm, self.nt) - # final=[-0.020, 0.185] - final = [0.06, 0.49] # AR6, SSP2-4.5 + final=[-0.020, 0.185] + # final = [0.06, 0.49] # AR6, SSP2-4.5 # For SMB+dyn during 2005-2010 Table 4.6 gives 0.41+-0.24 mm yr-1 (5-95% range) # For dyn at 2100 Chapter 13 gives [-20,185] mm for all scenarios @@ -525,8 +525,8 @@ def project_landwater(self): # The rate at start is the one for 1993-2010 from the budget table. # The final amount is the mean for 2081-2100. nyr=2100-2081+1 # number of years of the time-mean of the final amount - # final = [-0.01,0.09] # AR5 - final = [0.01, 0.04] # AR6 + final = [-0.01,0.09] # AR5 + # final = [0.01, 0.04] # AR6 return self.time_projection(0.38, 0.49-0.38, final, nfinal=nyr) def time_projection( From 4b39d7f5d736053033c19aff62a0a83dd22b445e Mon Sep 17 00:00:00 2001 From: mo-gregmunday Date: Wed, 17 Jul 2024 17:49:57 +0100 Subject: [PATCH 017/276] Update params for RCP comparison --- profsea/emulator/__init__.py | 54 +++++++++++++++++++++++++----------- 1 file changed, 38 insertions(+), 16 deletions(-) diff --git a/profsea/emulator/__init__.py b/profsea/emulator/__init__.py index 84df3d9..dd687fd 100644 --- a/profsea/emulator/__init__.py +++ b/profsea/emulator/__init__.py @@ -82,6 +82,8 @@ def __init__( tcv: float=1.0, glaciermip: bool=False, input_ensemble: bool=True, + T_percentile_95: np.ndarray=None, + OHC_percentile_95: np.ndarray=None, palmer_method: bool=False): self.T_change = T_change @@ -95,6 +97,8 @@ def __init__( self.tcv = tcv self.glaciermip = glaciermip self.input_ensemble = input_ensemble + self.T_percentile_95 = T_percentile_95 + self.OHC_percentile_95 = OHC_percentile_95 self.palmer_method = palmer_method # First year of AR5 projections @@ -243,11 +247,29 @@ def calculate_drivers(self): return self.T_change, therm_ens, T_int_ens, T_int_med - T_med = np.percentile(self.T_change, 50, axis=0) - T_std = np.std(self.T_change, axis=0) + if len(self.T_change.shape) > 1: + T_med = np.percentile(self.T_change, 50, axis=0) + T_std = np.std(self.T_change, axis=0) - therm_med = np.percentile(self.OHC_change, 50, axis=0) * self.exp_efficiency - therm_std = np.std(self.OHC_change * self.exp_efficiency, axis=0) + therm_med = np.percentile(self.OHC_change, 50, axis=0) * self.exp_efficiency + therm_std = np.std(self.OHC_change * self.exp_efficiency, axis=0) + else: + if self.T_percentile_95 is not None: + T_med = self.T_change + therm_med = self.OHC_change + + T_std = (self.T_percentile_95 - self.T_change) / 1.645 + # therm_std = (self.OHC_percentile_95 - self.OHC_change) * self.exp_efficiency / 1.645 + therm_std = (self.OHC_percentile_95 - self.OHC_change) / 1.645 + self.T_std = T_std + self.therm_std = therm_std + + else: + raise ValueError( + 'If input_ensemble is False, and T_change and OHC_change ' + 'are not 2D arrays, you must provide a 95th percentile ' + 'timeseries for T_change and OHC_change. Add this using ' + 'T_percentile_95 and OHC_percentile_95 keyword arguments.') # Time-integral of temperature anomaly T_int_med = np.cumsum(T_med) @@ -495,18 +517,18 @@ def project_antdyn(self, fraction=None): np.ndarray Antarctic rapid ice-sheet dynamics contribution to GMSLR. """ - # lcoeff=dict(rcp26=[-2.881, 0.923, 0.000],\ - # rcp45=[-2.676, 0.850, 0.000],\ - # rcp60=[-2.660, 0.870, 0.000],\ - # rcp85=[-2.399, 0.860, 0.000]) - # lcoeff = lcoeff['rcp85'] - - # from scipy.stats import norm - # ascale=norm.ppf(fraction) - # final=np.exp(lcoeff[2]*ascale**2+lcoeff[1]*ascale+lcoeff[0]) - # final = final.reshape(self.nm, self.nt) - - final=[-0.020, 0.185] + lcoeff=dict(rcp26=[-2.881, 0.923, 0.000],\ + rcp45=[-2.676, 0.850, 0.000],\ + rcp60=[-2.660, 0.870, 0.000],\ + rcp85=[-2.399, 0.860, 0.000]) + lcoeff = lcoeff['rcp85'] + + from scipy.stats import norm + ascale=norm.ppf(fraction) + final=np.exp(lcoeff[2]*ascale**2+lcoeff[1]*ascale+lcoeff[0]) + final = final.reshape(self.nm, self.nt) + + # final=[-0.020, 0.185] # final = [0.06, 0.49] # AR6, SSP2-4.5 # For SMB+dyn during 2005-2010 Table 4.6 gives 0.41+-0.24 mm yr-1 (5-95% range) From 88e680ef1ca761a5e8aafba8bf85d709fe5a0933 Mon Sep 17 00:00:00 2001 From: mo-gregmunday Date: Tue, 30 Jul 2024 09:57:15 +0100 Subject: [PATCH 018/276] Updated for RCP runs --- profsea/emulator/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/profsea/emulator/__init__.py b/profsea/emulator/__init__.py index dd687fd..45b5c21 100644 --- a/profsea/emulator/__init__.py +++ b/profsea/emulator/__init__.py @@ -521,7 +521,7 @@ def project_antdyn(self, fraction=None): rcp45=[-2.676, 0.850, 0.000],\ rcp60=[-2.660, 0.870, 0.000],\ rcp85=[-2.399, 0.860, 0.000]) - lcoeff = lcoeff['rcp85'] + lcoeff = lcoeff[self.scenario] from scipy.stats import norm ascale=norm.ppf(fraction) From 564c8d7ff85052338e53893eaf7d4b6112a7d5ae Mon Sep 17 00:00:00 2001 From: Gregory Munday Date: Mon, 27 Jan 2025 16:24:55 +1100 Subject: [PATCH 019/276] Enabled AntDyn contribution from any scenario --- .gitignore | 1 + profsea/emulator/__init__.py | 59 +++++++++++++++++++++++++----------- 2 files changed, 43 insertions(+), 17 deletions(-) diff --git a/.gitignore b/.gitignore index 398ce69..684ce96 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,4 @@ # Folders *__pycache__* profsea.egg-info +data/ \ No newline at end of file diff --git a/profsea/emulator/__init__.py b/profsea/emulator/__init__.py index 45b5c21..f3105f0 100644 --- a/profsea/emulator/__init__.py +++ b/profsea/emulator/__init__.py @@ -11,6 +11,7 @@ from collections.abc import Sequence import numpy as np +from scipy.stats import norm class GMSLREmulator: """Emulator for global mean sea level rise components. @@ -42,6 +43,12 @@ class GMSLREmulator: input_ensemble: bool If True, use an input ensemble of temperature and ocean heat content change. + T_percentile_95: np.ndarray + 95th percentile of temperature change timeseries. + OHC_percentile_95: np.ndarray + 95th percentile of ocean heat content change timeseries. + cum_emissions_total: float + Total cumulative emissions from 2015 to scenario end. palmer_method: bool If True, allow integration to end in any year up to 2300, with the contributions to GMLSR from ice-sheet dynamics, @@ -84,6 +91,7 @@ def __init__( input_ensemble: bool=True, T_percentile_95: np.ndarray=None, OHC_percentile_95: np.ndarray=None, + cum_emissions_total: np.ndarray=None, palmer_method: bool=False): self.T_change = T_change @@ -99,6 +107,7 @@ def __init__( self.input_ensemble = input_ensemble self.T_percentile_95 = T_percentile_95 self.OHC_percentile_95 = OHC_percentile_95 + self.cum_emissions_total = cum_emissions_total self.palmer_method = palmer_method # First year of AR5 projections @@ -128,6 +137,14 @@ def __init__( if input_ensemble: self.nt = self.T_change.shape[0] + + if self.scenario not in ['rcp26', 'rcp45', 'rcp85', + 'ssp126', 'ssp245', 'ssp585'] and self.cum_emissions_total is None: + raise ValueError( + 'If the scenario is not rcp26, rcp45, rcp85, ssp126, ssp245 or ssp585, ' + 'you must provide the total \ncumulative emissions from 2015 to the end ' + 'of the scenario using the cum_emissions_total keyword argument.\n' + 'This is required to calculate the Antarctic dynamic contribution to GMSLR.') def get_components(self): components_dict = { @@ -329,7 +346,8 @@ def project_glacier(self, T_int_med: np.ndarray, T_int_ens: np.ndarray): dict(name='RAD2014',factor=5.18,exponent=0.709,cvgl=0.135),\ dict(name='WAL2001',factor=2.66,exponent=0.730,cvgl=0.206)] cvgl=0.20 # unnecessary default - else: raise KeyError('glaciermip must be 1 or 2') + else: + raise KeyError('glaciermip must be 1 or 2') else: glparm=[dict(name='Marzeion',factor=4.96,exponent=0.685),\ dict(name='Radic',factor=5.45,exponent=0.676),\ @@ -517,22 +535,29 @@ def project_antdyn(self, fraction=None): np.ndarray Antarctic rapid ice-sheet dynamics contribution to GMSLR. """ - lcoeff=dict(rcp26=[-2.881, 0.923, 0.000],\ - rcp45=[-2.676, 0.850, 0.000],\ - rcp60=[-2.660, 0.870, 0.000],\ - rcp85=[-2.399, 0.860, 0.000]) - lcoeff = lcoeff[self.scenario] - - from scipy.stats import norm - ascale=norm.ppf(fraction) - final=np.exp(lcoeff[2]*ascale**2+lcoeff[1]*ascale+lcoeff[0]) - final = final.reshape(self.nm, self.nt) - - # final=[-0.020, 0.185] - # final = [0.06, 0.49] # AR6, SSP2-4.5 - - # For SMB+dyn during 2005-2010 Table 4.6 gives 0.41+-0.24 mm yr-1 (5-95% range) - # For dyn at 2100 Chapter 13 gives [-20,185] mm for all scenarios + # This is a naive solution to calculating the AntDyn contribution + # for any given scenario. Basically linear regressions through existing data + # to find rough relationship between cumulative emissions and AntDyn contribution. + if self.cum_emissions_total: + upper = (0.000110 * self.cum_emissions_total) + 0.375 # in metres + lower = (1.363e-05 * self.cum_emissions_total) + 0.0392 # in metres + final = [lower, upper] + else: + lcoeff=dict(rcp26=[-2.881, 0.923, 0.000],\ + rcp45=[-2.676, 0.850, 0.000],\ + rcp60=[-2.660, 0.870, 0.000],\ + rcp85=[-2.399, 0.860, 0.000]) + lcoeff = lcoeff[self.scenario] + + ascale=norm.ppf(fraction) + final=np.exp(lcoeff[2]*ascale**2+lcoeff[1]*ascale+lcoeff[0]) + final = final.reshape(self.nm, self.nt) + + # final=[-0.020, 0.185] + # final = [0.06, 0.49] # AR6, SSP2-4.5 + + # For SMB+dyn during 2005-2010 Table 4.6 gives 0.41+-0.24 mm yr-1 (5-95% range) + # For dyn at 2100 Chapter 13 gives [-20,185] mm for all scenarios return self.time_projection(0.41, 0.20, final, fraction=fraction) + self.dant From f8ec0d7e0c046e020117b57d6141b3fe1c184d05 Mon Sep 17 00:00:00 2001 From: Gregory Munday Date: Mon, 27 Jan 2025 17:20:55 +1100 Subject: [PATCH 020/276] Added cumulative emissions requirement --- .../step3_process_regional_sealevel_projections.py | 13 ++++++++++--- 1 file changed, 10 insertions(+), 3 deletions(-) diff --git a/profsea/step3_process_regional_sealevel_projections.py b/profsea/step3_process_regional_sealevel_projections.py index 8c2d2d0..ac5f8da 100644 --- a/profsea/step3_process_regional_sealevel_projections.py +++ b/profsea/step3_process_regional_sealevel_projections.py @@ -545,8 +545,8 @@ def main(): print(f'User specified lat(s) and lon(s) is (are): ' f'{settings["siteinfo"]["sitelatlon"]}') if settings["siteinfo"]["sitelatlon"] == [[]]: - print(f' No lat lon specified - use tide gauge metadata if ' - f'available') + print(' No lat lon specified - use tide gauge metadata if ' + 'available') print(f'User specified science method is: {settings["sciencemethod"]}') if {settings["cmipinfo"]["cmip_sea"]} == {'all'}: print('User specified all CMIP models') @@ -577,6 +577,12 @@ def main(): print(f'Projecting {scenario}...') T_change = read_csv_file(f'*{scenario}*_temperature*.csv') OHC_change = read_csv_file(f'*{scenario}*_ocean_heat_content_change*.csv') + + cum_emissions_file = f'*{scenario}*_cumulative_emissions_total.txt' + if glob.glob(os.path.join(settings["emulator_settings"]["emulator_input_dir"], cum_emissions_file)): + cum_emissions_total = read_csv_file(cum_emissions_file) + else: + raise FileNotFoundError('For any non-RCP scenario, a cumulative emissions total must be provided.') gmslr = GMSLREmulator( T_change, @@ -585,7 +591,8 @@ def main(): os.path.join(settings["baseoutdir"], 'emulator_output'), settings["projection_end_year"], palmer_method=palmer_method, - input_ensemble=settings["emulator_settings"]["use_input_ensemble"] + input_ensemble=settings["emulator_settings"]["use_input_ensemble"], + cum_emissions_total=cum_emissions_total ) gmslr.project() print('Saving components...') From 0475d398321ae3a20f5931c2900c032ebb2d8c08 Mon Sep 17 00:00:00 2001 From: Gregory Munday Date: Tue, 8 Apr 2025 11:51:24 +0100 Subject: [PATCH 021/276] Added code for spatial projections --- profsea/emulator/__init__.py | 35 +- ...2_extract_steric_dyn_regression_spatial.py | 139 +++++ ...s_regional_sealevel_projections_spatial.py | 534 ++++++++++++++++++ 3 files changed, 695 insertions(+), 13 deletions(-) create mode 100644 profsea/step2_extract_steric_dyn_regression_spatial.py create mode 100644 profsea/step3_process_regional_sealevel_projections_spatial.py diff --git a/profsea/emulator/__init__.py b/profsea/emulator/__init__.py index f3105f0..f9815e0 100644 --- a/profsea/emulator/__init__.py +++ b/profsea/emulator/__init__.py @@ -48,7 +48,7 @@ class GMSLREmulator: OHC_percentile_95: np.ndarray 95th percentile of ocean heat content change timeseries. cum_emissions_total: float - Total cumulative emissions from 2015 to scenario end. + Total cumulative emissions from 2015 to 2100. palmer_method: bool If True, allow integration to end in any year up to 2300, with the contributions to GMLSR from ice-sheet dynamics, @@ -87,7 +87,7 @@ def __init__( nt: int=450, nm: int=1000, tcv: float=1.0, - glaciermip: bool=False, + glaciermip: bool|int=False, input_ensemble: bool=True, T_percentile_95: np.ndarray=None, OHC_percentile_95: np.ndarray=None, @@ -331,21 +331,27 @@ def project_glacier(self, T_int_med: np.ndarray, T_int_ens: np.ndarray): if self.glaciermip: if self.glaciermip==1: - glparm=[dict(name='SLA2012',factor=3.39,exponent=0.722,cvgl=0.15),\ - dict(name='MAR2012',factor=4.35,exponent=0.658,cvgl=0.13),\ - dict(name='GIE2013',factor=3.57,exponent=0.665,cvgl=0.13),\ - dict(name='RAD2014',factor=6.21,exponent=0.648,cvgl=0.17),\ + glparm=[dict(name='SLA2012',factor=3.39,exponent=0.722,cvgl=0.15), + dict(name='MAR2012',factor=4.35,exponent=0.658,cvgl=0.13), + dict(name='GIE2013',factor=3.57,exponent=0.665,cvgl=0.13), + dict(name='RAD2014',factor=6.21,exponent=0.648,cvgl=0.17), dict(name='GloGEM',factor=2.88,exponent=0.753,cvgl=0.13)] cvgl=0.15 # unnecessary default elif self.glaciermip==2: - glparm=[dict(name='GLIMB',factor=3.70,exponent=0.662,cvgl=0.206),\ - dict(name='GloGEM',factor=4.08,exponent=0.716,cvgl=0.161),\ - dict(name='JULES',factor=5.50,exponent=0.564,cvgl=0.188),\ - dict(name='Mar-12',factor=4.89,exponent=0.651,cvgl=0.141),\ - dict(name='OGGM',factor=4.26,exponent=0.715,cvgl=0.164),\ - dict(name='RAD2014',factor=5.18,exponent=0.709,cvgl=0.135),\ + glparm=[dict(name='GLIMB',factor=3.70,exponent=0.662,cvgl=0.206), + dict(name='GloGEM',factor=4.08,exponent=0.716,cvgl=0.161), + dict(name='JULES',factor=5.50,exponent=0.564,cvgl=0.188), + dict(name='Mar-12',factor=4.89,exponent=0.651,cvgl=0.141), + dict(name='OGGM',factor=4.26,exponent=0.715,cvgl=0.164), + dict(name='RAD2014',factor=5.18,exponent=0.709,cvgl=0.135), dict(name='WAL2001',factor=2.66,exponent=0.730,cvgl=0.206)] cvgl=0.20 # unnecessary default + elif self.glaciermip==3: + glparm=[dict(name='GLIMB',factor=3.70,exponent=0.662,cvgl=0.206), + dict(name='GloGEMflow',factor=5.50,exponent=0.564,cvgl=0.188), + dict(name='OGGMv1.6',factor=2.66,exponent=0.730,cvgl=0.206), + dict(name='PyGEM-OGGMv1.3', factor=2.66, exponent=0.730, cvgl=0.206)] + else: raise KeyError('glaciermip must be 1 or 2') else: @@ -426,7 +432,7 @@ def project_greensmb(self, T_ens: np.ndarray): febound = [1, 1.15] # bounds of uniform pdf of SMB elevation feedback factor # random log-normal factor - fn = np.exp(np.random.standard_normal(self.nm)*fnlogsd) + fn = np.exp(np.random.standard_normal(self.nm) * fnlogsd) # elevation feedback factor fe = np.random.sample(self.nm) * (febound[1] - febound[0]) + febound[0] ff = fn * fe @@ -542,12 +548,15 @@ def project_antdyn(self, fraction=None): upper = (0.000110 * self.cum_emissions_total) + 0.375 # in metres lower = (1.363e-05 * self.cum_emissions_total) + 0.0392 # in metres final = [lower, upper] + # print(final) + # final=[-0.020, 0.185] else: lcoeff=dict(rcp26=[-2.881, 0.923, 0.000],\ rcp45=[-2.676, 0.850, 0.000],\ rcp60=[-2.660, 0.870, 0.000],\ rcp85=[-2.399, 0.860, 0.000]) lcoeff = lcoeff[self.scenario] + print('hi') ascale=norm.ppf(fraction) final=np.exp(lcoeff[2]*ascale**2+lcoeff[1]*ascale+lcoeff[0]) diff --git a/profsea/step2_extract_steric_dyn_regression_spatial.py b/profsea/step2_extract_steric_dyn_regression_spatial.py new file mode 100644 index 0000000..8a3d386 --- /dev/null +++ b/profsea/step2_extract_steric_dyn_regression_spatial.py @@ -0,0 +1,139 @@ +""" +Copyright (c) 2023, Met Office +All rights reserved. +""" +from profsea.config import settings +from profsea.directories import read_dir, makefolder +from profsea.slr_pkg import models, cmip, cubedata, process, get_cube_years + +import numpy as np +from sklearn.linear_model import LinearRegression + + +def extract_dyn_steric_regression(models, scenarios): + """ + Calculate, plot, and save regression between the global mean ( + thermosteric) and local (sterodynamic) sea level change from CMIP models. + :param models: list of model names to be extracted + :param scenarios: list of RCP scenarios + """ + # Base directory for CMIP "zos" and "zostoga" data + datadir = settings["cmipinfo"]["sealevelbasedir"] + # Dictionary of CMIP models and experiments + zos_dict = cmip.zos_dictionary() + zos_masks = np.empty((len(models), len(scenarios), 180, 360)) # (model, scenario, lat, lon) + regr_slopes = np.empty((len(models), len(scenarios), 180, 360)) # (model, scenario, lat, lon) + for m, model in enumerate(models): + print(f'Calculating regression for {model} all over the grid') + for s, scenario in enumerate(scenarios): + try: + print(f'Scenario: {scenario}') + # dynamic sea level (zos) + zos_date = zos_dict[model][scenario]['driftcorr'] + zos_file = f'{datadir}normalized_zos_Omon_{model}_' \ + f'{scenario}_{zos_date}_driftcorr.nc' + zos = cubedata.read_zos_cube(zos_file)[0] + zos_masks[m, s] = zos[0].data.mask + + # -------------------------------------------------------- + # global mean thermosteric (zostoga) + # Extract, and drift-correct CMIP "zostoga" data + # Normal (concatenated) + zostoga_date = zos_dict[model][scenario]['zostoga'] + zostoga_file = f'{datadir}zostoga_Omon_{model}_' \ + f'{scenario}_{zostoga_date}.nc' + zostoga_raw = cubedata.read_zos_cube(zostoga_file)[0] + # piControl (concatenated) + piControl_date = zos_dict[model][scenario]['piControl'] + zostoga_pic_file = f'{datadir}zostoga_Omon_' \ + f'{model}_piControl_{piControl_date}.nc' + zostoga_pic = cubedata.read_zos_cube(zostoga_pic_file)[0] + + regr = process.Regress('linear') + cube_drift, _ = regr.regress_t_scalar(zostoga_pic) + zostoga, _ = regr.detrend_scalar(zostoga_raw, cube_drift) + + # -------------------------------------------------------- + + yrs = get_cube_years(zos) + if model == 'bcc-csm1-1': + index = np.where(yrs >= 2100) + zostoga.data[index] = zostoga.data[index] + \ + zostoga.data[index[0][0] - 1] + # Calculate regression coefficients for periods 2005-2100 + # and 2050-2100 + idx = ((2005 <= yrs) & (yrs <= 2100)) + + zostoga.data = zostoga.data - zostoga.data[0:10].mean() + zos.data = zos.data - zos.data[0:10].mean() + + # Calculate the slope and intercepts of linear fits of the + # global and local sea level projections + regr = LinearRegression() + regr.fit( + zostoga.data[idx].reshape(-1, 1), + zos.data[idx].reshape(zos.data[idx].shape[0], -1)) + slopes = regr.coef_.reshape(180, 360) + + regr_slopes[m, s] = slopes + except Exception: + print(f'Error calculating regression for {model}, {scenario}') + print('Adding NaN to regression slope') + + regr_slopes[m, s] = np.nan + zos_masks[m, s] = np.nan + continue + + # Replace rcp26 regression slopes with all np.nan with rcp45 + for m in range(len(models)): + if regr_slopes[m, 0].all() == np.nan: + regr_slopes[m, 0] = regr_slopes[m, 1] + zos_masks[m, 0] = zos_masks[m, 1] + + assert regr_slopes.all() != np.nan, \ + 'Regression slopes contain NaN values. Check the data and try again.' + + # Create the output data file directory and filename + out_zosddir = read_dir()[2] + makefolder(out_zosddir) + outdatafile = f'{out_zosddir}zos_regression.npy' + outdatafile_mask = f'{out_zosddir}zos_regression_masks.npy' + + # Save the sea level regressions data to file + np.save(outdatafile, regr_slopes) + np.save(outdatafile_mask, zos_masks) + print(f'Saved regression data to {outdatafile}') + + # Plot the regression results + + + +def main(): + """ + Finds all CMIP model names, then calculates the regression parameters + between global and local sea level projections for the given locations. + :param df: DataFrame of site location's metadata + """ + print('running function extract_cmip5_steric_dyn_regression') + # Specify the emission scenarios + scenarios = ['rcp26', 'rcp45', 'rcp85'] + + # Select CMIP5 models to use + if settings["cmipinfo"]["cmip_sea"] == 'all': + model_names = models.cmip5_names() + elif settings["cmipinfo"]["cmip_sea"] == 'marginal': + model_names = models.cmip5_names_marginal() + else: + raise UnboundLocalError( + 'The selected CMIP5 models to use - cmip_sea = ' + + f'{settings["cmipinfo"]["cmip_sea"]} - ' + + 'is not recognised') + + # Calculate the regression parameters and plot the results + # Note the x and y limits of the plot are set to 0.6 - this can be + # updated in extract_dyn_steric_regression() function + extract_dyn_steric_regression(model_names, scenarios) + + +if __name__ == '__main__': + main() diff --git a/profsea/step3_process_regional_sealevel_projections_spatial.py b/profsea/step3_process_regional_sealevel_projections_spatial.py new file mode 100644 index 0000000..44a5cbc --- /dev/null +++ b/profsea/step3_process_regional_sealevel_projections_spatial.py @@ -0,0 +1,534 @@ +""" +Copyright (c) 2023, Met Office +All rights reserved. +""" + +import os +import iris +import glob +import json +import pickle +import numpy as np +import pandas as pd +from scipy.interpolate import RegularGridInterpolator +from netCDF4 import Dataset +import xarray as xr +import dask.array as da + +from profsea.config import settings +from profsea.slr_pkg import choose_montecarlo_dir # found in __init.py__ +from profsea.directories import read_dir, makefolder +from emulator import GMSLREmulator + + +def calc_baseline_period(sci_method, yrs): + """ + Baseline years used for UKCP18 -- 1981-2000 (offset required) + Baseline years used for IPCC AR5 and Palmer et al 2020 -- 1986-2005 + :param sci_method: scientific method used to select GIA estimates, GRD + fingerprints and CMIP regression (based on user input) + :param yrs: years of the projections + :return: baseline years + """ + if sci_method == 'global': + byr1 = 1986. + byr2 = 2005. + # offset not required + G_offset = 0.0 + print("Baseline period = ", byr1, "to", byr2) + elif sci_method == 'UK': + byr1 = 1981. + byr2 = 2000. + G_offset = 0.011 + print("Baseline period = ", byr1, "to", byr2) + + midyr = (byr2 - byr1 + 1) * 0.5 + byr1 + + return yrs[0] - midyr, G_offset + + +def calc_future_sea_level(scenario): + """ + Calculates future sea level at the given site and write to file. + :param df: Data frame of all metadata (tide gauge or site specific) for + each(all) location(s) + :param site_loc: name of the site location + :param scenario: emission scenario + """ + print('running function calc_future_sea_level') + + + # Set the UKCP*18* random seed so results are reproducible + np.random.seed(18) + + # Directory of Monte Carlo time series for new projections + mcdir = choose_montecarlo_dir() + + # Specify the sea level components to include. The GIA contribution is + # calculated separately. + components = ['exp', 'antdyn', 'antsmb', 'greendyn', 'greensmb', + 'glacier', 'landwater'] + + # Select dimensions from sample file, [time, realisation] + sample = np.load(os.path.join(mcdir, f'{scenario}_exp.npy')) + nesm = sample.shape[0] + nyrs = sample.shape[1] + yrs = np.arange(2007, 2007 + nyrs) + + grid_path = os.path.join(settings["baseoutdir"], "full_earth/data/zos_regression/zos_regression.npy") + grid_sample = np.load(grid_path)[0, 0] + + # Determine the number of samples you wish to make CHOGOM + # project = 100000, UKCP18 = 200000 + if nesm >= 200000: + nsmps = 200000 + else: + nsmps = nesm + + array_dims = [nesm, nsmps, nyrs, grid_sample.shape[0], grid_sample.shape[1]] + + # Get random samples of global and regional sea level components + calculate_sl_components(mcdir, components, scenario, yrs, array_dims) + + +def calc_gia_contribution(sci_method, yrs, nyrs, nsmps): + """ + Calculate the glacial isostatic adjustment (GIA) contribution to the + regional component of sea level rise. + Option to use the generic global GIA estimates or use the GIA estimates + developed for UK as part of UKCP18. + :param sci_method: scientific method used to select GIA estimates, GRD + fingerprints and CMIP regression (based on user input) + :param yrs: years of the projections + :param nyrs: number of years in each projection time series + :param nsmps: determine the number of samples + :param coords: coordinates of location of interest + :return: GIA estimates converted to mm/yr + """ + print('running function calc_gia_contribution') + + nGIA, GIA_vals = read_gia_estimates(sci_method) + + Tdelta, G_offset = calc_baseline_period(sci_method, yrs) + + # Unit series of mm/yr expressed as m/yr + unit_series = (np.arange(nyrs) + Tdelta) * 0.001 + GIA_unit_series = np.ones([5, nyrs]) * unit_series + + # rgiai is an array of random GIA indices the size of the sample years + rgiai = np.random.randint(nGIA, size=5) + + GIA_T = da.from_array(GIA_unit_series) + GIA_vals = da.from_array(GIA_vals) + + GIA_series = GIA_T[:, :, None, None] * GIA_vals[rgiai, None, :, :] + + return GIA_series, G_offset + + +def calculate_sl_components(mcdir, components, scenario, yrs, array_dims): + """ + Calculates global and regional component part contributions to sea level + change. + :param mcdir: location of Monte Carlo time series for new projections + :param components: sea level components + :param scenario: emission scenario + :param yrs: years of the projections + :param array_dims: Array of nesm, nsmps and nyrs + nesm --> Number of ensemble members in time series + nsmps --> Determine the number of samples you wish to make + nyrs --> Number of years in each projection time series + :return: montecarlo_G (global contribution to sea level rise) and + montecarlo_R (regional contribution to sea level change) + """ + print('running function calculate_sl_components') + + # Numbers of ensemble members, samples, years + nesm, nsmps, nyrs, lats, lons = array_dims + + # Scientific method + sci_method = settings["sciencemethod"] + + # Set up fingerprint interpolators for each component of sea level change + # Note that the first entry in the list is the Slangen, which includes the + # land water estimate + nFPs, FPlist = setup_FP_interpolators(components, sci_method) + + # Use the same resampling across all sea level components (to preserve + # correlations) + resamples = np.random.choice(nesm, nsmps) + rfpi = np.random.randint(nFPs, size=nsmps) + + # The "offset_slopes" scales the global_offset variables into the different + # sea level components. + # These offsets exist because a baseline period of 1981-2000 is used, + # rather than the AR5 baseline period of 1986-2005. + offset_slopes = {'exp': 0.405, + 'antnet': 0.095, + 'antsmb': -0.025, + 'antdyn': 0.120, + 'greennet': 0.125, + 'greensmb': 0.040, + 'greendyn': 0.085, + 'glacier': 0.270, + 'landwater': 0.105} + + # Calculate the GIA contribution to the regional component of sea level + # change + GIA_series, G_offset = calc_gia_contribution(sci_method, yrs, nyrs, nsmps) + + GIA_series = GIA_series.compute() + + sealev_ddir = read_dir()[4] + file_header = '_'.join(['gia', scenario, "projection", + f"{settings['projection_end_year']}"]) + R_file = '_'.join([file_header, 'regional']) + '.npy' + + np.save(os.path.join(sealev_ddir, R_file), GIA_series) + del GIA_series + + for cc, comp in enumerate(components): + # Monte Carlo for regional values (FPs applied) + GIA + montecarlo_R = da.zeros((nsmps, nyrs, lats, lons), dtype=np.float16) + # Monte Carlo for Global values (no FPs applied) + montecarlo_G = da.zeros((nsmps, nyrs, lats, lons), dtype=np.float16) + + print(f'cc = {cc:d}, comp = {comp}') + offset = G_offset * offset_slopes[comp] + + if settings["emulator_settings"]["emulator_mode"]: + # Input timeseries provided as numpy objects + mc_timeseries = np.load(os.path.join(mcdir, f'{scenario}_{comp}.npy')) + offset_mc = mc_timeseries[resamples, :nyrs] + offset + montecarlo_G[:, :] = da.from_array(offset_mc[:, :, None, None]) + else: + cube = iris.load_cube(os.path.join(mcdir, f'{scenario}_{comp}.nc')) + offset_mc = cube.data[:nyrs, resamples] + offset + montecarlo_G[:, :] = offset_mc[:, :, None, None] + + if comp == 'exp': + if sci_method == 'global': + if settings["emulator_settings"]["emulator_mode"]: + coeffs = load_CMIP5_slope_coeffs('rcp26') + else: + coeffs = load_CMIP5_slope_coeffs(scenario) + rand_samples = np.random.choice(coeffs.shape[0], size=nsmps, + replace=True) + rand_coeffs = coeffs[rand_samples, :, :] + rand_coeffs = da.from_array(rand_coeffs) + elif sci_method == 'UK': + if settings["emulator_settings"]["emulator_mode"]: + coeffs, weights = load_CMIP5_slope_coeffs_UK('rcp85') + else: + coeffs, weights = load_CMIP5_slope_coeffs_UK(scenario) + rand_coeffs = np.random.choice(coeffs, size=nsmps, + replace=True, p=weights) + + montecarlo_R[:, :, :, :] = montecarlo_G[:, :, :, :] * rand_coeffs[:, None, :, :] + del rand_coeffs + + elif comp == 'landwater': + landwater_FP_interpolator = FPlist[0]['landwater'] + # Interpolate values + vals = da.from_array(landwater_FP_interpolator.values.astype(np.float16).data) + vals = np.roll(vals, 180, axis=1) + montecarlo_R[:, :, :, :] = montecarlo_G[:, :, :, :] * vals[None, None, :, :] + else: + # Initiate an empty list for fingerprint values + FPvals = [] + for FP_dict in FPlist: + # Interpolate values to target lat/lon + val = FP_dict[comp].values + if val.shape != (lats, lons): + original_da = xr.DataArray( + val, + coords=[("lat", np.linspace(90, -90, 360)), ("lon", np.linspace(-180, 180, 720, endpoint=False))], + name="v") + target_lat = np.linspace(90, -90, 180) + target_lon = np.linspace(-180, 180, 360, endpoint=False) + val = original_da.interp(lat=target_lat, lon=target_lon, method="linear").data + val = np.roll(val, 180, axis=1) + else: + val = np.roll(val, 180, axis=1) + + FPvals.append(val) + FPvals = da.from_array(np.array(FPvals, dtype=np.float16)) + montecarlo_R[:, :, :, :] = montecarlo_G[:, :, :, :] * FPvals[rfpi][:, None, :, :] + del FPvals + + # Take the 0th, 25th, 50th, 75th and 100th percentiles + montecarlo_R = da.percentile(montecarlo_R, [0, 25, 50, 75, 100], axis=0) + + montecarlo_R = montecarlo_R.compute() + + # Create the output sea level projections file directory and filename + sealev_ddir = read_dir()[4] + file_header = '_'.join([comp, scenario, "projection", + f"{settings['projection_end_year']}"]) + # G_file = '_'.join([file_header, 'global']) + '.npy' + R_file = '_'.join([file_header, 'regional']) + '.npy' + + # Save the global and local projections + makefolder(sealev_ddir) + # montecarlo_R.to_netcdf(os.path.join(sealev_ddir, R_file)) + # np.save(os.path.join(sealev_ddir, G_file), montecarlo_G) + np.save(os.path.join(sealev_ddir, R_file), montecarlo_R) + + return montecarlo_R + + +def create_FP_interpolator(datadir, dfile, method='linear'): + """ + Generates a scipy Interpolator object from input NetCDF data of + gravitational fingerprints (takes inputs of Latitude and Longitude). + :param datadir: data directory + :param dfile: data filename + :param method: interpolation type --> 'linear' or 'nearest' + :return: 2D Interpolator object + """ + cube = iris.load_cube(os.path.join(datadir, dfile)) + lon = cube.coord('longitude').points + lat = cube.coord('latitude').points + + # Define linear interpolator object: + interp_object = RegularGridInterpolator((lat, lon), cube.data, + method=method, bounds_error=True, + fill_value=None) + + return interp_object + + +def get_projection_info(indir, scenario): + """ + Read in the dimensions of the Monte-Carlo data. These files are all + relative to midnight on 1st January 2007 + :param indir: directory of Monte Carlo time series for new projections + :param scenario: emission scenarios to be considered + :return: Number of ensemble members in time series, number of years in + each projection time series and the years of the projections + """ + sample_file = f'{scenario}_exp.nc' + f = Dataset(f'{indir}{sample_file}', 'r') + nesm = f.dimensions['realization'].size + t = f.variables['time'] + nyrs = t.size + unit_str = t.units + first_year = int(unit_str.split(' ')[2][:4]) + f.close() + + yrs = first_year + np.arange(nyrs) + + return nesm, nyrs, yrs + + +def load_CMIP5_slope_coeffs(scenario): + """ + Loads in the CMIP slope coefficients based on linear regression of + 'zos+zostoga' against 'zostoga' for the period 2005 to 2100. + Some models are missing regression slopes for RCP2.6. If so, use RCP4.5 + values instead. + :param site_loc: name of the site location + :param scenario: emissions scenario + :return: 1D array of regression coefficients + """ + print('running function load_CMIP5_slope_coeffs') + # Read in the sea level regressions + in_zosddir = read_dir()[2] + filename = os.path.join(in_zosddir, 'zos_regression.npy') + coeffs = np.load(filename) + scenario_index = ['rcp26', 'rcp45', 'rcp85'].index(scenario) + coeffs = coeffs[:, scenario_index, :, :] + coeffs[np.isnan(coeffs)] = 0 + coeffs[coeffs > 999] = 0 + coeffs[coeffs < -999] = 0 + + return coeffs + + +def load_CMIP5_slope_coeffs_UK(scenario): + """ + Loads in the CMIP5 slope coefficients developed for UKCP18 + :param scenario: emissions scenario + :return: 1D array of slope coefficients and 1D array of weights + """ + print('running function load_CMIP5_slope_coeffs_UK') + in_zosdir_uk = settings["cmipinfo"]["slopecoeffsuk"] + filename_uk = f'{scenario}_CMIP5_regress_coeffs_uk_mask_1.pickle' + + try: + with open(os.path.join(in_zosdir_uk, filename_uk), 'rb') as f: + data = pickle.load(f, encoding='latin1')['uk_mask_1'] + except FileNotFoundError: + raise FileNotFoundError(filename_uk, + '- scenario selected does not exist') + + # Keys are: 'coeffs', 'models', 'weights' + coeffs = data['coeffs'] + weights = data['weights'] + + return coeffs, weights + + +def read_gia_estimates(sci_method): + """ + Read in pre-processed interpolator objects of GIA estimates (Lambeck, + ICE5G or UK specific ones) + :param sci_method: scientific method used to select GIA estimates, GRD + fingerprints and CMIP regression (based on user input) + :param coords: latitude and longitude of tide gauge + :return: length of GIA_vals and numpy array of pre-processed interpolator + objects of GIA estimates + """ + print('running function read_gia_estimates') + # Directories containing GIA data (independent of scenario) + if sci_method == 'global': + gia_file = settings["giaestimates"]["global"] + elif sci_method == 'UK': + gia_file = settings["giaestimates"]["uk"] + else: + raise UnboundLocalError('The selected GIA estimate - ' + + f'{sci_method} - is not available') + + with open(gia_file, "rb") as ifp: + GIA_dict = pickle.load(ifp, encoding='latin1') + + GIA_vals = [] + # The GIA_dict contains interpolator objects + for key in list(GIA_dict.keys()): + val = GIA_dict[key].values + GIA_vals.append(val) + + nGIA = len(GIA_vals) + GIA_vals = np.array(GIA_vals) + + # Sort out the crazy values in the 0th GIA array + GIA_vals[0][GIA_vals[0] < -99999] = 0 + # AND shift them from -180, 180 to 0, 360 + GIA_vals = np.roll(GIA_vals, 180, axis=2) + + return nGIA, GIA_vals + + +def setup_FP_interpolators(components, sci_method): + """ + Create 2D Interpolator objects for the Slangen, Spada and Klemann + fingerprints + :param components: list of sea level components + :param sci_method: scientific method used to select GIA estimates, GRD + fingerprints and CMIP regression (based on user input) + :return nFPs: length of FPlist and interpolator objects of all sea level + components + """ + print('running function setup_FP_interpolators') + + # Directories for the Slangen, Spada and Klemann fingerprints + slangendir = settings["fingerprints"]["slangendir"] + spadadir = settings["fingerprints"]["spadadir"] + klemanndir = settings["fingerprints"]["klemanndir"] + + # Create empty dictionaries for the Slangen, Spada and Klemann fingerprints + # interpolator objects. + slangen_FPs = {} + spada_FPs = {} + klemann_FPs = {} + + # Only 1 fingerprint for Landwater + comp = 'landwater' + slangen_FPs[comp] = create_FP_interpolator(slangendir, + comp + '_slangen_nomask.nc') + + # Create interpolators for the remaining components. Expansion ('exp') + # is global so no interpolation is needed. + components_todo = [c for c in components if c not in ['exp', 'landwater']] + for comp in components_todo: + slangen_FPs[comp] = create_FP_interpolator(slangendir, + comp + '_slangen_nomask.nc') + spada_FPs[comp] = create_FP_interpolator(spadadir, + comp + '_spada_nomask.nc') + klemann_FPs[comp] = create_FP_interpolator(klemanndir, + comp + '_klemann_nomask.nc') + + if sci_method == 'UK': + # Klemann fingerprints were not used in UKCP18 + FPlist = [slangen_FPs, spada_FPs] + elif sci_method == 'global': + FPlist = [slangen_FPs, spada_FPs, klemann_FPs] + else: + raise UnboundLocalError('The selected GRD fingerprint method - ' + + f'{sci_method} - is not available') + + nFPs = len(FPlist) + + return nFPs, FPlist + + +def read_csv_file(file_pattern: str, start_yr: int=2007, + end_yr: int=settings["projection_end_year"]): + file = glob.glob( + os.path.join( + settings["emulator_settings"]["emulator_input_dir"], + file_pattern)) + if not file: + raise FileNotFoundError(f'File {file_pattern} not found') + df = pd.read_csv(file[0]) + return df.loc[:, str(start_yr):str(end_yr)].to_numpy() + + +def main(): + """ + Reads in and calculates global and local (regional) sea level change + (sum total), based on the different contributing factors e.g. thermal + expansion, GIA and mass balance. Writes out the selected emissions scenario + estimates of the various components and their sums. + """ + print(f'\nProjecting out to: {settings["projection_end_year"]}\n') + + # Extract site data from station list (e.g. tide gauge location) or + # construct based on user input + if settings["emulator_settings"]["emulator_mode"]: + print('\nInitiating ProFSea emulator') + if settings["projection_end_year"] > 2100: + palmer_method = True + else: + palmer_method = False + + makefolder(os.path.join(settings["baseoutdir"], 'emulator_output')) + + # Get the metadata of either the site location or tide gauge location + for scenario in settings["emulator_settings"]["emulator_scenario"]: + # print(f'Projecting {scenario}...') + # T_change = read_csv_file(f'*{scenario}*_temperature*.csv') + # OHC_change = read_csv_file(f'*{scenario}*_ocean_heat_content_change*.csv') + + # cum_emissions_file = 'cumulative_scenario_emissions.json' + # if glob.glob(os.path.join(settings["emulator_settings"]["emulator_input_dir"], cum_emissions_file)): + # with open('ngfs_data/cumulative_scenario_emissions.json') as f: + # cum_emissions_total = json.load(f)[scenario] + # else: + # raise FileNotFoundError('For any non-RCP scenario, a cumulative emissions total must be provided.') + + # gmslr = GMSLREmulator( + # T_change, + # OHC_change, + # scenario, + # os.path.join(settings["baseoutdir"], 'emulator_output'), + # settings["projection_end_year"], + # palmer_method=palmer_method, + # input_ensemble=settings["emulator_settings"]["use_input_ensemble"], + # cum_emissions_total=cum_emissions_total) + # gmslr.project() + # print('Saving components...') + # gmslr.save_components( + # os.path.join(settings["baseoutdir"], 'emulator_output'), + # scenario) + # print('Saved!\n') + + calc_future_sea_level(scenario) + else: + scenarios = ['rcp26', 'rcp45', 'rcp85'] + for scenario in scenarios: + calc_future_sea_level(scenario) + + +if __name__ == '__main__': + main() From 3fe0dcbbcb7a3adedf18b4ad0d6930a2c61476f7 Mon Sep 17 00:00:00 2001 From: Gregory Munday Date: Fri, 23 May 2025 08:46:23 +0100 Subject: [PATCH 022/276] Update to fp32 --- ...step3_process_regional_sealevel_projections_spatial.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/profsea/step3_process_regional_sealevel_projections_spatial.py b/profsea/step3_process_regional_sealevel_projections_spatial.py index 44a5cbc..05a032e 100644 --- a/profsea/step3_process_regional_sealevel_projections_spatial.py +++ b/profsea/step3_process_regional_sealevel_projections_spatial.py @@ -189,9 +189,9 @@ def calculate_sl_components(mcdir, components, scenario, yrs, array_dims): for cc, comp in enumerate(components): # Monte Carlo for regional values (FPs applied) + GIA - montecarlo_R = da.zeros((nsmps, nyrs, lats, lons), dtype=np.float16) + montecarlo_R = da.zeros((nsmps, nyrs, lats, lons), dtype=np.float32) # Monte Carlo for Global values (no FPs applied) - montecarlo_G = da.zeros((nsmps, nyrs, lats, lons), dtype=np.float16) + montecarlo_G = da.zeros((nsmps, nyrs, lats, lons), dtype=np.float32) print(f'cc = {cc:d}, comp = {comp}') offset = G_offset * offset_slopes[comp] @@ -230,7 +230,7 @@ def calculate_sl_components(mcdir, components, scenario, yrs, array_dims): elif comp == 'landwater': landwater_FP_interpolator = FPlist[0]['landwater'] # Interpolate values - vals = da.from_array(landwater_FP_interpolator.values.astype(np.float16).data) + vals = da.from_array(landwater_FP_interpolator.values.astype(np.float32).data) vals = np.roll(vals, 180, axis=1) montecarlo_R[:, :, :, :] = montecarlo_G[:, :, :, :] * vals[None, None, :, :] else: @@ -252,7 +252,7 @@ def calculate_sl_components(mcdir, components, scenario, yrs, array_dims): val = np.roll(val, 180, axis=1) FPvals.append(val) - FPvals = da.from_array(np.array(FPvals, dtype=np.float16)) + FPvals = da.from_array(np.array(FPvals, dtype=np.float32)) montecarlo_R[:, :, :, :] = montecarlo_G[:, :, :, :] * FPvals[rfpi][:, None, :, :] del FPvals From 3c24f41286158180e357d29d0152b61c8f7e617e Mon Sep 17 00:00:00 2001 From: Gregory Munday Date: Sat, 24 May 2025 14:09:24 +0100 Subject: [PATCH 023/276] Added CMIP6 patterns code + big refactor --- ...ions_spatial.py => spatial_projections.py} | 370 ++++++++---------- 1 file changed, 156 insertions(+), 214 deletions(-) rename profsea/{step3_process_regional_sealevel_projections_spatial.py => spatial_projections.py} (57%) diff --git a/profsea/step3_process_regional_sealevel_projections_spatial.py b/profsea/spatial_projections.py similarity index 57% rename from profsea/step3_process_regional_sealevel_projections_spatial.py rename to profsea/spatial_projections.py index 05a032e..9dd2687 100644 --- a/profsea/step3_process_regional_sealevel_projections_spatial.py +++ b/profsea/spatial_projections.py @@ -6,7 +6,6 @@ import os import iris import glob -import json import pickle import numpy as np import pandas as pd @@ -18,36 +17,23 @@ from profsea.config import settings from profsea.slr_pkg import choose_montecarlo_dir # found in __init.py__ from profsea.directories import read_dir, makefolder -from emulator import GMSLREmulator -def calc_baseline_period(sci_method, yrs): +def calc_baseline_period(yrs: np.array) -> float: """ - Baseline years used for UKCP18 -- 1981-2000 (offset required) Baseline years used for IPCC AR5 and Palmer et al 2020 -- 1986-2005 - :param sci_method: scientific method used to select GIA estimates, GRD - fingerprints and CMIP regression (based on user input) :param yrs: years of the projections :return: baseline years """ - if sci_method == 'global': - byr1 = 1986. - byr2 = 2005. - # offset not required - G_offset = 0.0 - print("Baseline period = ", byr1, "to", byr2) - elif sci_method == 'UK': - byr1 = 1981. - byr2 = 2000. - G_offset = 0.011 - print("Baseline period = ", byr1, "to", byr2) + byr1 = 1986. + byr2 = 2005. + print("Baseline period = ", byr1, "to", byr2) midyr = (byr2 - byr1 + 1) * 0.5 + byr1 + return yrs[0] - midyr - return yrs[0] - midyr, G_offset - -def calc_future_sea_level(scenario): +def calc_future_sea_level(scenario: str) -> None: """ Calculates future sea level at the given site and write to file. :param df: Data frame of all metadata (tide gauge or site specific) for @@ -71,45 +57,35 @@ def calc_future_sea_level(scenario): # Select dimensions from sample file, [time, realisation] sample = np.load(os.path.join(mcdir, f'{scenario}_exp.npy')) - nesm = sample.shape[0] + nesm = sample.shape[0] # also number of samples to make nyrs = sample.shape[1] yrs = np.arange(2007, 2007 + nyrs) - grid_path = os.path.join(settings["baseoutdir"], "full_earth/data/zos_regression/zos_regression.npy") - grid_sample = np.load(grid_path)[0, 0] - - # Determine the number of samples you wish to make CHOGOM - # project = 100000, UKCP18 = 200000 - if nesm >= 200000: - nsmps = 200000 - else: - nsmps = nesm - - array_dims = [nesm, nsmps, nyrs, grid_sample.shape[0], grid_sample.shape[1]] + grid_path = os.path.join( + settings["cmipinfo"]["sealevelbasedir"], + "ACCESS-CM2/zos_regression_ssp245_ACCESS-CM2.npy") + grid_sample = np.load(grid_path) + array_dims = [nesm, nesm, nyrs, grid_sample.shape[0], grid_sample.shape[1]] # Get random samples of global and regional sea level components calculate_sl_components(mcdir, components, scenario, yrs, array_dims) -def calc_gia_contribution(sci_method, yrs, nyrs, nsmps): +def calc_gia_contribution( + yrs: np.array, nyrs: int, nsmps: int, scenario: str) -> None: """ Calculate the glacial isostatic adjustment (GIA) contribution to the regional component of sea level rise. Option to use the generic global GIA estimates or use the GIA estimates developed for UK as part of UKCP18. - :param sci_method: scientific method used to select GIA estimates, GRD - fingerprints and CMIP regression (based on user input) :param yrs: years of the projections :param nyrs: number of years in each projection time series :param nsmps: determine the number of samples :param coords: coordinates of location of interest :return: GIA estimates converted to mm/yr """ - print('running function calc_gia_contribution') - - nGIA, GIA_vals = read_gia_estimates(sci_method) - - Tdelta, G_offset = calc_baseline_period(sci_method, yrs) + nGIA, GIA_vals = read_gia_estimates() + Tdelta = calc_baseline_period(yrs) # Unit series of mm/yr expressed as m/yr unit_series = (np.arange(nyrs) + Tdelta) * 0.001 @@ -123,10 +99,103 @@ def calc_gia_contribution(sci_method, yrs, nyrs, nsmps): GIA_series = GIA_T[:, :, None, None] * GIA_vals[rgiai, None, :, :] - return GIA_series, G_offset + GIA_series = GIA_series.compute() + + file_header = '_'.join(['gia', scenario, "projection", + f"{settings['projection_end_year']}"]) + R_file = '_'.join([file_header, 'regional']) + '.npy' + + np.save(os.path.join(read_dir()[4], R_file), GIA_series) + del GIA_series + + +def calc_expansion_contribution( + scenario: str, nsmps: int, nyrs: int) -> da.array: + """ + Calculate the thermal expansion contribution to the regional component of + sea level rise. + :param scenario: emission scenario + :param nsmps: determine the number of samples + :param nyrs: number of years in each projection time series + :return: expansion estimates converted to mm/yr + """ + # Select slope coefficients based on the MIP + if settings["cmipinfo"]["mip"] == "CMIP6": + coeffs = load_CMIP6_slopes('ssp585') + else: + coeffs = load_CMIP5_slope_coeffs('rcp85') + + rand_samples = np.random.choice( + coeffs.shape[0], size=nsmps, replace=True) + rand_coeffs = coeffs[rand_samples, :, :] + rand_coeffs = da.from_array(rand_coeffs) + return rand_coeffs + + +def calc_landwater_contribution(interpolator: dict) -> da.array: + """ + Calculate the regional landwater contribution to sea level rise. + :param interpolator: dictionary of interpolator objects + :return: numpy array of landwater values + """ + landwater_FP_interpolator = interpolator['landwater'] + landwater_vals = da.from_array( + landwater_FP_interpolator.values.astype(np.float32).data) + landwater_vals = da.roll(landwater_vals, 180, axis=1) + return landwater_vals + + +def calc_fingerprint_contributions( + FPlist: list, comp: str, lats: int, lons: int) -> da.array: + # Initiate an empty list for fingerprint values + FPvals = [] + for FP_dict in FPlist: + # Interpolate values to target lat/lon + val = FP_dict[comp].values + if val.shape != (lats, lons): + original_da = xr.DataArray( + val, + coords=[ + ("lat", np.linspace(90, -90, 360)), + ("lon", np.linspace(-180, 180, 720, endpoint=False)) + ], + name="v") + target_lat = np.linspace(90, -90, 180) + target_lon = np.linspace(-180, 180, 360, endpoint=False) + val = original_da.interp( + lat=target_lat, lon=target_lon, method="linear").data + val = np.roll(val, 180, axis=1) + else: + val = np.roll(val, 180, axis=1) + + FPvals.append(val) + + FPvals = da.from_array(np.array(FPvals, dtype=np.float32)) + return FPvals -def calculate_sl_components(mcdir, components, scenario, yrs, array_dims): +def save_projections( + montecarlo_R: da.array, component: str, scenario: str) -> None: + """ + Save the regional sea level projections to a file. + :param montecarlo_R: regional sea level projections + :param component: sea level component + :param scenario: emission scenario + """ + sealev_ddir = read_dir()[4] + file_header = '_'.join([component, scenario, "projection", + f"{settings['projection_end_year']}"]) + # G_file = '_'.join([file_header, 'global']) + '.npy' + R_file = '_'.join([file_header, 'regional']) + '.npy' + + # Save the global and local projections + makefolder(sealev_ddir) + np.save(os.path.join(sealev_ddir, R_file), montecarlo_R) + + +def calculate_sl_components( + mcdir: str, components: list, scenario: str, + yrs: np.array, array_dims: list) -> None: """ Calculates global and regional component part contributions to sea level change. @@ -139,142 +208,47 @@ def calculate_sl_components(mcdir, components, scenario, yrs, array_dims): nsmps --> Determine the number of samples you wish to make nyrs --> Number of years in each projection time series :return: montecarlo_G (global contribution to sea level rise) and - montecarlo_R (regional contribution to sea level change) - """ - print('running function calculate_sl_components') - + montecarlo_R (regional contribution to sea level change) + """ # Numbers of ensemble members, samples, years nesm, nsmps, nyrs, lats, lons = array_dims - - # Scientific method - sci_method = settings["sciencemethod"] - - # Set up fingerprint interpolators for each component of sea level change - # Note that the first entry in the list is the Slangen, which includes the - # land water estimate - nFPs, FPlist = setup_FP_interpolators(components, sci_method) - - # Use the same resampling across all sea level components (to preserve - # correlations) - resamples = np.random.choice(nesm, nsmps) + nFPs, FPlist = setup_FP_interpolators(components) + resamples = np.random.choice(nesm, nsmps) # Preserve correlations across comps rfpi = np.random.randint(nFPs, size=nsmps) - # The "offset_slopes" scales the global_offset variables into the different - # sea level components. - # These offsets exist because a baseline period of 1981-2000 is used, - # rather than the AR5 baseline period of 1986-2005. - offset_slopes = {'exp': 0.405, - 'antnet': 0.095, - 'antsmb': -0.025, - 'antdyn': 0.120, - 'greennet': 0.125, - 'greensmb': 0.040, - 'greendyn': 0.085, - 'glacier': 0.270, - 'landwater': 0.105} - - # Calculate the GIA contribution to the regional component of sea level - # change - GIA_series, G_offset = calc_gia_contribution(sci_method, yrs, nyrs, nsmps) - - GIA_series = GIA_series.compute() - - sealev_ddir = read_dir()[4] - file_header = '_'.join(['gia', scenario, "projection", - f"{settings['projection_end_year']}"]) - R_file = '_'.join([file_header, 'regional']) + '.npy' - - np.save(os.path.join(sealev_ddir, R_file), GIA_series) - del GIA_series - - for cc, comp in enumerate(components): - # Monte Carlo for regional values (FPs applied) + GIA - montecarlo_R = da.zeros((nsmps, nyrs, lats, lons), dtype=np.float32) - # Monte Carlo for Global values (no FPs applied) - montecarlo_G = da.zeros((nsmps, nyrs, lats, lons), dtype=np.float32) + # Calculate GIA contribution and save it out + calc_gia_contribution(yrs, nyrs, nsmps, scenario) - print(f'cc = {cc:d}, comp = {comp}') - offset = G_offset * offset_slopes[comp] + for comp in components: + print(f'\nComponent: {comp}') + montecarlo_R = da.zeros((nsmps, nyrs, lats, lons), dtype=np.float32) # (FPs applied) + GIA + montecarlo_G = da.zeros((nsmps, nyrs, lats, lons), dtype=np.float32) # (no FPs applied) - if settings["emulator_settings"]["emulator_mode"]: - # Input timeseries provided as numpy objects - mc_timeseries = np.load(os.path.join(mcdir, f'{scenario}_{comp}.npy')) - offset_mc = mc_timeseries[resamples, :nyrs] + offset - montecarlo_G[:, :] = da.from_array(offset_mc[:, :, None, None]) - else: - cube = iris.load_cube(os.path.join(mcdir, f'{scenario}_{comp}.nc')) - offset_mc = cube.data[:nyrs, resamples] + offset - montecarlo_G[:, :] = offset_mc[:, :, None, None] + # Load global projections in for the component + mc_timeseries = np.load(os.path.join(mcdir, f'{scenario}_{comp}.npy')) + sampled_mc = mc_timeseries[resamples, :nyrs] + montecarlo_G[:, :] = da.from_array(sampled_mc[:, :, None, None]) if comp == 'exp': - if sci_method == 'global': - if settings["emulator_settings"]["emulator_mode"]: - coeffs = load_CMIP5_slope_coeffs('rcp26') - else: - coeffs = load_CMIP5_slope_coeffs(scenario) - rand_samples = np.random.choice(coeffs.shape[0], size=nsmps, - replace=True) - rand_coeffs = coeffs[rand_samples, :, :] - rand_coeffs = da.from_array(rand_coeffs) - elif sci_method == 'UK': - if settings["emulator_settings"]["emulator_mode"]: - coeffs, weights = load_CMIP5_slope_coeffs_UK('rcp85') - else: - coeffs, weights = load_CMIP5_slope_coeffs_UK(scenario) - rand_coeffs = np.random.choice(coeffs, size=nsmps, - replace=True, p=weights) - - montecarlo_R[:, :, :, :] = montecarlo_G[:, :, :, :] * rand_coeffs[:, None, :, :] - del rand_coeffs + sampled_coeffs = calc_expansion_contribution(scenario, nsmps, nyrs) + montecarlo_R[:, :, :, :] = montecarlo_G[:, :, :, :] * sampled_coeffs[:, None, :, :] + del sampled_coeffs elif comp == 'landwater': - landwater_FP_interpolator = FPlist[0]['landwater'] - # Interpolate values - vals = da.from_array(landwater_FP_interpolator.values.astype(np.float32).data) - vals = np.roll(vals, 180, axis=1) - montecarlo_R[:, :, :, :] = montecarlo_G[:, :, :, :] * vals[None, None, :, :] + landwater_vals = calc_landwater_contribution(FPlist[0]) + montecarlo_R[:, :, :, :] = montecarlo_G[:, :, :, :] * landwater_vals[None, None, :, :] + del landwater_vals else: - # Initiate an empty list for fingerprint values - FPvals = [] - for FP_dict in FPlist: - # Interpolate values to target lat/lon - val = FP_dict[comp].values - if val.shape != (lats, lons): - original_da = xr.DataArray( - val, - coords=[("lat", np.linspace(90, -90, 360)), ("lon", np.linspace(-180, 180, 720, endpoint=False))], - name="v") - target_lat = np.linspace(90, -90, 180) - target_lon = np.linspace(-180, 180, 360, endpoint=False) - val = original_da.interp(lat=target_lat, lon=target_lon, method="linear").data - val = np.roll(val, 180, axis=1) - else: - val = np.roll(val, 180, axis=1) - - FPvals.append(val) - FPvals = da.from_array(np.array(FPvals, dtype=np.float32)) + FPvals = calc_fingerprint_contributions(FPlist, comp, lats, lons) montecarlo_R[:, :, :, :] = montecarlo_G[:, :, :, :] * FPvals[rfpi][:, None, :, :] del FPvals # Take the 0th, 25th, 50th, 75th and 100th percentiles montecarlo_R = da.percentile(montecarlo_R, [0, 25, 50, 75, 100], axis=0) - montecarlo_R = montecarlo_R.compute() # Create the output sea level projections file directory and filename - sealev_ddir = read_dir()[4] - file_header = '_'.join([comp, scenario, "projection", - f"{settings['projection_end_year']}"]) - # G_file = '_'.join([file_header, 'global']) + '.npy' - R_file = '_'.join([file_header, 'regional']) + '.npy' - - # Save the global and local projections - makefolder(sealev_ddir) - # montecarlo_R.to_netcdf(os.path.join(sealev_ddir, R_file)) - # np.save(os.path.join(sealev_ddir, G_file), montecarlo_G) - np.save(os.path.join(sealev_ddir, R_file), montecarlo_R) - - return montecarlo_R + save_projections(montecarlo_R, comp, scenario) def create_FP_interpolator(datadir, dfile, method='linear'): @@ -291,10 +265,10 @@ def create_FP_interpolator(datadir, dfile, method='linear'): lat = cube.coord('latitude').points # Define linear interpolator object: - interp_object = RegularGridInterpolator((lat, lon), cube.data, - method=method, bounds_error=True, - fill_value=None) - + interp_object = RegularGridInterpolator( + (lat, lon), cube.data, + method=method, bounds_error=True, + fill_value=None) return interp_object @@ -317,7 +291,6 @@ def get_projection_info(indir, scenario): f.close() yrs = first_year + np.arange(nyrs) - return nesm, nyrs, yrs @@ -341,59 +314,39 @@ def load_CMIP5_slope_coeffs(scenario): coeffs[np.isnan(coeffs)] = 0 coeffs[coeffs > 999] = 0 coeffs[coeffs < -999] = 0 - return coeffs -def load_CMIP5_slope_coeffs_UK(scenario): +def load_CMIP6_slopes(scenario): """ - Loads in the CMIP5 slope coefficients developed for UKCP18 + Load in the CMIP6 slope coefficients. + :param site_loc: name of the site location :param scenario: emissions scenario - :return: 1D array of slope coefficients and 1D array of weights + :return: 1D array of regression coefficients """ - print('running function load_CMIP5_slope_coeffs_UK') - in_zosdir_uk = settings["cmipinfo"]["slopecoeffsuk"] - filename_uk = f'{scenario}_CMIP5_regress_coeffs_uk_mask_1.pickle' - - try: - with open(os.path.join(in_zosdir_uk, filename_uk), 'rb') as f: - data = pickle.load(f, encoding='latin1')['uk_mask_1'] - except FileNotFoundError: - raise FileNotFoundError(filename_uk, - '- scenario selected does not exist') - - # Keys are: 'coeffs', 'models', 'weights' - coeffs = data['coeffs'] - weights = data['weights'] + # Read in the sea level regressions + cmip6_dir = settings["cmipinfo"]["sealevelbasedir"] + slope_files = glob.glob(cmip6_dir + f'/*/zos_regression_{scenario}_*.npy') + slopes = np.zeros((len(slope_files), 180, 360), dtype=np.float32) + for i, slope_file in enumerate(slope_files): + slopes[i, :, :] = np.load(slope_file) - return coeffs, weights + return slopes -def read_gia_estimates(sci_method): +def read_gia_estimates(): """ Read in pre-processed interpolator objects of GIA estimates (Lambeck, - ICE5G or UK specific ones) - :param sci_method: scientific method used to select GIA estimates, GRD - fingerprints and CMIP regression (based on user input) - :param coords: latitude and longitude of tide gauge + ICE5G) + :param: none :return: length of GIA_vals and numpy array of pre-processed interpolator objects of GIA estimates """ - print('running function read_gia_estimates') - # Directories containing GIA data (independent of scenario) - if sci_method == 'global': - gia_file = settings["giaestimates"]["global"] - elif sci_method == 'UK': - gia_file = settings["giaestimates"]["uk"] - else: - raise UnboundLocalError('The selected GIA estimate - ' + - f'{sci_method} - is not available') - + gia_file = settings["giaestimates"]["global"] with open(gia_file, "rb") as ifp: - GIA_dict = pickle.load(ifp, encoding='latin1') + GIA_dict = pickle.load(ifp, encoding='latin1') # Interp objects GIA_vals = [] - # The GIA_dict contains interpolator objects for key in list(GIA_dict.keys()): val = GIA_dict[key].values GIA_vals.append(val) @@ -403,19 +356,17 @@ def read_gia_estimates(sci_method): # Sort out the crazy values in the 0th GIA array GIA_vals[0][GIA_vals[0] < -99999] = 0 + # AND shift them from -180, 180 to 0, 360 GIA_vals = np.roll(GIA_vals, 180, axis=2) - return nGIA, GIA_vals -def setup_FP_interpolators(components, sci_method): +def setup_FP_interpolators(components): """ Create 2D Interpolator objects for the Slangen, Spada and Klemann fingerprints :param components: list of sea level components - :param sci_method: scientific method used to select GIA estimates, GRD - fingerprints and CMIP regression (based on user input) :return nFPs: length of FPlist and interpolator objects of all sea level components """ @@ -448,17 +399,8 @@ def setup_FP_interpolators(components, sci_method): klemann_FPs[comp] = create_FP_interpolator(klemanndir, comp + '_klemann_nomask.nc') - if sci_method == 'UK': - # Klemann fingerprints were not used in UKCP18 - FPlist = [slangen_FPs, spada_FPs] - elif sci_method == 'global': - FPlist = [slangen_FPs, spada_FPs, klemann_FPs] - else: - raise UnboundLocalError('The selected GRD fingerprint method - ' + - f'{sci_method} - is not available') - + FPlist = [slangen_FPs, spada_FPs, klemann_FPs] nFPs = len(FPlist) - return nFPs, FPlist From d4b5a1d6f07af00878a968c26a534e4e2fadf08e Mon Sep 17 00:00:00 2001 From: Gregory Munday Date: Sat, 24 May 2025 14:56:05 +0100 Subject: [PATCH 024/276] Further refactor --- profsea/spatial_projections.py | 97 ++++++++++++++++------------------ 1 file changed, 47 insertions(+), 50 deletions(-) diff --git a/profsea/spatial_projections.py b/profsea/spatial_projections.py index 9dd2687..f9723ae 100644 --- a/profsea/spatial_projections.py +++ b/profsea/spatial_projections.py @@ -251,7 +251,8 @@ def calculate_sl_components( save_projections(montecarlo_R, comp, scenario) -def create_FP_interpolator(datadir, dfile, method='linear'): +def create_FP_interpolator( + datadir: str, dfile: str, method: str='linear') -> RegularGridInterpolator: """ Generates a scipy Interpolator object from input NetCDF data of gravitational fingerprints (takes inputs of Latitude and Longitude). @@ -272,7 +273,7 @@ def create_FP_interpolator(datadir, dfile, method='linear'): return interp_object -def get_projection_info(indir, scenario): +def get_projection_info(indir: str, scenario: str) -> tuple: """ Read in the dimensions of the Monte-Carlo data. These files are all relative to midnight on 1st January 2007 @@ -294,7 +295,7 @@ def get_projection_info(indir, scenario): return nesm, nyrs, yrs -def load_CMIP5_slope_coeffs(scenario): +def load_CMIP5_slope_coeffs(scenario: str) -> np.ndarray: """ Loads in the CMIP slope coefficients based on linear regression of 'zos+zostoga' against 'zostoga' for the period 2005 to 2100. @@ -317,7 +318,7 @@ def load_CMIP5_slope_coeffs(scenario): return coeffs -def load_CMIP6_slopes(scenario): +def load_CMIP6_slopes(scenario: str) -> np.ndarray: """ Load in the CMIP6 slope coefficients. :param site_loc: name of the site location @@ -334,7 +335,7 @@ def load_CMIP6_slopes(scenario): return slopes -def read_gia_estimates(): +def read_gia_estimates() -> tuple: """ Read in pre-processed interpolator objects of GIA estimates (Lambeck, ICE5G) @@ -362,7 +363,7 @@ def read_gia_estimates(): return nGIA, GIA_vals -def setup_FP_interpolators(components): +def setup_FP_interpolators(components: list) -> tuple: """ Create 2D Interpolator objects for the Slangen, Spada and Klemann fingerprints @@ -404,8 +405,9 @@ def setup_FP_interpolators(components): return nFPs, FPlist -def read_csv_file(file_pattern: str, start_yr: int=2007, - end_yr: int=settings["projection_end_year"]): +def read_csv_file( + file_pattern: str, start_yr: int=2007, + end_yr: int=settings["projection_end_year"]) -> np.ndarray: file = glob.glob( os.path.join( settings["emulator_settings"]["emulator_input_dir"], @@ -427,49 +429,44 @@ def main(): # Extract site data from station list (e.g. tide gauge location) or # construct based on user input - if settings["emulator_settings"]["emulator_mode"]: - print('\nInitiating ProFSea emulator') - if settings["projection_end_year"] > 2100: - palmer_method = True - else: - palmer_method = False - - makefolder(os.path.join(settings["baseoutdir"], 'emulator_output')) - - # Get the metadata of either the site location or tide gauge location - for scenario in settings["emulator_settings"]["emulator_scenario"]: - # print(f'Projecting {scenario}...') - # T_change = read_csv_file(f'*{scenario}*_temperature*.csv') - # OHC_change = read_csv_file(f'*{scenario}*_ocean_heat_content_change*.csv') - - # cum_emissions_file = 'cumulative_scenario_emissions.json' - # if glob.glob(os.path.join(settings["emulator_settings"]["emulator_input_dir"], cum_emissions_file)): - # with open('ngfs_data/cumulative_scenario_emissions.json') as f: - # cum_emissions_total = json.load(f)[scenario] - # else: - # raise FileNotFoundError('For any non-RCP scenario, a cumulative emissions total must be provided.') - - # gmslr = GMSLREmulator( - # T_change, - # OHC_change, - # scenario, - # os.path.join(settings["baseoutdir"], 'emulator_output'), - # settings["projection_end_year"], - # palmer_method=palmer_method, - # input_ensemble=settings["emulator_settings"]["use_input_ensemble"], - # cum_emissions_total=cum_emissions_total) - # gmslr.project() - # print('Saving components...') - # gmslr.save_components( - # os.path.join(settings["baseoutdir"], 'emulator_output'), - # scenario) - # print('Saved!\n') - - calc_future_sea_level(scenario) + print('\nInitiating ProFSea emulator') + if settings["projection_end_year"] > 2100: + palmer_method = True else: - scenarios = ['rcp26', 'rcp45', 'rcp85'] - for scenario in scenarios: - calc_future_sea_level(scenario) + palmer_method = False + + makefolder(os.path.join(settings["baseoutdir"], 'emulator_output')) + + # Get the metadata of either the site location or tide gauge location + for scenario in settings["emulator_settings"]["emulator_scenario"]: + # print(f'Projecting {scenario}...') + # T_change = read_csv_file(f'*{scenario}*_temperature*.csv') + # OHC_change = read_csv_file(f'*{scenario}*_ocean_heat_content_change*.csv') + + # cum_emissions_file = 'cumulative_scenario_emissions.json' + # if glob.glob(os.path.join(settings["emulator_settings"]["emulator_input_dir"], cum_emissions_file)): + # with open('ngfs_data/cumulative_scenario_emissions.json') as f: + # cum_emissions_total = json.load(f)[scenario] + # else: + # raise FileNotFoundError('For any non-RCP scenario, a cumulative emissions total must be provided.') + + # gmslr = GMSLREmulator( + # T_change, + # OHC_change, + # scenario, + # os.path.join(settings["baseoutdir"], 'emulator_output'), + # settings["projection_end_year"], + # palmer_method=palmer_method, + # input_ensemble=settings["emulator_settings"]["use_input_ensemble"], + # cum_emissions_total=cum_emissions_total) + # gmslr.project() + # print('Saving components...') + # gmslr.save_components( + # os.path.join(settings["baseoutdir"], 'emulator_output'), + # scenario) + # print('Saved!\n') + + calc_future_sea_level(scenario) if __name__ == '__main__': From 827d0e4b079d51b0a7040c7270abe8fab19b1d65 Mon Sep 17 00:00:00 2001 From: Gregory Munday Date: Mon, 26 May 2025 16:43:06 +0100 Subject: [PATCH 025/276] Updated landwater component to AR6 --- profsea/emulator/__init__.py | 110 ++++++++++++------ .../emulator/landwater_projections/.DS_Store | Bin 0 -> 6148 bytes .../ssp_global_landwater_projections.nc | Bin 0 -> 379400 bytes profsea/spatial_projections.py | 1 + ...3_process_regional_sealevel_projections.py | 6 +- 5 files changed, 76 insertions(+), 41 deletions(-) create mode 100644 profsea/emulator/landwater_projections/.DS_Store create mode 100644 profsea/emulator/landwater_projections/ssp_global_landwater_projections.nc diff --git a/profsea/emulator/__init__.py b/profsea/emulator/__init__.py index f9815e0..20ff58b 100644 --- a/profsea/emulator/__init__.py +++ b/profsea/emulator/__init__.py @@ -9,9 +9,11 @@ import os import concurrent from collections.abc import Sequence +from pathlib import Path import numpy as np from scipy.stats import norm +import xarray as xr class GMSLREmulator: """Emulator for global mean sea level rise components. @@ -77,23 +79,24 @@ class GMSLREmulator: """ def __init__( - self, - T_change: np.ndarray, - OHC_change: np.ndarray, - scenario: str, - output_dir: str, - end_yr: int, - seed: int=0, - nt: int=450, - nm: int=1000, - tcv: float=1.0, - glaciermip: bool|int=False, - input_ensemble: bool=True, - T_percentile_95: np.ndarray=None, - OHC_percentile_95: np.ndarray=None, - cum_emissions_total: np.ndarray=None, - palmer_method: bool=False): - + self, + T_change: np.ndarray, + OHC_change: np.ndarray, + scenario: str, + output_dir: str, + end_yr: int, + seed: int=42, + nt: int=450, + nm: int=1000, + tcv: float=1.0, + glaciermip: bool|int=False, + input_ensemble: bool=True, + T_percentile_95: np.ndarray=None, + OHC_percentile_95: np.ndarray=None, + cum_emissions_total: np.ndarray=None, + palmer_method: bool=False) -> None: + + np.random.seed(None if seed is None else seed) self.T_change = T_change self.OHC_change = OHC_change self.scenario = scenario @@ -133,7 +136,9 @@ def __init__( self.mSLEoGt = 1e12 / 3.61e14 * 1e-3 # Sensitivity of thermosteric SLR to ocean heat content change - self.exp_efficiency = 0.12e-24 + # From Turner et al. (2023) + self.exp_efficiency = np.random.normal( + loc=0.113, scale=0.013, size=OHC_change.shape[0]) * 1e-24 # m/YJ if input_ensemble: self.nt = self.T_change.shape[0] @@ -146,7 +151,8 @@ def __init__( 'of the scenario using the cum_emissions_total keyword argument.\n' 'This is required to calculate the Antarctic dynamic contribution to GMSLR.') - def get_components(self): + def get_components(self) -> dict: + """Get all GMSLR components as a dictionary.""" components_dict = { 'exp': self.expansion, 'glacier': self.glacier, @@ -162,7 +168,7 @@ def get_components(self): } return components_dict - def save_components(self, output_dir: str, scenario_name: str): + def save_components(self, output_dir: str, scenario_name: str) -> None: """Save all SLR components as .npy files to a directory. Parameters @@ -184,14 +190,13 @@ def save_components(self, output_dir: str, scenario_name: str): component.T # Transpose to match the shape of original Monte Carlo simulations ) - def project(self): + def project(self) -> None: """Run the emulator to project GMSLR components. Returns ------- None """ - np.random.seed(self.seed) T_ens, Exp_ens, T_int_ens, T_int_med = self.calculate_drivers() self.expansion = np.tile(Exp_ens, (self.nm, 1)) @@ -204,8 +209,9 @@ def project(self): self.sheetdyn = self.greendyn + self.antdyn self.gmslr = self.glacier + self.greennet + self.antnet + self.landwater + self.expansion - def run_parallel_projections(self, T_int_med: np.ndarray, T_int_ens: np.ndarray, - T_ens: np.ndarray, fraction: np.ndarray): + def run_parallel_projections( + self, T_int_med: np.ndarray, T_int_ens: np.ndarray, + T_ens: np.ndarray, fraction: np.ndarray) -> None: """Run components of the emulator in parallel. Returns @@ -236,7 +242,7 @@ def run_parallel_projections(self, T_int_med: np.ndarray, T_int_ens: np.ndarray, self.antdyn = results['antdyn'] self.landwater = results['landwater'] - def calculate_drivers(self): + def calculate_drivers(self) -> tuple: """Calculate the drivers of GMSLR: temperature change and thermosteric sea level rise. @@ -258,7 +264,7 @@ def calculate_drivers(self): if self.OHC_change.shape[1] != self.nyr: self.OHC_change = self.OHC_change.T - therm_ens = self.OHC_change * self.exp_efficiency + therm_ens = self.OHC_change * self.exp_efficiency[:, None] T_int_ens = np.cumsum(self.T_change, axis=1) T_int_med = np.percentile(T_int_ens, 50, axis=0) # using median here instead of mean... CHECK THIS @@ -305,7 +311,8 @@ def calculate_drivers(self): return T_ens, therm_ens, T_int_ens, T_int_med - def project_glacier(self, T_int_med: np.ndarray, T_int_ens: np.ndarray): + def project_glacier( + self, T_int_med: np.ndarray, T_int_ens: np.ndarray) -> np.ndarray: """Project glacier contribution to GMSLR. Parameters @@ -393,7 +400,9 @@ def project_glacier(self, T_int_med: np.ndarray, T_int_ens: np.ndarray): return glacier - def _project_glacier1(self, T_int: np.ndarray, factor: float, exponent: float): + def _project_glacier1( + self, T_int: np.ndarray, factor: float, + exponent: float) -> np.ndarray: """Project glacier contribution by one glacier method. Parameters @@ -413,7 +422,7 @@ def _project_glacier1(self, T_int: np.ndarray, factor: float, exponent: float): scale=1e-3 # mm to m return scale * factor * (np.where(T_int<0, 0, T_int)**exponent) - def project_greensmb(self, T_ens: np.ndarray): + def project_greensmb(self, T_ens: np.ndarray) -> np.ndarray: """Project Greenland SMB contribution to GMSLR. Parameters @@ -452,7 +461,7 @@ def project_greensmb(self, T_ens: np.ndarray): return greensmb - def _fettweis(self, ztgreen): + def _fettweis(self, ztgreen: np.ndarray) -> np.ndarray: """Calculate Greenland SMB in m yr-1 SLE from global mean temperature anomaly, using Eq 2 of Fettweis et al. (2013). @@ -468,7 +477,9 @@ def _fettweis(self, ztgreen): """ return (71.5*ztgreen+20.4*(ztgreen**2)+2.8*(ztgreen**3))*self.mSLEoGt - def project_antsmb(self, T_int_ens, fraction=None): + def project_antsmb( + self, T_int_ens: np.ndarray, + fraction: np.ndarray=None) -> np.ndarray: """Project Antarctic SMB contribution to GMSLR. Parameters @@ -510,7 +521,7 @@ def project_antsmb(self, T_int_ens, fraction=None): return antsmb - def project_greendyn(self): + def project_greendyn(self) -> np.ndarray: """Project Greenland rapid ice-sheet dynamics contribution to GMSLR. Returns @@ -528,7 +539,7 @@ def project_greendyn(self): 0.63*self.fgreendyn, 0.17*self.fgreendyn, finalrange) + self.fgreendyn*self.dgreen - def project_antdyn(self, fraction=None): + def project_antdyn(self, fraction: np.ndarray=None) -> np.ndarray: """Project Antarctic rapid ice-sheet dynamics contribution to GMSLR. Parameters @@ -556,7 +567,6 @@ def project_antdyn(self, fraction=None): rcp60=[-2.660, 0.870, 0.000],\ rcp85=[-2.399, 0.860, 0.000]) lcoeff = lcoeff[self.scenario] - print('hi') ascale=norm.ppf(fraction) final=np.exp(lcoeff[2]*ascale**2+lcoeff[1]*ascale+lcoeff[0]) @@ -569,10 +579,36 @@ def project_antdyn(self, fraction=None): # For dyn at 2100 Chapter 13 gives [-20,185] mm for all scenarios return self.time_projection(0.41, 0.20, final, fraction=fraction) + self.dant - - def project_landwater(self): + + def project_landwater(self) -> np.ndarray: """Project land water storage contribution to GMSLR. + Returns + ------- + np.ndarray + Land water storage contribution to GMSLR. + """ + # Read in AR6 landwater contributions + lw_ds = xr.open_dataset( + Path(__file__).parent / 'landwater_projections' + / 'ssp_global_landwater_projections.nc') + + # Interpolate to annual projections + lw_ds = lw_ds.interp(years=np.arange(2005, 2301, 1), method='linear').squeeze() + lw = lw_ds['sea_level_change'].values * 1e-3 # mm to m + + # Go from shape (20000, 294) to (101000, 294) + full_repeats = (self.nt * self.nm) // lw.shape[0] + remainder = (self.nt * self.nm) % lw.shape[0] + lw = np.vstack([np.tile(lw, (full_repeats, 1)), lw[:remainder]]) + lw = lw.reshape(self.nt * self.nm, lw.shape[1]) + lw = lw[:, 1:-1] # Start at 2006, end at 2299 + return lw + + def _project_landwater_ar5(self) -> np.ndarray: + """Old projection function. Project land water storage + contribution to GMSLR. + Returns ------- np.ndarray @@ -587,7 +623,7 @@ def project_landwater(self): def time_projection( self, startratemean: float, startratepm: float, final, - nfinal: int=1, fraction: np.ndarray=None): + nfinal: int=1, fraction: np.ndarray=None) -> np.ndarray: """Project a quantity which is a quadratic function of time. 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z+d6Q`SmVTU#w6tf`=;!SRH%b#$Z(N%&h6H02GTgNo&!F}!fhxb-5Kdd-Xz`)Re1qJ Gys Date: Mon, 2 Jun 2025 18:06:21 +0100 Subject: [PATCH 026/276] Updated GIS component --- profsea/emulator/__init__.py | 105 +++++++++++++++--- .../aux_data/ISMIP_GIS_calibration.csv | 22 ++++ .../ssp_global_landwater_projections.nc | Bin 3 files changed, 109 insertions(+), 18 deletions(-) create mode 100644 profsea/emulator/aux_data/ISMIP_GIS_calibration.csv rename profsea/emulator/{landwater_projections => aux_data}/ssp_global_landwater_projections.nc (100%) diff --git a/profsea/emulator/__init__.py b/profsea/emulator/__init__.py index 20ff58b..70171d0 100644 --- a/profsea/emulator/__init__.py +++ b/profsea/emulator/__init__.py @@ -12,8 +12,9 @@ from pathlib import Path import numpy as np -from scipy.stats import norm +from scipy.stats import norm, truncnorm import xarray as xr +import pandas as pd class GMSLREmulator: """Emulator for global mean sea level rise components. @@ -77,7 +78,7 @@ class GMSLREmulator: exp_efficiency: float Sensitivity of thermosteric SLR to ocean heat content change. """ - + def __init__( self, T_change: np.ndarray, @@ -85,7 +86,7 @@ def __init__( scenario: str, output_dir: str, end_yr: int, - seed: int=42, + seed: int=1234, nt: int=450, nm: int=1000, tcv: float=1.0, @@ -150,7 +151,7 @@ def __init__( 'you must provide the total \ncumulative emissions from 2015 to the end ' 'of the scenario using the cum_emissions_total keyword argument.\n' 'This is required to calculate the Antarctic dynamic contribution to GMSLR.') - + def get_components(self) -> dict: """Get all GMSLR components as a dictionary.""" components_dict = { @@ -189,7 +190,7 @@ def save_components(self, output_dir: str, scenario_name: str) -> None: f'{scenario_name}_{name}.npy'), component.T # Transpose to match the shape of original Monte Carlo simulations ) - + def project(self) -> None: """Run the emulator to project GMSLR components. @@ -204,10 +205,8 @@ def project(self) -> None: self.run_parallel_projections(T_int_med, T_int_ens, T_ens, fraction) - self.greennet = self.greensmb + self.greendyn self.antnet = self.antsmb + self.antdyn - self.sheetdyn = self.greendyn + self.antdyn - self.gmslr = self.glacier + self.greennet + self.antnet + self.landwater + self.expansion + self.gmslr = self.glacier + self.greenland + self.antnet + self.landwater + self.expansion def run_parallel_projections( self, T_int_med: np.ndarray, T_int_ens: np.ndarray, @@ -221,9 +220,8 @@ def run_parallel_projections( with concurrent.futures.ThreadPoolExecutor() as executor: futures = { executor.submit(self.project_glacier, T_int_med, T_int_ens): 'glacier', - executor.submit(self.project_greensmb, T_ens): 'greensmb', executor.submit(self.project_antsmb, T_int_ens, fraction): 'antsmb', - executor.submit(self.project_greendyn): 'greendyn', + executor.submit(self.project_greenland_AR6, T_ens): 'greenland', executor.submit(self.project_antdyn, fraction): 'antdyn', executor.submit(self.project_landwater): 'landwater' } @@ -236,12 +234,11 @@ def run_parallel_projections( print(f"{key} generated an exception: {e}") self.glacier = results['glacier'] - self.greensmb = results['greensmb'] self.antsmb = results['antsmb'] - self.greendyn = results['greendyn'] + self.greenland = results['greenland'] self.antdyn = results['antdyn'] self.landwater = results['landwater'] - + def calculate_drivers(self) -> tuple: """Calculate the drivers of GMSLR: temperature change and thermosteric sea level rise. @@ -308,8 +305,7 @@ def calculate_drivers(self) -> tuple: therm_ens = z[:, np.newaxis] * therm_std + therm_med T_int_ens = z[:, np.newaxis] * T_int_std + T_int_med - return T_ens, therm_ens, T_int_ens, T_int_med - + return T_ens, therm_ens, T_int_ens, T_int_med def project_glacier( self, T_int_med: np.ndarray, T_int_ens: np.ndarray) -> np.ndarray: @@ -422,7 +418,7 @@ def _project_glacier1( scale=1e-3 # mm to m return scale * factor * (np.where(T_int<0, 0, T_int)**exponent) - def project_greensmb(self, T_ens: np.ndarray) -> np.ndarray: + def project_greensmb_AR5(self, T_ens: np.ndarray) -> np.ndarray: """Project Greenland SMB contribution to GMSLR. Parameters @@ -521,7 +517,80 @@ def project_antsmb( return antsmb - def project_greendyn(self) -> np.ndarray: + def project_greenland_AR6(self, T_ens: np.ndarray) -> np.ndarray: + """Project Greenland ice-sheet contribution to GMSLR. + This follows the IPCC AR6 methodology as closely as possible. + + Returns + ------- + np.ndarray + Total GIS contribution to GMSLR. + """ + df = pd.read_csv( + Path(__file__).parent / "aux_data" / "ISMIP_GIS_calibration.csv") + b0 = df['b0'].values[None, :, None] + b1 = df['b1'].values[None, :, None] + b2 = df['b2'].values[None, :, None] + b3 = df['b3'].values[None, :, None] + b4 = df['b4'].values[None, :, None] + b5 = df['b5'].values[None, :, None] + sigma = df['sigma'].values + time_delta = np.arange(self.nyr) + + # GIS trend values taken from FACTS GitHub repo + trend_mean = 0.19 + trend_std = 0.1 + + # Calculate trend contribution distribution + rng = np.random.default_rng(self.seed) + trend = truncnorm.ppf( + rng.random(self.nm), + a = 0.0, + b = 99999.9, + loc = trend_mean, + scale = trend_std + ) + trend = trend[:, None] * time_delta[None, :] + trend = trend[:, None, :] + trend /= 1e3 # convert mm to m SLE + + # Calculate GIS contribution rate + dsle = b0 + (b1 * T_ens[:, None, :]) + (b2 * T_ens[:, None, :]**2) \ + + (b3 * T_ens[:, None, :]**3) + (b4 * time_delta[None, None, :]) \ + + (b5 * time_delta[None, None, :]**2) + + + # Now integrate + sle = np.cumsum(dsle, axis=2) # mm SLE per K of global warming + sle = sle * 1e-3 # convert mm to m SLE + + # Make a Monte Carlo ensemble of projections for each model in the calibration + sle_ens = np.zeros((self.nm, self.nt, self.nyr)) + r_per_model = self.nm // sle.shape[1] + r_remainder = self.nm % sle.shape[1] + + # We want to distribute the remainder evenly across the models + unc = np.random.normal(scale=sigma) + current_ensemble_idx = 0 + for i in range(sle.shape[1]): + num_reals_for_model_i = r_per_model + 1 if i < r_remainder else r_per_model + ifirst = current_ensemble_idx + ilast = current_ensemble_idx + num_reals_for_model_i + model_term = sle[:, i, :] # Shape (nt, nyr) + uncertainty_term = model_term * unc[None, i, None] # Shape (num_reals_for_model_i, nt, nyr_param) + + sle_ens[ifirst:ilast, :, :] = model_term[None, :, :] + uncertainty_term + current_ensemble_idx = ilast + + sle_ens += trend + + # Persist 2100 rate of change + rate = np.diff(sle_ens, axis=2)[:, :, 94] + sle_ens[:, :, 95:] = sle_ens[:, :, 94:95] + (rate[:, :, None] * time_delta[None, None, 1:self.nyr - 94]) + sle_ens = sle_ens.reshape((self.nm * self.nt, self.nyr)) + return sle_ens + + def project_greendyn_AR5(self) -> np.ndarray: """Project Greenland rapid ice-sheet dynamics contribution to GMSLR. Returns @@ -590,7 +659,7 @@ def project_landwater(self) -> np.ndarray: """ # Read in AR6 landwater contributions lw_ds = xr.open_dataset( - Path(__file__).parent / 'landwater_projections' + Path(__file__).parent / 'aux_data' / 'ssp_global_landwater_projections.nc') # Interpolate to annual projections diff --git a/profsea/emulator/aux_data/ISMIP_GIS_calibration.csv b/profsea/emulator/aux_data/ISMIP_GIS_calibration.csv new file mode 100644 index 0000000..41ea482 --- /dev/null +++ b/profsea/emulator/aux_data/ISMIP_GIS_calibration.csv @@ -0,0 +1,22 @@ +institution,model,b0,b1,b2,b3,b4,b5,sigma +UCIJPL, ISSM1,0.11280511383425385,0.6821503460827252,-0.16400289768763443,0.029789425852056794,-0.009489849321525412,0.00014879916101584456,0.44138133304923294 +UAF, PISM1,0.10798850687485739,2.22736503017021,-0.692345687432411,0.08161212823174324,-0.04641834271523848,0.0004685291256141255,0.5278388471215101 +NCAR, CISM,0.31324843280109604,1.5993142312892896,-0.44199635991569686,0.05872843417961349,-0.030363605622934386,0.00027208434509873314,0.5786469779135103 +MUN, GSM2601,0.26176469764101284,1.9203005829347362,-0.64443813905498,0.06774621868079578,-0.04785788778603006,0.0005414869899329489,0.4804421954085724 +AWI, ISSM1,0.15203622407200434,0.5737828059597607,-0.06009509991088713,0.018698973452613288,-0.0017116999595181426,2.627912853014891e-05,0.5379227283666163 +JPL, ISSMPALEO,0.08192192746084714,0.6728440122964905,-0.12814016340042222,0.022584230858470278,-0.01643152901161038,0.0001855436066229288,0.40578701831731945 +BGC, BISICLES,0.14652938235787794,0.8228410010355418,-0.2424425609220453,0.04248976786373326,-0.01659422359712792,0.0002188231920179362,0.4866165180787471 +GSFC, ISSM,0.17216048421978947,1.940293458477143,-0.6163995281733783,0.07537669095303912,-0.034675558756788405,0.0003814767735637048,0.47471207710632624 +UCIJPL, ISSM2,0.21641138942280236,0.33642105388928734,0.04866982311529178,-0.015205305616245113,-0.004018009867746031,8.458271035394205e-05,0.42536807781497576 +IMAU, IMAUICE2,0.20036011456528868,2.2955765155739787,-0.7112950680247732,0.09145182217310044,-0.044411324549290754,0.00044883827336406057,0.6120063870228036 +VUB, GISMHOMv1,0.39396308078447184,0.8529109232648526,-0.19173147734077922,0.0360613162914003,-0.0023678409809129164,-1.1101471443453192e-05,0.5764299273835812 +IMAU, IMAUICE1,0.2800198843218169,0.8427979750531325,-0.12558217145775608,0.031145488329698878,-0.0023911906890941026,-7.900936685345528e-06,0.7439631769871697 +MUN, GSM2611,0.31961312362156136,0.3908080413807351,0.15162878131265423,-0.042695881436106475,-0.01605741445283293,0.00020608416355116788,0.45394137922358807 +UAF, PISM2,0.2167261842707333,0.22324315646728543,-0.0321449483341798,0.019635585944547174,0.0015780370372677766,-1.0150760214600041e-05,0.42575847295364555 +VUW, PISM,1.3145573843686975e-05,1.5421914530833938,-0.5162788335603761,0.0604074471391165,-0.03655888489420711,0.0004626783801828793,0.3706427862813395 +AWI, ISSM2,0.1513883454153806,0.576742644362664,-0.0562763456969293,0.018041192418930763,-0.0010313859576172035,1.64181719419787e-05,0.539335117427485 +ILTS_PIK, SICOPOLIS2,0.23327050371069546,0.635252150611775,-0.05725160435687826,0.01879619175116609,-0.0052735802812815535,6.885471476181237e-05,0.5382205194008777 +ILTS_PIK, SICOPOLIS1,0.17910675828310474,1.8256763597420758,-0.5246724854860112,0.06912592493435277,-0.03590566483921975,0.0003654973100086778,0.5533500350485601 +AWI, ISSM3,0.0894329235365312,2.117295179930542,-0.6491365405376079,0.08078620843845385,-0.042188539617928456,0.0004211035302041921,0.5679266897268976 +JPL, ISSM,0.19722907443354742,0.727228695939683,-0.1329472066135633,0.028647131912272528,-0.003898102886985555,4.903956863699932e-05,0.5329293558099194 +LSCE, GRISLI2,0.21625621392082017,0.3885561495874317,-0.03848012730510675,0.017660852688523576,-0.006097320636799353,5.743035870686697e-05,0.49405529384710106 diff --git a/profsea/emulator/landwater_projections/ssp_global_landwater_projections.nc b/profsea/emulator/aux_data/ssp_global_landwater_projections.nc similarity index 100% rename from profsea/emulator/landwater_projections/ssp_global_landwater_projections.nc rename to profsea/emulator/aux_data/ssp_global_landwater_projections.nc From 42516e265a4b5f26c71b6dace41814f310ba58f5 Mon Sep 17 00:00:00 2001 From: Gregory Munday Date: Tue, 23 Sep 2025 15:03:58 +0100 Subject: [PATCH 027/276] A few small updates --- profsea/emulator/__init__.py | 30 +++++++++++++----------------- 1 file changed, 13 insertions(+), 17 deletions(-) diff --git a/profsea/emulator/__init__.py b/profsea/emulator/__init__.py index 70171d0..6c9870c 100644 --- a/profsea/emulator/__init__.py +++ b/profsea/emulator/__init__.py @@ -90,12 +90,12 @@ def __init__( nt: int=450, nm: int=1000, tcv: float=1.0, - glaciermip: bool|int=False, + glaciermip: bool|int=2, input_ensemble: bool=True, T_percentile_95: np.ndarray=None, OHC_percentile_95: np.ndarray=None, cum_emissions_total: np.ndarray=None, - palmer_method: bool=False) -> None: + palmer_method: bool=True) -> None: np.random.seed(None if seed is None else seed) self.T_change = T_change @@ -148,9 +148,9 @@ def __init__( 'ssp126', 'ssp245', 'ssp585'] and self.cum_emissions_total is None: raise ValueError( 'If the scenario is not rcp26, rcp45, rcp85, ssp126, ssp245 or ssp585, ' - 'you must provide the total \ncumulative emissions from 2015 to the end ' - 'of the scenario using the cum_emissions_total keyword argument.\n' - 'This is required to calculate the Antarctic dynamic contribution to GMSLR.') + 'you must provide the total \ncumulative emissions from 2015 to 2100 ' + 'using the cum_emissions_total keyword argument. This is required \n' + 'to calculate the Antarctic dynamic contribution to GMSLR.') def get_components(self) -> dict: """Get all GMSLR components as a dictionary.""" @@ -199,7 +199,6 @@ def project(self) -> None: None """ T_ens, Exp_ens, T_int_ens, T_int_med = self.calculate_drivers() - self.expansion = np.tile(Exp_ens, (self.nm, 1)) fraction = np.random.rand(self.nm * self.nt) # correlation between antsmb and antdyn @@ -304,7 +303,6 @@ def calculate_drivers(self) -> tuple: T_ens = z[:, np.newaxis] * T_std + T_med therm_ens = z[:, np.newaxis] * therm_std + therm_med T_int_ens = z[:, np.newaxis] * T_int_std + T_int_med - return T_ens, therm_ens, T_int_ens, T_int_med def project_glacier( @@ -365,11 +363,9 @@ def project_glacier( cvgl=0.20 # random methodological error ngl=len(glparm) # number of glacier methods - if self.nm%ngl: - raise ValueError('number of realisations '+\ - 'must be a multiple of number of glacier methods') - nrpergl = self.nm // ngl + r_per_model = self.nm // ngl + r_remainder = self.nm % ngl r = np.random.standard_normal(self.nm) r = r[:, np.newaxis, np.newaxis] @@ -379,21 +375,23 @@ def project_glacier( cvgl_all = np.array([glparm[igl]['cvgl'] if self.glaciermip else cvgl for igl in range(ngl)]) # Make an ensemble of projections for each method + current_ensemble_idx = 0 for igl in range(ngl): mgl = mgl_all[igl] zgl = zgl_all[igl] cvgl = cvgl_all[igl] - ifirst = igl * nrpergl - ilast = ifirst + nrpergl + num_reals_for_model_i = r_per_model + 1 if igl < r_remainder else r_per_model + ifirst = current_ensemble_idx + ilast = current_ensemble_idx + num_reals_for_model_i glacier[ifirst:ilast, ...] = zgl + (mgl * r[ifirst:ilast] * cvgl) + current_ensemble_idx = ilast glacier += dmz np.clip(glacier, None, glmass, out=glacier) glacier = glacier.reshape(glacier.shape[0]*glacier.shape[1], glacier.shape[2]) - return glacier def _project_glacier1( @@ -454,7 +452,6 @@ def project_greensmb_AR5(self, T_ens: np.ndarray) -> np.ndarray: greensmb += (1 - self.fgreendyn) * self.dgreen greensmb = greensmb.reshape(greensmb.shape[0]*greensmb.shape[1], greensmb.shape[2]) - return greensmb def _fettweis(self, ztgreen: np.ndarray) -> np.ndarray: @@ -514,12 +511,12 @@ def project_antsmb( z = z[:, :, np.newaxis] antsmb = z * T_int_ens antsmb = antsmb.reshape(antsmb.shape[0] * antsmb.shape[1], antsmb.shape[2]) - return antsmb def project_greenland_AR6(self, T_ens: np.ndarray) -> np.ndarray: """Project Greenland ice-sheet contribution to GMSLR. This follows the IPCC AR6 methodology as closely as possible. + Projections are relative to 1996-2014 baseline. Returns ------- @@ -646,7 +643,6 @@ def project_antdyn(self, fraction: np.ndarray=None) -> np.ndarray: # For SMB+dyn during 2005-2010 Table 4.6 gives 0.41+-0.24 mm yr-1 (5-95% range) # For dyn at 2100 Chapter 13 gives [-20,185] mm for all scenarios - return self.time_projection(0.41, 0.20, final, fraction=fraction) + self.dant def project_landwater(self) -> np.ndarray: From f32223aefd3f1377ff0803d4f8c4338f49dd2ba0 Mon Sep 17 00:00:00 2001 From: Gregory Munday Date: Fri, 10 Oct 2025 16:10:49 +0100 Subject: [PATCH 028/276] Fixed spatial_projections.py --- profsea/config.py | 2 +- profsea/emulator/__init__.py | 25 ++- .../emulator/landwater_projections/.DS_Store | Bin 6148 -> 0 bytes profsea/spatial_projections.py | 155 ++++++++++-------- profsea/user-settings-emu.yml | 81 +++++++++ 5 files changed, 190 insertions(+), 73 deletions(-) delete mode 100644 profsea/emulator/landwater_projections/.DS_Store create mode 100644 profsea/user-settings-emu.yml diff --git a/profsea/config.py b/profsea/config.py index 99e26da..70229d2 100644 --- a/profsea/config.py +++ b/profsea/config.py @@ -10,5 +10,5 @@ path = pathlib.Path(__file__).parents[0].as_posix() print(path) -with open(os.path.join(path, "user-settings.yml"), "r") as f: +with open(os.path.join(path, "user-settings-emu.yml"), "r") as f: settings = yaml.load(f, Loader=yaml.SafeLoader) diff --git a/profsea/emulator/__init__.py b/profsea/emulator/__init__.py index 6c9870c..050a91c 100644 --- a/profsea/emulator/__init__.py +++ b/profsea/emulator/__init__.py @@ -46,6 +46,9 @@ class GMSLREmulator: input_ensemble: bool If True, use an input ensemble of temperature and ocean heat content change. + output_percentiles: list|np.ndarray + If not None, calculate percentiles from a 1D list/array for each + component T_percentile_95: np.ndarray 95th percentile of temperature change timeseries. OHC_percentile_95: np.ndarray @@ -92,6 +95,7 @@ def __init__( tcv: float=1.0, glaciermip: bool|int=2, input_ensemble: bool=True, + output_percentiles: list|np.ndarray=None, T_percentile_95: np.ndarray=None, OHC_percentile_95: np.ndarray=None, cum_emissions_total: np.ndarray=None, @@ -109,6 +113,7 @@ def __init__( self.tcv = tcv self.glaciermip = glaciermip self.input_ensemble = input_ensemble + self.output_percentiles = output_percentiles self.T_percentile_95 = T_percentile_95 self.OHC_percentile_95 = OHC_percentile_95 self.cum_emissions_total = cum_emissions_total @@ -140,7 +145,7 @@ def __init__( # From Turner et al. (2023) self.exp_efficiency = np.random.normal( loc=0.113, scale=0.013, size=OHC_change.shape[0]) * 1e-24 # m/YJ - + if input_ensemble: self.nt = self.T_change.shape[0] @@ -157,14 +162,11 @@ def get_components(self) -> dict: components_dict = { 'exp': self.expansion, 'glacier': self.glacier, - 'greensmb': self.greensmb, - 'greendyn': self.greendyn, - 'greennet': self.greennet, + 'greenland': self.greenland, 'antsmb': self.antsmb, 'antdyn': self.antdyn, 'antnet': self.antnet, 'landwater': self.landwater, - 'sheetdyn': self.sheetdyn, 'gmslr': self.gmslr } return components_dict @@ -188,7 +190,7 @@ def save_components(self, output_dir: str, scenario_name: str) -> None: os.path.join( output_dir, f'{scenario_name}_{name}.npy'), - component.T # Transpose to match the shape of original Monte Carlo simulations + component ) def project(self) -> None: @@ -203,6 +205,9 @@ def project(self) -> None: fraction = np.random.rand(self.nm * self.nt) # correlation between antsmb and antdyn self.run_parallel_projections(T_int_med, T_int_ens, T_ens, fraction) + + if self.output_percentiles is not None: + self.expansion = np.percentile(self.expansion, self.output_percentiles, axis=0) self.antnet = self.antsmb + self.antdyn self.gmslr = self.glacier + self.greenland + self.antnet + self.landwater + self.expansion @@ -237,6 +242,14 @@ def run_parallel_projections( self.greenland = results['greenland'] self.antdyn = results['antdyn'] self.landwater = results['landwater'] + + if self.output_percentiles is not None: + self.glacier = np.percentile(self.glacier, self.output_percentiles, axis=0) + self.antsmb = np.percentile(self.antsmb, self.output_percentiles, axis=0) + self.greenland = np.percentile(self.greenland, self.output_percentiles, axis=0) + self.antdyn = np.percentile(self.antdyn, self.output_percentiles, axis=0) + self.landwater = np.percentile(self.landwater, self.output_percentiles, axis=0) + def calculate_drivers(self) -> tuple: """Calculate the drivers of GMSLR: temperature change and diff --git a/profsea/emulator/landwater_projections/.DS_Store b/profsea/emulator/landwater_projections/.DS_Store deleted file mode 100644 index 7586fea4de99c0bb6d65fde98ea5907bf4dd925c..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 6148 zcmeHK%TB{E5FD2vRdDH%VyE8Js&Wo5KA7z4(@ zUt>V-?jtK?7=myc;B!+J)GjKp* float: @@ -84,6 +84,7 @@ def calc_gia_contribution( :param coords: coordinates of location of interest :return: GIA estimates converted to mm/yr """ + print('Calculating GIA contribution...') nGIA, GIA_vals = read_gia_estimates() Tdelta = calc_baseline_period(yrs) @@ -96,9 +97,7 @@ def calc_gia_contribution( GIA_T = da.from_array(GIA_unit_series) GIA_vals = da.from_array(GIA_vals) - GIA_series = GIA_T[:, :, None, None] * GIA_vals[rgiai, None, :, :] - GIA_series = GIA_series.compute() file_header = '_'.join(['gia', scenario, "projection", @@ -110,21 +109,23 @@ def calc_gia_contribution( def calc_expansion_contribution( - scenario: str, nsmps: int, nyrs: int) -> da.array: + scenario: str, nsmps: int) -> da.array: """ Calculate the thermal expansion contribution to the regional component of sea level rise. :param scenario: emission scenario :param nsmps: determine the number of samples - :param nyrs: number of years in each projection time series :return: expansion estimates converted to mm/yr """ # Select slope coefficients based on the MIP - if settings["cmipinfo"]["mip"] == "CMIP6": - coeffs = load_CMIP6_slopes('ssp585') - coeffs = np.roll(coeffs, 180, axis=2) + if settings["emulator_settings"]["emulator_mode"]: + if settings["cmipinfo"]["mip"] == "CMIP6": + coeffs = load_CMIP6_slopes('ssp585') + coeffs = np.roll(coeffs, 180, axis=2) + else: + coeffs = load_CMIP5_slope_coeffs('rcp85') else: - coeffs = load_CMIP5_slope_coeffs('rcp85') + coeffs = load_CMIP5_slope_coeffs(scenario) rand_samples = np.random.choice( coeffs.shape[0], size=nsmps, replace=True) @@ -231,8 +232,8 @@ def calculate_sl_components( montecarlo_G[:, :] = da.from_array(sampled_mc[:, :, None, None]) if comp == 'exp': - sampled_coeffs = calc_expansion_contribution(scenario, nsmps, nyrs) - montecarlo_R[:, :, :, :] = montecarlo_G[:, :, :, :] * sampled_coeffs[:, None, :, :] + sampled_coeffs = calc_expansion_contribution(scenario, nsmps) + montecarlo_R = montecarlo_G * sampled_coeffs[:, None, :, :] del sampled_coeffs elif comp == 'landwater': @@ -246,6 +247,7 @@ def calculate_sl_components( # Take the 0th, 25th, 50th, 75th and 100th percentiles montecarlo_R = da.percentile(montecarlo_R, [0, 25, 50, 75, 100], axis=0) + print('Computing result...') montecarlo_R = montecarlo_R.compute() # Create the output sea level projections file directory and filename @@ -406,17 +408,59 @@ def setup_FP_interpolators(components: list) -> tuple: return nFPs, FPlist -def read_csv_file( - file_pattern: str, start_yr: int=2007, - end_yr: int=settings["projection_end_year"]) -> np.ndarray: - file = glob.glob( - os.path.join( - settings["emulator_settings"]["emulator_input_dir"], - file_pattern)) - if not file: - raise FileNotFoundError(f'File {file_pattern} not found') - df = pd.read_csv(file[0]) - return df.loc[:, str(start_yr):str(end_yr)].to_numpy() +def calculate_global_components(scenario: str, palmer_method: bool) -> None: + """ + Calculate the global contributions for each of the sea-level components + using the GMSLR module. + :param scenario: string representing the scenario being simulated + :param palmer_method: boolean to determine whether to use the palmer_method + """ + # Check inputs are correctly set up + if not (os.path.exists(settings["scm_data"]["temperature"]) and + os.path.exists(settings["scm_data"]["ocean_heat_content"])): + raise Exception( + 'SCM data paths (temperature and ocean heat content) must be ' + 'correctly configured in user-settings.yml') + + if not os.path.exists(settings["scm_data"]["cumulative_emissions"]): + raise Exception('Cumulative emissions path must be correctly configured ' + 'in user-settings.yml') + + if (settings["scm_data"]["temperature"].split(".")[-1] != 'nc' or + settings["scm_data"]["ocean_heat_content"].split(".")[-1] != 'nc'): + raise Exception('SCM data must be saved in NetCDF format.') + + percentiles = np.arange(101) + + # Now run the simulations + print(f'Projecting global components for {scenario} scenario...') + T_change = xr.load_dataarray(settings["scm_data"]["temperature"]) + OHC_change = xr.load_dataarray(settings["scm_data"]["ocean_heat_content"]) + with open(settings["scm_data"]["cumulative_emissions"]) as f: + cumulative_emissions = json.load(f) + + T_change = T_change.sel(scenario=scenario).data.T + OHC_change = OHC_change.sel(scenario=scenario).data.T + T_change = np.percentile(T_change, percentiles, axis=0) + OHC_change = np.percentile(OHC_change, percentiles, axis=0) + + gmslr = GMSLREmulator( + T_change, + OHC_change, + scenario, + os.path.join(settings["baseoutdir"], 'emulator_output'), + settings["projection_end_year"], + palmer_method=palmer_method, + input_ensemble=settings["emulator_settings"]["use_input_ensemble"], + output_percentiles=np.arange(101), + cum_emissions_total=cumulative_emissions[scenario]) + gmslr.project() + + print('Saving components...') + gmslr.save_components( + os.path.join(settings["baseoutdir"], 'emulator_output'), + scenario) + print('Saved!\n') def main(): @@ -430,44 +474,23 @@ def main(): # Extract site data from station list (e.g. tide gauge location) or # construct based on user input - print('\nInitiating ProFSea emulator') - if settings["projection_end_year"] > 2100: - palmer_method = True - else: - palmer_method = False - - makefolder(os.path.join(settings["baseoutdir"], 'emulator_output')) - - # Get the metadata of either the site location or tide gauge location - for scenario in settings["emulator_settings"]["emulator_scenario"]: - # print(f'Projecting {scenario}...') - # T_change = read_csv_file(f'*{scenario}*_temperature*.csv') - # OHC_change = read_csv_file(f'*{scenario}*_ocean_heat_content_change*.csv') - - # cum_emissions_file = 'cumulative_scenario_emissions.json' - # if glob.glob(os.path.join(settings["emulator_settings"]["emulator_input_dir"], cum_emissions_file)): - # with open('ngfs_data/cumulative_scenario_emissions.json') as f: - # cum_emissions_total = json.load(f)[scenario] - # else: - # raise FileNotFoundError('For any non-RCP scenario, a cumulative emissions total must be provided.') + if settings["emulator_settings"]["emulator_mode"]: + print('\nInitiating ProFSea emulator') + if settings["projection_end_year"] > 2100: + palmer_method = True + else: + palmer_method = False + + makefolder(os.path.join(settings["baseoutdir"], 'emulator_output')) - # gmslr = GMSLREmulator( - # T_change, - # OHC_change, - # scenario, - # os.path.join(settings["baseoutdir"], 'emulator_output'), - # settings["projection_end_year"], - # palmer_method=palmer_method, - # input_ensemble=settings["emulator_settings"]["use_input_ensemble"], - # cum_emissions_total=cum_emissions_total) - # gmslr.project() - # print('Saving components...') - # gmslr.save_components( - # os.path.join(settings["baseoutdir"], 'emulator_output'), - # scenario) - # print('Saved!\n') - - calc_future_sea_level(scenario) + # Get the metadata of either the site location or tide gauge location + for scenario in settings["emulator_settings"]["emulator_scenario"]: + calculate_global_components(scenario, palmer_method) + calc_future_sea_level(scenario) + else: + scenarios = ['rcp26', 'rcp45', 'rcp85'] + for scenario in scenarios: + calc_future_sea_level(scenario) if __name__ == '__main__': diff --git a/profsea/user-settings-emu.yml b/profsea/user-settings-emu.yml new file mode 100644 index 0000000..225ada5 --- /dev/null +++ b/profsea/user-settings-emu.yml @@ -0,0 +1,81 @@ +# Site(s) information +# region: region of the world +# N.B. used to create output folder structure +# sitename: tide gauge name or other site name +# N.B. if site_name has an apostrophe use triple quotes +# # sitename: '''site'x''' +# sitelatlon: site location's latitude and longitude +# N.B. if tide gauge name: latlon taken from TG metadata + # sitelatlon: [[]]) +# N.B. if other site name: latlon needs to be specified + # sitelatlon: [[-51.69, -57.82]]) +siteinfo: + region: 'cmip6_patterns' # str + sitename: ['Stanley II'] # list + sitelatlon: [[]] # list + +# Base output directory +# N.B. the code will add region to the output directory +baseoutdir: '/add/your/directory/' + +# Set the end year of the projection(s) +projection_end_year: 2300 # int between 2050 and 2300 + +# Emulator settings +emulator_settings: + emulator_mode: true # bool + use_input_ensemble: true # bool + emulator_scenario: ['high-extension', 'high-overshoot', 'medium-extension', 'medium-overshoot', 'low', 'verylow', 'verylow-overshoot'] # str + +scm_data: + temperature: '/results/fair_output/cmip7_temperature.nc' # str + ocean_heat_content: '/results/fair_output/cmip7_ohc.nc' # str + cumulative_emissions: '/data/cumulative_cmip7_emissions.json' # str + +# Science method +# UKCP18 --> sciencemethod: 'UK' +# Palmer et al (2020) --> sciencemethod: 'global +# N.B. Used to identify which GIA estimates to use +sciencemethod: 'global' # str + +# Tide gauge information (recommend default below) +# source: source of tide gauge information +# N.B. Also used to identify which TG directory to use +# datafq: temporal scale of tide gauge data +# N.B. not set up monthly files for PSMSL +# psmsldir: TG base directory +tidegaugeinfo: + source: 'PSMSL' # str + datafq: ['annual'] # list + psmsldir: '/home/user/data/PSMSL/' + +# CMIP information +cmipinfo: + mip: 'CMIP6' # str +# cmip_sea: for non-UK locations, specify if site is within marginal sea +# Not in a marginal sea --> cmip_sea: 'all' +# Marginal sea --> cmip_sea: 'marginal' + cmip_sea: 'all' # str +# sealevelbasedir: Base directory for CMIP "zos" and "zostoga" data + sealevelbasedir: '/data/cmip6/' +# slopecoeffsuk: CMIP5 slope coefficients developed for UKCP18 + slopecoeffsuk: '/data/uk_cmip_slope_coefficients/' + + +# Directories for the GIA estimates (independent of scenario) +# UKCP18 --> UK specific ones +# Global locations --> Lambeck, ICE5G +giaestimates: + global: '/data/gia_estimates/global_GIA_interpolators.pickle' + uk: '‘/data/gia_estimates/Bradley_GIA_interpolator.pickle' + +# Directories for the Slangen, Spada and Klemann fingerprints +fingerprints: + slangendir: '/data/grd_fingerprints/' + spadadir: '/data/grd_fingerprints/' + klemanndir: '/data/grd_fingerprints/' + +# Directory of Monte Carlo time series for new projections +# N.B. Originally developed by Jonathan Gregory +short_montecarlodir: '/path/to/2100/montecarlo/simulations' # Required for projections up to 2100 +long_montecarlodir: '/data/runs/emulator_output' # Required for projections from 2100 up to 2300 From 5de40a2c295bae6353aa70ace4ef3bb35bb5e910 Mon Sep 17 00:00:00 2001 From: Gregory Munday Date: Fri, 10 Oct 2025 16:22:00 +0100 Subject: [PATCH 029/276] Updated .gitignore --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index bf31a12..8904e39 100644 --- a/.gitignore +++ b/.gitignore @@ -2,3 +2,4 @@ *__pycache__* profsea.egg-info data/ +*.DS_Store From 1b462e6c2d2ae8b051a0e48bf4cbdb3e1f0e6ca9 Mon Sep 17 00:00:00 2001 From: Gregory Munday Date: Fri, 10 Oct 2025 16:43:51 +0100 Subject: [PATCH 030/276] Small check update --- profsea/spatial_projections.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/profsea/spatial_projections.py b/profsea/spatial_projections.py index 239458a..94858d8 100644 --- a/profsea/spatial_projections.py +++ b/profsea/spatial_projections.py @@ -119,10 +119,12 @@ def calc_expansion_contribution( """ # Select slope coefficients based on the MIP if settings["emulator_settings"]["emulator_mode"]: - if settings["cmipinfo"]["mip"] == "CMIP6": + if settings["cmipinfo"]["mip"].lower() == "cmip6": + print("Using CMIP6 sterodynamic patterns...") coeffs = load_CMIP6_slopes('ssp585') coeffs = np.roll(coeffs, 180, axis=2) else: + print("Using CMIP5 sterodynamic patterns...") coeffs = load_CMIP5_slope_coeffs('rcp85') else: coeffs = load_CMIP5_slope_coeffs(scenario) From 54ea11b1f9337acbd361c30e958f8cf404d7de6d Mon Sep 17 00:00:00 2001 From: Gregory Munday Date: Wed, 15 Oct 2025 11:48:52 +0100 Subject: [PATCH 031/276] Added worked example plus some small updates --- profsea/emulator/__init__.py | 11 +- .../aux_data/cumulative_cmip6_emissions.json | 1 + profsea/spatial_projections.py | 31 +-- profsea/worked_example.ipynb | 177 ++++++++++++++++++ 4 files changed, 200 insertions(+), 20 deletions(-) create mode 100644 profsea/emulator/aux_data/cumulative_cmip6_emissions.json create mode 100644 profsea/worked_example.ipynb diff --git a/profsea/emulator/__init__.py b/profsea/emulator/__init__.py index 050a91c..cd77736 100644 --- a/profsea/emulator/__init__.py +++ b/profsea/emulator/__init__.py @@ -27,8 +27,6 @@ class GMSLREmulator: Array of ocean heat content change values. scenario: str Name of the scenario. - output_dir: str - Directory to save the components to. end_yr: int End year of the projections. seed: int @@ -87,7 +85,6 @@ def __init__( T_change: np.ndarray, OHC_change: np.ndarray, scenario: str, - output_dir: str, end_yr: int, seed: int=1234, nt: int=450, @@ -105,7 +102,6 @@ def __init__( self.T_change = T_change self.OHC_change = OHC_change self.scenario = scenario - self.output_dir = output_dir self.end_yr = end_yr self.seed = seed self.nt = nt @@ -160,7 +156,7 @@ def __init__( def get_components(self) -> dict: """Get all GMSLR components as a dictionary.""" components_dict = { - 'exp': self.expansion, + 'expansion': self.expansion, 'glacier': self.glacier, 'greenland': self.greenland, 'antsmb': self.antsmb, @@ -170,6 +166,11 @@ def get_components(self) -> dict: 'gmslr': self.gmslr } return components_dict + + def list_components(self) -> list: + """List the available SLR components.""" + component_dict = self.get_components() + print(list(component_dict.keys())) def save_components(self, output_dir: str, scenario_name: str) -> None: """Save all SLR components as .npy files to a directory. diff --git a/profsea/emulator/aux_data/cumulative_cmip6_emissions.json b/profsea/emulator/aux_data/cumulative_cmip6_emissions.json new file mode 100644 index 0000000..2cf587a --- /dev/null +++ b/profsea/emulator/aux_data/cumulative_cmip6_emissions.json @@ -0,0 +1 @@ +{"ssp126": 325.34, "ssp245": 810.20, "ssp370": 1506.5773134877386, "ssp585": 2176.3538993188017} \ No newline at end of file diff --git a/profsea/spatial_projections.py b/profsea/spatial_projections.py index 94858d8..8fdf5e0 100644 --- a/profsea/spatial_projections.py +++ b/profsea/spatial_projections.py @@ -2,14 +2,15 @@ Copyright (c) 2023, Met Office All rights reserved. """ -import dask.array as da import glob -import iris +import pickle import json +import os + +import dask.array as da +import iris from netCDF4 import Dataset import numpy as np -import os -import pickle from scipy.interpolate import RegularGridInterpolator import xarray as xr @@ -52,7 +53,7 @@ def calc_future_sea_level(scenario: str) -> None: # Specify the sea level components to include. The GIA contribution is # calculated separately. - components = ['exp', 'antdyn', 'antsmb', 'greendyn', 'greensmb', + components = ['expansion', 'antdyn', 'antsmb', 'greendyn', 'greensmb', 'glacier', 'landwater'] # Select dimensions from sample file, [time, realisation] @@ -233,7 +234,7 @@ def calculate_sl_components( sampled_mc = mc_timeseries[resamples, :nyrs] montecarlo_G[:, :] = da.from_array(sampled_mc[:, :, None, None]) - if comp == 'exp': + if comp == 'expansion': sampled_coeffs = calc_expansion_contribution(scenario, nsmps) montecarlo_R = montecarlo_G * sampled_coeffs[:, None, :, :] del sampled_coeffs @@ -376,7 +377,7 @@ def setup_FP_interpolators(components: list) -> tuple: :return nFPs: length of FPlist and interpolator objects of all sea level components """ - print('running function setup_FP_interpolators') + print("running function setup_FP_interpolators") # Directories for the Slangen, Spada and Klemann fingerprints slangendir = settings["fingerprints"]["slangendir"] @@ -390,20 +391,20 @@ def setup_FP_interpolators(components: list) -> tuple: klemann_FPs = {} # Only 1 fingerprint for Landwater - comp = 'landwater' + comp = "landwater" slangen_FPs[comp] = create_FP_interpolator(slangendir, - comp + '_slangen_nomask.nc') + comp + "_slangen_nomask.nc") - # Create interpolators for the remaining components. Expansion ('exp') + # Create interpolators for the remaining components. Expansion ('expansion') # is global so no interpolation is needed. - components_todo = [c for c in components if c not in ['exp', 'landwater']] + components_todo = [c for c in components if c not in ["expansion", "landwater"]] for comp in components_todo: slangen_FPs[comp] = create_FP_interpolator(slangendir, - comp + '_slangen_nomask.nc') + comp + "_slangen_nomask.nc") spada_FPs[comp] = create_FP_interpolator(spadadir, - comp + '_spada_nomask.nc') + comp + "_spada_nomask.nc") klemann_FPs[comp] = create_FP_interpolator(klemanndir, - comp + '_klemann_nomask.nc') + comp + "_klemann_nomask.nc") FPlist = [slangen_FPs, spada_FPs, klemann_FPs] nFPs = len(FPlist) @@ -460,7 +461,7 @@ def calculate_global_components(scenario: str, palmer_method: bool) -> None: print('Saving components...') gmslr.save_components( - os.path.join(settings["baseoutdir"], 'emulator_output'), + os.path.join(settings["baseoutdir"], 'gmslr_output'), scenario) print('Saved!\n') diff --git a/profsea/worked_example.ipynb b/profsea/worked_example.ipynb new file mode 100644 index 0000000..6b3b734 --- /dev/null +++ b/profsea/worked_example.ipynb @@ -0,0 +1,177 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "46a1a49f", + "metadata": {}, + "outputs": [], + "source": [ + "from profsea.emulator import GMSLREmulator" + ] + }, + { + "cell_type": "markdown", + "id": "52cdd115", + "metadata": {}, + "source": [ + "#### Introduction\n", + "The aim of this notebook is to break down the steps required to run the emulator version of ProFSea.\n", + "\n", + "All of this can't run in Jupyter Notebook but the steps should be clear enough to follow along!\n", + "\n", + "> If you want to use the CMIP6 patterns sterodynamic patterns instead of CMIP5, you need to run [this recipe](https://github.com/ESMValGroup/ESMValTool/blob/steric_patterns/esmvaltool/recipes/recipe_steric_patterns.yml) in ESMValTool. I recommend running with as much memory and processors as possible. \n", + "\n", + "> However, if you prefer not to go via ESMValTool feel free to reach out at gregory.munday\\@dtc.ox.ac.uk and I can just send them over (they're not large in terms of data size).\n", + "---\n", + "All other data required for the tool can be [downloaded here](https://catalogue.ceda.ac.uk/uuid/47c849b907414f3ab5e56ba9cf83e779/).\n", + "\n", + "I like keeping all my data in a `data/` folder next to the `profsea/` folder, which looks like this:\n", + "\n", + "  `data/` \\\n", + "   `cmip5/` \\\n", + "   `cmip6/` \\\n", + "   `gia_estimates/` \\\n", + "   `grd_fingerprints/` \\\n", + "   `monte_carlo_timeseries/` \\\n", + "   `uk_cmip_slope_coefficients/`\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "ef69532c", + "metadata": {}, + "source": [ + "#### Setting up the environment\n", + "The next step is to set up the conda environment. You should use the `ProFSea-emu-env.yml` environment file, building it with \n", + "\n", + "```bash\n", + "conda env create -f ProFSea-emu-env.yml\n", + "```\n", + "\n", + "and then activating with `conda activate profsea-emu`.\n", + "\n", + "---\n", + "\n", + "One final step here is to install ProFSea as an editable package, using\n", + "\n", + "```bash \n", + "pip install -e . \n", + "```\n", + "\n", + "while inside the highest level directory." + ] + }, + { + "cell_type": "markdown", + "id": "5616586f", + "metadata": {}, + "source": [ + "#### Setup FaIR output\n", + "Run FaIR for the scenarios you want to simulate and save out the `global surface air temperature` and `ocean heat content`. You'll also need to calculate the 2015-2100 cumulative CO2 emissions and save it to a `.json` file. The CMIP6 cumulative emissions are included in the repo: \n", + "\n", + "```profsea/emulator/aux_data/cumulative_cmip6_emissions.json```\n", + "\n", + "Here's some sample code to calculate it from scratch in FaIR. Perhaps this could be included in this branch at some point.\n", + "\n", + "```python\n", + "# Output cumulative emissions from 2015 to 2100\n", + "cum_emissions_dict = {}\n", + "for scenario in hist_gcam_ext.scenario.unique(): # loop over your scenarios\n", + " emissions = hist_gcam_ext.loc[\n", + " (hist_gcam_ext['scenario'] == scenario) & (hist_gcam_ext['variable'] == 'CO2 FFI'), \n", + " '2015':'2100'\n", + " ].to_numpy()[0] # select anthro emissions\n", + " cum_emissions = np.cumsum(emissions)\n", + " cum_emissions = cum_emissions / 1000 # Convert to PgC\n", + " print(f'Total cumulative carbon emissions from 2015 to 2100 for {scenario}: ', cum_emissions[-1])\n", + " cum_emissions_dict[scenario] = cum_emissions[-1]\n", + " # Save to json\n", + " with open('ngfs_data/cumulative_scenario_emissions.json', 'w') as f:\n", + " json.dump(cum_emissions_dict, f)\n", + "```\n", + "\n", + "You need to do this because the current Antarctic Ice-Sheet dynamics component uses the relationship between cumulative emissions and sea-level contribution to calculate the lower and upper bounds of the projection. This was implemented to remove scenario-dependence from the GMSLR emulator, and should be redundant when the component is updated.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "6cef2ba4", + "metadata": {}, + "source": [ + "#### Running global → local ProFSea projections\n", + "Point your paths in the `user-settings-emu.yml` file to the right places, fill in your scenario names and you should be ready to go!\n", + "\n", + "Run: \n", + "\n", + "```bash\n", + "python spatial_projections.py\n", + "```\n", + "\n", + "The global projections will be saved to `data/runs/gmslr_output/`, while the spatialised projections should be saved to `data/runs//`\n", + "\n", + "The way this is accomplished (so that it runs on a laptop) is by taking and saving 101 percentiles of the GMSLR module output for each component. This can be updated via the `output_percentiles` keyword in the `GMSLR` class, currently line 458 in `spatial_projections.py`." + ] + }, + { + "cell_type": "markdown", + "id": "84c2ef51", + "metadata": {}, + "source": [ + "You can also run the GMSLR module by itself:\n", + "\n", + "```python\n", + "from profsea.emulator import GMSLREmulator\n", + "\n", + "emulator = GMSLREmulator(\n", + " T_change, # your T_change anom from FaIR\n", + " OHC_change, # your OHC_change anom from FaIR\n", + " 'low-overshoot', # your scenario name (str)\n", + " 2300, # projection end year\n", + " palmer_method=palmer_method, # recommended for runs beyond 2100\n", + " input_ensemble=True, # must be true if providing a complete FaIR ensemble\n", + " output_percentiles=np.arange(101), # list/array of percentiles to sample\n", + " cum_emissions_total=1450) # int, cumulative emissions in scenario from 2015-2100\n", + "\n", + "# run the projections (auto-parallel)\n", + "emulator.project()\n", + "\n", + "# Save to desired folder with the name of the scenario\n", + "emulator.save_components(\n", + " os.path.join(settings[\"baseoutdir\"], 'gmslr_output'), # output dir\n", + " scenario) # scenario name\n", + "\n", + "# Lists the available components\n", + "emulator.list_components()\n", + "\n", + "# You can also access the data from each component directly:\n", + "emulator.greenland # returns greenland projection ensemble\n", + "\n", + "```" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "cmip7", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From d72ad0b1efae29d6c0c9ace662e88a2082ee1979 Mon Sep 17 00:00:00 2001 From: Gregory Munday Date: Mon, 20 Oct 2025 15:59:36 +0100 Subject: [PATCH 032/276] GMSLR fix --- profsea/spatial_projections.py | 1 - 1 file changed, 1 deletion(-) diff --git a/profsea/spatial_projections.py b/profsea/spatial_projections.py index 8fdf5e0..01fec2d 100644 --- a/profsea/spatial_projections.py +++ b/profsea/spatial_projections.py @@ -451,7 +451,6 @@ def calculate_global_components(scenario: str, palmer_method: bool) -> None: T_change, OHC_change, scenario, - os.path.join(settings["baseoutdir"], 'emulator_output'), settings["projection_end_year"], palmer_method=palmer_method, input_ensemble=settings["emulator_settings"]["use_input_ensemble"], From 3297cb9d41184b174db58c82aeda2c2d28edb1db Mon Sep 17 00:00:00 2001 From: Gregory Munday Date: Wed, 22 Oct 2025 11:25:33 +0100 Subject: [PATCH 033/276] Small fixes --- profsea/directories.py | 2 +- profsea/spatial_projections.py | 20 ++++++++++++++------ profsea/user-settings-emu.yml | 4 +++- 3 files changed, 18 insertions(+), 8 deletions(-) diff --git a/profsea/directories.py b/profsea/directories.py index 346f9e2..45dd50a 100644 --- a/profsea/directories.py +++ b/profsea/directories.py @@ -39,7 +39,7 @@ def read_dir(): 'zos_regression', '') # output sea level projections data directory - sealev_ddir = os.path.join(root_dir, data_region, 'data', + sealev_ddir = os.path.join(root_dir, settings['experiment_name'], 'data', 'sea_level_projections', '') # output sea level projections figure directory diff --git a/profsea/spatial_projections.py b/profsea/spatial_projections.py index 01fec2d..28ce006 100644 --- a/profsea/spatial_projections.py +++ b/profsea/spatial_projections.py @@ -6,6 +6,7 @@ import pickle import json import os +from pathlib import Path import dask.array as da import iris @@ -57,7 +58,7 @@ def calc_future_sea_level(scenario: str) -> None: 'glacier', 'landwater'] # Select dimensions from sample file, [time, realisation] - sample = np.load(os.path.join(mcdir, f'{scenario}_exp.npy')) + sample = np.load(os.path.join(mcdir, f'{scenario}_expansion.npy')) nesm = sample.shape[0] # also number of samples to make nyrs = sample.shape[1] yrs = np.arange(2007, 2007 + nyrs) @@ -194,7 +195,6 @@ def save_projections( R_file = '_'.join([file_header, 'regional']) + '.npy' # Save the global and local projections - makefolder(sealev_ddir) np.save(os.path.join(sealev_ddir, R_file), montecarlo_R) @@ -460,7 +460,7 @@ def calculate_global_components(scenario: str, palmer_method: bool) -> None: print('Saving components...') gmslr.save_components( - os.path.join(settings["baseoutdir"], 'gmslr_output'), + os.path.join(settings["emulator_settings"]["gmslr_output_dir"]), scenario) print('Saved!\n') @@ -474,6 +474,16 @@ def main(): """ print(f'\nProjecting out to: {settings["projection_end_year"]}\n') + # Sort out paths + Path( + os.path.join( + settings["baseoutdir"], + settings["experiment_name"]) + ).mkdir(parents=True, exist_ok=True) + Path( + read_dir()[4] + ).mkdir(parents=True, exist_ok=True) + # Extract site data from station list (e.g. tide gauge location) or # construct based on user input if settings["emulator_settings"]["emulator_mode"]: @@ -482,9 +492,7 @@ def main(): palmer_method = True else: palmer_method = False - - makefolder(os.path.join(settings["baseoutdir"], 'emulator_output')) - + # Get the metadata of either the site location or tide gauge location for scenario in settings["emulator_settings"]["emulator_scenario"]: calculate_global_components(scenario, palmer_method) diff --git a/profsea/user-settings-emu.yml b/profsea/user-settings-emu.yml index 225ada5..6968161 100644 --- a/profsea/user-settings-emu.yml +++ b/profsea/user-settings-emu.yml @@ -10,12 +10,13 @@ # N.B. if other site name: latlon needs to be specified # sitelatlon: [[-51.69, -57.82]]) siteinfo: - region: 'cmip6_patterns' # str + region: 'cmip7_example' # str sitename: ['Stanley II'] # list sitelatlon: [[]] # list # Base output directory # N.B. the code will add region to the output directory +experiment_name: 'cmip7_example' baseoutdir: '/add/your/directory/' # Set the end year of the projection(s) @@ -25,6 +26,7 @@ projection_end_year: 2300 # int between 2050 and 2300 emulator_settings: emulator_mode: true # bool use_input_ensemble: true # bool + gmslr_output_dir: '/ProFSea/ProFSea-tool/data/runs/gmslr' emulator_scenario: ['high-extension', 'high-overshoot', 'medium-extension', 'medium-overshoot', 'low', 'verylow', 'verylow-overshoot'] # str scm_data: From d10ae03b4f25a856288c9f29076f4deff9580760 Mon Sep 17 00:00:00 2001 From: Gregory Munday Date: Wed, 22 Oct 2025 11:36:26 +0100 Subject: [PATCH 034/276] New env file --- ProFSea-emu-env.yml | 15 +++++++++++++++ 1 file changed, 15 insertions(+) create mode 100644 ProFSea-emu-env.yml diff --git a/ProFSea-emu-env.yml b/ProFSea-emu-env.yml new file mode 100644 index 0000000..09a1fa1 --- /dev/null +++ b/ProFSea-emu-env.yml @@ -0,0 +1,15 @@ +name: profsea-emu +channels: + - conda-forge +dependencies: + - cartopy + - dask + - iris + - matplotlib + - netcdf4 + - numpy + - pandas + - python + - pyyaml + - scipy + - xarray \ No newline at end of file From d8669a54adbbfc5b08f5f369773de133c696eb06 Mon Sep 17 00:00:00 2001 From: Hemant Khatri Date: Thu, 23 Oct 2025 10:21:47 +0100 Subject: [PATCH 035/276] Update filepaths for gmslr folder --- profsea/spatial_projections.py | 21 ++++++++++++++++---- profsea/user-settings-emu.yml | 36 +++++++++++++++++----------------- 2 files changed, 35 insertions(+), 22 deletions(-) diff --git a/profsea/spatial_projections.py b/profsea/spatial_projections.py index 28ce006..4e5c2d4 100644 --- a/profsea/spatial_projections.py +++ b/profsea/spatial_projections.py @@ -58,10 +58,12 @@ def calc_future_sea_level(scenario: str) -> None: 'glacier', 'landwater'] # Select dimensions from sample file, [time, realisation] - sample = np.load(os.path.join(mcdir, f'{scenario}_expansion.npy')) + sample = np.load(os.path.join(settings["baseoutdir"],settings["experiment_name"], + 'data', 'gmslr', + f'{scenario}_expansion.npy')) nesm = sample.shape[0] # also number of samples to make nyrs = sample.shape[1] - yrs = np.arange(2007, 2007 + nyrs) + yrs = np.arange(2006, 2006 + nyrs) grid_path = os.path.join( settings["cmipinfo"]["sealevelbasedir"], @@ -230,7 +232,9 @@ def calculate_sl_components( montecarlo_G = da.zeros((nsmps, nyrs, lats, lons), dtype=np.float32) # (no FPs applied) # Load global projections in for the component - mc_timeseries = np.load(os.path.join(mcdir, f'{scenario}_{comp}.npy')) + #mc_timeseries = np.load(os.path.join(mcdir, f'{scenario}_{comp}.npy')) + mc_timeseries = np.load(os.path.join(settings["baseoutdir"],settings["experiment_name"], + 'data', 'gmslr', f'{scenario}_{comp}.npy')) sampled_mc = mc_timeseries[resamples, :nyrs] montecarlo_G[:, :] = da.from_array(sampled_mc[:, :, None, None]) @@ -460,7 +464,8 @@ def calculate_global_components(scenario: str, palmer_method: bool) -> None: print('Saving components...') gmslr.save_components( - os.path.join(settings["emulator_settings"]["gmslr_output_dir"]), + os.path.join(settings["baseoutdir"],settings["experiment_name"], + 'data', 'gmslr'), scenario) print('Saved!\n') @@ -480,6 +485,14 @@ def main(): settings["baseoutdir"], settings["experiment_name"]) ).mkdir(parents=True, exist_ok=True) + + Path( + os.path.join( + settings["baseoutdir"], + settings["experiment_name"], + 'data', 'gmslr') + ).mkdir(parents=True, exist_ok=True) + Path( read_dir()[4] ).mkdir(parents=True, exist_ok=True) diff --git a/profsea/user-settings-emu.yml b/profsea/user-settings-emu.yml index 6968161..aa21cc4 100644 --- a/profsea/user-settings-emu.yml +++ b/profsea/user-settings-emu.yml @@ -10,14 +10,14 @@ # N.B. if other site name: latlon needs to be specified # sitelatlon: [[-51.69, -57.82]]) siteinfo: - region: 'cmip7_example' # str + region: 'cmip6_patterns' # str sitename: ['Stanley II'] # list sitelatlon: [[]] # list # Base output directory # N.B. the code will add region to the output directory -experiment_name: 'cmip7_example' -baseoutdir: '/add/your/directory/' +experiment_name: 'fair_example' +baseoutdir: '/data/users/hemant.khatri/ProFSea-Runs/test-runs/FAIR-calibrated-ensemble/' # Set the end year of the projection(s) projection_end_year: 2300 # int between 2050 and 2300 @@ -26,13 +26,13 @@ projection_end_year: 2300 # int between 2050 and 2300 emulator_settings: emulator_mode: true # bool use_input_ensemble: true # bool - gmslr_output_dir: '/ProFSea/ProFSea-tool/data/runs/gmslr' - emulator_scenario: ['high-extension', 'high-overshoot', 'medium-extension', 'medium-overshoot', 'low', 'verylow', 'verylow-overshoot'] # str + gmslr_output_dir: '/data/users/hemant.khatri/ProFSea-Runs/test-runs/FAIR-calibrated-ensemble/gmslr' + emulator_scenario: ['high-overshoot', 'medium-overshoot', 'low', 'verylow-overshoot'] # str scm_data: - temperature: '/results/fair_output/cmip7_temperature.nc' # str - ocean_heat_content: '/results/fair_output/cmip7_ohc.nc' # str - cumulative_emissions: '/data/cumulative_cmip7_emissions.json' # str + temperature: '/data/users/hemant.khatri/ProFSea-Runs/test-runs/FAIR-calibrated-ensemble/FAIR-output/cmip6_temperature.nc' # str + ocean_heat_content: '/data/users/hemant.khatri/ProFSea-Runs/test-runs/FAIR-calibrated-ensemble/FAIR-output/cmip6_ohc.nc' # str + cumulative_emissions: '/data/users/hemant.khatri/ProFSea-Runs/test-runs/FAIR-calibrated-ensemble/FAIR-output/cumulative_scenario_emissions.json' # str # Science method # UKCP18 --> sciencemethod: 'UK' @@ -49,7 +49,7 @@ sciencemethod: 'global' # str tidegaugeinfo: source: 'PSMSL' # str datafq: ['annual'] # list - psmsldir: '/home/user/data/PSMSL/' + psmsldir: '/data/users/hemant.khatri/ProFSea-Runs/data-profsea/PSMSL/' # CMIP information cmipinfo: @@ -59,25 +59,25 @@ cmipinfo: # Marginal sea --> cmip_sea: 'marginal' cmip_sea: 'all' # str # sealevelbasedir: Base directory for CMIP "zos" and "zostoga" data - sealevelbasedir: '/data/cmip6/' + sealevelbasedir: '/data/users/hemant.khatri/ProFSea-Runs/data-profsea/cmip6/' # slopecoeffsuk: CMIP5 slope coefficients developed for UKCP18 - slopecoeffsuk: '/data/uk_cmip_slope_coefficients/' + slopecoeffsuk: '/data/users/hemant.khatri/ProFSea-Runs/data-profsea/uk_cmip_slope_coefficients/' # Directories for the GIA estimates (independent of scenario) # UKCP18 --> UK specific ones # Global locations --> Lambeck, ICE5G giaestimates: - global: '/data/gia_estimates/global_GIA_interpolators.pickle' - uk: '‘/data/gia_estimates/Bradley_GIA_interpolator.pickle' + global: '/data/users/hemant.khatri/ProFSea-Runs/data-profsea/gia_estimates/global_GIA_interpolators.pickle' + uk: '/data/users/hemant.khatri/ProFSea-Runs/data-profsea/gia_estimates/Bradley_GIA_interpolator.pickle' # Directories for the Slangen, Spada and Klemann fingerprints fingerprints: - slangendir: '/data/grd_fingerprints/' - spadadir: '/data/grd_fingerprints/' - klemanndir: '/data/grd_fingerprints/' + slangendir: '/data/users/hemant.khatri/ProFSea-Runs/data-profsea/grd_fingerprints/' + spadadir: '/data/users/hemant.khatri/ProFSea-Runs/data-profsea/grd_fingerprints/' + klemanndir: '/data/users/hemant.khatri/ProFSea-Runs/data-profsea/grd_fingerprints/' # Directory of Monte Carlo time series for new projections # N.B. Originally developed by Jonathan Gregory -short_montecarlodir: '/path/to/2100/montecarlo/simulations' # Required for projections up to 2100 -long_montecarlodir: '/data/runs/emulator_output' # Required for projections from 2100 up to 2300 +short_montecarlodir: '/data/users/hemant.khatri/ProFSea-Runs/data-profsea/monte_carlo_timeseries/' # Required for projections up to 2100 +long_montecarlodir: '/data/users/hemant.khatri/ProFSea-Runs/test-runs/FAIR-calibrated-ensemble/emulator_output' # Required for projections from 2100 up to 2300 From b42f6d4b44d0f2e11954acbf2b5c2eecbca9c7ed Mon Sep 17 00:00:00 2001 From: Hemant Khatri Date: Thu, 23 Oct 2025 10:27:29 +0100 Subject: [PATCH 036/276] Revert user-settings-emu to run_from_scm branch --- profsea/user-settings-emu.yml | 36 +++++++++++++++++------------------ 1 file changed, 18 insertions(+), 18 deletions(-) diff --git a/profsea/user-settings-emu.yml b/profsea/user-settings-emu.yml index aa21cc4..6968161 100644 --- a/profsea/user-settings-emu.yml +++ b/profsea/user-settings-emu.yml @@ -10,14 +10,14 @@ # N.B. if other site name: latlon needs to be specified # sitelatlon: [[-51.69, -57.82]]) siteinfo: - region: 'cmip6_patterns' # str + region: 'cmip7_example' # str sitename: ['Stanley II'] # list sitelatlon: [[]] # list # Base output directory # N.B. the code will add region to the output directory -experiment_name: 'fair_example' -baseoutdir: '/data/users/hemant.khatri/ProFSea-Runs/test-runs/FAIR-calibrated-ensemble/' +experiment_name: 'cmip7_example' +baseoutdir: '/add/your/directory/' # Set the end year of the projection(s) projection_end_year: 2300 # int between 2050 and 2300 @@ -26,13 +26,13 @@ projection_end_year: 2300 # int between 2050 and 2300 emulator_settings: emulator_mode: true # bool use_input_ensemble: true # bool - gmslr_output_dir: '/data/users/hemant.khatri/ProFSea-Runs/test-runs/FAIR-calibrated-ensemble/gmslr' - emulator_scenario: ['high-overshoot', 'medium-overshoot', 'low', 'verylow-overshoot'] # str + gmslr_output_dir: '/ProFSea/ProFSea-tool/data/runs/gmslr' + emulator_scenario: ['high-extension', 'high-overshoot', 'medium-extension', 'medium-overshoot', 'low', 'verylow', 'verylow-overshoot'] # str scm_data: - temperature: '/data/users/hemant.khatri/ProFSea-Runs/test-runs/FAIR-calibrated-ensemble/FAIR-output/cmip6_temperature.nc' # str - ocean_heat_content: '/data/users/hemant.khatri/ProFSea-Runs/test-runs/FAIR-calibrated-ensemble/FAIR-output/cmip6_ohc.nc' # str - cumulative_emissions: '/data/users/hemant.khatri/ProFSea-Runs/test-runs/FAIR-calibrated-ensemble/FAIR-output/cumulative_scenario_emissions.json' # str + temperature: '/results/fair_output/cmip7_temperature.nc' # str + ocean_heat_content: '/results/fair_output/cmip7_ohc.nc' # str + cumulative_emissions: '/data/cumulative_cmip7_emissions.json' # str # Science method # UKCP18 --> sciencemethod: 'UK' @@ -49,7 +49,7 @@ sciencemethod: 'global' # str tidegaugeinfo: source: 'PSMSL' # str datafq: ['annual'] # list - psmsldir: '/data/users/hemant.khatri/ProFSea-Runs/data-profsea/PSMSL/' + psmsldir: '/home/user/data/PSMSL/' # CMIP information cmipinfo: @@ -59,25 +59,25 @@ cmipinfo: # Marginal sea --> cmip_sea: 'marginal' cmip_sea: 'all' # str # sealevelbasedir: Base directory for CMIP "zos" and "zostoga" data - sealevelbasedir: '/data/users/hemant.khatri/ProFSea-Runs/data-profsea/cmip6/' + sealevelbasedir: '/data/cmip6/' # slopecoeffsuk: CMIP5 slope coefficients developed for UKCP18 - slopecoeffsuk: '/data/users/hemant.khatri/ProFSea-Runs/data-profsea/uk_cmip_slope_coefficients/' + slopecoeffsuk: '/data/uk_cmip_slope_coefficients/' # Directories for the GIA estimates (independent of scenario) # UKCP18 --> UK specific ones # Global locations --> Lambeck, ICE5G giaestimates: - global: '/data/users/hemant.khatri/ProFSea-Runs/data-profsea/gia_estimates/global_GIA_interpolators.pickle' - uk: '/data/users/hemant.khatri/ProFSea-Runs/data-profsea/gia_estimates/Bradley_GIA_interpolator.pickle' + global: '/data/gia_estimates/global_GIA_interpolators.pickle' + uk: '‘/data/gia_estimates/Bradley_GIA_interpolator.pickle' # Directories for the Slangen, Spada and Klemann fingerprints fingerprints: - slangendir: '/data/users/hemant.khatri/ProFSea-Runs/data-profsea/grd_fingerprints/' - spadadir: '/data/users/hemant.khatri/ProFSea-Runs/data-profsea/grd_fingerprints/' - klemanndir: '/data/users/hemant.khatri/ProFSea-Runs/data-profsea/grd_fingerprints/' + slangendir: '/data/grd_fingerprints/' + spadadir: '/data/grd_fingerprints/' + klemanndir: '/data/grd_fingerprints/' # Directory of Monte Carlo time series for new projections # N.B. Originally developed by Jonathan Gregory -short_montecarlodir: '/data/users/hemant.khatri/ProFSea-Runs/data-profsea/monte_carlo_timeseries/' # Required for projections up to 2100 -long_montecarlodir: '/data/users/hemant.khatri/ProFSea-Runs/test-runs/FAIR-calibrated-ensemble/emulator_output' # Required for projections from 2100 up to 2300 +short_montecarlodir: '/path/to/2100/montecarlo/simulations' # Required for projections up to 2100 +long_montecarlodir: '/data/runs/emulator_output' # Required for projections from 2100 up to 2300 From b81db53b38136a0f416bb7ee28fd64fb26420ef6 Mon Sep 17 00:00:00 2001 From: Gregory Munday Date: Thu, 23 Oct 2025 13:43:47 +0100 Subject: [PATCH 037/276] Fix for greenland --- profsea/emulator/__init__.py | 38 ++++++++++++++++++++++------------ profsea/slr_pkg/__init__.py | 2 +- profsea/spatial_projections.py | 2 +- 3 files changed, 27 insertions(+), 15 deletions(-) diff --git a/profsea/emulator/__init__.py b/profsea/emulator/__init__.py index cd77736..04bc6ac 100644 --- a/profsea/emulator/__init__.py +++ b/profsea/emulator/__init__.py @@ -159,6 +159,8 @@ def get_components(self) -> dict: 'expansion': self.expansion, 'glacier': self.glacier, 'greenland': self.greenland, + 'greendyn': self.greendyn, + 'greensmb': self.greensmb, 'antsmb': self.antsmb, 'antdyn': self.antdyn, 'antnet': self.antnet, @@ -186,6 +188,8 @@ def save_components(self, output_dir: str, scenario_name: str) -> None: ------- None """ + # Create directory if it doesn't exist + Path(output_dir).mkdir(parents=True, exist_ok=True) for name, component in self.get_components().items(): np.save( os.path.join( @@ -206,12 +210,21 @@ def project(self) -> None: fraction = np.random.rand(self.nm * self.nt) # correlation between antsmb and antdyn self.run_parallel_projections(T_int_med, T_int_ens, T_ens, fraction) - - if self.output_percentiles is not None: - self.expansion = np.percentile(self.expansion, self.output_percentiles, axis=0) self.antnet = self.antsmb + self.antdyn self.gmslr = self.glacier + self.greenland + self.antnet + self.landwater + self.expansion + + if self.output_percentiles is not None: + self.gmslr = np.percentile(self.gmslr, self.output_percentiles, axis=0) + self.expansion = np.percentile(self.expansion, self.output_percentiles, axis=0) + self.antnet = np.percentile(self.antnet, self.output_percentiles, axis=0) + self.antdyn = np.percentile(self.antdyn, self.output_percentiles, axis=0) + self.antsmb = np.percentile(self.antsmb, self.output_percentiles, axis=0) + self.glacier = np.percentile(self.glacier, self.output_percentiles, axis=0) + self.greenland = np.percentile(self.greenland, self.output_percentiles, axis=0) + self.greenland_ar6 = np.percentile(self.greenland_ar6, self.output_percentiles, axis=0) + self.landwater = np.percentile(self.landwater, self.output_percentiles, axis=0) + self.landwater_ar6 = np.percentile(self.landwater_ar6, self.output_percentiles, axis=0) def run_parallel_projections( self, T_int_med: np.ndarray, T_int_ens: np.ndarray, @@ -227,8 +240,11 @@ def run_parallel_projections( executor.submit(self.project_glacier, T_int_med, T_int_ens): 'glacier', executor.submit(self.project_antsmb, T_int_ens, fraction): 'antsmb', executor.submit(self.project_greenland_AR6, T_ens): 'greenland', + executor.submit(self.project_greendyn_AR5): 'greendyn', + executor.submit(self.project_greensmb_AR5, T_ens): 'greensmb', executor.submit(self.project_antdyn, fraction): 'antdyn', - executor.submit(self.project_landwater): 'landwater' + executor.submit(self._project_landwater_ar5): 'landwater', + executor.submit(self.project_landwater): 'landwater_ar6' } results = {} for future in concurrent.futures.as_completed(futures): @@ -240,16 +256,13 @@ def run_parallel_projections( self.glacier = results['glacier'] self.antsmb = results['antsmb'] - self.greenland = results['greenland'] + self.greenland_ar6 = results['greenland'] + self.greenland = results['greendyn'] + results['greensmb'] + self.greendyn = results['greendyn'] + self.greensmb = results['greensmb'] self.antdyn = results['antdyn'] self.landwater = results['landwater'] - - if self.output_percentiles is not None: - self.glacier = np.percentile(self.glacier, self.output_percentiles, axis=0) - self.antsmb = np.percentile(self.antsmb, self.output_percentiles, axis=0) - self.greenland = np.percentile(self.greenland, self.output_percentiles, axis=0) - self.antdyn = np.percentile(self.antdyn, self.output_percentiles, axis=0) - self.landwater = np.percentile(self.landwater, self.output_percentiles, axis=0) + self.landwater_ar6 = results['landwater_ar6'] def calculate_drivers(self) -> tuple: @@ -366,7 +379,6 @@ def project_glacier( dict(name='GloGEMflow',factor=5.50,exponent=0.564,cvgl=0.188), dict(name='OGGMv1.6',factor=2.66,exponent=0.730,cvgl=0.206), dict(name='PyGEM-OGGMv1.3', factor=2.66, exponent=0.730, cvgl=0.206)] - else: raise KeyError('glaciermip must be 1 or 2') else: diff --git a/profsea/slr_pkg/__init__.py b/profsea/slr_pkg/__init__.py index be4f2a5..b2cb7c8 100644 --- a/profsea/slr_pkg/__init__.py +++ b/profsea/slr_pkg/__init__.py @@ -325,7 +325,7 @@ def choose_montecarlo_dir(): Choose the Monte Carlo directory based on the projection end year. """ if settings["emulator_settings"]["emulator_mode"]: - return os.path.join(settings["baseoutdir"], 'emulator_output') + return os.path.join(settings["emulator_settings"]["gmslr_output_dir"]) end_yr = settings["projection_end_year"] if (end_yr >= 2050) & (end_yr <= 2100): diff --git a/profsea/spatial_projections.py b/profsea/spatial_projections.py index 28ce006..12aa5db 100644 --- a/profsea/spatial_projections.py +++ b/profsea/spatial_projections.py @@ -16,7 +16,7 @@ import xarray as xr from profsea.config import settings -from profsea.directories import read_dir, makefolder +from profsea.directories import read_dir from profsea.emulator import GMSLREmulator from profsea.slr_pkg import choose_montecarlo_dir # found in __init.py__ From 7cb0ca9fd74b96a0bb5d87fb801735895f4f2e30 Mon Sep 17 00:00:00 2001 From: Hemant Khatri Date: Mon, 27 Oct 2025 10:55:49 +0000 Subject: [PATCH 038/276] minor edits to code style --- profsea/spatial_projections.py | 13 ++++++++----- 1 file changed, 8 insertions(+), 5 deletions(-) diff --git a/profsea/spatial_projections.py b/profsea/spatial_projections.py index 83f290f..bfeee63 100644 --- a/profsea/spatial_projections.py +++ b/profsea/spatial_projections.py @@ -59,8 +59,8 @@ def calc_future_sea_level(scenario: str) -> None: # Select dimensions from sample file, [time, realisation] sample = np.load(os.path.join(settings["baseoutdir"],settings["experiment_name"], - 'data', 'gmslr', - f'{scenario}_expansion.npy')) + 'data', 'gmslr', f'{scenario}_expansion.npy')) + nesm = sample.shape[0] # also number of samples to make nyrs = sample.shape[1] yrs = np.arange(2006, 2006 + nyrs) @@ -234,7 +234,8 @@ def calculate_sl_components( # Load global projections in for the component #mc_timeseries = np.load(os.path.join(mcdir, f'{scenario}_{comp}.npy')) mc_timeseries = np.load(os.path.join(settings["baseoutdir"],settings["experiment_name"], - 'data', 'gmslr', f'{scenario}_{comp}.npy')) + 'data','gmslr',f'{scenario}_{comp}.npy')) + print("data read: ", mc_timeseries) sampled_mc = mc_timeseries[resamples, :nyrs] montecarlo_G[:, :] = da.from_array(sampled_mc[:, :, None, None]) @@ -465,9 +466,11 @@ def calculate_global_components(scenario: str, palmer_method: bool) -> None: print('Saving components...') gmslr.save_components( os.path.join(settings["baseoutdir"],settings["experiment_name"], - 'data', 'gmslr'), + 'data', 'gmslr'), scenario) - print('Saved!\n') + print('Saved at: ', + os.path.join(settings["baseoutdir"],settings["experiment_name"], + 'data', 'gmslr'),'\n') def main(): From 7dfca00747ae4b5ea4d9f5b4dce93efe092a0d92 Mon Sep 17 00:00:00 2001 From: Gregory Munday Date: Thu, 6 Nov 2025 10:13:21 +0000 Subject: [PATCH 039/276] Updated sampling, added new Greenland FP --- profsea/emulator/__init__.py | 44 ++++++++---- profsea/spatial_projections.py | 122 +++++++++++++++++++++------------ 2 files changed, 110 insertions(+), 56 deletions(-) diff --git a/profsea/emulator/__init__.py b/profsea/emulator/__init__.py index 04bc6ac..fed5c21 100644 --- a/profsea/emulator/__init__.py +++ b/profsea/emulator/__init__.py @@ -12,9 +12,13 @@ from pathlib import Path import numpy as np +import pandas as pd +from rich.console import Console +from scipy.spatial.distance import cdist from scipy.stats import norm, truncnorm import xarray as xr -import pandas as pd + +console = Console() class GMSLREmulator: """Emulator for global mean sea level rise components. @@ -158,7 +162,7 @@ def get_components(self) -> dict: components_dict = { 'expansion': self.expansion, 'glacier': self.glacier, - 'greenland': self.greenland, + 'greenland': self.greenland_ar6, 'greendyn': self.greendyn, 'greensmb': self.greensmb, 'antsmb': self.antsmb, @@ -173,6 +177,15 @@ def list_components(self) -> list: """List the available SLR components.""" component_dict = self.get_components() print(list(component_dict.keys())) + + def sample_members_2D( + self, array: np.ndarray) -> np.ndarray: + """Sample real ensemble members from a 2D numpy array.""" + # Caculate statistical timeseries, then match with closest real timeseries + array_percentiles = np.percentile(array, self.output_percentiles, axis=0) + distances = cdist(array_percentiles, array) + mem_indices = np.argmin(distances, axis=1) + return array[mem_indices] def save_components(self, output_dir: str, scenario_name: str) -> None: """Save all SLR components as .npy files to a directory. @@ -212,19 +225,22 @@ def project(self) -> None: self.run_parallel_projections(T_int_med, T_int_ens, T_ens, fraction) self.antnet = self.antsmb + self.antdyn - self.gmslr = self.glacier + self.greenland + self.antnet + self.landwater + self.expansion + self.gmslr = self.glacier + self.greenland_ar6 + self.antnet + self.landwater + self.expansion + # TODO Parallelise this section as it's a bit slow if self.output_percentiles is not None: - self.gmslr = np.percentile(self.gmslr, self.output_percentiles, axis=0) - self.expansion = np.percentile(self.expansion, self.output_percentiles, axis=0) - self.antnet = np.percentile(self.antnet, self.output_percentiles, axis=0) - self.antdyn = np.percentile(self.antdyn, self.output_percentiles, axis=0) - self.antsmb = np.percentile(self.antsmb, self.output_percentiles, axis=0) - self.glacier = np.percentile(self.glacier, self.output_percentiles, axis=0) - self.greenland = np.percentile(self.greenland, self.output_percentiles, axis=0) - self.greenland_ar6 = np.percentile(self.greenland_ar6, self.output_percentiles, axis=0) - self.landwater = np.percentile(self.landwater, self.output_percentiles, axis=0) - self.landwater_ar6 = np.percentile(self.landwater_ar6, self.output_percentiles, axis=0) + console.log(f"Sampling {len(self.output_percentiles)} members per component...") + self.gmslr = self.sample_members_2D(self.gmslr) + self.expansion = self.sample_members_2D(self.expansion) + self.antnet = self.sample_members_2D(self.antnet) + self.antdyn = self.sample_members_2D(self.antdyn) + self.antsmb = self.sample_members_2D(self.antsmb) + self.glacier = self.sample_members_2D(self.glacier) + self.greenland_ar6 = self.sample_members_2D(self.greenland_ar6) + self.landwater = self.sample_members_2D(self.landwater) + # self.greenland_ar5 = self.sample_members_2D(self.greenland_ar5) + # self.landwater_ar6 = self.sample_members_2D(self.landwater_ar6) + def run_parallel_projections( self, T_int_med: np.ndarray, T_int_ens: np.ndarray, @@ -257,7 +273,7 @@ def run_parallel_projections( self.glacier = results['glacier'] self.antsmb = results['antsmb'] self.greenland_ar6 = results['greenland'] - self.greenland = results['greendyn'] + results['greensmb'] + self.greenland_ar5 = results['greendyn'] + results['greensmb'] self.greendyn = results['greendyn'] self.greensmb = results['greensmb'] self.antdyn = results['antdyn'] diff --git a/profsea/spatial_projections.py b/profsea/spatial_projections.py index 12aa5db..c7b8843 100644 --- a/profsea/spatial_projections.py +++ b/profsea/spatial_projections.py @@ -7,11 +7,15 @@ import json import os from pathlib import Path +import warnings import dask.array as da import iris from netCDF4 import Dataset import numpy as np +from rich.console import Console +from rich.progress import track +import scipy from scipy.interpolate import RegularGridInterpolator import xarray as xr @@ -20,6 +24,8 @@ from profsea.emulator import GMSLREmulator from profsea.slr_pkg import choose_montecarlo_dir # found in __init.py__ +console = Console() +warnings.filterwarnings("ignore") def calc_baseline_period(yrs: np.array) -> float: """ @@ -30,7 +36,7 @@ def calc_baseline_period(yrs: np.array) -> float: byr1 = 1986. byr2 = 2005. - print("Baseline period = ", byr1, "to", byr2) + console.log("Baseline period = ", byr1, "to", byr2) midyr = (byr2 - byr1 + 1) * 0.5 + byr1 return yrs[0] - midyr @@ -43,9 +49,6 @@ def calc_future_sea_level(scenario: str) -> None: :param site_loc: name of the site location :param scenario: emission scenario """ - print('running function calc_future_sea_level') - - # Set the UKCP*18* random seed so results are reproducible np.random.seed(18) @@ -54,7 +57,7 @@ def calc_future_sea_level(scenario: str) -> None: # Specify the sea level components to include. The GIA contribution is # calculated separately. - components = ['expansion', 'antdyn', 'antsmb', 'greendyn', 'greensmb', + components = ['expansion', 'antdyn', 'antsmb', 'greenland', 'glacier', 'landwater'] # Select dimensions from sample file, [time, realisation] @@ -62,6 +65,7 @@ def calc_future_sea_level(scenario: str) -> None: nesm = sample.shape[0] # also number of samples to make nyrs = sample.shape[1] yrs = np.arange(2007, 2007 + nyrs) + console.log(f"Running with {nesm} ensemble members") grid_path = os.path.join( settings["cmipinfo"]["sealevelbasedir"], @@ -86,7 +90,7 @@ def calc_gia_contribution( :param coords: coordinates of location of interest :return: GIA estimates converted to mm/yr """ - print('Calculating GIA contribution...') + console.log('Calculating GIA contribution...') nGIA, GIA_vals = read_gia_estimates() Tdelta = calc_baseline_period(yrs) @@ -122,11 +126,9 @@ def calc_expansion_contribution( # Select slope coefficients based on the MIP if settings["emulator_settings"]["emulator_mode"]: if settings["cmipinfo"]["mip"].lower() == "cmip6": - print("Using CMIP6 sterodynamic patterns...") coeffs = load_CMIP6_slopes('ssp585') coeffs = np.roll(coeffs, 180, axis=2) else: - print("Using CMIP5 sterodynamic patterns...") coeffs = load_CMIP5_slope_coeffs('rcp85') else: coeffs = load_CMIP5_slope_coeffs(scenario) @@ -154,7 +156,7 @@ def calc_landwater_contribution(interpolator: dict) -> da.array: def calc_fingerprint_contributions( FPlist: list, comp: str, lats: int, lons: int) -> da.array: # Initiate an empty list for fingerprint values - FPvals = [] + fp_vals = [] for FP_dict in FPlist: # Interpolate values to target lat/lon val = FP_dict[comp].values @@ -174,10 +176,35 @@ def calc_fingerprint_contributions( else: val = np.roll(val, 180, axis=1) - FPvals.append(val) + fp_vals.append(val) + + fp_vals = da.from_array(np.array(fp_vals, dtype=np.float32)) + return fp_vals + + +def calc_greenland_fingerprint_ar6() -> da.array: + """Load and prepare the GIS fingerprint. + + This fingerprint was/is used by FACTS for AR6 projections, and was + originally calculated by Mitrovica et al., (2011). + + :return dask array containing GIS fingerprint + """ + # Load in the fingerprint + fp_path = Path(settings["fingerprints"]) / "greenland_ar6.nc" + fp_ds = xr.load_dataset(fp_path) + + # Interpolate to (180, 360) grid + fp_vals = fp_ds.fp.interp( + lat=np.linspace(-90, 90, 180, endpoint=False), + lon=np.linspace(0, 360, 360, endpoint=False), + method="linear").data * 1000 # convert mm to m SLE per m GMSLR - FPvals = da.from_array(np.array(FPvals, dtype=np.float32)) - return FPvals + # Flip vertically and roll by 180 degrees + fp_vals = np.flip(fp_vals) + fp_vals = np.roll(fp_vals, 180, axis=1) + fp_vals = da.from_array(np.array(fp_vals, dtype=np.float32)) + return fp_vals def save_projections( @@ -214,7 +241,7 @@ def calculate_sl_components( nyrs --> Number of years in each projection time series :return: montecarlo_G (global contribution to sea level rise) and montecarlo_R (regional contribution to sea level change) - """ + """ # Numbers of ensemble members, samples, years nesm, nsmps, nyrs, lats, lons = array_dims nFPs, FPlist = setup_FP_interpolators(components) @@ -224,8 +251,7 @@ def calculate_sl_components( # Calculate GIA contribution and save it out calc_gia_contribution(yrs, nyrs, nsmps, scenario) - for comp in components: - print(f'\nComponent: {comp}') + for comp in track(components, description="Calculating components..."): montecarlo_R = da.zeros((nsmps, nyrs, lats, lons), dtype=np.float32) # (FPs applied) + GIA montecarlo_G = da.zeros((nsmps, nyrs, lats, lons), dtype=np.float32) # (no FPs applied) @@ -234,23 +260,27 @@ def calculate_sl_components( sampled_mc = mc_timeseries[resamples, :nyrs] montecarlo_G[:, :] = da.from_array(sampled_mc[:, :, None, None]) - if comp == 'expansion': + if comp == "expansion": sampled_coeffs = calc_expansion_contribution(scenario, nsmps) montecarlo_R = montecarlo_G * sampled_coeffs[:, None, :, :] del sampled_coeffs - elif comp == 'landwater': + elif comp == "landwater": landwater_vals = calc_landwater_contribution(FPlist[0]) montecarlo_R[:, :, :, :] = montecarlo_G[:, :, :, :] * landwater_vals[None, None, :, :] del landwater_vals + + elif comp == "greenland": + greenland_fp = calc_greenland_fingerprint_ar6() + montecarlo_R[:, :, :, :] = montecarlo_G[:, :, :, :] * greenland_fp[None, None, :, :] + else: - FPvals = calc_fingerprint_contributions(FPlist, comp, lats, lons) - montecarlo_R[:, :, :, :] = montecarlo_G[:, :, :, :] * FPvals[rfpi][:, None, :, :] - del FPvals + fp_vals = calc_fingerprint_contributions(FPlist, comp, lats, lons) + montecarlo_R[:, :, :, :] = montecarlo_G[:, :, :, :] * fp_vals[rfpi][:, None, :, :] + del fp_vals # Take the 0th, 25th, 50th, 75th and 100th percentiles montecarlo_R = da.percentile(montecarlo_R, [0, 25, 50, 75, 100], axis=0) - print('Computing result...') montecarlo_R = montecarlo_R.compute() # Create the output sea level projections file directory and filename @@ -311,7 +341,6 @@ def load_CMIP5_slope_coeffs(scenario: str) -> np.ndarray: :param scenario: emissions scenario :return: 1D array of regression coefficients """ - print('running function load_CMIP5_slope_coeffs') # Read in the sea level regressions in_zosddir = read_dir()[2] filename = os.path.join(in_zosddir, 'zos_regression.npy') @@ -377,13 +406,6 @@ def setup_FP_interpolators(components: list) -> tuple: :return nFPs: length of FPlist and interpolator objects of all sea level components """ - print("running function setup_FP_interpolators") - - # Directories for the Slangen, Spada and Klemann fingerprints - slangendir = settings["fingerprints"]["slangendir"] - spadadir = settings["fingerprints"]["spadadir"] - klemanndir = settings["fingerprints"]["klemanndir"] - # Create empty dictionaries for the Slangen, Spada and Klemann fingerprints # interpolator objects. slangen_FPs = {} @@ -392,18 +414,18 @@ def setup_FP_interpolators(components: list) -> tuple: # Only 1 fingerprint for Landwater comp = "landwater" - slangen_FPs[comp] = create_FP_interpolator(slangendir, + slangen_FPs[comp] = create_FP_interpolator(settings["fingerprints"], comp + "_slangen_nomask.nc") # Create interpolators for the remaining components. Expansion ('expansion') # is global so no interpolation is needed. - components_todo = [c for c in components if c not in ["expansion", "landwater"]] + components_todo = [c for c in components if c not in ["expansion", "landwater", "greenland"]] for comp in components_todo: - slangen_FPs[comp] = create_FP_interpolator(slangendir, + slangen_FPs[comp] = create_FP_interpolator(settings["fingerprints"], comp + "_slangen_nomask.nc") - spada_FPs[comp] = create_FP_interpolator(spadadir, + spada_FPs[comp] = create_FP_interpolator(settings["fingerprints"], comp + "_spada_nomask.nc") - klemann_FPs[comp] = create_FP_interpolator(klemanndir, + klemann_FPs[comp] = create_FP_interpolator(settings["fingerprints"], comp + "_klemann_nomask.nc") FPlist = [slangen_FPs, spada_FPs, klemann_FPs] @@ -411,6 +433,21 @@ def setup_FP_interpolators(components: list) -> tuple: return nFPs, FPlist +def sample_members_2D(array: np.ndarray, percentile_seq: list|np.ndarray) -> np.ndarray: + """Sample real ensemble members from a 2D numpy array.""" + # Caculate statistical timeseries, then match with closest real timeseries + from scipy.spatial.distance import cdist + array_percentiles = np.percentile(array, percentile_seq, axis=0) + array_perc_diffs = array[None, :, :] - array_percentiles[:, None, :] + + # Calculate distances between statistical percentiles and real members + distances = scipy.linalg.norm(array_perc_diffs, axis=2) + mem_indices = np.argmin(distances, axis=1) + distances = cdist(array_percentiles, array) + mem_indices = np.argmin(distances, axis=1) + return array[mem_indices] + + def calculate_global_components(scenario: str, palmer_method: bool) -> None: """ Calculate the global contributions for each of the sea-level components @@ -436,16 +473,17 @@ def calculate_global_components(scenario: str, palmer_method: bool) -> None: percentiles = np.arange(101) # Now run the simulations - print(f'Projecting global components for {scenario} scenario...') + console.log(f'Projecting global components for {scenario} scenario...') T_change = xr.load_dataarray(settings["scm_data"]["temperature"]) OHC_change = xr.load_dataarray(settings["scm_data"]["ocean_heat_content"]) with open(settings["scm_data"]["cumulative_emissions"]) as f: cumulative_emissions = json.load(f) - T_change = T_change.sel(scenario=scenario).data.T + T_change = T_change.sel(scenario=scenario).data.T # (member, time) OHC_change = OHC_change.sel(scenario=scenario).data.T - T_change = np.percentile(T_change, percentiles, axis=0) - OHC_change = np.percentile(OHC_change, percentiles, axis=0) + + T_change = sample_members_2D(T_change, percentiles) + OHC_change = sample_members_2D(OHC_change, percentiles) gmslr = GMSLREmulator( T_change, @@ -458,11 +496,11 @@ def calculate_global_components(scenario: str, palmer_method: bool) -> None: cum_emissions_total=cumulative_emissions[scenario]) gmslr.project() - print('Saving components...') + console.log('Saving components...') gmslr.save_components( os.path.join(settings["emulator_settings"]["gmslr_output_dir"]), scenario) - print('Saved!\n') + console.log('Saved!\n') def main(): @@ -472,7 +510,7 @@ def main(): expansion, GIA and mass balance. Writes out the selected emissions scenario estimates of the various components and their sums. """ - print(f'\nProjecting out to: {settings["projection_end_year"]}\n') + console.log(f'\nProjecting out to: {settings["projection_end_year"]}\n') # Sort out paths Path( @@ -487,7 +525,7 @@ def main(): # Extract site data from station list (e.g. tide gauge location) or # construct based on user input if settings["emulator_settings"]["emulator_mode"]: - print('\nInitiating ProFSea emulator') + console.log('\nInitiating ProFSea emulator') if settings["projection_end_year"] > 2100: palmer_method = True else: From 71b21a5c8b4a55e354589fa47cc1e0dbbf38e803 Mon Sep 17 00:00:00 2001 From: Gregory Munday Date: Thu, 6 Nov 2025 10:23:03 +0000 Subject: [PATCH 040/276] Updated yml --- profsea/user-settings-emu.yml | 7 ++----- 1 file changed, 2 insertions(+), 5 deletions(-) diff --git a/profsea/user-settings-emu.yml b/profsea/user-settings-emu.yml index 6968161..0e05fa4 100644 --- a/profsea/user-settings-emu.yml +++ b/profsea/user-settings-emu.yml @@ -71,11 +71,8 @@ giaestimates: global: '/data/gia_estimates/global_GIA_interpolators.pickle' uk: '‘/data/gia_estimates/Bradley_GIA_interpolator.pickle' -# Directories for the Slangen, Spada and Klemann fingerprints -fingerprints: - slangendir: '/data/grd_fingerprints/' - spadadir: '/data/grd_fingerprints/' - klemanndir: '/data/grd_fingerprints/' +# Directories for all the fingerprints +fingerprints: '/data/grd_fingerprints/' # Directory of Monte Carlo time series for new projections # N.B. Originally developed by Jonathan Gregory From e4a06ca5a0f264e9f32ed5cce98f0385e31084c0 Mon Sep 17 00:00:00 2001 From: Gregory Munday Date: Thu, 6 Nov 2025 18:46:15 +0000 Subject: [PATCH 041/276] smol update --- profsea/spatial_projections.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/profsea/spatial_projections.py b/profsea/spatial_projections.py index c7b8843..d1d6fb3 100644 --- a/profsea/spatial_projections.py +++ b/profsea/spatial_projections.py @@ -17,12 +17,13 @@ from rich.progress import track import scipy from scipy.interpolate import RegularGridInterpolator +from scipy.spatial.distance import cdist import xarray as xr from profsea.config import settings from profsea.directories import read_dir from profsea.emulator import GMSLREmulator -from profsea.slr_pkg import choose_montecarlo_dir # found in __init.py__ +from profsea.slr_pkg import choose_montecarlo_dir console = Console() warnings.filterwarnings("ignore") @@ -436,7 +437,6 @@ def setup_FP_interpolators(components: list) -> tuple: def sample_members_2D(array: np.ndarray, percentile_seq: list|np.ndarray) -> np.ndarray: """Sample real ensemble members from a 2D numpy array.""" # Caculate statistical timeseries, then match with closest real timeseries - from scipy.spatial.distance import cdist array_percentiles = np.percentile(array, percentile_seq, axis=0) array_perc_diffs = array[None, :, :] - array_percentiles[:, None, :] From ecb9988bf227382e36548bc4e87ed58cb0d3c555 Mon Sep 17 00:00:00 2001 From: Gregory Munday Date: Wed, 19 Nov 2025 08:53:25 +0000 Subject: [PATCH 042/276] Added full probabilistic mode + separate spatialisation step --- profsea/config.py | 1 - profsea/emulator/__init__.py | 137 ++++++++--- profsea/fair_prof_prob.py | 300 ++++++++++++++++++++++++ profsea/spatialise.py | 434 +++++++++++++++++++++++++++++++++++ 4 files changed, 842 insertions(+), 30 deletions(-) create mode 100644 profsea/fair_prof_prob.py create mode 100644 profsea/spatialise.py diff --git a/profsea/config.py b/profsea/config.py index 70229d2..595e71b 100644 --- a/profsea/config.py +++ b/profsea/config.py @@ -8,7 +8,6 @@ import yaml path = pathlib.Path(__file__).parents[0].as_posix() -print(path) with open(os.path.join(path, "user-settings-emu.yml"), "r") as f: settings = yaml.load(f, Loader=yaml.SafeLoader) diff --git a/profsea/emulator/__init__.py b/profsea/emulator/__init__.py index fed5c21..84e3120 100644 --- a/profsea/emulator/__init__.py +++ b/profsea/emulator/__init__.py @@ -9,6 +9,8 @@ import os import concurrent from collections.abc import Sequence +import functools +import atexit from pathlib import Path import numpy as np @@ -20,6 +22,38 @@ console = Console() +@functools.lru_cache(maxsize=1) +def load_greenland_calibration(): + """Loads the CSV once and keeps it in memory.""" + path = Path(__file__).parent / "aux_data" / "ISMIP_GIS_calibration.csv" + return pd.read_csv(path) + +@functools.lru_cache(maxsize=1) +def load_landwater_projection(): + """Loads the NetCDF once and keeps the VALUES in memory.""" + path = Path(__file__).parent / 'aux_data' / 'ssp_global_landwater_projections.nc' + with xr.open_dataset(path) as ds: + ds.load() + return ds + +_SHARED_EXECUTOR = None + +def get_shared_executor(): + """Returns a singleton ThreadPoolExecutor.""" + global _SHARED_EXECUTOR + if _SHARED_EXECUTOR is None: + max_workers = min(8, os.cpu_count() or 1) + _SHARED_EXECUTOR = concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) + return _SHARED_EXECUTOR + +@atexit.register +def shutdown_executor(): + """Ensures the executor is cleaned up when the script exits.""" + global _SHARED_EXECUTOR + if _SHARED_EXECUTOR: + _SHARED_EXECUTOR.shutdown(wait=True) + + class GMSLREmulator: """Emulator for global mean sea level rise components. @@ -45,12 +79,17 @@ class GMSLREmulator: glaciermip: bool | int If False, use AR5 parameters. If 1, use GlacierMIP (Hock et al., 2019). If 2, use GlacierMIP2 (Marzeion et al., 2020). + parallel: bool + If True, project SLR components in parallel. input_ensemble: bool If True, use an input ensemble of temperature and ocean heat content change. output_percentiles: list|np.ndarray If not None, calculate percentiles from a 1D list/array for each component + random_sample: bool + If True, randomly sample a single ensemble member across all + components T_percentile_95: np.ndarray 95th percentile of temperature change timeseries. OHC_percentile_95: np.ndarray @@ -95,16 +134,18 @@ def __init__( nm: int=1000, tcv: float=1.0, glaciermip: bool|int=2, + parallel: bool=True, input_ensemble: bool=True, output_percentiles: list|np.ndarray=None, + random_sample: bool=False, T_percentile_95: np.ndarray=None, OHC_percentile_95: np.ndarray=None, cum_emissions_total: np.ndarray=None, palmer_method: bool=True) -> None: np.random.seed(None if seed is None else seed) - self.T_change = T_change - self.OHC_change = OHC_change + self.T_change = np.asarray(T_change) + self.OHC_change = np.asarray(OHC_change) self.scenario = scenario self.end_yr = end_yr self.seed = seed @@ -112,8 +153,10 @@ def __init__( self.nm = nm self.tcv = tcv self.glaciermip = glaciermip + self.parallel = parallel self.input_ensemble = input_ensemble self.output_percentiles = output_percentiles + self.random_sample = random_sample self.T_percentile_95 = T_percentile_95 self.OHC_percentile_95 = OHC_percentile_95 self.cum_emissions_total = cum_emissions_total @@ -222,11 +265,26 @@ def project(self) -> None: self.expansion = np.tile(Exp_ens, (self.nm, 1)) fraction = np.random.rand(self.nm * self.nt) # correlation between antsmb and antdyn - self.run_parallel_projections(T_int_med, T_int_ens, T_ens, fraction) + if self.parallel: + self.run_parallel_projections(T_int_med, T_int_ens, T_ens, fraction) + else: + self.run_serial_projections(T_int_med, T_int_ens, T_ens, fraction) self.antnet = self.antsmb + self.antdyn self.gmslr = self.glacier + self.greenland_ar6 + self.antnet + self.landwater + self.expansion + rng = np.random.default_rng() + if self.random_sample: + random_idx = rng.integers(low=0, high=self.nm) + self.gmslr = self.gmslr[random_idx][None, :] + self.expansion = self.expansion[random_idx][None, :] + self.antnet = self.antnet[random_idx][None, :] + self.antdyn = self.antdyn[random_idx][None, :] + self.antsmb = self.antsmb[random_idx][None, :] + self.glacier = self.glacier[random_idx][None, :] + self.greenland_ar6 = self.greenland_ar6[random_idx][None, :] + self.landwater = self.landwater[random_idx][None, :] + # TODO Parallelise this section as it's a bit slow if self.output_percentiles is not None: console.log(f"Sampling {len(self.output_percentiles)} members per component...") @@ -241,6 +299,28 @@ def project(self) -> None: # self.greenland_ar5 = self.sample_members_2D(self.greenland_ar5) # self.landwater_ar6 = self.sample_members_2D(self.landwater_ar6) + def run_serial_projections( + self, T_int_med: np.ndarray, T_int_ens: np.ndarray, + T_ens: np.ndarray, fraction: np.ndarray) -> None: + """Run components of the emulator sequentially. + + Returns + ------- + None + """ + self.glacier = self.project_glacier(T_int_med, T_int_ens) + self.antsmb = self.project_antsmb(T_int_ens, fraction) + self.greenland_ar6 = self.project_greenland_AR6(T_ens) + self.greendyn = self.project_greendyn_AR5() + self.greensmb = self.project_greensmb_AR5(T_ens) + self.antdyn = self.project_antdyn(fraction) + + # _project_landwater_ar5 corresponds to 'landwater' key in the parallel map + self.landwater = self._project_landwater_ar5() + # project_landwater corresponds to 'landwater_ar6' key in the parallel map + self.landwater_ar6 = self.project_landwater() + + self.greenland_ar5 = self.greendyn + self.greensmb def run_parallel_projections( self, T_int_med: np.ndarray, T_int_ens: np.ndarray, @@ -251,24 +331,24 @@ def run_parallel_projections( ------- None """ - with concurrent.futures.ThreadPoolExecutor() as executor: - futures = { - executor.submit(self.project_glacier, T_int_med, T_int_ens): 'glacier', - executor.submit(self.project_antsmb, T_int_ens, fraction): 'antsmb', - executor.submit(self.project_greenland_AR6, T_ens): 'greenland', - executor.submit(self.project_greendyn_AR5): 'greendyn', - executor.submit(self.project_greensmb_AR5, T_ens): 'greensmb', - executor.submit(self.project_antdyn, fraction): 'antdyn', - executor.submit(self._project_landwater_ar5): 'landwater', - executor.submit(self.project_landwater): 'landwater_ar6' - } - results = {} - for future in concurrent.futures.as_completed(futures): - key = futures[future] - try: - results[key] = future.result() - except Exception as e: - print(f"{key} generated an exception: {e}") + executor = get_shared_executor() + futures = { + executor.submit(self.project_glacier, T_int_med, T_int_ens): 'glacier', + executor.submit(self.project_antsmb, T_int_ens, fraction): 'antsmb', + executor.submit(self.project_greenland_AR6, T_ens): 'greenland', + executor.submit(self.project_greendyn_AR5): 'greendyn', + executor.submit(self.project_greensmb_AR5, T_ens): 'greensmb', + executor.submit(self.project_antdyn, fraction): 'antdyn', + executor.submit(self._project_landwater_ar5): 'landwater', + executor.submit(self.project_landwater): 'landwater_ar6' + } + results = {} + for future in concurrent.futures.as_completed(futures): + key = futures[future] + try: + results[key] = future.result() + except Exception as e: + print(f"{key} generated an exception: {e}") self.glacier = results['glacier'] self.antsmb = results['antsmb'] @@ -565,8 +645,7 @@ def project_greenland_AR6(self, T_ens: np.ndarray) -> np.ndarray: np.ndarray Total GIS contribution to GMSLR. """ - df = pd.read_csv( - Path(__file__).parent / "aux_data" / "ISMIP_GIS_calibration.csv") + df = load_greenland_calibration() b0 = df['b0'].values[None, :, None] b1 = df['b1'].values[None, :, None] b2 = df['b2'].values[None, :, None] @@ -696,13 +775,13 @@ def project_landwater(self) -> np.ndarray: Land water storage contribution to GMSLR. """ # Read in AR6 landwater contributions - lw_ds = xr.open_dataset( - Path(__file__).parent / 'aux_data' - / 'ssp_global_landwater_projections.nc') + lw_ds = load_landwater_projection() # Interpolate to annual projections - lw_ds = lw_ds.interp(years=np.arange(2005, 2301, 1), method='linear').squeeze() - lw = lw_ds['sea_level_change'].values * 1e-3 # mm to m + interp_ds = lw_ds.interp(years=np.arange(2005, 2301, 1), method='linear').squeeze() + lw = interp_ds['sea_level_change'].values * 1e-3 # mm to m + + del interp_ds # Go from shape (20000, 294) to (101000, 294) full_repeats = (self.nt * self.nm) // lw.shape[0] @@ -727,7 +806,7 @@ def _project_landwater_ar5(self) -> np.ndarray: final = [-0.01,0.09] # AR5 # final = [0.01, 0.04] # AR6 return self.time_projection(0.38, 0.49-0.38, final, nfinal=nyr) - + def time_projection( self, startratemean: float, startratepm: float, final, nfinal: int=1, fraction: np.ndarray=None) -> np.ndarray: diff --git a/profsea/fair_prof_prob.py b/profsea/fair_prof_prob.py new file mode 100644 index 0000000..33c42cf --- /dev/null +++ b/profsea/fair_prof_prob.py @@ -0,0 +1,300 @@ +import concurrent.futures +import json +import multiprocessing + + +from fair import FAIR +from fair.io import read_properties +from fair.interface import initialise +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +from rich.progress import Progress +from scipy.spatial.distance import cdist +import xarray as xr + +from profsea.emulator import GMSLREmulator + +worker_tas = None +worker_ohc = None +worker_emissions = None +worker_scenarios = None + +def init_worker(tas, ohc, emissions, scenarios): + """Initialize worker with read-only shared data.""" + global worker_tas, worker_ohc, worker_emissions, worker_scenarios + worker_tas = tas + worker_ohc = ohc + worker_emissions = emissions + worker_scenarios = scenarios + +def run_fair(baseline_start: int, baseline_end: int, scenarios: list) -> tuple[np.ndarray]: + f = FAIR() + f.define_time(1750, 2300, 1) + f.define_scenarios(scenarios) + species, properties = read_properties('../data/fair/fair-parameters/species_configs_properties_1.4.1.csv') + f.define_species(species, properties) + f.ch4_method='Thornhill2021' + df_configs = pd.read_csv('../data/fair/fair-parameters/calibrated_constrained_parameters_1.4.1.csv', index_col=0) + f.define_configs(df_configs.index) + f.allocate() + + f.fill_from_rcmip() + f.fill_species_configs('../data/fair/fair-parameters/species_configs_properties_1.4.1.csv') + f.override_defaults('../data/fair/fair-parameters/calibrated_constrained_parameters_1.4.1.csv') + initialise(f.concentration, f.species_configs["baseline_concentration"]) + initialise(f.forcing, 0) + initialise(f.temperature, 0) + initialise(f.cumulative_emissions, 0) + initialise(f.airborne_emissions, 0) + initialise(f.ocean_heat_content_change, 0) + + f.run() + + # Make anomaly with respect to baseline + tas_baseline = f.temperature.loc[dict(layer=0, timebounds=np.arange(baseline_start, baseline_end+1))].mean() + ohc_baseline = f.ocean_heat_content_change.loc[dict(timebounds=np.arange(baseline_start, baseline_end+1))].mean() + + tas = f.temperature.loc[dict(layer=0, timebounds=np.arange(2006, 2300))] - tas_baseline # shape (294, 7, 841) + ohc = f.ocean_heat_content_change.loc[dict(timebounds=np.arange(2006, 2300))] - ohc_baseline + return tas.values, ohc.values + + +def simulation_task(random_idx): + """Runs the emulator for a single ensemble member across all scenarios.""" + # Access data from global scope (initialized via init_worker) + tas_sampled = worker_tas[:, :, random_idx] + ohc_sampled = worker_ohc[:, :, random_idx] + results = {} + for scenario in worker_scenarios: + results[scenario] = {} + + for idx, scenario in enumerate(worker_scenarios): + emulator = GMSLREmulator( + np.expand_dims(tas_sampled[:, idx], axis=0), + np.expand_dims(ohc_sampled[:, idx], axis=0), + scenario, + 2300, + input_ensemble=True, + random_sample=True, + cum_emissions_total=worker_emissions[scenario], + parallel=False + ) + emulator.project() + + results[scenario]["gmslr"] = emulator.gmslr + results[scenario]["expansion"] = emulator.expansion + results[scenario]["antarctica"] = emulator.antnet + results[scenario]["greenland"] = emulator.greenland_ar6 + results[scenario]["glaciers"] = emulator.glacier + results[scenario]["landwater"] = emulator.landwater + + return tas_sampled, ohc_sampled, results + + +def plot_samples(tas: np.ndarray, ohc: np.ndarray) -> None: + fig = plt.figure(figsize=(12, 6), layout="constrained") + + ax = fig.add_subplot(121) + ax.plot(tas.T, color='seagreen', alpha=0.05) + ax.set_xlabel("Simulation years") + ax.set_ylabel("GMST ($\degree$C)") + + ax = fig.add_subplot(122) + ax.plot(ohc.T, color='seagreen', alpha=0.05) + ax.set_xlabel("Simulation years") + ax.set_ylabel("OHC (J)") + + plt.show() + plt.close() + + +def sample_members_2D(array: np.ndarray, percentiles: list|np.ndarray) -> np.ndarray: + """Sample real ensemble members from a 2D numpy array.""" + # Caculate statistical timeseries, then match with closest real timeseries + array_percentiles = np.percentile(array, percentiles, axis=0) + distances = cdist(array_percentiles, array) + mem_indices = np.argmin(distances, axis=1) + return array[mem_indices] + + +def process_global_ensemble(components: list, percentiles: list, scenario: str) -> None: + for comp, data in components.items(): + # Since the spatial projections won't change the ensemble order + # we can take percentiles here to pass to spatialise.py + sampled_ensemble = sample_members_2D(data, percentiles) + components[comp] = sampled_ensemble + return components + + +def save_to_netcdf(components: dict, filename: str) -> None: + """ + Saves the full ensemble results to a single NetCDF file. + + Structure: + Dimensions: (scenario, member, year) + Variables: gmslr, expansion, glaciers, etc. + """ + scenarios = list(components.keys()) + comp_names = list(components[scenarios[0]].keys()) + sample = components[scenarios[0]][comp_names[0]] + n_member, n_time = sample.shape + + # Create coords + years = np.arange(2006, 2006 + n_time) + members = np.arange(n_member) + data_vars = {} + for comp in comp_names: + # Stack data across scenarios + # Resulting shape: (n_scenario, n_member, n_time) + stacked_data = np.stack([components[s][comp] for s in scenarios], axis=0) + data_vars[comp] = xr.DataArray( + data=stacked_data, + dims=["scenario", "member", "year"], + coords={ + "scenario": scenarios, + "member": members, + "year": years + }, + attrs={ + "units": "m", + "description": f"Sea level contribution from {comp}" + } + ) + + ds = xr.Dataset(data_vars) + ds.attrs = { + "title": "ProFSea GMSLR Projections", + "source": "FAIR v2.2 + ProFSea Emulator", + } + + # Save with compression + encoding = {var: {"zlib": True, "complevel": 5} for var in data_vars} + ds.to_netcdf(filename, encoding=encoding) + print(f"Successfully saved full ensemble to {filename}") + + +def plot_component( + ax: plt.Axes, component_dict: dict, component: str, + scenarios: list, plot_legend: bool=False) -> None: + time = np.arange(2006, 2300) + ssp_colours = { + "ssp119": tuple(np.array([0, 173, 207]) / 255), + "ssp126": tuple(np.array([23, 60, 102]) / 255), + "ssp245": tuple(np.array([247, 148, 32]) / 255), + "ssp370": tuple(np.array([231, 29, 37]) / 255), + "ssp585": tuple(np.array([149, 27, 30]) / 255) + } + for scenario in reversed(scenarios): + plt.fill_between( + time, + component_dict[scenario][component][1], + component_dict[scenario][component][3], + color=ssp_colours[scenario], + edgecolor='none', + alpha=0.3) + plt.plot( + time, + component_dict[scenario][component][2], + label=f"{scenario}", + color=ssp_colours[scenario]) + + ax.set_xlabel("Year") + ax.set_ylabel("SLE (m)") + ax.set_title(component) + if plot_legend: + ax.legend(frameon=False, loc="upper left") + +def main(): + # Set up for main loop + percentiles = [5, 17, 50, 83, 95] + scenarios = ["ssp119", "ssp126", "ssp245", "ssp370", "ssp585"] + + emissions_path = "/Users/gregorymunday/Documents/Papers/ProFSea/cmip7-slr/data/cumulative_cmip6_emissions.json" + with open(emissions_path) as f: + cumulative_emissions = json.load(f) + + baseline_start = 1995 + baseline_end = 2014 # inclusive + + # Set up components data struct + components = {} + for scenario in scenarios: + components[scenario] = { + "gmslr": [], "expansion": [], "antarctica": [], + "greenland": [], "glaciers": [], "landwater": [] + } + + # Enter 1000x loop + tas, ohc = run_fair(baseline_start, baseline_end, scenarios) + + # Pre-generate the random ensemble member indices + n_iterations = 1000 + rng = np.random.default_rng() + random_indices = rng.integers(0, high=841, size=n_iterations) + + sampled_tas = [] + sampled_ohc = [] + + max_workers = 12 + with concurrent.futures.ProcessPoolExecutor( + max_workers=max_workers, + initializer=init_worker, + initargs=(tas, ohc, cumulative_emissions, scenarios) + ) as exectutor: + futures = [exectutor.submit(simulation_task, idx) for idx in random_indices] + + with Progress() as progress: + task = progress.add_task("[orange1]Simulating and sampling...", total=n_iterations) + for future in concurrent.futures.as_completed(futures): + tas_sampled, ohc_sampled, result_dict = future.result() + sampled_tas.append(tas_sampled) + sampled_ohc.append(ohc_sampled) + + # Now add the results into the components dictionary + for scenario in scenarios: + for key in components[scenario].keys(): + components[scenario][key].append(result_dict[scenario][key]) + + progress.update(task, advance=1) + + + # Convert to Numpy arrays + for scenario, data in components.items(): + for key in data: + data[key] = np.array(data[key]).squeeze() # Now shape (nens, time) + + sampled_components = {} + for scenario in scenarios: + sampled_components[scenario] = process_global_ensemble( + components[scenario], percentiles, scenario) + + save_to_netcdf(sampled_components, "probabilistic_projections/global/gmslr_projections.nc") + + fig = plt.figure(figsize=(16, 8), layout="constrained") + ax = fig.add_subplot(231) + plot_component(ax, sampled_components, "gmslr", scenarios, plot_legend=True) + ax = fig.add_subplot(232) + plot_component(ax, sampled_components, "expansion", scenarios) + ax = fig.add_subplot(233) + plot_component(ax, sampled_components, "glaciers", scenarios) + ax = fig.add_subplot(234) + plot_component(ax, sampled_components, "antarctica", scenarios) + ax = fig.add_subplot(235) + plot_component(ax, sampled_components, "greenland", scenarios) + ax = fig.add_subplot(236) + plot_component(ax, sampled_components, "landwater", scenarios) + + fig.savefig("probabilistic_components_ssps.png", dpi=300) + plt.show() + + + sampled_tas = np.asarray(sampled_tas) # shape (nens, time, nscen) + sampled_ohc = np.asarray(sampled_ohc) + + plot_samples(sampled_tas[:, :, 0], sampled_ohc[:, :, 0]) + + +if __name__ == "__main__": + multiprocessing.freeze_support() + main() \ No newline at end of file diff --git a/profsea/spatialise.py b/profsea/spatialise.py new file mode 100644 index 0000000..914f19d --- /dev/null +++ b/profsea/spatialise.py @@ -0,0 +1,434 @@ +""" +Copyright (c) 2023, Met Office +All rights reserved. +""" +import glob +import pickle +import json +import os +from pathlib import Path +import warnings + +import dask.array as da +import iris +from netCDF4 import Dataset +import numpy as np +from rich.console import Console +from rich.progress import track +import scipy +from scipy.interpolate import RegularGridInterpolator +from scipy.spatial.distance import cdist +import xarray as xr + +from profsea.config import settings +from profsea.directories import read_dir +from profsea.emulator import GMSLREmulator +from profsea.slr_pkg import choose_montecarlo_dir + +console = Console() +warnings.filterwarnings("ignore") + +def calc_baseline_period(yrs: np.array) -> float: + """ + Baseline years used for IPCC AR5 and Palmer et al 2020 -- 1986-2005 + :param yrs: years of the projections + :return: baseline years + """ + byr1 = 1986. + byr2 = 2005. + + console.log("Baseline period = ", byr1, "to", byr2) + midyr = (byr2 - byr1 + 1) * 0.5 + byr1 + return yrs[0] - midyr + + +def calc_future_sea_level(scenario: str) -> None: + """ + Calculates future sea level at the given site and write to file. + :param df: Data frame of all metadata (tide gauge or site specific) for + each(all) location(s) + :param site_loc: name of the site location + :param scenario: emission scenario + """ + # Set the UKCP*18* random seed so results are reproducible + np.random.seed(18) + + # Directory of Monte Carlo time series for new projections + mcdir = choose_montecarlo_dir() + + # Specify the sea level components to include. The GIA contribution is + # calculated separately. + components = ['expansion', 'antdyn', 'antsmb', 'greenland', + 'glacier', 'landwater'] + + # Select dimensions from sample file, [time, realisation] + sample = np.load(os.path.join(mcdir, f'{scenario}_expansion.npy')) + nesm = sample.shape[0] # also number of samples to make + nyrs = sample.shape[1] + yrs = np.arange(2007, 2007 + nyrs) + console.log(f"Running with {nesm} ensemble members") + + grid_path = os.path.join( + settings["cmipinfo"]["sealevelbasedir"], + "ACCESS-CM2/zos_regression_ssp245_ACCESS-CM2.npy") + grid_sample = np.load(grid_path) + array_dims = [nesm, nesm, nyrs, grid_sample.shape[0], grid_sample.shape[1]] + + # Get random samples of global and regional sea level components + calculate_sl_components(mcdir, components, scenario, yrs, array_dims) + + +def calc_gia_contribution( + yrs: np.array, nyrs: int, nsmps: int, scenario: str) -> None: + """ + Calculate the glacial isostatic adjustment (GIA) contribution to the + regional component of sea level rise. + Option to use the generic global GIA estimates or use the GIA estimates + developed for UK as part of UKCP18. + :param yrs: years of the projections + :param nyrs: number of years in each projection time series + :param nsmps: determine the number of samples + :param coords: coordinates of location of interest + :return: GIA estimates converted to mm/yr + """ + console.log('Calculating GIA contribution...') + nGIA, GIA_vals = read_gia_estimates() + Tdelta = calc_baseline_period(yrs) + + # Unit series of mm/yr expressed as m/yr + unit_series = (np.arange(nyrs) + Tdelta) * 0.001 + GIA_unit_series = np.ones([5, nyrs]) * unit_series + + # rgiai is an array of random GIA indices the size of the sample years + rgiai = np.random.randint(nGIA, size=5) + + GIA_T = da.from_array(GIA_unit_series) + GIA_vals = da.from_array(GIA_vals) + GIA_series = GIA_T[:, :, None, None] * GIA_vals[rgiai, None, :, :] + GIA_series = GIA_series.compute() + + file_header = '_'.join(['gia', scenario, "projection", + f"{settings['projection_end_year']}"]) + R_file = '_'.join([file_header, 'regional']) + '.npy' + + np.save(os.path.join(read_dir()[4], R_file), GIA_series) + del GIA_series + + +def calc_expansion_contribution( + scenario: str, nsmps: int) -> da.array: + """ + Calculate the thermal expansion contribution to the regional component of + sea level rise. + :param scenario: emission scenario + :param nsmps: determine the number of samples + :return: expansion estimates converted to mm/yr + """ + # Select slope coefficients based on the MIP + if settings["emulator_settings"]["emulator_mode"]: + if settings["cmipinfo"]["mip"].lower() == "cmip6": + coeffs = load_CMIP6_slopes('ssp585') + coeffs = np.roll(coeffs, 180, axis=2) + else: + coeffs = load_CMIP5_slope_coeffs('rcp85') + else: + coeffs = load_CMIP5_slope_coeffs(scenario) + + rand_samples = np.random.choice( + coeffs.shape[0], size=nsmps, replace=True) + rand_coeffs = coeffs[rand_samples, :, :] + rand_coeffs = da.from_array(rand_coeffs) + return rand_coeffs + + +def calc_landwater_contribution(interpolator: dict) -> da.array: + """ + Calculate the regional landwater contribution to sea level rise. + :param interpolator: dictionary of interpolator objects + :return: numpy array of landwater values + """ + landwater_FP_interpolator = interpolator['landwater'] + landwater_vals = da.from_array( + landwater_FP_interpolator.values.astype(np.float32).data) + landwater_vals = da.roll(landwater_vals, 180, axis=1) + return landwater_vals + + +def calc_fingerprint_contributions( + FPlist: list, comp: str, lats: int, lons: int) -> da.array: + # Initiate an empty list for fingerprint values + fp_vals = [] + for FP_dict in FPlist: + # Interpolate values to target lat/lon + val = FP_dict[comp].values + if val.shape != (lats, lons): + original_da = xr.DataArray( + val, + coords=[ + ("lat", np.linspace(90, -90, 360)), + ("lon", np.linspace(-180, 180, 720, endpoint=False)) + ], + name="v") + target_lat = np.linspace(90, -90, 180) + target_lon = np.linspace(-180, 180, 360, endpoint=False) + val = original_da.interp( + lat=target_lat, lon=target_lon, method="linear").data + val = np.roll(val, 180, axis=1) + else: + val = np.roll(val, 180, axis=1) + + fp_vals.append(val) + + fp_vals = da.from_array(np.array(fp_vals, dtype=np.float32)) + return fp_vals + + +def calc_greenland_fingerprint_ar6() -> da.array: + """Load and prepare the GIS fingerprint. + + This fingerprint was/is used by FACTS for AR6 projections, and was + originally calculated by Mitrovica et al., (2011). + + :return dask array containing GIS fingerprint + """ + # Load in the fingerprint + fp_path = Path(settings["fingerprints"]) / "greenland_ar6.nc" + fp_ds = xr.load_dataset(fp_path) + + # Interpolate to (180, 360) grid + fp_vals = fp_ds.fp.interp( + lat=np.linspace(-90, 90, 180, endpoint=False), + lon=np.linspace(0, 360, 360, endpoint=False), + method="linear").data * 1000 # convert mm to m SLE per m GMSLR + + # Flip vertically and roll by 180 degrees + fp_vals = np.flip(fp_vals) + fp_vals = np.roll(fp_vals, 180, axis=1) + fp_vals = da.from_array(np.array(fp_vals, dtype=np.float32)) + return fp_vals + + +def save_projections( + montecarlo_R: da.array, component: str, scenario: str) -> None: + """ + Save the regional sea level projections to a file. + :param montecarlo_R: regional sea level projections + :param component: sea level component + :param scenario: emission scenario + """ + sealev_ddir = read_dir()[4] + file_header = '_'.join([component, scenario, "projection", + f"{settings['projection_end_year']}"]) + # G_file = '_'.join([file_header, 'global']) + '.npy' + R_file = '_'.join([file_header, 'regional']) + '.npy' + + # Save the global and local projections + np.save(os.path.join(sealev_ddir, R_file), montecarlo_R) + + +def calculate_sl_components( + mcdir: str, components: list, scenario: str, + yrs: np.array, array_dims: list) -> None: + """ + Calculates global and regional component part contributions to sea level + change. + :param mcdir: location of Monte Carlo time series for new projections + :param components: sea level components + :param scenario: emission scenario + :param yrs: years of the projections + :param array_dims: Array of nesm, nsmps and nyrs + nesm --> Number of ensemble members in time series + nsmps --> Determine the number of samples you wish to make + nyrs --> Number of years in each projection time series + :return: montecarlo_G (global contribution to sea level rise) and + montecarlo_R (regional contribution to sea level change) + """ + # Numbers of ensemble members, samples, years + nesm, nsmps, nyrs, lats, lons = array_dims + nFPs, FPlist = setup_FP_interpolators(components) + resamples = np.random.choice(nesm, nsmps) # Preserve correlations across comps + rfpi = np.random.randint(nFPs, size=nsmps) + + # Calculate GIA contribution and save it out + calc_gia_contribution(yrs, nyrs, nsmps, scenario) + + for comp in track(components, description="Calculating components..."): + montecarlo_R = da.zeros((nsmps, nyrs, lats, lons), dtype=np.float32) # (FPs applied) + GIA + montecarlo_G = da.zeros((nsmps, nyrs, lats, lons), dtype=np.float32) # (no FPs applied) + + # Load global projections in for the component + mc_timeseries = np.load(os.path.join(mcdir, f'{scenario}_{comp}.npy')) + sampled_mc = mc_timeseries[resamples, :nyrs] + montecarlo_G[:, :] = da.from_array(sampled_mc[:, :, None, None]) + + if comp == "expansion": + sampled_coeffs = calc_expansion_contribution(scenario, nsmps) + montecarlo_R = montecarlo_G * sampled_coeffs[:, None, :, :] + del sampled_coeffs + + elif comp == "landwater": + landwater_vals = calc_landwater_contribution(FPlist[0]) + montecarlo_R[:, :, :, :] = montecarlo_G[:, :, :, :] * landwater_vals[None, None, :, :] + del landwater_vals + + elif comp == "greenland": + greenland_fp = calc_greenland_fingerprint_ar6() + montecarlo_R[:, :, :, :] = montecarlo_G[:, :, :, :] * greenland_fp[None, None, :, :] + + else: + fp_vals = calc_fingerprint_contributions(FPlist, comp, lats, lons) + montecarlo_R[:, :, :, :] = montecarlo_G[:, :, :, :] * fp_vals[rfpi][:, None, :, :] + del fp_vals + + # Take the 0th, 25th, 50th, 75th and 100th percentiles + montecarlo_R = da.percentile(montecarlo_R, [0, 25, 50, 75, 100], axis=0) + montecarlo_R = montecarlo_R.compute() + + # Create the output sea level projections file directory and filename + save_projections(montecarlo_R, comp, scenario) + + +def create_FP_interpolator( + datadir: str, dfile: str, method: str='linear') -> RegularGridInterpolator: + """ + Generates a scipy Interpolator object from input NetCDF data of + gravitational fingerprints (takes inputs of Latitude and Longitude). + :param datadir: data directory + :param dfile: data filename + :param method: interpolation type --> 'linear' or 'nearest' + :return: 2D Interpolator object + """ + cube = iris.load_cube(os.path.join(datadir, dfile)) + lon = cube.coord('longitude').points + lat = cube.coord('latitude').points + + # Define linear interpolator object: + interp_object = RegularGridInterpolator( + (lat, lon), cube.data, + method=method, bounds_error=True, + fill_value=None) + return interp_object + + +def get_projection_info(indir: str, scenario: str) -> tuple: + """ + Read in the dimensions of the Monte-Carlo data. These files are all + relative to midnight on 1st January 2007 + :param indir: directory of Monte Carlo time series for new projections + :param scenario: emission scenarios to be considered + :return: Number of ensemble members in time series, number of years in + each projection time series and the years of the projections + """ + sample_file = f'{scenario}_exp.nc' + f = Dataset(f'{indir}{sample_file}', 'r') + nesm = f.dimensions['realization'].size + t = f.variables['time'] + nyrs = t.size + unit_str = t.units + first_year = int(unit_str.split(' ')[2][:4]) + f.close() + + yrs = first_year + np.arange(nyrs) + return nesm, nyrs, yrs + + +def load_CMIP5_slope_coeffs(scenario: str) -> np.ndarray: + """ + Loads in the CMIP slope coefficients based on linear regression of + 'zos+zostoga' against 'zostoga' for the period 2005 to 2100. + Some models are missing regression slopes for RCP2.6. If so, use RCP4.5 + values instead. + :param site_loc: name of the site location + :param scenario: emissions scenario + :return: 1D array of regression coefficients + """ + # Read in the sea level regressions + in_zosddir = read_dir()[2] + filename = os.path.join(in_zosddir, 'zos_regression.npy') + coeffs = np.load(filename) + scenario_index = ['rcp26', 'rcp45', 'rcp85'].index(scenario) + coeffs = coeffs[:, scenario_index, :, :] + coeffs[np.isnan(coeffs)] = 0 + coeffs[coeffs > 999] = 0 + coeffs[coeffs < -999] = 0 + return coeffs + + +def load_CMIP6_slopes(scenario: str) -> np.ndarray: + """ + Load in the CMIP6 slope coefficients. + :param site_loc: name of the site location + :param scenario: emissions scenario + :return: 1D array of regression coefficients + """ + # Read in the sea level regressions + cmip6_dir = settings["cmipinfo"]["sealevelbasedir"] + slope_files = glob.glob(cmip6_dir + f'/*/zos_regression_{scenario}_*.npy') + slopes = np.zeros((len(slope_files), 180, 360), dtype=np.float32) + for i, slope_file in enumerate(slope_files): + slopes[i, :, :] = np.load(slope_file) + + return slopes + + +def read_gia_estimates() -> tuple: + """ + Read in pre-processed interpolator objects of GIA estimates (Lambeck, + ICE5G) + :param: none + :return: length of GIA_vals and numpy array of pre-processed interpolator + objects of GIA estimates + """ + gia_file = settings["giaestimates"]["global"] + with open(gia_file, "rb") as ifp: + GIA_dict = pickle.load(ifp, encoding='latin1') # Interp objects + + GIA_vals = [] + for key in list(GIA_dict.keys()): + val = GIA_dict[key].values + GIA_vals.append(val) + + nGIA = len(GIA_vals) + GIA_vals = np.array(GIA_vals) + + # Sort out the crazy values in the 0th GIA array + GIA_vals[0][GIA_vals[0] < -99999] = 0 + + # AND shift them from -180, 180 to 0, 360 + GIA_vals = np.roll(GIA_vals, 180, axis=2) + return nGIA, GIA_vals + + +def setup_FP_interpolators(components: list) -> tuple: + """ + Create 2D Interpolator objects for the Slangen, Spada and Klemann + fingerprints + :param components: list of sea level components + :return nFPs: length of FPlist and interpolator objects of all sea level + components + """ + # Create empty dictionaries for the Slangen, Spada and Klemann fingerprints + # interpolator objects. + slangen_FPs = {} + spada_FPs = {} + klemann_FPs = {} + + # Only 1 fingerprint for Landwater + comp = "landwater" + slangen_FPs[comp] = create_FP_interpolator(settings["fingerprints"], + comp + "_slangen_nomask.nc") + + # Create interpolators for the remaining components. Expansion ('expansion') + # is global so no interpolation is needed. + components_todo = [c for c in components if c not in ["expansion", "landwater", "greenland"]] + for comp in components_todo: + slangen_FPs[comp] = create_FP_interpolator(settings["fingerprints"], + comp + "_slangen_nomask.nc") + spada_FPs[comp] = create_FP_interpolator(settings["fingerprints"], + comp + "_spada_nomask.nc") + klemann_FPs[comp] = create_FP_interpolator(settings["fingerprints"], + comp + "_klemann_nomask.nc") + + FPlist = [slangen_FPs, spada_FPs, klemann_FPs] + nFPs = len(FPlist) + return nFPs, FPlist From 386756d30775308b8a33375ebec319919fef8fec Mon Sep 17 00:00:00 2001 From: Hemant Khatri Date: Mon, 24 Nov 2025 16:55:36 +0000 Subject: [PATCH 043/276] resolve merge conflicts --- profsea/spatial_projections.py | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/profsea/spatial_projections.py b/profsea/spatial_projections.py index c1befe3..2a6e3dd 100644 --- a/profsea/spatial_projections.py +++ b/profsea/spatial_projections.py @@ -67,12 +67,9 @@ def calc_future_sea_level(scenario: str) -> None: nesm = sample.shape[0] # also number of samples to make nyrs = sample.shape[1] -<<<<<<< HEAD + yrs = np.arange(2006, 2006 + nyrs) -======= - yrs = np.arange(2007, 2007 + nyrs) console.log(f"Running with {nesm} ensemble members") ->>>>>>> emu-update-greg grid_path = os.path.join( settings["cmipinfo"]["sealevelbasedir"], From 2e176463555929e2929397266d39e123c083244c Mon Sep 17 00:00:00 2001 From: Hemant Khatri Date: Wed, 26 Nov 2025 16:16:13 +0000 Subject: [PATCH 044/276] code for saving global components in netcdf file. update config file, so user can specify path for user-settings.yml file --- profsea/config.py | 19 ++++++++++++++++++- profsea/emulator/__init__.py | 13 +++++++++++++ 2 files changed, 31 insertions(+), 1 deletion(-) diff --git a/profsea/config.py b/profsea/config.py index 595e71b..2c2d1cf 100644 --- a/profsea/config.py +++ b/profsea/config.py @@ -6,8 +6,25 @@ import os import pathlib import yaml +import argparse + + +# Create argument parser +parser = argparse.ArgumentParser(description="Run script with optional custom path") +parser.add_argument("--path", type=str, help="Supply the directory path (optional)") + +# Parse arguments +args = parser.parse_args() + + +# Use user-supplied path if given, else fallback to script's directory +if args.path: + path = pathlib.Path(args.path).as_posix() +else: + path = pathlib.Path(__file__).parents[0].as_posix() -path = pathlib.Path(__file__).parents[0].as_posix() with open(os.path.join(path, "user-settings-emu.yml"), "r") as f: settings = yaml.load(f, Loader=yaml.SafeLoader) + +print(f"Using path: {path}") diff --git a/profsea/emulator/__init__.py b/profsea/emulator/__init__.py index 84e3120..65d32a4 100644 --- a/profsea/emulator/__init__.py +++ b/profsea/emulator/__init__.py @@ -254,6 +254,17 @@ def save_components(self, output_dir: str, scenario_name: str) -> None: component ) + # Save data in netcdf format (store all components in an xarray dataset) + # Assumes first dimension is percentile, but can be more general (percentile/ensemble) + ds = xr.Dataset() + for name, component in self.get_components().items(): + xr_dataArray = xr.DataArray(component, dims=["percentile", "time"], + coords={"percentile": np.arange(0,101), + "time": np.arange(2006, component.shape[1] + 2006)}) + xr_dataArray.attrs["units"] = "meter" + ds[name] = xr_dataArray + ds.to_netcdf(os.path.join(output_dir,f'{scenario_name}.nc')) + def project(self) -> None: """Run the emulator to project GMSLR components. @@ -296,6 +307,8 @@ def project(self) -> None: self.glacier = self.sample_members_2D(self.glacier) self.greenland_ar6 = self.sample_members_2D(self.greenland_ar6) self.landwater = self.sample_members_2D(self.landwater) + self.greensmb = self.sample_members_2D(self.greensmb) + self.greendyn = self.sample_members_2D(self.greendyn) # self.greenland_ar5 = self.sample_members_2D(self.greenland_ar5) # self.landwater_ar6 = self.sample_members_2D(self.landwater_ar6) From 23c75546bfdbd3b20831dfb4acdf94f53ffae8d0 Mon Sep 17 00:00:00 2001 From: Hemant Khatri Date: Fri, 28 Nov 2025 11:54:46 +0000 Subject: [PATCH 045/276] implement suggested changes --- profsea/emulator/__init__.py | 6 +++--- profsea/spatial_projections.py | 3 ++- 2 files changed, 5 insertions(+), 4 deletions(-) diff --git a/profsea/emulator/__init__.py b/profsea/emulator/__init__.py index 65d32a4..f940403 100644 --- a/profsea/emulator/__init__.py +++ b/profsea/emulator/__init__.py @@ -259,11 +259,11 @@ def save_components(self, output_dir: str, scenario_name: str) -> None: ds = xr.Dataset() for name, component in self.get_components().items(): xr_dataArray = xr.DataArray(component, dims=["percentile", "time"], - coords={"percentile": np.arange(0,101), + coords={"percentile": self.output_percentiles, "time": np.arange(2006, component.shape[1] + 2006)}) - xr_dataArray.attrs["units"] = "meter" + xr_dataArray.attrs["units"] = "m" ds[name] = xr_dataArray - ds.to_netcdf(os.path.join(output_dir,f'{scenario_name}.nc')) + ds.to_netcdf(os.path.join(output_dir,f'{scenario_name}_global.nc')) def project(self) -> None: """Run the emulator to project GMSLR components. diff --git a/profsea/spatial_projections.py b/profsea/spatial_projections.py index 2a6e3dd..83c40e6 100644 --- a/profsea/spatial_projections.py +++ b/profsea/spatial_projections.py @@ -477,6 +477,7 @@ def calculate_global_components(scenario: str, palmer_method: bool) -> None: raise Exception('SCM data must be saved in NetCDF format.') percentiles = np.arange(101) + # percentiles are hard coded. We could make it an user input in future updates. # Now run the simulations console.log(f'Projecting global components for {scenario} scenario...') @@ -498,7 +499,7 @@ def calculate_global_components(scenario: str, palmer_method: bool) -> None: settings["projection_end_year"], palmer_method=palmer_method, input_ensemble=settings["emulator_settings"]["use_input_ensemble"], - output_percentiles=np.arange(101), + output_percentiles=percentiles, cum_emissions_total=cumulative_emissions[scenario]) gmslr.project() From 8907b0cfdd3863335324849526ca72de9fd7e231 Mon Sep 17 00:00:00 2001 From: Gregory Munday Date: Fri, 28 Nov 2025 15:34:02 +0000 Subject: [PATCH 046/276] Various update4s --- profsea/emulator/__init__.py | 2 +- profsea/fair_prof_prob.py | 117 +++++++++++++++++++++++++++++------ profsea/spatialise.py | 2 - 3 files changed, 99 insertions(+), 22 deletions(-) diff --git a/profsea/emulator/__init__.py b/profsea/emulator/__init__.py index 84e3120..7a58061 100644 --- a/profsea/emulator/__init__.py +++ b/profsea/emulator/__init__.py @@ -161,7 +161,7 @@ def __init__( self.OHC_percentile_95 = OHC_percentile_95 self.cum_emissions_total = cum_emissions_total self.palmer_method = palmer_method - + # First year of AR5 projections self.endofhistory = 2006 diff --git a/profsea/fair_prof_prob.py b/profsea/fair_prof_prob.py index 33c42cf..0390f98 100644 --- a/profsea/fair_prof_prob.py +++ b/profsea/fair_prof_prob.py @@ -1,7 +1,6 @@ +import argparse import concurrent.futures import json -import multiprocessing - from fair import FAIR from fair.io import read_properties @@ -9,6 +8,7 @@ import matplotlib.pyplot as plt import numpy as np import pandas as pd +from rich_argparse import RichHelpFormatter from rich.progress import Progress from scipy.spatial.distance import cdist import xarray as xr @@ -23,10 +23,82 @@ def init_worker(tas, ohc, emissions, scenarios): """Initialize worker with read-only shared data.""" global worker_tas, worker_ohc, worker_emissions, worker_scenarios - worker_tas = tas - worker_ohc = ohc - worker_emissions = emissions - worker_scenarios = scenarios + worker_tas = tas.copy() + worker_ohc = ohc.copy() + worker_emissions = emissions.copy() + worker_scenarios = scenarios.copy() + + +def index_df(df: pd.DataFrame, baseline_start: int, baseline_end: int) -> tuple[pd.DataFrame]: + meta_cols = ['ensemble_member', 'scenario', 'region', 'variable', 'unit', 'climate_model'] + existing_meta = [c for c in meta_cols if c in df.columns] + df_indexed = df.set_index(existing_meta) + + years = df_indexed.columns.str[:4].astype(int) + mask = (years >= 1750) & (years <= 2301) + df_indexed = df_indexed.loc[:, mask] + + years = df_indexed.columns.str[:4].astype(int) + baseline_mask = (years >= baseline_start) & (years <= baseline_end) + baseline_means = df_indexed.loc[:, baseline_mask].mean(axis=1) + df_anom = df_indexed.sub(baseline_means, axis=0) + + # Now slice up again + mask = (years >= 2006) & (years <= 2300) + df_final = df_anom.loc[:, mask].reset_index() + return df_final + + +def df_to_arr(df, scenario_order): + meta_cols = ['ensemble_member', 'scenario', 'region', 'variable', 'unit', 'climate_model'] + existing_meta = [c for c in meta_cols if c in df.columns] + + # Set the index (this creates a MultiIndex including 'scenario') + df = df.set_index(existing_meta) + array = [] + for scenario in scenario_order: + try: + mask = df.index.get_level_values('scenario') == scenario + group_df = df.loc[mask] + except KeyError: + raise ValueError(f"Scenario '{scenario}' not found in the input DataFrame.") + + if group_df.empty: + raise ValueError(f"No data found for scenario '{scenario}'") + + # Sort by member to ensure internal alignment + group_sorted = group_df.sort_index(level='ensemble_member') + scenario_data = group_sorted.values + array.append(scenario_data) + + array = np.stack(array, axis=0) + array = array.transpose(2, 0, 1) # (n_scenarios, n_members, n_time) + + return array + + +def load_magicc_forcing( + input_path: str, scenarios: list, + baseline_start: int, baseline_end: int) -> tuple[np.ndarray]: + df = pd.read_csv(input_path, index_col=0) + + tas_condition = ( + (df["variable"] == "Surface Air Temperature Change") & + (df["scenario"].isin(scenarios)) + ) + ohc_condition = ( + (df["variable"] == "Heat Content|Ocean") & + (df["scenario"].isin(scenarios)) + ) + tas_df = df.loc[tas_condition] + ohc_df = df.loc[ohc_condition] + + tas_df = index_df(tas_df, baseline_start, baseline_end) + ohc_df = index_df(ohc_df, baseline_start, baseline_end) + tas_arr = df_to_arr(tas_df, scenarios) # shape (scenario, mem, time) + ohc_arr = df_to_arr(ohc_df, scenarios) * 1e21 # convert to J from ZJ + return tas_arr, ohc_arr + def run_fair(baseline_start: int, baseline_end: int, scenarios: list) -> tuple[np.ndarray]: f = FAIR() @@ -55,8 +127,8 @@ def run_fair(baseline_start: int, baseline_end: int, scenarios: list) -> tuple[n tas_baseline = f.temperature.loc[dict(layer=0, timebounds=np.arange(baseline_start, baseline_end+1))].mean() ohc_baseline = f.ocean_heat_content_change.loc[dict(timebounds=np.arange(baseline_start, baseline_end+1))].mean() - tas = f.temperature.loc[dict(layer=0, timebounds=np.arange(2006, 2300))] - tas_baseline # shape (294, 7, 841) - ohc = f.ocean_heat_content_change.loc[dict(timebounds=np.arange(2006, 2300))] - ohc_baseline + tas = f.temperature.loc[dict(layer=0, timebounds=np.arange(2006, 2301))] - tas_baseline # shape (294, 7, 841) + ohc = f.ocean_heat_content_change.loc[dict(timebounds=np.arange(2006, 2301))] - ohc_baseline return tas.values, ohc.values @@ -74,7 +146,7 @@ def simulation_task(random_idx): np.expand_dims(tas_sampled[:, idx], axis=0), np.expand_dims(ohc_sampled[:, idx], axis=0), scenario, - 2300, + 2301, input_ensemble=True, random_sample=True, cum_emissions_total=worker_emissions[scenario], @@ -88,7 +160,6 @@ def simulation_task(random_idx): results[scenario]["greenland"] = emulator.greenland_ar6 results[scenario]["glaciers"] = emulator.glacier results[scenario]["landwater"] = emulator.landwater - return tas_sampled, ohc_sampled, results @@ -177,12 +248,13 @@ def save_to_netcdf(components: dict, filename: str) -> None: def plot_component( ax: plt.Axes, component_dict: dict, component: str, scenarios: list, plot_legend: bool=False) -> None: - time = np.arange(2006, 2300) + time = np.arange(2006, 2301) ssp_colours = { "ssp119": tuple(np.array([0, 173, 207]) / 255), "ssp126": tuple(np.array([23, 60, 102]) / 255), "ssp245": tuple(np.array([247, 148, 32]) / 255), "ssp370": tuple(np.array([231, 29, 37]) / 255), + "ssp534-over": tuple(np.array([0, 79, 0]) / 255), "ssp585": tuple(np.array([149, 27, 30]) / 255) } for scenario in reversed(scenarios): @@ -205,10 +277,11 @@ def plot_component( if plot_legend: ax.legend(frameon=False, loc="upper left") -def main(): + +def main(args): # Set up for main loop percentiles = [5, 17, 50, 83, 95] - scenarios = ["ssp119", "ssp126", "ssp245", "ssp370", "ssp585"] + scenarios = ["ssp119", "ssp126", "ssp245", "ssp370", "ssp534-over", "ssp585"] emissions_path = "/Users/gregorymunday/Documents/Papers/ProFSea/cmip7-slr/data/cumulative_cmip6_emissions.json" with open(emissions_path) as f: @@ -226,12 +299,17 @@ def main(): } # Enter 1000x loop - tas, ohc = run_fair(baseline_start, baseline_end, scenarios) + if args.input.lower() == "magicc": + tas, ohc = load_magicc_forcing(args.input_path, scenarios, baseline_start, baseline_end) + elif args.input.lower() == "fair": + tas, ohc = run_fair(baseline_start, baseline_end, scenarios) + else: + raise ValueError("Input source not recognised.") # Pre-generate the random ensemble member indices n_iterations = 1000 rng = np.random.default_rng() - random_indices = rng.integers(0, high=841, size=n_iterations) + random_indices = rng.integers(0, high=tas.shape[2], size=n_iterations) sampled_tas = [] sampled_ohc = [] @@ -258,7 +336,6 @@ def main(): progress.update(task, advance=1) - # Convert to Numpy arrays for scenario, data in components.items(): for key in data: @@ -269,7 +346,7 @@ def main(): sampled_components[scenario] = process_global_ensemble( components[scenario], percentiles, scenario) - save_to_netcdf(sampled_components, "probabilistic_projections/global/gmslr_projections.nc") + save_to_netcdf(sampled_components, "probabilistic_projections/global/gmslr_projections_MAGICC_forcing.nc") fig = plt.figure(figsize=(16, 8), layout="constrained") ax = fig.add_subplot(231) @@ -296,5 +373,7 @@ def main(): if __name__ == "__main__": - multiprocessing.freeze_support() - main() \ No newline at end of file + p = argparse.ArgumentParser(formatter_class=RichHelpFormatter) + p.add_argument("--input", default="fair", required=False, help="Input climate forcing source (default: FaIR)", type=str) + p.add_argument("--input_path", required=False, help="Path to input climate forcing", type=str) + main(p.parse_args()) \ No newline at end of file diff --git a/profsea/spatialise.py b/profsea/spatialise.py index 914f19d..8e13a89 100644 --- a/profsea/spatialise.py +++ b/profsea/spatialise.py @@ -15,14 +15,12 @@ import numpy as np from rich.console import Console from rich.progress import track -import scipy from scipy.interpolate import RegularGridInterpolator from scipy.spatial.distance import cdist import xarray as xr from profsea.config import settings from profsea.directories import read_dir -from profsea.emulator import GMSLREmulator from profsea.slr_pkg import choose_montecarlo_dir console = Console() From 93f560b552b8a0aabd775eed315bc64641931cd1 Mon Sep 17 00:00:00 2001 From: Gregory Munday Date: Fri, 28 Nov 2025 15:36:29 +0000 Subject: [PATCH 047/276] smol change --- profsea/spatialise.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/profsea/spatialise.py b/profsea/spatialise.py index 8e13a89..88a8fff 100644 --- a/profsea/spatialise.py +++ b/profsea/spatialise.py @@ -4,7 +4,6 @@ """ import glob import pickle -import json import os from pathlib import Path import warnings @@ -16,7 +15,6 @@ from rich.console import Console from rich.progress import track from scipy.interpolate import RegularGridInterpolator -from scipy.spatial.distance import cdist import xarray as xr from profsea.config import settings From 770274e1c22073ea82381967c665de4c7deeb049 Mon Sep 17 00:00:00 2001 From: Gregory Munday Date: Fri, 28 Nov 2025 15:40:43 +0000 Subject: [PATCH 048/276] Remove print --- profsea/spatial_projections.py | 1 - 1 file changed, 1 deletion(-) diff --git a/profsea/spatial_projections.py b/profsea/spatial_projections.py index 83c40e6..da1a84d 100644 --- a/profsea/spatial_projections.py +++ b/profsea/spatial_projections.py @@ -263,7 +263,6 @@ def calculate_sl_components( #mc_timeseries = np.load(os.path.join(mcdir, f'{scenario}_{comp}.npy')) mc_timeseries = np.load(os.path.join(settings["baseoutdir"],settings["experiment_name"], 'data','gmslr',f'{scenario}_{comp}.npy')) - print("data read: ", mc_timeseries) sampled_mc = mc_timeseries[resamples, :nyrs] montecarlo_G[:, :] = da.from_array(sampled_mc[:, :, None, None]) From 7a27d0ee8b564e3a1c0474736681bce7017808b9 Mon Sep 17 00:00:00 2001 From: Hemant Khatri Date: Tue, 2 Dec 2025 09:59:14 +0000 Subject: [PATCH 049/276] write lalon spatial projections in netcdf files --- profsea/spatial_projections.py | 19 ++++++++++++++++--- 1 file changed, 16 insertions(+), 3 deletions(-) diff --git a/profsea/spatial_projections.py b/profsea/spatial_projections.py index da1a84d..05605d8 100644 --- a/profsea/spatial_projections.py +++ b/profsea/spatial_projections.py @@ -212,12 +212,13 @@ def calc_greenland_fingerprint_ar6() -> da.array: def save_projections( - montecarlo_R: da.array, component: str, scenario: str) -> None: + montecarlo_R: da.array, component: str, scenario: str, percentile: da.array) -> None: """ Save the regional sea level projections to a file. :param montecarlo_R: regional sea level projections :param component: sea level component :param scenario: emission scenario + :param percentile: percentiles used for spatial projections """ sealev_ddir = read_dir()[4] file_header = '_'.join([component, scenario, "projection", @@ -228,6 +229,17 @@ def save_projections( # Save the global and local projections np.save(os.path.join(sealev_ddir, R_file), montecarlo_R) + # Save data in netcdf format (Assuming first dimension is percentile, but can be more general percentile/ensemble) + xr_dataArray = xr.DataArray(montecarlo_R, dims=["percentile", "time", "lat", "lon"], + coords={"percentile": percentile, + "time": np.arange(2006, montecarlo_R.shape[1] + 2006), + "lat": np.arange(-90, 90) + 0.5, "lon": np.arange(0,360) + 0.5}) + xr_dataArray.attrs["units"] = "m" + ds = xr_dataArray.to_dataset(name = component) + + R_file = '_'.join([file_header, 'regional']) + '.nc' + ds.to_netcdf(os.path.join(sealev_ddir, R_file)) + def calculate_sl_components( mcdir: str, components: list, scenario: str, @@ -286,11 +298,12 @@ def calculate_sl_components( del fp_vals # Take the 0th, 25th, 50th, 75th and 100th percentiles - montecarlo_R = da.percentile(montecarlo_R, [0, 25, 50, 75, 100], axis=0) + percentile_regional = np.array([0, 25, 50, 75, 100]) + montecarlo_R = da.percentile(montecarlo_R, percentile_regional, axis=0) montecarlo_R = montecarlo_R.compute() # Create the output sea level projections file directory and filename - save_projections(montecarlo_R, comp, scenario) + save_projections(montecarlo_R, comp, scenario, percentile_regional) def create_FP_interpolator( From e6481bd9283cb3ff32e86b6bcb7bb495fd583f18 Mon Sep 17 00:00:00 2001 From: Gregory Munday Date: Fri, 5 Dec 2025 08:55:03 +0000 Subject: [PATCH 050/276] Added new antarctic component --- profsea/emulator/__init__.py | 2 + profsea/emulator/antarctica.py | 83 ++++++++++++++++++++++++++++++++++ profsea/fair_prof_prob.py | 27 +++++++---- 3 files changed, 102 insertions(+), 10 deletions(-) create mode 100644 profsea/emulator/antarctica.py diff --git a/profsea/emulator/__init__.py b/profsea/emulator/__init__.py index ab1cc9e..29db08d 100644 --- a/profsea/emulator/__init__.py +++ b/profsea/emulator/__init__.py @@ -20,6 +20,8 @@ from scipy.stats import norm, truncnorm import xarray as xr +from .antarctica import Antarctica + console = Console() @functools.lru_cache(maxsize=1) diff --git a/profsea/emulator/antarctica.py b/profsea/emulator/antarctica.py new file mode 100644 index 0000000..5370451 --- /dev/null +++ b/profsea/emulator/antarctica.py @@ -0,0 +1,83 @@ +from pathlib import Path + +from rich.progress import track +import numpy as np +import xarray as xr + +class Antarctica: + def __init__( + self, + params_path: Path, + n_samples: int=1000) -> None: + """ + """ + self.param_ds = xr.load_dataset(params_path) + self.n_samples = n_samples + + self.n_models = self.param_ds.coords["model"].shape[0] + n_params = self.param_ds.coords["param"].shape[0] + self.mv_dists = self._calc_mv_dists(n_params) + + def _calc_mv_dists(self, n_params: int): + """ + """ + mv_dists = [] + for m in range(self.n_models): + # Calculate covariance of param residuals + cov = np.cov(self.param_ds.param_residuals[m].T) + + # Add jitter in case of small param values + cov += np.eye(n_params) * 1e-8 + + # Draw samples centered at 0 + samples = np.random.multivariate_normal(np.zeros(n_params), cov, size=self.n_samples) + mv_dists.append(samples) + return np.array(mv_dists) + + def _inst_function(self, tas, t_int, params_gen, params_resid): + """ + """ + # params: [a, b] + p = params_gen + params_resid + return (p[:, 0, None] * tas) + (p[:, 1, None] * t_int) + + def _impulse_response(self, f_inst_vals, tau): + """ + """ + n = len(f_inst_vals) + # Avoid division by zero if tau is tiny + tau = max(tau, 1e-3) + decay_factors = np.exp(-np.arange(n) / tau) * (1 / tau) + + # Efficient convolution + # Note: scipy.signal.convolve is usually faster for large n, + # but keeping your implementation for consistency. + response = np.zeros(n) + for k in range(n): + response[k] = np.sum(decay_factors[:k+1] * f_inst_vals[k::-1]) + return response + + + def predict(self, tas: np.ndarray, tas_int: np.ndarray, display_progress=True): + """ + """ + n_time = tas.shape[0] + + # Shape: (n_models, n_samples, n_time) + all_preds = np.zeros((self.n_models, self.n_samples, n_time)) + + for model in track( + range(self.n_models), + description="Projecting AIS response... ", + disable=not display_progress): + tau = self.param_ds.tau[model].values + gen_p = self.param_ds.general_params[model].values # [alpha, beta, gamma] + resid_p = self.mv_dists[model] # [n_samples, n_params] + + # Calculate forcing for all samples at once + f_vals = self._inst_function(tas[None, :], tas_int[None, :], gen_p[None, :], resid_p) + + # Convolve + for sample in range(self.n_samples): + all_preds[model, sample, :] = self._impulse_response(f_vals[sample], tau) + return all_preds diff --git a/profsea/fair_prof_prob.py b/profsea/fair_prof_prob.py index 0390f98..e346e73 100644 --- a/profsea/fair_prof_prob.py +++ b/profsea/fair_prof_prob.py @@ -29,23 +29,30 @@ def init_worker(tas, ohc, emissions, scenarios): worker_scenarios = scenarios.copy() -def index_df(df: pd.DataFrame, baseline_start: int, baseline_end: int) -> tuple[pd.DataFrame]: +def index_df(df: pd.DataFrame, baseline_start: int, baseline_end: int) -> pd.DataFrame: meta_cols = ['ensemble_member', 'scenario', 'region', 'variable', 'unit', 'climate_model'] existing_meta = [c for c in meta_cols if c in df.columns] df_indexed = df.set_index(existing_meta) - years = df_indexed.columns.str[:4].astype(int) - mask = (years >= 1750) & (years <= 2301) - df_indexed = df_indexed.loc[:, mask] + years = df_indexed.columns.astype(str).str[:4].astype(int) + df_indexed.columns = years + + mask_full = (years >= 1750) & (years <= 2300) + df_indexed = df_indexed.loc[:, mask_full] + + years_sliced = df_indexed.columns + baseline_mask = (years_sliced >= baseline_start) & (years_sliced <= baseline_end) + + if not baseline_mask.any(): + raise ValueError(f"No years found in baseline range {baseline_start}-{baseline_end}") - years = df_indexed.columns.str[:4].astype(int) - baseline_mask = (years >= baseline_start) & (years <= baseline_end) baseline_means = df_indexed.loc[:, baseline_mask].mean(axis=1) df_anom = df_indexed.sub(baseline_means, axis=0) - - # Now slice up again - mask = (years >= 2006) & (years <= 2300) - df_final = df_anom.loc[:, mask].reset_index() + years_final = df_anom.columns + mask_final = (years_final >= 2006) & (years_final <= 2300) + + df_final = df_anom.loc[:, mask_final].reset_index() + return df_final From a29f5652345d0c1dd1b4bd649307ef1c4b7fcb81 Mon Sep 17 00:00:00 2001 From: Gregory Munday Date: Fri, 5 Dec 2025 09:20:27 +0000 Subject: [PATCH 051/276] Improve performance + add ais_params.nc --- profsea/emulator/antarctica.py | 21 ++++++++------------- profsea/emulator/aux_data/ais_params.nc | Bin 0 -> 22574 bytes 2 files changed, 8 insertions(+), 13 deletions(-) create mode 100644 profsea/emulator/aux_data/ais_params.nc diff --git a/profsea/emulator/antarctica.py b/profsea/emulator/antarctica.py index 5370451..5ca3bf9 100644 --- a/profsea/emulator/antarctica.py +++ b/profsea/emulator/antarctica.py @@ -2,6 +2,7 @@ from rich.progress import track import numpy as np +from scipy.signal import fftconvolve import xarray as xr class Antarctica: @@ -44,18 +45,13 @@ def _inst_function(self, tas, t_int, params_gen, params_resid): def _impulse_response(self, f_inst_vals, tau): """ """ - n = len(f_inst_vals) - # Avoid division by zero if tau is tiny - tau = max(tau, 1e-3) - decay_factors = np.exp(-np.arange(n) / tau) * (1 / tau) + n_time = f_inst_vals.shape[-1] + tau = max(tau, 1e-3) # avoid zero division if tau is v small + decay_factors = np.exp(-np.arange(n_time) / tau) * (1 / tau) - # Efficient convolution - # Note: scipy.signal.convolve is usually faster for large n, - # but keeping your implementation for consistency. - response = np.zeros(n) - for k in range(n): - response[k] = np.sum(decay_factors[:k+1] * f_inst_vals[k::-1]) - return response + kernel = decay_factors[None, :] + response = fftconvolve(f_inst_vals, kernel, mode='full', axes=-1) # shape (n_samples, 2*n_time - 1) + return response[:, :n_time] def predict(self, tas: np.ndarray, tas_int: np.ndarray, display_progress=True): @@ -78,6 +74,5 @@ def predict(self, tas: np.ndarray, tas_int: np.ndarray, display_progress=True): f_vals = self._inst_function(tas[None, :], tas_int[None, :], gen_p[None, :], resid_p) # Convolve - for sample in range(self.n_samples): - all_preds[model, sample, :] = self._impulse_response(f_vals[sample], tau) + all_preds[model, :, :] = self._impulse_response(f_vals, tau) return all_preds diff --git a/profsea/emulator/aux_data/ais_params.nc b/profsea/emulator/aux_data/ais_params.nc new file mode 100644 index 0000000000000000000000000000000000000000..3d951af5a32b704bf2196d1aab53ab186edc498c GIT binary patch literal 22574 zcmeHuc|29y+xSLgIED!9BX~lP0X2g# z+MvJ$u|TZQ5(o+;26_=7J{Tnl{{vnvpFn4Smq4Nmmgqu|3Gg5g0P3z^FoSH%!n{YC8d>Ab%HXL!wAS`u+rWl7BGPILODpX%R!Mz ziZWkN1(6V-v+@E269Xg!atO*n#33L8W)Cqfh3LhH!Wb>U&jj&982BH;n-BtBT-}s# zB$B@?(Z@NE5CHMO9f4LL2um3<=ny0{2W6@jJdkeV0SZo3Z21-{ltj(I^Qfx+ZBv}V z4s?7>za1a7i6zwjAsw^{q@$^-58a?03@kpNCPdvhBP|OHin@WY0I!arv95`Qp{WVp z0;gr93t_MT&jc}3^=hJJtV`hs`U3pkBv*nL1e#@n|F~e`z#~9~qeJv2y8b8@xC%&# 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zRtERe9dD(BlIlDw6z{zDl!S&iR1FogZB@$LqXkMstU7n&L+0fhqTcU4f_ICie!xt;h*Qi`sjQAkKeOQhlXlPwXE~-I#1P#g5$b-WMc--d=ilQ`^huQJ(0szopSeY^}D|Lv#L)h*=@rW{$}75W!TK5 z@gn#%%WeDD1Z7EeY*)|3-Rk~&G$7kbkbioDW9ZYT>5%VH?Y;FHGa3gSG;DCGC9}Pg z?sM+L4ZSh>qom&Ux#1;K-Tl+ue}2DAo;yN*rWrncl}z>`U!3B`%yLhW)3}GeCvM|s zpDo-!Hoz6p6Fx+~rg0mWpPI_?zOAk4&Yi}lIrmw$zwswar(!-=eSb1DyDj2ixR*9r bg6uLSh?^^mNDudGmYeG!ukN|jv+w@^UTo2H literal 0 HcmV?d00001 From a464312d42140a9a30ea551ffff36f37a8f4ff31 Mon Sep 17 00:00:00 2001 From: Hemant Khatri Date: Fri, 5 Dec 2025 11:35:12 +0000 Subject: [PATCH 052/276] Remove saving to .npy, add encoding to netcdf, save gia component in netcdf file --- profsea/spatial_projections.py | 32 ++++++++++++++++++++++---------- 1 file changed, 22 insertions(+), 10 deletions(-) diff --git a/profsea/spatial_projections.py b/profsea/spatial_projections.py index 05605d8..53e5601 100644 --- a/profsea/spatial_projections.py +++ b/profsea/spatial_projections.py @@ -112,9 +112,22 @@ def calc_gia_contribution( file_header = '_'.join(['gia', scenario, "projection", f"{settings['projection_end_year']}"]) - R_file = '_'.join([file_header, 'regional']) + '.npy' + sealev_ddir = read_dir()[4] + + # Save data in netcdf format (Assuming first dimension is percentile, but can be more general percentile/ensemble) + xr_dataArray = xr.DataArray(GIA_series, dims=["percentile", "time", "lat", "lon"], + coords={"time": np.arange(2006, GIA_series.shape[1] + 2006), + "lat": np.arange(-90, 90) + 0.5, "lon": np.arange(0, 360) + 0.5}) + xr_dataArray.attrs["units"] = "m" + xr_dataArray.attrs["long_name"] = f"Regional GIA sea-level projections" + ds = xr_dataArray.to_dataset(name='gia') + + ds.attrs["source"] = "ProFSea-Climate v0.1" + + R_file = '_'.join([file_header, 'regional']) + '.nc' + encoding = {'gia': {"zlib": True, "complevel": 5}} + ds.to_netcdf(os.path.join(sealev_ddir, R_file), encoding=encoding) - np.save(os.path.join(read_dir()[4], R_file), GIA_series) del GIA_series @@ -223,22 +236,21 @@ def save_projections( sealev_ddir = read_dir()[4] file_header = '_'.join([component, scenario, "projection", f"{settings['projection_end_year']}"]) - # G_file = '_'.join([file_header, 'global']) + '.npy' - R_file = '_'.join([file_header, 'regional']) + '.npy' - - # Save the global and local projections - np.save(os.path.join(sealev_ddir, R_file), montecarlo_R) # Save data in netcdf format (Assuming first dimension is percentile, but can be more general percentile/ensemble) xr_dataArray = xr.DataArray(montecarlo_R, dims=["percentile", "time", "lat", "lon"], coords={"percentile": percentile, "time": np.arange(2006, montecarlo_R.shape[1] + 2006), - "lat": np.arange(-90, 90) + 0.5, "lon": np.arange(0,360) + 0.5}) + "lat": np.arange(-90, 90) + 0.5, "lon": np.arange(0, 360) + 0.5}) xr_dataArray.attrs["units"] = "m" - ds = xr_dataArray.to_dataset(name = component) + xr_dataArray.attrs["long_name"] = f"Regional {component} sea-level projections" + ds = xr_dataArray.to_dataset(name=component) + + ds.attrs["source"] = "ProFSea-Climate v0.1" R_file = '_'.join([file_header, 'regional']) + '.nc' - ds.to_netcdf(os.path.join(sealev_ddir, R_file)) + encoding = {component: {"zlib": True, "complevel": 5}} + ds.to_netcdf(os.path.join(sealev_ddir, R_file), encoding=encoding) def calculate_sl_components( From 6deca3bbb30b29bd3943380d205148122d3e3d77 Mon Sep 17 00:00:00 2001 From: Gregory Munday Date: Fri, 5 Dec 2025 12:18:17 +0000 Subject: [PATCH 053/276] Add AIS and other notebooks --- profsea/notebooks/ais_fitting.ipynb | 1010 ++++++++++++++++++++++++ profsea/notebooks/compare_ais.ipynb | 615 +++++++++++++++ profsea/notebooks/evaluate_gmslr.ipynb | 343 ++++++++ profsea/notebooks/worked_example.ipynb | 177 +++++ 4 files changed, 2145 insertions(+) create mode 100644 profsea/notebooks/ais_fitting.ipynb create mode 100644 profsea/notebooks/compare_ais.ipynb create mode 100644 profsea/notebooks/evaluate_gmslr.ipynb create mode 100644 profsea/notebooks/worked_example.ipynb diff --git a/profsea/notebooks/ais_fitting.ipynb b/profsea/notebooks/ais_fitting.ipynb new file mode 100644 index 0000000..65b5487 --- /dev/null +++ b/profsea/notebooks/ais_fitting.ipynb @@ -0,0 +1,1010 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 137, + "id": "03acb605", + "metadata": {}, + "outputs": [], + "source": [ + "import glob \n", + "from rich.progress import track\n", + "\n", + "import h5py\n", + "import numpy as np\n", + "import xarray as xr\n", + "import matplotlib.pyplot as plt\n", + "from scipy.optimize import least_squares\n", + "from scipy.signal import fftconvolve" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "2b26565c", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/gregorymunday/anaconda3/envs/cmip7/lib/python3.11/site-packages/xarray/conventions.py:289: SerializationWarning: variable 'tas' has multiple fill values {1e+20, 1e+20} defined, decoding all values to NaN.\n", + " var = coder.decode(var, name=name)\n", + "/Users/gregorymunday/anaconda3/envs/cmip7/lib/python3.11/site-packages/xarray/conventions.py:289: SerializationWarning: variable 'tas' has multiple fill values {1e+20, 1e+20} defined, decoding all values to NaN.\n", + " var = coder.decode(var, name=name)\n", + "/Users/gregorymunday/anaconda3/envs/cmip7/lib/python3.11/site-packages/xarray/conventions.py:289: SerializationWarning: variable 'tas' has multiple fill values {1e+20, 1e+20} defined, decoding all values to NaN.\n", + " var = coder.decode(var, name=name)\n", + "/Users/gregorymunday/anaconda3/envs/cmip7/lib/python3.11/site-packages/xarray/conventions.py:289: SerializationWarning: variable 'tas' has multiple fill values {1e+20, 1e+20} defined, decoding all values to NaN.\n", + " var = coder.decode(var, name=name)\n", + "/Users/gregorymunday/anaconda3/envs/cmip7/lib/python3.11/site-packages/xarray/conventions.py:289: SerializationWarning: variable 'tas' has multiple fill values {1e+20, 1e+20} defined, decoding all values to NaN.\n", + " var = coder.decode(var, name=name)\n", + "/Users/gregorymunday/anaconda3/envs/cmip7/lib/python3.11/site-packages/xarray/conventions.py:289: SerializationWarning: variable 'tas' has multiple fill values {1e+20, 1e+20} defined, decoding all values to NaN.\n", + " var = coder.decode(var, name=name)\n" + ] + } + ], + "source": [ + "def load_global_temperature(path: str) -> np.array:\n", + " ds = xr.open_mfdataset(path).tas\n", + " ds = ds.groupby('time.year').mean('time')\n", + " global_weights = np.cos(np.deg2rad(ds.lat))\n", + " ds = ds.weighted(global_weights).mean(dim=['lat', 'lon']).compute()\n", + " tas = ds.sel(year=slice(2015, 2299)).values - ds.sel(year=slice(1995, 2014)).mean('year').values\n", + " return tas\n", + "\n", + "def load_stable_temperature(path: str) -> np.array:\n", + " tas = xr.open_mfdataset(path).tas.values\n", + " return tas\n", + "\n", + "def load_var(path: str, variable: str) -> np.array:\n", + " files = sorted(glob.glob(path))\n", + " anoms = []\n", + " for f in files:\n", + " ds = xr.open_dataset(f, decode_times=False)[f'{variable}']\n", + " ds = ds.sel(time=slice(2015, 2299)).values\n", + " if variable == 'sle':\n", + " ds -= ds[0]\n", + " anoms.append(ds)\n", + " return np.array(anoms)\n", + "\n", + "# High emissions temperatures\n", + "ccsm4_tas_85 = load_global_temperature('../data/AIS/forcing/global/CCSM4/*.nc')\n", + "cesm2_tas_585 = load_global_temperature('../data/AIS/forcing/global/CESM2-WACCM/*.nc')\n", + "ukesm_tas_585 = load_global_temperature('../data/AIS/forcing/global/UKESM1-0-LL/*.nc')\n", + "hadgem2_tas_85 = load_global_temperature('../data/AIS/forcing/global/HadGEM2-ES/*.nc')\n", + "\n", + "# Stabilisation temperatures\n", + "ukesm_stable_tas_585 = load_stable_temperature('../data/AIS/forcing/global/UKESM1-0-LL_STABLE/*.nc')\n", + "cesm2_stable_tas_585 = load_stable_temperature('../data/AIS/forcing/global/CESM2-WACCM_STABLE/*.nc')\n", + "hadgem2_stable_tas_85 = load_stable_temperature('../data/AIS/forcing/global/HadGEM2-ES_STABLE/*.nc')\n", + "noresm_stable_tas_85 = load_stable_temperature('../data/AIS/forcing/global/NorESM1-M_STABLE/tas_Amon_NorESM1-M_rcp85_stabilised.nc')\n", + "\n", + "# Low emissions temperatures\n", + "ukesm_tas_26 = load_global_temperature('../data/AIS/forcing/global/UKESM1-0-LL_126/*.nc')\n", + "noresm_tas_26 = load_stable_temperature('../data/AIS/forcing/global/NorESM1-M_STABLE/tas_Amon_NorESM1-M_rcp26_stabilised.nc')\n", + "\n", + "# SLE\n", + "noresm_sle_26 = load_var('../data/AIS/expAE01/sle/*.nc', 'sle')\n", + "ccsm4_sle_85 = load_var('../data/AIS/expAE02/sle/*.nc', 'sle')\n", + "hadgem2_sle_85 = load_var('../data/AIS/expAE03/sle/*.nc', 'sle')\n", + "cesm2_sle_585 = load_var('../data/AIS/expAE04/sle/*.nc', 'sle')\n", + "ukesm_sle_585 = load_var('../data/AIS/expAE05/sle/*.nc', 'sle')\n", + "ukesm_stable_sle_585 = load_var('../data/AIS/expAE06/sle/*.nc', 'sle')\n", + "noresm_stable_sle_85 = load_var('../data/AIS/expAE07/sle/*.nc', 'sle')\n", + "hadgem2_stable_sle_85 = load_var('../data/AIS/expAE08/sle/*.nc', 'sle')\n", + "cesm2_stable_sle_585 = load_var('../data/AIS/expAE09/sle/*.nc', 'sle')\n", + "ukesm_sle_26 = load_var('../data/AIS/expAE10/sle/*.nc', 'sle')\n", + "\n", + "model_list = sorted(glob.glob('../data/AIS/expAE04/sle/*.nc'))\n", + "model_names = [item.split(\"/\")[-1].split(\"sle_AIS_\")[-1].split(\"_exp\")[0] for item in model_list]" + ] + }, + { + "cell_type": "code", + "execution_count": 155, + "id": "671ba1e6", + "metadata": {}, + "outputs": [], + "source": [ + "# Training data\n", + "tas_train = np.array([\n", + " cesm2_tas_585, # high\n", + " ccsm4_tas_85, # high\n", + " hadgem2_tas_85, # high\n", + " ukesm_tas_585, # high ])\n", + " noresm_tas_26,\n", + " ukesm_stable_tas_585\n", + " ]) # stable\n", + "sle_train = np.array([\n", + " cesm2_sle_585,\n", + " ccsm4_sle_85,\n", + " hadgem2_sle_85, \n", + " ukesm_sle_585,\n", + " noresm_sle_26,\n", + " ukesm_stable_sle_585\n", + " ])\n", + "t_int_train = np.array([\n", + " np.cumsum(cesm2_tas_585), \n", + " np.cumsum(ccsm4_tas_85), \n", + " np.cumsum(hadgem2_tas_85),\n", + " np.cumsum(ukesm_tas_585),\n", + " np.cumsum(noresm_tas_26),\n", + " np.cumsum(ukesm_stable_tas_585)\n", + " ])\n", + "\n", + "# Test data\n", + "tas_test = np.array([\n", + " noresm_stable_tas_85, # stable\n", + " hadgem2_stable_tas_85, # stable\n", + " cesm2_stable_tas_585, # stable\n", + " ukesm_tas_26]) # low\n", + "\n", + "t_int_test = np.array([\n", + " np.cumsum(noresm_stable_tas_85), \n", + " np.cumsum(hadgem2_stable_tas_85), \n", + " np.cumsum(cesm2_stable_tas_585), \n", + " np.cumsum(ukesm_tas_26)])\n", + "sle_test = [\n", + " noresm_stable_sle_85,\n", + " hadgem2_stable_sle_85,\n", + " cesm2_stable_sle_585,\n", + " ukesm_sle_26]\n", + "\n", + "\n", + "# # Try to train with all data\n", + "# min_models = hadgem2_stable_sle_85.shape[0]\n", + "\n", + "# tas_train = np.array([\n", + "# cesm2_tas_585[:min], # high\n", + "# ccsm4_tas_85, # high\n", + "# hadgem2_tas_85, # high\n", + "# ukesm_tas_585, # high ])\n", + "# noresm_tas_26,\n", + "# ukesm_stable_tas_585\n", + "# ]) # stable\n", + "# sle_train = np.array([\n", + "# cesm2_sle_585,\n", + "# ccsm4_sle_85,\n", + "# hadgem2_sle_85, \n", + "# ukesm_sle_585,\n", + "# noresm_sle_26,\n", + "# ukesm_stable_sle_585\n", + "# ])\n", + "# t_int_train = np.array([\n", + "# np.cumsum(cesm2_tas_585), \n", + "# np.cumsum(ccsm4_tas_85), \n", + "# np.cumsum(hadgem2_tas_85),\n", + "# np.cumsum(ukesm_tas_585),\n", + "# np.cumsum(noresm_tas_26),\n", + "# np.cumsum(ukesm_stable_tas_585)\n", + "# ])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 156, + "id": "3498a908", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "66d77c7eac0446d8adf28e63d1edb7e5", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Output()" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training with Polynomial Model, Global Tau, and Regularization...\n" + ] + }, + { + "data": { + "text/html": [ + "

\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Complete.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# --- 1. Define New Model Functions ---\n",
+    "\n",
+    "def f_poly(tas, t_int, params):\n",
+    "    \"\"\"\n",
+    "    Polynomial forcing function.\n",
+    "    params[0] (alpha): Linear response to temperature\n",
+    "    params[1] (beta) : Quadratic response (non-linearity)\n",
+    "    params[2] (gamma): Response to integrated temperature\n",
+    "    \"\"\"\n",
+    "    alpha, beta = params\n",
+    "    return (alpha * tas) + (beta * t_int)\n",
+    "\n",
+    "def impulse_response_fitter(f_inst_vals, tau, dt=1):\n",
+    "    \"\"\"Same convolution logic as before.\"\"\"\n",
+    "    n = len(f_inst_vals)\n",
+    "    # Avoid division by zero if tau is tiny\n",
+    "    tau = max(tau, 1e-3)\n",
+    "    decay_factors = np.exp(-np.arange(n) * dt / tau) * (dt / tau)\n",
+    "    \n",
+    "    # Efficient convolution\n",
+    "    # Note: scipy.signal.convolve is usually faster for large n, \n",
+    "    # but keeping your implementation for consistency.\n",
+    "    response = fftconvolve(f_inst_vals, decay_factors, mode=\"full\", axes=-1)\n",
+    "    return response[:n]\n",
+    "\n",
+    "def residuals_global(params_all, tas_flat, t_int_flat, y_flat, splits):\n",
+    "    \"\"\"\n",
+    "    Fit Tau and ABC parameters simultaneously to ALL data for one model.\n",
+    "    params_all: [tau, alpha, beta, gamma]\n",
+    "    \"\"\"\n",
+    "    tau = params_all[0]\n",
+    "    abc = params_all[1:]\n",
+    "    \n",
+    "    # Reconstruct the forcing for the flattened array\n",
+    "    # Note: We process the flattened array directly for speed, assuming\n",
+    "    # the convolution restart at scenario boundaries is handled \n",
+    "    # or negligible for global fit. \n",
+    "    # BETTER APPROACH: Loop over splits to handle convolution boundaries correctly.\n",
+    "    all_res = []\n",
+    "    start_idx = 0\n",
+    "    for end_idx in splits:\n",
+    "        tas_s = tas_flat[start_idx:end_idx]\n",
+    "        t_int_s = t_int_flat[start_idx:end_idx]\n",
+    "        y_s = y_flat[start_idx:end_idx]\n",
+    "        \n",
+    "        f_vals = f_poly(tas_s, t_int_s, abc)\n",
+    "        y_pred = impulse_response_fitter(f_vals, tau)\n",
+    "        all_res.append(y_pred - y_s)\n",
+    "        start_idx = end_idx\n",
+    "        \n",
+    "    return np.concatenate(all_res)\n",
+    "\n",
+    "def residuals_specific_regularized(params_abc, tau_fixed, tas, t_int, y_obs, \n",
+    "                                   general_abc, lambda_reg):\n",
+    "    \"\"\"\n",
+    "    Fit specific ABC parameters with a fixed Tau and Shrinkage.\n",
+    "    \"\"\"\n",
+    "    # 1. Physics Error\n",
+    "    f_vals = f_poly(tas, t_int, params_abc)\n",
+    "    y_pred = impulse_response_fitter(f_vals, tau_fixed)\n",
+    "    phys_res = y_pred - y_obs\n",
+    "    \n",
+    "    # 2. Regularization Penalty (Shrinkage)\n",
+    "    # Penalize deviation from the general parameters\n",
+    "    # We use sqrt(lambda) because least_squares minimizes sums of squares\n",
+    "    reg_res = np.sqrt(lambda_reg) * (params_abc - general_abc)\n",
+    "    \n",
+    "    return np.concatenate([phys_res, reg_res])\n",
+    "\n",
+    "# --- 2. Setup Containers ---\n",
+    "general_params = []     # Stores [alpha, beta, gamma]\n",
+    "general_timescales = [] # Stores [tau]\n",
+    "specific_params = []    # Stores [model, scenario, 3]\n",
+    "impulse_mse = []\n",
+    "\n",
+    "# Hyperparameters\n",
+    "lambda_reg = 1  # Strength of shrinkage. 0.1 to 10.0 is a typical range.\n",
+    "tau_bounds = (5, 150.0) # Constrain tau to physics-compliant range\n",
+    "\n",
+    "# --- 3. Main Training Loop ---\n",
+    "\n",
+    "print(\"Training with Polynomial Model, Global Tau, and Regularization...\")\n",
+    "n_models = sle_train.shape[1]\n",
+    "for m_idx in track(range(n_models), description=\"Training... \"):\n",
+    "    \n",
+    "    # --- A. Prepare Data for Global Fit ---\n",
+    "    # We need to flatten the data but keep track of where scenarios split\n",
+    "    # so the convolution doesn't \"bleed\" between scenarios.\n",
+    "    tas_list = [tas_train[f_idx] for f_idx in range(tas_train.shape[0])]\n",
+    "    t_int_list = [t_int_train[f_idx] for f_idx in range(tas_train.shape[0])]\n",
+    "    y_list = [sle_train[f_idx, m_idx] for f_idx in range(tas_train.shape[0])]\n",
+    "    \n",
+    "    # Calculate split indices for the global residual function\n",
+    "    lengths = [len(x) for x in tas_list]\n",
+    "    splits = np.cumsum(lengths)\n",
+    "    \n",
+    "    tas_flat = np.concatenate(tas_list)\n",
+    "    t_int_flat = np.concatenate(t_int_list)\n",
+    "    y_flat = np.concatenate(y_list)\n",
+    "    \n",
+    "    # --- B. Fit Global Parameters (Tau + ABC) ---\n",
+    "    # Initial guess: tau=10, a=0.1, b=0, c=0.1\n",
+    "    x0_global = [5.0, 0.1, 0.1] \n",
+    "    \n",
+    "    res_global = least_squares(\n",
+    "        residuals_global,\n",
+    "        x0=x0_global,\n",
+    "        bounds=([tau_bounds[0], 0, 0], \n",
+    "                [tau_bounds[1], 1, 1]),\n",
+    "        args=(tas_flat, t_int_flat, y_flat, splits)\n",
+    "    )\n",
+    "    \n",
+    "    tau_best = res_global.x[0]\n",
+    "    abc_best = res_global.x[1:]\n",
+    "    \n",
+    "    general_timescales.append(tau_best)\n",
+    "    general_params.append(abc_best)\n",
+    "    \n",
+    "    # --- C. Fit Specific Scenarios (Fixed Tau + Shrinkage) ---\n",
+    "    model_scenario_params = []\n",
+    "    for f_idx in range(tas_train.shape[0]):\n",
+    "        \n",
+    "        # We start the optimization at the global parameters.\n",
+    "        res_spec = least_squares(\n",
+    "            residuals_specific_regularized,\n",
+    "            x0=abc_best, \n",
+    "            args=(tau_best, tas_train[f_idx], t_int_train[f_idx], \n",
+    "                  sle_train[f_idx, m_idx], abc_best, lambda_reg)\n",
+    "        )\n",
+    "        model_scenario_params.append(res_spec.x)\n",
+    "        \n",
+    "    specific_params.append(model_scenario_params)\n",
+    "\n",
+    "# --- 4. Format Outputs ---\n",
+    "\n",
+    "general_timescales = np.array(general_timescales)\n",
+    "general_params = np.array(general_params) # Shape (43, 3)\n",
+    "specific_params = np.array(specific_params) # Shape (43, n_scenarios, 3)\n",
+    "param_residuals = specific_params - general_params[:, None, :]\n",
+    "\n",
+    "print(\"Training Complete.\")\n",
+    "\n",
+    "# Remove models with timescales less than X years\n",
+    "timescale_cutoff = 50\n",
+    "temp_timescales = np.where(general_timescales >= timescale_cutoff, general_timescales, 0)\n",
+    "constrained_idx = np.argwhere(temp_timescales).squeeze()\n",
+    "\n",
+    "constr_timescales = general_timescales[constrained_idx]\n",
+    "constr_general_params = general_params[constrained_idx]\n",
+    "constr_specific_params = specific_params[constrained_idx]\n",
+    "constr_param_residuals = param_residuals[constrained_idx]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 165,
+   "id": "77afd548",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(43, 6, 2)"
+      ]
+     },
+     "execution_count": 165,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "param_residuals.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 157,
+   "id": "3e4c8cd4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 157,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.bar(model_names, general_timescales)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 158,
+   "id": "f9006f8b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Save timescales and params into a NetCDF\n",
+    "params = [\"a\", \"b\"]\n",
+    "data_vars = {}\n",
+    "data_vars[\"tau\"] = xr.DataArray(\n",
+    "    data=general_timescales,\n",
+    "    dims=[\"model\"],\n",
+    "    coords={\n",
+    "        \"model\": model_names,\n",
+    "    },\n",
+    "    attrs={\n",
+    "        \"units\": \"years\", \n",
+    "        \"description\": \"Estimated model response timescale.\"\n",
+    "    }\n",
+    ")\n",
+    "data_vars[\"general_params\"] = xr.DataArray(\n",
+    "    data=general_params,\n",
+    "    dims=[\"model\", \"param\"],\n",
+    "    coords={\n",
+    "        \"model\": model_names,\n",
+    "        \"param\": params\n",
+    "    },\n",
+    "    attrs={\n",
+    "        \"units\": \"N/A\", \n",
+    "        \"description\": \"Estimated general model parameters across all training data.\"\n",
+    "    }\n",
+    ")\n",
+    "data_vars[\"param_residuals\"] = xr.DataArray(\n",
+    "    data=param_residuals,\n",
+    "    dims=[\"model\", \"scenario\", \"param\"],\n",
+    "    coords={\n",
+    "        \"model\": model_names,\n",
+    "        \"scenario\": np.arange(param_residuals.shape[1]),\n",
+    "        \"param\": params,\n",
+    "    },\n",
+    "    attrs={\n",
+    "        \"units\": \"N/A\", \n",
+    "        \"description\": \"Estimated model parameter residuals between training scenario-specific parameters and general parameters.\"\n",
+    "    }\n",
+    ")\n",
+    "    \n",
+    "ds = xr.Dataset(data_vars)\n",
+    "ds.attrs = {\n",
+    "    \"title\": \"Antarctic ice-sheet emulator parameters\",\n",
+    "    \"source\": \"Gregory Munday, December 2025\",\n",
+    "}\n",
+    "\n",
+    "# Save with compression\n",
+    "encoding = {var: {\"zlib\": True, \"complevel\": 5} for var in data_vars}\n",
+    "ds.to_netcdf(\"ais_params.nc\", encoding=encoding)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "09752923",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_ds = xr.open_dataset(\"ais_params.nc\")\n",
+    "test_ds.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "id": "8c273d81",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Generating distributions from specific parameters...\n"
+     ]
+    }
+   ],
+   "source": [
+    "# --- 1. Define Prediction Helpers (Ensure these match your training logic) ---\n",
+    "def f_poly_mixed(tas, t_int, params_gen, params_perturb):\n",
+    "    # params: [alpha, beta, gamma]\n",
+    "    p = params_gen + params_perturb\n",
+    "    # Broadcasting: (N_samples, 1, 3) * (1, Time) -> (N_samples, Time)\n",
+    "    return (p[:, 0, None] * tas) + (p[:, 1, None] * t_int)\n",
+    "\n",
+    "def predict_ais_me_poly(X, general_params, general_taus, mv_dists):\n",
+    "    tas, t_int = X\n",
+    "    n_models = len(general_params)\n",
+    "    n_samples = mv_dists[0].shape[0]\n",
+    "    n_time = len(tas)\n",
+    "    \n",
+    "    \n",
+    "    # Shape: (n_models, n_samples, n_time)\n",
+    "    all_preds = np.zeros((n_models, n_samples, n_time))\n",
+    "    \n",
+    "    for m in track(range(n_models), description=\"Projecting AIS response... \"):\n",
+    "        tau = general_taus[m]\n",
+    "        gen_p = general_params[m]     # [alpha, beta, gamma]\n",
+    "        perturb_p = mv_dists[m]       # [1000, 3]\n",
+    "        \n",
+    "        # Calculate forcing for all samples at once\n",
+    "        f_vals = f_poly_mixed(tas[None, :], t_int[None, :], gen_p[None, :], perturb_p)\n",
+    "        \n",
+    "        # Convolve\n",
+    "        for i in range(n_samples):\n",
+    "            all_preds[m, i, :] = impulse_response_fitter(f_vals[i], tau, dt=1)\n",
+    "            \n",
+    "    return all_preds\n",
+    "\n",
+    "def calc_mv_dists(general_params, param_residuals, n_samples):\n",
+    "    mv_dists = []\n",
+    "    covariances = []\n",
+    "    for m in range(len(general_params)):\n",
+    "    # Calculate residuals: Specific - General\n",
+    "    # specific_params shape is (43, n_scenarios, 3)\n",
+    "        \n",
+    "        # Calculate covariance of these perturbations\n",
+    "        cov = np.cov(param_residuals[m].T)\n",
+    "        \n",
+    "        # Optional: Check for ill-conditioned matrix and add tiny jitter if needed\n",
+    "        cov += np.eye(2) * 1e-8 \n",
+    "        covariances.append(cov)\n",
+    "        \n",
+    "        # Generate samples centered at 0 (we add them to general params later)\n",
+    "\n",
+    "        samples = np.random.multivariate_normal(np.zeros(2), cov, size=n_samples)\n",
+    "\n",
+    "            \n",
+    "        mv_dists.append(samples)\n",
+    "    return mv_dists, np.array(covariances)\n",
+    "\n",
+    "print(\"Generating distributions from specific parameters...\")\n",
+    "n_samples = 1000\n",
+    "mv_dists, covs = calc_mv_dists(general_params, param_residuals, n_samples)\n",
+    "constr_mv_dists, constr_covs = calc_mv_dists(constr_general_params, constr_param_residuals, n_samples)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "1e8095ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(33, 2, 2)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "constr_covs.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "id": "e88eee24",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1b283b0cb5ed44e0b94ba9ed504c9a3d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running predictions for Test Scenario 1...\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Select a scenario index to validate\n",
+    "test_idx = 1\n",
+    "\n",
+    "# Handle case where user might not have 'test' vars loaded yet, fallback to train\n",
+    "if 'tas_test' not in locals():\n",
+    "    print(\"Test data not found, using Training data index 0 for visualization.\")\n",
+    "    X_input = (tas_train[test_idx], t_int_train[test_idx])\n",
+    "    y_true_scenario = sle_train[test_idx] \n",
+    "else:\n",
+    "    X_input = (tas_test[test_idx], t_int_test[test_idx])\n",
+    "    y_true_scenario = sle_test[test_idx] \n",
+    "\n",
+    "# X_input contains 1D arrays of shape (Time,)\n",
+    "# y_true_scenario contains array of shape (Model, Time) -> (43, Time)\n",
+    "\n",
+    "# Run Emulator\n",
+    "print(f\"Running predictions for Test Scenario {test_idx}...\")\n",
+    "# preds shape: (43 models, 1000 samples, Time)\n",
+    "preds = ais.predict(tas_test[test_idx], t_int_test[test_idx])\n",
+    "\n",
+    "# --- 4. Visualization ---\n",
+    "\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "\n",
+    "# Option A: Visualize ONE specific model (e.g., Model 0) to check fit quality\n",
+    "model_to_plot = 8\n",
+    "model_preds = preds[model_to_plot] # (1000, Time)\n",
+    "\n",
+    "# Calculate stats\n",
+    "p5 = np.percentile(model_preds, 5, axis=0)\n",
+    "p17 = np.percentile(model_preds, 17, axis=0)\n",
+    "median = np.median(model_preds, axis=0)\n",
+    "p83 = np.percentile(model_preds, 83, axis=0)\n",
+    "p95 = np.percentile(model_preds, 95, axis=0)\n",
+    "\n",
+    "time_axis = np.arange(len(median))\n",
+    "\n",
+    "# Plot Emulator Uncertainty\n",
+    "plt.fill_between(time_axis, p5, p95, color='C0', alpha=0.2, label='5-95% Confidence')\n",
+    "plt.fill_between(time_axis, p17, p83, color='C0', alpha=0.4, label='17-83% Confidence')\n",
+    "plt.plot(time_axis, median, color='C0', lw=2, label='Emulator Median')\n",
+    "\n",
+    "# Plot True Value\n",
+    "# Since y_true_scenario is shape (Model, Time), we slice the specific model\n",
+    "if y_true_scenario.ndim == 2:\n",
+    "    true_curve = y_true_scenario[model_to_plot] \n",
+    "    label_str = f'True Physics Model {model_to_plot}'\n",
+    "else:\n",
+    "    # Fallback if the user passed a 1D array\n",
+    "    true_curve = y_true_scenario\n",
+    "    label_str = 'True Physics Model'\n",
+    "\n",
+    "plt.plot(time_axis, true_curve, 'k--', lw=2, label=label_str)\n",
+    "\n",
+    "plt.title(f\"Emulator vs Truth: Model {model_to_plot}, Scenario {test_idx}\\n(Regularized Polynomial Fit)\")\n",
+    "plt.xlabel(\"Time (years)\")\n",
+    "plt.ylabel(\"Sea Level Equivalent (m)\")\n",
+    "plt.legend()\n",
+    "plt.grid(True, alpha=0.3)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 159,
+   "id": "7269a632",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "76b2d198914a45d1ac67ce9097a7d7a8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%reload_ext autoreload\n",
+    "from profsea.emulator import Antarctica\n",
+    "ais = Antarctica(\"ais_params.nc\")\n",
+    "test_idx = 3\n",
+    "ais_preds = ais.predict(tas_train[test_idx], t_int_train[test_idx], display_progress=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 166,
+   "id": "80c9bd2d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running predictions for Test Scenario 3...\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "X_input = (tas_train[test_idx], t_int_train[test_idx])\n",
+    "y_true_scenario = sle_train[test_idx] \n",
+    "\n",
+    "\n",
+    "# Run Emulator\n",
+    "print(f\"Running predictions for Test Scenario {test_idx}...\")\n",
+    "# preds shape: (43 models, 1000 samples, Time)\n",
+    "preds = ais_preds.copy()\n",
+    "# constr_preds = predict_ais_me_poly(X_input, constr_general_params, constr_timescales, constr_mv_dists)\n",
+    "\n",
+    "# Plot all test data against prediction plume\n",
+    "fig = plt.figure(figsize=(12, 6), layout=\"constrained\")\n",
+    "p5 = np.percentile(preds, 5, axis=(0, 1))\n",
+    "p17 = np.percentile(preds, 17, axis=(0, 1))\n",
+    "median = np.median(preds, axis=(0, 1))\n",
+    "p83 = np.percentile(preds, 83, axis=(0, 1))\n",
+    "p95 = np.percentile(preds, 95, axis=(0, 1))\n",
+    "\n",
+    "true_median = np.percentile(y_true_scenario, 50, axis=0)\n",
+    "\n",
+    "time_axis = np.arange(len(median))\n",
+    "\n",
+    "ax = fig.add_subplot(111)\n",
+    "# Plot Emulator Uncertainty\n",
+    "ax.fill_between(time_axis, p5, p95, color='C0', alpha=0.2, label='5-95% Confidence')\n",
+    "ax.fill_between(time_axis, p17, p83, color='C0', alpha=0.4, label='17-83% Confidence')\n",
+    "\n",
+    "\n",
+    "ax.plot(np.tile(time_axis, (y_true_scenario.shape[0], 1)).T, y_true_scenario.T, '--', lw=1, color=\"black\", alpha=0.4, label=\"ISMIP6 models\")\n",
+    "ax.plot(time_axis, true_median, lw=2, color='black', label=\"ISMIP6 median\")\n",
+    "ax.plot(time_axis, median, color='C0', lw=3, label='Emulator Median')\n",
+    "\n",
+    "ax.set_title(f\"Emulator vs ISMIP6 \\nScenario {test_idx}\")\n",
+    "ax.set_xlabel(\"Time (years)\")\n",
+    "ax.set_ylabel(\"Sea Level Equivalent (m)\")\n",
+    "handles, labels = ax.get_legend_handles_labels()\n",
+    "unique_labels = set(labels)\n",
+    "handles = [handles[labels.index(label)] for label in unique_labels if label in labels]\n",
+    "ax.legend(handles, unique_labels, frameon=False, loc='upper left')\n",
+    "\n",
+    "ax.grid(True, alpha=0.3)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "af37b397",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 179,
+   "id": "2d9df888",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9e6be923cda84256b69e49d827a1c3ce",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running idealised predictions...\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# --- 1. Helper Functions (copied for self-containment) ---\n",
+    "\n",
+    "def f_poly_mixed(tas, t_int, params_gen, params_perturb):\n",
+    "    # params: [alpha, beta, gamma]\n",
+    "    p = params_gen + params_perturb\n",
+    "    return (p[:, 0, None] * tas) + (p[:, 2, None] * t_int)\n",
+    "\n",
+    "def impulse_response_fitter(f_inst_vals, tau, dt=1):\n",
+    "    n = len(f_inst_vals)\n",
+    "    tau = max(tau, 1e-3)\n",
+    "    decay_factors = np.exp(-np.arange(n) * dt / tau) * (dt / tau)\n",
+    "    response = np.zeros(n)\n",
+    "    for k in range(n):\n",
+    "        response[k] = np.sum(decay_factors[:k+1] * f_inst_vals[k::-1])\n",
+    "    return response\n",
+    "\n",
+    "def predict_ais_me_poly(X, general_params, general_taus, mv_dists):\n",
+    "    tas, t_int = X\n",
+    "    n_models = len(general_params)\n",
+    "    n_samples = mv_dists[0].shape[0]\n",
+    "    n_time = len(tas)\n",
+    "    all_preds = np.zeros((n_models, n_samples, n_time))\n",
+    "    \n",
+    "    for m in range(n_models):\n",
+    "        tau = general_taus[m]\n",
+    "        gen_p = general_params[m]\n",
+    "        perturb_p = mv_dists[m]\n",
+    "        \n",
+    "        f_vals = f_poly_mixed(tas[None, :], t_int[None, :], gen_p[None, :], perturb_p)\n",
+    "        \n",
+    "        for i in range(n_samples):\n",
+    "            all_preds[m, i, :] = impulse_response_fitter(f_vals[i], tau, dt=1)\n",
+    "            \n",
+    "    return all_preds\n",
+    "\n",
+    "# --- 2. Construct Idealised Forcing ---\n",
+    "\n",
+    "n_years = 1000\n",
+    "peak_temp = 7.0  # Degrees warming\n",
+    "ramp_len = 300    # Years to ramp up/down\n",
+    "\n",
+    "# Create time axis\n",
+    "time = np.arange(n_years)\n",
+    "tas_ideal = np.zeros(n_years)\n",
+    "\n",
+    "# Ramp Up (0 to 50 years)\n",
+    "tas_ideal[:ramp_len] = np.linspace(0, peak_temp, ramp_len)\n",
+    "\n",
+    "# Ramp Down (50 to 100 years)\n",
+    "tas_ideal[ramp_len:ramp_len*2] = np.linspace(peak_temp, -5, ramp_len)\n",
+    "tas_ideal[ramp_len*2:] = -5\n",
+    "\n",
+    "# Stabilization (100+ years) -> Remains at 0\n",
+    "# Note: In reality, physics might suggest T approaches equilibrium, but 0 is good for testing.\n",
+    "\n",
+    "# Integrate Temperature\n",
+    "# We assume dt=1 year\n",
+    "t_int_ideal = np.cumsum(tas_ideal)\n",
+    "\n",
+    "# --- 3. Run Prediction ---\n",
+    "\n",
+    "# Check if model variables exist (for standalone running)\n",
+    "if 'general_params' not in locals():\n",
+    "    print(\"Warning: Training data not found. Generating dummy model for demonstration.\")\n",
+    "    general_params = np.array([[0.1, 0.01, 0.05]])  # 1 Model\n",
+    "    general_timescales = np.array([15.0])           # Tau = 15 years\n",
+    "    mv_dists = [np.random.normal(0, 0.01, size=(100, 3))] # Small uncertainty\n",
+    "\n",
+    "print(\"Running idealised predictions...\")\n",
+    "preds = ais.predict(tas_ideal, t_int_ideal)\n",
+    "\n",
+    "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 8), sharex=True)\n",
+    "\n",
+    "# Plot Forcing\n",
+    "ax1.plot(time, tas_ideal, 'r-', lw=2, label='Temperature (TAS)')\n",
+    "ax1.set_ylabel(\"Global Temperature Change (°C)\", color='r')\n",
+    "ax1.tick_params(axis='y', labelcolor='r')\n",
+    "ax1.set_title(\"Idealised Ramp-Up Ramp-Down Experiment\")\n",
+    "ax1.grid(True, alpha=0.3)\n",
+    "\n",
+    "# Add Integrated T to secondary axis to show why melt continues\n",
+    "ax1b = ax1.twinx()\n",
+    "ax1b.plot(time, t_int_ideal, 'k:', alpha=0.5, label='Integrated T')\n",
+    "ax1b.set_ylabel(\"Integrated Temperature (°C·yr)\", color='k')\n",
+    "ax1b.legend(loc='upper left')\n",
+    "\n",
+    "# Plot Response (Aggregate of all models if multiple exist)\n",
+    "# Flatten all models/samples to get the grand ensemble stats\n",
+    "flat_preds = preds.reshape(-1, preds.shape[-1])\n",
+    "p5 = np.percentile(flat_preds, 17, axis=0)\n",
+    "p50 = np.median(flat_preds, axis=0)\n",
+    "p95 = np.percentile(flat_preds, 83, axis=0)\n",
+    "\n",
+    "ax2.fill_between(time, p5, p95, color='C0', alpha=0.3, label='66% Confidence Interval')\n",
+    "ax2.plot(time, p50, color='C0', lw=2, label='Ensemble Median')\n",
+    "\n",
+    "# Highlight the \"Peak Melt\" vs \"Peak Temp\" lag\n",
+    "peak_melt_idx = np.argmax(p50)\n",
+    "lag_years = time[peak_melt_idx] - ramp_len\n",
+    "ax2.axvline(x=ramp_len, color='r', linestyle='--', alpha=0.5, label='Peak Temp')\n",
+    "ax2.axvline(x=time[peak_melt_idx], color='C0', linestyle='--', alpha=0.5, label=f'Peak Rate/Level (Lag: {lag_years} yrs)')\n",
+    "\n",
+    "ax2.set_xlabel(\"Time (years)\")\n",
+    "ax2.set_ylabel(\"Sea Level Equivalent (m)\")\n",
+    "ax2.legend()\n",
+    "ax2.grid(True, alpha=0.3)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "cmip7",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/profsea/notebooks/compare_ais.ipynb b/profsea/notebooks/compare_ais.ipynb
new file mode 100644
index 0000000..b516fdf
--- /dev/null
+++ b/profsea/notebooks/compare_ais.ipynb
@@ -0,0 +1,615 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "83d64d2f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "\n",
+    "import numpy as np \n",
+    "import matplotlib.pyplot as plt\n",
+    "import xarray as xr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "1ce6ed82",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "old_ais_path = \"../probabilistic_projections/global/gmslr_projections.nc\"\n",
+    "new_ais_path = \"../probabilistic_projections/global/gmslr_projections_NEW_AIS.nc\"\n",
+    "old_ds = xr.open_dataset(old_ais_path)\n",
+    "new_ds = xr.open_dataset(new_ais_path)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6dcb4835",
+   "metadata": {},
+   "source": [
+    "Timeseries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "ec09f575",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
<xarray.Dataset> Size: 427kB\n",
+       "Dimensions:     (scenario: 6, member: 5, year: 295)\n",
+       "Coordinates:\n",
+       "  * scenario    (scenario) <U11 264B 'ssp119' 'ssp126' ... 'ssp585'\n",
+       "  * member      (member) int64 40B 0 1 2 3 4\n",
+       "  * year        (year) int64 2kB 2006 2007 2008 2009 ... 2297 2298 2299 2300\n",
+       "Data variables:\n",
+       "    gmslr       (scenario, member, year) float64 71kB ...\n",
+       "    expansion   (scenario, member, year) float64 71kB ...\n",
+       "    antarctica  (scenario, member, year) float64 71kB ...\n",
+       "    greenland   (scenario, member, year) float64 71kB ...\n",
+       "    glaciers    (scenario, member, year) float64 71kB ...\n",
+       "    landwater   (scenario, member, year) float64 71kB ...\n",
+       "Attributes:\n",
+       "    title:    ProFSea GMSLR Projections\n",
+       "    source:   FAIR v2.2 + ProFSea Emulator
"
+      ],
+      "text/plain": [
+       " Size: 427kB\n",
+       "Dimensions:     (scenario: 6, member: 5, year: 295)\n",
+       "Coordinates:\n",
+       "  * scenario    (scenario) "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(16, 7), layout=\"constrained\")\n",
+    "\n",
+    "for scen_idx, scenario in enumerate(old_ds.scenario):\n",
+    "    ax = fig.add_subplot(2, 3, scen_idx+1)\n",
+    "    # Median\n",
+    "    ax.set_title(f\"{scenario.values}\")\n",
+    "    ax.plot(old_ds.year, old_ds.antarctica[scen_idx, 2], color=\"royalblue\", lw=2, label=\"AR5 emulator\")\n",
+    "    ax.plot(new_ds.year, new_ds.antarctica[scen_idx, 2], color=\"goldenrod\", lw=2, label=\"ISMIP6 emulator\")\n",
+    "    ax.fill_between(old_ds.year, old_ds.antarctica[scen_idx, 1], old_ds.antarctica[scen_idx, 3], color=\"royalblue\", alpha=0.2)\n",
+    "    ax.fill_between(new_ds.year, new_ds.antarctica[scen_idx, 1], new_ds.antarctica[scen_idx, 3], color=\"goldenrod\", alpha=0.2)\n",
+    "    ax.set_ylabel(\"SLE (m)\")\n",
+    "    ax.set_xlabel(\"Year\")\n",
+    "    if scen_idx == 0:\n",
+    "        ax.legend(loc=\"upper left\")\n",
+    "\n",
+    "\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ff3aa666",
+   "metadata": {},
+   "source": [
+    "End-of-century box and whisker"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "9c58124d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "medianprops = dict(linestyle='-', linewidth=1.5, color='k')\n",
+    "boxprops = dict(linestyle='-', linewidth=0.5)\n",
+    "\n",
+    "fig = plt.figure(figsize=(10, 5), layout=\"constrained\")\n",
+    "\n",
+    "# Antarctica\n",
+    "ax = fig.add_subplot(121)\n",
+    "old_eoc = []\n",
+    "new_eoc = []\n",
+    "for scen_idx, scenario in enumerate(old_ds.scenario):\n",
+    "    old_eoc.append(old_ds.isel(scenario=scen_idx, year=-1).antarctica.values)\n",
+    "    new_eoc.append(new_ds.isel(scenario=scen_idx, year=-1).antarctica.values)\n",
+    "\n",
+    "indices = np.arange(1, len(old_ds.scenario)+1)\n",
+    "width = 0.35\n",
+    "\n",
+    "old_bplot = ax.boxplot(old_eoc, positions=indices - 0.2, widths=width, patch_artist=True, medianprops=medianprops, boxprops=boxprops, label=\"AR5 emulator\")\n",
+    "new_bplot = ax.boxplot(new_eoc, positions=indices + 0.2, widths=width, patch_artist=True, medianprops=medianprops, boxprops=boxprops, label=\"ISMIP6 emulator\")\n",
+    "\n",
+    "for patch, color in zip(old_bplot['boxes'], ['royalblue' for i in range(len(old_eoc))]):\n",
+    "    patch.set_facecolor(color)\n",
+    "for patch, color in zip(new_bplot['boxes'], ['goldenrod' for i in range(len(old_eoc))]):\n",
+    "    patch.set_facecolor(color)\n",
+    "\n",
+    "ax.set_title(\"Year 2300 comparison - Antarctica\")\n",
+    "ax.set_ylabel(\"SLE (m)\")\n",
+    "ax.set_xlabel(\"Scenario\")\n",
+    "ax.set_ylim([-0.5, 5])\n",
+    "ax.set_xticks(indices)\n",
+    "ax.set_xticklabels(old_ds.scenario.values, rotation=45, ha='right')\n",
+    "ax.grid()\n",
+    "ax.legend(loc=\"upper left\")\n",
+    "\n",
+    "# Global\n",
+    "ax = fig.add_subplot(122)\n",
+    "old_eoc = []\n",
+    "new_eoc = []\n",
+    "for scen_idx, scenario in enumerate(old_ds.scenario):\n",
+    "    old_eoc.append(old_ds.isel(scenario=scen_idx, year=-1).gmslr.values)\n",
+    "    new_eoc.append(new_ds.isel(scenario=scen_idx, year=-1).gmslr.values)\n",
+    "\n",
+    "indices = np.arange(1, len(old_ds.scenario)+1)\n",
+    "width = 0.35\n",
+    "\n",
+    "old_bplot = ax.boxplot(old_eoc, positions=indices - 0.2, widths=width, patch_artist=True, medianprops=medianprops, boxprops=boxprops, label=\"AR5 emulator\")\n",
+    "new_bplot = ax.boxplot(new_eoc, positions=indices + 0.2, widths=width, patch_artist=True, medianprops=medianprops, boxprops=boxprops, label=\"ISMIP6 emulator\")\n",
+    "\n",
+    "for patch, color in zip(old_bplot['boxes'], ['royalblue' for i in range(len(old_eoc))]):\n",
+    "    patch.set_facecolor(color)\n",
+    "for patch, color in zip(new_bplot['boxes'], ['goldenrod' for i in range(len(old_eoc))]):\n",
+    "    patch.set_facecolor(color)\n",
+    "\n",
+    "ax.set_title(\"Year 2300 comparison - Global\")\n",
+    "ax.set_ylabel(\"SLE (m)\")\n",
+    "ax.set_xlabel(\"Scenario\")\n",
+    "ax.set_ylim([-0.5, 5.5])\n",
+    "ax.set_xticks(indices)\n",
+    "ax.set_xticklabels(old_ds.scenario.values, rotation=45, ha='right')\n",
+    "ax.grid()\n",
+    "plt.show()\n",
+    "ax.grid()\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "cmip7",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/profsea/notebooks/evaluate_gmslr.ipynb b/profsea/notebooks/evaluate_gmslr.ipynb
new file mode 100644
index 0000000..b9f2358
--- /dev/null
+++ b/profsea/notebooks/evaluate_gmslr.ipynb
@@ -0,0 +1,343 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "8875184d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import gc\n",
+    "\n",
+    "import matplotlib.pyplot as plt \n",
+    "import numpy as np\n",
+    "from tqdm import tqdm\n",
+    "import xarray as xr \n",
+    "\n",
+    "from profsea.emulator import GMSLREmulator"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "00b62b34",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def load_mc(scenario: str, var_name: str) -> xr.DataArray:\n",
+    "    ds = xr.load_dataset(f\"/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/data/long_monte_carlo/{scenario}_{var_name}.nc\")\n",
+    "    return ds.global_average_sea_level_change.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "10f68c5b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scenarios = ['rcp26', 'rcp45', 'rcp85']\n",
+    "t_mid_input = {}\n",
+    "t_upper_input = {}\n",
+    "\n",
+    "# Expansion\n",
+    "exp_mid_input = {}\n",
+    "exp_upper_input = {}\n",
+    "exp_lower_input = {}\n",
+    "\n",
+    "# GMSLR\n",
+    "ar5_gmslr_mid = {}\n",
+    "ar5_gmslr_upper = {}\n",
+    "ar5_gmslr_lower = {}\n",
+    "\n",
+    "# Antarctica\n",
+    "ar5_ant_mid = {}\n",
+    "ar5_ant_upper = {}\n",
+    "ar5_ant_lower = {}\n",
+    "\n",
+    "# Greenland\n",
+    "ar5_green_mid = {}\n",
+    "ar5_green_upper = {}\n",
+    "ar5_green_lower = {}\n",
+    "\n",
+    "# Glaciers\n",
+    "ar5_glac_mid = {}\n",
+    "ar5_glac_upper = {}\n",
+    "ar5_glac_lower = {}\n",
+    "\n",
+    "# Landwater\n",
+    "ar5_land_mid = {}\n",
+    "ar5_land_upper = {}\n",
+    "ar5_land_lower = {}\n",
+    "\n",
+    "for scenario in scenarios:\n",
+    "    # Temperature\n",
+    "    t_mid_input[scenario] = load_mc(scenario, \"temperaturemid\")\n",
+    "    t_upper_input[scenario] = load_mc(scenario, \"temperatureupper\")\n",
+    "\n",
+    "    # Thermal expansion\n",
+    "    exp_mid_input[scenario] = load_mc(scenario, \"expansionmid\")\n",
+    "    exp_upper_input[scenario] = load_mc(scenario, \"expansionupper\")\n",
+    "    exp_lower_input[scenario] = load_mc(scenario, \"expansionlower\")\n",
+    "\n",
+    "    # GMSLR\n",
+    "    ar5_gmslr_mid[scenario] = load_mc(scenario, \"summid\")\n",
+    "    ar5_gmslr_upper[scenario] = load_mc(scenario, \"sumupper\")\n",
+    "    ar5_gmslr_lower[scenario] = load_mc(scenario, \"sumlower\")\n",
+    "\n",
+    "    # Antarctica\n",
+    "    ar5_ant_mid[scenario] = load_mc(scenario, \"antnetmid\")\n",
+    "    ar5_ant_upper[scenario] = load_mc(scenario, \"antnetupper\")\n",
+    "    ar5_ant_lower[scenario] = load_mc(scenario, \"antnetlower\")\n",
+    "    \n",
+    "    # Greenland\n",
+    "    ar5_green_mid[scenario] = load_mc(scenario, \"greennetmid\")\n",
+    "    ar5_green_upper[scenario] = load_mc(scenario, \"greennetupper\")\n",
+    "    ar5_green_lower[scenario] = load_mc(scenario, \"greennetlower\")\n",
+    "\n",
+    "    # Glaciers\n",
+    "    ar5_glac_mid[scenario] = load_mc(scenario, \"glaciermid\")\n",
+    "    ar5_glac_upper[scenario] = load_mc(scenario, \"glacierupper\")\n",
+    "    ar5_glac_lower[scenario] = load_mc(scenario, \"glacierlower\")\n",
+    "\n",
+    "    # Landwater\n",
+    "    ar5_land_mid[scenario] = load_mc(scenario, \"landwatermid\")\n",
+    "    ar5_land_upper[scenario] = load_mc(scenario, \"landwaterupper\")\n",
+    "    ar5_land_lower[scenario] = load_mc(scenario, \"landwaterlower\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "f50b0202",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 3/3 [04:44<00:00, 94.88s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "gmslr_output = {}\n",
+    "exp_output = {}\n",
+    "ant_output = {}\n",
+    "green_output = {}\n",
+    "glac_output = {}\n",
+    "land_output = {}\n",
+    "ar6_green_output = {}\n",
+    "ar6_land_output = {}\n",
+    "for scenario in tqdm(scenarios):\n",
+    "    emulator = GMSLREmulator(\n",
+    "        t_mid_input[scenario],\n",
+    "        exp_mid_input[scenario],\n",
+    "        scenario,\n",
+    "        2300,  # IF YEAR BEYOND 2100 AUTO SET PALMER METHOD TO TRUE\n",
+    "        input_ensemble=False,\n",
+    "        glaciermip=None,\n",
+    "        T_percentile_95=t_upper_input[scenario],\n",
+    "        OHC_percentile_95=exp_upper_input[scenario],\n",
+    "        output_percentiles=np.linspace(0, 100, 1001),\n",
+    "    )\n",
+    "    emulator.project()\n",
+    "    gmslr_output[scenario] = emulator.gmslr\n",
+    "    exp_output[scenario] = emulator.expansion\n",
+    "    ant_output[scenario] = emulator.antnet\n",
+    "    green_output[scenario] = emulator.greenland\n",
+    "    glac_output[scenario] = emulator.glacier\n",
+    "    land_output[scenario] = emulator.landwater\n",
+    "    ar6_green_output[scenario] = emulator.greenland_ar6\n",
+    "    ar6_land_output[scenario] = emulator.landwater_ar6\n",
+    "    gc.collect()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "fdcec326",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# GMSLR\n",
+    "gmslr_mid = {}\n",
+    "gmslr_upper = {}\n",
+    "gmslr_lower = {}\n",
+    "\n",
+    "# Expansion\n",
+    "exp_mid = {}\n",
+    "exp_upper = {}\n",
+    "exp_lower = {}\n",
+    "\n",
+    "# Antarctica\n",
+    "ant_mid = {}\n",
+    "ant_upper = {}\n",
+    "ant_lower = {}\n",
+    "\n",
+    "# Greenland\n",
+    "green_mid = {}\n",
+    "green_upper = {}\n",
+    "green_lower = {}\n",
+    "\n",
+    "# Glaciers\n",
+    "glac_mid = {}\n",
+    "glac_upper = {}\n",
+    "glac_lower = {}\n",
+    "\n",
+    "# Landwater\n",
+    "land_mid = {}\n",
+    "land_upper = {}\n",
+    "land_lower = {}\n",
+    "\n",
+    "# Greenland AR6\n",
+    "ar6_green_mid = {}\n",
+    "ar6_green_upper = {}\n",
+    "ar6_green_lower = {}\n",
+    "\n",
+    "# Landwater AR6\n",
+    "ar6_land_mid = {}\n",
+    "ar6_land_upper = {}\n",
+    "ar6_land_lower = {}\n",
+    "for scenario in scenarios:\n",
+    "    # GMSLR\n",
+    "    gmslr_mid[scenario] = np.percentile(gmslr_output[scenario].T, 50, axis=1)\n",
+    "    gmslr_upper[scenario] = np.percentile(gmslr_output[scenario].T, 95, axis=1)\n",
+    "    gmslr_lower[scenario] = np.percentile(gmslr_output[scenario].T, 5, axis=1)\n",
+    "\n",
+    "    # Expansion\n",
+    "    exp_mid[scenario] = np.percentile(exp_output[scenario].T, 50, axis=1)\n",
+    "    exp_upper[scenario] = np.percentile(exp_output[scenario].T, 95, axis=1)\n",
+    "    exp_lower[scenario] = np.percentile(exp_output[scenario].T, 5, axis=1)\n",
+    "\n",
+    "    # Antarctica\n",
+    "    ant_mid[scenario] = np.percentile(ant_output[scenario].T, 50, axis=1)\n",
+    "    ant_upper[scenario] = np.percentile(ant_output[scenario].T, 95, axis=1)\n",
+    "    ant_lower[scenario] = np.percentile(ant_output[scenario].T, 5, axis=1)\n",
+    "    \n",
+    "    # Greenland\n",
+    "    green_mid[scenario] =  np.percentile(green_output[scenario].T, 50, axis=1)\n",
+    "    green_upper[scenario] = np.percentile(green_output[scenario].T, 95, axis=1)\n",
+    "    green_lower[scenario] = np.percentile(green_output[scenario].T, 5, axis=1)\n",
+    "\n",
+    "    # Glaciers\n",
+    "    glac_mid[scenario] = np.percentile(glac_output[scenario].T, 50, axis=1)\n",
+    "    glac_upper[scenario] = np.percentile(glac_output[scenario].T, 95, axis=1)\n",
+    "    glac_lower[scenario] = np.percentile(glac_output[scenario].T, 5, axis=1)\n",
+    "\n",
+    "    # Landwater\n",
+    "    land_mid[scenario] = np.percentile(land_output[scenario].T, 50, axis=1)\n",
+    "    land_upper[scenario] = np.percentile(land_output[scenario].T, 95, axis=1)\n",
+    "    land_lower[scenario] = np.percentile(land_output[scenario].T, 5, axis=1)\n",
+    "\n",
+    "    # Greenland AR6\n",
+    "    ar6_green_mid[scenario] = np.percentile(ar6_green_output[scenario].T, 50, axis=1)\n",
+    "    ar6_green_upper[scenario] = np.percentile(ar6_green_output[scenario].T, 95, axis=1)\n",
+    "    ar6_green_lower[scenario] = np.percentile(ar6_green_output[scenario].T, 5, axis=1)\n",
+    "\n",
+    "    ar6_land_mid[scenario] = np.percentile(ar6_land_output[scenario].T, 50, axis=1)\n",
+    "    ar6_land_upper[scenario] = np.percentile(ar6_land_output[scenario].T, 95, axis=1)\n",
+    "    ar6_land_lower[scenario] = np.percentile(ar6_land_output[scenario].T, 5, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "778341fb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sim_comp_dict = {\n",
+    "    \"gmslr\": [gmslr_mid, gmslr_upper, gmslr_lower],\n",
+    "    \"expansion\": [exp_mid, exp_upper, exp_lower],\n",
+    "    \"antarctica\": [ant_mid, ant_upper, ant_lower],\n",
+    "    \"greenland\": [green_mid, green_upper, green_lower],\n",
+    "    \"glaciers\": [glac_mid, glac_upper, glac_lower],\n",
+    "    \"landwater\": [land_mid, land_upper, land_lower],\n",
+    "    \"ar6_greenland\": [ar6_green_mid, ar6_green_upper, ar6_green_lower],\n",
+    "    \"ar6_landwater\": [ar6_land_mid, ar6_land_upper, ar6_land_lower]\n",
+    "}\n",
+    "ar5_comp_dict = {\n",
+    "    \"gmslr\": [ar5_gmslr_mid, ar5_gmslr_upper, ar5_gmslr_lower],\n",
+    "    \"expansion\": [exp_mid_input, exp_upper_input, exp_lower_input],\n",
+    "    \"antarctica\": [ar5_ant_mid, ar5_ant_upper, ar5_ant_lower],\n",
+    "    \"greenland\": [ar5_green_mid, ar5_green_upper, ar5_green_lower],\n",
+    "    \"glaciers\": [ar5_glac_mid, ar5_glac_upper, ar5_glac_lower],\n",
+    "    \"landwater\": [ar5_land_mid, ar5_land_upper, ar5_land_lower]\n",
+    "}\n",
+    "component = \"landwater\"\n",
+    "\n",
+    "fig = plt.figure(figsize=(14, 4), layout='constrained')\n",
+    "\n",
+    "for i, scenario in enumerate(scenarios):\n",
+    "    ax = fig.add_subplot(1, 3, i + 1)\n",
+    "\n",
+    "    # Simulation\n",
+    "    ax.plot(sim_comp_dict[component][0][scenario], color='goldenrod', label='JG-ProFSea')\n",
+    "    ax.plot(sim_comp_dict[component][1][scenario], color='goldenrod', ls='--')\n",
+    "    ax.plot(sim_comp_dict[component][2][scenario], color='goldenrod', ls='--')\n",
+    "\n",
+    "    if component == \"greenland\":\n",
+    "        # AR6 simulation\n",
+    "        ax.plot(sim_comp_dict[\"ar6_greenland\"][0][scenario], color='skyblue', label=\"AR6-ProFSea\")\n",
+    "        ax.plot(sim_comp_dict[\"ar6_greenland\"][1][scenario], color='skyblue', ls=\"--\")\n",
+    "        ax.plot(sim_comp_dict[\"ar6_greenland\"][2][scenario], color='skyblue', ls=\"--\")\n",
+    "\n",
+    "    elif component == \"landwater\":\n",
+    "        # AR6 sim\n",
+    "        ax.plot(sim_comp_dict[\"ar6_landwater\"][0][scenario], color='skyblue', label=\"AR6-ProFSea\")\n",
+    "        ax.plot(sim_comp_dict[\"ar6_landwater\"][1][scenario], color='skyblue', ls=\"--\")\n",
+    "        ax.plot(sim_comp_dict[\"ar6_landwater\"][2][scenario], color='skyblue', ls=\"--\")\n",
+    "\n",
+    "    # AR5\n",
+    "    ax.plot(ar5_comp_dict[component][0][scenario], color='navy', label='AR5')\n",
+    "    ax.plot(ar5_comp_dict[component][1][scenario], color='navy', ls='--')\n",
+    "    ax.plot(ar5_comp_dict[component][2][scenario], color='navy', ls='--')\n",
+    "    ax.plot()\n",
+    "    # ax.set_ylim([-0.1, 7.0])\n",
+    "    ax.set_xlabel(\"Simulation year\")\n",
+    "    if component == \"gmslr\":\n",
+    "        ax.set_ylabel(f\"{component.upper()} (m)\")\n",
+    "    else:\n",
+    "        ax.set_ylabel(f\"{component[0].upper() + component[1:]} (m)\")\n",
+    "    ax.text(0.54, 0.92, scenario, transform=ax.transAxes, weight='bold')\n",
+    "    ax.legend(loc='upper left')\n",
+    "fig.savefig(f\"eval_plots/rcp_eval_ar5_ar6_{component}.png\", dpi=300)\n",
+    "plt.show()\n",
+    "plt.close()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "cmip7",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/profsea/notebooks/worked_example.ipynb b/profsea/notebooks/worked_example.ipynb
new file mode 100644
index 0000000..45c04da
--- /dev/null
+++ b/profsea/notebooks/worked_example.ipynb
@@ -0,0 +1,177 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "46a1a49f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from profsea.emulator import GMSLREmulator"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "52cdd115",
+   "metadata": {},
+   "source": [
+    "#### Introduction\n",
+    "The aim of this notebook is to break down the steps required to run the emulator version of ProFSea.\n",
+    "\n",
+    "All of this can't run in Jupyter Notebook but the steps should be clear enough to follow along!\n",
+    "\n",
+    "> If you want to use the CMIP6 patterns sterodynamic patterns instead of CMIP5, you need to run [this recipe](https://github.com/ESMValGroup/ESMValTool/blob/steric_patterns/esmvaltool/recipes/recipe_steric_patterns.yml) in ESMValTool. I recommend running with as much memory and processors as possible. \n",
+    "\n",
+    "> However, if you prefer not to go via ESMValTool feel free to reach out at gregory.munday\\@dtc.ox.ac.uk and I can just send them over (they're not large in terms of data size).\n",
+    "---\n",
+    "All other data required for the tool can be [downloaded here](https://catalogue.ceda.ac.uk/uuid/47c849b907414f3ab5e56ba9cf83e779/).\n",
+    "\n",
+    "I like keeping all my data in a `data/` folder next to the `profsea/` folder, which looks like this:\n",
+    "\n",
+    "  `data/` \\\n",
+    "   `cmip5/` \\\n",
+    "   `cmip6/` \\\n",
+    "   `gia_estimates/` \\\n",
+    "   `grd_fingerprints/` \\\n",
+    "   `monte_carlo_timeseries/` \\\n",
+    "   `uk_cmip_slope_coefficients/`\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ef69532c",
+   "metadata": {},
+   "source": [
+    "#### Setting up the environment\n",
+    "The next step is to set up the conda environment. You should use the `ProFSea-emu-env.yml` environment file, building it with \n",
+    "\n",
+    "```bash\n",
+    "conda env create -f ProFSea-emu-env.yml\n",
+    "```\n",
+    "\n",
+    "and then activating with `conda activate profsea-emu`.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "One final step here is to install ProFSea as an editable package, using\n",
+    "\n",
+    "```bash \n",
+    "pip install -e . \n",
+    "```\n",
+    "\n",
+    "while inside the highest level directory."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5616586f",
+   "metadata": {},
+   "source": [
+    "#### Setup FaIR output\n",
+    "Run FaIR for the scenarios you want to simulate and save out the `global surface air temperature` and `ocean heat content`. You'll also need to calculate the 2015-2100 cumulative CO2 emissions and save it to a `.json` file. The CMIP6 cumulative emissions are included in the repo: \n",
+    "\n",
+    "```profsea/emulator/aux_data/cumulative_cmip6_emissions.json```\n",
+    "\n",
+    "Here's some sample code to calculate it from scratch in FaIR. Perhaps this could be included in this branch at some point.\n",
+    "\n",
+    "```python\n",
+    "# Output cumulative emissions from 2015 to 2100\n",
+    "cum_emissions_dict = {}\n",
+    "for scenario in hist_gcam_ext.scenario.unique():  # loop over your scenarios\n",
+    "        emissions = hist_gcam_ext.loc[\n",
+    "            (hist_gcam_ext['scenario'] == scenario) & (hist_gcam_ext['variable'] == 'CO2 FFI'), \n",
+    "            '2015':'2100'\n",
+    "        ].to_numpy()[0]  # select anthro emissions\n",
+    "        cum_emissions = np.cumsum(emissions)\n",
+    "        cum_emissions = cum_emissions / 1000  # Convert to PgC\n",
+    "        print(f'Total cumulative carbon emissions from 2015 to 2100 for {scenario}: ', cum_emissions[-1])\n",
+    "        cum_emissions_dict[scenario] = cum_emissions[-1]\n",
+    "        # Save to json\n",
+    "        with open('ngfs_data/cumulative_scenario_emissions.json', 'w') as f:\n",
+    "            json.dump(cum_emissions_dict, f)\n",
+    "```\n",
+    "\n",
+    "You need to do this because the current Antarctic Ice-Sheet dynamics component uses the relationship between cumulative emissions and sea-level contribution to calculate the lower and upper bounds of the projection. This was implemented to remove scenario-dependence from the GMSLR emulator, and should be redundant when the component is updated.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6cef2ba4",
+   "metadata": {},
+   "source": [
+    "#### Running global → local ProFSea projections\n",
+    "Point your paths in the `user-settings-emu.yml` file to the right places, fill in your scenario names and you should be ready to go!\n",
+    "\n",
+    "Run: \n",
+    "\n",
+    "```bash\n",
+    "python spatial_projections.py\n",
+    "```\n",
+    "\n",
+    "The global projections will be saved to `data/runs/gmslr_output/`, while the spatialised projections should be saved to `data/runs//`\n",
+    "\n",
+    "The way this is accomplished (so that it runs on a laptop) is by taking and saving 101 percentiles of the GMSLR module output for each component. This can be updated via the `output_percentiles` keyword in the `GMSLR` class, currently line 458 in `spatial_projections.py`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "84c2ef51",
+   "metadata": {},
+   "source": [
+    "You can also run the GMSLR module by itself:\n",
+    "\n",
+    "```python\n",
+    "from profsea.emulator import GMSLREmulator\n",
+    "\n",
+    "emulator = GMSLREmulator(\n",
+    "    T_change,  # your T_change anom from FaIR\n",
+    "    OHC_change,  # your OHC_change anom from FaIR\n",
+    "    'low-overshoot',  # your scenario name (str)\n",
+    "    2300,  #  projection end year\n",
+    "    palmer_method=palmer_method,  # recommended for runs beyond 2100\n",
+    "    input_ensemble=True,  # must be true if providing a complete FaIR ensemble\n",
+    "    output_percentiles=np.arange(101),  # list/array of percentiles to sample\n",
+    "    cum_emissions_total=1450)  # int, cumulative emissions in scenario from 2015-2100\n",
+    "\n",
+    "#  run the projections (auto-parallel)\n",
+    "emulator.project()\n",
+    "\n",
+    "# Save to desired folder with the name of the scenario\n",
+    "emulator.save_components(\n",
+    "    os.path.join(settings[\"baseoutdir\"], 'gmslr_output'),  # output dir\n",
+    "    scenario)  # scenario name\n",
+    "\n",
+    "# Lists the available components\n",
+    "emulator.list_components()\n",
+    "\n",
+    "# You can also access the data from each component directly:\n",
+    "emulator.greenland  # returns greenland projection ensemble\n",
+    "\n",
+    "```"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "cmip7",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 2c0922979dbbc402bde91dcf8d0a37d7817b63b7 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Sun, 7 Dec 2025 15:07:06 +0000
Subject: [PATCH 054/276] Updated code

---
 profsea/emulator/__init__.py            |  29 +-
 profsea/emulator/antarctica.py          |  40 +--
 profsea/emulator/aux_data/ais_params.nc | Bin 22574 -> 0 bytes
 profsea/fair_prof_prob.py               |   4 +-
 profsea/notebooks/ais_fitting.ipynb     |  38 +-
 profsea/notebooks/compare_ais.ipynb     | 444 +-----------------------
 profsea/worked_example.ipynb            | 177 ----------
 7 files changed, 81 insertions(+), 651 deletions(-)
 delete mode 100644 profsea/emulator/aux_data/ais_params.nc
 delete mode 100644 profsea/worked_example.ipynb

diff --git a/profsea/emulator/__init__.py b/profsea/emulator/__init__.py
index 29db08d..0b669b1 100644
--- a/profsea/emulator/__init__.py
+++ b/profsea/emulator/__init__.py
@@ -6,11 +6,12 @@
 # Calculate Monte Carlo projections of GMSLR using methods 
 # from Jonathon Gregory and AR5. Staying close to JG's original
 # code where possible.
+import atexit
 import os
 import concurrent
 from collections.abc import Sequence
 import functools
-import atexit
+import math
 from pathlib import Path
 
 import numpy as np
@@ -283,7 +284,8 @@ def project(self) -> None:
         else:
             self.run_serial_projections(T_int_med, T_int_ens, T_ens, fraction)
         
-        self.antnet = self.antsmb + self.antdyn
+        # self.antnet = self.antsmb + self.antdyn
+        self.antnet = self.new_ant
         self.gmslr = self.glacier + self.greenland_ar6 + self.antnet + self.landwater + self.expansion
 
         rng = np.random.default_rng()
@@ -335,8 +337,29 @@ def run_serial_projections(
         # project_landwater corresponds to 'landwater_ar6' key in the parallel map
         self.landwater_ar6 = self.project_landwater()
 
+        wa_path = Path(__file__).parent / "aux_data" / "wa_params_fastslow.nc"
+        ea_path = Path(__file__).parent / "aux_data" / "ea_params_fastslow.nc"
+        pen_path = Path(__file__).parent / "aux_data" / "pen_params_fastslow.nc"
+        is_samples = math.ceil(self.nm / 14)  # 43 IS models
+        for T, T_int in zip(T_ens, T_int_ens):
+            wa = Antarctica(wa_path, samples=is_samples)
+            ea = Antarctica(ea_path, samples=is_samples)
+            pen = Antarctica(pen_path, samples=is_samples)
+            self.wa = wa.predict(T, T_int, display_progress=False)  # shape (ismodel, n_samples, time) 
+            self.ea = ea.predict(T, T_int, display_progress=False)
+            self.pen = pen.predict(T, T_int, display_progress=False)
+            self.new_ant = self.wa + self.ea + self.pen
+            self.new_ant = np.reshape(self.new_ant,
+                (self.new_ant.shape[0] * self.new_ant.shape[1], 
+                self.new_ant.shape[2]))
+
+        # Resample to get exactly self.nm members
+        rng = np.random.default_rng()
+        rand_idx = rng.integers(low=0, high=self.nm, size=self.nm)
+        self.new_ant = self.new_ant[rand_idx, :]
+
         self.greenland_ar5 = self.greendyn + self.greensmb
-            
+    
     def run_parallel_projections(
             self, T_int_med: np.ndarray, T_int_ens: np.ndarray, 
             T_ens: np.ndarray, fraction: np.ndarray) -> None:
diff --git a/profsea/emulator/antarctica.py b/profsea/emulator/antarctica.py
index 5ca3bf9..c6a9151 100644
--- a/profsea/emulator/antarctica.py
+++ b/profsea/emulator/antarctica.py
@@ -9,11 +9,11 @@ class Antarctica:
     def __init__(
             self, 
             params_path: Path, 
-            n_samples: int=1000) -> None:
+            samples: int=1000) -> None:
         """
         """
         self.param_ds = xr.load_dataset(params_path)
-        self.n_samples = n_samples
+        self.n_samples = samples
 
         self.n_models = self.param_ds.coords["model"].shape[0]
         n_params = self.param_ds.coords["param"].shape[0]
@@ -35,24 +35,21 @@ def _calc_mv_dists(self, n_params: int):
             mv_dists.append(samples)
         return np.array(mv_dists)
 
-    def _inst_function(self, tas, t_int, params_gen, params_resid):
+    def _impulse_response_term(
+            self, 
+            tas: np.ndarray, 
+            tau: float, 
+            params: np.ndarray):
         """
         """
-        # params: [a, b]
-        p = params_gen + params_resid
-        return (p[:, 0, None] * tas) + (p[:, 1, None] * t_int)
+        n_time = tas.shape[-1]
+        a_lin = params[:, 0]
 
-    def _impulse_response(self, f_inst_vals, tau):
-        """
-        """
-        n_time = f_inst_vals.shape[-1]
-        tau = max(tau, 1e-3)  # avoid zero division if tau is v small
         decay_factors = np.exp(-np.arange(n_time) / tau) * (1 / tau)
+        t_conv = fftconvolve(tas, decay_factors, mode='full')[:n_time]
         
-        kernel = decay_factors[None, :]
-        response = fftconvolve(f_inst_vals, kernel, mode='full', axes=-1)  # shape (n_samples, 2*n_time - 1)
-        return response[:, :n_time]
-
+        term_slow = (a_lin[:, None] * t_conv[None, :])
+        return term_slow
 
     def predict(self, tas: np.ndarray, tas_int: np.ndarray, display_progress=True):
         """
@@ -67,12 +64,13 @@ def predict(self, tas: np.ndarray, tas_int: np.ndarray, display_progress=True):
                 description="Projecting AIS response... ", 
                 disable=not display_progress):
             tau = self.param_ds.tau[model].values
-            gen_p = self.param_ds.general_params[model].values  # [alpha, beta, gamma]
+            general_p = self.param_ds.general_params[model].values  # [alpha, beta, gamma]
             resid_p = self.mv_dists[model]  # [n_samples, n_params]
-            
-            # Calculate forcing for all samples at once
-            f_vals = self._inst_function(tas[None, :], tas_int[None, :], gen_p[None, :], resid_p)
-            
+
+            total_params = general_p + resid_p
+            term_slow = self._impulse_response_term(tas, tau, total_params)
+            term_fast = total_params[:, 1][:, None] * tas_int[None, :]
+
             # Convolve
-            all_preds[model, :, :] = self._impulse_response(f_vals, tau)
+            all_preds[model, :, :] = term_fast + term_slow
         return all_preds
diff --git a/profsea/emulator/aux_data/ais_params.nc b/profsea/emulator/aux_data/ais_params.nc
deleted file mode 100644
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diff --git a/profsea/fair_prof_prob.py b/profsea/fair_prof_prob.py
index e346e73..7c874d8 100644
--- a/profsea/fair_prof_prob.py
+++ b/profsea/fair_prof_prob.py
@@ -353,7 +353,7 @@ def main(args):
         sampled_components[scenario] = process_global_ensemble(
             components[scenario], percentiles, scenario)
 
-    save_to_netcdf(sampled_components, "probabilistic_projections/global/gmslr_projections_MAGICC_forcing.nc")
+    save_to_netcdf(sampled_components, "probabilistic_projections/global/gmslr_projections_NEW_AIS_FASTSLOW.nc")
     
     fig = plt.figure(figsize=(16, 8), layout="constrained")
     ax = fig.add_subplot(231)
@@ -376,7 +376,7 @@ def main(args):
     sampled_tas = np.asarray(sampled_tas) # shape (nens, time, nscen)
     sampled_ohc = np.asarray(sampled_ohc)
 
-    plot_samples(sampled_tas[:, :, 0], sampled_ohc[:, :, 0])
+    plot_samples(sampled_tas[:, :, -1], sampled_ohc[:, :, -1])
 
 
 if __name__ == "__main__":
diff --git a/profsea/notebooks/ais_fitting.ipynb b/profsea/notebooks/ais_fitting.ipynb
index 65b5487..9294c55 100644
--- a/profsea/notebooks/ais_fitting.ipynb
+++ b/profsea/notebooks/ais_fitting.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 137,
+   "execution_count": 1,
    "id": "03acb605",
    "metadata": {},
    "outputs": [],
@@ -20,7 +20,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "2b26565c",
    "metadata": {},
    "outputs": [
@@ -67,6 +67,22 @@
     "        anoms.append(ds)\n",
     "    return np.array(anoms)\n",
     "\n",
+    "def get_model_name(path: str):\n",
+    "    return path.split(\"/\")[-1].split(\"sle_AIS_\")[-1].split(\"_exp\")[0]\n",
+    "\n",
+    "def load_common_models(data_path: str, variable: str, common_models: list) -> np.array:\n",
+    "    exp_path = []\n",
+    "    models_by_experiment = [get_model_name(path) for path in exp_path]\n",
+    "    files = sorted(glob.glob(path))\n",
+    "    anoms = []\n",
+    "    for f in files:\n",
+    "        ds = xr.open_dataset(f, decode_times=False)[f'{variable}']\n",
+    "        ds = ds.sel(time=slice(2015, 2299)).values\n",
+    "        if variable == 'sle':\n",
+    "            ds -= ds[0]\n",
+    "        anoms.append(ds)\n",
+    "    return np.array(anoms)\n",
+    "\n",
     "# High emissions temperatures\n",
     "ccsm4_tas_85 = load_global_temperature('../data/AIS/forcing/global/CCSM4/*.nc')\n",
     "cesm2_tas_585 = load_global_temperature('../data/AIS/forcing/global/CESM2-WACCM/*.nc')\n",
@@ -182,7 +198,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 156,
+   "execution_count": null,
    "id": "3498a908",
    "metadata": {},
    "outputs": [
@@ -226,8 +242,6 @@
     }
    ],
    "source": [
-    "# --- 1. Define New Model Functions ---\n",
-    "\n",
     "def f_poly(tas, t_int, params):\n",
     "    \"\"\"\n",
     "    Polynomial forcing function.\n",
@@ -259,11 +273,7 @@
     "    tau = params_all[0]\n",
     "    abc = params_all[1:]\n",
     "    \n",
-    "    # Reconstruct the forcing for the flattened array\n",
-    "    # Note: We process the flattened array directly for speed, assuming\n",
-    "    # the convolution restart at scenario boundaries is handled \n",
-    "    # or negligible for global fit. \n",
-    "    # BETTER APPROACH: Loop over splits to handle convolution boundaries correctly.\n",
+    "    # Loop over splits to handle convolution boundaries correctly.\n",
     "    all_res = []\n",
     "    start_idx = 0\n",
     "    for end_idx in splits:\n",
@@ -296,14 +306,14 @@
     "    return np.concatenate([phys_res, reg_res])\n",
     "\n",
     "# --- 2. Setup Containers ---\n",
-    "general_params = []     # Stores [alpha, beta, gamma]\n",
+    "general_params = []     # Stores [alpha, beta]\n",
     "general_timescales = [] # Stores [tau]\n",
-    "specific_params = []    # Stores [model, scenario, 3]\n",
+    "specific_params = []    # Stores [model, scenario, param]\n",
     "impulse_mse = []\n",
     "\n",
     "# Hyperparameters\n",
-    "lambda_reg = 1  # Strength of shrinkage. 0.1 to 10.0 is a typical range.\n",
-    "tau_bounds = (5, 150.0) # Constrain tau to physics-compliant range\n",
+    "lambda_reg = 1 # regulariser\n",
+    "tau_bounds = (5, 150.0)  # prescribed maximum response timescale range\n",
     "\n",
     "# --- 3. Main Training Loop ---\n",
     "\n",
diff --git a/profsea/notebooks/compare_ais.ipynb b/profsea/notebooks/compare_ais.ipynb
index b516fdf..0f27bba 100644
--- a/profsea/notebooks/compare_ais.ipynb
+++ b/profsea/notebooks/compare_ais.ipynb
@@ -16,15 +16,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 33,
    "id": "1ce6ed82",
    "metadata": {},
    "outputs": [],
    "source": [
     "old_ais_path = \"../probabilistic_projections/global/gmslr_projections.nc\"\n",
-    "new_ais_path = \"../probabilistic_projections/global/gmslr_projections_NEW_AIS.nc\"\n",
+    "new_ais_path = \"../probabilistic_projections/global/gmslr_projections_NEW_AIS_FASTSLOW.nc\"\n",
+    "c_new_ais_path = \"../probabilistic_projections/global/gmslr_projections_NEW_AIS.nc\"\n",
     "old_ds = xr.open_dataset(old_ais_path)\n",
-    "new_ds = xr.open_dataset(new_ais_path)"
+    "new_ds = xr.open_dataset(new_ais_path)\n",
+    "new_ds = xr.open_dataset(c_new_ais_path)"
    ]
   },
   {
@@ -37,441 +39,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "ec09f575",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "
\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "
<xarray.Dataset> Size: 427kB\n",
-       "Dimensions:     (scenario: 6, member: 5, year: 295)\n",
-       "Coordinates:\n",
-       "  * scenario    (scenario) <U11 264B 'ssp119' 'ssp126' ... 'ssp585'\n",
-       "  * member      (member) int64 40B 0 1 2 3 4\n",
-       "  * year        (year) int64 2kB 2006 2007 2008 2009 ... 2297 2298 2299 2300\n",
-       "Data variables:\n",
-       "    gmslr       (scenario, member, year) float64 71kB ...\n",
-       "    expansion   (scenario, member, year) float64 71kB ...\n",
-       "    antarctica  (scenario, member, year) float64 71kB ...\n",
-       "    greenland   (scenario, member, year) float64 71kB ...\n",
-       "    glaciers    (scenario, member, year) float64 71kB ...\n",
-       "    landwater   (scenario, member, year) float64 71kB ...\n",
-       "Attributes:\n",
-       "    title:    ProFSea GMSLR Projections\n",
-       "    source:   FAIR v2.2 + ProFSea Emulator
"
-      ],
-      "text/plain": [
-       " Size: 427kB\n",
-       "Dimensions:     (scenario: 6, member: 5, year: 295)\n",
-       "Coordinates:\n",
-       "  * scenario    (scenario) "
       ]
@@ -489,8 +63,10 @@
     "    ax.set_title(f\"{scenario.values}\")\n",
     "    ax.plot(old_ds.year, old_ds.antarctica[scen_idx, 2], color=\"royalblue\", lw=2, label=\"AR5 emulator\")\n",
     "    ax.plot(new_ds.year, new_ds.antarctica[scen_idx, 2], color=\"goldenrod\", lw=2, label=\"ISMIP6 emulator\")\n",
+    "    ax.plot(c_new_ds.year, c_new_ds.antarctica[scen_idx, 2], color=\"seagreen\", lw=2, label=\"ISMIP6 emulator, common models\")\n",
     "    ax.fill_between(old_ds.year, old_ds.antarctica[scen_idx, 1], old_ds.antarctica[scen_idx, 3], color=\"royalblue\", alpha=0.2)\n",
     "    ax.fill_between(new_ds.year, new_ds.antarctica[scen_idx, 1], new_ds.antarctica[scen_idx, 3], color=\"goldenrod\", alpha=0.2)\n",
+    "    ax.fill_between(c_new_ds.year, c_new_ds.antarctica[scen_idx, 1], c_new_ds.antarctica[scen_idx, 3], color=\"seagreen\", alpha=0.2)\n",
     "    ax.set_ylabel(\"SLE (m)\")\n",
     "    ax.set_xlabel(\"Year\")\n",
     "    if scen_idx == 0:\n",
@@ -510,7 +86,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 34,
    "id": "9c58124d",
    "metadata": {},
    "outputs": [
diff --git a/profsea/worked_example.ipynb b/profsea/worked_example.ipynb
deleted file mode 100644
index 6b3b734..0000000
--- a/profsea/worked_example.ipynb
+++ /dev/null
@@ -1,177 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "46a1a49f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from profsea.emulator import GMSLREmulator"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "52cdd115",
-   "metadata": {},
-   "source": [
-    "#### Introduction\n",
-    "The aim of this notebook is to break down the steps required to run the emulator version of ProFSea.\n",
-    "\n",
-    "All of this can't run in Jupyter Notebook but the steps should be clear enough to follow along!\n",
-    "\n",
-    "> If you want to use the CMIP6 patterns sterodynamic patterns instead of CMIP5, you need to run [this recipe](https://github.com/ESMValGroup/ESMValTool/blob/steric_patterns/esmvaltool/recipes/recipe_steric_patterns.yml) in ESMValTool. I recommend running with as much memory and processors as possible. \n",
-    "\n",
-    "> However, if you prefer not to go via ESMValTool feel free to reach out at gregory.munday\\@dtc.ox.ac.uk and I can just send them over (they're not large in terms of data size).\n",
-    "---\n",
-    "All other data required for the tool can be [downloaded here](https://catalogue.ceda.ac.uk/uuid/47c849b907414f3ab5e56ba9cf83e779/).\n",
-    "\n",
-    "I like keeping all my data in a `data/` folder next to the `profsea/` folder, which looks like this:\n",
-    "\n",
-    "  `data/` \\\n",
-    "   `cmip5/` \\\n",
-    "   `cmip6/` \\\n",
-    "   `gia_estimates/` \\\n",
-    "   `grd_fingerprints/` \\\n",
-    "   `monte_carlo_timeseries/` \\\n",
-    "   `uk_cmip_slope_coefficients/`\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ef69532c",
-   "metadata": {},
-   "source": [
-    "#### Setting up the environment\n",
-    "The next step is to set up the conda environment. You should use the `ProFSea-emu-env.yml` environment file, building it with \n",
-    "\n",
-    "```bash\n",
-    "conda env create -f ProFSea-emu-env.yml\n",
-    "```\n",
-    "\n",
-    "and then activating with `conda activate profsea-emu`.\n",
-    "\n",
-    "---\n",
-    "\n",
-    "One final step here is to install ProFSea as an editable package, using\n",
-    "\n",
-    "```bash \n",
-    "pip install -e . \n",
-    "```\n",
-    "\n",
-    "while inside the highest level directory."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5616586f",
-   "metadata": {},
-   "source": [
-    "#### Setup FaIR output\n",
-    "Run FaIR for the scenarios you want to simulate and save out the `global surface air temperature` and `ocean heat content`. You'll also need to calculate the 2015-2100 cumulative CO2 emissions and save it to a `.json` file. The CMIP6 cumulative emissions are included in the repo: \n",
-    "\n",
-    "```profsea/emulator/aux_data/cumulative_cmip6_emissions.json```\n",
-    "\n",
-    "Here's some sample code to calculate it from scratch in FaIR. Perhaps this could be included in this branch at some point.\n",
-    "\n",
-    "```python\n",
-    "# Output cumulative emissions from 2015 to 2100\n",
-    "cum_emissions_dict = {}\n",
-    "for scenario in hist_gcam_ext.scenario.unique():  # loop over your scenarios\n",
-    "        emissions = hist_gcam_ext.loc[\n",
-    "            (hist_gcam_ext['scenario'] == scenario) & (hist_gcam_ext['variable'] == 'CO2 FFI'), \n",
-    "            '2015':'2100'\n",
-    "        ].to_numpy()[0]  # select anthro emissions\n",
-    "        cum_emissions = np.cumsum(emissions)\n",
-    "        cum_emissions = cum_emissions / 1000  # Convert to PgC\n",
-    "        print(f'Total cumulative carbon emissions from 2015 to 2100 for {scenario}: ', cum_emissions[-1])\n",
-    "        cum_emissions_dict[scenario] = cum_emissions[-1]\n",
-    "        # Save to json\n",
-    "        with open('ngfs_data/cumulative_scenario_emissions.json', 'w') as f:\n",
-    "            json.dump(cum_emissions_dict, f)\n",
-    "```\n",
-    "\n",
-    "You need to do this because the current Antarctic Ice-Sheet dynamics component uses the relationship between cumulative emissions and sea-level contribution to calculate the lower and upper bounds of the projection. This was implemented to remove scenario-dependence from the GMSLR emulator, and should be redundant when the component is updated.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6cef2ba4",
-   "metadata": {},
-   "source": [
-    "#### Running global → local ProFSea projections\n",
-    "Point your paths in the `user-settings-emu.yml` file to the right places, fill in your scenario names and you should be ready to go!\n",
-    "\n",
-    "Run: \n",
-    "\n",
-    "```bash\n",
-    "python spatial_projections.py\n",
-    "```\n",
-    "\n",
-    "The global projections will be saved to `data/runs/gmslr_output/`, while the spatialised projections should be saved to `data/runs//`\n",
-    "\n",
-    "The way this is accomplished (so that it runs on a laptop) is by taking and saving 101 percentiles of the GMSLR module output for each component. This can be updated via the `output_percentiles` keyword in the `GMSLR` class, currently line 458 in `spatial_projections.py`."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "84c2ef51",
-   "metadata": {},
-   "source": [
-    "You can also run the GMSLR module by itself:\n",
-    "\n",
-    "```python\n",
-    "from profsea.emulator import GMSLREmulator\n",
-    "\n",
-    "emulator = GMSLREmulator(\n",
-    "    T_change,  # your T_change anom from FaIR\n",
-    "    OHC_change,  # your OHC_change anom from FaIR\n",
-    "    'low-overshoot',  # your scenario name (str)\n",
-    "    2300,  #  projection end year\n",
-    "    palmer_method=palmer_method,  # recommended for runs beyond 2100\n",
-    "    input_ensemble=True,  # must be true if providing a complete FaIR ensemble\n",
-    "    output_percentiles=np.arange(101),  # list/array of percentiles to sample\n",
-    "    cum_emissions_total=1450)  # int, cumulative emissions in scenario from 2015-2100\n",
-    "\n",
-    "#  run the projections (auto-parallel)\n",
-    "emulator.project()\n",
-    "\n",
-    "# Save to desired folder with the name of the scenario\n",
-    "emulator.save_components(\n",
-    "    os.path.join(settings[\"baseoutdir\"], 'gmslr_output'),  # output dir\n",
-    "    scenario)  # scenario name\n",
-    "\n",
-    "# Lists the available components\n",
-    "emulator.list_components()\n",
-    "\n",
-    "# You can also access the data from each component directly:\n",
-    "emulator.greenland  # returns greenland projection ensemble\n",
-    "\n",
-    "```"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "cmip7",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

From c4df5e7deeb004e288a677a9c3419578d611900e Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Sun, 7 Dec 2025 15:14:36 +0000
Subject: [PATCH 055/276] Cleanup repo

---
 ProFSea-environment.lock                      |  99 --
 ProFSea-environment.yml                       |  14 -
 .../data/cmip5/STANLEYII_ij_1x1_coords.csv    |  25 -
 ...STANLEYII_rcp26_projection_2100_global.csv | 847 ----------------
 ...ANLEYII_rcp26_projection_2100_regional.csv | 847 ----------------
 ...STANLEYII_rcp45_projection_2100_global.csv | 847 ----------------
 ...ANLEYII_rcp45_projection_2100_regional.csv | 847 ----------------
 ...STANLEYII_rcp85_projection_2100_global.csv | 847 ----------------
 ...ANLEYII_rcp85_projection_2100_regional.csv | 847 ----------------
 .../STANLEYII_zos_regression.csv              |  64 --
 .../maps/STANLEYII_ACCESS1-0_ij_figure.png    | Bin 17550 -> 0 bytes
 .../maps/STANLEYII_CNRM-CM5_ij_figure.png     | Bin 16479 -> 0 bytes
 .../STANLEYII_CSIRO-Mk3-6-0_ij_figure.png     | Bin 18577 -> 0 bytes
 .../maps/STANLEYII_CanESM2_ij_figure.png      | Bin 17166 -> 0 bytes
 .../maps/STANLEYII_GFDL-ESM2G_ij_figure.png   | Bin 18060 -> 0 bytes
 .../maps/STANLEYII_GFDL-ESM2M_ij_figure.png   | Bin 17845 -> 0 bytes
 .../maps/STANLEYII_GISS-E2-R_ij_figure.png    | Bin 15809 -> 0 bytes
 .../maps/STANLEYII_HadGEM2-CC_ij_figure.png   | Bin 18250 -> 0 bytes
 .../maps/STANLEYII_HadGEM2-ES_ij_figure.png   | Bin 17954 -> 0 bytes
 .../maps/STANLEYII_IPSL-CM5A-LR_ij_figure.png | Bin 16721 -> 0 bytes
 .../maps/STANLEYII_IPSL-CM5A-MR_ij_figure.png | Bin 17908 -> 0 bytes
 .../STANLEYII_MIROC-ESM-CHEM_ij_figure.png    | Bin 18298 -> 0 bytes
 .../maps/STANLEYII_MIROC-ESM_ij_figure.png    | Bin 17317 -> 0 bytes
 .../maps/STANLEYII_MIROC5_ij_figure.png       | Bin 17208 -> 0 bytes
 .../maps/STANLEYII_MPI-ESM-LR_ij_figure.png   | Bin 17326 -> 0 bytes
 .../maps/STANLEYII_MPI-ESM-MR_ij_figure.png   | Bin 17963 -> 0 bytes
 .../maps/STANLEYII_MRI-CGCM3_ij_figure.png    | Bin 17711 -> 0 bytes
 .../maps/STANLEYII_NorESM1-ME_ij_figure.png   | Bin 17493 -> 0 bytes
 .../maps/STANLEYII_NorESM1-M_ij_figure.png    | Bin 17680 -> 0 bytes
 .../maps/STANLEYII_bcc-csm1-1_ij_figure.png   | Bin 17127 -> 0 bytes
 .../maps/STANLEYII_inmcm4_ij_figure.png       | Bin 17175 -> 0 bytes
 .../sea_level_projections/01_STANLEY II.png   | Bin 188486 -> 0 bytes
 .../sea_level_projections/02_STANLEY II.png   | Bin 140337 -> 0 bytes
 .../03_STANLEY II_rcp26.png                   | Bin 182428 -> 0 bytes
 .../03_STANLEY II_rcp45.png                   | Bin 186653 -> 0 bytes
 .../03_STANLEY II_rcp85.png                   | Bin 192573 -> 0 bytes
 .../sea_level_projections/04_STANLEY II.png   | Bin 182024 -> 0 bytes
 .../sea_level_projections/05_STANLEY II.png   | Bin 229370 -> 0 bytes
 .../sea_level_projections/06_STANLEY II.png   | Bin 224401 -> 0 bytes
 .../07_STANLEY II_rcp85.png                   | Bin 59851 -> 0 bytes
 .../figures/sea_level_projections/Thumbs.db   | Bin 52224 -> 0 bytes
 ...YII_ACCESS1-0_zos_regression_2005-2100.png | Bin 68181 -> 0 bytes
 ...EYII_CNRM-CM5_zos_regression_2005-2100.png | Bin 66931 -> 0 bytes
 ...CSIRO-Mk3-6-0_zos_regression_2005-2100.png | Bin 73254 -> 0 bytes
 ...LEYII_CanESM2_zos_regression_2005-2100.png | Bin 67639 -> 0 bytes
 ...II_GFDL-ESM2G_zos_regression_2005-2100.png | Bin 65435 -> 0 bytes
 ...II_GFDL-ESM2M_zos_regression_2005-2100.png | Bin 67501 -> 0 bytes
 ...YII_GISS-E2-R_zos_regression_2005-2100.png | Bin 74153 -> 0 bytes
 ...II_HadGEM2-CC_zos_regression_2005-2100.png | Bin 65435 -> 0 bytes
 ...II_HadGEM2-ES_zos_regression_2005-2100.png | Bin 68217 -> 0 bytes
 ..._IPSL-CM5A-LR_zos_regression_2005-2100.png | Bin 70862 -> 0 bytes
 ..._IPSL-CM5A-MR_zos_regression_2005-2100.png | Bin 69715 -> 0 bytes
 ...IROC-ESM-CHEM_zos_regression_2005-2100.png | Bin 68730 -> 0 bytes
 ...YII_MIROC-ESM_zos_regression_2005-2100.png | Bin 67271 -> 0 bytes
 ...NLEYII_MIROC5_zos_regression_2005-2100.png | Bin 70026 -> 0 bytes
 ...II_MPI-ESM-LR_zos_regression_2005-2100.png | Bin 73127 -> 0 bytes
 ...II_MPI-ESM-MR_zos_regression_2005-2100.png | Bin 72174 -> 0 bytes
 ...YII_MRI-CGCM3_zos_regression_2005-2100.png | Bin 67764 -> 0 bytes
 ...II_NorESM1-ME_zos_regression_2005-2100.png | Bin 67501 -> 0 bytes
 ...YII_NorESM1-M_zos_regression_2005-2100.png | Bin 69386 -> 0 bytes
 ...II_bcc-csm1-1_zos_regression_2005-2100.png | Bin 67005 -> 0 bytes
 ...NLEYII_inmcm4_zos_regression_2005-2100.png | Bin 60704 -> 0 bytes
 profsea/plotting_libraries.py                 | 229 -----
 ..._prof_prob.py => probabilistic_wrapper.py} |   0
 profsea/step1_extract_cmip.py                 | 339 -------
 .../step2_extract_steric_dyn_regression.py    |  71 --
 ...2_extract_steric_dyn_regression_spatial.py | 139 ---
 ...3_process_regional_sealevel_projections.py | 612 ------------
 profsea/step4_plot_regional_sealevel.py       | 940 ------------------
 profsea/surge/tide_gauge_library.py           | 175 ----
 profsea/user-settings.yml                     |  69 --
 profsea/{ => utils}/tide_gauge_locations.py   |   0
 profsea/worked_example.ipynb                  | 177 ----
 73 files changed, 8035 deletions(-)
 delete mode 100644 ProFSea-environment.lock
 delete mode 100755 ProFSea-environment.yml
 delete mode 100644 profsea/Falkland_example/data/cmip5/STANLEYII_ij_1x1_coords.csv
 delete mode 100644 profsea/Falkland_example/data/sea_level_projections/STANLEYII_rcp26_projection_2100_global.csv
 delete mode 100644 profsea/Falkland_example/data/sea_level_projections/STANLEYII_rcp26_projection_2100_regional.csv
 delete mode 100644 profsea/Falkland_example/data/sea_level_projections/STANLEYII_rcp45_projection_2100_global.csv
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 delete mode 100644 profsea/Falkland_example/data/sea_level_projections/STANLEYII_rcp85_projection_2100_global.csv
 delete mode 100644 profsea/Falkland_example/data/sea_level_projections/STANLEYII_rcp85_projection_2100_regional.csv
 delete mode 100644 profsea/Falkland_example/data/zos_regression/STANLEYII_zos_regression.csv
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 delete mode 100644 profsea/Falkland_example/figures/maps/STANLEYII_MIROC-ESM-CHEM_ij_figure.png
 delete mode 100644 profsea/Falkland_example/figures/maps/STANLEYII_MIROC-ESM_ij_figure.png
 delete mode 100644 profsea/Falkland_example/figures/maps/STANLEYII_MIROC5_ij_figure.png
 delete mode 100644 profsea/Falkland_example/figures/maps/STANLEYII_MPI-ESM-LR_ij_figure.png
 delete mode 100644 profsea/Falkland_example/figures/maps/STANLEYII_MPI-ESM-MR_ij_figure.png
 delete mode 100644 profsea/Falkland_example/figures/maps/STANLEYII_MRI-CGCM3_ij_figure.png
 delete mode 100644 profsea/Falkland_example/figures/maps/STANLEYII_NorESM1-ME_ij_figure.png
 delete mode 100644 profsea/Falkland_example/figures/maps/STANLEYII_NorESM1-M_ij_figure.png
 delete mode 100644 profsea/Falkland_example/figures/maps/STANLEYII_bcc-csm1-1_ij_figure.png
 delete mode 100644 profsea/Falkland_example/figures/maps/STANLEYII_inmcm4_ij_figure.png
 delete mode 100644 profsea/Falkland_example/figures/sea_level_projections/01_STANLEY II.png
 delete mode 100644 profsea/Falkland_example/figures/sea_level_projections/02_STANLEY II.png
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 delete mode 100644 profsea/Falkland_example/figures/sea_level_projections/03_STANLEY II_rcp45.png
 delete mode 100644 profsea/Falkland_example/figures/sea_level_projections/03_STANLEY II_rcp85.png
 delete mode 100644 profsea/Falkland_example/figures/sea_level_projections/04_STANLEY II.png
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 delete mode 100644 profsea/Falkland_example/figures/sea_level_projections/06_STANLEY II.png
 delete mode 100644 profsea/Falkland_example/figures/sea_level_projections/07_STANLEY II_rcp85.png
 delete mode 100755 profsea/Falkland_example/figures/sea_level_projections/Thumbs.db
 delete mode 100644 profsea/Falkland_example/figures/zos_regression/STANLEYII_ACCESS1-0_zos_regression_2005-2100.png
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 delete mode 100644 profsea/Falkland_example/figures/zos_regression/STANLEYII_CSIRO-Mk3-6-0_zos_regression_2005-2100.png
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 delete mode 100644 profsea/Falkland_example/figures/zos_regression/STANLEYII_GFDL-ESM2G_zos_regression_2005-2100.png
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 delete mode 100644 profsea/Falkland_example/figures/zos_regression/STANLEYII_HadGEM2-CC_zos_regression_2005-2100.png
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 delete mode 100644 profsea/Falkland_example/figures/zos_regression/STANLEYII_MIROC-ESM-CHEM_zos_regression_2005-2100.png
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 delete mode 100644 profsea/plotting_libraries.py
 rename profsea/{fair_prof_prob.py => probabilistic_wrapper.py} (100%)
 delete mode 100644 profsea/step1_extract_cmip.py
 delete mode 100644 profsea/step2_extract_steric_dyn_regression.py
 delete mode 100644 profsea/step2_extract_steric_dyn_regression_spatial.py
 delete mode 100644 profsea/step3_process_regional_sealevel_projections.py
 delete mode 100644 profsea/step4_plot_regional_sealevel.py
 delete mode 100644 profsea/surge/tide_gauge_library.py
 delete mode 100644 profsea/user-settings.yml
 rename profsea/{ => utils}/tide_gauge_locations.py (100%)
 delete mode 100644 profsea/worked_example.ipynb

diff --git a/ProFSea-environment.lock b/ProFSea-environment.lock
deleted file mode 100644
index e00b900..0000000
--- a/ProFSea-environment.lock
+++ /dev/null
@@ -1,99 +0,0 @@
-# This file may be used to create an environment using:
-# $ conda create --name ProFSea-env --file ProFSea-environment.lock
-# platform: linux-64
-@EXPLICIT
-https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2
-https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2023.11.17-hbcca054_0.conda
-https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.40-h41732ed_0.conda
-https://conda.anaconda.org/conda-forge/linux-64/libgfortran-3.0.0-1.tar.bz2
-https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-13.2.0-h7e041cc_3.conda
-https://conda.anaconda.org/conda-forge/linux-64/libgomp-13.2.0-h807b86a_3.conda
-https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_gnu.tar.bz2
-https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-13.2.0-h807b86a_3.conda
-https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.25.0-hd590300_0.conda
-https://conda.anaconda.org/conda-forge/linux-64/geos-3.9.1-h9c3ff4c_2.tar.bz2
-https://conda.anaconda.org/conda-forge/linux-64/giflib-5.2.1-h0b41bf4_3.conda
-https://conda.anaconda.org/conda-forge/linux-64/icu-67.1-he1b5a44_0.tar.bz2
-https://conda.anaconda.org/conda-forge/linux-64/keyutils-1.6.1-h166bdaf_0.tar.bz2
-https://conda.anaconda.org/conda-forge/linux-64/lerc-4.0.0-h27087fc_0.tar.bz2
-https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.19-hd590300_0.conda
-https://conda.anaconda.org/conda-forge/linux-64/libev-4.33-hd590300_2.conda
-https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.5.0-hcb278e6_1.conda
-https://conda.anaconda.org/conda-forge/linux-64/libffi-3.3-h58526e2_2.tar.bz2
-https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-13.2.0-ha4646dd_3.conda
-https://conda.anaconda.org/conda-forge/linux-64/libjpeg-turbo-3.0.0-hd590300_1.conda
-https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.3.2-hd590300_0.conda
-https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.2.13-hd590300_5.conda
-https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.4-h59595ed_2.conda
-https://conda.anaconda.org/conda-forge/linux-64/openssl-1.1.1w-hd590300_0.conda
-https://conda.anaconda.org/conda-forge/linux-64/xxhash-0.8.2-hd590300_0.conda
-https://conda.anaconda.org/conda-forge/linux-64/xz-5.2.6-h166bdaf_0.tar.bz2
-https://conda.anaconda.org/conda-forge/linux-64/yaml-0.2.5-h7f98852_2.tar.bz2
-https://conda.anaconda.org/conda-forge/linux-64/hdf4-4.2.15-h2a13503_7.conda
-https://conda.anaconda.org/conda-forge/linux-64/libedit-3.1.20191231-he28a2e2_2.tar.bz2
-https://conda.anaconda.org/conda-forge/linux-64/libgfortran-ng-13.2.0-h69a702a_3.conda
-https://conda.anaconda.org/conda-forge/linux-64/libnghttp2-1.51.0-hdcd2b5c_0.conda
-https://conda.anaconda.org/conda-forge/linux-64/libpng-1.6.39-h753d276_0.conda
-https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.44.2-h2797004_0.conda
-https://conda.anaconda.org/conda-forge/linux-64/libssh2-1.10.0-haa6b8db_3.tar.bz2
-https://conda.anaconda.org/conda-forge/linux-64/libudunits2-2.2.28-h40f5838_3.conda
-https://conda.anaconda.org/conda-forge/linux-64/readline-8.2-h8228510_1.conda
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-
diff --git a/ProFSea-environment.yml b/ProFSea-environment.yml
deleted file mode 100755
index 2a3e238..0000000
--- a/ProFSea-environment.yml
+++ /dev/null
@@ -1,14 +0,0 @@
-name: ProFSea-env
-channels:
-  - conda-forge
-dependencies:
-  - numpy
-  - pandas
-  - python=3.7.7
-  - scipy
-  - pyyaml
-  - netcdf4
-  - matplotlib
-  - libwebp>=1.3.2
-  - cartopy
-  - iris
\ No newline at end of file
diff --git a/profsea/Falkland_example/data/cmip5/STANLEYII_ij_1x1_coords.csv b/profsea/Falkland_example/data/cmip5/STANLEYII_ij_1x1_coords.csv
deleted file mode 100644
index 5971a41..0000000
--- a/profsea/Falkland_example/data/cmip5/STANLEYII_ij_1x1_coords.csv
+++ /dev/null
@@ -1,25 +0,0 @@
-Location: STANLEY II
-Latitude:  -51.692
-Longitude:  -57.821
-Model,i,j,box_lon,box_lat
-ACCESS1-0,302,38,302.0,-51.5
-bcc-csm1-1,303,38,303.0,-51.5
-CNRM-CM5,302,38,302.0,-51.5
-CSIRO-Mk3-6-0,302,38,302.0,-51.5
-CanESM2,302,38,302.0,-51.5
-GFDL-ESM2G,303,38,303.0,-51.5
-GFDL-ESM2M,303,38,303.0,-51.5
-GISS-E2-R,302,38,302.0,-51.5
-HadGEM2-CC,303,38,303.0,-51.5
-HadGEM2-ES,303,38,303.0,-51.5
-inmcm4,303,38,303.0,-51.5
-IPSL-CM5A-LR,302,38,302.0,-51.5
-IPSL-CM5A-MR,302,38,302.0,-51.5
-MIROC-ESM,302,38,302.0,-51.5
-MIROC-ESM-CHEM,302,38,302.0,-51.5
-MIROC5,302,38,302.0,-51.5
-MPI-ESM-LR,302,38,302.0,-51.5
-MPI-ESM-MR,303,38,303.0,-51.5
-MRI-CGCM3,302,38,302.0,-51.5
-NorESM1-M,303,38,303.0,-51.5
-NorESM1-ME,303,38,303.0,-51.5
diff --git a/profsea/Falkland_example/data/sea_level_projections/STANLEYII_rcp26_projection_2100_global.csv b/profsea/Falkland_example/data/sea_level_projections/STANLEYII_rcp26_projection_2100_global.csv
deleted file mode 100644
index 67d6f48..0000000
--- a/profsea/Falkland_example/data/sea_level_projections/STANLEYII_rcp26_projection_2100_global.csv
+++ /dev/null
@@ -1,847 +0,0 @@
-year,percentile,exp,antdyn,antsmb,greendyn,greensmb,glacier,landwater,antnet,greennet,sum
-2007,5,0.007747830357402563,0.002729416126385331,-0.00018738857907010236,0.0016894025029614568,0.0014912912156432867,0.009886452928185463,0.0002794466563500464,0.002601555286673829,0.0032219790271483363,0.024319663889400545
-2007,10,0.008736629970371723,0.002749401144683361,-0.0001577381044626236,0.0017016869969666004,0.0015035183168947697,0.01020332332700491,0.00028879407909698784,0.0026430128418724053,0.00324442470446229,0.02562091049621813
-2007,30,0.011505596339702606,0.0028308017645031214,-0.00010631662735249847,0.0017332754796370864,0.001536902622319758,0.01085098460316658,0.0003355311928316951,0.002742401135037653,0.003300822037272155,0.029088137270991864
-2007,33,0.01182605978101492,0.0028428561869077384,-0.0001010399209917523,0.0017402952071279287,0.0015414771623909473,0.01092148456722498,0.0003425417235121131,0.002756573385286174,0.003307720690499991,0.02951855673949467
-2007,50,0.013211064040660858,0.00291229784488678,-7.606841245433316e-05,0.0017701288452371955,0.001567732891999185,0.011307753156870604,0.00038226827746257186,0.002831636473274557,0.0033446088200435042,0.03128627405021689
-2007,67,0.014535723254084587,0.0029822924407199025,-5.334218803909607e-05,0.0017964525613933802,0.0015995744615793228,0.011766890995204449,0.0004219948314130306,0.00290754983203442,0.003383157136850059,0.03291255475152866
-2007,70,0.014775460585951805,0.002995399944484234,-4.9265647248830646e-05,0.0018034722888842225,0.0016066718380898237,0.011866511777043343,0.00042900542030110955,0.0029216647963039577,0.003390827146358788,0.03324935326745617
-2007,90,0.01760701648890972,0.0030842043925076723,-1.9583005268941633e-05,0.0018420804990455508,0.0016821788158267736,0.012778846453875304,0.0004734056710731238,0.0030230686061258893,0.0034707156009972095,0.03646935069991741
-2007,95,0.018689656630158424,0.0031117238104343414,-9.80908266683408e-06,0.0018508550710976124,0.0017289366805925965,0.013186596473678945,0.00048508995678275824,0.003058169371797703,0.0035174950724467633,0.03784863332439272
-2008,5,0.008809786289930344,0.0029567384626716375,-0.00037360007991082966,0.0019223066046833992,0.0015208865283057094,0.009989279322326183,0.0005511200288310647,0.0027049929194618016,0.0035271180677227676,0.026616155130250264
-2008,10,0.00983534287661314,0.0029973755590617657,-0.0003131370758637786,0.0019474038854241371,0.0015460723079741001,0.010526285506784916,0.0005707318778149784,0.002788511075777933,0.0035728064831346273,0.02822620734150405
-2008,30,0.01270724181085825,0.003162417095154524,-0.00020850003056693822,0.0020119398832321167,0.0016136295162141323,0.011656438000500202,0.0006687914719805121,0.0029905064147897065,0.0036875532008707524,0.032324369121852214
-2008,33,0.01303961779922247,0.003186901572626084,-0.00019831673125736415,0.0020262813195586205,0.0016221862751990557,0.011776070110499859,0.0006835003150627017,0.003019230734935263,0.003701493857661262,0.03280145770462695
-2008,50,0.014476108364760876,0.0033285808749496937,-0.00014754774747416377,0.002087231958284974,0.001675595180131495,0.012418901547789574,0.0007668508915230632,0.0031719293037895113,0.003776411642320454,0.03487115509051364
-2008,67,0.01585000939667225,0.003473451046738774,-0.00010184753045905381,0.002141011878848076,0.0017397503834217787,0.013145781224593521,0.0008502015843987465,0.0033291474464931525,0.0038547385041601957,0.03680496705841506
-2008,70,0.016098659485578537,0.0035010041203349827,-9.347584273200482e-05,0.0021553533151745796,0.0017538052052259445,0.013294204790145158,0.000864910485688597,0.0033588339429115877,0.0038700508535839616,0.03719890025313362
-2008,90,0.019035475328564644,0.0036975243128836155,-3.281182216596789e-05,0.0022342305164784193,0.0019061621278524399,0.014723778516054154,0.0009580671321600676,0.003579327036277391,0.004031265224330127,0.04088516818592325
-2008,95,0.02015835978090763,0.003768082708120346,-1.2909322322229855e-05,0.002252157311886549,0.001999214291572571,0.015381129458546638,0.0009825819870457053,0.003664510171802249,0.004125312727410346,0.042551113924855596
-2009,5,0.009925924241542816,0.003181967418640852,-0.0005756449216278269,0.002153712324798107,0.0015587951056659222,0.010254379361867905,0.0008150199428200722,0.002791649312712252,0.003841180296149105,0.029214699009025936
-2009,10,0.011036878451704979,0.0032439231872558594,-0.0004833759739995003,0.002192151267081499,0.0015973133267834783,0.010975790210068226,0.000845813425257802,0.0029193434165790677,0.0039110222132876515,0.03109469817718491
-2009,30,0.014147917740046978,0.0034948461689054966,-0.00032333868439309303,0.0022909934632480145,0.0017007326241582632,0.012485547177493572,0.0009997807210311294,0.0032272590906359255,0.004086900665424764,0.03582932110657566
-2009,33,0.014507969841361046,0.003532136103603989,-0.00030752088059671223,0.002312958473339677,0.0017140804254449904,0.012649044310674071,0.0010228757746517658,0.003271678124292521,0.004108313820324838,0.03635602352733258
-2009,50,0.016064075753092766,0.003748849965631962,-0.00022996746702119708,0.0024063095916062593,0.001796110300347209,0.013508155941963196,0.0011537481332197785,0.0035055707921856083,0.004223189316689968,0.038756343157729134
-2009,67,0.01755238138139248,0.003973476151004434,-0.00015988576342351735,0.0024886783212423325,0.001894370885565877,0.014470420433208349,0.0012846202589571476,0.0037493951733267752,0.004343107922468334,0.04103905962219869
-2009,70,0.01782173477113247,0.004016813402995467,-0.0001469960407121107,0.002510643098503351,0.0019161389209330082,0.014667678158730268,0.0013077154289931059,0.0037959426845191047,0.004366751865018159,0.04149348942155484
-2009,90,0.02100309729576111,0.004339959938079119,-5.453753292385951e-05,0.0026314505375921726,0.002149812411516905,0.016531893424689772,0.0014539845287799835,0.004154370010655839,0.004614380444400013,0.04574705990235089
-2009,95,0.02221948280930519,0.004469077102839947,-2.441359538352117e-05,0.002658906625583768,0.002293102676048875,0.017397857457399368,0.001492476207204163,0.0043016682852794474,0.004758716828655451,0.04771817427390487
-2010,5,0.011036466807126999,0.003405102528631687,-0.0008001990499906242,0.002383620012551546,0.0015909349313005805,0.010370799340307713,0.0010711464565247297,0.0028621499659493566,0.004151605535298586,0.03160007820260944
-2010,10,0.012227976694703102,0.0034890444949269295,-0.0006708314758725464,0.002435928676277399,0.001644184230826795,0.011296790093183517,0.0011140386341139674,0.003039592527784407,0.00424860151251778,0.033805799185938665
-2010,30,0.015564601868391037,0.003828088752925396,-0.00044731363013852395,0.002570436103269458,0.0017893636832013726,0.013248197734355927,0.0013284990563988686,0.0034612940362421796,0.004490448394790292,0.039232348382211055
-2010,33,0.0159507617354393,0.0038785597775131465,-0.00042527844198048115,0.002600326668471098,0.0018074813997372985,0.013455716893076897,0.0013606681022793055,0.0035196542739868164,0.00451978606870398,0.03982664288923843
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-2010,67,0.01921592652797699,0.0044823678303509955,-0.00021904123423155397,0.002839451190084219,0.0020598392002284527,0.015747706629335882,0.001725250855088234,0.004175565801674566,0.004843331546289846,0.04520464108645683
-2010,70,0.019504811614751816,0.004542827000841498,-0.00020109109755139798,0.002869341755285859,0.0020889343926683065,0.01599145494401455,0.0017574199009686708,0.004240909416694194,0.004876245697960258,0.04572657800308661
-2010,90,0.02291685715317726,0.005011511035263538,-7.147782889660448e-05,0.0030337399803102016,0.0024167730007320642,0.018289243429899217,0.001961157424375415,0.004756442940561101,0.005221306905150413,0.050585113454144445
-2010,95,0.024221446365118027,0.0052147069945931435,-2.919388862210326e-05,0.0030711032450199127,0.0026162220165133476,0.01937731821089983,0.0020147725008428097,0.0049824361303763,0.0054220715537667274,0.05285678950604051
-2011,5,0.011580809950828552,0.0036261440254747868,-0.0010376267600804567,0.0026120292022824287,0.0016213177586905658,0.0104464590549469,0.0013194996863603592,0.002921492385212332,0.004461290530161932,0.03337878862294019
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-2011,30,0.016563870012760162,0.004162145312875509,-0.0005778027116321027,0.002850267803296447,0.0018794205971062183,0.013971872627735138,0.0016549464780837297,0.003692031590617262,0.004897702438756824,0.04218189211678691
-2011,33,0.016988826915621758,0.004226172673515976,-0.0005488611641339958,0.0028883861377835274,0.0019034300930798054,0.014226524513214827,0.0016968772979453206,0.0037631906452588737,0.0049356955301482226,0.04285753955089604
-2011,50,0.018825439736247063,0.004601345397531986,-0.00040720826655160636,0.003050388302654028,0.0020531327463686466,0.015557492151856422,0.001934485393576324,0.004172714667220134,0.00513838604092598,0.046022153706871904
-2011,67,0.020582029595971107,0.005000126007944346,-0.0002795084146782756,0.00319333141669631,0.0022315748501569033,0.016978813726454975,0.002172093605622649,0.004606513801263645,0.005353105859830976,0.04905009061476449
-2011,70,0.02089993841946125,0.0050790454261004925,-0.0002564470923971385,0.003231449518352747,0.0022703062277287245,0.01727275550365448,0.002214024541899562,0.0046934572710597405,0.005396237573586404,0.04964837678708136
-2011,90,0.024654779583215714,0.005712177604436874,-8.722769416635834e-05,0.0034410993102937937,0.00269849575124681,0.019991185516119003,0.0024795865174382925,0.005385389519506135,0.0058442068751901385,0.05521986925741658
-2011,95,0.026090435683727264,0.006004971917718649,-3.080375681747705e-05,0.0034887471701949835,0.002958235563710332,0.021290436387062073,0.0025494713336229324,0.00570445707417093,0.006108361214864999,0.05783235537819564
-2012,5,0.012819323688745499,0.0038450919091701508,-0.0013002261985093355,0.0028389403596520424,0.0016478211618959904,0.010455084359273314,0.0015600797487422824,0.002956699696369469,0.00476907750708051,0.03577534900250612
-2012,10,0.014240577816963196,0.003975007217377424,-0.0010895669693127275,0.0029205738101154566,0.0017339733894914389,0.011796928942203522,0.0016299202106893063,0.003246404812671244,0.0049250290147028865,0.03871398430128466
-2012,30,0.018220558762550354,0.004497014917433262,-0.0007200284744612873,0.0031304885633289814,0.0019729668274521828,0.014697497338056564,0.0019791231025010347,0.003917214984539896,0.005311694426927716,0.04579181682493072
-2012,33,0.018681176006793976,0.004574975022114814,-0.0006830458296462893,0.003177136415615678,0.0020042965188622475,0.014993047341704369,0.002031503478065133,0.004002376226708293,0.005359111120924354,0.04655886352644302
-2012,50,0.020671917125582695,0.005033571273088455,-0.0005045482539571822,0.0033753893803805113,0.0021942530293017626,0.016571104526519775,0.0023283257614821196,0.004505023593083024,0.005610675434581935,0.05013601934479084
-2012,67,0.022575918585062027,0.005526750059798361,-0.0003439585561864078,0.0035503183025866747,0.002418983089737595,0.018236864954233174,0.002625148044899106,0.005040757154347375,0.005878755414159969,0.05357345922326204
-2012,70,0.022920506075024605,0.00562546863220632,-0.00031456921715289354,0.003596966154873371,0.0024673165753483772,0.018579966388642788,0.002677528653293848,0.005150104523636401,0.005933039379306138,0.05424960755917709
-2012,90,0.02699045091867447,0.006441960111260414,-0.0001015606612781994,0.0038535285275429487,0.0030093290843069553,0.021734103560447693,0.003009271342307329,0.006037805491359904,0.006497916067019105,0.06054090707038995
-2012,95,0.028546586632728577,0.00683987233787775,-2.9286128210515647e-05,0.003911837935447693,0.0033414247445762157,0.02325023151934147,0.0030965718906372786,0.006465218681842084,0.006831627967767416,0.0634869208181044
-2013,5,0.014244738966226578,0.004061946179717779,-0.0015966960927471519,0.0030643530189990997,0.0016981662483885884,0.010816581826657058,0.0017928860615938902,0.0029560162220150232,0.005102688551414758,0.03883128772868076
-2013,10,0.015743613243103027,0.004215848632156849,-0.0013380789896473289,0.003161441534757614,0.0018020125571638346,0.012351608369499445,0.0018775764619931579,0.0033090042765252297,0.005290249828249216,0.04203686548571568
-2013,30,0.01994095742702484,0.004832698963582516,-0.0008896735380403697,0.0034110983833670616,0.0020946611184626818,0.015616120770573616,0.0023010284639894962,0.004113615192181896,0.005755482614040375,0.04969107315992005
-2013,33,0.020426731556653976,0.004924966362304986,-0.0008454069029539824,0.003466577734798193,0.002132551970425993,0.015958702396601437,0.002364546060562134,0.004214885761030018,0.005812402979936451,0.05053611986717442
-2013,50,0.022526195272803307,0.005469783209264278,-0.000627555069513619,0.003702364629134536,0.0023652338422834873,0.017755300737917423,0.002724480116739869,0.004815676831640303,0.00611586298327893,0.054440485590021126
-2013,67,0.024534182623028755,0.0060622409218922275,-0.0004313429817557335,0.003910412080585957,0.0026411041617393494,0.019649269059300423,0.0030844141729176044,0.005460367666673847,0.006441213921643795,0.05822976506431587
-2013,70,0.024897588416934013,0.00618209633976221,-0.00039572292007505894,0.003965891432017088,0.0027004596777260303,0.020033824071288106,0.0031479322351515293,0.005593524081632495,0.006507645105011761,0.05897341959644109
-2013,90,0.029189810156822205,0.0072008585557341576,-0.00013581581879407167,0.004271027632057667,0.0033644975628703833,0.02359943278133869,0.0035502114333212376,0.006700445606838912,0.007197301811538637,0.0658560055133421
-2013,95,0.030830930918455124,0.007719407789409161,-4.9211746954824775e-05,0.004340376704931259,0.0037727414164692163,0.025314992573112247,0.0036560744047164917,0.00724978577636648,0.007607888232450931,0.06907740183523856
-2014,5,0.015892239287495613,0.004276706837117672,-0.001879818388260901,0.0032882671803236008,0.0017630320508033037,0.011391282826662064,0.0020179194398224354,0.002964482642710209,0.005450741620734334,0.042366810273961164
-2014,10,0.017387934029102325,0.00445526372641325,-0.0015811462653800845,0.003401339752599597,0.0018869723426178098,0.013071123510599136,0.002118376549333334,0.0033701335196383297,0.005669838166795671,0.0456714068481233
-2014,30,0.021576369181275368,0.005169196054339409,-0.001060834270901978,0.0036920972634106874,0.0022274604532867667,0.016569087281823158,0.0026206630282104015,0.004304061934817582,0.006210821284912527,0.05352719603688456
-2014,33,0.022061113268136978,0.005276146996766329,-0.001008195336908102,0.0037567103281617165,0.002270893193781376,0.016941889077425002,0.0026960058603435755,0.004422789963427931,0.00627725466620177,0.05439660066025681
-2014,50,0.0241561196744442,0.005909981206059456,-0.0007559303776361048,0.004031314514577389,0.0025412742979824543,0.018908094614744186,0.0031229492742568254,0.005123031965922564,0.0066308724926784635,0.05850674584507942
-2014,67,0.026159847155213356,0.006606598282232883,-0.0005281163612380624,0.004273612983524799,0.0028650397434830666,0.020988566819578416,0.0035498926881700754,0.005887461360543966,0.007010257558431476,0.06249652673024685
-2014,70,0.026522481814026833,0.006748929154127835,-0.0004862589412368834,0.004338225349783897,0.002934727119281888,0.02141612712293863,0.0036252355203032494,0.006040505855344235,0.007087722979485988,0.06328446977131534
-2014,90,0.030805593356490135,0.00798887200653553,-0.000185929486178793,0.0046935961581766605,0.0037124447990208864,0.02532110307365656,0.004102407954633236,0.007381180941592902,0.007895627152174713,0.07048782731872053
-2014,95,0.03244323283433914,0.008643578737974167,-8.766335668042302e-05,0.004774361848831177,0.004183367546647776,0.027190981619060032,0.0042279791086912155,0.008069614199484931,0.00837394897826016,0.07389311579463538
-2015,5,0.017370635643601418,0.004489373415708542,-0.0021850518649443977,0.00351068377494812,0.001819094514939934,0.011771785095334053,0.0022351795341819525,0.00295342446770519,0.0057898408500477675,0.04547884239291307
-2015,10,0.018861660733819008,0.00469325203448534,-0.001839303644374013,0.0036402682308107615,0.0019626212306320667,0.013621756806969643,0.0023523208219558,0.00342015631031245,0.006042637582868338,0.04895075588137843
-2015,30,0.023037021979689598,0.005506507121026516,-0.0012395823141559958,0.00397348590195179,0.002357349032536149,0.017454149015247823,0.0029380270279943943,0.00449662865139544,0.006666014133952558,0.05714461475145072
-2015,33,0.023520251736044884,0.005628516934812069,-0.001178052625618875,0.004047533962875605,0.0024084524600766597,0.01786596141755581,0.003025882877409458,0.004629522329196334,0.006742825265973806,0.05803433317894814
-2015,50,0.025608718395233154,0.006354164332151413,-0.0008868009899742901,0.0043622395023703575,0.0027217762544751167,0.020031416788697243,0.0035237332340329885,0.005434655700810254,0.007149776793085039,0.06237044354202226
-2015,67,0.027606191113591194,0.007159822760149839,-0.0006217677082167934,0.004639920778572559,0.0030985691118985415,0.022317582722753287,0.004021583124995232,0.0063189034117385745,0.007588989448267967,0.06659008572576568
-2015,70,0.02796769328415394,0.007325966469943523,-0.0005742505309171975,0.0047139693051576614,0.003180199721828103,0.02279297187924385,0.004109439440071583,0.006498115719296038,0.007678936421871185,0.06741924664238468
-2015,90,0.03223743662238121,0.008806001394987106,-0.0002279469044879079,0.005121234804391861,0.004082545638084412,0.0270682442933321,0.004665860440582037,0.008088253671303391,0.008616724144667388,0.07500444285105914
-2015,95,0.03386996313929558,0.00961238518357277,-0.00011403780081309378,0.005213795229792595,0.004630396957509216,0.029114042408764355,0.004812286701053381,0.008932961376558524,0.009171876590698957,0.07860951451584697
-2016,5,0.018640216439962387,0.004699947312474251,-0.002483146730810404,0.003731601405888796,0.001876448281109333,0.012159384787082672,0.0024446656461805105,0.0029473223257809877,0.006128997216001153,0.048384376177273224
-2016,10,0.02017526887357235,0.004929813556373119,-0.0020918778609484434,0.003878226736560464,0.0020400692941620947,0.014159809798002243,0.002579407999292016,0.0034743912052363165,0.006415502610616386,0.052076086394663434
-2016,30,0.024473926052451134,0.00584463169798255,-0.001414960017427802,0.004255262669175863,0.002486974000930786,0.018300170078873634,0.0032531195320189,0.004690617322921753,0.007121837511658668,0.0607044885342475
-2016,33,0.024971425533294678,0.005982076171785593,-0.0013445350364781918,0.0043390486389398575,0.0025442258920520544,0.018746858462691307,0.0033541761804372072,0.004840501511353068,0.007208958948031067,0.061640961291850545
-2016,50,0.027121564373373985,0.006802333518862724,-0.0010156690841540694,0.0046951379626989365,0.0029000979848206043,0.02109602279961109,0.0039268312975764275,0.00575132470112294,0.007669689133763313,0.06624641621601768
-2016,67,0.029178019613027573,0.007721913112327459,-0.0007159688393585384,0.005009335000067949,0.0033282185904681683,0.023574715480208397,0.004499485716223717,0.006759870913811028,0.008167663742788135,0.07075845940271393
-2016,70,0.02955019846558571,0.007913208566606045,-0.0006629652925767004,0.005093120504170656,0.0034204809926450253,0.024087753891944886,0.004600542597472668,0.006968950852751732,0.008270163391716778,0.07163300631800666
-2016,90,0.033946022391319275,0.009652245789766312,-0.0002705751336179674,0.0055539426393806934,0.004443589597940446,0.028707711026072502,0.005240568425506353,0.008830236960784534,0.009334858856163919,0.07968212433625013
-2016,95,0.03562675788998604,0.010625827126204967,-0.000140847244620091,0.005658674519509077,0.005068389233201742,0.030907876789569855,0.0054089962504804134,0.00984116131439805,0.009963839803822337,0.08354780399240554
-2017,5,0.01997438445687294,0.0049084266647696495,-0.0027909182477742433,0.003951020538806915,0.0019437840674072504,0.012639591470360756,0.0026463791728019714,0.0029322446789592505,0.006478744640480727,0.05147014442368345
-2017,10,0.02157832309603691,0.005164949223399162,-0.002354026073589921,0.004115215037018061,0.002127833431586623,0.014781969506293536,0.0027996397111564875,0.0035202415776439014,0.006798499962314963,0.05537888132967055
-2017,30,0.026069877669215202,0.006183570250868797,-0.001597638358362019,0.004537428729236126,0.0026276465505361557,0.019183552637696267,0.003565941471606493,0.004875577753409743,0.007589100394397974,0.06448165538022295
-2017,33,0.02658970281481743,0.006336824395693838,-0.001521326950751245,0.004631254356354475,0.0026912346947938204,0.019658135678619147,0.0036808867007493973,0.005041728162614163,0.007686727386899292,0.0654649227799382
-2017,50,0.02883632481098175,0.007254487834870815,-0.0011526059824973345,0.005030011758208275,0.0030892796348780394,0.02218412607908249,0.004332243464887142,0.006063560955226421,0.008203130215406418,0.07036577741382644
-2017,67,0.030985062941908836,0.00829286965541542,-0.000817653548438102,0.005381856579333544,0.0035693359095603228,0.024851683527231216,0.0049835997633636,0.007200761072454046,0.008761752599384637,0.07518030983163045
-2017,70,0.03137394040822983,0.008510654792189598,-0.0007596362847834826,0.0054756817407906055,0.003673095256090164,0.025396160781383514,0.005098544992506504,0.007438742078375065,0.008875118475407362,0.07611710336059332
-2017,90,0.03596702218055725,0.01052760612219572,-0.00032271334202960134,0.00599172106012702,0.004821382462978363,0.030344752222299577,0.0058265323750674725,0.00959192545269616,0.010069676954299212,0.08469828930683435
-2017,95,0.037723179906606674,0.011683903634548187,-0.00017647925415076315,0.00610900204628706,0.005519814998842775,0.03270019385963678,0.006018107756972313,0.010785361751914024,0.010775092686526476,0.08884082897566259
-2018,5,0.021197929978370667,0.005114813335239887,-0.003126322291791439,0.00416894257068634,0.0019895550794899464,0.012849139934405686,0.0028403187170624733,0.00289191585034132,0.006811336148530245,0.054139761108672246
-2018,10,0.022891942411661148,0.005398658104240894,-0.002636844990774989,0.004351234063506126,0.002196262124925852,0.015183189138770104,0.003013014327734709,0.003551082219928503,0.007168419426307082,0.05836181177292019
-2018,30,0.027635738253593445,0.006523321382701397,-0.0017890594899654388,0.004819984547793865,0.0027584435883909464,0.019986937940120696,0.0038764923810958862,0.005060431419406085,0.008052930748090149,0.06815227094339207
-2018,33,0.02818475477397442,0.006692761918529868,-0.0017023790860548615,0.004924151115119457,0.0028306334279477596,0.020506203174591064,0.004006013739854097,0.005246582620311528,0.0081617741426453,0.06921482509293128
-2018,50,0.030557546764612198,0.007710629142820835,-0.001289184670895338,0.005366859491914511,0.0032791525591164827,0.023244705982506275,0.004739969968795776,0.006382883835613029,0.008737974101677537,0.074483524353127
-2018,67,0.03282695263624191,0.008872693004086617,-0.0009136950829997659,0.0057574850507080555,0.0038197182584553957,0.026130635291337967,0.005473926197737455,0.007653931868844666,0.009364158823154866,0.07966910008573906
-2018,70,0.033237673342227936,0.009118306171149015,-0.0008476041257381439,0.0058616516180336475,0.003936156630516052,0.026716114580631254,0.0056034475564956665,0.007922830292955041,0.009493044344708323,0.0806924190663267
-2018,90,0.03808869794011116,0.011432083323597908,-0.00035694900143425876,0.006434568203985691,0.005230538081377745,0.032038770616054535,0.006423751823604107,0.010386114125140011,0.010836137551814319,0.08994342711521312
-2018,95,0.039943479001522064,0.012786616571247578,-0.000193462532479316,0.006564776878803968,0.0060174292884767056,0.03456905484199524,0.0066396212205290794,0.011770144570618868,0.011630155355669558,0.09439564542844892
-2019,5,0.022549010813236237,0.005319105461239815,-0.0034826539922505617,0.0043853651732206345,0.002023420762270689,0.012901505455374718,0.0030264852102845907,0.002835599472746253,0.007132293656468391,0.05676179115034756
-2019,10,0.024303890764713287,0.005630939733237028,-0.002933947369456291,0.00458628311753273,0.0022517877630889417,0.015452201664447785,0.0032195330131798983,0.0035776759032160044,0.007529187691397965,0.06130533281248063
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diff --git a/profsea/Falkland_example/data/sea_level_projections/STANLEYII_rcp26_projection_2100_regional.csv b/profsea/Falkland_example/data/sea_level_projections/STANLEYII_rcp26_projection_2100_regional.csv
deleted file mode 100644
index 8cc301f..0000000
--- a/profsea/Falkland_example/data/sea_level_projections/STANLEYII_rcp26_projection_2100_regional.csv
+++ /dev/null
@@ -1,847 +0,0 @@
-year,percentile,exp,antdyn,antsmb,greendyn,greensmb,glacier,landwater,GIA,antnet,greennet,sum
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-2009,67,0.01619601740911603,-0.00020304405958541892,-0.00012359106291640724,0.0036146949418487877,0.0025798325930285397,0.011760088883001537,0.0016751935737664384,0.0007595153574514231,-0.000439630119374933,0.006149836835844892,0.034295047829109034
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-2012,10,0.012512364193797112,-0.0006739622200934845,-0.0008417891383475385,0.004194551211450482,0.002352356109249326,0.00958703215981951,0.002125477816234445,-0.0030497896857342964,-0.00132407297031078,0.0069245989680586425,0.0310400315688184
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-2012,33,0.016507816140912474,-0.0005373221042507477,-0.0005283957769392974,0.004594863772430033,0.0027243983866436557,0.01217915650426963,0.002649157638461626,-0.0030497896857342964,-0.0010071970230005542,0.007556549304651271,0.03801256836774221
-2012,50,0.01859966553077102,-0.0004635552707245983,-0.0003904380591439653,0.004878474823416621,0.002979989735933894,0.013463699190652989,0.0030362251615399793,0.0009347881322479053,-0.0008509922845396354,0.0079293412137416,0.04157197910946009
-2012,67,0.020838798129931092,-0.00033105826999704855,-0.00026585239874110054,0.005160263584640257,0.003289835176149263,0.014818004445854337,0.003423292684618333,0.0009347881322479053,-0.0007024145908551483,0.00832876673587039,0.04509151133653364
-2012,70,0.02134277801141143,-0.00027804296372508627,-0.00024317108819139,0.005208726112381527,0.0033581858144058636,0.015100088741587669,0.003491598986002745,0.0009347881322479053,-0.0006748198404718026,0.008407354719575419,0.04577873644587321
-2012,90,0.02588779024034739,-0.00019439198508543114,-7.837101930872365e-05,0.005612790281252945,0.004098577484099669,0.01766239730448964,0.003924204043337373,0.0009347881322479053,-0.00044113716534792093,0.009207998334095314,0.052320316475437556
-2012,95,0.02826680339872837,-0.00017741136369919187,-2.2559967138933828e-05,0.005743213437135214,0.004549063261186544,0.018888154618384684,0.004038047271738067,0.0009347881322479053,-0.0003419887804711829,0.00967507739187498,0.05557009892665286
-2013,5,0.012407807644829155,-0.0008182387419788244,-0.0012339278519813586,0.004398484780709878,0.002300229287583461,0.008783879686557982,0.0023379914709703267,-0.00324040154109269,-0.0017481056774220636,0.0071685338167533546,0.030800996278437694
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-2013,90,0.028045103209093213,-0.00020889804262529753,-0.00010503396457841537,0.006211376661010023,0.004582302213061784,0.019179620698627404,0.004629610452695082,0.0009932123905133994,-0.0005102984603525803,0.010193029389315555,0.05702463661193243
-2013,95,0.030614089226722718,-0.0001888328194324456,-3.817444858747244e-05,0.0063641872088604735,0.005137418147831598,0.020569678568033253,0.004767659785285489,0.0009932123905133994,-0.00039518743361592926,0.010765115986805811,0.06055787274647353
-2014,5,0.013787954503670334,-0.0009078492998107129,-0.0014531391486342588,0.00473395769680235,0.0023915678961419326,0.009253675827384135,0.002631443537028695,-0.003431013396451084,-0.0020069376978193213,0.00766445897694854,0.033684094162604324
-2014,10,0.01523051055148244,-0.0008235284185247615,-0.0012227022300700138,0.0049027230088967335,0.0025607063482506495,0.010618223560760434,0.0027624434205494658,-0.003431013396451084,-0.0017875857727015446,0.007983669165857793,0.03683351319189551
-2014,30,0.019024443509988487,-0.0006507995530070332,-0.0008203796588735065,0.005367163792243254,0.0030262146282227535,0.013460558863563662,0.003417444052633304,-0.003431013396451084,-0.00138627253967639,0.008765230394428988,0.043912403165487786
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-2014,50,0.021697763298638165,-0.0005300962138453532,-0.0005843810165944629,0.005830363768905318,0.0034528805996264364,0.015364820338300045,0.004072444381097146,0.0010516366487788936,-0.0011310236377004555,0.009376010626804161,0.048757470139255396
-2014,67,0.024164213712327184,-0.00040065469516446225,-0.00040798443392093045,0.006210750523165267,0.003896713710221631,0.017060069584353584,0.00462919479692041,0.0010516366487788936,-0.0009292752060966668,0.009931662390828294,0.052811882218075694
-2014,70,0.024681410422548653,-0.00034463478461604083,-0.0003759224474550823,0.006280751883354175,0.003992765884103861,0.017405340389365895,0.004727444709560988,0.0010516366487788936,-0.0008923240952134435,0.010040993951530715,0.05360465587080653
-2014,90,0.029693406416103244,-0.00022343908798578665,-0.00014373216133997625,0.006818633323016394,0.0050544858756226265,0.020583536104843838,0.0053496956743846295,0.0010516366487788936,-0.0005884271198150568,0.011171449746997464,0.061107649981766585
-2014,95,0.032401160533726214,-0.0002002165451761453,-6.773084511003878e-05,0.007004929104890892,0.005701446300677771,0.02211597374930685,0.005513445225165597,0.0010516366487788936,-0.0004598744028749108,0.011842274773123102,0.06483508160636815
-2015,5,0.014988405083119868,-0.0010028288579199743,-0.0016886162527446655,0.005055860494753963,0.0024673310689018626,0.00956093777562033,0.002914758945897001,-0.003621625251809477,-0.0022840188629081088,0.008148815086137807,0.03623577640653403
-2015,10,0.016484888938069344,-0.0009019736053024292,-0.0014227751828771121,0.00524346930064616,0.0026645820491944215,0.011070311366568386,0.003067515631098881,-0.003621625251809477,-0.0020311079037177698,0.008513359957543779,0.039536644613619734
-2015,30,0.020271166038513186,-0.0007013131407480598,-0.000957803985893561,0.005760855303646522,0.0032034417161449116,0.014180984645370575,0.0038312987534882847,-0.003621625251809477,-0.0015673525468292958,0.009410775770164751,0.04696572320729671
-2015,33,0.02069501005858183,-0.0006785911058040101,-0.0009114423430522868,0.005863131174459999,0.0032736685393785353,0.014516846071660061,0.003945866115579697,-0.003621625251809477,-0.0015189571328968541,0.009519380952505676,0.047793857278240905
-2015,50,0.02300981916747987,-0.0005636473511601373,-0.0006852430724506475,0.00629311955239139,0.003698415602615769,0.01627859785502364,0.004595081875877688,0.0011100609070443875,-0.0012763062818358217,0.01011164720458041,0.052063770215810594
-2015,67,0.025512241002172233,-0.00042588561063214487,-0.00048088315392832574,0.006744338399442985,0.004212944632063068,0.018143025726340672,0.005244297028935688,0.0011100609070443875,-0.001045536950305836,0.010748861159291552,0.056339766295504835
-2015,70,0.026023414623737334,-0.0003797946652658414,-0.0004437558948606231,0.006825383096462235,0.004324515884625859,0.01852056975006085,0.005358864998267092,0.0011100609070443875,-0.0010042177865186588,0.010877113436591085,0.05717234949213872
-2015,90,0.031219507664442064,-0.00023801510908685918,-0.00017648622747807905,0.007441170336562145,0.005558095826986659,0.021996972475743625,0.006084459101166024,0.0011100609070443875,-0.0006609104705068401,0.01218490889044907,0.06507679222841305
-2015,95,0.03394054442290217,-0.00021156256106368988,-8.793947271326885e-05,0.007637785570467427,0.006309269791503907,0.023675124634767057,0.006275404502238379,0.0011100609070443875,-0.0005171068131271095,0.012964334648584876,0.06899949876578106
-2016,5,0.016068565603345634,-0.0011005722707064826,-0.001919353745068739,0.0053721741555218805,0.002545733292950418,0.00987555718637866,0.0031879367867152556,-0.003812237107167871,-0.0025584450421950898,0.008633542148661058,0.038625478068986606
-2016,10,0.017603077159635723,-0.000981953463233194,-0.001618436181657098,0.005583546987578978,0.0027688783409234403,0.011512113826224074,0.003363645929142918,-0.003812237107167871,-0.002272969331612386,0.00904493146502186,0.04211757594005855
-2016,30,0.02152880383003503,-0.0007527380592677668,-0.001093389705916123,0.006184600586725834,0.0033787678382247244,0.014867567219454314,0.004242191337661235,-0.003812237107167871,-0.0017489691666115264,0.010056620676599896,0.049969145765792776
-2016,33,0.021945314079895615,-0.0007265880206877239,-0.001040787914898392,0.006280897686130596,0.0034585609646750363,0.01523019290246599,0.004373973042671985,-0.003812237107167871,-0.0016944598326672263,0.010179731678126596,0.05085080297745963
-2016,50,0.024353102810867132,-0.0005976222460591534,-0.0007850053020140496,0.0067671536732453776,0.003940191960956746,0.017143332730799106,0.005120737049799548,0.0011684851653098816,-0.0014214102163714968,0.010849448104620314,0.05538389138216978
-2016,67,0.026958559445105494,-0.0004523943715019114,-0.0005535729503476569,0.007287182275293309,0.004525007294970448,0.019167338430491943,0.005867500146067124,0.0011684851653098816,-0.0011619112851008096,0.011568158788690528,0.059920886118683445
-2016,70,0.027475358065217732,-0.00041619713902946046,-0.0005116647697960138,0.007374227892529515,0.004650905072518815,0.019572491098113903,0.005999282154697869,0.0011684851653098816,-0.0011162181754454774,0.011714458599088989,0.06079941037255142
-2016,90,0.032987871516495945,-0.0002526261180085544,-0.00020884454236752572,0.008055971550921965,0.006052020806444753,0.02332351357193835,0.006833900125799272,0.0011684851653098816,-0.0007338891618259258,0.013193707519437707,0.0691875149445746
-2016,95,0.03577746518775821,-0.00022287084696168044,-0.00010941332177388192,0.00827785648869491,0.006904112863483389,0.0251390579865203,0.007053536402023853,0.0011684851653098816,-0.0005752915287190378,0.014078038544469422,0.073334710401792
-2017,5,0.017203184843063355,-0.0012014133890669882,-0.0021560901475767896,0.005700451994143641,0.0026374441283666837,0.01025965293012662,0.003450978881203436,-0.004002848962526264,-0.002840038762893597,0.009129359561838614,0.041161602610625764
-2017,10,0.01880470642000437,-0.001064141808069809,-0.0018212300138481738,0.005923924965206549,0.002887612220694643,0.012013471911739515,0.0036508364400215506,-0.004002848962526264,-0.0025233140123585647,0.009588736512963757,0.044857361124093144
-2017,30,0.022927205380797386,-0.0008046972043069122,-0.0012358633686531542,0.006582415016585006,0.0035677687406569306,0.015587706685838566,0.0046501230196321405,-0.004002848962526264,-0.001938372682827051,0.01071926297116528,0.05316577162315977
-2017,33,0.023347741947323083,-0.0007752691228723752,-0.001176887892298647,0.006704990445555015,0.003658175166414584,0.015974248055339098,0.004800015961030729,-0.004002848962526264,-0.001877367024833096,0.010857897190657747,0.05410418290890706
-2017,50,0.025887029647082088,-0.000631705617890442,-0.0008914088174657336,0.007260068648547005,0.004197362002057845,0.01803042805860515,0.005649409902862726,0.0012269094235753757,-0.001574631550291491,0.011605364654022314,0.058912088318162914
-2017,67,0.02864385134652257,-0.00048026176770686694,-0.0006327688709179294,0.007824770822560476,0.004853820110051703,0.020202801715526197,0.00649880323745473,0.0012269094235753757,-0.0012863127737013473,0.012408340495702096,0.06374369980567457
-2017,70,0.02915694209560752,-0.000449590837393435,-0.0005863150476904965,0.007931258342271399,0.004993535156027664,0.02063730658896845,0.006648696178853319,0.0012269094235753757,-0.0012353719784218445,0.01257106450326261,0.06467141198471586
-2017,90,0.03500935236029327,-0.000267272102670833,-0.0002489614731501643,0.008676671684921875,0.006566280300929909,0.024655517133662864,0.007598019355524368,0.0012269094235753757,-0.000813914936935936,0.014227579644661148,0.0735959829969092
-2017,95,0.03792779001966119,-0.00023414140287011693,-0.00013641396334852953,0.008925143268921383,0.007521609720510583,0.02658778271549161,0.007847840924522016,0.0012269094235753757,-0.0006398558161140477,0.015217837809951035,0.07799607458048487
-2018,5,0.018245853126049042,-0.001307073503965327,-0.0024163482164051304,0.006027388768552067,0.002700399556837327,0.01042718066141678,0.0037038834076415653,-0.004193460817884657,-0.0031456023256962794,0.009604796155730214,0.04336237220315513
-2018,10,0.0199484159950912,-0.001149068042167519,-0.0020405873239600666,0.006261883202922126,0.0029821565890397164,0.01234300889957995,0.003929085038394806,-0.004193460817884657,-0.0027895739379900438,0.010115501051752449,0.04732902496743591
-2018,30,0.02429726463854313,-0.0008570295943245846,-0.0013834265571820868,0.007001367469260569,0.003745485484078926,0.01623989988879086,0.005055093192161008,-0.004193460817884657,-0.002132726389215615,0.01137656874902794,0.056267002992683744
-2018,33,0.02474163197502494,-0.0008247477723672515,-0.0013171849947836176,0.007129037543369594,0.0038487749449784827,0.016659791558279766,0.005223993959795944,-0.004193460817884657,-0.002065201391897903,0.011530572135760055,0.05727564430478506
-2018,50,0.027437200386077165,-0.0006663283262453064,-0.0009969623020864302,0.007758906745385124,0.004454755118898951,0.018888476052933353,0.006181100738687217,0.0012853336818408696,-0.0017282434107899491,0.012362703338912732,0.06243424228972759
-2018,67,0.03035125403702259,-0.000508407120973087,-0.0007064911836266474,0.008372152426746679,0.005193111178959991,0.02123416088998508,0.007138207517578491,0.0012853336818408696,-0.001406660415482097,0.013261372565818143,0.0676230176816676
-2018,70,0.030883203428238633,-0.0004775093827513884,-0.0006543295030143533,0.008486322510047753,0.005351826009001607,0.021704145696457292,0.007307108285213427,0.0012853336818408696,-0.0013495028147327403,0.013443346997373587,0.06861178690155742
-2018,90,0.03709034727141261,-0.00028195307515373435,-0.0002755778867438511,0.009315773872320207,0.007121266710952606,0.026029654046767915,0.008376816183101318,0.0012853336818408696,-0.0008813333000187711,0.015305123623605213,0.0781802104391834
-2018,95,0.04016860171407462,-0.00024537422878899944,-0.00014965367588848025,0.00957969067452306,0.008200638707841053,0.028117288414268058,0.00865831806973287,0.0012853336818408696,-0.0006906269903159973,0.01641674991436496,0.08287647201904412
-2019,5,0.019383239809423684,-0.0014153354710419953,-0.002691763847135591,0.006352981923878022,0.0027488886265739274,0.010472382563262788,0.003946651580509628,-0.004384072673243051,-0.0034658711754707566,0.010064776214324037,0.045516892292425774
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-2099,33,0.12280526325851679,-0.007102621655604943,-0.020261406579578516,0.04479953014473432,0.03197702938643458,0.07402694692446764,0.03271010921814365,-0.019633021101914536,-0.027975192158290387,0.0869939664466769,0.3183494439457065
-2099,50,0.13616470539793374,-0.004626133211412705,-0.015752882317965945,0.05633288714126163,0.041753576262573824,0.08732712015086991,0.059271058521190655,0.006017698601345891,-0.022900543638917033,0.10138942788568908,0.3566540640765493
-2099,67,0.15032407987713814,-0.0029968284017995016,-0.011721359763758378,0.06778161055204523,0.05373491010408352,0.10058549532014362,0.0858320126821576,0.006017698601345891,-0.01825451847788986,0.11642802274791657,0.39615324357950765
-2099,70,0.15288261492550373,-0.0027619973970641463,-0.01100768783090236,0.07036941318292754,0.056363530897672535,0.10313807791287048,0.09051924474495404,0.006017698601345891,-0.01742288983684322,0.11936024109020926,0.4036941170957627
-2099,90,0.18354737984091044,-0.0013347893721965913,-0.005751260108331998,0.08562384973508141,0.08563834883879451,0.12624730090254654,0.12020503161626514,0.006017698601345891,-0.010924841438235938,0.15013245011926996,0.47360026683397455
-2099,95,0.19864719637632372,-0.0009564429940881856,-0.0037973152901749833,0.0894980037673724,0.10368646731706234,0.13720527321257014,0.12801707209980606,0.006017698601345891,-0.0083783433033278,0.168464360900473,0.5082589027435137
-2100,5,0.09175164632499218,-0.023229288988125634,-0.035858399815714814,0.02488567024467433,0.014188692591185206,0.03777944778326466,-0.010053249133691869,-0.01982363295727293,-0.0462960237145146,0.05445722102110567,0.2204519521312553
-2100,10,0.10013713685050607,-0.016365414007907083,-0.030613276111826196,0.028950195997150676,0.01849502020074547,0.04886160234094399,-0.0036802059058650767,-0.01982363295727293,-0.04008552485752377,0.06311674431571668,0.2495262862143255
-2100,30,0.12159980932176113,-0.007849373705509827,-0.021444980600122832,0.04286155224304489,0.03068522129805891,0.0720368251748878,0.02818501509118882,-0.01982363295727293,-0.029397076552257744,0.08531064137413195,0.313976532350874
-2100,33,0.12377075560688973,-0.00721031077249586,-0.020521516541833984,0.0453056190578918,0.03235434982783987,0.07461390451080119,0.03296479538671895,-0.01982363295727293,-0.028357390979895276,0.0880360918735007,0.32126314328851985
-2100,50,0.1371753405481577,-0.004688527449167295,-0.01595154870289918,0.057060239818336043,0.04226660958100401,0.08804114934415037,0.06005022515792286,0.0060761228596113845,-0.02320829742242886,0.10267926269663456,0.36014036175584885
-2100,67,0.15160728479558275,-0.003031108872064509,-0.011865975841770335,0.06871562384175436,0.05441555138163438,0.10142554097470093,0.08713565492912678,0.0060761228596113845,-0.018491842284944535,0.11793867237885099,0.40022884057310754
-2100,70,0.1542211723163724,-0.0027912444938084104,-0.011142226292670651,0.07132337268876317,0.05707100966204223,0.10399159393701769,0.09191544008257684,0.0060761228596113845,-0.01764966701461718,0.12092482978401622,0.4079433556820319
-2100,90,0.1851145754635334,-0.0013437794695178606,-0.0058187205650782675,0.08686685271078348,0.08675761533817151,0.12730323788782133,0.122187403005254,0.0060761228596113845,-0.01105724998192351,0.15214150923746134,0.47877771572391814
-2100,95,0.20033668273687363,-0.0009598030562998854,-0.0038385077186049183,0.09079724790725203,0.1050330476519194,0.13835207546394795,0.13015370187849756,0.0060761228596113845,-0.008470499424715762,0.1707328706946201,0.5139132030987965
diff --git a/profsea/Falkland_example/data/sea_level_projections/STANLEYII_rcp45_projection_2100_global.csv b/profsea/Falkland_example/data/sea_level_projections/STANLEYII_rcp45_projection_2100_global.csv
deleted file mode 100644
index c2fbce5..0000000
--- a/profsea/Falkland_example/data/sea_level_projections/STANLEYII_rcp45_projection_2100_global.csv
+++ /dev/null
@@ -1,847 +0,0 @@
-year,percentile,exp,antdyn,antsmb,greendyn,greensmb,glacier,landwater,antnet,greennet,sum
-2007,5,0.008229974657297134,0.0027299541980028152,-0.00019024289213120937,0.0016894025029614568,0.0014904462615959347,0.009865183383226395,0.0002794466563500464,0.0026003383100032806,0.0032215165556408465,0.02476743524371159
-2007,10,0.009173440746963024,0.0027500782161951065,-0.0001599828974576667,0.0017016869969666004,0.0015029923524707556,0.010186166502535343,0.00028879407909698784,0.0026425049582030624,0.003244227496907115,0.026038945989057538
-2007,30,0.011815459467470646,0.002831880934536457,-0.0001073703242582269,0.0017332754796370864,0.0015370583743788302,0.010853993147611618,0.0003355311928316951,0.0027429508820205227,0.00330113674281165,0.029400606065246394
-2007,33,0.012121230363845825,0.0028439889079891144,-0.00010205431317444891,0.0017402952071279287,0.00154157227370888,0.010924169793725014,0.0003425417235121131,0.0027571243190323003,0.003308123027672991,0.029818229429984057
-2007,50,0.013442737981677055,0.002913743257522583,-7.654178625671193e-05,0.0017701288452371955,0.0015684564714320004,0.011318678501993418,0.00038226827746257186,0.002832439073245041,0.0033452652278356254,0.03152719994614017
-2007,67,0.014706665650010109,0.002984089271631092,-5.35062063136138e-05,0.0017964525613933802,0.001600855146534741,0.011782738026231528,0.0004219948314130306,0.0029089518775435863,0.0033842173870652914,0.033106355798154255
-2007,70,0.01493541244417429,0.002997269877232611,-4.925956091028638e-05,0.0018034722888842225,0.0016080625355243683,0.01188282947987318,0.00042900542030110955,0.002923197029485891,0.0033920287154614926,0.0334307287223055
-2007,90,0.01763715222477913,0.0030866435263305902,-1.887162943603471e-05,0.0018420804990455508,0.0016849985113367438,0.012807788979262113,0.0004734056710731238,0.0030250579366111197,0.003473164769820869,0.0365335078415228
-2007,95,0.018670156598091125,0.0031142900697886944,-8.972326759249077e-06,0.0018508550710976124,0.0017325662774965167,0.013223980087786913,0.00048508995678275824,0.003060682878640364,0.0035207918379455802,0.03787797319309902
-2008,5,0.00942226778715849,0.0029588909819722176,-0.0003722738183569163,0.0019223066046833992,0.0015177444263827055,0.009918984957039356,0.0005511200288310647,0.002709460473852232,0.0035240621014963835,0.027123401036715223
-2008,10,0.010384301654994488,0.003000084310770035,-0.00031190270092338316,0.0019474038854241371,0.0015427843434736132,0.010459965839982033,0.0005707318778149784,0.0027935943799093366,0.0035700987791642547,0.02869841980791534
-2008,30,0.013078315183520317,0.003166733542457223,-0.00020666010095737875,0.0020119398832321167,0.0016108984127640724,0.011618562042713165,0.0006687914719805121,0.0029973652126500383,0.0036848414922133086,0.03265678184252465
-2008,33,0.013390103355050087,0.003191432380117476,-0.00019625165441539139,0.0020262813195586205,0.0016195097705349326,0.011739669367671013,0.0006835003150627017,0.0030262009386206046,0.0036987857951316983,0.03310978279536357
-2008,50,0.01473761722445488,0.003334362991154194,-0.00014519999967887998,0.002087231958284974,0.0016730890492908657,0.012390101328492165,0.0007668508915230632,0.0031801401200937107,0.0037737214006483555,0.03510122626175871
-2008,67,0.016026418656110764,0.0034806386055424815,-9.931124077411368e-05,0.002141011878848076,0.0017373837938066575,0.013119114385917783,0.0008502015843987465,0.0033385501301381737,0.0038522755319718273,0.03696701826731442
-2008,70,0.01625966653227806,0.0035084845032542942,-9.102411422645673e-05,0.0021553533151745796,0.001751153962686658,0.013269010931253433,0.000864910485688597,0.003368482786754612,0.0038676489843055607,0.0373405848396942
-2008,90,0.019014576449990273,0.0037072808481752872,-3.0072495064814575e-05,0.0022342305164784193,0.0019043567590415478,0.014703273307532072,0.0009580671321600676,0.003590644191717729,0.004029212985187769,0.04087927782384213
-2008,95,0.020067911595106125,0.0037783479783684015,-9.629442502046004e-06,0.002252157311886549,0.0019974501337856054,0.015367973549291487,0.0009825819870457053,0.0036765826298506,0.004123428312595934,0.04248074392889976
-2009,5,0.010274065658450127,0.0031868102960288525,-0.0005692910635843873,0.002153712324798107,0.0015511831734329462,0.01009910828433931,0.0008150199428200722,0.0028050837499904446,0.0038332093332428484,0.029350616334522784
-2009,10,0.011347959749400616,0.003250018460676074,-0.00047732802340760827,0.002192151267081499,0.0015892701921984553,0.010831926017999649,0.000845813425257802,0.0029333486745599657,0.0039036007947288454,0.031242374316479984
-2009,30,0.014355217106640339,0.003504558000713587,-0.0003167155955452472,0.0022909934632480145,0.0016932578524574637,0.012393906712532043,0.0009997807210311294,0.003244493762758793,0.004079043201636523,0.03594311278720852
-2009,33,0.014703258872032166,0.00354233036050573,-0.0003008127387147397,0.002312958473339677,0.0017063368577510118,0.012557171313092112,0.0010228757746517658,0.0032890866277739406,0.004100471858400852,0.036462478659814224
-2009,50,0.01620745286345482,0.003761859377846122,-0.00022301705030258745,0.0024063095916062593,0.0017881286912597716,0.013431687373667955,0.0011537481332197785,0.0035254467802587897,0.004215159686282277,0.03882661568786716
-2009,67,0.01764610968530178,0.0039896487211808565,-0.00015299892402254045,0.0024886783212423325,0.0018863402074202895,0.014393134973943233,0.0012846202589571476,0.0037716275546699762,0.004335137617308646,0.04107562266290188
-2009,70,0.01790647953748703,0.004033643938601017,-0.00014021109091117982,0.002510643098503351,0.0019073542673140763,0.014589563012123108,0.0013077154289931059,0.0038195444285520352,0.0043585945153608915,0.041521356368320994
-2009,90,0.020981712266802788,0.0043619126081466675,-4.730471955554092e-05,0.0026314505375921726,0.0021412610076367855,0.01645800843834877,0.0014539845287799835,0.004181875416543335,0.0046054774429649115,0.04569964717666153
-2009,95,0.022157521918416023,0.004492173902690411,-1.602126758371014e-05,0.002658906625583768,0.0022834530100226402,0.017332224547863005,0.001492476207204163,0.00433038741175551,0.004749444650951773,0.047645570761960694
-2010,5,0.011335372924804688,0.0034137119073420763,-0.0008042361587285995,0.002383620012551546,0.001569479936733842,0.00996096059679985,0.0010711464565247297,0.0028763834852725267,0.004131592338671908,0.031343098846264184
-2010,10,0.012474281713366508,0.0034998804330825806,-0.0006716916686855257,0.002435928676277399,0.0016231626505032182,0.01092272037640214,0.0011140386341139674,0.003058027272345498,0.004230084456503391,0.03361100545262161
-2010,30,0.015663601458072662,0.0038453543093055487,-0.0004397040174808353,0.002570436103269458,0.001772689982317388,0.013067109510302544,0.0013284990563988686,0.0034931106289150192,0.004474669927731156,0.03914268588705454
-2010,33,0.016032714396715164,0.003896683161146939,-0.00041662759031169116,0.002600326668471098,0.001792507478967309,0.013290062360465527,0.0013606681022793055,0.0035487159329932183,0.004504478538874537,0.0397384151937149
-2010,50,0.017627974972128868,0.004196232184767723,-0.000305041583487764,0.0027273616287857294,0.0019100001081824303,0.014435678720474243,0.0015429593622684479,0.00387461505306419,0.004663931322284043,0.04248530519544147
-2010,67,0.01915372908115387,0.00451111821923405,-0.00020440792082808892,0.002839451190084219,0.0020494188647717237,0.01566347222775221,0.001725250855088234,0.004214459942886606,0.004832559503847734,0.04509396663677762
-2010,70,0.01942986063659191,0.004572747927159071,-0.00018603239150252204,0.002869341755285859,0.00207995749078691,0.01591876894235611,0.0017574199009686708,0.004282394569600001,0.0048662517219781876,0.045616937745944594
-2010,90,0.02269127406179905,0.005050537642091513,-5.179455547477119e-05,0.0030337399803102016,0.0024151925463229418,0.018270509690046313,0.001961157424375415,0.004807468850049191,0.005217508296482265,0.050437325466191396
-2010,95,0.023938268423080444,0.005255767144262791,-5.994080311211292e-06,0.0030711032450199127,0.002621992025524378,0.019398171454668045,0.0020147725008428097,0.0050360107303276894,0.005425760697107762,0.05269900935381883
-2011,5,0.01258755661547184,0.00363959651440382,-0.0010417817626148461,0.0026120292022824287,0.0016022146446630359,0.010102792596444487,0.0013194996863603592,0.0029397746548056602,0.004443408892257139,0.03395551033463562
-2011,10,0.013784435577690601,0.0037496699951589108,-0.000871053955052048,0.002678736113011837,0.001671685604378581,0.01126901712268591,0.0013754075625911355,0.0031745391024742275,0.004569892887957394,0.03648687543900451
-2011,30,0.017136093229055405,0.0041891224682331085,-0.0005710058030672371,0.002850267803296447,0.0018651392310857773,0.013825938105583191,0.0016549464780837297,0.0037316292873583734,0.00488399596652016,0.04260319561581127
-2011,33,0.0175239946693182,0.004254490467719734,-0.0005412993737263605,0.0028883861377835274,0.0018906547408550976,0.014091147109866142,0.0016968772979453206,0.003801759914495051,0.004922507768496871,0.04326336213038303
-2011,50,0.019200453534722328,0.004637482110410929,-0.0003970886755269021,0.003050388302654028,0.0020427757408469915,0.015461264178156853,0.001934485393576324,0.004220060360239586,0.0051265505608171225,0.04631940215767827
-2011,67,0.020803870633244514,0.005045048967003823,-0.00026714990963228047,0.00319333141669631,0.0022234232164919376,0.01691387651488185,0.002172093605622649,0.004660432845412288,0.005343981068581343,0.04925737610523356
-2011,70,0.021094055846333504,0.0051257970277220006,-0.00024335998750757426,0.003231449518352747,0.002263067988678813,0.017214768193662165,0.002214024541899562,0.004750017251353711,0.005387611221522093,0.04983101422694745
-2011,90,0.0245214756578207,0.0057731568813323975,-6.991623376961797e-05,0.0034410993102937937,0.0026972435880452394,0.019975844770669937,0.0024795865174382925,0.005454560334328562,0.005841541104018688,0.05520343067473732
-2011,95,0.02583194151520729,0.006069128401577473,-1.168072899417904e-05,0.0034887471701949835,0.002965169493108988,0.02130740825086832,0.0025494713336229324,0.005777028450393113,0.0061112570110708475,0.05772272029716986
-2012,5,0.013811971992254257,0.0038644634187221527,-0.0012820952164474873,0.0028389403596520424,0.0016422970220446587,0.010354802943766117,0.0015600797487422824,0.0029992766212671995,0.004762134229531511,0.03667819046204386
-2012,10,0.01508180983364582,0.0039993878453969955,-0.0010737013071775436,0.0029205738101154566,0.0017275080317631364,0.011696481425315142,0.0016299202106893063,0.00328559527406469,0.0049171637743711475,0.03947396023577312
-2012,30,0.018637778237462044,0.004535862244665623,-0.0007074878667481244,0.0031304885633289814,0.0019637443358078597,0.01460636593401432,0.0019791231025010347,0.003966030897572637,0.00530172202270478,0.04613530499336775
-2012,33,0.019049324095249176,0.00461575236171484,-0.0006710535380989313,0.003177136415615678,0.0019951085560023785,0.014902220461517573,0.002031503478065133,0.004053205426316708,0.005348665817873553,0.04685830103699118
-2012,50,0.02082797884941101,0.005085608456283808,-0.0004947070847265422,0.0033753893803805113,0.002182349213398993,0.01647297292947769,0.0023283257614821196,0.004566072602756321,0.0055986211518757045,0.0502197781461291
-2012,67,0.022529134526848793,0.005591439562849702,-0.0003359550901222974,0.0035503183025866747,0.002403837065212432,0.018128657005727293,0.002625148044899106,0.005113495804835111,0.00586478325072676,0.05347386685141828
-2012,70,0.022837011143565178,0.005692790821194648,-0.0003063787240535021,0.003596966154873371,0.0024521765299141407,0.018472920730710028,0.002677528653293848,0.005227342975558713,0.005918369954451918,0.054113245996995823
-2012,90,0.026473358273506165,0.006529770791530609,-9.538193990010768e-05,0.0038535285275429487,0.002985897706821561,0.02160222101956606,0.003009271342307329,0.006134006136562675,0.006476210663095117,0.060030100954463705
-2012,95,0.02786370925605297,0.006932257674634457,-2.3919874092827008e-05,0.003911837935447693,0.003313835710287094,0.023110881447792053,0.0030965718906372786,0.006565365250571625,0.0068056206451728935,0.06279431478469633
-2013,5,0.015280546620488167,0.004088312853127718,-0.0015443468000739813,0.0030643530189990997,0.0016888019163161516,0.010684386175125838,0.0017928860615938902,0.003038609051145613,0.005087736458517611,0.03975069583248114
-2013,10,0.016588643193244934,0.004249033518135548,-0.0012940007727593184,0.003161441534757614,0.001789835630916059,0.012189896684139967,0.0018775764619931579,0.003382223891094327,0.0052725363988429304,0.042746630977489986
-2013,30,0.020251745358109474,0.0048855748027563095,-0.0008578930806834251,0.0034110983833670616,0.0020729871466755867,0.015427292510867118,0.0023010284639894962,0.004191093612462282,0.0057316951337270435,0.04982514012372121
-2013,33,0.020675690844655037,0.004980468843132258,-0.0008145375759340823,0.003466577734798193,0.0021098130382597446,0.015759754925966263,0.002364546060562134,0.004296757921110839,0.005787645471282303,0.050596962589479516
-2013,50,0.022507932037115097,0.00554061122238636,-0.0006031258380971849,0.003702364629134536,0.002334874588996172,0.017529986798763275,0.002724480116739869,0.004908201604848728,0.0060862284153699875,0.054234451410593465
-2013,67,0.024260342121124268,0.006150290630757811,-0.0004129501467105001,0.003910412080585957,0.0026014975737780333,0.019387178122997284,0.0030844141729176044,0.0055701728485291835,0.006404350702650846,0.057763848977629095
-2013,70,0.024577492848038673,0.0062737294472753995,-0.0003783259307965636,0.003965891432017088,0.0026585781015455723,0.019768528640270233,0.0031479322351515293,0.005706168652977794,0.006468834145925939,0.058459646825212985
-2013,90,0.028323397040367126,0.007320377975702286,-0.00012589397374540567,0.004271027632057667,0.0033002041280269633,0.023271748051047328,0.0035502114333212376,0.006837628781795502,0.007138632261194289,0.06483348868787289
-2013,95,0.029755637049674988,0.007845154963433743,-4.112490205443464e-05,0.004340376704931259,0.0036933892406523228,0.024963125586509705,0.0036560744047164917,0.007391141899279316,0.007533663418143988,0.06783768222667275
-2014,5,0.01662345789372921,0.004311145283281803,-0.0018155163852497935,0.0032882671803236008,0.0017540724948048592,0.011280372738838196,0.0020179194398224354,0.0030664439545944333,0.005434273037826643,0.04301788471639156
-2014,10,0.017991479486227036,0.004498607013374567,-0.0015257358551025388,0.003401339752599597,0.0018741115927696228,0.01292260829359293,0.002118376549333334,0.003464116482064128,0.005649811844341457,0.04618074678000994
-2014,30,0.02182239294052124,0.00523825827986002,-0.0010216932278126478,0.0036920972634106874,0.0022027287632226944,0.016361549496650696,0.0026206630282104015,0.004402719480276573,0.0061827467288821936,0.053622314298991114
-2014,33,0.022265758365392685,0.0053486399073153735,-0.0009713359177112579,0.0037567103281617165,0.0022449686657637358,0.016727838665246964,0.0026960058603435755,0.004526247386820614,0.006248184269061312,0.05443825130176265
-2014,50,0.02418193593621254,0.006002491340041161,-0.000727359380107373,0.004031314514577389,0.0025052414275705814,0.018654602579772472,0.0031229492742568254,0.005243040563073009,0.006595137063413858,0.0583382076874841
-2014,67,0.026014626026153564,0.006721601393073799,-0.0005063842399977148,0.004273612983524799,0.0028173751197755337,0.020693971440196038,0.0035498926881700754,0.006023717622156255,0.006965880515053868,0.0621392862754874
-2014,70,0.02634630724787712,0.0068686121143400666,-0.00046595205203630034,0.004338225349783897,0.0028848249930888414,0.021111510694026947,0.0036252355203032494,0.006187218421837314,0.007041030446998774,0.06289063442964107
-2014,90,0.030263813212513924,0.008144979365170002,-0.00017515594663564116,0.0046935961581766605,0.0036316460464149714,0.02494118791073561,0.004102407954633236,0.007555073680123314,0.007820739410817623,0.06971883022342809
-2014,95,0.031761664897203445,0.00880782026797533,-8.008680742932484e-05,0.004774361848831177,0.004085642751306295,0.026772363111376762,0.0042279791086912155,0.00825242690189043,0.008281651232391595,0.07294442859711125
-2015,5,0.017366506159305573,0.004532960243523121,-0.002079466823488474,0.00351068377494812,0.0018132282420992851,0.011716378852725029,0.0022351795341819525,0.0030997053254395723,0.005772088875528425,0.045523549139034
-2015,10,0.018873430788517,0.004748108796775341,-0.0017501550260931253,0.0036402682308107615,0.0019513657316565514,0.013498125597834587,0.0023523208219558,0.0035531908506527543,0.006017377856187523,0.048941876069875434
-2015,30,0.023093316704034805,0.005593914072960615,-0.0011800553184002638,0.00397348590195179,0.0023239082656800747,0.017183198034763335,0.0029380270279943943,0.004623773391358554,0.006625706912018359,0.05703687370987609
-2015,33,0.023581702262163162,0.005720265870913863,-0.0011215585982427,0.004047533962875605,0.0023711302783340216,0.0175820055603981,0.003025882877409458,0.004769174054672476,0.006701157430652529,0.05791284357823315
-2015,50,0.025692438706755638,0.006471247412264347,-0.0008446788997389376,0.0043622395023703575,0.0026674806140363216,0.0196688249707222,0.0035237332340329885,0.005592022149357945,0.00709827896207571,0.06219311218592338
-2015,67,0.027711210772395134,0.007305373558774593,-0.0005927803576923907,0.004639920778572559,0.0030245224665850405,0.02188016287982464,0.004021583124995232,0.006495384615845978,0.007520778498146683,0.06635953935212456
-2015,70,0.028076568618416786,0.0074774413369596,-0.0005478465463966131,0.0047139693051576614,0.0031008378136903048,0.02234296128153801,0.004109439440071583,0.00668697280343622,0.0076060391729697585,0.06717792631097837
-2015,90,0.03239184245467186,0.009003574959933758,-0.0002189711231039837,0.005121234804391861,0.003952709026634693,0.026479313150048256,0.004665860440582037,0.008315211877925333,0.0084965338697657,0.07465752956923098
-2015,95,0.03404177725315094,0.00982025358825922,-0.00011003372019331531,0.005213795229792595,0.004470566287636757,0.028456887789070604,0.004812286701053381,0.009168889533611922,0.009021497331559658,0.0782126677047927
-2016,5,0.018651317805051804,0.004753757268190384,-0.0023512807674705982,0.003731601405888796,0.001855918439105153,0.011920850165188313,0.0024446656461805105,0.003132798708975315,0.006093581416644156,0.0482459348422708
-2016,10,0.020192530006170273,0.004997537471354008,-0.0019789044745266438,0.003878226736560464,0.0020110870245844126,0.013866722583770752,0.002579407999292016,0.0036456503439694643,0.006372261303476989,0.05191325515625067
-2016,30,0.024508439004421234,0.005952541716396809,-0.0013343699974939227,0.004255262669175863,0.0024337880313396454,0.0178974162787199,0.0032531195320189,0.004857043270021677,0.007060783333145082,0.060448012710548935
-2016,33,0.025007937103509903,0.006095345951616764,-0.0012681714724749327,0.0043390486389398575,0.0024872056674212217,0.018328799698501826,0.0033541761804372072,0.005021231321879895,0.007145951967686415,0.06138119077542797
-2016,50,0.02716670371592045,0.006946881301701069,-0.0009550847462378442,0.0046951379626989365,0.002822623122483492,0.02060466632246971,0.0039268312975764275,0.00595385511405766,0.007595171686261892,0.06591380630561616
-2016,67,0.029231412336230278,0.007901605572551491,-0.000670295616146177,0.005009335000067949,0.0032264976762235165,0.02300497664138675,0.004499485716223717,0.006985937128774822,0.008073621348012238,0.07036589432915208
-2016,70,0.029605085030198097,0.008100214321166276,-0.0006195319001562893,0.005093120504170656,0.0033136624842882156,0.023501979373395442,0.004600542597472668,0.0072075197356753044,0.008170087728649378,0.07122667668736539
-2016,90,0.03401855006814003,0.009896163828670979,-0.00024764821864664555,0.0055539426393806934,0.004277213476598263,0.02797578386962414,0.005240568425506353,0.009118438661971595,0.009178611426614225,0.0791542261140421
-2016,95,0.03570602834224701,0.010882454924285412,-0.0001244613384187702,0.005658674519509077,0.0048637050203979015,0.030120916664600372,0.0054089962504804134,0.010140347178094089,0.009773105848580599,0.0829655856708996
-2017,5,0.02026953548192978,0.0049735368229448795,-0.0026249561924487352,0.003951020538806915,0.0019336536061018705,0.012537021189928055,0.0026463791728019714,0.00315349199809134,0.006448140583233908,0.05176977821975015
-2017,10,0.021768666803836823,0.005246895365417004,-0.0022146659437566997,0.004115215037018061,0.002104727551341057,0.014587870799005032,0.0027996397111564875,0.00371848966460675,0.006756717420648784,0.05549945685779676
-2017,30,0.02596673183143139,0.006314140744507313,-0.0015037873527035117,0.004537428729236126,0.0025718828430399297,0.018786277249455453,0.003565941471606493,0.005071714171208441,0.007524915761314333,0.0640988074010238
-2017,33,0.02645258978009224,0.0064738809317350385,-0.0014323430368676782,0.004631254356354475,0.0026323236525058746,0.019237685948610306,0.0036808867007493973,0.005254876450635493,0.007619673386216164,0.06503508828696795
-2017,50,0.02855241298675537,0.007429390214383602,-0.0010860587935894728,0.005030011758208275,0.003004646860063076,0.021653050556778908,0.004332243464887142,0.006299533706624061,0.008121320512145758,0.06967855177936144
-2017,67,0.030560744926333427,0.00851029775105417,-0.0007711967919021845,0.005381856579333544,0.0034503524657338858,0.024210499599575996,0.0049835997633636,0.007470806069322866,0.008652963405475021,0.07426995906746016
-2017,70,0.03092421405017376,0.00873693162575364,-0.0007167526986449958,0.0054756817407906055,0.003549701999872923,0.02473936695605516,0.005098544992506504,0.0077174947946332395,0.008760115830227733,0.075166730734054
-2017,90,0.03521716967225075,0.010822746902704239,-0.00030622168560512364,0.00599172106012702,0.00461588054895401,0.02949514426290989,0.0058265323750674725,0.009933639405062422,0.00987975592724979,0.08330797237576917
-2017,95,0.03685857355594635,0.011994422413408756,-0.00016955693572527048,0.00610900204628706,0.005268503911793232,0.031745828688144684,0.006018107756972313,0.011130439233966172,0.010536304418928921,0.08723732222570105
-2018,5,0.021380890160799026,0.005192299373447895,-0.0029216359835118055,0.00416894257068634,0.00200219196267426,0.012992304004728793,0.0028403187170624733,0.003162941662594676,0.006795489671640098,0.05465789446316194
-2018,10,0.022935254499316216,0.0054961806163191795,-0.0024668530095368624,0.004351234063506126,0.0021903133019804955,0.015171277243644,0.003013014327734709,0.0037858900614082813,0.007136335968971253,0.05856712833920028
-2018,30,0.027287987992167473,0.006678711622953415,-0.001681926310993731,0.004819984547793865,0.0027069145813584327,0.019630011916160584,0.0038764923810958862,0.005285142920911312,0.007989380368962884,0.06758615153376013
-2018,33,0.027791745960712433,0.006855870652943849,-0.0016025180928409101,0.004924151115119457,0.0027756355702877045,0.020107731223106384,0.004006013739854097,0.005488553550094366,0.008094900213181972,0.06858095715753734
-2018,50,0.029968932271003723,0.00791877694427967,-0.0012195026502013206,0.005366859491914511,0.0031869359081611037,0.022687848657369614,0.004739969968795776,0.006652418524026871,0.008651808719150722,0.07348664943128824
-2018,67,0.03205125778913498,0.009131450084969406,-0.0008704272331669927,0.0057574850507080555,0.0036831796169281006,0.025423036888241768,0.005473926197737455,0.007967557176016271,0.009241671129129827,0.07837531387573109
-2018,70,0.03242811560630798,0.009387594275176525,-0.0008103172294795513,0.0058616516180336475,0.0037928109755739556,0.025979829765856267,0.0056034475564956665,0.008242755895480514,0.00936077805235982,0.07932040579617022
-2018,90,0.03687924146652222,0.011783325113356113,-0.00035702457826118915,0.006434568203985691,0.004976685158908367,0.03104409947991371,0.006423751823604107,0.010781583259813488,0.010604447592049837,0.08797548685106449
-2018,95,0.03858111798763275,0.013156159780919552,-0.00020322682394180447,0.006564776878803968,0.005702887196093798,0.03344689682126045,0.0066396212205290794,0.012175223033409566,0.01133186318911612,0.09211874342290685
-2019,5,0.022721588611602783,0.005410044454038143,-0.0032360367709770798,0.0043853651732206345,0.0020391986588947477,0.01311510126106441,0.0030264852102845907,0.0031604247633367777,0.0071169576607644554,0.0573773228097707
-2019,10,0.024371201172471046,0.005745393224060535,-0.002729189395904541,0.00458628311753273,0.0022502425126731396,0.01548346057534218,0.0032195330131798983,0.003855475690215826,0.007496305904351175,0.06161327646113932
-2019,30,0.028990665450692177,0.00704625528305769,-0.001857791212387383,0.005102928727865219,0.002826181310229003,0.020330512896180153,0.004184771794825792,0.005511356212082319,0.008439373271539808,0.07132858083350584
-2019,33,0.02952529489994049,0.007241314956918358,-0.0017684996128082275,0.005217738915234804,0.0029011489171534774,0.02085633100941777,0.004329557530581951,0.005737341823987663,0.00855643104761839,0.07239732780202758
-2019,50,0.0318358950316906,0.008415039628744125,-0.0013434126740321517,0.005705682095140219,0.0033595175482332706,0.023638373240828514,0.00515001080930233,0.0070219687186181545,0.009172888938337564,0.07769084366736934
-2019,67,0.03404582291841507,0.00976506289094687,-0.000955394993070513,0.006136219948530197,0.003912833519279957,0.02657828086987139,0.005970463622361422,0.008483226411044598,0.009827284701168537,0.08295164402690716
-2019,70,0.03444577753543854,0.010052201896905899,-0.0008883664268068969,0.006251030135899782,0.00403442420065403,0.027170580998063087,0.00611524935811758,0.008794157110969536,0.009959728317335248,0.0839694777969271
-2019,90,0.039169661700725555,0.012777895666658878,-0.0003832941874861717,0.006882485933601856,0.005354864522814751,0.032584314793348314,0.007032226305454969,0.011672406952129677,0.011343763442710042,0.0932782621937804
-2019,95,0.04097582772374153,0.0143676633015275,-0.0002146402730431877,0.007025999017059803,0.006162866763770576,0.03516892828047273,0.007273536175489426,0.013280619779834508,0.012157599627971647,0.09776535920100285
-2020,5,0.024134736508131027,0.005626772064715624,-0.00355898542329669,0.004600290209054947,0.00208547244546935,0.013307245448231697,0.0032048781868070364,0.0031487171538174155,0.007446351030375808,0.060274421604117376
-2020,10,0.0258769728243351,0.005994534119963646,-0.0030026815365999937,0.004820362199097872,0.002317892387509346,0.015855735540390013,0.0034191953018307686,0.003915007691830397,0.007863550912588835,0.0647998575004749
-2020,30,0.03075581043958664,0.007416769862174988,-0.002039942890405655,0.005386261735111475,0.002954017836600542,0.021068700589239597,0.004490780644118786,0.005733085563406348,0.008899628114886583,0.07518013240769506
-2020,33,0.03132045641541481,0.007630213852971792,-0.0019429050153121352,0.0055120172910392284,0.0030342251993715763,0.021633028984069824,0.004651518538594246,0.005980399437248707,0.009028313192538917,0.07632021704455838
-2020,50,0.03376079350709915,0.00891817919909954,-0.0014737371820956469,0.006046478636562824,0.003541152458637953,0.02462068200111389,0.005562365986406803,0.007392010185867548,0.0097053786739707,0.0819889125705231
-2020,67,0.03609480336308479,0.010411135861650114,-0.0010470097186043859,0.006518061738461256,0.004152971785515547,0.027751924470067024,0.00647321343421936,0.00900372548494488,0.010425924723967911,0.08761808759067208
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diff --git a/profsea/Falkland_example/data/sea_level_projections/STANLEYII_rcp45_projection_2100_regional.csv b/profsea/Falkland_example/data/sea_level_projections/STANLEYII_rcp45_projection_2100_regional.csv
deleted file mode 100644
index dd1d968..0000000
--- a/profsea/Falkland_example/data/sea_level_projections/STANLEYII_rcp45_projection_2100_regional.csv
+++ /dev/null
@@ -1,847 +0,0 @@
-year,percentile,exp,antdyn,antsmb,greendyn,greensmb,glacier,landwater,GIA,antnet,greennet,sum
-2007,5,0.007412072566151619,-0.0003583246571653038,-0.00014704131912452684,0.002366923697449878,0.00199068598193268,0.00800727265439921,0.00036440904591380244,-0.0020967304089423286,-0.00045503213358600995,0.004411534151456475,0.019298801365740643
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-2007,30,0.010795652567315846,-0.00032193769916562124,-8.303626753591746e-05,0.002490901584624222,0.0020838193840373074,0.008813965493464143,0.00043754541010129994,-0.0020967304089423286,-0.0003821059491855392,0.004595516930514147,0.023800073933863318
-2007,33,0.011067471873760224,-0.0003174986573784844,-7.896366541384994e-05,0.002503056241501685,0.0020919293995003223,0.008874409096323383,0.00044668740818411273,-0.0020967304089423286,-0.00037611647631145834,0.004613834591018041,0.02415013744962811
-2007,50,0.012451514224708079,-0.00028251021642937543,-5.917973702878497e-05,0.0025601984010806883,0.0021376609998954308,0.009198440908561863,0.0004984923423050482,0.0006426668409204348,-0.0003408424777827109,0.0047117625201758,0.02599218265728094
-2007,67,0.013921686838008464,-0.0001361622070164918,-4.132737593923555e-05,0.002618634983485276,0.002187458963259153,0.009580240695967477,0.0005502972764259838,0.0006426668409204348,-0.0002780442461983347,0.004806855293159701,0.027800050221081035
-2007,70,0.014207613454665988,-0.00013341446113369259,-3.8086091259450464e-05,0.0026338791554432355,0.002198006996526057,0.009659855831112368,0.0005594393504137956,0.0006426668409204348,-0.0002450263507173884,0.004825304992593686,0.028151703193718264
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-2008,5,0.008465001151897014,-0.0004270876130440148,-0.0002879349051825689,0.002717436374717038,0.002041886932157613,0.008053275671443828,0.0007186814346375422,-0.0022873422643007223,-0.0006246518162809496,0.004872115688781906,0.021355243152348286
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-2008,70,0.015456584729626775,-0.00016019158974736098,-7.029985582665645e-05,0.003124685512500717,0.0023875724236274726,0.010786458134396312,0.0011278760998872576,0.0007010910991859289,-0.0003379063552058959,0.005484993841218609,0.031526263944456116
-2008,90,0.01844888522028923,-0.00013690414491299903,-2.3225797778349074e-05,0.003276177450996425,0.0026013488533936815,0.011952192946661689,0.0012493559025250622,0.0007010910991859289,-0.00021543012702559207,0.005740785562163118,0.03537131509575777
-2008,95,0.020026943547371775,-0.00013148474531523443,-7.42094298266918e-06,0.0033538794542432304,0.0027277518320416626,0.012495667744615899,0.0012813242037252746,0.0007010910991859289,-0.00018231103732935358,0.005880514884727838,0.037331920293149426
-2009,5,0.00923212705012411,-0.0004991383920875932,-0.00044010321132081313,0.003067023741579107,0.0020942648501977154,0.008200537453789446,0.001062816938456222,-0.002477954119659116,-0.0008098960827768964,0.005329706771578441,0.023232108308289603
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-2009,30,0.013103122517466545,-0.00040789307580538394,-0.00024489004838302,0.0033180399099444918,0.002299046308647517,0.01006919698742632,0.0013037520055977827,-0.002477954119659116,-0.0006100448910351719,0.005729511293826339,0.029368063285047846
-2009,33,0.013418932667467744,-0.0004010672097177025,-0.00023252773037660322,0.0033422590272613557,0.002318515501478466,0.010203386066987181,0.0013338688320617285,-0.002477954119659116,-0.000595501774111467,0.005762472291218341,0.029849014887424707
-2009,50,0.015011734015867114,-0.00036468257622430163,-0.0001724234187800086,0.00347583233313415,0.0024308567035758706,0.010914910651813797,0.0015045313547240814,0.0007595153574514231,-0.0005181185604732274,0.005944839781131493,0.032283736525529794
-2009,67,0.01669024397712201,-0.00020389006500754294,-0.00011827433794909751,0.0036146949418487877,0.002568773570353211,0.01169786327244565,0.0016751935737664384,0.0007595153574514231,-0.000434890409923887,0.006138882103212603,0.03468308753156163
-2009,70,0.017028834213502705,-0.00018842173569353726,-0.00010836745355362723,0.0036414104146824055,0.00259848615281274,0.011858231316061037,0.0017053105520403822,0.0007595153574514231,-0.0004189798335411419,0.006176834074124737,0.03514920200599326
-2009,90,0.020347638005018234,-0.00015150339995641165,-3.653591280935881e-05,0.0038494848583949257,0.002922854738709164,0.013376936278955231,0.001896051009615365,0.0007595153574514231,-0.00027209270936817746,0.006547640392122518,0.03961510462542677
-2009,95,0.022105566896125674,-0.00014322770122895433,-1.2486637942006336e-05,0.003940561802103454,0.0031157553106705926,0.014091665569563717,0.0019462456191819428,0.0007595153574514231,-0.0002222922238420774,0.006754243951220406,0.04185383165811704
-2010,5,0.010178911929577589,-0.0005751536192947083,-0.0006219316694168743,0.003415686120556269,0.002121807933042845,0.00808710588903188,0.0013968156332748415,-0.0026685659750175094,-0.0010266636716786237,0.005769279356152015,0.025004442438178127
-2010,10,0.011357032814621925,-0.0005391965794617483,-0.0005188307779521535,0.0034881447949547508,0.0021992882488936875,0.008872592008845173,0.0014527486607678608,-0.0026685659750175094,-0.0009267096337166693,0.00592684180863463,0.027123460794248024
-2010,30,0.014292804495804012,-0.0004565841774554674,-0.00033980491038797207,0.003726222421137837,0.0024073027687436287,0.010616101785117573,0.0017324131909929655,-0.0026685659750175094,-0.0007427743609211066,0.006295346016895452,0.032097700708216645
-2010,33,0.014632237937673926,-0.0004478006382214436,-0.00032220918970150483,0.003757275954439939,0.002435718826230294,0.01079261788669936,0.0017743628477552315,-0.0026685659750175094,-0.0007227661568579864,0.006340846572640763,0.03264594412922462
-2010,50,0.016330828112363815,-0.0003988480902885986,-0.0002358485835021077,0.00393777319933963,0.0025950669107383416,0.011730243470854941,0.002012077569408072,0.0008179396157169171,-0.0006187689290934831,0.006583790520037775,0.03543568918009811
-2010,67,0.01811827798280865,-0.00024354607590133386,-0.00015808519662881696,0.004121690922433289,0.002789541442191525,0.012726850753421138,0.002249792594680909,0.0008179396157169171,-0.0005153256425044476,0.006844026501095051,0.038167869389701396
-2010,70,0.018469981818459928,-0.00021810490903892085,-0.00014373260613241145,0.004160973177447996,0.0028306547330433206,0.012935920882744329,0.002291742251443175,0.0008179396157169171,-0.0004956383761183273,0.006895551950269936,0.03869153953168324
-2010,90,0.022020393151044846,-0.00016623088562414074,-4.0023140795885685e-05,0.004432571547265073,0.003292956459185325,0.01484866016837358,0.0025574237145575226,0.0008179396157169171,-0.0003219390603888674,0.007406041820758107,0.043766988857916786
-2010,95,0.023904178152792156,-0.0001550011793754191,-4.623209639146517e-06,0.0045343447977598565,0.0035737589234659637,0.015759928222872036,0.002627339809161299,0.0008179396157169171,-0.0002522747311916488,0.007699519349893916,0.04628905656200843
-2011,5,0.011271715378575027,-0.0006560061636110428,-0.0008057895010138452,0.003750222760557428,0.0021683961320322835,0.008206637882018282,0.001720677670903398,-0.002859177830375903,-0.0012477425871625382,0.006220885408126048,0.027170726380281042
-2011,10,0.012541702441871166,-0.0006091171571905685,-0.0006726022669367692,0.003842626083073998,0.0022663766706006185,0.009152380789832876,0.0017935836634188484,-0.002859177830375903,-0.0011190314344806278,0.006415834039380299,0.029553402053004423
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-2011,90,0.023829992832615974,-0.0001810866190295754,-5.408197213127563e-05,0.005019862381144451,0.003676694354603399,0.01623749511875813,0.0032334749282115225,0.0008763638739824111,-0.00037845703984136633,0.00828485843212132,0.04795411328394687
-2011,95,0.02587502769660205,-0.00016680515962122985,-9.059275916686284e-06,0.005135228793549446,0.004038604350068798,0.017311368851300957,0.003324607380903336,0.0008763638739824111,-0.0002932260113079914,0.00866296840331992,0.05074220575712343
-2012,5,0.012349708193913102,-0.0007411619059170862,-0.0009917453545257647,0.0040763644256672504,0.0022241852073253414,0.008404583339004979,0.00203440320315189,-0.0030497896857342964,-0.0014728584638352916,0.006681200898781857,0.029411082272880616
-2012,10,0.013726343965716661,-0.0006823771364058784,-0.000829432440653824,0.004194551211450482,0.0023439956741928064,0.009507561150745714,0.002125477816234445,-0.0030497896857342964,-0.0013178434364425344,0.0069128350495216324,0.03205698088439652
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-2012,33,0.01738376374728978,-0.0005427992261788894,-0.0005192506045190963,0.004594863772430033,0.0027119111687692442,0.012106156762538796,0.002649157638461626,-0.0030497896857342964,-0.0010033201471144864,0.007542012297989587,0.038748314296077
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-2012,67,0.02130189306512475,-0.00033444229168554465,-0.0002594638835045693,0.005160263584640257,0.0032693284603365308,0.014728156311245648,0.003423292684618333,0.0009347881322479053,-0.0006999047941151479,0.008309422742866135,0.045421184282663174
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-2012,90,0.02573475367948413,-0.00019607058305932655,-7.352792978786479e-05,0.005612790281252945,0.00406751108561189,0.01755734180844842,0.003924204043337373,0.0009347881322479053,-0.00043819992859173195,0.009178346712479985,0.052195003069038085
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-2013,67,0.022923589065670966,-0.00037858698346572984,-0.0003191753601152964,0.005685733463071244,0.0035382207361159368,0.01575177292292371,0.004022193146401294,0.0009932123905133994,-0.0008019739291995191,0.00907469967575489,0.04913420925234148
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-2100,95,0.2622400231808424,-0.001250298761529239,-0.007255482109758254,0.09079724790725203,0.1517381463342976,0.16384344908211965,0.13015370187849756,0.0060761228596113845,-0.013168450362408693,0.2155101050160795,0.6245105528685639
diff --git a/profsea/Falkland_example/data/sea_level_projections/STANLEYII_rcp85_projection_2100_global.csv b/profsea/Falkland_example/data/sea_level_projections/STANLEYII_rcp85_projection_2100_global.csv
deleted file mode 100644
index c96e42c..0000000
--- a/profsea/Falkland_example/data/sea_level_projections/STANLEYII_rcp85_projection_2100_global.csv
+++ /dev/null
@@ -1,847 +0,0 @@
-year,percentile,exp,antdyn,antsmb,greendyn,greensmb,glacier,landwater,antnet,greennet,sum
-2007,5,0.00803840346634388,0.002730532782152295,-0.00019164537661708891,0.0016901363851502538,0.0014884062111377716,0.009809032594785095,0.0002794466563500464,0.002600881562102586,0.0032208205200731756,0.024494150351438294
-2007,10,0.00900394655764103,0.002750872168689966,-0.0001606561374501325,0.001702548936009407,0.001501323888078332,0.010140316374599934,0.00028879407909698784,0.0026434402461745776,0.00324392132461071,0.02581651372820488
-2007,30,0.011707784608006477,0.0028334427624940872,-0.00010707565525081009,0.0017344666412100196,0.0015359563520178198,0.010834051575511695,0.0003355311928316951,0.0027452007416286506,0.0033014778164215386,0.029272809398389655
-2007,33,0.012020709924399853,0.0028456713422201573,-0.00010182185360463336,0.0017415594775229692,0.0015403234865516424,0.010907737696543336,0.0003425417235121131,0.0027594000301178314,0.003308492712676525,0.029701532101680642
-2007,50,0.013373138383030891,0.0029162338469177485,-7.581615500384942e-05,0.0017717040609568357,0.001567708735819906,0.011309161316603422,0.00038226827746257186,0.0028357511619105935,0.0033460151171311736,0.0314538227430603
-2007,67,0.014666641131043434,0.00298776590032503,-5.241474718786776e-05,0.0017983021680265665,0.0016006462974473834,0.011778074316680431,0.0004219948314130306,0.0029134345895727165,0.003385582937626168,0.03306750724725134
-2007,70,0.014900739304721355,0.0030012449249625208,-4.811262260773219e-05,0.001805395120754838,0.0016077874461188912,0.011877435259521008,0.00042900542030110955,0.0029278158181114122,0.003393467154819518,0.033401016602147136
-2007,90,0.017665695399045944,0.0030943932943046093,-1.7039590602507815e-05,0.0018444056622684002,0.001685899100266397,0.012814879603683948,0.0004734056710731238,0.003033706899259414,0.003475873882416636,0.03658420709471102
-2007,95,0.01872287131845951,0.00312490900978446,-6.926881746949275e-06,0.0018532717367634177,0.0017335314769297838,0.01323755457997322,0.00048508995678275824,0.0030717053268745076,0.003523893578676507,0.037954177132633035
-2008,5,0.009411504492163658,0.002961205318570137,-0.0003713573096320033,0.001925242249853909,0.0015166203957051039,0.00989010906778276,0.0005511200288310647,0.0027138765988638626,0.0035265108745079488,0.027082726252046996
-2008,10,0.010384941473603249,0.003003259189426899,-0.00031120917992666364,0.0019508516415953636,0.001541546534281224,0.010434946976602077,0.0005707318778149784,0.0027983836480416358,0.0035731815733015537,0.02868853168838541
-2008,30,0.013110887259244919,0.0031729806214571,-0.0002056582015939057,0.0020167045295238495,0.0016096584731712937,0.011602311674505473,0.0006687914719805121,0.003004676487762481,0.003689144621603191,0.03269170137718902
-2008,33,0.013426371850073338,0.003198161961045116,-0.0001951091253431514,0.002031338633969426,0.00161837472114712,0.011724714189767838,0.0006835003150627017,0.0030340651984442956,0.0037033731420524417,0.03315075097190857
-2008,50,0.014789857901632786,0.003344325814396143,-0.0001441341155441478,0.0020935330539941788,0.0016719467239454389,0.012377348728477955,0.0007668508915230632,0.0031912210397422314,0.0037792642833665013,0.03516171635783394
-2008,67,0.016093937680125237,0.0034953451948240405,-9.827991743804887e-05,0.002148410538211465,0.0017362330749165268,0.013106387108564377,0.0008502015843987465,0.0033539441792527214,0.0038583510322496295,0.03704212242635549
-2008,70,0.016329949721693993,0.003524384251795709,-8.986520697362721e-05,0.002163044409826398,0.0017499258974567056,0.013255969528108836,0.000864910485688597,0.0033855779620353132,0.0038738317438401283,0.037422930102911776
-2008,90,0.019117513671517372,0.003738280152902007,-2.905346973420802e-05,0.0022435314022004604,0.0019029773538932204,0.014690252486616375,0.0009580671321600676,0.003622281386924442,0.004035602603107691,0.04099382563872496
-2008,95,0.02018333226442337,0.003820823971182108,-8.405218977713957e-06,0.0022618239745497704,0.0019962547812610865,0.015359122492372985,0.0009825819870457053,0.0037170939904171973,0.004130265908315778,0.042607052180756
-2009,5,0.010290154255926609,0.003192017786204815,-0.0005858021904714406,0.0021603177301585674,0.0015477625420317054,0.010011195205152035,0.0008150199428200722,0.0028005468659102915,0.0038399543846026063,0.029277819439903395
-2009,10,0.011371897533535957,0.0032571617048233747,-0.0004908435512334108,0.0021999087184667587,0.001586676575243473,0.010772752575576305,0.000845813425257802,0.0029330099059734493,0.003912837244570255,0.031231266162649263
-2009,30,0.014401137828826904,0.003518613986670971,-0.0003239485959056765,0.0023017139174044132,0.001694315462373197,0.012410992756485939,0.0009997807210311294,0.0032550626492593437,0.004094056936446577,0.036050846669240855
-2009,33,0.014751723036170006,0.0035574725014157595,-0.0003072011168114841,0.0023243376053869724,0.001707959221675992,0.012581178126856685,0.0010228757746517658,0.0033003796052071266,0.0041160382912494246,0.03657915241637966
-2009,50,0.01626691408455372,0.0037842760793864727,-0.0002266397641506046,0.0024204871151596308,0.0017930028261616826,0.013482754118740559,0.0011537481332197785,0.003544527731719427,0.004234537249431014,0.038986539460893255
-2009,67,0.01771608740091324,0.004022738137282432,-0.0001542352547403425,0.002505325013771653,0.0018942529568448663,0.01446848176419735,0.0012846202589571476,0.0038014798659423837,0.004358654170064256,0.04127459936629748
-2009,70,0.017978359013795853,0.004069418972358107,-0.00014080027176532894,0.0025279484689235687,0.0019161889795213938,0.014670481346547602,0.0013077154289931059,0.0038532421240233816,0.004382761637680232,0.04173201532103121
-2009,90,0.02107607200741768,0.004431660752743483,-4.467406542971722e-05,0.0026523773558437824,0.00215870700776577,0.01658243089914322,0.0014539845287799835,0.004248249460943043,0.004638119414448738,0.045984577719355005
-2009,95,0.022260475903749466,0.004587744828313589,-1.1976075256825425e-05,0.002680656500160694,0.0023056436330080032,0.01748741725459695,0.001492476207204163,0.004421421261940849,0.004787939076777548,0.04795845033368096
-2010,5,0.011235869489610195,0.0034229697193950415,-0.0008270868274848908,0.0023953625932335854,0.001570083317346871,0.009975207131356,0.0010711464565247297,0.002869997697416693,0.004150049644522369,0.03133935666655816
-2010,10,0.012390912510454655,0.00351257948204875,-0.0006907949689775705,0.002449719700962305,0.0016254376387223601,0.010959106031805277,0.0011140386341139674,0.003058617818169296,0.004252151737455278,0.03364833247360366
-2010,30,0.01562541536986828,0.003870342392474413,-0.00045244290959089994,0.0025894949212670326,0.0017794771119952202,0.01314303260296583,0.0013284990563988686,0.00350751361111179,0.004505101242102683,0.039263873448362574
-2010,33,0.01599975675344467,0.003923602108843624,-0.0004286227631382644,0.0026205561589449644,0.0017997920257039367,0.01337085073813796,0.0013606681022793055,0.003567107836715877,0.0045358658686745915,0.03987100744561758
-2010,50,0.017617616802453995,0.004236084409058094,-0.00031393469544127584,0.0027525662444531918,0.0019207592122256756,0.014539130963385105,0.0015429593622684479,0.003905613935785368,0.0047010029666125774,0.04266255269612884
-2010,67,0.01916498690843582,0.004569944422692061,-0.00021050864597782493,0.0028690455947071314,0.002064510714262724,0.01578814508393407,0.001725250855088234,0.004265406576450914,0.00487518883543089,0.045318402842676735
-2010,70,0.019445030018687248,0.004636347573250532,-0.00019165633857483064,0.002900106832385063,0.0020960418740287425,0.016047475859522816,0.0017574199009686708,0.004339113045716658,0.004910182429011911,0.04585124837612966
-2010,90,0.02275264635682106,0.005174534395337105,-5.352480002329685e-05,0.0030709432903677225,0.002441226039081812,0.018440309911966324,0.001961157424375415,0.004922432359308004,0.0052715063793584704,0.050761525408597664
-2010,95,0.02401730604469776,0.005425671581178904,-6.674962605757173e-06,0.0031097696628421545,0.0026540211401879787,0.019588085915893314,0.0020147725008428097,0.005192782560698104,0.005486812372691928,0.05306166249938542
-2011,5,0.012304658070206642,0.0036540618166327477,-0.0010673441458493472,0.0026303771883249283,0.0015995572146493941,0.010070037329569459,0.0013194996863603592,0.0029403689899481834,0.004467730899341405,0.03374899226673733
-2011,10,0.013479082845151424,0.0037695125211030245,-0.0008914593490771949,0.0027002845890820026,0.0016710071358829737,0.011252051126211882,0.0013754075625911355,0.0031824413454160094,0.004599273286294192,0.03627612994423544
-2011,30,0.01676785945892334,0.0042281667701900005,-0.0005838759243488312,0.002880047308281064,0.0018700231215916575,0.013879084959626198,0.0016549464780837297,0.0037611786974593997,0.00492530781775713,0.0424111470230855
-2011,33,0.01714848354458809,0.004296551635488868,-0.0005531262722797692,0.0029199945274740458,0.001896224683150649,0.014148298110812902,0.0016968772979453206,0.0038370608453988098,0.004965119906701148,0.043066375092021186
-2011,50,0.018793489784002304,0.004699750803411007,-0.0004051318974234164,0.003089770209044218,0.002052728785201907,0.015550626441836357,0.001934485393576324,0.004274396749678999,0.005177825049031526,0.04613014007918537
-2011,67,0.02036682516336441,0.00513696458656341,-0.00027166816289536655,0.003239572513848543,0.0022377818822860718,0.01703088792040944,0.002172093605622649,0.0047460585192311555,0.005402392465621233,0.04907640670193359
-2011,70,0.020651567727327347,0.005225171707570552,-0.00024733183963689977,0.003279519733041525,0.0022791684605181217,0.017338179051876068,0.002214024541899562,0.004843288799747825,0.005447656277101487,0.04965404030226637
-2011,90,0.024014683440327644,0.005966902244836092,-6.91205495968461e-05,0.0034992294386029243,0.0027254330925643444,0.020147496834397316,0.0024795865174382925,0.005641670111799613,0.0059147253166884186,0.055034569231793284
-2011,95,0.025300564244389534,0.006334604695439339,-8.636193342681509e-06,0.0035491634625941515,0.003001577220857138,0.02149893194437027,0.0025494713336229324,0.006032119083101861,0.006192627141717821,0.05755328136292519
-2012,5,0.013517240062355995,0.0038852933794260025,-0.0013041174970567226,0.002865361049771309,0.0016297316178679466,0.010171101009473205,0.0015600797487422824,0.003014154441189021,0.004787169606424868,0.0363343035653088
-2012,10,0.014778546057641506,0.004027960821986198,-0.0010889972327277064,0.0029516038484871387,0.0017170023638755083,0.011536415666341782,0.0016299202106893063,0.0033109181094914675,0.0049481257563456895,0.03913718015246559
-2012,30,0.01831061765551567,0.004592086188495159,-0.0007134063635021448,0.0031733710784465075,0.00196022167801857,0.014564856886863708,0.0019791231025010347,0.004020359338028356,0.005349118262529373,0.045935862135957
-2012,33,0.018719399347901344,0.004676320306025446,-0.0006758983363397419,0.0032226527109742165,0.001992114819586277,0.014874715358018875,0.002031503478065133,0.004114924115128815,0.005397957140812651,0.04666315045098599
-2012,50,0.020486099645495415,0.005175275262445211,-0.0004952334566041827,0.003432099474593997,0.002183576114475727,0.016488246619701385,0.0023283257614821196,0.0046567413955926895,0.0056589661398902535,0.05008336348691955
-2012,67,0.022175824269652367,0.005723798321560026,-0.0003324026765767485,0.003616905305534601,0.0024093652609735727,0.01818103585392237,0.002625148044899106,0.005246118089416996,0.005934542044997215,0.05336436648445669
-2012,70,0.022481631487607956,0.0058358904439955945,-0.0003025465877726678,0.0036661869380623102,0.002460082061588764,0.018527686595916748,0.002677528653293848,0.005370002472773194,0.00599004765972495,0.054027229716302826
-2012,90,0.02609354257583618,0.00680876336991787,-8.506228914484382e-05,0.003937236033380032,0.0030055639799684286,0.021712355315685272,0.003009271342307329,0.006409147579688579,0.006560770072974264,0.060007136870990505
-2012,95,0.027474550530314445,0.007314542308449745,-1.1491801660668531e-05,0.003998837433755398,0.0033442394342273474,0.023249991703778505,0.0030965718906372786,0.006935311794222798,0.006901128345634788,0.06281597865745425
-2013,5,0.015132676810026169,0.00411666464060545,-0.0015486970078200102,0.0031003146432340145,0.0016770695801824331,0.010506565682590008,0.0017928860615938902,0.003077074186876416,0.005124419694766402,0.03961249889780447
-2013,10,0.01644059270620346,0.004287924617528915,-0.0012974385172128677,0.003203677013516426,0.0017797958571463823,0.01203794488683343,0.0018775764619931579,0.003427056421060115,0.005315900081768632,0.042633500743977495
-2013,30,0.02010319009423256,0.004962100647389889,-0.0008555626263841987,0.003469466231763363,0.002065651398152113,0.01535971201956272,0.0023010284639894962,0.004271295212674886,0.005792850395664573,0.04984156463833642
-2013,33,0.020527075976133347,0.005062908898107708,-0.0008112997747957706,0.003528530476614833,0.002103895414620638,0.01570136422291398,0.002364546060562134,0.00438710511662066,0.0058506999630481005,0.050621320099162405
-2013,50,0.02235906384885311,0.00566265732049942,-0.0005983777809888124,0.0037795535754412413,0.0023294996935874224,0.017494864761829376,0.002724480116739869,0.005036963848397136,0.0061625209636986256,0.05430140841053799
-2013,67,0.024111231788992882,0.006330445008352401,-0.0004070596187375486,0.0040010446682572365,0.0025972307194024324,0.0193715918622911,0.0030844141729176044,0.00575213165895548,0.006489574648439885,0.057856188986042986
-2013,70,0.024428339675068855,0.006468503922224044,-0.00037105951923877,0.004060108680278063,0.002656030934303999,0.01975718289613724,0.0031479322351515293,0.005906816804781556,0.006554763996973634,0.05857259726035409
-2013,90,0.028173726052045822,0.007700118236243725,-0.00011667233775369823,0.004384961910545826,0.0033008805476129055,0.023280936852097513,0.0035502114333212376,0.007216451282147318,0.007231329334899783,0.06499834537971766
-2013,95,0.029605768620967865,0.00836548674851656,-3.0315703043015674e-05,0.004458792507648468,0.003699328750371933,0.02498372113332152,0.0036560744047164917,0.007907059683930129,0.007632906630169598,0.06803572672943119
-2014,5,0.016829488798975945,0.0043481760658323765,-0.001829636632464826,0.0033352377358824015,0.0017344130901619792,0.010962268337607384,0.0020179194398224354,0.003111088299192488,0.005475935223512351,0.04312333765310541
-2014,10,0.018148086965084076,0.004549403674900532,-0.0015333117917180061,0.0034565043170005083,0.001853747060522437,0.012655663304030895,0.002118376549333334,0.0035211622598581016,0.0057005391921848055,0.04630146126146428
-2014,30,0.021840602159500122,0.005338210612535477,-0.001019673002883792,0.0037683327682316303,0.002188133541494608,0.01625185031443834,0.0026206630282104015,0.004507158824708313,0.006259503401815891,0.05380445834016427
-2014,33,0.02226795069873333,0.005456316941417754,-0.0009691778104752302,0.0038376280572265387,0.002231540158390999,0.016626043487340213,0.0026960058603435755,0.004645178560167551,0.006327874579001218,0.054627763575699644
-2014,50,0.024114903062582016,0.006161898374557495,-0.0007195415091700852,0.004132132977247238,0.0024974269326776266,0.018602831289172173,0.0031229492742568254,0.005411980419012252,0.006692674709483981,0.058540443977108225
-2014,67,0.025881383568048477,0.006956906197592618,-0.0004947074339725077,0.004391989670693874,0.0028131986036896706,0.02067461423575878,0.0035498926881700754,0.006263645255239682,0.00707754134433344,0.062343413644703104
-2014,70,0.02620108053088188,0.007123012002557516,-0.00045380849041976035,0.004461285192519426,0.0028809471987187862,0.021099988929927347,0.0036252355203032494,0.00644876720616594,0.007153153209947049,0.06308549759560264
-2014,90,0.029977060854434967,0.00864096637815237,-0.00015620363410562277,0.004842408932745457,0.003640657872892916,0.024979694187641142,0.004102407954633236,0.008056519960518926,0.007949409703724087,0.0699147202540189
-2014,95,0.03142079710960388,0.009487438015639782,-5.721960769733414e-05,0.004929027520120144,0.004105629865080118,0.02685219096019864,0.0042279791086912155,0.008927806600695476,0.008419886149931697,0.07316582870844286
-2015,5,0.017872951924800873,0.004579827189445496,-0.0021056639379821718,0.0035701305605471134,0.0017842391971498728,0.011312860064208508,0.0022351795341819525,0.0031525110825896263,0.005821943806950003,0.04586781238322146
-2015,10,0.019300103187561035,0.0048123979941010475,-0.00176658189157024,0.003710085991770029,0.0019223936833441257,0.013151461351662874,0.0023523208219558,0.003621506621129811,0.006080910668242723,0.049321849504485725
-2015,30,0.023296598345041275,0.0057204170152544975,-0.001179371471516788,0.004069970920681953,0.0023054382763803005,0.017054662108421326,0.0029380270279943943,0.004756988608278334,0.006724633951671421,0.05743647182243876
-2015,33,0.023759128525853157,0.005856544286943972,-0.0011213284451514482,0.004149945452809334,0.002355563687160611,0.017466310411691666,0.003025882877409458,0.004917043514724355,0.006802851362153888,0.0583322883863002
-2015,50,0.025758128613233566,0.006672997958958149,-0.0008363813394680619,0.0044898372143507,0.002659519435837865,0.019625820219516754,0.0035237332340329885,0.005803309381008148,0.00722364685498178,0.06259686435805634
-2015,67,0.027670031413435936,0.007603180650621656,-0.0005789885763078928,0.004789741709828377,0.003022577613592148,0.021890610456466675,0.004021583124995232,0.006799459632020444,0.0076660772366449245,0.06677622667164543
-2015,70,0.028016047552227974,0.007799415476620197,-0.0005323281511664391,0.004869716241955757,0.0031006978591904036,0.022350232675671575,0.004109439440071583,0.007016794639639556,0.007752945646643639,0.0675897026900202
-2015,90,0.032102879136800766,0.009631308726966381,-0.00019153508765157312,0.005309575702995062,0.003973783925175667,0.026572374626994133,0.004665860440582037,0.008943326742155477,0.008668448124080897,0.07505128881894052
-2015,95,0.03366547077894211,0.010680394247174263,-8.053356577875093e-05,0.005409543868154287,0.004508966580033302,0.028605666011571884,0.004812286701053381,0.010015321662649512,0.009209373989142476,0.07862821737653576
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-2016,10,0.020758869126439095,0.005076907575130463,-0.002004891401156783,0.003964421339333057,0.001998616848140955,0.013724676705896856,0.002579407999292016,0.003720595734193921,0.006471244734711945,0.05273440238379408
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-2016,33,0.0255286768078804,0.006263591395691037,-0.0012780196266248822,0.004465482663363218,0.0024853999190963803,0.018320602737367152,0.0033541761804372072,0.005188671057112515,0.007289795204997063,0.06235376924159937
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-2016,67,0.02971215918660164,0.008269268050789836,-0.0006689384061610325,0.0051942989230155945,0.0032381860073655844,0.023086421191692352,0.004499485716223717,0.00734763516811654,0.008267826077062636,0.07148333112592808
-2016,70,0.03008229285478592,0.008497713692486286,-0.0006158541073091328,0.005285400897264481,0.003326933365315199,0.023589568212628365,0.004600542597472668,0.007598355645313859,0.008365905843675137,0.07236270322464407
-2016,90,0.034453969448804855,0.010671144351363182,-0.00023512591724283993,0.005786462686955929,0.004311460070312023,0.02812519297003746,0.005240568425506353,0.00987963200896047,0.00940058280248195,0.080425617902074
-2016,95,0.03612546995282173,0.011944356374442577,-0.00011020154488505796,0.0059003401547670364,0.004910733550786972,0.030305245891213417,0.0054089962504804134,0.011168814609845838,0.010010719811543821,0.08431326228310354
-2017,5,0.020763233304023743,0.005043548531830311,-0.0026855082251131535,0.00403982400894165,0.0019134991860482841,0.012284976057708263,0.0026463791728019714,0.0032075901981443167,0.006551367929205298,0.05250609767535934
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-2017,33,0.02725469134747982,0.006677458118647337,-0.0014531207270920277,0.0047842394560575485,0.0026345248334109783,0.019269440323114395,0.0036808867007493973,0.005450834520161152,0.007800189312547445,0.06643785671272781
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-2018,5,0.02212337777018547,0.005275619216263294,-0.003010270418599248,0.004274625796824694,0.001990043092519045,0.012852878170087933,0.0028403187170624733,0.003209757851436734,0.0069321696646511555,0.055820955021772534
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-2018,33,0.02880648896098137,0.0070981436688452955,-0.0016423602867871527,0.005106216296553612,0.0027940000873059034,0.020246927384287117,0.004006013739854097,0.0057077623164514075,0.008326806931290776,0.07041620614356361
-2018,50,0.031076133251190186,0.00827744323760271,-0.0012453595991246402,0.005593699868768454,0.003222502302378416,0.0229021068662405,0.004739969968795776,0.006987208733335137,0.008927225833758712,0.07554875581990927
-2018,67,0.03324688971042633,0.009660884495824579,-0.0008842573023866858,0.006023833062499762,0.003738952102139592,0.02570912891998887,0.005473926197737455,0.008468393934890628,0.00955457023344934,0.08065412591793576
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-2018,90,0.038279905915260315,0.01289929635822773,-0.00035088349250145257,0.006769396830350161,0.005084156524389982,0.03146451376378536,0.006423751823604107,0.011874352116137743,0.010979144461452962,0.09072062660707161
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-2019,5,0.023860029876232147,0.00550782959908247,-0.003354062791913748,0.004509396851062775,0.0020448178984224796,0.013145809061825275,0.0030264852102845907,0.003202242311090231,0.0072973902802914384,0.05922412969521247
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diff --git a/profsea/Falkland_example/data/sea_level_projections/STANLEYII_rcp85_projection_2100_regional.csv b/profsea/Falkland_example/data/sea_level_projections/STANLEYII_rcp85_projection_2100_regional.csv
deleted file mode 100644
index bef1db3..0000000
--- a/profsea/Falkland_example/data/sea_level_projections/STANLEYII_rcp85_projection_2100_regional.csv
+++ /dev/null
@@ -1,847 +0,0 @@
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-2009,5,0.009138995303958654,-0.0005061726539438843,-0.0004532150235726104,0.0030786815561146246,0.0020892958423398204,0.008131191138745934,0.001062816938456222,-0.002477954119659116,-0.0008256334833508757,0.005342376351441652,0.023041560652277998
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-2009,90,0.019890855669975283,-0.00015211054273388838,-3.449014935648754e-05,0.003880973671146101,0.002946273917653976,0.01347921843793272,0.001896051009615365,0.0007595153574514231,-0.000273533473656826,0.0065928216212033,0.039348500449897454
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-2011,5,0.010883316929638386,-0.0006776904521854411,-0.0008253380241970439,0.003779263609307391,0.0021652877576305136,0.00817931153714046,0.001720677670903398,-0.002859177830375903,-0.0012769967492687994,0.006257875209989242,0.026745639598423198
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-2011,90,0.02270116546265781,-0.00018277313144258162,-5.3501704840560003e-05,0.005098833084918001,0.0037150174144922107,0.01637544872552535,0.0032334749282115225,0.0008763638739824111,-0.0003833440336799871,0.008389155728506063,0.04711832409858945
-2011,95,0.024334226793050764,-0.00016786974328860112,-6.689706266204649e-06,0.0052197357687873615,0.004087486791278423,0.017468877870822457,0.003324607380903336,0.0008763638739824111,-0.0002970840249716352,0.008778746317824502,0.04973164736772844
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-2012,90,0.024673120184987782,-0.00019849919040935155,-6.589448882788513e-05,0.005734394441733124,0.004094036374421817,0.017648436884621414,0.003924204043337373,0.0009347881322479053,-0.0004373317500711655,0.009303642073270436,0.051413251712441835
-2012,95,0.026455196274630725,-0.00018017265935945628,-8.882908802283432e-06,0.005864903241888643,0.004550566252815657,0.018892985056498137,0.004038047271738067,0.0009347881322479053,-0.0003360053342663687,0.009778297825711829,0.054309157008066836
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-2013,90,0.026668276676908135,-0.0002144883908865122,-8.998184479882485e-05,0.0063697936096920055,0.004495686161705048,0.018921981367993192,0.004629610452695082,0.0009932123905133994,-0.0005036363393371733,0.010250214053190368,0.055746171707147546
-2013,95,0.028599478569068013,-0.00019259126194043818,-2.3362086424823096e-05,0.00654081715020944,0.005036185555472221,0.020300799956544355,0.004767659785285489,0.0009932123905133994,-0.00038819180268671806,0.010810221619699778,0.05887190239725284
-2014,5,0.01479764774441719,-0.0009844152924042657,-0.0014139833371023165,0.004803575491101715,0.0023499556387443533,0.008901570917927907,0.002631443537028695,-0.003431013396451084,-0.002011252471336928,0.007704453706627124,0.034374033778689105
-2014,10,0.016175421476364134,-0.0008810251974696078,-0.0011857355682161183,0.004979141928685441,0.0025164433157858525,0.010284313959392947,0.0027624434205494658,-0.003431013396451084,-0.0017943018974146926,0.008031007686440673,0.03734200801892502
-2014,30,0.019473775626719,-0.0006809142784940216,-0.0007885309848018762,0.005466585897859166,0.002973008321257695,0.013201583244108011,0.003417444052633304,-0.003431013396451084,-0.0013895133997390723,0.008839489136486578,0.044144712311491974
-2014,33,0.01992400862276554,-0.0006584412002240657,-0.0007489781524959822,0.0055559068336378855,0.0030341214980836857,0.01350785386875952,0.003515693965273882,-0.003431013396451084,-0.001346578109855416,0.008935341726861954,0.04487859362496077
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-2014,67,0.023988024355284868,-0.00041444698007262186,-0.00038261322587577394,0.006384135249245877,0.00382635598975205,0.016797363733312556,0.00462919479692041,0.0010516366487788936,-0.0009264110772862453,0.010032100297761231,0.05244254248936633
-2014,70,0.02438233627155423,-0.00037261646428629055,-0.0003510888103986889,0.0064627825076427154,0.0039189815434355554,0.01714595017160484,0.004727444709560988,0.0010516366487788936,-0.0008887461825461265,0.010141344263540374,0.053174466289814495
-2014,90,0.028401997400075197,-0.00023074073488740346,-0.00012085066541769921,0.00703577416214876,0.004957700423880224,0.020302853498443744,0.0053496956743846295,0.0010516366487788936,-0.0005784989791044678,0.011261755740080513,0.05999710444304166
-2014,95,0.030488129273056983,-0.0002051255510315468,-4.4461956824046745e-05,0.007223038399246785,0.005591756267633365,0.021819823942151637,0.005513445225165597,0.0010516366487788936,-0.00044882716061422716,0.011917775670054379,0.06332994035051884
-2015,5,0.015716381541825832,-0.0010990940315919186,-0.001627099239152841,0.005139781222929659,0.002419501183895799,0.009181589269760683,0.002914758945897001,-0.003621625251809477,-0.0022785228202559724,0.008200938342840608,0.03656796886939404
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diff --git a/profsea/Falkland_example/data/zos_regression/STANLEYII_zos_regression.csv b/profsea/Falkland_example/data/zos_regression/STANLEYII_zos_regression.csv
deleted file mode 100644
index 5f12cf8..0000000
--- a/profsea/Falkland_example/data/zos_regression/STANLEYII_zos_regression.csv
+++ /dev/null
@@ -1,64 +0,0 @@
-Model,Scenario,i,j,lon,lat,slope_05_00,slope_50_00
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diff --git a/profsea/Falkland_example/figures/maps/STANLEYII_ACCESS1-0_ij_figure.png b/profsea/Falkland_example/figures/maps/STANLEYII_ACCESS1-0_ij_figure.png
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diff --git a/profsea/Falkland_example/figures/maps/STANLEYII_GFDL-ESM2M_ij_figure.png b/profsea/Falkland_example/figures/maps/STANLEYII_GFDL-ESM2M_ij_figure.png
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diff --git a/profsea/Falkland_example/figures/maps/STANLEYII_MIROC-ESM-CHEM_ij_figure.png b/profsea/Falkland_example/figures/maps/STANLEYII_MIROC-ESM-CHEM_ij_figure.png
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diff --git a/profsea/Falkland_example/figures/zos_regression/STANLEYII_CanESM2_zos_regression_2005-2100.png b/profsea/Falkland_example/figures/zos_regression/STANLEYII_CanESM2_zos_regression_2005-2100.png
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zul8gQ~WD8>_L9NDu-I@9ZeY*CX -0.05:
-            tg_ymin = -0.05
-        ymin = min(tg_ymin, ymin)
-
-    return [ymin-0.1, ymax+0.1]
-
-
-def location_string(loc_string):
-    """
-    Re-format location string for use in plotting.
-    Deals with , - _ ( ) and space
-    :param loc_string: site location
-    :return: simplified site location string
-    """
-    loc_title = []
-    title_temp = re.split(r"[, |\-|_|)|(|\s]", loc_string)
-    for string in title_temp:
-        loc_title.append(string.capitalize())
-    loc_title = " ".join(loc_title)
-
-    if loc_title == 'Stanley Ii':
-        loc_title = 'Stanley II'
-
-    return loc_title
-
-
-def plot_zeroline(ax, xvalues):
-    """
-    Plots a horizontal line where x=0
-    :param ax: number of subplot
-    :param xvalues: range of years
-    """
-    ax.plot(xvalues, [0., 0.], 'grey', linewidth=0.5)
-
-
-def scenario_string(rcp_scen, i):
-    """
-    Re-format scenario string for use in plotting
-    :param rcp_scen: RCP emission scenario
-    :param i: loop level; -999 if not required
-    :return: simplified scenario string
-    """
-    if i >= 0:
-        rcp_label = rcp_scen[i].upper()
-        rcp_label = rcp_label[:3] + ' ' + rcp_label[3] + '.' + rcp_label[4:]
-    else:
-        rcp_label = rcp_scen.upper()
-        rcp_label = rcp_label[:3] + ' ' + rcp_label[3] + '.' + rcp_label[4:]
-
-    return rcp_label
-
-
-def ukcp18_colours():
-    """
-    Define plotting colour schemes as used in UKCP18 Marine Report
-    :return: UKCP18 colour schemes
-    """
-    rcp_colours = {'rcp85': 'firebrick',
-                   'rcp45': 'steelblue',
-                   'rcp26': 'darkblue',
-                   }
-
-    comp_parts_colours = {'antnet': 'b',
-                          'greennet': 'g',
-                          'glacier': 'c',
-                          'landwater': 'purple',
-                          'sum': 'k',
-                          'gia': 'orange',
-                          'ocean': 'r'}
-
-    return rcp_colours, comp_parts_colours
-
-
-def get_emulator_colors(num_scenarios, get_all=False):
-        colors = [
-            '#031326', '#13385a', '#45587a', '#6a5e76', 
-            '#8d616d', '#b86462', '#e37861', '#e8a077']
-        
-        def get_equidistant_indices(num_colors, num_iterations):
-            if num_iterations >= num_colors:
-                return list(range(num_colors))
-            
-            # Avoid first and last index if possible
-            if num_iterations > 1:
-                start = 1
-                stop = num_colors - 2
-                indices = np.linspace(start, stop, num=num_iterations, dtype=int)
-            else:
-                # If only one iteration, take the middle color
-                indices = [num_colors // 2]
-            
-            return indices
-        
-        # Return a cyclical color generator 
-        if (num_scenarios >= len(colors)) or get_all:
-            return itertools.cycle(colors)
-        
-        color_idxs = get_equidistant_indices(len(colors), num_scenarios)
-        return itertools.cycle([colors[i] for i in color_idxs])
-
-
-def ukcp18_labels():
-    """
-    Define sea level component labels as used in UKCP18 Marine Report
-    :return: UKCP18 component labels
-    """
-    comp_parts_labels = {'antnet': 'Antarctica',
-                         'greennet': 'Greenland',
-                         'glacier': 'Glaciers',
-                         'landwater': 'Land Water',
-                         'sum': 'Local Total',
-                         'gia': 'GIA',
-                         'ocean': 'Ocean'}
-
-    return comp_parts_labels
-
-
-def multi_index_values(df_list):
-    """
-    Get the values of the multi-index: years and percentile values.
-    :param df_list: DataFrame list of regional sea level projections
-    :return: years and percentile values
-    """
-    df = df_list[0]
-    proj_years = np.sort(list(set(list(df.index.get_level_values('year')))))
-    percentiles_all = np.sort(
-        [float(v) for v in list(set(list(
-            df.index.get_level_values('percentile'))))])
-
-    return proj_years, percentiles_all
-
-
-def extract_comp_sl(df, percentiles, comp):
-    """
-    Get the sums of all components of local sea level projections.
-    :param df: global or regional DataFrame of sea level projections
-    :param percentiles: specified percentiles
-    :param comp: components of sea level
-    :return: sum of sea level components at lower, middle and upper percentile
-    """
-    # 5th percentile - based on UKCP18 percentile levels
-    rlow = df.xs(percentiles[0], level='percentile')[comp].to_numpy(copy=True)
-    # 50th percentile
-    rmid = df.xs(percentiles[4], level='percentile')[comp].to_numpy(copy=True)
-    # 95th percentile
-    rupp = df.xs(percentiles[8], level='percentile')[comp].to_numpy(copy=True)
-
-    return rlow, rmid, rupp
-
-
-def plot_tg_data(ax, nflag, flag, tg_years, non_missing, tg_amsl, tg_name):
-    """
-    Plot the annual mean sea levels from the tide gauge data.
-    :param ax: subplot number
-    :param nflag: number of flagged years
-    :param flag: flagged data
-    :param tg_years: tide gauge years
-    :param non_missing: boolean to indicate NaN values
-    :param tg_amsl: annual mean sea level data
-    :param tg_name: tide gauge name
-    """
-    if nflag > 0:
-        # There are some years with less than min_valid_fraction of flag data;
-        # plot these annual means as open symbols.
-        print(f'Tide gauge data has been flagged for attention - ' +
-              f'{tg_years[(flag & non_missing)]}')
-        ax.plot(tg_years[(flag & non_missing)], tg_amsl[(flag & non_missing)],
-                marker='o', mec='black', mfc='None',
-                markersize=3, linestyle='None', label='TG flagged')
-    if nflag < len(flag):
-        ax.plot(tg_years[(~flag & non_missing)],
-                tg_amsl[(~flag & non_missing)], 'ko', markersize=3,
-                label=f'{location_string(tg_name)} TG')
diff --git a/profsea/fair_prof_prob.py b/profsea/probabilistic_wrapper.py
similarity index 100%
rename from profsea/fair_prof_prob.py
rename to profsea/probabilistic_wrapper.py
diff --git a/profsea/step1_extract_cmip.py b/profsea/step1_extract_cmip.py
deleted file mode 100644
index 276c065..0000000
--- a/profsea/step1_extract_cmip.py
+++ /dev/null
@@ -1,339 +0,0 @@
-"""
-Copyright (c) 2023, Met Office
-All rights reserved.
-"""
-
-import os
-import numpy as np
-import pandas as pd
-
-from profsea.config import settings
-from profsea.directories import read_dir, makefolder
-from profsea.slr_pkg import abbreviate_location_name, plot_ij  # found in __init.py__
-from profsea.slr_pkg import cmip, cubeutils, models, whichbox
-from profsea.tide_gauge_locations import extract_site_info
-
-
-def accept_reject_cmip(cube, model, site_loc, cmip_i, cmip_j, site_lat,
-                       site_lon, unit_test=False):
-    """
-    Accept or reject selected CMIP grid box based on a user input.
-    If CMIP grid box is rejected, search neighbouring grid boxes until a
-    suitable one is found.
-    :param cube: cube containing zos field from CMIP models
-    :param model: CMIP model name
-    :param site_loc: name of the site location
-    :param cmip_i: CMIP coord of site location's latitude
-    :param cmip_j: CMIP coord of site location's longitude
-    :param site_lat: latitude of the site location
-    :param site_lon: longitude of the site location
-    :param unit_test: flag to disable plotting for unit testing purposes
-    :return: Selected CMIP coords or None if user doesn't confirm grid box
-    selection
-    """
-    # Plot a map of the site location and selected CMIP grid box for user
-    # validation. At this stage don't save the figure to file.
-    if not unit_test:
-        plot_ij(cube, model, site_loc, [cmip_i, cmip_j], site_lat, site_lon,
-                save_map=False)
-
-    # Ask user to accept cmip grid box or re-select from neighbouring cells
-    decision = input(f'Is selected CMIP grid box {cmip_i, cmip_j} '
-                     f'appropriate for {model}? Y or N: ')
-    if decision == 'Y' or decision == 'y':
-        # Save map to file and return CMIP grid box coords
-        if not unit_test:
-            plot_ij(cube, model, site_loc, [cmip_i, cmip_j],
-                    site_lat, site_lon)
-        return cmip_i, cmip_j
-    elif decision == 'N' or decision == 'n':
-        print('Selecting another CMIP grid box')
-        return None, None
-    else:
-        raise TypeError('Response needs to be Y or N')
-
-
-def calc_radius_range(radius):
-    """
-    Calculate the maximum distance to search for ocean grid point.
-    :param radius: Maximum range to search for ocean point
-    :return: x_radius_range, y_radius_range
-    """
-    # if radius > 1:
-    #     rm1 = radius - 1
-    # else:
-    #     rm1 = radius
-
-    x_radius_range = list(range(-radius, radius + 1))
-    y_radius_range = list(range(-radius, radius + 1))
-
-    return x_radius_range, y_radius_range
-
-
-def check_cube_mask(cube):
-    """
-    Check if the cube has a scalar mask. If so, then re-mask the cube to
-    remove grid points that are exactly equal to 0.
-    **NOTES:
-    - Land points have a value of 0, ocean points have non-zero (both
-    positive and negative) values.
-    - Because the CMIP models were interpolated onto a 1x1 grid before
-    the mask was checked some interpolation issues may still arise.
-    :param cube: cube containing zos field from CMIP models
-    :return: original cube, or re-masked cube
-    """
-    apply_mask = False
-    try:
-        cube.data.mask
-        apply_mask = not isinstance(cube.data.mask, (list, tuple, np.ndarray))
-    except AttributeError:
-        apply_mask = True
-
-    if apply_mask:
-        new_mask = (cube.data == 0.0)
-        print(f'Scalar mask: Re-masking the cube to mask cells = 0')
-        cube = cube.copy(data=np.ma.array(cube.data, mask=new_mask))
-
-    return cube
-
-
-def extract_lat_lon(df):
-    """
-    Extracts site location's metadata, calls wraplongitude() to convert
-    longitude.
-    :param df: DataFrame of site location's metadata
-    :return: latitude, longitude, converted longitude
-    """
-    site = df.name
-    station_id = df['Station ID']
-    lat = df['Latitude']
-    lon_orig = df['Longitude']
-    print(f'Site location: {site}, ID: {station_id}, ' +
-          f'Lat: {lat:.2f}, Lon: {lon_orig:.2f}')
-
-    lon = whichbox.wraplongitude(lon_orig)
-
-    return lat, lon_orig, lon
-
-
-def extract_ssh_data(cmip_sea):
-    """
-    Get a list of appropriate CMIP models to use. Load SSH data for each
-    model on a re-gridded 1x1 grid.
-    :param cmip_sea: variable to distinguish between which CMIP models to use
-    :return: CMIP model names, and associated SSH data cubes
-    """
-    cmip_dir = settings["cmipinfo"]["sealevelbasedir"]
-    # Select CMIP models to use depending on whether location is within a
-    # marginal sea
-    if cmip_sea == 'all':
-        model_names = models.cmip5_names()
-    elif cmip_sea == 'marginal':
-        model_names = models.cmip5_names_marginal()
-    else:
-        raise UnboundLocalError(f'The selected CMIP5 models to use - ' +
-                                f'cmip_sea = {cmip_sea} - is not recognised')
-
-    # Load SSH data for each model on a re-gridded 1x1 grid, store in a list
-    cubes = []
-    cmip_dict = cmip.model_dictionary()
-    for model in model_names:
-        print(f'Getting data for {model} model')
-        cmip_date = cmip_dict[model]['historical']
-        cmip_file = f'{cmip_dir}zos_Omon_{model}_historical_{cmip_date}.nc'
-        cube = cubeutils.loadcube(cmip_file, ncvar='zos')[0]
-        cubes.append(cube.slices(['latitude', 'longitude']).next())
-
-    return model_names, cubes
-
-
-def find_ocean_pt(zos_cube_in, model, site_loc, site_lat, site_lon):
-    """
-    Searches for the nearest appropriate ocean point(s) in the CMIP model
-    adjacent to the site location. Initially, finds the model grid box
-    indices of the given location. Then, searches surrounding boxes until an
-    appropriate ocean point is found - needs to be accepted by the user.
-    **NOTES:
-    - The GCM data have been interpolated to a common 1 x 1 degree grid
-    :param zos_cube_in: cube containing zos field from CMIP models
-    :param model: CMIP model name
-    :param site_loc: name of the site location
-    :param site_lat: latitude of the site location
-    :param site_lon: longitude of the site location
-    :return: model grid box indices
-    """
-    # Find grid box indices of location
-    (i, j), = whichbox.find_gridbox_indicies(zos_cube_in,
-                                             [(site_lon, site_lat)])
-    grid_lons = zos_cube_in.coord('longitude').points
-    grid_lats = zos_cube_in.coord('latitude').points
-
-    # Check to see if the cube has a scalar mask, and add mask where cmip
-    zos_cube = check_cube_mask(zos_cube_in)
-
-    # If the CMIP grid box of the exact site location is an ocean point
-    # Get the user to check if it's appropriate and if so return the indices
-    if not zos_cube.data.mask[j, i]:
-        print('Checking CMIP grid box at site location')
-        i_out, j_out = accept_reject_cmip(zos_cube, model, site_loc, i, j,
-                                          site_lat, site_lon)
-        if i_out is not None:
-            pt_lon = grid_lons[i_out]
-            pt_lat = grid_lats[j_out]
-            return i_out, j_out, pt_lon, pt_lat
-
-        # If no indices are returned then the CMIP grid box is not appropriate
-        # for use. Check the CMIP grid boxes surrounding the site location
-        # until an appropriate one is found.
-        else:
-            i_out, j_out, pt_lon, pt_lat = search_for_next_cmip(i, j,
-                                                                zos_cube,
-                                                                model,
-                                                                site_loc,
-                                                                site_lat,
-                                                                site_lon)
-
-    # If the CMIP grid box of the exact site location is masked, start by
-    # checking the next set of cmip grid boxes
-    else:
-        i_out, j_out, pt_lon, pt_lat = search_for_next_cmip(i, j, zos_cube,
-                                                            model, site_loc,
-                                                            site_lat, site_lon)
-
-    return i_out, j_out, pt_lon, pt_lat
-
-
-def ocean_point_wrapper(df, model_names, cubes):
-    """
-    Wrapper script to extract relevant metadata for site location, needed to
-    select the nearest CMIP grid box. Collates the CMIP model name, i and j
-    coords selected and lat and lon value of the site.
-    Writes the results to a single .csv file assuming write=True.
-    :param df: DataFrame of site location's metadata
-    :param model_names: CMIP model names
-    :param cubes: cube containing zos field from CMIP models
-    """
-
-    # Get the metadata of either the site location or tide gauge location
-    for site_loc in df.index.values:
-        df_site = df.loc[site_loc]
-        lat, lon_orig, lon = extract_lat_lon(df_site)
-
-        # Setup empty 2D list to store results for each model
-        # [name, i and j coords, lat and lon value]
-        result = []
-        for n, zos_cube in enumerate(cubes):
-            model = model_names[n]
-            i, j, pt_lon, pt_lat = find_ocean_pt(zos_cube, model, site_loc,
-                                                 lat, lon)
-            result.append([model, i, j, pt_lon, pt_lat])
-
-        # Write the data to a file
-        write_i_j(site_loc, result, lat, lon_orig)
-
-
-def search_for_next_cmip(cmip_i, cmip_j, cube, model, site_loc, site_lat,
-                         site_lon, unit_test=False):
-    """
-    Iteratively check the CMIP grid boxes surrounding the site location
-    until a suitable option is found.
-    :param cmip_i: CMIP coord of site location's latitude
-    :param cmip_j: CMIP coord of site location's longitude
-    :param cube: cube containing zos field from CMIP models
-    :param model: CMIP model name
-    :param site_loc: name of the site location
-    :param site_lat: latitude of the site location
-    :param site_lon: longitude of the site location
-    :param unit_test: flag to disable plotting for unit testing purposes
-    :return: Selected CMIP coords
-    """
-    grid_lons = cube.coord('longitude').points
-    grid_lats = cube.coord('latitude').points
-
-    # The radius limit of 7 is arbitrary but should be large enough.
-    for radius in range(1, 8):  # grid boxes
-        print(f'Checking CMIP grid boxes {radius} box removed ' +
-              'from site location')
-        x_radius_range, y_radius_range = calc_radius_range(radius)
-        for ix in x_radius_range:
-            for iy in y_radius_range:
-                # Search the nearest grid cells.  If the new mask is False,
-                # that grid cell is an ocean point
-                limit_lo = radius * radius
-                dd = ix * ix + iy * iy
-                if dd >= limit_lo:
-                    # modulus for when grid cell is close to 0deg.
-                    i_try = (cmip_i + ix) % len(grid_lons)
-                    j_try = cmip_j + iy
-
-                    if not cube.data.mask[j_try, i_try]:
-                        i_out, j_out = accept_reject_cmip(
-                            cube, model, site_loc, i_try, j_try, site_lat,
-                            site_lon, unit_test)
-                        if i_out is not None:
-                            pt_lon = grid_lons[i_out]
-                            pt_lat = grid_lats[j_out]
-                            return i_out, j_out, pt_lon, pt_lat
-
-
-def write_i_j(site_loc, result, site_lat, lon_orig):
-    """
-    Convert the grid indices to a data frame and writes to file.
-    :param site_loc: name of site location
-    :param result: grid indices and coordinates of nearest ocean point
-    :param site_lat: latitude of the site location
-    :param lon_orig: longitude of site
-    """
-    # Store the grid indices and coordinates in a dataframe
-    df_out = pd.DataFrame(result,
-                          columns=['Model', 'i', 'j', 'box_lon', 'box_lat'])
-
-    # Create the output file directory location
-    out_cmipdir = read_dir()[0]
-    makefolder(out_cmipdir)
-
-    # Abbreviate the site location name suitable to use as a filename
-    loc_abbrev = abbreviate_location_name(site_loc)
-    outfile = os.path.join(out_cmipdir, f'{loc_abbrev}_ij_1x1_coords.csv')
-
-    # Save the CMIP grid box selection (i and j coordinates) to file
-    with open(outfile, 'w') as ofp:
-        ofp.write(f'Location: {site_loc}' + '\n')
-        ofp.write(f'Latitude: {site_lat:8.3f}' + '\n')
-        ofp.write(f'Longitude: {lon_orig:8.3f}' + '\n')
-        df_out.to_csv(ofp, index=False)
-
-
-def main():
-    """
-    Find the nearest CMIP model grid box, to the user defined site
-    """
-    print(f'User specified site name(s) is (are):'
-          f' {settings["siteinfo"]["sitename"]}')
-    print(f'User specified lat(s) and lon(s) is (are): '
-          f'{settings["siteinfo"]["sitelatlon"]}')
-    if settings["siteinfo"]["sitelatlon"] == [[]]:
-        print(f'    No lat lon specified - use tide gauge metadata if '
-              f'available')
-    print(f'User specified science method is: {settings["sciencemethod"]}')
-    if {settings["cmipinfo"]["cmip_sea"]} == {'all'}:
-        print('User specified all CMIP models')
-    elif {settings["cmipinfo"]["cmip_sea"]} == {'marginal'}:
-        print('User specified CMIP models for marginal seas only')
-
-    # Extract site data from station list (e.g. tide gauge location) or
-    # construct based on user input
-    df_site_data = extract_site_info(settings["tidegaugeinfo"]["source"],
-                                     settings["tidegaugeinfo"]["datafq"],
-                                     settings["siteinfo"]["region"],
-                                     settings["siteinfo"]["sitename"],
-                                     settings["siteinfo"]["sitelatlon"])
-
-    # Find the nearest, appropriate ocean point in CMIP models to specified
-    # site location
-    cmip_models, ssh_cubes = extract_ssh_data(settings["cmipinfo"]["cmip_sea"])
-    ocean_point_wrapper(df_site_data, cmip_models, ssh_cubes)
-
-
-if __name__ == '__main__':
-    main()
diff --git a/profsea/step2_extract_steric_dyn_regression.py b/profsea/step2_extract_steric_dyn_regression.py
deleted file mode 100644
index 599b1b9..0000000
--- a/profsea/step2_extract_steric_dyn_regression.py
+++ /dev/null
@@ -1,71 +0,0 @@
-"""
-Copyright (c) 2023, Met Office
-All rights reserved.
-"""
-
-from profsea.config import settings
-from profsea.slr_pkg import extract_dyn_steric_regression  # found in __init.py__
-from profsea.slr_pkg import models
-from profsea.tide_gauge_locations import extract_site_info
-
-
-def extract_cmip5_steric_dyn_regression(df):
-    """
-    Finds all CMIP model names, then calculates the regression parameters
-    between global and local sea level projections for the given locations.
-    :param df: DataFrame of site location's metadata
-    """
-    print('running function extract_cmip5_steric_dyn_regression')
-    # Specify the emission scenarios
-    scenarios = ['rcp26', 'rcp45', 'rcp85']
-
-    # Select CMIP5 models to use
-    if settings["cmipinfo"]["cmip_sea"] == 'all':
-        model_names = models.cmip5_names()
-    elif settings["cmipinfo"]["cmip_sea"] == 'marginal':
-        model_names = models.cmip5_names_marginal()
-    else:
-        raise UnboundLocalError(
-            'The selected CMIP5 models to use - cmip_sea = ' +
-            f'{settings["cmipinfo"]["cmip_sea"]} - ' +
-            'is not recognised')
-
-    # Calculate the regression parameters and plot the results
-    # Note the x and y limits of the plot are set to 0.6 - this can be
-    # updated in extract_dyn_steric_regression() function
-    extract_dyn_steric_regression(model_names, df, scenarios)
-
-
-def main():
-    """
-    Calculate the regression between local sea level change and global mean
-    sea level rise from thermal expansion.
-    If the regression slope is steeper/shallower than 1, then the local change
-    is increasing faster/slower than the global average.
-    """
-    print(f'User specified site name(s) is (are):'
-          f' {settings["siteinfo"]["sitename"]}')
-    print(f'User specified lat(s) and lon(s) is (are): '
-          f'{settings["siteinfo"]["sitelatlon"]}')
-    if settings["siteinfo"]["sitelatlon"] == [[]]:
-        print(f'    No lat lon specified - use tide gauge metadata if '
-              f'available')
-    print(f'User specified science method is: {settings["sciencemethod"]}')
-    if {settings["cmipinfo"]["cmip_sea"]} == {'all'}:
-        print('User specified all CMIP models')
-    elif {settings["cmipinfo"]["cmip_sea"]} == {'marginal'}:
-        print('User specified CMIP models for marginal seas only')
-
-    # Extract site data from station list (e.g. tide gauge location) or
-    # construct based on user input
-    df_site_data = extract_site_info(settings["tidegaugeinfo"]["source"],
-                                     settings["tidegaugeinfo"]["datafq"],
-                                     settings["siteinfo"]["region"],
-                                     settings["siteinfo"]["sitename"],
-                                     settings["siteinfo"]["sitelatlon"])
-
-    extract_cmip5_steric_dyn_regression(df_site_data)
-
-
-if __name__ == '__main__':
-    main()
diff --git a/profsea/step2_extract_steric_dyn_regression_spatial.py b/profsea/step2_extract_steric_dyn_regression_spatial.py
deleted file mode 100644
index 8a3d386..0000000
--- a/profsea/step2_extract_steric_dyn_regression_spatial.py
+++ /dev/null
@@ -1,139 +0,0 @@
-"""
-Copyright (c) 2023, Met Office
-All rights reserved.
-"""
-from profsea.config import settings
-from profsea.directories import read_dir, makefolder
-from profsea.slr_pkg import models, cmip, cubedata, process, get_cube_years
-
-import numpy as np
-from sklearn.linear_model import LinearRegression
-
-
-def extract_dyn_steric_regression(models, scenarios):
-    """
-    Calculate, plot, and save regression between the global mean (
-    thermosteric) and local (sterodynamic) sea level change from CMIP models.
-    :param models: list of model names to be extracted
-    :param scenarios: list of RCP scenarios
-    """
-    # Base directory for CMIP "zos" and "zostoga" data
-    datadir = settings["cmipinfo"]["sealevelbasedir"]
-    # Dictionary of CMIP models and experiments
-    zos_dict = cmip.zos_dictionary()
-    zos_masks = np.empty((len(models), len(scenarios), 180, 360)) # (model, scenario, lat, lon)
-    regr_slopes = np.empty((len(models), len(scenarios), 180, 360)) # (model, scenario, lat, lon)
-    for m, model in enumerate(models):
-        print(f'Calculating regression for {model} all over the grid')
-        for s, scenario in enumerate(scenarios):
-            try:
-                print(f'Scenario: {scenario}')
-                # dynamic sea level (zos)
-                zos_date = zos_dict[model][scenario]['driftcorr']
-                zos_file = f'{datadir}normalized_zos_Omon_{model}_' \
-                            f'{scenario}_{zos_date}_driftcorr.nc'
-                zos = cubedata.read_zos_cube(zos_file)[0]
-                zos_masks[m, s] = zos[0].data.mask
-
-                # --------------------------------------------------------
-                # global mean thermosteric (zostoga)
-                # Extract, and drift-correct CMIP "zostoga" data
-                # Normal (concatenated)
-                zostoga_date = zos_dict[model][scenario]['zostoga']
-                zostoga_file = f'{datadir}zostoga_Omon_{model}_' \
-                                f'{scenario}_{zostoga_date}.nc'
-                zostoga_raw = cubedata.read_zos_cube(zostoga_file)[0]
-                # piControl (concatenated)
-                piControl_date = zos_dict[model][scenario]['piControl']
-                zostoga_pic_file = f'{datadir}zostoga_Omon_' \
-                                    f'{model}_piControl_{piControl_date}.nc'
-                zostoga_pic = cubedata.read_zos_cube(zostoga_pic_file)[0]
-
-                regr = process.Regress('linear')
-                cube_drift, _ = regr.regress_t_scalar(zostoga_pic)
-                zostoga, _ = regr.detrend_scalar(zostoga_raw, cube_drift)
-
-                # --------------------------------------------------------
-
-                yrs = get_cube_years(zos)
-                if model == 'bcc-csm1-1':
-                    index = np.where(yrs >= 2100)
-                    zostoga.data[index] = zostoga.data[index] +  \
-                        zostoga.data[index[0][0] - 1]
-                # Calculate regression coefficients for periods 2005-2100
-                # and 2050-2100
-                idx = ((2005 <= yrs) & (yrs <= 2100))
-
-                zostoga.data = zostoga.data - zostoga.data[0:10].mean()
-                zos.data = zos.data - zos.data[0:10].mean()
-
-                # Calculate the slope and intercepts of linear fits of the
-                # global and local sea level projections
-                regr = LinearRegression()
-                regr.fit(
-                    zostoga.data[idx].reshape(-1, 1),
-                    zos.data[idx].reshape(zos.data[idx].shape[0], -1))
-                slopes = regr.coef_.reshape(180, 360)
-
-                regr_slopes[m, s] = slopes
-            except Exception: 
-                print(f'Error calculating regression for {model}, {scenario}')
-                print('Adding NaN to regression slope')
-
-                regr_slopes[m, s] = np.nan
-                zos_masks[m, s] = np.nan
-                continue
-
-    # Replace rcp26 regression slopes with all np.nan with rcp45
-    for m in range(len(models)):
-        if regr_slopes[m, 0].all() == np.nan:
-            regr_slopes[m, 0] = regr_slopes[m, 1]
-            zos_masks[m, 0] = zos_masks[m, 1]
-        
-    assert regr_slopes.all() != np.nan, \
-        'Regression slopes contain NaN values. Check the data and try again.'
-
-    # Create the output data file directory and filename
-    out_zosddir = read_dir()[2]
-    makefolder(out_zosddir)
-    outdatafile = f'{out_zosddir}zos_regression.npy'
-    outdatafile_mask = f'{out_zosddir}zos_regression_masks.npy'
-
-    # Save the sea level regressions data to file
-    np.save(outdatafile, regr_slopes)
-    np.save(outdatafile_mask, zos_masks)
-    print(f'Saved regression data to {outdatafile}')
-
-    # Plot the regression results
-
-
-
-def main():
-    """
-    Finds all CMIP model names, then calculates the regression parameters
-    between global and local sea level projections for the given locations.
-    :param df: DataFrame of site location's metadata
-    """
-    print('running function extract_cmip5_steric_dyn_regression')
-    # Specify the emission scenarios
-    scenarios = ['rcp26', 'rcp45', 'rcp85']
-
-    # Select CMIP5 models to use
-    if settings["cmipinfo"]["cmip_sea"] == 'all':
-        model_names = models.cmip5_names()
-    elif settings["cmipinfo"]["cmip_sea"] == 'marginal':
-        model_names = models.cmip5_names_marginal()
-    else:
-        raise UnboundLocalError(
-            'The selected CMIP5 models to use - cmip_sea = ' +
-            f'{settings["cmipinfo"]["cmip_sea"]} - ' +
-            'is not recognised')
-
-    # Calculate the regression parameters and plot the results
-    # Note the x and y limits of the plot are set to 0.6 - this can be
-    # updated in extract_dyn_steric_regression() function
-    extract_dyn_steric_regression(model_names, scenarios)
-
-
-if __name__ == '__main__':
-    main()
diff --git a/profsea/step3_process_regional_sealevel_projections.py b/profsea/step3_process_regional_sealevel_projections.py
deleted file mode 100644
index 14c614e..0000000
--- a/profsea/step3_process_regional_sealevel_projections.py
+++ /dev/null
@@ -1,612 +0,0 @@
-"""
-Copyright (c) 2023, Met Office
-All rights reserved.
-"""
-
-import os
-import iris
-import glob
-import pickle
-import numpy as np
-import pandas as pd
-from scipy.interpolate import RegularGridInterpolator
-from netCDF4 import Dataset
-
-from profsea.config import settings
-from profsea.tide_gauge_locations import extract_site_info
-from profsea.slr_pkg import abbreviate_location_name, choose_montecarlo_dir  # found in __init.py__
-from profsea.directories import read_dir, makefolder
-from emulator import GMSLREmulator
-
-
-def calc_baseline_period(sci_method, yrs):
-    """
-    Baseline years used for UKCP18 -- 1981-2000 (offset required)
-    Baseline years used for IPCC AR5 and Palmer et al 2020 -- 1986-2005
-    :param sci_method: scientific method used to select GIA estimates, GRD
-    fingerprints and CMIP regression (based on user input)
-    :param yrs: years of the projections
-    :return: baseline years
-    """
-    if sci_method == 'global':
-        byr1 = 1986.
-        byr2 = 2005.
-        # offset not required
-        G_offset = 0.0
-        print("Baseline period = ", byr1, "to", byr2)
-    elif sci_method == 'UK':
-        byr1 = 1981.
-        byr2 = 2000.
-        G_offset = 0.011
-        print("Baseline period = ", byr1, "to", byr2)
-
-    midyr = (byr2 - byr1 + 1) * 0.5 + byr1
-
-    return yrs[0] - midyr, G_offset
-
-
-def calc_future_sea_level_at_site(df, site_loc, scenario):
-    """
-    Calculates future sea level at the given site and write to file.
-    :param df: Data frame of all metadata (tide gauge or site specific) for
-               each(all) location(s)
-    :param site_loc: name of the site location
-    :param scenario: emission scenario
-    """
-    print('running function calc_future_sea_level_at_site')
-    # Get the location's latitude and longitude.
-    loc_coords = [df.at[site_loc, 'Latitude'], df.at[site_loc, 'Longitude']]
-
-    # Set the UKCP*18* random seed so results are reproducible
-    np.random.seed(18)
-
-    # Directory of Monte Carlo time series for new projections
-    mcdir = choose_montecarlo_dir()
-
-    # Specify the sea level components to include. The GIA contribution is
-    # calculated separately.
-    components = ['exp', 'antdyn', 'antsmb', 'greendyn', 'greensmb',
-                  'glacier', 'landwater']
-
-    # Select dimensions from sample file, [time, realisation]
-    sample = np.load(os.path.join(mcdir, f'{scenario}_exp.npy'))
-    nesm = sample.shape[1]
-    nyrs = sample.shape[0]
-    yrs = np.arange(2007, 2007 + nyrs)
-
-    # Determine the number of samples you wish to make CHOGOM
-    # project = 100000, UKCP18 = 200000
-    if nesm >= 200000:
-        nsmps = 200000
-    else:
-        nsmps = nesm
-        
-    array_dims = [nesm, nsmps, nyrs]
-
-    # Get random samples of global and regional sea level components
-    montecarlo_G, montecarlo_R = calculate_sl_components(mcdir, components,
-                                                         scenario, site_loc,
-                                                         loc_coords, yrs,
-                                                         array_dims)
-
-    # Calculate the summary time series of global and local sea level
-    # change, i.e. mean and percentile values.
-    G_df, R_df = calculate_summary_timeseries(components, yrs, montecarlo_G,
-                                              montecarlo_R)
-
-    # Create the output sea level projections file directory and filename
-    sealev_ddir = read_dir()[4]
-    loc_abbrev = abbreviate_location_name(site_loc)
-    file_header = '_'.join([loc_abbrev, scenario, "projection", 
-                            f"{settings['projection_end_year']}"])
-    G_file = '_'.join([file_header, 'global']) + '.csv'
-    R_file = '_'.join([file_header, 'regional']) + '.csv'
-
-    # Save the global and local projections
-    makefolder(sealev_ddir)
-    G_df.to_csv(os.path.join(sealev_ddir, G_file))
-    R_df.to_csv(os.path.join(sealev_ddir, R_file))
-
-
-def calc_gia_contribution(sci_method, yrs, nyrs, nsmps, coords):
-    """
-    Calculate the glacial isostatic adjustment (GIA) contribution to the
-    regional component of sea level rise.
-    Option to use the generic global GIA estimates or use the GIA estimates
-    developed for UK as part of UKCP18.
-    :param sci_method: scientific method used to select GIA estimates, GRD
-    fingerprints and CMIP regression (based on user input)
-    :param yrs: years of the projections
-    :param nyrs: number of years in each projection time series
-    :param nsmps: determine the number of samples
-    :param coords: coordinates of location of interest
-    :return: GIA estimates converted to mm/yr
-    """
-    print('running function calc_gia_contribution')
-
-    nGIA, GIA_vals = read_gia_estimates(sci_method, coords)
-
-    Tdelta, G_offset = calc_baseline_period(sci_method, yrs)
-
-    # Unit series of mm/yr expressed as m/yr
-    unit_series = (np.arange(nyrs) + Tdelta) * 0.001
-    GIA_unit_series = np.ones([nsmps, nyrs]) * unit_series
-
-    # rgiai is an array of random GIA indices the size of the sample years
-    rgiai = np.random.randint(nGIA, size=nsmps)
-
-    GIA_series = GIA_unit_series.transpose() * GIA_vals[rgiai]
-
-    print('GIA_vals (mm/yr) = ', GIA_series)
-
-    return GIA_series, G_offset
-
-
-def calculate_sl_components(mcdir, components, scenario, site_loc, loc_coords,
-                            yrs, array_dims):
-    """
-    Calculates global and regional component part contributions to sea level
-    change.
-    :param mcdir: location of Monte Carlo time series for new projections
-    :param components: sea level components
-    :param scenario: emission scenario
-    :param site_loc: name of the site location
-    :param loc_coords: latitude and longitude of site
-    :param yrs: years of the projections
-    :param array_dims: Array of nesm, nsmps and nyrs
-        nesm --> Number of ensemble members in time series
-        nsmps --> Determine the number of samples you wish to make
-        nyrs --> Number of years in each projection time series
-    :return: montecarlo_G (global contribution to sea level rise) and
-    montecarlo_R (regional contribution to sea level change)
-    """
-    print('running function calculate_sl_components')
-    ncomps = len(components)
-
-    # Numbers of ensemble members, samples, years
-    nesm, nsmps, nyrs = array_dims
-
-    # Coordinates of location of interest
-    tlat, tlon = loc_coords
-
-    # Scientific method
-    sci_method = settings["sciencemethod"]
-
-    # Set up fingerprint interpolators for each component of sea level change
-    # Note that the first entry in the list is the Slangen, which includes the
-    # land water estimate
-    nFPs, FPlist = setup_FP_interpolators(components, sci_method)
-
-    # Use the same resampling across all sea level components (to preserve
-    # correlations)
-    resamples = np.random.choice(nesm, nsmps)
-    rfpi = np.random.randint(nFPs, size=nsmps)
-
-    # The "offset_slopes" scales the global_offset variables into the different
-    # sea level components.
-    # These offsets exist because a baseline period of 1981-2000 is used,
-    # rather than the AR5 baseline period of 1986-2005.
-    offset_slopes = {'exp': 0.405,
-                     'antnet': 0.095,
-                     'antsmb': -0.025,
-                     'antdyn': 0.120,
-                     'greennet': 0.125,
-                     'greensmb': 0.040,
-                     'greendyn': 0.085,
-                     'glacier': 0.270,
-                     'landwater': 0.105}
-
-    # Monte Carlo for regional values (FPs applied) + GIA
-    montecarlo_R = np.zeros((ncomps + 1, nyrs, nsmps))
-    # Monte Carlo for Global values (no FPs applied)
-    montecarlo_G = np.zeros((ncomps, nyrs, nsmps))
-
-    # Calculate the GIA contribution to the regional component of sea level
-    # change
-    GIA_series, G_offset = calc_gia_contribution(sci_method, yrs, nyrs, nsmps,
-                                                 loc_coords)
-    montecarlo_R[-1, :, :] = GIA_series[:, :]
-
-    for cc, comp in enumerate(components):
-        print(f'cc = {cc:d}, comp = {comp}')
-
-        offset = G_offset * offset_slopes[comp]
-
-        if settings["emulator_settings"]["emulator_mode"]:
-            # Input timeseries provided as numpy objects
-            mc_timeseries = np.load(os.path.join(mcdir, f'{scenario}_{comp}.npy'))
-            montecarlo_G[cc, :, :] = mc_timeseries[:nyrs, resamples] + offset
-        else:
-            cube = iris.load_cube(os.path.join(mcdir, f'{scenario}_{comp}.nc'))
-            montecarlo_G[cc, :, :] = cube.data[:nyrs, resamples] + offset
-
-        if comp == 'exp':
-            if sci_method == 'global':
-                if settings["emulator_settings"]["emulator_mode"]:
-                    coeffs = load_CMIP5_slope_coeffs(site_loc, 'rcp85')
-                else:
-                    coeffs = load_CMIP5_slope_coeffs(site_loc, scenario)
-                rand_coeffs = np.random.choice(coeffs, size=nsmps,
-                                               replace=True)
-            elif sci_method == 'UK':
-                if settings["emulator_settings"]["emulator_mode"]:
-                    coeffs, weights = load_CMIP5_slope_coeffs_UK('rcp85')
-                else:
-                    coeffs, weights = load_CMIP5_slope_coeffs_UK(scenario)
-                rand_coeffs = np.random.choice(coeffs, size=nsmps,
-                                               replace=True, p=weights)
-            montecarlo_R[cc, :, :] = montecarlo_G[cc, :, :] * rand_coeffs
-        elif comp == 'landwater':
-            landwater_FP_interpolator = FPlist[0]['landwater']
-            # Interpolate values to target lat/lon
-            val = landwater_FP_interpolator([tlat, tlon])[0]
-            montecarlo_R[cc, :, :] = montecarlo_G[cc, :, :] * val
-        else:
-            # Initiate an empty list for fingerprint values
-            FPvals = []
-            for FP_dict in FPlist:
-                # Interpolate values to target lat/lon
-                val = FP_dict[comp]([tlat, tlon])[0]
-                FPvals.append(val)
-            FPvals = np.array(FPvals)
-            montecarlo_R[cc, :, :] = montecarlo_G[cc, :, :] * FPvals[rfpi]
-
-    return montecarlo_G, montecarlo_R
-
-
-def calculate_summary_timeseries(components, years, montecarlo_G,
-                                 montecarlo_R):
-    """
-    Calculates summary timeseries of each sea level rise component, plus sums
-    of the contributions from Greenland and Antarctica, and sum of all
-    components.
-    :param components: sea level components
-    :param years: years of the projections
-    :param montecarlo_G: global sea level components
-    :param montecarlo_R: regional sea level components
-    :return: DataFrame of global and regional summary timeseries of sea level
-    """
-    print('running function calculate_summary_timeseries')
-    percentiles = [5, 10, 30, 33, 50, 67, 70, 90, 95]
-
-    # Define lists to store the summary timeseries. They will be converted to
-    # Pandas dataframes.
-    R_list = []
-    G_list = []
-
-    for cc, _ in enumerate(components):
-        # Calculate and store the global component time series
-        cgout = np.percentile(montecarlo_G[cc, :, :], percentiles, axis=1)
-        G_list.append(cgout.flatten(order='F'))
-
-        # Calculate and store the regional component time series
-        crout = np.percentile(montecarlo_R[cc, :, :], percentiles, axis=1)
-        R_list.append(crout.flatten(order='F'))
-
-    # Set up a multi-level index for the dataframes, consisting of the years
-    # and percentiles.
-    iterables = [years, percentiles]
-    idx = pd.MultiIndex.from_product(iterables, names=['year', 'percentile'])
-    G_df = pd.DataFrame(np.asarray(G_list).T, columns=components, index=idx)
-    R_df = pd.DataFrame(np.asarray(R_list).T, columns=components, index=idx)
-    R_df.rename(columns={"exp": "ocean"})
-
-    # Store the regional GIA component time series
-    ncomp = len(components)
-    crout = np.percentile(montecarlo_R[ncomp, :, :], percentiles, axis=1)
-    R_df['GIA'] = crout.flatten(order='F')
-
-    # Add sums of contributions from Antarctica and Greenland to the saved
-    # series. This needs to be calculated across the full montecarlo range not
-    # a pandas addition.
-    # calc_future_sea_level_at_site states: components = ['exp', 'antdyn',
-    # 'antsmb', 'greendyn', 'greensmb', 'glacier', 'landwater']
-    antnet_tmpg = np.percentile(
-        montecarlo_G[1, :, :] + montecarlo_G[2, :, :],
-        percentiles, axis=1).flatten(order='F')
-    G_df['antnet'] = pd.DataFrame(antnet_tmpg, columns=['antnet'], index=idx)
-    greennet_tmpg = np.percentile(
-        montecarlo_G[3, :, :] + montecarlo_G[4, :, :],
-        percentiles, axis=1).flatten(order='F')
-    G_df['greennet'] = pd.DataFrame(
-        greennet_tmpg, columns=['greennet'], index=idx)
-    antnet_tmpr = np.percentile(
-        montecarlo_R[1, :, :] + montecarlo_R[2, :, :],
-        percentiles, axis=1).flatten(order='F')
-    R_df['antnet'] = pd.DataFrame(antnet_tmpr, columns=['antnet'], index=idx)
-    greennet_tmpr = np.percentile(
-        montecarlo_R[3, :, :] + montecarlo_R[4, :, :],
-        percentiles, axis=1).flatten(order='F')
-    R_df['greennet'] = pd.DataFrame(
-        greennet_tmpr, columns=['greennet'], index=idx)
-
-    # Sum of all components (net sea level change)
-    montecarlo_Gsum = np.sum(montecarlo_G, axis=0)
-    montecarlo_Rsum = np.sum(montecarlo_R, axis=0)
-    # Store the percentiles of the sums of all components to global and local
-    # sea level change
-    cgout = np.percentile(montecarlo_Gsum, percentiles, axis=1)
-    crout = np.percentile(montecarlo_Rsum, percentiles, axis=1)
-    G_df['sum'] = cgout.flatten(order='F')
-    R_df['sum'] = crout.flatten(order='F')
-
-    return G_df, R_df
-
-
-def create_FP_interpolator(datadir, dfile, method='linear'):
-    """
-    Generates a scipy Interpolator object from input NetCDF data of
-    gravitational fingerprints (takes inputs of Latitude and Longitude).
-    :param datadir: data directory
-    :param dfile: data filename
-    :param method: interpolation type --> 'linear' or 'nearest'
-    :return: 2D Interpolator object
-    """
-    cube = iris.load_cube(os.path.join(datadir, dfile))
-    lon = cube.coord('longitude').points
-    lat = cube.coord('latitude').points
-
-    # Define linear interpolator object:
-    interp_object = RegularGridInterpolator((lat, lon), cube.data,
-                                            method=method, bounds_error=True,
-                                            fill_value=None)
-
-    return interp_object
-
-
-def get_projection_info(indir, scenario):
-    """
-    Read in the dimensions of the Monte-Carlo data. These files are all
-    relative to midnight on 1st January 2007
-    :param indir: directory of Monte Carlo time series for new projections
-    :param scenario: emission scenarios to be considered
-    :return: Number of ensemble members in time series, number of years in
-    each projection time series and the years of the projections
-    """
-    sample_file = f'{scenario}_exp.nc'
-    f = Dataset(f'{indir}{sample_file}', 'r')
-    nesm = f.dimensions['realization'].size
-    t = f.variables['time']
-    nyrs = t.size
-    unit_str = t.units
-    first_year = int(unit_str.split(' ')[2][:4])
-    f.close()
-
-    yrs = first_year + np.arange(nyrs)
-
-    return nesm, nyrs, yrs
-
-
-def load_CMIP5_slope_coeffs(site_loc, scenario):
-    """
-    Loads in the CMIP slope coefficients based on linear regression of
-    'zos+zostoga' against 'zostoga' for the period 2005 to 2100.
-    Some models are missing regression slopes for RCP2.6. If so, use RCP4.5
-    values instead.
-    :param site_loc: name of the site location
-    :param scenario: emissions scenario
-    :return: 1D array of regression coefficients
-    """
-    print('running function load_CMIP5_slope_coeffs')
-
-    # Read in the sea level regressions
-    in_zosddir = read_dir()[2]
-    loc_abbrev = abbreviate_location_name(site_loc)
-    filename = os.path.join(in_zosddir, f'{loc_abbrev}_zos_regression.csv')
-    df = pd.read_csv(filename, header=0)
-
-    coeffs = df.loc[(df['Scenario'] == scenario)]['slope_05_00'].values
-
-    # Some models are missing regression slopes for RCP2.6. If so, use RCP4.5
-    # values instead
-    if scenario == 'rcp26':
-        rcp45_coeffs = df.loc[(df['Scenario'] ==
-                               'rcp45')]['slope_05_00'].values
-        msgi = np.where(np.isnan(coeffs))[0]
-        coeffs[msgi] = rcp45_coeffs[msgi]
-
-    return coeffs
-
-
-def load_CMIP5_slope_coeffs_UK(scenario):
-    """
-    Loads in the CMIP5 slope coefficients developed for UKCP18
-    :param scenario: emissions scenario
-    :return: 1D array of slope coefficients and 1D array of weights
-    """
-    print('running function load_CMIP5_slope_coeffs_UK')
-    in_zosdir_uk = settings["cmipinfo"]["slopecoeffsuk"]
-    filename_uk = f'{scenario}_CMIP5_regress_coeffs_uk_mask_1.pickle'
-
-    try:
-        with open(os.path.join(in_zosdir_uk, filename_uk), 'rb') as f:
-            data = pickle.load(f, encoding='latin1')['uk_mask_1']
-    except FileNotFoundError:
-        raise FileNotFoundError(filename_uk,
-                                '- scenario selected does not exist')
-
-    # Keys are: 'coeffs', 'models', 'weights'
-    coeffs = data['coeffs']
-    weights = data['weights']
-
-    return coeffs, weights
-
-
-def read_gia_estimates(sci_method, coords):
-    """
-    Read in pre-processed interpolator objects of GIA estimates (Lambeck,
-    ICE5G or UK specific ones)
-    :param sci_method: scientific method used to select GIA estimates, GRD
-    fingerprints and CMIP regression (based on user input)
-    :param coords: latitude and longitude of tide gauge
-    :return: length of GIA_vals and numpy array of pre-processed interpolator
-    objects of GIA estimates
-    """
-    print('running function read_gia_estimates')
-    # Directories containing GIA data (independent of scenario)
-    if sci_method == 'global':
-        gia_file = settings["giaestimates"]["global"]
-    elif sci_method == 'UK':
-        gia_file = settings["giaestimates"]["uk"]
-    else:
-        raise UnboundLocalError('The selected GIA estimate - ' +
-                                f'{sci_method} - is not available')
-
-    with open(gia_file, "rb") as ifp:
-        GIA_dict = pickle.load(ifp, encoding='latin1')
-
-    GIA_vals = []
-    lat, lon = coords
-    # The GIA_dict contains interpolator objects
-    for key in list(GIA_dict.keys()):
-        val = GIA_dict[key]([lat, lon])[0]
-        GIA_vals.append(val)
-
-    nGIA = len(GIA_vals)
-    GIA_vals = np.array(GIA_vals)
-
-    return nGIA, GIA_vals
-
-
-def setup_FP_interpolators(components, sci_method):
-    """
-    Create 2D Interpolator objects for the Slangen, Spada and Klemann
-    fingerprints
-    :param components: list of sea level components
-    :param sci_method: scientific method used to select GIA estimates, GRD
-    fingerprints and CMIP regression (based on user input)
-    :return nFPs: length of FPlist and interpolator objects of all sea level
-    components
-    """
-    print('running function setup_FP_interpolators')
-
-    # Directories for the Slangen, Spada and Klemann fingerprints
-    slangendir = settings["fingerprints"]["slangendir"]
-    spadadir = settings["fingerprints"]["spadadir"]
-    klemanndir = settings["fingerprints"]["klemanndir"]
-
-    # Create empty dictionaries for the Slangen, Spada and Klemann fingerprints
-    # interpolator objects.
-    slangen_FPs = {}
-    spada_FPs = {}
-    klemann_FPs = {}
-
-    # Only 1 fingerprint for Landwater
-    comp = 'landwater'
-    slangen_FPs[comp] = create_FP_interpolator(slangendir,
-                                               comp + '_slangen_nomask.nc')
-
-    # Create interpolators for the remaining components. Expansion ('exp')
-    # is global so no interpolation is needed.
-    components_todo = [c for c in components if c not in ['exp', 'landwater']]
-    for comp in components_todo:
-        slangen_FPs[comp] = create_FP_interpolator(slangendir,
-                                                   comp + '_slangen_nomask.nc')
-        spada_FPs[comp] = create_FP_interpolator(spadadir,
-                                                 comp + '_spada_nomask.nc')
-        klemann_FPs[comp] = create_FP_interpolator(klemanndir,
-                                                   comp + '_klemann_nomask.nc')
-
-    if sci_method == 'UK':
-        # Klemann fingerprints were not used in UKCP18
-        FPlist = [slangen_FPs, spada_FPs]
-    elif sci_method == 'global':
-        FPlist = [slangen_FPs, spada_FPs, klemann_FPs]
-    else:
-        raise UnboundLocalError('The selected GRD fingerprint method - ' +
-                                f'{sci_method} - is not available')
-
-    nFPs = len(FPlist)
-
-    return nFPs, FPlist
-
-
-def read_csv_file(file_pattern: str, start_yr: int=2007, 
-                  end_yr: int=settings["projection_end_year"]):
-    file = glob.glob(
-        os.path.join(
-            settings["emulator_settings"]["emulator_input_dir"], 
-            file_pattern))
-    if not file:
-        raise FileNotFoundError(f'File {file_pattern} not found')
-    df = pd.read_csv(file[0])
-    return df.loc[:, str(start_yr):str(end_yr)].to_numpy()
-
-
-def main():
-    """
-    Reads in and calculates global and local (regional) sea level change
-    (sum total), based on the different contributing factors e.g. thermal
-    expansion, GIA and mass balance. Writes out the selected emissions scenario
-    estimates of the various components and their sums.
-    """
-    print(f'User specified site name(s) is (are):'
-          f' {settings["siteinfo"]["sitename"]}')
-    print(f'User specified lat(s) and lon(s) is (are): '
-          f'{settings["siteinfo"]["sitelatlon"]}')
-    if settings["siteinfo"]["sitelatlon"] == [[]]:
-        print('    No lat lon specified - use tide gauge metadata if '
-              'available')
-    print(f'User specified science method is: {settings["sciencemethod"]}')
-    if {settings["cmipinfo"]["cmip_sea"]} == {'all'}:
-        print('User specified all CMIP models')
-    elif {settings["cmipinfo"]["cmip_sea"]} == {'marginal'}:
-        print('User specified CMIP models for marginal seas only')
-
-    print(f'\nProjecting out to: {settings["projection_end_year"]}\n')
-
-    # Extract site data from station list (e.g. tide gauge location) or
-    # construct based on user input
-    df_site_data = extract_site_info(settings["tidegaugeinfo"]["source"],
-                                     settings["tidegaugeinfo"]["datafq"],
-                                     settings["siteinfo"]["region"],
-                                     settings["siteinfo"]["sitename"],
-                                     settings["siteinfo"]["sitelatlon"])
-
-    if settings["emulator_settings"]["emulator_mode"]:
-        print('\nInitiating ProFSea emulator')
-        if settings["projection_end_year"] > 2100:
-            palmer_method = True
-        else:
-            palmer_method = False
-            
-        makefolder(os.path.join(settings["baseoutdir"], 'emulator_output'))
-        
-        # Get the metadata of either the site location or tide gauge location
-        for scenario in settings["emulator_settings"]["emulator_scenario"]:
-            print(f'Projecting {scenario}...')
-            T_change = read_csv_file(f'*{scenario}*_temperature*.csv')
-            OHC_change = read_csv_file(f'*{scenario}*_ocean_heat_content_change*.csv')
-
-            cum_emissions_file = f'*{scenario}*_cumulative_emissions_total.txt'
-            if glob.glob(os.path.join(settings["emulator_settings"]["emulator_input_dir"], cum_emissions_file)):
-                cum_emissions_total = read_csv_file(cum_emissions_file)
-            else:
-                raise FileNotFoundError('For any non-RCP scenario, a cumulative emissions total must be provided.')
-            
-            gmslr = GMSLREmulator(
-                T_change,
-                OHC_change,
-                scenario,
-                os.path.join(settings["baseoutdir"], 'emulator_output'),
-                settings["projection_end_year"],
-                palmer_method=palmer_method,
-                input_ensemble=settings["emulator_settings"]["use_input_ensemble"],
-                cum_emissions_total=cum_emissions_total)
-            gmslr.project()
-            print('Saving components...')
-            gmslr.save_components(
-                os.path.join(settings["baseoutdir"], 'emulator_output'),
-                scenario)
-            print('Saved!\n')
-            for loc_name in df_site_data.index.values:
-                calc_future_sea_level_at_site(df_site_data, loc_name, scenario)
-    else:
-        scenarios = ['rcp26', 'rcp45', 'rcp85']
-        for scenario in scenarios:
-            for loc_name in df_site_data.index.values:
-                calc_future_sea_level_at_site(df_site_data, loc_name, scenario)
-
-
-if __name__ == '__main__':
-    main()
diff --git a/profsea/step4_plot_regional_sealevel.py b/profsea/step4_plot_regional_sealevel.py
deleted file mode 100644
index d15112b..0000000
--- a/profsea/step4_plot_regional_sealevel.py
+++ /dev/null
@@ -1,940 +0,0 @@
-"""
-Copyright (c) 2023, Met Office
-All rights reserved.
-"""
-
-import os
-import iris
-import numpy as np
-import pandas as pd
-import matplotlib
-import matplotlib.pyplot as plt
-
-from profsea.config import settings
-from profsea.tide_gauge_locations import extract_site_info, find_nearest_station_id
-from profsea.slr_pkg import abbreviate_location_name, choose_montecarlo_dir  # found in __init.py__
-from profsea.surge import tide_gauge_library as tgl
-from profsea.directories import read_dir, makefolder
-from profsea.plotting_libraries import (
-    location_string, scenario_string, ukcp18_colours, ukcp18_labels, 
-    calc_xlim, calc_ylim, plot_zeroline, multi_index_values, extract_comp_sl,
-    plot_tg_data)
-from profsea.emulator.plotting import plot_slr, plot_slr_components
-
-
-def compute_uncertainties(df_r_list, scenarios, tg_years, tg_amsl):
-    """
-    Function to estimate uncertainties following Hawkins and Sutton (2009).
-    :param df_r_list: regional sea level projections
-    :param scenarios: emissions scenario
-    :param tg_years: tide gauge years
-    :param tg_amsl: annual mean sea level data
-    :return: years, scenario uncertainty, model uncertainty, internal
-    variability
-    """
-    nyrs = np.range(2007, settings["projection_end_year"] + 1).size
-    allpmid = np.zeros([3, nyrs])
-    allunc = np.zeros([3, nyrs])
-
-    # Estimate internal variability from de-trended gauge data
-    tg_years_arr = np.array(tg_years, dtype='int')
-    vals = np.ma.masked_values(tg_amsl, -99999.)
-    IntV = compute_variability(tg_years_arr / 1000., vals / 1.)
-
-    # Get the values of the multi-index: years and percentile values
-    years, percentiles = multi_index_values(df_r_list)
-
-    for rcp_count, _ in enumerate(scenarios):
-        # Get the sums of local sea level projections
-        df_R = df_r_list[rcp_count]
-        rlow, rmid, rupp = extract_comp_sl(df_R, percentiles, 'sum')
-
-        allpmid[rcp_count, :] = rmid
-        allunc[rcp_count, :] = (rupp - rlow) * 0.5
-
-    # Scenario Uncertainty (expressed as 90% confidence interval)
-    UncS = np.std(allpmid, axis=0) * 1.645
-    # Model Uncertainty (already expressed as 90% confidence interval)
-    UncM = np.mean(allunc, axis=0)
-
-    return years, UncS, UncM, IntV
-
-
-def compute_variability(x, y, factor=1.645):
-    """
-    Estimate internal variability from de-trended gauge data.
-    :param x: tide gauge temporal data
-    :param y: tide gauge data
-    :param factor: multiplication factor
-    :return: internal variability component of uncertainty
-    """
-    mask = np.ma.getmask(y)
-    index = np.where(mask is True)
-    new_x = np.delete(x, index)
-    new_y = np.delete(y, index)
-    fit = np.polyfit(new_x, new_y, 1)
-    fit_data = new_x * fit[0] + fit[1]
-    stdev = np.std(new_y - fit_data)
-
-    return stdev * factor
-
-
-def plot_figure_one(r_df_list, site_name, scenarios, fig_dir):
-    """
-    Subplot 1 - Local sea level projections for all RCPs - sum total.
-    Subplot 2 - Local sea level projections for RCP8.5 - sum total and
-    component parts.
-    :param r_df_list: regional sea level projections
-    :param site_name: site location
-    :param scenarios: emissions scenario
-    :param fig_dir: figure directory
-    """
-    # Get the values of the multi-index: years and percentile values
-    years, percentiles = multi_index_values(r_df_list)
-
-    # UKCP18 colour scheme for sea level components
-    rcp_colours = ukcp18_colours()[0]
-
-    # Calculate the x-axis and y-axis limit
-    xlim = calc_xlim('proj', [], years)
-    ylim = calc_ylim('proj', [], r_df_list)
-
-    fig = plt.figure(figsize=(7.68, 3.5))
-    matplotlib.rcParams['font.size'] = 7.5
-
-    # First figure, regional sea level projections and uncertainties under each
-    # scenario
-    ax = fig.add_subplot(1, 2, 1)
-
-    for rcp_count, rcp_str in enumerate(scenarios):
-        # Get the sums of all components of local sea level projections
-        r_df_rcp = r_df_list[rcp_count]
-        rlow, rmid, rupp = extract_comp_sl(r_df_rcp, percentiles, 'sum')
-
-        label = scenario_string(scenarios, rcp_count)
-        ax.fill_between(years, rlow, rupp, alpha=0.3,
-                        color=rcp_colours[rcp_str], linewidth=0)
-        ax.plot(years, rmid, color=rcp_colours[rcp_str], label=label)
-
-    plot_zeroline(ax, xlim)
-
-    ax.set_xlim(xlim)
-    ax.set_ylim(ylim)
-    ax.yaxis.set_ticks_position('both')
-    ax.yaxis.set_label_position('left')
-
-    ax.set_title(f'Local sea level - {location_string(site_name)}')
-    ax.set_xlabel('Year')
-    ax.set_ylabel('Sea Level Change (m)')
-
-    ax.legend(loc='upper left', frameon=False)
-
-    # Second figure showing the various components of sea level projections
-    bx = fig.add_subplot(1, 2, 2)
-
-    # UKCP18 colour scheme for sea level components
-    comp_colours = ukcp18_colours()[1]
-    # UKCP18 labels for sea level components
-    comp_labels = ukcp18_labels()
-
-    # Show components for the RCP8.5 scenario (only plot the uncertainty for
-    # sum and thermal expansion)
-    for rcp_count, rcp_str in enumerate(scenarios):
-        if scenarios[rcp_count] == 'rcp85':
-            break
-    else:
-        raise ValueError('RCP8.5 scenario not in list')
-
-    r_df_rcp = r_df_list[rcp_count]
-
-    for comp in ['sum', 'ocean', 'greennet', 'antnet', 'glacier', 'landwater',
-                 'gia']:
-        # Get the components of regional sea level projections
-        clow_85, cmid_85, cupp_85 = extract_comp_sl(r_df_rcp, percentiles,
-                                                    comp)
-        colour = comp_colours[comp]
-        label = comp_labels[comp]
-
-        if comp in ['sum', 'ocean']:
-            bx.fill_between(years, cupp_85, clow_85, facecolor=colour,
-                            alpha=0.3, edgecolor='None')
-
-            if comp == 'sum':
-                bx.plot(years, cmid_85, colour, linewidth=1.5, label=label)
-            else:
-                bx.plot(years, cmid_85, colour, linewidth=1.5, label=label)
-        else:
-            bx.plot(years, cmid_85, colour, linewidth=1.5, label=label)
-
-    plot_zeroline(bx, xlim)
-    bx.set_xlim(xlim)
-    bx.set_ylim(ylim)
-    bx.yaxis.set_ticks_position('both')
-    bx.yaxis.set_label_position('left')
-
-    bx.legend(loc='upper left', frameon=False)
-    bx.set_title(f'Sea level components - {scenario_string(rcp_str, -999)}')
-    bx.set_xlabel('Year')
-
-    # Save the figure
-    fig.tight_layout()
-    ffile = f'{fig_dir}01_{site_name}.png'
-    plt.savefig(ffile, dpi=300, format='png')
-    plt.close()
-
-
-def plot_figure_two(r_df_list, tg_name, nflag, flag, tg_years, non_missing,
-                    tg_amsl, site_name, scenarios, fig_dir):
-    """
-    Local sea level projections for specified RCPs - sum total, and annual
-    mean tide gauge observations.
-    :param r_df_list: regional sea level projections
-    :param tg_name: tide gauge name
-    :param nflag: number of flagged years
-    :param flag: flagged data
-    :param tg_years: tide gauge years
-    :param non_missing: boolean to indicate NaN values
-    :param tg_amsl: annual mean sea level data
-    :param site_name: site location
-    :param scenarios: emissions scenario
-    :param fig_dir:figure directory
-    """
-    # Get the values of the multi-index: years and percentile values
-    years, percentiles = multi_index_values(r_df_list)
-
-    # UKCP18 colour scheme for RCP scenarios
-    rcp_colours = ukcp18_colours()[0]
-
-    fig = plt.figure(figsize=(5, 4.5))
-    matplotlib.rcParams['font.size'] = 10
-
-    # Draw regional sea level projections and uncertainties under each scenario
-    ax = fig.add_subplot(1, 1, 1)
-
-    for rcp_count, rcp_str in enumerate(scenarios):
-        # Get the sums of all components of local sea level projections
-        r_df_rcp = r_df_list[rcp_count]
-        rlow, rmid, rupp = extract_comp_sl(r_df_rcp, percentiles, 'sum')
-
-        label = scenario_string(scenarios, rcp_count)
-        ax.fill_between(years, rlow, rupp, alpha=0.3,
-                        color=rcp_colours[rcp_str], linewidth=0)
-        ax.plot(years, rmid, color=rcp_colours[rcp_str], label=label)
-
-    # Calculate sensible x-axis range and y-axis range based on tide gauge data
-    # and projections
-    xlim = calc_xlim('tide', tg_years, years)
-    df_tmp = pd.DataFrame([rlow, rmid, rupp])
-    ylim = calc_ylim('tide', tg_amsl, df_tmp)
-
-    plot_zeroline(ax, xlim)
-
-    # Plot the annual mean sea levels from the tide gauge data
-    plot_tg_data(ax, nflag, flag, tg_years, non_missing, tg_amsl, tg_name)
-
-    ax.set_xlim(xlim)
-    ax.set_ylim(ylim)
-    ax.yaxis.set_ticks_position('both')
-    ax.yaxis.set_label_position('left')
-    ax.set_title(f'Local sea level - {location_string(site_name)}')
-    ax.set_ylabel('Sea Level Change (m)')
-    ax.set_xlabel('Year')
-    ax.legend(loc='upper left', frameon=False)
-
-    # Save the figure
-    fig.tight_layout()
-    ffile = f'{fig_dir}02_{site_name}.png'
-    plt.savefig(ffile, dpi=300, format='png')
-    plt.close()
-
-
-def plot_figure_three(g_df_list, r_df_list, global_low, global_mid, global_upp,
-                      site_name, scenarios, fig_dir):
-    """
-    Subplot 1 - Comparison of IPCC AR5 + Levermann global projections and
-    global projections developed here.
-    Subplot 2 - Comparison of global projections developed here and local sea
-    level projections.
-    :param g_df_list: global sea level projections
-    :param r_df_list: regional sea level projections
-    :param global_low: IPCC AR5 + Levermann global projections 5th percentile
-    :param global_mid: IPCC AR5 + Levermann global projections 50th percentile
-    :param global_upp: IPCC AR5 + Levermann global projections 95th percentile
-    :param site_name: site location
-    :param scenarios: emissions scenario
-    :param fig_dir: figure directory
-    """
-    # Get the values of the multi-index: years and percentile values
-    years, percentiles = multi_index_values(r_df_list)
-
-    # UKCP18 colour scheme for RCP scenarios
-    rcp_colours = ukcp18_colours()[0]
-
-    # Calculate sensible x-axis range based on projections
-    xlim = calc_xlim('proj', [], years)
-
-    for rcp_count, rcp_str in enumerate(scenarios):
-        # Get the sums of all components of global sea level projections
-        g_df_rcp = g_df_list[rcp_count]
-        glow, gmid, gupp = extract_comp_sl(g_df_rcp, percentiles, 'sum')
-
-        # Get the sums of all components of global sea level projections
-        r_df_rcp = r_df_list[rcp_count]
-        rlow, rmid, rupp = extract_comp_sl(r_df_rcp, percentiles, 'sum')
-
-        # Calculate sensible y-axis range based on projections
-        r_ylim = calc_ylim('proj', [], r_df_rcp)
-        g_ylim = calc_ylim('proj', [], g_df_rcp)
-        ylim = [min(r_ylim[0], g_ylim[0]), max(r_ylim[1], g_ylim[1])]
-
-        # Plot comparison of global sea level projections
-        # Check that global is equivalent to the IPCC AR5 + Levermann sea
-        # level projections
-        fig = plt.figure(figsize=(7.68, 3.5))
-        matplotlib.rcParams['font.size'] = 7.5
-
-        ax = fig.add_subplot(1, 2, 1)
-        # IPCC
-        ax.plot(years, global_mid[rcp_count], color='k', linewidth=1.5,
-                linestyle='-', label='IPCC AR5 + Levermann')
-        ax.plot(years, global_low[rcp_count], color='k', linewidth=1.5,
-                linestyle=':')
-        ax.plot(years, global_upp[rcp_count], color='k', linewidth=1.5,
-                linestyle=':')
-        # Global
-        ax.plot(years, gmid, color='orange', linewidth=1.5, linestyle='--',
-                label='Global Total')
-        ax.fill_between(years, glow, gupp, color='orange', alpha=0.3,
-                        linewidth=0, edgecolor='None')
-
-        plot_zeroline(ax, xlim)
-        ax.set_xlim(xlim)
-        ax.set_ylim(ylim)
-        ax.yaxis.set_ticks_position('both')
-        ax.yaxis.set_label_position('left')
-
-        ax.set_title(f'Global sea level - {scenario_string(rcp_str, -999)}')
-        ax.set_ylabel('Sea Level Change (m)')
-        ax.set_xlabel('Year')
-        ax.legend(loc='upper left', frameon=False)
-
-        # Plot regional sea level projections, showing both error ranges
-        bx = fig.add_subplot(1, 2, 2)
-        # Global
-        bx.plot(years, gmid, color='k', linewidth=1.5, linestyle='-',
-                label='Global Total')
-        bx.plot(years, glow, color='k', linewidth=1.5, linestyle=':')
-        bx.plot(years, gupp, color='k', linewidth=1.5, linestyle=':')
-        # Local
-        bx.plot(years, rmid, color=rcp_colours[rcp_str], linewidth=1.5,
-                linestyle='--', label=location_string(site_name))
-        bx.fill_between(years, rlow, rupp, color=rcp_colours[rcp_str],
-                        alpha=0.3, linewidth=0, edgecolor='None')
-
-        plot_zeroline(bx, xlim)
-        bx.set_xlim(xlim)
-        bx.set_ylim(ylim)
-        bx.yaxis.set_ticks_position('both')
-        bx.yaxis.set_label_position('left')
-
-        bx.set_title(f'Local sea level - {location_string(site_name)} - '
-                     f'{scenario_string(rcp_str, -999)}')
-        bx.set_xlabel('Year')
-        bx.legend(loc='upper left', frameon=False)
-
-        # Save the figure
-        fig.tight_layout()
-        outfile = f'{fig_dir}03_{site_name}_{rcp_str}.png'
-        plt.savefig(outfile, dpi=300, format='png')
-        plt.close()
-
-
-def plot_figure_four(r_df_list, site_name, scenarios, fig_dir):
-    """
-    Local sea level projections for all RCPs - sum total.
-    Same style as Figure 3.1.4 of UKCP18 Marine Report (pg 16).
-    :param r_df_list: regional sea level projections
-    :param site_name: site location
-    :param scenarios: emissions scenario
-    :param fig_dir: figure directory
-    """
-    # Get the values of the multi-index: years and percentile values
-    years, percentiles = multi_index_values(r_df_list)
-
-    # UKCP18 colour scheme for RCP scenarios
-    rcp_colours = ukcp18_colours()[0]
-
-    fig = plt.figure(figsize=(5, 4.5))
-    matplotlib.rcParams['font.size'] = 10
-
-    # First figure, regional sea level projections and uncertainties under each
-    # scenario
-    ax = fig.add_subplot(1, 1, 1)
-
-    for rcp_count, rcp_str in enumerate(scenarios):
-        # Get the sums of regional sea level projections
-        r_df_rcp = r_df_list[rcp_count]
-        rlow, rmid, rupp = extract_comp_sl(r_df_rcp, percentiles, 'sum')
-
-        label = scenario_string(scenarios, rcp_count)
-        plt.plot(years, rmid, color=rcp_colours[rcp_str], linewidth=4.0,
-                 label=label)
-        plt.plot(years, rupp, color=rcp_colours[rcp_str], linewidth=1.0,
-                 linestyle='--')
-        plt.plot(years, rlow, color=rcp_colours[rcp_str], linewidth=1.0,
-                 linestyle='--')
-
-    # Calculate sensible x-axis range and y-axis range based on tide gauge data
-    # and projections
-    xlim = calc_xlim('proj', [], years)
-    df_tmp = pd.DataFrame([rlow, rmid, rupp])
-    ylim = calc_ylim('proj', [], df_tmp)
-
-    plot_zeroline(ax, xlim)
-    plt.xlim(xlim)
-    plt.ylim(ylim)
-    ax.yaxis.set_ticks_position('both')
-    ax.yaxis.set_label_position('left')
-
-    plt.title(f'Local sea level - {location_string(site_name)}')
-    plt.ylabel('Sea Level Change (m)')
-    plt.xlabel('Year')
-    plt.legend(loc='upper left', frameon=False)
-
-    # Save the figure
-    fig.tight_layout()
-    outfile = f'{fig_dir}04_{site_name}.png'
-    plt.savefig(outfile, dpi=300, format='png')
-    plt.close()
-
-
-def plot_figure_five(g_df_list, r_df_list, site_name, scenarios, fig_dir):
-    """
-    Comparison of global mean sea level projections calculated by the tool and
-    local sea level projections - sum total
-    Also shown local sea level projections - component parts
-    Subplot 1, 2, 3 - RCP2.6, RCP4.5, RCP8.5 respectively.
-    :param g_df_list: global sea level projections
-    :param r_df_list: regional sea level projections
-    :param site_name: site location
-    :param scenarios: emissions scenario
-    :param fig_dir: figure directory
-    """
-    # Get the values of the multi-index: years and percentile values
-    years, percentiles = multi_index_values(r_df_list)
-
-    # UKCP18 colour scheme for sea level components
-    comp_colours = ukcp18_colours()[1]
-    # UKCP18 labels for sea level components
-    comp_labels = ukcp18_labels()
-
-    # Calculate sensible x-axis range and y-axis range based on projections
-    xlim = calc_xlim('proj', [], years)
-    r_ylim = calc_ylim('proj', [], r_df_list)
-    g_ylim = calc_ylim('proj', [], g_df_list)
-    ylim = [min(r_ylim[0], g_ylim[0]), max(r_ylim[1], g_ylim[1])]
-
-    fig = plt.figure(figsize=(7.68, 3.5))
-    matplotlib.rcParams['font.size'] = 7.5
-
-    # Loop through RCPs for subplots
-    for rcp_count, _ in enumerate(scenarios):
-        ax = fig.add_subplot(1, 3, rcp_count + 1)
-
-        # Get the sums of global sea level projections
-        g_df_rcp = g_df_list[rcp_count]
-        glow, gmid, gupp = extract_comp_sl(g_df_rcp, percentiles, 'sum')
-
-        ax.plot(years, gmid, color='k', linewidth=1.5, linestyle='--',
-                label='Global Total')
-        ax.plot(years, glow, color='k', linewidth=1.5, linestyle=':')
-        ax.plot(years, gupp, color='k', linewidth=1.5, linestyle=':')
-
-        # Get the sums of local sea level projections
-        r_df_rcp = r_df_list[rcp_count]
-
-        for comp in ['sum', 'ocean', 'greennet', 'antnet', 'glacier',
-                     'landwater', 'gia']:
-            # Get the components of regional sea level projections
-            clow, cmid, cupp = extract_comp_sl(r_df_rcp, percentiles, comp)
-
-            colour = comp_colours[comp]
-            label = comp_labels[comp]
-
-            if comp in ['sum', 'ocean']:
-                ax.plot(years, cmid, colour, linewidth=1.5, label=label)
-                ax.fill_between(years, cupp, clow, facecolor=colour, alpha=0.3,
-                                edgecolor='None')
-            else:
-                ax.plot(years, cmid, colour, linewidth=1.5, label=label)
-
-        plot_zeroline(ax, xlim)
-        ax.set_xlim(xlim)
-        ax.set_ylim(ylim)
-        ax.yaxis.set_ticks_position('both')
-        ax.yaxis.set_label_position('left')
-
-        ax.set_xlabel('Year')
-        ax.set_title(f'{location_string(site_name)} - '
-                     f'{scenario_string(scenarios[rcp_count], -999)}')
-        if rcp_count == 0:
-            ax.set_ylabel('Sea Level Change (m)')
-
-    # Put legend to the right of the current axis
-    ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
-
-    # Save the figure
-    fig.tight_layout()
-    outfile = f'{fig_dir}05_{site_name}.png'
-    plt.savefig(outfile, dpi=300, format='png')
-    plt.close()
-
-
-def plot_figure_six(r_df_list, global_low, global_mid, global_upp,
-                    site_name, scenarios, fig_dir):
-    """
-    Comparison of IPCC AR5 + Levermann projections and local sea level
-    projections - sum total and component parts.
-    Subplot 1, 2, 3 - RCP2.6, RCP4.5, RCP8.5 respectively.
-    :param r_df_list: regional sea level projections
-    :param global_low: IPCC AR5 + Levermann global projections 5th percentile
-    :param global_mid: IPCC AR5 + Levermann global projections 50th percentile
-    :param global_upp: IPCC AR5 + Levermann global projections 95th percentile
-    :param site_name: site location
-    :param scenarios: emissions scenario
-    :param fig_dir: figure directory
-    """
-    # Get the values of the multi-index: years and percentile values
-    years, percentiles = multi_index_values(r_df_list)
-
-    # UKCP18 colour scheme for sea level components
-    comp_colours = ukcp18_colours()[1]
-    # UKCP18 labels for sea level components
-    comp_labels = ukcp18_labels()
-
-    # Calculate sensible x-axis range and y-axis range based on projections
-    xlim = calc_xlim('proj', [], years)
-    r_ylim = calc_ylim('proj', [], r_df_list)
-    # IPCC AR5 + Levermann projections are read in as an array
-    g_ylim = [min(global_low[0]-0.1), max(global_upp[2])+0.1]
-    ylim = [min(r_ylim[0], g_ylim[0]), max(r_ylim[1], g_ylim[1])]
-
-    fig = plt.figure(figsize=(7.68, 3.5))
-    matplotlib.rcParams['font.size'] = 7.5
-
-    # Loop through RCPs for subplots
-    for rcp_count, _ in enumerate(scenarios):
-        ax = fig.add_subplot(1, 3, rcp_count + 1)
-
-        # Get the sums of all components of global sea level projections
-        cmid_g = global_mid[rcp_count]
-        clow_g = global_low[rcp_count]
-        cupp_g = global_upp[rcp_count]
-
-        ax.plot(years, cmid_g, color='k', linewidth=1.5, linestyle='--',
-                label='IPCC AR5 + Levermann')
-        ax.plot(years, clow_g, color='k', linewidth=1.5, linestyle=':')
-        ax.plot(years, cupp_g, color='k', linewidth=1.5, linestyle=':')
-
-        # Get the sums of all components of local sea level projections
-        r_df_rcp = r_df_list[rcp_count]
-        for comp in ['sum', 'ocean', 'greennet', 'antnet', 'glacier',
-                     'landwater', 'gia']:
-            # Get the sums of regional sea level projections
-            clow, cmid, cupp = extract_comp_sl(r_df_rcp, percentiles, comp)
-
-            colour = comp_colours[comp]
-            label = comp_labels[comp]
-
-            if comp in ['sum', 'ocean']:
-                ax.plot(years, cmid, colour, linewidth=1.5, label=label)
-                ax.fill_between(years, cupp, clow, facecolor=colour, alpha=0.3,
-                                edgecolor='None')
-            else:
-                ax.plot(years, cmid, colour, linewidth=1.5, label=label)
-
-        plot_zeroline(ax, xlim)
-        ax.set_xlim(xlim)
-        ax.set_ylim(ylim)
-        ax.yaxis.set_ticks_position('both')
-        ax.yaxis.set_label_position('left')
-
-        ax.set_xlabel('Year')
-        ax.set_title(f'{location_string(site_name)} - '
-                     f'{scenario_string(scenarios[rcp_count], -999)}')
-        if rcp_count == 0:
-            ax.set_ylabel('Sea Level Change (m)')
-
-    # Put legend to the right of the current axis
-    ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
-
-    # Save the figure
-    fig.tight_layout()
-    outfile = f'{fig_dir}06_{site_name}.png'
-    plt.savefig(outfile, dpi=300, format='png')
-    plt.close()
-
-
-def plot_figure_seven(g_df_list, r_df_list, site_name, scenarios, fig_dir):
-    """
-    Subplot 1 - Global sea level projections for RCP8.5 - total and component
-    parts, including uncertainty range.
-    Subplot 2 - Local sea level projections for RCP8.5 - total and component
-    parts, including uncertainty range.
-    Same style as Figure 4 Howard and Palmer (2019).
-    :param g_df_list: global sea level projections
-    :param r_df_list: regional sea level projections
-    :param site_name: site location
-    :param scenarios: emission scenario
-    :param fig_dir: figure directory
-    """
-    # Get the values of the multi-index: years and percentile values
-    _, percentiles = multi_index_values(r_df_list)
-
-    # UKCP18 colour scheme for sea level components
-    comp_colours = ukcp18_colours()[1]
-    # UKCP18 labels for sea level components
-    comp_labels = ukcp18_labels()
-
-    # Calculate sensible x-axis range and y-axis range
-    xlim = ([-0.4, 7.4])
-    r_ylim = calc_ylim('proj', [], r_df_list)
-    g_ylim = calc_ylim('proj', [], g_df_list)
-    ylim = [min(r_ylim[0], g_ylim[0]), max(r_ylim[1], g_ylim[1])]
-
-    # Show components for the RCP8.5 scenario (only plot the uncertainty for
-    # sum and thermal expansion)
-    for rcp_count, rcp_str in enumerate(scenarios):
-        if scenarios[rcp_count] == 'rcp85':
-            break
-    else:
-        raise ValueError('RCP8.5 scenario not in list')
-    r_df_rcp = r_df_list[rcp_count]
-    g_df_rcp = g_df_list[rcp_count]
-
-    # -------------------------------------------------------------------------
-    # First figure, global sea level projections and uncertainties under
-    # RCP 8.5 and component parts
-    fig = plt.figure(figsize=(7.68, 4.5))
-    matplotlib.rcParams['font.size'] = 9
-
-    ax_labels = []
-    ax = fig.add_subplot(1, 2, 1)
-    for cc, comp in enumerate(['sum', 'ocean', 'antnet', 'greennet', 'glacier',
-                               'landwater']):
-        # Get the components of regional sea level projections
-        clow_85_G, cmid_85_G, cupp_85_G = extract_comp_sl(g_df_rcp,
-                                                          percentiles, comp)
-
-        colour = comp_colours[comp]
-        label = comp_labels[comp]
-        ax_labels.append(label)
-        xpts = [cc - 0.4, cc + 0.4]
-
-        ax.fill_between(xpts, clow_85_G[-1], cupp_85_G[-1],
-                        facecolor=colour, alpha=0.35, label=label)
-        ax.plot(xpts, [cmid_85_G[-1], cmid_85_G[-1]], linewidth=2.0,
-                color=colour)
-
-    plot_zeroline(ax, xlim)
-    ax.set_xticks([0, 1, 2, 3, 4, 5])
-    ax.set_xticklabels(ax_labels, rotation=90)
-    ax.yaxis.set_ticks_position('both')
-    ax.yaxis.set_label_position('left')
-    plt.ylim(ylim)
-    plt.ylabel('Sea Level Change (m)')
-    plt.title(f'Global sea level - {scenario_string(rcp_str, -999)}')
-
-    # -------------------------------------------------------------------------
-    # Second figure, regional sea level projections and uncertainties under
-    # RCP 8.5 and component parts
-    bx = fig.add_subplot(1, 2, 2)
-    bx_labels = []
-    for cc, comp in enumerate(['sum', 'ocean', 'antnet', 'greennet', 'glacier',
-                               'landwater', 'gia']):
-        # Get the components of local sea level projections
-        clow_85_R, cmid_85_R, cupp_85_R = extract_comp_sl(r_df_rcp,
-                                                          percentiles, comp)
-
-        colour = comp_colours[comp]
-        label = comp_labels[comp]
-        bx_labels.append(label)
-        xpts = [cc - 0.4, cc + 0.4]
-
-        bx.fill_between(xpts, clow_85_R[-1], cupp_85_R[-1], facecolor=colour,
-                        alpha=0.35, label=label)
-        bx.plot(xpts, [cmid_85_R[-1], cmid_85_R[-1]], linewidth=2.0,
-                color=colour)
-
-    plot_zeroline(bx, xlim)
-    bx.set_xticks([0, 1, 2, 3, 4, 5, 6])
-    bx.set_xticklabels(bx_labels, rotation=90)
-    bx.yaxis.set_ticks_position('both')
-    bx.yaxis.set_label_position('left')
-    plt.ylim(ylim)
-    plt.title(f'Local sea level - {location_string(site_name)} - '
-              f'{scenario_string(scenarios[rcp_count], -999)}')
-
-    # Save the figure
-    fig.tight_layout()
-    outfile = f'{fig_dir}07_{site_name}_rcp85.png'
-    plt.savefig(outfile, dpi=200, format='png')
-    plt.close()
-
-
-def read_G_R_sl_projections(site_name, scenarios):
-    """
-    Reads in the global and regional sea level projections calculated from
-    the CMIP projections (in metres). These data are relative to a baseline
-    of 1981-2010.
-    :param site_name: site location
-    :param scenarios: emission scenarios
-    :return: DataFrame of global and regional sea level projections
-    """
-    print('running function read_regional_sea_level_projections')
-
-    # Read in the global and regional sea level projections
-    in_slddir = read_dir()[4]
-    loc_abbrev = abbreviate_location_name(site_name)
-
-    G_df_list = []
-    R_df_list = []
-    for sce in scenarios:
-        G_filename = '{}{}_{}_projection_{}_global.csv'.format(
-            in_slddir, loc_abbrev, sce, settings["projection_end_year"])
-        R_filename = '{}{}_{}_projection_{}_regional.csv'.format(
-            in_slddir, loc_abbrev, sce, settings["projection_end_year"])
-        try:
-            G_df = pd.read_csv(G_filename, header=0,
-                               index_col=['year', 'percentile'])
-            G_df.rename(columns={'exp': 'ocean', 'GIA': 'gia'}, inplace=True)
-            R_df = pd.read_csv(R_filename, header=0,
-                               index_col=['year', 'percentile'])
-            R_df.rename(columns={'exp': 'ocean', 'GIA': 'gia'}, inplace=True)
-        except FileNotFoundError:
-            raise FileNotFoundError(
-                sce, '- scenario selected does not currently exist')
-
-        G_df_list.append(G_df)
-        R_df_list.append(R_df)
-
-    return G_df_list, R_df_list
-
-
-def read_IPCC_AR5_Levermann_proj(scenarios, refname='sum'):
-    """
-    Read in the IPCC AR5 + Levermann global sea level projections.
-    :param scenarios: emission scenario
-    :param refname: name of the component to plot e.g. "expansion",
-    "antsmb", "sum"
-    :return: lower, middle and upper time series of IPCC AR5 + Levermann
-    global sea level projections
-    """
-    print('running function read_IPCC_AR5_Levermann_proj')
-
-    # Directory of Monte Carlo time series for new projections
-    mcdir = choose_montecarlo_dir()
-    
-    nyrs = np.arange(2007, settings["projection_end_year"] + 1).size
-
-    ar5_low = []
-    ar5_mid = []
-    ar5_upp = []
-
-    for sce in scenarios:
-        reflow = iris.load_cube(
-            os.path.join(mcdir, sce + '_' + refname + 'lower.nc'))[:nyrs].data
-        refmid = iris.load_cube(
-            os.path.join(mcdir, sce + '_' + refname + 'mid.nc'))[:nyrs].data
-        refupp = iris.load_cube(
-            os.path.join(mcdir, sce + '_' + refname + 'upper.nc'))[:nyrs].data
-
-        ar5_low.append(reflow)
-        ar5_mid.append(refmid)
-        ar5_upp.append(refupp)
-
-    return ar5_low, ar5_mid, ar5_upp
-
-
-def read_PSMSL_tide_gauge_obs(root_dir, data_source, data_type, region,
-                              df_site, site_name, base_fdir):
-    """
-    Read annual mean sea level observations from PSMSL.
-    :param root_dir: base directory
-    :param data_source: data source of tide gauge data
-    :param data_type: data type of tide gauge data
-    :param region: user specified region (for folder structure)
-    :param df_site: DataFrame of site metadata
-    :param site_name: site locaiton
-    :param base_fdir: figure directory
-    :return: annual mean sea level data and relevant metadata on missing /
-    flagged data
-    """
-    # Determine Station ID - indicator for .rlr file
-    station_id = df_site.loc[site_name, 'Station ID']
-
-    if station_id == 'NA':
-        print(f'{site_name} is not a recognised tide gauge on PSMSLs ' +
-              'station list')
-        latitude = df_site.loc[site_name, 'Latitude']
-        longitude = df_site.loc[site_name, 'Longitude']
-        station_id, tg_name = find_nearest_station_id(
-            root_dir, data_source, data_type, region, latitude, longitude)
-    else:
-        tg_name = location_string(site_name)
-
-    baseline_years = [1981, 2010]
-    min_valid_fraction = 0.5
-
-    # Read in the annual mean sea levels, downloaded from PSMSL
-    tg_years, tg_amsl, _, tg_flag_ex = \
-        tgl.read_rlr_annual_mean_sea_level(station_id)
-    non_missing = ~np.isnan(tg_amsl)
-
-    # Find the years with 'flagged' data, flagged for attention '001' or MTL
-    # in MSL time series '010'
-    flag = (tg_flag_ex >= min_valid_fraction)
-
-    nflag = sum(flag)
-
-    # Calculate the baseline sea level and difference between the observed
-    # period and the baseline.
-    # Latter will be zero if sufficent observations lie within the baseline
-    loc_abbrev = abbreviate_location_name(site_name)
-    baseline_sl, _ = tgl.calc_baseline_sl(root_dir, region, loc_abbrev,
-                                          tg_years, tg_amsl, baseline_years)
-
-    # Adjust the tide gauge data to be sea levels relative to the baseline,
-    # to match the regional projections
-    tg_amsl = tg_amsl - baseline_sl
-
-    # Convert the tide gauge sea levels from mm to metres.
-    tg_amsl /= 1000.0
-
-    return tg_name, nflag, flag, tg_years, non_missing, tg_amsl
-
-
-def main():
-    """
-    Read in the global and regional sea level projections to create several
-    graphs.
-    """
-    print(f'User specified site name(s) is (are):'
-          f' {settings["siteinfo"]["sitename"]}')
-    print(f'User specified lat(s) and lon(s) is (are): '
-          f'{settings["siteinfo"]["sitelatlon"]}')
-    if settings["siteinfo"]["sitelatlon"] == [[]]:
-        print(f'    No lat lon specified - use tide gauge metadata if '
-              f'available')
-    print(f'User specified science method is: {settings["sciencemethod"]}')
-    if {settings["cmipinfo"]["cmip_sea"]} == {'all'}:
-        print('User specified all CMIP models')
-    elif {settings["cmipinfo"]["cmip_sea"]} == {'marginal'}:
-        print('User specified CMIP models for marginal seas only')
-
-    # Extract site data from station list (e.g. tide gauge location) or
-    # construct based on user input
-    df_site_data = extract_site_info(settings["tidegaugeinfo"]["source"],
-                                     settings["tidegaugeinfo"]["datafq"],
-                                     settings["siteinfo"]["region"],
-                                     settings["siteinfo"]["sitename"],
-                                     settings["siteinfo"]["sitelatlon"])
-
-    # Create the output sea level projections figure directory to use across
-    # multiple functions
-    sealev_fdir = read_dir()[5]
-    makefolder(sealev_fdir)
-    
-    if settings["emulator_settings"]["emulator_mode"]:
-        emulator_scenarios = settings["emulator_settings"]["emulator_scenario"]
-        for df_loc in df_site_data.index.values:
-            # Read global and regional sea level projections calculated previously
-            g_df_list, r_df_list = read_G_R_sl_projections(df_loc, emulator_scenarios)
-
-            # Read annual mean sea level observations from PSMSL
-            tg_name, nflag, flag, tg_years, non_missing, tg_amsl = \
-                read_PSMSL_tide_gauge_obs(settings["baseoutdir"], settings[
-                    "tidegaugeinfo"]["source"], settings["tidegaugeinfo"][
-                    "datafq"], settings["siteinfo"]["region"], df_site_data,
-                                        df_loc, sealev_fdir)
-                
-            plot_slr(r_df_list, tg_name, nflag, flag, tg_years,
-                            non_missing, tg_amsl, df_loc, emulator_scenarios,
-                            sealev_fdir)
-            
-            plot_slr_components(g_df_list, r_df_list, df_loc, emulator_scenarios,
-                          sealev_fdir)
-            
-    else:
-        rcp_scenarios = ['rcp26', 'rcp45', 'rcp85']
-        for df_loc in df_site_data.index.values:
-            # Read global and regional sea level projections calculated previously
-            g_df_list, r_df_list = read_G_R_sl_projections(df_loc, rcp_scenarios)
-
-            # Read in the IPCC AR5 + Levermann global sea level projections
-            ar5_low, ar5_mid, ar5_upp = read_IPCC_AR5_Levermann_proj(rcp_scenarios)
-
-            # Read annual mean sea level observations from PSMSL
-            tg_name, nflag, flag, tg_years, non_missing, tg_amsl = \
-                read_PSMSL_tide_gauge_obs(settings["baseoutdir"], settings[
-                    "tidegaugeinfo"]["source"], settings["tidegaugeinfo"][
-                    "datafq"], settings["siteinfo"]["region"], df_site_data,
-                                        df_loc, sealev_fdir)
-        # -------------------------------------------------------------------------
-        # Plotting section - comment functions in and out depending on which graphs
-        # you wish to save
-            # Figure 1
-            # Subplot 1 - Local sea level projections for all RCPs - sum total
-            # Subplot 2 - Local sea level projections for RCP8.5 - total and
-            # component parts
-            plot_figure_one(r_df_list, df_loc, rcp_scenarios, sealev_fdir)
-
-            # Figure 2
-            # Local sea level projections for specified RCPs - sum total,
-            # and annual mean tide gauge observations
-            plot_figure_two(r_df_list, tg_name, nflag, flag, tg_years,
-                            non_missing, tg_amsl, df_loc, rcp_scenarios,
-                            sealev_fdir)
-
-            # Figure 3 (one output per RCP)
-            # Subplot 1 - Comparison of IPCC AR5 + Levermann global projections
-            # and global projections developed here
-            # Subplot 2 - Comparison of global projections and local sea level
-            # projections
-            plot_figure_three(g_df_list, r_df_list, ar5_low, ar5_mid, ar5_upp,
-                            df_loc, rcp_scenarios, sealev_fdir)
-
-            # Figure 4
-            # Local sea level projections for all RCPs - sum total
-            # Same style as Figure 3.1.4 of UKCP18 Marine Report (pg 16)
-            plot_figure_four(r_df_list, df_loc, rcp_scenarios, sealev_fdir)
-
-            # Figure 5
-            # Comparison of global mean sea level projections calculated by the
-            # tool and local sea level projections - sum total
-            # Also shown local sea level projections - component parts
-            # Subplot 1, 2, 3 - RCP2.6, RCP4.5, RCP8.5 respectively
-            plot_figure_five(g_df_list, r_df_list, df_loc, rcp_scenarios,
-                            sealev_fdir)
-
-            # Figure 6
-            # Comparison of IPCC AR5 + Levermann projections and local sea level
-            # projections - sum total
-            # Also shown local sea level projections - component parts
-            # Subplot 1, 2, 3 - RCP2.6, RCP4.5, RCP8.5 respectively
-            if settings["projection_end_year"] <= 2100: 
-                plot_figure_six(r_df_list, ar5_low, ar5_mid, ar5_upp, df_loc,
-                                rcp_scenarios, sealev_fdir)
-
-            # Figure 7
-            # Subplot 1 - Global sea level projections for RCP8.5 - total and
-            # component parts, including uncertainty range
-            # Subplot 2 - Local sea level projections for RCP8.5 - total and
-            # component parts, including uncertainty range
-            # Same style as Figure 4 Howard and Palmer (2019)
-            plot_figure_seven(g_df_list, r_df_list, df_loc, rcp_scenarios,
-                            sealev_fdir)
-
-
-if __name__ == '__main__':
-    main()
diff --git a/profsea/surge/tide_gauge_library.py b/profsea/surge/tide_gauge_library.py
deleted file mode 100644
index a358217..0000000
--- a/profsea/surge/tide_gauge_library.py
+++ /dev/null
@@ -1,175 +0,0 @@
-"""
-Copyright (c) 2023, Met Office
-All rights reserved.
-"""
-
-import os
-import pandas as pd
-import numpy as np
-from scipy.stats import linregress
-
-from profsea.config import settings
-
-
-def calc_baseline_sl(root_dir, data_region, loc_abbrev, years, annual_means,
-                     baseline_years, min_overlapping_years=14):
-    """
-    Calculates or estimates the baseline sea levels, using observed or
-    modelled values. If there are sufficient observed values, use those to
-    estimate the baseline sea level, otherwise read in and extrapolate back
-    from the RCP 2.6 projections, which are nearly linear.
-    :param root_dir: base directory for all input and output data
-    :param data_region: region (used for file paths)
-    :param loc_abbrev: abbreviated name of tide gauge
-    :param years: Numpy array of available years for requested tide gauge
-    :param annual_means: annual mean sea level (mm)
-    :param baseline_years: first and last year of the baseline period,
-    should be 1981-2000
-    :param min_overlapping_years: minimum number of years of the observed
-    sea levels that must lie inside the baseline period
-    :return: the baseline sea level and the difference between the observed
-    period and the baseline
-    """
-    print('running function calc_baseline_sl')
-    # Check which years from this gauge lie within the baseline period.
-    common_years = find_common_years(years, baseline_years)
-    nvalid = 0
-
-    if len(common_years) >= min_overlapping_years:
-        obs_amsl = annual_means[(years >= common_years[0]) &
-                                (years <= common_years[-1])]
-        # Check for number of valid data values
-        nvalid = sum(~np.isnan(obs_amsl))
-
-    if nvalid >= min_overlapping_years:
-        # Calculate the mean sea level for the baseline using these
-        # observations.
-        baseline_sea_level = np.nanmean(obs_amsl)
-        delta_sl = 0.0
-    else:
-        # (a) Get obs years
-        # (b) read in the RCP2.6 sea level projections - percentile = 50.0
-        df_m_rcp26 = read_regional_sea_level_projections(
-            root_dir, data_region, loc_abbrev, 'rcp26', pcile=50.0)
-        sl_rcp26 = df_m_rcp26['sum'].values
-        years_rcp26 = df_m_rcp26['year'].values
-
-        # (c) Fit a straight line to the mid-point estimates
-        m, c, _, _, _ = linregress(years_rcp26, sl_rcp26)
-
-        # Extrapolate fitted straight line to get values for the baseline
-        # years and hence the mean value for the baseline.
-        model_sl_in_baseline = [m * base_year + c for base_year in
-                                list(range(baseline_years[0],
-                                           baseline_years[1] + 1))]
-        model_mean_baseline_sl = np.mean(np.array(model_sl_in_baseline))
-
-        # Extrapolate fitted straight line to get modelled values for the
-        # years of the observations, then the mean value for this period
-        model_sl_in_obs_years = [m * obs_year + c for obs_year in years]
-        model_mean_obs_sl = np.mean(np.array(model_sl_in_obs_years))
-
-        # Calculate the change in sea level between the baseline and
-        # observed periods using the modelled data
-        delta_sl = model_mean_baseline_sl - model_mean_obs_sl
-
-        # (d) Add modelled SL change from (c) to mean sea level from
-        # tide gauge data
-        obs_mean_sl = np.nanmean(annual_means)
-        baseline_sea_level = obs_mean_sl + delta_sl
-
-    return baseline_sea_level, delta_sl
-
-
-def find_common_years(years, baseline_years):
-    """
-    Finds the years that lie within the specified baseline period
-    :param years: List of years in the observed data
-    :param baseline_years: 2-element list holding the first and last years
-    of the period of interest
-    :return: list of all common years within the baseline period
-    """
-    common_years = sorted(list(set(list(range(baseline_years[0],
-                                              baseline_years[1] + 1)))
-                               & set(years)))
-    if not common_years:
-        print(f'No common years found between tide gauge data and ' +
-              'baseline years')
-    else:
-        print(f'The common years in the tide gauge data are ' +
-              f'between {common_years[0]} and {common_years[-1]}')
-
-    return common_years
-
-
-def read_regional_sea_level_projections(root_dir, data_region, loc_abbrev,
-                                        scenario, pcile=None):
-    """
-    Reads in the regional sea level projections calculated from the CMIP
-    projections in a previous step. These data are changes in sea level
-    relative to a baseline of 1981-2010.
-    :param root_dir: root directory containing all code and input data
-    :param data_region: region (used for file paths)
-    :param loc_abbrev: abbreviated name of tide gauge
-    :param scenario: RCP scenario
-    :param pcile: If not None, return data for this percentile; otherwise,
-    return data for all percentiles
-    :return df: DataFrame containing regional sea level projections,
-    as annual means.
-    """
-    path = os.path.join(root_dir, data_region, 'data', 'sea_level_projections')
-    end_yr = settings['projection_end_year']
-    filename = os.path.join(
-        path, f'{loc_abbrev}_{scenario}_projection_{end_yr}_regional.csv')
-    df = pd.read_csv(filename, header=0, usecols=['year', 'percentile', 'sum'])
-
-    if pcile is not None:
-        df = df.loc[df['percentile'] == pcile]
-        df.reset_index(inplace=True, drop=True)
-
-    # Convert the projections from metres to mm.
-    df['sum'] = df['sum'] * 1000.0
-
-    return df
-
-
-def read_rlr_annual_mean_sea_level(station_id):
-    """
-    Reads in annual mean sea levels from a .rlrdata file - downloaded
-    from PSMSL (https://www.psmsl.org/data/obtaining/complete.php and
-    https://www.psmsl.org/data/obtaining/notes.php). Replaces all -99999 with
-    NAN values - for use in plotting
-    :param station_id: PSMSL Station ID for location
-    :return years: numpy array of available years for requested station
-    :return annual_means: corresponding annual mean sea level (mm)
-    :return flag_missing: corresponding flag for missing data
-    :return flag_exception: corresponding 'flag for attention'
-    """
-    print('running function read_rlr_annual_mean_sea_level')
-    psmsl_basedir = settings["tidegaugeinfo"]["psmsldir"]
-    path = os.path.join(psmsl_basedir, 'rlr_annual', 'data')
-
-    filename = os.path.join(path, f'{station_id}.rlrdata')
-    print(f'Reading in data for {filename}')
-    rlr_data = np.genfromtxt(filename, dtype=None, skip_header=0, names=[
-        'Year', 'RLR_data', 'Flag_missing', 'Flag_exception'],
-                             delimiter=";", encoding=None)
-    years = []
-    annual_means = []
-    flag_missing = []
-    flag_exception = []
-    for row in rlr_data:
-        year = row[0]
-        rlr = row[1]
-        missing = row[2]
-        exception = row[3]
-        years = np.append(years, int(year))
-        if float(rlr) != -99999:
-            annual_means = np.append(annual_means, float(rlr))
-        else:
-            annual_means = np.append(annual_means, np.nan)
-
-        flag_missing = np.append(flag_missing, str(missing))
-        flag_exception = np.append(flag_exception, float(exception))
-
-    return years, annual_means, flag_missing, flag_exception
diff --git a/profsea/user-settings.yml b/profsea/user-settings.yml
deleted file mode 100644
index 7fce703..0000000
--- a/profsea/user-settings.yml
+++ /dev/null
@@ -1,69 +0,0 @@
-# Site(s) information
-# region: region of the world
-#   N.B. used to create output folder structure
-# sitename: tide gauge name or other site name
-#   N.B. if site_name has an apostrophe use triple quotes
-#       # sitename: '''site'x'''
-# sitelatlon: site location's latitude and longitude
-#   N.B. if tide gauge name: latlon taken from TG metadata
-        # sitelatlon: [[]])
-#   N.B. if other site name: latlon needs to be specified
-        # sitelatlon: [[-51.69, -57.82]])
-siteinfo:
-    region: 'Falkland_example'   # str
-    sitename: ['Stanley II']     # list
-    sitelatlon: [[]]             # list
-
-# Base output directory
-#   N.B. the code will add region to the output directory
-baseoutdir: '/home/user/sl_output/'
-
-# Set the end year of the projection(s)
-projection_end_year: 2300 # int between 2050 and 2300
-
-# Science method
-#   UKCP18 --> sciencemethod: 'UK'
-#   Palmer et al (2020) --> sciencemethod: 'global
-#       N.B. Used to identify which GIA estimates to use
-sciencemethod: 'global'  # str
-
-# Tide gauge information (recommend default below)
-# source: source of tide gauge information
-#   N.B. Also used to identify which TG directory to use
-# datafq: temporal scale of tide gauge data
-#   N.B. not set up monthly files for PSMSL
-# psmsldir: TG base directory
-tidegaugeinfo:
-    source: 'PSMSL'    # str
-    datafq: ['annual'] # list
-    psmsldir: '/home/user/data/PSMSL/'
-
-# CMIP information
-cmipinfo:
-# cmip_sea: for non-UK locations, specify if site is within marginal sea
-#   Not in a marginal sea --> cmip_sea: 'all'
-#   Marginal sea --> cmip_sea: 'marginal'
-    cmip_sea: 'all'     # str
-# sealevelbasedir: Base directory for CMIP "zos" and "zostoga" data
-    sealevelbasedir: '/deposited2023/profsea_tool/cmip5/'
-# slopecoeffsuk: CMIP5 slope coefficients developed for UKCP18
-    slopecoeffsuk: '/deposited2023/profsea_tool/uk_cmip_slope_coefficients/'
-
-
-# Directories for the GIA estimates (independent of scenario)
-#   UKCP18 --> UK specific ones
-#   Global locations --> Lambeck, ICE5G
-giaestimates:
-    global: '/deposited2023/profsea_tool/gia_estimates /global_GIA_interpolators.pickle'
-    uk: '‘/deposited2023/profsea_tool/gia_estimates/Bradley_GIA_interpolator.pickle'
-
-# Directories for the Slangen, Spada and Klemann fingerprints
-fingerprints:
-    slangendir: '/deposited2023/profsea_tool/grd_fingerprints/'
-    spadadir: '/deposited2023/profsea_tool/grd_fingerprints/'
-    klemanndir: '/deposited2023/profsea_tool/grd_fingerprints/'
-
-# Directory of Monte Carlo time series for new projections
-#   N.B. Originally developed by Jonathan Gregory
-short_montecarlodir: '/path/to/2100/montecarlo/simulations' # Required for projections up to 2100
-long_montecarlodir: '/path/to/2300/montecarlo/simulations' # Required for projections from 2100 up to 2300
diff --git a/profsea/tide_gauge_locations.py b/profsea/utils/tide_gauge_locations.py
similarity index 100%
rename from profsea/tide_gauge_locations.py
rename to profsea/utils/tide_gauge_locations.py
diff --git a/profsea/worked_example.ipynb b/profsea/worked_example.ipynb
deleted file mode 100644
index 6b3b734..0000000
--- a/profsea/worked_example.ipynb
+++ /dev/null
@@ -1,177 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "46a1a49f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from profsea.emulator import GMSLREmulator"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "52cdd115",
-   "metadata": {},
-   "source": [
-    "#### Introduction\n",
-    "The aim of this notebook is to break down the steps required to run the emulator version of ProFSea.\n",
-    "\n",
-    "All of this can't run in Jupyter Notebook but the steps should be clear enough to follow along!\n",
-    "\n",
-    "> If you want to use the CMIP6 patterns sterodynamic patterns instead of CMIP5, you need to run [this recipe](https://github.com/ESMValGroup/ESMValTool/blob/steric_patterns/esmvaltool/recipes/recipe_steric_patterns.yml) in ESMValTool. I recommend running with as much memory and processors as possible. \n",
-    "\n",
-    "> However, if you prefer not to go via ESMValTool feel free to reach out at gregory.munday\\@dtc.ox.ac.uk and I can just send them over (they're not large in terms of data size).\n",
-    "---\n",
-    "All other data required for the tool can be [downloaded here](https://catalogue.ceda.ac.uk/uuid/47c849b907414f3ab5e56ba9cf83e779/).\n",
-    "\n",
-    "I like keeping all my data in a `data/` folder next to the `profsea/` folder, which looks like this:\n",
-    "\n",
-    "  `data/` \\\n",
-    "   `cmip5/` \\\n",
-    "   `cmip6/` \\\n",
-    "   `gia_estimates/` \\\n",
-    "   `grd_fingerprints/` \\\n",
-    "   `monte_carlo_timeseries/` \\\n",
-    "   `uk_cmip_slope_coefficients/`\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ef69532c",
-   "metadata": {},
-   "source": [
-    "#### Setting up the environment\n",
-    "The next step is to set up the conda environment. You should use the `ProFSea-emu-env.yml` environment file, building it with \n",
-    "\n",
-    "```bash\n",
-    "conda env create -f ProFSea-emu-env.yml\n",
-    "```\n",
-    "\n",
-    "and then activating with `conda activate profsea-emu`.\n",
-    "\n",
-    "---\n",
-    "\n",
-    "One final step here is to install ProFSea as an editable package, using\n",
-    "\n",
-    "```bash \n",
-    "pip install -e . \n",
-    "```\n",
-    "\n",
-    "while inside the highest level directory."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5616586f",
-   "metadata": {},
-   "source": [
-    "#### Setup FaIR output\n",
-    "Run FaIR for the scenarios you want to simulate and save out the `global surface air temperature` and `ocean heat content`. You'll also need to calculate the 2015-2100 cumulative CO2 emissions and save it to a `.json` file. The CMIP6 cumulative emissions are included in the repo: \n",
-    "\n",
-    "```profsea/emulator/aux_data/cumulative_cmip6_emissions.json```\n",
-    "\n",
-    "Here's some sample code to calculate it from scratch in FaIR. Perhaps this could be included in this branch at some point.\n",
-    "\n",
-    "```python\n",
-    "# Output cumulative emissions from 2015 to 2100\n",
-    "cum_emissions_dict = {}\n",
-    "for scenario in hist_gcam_ext.scenario.unique():  # loop over your scenarios\n",
-    "        emissions = hist_gcam_ext.loc[\n",
-    "            (hist_gcam_ext['scenario'] == scenario) & (hist_gcam_ext['variable'] == 'CO2 FFI'), \n",
-    "            '2015':'2100'\n",
-    "        ].to_numpy()[0]  # select anthro emissions\n",
-    "        cum_emissions = np.cumsum(emissions)\n",
-    "        cum_emissions = cum_emissions / 1000  # Convert to PgC\n",
-    "        print(f'Total cumulative carbon emissions from 2015 to 2100 for {scenario}: ', cum_emissions[-1])\n",
-    "        cum_emissions_dict[scenario] = cum_emissions[-1]\n",
-    "        # Save to json\n",
-    "        with open('ngfs_data/cumulative_scenario_emissions.json', 'w') as f:\n",
-    "            json.dump(cum_emissions_dict, f)\n",
-    "```\n",
-    "\n",
-    "You need to do this because the current Antarctic Ice-Sheet dynamics component uses the relationship between cumulative emissions and sea-level contribution to calculate the lower and upper bounds of the projection. This was implemented to remove scenario-dependence from the GMSLR emulator, and should be redundant when the component is updated.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6cef2ba4",
-   "metadata": {},
-   "source": [
-    "#### Running global → local ProFSea projections\n",
-    "Point your paths in the `user-settings-emu.yml` file to the right places, fill in your scenario names and you should be ready to go!\n",
-    "\n",
-    "Run: \n",
-    "\n",
-    "```bash\n",
-    "python spatial_projections.py\n",
-    "```\n",
-    "\n",
-    "The global projections will be saved to `data/runs/gmslr_output/`, while the spatialised projections should be saved to `data/runs//`\n",
-    "\n",
-    "The way this is accomplished (so that it runs on a laptop) is by taking and saving 101 percentiles of the GMSLR module output for each component. This can be updated via the `output_percentiles` keyword in the `GMSLR` class, currently line 458 in `spatial_projections.py`."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "84c2ef51",
-   "metadata": {},
-   "source": [
-    "You can also run the GMSLR module by itself:\n",
-    "\n",
-    "```python\n",
-    "from profsea.emulator import GMSLREmulator\n",
-    "\n",
-    "emulator = GMSLREmulator(\n",
-    "    T_change,  # your T_change anom from FaIR\n",
-    "    OHC_change,  # your OHC_change anom from FaIR\n",
-    "    'low-overshoot',  # your scenario name (str)\n",
-    "    2300,  #  projection end year\n",
-    "    palmer_method=palmer_method,  # recommended for runs beyond 2100\n",
-    "    input_ensemble=True,  # must be true if providing a complete FaIR ensemble\n",
-    "    output_percentiles=np.arange(101),  # list/array of percentiles to sample\n",
-    "    cum_emissions_total=1450)  # int, cumulative emissions in scenario from 2015-2100\n",
-    "\n",
-    "#  run the projections (auto-parallel)\n",
-    "emulator.project()\n",
-    "\n",
-    "# Save to desired folder with the name of the scenario\n",
-    "emulator.save_components(\n",
-    "    os.path.join(settings[\"baseoutdir\"], 'gmslr_output'),  # output dir\n",
-    "    scenario)  # scenario name\n",
-    "\n",
-    "# Lists the available components\n",
-    "emulator.list_components()\n",
-    "\n",
-    "# You can also access the data from each component directly:\n",
-    "emulator.greenland  # returns greenland projection ensemble\n",
-    "\n",
-    "```"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "cmip7",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

From b8c6776a61e7948a7bd6d9f35a9eb8783db86ab7 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Sun, 7 Dec 2025 15:29:23 +0000
Subject: [PATCH 056/276] Small README update

---
 README.md | 18 ++++++++++--------
 1 file changed, 10 insertions(+), 8 deletions(-)

diff --git a/README.md b/README.md
index b0f37e4..8878f8b 100644
--- a/README.md
+++ b/README.md
@@ -1,15 +1,17 @@
-# ProFSea-tool
+# ProFSea-Climate
 
 [![DOI](https://zenodo.org/badge/713453340.svg)](https://zenodo.org/doi/10.5281/zenodo.10255467)
 
-The ProFSea tool should be cited as: Perks, R., & Weeks, J. (2023). MetOffice/ProFSea-tool: v1.0.0 (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.10255468
+## About
+This is the experimental science branch of ProFSea, dedicated to extending the capabilities of the existing ProFSea-tool. These extensions have not yet been published or peer-reviewed.
 
-## User Guide
-A user guide summarising the underpinning science on sea-level projections and providing guidance on how to set-up and run the Met Office Projecting Future Sea Level (ProFSea) tool can be found here: [10.5281/zenodo.10134070](https://zenodo.org/doi/10.5281/zenodo.10134070).
-
-Users are advised to read this guide before using any products as it describes the data availability, the main outputs from the tool and the caveats and limitations.
-
-The ProFSea tool user guide should be cited as: Perks, R. J. (2023). Met Office Projecting Future Sea Level (ProFSea) tool - User Guide (1.1). Met Office. https://doi.org/10.5281/zenodo.10245409
+Ongoing developments include:
+    - [x] Full spatial field projections of regional sea-level change
+    - [x] Use any input climate forcing to produce ProFSea projections
+    - [ ] Updates to sea-level components based on the latest evidence in the literature
+    - [ ] Observational constraints to projection ensembles
+    - [ ] Structural enhancements such as updated workflows, GitHub Actions and general reformatting
+    - [ ] Full code documentation
 
 ## Contributors
 Several people have contributed to the development of the ProFSea tool and User Guide documentation, namely: Rachel Perks, Jacob Cheung, Benjamin Harrison, Katie Hodge, Mathew Palmer, Michael Sanderson, Hamish Steptoe, Jennifer Weeks and Gregory Munday.

From 1092156ef4fddee4f0b50b6b62b2d483e9cca77a Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Sun, 7 Dec 2025 15:32:52 +0000
Subject: [PATCH 057/276] Update readme

---
 README.md | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/README.md b/README.md
index 8878f8b..b91b906 100644
--- a/README.md
+++ b/README.md
@@ -6,12 +6,12 @@
 This is the experimental science branch of ProFSea, dedicated to extending the capabilities of the existing ProFSea-tool. These extensions have not yet been published or peer-reviewed.
 
 Ongoing developments include:
-    - [x] Full spatial field projections of regional sea-level change
-    - [x] Use any input climate forcing to produce ProFSea projections
-    - [ ] Updates to sea-level components based on the latest evidence in the literature
-    - [ ] Observational constraints to projection ensembles
-    - [ ] Structural enhancements such as updated workflows, GitHub Actions and general reformatting
-    - [ ] Full code documentation
+    -[x][🛠️] Full spatial field projections of regional sea-level change
+    -[x][🛠️] Use any input climate forcing to produce ProFSea projections
+    -[][🛠️] Updates to sea-level components based on the latest evidence in the literature
+    -[][🛠️] Observational constraints to projection ensembles
+    -[][🛠️] Structural enhancements such as updated workflows, GitHub Actions and general reformatting
+    -[][🛠️] Full code documentation
 
 ## Contributors
 Several people have contributed to the development of the ProFSea tool and User Guide documentation, namely: Rachel Perks, Jacob Cheung, Benjamin Harrison, Katie Hodge, Mathew Palmer, Michael Sanderson, Hamish Steptoe, Jennifer Weeks and Gregory Munday.

From a3984639f935f27f5ee24bc2937686567bbc4f40 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Sun, 7 Dec 2025 15:33:36 +0000
Subject: [PATCH 058/276] Update readme

---
 README.md | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/README.md b/README.md
index b91b906..4df45e6 100644
--- a/README.md
+++ b/README.md
@@ -6,12 +6,12 @@
 This is the experimental science branch of ProFSea, dedicated to extending the capabilities of the existing ProFSea-tool. These extensions have not yet been published or peer-reviewed.
 
 Ongoing developments include:
-    -[x][🛠️] Full spatial field projections of regional sea-level change
-    -[x][🛠️] Use any input climate forcing to produce ProFSea projections
-    -[][🛠️] Updates to sea-level components based on the latest evidence in the literature
-    -[][🛠️] Observational constraints to projection ensembles
-    -[][🛠️] Structural enhancements such as updated workflows, GitHub Actions and general reformatting
-    -[][🛠️] Full code documentation
+-[x][🛠️] Full spatial field projections of regional sea-level change
+-[x][🛠️] Use any input climate forcing to produce ProFSea projections
+-[][🛠️] Updates to sea-level components based on the latest evidence in the literature
+-[][🛠️] Observational constraints to projection ensembles
+-[][🛠️] Structural enhancements such as updated workflows, GitHub Actions and general reformatting
+-[][🛠️] Full code documentation
 
 ## Contributors
 Several people have contributed to the development of the ProFSea tool and User Guide documentation, namely: Rachel Perks, Jacob Cheung, Benjamin Harrison, Katie Hodge, Mathew Palmer, Michael Sanderson, Hamish Steptoe, Jennifer Weeks and Gregory Munday.

From f11850a22c1f511f59b54430c3dff7aa7cde1e0d Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Sun, 7 Dec 2025 15:34:25 +0000
Subject: [PATCH 059/276] Update readme

---
 README.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/README.md b/README.md
index 4df45e6..7e7bc4d 100644
--- a/README.md
+++ b/README.md
@@ -6,6 +6,7 @@
 This is the experimental science branch of ProFSea, dedicated to extending the capabilities of the existing ProFSea-tool. These extensions have not yet been published or peer-reviewed.
 
 Ongoing developments include:
+
 -[x][🛠️] Full spatial field projections of regional sea-level change
 -[x][🛠️] Use any input climate forcing to produce ProFSea projections
 -[][🛠️] Updates to sea-level components based on the latest evidence in the literature

From 788f54aaf32213a9975931e5f5dde1caf770e4f1 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Sun, 7 Dec 2025 15:35:08 +0000
Subject: [PATCH 060/276] Update readme

---
 README.md | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/README.md b/README.md
index 7e7bc4d..71bf44c 100644
--- a/README.md
+++ b/README.md
@@ -7,12 +7,12 @@ This is the experimental science branch of ProFSea, dedicated to extending the c
 
 Ongoing developments include:
 
--[x][🛠️] Full spatial field projections of regional sea-level change
--[x][🛠️] Use any input climate forcing to produce ProFSea projections
--[][🛠️] Updates to sea-level components based on the latest evidence in the literature
--[][🛠️] Observational constraints to projection ensembles
--[][🛠️] Structural enhancements such as updated workflows, GitHub Actions and general reformatting
--[][🛠️] Full code documentation
+- [x][🛠️] Full spatial field projections of regional sea-level change
+- [x][🛠️] Use any input climate forcing to produce ProFSea projections
+- [][🛠️] Updates to sea-level components based on the latest evidence in the literature
+- [][🛠️] Observational constraints to projection ensembles
+- [][🛠️] Structural enhancements such as updated workflows, GitHub Actions and general reformatting
+- [][🛠️] Full code documentation
 
 ## Contributors
 Several people have contributed to the development of the ProFSea tool and User Guide documentation, namely: Rachel Perks, Jacob Cheung, Benjamin Harrison, Katie Hodge, Mathew Palmer, Michael Sanderson, Hamish Steptoe, Jennifer Weeks and Gregory Munday.

From fdefa8b215000cc3cdf9790a7d862d8d3dfcfe71 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Sun, 7 Dec 2025 15:36:09 +0000
Subject: [PATCH 061/276] Update readme

---
 README.md | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/README.md b/README.md
index 71bf44c..004d7cc 100644
--- a/README.md
+++ b/README.md
@@ -7,12 +7,12 @@ This is the experimental science branch of ProFSea, dedicated to extending the c
 
 Ongoing developments include:
 
-- [x][🛠️] Full spatial field projections of regional sea-level change
-- [x][🛠️] Use any input climate forcing to produce ProFSea projections
-- [][🛠️] Updates to sea-level components based on the latest evidence in the literature
-- [][🛠️] Observational constraints to projection ensembles
-- [][🛠️] Structural enhancements such as updated workflows, GitHub Actions and general reformatting
-- [][🛠️] Full code documentation
+- [x] [🛠️] Full spatial field projections of regional sea-level change
+- [x] [🛠️] Use any input climate forcing to produce ProFSea projections
+- [ ] [🛠️] Updates to sea-level components based on the latest evidence in the literature
+- [ ] [🛠️] Observational constraints to projection ensembles
+- [ ] [🛠️] Structural enhancements such as updated workflows, GitHub Actions and general reformatting
+- [ ] [🛠️] Full code documentation
 
 ## Contributors
 Several people have contributed to the development of the ProFSea tool and User Guide documentation, namely: Rachel Perks, Jacob Cheung, Benjamin Harrison, Katie Hodge, Mathew Palmer, Michael Sanderson, Hamish Steptoe, Jennifer Weeks and Gregory Munday.

From 5287cf586aa0d64f80fdf5313bbe4fa241d18a19 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Sun, 7 Dec 2025 15:37:02 +0000
Subject: [PATCH 062/276] Update readme

---
 README.md | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/README.md b/README.md
index 004d7cc..94bab9b 100644
--- a/README.md
+++ b/README.md
@@ -7,12 +7,12 @@ This is the experimental science branch of ProFSea, dedicated to extending the c
 
 Ongoing developments include:
 
-- [x] [🛠️] Full spatial field projections of regional sea-level change
-- [x] [🛠️] Use any input climate forcing to produce ProFSea projections
-- [ ] [🛠️] Updates to sea-level components based on the latest evidence in the literature
-- [ ] [🛠️] Observational constraints to projection ensembles
-- [ ] [🛠️] Structural enhancements such as updated workflows, GitHub Actions and general reformatting
-- [ ] [🛠️] Full code documentation
+- [x]🛠️ Full spatial field projections of regional sea-level change
+- [x]🛠️ Use any input climate forcing to produce ProFSea projections
+- [ ]🛠️ Updates to sea-level components based on the latest evidence in the literature
+- [ ]🛠️ Observational constraints to projection ensembles
+- [ ]🛠️ Structural enhancements such as updated workflows, GitHub Actions and general reformatting
+- [ ]🛠️ Full code documentation
 
 ## Contributors
 Several people have contributed to the development of the ProFSea tool and User Guide documentation, namely: Rachel Perks, Jacob Cheung, Benjamin Harrison, Katie Hodge, Mathew Palmer, Michael Sanderson, Hamish Steptoe, Jennifer Weeks and Gregory Munday.

From a4f7903b276edb58c2cf2ebbe397209ef0addeac Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Sun, 7 Dec 2025 15:37:35 +0000
Subject: [PATCH 063/276] Update readme

---
 README.md | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/README.md b/README.md
index 94bab9b..4d5d2ee 100644
--- a/README.md
+++ b/README.md
@@ -7,12 +7,12 @@ This is the experimental science branch of ProFSea, dedicated to extending the c
 
 Ongoing developments include:
 
-- [x]🛠️ Full spatial field projections of regional sea-level change
-- [x]🛠️ Use any input climate forcing to produce ProFSea projections
-- [ ]🛠️ Updates to sea-level components based on the latest evidence in the literature
-- [ ]🛠️ Observational constraints to projection ensembles
-- [ ]🛠️ Structural enhancements such as updated workflows, GitHub Actions and general reformatting
-- [ ]🛠️ Full code documentation
+- [x] 🛠️ Full spatial field projections of regional sea-level change
+- [x] 🛠️ Use any input climate forcing to produce ProFSea projections
+- [ ] 🛠️ Updates to sea-level components based on the latest evidence in the literature
+- [ ] 🛠️ Observational constraints to projection ensembles
+- [ ] 🛠️ Structural enhancements such as updated workflows, GitHub Actions and general reformatting
+- [ ] 🛠️ Full code documentation
 
 ## Contributors
 Several people have contributed to the development of the ProFSea tool and User Guide documentation, namely: Rachel Perks, Jacob Cheung, Benjamin Harrison, Katie Hodge, Mathew Palmer, Michael Sanderson, Hamish Steptoe, Jennifer Weeks and Gregory Munday.

From 0c33c5f98da32a7f0555058d675c9c06148ebae9 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Sun, 7 Dec 2025 20:02:56 +0000
Subject: [PATCH 064/276] Added notebooks

---
 profsea/notebooks/check_patterns.ipynb        | 308 ++++++++++++++++++
 .../eval_plots/rcp_eval_ar5_antarctica.png    | Bin 0 -> 258936 bytes
 .../eval_plots/rcp_eval_ar5_ar6_greenland.png | Bin 0 -> 426679 bytes
 .../eval_plots/rcp_eval_ar5_ar6_landwater.png | Bin 0 -> 203527 bytes
 .../eval_plots/rcp_eval_ar5_expansion.png     | Bin 0 -> 364344 bytes
 .../eval_plots/rcp_eval_ar5_glaciers.png      | Bin 0 -> 312904 bytes
 .../eval_plots/rcp_eval_ar5_gmslr.png         | Bin 0 -> 312691 bytes
 .../eval_plots/rcp_eval_ar5_greenland.png     | Bin 0 -> 347737 bytes
 .../eval_plots/rcp_eval_ar5_landwater.png     | Bin 0 -> 177730 bytes
 profsea/notebooks/worked_example.ipynb        | 177 ++++++++++
 10 files changed, 485 insertions(+)
 create mode 100644 profsea/notebooks/check_patterns.ipynb
 create mode 100644 profsea/notebooks/eval_plots/rcp_eval_ar5_antarctica.png
 create mode 100644 profsea/notebooks/eval_plots/rcp_eval_ar5_ar6_greenland.png
 create mode 100644 profsea/notebooks/eval_plots/rcp_eval_ar5_ar6_landwater.png
 create mode 100644 profsea/notebooks/eval_plots/rcp_eval_ar5_expansion.png
 create mode 100644 profsea/notebooks/eval_plots/rcp_eval_ar5_glaciers.png
 create mode 100644 profsea/notebooks/eval_plots/rcp_eval_ar5_gmslr.png
 create mode 100644 profsea/notebooks/eval_plots/rcp_eval_ar5_greenland.png
 create mode 100644 profsea/notebooks/eval_plots/rcp_eval_ar5_landwater.png
 create mode 100644 profsea/notebooks/worked_example.ipynb

diff --git a/profsea/notebooks/check_patterns.ipynb b/profsea/notebooks/check_patterns.ipynb
new file mode 100644
index 0000000..53e37ea
--- /dev/null
+++ b/profsea/notebooks/check_patterns.ipynb
@@ -0,0 +1,308 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "628f460e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import iris\n",
+    "import iris.quickplot as qplt\n",
+    "import xarray as xr\n",
+    "import glob\n",
+    "import cartopy.crs as ccrs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "6a8a8e38",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['../data/cmip6/ACCESS-CM2/zos_regression_ssp245_ACCESS-CM2.npy', '../data/cmip6/ACCESS-ESM1-5/zos_regression_ssp245_ACCESS-ESM1-5.npy', '../data/cmip6/CMCC-CM2-SR5/zos_regression_ssp245_CMCC-CM2-SR5.npy', '../data/cmip6/CNRM-CM6-1-HR/zos_regression_ssp245_CNRM-CM6-1-HR.npy', '../data/cmip6/CNRM-CM6-1/zos_regression_ssp245_CNRM-CM6-1.npy', '../data/cmip6/CNRM-ESM2-1/zos_regression_ssp245_CNRM-ESM2-1.npy', '../data/cmip6/CanESM5-1/zos_regression_ssp245_CanESM5-1.npy', '../data/cmip6/CanESM5-CanOE/zos_regression_ssp245_CanESM5-CanOE.npy', '../data/cmip6/CanESM5/zos_regression_ssp245_CanESM5.npy', '../data/cmip6/EC-Earth3/zos_regression_ssp245_EC-Earth3.npy', '../data/cmip6/GISS-E2-1-G/zos_regression_ssp245_GISS-E2-1-G.npy', '../data/cmip6/GISS-E2-1-H/zos_regression_ssp245_GISS-E2-1-H.npy', '../data/cmip6/GISS-E2-2-G/zos_regression_ssp245_GISS-E2-2-G.npy', '../data/cmip6/INM-CM4-8/zos_regression_ssp245_INM-CM4-8.npy', '../data/cmip6/INM-CM5-0/zos_regression_ssp245_INM-CM5-0.npy', '../data/cmip6/MIROC6/zos_regression_ssp245_MIROC6.npy', '../data/cmip6/MPI-ESM1-2-HR/zos_regression_ssp245_MPI-ESM1-2-HR.npy', '../data/cmip6/MPI-ESM1-2-LR/zos_regression_ssp245_MPI-ESM1-2-LR.npy', '../data/cmip6/MRI-ESM2-0/zos_regression_ssp245_MRI-ESM2-0.npy', '../data/cmip6/NorESM2-LM/zos_regression_ssp245_NorESM2-LM.npy', '../data/cmip6/NorESM2-MM/zos_regression_ssp245_NorESM2-MM.npy', '../data/cmip6/UKESM1-0-LL/zos_regression_ssp245_UKESM1-0-LL.npy']\n"
+     ]
+    }
+   ],
+   "source": [
+    "cmip6_files = sorted(glob.glob('../data/cmip6/*/zos_regression_ssp245_*.npy'))\n",
+    "cmip6_patts = np.zeros((len(cmip6_files), 180, 360))\n",
+    "for i, f in enumerate(cmip6_files):\n",
+    "    cmip6_patts[i] = np.load(f).data\n",
+    "    \n",
+    "cmip5_patt = np.load('../data/runs/cmip7_ensemble/data/zos_regression/zos_regression.npy')[2, 0]\n",
+    "\n",
+    "print(cmip6_files)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "d12c5863",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'../data/cmip6/UKESM1-0-LL/zos_regression_ssp245_UKESM1-0-LL.npy'"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cmip6_files[-1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "4d21ccf1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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P7TyHswBlU9aCF134CETNUJeaUakZ6kqdo1qzLSG78qVv7ZrJvmeFldq4mqrraq7tzuEYpW57KRhKwQmjwlEKhuVaza8IhmXLvABLVY5Rre6jpRpYqSQq3ZUbC4GRMOykHgsnXdOccdL7ZvbeLRjT969aN+AMg8xNF9wxeAXXf1a7RDUSaOiRAMdu0V6nGsf3e+5tPd8QJqqMsgfuuFSMYFubKLEgddvoPmtPc8TIvPlrUhNCqv2a5z3n6vnekquNzXWP4afOPqzaFQlkFMG+OJNgDMi1ZiFjEpnRhgXnXUqmj0c/M4LZeMpKcFSCeXoqcz9O0lpRzZZjEtYY6tMTsVtC3SdujYv8IWXIVfeXh/U07z+fYXHaN+ltwwPdi6+Bqck6Ffnn9u1HAupoQGHWKW0ajRysjF8SJEZiGc6LvhuJQUlISEhISEjYcNjUDMp3L2wBx5zn5UC/7My4GR1joz0zGnduIKLfuwHW+OEdU9ar5aZ61ijLocaG6Ti58QQBTM/SjKmrCJlcV2h6j4DpWToWxPQyh0QrEk4bTcIwEx4rMsjd9D98eycA4DnnfFO1IRfICgleaH3IAOCFPr4cYDkjsn7gU5+eDYNxPPCiCx+xDEldctQVx1/tPxPA7JiYjYgnR4PGMnPPznGBOQ5sI28Uoxs6WmU4VmU4atiVmuk/tXUluY0OKkVzXJ+x8P7V+8wk5jJgzjJ8AnN6n3OZwHwmnM6JCwwyYXvxKnJEsyncjdEDprfNUGt9DZ02vXbaUxekdx5GV9FzxCxLYlgcF4HmdBvx949iTNw+G0yCpOt8JiFsj42IY0oLtjVXz/C2osLWYoztcyraauu2Eea2Kw3K5/5WsZhXXvaIOk4VPgJtZQNRkukagAgikjIJE2TIONGaONspsq3+H4CsGETtP2uVZiTLKkNZKsqsFBzjKkNlGT5u/8x5MOek0tOOLWCdzFcX+kQEddUtg9+CPu1Yq54mjDIMowMBwxT6LKLPUJFpAY+1KwWzrAoAVAIkahBYroHPHe7X1sSgJCQkJCQkJGw4bGoG5Rk7FGtQCzLWCPdlZ8bXQvU0RVtehq6cDtPE0U9DttixaTsOLYMxaV8pnzF/TN1EyajlLmqGRtAUTGKY+VEyQ14j1+PNg6zGQPeo8rxGUdTIiFYkHxJWpACY7lT/w58rBmXHM3QPNWdAnrmomVCeL6WNkvnEn+zFRsKPPuNR1CVHVarG//UjZ3rrT2aWZFZYyGuMahNVpu7NrXrdkEtszRkqy3qyKGsCOB8cGgE2CFgSw/CF7F7GHCORceHdgoy3d0P/7ptPAQA8a9eTqn2EManqDLVgQc9caSMMaA/Vdy6WnptxxqVlcbhmUxhhVMJ3ldFlSKlYJ8PqlIKj0usMs1IG+grrvMxcRNxcVmMhrzDUz/vW4RgLC2MMtygqJF8QyLf5beA7gp+MSmuHxhJSv4RlqaYNo2LaTFkTautEUoGBa1bmf/+fpu7ssh/eD1kBwrKXDLV+Rg2zUhtWruIoa8eo1JRNEcwyX4BjUNq8jEKwPiz7BHTVP2mflGVrumNL3ytmirZ6UVGasRPBvH0OAgYv1P+UZH1J1lUSOFYx4PF+bUoMSkJCQkJCQsKGw6ZmUL5v6wgLuRr/omNn4dhi7Gs1No7X9CCQreu71sXQ7Zsgo+U4cy3PuQQDcU3lEhlr+oUA2m01q22UDJ02ETOZZUGkrw8ZAEzTNGzANBOivVeyHMg5mBG3cLcOf67+y55m+ska7hOc9LZqoJYbijkxrAkAjJZylGVm76kLz/g2pAS+/MTGirY5kThjbsVjDswYv+kpLdeOdQAMi+eYTQqPVdC6COPpMuDCap8An+ErtCdOUaiuekjSPfCN3Ws6RqOrCvFd247CjNxzyZBJafNIxdJyhT1aqkFhluGRyLggmhS1jvaWbY9fMo/VEZKhqmk0i8+uAL6j8tCwTJnAMK8wHKjzNxxUyHLh3kcCECv+scgVLQ7hzE//lTOfFcnhGJUqYJIpgwLtN6TZlrsjzInBpGi+H33Go5aVFTWHEES/Ujt2pa7V+amFYaSY9gtx53MSurRC08CL1Gr5HTD3QcadfsowgIoZdKwJ59KbB9rZQhnohOi9KyWznk5mXkpmz6GUsOfP3It1wFBV0p8HFAt4uIx7Z8WQGJSEhISEhISEDYdNzaB87+nfwZZ8qGPnXe8ipkrui9hXLCO9mvg82T74Wm370o7tz8yHX8MAwDPdo8rcPM+cHoTlfpQMHzCTzBgsZ2A6bIflDMgKx4rkXE1nwbxqgJsOD7TteLYoh1pUNSAkZBVx3eUMEAwvefk3AOieViVVZAAA1LDTn/5sd69prXjB+V8HAJSjjPiVMO+e2mg+JRsBJkKiLZoFILoH3fOzHWytt3BOo4oNBJyLcG6ibzI1TXVRhg3kXHrP218+PDtG7umnLXqROQbmntCPC7KASaXsUIOR1T1h+0wDhEERnn6AaUaFkXeMdU/OpOdmTNmBquIYV7mnt6Dtz7hs7NOwDCYiRuiedVHXyGp6RQE50u0ZwrO+ZTmzDCmgtX0mVdkAalqfM0bSkTOup3XZKy97BGIsIUb62EYqkgdQnkJSwPklZRLQ1/9zf3su/vz/dzZCXPq8/ao9slmP0OdMCOadT8MemO3MMu88TPG7wiIsCWOT3++GZWJMguX03oCLiGKIa3p6oGHnJYJ1JKLOzlu3bXc+pWQQFWGgamYdtgF1f7rzyfCdFSJOmoDEoCQkJCQkJCRsOGxqBuWMs49i+2Ac5EyZro6QFIh+ffJm7D5VntNpNc/aw3c6PgkZtZgE/HFervUgZoHRgphtyDTLuJ/YgzPFhphp+n+WeWU//uvtGYpjuPptQa6OFZ0HqRbAuHYMSiVgrAZlJTW74sa6ZeXCrSQJzt93yX58+vOzYVGMyysA+8Vv5qVg3lf+gwfOmMk+T1bUknuaE8Oe0NvbMieaMQn1UzYjNScMimZMCuM6nKu8S5Y1yYTHMlBc/D3fsNdQ1AxVxfHFxybrUL5nx2EI6bxOOFMMEWVMzXEZZsdgEmMbMrJUV+JPO9bEHrf+A4B8IJAN/eg5Poho61akZh10e2oGcL/3Tb1H6LvTrDNMLB9oZoSAbyHvn4KDDWxiLp9tpRaldBqOHbXryhpiRdh5lqtoHnNeTC4Z46XC5zWLtT3HXf/fdtbsqp/+FmQ15++L/C+1cYccC/Wn2SGxonxdAKAeKVfoNrYlhGFCTNuzwuUG44XzguGZ8YPSx5krxrvxGwDYd3f8tyl4n4flerDe5mA8RqVxzcwyM++m3fbmGjrGxW3riuYrSYOSkJCQkJCQsImxqRmU4Vk5hnPBIUTCabrG5LrS6jS+REn0CuP+vNVwGDaCtsObDhoTttcLFQq2a9TLo8cLwB8MF8LNV7VOeGLW14Bg+Ph7htFqrn7TMul5iEbPSB4y9ZymFn17Sf2/XOOTHz8r3jaNfZfuD9rsJmfBmlz6vP1+T0cwkunYeDO4AnoVHviG73uSMBlcsww06sw6lmpNic1IzYSNWlHzZJpLZJkAJxELWUdOGG98P5M2h5PMGbJc4IfPUYYLVcVR1Zl1IaVRMIDqqRm2ws7bNrjcMYB5fBzbBsHtrRtqykxdblvHOjEmLVWQZyryrig0kzSskA8lsnnNMm0B+Lx+xwwUe+HpyML3QIsY5k8/8JRG+wxe+tqDzfea2fbP9KLdW9wGjJEeP2t/FwVolBISPHhffvy3t4al1Lso463vqhB/+sHpWNCXvuoAAEAuV5bRyZYlxEii1mm6RckhKqdfARyTZ7KnU8aEDxUTBQBs6GsBWUGuW4QJaaBH6CnrqmfC9WEhswXHdjHz3vfSLLdFB3XsREgMV7oK+EgMSkJCQkJCQsKGA5NyWtXGicfhw4exY8cOHHrXv8f2+UE7WzEtYgYN5n/OlLYD0OOtmetpZGQ615oORua9uiYwKH0l2ID6VA3ZDUCxJHXt5seVmgd0YoTaeZLUQi9T89P2Otrw4qu/DrksIMaqDXf/+fpG40zC/+sHtJqfjIWC+iSUZFo7Uho9g4nvN4j5B5wqHim7hiq8wriXUqdJ49UDqLxNRq8x5DUyLpFrXYlxeKXRcJMi4RjRbWSmh8oleObG+LNCegyGlMxe72rMUZWOQamqzGpOTMRWqA2xPeHAi0TUzvOhFBy14MRDw7VbUqZEgzJHBRcoCp3nalChGNQYLOhzNK9730Ndx4D0vjV7wiJMx5/+t9Xfh1dd84TPhGTc9sg//vs7AABXv/6w22AK1kSVD3RwAD5+y/yq27teeMnLvwE51u/HkYQYS0gtmxClcrJ17xHm7s1cghfNaEpWGJ0gs5oepTnhuOuP9qyqjVdd84S/oIuJ6aNDiYEy7W3/I9AUBesabQFwZFTi9LfdjsXFRWzfvr2zCYlBSUhISEhISNhw2NQaFAD9xu5imJQSsmli4O8r54o5AYBBHrApmd9bYDzOoExiU8J2CqH+AMeQjLXcvKwUUwIAoxIYV5AjPb9SKfdWIPrF/tJ//Xh02vSQ7vpIeySEGbc19RpvEwgAQ4ZM9x6u3PdIkDbT//JmnAXqfl1MK8C7HCYvf75iRz7zV36Zy39kf7OwlzXVvwd8vwLnB8GF1K6KpmcpfcdIwXDhGd9W7dX6hDYX0s0O468x1j4o5pLmRG8CqLw4BdFX8IDZqINol3BUmkbDeAwE8yNdCuKLAtTITARKptgcUw3PJHgtG2yG3huyTHgsDe10SgkIfdyiZijrrJGbh4K6wzLSXhPFQ6ObDKvEuUQ+FOA6UiebVxEzHmsSMLrmebmrB+vZ6HGberx57jPFsfcR9UhqK9Pybv34rQsT23ki4N5ZWndBbDpYwZSuScteMvPeotEspqyJ6jSRT5nWC7kQMD83mZD2/Qmod+hL/8033c71ub3r//NUACoqyUL4ZTxwFmVNWvUp06RkjjAnzNM0oqlVWUP65cSgJCQkJCQkJGw4pA+UhISEhISEhA2HzT3EQ6mpPjRSUKYh7gngUWJCOsMgs50OH8MYgBFS5lINv7AWCjQc1omVaWu7EE2xa2mGcUo3pDOuIVcqyNIk5xOdIb93/T/dYbUv/anH1IShDamuWjfnJa94DJ/4k734xEefpuZf/g2wHPjEx7pDjfvgxVd/vXN9OLRjl/9Fc7kJbWaVTmKWayFkJdWwD4CskqhL4ZkzidpPlGWudyw52KT0BpsN2ws1jFhJjmOVemWMhJ+OfS4TKODs6wUYhA4oHdeZl/bdLI/J89uSrdEEe2ZoJq9rDCpuk90pqIvImNSW4LoshLYSV9etKITdvzFJowZbUvrW3VVlyioDNxoqnGfSlqUJ//KsxqCorRDWGK95dua6fdkQ4EMg00ZobM4PJY6Fk/YRtLthgZZ3DLWrz/hk+t8YL8YS0Omb4ePv6xY+bjSYd1YfvOTl35iu/Cses9Ne/IPyOHC/SZzp4Z7mb9pLX3tQbWLM68LfvbUEhqzWDkOQFLzWoI0MAwmSuiQU207R3sSgJCQkJCQkJGw4bG4GZUDCe9tEOaGoJ7RflnS924xxuD6fYQ70viRnflK9jFo+kzbRbe1OwjaQnXaFawXGOTDhwVbY5UKFZSkgK2lD5T716XOwFnTZSa8XrHANQHvegOlhkg9eue8RlZTQpIQXADcdcSGR1S7RodAhyHXpGJVab1fXDIJx1/PXgskLnvJtu8/NHIK8kNc4olmTlZpjRbNII8FQcImh7kmrUF2gMlbzkqMKwrP92zl+TWVwrU3YcoxBGWrhsqSmaWY7JpEPpbNtn5MoIEHluJMMFmwoaaXuAQCoyzEECUFX5Zhv1kUYkqxwCT35MLA3L5h9b7ABV0n0WhgTwPWcp2NO0NFLDsLlhcSf6nDiNmw2dmTWmIY9AYBP/Il7d3oBCED0WnQFJPTFVf/h29HgEVn7rA3TIwOdNh29hLU8MPBUv0utxm+D/ulUEoOSkJCQkJCQsOEwtVHbPffcg/e85z144IEH8Pjjj+POO+/Ey1/+cq/MV77yFbztbW/D5z//eQgh8OxnPxv/43/8D5xzjurJj0YjXH/99fjIRz6C5eVlXHbZZXjf+96Hs87qp1ewRm2/9X8po7bQfr0iY2J0nZSOJakE5JgkrAvCx0KEyY8+8xfn4ooXaD3DANaMh8/p0EBi6tawjzYwY3W0vZWw2hGaUE8l2APQdbWCj1p6LJ/6TDuLcsWPunDcWRqqeSwISXxoGBnTo+jSwLzkZV+fiY5lGlx52SOKgTIJ1ypn0AQoUzfDptQVR1X56cQpO0ATi335iV141q4nN10IstFmHCkzLGkGpZIMOZOY12zBfFaj4NKGGzM0tTirHSpnRNuSMWeZX3CBQSYw0KG6w6LC3JzSywzmKhRzwlrFK8txZp8RljOAmGYZ46xoQ4V0iS510jSX6FL620R0bdaca8CBInMGa5Rpje0z7IEaBqXDrh4glvWxumPTpt4/OL2z3oTNiav+g2NzqZHfVa970gtJtmlaAKdNaksyS9EwcRPx+xfqN/bw8hinv+EP1seo7dixY3juc5+L2267Lbr+n/7pn/DCF74Qz3jGM/C5z30Of/u3f4t3vOMdmJtzWSWvu+463Hnnnbjjjjtw77334ujRo7j66qtRGwFoQkJCQkJCwimNNVndM8YaDMqrX/1qFEWBD37wg9FtFhcXccYZZ+CDH/wgXvWqVwEAHnvsMZx99tm46667cOWVV07cr2VQ3vs6bC9yn4UgDIQcC8c8GJiPPG1fXI/0fMlQjTnqyhky/fUjs08a9+IXP+p6bpHPQykAENbEpgTXUSdtDA/91DTj3Czytdu0JW7uf71s6W00UIBJGhcaxTMpAeEsceVljwBQ10GMAUHvlZFmUypurfEBWEM3m0yOMCib1cDNkAVHqgwl0V4MuUShtReFTg7ofKjcfcah7erNvLW4j++PEZrQ2OLTxIPUAG6Q1Sg0gzIYVBgM1XQxrFEsCGQ6tx2fZ47dBBRbYpLt6YiYq173JADgT/9wumt09c8tuhkzrg/9rNHXK2PxxH5APPLBLJ+AsL2e/sS0KZbUTyf7+/gE3UnCqYOr/t/fARBJekvZlDaI4Mck1HtCMyhLI5z+i//1+FvdCyHwp3/6p3j605+OK6+8Ek996lPx/Oc/Hx/96EdtmQceeABlWWLfvn122d69e3HBBRfgvvvui9Y7Go1w+PBh7y8hISEhISHh5MVMo3gOHjyIo0eP4jd+4zfwa7/2a3jXu96FT37yk3jFK16Bz372s7jkkktw4MABDAYDnH66P965e/duHDhwIFrvzTffjBtvvLG5wvSCKtHSKwFk6RI9GS0BoKzO6xFHNdZj6mWG0TjH332ze3x3rfjkJ8/uXXbfJfutZmY9k+3tu8RpUBh3mpRZ7jNkT6aJDPrkx8+yLIr5v4tJecnL4r4pUkzHwHzqz5xu54of3Q+mRyCZkOC50ZwYfxxtX84ZhGDWJkJyhqpSqvV/eGLzsSdLJHKnkrBROwWXGHC/x0QlXJwIoszZob2hUJ/ionXUdM5N1I5iTEziwSKj08rm3viM5EWNfKDXafbE+IrwrRkwzK3+I6bjmJY5MQgZiKvftKzqu2VeTYfpKiiohoUzP/KhC+R9d/XPHwnefy4B38d/e+vEqgwDlJiUhD99/2nrvo8f+w+RtAstmDmDAgA/8RM/gTe+8Y143vOeh7e//e24+uqr8fu///ud20opwVqyLt5www1YXFy0f48++ugsm52QkJCQkJCwwTBTBuUpT3kK8jzHs571LG/5M5/5TNx7770AgD179mA8HuPQoUMei3Lw4EFcfPHF0XqHwyGGw2Fj+adu34GFzF9+1eueBFZU+AUrarBBDbHiUmcbGG+L8Ug7Y47yDedX8enPrx9rciL2sxZMw3yYqJ82JmU14AOGu/9cMSpXvGA/uO6pZ2OgHksIHfFjPDJErT62q8r1AZ658xC+8uTGi5QwGpNKcoxqhhWtMzlWqWSAA81mLGQSQx21kzP9TMH4nsCbZ5K6ykpwT1lipGCONQmjdKzOhAsUJFInzwTyXE9rZ9bcMChDaaN2si0A35KBz+nzX2Q26dp64+O3zEenr37TMj5+61Zcfd0SKR0wKmE0UCTJ6KyT7iXmJOF44pP/dVvvsjNlUAaDAX7oh34IX/3qV73lX/va13DuuepH8KKLLkJRFLj77rvt+scffxxf/vKXWz9QEhISEhISEk4tTM2gHD16FA899JCdf/jhh/Hggw9i586dOOecc/CWt7wFr3rVq/BjP/ZjuPTSS/HJT34S/+t//S987nOfAwDs2LEDr3vd6/DmN78Zu3btws6dO3H99dfjwgsvxOWXX77mA6LjyFf97CGwcQ02Vj0suVyBHdXTUkLUKs06sLndPjcDToQbLeC7A4d4ycu+btdPYmioHuXu/+MYp32X7AdfFiQaTPmkSKF0J0JwVFrDcSLZkxWbO0jNtwWSZEzCkA5ZISCkDTrDgOSZ4cxY7RheRDnJUp7E5qsBUBPDykoCOWMQ1B02c9PG3wQABlltc9oAAOcCea7ZlUKgmKsta5JvAdgw7kc0C4fOtcKwKZQBufrnj8QLc9ZLP5KQcDJj6g+UL3zhC7j00kvt/Jve9CYAwDXXXIPbb78dP/mTP4nf//3fx80334w3vOEN+L7v+z788R//MV74whfabd773vciz3O88pWvtEZtt99+O7KsvwVuQkJCQkJCwsmLNfmgnCgYH5Q7fuD6hgZlEoy3xfLjDEcODfE3jx+fcemE44dJ2Y8BFa00K4fafZfuR31MTYsxUI85xsvqY3tlucBorPoBJ4pBKbjwPEiywFfE5LbhUJ4jjy35GoctuRLYSDDLkGRM4nBZeGVqySxrUpFputyAgbZBYqg1J3NZrf60zmSYVxjktdWdFEWNTDMog/ka+RaJfJtmauadzwnLlLDlrj/as+rztt6g7MnHb+s/Lp+QsJmxVI/w6i/+5vH3QUlISEhISEhImAU2dzbjKXHlZY/g2KOqt/Xkt7dsSlfPhHb0YU7Wy4mW6dHJ0B2YOqp+72mLeOg7xydi4uwtitLJM4GMaEcyzVSYzL+14Ci1PsVkJA5xrJr8mjhW5dg1HNm6xoJjpKctq0L0QJwxl7lYCHDdVzLsTqYbkgmOTAj7ouKZRDGnNShbJPLtDHxBsyY0BxZwwtmTNn2JYUsSa5KQ0I3EoCQkJCQkJCRsOGxqBuVZu55EjgWMKyeunctrfK2ll3roHwssHlFK+qVxgTPmVmyPT0jmjaknbD6E7MiLX+oM/T55V38H30l48YsfdTlTBCBJjkspdLZj7X9SC25ZlIxJfO9pi6j1Pffw4bX1oHcOlUXyfF5hLq8w1FqR4aAi7qoq0sUwO3XJMF7Osbyi7vV6xJ2rK5NgkuGcrUcBAI8cnS6KpOCC5NnxqZhSsEZuKDprWJZScHAGx6DUHIPMFWRMghfatXeoMwXrimYVKbbvkv0z8QZKDElCwtqQGJSEhISEhISEDYdNzaAcWp5DWW/F4TLDWLhvLTPePpcJLGS1HUOn2YmfOr8CgPbiJLYXpR0XP1Ju6lNz0uHFL/bTGxgG41OfPsdbR3Mdycr14q/c90i0XtOrnyZHkjYA0TvROZ60D0q1wlGNM9TaSZYzaaNkAAEIDslmEzi3Y6h2unU4BuMSw4FiUIpBjcGCYlCyeQlOA92+A8glWKdbAfe8MCYhJQNvSTkR4mlblsDhb2s4yIILzGWOWhJgqIWL6ikFt6yJkMwyLgWXmMsqDLnOUJzXyDOXb6eYE8jnVJ18yMAGTLEoa8QVP7rfskybwVk5IeFUQGJQEhISEhISEjYcNjVNsFLlOFZlOFZlzrWSSeS6ByjBMBYche6dDUiOj+Uq91wvk/5kY8GyIhyK5qKOsGT6yn2PeG6xV17+iF+OkBXUDTbElfsemcim2Cgh4dogKwkxUswJAFRjlYvHgDFpPTwyyVDXEplOqvm9py1iXGVTaT32Lqg8LguDEgtDlZq7yGtwLsG5O9i6NDlcJKRQLA8AjJcylGWGsm6aInImwbiLmHnWrie99VKGxyWQZy4vTl1z1No1t645jMOSIWS+9C3n1nze9iMYae2YkMw+l8OiUu6x2usk4wKDQY25repYB6dJZDt0huKFbE26kyte4LJ4r2e28ISEhNUhMSgJCQkJCQkJGw6bmkE5NB5ASN5wqTRQuUEA3XkEkwzQWpWcS2QsaU02KtiC6l0zrvPp2KgZxQgww5oQ9kQK2dCHGHSxJ4DSshhcue8RLzrEaBMk3ad0y8J8P4xJMOYYPcNsCMFULhl9v2aVnLqHYHLUzOWVZS+yTIAxgJH9iJE6f+UogxQMI521+8jyEEfGA4w108EZMJfp6J+81vUKWy/nwubJ4VxaF9esEOAZyQ5eM9QlR1VqJqniECRCjjOJ5+x+AgDwd998CuaLElt1FFJRVBgMtcZkWINn0mNreCFQaMPJbEe2Ll42V7xgv5djKSHhVMBpgzG+Mx7MvN5tRYVSM8kjwe3znDGJgomuTT0kBiUhISEhISFhw2FT0wdHqgxznNkoBED1CI22xETomMicWrovMi4Vg7K9UGPb661BGegxdhptlBDHS3/qMc8RlDF34WSlr6++uJRdYVqrIg29QZiOKy97ZCKLYmDYFOOj4hxPpd4noWZ0Z8CwCTyTEDVslmy6LmcSNEBGyhLzFcdztJZkZVRgrLUhpeD4xjE/J87ZW455mg+j0+CZtFE0gGYzNEMiBMNonOPoSPWSjpYDHC1zlLpszqQlpxhT0TeGQeFcoCgEeOb2Y46FcaVzqUrV3tFKjrLMIKSLXjJli7xGMagtk/QjW74BxhQLAwD5nEA2r9uQG9ZM2nk+z8E1ozbLrNiGMbniBfs7s14nJJxMMP5J5rk3v4ESbE0jCvQ37slx4TlI23cogOW6/z7Sr2VCQkJCQkLChsOmZlBoRlSDwKwSEgxChxMwALXtDXMIKa3/wpa86pVzZBKM98OK7gmbr0oDOh+6bZre56nOskgBMCIuCTUoDXDWvk6jL3sShZCtTrTUP8OAcQkIZqedbkO5oHITQKP1Mgu16tFUyxzjFXUPLi0NMJdXHiMxyGrrdTIYVMg1A8G4hBTMepvQaJpxmWG5LLCiey2jWmm2TJMLLm0EjcpsLCz7wxggJSAMG1M7nYsUzLIzAFCWGWrBMTButsMKwzntyzJXgxcSLDfnJzhfzC1jOcAKhmxO59eZz8G2DsAKddKuuuYJ/OkHnhK9FquFYU8uf/5+b/ln/ippUhJOLmwvSlQkylDCZRpfqvzIvp3DsXVar3WeLjNfBhnKhQSW9DtGSPjr4DMhlE2ZhFP7lzAhISEhISFhQ2JTMyhAkzGhMB1q44xZSxfYYawscluWYS6rbX0Mck2MimFSRCTCyOUrcfsy80I6lkXCfbGeSvjEn+zFS17mMhP7DIpeGDstM9YR9Mnfc/efn4srfnQ/xNhpUOrKrWdEi5ENlNaCaydUljEv6mhQCgyXFZsyPFxhYUnpOgDlQZJlTneSD2qb40dKBilhfUfMfimM4+swU5mNc27clisMtU/LfFFiULh6hWCo69yyMbR+IZnOfuzu72FRYW5OjWcP50sUC1q7EgQJSAH87y/47MTlP6LYC86ZF0HV+H84u1dWyJjYe0ok9iRhY2FbUdlXYMhA0JEEziQEYTfqgOlYqTOPuV+J+CEZlIJjVDvGpBQMldRResKxJAK+R1L4GuYAhGlfv8P1tk1ISEhISEhI2FDY1AyK0pd0sygAlSZQ71gfHIqxoGUNk2G+ONu+NkOdCe1VhvvraiqDBGcuL4mQADtFo38+8THldfHiq7+uToRxbjUXqGrZsOU07btE9ZbXI8+KYVEUBKRwug2eCWSFi0gJ9RdquWEJgFw/kbwQyOfGKJf0GHClvARMJEzIkKh9mXXCutlKnWrZ6Exq4kcAADlxbTURQqY3VFUZqprb7Mv0LufQLq+5iSqqURQ1Cu1nkg9lwz/GuNnKyEP4mb/QETU/ul8/z2Zvlequzem8PjnHS//NN1U9yxXkioAY6bLC35epsxXBtZhYPiHhBGBbUaGWzGpHmr8pDCWZlwAq+hvkuaxLL3N4F2rJbLTfWHCMasfGlFqTYvbXNVKgNChmuEBGn/82nFq/egkJCQkJCQmbApuaQVGaEhelA/hsSoxZMRyKcZk1/0o01caOyWBRBqMgWhGzKyGbLA3N1kq/Np1GJt5mNe3yCNF6TxVtSptrqM1OXAcrggFQab1L1jffCh86d1gpBUQZKSSAegQwvY4xFdnCcsOwkFw3A/Vw8oG+xyrFzNhbXTDHFkh4rq6AYxKKQY156XpRUvgPBeP+dqJmqCp3b9HxbCEZMu5y5AwHFQYmg/KwtlFKAMAz2Kgd0x7DqHz2iz2ugzm2sYCsJFhpDGeY86PR7MmnPzv9db38+fs9PVNiTxI2Ehby2jImKzWHkKzBYLYh1ITQbab53ViqMpiE8KNa/QaaeaVtcfuL7cuxtc6bSgSamEk4NX7lEhISEhISEjYVNjmDwlBJNb7lrDDkRE2KAWVfurYx7ElBtCYiiGBYC/x64gN0Rp1t1hZcnDIsSgw0d47BlfseaWQwNmDrfKd/6jMkl89lj6BeUveKKGHZFFEyz7GUFxIZbRtzLArjDJgHMqupkJC1dDqOSjqWxlJxuhoOsMxMC4D72hdZEa1G7bQhomSoS4ZsrD1IdH4fXrqsw4ZBGRQ1BoPKaU4GAiyXnscLzV1E2Z8XPXc/Pve3ccbi7j8/V+mFzDVkALgEKqfTsexK6c5HHzSidgyEWpcidxJOFLbkldVUAkAlmnoPaTn/dhEHJ4+JASMRPtOwF5UElrXucrlmqIjupJaAIIrKUFfCmIscBFzbMyYtC9MHp+4vXEJCQkJCQsKGRfpASUhISEhISNhw2NRDPIASAwnmHNg4a4pmY5TYpGEgamaTEbEsML0f2DRfgZOGjcxaCX/I6VQe7jEwwz5XXqYEtFLIVQko19wOYqu/79L9qI+p6Xo5FK8ZkaymbgcMbKCvI2f+sIwAUCnBKADIsQSnKjWujd9MPXOKmmXDDCznXjoAOa4htehUjgXkWE+XEnIsIfS64XKNauQSAhorfUCJcrOchlDr4Z1gKAkAPvtg8xq86MJH1NAN0Bju+fTnXdg2ywEmmZ+g0YQ3dqQ2MKDDOmkIJ2Gj4liVYyGvvRQp9HeGmrPRoaBwHTMSh4gx2jSShEoCx6oMS9aoTUkqPLNGr243zZl6tIXJjEo2Yp0DVE2kX7WEhISEhISEDYdNz6BMg5BNibEo5quw7sFItIVXUTAmVTmS9I1+Qa5WZjsbee7JCcNg7Lt0P/Zdqg3aTgCTYvZrGB1AQh5zTISsGUQpwQaahSAhwCxjAGE+lHeZBKs02zEnlYGZQc7BCi1u3ToAmy/U8rkCyIjB4KgEWykhl3WK9XHt6pHSSysgK6kYlpEqK1aI4FeHd5tOEuPNsGJZr+EupeZuXHriYrPu7v8z+Zom1iRhs4Am66O2Fhw6WahmzFcER00s5yk4CxgKJsENcxIxdwyxove7VHGMBIMmV1WywJZtQiJTaN+NNhP9ad4KiUFJSEhISEhI2HDY1AwKh07G1lFG2ce7abtt8BlnWI4ew9qtddh64L78pGSORdH7oPoWavK2Fgy4OOXs8DcLqFmclMwyKKJmkILGRQtwEyqcM6UdGeh+SGZ84zXbUgm/65Jzx5psmQMWdIa+4QDgHBhr6oMzQAig1BRIJRRTo9fxQebmGVP7W1FiEnGshDhqplWYs2E2aGhzX7SFGgOOHbn8R/ars0NM6f7srxMrknDqwbzfBSFPw7BhZQQqrf5DMKehnPRLc7TilkEZC4aRcGZsXQh/B7n936TeoDk/+9uA0LoSEhISEhISEjYMpv5Aueeee/DjP/7j2Lt3Lxhj+OhHP9pa9tprrwVjDLfeequ3fDQa4Rd+4RfwlKc8BVu2bMHLXvYyfP3rX5+2KZY94forMWPSMiacNb/sqNpZSP/PpKVu+1M1u7+wjhMB9TXq/gZc2L8Epf8wfycSjDP95yf5k4KhGnPUywz1MoNYUtE5cqzLaFaEzRdg2+fATt8CdtqC+tu1xf/buQU4fav7267/tm0B5ueAQaH+OFPdr3Ht/irhumScgQ1z9bd1CLZlCLZN/fEtBfhCpv7mGFimmRPNnphkiGFCxB971qO45NmP+At5z4dGKDO5z/zVufjMX52b2JOEUx6V5Ch1Ir+a/G+MS9t+w0rN5odYEdz91QwrtWNPwt/JGHjwx/TvMWNqOie/zxmb7qNj6g+UY8eO4bnPfS5uu+22znIf/ehH8Vd/9VfYu3dvY911112HO++8E3fccQfuvfdeHD16FFdffTXqOkyskpCQkJCQkHAqYmoNykte8hK85CUv6SzzjW98Az//8z+PT33qU7jqqqu8dYuLi/jDP/xDfPCDH8Tll18OAPjQhz6Es88+G5/5zGdw5ZVX9m6LYU9CbUnXGJdsGYebxIII6dcrSUR32xgcQGyGW+qlm/LI1+2s7PQTThxMVNHlP7IfvJDItaBC1NrrxLAJpgtiwBkw0I/oljmgyGFFKiLUoGSu7HCoygJAngMlyVwoJGRZQ2pdiRy5TgGT6iaXWu9iPXdoqIC52YNkgCFrAgAsM9FJav5FF2oWpS97ghSFk5AQQmlKnCeXcLPggS8Yg4rsUVBW90aTIqViSpZ0ctCVmmEsgnoJun/n6HLNlJhXBXO/ukY32hcz16AIIfDTP/3TeMtb3oJnP/vZjfUPPPAAyrLEvn377LK9e/figgsuwH333RetczQa4fDhw95fQkJCQkJCwsmLmUfxvOtd70Ke53jDG94QXX/gwAEMBgOcfvrp3vLdu3fjwIED0W1uvvlm3Hjjja37lFP70zVBsrivrR79P/1K9DvF1FXPn29EFkm3fhom5QRJYk4pvPjFj/oLyEX+5F1nR7f5zF8oNuCyH1beLMYVlg91FXM6SSBUdAyjtF2WAXMDNO1lNRhXLAqg/qdMS1WrPwAoK+VIWzuvExdiJgBWOeaERvpAe6boh4RxBpYHtpKi2SRAucxCsAZzYhkVoNVVNiEhwceACxSaspDan8T+djHCogDKf8smE1Xb2CSEOlJnRZCkhLqa1bAnYdSO2YRFtu+LmTIoDzzwAH7rt34Lt99+OxibrkVSytZtbrjhBiwuLtq/Rx99NFouISEhISEh4eTATBmUP//zP8fBgwdxzjkuF0ld13jzm9+MW2+9Ff/yL/+CPXv2YDwe49ChQx6LcvDgQVx88cXReofDIYbDYa82hDkBVN6Cybl4pmVPQsYmpjsxyyexJm3sT8aoZqbJpHSxKilPz3HCKk4t13YlAqq34ZEiFfE5odE1UmtOjNeIDCK1pAAqusBYvgrFmiyP1PxYMSj2hhckp80YAGqXOyPIpWGcZWk7Q9BjMb4oXKgOiG2yZlo++6VzGtsnJCTEYbQjS7WK4gEc+WmdlLQexbjHKj2KY2VLksen1hqU2KNsfUta2hJqTugyjsjvnN1uuh/amf5y/fRP/zT+7u/+Dg8++KD927t3L97ylrfgU5/6FADgoosuQlEUuPvuu+12jz/+OL785S+3fqAkJCQkJCQknFqYmkE5evQoHnroITv/8MMP48EHH8TOnTtxzjnnYNeuXV75oiiwZ88efN/3fR8AYMeOHXjd616HN7/5zdi1axd27tyJ66+/HhdeeKGN6pkGZpjeMA2UjZi0bhrExtBiX5mhsy1nshGpEzrbhvO+X4sp0+1y23U4BReJRZk1wmgbAyHx4pc+2qpDodsahsEyC2OAzbXUO64ANvayEjdYlJg+RUi17fJYLV4uVTZjkxVZSKCGmwYgjV6F7gtQzIfpbkl0JqPiOfxzJFx2Y1m5nD4JCQlx0MeLw2lHxoJ7DrJUP2kfWbNSMtg4PfMYEwalll6y4V6MiSonvXVtr8PwWPiUUalTf6B84QtfwKWXXmrn3/SmNwEArrnmGtx+++296njve9+LPM/xyle+EsvLy7jssstw++23I8um9MpOSEhISEhIOCnBpIwINDY4Dh8+jB07duD9F74N89nQ+2KjuXcANNaF6KNFCcs09gHf76Sv5iSWIygEda2VZPxQyOZWzNsuvi7l65kNuqJ4PF0HTbUDvVzQeTfNOMC3qIr46UPwbUOXU6fI/a6SEEBNNm6j1yrlFGsicuRSCTmqIZf0fCl9PQl3kURgaNxI0tOuoNHNsyQO1xmZ6bY2cggQI6DWsphqmaPWXgx/8c9Pix9HgsU/HFb3xLO2j09wSxLWEyGDYnC0choUHpRtRPWA6lNYMA/lFtszGxwPXgZUcwLQ3zN/5CD8rVwRK/i5L70Li4uL2L59+4R9JiQkJCQkJCRsMGzqbMZtCMfjptmua5uQ+aDKZPcVqRXNwXwbW+K74E72PEnOshsDn/yk05hcue8RMNILobqOEIwzF4kDfZ8Y3cYcB9uiQnzYQqGyChs9SFV7aUxlTSJ8JgiqJIkGkqMackVAlvpeG0urDXFt7DEoDbSzJ2a/YSpUEsVj8nQASq+yCYlci+/adtRO/8uRrd667z1t0U4/9J0dU9dt2BLAnb4Lto/xlSODxKSchKCPVPg7Zlj6uUygIL8DEsy+CkoAIOy68Kb9+uUq2BMWsCF2mjV/82aBxKAkJCQkJCQkbDhsagaFut61MR90Xcxxtk80T1S7gvaY7tVyHCE7QjUndFrNN7F5+6CbHAKQViKv/vM0KAZU30HnaTfBMB1LJbDiUxseE2JSjQLNB0BnTrbbCVev0OyJWNHranj+JCETwhii3RiqNfH2Y6bH7thFjcZ5oHXwQkCukzbq6ZrB+Noq2IsuUNaE4rztRxo9yNUwJ4DPngD+ZXjmtsSanGyI5b8B3HWX+vchZ5RtByqdrRhwkTlmPsaa+HVLj0UJdSYGod4ECPWdQfloLdMjMSgJCQkJCQkJGw6bmkGJgbIkkzQl06LNXa/PV54bT/Q9TnjE3t9nSsiXcDC26LetnT9hSNE70+LKyx7xugGf+ozverrv0v12WgaeHp49ScgccOkYBMkc0wLlMcLG5kap/Kgd+HlzQn0H8waBfaaGamLEWEKOnQ+JYjco+yJtb4lxQHLA3Fr0VpWIZDAmXieiBOqR2qAac/yffzwL64ULz/i2nf7St3wfJsOcPP20xZmzKAaT3DFXq0N51vaxx6JMmT0k4SSGefRWBMdKzVESbxMalNdH2hV6mkzaJxBnT/pqT+QUOsr0q5WQkJCQkJCw4XDSMSgxUDV0zFW2DX6ETf/9SbivSsN00DFDqomhHWEGGbjI+kyJRJM5cWWby2eppj6VoNgRXyty5b5HPBbCaDhCN9VJPRbGAKafOsllM/KF3HPUZ8RMi5boIJ4RZiZXNVkEzIaoAVlpTwTJLOPDOMAEAJOlWARWKLy7hyVJ+0TJUJeqcDmanQHj8/Z8y06be/7vvvkUAD6TEuJr39mBp5+2iEznqPrKk6evug3nbT9ip6d9xqZlU2Ln+6tHB/i+rUmDstlBmTcpGThobh2GouXeOlJlGJkMxIKhJG6wXVE5MX3JJFZuGgaD/tb5v4GrH8lIDEpCQkJCQkLChsNJwaC0ucV2fbVNw6TEQL9+dfCDmpbMfkG6NoUMR9s+jd6ERUsK7RLYBuoqyEk9GZOYy1TXdqVO6QR6gV4uoRxX62U1W498BiKmQWjoMwCr72BGsxLzDiERNVQfImrWSL1jq80kRE60I8FTLStA1q4eUcXHgTlUV4cZ1obLZn+MRuCQ9ksRORbJvP9ngSwTLtmyZF7dX/rWLlzwFMeifPmJuB4FAJ6160nLfoTlJuHhw9vwPTsO2/l/Wux2wzSg7ElfmGidrxzxI3oSi7L5EOZpoxBMeveyoBQEFGuypJ/hlZpZBiUo1vjN8wyumXGf7f+b1+Zm648EKKyHRCoxKAkJCQkJCQkbDpuaQVF5b9R0F3MyDZPSV68S9pxttkb4DMhkNiXWJgdatos9ia1PGpT+uOIF+51OYwlguXQJgUugXmYYLyv2aTzKIXQPhjHFXHAuvXkWzANqGWNSZfrVCMeh7T5rhrrikKanJP17ju6DcwmeCVsvK5t1Ct37koLZusL2SV23tO2NMyO27dyf7iq7WvzA3m8iy6Q9v1ICdc3tdBe62JR/+PbOTs3KJPRlTShW64cCKCblq0ebLAqAxKRscNA8bRl53mviX2KQM/O/f3OXgmGxVPf9cu3/TmX6D3CMCs1q3PVIStmuQ6HPF2NNNqXNsd2MIrRhUtQbRWJQEhISEhISEjYcNjWDwnqwJxPrWIP/Ku2FGlAmBWh+TU6zty72pE37QPedMBmX/bDyM1n5NseSHucXgilWQvfa65qhLHOMSvW4lIJ7154zaXtGjElkXCAzDAqXNnKEMYksk1EWBFDshmFm6ppBCO4ykAoGxqXL9cQk8ly7ynKp8mroCJpYnZSJMfsGoG8Ww04oDyF7bELrUMKbmiJMpxqBEFM+lASW3ZHmf2ana+EYpkm44CnfbrAo5jw9Z/cTNhKI4pk7D9nptUT9zBrJDuXEYSGvsVT11/HF8rQBij0pJUMpTOSL/1uUBxe5lMBYuG0dAj8kNLMH+/lz+nmetLGTfZxkTSbjsB1qn+06nEn7S0hISEhISEjYENjcDAokGORUmpNw+xBtEUFUtRyO68XYjDZNitoHrbe7sV26ky4WpQ3T9gBOZlz6vP049i2VPXhxcR7Hxmq60q67tPdeSY7aKueZN/7KIC1jwvV0pmmHjDAomWZXbIbrzL92Rh8CaNdgyXwfnRq2Lp5JQGtmOJfIWsaSqd4kWKN3xMAyyri4EgLKm8FoU0wWYn2gdpl/DP7xrBVKc8LAmGOAhL4+5nw9a9eTtnxNHJPpsxGOiVP9iRQMF57x7YYLrdk+4xLP2f0EAESZloSTGyYCElC/A3Qe6I6MlJJBmHuPaE5KPe1yrTkdiVvicKzi1jMrY9KWLbjazjEU3SxJX0YifHQdC6TrpesMqxuUaWNNpnFETgxKQkJCQkJCwobDpmZQOAPy4BNrtZqSabUrMRYFaCqUY8P3k1iTcFuKsDfctl8zDmimZ5mT6GTB0qEBvnN4HgCwuDK0PaFa+jeVkIo1qVoYgZxLMN29Uap6Yc93xoTVp2RcouDCaVKq5nhsG2Nm2TJBqQs3LSX3c+UEj4E3Ds3jz4gUzJfnw7EogNJsSLp7ErUjBRo+LTTKaFoYx1gRySFlnp+Q8QB83QigonVikBJeNzE25k51RBshIs5E63wtiOZJWF+EDMmAsKBmHpic78zoTsw0zbk2x9u58oOjHALAvG6GidwBFJtCo1mB1WuU6B1un3m6jBxz6L3iMyQuasmsd9tJL5JpEhKDkpCQkJCQkLDhkD5QEhISEhISEjYcNvUQT8biItnVYtLwUGhME7MBjgkSGZOt1P16fiFaMWYQwpaEssC/Ou8xHD0yxNGxosuPVYUN+TNCNjOUIOBf13B4r5R+KCEDt+c+Y9xSmgUXKDlHzpxo1hdy+m00IcyAssLmkgE26aSzpK/J/kK4+l1ocwh3bBKSCFKZlGDUc5tLcGN1z9SJsHvtUHNnucDzz30cpTaa+uJju9sLQwlYHzxwRmeZNphwYDPUQwW0FEKyhuU3NXZrE/I9b8+3Vt22WeHpyZjthEIino6k4AJlMMxDBaJUWqt+t1jn0A5FwaR9DKkxWyykV6zDaGQofAUCEXpQts36gzNgmhHfxKAkJCQkJCQkbDhsagZFiYVCcej6fEFSROtv6XFx+L3vNhGtKRsuo5gm6RrzpkMzn5Q8cDzKsFwWOKJDi1dqbns/Av41pmHFMXDS3WaQyJm7zjkxcZNgyKRArctnXIKDmq+5EEHV82K+yJpJF3YMoNYruVRiVtrrtwJVdFtLU7GohDKDc8n4oNkUYzTHnB2+zg3v3ZHCFo0mNjTtC0N6Q8v5mPh1WnzlydNb2RMDL4Q7Ahbp6nEu8AN7v+ktkyRpoZDMC6+exbEkbBxk+hmM3TOx4AcpGUq7XjMhgH5w+v9QZQxEeO/CjKPeiWxyWhRbdsL6LpbEloFsLGuwweRYk0g2ISEhISEhYVNjUzMomQ6fDb+yvPG4ju1nybSEdYU6FapRmdZczYAFKblDxCyVG2WYbzp3qsH01g8vzeHQyhyOVMa+ntnkkCasmF7TSfeKHaNlDBLSSEUgGTFqgoBg3M5LazRo9CrSJepTaSXBTZugWRWii7Fj2+aeshopaVkRweMsCg1PpyG2sva5N2Ump/bEcwGpC0tj2kbDjCW8ZIcmQWFdcVQVR6V1TyGrsJ4sQx9TxC4jxXBeSpUGIcsMq+QnMGSCoYYzkks4uWB/Twj71mUbUUpmWZNp3rlHqgwVuQ0VgyLdtF4e+y2RknUyDw1LipZyXSxIjC3pSjmTGJSEhISEhISEkwabmkEJ01eHiH1JepqPyBftrFiVtlTUndtg+i/GPmyMBGt83QrdE17IawjpepGTzIY2K56160nUguPIyhAAcHg8wOLYRe5IMBK941gUO99RN4cbTRZSQjBGbKtJFIxQ7InkhoVQDErOpd3WmIPlELonrq3twVRyPGIrHWqbbMQPiN21YEAmvUgdP3lfByPHJUTt7jFRATxz9cRgGBhRM1SlYkxWVooTosX4h2/v9CJzQrSxkfb8maguco6ULkUgM6ZZmdAaFH2Okv7kpIbVGhH7egrKclP2JIbFMpuYysTAjBYAk3Vlk5LItqVzCdfFlvdlSVrrwXS/cSfnr1FCQkJCQkLCpsamZlDaUjcLsh7wv0R5pByF1Y6sYyRQmz19W5so+jAmRmUuSK/ZRJoIKRtf0GqdqnfAxUnFohg/DCkZKsGxonUQK3WGWvq6EwMhtRW1npd6OnZPcKZ9EYySXXEb9nwrB3yzoS9MEtrPwGgVMiaIaImDSwljPCKlVHoH5u4dy6ZobYqXoNJO66gDr9X9bm4hGDKSSJA1xF7w1onSaVDqiqPUDMr4BHrufPkJn8Voi+wx/iltMEyMrBmkcMdjnuVaM0cyiOJJODlhrzv835mKMK+lYCiChKCL+pkw3kp9om3arOSByQxDV7RNFwMSWx+WaSTpbWlD+Js1jQZzU3+gJCQk9EcNgfu3Po6DgyU8tZzH84/uQZZI1ISEhA2KqT9Q7rnnHrznPe/BAw88gMcffxx33nknXv7ylwMAyrLEr/zKr+Cuu+7CP//zP2PHjh24/PLL8Ru/8RvYu3evrWM0GuH666/HRz7yESwvL+Oyyy7D+973Ppx11llTtaVVfaz/72JSTLlWh9eOTlAfdiXcPpY0cC0RPX3gdBGOIeGMWf0JhWvvOpvIHEc8c+chm5hPSIay5ljSUTvLNbcpzw0q0/vXy42KXqI9AkRCuQTnhokx/iXSnW9zRiup9CjmXsh1vbn12yB3pEBIhWi/E8fUeIxJEOFDtVlSMHxi1z/jnWffh8cHx+zyM8dbcOPX/xVeunied0zGr4Rzxdpw3QtkHGB6mmcAy0mywEr/r9sgBUNVqZVfPXRa9NydCNDkgYZN6eMvRJmYC57ybRuR9DePP3XGLUzYDKDPniTPHUWoj6Sak0n3XPjb1sWa9GU6Qh3JahiUPu2bVH5dNSjHjh3Dc5/7XNx2222NdUtLS/jiF7+Id7zjHfjiF7+IP/mTP8HXvvY1vOxlL/PKXXfddbjzzjtxxx134N5778XRo0dx9dVXo67rRp0JCQlrwydP/2f83HffjceLY97yA8UxXHven+GuHQ+foJYlJCQktGNqBuUlL3kJXvKSl0TX7dixA3fffbe37Hd+53fwwz/8w3jkkUdwzjnnYHFxEX/4h3+ID37wg7j88ssBAB/60Idw9tln4zOf+QyuvPLKVRyGjzZWJOYjMkmTEkNX9M+kqJ0wUmeWLIrXoyZuhz4DoPQR5mtZRqI4+qYP3+hgTNref11yLFc5jmkGRTnH+sdu2BTFrMCL6gnhtEpMuUvq5ZkuH4uT4YxBIPAyYdLWz2D8T7S2RUcEAQDX04ZsEZCeR0pbNFsNgV899/+oqx1capPa551n/SVeevQcZOBgTDoGJRPIcoGs0P4LQwle6LYOFIMSwvqgCIZyg7sUUzZlGoS6loRTD/adwPxl9H3KGTASrPGeoeir22hzcVVNaGc+mnrDfkxJnzf/pGihWWDdf4EWFxfBGMNpp50GAHjggQdQliX27dtny+zduxcXXHAB7rvvvmgdo9EIhw8f9v4SEhIm4/5tj6thnZZ3pGTAY4Nj+Kst34wXSEhISDhBWFeR7MrKCt7+9rfjNa95DbZv3w4AOHDgAAaDAU4/3VfN7969GwcOHIjWc/PNN+PGG29sLCepP3phmlw202JavxPAd5elaPsy7cu2hG6Hqn3Ga8OPMtlseNqWJXBIPHpsy8SynEnrZrpcFlipc5RGfR/oT8wy9b9S4PfJQG0zRnvL4tOm50KvISNl4j0Z6U2bbTkDmfbr5cQT5VtzSy1H4eOJuaMoyhqMO9YpKyRYJpENdVtzxZwAANONlVqoIyugXmYoR4o1GY3zDaU9SUiYJVz0n7TTtWSeS3eo9ag7Xt2T3F37epdM8iZZTRRODF2/Q5N+k6f5zV43BqUsS7z61a+GEALve9/7JpZXYZTxH80bbrgBi4uL9u/RRx+ddXMTEk5KPLVc6FVudzW/zi1JSEhImA7rwqCUZYlXvvKVePjhh/G///f/tuwJAOzZswfj8RiHDh3yWJSDBw/i4osvjtY3HA4xHA6j69bCikz80iMfiWvNkhzVrSD+hdh1TF3r2jIle5oX+FE9sbaZ3veAq5iR8jhoUfYuLHmRMjT/Cf1ulRIQjOHsLU7wKXQP5hvH/B/jLz+xy/qgCKl8Royz49j2apyOw/RwqAcKhTkLsbwYmZ2XXpZtbxomE6ljOmLzgMt0nNmsxIo9sfOMsCmAXifstiZXzAuWz8CZ4wUcKJZC00t1biWwt17Aj8mnIF+owTJptSW80KyJnmfkRpFCQlaAGKn58ijHscUBHvjG7siZS5gFThuMAQDfGQ9OcEtObawITjR83P4uGAbFwGj9Yjo/gz6eJl0urmuJxAnR9c4L0Zf1Xytm/stjPk7+8R//EZ/5zGewa5cvKLvoootQFIUnpn388cfx5S9/ufUDJSEhYXXIwPFrj/8rAED4DjHzv/HkDyU/lISEhA2HqRmUo0eP4qGHHrLzDz/8MB588EHs3LkTe/fuxU/91E/hi1/8Ij7+8Y+jrmurK9m5cycGgwF27NiB173udXjzm9+MXbt2YefOnbj++utx4YUX2qievmiLP++LGCPS9rUbeod0fRUDzS/Z1eTmmRZtX7UC8LJvqva4+fV0ze2DPfPLqCT3suoaZoMxFcFiIMAACeTMZZPtQlGo0PXtXDSyXAuZQZCoE8rg5N6Yr2YsyDpzbnPmsyIhg5LT7bjKvWPXmXnCkjg9iWJ8bO9J60qYtx+nPaGsCecCea7ZlFzg5aOzURy8BG/fdT8ey50m5WliAe868oP4SX42sFUC3Pc2YTnzxbXSaU7EWLEn5ZIqPFrKsbScevbrCcOcbC9KHC6LE9yaUxdKZ+Ii/Mz7s+096rQojmGh7GcbYhE4betWgz4Z2ns53WK6kYxpyk79gfKFL3wBl156qZ1/05veBAC45ppr8M53vhMf+9jHAADPe97zvO0++9nP4kUvehEA4L3vfS/yPMcrX/lKa9R2++23I8s2dlhiQsJmxcuWzsVVS2fjLxe+iW8WS9gj5/GC8VORca5ioxMSEhI2GJiUEVvRDY7Dhw9jx44d+ND3X4+FLK5NASazHMDsc+7MKn8B0D2u1+crNDa+aXvdwXwMhlEwJdZLi/LU+RXUgnnXyz9nbrqW3FufcWnZFAB4bMnXoZgcKitVjlpwHNM9z6NlgaNVhpVa1bdSc885lsGxKIxJy5QAoY5EzefBupiuxExTlqQrMofmmjIMitGZMAaPMckyCa6ZGOVdotdlErwguhLtAGsqZpxOa8aEWjFHWBMAqEcM4+UMoxXtzLsymJjP5lTDejEd24vSm09syvHD0Yo3mIxQgxLTjJj1BpMy13fl0Glb36euadGXnZlmH0v1CK/54m9icXHR06dG6+1fbUJCQkJCQkLC8cGmThYo0fSyCNf3RZdb6DRwOVgml502n0Gs7Pp6u6hMuGYPGZPe+Y45864GUjswhgp4A6/nIf31tQBMxl8OiT3zyziw7EJmv3lU+aU8MRpiIasxl9X2WIbcH2E1jo+GFTHMR8FllCUx00ZLAjhGJ7MsiRs7DnUklB1xx930NAE0E8IkuInUIYyJcXzlNvpGWMdXnvmurw2WJAadcUJWUrEmusNeLzOUK2o8qBxnWFkpsFyqih/6zo6OCk9NHC4LnDYYzzzq5nBZYHtR2nsjaVKOPyhLkulp9W5qvg/p71BfvSRlYiRUNBDVMdLpzighdGsM27Z1zIm/vu13rcvjpVm2/29GYlASEhISEhISNhw2NYNSCoYiEsZxvKJSYl+T5mu3qyz9KpzkINiGSZmau+LrV8t58IBBoc62q2FSnjq/AgCoO3JVNNvQXGZUVDVUJM6u4ciue3KkNEpHStXzLwhrQlkUDkBkbnnOJAruWJKCC2Ra65IxP/Im41QbonQjGdlPyIaEiEUiMR6wLTpKx7q85sJzfOUFyZNDvUtyBmS+h4l37oR0N5N0rAmgmBMxYihX1B1UjTOUpZouyxzLZY5qg+fbOVlxuCywJVcppE1+KQqzLtZDpnm46LsqVk+CwhGdvXpbrujFpdryn74GBbCiPRO108aaTGRTvNcF8xxpzXULf+uaj/lq3/bN7TibjilpQ2JQEhISEhISEjY1NvUns8mnMgvGJObIN/nrtImQPWk4+6Gfe+C0mKRf6YrWmbQd9QcJdSh0/9OyKGH8WB9letgewEX2VELdD6bXaOYBYC5TLrLhsRityID4pGRaQ2JYEsOeFJljSWhkjtGWqHW6/Xz68+2752rn2IwwKLkA123IColsqNcZx1eTJ8fY20IzJxxed00K6XQmQkJqxkRWijWpR1rfUzKUo9yyJlWdoarIdJ3hX45snfo4TyVwBuwcqhP85Gi2WpQuxoOuW8jrqA9T+IxtyavEokRwpMosc2Jg9GrUddrqRphjN9ai0auDeeFdL62XC8qs5+DBLNgTAJ3ZnUMkBiUhISEhISFhw2FTfy5XgqFcZVbeRt4a6X+t0SiSSZqSsEwzt82qmtiJoFMcXe/2T3rmkWWew2qPr+Q2tmYtUT3NHBJ94+8lvrXS7oVjMNSMhqehAbMeKgUXVp+SWe2JXpcJ5FxYXYliTXS7JzAlcoreguAhi0L0K5lQuhSrLZFEZ2IcYCMRBEICFWzXSlYSUsCyJqJ2UTqi5KhGHHVldCa8wZrYjM+CH5ccTZsdT44GlkHZORzPnEXpg6Uqw4JmAEJH0hDbigpHyk39szBTGO1JCPPuLSUCRte4KOlyq2QdmlwJiBt4vBwwHcuxXvlzDNp+C6rEoCQkJCQkJCRsZmzqT+VSAsUUH4Fez0E2x/BqkPw1hFFxeRfiHierzYWwVkz6uuxiTtp6UeFyNa5qzglT02vMgWTbpPfFIV3dPWFYDuoi2wWjV6LR/TlXHiYAMJfXhEERXnsMexLqTABA1P4JE5J5PYe2DM2qniC6RzAYUiJj0tPoSJ2BWtoBb2YdXs0upO6uMXJjSKG0JdJ6mxjWRBWqS2YZk7riqCqOSvcaq1qxJLVuVC0YyX/Fpoq+OpVhWBPDpJwIWF+e4F0Vi/LZVqgIoFOJSVksM3uOJuFI5ZhEEbxTJByLEWrlZgVJruGa3sGR9q3O+2uKXUL9bvdFYlASEhISEhISNhw29SeykPGY6raeuIofD+qw2+gJwqzUtozytrDupVKxKVt1xtil2uVnMNE/1AVQSDfuKwBw2sOOtHWar8ZWb42Ocl0MUNijoq6FjX2j+QU/jS/KweU5O33G3EpruW+tzDWW7V1QWXkzJvG0LWpa9fb9KB4zXeqIHqqbyHmNLbkSYDx6bItX/9NPW3T2IJKhFhySMEkg6wB4ZcMyYXRO6IMjLTMjbd6+GhycSVSqM4tMSkjBbBSPFAJcsze8lGCZbDAnqj2ArJhleuqSQwjCmtTMeplUlWJLzDNV1RlqwVQGaV2XIAxKQj8Y5oRBYtdwhG+PJmum1gqjOcmY1LoTx5zS51nIdjfRbUXlvRtm6VZrtB1GE2Pfj3p3C9la+dn4/gDnZfLkWP38FVx5HsW0eEJKSLBWLYoMpmOu17MAbZvn5j2hTTF0PblUwzJt5Gdfxmia85IYlISEhISEhIQNh03NoEjEvxbNF2bse67tq7DxVUedXyVDDT8OnQdKbcqYqGWUsWD2y5RBNur2dsukY3V6tL3tm7UvY9KsrzlG7bb3tSKzHGP91spcJ4tC8bQtS+BwrIMJpFGROK4c7fGXmhkw85xJzGU15vV4ewxGe1ERx1xXd6g9iS83bVT79BkTQI9bm/uVSZjk4kwYJ1kTQaPcarnJO1T6uXjCYDZJ2iNqhlo7Xwqh2CATmUMZk1pwzRY5liSxJmsH1aAYFgXAujIpRptFs2kbOE3duu0+iqP6niu4xMDovQzjqu+tjKn3t3FqXSuTslg65iN8D8Y8kADzPqfvd/9Ebc0FKltv04drVtwPff8f72vF2XSOr9NgmkNJDEpCQkJCQkLChsOmZlCE7M5FECNF2rQBfvS6z4oIqC852tMFXI/AlPfbEf8CV/kZSGHW/pVo9hu21RxLbLnbjz/fxZrEInfodl3ZMk37YlqUaT1RYlqTNsRi+DPba9Rtku6ahRE4HBJ5R++sFBxjrc0IHW+nhckHLaXyT5HUY4dJq3UKtSqqnW6ers+48LxYwvvA3OdSKBbEsEFCRzOZ+S7GxCxrw1rPy6mGJ0cDL0/UeuG0wdhGpJl7xFwrQZy3hWWafRZjPbAiuNXsFUEWcXVP6mndnnpGbaFsNH2vHSoz641kcm659U0dnmurqy/cRwxdz0iXhde0OdlmBfs+b9MdRs7FtEgalISEhISEhIRNjfSBkpCQkJCQkLDhsKmHeGh4Whf6iGXdkEmkrClDaPeuYZHmvlxacyrAssvIfiQRZMa+Hulu1zK809eQpy/tG4Ycy0BYupakWdF29aiPMydsNsMsVFwrJbPDOOdsPeptO6oyG5LcJQamtvd2WSNBpJqvwcDAGnbUYXmD2DCWl+zNE0K3ib9D4zgtnE2hwyccZ8ytTDWs2YXTBiqUOWNSJbckKRhqweyQSdfz3D0MvPrxvFIybMsrO5QS2hpUwjeDrGdofXl6oQZQqVj2SKWGd6hQV5KkfxRhWLYZpmobWgnfg5MysXQxBGHodd/tVzvsM9n4k+xjmmGa8P04xWsmMSgJCQkJCQkJGw6bmkFh6DadMYhZvrt1wTytn/ZQI9vQ3sisvvm7Ejj1/fCkAl9AtTNs3yzC1jiTnewCZVEmhemuBbbnL5myzYcL67XXSkoIMK9sJXz2gAraasl7naNaqjBsZvepBH6eHb41/5PRGLtyFf2ENtZlEmIsSZuQr6v3J+V0ouYEBfosCslsaP1qzqUR3FIWL2dKPM0tO9uHaZy9bYDat8K2vLJshQG1Y1AshROzCwCzzmO3o6gti8KgjNlyK5jXhpkkGd80SfdcIIN6RxuuZlIVfc921vUcdrQHmPyen8RmtK3OWDwIpQ+mucsSg5KQkJCQkJCw4bCpGZSMYWKCpy6GBGhnLGKMid1mwrcxLU971+E8J1/w7XV1hULHQ6bVurCe+HfrtMwPZU0k/N4XHX/tYoI40Ogi0fZTO/oYvnFsoXXd07YsISPH5BgceMmxusL/aChm23p/3l1Ly6jYxJJEC6CZlXDfq+m5Vh13TZ9ruhZNQcLq8eRo4FnfrxZPnV/BkCt9RVdIv5SKRbFMoWx/b8yaSdmqLeUHvG7oF9pMwBjU+8GwG5VsMtX5Kpu3Q+tRjlYcDNJL1imkM6QUUlrWIjRtC5ExVzamY+zWJvZr9yQWpGt1F/vShfVUo03TpsSgJCQkJCQkJGw4bGoGxSR5akMfhXSIaSJe+tQRY1Da2heL3qFjs4D7Wg53Hev10N5R+1c46/0lbxTtfr1q44ypJOCz+OKlRk6T2JQYbOLGwO7f6FDUOrXcMHA8WMdl8xhpXX7CNYZaUtM+l3isof/p6O7MyigrrGeanvqkqIOEtcNY30+Lp84rvYpK6SCQkefE3KO1YF6Uloriit9b68miLeQ15jLN8ESM4Bh5k6lnhTCxXroPRtaZOtbWbqNN81hygLC6zkzTGGuG58+yOOT3x2juvDfWLJ4nNskMbm076WK71wPVFA6PiUFJSEhISEhI2HDY1AzKgEtrVxzDNPHW01jBd5VfreYlVnatmCb9daw3FfWEacS0x1maWY1jD7jAeAoWhZOxZdrDrAVHxp02BAiYkKC5Ma8QFxHhe0xwBlTCrGGQYHaMnbIpTTatWzcU6/WuRjPUqKMvYxZGfiWflOMOE+XDmEsAaNgTz2eIpi4gSTGN/oTeW5OYkzYtymmDsb2Xu1igjEnkTNjUE7G63L7MM+KeLaNnU3PrYOeutYs2/QV8bV3oZUKxIjjmuMC23OlZ7HPK1NNPn/dJmIld/BoZkGl+J2eBOjEoCQkJCQkJCZsZm5pBmcsE5laZjrvPl1mfCB+77DiM43V96LYliwvR1sqYqt4mxmpE3LS1oX2baWDGoc106KEAIMqqnL3lGDIuLXNCS0g9rs3pODhR4DfhIiNC91WKgisWJdM9wFJwT48ivXTs0nMVVm1wdYURaTLS04ixH6v1REnYeDCMCX0GAMXimcitTEhk5OauhfPsMeyJY/DYRG3TLBLA+fVJ5Nx3WOaQ5N3g60r8BKrquL1mmHcT8xOvrhYMSieSkXcMdVymbxuje6HnZanmWMhcYlIRsLLUMTfUq4Xg3O1nUtkY1qJbW0sU2VpYFzEFJ5YYlISEhISEhIQNh6kZlHvuuQfvec978MADD+Dxxx/HnXfeiZe//OV2vZQSN954I97//vfj0KFDeP7zn4/f/d3fxbOf/WxbZjQa4frrr8dHPvIRLC8v47LLLsP73vc+nHXWWVO1ZcAEhiat+LQHotG3pz+NDiVE+JU7jceG34Ypxu46XF7p8knfshyOXTH7Ny6Lsx66NPWv1pvBsCdh7iTA+SuY45W6x9TKkoWRBEwSbxNXjjGJmrneF9N6lErEGCnVczTj7UK68XpXn2kfdI8xjCCKsy+T9CJ9O56d9x/8SKdZ5pI51WFcYb1nM7gW314ZAgD2LiypdSRyx7B2leRefiWDaSP11PTqetgMhqUgWq2+Ob30/mlp+4xG7s3VOFQr7xLfz0lAea6oecpAqfef0ZyEWMgEjlaun091ZrnWunQdep9z3P3u0+/HibWQfU5RtrHtDEYKKrGODMqxY8fw3Oc+F7fddlt0/bvf/W7ccsstuO2223D//fdjz549uOKKK3DkyBFb5rrrrsOdd96JO+64A/feey+OHj2Kq6++GnUdvwkSEhISEhISTi0wGRvo7rsxYx6DIqXE3r17cd111+Ftb3sbAMWW7N69G+9617tw7bXXYnFxEWeccQY++MEP4lWvehUA4LHHHsPZZ5+Nu+66C1deeeXE/R4+fBg7duzAx37oOmzJhxPL98t4PP1p6PqapF+9lEGh7Mk044euVzO5THP//nIBNMZbp3E8jGZZnkKvE99HwHYEzIknWifrV+oMFN972qLvI6P/l8E4878c2dqzZZPxPTsOez4U49plQm679oCJJhCNPCqAz1bEwD0dS/vFC3uUkyJxJrnctm1/cDkxKccLexeWMMgE0Z1w1Jqxo9qT1aLtng3fA22RPAUX2JJXGBidhr6XTRuppsNoZARhLyrh3sbmfWWOTUjnQdLloBtiRBjNjPnRn7VkqKQ7d2Y/pj1t7EmIpZorby4TRaiZGgOfGW5uH/4Gdf0+zEIr1AezioSlOFaN8LL7b8Xi4iK2b9/evf/+u5+Mhx9+GAcOHMC+ffvssuFwiEsuuQT33XcfAOCBBx5AWZZemb179+KCCy6wZUKMRiMcPnzY+0tISEhISEg4eTHTKJ4DBw4AAHbv3u0t3717N/bv32/LDAYDnH766Y0yZvsQN998M2688cbG8vALdRpM9gJw06v5Wl23DKE99SsymPeYBFJPqFA3ywwYZGdmT3uejAYjcM2lfY+ur2E6KsnR3ytyLqs9FuWh7+zA9+xwH7D/tOi+0L9r29GetU6Hf1rcju89bRGAy9thrn9FI3pkM6qiltx5s3DuHGxbTlYbcxL2dMw1DXuZXRE/IsjCLGW3loniqfMriUU5jqAOy7VgnjbIXLfVwmdNpn9/lYKjFNw6Qgvmu9nSfEDm/UPX9UWTHfTB4XQljTZ6Wh/FmlDdSdielRYvpjkSYbiQCZRE12a8VjzNWpCHLVzug0RxBWxKtorfp9U4TK82Wqer7ml+s9cliocFN7WUsrEsRFeZG264AYuLi/bv0UcfnVlbExISEhISEjYeZsqg7NmzB4BiSc4880y7/ODBg5ZV2bNnD8bjMQ4dOuSxKAcPHsTFF18crXc4HGI4jGtNVhsHXkvW+ZU31Zd8pA1tWpNZua12NY+O2wKONaH7pz2E7iij7jbWXm/LlTfMi8dEteQkAgBOeh4CiCr21XY+M8WZtDk/AKVJoawJxSx1JyEe+s4OO33e9iMw/bmM5PgxDIob6+a6N2n0KtLek0JHQVi3S306TF0cPjMiWu4IpzWarGcJdS9UDwNobw7bvmZ9iUVZX+yZXwag7336Hgl7xmskbSnL1qWZ2zkcWx3K9qK064+U6mfFRBYxncsm1J2Y+pvr3PM9rcuV936NtNkcRy39bajuhGJHUWMk/N8KCeYxJxTGYyXWpi7WZDLjFWcyZSSXWttv1yTGZJaOsvG8S9NjpgzKeeedhz179uDuu++2y8bjMT7/+c/bj4+LLroIRVF4ZR5//HF8+ctfbv1ASUhISEhISDi1MDWDcvToUTz00EN2/uGHH8aDDz6InTt34pxzzsF1112Hm266Ceeffz7OP/983HTTTVhYWMBrXvMaAMCOHTvwute9Dm9+85uxa9cu7Ny5E9dffz0uvPBCXH755VO1pZK89cu3H2bzyRj/sp1cBoiPm/YpF0PXmGzInFAw+Nk6u/Q3befaZTp2+2HEPVKVoUyS7Pxidx4k4f59h03FhJn6JQbc5xLoeY+55a4HOJO24VKS/D9MXSNu2iEAwLmAloKTzK8SGROOMZHSP+7GeHL82CbF6DE2ObKHHpfLV9IdZZSwfljv8069eNq0dOaePW0wbiwDgGNVDs4qACpSLcaamGnDmpj9hO+68L1mdCV5x2no0rTEWORpoisn5W2jmefp+0lIILfOsTJ4H4b1+AvC898VHbj6UYVVbdYD4fXrz4tM/YHyhS98AZdeeqmdf9Ob3gQAuOaaa3D77bfjrW99K5aXl/H617/eGrV9+tOfxrZt2+w2733ve5HnOV75yldao7bbb78dWZY19peQkJCQkJBw6mFNPignCsYH5f+56M1YyLp9UGadCRPo51a4Hvvt3F/HVZzGyTZWvu92AKKK9ViZMNqHflPHfD1WOxZJmZjjxaAAsJFEdaD+N3l9oNtjIh7UvCvLtP7EKN45k2vWFhjwqca+fUzyRaF1H1ieX0XrEmJ46vyKnQ7f2LO6L2J1G4R+JW1lAOBwWQAAthWKQTEMbSxyMKY5kQGL0rbPkEERgBeJ04YYuxKW35q7FoyEr/MomPSig8J20CiVMDqRexE+bl34ruzrdxU/lhY2tb3KVaE9Q3WXtpNhqR7hXz/wX46/D0pCQkJCQkJCwiywqbMZl5LZePaYUno93faMxoLOb1RMex6cDiLoHU9xiJOU2+H5U5mG9TryZe5F9qwBAqrXcrxYlLZIIoD4sQgANBpABN41gRsxa+neTsOCGM0JjcyZhC5Pny5fFRN1shom5WlblqLLpQQeW1qYur6TAZKwDl3XfC2ceHvvu/s+odsZ5sTAPHNxHxRfdxJjTyhDQf1LSgnr2gpoltRMM7d9H3RlEqaus4DSwPjRiW7/LGBPau8ZdmXNNaSxLqodavu+ehCq3zHz3noy3dd1N9xuakzYTzlFOxKDkpCQkJCQkLDhsKkZFOr+55Ydv/2vRauxkbEWNmiSwj0GE/0Ti9zpyji8WWH8WM7ZehQZ4LEoRodCe5lmPnT1ZD1YkLCnLaUfudPFgLh9r4112jO/7LEoexeWptZNUFbAsCvfOHbyMylnzCntSZihmLIo68GYGKzlXRCLlomW66gjfC8UTAYsipo2TErI/sYYdaOJCddR3QkF1ZuoKB0yT8oxy344PU3oy8KNJ5IuF77beIyljLz/ujLSy5Z1sfN/Ipj/aVjsxKAkJCQkJCQkbDhsagaFxq/P0gVvrejbFuob0lVm2nr77LcLa3H+C9HW5jCKp7UtU0b0dPnK8OOoQemDR45udSwKAMEkhDQq/8nX2/R+uh2Ru/UKVI8SYpIXQ19wJrF3Ia4paUMbK0CZl5hO5WRiVQx7AqydxaLoqqtvxF+XL0dsuzDKhPqecDSjXbxtyf+GRQHiWgajVykj7aa6lND7aSGLsyeWvWljf0l72zLE0+fUngddX8iY0NxlrEP7RdHGmNA20HY0jqHlGnc51K5F6zlN+cSgJCQkJCQkJGw4bGoGJRwPnBZ9Ik36lFstsh495D6+IrPe73qh7VqFX8mNcdloXS3noiM2v2zJSHoiwRkAeyyu/yPAIGW/TMLd9fvnKcyvM6l833WzxOZzZpo9dg1HjSgugz7aoz6YzHqsnmmJOZ3SyJ02hNm3Q1DGgupRSsm8iJ4umIidtnw6XllgouE41ch4TBBzbA1d1her9dualItntdGZre/vKW/DacpvvDd2QkJCQkJCwimPTc2gMJL9db3q70Jft79pdCaz2n/ndqvbbE3oE4kzFVPSVgfzWQez/UZkTwAV0XP2lmN2PtOeC1xnQaZ5UaZBW5RMyJxMOr+Tom1smiE2XRtNNFFbfdNud7Jg59DltllLhMU0eoNJepK27Sbtw2eA4vublHcnpqkwy3KGVtYk1JhQFExOlYYtZEZMO0IINN/JbRE/02IaX5JJ+pD1/M3sg2n2vzHf2gkJCQkJCQmnNDY1g7JWDcp6YqpxtuN4DH2+SFfrOzLt125X752ekliprlNG692ozAmFOd++g65mO0xvdEraYDUak9UwE3Sbk5nZWG+cNhh774Eu9mRaF+tJ2pI+kTt9WJVmvfF1Xe6m02gqjhdiEUb0WlVB++gbRwTzfd+t07i+dmEj/j4mDUpCQkJCQkLCpsamZlAS1gfm631aJqXLy2C1GAuOgoc5c7thym6WYJBHjjpnWZP2Y7oj7naEnTVrsp5YS3tOJg+USaAsSpfeAujOM7NarIY9mYY56cPihLl4KNQxd5+X1SJkTLoQvgf7vlsnRTMZrOW6bkR2JURiUBISEhISEhI2HNIHSkJCQkJCQsKGQxriOc5YL8FXb3t9U75H2bUk6uuynLdlAgqTDkWYqUKbKQUO0dFtwjo3g0CWwgz1UDx1fiVScnKSv77h2TSsd6MN95wKOG2gwoqnodtjYZptoZuxpHl0AEbIpuC2LcFerD6XamTtpoJt6BLxjvQ+h7z/e2oaU7fY+zKfwWG2DfWEdvnRNk0R8t2FWk4fctyV8HXabXrtb/WbJiQkJCQkJCSsDzY1g2KSBXYlNdrooGK3SV/B3Unh+u+TpjC3y9rat8ZekU141adwx75sann4LMqkZHibHQeX5wA0mRRlhe/mbVgyVm9JH5qkbTZG5WlbljaVUHZ7UU5VfjUGW+E2lCFR787+bIoq07Xer8vAsCvmHSPQDK2n62YBDvO7oI0ap3yPTWrHSBjb/mYKgj6MQRGxFqCg7+Outq/9t45ep8ml64BR64OuxJGTkBiUhISEhISEhA2HTc2gSG12L1r8sdcjxGw1mPzF2Led/Y+n24a/2aAwydXMQHtSE4rGUo1H6yHrGFxv7VRgUmLYORyDSWlPkQBbM5sCrM5WPvYoricTE7bxaVuWAGzskOMYcxJjgiliPdaua9tHDxLTkdBtu9gXVa4/m+K1lZq2hYlBzboe+/G0KIJFdSjmvRYeSyxUOPf2Sdo0xXUZcpfA0FxTw5ZMy+IYhHqZFaKtc2kFJrc1DDcPy9Zk3WSGpLmjvr8b0/wuJwYlISEhISEhYcNhUzMoBn2smvvW0VcPcjzQpa+Z9LVad5yD2Ka0vq5tuzDxq3uaUxqOTbf0wDhzV2q9ogg2Op4cDQC4iJCMSXfi5NpZlLViUh19GJZp27FR9SjbispOr/a69NkuZEMMYhb5bUxKuH13tE83mxJL4AnoRHy0jUxCAMjMlpIwJrYuU49/bG0sijnGSb8L0xiwmf0YLQptA4WQLtLIKyf9c9Q3qggA5rjju49WXI8iqPnw3b2jqLFYqrPJmX8Nw/PhX7PpGZK2kYxmuV7F1D77F01ISEhISEhIOD7Y1AyKieLpiy5WxNWzsXrhbccXjj1STPrSDXseQH/WpIslidVL0fcLO9ihAmFUQjbF9Mg21pU7/vjOeGCndw4VmzLgdbTsiWabPM+bNbA0my3SKHx+1ttuvMsnaJK3Sbh9uG3b+zRWr4rkaZYVkinGT0MiYFU0owI4VoXDeIX4bABnzOo8YozEajw8QlRSHVes/mlYdwb/fKzGzwUACi4xFgy13n7noPLWPznOXSSlVHumrEQbcx57z3fpVzjrd/xT+65MVTohISEhISEh4ThgUzMoMXSNNca+3uhX/mbyTzFYTQ9sbQmm2ntNk9wEwy/sPl/ToeZGQCn9PX8VEsVD9zDgAuNN5iY7K3Rdi77sybQJC6fCNOPQq9RqbEQdCvU96gNadhZRajFGpQ+TMrneZvlJniqmPbHEg1mgU6HrEGFMORgEeQcZPUkv19ceZWiUI4OL1Ikddx9X1bZ3odGw9GVShlyiFBLmIA5pvQn1V6HTOwcVnhyrn33GZMdz6N8LYTtDNqVv1NO0ODXf3gkJCQkJCQkbGpuaQVFjm/HlFDHlOsgSVw4d5TYeTgTj0/alHDtfkxTjXec47GVSJsWwKHad6W0FfgsMikUBcMoxKS7FPfMieSaxJzHWZBaRPCFqsE79CPfuHb9gqF/ZbDoUg0k9zS72ZLXH3Oda0mc1PPdtz+zaWNlwvluvYlZRrYpgUr0TAvanD5MyjYuta6tjEjLmlhgtjFlHj2NrLnC04rae8P3JIKfWoJh6R8KxYEI6PUklfQbFsCcGAt0sRchgh9Gupsw09/I0OLXe2gkJCQkJCQmbAjP/QKmqCr/yK7+C8847D/Pz8/ju7/5u/Oqv/iqEcN+nUkq8853vxN69ezE/P48XvehF+Pu///tV75NZT1n/L2Pqz3ytcga7bNJfzkTn+kn77PvXVs+kPwDecYV/GwH9oqbat41tb6OX0OzxCKgelfmrT3CkyokEPX99tQvTsCf0PK/mz9Qd+zNtMX9t+6ZtXA+WZz0Q02m0/XHm/hiD9zctYufIXgcYR27T62fNc9zyPPba9yq2i73LaLQeI8s4k+BQmgoOl4eHbltJ/y+6z1X85cz9FUwxIHNcYI4LLGTCa8PRinvtWi3rNODC/hnMZUL9cfW/WR9jjhiTVsMXfgBM+j2hvz90my6sJXJt5h8o73rXu/D7v//7uO222/CVr3wF7373u/Ge97wHv/M7v2PLvPvd78Ytt9yC2267Dffffz/27NmDK664AkeOHJl1cxISEhISEhI2IWauQfmLv/gL/MRP/ASuuuoqAMB3fdd34SMf+Qi+8IUvAFDsya233opf/uVfxite8QoAwAc+8AHs3r0bH/7wh3HttdeuuQ2d+QhalovodnEdBODGHadF2JuY5muS7r8rImmaervdBKfHasca11KPGWNlxDMBUNfa1HKqRfRYls2Ola/uwjIWZydMhtrVYpKuhO6fsiihNmWz6VGOVTlxk22e2C62i3c8E20RV22siV1PXEjb1h8PTBN11laUeiQZjVpnHh3v3okXDHOCTcruXklY5rbuaENbu6gTLT33cwFjYlpVcIFScK9dBZMw1TBI+0yEbXcZnx3a3ruTvbVmU0+j/HTFJ+OFL3wh/uzP/gxf+9rXAAB/+7d/i3vvvRcvfelLAQAPP/wwDhw4gH379tlthsMhLrnkEtx3333ROkejEQ4fPuz9JSQkJCQkJJy8mDmD8ra3vQ2Li4t4xjOegSzLUNc1fv3Xfx3/5t/8GwDAgQMHAAC7d+/2ttu9ezf2798frfPmm2/GjTfe2LrP0FugixXoo9RuQ3eG4H51dDEDk3orfu6L/nV31bsWxmRqV8Ce+5oVC0M9UrKeOoyTAQt5jWGmHGQbvhcz7BGvlUWhaMtIHbIiNFOz2W6zZbGmLtAhE9t2PjkkhD4RORNe2fCa9mFNwrZMKjcJ0/pItTtkt+8ztgkt73mVxJxkuxo04R6K6d3Ceg17Mg2T3fWua3+fAxU57ozJTr1dmM/MzJu2UU2jt90aI3NmoYWcOYPyR3/0R/jQhz6ED3/4w/jiF7+ID3zgA/jN3/xNfOADH/DKMRY+VLKxzOCGG27A4uKi/Xv00Udn3eyEhISEhISEDYSZMyhvectb8Pa3vx2vfvWrAQAXXngh9u/fj5tvvhnXXHMN9uzZA0AxKWeeeabd7uDBgw1WxWA4HGI4HDaW06/w7oiRyZ/203hyTINZjuH2iXw5HuhzPlbz9bwe9dqejmQnvQ7FsERzWe0xRpXknVEuXbqNru3WI59PjBGZpEmh7Ispa7QzexeWAACPLW0cR1mrTZAcbZyukC6yxqAwGoTMsSgGchVMiGNy1q47ib2b+maZ76sxCcuFZ45qLNqiVGYNIZWnj5kO2ZPwnRb6nFDNyaR3W663LQWzxyoA1PCPM2RxbP2R/ahoMVImYFW62tZ2PSeNNJzQbMZLS0vg3K82yzIbZnzeeedhz549uPvuu+368XiMz3/+87j44otn3ZyEhISEhISETYiZMyg//uM/jl//9V/HOeecg2c/+9n4m7/5G9xyyy34mZ/5GQBqaOe6667DTTfdhPPPPx/nn38+brrpJiwsLOA1r3nN1Pvr9zXWNa7Zp7ego0RmpI1YLSa1db2dZdvySADdzq/HC10Ke6E9Ek5mLORKdzLgteccK+UEtmODSThoW/uwKV3RLRuJOQmhnhFOpt2zVQmVmdcwfoX2TQKAHaz0upb0+krSm/f3NflB7PIdmmabtu1ibZikKwG6mZKYFmSt6LM/er3CazeJNYmto0xKG0y9DCQXmVBsSZizvCs3ju9Q3GRU2rYLsdpI0Wkw8w+U3/md38E73vEOvP71r8fBgwexd+9eXHvttfhP/+k/2TJvfetbsby8jNe//vU4dOgQnv/85+PTn/40tm3bNuvmJCQkJCQkJGxCMCk3iwejw+HDh7Fjxw7c/ry3YiEbruoLLdxmEjsRY0+miW+fNbvRp76+Y8mrzpMwxfF37SO2zWri6GM9KEHWZaQXejJpUUaC4fRBhflM+WsUxC+hlgy15OvGrq0XQ9Z9v7h1jDl/kHC5lBubQZnLXL9XaU5oZI66RwtyTKaXffqgwlxW23NEe/Ehk6KW+fsNr1nbvdGHUZk26qZtmy7WIiwTa685JvNU99Gg9GVmQm0JOtbFGIiiR5TZSMSzPRvQOhbyGqW+F0rBUUrW+nsWaj5iepNY2/usD/dl0PWukWBYqkf49w++G4uLi9i+fXt7Yay/highISEhISEhYWps6mzGlQCq4CuvL2tAc7r0AY/lKYnW27+rOouom7494+7j7NeO8Gu2ls2vbJNFs/k1zoJ5f5uu8rPwuBFQR3myfpF75wgMtXA96SoY355ltFfb9e5CH9+S8H6h8HqEkK2370ZmTwDFblHGREpm7+FaMpSCocjcwZpe83LNYfJ+AU032JA9CTU95pr1yQnTFWHTxZ50PYshOxIr2ycqCHD3XZhRV0rWcJae1J6QNTH77GJQKOa4v7dSsqmei75YqjKrNxPSHKOvPTL7zdh0ruezZETbGLgTGsWTkJCQkJCQkLBWbGoGhWbh7Av6jTsprwJFqJJmTMbHQtfZi2SaHsFqt29DeA44XA8aCN0bmS2DoBxnTdaE9pjNelWLBNAcn6U9J69NnZ4eDCYLislhcTJA9ba5054I57Vh/DQM+vZMp0f/SLeuHCWmjnaPBWn3ZdxL7a0j+7EzGwU043boQjrHhceeAO68jQW3miqDNmbDMDMGbJXRbH00Il3lVbuay6bRL4TgzI+cEZJozhisizTQ7InHnGZpG6iGY9JvTNt93Ed7YjDkEuUUv0fm2hdcQJD32Gqe5L7Pf+jY7vYpp4owTQxKQkJCQkJCwqbGpmZQaE9rLczAatDWg6jR/XXet57VoFOD0arUXz1qBF/sjQy1PusSsilUZsDJGDJlVwyzYtoZLqfLgO74f8Cd7wqTc1hsFpTaM6OWNuuGF80xyXF5Gg3CZEwub1iOkFWjdXRFDLh1EpwxmEBEwbo9UTYKDNNlIjAAxY4oDUN7+829qrQo3GNQujQoYZRJm1voLNH3PmvzbelC7D1BWTWzjkK9q6QrI5nXO5/m9yN8x4Tak9WiYD6LYtqbRx6pI6X66V7Ia+TkPuhi8NtbGWejZwmqd5pG55IYlISEhISEhIQNh03NoBgXPCHR+TXc+hXWU+UNTMd0zJIVCdGnvfGeSlhmev+CLsRqqwNNAGVTGGAZF8O0UMNFQ5CYZa7XZ8aezbyvSQjZlM68EPDH8Tcrm2IiPlz73R0fulxOg0k5UqaN4nAMWsi0SV/LRP5tOMnC3WvqmASY1TtJm6dnozIpBRc2E21FIhr69F4rXaYUTN/bTW1Rm6bD+QH5eq5p/YmmWd+GppdKe9k4E9PUupn2hMyJf6ymTl0Pwnsrfj5j+omt+fpw9tPoVgDoaC5ybEGGcdrKDN2/TZw1n9vVsCpWG0bOL3XTXZni1CUGJSEhISEhIWHDYVMzKMYZNPwmbBuHi/UWMv3/xDHTgG1Zr0iZadHd+4iV7+4V9627L7qzRCs4Xwa3QxZEepgPcMZ8vUrGwjFpPyPnpJxAXg/DaiM2H5NiWBQAYKS30oc5aXMW5UFvjKHDHdTsr8MBNKZHAvzzHTqA1kF2YyGBnJtppUih49uWedtADMpAaxRMtJVhQmoSxTPpWXt8pbDnodTbmXPVlSsGaF4z1jIX3gddkS9tmXHXin7alebxhhGFoRYlZFrV+vizsdqIkxMFdTxOR0KPiiN455PfsfD6CgA8oiOk673yHuvpv3MMixJDNgVLlBiUhISEhISEhA2HTc2gFFyi4DHPBN2zmGrc3a9ESOfAZ5TwnKxr06/IQB1u6+vZitV+sXczIevDmqxWDwPEv4xj/hjmY9v0EJhUX+eGJKiYU7kzE9VhtiV1Cam6Fma/kvRAw+PZLNE9B0fq8S241GPlLiKkzSW0K++RRPP8N58LZpdShoUj0sPytmtf57VPt6G287pnZ9tPWTGTv0YtyJjLdiymevbXD3NZbf12DPOxGl2QBGWapDo/lDnpdHydrs0x3YZZHs7HdGNA/57vNLnLYixdyM65ODafiYPHtMV9dvqwN5wB2/LQEerE4ViVY0teec8ufUaEZA2/nLZrQ59hESlHz46UTW8q90zLRvkYU90HiUFJSEhISEhI2HBIHygJCQkJCQkJGw6be4iHSQy4aBWshmFk3rqWVYYqy4jIJ9NhrS4RkwIV49r6jFgpqJdHaMm+bXJt60Zb2GdcLDuhsq79rH5TizAccFL99PxRqpkzoLbCSEVnUgEtHe4BSSzH0RR0TmOwtxFghrYKJpEzd9ZEJP26g6a+GV1ChbBNsVtse7Mfl7AOasiB3P+e4I6K7YK2+eGdwUqm6hHm2khGrn0zTNQ+owzYM7+MA8vz4QEcN2zJK4wDM7a1oLKH69fTNRwCuGGvZpl4ezjZwDdTdPcDg28jwICmbUB8t95zZofrWsrGLPb7DB1yPSQviOiaDg3Gtm3b5+nFxhnSieFY5X7G57KaiICbzwhF7Pjdb1tTQpyRacn890C43quMgDOJcoofn8SgJCQkJCQkJGw4bGoGJWcCQy5amQOgIz24EUyF5WmImdfrk5DBl7e063xBLdAMXzZsjqDrgjTfsdCstYhf+3yotoWcdWE1FtFtvbgYYj1Nrw8jaRgps+eeM3imRTlzPfyMAZIzZPY6SI8d8BiVTcCkfHOUo9DNzbliU3gghgPcPeLs5RUDQUOJDWjyNaAlLJ+Z+pvhv43wRjLP4M5v0xCqKVrsYwJm9mmN2ujxS0Awhj3zywBwQpmU0IwtZsfeZvz1TS2ErqR7hkzCyz7PVIwlmbRdbazyGOyNxCHV+5Bsq95XlNk09xi0hZ5f1jWAbGP36dDGZIaMZyyUug9H1ZYeZaMzJX2wUmeYy9xxUPNK9X70mdbQnM4xovoe6Eg54babfCPS6z+YIjVAYlASEhISEhISNhw2NYNiwozbeIZmOF9z7NPN+73nkHkRNISRhKsBsMxKrF6jXTElMvisyqSx+mbLg/ZG1vcN6QTUMbFGj9ZH11esC+vrBh3LnoZNCcub/ZSBJsW0hRHWJGeux18whlpKO59rNsX2EFj/YzmReHylAIAGeyHg3xNNU6VmrxVo3jc+o9LeiwqXZUz1vmI9KyGZuu8j49amrZl3fzRDwGmYMQW5ghBSevoC1etX8ydCj8KZekeN9MWorBbFlZnEcoqIfoVL6TEj0z5P6wXK1vVlcwUibFmLVYMp3wXKave14l/P1CQnAit1RlgKiUKfzIKLiQxKF3jw2+R+MyeHzNP3BdXLTUJiUBISEhISEhI2HDY1g5Ix/4sQaH7lqf8j47DMXxaOo4U9QSGZ7QGa+gyTEtOtuK9RpV3xk0855TsHiXyQ7WP1zTF6vy3B8LBa1zjqeG+rrQdmTtGsGQXW8rFt2hHalAvGWtso4XqkpTRjrmoHlEEpGVBwxaQAQC1Vz8JpKmCvQ0ONfgJhGJMQgkS2CNkeIRLrSdJTSS3TOfxkfIZRoUnpaE8o3kN1650uyx/XpsvMsXg19NQAqe1oi53yhetnzrBpJ8K4LYxQUb3OeNmjFe9MQEefCQH1PPR9LqfphYbPptlvYzlr6j26IkbCNXTb6DlpuVyzsp4XaGoATyaMtTnggAuYuCeVWNCdQHVN2/Q+zWWMjCJkLPLc9nzGct7/IiYGJSEhISEhIWHDYVMzKDTeuyviIFQmQ8/Rr/HmF6EbT2fwx9cNo2K/7ojldwZ/TF3F7UuvN0mjgYxGxeyHg0UjfoznSugBwCPj+iYaKKapoF/Bk9CwpFjnTmhb/Vw6MQ7TvUcbhSXb9TcSbtw/12yU8UwxHiLmJHX1/k4EQuYk1gM1x6qS0AF1ywlk5EIajwpXj/T8K3iEUaERP9zTW/mRUG2Kf6cvcet9BlMG1619XDzstTkWRbXAXFAhJXIOSMNWngCZgUkGaPxLQqZrkkbiyXHujoyU5VJpa0xHdBoGYNJ5CNlLl3xR/+9kB2t6ZroYFaCdKZm0R4+ZgdP3iVC7FPipnKwYC24ZiyHzo5Q6tSD2/cqiy4HmvSQ7WFG/isSgJCQkJCQkJGxibGoGhXkafofwy97pNILIgEZ3ItxO/Z9ZlTLRjjD/K9/UJGFcC01Xw+dtKEMSjqCHjrUmSSHgIn48D5VAb0N1KdRFlbooAvD8XVRd6AUT8XO8EfYeQXQmgO8vISSJtgorEk5HBK56oKYXmklpfUVOpKrfMCc0GiEGTq5vLRlES2eIQ93nbdoRT1+iGRFyinxGRTrvEwblv0LT2Tf303Vj+escw6ieitDR2SVD89kWLxpImpap7SoBgKs+WHYc+8smgqIU3LIogNaf0YLB6TlSZTYR3WKZEefYoCfJAEgXodQ1pN+mKekqE9tnM1Gguw/WAhn8PwldPXOgvV2xBIOnCiphGHs/caBBlyaw6xn2dFHk90hoR+/Y78qk69fcR0JCQkJCQkLCBsOmZlCAuONd3680Bt+3IebkGiurysmAgXHshSpDB9XJ1zuTdlxURelQlxZGjshEIhDWBiTHimZmnPtp2FYHw6iYVoaMCtiJ6VWs6uuYmbFl03V3WqBKxlxKFSTUcsO8VFIiky6/yWA1bZkxHl8pGsxJm9OvivhSx1JCgpF7LLi0igmhDrFMNnrHXlnjfxJhVJwmCpCCxM+wIJ6GUWZLevvpgmMBSbQBYS8l4c6Mm200F40Mn5/jw4oVXGBFR1AY9sRjRIN3QdjmQ2Vmtw3ZVQOnuJHefAyx3nGnr1G75ACcNeM0+nqNhAjzmhl0HYuwTFQcsabE3gWzigTaLHAsVeS3CbP30eFQPj3m3qP1T6tbSgxKQkJCQkJCwobDpmdQDCblEAnLxNA2th1uu9YvcDOmL00kkK1a2p6f2o9jaQxr47EtErZ7S9mUkEXwM9SaZf7xxb5Up8nlMw36fBV39sxI15JB2twh+ZTtEdL5q9TSaYNO5Fc7ZU44aUtXnqaJPcugB8NIVloOoiNhSs8QMiqUfaN6H6pPyeF7rkj4+ako00Hzg7SBMga+t5H09CoSDGEeLFfW3eer7elPi0oy60Exqn3NSSNyJHBMVW7AepVeFutxCnP93MPcij7H3WRFZHRdWNeanhPvmnYX7WLEhXdv+ww2vR9PNdakDZzJhqv3tLqQSQg9h6aJHA2RGJSEhISEhISEDYd1+UD5xje+gX/7b/8tdu3ahYWFBTzvec/DAw88YNdLKfHOd74Te/fuxfz8PF70ohfh7//+72e2f2nje/w/4+TY9geor0nXI41vG6+PbudgXWcxPdtgepo0G6X5U1ETZBkUM2P+TO+bw2S6db1hkzfF/HEynelxZgZaVv3x2B+m/4sdZ/hHwYK/LNi/dyyk3SyoQ+1f6j+/pxjeBycCdNeqfS7flDq++DmSCO85BPer+xNaF1FLxZxU0t2bpodvtjMailK4v0pyVJLraVqGo7R1M1TCTas//8qHbCbX92Hsjz7F9FrnXNr8IuqP3tNuPxKqPTuH49lcqA6MBXfnHsBKzTEWDGPBUOpzTq9Npf/MOqdb8f9iiD034TPbfHZk44833gFu+5z8hc8aW8NfyA4ahEc6KWN727MgtIaHPtP0OQjrPJUIFve8iM6oTOVBO/1fiLVoXGb+gXLo0CG84AUvQFEU+MQnPoF/+Id/wH/5L/8Fp512mi3z7ne/G7fccgtuu+023H///dizZw+uuOIKHDlyZNbNSUhISEhISNiEmLkG5V3vehfOPvts/Lf/9t/ssu/6ru+y01JK3HrrrfjlX/5lvOIVrwAAfOADH8Du3bvx4Q9/GNdee+1M2zPJiTJEm9YkXE7raWNM7Pq2PCkT29reTqNJoZFFtCFhhAAP2mDHZiP7cs67kTa31NMs1932GLquTJv6m8GN4wsoFom6dba1gzJJgPL0yLtvjeOKTLMn9lpIhopEZ3Xdx9PoLaiuBFpXYvwmGVP0C9VEuX0oFsa2B9KLFOJqIQB1btW27kpN08a2a099ilQ5uk7F+5jnQ7EosCzKk6PZxm0Z75NDo8LeR1IyjAXAjI4MzXaGx9KGvnlOQnRFTTS1J/p/WqYlq/RqEGOQecvyNgSvOVdPy+lpO/rwnc2ZxDdH6udw97CaokUbH+YdwqD0J/65EjBeQeuhRwE2mAblYx/7GH7wB38Q//pf/2s89alPxfd///fjD/7gD+z6hx9+GAcOHMC+ffvssuFwiEsuuQT33XdftM7RaITDhw97fwkJCQkJCQknL2bOoPzzP/8zfu/3fg9vetOb8Eu/9Ev467/+a7zhDW/AcDjEv/t3/w4HDhwAAOzevdvbbvfu3di/f3+0zptvvhk33nhjdF3oZdKFPtkXadbhcFnImngulmZ5mCukpf62drSxNuG6ST2uMDog7AnZchEflbZ9AiYnUaxcP2aFoq1IrNfXVpa2x7EpbkksH1FbPScCB0e5dz4NGzDU2hOjP1KshosCySL+H21H0Lw27cdqoofovKmY+opAt8fMZcywJOZ+cl4rlWCERXG+JtZvZZUKgOZxkJxYzLGMAGE21+Eyb8krfHusGJljFbfXqDYMip6flqFjDNats5aBo3JQlkZPAW6fUV+mNSB8jmbVw22LtuEkz5nBavv4XXqW9WAONgrMu22Y1Up7wslVFNzm0BGM+U7D9PemEfXpXyjhsafNddQTZZp7ceYMihACP/ADP4CbbroJ3//9349rr70WP/uzP4vf+73f88qxQJ0jpWwsM7jhhhuwuLho/x599NFZNzshISEhISFhA2HmDMqZZ56JZz3rWd6yZz7zmfjjP/5jAMCePXsAAAcOHMCZZ55pyxw8eLDBqhgMh0MMh8PGck7G1gz6silt7EQXc0LH/9s0JtTLoE+b1sKY9PVm6RrnZUw6P5awbWgyIcpbIrKPlvxHXWjXlcTKTqwOzkHXFCZsT6T3JEiHn04fLzw59h+/AdGcDLmfKZhLCU4iEmjGbKA914iQq2eHJPE5MXX5Hj1orDOMBQeDGcnPNdvj/FSaSoJZ9fTDyCyqT6HPyM7heCY6lLmsxjdXBnhsRV3L74wZKsvaAHMZw0Any+njI+KxJAzIiH7FrOKQqIOynh9IUCdnLosy85aH18A0wtUTMsIhE7seDtTh3UFzjE2DrvKe9gomMlLhsZUCe+fKqfa1kWHuu4xJFJmw7wPDrNuM1RI2vxOg7jP7XHawnALM05jUmjEx2xhnWWD6XG4zZ1Be8IIX4Ktf/aq37Gtf+xrOPfdcAMB5552HPXv24O6777brx+MxPv/5z+Piiy+edXMSEhISEhISNiFmzqC88Y1vxMUXX4ybbroJr3zlK/HXf/3XeP/734/3v//9ANTQznXXXYebbroJ559/Ps4//3zcdNNNWFhYwGte85qp9xeOUU7SpHSxDsbbxK2Pa05CvUnYY520jxBd0TvTuN92oe1LNKZPsY60sQ06muP3SPr3hKdhTCblEVHXwnUFKzvlwzAmGyGzqWnbkEsbDWK8blyuKZ8FaNxXZF0GEK1KO7uynqCZt4Vktv1q3mTUMcyCnwW5by9rUmQAfTeYbMtt529aFPo6Pb4ywOPLGQ6sqOWHxwK1bth8ziEho+yFa6P733gbmbI5g808njF379daWxWtT7MrBixgjoy/DqBYlRijEuaBou0Mmci257FN8xYDZXjNPU73Iu267jrXoiOhrrNrhclIHoM5glIA5y6sP0ujPGvU2aU+NKoxTXaMZsYONSn0/NJnS0qglrwRCWrljWs4rzP/QPmhH/oh3Hnnnbjhhhvwq7/6qzjvvPNw66234rWvfa0t89a3vhXLy8t4/etfj0OHDuH5z38+Pv3pT2Pbtm2zbk5CQkJCQkLCJgSTci0+bycGhw8fxo4dO/CZH/l5bMmHAdPR1HG0rY8xFW0RO3SZcd209UTq6vJQCdFXh3K8Ec0K3JUXg05P0e5Yz2W1PTOa78Q4cwKwrqemR2B8Row+YD6TmOPHh19YJBlrK6naYdqQM8eghKDsXBczFzriNtYF9U7SGcQdP52TMZ33XI91WepWbNbRnp2/nfTGr2OIuVWq44iU7Xj2vzNenQal4AJPjFQv+RvLGQ6uAMd0WuxR7ZRPA84wnzPMZa1VWRi9iWlhzmFdnQHFplitAEPAfJDpoF67TYSh6uPkGrIWXZ3hteTqaXvy+kaqTay/TYOmEbZ9Gg2KYUxi2Z5DmF0q9+X1Z1G2FRW25Gof80VlmT/Thlrw1ne1epc6liREpd2ha6GcpMN8eDk371rfsfZoOcLlf3EbFhcXsX379s72p1w8CQkJCQkJCRsOmz6bMc2b45Y117eV6RsJExvfjDIMEfZkkkPs8WZN+o61Wg8R2h4mW3s73BujnE7v0Oz5tZ+Erq9qwSRch9XpHFRuDldn2As9USi4xHym2pUz0eqVofKXkOMJylF/EsnCe1p2PB+w6Y77RMCFeaHofJufifFWCTPNOr5AehFJFDHdQdu4uGhYF3RHgJw2UK6yfZiU7YXqhY4FxxOjAo+tqLvsO6ViJLYWqv6thWPtaqHOSxAcQxhZ0lb4kVFSMKUBsHoR6UUkqdxbbltTdyPyTqqyJuO3ylitdmwjgYJT7EdnxS9M7LrYrNikLWF9jXrQ/Z4II8X6MhTNevx7hmqiQvR5/xrXWQAeM9i3Pga03vOzwEKulEjbB2MMMjVdcGGjNila2w2nSRGMNZxmaSb4WjLv/eF8kYw/1ep+1BKDkpCQkJCQkLDhsKkZFMqeTMOa0OVduXrCb76+46ThfvuWaSvX1Ya+X5iTWJMuN0/PbwO+Z0oXuxLm/+ncf5vTbUv51uMJe6WkfOPcMefuuV76kyfGua17mKmIDhe5IbUORq3v8ivhgHd+Q1mDH61D6tGLRMN3xE1L0refpIeK6VC8dhJ9g7mm4TU0ninSm3d7VfXo3ldEQ0Hh+YYQFklIpqO0mN0+jLAzOG0wbjAFnPnHNqrVUSzVGY5UGcb6Qgw4w3wmUeiDLLg7t6UAqtBZmuiDVC4jaZeH5ej/9HxlkA3HXxr9MykihZ4TNa/riJQN2R/XPseGtW1jUHcxH23MmddevWzC66SLYaFtirFq/nrlhUKb5zy3jF6ou620TbF9mP18fbmw20kJlLpN37tl9Zm3Cy6wtTC6k9IyKJxJ1IJ7ucra4CK5nAZFBJoUk6FcaPaE6jcFAwTT65nTozA0nYG7kBiUhISEhISEhA2HTc2gmLEvChGMhfV3am2qvFvXRb4A28bvp9WThI6XjfWm3uB/um4ahMzJZH2K38vmYZA7PaerdAWNHUdv3Qwds4bLsJvpL/fa9gyODwomsXOg3FhyLrFY5s5hlSvdSRtzEj0PkWXmWCgLFZajjMsk199pXJK9tjG3X0Zy8bRFN1B2wGloODIIr5efUe8G+Nc48/QofrsFmF0f9gBrwYJn1jEmmb4m5jxVkqMUXLcHWMgEnjrkdruMSQx1D3GYuUZUQmlWSjJWb6LJAM2w6JOtltPW+IgxnF7WYXJuqD4lZJ5i+XzaonimyYlFEbtFPKKVTLt7oL2+aVjiruc6lufHX99kheh5bWvbJGa967fEq4e55/RrRwd4+tbVsSjzWY1tWl81l1fIc2H3LUKWEc3nxq4HQ63FbsYtlo5aVHpdLZkXLSsBlILb97/kEnXtdDrmWeqDxKAkJCQkJCQkbDhscgaFd45nxcacuzQnQPvX7qQet+nhhPvrYkS6tom11SsTaVPXWHLbvrvQh7UQQYC8x6hQNqVHm/rqTSa1XdreN4mYiJQ7Hh4zArDjrwNeI2MZcn1AJmqH9lpC3Uabe6bn6ojJ90LYKzTzqilufDgGen+aXpOEYSR0GeOWStrvj9vHGZDm8RiXWQcVeWKmyXEQlgNo6nJovVL38qz7M3f5Q4Rknk9LnqmMr6ZsWft3ZsGFrTdjEgUXGGZGR+R7PlSCez1N5X+h6lsRHCu67uWaodLeGIC6fjXRqzQZFV/vY9rOtK4qpp2IYZJ3yTSOsAYxh+1pMKkNXSxieA/QZ8u+V8NtdfWtTGannieyrIUJm6RvlNIxXvka7FeHWY1hrnjaoqjBjf5DMFTg3jNbC19X4ry+WKAzaR+dUAwK1ZSp90Kt7/OxcM7LDM4/pQ8Sg5KQkJCQkJCw4ZA+UBISEhISEhI2HDb1EM+0mETp9xEzxRAO46h5N3wzycAnpNbtEAWLl7f76d3C9UPTSCk+bNUH0wxRdcGZhzEXXqvDe6lIVg1TrHFnLTD21wuZwICrML9BJjAMwpnbRM6hsDRMihkaT1FQWtskjqPntmlfruaNMNS3r3fp2EORXCk4Sk8oxzppfVovnQ/RMDcULq+ZSoRohpGYNiZz7bdDTBEBZFfSxIxJZPraGFGh1MdW1RxFrZbNZZVXT54JFFzYbTPuhngYb95cUjBUelhnpcpxrFT3ybEyx0qdYUXT4qVgVlQLOCGiOj8+wuEeX6AcDwcH4ue/z9DvJJjrN026D4ou4X/bcC9ved/Q8Ho6DOQFOnQN4cjVvyMmbUbTTtjhRmKo98/HlIHgd/cIOTb1nDEc4fS5EeaGKsy4KIQzVKtz1IKjqtVA2LjmHaHDfgLAWDAKPc5JgSRmfcHFVPfYRviNS0hISEhISEjwsKkZlNiX2LQ9d2raw9AvGZWxC6YhvzSVPGVNDJPSCMdFu4A2FJ4aZGx14czTpBGnJk9rZRcmhUy3tgHdX85KRtmvYq8nKf0wSwZgR1HHNpsKT4zdY6SE2Q4LucCu+RUAQM4FKsEhxqrXXGvGgYbm5kTUmAc9cJ81CcIiSch9NBzYlgW4ZDYEMGMSuW6A477UlGIG/KR+dP+V4BiZ3pjIlJkZZSmIkLQLXc9xaBilpkyPT7E71tqf+2kEM91+IM5mGHDNvBgxoflfaAYlFwxDWXnH5Izk1HaZFsnyzLE4jKn9ch16nBVSqX11veUKx8qSuheWVwosjQdYqdz5rAQjIcnOYIsyK+3HRKbJ8jAUPcbUUUzDJBvEBNT0nuxiU2KpQULE32fNjYR0hmpCOgGthHoZOAa7GU4/S3SJY71yIMJtsvyhYwPvvZwx90zlDNiWC+waKMZkLq8xNyxRFOZ+FKh0ctJxmWG5zDHWz6xJ8OeE7+QdIh0rGrZnEroSyk6LxKAkJCQkJCQkbDhsagZF9fW6v9ZUiri4pgOgRkHSsihm3pVhjS85akLGG+OZ7uvcsR5+79cgTBjVpllx24Q9nPZ6YujDPByP8NtYaK3XBv0/D9pjk6mR42s7JrWc2XoM06D2348tmxamThfeKbGgezcLC2PFoOhCK3WGOrjnJuk0qLmZ2VvDaE2680O1CwY0sV/GJLTkQjEmjFnrfVW2Btc3f57VJDGass3OtdMYrySA3IbNVkTzIaBeNKYdOTeW1+5YWPB8tOu2XOKxGkxxaSb5ncgsY1IziUwyZHonhk0xTIphTYDm/SeCTIyKUdHbmV43c+sYcyxJOJ0VEizX2xYAz+DqKmrFqgAoBjWGKxVK3dst60yFgdoerdOuKAaFWxOtWC84NKGMhXkzbTJHNTxUc8QhQTmpFmK3gdD63GgZKCtGTb3MNnZ7+KHKsfvXTof79trhpn3WRgbW7Oq1EGNUQvO3Wb0bo6lPiCYlFqpslhVMorBsinqOlqyuKYOoiQat4ljWdvpHRwMcKwuMRWbr86+Tz5hMssQPmbmwfCwFwLSMXGJQEhISEhISEjYcNjWDQr8yo1bQbPJ4pl8+nsgottz7sguSuElJNCgAwr569zekjLIfWYueI6ZtUeX6HfjxYEtChGOUUrJWQ6hQjxLrTZnzFb8HTD0SjDBhJZhnLT4tqO4kbBcDUNieOmxPNx/U2LFl2ZY7Nla9GZpkazXRTxK6p2nnqXZB6RVC8yxzHjLC2hScI2cChaZM5jOh26P0F4xJZDrxGGMSeVZjoOupJUcp3NXyevGSQTJ3jaXQKdjNPNwz4fRF5vwxrSXwGU21TjNI9JzZ7q4yobJsgWYHsghr0maGZ2uKrLdGd5mOfDJRPJkE1zdWlgswLpBZsZpE6FFlo464xGBQ2wiioai8cpJcw1pwiJpZI6ySRGaUgmNcO2t+xV747acMSs4lct12xaA53U4WSaDpaTU6esNS+gyKlM6gq5lmAI1ngOpV/HVxO/5QN9eHoaFsRZNJMPcm86JtTLRQH0t9w3HGkhO6dpjp4J0YqZcuO3Ou9NaZ+yjjitkcj9X9MBrnWFyZAwAcGReWtfXbEGerwmvUeL/S1cEoQymZTfEAwCbT5JjMzFAkBiUhISEhISFhw2FTMyiA82oIIwzM/5z5PeVprO7bygDNXhenZQNGBYhbLreD9BbJ0ixyjDT2wiCsfppeeUxVT9E3eoZqfwB4duwmCsrbzwQWpRVkk1DLQKE0KOTcS0yMhGhDjD2hvYIhl1jQUR1zXGBZe11sHWXIMol5nQpdSAaU8MaEY5EO4b7Mtqb5ldYq0CgP409iGJSKqPNpz40x5kU65Yxb9mc+k5jjGRZy1b75usKCttA2dvCmF2+T8UXaAABVqHth3bb4GekZZ0zYm0AyEokllU6Ckoi0J8olTcAX9l59bYXfw1Y6FxepAy/pHmPSsgycKQaiMCntM8UsUTDCRDAh7bmXFUNdOk0U467eXGtZ6DNhe7q10vcYrUFVcZSluidHZY6MZWC1mh/X6gz7mhR9frSOyRwbh0SRCceoZLXV04QsgBTN+9Poe8w6678hFKPj7hWXhC5EmJSuDu6pEKFmy54r68XitDnh8YeaGBoFV3v7B9ECyUZdYXLN0FI/1Dmadjd+i0hZw76Y6Um/GfPmfZNVqAXH4SXDmgxwVL9/lmvuPZOxd0yX3qetbD496dsbiUFJSEhISEhI2HDY1AxKbcfspffl1+bjEarHY8kA+8RwM/0l3PV11xX104VwO5r8KsamuPYS1ib4Oo+zQkE9LV/TIRPS5kESizZR7BZZYcdbV5eELAaPXZFNFoWRfggHSaI2oxie0PeEM4kBF5jL3Li+cQvNDi+Aw6UbH1UZxiIjegEXjREDvWfp2HElfd1JJRw7ZNgTk4SulMxPQhcOKzNmx4sLJjGXcSxob475LMN8po5lLlN6BXPdS534bqTbP6oZShsxpf6nrAh1Nw2jihRr4piEjLEmowLj/irBW8JLarBOpoRGyIS6B3oP0fapecfwFFw5yQ60mUxeC0jbpay0dkTNZbkAwDwmRNSOgWJM+VaYsjx3z4mUDEJfVBMJxPUJVEwLOW7JUAlVT8U4SuEfq0HOJZhgNnklYNgh56hbFE5zRBG+Jxnz2wC4aKi6ZijqDFVlNCi8NaFgmPiyFiRaibAcap90O9lgZj1fEek7pobnos0PxHjRmDYZRoWyLz4TE2FYyLva/A4oZo/BKI242Y78dpl3s0kGaZ7LnEks6mivgqv3jXGr5gxYLnMsVeqn/UhZeO7Efdn0SUw5Z+3sRhmcIxqxV4MlDUpCQkJCQkLC5samZlDGkmNUc+ScBT0wouGQaKQ7B9x4X+hY2eZySBkC0/ujXhXUDZRi0hdgbH1bVEG0LjI2bNgWAe2/QtgVkHWA77BIS9BvWxNBQRXt9P+ur+zQSdfUZ44j6qDbWpuPWO/Nbi/dfsJesCC99mwN6cwB/7xRn4mCAcNM2pw7GZMY617y4soQjCkHVkDdl5XkpOfWHRnRprg3TKK2JFHzHlPoNBe1hO5Ru7Jmu/CSZIyh4BJz+saa4xzzWpMw5BIFd0yC2b9hTcbCd6UE6HVBxMFU/6/PmWVQuD+v9Cvq/BVcIhfCOu6GbF9FeoxhpEYsPXxbrz4Gy6AwibmsxrB2+ZYGZrrKUGQuMsdE+thrIxlqfW8o/YlATgb0RU10HBW3DASgo4WIO66ZZtzXrRj9hGUEKGsggCwwUJLk3gGITigL7w7psSY8E9CXBYyruqU+kVWZIaskOFM3UlU5LYbZV1sOJQBWu2LyKbXl6mkyKG5bui8hm++QkG1xGhTu3UdNTUq3Y67/GyO9Z7aUgDmtShsiYZ2Yvd8UFQUz0Nd4jjC085nAfFZjoOdXNCt7VGuSVgTH2J6/9t+0cJlhER3TLr31sd+iMnje6fvXnINKSozDULYOJAYlISEhISEhYcNhUzMogOr1VYLZfCJUj6IiCbj9sit1jxVQ4/Rejwqx2HgfVr/AXCSCWu7ymcTYFaD9S7BGRI/hRfxQPQWiywHjKKjH+m01tCflr7PL7fiom6fsgOvz+mxIDF1f5NSbhY6vqqWOWXFlg3Z27DfshYX12raAMEes6SXQhTByx9+P61Fl2lFxpHstnAGZPu6aOIECrnfbxpqE0QF0Ge2Z9clf4tVLeoBKr+LGxQXRTBhXE3NvF5xjTnf5Blz16tw4uXIdtdmiJzw/YVQNfT4y0jPP9bNmShdcotDXeZip8XfDVtH7z2gHHHPgn6eGTiB8BIP1tB66ryGXGPDM69HS7NVGowI4LxYblRLojTIuMdARQCYqyDAA4zpDafUqDHkmMEciqgzGVYZxlXk5kmgeH89XiCkvbsvSSIZx5d4QrHRtzbMajCHIOUQ0M8QxlzF1D8nKsCQCUrgoPWKCHIXxljHI9V0vJItGDwHNXEtS+NFAkjxnIQtjtyGsSG1Zd6WBCTP+xryLYiwdyKGK4H6kkW5jxlFqdlPtlxxbcMjUQd3UaXI41ZJhpc6sFkwxtG47ilq2s+DGX4pmp8+Y7NSdmGMLf/vCbNrTsCKJQUlISEhISEjYcNjUDEoGacen6diZ84BQ428jO888NoX2fCtpvo5pj0tBBL0mlU3SzyjpxqR9d04zHeohKEIChbIu1PmzkR8oyK/RzERKzlWbx4j5yifrK+l6C9TP0mlSolV5CL/MaSbmZpSV9OajWZtJdTQiio4lW5V8cL0onOfHFJSD2W/HJtR7pxQMudXbcKKnaFZg9BhqW9ZgTcI8IKFmahJU/WjtsYaLawnUpBcnAEXzQbEkjk0xz4C5psGYvgx1W92eCm1lGYCcMduLzJhzpVzIBBYybnMHFeT8loJ5z7uLvpgMc7rMs1FKp2Mr9fkx7VHaAI456h2jWYUBFypvCmF46HNRk9632q8fHUTvUeXU66IxMq19AVQGW5ufSDKM6gwrmkEZ6cgqz5OIeM6o96XTeHAm7fxymZPjFBhkNQaatSmKGkVRWw0F48L1drUGxXi8GP2MYYMq4nTb0KAAyISwkUSUTeFMiTYoU9oaDaiJoJjexzArodaGztc2sk55/bioNwYB4UW/ucg6996kCKOFAMfwGX3IyPxW1eY+a3e6riSIn4kaIbDsMwKtJWVi0IwwgndfgDCg5pyb9bL1GTbsCdWahe9/qtGclN08tl1CQkJCQkJCwobBpmZQ1Hgf0658bqzM9XYYRsJ9paoeFex0LUF6WKpnFK5X60I2gFkWBVC9qIIbDYJS9pt1hmmxehUyjhdzQfTG6ggzYzxFMtLTkKDfv81MuGGGYwq3H5+9kGDIrCaBqfw19jzA22PMZ6arlxxjVcw+aZvCXEJUsyLBGmxKU5HvDm2ya28/PGVQ4eDIPS50XJmeB5VpVtp7rpbS9uwzqfQTXexNGKUT81uYBHXM+rqCgUnn+6Jy4ABMmnuEgWqMMuZ0SSoCTtox7HAfMa1VG5h+ZgA/OsFvc7PHZKNEyPNk2KmtOcdKLjGvKx5y5wWkeqfMsqe1RKNHavugTO2XZvE1dQDASABjXfFKzVAKaa9/xoC5jDAoOTDUuYwGWi9jr3/E8dr1qMPj9P1gaHtKnWPIMEdzpdO5qPW+7o76TsSzY5v3o1oWyzpccIG5rLZOwsOqwlxZWZ+UvBReJJEUzPme1BzjMrP5gsZ1RjQeDJQNZkz53BRG21I5Xxbj4EszUtvj4tJmlo5BSmajqITgDT0L1bBIwew+a8FRE2dwqmWhdVOEeZ6ibsCawSmtxohjpXbZwEfCub46V1y3DzM9qhk4Y9673//dcO9Ok2PIY1G8A/GOQm9jDkTVaTRYVD8T+gYZB3ezJA+ZffRHYlASEhISEhISNhzWnUG5+eab8Uu/9Ev4xV/8Rdx6660AACklbrzxRrz//e/HoUOH8PznPx+/+7u/i2c/+9lT1b0iOArBUUjqx+A0FKb3QF01w3wL5guxFMq7YaTH28dC6jwWZtyZfgVKZJyhIAyKG5tnKDizX405A3LOLIOSEXZF9UAdjDcE7U0yUg+NFjLaG6tvgK+V6dK9hK6YYa+O5r5gxEdAucOqGtyZcL1vipgXSRsa45UdHiWml297ARKdLr2xfBfAdBk1DWhVniMx/MgXWXOsGN0Gud5DbqJOXI+atkmSezX0NjGg7qux5aptzsE3g4RgjuGrJVATlkQGlVBX1lpa+Ymelx6bKCVtu9R+K9Kuo2WVp4raac6ZZiEdc9bG9oVXNmPuWRsLl4cIAFa4JCwDsFKrZ9ocS8jw0eeQPpeZ7pEaLc5IAMv6RKzU6r1QEwZqJXNeMSuCYU63b5CxBpva1ImR+4isMu+GGNtmGIdCG4/MZcKyNCxgf0zvmzKmXo8ekcy9pE32XVqraCDT4x/WGZazAsVIu85mdcMLynqJaDfYyjLZHLWNpmzqcKhTb8Yl8sA52OQ6yphs5EDK9HXghGkxR2qcd4WQqGsG2UL7SUZZEs3ekHPSxtKY9zW3bRe+hiaSHVoYXU7FMa5yjC3LxG0klvqd4h6DZq6DiU4LPbmopoe+8xTzoeYV0drCPqP5PgLcPUqjdij7aLaj0WIFd8x/xqRlifpgXRmU+++/H+9///vxnOc8x1v+7ne/G7fccgtuu+023H///dizZw+uuOIKHDlyZD2bk5CQkJCQkLBJsG4MytGjR/Ha174Wf/AHf4Bf+7Vfs8ullLj11lvxy7/8y3jFK14BAPjABz6A3bt348Mf/jCuvfba3vsY1QxDziGY8HrdNBMlddWcBEF6feMaWNb0ykolMRai0cMxPUDTIwR0boSM5DPhasyczvs9KjoW7r5IATMe6xiUjEkYy4NC9zLo+LbP0vi5TkJHV5PPxOVF8TUK6oQAkkvrCFmzII+L57/BGipw6ncR66y0sSuhA2Goy6Hutpwx280O+ydtjpOqfdFdR3FwlHtRHapdfj2CMDq1pIyK2yZjyo3V5ulhihGLOi7q/CmUXWFM2gdWSEm8f5owdRYM4KQbNeDwXDMBaVkSzhiYYDCiI5VHRmn/ARWZIe2zJVEKx6iUUug/7d+BGmMdAyaZQCZyzNcDAMAczzHgHIXWavjRQP5zGEZEZIx6Hvn3XMaYZUxWaomVWmlo4A4/eLbUtHkmDcNTcD/iYVQDY/Mu0NM0Q63Sr5HzousZCJVHyD6XXD1fMcRuRwantzFtdudBMWGA8row7wHDtDb1StJuZyNbmLR6AQqjn5rLanvPrwiOMYCl2vj7FJ4vRsGF10umzKxhZRwD4Puy0Gg1y0LY45Q2+i3TEVE22zLxmBlkNfKMY6A1MVkmkGXCnjNGGBUmgSwDRPDCoPea2b9k2r+F6ngQf7eYPEY2I3WuXIQz6yQsrW8M47DOuwZ1yVAb3Q5xDq4qpdkxGp6VKsOKzla9UmWopJ+tmr776b1gdCOl3nGYl0tIgBP9UQn6WwrrIQYEbLI6UXYZjegBgEww73dqtBEYlP/4H/8jrrrqKlx++eXe8ocffhgHDhzAvn377LLhcIhLLrkE9913X7Su0WiEw4cPe38JCQkJCQkJJy/WhUG544478MUvfhH3339/Y92BAwcAALt37/aW7969G/v374/Wd/PNN+PGG29sLK/0F6FgvKGjAExEhf+1a4rlTPcgzNcl/F6+ylliek0CK6JGqakEM86eW0UGI71BNV0QdmWYSZsxtODOxyHXkQ2xrK5m3vQsTS+TZpotuERhGBVOPRR8fQr1UDHj2oIRp1MmIvHxzTFpE3Xg8ir4CvZa0vFZFf1jji3mWtimNekTeUNz/dh6gmvt5eUIxo0zBjyyXOCc+clusmHvNpxveNQA9kajY77qnmJYEToTKTP5bJr7NJ4dvhslHct10VZ5sH2YKTgP5k0p1Sh6XPqCaTZAcIkBKcugelXmIAUHas0sCClRS4FSsyYjNkbJxgCAChVylkOYLquYA0fhMkoToxbDPtlxc2HG800bpG1PLRXT6Z53xZwAwLFSsZ40coeOlVMNTM7Vc2pynbhnVLNDgjh8CukzPIA+X3pHFUOt6ym507OY82f0Ye5Y4jC6HONDlHHnl8QNC2I1FoywnK4XbKCeSc1Q0edQcIALjw0CgIVc3RSCXgf9rqWahNBXxjHD0rKDZj6MSGu8Y8xx22N3PW76XssZZYsEhlqDMq45BlmGSudBynUOJKNXYQxe7qJJcK6zwXsEzUgeSd+t0t0LjEkbXQQAWSGQFfqdXEiwHOCFO3CVw6i2OzLXVNZqWozUPsdLGVaW1U/3seUhlsYFRvqdYrJiG92Ol+Vaa1ds5JBmspzvFdVrcqB2LJ3JXdXmeq3WuXNEweF8WwSbTv83cwbl0UcfxS/+4i/iQx/6EObm5lrLseDHSUrZWGZwww03YHFx0f49+uijM21zQkJCQkJCwsbCzBmUBx54AAcPHsRFF11kl9V1jXvuuQe33XYbvvrVrwJQTMqZZ55pyxw8eLDBqhgMh0MMh8PoOtrDt8v0/7GcC9S1M2OuLGPNHo1z1JQYyRpjqfqPFQQEBLj+vuNgyPT4YA6OjHFkMIwJR8G4Hd8eZAwDwqZkTPWOVD36Sz/ocQGOPcltb0JFRRRknkYOUUal6acCb4w3Y5y4NYYMlIt3rySP9qI8hJ4Lpndmj4iwLN46n20xiPUyaRtpGaNH6cqzQZ1k+YQv+W8S3xM/+22zPQZ750o8tlK4MszRSGa81xyf1GPAvgJe70PrRGjuHXuMMOyLZgC0/shdb+crofLYSJu1VmkguNczFoThMX47als1dkx1Rq7PrB8effMKSMV22PYLVEaDoqdrpnqHlRQQkJaJzOCi6cz4vvQ0U/R+9TVJI8JujIXEkr45j1W11o3RephlFzLiTpszphkUHaGSKUbFd+d056iWNH+NYuccyyitB0kmfD1A7PmmUAyqY0xNJmcAqGoQLyXNVhgfCnKPKU1JkCmaaI6EBIQ+zppJVJJ72jV137h3pIHhTZ0mRjnOLlvnU7c/4wVl2F7D2hrQZymEKZuR+9ycA+MpZHJHFczpWjImUdQCQ30/DrIMeV2j4Oq9nHFfj9IWHQUoHxSrKRJcP6fMlqlFS8Zn5s9ngkMICY+44e5GZxnABnp2yMHmOJil1zlY7qYBWLOc+WMltn57BQCw7YkRlg4PcGxJVVTVmRdBo45bs32SoSYMSi1UdJC5NyrJbeb1pSrHMZYBldGruHvLnAd6WJQFs4dKp4Nz1BczZ1Auu+wyfOlLX8KDDz5o/37wB38Qr33ta/Hggw/iu7/7u7Fnzx7cfffddpvxeIzPf/7zuPjii2fdnISEhISEhIRNiJkzKNu2bcMFF1zgLduyZQt27dpll1933XW46aabcP755+P888/HTTfdhIWFBbzmNa+Zen9UDQ74vds+I47SK+8iFgSc1qTW4+smKqFkJWpWgUvDC3DLpmQyA5fc9dTqDDm49Swoqsz21FS0j/OHyJga+opFGuRMYsxdD0aNk0urdck43LTuXZuP1dDBsstfJYwoCTO5UtakjnxF233QzjZCvxd4YC3LVVsZPGdGNB1Bu9AWycPgdCgAJmpRusZYlfrdn3csHqzWgjNfuW/G26mGwkY6SMXghfty2ZjJWLc+2UZPlUGC2UF9xazZqB6u7meqUzAMhMlPRaF6zfpYGCDsRdK6BjNOzrn2PlHns5I1aqafFygtSm2eH1QoZQ5unTyFbU+oBePMuNAyO29QCRWBZNpcComx8J9Z+15AIJiQTquRgSFn3EaHDGuOQeb2CdBrJFEJ6iwqdfs00yGYjeIwbE94f5uhbKqJceyKu06V9DVotWXF1LVwQRXuJgrzWDGo+8rUW0mXn8hE/oV6D+p6bUAZWUDpF0ohrX5hpXb3LoPU3hewberyNqLPjspk7TymwMkzINSxGX2FDBgUk70eAIpaoOAZuRYuuibTeYPa/Exo5GfImBjnWxqd555v2WSU2yAUU2XeB7KQYJyBzamfZLZtCGzRowZb54DhAMg1GzSukC0eU8d5YBFz31jClm+NAADjo7mNBAI0W9ShvWFcQmgWrCozy8QsrswhHw1hPhGWKo4SNN+OYw0l/Pd9+M4y71oAnec9hhNidf/Wt74Vy8vLeP3rX2+N2j796U9j27ZtJ6I5CQkJCQkJCRsMx+UD5XOf+5w3zxjDO9/5Trzzne9cU71GdUx78jF9AP12p+VstlY4fYDtKQlJ9AASNWo7hl6yseoN2rFkx6DkyMFIn0CtYcikOtWFzDHU00ORIecuAqhgqvdluI+ceKbUnCGTKrLCHIeQDCaknAnqUOvnCor1Xjw2geoX0D5GblgEyg4YxDJ5tnWawnHFsJw/dul7R9BoJLM+3DamQRESjQPiTNpIrBh2D1WP/5uj3NMDGa8TChMMs3+pAGO+loRmuva0QUH7S8GgjTmRCaBkfkZT2gba+zaqAedBw9xJFBLg3DIoY8GxIhiO6V7T4phZvx8TtUa1DlLCao6oNsT0mnJ7AByMMehHBNn/v72zjZWrOu/9f62198w5Pj5+w/gtBF8rCU2ICW0MoUZAuFSxikQbhFSlrVQRRapKC1QWkapSVOFKUUz5QFvdNFRVe2n6ISIfCm2ktlFcFZwglHsT4lwckku5iYMdYmNe/H58Zmbv9dwP6+1Ze88cHxPjM8fn+UlHnv0ye/Zes/Z4r/96nv9jNQqfWdAL90toK1VhlvqwcGpL1xqmFDh5IGTt8OsMlxMye4LCGb0bQPH8jFIolYm+LOHcK987nN7EYsigUYSRpHWuqfwceFZRTSl+JnzPPDuosOlalGpn1aQsD36vqZi5A/iYMp0qNHOvGOP7UUgA4U7BFoAmat97jRgswP0uuNFtUvhCBg6Qx8CEtg/xaDO1xhnmmDxTqZjZpJVz8A3hFEa1HbI5vB0slC9EHDqdyt5AFoD/vSTktZdqYkoWafQtZX5PhmW2aO791GirUO04nA9fzr2fQjv66wDFGjtAcNBNfiZlpVFW3qelsCgGFtbLl0VFgK2SwjpRQPnrRLcDrFoBml7ut3WBvlN81dvHUP7sDRQ/PeY2vXEW9kwVM4AAQPn/5ZX/z1B5q2M9oYGyAAbeu+hYH8uOOLWzeNOitip6lvSUq2tVM9WEMywGJbVP2vt81BPgXYhBEQRBEARB+HlZ1NWMB+QjrgnZnGBAsydst4zGEzdSvR2fDRAzLNi+IRMgzGjXqFBjABuezknH0ZhV1qsmPlLab69Und7r5+lrKlGSRuVHmn1frySMlGpK8SnwHhUxCJzceYXRLXeADaOVYTEd3DUwtkumtox2um0yKv7kXIyajx62VucDKB+jw5/I21lAo86Xz8XbOF/uODxbZurMum4a8a/vVni9V7Tindxr1VaNiKsZccCH0hCWGRur0Dpfh/TmilLl7dnaZUhwJ0de86d9bQqbJgbpWvx+pQqRVe44A6twptI40Q8KCuFMlRQUzUbjQR0JqkRNqeouIbiQumXn06FglOvLXasxEfq17aJHNWpmusJVxpostB83Q7WzdppVj2N4Tfw+kpoR4jsK+JiZcCSyGLAxt/W5RYBTOCwZBP9SC7jsC68WcEWsIosaXEEJo3AVjxUy+AqlW+ff/P5SDIpTjcK955x2gYrFp0U1Red1xEqd1F4TvFdG9JNho9yg8ARlYVStsJpcrSHAxZzMVCrG/PC4HHe+lDn8cr+nptcTS0KCVe6PYsNQrDocYm9sPG76LXFxdqzae8yg4vmD4TMp85yaK7OE+7WMIlWPp6jkAE5FrGqNwhcIKiuLwv+H0ylrmJ5FcdYrKJ0a5WyN8qy7h82shfH7KmudCtpx/2/Q1BRo1ZT78KkpqKllUNPL3HEuOw463YuqSHaBLqALaplPHZqeBCa7wMB9pn7jJNTkCf+mGQwGBjOVj0GpNQqbK8dZdWXkqrE78fRbm77zlBU6H0RBEQRBEARh7FjUCsps7UZ5TUfDwLCRBI85qW3K33f+HhS9T3KFQUFR/ixnlYX1I0KrahReFaEsCx0x0yc/hzSHXoGcVSDcaK+uWV0fUiD/fhsdClU8P+4gmmW6NJSEptKR1dNhIy8gzHW71yESn498mqONpnLSHGnMp5oxP+dhqMZ2l9nTfm9UjsL7svf4efkRNTU0O3eFFHcSsOTmYAFXRTf2E7TjeTildvVvAGC6qLG6U2F56UYsE6aC0RTnrPu1jiOW2dpgtjbxMwfWZV+E0UczgwoAfuozkpo+MUZRGr8op7qFke+ZinDSj9QG5LJpUl0p1YinSYpBUE947IjRiPFUMMByr4oMbIHKUoxxCTEcfOQestNUGG2zbU3vE+68WxNFP5CatUiorBzUz8qrHvzes+zb17CsnlKuM1hKsS0VLGqvoQIAhbgG0vFIBcvoMypl9DXjozRSfFWhNAyltieyqKzCgFlh9G147fxpkjdLikUryGXhRAFiyP3Kv8+wLp5TowPzDLPwGwkEtTnvgfy+y/ugi83gLsa5opH/xhiVftsMpYweo4BBQzXh2SHNGjSjXHuDejIqs3Eumr/mznPG9z//+aFSs1HWxTL5hii1hamSmmJUqsxcFBblTI3ypFvuHq9QvuUydYo3zsJcdhLq8rfdtaw5CrV6hTuBbheoqpjhg1XLoaYmkBUa0o2z7vropdUrQKtWAoXPHFp/HMXUzwAAy4qjWFufjllRoSaa8fE0PatQh+umXNELvw285hPPiJsdFawyBFFQBEEQBEEYOxa1gnK2VigbikBzpNV8AuOjhjAqABDVk2btDncc5bxMQuyI6rg4g1SZJM6ph4yekLXjXpvMdbb0zV4qAwOFVMU4n1N3Ad5hzlwB0HHUUmiFCk2VYDh8frAmitce2oG3G8/MKBX5Oe3Unnzf4cw/GGVUZddh8M+ca/TF9+XKS1BdmupScxQFhJFZPmqfrRVOVek1bzeuMoVRXVBNlhnCWh/PsmFiFuumZrBi2rlAdibd+qrvdj4708Gps8774FS/g05lW5W5w4imZ1O8SqiJFPo297LomvDaj+IUYcJQVDpcNV63bdbWqMmmOI46KEp+JMRjf3y/DSOjrtboQMNPt3uXZLdtslDeIyXFsvCMufmQ+xv549jgd+LVDfY6+KBU4V7yMTBJQZl7GMczgiqkGkMDNUCNCpWq2L42xjpo6KiaGhTZvV9A+9+HdL+rqJgQEOJw4GJgCubz0bdgypYz+AxVk0tNsb8F1TN5m+R9nKvNoT4Oj0sj1tYEptJY1748a6lQyeSUZ9vNlTmZ1lG2Lv/dZh4qQ7IT072msntWqVQ52t37NPI+5xWTNWuvdP5zL4f31pR8mjQ5B+cqtoOLifEhZ5gFq8zsFRTu01KaVBG6c6pC+baPV/lZjbI7i2JyBgBQLDsKM+U/Y0UBPd2BmvSqyETpFJKO/6+90Ig3pdZOWQlqizHA5CRo9WoAAK1bB7V6lds0PYnp7k9hyhMAgPI1C3N6Cm9761s7MKiii7DK4jeBRgwnmLoCoFfz3jE3oqAIgiAIgjB2yAOKIAiCIAhjx6Kf4gn23C2bdbSDKzlhmoPLzhXlQWypYJVGRxlYcvK7tho9VaSURWVbsm7HN62BiVM5QEo9BEJaXyMorTFFwoPUXPn4EDRJWWrxqGsMxPLwPngwFazLP08xeTWkPEe5VauWcdZcT7gjjdri+vPQ+Ed8XjNYtvmdN2XnLOOuESiX5GCVBTT2rcKZijDrVf2akuzcMcCEyVO8S+2mdgBgTafG5V1nQ71uagZrLjuDyTXerGm5n14Mc0dvAmd7PtiaQiAsk/FhMWlCwT3NpifbxcuSfO2OEFp6wgDLrcaqjp/WqRVmfeDeYGDRowp1DNrOLfG163HxtYFG0Sq4GKaOeD8Ka8MUZh6syQ0TyU9B8mmBMC0Z2r6yYQrUTelUFMzXKE7pkA/EDanNIUA2TO1o1pMKaBgYFpibT7XWqDFQbjp3oPoYoB8D5Ak22g0ALihexekeA6MKFBR+C/IpnxIFihAEr5Qvouj7KykXdJmlAPvpHj+NEwuQaoV+TA0ndA0vkaFalgNzTenw6d+QSMDhwfQdrdANswWsyGDzc9yxVfZbltk5UEpfVwoYsFRnPtWabA/SVG7zng3np9i61H4UXxeqWQqEpSgjBeKmYFt2f1EjGDcEi8J9b6kYqvLHSg0RAma1Mj4gOEzZE9QgTfkU/TJN02kXTJuWLYrCWxUUNSanzmLiMm99f7mBvmwSasqnEocpHyBN7dQ+BfnMWeDsWWDlSrc8NQXasMFdV78PfWYWEycOAwCmT8zi2NmJeB0DqzDrp2p6Q0IjLFKfqykZWdYW0dxvPoiCIgiCIAjC2LGoFRTnL0WttNlhKajAcOt2ni7KxQSt0tN4BwpAgSIUoiKDSeoktYPS6KZUGkaz4oBehRimJlBDsSFfxC0VpUtpkbChkNpo1aEZdNi04w7rgori3pO/iQc+hpTTYPkd0j+HpTA21ZymstMaZ88/Tio/zjyKjuUF2PLA2dY+Qw6XAqjdcq+26NUpALPUChNGxdcKQMePfCaMU0+m/QhnRVFhOqQVdwcwJSXr6a6C6mgU3sSvc6aGOeHNAEljpjZRMSsVocuC6IwiGJMCDUMRM6CtS/HlUhOmipr1Ox0DPIuzHZi+wpnanW8PA1Sos1TaIgZyGmh2s1EIfPWfVlkVAygtuXspjoRZG7vjslE7nBFfKmDoFJOeT5vsW4uBV0wG5IJec4sw/5ksnZgTzt/ApHRgpbOUX3ceFEd9XHmxcPYCXD21zIDOqvRaw6CmAWpV+s8sUKBAGUzqyZ2RayOCVTqaz+m43bcRD6a37r7sq6SodFh/5G3vSiskNXiYkkKtHhPOH/GGCepEwY6Tp3vnN1LbkoAytQPRrCsp2ABT1dj7kpoWfmOCusHONdzPQYkBNRSWvFQAL5IYgmvD9XCl1ShAUx6IrxUPmVVQ7DdUK2RWBuE9rfNEW+nkadKGFfgstLPs54pKsO03mjBxtoPJ086ifvmJPrrHTqNY7fq5miqhlrn+pkoTsiDch/RdOK/y9xZNTwMDdxy8fRw4dgaV/z2ameng1KDEqYE7Li9zMKC8zUIyAv+Gw/+zFSEWmJwPoqAIgiAIgjB2LGoFBUjpilnabGNAMGyU3DRNCsWOUtoafEl7tzJYUQNAx+qoRAC50VQR4zbyEUsYfbt5c7/OUjbC48UJ2+tspnaEcWO+b3rN12f7jEivjLO1TA1yCoqGrsM+ys/rhpFIwzCNqShKqezpt7kfRmyL+4x8yE6qwbB988KCo+ORmkoMsTgHgpsT53E7QD7nP+nzGZcVCtMlxZiTSUOYNE6lAICuSXEFvX6BzpkaunSjFD1BUB1AdXx7l2lu2SiLwltnAy6Vz9Z66IjCIphoBVO/5oivPYqb8EPNVSUQen2hNLqmxPG+GyWdGhj0qY4xHqE/AEDhXwelMCht4bslJNPDlHbJzpmQGYaFeyIYuvX9ip616FuLWevTfFGh71N+SeV9WVEqMRHOI6gkyhuohfM3UCiCDbpXBmPMiTdm4/dBOq6GQRktBjS5dVYNv6/c9bFJd5VM3XhwUAEN2/jM7NqUYvsqVJTO30JFS//aAtYAZYz3UD7lOyhv6ZhDfxfZOlIp8TncPyk9WDViTFichVJQSCqOaaiYlpJxV7BBD2r1sLTjtM79dg6zJ2gW79RMLQnH421QM0WlUM5MrGLLQaiuVVJRAMRSFvw+bJrbtS0U2/2/GQMXXvM4mGTbT+5e8/essQQTCsySK+oX7AcGgwKTp/vovuHukXL5LMykszXQXQXVUVD+t0tNnoZacRLqtTfcclkAZ128nD18Cr1X+3jroLPQP3xqOd7qd3DKG7XN1snkNFxPKg6Zt49FSzCbN6KgCIIgCIIwdixqBaUIT8HsUZ4rI8MK4wVClHE2Ekd60jNIj82avPlROF5j6MHnN9tzs240Hmy++zXFOXSXnUCtOeC8GF0Y1dkhWQkWtQrLNhtR8hHdMFMqPhbnxQ75tphBwGy8NaVR8jDTqSJuC+3Bt/PPby/H1/OfomzBlRue+dCMORn2Edw8iofmOAt3hY5/82QBTPlRyFRBmC4IU175WGYsJoyNJesBoA4W+bXB2dkS+lQo+16h6FcgP+179kQZjdoGVqPUBMW+O0Kav+WKiXvdjgMAeMxN3scGzEI/9NcJQ1jRUfFdpeq4mA+b4jn499RUvmpLoDCvTyqqIMEG3zRGtfF9lCuM/ZrQ9/PiPaqjyRrgTNMChoL1mT8uTFRIQo/m/dA01L/8HEbfgxoKhoxfX0BDo4rqhZ+/p9EKSiCYOKaChTp+vxaK2bT5/dk58js0KJlJ4M2vhZvghdig8F5S7b7fVCRGqSyWeDvlv1pctYsKiU0HMcw0zSQxKL7mBQA5TQO4UTTjaZpKiyV+gFwZJQ2vSLnlmisZpFBnbaZGxgGGfZqKyrB7MGQKpYwk10Y1s/GPRQcVwapU8NMoQsl/FxRQ+5oI/crgdK+D8qQvQmhqdEp375RlDa0J2nhF1FTQZgBTnorHGsy648yc7uDY6cvwhs/cebtf4uRAo88K/fEyA4XmCoozh0yxlHkfLUa03zBEQREEQRAEYexY1ArKhCYf0ZysdJ3FtXsdRsLDS4z7f/nDLnvot+RGgYBTTmoimBHRx1oNj2kIx6ks824gi4EfHdagqIwEmvEiPBuhVlUsUuZeV3E0VmMQVZOQZWBpdMK5Vj7KGxpQLuMAQFbcMHpeKJOt4/PxYX83x29ytQUqZiUMU1t4EbU8dkS1RoWcZmZGfJ9XZYZ5RzQ9XMLrpKioeA5GwbWJ3+YK/ilM+rtlWaEwVbhzmPIZOyHmZHlRo6NttLQutMWE39YxNcgqnDnjVJKzMx3ooxbWzx/PzHbQr32WiSJMmDoGAbjRq8asrwpXs5gE18+5z0SaBQ+xVcEnxcLZzlcx5sP5GADOE6W2KT5gogBUreO1OF+M1B+56FCzkauDshG+IZed466trV7kMVrJ28Ttr2F8n+sArX7DY0lMQ3WYC57l5ryBcrWS97OYvUQu9iWUvQg0i4nGVhgSmxL2LZj3SonCX6df1hq8cCP/jQl9N1dtw/tyq/hSIytXMcw7qdlKo5QKhaQOBDWlqaKE97st4d53Y/+gUFgAJvyuKQUzJKqk6X0SjjOXjb7zWlFsOSecX3x/eGGbMXLpfgkxJ+2W4PunHdxvTroG12ZJVYxtq9wJpvZ02UEpzlCx/ud0tnBLOBEjxZvVSkX/lBoKqIGzzVP258Gv06lr+bX0revns7XBTGUw439vZmqNipJXTFcnbTfEywQFpVDUUt4q9n0244XmQhQUQRAEQRDGjkWtoEwWwdEw+SaE0s/htXuSTmoG51xzmmH32rqRJ3dVnGvGmShFOIfRYZjHH1iLQVA9/EgtzUlTQ01JcSWVV0VCkbIaFQaqF7MJahqAvGJSowJRnebH0VZSlJ9TD2qI4ooKo5k34sY7SVkJrpkFlU4lUcFnohiqsLjPduszRYUrLKRaitR8cIpNUmMKpeO8r7UKhR+tAYBV5EduaeTZzEAKWQgTBpgqgGk/aJ4u6pi1M2EsJrTFMq+STBUDTHUGmPTeJ91uhbKT2r+uNHp9d9ud7ZXo1wZVUEUoxZUASTUJ2wZWoefVlr7VsejWwJc6D3ElNbl14bUr9Ia0zEqjO5+R9HrgFT/AqSs06p4hBZvNqTfiIBrDThefErY1Ms5YbIP1ykvIDuoOiVUalSk2F9ypNnxmk1SsU6EAUKgQd9KMDkkjO+ehkjL8mt2WGtdmieIAnCuFhVboap0KdWqXfROWuZLQVP+A3O+DqyTKr+MqxLkg9l2OKugYIueG+U8pOBWn9KP60scnxK4DJL8STagoL77prjeobcjVIJUUi5YaALB+7f746fOvRjOlI7Rl0yupeWzeHvlxk5KlKS882Iznic6xyn0X4TemmW2Xnx+1zqkZsxMz4lpKfnqd/l90R6usikVIw3Ye49a3Om7TcMqI8o2UFw516kzJvrumglKw8+2fR5VQUVAEQRAEQRg7FrWC0tWufHVNac6VwOfJk4ritp17VN6sUwEAtfI1KtjTqkU+ekgjND8S5aNSmzJ3BrCoQxYPalTemzJAised2KiuVKpCjSoqJpUaoKYBbFBQUKGmgT/3gXsvpXohzSyDoHwAuWqiVD5a1Nm2oIakeBXFFBOurhhVQnu/Tncc9lo5BSVtUy0PCx67MozQLnn2kYrxLYAbLRt2TIs06jJAlrvfMQrd+Nr52kx4lWS6BFaVNVb7aPjpssKEqfy+Fh1TR8VkcmKAF15bP/Scb/7gIZBVIK909KoCJ/sdnB64duhbzZQP5b1Y0vKgoZKEbS6Dp1FDJWyzoc6Un6O27jVl+6b4kcqyTAjbjjUJbWsUoho1DE3t0XdcbmXMpLnxEjqLtwjuxanGSlIHRrkAz7U8LFOrJmqoOg3VsJEZxn2OXIyHSqNHnd5t4dowqKmVV6tCezdjRzpaoeNvvwmj0NEU+2ShUwyHacQSAPxalPt+2fU3VbCm+ttUBUIcxFxKioKLR+CxLUnpIFc7SodlP6rm7aK5yp0fvFCIcU+lQlJiFHln3NR+XBEKKiMAzFpXqyq0fbsN0rVp3/+asSMBQlIgQ42srC2i8kKZ4mP8+RasXcK5l9E51qtBaMOzhfi18veF1/wc+LXl6mNQnsJvpwKsjt9zTSn2hvx9Ft2eY12otvoR1B6TnRNrn0Zbdc5DFhEFRRAEQRCEsWNRKyhahVEFQQcnQqhsrpsrKHVj3tu9My03K27ynHo3mvTbbJhHT8fNK3VS5nvSrB+SPB3q6GPizte7m8RsHIvau2Za1KhVUlAs1bAYxO01DWCJxaMw1YRQt30amiOioKjw0TIMLFdavGKiY8wMoH1dEUINpZJKQmRhVBlrkxQs68FCo1AFQBVb5qM0rorM7S/BnXGDm+acI3v/r1HO1yTU1JkqXZwJ4P6dMhYrvWKysqwwXVZY7h1gV0z08P03L5vzvAI3f/BQ9CjozRTo9wv0vGIyUxU40S/xto9JOVVpzHiZrm9zJSR4W+T9tRnH4duEmH+OPw6PweD3RHgv4N2JG/1CI8WatCMxEtwVNr433IfnmHIeFlcSPVMaWSlcdQij3vk4EXO9MJxniCkL7cPjU5pZM6mit1MECqZ8tNWC1CYVue8SAGZrjb6lOMrnnkmuLxImfCMvMxYTmtDxwRpdQ9kIteklEbIxBl5FCJ/hVLm8H8UsR5/RNSq+oqlsNOFKA1cHutrFIzRH1LwfxPMJx/L/BtWh61WTjrZYViS1kmfMWKjWchUcVa1Gr9bo++WKzq2gD4v/CKQYLuXrXqVt3DE3/AEuZiNTTbSNNXSc+3j+28YzasJ3E89tjlPXjR9zvtxUV8I1uBUaBEId02pSZk5Qo/h1D8uMctvC95hOcq6ecz4+V6KgCIIgCIIwdixqBaU5EgzEJ0b/ZDdXLZ46ezJOEcYDm0ZYA/JZCP69aTQ7ej47eJ1UlqIbJuDiTgbKx5EEvxKmmPC4E141tVJOFQlxJuQjWJpxJ26bU09C9g5XT4Y5Xiql43oemxK3j3iObe6bxbLE2I+UHZTWtWNQgitoWE77Dn/c5lkifB+jdHSzLZVhWREapUpVXztGYZlxniYAMF06R1jAx5t0KqzwcSUrun1MFBUK78BYFO2sqP/+i69my+SVkKqXRkJ1rdEfGJzuOzXpeK+D44MCb/sKoScHwBk3WES/9qO+mAEymqZ6wesKAblTJ9C+ZygbcbENftQ7TJEaVjeKw7OwuOPk8H3T6+Bl03RmDlkypUKMRwnz/VmVWpYVMSzzhZ9/9IJpxPs4z848EybEQRiVVBMAPk6EYjXrro+/CNSkYrbVrFXo2ZQ14a4nKQWThjDh+9iEtugai44OHjo21mIptUVh6ixbhFisUr8ysTZL3xr0ah2XXV9gKhJTA4KCHJSGufpMyHrJqmuz+AqnFgzvG83YFtfaSWUIfwAwWVb40YkVQ48zF1df9jb6tUG/cvdWZXW87lA5OHiHhEy/OsZ3tXtq2qYypUOz6zaafDxLfj+ZUENHpW1NN9pYibyhLAWIqyukGsvt/Wz8LU0bVUsJta7eUlAk2bGcP5LK/n9sftao36T4O87uH85grh+z1rEEQRAEQRDGjEWtoJytnY/FqKh0oD0X1qygyj1TePbNbJ38IHo1ZQ61YYQ6qhKxG53lWTsDHysScnEA5wYbVBN3rjZTTaxyy3Eb1VEV4Vk6TYI7LDMiTNuGKiQm2x7UCxdvorPsHbecVJHoo+IzeIxy6oCGgaECxseoFChgKCgmGoZM9EUx3lGzWUfFHXe4gtKsXxT2LZSOI4KO0bF+TtcodI2Kc/yTRmE5q6EzXRBWlO47WllWWNXtY3nHxZx0OxUKU+O7PxuenQMAz3xvMwDg4x8+CCIVXR+Jko+HMRZGEwo/oio0Of+DYV8UQsZAWh418CBqjsba+zQrqeb7jFCpqF0bJbZ7U11hMSJuMb2x0MmPA8hjO4bRdPVUimWwDI3/oLicKRlshN+MhwneMQB8tkdadvtQpr5EZ2DlYgu68TOd6tGN6gZlsQUExTKvnM9EcilNsSQdrxp0vILS1TUKppp0imrecU8f3fR6dCeua43a5p/JqZnXRRx9s+UBi+no13pO9SBcd1AnTOznFkZb/OCtNa1z3br2LdRWN1QH9toQcGJel53R/Kxf3PBG69y/d+Tyoe/9yPo3Yet2WwA+m4Wdn1LuvgYQj/eR9W9mx3vx9bXzPu+ta98aup6fOzEVp/b9KS5btz38pvCMHsRrSauUYhlTZFHG4wZn5fB+ypQ/F3eSjlex+JRhaptir2drhfkiCoogCIIgCGPHolZQ+vXoKH5geGYBHyU1HTX71h0T8LEjfmPlvUySXwQlV0g0singVJTgdRIqECfVJCkkbnsec3IuogcJAFIGcbpblUCIR/HqSVJL2l9zUEG4QhKOz91lFXRUQYJTLI8v4U6xBiWKoJL4GJNQBbZwtWb9a53VHRlWafZcbqHD1ISQ/VFE1cQpJ0Bygw2ZOssLiyljscwrKFOmxnKvoCwv++eVqcMhUrAVG43WCtY7xVaVjnPZgIvinzAWkya5xYatfa0wsMm/pFlXati8c6A9fz2/c5/f1HD+WZkbJ9K9WCjmOaPyTBynqLTdJuP5ch8HllkRjpV8MZxiwlWTiahAUFYTSfnIkqigMOVg4DMzgqNu+Hz+2TzWotQUY0e6XuUIsSKltlENIFJZNkYV61Ql1aFj0vuMtjHOaf8b59/3AnMpfU2uf+8RWJt/p7HKL1N7+gPjXY/dPVxbBcXiOML5u+uy87533sk99k4YpZYM43wUjwv9/vNtjw+tOZbFpBjtVLGo+LCbP/THQFBdePzPZIxdcfcZV/+cAoi4zN3bFVsO60Zl7/Xn90MTz0MQBEEQBGGsWNQKShVGlCOizUP2Ao9KzxWUFFfiYlBY3QzKFRheJycoJHE0C2LZFm6/UfV1nKOpH0kRUKgixqAo7wMYvQEoOblaZaGUjlk7gIvGtjE+xKZ9YVthBU1VhMeOGFUOVUUMymzfgoqW42uKK1HeKzY4yTqFpNDhc1O0eOEVE+5nwVWT5pwlr1CrVHsOlY/iQ90PIHlLAC7GZFVpsabjVJLpcoAJU8cR7ERRo+sVlKKooRRh23teB4CRzrCcW64+lM5JI3ZEpRRUyAAxhKKw6NS+8rEdoCYV4yuWGY3lRci+UOgzH4LKJsdYoO3Zg/SRLX+Un5fcFTJF6PO4kuAqymNFkqMmUCqbOWwWQ+JDRsHVFq5mhGyRMsaHpJgNo9225J5J2QjSzZt75coGbwu/zbctP6/gX6GVi6ngGTUdU6Pw/YjHPjRHtwDwX8dXzn2xF5lvH9ow732vufwt1CZkBoYRu2uHi6WECDk/fHv1ee2/ZcWpqOJSqMOTTMSiEmgUeVUx3BMu2y330/EKpI/h6geHbOveF+vR2ZQxF947Xy64grJ7925cf/31mJ6exrp163DnnXfi5ZdfzvYhIuzatQubNm3C5OQkbr31Vrz00ksX+lQEQRAEQVikXHAFZe/evbj33ntx/fXXo6oqPPTQQ9ixYwd+8IMfYGpqCgDw6KOP4rHHHsM//MM/4KqrrsLnPvc5fOITn8DLL7+M6enpeX9WcG3kc/NA29skuLoSkrdJcJJM3hHBATa8NykfzSwJwNcEYdkXYRo3WSCETBeCho0jN8v8P6JzbFRmcifZClVUYhTVTsvwsSM1BrCUKuXQENWkWRcHAAwVKNGNsSIGBYxtZ9S49+lWRo1myofLvAkjKRXrprjlXBUxqlHHhY22m5VZ82vw7dq6tjSSz2qAKKR6JtrGLJ2VZY213T5WT8wCAKYm+iiLel7qyHzg9jJkWdS/ZpVIC4suqqyORsdYTFXuu1hRmeh82bPOBTPMAQ/8KH/A+jafD+a+HkE5BOYffxLIMmgaKomLMUnnzlUQ41+H2AzuBhpUjrSvzXwoCmXj53ZMjQMn02/AlctP+0yopLZwLwle6ZVnWITX8xnZf2jNsWwketWqE614nOh0qu28R63nO7odd/a/cVmWofLzxmoIFx9+bzW5cvnp6AFT2zxeJc4mNFRGwMVzDazGrP/tmq01Zmods3X6SjWqqc//fC/4A8rXvva1bPmJJ57AunXr8MILL+CWW24BEeEv//Iv8dBDD+Guu+4CAHzpS1/C+vXr8eUvfxm/93u/1zpmr9dDr9eLyydPnrzQpy0IgiAIwhjxrsegnDhxAgCwZo2bmz1w4ACOHDmCHTt2xH263S4+/vGP4/nnnx/6gLJ792782Z/9WWv9soIwaUJmTYg2Tpk4PV/3ImXqNCq+DnGH5Zk6zdFndCxVIZbEj9aIjZLVEF8WFqMSltN6my075xM/D4iCOclW3vvEz32jm2X9BIXEnZ7OfEcKMih9LZwSBUplUPrYldL7hgSnTq6CBGWDqxVcsVAqr+IaXDbdcVQcVQN5bAiv+BngCkCIBwqvc7fLHIV8FF9o7s7JRvSKwCufvlujP66euGUeP0MwReodWlt06wqT3kl2YHXMkgijklBbpLIKFek4J1yz+eDKx1NUsc3ymhrznfPlSkk4X16lNGTfAKn2SjObJXi8dHWK6emY2vliNPwi5sPB08vnve87pal0jFucyDghqsmly3zutfevcv+fN51tq1pj1v92zVQFzlQFTldeUbE6xqdU1HbRnYt3NYuHiPDAAw/gpptuwtatWwEAR44cAQCsX59L6+vXr4/bmjz44IM4ceJE/Dt06NDQ/QRBEARBuDR4VxWU++67Dy+++CKee+651jZeqwNwDzPNdYFut4tut9taf1nHYkIHp9U0Vz/rh+cztXJPa1WIWs7dYAlg3geUKSjNuBZe1VWTm5wPZ2uUaqktzRolMZ4lc0GBzwZiigosrPcOCYoKAHSoff0aOmbNKPbaqSIapQ4xKLlCwjNoCj08PgRIVT0VW+a1TxRS7RNXuRPRqXXK1M5rxNetmTSpNofLyqDMHbFv0xP4bCP2ouIR45T717h2gD93fw4+tmGqqLHcf/5UUWFZUcWsnQvNN//vewEAN111KFNNmv2CqyhaKxSFRddnFlmb+hGRQm11dLQc1MZdt01zxHUjCyW4ftaUPD4qq1r1V0ah4eM6wrKvQtuMHQGC70Xy8eiYGqWpnfMnZKQtCJci/2+EunjVqhNRMZ2sKkzVBaZ9bB2vKj0ghVPVXL9COe+agnL//ffjq1/9Kp555hlcccUVcf2GDS6tramWHD16tKWqCIIgCIKwNLngCgoR4f7778fTTz+NZ599Flu2bMm2b9myBRs2bMCePXvwS7/0SwCAfr+PvXv34s///M/P67PWd3tYVuQVOAdWYcaPxE9X2qsJbn+tFJQfevcRMiLccmWd2hIdQBUxl1ZXTyUtt6u82ta8WvJbYIstZYXIYBSqEQ9SKJUpHaVWKHVQQhQ6/nFzwjgPEF6BdVSWjEbuWdF05uXVMMNxTKy5kR+rVBSdWVeWFdZNnsVPTrl5zZlq9HU2mdAWEyMend87dSbGZwB5dVHtfTEm/Ki+W1ZY1nW+MS++vhbH+x1cv+YUAOBjyw8DAP73wY3zPq/5oDSgYVMWD9BSVGLdER17CQDEuixA+g6Cyyd5daX2rrRErIKtV1qSusLax9dhqRtzxsNwWTHpOw9Ooe+kHowgCEuD9608mVfoNhZGDzBhnKLCf38GVuPkYDD0OMO44A8o9957L7785S/jX/7lXzA9PR2VkpUrV2JychJKKezcuROf//zn8YEPfAAf+MAH8PnPfx7Lli3Db//2b1/o0xEEQRAEYRFywR9QHn/8cQDArbfemq1/4okn8OlPfxoA8Ed/9Ec4e/Ys/uAP/gDHjh3DDTfcgK9//evn5YECACs6fSwrdOYK2a81+PiQRxu7+gJuuaddnZNYa8Dm2Q+hYjGQ+0oAwZ12fm6dzequPAsmeIfEGiWq6cCpwN03eZZMqYOTZnivjRlNE8ZiZVllqsWKcuCvRcUYkHA+b/c6cb9VvoKvu672SDvEJbhral+4ZjEKhaljdc4LNfI+dGZqzu2/uOENaD/iH1aT5HycM98JSrs/Yp2CmDLiKh0HVaydKZb28wpakO2CZ0jZnr8Nako4rrUKdajGanWsaMvPIZ7vkCqyXEFTrCrtvsPr5r54QRCWDO9bmdt9ZL8lQIxHs6RgKCj9FjX6mC/vyhTPuVBKYdeuXdi1a9eF/nhBEARBEC4BFnUtHqMJRlkff+JGebVS6Hq/hZoU6iLFbXS0Rt8m35SstonP2kmqiWq9HqWoNOG1ZFyWDGV+ISFWpIyqSO7OyZeDz8SEqWOWy3xoxnycHJTzet/xfufcOzHO9RTdry5uFzsfj40Lya3XHATAHGW1isuhWDRZ3z48vmZExg9ROzOMw+NamihFMCa8ttDaYtS3z1Wad1tdEgRhcRJ+5+1csWwjYtsA7kB97tpbHKlmLAiCIAjC2LGoFZTaKpDOawYYTegEh9XCxUJMGp+DbVM0cVsV8XP2PNaEfVbmnIfRCkrwjQh1ZjraYpmxWZTzO+F81JOLyY9OrFjoUxgLnt1/5dD1N38wNxUc5jLLvU/m8lA5H+KIxZz7IBc6k0kQhEuLH51YgfevOhEVjWaV7nPB/7+s51B/m4iCIgiCIAjC2CEPKIIgCIIgjB2LeoonoEEpKhUWWnPjLo2OZUGzwYiNcgvwOY/P7L/D8uh9Xcpv4QMP3+51fu7pHWHxEizwOTe+77X4mgiwtc6WA8103/kyVwDt/3pVpnMEQZgfoTgg0LDsUDQ6BIKaIRHv/P8/UVAEQRAEQRg7FrWCYjRlaa0AYMCKm0GByMLqUCgtpW5aFizrlvNj81SoYEj2Vq9dsG/Emc3/IoQlx/M/es/Q9TdddahlotYMRGsG1rrX+T5KE751YNMFOltBEJYq/Lfl50mI+G/Tp+Nrcx5qsCgogiAIgiCMHYtaQTl4ejmWmfmqGnPznqkZAMBrZ5ZdkOMJwvny3H+141UEQRAWigtlIxGKxgLATD0/01BAFBRBEARBEMaQRa2gXEhEOREEQRCE8UEUFEEQBEEQxg55QBEEQRAEYeyQBxRBEARBEMYOeUARBEEQBGHskAcUQRAEQRDGDnlAEQRBEARh7JAHFEEQBEEQxg55QBEEQRAEYeyQBxRBEARBEMYOeUARBEEQBGHskAcUQRAEQRDGDnlAEQRBEARh7JAHFEEQBEEQxg55QBEEQRAEYeyQBxRBEARBEMYOeUARBEEQBGHskAcUQRAEQRDGDnlAEQRBEARh7FjQB5QvfvGL2LJlCyYmJrBt2zZ885vfXMjTEQRBEARhTFiwB5SvfOUr2LlzJx566CHs27cPN998M26//XYcPHhwoU5JEARBEIQxQRERLcQH33DDDfjoRz+Kxx9/PK770Ic+hDvvvBO7d+/O9u31euj1enH5xIkTuPLKK/E/r70fy0z3op2zIAiCIAjvnJm6h8/8n/+B48ePY+XKlXPvTAtAr9cjYww99dRT2fo//MM/pFtuuaW1/8MPP0wA5E/+5E/+5E/+5O8S+Dt06NA5nxUKLABvvvkm6rrG+vXrs/Xr16/HkSNHWvs/+OCDeOCBB+Ly8ePHsXnzZhw8ePDcT2BLhJMnT+K9730vDh06hBUrViz06YwF0iZtpE3aSJu0kTZpI23S5p20CRHh1KlT2LRp0zn3XZAHlIBSKlsmotY6AOh2u+h221M5K1eulI7SYMWKFdImDaRN2kibtJE2aSNt0kbapM35tsl8hYUFCZJdu3YtjDEtteTo0aMtVUUQBEEQhKXHgjygdDodbNu2DXv27MnW79mzBzfeeONCnJIgCIIgCGPEgk3xPPDAA/id3/kdXHfdddi+fTv+9m//FgcPHsQ999xzzvd2u108/PDDQ6d9lirSJm2kTdpIm7SRNmkjbdJG2qTNu90mC5ZmDDijtkcffRSHDx/G1q1b8Rd/8Re45ZZbFup0BEEQBEEYExb0AUUQBEEQBGEYUotHEARBEISxQx5QBEEQBEEYO+QBRRAEQRCEsUMeUARBEARBGDsW5QPKF7/4RWzZsgUTExPYtm0bvvnNby70KV0Udu3aBaVU9rdhw4a4nYiwa9cubNq0CZOTk7j11lvx0ksvLeAZX3i+8Y1v4Nd+7dewadMmKKXwz//8z9n2+bRBr9fD/fffj7Vr12Jqagq//uu/jp/+9KcX8SouLOdqk09/+tOtfvPLv/zL2T6XWpvs3r0b119/Paanp7Fu3TrceeedePnll7N9llJfmU97LLV+8vjjj+MjH/lIdEHdvn07/v3f/z1uX0r9I3CuNrnYfWTRPaB85Stfwc6dO/HQQw9h3759uPnmm3H77bfj4MGDC31qF4UPf/jDOHz4cPzbv39/3Pboo4/isccewxe+8AV8+9vfxoYNG/CJT3wCp06dWsAzvrCcOXMG1157Lb7whS8M3T6fNti5cyeefvppPPnkk3juuedw+vRp3HHHHajr+mJdxgXlXG0CAL/6q7+a9Zt/+7d/y7Zfam2yd+9e3HvvvfjWt76FPXv2oKoq7NixA2fOnIn7LKW+Mp/2AJZWP7niiivwyCOP4Dvf+Q6+853v4LbbbsMnP/nJ+BCylPpH4FxtAlzkPvJzFia+6HzsYx+je+65J1v3wQ9+kP74j/94gc7o4vHwww/TtddeO3SbtZY2bNhAjzzySFw3OztLK1eupL/5m7+5SGd4cQFATz/9dFyeTxscP36cyrKkJ598Mu7z2muvkdaavva1r120c3+3aLYJEdHdd99Nn/zkJ0e+51JvEyKio0ePEgDau3cvEUlfabYHkfQTIqLVq1fT3/3d3y35/sEJbUJ08fvIolJQ+v0+XnjhBezYsSNbv2PHDjz//PMLdFYXl1deeQWbNm3Cli1b8Ju/+Zv48Y9/DAA4cOAAjhw5krVNt9vFxz/+8SXTNvNpgxdeeAGDwSDbZ9OmTdi6desl3U7PPvss1q1bh6uuugq/+7u/i6NHj8ZtS6FNTpw4AQBYs2YNAOkrzfYILNV+Utc1nnzySZw5cwbbt29f8v0DaLdJ4GL2kQWtZny+vPnmm6jrulVQcP369a3Cg5ciN9xwA/7xH/8RV111FV5//XV87nOfw4033oiXXnopXv+wtnn11VcX4nQvOvNpgyNHjqDT6WD16tWtfS7VPnT77bfjN37jN7B582YcOHAAf/qnf4rbbrsNL7zwArrd7iXfJkSEBx54ADfddBO2bt0KYGn3lWHtASzNfrJ//35s374ds7OzWL58OZ5++mlcffXV8T/Tpdg/RrUJcPH7yKJ6QAkopbJlImqtuxS5/fbb4+trrrkG27dvx/ve9z586UtfioFKS7VtOO+kDS7ldvrUpz4VX2/duhXXXXcdNm/ejH/913/FXXfdNfJ9l0qb3HfffXjxxRfx3HPPtbYtxb4yqj2WYj/5hV/4BXzve9/D8ePH8U//9E+4++67sXfv3rh9KfaPUW1y9dVXX/Q+sqimeNauXQtjTOtJ7OjRo60n3aXA1NQUrrnmGrzyyisxm2cpt8182mDDhg3o9/s4duzYyH0udTZu3IjNmzfjlVdeAXBpt8n999+Pr371q3jmmWdwxRVXxPVLta+Mao9hLIV+0ul08P73vx/XXXcddu/ejWuvvRZ/9Vd/tWT7BzC6TYbxbveRRfWA0ul0sG3bNuzZsydbv2fPHtx4440LdFYLR6/Xww9/+ENs3LgRW7ZswYYNG7K26ff72Lt375Jpm/m0wbZt21CWZbbP4cOH8f3vf3/JtNNbb72FQ4cOYePGjQAuzTYhItx333146qmn8J//+Z/YsmVLtn2p9ZVztccwlkI/aUJE6PV6S65/zEVok2G8633kvMNqF5gnn3ySyrKkv//7v6cf/OAHtHPnTpqamqKf/OQnC31q7zqf/exn6dlnn6Uf//jH9K1vfYvuuOMOmp6ejtf+yCOP0MqVK+mpp56i/fv302/91m/Rxo0b6eTJkwt85heOU6dO0b59+2jfvn0EgB577DHat28fvfrqq0Q0vza455576IorrqD/+I//oO9+97t022230bXXXktVVS3UZf1czNUmp06dos9+9rP0/PPP04EDB+iZZ56h7du303ve855Luk1+//d/n1auXEnPPvssHT58OP7NzMzEfZZSXzlXeyzFfvLggw/SN77xDTpw4AC9+OKL9Cd/8iektaavf/3rRLS0+kdgrjZZiD6y6B5QiIj++q//mjZv3kydToc++tGPZqlylzKf+tSnaOPGjVSWJW3atInuuusueumll+J2ay09/PDDtGHDBup2u3TLLbfQ/v37F/CMLzzPPPMMAWj93X333UQ0vzY4e/Ys3XfffbRmzRqanJykO+64gw4ePLgAV3NhmKtNZmZmaMeOHXT55ZdTWZZ05ZVX0t1339263kutTYa1BwB64okn4j5Lqa+cqz2WYj/5zGc+E/8fufzyy+lXfuVX4sMJ0dLqH4G52mQh+ogiIjp/3UUQBEEQBOHdY1HFoAiCIAiCsDSQBxRBEARBEMYOeUARBEEQBGHskAcUQRAEQRDGDnlAEQRBEARh7JAHFEEQBEEQxg55QBEEQRAEYeyQBxRBEARBEMYOeUARBEEQBGHskAcUQRAEQRDGDnlAEQRBEARh7Pj/S5b5FDXAA8EAAAAASUVORK5CYII=",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.pcolormesh(cmip6_patts[-1], cmap='magma')\n",
+    "# plt.scatter(26, 30, color='green')\n",
+    "plt.scatter(150, 120, color='green')\n",
+    "# plt.scatter(340, 170, color='green')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "e07d901d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Check the original zos and zostoga maps (UKESM1)\n",
+    "zos_ukesm = '/Users/gregorymunday/climate_data/CMIP6/ScenarioMIP/MOHC/UKESM1-0-LL/ssp585/r1i1p1f2/Omon/zos/gn/v20190726/zos_Omon_UKESM1-0-LL_ssp585_r1i1p1f2_gn_201501-204912.nc'\n",
+    "zostoga_ukesm = '/Users/gregorymunday/climate_data/CMIP6/ScenarioMIP/MOHC/UKESM1-0-LL/ssp585/r1i1p1f2/Omon/zostoga/gm/v20190819/zostoga_Omon_UKESM1-0-LL_ssp585_r1i1p1f2_gm_201501-204912.nc'\n",
+    "\n",
+    "cube = iris.load_cube(zos_ukesm)\n",
+    "\n",
+    "qplt.pcolormesh(cube[-2], cmap='magma')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "b9700bdd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(12, 6))\n",
+    "\n",
+    "\n",
+    "for i in range(len(cmip6_patts)):\n",
+    "    cmip6_patts[i] = np.roll(cmip6_patts[i], 180, axis=1)\n",
+    "    # Plot each pattern\n",
+    "    ax = plt.subplot(5, 5, i+ 1)\n",
+    "    ax.pcolormesh(cmip6_patts[i], cmap='RdBu_r', vmin=-0.2, vmax=0.2)\n",
+    "\n",
+    "\n",
+    "# plt.pcolormesh(np.roll(cmip6_patt, 180, axis=1), cmap='RdBu_r', vmin=-0.2, vmax=0.2)\n",
+    "# plt.colorbar(label='Sea Surface Height Regression (m)')\n",
+    "# plt.title('CMIP6')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "19cb8e30",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.pcolormesh(cmip5_patt, cmap='RdBu_r', vmin=-0.2, vmax=0.2)\n",
+    "plt.colorbar(label='Sea Surface Height Regression (m)')\n",
+    "plt.title('CMIP5')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "91a7af3c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "files = glob.glob('landwater/*.nc')\n",
+    "vals = []\n",
+    "lowers = []\n",
+    "uppers = []\n",
+    "labels = []\n",
+    "for f in files:\n",
+    "    ds = xr.load_dataset(f)\n",
+    "    ds = ds.interp(years=np.arange(2005, 2301, 1), method='linear').squeeze()\n",
+    "    vals.append(np.percentile(ds['sea_level_change'].values, 50, axis=0))\n",
+    "    lowers.append(np.percentile(ds['sea_level_change'].values, 17, axis=0))\n",
+    "    uppers.append(np.percentile(ds['sea_level_change'].values, 83, axis=0))\n",
+    "    labels.append(f.split('/')[-1].split('-')[-1][:6])\n",
+    "    \n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "90991bfc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2300\n",
+      "SSP585 lower at 2100: 0.047m\n",
+      "SSP585 upper at 2100: 0.101m\n",
+      "SSP585 median at 2100: 0.074m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "time = np.arange(2005, 2301, 1)\n",
+    "print(time[-1])\n",
+    "\n",
+    "plt.figure(figsize=(10, 5))\n",
+    "colors = plt.cm.viridis(np.linspace(0, 1, len(vals)))\n",
+    "for i, val in enumerate(vals):\n",
+    "    if not labels[i] == 'ssp585':\n",
+    "        continue\n",
+    "    plt.plot(time, val*0.001, color=colors[i], label=labels[i])\n",
+    "    plt.plot(time, lowers[i]*0.001, color=colors[i], linestyle='--')\n",
+    "    plt.plot(time, uppers[i]*0.001, color=colors[i], linestyle='--')\n",
+    "    plt.fill_between(time, np.array(lowers[i])*0.001, np.array(uppers[i])*0.001, color=colors[i], alpha=0.2)\n",
+    "\n",
+    "    print(f'SSP585 lower at 2100: {lowers[i][-1] * 1e-3}m')\n",
+    "    print(f'SSP585 upper at 2100: {uppers[i][-1]* 1e-3}m')\n",
+    "    print(f'SSP585 median at 2100: {val[-1]* 1e-3}m')\n",
+    "\n",
+    "plt.xlabel('Year')\n",
+    "plt.ylabel('Sea Level Change (m)')\n",
+    "plt.title('Sea Level Change from Land Water Storage')\n",
+    "plt.legend()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "de7322b9",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "cmip7",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/profsea/notebooks/eval_plots/rcp_eval_ar5_antarctica.png b/profsea/notebooks/eval_plots/rcp_eval_ar5_antarctica.png
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zo%&<~5|u5UljF-&&but+&m0!=+Aqjqg$!umcEz%335x9@P@WWBzaw}TUJkPe=r0wn
zc)Qo7^@4F%sf!VI?V)61QHK}}fnPm+-+Mm4@8aM;@0&j3&;Qi4g8vT;DjJ{aZTgwM
z!|NN*?gdNMU;Fd>1b?~ozsxuK@gMw|#h<_L|LB|kZu$G>?DE|y>hz6!_Z--r^xcoY
F{6A<+@0 If you want to use the CMIP6 patterns sterodynamic patterns instead of CMIP5, you need to run [this recipe](https://github.com/ESMValGroup/ESMValTool/blob/steric_patterns/esmvaltool/recipes/recipe_steric_patterns.yml) in ESMValTool. I recommend running with as much memory and processors as possible. \n",
+    "\n",
+    "> However, if you prefer not to go via ESMValTool feel free to reach out at gregory.munday\\@dtc.ox.ac.uk and I can just send them over (they're not large in terms of data size).\n",
+    "---\n",
+    "All other data required for the tool can be [downloaded here](https://catalogue.ceda.ac.uk/uuid/47c849b907414f3ab5e56ba9cf83e779/).\n",
+    "\n",
+    "I like keeping all my data in a `data/` folder next to the `profsea/` folder, which looks like this:\n",
+    "\n",
+    "  `data/` \\\n",
+    "   `cmip5/` \\\n",
+    "   `cmip6/` \\\n",
+    "   `gia_estimates/` \\\n",
+    "   `grd_fingerprints/` \\\n",
+    "   `monte_carlo_timeseries/` \\\n",
+    "   `uk_cmip_slope_coefficients/`\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ef69532c",
+   "metadata": {},
+   "source": [
+    "#### Setting up the environment\n",
+    "The next step is to set up the conda environment. You should use the `ProFSea-emu-env.yml` environment file, building it with \n",
+    "\n",
+    "```bash\n",
+    "conda env create -f ProFSea-emu-env.yml\n",
+    "```\n",
+    "\n",
+    "and then activating with `conda activate profsea-emu`.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "One final step here is to install ProFSea as an editable package, using\n",
+    "\n",
+    "```bash \n",
+    "pip install -e . \n",
+    "```\n",
+    "\n",
+    "while inside the highest level directory."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5616586f",
+   "metadata": {},
+   "source": [
+    "#### Setup FaIR output\n",
+    "Run FaIR for the scenarios you want to simulate and save out the `global surface air temperature` and `ocean heat content`. You'll also need to calculate the 2015-2100 cumulative CO2 emissions and save it to a `.json` file. The CMIP6 cumulative emissions are included in the repo: \n",
+    "\n",
+    "```profsea/emulator/aux_data/cumulative_cmip6_emissions.json```\n",
+    "\n",
+    "Here's some sample code to calculate it from scratch in FaIR. Perhaps this could be included in this branch at some point.\n",
+    "\n",
+    "```python\n",
+    "# Output cumulative emissions from 2015 to 2100\n",
+    "cum_emissions_dict = {}\n",
+    "for scenario in hist_gcam_ext.scenario.unique():  # loop over your scenarios\n",
+    "        emissions = hist_gcam_ext.loc[\n",
+    "            (hist_gcam_ext['scenario'] == scenario) & (hist_gcam_ext['variable'] == 'CO2 FFI'), \n",
+    "            '2015':'2100'\n",
+    "        ].to_numpy()[0]  # select anthro emissions\n",
+    "        cum_emissions = np.cumsum(emissions)\n",
+    "        cum_emissions = cum_emissions / 1000  # Convert to PgC\n",
+    "        print(f'Total cumulative carbon emissions from 2015 to 2100 for {scenario}: ', cum_emissions[-1])\n",
+    "        cum_emissions_dict[scenario] = cum_emissions[-1]\n",
+    "        # Save to json\n",
+    "        with open('ngfs_data/cumulative_scenario_emissions.json', 'w') as f:\n",
+    "            json.dump(cum_emissions_dict, f)\n",
+    "```\n",
+    "\n",
+    "You need to do this because the current Antarctic Ice-Sheet dynamics component uses the relationship between cumulative emissions and sea-level contribution to calculate the lower and upper bounds of the projection. This was implemented to remove scenario-dependence from the GMSLR emulator, and should be redundant when the component is updated.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6cef2ba4",
+   "metadata": {},
+   "source": [
+    "#### Running global → local ProFSea projections\n",
+    "Point your paths in the `user-settings-emu.yml` file to the right places, fill in your scenario names and you should be ready to go!\n",
+    "\n",
+    "Run: \n",
+    "\n",
+    "```bash\n",
+    "python spatial_projections.py\n",
+    "```\n",
+    "\n",
+    "The global projections will be saved to `data/runs/gmslr_output/`, while the spatialised projections should be saved to `data/runs//`\n",
+    "\n",
+    "The way this is accomplished (so that it runs on a laptop) is by taking and saving 101 percentiles of the GMSLR module output for each component. This can be updated via the `output_percentiles` keyword in the `GMSLR` class, currently line 458 in `spatial_projections.py`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "84c2ef51",
+   "metadata": {},
+   "source": [
+    "You can also run the GMSLR module by itself:\n",
+    "\n",
+    "```python\n",
+    "from profsea.emulator import GMSLREmulator\n",
+    "\n",
+    "emulator = GMSLREmulator(\n",
+    "    T_change,  # your T_change anom from FaIR\n",
+    "    OHC_change,  # your OHC_change anom from FaIR\n",
+    "    'low-overshoot',  # your scenario name (str)\n",
+    "    2300,  #  projection end year\n",
+    "    palmer_method=palmer_method,  # recommended for runs beyond 2100\n",
+    "    input_ensemble=True,  # must be true if providing a complete FaIR ensemble\n",
+    "    output_percentiles=np.arange(101),  # list/array of percentiles to sample\n",
+    "    cum_emissions_total=1450)  # int, cumulative emissions in scenario from 2015-2100\n",
+    "\n",
+    "#  run the projections (auto-parallel)\n",
+    "emulator.project()\n",
+    "\n",
+    "# Save to desired folder with the name of the scenario\n",
+    "emulator.save_components(\n",
+    "    os.path.join(settings[\"baseoutdir\"], 'gmslr_output'),  # output dir\n",
+    "    scenario)  # scenario name\n",
+    "\n",
+    "# Lists the available components\n",
+    "emulator.list_components()\n",
+    "\n",
+    "# You can also access the data from each component directly:\n",
+    "emulator.greenland  # returns greenland projection ensemble\n",
+    "\n",
+    "```"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "cmip7",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 14363305cc253e8d867a74caf02237336ab25b52 Mon Sep 17 00:00:00 2001
From: Greg Munday <100290135+gregrmunday@users.noreply.github.com>
Date: Mon, 8 Dec 2025 13:51:54 +0000
Subject: [PATCH 065/276] Add warning label

---
 README.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/README.md b/README.md
index 4d5d2ee..3774f2c 100644
--- a/README.md
+++ b/README.md
@@ -3,6 +3,8 @@
 [![DOI](https://zenodo.org/badge/713453340.svg)](https://zenodo.org/doi/10.5281/zenodo.10255467)
 
 ## About
+🚧🚧 **Projections created using this branch are currently not fit for any use other than the development of this repository** 🚧🚧
+
 This is the experimental science branch of ProFSea, dedicated to extending the capabilities of the existing ProFSea-tool. These extensions have not yet been published or peer-reviewed.
 
 Ongoing developments include:

From 311cfa0010cfd29fd8205f91800d983fa55012b1 Mon Sep 17 00:00:00 2001
From: Greg Munday <100290135+gregrmunday@users.noreply.github.com>
Date: Mon, 8 Dec 2025 13:56:56 +0000
Subject: [PATCH 066/276] Extra loud warning

---
 README.md | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 3774f2c..f2f5a8a 100644
--- a/README.md
+++ b/README.md
@@ -3,7 +3,9 @@
 [![DOI](https://zenodo.org/badge/713453340.svg)](https://zenodo.org/doi/10.5281/zenodo.10255467)
 
 ## About
-🚧🚧 **Projections created using this branch are currently not fit for any use other than the development of this repository** 🚧🚧
+🚧   🚧   🚧   🚧   🚧   🚧   🚧   🚧   🚧   🚧   🚧   🚧   🚧   🚧   🚧   🚧   🚧 
      
+**Projections created using this branch are currently not fit for any use other than in the development of this repository.** 
      
+🚧   🚧   🚧   🚧   🚧   🚧   🚧   🚧   🚧   🚧   🚧   🚧   🚧   🚧   🚧   🚧   🚧
 
 This is the experimental science branch of ProFSea, dedicated to extending the capabilities of the existing ProFSea-tool. These extensions have not yet been published or peer-reviewed.
 

From 736a2b332b1a81e1847ca54d5f44881cefd72712 Mon Sep 17 00:00:00 2001
From: Greg Munday <100290135+gregrmunday@users.noreply.github.com>
Date: Mon, 8 Dec 2025 13:59:21 +0000
Subject: [PATCH 067/276] Place higher warning

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index f2f5a8a..731e75a 100644
--- a/README.md
+++ b/README.md
@@ -2,11 +2,11 @@
 
 [![DOI](https://zenodo.org/badge/713453340.svg)](https://zenodo.org/doi/10.5281/zenodo.10255467)
 
-## About
 🚧   🚧   🚧   🚧   🚧   🚧   🚧   🚧   🚧   🚧   🚧   🚧   🚧   🚧   🚧   🚧   🚧 
      
 **Projections created using this branch are currently not fit for any use other than in the development of this repository.** 
      
 🚧   🚧   🚧   🚧   🚧   🚧   🚧   🚧   🚧   🚧   🚧   🚧   🚧   🚧   🚧   🚧   🚧
 
+## About
 This is the experimental science branch of ProFSea, dedicated to extending the capabilities of the existing ProFSea-tool. These extensions have not yet been published or peer-reviewed.
 
 Ongoing developments include:

From 198652a20c39e51fed5e8bbba35918e8562ccbee Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Mon, 8 Dec 2025 14:52:50 +0000
Subject: [PATCH 068/276] Dev updates

---
 profsea/emulator/__init__.py        |  6 +++---
 profsea/emulator/antarctica.py      |  6 +++---
 profsea/fair_prof_prob.py           |  2 +-
 profsea/notebooks/compare_ais.ipynb | 16 ++++++++--------
 4 files changed, 15 insertions(+), 15 deletions(-)

diff --git a/profsea/emulator/__init__.py b/profsea/emulator/__init__.py
index 0b669b1..7a3acb5 100644
--- a/profsea/emulator/__init__.py
+++ b/profsea/emulator/__init__.py
@@ -337,9 +337,9 @@ def run_serial_projections(
         # project_landwater corresponds to 'landwater_ar6' key in the parallel map
         self.landwater_ar6 = self.project_landwater()
 
-        wa_path = Path(__file__).parent / "aux_data" / "wa_params_fastslow.nc"
-        ea_path = Path(__file__).parent / "aux_data" / "ea_params_fastslow.nc"
-        pen_path = Path(__file__).parent / "aux_data" / "pen_params_fastslow.nc"
+        wa_path = Path(__file__).parent / "aux_data" / "wa_params_rate.nc"
+        ea_path = Path(__file__).parent / "aux_data" / "ea_params_rate.nc"
+        pen_path = Path(__file__).parent / "aux_data" / "pen_params_rate.nc"
         is_samples = math.ceil(self.nm / 14)  # 43 IS models
         for T, T_int in zip(T_ens, T_int_ens):
             wa = Antarctica(wa_path, samples=is_samples)
diff --git a/profsea/emulator/antarctica.py b/profsea/emulator/antarctica.py
index c6a9151..02eca81 100644
--- a/profsea/emulator/antarctica.py
+++ b/profsea/emulator/antarctica.py
@@ -38,7 +38,7 @@ def _calc_mv_dists(self, n_params: int):
     def _impulse_response_term(
             self, 
             tas: np.ndarray, 
-            tau: float, 
+            tau: float,
             params: np.ndarray):
         """
         """
@@ -46,9 +46,9 @@ def _impulse_response_term(
         a_lin = params[:, 0]
 
         decay_factors = np.exp(-np.arange(n_time) / tau) * (1 / tau)
-        t_conv = fftconvolve(tas, decay_factors, mode='full')[:n_time]
+        t_conv = np.cumsum(fftconvolve(tas, decay_factors, mode='full')[:n_time], axis=0)
         
-        term_slow = (a_lin[:, None] * t_conv[None, :])
+        term_slow = (a_lin[:, None][None, :] * t_conv[None, :])
         return term_slow
 
     def predict(self, tas: np.ndarray, tas_int: np.ndarray, display_progress=True):
diff --git a/profsea/fair_prof_prob.py b/profsea/fair_prof_prob.py
index 7c874d8..4551e7a 100644
--- a/profsea/fair_prof_prob.py
+++ b/profsea/fair_prof_prob.py
@@ -353,7 +353,7 @@ def main(args):
         sampled_components[scenario] = process_global_ensemble(
             components[scenario], percentiles, scenario)
 
-    save_to_netcdf(sampled_components, "probabilistic_projections/global/gmslr_projections_NEW_AIS_FASTSLOW.nc")
+    save_to_netcdf(sampled_components, "probabilistic_projections/global/gmslr_projections_NEW_AIS_RATE.nc")
     
     fig = plt.figure(figsize=(16, 8), layout="constrained")
     ax = fig.add_subplot(231)
diff --git a/profsea/notebooks/compare_ais.ipynb b/profsea/notebooks/compare_ais.ipynb
index 0f27bba..13e9235 100644
--- a/profsea/notebooks/compare_ais.ipynb
+++ b/profsea/notebooks/compare_ais.ipynb
@@ -16,17 +16,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 3,
    "id": "1ce6ed82",
    "metadata": {},
    "outputs": [],
    "source": [
     "old_ais_path = \"../probabilistic_projections/global/gmslr_projections.nc\"\n",
-    "new_ais_path = \"../probabilistic_projections/global/gmslr_projections_NEW_AIS_FASTSLOW.nc\"\n",
+    "new_ais_path = \"../probabilistic_projections/global/gmslr_projections_NEW_AIS_RATE.nc\"\n",
     "c_new_ais_path = \"../probabilistic_projections/global/gmslr_projections_NEW_AIS.nc\"\n",
     "old_ds = xr.open_dataset(old_ais_path)\n",
     "new_ds = xr.open_dataset(new_ais_path)\n",
-    "new_ds = xr.open_dataset(c_new_ais_path)"
+    "c_new_ds = xr.open_dataset(c_new_ais_path)"
    ]
   },
   {
@@ -39,13 +39,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 4,
    "id": "4d0b2f15",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
      "
       ]
@@ -86,13 +86,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 8,
    "id": "9c58124d",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
      "
       ]
@@ -157,7 +157,7 @@
     "ax.set_title(\"Year 2300 comparison - Global\")\n",
     "ax.set_ylabel(\"SLE (m)\")\n",
     "ax.set_xlabel(\"Scenario\")\n",
-    "ax.set_ylim([-0.5, 5.5])\n",
+    "ax.set_ylim([-0.5, 6])\n",
     "ax.set_xticks(indices)\n",
     "ax.set_xticklabels(old_ds.scenario.values, rotation=45, ha='right')\n",
     "ax.grid()\n",

From a2e6abaf8557d0740cbca7faafc8ba57eb4b6f62 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Mon, 8 Dec 2025 14:54:36 +0000
Subject: [PATCH 069/276] Update conda env.yml

---
 ProFSea-emu-env.yml | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/ProFSea-emu-env.yml b/ProFSea-emu-env.yml
index 09a1fa1..9f13267 100644
--- a/ProFSea-emu-env.yml
+++ b/ProFSea-emu-env.yml
@@ -9,7 +9,8 @@ dependencies:
   - netcdf4
   - numpy
   - pandas
-  - python
+  - python=12
   - pyyaml
+  - rich
   - scipy
   - xarray
\ No newline at end of file

From 7a766d0b03756376a5f6c42391963821cb150661 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Mon, 8 Dec 2025 14:57:25 +0000
Subject: [PATCH 070/276] Update env

---
 ProFSea-emu-env.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ProFSea-emu-env.yml b/ProFSea-emu-env.yml
index 9f13267..e4bd032 100644
--- a/ProFSea-emu-env.yml
+++ b/ProFSea-emu-env.yml
@@ -9,7 +9,7 @@ dependencies:
   - netcdf4
   - numpy
   - pandas
-  - python=12
+  - python=3.12
   - pyyaml
   - rich
   - scipy

From f4e68c96628f64872d4b2ab6c7b7a88208425d7a Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Tue, 9 Dec 2025 12:44:20 +0000
Subject: [PATCH 071/276] model update

---
 profsea/emulator/antarctica.py | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/profsea/emulator/antarctica.py b/profsea/emulator/antarctica.py
index 02eca81..32ea885 100644
--- a/profsea/emulator/antarctica.py
+++ b/profsea/emulator/antarctica.py
@@ -39,6 +39,7 @@ def _impulse_response_term(
             self, 
             tas: np.ndarray, 
             tau: float,
+            gamma: float,
             params: np.ndarray):
         """
         """
@@ -46,7 +47,8 @@ def _impulse_response_term(
         a_lin = params[:, 0]
 
         decay_factors = np.exp(-np.arange(n_time) / tau) * (1 / tau)
-        t_conv = np.cumsum(fftconvolve(tas, decay_factors, mode='full')[:n_time], axis=0)
+        forcing_base = np.sign(tas) * (tas ** gamma)
+        t_conv = np.cumsum(fftconvolve(forcing_base, decay_factors, mode='full')[:n_time], axis=0)
         
         term_slow = (a_lin[:, None][None, :] * t_conv[None, :])
         return term_slow
@@ -55,7 +57,7 @@ def predict(self, tas: np.ndarray, tas_int: np.ndarray, display_progress=True):
         """
         """
         n_time = tas.shape[0]
-        
+
         # Shape: (n_models, n_samples, n_time)
         all_preds = np.zeros((self.n_models, self.n_samples, n_time))
         
@@ -64,11 +66,12 @@ def predict(self, tas: np.ndarray, tas_int: np.ndarray, display_progress=True):
                 description="Projecting AIS response... ", 
                 disable=not display_progress):
             tau = self.param_ds.tau[model].values
+            gamma = self.param_ds.gamma[model].values
             general_p = self.param_ds.general_params[model].values  # [alpha, beta, gamma]
             resid_p = self.mv_dists[model]  # [n_samples, n_params]
 
             total_params = general_p + resid_p
-            term_slow = self._impulse_response_term(tas, tau, total_params)
+            term_slow = self._impulse_response_term(tas, tau, gamma, total_params)
             term_fast = total_params[:, 1][:, None] * tas_int[None, :]
 
             # Convolve

From 5cb75df66c85d2e640d3742535491328240ee0d1 Mon Sep 17 00:00:00 2001
From: Hemant Khatri 
Date: Tue, 9 Dec 2025 15:37:38 +0000
Subject: [PATCH 072/276] streamline file paths and remove hard-coded directory
 paths

---
 profsea/spatial_projections.py | 19 ++++++++++++-------
 profsea/user-settings-emu.yml  | 13 +++++++------
 2 files changed, 19 insertions(+), 13 deletions(-)

diff --git a/profsea/spatial_projections.py b/profsea/spatial_projections.py
index 53e5601..17fdbd8 100644
--- a/profsea/spatial_projections.py
+++ b/profsea/spatial_projections.py
@@ -63,7 +63,7 @@ def calc_future_sea_level(scenario: str) -> None:
 
     # Select dimensions from sample file, [time, realisation]
     sample = np.load(os.path.join(settings["baseoutdir"],settings["experiment_name"],
-                                  'data', 'gmslr', f'{scenario}_expansion.npy'))
+                                  settings['emulator_settings']['gmslr_output_dir'], f'{scenario}_expansion.npy'))
     
     nesm = sample.shape[0] # also number of samples to make
     nyrs = sample.shape[1]
@@ -112,7 +112,8 @@ def calc_gia_contribution(
 
     file_header = '_'.join(['gia', scenario, "projection", 
                     f"{settings['projection_end_year']}"])
-    sealev_ddir = read_dir()[4]
+    sealev_ddir = os.path.join(settings["baseoutdir"],settings["experiment_name"],
+                               settings['emulator_settings']['spatial_output_dir'])
 
     # Save data in netcdf format (Assuming first dimension is percentile, but can be more general percentile/ensemble)
     xr_dataArray = xr.DataArray(GIA_series, dims=["percentile", "time", "lat", "lon"], 
@@ -233,7 +234,8 @@ def save_projections(
     :param scenario: emission scenario
     :param percentile: percentiles used for spatial projections
     """
-    sealev_ddir = read_dir()[4]
+    sealev_ddir = os.path.join(settings["baseoutdir"],settings["experiment_name"],
+                               settings['emulator_settings']['spatial_output_dir'])
     file_header = '_'.join([component, scenario, "projection", 
                             f"{settings['projection_end_year']}"])
 
@@ -286,7 +288,7 @@ def calculate_sl_components(
         # Load global projections in for the component
         #mc_timeseries = np.load(os.path.join(mcdir, f'{scenario}_{comp}.npy'))
         mc_timeseries = np.load(os.path.join(settings["baseoutdir"],settings["experiment_name"],
-                                             'data','gmslr',f'{scenario}_{comp}.npy'))
+                                             settings['emulator_settings']['gmslr_output_dir'],f'{scenario}_{comp}.npy'))
         sampled_mc = mc_timeseries[resamples, :nyrs]
         montecarlo_G[:, :] = da.from_array(sampled_mc[:, :, None, None])
 
@@ -530,7 +532,7 @@ def calculate_global_components(scenario: str, palmer_method: bool) -> None:
     console.log('Saving components...')
     gmslr.save_components(
         os.path.join(settings["baseoutdir"],settings["experiment_name"],
-                     'data', 'gmslr'),
+                     settings['emulator_settings']['gmslr_output_dir']),
         scenario)
 
 
@@ -558,11 +560,14 @@ def main():
         os.path.join(
             settings["baseoutdir"],
             settings["experiment_name"],
-            'data', 'gmslr')
+            settings['emulator_settings']['gmslr_output_dir'])
     ).mkdir(parents=True, exist_ok=True)
 
     Path(
-        read_dir()[4]
+        os.path.join(
+            settings["baseoutdir"],
+            settings["experiment_name"],
+            settings['emulator_settings']['spatial_output_dir'])
     ).mkdir(parents=True, exist_ok=True)
 
     # Extract site data from station list (e.g. tide gauge location) or
diff --git a/profsea/user-settings-emu.yml b/profsea/user-settings-emu.yml
index 0e05fa4..4815ea8 100644
--- a/profsea/user-settings-emu.yml
+++ b/profsea/user-settings-emu.yml
@@ -16,8 +16,8 @@ siteinfo:
 
 # Base output directory
 #   N.B. the code will add region to the output directory
-experiment_name: 'cmip7_example'
-baseoutdir: '/add/your/directory/'
+experiment_name: 'exp_name' # str
+baseoutdir: '/add/your/directory/' # all output will be saved inside baseoutdir/exp_name/
 
 # Set the end year of the projection(s)
 projection_end_year: 2300 # int between 2050 and 2300
@@ -26,13 +26,14 @@ projection_end_year: 2300 # int between 2050 and 2300
 emulator_settings:
     emulator_mode: true # bool
     use_input_ensemble: true # bool
-    gmslr_output_dir: '/ProFSea/ProFSea-tool/data/runs/gmslr'
+    gmslr_output_dir: 'gmslr' # str # directory name to save global projections
+    spatial_output_dir: 'sea_level_projections' # str # directory name to save spatial projections
     emulator_scenario: ['high-extension', 'high-overshoot', 'medium-extension', 'medium-overshoot', 'low', 'verylow', 'verylow-overshoot'] # str
     
 scm_data:
-    temperature: '/results/fair_output/cmip7_temperature.nc' # str
-    ocean_heat_content: '/results/fair_output/cmip7_ohc.nc' # str
-    cumulative_emissions: '/data/cumulative_cmip7_emissions.json' # str
+    temperature: '/path/to/temperature.nc' # str # global-mean surface temperature file
+    ocean_heat_content: '/path/to/ohc.nc' # str # ocean heat content file 
+    cumulative_emissions: '/path/to/emissions.json' # str # cumulative emissions file
 
 # Science method
 #   UKCP18 --> sciencemethod: 'UK'

From 119d85e414891b3680bdd86fab959f521c4e99ce Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Tue, 9 Dec 2025 16:06:21 +0000
Subject: [PATCH 073/276] Remove GridInterpolator, update lat/lon interp

---
 profsea/spatial_projections.py | 67 ++++++++++++----------------------
 1 file changed, 24 insertions(+), 43 deletions(-)

diff --git a/profsea/spatial_projections.py b/profsea/spatial_projections.py
index 53e5601..0c49a0a 100644
--- a/profsea/spatial_projections.py
+++ b/profsea/spatial_projections.py
@@ -10,13 +10,11 @@
 import warnings
 
 import dask.array as da
-import iris
 from netCDF4 import Dataset
 import numpy as np
 from rich.console import Console
 from rich.progress import track
 import scipy
-from scipy.interpolate import RegularGridInterpolator
 from scipy.spatial.distance import cdist
 import xarray as xr
 
@@ -119,7 +117,7 @@ def calc_gia_contribution(
                                 coords={"time": np.arange(2006, GIA_series.shape[1] + 2006),
                                         "lat": np.arange(-90, 90) + 0.5, "lon": np.arange(0, 360) + 0.5})
     xr_dataArray.attrs["units"] = "m"
-    xr_dataArray.attrs["long_name"] = f"Regional GIA sea-level projections"
+    xr_dataArray.attrs["long_name"] = "Regional GIA sea-level projections"
     ds = xr_dataArray.to_dataset(name='gia')
 
     ds.attrs["source"] = "ProFSea-Climate v0.1"
@@ -185,8 +183,8 @@ def calc_fingerprint_contributions(
                     ("lon", np.linspace(-180, 180, 720, endpoint=False))
                 ],
                 name="v")
-            target_lat = np.linspace(90, -90, 180)
-            target_lon = np.linspace(-180, 180, 360, endpoint=False)
+            target_lat = np.linspace(90, -90, 180) + 0.5
+            target_lon = np.linspace(-180, 180, 360, endpoint=False) + 0.5
             val = original_da.interp(
                 lat=target_lat, lon=target_lon, method="linear").data
             val = np.roll(val, 180, axis=1)
@@ -213,8 +211,8 @@ def calc_greenland_fingerprint_ar6() -> da.array:
 
     # Interpolate to (180, 360) grid
     fp_vals = fp_ds.fp.interp(
-        lat=np.linspace(-90, 90, 180, endpoint=False), 
-        lon=np.linspace(0, 360, 360, endpoint=False), 
+        lat=np.linspace(-90, 90, 180, endpoint=False) + 0.5, 
+        lon=np.linspace(0, 360, 360, endpoint=False) + 0.5, 
         method="linear").data * 1000  # convert mm to m SLE per m GMSLR
 
     # Flip vertically and roll by 180 degrees
@@ -272,7 +270,7 @@ def calculate_sl_components(
     """  
     # Numbers of ensemble members, samples, years
     nesm, nsmps, nyrs, lats, lons = array_dims
-    nFPs, FPlist = setup_FP_interpolators(components)
+    nFPs, FPlist = load_fingerprints(components)
     resamples = np.random.choice(nesm, nsmps) # Preserve correlations across comps
     rfpi = np.random.randint(nFPs, size=nsmps)
 
@@ -318,28 +316,6 @@ def calculate_sl_components(
         save_projections(montecarlo_R, comp, scenario, percentile_regional)
 
 
-def create_FP_interpolator(
-    datadir: str, dfile: str, method: str='linear') -> RegularGridInterpolator:
-    """
-    Generates a scipy Interpolator object from input NetCDF data of
-    gravitational fingerprints (takes inputs of Latitude and Longitude).
-    :param datadir: data directory
-    :param dfile: data filename
-    :param method: interpolation type --> 'linear' or 'nearest'
-    :return: 2D Interpolator object
-    """
-    cube = iris.load_cube(os.path.join(datadir, dfile))
-    lon = cube.coord('longitude').points
-    lat = cube.coord('latitude').points
-
-    # Define linear interpolator object:
-    interp_object = RegularGridInterpolator(
-        (lat, lon), cube.data,
-        method=method, bounds_error=True,
-        fill_value=None)
-    return interp_object
-
-
 def get_projection_info(indir: str, scenario: str) -> tuple:
     """
     Read in the dimensions of the Monte-Carlo data. These files are all
@@ -429,7 +405,7 @@ def read_gia_estimates() -> tuple:
     return nGIA, GIA_vals
 
 
-def setup_FP_interpolators(components: list) -> tuple:
+def load_fingerprints(components: list) -> tuple:
     """
     Create 2D Interpolator objects for the Slangen, Spada and Klemann
     fingerprints
@@ -445,19 +421,24 @@ def setup_FP_interpolators(components: list) -> tuple:
 
     # Only 1 fingerprint for Landwater
     comp = "landwater"
-    slangen_FPs[comp] = create_FP_interpolator(settings["fingerprints"],
-                                               comp + "_slangen_nomask.nc")
-
-    # Create interpolators for the remaining components. Expansion ('expansion')
-    # is global so no interpolation is needed.
-    components_todo = [c for c in components if c not in ["expansion", "landwater", "greenland"]]
+    slangen_FPs[comp] = xr.load_dataset(
+        settings["fingerprints"],
+        comp + "_slangen_nomask.nc").values
+
+    # Other FPs have multiple components
+    components_todo = [
+        c for c in components 
+        if c not in ["expansion", "landwater", "greenland"]]
     for comp in components_todo:
-        slangen_FPs[comp] = create_FP_interpolator(settings["fingerprints"],
-                                                   comp + "_slangen_nomask.nc")
-        spada_FPs[comp] = create_FP_interpolator(settings["fingerprints"],
-                                                 comp + "_spada_nomask.nc")
-        klemann_FPs[comp] = create_FP_interpolator(settings["fingerprints"],
-                                                   comp + "_klemann_nomask.nc")
+        slangen_FPs[comp] = xr.load_dataset(
+            settings["fingerprints"],
+            comp + "_slangen_nomask.nc").values
+        spada_FPs[comp] = xr.load_dataset(
+            settings["fingerprints"],
+            comp + "_spada_nomask.nc").values
+        klemann_FPs[comp] = xr.load_dataset(
+            settings["fingerprints"],
+            comp + "_klemann_nomask.nc").values
 
     FPlist = [slangen_FPs, spada_FPs, klemann_FPs]
     nFPs = len(FPlist)

From 10bf7fd0513c4bd11010e60c5bcc4fc115da59f8 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Tue, 9 Dec 2025 16:06:54 +0000
Subject: [PATCH 074/276] Refactor

---
 profsea/spatial_projections.py | 46 +++++++++++++++++++---------------
 1 file changed, 26 insertions(+), 20 deletions(-)

diff --git a/profsea/spatial_projections.py b/profsea/spatial_projections.py
index 0c49a0a..fe8d8cc 100644
--- a/profsea/spatial_projections.py
+++ b/profsea/spatial_projections.py
@@ -168,6 +168,26 @@ def calc_landwater_contribution(interpolator: dict) -> da.array:
     return landwater_vals
 
 
+def interpolate(data: np.ndarray, lats: int, lons: int) -> np.ndarray:
+    """
+    """
+    original_da = xr.DataArray(
+        data,
+        coords=[
+            ("lat", np.linspace(90, -90, data.shape[0])), 
+            ("lon", np.linspace(-180, 180, data.shape[1], endpoint=False))
+        ],
+        name="v")
+
+    target_lat = np.linspace(90, -90, lats) + 0.5
+    target_lon = np.linspace(-180, 180, lons, endpoint=False) + 0.5
+    data_interp = original_da.interp(
+        lat=target_lat, lon=target_lon, method="linear").data
+
+    data_interp = np.roll(data_interp, 180, axis=1)
+    return data_interp
+
+
 def calc_fingerprint_contributions(
     FPlist: list, comp: str, lats: int, lons: int) -> da.array:
     # Initiate an empty list for fingerprint values
@@ -175,29 +195,15 @@ def calc_fingerprint_contributions(
     for FP_dict in FPlist:
         # Interpolate values to target lat/lon
         val = FP_dict[comp].values
-        if val.shape != (lats, lons):
-            original_da = xr.DataArray(
-                val,
-                coords=[
-                    ("lat", np.linspace(90, -90, 360)), 
-                    ("lon", np.linspace(-180, 180, 720, endpoint=False))
-                ],
-                name="v")
-            target_lat = np.linspace(90, -90, 180) + 0.5
-            target_lon = np.linspace(-180, 180, 360, endpoint=False) + 0.5
-            val = original_da.interp(
-                lat=target_lat, lon=target_lon, method="linear").data
-            val = np.roll(val, 180, axis=1)
-        else:
-            val = np.roll(val, 180, axis=1)
-            
+        val = interpolate(val, lats, lons)
+        val = np.roll(val, 180, axis=1)
         fp_vals.append(val)
 
     fp_vals = da.from_array(np.array(fp_vals, dtype=np.float32))
     return fp_vals
 
 
-def calc_greenland_fingerprint_ar6() -> da.array:
+def calc_greenland_fingerprint_ar6(lats: int, lons: int) -> da.array:
     """Load and prepare the GIS fingerprint.
 
     This fingerprint was/is used by FACTS for AR6 projections, and was 
@@ -211,8 +217,8 @@ def calc_greenland_fingerprint_ar6() -> da.array:
 
     # Interpolate to (180, 360) grid
     fp_vals = fp_ds.fp.interp(
-        lat=np.linspace(-90, 90, 180, endpoint=False) + 0.5, 
-        lon=np.linspace(0, 360, 360, endpoint=False) + 0.5, 
+        lat=np.linspace(-90, 90, lats, endpoint=False) + 0.5, 
+        lon=np.linspace(0, 360, lons, endpoint=False) + 0.5, 
         method="linear").data * 1000  # convert mm to m SLE per m GMSLR
 
     # Flip vertically and roll by 180 degrees
@@ -299,7 +305,7 @@ def calculate_sl_components(
             del landwater_vals
 
         elif comp == "greenland":
-            greenland_fp = calc_greenland_fingerprint_ar6()
+            greenland_fp = calc_greenland_fingerprint_ar6(lats, lons)
             montecarlo_R[:, :, :, :] = montecarlo_G[:, :, :, :] * greenland_fp[None, None, :, :]
 
         else:

From efb2eee76974d2df9094fca7b548ae201b27f860 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Tue, 9 Dec 2025 16:53:17 +0000
Subject: [PATCH 075/276] Fixed loading issues

---
 profsea/spatial_projections.py | 42 +++++++++++++++++-----------------
 1 file changed, 21 insertions(+), 21 deletions(-)

diff --git a/profsea/spatial_projections.py b/profsea/spatial_projections.py
index fe8d8cc..e1c67cf 100644
--- a/profsea/spatial_projections.py
+++ b/profsea/spatial_projections.py
@@ -26,6 +26,9 @@
 console = Console()
 warnings.filterwarnings("ignore")
 
+# from dask.distributed import Client
+# client = Client()  # start distributed scheduler locally.
+
 def calc_baseline_period(yrs: np.array) -> float:
     """
     Baseline years used for IPCC AR5 and Palmer et al 2020 -- 1986-2005
@@ -155,15 +158,13 @@ def calc_expansion_contribution(
     return rand_coeffs
 
 
-def calc_landwater_contribution(interpolator: dict) -> da.array:
+def calc_landwater_contribution(data: dict, lats: int, lons: int) -> da.array:
     """
     Calculate the regional landwater contribution to sea level rise.
     :param interpolator: dictionary of interpolator objects
     :return: numpy array of landwater values
     """
-    landwater_FP_interpolator = interpolator['landwater']
-    landwater_vals = da.from_array(
-        landwater_FP_interpolator.values.astype(np.float32).data)
+    landwater_vals = interpolate(data, lats, lons)
     landwater_vals = da.roll(landwater_vals, 180, axis=1)
     return landwater_vals
 
@@ -194,7 +195,7 @@ def calc_fingerprint_contributions(
     fp_vals = []
     for FP_dict in FPlist:
         # Interpolate values to target lat/lon
-        val = FP_dict[comp].values
+        val = FP_dict[comp]
         val = interpolate(val, lats, lons)
         val = np.roll(val, 180, axis=1)
         fp_vals.append(val)
@@ -254,7 +255,7 @@ def save_projections(
 
     R_file = '_'.join([file_header, 'regional']) + '.nc'
     encoding = {component: {"zlib": True, "complevel": 5}}
-    ds.to_netcdf(os.path.join(sealev_ddir, R_file), encoding=encoding)
+    ds.to_netcdf(os.path.join(sealev_ddir, R_file), encoding=encoding, compute=True)
 
 
 def calculate_sl_components(
@@ -300,7 +301,7 @@ def calculate_sl_components(
             del sampled_coeffs
 
         elif comp == "landwater":
-            landwater_vals = calc_landwater_contribution(FPlist[0])
+            landwater_vals = calc_landwater_contribution(FPlist[0], lats, lons)
             montecarlo_R[:, :, :, :] = montecarlo_G[:, :, :, :] * landwater_vals[None, None, :, :]
             del landwater_vals
 
@@ -316,7 +317,6 @@ def calculate_sl_components(
         # Take the 0th, 25th, 50th, 75th and 100th percentiles
         percentile_regional = np.array([0, 25, 50, 75, 100])
         montecarlo_R = da.percentile(montecarlo_R, percentile_regional, axis=0)
-        montecarlo_R = montecarlo_R.compute()
 
         # Create the output sea level projections file directory and filename
         save_projections(montecarlo_R, comp, scenario, percentile_regional)
@@ -427,24 +427,24 @@ def load_fingerprints(components: list) -> tuple:
 
     # Only 1 fingerprint for Landwater
     comp = "landwater"
-    slangen_FPs[comp] = xr.load_dataset(
-        settings["fingerprints"],
-        comp + "_slangen_nomask.nc").values
+    slangen_FPs[comp] = da.from_array(xr.load_dataarray(
+        os.path.join(settings["fingerprints"],
+        comp + "_slangen_nomask.nc")).values)
 
     # Other FPs have multiple components
     components_todo = [
         c for c in components 
         if c not in ["expansion", "landwater", "greenland"]]
     for comp in components_todo:
-        slangen_FPs[comp] = xr.load_dataset(
-            settings["fingerprints"],
-            comp + "_slangen_nomask.nc").values
-        spada_FPs[comp] = xr.load_dataset(
-            settings["fingerprints"],
-            comp + "_spada_nomask.nc").values
-        klemann_FPs[comp] = xr.load_dataset(
-            settings["fingerprints"],
-            comp + "_klemann_nomask.nc").values
+        slangen_FPs[comp] = da.from_array(xr.load_dataarray(
+            os.path.join(settings["fingerprints"],
+            comp + "_slangen_nomask.nc")).values)
+        spada_FPs[comp] = da.from_array(xr.load_dataarray(
+            os.path.join(settings["fingerprints"],
+            comp + "_spada_nomask.nc")).values)
+        klemann_FPs[comp] = da.from_array(xr.load_dataarray(
+            os.path.join(settings["fingerprints"],
+            comp + "_klemann_nomask.nc")).values)
 
     FPlist = [slangen_FPs, spada_FPs, klemann_FPs]
     nFPs = len(FPlist)
@@ -514,7 +514,7 @@ def calculate_global_components(scenario: str, palmer_method: bool) -> None:
         cum_emissions_total=cumulative_emissions[scenario])
     gmslr.project()
 
-    console.log('Saving components...')
+    console.log('Saving global components...')
     gmslr.save_components(
         os.path.join(settings["baseoutdir"],settings["experiment_name"],
                      'data', 'gmslr'),

From 5eb1637d0120b1850844fe974bd20f0530c8ee45 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Tue, 9 Dec 2025 18:10:22 +0000
Subject: [PATCH 076/276] Added memory optimisations

---
 profsea/spatial_projections.py | 91 +++++++++++++++++++---------------
 1 file changed, 50 insertions(+), 41 deletions(-)

diff --git a/profsea/spatial_projections.py b/profsea/spatial_projections.py
index e1c67cf..880dd68 100644
--- a/profsea/spatial_projections.py
+++ b/profsea/spatial_projections.py
@@ -83,7 +83,8 @@ def calc_future_sea_level(scenario: str) -> None:
 
 
 def calc_gia_contribution(
-        yrs: np.array, nyrs: int, nsmps: int, scenario: str) -> None:
+        yrs: np.array, nyrs: int, nsmps: int, 
+        scenario: str) -> None:
     """
     Calculate the glacial isostatic adjustment (GIA) contribution to the
     regional component of sea level rise.
@@ -101,24 +102,28 @@ def calc_gia_contribution(
 
     # Unit series of mm/yr expressed as m/yr
     unit_series = (np.arange(nyrs) + Tdelta) * 0.001
-    GIA_unit_series = np.ones([5, nyrs]) * unit_series
+    GIA_unit_series = np.ones([nsmps, nyrs]) * unit_series
 
     # rgiai is an array of random GIA indices the size of the sample years
-    rgiai = np.random.randint(nGIA, size=5)
+    rgiai = np.random.randint(nGIA, size=nsmps)
 
     GIA_T = da.from_array(GIA_unit_series)
     GIA_vals = da.from_array(GIA_vals)
     GIA_series = GIA_T[:, :, None, None] * GIA_vals[rgiai, None, :, :]
-    GIA_series = GIA_series.compute()
 
     file_header = '_'.join(['gia', scenario, "projection", 
                     f"{settings['projection_end_year']}"])
     sealev_ddir = read_dir()[4]
 
     # Save data in netcdf format (Assuming first dimension is percentile, but can be more general percentile/ensemble)
-    xr_dataArray = xr.DataArray(GIA_series, dims=["percentile", "time", "lat", "lon"], 
-                                coords={"time": np.arange(2006, GIA_series.shape[1] + 2006),
-                                        "lat": np.arange(-90, 90) + 0.5, "lon": np.arange(0, 360) + 0.5})
+    xr_dataArray = xr.DataArray(
+        GIA_series, 
+        dims=["samples", "time", "lat", "lon"], 
+        coords={
+            "samples": np.arange(nsmps),
+            "time": np.arange(2006, GIA_series.shape[1] + 2006),
+            "lat": np.arange(-90, 90) + 0.5, 
+            "lon": np.arange(0, 360) + 0.5})
     xr_dataArray.attrs["units"] = "m"
     xr_dataArray.attrs["long_name"] = "Regional GIA sea-level projections"
     ds = xr_dataArray.to_dataset(name='gia')
@@ -126,8 +131,8 @@ def calc_gia_contribution(
     ds.attrs["source"] = "ProFSea-Climate v0.1"
 
     R_file = '_'.join([file_header, 'regional']) + '.nc'
-    encoding = {'gia': {"zlib": True, "complevel": 5}}
-    ds.to_netcdf(os.path.join(sealev_ddir, R_file), encoding=encoding)
+    encoding = {'gia': {"zlib": True, "complevel": 5, "dtype": "float32"}}
+    ds.to_netcdf(os.path.join(sealev_ddir, R_file), encoding=encoding, compute=True)
     
     del GIA_series
 
@@ -145,7 +150,7 @@ def calc_expansion_contribution(
     if settings["emulator_settings"]["emulator_mode"]:
         if settings["cmipinfo"]["mip"].lower() == "cmip6":
             coeffs = load_CMIP6_slopes('ssp585')
-            coeffs = np.roll(coeffs, 180, axis=2)
+            coeffs = da.roll(coeffs, 180, axis=2)
         else:
             coeffs = load_CMIP5_slope_coeffs('rcp85')
     else:
@@ -154,7 +159,6 @@ def calc_expansion_contribution(
     rand_samples = np.random.choice(
         coeffs.shape[0], size=nsmps, replace=True)               
     rand_coeffs = coeffs[rand_samples, :, :]
-    rand_coeffs = da.from_array(rand_coeffs)
     return rand_coeffs
 
 
@@ -169,11 +173,11 @@ def calc_landwater_contribution(data: dict, lats: int, lons: int) -> da.array:
     return landwater_vals
 
 
-def interpolate(data: np.ndarray, lats: int, lons: int) -> np.ndarray:
+def interpolate(data: da.array, lats: int, lons: int) -> np.ndarray:
     """
     """
     original_da = xr.DataArray(
-        data,
+        data.data,
         coords=[
             ("lat", np.linspace(90, -90, data.shape[0])), 
             ("lon", np.linspace(-180, 180, data.shape[1], endpoint=False))
@@ -185,7 +189,7 @@ def interpolate(data: np.ndarray, lats: int, lons: int) -> np.ndarray:
     data_interp = original_da.interp(
         lat=target_lat, lon=target_lon, method="linear").data
 
-    data_interp = np.roll(data_interp, 180, axis=1)
+    data_interp = da.roll(data_interp, 180, axis=1)
     return data_interp
 
 
@@ -197,10 +201,9 @@ def calc_fingerprint_contributions(
         # Interpolate values to target lat/lon
         val = FP_dict[comp]
         val = interpolate(val, lats, lons)
-        val = np.roll(val, 180, axis=1)
         fp_vals.append(val)
 
-    fp_vals = da.from_array(np.array(fp_vals, dtype=np.float32))
+    fp_vals = da.stack(fp_vals, axis=0)
     return fp_vals
 
 
@@ -214,7 +217,7 @@ def calc_greenland_fingerprint_ar6(lats: int, lons: int) -> da.array:
     """
     # Load in the fingerprint
     fp_path = Path(settings["fingerprints"]) / "greenland_ar6.nc"
-    fp_ds = xr.load_dataset(fp_path)
+    fp_ds = xr.open_dataset(fp_path, chunks={})
 
     # Interpolate to (180, 360) grid
     fp_vals = fp_ds.fp.interp(
@@ -223,9 +226,8 @@ def calc_greenland_fingerprint_ar6(lats: int, lons: int) -> da.array:
         method="linear").data * 1000  # convert mm to m SLE per m GMSLR
 
     # Flip vertically and roll by 180 degrees
-    fp_vals = np.flip(fp_vals)
-    fp_vals = np.roll(fp_vals, 180, axis=1)
-    fp_vals = da.from_array(np.array(fp_vals, dtype=np.float32))
+    fp_vals = da.flip(fp_vals)
+    fp_vals = da.roll(fp_vals, 180, axis=1)
     return fp_vals
 
 
@@ -254,7 +256,7 @@ def save_projections(
     ds.attrs["source"] = "ProFSea-Climate v0.1"
 
     R_file = '_'.join([file_header, 'regional']) + '.nc'
-    encoding = {component: {"zlib": True, "complevel": 5}}
+    encoding = {component: {"zlib": True, "complevel": 5, "dtype": "float32"}}
     ds.to_netcdf(os.path.join(sealev_ddir, R_file), encoding=encoding, compute=True)
 
 
@@ -280,9 +282,11 @@ def calculate_sl_components(
     nFPs, FPlist = load_fingerprints(components)
     resamples = np.random.choice(nesm, nsmps) # Preserve correlations across comps
     rfpi = np.random.randint(nFPs, size=nsmps)
+    # Take the 0th, 25th, 50th, 75th and 100th percentiles
+    output_percentiles = np.array([0, 25, 50, 75, 100])
 
     # Calculate GIA contribution and save it out
-    calc_gia_contribution(yrs, nyrs, nsmps, scenario)
+    calc_gia_contribution(yrs, nyrs, len(output_percentiles), scenario)
 
     for comp in track(components, description="Calculating components..."):
         montecarlo_R = da.zeros((nsmps, nyrs, lats, lons), dtype=np.float32) # (FPs applied) + GIA
@@ -291,9 +295,9 @@ def calculate_sl_components(
         # Load global projections in for the component
         #mc_timeseries = np.load(os.path.join(mcdir, f'{scenario}_{comp}.npy'))
         mc_timeseries = np.load(os.path.join(settings["baseoutdir"],settings["experiment_name"],
-                                             'data','gmslr',f'{scenario}_{comp}.npy'))
+                                             'data','gmslr',f'{scenario}_{comp}.npy'), mmap_mode='r')
         sampled_mc = mc_timeseries[resamples, :nyrs]
-        montecarlo_G[:, :] = da.from_array(sampled_mc[:, :, None, None])
+        montecarlo_G[:, :] = da.from_array(sampled_mc[:, :, None, None], chunks="auto")
 
         if comp == "expansion":
             sampled_coeffs = calc_expansion_contribution(scenario, nsmps)
@@ -301,7 +305,7 @@ def calculate_sl_components(
             del sampled_coeffs
 
         elif comp == "landwater":
-            landwater_vals = calc_landwater_contribution(FPlist[0], lats, lons)
+            landwater_vals = calc_landwater_contribution(FPlist[0]["landwater"], lats, lons)
             montecarlo_R[:, :, :, :] = montecarlo_G[:, :, :, :] * landwater_vals[None, None, :, :]
             del landwater_vals
 
@@ -314,12 +318,11 @@ def calculate_sl_components(
             montecarlo_R[:, :, :, :] = montecarlo_G[:, :, :, :] * fp_vals[rfpi][:, None, :, :]
             del fp_vals
 
-        # Take the 0th, 25th, 50th, 75th and 100th percentiles
-        percentile_regional = np.array([0, 25, 50, 75, 100])
-        montecarlo_R = da.percentile(montecarlo_R, percentile_regional, axis=0)
+        montecarlo_R = da.percentile(montecarlo_R, output_percentiles, axis=0)
+        montecarlo_R = montecarlo_R.astype(np.float32)
 
         # Create the output sea level projections file directory and filename
-        save_projections(montecarlo_R, comp, scenario, percentile_regional)
+        save_projections(montecarlo_R, comp, scenario, output_percentiles)
 
 
 def get_projection_info(indir: str, scenario: str) -> tuple:
@@ -376,11 +379,17 @@ def load_CMIP6_slopes(scenario: str) -> np.ndarray:
     # Read in the sea level regressions
     cmip6_dir = settings["cmipinfo"]["sealevelbasedir"]
     slope_files = glob.glob(cmip6_dir + f'/*/zos_regression_{scenario}_*.npy')
-    slopes = np.zeros((len(slope_files), 180, 360), dtype=np.float32)
-    for i, slope_file in enumerate(slope_files):
-        slopes[i, :, :] = np.load(slope_file)
 
-    return slopes
+    def load_one_slope(f):
+        return np.load(f, mmap_mode='r')
+
+    # Create a list of lazy dask arrays
+    lazy_slopes = [
+        da.from_array(load_one_slope(f), chunks=(180, 360)) 
+        for f in slope_files]
+    slopes_stack = da.stack(lazy_slopes, axis=0)
+
+    return slopes_stack
 
 
 def read_gia_estimates() -> tuple:
@@ -427,24 +436,24 @@ def load_fingerprints(components: list) -> tuple:
 
     # Only 1 fingerprint for Landwater
     comp = "landwater"
-    slangen_FPs[comp] = da.from_array(xr.load_dataarray(
+    slangen_FPs[comp] = xr.open_dataarray(
         os.path.join(settings["fingerprints"],
-        comp + "_slangen_nomask.nc")).values)
+        comp + "_slangen_nomask.nc"), chunks={})
 
     # Other FPs have multiple components
     components_todo = [
         c for c in components 
         if c not in ["expansion", "landwater", "greenland"]]
     for comp in components_todo:
-        slangen_FPs[comp] = da.from_array(xr.load_dataarray(
+        slangen_FPs[comp] = xr.open_dataarray(
             os.path.join(settings["fingerprints"],
-            comp + "_slangen_nomask.nc")).values)
-        spada_FPs[comp] = da.from_array(xr.load_dataarray(
+            comp + "_slangen_nomask.nc"), chunks={})
+        spada_FPs[comp] = xr.open_dataarray(
             os.path.join(settings["fingerprints"],
-            comp + "_spada_nomask.nc")).values)
-        klemann_FPs[comp] = da.from_array(xr.load_dataarray(
+            comp + "_spada_nomask.nc"), chunks={})
+        klemann_FPs[comp] = xr.open_dataarray(
             os.path.join(settings["fingerprints"],
-            comp + "_klemann_nomask.nc")).values)
+            comp + "_klemann_nomask.nc"), chunks={})
 
     FPlist = [slangen_FPs, spada_FPs, klemann_FPs]
     nFPs = len(FPlist)

From d9774fcd2e5694f70dfee28d008ee66ff914d07e Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Tue, 9 Dec 2025 18:19:49 +0000
Subject: [PATCH 077/276] Remove redundant line

---
 profsea/spatial_projections.py | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/profsea/spatial_projections.py b/profsea/spatial_projections.py
index 880dd68..c2108c7 100644
--- a/profsea/spatial_projections.py
+++ b/profsea/spatial_projections.py
@@ -26,9 +26,6 @@
 console = Console()
 warnings.filterwarnings("ignore")
 
-# from dask.distributed import Client
-# client = Client()  # start distributed scheduler locally.
-
 def calc_baseline_period(yrs: np.array) -> float:
     """
     Baseline years used for IPCC AR5 and Palmer et al 2020 -- 1986-2005

From 489b0a0b859b2a8f3476aa55583f1cc2fd39fb41 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Tue, 9 Dec 2025 21:01:20 +0000
Subject: [PATCH 078/276] Separating emulator into components

---
 profsea/emulator/__init__.py | 901 +----------------------------------
 profsea/emulator/gmslr.py    | 899 ++++++++++++++++++++++++++++++++++
 profsea/emulator/spatial.py  | 411 ++++++++++++++++
 profsea/spatialise.py        | 430 -----------------
 4 files changed, 1312 insertions(+), 1329 deletions(-)
 create mode 100644 profsea/emulator/gmslr.py
 create mode 100644 profsea/emulator/spatial.py
 delete mode 100644 profsea/spatialise.py

diff --git a/profsea/emulator/__init__.py b/profsea/emulator/__init__.py
index ab1cc9e..ea227fb 100644
--- a/profsea/emulator/__init__.py
+++ b/profsea/emulator/__init__.py
@@ -1,899 +1,2 @@
-"""
-Copyright (c) 2023, Met Office
-All rights reserved.
-"""
-
-# Calculate Monte Carlo projections of GMSLR using methods 
-# from Jonathon Gregory and AR5. Staying close to JG's original
-# code where possible.
-import os
-import concurrent
-from collections.abc import Sequence
-import functools
-import atexit
-from pathlib import Path
-
-import numpy as np
-import pandas as pd
-from rich.console import Console
-from scipy.spatial.distance import cdist
-from scipy.stats import norm, truncnorm
-import xarray as xr
-
-console = Console()
-
-@functools.lru_cache(maxsize=1)
-def load_greenland_calibration():
-    """Loads the CSV once and keeps it in memory."""
-    path = Path(__file__).parent / "aux_data" / "ISMIP_GIS_calibration.csv"
-    return pd.read_csv(path)
-
-@functools.lru_cache(maxsize=1)
-def load_landwater_projection():
-    """Loads the NetCDF once and keeps the VALUES in memory."""
-    path = Path(__file__).parent / 'aux_data' / 'ssp_global_landwater_projections.nc'
-    with xr.open_dataset(path) as ds:
-        ds.load() 
-    return ds
-
-_SHARED_EXECUTOR = None
-
-def get_shared_executor():
-    """Returns a singleton ThreadPoolExecutor."""
-    global _SHARED_EXECUTOR
-    if _SHARED_EXECUTOR is None:
-        max_workers = min(8, os.cpu_count() or 1)
-        _SHARED_EXECUTOR = concurrent.futures.ThreadPoolExecutor(max_workers=max_workers)
-    return _SHARED_EXECUTOR
-
-@atexit.register
-def shutdown_executor():
-    """Ensures the executor is cleaned up when the script exits."""
-    global _SHARED_EXECUTOR
-    if _SHARED_EXECUTOR:
-        _SHARED_EXECUTOR.shutdown(wait=True)
-
-
-class GMSLREmulator:
-    """Emulator for global mean sea level rise components.
-    
-    Parameters
-    ----------
-    T_change: np.ndarray
-        Array of temperature change values.
-    OHC_change: np.ndarray
-        Array of ocean heat content change values.
-    scenario: str
-        Name of the scenario.
-    end_yr: int
-        End year of the projections.
-    seed: int
-        Seed for numpy.random.
-    nt: int
-        Number of realisations of the input timeseries
-    nm: int
-        Number of realisations of for each component. 
-        Must be a multiple of the number of glacier methods.
-    tcv: float
-        Multiplier for the standard deviation in the input fields.
-    glaciermip: bool | int
-        If False, use AR5 parameters. If 1, use GlacierMIP 
-        (Hock et al., 2019). If 2, use GlacierMIP2 (Marzeion et al., 2020).
-    parallel: bool
-        If True, project SLR components in parallel.
-    input_ensemble: bool
-        If True, use an input ensemble of temperature and 
-        ocean heat content change.
-    output_percentiles: list|np.ndarray
-        If not None, calculate percentiles from a 1D list/array for each 
-        component
-    random_sample: bool
-        If True, randomly sample a single ensemble member across all 
-        components
-    T_percentile_95: np.ndarray
-        95th percentile of temperature change timeseries.
-    OHC_percentile_95: np.ndarray   
-        95th percentile of ocean heat content change timeseries.
-    cum_emissions_total: float
-        Total cumulative emissions from 2015 to 2100.
-    palmer_method: bool
-        If True, allow integration to end in any year up to 2300, 
-        with the contributions to GMLSR from ice-sheet dynamics, 
-        Greenland SMB and land water storage held at the 2100 rate 
-        beyond 2100.
-    
-    Attributes
-    ----------
-    endofhistory: int
-        First year of AR5 projections.
-    endofAR5: int
-        Last year of AR5 projections.
-    nyr: int
-        Length of projections.
-    fgreendyn: float
-        Fraction of SLE from Greenland during 1996 to 2005 assumed 
-        to result from rapid dynamical change.
-    dgreen: float
-        m SLE from Greenland during 1996 to 2005 according to AR5 chapter 4.
-    dant: float
-        m SLE from Antarctica during 1996 to 2005 according to AR5 chapter 4.
-    mSLEoGt: float
-        Conversion factor for Gt to m SLE.
-    exp_efficiency: float
-        Sensitivity of thermosteric SLR to ocean heat content change.
-    """
-
-    def __init__(
-            self, 
-            T_change: np.ndarray,
-            OHC_change: np.ndarray,
-            scenario: str,
-            end_yr: int,
-            seed: int=1234,
-            nt: int=450,
-            nm: int=1000,
-            tcv: float=1.0,
-            glaciermip: bool|int=2,
-            parallel: bool=True,
-            input_ensemble: bool=True,
-            output_percentiles: list|np.ndarray=None,
-            random_sample: bool=False,
-            T_percentile_95: np.ndarray=None,
-            OHC_percentile_95: np.ndarray=None,
-            cum_emissions_total: np.ndarray=None,
-            palmer_method: bool=True) -> None:
-        
-        np.random.seed(None if seed is None else seed)
-        self.T_change = np.asarray(T_change)
-        self.OHC_change = np.asarray(OHC_change)
-        self.scenario = scenario
-        self.end_yr = end_yr
-        self.seed = seed
-        self.nt = nt
-        self.nm = nm
-        self.tcv = tcv
-        self.glaciermip = glaciermip
-        self.parallel = parallel
-        self.input_ensemble = input_ensemble
-        self.output_percentiles = output_percentiles
-        self.random_sample = random_sample
-        self.T_percentile_95 = T_percentile_95
-        self.OHC_percentile_95 = OHC_percentile_95
-        self.cum_emissions_total = cum_emissions_total
-        self.palmer_method = palmer_method
-
-        # First year of AR5 projections
-        self.endofhistory = 2006
-
-        # Last year of AR5 projections
-        self.endofAR5 = 2100
-        
-        # Length of projections
-        self.nyr = self.end_yr - self.endofhistory
-
-        # Fraction of SLE from Greenland during 1996 to 2005 assumed to result from
-        # rapid dynamical change, with the remainder assumed to result from SMB change
-        self.fgreendyn = 0.5
-
-        # m SLE from Greenland during 1996 to 2005 according to AR5 chapter 4
-        self.dgreen = (3.21 - 0.30) * 1e-3
-
-        # m SLE from Antarctica during 1996 to 2005 according to AR5 chapter 4
-        self.dant = (2.37 + 0.13) * 1e-3
-
-        # Conversion factor for Gt to m SLE
-        self.mSLEoGt = 1e12 / 3.61e14 * 1e-3
-
-        # Sensitivity of thermosteric SLR to ocean heat content change
-        # From Turner et al. (2023)
-        self.exp_efficiency = np.random.normal(
-            loc=0.113, scale=0.013, size=OHC_change.shape[0]) * 1e-24 # m/YJ
-
-        if input_ensemble:
-            self.nt = self.T_change.shape[0]
-
-        if self.scenario not in ['rcp26', 'rcp45', 'rcp85', 
-            'ssp126', 'ssp245', 'ssp585'] and self.cum_emissions_total is None:
-            raise ValueError(
-                'If the scenario is not rcp26, rcp45, rcp85, ssp126, ssp245 or ssp585, '
-                'you must provide the total \ncumulative emissions from 2015 to 2100 '
-                'using the cum_emissions_total keyword argument. This is required \n'
-                'to calculate the Antarctic dynamic contribution to GMSLR.')
-    
-    def get_components(self) -> dict:
-        """Get all GMSLR components as a dictionary."""
-        components_dict = {
-            'expansion': self.expansion,
-            'glacier': self.glacier,
-            'greenland': self.greenland_ar6,
-            'greendyn': self.greendyn,
-            'greensmb': self.greensmb,
-            'antsmb': self.antsmb,
-            'antdyn': self.antdyn,
-            'antnet': self.antnet,
-            'landwater': self.landwater,
-            'gmslr': self.gmslr
-        }
-        return components_dict
-
-    def list_components(self) -> list:
-        """List the available SLR components."""
-        component_dict = self.get_components()
-        print(list(component_dict.keys()))
-
-    def sample_members_2D(
-            self, array: np.ndarray) -> np.ndarray:
-        """Sample real ensemble members from a 2D numpy array."""
-        # Caculate statistical timeseries, then match with closest real timeseries 
-        array_percentiles = np.percentile(array, self.output_percentiles, axis=0)
-        distances = cdist(array_percentiles, array)
-        mem_indices = np.argmin(distances, axis=1)
-        return array[mem_indices]
-    
-    def save_components(self, output_dir: str, scenario_name: str) -> None:
-        """Save all SLR components as .npy files to a directory.
-        
-        Parameters
-        ----------
-        output_directory: str
-            Directory to save components to.
-        scenario_name: str
-            Name of the scenario you've run the emulator for. 
-            
-        Returns
-        -------
-        None
-        """
-        # Create directory if it doesn't exist
-        Path(output_dir).mkdir(parents=True, exist_ok=True)
-        for name, component in self.get_components().items():
-            np.save(
-                os.path.join(
-                    output_dir, 
-                    f'{scenario_name}_{name}.npy'), 
-                component
-            )
-
-        # Save data in netcdf format (store all components in an xarray dataset)
-        # Assumes first dimension is percentile, but can be more general (percentile/ensemble)
-        ds = xr.Dataset()
-        for name, component in self.get_components().items():
-            xr_dataArray = xr.DataArray(component, dims=["percentile", "time"], 
-                                        coords={"percentile": self.output_percentiles, 
-                                                "time": np.arange(2006, component.shape[1] + 2006)})
-            xr_dataArray.attrs["units"] = "m"
-            ds[name] = xr_dataArray
-        ds.to_netcdf(os.path.join(output_dir,f'{scenario_name}_global.nc'))
-
-    def project(self) -> None:
-        """Run the emulator to project GMSLR components.
-
-        Returns
-        -------
-        None
-        """
-        T_ens, Exp_ens, T_int_ens, T_int_med = self.calculate_drivers() 
-        self.expansion = np.tile(Exp_ens, (self.nm, 1))
-        fraction = np.random.rand(self.nm * self.nt) # correlation between antsmb and antdyn
-        
-        if self.parallel:
-            self.run_parallel_projections(T_int_med, T_int_ens, T_ens, fraction)
-        else:
-            self.run_serial_projections(T_int_med, T_int_ens, T_ens, fraction)
-        
-        self.antnet = self.antsmb + self.antdyn
-        self.gmslr = self.glacier + self.greenland_ar6 + self.antnet + self.landwater + self.expansion
-
-        rng = np.random.default_rng()
-        if self.random_sample:
-            random_idx = rng.integers(low=0, high=self.nm)
-            self.gmslr = self.gmslr[random_idx][None, :]
-            self.expansion = self.expansion[random_idx][None, :]
-            self.antnet = self.antnet[random_idx][None, :]
-            self.antdyn = self.antdyn[random_idx][None, :]
-            self.antsmb = self.antsmb[random_idx][None, :]
-            self.glacier = self.glacier[random_idx][None, :]
-            self.greenland_ar6 = self.greenland_ar6[random_idx][None, :]
-            self.landwater = self.landwater[random_idx][None, :]
-
-        # TODO Parallelise this section as it's a bit slow
-        if self.output_percentiles is not None:
-            console.log(f"Sampling {len(self.output_percentiles)} members per component...")
-            self.gmslr = self.sample_members_2D(self.gmslr)
-            self.expansion = self.sample_members_2D(self.expansion)
-            self.antnet = self.sample_members_2D(self.antnet)
-            self.antdyn = self.sample_members_2D(self.antdyn)
-            self.antsmb = self.sample_members_2D(self.antsmb)
-            self.glacier = self.sample_members_2D(self.glacier)
-            self.greenland_ar6 = self.sample_members_2D(self.greenland_ar6)
-            self.landwater = self.sample_members_2D(self.landwater)
-            self.greensmb = self.sample_members_2D(self.greensmb)
-            self.greendyn = self.sample_members_2D(self.greendyn)
-            # self.greenland_ar5 = self.sample_members_2D(self.greenland_ar5)
-            # self.landwater_ar6 = self.sample_members_2D(self.landwater_ar6)
-
-    def run_serial_projections(
-            self, T_int_med: np.ndarray, T_int_ens: np.ndarray, 
-            T_ens: np.ndarray, fraction: np.ndarray) -> None:
-        """Run components of the emulator sequentially.
-        
-        Returns
-        -------
-        None
-        """
-        self.glacier = self.project_glacier(T_int_med, T_int_ens)
-        self.antsmb = self.project_antsmb(T_int_ens, fraction)
-        self.greenland_ar6 = self.project_greenland_AR6(T_ens)
-        self.greendyn = self.project_greendyn_AR5()
-        self.greensmb = self.project_greensmb_AR5(T_ens)
-        self.antdyn = self.project_antdyn(fraction)
-        
-        # _project_landwater_ar5 corresponds to 'landwater' key in the parallel map
-        self.landwater = self._project_landwater_ar5()
-        # project_landwater corresponds to 'landwater_ar6' key in the parallel map
-        self.landwater_ar6 = self.project_landwater()
-
-        self.greenland_ar5 = self.greendyn + self.greensmb
-            
-    def run_parallel_projections(
-            self, T_int_med: np.ndarray, T_int_ens: np.ndarray, 
-            T_ens: np.ndarray, fraction: np.ndarray) -> None:
-        """Run components of the emulator in parallel.
-        
-        Returns
-        -------
-        None
-        """
-        executor = get_shared_executor()
-        futures = {
-            executor.submit(self.project_glacier, T_int_med, T_int_ens): 'glacier',
-            executor.submit(self.project_antsmb, T_int_ens, fraction): 'antsmb',
-            executor.submit(self.project_greenland_AR6, T_ens): 'greenland',
-            executor.submit(self.project_greendyn_AR5): 'greendyn',
-            executor.submit(self.project_greensmb_AR5, T_ens): 'greensmb',
-            executor.submit(self.project_antdyn, fraction): 'antdyn',
-            executor.submit(self._project_landwater_ar5): 'landwater',
-            executor.submit(self.project_landwater): 'landwater_ar6'
-        }
-        results = {}
-        for future in concurrent.futures.as_completed(futures):
-            key = futures[future]
-            try:
-                results[key] = future.result()
-            except Exception as e:
-                print(f"{key} generated an exception: {e}")
-
-        self.glacier = results['glacier']
-        self.antsmb = results['antsmb']
-        self.greenland_ar6 = results['greenland']
-        self.greenland_ar5 = results['greendyn'] + results['greensmb']
-        self.greendyn = results['greendyn']
-        self.greensmb = results['greensmb']
-        self.antdyn = results['antdyn']
-        self.landwater = results['landwater']
-        self.landwater_ar6 = results['landwater_ar6']
-
-  
-    def calculate_drivers(self) -> tuple:
-        """Calculate the drivers of GMSLR: temperature change and 
-        thermosteric sea level rise.
-        
-        Returns
-        -------
-        T_ens: np.ndarray
-            Ensemble of temperature changes.
-        therm_ens: np.ndarray
-            Ensemble of thermosteric sea level rise.
-        T_int_ens: np.ndarray
-            Ensemble of time-integral temperature anomalies.
-        T_int_med: np.ndarray
-            Median of time-integral temperature anomalies.
-        """
-        if self.input_ensemble:
-            # Check if dimensions are the right way around 
-            if self.T_change.shape[1] != self.nyr: 
-                self.T_change = self.T_change.T
-            if self.OHC_change.shape[1] != self.nyr: 
-                self.OHC_change = self.OHC_change.T
-                
-            therm_ens = self.OHC_change * self.exp_efficiency[:, None]
-            T_int_ens = np.cumsum(self.T_change, axis=1)
-            T_int_med = np.percentile(T_int_ens, 50, axis=0) # using median here instead of mean... CHECK THIS
-
-            return self.T_change, therm_ens, T_int_ens, T_int_med
-        
-        if len(self.T_change.shape) > 1:
-            T_med = np.percentile(self.T_change, 50, axis=0)
-            T_std = np.std(self.T_change, axis=0)
-
-            therm_med = np.percentile(self.OHC_change, 50, axis=0) * self.exp_efficiency
-            therm_std = np.std(self.OHC_change * self.exp_efficiency, axis=0)
-        else:
-            if self.T_percentile_95 is not None:
-                T_med = self.T_change
-                therm_med = self.OHC_change 
-                
-                T_std = (self.T_percentile_95 - self.T_change) / 1.645
-                # therm_std = (self.OHC_percentile_95 - self.OHC_change) * self.exp_efficiency / 1.645
-                therm_std = (self.OHC_percentile_95 - self.OHC_change) / 1.645
-                self.T_std = T_std
-                self.therm_std = therm_std
-            
-            else:
-                raise ValueError(
-                    'If input_ensemble is False, and T_change and OHC_change '
-                    'are not 2D arrays, you must provide a 95th percentile '
-                    'timeseries for T_change and OHC_change. Add this using '
-                    'T_percentile_95 and OHC_percentile_95 keyword arguments.')
-        
-        # Time-integral of temperature anomaly
-        T_int_med = np.cumsum(T_med)
-        T_int_std = np.cumsum(T_std)
-        
-        # Generate a sample of perfectly correlated timeseries fields of temperature,
-        # time-integral temperature and expansion, each of them [realisation,time]
-        z = np.random.standard_normal(self.nt) * self.tcv
-
-        # For each quantity, mean + standard deviation * normal random number
-        # reshape to [realisation,time]
-        T_ens = z[:, np.newaxis] * T_std + T_med
-        therm_ens = z[:, np.newaxis] * therm_std + therm_med
-        T_int_ens = z[:, np.newaxis] * T_int_std + T_int_med
-        return T_ens, therm_ens, T_int_ens, T_int_med  
-
-    def project_glacier(
-            self, T_int_med: np.ndarray, T_int_ens: np.ndarray) -> np.ndarray:
-        """Project glacier contribution to GMSLR.
-        
-        Parameters
-        ----------
-        T_int_med: np.ndarray
-            Time-integral of median temperature anomaly timeseries.
-        T_int_ens: np.ndarray
-            Ensemble of time-integral temperature anomaly timeseries.
-        
-        Returns
-        -------
-        glacier: np.ndarray
-            Glacier contribution to GMSLR.
-        """
-        # glaciermip -- False => AR5 parameters, 1 => fit to Hock et al. (2019),
-        #   2 => fit to Marzeion et al. (2020)
-        dmzdtref=0.95 # mm yr-1 in Marzeion's CMIP5 ensemble mean for AR5 ref period
-        dmz=dmzdtref*(self.endofhistory-1996)*1e-3 # m from glacier at start wrt AR5 ref period
-        glmass=412.0-96.3 # initial glacier mass, used to set a limit, from Tab 4.2
-        glmass=1e-3*glmass # m SLE
-        
-        glacier = np.full((self.nm, self.nt, self.nyr), np.nan)
-
-        if self.glaciermip:
-            if self.glaciermip==1:
-                glparm=[dict(name='SLA2012',factor=3.39,exponent=0.722,cvgl=0.15),
-                    dict(name='MAR2012',factor=4.35,exponent=0.658,cvgl=0.13),
-                    dict(name='GIE2013',factor=3.57,exponent=0.665,cvgl=0.13),
-                    dict(name='RAD2014',factor=6.21,exponent=0.648,cvgl=0.17),
-                    dict(name='GloGEM',factor=2.88,exponent=0.753,cvgl=0.13)]
-                cvgl=0.15 # unnecessary default
-            elif self.glaciermip==2:
-                glparm=[dict(name='GLIMB',factor=3.70,exponent=0.662,cvgl=0.206),
-                    dict(name='GloGEM',factor=4.08,exponent=0.716,cvgl=0.161),
-                    dict(name='JULES',factor=5.50,exponent=0.564,cvgl=0.188),
-                    dict(name='Mar-12',factor=4.89,exponent=0.651,cvgl=0.141),
-                    dict(name='OGGM',factor=4.26,exponent=0.715,cvgl=0.164),
-                    dict(name='RAD2014',factor=5.18,exponent=0.709,cvgl=0.135),
-                    dict(name='WAL2001',factor=2.66,exponent=0.730,cvgl=0.206)]
-                cvgl=0.20 # unnecessary default
-            elif self.glaciermip==3:
-                glparm=[dict(name='GLIMB',factor=3.70,exponent=0.662,cvgl=0.206),
-                    dict(name='GloGEMflow',factor=5.50,exponent=0.564,cvgl=0.188),
-                    dict(name='OGGMv1.6',factor=2.66,exponent=0.730,cvgl=0.206),
-                    dict(name='PyGEM-OGGMv1.3', factor=2.66, exponent=0.730, cvgl=0.206)]
-            else: 
-                raise KeyError('glaciermip must be 1 or 2')
-        else:
-            glparm=[dict(name='Marzeion',factor=4.96,exponent=0.685),\
-            dict(name='Radic',factor=5.45,exponent=0.676),\
-            dict(name='Slangen',factor=3.44,exponent=0.742),\
-            dict(name='Giesen',factor=3.02,exponent=0.733)]
-            cvgl=0.20 # random methodological error
-            
-        ngl=len(glparm) # number of glacier methods
-            
-        r_per_model = self.nm // ngl
-        r_remainder = self.nm % ngl
-        r = np.random.standard_normal(self.nm)
-        r = r[:, np.newaxis, np.newaxis]
-        
-        # Precompute mgl and zgl for all glacier methods
-        mgl_all = np.array([self._project_glacier1(T_int_med, glparm[igl]['factor'], glparm[igl]['exponent']) for igl in range(ngl)])
-        zgl_all = np.array([self._project_glacier1(T_int_ens, glparm[igl]['factor'], glparm[igl]['exponent']) for igl in range(ngl)])
-        cvgl_all = np.array([glparm[igl]['cvgl'] if self.glaciermip else cvgl for igl in range(ngl)])
-
-        # Make an ensemble of projections for each method
-        current_ensemble_idx = 0
-        for igl in range(ngl):
-            mgl = mgl_all[igl]
-            zgl = zgl_all[igl]
-            cvgl = cvgl_all[igl]
-            
-            num_reals_for_model_i = r_per_model + 1 if igl < r_remainder else r_per_model
-            ifirst = current_ensemble_idx
-            ilast = current_ensemble_idx + num_reals_for_model_i
-
-            glacier[ifirst:ilast, ...] = zgl + (mgl * r[ifirst:ilast] * cvgl)
-            current_ensemble_idx = ilast
-
-        glacier += dmz
-        np.clip(glacier, None, glmass, out=glacier)
-        
-        glacier = glacier.reshape(glacier.shape[0]*glacier.shape[1], glacier.shape[2])
-        return glacier
-
-    def _project_glacier1(
-            self, T_int: np.ndarray, factor: float, 
-            exponent: float) -> np.ndarray:
-        """Project glacier contribution by one glacier method.
-        
-        Parameters
-        ----------
-        T_int: np.ndarray
-            Time-integral temperature anomaly timeseries.
-        factor: float
-            Factor for the glacier method.
-        exponent: float
-            Exponent for the glacier method.
-            
-        Returns
-        -------
-        np.ndarray
-            Projection of glacier contribution.
-        """
-        scale=1e-3 # mm to m
-        return scale * factor * (np.where(T_int<0, 0, T_int)**exponent)
-
-    def project_greensmb_AR5(self, T_ens: np.ndarray) -> np.ndarray:
-        """Project Greenland SMB contribution to GMSLR.
-        
-        Parameters
-        ----------
-        T_ens: np.ndarray
-            Ensemble of temperature anomaly timeseries.
-            
-        Returns
-        -------
-        greensmb: np.ndarray
-            Greenland SMB contribution to GMSLR.
-        
-        """
-        dtgreen = -0.146 # Delta_T of Greenland ref period wrt AR5 ref period  
-        fnlogsd = 0.4 # random methodological error of the log factor
-        febound = [1, 1.15] # bounds of uniform pdf of SMB elevation feedback factor
-
-        # random log-normal factor
-        fn = np.exp(np.random.standard_normal(self.nm) * fnlogsd)
-        # elevation feedback factor
-        fe = np.random.sample(self.nm) * (febound[1] - febound[0]) + febound[0]
-        ff = fn * fe
-        
-        ztgreen = T_ens - dtgreen
-        
-        greensmb = ff[:, np.newaxis, np.newaxis] * self._fettweis(ztgreen)
-
-        if self.palmer_method and self.end_yr > self.endofAR5:
-            greensmb[:, :, 95:] = greensmb[:, :, 94:95]
-
-        greensmb = np.cumsum(greensmb, axis=-1)
-
-        greensmb += (1 - self.fgreendyn) * self.dgreen
-
-        greensmb = greensmb.reshape(greensmb.shape[0]*greensmb.shape[1], greensmb.shape[2])
-        return greensmb
-
-    def _fettweis(self, ztgreen: np.ndarray) -> np.ndarray:
-        """Calculate Greenland SMB in m yr-1 SLE from global mean temperature 
-        anomaly, using Eq 2 of Fettweis et al. (2013).
-        
-        Parameters
-        ----------
-        ztgreen: np.ndarray
-            Global mean temperature anomaly.
-            
-        Returns
-        -------
-        np.ndarray
-            Greenland SMB in m yr-1 SLE.
-        """
-        return (71.5*ztgreen+20.4*(ztgreen**2)+2.8*(ztgreen**3))*self.mSLEoGt
-
-    def project_antsmb(
-            self, T_int_ens: np.ndarray, 
-            fraction: np.ndarray=None) -> np.ndarray:
-        """Project Antarctic SMB contribution to GMSLR.
-        
-        Parameters
-        ----------
-        T_int_ens: np.ndarray
-            Ensemble of time-integral temperature anomaly timeseries.
-        fraction: np.ndarray
-            Random numbers for the SMB-dynamic feedback.
-            
-        Returns
-        -------
-        antsmb: np.ndarray
-            Antarctic SMB contribution to GMSLR.
-        """
-        # The following are [mean,SD]
-        pcoK=[5.1,1.5] # % change in Ant SMB per K of warming from G&H06
-        KoKg=[1.1,0.2] # ratio of Antarctic warming to global warming from G&H06
-
-        # Generate a distribution of products of the above two factors
-        pcoKg = (pcoK[0] + np.random.standard_normal([self.nm, self.nt]) * pcoK[1]) * \
-            (KoKg[0] + np.random.standard_normal([self.nm, self.nt]) * KoKg[1])
-        meansmb = 1923 # model-mean time-mean 1979-2010 Gt yr-1 from 13.3.3.2
-        moaoKg = -pcoKg * 1e-2 * meansmb * self.mSLEoGt # m yr-1 of SLE per K of global warming
-
-        if fraction is None:
-            fraction = np.random.rand(self.nm, self.nt)
-        elif fraction.size != self.nm * self.nt:
-            raise ValueError('fraction is the wrong size')
-        else:
-            fraction.shape = (self.nm, self.nt)
-
-        smax = 0.35 # max value of S in 13.SM.1.5
-        ainterfactor = 1 - fraction * smax
-
-        z = moaoKg * ainterfactor
-        z = z[:, :, np.newaxis]
-        antsmb = z * T_int_ens
-        antsmb = antsmb.reshape(antsmb.shape[0] * antsmb.shape[1], antsmb.shape[2])
-        return antsmb
-
-    def project_greenland_AR6(self, T_ens: np.ndarray) -> np.ndarray:
-        """Project Greenland ice-sheet contribution to GMSLR.
-        This follows the IPCC AR6 methodology as closely as possible.
-        Projections are relative to 1996-2014 baseline.
-        
-        Returns
-        -------
-        np.ndarray
-            Total GIS contribution to GMSLR.
-        """
-        df = load_greenland_calibration()
-        b0 = df['b0'].values[None, :, None]
-        b1 = df['b1'].values[None, :, None]
-        b2 = df['b2'].values[None, :, None]
-        b3 = df['b3'].values[None, :, None]
-        b4 = df['b4'].values[None, :, None]
-        b5 = df['b5'].values[None, :, None]
-        sigma = df['sigma'].values
-        time_delta = np.arange(self.nyr)
-
-        # GIS trend values taken from FACTS GitHub repo
-        trend_mean = 0.19
-        trend_std = 0.1
-
-        # Calculate trend contribution distribution
-        rng = np.random.default_rng(self.seed)
-        trend = truncnorm.ppf(
-            rng.random(self.nm), 
-            a = 0.0,
-            b = 99999.9,
-            loc = trend_mean,
-            scale = trend_std
-        )
-        trend = trend[:, None] * time_delta[None, :]
-        trend = trend[:, None, :] 
-        trend /= 1e3 # convert mm to m SLE
-
-        # Calculate GIS contribution rate
-        dsle = b0 + (b1 * T_ens[:, None, :]) + (b2 * T_ens[:, None, :]**2) \
-            + (b3 * T_ens[:, None, :]**3) + (b4 * time_delta[None, None, :]) \
-            + (b5 * time_delta[None, None, :]**2)
-
-
-        # Now integrate
-        sle = np.cumsum(dsle, axis=2)  # mm SLE per K of global warming
-        sle = sle * 1e-3  # convert mm to m SLE
-
-        # Make a Monte Carlo ensemble of projections for each model in the calibration
-        sle_ens = np.zeros((self.nm, self.nt, self.nyr))
-        r_per_model = self.nm // sle.shape[1]
-        r_remainder = self.nm % sle.shape[1]
-
-        # We want to distribute the remainder evenly across the models
-        unc = np.random.normal(scale=sigma)
-        current_ensemble_idx = 0
-        for i in range(sle.shape[1]):
-            num_reals_for_model_i = r_per_model + 1 if i < r_remainder else r_per_model
-            ifirst = current_ensemble_idx
-            ilast = current_ensemble_idx + num_reals_for_model_i
-            model_term = sle[:, i, :] # Shape (nt, nyr)
-            uncertainty_term = model_term * unc[None, i, None] # Shape (num_reals_for_model_i, nt, nyr_param)
-                
-            sle_ens[ifirst:ilast, :, :] = model_term[None, :, :] + uncertainty_term
-            current_ensemble_idx = ilast
-
-        sle_ens += trend
-
-        # Persist 2100 rate of change
-        rate = np.diff(sle_ens, axis=2)[:, :, 94]
-        sle_ens[:, :, 95:] = sle_ens[:, :, 94:95] + (rate[:, :, None] * time_delta[None, None, 1:self.nyr - 94])
-        sle_ens = sle_ens.reshape((self.nm * self.nt, self.nyr))
-        return sle_ens
-
-    def project_greendyn_AR5(self) -> np.ndarray:
-        """Project Greenland rapid ice-sheet dynamics contribution to GMSLR.
-        
-        Returns
-        -------
-        np.ndarray
-            Greenland rapid ice-sheet dynamics contribution to GMSLR.
-        """
-        # For SMB+dyn during 2005-2010 Table 4.6 gives 0.63+-0.17 mm yr-1 (5-95% range)
-        # For dyn at 2100 Chapter 13 gives [20,85] mm for rcp85, [14,63] mm otherwise
-        if self.scenario in ['rcp85','ssp585']:
-            finalrange=[0.020,0.085]
-        else:
-            finalrange=[0.014,0.063]
-        return self.time_projection(
-            0.63*self.fgreendyn, 0.17*self.fgreendyn, 
-            finalrange) + self.fgreendyn*self.dgreen
-
-    def project_antdyn(self, fraction: np.ndarray=None) -> np.ndarray:
-        """Project Antarctic rapid ice-sheet dynamics contribution to GMSLR.
-        
-        Parameters
-        ----------
-        fraction: np.ndarray
-            Random numbers for the dynamic contribution.
-        
-        Returns
-        -------
-        np.ndarray
-            Antarctic rapid ice-sheet dynamics contribution to GMSLR.
-        """
-        # This is a naive solution to calculating the AntDyn contribution 
-        # for any given scenario. Basically linear regressions through existing data 
-        # to find rough relationship between cumulative emissions and AntDyn contribution.
-        if self.cum_emissions_total:
-            upper = (0.000110 * self.cum_emissions_total) + 0.375 # in metres
-            lower = (1.363e-05 * self.cum_emissions_total) +  0.0392 # in metres
-            final = [lower, upper]
-            # print(final)
-            # final=[-0.020, 0.185]
-        else:
-            lcoeff=dict(rcp26=[-2.881, 0.923, 0.000],\
-            rcp45=[-2.676, 0.850, 0.000],\
-            rcp60=[-2.660, 0.870, 0.000],\
-            rcp85=[-2.399, 0.860, 0.000])
-            lcoeff = lcoeff[self.scenario]
-
-            ascale=norm.ppf(fraction)
-            final=np.exp(lcoeff[2]*ascale**2+lcoeff[1]*ascale+lcoeff[0])
-            final = final.reshape(self.nm, self.nt)
-            
-            # final=[-0.020, 0.185]
-            # final = [0.06, 0.49] # AR6, SSP2-4.5
-
-            # For SMB+dyn during 2005-2010 Table 4.6 gives 0.41+-0.24 mm yr-1 (5-95% range)
-            # For dyn at 2100 Chapter 13 gives [-20,185] mm for all scenarios
-        return self.time_projection(0.41, 0.20, final, fraction=fraction) + self.dant
-
-    def project_landwater(self) -> np.ndarray:
-        """Project land water storage contribution to GMSLR.
-        
-        Returns
-        -------
-        np.ndarray
-            Land water storage contribution to GMSLR.
-        """
-        # Read in AR6 landwater contributions
-        lw_ds = load_landwater_projection()
-
-        # Interpolate to annual projections
-        interp_ds = lw_ds.interp(years=np.arange(2005, 2301, 1), method='linear').squeeze()
-        lw = interp_ds['sea_level_change'].values * 1e-3  # mm to m
-
-        del interp_ds
-        
-        # Go from shape (20000, 294) to (101000, 294)
-        full_repeats = (self.nt * self.nm) // lw.shape[0]
-        remainder = (self.nt * self.nm) % lw.shape[0]
-        lw = np.vstack([np.tile(lw, (full_repeats, 1)), lw[:remainder]])
-        lw = lw.reshape(self.nt * self.nm, lw.shape[1])
-        lw = lw[:, 1:-1] # Start at 2006, end at 2299
-        return lw
-  
-    def _project_landwater_ar5(self) -> np.ndarray:
-        """Old projection function. Project land water storage 
-        contribution to GMSLR.
-        
-        Returns
-        -------
-        np.ndarray
-            Land water storage contribution to GMSLR.
-        """
-        # The rate at start is the one for 1993-2010 from the budget table.
-        # The final amount is the mean for 2081-2100.
-        nyr=2100-2081+1 # number of years of the time-mean of the final amount
-        final = [-0.01,0.09] # AR5
-        # final = [0.01, 0.04] # AR6
-        return self.time_projection(0.38, 0.49-0.38, final, nfinal=nyr)
-
-    def time_projection(
-        self, startratemean: float, startratepm: float, final,
-        nfinal: int=1, fraction: np.ndarray=None) -> np.ndarray:
-        """Project a quantity which is a quadratic function of time.
-        
-        Parameters
-        ----------
-        startratemean: float
-            Rate of GMSLR at the start in mm yr-1.
-        startratepm: float
-            Start rate error in mm yr-1.
-        final: list | np.ndarray
-            Likely range in m for GMSLR at the end of AR5.
-        nfinal: int
-            Number of years at the end over which final is a time-mean.
-        fraction: np.ndarray
-            Random numbers in the range 0 to 1.
-            
-        Returns
-        -------
-        np.ndarray
-            Projection of the quantity.
-        """
-        # Create a field of elapsed time since start in years
-        timeendofAR5 = self.endofAR5 - self.endofhistory + 1
-        time = np.arange(self.end_yr - self.endofhistory) + 1
-        
-        if fraction is None:
-            fraction = np.random.rand(self.nm, self.nt)
-        elif fraction.size != self.nm * self.nt:
-            raise ValueError('fraction is the wrong size')
-        
-        fraction = fraction.reshape(self.nm, self.nt)
-
-        # Convert inputs to startrate (m yr-1) and afinal (m), where both are
-        # arrays with the size of fraction
-        startrate = (startratemean + \
-            startratepm * np.array([-1,1],dtype=float)) * 1e-3 # convert mm yr-1 to m yr-1
-        finalisrange = isinstance(final, Sequence)
-        
-        if finalisrange:
-            if len(final) != 2:
-                raise ValueError('final range is the wrong size')
-            afinal = (1 - fraction) * final[0] + fraction * final[1]
-        else:
-            if final.shape != fraction.shape:
-                raise ValueError('final array is the wrong shape')
-            afinal = final
-            
-        startrate = (1 - fraction) * startrate[0] + fraction * startrate[1]
-
-        # For terms where the rate increases linearly in time t, we can write GMSLR as
-        #   S(t) = a*t**2 + b*t
-        # where a is 0.5*acceleration and b is start rate. Hence
-        #   a = S/t**2-b/t = (S-b*t)/t**2
-        # If nfinal=1, the following two lines are equivalent to
-        # halfacc=(final-startyr*nyr)/nyr**2
-        finalyr = np.arange(nfinal) - nfinal + 94 + 1  # last element ==nyr
-        halfacc = (afinal - startrate * finalyr.mean()) / (finalyr**2).mean()
-        quadratic = halfacc[:, :, np.newaxis] * (time**2)
-        linear = startrate[:, :, np.newaxis] * time
-
-        # If acceleration ceases for t>t0, the rate is 2*a*t0+b thereafter, so
-        #   S(t) = a*t0**2 + b*t0 + (2*a*t0+b)*(t-t0)
-        #        = a*t0*(2*t - t0) + b*t
-        # i.e. the quadratic term is replaced, the linear term unaffected
-        # The quadratic also = a*t**2-a*(t-t0)**2
-
-        if self.palmer_method:
-            y = halfacc[:, :, np.newaxis] * timeendofAR5 * ((2 * time) - timeendofAR5)
-            quadratic[:, :, 95:] = y[:, :, 95:]
-
-        quadratic += linear
-        
-        quadratic = quadratic.reshape(quadratic.shape[0]*quadratic.shape[1], quadratic.shape[2])
-        
-        return quadratic
+from .gmslr import Global
+from .spatial import Spatial
diff --git a/profsea/emulator/gmslr.py b/profsea/emulator/gmslr.py
new file mode 100644
index 0000000..4540c07
--- /dev/null
+++ b/profsea/emulator/gmslr.py
@@ -0,0 +1,899 @@
+"""
+Copyright (c) 2023, Met Office
+All rights reserved.
+"""
+
+# Calculate Monte Carlo projections of GMSLR using methods 
+# from Jonathon Gregory and AR5. Staying close to JG's original
+# code where possible.
+import os
+import concurrent
+from collections.abc import Sequence
+import functools
+import atexit
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+from rich.console import Console
+from scipy.spatial.distance import cdist
+from scipy.stats import norm, truncnorm
+import xarray as xr
+
+console = Console()
+
+@functools.lru_cache(maxsize=1)
+def load_greenland_calibration():
+    """Loads the CSV once and keeps it in memory."""
+    path = Path(__file__).parent / "aux_data" / "ISMIP_GIS_calibration.csv"
+    return pd.read_csv(path)
+
+@functools.lru_cache(maxsize=1)
+def load_landwater_projection():
+    """Loads the NetCDF once and keeps the VALUES in memory."""
+    path = Path(__file__).parent / 'aux_data' / 'ssp_global_landwater_projections.nc'
+    with xr.open_dataset(path) as ds:
+        ds.load() 
+    return ds
+
+_SHARED_EXECUTOR = None
+
+def get_shared_executor():
+    """Returns a singleton ThreadPoolExecutor."""
+    global _SHARED_EXECUTOR
+    if _SHARED_EXECUTOR is None:
+        max_workers = min(8, os.cpu_count() or 1)
+        _SHARED_EXECUTOR = concurrent.futures.ThreadPoolExecutor(max_workers=max_workers)
+    return _SHARED_EXECUTOR
+
+@atexit.register
+def shutdown_executor():
+    """Ensures the executor is cleaned up when the script exits."""
+    global _SHARED_EXECUTOR
+    if _SHARED_EXECUTOR:
+        _SHARED_EXECUTOR.shutdown(wait=True)
+
+
+class Global:
+    """Global sea level rise component emulator.
+
+    Parameters
+    ----------
+    T_change: np.ndarray
+        Array of temperature change values.
+    OHC_change: np.ndarray
+        Array of ocean heat content change values.
+    scenario: str
+        Name of the scenario.
+    end_yr: int
+        End year of the projections.
+    seed: int
+        Seed for numpy.random.
+    nt: int
+        Number of realisations of the input timeseries
+    nm: int
+        Number of realisations of for each component. 
+        Must be a multiple of the number of glacier methods.
+    tcv: float
+        Multiplier for the standard deviation in the input fields.
+    glaciermip: bool | int
+        If False, use AR5 parameters. If 1, use GlacierMIP 
+        (Hock et al., 2019). If 2, use GlacierMIP2 (Marzeion et al., 2020).
+    parallel: bool
+        If True, project SLR components in parallel.
+    input_ensemble: bool
+        If True, use an input ensemble of temperature and 
+        ocean heat content change.
+    output_percentiles: list|np.ndarray
+        If not None, calculate percentiles from a 1D list/array for each 
+        component
+    random_sample: bool
+        If True, randomly sample a single ensemble member across all 
+        components
+    T_percentile_95: np.ndarray
+        95th percentile of temperature change timeseries.
+    OHC_percentile_95: np.ndarray   
+        95th percentile of ocean heat content change timeseries.
+    cum_emissions_total: float
+        Total cumulative emissions from 2015 to 2100.
+    palmer_method: bool
+        If True, allow integration to end in any year up to 2300, 
+        with the contributions to GMLSR from ice-sheet dynamics, 
+        Greenland SMB and land water storage held at the 2100 rate 
+        beyond 2100.
+    
+    Attributes
+    ----------
+    endofhistory: int
+        First year of AR5 projections.
+    endofAR5: int
+        Last year of AR5 projections.
+    nyr: int
+        Length of projections.
+    fgreendyn: float
+        Fraction of SLE from Greenland during 1996 to 2005 assumed 
+        to result from rapid dynamical change.
+    dgreen: float
+        m SLE from Greenland during 1996 to 2005 according to AR5 chapter 4.
+    dant: float
+        m SLE from Antarctica during 1996 to 2005 according to AR5 chapter 4.
+    mSLEoGt: float
+        Conversion factor for Gt to m SLE.
+    exp_efficiency: float
+        Sensitivity of thermosteric SLR to ocean heat content change.
+    """
+
+    def __init__(
+            self, 
+            T_change: np.ndarray,
+            OHC_change: np.ndarray,
+            scenario: str,
+            end_yr: int,
+            seed: int=1234,
+            nt: int=450,
+            nm: int=1000,
+            tcv: float=1.0,
+            glaciermip: bool|int=2,
+            parallel: bool=True,
+            input_ensemble: bool=True,
+            output_percentiles: list|np.ndarray=None,
+            random_sample: bool=False,
+            T_percentile_95: np.ndarray=None,
+            OHC_percentile_95: np.ndarray=None,
+            cum_emissions_total: np.ndarray=None,
+            palmer_method: bool=True) -> None:
+        
+        np.random.seed(None if seed is None else seed)
+        self.T_change = np.asarray(T_change)
+        self.OHC_change = np.asarray(OHC_change)
+        self.scenario = scenario
+        self.end_yr = end_yr
+        self.seed = seed
+        self.nt = nt
+        self.nm = nm
+        self.tcv = tcv
+        self.glaciermip = glaciermip
+        self.parallel = parallel
+        self.input_ensemble = input_ensemble
+        self.output_percentiles = output_percentiles
+        self.random_sample = random_sample
+        self.T_percentile_95 = T_percentile_95
+        self.OHC_percentile_95 = OHC_percentile_95
+        self.cum_emissions_total = cum_emissions_total
+        self.palmer_method = palmer_method
+
+        # First year of AR5 projections
+        self.endofhistory = 2006
+
+        # Last year of AR5 projections
+        self.endofAR5 = 2100
+        
+        # Length of projections
+        self.nyr = self.end_yr - self.endofhistory
+
+        # Fraction of SLE from Greenland during 1996 to 2005 assumed to result from
+        # rapid dynamical change, with the remainder assumed to result from SMB change
+        self.fgreendyn = 0.5
+
+        # m SLE from Greenland during 1996 to 2005 according to AR5 chapter 4
+        self.dgreen = (3.21 - 0.30) * 1e-3
+
+        # m SLE from Antarctica during 1996 to 2005 according to AR5 chapter 4
+        self.dant = (2.37 + 0.13) * 1e-3
+
+        # Conversion factor for Gt to m SLE
+        self.mSLEoGt = 1e12 / 3.61e14 * 1e-3
+
+        # Sensitivity of thermosteric SLR to ocean heat content change
+        # From Turner et al. (2023)
+        self.exp_efficiency = np.random.normal(
+            loc=0.113, scale=0.013, size=OHC_change.shape[0]) * 1e-24 # m/YJ
+
+        if input_ensemble:
+            self.nt = self.T_change.shape[0]
+
+        if self.scenario not in ['rcp26', 'rcp45', 'rcp85', 
+            'ssp126', 'ssp245', 'ssp585'] and self.cum_emissions_total is None:
+            raise ValueError(
+                'If the scenario is not rcp26, rcp45, rcp85, ssp126, ssp245 or ssp585, '
+                'you must provide the total \ncumulative emissions from 2015 to 2100 '
+                'using the cum_emissions_total keyword argument. This is required \n'
+                'to calculate the Antarctic dynamic contribution to GMSLR.')
+    
+    def get_components(self) -> dict:
+        """Get all GMSLR components as a dictionary."""
+        components_dict = {
+            'expansion': self.expansion,
+            'glacier': self.glacier,
+            'greenland': self.greenland_ar6,
+            'greendyn': self.greendyn,
+            'greensmb': self.greensmb,
+            'antsmb': self.antsmb,
+            'antdyn': self.antdyn,
+            'antnet': self.antnet,
+            'landwater': self.landwater,
+            'gmslr': self.gmslr
+        }
+        return components_dict
+
+    def list_components(self) -> list:
+        """List the available SLR components."""
+        component_dict = self.get_components()
+        print(list(component_dict.keys()))
+
+    def sample_members_2D(
+            self, array: np.ndarray) -> np.ndarray:
+        """Sample real ensemble members from a 2D numpy array."""
+        # Caculate statistical timeseries, then match with closest real timeseries 
+        array_percentiles = np.percentile(array, self.output_percentiles, axis=0)
+        distances = cdist(array_percentiles, array)
+        mem_indices = np.argmin(distances, axis=1)
+        return array[mem_indices]
+    
+    def save_components(self, output_dir: str, scenario_name: str) -> None:
+        """Save all SLR components as .npy files to a directory.
+        
+        Parameters
+        ----------
+        output_directory: str
+            Directory to save components to.
+        scenario_name: str
+            Name of the scenario you've run the emulator for. 
+            
+        Returns
+        -------
+        None
+        """
+        # Create directory if it doesn't exist
+        Path(output_dir).mkdir(parents=True, exist_ok=True)
+        for name, component in self.get_components().items():
+            np.save(
+                os.path.join(
+                    output_dir, 
+                    f'{scenario_name}_{name}.npy'), 
+                component
+            )
+
+        # Save data in netcdf format (store all components in an xarray dataset)
+        # Assumes first dimension is percentile, but can be more general (percentile/ensemble)
+        ds = xr.Dataset()
+        for name, component in self.get_components().items():
+            xr_dataArray = xr.DataArray(component, dims=["percentile", "time"], 
+                                        coords={"percentile": self.output_percentiles, 
+                                                "time": np.arange(2006, component.shape[1] + 2006)})
+            xr_dataArray.attrs["units"] = "m"
+            ds[name] = xr_dataArray
+        ds.to_netcdf(os.path.join(output_dir,f'{scenario_name}_global.nc'))
+
+    def project(self) -> None:
+        """Run the emulator to project GMSLR components.
+
+        Returns
+        -------
+        None
+        """
+        T_ens, Exp_ens, T_int_ens, T_int_med = self.calculate_drivers() 
+        self.expansion = np.tile(Exp_ens, (self.nm, 1))
+        fraction = np.random.rand(self.nm * self.nt) # correlation between antsmb and antdyn
+        
+        if self.parallel:
+            self.run_parallel_projections(T_int_med, T_int_ens, T_ens, fraction)
+        else:
+            self.run_serial_projections(T_int_med, T_int_ens, T_ens, fraction)
+        
+        self.antnet = self.antsmb + self.antdyn
+        self.gmslr = self.glacier + self.greenland_ar6 + self.antnet + self.landwater + self.expansion
+
+        rng = np.random.default_rng()
+        if self.random_sample:
+            random_idx = rng.integers(low=0, high=self.nm)
+            self.gmslr = self.gmslr[random_idx][None, :]
+            self.expansion = self.expansion[random_idx][None, :]
+            self.antnet = self.antnet[random_idx][None, :]
+            self.antdyn = self.antdyn[random_idx][None, :]
+            self.antsmb = self.antsmb[random_idx][None, :]
+            self.glacier = self.glacier[random_idx][None, :]
+            self.greenland_ar6 = self.greenland_ar6[random_idx][None, :]
+            self.landwater = self.landwater[random_idx][None, :]
+
+        # TODO Parallelise this section as it's a bit slow
+        if self.output_percentiles is not None:
+            console.log(f"Sampling {len(self.output_percentiles)} members per component...")
+            self.gmslr = self.sample_members_2D(self.gmslr)
+            self.expansion = self.sample_members_2D(self.expansion)
+            self.antnet = self.sample_members_2D(self.antnet)
+            self.antdyn = self.sample_members_2D(self.antdyn)
+            self.antsmb = self.sample_members_2D(self.antsmb)
+            self.glacier = self.sample_members_2D(self.glacier)
+            self.greenland_ar6 = self.sample_members_2D(self.greenland_ar6)
+            self.landwater = self.sample_members_2D(self.landwater)
+            self.greensmb = self.sample_members_2D(self.greensmb)
+            self.greendyn = self.sample_members_2D(self.greendyn)
+            # self.greenland_ar5 = self.sample_members_2D(self.greenland_ar5)
+            # self.landwater_ar6 = self.sample_members_2D(self.landwater_ar6)
+
+    def run_serial_projections(
+            self, T_int_med: np.ndarray, T_int_ens: np.ndarray, 
+            T_ens: np.ndarray, fraction: np.ndarray) -> None:
+        """Run components of the emulator sequentially.
+        
+        Returns
+        -------
+        None
+        """
+        self.glacier = self.project_glacier(T_int_med, T_int_ens)
+        self.antsmb = self.project_antsmb(T_int_ens, fraction)
+        self.greenland_ar6 = self.project_greenland_AR6(T_ens)
+        self.greendyn = self.project_greendyn_AR5()
+        self.greensmb = self.project_greensmb_AR5(T_ens)
+        self.antdyn = self.project_antdyn(fraction)
+        
+        # _project_landwater_ar5 corresponds to 'landwater' key in the parallel map
+        self.landwater = self._project_landwater_ar5()
+        # project_landwater corresponds to 'landwater_ar6' key in the parallel map
+        self.landwater_ar6 = self.project_landwater()
+
+        self.greenland_ar5 = self.greendyn + self.greensmb
+            
+    def run_parallel_projections(
+            self, T_int_med: np.ndarray, T_int_ens: np.ndarray, 
+            T_ens: np.ndarray, fraction: np.ndarray) -> None:
+        """Run components of the emulator in parallel.
+        
+        Returns
+        -------
+        None
+        """
+        executor = get_shared_executor()
+        futures = {
+            executor.submit(self.project_glacier, T_int_med, T_int_ens): 'glacier',
+            executor.submit(self.project_antsmb, T_int_ens, fraction): 'antsmb',
+            executor.submit(self.project_greenland_AR6, T_ens): 'greenland',
+            executor.submit(self.project_greendyn_AR5): 'greendyn',
+            executor.submit(self.project_greensmb_AR5, T_ens): 'greensmb',
+            executor.submit(self.project_antdyn, fraction): 'antdyn',
+            executor.submit(self._project_landwater_ar5): 'landwater',
+            executor.submit(self.project_landwater): 'landwater_ar6'
+        }
+        results = {}
+        for future in concurrent.futures.as_completed(futures):
+            key = futures[future]
+            try:
+                results[key] = future.result()
+            except Exception as e:
+                print(f"{key} generated an exception: {e}")
+
+        self.glacier = results['glacier']
+        self.antsmb = results['antsmb']
+        self.greenland_ar6 = results['greenland']
+        self.greenland_ar5 = results['greendyn'] + results['greensmb']
+        self.greendyn = results['greendyn']
+        self.greensmb = results['greensmb']
+        self.antdyn = results['antdyn']
+        self.landwater = results['landwater']
+        self.landwater_ar6 = results['landwater_ar6']
+
+  
+    def calculate_drivers(self) -> tuple:
+        """Calculate the drivers of GMSLR: temperature change and 
+        thermosteric sea level rise.
+        
+        Returns
+        -------
+        T_ens: np.ndarray
+            Ensemble of temperature changes.
+        therm_ens: np.ndarray
+            Ensemble of thermosteric sea level rise.
+        T_int_ens: np.ndarray
+            Ensemble of time-integral temperature anomalies.
+        T_int_med: np.ndarray
+            Median of time-integral temperature anomalies.
+        """
+        if self.input_ensemble:
+            # Check if dimensions are the right way around 
+            if self.T_change.shape[1] != self.nyr: 
+                self.T_change = self.T_change.T
+            if self.OHC_change.shape[1] != self.nyr: 
+                self.OHC_change = self.OHC_change.T
+                
+            therm_ens = self.OHC_change * self.exp_efficiency[:, None]
+            T_int_ens = np.cumsum(self.T_change, axis=1)
+            T_int_med = np.percentile(T_int_ens, 50, axis=0) # using median here instead of mean... CHECK THIS
+
+            return self.T_change, therm_ens, T_int_ens, T_int_med
+        
+        if len(self.T_change.shape) > 1:
+            T_med = np.percentile(self.T_change, 50, axis=0)
+            T_std = np.std(self.T_change, axis=0)
+
+            therm_med = np.percentile(self.OHC_change, 50, axis=0) * self.exp_efficiency
+            therm_std = np.std(self.OHC_change * self.exp_efficiency, axis=0)
+        else:
+            if self.T_percentile_95 is not None:
+                T_med = self.T_change
+                therm_med = self.OHC_change 
+                
+                T_std = (self.T_percentile_95 - self.T_change) / 1.645
+                # therm_std = (self.OHC_percentile_95 - self.OHC_change) * self.exp_efficiency / 1.645
+                therm_std = (self.OHC_percentile_95 - self.OHC_change) / 1.645
+                self.T_std = T_std
+                self.therm_std = therm_std
+            
+            else:
+                raise ValueError(
+                    'If input_ensemble is False, and T_change and OHC_change '
+                    'are not 2D arrays, you must provide a 95th percentile '
+                    'timeseries for T_change and OHC_change. Add this using '
+                    'T_percentile_95 and OHC_percentile_95 keyword arguments.')
+        
+        # Time-integral of temperature anomaly
+        T_int_med = np.cumsum(T_med)
+        T_int_std = np.cumsum(T_std)
+        
+        # Generate a sample of perfectly correlated timeseries fields of temperature,
+        # time-integral temperature and expansion, each of them [realisation,time]
+        z = np.random.standard_normal(self.nt) * self.tcv
+
+        # For each quantity, mean + standard deviation * normal random number
+        # reshape to [realisation,time]
+        T_ens = z[:, np.newaxis] * T_std + T_med
+        therm_ens = z[:, np.newaxis] * therm_std + therm_med
+        T_int_ens = z[:, np.newaxis] * T_int_std + T_int_med
+        return T_ens, therm_ens, T_int_ens, T_int_med  
+
+    def project_glacier(
+            self, T_int_med: np.ndarray, T_int_ens: np.ndarray) -> np.ndarray:
+        """Project glacier contribution to GMSLR.
+        
+        Parameters
+        ----------
+        T_int_med: np.ndarray
+            Time-integral of median temperature anomaly timeseries.
+        T_int_ens: np.ndarray
+            Ensemble of time-integral temperature anomaly timeseries.
+        
+        Returns
+        -------
+        glacier: np.ndarray
+            Glacier contribution to GMSLR.
+        """
+        # glaciermip -- False => AR5 parameters, 1 => fit to Hock et al. (2019),
+        #   2 => fit to Marzeion et al. (2020)
+        dmzdtref=0.95 # mm yr-1 in Marzeion's CMIP5 ensemble mean for AR5 ref period
+        dmz=dmzdtref*(self.endofhistory-1996)*1e-3 # m from glacier at start wrt AR5 ref period
+        glmass=412.0-96.3 # initial glacier mass, used to set a limit, from Tab 4.2
+        glmass=1e-3*glmass # m SLE
+        
+        glacier = np.full((self.nm, self.nt, self.nyr), np.nan)
+
+        if self.glaciermip:
+            if self.glaciermip==1:
+                glparm=[dict(name='SLA2012',factor=3.39,exponent=0.722,cvgl=0.15),
+                    dict(name='MAR2012',factor=4.35,exponent=0.658,cvgl=0.13),
+                    dict(name='GIE2013',factor=3.57,exponent=0.665,cvgl=0.13),
+                    dict(name='RAD2014',factor=6.21,exponent=0.648,cvgl=0.17),
+                    dict(name='GloGEM',factor=2.88,exponent=0.753,cvgl=0.13)]
+                cvgl=0.15 # unnecessary default
+            elif self.glaciermip==2:
+                glparm=[dict(name='GLIMB',factor=3.70,exponent=0.662,cvgl=0.206),
+                    dict(name='GloGEM',factor=4.08,exponent=0.716,cvgl=0.161),
+                    dict(name='JULES',factor=5.50,exponent=0.564,cvgl=0.188),
+                    dict(name='Mar-12',factor=4.89,exponent=0.651,cvgl=0.141),
+                    dict(name='OGGM',factor=4.26,exponent=0.715,cvgl=0.164),
+                    dict(name='RAD2014',factor=5.18,exponent=0.709,cvgl=0.135),
+                    dict(name='WAL2001',factor=2.66,exponent=0.730,cvgl=0.206)]
+                cvgl=0.20 # unnecessary default
+            elif self.glaciermip==3:
+                glparm=[dict(name='GLIMB',factor=3.70,exponent=0.662,cvgl=0.206),
+                    dict(name='GloGEMflow',factor=5.50,exponent=0.564,cvgl=0.188),
+                    dict(name='OGGMv1.6',factor=2.66,exponent=0.730,cvgl=0.206),
+                    dict(name='PyGEM-OGGMv1.3', factor=2.66, exponent=0.730, cvgl=0.206)]
+            else: 
+                raise KeyError('glaciermip must be 1 or 2')
+        else:
+            glparm=[dict(name='Marzeion',factor=4.96,exponent=0.685),\
+            dict(name='Radic',factor=5.45,exponent=0.676),\
+            dict(name='Slangen',factor=3.44,exponent=0.742),\
+            dict(name='Giesen',factor=3.02,exponent=0.733)]
+            cvgl=0.20 # random methodological error
+            
+        ngl=len(glparm) # number of glacier methods
+            
+        r_per_model = self.nm // ngl
+        r_remainder = self.nm % ngl
+        r = np.random.standard_normal(self.nm)
+        r = r[:, np.newaxis, np.newaxis]
+        
+        # Precompute mgl and zgl for all glacier methods
+        mgl_all = np.array([self._project_glacier1(T_int_med, glparm[igl]['factor'], glparm[igl]['exponent']) for igl in range(ngl)])
+        zgl_all = np.array([self._project_glacier1(T_int_ens, glparm[igl]['factor'], glparm[igl]['exponent']) for igl in range(ngl)])
+        cvgl_all = np.array([glparm[igl]['cvgl'] if self.glaciermip else cvgl for igl in range(ngl)])
+
+        # Make an ensemble of projections for each method
+        current_ensemble_idx = 0
+        for igl in range(ngl):
+            mgl = mgl_all[igl]
+            zgl = zgl_all[igl]
+            cvgl = cvgl_all[igl]
+            
+            num_reals_for_model_i = r_per_model + 1 if igl < r_remainder else r_per_model
+            ifirst = current_ensemble_idx
+            ilast = current_ensemble_idx + num_reals_for_model_i
+
+            glacier[ifirst:ilast, ...] = zgl + (mgl * r[ifirst:ilast] * cvgl)
+            current_ensemble_idx = ilast
+
+        glacier += dmz
+        np.clip(glacier, None, glmass, out=glacier)
+        
+        glacier = glacier.reshape(glacier.shape[0]*glacier.shape[1], glacier.shape[2])
+        return glacier
+
+    def _project_glacier1(
+            self, T_int: np.ndarray, factor: float, 
+            exponent: float) -> np.ndarray:
+        """Project glacier contribution by one glacier method.
+        
+        Parameters
+        ----------
+        T_int: np.ndarray
+            Time-integral temperature anomaly timeseries.
+        factor: float
+            Factor for the glacier method.
+        exponent: float
+            Exponent for the glacier method.
+            
+        Returns
+        -------
+        np.ndarray
+            Projection of glacier contribution.
+        """
+        scale=1e-3 # mm to m
+        return scale * factor * (np.where(T_int<0, 0, T_int)**exponent)
+
+    def project_greensmb_AR5(self, T_ens: np.ndarray) -> np.ndarray:
+        """Project Greenland SMB contribution to GMSLR.
+        
+        Parameters
+        ----------
+        T_ens: np.ndarray
+            Ensemble of temperature anomaly timeseries.
+            
+        Returns
+        -------
+        greensmb: np.ndarray
+            Greenland SMB contribution to GMSLR.
+        
+        """
+        dtgreen = -0.146 # Delta_T of Greenland ref period wrt AR5 ref period  
+        fnlogsd = 0.4 # random methodological error of the log factor
+        febound = [1, 1.15] # bounds of uniform pdf of SMB elevation feedback factor
+
+        # random log-normal factor
+        fn = np.exp(np.random.standard_normal(self.nm) * fnlogsd)
+        # elevation feedback factor
+        fe = np.random.sample(self.nm) * (febound[1] - febound[0]) + febound[0]
+        ff = fn * fe
+        
+        ztgreen = T_ens - dtgreen
+        
+        greensmb = ff[:, np.newaxis, np.newaxis] * self._fettweis(ztgreen)
+
+        if self.palmer_method and self.end_yr > self.endofAR5:
+            greensmb[:, :, 95:] = greensmb[:, :, 94:95]
+
+        greensmb = np.cumsum(greensmb, axis=-1)
+
+        greensmb += (1 - self.fgreendyn) * self.dgreen
+
+        greensmb = greensmb.reshape(greensmb.shape[0]*greensmb.shape[1], greensmb.shape[2])
+        return greensmb
+
+    def _fettweis(self, ztgreen: np.ndarray) -> np.ndarray:
+        """Calculate Greenland SMB in m yr-1 SLE from global mean temperature 
+        anomaly, using Eq 2 of Fettweis et al. (2013).
+        
+        Parameters
+        ----------
+        ztgreen: np.ndarray
+            Global mean temperature anomaly.
+            
+        Returns
+        -------
+        np.ndarray
+            Greenland SMB in m yr-1 SLE.
+        """
+        return (71.5*ztgreen+20.4*(ztgreen**2)+2.8*(ztgreen**3))*self.mSLEoGt
+
+    def project_antsmb(
+            self, T_int_ens: np.ndarray, 
+            fraction: np.ndarray=None) -> np.ndarray:
+        """Project Antarctic SMB contribution to GMSLR.
+        
+        Parameters
+        ----------
+        T_int_ens: np.ndarray
+            Ensemble of time-integral temperature anomaly timeseries.
+        fraction: np.ndarray
+            Random numbers for the SMB-dynamic feedback.
+            
+        Returns
+        -------
+        antsmb: np.ndarray
+            Antarctic SMB contribution to GMSLR.
+        """
+        # The following are [mean,SD]
+        pcoK=[5.1,1.5] # % change in Ant SMB per K of warming from G&H06
+        KoKg=[1.1,0.2] # ratio of Antarctic warming to global warming from G&H06
+
+        # Generate a distribution of products of the above two factors
+        pcoKg = (pcoK[0] + np.random.standard_normal([self.nm, self.nt]) * pcoK[1]) * \
+            (KoKg[0] + np.random.standard_normal([self.nm, self.nt]) * KoKg[1])
+        meansmb = 1923 # model-mean time-mean 1979-2010 Gt yr-1 from 13.3.3.2
+        moaoKg = -pcoKg * 1e-2 * meansmb * self.mSLEoGt # m yr-1 of SLE per K of global warming
+
+        if fraction is None:
+            fraction = np.random.rand(self.nm, self.nt)
+        elif fraction.size != self.nm * self.nt:
+            raise ValueError('fraction is the wrong size')
+        else:
+            fraction.shape = (self.nm, self.nt)
+
+        smax = 0.35 # max value of S in 13.SM.1.5
+        ainterfactor = 1 - fraction * smax
+
+        z = moaoKg * ainterfactor
+        z = z[:, :, np.newaxis]
+        antsmb = z * T_int_ens
+        antsmb = antsmb.reshape(antsmb.shape[0] * antsmb.shape[1], antsmb.shape[2])
+        return antsmb
+
+    def project_greenland_AR6(self, T_ens: np.ndarray) -> np.ndarray:
+        """Project Greenland ice-sheet contribution to GMSLR.
+        This follows the IPCC AR6 methodology as closely as possible.
+        Projections are relative to 1996-2014 baseline.
+        
+        Returns
+        -------
+        np.ndarray
+            Total GIS contribution to GMSLR.
+        """
+        df = load_greenland_calibration()
+        b0 = df['b0'].values[None, :, None]
+        b1 = df['b1'].values[None, :, None]
+        b2 = df['b2'].values[None, :, None]
+        b3 = df['b3'].values[None, :, None]
+        b4 = df['b4'].values[None, :, None]
+        b5 = df['b5'].values[None, :, None]
+        sigma = df['sigma'].values
+        time_delta = np.arange(self.nyr)
+
+        # GIS trend values taken from FACTS GitHub repo
+        trend_mean = 0.19
+        trend_std = 0.1
+
+        # Calculate trend contribution distribution
+        rng = np.random.default_rng(self.seed)
+        trend = truncnorm.ppf(
+            rng.random(self.nm), 
+            a = 0.0,
+            b = 99999.9,
+            loc = trend_mean,
+            scale = trend_std
+        )
+        trend = trend[:, None] * time_delta[None, :]
+        trend = trend[:, None, :] 
+        trend /= 1e3 # convert mm to m SLE
+
+        # Calculate GIS contribution rate
+        dsle = b0 + (b1 * T_ens[:, None, :]) + (b2 * T_ens[:, None, :]**2) \
+            + (b3 * T_ens[:, None, :]**3) + (b4 * time_delta[None, None, :]) \
+            + (b5 * time_delta[None, None, :]**2)
+
+
+        # Now integrate
+        sle = np.cumsum(dsle, axis=2)  # mm SLE per K of global warming
+        sle = sle * 1e-3  # convert mm to m SLE
+
+        # Make a Monte Carlo ensemble of projections for each model in the calibration
+        sle_ens = np.zeros((self.nm, self.nt, self.nyr))
+        r_per_model = self.nm // sle.shape[1]
+        r_remainder = self.nm % sle.shape[1]
+
+        # We want to distribute the remainder evenly across the models
+        unc = np.random.normal(scale=sigma)
+        current_ensemble_idx = 0
+        for i in range(sle.shape[1]):
+            num_reals_for_model_i = r_per_model + 1 if i < r_remainder else r_per_model
+            ifirst = current_ensemble_idx
+            ilast = current_ensemble_idx + num_reals_for_model_i
+            model_term = sle[:, i, :] # Shape (nt, nyr)
+            uncertainty_term = model_term * unc[None, i, None] # Shape (num_reals_for_model_i, nt, nyr_param)
+                
+            sle_ens[ifirst:ilast, :, :] = model_term[None, :, :] + uncertainty_term
+            current_ensemble_idx = ilast
+
+        sle_ens += trend
+
+        # Persist 2100 rate of change
+        rate = np.diff(sle_ens, axis=2)[:, :, 94]
+        sle_ens[:, :, 95:] = sle_ens[:, :, 94:95] + (rate[:, :, None] * time_delta[None, None, 1:self.nyr - 94])
+        sle_ens = sle_ens.reshape((self.nm * self.nt, self.nyr))
+        return sle_ens
+
+    def project_greendyn_AR5(self) -> np.ndarray:
+        """Project Greenland rapid ice-sheet dynamics contribution to GMSLR.
+        
+        Returns
+        -------
+        np.ndarray
+            Greenland rapid ice-sheet dynamics contribution to GMSLR.
+        """
+        # For SMB+dyn during 2005-2010 Table 4.6 gives 0.63+-0.17 mm yr-1 (5-95% range)
+        # For dyn at 2100 Chapter 13 gives [20,85] mm for rcp85, [14,63] mm otherwise
+        if self.scenario in ['rcp85','ssp585']:
+            finalrange=[0.020,0.085]
+        else:
+            finalrange=[0.014,0.063]
+        return self.time_projection(
+            0.63*self.fgreendyn, 0.17*self.fgreendyn, 
+            finalrange) + self.fgreendyn*self.dgreen
+
+    def project_antdyn(self, fraction: np.ndarray=None) -> np.ndarray:
+        """Project Antarctic rapid ice-sheet dynamics contribution to GMSLR.
+        
+        Parameters
+        ----------
+        fraction: np.ndarray
+            Random numbers for the dynamic contribution.
+        
+        Returns
+        -------
+        np.ndarray
+            Antarctic rapid ice-sheet dynamics contribution to GMSLR.
+        """
+        # This is a naive solution to calculating the AntDyn contribution 
+        # for any given scenario. Basically linear regressions through existing data 
+        # to find rough relationship between cumulative emissions and AntDyn contribution.
+        if self.cum_emissions_total:
+            upper = (0.000110 * self.cum_emissions_total) + 0.375 # in metres
+            lower = (1.363e-05 * self.cum_emissions_total) +  0.0392 # in metres
+            final = [lower, upper]
+            # print(final)
+            # final=[-0.020, 0.185]
+        else:
+            lcoeff=dict(rcp26=[-2.881, 0.923, 0.000],\
+            rcp45=[-2.676, 0.850, 0.000],\
+            rcp60=[-2.660, 0.870, 0.000],\
+            rcp85=[-2.399, 0.860, 0.000])
+            lcoeff = lcoeff[self.scenario]
+
+            ascale=norm.ppf(fraction)
+            final=np.exp(lcoeff[2]*ascale**2+lcoeff[1]*ascale+lcoeff[0])
+            final = final.reshape(self.nm, self.nt)
+            
+            # final=[-0.020, 0.185]
+            # final = [0.06, 0.49] # AR6, SSP2-4.5
+
+            # For SMB+dyn during 2005-2010 Table 4.6 gives 0.41+-0.24 mm yr-1 (5-95% range)
+            # For dyn at 2100 Chapter 13 gives [-20,185] mm for all scenarios
+        return self.time_projection(0.41, 0.20, final, fraction=fraction) + self.dant
+
+    def project_landwater(self) -> np.ndarray:
+        """Project land water storage contribution to GMSLR.
+        
+        Returns
+        -------
+        np.ndarray
+            Land water storage contribution to GMSLR.
+        """
+        # Read in AR6 landwater contributions
+        lw_ds = load_landwater_projection()
+
+        # Interpolate to annual projections
+        interp_ds = lw_ds.interp(years=np.arange(2005, 2301, 1), method='linear').squeeze()
+        lw = interp_ds['sea_level_change'].values * 1e-3  # mm to m
+
+        del interp_ds
+        
+        # Go from shape (20000, 294) to (101000, 294)
+        full_repeats = (self.nt * self.nm) // lw.shape[0]
+        remainder = (self.nt * self.nm) % lw.shape[0]
+        lw = np.vstack([np.tile(lw, (full_repeats, 1)), lw[:remainder]])
+        lw = lw.reshape(self.nt * self.nm, lw.shape[1])
+        lw = lw[:, 1:-1] # Start at 2006, end at 2299
+        return lw
+  
+    def _project_landwater_ar5(self) -> np.ndarray:
+        """Old projection function. Project land water storage 
+        contribution to GMSLR.
+        
+        Returns
+        -------
+        np.ndarray
+            Land water storage contribution to GMSLR.
+        """
+        # The rate at start is the one for 1993-2010 from the budget table.
+        # The final amount is the mean for 2081-2100.
+        nyr=2100-2081+1 # number of years of the time-mean of the final amount
+        final = [-0.01,0.09] # AR5
+        # final = [0.01, 0.04] # AR6
+        return self.time_projection(0.38, 0.49-0.38, final, nfinal=nyr)
+
+    def time_projection(
+        self, startratemean: float, startratepm: float, final,
+        nfinal: int=1, fraction: np.ndarray=None) -> np.ndarray:
+        """Project a quantity which is a quadratic function of time.
+        
+        Parameters
+        ----------
+        startratemean: float
+            Rate of GMSLR at the start in mm yr-1.
+        startratepm: float
+            Start rate error in mm yr-1.
+        final: list | np.ndarray
+            Likely range in m for GMSLR at the end of AR5.
+        nfinal: int
+            Number of years at the end over which final is a time-mean.
+        fraction: np.ndarray
+            Random numbers in the range 0 to 1.
+            
+        Returns
+        -------
+        np.ndarray
+            Projection of the quantity.
+        """
+        # Create a field of elapsed time since start in years
+        timeendofAR5 = self.endofAR5 - self.endofhistory + 1
+        time = np.arange(self.end_yr - self.endofhistory) + 1
+        
+        if fraction is None:
+            fraction = np.random.rand(self.nm, self.nt)
+        elif fraction.size != self.nm * self.nt:
+            raise ValueError('fraction is the wrong size')
+        
+        fraction = fraction.reshape(self.nm, self.nt)
+
+        # Convert inputs to startrate (m yr-1) and afinal (m), where both are
+        # arrays with the size of fraction
+        startrate = (startratemean + \
+            startratepm * np.array([-1,1],dtype=float)) * 1e-3 # convert mm yr-1 to m yr-1
+        finalisrange = isinstance(final, Sequence)
+        
+        if finalisrange:
+            if len(final) != 2:
+                raise ValueError('final range is the wrong size')
+            afinal = (1 - fraction) * final[0] + fraction * final[1]
+        else:
+            if final.shape != fraction.shape:
+                raise ValueError('final array is the wrong shape')
+            afinal = final
+            
+        startrate = (1 - fraction) * startrate[0] + fraction * startrate[1]
+
+        # For terms where the rate increases linearly in time t, we can write GMSLR as
+        #   S(t) = a*t**2 + b*t
+        # where a is 0.5*acceleration and b is start rate. Hence
+        #   a = S/t**2-b/t = (S-b*t)/t**2
+        # If nfinal=1, the following two lines are equivalent to
+        # halfacc=(final-startyr*nyr)/nyr**2
+        finalyr = np.arange(nfinal) - nfinal + 94 + 1  # last element ==nyr
+        halfacc = (afinal - startrate * finalyr.mean()) / (finalyr**2).mean()
+        quadratic = halfacc[:, :, np.newaxis] * (time**2)
+        linear = startrate[:, :, np.newaxis] * time
+
+        # If acceleration ceases for t>t0, the rate is 2*a*t0+b thereafter, so
+        #   S(t) = a*t0**2 + b*t0 + (2*a*t0+b)*(t-t0)
+        #        = a*t0*(2*t - t0) + b*t
+        # i.e. the quadratic term is replaced, the linear term unaffected
+        # The quadratic also = a*t**2-a*(t-t0)**2
+
+        if self.palmer_method:
+            y = halfacc[:, :, np.newaxis] * timeendofAR5 * ((2 * time) - timeendofAR5)
+            quadratic[:, :, 95:] = y[:, :, 95:]
+
+        quadratic += linear
+        
+        quadratic = quadratic.reshape(quadratic.shape[0]*quadratic.shape[1], quadratic.shape[2])
+        
+        return quadratic
diff --git a/profsea/emulator/spatial.py b/profsea/emulator/spatial.py
new file mode 100644
index 0000000..8453fad
--- /dev/null
+++ b/profsea/emulator/spatial.py
@@ -0,0 +1,411 @@
+"""
+Copyright (c) 2023, Met Office
+All rights reserved.
+"""
+import glob
+import pickle
+import os
+from pathlib import Path
+import warnings
+
+import dask.array as da
+from netCDF4 import Dataset
+import numpy as np
+from rich.console import Console
+from rich.progress import track
+import xarray as xr
+
+from profsea.config import settings
+from profsea.directories import read_dir
+
+console = Console()
+warnings.filterwarnings("ignore")
+
+class Spatial:
+    """
+    """
+    def __init__(
+            self, 
+            components: dict, 
+            end_year=2301, 
+            output_ensemble: list|np.ndarray=[],
+            output_dir: str|Path=None
+        ):
+        self.n_samples = components["gmslr"].shape[0]
+        self.end_year = end_year
+        self.start_year = 2006
+        self.n_years = self.end_year - self.start_year
+
+    def _calc_baseline_period(self) -> float:
+        """
+        Baseline years used for IPCC AR5 and Palmer et al 2020 -- 1986-2005
+        :param yrs: years of the projections
+        :return: baseline years
+        """
+        byr1 = 1986.
+        byr2 = 2005.
+
+        console.log("Baseline period = ", byr1, "to", byr2)
+        midyr = (byr2 - byr1 + 1) * 0.5 + byr1
+        return self.start_year - midyr
+
+    def _calc_gia_contribution(self, scenario: str) -> None:
+        """
+        Calculate the glacial isostatic adjustment (GIA) contribution to the
+        regional component of sea level rise.
+        Option to use the generic global GIA estimates or use the GIA estimates
+        developed for UK as part of UKCP18.
+        :return: GIA estimates converted to mm/yr
+        """
+        console.log('Calculating GIA contribution...')
+        nGIA, GIA_vals = self._read_gia_estimates()
+        Tdelta = self._calc_baseline_period()
+        num_percs = len(self.output_percentiles)
+
+        # Unit series of mm/yr expressed as m/yr
+        unit_series = (np.arange(self.n_years) + Tdelta) * 0.001
+        GIA_unit_series = np.ones([num_percs, self.n_years]) * unit_series
+
+        # rgiai is an array of random GIA indices the size of the sample years
+        rgiai = np.random.randint(nGIA, size=num_percs)
+
+        GIA_T = da.from_array(GIA_unit_series)
+        GIA_vals = da.from_array(GIA_vals)
+        GIA_series = GIA_T[:, :, None, None] * GIA_vals[rgiai, None, :, :]
+
+        file_header = '_'.join(
+            ['gia', scenario, "projection", self.end_year])
+
+        # Save data in netcdf format (Assuming first dimension is percentile, but can be more general percentile/ensemble)
+        xr_dataArray = xr.DataArray(
+            GIA_series, 
+            dims=["samples", "time", "lat", "lon"], 
+            coords={
+                "samples": np.arange(num_percs),
+                "time": np.arange(self.start_year, self.end_year),
+                "lat": np.arange(-90, 90) + 0.5, 
+                "lon": np.arange(0, 360) + 0.5})
+        xr_dataArray.attrs["units"] = "m"
+        xr_dataArray.attrs["long_name"] = "Regional GIA sea-level projections"
+        ds = xr_dataArray.to_dataset(name='gia')
+
+        ds.attrs["source"] = "ProFSea-Climate v0.1"
+
+        R_file = '_'.join([file_header, 'regional']) + '.nc'
+        encoding = {'gia': {"zlib": True, "complevel": 5, "dtype": "float32"}}
+        ds.to_netcdf(os.path.join(self.output_dir, R_file), encoding=encoding, compute=True)
+        del GIA_series
+
+    def project(self) -> None:
+        """
+        Calculates global and regional component part contributions to sea level
+        change.
+        :param mcdir: location of Monte Carlo time series for new projections
+        :param components: sea level components
+        :param scenario: emission scenario
+        :param yrs: years of the projections
+        :param array_dims: Array of nesm, nsmps and nyrs
+            nesm --> Number of ensemble members in time series
+            nsmps --> Determine the number of samples you wish to make
+            nyrs --> Number of years in each projection time series
+        :return: montecarlo_G (global contribution to sea level rise) and
+            montecarlo_R (regional contribution to sea level change)
+        """  
+        # Numbers of ensemble members, samples, years
+        # Select dimensions from sample file, [time, realisation]
+        console.log(f"Running with {self.n_samples} ensemble members")
+
+        grid_path = os.path.join(
+            settings["cmipinfo"]["sealevelbasedir"], 
+            "ACCESS-CM2/zos_regression_ssp245_ACCESS-CM2.npy")
+        nFPs, FPlist = self._load_fingerprints(components)
+        resamples = np.random.choice(self.n_samples, self.n_samples) # Preserve correlations across comps
+        rfpi = np.random.randint(nFPs, size=self.n_samples)
+
+        # Calculate GIA contribution and save it out
+        self._calc_gia_contribution(scenario)
+        components = [
+            'expansion', 'antdyn', 'antsmb', 
+            'greenland', 'glacier', 'landwater']
+        for comp in track(components, description="Calculating components..."):
+            montecarlo_R = da.zeros((nsmps, nyrs, lats, lons), dtype=np.float32) # (FPs applied) + GIA
+            montecarlo_G = da.zeros((nsmps, nyrs, lats, lons), dtype=np.float32) # (no FPs applied)
+
+            # Load global projections in for the component
+            #mc_timeseries = np.load(os.path.join(mcdir, f'{scenario}_{comp}.npy'))
+            mc_timeseries = np.load(os.path.join(settings["baseoutdir"],settings["experiment_name"],
+                                                'data','gmslr',f'{scenario}_{comp}.npy'), mmap_mode='r')
+            sampled_mc = mc_timeseries[resamples, :nyrs]
+            montecarlo_G[:, :] = da.from_array(sampled_mc[:, :, None, None], chunks="auto")
+
+            if comp == "expansion":
+                sampled_coeffs = self._calc_expansion_contribution(scenario, nsmps)
+                montecarlo_R = montecarlo_G * sampled_coeffs[:, None, :, :]
+                del sampled_coeffs
+
+            elif comp == "landwater":
+                landwater_vals = self._calc_landwater_contribution(FPlist[0]["landwater"], lats, lons)
+                montecarlo_R[:, :, :, :] = montecarlo_G[:, :, :, :] * landwater_vals[None, None, :, :]
+                del landwater_vals
+
+            elif comp == "greenland":
+                greenland_fp = self._calc_greenland_fingerprint_ar6(lats, lons)
+                montecarlo_R[:, :, :, :] = montecarlo_G[:, :, :, :] * greenland_fp[None, None, :, :]
+
+            else:
+                fp_vals = self._calc_fingerprint_contributions(FPlist, comp, lats, lons)
+                montecarlo_R[:, :, :, :] = montecarlo_G[:, :, :, :] * fp_vals[rfpi][:, None, :, :]
+                del fp_vals
+
+            montecarlo_R = da.percentile(montecarlo_R, self.output_percentiles, axis=0)
+            montecarlo_R = montecarlo_R.astype(np.float32)
+
+            # Create the output sea level projections file directory and filename
+            self._save_projections(montecarlo_R, comp, scenario)
+
+    def _save_projections(self, montecarlo_R: da.array, component: str, scenario: str) -> None:
+        """
+        Save the regional sea level projections to a file.
+        :param montecarlo_R: regional sea level projections
+        :param component: sea level component
+        :param scenario: emission scenario
+        :param percentile: percentiles used for spatial projections
+        """
+        file_header = '_'.join([component, scenario, "projection", 
+                                f"{settings['projection_end_year']}"])
+
+        # Save data in netcdf format (Assuming first dimension is percentile, but can be more general percentile/ensemble)
+        xr_dataArray = xr.DataArray(montecarlo_R, dims=["percentile", "time", "lat", "lon"], 
+                                    coords={"percentile": self.output_percentiles, 
+                                            "time": np.arange(2006, montecarlo_R.shape[1] + 2006),
+                                            "lat": np.arange(-90, 90) + 0.5, "lon": np.arange(0, 360) + 0.5})
+        xr_dataArray.attrs["units"] = "m"
+        xr_dataArray.attrs["long_name"] = f"Regional {component} sea-level projections"
+        ds = xr_dataArray.to_dataset(name=component)
+
+        ds.attrs["source"] = "ProFSea-Climate v0.1"
+
+        R_file = '_'.join([file_header, 'regional']) + '.nc'
+        encoding = {component: {"zlib": True, "complevel": 5, "dtype": "float32"}}
+        ds.to_netcdf(os.path.join(self.output_dir, R_file), encoding=encoding, compute=True)
+
+    def _read_gia_estimates(self) -> tuple:
+        """
+        Read in pre-processed interpolator objects of GIA estimates (Lambeck,
+        ICE5G)
+        :param: none
+        :return: length of GIA_vals and numpy array of pre-processed interpolator
+        objects of GIA estimates
+        """
+        gia_file = settings["giaestimates"]["global"]
+        with open(gia_file, "rb") as ifp:
+            GIA_dict = pickle.load(ifp, encoding='latin1') # Interp objects
+
+        GIA_vals = []
+        for key in list(GIA_dict.keys()):
+            val = GIA_dict[key].values
+            GIA_vals.append(val)
+
+        nGIA = len(GIA_vals)
+        GIA_vals = np.array(GIA_vals)
+
+        # Sort out the crazy values in the 0th GIA array
+        GIA_vals[0][GIA_vals[0] < -99999] = 0
+
+        # AND shift them from -180, 180 to 0, 360
+        GIA_vals = np.roll(GIA_vals, 180, axis=2)
+        return nGIA, GIA_vals
+
+    def _calc_expansion_contribution(self, scenario: str, nsmps: int) -> da.array:
+        """
+        Calculate the thermal expansion contribution to the regional component of
+        sea level rise.
+        :param scenario: emission scenario
+        :param nsmps: determine the number of samples
+        :return: expansion estimates converted to mm/yr
+        """
+        # Select slope coefficients based on the MIP
+        if settings["emulator_settings"]["emulator_mode"]:
+            if settings["cmipinfo"]["mip"].lower() == "cmip6":
+                coeffs = self._load_CMIP6_slopes('ssp585')
+                coeffs = da.roll(coeffs, 180, axis=2)
+            else:
+                coeffs = self._load_CMIP5_slope_coeffs('rcp85')
+        else:
+            coeffs = self._load_CMIP5_slope_coeffs(scenario)
+
+        rand_samples = np.random.choice(
+            coeffs.shape[0], size=nsmps, replace=True)               
+        rand_coeffs = coeffs[rand_samples, :, :]
+        return rand_coeffs
+
+
+    def _calc_landwater_contribution(self, data: dict, lats: int, lons: int) -> da.array:
+        """
+        Calculate the regional landwater contribution to sea level rise.
+        :param interpolator: dictionary of interpolator objects
+        :return: numpy array of landwater values
+        """
+        landwater_vals = self._interpolate(data, lats, lons)
+        landwater_vals = da.roll(landwater_vals, 180, axis=1)
+        return landwater_vals
+
+
+    def _interpolate(self, data: da.array, lats: int, lons: int) -> np.ndarray:
+        """
+        """
+        original_da = xr.DataArray(
+            data.data,
+            coords=[
+                ("lat", np.linspace(90, -90, data.shape[0])), 
+                ("lon", np.linspace(-180, 180, data.shape[1], endpoint=False))
+            ],
+            name="v")
+
+        target_lat = np.linspace(90, -90, lats) + 0.5
+        target_lon = np.linspace(-180, 180, lons, endpoint=False) + 0.5
+        data_interp = original_da.interp(
+            lat=target_lat, lon=target_lon, method="linear").data
+
+        data_interp = da.roll(data_interp, 180, axis=1)
+        return data_interp
+
+    def _calc_fingerprint_contributions(
+            self, FPlist: list, comp: str,
+            lats: int, lons: int) -> da.array:
+        # Initiate an empty list for fingerprint values
+        fp_vals = []
+        for FP_dict in FPlist:
+            # Interpolate values to target lat/lon
+            val = FP_dict[comp]
+            val = self._interpolate(val, lats, lons)
+            fp_vals.append(val)
+
+        fp_vals = da.stack(fp_vals, axis=0)
+        return fp_vals
+
+    def _calc_greenland_fingerprint_ar6(self, lats: int, lons: int) -> da.array:
+        """Load and prepare the GIS fingerprint.
+
+        This fingerprint was/is used by FACTS for AR6 projections, and was 
+        originally calculated by Mitrovica et al., (2011).
+        
+        :return dask array containing GIS fingerprint
+        """
+        # Load in the fingerprint
+        fp_path = Path(settings["fingerprints"]) / "greenland_ar6.nc"
+        fp_ds = xr.open_dataset(fp_path, chunks={})
+
+        # Interpolate to (180, 360) grid
+        fp_vals = fp_ds.fp.interp(
+            lat=np.linspace(-90, 90, lats, endpoint=False) + 0.5, 
+            lon=np.linspace(0, 360, lons, endpoint=False) + 0.5, 
+            method="linear").data * 1000  # convert mm to m SLE per m GMSLR
+
+        # Flip vertically and roll by 180 degrees
+        fp_vals = da.flip(fp_vals)
+        fp_vals = da.roll(fp_vals, 180, axis=1)
+        return fp_vals
+
+    def _get_projection_info(self, indir: str, scenario: str) -> tuple:
+        """
+        Read in the dimensions of the Monte-Carlo data. These files are all
+        relative to midnight on 1st January 2007
+        :param indir: directory of Monte Carlo time series for new projections
+        :param scenario: emission scenarios to be considered
+        :return: Number of ensemble members in time series, number of years in
+        each projection time series and the years of the projections
+        """
+        sample_file = f'{scenario}_exp.nc'
+        f = Dataset(f'{indir}{sample_file}', 'r')
+        nesm = f.dimensions['realization'].size
+        t = f.variables['time']
+        nyrs = t.size
+        unit_str = t.units
+        first_year = int(unit_str.split(' ')[2][:4])
+        f.close()
+
+        yrs = first_year + np.arange(nyrs)
+        return nesm, nyrs, yrs
+
+    def _load_CMIP5_slope_coeffs(self, scenario: str) -> np.ndarray:
+        """
+        Loads in the CMIP slope coefficients based on linear regression of
+        'zos+zostoga' against 'zostoga' for the period 2005 to 2100.
+        Some models are missing regression slopes for RCP2.6. If so, use RCP4.5
+        values instead.
+        :param site_loc: name of the site location
+        :param scenario: emissions scenario
+        :return: 1D array of regression coefficients
+        """
+        # Read in the sea level regressions
+        in_zosddir = read_dir()[2]
+        filename = os.path.join(in_zosddir, 'zos_regression.npy')
+        coeffs = np.load(filename)
+        scenario_index = ['rcp26', 'rcp45', 'rcp85'].index(scenario)
+        coeffs = coeffs[:, scenario_index, :, :]
+        coeffs[np.isnan(coeffs)] = 0
+        coeffs[coeffs > 999] = 0
+        coeffs[coeffs < -999] = 0
+        return coeffs
+
+    def _load_CMIP6_slopes(self, scenario: str) -> np.ndarray:
+        """
+        Load in the CMIP6 slope coefficients.
+        :param site_loc: name of the site location
+        :param scenario: emissions scenario
+        :return: 1D array of regression coefficients
+        """
+        # Read in the sea level regressions
+        cmip6_dir = settings["cmipinfo"]["sealevelbasedir"]
+        slope_files = glob.glob(cmip6_dir + f'/*/zos_regression_{scenario}_*.npy')
+
+        def load_one_slope(f):
+            return np.load(f, mmap_mode='r')
+
+        # Create a list of lazy dask arrays
+        lazy_slopes = [
+            da.from_array(load_one_slope(f), chunks=(180, 360)) 
+            for f in slope_files]
+        slopes_stack = da.stack(lazy_slopes, axis=0)
+
+        return slopes_stack
+
+    def _load_fingerprints(self, components: list) -> tuple:
+        """
+        Create 2D Interpolator objects for the Slangen, Spada and Klemann
+        fingerprints
+        :param components: list of sea level components
+        :return nFPs: length of FPlist and interpolator objects of all sea level
+        components
+        """
+        # Create empty dictionaries for the Slangen, Spada and Klemann fingerprints
+        # interpolator objects.
+        slangen_FPs = {}
+        spada_FPs = {}
+        klemann_FPs = {}
+
+        # Only 1 fingerprint for Landwater
+        comp = "landwater"
+        slangen_FPs[comp] = xr.open_dataarray(
+            os.path.join(settings["fingerprints"],
+            comp + "_slangen_nomask.nc"), chunks={})
+
+        # Other FPs have multiple components
+        components_todo = [
+            c for c in components 
+            if c not in ["expansion", "landwater", "greenland"]]
+        for comp in components_todo:
+            slangen_FPs[comp] = xr.open_dataarray(
+                os.path.join(settings["fingerprints"],
+                comp + "_slangen_nomask.nc"), chunks={})
+            spada_FPs[comp] = xr.open_dataarray(
+                os.path.join(settings["fingerprints"],
+                comp + "_spada_nomask.nc"), chunks={})
+            klemann_FPs[comp] = xr.open_dataarray(
+                os.path.join(settings["fingerprints"],
+                comp + "_klemann_nomask.nc"), chunks={})
+
+        FPlist = [slangen_FPs, spada_FPs, klemann_FPs]
+        nFPs = len(FPlist)
+        return nFPs, FPlist
diff --git a/profsea/spatialise.py b/profsea/spatialise.py
deleted file mode 100644
index 88a8fff..0000000
--- a/profsea/spatialise.py
+++ /dev/null
@@ -1,430 +0,0 @@
-"""
-Copyright (c) 2023, Met Office
-All rights reserved.
-"""
-import glob
-import pickle
-import os
-from pathlib import Path
-import warnings
-
-import dask.array as da
-import iris
-from netCDF4 import Dataset
-import numpy as np
-from rich.console import Console
-from rich.progress import track
-from scipy.interpolate import RegularGridInterpolator
-import xarray as xr
-
-from profsea.config import settings
-from profsea.directories import read_dir
-from profsea.slr_pkg import choose_montecarlo_dir
-
-console = Console()
-warnings.filterwarnings("ignore")
-
-def calc_baseline_period(yrs: np.array) -> float:
-    """
-    Baseline years used for IPCC AR5 and Palmer et al 2020 -- 1986-2005
-    :param yrs: years of the projections
-    :return: baseline years
-    """
-    byr1 = 1986.
-    byr2 = 2005.
-
-    console.log("Baseline period = ", byr1, "to", byr2)
-    midyr = (byr2 - byr1 + 1) * 0.5 + byr1
-    return yrs[0] - midyr
-
-
-def calc_future_sea_level(scenario: str) -> None:
-    """
-    Calculates future sea level at the given site and write to file.
-    :param df: Data frame of all metadata (tide gauge or site specific) for
-               each(all) location(s)
-    :param site_loc: name of the site location
-    :param scenario: emission scenario
-    """
-    # Set the UKCP*18* random seed so results are reproducible
-    np.random.seed(18)
-
-    # Directory of Monte Carlo time series for new projections
-    mcdir = choose_montecarlo_dir()
-
-    # Specify the sea level components to include. The GIA contribution is
-    # calculated separately.
-    components = ['expansion', 'antdyn', 'antsmb', 'greenland',
-                  'glacier', 'landwater']
-
-    # Select dimensions from sample file, [time, realisation]
-    sample = np.load(os.path.join(mcdir, f'{scenario}_expansion.npy'))
-    nesm = sample.shape[0] # also number of samples to make
-    nyrs = sample.shape[1]
-    yrs = np.arange(2007, 2007 + nyrs)
-    console.log(f"Running with {nesm} ensemble members")
-
-    grid_path = os.path.join(
-        settings["cmipinfo"]["sealevelbasedir"], 
-        "ACCESS-CM2/zos_regression_ssp245_ACCESS-CM2.npy")
-    grid_sample = np.load(grid_path)
-    array_dims = [nesm, nesm, nyrs, grid_sample.shape[0], grid_sample.shape[1]]
-
-    # Get random samples of global and regional sea level components
-    calculate_sl_components(mcdir, components, scenario, yrs, array_dims)
-
-
-def calc_gia_contribution(
-        yrs: np.array, nyrs: int, nsmps: int, scenario: str) -> None:
-    """
-    Calculate the glacial isostatic adjustment (GIA) contribution to the
-    regional component of sea level rise.
-    Option to use the generic global GIA estimates or use the GIA estimates
-    developed for UK as part of UKCP18.
-    :param yrs: years of the projections
-    :param nyrs: number of years in each projection time series
-    :param nsmps: determine the number of samples
-    :param coords: coordinates of location of interest
-    :return: GIA estimates converted to mm/yr
-    """
-    console.log('Calculating GIA contribution...')
-    nGIA, GIA_vals = read_gia_estimates()
-    Tdelta = calc_baseline_period(yrs)
-
-    # Unit series of mm/yr expressed as m/yr
-    unit_series = (np.arange(nyrs) + Tdelta) * 0.001
-    GIA_unit_series = np.ones([5, nyrs]) * unit_series
-
-    # rgiai is an array of random GIA indices the size of the sample years
-    rgiai = np.random.randint(nGIA, size=5)
-
-    GIA_T = da.from_array(GIA_unit_series)
-    GIA_vals = da.from_array(GIA_vals)
-    GIA_series = GIA_T[:, :, None, None] * GIA_vals[rgiai, None, :, :]
-    GIA_series = GIA_series.compute()
-
-    file_header = '_'.join(['gia', scenario, "projection", 
-                    f"{settings['projection_end_year']}"])
-    R_file = '_'.join([file_header, 'regional']) + '.npy'
-    
-    np.save(os.path.join(read_dir()[4], R_file), GIA_series)
-    del GIA_series
-
-
-def calc_expansion_contribution(
-        scenario: str, nsmps: int) -> da.array:
-    """
-    Calculate the thermal expansion contribution to the regional component of
-    sea level rise.
-    :param scenario: emission scenario
-    :param nsmps: determine the number of samples
-    :return: expansion estimates converted to mm/yr
-    """
-    # Select slope coefficients based on the MIP
-    if settings["emulator_settings"]["emulator_mode"]:
-        if settings["cmipinfo"]["mip"].lower() == "cmip6":
-            coeffs = load_CMIP6_slopes('ssp585')
-            coeffs = np.roll(coeffs, 180, axis=2)
-        else:
-            coeffs = load_CMIP5_slope_coeffs('rcp85')
-    else:
-        coeffs = load_CMIP5_slope_coeffs(scenario)
-
-    rand_samples = np.random.choice(
-        coeffs.shape[0], size=nsmps, replace=True)               
-    rand_coeffs = coeffs[rand_samples, :, :]
-    rand_coeffs = da.from_array(rand_coeffs)
-    return rand_coeffs
-
-
-def calc_landwater_contribution(interpolator: dict) -> da.array:
-    """
-    Calculate the regional landwater contribution to sea level rise.
-    :param interpolator: dictionary of interpolator objects
-    :return: numpy array of landwater values
-    """
-    landwater_FP_interpolator = interpolator['landwater']
-    landwater_vals = da.from_array(
-        landwater_FP_interpolator.values.astype(np.float32).data)
-    landwater_vals = da.roll(landwater_vals, 180, axis=1)
-    return landwater_vals
-
-
-def calc_fingerprint_contributions(
-    FPlist: list, comp: str, lats: int, lons: int) -> da.array:
-    # Initiate an empty list for fingerprint values
-    fp_vals = []
-    for FP_dict in FPlist:
-        # Interpolate values to target lat/lon
-        val = FP_dict[comp].values
-        if val.shape != (lats, lons):
-            original_da = xr.DataArray(
-                val,
-                coords=[
-                    ("lat", np.linspace(90, -90, 360)), 
-                    ("lon", np.linspace(-180, 180, 720, endpoint=False))
-                ],
-                name="v")
-            target_lat = np.linspace(90, -90, 180)
-            target_lon = np.linspace(-180, 180, 360, endpoint=False)
-            val = original_da.interp(
-                lat=target_lat, lon=target_lon, method="linear").data
-            val = np.roll(val, 180, axis=1)
-        else:
-            val = np.roll(val, 180, axis=1)
-            
-        fp_vals.append(val)
-
-    fp_vals = da.from_array(np.array(fp_vals, dtype=np.float32))
-    return fp_vals
-
-
-def calc_greenland_fingerprint_ar6() -> da.array:
-    """Load and prepare the GIS fingerprint.
-
-    This fingerprint was/is used by FACTS for AR6 projections, and was 
-    originally calculated by Mitrovica et al., (2011).
-    
-    :return dask array containing GIS fingerprint
-    """
-    # Load in the fingerprint
-    fp_path = Path(settings["fingerprints"]) / "greenland_ar6.nc"
-    fp_ds = xr.load_dataset(fp_path)
-
-    # Interpolate to (180, 360) grid
-    fp_vals = fp_ds.fp.interp(
-        lat=np.linspace(-90, 90, 180, endpoint=False), 
-        lon=np.linspace(0, 360, 360, endpoint=False), 
-        method="linear").data * 1000  # convert mm to m SLE per m GMSLR
-
-    # Flip vertically and roll by 180 degrees
-    fp_vals = np.flip(fp_vals)
-    fp_vals = np.roll(fp_vals, 180, axis=1)
-    fp_vals = da.from_array(np.array(fp_vals, dtype=np.float32))
-    return fp_vals
-
-
-def save_projections(
-        montecarlo_R: da.array, component: str, scenario: str) -> None:
-    """
-    Save the regional sea level projections to a file.
-    :param montecarlo_R: regional sea level projections
-    :param component: sea level component
-    :param scenario: emission scenario
-    """
-    sealev_ddir = read_dir()[4]
-    file_header = '_'.join([component, scenario, "projection", 
-                            f"{settings['projection_end_year']}"])
-    # G_file = '_'.join([file_header, 'global']) + '.npy'
-    R_file = '_'.join([file_header, 'regional']) + '.npy'
-
-    # Save the global and local projections
-    np.save(os.path.join(sealev_ddir, R_file), montecarlo_R)
-
-
-def calculate_sl_components(
-        mcdir: str, components: list, scenario: str, 
-        yrs: np.array, array_dims: list) -> None:
-    """
-    Calculates global and regional component part contributions to sea level
-    change.
-    :param mcdir: location of Monte Carlo time series for new projections
-    :param components: sea level components
-    :param scenario: emission scenario
-    :param yrs: years of the projections
-    :param array_dims: Array of nesm, nsmps and nyrs
-        nesm --> Number of ensemble members in time series
-        nsmps --> Determine the number of samples you wish to make
-        nyrs --> Number of years in each projection time series
-    :return: montecarlo_G (global contribution to sea level rise) and
-        montecarlo_R (regional contribution to sea level change)
-    """  
-    # Numbers of ensemble members, samples, years
-    nesm, nsmps, nyrs, lats, lons = array_dims
-    nFPs, FPlist = setup_FP_interpolators(components)
-    resamples = np.random.choice(nesm, nsmps) # Preserve correlations across comps
-    rfpi = np.random.randint(nFPs, size=nsmps)
-
-    # Calculate GIA contribution and save it out
-    calc_gia_contribution(yrs, nyrs, nsmps, scenario)
-
-    for comp in track(components, description="Calculating components..."):
-        montecarlo_R = da.zeros((nsmps, nyrs, lats, lons), dtype=np.float32) # (FPs applied) + GIA
-        montecarlo_G = da.zeros((nsmps, nyrs, lats, lons), dtype=np.float32) # (no FPs applied)
-
-        # Load global projections in for the component
-        mc_timeseries = np.load(os.path.join(mcdir, f'{scenario}_{comp}.npy'))
-        sampled_mc = mc_timeseries[resamples, :nyrs]
-        montecarlo_G[:, :] = da.from_array(sampled_mc[:, :, None, None])
-
-        if comp == "expansion":
-            sampled_coeffs = calc_expansion_contribution(scenario, nsmps)
-            montecarlo_R = montecarlo_G * sampled_coeffs[:, None, :, :]
-            del sampled_coeffs
-
-        elif comp == "landwater":
-            landwater_vals = calc_landwater_contribution(FPlist[0])
-            montecarlo_R[:, :, :, :] = montecarlo_G[:, :, :, :] * landwater_vals[None, None, :, :]
-            del landwater_vals
-
-        elif comp == "greenland":
-            greenland_fp = calc_greenland_fingerprint_ar6()
-            montecarlo_R[:, :, :, :] = montecarlo_G[:, :, :, :] * greenland_fp[None, None, :, :]
-
-        else:
-            fp_vals = calc_fingerprint_contributions(FPlist, comp, lats, lons)
-            montecarlo_R[:, :, :, :] = montecarlo_G[:, :, :, :] * fp_vals[rfpi][:, None, :, :]
-            del fp_vals
-
-        # Take the 0th, 25th, 50th, 75th and 100th percentiles
-        montecarlo_R = da.percentile(montecarlo_R, [0, 25, 50, 75, 100], axis=0)
-        montecarlo_R = montecarlo_R.compute()
-
-        # Create the output sea level projections file directory and filename
-        save_projections(montecarlo_R, comp, scenario)
-
-
-def create_FP_interpolator(
-    datadir: str, dfile: str, method: str='linear') -> RegularGridInterpolator:
-    """
-    Generates a scipy Interpolator object from input NetCDF data of
-    gravitational fingerprints (takes inputs of Latitude and Longitude).
-    :param datadir: data directory
-    :param dfile: data filename
-    :param method: interpolation type --> 'linear' or 'nearest'
-    :return: 2D Interpolator object
-    """
-    cube = iris.load_cube(os.path.join(datadir, dfile))
-    lon = cube.coord('longitude').points
-    lat = cube.coord('latitude').points
-
-    # Define linear interpolator object:
-    interp_object = RegularGridInterpolator(
-        (lat, lon), cube.data,
-        method=method, bounds_error=True,
-        fill_value=None)
-    return interp_object
-
-
-def get_projection_info(indir: str, scenario: str) -> tuple:
-    """
-    Read in the dimensions of the Monte-Carlo data. These files are all
-    relative to midnight on 1st January 2007
-    :param indir: directory of Monte Carlo time series for new projections
-    :param scenario: emission scenarios to be considered
-    :return: Number of ensemble members in time series, number of years in
-    each projection time series and the years of the projections
-    """
-    sample_file = f'{scenario}_exp.nc'
-    f = Dataset(f'{indir}{sample_file}', 'r')
-    nesm = f.dimensions['realization'].size
-    t = f.variables['time']
-    nyrs = t.size
-    unit_str = t.units
-    first_year = int(unit_str.split(' ')[2][:4])
-    f.close()
-
-    yrs = first_year + np.arange(nyrs)
-    return nesm, nyrs, yrs
-
-
-def load_CMIP5_slope_coeffs(scenario: str) -> np.ndarray:
-    """
-    Loads in the CMIP slope coefficients based on linear regression of
-    'zos+zostoga' against 'zostoga' for the period 2005 to 2100.
-    Some models are missing regression slopes for RCP2.6. If so, use RCP4.5
-    values instead.
-    :param site_loc: name of the site location
-    :param scenario: emissions scenario
-    :return: 1D array of regression coefficients
-    """
-    # Read in the sea level regressions
-    in_zosddir = read_dir()[2]
-    filename = os.path.join(in_zosddir, 'zos_regression.npy')
-    coeffs = np.load(filename)
-    scenario_index = ['rcp26', 'rcp45', 'rcp85'].index(scenario)
-    coeffs = coeffs[:, scenario_index, :, :]
-    coeffs[np.isnan(coeffs)] = 0
-    coeffs[coeffs > 999] = 0
-    coeffs[coeffs < -999] = 0
-    return coeffs
-
-
-def load_CMIP6_slopes(scenario: str) -> np.ndarray:
-    """
-    Load in the CMIP6 slope coefficients.
-    :param site_loc: name of the site location
-    :param scenario: emissions scenario
-    :return: 1D array of regression coefficients
-    """
-    # Read in the sea level regressions
-    cmip6_dir = settings["cmipinfo"]["sealevelbasedir"]
-    slope_files = glob.glob(cmip6_dir + f'/*/zos_regression_{scenario}_*.npy')
-    slopes = np.zeros((len(slope_files), 180, 360), dtype=np.float32)
-    for i, slope_file in enumerate(slope_files):
-        slopes[i, :, :] = np.load(slope_file)
-
-    return slopes
-
-
-def read_gia_estimates() -> tuple:
-    """
-    Read in pre-processed interpolator objects of GIA estimates (Lambeck,
-    ICE5G)
-    :param: none
-    :return: length of GIA_vals and numpy array of pre-processed interpolator
-    objects of GIA estimates
-    """
-    gia_file = settings["giaestimates"]["global"]
-    with open(gia_file, "rb") as ifp:
-        GIA_dict = pickle.load(ifp, encoding='latin1') # Interp objects
-
-    GIA_vals = []
-    for key in list(GIA_dict.keys()):
-        val = GIA_dict[key].values
-        GIA_vals.append(val)
-
-    nGIA = len(GIA_vals)
-    GIA_vals = np.array(GIA_vals)
-
-     # Sort out the crazy values in the 0th GIA array
-    GIA_vals[0][GIA_vals[0] < -99999] = 0
-
-    # AND shift them from -180, 180 to 0, 360
-    GIA_vals = np.roll(GIA_vals, 180, axis=2)
-    return nGIA, GIA_vals
-
-
-def setup_FP_interpolators(components: list) -> tuple:
-    """
-    Create 2D Interpolator objects for the Slangen, Spada and Klemann
-    fingerprints
-    :param components: list of sea level components
-    :return nFPs: length of FPlist and interpolator objects of all sea level
-    components
-    """
-    # Create empty dictionaries for the Slangen, Spada and Klemann fingerprints
-    # interpolator objects.
-    slangen_FPs = {}
-    spada_FPs = {}
-    klemann_FPs = {}
-
-    # Only 1 fingerprint for Landwater
-    comp = "landwater"
-    slangen_FPs[comp] = create_FP_interpolator(settings["fingerprints"],
-                                               comp + "_slangen_nomask.nc")
-
-    # Create interpolators for the remaining components. Expansion ('expansion')
-    # is global so no interpolation is needed.
-    components_todo = [c for c in components if c not in ["expansion", "landwater", "greenland"]]
-    for comp in components_todo:
-        slangen_FPs[comp] = create_FP_interpolator(settings["fingerprints"],
-                                                   comp + "_slangen_nomask.nc")
-        spada_FPs[comp] = create_FP_interpolator(settings["fingerprints"],
-                                                 comp + "_spada_nomask.nc")
-        klemann_FPs[comp] = create_FP_interpolator(settings["fingerprints"],
-                                                   comp + "_klemann_nomask.nc")
-
-    FPlist = [slangen_FPs, spada_FPs, klemann_FPs]
-    nFPs = len(FPlist)
-    return nFPs, FPlist

From bc7d2dc1bda377053201779dc26ccb672ec073e8 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Tue, 9 Dec 2025 23:13:58 +0000
Subject: [PATCH 079/276] Decouple spatial class from user-settings

---
 profsea/emulator/gmslr.py        |  35 ++---
 profsea/emulator/spatial.py      | 232 ++++++++++++++-----------------
 profsea/probabilistic_wrapper.py |  10 +-
 profsea/spatial_projections.py   |   4 +-
 profsea/utils/__init__.py        |   1 +
 profsea/utils/utils.py           |  10 ++
 6 files changed, 131 insertions(+), 161 deletions(-)
 create mode 100644 profsea/utils/__init__.py
 create mode 100644 profsea/utils/utils.py

diff --git a/profsea/emulator/gmslr.py b/profsea/emulator/gmslr.py
index 4540c07..5c7ef01 100644
--- a/profsea/emulator/gmslr.py
+++ b/profsea/emulator/gmslr.py
@@ -20,6 +20,8 @@
 from scipy.stats import norm, truncnorm
 import xarray as xr
 
+from profsea.utils import sample_members_2D
+
 console = Console()
 
 @functools.lru_cache(maxsize=1)
@@ -220,15 +222,6 @@ def list_components(self) -> list:
         """List the available SLR components."""
         component_dict = self.get_components()
         print(list(component_dict.keys()))
-
-    def sample_members_2D(
-            self, array: np.ndarray) -> np.ndarray:
-        """Sample real ensemble members from a 2D numpy array."""
-        # Caculate statistical timeseries, then match with closest real timeseries 
-        array_percentiles = np.percentile(array, self.output_percentiles, axis=0)
-        distances = cdist(array_percentiles, array)
-        mem_indices = np.argmin(distances, axis=1)
-        return array[mem_indices]
     
     def save_components(self, output_dir: str, scenario_name: str) -> None:
         """Save all SLR components as .npy files to a directory.
@@ -299,18 +292,18 @@ def project(self) -> None:
         # TODO Parallelise this section as it's a bit slow
         if self.output_percentiles is not None:
             console.log(f"Sampling {len(self.output_percentiles)} members per component...")
-            self.gmslr = self.sample_members_2D(self.gmslr)
-            self.expansion = self.sample_members_2D(self.expansion)
-            self.antnet = self.sample_members_2D(self.antnet)
-            self.antdyn = self.sample_members_2D(self.antdyn)
-            self.antsmb = self.sample_members_2D(self.antsmb)
-            self.glacier = self.sample_members_2D(self.glacier)
-            self.greenland_ar6 = self.sample_members_2D(self.greenland_ar6)
-            self.landwater = self.sample_members_2D(self.landwater)
-            self.greensmb = self.sample_members_2D(self.greensmb)
-            self.greendyn = self.sample_members_2D(self.greendyn)
-            # self.greenland_ar5 = self.sample_members_2D(self.greenland_ar5)
-            # self.landwater_ar6 = self.sample_members_2D(self.landwater_ar6)
+            self.gmslr = sample_members_2D(self.gmslr)
+            self.expansion = sample_members_2D(self.expansion, self.output_percentiles)
+            self.antnet = sample_members_2D(self.antnet, self.output_percentiles)
+            self.antdyn = sample_members_2D(self.antdyn, self.output_percentiles)
+            self.antsmb = sample_members_2D(self.antsmb, self.output_percentiles)
+            self.glacier = sample_members_2D(self.glacier, self.output_percentiles)
+            self.greenland_ar6 = sample_members_2D(self.greenland_ar6, self.output_percentiles)
+            self.landwater = sample_members_2D(self.landwater, self.output_percentiles)
+            self.greensmb = sample_members_2D(self.greensmb, self.output_percentiles)
+            self.greendyn = sample_members_2D(self.greendyn, self.output_percentiles)
+            # self.greenland_ar5 = self.sample_members_2D(self.greenland_ar5, self.output_percentiles)
+            # self.landwater_ar6 = self.sample_members_2D(self.landwater_ar6, self.output_percentiles)
 
     def run_serial_projections(
             self, T_int_med: np.ndarray, T_int_ens: np.ndarray, 
diff --git a/profsea/emulator/spatial.py b/profsea/emulator/spatial.py
index 8453fad..fb59298 100644
--- a/profsea/emulator/spatial.py
+++ b/profsea/emulator/spatial.py
@@ -9,7 +9,6 @@
 import warnings
 
 import dask.array as da
-from netCDF4 import Dataset
 import numpy as np
 from rich.console import Console
 from rich.progress import track
@@ -21,35 +20,69 @@
 console = Console()
 warnings.filterwarnings("ignore")
 
+# TODO change numpy.random to random generators
+
 class Spatial:
-    """
-    """
+
     def __init__(
             self, 
-            components: dict, 
-            end_year=2301, 
-            output_ensemble: list|np.ndarray=[],
-            output_dir: str|Path=None
+            scenario: str,
+            expansion_patterns_dir: str|Path,
+            fingerprint_dir: str|Path,
+            gia_dir: str|Path,
+            components_dir: str|Path=None, 
+            components: dict=None, 
+            end_year: int=2301, 
+            baseline_yrs: tuple=(1986, 2005),
+            output_percentiles: list|np.ndarray=[5, 17, 50, 83, 95],
+            output_dir: str|Path=None,
+            cmip5_patterns: bool=False
         ):
-        self.n_samples = components["gmslr"].shape[0]
+        """
+        """
+        if not components_dir and not components:
+            raise ValueError(
+                "Provide either an input directory or "
+                "dictionary with global projection components")
+        if components:
+            self.n_samples = components["gmslr"].shape[0]  # ens dimension
+        else:
+            sample_filepath = next(Path(components_dir).glob("*.npy"))
+            sample = np.load(sample_filepath, mmap_mode="r")
+            self.n_samples = sample.shape[0]
+
+        self.scenario = scenario
+        self.expansion_patterns_dir = expansion_patterns_dir
+        self.fingerprint_dir = fingerprint_dir
+        self.gia_dir = gia_dir
+        self.components_dir = components_dir
+        self.components = components
         self.end_year = end_year
+        self.baseline_yrs = baseline_yrs
+        self.output_percentiles = output_percentiles
+        self.output_dir = output_dir
         self.start_year = 2006
         self.n_years = self.end_year - self.start_year
 
+        # Get lat/lon sizes
+        sample_pattern_filepath = Path(expansion_patterns_dir) \
+            / "ACCESS-CM2/zos_regression_ssp245_ACCESS-CM2.npy"
+        sample_pattern = np.load(sample_pattern_filepath, mmap_mode="r")
+        self.nlat, self.nlon = sample_pattern.shape[0], sample_pattern.shape[1]
+
+
+        console.log(f"Baseline period = {self.baseline_yrs[0]} to {self.baseline_yrs[1]}")
+
     def _calc_baseline_period(self) -> float:
         """
         Baseline years used for IPCC AR5 and Palmer et al 2020 -- 1986-2005
         :param yrs: years of the projections
         :return: baseline years
         """
-        byr1 = 1986.
-        byr2 = 2005.
-
-        console.log("Baseline period = ", byr1, "to", byr2)
-        midyr = (byr2 - byr1 + 1) * 0.5 + byr1
+        midyr = (self.baseline_yrs[1] - self.baseline_yrs[0] + 1) * 0.5 + self.baseline_yrs[0]
         return self.start_year - midyr
 
-    def _calc_gia_contribution(self, scenario: str) -> None:
+    def _calc_gia_contribution(self) -> None:
         """
         Calculate the glacial isostatic adjustment (GIA) contribution to the
         regional component of sea level rise.
@@ -74,7 +107,7 @@ def _calc_gia_contribution(self, scenario: str) -> None:
         GIA_series = GIA_T[:, :, None, None] * GIA_vals[rgiai, None, :, :]
 
         file_header = '_'.join(
-            ['gia', scenario, "projection", self.end_year])
+            ['gia', self.scenario, "projection", self.end_year])
 
         # Save data in netcdf format (Assuming first dimension is percentile, but can be more general percentile/ensemble)
         xr_dataArray = xr.DataArray(
@@ -111,49 +144,45 @@ def project(self) -> None:
         :return: montecarlo_G (global contribution to sea level rise) and
             montecarlo_R (regional contribution to sea level change)
         """  
-        # Numbers of ensemble members, samples, years
-        # Select dimensions from sample file, [time, realisation]
         console.log(f"Running with {self.n_samples} ensemble members")
 
-        grid_path = os.path.join(
-            settings["cmipinfo"]["sealevelbasedir"], 
-            "ACCESS-CM2/zos_regression_ssp245_ACCESS-CM2.npy")
-        nFPs, FPlist = self._load_fingerprints(components)
-        resamples = np.random.choice(self.n_samples, self.n_samples) # Preserve correlations across comps
-        rfpi = np.random.randint(nFPs, size=self.n_samples)
+        resamples = np.random.choice(self.n_samples, size=self.n_samples) # Preserve correlations across comps
 
         # Calculate GIA contribution and save it out
-        self._calc_gia_contribution(scenario)
-        components = [
-            'expansion', 'antdyn', 'antsmb', 
-            'greenland', 'glacier', 'landwater']
-        for comp in track(components, description="Calculating components..."):
-            montecarlo_R = da.zeros((nsmps, nyrs, lats, lons), dtype=np.float32) # (FPs applied) + GIA
-            montecarlo_G = da.zeros((nsmps, nyrs, lats, lons), dtype=np.float32) # (no FPs applied)
+        self._calc_gia_contribution()
+        nFPs, FPlist = self._load_fingerprints()
+        rfpi = np.random.randint(nFPs, size=self.n_samples)
+        for comp in track(self.components.keys(), description="Calculating components..."):
+            montecarlo_R = da.zeros(
+                (self.n_samples, self.n_years, self.nlat, self.nlon),
+                dtype=np.float32) # (FPs applied) + GIA
+            montecarlo_G = da.zeros(
+                (self.n_samples, self.n_years, self.nlat, self.nlon),
+                dtype=np.float32) # (no FPs applied)
 
             # Load global projections in for the component
-            #mc_timeseries = np.load(os.path.join(mcdir, f'{scenario}_{comp}.npy'))
-            mc_timeseries = np.load(os.path.join(settings["baseoutdir"],settings["experiment_name"],
-                                                'data','gmslr',f'{scenario}_{comp}.npy'), mmap_mode='r')
-            sampled_mc = mc_timeseries[resamples, :nyrs]
+            if self.components_dir:
+                component_path = Path(self.components_dir) / f"{self.scenario}_{comp}.npy"
+                mc_timeseries = np.load(component_path, mmap_mode='r')
+            sampled_mc = mc_timeseries[resamples, :self.n_years]
             montecarlo_G[:, :] = da.from_array(sampled_mc[:, :, None, None], chunks="auto")
 
             if comp == "expansion":
-                sampled_coeffs = self._calc_expansion_contribution(scenario, nsmps)
+                sampled_coeffs = self._calc_expansion_contribution()
                 montecarlo_R = montecarlo_G * sampled_coeffs[:, None, :, :]
                 del sampled_coeffs
 
             elif comp == "landwater":
-                landwater_vals = self._calc_landwater_contribution(FPlist[0]["landwater"], lats, lons)
+                landwater_vals = self._calc_landwater_contribution(FPlist[0]["landwater"])
                 montecarlo_R[:, :, :, :] = montecarlo_G[:, :, :, :] * landwater_vals[None, None, :, :]
                 del landwater_vals
 
             elif comp == "greenland":
-                greenland_fp = self._calc_greenland_fingerprint_ar6(lats, lons)
+                greenland_fp = self._calc_greenland_fingerprint_ar6()
                 montecarlo_R[:, :, :, :] = montecarlo_G[:, :, :, :] * greenland_fp[None, None, :, :]
 
             else:
-                fp_vals = self._calc_fingerprint_contributions(FPlist, comp, lats, lons)
+                fp_vals = self._calc_fingerprint_contributions(FPlist)
                 montecarlo_R[:, :, :, :] = montecarlo_G[:, :, :, :] * fp_vals[rfpi][:, None, :, :]
                 del fp_vals
 
@@ -161,9 +190,9 @@ def project(self) -> None:
             montecarlo_R = montecarlo_R.astype(np.float32)
 
             # Create the output sea level projections file directory and filename
-            self._save_projections(montecarlo_R, comp, scenario)
+            self._save_projections(montecarlo_R, comp)
 
-    def _save_projections(self, montecarlo_R: da.array, component: str, scenario: str) -> None:
+    def _save_projections(self, montecarlo_R: da.array, component: str) -> None:
         """
         Save the regional sea level projections to a file.
         :param montecarlo_R: regional sea level projections
@@ -171,7 +200,7 @@ def _save_projections(self, montecarlo_R: da.array, component: str, scenario: st
         :param scenario: emission scenario
         :param percentile: percentiles used for spatial projections
         """
-        file_header = '_'.join([component, scenario, "projection", 
+        file_header = '_'.join([component, self.scenario, "projection", 
                                 f"{settings['projection_end_year']}"])
 
         # Save data in netcdf format (Assuming first dimension is percentile, but can be more general percentile/ensemble)
@@ -197,8 +226,8 @@ def _read_gia_estimates(self) -> tuple:
         :return: length of GIA_vals and numpy array of pre-processed interpolator
         objects of GIA estimates
         """
-        gia_file = settings["giaestimates"]["global"]
-        with open(gia_file, "rb") as ifp:
+        gia_path = next(Path(self.gia_dir).glob("global*"))
+        with open(gia_path, "rb") as ifp:
             GIA_dict = pickle.load(ifp, encoding='latin1') # Interp objects
 
         GIA_vals = []
@@ -216,7 +245,7 @@ def _read_gia_estimates(self) -> tuple:
         GIA_vals = np.roll(GIA_vals, 180, axis=2)
         return nGIA, GIA_vals
 
-    def _calc_expansion_contribution(self, scenario: str, nsmps: int) -> da.array:
+    def _calc_expansion_contribution(self) -> da.array:
         """
         Calculate the thermal expansion contribution to the regional component of
         sea level rise.
@@ -225,33 +254,25 @@ def _calc_expansion_contribution(self, scenario: str, nsmps: int) -> da.array:
         :return: expansion estimates converted to mm/yr
         """
         # Select slope coefficients based on the MIP
-        if settings["emulator_settings"]["emulator_mode"]:
-            if settings["cmipinfo"]["mip"].lower() == "cmip6":
-                coeffs = self._load_CMIP6_slopes('ssp585')
-                coeffs = da.roll(coeffs, 180, axis=2)
-            else:
-                coeffs = self._load_CMIP5_slope_coeffs('rcp85')
-        else:
-            coeffs = self._load_CMIP5_slope_coeffs(scenario)
+        coeffs = self._load_CMIP6_slopes()
+        coeffs = da.roll(coeffs, 180, axis=2)
 
         rand_samples = np.random.choice(
-            coeffs.shape[0], size=nsmps, replace=True)               
+            coeffs.shape[0], size=self.n_samples, replace=True)               
         rand_coeffs = coeffs[rand_samples, :, :]
         return rand_coeffs
 
-
-    def _calc_landwater_contribution(self, data: dict, lats: int, lons: int) -> da.array:
+    def _calc_landwater_contribution(self, data: xr.DataArray) -> da.array:
         """
         Calculate the regional landwater contribution to sea level rise.
         :param interpolator: dictionary of interpolator objects
         :return: numpy array of landwater values
         """
-        landwater_vals = self._interpolate(data, lats, lons)
+        landwater_vals = self._interpolate(data, self.nlat, self.nlon)
         landwater_vals = da.roll(landwater_vals, 180, axis=1)
         return landwater_vals
 
-
-    def _interpolate(self, data: da.array, lats: int, lons: int) -> np.ndarray:
+    def _interpolate(self, data: da.array) -> np.ndarray:
         """
         """
         original_da = xr.DataArray(
@@ -262,29 +283,27 @@ def _interpolate(self, data: da.array, lats: int, lons: int) -> np.ndarray:
             ],
             name="v")
 
-        target_lat = np.linspace(90, -90, lats) + 0.5
-        target_lon = np.linspace(-180, 180, lons, endpoint=False) + 0.5
+        target_lat = np.linspace(90, -90, self.nlat) + 0.5
+        target_lon = np.linspace(-180, 180, self.nlon, endpoint=False) + 0.5
         data_interp = original_da.interp(
             lat=target_lat, lon=target_lon, method="linear").data
 
         data_interp = da.roll(data_interp, 180, axis=1)
         return data_interp
 
-    def _calc_fingerprint_contributions(
-            self, FPlist: list, comp: str,
-            lats: int, lons: int) -> da.array:
+    def _calc_fingerprint_contributions(self, FPlist: list, comp: str) -> da.array:
         # Initiate an empty list for fingerprint values
         fp_vals = []
         for FP_dict in FPlist:
             # Interpolate values to target lat/lon
             val = FP_dict[comp]
-            val = self._interpolate(val, lats, lons)
+            val = self._interpolate(val, self.nlat, self.nlon)
             fp_vals.append(val)
 
         fp_vals = da.stack(fp_vals, axis=0)
         return fp_vals
 
-    def _calc_greenland_fingerprint_ar6(self, lats: int, lons: int) -> da.array:
+    def _calc_greenland_fingerprint_ar6(self) -> da.array:
         """Load and prepare the GIS fingerprint.
 
         This fingerprint was/is used by FACTS for AR6 projections, and was 
@@ -293,13 +312,13 @@ def _calc_greenland_fingerprint_ar6(self, lats: int, lons: int) -> da.array:
         :return dask array containing GIS fingerprint
         """
         # Load in the fingerprint
-        fp_path = Path(settings["fingerprints"]) / "greenland_ar6.nc"
+        fp_path = Path(self.fingerprint_dir) / "greenland_ar6.nc"
         fp_ds = xr.open_dataset(fp_path, chunks={})
 
         # Interpolate to (180, 360) grid
         fp_vals = fp_ds.fp.interp(
-            lat=np.linspace(-90, 90, lats, endpoint=False) + 0.5, 
-            lon=np.linspace(0, 360, lons, endpoint=False) + 0.5, 
+            lat=np.linspace(-90, 90, self.nlat, endpoint=False) + 0.5, 
+            lon=np.linspace(0, 360, self.nlon, endpoint=False) + 0.5, 
             method="linear").data * 1000  # convert mm to m SLE per m GMSLR
 
         # Flip vertically and roll by 180 degrees
@@ -307,49 +326,7 @@ def _calc_greenland_fingerprint_ar6(self, lats: int, lons: int) -> da.array:
         fp_vals = da.roll(fp_vals, 180, axis=1)
         return fp_vals
 
-    def _get_projection_info(self, indir: str, scenario: str) -> tuple:
-        """
-        Read in the dimensions of the Monte-Carlo data. These files are all
-        relative to midnight on 1st January 2007
-        :param indir: directory of Monte Carlo time series for new projections
-        :param scenario: emission scenarios to be considered
-        :return: Number of ensemble members in time series, number of years in
-        each projection time series and the years of the projections
-        """
-        sample_file = f'{scenario}_exp.nc'
-        f = Dataset(f'{indir}{sample_file}', 'r')
-        nesm = f.dimensions['realization'].size
-        t = f.variables['time']
-        nyrs = t.size
-        unit_str = t.units
-        first_year = int(unit_str.split(' ')[2][:4])
-        f.close()
-
-        yrs = first_year + np.arange(nyrs)
-        return nesm, nyrs, yrs
-
-    def _load_CMIP5_slope_coeffs(self, scenario: str) -> np.ndarray:
-        """
-        Loads in the CMIP slope coefficients based on linear regression of
-        'zos+zostoga' against 'zostoga' for the period 2005 to 2100.
-        Some models are missing regression slopes for RCP2.6. If so, use RCP4.5
-        values instead.
-        :param site_loc: name of the site location
-        :param scenario: emissions scenario
-        :return: 1D array of regression coefficients
-        """
-        # Read in the sea level regressions
-        in_zosddir = read_dir()[2]
-        filename = os.path.join(in_zosddir, 'zos_regression.npy')
-        coeffs = np.load(filename)
-        scenario_index = ['rcp26', 'rcp45', 'rcp85'].index(scenario)
-        coeffs = coeffs[:, scenario_index, :, :]
-        coeffs[np.isnan(coeffs)] = 0
-        coeffs[coeffs > 999] = 0
-        coeffs[coeffs < -999] = 0
-        return coeffs
-
-    def _load_CMIP6_slopes(self, scenario: str) -> np.ndarray:
+    def _load_CMIP6_slopes(self) -> np.ndarray:
         """
         Load in the CMIP6 slope coefficients.
         :param site_loc: name of the site location
@@ -357,21 +334,22 @@ def _load_CMIP6_slopes(self, scenario: str) -> np.ndarray:
         :return: 1D array of regression coefficients
         """
         # Read in the sea level regressions
-        cmip6_dir = settings["cmipinfo"]["sealevelbasedir"]
-        slope_files = glob.glob(cmip6_dir + f'/*/zos_regression_{scenario}_*.npy')
+        slope_files = list(Path(
+            self.expansion_patterns_dir
+        ).glob("/*/zos_regression_ssp585_*.npy"))
 
-        def load_one_slope(f):
+        def _load_one_slope(f):
             return np.load(f, mmap_mode='r')
 
         # Create a list of lazy dask arrays
         lazy_slopes = [
-            da.from_array(load_one_slope(f), chunks=(180, 360)) 
+            da.from_array(_load_one_slope(f), chunks=(180, 360)) 
             for f in slope_files]
         slopes_stack = da.stack(lazy_slopes, axis=0)
 
         return slopes_stack
 
-    def _load_fingerprints(self, components: list) -> tuple:
+    def _load_fingerprints(self) -> tuple:
         """
         Create 2D Interpolator objects for the Slangen, Spada and Klemann
         fingerprints
@@ -387,24 +365,20 @@ def _load_fingerprints(self, components: list) -> tuple:
 
         # Only 1 fingerprint for Landwater
         comp = "landwater"
-        slangen_FPs[comp] = xr.open_dataarray(
-            os.path.join(settings["fingerprints"],
-            comp + "_slangen_nomask.nc"), chunks={})
+        landwater_path = Path(self.fingerprint_dir) / f"{comp}_slangen_nomask.nc"
+        slangen_FPs[comp] = xr.open_dataarray(landwater_path, chunks={})
 
         # Other FPs have multiple components
         components_todo = [
-            c for c in components 
+            c for c in self.components.keys() 
             if c not in ["expansion", "landwater", "greenland"]]
         for comp in components_todo:
-            slangen_FPs[comp] = xr.open_dataarray(
-                os.path.join(settings["fingerprints"],
-                comp + "_slangen_nomask.nc"), chunks={})
-            spada_FPs[comp] = xr.open_dataarray(
-                os.path.join(settings["fingerprints"],
-                comp + "_spada_nomask.nc"), chunks={})
-            klemann_FPs[comp] = xr.open_dataarray(
-                os.path.join(settings["fingerprints"],
-                comp + "_klemann_nomask.nc"), chunks={})
+            slangen_path = Path(self.fingerprint_dir) / f"{comp}_slangen_nomask.nc"
+            spada_path = Path(self.fingerprint_dir) / f"{comp}_spada_nomask.nc"
+            klemann_path = Path(self.fingerprint_dir) / f"{comp}_klemann_nomask.nc"
+            slangen_FPs[comp] = xr.open_dataarray(slangen_path, chunks={})
+            spada_FPs[comp] = xr.open_dataarray(spada_path, chunks={})
+            klemann_FPs[comp] = xr.open_dataarray(klemann_path, chunks={})
 
         FPlist = [slangen_FPs, spada_FPs, klemann_FPs]
         nFPs = len(FPlist)
diff --git a/profsea/probabilistic_wrapper.py b/profsea/probabilistic_wrapper.py
index 0390f98..9932dd4 100644
--- a/profsea/probabilistic_wrapper.py
+++ b/profsea/probabilistic_wrapper.py
@@ -14,6 +14,7 @@
 import xarray as xr
 
 from profsea.emulator import GMSLREmulator
+from profsea.utils import sample_members_2D
 
 worker_tas = None
 worker_ohc = None
@@ -180,15 +181,6 @@ def plot_samples(tas: np.ndarray, ohc: np.ndarray) -> None:
     plt.close()
 
 
-def sample_members_2D(array: np.ndarray, percentiles: list|np.ndarray) -> np.ndarray:
-    """Sample real ensemble members from a 2D numpy array."""
-    # Caculate statistical timeseries, then match with closest real timeseries 
-    array_percentiles = np.percentile(array, percentiles, axis=0)
-    distances = cdist(array_percentiles, array)
-    mem_indices = np.argmin(distances, axis=1)
-    return array[mem_indices]
-
-
 def process_global_ensemble(components: list, percentiles: list, scenario: str) -> None:
     for comp, data in components.items():
         # Since the spatial projections won't change the ensemble order
diff --git a/profsea/spatial_projections.py b/profsea/spatial_projections.py
index c2108c7..f19507d 100644
--- a/profsea/spatial_projections.py
+++ b/profsea/spatial_projections.py
@@ -20,7 +20,7 @@
 
 from profsea.config import settings
 from profsea.directories import read_dir
-from profsea.emulator import GMSLREmulator
+from profsea.emulator import Global
 from profsea.slr_pkg import choose_montecarlo_dir
 
 console = Console()
@@ -509,7 +509,7 @@ def calculate_global_components(scenario: str, palmer_method: bool) -> None:
     T_change = sample_members_2D(T_change, percentiles)
     OHC_change = sample_members_2D(OHC_change, percentiles)
 
-    gmslr = GMSLREmulator(
+    gmslr = Global(
         T_change,
         OHC_change,
         scenario,
diff --git a/profsea/utils/__init__.py b/profsea/utils/__init__.py
new file mode 100644
index 0000000..e0fe9da
--- /dev/null
+++ b/profsea/utils/__init__.py
@@ -0,0 +1 @@
+from .utils import *  # noqa: F403
diff --git a/profsea/utils/utils.py b/profsea/utils/utils.py
new file mode 100644
index 0000000..ce41913
--- /dev/null
+++ b/profsea/utils/utils.py
@@ -0,0 +1,10 @@
+import numpy as np
+from scipy.spatial.distance import cdist
+
+def sample_members_2D(array: np.ndarray, percentiles: list|np.ndarray) -> np.ndarray:
+    """Sample real ensemble members from a 2D numpy array."""
+    # Caculate statistical timeseries, then match with closest real timeseries 
+    array_percentiles = np.percentile(array, percentiles, axis=0)
+    distances = cdist(array_percentiles, array)
+    mem_indices = np.argmin(distances, axis=1)
+    return array[mem_indices]

From 80812a29cd087c553c5d7614e833a1edb1be63f9 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Tue, 9 Dec 2025 23:16:44 +0000
Subject: [PATCH 080/276] Small tweaks

---
 profsea/emulator/spatial.py    |  7 +------
 profsea/spatial_projections.py | 17 +----------------
 2 files changed, 2 insertions(+), 22 deletions(-)

diff --git a/profsea/emulator/spatial.py b/profsea/emulator/spatial.py
index fb59298..50576a8 100644
--- a/profsea/emulator/spatial.py
+++ b/profsea/emulator/spatial.py
@@ -2,7 +2,6 @@
 Copyright (c) 2023, Met Office
 All rights reserved.
 """
-import glob
 import pickle
 import os
 from pathlib import Path
@@ -14,9 +13,6 @@
 from rich.progress import track
 import xarray as xr
 
-from profsea.config import settings
-from profsea.directories import read_dir
-
 console = Console()
 warnings.filterwarnings("ignore")
 
@@ -200,8 +196,7 @@ def _save_projections(self, montecarlo_R: da.array, component: str) -> None:
         :param scenario: emission scenario
         :param percentile: percentiles used for spatial projections
         """
-        file_header = '_'.join([component, self.scenario, "projection", 
-                                f"{settings['projection_end_year']}"])
+        file_header = '_'.join([component, self.scenario, "projection", self.end_year])
 
         # Save data in netcdf format (Assuming first dimension is percentile, but can be more general percentile/ensemble)
         xr_dataArray = xr.DataArray(montecarlo_R, dims=["percentile", "time", "lat", "lon"], 
diff --git a/profsea/spatial_projections.py b/profsea/spatial_projections.py
index f19507d..940f0d3 100644
--- a/profsea/spatial_projections.py
+++ b/profsea/spatial_projections.py
@@ -14,13 +14,12 @@
 import numpy as np
 from rich.console import Console
 from rich.progress import track
-import scipy
-from scipy.spatial.distance import cdist
 import xarray as xr
 
 from profsea.config import settings
 from profsea.directories import read_dir
 from profsea.emulator import Global
+from profsea.utils import sample_members_2D
 from profsea.slr_pkg import choose_montecarlo_dir
 
 console = Console()
@@ -457,20 +456,6 @@ def load_fingerprints(components: list) -> tuple:
     return nFPs, FPlist
 
 
-def sample_members_2D(array: np.ndarray, percentile_seq: list|np.ndarray) -> np.ndarray:
-        """Sample real ensemble members from a 2D numpy array."""
-        # Caculate statistical timeseries, then match with closest real timeseries 
-        array_percentiles = np.percentile(array, percentile_seq, axis=0)
-        array_perc_diffs = array[None, :, :] - array_percentiles[:, None, :]
-
-        # Calculate distances between statistical percentiles and real members
-        distances = scipy.linalg.norm(array_perc_diffs, axis=2)
-        mem_indices = np.argmin(distances, axis=1)
-        distances = cdist(array_percentiles, array)
-        mem_indices = np.argmin(distances, axis=1)
-        return array[mem_indices]
-
-
 def calculate_global_components(scenario: str, palmer_method: bool) -> None:
     """
     Calculate the global contributions for each of the sea-level components

From 7b3f7f42299bd1ab8c93b11ff9492ebf3a91691d Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Wed, 10 Dec 2025 09:05:10 +0000
Subject: [PATCH 081/276] Add error handling

---
 profsea/emulator/spatial.py | 15 ++++++++++++---
 1 file changed, 12 insertions(+), 3 deletions(-)

diff --git a/profsea/emulator/spatial.py b/profsea/emulator/spatial.py
index 50576a8..d021729 100644
--- a/profsea/emulator/spatial.py
+++ b/profsea/emulator/spatial.py
@@ -36,14 +36,18 @@ def __init__(
         ):
         """
         """
-        if not components_dir and not components:
+        if ((not components_dir and not components) or
+            (components_dir and components)):
             raise ValueError(
                 "Provide either an input directory or "
                 "dictionary with global projection components")
         if components:
             self.n_samples = components["gmslr"].shape[0]  # ens dimension
         else:
-            sample_filepath = next(Path(components_dir).glob("*.npy"))
+            try:
+                sample_filepath = next(Path(components_dir).glob("*.npy"))
+            except StopIteration:
+                raise FileNotFoundError("Nothing found in components_dir")
             sample = np.load(sample_filepath, mmap_mode="r")
             self.n_samples = sample.shape[0]
 
@@ -63,7 +67,12 @@ def __init__(
         # Get lat/lon sizes
         sample_pattern_filepath = Path(expansion_patterns_dir) \
             / "ACCESS-CM2/zos_regression_ssp245_ACCESS-CM2.npy"
-        sample_pattern = np.load(sample_pattern_filepath, mmap_mode="r")
+        try:
+            sample_pattern = np.load(sample_pattern_filepath, mmap_mode="r")
+        except FileNotFoundError:
+            raise FileNotFoundError(
+                "expansion_patterns_dir expects the " 
+                "patterns' parent directory")
         self.nlat, self.nlon = sample_pattern.shape[0], sample_pattern.shape[1]
 
 

From 0949410e5b867a4de420706e9621550b69e13f80 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Wed, 10 Dec 2025 11:07:39 +0000
Subject: [PATCH 082/276] Fixed bugs, now runs!

---
 profsea/emulator/spatial.py | 59 +++++++++++++++++++++----------------
 1 file changed, 33 insertions(+), 26 deletions(-)

diff --git a/profsea/emulator/spatial.py b/profsea/emulator/spatial.py
index d021729..979a9b8 100644
--- a/profsea/emulator/spatial.py
+++ b/profsea/emulator/spatial.py
@@ -36,33 +36,46 @@ def __init__(
         ):
         """
         """
+        # Start off with some error handling
         if ((not components_dir and not components) or
             (components_dir and components)):
             raise ValueError(
                 "Provide either an input directory or "
                 "dictionary with global projection components")
+
         if components:
-            self.n_samples = components["gmslr"].shape[0]  # ens dimension
+            if components["gmslr"]: 
+                raise ValueError(
+                    "Remove the GMSLR component from "
+                    "the component dictionary")
+            self.n_samples = components["expansion"].shape[0]  # ens dimension
+            self.components = components
         else:
-            try:
-                sample_filepath = next(Path(components_dir).glob("*.npy"))
-            except StopIteration:
-                raise FileNotFoundError("Nothing found in components_dir")
-            sample = np.load(sample_filepath, mmap_mode="r")
-            self.n_samples = sample.shape[0]
-
+            # Read in the components
+            component_list = [  # currently allowed components
+                "expansion", "antdyn", 
+                "antsmb", "glacier", "greenland"]
+            self.components = {}
+            for comp in component_list:
+                component_path = Path(components_dir) / f"{scenario}_{comp}.npy"
+                self.components[comp] = np.load(component_path, mmap_mode='r')
+  
+        if not output_dir:
+            output_dir = Path.cwd()
+
+        # Assign attributes
         self.scenario = scenario
         self.expansion_patterns_dir = expansion_patterns_dir
         self.fingerprint_dir = fingerprint_dir
         self.gia_dir = gia_dir
         self.components_dir = components_dir
-        self.components = components
         self.end_year = end_year
         self.baseline_yrs = baseline_yrs
         self.output_percentiles = output_percentiles
         self.output_dir = output_dir
         self.start_year = 2006
         self.n_years = self.end_year - self.start_year
+        self.n_samples = self.components["expansion"].shape[0]
 
         # Get lat/lon sizes
         sample_pattern_filepath = Path(expansion_patterns_dir) \
@@ -75,7 +88,6 @@ def __init__(
                 "patterns' parent directory")
         self.nlat, self.nlon = sample_pattern.shape[0], sample_pattern.shape[1]
 
-
         console.log(f"Baseline period = {self.baseline_yrs[0]} to {self.baseline_yrs[1]}")
 
     def _calc_baseline_period(self) -> float:
@@ -111,9 +123,6 @@ def _calc_gia_contribution(self) -> None:
         GIA_vals = da.from_array(GIA_vals)
         GIA_series = GIA_T[:, :, None, None] * GIA_vals[rgiai, None, :, :]
 
-        file_header = '_'.join(
-            ['gia', self.scenario, "projection", self.end_year])
-
         # Save data in netcdf format (Assuming first dimension is percentile, but can be more general percentile/ensemble)
         xr_dataArray = xr.DataArray(
             GIA_series, 
@@ -129,6 +138,7 @@ def _calc_gia_contribution(self) -> None:
 
         ds.attrs["source"] = "ProFSea-Climate v0.1"
 
+        file_header = f"gia_{self.scenario}_projection_{self.end_year}"
         R_file = '_'.join([file_header, 'regional']) + '.nc'
         encoding = {'gia': {"zlib": True, "complevel": 5, "dtype": "float32"}}
         ds.to_netcdf(os.path.join(self.output_dir, R_file), encoding=encoding, compute=True)
@@ -151,24 +161,22 @@ def project(self) -> None:
         """  
         console.log(f"Running with {self.n_samples} ensemble members")
 
-        resamples = np.random.choice(self.n_samples, size=self.n_samples) # Preserve correlations across comps
+        resamples = np.random.choice(self.n_samples, size=self.n_samples)  # Preserve correlations across comps
 
         # Calculate GIA contribution and save it out
         self._calc_gia_contribution()
         nFPs, FPlist = self._load_fingerprints()
         rfpi = np.random.randint(nFPs, size=self.n_samples)
-        for comp in track(self.components.keys(), description="Calculating components..."):
+        for comp in track(list(self.components.keys()), description="Calculating components..."):
             montecarlo_R = da.zeros(
                 (self.n_samples, self.n_years, self.nlat, self.nlon),
-                dtype=np.float32) # (FPs applied) + GIA
+                dtype=np.float32)  # (FPs applied) + GIA
             montecarlo_G = da.zeros(
                 (self.n_samples, self.n_years, self.nlat, self.nlon),
-                dtype=np.float32) # (no FPs applied)
+                dtype=np.float32)  # (no FPs applied)
 
             # Load global projections in for the component
-            if self.components_dir:
-                component_path = Path(self.components_dir) / f"{self.scenario}_{comp}.npy"
-                mc_timeseries = np.load(component_path, mmap_mode='r')
+            mc_timeseries = self.components[comp]
             sampled_mc = mc_timeseries[resamples, :self.n_years]
             montecarlo_G[:, :] = da.from_array(sampled_mc[:, :, None, None], chunks="auto")
 
@@ -187,7 +195,7 @@ def project(self) -> None:
                 montecarlo_R[:, :, :, :] = montecarlo_G[:, :, :, :] * greenland_fp[None, None, :, :]
 
             else:
-                fp_vals = self._calc_fingerprint_contributions(FPlist)
+                fp_vals = self._calc_fingerprint_contributions(FPlist, comp)
                 montecarlo_R[:, :, :, :] = montecarlo_G[:, :, :, :] * fp_vals[rfpi][:, None, :, :]
                 del fp_vals
 
@@ -205,8 +213,6 @@ def _save_projections(self, montecarlo_R: da.array, component: str) -> None:
         :param scenario: emission scenario
         :param percentile: percentiles used for spatial projections
         """
-        file_header = '_'.join([component, self.scenario, "projection", self.end_year])
-
         # Save data in netcdf format (Assuming first dimension is percentile, but can be more general percentile/ensemble)
         xr_dataArray = xr.DataArray(montecarlo_R, dims=["percentile", "time", "lat", "lon"], 
                                     coords={"percentile": self.output_percentiles, 
@@ -218,6 +224,7 @@ def _save_projections(self, montecarlo_R: da.array, component: str) -> None:
 
         ds.attrs["source"] = "ProFSea-Climate v0.1"
 
+        file_header = f"{component}_{self.scenario}_projection_{self.end_year}"
         R_file = '_'.join([file_header, 'regional']) + '.nc'
         encoding = {component: {"zlib": True, "complevel": 5, "dtype": "float32"}}
         ds.to_netcdf(os.path.join(self.output_dir, R_file), encoding=encoding, compute=True)
@@ -301,7 +308,7 @@ def _calc_fingerprint_contributions(self, FPlist: list, comp: str) -> da.array:
         for FP_dict in FPlist:
             # Interpolate values to target lat/lon
             val = FP_dict[comp]
-            val = self._interpolate(val, self.nlat, self.nlon)
+            val = self._interpolate(val)
             fp_vals.append(val)
 
         fp_vals = da.stack(fp_vals, axis=0)
@@ -340,7 +347,7 @@ def _load_CMIP6_slopes(self) -> np.ndarray:
         # Read in the sea level regressions
         slope_files = list(Path(
             self.expansion_patterns_dir
-        ).glob("/*/zos_regression_ssp585_*.npy"))
+        ).glob("*/zos_regression_ssp585_*.npy"))
 
         def _load_one_slope(f):
             return np.load(f, mmap_mode='r')
@@ -374,7 +381,7 @@ def _load_fingerprints(self) -> tuple:
 
         # Other FPs have multiple components
         components_todo = [
-            c for c in self.components.keys() 
+            c for c in list(self.components.keys()) 
             if c not in ["expansion", "landwater", "greenland"]]
         for comp in components_todo:
             slangen_path = Path(self.fingerprint_dir) / f"{comp}_slangen_nomask.nc"

From 1c8e0f4f76d2d1a54dbe51ddfa06994732b1cff5 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Wed, 10 Dec 2025 11:18:28 +0000
Subject: [PATCH 083/276] Added FP lat lons

---
 profsea/spatial_projections.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/profsea/spatial_projections.py b/profsea/spatial_projections.py
index c2108c7..c688340 100644
--- a/profsea/spatial_projections.py
+++ b/profsea/spatial_projections.py
@@ -176,8 +176,8 @@ def interpolate(data: da.array, lats: int, lons: int) -> np.ndarray:
     original_da = xr.DataArray(
         data.data,
         coords=[
-            ("lat", np.linspace(90, -90, data.shape[0])), 
-            ("lon", np.linspace(-180, 180, data.shape[1], endpoint=False))
+            ("lat", data[data.dims[0]].values), 
+            ("lon", data[data.dims[1]].values)
         ],
         name="v")
 

From 18bce21e9526553c5e9604df20615504d0cfb6e5 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Wed, 10 Dec 2025 14:26:15 +0000
Subject: [PATCH 084/276] Add warning

---
 profsea/emulator/spatial.py | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/profsea/emulator/spatial.py b/profsea/emulator/spatial.py
index 979a9b8..1d7389b 100644
--- a/profsea/emulator/spatial.py
+++ b/profsea/emulator/spatial.py
@@ -87,7 +87,9 @@ def __init__(
                 "expansion_patterns_dir expects the " 
                 "patterns' parent directory")
         self.nlat, self.nlon = sample_pattern.shape[0], sample_pattern.shape[1]
-
+        console.log("INFO: Spatial() expects sterodynamic patterns on half-integer grids:\n"
+                    "\tlat: (-89.5, ..., 89.5)\n"
+                    "\tlon: (-179.5, ..., 179.5)")
         console.log(f"Baseline period = {self.baseline_yrs[0]} to {self.baseline_yrs[1]}")
 
     def _calc_baseline_period(self) -> float:
@@ -289,8 +291,8 @@ def _interpolate(self, data: da.array) -> np.ndarray:
         original_da = xr.DataArray(
             data.data,
             coords=[
-                ("lat", np.linspace(90, -90, data.shape[0])), 
-                ("lon", np.linspace(-180, 180, data.shape[1], endpoint=False))
+                ("lat", data[data.dims[0]].values), 
+                ("lon", data[data.dims[1]].values)
             ],
             name="v")
 

From 7d4143292139556362fae70307fe6166f59f7c57 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Wed, 10 Dec 2025 15:07:45 +0000
Subject: [PATCH 085/276] Add grid warning

---
 profsea/spatial_projections.py | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/profsea/spatial_projections.py b/profsea/spatial_projections.py
index c688340..1bf95e5 100644
--- a/profsea/spatial_projections.py
+++ b/profsea/spatial_projections.py
@@ -75,6 +75,11 @@ def calc_future_sea_level(scenario: str) -> None:
     grid_sample = np.load(grid_path)
     array_dims = [nesm, nesm, nyrs, grid_sample.shape[0], grid_sample.shape[1]]
 
+    console.log(
+        "INFO: This module expects sterodynamic patterns on half-integer grids:\n"
+        "\tlat: (-89.5, ..., 89.5)\n"
+        "\tlon: (-179.5, ..., 179.5)")
+
     # Get random samples of global and regional sea level components
     calculate_sl_components(mcdir, components, scenario, yrs, array_dims)
 

From 83434d817bd6e7564e1fc2a507be14653bcfb72e Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Thu, 11 Dec 2025 23:17:25 +0000
Subject: [PATCH 086/276] Move interp into utils

---
 profsea/emulator/spatial.py    | 25 ++++---------------------
 profsea/spatial_projections.py | 22 +---------------------
 profsea/utils/utils.py         | 22 ++++++++++++++++++++++
 3 files changed, 27 insertions(+), 42 deletions(-)

diff --git a/profsea/emulator/spatial.py b/profsea/emulator/spatial.py
index 1d7389b..87f6b71 100644
--- a/profsea/emulator/spatial.py
+++ b/profsea/emulator/spatial.py
@@ -13,6 +13,8 @@
 from rich.progress import track
 import xarray as xr
 
+from profsea.utils import interpolate
+
 console = Console()
 warnings.filterwarnings("ignore")
 
@@ -281,36 +283,17 @@ def _calc_landwater_contribution(self, data: xr.DataArray) -> da.array:
         :param interpolator: dictionary of interpolator objects
         :return: numpy array of landwater values
         """
-        landwater_vals = self._interpolate(data, self.nlat, self.nlon)
+        landwater_vals = interpolate(data, self.nlat, self.nlon)
         landwater_vals = da.roll(landwater_vals, 180, axis=1)
         return landwater_vals
 
-    def _interpolate(self, data: da.array) -> np.ndarray:
-        """
-        """
-        original_da = xr.DataArray(
-            data.data,
-            coords=[
-                ("lat", data[data.dims[0]].values), 
-                ("lon", data[data.dims[1]].values)
-            ],
-            name="v")
-
-        target_lat = np.linspace(90, -90, self.nlat) + 0.5
-        target_lon = np.linspace(-180, 180, self.nlon, endpoint=False) + 0.5
-        data_interp = original_da.interp(
-            lat=target_lat, lon=target_lon, method="linear").data
-
-        data_interp = da.roll(data_interp, 180, axis=1)
-        return data_interp
-
     def _calc_fingerprint_contributions(self, FPlist: list, comp: str) -> da.array:
         # Initiate an empty list for fingerprint values
         fp_vals = []
         for FP_dict in FPlist:
             # Interpolate values to target lat/lon
             val = FP_dict[comp]
-            val = self._interpolate(val)
+            val = interpolate(val)
             fp_vals.append(val)
 
         fp_vals = da.stack(fp_vals, axis=0)
diff --git a/profsea/spatial_projections.py b/profsea/spatial_projections.py
index 8e5e16d..0c45077 100644
--- a/profsea/spatial_projections.py
+++ b/profsea/spatial_projections.py
@@ -19,7 +19,7 @@
 from profsea.config import settings
 from profsea.directories import read_dir
 from profsea.emulator import Global
-from profsea.utils import sample_members_2D
+from profsea.utils import sample_members_2D, interpolate
 from profsea.slr_pkg import choose_montecarlo_dir
 
 console = Console()
@@ -175,26 +175,6 @@ def calc_landwater_contribution(data: dict, lats: int, lons: int) -> da.array:
     return landwater_vals
 
 
-def interpolate(data: da.array, lats: int, lons: int) -> np.ndarray:
-    """
-    """
-    original_da = xr.DataArray(
-        data.data,
-        coords=[
-            ("lat", data[data.dims[0]].values), 
-            ("lon", data[data.dims[1]].values)
-        ],
-        name="v")
-
-    target_lat = np.linspace(90, -90, lats) + 0.5
-    target_lon = np.linspace(-180, 180, lons, endpoint=False) + 0.5
-    data_interp = original_da.interp(
-        lat=target_lat, lon=target_lon, method="linear").data
-
-    data_interp = da.roll(data_interp, 180, axis=1)
-    return data_interp
-
-
 def calc_fingerprint_contributions(
     FPlist: list, comp: str, lats: int, lons: int) -> da.array:
     # Initiate an empty list for fingerprint values
diff --git a/profsea/utils/utils.py b/profsea/utils/utils.py
index ce41913..1594d2d 100644
--- a/profsea/utils/utils.py
+++ b/profsea/utils/utils.py
@@ -1,5 +1,7 @@
+import dask.array as da
 import numpy as np
 from scipy.spatial.distance import cdist
+import xarray as xr
 
 def sample_members_2D(array: np.ndarray, percentiles: list|np.ndarray) -> np.ndarray:
     """Sample real ensemble members from a 2D numpy array."""
@@ -8,3 +10,23 @@ def sample_members_2D(array: np.ndarray, percentiles: list|np.ndarray) -> np.nda
     distances = cdist(array_percentiles, array)
     mem_indices = np.argmin(distances, axis=1)
     return array[mem_indices]
+
+
+def interpolate(data: da.array, lats: int, lons: int) -> np.ndarray:
+    """
+    """
+    original_da = xr.DataArray(
+        data.data,
+        coords=[
+            ("lat", data[data.dims[0]].values), 
+            ("lon", data[data.dims[1]].values)
+        ],
+        name="v")
+
+    target_lat = np.linspace(90, -90, lats) + 0.5
+    target_lon = np.linspace(-180, 180, lons, endpoint=False) + 0.5
+    data_interp = original_da.interp(
+        lat=target_lat, lon=target_lon, method="linear").data
+
+    data_interp = da.roll(data_interp, 180, axis=1)
+    return data_interp

From 3061a2faffe6089c9f7eaeb98d240457a4b7b7a9 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Thu, 11 Dec 2025 23:22:53 +0000
Subject: [PATCH 087/276] Small fix

---
 profsea/emulator/spatial.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/profsea/emulator/spatial.py b/profsea/emulator/spatial.py
index 87f6b71..3edbcc0 100644
--- a/profsea/emulator/spatial.py
+++ b/profsea/emulator/spatial.py
@@ -293,7 +293,7 @@ def _calc_fingerprint_contributions(self, FPlist: list, comp: str) -> da.array:
         for FP_dict in FPlist:
             # Interpolate values to target lat/lon
             val = FP_dict[comp]
-            val = interpolate(val)
+            val = interpolate(val, self.nlat, self.nlon)
             fp_vals.append(val)
 
         fp_vals = da.stack(fp_vals, axis=0)

From 61d64a4eca74f02e33bc6d2d72e7c61b2a367a45 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Thu, 11 Dec 2025 23:38:06 +0000
Subject: [PATCH 088/276] Use NumPy RNG instead of legacy

---
 profsea/emulator/spatial.py | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/profsea/emulator/spatial.py b/profsea/emulator/spatial.py
index 3edbcc0..ba9eb06 100644
--- a/profsea/emulator/spatial.py
+++ b/profsea/emulator/spatial.py
@@ -18,8 +18,6 @@
 console = Console()
 warnings.filterwarnings("ignore")
 
-# TODO change numpy.random to random generators
-
 class Spatial:
 
     def __init__(
@@ -34,7 +32,8 @@ def __init__(
             baseline_yrs: tuple=(1986, 2005),
             output_percentiles: list|np.ndarray=[5, 17, 50, 83, 95],
             output_dir: str|Path=None,
-            cmip5_patterns: bool=False
+            cmip5_patterns: bool=False,
+            random_seed: int=None
         ):
         """
         """
@@ -78,6 +77,7 @@ def __init__(
         self.start_year = 2006
         self.n_years = self.end_year - self.start_year
         self.n_samples = self.components["expansion"].shape[0]
+        self.rng = np.random.default_rng(seed=random_seed)
 
         # Get lat/lon sizes
         sample_pattern_filepath = Path(expansion_patterns_dir) \
@@ -121,7 +121,7 @@ def _calc_gia_contribution(self) -> None:
         GIA_unit_series = np.ones([num_percs, self.n_years]) * unit_series
 
         # rgiai is an array of random GIA indices the size of the sample years
-        rgiai = np.random.randint(nGIA, size=num_percs)
+        rgiai = self.rng.integers(nGIA, size=num_percs)
 
         GIA_T = da.from_array(GIA_unit_series)
         GIA_vals = da.from_array(GIA_vals)
@@ -165,12 +165,12 @@ def project(self) -> None:
         """  
         console.log(f"Running with {self.n_samples} ensemble members")
 
-        resamples = np.random.choice(self.n_samples, size=self.n_samples)  # Preserve correlations across comps
+        resamples = self.rng.choice(self.n_samples, size=self.n_samples)  # Preserve correlations across comps
 
         # Calculate GIA contribution and save it out
         self._calc_gia_contribution()
         nFPs, FPlist = self._load_fingerprints()
-        rfpi = np.random.randint(nFPs, size=self.n_samples)
+        rfpi = self.rng.integers(nFPs, size=self.n_samples)
         for comp in track(list(self.components.keys()), description="Calculating components..."):
             montecarlo_R = da.zeros(
                 (self.n_samples, self.n_years, self.nlat, self.nlon),
@@ -272,7 +272,7 @@ def _calc_expansion_contribution(self) -> da.array:
         coeffs = self._load_CMIP6_slopes()
         coeffs = da.roll(coeffs, 180, axis=2)
 
-        rand_samples = np.random.choice(
+        rand_samples = self.rng.choice(
             coeffs.shape[0], size=self.n_samples, replace=True)               
         rand_coeffs = coeffs[rand_samples, :, :]
         return rand_coeffs

From cac0e7dcfb440898c6f476203f94c34a58cebaa4 Mon Sep 17 00:00:00 2001
From: Hemant Khatri 
Date: Thu, 18 Dec 2025 10:36:53 +0000
Subject: [PATCH 089/276] correct typo

---
 profsea/emulator/gmslr.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/profsea/emulator/gmslr.py b/profsea/emulator/gmslr.py
index 5c7ef01..e473cbe 100644
--- a/profsea/emulator/gmslr.py
+++ b/profsea/emulator/gmslr.py
@@ -292,7 +292,7 @@ def project(self) -> None:
         # TODO Parallelise this section as it's a bit slow
         if self.output_percentiles is not None:
             console.log(f"Sampling {len(self.output_percentiles)} members per component...")
-            self.gmslr = sample_members_2D(self.gmslr)
+            self.gmslr = sample_members_2D(self.gmslr, self.output_percentiles)
             self.expansion = sample_members_2D(self.expansion, self.output_percentiles)
             self.antnet = sample_members_2D(self.antnet, self.output_percentiles)
             self.antdyn = sample_members_2D(self.antdyn, self.output_percentiles)

From 78f78aff4b39f64a3a149b6d3175f94b6b2337bb Mon Sep 17 00:00:00 2001
From: Hemant Khatri 
Date: Fri, 19 Dec 2025 14:45:48 +0000
Subject: [PATCH 090/276] reduce default self.nm to 100 and remove calculations
 that are not used in the code

---
 profsea/emulator/gmslr.py | 24 ++++++++----------------
 1 file changed, 8 insertions(+), 16 deletions(-)

diff --git a/profsea/emulator/gmslr.py b/profsea/emulator/gmslr.py
index e473cbe..bb9cd75 100644
--- a/profsea/emulator/gmslr.py
+++ b/profsea/emulator/gmslr.py
@@ -132,7 +132,7 @@ def __init__(
             scenario: str,
             end_yr: int,
             seed: int=1234,
-            nt: int=450,
+            nt: int=100,
             nm: int=1000,
             tcv: float=1.0,
             glaciermip: bool|int=2,
@@ -186,13 +186,14 @@ def __init__(
         # Conversion factor for Gt to m SLE
         self.mSLEoGt = 1e12 / 3.61e14 * 1e-3
 
+        if input_ensemble:
+            self.nt = self.T_change.shape[0]
+
         # Sensitivity of thermosteric SLR to ocean heat content change
         # From Turner et al. (2023)
         self.exp_efficiency = np.random.normal(
-            loc=0.113, scale=0.013, size=OHC_change.shape[0]) * 1e-24 # m/YJ
+            loc=0.113, scale=0.013, size=self.nt)[:, None] * 1e-24 # m/YJ
 
-        if input_ensemble:
-            self.nt = self.T_change.shape[0]
 
         if self.scenario not in ['rcp26', 'rcp45', 'rcp85', 
             'ssp126', 'ssp245', 'ssp585'] and self.cum_emissions_total is None:
@@ -389,28 +390,19 @@ def calculate_drivers(self) -> tuple:
             if self.OHC_change.shape[1] != self.nyr: 
                 self.OHC_change = self.OHC_change.T
                 
-            therm_ens = self.OHC_change * self.exp_efficiency[:, None]
-            T_int_ens = np.cumsum(self.T_change, axis=1)
-            T_int_med = np.percentile(T_int_ens, 50, axis=0) # using median here instead of mean... CHECK THIS
-
-            return self.T_change, therm_ens, T_int_ens, T_int_med
-        
-        if len(self.T_change.shape) > 1:
             T_med = np.percentile(self.T_change, 50, axis=0)
             T_std = np.std(self.T_change, axis=0)
 
             therm_med = np.percentile(self.OHC_change, 50, axis=0) * self.exp_efficiency
             therm_std = np.std(self.OHC_change * self.exp_efficiency, axis=0)
+
         else:
             if self.T_percentile_95 is not None:
                 T_med = self.T_change
-                therm_med = self.OHC_change 
+                therm_med = self.OHC_change * self.exp_efficiency 
                 
                 T_std = (self.T_percentile_95 - self.T_change) / 1.645
-                # therm_std = (self.OHC_percentile_95 - self.OHC_change) * self.exp_efficiency / 1.645
-                therm_std = (self.OHC_percentile_95 - self.OHC_change) / 1.645
-                self.T_std = T_std
-                self.therm_std = therm_std
+                therm_std = (self.OHC_percentile_95 - self.OHC_change) * self.exp_efficiency / 1.645
             
             else:
                 raise ValueError(

From 5662e012730dbe1e06e2b9134557c10ba598d338 Mon Sep 17 00:00:00 2001
From: Hemant Khatri 
Date: Thu, 15 Jan 2026 19:06:21 +0000
Subject: [PATCH 091/276] Bug correction in spatialize.py

---
 profsea/emulator/gmslr.py   |  7 ----
 profsea/emulator/spatial.py | 71 ++++++++++++++++++++++---------------
 profsea/utils/utils.py      |  3 +-
 3 files changed, 44 insertions(+), 37 deletions(-)

diff --git a/profsea/emulator/gmslr.py b/profsea/emulator/gmslr.py
index bb9cd75..c49b130 100644
--- a/profsea/emulator/gmslr.py
+++ b/profsea/emulator/gmslr.py
@@ -240,13 +240,6 @@ def save_components(self, output_dir: str, scenario_name: str) -> None:
         """
         # Create directory if it doesn't exist
         Path(output_dir).mkdir(parents=True, exist_ok=True)
-        for name, component in self.get_components().items():
-            np.save(
-                os.path.join(
-                    output_dir, 
-                    f'{scenario_name}_{name}.npy'), 
-                component
-            )
 
         # Save data in netcdf format (store all components in an xarray dataset)
         # Assumes first dimension is percentile, but can be more general (percentile/ensemble)
diff --git a/profsea/emulator/spatial.py b/profsea/emulator/spatial.py
index ba9eb06..07290e1 100644
--- a/profsea/emulator/spatial.py
+++ b/profsea/emulator/spatial.py
@@ -19,6 +19,31 @@
 warnings.filterwarnings("ignore")
 
 class Spatial:
+    """ Spatial sea level rise component emulator.
+
+    Parameters
+    ----------
+    scenario: str
+        Name of the scenario.
+    expansion_patterns_dir: str
+        Direcotry path of regression patterns of thermal expansion component from cmip models.
+    fingerprint_dir: str
+        Direcotry path of GRD (Gravitational, Rotational, Deformational) fingerprint data
+    gia_dir: str
+        Direcotry path of GIA (Glacial Isostatic Adjustment) data
+    components_dir: str
+        Path to global sea-level rise components (output of ProFSea's gmslr module)
+    component_list: str
+        Namelist of components for spatial projections
+    end_year: int
+        End year of the projections.
+    output_percentiles: int
+        List of percentiles for output
+    output_dir: str
+        Path to output directory for saving spatial projections.
+    random_seed: bool
+        Seed for numpy.random.
+    """
 
     def __init__(
             self, 
@@ -27,7 +52,7 @@ def __init__(
             fingerprint_dir: str|Path,
             gia_dir: str|Path,
             components_dir: str|Path=None, 
-            components: dict=None, 
+            component_list: dict=None, 
             end_year: int=2301, 
             baseline_yrs: tuple=(1986, 2005),
             output_percentiles: list|np.ndarray=[5, 17, 50, 83, 95],
@@ -35,31 +60,23 @@ def __init__(
             cmip5_patterns: bool=False,
             random_seed: int=None
         ):
-        """
-        """
+
         # Start off with some error handling
-        if ((not components_dir and not components) or
-            (components_dir and components)):
+        if not components_dir:
+            raise ValueError(
+                "Provide an input directory")
+
+        if not component_list:
             raise ValueError(
-                "Provide either an input directory or "
-                "dictionary with global projection components")
-
-        if components:
-            if components["gmslr"]: 
-                raise ValueError(
-                    "Remove the GMSLR component from "
-                    "the component dictionary")
-            self.n_samples = components["expansion"].shape[0]  # ens dimension
-            self.components = components
-        else:
-            # Read in the components
-            component_list = [  # currently allowed components
-                "expansion", "antdyn", 
-                "antsmb", "glacier", "greenland"]
-            self.components = {}
-            for comp in component_list:
-                component_path = Path(components_dir) / f"{scenario}_{comp}.npy"
-                self.components[comp] = np.load(component_path, mmap_mode='r')
+                "Provide namelist of global projection components. "
+                "Currently allowed components are expansion, antdyn, "
+                "antsmb, glacier, greenland and landwater")
+
+        self.components = {}
+        component_path = Path(components_dir) / f"{scenario}_global.nc"
+        ds_component = xr.load_dataset(component_path)
+        for comp in component_list:
+            self.components[comp] = ds_component[comp].data
   
         if not output_dir:
             output_dir = Path.cwd()
@@ -284,7 +301,7 @@ def _calc_landwater_contribution(self, data: xr.DataArray) -> da.array:
         :return: numpy array of landwater values
         """
         landwater_vals = interpolate(data, self.nlat, self.nlon)
-        landwater_vals = da.roll(landwater_vals, 180, axis=1)
+        landwater_vals = da.roll(landwater_vals, 180, axis=1) # change lons from (-180,180) to (0,360)
         return landwater_vals
 
     def _calc_fingerprint_contributions(self, FPlist: list, comp: str) -> da.array:
@@ -294,6 +311,7 @@ def _calc_fingerprint_contributions(self, FPlist: list, comp: str) -> da.array:
             # Interpolate values to target lat/lon
             val = FP_dict[comp]
             val = interpolate(val, self.nlat, self.nlon)
+            val = da.roll(val, 180, axis=1) # change lons from (-180,180) to (0,360)
             fp_vals.append(val)
 
         fp_vals = da.stack(fp_vals, axis=0)
@@ -317,9 +335,6 @@ def _calc_greenland_fingerprint_ar6(self) -> da.array:
             lon=np.linspace(0, 360, self.nlon, endpoint=False) + 0.5, 
             method="linear").data * 1000  # convert mm to m SLE per m GMSLR
 
-        # Flip vertically and roll by 180 degrees
-        fp_vals = da.flip(fp_vals)
-        fp_vals = da.roll(fp_vals, 180, axis=1)
         return fp_vals
 
     def _load_CMIP6_slopes(self) -> np.ndarray:
diff --git a/profsea/utils/utils.py b/profsea/utils/utils.py
index 1594d2d..a195020 100644
--- a/profsea/utils/utils.py
+++ b/profsea/utils/utils.py
@@ -23,10 +23,9 @@ def interpolate(data: da.array, lats: int, lons: int) -> np.ndarray:
         ],
         name="v")
 
-    target_lat = np.linspace(90, -90, lats) + 0.5
+    target_lat = np.linspace(-90, 90, lats, endpoint=False) + 0.5
     target_lon = np.linspace(-180, 180, lons, endpoint=False) + 0.5
     data_interp = original_da.interp(
         lat=target_lat, lon=target_lon, method="linear").data
 
-    data_interp = da.roll(data_interp, 180, axis=1)
     return data_interp

From 2e5d9cb6d063f5e6e7807bbf5ceebed2bdff0fb8 Mon Sep 17 00:00:00 2001
From: Hemant Khatri 
Date: Fri, 16 Jan 2026 14:25:28 +0000
Subject: [PATCH 092/276] Correct typos

---
 profsea/emulator/spatial.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/profsea/emulator/spatial.py b/profsea/emulator/spatial.py
index 07290e1..e439c62 100644
--- a/profsea/emulator/spatial.py
+++ b/profsea/emulator/spatial.py
@@ -33,7 +33,7 @@ class Spatial:
         Direcotry path of GIA (Glacial Isostatic Adjustment) data
     components_dir: str
         Path to global sea-level rise components (output of ProFSea's gmslr module)
-    component_list: str
+    component_list: list
         Namelist of components for spatial projections
     end_year: int
         End year of the projections.
@@ -52,7 +52,7 @@ def __init__(
             fingerprint_dir: str|Path,
             gia_dir: str|Path,
             components_dir: str|Path=None, 
-            component_list: dict=None, 
+            component_list: list=None, 
             end_year: int=2301, 
             baseline_yrs: tuple=(1986, 2005),
             output_percentiles: list|np.ndarray=[5, 17, 50, 83, 95],

From 27da3321ae90b885a21a2278a5cfcbaf22f97f41 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Sun, 18 Jan 2026 21:29:46 +0000
Subject: [PATCH 093/276] More AIS fitting...

---
 profsea/emulator/__init__.py     |  1 +
 profsea/emulator/antarctica.py   | 88 ++++++++++++--------------------
 profsea/emulator/gmslr.py        | 10 +++-
 profsea/probabilistic_wrapper.py |  9 ++--
 4 files changed, 48 insertions(+), 60 deletions(-)

diff --git a/profsea/emulator/__init__.py b/profsea/emulator/__init__.py
index ea227fb..65c36f7 100644
--- a/profsea/emulator/__init__.py
+++ b/profsea/emulator/__init__.py
@@ -1,2 +1,3 @@
 from .gmslr import Global
 from .spatial import Spatial
+from .antarctica import AntarcticaISMIP6
\ No newline at end of file
diff --git a/profsea/emulator/antarctica.py b/profsea/emulator/antarctica.py
index 32ea885..98c7c7a 100644
--- a/profsea/emulator/antarctica.py
+++ b/profsea/emulator/antarctica.py
@@ -1,79 +1,59 @@
 from pathlib import Path
-
 from rich.progress import track
 import numpy as np
 from scipy.signal import fftconvolve
 import xarray as xr
 
-class Antarctica:
-    def __init__(
-            self, 
-            params_path: Path, 
-            samples: int=1000) -> None:
-        """
-        """
+class AntarcticaISMIP6:
+    """ISMIP6 2300 Antarctic ice-sheet emulator."""
+    def __init__(self, params_path: Path, samples: int=1000):
         self.param_ds = xr.load_dataset(params_path)
         self.n_samples = samples
-
         self.n_models = self.param_ds.coords["model"].shape[0]
-        n_params = self.param_ds.coords["param"].shape[0]
-        self.mv_dists = self._calc_mv_dists(n_params)
-
-    def _calc_mv_dists(self, n_params: int):
-        """
-        """
-        mv_dists = []
-        for m in range(self.n_models):
-            # Calculate covariance of param residuals
-            cov = np.cov(self.param_ds.param_residuals[m].T)
-            
-            # Add jitter in case of small param values
-            cov += np.eye(n_params) * 1e-8 
-            
-            # Draw samples centered at 0 
-            samples = np.random.multivariate_normal(np.zeros(n_params), cov, size=self.n_samples) 
-            mv_dists.append(samples)
-        return np.array(mv_dists)
 
-    def _impulse_response_term(
-            self, 
-            tas: np.ndarray, 
-            tau: float,
-            gamma: float,
-            params: np.ndarray):
-        """
-        """
+    def _impulse_response_term(self, tas: np.ndarray, tau: float, gamma: float, params: np.ndarray, dt: float):
         n_time = tas.shape[-1]
-        a_lin = params[:, 0]
+        alphas = params[:, 0] 
 
-        decay_factors = np.exp(-np.arange(n_time) / tau) * (1 / tau)
-        forcing_base = np.sign(tas) * (tas ** gamma)
-        t_conv = np.cumsum(fftconvolve(forcing_base, decay_factors, mode='full')[:n_time], axis=0)
+        decay_factors = np.exp(-np.arange(n_time) * dt / tau) * (dt / tau)
+        forcing_base = np.sign(tas) * (np.abs(tas) ** gamma)
+        rate_delayed = fftconvolve(forcing_base, decay_factors, mode='full')[:n_time]
+        
+        t_conv = np.cumsum(rate_delayed, axis=0) * dt
+        term_slow = alphas[:, None] * t_conv[None, :]
         
-        term_slow = (a_lin[:, None][None, :] * t_conv[None, :])
         return term_slow
 
-    def predict(self, tas: np.ndarray, tas_int: np.ndarray, display_progress=True):
+    def predict(self, tas: np.ndarray, tas_int: np.ndarray, dt: float=1.0, display_progress=True):
         """
+        Projects AIS response using empirical additive bootstrapping.
         """
         n_time = tas.shape[0]
-
-        # Shape: (n_models, n_samples, n_time)
-        all_preds = np.zeros((self.n_models, self.n_samples, n_time))
         
+        # Output shape: (n_models, n_samples, n_time)
+        all_preds = np.zeros((self.n_models, self.n_samples, n_time))
+
+        # Shape assumed: (n_models, n_training_scenarios, 2)
+        all_residuals = self.param_ds.param_residuals.values 
+        n_train_scenarios = all_residuals.shape[1]
         for model in track(
                 range(self.n_models), 
                 description="Projecting AIS response... ", 
                 disable=not display_progress):
-            tau = self.param_ds.tau[model].values
-            gamma = self.param_ds.gamma[model].values
-            general_p = self.param_ds.general_params[model].values  # [alpha, beta, gamma]
-            resid_p = self.mv_dists[model]  # [n_samples, n_params]
 
-            total_params = general_p + resid_p
-            term_slow = self._impulse_response_term(tas, tau, gamma, total_params)
-            term_fast = total_params[:, 1][:, None] * tas_int[None, :]
+            tau = float(self.param_ds.tau[model].values)
+            gamma = float(self.param_ds.gamma[model].values)
+            general_p = self.param_ds.general_params[model].values  # [alpha, beta, drift]
+            
+            rand_indices = np.random.randint(0, n_train_scenarios, size=self.n_samples)
+            sampled_residuals = all_residuals[model, rand_indices, :]
+            
+            total_params = general_p + sampled_residuals
+            term_slow = self._impulse_response_term(tas, tau, gamma, total_params, dt)
 
-            # Convolve
-            all_preds[model, :, :] = term_fast + term_slow
-        return all_preds
+            betas = total_params[:, 1]
+            term_fast = betas[:, None] * tas_int[None, :]
+            term_drift = total_params[:, 2:3] * np.arange(n_time)[None, :]
+            all_preds[model, :, :] = term_fast + term_slow + term_drift
+            
+        return all_preds
\ No newline at end of file
diff --git a/profsea/emulator/gmslr.py b/profsea/emulator/gmslr.py
index c49b130..c9ba2e7 100644
--- a/profsea/emulator/gmslr.py
+++ b/profsea/emulator/gmslr.py
@@ -21,6 +21,7 @@
 import xarray as xr
 
 from profsea.utils import sample_members_2D
+from .antarctica import AntarcticaISMIP6
 
 console = Console()
 
@@ -268,7 +269,14 @@ def project(self) -> None:
         else:
             self.run_serial_projections(T_int_med, T_int_ens, T_ens, fraction)
         
-        self.antnet = self.antsmb + self.antdyn
+        # self.antnet = self.antsmb + self.antdyn
+        wa = Antarctica("/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/emulator/aux_data/wa_params_JAN.nc")
+        ea = Antarctica("/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/emulator/aux_data/ea_params_JAN.nc")
+        pen = Antarctica("/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/emulator/aux_data/pen_params_JAN.nc")
+        self.antnet = wa.predict(T_ens.squeeze(), T_int_ens.squeeze(), display_progress=False) + ea.predict(T_ens.squeeze(), T_int_ens.squeeze(), display_progress=False) + pen.predict(T_ens.squeeze(), T_int_ens.squeeze(), display_progress=False)
+        ais_rng = np.random.default_rng()
+        random_ais_idx = ais_rng.integers(low=0, high=43)
+        self.antnet = self.antnet[random_ais_idx, :, :]
         self.gmslr = self.glacier + self.greenland_ar6 + self.antnet + self.landwater + self.expansion
 
         rng = np.random.default_rng()
diff --git a/profsea/probabilistic_wrapper.py b/profsea/probabilistic_wrapper.py
index f0c5f28..bc50deb 100644
--- a/profsea/probabilistic_wrapper.py
+++ b/profsea/probabilistic_wrapper.py
@@ -10,10 +10,9 @@
 import pandas as pd
 from rich_argparse import RichHelpFormatter
 from rich.progress import Progress
-from scipy.spatial.distance import cdist
 import xarray as xr
 
-from profsea.emulator import GMSLREmulator
+from profsea.emulator import Global
 from profsea.utils import sample_members_2D
 
 worker_tas = None
@@ -150,7 +149,7 @@ def simulation_task(random_idx):
         results[scenario] = {}
 
     for idx, scenario in enumerate(worker_scenarios):
-        emulator = GMSLREmulator(
+        emulator = Global(
             np.expand_dims(tas_sampled[:, idx], axis=0),
             np.expand_dims(ohc_sampled[:, idx], axis=0),
             scenario,
@@ -345,7 +344,7 @@ def main(args):
         sampled_components[scenario] = process_global_ensemble(
             components[scenario], percentiles, scenario)
 
-    save_to_netcdf(sampled_components, "probabilistic_projections/global/gmslr_projections_NEW_AIS_RATE.nc")
+    save_to_netcdf(sampled_components, "probabilistic_projections/global/gmslr_projections_AIS_JAN_pen.nc")
     
     fig = plt.figure(figsize=(16, 8), layout="constrained")
     ax = fig.add_subplot(231)
@@ -368,7 +367,7 @@ def main(args):
     sampled_tas = np.asarray(sampled_tas) # shape (nens, time, nscen)
     sampled_ohc = np.asarray(sampled_ohc)
 
-    plot_samples(sampled_tas[:, :, -1], sampled_ohc[:, :, -1])
+    plot_samples(sampled_tas[:, :, -2], sampled_ohc[:, :, -2])
 
 
 if __name__ == "__main__":

From b2ad50601d66352f32cf7ee3d8f1dfba77c08f58 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Sun, 18 Jan 2026 21:33:14 +0000
Subject: [PATCH 094/276] Update comparison notebook

---
 profsea/notebooks/compare_ais.ipynb | 20 ++++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/profsea/notebooks/compare_ais.ipynb b/profsea/notebooks/compare_ais.ipynb
index 13e9235..951ddb5 100644
--- a/profsea/notebooks/compare_ais.ipynb
+++ b/profsea/notebooks/compare_ais.ipynb
@@ -16,14 +16,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 13,
    "id": "1ce6ed82",
    "metadata": {},
    "outputs": [],
    "source": [
     "old_ais_path = \"../probabilistic_projections/global/gmslr_projections.nc\"\n",
-    "new_ais_path = \"../probabilistic_projections/global/gmslr_projections_NEW_AIS_RATE.nc\"\n",
-    "c_new_ais_path = \"../probabilistic_projections/global/gmslr_projections_NEW_AIS.nc\"\n",
+    "new_ais_path = \"../probabilistic_projections/global/gmslr_projections_AIS_JAN_pen.nc\"\n",
+    "c_new_ais_path = \"../probabilistic_projections/global/gmslr_projections_AIS_JAN.nc\"\n",
     "old_ds = xr.open_dataset(old_ais_path)\n",
     "new_ds = xr.open_dataset(new_ais_path)\n",
     "c_new_ds = xr.open_dataset(c_new_ais_path)"
@@ -39,13 +39,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 15,
    "id": "4d0b2f15",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
      "
       ]
@@ -64,9 +64,9 @@
     "    ax.plot(old_ds.year, old_ds.antarctica[scen_idx, 2], color=\"royalblue\", lw=2, label=\"AR5 emulator\")\n",
     "    ax.plot(new_ds.year, new_ds.antarctica[scen_idx, 2], color=\"goldenrod\", lw=2, label=\"ISMIP6 emulator\")\n",
     "    ax.plot(c_new_ds.year, c_new_ds.antarctica[scen_idx, 2], color=\"seagreen\", lw=2, label=\"ISMIP6 emulator, common models\")\n",
-    "    ax.fill_between(old_ds.year, old_ds.antarctica[scen_idx, 1], old_ds.antarctica[scen_idx, 3], color=\"royalblue\", alpha=0.2)\n",
-    "    ax.fill_between(new_ds.year, new_ds.antarctica[scen_idx, 1], new_ds.antarctica[scen_idx, 3], color=\"goldenrod\", alpha=0.2)\n",
-    "    ax.fill_between(c_new_ds.year, c_new_ds.antarctica[scen_idx, 1], c_new_ds.antarctica[scen_idx, 3], color=\"seagreen\", alpha=0.2)\n",
+    "    ax.fill_between(old_ds.year, old_ds.antarctica[scen_idx, 0], old_ds.antarctica[scen_idx, 4], color=\"royalblue\", alpha=0.2)\n",
+    "    ax.fill_between(new_ds.year, new_ds.antarctica[scen_idx, 0], new_ds.antarctica[scen_idx, 4], color=\"goldenrod\", alpha=0.2)\n",
+    "    ax.fill_between(c_new_ds.year, c_new_ds.antarctica[scen_idx, 0], c_new_ds.antarctica[scen_idx, 4], color=\"seagreen\", alpha=0.2)\n",
     "    ax.set_ylabel(\"SLE (m)\")\n",
     "    ax.set_xlabel(\"Year\")\n",
     "    if scen_idx == 0:\n",
@@ -86,13 +86,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 16,
    "id": "9c58124d",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
      "
       ]

From a9eff7ae29c9cb314f02ec541b1cda626901ee74 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Thu, 5 Mar 2026 11:09:12 +0000
Subject: [PATCH 095/276] Update for latest version

---
 profsea/emulator/antarctica.py   |  78 ++++++++----
 profsea/emulator/gmslr.py        | 205 ++++++++++++++++---------------
 profsea/probabilistic_wrapper.py |  94 +++++++++-----
 3 files changed, 223 insertions(+), 154 deletions(-)

diff --git a/profsea/emulator/antarctica.py b/profsea/emulator/antarctica.py
index 98c7c7a..e125ac0 100644
--- a/profsea/emulator/antarctica.py
+++ b/profsea/emulator/antarctica.py
@@ -5,55 +5,91 @@
 import xarray as xr
 
 class AntarcticaISMIP6:
-    """ISMIP6 2300 Antarctic ice-sheet emulator."""
+    """ISMIP6 2300 Antarctic ice-sheet emulator with two-timescale response."""
     def __init__(self, params_path: Path, samples: int=1000):
         self.param_ds = xr.load_dataset(params_path)
         self.n_samples = samples
         self.n_models = self.param_ds.coords["model"].shape[0]
 
-    def _impulse_response_term(self, tas: np.ndarray, tau: float, gamma: float, params: np.ndarray, dt: float):
-        n_time = tas.shape[-1]
-        alphas = params[:, 0] 
+    def _impulse_response_term(
+            self, 
+            tas: np.ndarray, 
+            tau1: float, 
+            tau2: float, 
+            gamma: float, 
+            params: np.ndarray, 
+            dt: float):
+        n_time = tas.shape[0]
+        
+        # Two slow coeffs
+        alphas1 = params[:, 0] 
+        alphas2 = params[:, 1] 
 
-        decay_factors = np.exp(-np.arange(n_time) * dt / tau) * (dt / tau)
         forcing_base = np.sign(tas) * (np.abs(tas) ** gamma)
-        rate_delayed = fftconvolve(forcing_base, decay_factors, mode='full')[:n_time]
-        
-        t_conv = np.cumsum(rate_delayed, axis=0) * dt
-        term_slow = alphas[:, None] * t_conv[None, :]
+
+        # Reservoir 1
+        decay_factors1 = np.exp(-np.arange(n_time) * dt / tau1) * (dt / tau1)
+        rate_delayed1 = fftconvolve(forcing_base, decay_factors1, mode='full')[:n_time]
+        t_conv1 = np.cumsum(rate_delayed1, axis=0) * dt
+        term_slow1 = alphas1[:, None] * t_conv1[None, :]
+
+        # Reservoir 2 
+        decay_factors2 = np.exp(-np.arange(n_time) * dt / tau2) * (dt / tau2)
+        rate_delayed2 = fftconvolve(forcing_base, decay_factors2, mode='full')[:n_time]
+        t_conv2 = np.cumsum(rate_delayed2, axis=0) * dt
+        term_slow2 = alphas2[:, None] * t_conv2[None, :]
         
-        return term_slow
+        return term_slow1 + term_slow2
 
-    def predict(self, tas: np.ndarray, tas_int: np.ndarray, dt: float=1.0, display_progress=True):
+    def predict(self, tas: np.ndarray, dt: float=1.0, display_progress=True, seed=None):
         """
         Projects AIS response using empirical additive bootstrapping.
         """
-        n_time = tas.shape[0]
-        
+        tas = np.squeeze(tas)
+        n_time = len(tas)
+        physical_time = np.arange(n_time) * dt
+
+        # RNG
+        rng = np.random.default_rng(seed)
+
+        # Integrated temperature term
+        tas_int = np.cumsum(tas) * dt
+
         # Output shape: (n_models, n_samples, n_time)
         all_preds = np.zeros((self.n_models, self.n_samples, n_time))
 
-        # Shape assumed: (n_models, n_training_scenarios, 2)
+        # Shape assumed: (n_models, n_training_scenarios, 4)
         all_residuals = self.param_ds.param_residuals.values 
         n_train_scenarios = all_residuals.shape[1]
+        
         for model in track(
                 range(self.n_models), 
                 description="Projecting AIS response... ", 
                 disable=not display_progress):
 
-            tau = float(self.param_ds.tau[model].values)
+            tau1 = float(self.param_ds.tau1[model].values)
+            tau2 = float(self.param_ds.tau2[model].values)
             gamma = float(self.param_ds.gamma[model].values)
-            general_p = self.param_ds.general_params[model].values  # [alpha, beta, drift]
             
-            rand_indices = np.random.randint(0, n_train_scenarios, size=self.n_samples)
+            # [alpha1, alpha2, beta, drift]
+            general_p = self.param_ds.general_params[model].values  
+            
+            rand_indices = rng.integers(0, n_train_scenarios, size=self.n_samples)
             sampled_residuals = all_residuals[model, rand_indices, :]
             
             total_params = general_p + sampled_residuals
-            term_slow = self._impulse_response_term(tas, tau, gamma, total_params, dt)
+            
+            # Slow term
+            term_slow = self._impulse_response_term(tas, tau1, tau2, gamma, total_params, dt)
 
-            betas = total_params[:, 1]
+            # Fast term
+            betas = total_params[:, 2]
             term_fast = betas[:, None] * tas_int[None, :]
-            term_drift = total_params[:, 2:3] * np.arange(n_time)[None, :]
-            all_preds[model, :, :] = term_fast + term_slow + term_drift
             
+            # Drift term
+            drift_coeffs = total_params[:, 3]
+            term_drift = drift_coeffs[:, None] * physical_time[None, :]
+            
+            all_preds[model, :, :] = term_fast + term_slow + term_drift
+
         return all_preds
\ No newline at end of file
diff --git a/profsea/emulator/gmslr.py b/profsea/emulator/gmslr.py
index c9ba2e7..137d1e7 100644
--- a/profsea/emulator/gmslr.py
+++ b/profsea/emulator/gmslr.py
@@ -21,7 +21,7 @@
 import xarray as xr
 
 from profsea.utils import sample_members_2D
-from .antarctica import AntarcticaISMIP6
+from .antarctica import AntarcticaISMIP6 
 
 console = Console()
 
@@ -128,9 +128,6 @@ class Global:
 
     def __init__(
             self, 
-            T_change: np.ndarray,
-            OHC_change: np.ndarray,
-            scenario: str,
             end_yr: int,
             seed: int=1234,
             nt: int=100,
@@ -143,15 +140,9 @@ def __init__(
             random_sample: bool=False,
             T_percentile_95: np.ndarray=None,
             OHC_percentile_95: np.ndarray=None,
-            cum_emissions_total: np.ndarray=None,
             palmer_method: bool=True) -> None:
-        
-        np.random.seed(None if seed is None else seed)
-        self.T_change = np.asarray(T_change)
-        self.OHC_change = np.asarray(OHC_change)
-        self.scenario = scenario
+
         self.end_yr = end_yr
-        self.seed = seed
         self.nt = nt
         self.nm = nm
         self.tcv = tcv
@@ -162,7 +153,6 @@ def __init__(
         self.random_sample = random_sample
         self.T_percentile_95 = T_percentile_95
         self.OHC_percentile_95 = OHC_percentile_95
-        self.cum_emissions_total = cum_emissions_total
         self.palmer_method = palmer_method
 
         # First year of AR5 projections
@@ -187,22 +177,15 @@ def __init__(
         # Conversion factor for Gt to m SLE
         self.mSLEoGt = 1e12 / 3.61e14 * 1e-3
 
-        if input_ensemble:
-            self.nt = self.T_change.shape[0]
-
-        # Sensitivity of thermosteric SLR to ocean heat content change
-        # From Turner et al. (2023)
-        self.exp_efficiency = np.random.normal(
-            loc=0.113, scale=0.013, size=self.nt)[:, None] * 1e-24 # m/YJ
+        self.seed_seq = np.random.SeedSequence(seed if seed is not None else 1234)
+        self.rng = np.random.default_rng(self.seed_seq)
+        child_seeds = self.seed_seq.spawn(8)
+        self.rngs = [np.random.default_rng(s) for s in child_seeds]
 
+        self.wa = AntarcticaISMIP6("/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/emulator/aux_data/wa_params_MAR_AD_2term.nc")
+        self.ea = AntarcticaISMIP6("/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/emulator/aux_data/ea_params_MAR_AD_2term.nc")
+        self.pen = AntarcticaISMIP6("/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/emulator/aux_data/pen_params_MAR_AD_2term.nc")
 
-        if self.scenario not in ['rcp26', 'rcp45', 'rcp85', 
-            'ssp126', 'ssp245', 'ssp585'] and self.cum_emissions_total is None:
-            raise ValueError(
-                'If the scenario is not rcp26, rcp45, rcp85, ssp126, ssp245 or ssp585, '
-                'you must provide the total \ncumulative emissions from 2015 to 2100 '
-                'using the cum_emissions_total keyword argument. This is required \n'
-                'to calculate the Antarctic dynamic contribution to GMSLR.')
     
     def get_components(self) -> dict:
         """Get all GMSLR components as a dictionary."""
@@ -253,16 +236,37 @@ def save_components(self, output_dir: str, scenario_name: str) -> None:
             ds[name] = xr_dataArray
         ds.to_netcdf(os.path.join(output_dir,f'{scenario_name}_global.nc'))
 
-    def project(self) -> None:
+    def project(
+            self, 
+            scenario: str, 
+            T_change: np.ndarray, 
+            OHC_change: np.ndarray, 
+            cum_emissions_total: int=None) -> None:
         """Run the emulator to project GMSLR components.
 
         Returns
         -------
         None
         """
+        self.scenario = scenario
+        self.T_change = np.asarray(T_change)
+        self.OHC_change = np.asarray(OHC_change)
+        self.cum_emissions_total = cum_emissions_total
+
+        if self.input_ensemble:
+            self.nt = self.T_change.shape[0]
+
+        if self.scenario not in ['rcp26', 'rcp45', 'rcp85', 
+            'ssp126', 'ssp245', 'ssp585'] and self.cum_emissions_total is None:
+            raise ValueError(
+                'If the scenario is not rcp26, rcp45, rcp85, ssp126, ssp245 or ssp585, '
+                'you must provide the total \ncumulative emissions from 2015 to 2100 '
+                'using the cum_emissions_total keyword argument. This is required \n'
+                'to calculate the Antarctic dynamic contribution to GMSLR.')
+
         T_ens, Exp_ens, T_int_ens, T_int_med = self.calculate_drivers() 
         self.expansion = np.tile(Exp_ens, (self.nm, 1))
-        fraction = np.random.rand(self.nm * self.nt) # correlation between antsmb and antdyn
+        fraction = self.rng.random(self.nm * self.nt) # correlation between antsmb and antdyn
         
         if self.parallel:
             self.run_parallel_projections(T_int_med, T_int_ens, T_ens, fraction)
@@ -270,19 +274,13 @@ def project(self) -> None:
             self.run_serial_projections(T_int_med, T_int_ens, T_ens, fraction)
         
         # self.antnet = self.antsmb + self.antdyn
-        wa = Antarctica("/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/emulator/aux_data/wa_params_JAN.nc")
-        ea = Antarctica("/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/emulator/aux_data/ea_params_JAN.nc")
-        pen = Antarctica("/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/emulator/aux_data/pen_params_JAN.nc")
-        self.antnet = wa.predict(T_ens.squeeze(), T_int_ens.squeeze(), display_progress=False) + ea.predict(T_ens.squeeze(), T_int_ens.squeeze(), display_progress=False) + pen.predict(T_ens.squeeze(), T_int_ens.squeeze(), display_progress=False)
-        ais_rng = np.random.default_rng()
-        random_ais_idx = ais_rng.integers(low=0, high=43)
-        self.antnet = self.antnet[random_ais_idx, :, :]
-        self.gmslr = self.glacier + self.greenland_ar6 + self.antnet + self.landwater + self.expansion
+        self.antnet = self.wa.predict(T_ens.squeeze(), display_progress=False) + self.ea.predict(T_ens.squeeze(), display_progress=False) + self.pen.predict(T_ens.squeeze(), display_progress=False)
+        random_ais_idx = self.rng.integers(low=0, high=43)
+        self.antnet = self.antnet[random_ais_idx, :, :] # size (1000, 295)
+        self.antnet = np.repeat(self.antnet, self.nt, axis=0)
 
-        rng = np.random.default_rng()
         if self.random_sample:
-            random_idx = rng.integers(low=0, high=self.nm)
-            self.gmslr = self.gmslr[random_idx][None, :]
+            random_idx = self.rng.integers(low=0, high=self.nm)
             self.expansion = self.expansion[random_idx][None, :]
             self.antnet = self.antnet[random_idx][None, :]
             self.antdyn = self.antdyn[random_idx][None, :]
@@ -291,6 +289,8 @@ def project(self) -> None:
             self.greenland_ar6 = self.greenland_ar6[random_idx][None, :]
             self.landwater = self.landwater[random_idx][None, :]
 
+        self.gmslr = self.glacier + self.greenland_ar6 + self.antnet + self.landwater + self.expansion
+
         # TODO Parallelise this section as it's a bit slow
         if self.output_percentiles is not None:
             console.log(f"Sampling {len(self.output_percentiles)} members per component...")
@@ -316,47 +316,51 @@ def run_serial_projections(
         -------
         None
         """
-        self.glacier = self.project_glacier(T_int_med, T_int_ens)
-        self.antsmb = self.project_antsmb(T_int_ens, fraction)
-        self.greenland_ar6 = self.project_greenland_AR6(T_ens)
-        self.greendyn = self.project_greendyn_AR5()
-        self.greensmb = self.project_greensmb_AR5(T_ens)
-        self.antdyn = self.project_antdyn(fraction)
+        child_seeds = self.seed_seq.spawn(8)
+        rngs = [np.random.default_rng(s) for s in child_seeds]
+        self.glacier = self.project_glacier(T_int_med, T_int_ens, rngs[0])
+        self.antsmb = self.project_antsmb(T_int_ens, rngs[1], fraction)
+        self.greenland_ar6 = self.project_greenland_AR6(T_ens, rngs[2])
+        self.greendyn = self.project_greendyn_AR5(rngs[3])
+        self.greensmb = self.project_greensmb_AR5(T_ens, rngs[4])
+        self.antdyn = self.project_antdyn(rngs[5], fraction)
         
         # _project_landwater_ar5 corresponds to 'landwater' key in the parallel map
-        self.landwater = self._project_landwater_ar5()
+        self.landwater = self._project_landwater_ar5(rngs[6])
         # project_landwater corresponds to 'landwater_ar6' key in the parallel map
         self.landwater_ar6 = self.project_landwater()
 
         self.greenland_ar5 = self.greendyn + self.greensmb
             
     def run_parallel_projections(
-            self, T_int_med: np.ndarray, T_int_ens: np.ndarray, 
-            T_ens: np.ndarray, fraction: np.ndarray) -> None:
-        """Run components of the emulator in parallel.
-        
-        Returns
-        -------
-        None
-        """
-        executor = get_shared_executor()
-        futures = {
-            executor.submit(self.project_glacier, T_int_med, T_int_ens): 'glacier',
-            executor.submit(self.project_antsmb, T_int_ens, fraction): 'antsmb',
-            executor.submit(self.project_greenland_AR6, T_ens): 'greenland',
-            executor.submit(self.project_greendyn_AR5): 'greendyn',
-            executor.submit(self.project_greensmb_AR5, T_ens): 'greensmb',
-            executor.submit(self.project_antdyn, fraction): 'antdyn',
-            executor.submit(self._project_landwater_ar5): 'landwater',
-            executor.submit(self.project_landwater): 'landwater_ar6'
-        }
-        results = {}
-        for future in concurrent.futures.as_completed(futures):
-            key = futures[future]
-            try:
-                results[key] = future.result()
-            except Exception as e:
-                print(f"{key} generated an exception: {e}")
+        self, T_int_med: np.ndarray, T_int_ens: np.ndarray, 
+        T_ens: np.ndarray, fraction: np.ndarray) -> None:
+        child_seeds = self.seed_seq.spawn(8)
+        rngs = [np.random.default_rng(s) for s in child_seeds]
+
+        # Use a context manager for the executor
+        with concurrent.futures.ThreadPoolExecutor() as executor:
+            futures = {
+                executor.submit(self.project_glacier, T_int_med, T_int_ens, rngs[0]): 'glacier',
+                executor.submit(self.project_antsmb, T_int_ens, rngs[1], fraction): 'antsmb',
+                executor.submit(self.project_greenland_AR6, T_ens, rngs[2]): 'greenland',
+                executor.submit(self.project_greendyn_AR5, rngs[3]): 'greendyn',
+                executor.submit(self.project_greensmb_AR5, T_ens, rngs[4]): 'greensmb',
+                executor.submit(self.project_antdyn, rngs[5], fraction): 'antdyn',
+                executor.submit(self._project_landwater_ar5, rngs[6]): 'landwater',
+                executor.submit(self.project_landwater): 'landwater_ar6' # Assuming no RNG needed here
+            }
+            
+            results = {}
+            for future in concurrent.futures.as_completed(futures):
+                key = futures[future]
+                try:
+                    # Letting the exception propagate is safer than silently masking it
+                    results[key] = future.result() 
+                except Exception as e:
+                    raise RuntimeError(f"Component '{key}' failed during projection.") from e
+
+        self.glacier = results['glacier']
 
         self.glacier = results['glacier']
         self.antsmb = results['antsmb']
@@ -384,6 +388,11 @@ def calculate_drivers(self) -> tuple:
         T_int_med: np.ndarray
             Median of time-integral temperature anomalies.
         """
+        # Sensitivity of thermosteric SLR to ocean heat content change
+        # From Turner et al. (2023)
+        exp_efficiency = self.rng.normal(
+            loc=0.113, scale=0.013, size=self.nt)[:, None] * 1e-24 # m/YJ
+
         if self.input_ensemble:
             # Check if dimensions are the right way around 
             if self.T_change.shape[1] != self.nyr: 
@@ -394,16 +403,16 @@ def calculate_drivers(self) -> tuple:
             T_med = np.percentile(self.T_change, 50, axis=0)
             T_std = np.std(self.T_change, axis=0)
 
-            therm_med = np.percentile(self.OHC_change, 50, axis=0) * self.exp_efficiency
-            therm_std = np.std(self.OHC_change * self.exp_efficiency, axis=0)
+            therm_med = np.percentile(self.OHC_change, 50, axis=0) * exp_efficiency
+            therm_std = np.std(self.OHC_change * exp_efficiency, axis=0)
 
         else:
             if self.T_percentile_95 is not None:
                 T_med = self.T_change
-                therm_med = self.OHC_change * self.exp_efficiency 
+                therm_med = self.OHC_change * exp_efficiency 
                 
                 T_std = (self.T_percentile_95 - self.T_change) / 1.645
-                therm_std = (self.OHC_percentile_95 - self.OHC_change) * self.exp_efficiency / 1.645
+                therm_std = (self.OHC_percentile_95 - self.OHC_change) * exp_efficiency / 1.645
             
             else:
                 raise ValueError(
@@ -418,7 +427,7 @@ def calculate_drivers(self) -> tuple:
         
         # Generate a sample of perfectly correlated timeseries fields of temperature,
         # time-integral temperature and expansion, each of them [realisation,time]
-        z = np.random.standard_normal(self.nt) * self.tcv
+        z = self.rng.standard_normal(self.nt) * self.tcv
 
         # For each quantity, mean + standard deviation * normal random number
         # reshape to [realisation,time]
@@ -428,7 +437,7 @@ def calculate_drivers(self) -> tuple:
         return T_ens, therm_ens, T_int_ens, T_int_med  
 
     def project_glacier(
-            self, T_int_med: np.ndarray, T_int_ens: np.ndarray) -> np.ndarray:
+            self, T_int_med: np.ndarray, T_int_ens: np.ndarray, rng: np.random.Generator) -> np.ndarray:
         """Project glacier contribution to GMSLR.
         
         Parameters
@@ -487,7 +496,7 @@ def project_glacier(
             
         r_per_model = self.nm // ngl
         r_remainder = self.nm % ngl
-        r = np.random.standard_normal(self.nm)
+        r = self.rng.standard_normal(self.nm)
         r = r[:, np.newaxis, np.newaxis]
         
         # Precompute mgl and zgl for all glacier methods
@@ -537,7 +546,7 @@ def _project_glacier1(
         scale=1e-3 # mm to m
         return scale * factor * (np.where(T_int<0, 0, T_int)**exponent)
 
-    def project_greensmb_AR5(self, T_ens: np.ndarray) -> np.ndarray:
+    def project_greensmb_AR5(self, T_ens: np.ndarray, rng: np.random.Generator) -> np.ndarray:
         """Project Greenland SMB contribution to GMSLR.
         
         Parameters
@@ -556,9 +565,9 @@ def project_greensmb_AR5(self, T_ens: np.ndarray) -> np.ndarray:
         febound = [1, 1.15] # bounds of uniform pdf of SMB elevation feedback factor
 
         # random log-normal factor
-        fn = np.exp(np.random.standard_normal(self.nm) * fnlogsd)
+        fn = np.exp(self.rng.standard_normal(self.nm) * fnlogsd)
         # elevation feedback factor
-        fe = np.random.sample(self.nm) * (febound[1] - febound[0]) + febound[0]
+        fe = self.rng.random(self.nm) * (febound[1] - febound[0]) + febound[0]
         ff = fn * fe
         
         ztgreen = T_ens - dtgreen
@@ -592,8 +601,7 @@ def _fettweis(self, ztgreen: np.ndarray) -> np.ndarray:
         return (71.5*ztgreen+20.4*(ztgreen**2)+2.8*(ztgreen**3))*self.mSLEoGt
 
     def project_antsmb(
-            self, T_int_ens: np.ndarray, 
-            fraction: np.ndarray=None) -> np.ndarray:
+            self, T_int_ens: np.ndarray, rng: np.random.Generator, fraction: np.ndarray=None) -> np.ndarray:
         """Project Antarctic SMB contribution to GMSLR.
         
         Parameters
@@ -613,13 +621,13 @@ def project_antsmb(
         KoKg=[1.1,0.2] # ratio of Antarctic warming to global warming from G&H06
 
         # Generate a distribution of products of the above two factors
-        pcoKg = (pcoK[0] + np.random.standard_normal([self.nm, self.nt]) * pcoK[1]) * \
-            (KoKg[0] + np.random.standard_normal([self.nm, self.nt]) * KoKg[1])
+        pcoKg = (pcoK[0] + self.rng.standard_normal([self.nm, self.nt]) * pcoK[1]) * \
+            (KoKg[0] + self.rng.standard_normal([self.nm, self.nt]) * KoKg[1])
         meansmb = 1923 # model-mean time-mean 1979-2010 Gt yr-1 from 13.3.3.2
         moaoKg = -pcoKg * 1e-2 * meansmb * self.mSLEoGt # m yr-1 of SLE per K of global warming
 
         if fraction is None:
-            fraction = np.random.rand(self.nm, self.nt)
+            fraction = self.rng.random((self.nm, self.nt))
         elif fraction.size != self.nm * self.nt:
             raise ValueError('fraction is the wrong size')
         else:
@@ -634,7 +642,7 @@ def project_antsmb(
         antsmb = antsmb.reshape(antsmb.shape[0] * antsmb.shape[1], antsmb.shape[2])
         return antsmb
 
-    def project_greenland_AR6(self, T_ens: np.ndarray) -> np.ndarray:
+    def project_greenland_AR6(self, T_ens: np.ndarray, rng: np.random.Generator) -> np.ndarray:
         """Project Greenland ice-sheet contribution to GMSLR.
         This follows the IPCC AR6 methodology as closely as possible.
         Projections are relative to 1996-2014 baseline.
@@ -659,9 +667,8 @@ def project_greenland_AR6(self, T_ens: np.ndarray) -> np.ndarray:
         trend_std = 0.1
 
         # Calculate trend contribution distribution
-        rng = np.random.default_rng(self.seed)
         trend = truncnorm.ppf(
-            rng.random(self.nm), 
+            self.rng.random(self.nm), 
             a = 0.0,
             b = 99999.9,
             loc = trend_mean,
@@ -687,7 +694,7 @@ def project_greenland_AR6(self, T_ens: np.ndarray) -> np.ndarray:
         r_remainder = self.nm % sle.shape[1]
 
         # We want to distribute the remainder evenly across the models
-        unc = np.random.normal(scale=sigma)
+        unc = self.rng.normal(scale=sigma)
         current_ensemble_idx = 0
         for i in range(sle.shape[1]):
             num_reals_for_model_i = r_per_model + 1 if i < r_remainder else r_per_model
@@ -707,7 +714,7 @@ def project_greenland_AR6(self, T_ens: np.ndarray) -> np.ndarray:
         sle_ens = sle_ens.reshape((self.nm * self.nt, self.nyr))
         return sle_ens
 
-    def project_greendyn_AR5(self) -> np.ndarray:
+    def project_greendyn_AR5(self, rng: np.random.Generator) -> np.ndarray:
         """Project Greenland rapid ice-sheet dynamics contribution to GMSLR.
         
         Returns
@@ -723,9 +730,9 @@ def project_greendyn_AR5(self) -> np.ndarray:
             finalrange=[0.014,0.063]
         return self.time_projection(
             0.63*self.fgreendyn, 0.17*self.fgreendyn, 
-            finalrange) + self.fgreendyn*self.dgreen
+            finalrange, rng) + self.fgreendyn*self.dgreen
 
-    def project_antdyn(self, fraction: np.ndarray=None) -> np.ndarray:
+    def project_antdyn(self, rng: np.random.Generator, fraction: np.ndarray=None) -> np.ndarray:
         """Project Antarctic rapid ice-sheet dynamics contribution to GMSLR.
         
         Parameters
@@ -763,7 +770,7 @@ def project_antdyn(self, fraction: np.ndarray=None) -> np.ndarray:
 
             # For SMB+dyn during 2005-2010 Table 4.6 gives 0.41+-0.24 mm yr-1 (5-95% range)
             # For dyn at 2100 Chapter 13 gives [-20,185] mm for all scenarios
-        return self.time_projection(0.41, 0.20, final, fraction=fraction) + self.dant
+        return self.time_projection(0.41, 0.20, final, rng, fraction=fraction) + self.dant
 
     def project_landwater(self) -> np.ndarray:
         """Project land water storage contribution to GMSLR.
@@ -790,7 +797,7 @@ def project_landwater(self) -> np.ndarray:
         lw = lw[:, 1:-1] # Start at 2006, end at 2299
         return lw
   
-    def _project_landwater_ar5(self) -> np.ndarray:
+    def _project_landwater_ar5(self, rng: np.random.Generator) -> np.ndarray:
         """Old projection function. Project land water storage 
         contribution to GMSLR.
         
@@ -804,11 +811,11 @@ def _project_landwater_ar5(self) -> np.ndarray:
         nyr=2100-2081+1 # number of years of the time-mean of the final amount
         final = [-0.01,0.09] # AR5
         # final = [0.01, 0.04] # AR6
-        return self.time_projection(0.38, 0.49-0.38, final, nfinal=nyr)
+        return self.time_projection(0.38, 0.49-0.38, final, rng, nfinal=nyr)
 
     def time_projection(
         self, startratemean: float, startratepm: float, final,
-        nfinal: int=1, fraction: np.ndarray=None) -> np.ndarray:
+        rng: np.random.Generator, nfinal: int=1, fraction: np.ndarray=None) -> np.ndarray:
         """Project a quantity which is a quadratic function of time.
         
         Parameters
@@ -834,7 +841,7 @@ def time_projection(
         time = np.arange(self.end_yr - self.endofhistory) + 1
         
         if fraction is None:
-            fraction = np.random.rand(self.nm, self.nt)
+            fraction = self.rng.random((self.nm, self.nt))
         elif fraction.size != self.nm * self.nt:
             raise ValueError('fraction is the wrong size')
         
diff --git a/profsea/probabilistic_wrapper.py b/profsea/probabilistic_wrapper.py
index bc50deb..ed00c01 100644
--- a/profsea/probabilistic_wrapper.py
+++ b/profsea/probabilistic_wrapper.py
@@ -1,6 +1,7 @@
 import argparse
 import concurrent.futures
 import json
+from pathlib import Path
 
 from fair import FAIR
 from fair.io import read_properties
@@ -19,15 +20,22 @@
 worker_ohc = None
 worker_emissions = None
 worker_scenarios = None
+worker_emulator = None
 
 def init_worker(tas, ohc, emissions, scenarios):
     """Initialize worker with read-only shared data."""
-    global worker_tas, worker_ohc, worker_emissions, worker_scenarios
+    global worker_tas, worker_ohc, worker_emissions, worker_scenarios, worker_emulator
     worker_tas = tas.copy()
     worker_ohc = ohc.copy()
     worker_emissions = emissions.copy()
     worker_scenarios = scenarios.copy()
 
+    worker_emulator = Global(
+        2301,
+        input_ensemble=True,
+        random_sample=True,
+        parallel=False 
+    )
 
 def index_df(df: pd.DataFrame, baseline_start: int, baseline_end: int) -> pd.DataFrame:
     meta_cols = ['ensemble_member', 'scenario', 'region', 'variable', 'unit', 'climate_model']
@@ -107,6 +115,25 @@ def load_magicc_forcing(
     return tas_arr, ohc_arr
 
 
+def load_fair_forcing(
+        input_path: str, baseline_start: int, baseline_end: int) -> tuple[np.ndarray]:
+    tas_path = Path(input_path) / "tas.nc"
+    ohc_path = Path(input_path) / "ohc.nc"
+
+    tas = xr.load_dataarray(tas_path)
+    ohc= xr.laod_dataarray(ohc_path)
+
+    tas_baseline = tas.loc[dict(layer=0, timebounds=np.arange(baseline_start, baseline_end+1))].mean()
+    ohc_baseline = ohc.loc[dict(timebounds=np.arange(baseline_start, baseline_end+1))].mean()
+
+    tas = tas.loc[dict(layer=0, timebounds=np.arange(2006, 2301))] - tas_baseline  # shape (294, 7, 841)
+    ohc = ohc.loc[dict(timebounds=np.arange(2006, 2301))] - ohc_baseline
+
+
+
+    return tas.values, ohc.values
+
+
 def run_fair(baseline_start: int, baseline_end: int, scenarios: list) -> tuple[np.ndarray]:
     f = FAIR()
     f.define_time(1750, 2300, 1)
@@ -134,40 +161,35 @@ def run_fair(baseline_start: int, baseline_end: int, scenarios: list) -> tuple[n
     tas_baseline = f.temperature.loc[dict(layer=0, timebounds=np.arange(baseline_start, baseline_end+1))].mean()
     ohc_baseline = f.ocean_heat_content_change.loc[dict(timebounds=np.arange(baseline_start, baseline_end+1))].mean()
 
-    tas = f.temperature.loc[dict(layer=0, timebounds=np.arange(2006, 2301))] - tas_baseline  # shape (294, 7, 841)
+    tas = f.temperature.loc[dict(layer=0, timebounds=np.arange(2006, 2301))] - tas_baseline
     ohc = f.ocean_heat_content_change.loc[dict(timebounds=np.arange(2006, 2301))] - ohc_baseline
+
+    print(tas.values.shape)
     return tas.values, ohc.values
 
 
-def simulation_task(random_idx):
+def simulation_task(random_idx: int):
     """Runs the emulator for a single ensemble member across all scenarios."""
     # Access data from global scope (initialized via init_worker)
     tas_sampled = worker_tas[:, :, random_idx]
     ohc_sampled = worker_ohc[:, :, random_idx]
-    results = {}
-    for scenario in worker_scenarios:
-        results[scenario] = {}
+    results = {scenario: {} for scenario in worker_scenarios}
 
     for idx, scenario in enumerate(worker_scenarios):
-        emulator = Global(
+        worker_emulator.project(
+            scenario,
             np.expand_dims(tas_sampled[:, idx], axis=0),
             np.expand_dims(ohc_sampled[:, idx], axis=0),
-            scenario,
-            2301,
-            input_ensemble=True,
-            random_sample=True,
-            cum_emissions_total=worker_emissions[scenario],
-            parallel=False 
+            cum_emissions_total=worker_emissions[scenario]
         )
-        emulator.project()
 
-        results[scenario]["gmslr"] = emulator.gmslr
-        results[scenario]["expansion"] = emulator.expansion
-        results[scenario]["antarctica"] = emulator.antnet
-        results[scenario]["greenland"] = emulator.greenland_ar6
-        results[scenario]["glaciers"] = emulator.glacier
-        results[scenario]["landwater"] = emulator.landwater
-    return tas_sampled, ohc_sampled, results
+        results[scenario]["gmslr"] = worker_emulator.gmslr
+        results[scenario]["expansion"] = worker_emulator.expansion
+        results[scenario]["antarctica"] = worker_emulator.antnet
+        results[scenario]["greenland"] = worker_emulator.greenland_ar6
+        results[scenario]["glaciers"] = worker_emulator.glacier
+        results[scenario]["landwater"] = worker_emulator.landwater
+    return results, random_idx
 
 
 def plot_samples(tas: np.ndarray, ohc: np.ndarray) -> None:
@@ -183,6 +205,9 @@ def plot_samples(tas: np.ndarray, ohc: np.ndarray) -> None:
     ax.set_xlabel("Simulation years")
     ax.set_ylabel("OHC (J)")
 
+    ax.plot(np.arange(tas.shape[1]), np.median(tas, axis=0), color="black")
+
+    fig.savefig("forcing.png", dpi=200)
     plt.show()
     plt.close()
 
@@ -256,14 +281,14 @@ def plot_component(
         "ssp585": tuple(np.array([149, 27, 30]) / 255)
     }
     for scenario in reversed(scenarios):
-        plt.fill_between(
+        ax.fill_between(
             time, 
             component_dict[scenario][component][1], 
             component_dict[scenario][component][3],  
             color=ssp_colours[scenario], 
             edgecolor='none',
             alpha=0.3)
-        plt.plot(
+        ax.plot(
             time, 
             component_dict[scenario][component][2], 
             label=f"{scenario}", 
@@ -300,6 +325,8 @@ def main(args):
     if args.input.lower() == "magicc":
         tas, ohc = load_magicc_forcing(args.input_path, scenarios, baseline_start, baseline_end)
     elif args.input.lower() == "fair":
+        tas, ohc = load_fair_forcing(args.input_path, scenarios, baseline_start, baseline_end)
+    elif args.input.lower() == "run_fair":
         tas, ohc = run_fair(baseline_start, baseline_end, scenarios)
     else:
         raise ValueError("Input source not recognised.")
@@ -323,9 +350,9 @@ def main(args):
         with Progress() as progress:
             task = progress.add_task("[orange1]Simulating and sampling...", total=n_iterations)
             for future in concurrent.futures.as_completed(futures):
-                tas_sampled, ohc_sampled, result_dict = future.result()
-                sampled_tas.append(tas_sampled)
-                sampled_ohc.append(ohc_sampled)
+                result_dict, idx_returned = future.result()
+                sampled_tas.append(tas[:, :, idx_returned])
+                sampled_ohc.append(ohc[:, :, idx_returned])
 
                 # Now add the results into the components dictionary
                 for scenario in scenarios:
@@ -334,6 +361,11 @@ def main(args):
 
                 progress.update(task, advance=1)
 
+    sampled_tas = np.asarray(sampled_tas) # shape (nens, time, nscen)
+    sampled_ohc = np.asarray(sampled_ohc)
+
+    plot_samples(sampled_tas[:, :, -1], sampled_ohc[:, :, -1])
+
     # Convert to Numpy arrays
     for scenario, data in components.items():
         for key in data:
@@ -344,7 +376,7 @@ def main(args):
         sampled_components[scenario] = process_global_ensemble(
             components[scenario], percentiles, scenario)
 
-    save_to_netcdf(sampled_components, "probabilistic_projections/global/gmslr_projections_AIS_JAN_pen.nc")
+    save_to_netcdf(sampled_components, "probabilistic_projections/global/gmslr_projections_AIS_MAR_AD.nc")
     
     fig = plt.figure(figsize=(16, 8), layout="constrained")
     ax = fig.add_subplot(231)
@@ -362,16 +394,10 @@ def main(args):
 
     fig.savefig("probabilistic_components_ssps.png", dpi=300)
     plt.show()
-    
-
-    sampled_tas = np.asarray(sampled_tas) # shape (nens, time, nscen)
-    sampled_ohc = np.asarray(sampled_ohc)
-
-    plot_samples(sampled_tas[:, :, -2], sampled_ohc[:, :, -2])
 
 
 if __name__ == "__main__":
     p = argparse.ArgumentParser(formatter_class=RichHelpFormatter)
-    p.add_argument("--input", default="fair", required=False, help="Input climate forcing source (default: FaIR)", type=str)
+    p.add_argument("--input", default="run_fair", required=False, help="Input climate forcing source (default: FaIR)", type=str)
     p.add_argument("--input_path", required=False, help="Path to input climate forcing", type=str)
     main(p.parse_args())
\ No newline at end of file

From 41f3284dc22aa003abd7a50aeeac779f8b203af6 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Thu, 5 Mar 2026 17:18:42 +0000
Subject: [PATCH 096/276] Mo' updates

---
 profsea/emulator/gmslr.py        | 241 ++++++++++++++++---------------
 profsea/emulator/spatial.py      |  39 ++++-
 profsea/probabilistic_wrapper.py |  74 ++++++----
 3 files changed, 203 insertions(+), 151 deletions(-)

diff --git a/profsea/emulator/gmslr.py b/profsea/emulator/gmslr.py
index 137d1e7..c73e2a4 100644
--- a/profsea/emulator/gmslr.py
+++ b/profsea/emulator/gmslr.py
@@ -140,7 +140,8 @@ def __init__(
             random_sample: bool=False,
             T_percentile_95: np.ndarray=None,
             OHC_percentile_95: np.ndarray=None,
-            palmer_method: bool=True) -> None:
+            palmer_method: bool=True,
+            active_components: list[str]=None) -> None:
 
         self.end_yr = end_yr
         self.nt = nt
@@ -179,28 +180,39 @@ def __init__(
 
         self.seed_seq = np.random.SeedSequence(seed if seed is not None else 1234)
         self.rng = np.random.default_rng(self.seed_seq)
-        child_seeds = self.seed_seq.spawn(8)
-        self.rngs = [np.random.default_rng(s) for s in child_seeds]
+        self._stochastic_components = [
+            "glacier", "antsmb", "greenland_ar6", 
+            "greendyn", "greensmb", "antdyn", "antarctica"
+        ]
+        child_seeds = self.seed_seq.spawn(len(self._stochastic_components))
+        self.component_rngs = {
+            comp: np.random.default_rng(s) 
+            for comp, s in zip(self._stochastic_components, child_seeds)
+        }
+
+        self.active_components = active_components or [
+            'expansion', 'glacier', 'greenland_ar6', 'antarctica', 'landwater'
+        ]
 
-        self.wa = AntarcticaISMIP6("/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/emulator/aux_data/wa_params_MAR_AD_2term.nc")
-        self.ea = AntarcticaISMIP6("/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/emulator/aux_data/ea_params_MAR_AD_2term.nc")
-        self.pen = AntarcticaISMIP6("/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/emulator/aux_data/pen_params_MAR_AD_2term.nc")
+        # Setup AIS emulator, if needed
+        if "antarctica" in self.active_components:
+            wais_path = Path(__file__).parent / 'aux_data' / "wais_params.nc"
+            eais_path = Path(__file__).parent / 'aux_data' / "eais_params.nc"
+            aispen_path = Path(__file__).parent / 'aux_data' / "aispen_params.nc"
+            self.wais_model = AntarcticaISMIP6(wais_path)
+            self.eais_model = AntarcticaISMIP6(eais_path)
+            self.aispen_model = AntarcticaISMIP6(aispen_path)
 
     
     def get_components(self) -> dict:
         """Get all GMSLR components as a dictionary."""
-        components_dict = {
-            'expansion': self.expansion,
-            'glacier': self.glacier,
-            'greenland': self.greenland_ar6,
-            'greendyn': self.greendyn,
-            'greensmb': self.greensmb,
-            'antsmb': self.antsmb,
-            'antdyn': self.antdyn,
-            'antnet': self.antnet,
-            'landwater': self.landwater,
-            'gmslr': self.gmslr
-        }
+        components_dict = {}
+        # Explicitly check for sub-components alongside active ones
+        export_list = self.active_components + ['gmslr', 'wais', 'eais', 'aispen']
+        
+        for comp in export_list:
+            if hasattr(self, comp):
+                components_dict[comp] = getattr(self, comp)
         return components_dict
 
     def list_components(self) -> list:
@@ -242,12 +254,7 @@ def project(
             T_change: np.ndarray, 
             OHC_change: np.ndarray, 
             cum_emissions_total: int=None) -> None:
-        """Run the emulator to project GMSLR components.
-
-        Returns
-        -------
-        None
-        """
+        """Run the emulator to project GMSLR components."""
         self.scenario = scenario
         self.T_change = np.asarray(T_change)
         self.OHC_change = np.asarray(OHC_change)
@@ -256,122 +263,113 @@ def project(
         if self.input_ensemble:
             self.nt = self.T_change.shape[0]
 
-        if self.scenario not in ['rcp26', 'rcp45', 'rcp85', 
-            'ssp126', 'ssp245', 'ssp585'] and self.cum_emissions_total is None:
-            raise ValueError(
-                'If the scenario is not rcp26, rcp45, rcp85, ssp126, ssp245 or ssp585, '
-                'you must provide the total \ncumulative emissions from 2015 to 2100 '
-                'using the cum_emissions_total keyword argument. This is required \n'
-                'to calculate the Antarctic dynamic contribution to GMSLR.')
-
         T_ens, Exp_ens, T_int_ens, T_int_med = self.calculate_drivers() 
-        self.expansion = np.tile(Exp_ens, (self.nm, 1))
         fraction = self.rng.random(self.nm * self.nt) # correlation between antsmb and antdyn
         
+        # Evaluate Inline Components
+        if 'expansion' in self.active_components:
+            self.expansion = np.tile(Exp_ens, (self.nm, 1))
+
+        # Evaluate Standard Components
         if self.parallel:
             self.run_parallel_projections(T_int_med, T_int_ens, T_ens, fraction)
         else:
             self.run_serial_projections(T_int_med, T_int_ens, T_ens, fraction)
-        
-        # self.antnet = self.antsmb + self.antdyn
-        self.antnet = self.wa.predict(T_ens.squeeze(), display_progress=False) + self.ea.predict(T_ens.squeeze(), display_progress=False) + self.pen.predict(T_ens.squeeze(), display_progress=False)
-        random_ais_idx = self.rng.integers(low=0, high=43)
-        self.antnet = self.antnet[random_ais_idx, :, :] # size (1000, 295)
-        self.antnet = np.repeat(self.antnet, self.nt, axis=0)
 
+        # Handle Random Sampling
         if self.random_sample:
             random_idx = self.rng.integers(low=0, high=self.nm)
-            self.expansion = self.expansion[random_idx][None, :]
-            self.antnet = self.antnet[random_idx][None, :]
-            self.antdyn = self.antdyn[random_idx][None, :]
-            self.antsmb = self.antsmb[random_idx][None, :]
-            self.glacier = self.glacier[random_idx][None, :]
-            self.greenland_ar6 = self.greenland_ar6[random_idx][None, :]
-            self.landwater = self.landwater[random_idx][None, :]
-
-        self.gmslr = self.glacier + self.greenland_ar6 + self.antnet + self.landwater + self.expansion
-
-        # TODO Parallelise this section as it's a bit slow
+            
+            for comp in self.active_components + ['wais', 'eais', 'aispen']:
+                if hasattr(self, comp):
+                    comp_data = getattr(self, comp)
+                    if comp_data.ndim > 1:
+                        setattr(self, comp, comp_data[random_idx][None, :])
+
+        # Calculate GMSLR
+        components_to_sum = [getattr(self, comp) for comp in self.active_components if hasattr(self, comp)]
+        if not components_to_sum:
+            raise RuntimeError("No active components were evaluated. Cannot compute GMSLR.")
+        self.gmslr = np.sum(components_to_sum, axis=0)
+
+        # Output percentiles
         if self.output_percentiles is not None:
             console.log(f"Sampling {len(self.output_percentiles)} members per component...")
             self.gmslr = sample_members_2D(self.gmslr, self.output_percentiles)
-            self.expansion = sample_members_2D(self.expansion, self.output_percentiles)
-            self.antnet = sample_members_2D(self.antnet, self.output_percentiles)
-            self.antdyn = sample_members_2D(self.antdyn, self.output_percentiles)
-            self.antsmb = sample_members_2D(self.antsmb, self.output_percentiles)
-            self.glacier = sample_members_2D(self.glacier, self.output_percentiles)
-            self.greenland_ar6 = sample_members_2D(self.greenland_ar6, self.output_percentiles)
-            self.landwater = sample_members_2D(self.landwater, self.output_percentiles)
-            self.greensmb = sample_members_2D(self.greensmb, self.output_percentiles)
-            self.greendyn = sample_members_2D(self.greendyn, self.output_percentiles)
-            # self.greenland_ar5 = self.sample_members_2D(self.greenland_ar5, self.output_percentiles)
-            # self.landwater_ar6 = self.sample_members_2D(self.landwater_ar6, self.output_percentiles)
+            
+            for comp in self.active_components + ['wais', 'eais', 'aispen']:
+                if hasattr(self, comp):
+                    sampled_data = sample_members_2D(getattr(self, comp), self.output_percentiles)
+                    setattr(self, comp, sampled_data)
+
+    def _build_task_registry(
+            self, 
+            T_int_med: np.ndarray, 
+            T_int_ens: np.ndarray, 
+            T_ens: np.ndarray, 
+            fraction: np.ndarray) -> dict:
+        """Helper to map active component names to their functions and arguments."""
+        registry = {}
+        for comp in self.active_components:
+            rng = self.component_rngs.get(comp)
+
+            if comp == "glacier":
+                registry[comp] = (self.project_glacier, (T_int_med, T_int_ens, rng))
+            elif comp == "antsmb":
+                registry[comp] = (self.project_antsmb, (T_int_ens, rng, fraction))
+            elif comp == "greenland_ar6":
+                registry[comp] = (self.project_greenland_AR6, (T_ens, rng))
+            elif comp == "greendyn":
+                registry[comp] = (self.project_greendyn_AR5, (rng,))
+            elif comp == "greensmb":
+                registry[comp] = (self.project_greensmb_AR5, (T_ens, rng))
+            elif comp == "antdyn":
+                registry[comp] = (self.project_antdyn, (rng, fraction))
+            elif comp == "landwater":
+                registry[comp] = (self.project_landwater_ar6, ())
+            elif comp == "antarctica":
+                registry[comp] = (self.project_antarctica_ismip6, (T_ens, rng))
+
+        return registry
 
     def run_serial_projections(
             self, T_int_med: np.ndarray, T_int_ens: np.ndarray, 
             T_ens: np.ndarray, fraction: np.ndarray) -> None:
-        """Run components of the emulator sequentially.
+        """Run components of the emulator sequentially."""        
+        registry = self._build_task_registry(T_int_med, T_int_ens, T_ens, fraction)
         
-        Returns
-        -------
-        None
-        """
-        child_seeds = self.seed_seq.spawn(8)
-        rngs = [np.random.default_rng(s) for s in child_seeds]
-        self.glacier = self.project_glacier(T_int_med, T_int_ens, rngs[0])
-        self.antsmb = self.project_antsmb(T_int_ens, rngs[1], fraction)
-        self.greenland_ar6 = self.project_greenland_AR6(T_ens, rngs[2])
-        self.greendyn = self.project_greendyn_AR5(rngs[3])
-        self.greensmb = self.project_greensmb_AR5(T_ens, rngs[4])
-        self.antdyn = self.project_antdyn(rngs[5], fraction)
-        
-        # _project_landwater_ar5 corresponds to 'landwater' key in the parallel map
-        self.landwater = self._project_landwater_ar5(rngs[6])
-        # project_landwater corresponds to 'landwater_ar6' key in the parallel map
-        self.landwater_ar6 = self.project_landwater()
-
-        self.greenland_ar5 = self.greendyn + self.greensmb
+        for comp in self.active_components:
+            if comp in registry:
+                func, args = registry[comp]
+                setattr(self, comp, func(*args))
+
+        # Handle AR5 greenland combination if both are active
+        if 'greendyn' in self.active_components and 'greensmb' in self.active_components:
+            self.greenland_ar5 = self.greendyn + self.greensmb
             
     def run_parallel_projections(
         self, T_int_med: np.ndarray, T_int_ens: np.ndarray, 
         T_ens: np.ndarray, fraction: np.ndarray) -> None:
-        child_seeds = self.seed_seq.spawn(8)
-        rngs = [np.random.default_rng(s) for s in child_seeds]
+        """Run components of the emulator in parallel."""
+        registry = self._build_task_registry(T_int_med, T_int_ens, T_ens, fraction)
 
-        # Use a context manager for the executor
         with concurrent.futures.ThreadPoolExecutor() as executor:
-            futures = {
-                executor.submit(self.project_glacier, T_int_med, T_int_ens, rngs[0]): 'glacier',
-                executor.submit(self.project_antsmb, T_int_ens, rngs[1], fraction): 'antsmb',
-                executor.submit(self.project_greenland_AR6, T_ens, rngs[2]): 'greenland',
-                executor.submit(self.project_greendyn_AR5, rngs[3]): 'greendyn',
-                executor.submit(self.project_greensmb_AR5, T_ens, rngs[4]): 'greensmb',
-                executor.submit(self.project_antdyn, rngs[5], fraction): 'antdyn',
-                executor.submit(self._project_landwater_ar5, rngs[6]): 'landwater',
-                executor.submit(self.project_landwater): 'landwater_ar6' # Assuming no RNG needed here
-            }
+            futures = {}
+            for comp in self.active_components:
+                if comp in registry:
+                    func, args = registry[comp]
+                    futures[executor.submit(func, *args)] = comp
             
-            results = {}
             for future in concurrent.futures.as_completed(futures):
-                key = futures[future]
+                comp_name = futures[future]
                 try:
-                    # Letting the exception propagate is safer than silently masking it
-                    results[key] = future.result() 
+                    setattr(self, comp_name, future.result()) 
                 except Exception as e:
-                    raise RuntimeError(f"Component '{key}' failed during projection.") from e
-
-        self.glacier = results['glacier']
-
-        self.glacier = results['glacier']
-        self.antsmb = results['antsmb']
-        self.greenland_ar6 = results['greenland']
-        self.greenland_ar5 = results['greendyn'] + results['greensmb']
-        self.greendyn = results['greendyn']
-        self.greensmb = results['greensmb']
-        self.antdyn = results['antdyn']
-        self.landwater = results['landwater']
-        self.landwater_ar6 = results['landwater_ar6']
+                    raise RuntimeError(f"Component '{comp_name}' failed during projection.") from e
 
+        # Handle AR5 greenland combination if both are active
+        if 'greendyn' in self.active_components and 'greensmb' in self.active_components:
+            self.greenland_ar5 = self.greendyn + self.greensmb
   
     def calculate_drivers(self) -> tuple:
         """Calculate the drivers of GMSLR: temperature change and 
@@ -436,6 +434,19 @@ def calculate_drivers(self) -> tuple:
         T_int_ens = z[:, np.newaxis] * T_int_std + T_int_med
         return T_ens, therm_ens, T_int_ens, T_int_med  
 
+    def project_antarctica_ismip6(self, T_ens: np.ndarray, rng) -> np.ndarray:
+        wais_raw = self.wais_model.predict(T_ens.squeeze(), display_progress=False)
+        eais_raw = self.eais_model.predict(T_ens.squeeze(), display_progress=False)
+        aispen_raw = self.aispen_model.predict(T_ens.squeeze(), display_progress=False)
+
+        random_ais_idx = rng.integers(low=0, high=43)
+
+        # Match the correct output shape
+        self.wais = np.repeat(wais_raw[random_ais_idx, :, :], self.nt, axis=0)
+        self.eais = np.repeat(eais_raw[random_ais_idx, :, :], self.nt, axis=0)
+        self.aispen = np.repeat(aispen_raw[random_ais_idx, :, :], self.nt, axis=0)
+        return self.wais + self.eais + self.aispen
+
     def project_glacier(
             self, T_int_med: np.ndarray, T_int_ens: np.ndarray, rng: np.random.Generator) -> np.ndarray:
         """Project glacier contribution to GMSLR.
@@ -631,7 +642,7 @@ def project_antsmb(
         elif fraction.size != self.nm * self.nt:
             raise ValueError('fraction is the wrong size')
         else:
-            fraction.shape = (self.nm, self.nt)
+            fraction = fraction.reshape((self.nm, self.nt))
 
         smax = 0.35 # max value of S in 13.SM.1.5
         ainterfactor = 1 - fraction * smax
@@ -772,7 +783,7 @@ def project_antdyn(self, rng: np.random.Generator, fraction: np.ndarray=None) ->
             # For dyn at 2100 Chapter 13 gives [-20,185] mm for all scenarios
         return self.time_projection(0.41, 0.20, final, rng, fraction=fraction) + self.dant
 
-    def project_landwater(self) -> np.ndarray:
+    def project_landwater_ar6(self) -> np.ndarray:
         """Project land water storage contribution to GMSLR.
         
         Returns
@@ -794,10 +805,10 @@ def project_landwater(self) -> np.ndarray:
         remainder = (self.nt * self.nm) % lw.shape[0]
         lw = np.vstack([np.tile(lw, (full_repeats, 1)), lw[:remainder]])
         lw = lw.reshape(self.nt * self.nm, lw.shape[1])
-        lw = lw[:, 1:-1] # Start at 2006, end at 2299
+        lw = lw[:, 1:] # Start at 2006, end at 2299
         return lw
   
-    def _project_landwater_ar5(self, rng: np.random.Generator) -> np.ndarray:
+    def project_landwater_ar5(self, rng: np.random.Generator) -> np.ndarray:
         """Old projection function. Project land water storage 
         contribution to GMSLR.
         
diff --git a/profsea/emulator/spatial.py b/profsea/emulator/spatial.py
index e439c62..8ed8e40 100644
--- a/profsea/emulator/spatial.py
+++ b/profsea/emulator/spatial.py
@@ -70,11 +70,11 @@ def __init__(
             raise ValueError(
                 "Provide namelist of global projection components. "
                 "Currently allowed components are expansion, antdyn, "
-                "antsmb, glacier, greenland and landwater")
+                "antsmb, wais, eais, glacier, greenland and landwater")
 
         self.components = {}
-        component_path = Path(components_dir) / f"{scenario}_global.nc"
-        ds_component = xr.load_dataset(component_path)
+        component_path = Path(components_dir)
+        ds_component = xr.load_dataset(component_path).sel(scenario=scenario)
         for comp in component_list:
             self.components[comp] = ds_component[comp].data
   
@@ -197,7 +197,7 @@ def project(self) -> None:
                 dtype=np.float32)  # (no FPs applied)
 
             # Load global projections in for the component
-            mc_timeseries = self.components[comp]
+            mc_timeseries = self.components[comp] # shape (mem, time)
             sampled_mc = mc_timeseries[resamples, :self.n_years]
             montecarlo_G[:, :] = da.from_array(sampled_mc[:, :, None, None], chunks="auto")
 
@@ -214,6 +214,16 @@ def project(self) -> None:
             elif comp == "greenland":
                 greenland_fp = self._calc_greenland_fingerprint_ar6()
                 montecarlo_R[:, :, :, :] = montecarlo_G[:, :, :, :] * greenland_fp[None, None, :, :]
+            
+            elif comp == "wais":
+                wais_fp = self._calc_antarctic_fingerprint(comp)
+                montecarlo_R[:, :, :, :] = montecarlo_G[:, :, :, :] * wais_fp[None, None, :, :]
+                del wais_fp
+
+            elif comp == "eais":
+                eais_fp = self._calc_antarctic_fingerprint(comp)
+                montecarlo_R[:, :, :, :] = montecarlo_G[:, :, :, :] * eais_fp[None, None, :, :]
+                del eais_fp
 
             else:
                 fp_vals = self._calc_fingerprint_contributions(FPlist, comp)
@@ -334,7 +344,26 @@ def _calc_greenland_fingerprint_ar6(self) -> da.array:
             lat=np.linspace(-90, 90, self.nlat, endpoint=False) + 0.5, 
             lon=np.linspace(0, 360, self.nlon, endpoint=False) + 0.5, 
             method="linear").data * 1000  # convert mm to m SLE per m GMSLR
+        return fp_vals
 
+    def _calc_antarctic_fingerprint(self, fname: str) -> da.array:
+        """Load and prepare the WAIS fingerprint.
+
+        Fingerprints from Kopp, R. E. (2022). Framework for Assessing Changes 
+        To Sea-level (FACTS) Module Data (1.0) [Data set]. Zenodo.
+        
+        :return dask array containing Antarctic fingerprint
+        """
+        # Load in the fingerprint
+        fp_path = Path(self.fingerprint_dir) / os.path.join(fname, ".nc")
+        print(fp_path)
+        fp_ds = xr.open_dataset(fp_path, chunks={})
+
+        # Interpolate to (180, 360) grid
+        fp_vals = fp_ds.fp.interp(
+            lat=np.linspace(-90, 90, self.nlat, endpoint=False) + 0.5, 
+            lon=np.linspace(0, 360, self.nlon, endpoint=False) + 0.5, 
+            method="linear").data * 1000  # convert mm to m SLE per m GMSLR
         return fp_vals
 
     def _load_CMIP6_slopes(self) -> np.ndarray:
@@ -382,7 +411,7 @@ def _load_fingerprints(self) -> tuple:
         # Other FPs have multiple components
         components_todo = [
             c for c in list(self.components.keys()) 
-            if c not in ["expansion", "landwater", "greenland"]]
+            if c not in ["expansion", "landwater", "greenland", "wais", "eais"]]
         for comp in components_todo:
             slangen_path = Path(self.fingerprint_dir) / f"{comp}_slangen_nomask.nc"
             spada_path = Path(self.fingerprint_dir) / f"{comp}_spada_nomask.nc"
diff --git a/profsea/probabilistic_wrapper.py b/profsea/probabilistic_wrapper.py
index ed00c01..a2d8a9a 100644
--- a/profsea/probabilistic_wrapper.py
+++ b/profsea/probabilistic_wrapper.py
@@ -60,7 +60,6 @@ def index_df(df: pd.DataFrame, baseline_start: int, baseline_end: int) -> pd.Dat
     mask_final = (years_final >= 2006) & (years_final <= 2300)
     
     df_final = df_anom.loc[:, mask_final].reset_index()
-    
     return df_final
 
 
@@ -88,7 +87,6 @@ def df_to_arr(df, scenario_order):
 
     array = np.stack(array, axis=0)
     array = array.transpose(2, 0, 1)  # (n_scenarios, n_members, n_time)
-    
     return array
 
 
@@ -116,21 +114,22 @@ def load_magicc_forcing(
 
 
 def load_fair_forcing(
-        input_path: str, baseline_start: int, baseline_end: int) -> tuple[np.ndarray]:
+        input_path: str, scenarios: list,
+        baseline_start: int, baseline_end: int) -> tuple[np.ndarray]:
     tas_path = Path(input_path) / "tas.nc"
     ohc_path = Path(input_path) / "ohc.nc"
 
     tas = xr.load_dataarray(tas_path)
-    ohc= xr.laod_dataarray(ohc_path)
+    ohc= xr.load_dataarray(ohc_path)
 
-    tas_baseline = tas.loc[dict(layer=0, timebounds=np.arange(baseline_start, baseline_end+1))].mean()
-    ohc_baseline = ohc.loc[dict(timebounds=np.arange(baseline_start, baseline_end+1))].mean()
+    tas_baseline = tas.loc[dict(timebounds=np.arange(baseline_start, baseline_end+1))].mean("timebounds")
+    ohc_baseline = ohc.loc[dict(timebounds=np.arange(baseline_start, baseline_end+1))].mean(["timebounds"])
 
-    tas = tas.loc[dict(layer=0, timebounds=np.arange(2006, 2301))] - tas_baseline  # shape (294, 7, 841)
+    tas = tas.loc[dict(timebounds=np.arange(2006, 2301))] - tas_baseline  # shape (294, 7, 841)
     ohc = ohc.loc[dict(timebounds=np.arange(2006, 2301))] - ohc_baseline
 
-
-
+    tas = tas.sel(scenario=scenarios).transpose("scenario", "config", "timebounds")
+    ohc = ohc.sel(scenario=scenarios).transpose("scenario", "config", "timebounds")
     return tas.values, ohc.values
 
 
@@ -161,33 +160,32 @@ def run_fair(baseline_start: int, baseline_end: int, scenarios: list) -> tuple[n
     tas_baseline = f.temperature.loc[dict(layer=0, timebounds=np.arange(baseline_start, baseline_end+1))].mean()
     ohc_baseline = f.ocean_heat_content_change.loc[dict(timebounds=np.arange(baseline_start, baseline_end+1))].mean()
 
-    tas = f.temperature.loc[dict(layer=0, timebounds=np.arange(2006, 2301))] - tas_baseline
+    tas = f.temperature.loc[dict(layer=0, timebounds=np.arange(2006, 2301))] - tas_baseline  # shape (294, 7, 841)
     ohc = f.ocean_heat_content_change.loc[dict(timebounds=np.arange(2006, 2301))] - ohc_baseline
-
-    print(tas.values.shape)
     return tas.values, ohc.values
 
 
 def simulation_task(random_idx: int):
     """Runs the emulator for a single ensemble member across all scenarios."""
     # Access data from global scope (initialized via init_worker)
-    tas_sampled = worker_tas[:, :, random_idx]
-    ohc_sampled = worker_ohc[:, :, random_idx]
+    tas_sampled = worker_tas[:, random_idx, :]
+    ohc_sampled = worker_ohc[:, random_idx, :]
     results = {scenario: {} for scenario in worker_scenarios}
 
     for idx, scenario in enumerate(worker_scenarios):
         worker_emulator.project(
             scenario,
-            np.expand_dims(tas_sampled[:, idx], axis=0),
-            np.expand_dims(ohc_sampled[:, idx], axis=0),
-            cum_emissions_total=worker_emissions[scenario]
+            np.expand_dims(tas_sampled[idx, :], axis=0),
+            np.expand_dims(ohc_sampled[idx, :], axis=0),
         )
 
         results[scenario]["gmslr"] = worker_emulator.gmslr
         results[scenario]["expansion"] = worker_emulator.expansion
-        results[scenario]["antarctica"] = worker_emulator.antnet
+        results[scenario]["antarctica"] = worker_emulator.antarctica
+        results[scenario]["wais"] = worker_emulator.wais
+        results[scenario]["eais"] = worker_emulator.eais
         results[scenario]["greenland"] = worker_emulator.greenland_ar6
-        results[scenario]["glaciers"] = worker_emulator.glacier
+        results[scenario]["glacier"] = worker_emulator.glacier
         results[scenario]["landwater"] = worker_emulator.landwater
     return results, random_idx
 
@@ -272,12 +270,20 @@ def plot_component(
         ax: plt.Axes, component_dict: dict, component: str, 
         scenarios: list, plot_legend: bool=False) -> None:
     time = np.arange(2006, 2301)
+    # ssp_colours = {
+    #     "ssp119": tuple(np.array([0, 173, 207]) / 255),
+    #     "ssp126": tuple(np.array([23, 60, 102]) / 255),
+    #     "ssp245": tuple(np.array([247, 148, 32]) / 255),
+    #     "ssp370": tuple(np.array([231, 29, 37]) / 255),
+    #     "ssp534-over": tuple(np.array([0, 79, 0]) / 255),
+    #     "ssp585": tuple(np.array([149, 27, 30]) / 255)
+    # }
     ssp_colours = {
-        "ssp119": tuple(np.array([0, 173, 207]) / 255),
+        "RESCUE-Tier1-Extension-CB1700_2110_2.0": tuple(np.array([0, 173, 207]) / 255),
         "ssp126": tuple(np.array([23, 60, 102]) / 255),
-        "ssp245": tuple(np.array([247, 148, 32]) / 255),
+        "RESCUE-Tier1-Extension-CB1700_2150_1.5": tuple(np.array([247, 148, 32]) / 255),
         "ssp370": tuple(np.array([231, 29, 37]) / 255),
-        "ssp534-over": tuple(np.array([0, 79, 0]) / 255),
+        "RESCUE-Tier1-Extension-CB500st_2110_1.5": tuple(np.array([0, 79, 0]) / 255),
         "ssp585": tuple(np.array([149, 27, 30]) / 255)
     }
     for scenario in reversed(scenarios):
@@ -303,8 +309,12 @@ def plot_component(
 
 def main(args):
     # Set up for main loop
-    percentiles = [5, 17, 50, 83, 95]
-    scenarios = ["ssp119", "ssp126", "ssp245", "ssp370", "ssp534-over", "ssp585"]
+    percentiles = np.arange(0, 101)
+    # scenarios = ["ssp119", "ssp126", "ssp245", "ssp370", "ssp534-over", "ssp585"]
+    scenarios = [
+        'RESCUE-Tier1-Extension-CB1700_2110_2.0',
+        'RESCUE-Tier1-Extension-CB1700_2150_1.5',
+        'RESCUE-Tier1-Extension-CB500st_2110_1.5']
 
     emissions_path = "/Users/gregorymunday/Documents/Papers/ProFSea/cmip7-slr/data/cumulative_cmip6_emissions.json"
     with open(emissions_path) as f:
@@ -318,7 +328,8 @@ def main(args):
     for scenario in scenarios:
         components[scenario] = {
             "gmslr": [], "expansion": [], "antarctica": [],
-            "greenland": [], "glaciers": [], "landwater": []
+            "wais": [], "eais": [], "greenland": [], "glacier": [], 
+            "landwater": []
         }
 
     # Enter 1000x loop
@@ -332,9 +343,10 @@ def main(args):
         raise ValueError("Input source not recognised.")
 
     # Pre-generate the random ensemble member indices
+    # Shape is # (scenario, ens, time)
     n_iterations = 1000
     rng = np.random.default_rng()
-    random_indices = rng.integers(0, high=tas.shape[2], size=n_iterations)
+    random_indices = rng.integers(0, high=tas.shape[1], size=n_iterations)
 
     sampled_tas = []
     sampled_ohc = []
@@ -351,8 +363,8 @@ def main(args):
             task = progress.add_task("[orange1]Simulating and sampling...", total=n_iterations)
             for future in concurrent.futures.as_completed(futures):
                 result_dict, idx_returned = future.result()
-                sampled_tas.append(tas[:, :, idx_returned])
-                sampled_ohc.append(ohc[:, :, idx_returned])
+                sampled_tas.append(tas[:, idx_returned, :])
+                sampled_ohc.append(ohc[:, idx_returned, :])
 
                 # Now add the results into the components dictionary
                 for scenario in scenarios:
@@ -364,7 +376,7 @@ def main(args):
     sampled_tas = np.asarray(sampled_tas) # shape (nens, time, nscen)
     sampled_ohc = np.asarray(sampled_ohc)
 
-    plot_samples(sampled_tas[:, :, -1], sampled_ohc[:, :, -1])
+    plot_samples(sampled_tas[:, -1, :], sampled_ohc[:, -1, :])
 
     # Convert to Numpy arrays
     for scenario, data in components.items():
@@ -376,7 +388,7 @@ def main(args):
         sampled_components[scenario] = process_global_ensemble(
             components[scenario], percentiles, scenario)
 
-    save_to_netcdf(sampled_components, "probabilistic_projections/global/gmslr_projections_AIS_MAR_AD.nc")
+    save_to_netcdf(sampled_components, "probabilistic_projections/global/gmslr_projections_RESCUE_101mem.nc")
     
     fig = plt.figure(figsize=(16, 8), layout="constrained")
     ax = fig.add_subplot(231)
@@ -384,7 +396,7 @@ def main(args):
     ax = fig.add_subplot(232)
     plot_component(ax, sampled_components, "expansion", scenarios)
     ax = fig.add_subplot(233)
-    plot_component(ax, sampled_components, "glaciers", scenarios)
+    plot_component(ax, sampled_components, "glacier", scenarios)
     ax = fig.add_subplot(234)
     plot_component(ax, sampled_components, "antarctica", scenarios)
     ax = fig.add_subplot(235)

From cd616ce84f384a34035a9fe1e31306eac4dd45b3 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Thu, 5 Mar 2026 17:27:26 +0000
Subject: [PATCH 097/276] Smol fix

---
 profsea/emulator/spatial.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/profsea/emulator/spatial.py b/profsea/emulator/spatial.py
index 8ed8e40..0ffc608 100644
--- a/profsea/emulator/spatial.py
+++ b/profsea/emulator/spatial.py
@@ -354,9 +354,10 @@ def _calc_antarctic_fingerprint(self, fname: str) -> da.array:
         
         :return dask array containing Antarctic fingerprint
         """
+        fname += ".nc"
+
         # Load in the fingerprint
-        fp_path = Path(self.fingerprint_dir) / os.path.join(fname, ".nc")
-        print(fp_path)
+        fp_path = Path(self.fingerprint_dir) / fname
         fp_ds = xr.open_dataset(fp_path, chunks={})
 
         # Interpolate to (180, 360) grid

From eff2802322ea15202b475735935b1dba14e9e388 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Mon, 9 Mar 2026 11:27:34 +0000
Subject: [PATCH 098/276] Optional spatial sampling

---
 profsea/emulator/spatial.py      | 129 +++++++++++++++++++------------
 profsea/probabilistic_wrapper.py |   4 +-
 2 files changed, 80 insertions(+), 53 deletions(-)

diff --git a/profsea/emulator/spatial.py b/profsea/emulator/spatial.py
index 0ffc608..7cd1ef3 100644
--- a/profsea/emulator/spatial.py
+++ b/profsea/emulator/spatial.py
@@ -58,7 +58,8 @@ def __init__(
             output_percentiles: list|np.ndarray=[5, 17, 50, 83, 95],
             output_dir: str|Path=None,
             cmip5_patterns: bool=False,
-            random_seed: int=None
+            random_seed: int=None,
+            sample_spatial: bool=False
         ):
 
         # Start off with some error handling
@@ -93,9 +94,16 @@ def __init__(
         self.output_dir = output_dir
         self.start_year = 2006
         self.n_years = self.end_year - self.start_year
-        self.n_samples = self.components["expansion"].shape[0]
+        self.sample_spatial = sample_spatial
         self.rng = np.random.default_rng(seed=random_seed)
 
+        if self.sample_spatial:
+            # Get full ensemble
+            self.n_samples = self.components["expansion"].shape[0]
+        else:
+            # Use global trajectories
+            self.n_samples = len(self.output_percentiles)
+
         # Get lat/lon sizes
         sample_pattern_filepath = Path(expansion_patterns_dir) \
             / "ACCESS-CM2/zos_regression_ssp245_ACCESS-CM2.npy"
@@ -131,25 +139,25 @@ def _calc_gia_contribution(self) -> None:
         console.log('Calculating GIA contribution...')
         nGIA, GIA_vals = self._read_gia_estimates()
         Tdelta = self._calc_baseline_period()
-        num_percs = len(self.output_percentiles)
 
         # Unit series of mm/yr expressed as m/yr
         unit_series = (np.arange(self.n_years) + Tdelta) * 0.001
-        GIA_unit_series = np.ones([num_percs, self.n_years]) * unit_series
+        GIA_unit_series = np.ones([self.n_samples, self.n_years]) * unit_series
 
         # rgiai is an array of random GIA indices the size of the sample years
-        rgiai = self.rng.integers(nGIA, size=num_percs)
+        rgiai = self.rng.integers(nGIA, size=self.n_samples)
 
         GIA_T = da.from_array(GIA_unit_series)
         GIA_vals = da.from_array(GIA_vals)
         GIA_series = GIA_T[:, :, None, None] * GIA_vals[rgiai, None, :, :]
+        GIA_series_out = da.percentile(GIA_series, self.output_percentiles, axis=0)
 
         # Save data in netcdf format (Assuming first dimension is percentile, but can be more general percentile/ensemble)
         xr_dataArray = xr.DataArray(
-            GIA_series, 
-            dims=["samples", "time", "lat", "lon"], 
+            GIA_series_out, 
+            dims=["percentile", "time", "lat", "lon"], 
             coords={
-                "samples": np.arange(num_percs),
+                "percentile": self.output_percentiles,
                 "time": np.arange(self.start_year, self.end_year),
                 "lat": np.arange(-90, 90) + 0.5, 
                 "lon": np.arange(0, 360) + 0.5})
@@ -163,7 +171,7 @@ def _calc_gia_contribution(self) -> None:
         R_file = '_'.join([file_header, 'regional']) + '.nc'
         encoding = {'gia': {"zlib": True, "complevel": 5, "dtype": "float32"}}
         ds.to_netcdf(os.path.join(self.output_dir, R_file), encoding=encoding, compute=True)
-        del GIA_series
+        return GIA_series
 
     def project(self) -> None:
         """
@@ -180,61 +188,76 @@ def project(self) -> None:
         :return: montecarlo_G (global contribution to sea level rise) and
             montecarlo_R (regional contribution to sea level change)
         """  
-        console.log(f"Running with {self.n_samples} ensemble members")
+        console.log(f"Running in {'PROBABILISTIC' if self.sample_spatial else 'STORYLINE'} mode.")
+        console.log(f"Processing {self.n_samples} input trajectories...")
 
-        resamples = self.rng.choice(self.n_samples, size=self.n_samples)  # Preserve correlations across comps
+        if self.sample_spatial:
+            resamples = self.rng.choice(self.n_samples, size=self.n_samples)
+        else:
+            resamples = np.arange(self.n_samples)
 
-        # Calculate GIA contribution and save it out
-        self._calc_gia_contribution()
+        # Calculate GIA contribution. 
+        gia_raw_samples = self._calc_gia_contribution() 
+        
         nFPs, FPlist = self._load_fingerprints()
-        rfpi = self.rng.integers(nFPs, size=self.n_samples)
+
+        if self.sample_spatial:
+            rfpi = self.rng.integers(nFPs, size=self.n_samples)
+        
+        # Initialise the Total array
+        total_montecarlo_R = gia_raw_samples
+
         for comp in track(list(self.components.keys()), description="Calculating components..."):
-            montecarlo_R = da.zeros(
-                (self.n_samples, self.n_years, self.nlat, self.nlon),
-                dtype=np.float32)  # (FPs applied) + GIA
-            montecarlo_G = da.zeros(
-                (self.n_samples, self.n_years, self.nlat, self.nlon),
-                dtype=np.float32)  # (no FPs applied)
-
-            # Load global projections in for the component
-            mc_timeseries = self.components[comp] # shape (mem, time)
+            mc_timeseries = self.components[comp] 
             sampled_mc = mc_timeseries[resamples, :self.n_years]
-            montecarlo_G[:, :] = da.from_array(sampled_mc[:, :, None, None], chunks="auto")
+            montecarlo_G = da.from_array(sampled_mc[:, :, None, None], chunks="auto")
 
+            # Apply regional scaling/fingerprints (Fixes the slice assignment bug)
             if comp == "expansion":
                 sampled_coeffs = self._calc_expansion_contribution()
                 montecarlo_R = montecarlo_G * sampled_coeffs[:, None, :, :]
-                del sampled_coeffs
-
             elif comp == "landwater":
                 landwater_vals = self._calc_landwater_contribution(FPlist[0]["landwater"])
-                montecarlo_R[:, :, :, :] = montecarlo_G[:, :, :, :] * landwater_vals[None, None, :, :]
-                del landwater_vals
-
+                montecarlo_R = montecarlo_G * landwater_vals[None, None, :, :]
             elif comp == "greenland":
                 greenland_fp = self._calc_greenland_fingerprint_ar6()
-                montecarlo_R[:, :, :, :] = montecarlo_G[:, :, :, :] * greenland_fp[None, None, :, :]
-            
+                montecarlo_R = montecarlo_G * greenland_fp[None, None, :, :]
             elif comp == "wais":
                 wais_fp = self._calc_antarctic_fingerprint(comp)
-                montecarlo_R[:, :, :, :] = montecarlo_G[:, :, :, :] * wais_fp[None, None, :, :]
-                del wais_fp
-
+                montecarlo_R = montecarlo_G * wais_fp[None, None, :, :]
             elif comp == "eais":
                 eais_fp = self._calc_antarctic_fingerprint(comp)
-                montecarlo_R[:, :, :, :] = montecarlo_G[:, :, :, :] * eais_fp[None, None, :, :]
-                del eais_fp
+                montecarlo_R = montecarlo_G * eais_fp[None, None, :, :]
+            else:
+                if self.sample_spatial:
+                    fp_vals = self._calc_fingerprint_contributions(FPlist, comp)
+                    montecarlo_R = montecarlo_G * fp_vals[rfpi][:, None, :, :]
+                else:
+                    fp_vals = self._calc_fingerprint_contributions(FPlist, comp)
+                    fp_mean = da.nanmean(fp_vals, axis=0)
+                    montecarlo_R = montecarlo_G * fp_mean[None, None, :, :]
+
+            # Accumulate overall pattern
+            if total_montecarlo_R is None:
+                total_montecarlo_R = montecarlo_R
+            else:
+                total_montecarlo_R = total_montecarlo_R + montecarlo_R
 
+            # Calculate and save percentiles for the individual component
+            if self.sample_spatial:
+                comp_percentiles = da.percentile(montecarlo_R, self.output_percentiles, axis=0)
             else:
-                fp_vals = self._calc_fingerprint_contributions(FPlist, comp)
-                montecarlo_R[:, :, :, :] = montecarlo_G[:, :, :, :] * fp_vals[rfpi][:, None, :, :]
-                del fp_vals
+                comp_percentiles = montecarlo_R
+
+            self._save_projections(comp_percentiles.astype(np.float32), comp)
 
-            montecarlo_R = da.percentile(montecarlo_R, self.output_percentiles, axis=0)
-            montecarlo_R = montecarlo_R.astype(np.float32)
+        console.log("Processing total sea-level rise grids...")
+        if self.sample_spatial:
+            total_out = da.percentile(total_montecarlo_R, self.output_percentiles, axis=0)
+        else:
+            total_out = total_montecarlo_R
 
-            # Create the output sea level projections file directory and filename
-            self._save_projections(montecarlo_R, comp)
+        self._save_projections(total_out.astype(np.float32), "total")
 
     def _save_projections(self, montecarlo_R: da.array, component: str) -> None:
         """
@@ -299,10 +322,14 @@ def _calc_expansion_contribution(self) -> da.array:
         coeffs = self._load_CMIP6_slopes()
         coeffs = da.roll(coeffs, 180, axis=2)
 
-        rand_samples = self.rng.choice(
-            coeffs.shape[0], size=self.n_samples, replace=True)               
-        rand_coeffs = coeffs[rand_samples, :, :]
-        return rand_coeffs
+        if self.sample_spatial:
+            rand_samples = self.rng.choice(
+                coeffs.shape[0], size=self.n_samples, replace=True)               
+            return coeffs[rand_samples, :, :]
+        else:
+            # Calc pattern ensemble mean
+            mean_coeff = da.nanmean(coeffs, axis=0)
+            return da.broadcast_to(mean_coeff, (self.n_samples, self.nlat, self.nlon))
 
     def _calc_landwater_contribution(self, data: xr.DataArray) -> da.array:
         """
@@ -384,7 +411,7 @@ def _load_one_slope(f):
 
         # Create a list of lazy dask arrays
         lazy_slopes = [
-            da.from_array(_load_one_slope(f), chunks=(180, 360)) 
+            da.from_array(_load_one_slope(f), chunks=(45, 45)) 
             for f in slope_files]
         slopes_stack = da.stack(lazy_slopes, axis=0)
 
@@ -417,9 +444,9 @@ def _load_fingerprints(self) -> tuple:
             slangen_path = Path(self.fingerprint_dir) / f"{comp}_slangen_nomask.nc"
             spada_path = Path(self.fingerprint_dir) / f"{comp}_spada_nomask.nc"
             klemann_path = Path(self.fingerprint_dir) / f"{comp}_klemann_nomask.nc"
-            slangen_FPs[comp] = xr.open_dataarray(slangen_path, chunks={})
-            spada_FPs[comp] = xr.open_dataarray(spada_path, chunks={})
-            klemann_FPs[comp] = xr.open_dataarray(klemann_path, chunks={})
+            slangen_FPs[comp] = xr.open_dataarray(slangen_path, chunks={"lat": 45, "lon": 45})
+            spada_FPs[comp] = xr.open_dataarray(spada_path, chunks={"lat": 45, "lon": 45})
+            klemann_FPs[comp] = xr.open_dataarray(klemann_path, chunks={"lat": 45, "lon": 45})
 
         FPlist = [slangen_FPs, spada_FPs, klemann_FPs]
         nFPs = len(FPlist)
diff --git a/profsea/probabilistic_wrapper.py b/profsea/probabilistic_wrapper.py
index a2d8a9a..ad6f0c9 100644
--- a/profsea/probabilistic_wrapper.py
+++ b/profsea/probabilistic_wrapper.py
@@ -309,7 +309,7 @@ def plot_component(
 
 def main(args):
     # Set up for main loop
-    percentiles = np.arange(0, 101)
+    percentiles = [0, 5, 17, 50, 83, 95, 100]
     # scenarios = ["ssp119", "ssp126", "ssp245", "ssp370", "ssp534-over", "ssp585"]
     scenarios = [
         'RESCUE-Tier1-Extension-CB1700_2110_2.0',
@@ -388,7 +388,7 @@ def main(args):
         sampled_components[scenario] = process_global_ensemble(
             components[scenario], percentiles, scenario)
 
-    save_to_netcdf(sampled_components, "probabilistic_projections/global/gmslr_projections_RESCUE_101mem.nc")
+    save_to_netcdf(sampled_components, "probabilistic_projections/global/gmslr_projections_RESCUE_test.nc")
     
     fig = plt.figure(figsize=(16, 8), layout="constrained")
     ax = fig.add_subplot(231)

From e80366e1f8d8cdab72c30a813530de1afb91ac95 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Mon, 9 Mar 2026 11:28:09 +0000
Subject: [PATCH 099/276] Remove import

---
 profsea/emulator/gmslr.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/profsea/emulator/gmslr.py b/profsea/emulator/gmslr.py
index c73e2a4..b18ea03 100644
--- a/profsea/emulator/gmslr.py
+++ b/profsea/emulator/gmslr.py
@@ -16,7 +16,6 @@
 import numpy as np
 import pandas as pd
 from rich.console import Console
-from scipy.spatial.distance import cdist
 from scipy.stats import norm, truncnorm
 import xarray as xr
 

From 792ad55af152c976d1ed01c85895b62badce0aac Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Mon, 9 Mar 2026 11:29:35 +0000
Subject: [PATCH 100/276] Add params

---
 profsea/emulator/aux_data/aispen_params.nc | Bin 0 -> 33392 bytes
 profsea/emulator/aux_data/eais_params.nc   | Bin 0 -> 33471 bytes
 profsea/emulator/aux_data/wais_params.nc   | Bin 0 -> 33445 bytes
 3 files changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 profsea/emulator/aux_data/aispen_params.nc
 create mode 100644 profsea/emulator/aux_data/eais_params.nc
 create mode 100644 profsea/emulator/aux_data/wais_params.nc

diff --git a/profsea/emulator/aux_data/aispen_params.nc b/profsea/emulator/aux_data/aispen_params.nc
new file mode 100644
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diff --git a/profsea/emulator/aux_data/wais_params.nc b/profsea/emulator/aux_data/wais_params.nc
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HcmV?d00001


From 4670d382bf873b00fe6506976021fc0433206ba2 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Fri, 3 Apr 2026 11:11:22 +0100
Subject: [PATCH 101/276] Add dim check and reformat

---
 profsea/emulator/gmslr.py | 673 +++++++++++++++++++++++---------------
 1 file changed, 418 insertions(+), 255 deletions(-)

diff --git a/profsea/emulator/gmslr.py b/profsea/emulator/gmslr.py
index b18ea03..4009c0a 100644
--- a/profsea/emulator/gmslr.py
+++ b/profsea/emulator/gmslr.py
@@ -3,7 +3,7 @@
 All rights reserved.
 """
 
-# Calculate Monte Carlo projections of GMSLR using methods 
+# Calculate Monte Carlo projections of GMSLR using methods
 # from Jonathon Gregory and AR5. Staying close to JG's original
 # code where possible.
 import os
@@ -20,34 +20,41 @@
 import xarray as xr
 
 from profsea.utils import sample_members_2D
-from .antarctica import AntarcticaISMIP6 
+from .antarctica import AntarcticaISMIP6
 
 console = Console()
 
+
 @functools.lru_cache(maxsize=1)
 def load_greenland_calibration():
     """Loads the CSV once and keeps it in memory."""
     path = Path(__file__).parent / "aux_data" / "ISMIP_GIS_calibration.csv"
     return pd.read_csv(path)
 
+
 @functools.lru_cache(maxsize=1)
 def load_landwater_projection():
     """Loads the NetCDF once and keeps the VALUES in memory."""
-    path = Path(__file__).parent / 'aux_data' / 'ssp_global_landwater_projections.nc'
+    path = Path(__file__).parent / "aux_data" / "ssp_global_landwater_projections.nc"
     with xr.open_dataset(path) as ds:
-        ds.load() 
+        ds.load()
     return ds
 
+
 _SHARED_EXECUTOR = None
 
+
 def get_shared_executor():
     """Returns a singleton ThreadPoolExecutor."""
     global _SHARED_EXECUTOR
     if _SHARED_EXECUTOR is None:
         max_workers = min(8, os.cpu_count() or 1)
-        _SHARED_EXECUTOR = concurrent.futures.ThreadPoolExecutor(max_workers=max_workers)
+        _SHARED_EXECUTOR = concurrent.futures.ThreadPoolExecutor(
+            max_workers=max_workers
+        )
     return _SHARED_EXECUTOR
 
+
 @atexit.register
 def shutdown_executor():
     """Ensures the executor is cleaned up when the script exits."""
@@ -74,36 +81,36 @@ class Global:
     nt: int
         Number of realisations of the input timeseries
     nm: int
-        Number of realisations of for each component. 
+        Number of realisations of for each component.
         Must be a multiple of the number of glacier methods.
     tcv: float
         Multiplier for the standard deviation in the input fields.
     glaciermip: bool | int
-        If False, use AR5 parameters. If 1, use GlacierMIP 
+        If False, use AR5 parameters. If 1, use GlacierMIP
         (Hock et al., 2019). If 2, use GlacierMIP2 (Marzeion et al., 2020).
     parallel: bool
         If True, project SLR components in parallel.
     input_ensemble: bool
-        If True, use an input ensemble of temperature and 
+        If True, use an input ensemble of temperature and
         ocean heat content change.
     output_percentiles: list|np.ndarray
-        If not None, calculate percentiles from a 1D list/array for each 
+        If not None, calculate percentiles from a 1D list/array for each
         component
     random_sample: bool
-        If True, randomly sample a single ensemble member across all 
+        If True, randomly sample a single ensemble member across all
         components
     T_percentile_95: np.ndarray
         95th percentile of temperature change timeseries.
-    OHC_percentile_95: np.ndarray   
+    OHC_percentile_95: np.ndarray
         95th percentile of ocean heat content change timeseries.
     cum_emissions_total: float
         Total cumulative emissions from 2015 to 2100.
     palmer_method: bool
-        If True, allow integration to end in any year up to 2300, 
-        with the contributions to GMLSR from ice-sheet dynamics, 
-        Greenland SMB and land water storage held at the 2100 rate 
+        If True, allow integration to end in any year up to 2300,
+        with the contributions to GMLSR from ice-sheet dynamics,
+        Greenland SMB and land water storage held at the 2100 rate
         beyond 2100.
-    
+
     Attributes
     ----------
     endofhistory: int
@@ -113,7 +120,7 @@ class Global:
     nyr: int
         Length of projections.
     fgreendyn: float
-        Fraction of SLE from Greenland during 1996 to 2005 assumed 
+        Fraction of SLE from Greenland during 1996 to 2005 assumed
         to result from rapid dynamical change.
     dgreen: float
         m SLE from Greenland during 1996 to 2005 according to AR5 chapter 4.
@@ -126,21 +133,22 @@ class Global:
     """
 
     def __init__(
-            self, 
-            end_yr: int,
-            seed: int=1234,
-            nt: int=100,
-            nm: int=1000,
-            tcv: float=1.0,
-            glaciermip: bool|int=2,
-            parallel: bool=True,
-            input_ensemble: bool=True,
-            output_percentiles: list|np.ndarray=None,
-            random_sample: bool=False,
-            T_percentile_95: np.ndarray=None,
-            OHC_percentile_95: np.ndarray=None,
-            palmer_method: bool=True,
-            active_components: list[str]=None) -> None:
+        self,
+        end_yr: int,
+        seed: int = 1234,
+        nt: int = 100,
+        nm: int = 1000,
+        tcv: float = 1.0,
+        glaciermip: bool | int = 2,
+        parallel: bool = True,
+        input_ensemble: bool = True,
+        output_percentiles: list | np.ndarray = None,
+        random_sample: bool = False,
+        T_percentile_95: np.ndarray = None,
+        OHC_percentile_95: np.ndarray = None,
+        palmer_method: bool = True,
+        active_components: list[str] = None,
+    ) -> None:
 
         self.end_yr = end_yr
         self.nt = nt
@@ -160,7 +168,7 @@ def __init__(
 
         # Last year of AR5 projections
         self.endofAR5 = 2100
-        
+
         # Length of projections
         self.nyr = self.end_yr - self.endofhistory
 
@@ -180,35 +188,43 @@ def __init__(
         self.seed_seq = np.random.SeedSequence(seed if seed is not None else 1234)
         self.rng = np.random.default_rng(self.seed_seq)
         self._stochastic_components = [
-            "glacier", "antsmb", "greenland_ar6", 
-            "greendyn", "greensmb", "antdyn", "antarctica"
+            "glacier",
+            "antsmb",
+            "greenland_ar6",
+            "greendyn",
+            "greensmb",
+            "antdyn",
+            "antarctica",
         ]
         child_seeds = self.seed_seq.spawn(len(self._stochastic_components))
         self.component_rngs = {
-            comp: np.random.default_rng(s) 
+            comp: np.random.default_rng(s)
             for comp, s in zip(self._stochastic_components, child_seeds)
         }
 
         self.active_components = active_components or [
-            'expansion', 'glacier', 'greenland_ar6', 'antarctica', 'landwater'
+            "expansion",
+            "glacier",
+            "greenland_ar6",
+            "antarctica",
+            "landwater",
         ]
 
         # Setup AIS emulator, if needed
         if "antarctica" in self.active_components:
-            wais_path = Path(__file__).parent / 'aux_data' / "wais_params.nc"
-            eais_path = Path(__file__).parent / 'aux_data' / "eais_params.nc"
-            aispen_path = Path(__file__).parent / 'aux_data' / "aispen_params.nc"
+            wais_path = Path(__file__).parent / "aux_data" / "wais_params.nc"
+            eais_path = Path(__file__).parent / "aux_data" / "eais_params.nc"
+            aispen_path = Path(__file__).parent / "aux_data" / "aispen_params.nc"
             self.wais_model = AntarcticaISMIP6(wais_path)
             self.eais_model = AntarcticaISMIP6(eais_path)
             self.aispen_model = AntarcticaISMIP6(aispen_path)
 
-    
     def get_components(self) -> dict:
         """Get all GMSLR components as a dictionary."""
         components_dict = {}
         # Explicitly check for sub-components alongside active ones
-        export_list = self.active_components + ['gmslr', 'wais', 'eais', 'aispen']
-        
+        export_list = self.active_components + ["gmslr", "wais", "eais", "aispen"]
+
         for comp in export_list:
             if hasattr(self, comp):
                 components_dict[comp] = getattr(self, comp)
@@ -218,17 +234,17 @@ def list_components(self) -> list:
         """List the available SLR components."""
         component_dict = self.get_components()
         print(list(component_dict.keys()))
-    
+
     def save_components(self, output_dir: str, scenario_name: str) -> None:
         """Save all SLR components as .npy files to a directory.
-        
+
         Parameters
         ----------
         output_directory: str
             Directory to save components to.
         scenario_name: str
-            Name of the scenario you've run the emulator for. 
-            
+            Name of the scenario you've run the emulator for.
+
         Returns
         -------
         None
@@ -240,33 +256,79 @@ def save_components(self, output_dir: str, scenario_name: str) -> None:
         # Assumes first dimension is percentile, but can be more general (percentile/ensemble)
         ds = xr.Dataset()
         for name, component in self.get_components().items():
-            xr_dataArray = xr.DataArray(component, dims=["percentile", "time"], 
-                                        coords={"percentile": self.output_percentiles, 
-                                                "time": np.arange(2006, component.shape[1] + 2006)})
+            xr_dataArray = xr.DataArray(
+                component,
+                dims=["percentile", "time"],
+                coords={
+                    "percentile": self.output_percentiles,
+                    "time": np.arange(2006, component.shape[1] + 2006),
+                },
+            )
             xr_dataArray.attrs["units"] = "m"
             ds[name] = xr_dataArray
-        ds.to_netcdf(os.path.join(output_dir,f'{scenario_name}_global.nc'))
+        ds.to_netcdf(os.path.join(output_dir, f"{scenario_name}_global.nc"))
+
+    def check_shapes(
+        self, T_change: np.ndarray, OHC_change: np.ndarray, n_time: int
+    ) -> None:
+        """Check that the input arrays have the correct shape.
+
+        Parameters
+        ----------
+        T_change: np.ndarray
+            Array of surface temperature changes.
+        OHC_change: np.ndarray
+            Array of ocean heat content changes.
+        n_time: int
+            Expected number of time steps.
+
+        Returns
+        -------
+        None
+        """
+        if T_change.ndim == 1:
+            T_change = T_change[np.newaxis, :]
+        if OHC_change.ndim == 1:
+            OHC_change = OHC_change[np.newaxis, :]
+
+        if T_change.shape[1] != n_time:
+            # Split over lines for readability
+            raise ValueError(
+                f"T_change should have shape (realisation, time) with time \
+                dimension of length {n_time}. Got {T_change.shape}."
+            )
+        if OHC_change.shape[1] != n_time:
+            raise ValueError(
+                f"OHC_change should have shape (realisation, time) with time \
+                dimension of length {n_time}. Got {OHC_change.shape}."
+            )
 
     def project(
-            self, 
-            scenario: str, 
-            T_change: np.ndarray, 
-            OHC_change: np.ndarray, 
-            cum_emissions_total: int=None) -> None:
+        self,
+        scenario: str,
+        T_change: np.ndarray,
+        OHC_change: np.ndarray,
+        cum_emissions_total: int = None,
+    ) -> None:
         """Run the emulator to project GMSLR components."""
         self.scenario = scenario
         self.T_change = np.asarray(T_change)
         self.OHC_change = np.asarray(OHC_change)
         self.cum_emissions_total = cum_emissions_total
 
+        # Check input shapes are correct
+        self.check_shapes(self.T_change, self.OHC_change, self.nyr)
+
         if self.input_ensemble:
             self.nt = self.T_change.shape[0]
 
-        T_ens, Exp_ens, T_int_ens, T_int_med = self.calculate_drivers() 
-        fraction = self.rng.random(self.nm * self.nt) # correlation between antsmb and antdyn
-        
+        T_ens, Exp_ens, T_int_ens, T_int_med = self.calculate_drivers()
+        fraction = self.rng.random(
+            self.nm * self.nt
+        )  # correlation between antsmb and antdyn
+
         # Evaluate Inline Components
-        if 'expansion' in self.active_components:
+        if "expansion" in self.active_components:
             self.expansion = np.tile(Exp_ens, (self.nm, 1))
 
         # Evaluate Standard Components
@@ -278,35 +340,46 @@ def project(
         # Handle Random Sampling
         if self.random_sample:
             random_idx = self.rng.integers(low=0, high=self.nm)
-            
-            for comp in self.active_components + ['wais', 'eais', 'aispen']:
+
+            for comp in self.active_components + ["wais", "eais", "aispen"]:
                 if hasattr(self, comp):
                     comp_data = getattr(self, comp)
                     if comp_data.ndim > 1:
                         setattr(self, comp, comp_data[random_idx][None, :])
 
         # Calculate GMSLR
-        components_to_sum = [getattr(self, comp) for comp in self.active_components if hasattr(self, comp)]
+        components_to_sum = [
+            getattr(self, comp)
+            for comp in self.active_components
+            if hasattr(self, comp)
+        ]
         if not components_to_sum:
-            raise RuntimeError("No active components were evaluated. Cannot compute GMSLR.")
+            raise RuntimeError(
+                "No active components were evaluated. Cannot compute GMSLR."
+            )
         self.gmslr = np.sum(components_to_sum, axis=0)
 
         # Output percentiles
         if self.output_percentiles is not None:
-            console.log(f"Sampling {len(self.output_percentiles)} members per component...")
+            console.log(
+                f"Sampling {len(self.output_percentiles)} members per component..."
+            )
             self.gmslr = sample_members_2D(self.gmslr, self.output_percentiles)
-            
-            for comp in self.active_components + ['wais', 'eais', 'aispen']:
+
+            for comp in self.active_components + ["wais", "eais", "aispen"]:
                 if hasattr(self, comp):
-                    sampled_data = sample_members_2D(getattr(self, comp), self.output_percentiles)
+                    sampled_data = sample_members_2D(
+                        getattr(self, comp), self.output_percentiles
+                    )
                     setattr(self, comp, sampled_data)
 
     def _build_task_registry(
-            self, 
-            T_int_med: np.ndarray, 
-            T_int_ens: np.ndarray, 
-            T_ens: np.ndarray, 
-            fraction: np.ndarray) -> dict:
+        self,
+        T_int_med: np.ndarray,
+        T_int_ens: np.ndarray,
+        T_ens: np.ndarray,
+        fraction: np.ndarray,
+    ) -> dict:
         """Helper to map active component names to their functions and arguments."""
         registry = {}
         for comp in self.active_components:
@@ -332,23 +405,34 @@ def _build_task_registry(
         return registry
 
     def run_serial_projections(
-            self, T_int_med: np.ndarray, T_int_ens: np.ndarray, 
-            T_ens: np.ndarray, fraction: np.ndarray) -> None:
-        """Run components of the emulator sequentially."""        
+        self,
+        T_int_med: np.ndarray,
+        T_int_ens: np.ndarray,
+        T_ens: np.ndarray,
+        fraction: np.ndarray,
+    ) -> None:
+        """Run components of the emulator sequentially."""
         registry = self._build_task_registry(T_int_med, T_int_ens, T_ens, fraction)
-        
+
         for comp in self.active_components:
             if comp in registry:
                 func, args = registry[comp]
                 setattr(self, comp, func(*args))
 
         # Handle AR5 greenland combination if both are active
-        if 'greendyn' in self.active_components and 'greensmb' in self.active_components:
+        if (
+            "greendyn" in self.active_components
+            and "greensmb" in self.active_components
+        ):
             self.greenland_ar5 = self.greendyn + self.greensmb
-            
+
     def run_parallel_projections(
-        self, T_int_med: np.ndarray, T_int_ens: np.ndarray, 
-        T_ens: np.ndarray, fraction: np.ndarray) -> None:
+        self,
+        T_int_med: np.ndarray,
+        T_int_ens: np.ndarray,
+        T_ens: np.ndarray,
+        fraction: np.ndarray,
+    ) -> None:
         """Run components of the emulator in parallel."""
         registry = self._build_task_registry(T_int_med, T_int_ens, T_ens, fraction)
 
@@ -358,22 +442,27 @@ def run_parallel_projections(
                 if comp in registry:
                     func, args = registry[comp]
                     futures[executor.submit(func, *args)] = comp
-            
+
             for future in concurrent.futures.as_completed(futures):
                 comp_name = futures[future]
                 try:
-                    setattr(self, comp_name, future.result()) 
+                    setattr(self, comp_name, future.result())
                 except Exception as e:
-                    raise RuntimeError(f"Component '{comp_name}' failed during projection.") from e
+                    raise RuntimeError(
+                        f"Component '{comp_name}' failed during projection."
+                    ) from e
 
         # Handle AR5 greenland combination if both are active
-        if 'greendyn' in self.active_components and 'greensmb' in self.active_components:
+        if (
+            "greendyn" in self.active_components
+            and "greensmb" in self.active_components
+        ):
             self.greenland_ar5 = self.greendyn + self.greensmb
-  
+
     def calculate_drivers(self) -> tuple:
-        """Calculate the drivers of GMSLR: temperature change and 
+        """Calculate the drivers of GMSLR: temperature change and
         thermosteric sea level rise.
-        
+
         Returns
         -------
         T_ens: np.ndarray
@@ -387,16 +476,17 @@ def calculate_drivers(self) -> tuple:
         """
         # Sensitivity of thermosteric SLR to ocean heat content change
         # From Turner et al. (2023)
-        exp_efficiency = self.rng.normal(
-            loc=0.113, scale=0.013, size=self.nt)[:, None] * 1e-24 # m/YJ
+        exp_efficiency = (
+            self.rng.normal(loc=0.113, scale=0.013, size=self.nt)[:, None] * 1e-24
+        )  # m/YJ
 
         if self.input_ensemble:
-            # Check if dimensions are the right way around 
-            if self.T_change.shape[1] != self.nyr: 
+            # Check if dimensions are the right way around
+            if self.T_change.shape[1] != self.nyr:
                 self.T_change = self.T_change.T
-            if self.OHC_change.shape[1] != self.nyr: 
+            if self.OHC_change.shape[1] != self.nyr:
                 self.OHC_change = self.OHC_change.T
-                
+
             T_med = np.percentile(self.T_change, 50, axis=0)
             T_std = np.std(self.T_change, axis=0)
 
@@ -406,22 +496,25 @@ def calculate_drivers(self) -> tuple:
         else:
             if self.T_percentile_95 is not None:
                 T_med = self.T_change
-                therm_med = self.OHC_change * exp_efficiency 
-                
+                therm_med = self.OHC_change * exp_efficiency
+
                 T_std = (self.T_percentile_95 - self.T_change) / 1.645
-                therm_std = (self.OHC_percentile_95 - self.OHC_change) * exp_efficiency / 1.645
-            
+                therm_std = (
+                    (self.OHC_percentile_95 - self.OHC_change) * exp_efficiency / 1.645
+                )
+
             else:
                 raise ValueError(
-                    'If input_ensemble is False, and T_change and OHC_change '
-                    'are not 2D arrays, you must provide a 95th percentile '
-                    'timeseries for T_change and OHC_change. Add this using '
-                    'T_percentile_95 and OHC_percentile_95 keyword arguments.')
-        
+                    "If input_ensemble is False, and T_change and OHC_change "
+                    "are not 2D arrays, you must provide a 95th percentile "
+                    "timeseries for T_change and OHC_change. Add this using "
+                    "T_percentile_95 and OHC_percentile_95 keyword arguments."
+                )
+
         # Time-integral of temperature anomaly
         T_int_med = np.cumsum(T_med)
         T_int_std = np.cumsum(T_std)
-        
+
         # Generate a sample of perfectly correlated timeseries fields of temperature,
         # time-integral temperature and expansion, each of them [realisation,time]
         z = self.rng.standard_normal(self.nt) * self.tcv
@@ -431,7 +524,7 @@ def calculate_drivers(self) -> tuple:
         T_ens = z[:, np.newaxis] * T_std + T_med
         therm_ens = z[:, np.newaxis] * therm_std + therm_med
         T_int_ens = z[:, np.newaxis] * T_int_std + T_int_med
-        return T_ens, therm_ens, T_int_ens, T_int_med  
+        return T_ens, therm_ens, T_int_ens, T_int_med
 
     def project_antarctica_ismip6(self, T_ens: np.ndarray, rng) -> np.ndarray:
         wais_raw = self.wais_model.predict(T_ens.squeeze(), display_progress=False)
@@ -447,16 +540,17 @@ def project_antarctica_ismip6(self, T_ens: np.ndarray, rng) -> np.ndarray:
         return self.wais + self.eais + self.aispen
 
     def project_glacier(
-            self, T_int_med: np.ndarray, T_int_ens: np.ndarray, rng: np.random.Generator) -> np.ndarray:
+        self, T_int_med: np.ndarray, T_int_ens: np.ndarray, rng: np.random.Generator
+    ) -> np.ndarray:
         """Project glacier contribution to GMSLR.
-        
+
         Parameters
         ----------
         T_int_med: np.ndarray
             Time-integral of median temperature anomaly timeseries.
         T_int_ens: np.ndarray
             Ensemble of time-integral temperature anomaly timeseries.
-        
+
         Returns
         -------
         glacier: np.ndarray
@@ -464,55 +558,83 @@ def project_glacier(
         """
         # glaciermip -- False => AR5 parameters, 1 => fit to Hock et al. (2019),
         #   2 => fit to Marzeion et al. (2020)
-        dmzdtref=0.95 # mm yr-1 in Marzeion's CMIP5 ensemble mean for AR5 ref period
-        dmz=dmzdtref*(self.endofhistory-1996)*1e-3 # m from glacier at start wrt AR5 ref period
-        glmass=412.0-96.3 # initial glacier mass, used to set a limit, from Tab 4.2
-        glmass=1e-3*glmass # m SLE
-        
+        dmzdtref = 0.95  # mm yr-1 in Marzeion's CMIP5 ensemble mean for AR5 ref period
+        dmz = (
+            dmzdtref * (self.endofhistory - 1996) * 1e-3
+        )  # m from glacier at start wrt AR5 ref period
+        glmass = 412.0 - 96.3  # initial glacier mass, used to set a limit, from Tab 4.2
+        glmass = 1e-3 * glmass  # m SLE
+
         glacier = np.full((self.nm, self.nt, self.nyr), np.nan)
 
         if self.glaciermip:
-            if self.glaciermip==1:
-                glparm=[dict(name='SLA2012',factor=3.39,exponent=0.722,cvgl=0.15),
-                    dict(name='MAR2012',factor=4.35,exponent=0.658,cvgl=0.13),
-                    dict(name='GIE2013',factor=3.57,exponent=0.665,cvgl=0.13),
-                    dict(name='RAD2014',factor=6.21,exponent=0.648,cvgl=0.17),
-                    dict(name='GloGEM',factor=2.88,exponent=0.753,cvgl=0.13)]
-                cvgl=0.15 # unnecessary default
-            elif self.glaciermip==2:
-                glparm=[dict(name='GLIMB',factor=3.70,exponent=0.662,cvgl=0.206),
-                    dict(name='GloGEM',factor=4.08,exponent=0.716,cvgl=0.161),
-                    dict(name='JULES',factor=5.50,exponent=0.564,cvgl=0.188),
-                    dict(name='Mar-12',factor=4.89,exponent=0.651,cvgl=0.141),
-                    dict(name='OGGM',factor=4.26,exponent=0.715,cvgl=0.164),
-                    dict(name='RAD2014',factor=5.18,exponent=0.709,cvgl=0.135),
-                    dict(name='WAL2001',factor=2.66,exponent=0.730,cvgl=0.206)]
-                cvgl=0.20 # unnecessary default
-            elif self.glaciermip==3:
-                glparm=[dict(name='GLIMB',factor=3.70,exponent=0.662,cvgl=0.206),
-                    dict(name='GloGEMflow',factor=5.50,exponent=0.564,cvgl=0.188),
-                    dict(name='OGGMv1.6',factor=2.66,exponent=0.730,cvgl=0.206),
-                    dict(name='PyGEM-OGGMv1.3', factor=2.66, exponent=0.730, cvgl=0.206)]
-            else: 
-                raise KeyError('glaciermip must be 1 or 2')
+            if self.glaciermip == 1:
+                glparm = [
+                    dict(name="SLA2012", factor=3.39, exponent=0.722, cvgl=0.15),
+                    dict(name="MAR2012", factor=4.35, exponent=0.658, cvgl=0.13),
+                    dict(name="GIE2013", factor=3.57, exponent=0.665, cvgl=0.13),
+                    dict(name="RAD2014", factor=6.21, exponent=0.648, cvgl=0.17),
+                    dict(name="GloGEM", factor=2.88, exponent=0.753, cvgl=0.13),
+                ]
+                cvgl = 0.15  # unnecessary default
+            elif self.glaciermip == 2:
+                glparm = [
+                    dict(name="GLIMB", factor=3.70, exponent=0.662, cvgl=0.206),
+                    dict(name="GloGEM", factor=4.08, exponent=0.716, cvgl=0.161),
+                    dict(name="JULES", factor=5.50, exponent=0.564, cvgl=0.188),
+                    dict(name="Mar-12", factor=4.89, exponent=0.651, cvgl=0.141),
+                    dict(name="OGGM", factor=4.26, exponent=0.715, cvgl=0.164),
+                    dict(name="RAD2014", factor=5.18, exponent=0.709, cvgl=0.135),
+                    dict(name="WAL2001", factor=2.66, exponent=0.730, cvgl=0.206),
+                ]
+                cvgl = 0.20  # unnecessary default
+            elif self.glaciermip == 3:
+                glparm = [
+                    dict(name="GLIMB", factor=3.70, exponent=0.662, cvgl=0.206),
+                    dict(name="GloGEMflow", factor=5.50, exponent=0.564, cvgl=0.188),
+                    dict(name="OGGMv1.6", factor=2.66, exponent=0.730, cvgl=0.206),
+                    dict(
+                        name="PyGEM-OGGMv1.3", factor=2.66, exponent=0.730, cvgl=0.206
+                    ),
+                ]
+            else:
+                raise KeyError("glaciermip must be 1 or 2")
         else:
-            glparm=[dict(name='Marzeion',factor=4.96,exponent=0.685),\
-            dict(name='Radic',factor=5.45,exponent=0.676),\
-            dict(name='Slangen',factor=3.44,exponent=0.742),\
-            dict(name='Giesen',factor=3.02,exponent=0.733)]
-            cvgl=0.20 # random methodological error
-            
-        ngl=len(glparm) # number of glacier methods
-            
+            glparm = [
+                dict(name="Marzeion", factor=4.96, exponent=0.685),
+                dict(name="Radic", factor=5.45, exponent=0.676),
+                dict(name="Slangen", factor=3.44, exponent=0.742),
+                dict(name="Giesen", factor=3.02, exponent=0.733),
+            ]
+            cvgl = 0.20  # random methodological error
+
+        ngl = len(glparm)  # number of glacier methods
+
         r_per_model = self.nm // ngl
         r_remainder = self.nm % ngl
         r = self.rng.standard_normal(self.nm)
         r = r[:, np.newaxis, np.newaxis]
-        
+
         # Precompute mgl and zgl for all glacier methods
-        mgl_all = np.array([self._project_glacier1(T_int_med, glparm[igl]['factor'], glparm[igl]['exponent']) for igl in range(ngl)])
-        zgl_all = np.array([self._project_glacier1(T_int_ens, glparm[igl]['factor'], glparm[igl]['exponent']) for igl in range(ngl)])
-        cvgl_all = np.array([glparm[igl]['cvgl'] if self.glaciermip else cvgl for igl in range(ngl)])
+        mgl_all = np.array(
+            [
+                self._project_glacier1(
+                    T_int_med, glparm[igl]["factor"], glparm[igl]["exponent"]
+                )
+                for igl in range(ngl)
+            ]
+        )
+        zgl_all = np.array(
+            [
+                self._project_glacier1(
+                    T_int_ens, glparm[igl]["factor"], glparm[igl]["exponent"]
+                )
+                for igl in range(ngl)
+            ]
+        )
+        cvgl_all = np.array(
+            [glparm[igl]["cvgl"] if self.glaciermip else cvgl for igl in range(ngl)]
+        )
 
         # Make an ensemble of projections for each method
         current_ensemble_idx = 0
@@ -520,8 +642,10 @@ def project_glacier(
             mgl = mgl_all[igl]
             zgl = zgl_all[igl]
             cvgl = cvgl_all[igl]
-            
-            num_reals_for_model_i = r_per_model + 1 if igl < r_remainder else r_per_model
+
+            num_reals_for_model_i = (
+                r_per_model + 1 if igl < r_remainder else r_per_model
+            )
             ifirst = current_ensemble_idx
             ilast = current_ensemble_idx + num_reals_for_model_i
 
@@ -530,15 +654,15 @@ def project_glacier(
 
         glacier += dmz
         np.clip(glacier, None, glmass, out=glacier)
-        
-        glacier = glacier.reshape(glacier.shape[0]*glacier.shape[1], glacier.shape[2])
+
+        glacier = glacier.reshape(glacier.shape[0] * glacier.shape[1], glacier.shape[2])
         return glacier
 
     def _project_glacier1(
-            self, T_int: np.ndarray, factor: float, 
-            exponent: float) -> np.ndarray:
+        self, T_int: np.ndarray, factor: float, exponent: float
+    ) -> np.ndarray:
         """Project glacier contribution by one glacier method.
-        
+
         Parameters
         ----------
         T_int: np.ndarray
@@ -547,41 +671,43 @@ def _project_glacier1(
             Factor for the glacier method.
         exponent: float
             Exponent for the glacier method.
-            
+
         Returns
         -------
         np.ndarray
             Projection of glacier contribution.
         """
-        scale=1e-3 # mm to m
-        return scale * factor * (np.where(T_int<0, 0, T_int)**exponent)
+        scale = 1e-3  # mm to m
+        return scale * factor * (np.where(T_int < 0, 0, T_int) ** exponent)
 
-    def project_greensmb_AR5(self, T_ens: np.ndarray, rng: np.random.Generator) -> np.ndarray:
+    def project_greensmb_AR5(
+        self, T_ens: np.ndarray, rng: np.random.Generator
+    ) -> np.ndarray:
         """Project Greenland SMB contribution to GMSLR.
-        
+
         Parameters
         ----------
         T_ens: np.ndarray
             Ensemble of temperature anomaly timeseries.
-            
+
         Returns
         -------
         greensmb: np.ndarray
             Greenland SMB contribution to GMSLR.
-        
+
         """
-        dtgreen = -0.146 # Delta_T of Greenland ref period wrt AR5 ref period  
-        fnlogsd = 0.4 # random methodological error of the log factor
-        febound = [1, 1.15] # bounds of uniform pdf of SMB elevation feedback factor
+        dtgreen = -0.146  # Delta_T of Greenland ref period wrt AR5 ref period
+        fnlogsd = 0.4  # random methodological error of the log factor
+        febound = [1, 1.15]  # bounds of uniform pdf of SMB elevation feedback factor
 
         # random log-normal factor
         fn = np.exp(self.rng.standard_normal(self.nm) * fnlogsd)
         # elevation feedback factor
         fe = self.rng.random(self.nm) * (febound[1] - febound[0]) + febound[0]
         ff = fn * fe
-        
+
         ztgreen = T_ens - dtgreen
-        
+
         greensmb = ff[:, np.newaxis, np.newaxis] * self._fettweis(ztgreen)
 
         if self.palmer_method and self.end_yr > self.endofAR5:
@@ -591,59 +717,70 @@ def project_greensmb_AR5(self, T_ens: np.ndarray, rng: np.random.Generator) -> n
 
         greensmb += (1 - self.fgreendyn) * self.dgreen
 
-        greensmb = greensmb.reshape(greensmb.shape[0]*greensmb.shape[1], greensmb.shape[2])
+        greensmb = greensmb.reshape(
+            greensmb.shape[0] * greensmb.shape[1], greensmb.shape[2]
+        )
         return greensmb
 
     def _fettweis(self, ztgreen: np.ndarray) -> np.ndarray:
-        """Calculate Greenland SMB in m yr-1 SLE from global mean temperature 
+        """Calculate Greenland SMB in m yr-1 SLE from global mean temperature
         anomaly, using Eq 2 of Fettweis et al. (2013).
-        
+
         Parameters
         ----------
         ztgreen: np.ndarray
             Global mean temperature anomaly.
-            
+
         Returns
         -------
         np.ndarray
             Greenland SMB in m yr-1 SLE.
         """
-        return (71.5*ztgreen+20.4*(ztgreen**2)+2.8*(ztgreen**3))*self.mSLEoGt
+        return (
+            71.5 * ztgreen + 20.4 * (ztgreen**2) + 2.8 * (ztgreen**3)
+        ) * self.mSLEoGt
 
     def project_antsmb(
-            self, T_int_ens: np.ndarray, rng: np.random.Generator, fraction: np.ndarray=None) -> np.ndarray:
+        self,
+        T_int_ens: np.ndarray,
+        rng: np.random.Generator,
+        fraction: np.ndarray = None,
+    ) -> np.ndarray:
         """Project Antarctic SMB contribution to GMSLR.
-        
+
         Parameters
         ----------
         T_int_ens: np.ndarray
             Ensemble of time-integral temperature anomaly timeseries.
         fraction: np.ndarray
             Random numbers for the SMB-dynamic feedback.
-            
+
         Returns
         -------
         antsmb: np.ndarray
             Antarctic SMB contribution to GMSLR.
         """
         # The following are [mean,SD]
-        pcoK=[5.1,1.5] # % change in Ant SMB per K of warming from G&H06
-        KoKg=[1.1,0.2] # ratio of Antarctic warming to global warming from G&H06
+        pcoK = [5.1, 1.5]  # % change in Ant SMB per K of warming from G&H06
+        KoKg = [1.1, 0.2]  # ratio of Antarctic warming to global warming from G&H06
 
         # Generate a distribution of products of the above two factors
-        pcoKg = (pcoK[0] + self.rng.standard_normal([self.nm, self.nt]) * pcoK[1]) * \
-            (KoKg[0] + self.rng.standard_normal([self.nm, self.nt]) * KoKg[1])
-        meansmb = 1923 # model-mean time-mean 1979-2010 Gt yr-1 from 13.3.3.2
-        moaoKg = -pcoKg * 1e-2 * meansmb * self.mSLEoGt # m yr-1 of SLE per K of global warming
+        pcoKg = (pcoK[0] + self.rng.standard_normal([self.nm, self.nt]) * pcoK[1]) * (
+            KoKg[0] + self.rng.standard_normal([self.nm, self.nt]) * KoKg[1]
+        )
+        meansmb = 1923  # model-mean time-mean 1979-2010 Gt yr-1 from 13.3.3.2
+        moaoKg = (
+            -pcoKg * 1e-2 * meansmb * self.mSLEoGt
+        )  # m yr-1 of SLE per K of global warming
 
         if fraction is None:
             fraction = self.rng.random((self.nm, self.nt))
         elif fraction.size != self.nm * self.nt:
-            raise ValueError('fraction is the wrong size')
+            raise ValueError("fraction is the wrong size")
         else:
             fraction = fraction.reshape((self.nm, self.nt))
 
-        smax = 0.35 # max value of S in 13.SM.1.5
+        smax = 0.35  # max value of S in 13.SM.1.5
         ainterfactor = 1 - fraction * smax
 
         z = moaoKg * ainterfactor
@@ -652,24 +789,26 @@ def project_antsmb(
         antsmb = antsmb.reshape(antsmb.shape[0] * antsmb.shape[1], antsmb.shape[2])
         return antsmb
 
-    def project_greenland_AR6(self, T_ens: np.ndarray, rng: np.random.Generator) -> np.ndarray:
+    def project_greenland_AR6(
+        self, T_ens: np.ndarray, rng: np.random.Generator
+    ) -> np.ndarray:
         """Project Greenland ice-sheet contribution to GMSLR.
         This follows the IPCC AR6 methodology as closely as possible.
         Projections are relative to 1996-2014 baseline.
-        
+
         Returns
         -------
         np.ndarray
             Total GIS contribution to GMSLR.
         """
         df = load_greenland_calibration()
-        b0 = df['b0'].values[None, :, None]
-        b1 = df['b1'].values[None, :, None]
-        b2 = df['b2'].values[None, :, None]
-        b3 = df['b3'].values[None, :, None]
-        b4 = df['b4'].values[None, :, None]
-        b5 = df['b5'].values[None, :, None]
-        sigma = df['sigma'].values
+        b0 = df["b0"].values[None, :, None]
+        b1 = df["b1"].values[None, :, None]
+        b2 = df["b2"].values[None, :, None]
+        b3 = df["b3"].values[None, :, None]
+        b4 = df["b4"].values[None, :, None]
+        b5 = df["b5"].values[None, :, None]
+        sigma = df["sigma"].values
         time_delta = np.arange(self.nyr)
 
         # GIS trend values taken from FACTS GitHub repo
@@ -678,21 +817,21 @@ def project_greenland_AR6(self, T_ens: np.ndarray, rng: np.random.Generator) ->
 
         # Calculate trend contribution distribution
         trend = truncnorm.ppf(
-            self.rng.random(self.nm), 
-            a = 0.0,
-            b = 99999.9,
-            loc = trend_mean,
-            scale = trend_std
+            self.rng.random(self.nm), a=0.0, b=99999.9, loc=trend_mean, scale=trend_std
         )
         trend = trend[:, None] * time_delta[None, :]
-        trend = trend[:, None, :] 
-        trend /= 1e3 # convert mm to m SLE
+        trend = trend[:, None, :]
+        trend /= 1e3  # convert mm to m SLE
 
         # Calculate GIS contribution rate
-        dsle = b0 + (b1 * T_ens[:, None, :]) + (b2 * T_ens[:, None, :]**2) \
-            + (b3 * T_ens[:, None, :]**3) + (b4 * time_delta[None, None, :]) \
-            + (b5 * time_delta[None, None, :]**2)
-
+        dsle = (
+            b0
+            + (b1 * T_ens[:, None, :])
+            + (b2 * T_ens[:, None, :] ** 2)
+            + (b3 * T_ens[:, None, :] ** 3)
+            + (b4 * time_delta[None, None, :])
+            + (b5 * time_delta[None, None, :] ** 2)
+        )
 
         # Now integrate
         sle = np.cumsum(dsle, axis=2)  # mm SLE per K of global warming
@@ -710,9 +849,11 @@ def project_greenland_AR6(self, T_ens: np.ndarray, rng: np.random.Generator) ->
             num_reals_for_model_i = r_per_model + 1 if i < r_remainder else r_per_model
             ifirst = current_ensemble_idx
             ilast = current_ensemble_idx + num_reals_for_model_i
-            model_term = sle[:, i, :] # Shape (nt, nyr)
-            uncertainty_term = model_term * unc[None, i, None] # Shape (num_reals_for_model_i, nt, nyr_param)
-                
+            model_term = sle[:, i, :]  # Shape (nt, nyr)
+            uncertainty_term = (
+                model_term * unc[None, i, None]
+            )  # Shape (num_reals_for_model_i, nt, nyr_param)
+
             sle_ens[ifirst:ilast, :, :] = model_term[None, :, :] + uncertainty_term
             current_ensemble_idx = ilast
 
@@ -720,13 +861,15 @@ def project_greenland_AR6(self, T_ens: np.ndarray, rng: np.random.Generator) ->
 
         # Persist 2100 rate of change
         rate = np.diff(sle_ens, axis=2)[:, :, 94]
-        sle_ens[:, :, 95:] = sle_ens[:, :, 94:95] + (rate[:, :, None] * time_delta[None, None, 1:self.nyr - 94])
+        sle_ens[:, :, 95:] = sle_ens[:, :, 94:95] + (
+            rate[:, :, None] * time_delta[None, None, 1 : self.nyr - 94]
+        )
         sle_ens = sle_ens.reshape((self.nm * self.nt, self.nyr))
         return sle_ens
 
     def project_greendyn_AR5(self, rng: np.random.Generator) -> np.ndarray:
         """Project Greenland rapid ice-sheet dynamics contribution to GMSLR.
-        
+
         Returns
         -------
         np.ndarray
@@ -734,57 +877,66 @@ def project_greendyn_AR5(self, rng: np.random.Generator) -> np.ndarray:
         """
         # For SMB+dyn during 2005-2010 Table 4.6 gives 0.63+-0.17 mm yr-1 (5-95% range)
         # For dyn at 2100 Chapter 13 gives [20,85] mm for rcp85, [14,63] mm otherwise
-        if self.scenario in ['rcp85','ssp585']:
-            finalrange=[0.020,0.085]
+        if self.scenario in ["rcp85", "ssp585"]:
+            finalrange = [0.020, 0.085]
         else:
-            finalrange=[0.014,0.063]
-        return self.time_projection(
-            0.63*self.fgreendyn, 0.17*self.fgreendyn, 
-            finalrange, rng) + self.fgreendyn*self.dgreen
+            finalrange = [0.014, 0.063]
+        return (
+            self.time_projection(
+                0.63 * self.fgreendyn, 0.17 * self.fgreendyn, finalrange, rng
+            )
+            + self.fgreendyn * self.dgreen
+        )
 
-    def project_antdyn(self, rng: np.random.Generator, fraction: np.ndarray=None) -> np.ndarray:
+    def project_antdyn(
+        self, rng: np.random.Generator, fraction: np.ndarray = None
+    ) -> np.ndarray:
         """Project Antarctic rapid ice-sheet dynamics contribution to GMSLR.
-        
+
         Parameters
         ----------
         fraction: np.ndarray
             Random numbers for the dynamic contribution.
-        
+
         Returns
         -------
         np.ndarray
             Antarctic rapid ice-sheet dynamics contribution to GMSLR.
         """
-        # This is a naive solution to calculating the AntDyn contribution 
-        # for any given scenario. Basically linear regressions through existing data 
+        # This is a naive solution to calculating the AntDyn contribution
+        # for any given scenario. Basically linear regressions through existing data
         # to find rough relationship between cumulative emissions and AntDyn contribution.
         if self.cum_emissions_total:
-            upper = (0.000110 * self.cum_emissions_total) + 0.375 # in metres
-            lower = (1.363e-05 * self.cum_emissions_total) +  0.0392 # in metres
+            upper = (0.000110 * self.cum_emissions_total) + 0.375  # in metres
+            lower = (1.363e-05 * self.cum_emissions_total) + 0.0392  # in metres
             final = [lower, upper]
             # print(final)
             # final=[-0.020, 0.185]
         else:
-            lcoeff=dict(rcp26=[-2.881, 0.923, 0.000],\
-            rcp45=[-2.676, 0.850, 0.000],\
-            rcp60=[-2.660, 0.870, 0.000],\
-            rcp85=[-2.399, 0.860, 0.000])
+            lcoeff = dict(
+                rcp26=[-2.881, 0.923, 0.000],
+                rcp45=[-2.676, 0.850, 0.000],
+                rcp60=[-2.660, 0.870, 0.000],
+                rcp85=[-2.399, 0.860, 0.000],
+            )
             lcoeff = lcoeff[self.scenario]
 
-            ascale=norm.ppf(fraction)
-            final=np.exp(lcoeff[2]*ascale**2+lcoeff[1]*ascale+lcoeff[0])
+            ascale = norm.ppf(fraction)
+            final = np.exp(lcoeff[2] * ascale**2 + lcoeff[1] * ascale + lcoeff[0])
             final = final.reshape(self.nm, self.nt)
-            
+
             # final=[-0.020, 0.185]
             # final = [0.06, 0.49] # AR6, SSP2-4.5
 
             # For SMB+dyn during 2005-2010 Table 4.6 gives 0.41+-0.24 mm yr-1 (5-95% range)
             # For dyn at 2100 Chapter 13 gives [-20,185] mm for all scenarios
-        return self.time_projection(0.41, 0.20, final, rng, fraction=fraction) + self.dant
+        return (
+            self.time_projection(0.41, 0.20, final, rng, fraction=fraction) + self.dant
+        )
 
     def project_landwater_ar6(self) -> np.ndarray:
         """Project land water storage contribution to GMSLR.
-        
+
         Returns
         -------
         np.ndarray
@@ -794,23 +946,25 @@ def project_landwater_ar6(self) -> np.ndarray:
         lw_ds = load_landwater_projection()
 
         # Interpolate to annual projections
-        interp_ds = lw_ds.interp(years=np.arange(2005, 2301, 1), method='linear').squeeze()
-        lw = interp_ds['sea_level_change'].values * 1e-3  # mm to m
+        interp_ds = lw_ds.interp(
+            years=np.arange(2005, 2301, 1), method="linear"
+        ).squeeze()
+        lw = interp_ds["sea_level_change"].values * 1e-3  # mm to m
 
         del interp_ds
-        
+
         # Go from shape (20000, 294) to (101000, 294)
         full_repeats = (self.nt * self.nm) // lw.shape[0]
         remainder = (self.nt * self.nm) % lw.shape[0]
         lw = np.vstack([np.tile(lw, (full_repeats, 1)), lw[:remainder]])
         lw = lw.reshape(self.nt * self.nm, lw.shape[1])
-        lw = lw[:, 1:] # Start at 2006, end at 2299
+        lw = lw[:, 1:]  # Start at 2006, end at 2299
         return lw
-  
+
     def project_landwater_ar5(self, rng: np.random.Generator) -> np.ndarray:
-        """Old projection function. Project land water storage 
+        """Old projection function. Project land water storage
         contribution to GMSLR.
-        
+
         Returns
         -------
         np.ndarray
@@ -818,16 +972,22 @@ def project_landwater_ar5(self, rng: np.random.Generator) -> np.ndarray:
         """
         # The rate at start is the one for 1993-2010 from the budget table.
         # The final amount is the mean for 2081-2100.
-        nyr=2100-2081+1 # number of years of the time-mean of the final amount
-        final = [-0.01,0.09] # AR5
+        nyr = 2100 - 2081 + 1  # number of years of the time-mean of the final amount
+        final = [-0.01, 0.09]  # AR5
         # final = [0.01, 0.04] # AR6
-        return self.time_projection(0.38, 0.49-0.38, final, rng, nfinal=nyr)
+        return self.time_projection(0.38, 0.49 - 0.38, final, rng, nfinal=nyr)
 
     def time_projection(
-        self, startratemean: float, startratepm: float, final,
-        rng: np.random.Generator, nfinal: int=1, fraction: np.ndarray=None) -> np.ndarray:
+        self,
+        startratemean: float,
+        startratepm: float,
+        final,
+        rng: np.random.Generator,
+        nfinal: int = 1,
+        fraction: np.ndarray = None,
+    ) -> np.ndarray:
         """Project a quantity which is a quadratic function of time.
-        
+
         Parameters
         ----------
         startratemean: float
@@ -840,7 +1000,7 @@ def time_projection(
             Number of years at the end over which final is a time-mean.
         fraction: np.ndarray
             Random numbers in the range 0 to 1.
-            
+
         Returns
         -------
         np.ndarray
@@ -849,29 +1009,30 @@ def time_projection(
         # Create a field of elapsed time since start in years
         timeendofAR5 = self.endofAR5 - self.endofhistory + 1
         time = np.arange(self.end_yr - self.endofhistory) + 1
-        
+
         if fraction is None:
             fraction = self.rng.random((self.nm, self.nt))
         elif fraction.size != self.nm * self.nt:
-            raise ValueError('fraction is the wrong size')
-        
+            raise ValueError("fraction is the wrong size")
+
         fraction = fraction.reshape(self.nm, self.nt)
 
         # Convert inputs to startrate (m yr-1) and afinal (m), where both are
         # arrays with the size of fraction
-        startrate = (startratemean + \
-            startratepm * np.array([-1,1],dtype=float)) * 1e-3 # convert mm yr-1 to m yr-1
+        startrate = (
+            startratemean + startratepm * np.array([-1, 1], dtype=float)
+        ) * 1e-3  # convert mm yr-1 to m yr-1
         finalisrange = isinstance(final, Sequence)
-        
+
         if finalisrange:
             if len(final) != 2:
-                raise ValueError('final range is the wrong size')
+                raise ValueError("final range is the wrong size")
             afinal = (1 - fraction) * final[0] + fraction * final[1]
         else:
             if final.shape != fraction.shape:
-                raise ValueError('final array is the wrong shape')
+                raise ValueError("final array is the wrong shape")
             afinal = final
-            
+
         startrate = (1 - fraction) * startrate[0] + fraction * startrate[1]
 
         # For terms where the rate increases linearly in time t, we can write GMSLR as
@@ -896,7 +1057,9 @@ def time_projection(
             quadratic[:, :, 95:] = y[:, :, 95:]
 
         quadratic += linear
-        
-        quadratic = quadratic.reshape(quadratic.shape[0]*quadratic.shape[1], quadratic.shape[2])
-        
+
+        quadratic = quadratic.reshape(
+            quadratic.shape[0] * quadratic.shape[1], quadratic.shape[2]
+        )
+
         return quadratic

From 4f29b6d477151a3f44de50862c301cfa1b870425 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Fri, 3 Apr 2026 11:19:18 +0100
Subject: [PATCH 102/276] Fix bugs in probabilistic_wrapper.py

---
 profsea/probabilistic_wrapper.py | 86 ++++++++++++++++++++------------
 1 file changed, 54 insertions(+), 32 deletions(-)

diff --git a/profsea/probabilistic_wrapper.py b/profsea/probabilistic_wrapper.py
index ad6f0c9..d0b13f1 100644
--- a/profsea/probabilistic_wrapper.py
+++ b/profsea/probabilistic_wrapper.py
@@ -10,12 +10,15 @@
 import numpy as np
 import pandas as pd
 from rich_argparse import RichHelpFormatter
+from rich.console import Console
 from rich.progress import Progress
 import xarray as xr
 
 from profsea.emulator import Global
 from profsea.utils import sample_members_2D
 
+console = Console()
+
 worker_tas = None
 worker_ohc = None
 worker_emissions = None
@@ -133,7 +136,12 @@ def load_fair_forcing(
     return tas.values, ohc.values
 
 
-def run_fair(baseline_start: int, baseline_end: int, scenarios: list) -> tuple[np.ndarray]:
+def run_fair(
+        baseline_start: int, 
+        baseline_end: int, 
+        scenarios: list, 
+        args: argparse.Namespace
+    ) -> tuple[np.ndarray]:
     f = FAIR()
     f.define_time(1750, 2300, 1)
     f.define_scenarios(scenarios)
@@ -144,7 +152,14 @@ def run_fair(baseline_start: int, baseline_end: int, scenarios: list) -> tuple[n
     f.define_configs(df_configs.index)
     f.allocate()
 
-    f.fill_from_rcmip()
+    if args.emissions_file:
+        f.fill_from_csv(
+            emissions_file=args.emissions_file,
+            forcing_file=args.forcing_file
+        )
+    else:
+        f.fill_from_rcmip()
+
     f.fill_species_configs('../data/fair/fair-parameters/species_configs_properties_1.4.1.csv')
     f.override_defaults('../data/fair/fair-parameters/calibrated_constrained_parameters_1.4.1.csv')
     initialise(f.concentration, f.species_configs["baseline_concentration"])
@@ -162,6 +177,9 @@ def run_fair(baseline_start: int, baseline_end: int, scenarios: list) -> tuple[n
 
     tas = f.temperature.loc[dict(layer=0, timebounds=np.arange(2006, 2301))] - tas_baseline  # shape (294, 7, 841)
     ohc = f.ocean_heat_content_change.loc[dict(timebounds=np.arange(2006, 2301))] - ohc_baseline
+
+    tas = tas.sel(scenario=scenarios).transpose("scenario", "config", "timebounds")
+    ohc = ohc.sel(scenario=scenarios).transpose("scenario", "config", "timebounds")
     return tas.values, ohc.values
 
 
@@ -197,14 +215,13 @@ def plot_samples(tas: np.ndarray, ohc: np.ndarray) -> None:
     ax.plot(tas.T, color='seagreen', alpha=0.05)
     ax.set_xlabel("Simulation years")
     ax.set_ylabel("GMST ($\degree$C)")
+    ax.plot(np.arange(tas.shape[1]), np.median(tas, axis=0), color="black")
 
     ax = fig.add_subplot(122)
     ax.plot(ohc.T, color='seagreen', alpha=0.05)
     ax.set_xlabel("Simulation years")
     ax.set_ylabel("OHC (J)")
 
-    ax.plot(np.arange(tas.shape[1]), np.median(tas, axis=0), color="black")
-
     fig.savefig("forcing.png", dpi=200)
     plt.show()
     plt.close()
@@ -263,42 +280,35 @@ def save_to_netcdf(components: dict, filename: str) -> None:
     # Save with compression
     encoding = {var: {"zlib": True, "complevel": 5} for var in data_vars}
     ds.to_netcdf(filename, encoding=encoding)
-    print(f"Successfully saved full ensemble to {filename}")
+    console.log(f"Successfully saved full ensemble to {filename}")
 
 
 def plot_component(
         ax: plt.Axes, component_dict: dict, component: str, 
         scenarios: list, plot_legend: bool=False) -> None:
     time = np.arange(2006, 2301)
-    # ssp_colours = {
-    #     "ssp119": tuple(np.array([0, 173, 207]) / 255),
-    #     "ssp126": tuple(np.array([23, 60, 102]) / 255),
-    #     "ssp245": tuple(np.array([247, 148, 32]) / 255),
-    #     "ssp370": tuple(np.array([231, 29, 37]) / 255),
-    #     "ssp534-over": tuple(np.array([0, 79, 0]) / 255),
-    #     "ssp585": tuple(np.array([149, 27, 30]) / 255)
-    # }
-    ssp_colours = {
-        "RESCUE-Tier1-Extension-CB1700_2110_2.0": tuple(np.array([0, 173, 207]) / 255),
-        "ssp126": tuple(np.array([23, 60, 102]) / 255),
-        "RESCUE-Tier1-Extension-CB1700_2150_1.5": tuple(np.array([247, 148, 32]) / 255),
-        "ssp370": tuple(np.array([231, 29, 37]) / 255),
-        "RESCUE-Tier1-Extension-CB500st_2110_1.5": tuple(np.array([0, 79, 0]) / 255),
-        "ssp585": tuple(np.array([149, 27, 30]) / 255)
+    scenario_colors = {
+        scenarios[0]: '#800000',
+        scenarios[1]: '#ff0000',
+        scenarios[2]: '#fc7b03',
+        scenarios[3]: '#d3a640',
+        scenarios[4]: '#098740',
+        scenarios[5]: '#0080d0',
+        scenarios[6]: '#100060',
     }
     for scenario in reversed(scenarios):
         ax.fill_between(
             time, 
             component_dict[scenario][component][1], 
             component_dict[scenario][component][3],  
-            color=ssp_colours[scenario], 
+            color=scenario_colors[scenario], 
             edgecolor='none',
             alpha=0.3)
         ax.plot(
             time, 
             component_dict[scenario][component][2], 
             label=f"{scenario}", 
-            color=ssp_colours[scenario])
+            color=scenario_colors[scenario])
 
     ax.set_xlabel("Year")
     ax.set_ylabel("SLE (m)")
@@ -311,14 +321,20 @@ def main(args):
     # Set up for main loop
     percentiles = [0, 5, 17, 50, 83, 95, 100]
     # scenarios = ["ssp119", "ssp126", "ssp245", "ssp370", "ssp534-over", "ssp585"]
-    scenarios = [
-        'RESCUE-Tier1-Extension-CB1700_2110_2.0',
-        'RESCUE-Tier1-Extension-CB1700_2150_1.5',
-        'RESCUE-Tier1-Extension-CB500st_2110_1.5']
+    # scenarios = [
+    #     'RESCUE-Tier1-Extension-CB1700_2110_2.0',
+    #     'RESCUE-Tier1-Extension-CB1700_2150_1.5',
+    #     'RESCUE-Tier1-Extension-CB500st_2110_1.5']
+    if args.emissions_file:
+        scen_df = pd.read_csv(args.emissions_file)
+        scenarios = scen_df["scenario"].unique().tolist()
+
+    console.log(f"Using scenarios: {scenarios}")
 
-    emissions_path = "/Users/gregorymunday/Documents/Papers/ProFSea/cmip7-slr/data/cumulative_cmip6_emissions.json"
-    with open(emissions_path) as f:
-        cumulative_emissions = json.load(f)
+    if "ssp" in scenarios[0].lower():
+        emissions_path = args.cumulative_emissions_file
+        with open(emissions_path) as f:
+            cumulative_emissions = json.load(f)
 
     baseline_start = 1995
     baseline_end = 2014 # inclusive
@@ -338,7 +354,7 @@ def main(args):
     elif args.input.lower() == "fair":
         tas, ohc = load_fair_forcing(args.input_path, scenarios, baseline_start, baseline_end)
     elif args.input.lower() == "run_fair":
-        tas, ohc = run_fair(baseline_start, baseline_end, scenarios)
+        tas, ohc = run_fair(baseline_start, baseline_end, scenarios, args)
     else:
         raise ValueError("Input source not recognised.")
 
@@ -388,7 +404,8 @@ def main(args):
         sampled_components[scenario] = process_global_ensemble(
             components[scenario], percentiles, scenario)
 
-    save_to_netcdf(sampled_components, "probabilistic_projections/global/gmslr_projections_RESCUE_test.nc")
+    output_dir = Path(args.output_dir) / args.output_filename
+    save_to_netcdf(sampled_components, output_dir)
     
     fig = plt.figure(figsize=(16, 8), layout="constrained")
     ax = fig.add_subplot(231)
@@ -404,7 +421,7 @@ def main(args):
     ax = fig.add_subplot(236)
     plot_component(ax, sampled_components, "landwater", scenarios)
 
-    fig.savefig("probabilistic_components_ssps.png", dpi=300)
+    fig.savefig(f"{args.output_dir}{args.output_filename.replace('.nc', '_components.png')}", dpi=300)
     plt.show()
 
 
@@ -412,4 +429,9 @@ def main(args):
     p = argparse.ArgumentParser(formatter_class=RichHelpFormatter)
     p.add_argument("--input", default="run_fair", required=False, help="Input climate forcing source (default: FaIR)", type=str)
     p.add_argument("--input_path", required=False, help="Path to input climate forcing", type=str)
+    p.add_argument("--emissions_file", required=False, help="Path to CSV file containing emissions scenarios (optional)", type=str)
+    p.add_argument("--forcing_file", required=False, help="Path to CSV file containing climate forcing time series (optional)", type=str)
+    p.add_argument("--output_dir", default="", help="Directory to save outputs (default: current directory)", type=str)
+    p.add_argument("--output_filename", default="gmslr_projections.nc", help="Filename for output NetCDF (default: gmslr_projections.nc)", type=str)
+    p.add_argument("--cumulative_emissions_file", default="cumulative_cmip6_emissions.json", help="Path to JSON file containing cumulative emissions for SSP scenarios (default: cumulative_cmip6_emissions.json)", type=str)
     main(p.parse_args())
\ No newline at end of file

From 763db367abec7debdad4a6db1980cb9deec0d4f4 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Fri, 3 Apr 2026 11:26:01 +0100
Subject: [PATCH 103/276] Remove unnecessary notebooks

---
 profsea/notebooks/ais_fitting.ipynb    | 1020 ------------------------
 profsea/notebooks/check_patterns.ipynb |  308 -------
 profsea/notebooks/compare_ais.ipynb    |  191 -----
 profsea/notebooks/evaluate_gmslr.ipynb |  343 --------
 4 files changed, 1862 deletions(-)
 delete mode 100644 profsea/notebooks/ais_fitting.ipynb
 delete mode 100644 profsea/notebooks/check_patterns.ipynb
 delete mode 100644 profsea/notebooks/compare_ais.ipynb
 delete mode 100644 profsea/notebooks/evaluate_gmslr.ipynb

diff --git a/profsea/notebooks/ais_fitting.ipynb b/profsea/notebooks/ais_fitting.ipynb
deleted file mode 100644
index 9294c55..0000000
--- a/profsea/notebooks/ais_fitting.ipynb
+++ /dev/null
@@ -1,1020 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "03acb605",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import glob \n",
-    "from rich.progress import track\n",
-    "\n",
-    "import h5py\n",
-    "import numpy as np\n",
-    "import xarray as xr\n",
-    "import matplotlib.pyplot as plt\n",
-    "from scipy.optimize import least_squares\n",
-    "from scipy.signal import fftconvolve"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2b26565c",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/gregorymunday/anaconda3/envs/cmip7/lib/python3.11/site-packages/xarray/conventions.py:289: SerializationWarning: variable 'tas' has multiple fill values {1e+20, 1e+20} defined, decoding all values to NaN.\n",
-      "  var = coder.decode(var, name=name)\n",
-      "/Users/gregorymunday/anaconda3/envs/cmip7/lib/python3.11/site-packages/xarray/conventions.py:289: SerializationWarning: variable 'tas' has multiple fill values {1e+20, 1e+20} defined, decoding all values to NaN.\n",
-      "  var = coder.decode(var, name=name)\n",
-      "/Users/gregorymunday/anaconda3/envs/cmip7/lib/python3.11/site-packages/xarray/conventions.py:289: SerializationWarning: variable 'tas' has multiple fill values {1e+20, 1e+20} defined, decoding all values to NaN.\n",
-      "  var = coder.decode(var, name=name)\n",
-      "/Users/gregorymunday/anaconda3/envs/cmip7/lib/python3.11/site-packages/xarray/conventions.py:289: SerializationWarning: variable 'tas' has multiple fill values {1e+20, 1e+20} defined, decoding all values to NaN.\n",
-      "  var = coder.decode(var, name=name)\n",
-      "/Users/gregorymunday/anaconda3/envs/cmip7/lib/python3.11/site-packages/xarray/conventions.py:289: SerializationWarning: variable 'tas' has multiple fill values {1e+20, 1e+20} defined, decoding all values to NaN.\n",
-      "  var = coder.decode(var, name=name)\n",
-      "/Users/gregorymunday/anaconda3/envs/cmip7/lib/python3.11/site-packages/xarray/conventions.py:289: SerializationWarning: variable 'tas' has multiple fill values {1e+20, 1e+20} defined, decoding all values to NaN.\n",
-      "  var = coder.decode(var, name=name)\n"
-     ]
-    }
-   ],
-   "source": [
-    "def load_global_temperature(path: str) -> np.array:\n",
-    "    ds = xr.open_mfdataset(path).tas\n",
-    "    ds = ds.groupby('time.year').mean('time')\n",
-    "    global_weights = np.cos(np.deg2rad(ds.lat))\n",
-    "    ds = ds.weighted(global_weights).mean(dim=['lat', 'lon']).compute()\n",
-    "    tas = ds.sel(year=slice(2015, 2299)).values - ds.sel(year=slice(1995, 2014)).mean('year').values\n",
-    "    return tas\n",
-    "\n",
-    "def load_stable_temperature(path: str) -> np.array:\n",
-    "    tas = xr.open_mfdataset(path).tas.values\n",
-    "    return tas\n",
-    "\n",
-    "def load_var(path: str, variable: str) -> np.array:\n",
-    "    files = sorted(glob.glob(path))\n",
-    "    anoms = []\n",
-    "    for f in files:\n",
-    "        ds = xr.open_dataset(f, decode_times=False)[f'{variable}']\n",
-    "        ds = ds.sel(time=slice(2015, 2299)).values\n",
-    "        if variable == 'sle':\n",
-    "            ds -= ds[0]\n",
-    "        anoms.append(ds)\n",
-    "    return np.array(anoms)\n",
-    "\n",
-    "def get_model_name(path: str):\n",
-    "    return path.split(\"/\")[-1].split(\"sle_AIS_\")[-1].split(\"_exp\")[0]\n",
-    "\n",
-    "def load_common_models(data_path: str, variable: str, common_models: list) -> np.array:\n",
-    "    exp_path = []\n",
-    "    models_by_experiment = [get_model_name(path) for path in exp_path]\n",
-    "    files = sorted(glob.glob(path))\n",
-    "    anoms = []\n",
-    "    for f in files:\n",
-    "        ds = xr.open_dataset(f, decode_times=False)[f'{variable}']\n",
-    "        ds = ds.sel(time=slice(2015, 2299)).values\n",
-    "        if variable == 'sle':\n",
-    "            ds -= ds[0]\n",
-    "        anoms.append(ds)\n",
-    "    return np.array(anoms)\n",
-    "\n",
-    "# High emissions temperatures\n",
-    "ccsm4_tas_85 = load_global_temperature('../data/AIS/forcing/global/CCSM4/*.nc')\n",
-    "cesm2_tas_585 = load_global_temperature('../data/AIS/forcing/global/CESM2-WACCM/*.nc')\n",
-    "ukesm_tas_585 = load_global_temperature('../data/AIS/forcing/global/UKESM1-0-LL/*.nc')\n",
-    "hadgem2_tas_85 = load_global_temperature('../data/AIS/forcing/global/HadGEM2-ES/*.nc')\n",
-    "\n",
-    "# Stabilisation temperatures\n",
-    "ukesm_stable_tas_585 = load_stable_temperature('../data/AIS/forcing/global/UKESM1-0-LL_STABLE/*.nc')\n",
-    "cesm2_stable_tas_585 = load_stable_temperature('../data/AIS/forcing/global/CESM2-WACCM_STABLE/*.nc')\n",
-    "hadgem2_stable_tas_85 = load_stable_temperature('../data/AIS/forcing/global/HadGEM2-ES_STABLE/*.nc')\n",
-    "noresm_stable_tas_85 = load_stable_temperature('../data/AIS/forcing/global/NorESM1-M_STABLE/tas_Amon_NorESM1-M_rcp85_stabilised.nc')\n",
-    "\n",
-    "# Low emissions temperatures\n",
-    "ukesm_tas_26 = load_global_temperature('../data/AIS/forcing/global/UKESM1-0-LL_126/*.nc')\n",
-    "noresm_tas_26 = load_stable_temperature('../data/AIS/forcing/global/NorESM1-M_STABLE/tas_Amon_NorESM1-M_rcp26_stabilised.nc')\n",
-    "\n",
-    "# SLE\n",
-    "noresm_sle_26 = load_var('../data/AIS/expAE01/sle/*.nc', 'sle')\n",
-    "ccsm4_sle_85 = load_var('../data/AIS/expAE02/sle/*.nc', 'sle')\n",
-    "hadgem2_sle_85 = load_var('../data/AIS/expAE03/sle/*.nc', 'sle')\n",
-    "cesm2_sle_585 = load_var('../data/AIS/expAE04/sle/*.nc', 'sle')\n",
-    "ukesm_sle_585 = load_var('../data/AIS/expAE05/sle/*.nc', 'sle')\n",
-    "ukesm_stable_sle_585 = load_var('../data/AIS/expAE06/sle/*.nc', 'sle')\n",
-    "noresm_stable_sle_85 = load_var('../data/AIS/expAE07/sle/*.nc', 'sle')\n",
-    "hadgem2_stable_sle_85 = load_var('../data/AIS/expAE08/sle/*.nc', 'sle')\n",
-    "cesm2_stable_sle_585 = load_var('../data/AIS/expAE09/sle/*.nc', 'sle')\n",
-    "ukesm_sle_26 = load_var('../data/AIS/expAE10/sle/*.nc', 'sle')\n",
-    "\n",
-    "model_list = sorted(glob.glob('../data/AIS/expAE04/sle/*.nc'))\n",
-    "model_names = [item.split(\"/\")[-1].split(\"sle_AIS_\")[-1].split(\"_exp\")[0] for item in model_list]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 155,
-   "id": "671ba1e6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Training data\n",
-    "tas_train = np.array([\n",
-    "    cesm2_tas_585, # high\n",
-    "    ccsm4_tas_85, # high\n",
-    "    hadgem2_tas_85, # high\n",
-    "    ukesm_tas_585, # high ])\n",
-    "    noresm_tas_26,\n",
-    "    ukesm_stable_tas_585\n",
-    "    ]) # stable\n",
-    "sle_train = np.array([\n",
-    "    cesm2_sle_585,\n",
-    "    ccsm4_sle_85,\n",
-    "    hadgem2_sle_85, \n",
-    "    ukesm_sle_585,\n",
-    "    noresm_sle_26,\n",
-    "    ukesm_stable_sle_585\n",
-    "    ])\n",
-    "t_int_train = np.array([\n",
-    "    np.cumsum(cesm2_tas_585), \n",
-    "    np.cumsum(ccsm4_tas_85), \n",
-    "    np.cumsum(hadgem2_tas_85),\n",
-    "    np.cumsum(ukesm_tas_585),\n",
-    "    np.cumsum(noresm_tas_26),\n",
-    "    np.cumsum(ukesm_stable_tas_585)\n",
-    "    ])\n",
-    "\n",
-    "# Test data\n",
-    "tas_test = np.array([\n",
-    "    noresm_stable_tas_85, # stable\n",
-    "    hadgem2_stable_tas_85, # stable\n",
-    "    cesm2_stable_tas_585, # stable\n",
-    "    ukesm_tas_26]) # low\n",
-    "\n",
-    "t_int_test = np.array([\n",
-    "    np.cumsum(noresm_stable_tas_85), \n",
-    "    np.cumsum(hadgem2_stable_tas_85), \n",
-    "    np.cumsum(cesm2_stable_tas_585), \n",
-    "    np.cumsum(ukesm_tas_26)])\n",
-    "sle_test = [\n",
-    "    noresm_stable_sle_85,\n",
-    "    hadgem2_stable_sle_85,\n",
-    "    cesm2_stable_sle_585,\n",
-    "    ukesm_sle_26]\n",
-    "\n",
-    "\n",
-    "# # Try to train with all data\n",
-    "# min_models = hadgem2_stable_sle_85.shape[0]\n",
-    "\n",
-    "# tas_train = np.array([\n",
-    "#     cesm2_tas_585[:min], # high\n",
-    "#     ccsm4_tas_85, # high\n",
-    "#     hadgem2_tas_85, # high\n",
-    "#     ukesm_tas_585, # high ])\n",
-    "#     noresm_tas_26,\n",
-    "#     ukesm_stable_tas_585\n",
-    "#     ]) # stable\n",
-    "# sle_train = np.array([\n",
-    "#     cesm2_sle_585,\n",
-    "#     ccsm4_sle_85,\n",
-    "#     hadgem2_sle_85, \n",
-    "#     ukesm_sle_585,\n",
-    "#     noresm_sle_26,\n",
-    "#     ukesm_stable_sle_585\n",
-    "#     ])\n",
-    "# t_int_train = np.array([\n",
-    "#     np.cumsum(cesm2_tas_585), \n",
-    "#     np.cumsum(ccsm4_tas_85), \n",
-    "#     np.cumsum(hadgem2_tas_85),\n",
-    "#     np.cumsum(ukesm_tas_585),\n",
-    "#     np.cumsum(noresm_tas_26),\n",
-    "#     np.cumsum(ukesm_stable_tas_585)\n",
-    "#     ])\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3498a908",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "66d77c7eac0446d8adf28e63d1edb7e5",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Output()"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training with Polynomial Model, Global Tau, and Regularization...\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "
      \n"
-      ],
-      "text/plain": []
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training Complete.\n"
-     ]
-    }
-   ],
-   "source": [
-    "def f_poly(tas, t_int, params):\n",
-    "    \"\"\"\n",
-    "    Polynomial forcing function.\n",
-    "    params[0] (alpha): Linear response to temperature\n",
-    "    params[1] (beta) : Quadratic response (non-linearity)\n",
-    "    params[2] (gamma): Response to integrated temperature\n",
-    "    \"\"\"\n",
-    "    alpha, beta = params\n",
-    "    return (alpha * tas) + (beta * t_int)\n",
-    "\n",
-    "def impulse_response_fitter(f_inst_vals, tau, dt=1):\n",
-    "    \"\"\"Same convolution logic as before.\"\"\"\n",
-    "    n = len(f_inst_vals)\n",
-    "    # Avoid division by zero if tau is tiny\n",
-    "    tau = max(tau, 1e-3)\n",
-    "    decay_factors = np.exp(-np.arange(n) * dt / tau) * (dt / tau)\n",
-    "    \n",
-    "    # Efficient convolution\n",
-    "    # Note: scipy.signal.convolve is usually faster for large n, \n",
-    "    # but keeping your implementation for consistency.\n",
-    "    response = fftconvolve(f_inst_vals, decay_factors, mode=\"full\", axes=-1)\n",
-    "    return response[:n]\n",
-    "\n",
-    "def residuals_global(params_all, tas_flat, t_int_flat, y_flat, splits):\n",
-    "    \"\"\"\n",
-    "    Fit Tau and ABC parameters simultaneously to ALL data for one model.\n",
-    "    params_all: [tau, alpha, beta, gamma]\n",
-    "    \"\"\"\n",
-    "    tau = params_all[0]\n",
-    "    abc = params_all[1:]\n",
-    "    \n",
-    "    # Loop over splits to handle convolution boundaries correctly.\n",
-    "    all_res = []\n",
-    "    start_idx = 0\n",
-    "    for end_idx in splits:\n",
-    "        tas_s = tas_flat[start_idx:end_idx]\n",
-    "        t_int_s = t_int_flat[start_idx:end_idx]\n",
-    "        y_s = y_flat[start_idx:end_idx]\n",
-    "        \n",
-    "        f_vals = f_poly(tas_s, t_int_s, abc)\n",
-    "        y_pred = impulse_response_fitter(f_vals, tau)\n",
-    "        all_res.append(y_pred - y_s)\n",
-    "        start_idx = end_idx\n",
-    "        \n",
-    "    return np.concatenate(all_res)\n",
-    "\n",
-    "def residuals_specific_regularized(params_abc, tau_fixed, tas, t_int, y_obs, \n",
-    "                                   general_abc, lambda_reg):\n",
-    "    \"\"\"\n",
-    "    Fit specific ABC parameters with a fixed Tau and Shrinkage.\n",
-    "    \"\"\"\n",
-    "    # 1. Physics Error\n",
-    "    f_vals = f_poly(tas, t_int, params_abc)\n",
-    "    y_pred = impulse_response_fitter(f_vals, tau_fixed)\n",
-    "    phys_res = y_pred - y_obs\n",
-    "    \n",
-    "    # 2. Regularization Penalty (Shrinkage)\n",
-    "    # Penalize deviation from the general parameters\n",
-    "    # We use sqrt(lambda) because least_squares minimizes sums of squares\n",
-    "    reg_res = np.sqrt(lambda_reg) * (params_abc - general_abc)\n",
-    "    \n",
-    "    return np.concatenate([phys_res, reg_res])\n",
-    "\n",
-    "# --- 2. Setup Containers ---\n",
-    "general_params = []     # Stores [alpha, beta]\n",
-    "general_timescales = [] # Stores [tau]\n",
-    "specific_params = []    # Stores [model, scenario, param]\n",
-    "impulse_mse = []\n",
-    "\n",
-    "# Hyperparameters\n",
-    "lambda_reg = 1 # regulariser\n",
-    "tau_bounds = (5, 150.0)  # prescribed maximum response timescale range\n",
-    "\n",
-    "# --- 3. Main Training Loop ---\n",
-    "\n",
-    "print(\"Training with Polynomial Model, Global Tau, and Regularization...\")\n",
-    "n_models = sle_train.shape[1]\n",
-    "for m_idx in track(range(n_models), description=\"Training... \"):\n",
-    "    \n",
-    "    # --- A. Prepare Data for Global Fit ---\n",
-    "    # We need to flatten the data but keep track of where scenarios split\n",
-    "    # so the convolution doesn't \"bleed\" between scenarios.\n",
-    "    tas_list = [tas_train[f_idx] for f_idx in range(tas_train.shape[0])]\n",
-    "    t_int_list = [t_int_train[f_idx] for f_idx in range(tas_train.shape[0])]\n",
-    "    y_list = [sle_train[f_idx, m_idx] for f_idx in range(tas_train.shape[0])]\n",
-    "    \n",
-    "    # Calculate split indices for the global residual function\n",
-    "    lengths = [len(x) for x in tas_list]\n",
-    "    splits = np.cumsum(lengths)\n",
-    "    \n",
-    "    tas_flat = np.concatenate(tas_list)\n",
-    "    t_int_flat = np.concatenate(t_int_list)\n",
-    "    y_flat = np.concatenate(y_list)\n",
-    "    \n",
-    "    # --- B. Fit Global Parameters (Tau + ABC) ---\n",
-    "    # Initial guess: tau=10, a=0.1, b=0, c=0.1\n",
-    "    x0_global = [5.0, 0.1, 0.1] \n",
-    "    \n",
-    "    res_global = least_squares(\n",
-    "        residuals_global,\n",
-    "        x0=x0_global,\n",
-    "        bounds=([tau_bounds[0], 0, 0], \n",
-    "                [tau_bounds[1], 1, 1]),\n",
-    "        args=(tas_flat, t_int_flat, y_flat, splits)\n",
-    "    )\n",
-    "    \n",
-    "    tau_best = res_global.x[0]\n",
-    "    abc_best = res_global.x[1:]\n",
-    "    \n",
-    "    general_timescales.append(tau_best)\n",
-    "    general_params.append(abc_best)\n",
-    "    \n",
-    "    # --- C. Fit Specific Scenarios (Fixed Tau + Shrinkage) ---\n",
-    "    model_scenario_params = []\n",
-    "    for f_idx in range(tas_train.shape[0]):\n",
-    "        \n",
-    "        # We start the optimization at the global parameters.\n",
-    "        res_spec = least_squares(\n",
-    "            residuals_specific_regularized,\n",
-    "            x0=abc_best, \n",
-    "            args=(tau_best, tas_train[f_idx], t_int_train[f_idx], \n",
-    "                  sle_train[f_idx, m_idx], abc_best, lambda_reg)\n",
-    "        )\n",
-    "        model_scenario_params.append(res_spec.x)\n",
-    "        \n",
-    "    specific_params.append(model_scenario_params)\n",
-    "\n",
-    "# --- 4. Format Outputs ---\n",
-    "\n",
-    "general_timescales = np.array(general_timescales)\n",
-    "general_params = np.array(general_params) # Shape (43, 3)\n",
-    "specific_params = np.array(specific_params) # Shape (43, n_scenarios, 3)\n",
-    "param_residuals = specific_params - general_params[:, None, :]\n",
-    "\n",
-    "print(\"Training Complete.\")\n",
-    "\n",
-    "# Remove models with timescales less than X years\n",
-    "timescale_cutoff = 50\n",
-    "temp_timescales = np.where(general_timescales >= timescale_cutoff, general_timescales, 0)\n",
-    "constrained_idx = np.argwhere(temp_timescales).squeeze()\n",
-    "\n",
-    "constr_timescales = general_timescales[constrained_idx]\n",
-    "constr_general_params = general_params[constrained_idx]\n",
-    "constr_specific_params = specific_params[constrained_idx]\n",
-    "constr_param_residuals = param_residuals[constrained_idx]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 165,
-   "id": "77afd548",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(43, 6, 2)"
-      ]
-     },
-     "execution_count": 165,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "param_residuals.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 157,
-   "id": "3e4c8cd4",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       ""
-      ]
-     },
-     "execution_count": 157,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "
      "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.bar(model_names, general_timescales)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 158,
-   "id": "f9006f8b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Save timescales and params into a NetCDF\n",
-    "params = [\"a\", \"b\"]\n",
-    "data_vars = {}\n",
-    "data_vars[\"tau\"] = xr.DataArray(\n",
-    "    data=general_timescales,\n",
-    "    dims=[\"model\"],\n",
-    "    coords={\n",
-    "        \"model\": model_names,\n",
-    "    },\n",
-    "    attrs={\n",
-    "        \"units\": \"years\", \n",
-    "        \"description\": \"Estimated model response timescale.\"\n",
-    "    }\n",
-    ")\n",
-    "data_vars[\"general_params\"] = xr.DataArray(\n",
-    "    data=general_params,\n",
-    "    dims=[\"model\", \"param\"],\n",
-    "    coords={\n",
-    "        \"model\": model_names,\n",
-    "        \"param\": params\n",
-    "    },\n",
-    "    attrs={\n",
-    "        \"units\": \"N/A\", \n",
-    "        \"description\": \"Estimated general model parameters across all training data.\"\n",
-    "    }\n",
-    ")\n",
-    "data_vars[\"param_residuals\"] = xr.DataArray(\n",
-    "    data=param_residuals,\n",
-    "    dims=[\"model\", \"scenario\", \"param\"],\n",
-    "    coords={\n",
-    "        \"model\": model_names,\n",
-    "        \"scenario\": np.arange(param_residuals.shape[1]),\n",
-    "        \"param\": params,\n",
-    "    },\n",
-    "    attrs={\n",
-    "        \"units\": \"N/A\", \n",
-    "        \"description\": \"Estimated model parameter residuals between training scenario-specific parameters and general parameters.\"\n",
-    "    }\n",
-    ")\n",
-    "    \n",
-    "ds = xr.Dataset(data_vars)\n",
-    "ds.attrs = {\n",
-    "    \"title\": \"Antarctic ice-sheet emulator parameters\",\n",
-    "    \"source\": \"Gregory Munday, December 2025\",\n",
-    "}\n",
-    "\n",
-    "# Save with compression\n",
-    "encoding = {var: {\"zlib\": True, \"complevel\": 5} for var in data_vars}\n",
-    "ds.to_netcdf(\"ais_params.nc\", encoding=encoding)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "09752923",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "test_ds = xr.open_dataset(\"ais_params.nc\")\n",
-    "test_ds.close()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 106,
-   "id": "8c273d81",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Generating distributions from specific parameters...\n"
-     ]
-    }
-   ],
-   "source": [
-    "# --- 1. Define Prediction Helpers (Ensure these match your training logic) ---\n",
-    "def f_poly_mixed(tas, t_int, params_gen, params_perturb):\n",
-    "    # params: [alpha, beta, gamma]\n",
-    "    p = params_gen + params_perturb\n",
-    "    # Broadcasting: (N_samples, 1, 3) * (1, Time) -> (N_samples, Time)\n",
-    "    return (p[:, 0, None] * tas) + (p[:, 1, None] * t_int)\n",
-    "\n",
-    "def predict_ais_me_poly(X, general_params, general_taus, mv_dists):\n",
-    "    tas, t_int = X\n",
-    "    n_models = len(general_params)\n",
-    "    n_samples = mv_dists[0].shape[0]\n",
-    "    n_time = len(tas)\n",
-    "    \n",
-    "    \n",
-    "    # Shape: (n_models, n_samples, n_time)\n",
-    "    all_preds = np.zeros((n_models, n_samples, n_time))\n",
-    "    \n",
-    "    for m in track(range(n_models), description=\"Projecting AIS response... \"):\n",
-    "        tau = general_taus[m]\n",
-    "        gen_p = general_params[m]     # [alpha, beta, gamma]\n",
-    "        perturb_p = mv_dists[m]       # [1000, 3]\n",
-    "        \n",
-    "        # Calculate forcing for all samples at once\n",
-    "        f_vals = f_poly_mixed(tas[None, :], t_int[None, :], gen_p[None, :], perturb_p)\n",
-    "        \n",
-    "        # Convolve\n",
-    "        for i in range(n_samples):\n",
-    "            all_preds[m, i, :] = impulse_response_fitter(f_vals[i], tau, dt=1)\n",
-    "            \n",
-    "    return all_preds\n",
-    "\n",
-    "def calc_mv_dists(general_params, param_residuals, n_samples):\n",
-    "    mv_dists = []\n",
-    "    covariances = []\n",
-    "    for m in range(len(general_params)):\n",
-    "    # Calculate residuals: Specific - General\n",
-    "    # specific_params shape is (43, n_scenarios, 3)\n",
-    "        \n",
-    "        # Calculate covariance of these perturbations\n",
-    "        cov = np.cov(param_residuals[m].T)\n",
-    "        \n",
-    "        # Optional: Check for ill-conditioned matrix and add tiny jitter if needed\n",
-    "        cov += np.eye(2) * 1e-8 \n",
-    "        covariances.append(cov)\n",
-    "        \n",
-    "        # Generate samples centered at 0 (we add them to general params later)\n",
-    "\n",
-    "        samples = np.random.multivariate_normal(np.zeros(2), cov, size=n_samples)\n",
-    "\n",
-    "            \n",
-    "        mv_dists.append(samples)\n",
-    "    return mv_dists, np.array(covariances)\n",
-    "\n",
-    "print(\"Generating distributions from specific parameters...\")\n",
-    "n_samples = 1000\n",
-    "mv_dists, covs = calc_mv_dists(general_params, param_residuals, n_samples)\n",
-    "constr_mv_dists, constr_covs = calc_mv_dists(constr_general_params, constr_param_residuals, n_samples)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "1e8095ce",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(33, 2, 2)"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "constr_covs.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 178,
-   "id": "e88eee24",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1b283b0cb5ed44e0b94ba9ed504c9a3d",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Output()"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Running predictions for Test Scenario 1...\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "
      \n"
-      ],
-      "text/plain": []
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "
      "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Select a scenario index to validate\n",
-    "test_idx = 1\n",
-    "\n",
-    "# Handle case where user might not have 'test' vars loaded yet, fallback to train\n",
-    "if 'tas_test' not in locals():\n",
-    "    print(\"Test data not found, using Training data index 0 for visualization.\")\n",
-    "    X_input = (tas_train[test_idx], t_int_train[test_idx])\n",
-    "    y_true_scenario = sle_train[test_idx] \n",
-    "else:\n",
-    "    X_input = (tas_test[test_idx], t_int_test[test_idx])\n",
-    "    y_true_scenario = sle_test[test_idx] \n",
-    "\n",
-    "# X_input contains 1D arrays of shape (Time,)\n",
-    "# y_true_scenario contains array of shape (Model, Time) -> (43, Time)\n",
-    "\n",
-    "# Run Emulator\n",
-    "print(f\"Running predictions for Test Scenario {test_idx}...\")\n",
-    "# preds shape: (43 models, 1000 samples, Time)\n",
-    "preds = ais.predict(tas_test[test_idx], t_int_test[test_idx])\n",
-    "\n",
-    "# --- 4. Visualization ---\n",
-    "\n",
-    "plt.figure(figsize=(12, 6))\n",
-    "\n",
-    "# Option A: Visualize ONE specific model (e.g., Model 0) to check fit quality\n",
-    "model_to_plot = 8\n",
-    "model_preds = preds[model_to_plot] # (1000, Time)\n",
-    "\n",
-    "# Calculate stats\n",
-    "p5 = np.percentile(model_preds, 5, axis=0)\n",
-    "p17 = np.percentile(model_preds, 17, axis=0)\n",
-    "median = np.median(model_preds, axis=0)\n",
-    "p83 = np.percentile(model_preds, 83, axis=0)\n",
-    "p95 = np.percentile(model_preds, 95, axis=0)\n",
-    "\n",
-    "time_axis = np.arange(len(median))\n",
-    "\n",
-    "# Plot Emulator Uncertainty\n",
-    "plt.fill_between(time_axis, p5, p95, color='C0', alpha=0.2, label='5-95% Confidence')\n",
-    "plt.fill_between(time_axis, p17, p83, color='C0', alpha=0.4, label='17-83% Confidence')\n",
-    "plt.plot(time_axis, median, color='C0', lw=2, label='Emulator Median')\n",
-    "\n",
-    "# Plot True Value\n",
-    "# Since y_true_scenario is shape (Model, Time), we slice the specific model\n",
-    "if y_true_scenario.ndim == 2:\n",
-    "    true_curve = y_true_scenario[model_to_plot] \n",
-    "    label_str = f'True Physics Model {model_to_plot}'\n",
-    "else:\n",
-    "    # Fallback if the user passed a 1D array\n",
-    "    true_curve = y_true_scenario\n",
-    "    label_str = 'True Physics Model'\n",
-    "\n",
-    "plt.plot(time_axis, true_curve, 'k--', lw=2, label=label_str)\n",
-    "\n",
-    "plt.title(f\"Emulator vs Truth: Model {model_to_plot}, Scenario {test_idx}\\n(Regularized Polynomial Fit)\")\n",
-    "plt.xlabel(\"Time (years)\")\n",
-    "plt.ylabel(\"Sea Level Equivalent (m)\")\n",
-    "plt.legend()\n",
-    "plt.grid(True, alpha=0.3)\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 159,
-   "id": "7269a632",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "76b2d198914a45d1ac67ce9097a7d7a8",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Output()"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "
      \n"
-      ],
-      "text/plain": []
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "%reload_ext autoreload\n",
-    "from profsea.emulator import Antarctica\n",
-    "ais = Antarctica(\"ais_params.nc\")\n",
-    "test_idx = 3\n",
-    "ais_preds = ais.predict(tas_train[test_idx], t_int_train[test_idx], display_progress=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 166,
-   "id": "80c9bd2d",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Running predictions for Test Scenario 3...\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "
      "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "X_input = (tas_train[test_idx], t_int_train[test_idx])\n",
-    "y_true_scenario = sle_train[test_idx] \n",
-    "\n",
-    "\n",
-    "# Run Emulator\n",
-    "print(f\"Running predictions for Test Scenario {test_idx}...\")\n",
-    "# preds shape: (43 models, 1000 samples, Time)\n",
-    "preds = ais_preds.copy()\n",
-    "# constr_preds = predict_ais_me_poly(X_input, constr_general_params, constr_timescales, constr_mv_dists)\n",
-    "\n",
-    "# Plot all test data against prediction plume\n",
-    "fig = plt.figure(figsize=(12, 6), layout=\"constrained\")\n",
-    "p5 = np.percentile(preds, 5, axis=(0, 1))\n",
-    "p17 = np.percentile(preds, 17, axis=(0, 1))\n",
-    "median = np.median(preds, axis=(0, 1))\n",
-    "p83 = np.percentile(preds, 83, axis=(0, 1))\n",
-    "p95 = np.percentile(preds, 95, axis=(0, 1))\n",
-    "\n",
-    "true_median = np.percentile(y_true_scenario, 50, axis=0)\n",
-    "\n",
-    "time_axis = np.arange(len(median))\n",
-    "\n",
-    "ax = fig.add_subplot(111)\n",
-    "# Plot Emulator Uncertainty\n",
-    "ax.fill_between(time_axis, p5, p95, color='C0', alpha=0.2, label='5-95% Confidence')\n",
-    "ax.fill_between(time_axis, p17, p83, color='C0', alpha=0.4, label='17-83% Confidence')\n",
-    "\n",
-    "\n",
-    "ax.plot(np.tile(time_axis, (y_true_scenario.shape[0], 1)).T, y_true_scenario.T, '--', lw=1, color=\"black\", alpha=0.4, label=\"ISMIP6 models\")\n",
-    "ax.plot(time_axis, true_median, lw=2, color='black', label=\"ISMIP6 median\")\n",
-    "ax.plot(time_axis, median, color='C0', lw=3, label='Emulator Median')\n",
-    "\n",
-    "ax.set_title(f\"Emulator vs ISMIP6 \\nScenario {test_idx}\")\n",
-    "ax.set_xlabel(\"Time (years)\")\n",
-    "ax.set_ylabel(\"Sea Level Equivalent (m)\")\n",
-    "handles, labels = ax.get_legend_handles_labels()\n",
-    "unique_labels = set(labels)\n",
-    "handles = [handles[labels.index(label)] for label in unique_labels if label in labels]\n",
-    "ax.legend(handles, unique_labels, frameon=False, loc='upper left')\n",
-    "\n",
-    "ax.grid(True, alpha=0.3)\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "af37b397",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 179,
-   "id": "2d9df888",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9e6be923cda84256b69e49d827a1c3ce",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Output()"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Running idealised predictions...\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "
      \n"
-      ],
-      "text/plain": []
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "
      "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# --- 1. Helper Functions (copied for self-containment) ---\n",
-    "\n",
-    "def f_poly_mixed(tas, t_int, params_gen, params_perturb):\n",
-    "    # params: [alpha, beta, gamma]\n",
-    "    p = params_gen + params_perturb\n",
-    "    return (p[:, 0, None] * tas) + (p[:, 2, None] * t_int)\n",
-    "\n",
-    "def impulse_response_fitter(f_inst_vals, tau, dt=1):\n",
-    "    n = len(f_inst_vals)\n",
-    "    tau = max(tau, 1e-3)\n",
-    "    decay_factors = np.exp(-np.arange(n) * dt / tau) * (dt / tau)\n",
-    "    response = np.zeros(n)\n",
-    "    for k in range(n):\n",
-    "        response[k] = np.sum(decay_factors[:k+1] * f_inst_vals[k::-1])\n",
-    "    return response\n",
-    "\n",
-    "def predict_ais_me_poly(X, general_params, general_taus, mv_dists):\n",
-    "    tas, t_int = X\n",
-    "    n_models = len(general_params)\n",
-    "    n_samples = mv_dists[0].shape[0]\n",
-    "    n_time = len(tas)\n",
-    "    all_preds = np.zeros((n_models, n_samples, n_time))\n",
-    "    \n",
-    "    for m in range(n_models):\n",
-    "        tau = general_taus[m]\n",
-    "        gen_p = general_params[m]\n",
-    "        perturb_p = mv_dists[m]\n",
-    "        \n",
-    "        f_vals = f_poly_mixed(tas[None, :], t_int[None, :], gen_p[None, :], perturb_p)\n",
-    "        \n",
-    "        for i in range(n_samples):\n",
-    "            all_preds[m, i, :] = impulse_response_fitter(f_vals[i], tau, dt=1)\n",
-    "            \n",
-    "    return all_preds\n",
-    "\n",
-    "# --- 2. Construct Idealised Forcing ---\n",
-    "\n",
-    "n_years = 1000\n",
-    "peak_temp = 7.0  # Degrees warming\n",
-    "ramp_len = 300    # Years to ramp up/down\n",
-    "\n",
-    "# Create time axis\n",
-    "time = np.arange(n_years)\n",
-    "tas_ideal = np.zeros(n_years)\n",
-    "\n",
-    "# Ramp Up (0 to 50 years)\n",
-    "tas_ideal[:ramp_len] = np.linspace(0, peak_temp, ramp_len)\n",
-    "\n",
-    "# Ramp Down (50 to 100 years)\n",
-    "tas_ideal[ramp_len:ramp_len*2] = np.linspace(peak_temp, -5, ramp_len)\n",
-    "tas_ideal[ramp_len*2:] = -5\n",
-    "\n",
-    "# Stabilization (100+ years) -> Remains at 0\n",
-    "# Note: In reality, physics might suggest T approaches equilibrium, but 0 is good for testing.\n",
-    "\n",
-    "# Integrate Temperature\n",
-    "# We assume dt=1 year\n",
-    "t_int_ideal = np.cumsum(tas_ideal)\n",
-    "\n",
-    "# --- 3. Run Prediction ---\n",
-    "\n",
-    "# Check if model variables exist (for standalone running)\n",
-    "if 'general_params' not in locals():\n",
-    "    print(\"Warning: Training data not found. Generating dummy model for demonstration.\")\n",
-    "    general_params = np.array([[0.1, 0.01, 0.05]])  # 1 Model\n",
-    "    general_timescales = np.array([15.0])           # Tau = 15 years\n",
-    "    mv_dists = [np.random.normal(0, 0.01, size=(100, 3))] # Small uncertainty\n",
-    "\n",
-    "print(\"Running idealised predictions...\")\n",
-    "preds = ais.predict(tas_ideal, t_int_ideal)\n",
-    "\n",
-    "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 8), sharex=True)\n",
-    "\n",
-    "# Plot Forcing\n",
-    "ax1.plot(time, tas_ideal, 'r-', lw=2, label='Temperature (TAS)')\n",
-    "ax1.set_ylabel(\"Global Temperature Change (°C)\", color='r')\n",
-    "ax1.tick_params(axis='y', labelcolor='r')\n",
-    "ax1.set_title(\"Idealised Ramp-Up Ramp-Down Experiment\")\n",
-    "ax1.grid(True, alpha=0.3)\n",
-    "\n",
-    "# Add Integrated T to secondary axis to show why melt continues\n",
-    "ax1b = ax1.twinx()\n",
-    "ax1b.plot(time, t_int_ideal, 'k:', alpha=0.5, label='Integrated T')\n",
-    "ax1b.set_ylabel(\"Integrated Temperature (°C·yr)\", color='k')\n",
-    "ax1b.legend(loc='upper left')\n",
-    "\n",
-    "# Plot Response (Aggregate of all models if multiple exist)\n",
-    "# Flatten all models/samples to get the grand ensemble stats\n",
-    "flat_preds = preds.reshape(-1, preds.shape[-1])\n",
-    "p5 = np.percentile(flat_preds, 17, axis=0)\n",
-    "p50 = np.median(flat_preds, axis=0)\n",
-    "p95 = np.percentile(flat_preds, 83, axis=0)\n",
-    "\n",
-    "ax2.fill_between(time, p5, p95, color='C0', alpha=0.3, label='66% Confidence Interval')\n",
-    "ax2.plot(time, p50, color='C0', lw=2, label='Ensemble Median')\n",
-    "\n",
-    "# Highlight the \"Peak Melt\" vs \"Peak Temp\" lag\n",
-    "peak_melt_idx = np.argmax(p50)\n",
-    "lag_years = time[peak_melt_idx] - ramp_len\n",
-    "ax2.axvline(x=ramp_len, color='r', linestyle='--', alpha=0.5, label='Peak Temp')\n",
-    "ax2.axvline(x=time[peak_melt_idx], color='C0', linestyle='--', alpha=0.5, label=f'Peak Rate/Level (Lag: {lag_years} yrs)')\n",
-    "\n",
-    "ax2.set_xlabel(\"Time (years)\")\n",
-    "ax2.set_ylabel(\"Sea Level Equivalent (m)\")\n",
-    "ax2.legend()\n",
-    "ax2.grid(True, alpha=0.3)\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "cmip7",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/profsea/notebooks/check_patterns.ipynb b/profsea/notebooks/check_patterns.ipynb
deleted file mode 100644
index 53e37ea..0000000
--- a/profsea/notebooks/check_patterns.ipynb
+++ /dev/null
@@ -1,308 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "628f460e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import iris\n",
-    "import iris.quickplot as qplt\n",
-    "import xarray as xr\n",
-    "import glob\n",
-    "import cartopy.crs as ccrs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "6a8a8e38",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['../data/cmip6/ACCESS-CM2/zos_regression_ssp245_ACCESS-CM2.npy', '../data/cmip6/ACCESS-ESM1-5/zos_regression_ssp245_ACCESS-ESM1-5.npy', '../data/cmip6/CMCC-CM2-SR5/zos_regression_ssp245_CMCC-CM2-SR5.npy', '../data/cmip6/CNRM-CM6-1-HR/zos_regression_ssp245_CNRM-CM6-1-HR.npy', '../data/cmip6/CNRM-CM6-1/zos_regression_ssp245_CNRM-CM6-1.npy', '../data/cmip6/CNRM-ESM2-1/zos_regression_ssp245_CNRM-ESM2-1.npy', '../data/cmip6/CanESM5-1/zos_regression_ssp245_CanESM5-1.npy', '../data/cmip6/CanESM5-CanOE/zos_regression_ssp245_CanESM5-CanOE.npy', '../data/cmip6/CanESM5/zos_regression_ssp245_CanESM5.npy', '../data/cmip6/EC-Earth3/zos_regression_ssp245_EC-Earth3.npy', '../data/cmip6/GISS-E2-1-G/zos_regression_ssp245_GISS-E2-1-G.npy', '../data/cmip6/GISS-E2-1-H/zos_regression_ssp245_GISS-E2-1-H.npy', '../data/cmip6/GISS-E2-2-G/zos_regression_ssp245_GISS-E2-2-G.npy', '../data/cmip6/INM-CM4-8/zos_regression_ssp245_INM-CM4-8.npy', '../data/cmip6/INM-CM5-0/zos_regression_ssp245_INM-CM5-0.npy', '../data/cmip6/MIROC6/zos_regression_ssp245_MIROC6.npy', '../data/cmip6/MPI-ESM1-2-HR/zos_regression_ssp245_MPI-ESM1-2-HR.npy', '../data/cmip6/MPI-ESM1-2-LR/zos_regression_ssp245_MPI-ESM1-2-LR.npy', '../data/cmip6/MRI-ESM2-0/zos_regression_ssp245_MRI-ESM2-0.npy', '../data/cmip6/NorESM2-LM/zos_regression_ssp245_NorESM2-LM.npy', '../data/cmip6/NorESM2-MM/zos_regression_ssp245_NorESM2-MM.npy', '../data/cmip6/UKESM1-0-LL/zos_regression_ssp245_UKESM1-0-LL.npy']\n"
-     ]
-    }
-   ],
-   "source": [
-    "cmip6_files = sorted(glob.glob('../data/cmip6/*/zos_regression_ssp245_*.npy'))\n",
-    "cmip6_patts = np.zeros((len(cmip6_files), 180, 360))\n",
-    "for i, f in enumerate(cmip6_files):\n",
-    "    cmip6_patts[i] = np.load(f).data\n",
-    "    \n",
-    "cmip5_patt = np.load('../data/runs/cmip7_ensemble/data/zos_regression/zos_regression.npy')[2, 0]\n",
-    "\n",
-    "print(cmip6_files)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "d12c5863",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'../data/cmip6/UKESM1-0-LL/zos_regression_ssp245_UKESM1-0-LL.npy'"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cmip6_files[-1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "4d21ccf1",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       ""
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "
      "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.pcolormesh(cmip6_patts[-1], cmap='magma')\n",
-    "# plt.scatter(26, 30, color='green')\n",
-    "plt.scatter(150, 120, color='green')\n",
-    "# plt.scatter(340, 170, color='green')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "id": "e07d901d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       ""
-      ]
-     },
-     "execution_count": 64,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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uQN++ffGNb3wDV155JdZcc02sv/76mDNnDqZPn4411lijbn+OOeYYtLa2YvTo0RgyZAgWLFiAadOmoV+/fmZV/q1vfQsDBgzApEmTcOGFFyIMQ8ycORPvvPPOUj+PvfbaC9dccw0OO+wwHHvssfjwww9x1VVXpSbj9ddfH1OmTMFFF12E9vZ2HHrooejXrx9ee+01fPDBB7jgggvQu3dv3HDDDRg/fjw++ugjHHjggVh77bXx/vvv409/+hPef//9XMLUKJamnQEDBuDUU0/FtGnT0L9/f+y333549913ccEFF2DIkCHe0ASNYMSIEbj//vtx0003Yeuttwbn3JBiF6NGjUL//v3xve99D1OnTkWlUsEvfvEL/OlPf/KWb2pqwtVXX43PP/8c2267LebOnYuLL74Ye+65J3bccUcAwK677ordd98dZ555Jj799FOMHj0af/7znzF16lRsueWWOOKII6w6Dz30UJx66qk49NBD0dHRkbKNGT16NI499lhMnDgRL7/8Mr7xjW+gra0N7733Hp577jmMGDECxx9/fObzuPDCC7HXXnth9913x8knn4woinDllVeid+/eluSz0XbWWGMNnHvuuZgyZQqOPPJIHHroofjwww9xwQUXoKWlBVOnTs3sy1e/+lUcfvjhuO6661CpVLDLLrvg1VdfxVVXXYW+fftmXldiNcEKNJguUWKZ4p577pGHHXaY3HjjjWXv3r1lpVKR6623njziiCPka6+9lir/m9/8Ru68886yb9++srm5WQ4fPlweeOCBluvzu+++Kw844ADZv39/2adPH7nHHnvIV199VQ4fPryuB8jtt98ud955Zzlo0CDZ1NQkhw4dKg8++GD55z//2Sr3+9//Xo4aNUq2tbXJddZZR06dOlX+7Gc/83pp7bXXXt62sry0brvtNvmVr3xFNjc3yw033FBOmzZNTp8+PVW3lFLecccdctttt5UtLS2yd+/ecsstt5QzZsywysyZM0futddecsCAAbJSqch11llH7rXXXvLee+/NfRbaK8ot99Zbb0kAXWrH58kmhJAXX3yxXHfddWVTU5PcfPPN5cMPPyy32GILud9++3WpPx999JE88MAD5RprrCEZY16vOoq5c+fKkSNHyl69esm11lpLHn300fKVV15J1Tt+/HjZ1tYm//znP8uxY8fK1tZWOWDAAHn88cfLzz//3Kqzvb1dnnnmmXL48OGyUqnIIUOGyOOPP15+/PHH3j4cdthhEoAcPXp0Zj9vu+02uf3228u2tjbZ2toqN9poI3nkkUfKl19+Off+pJTygQcekCNGjDBu9Zdddpn8wQ9+IPv379/ldn72s5/JzTffXDY1Ncl+/frJffbZxwoRIaXfq7Gjo0NOnjxZrr322rKlpUXusMMOct68eQ19oyVWbTApPRHHSpQoUWIVxVtvvYVNNtkEU6dOtYI+lug+VKtVfP3rX8c666yDxx9/fEV3p0QJAKVKq0SJEqsw/vSnP+GXv/wlRo0ahb59++L111/HFVdcgb59+2LSpEkrunurDCZNmoRdd93VqGpvvvlm/O1vf8P111+/ortWooRBSXhKlCixyqKtrQ0vv/wypk+fjk8++QT9+vXD2LFjcckll3SLF1kJhc8++wynnXYa3n//fVQqFWy11Vb47W9/i1122WVFd61ECYNSpVWiRIkSJUqUWOVRuqWXKFGiRIkSJVZ5lISnRIkSJUqUKLHKoyQ8JUqUKFGiRIlVHqXRMgAhBP7zn/+gT58+hUL1lyhRokSJEiVWHKSU+OyzzzB06NC6wURLwgOVnyUrZ1CJEiVKlChRomfjnXfeyU14DJSEBwDQp08fAOqBleHHi+Hv+5yROiYk0BkF6KipfD3tUYhOwdEhlPSsJjmqelsw1CRAXQUjCVSFrkudB1SZqlDndTsSiUSO1sEhUeFAJSb8zVyiyWwDFS5Q4eqKgEmEDFBBc4GA1MqZRMAkeHwgYAKcqX8BgDGAxS0HXIJDmnp4HWGhIB2WkkGAIYqfiyTbkWQQkple7TTrAnPdnF2mQoJZdX3zyfOtdmbvYu/TskIy7PJUdqj+Ej0b+vencH/fRiBhf0vMqZUz25mXvtsM0vomuPNNqPP1nYH1Oy5lsh9J/Q1wCAmyr8rq/ZpU4woAVAVDp2DojMcQva3HlKoARHx3whl7knuyn012n5PtgAGtgTrQEthjTmsg0KcSoXeg8nk1B5F5JnqM2fyRy+o8oRI+fPrppxg2bJiZx/NQEh4kCf769u1bEp6C6FOpAFDGYIIcjwRHR5ygcEkUoio4OqJ4QJIMS+LtJZEiQnrgklCDSBiPOJFkpl4hgSaeDFy6rNpWk74eiFlMeJoI4WmJB6MmLlFhMpPwcCQDvCY7ZgBn0gxQALzblPCwOgO9lGTgjUmPen7JM9EDv5686DvaFjZbU5OQSL3DbaE/27WQqu7ynV95MHv0WdZ+n0qzNekCCXERhDj4sPsLl5rtR3eY4i2XR1ToosD3jdBtbSnAkXwrGrRdkUF49DYlPPS8RXgYQ8AYwrjRkAEBY6jEzVR5QpxqIp/QUChyZJemY14TZ+gVjzFtoURbqMYZ/RwqTKDCo7hPAhWuCaJ6Pq9/O1nIBPHYtNXT1zTYuxKNmKOURssllgo8/mNMoimI0BREaAlraAlrCLlAyAWaeIQmHqFXWEOvsIZmrqQknKkBQ5I/PUgzpv4CJhEwtXoKyb8hUwOurkevMk2/mCYumnjk34fIOi5t6YmeTCRZAwskRMX7jOJB3/fH4r+ASyMh4oj3jXTJvjc66dGJBgB2nTct1f64udMwbm76eImVD8y8edL87q4kUZ+rJ2Gk2CMmP8L9i4mFkrTaf1X9J5TEVv3LUZMcUfxXFfG2UBJL/a3Qerv2HIpBjwGM2dcWeUaNtNEUqL/WQKJPGOHwP1yAw/9wAQ595UIAMM+oU3AsiQIsiQJ0RAE6o2S/U6j9zojjlZ1P7b4OliglPCWWDl95/Aaz/X97nKg2mEQYCPRu7gQA1KJADYSxVKcliNAUr26aeIj2KEhUXEbSoaDE2Gq7KgEIBqlpuoBRdwH2xM9Qn+Qk5AUQUgJ0Py6TXhFIUHkWAzMSHtUlhiBjzUilPbpefa2Aum+9SpGSgcf94ZKBSdUWkKxOAWDs85dh9uizzDNy8eSos619LdXRoKv8Ej0Xz+6oVMd6gta/IYOEBMM352arQ2aNPNu6xvebP7z9OQASiaNGFP9LFwT7/P7ihvv96A5TUJGJFJQJiZBTska+WQa4YXCT+1Xb+rQr1XJBv1vGAE6kvyGjkhpbGpZXrbqeIWDJvvqe1H6FS3OuwtOEU4KpMQxAFAVmPAiZjBdvCYENmFbPCbzwjdOssUxKYIdnrsp/ACW8KAlPiW7Dlx79CQDgX3sdjyAQiDVaSloRcXSy+EANRpzbzIWa6OOBhwmGKtQkD+hBRg0EkWRqheYZlfTgw8lgBPhFmFIySKZJDqAHMbPPkvYFk4reOP2h+zpYuXDIjw9ZIlVO2lJ1JYSNgYFLCT0XRc6k5Bv8NdHx2XOUJGflA5083d977PNdt/3QRAdIkx2g60RHY48XLlXqsrjqgKlKfZIVre7S76lrm2aVZdJamKCgpIiZfyUalRdpiXKiCld/+l6aONArUD2qMEXq9PPd+8VLsP9LF+HB7X4ExP02Y0x8P/q+hYR5XoJxCMkQxm0ySIx69spC91oiQUl4SnQ71n/kJmv/3X2ORRAIQAl8EAlupCtVwVGVrokkMP6P55vt6Vsk2zLDyBDQ6h17n0IQEiOkO8wxsp+c5LG0Sa++9HVBzhhpJo465Me17+GSQRA7ImmkPYrsaLUZZ2lbDj0x6NV8cjyRe+1REp0eg5fGTM48p39nasuSNSmPef7ypeqHj+Qk/ege0PfusR2mADyp2Ufk3LFgWYJKjXQfqGo9pSYnY4y2EWyJyYgyUo4XcoE0dogUmjRq4gPEz1kyS9pl2S6xhNppslWiayhzaUFZeffr1w+LFi0qDTiXA97Y8wR80t4CAPhveysWRwFqZHSNJMPhf7gAgCI7tfjjXxIpjy2tvpEye1DmAAKeEJMKs8XNAbPVStQWJuSuAWaiegpZYlujz4dmxSdSBpsBp0bM0jKM9hk0S7Lq09uRUDYQVcHN80m21b96gKbGzYAavEui0zMxf6zfPkPExuuU8LiG6fQ3bpT0PLbDFGu/EUkfnZi7IuHxYdbIs63vxzWMphKe5Jgt8dFEXo8NVWLk3xnbFGmv0E4BdAhmxpiqTJOrpF7XGUKapZAmO2E8cLRwiZYAKWcIQI0xTcQTVDtC+L7Fh+JnnDUeCAB9QuXdlae6XF1RZP4ujZZLLHds9Lsb0RQINAUCbWENrUGE5kCav8BZ6SgPjOR6aoisjabdP9d+RxlLKiKhvD1g7ddkYoisPDcSt1ftQSWJZwj9qwn1pww1iUFm7FpOrzXb8K+utRGz78NMDJ2TZ6BdgemzoSjJTs+GNqSl25zp8AbJPjVSBpJ3YVljn99fbP66C7vOm2aZXlMD5khycty2kSkK4wgBTY7UnyDfelb9Mv6PbkdSGjIk4rqqUpGpjtgVXm+7Y4T+Sh/bYUqKeH779xeb8UD/UQRMoiWI0BJEeHefY7v2MEoAKFVaJVYQWitVAMBAJtFSrWBJHLOnU3BEsmLK0bFIExwqbs6U8LC0EsCsmKEVWInBcAiGmpbwMGbseTjTg6Vt5OgVugsAnDjoC1j++gG3jaEFk0aP71vd6WMsJjlmpSklITocQkrweNaMpJoMfd5aWXg0HoAbUSSU6rHuw9azrzFeOJSgbz3bdkV+4RunWfucAaOfvaJLbS4PklQE6lumEsnst1B5njFT0rVls+ol21ICEXE/tyVF9nXSc8ztr261JhmotwAHjN1iIpHSEh5bNf7YDlMsCVsWoXx0hyloi71bAaAWj5MluoYuE57Ozk4sXLgQQthTznrrrbfUnSqx6kMbOLuYM/pMMEhit5MMahwSYMwy7HVtaVzdu09SYniIMTyWqElFetR5asiotm0lUbJiS31AhvTEO7qxeDfgtO/JYKk8tOoZPMfG26Q32v04b/DvKrJipZToPjQSZ6URjxzXdsuH7nTBzsNvtz/HvI9Uhav6oI1vEzWRfreoJMSFVgklMa98ZMVvzE8lufq4j+jUg+6zLlsTAOOKTAGA4MySwgmZ2OEJ3Tsy5mhJT55qsYkrSXgYG0OXBihLh8KE55///CeOOuoozJ071zoupQRjDFEUZVxZokR9VLhAnzAyg7MOTAikA3/lTcK+ccH2WEomgEjaQ5kAg7YNFI6hYsQYasTex47CLBEyiYrU5zgqXCCQwpw37uwMADgkkeLwjPvhTEIyZhaTnMkk7o/Ug7+eSNT/XbE50DXvrNIeqGdDe+NRIkDhenA9NeosQwxcKQPFo877IwgZoXZmAIhqNz6P0Hxr2havu/Db2OupwoWyndNtMmbGBiULcW2ekCnVsUsn7udu2bSbuVaHq0JVASyJCU5NMoRColkbNMeq+sQdP/HofHSHKU77to0gR6K+LBceS4fChGfChAkIwxAPP/wwhgwZUibbLNGtqHCB5kCgSlZ9GnTFppBt9KvL0fgarpzGuo4cE5aAh0FAmpg4apAjpzkD027pDJYKC1y52TOuV7vJ/QSIV2tWDB9LGETuO/sb4ywt2qeu/PT6vAlOX0Frsp89w7devCTz2hIrBr7gg3lu6t+ce5khSY1IGQQhN1RCQtM6aDIROURoWUC/g4/tMAWcOAxICIjYkJ/HUmAaooI6HrjfE2fABf881+yf+6WLzNdjp9BIe23pKO2qDyqNBaBIV8DscabCqUQ6GVO0tJd7SCsNUAoo+8cSXUdhwvPHP/4R8+fPxyabbLIs+lNiNce2c67G8zudgTAOvBUyQVRE+VADrx4AmRXPQhKX70imJRfq+kTcbFZ2DABx7RZ6gtEESDhqNcJamAAYZ0lMIZlIZlQ8IQmmz0FJeWzbgzTZqwct7eHWQiQZ6N2Vuwv3WZQkp+fhyVFnW6oTIHk/utOLx0rtADuPlUVwpE1yjuxmqY4PkWSQwpHM6H9ZbPcWn9XxciJCgKjqm5KdPIQcsZQmuZbG4VH9iv+FkhRrUztTJO6kG69L2Q4l4w8la9p2scTSozDh2XTTTfHBBx8si76UKAHANgyscAkuSdwOWg42EZCMWRIdKsaOQHL/xAbKdHKnw48gMTEs4qMaAQeNQOvKRWDUYRwqtk5gJgoOphMsSuXwSg2pswa1RIWQL+lRbWilVkL8KBjqJUNMypdkp2fC9tZS72gRoqPTjChJT2JESyddATt3lZu4U0dEB+yYWcsL7rv56A5TEiP/WHpiJCbx0UZBCZH7dTMkCYldsuMmIlXenUk9ruQrFYeL9D8JkSFSnpgluo7ChOfyyy/HGWecgUsvvRQjRoxApVKxzpdxbEo0gjmjzzTbvjgczTpiqbRjZVByIiSzJDg0YKCANKJ3VVaa7ZqQCMhgrsmPlcTQEXtT2yGXPBnSwhKJUnwSEDwZFAWhNVz1Ugi9ChXGwNFF4haf9M0OSkcHXgGGJG4LTTkhkcQtoaBEqyQ5Kwe6wwCZ5lebNfJsdMQ5ngAd64mZcBA03QsATFwBJMcHbazNkLhzy9ipwMqsbhG25Pv1SXcqPFkmRI4ki34+IbO961wHCo4k9pfWdifxvFRML3MuI7aX6jdHJEvS0x0oTHh22WUXAMC4ceOs46XRcokioJKHqkgmaO11lBgFJ6smrUOvCSL6jc+lVk/QrqDJoGLm/pSLhxrY8wwDqbjZPg4zeuo2E0ImIUh/lXorlsQIBnCOMO55BB5/Q367JJ1wUd2rK7VJyJy233Htb7LuS9VXYmXCsvC20jGi9GdRi/PU0cl+WbbfFTw56mzSF0dFRO4lSYQa75OiUze+CAGTOO8f53nboPZwbqoLAWWLx8lYRR0YgIRYhSz93Mw5bid7dceYSHQ9yWoJG4UJz9NPP70s+lFiNYM2rFSSHmpPo2JX6JVRyCUCpkh0JDlqxAhYl9dbgD2Y0ajDgtnSjizSo06lDX6LwIjDmVpZak8sSUiLzsFViylbCAHBGHw5gRK1F7P2KbJUWFTM7kuImhX9tcTqB4Hk/ZFEYuLD7V8/32yvCJWWBrOWQwrapsjY00htt6f2qbS2GrthXvhllc3cjd8VkH0TiJCSP9gR3CuE8NgGz9nZ6xlkymjZtdHS597+9vcw/KGb/RWVqIvChGfMmDHLoh8lVlOMef7yOC+UXiXZ8oaACSM2DiBiqUhStsLt8rU41UJN8Dj6cSLWNiqseDsgwfq0mguAFQVZUANmgtw4P2ag1fo1nf5B6k1EUiJgiZ1BjfF4UEwa0/ftqq/qQUh7JZqsMhOROYBCwQlL9EwsTeJQIEk34dp+McTGsvqdI6+e+87P+Pr5K0TFJWSSWqVDcHxRU9tLBMcXEcOSWLL6RU15T7lhKQBFJKqCqcUGEs8rqp5yM7YHxIZHRz3X+zSXVoXJlHFyHoxKi8tEasQlmoIIYVxvSXaWDl0y/v7kk09w9dVX4+ijj8YxxxyDa6+9FosWLSpcz0033YTNN98cffv2Rd++fTFy5Ej87ne/M+ellDj//PMxdOhQtLa2YuzYsfjrX/9q1dHR0YGTTjoJa665Jtra2vCd73wH7777blduq8QKwtjnLzOGenrA0a6kQqr0DJGwB2RA67oFQi7QHEZoChQBqsTHlHQo/VdhyWCk/7QHhnYz1dsciTha/4UMJIWFtMpS0BD0Wj1gpaSQPNbPJ+QsIsf0fbsh97VLcCSTfRe6rxUuzTMJCg7AJXomxj5/mflz8dSos8xfUWhXbvMHO9WJ/uspEJKhFv91xvZH6i9O8xABHZGS4ugYXlpKwwlRUWkjEkmQNhnWX4q5d5Z4fKlcfOq5MCQ2OhqpMcf5BnUcoZAkA6XfsnZHD5hQcbhkvsStRGMoLOF5+eWXsfvuu6O1tRXbbbcdpJS45pprcMkll+Dxxx/HVltt1XBd6667Li677DJ86UtfAgDcfvvt2GefffCHP/wBX/va13DFFVfgmmuuwcyZM/HlL38ZF198MXbddVe8/vrr6NOnDwDglFNOwUMPPYS7774bAwcOxOTJk7H33ntj/vz5CIIyDPfKAjcB4tydTjfbVOqhtqXZ3uKp68y513b9gWUDo8TNapCoCk68pZiRulAwI31J+3S4+7ZaSJeR1n4yoGrJkbSMhqnbPGcqng+tKyvbu31MD47M2qcrWL1a5XGurxKrB2aPPquuBEh/W9qLiJn3Jg6MRySdLvQEvCJJkJHEyiTfXlWoP/2tqREjm+gz5+sWMm2ADCQ2OsYQ2ezHUrF44aTK+tVU1DBZQ7uha+n29nOSyNp/GXdK/YdQomEUzpa+00474Utf+hJuvfVWhKHiS7VaDUcffTTefPNNPPPMM0vVoQEDBuDKK6/EUUcdhaFDh+KUU07BmWcqj56Ojg4MGjQIl19+OY477jgsWrQIa621Fu68805897vfBQD85z//wbBhw/Db3/4Wu+++e0NtltnSey7m7nS6GTRc6UTAZWZo/r+MO8USeUuZqLs6I46q4Fb2cRrW3nXHpd5eLlwXYfqvPu8jMVoilLii2vE3/IQn2c4y8vYhcVlXhId655RYNUAlOu670Yja67EdpijPLLIoqAoa5sEm6kBiEyYlWyF2PI/tMMV4lX1e41gcqe3FNY7Pa8CSKFFZ06znLnzBBfVYQ4MFUgkPACUp5hItJJpyS6x66hVGCGPpjA8Bkxj17JUN3SddyH318RsaumZ1QpH5u0sSHkp2ACAMQ5xxxhnYZpttivc2RhRFuPfee7F48WKMHDkSb731FhYsWIDddtvNlGlubsaYMWMwd+5cHHfccZg/fz6q1apVZujQodhss80wd+7cTMLT0dGBjo4Os//pp592ud8lli0kmFn5UN223n91l5OTsjLJRxUGwlphCclQiwfHzlqADhGYfa1OogSIkh+X8GQFAUx7YbiGh+S+4kCIVGpDU0u4K0BfrCB9TpMl2gYtTaU6JdlZtaCJDiU59N3JI8I+WIsKbsej0dsMtsfSijRaNlIdyfBFTHi+iJTNjlFJMSAtx6Hn0osUQ3LIuQoDmrgiOYAiPs1com9FJbPo31RFW6iSIn/96Wu75f4AIBK8bp69Eo2hMOHp27cv/v3vf6ciLb/zzjtGzVQEf/nLXzBy5EgsWbIEvXv3xgMPPIBNN93U5OoaNGiQVX7QoEF4++23AQALFixAU1MT+vfvnyqzYMGCzDanTZuGCy64oHBfSyw/6Dg9oUdezhwyoI9xci7gIg7JrspFItGBB1wilMLI4hlUNGSaW0u7kocx2dDu7TKO/ZMX+dgKDZ/Rb/cc4LiHk+CHjaihEuKkVvV6AiwJzuoBO7dbQvK7Cg7E+i0t6bTVpgBWuE2JRPJtLIk4OuMPqCrs4KUabloIDVeaysh5RiQ6YUx2mmLCEzKgNYjQO1RepC1BrVuJDpCotPSzfm3XH2DTWT/u1jZWJxQmPN/97ncxadIkXHXVVRg1ahQYY3juuedw+umn49BDDy3cga985Sv44x//iE8++QS//vWvMX78eMyZM8ecd3N16Xg/eahX5uyzz8app55q9j/99FMMGzascN9LLDvQgIF6UONSWq7bShsbx1FlEuACFSPpkOABIUaEawRcoAlAEI/cEYsNiIW2Z2CoMWa2qYSnFq8cjX2AE5DNJTiuCzgFfUPVCpoYJlqr9PT1tgQoqackOKseihggh1xi9LNXdKkd7tjscEL0OUle21MgYmNlAOgUykAZSEhQFsEBbHs7SnD09xqYxRAhPExJefQY0xxI9A4jtIZKwtOdDgF/+uYp3n6XWDoUJjxXXXUVGGM48sgjUaupH7pSqeD444/HZZcVd5FsamoyRsvbbLMNXnrpJVx//fXGbmfBggUYMmSIKb9w4UIj9Rk8eDA6Ozvx8ccfW1KehQsXYtSoUZltNjc3o7m5uXBfSyw/ZNkdUGNmnw78tV1/AAAIBEcgBALith4GakRs0ioiEpAvcvapMaT2mAJgbH/0oFoVPFNpkF5h2jY8gB3YrcITdcTSuhuXWPF4aLsfkSzmturRh6wwAXm2XL4yL445TR2L38xt51yd2aaOVKxVPqZ/scpV54xThvwJ+akSlj99i/Mx6U/nZ7axLPDb7c/B4ijAZzWtxmKWQwCVDPtUy7qkjpacqIITDzVAS3iS6wWSsi1coG9T1TIy7i5QZ4wS3YfChKepqQnXX389pk2bhjfeeANSSnzpS19Cr169uqVDUkp0dHRggw02wODBgzFr1ixsueWWAIDOzk7MmTMHl1+uPHq23nprVCoVzJo1CwcffDAA4L333sOrr76KK67o2iqnRM9GPUM/Ku79x+7fT5JzElF/MohpNRVUqgmhDZyZZZDJBczAr8HJKtIXjVbtp5dnSZwT7XaanNPuqiVWbjy8/TkA7BQFvkzYjYJO2FnG8xpCwpDzIu5T2k4sq3YVzVvHk2q83u6GJmiRDNARJcSLErAsI36X1ACJyopKeKhygCOR0urDVtTpgjZSJVYsChMejV69emHEiBFL1fiUKVOw5557YtiwYfjss89w9913Y/bs2Xj00UfBGMMpp5yCSy+9FBtvvDE23nhjXHrppejVqxcOO+wwAEC/fv0wadIkTJ48GQMHDsSAAQNw2mmnYcSIESYFRonVFzR2hVqZOSolY3woEUUc2opGSmaIkpsoRRMnqnKj+XLc8PP2tcnAqmIEJUEVOWSu11mJlQOP7jDFRNvNk+YUwZjnL7dyz6m67TJuSASNItIHTXrotp7sI0KFXK/G5Snd0f2rxh5l2qvM96RpwMDUOVLGlehQEuNaRnAk0qMKFwh4SXhWJjREePbff3/MnDkTffv2xf77759b9v7772+48f/+97844ogj8N5776Ffv37YfPPN8eijj2LXXXcFAJxxxhlob2/HCSecgI8//hjbb789Hn/8ccs4+tprr0UYhjj44IPR3t6OcePGYebMmWUMnhL4yuM34PXdTgKg7GmkkfDE0p54JaxD8lAjYZOI0MlfVSOBAk3ZDA8u34SXGJeqQGSU8HS3wWOJ5YvHdphSyI7DlUK4tjpZGdBdg1t1LJEcNuruDCRqNC05od8AB1CNywmp8msBalmwojyzdKC+ilCSF19+LNcI2SWBnCVEhiEt1XFBc1ypwKTqQTSVmcxXOjQUh2fixIn48Y9/jD59+mDixIm5ZWfMmNFtnVteKOPwrJ74vz1OBJD2noqIjY4mPFrd1RlxdEQBOoUi1NptHUgyuRv7gNgmh05KekJsJuHigVJnvzIjIQuNq5+yJDJZZV1SnUWGlgZPjjrbSotSFRwdMbHvFNy4gEeSrfBs6b/d/hz8t6OCDzvjdBKRTXqomgqwc8rRWDpBhoRHEyAddR1QLum9Q4F+sRt630on2pqqDXtNiZ+eYDrFj/lJ4Xsu4Ue3x+GhJGZlJDQlSvhgbGyIC7iGMX/QK+fY4FlFUxWoRIqs0ACGNSL5seoigRO1CLyOo2GJlQSzRiYZu7VkIYlXkyY/vkCVWaBlaXJYzhDnn1NYFgbuIlZZJfnnVrwbOpCQyyUiRKdgiDwCFldNBQBgtjQoUdUl54HYmzOlLozPAZaEp7C93Yo0fioBoAs2PO3t7ZBSGiPlt99+28TOoQEAS5To6fjyY//P2v/H7t8321Tq45YDEm+wasTRGSlpT6cIUGW80LhWup2u3KBSHb3NmS2NAYoFAHTVVLakQqaM4eeMPjOVmqWrEMTmjUYcp4b8K1K6oxcXX9RUglArJASV2sAxPpZ2nCtBjlt2ekz5qwUkiSoNTmhFR2cqsWeJlQeFCc8+++yD/fffH9/73vfwySefYLvttkNTUxM++OADXHPNNTj++OOXRT9LlFjm8BGbLFAxto72XImEE7HZJj8yNmsuseognTok8f4TkqWC9dWrS8MOYZBM1QFLe/9FkhmJT1elPVqdpQmOTi2h1Vg1ueLJ+aM7TMFnNTVlLapxdEaUUKY9rSyiiGSy88ll3J8nMWpOvLSCOJFql1FKeFY4ChOeV155Bddeq4wr77vvPgwePBh/+MMf8Otf/xrnnXdeSXhKrHbY7Inrvcdf3eVklfE8zuEVCUbUWwKVQFhl8+oq0XOhiYr2vEs8nRJDXx/xEXVIhK63wgW2nm177/1x5x+qOsAQCWaiDDcCmo7CtdnRXk8dEcMSwU2wQYZlL9mZNfJsi8xJJBKdJYKjPaqgPdKBBhkYA/GIS8e9oggYwGPmkhfHKGA68afaV+kkEiPlCrMNoiNPEuJM1PvBSyxzFCY8X3zxhfGSevzxx7H//vuDc44ddtjBpHwoUaJEQl40mYmIjD3g9mpxszJc/EqJb869DM/ueAaAtCoqIrZheQlofVB2KNkSAerR98rOp4LFs3gjGdIphLHRid28iZFyJJPz3RlF2IUmYEIqwiWJ3VBECFgnsY+rsDR5oNRDwPWYtF3RaTmq/tJxd3wZ0N3fpEs2TaWUZ4WiMOH50pe+hN/85jfYb7/98Nhjj+GHP1QrjYULF5YeTiVKeEClNpr8aFH5V8rsx6sc8nJY5cWGcWFcp+vMqzqGEwAwITPVW3NGnwnlMxjHshGJ+rVDKLKjUzVUCdkAlLTl9q+fD6D7XdJ1fzoFxxLBjVTMJmBJzB2NLNkKY7bxsfus6fPUwUt07B1XFcbh5ilLVIwBlw1/v9H1x4KRisVPTwA/7saGri3RfShMeM477zwcdthh+OEPf4hx48Zh5MiRAJS0R0dELlFidcV/D5xkVn6Df/2z1Hnt2REGEQIu8da3lAq4GgWWYWgkuKlnxJPXLfN+l1h6SDCLJAiZTX58gQOpkXLAhPEGqhfr5etPX4v5Y1VuwOYA6IiN6J8adZajtgosV/MlUbIdxSEVkrJxv4x9ErPyuxXFYztMAZC4ty+JVVOf1QJEsgWAIjiRTNp2NUAMwOTXzmuoveu/dmGK6CRSm2yyxJn2xEr2NUIm0RIkubOa438bQXDyLYiuP9Y8YMYZ5B1q8cOOtNXY8tGpQEuT2qmEYE7QyRJdR2HCc+CBB2LHHXfEe++9hy222MIcHzduHPbbb79u7VyJEisTFh40CTyQYPEE9f53j4KIGAbdN92UaY5jeDQ1qX91FKxQCNRq3NgEcDSWJb1Ez4Mv+KRK2yBTai2fm3rARGxHos41EqNJ2/i8NGayIdVSqPQLJu9bnGyzPSJSE6ImEkjeRx1ok+sDLB2vqlHMGnk2lsRxqz6rBvgi4lhiSFeSFkLXrnsUsmRHH/vxZhcCAH7wamPER4OqrULH3od6out0L/Reaf67Jh4Z27siTg4azHa5U+3fPRmohElDTSHY2HMK112iPrqUWmLw4MEYPHiwdWy77bbrlg6VKLGy4f3vHgUA4IE0fxpSAB8eOhEAENU42nqrc2vdc5shRAAQae/WeNEoJMOmpbprpcBOzyV5+57f6Qyv2irJVeUnDZwEqNTxmroSeTvg0mQ4r0B5DUZRYjBdEzAkpyrSGdA10THEJ2YJcim8C6uCG4nOEsGwRDAsjt/zJSQflqKDDM2xnil0cs0BSPU3D1pKo3PnhYS4uIRHI/CUTQIPCjQFUZftmYKTb4G44Ti1Q/PMcAYIbU0EsL0u7lL9JeqjMOFZvHgxLrvsMjz55JNYuHAhhLDFrW+++Wa3da5EiZUBjIjKGUsIDwv1wJh8I7KmBrlPjhwPzm0zS0FW211ZPdZD7eIJkLXUDAfpMShhnKFy4e3d3odVHaOfvQLP76SMmN3FPM1Vpc7bUoREpSW7nLKAIwmMF4EhZAI1Y7SSLTEUYJAkVo1+JfQbSg2If7nVeTj0lQsb6o+S7oT4pKpYzBcRQ2cEfFHTUiZppayocIlK3E6e6smHqzdVfQpZkoJC30PAVN1A2gCbGh9rgqMNlQMm0RxLzJpjNfTS2N3xk34KABA3He8YdMnEjazEMkNhwnP00Udjzpw5OOKIIzBkyBCwMmRsidUca949A4CS5DDPmGXsIDiSfF6hIkZGwlPliCKODX57U7f3r3reeACArEqITh/h8fSZS0STjwAL9b7SAzRdeke3929Vw+hnE4nP3J1OB5C4pWd5XjGSzkD/+5dxpwDoug1XcxChwgWa4+B4Ya0CCYZqLL7RnlkAjO1MRMiHkImUBGDGBby5AdfqB7f7EQDgk85mvN/J8VGH9rySqAqgGjOqmpCGXAVxvc3xNxQRAqaCCSbP6KYRF6Ta7B3qskqC40ppaLe/O1+Ro19udR4kIUOJ63nyO1UIeequaNOstZLs1OzghfLe08EOajwfWonGUZjw/O53v8MjjzyC0aNHL4v+lCixTPDGnieYwepLjy59HpsPDplotinnlwKQ8cjKBGJ3kQSGEMUSbD2u8kAgDAXe3edYAMC6D97S5b5VzxufSG0EIGM9gFgCyIgQHK1K8BGeUPWV1XS/JVgIdE45Mu4wwOIZpXJ+KQnKAnVLp5Ooa8ysvPZiCY+T/LIotAQj4NIiS8/ueAYCFiR5o8g1muxUicGwjK2OVIEk5k1NAvdtcy4OfPmizD4kRskMi2sMHfE72CEUydGqqZr17kkIzlCT2q0/UWHxuMOJqs1ujyYSjU2sAeJ5FcCWuN27jbIBUsekibXTwoUlHaLCz0hyRILhb3FC4q92QdIjfvI9tdEcmlAC4AwIOdghVxeur0QxFCY8/fv3x4ABA5ZFX0qU6Hb8ay/lBRVwQOfJ1eRHGwgXEVF/eOhESOmu9JKBVUQcybpUZZwGsenRs40ekBOBEEMQCVS6K0yHJjORhOyMD3UAIkKSJT5iiqDpspIZdRwPZCyFSvrNqgCPB2nelEyG1fPHl6QnAzs8cxUA4IVvnGanOnBsYhhRaTEmERTN0xQjz+4niEMh6IlfghAIqciOtu/Rc3EtflMFl0BNq5rUW3vfNucCUBKQfX6f2J08uN2PsCRSU8viGsPimsSSWIjRKSSqRKoDUCJisxjl5RZvMwCSJc/Q+U4iJM80kunkodr7SrVCJDaMWWqrClcG40nuM7s/VcERFgk26EDGDI81SfPdMZqArcQyRWHCc9FFF+G8887D7bffbvJplSjR3aASFK0yKoK3vx2vpBwjTIoiU4o2PBaCQQpm1afVusysDBMxDmNwSE5ShK5COZfWx7jwoElY+97p6AokTQ0tAFGNN6sMUZUZNZqIGETErXsxhIdL8ECAa5UWk+CVRKUgIBN1V8gQXXEUgjNu61J/Vwfs8MxVeGnMZLMvHFMAqq7hsS1YVyQIeWgOIrQEET6rKRYrJEgGdJ07S+87di6C0ISqcnHXNj19Qlsl0yk4Po/fsS8ihiU1ic54Qu+IpFU3BzPfT5KiI2E1lJAlZr1+aJKSxMtRx8NYZVUhhDIJQihi76uE8ChXfk2OHBIWR7YGgNd3Oym1WFpwwNGqzUrkHbeCk5XkVtx6ou2xVWK5gEnpmwqyseWWW+KNN96AlBLrr78+KpWKdf6VV17p1g4uDxRJL19i2eKDQyaiVg1QrepB2TbyrFQihBU1wFIbmFo1QBQlUVojwSGJ3FvKJAy8Vml9OlGpZ0TEEFWTiT+qKXsafZ36l1n/ajCSX4cxIAhEMnEFwvLa0lITXda195ECkMamJyEmUjClUqJGriFMnX2mJ3Y1nWcfadnpyBpQ+1zV07E4sO4tinjq+eo2OJe59xJUJILWeLsN4K3cqLikACrnzUSJfLyy86lGKhHwRKoTsKUzjM3D8zudgYVLVIyXDztDfFbTyTiVx1Snsa1JyJCGnX4BaIkZRa9AoilIylUF8GlVnfu0U6I9kkalpclOwJj5txJ/B00BQ0sANGlbnkB5aum2qdSGkWOqHmlUbi2BQDOnJEaRnZAYLdN0IK1hzSQB5UyisxagQyQ3ZFSNjv1VyAWawsiEAahUIjTH4SaCUKC1XxW9b7nT9zNA/PQEsEpgH4wfIjv8Ou81JfwoMn8XlvDsu+++Xe1XiRJeLDxokvFQEiJEtRagVkuv5dx0DKjCXCclIEQymWskK0SWst0x5KjKUavaRMAlPGlioLYZJwHjuEAUcQRxnA4plcrIyuhMNqSUtv0PlRpxGB0/015fPDlnPMHif5dMPkL1swOIOpCoogDUYoPRqMZRq3EIkdynlIkNkuCJ2D9R28U2KJaqTt+D7gMAiGTFGpRqrkZAIySHQTJxdoeNWRaaeES8lex0C27KBeq1Rd+TCLGKWKs0BUMlSq6vCqA9ZkvaC0u/20qik5QNeWKs7KqhhLQzmbuxdEKeuI83cYlmYnjczBN3ch4bIWuSwyDNsw7i/Gc0GvrfdjsJrKYJGidRmG3Co9NVhPH33qtXJ9a6J5FyLj7hCLgwbukhh9QW4oyV0p7lhMKEZ+rUqcuiHyVWUyw8aBJqNY6qFrNHDILY1wAwK18pgVotsKQsmohoNVOSuNEmExv9Lh3GPaomRKBaDVCL+1CLkgCAPrVXQLw4AilMfHoWEwTdPxHp48m1Wm/PQzVYSseoWYMxCU4kJlQaxANpyIZeQcrYuDhqZ6h1MKt8LZaW1WoctShIVFr6WWllhWCQWj3n8SaSkpl7AgBWVY3UvhDgVYDHBki8BeUA3gCodJBj2RIdDZr9myNx3w4Ei72ZErWQSPiu+g60tBMqbIyJysyklbw0kkBnlGxL2N+ikswkJEdLadxXRhl6J+otmyjJWIqjOtHMJVpi4tEc2+HY920/A0M0mTDbGl99/AaTAgYk7IoulyQAlmgKayaIaBA6o4WzG11xFNCsplxGDZRii+xSsrPs0aXAg5988gnuu+8+vPHGGzj99NMxYMAAvPLKKxg0aBDWWWed7u5jiVUMOlAfoCbyIBBmBdgZhWpSJuVlHCqfxUQjMlKdNPFpiu0JwjCq6+kUxVKkjo4Q7R0VdEaE8EibFGjoFaEe/CpcIIgHRcGV0a+MWQ1jEiyCkfiIiA6KAgCDDrfCOAAuE2lOAFDLTEv9xYG2GxXRWfLDIyCqiugAQHUJN7Y5gJKAafVgZzVOX+FIqxLX3zTJoZIsSoKkYIiqepsnsUQA8CZA1iQ6f6RUhk0Xl67sPlA11vLC1rOvwSs76zQUAh91KpOERZyrCMix6mlJpFRc1LvKsu+RSTLcGtR7Igk5iuLJXEhlB0NJjWVfwxlRS6UjBSUJWdV5TXBauESvQBiS0xoIo8JKxdkxBshJXUaVmGEgbtRX3D5G7X8CLpwgo8yoyfvOUO+8lrzyVgbewsHiyKL85NLebUWgMOH585//jF122QX9+vXDv/71LxxzzDEYMGAAHnjgAbz99tu4445ycCuRD7oSkrERMAVjEiA2M0IPPnEm5awQ95xJhDHhacSt2xCezhAdtRCdMUmoxe6nPgjIuGsibpMpXVDcbyaAKB4S1UAqjXosCGhwQQ4pEy8oJrUbuz5N1WawB95AER1Aqa9EB7PUViJilnpOqwddsmPqKxA5Ni/sliZlUgCIZCzxIq7sKOP4UBR57t2JrZ5O0lBoMISxtEVLSBU9MO7jMiE0WsinzT8FY5bXFDULdVVlie2Nli6mvaloAGIKmvFcqbSSGDkVR4VFkXh/JyotiloU4B+7fx9AEvBz01k/BgC8tusP7AUCaKBRpx2R2N0BQNvNfvudEisOhf3rTj31VEyYMAH//Oc/0dLSYo7vueeeeOaZZ7q1cyVWTQz4xUzzxwOBIFQxaPRfwAXoHC9joqOTa+ptCh6v1KiaIA8fHDIRS5ZUsGRJBYs7K/iiFmJJpP46YwlPJFU26apI9iPBFCHSf4KrIG1QRtGR9uISSi0nBI/tYWIj4UgNioaYVDmiKoeoOW7isYpOSvUgqIRHSqC2WP1pFVatGijD7dhOR9shacNk/Scd6Q59VlqKw2J1YJHcSbrfMn4YsgaIThXoUFalco8vkhdgNYD17FcA+dl2ztXoXamqvzBC71CgVyCVETLXqR0YAsZU/inz17i6kjl/muxoVVbAE7WWm0YCSAiQHg8SCVEcETn+02pmToIMJn/6nLufPPPOKDAS3nQfpPlWAi7In0yRKxrmYXXEU6POWtFdyEVhCc9LL72En/70p6nj66yzDhYsWNAtnSqx+qD/z5Vh68eHjwegpD9BR4gvYhVNFAWWXY4LGrtED8iNoLMjxBexOL+9FqIjClKJHSmsMBlCGV8C2ggzTvjJRRzfRxULEAt/iARI0zjO1TZ1ZecQJmghhDQusaKqJEF6IBVRItERETckB4jd5iWzjK4pyWFkoGdmII97UETS49g9SMlM2ooIsdG1brYpL6nB6ouvPn6DkSwsb9WWRjNX8pq2sIaaZOiIJRQVJlHhiQdVTdrhpCRYZlgHxpgnzhAxTIYiOnqfqrs0GVLnki9Sf9uJFCeOTq3Pw5XwSDJu2CqtgLzzOm6Rttl5dZeTwZk0Ep5NZ/3YnHMlSO43wJg049nqhJ5OcigKE56WlhZ8+umnqeOvv/461lprrW7pVInVD0291fDZdvPtymuL2NAYbyqtztK6eiQr4yB2o+YZRsBAEiPj089b8EVnbyyuKcKzJApiA0lVjjmRbl0RuESSgZpLBkbUVKovcS4jEdsWB8lZmnqOkQFdExNR04Mpg6wmPRARTzzZIpvQCMFTxtsagqgAA4fgaA8z5pBGQBEy5TqfPkf7r9pU+1FnTOyqUhEebY8UAdyOXlEixrLImVYEesKfM/pMRZT1dwfbZoYmPq2JJPWEhvXOAYbtJvYuMPtaqgPYbulaakNtemh6iCaeRHvWCT5DYkicEHlYcY20io0Se63W09AqbM6SnGEa1IPr9d1OSqRxEcBDaWz0uhIvbFWAq3p8atRZ+Obcy1ZMZ+qgMOHZZ599cOGFF+JXv/oVAMXc//3vf+Oss87CAQcc0O0dLLF6gOq7azVuDTpWVFYQDy4uwI2tj/Tap2gsPGgS2ttV/JH2aoglUYCq9sSSyYoR0CvNhJm4HzQlQLaaqL6EhMa5oQQiGZzj+4lgbJukhE14BCPu+LGqKsPmiEptKNHR59w4QpwQHG//PYQyFU9IsvhHS56LjMss+eERaLm2tG3oaYjivFpWCAVCOEKeEAHJJZhMyIwiOMl19NXR6ij97mk7IU1qKlwawlPhtsRHR4bW16myVEpj2woZQoOY/GgjakO+sr9P41lZJ62HIGX1jQ66r2sBQld2aMkONRKXYOAsOdfTiE9hwnPVVVfhW9/6FtZee220t7djzJgxWLBgAUaOHIlLLrlkWfSxxCqOf+z+fTMxf+nRn1irRaqOUTY80qiTaHoIAGDMzjgOJB5hX3zRjM87mgEkKixKqqhUJ2DCkfakB0oq3s4zonZTBiQJItPEwg2UKBy1VBQl952XxDAliaGZox2JDi3PuU1+mGc/aUNaRCeb9AAAM8EQpQDaTz4CrdeXpKcnQUUXTva1zQwj5EMbGpti9rxv4Nri2AECmZW5vMJMvL1YjUZTQCSEi8USHWqkrG13XNCs83aHG0Neslb3uxv2v2nzjtUBT406ixiXpxebPTUqRWHC07dvXzz33HN46qmn8Morr0AIga222gq77LLLsuhfidUAXrfQ+F/qsQUkpAcAIDhYHCGVxQbFLuGpdqrRtL0aYkkcZ0cbIdPcfVSqQ7e1eNzqG1X9UKLkGE37SAJVuTGexOwxEZ2JVCfLDsdHdlzJi4+kuQbdVKKTnE/qy1Nj5ZEdlwBKIgLQ6oX2k5WXWUl8egZ0slDrGGxVlVkESBWzx2Q59xAcDRav+PUh5U2loigDiNVUMt5WEpxEqiPJpJqovABNjuxggnkSHBfzx56a9JGpGgC4uX5zscFvb2q88CoIKhHX78LY55VEZ/bonmnXUzi1xKqIMrXEisUbe55gBQb8z37H4ItY/bSkFqKmXazjiT5ZdSbxcLRnlwnPzwWEZOioxUkMY08sXY8yCbZVWDTLdOgQHqOKYh7D37jf2nujURsZCuHY3uRJcVxC4ZIYt/50ebKdUc6V8KiyCblxjxcCUefxJqDX/ytJT3di1sizASh1q9/QX/07bu40c2z26LNQFQyfxd/Ix50B2iOGJTEB74iAmrGr0wQpQcBsg2JjiAxtbKz2Q5YQG8COkKxTQFjSU9NnaamdA6ZSReiFCQ0GirhN15YNpC4XbmJX+r3rMWVZ5Ddb3nh2xzMAJO/GTs9dUbiO2aPPSkm+9fMd8/zl3dPRAlimqSUA4Pe//z1mz56NhQsXQgh7dX7NNddkXFWiRBpv7HmC9eH898BJiKLQqKqURKe+fFRKBhDRqohVVp1EqqOhBnxJCE9sLKltW2BLcGhKCzf4mEt+6vZRp2pwJFFuzi4ffG7MeVIacywnmrOuw1eWqq1comMHQkzXQ+/Jh0SilV2mxNLDR3oo0dHQK/Pfbn8OAKBXwECjlgiiopQMloQU0GRFbdtGyfE3RPYrDsnRiws35xVVM+sJlu4HPJHquJHVAWoXV98DUb+PPi9P7bG1suD5nc4w20LCS2pWVAyoFY3ChOfSSy/Fj370I3zlK1/BoEGDrJgMReIzlFi98da3jgdgGxsCymC5ozO0DIopfComDRX+PrFzqUbcITr6Onu1qAdLV6oDJPm7XImOS4BoPT6XbX1tPQPnPNKUZVzselPZ12RWp85nSXg8RMclOTZR8tRNtimxsYiQAL74/hGllGcZoejEpqUrzYGEm5+8I/7BVWZ1aYISMiiDY2N8zKTjeZXUG8bbiQ0PUVPFKSHsHFi6DSeEgrNfBPWIuQDDVk9dW7ziHgJrXMtZLHannU1eSI+ehMKE5/rrr8dtt92GCRMmLHXj06ZNw/3334+///3vaG1txahRo3D55ZfjK1/5iikzYcIE3H777dZ122+/PV544QWz39HRgdNOOw2//OUv0d7ejnHjxuHGG2/Euuuuu9R9LNH9eOtbx1urLzrp1mLvKU0MlH1NMlu6BIi6rNPzWjLEyWCqPaONMTFJCqhVVfq8BpXmJGXTJMec565oPVtlROFzp2fkvrNcxH1qp1Q9DaqcvPVwu8/J8XT99DdNgWbUoOTH2S+x9GiJ7doCJiHBDOlXgTQZHt1hCgDEgQAT1Q3MUUVEWJAQk9aAoz02ml8iGGpEQskYrLxWFSZjwqSzmNskJkgRGcRlhSVpDZmwCE6hQJjUy9P52NwFEz2v311q47P17JVHa/HCN04jIQDUODF3p9MBJB5UGgzSnAOAUc9e2VAbY5+/zKjGVjYUJjycc4wePbpbGp8zZw5OPPFEbLvttqjVajjnnHOw22674bXXXkNbW5spt8cee2DGjBlmv6mpyarnlFNOwUMPPYS7774bAwcOxOTJk7H33ntj/vz5CAJ/9MwSyx9v7HkCADW4uiLkd/c5FgAgRWDZ4lheTJKBEdsWmn8vC9TLxCU41FjaR2w0fASGxvzI8oJy67HsBDwSmVzVkkt4MlRNRZEiQzyrP0iRHGseoecAm9TIpB2vCqskPN0KTXg4kypSuKOasrwT9fsc7+tzNamVvtT2Rf0bMgCWcbGtpmriwpLgVLggpCZRJes+Ask5zhIVF3U9ryeNSEuCE2mqTyJsfd8SRm2ubX+ozY8mPysT8fHBjSdGsbQqLl337NFnGfVoT0RhwvPDH/4QP/nJT3DdddctdeOPPvqotT9jxgysvfbamD9/Pr7xjW+Y483NzRg8eLC3jkWLFmH69Om48847jafYz3/+cwwbNgxPPPEEdt9996XuZ4mlx//tcWIqKzGg8kvRHFNSqsGoyeO5FZGVaj0wJhFQG4A6aikgm+Soc2Sb+8v5XM19Njb0XD2jYX28rsFwEfeSDHhVVI2SHNctndYr0lIfSnhLO57uxbZzrjbbL445zWxrUiDIvg7xoPNjRURiShcUlFAkaqpkX3tNAVrao+1yhGVQTFXJLowtHfmOfGMGkLZ18xEiQdrJy8EHJKRIE59lbePy4HY/Ms90v5cu7ta6TRJXxjLt6Hz3R9+V7edc1a196ikoTHhOO+007LXXXthoo42w6aabolKxQ6jef//9Xe7MokWLAAADBgywjs+ePRtrr7021lhjDYwZMwaXXHIJ1l57bQDA/PnzUa1Wsdtuu5nyQ4cOxWabbYa5c+d6CU9HRwc6OjrMvi9ydInuBfVeso4HAkMfuBX/PXCSOeaW01FQdWTiZFVqF/QRFrqCpVIjnyqKIk148vdpv/Ncvd3r3XPUKHhp7GcKIUcNlvrNskiO65qObCJje6M12McSXYK0SEs6HYQVLZnE4kk8sZIXwHgxsvh70nY5PCE9gJbw+AlPns1N2kHAdz+NLXZUfUSt5ZCf/Otpe3Wb6zL0/d2/7bnY/6WLlro+SlgAf98b+R0awU7PXZGp1lqlIi2fdNJJePrpp7Hzzjtj4MCB3WaoLKXEqaeeih133BGbbbaZOb7nnnvioIMOwvDhw/HWW2/h3HPPxTe/+U3Mnz8fzc3NWLBgAZqamtC/f3+rvkGDBmXm9po2bRouuOCCbul3iQTaEHmD396Ed75znBl4wlCgV69ExByEIhUZ2I1WqtNALFliE2oOgDvERW8HXBaKIpz16vpy5DSKPMmMW1eepxNVIaXj3NDrGu6av78plVY3XCeUhMcyz45FCFIAspaoJRmT6DvjjiJdXu3x2+3PSRPy+N+QkmpIcJYM8SGX4CQiuUSiuolMfKvkHACIeNaskN9XS2H0hK3VVklQQIEw/kapHU4WsmzpKBoJ0ZDKku5cW4slxDXJzSKKenp1Ffdve673uBXIkcEiNfv8/mI8uN2P1D1Amm19rivgkIg8xsM+25wXvqHI0dJM39r769kdzzDPXt0zM3F4epp6q3Acnj59+uDuu+/GXnvt1a0dOfHEE/HII4/gueeeyzU2fu+99zB8+HDcfffd2H///XHXXXdh4sSJlsQGAHbddVdstNFGuPnmm1N1+CQ8w4YNK+PwLAUWHjTJTPRSsJQxLUVTa4Soqr40N9neggOOBmPSJMOsRQFElDXYweSx4VwgCBqPFJyHlDSnAKlQah+H1HjctrMIgyVZyVEZudc10l8pCkqDGiY/8SRJR3iaNTrOng4AUQcgqsz8/owDPChJT6NwyY6W2hgNpGUgbIf9Z5CWxEZIW4VVI/Y9kWPro68HkoCAdhBAgeb4WwyJhEcbG+dJVYIMtS6FN9imQ3Zcez9D7OKgpNp4m3puVriwVIBdwX3bpAmPDsann7c2zvZJcijZAWCp2/d+sVj2ghfHnGZJdnZ4ZtmrpqgbfE3Ydl/Lg/As0zg8AwYMwEYbbdTlzvlw0kkn4X//93/xzDPP1PWsGjJkCIYPH45//vOfAIDBgwejs7MTH3/8sSXlWbhwIUaNGuWto7m5Gc3Nzd13A6sxPjhkIgCgtW9kkkWKKoOImJnwZJzrqdKilvlhm0T0SfJRvLvPsQjDRGrDGMg+IB27c2OIHAjwuE3OJXggwEMk9XgkKAY0jYMj52/EkDdrmWB5MgEAR7Z6CHY5q21eh5y4Up8GfEy7RxabXRntA51a6fN170lEbOlVcqsR3DD+UUx4JDmvwWKzY9t+JvkxJBJvq0gyMMFBXj+LIFFvKq3Oou7kVKqj1FhJ1HLAVinZ90NUUEZKQN6jHMlOljek2afODbDjeaXSTywFshINc8aw/0sXAlCkSMhEGuRKe4CE+AjA5AgsgpfGTAbAlgvJobDvOX6XllJqtqxQmPCcf/75mDp1KmbMmIFevXotVeNSSpx00kl44IEHMHv2bGywwQZ1r/nwww/xzjvvYMiQIQCArbfeGpVKBbNmzcLBBx8MQEmBXn31VVxxRfEokqsztB1NI8nw3v/uUag0R2hdI/aoaAVEnN1bSglGP1ihXFyD1ljHPzhAr/8305xec8N2RO1qm1egRlRDlmxJgSoU/xMAWmLPmgAWMDCdfMclAIJBxpnIIQFZk+l6KSgBWYoJmdmjYQLfeFBHVZVLarraxyyD4az6GjAwlkIqVTc1qNbPUwIskkBVS4UYCic7Wk3wwLbJyt8QB2n/MFrS0hyTjSYunMB9CTHR6htKDqK4vkiw2G2dm+NUQqET7Op6daRjIJHoaJJTCeyXREhm2dO4ZMUt2wjJcb3LUulMJIOMj0nGIGXi/cU57Yu3qVw8FBMTbZvURhZkuhc1yVD1fCtaCHrfNudaz3O/ly5OER8AeHj7c1JSHh1WQNXH8K34/NJKqroKag+pvuee64VQmPD8+Mc/xhtvvIFBgwZh/fXXTxktv/LKKw3XdeKJJ+Kuu+7Cgw8+iD59+hibm379+qG1tRWff/45zj//fBxwwAEYMmQI/vWvf2HKlClYc801sd9++5mykyZNwuTJkzFw4EAMGDAAp512GkaMGFHm92oA/z1wUmJsG6uEtNQmCIVZfa9xh616CkKBsEWAE0GZVlvImj2K8FDVHcaRBlhzOlRAEJ8L+vCEtCAmJpqoxLJ7RpaarEl1kIVMhXhNEZ342kiYemRNgtVkooKhS2RAEQ9DeOKJO4tsOGocLxz7llxkqq4aG5kbkZZYEq2M8imvK4dsAsiMCcBi2YC+V/UM44kqsKVgrLRabggmICC3JTw89mik6iQaQBOwbWQCJ05UED//iDFEkpt2IikM6QHSqRlovBwt4ckKJeEe6yrZccHhr1c4bZqwDtoDi0okukC2qWSLep25CVgjRp+ZXYdrLE7JLZAME6FHlEqXCJFkhoB9u4u2P90JRbK7VZbcrShMePbdd99ua/ymm1TytbFjx1rHZ8yYgQkTJiAIAvzlL3/BHXfcgU8++QRDhgzBzjvvjHvuuQd9+vQx5a+99lqEYYiDDz7YBB6cOXNmGYOnAbhGtjyQCJu01EYaqY0LGed+ygx0J6SS1gCxdAfgfdTvwVrs1441ATxeJvF+Tfb5moCsxZ9/vMxMCA9TLiIAEPCUBERGAtDX1pJtVo2UPUu8L83EjKRentiYeJeBZKK2CEReYKB6QYNcdDUUah1y1hUVkrmG3EN2dFWZkB7ACtjrGnFLwSBqEp9NOhIA0Gd6acsDKNWHdvtu5gJtoVpNVOIccVTVVCE2M2EgclOduCQhCfDJwEXi2cQlA5N+1a2OSk4lRwEXlr1QkTU+tbVxDX2tvhM7nZTW2SE+VC0kWSxhjNMgSTSWriYLVEVo3bNMUmgAyrYpkQbpMjD79PtxbauoKvC3259jpDg++DLGL08oCd7KsXApTHimTp3abY3Xs5dubW3FY489VreelpYW3HDDDbjhhpU7sduKwNr3TjcSHcaAoCLBwngga1ZqIwD4+PDxloHxwF/OwGfHHGFNZNyEMlarey394U0Aa2bgrXFlYTJcdf7oSAT9QvB+qjDr1wI02YSH1SJYCON6CDHx+LKr6zSpqUaG4CDk6njsemKuJETKIk/eIB/aQFuCucFKsohNFkmK0RUiUsgQeSliyef217lfCSUVY1rih0TFKbkEiyV+ACAiGdt4rRwD5vICVVP1rVTRVlErD1ddpMu6IRcohCM1oed1bCsIDnDCTAXAmPASHh0Q0E3cq8lIOkZO9m9LpTpFX4EsOx4Z5w/TT4oBgGQWiViasFXM/GtXYn9eyqaQSsgiyYzpoIpzlFy/30sXe729hLTMDcnx+JkBWNHL+lHPXmlFbO6p9jsAymzpQJktnaLz7CPBWomaKP5ixWKB6scSbTcnOY8Wn3AEKv3ilWaFpZZdvE0RF9a3Gfy4G+FD7bKjwNfsBTawtzrQry0hNABQi9L+nWabm1WbGhkEEMXkqLOmSI3erwlVly5rdZSl63Xbs1RXwiY3hPxYgU+c8249SzXJ513bXUlyfP1OcneYUykypJ9HLbnGbEeAWCJRWxzvdnJE1SQoXFApPbYe2u5HaAkirNWyBACwRq8laGlRhIdzmSIhrgeiJEE8pWSIHC9Hk45FcOMBWY0CVdaQDy35IeovIjlyvaJovRaJybhHX1kf3Jx13DmXJc2idUaCK621r3/SLv/1p6/N7MuskWdbxtq6X75n5UvYqTPSA8pTzHWb1xCkXzRCNZAMsVWp62HmXGsgQMMFUENzFwxyhWQ2Xxbodi+tAQMG4B//+AfWXHNN9O/fPzf2zkcffVSstyV6DDrOPAK8lXtVOwgEmPO2uN5IhuC0hEryEaumssgOoOx5WN8WRXQAoK2Xh/CQYZOSESGSc1GUEBoACD2zQsjTx2idDdnpCEByh/DEq2tNblxSI2lZsiL1t5ZusyvnNbpKfmK5uqQjb4a9EgtgEyCtbonfF1lL3hWpwvuCx9lhZCQhakldUjIlOQTQ59bVM6FoJVZh9WlRoTN69epE2KTe7Xou3lIQY13JICKAavaF8Eff5UwiksnkGThEIi9/nGrL9ikw256+NqruqpdI10e66HlqP8QlgyCkIemrUqPpev70zVOwJArRXlMvLzXcrnCJgAkvmWkEARNk/BIJuTRH4nJIVIsuKRLxfdHwARodglt5y0IOS4rUnclCV1Y0RHiuvfZaYzPTHSklSvRMsAqzDIZTE7SrU28GeFssDWqrJATn+Jty26meNx68d2yzs2Yb0LcN6EMIjyW5EenJnZ4zEh1PQ5wlxMdHEPLUVhlSGdMfQ3hiAqTLEQIEIWM1GrUl6CapTlelQ3mjnitJE7aVjtWiS4CQ7GsCZIgPl5D6vRJKpaXVXSyQ4CHzqsxWNzwWe99UuERbpYrebYrwNLfVjApQQTohH2yjckkkOuqbTQiQlvoAiSRIlZNWbjl9LC9nnKUqE4kdB40LBKdc/SjHrpqISJVyr8yHJj1AkkYjacPe337OVSa2jPtJjH62656/Oz13BeaMPjOuK7FX4oSIqTYTcqY+Jfv5uZ+KIT3Wgwd4bFNUEp0EDRGe8ePHe7dLrFpg1AgYgOy0yQYLgS++r1bfQSsQ9g/A+7cAAPiJdoDHzilHounSRDVRu3QiWKt63YJBrWBt8RJ/rb7AwH5AX0WoZUuzX1UFAEKACZFIE2q1hOg0AajRr52rfZEzk/KcIZS2HyCZUQRz+kQmfiYUoTESC6GXWeo0d8ik1ReP6sxFvFKXuk+A36o0z/IzDykSY/eLkX1pWVb6+pBMkDLkxH4qFv9biZ0EhHZTl8wYLXx+7BFgHJYadVXFk6PORktsn9OnqYqBfb5AS2/1IoUtwprIZA2J7ZwABBojjEq6Q0gOTwxEOCRg2cOQMoBXmkInaOs4sZ+h5YD453YDGmaQHN+5YlHPiUTHTZhah3gtDbEpCt0TfWfc6Z8g9ywliyVU+ndhqXWQ5rGupr5EgzY8RXJNrYw2MKUNT4LaZUeBxca8YnEN0aJYlM5jw+Ne2ptKGRlToiNuPVGVXaMXsNYawBrxsxQCCEOgSVk1yyAw22hugWxuVudNRURVJQRYFDMIrd6qxq5jtRqYkfBUFQEy6iNHMuQjPvSYkITUeK4TTr20LLUNomSoK+ZxWYQn616yDKW7YuPjsy+S7miaJflyrnXPRcQjrkZCBHRKiA4JGRNXEcHWjThou3HVIT/P73SGCdbXp6kT/dpUMKrea3Qi7CUSL0dux6ISURICwg3yKSJufjId8JPuuyqtRuxnssqmbE88den2tXG0Nt5tKFWE53i9HHe0jCtZMn0AMdYGsNkT13v7sizx/E5nmKCPgB3ZWnnhKTColCDj5k4zZR/bYYoV74eqtThgPPZagwghl1ZkaV3zqmK/AywDG5411lij4ZxZUeQKDEusTAjPug21SyeqHWFHK2acGSkNmgJ7kpv5A7ANBqmdtQdC9ukN6GjWro0M55CBrqcpLWkRQpEXU7m+PiYTpB6j0tJ2P5oMMKblwXElliFDmkBwBuiBwRLtJzr3ZF8kZRFZenlVpEG/CR8p0ZdSUiOlLX3h5Nlrd1tX2pIlfWk0no9uOmupXudazzSkzjEJqSwnrDMyVj2yTtfNP/kJVyW11wvfOA1NXKJ3s2J6/dqWoK2f2q70Fipeked5SwnrmSTHMwgEkeIA0jGBy84g3ghXz5L4AH7y47tWupIXUk+e5KcRZMXlQezF1Ug/lxVcCdKc0WeST1YaEhMwmUrEufsLl5rtWSPPNvcgoe5H31FN8tL70UFDhOfpp5822//6179w1llnYcKECRg5ciQAYN68ebj99tsxbdq0rCpKrKygo1jIwGKVF2sOgTCA/FWcoXe9tYChivDIvn0hW1ttqY1VJ09IAmOOFEHYk70j7YGkHlLCNiziLCEbxt3WqUu37yq8LdJAVWOuuEHAltfT9gM/iWkUVl9dI2GRBPPQBAiIyxSJbIh8MuQYKVt5smj0ZG//k2szbX8QkylOSCxXgSABQAYSMkrUXbKGREXIlWcgsPJLegIm0RJG6N2iSE6vPp2o9Fa/nYk2riFgflZpJDyJCjCLCGrDXWN7ExMc/bkFQdoAWghNDuL2yI9XL89Vllt6SuWV3E5KotNIgMKs/tiqMNqetL52KZmx41kR0h0fqMSFkp962HVeMuc+OepsCJl4ZlGpUAmFwm7p48aNw9FHH41DDz3UOn7XXXfhlltuwezZs7uzf8sFpUrLhpbwyFoi4WFNHKxfC9jgfurAgH5AW6syMgYg+/SBbIsNj5ubleSGwvWo0tu1mlJNSYeYGDURscg0BsOxV5QlBZLwkhuzT85rOyDXTsinKjL1e2yKfOXy0Gj99dpxz7mu8PX6lWXXlFcHdb2n7bp99v1LIH1liCebVnnpbdkpIDvi1XgnIIkAudf/W36kZ+FBKu2KEMyQgkZgzM2iAMMfStS/f9z5h+jVVMWA/so/v3WNmolbZbwhNcmpwQQATeWpE4nqym1TbTtqKKfv9SRFIqNeb91UJUNc4/Uxt4wLn4t5o1IdOzGvo4oTKnmobrsquFFp9RTCszJCBwsFT7z2et+y/BciyzR56Lx587wZyLfZZhscffTRRasr0cNQu+woMxmxJm7SQLA+zWCD+gGDBwIA5ID+QGursr8BFMHRaUZcyY5WUZmYODWjsmJS2ComTTy03Y7XnsaZ7N226kGTHdpmPWjmx0VapZQHq26PJMYYPFNDDcRSGCKlcg2KuSPBSoeltes3zUqkXPvpNVlEKe9cFjx9YNrV3fWQE2Sy0/2rxeoYraUU0hJgtZ98BFqvX/YD7MKDJnlVPFRS4kMjbtUaIiLCREJmdDsWwRH0XDaB8WYY59LxzrKv9UVh9sH1HAIaUw/5IiU3Kqmx6mnA21HHGBKSoRYFiSu34Bjx5HV1ry+Rj8SujIFVVg7VWWHCM2zYMNx88824+mo7UdlPf/pTDBs2rNs6VmLFgHEAmuT0bgLrExOagX2Ubc7aawIAZK82oKXF7+mkSYsVI4eQnKiWJjgZUpxUvYDy1ALyVWHmuEyfd9vwXZdFGDTxMV5SOWRJkwtKSrpDp26pnjyqO4qAeUhhRh8a6Jul4uoi3CjWKY8vTX4gbMITJYRnednzaMmOdguXIq264dqTKRA2ERCJOilwfoMtnroOr+36A3z2aUtcVwdamRLjBICl95HUjinDMNgHH+GyVFwuPNIcfW+2pMeW1uRBa6xTkprM+Dlk23GD95dPq9GEVFIwTcBqUYCq4KU0p5uhA4QaSQ+A9pOOQOsNPVfdXJjwXHvttTjggAPw2GOPYYcddgAAvPDCC3jjjTfw61//uts7WGL5Ibr6aKA5BNckp08L0EeprDCwn5HqAFBkJwhsryiNajWR6iCW4tAAgnkER7ue50F6SAzgn7B9MXvcYIZ5qOfR5EpmsvrhO26phwrO4CmJjmMo7ZOMmVmMkAtKyLoLKWlUPtGjBMgqJZi6Nh6lWI0lcZViAeCSHyqbnpZrl80gy7g0yScBACTSsVYl6aCAPJDWz0hVTZJLvPOd4zDsf39qzi+phUa1EoYCTa1xPU0CjJKcGuDNL0D6KB3bm8yylgTH7qtFhOIs9o2Qmqw+ATHpy6kij9QwS9Ljf3+o/ZB+llIoshOZzO9KpVUiCSvCGLqNmPSZfgcWf0/VK2X8TcbrsJarVRudZytS1DRtxUZR71JqiXfffRc33XQT/va3v0FKiU033RTf+973VloJz+puwyNuOA4AVB6r1qYkl1W/3sDaWoU1QKmvqESnVktcxlM2MjlkhBAVE1cnV9ri8eLy1ekrkxhRpCU8pv4uDIbGbdtzj7n33SDJyXL/Bur3N8v+Rx/Psr2hffeSR7tPXnse37W59kR2H6SQScJXERswa5uemoTsjMsRaY9G85V3onNKstrUg27TxcUH2f8eqCQ7QSjAg+yIvkAi9FP55BJ3cgAQHbHdyBKOWmeAajV2jRYc/Qa2o3ntWILyBVBTmSQQNKvgjdpeSXSSx6sJVBc4ahJwEOZfGoTQJUA+l3a1bdeX1Y6GL5aOZVRMSE+9ODtGfSbsIIqR4KjFcbgiwRHFaiwApfrKg/aTjgALu3+hsPgEFTuLkfVX6/V3ouN0RYjyTACYSj1vldOLocqFt2deV2T+LnNpYfUmPPLuyUCfWGrTpxfYN6ZAPn6+2h+6NuSasQqrrc0vtdGGwy7BATwTn0eKY+xpcibaRlM+pM6LbMJj1V8ghqvbTyny+1GU8GQRjaxnkBUhOkvaVSPPuh7xacCIGSDqrayhJI88aaJDSA49p4hNXKZTWDm5fGq15svjFaVDfIqSHq3KUoTH/p2yJnTGlZSn0jtWY/VOBnAda8gYH0dApTfA2+IJ+zMJ0RF3twKwJpBYO+iSKi+LJFHiY0dstglOFuGx6m4QSRaX+FmRCM6mjMfY2L0fSniEILF1IiLRiQlPNVL7Wzx1XbHOrgZoPzkmJvHatvnK7iM+WooEpB0LDPFB/O4JEHKDJNJ/bMJII/9XzveTnpLwFMTqSnjkQ+cAlVB5WwFgO52ljr90DQBAbLgBEKcUQRQBX3wBpoP+acPiRj2WHA+qFOGhebAsQ1ruGLjyxtp0yY3rlVUEPumRj7hkGUHnSX7y1GF5UqhGU0S4xy1jbY/Ex+1rUdJjDtSR8jheWlL4+uAQnppUhsy6PQGjA9PXW/ZB9BEVJD3vf/codRmXKUPfvBGTBxLN/dS73Dw0MAE6dcRt2WkHqdQu+NGHVZNQlQcAbyFkpIaE8NR5dVMxjIRNfGypTprw0Hu0VHKEbACo66mWJbVxU1TUDW7otE+lTEJwRJEmOa4Kq7TZyYNLeFjIlru6acnkI2wpLQd4U/z7B/G3rNeiWtJz3sxUPcvUS6vEygc5/zpgSUdyQEs01h6gCA+RcMhnL4Ncb6ja6dMnOdfersiOK9HJmuRdqUk9WxHXrsQ9TuvUXmBCOI5PlCg5MXGESLIoNpLPKq8ugITlEbACFvq8oMw9eAyYk6ya2X0pQnZo3d56aBQUt09IH88LaEjTTlA7HCHT8ZUoumjwzDhMDEoGBolEraW9vyjxMn3qgsYyMYYFZMTgU/34YtVENaBZxKSGpGrhx98EceuJCJwULOKnJ5h7YxX1bclIggXM2PVKLo20x1Xjua8PfexS37omNVyCkZxn1nskJMATgmFycJn3nIFaWGV5prmoR3DykBclWggOIR2J1AoKIrgyovX6OxXhWIFoufpOY4MHxO+y/mQ12YnfUf2eVy+cAMBPfBpBKeFB90t45FMXqQ0hgfYO4DMVMh6VAOAc7IDuDest/36LNUGzTb+njgGKoIRh4j5eqSSjY6ySYh0xGWpfArS2QA4YoPZDMtFXO9PGvlkkJyv7uM9jCrCNlN3ZBIC0CFCGPY8HqXqz7HxcFLEN8rnU03ryVF71VHIa9bK5+8pqdFYTY20tPaHSG7pP4/n4pEEuMohLZloKup1lD+RIeCBlosYSMs0LrXocSZPD6xqxB3DxwSETU+karOYFw9AHbrWOtZ+sBvGm9VvAmmiEb5mShDGSu46maYmunATxWUyAatKWZDn3pSU5qg1nW6bPuRIfel8++x617/EOW4qZw5fA1JXi0DY0oRHkOirVcZNqlhKexqCNiTXRoPkPlxUo0WIhwCoxqWniiQrLXWjF376JPk4ShX26pBMDL/hFKeFZnpCPTlUbHSRtd2cNaO+0B+VQQt57OgCAHXTlUrUp3lIDN2MMMo6BwzeeZBcKQ0Ue9KTW0ZEE7KtWVX87iZqqFiVpBXQgQX0ui6TAISWAf3Kmkg56baqcRzrkgxC59jfSkSoxN+hIFnnidFtHbtarYm4/iyBIJEaCKWmPIDOM4LYLO63P147vno2kogFbI84UqR6p3jE5+xKgPUNdqPezVFoeYpKFXJVWI2THV7f0EBwXlgQQYOTR69dMOhKoIljz7hn44JCJKUKgMfjXP7P2PzvmCDQPcgZtIH2fru0SZ4iuPcYUl9WkIXcSMpMU/HZMFEwL9ZAh8dESM02USJ8ZycrOHImOJCkMXINnH1zy5COQriGy62ruI0c+cACyYBqKEgA40Pkj9W51xdC/UZh3LmRgFQam1VghBwvs35T+isydKbogKS4JTzdAPnSOkeLIRe1KkqMRJWoBJqTKQRUGvmqKQ8dECW21lHj7jhQhYVQioQlOtWpLABgHG326UoEBQGtrQo6yRjSm7004ryOxvXHVWTSonj5f5x6TmyOt+NRkeQSIdtsnUdEqL0qGKHlJGrHPG9ISL7dpegtLokPtS6TTptHNpPtVxF4nioBqTREdQP2+ukwtSpMan1THbCfnvBNrPZVVFlnKs/3JIj8ZcB+X1NMwT9phnFk/X/W88YWkPJr0AOmJ30XYCybBrmWzo+GSvcjzXASRanU3ONHwUWlPhrrLp3HVsYga8dJyIy7r8yLDNohKcej1umyjua+KqM5WZ2i7HcvIfxmi48wjwJqJRKeJkBya3j0eBzI1/lSlXgANqbS23HLLhpOHvvLKK4U7saLRFZWWltLIRe2Qn3UkrrQhT8TYoW1wywKujrUoaQw7+Kpu6b945+eGxLCODmWvQ1VDNPKxm1Gc84S0bHWyuqdXb1T/9usLtvgLVXZJR1qiwh1SE5+TQWDte1/Mrthx5Lmu0z7Vuz5GZrwfN6Ch9a+09339yYv+nDIKdiUudGZwrrPISLwdxSQmL2O7j9BYZT0ERDrnXMmEj8BkEZwGdB+2sa2jwvKpqRxjxhTx8dWXB6PKkUu1ul0y+QgEbfF3ULMJDePMSPt4ixoLrAE9Q31Iz8maUmnRfGOmTLyfp9KiZVUiUkou/KotF64HFz2m681yH3ejMucZg7vkxkew3JQVFF95/IZ050tkQkt3ANtgOMs7Kg9abaU9r4zaqleQRO9vchb+rmq7ljBy9d4TiS8hPJ+JWveqtPbdd9/kRpYswY033ohNN93UJA994YUX8Ne//hUnnHBCI9Wt9JB3nQr5mQqaIRctUTmnwhxC6Ca41IPXvacnE3QUAZ01sMOvK9wfPuxwRXoAoKMjjmxMsoiHYSJVCgPbI8qreor7RN3Oq1WbVWvpijEkJrcLAEIoyRMA19DSi0ZtcYD8CTSPDLnXZ9rw5BAUd9vXl9w4PBnnfLY+rnrJ2F6RevR25CFDZj+HrLiEyLkuZVvjsc8xE7JwjjUA7wrOtKnP2fsGnFxPDByT/pLrBRIRH9PXalF6YpDLOEP1vPHkPuzftx4Zkp1AZNKGIDE2VmfB4hRzTHt/+Syqi0TkjoWK5jnE7QKE7BDyQklO2k6HkiGHbGTY1ehzPpKjy/ikNt7PxhdFuouGyCXZ6SL074+uq4E7pxyJoI+92GVNap81B2At8bwQey7m2gkS2IsC1ctGrqNoiPBMnTrVbB999NH4wQ9+gIsuuihV5p133mm44ZUV8q5TIRd3QC5ShEcsEXGwXaK2yrBnkAJgnZElDSoUAyYHfNjhqo2/3+JIA+L6Y9WJ5NzkvGJCQHKeuJprxISItbcn6i9tsGxluWb2hK9tWbgqxxpY2XuRJz2p93IXUf80YsTsI0dZfWjEfT0l4ZENlJWJOgpIpDq+cy6pkc77mEdqrP7E19GknpaKi5Z1JT76HuAHNaXyndddTxEfUgVH7MURHwg8tenrIuXlRMkHCwGuyQdgS4w86jDdF7oK9pGfvOi12phZ3xvzqfPoffqeqe4/TfHhON4ZaZdQT8SV6OgcU3lxePQxiqzEo64HFb1Wkx2fakvdYveQnFKFtfRouviORLVFiHIj0N5TvIUjWKs5kd7E44sxzg85EFAtAfzjaRENQHcTHop7770XL7/8cur44Ycfjm222Qa33XZb0Sp7DKIbvo+opQnB5J9lF+qsQX5RhezUK2wJGTK/56ujDzdoJJDe0sKZWI1RMVFvScv2BJCvXK/UWloaVIvsdAxSWPY+CInBLmlL1RcVUi/lnvepmLLQaJv1yI97PEViPeV9dfokLvp6a3XjEiBynSYtrtoq3paEmKQkMa66ykN4UtIaci7xkCKSEgCQ+YTEUq04qGd7naqHdNm4gmvpTpj0wXWN1wRH1gAZ2YSHC0Dqd5chMZw0BWwvEV8Osc4fHVlI/cVCh4x0SmLwnpTLlGiZAnaZRp+7icNDfi8RaS+nhPTQ/Ty43lQuyUnuxyZDjaiqsspScCcBa4nuQ9OldxhyXz1/vFetVbt4gtoIOXg/tXrgfZqNyQYA5bhTS15IV2pE42wB6N450IPChKe1tRXPPfccNt54Y+v4c889h5aWlm7rWE+FrAmgo5bYmDYpQyvLXS4P1DCLGi/XIuvFAKDUVGSy5MOPRGEIoVzPacBADa36qiZSHPni1WDbT1ZF35iRELnOKtAR2S9kFCT30smLSVco6uWiyrOf0dD2RF1KQ+HpW90++VYlvrYcwpMiJsLeJ9fl2tPUIzKxisu1DclTHZm6gCSSsZ4ca456RGZPtD7bEA0lmbFTC1irfs91ybeVXMdCx7GNw4rLQ6+XMVfXUhweJAHXVAGYlBU+AkIFR66Ki3pMaVWZjwR1nn0kIAChSdcSaVdMm5POs6bPLyZN1C5HOHbRmc/WA5PzKkqIDgBEEY+lNrbNDQBDMopIVWjMIs5kJpFp1Cg5Xb9Nfv611/FY/5GbulTX6gzqEajf484pR1qSTdeOjnFpBACsKQDr1yspG3IwSnrouBaJWCUfv4NkWy/E3LEjUQF3jRgVJjynnHIKjj/+eMyfP99KHnrbbbfhvPPO61Inehqi649FcPIt2QUCbnTxAJSVeRF9Z5bOMuWRRF6ARut37URcbyPq2aTJ0NanmOI6yjIA8I0mQv7xhqSvVMrAmUrgqEkbF4Az8BYmGfXKuRGNKThTX0SWZ1hEOtcIeWm0T757zHPJpmXrSWrodZY6yVNn1kBhSWlsQpCuyznuSAOotIVOyFloxI6nXo4oTZCAOIhdPGLxALZKKweMwcrt415nPQP9mniIk3scgOXaDQ4wyYxagE4esqZ+E50fyxBI2k9SDyUyskaC6nUiBSq1odIzV5KWmw+MAYA0zilBoBhiFKU9pnTgCk2WOEvnGqPkQ9A+xfVwMmEZoaFkuWTI32/aB12P+ved76gcgTRha4kuQCKdyoUKrYU0SUKja49RNpdm3vGMXdreMCY49UIr6HpS40kXbIwKE56zzjoLG264Ia6//nrcddddAICvfvWrmDlzJg4++ODCHehRcNUBHjDOIENuq7CsWCAuaSF1ufrKWpRZVrx7l10f5+YYX/cw+7K370jsbighia+zC9tvjfbMSvrolKfGzh0gpIPDiiPjzjwpw9kcdVJXk2i6HxaXgB4sfaQmT0JUD3nEpdHEma6djjsQZKVjqPNONtxvwGPvQSY0N3Kxta/GPUZ/bsfg2NQkAAQyWQGST4I5ZIPF5XXcFOY4btDyNCkhDYtP78vcrXAMeZEmAK73Et3OIjlZ6jVdv+QyCY9PEavgNOHR7uFWO0QC1XpjY7mNPvqfCXF/Y8JYS5cJwnj1TSXR5h70ggqAYCZvmCJYdMHADdFxSaoeCrLgqp0EhxXDR5MfdySpp86iUh3rHWOKjHVVWlTCRtO0O9B59pEmT10ejO0ONYWoCUjqVOFKe3zjnPR4aFLkzbl5/SsjLSdu6R9deDj6tjQZd3It5RE/+Z558Pykn0LceiKwxDOyaGT9GFqdpd3AqXorBpvwY7Mt3vm5TYo0+QhC8KGHQCz4ldpvbwdbHCfiWdxuE4EwUEbKPgIkBNhmJ0D++Sdqn7qs6/5Q6c+zl6n69b2EASzX8yyJjiY85mUW2S/y0kBLefQ2hZCErBVou15yTq9Hl4eYaRRQaZlLUgSxjoRHeLyrfP1y6nLVXdoVVO3bKi4v3Orpo8uaEYXnOvd6S2rhGtIm/cu1bdFlzHVkO8ogP/DUAYc4eSQqhpSFAI9jjmhpD02eSBM20hD73Z3BWidCZVya3GCAIjspAmRJvmAMnKMaR7VT3ZhO2mkcNbhEwBtfRLgBBZP2sm19pGReKVJW8lEqKdKkqJT0LHuIn54A1qfFzFuyMwKqtlOFd2zS+66TBD2XgU87qxh40V3LNtJyZ2cnFi5cCOGsltdbb72uVtlzIKQ90WWpUNxr3G1NAlL5iNQ/UpA5oRGWqp81ExD/uTvZr9XS0iITnTdD0gPYZKdRuHF3aBRgTiZvqlJyczgJUrYosixeXULn+320fUg92wNf/i5Tj4c0ufFyXPiIoEt4Ig8JjM8lkpOcuh1JEcsbVByYxJtOfaxiD0DMrcd9r7NQJw5TV4ybjVRJGybH0ihjXEy1/VmvmqOuyy2bBaX58V8rAB2Os3PKkWi69I7MrNTdTXIo1r53OgBFfEh3LYkziyVQNPM7AoAHhBzF52rVACySkDTGmEM+NOotp1UXiIqMkB5LMuT5ZvPITur6EssU4qbj1UbIlS2oDripxzU6PuVJwSnoIjpvfiwg4SlMeP75z3/iqKOOwty5c63jUir9bxS5hhwrKYREdP2xANKrSgCND/a0TAM/jLzjZNXmkdc7dYhk9aknGz1Y0WfOmZKJ07xW9dp11VxEqpMCJRScJZGCAVhJNAHYaRmEvV8n23JSp8wmICnvt5x7pW3W+z18aTLM76zvwTMz5kl23PNaypUVMJC25Q9a4t1mWbZBpt30YGMRKqdeLxlyt7P61qARe6PhCywDRi3J0lIVXzJRJH2nRstF3G27AmP3BIB36iYlOn90ZDKWcKjYXbG7bnjWbcu2U1DER5OeuAtUfwSAqBRjA3FLhUgWKYzxht3HXW8uGqWZMWnn/41Va43CR3RKrACQBbbsjLLtdPLGEN840MVYQFkoTHgmTJiAMAzx8MMPY8iQIQ1HYPZh2rRpuP/++/H3v/8dra2tGDVqFC6//HJ85StfMWWklLjgggtwyy234OOPP8b222+Pn/zkJ/ja175mynR0dOC0007DL3/5S7S3t2PcuHG48cYbse6663a5b9YKFDbp4cf8JGG09erI+cF8Kq0UDLECkQ7EK3+677iYJ5nBG7Dq1PAl2MzqTxjEeaQoAYn/FY56K4B9n0UIT6p9h9SwDALkGmszmU7j4Ksj1V5cp54I9H1l1UXL+MiR+fCZ6hMlzrUI5iHWWwUxUmcAoipjNnFyCY1bZ0DUQDw9GKWkOnl9agDeVBJ5oMSIEYkTTSFhjiQkxyftkWSmp/vaPklvN0yIGvi0TOL0TmXfg9hjl4UsLTVbTjD2Piz5DiUYOISR2jABJeEjz1fznSAUcXo+ZtXnbUuqzOpuzqwsPSYHLCNn61wdQpNllK3bfvvb3wMADH/oZm+5Et0AE0LDsdNpBIyZ77uRtIHmsgJlCxOeP/7xj5g/fz422WSTopemMGfOHJx44onYdtttUavVcM4552C33XbDa6+9hrY4ceUVV1yBa665BjNnzsSXv/xlXHzxxdh1113x+uuvo0+fPgCU59hDDz2Eu+++GwMHDsTkyZOx9957Y/78+QioBKIeGiEggLLpMTtdGLB8JNHTtg4mCEAZJmvXP5P9mhCerISbrqs252CbHEv6T8hSLQLbfjLki1eba9m2p9p9jIMWKhueMENdkfEG5gUUbARuNnbdD/d8VtvdNbm4Uhj3XujA60sYChBiQtRsKTsouu2QJjfZqC7LREyAPMQpJUFSxyz1l+62yfWlD9SRMjV4zjvB59STdrmP64mjFGvDZWWsG+eDAhLiA6hFGXUY0NIeLR2qZ++zFK+N+dTrDMrRlZMQnD696w01iLXvnY73v3sUAJgYPEAsealx8HiiCpsEWCDB9QwRwhgXMw6ISBgXexq/R9flgkZl5lxmBjgUglmvXFfX07ZxuSJcQfxOv/Od40p7nm6EuPXE5NvK8rwqQnp8yJuXg8YZT2HCs+mmm+KDDz4oepkXjz76qLU/Y8YMrL322pg/fz6+8Y1vQEqJ6667Dueccw72339/AMDtt9+OQYMG4a677sJxxx2HRYsWYfr06bjzzjuxyy67AAB+/vOfY9iwYXjiiSew++67N96hBgiPuOG49MSUe0G+lCernLzrVLDDriF948mEHkXpiYtKNNwcV/RfHzSrjuPvWP3Q5Meyx0HShrfeDDLDPeqvenBd7H1qLI08IwKd2NOq2/kNG1FT5hG1rOuybHiKopH6gaWboWMwztKeY6ls8zl2bnnkrUE0vDKEltIkkgMGZl/PbIlPfCjed1aJjl0O08e6Ak2qQtikhzttLkffkbXuUeqzhQdNsuLuBIEwJEhEMtWlpL9aDaVJjISkair4pD5aZabKJfZViTGylgaZKzyGyvQcYEt10lIkfTwt/Xl3H7XoW/fBnPAjdfDBIRONR5juS/+f397l+lZp+MZW18a1keuLnnNQmPBcfvnlOOOMM3DppZdixIgRqFQq1vlGk2/6sGjRIgDAgAEDAABvvfUWFixYgN12282UaW5uxpgxYzB37lwcd9xxmD9/PqrVqlVm6NCh2GyzzTB37lwv4eno6EBHR4fZ//TTT+0CdW1e0h8hHVxzc5A40WATo8H8H58POxzijRn5/QL8BCSH7LCvn5RkRwesODx2f506tIeWL8pZVntW9DFyPFfa45Hm+Op3yY4bhyeXqFhWDfYxVzXmHq9RFx9f3Y5UxUgZ6hAfep3vfovYCzVYRgonfw5NS8HJb1evL0VQ51taGlgER8AwHEPmNBlxuTC31V25hskUlkUwuR7aBoYl5xuUJi9LaGNmigUHHA0AEBFT8X8qdIJS/zAoU4PEJCo9mems8oaEEAKiJC5JuSyipLzI3Hc0TXRoedWm/cnQzOxJ+11/9lpCZjQwuk9B97y3Kx18NoMusaHbPtLjg8/buRH7wBwUJjxaijJu3Djr+NIaLUspceqpp2LHHXfEZpttBgBYsGABAGDQoEFW2UGDBuHtt982ZZqamtC/f/9UGX29i2nTpuGCCy5In+jiIFR4gCaZzPnR/y85PPMHpC8F6vNJL4rY7sRkQb54dbaKSHhSRTBmEwKX/GSJJwNHDeSd0B2JUla5LDSiMnNVgl5DZIfouMfzXOx9BMP1psuaQa0YQ44hsq9uU59M92cZS1e8KGCbk9meq2prpE/mfZFIGTDrR8bSixLp2JS4+ajMcU+Tlm1QlhSH2xcXsTtYUZCSTEbOfakRR5pTAkmMHysQYkwuXOLiEiDVXjZpMGslUzZHwuPYCKlPm1nrLX3df/Y7BkMfuDXV3n8PnGTapcTw/e8eVebtiiF+9v3UsYbGjSLkRduUmcULrP261zsoTHiefvrpopc0hO9///v485//jOeeey51zjWM1uQqD3llzj77bJx6amKb8umnn2LYsGFgAU+SnAH+AdbDSBuV6Jg6yWpZ3HqiKnbMT8Am/Nh4aUEIyJ+f4s+erolG1qTn2OwAsO12COQr1xPJjNtXMumGdUiK2x+vnVK6XymiBKSXaI0gy+DalahY54iUxY3PT2P2+FQ31rUN6jto0k8X+r2yvLboNmkjEvZ1Vr9k+ndy+1ezr5d6n0Zo9iYdbVCllgdXv1+PnNF9J9R83XYJaaFeW/oYRdY3rLJGk/LktfRFN7b2OamXSJeS/q1YCY8Pg3+t8gguPGgSRE0kRtfmf4mRt75NyVR8H2MAzWCMn5VKMB0llzmqK8BPJuuptGg5aickBLUrkhmG0wr/2e8Ys83jWEXUHf+DQybG20AQJu75IuIpgeeiCeNVPYEEdQLoc+uyCzuwwuDGFKNw3u0sT2eXIKXKGal+/I54SBJbljY8Y8aMKXpJXZx00kn43//9XzzzzDOWZ9XgwYMBKCnOkCFDzPGFCxcaqc/gwYPR2dmJjz/+2JLyLFy4EKNGjfK219zcjObm5uwO9YSBqBHdpmu3o7cLt+WoVxp1185rL2UA4CFT3QmfUbKbZiNLlJrnaSViPUWWCqoRY2hdf63OPVPvBrcNwB5gcvtboH/doY6qJ5ImA2Jd99QceyR/HrBuWm1Tgx562FF/qTb1uYy6VgLpTT2sfe90fHz4eEJi7CCFlmcbkJaEsWQskSL9rKwQA3kpL7KepTEFSB+TiG1/iIOAa8eTtZ7SgRlNPCJio+P2lXFpcpC59yWZsiODK9laRSBvO6nhsimS34j5h0N0UvY+XVw4dOnT/OSTT3D11Vfj6KOPxjHHHINrr73W2N8UgZQS3//+93H//ffjqaeewgYbbGCd32CDDTB48GDMmjXLHOvs7MScOXMMmdl6661RqVSsMu+99x5effXVTMKTCarS0tsZzDTzWvfPBw8j1uJBduT16Rg8uomNJoJvNLHOPaR/0oakO3X6VyiqmJmYRfKnJTBCKEmHSq9sX+Meqwdav5Zs1GrkL872rv+0rF2roajsXd+z/qvFiVX1tjkn0u3SenVGedpeLf5LXUtGfdOep4x+LpaunLRvXSfTf+7v6v7R+t22sv5q9L4aKG/9bjnlpP1HH6t1rd7Wr4/3PLBMYu4QDy/vuRiMM8UGWPrcygL97E1gwrx74EqqkRe9mR6rV59LsPL27TrjGEJxsMRkn/5J64+TKNQ6iKImO1ZZj1af8bStERC/k4IBguHzY49InV+ZkVJdxfOdJjAs3mbufEq38/58KFreg8ISnpdffhm77747Wltbsd1220FKiWuuuQaXXHIJHn/8cWy11VYN13XiiSfirrvuwoMPPog+ffoYm5t+/fqhtbUVjDGccsopuPTSS7Hxxhtj4403xqWXXopevXrhsMMOM2UnTZqEyZMnY+DAgRgwYABOO+00jBgxwtgbNQz34eURlizRWzeBHXk95M9PyS/kM8ptxCsrVU+GdIBER2bbnqqMm6mkI+/5pI45wRGL9s065rlvTTzc6zLVcI6nm0UMcqQieVnUM6UsORIZE31aJEQjq6xPyiQy1GSeY1b2dVrOew9pkkKNmr0RU+u0b52jIeTz2k3dQ512yPm0a6xbWVYbDZJu70RO3m36mtf5HIMzbmuszeWMRE0kk+cXCz0NOCxPNsmT8AAQ2oaH1JlSJ9rnGiVBqb7qITCQqitEpcUclZY7AlmSGyLVsSU6GX3ySKgSe6L43EpIdrsESnLIsSLX5+5nhXRpEIUJzw9/+EN85zvfwa233oowVJfXajUcffTROOWUU/DMM880XNdNN90EABg7dqx1fMaMGZgwYQIA4IwzzkB7eztOOOEEE3jw8ccfNzF4AODaa69FGIY4+OCDTeDBmTNnFovBA6QJD1XxuOqQevXQ6xuE+Nn3LSNmCAl5t3IVZ4dc7RRubOmaJd0BALbVyQ6JcUiTcEc2+CdHF/Vsi4o8lyxj4aw+ZJK3nH5pyY2vzjyC4+5nqr3qkDYgw2bGKd+oGggZk3ZeJvZU/+xjmuAUsr/Jkda5EpkU8akHz0+ZSVTqEJ3u8grrCnqy8TIj3mpSwop16fNIy6zH8Yqi9fra7A7UU2lZ/XHIThb8edWSa3mYr55bVeBVZ2XNeT4VVKOwFg9pmyDz3RYI1tQlCQ8lOwAQhiHOOOMMbLPNNoXqaiRvKWMM559/Ps4///zMMi0tLbjhhhtwww03FGq/EFwi1MgPWE8/CVg/Vq7xM0Ujzy2H6NgFPW16JCipeDxdQUOu4XnX+yZxh6hkEZd6/eoqydHX+87lka+8zOi+a+qQlMxJOy/RaN62W2eWasrTjncyy7jWJAnUEyuVJNSb/Oq9MkUkOF19rYtO0D2Y4OQh6uCAdlHnUoXUauCZ5ZGbIshK4uo73kh73UVMqJrLp/JaLUAFAo2agOTVReEhOr7tRlGY8PTt2xf//ve/U5GW33nnHUvqslJCK24BWIHVsliru+3DUqwe2ZHXGwmPfOoisG+e2+W6cpFntJw5AXejcUS9Z1Qvzo3ZziE7PtWWe995SUDz6qNl8splkRzr+ox6ff/CM3k30kZWvW59WeouT1u53lPCU8607ZAcSfoQa0F9A1uXpDI5r1G9SbLbJ7L4nnqqKgvINjTWRGN5T+5um+5vJq1PzY7+nKorQ8KTV06X9bnEe2EZO+cXXRmgJTupgKQ+LK3JRw7RSRVdliqt7373u5g0aRKuuuoqjBo1CowxPPfcczj99NNx6KGHFq2uR0EbWUkh7Qe+NB92PckQickjhQTil4odpaRVWpUlZ18C+eqNqmyY/bOxTb+Xec5bfquTIZ+5NN7xqJyMhxH8L21XJp56EpRUGw2Sq0bUSwByXdDz6spqix7zqaq8dTYgyUmV1VKRHJJTT4qTV08e2XFtbrz9yrkPV4JjvWJUwpOUNeezRDVum2h8Ei5KcGisHasMrYfDDt4okTYWWYnQb+bt+OyY2NiWkgfBVF6wBj7LIhKYer+JFRcI2aTGnYtd0mIFN+RuEEL9Tqfrdcv6IkFnhSpY2SFvOyl/kdGINsOHHCaYm7Qb6NLcU5jwXHXVVWCM4cgjj0StppKpVCoVHH/88bjssssKd6AnwtIPAqBJzZYKrmtdkT6NPSchPN0tbYnvlY09CwAgn01+R/aNKerY85dnXlcXjUhouukeCqm0siQ6dQlYHSmKqb+eCqmIfVBX20jX2ZAdjpF6OWQm41qjmjLnSBfd41SKQ4lOvN+QCqSLk2ajcJ82DURIy2SlpKiLLo4DyxsmDRxkigSQtZq1n4c8qUzuddp4mhKO2DbHLZNuM0fSo6Mkx9IYTWLce9Nlfd5YtH2LAAm2yrqlW3BtXPUxH+qIugqpqZaHSqupqQnXX389pk2bhjfeeANSSnzpS19Cr169Cje+2sG3+l6aQa8b1Epy9iWp6L9sp7OS8/Ou9F7HRp9pEaN0xXX6Vk+CklW2UXSV7KTq6YLRclfIcQNkp9ujH9eTBuUF+qtDdlIkxxR2VFjm+qw++w+nVvEZNh5dBXMnZl+9wkN6kNw74yzTjggAwrN6rjrLQNunCCipDgDtag1DEpx5LEWMfCrJ+k275CZ9vn4ZWjYLjNnEhxIX997ySI8UACvoJ7MyICXd8T3MbiDvdclOV6VIBIUJz6JFixBFEQYMGIARI0aY4x999BHCMFyqXFo9Bd3mtdFIPcZuBvXZ72YnAEAi6VlKGFUWPfbi1Z6SpA+jz2ys8jxrxe56vql6u2jBaK4vQMK6Umd3oq6qbCnbzZoh6tjjqOP2vjronKN1uOTHtJXfrbwM597zRQiQG2/FkeyY4579JO8WUW+50p+VQLoDEAmPAKReFzUgtci1r0lpfetLYFS5vPZYw+sMVxJDSY2P0KQkWQ45MhJyTu5FrKYGzF1AoUwFPhQY6wr/JIcccgjuvvvu1PFf/epXOOSQQ4pW16MgXXG/OeE5pm0c8v6yymYFbZPS9MHn+idfuxnytZvz+1IEhmyxDPuc7ElfS3fYTmcpiZDv+rqRyroRWX3trj5k/ab10MMmtrp2O1ZZZL7T1jnASHUssiOTP/ecFUwQzr5LgmTyZ7VPyxLVWOZfrbE/uGUFVLijOF6mbq/1+jsTwubaIHUTX17RaL3hTrTecCd4syI/PFATv/6X+nn4oOxs7N/BHI//TNk4HYT1F//uQjDrGikZRMQhIqYSnUqkzrt/UcQRRdxblxXv0jmf3ItzbyYpKUvdpzoPIw3rfcsqmFpiKWEFJlxOKDwTvPjii9h5551Tx8eOHYsXX3yxWzrV49EosWhkkswgK3WlTF0lOVYd8ZfJeWOBCklbWu0ln7/c2PdY5MdyvXfzbi2Dl7xo7i1vHaTf7jPxBaXsYWSmESxVULBGkTfZr0Ai4ARx9hIpIE2UKKmSwk++ctVyFCvrexMCOsK03mZh/Fc47Z2f5KTLwSIdPjLkq1PEubTcPwr3uEtusvvu7Geo0ZI+FxMy9yTImT9Qf9SerztsWVcgCqu0Ojo6jLEyRbVaRXt7e7d0qseg0R+3iJFrnnGXkAkF9SybtAdWSqVFcmnJV64H2+rkRnqdCbZ97Arv2u9IAfnURXG/ucp6rq8hqi42+ky/kXMW8hKhdhWuIV2eP2t3tgM0bsW5PO670XM9FN01vtarJy/1m+UOLWwVVkpVRc4rdVes7gjs7zmcMqOhfvcUtFydSCjaTzrCUgMVeYXrkYrMbDWGmGTX55KaRiEEU8lPPe1Rz6x6bug+9dYqE4hwRRCdZRDypTDh2XbbbXHLLbekgvzdfPPN2HrrrQt3oEciS4WVt++Dx54iS6XA9MpPZ2vnSEdeptekEmM2bjBrSEsMNvacZHv7yYnExrHXcWMByWcuBRs9xd8Id3x2RR3CQaUp7gjaqB7XrcO9Tj/7lMQpb8R2SIkvNpNpx4nvo7376DWNRuF2g3kJJ61DEdeYIihSXxcjigPJTyA1YaAqKlrO+Qkl+QklJRvdzBvpY1BGrWQ//nfxCUcojan+9JDm1WZ/FZn3AKXmAoCOM5XLulwMFbZCPweSOT0Lee7kPkPkLKJDSU5XPwXGknrUECK9Nj2uB5eBhxxJ+u2uhJA/PwWy1vicUhddCbBLkTPGFLG5LUx4LrnkEuyyyy7405/+hHHjxgEAnnzySbz00kt4/PHHi1bXs9CoyK6IRIdW7yM7ZBJj7go88L8YbPMT1TWvXJ/0W8PxuFoayOcvt0kP54mhs/Ycmn1JfI4ZF3Zvn6nn17OXJSQjUxrhkId6cCdf3/VZMRxcA+tUKhGPJGYpJntvG92JrLp7oHSnuyLxatJkESnaDpXYFHwMKS8kDe2lFTj7Tp/MNlat+CysEk/0gfLm0/ctIyoRk5BOMCKX7NST6iTluk4esq7VBCaTWDfQpOk/JUfCfrcXTRiPfjNv70LPVxAEmQvpQm1ZI2uR2k0oTHhGjx6NefPm4YorrsCvfvUrtLa2YvPNN8f06dOx8cYbd2vnegy6SHAyz/lUXN5YBg3K4btbLaIhZOJ67s4gnv5SQ2ZVxj+6s53OSoiT976XYlbIIj4UrvRHyGyVl4mdlCGB8khiCpO17kYPJDZLBSLFoYKtFFlyAwFmoLvCamk0MjlKIVNqrZUdLIylIk0SEDa3EySAXxFSmwoSmDe0NurBlecF5qidlHrLVl/51FsU1IXdSLY4sFJ7avnsT5c16SlYf1e8qQsTHgD4+te/jrvuuqsrl65c6KZVcqMJF63VYQNgW5+irsuIldMtyBupXLKSUZaNPD19TAc0dF3jl4YkUElMri2LQ0hcksS4LYHySXxof5dW2kP7YOpdNqRluSbKtOzWXVF/fEJr/zIkM25WblOeJaSHwrKfIXXnSXys6z2Pp2haANMu7c/KOvHloHK+klh0TjkSTEjwTnXc1jRKyGoe4fAcK2CLkykdKigNoqRVk574TModXYOqutS1eWqXQt1ZYTCpjLQHsUY3kZ1CThONjlUFVi9dIjxvvPEGZsyYgTfffBPXXXcd1l57bTz66KMYNmwYvva1r3Wlyp6BLK+nrtjv5F3nI0BaRaRfgJCDARC3KvUVP+YnqeotA2J9HeNK0uKkS6B2OvaLnNN/JzgfG3uO3SapV86+xHp5fSTHB0N8tGqsCHzSG+tYnVHGBBlxJ2EBID7HpHoOWYSEB+R5emZrSuDoCt98pBk/gIjStj/E3svKFGzdckEbnByDa8aRUkdYRpn1BnGffY17u9wmJ6n4NxmEBkAqOGDqvL5WRybI6K9ln9OICoOSNGrEzJGK12PUPLVVk/QAQNOld2SeW3zCEUpSrG1xYo27l+jkSHfccz4D5cxIy4XIj2o0ijSJsQ2aLclP5BAgxsADPe4yI+UB1G+/Uqi1qEdWnvNDEeR9VA2OVw2Hi6mDwp/gnDlzMGLECLz44ov49a9/jc8//xwA8Oc//xlTp04t3IGVDo0aK2sCU4Sl6pdMyHSMnrx2BCnr84MUyrvKNVZupP/WvbikJKPe3AjMGWBjz4FFyroCbfhNXcuLXscZjEu6JpI6lg/987kXU+JpHSN/9dqmx91tUs6KYcFyyhaBFUpgKVZ0sfuytc+QkvhYqz3t8uz7ydz66HFn24RdIn+U+Pjq6S4iol3WDVzXdvg/z1UZPADAZfLpeKQgrut5HmxXcruOvHqtOiQzf7Sc/0+RnyhS7epYPlHEU2Xz2lxpfnftDJM399Bz7l83wsSkq0d2CrRb+FM/66yzcPHFF2PWrFloamoyx3feeWfMmzevaHU9C8vhR0y150BGEjJS7UoSoFD89ITidWsyRPblExfa5cikkwommFt/mlRR0tUV0gMA3ozwS/N7uIQjjwRRkkPJTxikiYlLfKz4PS6B8pAp5jkH1N929i3Sw8g5c091iEte+4yl4zZapEuTC0fKlIMiwcaYh6BQzqn6Q8rk9CFFaurVWwQuoZHpxaea8JajOrGHoPWGO+OAhRI80MSn8efQaHycRkBJDj3may+L/NA/TYL0nwlaWCDqc4+Db5wt8t4WfMcpqXH/7ILOw6d9LYDCKq2//OUvXvudtdZaCx9++GHR6lY+1POuoTY5WSosy+5FS4Ls66WQYDL54VmTJ0mLIR2OO0heAk3L4LbO6O67T9f+xTLY1ccAcNmwB5cL9s1zbWLm9vmzL5L9Pr2cfjXwAdS7byvgoN4gMfZpO4IptZcuQn2lvb8/EXm75wL4fzvuHHcMpZklUYzLZ9gVuZ6AUqvLXOhyWn0TqHq9OasQe+nkEBmT8VwAPhdtY8ODtB0bPUdfc6oK0yo2I5giP2HK3ofW50LYpCcvR1cqMzZxTzf3Ikkfali2C6geirab78Ti7x1h9tXvpH6oerFzEoPgpSM9LrHJO5dlq6PBiZSKMWn6GEUcQeB5YaD6zyCxaMJ4AOiRqi356zPVAhuI1ZBaHVdnzgPsMcQdUyQdb+A93jCW8vspvJ5ZY4018N5776WO/+EPf8A666yzVJ1Z4chSL3S1nAsfa3XPkz9Zk0AkgEhA1gTEDcdB3HBcurxVh/Cfc+P2+PokhTe/VkP3k0eshCxWr1uP9VwE2D7TwPaZlrTrFbtm3G+jyJPQ0PNhkPy56q8gPhZy9UclPKHvHDkfcPtcGCClastSdQU8kfjkSZvgkJQsaY+RajFbEpIqC/vP1EskO1q9Bfu8hquOKv67xe0xW+gFpPczrydwJT7M6atXIuRRYa20K/5uQtvNd6Lt5jvBAglOltlFA/NxLo1Njf3bpuuhZMSFN41FVsRn5zxViaUlQNkqrh6v0hIiW4VU99oGpS1Z4c1pHctQZVZYwnPYYYfhzDPPxL333gvGGIQQeP7553HaaafhyCOPXOoO9Sj4yIxX6mGvuBuVAAF1PgIh1aoQAGPG0RPR9cciOPmWxtpoYNK3yEgWcSmYeiI+0Pi1DYDtcYGnTX1/jgTM9MUj0fI9E7eP9F7c39SNy2PeEypp0/VQaZHr0eVIkiyPL9Imi2dM77UZnnHxv1ZwPh+E9EpmzN2mJEV033nn3bI+GxslDwJi41VjgE2JBO07vScOy+vJlQZZhtSO0NNn4JwpyXF+xu6w8Wk49cQqjt633GmkHEWg4uUkvzbnMiUdshOC2uc4k7lSHhdZBNXNrC4kI8HxqSTDJk9F1Hg9BZkS26w5UJ9rRCK0gtClwIMTJkzAOuusAyklNt10U0RRhMMOOww/+tGPlkUflx/0ijXvx3Jfgq78sI1EQzaurbFItyrA9HUBh/jJ98A2XCsuHWQTFR9SMWXsEd1r3EzSV5g6RJ0ghxbpKDZrsF3OU315/PzUzCcfIPZFRm0koDymRLKvOhr/w/KfSSqwIH1GDT5bzpC+Tyrp8pAfi0g5Hl8u+THkE+lzhlzadVJxtGSelZXbByrGJpC+urU6FnaGaQmXJHlgWI2vLWkVaSiasodrZr1yviDVmW7xSwPntS9JjwJjMnHpjpiltmI88ehy4yW5pEeXUdcW7IMJEOgh+zl12UbpzJAeAGCSkSDrMl02SA58OvFI9J1xR7FOL2twnoSP8M2DWfu+BX+eiisP7kIzt7/F1ZyFCU+lUsEvfvELXHTRRXjllVcghMCWW265agQd1DLvRgKEyYwfmE4Iuqh7rR706n2lQpIBWELW4g8rlGAtIdieaWIin7jQkAUAkL+LjYAZmfidNlLEJXNCJ/ep1SqUcGRCzUbapqewN5aesB86R0WSrpJcbu6HpSNNV2u2yqXoMr3Ix+QmGaX9cWMDGRC3d6suh6jpeqx/hX2MutG77qSEKBlbH9eGJ8vjwX2X6XW1hJCxmiLDhqRrlSyRv1BCYUlbfK+NIP9SMQ5P+sA4i214pH1dRt2+bzB3smyEZDUCpz+iXR2onjcelQtvX8rKV15oksNYYuCrCY0lDXGCC1JyRKU8biRtHzny9oMQrKRvjREoKTVhU/sROKReBEhYQQ2V0TaVQBafrJc5Qg5oW9GqZzHrjhsxvHeytBKeLLtCXxsFnmWX4vAAwIYbbogNN9wQURThL3/5Cz7++GP079+/q9X1KDTiRZISgefVZ65JvwR1V5bWcWmOyc4I4qbjVfPH35TUR8gOALA9L4J85EfJRCp8S2HaHuljyr1dKlsSIG3rsNv5ShrT3QiJ1EM4SWuFBDrjY5wDUZSUrcVxbIzNTcEPUH/vjXxM1uqmWDPpj9jzMhljaHMRbImOsxrTKjB6XvfTIUuWBEjIhOxTFZq+lpRlIU+u03Uxso9EOknv08SucXmdK5WCLmcTmsQrzSkLJRnKSvJJn7Kbv4tOcN2W6sIDEQFYEu9wgeqFE1A5b+ayaayHQ/9OTErzflJCo/aZJYmxfqeY0Gh7njzjZ1M2rrsR1VYRyRFNLWE+33ic1cbZjAnQ5KI9EtoWEbAXi0Dj/c6S5uSpweodq1e2QAiNwoTnlFNOwYgRIzBp0iREUYQxY8Zg7ty56NWrFx5++GGMHTu2aJU9BolhZSMrAyrRIYHgXA8ZOpCHXJWzVud6xeqsiKH3E5JDDSFZNbJseihc+x6218VKOqIrCp1JtVHjYwCI9AfRbB1uhOw0ItmRf1d9Z5uoe2LfPNdWsXGWSHEEeShLOh1y5NgPFV1RNVqeO+TDd22WdMkyGI4lQR5JoalDmylxYZMY6sUlpK0aEyIhMTqpo/uM9DvoeQ/Mex0wmwTVkvfCvNfxMQZAeXRlD16ptihxIVJNRr4JmZbTWAQzRX5IMUnK+hKPZk5wddYHuXCvpXb0SwBWKfVbjCW/lPsbUCmNlsQsi5y5PilPFnxkiRupE8x3qfgONwROCAYRMRPrlPne5RUNzpSzBAAWcMi8aPN5KKLCaqRPPnQxGWlhwnPffffh8MMPBwA89NBDePPNN/H3v/8dd9xxB8455xw8//zzRavsOXDJTiMPMn4RmGeikkzGkwxZUdeE/aqTNtxJgMnYU8upV3vMZL2A0bXHWPVASASnT1d9uutUsMOuqX9f+h5iexm2n4qrIx88W+27ruMNuLgbUiRkygBZ/FP1j9UcKQ6QrDoAQAREbRXZyVJ90UH1yFgkp6q7YkgZNFN7Jo/xsktkKCGyVk2cEBzXhoilf3N6nUV+YpIDJFIcn/GzJj9a2qXPmVuh9kZ+EmQIEJHwQEgwQehI/M5leuA40qOUSsHUq+1e4r54hOcWCXLIDz1Hlg7Jvs/AGfWlPNIp2ygsb60qEHRfnt+VDpaKk9p/OaQmzy29UbWViyIGzI2ot3RdnNrtxNIe/XpEEUMQum9hDwPjyVgbciDSTh7pcXWp6EyjUpzUwtEZJ7pAqpiUxbhyS0sL/u///g/rrrsujj32WPTq1QvXXXcd3nrrLWyxxRb49NNPC3diRePTTz9Fv3798Mkt30Pf1ub0BFfvh8gz7krZWziTGJ0kQ4+nEZ3Qsxi2kJBRPAr7cqBUguTl4EzZXwDK5V1IyCWKZAQ/vBXR1UcnMX9CbtRl8raTgP5tQL+2uB7PSJ9rFCxtiUIU39eSKtgBl0O+drPa76wCHZ3Jc6lUwL5+kqlGvng18NEnaufjxYlKKwuNimVdotsdnmUuAWKk3pDDisxcz9bIJw3ScNWQGTY8yb7zfppr897jtDrMkgJRspn1ry7rq5tuu3E7yDUpIuKovFI2PQAg4+P0Vh2JqVWH8zitutzyOUilxRBI0hFwibAXEPZThVYXe55PJx6pXLUzniclMDSAnxuJWdvIaAhhB/uz6om3fSSnqNFyEU8vzpI8XEEg0NRUQxCqmw1Cgf4/73m/uXwkdjzqrAEdVbVdi+xxw5X++r797pDw0DqYG5k9mTs/be/AGt/7KRYtWoS+ffvmVllYwjNo0CC89tprGDJkCB599FHceOONAIAvvvgCQeAJjrcyIXB0mBRZekLO0wHj6GOQPD2g00kuIIzaR3jMds5EFgkwTYxcwtMUpOo1ZbUaIu6PuOE4sF4VsObQ9E/ecbLa1mSnOYmunZpoeeA/pw7A0iHogaNXk0p+2q9Pcp8dHcllnVXIl2KJFOdALcNgOQs+kuN+lKl/60mrGpzxjASBqNXoaOoagetj5nyD0qB6oO74qWfmeuz5T6l9R1VGpUBCKommvgf6nluqW2ad88b8sCQofuNnQ35oGzyRAlmu7sKR+Dj2Q5a0B/mrepfs5CUbVdILfz0AICMkEtxVHJ9OPBIAIHSOqqznIrKlNj5ykpVmwiJDOWkrsn7nRohNXuZ0H6KIgweJxOqTI8cDANa4owcRH/09NYW2+praRwJKgmu2hVFny5qzYiiSoqYR2Qsdq7NMCHJQmPBMnDgRBx98MIYMGQLGGHbddVcAwIsvvohNNtmkaHU9CwE3Osy6Uh4XIXmUOZIYq54wSOxpKpW0ZCG1MicSErqSrznGurT/WpJAQQmPIO7uxl4jLt8UJgSnV3PcRzJ5gqp2AvtcLUpmByFt1Q4ICZQCbOTpEO+q6N1syfvqXGc16W9HZ/K8siRk9BhF1m9hfTA8XVeWsYBrI2OdyyAiros8ACCCSjDoEJ2UOkz3VcBr0OwkibX6mQWLJBCVl091Z65JVQI4xi1UrZtygc9oM1fIL2S8sosnCUeQTu0gknO6Xseujhg8G+s48rNYVxObHje6NFV3ZWfqpvfgPy5rDBEkeLs6uDp7bWU5bjBtcwYQSY/+l2VKdaSTQkJ6SJEvpUR3wCJaAHj8PgrJwKVNunpkIEL9Y1RYMvbXIpvwpNIKSTOnsI6qZd8HoL6U3Rxj/gUQkNjWWoQn7msBQUthwnP++edjs802wzvvvIODDjoIzc3NcZsBzjqrTv6lno6mMGa2noklj/AUNVI1ZCRIjgUkkq5GvQnUlbDQ9rOkFXlpJ9x+hmF2PTXVf7btqf46CHIzoes+NFXUvy3NiuBowlMlolX3HoIAaHO8CvLuV8M1ZObM/s3ySJMmmh5JR0p95BJJt06ORLXnM5rMcnd39/PITT3kEiPPe6x9yrmEYUH6eZB7Z9YzoioqmRKBM1pGEDshU0csFaAkiseB3YzkKL5WUxeedFV5bCV9YDrDvGknIV2uFxnTgklqc6IrcomPnljpKwA/pGSI2pmR8FRqAvJHSgrSdPEdGVetfPjsmCMgqhyi5pHSxTAEUmSrtMwxrZ4SSWRjfZxKctQrlkGA3Hxanvqz4Epw3C/EOi9ZIvWEgGAcMr5ZKRgG/HJmblsrFK0tQJ/YfCGK1HhMx7YwWQyzr58E+fzl6vhnXwDtHbYphquC13DHRyHBfCmLdB2U8FDzgAJWOV1ySz/wwANTx8aPH9+VqnoWaNj+zDIZ57ImTN+kRSdc9+uvKyWg19MCQfq8r78+KUFqMvWQLp9R7dJAX68N/tY+SDVTu1up3MwHsySJu+NKIppIP0OekMZG+5f1nHyElUpStARL70e2xMxAT9jMQ4IAv3G0dT3ddstlSREFvETFXLu0vxuV8Oljcb1WfjCiPqMqrfj/KfUXgSVtcY23jV2OjKMt25IjE6iTqrtiby9r9cjSEh9zHYjUAbC8/K1oz7DJDYhEwgd7Mo+7Hat4akskWNMqqN4SLFeSQbOI59npuOSHpnEAFLnR10WCeUlOHrHJI0QUlNBwJhEhTXIAMjSn8m7F2z008rJ2JpGzLyHmHdzW8woBBIFlW2k0HC1N6getZUhdfAtuuniU3D7mEhyfU5FrCpKDLsfhWSWhcxcVgTs5umTHlTrkSRN8okIN6XlRGrE5aUT1wp0X27QpTd1s61OsS+T864rpZ10bpAzwoYdARD8Hq8ZSHd99WxeQlz4Mkmebeu51ftdGSJ/uQ60GVOK+RJEtcRPCJkC1yD8JuupN0y7xtrLKZxAhH7JIj7e9rhIgz/PRhzT5MfeQJj8WwdCEiZwHiG2Qr3lDTIi6izxTndCUdtfY90DCthNK7HuMio0QH0163Fs3sicq6eGJK31KRWO5WdvnZI1BdKg+dJx+BJqvvNN31yslXK83Sy0o/ETHhSY4ic2OTWpERCQ8JM+VhoCjbsqT+OQNU/Q6FgcWJF5apg69HS8wTaDF+H0d8IuZOY30AFAJjSYUVLKeZfIRBMr0ISBjIkXeeF6L0sfqCQ8AgNdxXCFYoYTnmWeewZVXXon58+fjvffewwMPPIB9993XnJ8wYQJuv/1265rtt98eL7zwgtnv6OjAaaedhl/+8pdob2/HuHHjcOONN2LdddftQo94MT/TwtXXUZ0AfvUJ4ExykfrxaXA8OtE3bFTrvFwhGlMJIU2A8sDGnqNc2BtRpwHqXiqxiqtSSe4titJlqXdTSD7EMLAJWSNeVw3lCxOxLVF8L7UofV5LpDqragCg/W5U/eQ+q1yD8Bz4VkR5OcXy1Jyu/ZQrccu0+aE7mhzFE78mNdTexiVHeeouQnwM6YnrN9IeQ4bisjqfl5EWeex7SI9T+8T2RyJ5PVksrZA6uSWNIaT7yZxnSBB1JGXlD49Ay7WrFunRcL2yLLsWmX3OtdkRqWuJtKcg2RFOuxTCsR3TdjmRhMnPq/aTyMqIAx0K4lYfCd5wvJ8VBXnv6Wpj6JoeghGTt81PzK4gDAAZesZF/YDJ2OkKZgISitpd1AM5piIriYRn8eLF2GKLLTBx4kQccMAB3jJ77LEHZsyYYfabmpqs86eccgoeeugh3H333Rg4cCAmT56MvffeG/Pnzy/uNeYzgtVoeKIiK3edp0qnbuAMlheOKzp1DZOpVMR1N/b1HbCJj7ecKxYky61apOxyRp6ee4tdgRsBOg983cMgqorosqJZz41Kq47kLeu6LFhJSitEqsHBNv2eKSb/eENCcDqraePteqQvi4TkrYzqvZs+0qN2/G1l1lvvd3CkPjTgoTnuqqhUnb6gh4bgEF0/cwgPJUCW3RBArkvKA9oOh/RDwNSj7XsSCZQiRq7KS/UZtnu7No4m95GOKJ08f8voWsCkjolqgOgAasceocqFyWfd6/+tXCQoqjLlmWS88tJSnUx3co9bOgVnkkQ2ZgjiQHnMkf5wyQAmXec8dVlG3S7JUX2I74mek8xeV2lJIZOIwBLNfdyfStizgi/Ju2IbzLZmoE8vYMN11H7vNshYTcWqVWuMS9Xx4tUkfg/UWKqDavrsTa2LM8a1eosxb/Lm+lihhGfPPffEnnvumVumubkZgwcP9p5btGgRpk+fjjvvvBO77LILAODnP/85hg0bhieeeAK7776797qOjg50ENdnEztIRH4pQlHoKlhMdgwBAnJTHPgIjk/Ck6faAdKkxj2POqSoJ0B/QE0VYIle+sZLqjDjtc2zX0kRUaTVVkVi75Cy8tUbSRukjqa4HLWDor+vvsaCIz7WZejK0LIZEGnpS2afHVLsLZNBatx7c13crecr7fum+v/UO2dmLG+zDEiTBnMpS0t/PMSJMSXdMUkpjarM0Jh0OgsjOWLxtYnKC+YqW/rDnFuVtLwhRQ7509em7k16f6Yvvn/ESkF6PvqfCQCAIBbQyi4OqwmZkPErRX4zJsE0cWIJwVE2V+RcTDYCcyUs8qOWpVQiJG2OTiJBZ6nb9Hld1sT21F5acRC/KCowxixjyJ+fksRVW3MNoK2XMUSWYdj4QpOz5IcKAqgJ0L3PrHGFSpE8emNXAGCuoz9Q4y9XYcKTFViQMYbm5uaUBGZpMXv2bKy99tpYY401MGbMGFxyySVYe+21AQDz589HtVrFbrvtZsoPHToUm222GebOnZtJeKZNm4YLLrggfaIm0i51jcJnVKxtF6jqiRIqV7ntEhx3APe15UK7f/vc94B4stQTfloCtiykO10BH6aieYt/Tk/iBlWr9vOjXmSAX7SZJbGgqr88FY8+n0mkhP17qwuSc2GQTGyCGiXHol03bockv41lA+Psm/Y1eXX67t5ro6A+wpbaylVpOQbWvrL0uVI3/3plnf5a0hUrTYZ9j66Ex1V/GXVDTH5SEZ3VVXGsIFKHSFReLvmhpMZrnGvUaBnnG4RrTN3TEcQ2bryiJXjquARLSVRsU0D6m5LDgsVGv4mKSAiaaT1NfkybUqpQaYQAmbKwbauEZCaLiumfTNoEITWNgN5rNeLorKox6j/7HYOhD9zacD3dDXnHyUBrU0J4+vWBbLbTBbGGCY9vfNRaDU8Ij6xxw4XlARtvW9Ly+FyBObsw4VljjTVMQjQf1l13XUyYMAFTp04FX8potXvuuScOOuggDB8+HG+99RbOPfdcfPOb38T8+fPR3NyMBQsWoKmpKZW0dNCgQViwYEFmvWeffTZOPfVUs//pp59i2LBhKrpkAYtvL7zeVFkqBfdah/D4UiX46nHF+CzRt8YFEjKgZg1zHftmwczlyxl840lmW/7xBvVcdFyeKEpsfZoq8BKWzI+WSMFSRtx13ltLYkEm/hQJiPe1apV68VCbFdoH74qHO0SAthkPGG4wE48RcCJfp3qZjHvVZNyS2rgSHYcoumWF5xnVi9XvI1K6SyAkxjmXOiZI1B7O4Oaw01IfIIP8ELscK4IzIT+uvY8hQORna5TkpAyciSebK1Xq6fjgkIloao0Jjx5qtKpHOnnUUoKAjLt1VIKa/NjRluOrYvIjyTxFCRCQpICIBFdl4zOBzn1Fe+HYBjHy/jYav0dAxeHpjHMRhl1dWHcXOFOx1XorwiNbW9XxOLAro84Y9WCNcfqjIvMYsf+xFog+R5bUgj/ug44D5J4H6kfbJyhMeGbOnIlzzjkHEyZMwHbbbQcpJV566SXcfvvt+NGPfoT3338fV111FZqbmzFlypSi1Vv47ne/a7Y322wzbLPNNhg+fDgeeeQR7L///pnXSSlzSVlzc7OJH2ThiyWWvUDDaLQslbrUq4vaJ8Sg6SFyY8XQYIOcAQEH22daY33swWBfPwnyqYsSwlOpAP16q+0wIXEG1P4nFUDQR0z1h0jUkCapJ7W6zGjDbZ9e70IPAlmDSr2Z0pWI5Nn1iIx3mjOkkoxlEeki34OR2jgqLt1f2k6R1R9ntjRFum04kiMCRo4Z8qMlNQHph0zUWaZeqlIk6i6d0NfYqdakOQZAxapyeGhSL3NsWZJTOkIzi13WlQ1MTCB6sITn48PHAwAqLQCPJTxMB203pQR4wCyJmvUMgsTuyefGL4lEVJ1P9nmO8TMA0KjM+lgoBagRM3ckrtQwWsf2ieJFiZQMkWMM7RpB631t29MZu2tLyfDGnieoPoQCwx+6GcsVzRWApFFinZ3qBrX5ABnzLPdzAhMBnwak1WDO+ElB1VSuY06eRMcNsKt/5OoyJDy33347rr76ahx88MHm2He+8x2MGDECP/3pT/Hkk09ivfXWwyWXXLLUhMfFkCFDMHz4cPzzn/8EAAwePBidnZ34+OOPLSnPwoULMWrUqOINCJgBziIbtmtAxrUFSE+DhMeFpITHyS1C+8uEBMJ4PwyALP3pSgj2zXOTfC+1yP7QUnpeYa80XOhroyhWwFMVV75tiT+ehEifS5GjnLL0A6f1difc2D4pr7ycNrvq/efClfJkSYPoeV+9jWR2JEhFcfXVG0d2TuyGYokC9fii0ZyJtxcL1X3Imt6HIj0AZGwInWfLQid6GSWTpABLCMOyeCe6iEUTxlv7gSE5Ejy2bGDx8KPDq3DEpIVq2Q2ZcSQmjiSIJhaNr7A7ROsEEpstLSmz1GFxDVKCw3XolIYcqaxBiSpMSAYeqXqMdEgm7ZhtbTiNpE36byQ4arG0xyVZywVaDaVNBDo6lXSHhtSIF5Hyjzdkkh4v3HGXjsteO1VCajLr7OI5B4UJz7x583DzzWk2uuWWW2LevHkAgB133BH//ve/i1ZdFx9++CHeeecdDBkyBACw9dZbo1KpYNasWYaAvffee3j11VdxxRVXdLmdzGSI7jbZl3llKKiI3kN8vMkV4+ssCU/IMwcHKZ280j3ZOLkLYHtdDADKhbIlHll1ag496tVq8W+YQyCo9xwFvc6X48qqw0OwMo3rcqRBPiPm3L53EzEq4gGXW4+nD430qxFpkFseGeqtos8hp3wq3QGN70ONnZHE8DHqLhMbCOZ11MaxiS2LXb/ruWSRLiDl0LkioXNA6bxQjAMsSIyseSWR7Ogb11zEeLJZvDshkPRdsEwchU2SXPJDM6trsMA/JkqRLmuuYfpfIjniiYSICQYeF2JRIvUBFImRSK6PBDfG0AK2+i0SDNX4gQW+YLDLGpzZKRmq1cSrtAHIeVc6UmayLSUJzCpskgM4djgZEh5drpsJfmHCs+6662L69Om47LLLrOPTp09XdjBQxMS1q/Hh888/x//93/+Z/bfeegt//OMfMWDAAAwYMADnn38+DjjgAAwZMgT/+te/MGXKFKy55prYb7/9AAD9+vXDpEmTMHnyZAwcOBADBgzAaaedhhEjRhivrUJw9Py+ba8niCMNsj5S+q8D39F0PhlyTg8EIQMqARkYnNGQM8hQDyKArEYFzOxWHrCDroS8e7LaqYRA/34wy23p+5jqkIQ8Yphyc/cMUq7UxuidGyQnWeTDvcZ9SXLVWTll80gUsPT6k0x1XTcMYka9RdRUHH6pjVZdUZWW1c+0NLeIgTG16aE5chlgRlhZi1VfxsRBApYKxlGd1Dh4TJwCLsDi77nt5hXjofXR/0wwBCeoSLDAluIwnpAcFrKU4ZF+5lr6RTPVc/PJKumYKSsBmq5DEgGoJNIaAA2MbzKJgeMsXvJeR5rZnXNpqcOCQKTjBmnyE3FEghkpTiQZqhFHJLU6DBDR8k22LWf+ABgcz8sD+yq7R5PCp+pI1FhyTgjleq7hftdSJgmfdZT8rMj0+hiQJjR5Y6UrGc+zb81BYcJz1VVX4aCDDsLvfvc7bLvttmCM4aWXXsLf//533HfffQCAl156ybK/ycLLL7+MnXfe2exrQ+Lx48fjpptuwl/+8hfccccd+OSTTzBkyBDsvPPOuOeee9CnTx9zzbXXXoswDHHwwQebwIMzZ87s3sztHnua9CREBkrnh0oToOymMsXuQPKhiniwj5N8SmeiZiFPCFq06qizvNDPK8vILi+egzurRc75LNQLhpXSOzs666JoRJybR2zyjmep3wDHo3ApKHNRglPEHZaQGTeycrrejH50RTpEQA2as6yKTY4uagzNYZlQ0ZgzUiSpELIiBSxvmACLgUTQDLAmcpyThReLSU8MKRKbSqa93MyYCGvbcusXSLLJ89jdPK5W6t/bp/rPEIdlpnPIyaLOHPUbiGcYc0gXkKjCGJNgVHITBRBcQmp1GJIYQ9HyCkbYpxXo31dt12rqz12U+xZ97mIxlVeRjHEm0SjZd21xUmp9j2Ag0/bSQ4IKjKmFCc93vvMdvP7667j55pvxj3/8A1JK7LnnnvjNb36D9ddfHwBw/PHHN1TX2LFj1UonA4899ljdOlpaWnDDDTfghhtuaKjNLsEylswfGFP5fVzQ+cX68fRBu7j+QEC0NapP8v9v78vD7Cjqtd/qPmeWLEw2shpCCCFCEhCiQEB2CItggCuicENYREAWI2F1uYAX2ZQoCgFRPhblinwS/FwQ4UJIxABCCLJDhCQoJEQgZJnlLN31/VFd1VXV1X36TGaSyeT3Ps88c7q7urqqurrqrd9W0IZE855Q81CxJVC9FbKeatDVjOYA5X2Qeq9x7LK1sVY/6rfnHiTkPYnVTgpBqRe1CHje/PPYp4mE4t/GqkfzEjH9XB4Vccgj0uN4T7Wkefr1aMZN28G7O6EMXiNjZR79D7P2TttEYIzHKqyCIDteQ0xwAMSSrSh2kbrXcvNXm7fCUk1WYRIgQM1QLARCxNt2yG/d+Z7qaCvOmUGEXJGQzejY2nlI1VoskTK5QKgtRgMzyGEIZfDMOcPfD56F3R7/Ue5ydwp2xPe8nTuNDOkSbZm3JDhSomPHk7MlPbq9j/6sesazOt53pwIPbr/99gmVVq9DrVVfwjskOm8bY8Jh8Gh8nNpvnkKCALHCUbEjOHTZubEtHmPWxCp+Bzd9FQDgf/329DptidBXEtWq2G0dECOPkBvHx7VIgE5u0jwJAKDCLdIjB35r5VPPzuobg6xVUB44yYbLfiZl4t0YElRrYMtbF822B0DsxaVdk0jY/+TJu4sIh74pqQ57ojU8lHrIekXaxPiNgNfIwBq0MmuqPACOPhGRJTDosYyMGEcFodJy9SaOSMKjb9uRtgipASMqg+XaLmynkvZBenoDlvRH1lMOP77cUwoeigbB8NX4Ll3W/37wLADocuJTveY08cTdRsUnA83GRsL+vqVoMW2Da126btjs8OSGyq5xT6qlXJKdWgs641r6JRudIjwff/wx/va3v2H16tUIrQHqlFNO6UyWPQpKPC43EtVeSmLvn+Td2n4quoFjRHw00buM8QHEYt40OwNehTKOZB7AKxysLFdc+uQLsAat43os2al6ExqiLhxFCDVXEKH798ZA6aRzEhd79ZIlOepseeo5nxNp/dtlaJ/wPMwz+eQpd54Fh36sSfgEqZEXU/Jxiu+5oVexY4NuFDQVFvOEzYpsT+bYaV2OI8WmEP3vuKeLCpEf/z7xdBQbRYEbtglRiCwJvL4+vKZ4EnSqluzxS46DnrjGtOM4kCMT3qWyvTWhLIvWlLFHFwfzNdUTtPbz3e9M37g1TSJkS3xE3lY+GbGCpFQuLndEEiPio44ZRzVSeQWhsPeR9j1difAnZ8Hfc6w4GDIwHnfKftJux2YO6lAjL+qaZWtjS7FD61i/Tw/9YqfV89Tfi2MsYLrQISfqJjy///3vcfLJJ6O1tRX9+/c34t0wxnoF4QEsmwBHP4y1tjAGWrvxDRWXfd2LVwQJF1fPPtYM9sJoQJcfm/5IBugd1yBDAKrXnY7CZf/HWectGh4zpTjSYFiqslwbeNofbV7jYhnN2k4XWOeySE0edU0GMiUUaSKBLiC9TvuI0AyR4Iw3lffZ9kSZRrw6m7+831Z/bSbE9iC69GAT2XRk4IMvnQa/wFHsI/pwsYXB6xtJlBs8YT8oiYrHk2OcNR4mvd5kWu2hTBv3ojT6OOshtoFSMZDk9RBAJIHiXNpM6XZEps2NXh5DgFGjT+iESEqC0j43+xrz4i0rOONxkEPGhUQp+rYWH3ghpjwxJ7MceVC5ciYKu42IY5WFYRyzRo6POlIXISnjWN6VQF4JaZ2S1M58v3UTntmzZ+P000/HNddcgz59+tT9wC0JsbQFbiNISOIB94tyxRuBpuIKoQ3abhdX8xjqPn3TQsPrwANQQnwxdEw+G4GO2TNUeVQdJOQjK5vGmyS85WywbeXHbBOWUJAd+YFn2eU4XcIzVjP25G6vFqu6ODd7lRJf7OTkm0Wm8qavBykSHhF237Snyfus1HbJaBMeWrTAmmCNTUfrhR5JN2tMjxYsUlohFyip3mDGvZGKJk9xNqEdkQRjwhvLkxrivh5Ys4wiaNtymAbB3EXYNOJjXk9vP3hq9Ets48GYID/GlhuhljaLADlUYXm27ki4s3tJVaS+95ch7UdE2KTxtrZNhsc4OGPKnnVjfWpLF4sxumHP4cCYEfGFIIg9r8SDHaQnpbPp6Vwd0s5H324n7TtQ/SglDIVdPvt7Zjm+Mwt1E553330XF1xwQe8mO06dv7xmpU286OSqMzOmh22nkzZ4pkh7jHxlWcGBsixD9L+T28TKjycxIOiut1Y5PACtX5uBvnO7mfQ0FkTEUPlwXYpTrkTBtKJz+orG1ivr52xIPXNVswUqeGYMCwn7Aw258JKrh2R0lpB0QrXVmRWSU5IjSaAeCHNjAsfkWg1CGPTLCUQ+t4ukNrmMYHuAhKir8cGXInuPQgivGMJrEudZgxer+IEkadBspozxyRFnKXG9RiBHcZ/2B0DG9zFsbQxJTZxWjt/K/sdFfjphA6Q8uLQNTfUtMITburgmHZ10mx5DEs845JY/4UZ4bbV+bQb67C/2msS4TwibRl3N3xCNl2WIOttGxLX2FdSRRWRSr2lSeD1SurgIY/Vo52ELEDqxoKl7Gjz88MPx3HPPYYcddqj7YVscNNG8sSuzvgFnwTM2LUwOuPHLZz4A62PPu4eP7tnAo0Ej1VgrjNVdPIhW29X0zlG6dIbzPPOYUok5vZKtMijTpQIHKwPt54t8w0CrH4B+t28cEQp/fp543rAWER4dEG28vjXeV6VUFr91NZMycBYkJDWatkwfpeXlwNygrsEHaxKfDmsuxnZEctLXVToB4nvrnCCzJm/blqZm2IS08/WWST9Is9tJiw+lp8vx3Ey9vcxLLQisMjjsAVKDg+qSOBlHKyWtcU1+n7JrcPNYl8Jy9T0jvjelCcS3xjcqEkBnsOY/Z6o9sAp9OQoDGPwWMUHK/g5k9EvdvMGQOjMkFntG+8p8YQQkTLisA6oD8pAbae17jbbn1qJVl/6oNPZ34Wh8aRukrXXEbWZeZr66Wi12RbfjLnHOEET2P9XAxz+OOBeA2Hsr0NzbC36A5mIVDQ1inOvTv4w+24lMi5O2RZ+v7gk+eGBUh2ixJffHqlRiQiPJjm9tyWPHz1HtoR9HG4Kq7eeticiQgnvRPKHnZX2n8sX5iFmr/Caj+9QcmzrP5kPdhOdzn/scLr74Yrz66quYPHkyinLzxgif//znO1WQHgeXUaSErdaQ5yDVUCkIxYo0TfzLvDgf6dFlS3wAgPlMDK6uJ7mEFFbnyCI4CWlqLTs6hy6eeQxoiFc7XuRuGlbsm+tHcNNX4Q2OpIseiz+kcijUV4rwRFFDNbLBDfKjTXJZUpCqJaHhHChVjTaNvUysDfQccT9S90dzXMssl32is4QnZdatpUZhHoBAuze0iIhexnrVqVn1tgiPEX08pQ0S0ctd792Oo2WTHD1PNUFHv7l5bJCaFIJTs32ZkLIAUO7gXY31Z5yiXODDKuAVAL8xIjz9Gbx+BaBB9mkmvgVALRi0YDU1n2UEUA2RlA4CCacOIDbqtskl85lp8KwtvpS607b3kfsn25IgWEQLsPbsctdDxEPTLurG0ooU6fnYi5T4P+dMvYtiEKJYEJKXQrmAauAp9Vf/fiX0HVxGcZBIWxjRDLb9EJHRqKGC7BQjB44wBEolsREoYMbLAWD40CtSEhWc8eScp0vFdBJjR4p2Bo5yNLq6pJEumafPxPhZdd+jt6RzYZWCugnPmWeeCQD47ne/m7jGGEOgqxW2NNjeJilg1kep7gWg7BfgmJSUx5Sel05wtPMqhH1MpBIeXT4ziiB+pk10jnOeRXJcxtk1JQlGoVV5mMfi/byidDL71rNndNrGhzX4sVQH0OI/hEBHGahEx+WqGKBtkgONxKSt+GsYw/IQYOo5HrgfraAKYbIPMYYsqUYukpNnNZMnHwe5SUy8OVdOifv0vhNwc2IIUHtSzCuetoiVsb9cIm1tggPYUhtrta6rVFR67Rw3j11SHfk4w+7OrosGOSZ4kfpjmzu7z0NLV1V7xRB+tGk2a/bAGv14LKuGavHAK9YiAI4FIOJ8XedsiY9xXRtTE+QnYwzUvVh1spmQhuvjr7w9YaqgpUkhp9x2UUdMqtSxkW/64oJzgFcj8hmE8CviRt8PEQQeig1ivOk3tIKG7RrgjYhc5oYOAIYOFvkMbBFkR5KGUgmsVIo3BNW9shLhH2wSwkwi48Mcx8IQ8YrWul+/T5Ifg+DqUqUa5Ed5SjMYhEgfv7uT8Nhu6L0KHkx1RAbiud0zV/Lai2DyWL8Ga8Wpq7skc4YQfwqxb9xR1Adsx6DQP07k9z7Jw4wzCQ6Q9g0b+bOGSAQtI7OWgbbzhJSpz821iU/5m6cAALx+PrzBzTHJKWs66FIFvL0ClMVxgtRoK1LXKt6ARVoS4XXKIQr/dZc6rvzXzKieDKy5ANYvqqiviY/l/zSS1RXnALOOaWmtY6e0oZMiY5WnfuCa1Ow2zTmsGPdp7ykzvyyJjbyuflt9XqbVpThG2jiNUltx7dhOK7OTaaxNJSXS9nrqCqw/U3x7pdYCyiUxBTT3LaPYD/BbRENKt3Muv6eKRnjKXERAtotom2PI0x4zCXHWt1XP9KITGaaRn+iaGqNtqaotzQm1ojsl5CnPr1VWz+6vpg1k+vjLEYfgDsAaGbyBYpHnDRsIDGkB+vcVlxsblKSGrVkLBB/FBKe9JEhONaWgLi0G08Yszx6/LPuehP2NhIP8cO0eg6i4pEzaNWOuDJMqL0DUMSc6acq6FaDWgK/EsNYHoZMmKa7VX3CoyXI8hqS4XfxLuHoCpvjUs9zmXSoWLb964CRMdRAdF3Q3YNYQd/P2ryfVa/ogwQqA1xx90I0+WNGP61qqKBUWb6+Ad1TjjzsIa096KVIdu51V+rxQ79uxAszy4MmrlqrhvZS4N0s9kyiDnd/GT7zO3TeyVKUZbc0TE0hE8FJUzIBOcFPytAkOd1wzJDxx2gSp4eZ1/R7jtYXi+3XGiwktgtCFWH/GKaiWROaljoLa66mPJ741I4xFVSt0NVTbPPAqB69wZz0BWGQnkrxI20RppOyQrNSUJuvPchFQDcwgVdY3Z7Wt/lRXbixlLoc1l2feJ8mObAc/fseswCL1bJS24MW/iz5YnyJY/8h6vKWPsBmU4TZ0T9RyxRgTUanW3gw0sUdg1Bq+p6nomfhtSK5rdFC9URLSdFu65Gh1p0mJZvCsP75WWTR0ivAsWLAAP/jBD/Daa6+BMYadd94ZF198Mfbbb7/OZNcz0dmBXrckT2PwuvorOpV4WhiRnjyiX+26U9WQVZdaE1wG0alnMmSavQUPebybsi3ml9BWQonnVGM1Fi9FhKetAlSCeO8dWzWhnVPPNaBNkGBI2BzkqautznKpUiIoW696+lk9KikXyUl5t6nvsV6ynMbPnANajvyy+gWiydRLnhf3OqQ2OUh7JsFReVnH3EobJu/TN8KsBeYJl+U6xvFUSGkOIJ5fLTGU2oXdZUepCN8OaSHLGwIAj6U6VQ4eBTrllWiTTxnxocacaixgIs8m29tTpMv/Ldhky/4OmMeNHdv18Uek7+T47lLR1ZleFEBPI5xDWDFKXPCE6h4AGgvCYLxRs5XtKGuLvrIgOQDQURESOalut4lGxqIgkabgKQk1K3jiWCdAQKwP1LfXcT3DJjt51Ne2CjxPpPwcqJvw/PKXv8Rpp52G448/HhdccAE451i0aJHatPOkk07a6EJtNugTW25bBiWbTk+UJfGJEFvrmOWJg1whOZk6Jlfn3jI1bFJSUQ/ZyeqLLtKS89liUI3aoBQIgqNEmYEgOhAid14OE5IyF5yGw5pKWtxvSdMkkcrwdnPe6zrWkdY3aiCdyGUQPRfBSWuneshLnWAeMzbNzELiXXFr0lKSg4z70qQ2+nXjXvNcklRG57l13Zp8bZJj52NsacDiDTYBsW/VgHvuxsZCX38FFYZKqYD2iPC0V4poKlRV2QSJicYRRBJSKdWpcISS8FQhHBCi+ggvTGYRvHQVnZB0cFU+U82TLL8Ltk2ULUHzfCjC4xUh7AkzCGTaO0yQzo0gobrqjOsOKeVo8FFu83pnjQiDlOJsiCraEY17paoaA1EOHPZVcC+808YJqbkoxB663PciqZNGhvTFHWOCFMlnZRGrWmOj5ZSQUKe57CHrEL8znrV7pwM777wzvvrVr+Ib3/iGcX7OnDn42c9+htdee62e7HoE1q1bh5aWFqz54RnYprmhPtKTNUnVMTk4PUlsu5O0vGvZZORcwXdKulOPzjslb33gCgMk4vfIYId+fw9eH1+NgorkIBqorYktgbSlmCFuh6mWjMT3DdfGhqPtX5+hBkJWAFhR2ioxEa8ky4CTWYOGjrwDQ5rBbZQur1eROudCjXfotvvJkQZJUX8mahBpl6TAdW8aaUlcs8hMnntdqip1XdropKrTHOe4CFg38Jf1Ex5DohOVJ6iIMpTaCtjQ2oj1HcIepK1aQDEyKt22XxsGDG5DsZ849ooAQiCIzEGCsqfyCQNmuFWLMsd1DVPO69AjTHuK/PCIAHFnOpcESJYhqHrqudK924+824oNAYpNAfzI5d4rmn2QVzXyWnW8U1WWlL7rUHMxL/rULUKe+U3o98oxpcCAgrVJajQmAUBY5krSZksbXWTRIOmatFKRc6Pto/9yA9cUMipUofI3A3xNNZpiY2eUKeMbV2FRCixSs2lSpyjvdaUKBn7jDqxduxbbbLNNembohITn7bffxjHHHJM4//nPfx7f/OY3682uR4GXquDyzaRNPk7dItxp65ZqmJNaXsPTrNV8FnLZ6nQlakwyaTAHJ65Em7zKY8mL2pzXIb1RN+sXks8x9vSRK6CqSXbichhH6h+vBknRuVwtFuINFxngJj154Irgbdhw5SOdTjWPulbj5lpEN1PCph1sjGpLl8rVYRPktLHh2WncxESmMaU4ZuyV9PttKBJdr4Gcngczy8VDIIg8fsrlAtrKDWitRiqtwEcl+pbWdzSisDZAc2QA6heFhCeoinurFR/V6HdguSHr8WQkQmPzU7OPh5zB0yU+WsRhndh4WjRiSYY8i/RIkhOGDNWqULlUA894RrEcoKlSQUMkJSk0h8oZSPYjqZbjVUnmZN20tnVIoGyo8noA83mCLKTZdxl5eAAraPW2ZmkeAlyVNy57HFU/bRGj1YszhFWdkDtIqZWP6z0DgOdzFTrBL/Ko3tE7jGLPpUnxUtXFYZweiGw5GxhYoyaBahAXecUYjDNRN+EZPXo0HnvsMey4447G+cceewyjR4+uN7seBd5aEZu/1VIteKaNR4JK21KZvMQnjcTUSCuOtcdnTGSdRo4q5PVmqLWibj17hrHaka6yCCPCI8XsIU8lOvGzdCJgkZ80cbl2T8M1JtnpmD0DPNBE3hxg0ffGIrG5robk1biOrIHDiwYNz2Mx6QFqEx+7T8oIw9LOK0NtmeVGLcvpRL1SngwCYd+jvp/ORrEIosndGhhTkVAnJcvkSuuS4Bj5ZEhzslQ8Lkii0xW2OxJBhaFSFrN7W0cD1peLWF8Rw34l9OBHDyuUOfy2EJWKSFuIYgBJklMJfARRHWzCkwUl9ckeTgGIyd0mQh7Ma34kkVLu5HK9E3qoROUqV31jI85iJUSpXECfigg/31ipGjGOdJJqS69siUkaCZBplVmLH4po1VKtZsVSCgOTVNn9Q6YXkiKeIAl6X9NJjCBc7r4WBl68b2f0W5fKpX6zXLRJqLWRTmg9phEeL0ShECpi6hdCeH7cDi7bNJuEqd8BM9qz0MjhNYrEfiPAIlf9EN1IeGbPno0LLrgAL7zwAvbZZx8wxvDkk0/irrvuwk033VRvdj0KpX9VUWrwEowaSIrvlMjOR9LtUlchBNZkHMI94TuYP4CEkabrvBMpK4haOumaOus8aotOkC3FGSPRriFOlasjjewAEFsL5H2WZUfEPJYgcZIQFb9rqhJKF89AGG3VEZaAsBIHCXOtzuI6RcQnGhw8n8MviXOFIIDXl4OlBW+rQ9NcS6KT5hlWU4qRJdVwIE3C45SOpGeTC8L7p3NI2tJYCXJIbNz5JFfLeV8jY1BxXTxthZwXSpWlreLDwLTZ+bC9Cas6mrA+IjUhgGL0zLbAw9pyEU2+mESkqktObAFnymtUaNutCdpyo0/j70lBtTVxavcxxMfid7zhpu9xeFoPCMHU7uOlwENZI0CynrJuTX6Axui3z7hhuO0iXap8dl0Q73QOwCBZPgvRUAiUjZQgAXGHCUMPQRBJzyKJlIsU+15olMmlptJJmC4VA5BKVILQE9e0dIBbMid2cmeqPStR2waO8npMtKnsPwUvRKMXoBDFlCpG9ZFllWUR7cdQjqR0pdBHNfRUfyl6IRr8ULVnY6GKpkYhjSz7bclGSUHdhOecc87B8OHDceONN+L+++8HIOx6fv3rX2P69On1Ztej0P5xEYViEX4hNJioZNkA4BWFiNErRscNSG1FXpVh6qNjqTe19MUAlA41lZx4Zqh5XeebSKuXIXNCthJHk0guYlVLpZHyzARswmXdwwqaZEbpn5Nqu1Qjw5TyciuKK7wk0VG3dgDVNpG2WmLGSglIX71LMbyEXwzB/Fj3zoo8LlhW4DyJnDGi6kIaAU9T8aTkYSPhoaTSbkTZ9QjlgXvwdz7ThQxJjbjfLa1xves0gpNLqqNIsfZMXp9aa/0Zp0B+Ofbqn1njRqitGQLOEKhPQExgpaguBYbkxKnl4dwgVNYpR9nT7tfv1deRUtojCU/B4wYhAoBKVPZSKAhPKYjJGgAUIz1WgxeiGPWlQkSiFJFiHAWPoxBN2B7SyYaLCMh6+YyjoRKgKYqY3OAHxmQfcBZP9CGLJncXgRBli0lCOinzkLymEyn9mSGX79T6DrRXV40IHOcxyZH1rHAWS/w4S1h9SCJdjNq6wYuJtK8RnlArQzX00BGFSSgFHkohM9qzyQtj0loI0BR56NYhcOycW/pxxx2H4447rjO39mh8vLYPqoUGNBQCFApBLJbzQyWyKxQD+EVu2Mow2wOgBsGJj2NJgRBxmnutGJ4NmjjVFgvqhKymONxFnIzrGtmoc25KFVZY6WpLJGC0Z6aEzC5DTokPA+LIrH56RcvfPAXVNoaODeJTCQOmVmbqmdYkJwcYxjg8j2sbBgodNwDwMkdYCMFCqZqyJ2CHKk5O+BmB28S9MPpnbs8qvW/msDVIPLeGpCRO1znSoweMUxIe217BQahSDYad5CV5j8tAN09+LruIBNS3Zmba/2f5I5Eny2qXS3ucThA4AG3SKoccxahD6SRA5aNJeAAkpAM6agVOzFokqLJq5ZXkxiAmFimTE3AlZCiHHtrl3lScoaoN0R7zVT4MgtxJAtTghWjyQ2PC1stg1AEMlZChpIhATALEpF9AUzWWKjX4oSGViglDusQkrneo6u1Z84LM00WOQh5LnoIwJhfcIaWz6ybfc8BF2apRf65wUW9JiKphXBdbw+wzD0WPozEimybhkeWT781ThLsc/ZZlKDCgyQ/REI29xWoBxSiSbT1BOinwoIY17Y0oFxpF5ywEiuUX/QDFYqQvDBiKPNoNE2IyYUGKIVZoEhwpwdFJjvwdVIWI047AKqFLCyQRU94Nnm7cl9Qzy/tFWgZb7VJTklQn1NPlYM5NEuTqnplSpQy1X03JTkYZVZEiw8Xyt0+J8mLKC6L8IUfHhiJKHeJT0UXE4vnx7yBkhn2DxzgKfohCITZU8UpSosNRAGLVhcdTiaAiulKNyuLRmyNJlkS5TJKoEmfADppXy34lflZafm7C4zzOMQHqfVdIeHimxCht76JEuoyy6IuQtHKmEZtaUiZhgxQRXt800K0Hdr+JVVoegqqHSrRqtqUqXJtsygA8MI1QMCFNiNLK1bjIJ8pfIz6cIyEtAOLJOK/GlmkJTcIjJ3tE5ROERydl8QQtJFXlqG07QoZKGG/9JtJpz2FAZP+KPr6HvoUQzXKBy0LjmbJc8jkVzpQkqRx6SnrGIbaDaorcqZt8H82+OdlLAhFE0pL4Xm1BYxG9AhNXPS0fhvi3z0KjTWziEjqeoT9Lv09vV1E+s7x6XrL76aQPUTn9EOhQBNJLJTy6hLESMlR43E98BlR4bHMW8pikAtpWQzWQi/AMHDjQ6IhZ+Oijj3I/vKfhX+190MdvRLMfoNkPlRiuyY9Fk83FChrLVbVqLxRCg3DYg6ULuvtkKA3tKj6qlmFg2sJcN/IDhJ5XMWqHzpd5+jE3CJNOpJgnruUNAGbXz3YnVVHKfa68FkQ6JNw3OeJj8xnagZTuONQu0muhHqmUVLmzkMNrYOCRfU1lPUfHWvFptLcVUSoXjPdq67llEaU+36XLB4CGcoCGssi32B4Igz5X7A0LjHF4Bc3w0eOGl4nZ7tbNGZ4lCQlkCOV9Jvf2sY0j65nwjTS2FCyH67KEi8zbLsxZJCFN8uKadGvZ4dQjnQoz2sXzeGTkGo8bsRq3PsLT/457IrWWcCOXnlVB1UOl4ivbFgaOPn6AMIph1+h5aI1WzBuqDG1BrMaQIVniydSsiz7hBRyohDxBhKLaWMTJLDuziIc4Fx/r0hVJwgAxAfradVtIK8okfldCoCNgKEUF66gCHRH7kWkao++pb4GhX7GAPpLweEK6IJ7B1XMlKjwmLh0BUFLPFOeKEeFp9IDmgq7q0YkaQ8BjQqYTBsZicqeXwfSZiYmYUMnFbaYTHl0VqZMkCf1929f0eyVJ0QlmxSKb8b7CLCp3lCe0hTtisgwIwhz/NlHhQFsQ56sbiNcjt8mV8kc/+lHuDLdktFY9hNxDNRQdttmPRY6h/rJDDwVp9OYgCPpgaRuUiTxiCY9cfVVDD5XAU4NTlXuZYkddrKmLOaVIUxriSSM2XW8qPR08X9znaZ4PniUtMuqVMUGI9ObEyzRiJSz1mUoXu11GpEfriQzItB2yY0kY5clh16QgPbwCCHur6LjS5inPlmrVT3pQMB7rwhEb3Ul1lvpoOUOA+H5BiKLJKPCEqtS5s3BUfk2ixzRPCNmeso66SlPVO2dgN9vGjGvG2IYnCU+qXOM88pMASW7SYrVkBazzPG7EbPF8Dnk5z4Isjbi4yE3ClboTtkdZEh6Zn4ynphN7RWZzYv0Zp2gxcjyEQdwfAShJdZ/I4LMp6jsdgYfGqvzwfFRDhiisFcp2eAeNmHBwBJyrfXmrHNGYGZE3q3w+i99PbJMT56afM+12TBIkJBjxBKpLPuR1HTqBCHi8j3CFx/XrCDiCkCv1l1CrAK3ROyh68TOLHkOBcciAyB4TecrJvqSRqkrIEfKYLDX4QtIkJUk+M/tcCFMCpde7wJjals9nLCI8eivLcRWG1Es+QU8p5xPPSqOHIBN58dQhOIgWeYE+zkW/KxZ5U6RI1cckuHm892Q+ATe/KUlWg4w8bOQiPDNnzsyf4xaMNWWGdt9Dk8/QGHhoigbXPoUQfaNBpE/BQzkIlBW6H01GpgQgzlMamYnfZtqQM2XhX42MwqRhmBQ/2npzF8wPXxjdSZ2v+AhiMafvxYTHr3IlEVLX/BC6aN2YGFKkHMnySMlRlK/PwZiXOmF7PgeLeq1XhIrdIK8bahauSSFslQugSIyUGmWpynRpBgCEMl5Je0G55wa2FMvRJrY3Q1XTmQOa0aT1vnV9uwu6VE6+GwDwvFBNGOJ9OaRrcoDxuOHi6vk81e7FNnjVVa4uwlOLBOgxU2yCEyrjSTPfNMNNUS+YbRDExy5VrkvqKuom66Sd0/t2Ti+rztgi6UbK9rNkW9eDDV+dIVRXlViqYwfgK/pSOs1Q8EPVB8uBryQNgrD4avIuh2JCiVfcukRCTDJVNbmLCV6SCM65EUzeZ5p7MRh0biqkCpKYmcSFMWaSHyBeyEHEoZNZ6dIfe6IXhto8NtYOY7JWDkKUwkCNG+XAR3vVQ2PEMOSuCgBQZIK4SMLDIhlKLEniaiu/ckR4ZJkaAoYOn2v3xtfEOJ+cN2Q6nzF1X9GTKi3HdwIpEYraM7M7We8hkrzEdk46NUmqNKsa+ZAESPwWJFA3cNfrI8uZWirZT1LI0KWv/xcA4PpPfle93446QltslA1Pe3s7KtZOpbUiHfZkfFxhaAxY1MG4pdcVE2D/go8+fohGPzYik6JDCbvjSvJhiww5WEKPKyfHajSR2h1H3qdD91YQ4k9doiPFoJE9kmeSn6IXxgSIcfiB6TaYFkBMdf4UCY/uHinjNCi9sxeqKKhK+iNFyA0heDEZsAuIVS66FML2pkm1T/J4cpdkOXlHxuPx5G5OvAnPB8BQY+neC4HmWVC1CEHAPfhRoLdywBOutSJvuRI2CYxBVHWJnmcSHpts6sHa7LaW7eMyKJbxSHTCEwSaiqsG+fUYR+gYsaQNlLJji/q5LjFLk/L4jMOLFh4FP4Tvh4ahvC6dFOfSiUstgpMgSZ00tLbrkcxHPDAIWKyKKoXomD0DTTfWNlxO1susjy4ZbEAVBY2gF4NQ9Tfpyt0hPZ0Chgo0kqCt2oOQKzUWIIhPKQxRjvp2oHUoBgaPMXiaFAKAOjbVVCyadGVa3S2dCUIUTW42kdLte1wTqpRCxXWJyBoP0cGrCCLG0xZ4aAwLKEbvosg8FD1JyBgaPE+pv0SQR67apRrGJDDgQgomyUfJY/CrTJGnWvC1NvE1qVJBkzil3Sef4TNukRbtd+JO5pSoxWm17wWiDZUND4dBeOy5ioEbUpg0J1OmlYtzk9DK+27Y+bvqWM6VpTqkr3UTntbWVlx66aW4//778eGHHyauB0FnI4ltfqwtczR44uWUA64+aGHUJhq1b7GIvgWGJjmIeKYu2YbUqwLxKsVeiUi4FpNO21Mroc7MRVm49sFwwwuhwGKXzCKLXDB1MqQdG4bG1gpc17emwV5BqN+aBMJnoSJeANBYrKJYrKJYjCb3yEZKtUHANJE9lMdUWih4JRHwuQo25he5EQhM96oCgEIxRB+/rJ4nJ3sAqAY+KmUfpSh4W1u1gHIoRsBKKCbzeGVpvmHDu8LdZE7I+CQyZ1uFqben7rEh20Ce8z2Ogh+o/lj0gwRZsg2yAyUNEaQk7oPMSXrFM3nCrkxHqBF72+7J9iSRdQCSksqCr0tak8RPL48LqXZZnYijoyNLu5Zmq+UxjiguHoqNQU07Hmmzw6OtIxRZCjyLwGlRjX3RhvKbqYSe+tb7FAL0Dz0l4WHw4FUY2qOZqq0aT+alQEhzypGYpMJDlHiAMheL3ypChIZnQXo9PHgoRD3Fhw+feSgoOxNmSDKEUXVMnAqMGaoyW4qUsEFR0qsQHVHZO8IqNrA2tLO2qOwlhCyEzyNbO96IQqT281FAkRdVeZlFG7hWZykR8+GrMnlg6nyAwJCa+fDjdmAe/KgNip5nkC4/Um+5bJ/EdWhpBWlQ9jMsfdyxX5FOeJh1l7TWyuP86TGeWlb9WRK6/ZHHTZWbtPkBBLEqRVSjVIdOq27Cc8kll2D+/PmYO3cuTjnlFNxyyy1499138dOf/hTXXXddvdn1KBS8SFwZipfZXo0/aLnq2FAR4s6mSF7YEBm1+epFmS9V/2glMdINxvIz8KQhV7qumxmEp8hi0lX0GBol4fE4iiFXH0g5FC6EukTKeL4l+dAN4uqF+ZGKoFIA0FgpoLFYVQGmisXAkFbo6hDdqy1N4iRF4MViFUU1EQTquvqvu3rz+D6/gCjiZySBiNxHVVTX0EeHNoGESB8IXGLoLMmdKg8kWY3rVTBsF5IB28zniv8+C6O4GJpET5Mc2cTAJjU6GdF/2xDExIvbkJshFnSJjksNqFxcLXuDKvNiz0mEYhKLOrofeoL0qEWKTnjM8sWRsN3ExiaqeTzIzBvin3ZAPimdtMsUerGmMah44OUQwY1fAQD4s3/ufo7MM2TGFguurRwAYMzvb8OKY842pFi6kTKDGCsAMQ6KxV/0vrXfFauDh5wLNZacCBEiYObCVxIgSQpCOcZwTxGHAnz4vBBLf+DD0z1+EBOcApiQ+kTfpSkZin8DpuoIEON5JTLYq6CKAFVUITYNK6ENIa+ARUSlAx58rxg934fHPfgQxwwevBoRYOV1T7ZzVO8KygijcciL6E5BkiwUUeTiGcWggCLz4Uv1pOcJqZiUBBvjqCCEFU0aVODxXKCTQhu6lCaZlqs0Lth5Ju2w4pEt4Ml5xbAPC+NzhtE6N8e4gGuSwZxSM6AThOf3v/897rnnHhx44IE4/fTTsd9++2HHHXfEmDFjcO+99+Lkk0+uN8seg6IHQ3QoP+C2sKrEtBtCD8WKp+IKNPgeipZRmUk+9F2QzY6kS4bkeVukaLNrHZ4jn9iNMS6PzvqLIUdFJzwsJjxFj8MPuRpE7DgcgEYsYE4M6aa3AmnqOVkP6b3Q6IdoqgZo8mVEzSA2sraeZU/IMpiWBGMcRUmkQgZArECFV11gGlFrpIdZBfR8xPvVRM+ShKcUeGiXEVN5umedDds9NGOnEE2vLv7r4nv5vvMEe5MGjZLwSHKbFr1WRxC1tR47xK6DLoEqGMHGmCI/Uq2j2zdVuWasrxEeNUxKAQWLbUM4F9+qMsz1QwScGX3WjuiqCIorVk9O+zTzJnc6neTIutjkyG5nHjCETJNclhBNq/VDfRMqxpN41opjzgYPY0eJSuAZ74EjJqY+42j0Waz68YCitE9hzPC2qVkea3SosmpMgDQ9M4OHAiso6YqgE/pCxFPEoRDRBE+1r0YCkJQO6QgiiRQQSaO0MnCECFAF5yVoJ6NnmNbkPgpg8BOSHpmPrFP0wzhfRQmcy4WXjwIaUfCEe3WRN6LI5O+GSKoknu0HQhIk3bNlXQGgwDwUPIZi1EbFkBk2SLrxuConj6U1WSOIa8x2LdaZMqqWx0kCJAUyMYGJF2/6Y5hGamQtbfIq65UXdROejz76CGPHjgUg7HWkG/pnP/tZnHPOOfVm16NQCcWL6QiAjipHR6Se28DbscFbDwBqJcCiTuVXi2jgDWjgooMW4KMI+cEy40MU52LYKxSdufue7DjaqoVp9+kGe57LuC+6xsTEUIg6eqMPFOUqKSJ4RTXIyUkwliikTaS2dMdFePRJpMr1eAumB4V+rx+Rn4ZokG72ORqMCTrde8AuR4Fx5ZHSt1DBNtFA3ycsozFkKEQDjvSKsT2SALFiDgOmjJjFfkQNWFcR09HaSiGW8Gh1BNwqy7TYJfYKywXzHcdSLxtZ+eoq1ti1V3v/ShJoRqAV5WfK3VbEyYjfp17fRk+0u2z7Pn6g2byFSlIHxOqt2Fg/accmISRbnipfgYUoSClSEBqqPrONYomqfpwHedzldeTdlsAmYwAQRp5WrL0IfAQ0DI028nQ8u/8d9wAQ+85xHtsyhSGLvaeC2CsQiO2jKlF/LethMKIFg0zdGLnNh35MPmO7F2bEWykwDwE4ijwe95TkCCGKKKpxrIoADB5KrEM8FxVUmBhTQx6AI1TSVI/FZEJKV/RjXSqiS1s8MDE+y4nVav0QoSJaFVRQ1fZikvkH0eIo4FWEkaqO8xDc2vyNaW+HI0AYbQYV8KoiNDok4eFcr2cRBa8BviQ8XjMKrClq20b4XjHRDlLKVEQDilwE4CuGRTSGRTRGxKzIfCX1AZLzhCiHbBNhmyUX+bY0R1e/SZWhnFMkyZHXfM3OSKjVkvMTtGOVb+JTc3xfej5R+i730tKxww47YPny5RgzZgx22WUX3H///dhzzz3x+9//HgMGDKg3ux6FtgpH1ePoCDhaqwHaoo7e4bWjzNoBAGXepj4GINJBe00oKsLTiIaoAzJ4iplLMM1v2oOnVig+/EhHHa1gAlNkqxvoFaJOHOuzY+M4KQaUwX0Lngi61RjZrARcs/jnwqugLCU8THhnmK6fMfvWIfuYqeJyI+TS0E1OlnEnlb9jNYZQ/cSRT0XQLlE+Hk3EiMoXkx8p5dClDQUPqMpVFLT9XfxAeDcp1Ua0ypGr3apn7AxdqfjoiPT4rZUi1pWL2BDt+dIReGhTIexNwpNoB+iSHdOuwKUKM1Y72m9PI8JA0tZKdxnV42XEXiNxXxESItlmsYSzyReTniSeUn8uCU85hEF4Qh6vtBo9jmZdUsMZAi6lPZ5SmYoysWgy1SU+5iSs6o2YAIWcI/Ri41OfMbXdgKibadeUFp1245CU3nCmvxf9vCVl4sywSRDlFKhWfVQrIVrfjgjjt0+BP1CMKd7oAWBf/IG6p+9tv8CGr85AtRRNiFZ8Fz1/KRGNVQEcaVERGGJCLI71705I7aRRfsGL44kBgMcZ5JMDBPDA8MTHNwIADhwwO5KiiO+pihJCJW0pGXYw4NAmes+QpvisiAIvoiLHRxQ1wiNWg5421upSGK5JdUKExnFWt+AIInKpN5omreKhIkdhWEXIq1Za8cSolTTCI8iRnlYSsoBVBNGLSIwkdp4kPKwJDWiOcm5CyEOE0fxTBY9UWlEb8aTxuO6FJwytkx3CZUrhMw9VuTi35qYCmBKihoxFxtvRvZaUyeNxWbKIS5bdajmlD7tQN+E57bTT8Pe//x0HHHAALr/8cnzuc5/DT37yE1SrVcyZM6euvBYuXIjvf//7WLx4MVauXIkHH3wQxx57rLrOOcdVV12F22+/HWvWrMFee+2FW265BRMnTlRpSqUSLrroIvzqV79Ce3s7DjnkEMydOxef+MQn6q0a2qocFU8YtG0IK+iIViEltKPMhVFbhbehykuqk3vw4LF2+Ex0QF00WeBF40Pz4Bszl7kq8SL9teycfqTTjnYv1sSYfkSEYmLC4EejmAezA0o1XbyfTOx5UQmluite4ZsxH2y9uDaYWhTIpd/V+6F0ZRXPhZrUKqFw4ZR2AdIVVi+/9JYTHhKxnYGyuUJMfkx3zthN1EO8IWKhEsJjXJXZ97lYGUcr31K5gHIkDSpXfXQEPjoCaaTsozXwsSEiRB1BHNG1wk0SY7eDLjaWIQfMoGDWffpkb/EoZp3X0+peKIHmRaK7i0rYZZXi7yZfBGHro9mqAXHsi3KIuN7ROTmoNflSRRKXTT63kYeG2lde16U6ttRBkVhNjx9C2FMVlHRKDNimqi+W7DAH6ZHYGPLjMa5UW1JcH+cXq4i4tjiR9wXQ3iNM9Vel4sNrj+5dXkXDB0IK0oSPExKfsMJUJPBSqYD2SPpYCTynQbkeRsHwMNQIEodFxrQVtSRCDb5eejGhiryYUrN4XBjqHjTgYpWXGOci9bLmVhnyCgJUEETxInTJjMcEoZETv0hbhBcd+6gqgqNIkqaCsgmPeiYCBKyqFrEhTK/jLIQIIwIUqHylhEeSHc5j6RG3vj65+OXKSSQiNWFJnfNYRZw3iL+HQqTyClFByOLnczSp5wS8aKj9fBarCG2Jo1gkhahatlaqrNodBXiCTCH+ZuXCnLNo8aONyz5jRhwe87s0xzbTs5Ib41ra+GjHjMpC3YTnG9/4hvp90EEH4fXXX8dzzz2HcePGYbfddqsrr9bWVuy222447bTT8B//8R+J6zfccAPmzJmDu+66CzvttBOuvvpqHHbYYXjjjTfQv39/AMCsWbPw+9//Hvfddx8GDx6M2bNn4+ijj8bixYvh+/VF8NpQDVBkAdp5FW28hHYvkuqwdlRCQXjKvB1BWFKiTc5D8TFGftQ+K6rOyJgQu+pgtsRHW7F4zFf6az/6wKW4Uhr0AUARBRS4RYBkNFUWrXajjlMNGSpeLNWpekBFWfELd8eymrBYwq1RN3hLynlMZBFtnfAIFYb4Lbw9zEBglTCepD3EA2uDx9DgMzRKAuTH3nNCNRdPZMIuKJYWiHOR8WH0wUlbBt8LEYSeIjmlqo+2SKLTEfgohZ5SW8k9eqT7bkfAFKmqRnW0QxTYUh3xmwtjTy1dPR5BtirLFE3r5FIjPFGwOEmGpBhbJ0eSRDcXPGzT4KN/ZEjS7LOIVEgJD0c5ylgSaNnHBFHhxmQm26QSGc3r0WFlWeS9urTPlPLERClkQMAYAt1bDbG6Tjfkdtk51Tb0jtqohj2PS3IUKAla7KEiiZBazUZ1iQmRzljlpB61Q8hQjT6Y6ksVNH/zFLXNSLA+RLm9gLWtQgWytqNRkXNb2mhvtlkKPHRE40ZbNdphXFsMVbWIuS4bDl0S3Oh5sTGzF0tNZdyD0GpfFwEJEaIalhBGJEGovyISwErwWFGNpwF8+KyqxsQAFYPo2HY1njXuSoNhjhAhDxTRCVBFwCtK2pKQvPDYA02l0Y5NomDaBiWkPap/RlIm6eUWQklBuRckZnoGHwGLCJouomOILJDE/FNBJfL9kguGWOPgWWN5CI4qAkW0EiosHi/OOTww7kHK1xmL1Z1+RH58RbLEAlq+Dd8ao5n2TXsMxiBojp3ckojHC9ZSHYSHcV7PMNt9YIwZEh7OOUaOHIlZs2bh0ksvBSCkOcOGDcP111+Ps846C2vXrsW2226LX/ziFzjxxBMBAO+99x5Gjx6Nhx56CIcffniuZ69btw4tLS2YNGAGfNaADtaKEl+PMheEpxK2IYiM2CpBB0LjgxC6WDm4M3jwIqt+nxWMQd8mO6Le8X2M+fBZQaX1tQ/cQxHFSK9b5I2G/tpHQZME+YaLY5EJYlTUVGO2i6Otb9VJjmTqHky9aVynfDAmex5PktVIwtMRnWgLArQHVbRHg15FExkX4KMRRTR5ot5Nno+mqIANPkOTH8e5kIbbKpxAgaMl2rhzm2KI/oVAeat5jKPKxYaDgBj8VTySaOCvaAO/HrZeRnEF4vD6egRaO0aFbSRo2EGlfIncHvEciMO8yxgp0QQZcmWgWeEBKqiiEg2WFVRQYh3oYBsACHWtHLAbWT8MCIdiMFoAAC2FIpoLnprA5QAEQMXMkP2q2WdoLkBJh5p9rkL1N3ixsTxgGmDH7RITHml3Aojfpv4/Drlv7zlke0N6SNrU6OpafdWZ1rY2dEKln9PVaq7z8pouNbVVcLqHoe+FaIiCBzYUxb5+MsRCpezjw3V9sWJDXwDAqo4iWiPpY5XHdXdBV33Kfq0vSuS3CYhFkQzw1lHlKIehSitX4nLSK4UBylGfK6GCChO+UECsTqpA+OCXWBtKXPS/jnAdqmEbKkFHVKZY2iLHVTk+eqwIxrx4EQN9DPbVuVqQY4tSRUXEw1BNyTRhxSA0Ur1lEJ5o3JLnY5Jj2/PEc4HHCmCsAM+L7ZFUXZQhu2bcrdW74DWg4DVHvxtRYE1qzpAqQJekSxJA23tM9xxTJeXSMrWgjoVGIm7nWHLElHkGELvYS7W5HVrA0wiPK75QoI2XMrYRAFR5qPpYO2/FX9fdgrVr19aMA9hjNw9dtmwZVq1ahWnTpqlzjY2NOOCAA7Bo0SKcddZZWLx4MSqVipFm5MiRmDRpEhYtWpRKeEqlEkql2Ap/3bp1AIAN+AgeiugI16EcbkAQijQBryIMow9Aiip14zPEBnLCf0F2HiGO1EmPDflRStIUqBWN6NTyA/dRVKuQCjpQ1Dq2jyIKLO6MujSowj0UeUHpuovMU7YXPmMohLGrp89i10YgJg3ytyvyaRZs2xIJfaCthlKiw1W6EILoAEAHa0eVxWLhIm9AYyhWMM1hA5rCiPwEPsq+8FIA4qitUuogVDCRyipkaA88wz6lymMVTTlkanCXhsi26skmGEAcZVSXqOiSLX1SEPWMGyVNDZYGbq+ENGND4XYrcijxKkrR5FJhFVRYGZXI8L7M2lHiG1AJInVt2K4klyVvA0JtdRlU+qNPUFTtqw9UQOwmDMQePbqqSw548t3LDef9qE/Zbusyra3q06cNxmNRuc+EPUCg7JFi26SYbJjH8SCsqZpS2ttlayPyivNl1rEtQUoeu1Vwkgh5oSSFHqpSxRoEKGpGC+2VIt5r7YP32oUkeHWJoTX6XGQfzQpUp0MnsTFxRvRcrvafKgUhSmGoTUZc3Q+I/tce9bkO1o4S6zDsHkMWquMq70A1WkxyHkS2LzHZ0L2duGZszFgVPisgjMg7g28sHkUajVRYbzY0iEtgjOchj9VqOsmR5bElPjDulWOVKRkS5dHLEECSHo4QTFOHMRYbZ9trHbnA1r3b1DWECKJ2kW3iaXOT3SaGFEwJ53QTDEGqAubDRxU8slMNWQgPHkK1GI8Jj5AExWFcfB7ZpiqixVT4BWmjGhN9MyK3LsWRNkZyXJOLNwBoi0xP8qDHEp5Vq1YBAIYNG2acHzZsGFasWKHSNDQ0YODAgYk08n4Xrr32Wlx11VWJ82XeCg8FlMMNKAetivDohmquDq9b3HOE4GH8kerSHxsMcSA3SYx0AhSyCkImO12sZy6wJoS8ojqkNOCTv30UYjaOAsq8igZFgPzYMBpuw+iCIkBmB/S9WESv6/RFXfLDJgw64QmieB5yQKqyKsoQkraQhSijHRXWHNWlCaVQDPQl3oByWIgJD2OG90Bb1UNHpJ7pCIDWAkOjdENnkiiI66WAoarKEw/oqvya1MZU1UFFoZV1EXYzcV66hEf/oCVs0b/9XBuhUo1FKx8Iw8NS1FdKrKQ8YsqsHRXeoTwNK2EbKmG7QewlGHyU2Qa0srWiPeEjDJvRGK3qdHuAWEIY1TMEqh5TKg6fMUO6wVksFYtjU7l7kF5ll02T5EkBM+0BPM1+Rkhz4vwl2THJRkyU6oHHTOVJmmdWcu8iFwHSf6eQoUjVWo4Y49pKEe+1N+DddnH8YYmjvRr3MV3qJZ4b20lkLVikujNWPYcqLllHKCSwgUY/fU0iUEEVHZGTRztrRQc2qD7HERovNeAVRXiCyDZSjq+60S9jHngYarYtBXCmeTrBHGcZ0xehfkK+Yntb6RL7EKGxwA11qY1GcOLyBVoe0vjZVl95xvdrzwnGvfDAuf129DlH0xFpj+E8hOdVjEWzqH/yTUvioxMcvUw+imDRQtPnBSH5ifpjyAN48NVCVJpRy9+6baogPLFzjpQAyd8VhChI2zVLEqSPhQEPlZ0RAEtSXU7ULw09lvBIuOIG1NoosFaayy+/HBdeeKE6XrduHUaPHo0yb4eHAqpBu2D22kdglslLnEuyfo2pa9IfOx/1OxoIZHzamABFncqLyQ9nIQJWhM+kgZww4AOE/tpHMSH9qUSER5f+FKLOWZDGfpGFvR7XQTajzxi8wDSONg0/tQklpelNKU8skZCkBxCiSttToMrkajCaqCM31jIaUYrIT4k3oRw2ohhKGyjxycm6NHo+StIoueihvcrQEI3Ryr0xeqxu9xIPQXrZYwRhrHCqhqbYNSZAXNVZX7HoRMWVtw1TIsSN83LyqUarvFLURh1oRQnS4L5drKjDeIKphmU1uANQ6lgJ2fYllES/kUQFHEWdnHOm1VuoQ6pRu1Y0CQ8C4V3lkiICsYrHXX+mritXeElwIu8j3RTGjmkVP0OQIU+liwlZWuwWF4TNga2qMokVtCOzDLL8ScmSDNRmBy2UqIQeWiOJz0dlH++1e3i/XfScj8uhCqch8jUltkkpbUz6geQ3Ktu5IwjQEcowHR0JyWuBF9T4FrAqStEipR3rUOIbDCmODtN2pewYd3V1UTzR26YEYWJsrc9+Uy+XbnysL3B5RHCSUpyY5KSqn3kgxniWnHI5D80VIweyRgLGoMoHT3s8C8B41SCB6XmYKjOlBozKF7CqmkM4ivBY3A5iUV00jMQ9XTOhEZ7AIkAqZEBUvgL3UNGu+SllloEt43EuQEVpPErOe1zosYRn+PDhAIQUZ8SIEer86tWrldRn+PDhKJfLWLNmjSHlWb16NfbZZ5/UvBsbG9HY2Jg4Xw1awZgvRPuWztTWpSoRKJOW+mrNr30g8f0mkqxakiOVNuq4igDxqiI4QWTAp8dxUB0q+q3rtj0UVbTQAtfuU9Y+0XEU50IatrHQNHLTdbM2ORLPzjdZ6BITHrWkJDlVhAZ7l0aE4loJlbDNEEdL8W2BNaHAGpXBuBcROxmvoiFsQFMgyFFzuQFNXgEN0VbVeoh6wCQTsq6qjjCHIs65Jg0yXTtlvXTPhzgabXRkiaazJDxGtFq52mLRijS6JuOaVHhsZF8OhY1EEEYER1MZiPpF7eA3oxjZAzT4fUV78pgABayq1KEeLyqDYY9zw6C5EkrphFRNxZ6BsdpUHkuDYtXaCTsaayqLyhxdY/rZJLFRdzGTTOkTfzKIWraUx+7lLrsfd9mz4VLrAbFqDxAeMW0Bw/qIo64pAx92BFhTFifWhx0osWhbFGFemljg6PYW8TPMOnvauwOELY6U2rR569GOtcrGUUpLlI0ij21iKmG7IUV0QSc4pgQ9qToKArPPuvIRRchWCtvju/ptvTHbZkeQnHj3YkFwJOPI6Dcsor8qzDWDttFf5JkqtQQ23I4uAMCDqgpYCaRrE+JiuOsqTCgKiij6mk2RcMrRzCt40ZhzdNvUhM0QE2TIIEcJF3spDdLmIvsrY/GYCcBQzXfw9Zl11lE34bnjjjtwxhlnJM5Xq1V85zvfwbXXXltvlk6MHTsWw4cPx6OPPordd98dAFAul7FgwQJcf/31AIApU6agWCzi0UcfxRe/+EUAwMqVK/Hyyy/jhhtuqPuZkth4rJgQeepp7FgRYEkpkLgUrUkTEiItjbkIjKVBUook03Koju3xyD4IMQlT1xBGQ5oMWiZEk2oVFRlDi8dZnTHqnMpQmnlG1FMpnhRlEDEdmNZBvQyPFntANaQVGhGoIlC2JoCYwGM3ywoCXlErrJBXUI3qXWHt8DQPOZ+JkACK8Hh9UOaRLQvvg0rYiAYuQwnEsSps2CtgVWZLUiPKyhMER0pcVFqd/DBrzyGjfdz9Kb5uGoHK4yAyuDQM7qPJphqWhYehRdo9bdUpB0QR10QLeMZTBiKtPSRR5JGEJ5ZwxDZQYhuW2PtPemk4CYum0rGvSW4Qq7Bk+ZNliyUxzDg2JR0mGcpC2pSSRXwksh1KJMkQiPtY7NVYDYG2gGFDpH/dUIlihkVSunZWUt9ObOwfG8PaccH07SCMuli1rLAyOtAqnoG1wvYrjFTNmnGxKHdMWqTXVWioS7NJhWssdaWznxnDkrw4M4uuKVWTKfFw3iLzVN9PRHYsGyYdakGoYm3Iez0wndg5tAYCYokVl8tPmFDEjw2N+cRNCs28dbWXUIlplMAl3o6eKecZ8RyL8Bjxj0yPOVcwSTlGV1nV6HcuCVW8sKuqcdUV4DENdROe2bNn46GHHsLPfvYzDBo0CADw+uuv46STTsLatWvrIjwbNmzAP/7xD3W8bNkyvPDCCxg0aBC22247zJo1C9dccw3Gjx+P8ePH45prrkGfPn1w0kknAQBaWlpwxhlnYPbs2Rg8eDAGDRqEiy66CJMnT8ahhx5ab9XApGs4E6zZSWK4iBRqiyBd1vTikkV2NAIk7jGlQTrBcet5gZABnt5xNaIU8BJC5hkxHhhiY2ifFZXExA7mJTujFE/6vIA4zkUBHvMsMaZprCaRZSAoy2Rej4lAwKqoIrZXqrCSKi+HMC502pywMnxWQDXaBNBelQSoKFfOABUE6GtEx5bGdaL8umRASjTM2SxWycXRSQMExsaJAYvOMCm9io0wQwQmgbG8JFww0muxPzgPDCJl20UEGkE0RfJmH5P6d3XMfHhcG8wSdgVme0g36kAZOcWdWe3BxQCfm/uCpRGemKBIHX+SEDFtBPe07yBLCmOTI5cNURp5qUdiY8MeTdJijEj3W5leD2hZCoD2gKM1IjzrKyHag6qy2RIrX0l4IhIcTQwMniH8F1+dSZwl9KjGIt9YaijJjv4d2vFo0qU26dIX93gb9+vkdbcdjiQlJgFJIT88gLQYEXlAk7zYaXWiI5+TVEsbt0DabjGN9Ih7jZjx2qRt9EVL1ehSPaZKsxxzSJytnHvkvOXB8wqmqkwiNI85PIQ64YnmEQnTLiiW5gCyX2kSHuajCjkf+clYShnhBHRD+Lyom/AsWbIEM2bMwOTJk3HXXXfhzTffxMUXX4wvfOELuOWWW+rK67nnnsNBBx2kjqVdzcyZM3HXXXfhkksuQXt7O772ta+pwIOPPPKIisEDAD/84Q9RKBTwxS9+UQUevOuuu+qOwQOIla7HCij48eQAmB+iIkIaQTE/RFvpUfOpxpHKKxq8TXug6DcXxs6qM1nGzvpAFUaqMXmuot2n3yPyFRIcO6qnSCvIoK4OA+KVP1j2yg2AMaDaE7siApHHhAoEFsaTd5WXEITJCVy2W9URHkCWv+y1oiMKCNnAmtHGGlHwdDd/PQRAktilIWSxx57cI0iFpWdVhAg0whaHppcRXuU18IjQpaxuE881Vruml4kd4l6uwIOwnFhBZ61oRXySQNUzQKC2DACqQCT9CyEiukoVV4Uz5QEImJ5/cqfneFfk2LYEMG3D7P2a7D15kqqo+HcWgXGRKztdlvFyliQnGS2bGdcSkbalhIXHQ4o0hI8N+2PvvnIg4h+1RXY67V3uGekAAEkQSURBVEEVG3hJqZsqKCsbtwAV9T2JZwaq34l8NffriDjr36yutpDu2qJ8lUxJjA5lPGxItbOkM5a0B/oYHDjucZTBoWrKIiVMv8qYQT6SeZvq+LwQMeCZyVQcnlYirUmERF8OtWv55hihJYi9wcyL9uJbqLilhCcMqwnSE3vIeWDcj1VpVjMkbVV9mCQoVoW5okjLa3ZeHvPVQg+IJYtBxiIxUe3OxOEJwxDf+MY3cPPNN8P3fdxzzz340pe+VG82PQYyDs+QbfYS1v88EBb4mvW9kq7AHV3TcF1Mia4Zu0vGOld1jdnXXB0nmV6ms8lPmlGaK3/bfdPokFqaWh0yDfqAayMh/YkGXkkaOI9JgW1k6wrdnrSJinXScq8ajxVUzApARMeWhniAubr1UjYH1Mtrr5IVWYsCmtnSF9kWehAzfb+eWkjzMJG/dS+TZPTXqnaneNcFPyJ+XjOKft/odx80sn5oQj/VRo28SalEijzeH8mHb3ptwYwE7vL8MwxpYQYf0wlJWigE24bHpdLKihmVvJZPwlMPTAkOT0ba5nE8Ks618AVwG8OL3xylMEQ58gRtCytoRwkdUZDUEtoV4alyESA1gCS/FYNUi+O4/4lvLe4fPisoW0BRB90bCcaYaJ63CLhGXPR09m/Y+RhSHXsxYH0nhgSJm2lqEB7Alqqk6K8t1EN4nF6ImapTa+w3yuclrpvZ6td853nXXJSMJ2fFPNIX1RkLpURw3ZS5Ss41rrhJ9nxjQyfulbAdq9Yu7L44PH/4wx/wq1/9Cvvssw/eeOMN/OxnP8P++++PkSNHdia7HgPxobJ0EWFO6OqtLNJiH2eRHZmf+4EwWLau8lL6XiUK1UWnkrTog4UunqxqBMcttszyhtCflSQ26ZO2XG2KdNb+NGFsw2MPpAl7KV3VJzIW93mB4eIasIoh4XGp+eJ6J0WsehnkpCLrEXKd8ARqQpGTQuyCWzEIUD0wJxA732ospoYZQsG+Xx9EhPowUF5aPi8K1Ygm2ZT2XeI5XAW7DBmDMOYWna7APC0ictSToiIIiQ6PVaNMl7Iwyz1bu6bi98T10GtlzyUulZaOtA1FO0N8DKKjkRh77zQ9TEHAoSJ2c7gjYgNRjKUwdAT2kw4NVYNU60b/si+6NsO0JTach6jyMpjmepyFLJWWnc48nyQ/LqlOwibHaTNpqbA6G1M3531poRTy3Zyf7NSXrUl2kmRDv56PNHEeWMfZY1Ri/tRUaww+ICX0kemFTYAAgHEZ68eNUJeW8/yqrboJz1lnnYW7774bV199NWbPno33338fp59+OiZPnoxbb71VGQ9viYjtIpIrGXsVkwe2+7pLsmNfyyJI6SsjxMtXHk16msQHmgrOHLiqscgZiNk2jz0hlHiVJ8mNHdRKh61ySYP9ccSDXtSZrUinulQncS9igmmnYRqJ8hDCZ6GxX03VinKtS7JqqbX0OnPtOfKjdK2ohS1NRbtWTagU6ulr+j0JGwpb4qi9b9f98n/AqvB4bPckblIZqd9hFHZehZqPws5LNVXAuQo1z5kHxjiCIJb4CEmOyEvfTFASGp0ASXgptj/K4Jgnv7IECUrU3o2cC34n5BuUNjmSuEiVlXvPs0j5aVyL1KbgRtC1UmSkLOPcGEb+EdmxowgbdZMEntnjS2D811XoNmySbdvruMet2mlNqU6YIsXRkaYi6iTx6QzqCGtgIktiYkt39MfZ92WRHPfiWj9Ok2YLxxittDy5iE2DZ6jOzAWqGF9jdV0s7XHn7ppXgjoIT90qrUmTJuHee+9N7Jt1yy234NJLL8WGDRvqya5HQKq0+vf5JBjzMycafRUTnwsNqYN+XkdSzeQIliV/uyQ8OUhXLbfERHpbjGgbsdaZnw5bapMnnd2+dnvWkr6lfrCWJEgXy0qRrSI8mpg1MTA49NPp9XJLcYAkwbGNibNtJGqssFIMRKV0JxZnC9dTtQ+c14hipN7ymdgEt8j6iGMUDTd1X/PgKqAQeVtIGyiW2LcnTiuIkNx4UAYbc8VxkpFXlTG5Zetjp9WPddRSX3UX7O03zND4HGbMphBVxL/tmCNBtEFkgCqqrKpcckWogA7Ye0GJ5weqn4njEP9e+4wq37Yte8Vl5ckIw1mLC3mP67dLahO3if1d23Y5bimOYZNjFiJ5zk6Sab9TR19IJTPuMadm3nWNrfY8kVRVuSQ4tvopSz1fay7KG9uo1pxh2/OYzzKfwRP9I/k74BWsb3u9e1RaixcvdsawOffcczvlGdWTEIYVg4FmQUkSlATBkxc0Nuu2y7HtTMTv7C0ogKT0Y2NVbzKPWCpiSmJCBjXo5JFw2HDp9tPKYByndOys+uayJdIHb92zjpnh2nV9tfNZqQODDBVvfqS6yki3rdGvpa+EJdLbKFHPGu2ZpVZV5YniS4WaZIfxWDUKbra5x+I1mfDu46ocIr6G5k3FGdQO6IxFIQ608shYP9E1FZyPxzpKex8eIAo2qs1taW8wTX2VF/YcmyUBiiU8XJEcIJba6CTHDh4p95+SnouAeLcy1pK4r5II3WAH+Vu99qmMuiTJuHFdSVd12680qWw2KRIIHOej3yqfDOlNHevzNKJTN+HdKFubfPeJx6RLV7Lsckx7nOQi2o6301nY47fhjm4tlG3IscG+lkaIXdfDxBhpSiHzoG7C09jYiLfeegt33nkn3nrrLdx0000YOnQoHn74YYwePbre7HoU7FVEVkhu2chZLurxPWaHk4aAtlQhs2w8VM+RKjYXkcgSO9dCFoPX3RC7EqkqLcfgm3d14sov61yIqlDDaITHjtzqMj4Xad0fvf6sLNsGe8XbGTWWXT77+WlpXcO4odJCJa4TFzp3OYdwFiojb87DaK83nfR7xnEoNymEsPWR1zzOIB2DASnBiQmOB3WopEHyGmPcCBdQi8ikxRCqB1mBIe1gfUA8P8stU3TVVMBDpZpySXH0YJJxLKfAUFMFEBIc3fj9/Y+fzF0fnZwbYwpCyykgTPTZuI5uUpOeJkuKA3c6kUlaNZzoPhubHM9LMV9wj1PZkhN7XM5SWbkkOmkLNBtpY4++KNbLE3vs1s7fy3h1ac811VfJuS62N8zfL+omPAsWLMCRRx6JfffdFwsXLsT3vvc9DB06FC+++CJ+/vOf4ze/+U29WfY42CI8+yV68CBDhCeMcV0TtdYxbMIjf7//8ZMYNuCzWj5Bfp25NcAwJKVFetq8k2peUWMa5N1SkhHmICUy1s7GqObivCx1WIr9gAw81tnVT15VpP7MRJqa78RDuo2CK/+s9yPLaEuZAvWOmAr3H+UbBUn0tf11pF2Pz4qCEMvYT9H7kwMh5wXjGwh09RW3Bk1uEhND1QVzTx4gVnHZadNqnBdZbyMtKrErndo7DdKEOCIxrIoqCzQiE8beVKigyipGOAP9NxAP/LadmEt1rKuuxL3JsUDG0jEdBKqqJUzykzaGpLRaYhVfg9jEBXWf31h02s4mT94m+YiR7iVrn7eRZhrheo4t8amlRbDHXmeVHATH9OjKoepSpnVm38ka9zzNkkfaw+oCB1vLkgd1E57LLrsMV199NS688EIjHs5BBx2Em266qd7sehT00NppIjq7wyT2/sj4lqRtiK5z1VdjeVdmwwZ8FrrRqPHhQFi9J4L/sXig1AP/ZRE045xDhec2yI6hrrOo7mmdW1MDpgV87BT0NuKO7UEM1WPY6fFVz8eQBGXUpVYd3cbqMVHJzi+fiFcMIOZ70q9xFihD5BAhGALITQ0DFq9LQ6nqki7MvBJJfAT0CKpS8qP6p5JqSINyZpSecV29GO/C7A54Wd9E5pLIuFDrehbiQJM8txRHxs+R6kTDe04jOiL//AsYcX+Ya7GSx14whiNNqrQgJ9kBALmrb2fRncQGgPwesyVJ7o0883pLibTu59r5SHJjm0y44rXFN8Xfd/K55hyYtEGtbefo8tiq5zP1U/IyFs91TBd1E56XXnoJ//M//5M4v+222+LDDz+sN7seBV8jPECyM6nfjjgDaSt7Fymo1wjMhpQGKZsRS6XlwcPKjxca94wceGBUJtP+3TX4ZZVLf2baqkTaDAxtmaqeabBz7Zkqv2giYBmGyRvrsh1DEJxk+W1i4S5FMq+sZ+WbNLJWeJnEqe42sSR1uqpU76/RM003dU8RZ4/Hgw5DiFBX5TKA8cBNz5g4k9wNWiCAJSVjgTFI20THda2rkUZ48qh5FTFhYUR4pAqrquxxALFJq26Ho8fPSdjWIUla5HOyFk0JewkepvarPDZxXYN0yaUoSFe9001Qn8iGBpDfgrm4rSWZkUi+J+sxGcbFpuTFM6Q6LtIin+VxWX5HtRxEJ0uq7bIZk3MBtFAhHOlziHyODjlHiLroC81uVGkNGDAAK1euxNixY43zS5YswahRo+rNrkfB8wrOyZ45LOLja6JTuQaaYQM+m2s11Rnoz9NVYWl4b80TAIBRAw8x1tA2AUobwCWBks+S7ZQ1wNodX4rWnbZRugSJpRCxOuxclChUF+9C/8hdA22eQTH/wFmPFKcWskiPia7pbyHCxOpKSnkEPIMMMejSLT9KL1PGLS1JQpjDOSDecTk65p767bIfSCM8aVti2FucpCFLwlMryqsedNIl0bGDVMp79PAGej6JOkT2fFnfofTMGrLNZ+oaj5g2gbvUKqbk0Sqfsv2y7oMR11i7X8fGSnhrSEy6mhin2OVkGRRnEQZbUux8pOP+NBWWK2CgHo5APTNDdeaaA/VyZxnG69d19SqD71z82s+266mdiJB/HK2b8Jx00km49NJL8X//7/8FYwxhGOKvf/0rLrroIpxyyin1Ztej4HvNKtKyDjsSMYCEBMWFeowHNwb1POfdNY9tsmfZ0F1ia8G2OwA0aYamD06kiT5aP5qAlehT+5aZZw4oXaZCc+SXRmrqITtZ5TOJBiAkOHa7pHt11DSWR2ycL2zHIhE4C5Uhomjjilq5gYlznpIGau0Br9OUzLONN62BLnXjwTrmNxexSCUbFqlOuy7yCNRWI0C8rYNOiGyCk7b44AjUAqYz0FW6uicl5y71WEQsWUEjuKbEVsDtPhyTAb0P6IVxtW3nJN8J5PrGkmTDRn02j7Gax1QvFVK/vawteaQENm1MSUp4TImO5xUM1VOyrxbiZ6Z8J7WkfbXIjg59DhiyzWfqJjlO7UMd0UHrJjzf+973cOqpp2LUqFHgnGOXXXZBEAQ46aST8O1vf7ve7HoU1Fb1zEsdcBjzNmqwIXQeiQ/CtfC2g8spr6JYXiFdrk0bHjvrzpEgl9u3KlqeVUvO59siafPYPWHYg7DLqyMveORb5S6zeL7aL80KWGajlrRHGkMHVpskJ4q43raXXS2kq1E3LgSEvims8HySNjhm1ON6iBYAjBiwP4B8C68h23wGQP32PjZUP+FJKYS5S7dJumOJqo5Yddr9yFYXC2RNvGltJuuYFhOnoBEe8e3J7Rp02xp5XX3L8GPyy8x8E+V35KP2P4zIjqc8gZPjguFtnBP1EJwsfLDu2czr27bs5bYBimDvCZkHndpLCwDeeustLFmyBGEYYvfdd8f48eM7k02PgAw8OHSbqapz6NA7G5GdTQtTBJresZ2BCrUB3rmatT6mztrg1DMZ1jOw1PIyU+drSKtcen5xXgzIss/bRvu+NmB7TAQa9PWQCpC/ffjQVpLwEltySHg2Gcqos15mF+xtPjxu1rMemAaRZhnSNrqtB3ITz7RtR0S+cvLJlnDYcZ5qkZ7B2+xhpLdjm8Tn429C7g/oDpfg+tZqfUsp77uLpasuuMmCW+rpSl/Lfs59r3tvKpfHVMLBxPE954mB4/Ke0r/prPg4Wc/YVJqKejC0ZaoqX8ir+GDdM923lxYAjBs3DuPGjevs7T0SWatdIjqbB3nVYENbphru7JyHYuJwSBdCltRZpxlhyzyzXCnrmVyzpCn5B3/TZkJXa9WSIul2GfY18wn1EQZh8yON0oURs0vSbEtpABg7IMv7zfKlTJY2MdHqbpOhLGQRmqyy5UEizg3iCSzNeUDYRKWEpNgIG616yAVjHtI8auQSWbcndu5j57gn+ZzcReoE0iWd6rdaAKQTHr2Mpno2I99oIVGPAbG610Ek7ZAezt3HtcWJ/sy837ItAeqJRGdjkYvwXHjhhbkznDNnTqcL09NAJGfLQS0DaV1HLa1I8kh0ZDA2Y0JKcevvlGrIut9VpkwbnuiefEEZzYFRGTmq42Scjixpg4qs7IgwDUAF2bNhkwDzWn4Jjy7edqqf65CU2OVJD+RYH+GoZ9NcCaEuSpfiSHVWZ+CS7shnRgWMTpj90ZAiWkTTJXmMyYBld5eob6FuElkL6fsRuu1nXJ5NyXuTqCVNreXKnarOdqh4U73pHDY8+vOzpH8uZ5ctheTo473U0ORBLsKzZMkS43jx4sUIggATJkwAALz55pvwfR9TpkzJW94eDyI7WzakZEgRH0tHLs7Fg4PhLaClFe7XVuYcuSUqWbAlUp1FlgdXcrWabiyZlq4WOA8Apjv1e6mSiDRCkb3BbKTqyQiXICVHZp3yt6mpFkwnQvUgy6g+874MOzAgn91OZ6HamJv904AtrcmQ0tTahsOZXyfgbq8kwQeQW/Ji5yvdsV2BG11p7fKlRWhP1EW34bHytr8bV555SYsd7HZrQC7CM3/+fPV7zpw56N+/P+6++24MHDgQALBmzRqcdtpp2G+//bqnlJsIb77zUE0dIGHLgk58snTzCWmAvTOwtUJ1RfnM41GQV0JQe9Urn5VOvOy0NolREp407yYLeeNGuchBFqHITSZ47bg3GxsGolZZ6snfRW7S8s/aY6irCU5aGYxFAfNNIpKleuI50sikDnIT98nOkH7HgiMhVXHZ0xSju91Rg23JiTgnwo9ICVtoedbl+T7095xX+mIQkwSB3Dg11JYi0ekq1G20PGrUKDzyyCOYOHGicf7ll1/GtGnT8N5773VpATcFpEgsj9EToXfA5fauo5bxcz3GhKn5WrulG/8zDJHrtsXQfueNxCo9POyVsBHTA6b4PA21Al1m1akzasK0vLsiL1e+NtJj5qRPjnb7dSXJGdT/U9Hzw8zyueBWsbrtmvKqY/OqsGqpjIzzSCfyjPmGAb6wrzHtXCS2NgLQG1DP/F230fK6devw/vvvJwjP6tWrsX79+nqzIxA2C1zG0IZHWE5VkzNQZcbEmpavrZayDZE7ayvkMtDMK87Pgh6hmcFPkep0TqWzMel12GXK2sBQot62zVu+BEnI2Cuvu1BPefV7JAzpD+I6KbKR1wA55xK71rtwqWtlefR+7asYOJG3kua5RARn60LdhOe4447DaaedhhtvvBF77703AODpp5/GxRdfjOOPP77LC0ggbCrYdj9ANIha3iouFVcaslxOdbVBPZFVaxkxG8eOlW9nwXmg6u3pXjkpE113uhx3xqYmQYAyPHPU+Rqb5naGRIj7ssnOiAH7d5utjqx3ngjrEmGaPY+OHHGxugqufm5LKm2Jjq2aImx9qJvw3Hbbbbjooovwn//5n6hURNCsQqGAM844A9///ve7vIAEwqaGLv1JU32pSSNjQHdNIvp9WaHcbc8re1PSNKR7qJgrYHFshp53QSc1et7GZJmyYs8iJfW6vdfKrx7vKZts1OOZpl+LjalNqZ2LUKQRHBfp6mqi+NH6FwDEqi3Xs2vBRdwT/VV3AsgKaLcRJCjNTs7lHeVpMaNs1RVh60SnAw+2trbirbfeAuccO+64I/r27dvVZdtkIBseQhok4dE9M/Rj+7eNLENp3RZIXAsybXfyIlvK4zsnB9e9tfbPyS5D7Qk+L/LYxKhzaQa5Vhv4LA6UWGu7lVEDD0m9lsfNHEjfKiKNvHaHl+ig/p/qsj4F1GfDlEXuOwPdlkz+t12+SW21daBbbXgk+vbti1133bWztxMIWxT0OCX6Lr+1XIjT8uFcbqIJLR9TxZVFlhLlss+nuMDaZCctr/TAdy7379rIehaQTe5cEpM0uKRiaWXNu6/cu2seyyQ9eVAP2QGAkQMPVG3SnW7oWajXhsaWcEm41GGdIb72M10kJ75GaitCEnUTntbWVlx33XV47LHHsHr1aoShOQi9/fbbXVY4AqEnQt/lt5ZtTdqk4DJENtQCltw1NSYK3BN6ljF1HtuVzqAetU1a2XTY7enBS6iJslRZuTYezIkscjRy4IHaM/LZ9Mj66pIcPR89TXehHhuePEj7Fuole3nyt93HPVYkgkOoiboJz1e+8hUsWLAAM2bMwIgRI8C6NzY4gbBZkSbBYY7ownkG8Sx7j0QcFFWGoNOqpKyVtR2vJKu86jgHuao1wWXZyLjaM23yFG2WkVedUZE7C5u0ZNniZLW1zMcmPiMHHtjtgVCzdmdPQ1pd8kg9s+3QahNTW8VKZIeQB3Xb8AwYMAB//OMfse+++3ZXmTY5yIaHYEPuMF2P7YIN18Rn2wDZeWUb5tYfq6aWKimLiKQFX7PzzevhpJfpn2seBQCMHnhY4nqqzU5G0L+sd5LmneWSsnQldLWUjU2pphrYz216UI9RuZP0d0ZK4+gT9Rjhm/dR/BxCN9vwDBw4EIMGDep04QiEngq5q3StFWaW2iJrYsizb1baKjuXW3CNPNLjlrhtfFx2QDZZsO+vt2yS+GRB2s8YqkRLwpZFTG1V2KaCTqQ2Zv+rrkaetrAlPrXiQNXTB9LzyEdudBDRIdSDuiU8v/zlL/H//t//w913340+ffp0V7k2KUjCQ5BkB4ARYTgNedQVevBAeY+9KWSefDcGWRIdUUa/JskBHN5cOaMsu5CH5LhgGw67jInrCXa4uYyBNzV0CU9iM9MaRCaLPHeG9OZRkZrpydOKkI165u+6Cc/uu++u3NG33357FItF4/rzzz9ff4k3M4jwbN2Q8UlqSTyAfC7oqbsg61tSpLgz16smyHNfmqrKpabKS2oMzy94WPHRQ3WVu7th28G43KLzuqVv6WjpN0n9rh053N2P6rX9Umkz1KTier7+ru+OTSDo6FaV1rHHHtvZctWNK6+8EldddZVxbtiwYVi1ahUAgHOOq666CrfffjvWrFmDvfbaC7fcckti2wsCYVMht3Gxpp4BOu+RkydoXpYEx4hI6yA4CSNoR/16Gtlxobs9nrYEdEZ6uLHeWwmJW7S8VmpehLm/GQJhY1E34bniiiu6oxypmDhxIv73f/9XHft+PADfcMMNmDNnDu666y7stNNOuPrqq3HYYYfhjTfeQP/+/TdpOQlbHnRRv2FTwzQbkTpcjJPnHXsO6XF36rR7yINathGuDUBd5CZtEtoSyI1EmgRHSn82dmf1LQVrN7wMANim7y6Z6TbGs8pG6ncjnXo14mPHS8q7uSiBUC86HXhwU6FQKGD48OGJ85xz/OhHP8K3vvUttYfX3XffjWHDhuF//ud/cNZZZ23qohJ6EWzj2HqQJ95LrW0EOi3xqeE2bkt0PC0ibU9UTW0M0ghNd7t491Ssa301lfTU2986670oXdbVu+GmtMeGa5NfAqGzyEV4Bg0ahDfffBNDhgzBwIEDM2PvfPTRR11WOABYunQpRo4cicbGRuy111645pprsMMOO2DZsmVYtWoVpk2bptI2NjbigAMOwKJFizIJT6lUQqlUUsfr1q3r0jITej5sV109dojxG0HdpKe+YHj5J5o8RCpP9GRdotPbSI7E1kpqamFd66vGsW7fYyMr9IAr1k5WEE474CapGAmbA7kIzw9/+EOlIvrRj37UneUxsNdee+Gee+7BTjvthPfffx9XX3019tlnH7zyyivKjmfYsGHGPcOGDcOKFSsy87322msTtkEEQmcNh7sC9UYBzu16bpE1XaIDbFkqKkLXQ6q70mL1ZCHre7ElmGlbQmSBpDuErkanNw/dHGhtbcW4ceNwySWXYO+998a+++6L9957DyNGjFBpzjzzTPzzn//Eww8/nJqPS8IzevRo8tLaipA1wNcanA1j4wyPrkS+dW51kMdgNA/BSXvmOx+lfyOErQ+ub6KeKNxZsImPvTWEBJEcQr3YJJuHbg707dsXkydPxtKlS5W32KpVqwzCs3r16oTUx0ZjYyMaGxu7s6iEHo41G15UA3zNmDtZEX6NuDpmusQqlqUbA+eJbOuCS02VBSI5hDSs2fCicSzDNaQhU8JjBYb0mBm+RL+HSA5hU2GLIjylUgmvvfYa9ttvP4wdOxbDhw/Ho48+it133x0AUC6XsWDBAlx//fWbuaSELQGdVV3ljWXiMnyutYN3Z/YY0tVURGgIXYWP1r+gSE9n7W5qhUkgskPYlOjRhOeiiy7CMcccg+222w6rV6/G1VdfjXXr1mHmzJlgjGHWrFm45pprMH78eIwfPx7XXHMN+vTpg5NOOmlzF53QS5FmjCnh2gU9CxuzJ5V9P4HQ1TD2gUuR6NRDhmQ6IjqEzYEeTXj+9a9/4ctf/jI++OADbLvttth7773x9NNPY8yYMQCASy65BO3t7fja176mAg8+8sgjFIOH0GWoN1hbPWSnFrojTg+BUA+kmku376lFcEiqQ+ip2KKMlrsLtLXE1g3bVuGj9S90af7btuylftez63iWCktHZ/emIhC6C0O2+YxxTJIdQnehy42WZWC/PJg3b17utATC1gA5yOvEB+gc2ZEgkkPoyfhg3bObuwgEQgK5CE9LS0t3l4NA2GzQjTO7E51d3do7ghPZIRAIhPpBKi2QSotAIBAIhC0R9czf5OJBIBAIBAKh1yOXSmv33XfP3D9Lx/PPP79RBSIQCAQCgUDoauQiPDKqMYFAIBAIBMKWCLLhAdnwEAgEAoGwJaLbbXg+/vhj/PznP8fll1+Ojz76CIBQZb377rudyY5AIBAIBAKhW1F3pOUXX3wRhx56KFpaWrB8+XKceeaZGDRoEB588EGsWLEC99xzT3eUk0AgEAgEAqHTqFvCc+GFF+LUU0/F0qVL0dTUpM4feeSRWLhwYZcWjkAgEAgEAqErUDfhefbZZ3HWWWclzo8aNQqrVq3qkkIRCAQCgUAgdCXqJjxNTU1Yt25d4vwbb7yBbbfdtksKRSAQCAQCgdCVqJvwTJ8+Hd/97ndRqVQAAIwxvPPOO7jsssvwH//xH11eQAKBQCAQCISNRd2E5wc/+AH+/e9/Y+jQoWhvb8cBBxyAHXfcEf3798f3vve97igjgUAgEAgEwkahbi+tbbbZBk8++SQef/xxPP/88wjDEHvssQcOPfTQ7igfgUAgEAgEwkaDAg+CAg8SCAQCgbAlolsCDz7++OPYZZddnAbLa9euxcSJE/GXv/yl/tISCAQCgUAgdDNyE54f/ehHOPPMM50MqqWlBWeddRbmzJnTpYUjEAgEAoFA6ArkJjx///vfccQRR6RenzZtGhYvXtwlhSIQCAQCgUDoSuQmPO+//z6KxWLq9UKhgH//+99dUigCgUAgEAiErkRuwjNq1Ci89NJLqddffPFFjBgxoksKRSAQCAQCgdCVyE14jjrqKPzXf/0XOjo6Etfa29txxRVX4Oijj+7SwhEIBAKBQCB0BXK7pb///vvYY4894Ps+zjvvPEyYMAGMMbz22mu45ZZbEAQBnn/+eQwbNqy7y9zlILd0AoFAIBC2PNQzf+cOPDhs2DAsWrQI55xzDi6//HJInsQYw+GHH465c+dukWSHQCAQCARC70ddkZbHjBmDhx56CGvWrME//vEPcM4xfvx4DBw4sLvKRyAQCAQCgbDRqHtrCQAYOHAgPvOZz3R1WQgEAoFAIBC6BXVvHkogEAgEAoGwpYEID4FAIBAIhF6PXkN45s6di7Fjx6KpqQlTpkyhfb0IBAKBQCAo9ArC8+tf/xqzZs3Ct771LSxZsgT77bcfjjzySLzzzjubu2gEAoFAIBB6AHLH4enJ2GuvvbDHHnvg1ltvVed23nlnHHvssbj22mtr3k9xeAgEAoFA2PLQLXF4eirK5TIWL16Myy67zDg/bdo0LFq0yHlPqVRCqVRSx2vXrgUgGo5AIBAIBMKWATlv55HdbPGE54MPPkAQBImgh8OGDcOqVauc91x77bW46qqrEudHjx7dLWUkEAgEAoHQfVi/fj1aWloy02zxhEeCMWYcc84T5yQuv/xyXHjhheo4DEOsWLECn/rUp/DPf/6T1FoQrHn06NHUHhqoTZKgNkmC2iQJapMkqE2S6EybcM6xfv16jBw5smbaLZ7wDBkyBL7vJ6Q5q1evTt3qorGxEY2NjcY5zxP229tssw11Pg3UHklQmyRBbZIEtUkS1CZJUJskUW+b1JLsSGzxXloNDQ2YMmUKHn30UeP8o48+in322WczlYpAIBAIBEJPwhYv4QGACy+8EDNmzMCnP/1pTJ06FbfffjveeecdnH322Zu7aAQCgUAgEHoAegXhOfHEE/Hhhx/iu9/9LlauXIlJkybhoYcewpgxY3Ln0djYiCuuuCKh6tpaQe2RBLVJEtQmSVCbJEFtkgS1SRLd3Sa9Ig4PgUAgEAgEQha2eBseAoFAIBAIhFogwkMgEAgEAqHXgwgPgUAgEAiEXg8iPAQCgUAgEHo9iPAQCAQCgUDo9SDCA2Du3LkYO3YsmpqaMGXKFPzlL3/Z3EXaZLjyyivBGDP+hg8frq5zznHllVdi5MiRaG5uxoEHHohXXnllM5a467Fw4UIcc8wxGDlyJBhj+O1vf2tcz9MGpVIJ559/PoYMGYK+ffvi85//PP71r39twlp0LWq1yamnnproN3vvvbeRpje1ybXXXovPfOYz6N+/P4YOHYpjjz0Wb7zxhpFma+snedpka+snt956K3bddVcVKXjq1Kn405/+pK5vbX0EqN0mm7KPbPWE59e//jVmzZqFb33rW1iyZAn2228/HHnkkXjnnXc2d9E2GSZOnIiVK1eqv5deekldu+GGGzBnzhzcfPPNePbZZzF8+HAcdthhWL9+/WYscdeitbUVu+22G26++Wbn9TxtMGvWLDz44IO477778OSTT2LDhg04+uijEQTBpqpGl6JWmwDAEUccYfSbhx56yLjem9pkwYIFOPfcc/H000/j0UcfRbVaxbRp09Da2qrSbG39JE+bAFtXP/nEJz6B6667Ds899xyee+45HHzwwZg+fboiNVtbHwFqtwmwCfsI38qx55578rPPPts498lPfpJfdtllm6lEmxZXXHEF32233ZzXwjDkw4cP59ddd50619HRwVtaWvhtt922iUq4aQGAP/jgg+o4Txt8/PHHvFgs8vvuu0+leffdd7nnefzhhx/eZGXvLthtwjnnM2fO5NOnT0+9p7e3yerVqzkAvmDBAs459RPOk23COfUTzjkfOHAg//nPf059RINsE843bR/ZqiU85XIZixcvxrRp04zz06ZNw6JFizZTqTY9li5dipEjR2Ls2LH40pe+hLfffhsAsGzZMqxatcpon8bGRhxwwAFbTfvkaYPFixejUqkYaUaOHIlJkyb16nZ64oknMHToUOy0004488wzsXr1anWtt7fJ2rVrAQCDBg0CQP0ESLaJxNbaT4IgwH333YfW1lZMnTqV+giSbSKxqfpIr9haorP44IMPEARBYlf1YcOGJXZf763Ya6+9cM8992CnnXbC+++/j6uvvhr77LMPXnnlFdUGrvZZsWLF5ijuJkeeNli1ahUaGhowcODARJre2o+OPPJInHDCCRgzZgyWLVuG73znOzj44IOxePFiNDY29uo24ZzjwgsvxGc/+1lMmjQJAPUTV5sAW2c/eemllzB16lR0dHSgX79+ePDBB7HLLruoyXlr7CNpbQJs2j6yVRMeCcaYccw5T5zrrTjyyCPV78mTJ2Pq1KkYN24c7r77bmU4tjW3j0Rn2qA3t9OJJ56ofk+aNAmf/vSnMWbMGPzxj3/E8ccfn3pfb2iT8847Dy+++CKefPLJxLWttZ+ktcnW2E8mTJiAF154AR9//DEeeOABzJw5EwsWLFDXt8Y+ktYmu+yyyybtI1u1SmvIkCHwfT/BElevXp1g4VsL+vbti8mTJ2Pp0qXKW2trbp88bTB8+HCUy2WsWbMmNU1vx4gRIzBmzBgsXboUQO9tk/PPPx+/+93vMH/+fHziE59Q57fmfpLWJi5sDf2koaEBO+64Iz796U/j2muvxW677Yabbrppq+4jaW3iQnf2ka2a8DQ0NGDKlCl49NFHjfOPPvoo9tlnn81Uqs2LUqmE1157DSNGjMDYsWMxfPhwo33K5TIWLFiw1bRPnjaYMmUKisWikWblypV4+eWXt5p2+vDDD/HPf/4TI0aMAND72oRzjvPOOw/z5s3D448/jrFjxxrXt8Z+UqtNXOjt/cQFzjlKpdJW2UfSINvEhW7tI3WZOPdC3HfffbxYLPI77riDv/rqq3zWrFm8b9++fPny5Zu7aJsEs2fP5k888QR/++23+dNPP82PPvpo3r9/f1X/6667jre0tPB58+bxl156iX/5y1/mI0aM4OvWrdvMJe86rF+/ni9ZsoQvWbKEA+Bz5szhS5Ys4StWrOCc52uDs88+m3/iE5/g//u//8uff/55fvDBB/PddtuNV6vVzVWtjUJWm6xfv57Pnj2bL1q0iC9btozPnz+fT506lY8aNarXtsk555zDW1pa+BNPPMFXrlyp/tra2lSara2f1GqTrbGfXH755XzhwoV82bJl/MUXX+Tf/OY3ued5/JFHHuGcb319hPPsNtnUfWSrJzycc37LLbfwMWPG8IaGBr7HHnsYbpW9HSeeeCIfMWIELxaLfOTIkfz444/nr7zyiroehiG/4oor+PDhw3ljYyPff//9+UsvvbQZS9z1mD9/PgeQ+Js5cybnPF8btLe38/POO48PGjSINzc386OPPpq/8847m6E2XYOsNmlra+PTpk3j2267LS8Wi3y77bbjM2fOTNS3N7WJqy0A8DvvvFOl2dr6Sa022Rr7yemnn67mkm233ZYfcsghiuxwvvX1Ec6z22RT9xHGOef1yYQIBAKBQCAQtixs1TY8BAKBQCAQtg4Q4SEQCAQCgdDrQYSHQCAQCARCrwcRHgKBQCAQCL0eRHgIBAKBQCD0ehDhIRAIBAKB0OtBhIdAIBAIBEKvBxEeAmEz4corr8SnPvUpdXzqqafi2GOPTU1/1113YcCAARv93AMPPBCzZs3a6Hw2Bl1Vl67AqlWrcNhhh6Fv376qTIwx/Pa3v90s5bHbxu4nPRU9oV8RCFkgwkMgdAKrVq3C+eefjx122AGNjY0YPXo0jjnmGDz22GPd9swTTzwRb775Zrflv7Xihz/8IVauXIkXXnhBte/KlStx5JFHdjrPJ554AowxfPzxxxtdvosuuqhb+1VXYd68efjv//7vzV0MAiEVhc1dAAJhS8Py5cux7777YsCAAbjhhhuw6667olKp4M9//jPOPfdcvP76693y3ObmZjQ3N3dL3lsz3nrrLUyZMgXjx49X5+TO1mmoVCooFovdXTQAQL9+/dCvX79N8qyNwaBBgzZ3EQiETJCEh0CoE1/72tfAGMPf/vY3fOELX8BOO+2EiRMn4sILL8TTTz+t0q1duxZf/epXMXToUGyzzTY4+OCD8fe//73Tz01TdfziF7/A9ttvj5aWFnzpS1/C+vXrVZrW1laccsop6NevH0aMGIEbb7wxkW+5XMYll1yCUaNGoW/fvthrr73wxBNPAAA6OjowceJEfPWrX1Xply1bhpaWFvzsZz9LLeucOXMwefJk9O3bF6NHj8bXvvY1bNiwIbN+t956K8aNG4eGhgZMmDABv/jFL4zrjDH8/Oc/x3HHHYc+ffpg/Pjx+N3vfmek+d3vfofx48ejubkZBx10EO6+++5MScv222+PBx54APfccw8YYzj11FPVs6RKa/ny5WCM4f7778eBBx6IpqYm/PKXv8SKFStwzDHHYODAgejbty8mTpyIhx56CMuXL8dBBx0EABg4cKCRrwt33XUXtttuO/Tp0wfHHXccPvzwQ+N6murzmmuuwbBhwzBgwABcddVVqFaruPjiizFo0CB84hOfwP/5P//HyOfdd9/FiSeeiIEDB2Lw4MGYPn06li9fnsj3Bz/4AUaMGIHBgwfj3HPPRaVSUWnmzp2L8ePHo6mpCcOGDcMXvvAFdc1Waa1ZswannHIKBg4ciD59+uDII4/E0qVLjXoPGDAAf/7zn7HzzjujX79+OOKII7By5crUtiIQNgZEeAiEOvDRRx/h4Ycfxrnnnou+ffsmrktCwjnH5z73OaxatQoPPfQQFi9ejD322AOHHHIIPvrooy4rz1tvvYXf/va3+MMf/oA//OEPWLBgAa677jp1/eKLL8b8+fPx4IMP4pFHHsETTzyBxYsXG3mcdtpp+Otf/4r77rsPL774Ik444QQcccQRWLp0KZqamnDvvffi7rvvxm9/+1sEQYAZM2bgoIMOwplnnplaLs/z8OMf/xgvv/wy7r77bjz++OO45JJLUtM/+OCD+PrXv47Zs2fj5ZdfxllnnYXTTjsN8+fPN9JdddVV+OIXv4gXX3wRRx11FE4++WTVnsuXL8cXvvAFHHvssXjhhRdw1lln4Vvf+lZm+z377LM44ogj8MUvfhErV67ETTfdlJr20ksvxQUXXIDXXnsNhx9+OM4991yUSiUsXLgQL730Eq6//nr069cPo0ePxgMPPAAAeOONNzLzfeaZZ3D66afja1/7Gl544QUcdNBBuPrqqzPLDACPP/443nvvPSxcuBBz5szBlVdeiaOPPhoDBw7EM888g7PPPhtnn302/vnPfwIA2tracNBBB6Ffv35YuHAhnnzySUUwyuWyynf+/Pl46623MH/+fNx999246667cNdddwEAnnvuOVxwwQX47ne/izfeeAMPP/ww9t9//9QynnrqqXjuuefwu9/9Dk899RQ45zjqqKMMAtXW1oYf/OAH+MUvfoGFCxfinXfewUUXXVSz/gRCp7DRW6ESCFsRnnnmGQ6Az5s3LzPdY489xrfZZhve0dFhnB83bhz/6U9/yjnn/IorruC77babujZz5kw+ffr01DzvvPNO3tLSoo6vuOIK3qdPH75u3Tp17uKLL+Z77bUX55zz9evX84aGBn7fffep6x9++CFvbm7mX//61znnnP/jH//gjDH+7rvvGs865JBD+OWXX66Ob7jhBj5kyBB+/vnn8+HDh/N///vfmfW3cf/99/PBgwen1mWfffbhZ555pnHPCSecwI866ih1DIB/+9vfVscbNmzgjDH+pz/9iXPO+aWXXsonTZpk5PGtb32LA+Br1qxJLdv06dP5zJkzjXMA+IMPPsg553zZsmUcAP/Rj35kpJk8eTK/8sornXnK3eaznss551/+8pf5EUccYZw78cQTE+/Z7idjxozhQRCocxMmTOD77befOq5Wq7xv3778V7/6Feec8zvuuINPmDCBh2Go0pRKJd7c3Mz//Oc/G/lWq1WV5oQTTuAnnngi55zzBx54gG+zzTZGf9NxwAEHqH715ptvcgD8r3/9q7r+wQcf8ObmZn7//fdzzkUfAMD/8Y9/qDS33HILHzZsWHqDEQgbAZLwEAh1gHMOQKg8srB48WJs2LABgwcPVjYY/fr1w7Jly/DWW291WXm233579O/fXx2PGDECq1evBiCkP+VyGVOnTlXXBw0ahAkTJqjj559/Hpxz7LTTTkY5FyxYYJRz9uzZmDBhAn7yk5/gzjvvxJAhQzLLNX/+fBx22GEYNWoU+vfvj1NOOQUffvghWltbnelfe+017Lvvvsa5fffdF6+99ppxbtddd1W/+/bti/79+6v6vvHGG/jMZz5jpN9zzz0zy1kPPv3pTxvHF1xwAa6++mrsu+++uOKKK/Diiy/Wnedrr71mvB8AiWMXJk6cCM+Lh+9hw4Zh8uTJ6tj3fQwePFi1zeLFi/GPf/wD/fv3V+940KBB6OjoMN7zxIkT4fu+Otb702GHHYYxY8Zghx12wIwZM3Dvvfeira0ttV6FQgF77bWXOjd48GBMmDDBeKd9+vTBuHHjnM8jELoaZLRMINSB8ePHgzGG1157LdOFPAxDjBgxQtnC6OhKd2zbcJYxhjAMAcTkLAthGML3fSxevNiY6AAYhrKrV6/GG2+8Ad/3sXTpUhxxxBGpea5YsQJHHXUUzj77bPz3f/83Bg0ahCeffBJnnHGGoc6wYZNIznniXK36uvLoKtgqzK985Ss4/PDD8cc//hGPPPIIrr32Wtx44404//zzc+fZ2fK52iGrbcIwxJQpU3Dvvfcm8tp2220z85V59O/fH88//zyeeOIJPPLII/iv//ovXHnllXj22WcTfTqtXvY7cj2vK98ZgaCDJDwEQh0YNGgQDj/8cNxyyy1OaYU0jt1jjz2watUqFAoF7LjjjsZfLelIV2HHHXdEsVg0DKnXrFljuLbvvvvuCIIAq1evTpRT91Q6/fTTMWnSJNxzzz245JJL8Oqrr6Y+97nnnkO1WsWNN96IvffeGzvttBPee++9zLLuvPPOePLJJ41zixYtws4775y7vp/85Cfx7LPPJsrSnRg9ejTOPvtszJs3D7Nnz1aG3A0NDQCAIAgy799ll12M9wMgcdwV2GOPPbB06VIMHTo08Z5bWlpy51MoFHDooYfihhtuwIsvvojly5fj8ccfT6TbZZddUK1W8cwzz6hzH374Id5888263imB0JUgwkMg1Im5c+ciCALsueeeeOCBB7B06VK89tpr+PGPf6zUEYceeiimTp2KY489Fn/+85+xfPlyLFq0CN/+9re7fRKW6NevH8444wxcfPHFeOyxx/Dyyy/j1FNPNVQhO+20E04++WSccsopmDdvHpYtW4Znn30W119/PR566CEAwC233IKnnnoK99xzD0466SR84QtfwMknn2wYu+oYN24cqtUqfvKTn+Dtt9/GL37xC9x2222ZZb344otx11134bbbbsPSpUsxZ84czJs3ry4D1rPOOguvv/46Lr30Urz55pu4//77lcFtLRVkZzBr1iz8+c9/xrJly/D888/j8ccfV5P5mDFjwBjDH/7wB/z73/9O9VC74IIL8PDDD+OGG27Am2++iZtvvhkPP/xwl5f15JNPxpAhQzB9+nT85S9/wbJly7BgwQJ8/etfx7/+9a9cefzhD3/Aj3/8Y7zwwgtYsWIF7rnnHoRhaKhIJcaPH4/p06fjzDPPxJNPPom///3v+M///E+MGjUK06dP7+rqEQi5QISHQKgTY8eOxfPPP4+DDjoIs2fPxqRJk3DYYYfhsccew6233gpATLAPPfQQ9t9/f5x++unYaaed8KUvfQnLly/HsGHDNllZv//972P//ffH5z//eRx66KH47Gc/iylTphhp7rzzTpxyyinKTufzn/88nnnmGYwePRqvv/46Lr74YsydOxejR48GIAjQxx9/jO985zvOZ37qU5/CnDlzcP3112PSpEm49957ce2112aW89hjj8VNN92E73//+5g4cSJ++tOf4s4778SBBx6Yu65jx47Fb37zG8ybNw+77rorbr31VuWl1djYmDufvAiCAOeeey523nlnHHHEEZgwYQLmzp0LABg1ahSuuuoqXHbZZRg2bBjOO+88Zx577703fv7zn+MnP/kJPvWpT+GRRx7Bt7/97S4va58+fbBw4UJst912OP7447Hzzjvj9NNPR3t7O7bZZptceQwYMADz5s3DwQcfjJ133hm33XYbfvWrX2HixInO9HfeeSemTJmCo48+GlOnTgXnHA899NAmi19EINhgnBSmBAKhl+J73/sebrvtNuWeTSAQtl6Q0TKBQOg1mDt3Lj7zmc9g8ODB+Otf/4rvf//7qdIVAoGwdYEID4FA6DVYunQprr76anz00UfYbrvtMHv2bFx++eWbu1gEAqEHgFRaBAKBQCAQej3IaJlAIBAIBEKvBxEeAoFAIBAIvR5EeAgEAoFAIPR6EOEhEAgEAoHQ60GEh0AgEAgEQq8HER4CgUAgEAi9HkR4CAQCgUAg9HoQ4SEQCAQCgdDrQYSHQCAQCARCrwcRHgKBQCAQCL0etJdWhI6ODpTL5c1dDAKBQCAQCHWgoaEBTU1NNdMR4YEgO83NzZu7GAQCgUAgEOrE8OHDsWzZspqkhwgPoEl2fAAs+i3/e9FRdMw8dZ6p3764xFiUNrpHS6sfq/+p56N8mG+c97T0yWe77/XsZ8A+7xv3qfMy3+jYi9KJFFEe3LPute6x/3PPeV2VmTPrPmb8Z/CS55idBmaZ7OsMzvQqX/ma1X1Q9zE7D5kW5nHaeVee+jGs+/V0qXna5U5cl/95dpntMmnp0tLIPO161CpLMj+emn+yTe16mMepecqyqjJk5yOenZbGrHfaeU+1j5VP2rFWdsase622Zp79TPt/VAbPfZ6lnIfHHWnNcsbDmjudrEh8n6xYdjp4AEt0FpnG3QmZ3ek867zdoe10+vWUNPGzUj5Qz6pYyv3wUq57XvIc0tKm5JV4tmf+t8tg1DvfvdyzXljafYn6utNx5jnySrknNU8P69a1YfsxX0S5XCbCUy9iYmN+pa7zzE6TQmTqJzwWGXGktwlM2r35CY9NQtyEh+mEB551bw3Ck0hvEpu08/UQnniCy0l4rOMscpK4J4V0dJbwJMZm/dmdJDzpE3r6s+x0qWlyEx6bbNj5mRO+STpq1SMf4UmWoesIj9dpwmOTmvh64loKYelqwsN0wpMgLhtJeBwExz7OTXhsYtNZwqMf1014apCQjSE8qQSmk4Qni5TUS3hq5p0vHWeeI03K/wzCUw/qS00gEAgEAoGwBYIID4FAIBAIhF4PIjwEAoFAIBB6PYjwEAgEAoFA6PUgwkMgEAgEAqHXgwgPgUAgEAiEXg8iPAQCgUAgEHo9iPAQCAQCgUDo9SDCQyAQCAQCodeDCA+BQCAQCIReDyI8BAKBQCAQej2I8BAIBAKBQOj1IMJDIBAIBAKh14MID4FAIBAIhF4PIjwEAoFAIBB6PYjwEAgEAoFA6PUgwkMgEAgEAqHXgwgPgUAgEAiEXg8iPAQCgUAgEHo9iPAQCAQCgUDo9SDCQyAQCAQCodejsLkL0NPAweUPWD+sY279BoCwy0rhOuaOZ8szLPrFojIwdcWLjr2U8/I+Zp0Pjfviunng0W/OPeNeDt+4h9v/uee8Hkb3M86iJ3jRf2b8Z/Ac5+xjOM/b19UxN4+9KAFj8j55HOcZXYrTwjxOO+/KUz+Gdb+eLjVPu9yJ6/I/zy6zXSYtXVoa1Xes67XKksyPp+afbFO7HuZxap6qn8v8svMRz05LY9Y77byn2sfKJ+1YKztj1r1WWzPPfqb9PyqD5z7PUs7D4460ZjmZF6d1pZMVie+TFctOBw9gic4i07g7IbM7nWedtzu0nU6/npImflbKB+pZFUu5H17Kdc9LnkNa2pS8Es/2zP92GYx657uXe9YLS7svUV93Os48R14p96Tm6WHdujbkBREeAA0NDRg+fDhWrVqVmsbFf7jNSwgEAoFAIGxSDB8+HA0NDTXTMc5p2gaAjo4OlMvlzV0MAoFAIBAIdaChoQFNTU010xHhIRAIBAKB0OtBRssEAoFAIBB6PYjwEAgEAoFA6PUgwkMgEAgEAqHXgwhPDSxfvhxnnHEGxo4di+bmZowbNw5XXHFFTQNnzjmuvPJKjBw5Es3NzTjwwAPxyiuvbKJS9xx873vfwz777IM+ffpgwIABue459dRTwRgz/vbee+/uLWgPRGfajvodsGbNGsyYMQMtLS1oaWnBjBkz8PHHH2fes7X2ublz52Ls2LFoamrClClT8Je//CUz/YIFCzBlyhQ0NTVhhx12wG233baJStrzUE/bPfHEE4n+xRjD66+/vglLvPmxcOFCHHPMMRg5ciQYY/jtb39b856u7HNEeGrg9ddfRxiG+OlPf4pXXnkFP/zhD3Hbbbfhm9/8ZuZ9N9xwA+bMmYObb74Zzz77LIYPH47DDjsM69ev30Ql7xkol8s44YQTcM4559R13xFHHIGVK1eqv4ceeqibSthz0Zm2o34HnHTSSXjhhRfw8MMP4+GHH8YLL7yAGTNm1Lxva+tzv/71rzFr1ix861vfwpIlS7DffvvhyCOPxDvvvONMv2zZMhx11FHYb7/9sGTJEnzzm9/EBRdcgAceeGATl3zzo962k3jjjTeMPjZ+/PhNVOKegdbWVuy22264+eabc6Xv8j7HCXXjhhtu4GPHjk29HoYhHz58OL/uuuvUuY6ODt7S0sJvu+22TVHEHoc777yTt7S05Eo7c+ZMPn369G4tz5aEvG1H/Y7zV199lQPgTz/9tDr31FNPcQD89ddfT71va+xze+65Jz/77LONc5/85Cf5ZZdd5kx/ySWX8E9+8pPGubPOOovvvffe3VbGnop6227+/PkcAF+zZs0mKN2WAQD8wQcfzEzT1X2OJDydwNq1azFo0KDU68uWLcOqVaswbdo0da6xsREHHHAAFi1atCmKuMXjiSeewNChQ7HTTjvhzDPPxOrVqzd3kXo8qN8BTz31FFpaWrDXXnupc3vvvTdaWlpqtsHW1OfK5TIWL15s9BUAmDZtWmo7PfXUU4n0hx9+OJ577jlUKpVuK2tPQ2faTmL33XfHiBEjcMghh2D+/PndWcxega7uc0R46sRbb72Fn/zkJzj77LNT08iIzcOGDTPODxs2LDOaM0HgyCOPxL333ovHH38cN954I5599lkcfPDBKJVKm7toPRrU70QbDB06NHF+6NChmW2wtfW5Dz74AEEQ1NVXVq1a5UxfrVbxwQcfdFtZexo603YjRozA7bffjgceeADz5s3DhAkTcMghh2DhwoWboshbLLq6z221hOfKK690GpHpf88995xxz3vvvYcjjjgCJ5xwAr7yla/UfIbapyUC5zxxbktEZ9quHpx44on43Oc+h0mTJuGYY47Bn/70J7z55pv44x//2IW12Dzo7rYDeme/q6fdXHWt1Qa9uc9lod6+4krvOr81oJ62mzBhAs4880zssccemDp1KubOnYvPfe5z+MEPfrApirpFoyv73Fa7l9Z5552HL33pS5lptt9+e/X7vffew0EHHYSpU6fi9ttvz7xv+PDhAAQ7HTFihDq/evXqBFvdElFv220sRowYgTFjxmDp0qVdlufmQne2XW/ud3nb7cUXX8T777+fuPbvf/+7rjboTX3OhSFDhsD3/YREIquvuPYbXL16NQqFAgYPHtxtZe1p6EzbubD33nvjl7/8ZVcXr1ehq/vcVkt4hgwZgiFDhuRK++677+Kggw7ClClTcOedd8LzsgVjY8eOxfDhw/Hoo49i9913ByD0vgsWLMD111+/0WXf3Kin7boCH374If75z38ak/iWiu5su97c7/K229SpU7F27Vr87W9/w5577gkAeOaZZ7B27Vrss88+uZ/Xm/qcCw0NDZgyZQoeffRRHHfccer8o48+iunTpzvvmTp1Kn7/+98b5x555BF8+tOfRrFY7Nby9iR0pu1cWLJkSa/tX12FLu9znTJ13orw7rvv8h133JEffPDB/F//+hdfuXKl+tMxYcIEPm/ePHV83XXX8ZaWFj5v3jz+0ksv8S9/+ct8xIgRfN26dZu6CpsVK1as4EuWLOFXXXUV79evH1+yZAlfsmQJX79+vUqjt9369ev57Nmz+aJFi/iyZcv4/Pnz+dSpU/moUaOo7Wq0HefU7zjn/IgjjuC77rorf+qpp/hTTz3FJ0+ezI8++mgjDfU5zu+77z5eLBb5HXfcwV999VU+a9Ys3rdvX758+XLOOeeXXXYZnzFjhkr/9ttv8z59+vBvfOMb/NVXX+V33HEHLxaL/De/+c3mqsJmQ71t98Mf/pA/+OCD/M033+Qvv/wyv+yyyzgA/sADD2yuKmwWrF+/Xo1jAPicOXP4kiVL+IoVKzjn3d/niPDUwJ133skBOP90AOB33nmnOg7DkF9xxRV8+PDhvLGxke+///78pZde2sSl3/yYOXOms+3mz5+v0uht19bWxqdNm8a33XZbXiwW+XbbbcdnzpzJ33nnnc1Tgc2IetuOc+p3nHP+4Ycf8pNPPpn379+f9+/fn5988skJd2DqcwK33HILHzNmDG9oaOB77LEHX7Bggbo2c+ZMfsABBxjpn3jiCb777rvzhoYGvv322/Nbb711E5e456Cetrv++uv5uHHjeFNTEx84cCD/7Gc/y//4xz9uhlJvXkj3fPtv5syZnPPu73O0WzqBQCAQCIRej63WS4tAIBAIBMLWAyI8BAKBQCAQej2I8BAIBAKBQOj1IMJDIBAIBAKh14MID4FAIBAIhF4PIjwEAoFAIBB6PYjwEAgEAoFA6PUgwkMgEAgEAqHXgwgPgUAgEAiEXg8iPAQCgUAgEHo9iPAQCAQCgUDo9SDCQyAQeg0OPPBAnH/++Zg1axYGDhyIYcOG4fbbb0draytOO+009O/fH+PGjcOf/vSnzV1UAoGwiUGEh0Ag9CrcfffdGDJkCP72t7/h/PPPxznnnIMTTjgB++yzD55//nkcfvjhmDFjBtra2jZ3UQkEwiYE7ZZOIBB6DQ488EAEQYC//OUvAIAgCNDS0oLjjz8e99xzDwBg1apVGDFiBJ566insvffem7O4BAJhE4IkPAQCoVdh1113Vb9938fgwYMxefJkdW7YsGEAgNWrV2/yshEIhM0HIjwEAqFXoVgsGseMMeMcYwwAEIbhJi0XgUDYvCDCQyAQCAQCodeDCA+BQCAQCIReDyI8BAKBQCAQej3IS4tAIBAIBEKvB0l4CAQCgUAg9HoQ4SEQCAQCgdDrQYSHQCAQCARCrwcRHgKBQCAQCL0eRHgIBAKBQCD0ehDhIRAIBAKB0OtBhIdAIBAIBEKvBxEeAoFAIBAIvR5EeAgEAoFAIPR6EOEhEAgEAoHQ60GEh0AgEAgEQq/H/wfpVETEDhC/kgAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "
      "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Check the original zos and zostoga maps (UKESM1)\n",
-    "zos_ukesm = '/Users/gregorymunday/climate_data/CMIP6/ScenarioMIP/MOHC/UKESM1-0-LL/ssp585/r1i1p1f2/Omon/zos/gn/v20190726/zos_Omon_UKESM1-0-LL_ssp585_r1i1p1f2_gn_201501-204912.nc'\n",
-    "zostoga_ukesm = '/Users/gregorymunday/climate_data/CMIP6/ScenarioMIP/MOHC/UKESM1-0-LL/ssp585/r1i1p1f2/Omon/zostoga/gm/v20190819/zostoga_Omon_UKESM1-0-LL_ssp585_r1i1p1f2_gm_201501-204912.nc'\n",
-    "\n",
-    "cube = iris.load_cube(zos_ukesm)\n",
-    "\n",
-    "qplt.pcolormesh(cube[-2], cmap='magma')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "b9700bdd",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "
      "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig = plt.figure(figsize=(12, 6))\n",
-    "\n",
-    "\n",
-    "for i in range(len(cmip6_patts)):\n",
-    "    cmip6_patts[i] = np.roll(cmip6_patts[i], 180, axis=1)\n",
-    "    # Plot each pattern\n",
-    "    ax = plt.subplot(5, 5, i+ 1)\n",
-    "    ax.pcolormesh(cmip6_patts[i], cmap='RdBu_r', vmin=-0.2, vmax=0.2)\n",
-    "\n",
-    "\n",
-    "# plt.pcolormesh(np.roll(cmip6_patt, 180, axis=1), cmap='RdBu_r', vmin=-0.2, vmax=0.2)\n",
-    "# plt.colorbar(label='Sea Surface Height Regression (m)')\n",
-    "# plt.title('CMIP6')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "19cb8e30",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "
      "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.pcolormesh(cmip5_patt, cmap='RdBu_r', vmin=-0.2, vmax=0.2)\n",
-    "plt.colorbar(label='Sea Surface Height Regression (m)')\n",
-    "plt.title('CMIP5')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "91a7af3c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "files = glob.glob('landwater/*.nc')\n",
-    "vals = []\n",
-    "lowers = []\n",
-    "uppers = []\n",
-    "labels = []\n",
-    "for f in files:\n",
-    "    ds = xr.load_dataset(f)\n",
-    "    ds = ds.interp(years=np.arange(2005, 2301, 1), method='linear').squeeze()\n",
-    "    vals.append(np.percentile(ds['sea_level_change'].values, 50, axis=0))\n",
-    "    lowers.append(np.percentile(ds['sea_level_change'].values, 17, axis=0))\n",
-    "    uppers.append(np.percentile(ds['sea_level_change'].values, 83, axis=0))\n",
-    "    labels.append(f.split('/')[-1].split('-')[-1][:6])\n",
-    "    \n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "id": "90991bfc",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2300\n",
-      "SSP585 lower at 2100: 0.047m\n",
-      "SSP585 upper at 2100: 0.101m\n",
-      "SSP585 median at 2100: 0.074m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       ""
-      ]
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "
      "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "time = np.arange(2005, 2301, 1)\n",
-    "print(time[-1])\n",
-    "\n",
-    "plt.figure(figsize=(10, 5))\n",
-    "colors = plt.cm.viridis(np.linspace(0, 1, len(vals)))\n",
-    "for i, val in enumerate(vals):\n",
-    "    if not labels[i] == 'ssp585':\n",
-    "        continue\n",
-    "    plt.plot(time, val*0.001, color=colors[i], label=labels[i])\n",
-    "    plt.plot(time, lowers[i]*0.001, color=colors[i], linestyle='--')\n",
-    "    plt.plot(time, uppers[i]*0.001, color=colors[i], linestyle='--')\n",
-    "    plt.fill_between(time, np.array(lowers[i])*0.001, np.array(uppers[i])*0.001, color=colors[i], alpha=0.2)\n",
-    "\n",
-    "    print(f'SSP585 lower at 2100: {lowers[i][-1] * 1e-3}m')\n",
-    "    print(f'SSP585 upper at 2100: {uppers[i][-1]* 1e-3}m')\n",
-    "    print(f'SSP585 median at 2100: {val[-1]* 1e-3}m')\n",
-    "\n",
-    "plt.xlabel('Year')\n",
-    "plt.ylabel('Sea Level Change (m)')\n",
-    "plt.title('Sea Level Change from Land Water Storage')\n",
-    "plt.legend()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "de7322b9",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "cmip7",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/profsea/notebooks/compare_ais.ipynb b/profsea/notebooks/compare_ais.ipynb
deleted file mode 100644
index 951ddb5..0000000
--- a/profsea/notebooks/compare_ais.ipynb
+++ /dev/null
@@ -1,191 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "83d64d2f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pathlib import Path\n",
-    "\n",
-    "import numpy as np \n",
-    "import matplotlib.pyplot as plt\n",
-    "import xarray as xr"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "1ce6ed82",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "old_ais_path = \"../probabilistic_projections/global/gmslr_projections.nc\"\n",
-    "new_ais_path = \"../probabilistic_projections/global/gmslr_projections_AIS_JAN_pen.nc\"\n",
-    "c_new_ais_path = \"../probabilistic_projections/global/gmslr_projections_AIS_JAN.nc\"\n",
-    "old_ds = xr.open_dataset(old_ais_path)\n",
-    "new_ds = xr.open_dataset(new_ais_path)\n",
-    "c_new_ds = xr.open_dataset(c_new_ais_path)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6dcb4835",
-   "metadata": {},
-   "source": [
-    "Timeseries"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "4d0b2f15",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "
      "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig = plt.figure(figsize=(16, 7), layout=\"constrained\")\n",
-    "\n",
-    "for scen_idx, scenario in enumerate(old_ds.scenario):\n",
-    "    ax = fig.add_subplot(2, 3, scen_idx+1)\n",
-    "    # Median\n",
-    "    ax.set_title(f\"{scenario.values}\")\n",
-    "    ax.plot(old_ds.year, old_ds.antarctica[scen_idx, 2], color=\"royalblue\", lw=2, label=\"AR5 emulator\")\n",
-    "    ax.plot(new_ds.year, new_ds.antarctica[scen_idx, 2], color=\"goldenrod\", lw=2, label=\"ISMIP6 emulator\")\n",
-    "    ax.plot(c_new_ds.year, c_new_ds.antarctica[scen_idx, 2], color=\"seagreen\", lw=2, label=\"ISMIP6 emulator, common models\")\n",
-    "    ax.fill_between(old_ds.year, old_ds.antarctica[scen_idx, 0], old_ds.antarctica[scen_idx, 4], color=\"royalblue\", alpha=0.2)\n",
-    "    ax.fill_between(new_ds.year, new_ds.antarctica[scen_idx, 0], new_ds.antarctica[scen_idx, 4], color=\"goldenrod\", alpha=0.2)\n",
-    "    ax.fill_between(c_new_ds.year, c_new_ds.antarctica[scen_idx, 0], c_new_ds.antarctica[scen_idx, 4], color=\"seagreen\", alpha=0.2)\n",
-    "    ax.set_ylabel(\"SLE (m)\")\n",
-    "    ax.set_xlabel(\"Year\")\n",
-    "    if scen_idx == 0:\n",
-    "        ax.legend(loc=\"upper left\")\n",
-    "\n",
-    "\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ff3aa666",
-   "metadata": {},
-   "source": [
-    "End-of-century box and whisker"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "9c58124d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "
      "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "medianprops = dict(linestyle='-', linewidth=1.5, color='k')\n",
-    "boxprops = dict(linestyle='-', linewidth=0.5)\n",
-    "\n",
-    "fig = plt.figure(figsize=(10, 5), layout=\"constrained\")\n",
-    "\n",
-    "# Antarctica\n",
-    "ax = fig.add_subplot(121)\n",
-    "old_eoc = []\n",
-    "new_eoc = []\n",
-    "for scen_idx, scenario in enumerate(old_ds.scenario):\n",
-    "    old_eoc.append(old_ds.isel(scenario=scen_idx, year=-1).antarctica.values)\n",
-    "    new_eoc.append(new_ds.isel(scenario=scen_idx, year=-1).antarctica.values)\n",
-    "\n",
-    "indices = np.arange(1, len(old_ds.scenario)+1)\n",
-    "width = 0.35\n",
-    "\n",
-    "old_bplot = ax.boxplot(old_eoc, positions=indices - 0.2, widths=width, patch_artist=True, medianprops=medianprops, boxprops=boxprops, label=\"AR5 emulator\")\n",
-    "new_bplot = ax.boxplot(new_eoc, positions=indices + 0.2, widths=width, patch_artist=True, medianprops=medianprops, boxprops=boxprops, label=\"ISMIP6 emulator\")\n",
-    "\n",
-    "for patch, color in zip(old_bplot['boxes'], ['royalblue' for i in range(len(old_eoc))]):\n",
-    "    patch.set_facecolor(color)\n",
-    "for patch, color in zip(new_bplot['boxes'], ['goldenrod' for i in range(len(old_eoc))]):\n",
-    "    patch.set_facecolor(color)\n",
-    "\n",
-    "ax.set_title(\"Year 2300 comparison - Antarctica\")\n",
-    "ax.set_ylabel(\"SLE (m)\")\n",
-    "ax.set_xlabel(\"Scenario\")\n",
-    "ax.set_ylim([-0.5, 5])\n",
-    "ax.set_xticks(indices)\n",
-    "ax.set_xticklabels(old_ds.scenario.values, rotation=45, ha='right')\n",
-    "ax.grid()\n",
-    "ax.legend(loc=\"upper left\")\n",
-    "\n",
-    "# Global\n",
-    "ax = fig.add_subplot(122)\n",
-    "old_eoc = []\n",
-    "new_eoc = []\n",
-    "for scen_idx, scenario in enumerate(old_ds.scenario):\n",
-    "    old_eoc.append(old_ds.isel(scenario=scen_idx, year=-1).gmslr.values)\n",
-    "    new_eoc.append(new_ds.isel(scenario=scen_idx, year=-1).gmslr.values)\n",
-    "\n",
-    "indices = np.arange(1, len(old_ds.scenario)+1)\n",
-    "width = 0.35\n",
-    "\n",
-    "old_bplot = ax.boxplot(old_eoc, positions=indices - 0.2, widths=width, patch_artist=True, medianprops=medianprops, boxprops=boxprops, label=\"AR5 emulator\")\n",
-    "new_bplot = ax.boxplot(new_eoc, positions=indices + 0.2, widths=width, patch_artist=True, medianprops=medianprops, boxprops=boxprops, label=\"ISMIP6 emulator\")\n",
-    "\n",
-    "for patch, color in zip(old_bplot['boxes'], ['royalblue' for i in range(len(old_eoc))]):\n",
-    "    patch.set_facecolor(color)\n",
-    "for patch, color in zip(new_bplot['boxes'], ['goldenrod' for i in range(len(old_eoc))]):\n",
-    "    patch.set_facecolor(color)\n",
-    "\n",
-    "ax.set_title(\"Year 2300 comparison - Global\")\n",
-    "ax.set_ylabel(\"SLE (m)\")\n",
-    "ax.set_xlabel(\"Scenario\")\n",
-    "ax.set_ylim([-0.5, 6])\n",
-    "ax.set_xticks(indices)\n",
-    "ax.set_xticklabels(old_ds.scenario.values, rotation=45, ha='right')\n",
-    "ax.grid()\n",
-    "plt.show()\n",
-    "ax.grid()\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "cmip7",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/profsea/notebooks/evaluate_gmslr.ipynb b/profsea/notebooks/evaluate_gmslr.ipynb
deleted file mode 100644
index b9f2358..0000000
--- a/profsea/notebooks/evaluate_gmslr.ipynb
+++ /dev/null
@@ -1,343 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "8875184d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import gc\n",
-    "\n",
-    "import matplotlib.pyplot as plt \n",
-    "import numpy as np\n",
-    "from tqdm import tqdm\n",
-    "import xarray as xr \n",
-    "\n",
-    "from profsea.emulator import GMSLREmulator"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "00b62b34",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def load_mc(scenario: str, var_name: str) -> xr.DataArray:\n",
-    "    ds = xr.load_dataset(f\"/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/data/long_monte_carlo/{scenario}_{var_name}.nc\")\n",
-    "    return ds.global_average_sea_level_change.values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "10f68c5b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "scenarios = ['rcp26', 'rcp45', 'rcp85']\n",
-    "t_mid_input = {}\n",
-    "t_upper_input = {}\n",
-    "\n",
-    "# Expansion\n",
-    "exp_mid_input = {}\n",
-    "exp_upper_input = {}\n",
-    "exp_lower_input = {}\n",
-    "\n",
-    "# GMSLR\n",
-    "ar5_gmslr_mid = {}\n",
-    "ar5_gmslr_upper = {}\n",
-    "ar5_gmslr_lower = {}\n",
-    "\n",
-    "# Antarctica\n",
-    "ar5_ant_mid = {}\n",
-    "ar5_ant_upper = {}\n",
-    "ar5_ant_lower = {}\n",
-    "\n",
-    "# Greenland\n",
-    "ar5_green_mid = {}\n",
-    "ar5_green_upper = {}\n",
-    "ar5_green_lower = {}\n",
-    "\n",
-    "# Glaciers\n",
-    "ar5_glac_mid = {}\n",
-    "ar5_glac_upper = {}\n",
-    "ar5_glac_lower = {}\n",
-    "\n",
-    "# Landwater\n",
-    "ar5_land_mid = {}\n",
-    "ar5_land_upper = {}\n",
-    "ar5_land_lower = {}\n",
-    "\n",
-    "for scenario in scenarios:\n",
-    "    # Temperature\n",
-    "    t_mid_input[scenario] = load_mc(scenario, \"temperaturemid\")\n",
-    "    t_upper_input[scenario] = load_mc(scenario, \"temperatureupper\")\n",
-    "\n",
-    "    # Thermal expansion\n",
-    "    exp_mid_input[scenario] = load_mc(scenario, \"expansionmid\")\n",
-    "    exp_upper_input[scenario] = load_mc(scenario, \"expansionupper\")\n",
-    "    exp_lower_input[scenario] = load_mc(scenario, \"expansionlower\")\n",
-    "\n",
-    "    # GMSLR\n",
-    "    ar5_gmslr_mid[scenario] = load_mc(scenario, \"summid\")\n",
-    "    ar5_gmslr_upper[scenario] = load_mc(scenario, \"sumupper\")\n",
-    "    ar5_gmslr_lower[scenario] = load_mc(scenario, \"sumlower\")\n",
-    "\n",
-    "    # Antarctica\n",
-    "    ar5_ant_mid[scenario] = load_mc(scenario, \"antnetmid\")\n",
-    "    ar5_ant_upper[scenario] = load_mc(scenario, \"antnetupper\")\n",
-    "    ar5_ant_lower[scenario] = load_mc(scenario, \"antnetlower\")\n",
-    "    \n",
-    "    # Greenland\n",
-    "    ar5_green_mid[scenario] = load_mc(scenario, \"greennetmid\")\n",
-    "    ar5_green_upper[scenario] = load_mc(scenario, \"greennetupper\")\n",
-    "    ar5_green_lower[scenario] = load_mc(scenario, \"greennetlower\")\n",
-    "\n",
-    "    # Glaciers\n",
-    "    ar5_glac_mid[scenario] = load_mc(scenario, \"glaciermid\")\n",
-    "    ar5_glac_upper[scenario] = load_mc(scenario, \"glacierupper\")\n",
-    "    ar5_glac_lower[scenario] = load_mc(scenario, \"glacierlower\")\n",
-    "\n",
-    "    # Landwater\n",
-    "    ar5_land_mid[scenario] = load_mc(scenario, \"landwatermid\")\n",
-    "    ar5_land_upper[scenario] = load_mc(scenario, \"landwaterupper\")\n",
-    "    ar5_land_lower[scenario] = load_mc(scenario, \"landwaterlower\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "f50b0202",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 3/3 [04:44<00:00, 94.88s/it]\n"
-     ]
-    }
-   ],
-   "source": [
-    "gmslr_output = {}\n",
-    "exp_output = {}\n",
-    "ant_output = {}\n",
-    "green_output = {}\n",
-    "glac_output = {}\n",
-    "land_output = {}\n",
-    "ar6_green_output = {}\n",
-    "ar6_land_output = {}\n",
-    "for scenario in tqdm(scenarios):\n",
-    "    emulator = GMSLREmulator(\n",
-    "        t_mid_input[scenario],\n",
-    "        exp_mid_input[scenario],\n",
-    "        scenario,\n",
-    "        2300,  # IF YEAR BEYOND 2100 AUTO SET PALMER METHOD TO TRUE\n",
-    "        input_ensemble=False,\n",
-    "        glaciermip=None,\n",
-    "        T_percentile_95=t_upper_input[scenario],\n",
-    "        OHC_percentile_95=exp_upper_input[scenario],\n",
-    "        output_percentiles=np.linspace(0, 100, 1001),\n",
-    "    )\n",
-    "    emulator.project()\n",
-    "    gmslr_output[scenario] = emulator.gmslr\n",
-    "    exp_output[scenario] = emulator.expansion\n",
-    "    ant_output[scenario] = emulator.antnet\n",
-    "    green_output[scenario] = emulator.greenland\n",
-    "    glac_output[scenario] = emulator.glacier\n",
-    "    land_output[scenario] = emulator.landwater\n",
-    "    ar6_green_output[scenario] = emulator.greenland_ar6\n",
-    "    ar6_land_output[scenario] = emulator.landwater_ar6\n",
-    "    gc.collect()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "fdcec326",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# GMSLR\n",
-    "gmslr_mid = {}\n",
-    "gmslr_upper = {}\n",
-    "gmslr_lower = {}\n",
-    "\n",
-    "# Expansion\n",
-    "exp_mid = {}\n",
-    "exp_upper = {}\n",
-    "exp_lower = {}\n",
-    "\n",
-    "# Antarctica\n",
-    "ant_mid = {}\n",
-    "ant_upper = {}\n",
-    "ant_lower = {}\n",
-    "\n",
-    "# Greenland\n",
-    "green_mid = {}\n",
-    "green_upper = {}\n",
-    "green_lower = {}\n",
-    "\n",
-    "# Glaciers\n",
-    "glac_mid = {}\n",
-    "glac_upper = {}\n",
-    "glac_lower = {}\n",
-    "\n",
-    "# Landwater\n",
-    "land_mid = {}\n",
-    "land_upper = {}\n",
-    "land_lower = {}\n",
-    "\n",
-    "# Greenland AR6\n",
-    "ar6_green_mid = {}\n",
-    "ar6_green_upper = {}\n",
-    "ar6_green_lower = {}\n",
-    "\n",
-    "# Landwater AR6\n",
-    "ar6_land_mid = {}\n",
-    "ar6_land_upper = {}\n",
-    "ar6_land_lower = {}\n",
-    "for scenario in scenarios:\n",
-    "    # GMSLR\n",
-    "    gmslr_mid[scenario] = np.percentile(gmslr_output[scenario].T, 50, axis=1)\n",
-    "    gmslr_upper[scenario] = np.percentile(gmslr_output[scenario].T, 95, axis=1)\n",
-    "    gmslr_lower[scenario] = np.percentile(gmslr_output[scenario].T, 5, axis=1)\n",
-    "\n",
-    "    # Expansion\n",
-    "    exp_mid[scenario] = np.percentile(exp_output[scenario].T, 50, axis=1)\n",
-    "    exp_upper[scenario] = np.percentile(exp_output[scenario].T, 95, axis=1)\n",
-    "    exp_lower[scenario] = np.percentile(exp_output[scenario].T, 5, axis=1)\n",
-    "\n",
-    "    # Antarctica\n",
-    "    ant_mid[scenario] = np.percentile(ant_output[scenario].T, 50, axis=1)\n",
-    "    ant_upper[scenario] = np.percentile(ant_output[scenario].T, 95, axis=1)\n",
-    "    ant_lower[scenario] = np.percentile(ant_output[scenario].T, 5, axis=1)\n",
-    "    \n",
-    "    # Greenland\n",
-    "    green_mid[scenario] =  np.percentile(green_output[scenario].T, 50, axis=1)\n",
-    "    green_upper[scenario] = np.percentile(green_output[scenario].T, 95, axis=1)\n",
-    "    green_lower[scenario] = np.percentile(green_output[scenario].T, 5, axis=1)\n",
-    "\n",
-    "    # Glaciers\n",
-    "    glac_mid[scenario] = np.percentile(glac_output[scenario].T, 50, axis=1)\n",
-    "    glac_upper[scenario] = np.percentile(glac_output[scenario].T, 95, axis=1)\n",
-    "    glac_lower[scenario] = np.percentile(glac_output[scenario].T, 5, axis=1)\n",
-    "\n",
-    "    # Landwater\n",
-    "    land_mid[scenario] = np.percentile(land_output[scenario].T, 50, axis=1)\n",
-    "    land_upper[scenario] = np.percentile(land_output[scenario].T, 95, axis=1)\n",
-    "    land_lower[scenario] = np.percentile(land_output[scenario].T, 5, axis=1)\n",
-    "\n",
-    "    # Greenland AR6\n",
-    "    ar6_green_mid[scenario] = np.percentile(ar6_green_output[scenario].T, 50, axis=1)\n",
-    "    ar6_green_upper[scenario] = np.percentile(ar6_green_output[scenario].T, 95, axis=1)\n",
-    "    ar6_green_lower[scenario] = np.percentile(ar6_green_output[scenario].T, 5, axis=1)\n",
-    "\n",
-    "    ar6_land_mid[scenario] = np.percentile(ar6_land_output[scenario].T, 50, axis=1)\n",
-    "    ar6_land_upper[scenario] = np.percentile(ar6_land_output[scenario].T, 95, axis=1)\n",
-    "    ar6_land_lower[scenario] = np.percentile(ar6_land_output[scenario].T, 5, axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "778341fb",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "
      "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "sim_comp_dict = {\n",
-    "    \"gmslr\": [gmslr_mid, gmslr_upper, gmslr_lower],\n",
-    "    \"expansion\": [exp_mid, exp_upper, exp_lower],\n",
-    "    \"antarctica\": [ant_mid, ant_upper, ant_lower],\n",
-    "    \"greenland\": [green_mid, green_upper, green_lower],\n",
-    "    \"glaciers\": [glac_mid, glac_upper, glac_lower],\n",
-    "    \"landwater\": [land_mid, land_upper, land_lower],\n",
-    "    \"ar6_greenland\": [ar6_green_mid, ar6_green_upper, ar6_green_lower],\n",
-    "    \"ar6_landwater\": [ar6_land_mid, ar6_land_upper, ar6_land_lower]\n",
-    "}\n",
-    "ar5_comp_dict = {\n",
-    "    \"gmslr\": [ar5_gmslr_mid, ar5_gmslr_upper, ar5_gmslr_lower],\n",
-    "    \"expansion\": [exp_mid_input, exp_upper_input, exp_lower_input],\n",
-    "    \"antarctica\": [ar5_ant_mid, ar5_ant_upper, ar5_ant_lower],\n",
-    "    \"greenland\": [ar5_green_mid, ar5_green_upper, ar5_green_lower],\n",
-    "    \"glaciers\": [ar5_glac_mid, ar5_glac_upper, ar5_glac_lower],\n",
-    "    \"landwater\": [ar5_land_mid, ar5_land_upper, ar5_land_lower]\n",
-    "}\n",
-    "component = \"landwater\"\n",
-    "\n",
-    "fig = plt.figure(figsize=(14, 4), layout='constrained')\n",
-    "\n",
-    "for i, scenario in enumerate(scenarios):\n",
-    "    ax = fig.add_subplot(1, 3, i + 1)\n",
-    "\n",
-    "    # Simulation\n",
-    "    ax.plot(sim_comp_dict[component][0][scenario], color='goldenrod', label='JG-ProFSea')\n",
-    "    ax.plot(sim_comp_dict[component][1][scenario], color='goldenrod', ls='--')\n",
-    "    ax.plot(sim_comp_dict[component][2][scenario], color='goldenrod', ls='--')\n",
-    "\n",
-    "    if component == \"greenland\":\n",
-    "        # AR6 simulation\n",
-    "        ax.plot(sim_comp_dict[\"ar6_greenland\"][0][scenario], color='skyblue', label=\"AR6-ProFSea\")\n",
-    "        ax.plot(sim_comp_dict[\"ar6_greenland\"][1][scenario], color='skyblue', ls=\"--\")\n",
-    "        ax.plot(sim_comp_dict[\"ar6_greenland\"][2][scenario], color='skyblue', ls=\"--\")\n",
-    "\n",
-    "    elif component == \"landwater\":\n",
-    "        # AR6 sim\n",
-    "        ax.plot(sim_comp_dict[\"ar6_landwater\"][0][scenario], color='skyblue', label=\"AR6-ProFSea\")\n",
-    "        ax.plot(sim_comp_dict[\"ar6_landwater\"][1][scenario], color='skyblue', ls=\"--\")\n",
-    "        ax.plot(sim_comp_dict[\"ar6_landwater\"][2][scenario], color='skyblue', ls=\"--\")\n",
-    "\n",
-    "    # AR5\n",
-    "    ax.plot(ar5_comp_dict[component][0][scenario], color='navy', label='AR5')\n",
-    "    ax.plot(ar5_comp_dict[component][1][scenario], color='navy', ls='--')\n",
-    "    ax.plot(ar5_comp_dict[component][2][scenario], color='navy', ls='--')\n",
-    "    ax.plot()\n",
-    "    # ax.set_ylim([-0.1, 7.0])\n",
-    "    ax.set_xlabel(\"Simulation year\")\n",
-    "    if component == \"gmslr\":\n",
-    "        ax.set_ylabel(f\"{component.upper()} (m)\")\n",
-    "    else:\n",
-    "        ax.set_ylabel(f\"{component[0].upper() + component[1:]} (m)\")\n",
-    "    ax.text(0.54, 0.92, scenario, transform=ax.transAxes, weight='bold')\n",
-    "    ax.legend(loc='upper left')\n",
-    "fig.savefig(f\"eval_plots/rcp_eval_ar5_ar6_{component}.png\", dpi=300)\n",
-    "plt.show()\n",
-    "plt.close()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "cmip7",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

From dd7ef43282412e0ba7ef0fcd559780646fb83be2 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Fri, 3 Apr 2026 13:07:30 +0100
Subject: [PATCH 104/276] Fix source attribute bug

---
 profsea/emulator/spatial.py | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/profsea/emulator/spatial.py b/profsea/emulator/spatial.py
index 7cd1ef3..8230b0e 100644
--- a/profsea/emulator/spatial.py
+++ b/profsea/emulator/spatial.py
@@ -163,10 +163,9 @@ def _calc_gia_contribution(self) -> None:
                 "lon": np.arange(0, 360) + 0.5})
         xr_dataArray.attrs["units"] = "m"
         xr_dataArray.attrs["long_name"] = "Regional GIA sea-level projections"
+        xr_dataArray.attrs["source"] = "ProFSea-Climate v0.1"
         ds = xr_dataArray.to_dataset(name='gia')
 
-        ds.attrs["source"] = "ProFSea-Climate v0.1"
-
         file_header = f"gia_{self.scenario}_projection_{self.end_year}"
         R_file = '_'.join([file_header, 'regional']) + '.nc'
         encoding = {'gia': {"zlib": True, "complevel": 5, "dtype": "float32"}}
@@ -274,10 +273,9 @@ def _save_projections(self, montecarlo_R: da.array, component: str) -> None:
                                             "lat": np.arange(-90, 90) + 0.5, "lon": np.arange(0, 360) + 0.5})
         xr_dataArray.attrs["units"] = "m"
         xr_dataArray.attrs["long_name"] = f"Regional {component} sea-level projections"
+        xr_dataArray.attrs["source"] = "ProFSea-Climate v0.1"
         ds = xr_dataArray.to_dataset(name=component)
 
-        ds.attrs["source"] = "ProFSea-Climate v0.1"
-
         file_header = f"{component}_{self.scenario}_projection_{self.end_year}"
         R_file = '_'.join([file_header, 'regional']) + '.nc'
         encoding = {component: {"zlib": True, "complevel": 5, "dtype": "float32"}}

From 64cdbb301006c3954068842ad501c6a910c6b266 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Fri, 10 Apr 2026 16:34:56 +0100
Subject: [PATCH 105/276] Add comments and reformat

---
 profsea/emulator/antarctica.py | 133 ++++++++++++++++++++++++---------
 1 file changed, 96 insertions(+), 37 deletions(-)

diff --git a/profsea/emulator/antarctica.py b/profsea/emulator/antarctica.py
index e125ac0..dfae460 100644
--- a/profsea/emulator/antarctica.py
+++ b/profsea/emulator/antarctica.py
@@ -4,46 +4,100 @@
 from scipy.signal import fftconvolve
 import xarray as xr
 
+
 class AntarcticaISMIP6:
-    """ISMIP6 2300 Antarctic ice-sheet emulator with two-timescale response."""
-    def __init__(self, params_path: Path, samples: int=1000):
+    """
+    ISMIP6 2300 Antarctic ice-sheet emulator with two-timescale response.
+
+    This emulation of the ISMIP6 ice-sheet model ensemble aims to capture
+    slow, fast and drift responses of the Antarctic ice sheet to GMST.
+    The slow response is modelled as the impulse response to temperature
+    forcing, convolved with two exponential decay kernels representing
+    different ice-sheet response timescales, depending on the region being modelled.
+    The fast response is modelled as a direct proportionality to the integrated 
+    temperature anomaly, while the drift term captures any linear time-dependent 
+    trends not explained by the temperature forcing.
+
+    Parameters provided are for the WAIS, EAIS and AIS Peninsula regions, since each has
+    different response characteristics to warming.
+    """
+
+    def __init__(self, params_path: Path, samples: int = 1000):
         self.param_ds = xr.load_dataset(params_path)
         self.n_samples = samples
         self.n_models = self.param_ds.coords["model"].shape[0]
 
     def _impulse_response_term(
-            self, 
-            tas: np.ndarray, 
-            tau1: float, 
-            tau2: float, 
-            gamma: float, 
-            params: np.ndarray, 
-            dt: float):
+        self,
+        tas: np.ndarray,
+        tau1: float,
+        tau2: float,
+        gamma: float,
+        params: np.ndarray,
+        dt: float,
+    ) -> np.ndarray:
+        """Computes the slow response term using convolution with two exponential decay kernels.
+        Parameters
+        ----------
+        tas : np.ndarray
+            Time series of global mean surface air temperature anomalies (shape: n_time).
+        tau1 : float
+            Timescale of the first response component (in years).
+        tau2 : float
+            Timescale of the second response component (in years).
+        gamma : float
+            Exponent for the temperature forcing term.
+        params : np.ndarray
+            Array of shape (n_samples, 4) containing [alpha1, alpha2, beta, drift] parameters for each sample.
+        dt : float
+            Time step in years.
+        Returns
+        -------
+        np.ndarray
+            Slow response term for each sample (shape: n_samples x n_time).
+        """
         n_time = tas.shape[0]
-        
+
         # Two slow coeffs
-        alphas1 = params[:, 0] 
-        alphas2 = params[:, 1] 
+        alphas1 = params[:, 0]
+        alphas2 = params[:, 1]
 
+        # Base forcing term to power of gamma, but preserving sign
         forcing_base = np.sign(tas) * (np.abs(tas) ** gamma)
 
-        # Reservoir 1
+        # Impulse response function for timescale 1
         decay_factors1 = np.exp(-np.arange(n_time) * dt / tau1) * (dt / tau1)
-        rate_delayed1 = fftconvolve(forcing_base, decay_factors1, mode='full')[:n_time]
+        rate_delayed1 = fftconvolve(forcing_base, decay_factors1, mode="full")[:n_time]
         t_conv1 = np.cumsum(rate_delayed1, axis=0) * dt
         term_slow1 = alphas1[:, None] * t_conv1[None, :]
 
-        # Reservoir 2 
+        # Impulse response function for timescale 2
         decay_factors2 = np.exp(-np.arange(n_time) * dt / tau2) * (dt / tau2)
-        rate_delayed2 = fftconvolve(forcing_base, decay_factors2, mode='full')[:n_time]
+        rate_delayed2 = fftconvolve(forcing_base, decay_factors2, mode="full")[:n_time]
         t_conv2 = np.cumsum(rate_delayed2, axis=0) * dt
         term_slow2 = alphas2[:, None] * t_conv2[None, :]
-        
-        return term_slow1 + term_slow2
 
-    def predict(self, tas: np.ndarray, dt: float=1.0, display_progress=True, seed=None):
+        return term_slow1 + term_slow2  # Slow terms are linearly combined
+
+    def predict(
+        self, tas: np.ndarray, dt: float = 1.0, display_progress=True, seed=None
+    ) -> np.ndarray:
         """
         Projects AIS response using empirical additive bootstrapping.
+        Parameters
+        ----------
+        tas : np.ndarray
+            Time series of global mean surface air temperature anomalies (shape: n_time).
+        dt : float, optional
+            Time step in years (default: 1.0).
+        display_progress : bool, optional
+            Whether to show a progress bar during prediction (default: True).
+        seed : int or None, optional
+            Random seed for reproducibility (default: None).
+        Returns
+        -------
+        np.ndarray
+            Projected AIS contributions (shape: n_models x n_samples x n_time).
         """
         tas = np.squeeze(tas)
         n_time = len(tas)
@@ -59,37 +113,42 @@ def predict(self, tas: np.ndarray, dt: float=1.0, display_progress=True, seed=No
         all_preds = np.zeros((self.n_models, self.n_samples, n_time))
 
         # Shape assumed: (n_models, n_training_scenarios, 4)
-        all_residuals = self.param_ds.param_residuals.values 
+        all_residuals = self.param_ds.param_residuals.values
         n_train_scenarios = all_residuals.shape[1]
-        
-        for model in track(
-                range(self.n_models), 
-                description="Projecting AIS response... ", 
-                disable=not display_progress):
 
+        for model in track(
+            range(self.n_models),
+            description="Projecting AIS response... ",
+            disable=not display_progress,
+        ):
+            # Unpack model-specific parameters
             tau1 = float(self.param_ds.tau1[model].values)
             tau2 = float(self.param_ds.tau2[model].values)
             gamma = float(self.param_ds.gamma[model].values)
-            
-            # [alpha1, alpha2, beta, drift]
-            general_p = self.param_ds.general_params[model].values  
-            
+
+            # Unpack general parameters [alpha1, alpha2, beta, drift]
+            general_p = self.param_ds.general_params[model].values
+
+            # Sample random parameter residuals for this model
             rand_indices = rng.integers(0, n_train_scenarios, size=self.n_samples)
             sampled_residuals = all_residuals[model, rand_indices, :]
-            
+
+            # Add sampled parameter residuals to general parameters
             total_params = general_p + sampled_residuals
-            
-            # Slow term
-            term_slow = self._impulse_response_term(tas, tau1, tau2, gamma, total_params, dt)
 
-            # Fast term
+            # Slow response term
+            term_slow = self._impulse_response_term(
+                tas, tau1, tau2, gamma, total_params, dt
+            )
+
+            # Fast response term
             betas = total_params[:, 2]
             term_fast = betas[:, None] * tas_int[None, :]
-            
+
             # Drift term
             drift_coeffs = total_params[:, 3]
             term_drift = drift_coeffs[:, None] * physical_time[None, :]
-            
+
             all_preds[model, :, :] = term_fast + term_slow + term_drift
 
-        return all_preds
\ No newline at end of file
+        return all_preds

From 11e388ee828321cbc1316b0fe6af8a3cdb867c83 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Fri, 10 Apr 2026 16:35:30 +0100
Subject: [PATCH 106/276] Smol fix

---
 profsea/emulator/antarctica.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/profsea/emulator/antarctica.py b/profsea/emulator/antarctica.py
index dfae460..16dcb63 100644
--- a/profsea/emulator/antarctica.py
+++ b/profsea/emulator/antarctica.py
@@ -14,8 +14,8 @@ class AntarcticaISMIP6:
     The slow response is modelled as the impulse response to temperature
     forcing, convolved with two exponential decay kernels representing
     different ice-sheet response timescales, depending on the region being modelled.
-    The fast response is modelled as a direct proportionality to the integrated 
-    temperature anomaly, while the drift term captures any linear time-dependent 
+    The fast response is modelled as a direct proportionality to the integrated
+    temperature anomaly, while the drift term captures any linear time-dependent
     trends not explained by the temperature forcing.
 
     Parameters provided are for the WAIS, EAIS and AIS Peninsula regions, since each has

From dfef6e9b2bdd9322f9e6ba1b40b83b8b63ff20af Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Fri, 10 Apr 2026 16:56:22 +0100
Subject: [PATCH 107/276] Address review comments

---
 profsea/emulator/gmslr.py   | 11 ++-----
 profsea/emulator/spatial.py | 65 ++++++++++++++++++++-----------------
 2 files changed, 37 insertions(+), 39 deletions(-)

diff --git a/profsea/emulator/gmslr.py b/profsea/emulator/gmslr.py
index 4009c0a..106e380 100644
--- a/profsea/emulator/gmslr.py
+++ b/profsea/emulator/gmslr.py
@@ -146,6 +146,7 @@ def __init__(
         random_sample: bool = False,
         T_percentile_95: np.ndarray = None,
         OHC_percentile_95: np.ndarray = None,
+        cum_emissions_total: float = None,
         palmer_method: bool = True,
         active_components: list[str] = None,
     ) -> None:
@@ -161,6 +162,7 @@ def __init__(
         self.random_sample = random_sample
         self.T_percentile_95 = T_percentile_95
         self.OHC_percentile_95 = OHC_percentile_95
+        self.cum_emissions_total = cum_emissions_total
         self.palmer_method = palmer_method
 
         # First year of AR5 projections
@@ -588,15 +590,6 @@ def project_glacier(
                     dict(name="WAL2001", factor=2.66, exponent=0.730, cvgl=0.206),
                 ]
                 cvgl = 0.20  # unnecessary default
-            elif self.glaciermip == 3:
-                glparm = [
-                    dict(name="GLIMB", factor=3.70, exponent=0.662, cvgl=0.206),
-                    dict(name="GloGEMflow", factor=5.50, exponent=0.564, cvgl=0.188),
-                    dict(name="OGGMv1.6", factor=2.66, exponent=0.730, cvgl=0.206),
-                    dict(
-                        name="PyGEM-OGGMv1.3", factor=2.66, exponent=0.730, cvgl=0.206
-                    ),
-                ]
             else:
                 raise KeyError("glaciermip must be 1 or 2")
         else:
diff --git a/profsea/emulator/spatial.py b/profsea/emulator/spatial.py
index 8230b0e..4f9aca4 100644
--- a/profsea/emulator/spatial.py
+++ b/profsea/emulator/spatial.py
@@ -19,31 +19,7 @@
 warnings.filterwarnings("ignore")
 
 class Spatial:
-    """ Spatial sea level rise component emulator.
-
-    Parameters
-    ----------
-    scenario: str
-        Name of the scenario.
-    expansion_patterns_dir: str
-        Direcotry path of regression patterns of thermal expansion component from cmip models.
-    fingerprint_dir: str
-        Direcotry path of GRD (Gravitational, Rotational, Deformational) fingerprint data
-    gia_dir: str
-        Direcotry path of GIA (Glacial Isostatic Adjustment) data
-    components_dir: str
-        Path to global sea-level rise components (output of ProFSea's gmslr module)
-    component_list: list
-        Namelist of components for spatial projections
-    end_year: int
-        End year of the projections.
-    output_percentiles: int
-        List of percentiles for output
-    output_dir: str
-        Path to output directory for saving spatial projections.
-    random_seed: bool
-        Seed for numpy.random.
-    """
+    """ Spatial sea level rise component emulator."""
 
     def __init__(
             self, 
@@ -51,7 +27,7 @@ def __init__(
             expansion_patterns_dir: str|Path,
             fingerprint_dir: str|Path,
             gia_dir: str|Path,
-            components_dir: str|Path=None, 
+            components_path: str|Path=None, 
             component_list: list=None, 
             end_year: int=2301, 
             baseline_yrs: tuple=(1986, 2005),
@@ -61,9 +37,33 @@ def __init__(
             random_seed: int=None,
             sample_spatial: bool=False
         ):
+        """
+        Parameters
+        ----------
+        scenario: str
+            Name of the scenario.
+        expansion_patterns_dir: str
+            Direcotry path of regression patterns of thermal expansion component from cmip models.
+        fingerprint_dir: str
+            Direcotry path of GRD (Gravitational, Rotational, Deformational) fingerprint data
+        gia_dir: str
+            Direcotry path of GIA (Glacial Isostatic Adjustment) data
+        components_path: str
+            Path to global sea-level rise components (output of ProFSea's gmslr module)
+        component_list: list
+            Namelist of components for spatial projections
+        end_year: int
+            End year of the projections.
+        output_percentiles: int
+            List of percentiles for output
+        output_dir: str
+            Path to output directory for saving spatial projections.
+        random_seed: bool
+            Seed for numpy.random.
+        """
 
         # Start off with some error handling
-        if not components_dir:
+        if not components_path:
             raise ValueError(
                 "Provide an input directory")
 
@@ -71,10 +71,15 @@ def __init__(
             raise ValueError(
                 "Provide namelist of global projection components. "
                 "Currently allowed components are expansion, antdyn, "
-                "antsmb, wais, eais, glacier, greenland and landwater")
+                "antsmb, wais, eais, aispen, glacier, greenland and, "
+                "landwater. antdyn and antsmb are the AR5-related dynamic "
+                "and SMB contributions to Antarctic sea-level rise. wais, "
+                "eais and aispen are the ISMIP6-related contributions to "
+                "sea-level rise from the WAIS, EAIS and AIS Peninsula regions "
+                "respectively.")
 
         self.components = {}
-        component_path = Path(components_dir)
+        component_path = Path(components_path)
         ds_component = xr.load_dataset(component_path).sel(scenario=scenario)
         for comp in component_list:
             self.components[comp] = ds_component[comp].data
@@ -87,7 +92,7 @@ def __init__(
         self.expansion_patterns_dir = expansion_patterns_dir
         self.fingerprint_dir = fingerprint_dir
         self.gia_dir = gia_dir
-        self.components_dir = components_dir
+        self.components_path = components_path
         self.end_year = end_year
         self.baseline_yrs = baseline_yrs
         self.output_percentiles = output_percentiles

From 088ff76f889251398fadf354659bb0ae47a5aa7c Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Mon, 13 Apr 2026 15:51:09 +0100
Subject: [PATCH 108/276] Restructure

---
 .../aux_data/ISMIP_GIS_calibration.csv        |   0
 .../aux_data/aispen_params.nc                 | Bin
 .../aux_data/cumulative_cmip6_emissions.json  |   0
 .../aux_data/eais_params.nc                   | Bin
 .../ssp_global_landwater_projections.nc       | Bin
 .../aux_data/wais_params.nc                   | Bin
 profsea/components/core/base.py               |  10 ++
 profsea/components/core/global_model.py       | 119 ++++++++++++++++++
 profsea/components/core/state.py              |  15 +++
 .../global/antarctica}/antarctica.py          |   0
 .../{emulator => components/global}/gmslr.py  |   0
 profsea/{emulator => components}/plotting.py  |   0
 .../spatial}/spatial.py                       |   0
 profsea/emulator/__init__.py                  |   3 -
 14 files changed, 144 insertions(+), 3 deletions(-)
 rename profsea/{emulator => components}/aux_data/ISMIP_GIS_calibration.csv (100%)
 rename profsea/{emulator => components}/aux_data/aispen_params.nc (100%)
 rename profsea/{emulator => components}/aux_data/cumulative_cmip6_emissions.json (100%)
 rename profsea/{emulator => components}/aux_data/eais_params.nc (100%)
 rename profsea/{emulator => components}/aux_data/ssp_global_landwater_projections.nc (100%)
 rename profsea/{emulator => components}/aux_data/wais_params.nc (100%)
 create mode 100644 profsea/components/core/base.py
 create mode 100644 profsea/components/core/global_model.py
 create mode 100644 profsea/components/core/state.py
 rename profsea/{emulator => components/global/antarctica}/antarctica.py (100%)
 rename profsea/{emulator => components/global}/gmslr.py (100%)
 rename profsea/{emulator => components}/plotting.py (100%)
 rename profsea/{emulator => components/spatial}/spatial.py (100%)
 delete mode 100644 profsea/emulator/__init__.py

diff --git a/profsea/emulator/aux_data/ISMIP_GIS_calibration.csv b/profsea/components/aux_data/ISMIP_GIS_calibration.csv
similarity index 100%
rename from profsea/emulator/aux_data/ISMIP_GIS_calibration.csv
rename to profsea/components/aux_data/ISMIP_GIS_calibration.csv
diff --git a/profsea/emulator/aux_data/aispen_params.nc b/profsea/components/aux_data/aispen_params.nc
similarity index 100%
rename from profsea/emulator/aux_data/aispen_params.nc
rename to profsea/components/aux_data/aispen_params.nc
diff --git a/profsea/emulator/aux_data/cumulative_cmip6_emissions.json b/profsea/components/aux_data/cumulative_cmip6_emissions.json
similarity index 100%
rename from profsea/emulator/aux_data/cumulative_cmip6_emissions.json
rename to profsea/components/aux_data/cumulative_cmip6_emissions.json
diff --git a/profsea/emulator/aux_data/eais_params.nc b/profsea/components/aux_data/eais_params.nc
similarity index 100%
rename from profsea/emulator/aux_data/eais_params.nc
rename to profsea/components/aux_data/eais_params.nc
diff --git a/profsea/emulator/aux_data/ssp_global_landwater_projections.nc b/profsea/components/aux_data/ssp_global_landwater_projections.nc
similarity index 100%
rename from profsea/emulator/aux_data/ssp_global_landwater_projections.nc
rename to profsea/components/aux_data/ssp_global_landwater_projections.nc
diff --git a/profsea/emulator/aux_data/wais_params.nc b/profsea/components/aux_data/wais_params.nc
similarity index 100%
rename from profsea/emulator/aux_data/wais_params.nc
rename to profsea/components/aux_data/wais_params.nc
diff --git a/profsea/components/core/base.py b/profsea/components/core/base.py
new file mode 100644
index 0000000..3375e6c
--- /dev/null
+++ b/profsea/components/core/base.py
@@ -0,0 +1,10 @@
+from abc import ABC, abstractmethod
+import numpy as np
+
+from .state import ClimateState
+
+class Component(ABC):
+    @abstractmethod
+    def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
+        """Calculate and return the SLR projection for this component"""
+        pass
\ No newline at end of file
diff --git a/profsea/components/core/global_model.py b/profsea/components/core/global_model.py
new file mode 100644
index 0000000..d3d2984
--- /dev/null
+++ b/profsea/components/core/global_model.py
@@ -0,0 +1,119 @@
+import numpy as np
+from typing import Dict
+
+from .state import ClimateState
+from .base import SLRComponent
+
+class Global:
+    def __init__(
+        self,
+        components: Dict[str, SLRComponent],
+        end_yr: int,
+        seed: int = 42,
+        nt: int = 100,
+        nm: int = 1000,
+    ):
+        self.components = components
+        self.end_yr = end_yr
+        self.nt = nt
+        self.nm = nm
+        
+        self.seed_seq = np.random.SeedSequence(seed)
+        self.rng = np.random.default_rng(self.seed_seq)
+        
+        # Independent RNGs for each provided component
+        child_seeds = self.seed_seq.spawn(len(self.components))
+        self.component_rngs = {
+            name: np.random.default_rng(s) 
+            for name, s in zip(self.components.keys(), child_seeds)
+        }
+
+    def run(self, scenario: str, T_change: np.ndarray, OHC_change: np.ndarray):
+        T_ens, therm_ens, T_int_ens, T_int_med = self._calculate_drivers(T_change, OHC_change)
+        
+        # Shared correlation arrays
+        fraction = self.rng.random(self.nm * self.nt)
+        
+        # Add context to shared state
+        state = ClimateState(
+            scenario=scenario, T_ens=T_ens, T_int_ens=T_int_ens, 
+            T_int_med=T_int_med, therm_ens=therm_ens, fraction=fraction,
+            nyr=self.end_yr - 2006, nt=self.nt, nm=self.nm
+        )
+
+        # Project components!
+        results = {}
+        for name, comp in self.components.items():
+            comp_rng = self.component_rngs[name]
+            results[name] = comp.project(state, comp_rng)
+
+        # Calculate total GMSLR
+        results["gmslr"] = np.sum(list(results.values()), axis=0)
+        return results
+
+    def _calculate_drivers(self, T_change: np.ndarray, OHC_change: np.ndarray) -> tuple:
+        """Calculate the drivers of GMSLR: temperature change and
+        thermosteric sea level rise.
+
+        Returns
+        -------
+        T_ens: np.ndarray
+            Ensemble of temperature changes.
+        therm_ens: np.ndarray
+            Ensemble of thermosteric sea level rise.
+        T_int_ens: np.ndarray
+            Ensemble of time-integral temperature anomalies.
+        T_int_med: np.ndarray
+            Median of time-integral temperature anomalies.
+        """
+        # Sensitivity of thermosteric SLR to ocean heat content change
+        # From Turner et al. (2023)
+        exp_efficiency = (
+            self.rng.normal(loc=0.113, scale=0.013, size=self.nt)[:, None] * 1e-24
+        )  # m/YJ
+
+        if self.input_ensemble:
+            # Check if dimensions are the right way around
+            if T_change.shape[1] != self.nyr:
+                T_change = T_change.T
+            if OHC_change.shape[1] != self.nyr:
+                OHC_change = OHC_change.T
+
+            T_med = np.percentile(T_change, 50, axis=0)
+            T_std = np.std(T_change, axis=0)
+
+            therm_med = np.percentile(OHC_change, 50, axis=0) * exp_efficiency
+            therm_std = np.std(OHC_change * exp_efficiency, axis=0)
+
+        else:
+            if self.T_percentile_95 is not None:
+                T_med = T_change
+                therm_med = OHC_change * exp_efficiency
+
+                T_std = (self.T_percentile_95 - T_change) / 1.645
+                therm_std = (
+                    (self.OHC_percentile_95 - OHC_change) * exp_efficiency / 1.645
+                )
+
+            else:
+                raise ValueError(
+                    "If input_ensemble is False, and T_change and OHC_change "
+                    "are not 2D arrays, you must provide a 95th percentile "
+                    "timeseries for T_change and OHC_change. Add this using "
+                    "T_percentile_95 and OHC_percentile_95 keyword arguments."
+                )
+
+        # Time-integral of temperature anomaly
+        T_int_med = np.cumsum(T_med)
+        T_int_std = np.cumsum(T_std)
+
+        # Generate a sample of perfectly correlated timeseries fields of temperature,
+        # time-integral temperature and expansion, each of them [realisation,time]
+        z = self.rng.standard_normal(self.nt) * self.tcv
+
+        # For each quantity, mean + standard deviation * normal random number
+        # reshape to [realisation,time]
+        T_ens = z[:, np.newaxis] * T_std + T_med
+        therm_ens = z[:, np.newaxis] * therm_std + therm_med
+        T_int_ens = z[:, np.newaxis] * T_int_std + T_int_med
+        return T_ens, therm_ens, T_int_ens, T_int_med
\ No newline at end of file
diff --git a/profsea/components/core/state.py b/profsea/components/core/state.py
new file mode 100644
index 0000000..fc6785d
--- /dev/null
+++ b/profsea/components/core/state.py
@@ -0,0 +1,15 @@
+from dataclasses import dataclass
+import numpy as np
+
+@dataclass
+class ClimateState:
+    """ClimateState context object to hold relevant state information for SLR projections."""
+    scenario: str
+    T_ens: np.ndarray
+    T_int_ens: np.ndarray
+    T_int_med: np.ndarray
+    therm_ens: np.ndarray
+    fraction: np.ndarray # shared correlation array for AntSMB/AntDyn
+    nyr: int
+    nt: int
+    nm: int
diff --git a/profsea/emulator/antarctica.py b/profsea/components/global/antarctica/antarctica.py
similarity index 100%
rename from profsea/emulator/antarctica.py
rename to profsea/components/global/antarctica/antarctica.py
diff --git a/profsea/emulator/gmslr.py b/profsea/components/global/gmslr.py
similarity index 100%
rename from profsea/emulator/gmslr.py
rename to profsea/components/global/gmslr.py
diff --git a/profsea/emulator/plotting.py b/profsea/components/plotting.py
similarity index 100%
rename from profsea/emulator/plotting.py
rename to profsea/components/plotting.py
diff --git a/profsea/emulator/spatial.py b/profsea/components/spatial/spatial.py
similarity index 100%
rename from profsea/emulator/spatial.py
rename to profsea/components/spatial/spatial.py
diff --git a/profsea/emulator/__init__.py b/profsea/emulator/__init__.py
deleted file mode 100644
index 65c36f7..0000000
--- a/profsea/emulator/__init__.py
+++ /dev/null
@@ -1,3 +0,0 @@
-from .gmslr import Global
-from .spatial import Spatial
-from .antarctica import AntarcticaISMIP6
\ No newline at end of file

From 7c97dcb5eb93873bf1908f0f3f211b337bdec616 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Mon, 13 Apr 2026 16:12:25 +0100
Subject: [PATCH 109/276] Update core global model

---
 profsea/components/core/global_model.py | 113 ++++++++++++++++++------
 1 file changed, 87 insertions(+), 26 deletions(-)

diff --git a/profsea/components/core/global_model.py b/profsea/components/core/global_model.py
index d3d2984..16856e3 100644
--- a/profsea/components/core/global_model.py
+++ b/profsea/components/core/global_model.py
@@ -1,54 +1,115 @@
-import numpy as np
+import concurrent.futures
 from typing import Dict
 
+import numpy as np
+from rich.console import Console
+
 from .state import ClimateState
 from .base import SLRComponent
+from profsea.utils import sample_members_2D
+
+console = Console()
 
 class Global:
     def __init__(
         self,
         components: Dict[str, SLRComponent],
         end_yr: int,
-        seed: int = 42,
         nt: int = 100,
         nm: int = 1000,
+        tcv: float = 1.0,
+        parallel: bool = True,
+        input_ensemble: bool = True,
+        output_percentiles: list | np.ndarray = None,
+        random_sample: bool = False,
     ):
         self.components = components
         self.end_yr = end_yr
         self.nt = nt
         self.nm = nm
+        self.tcv = tcv
+        self.parallel = parallel
+        self.input_ensemble = input_ensemble
+        self.output_percentiles = output_percentiles
+        self.random_sample = random_sample
+
+        self.endofhistory = 2006
+        self.endofAR5 = 2100
+        self.nyr = self.end_yr - self.endofhistory
+
+    def run(
+        self, 
+        scenario: str, 
+        T_change: np.ndarray, 
+        OHC_change: np.ndarray, 
+        member_seed: int = 42
+    ) -> Dict[str, np.ndarray]:
+        """Run the emulator to project GMSLR components for a specific state."""
+        seed_seq = np.random.SeedSequence(member_seed)
+        run_rng = np.random.default_rng(seed_seq)
         
-        self.seed_seq = np.random.SeedSequence(seed)
-        self.rng = np.random.default_rng(self.seed_seq)
-        
-        # Independent RNGs for each provided component
-        child_seeds = self.seed_seq.spawn(len(self.components))
-        self.component_rngs = {
-            name: np.random.default_rng(s) 
-            for name, s in zip(self.components.keys(), child_seeds)
-        }
-
-    def run(self, scenario: str, T_change: np.ndarray, OHC_change: np.ndarray):
-        T_ens, therm_ens, T_int_ens, T_int_med = self._calculate_drivers(T_change, OHC_change)
+        T_ens, therm_ens, T_int_ens, T_int_med = self._calculate_drivers(
+            T_change, OHC_change, run_rng
+        )
         
-        # Shared correlation arrays
-        fraction = self.rng.random(self.nm * self.nt)
+        # Shared physical correlation state
+        fraction = run_rng.random(self.nm * self.nt)
         
-        # Add context to shared state
         state = ClimateState(
-            scenario=scenario, T_ens=T_ens, T_int_ens=T_int_ens, 
-            T_int_med=T_int_med, therm_ens=therm_ens, fraction=fraction,
-            nyr=self.end_yr - 2006, nt=self.nt, nm=self.nm
+            scenario=scenario,
+            T_ens=T_ens,
+            T_int_ens=T_int_ens,
+            T_int_med=T_int_med,
+            therm_ens=therm_ens,
+            fraction=fraction,
+            endofAR5=self.endofAR5,
+            endofhistory=self.endofhistory,
+            end_yr=self.end_yr,
+            nyr=self.nyr,
+            nt=self.nt,
+            nm=self.nm
         )
 
-        # Project components!
+        # Child RNGs for each component
+        child_seeds = seed_seq.spawn(len(self.components))
+        comp_rngs = {
+            name: np.random.default_rng(s) 
+            for name, s in zip(self.components.keys(), child_seeds)
+        }
+
         results = {}
-        for name, comp in self.components.items():
-            comp_rng = self.component_rngs[name]
-            results[name] = comp.project(state, comp_rng)
+        if self.parallel:
+            with concurrent.futures.ThreadPoolExecutor() as executor:
+                futures = {
+                    executor.submit(comp.project, state, comp_rngs[name]): name 
+                    for name, comp in self.components.items()
+                }
+                for future in concurrent.futures.as_completed(futures):
+                    comp_name = futures[future]
+                    try:
+                        results[comp_name] = future.result()
+                    except Exception as e:
+                        raise RuntimeError(f"Component '{comp_name}' failed.") from e
+        else:
+            for name, comp in self.components.items():
+                results[name] = comp.project(state, comp_rngs[name])
+
+        # Random Sampling
+        if self.random_sample:
+            random_idx = run_rng.integers(low=0, high=self.nm)
+            for comp_name, data in results.items():
+                if data.ndim > 1:
+                    results[comp_name] = data[random_idx][None, :]
 
-        # Calculate total GMSLR
+        # Sum GMSLR
         results["gmslr"] = np.sum(list(results.values()), axis=0)
+
+        # Output percentiles
+        if self.output_percentiles is not None:
+            console.log(f"Sampling {len(self.output_percentiles)} members per component...")
+            for comp_name, data in results.items():
+                results[comp_name] = sample_members_2D(data, self.output_percentiles)
+
         return results
 
     def _calculate_drivers(self, T_change: np.ndarray, OHC_change: np.ndarray) -> tuple:
@@ -116,4 +177,4 @@ def _calculate_drivers(self, T_change: np.ndarray, OHC_change: np.ndarray) -> tu
         T_ens = z[:, np.newaxis] * T_std + T_med
         therm_ens = z[:, np.newaxis] * therm_std + therm_med
         T_int_ens = z[:, np.newaxis] * T_int_std + T_int_med
-        return T_ens, therm_ens, T_int_ens, T_int_med
\ No newline at end of file
+        return T_ens, therm_ens, T_int_ens, T_int_med

From 77b478ef09386885a61c06bc30e0a4e47cacd686 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Mon, 13 Apr 2026 16:53:15 +0100
Subject: [PATCH 110/276] Add component + test_build.py

---
 profsea/components/core/global_model.py       | 51 +++++++++-
 profsea/components/core/state.py              |  5 +-
 .../antarctica => global_}/antarctica.py      |  0
 .../components/{global => global_}/gmslr.py   |  0
 profsea/components/global_/greenland.py       | 98 +++++++++++++++++++
 test_build.py                                 | 17 ++++
 6 files changed, 165 insertions(+), 6 deletions(-)
 rename profsea/components/{global/antarctica => global_}/antarctica.py (100%)
 rename profsea/components/{global => global_}/gmslr.py (100%)
 create mode 100644 profsea/components/global_/greenland.py
 create mode 100644 test_build.py

diff --git a/profsea/components/core/global_model.py b/profsea/components/core/global_model.py
index 16856e3..dfb99ca 100644
--- a/profsea/components/core/global_model.py
+++ b/profsea/components/core/global_model.py
@@ -5,7 +5,7 @@
 from rich.console import Console
 
 from .state import ClimateState
-from .base import SLRComponent
+from .base import Component
 from profsea.utils import sample_members_2D
 
 console = Console()
@@ -13,7 +13,7 @@
 class Global:
     def __init__(
         self,
-        components: Dict[str, SLRComponent],
+        components: Dict[str, Component],
         end_yr: int,
         nt: int = 100,
         nm: int = 1000,
@@ -36,6 +36,42 @@ def __init__(
         self.endofhistory = 2006
         self.endofAR5 = 2100
         self.nyr = self.end_yr - self.endofhistory
+        
+
+    def _check_shapes(
+        self, T_change: np.ndarray, OHC_change: np.ndarray, n_time: int
+    ) -> None:
+        """Check that the input arrays have the correct shape.
+
+        Parameters
+        ----------
+        T_change: np.ndarray
+            Array of surface temperature changes.
+        OHC_change: np.ndarray
+            Array of ocean heat content changes.
+        n_time: int
+            Expected number of time steps.
+
+        Returns
+        -------
+        None
+        """
+        if T_change.ndim == 1:
+            T_change = T_change[np.newaxis, :]
+        if OHC_change.ndim == 1:
+            OHC_change = OHC_change[np.newaxis, :]
+
+        if T_change.shape[1] != n_time:
+            # Split over lines for readability
+            raise ValueError(
+                f"T_change should have shape (realisation, time) with time \
+                dimension of length {n_time}. Got {T_change.shape}."
+            )
+        if OHC_change.shape[1] != n_time:
+            raise ValueError(
+                f"OHC_change should have shape (realisation, time) with time \
+                dimension of length {n_time}. Got {OHC_change.shape}."
+            )
 
     def run(
         self, 
@@ -47,6 +83,11 @@ def run(
         """Run the emulator to project GMSLR components for a specific state."""
         seed_seq = np.random.SeedSequence(member_seed)
         run_rng = np.random.default_rng(seed_seq)
+
+        self._check_shapes(T_change, OHC_change, self.nyr)
+
+        if self.input_ensemble:
+            self.nt = T_change.shape[0]
         
         T_ens, therm_ens, T_int_ens, T_int_med = self._calculate_drivers(
             T_change, OHC_change, run_rng
@@ -112,7 +153,7 @@ def run(
 
         return results
 
-    def _calculate_drivers(self, T_change: np.ndarray, OHC_change: np.ndarray) -> tuple:
+    def _calculate_drivers(self, T_change: np.ndarray, OHC_change: np.ndarray, rng: np.random.Generator) -> tuple:
         """Calculate the drivers of GMSLR: temperature change and
         thermosteric sea level rise.
 
@@ -130,7 +171,7 @@ def _calculate_drivers(self, T_change: np.ndarray, OHC_change: np.ndarray) -> tu
         # Sensitivity of thermosteric SLR to ocean heat content change
         # From Turner et al. (2023)
         exp_efficiency = (
-            self.rng.normal(loc=0.113, scale=0.013, size=self.nt)[:, None] * 1e-24
+            rng.normal(loc=0.113, scale=0.013, size=self.nt)[:, None] * 1e-24
         )  # m/YJ
 
         if self.input_ensemble:
@@ -170,7 +211,7 @@ def _calculate_drivers(self, T_change: np.ndarray, OHC_change: np.ndarray) -> tu
 
         # Generate a sample of perfectly correlated timeseries fields of temperature,
         # time-integral temperature and expansion, each of them [realisation,time]
-        z = self.rng.standard_normal(self.nt) * self.tcv
+        z = rng.standard_normal(self.nt) * self.tcv
 
         # For each quantity, mean + standard deviation * normal random number
         # reshape to [realisation,time]
diff --git a/profsea/components/core/state.py b/profsea/components/core/state.py
index fc6785d..63090c2 100644
--- a/profsea/components/core/state.py
+++ b/profsea/components/core/state.py
@@ -9,7 +9,10 @@ class ClimateState:
     T_int_ens: np.ndarray
     T_int_med: np.ndarray
     therm_ens: np.ndarray
-    fraction: np.ndarray # shared correlation array for AntSMB/AntDyn
+    fraction: np.ndarray  # shared correlation array
+    endofAR5: int
+    endofhistory: int
+    end_yr: int
     nyr: int
     nt: int
     nm: int
diff --git a/profsea/components/global/antarctica/antarctica.py b/profsea/components/global_/antarctica.py
similarity index 100%
rename from profsea/components/global/antarctica/antarctica.py
rename to profsea/components/global_/antarctica.py
diff --git a/profsea/components/global/gmslr.py b/profsea/components/global_/gmslr.py
similarity index 100%
rename from profsea/components/global/gmslr.py
rename to profsea/components/global_/gmslr.py
diff --git a/profsea/components/global_/greenland.py b/profsea/components/global_/greenland.py
new file mode 100644
index 0000000..aeaab43
--- /dev/null
+++ b/profsea/components/global_/greenland.py
@@ -0,0 +1,98 @@
+import functools
+
+from pathlib import Path
+import numpy as np
+import pandas as pd
+from scipy.stats import truncnorm
+
+from profsea.components.core.base import Component
+from profsea.components.core.global_model import ClimateState
+
+@functools.lru_cache(maxsize=1)
+def load_greenland_calibration():
+    """Loads the CSV once and keeps it in memory."""
+    # path = Path(__file__).parent / "aux_data" / "ISMIP_GIS_calibration.csv"
+    # Path is actually relative to the project root, not the component file
+    path = Path(__file__).parent.parent / "aux_data" / "ISMIP_GIS_calibration.csv"
+    return pd.read_csv(path)
+
+
+class GreenlandAR6(Component):
+    def __init__(self):
+        self.df = load_greenland_calibration()
+
+    def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
+        """Project Greenland ice-sheet contribution to GMSLR.
+        This follows the IPCC AR6 methodology as closely as possible.
+        Projections are relative to 1996-2014 baseline.
+
+        Returns
+        -------
+        np.ndarray
+            Total GIS contribution to GMSLR.
+        """
+        df = load_greenland_calibration()
+        b0 = df["b0"].values[None, :, None]
+        b1 = df["b1"].values[None, :, None]
+        b2 = df["b2"].values[None, :, None]
+        b3 = df["b3"].values[None, :, None]
+        b4 = df["b4"].values[None, :, None]
+        b5 = df["b5"].values[None, :, None]
+        sigma = df["sigma"].values
+        time_delta = np.arange(state.nyr)
+
+        # GIS trend values taken from FACTS GitHub repo
+        trend_mean = 0.19
+        trend_std = 0.1
+
+        # Calculate trend contribution distribution
+        trend = truncnorm.ppf(
+            rng.random(state.nm), a=0.0, b=99999.9, loc=trend_mean, scale=trend_std
+        )
+        trend = trend[:, None] * time_delta[None, :]
+        trend = trend[:, None, :]
+        trend /= 1e3  # convert mm to m SLE
+
+        # Calculate GIS contribution rate
+        dsle = (
+            b0
+            + (b1 * state.T_ens[:, None, :])
+            + (b2 * state.T_ens[:, None, :] ** 2)
+            + (b3 * state.T_ens[:, None, :] ** 3)
+            + (b4 * time_delta[None, None, :])
+            + (b5 * time_delta[None, None, :] ** 2)
+        )
+
+        # Now integrate
+        sle = np.cumsum(dsle, axis=2)  # mm SLE per K of global warming
+        sle = sle * 1e-3  # convert mm to m SLE
+
+        # Make a Monte Carlo ensemble of projections for each model in the calibration
+        sle_ens = np.zeros((state.nm, state.nt, state.nyr))
+        r_per_model = state.nm // sle.shape[1]
+        r_remainder = state.nm % sle.shape[1]
+
+        # We want to distribute the remainder evenly across the models
+        unc = rng.normal(scale=sigma)
+        current_ensemble_idx = 0
+        for i in range(sle.shape[1]):
+            num_reals_for_model_i = r_per_model + 1 if i < r_remainder else r_per_model
+            ifirst = current_ensemble_idx
+            ilast = current_ensemble_idx + num_reals_for_model_i
+            model_term = sle[:, i, :]  # Shape (nt, nyr)
+            uncertainty_term = (
+                model_term * unc[None, i, None]
+            )  # Shape (num_reals_for_model_i, nt, nyr_param)
+
+            sle_ens[ifirst:ilast, :, :] = model_term[None, :, :] + uncertainty_term
+            current_ensemble_idx = ilast
+
+        sle_ens += trend
+
+        # Persist 2100 rate of change
+        rate = np.diff(sle_ens, axis=2)[:, :, 94]
+        sle_ens[:, :, 95:] = sle_ens[:, :, 94:95] + (
+            rate[:, :, None] * time_delta[None, None, 1 : state.nyr - 94]
+        )
+        sle_ens = sle_ens.reshape((state.nm * state.nt, state.nyr))
+        return sle_ens
diff --git a/test_build.py b/test_build.py
new file mode 100644
index 0000000..7b8e07f
--- /dev/null
+++ b/test_build.py
@@ -0,0 +1,17 @@
+import numpy as np
+
+from profsea.components.core.global_model import Global
+from profsea.components.global_.greenland import GreenlandAR6
+
+
+slr_components = {
+    "greenland": GreenlandAR6(),
+}
+
+model = Global(
+    components=slr_components,
+    end_yr=2301
+)
+
+projections = model.run(scenario="test", T_change=np.arange(295).reshape(1, -1), OHC_change=np.arange(295).reshape(1, -1), member_seed=42)
+

From 1325395901b9184747f89c638d15af59b17a6bad Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Mon, 20 Apr 2026 20:47:07 +0100
Subject: [PATCH 111/276] Add fix

---
 profsea/emulator/antarctica.py | 60 ++++++++++++++++++++++++----------
 profsea/emulator/gmslr.py      | 30 ++++++++++++-----
 2 files changed, 63 insertions(+), 27 deletions(-)

diff --git a/profsea/emulator/antarctica.py b/profsea/emulator/antarctica.py
index 16dcb63..a9b88bd 100644
--- a/profsea/emulator/antarctica.py
+++ b/profsea/emulator/antarctica.py
@@ -56,7 +56,7 @@ def _impulse_response_term(
         np.ndarray
             Slow response term for each sample (shape: n_samples x n_time).
         """
-        n_time = tas.shape[0]
+        _, n_time = tas.shape
 
         # Two slow coeffs
         alphas1 = params[:, 0]
@@ -67,20 +67,29 @@ def _impulse_response_term(
 
         # Impulse response function for timescale 1
         decay_factors1 = np.exp(-np.arange(n_time) * dt / tau1) * (dt / tau1)
-        rate_delayed1 = fftconvolve(forcing_base, decay_factors1, mode="full")[:n_time]
-        t_conv1 = np.cumsum(rate_delayed1, axis=0) * dt
-        term_slow1 = alphas1[:, None] * t_conv1[None, :]
+        rate_delayed1 = fftconvolve(
+            forcing_base, decay_factors1[None, :], mode="full", axes=1
+        )[:, :n_time]
+        t_conv1 = np.cumsum(rate_delayed1, axis=1) * dt
+        term_slow1 = alphas1[:, None, None] * t_conv1[None, :, :]
 
         # Impulse response function for timescale 2
         decay_factors2 = np.exp(-np.arange(n_time) * dt / tau2) * (dt / tau2)
-        rate_delayed2 = fftconvolve(forcing_base, decay_factors2, mode="full")[:n_time]
-        t_conv2 = np.cumsum(rate_delayed2, axis=0) * dt
-        term_slow2 = alphas2[:, None] * t_conv2[None, :]
+        rate_delayed2 = fftconvolve(
+            forcing_base, decay_factors2[None, :], mode="full", axes=1
+        )[:, :n_time]
+        t_conv2 = np.cumsum(rate_delayed2, axis=1) * dt
+        term_slow2 = alphas2[:, None, None] * t_conv2[None, :, :]
 
         return term_slow1 + term_slow2  # Slow terms are linearly combined
 
     def predict(
-        self, tas: np.ndarray, dt: float = 1.0, display_progress=True, seed=None
+        self,
+        tas: np.ndarray,
+        dt: float = 1.0,
+        display_progress=True,
+        seed=None,
+        model_idx=None,
     ) -> np.ndarray:
         """
         Projects AIS response using empirical additive bootstrapping.
@@ -99,25 +108,33 @@ def predict(
         np.ndarray
             Projected AIS contributions (shape: n_models x n_samples x n_time).
         """
-        tas = np.squeeze(tas)
-        n_time = len(tas)
+        # Ensure array is 2D
+        if tas.ndim == 1:
+            tas = tas[None, :]
+
+        nt, n_time = tas.shape
         physical_time = np.arange(n_time) * dt
 
         # RNG
         rng = np.random.default_rng(seed)
 
         # Integrated temperature term
-        tas_int = np.cumsum(tas) * dt
-
-        # Output shape: (n_models, n_samples, n_time)
-        all_preds = np.zeros((self.n_models, self.n_samples, n_time))
+        tas_int = np.cumsum(tas, axis=1) * dt
 
         # Shape assumed: (n_models, n_training_scenarios, 4)
         all_residuals = self.param_ds.param_residuals.values
         n_train_scenarios = all_residuals.shape[1]
 
+        if model_idx is not None:
+            # Run a specific model
+            models_to_run = [model_idx]
+        else:
+            models_to_run = range(self.n_models)
+
+        all_preds = np.zeros((len(models_to_run), self.n_samples, nt, n_time))
+
         for model in track(
-            range(self.n_models),
+            models_to_run,
             description="Projecting AIS response... ",
             disable=not display_progress,
         ):
@@ -143,12 +160,19 @@ def predict(
 
             # Fast response term
             betas = total_params[:, 2]
-            term_fast = betas[:, None] * tas_int[None, :]
+            term_fast = betas[:, None, None] * tas_int[None, :, :]
 
             # Drift term
             drift_coeffs = total_params[:, 3]
-            term_drift = drift_coeffs[:, None] * physical_time[None, :]
+            term_drift = drift_coeffs[:, None, None] * physical_time[None, None, :]
+
+            if model_idx is not None:
+                all_preds[0, :, :, :] = term_fast + term_slow + term_drift
+            else:
+                all_preds[model, :, :, :] = term_fast + term_slow + term_drift
 
-            all_preds[model, :, :] = term_fast + term_slow + term_drift
+        all_preds = (
+            all_preds.squeeze()
+        )  # Remove extra dimension if only one model is run
 
         return all_preds
diff --git a/profsea/emulator/gmslr.py b/profsea/emulator/gmslr.py
index 106e380..5a07f92 100644
--- a/profsea/emulator/gmslr.py
+++ b/profsea/emulator/gmslr.py
@@ -304,6 +304,7 @@ def check_shapes(
                 f"OHC_change should have shape (realisation, time) with time \
                 dimension of length {n_time}. Got {OHC_change.shape}."
             )
+        return T_change, OHC_change
 
     def project(
         self,
@@ -319,7 +320,9 @@ def project(
         self.cum_emissions_total = cum_emissions_total
 
         # Check input shapes are correct
-        self.check_shapes(self.T_change, self.OHC_change, self.nyr)
+        self.T_change, self.OHC_change = self.check_shapes(
+            self.T_change, self.OHC_change, self.nyr
+        )
 
         if self.input_ensemble:
             self.nt = self.T_change.shape[0]
@@ -529,16 +532,25 @@ def calculate_drivers(self) -> tuple:
         return T_ens, therm_ens, T_int_ens, T_int_med
 
     def project_antarctica_ismip6(self, T_ens: np.ndarray, rng) -> np.ndarray:
-        wais_raw = self.wais_model.predict(T_ens.squeeze(), display_progress=False)
-        eais_raw = self.eais_model.predict(T_ens.squeeze(), display_progress=False)
-        aispen_raw = self.aispen_model.predict(T_ens.squeeze(), display_progress=False)
-
         random_ais_idx = rng.integers(low=0, high=43)
 
-        # Match the correct output shape
-        self.wais = np.repeat(wais_raw[random_ais_idx, :, :], self.nt, axis=0)
-        self.eais = np.repeat(eais_raw[random_ais_idx, :, :], self.nt, axis=0)
-        self.aispen = np.repeat(aispen_raw[random_ais_idx, :, :], self.nt, axis=0)
+        wais_raw = self.wais_model.predict(
+            T_ens, model_idx=random_ais_idx, display_progress=False
+        )
+        eais_raw = self.eais_model.predict(
+            T_ens, model_idx=random_ais_idx, display_progress=False
+        )
+        aispen_raw = self.aispen_model.predict(
+            T_ens, model_idx=random_ais_idx, display_progress=False
+        )
+
+        # Flatten the sample and ensemble axes to match (nm * nt, nyr)
+        target_shape = (self.nm * self.nt, self.nyr)
+
+        self.wais = wais_raw.reshape(target_shape)
+        self.eais = eais_raw.reshape(target_shape)
+        self.aispen = aispen_raw.reshape(target_shape)
+
         return self.wais + self.eais + self.aispen
 
     def project_glacier(

From 111c2a3f4fc9f1a0cb03110098b67c1702251f97 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Mon, 20 Apr 2026 21:06:30 +0100
Subject: [PATCH 112/276] Add review changes

---
 profsea/components/core/global_model.py | 52 ++++++++++++-------------
 test_build.py                           |  2 +-
 2 files changed, 27 insertions(+), 27 deletions(-)

diff --git a/profsea/components/core/global_model.py b/profsea/components/core/global_model.py
index dfb99ca..4521114 100644
--- a/profsea/components/core/global_model.py
+++ b/profsea/components/core/global_model.py
@@ -10,6 +10,7 @@
 
 console = Console()
 
+
 class Global:
     def __init__(
         self,
@@ -36,7 +37,6 @@ def __init__(
         self.endofhistory = 2006
         self.endofAR5 = 2100
         self.nyr = self.end_yr - self.endofhistory
-        
 
     def _check_shapes(
         self, T_change: np.ndarray, OHC_change: np.ndarray, n_time: int
@@ -74,11 +74,11 @@ def _check_shapes(
             )
 
     def run(
-        self, 
-        scenario: str, 
-        T_change: np.ndarray, 
-        OHC_change: np.ndarray, 
-        member_seed: int = 42
+        self,
+        scenario: str,
+        T_change: np.ndarray,
+        OHC_change: np.ndarray,
+        member_seed: int = 42,
     ) -> Dict[str, np.ndarray]:
         """Run the emulator to project GMSLR components for a specific state."""
         seed_seq = np.random.SeedSequence(member_seed)
@@ -88,14 +88,14 @@ def run(
 
         if self.input_ensemble:
             self.nt = T_change.shape[0]
-        
+
         T_ens, therm_ens, T_int_ens, T_int_med = self._calculate_drivers(
             T_change, OHC_change, run_rng
         )
-        
+
         # Shared physical correlation state
         fraction = run_rng.random(self.nm * self.nt)
-        
+
         state = ClimateState(
             scenario=scenario,
             T_ens=T_ens,
@@ -108,13 +108,13 @@ def run(
             end_yr=self.end_yr,
             nyr=self.nyr,
             nt=self.nt,
-            nm=self.nm
+            nm=self.nm,
         )
 
         # Child RNGs for each component
         child_seeds = seed_seq.spawn(len(self.components))
         comp_rngs = {
-            name: np.random.default_rng(s) 
+            name: np.random.default_rng(s)
             for name, s in zip(self.components.keys(), child_seeds)
         }
 
@@ -122,7 +122,7 @@ def run(
         if self.parallel:
             with concurrent.futures.ThreadPoolExecutor() as executor:
                 futures = {
-                    executor.submit(comp.project, state, comp_rngs[name]): name 
+                    executor.submit(comp.project, state, comp_rngs[name]): name
                     for name, comp in self.components.items()
                 }
                 for future in concurrent.futures.as_completed(futures):
@@ -142,18 +142,24 @@ def run(
                 if data.ndim > 1:
                     results[comp_name] = data[random_idx][None, :]
 
-        # Sum GMSLR
-        results["gmslr"] = np.sum(list(results.values()), axis=0)
-
         # Output percentiles
         if self.output_percentiles is not None:
-            console.log(f"Sampling {len(self.output_percentiles)} members per component...")
+            console.log(
+                f"Sampling {len(self.output_percentiles)} members per component..."
+            )
             for comp_name, data in results.items():
                 results[comp_name] = sample_members_2D(data, self.output_percentiles)
 
         return results
 
-    def _calculate_drivers(self, T_change: np.ndarray, OHC_change: np.ndarray, rng: np.random.Generator) -> tuple:
+    def sum_components(self, components: Dict[str, np.ndarray]) -> np.ndarray:
+        """Sum the components to get total GMSLR."""
+        components["gmslr"] = np.sum(list(components.values()), axis=0)
+        return components["gmslr"]
+
+    def _calculate_drivers(
+        self, T_change: np.ndarray, OHC_change: np.ndarray, rng: np.random.Generator
+    ) -> tuple:
         """Calculate the drivers of GMSLR: temperature change and
         thermosteric sea level rise.
 
@@ -175,17 +181,11 @@ def _calculate_drivers(self, T_change: np.ndarray, OHC_change: np.ndarray, rng:
         )  # m/YJ
 
         if self.input_ensemble:
-            # Check if dimensions are the right way around
-            if T_change.shape[1] != self.nyr:
-                T_change = T_change.T
-            if OHC_change.shape[1] != self.nyr:
-                OHC_change = OHC_change.T
-
-            T_med = np.percentile(T_change, 50, axis=0)
+            T_med = sample_members_2D(T_change, [50])
             T_std = np.std(T_change, axis=0)
 
-            therm_med = np.percentile(OHC_change, 50, axis=0) * exp_efficiency
-            therm_std = np.std(OHC_change * exp_efficiency, axis=0)
+            therm_med = sample_members_2D(OHC_change, [50]) * exp_efficiency
+            therm_std = np.std(OHC_change, axis=0) * exp_efficiency
 
         else:
             if self.T_percentile_95 is not None:
diff --git a/test_build.py b/test_build.py
index 7b8e07f..e6b9187 100644
--- a/test_build.py
+++ b/test_build.py
@@ -14,4 +14,4 @@
 )
 
 projections = model.run(scenario="test", T_change=np.arange(295).reshape(1, -1), OHC_change=np.arange(295).reshape(1, -1), member_seed=42)
-
+gmslr = model.sum_components(projections)

From 7425cd88dff1ee267208a37d38e1a56fd199bc33 Mon Sep 17 00:00:00 2001
From: Hemant Khatri 
Date: Tue, 21 Apr 2026 09:52:59 +0100
Subject: [PATCH 113/276] landwater AR6 compoenent

---
 .gitignore                              |  1 +
 profsea/components/global_/greenland.py |  2 +-
 profsea/components/global_/landwater.py | 54 +++++++++++++++++++++++++
 3 files changed, 56 insertions(+), 1 deletion(-)
 create mode 100644 profsea/components/global_/landwater.py

diff --git a/.gitignore b/.gitignore
index 8904e39..38fdc3d 100644
--- a/.gitignore
+++ b/.gitignore
@@ -3,3 +3,4 @@
 profsea.egg-info
 data/
 *.DS_Store
+.ipynb_checkpoints/
diff --git a/profsea/components/global_/greenland.py b/profsea/components/global_/greenland.py
index aeaab43..a1b4bec 100644
--- a/profsea/components/global_/greenland.py
+++ b/profsea/components/global_/greenland.py
@@ -31,7 +31,7 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
         np.ndarray
             Total GIS contribution to GMSLR.
         """
-        df = load_greenland_calibration()
+        df = self.df
         b0 = df["b0"].values[None, :, None]
         b1 = df["b1"].values[None, :, None]
         b2 = df["b2"].values[None, :, None]
diff --git a/profsea/components/global_/landwater.py b/profsea/components/global_/landwater.py
new file mode 100644
index 0000000..0378ec1
--- /dev/null
+++ b/profsea/components/global_/landwater.py
@@ -0,0 +1,54 @@
+import functools
+
+from pathlib import Path
+import numpy as np
+import pandas as pd
+import xarray as xr
+from scipy.stats import truncnorm
+
+from profsea.components.core.base import Component
+from profsea.components.core.global_model import ClimateState
+
+@functools.lru_cache(maxsize=1)
+def load_landwater_projection():
+    """Loads the NetCDF once and keeps the VALUES in memory."""
+    path = Path(__file__).parent.parent/ "aux_data" / "ssp_global_landwater_projections.nc"
+    with xr.open_dataset(path) as ds:
+        ds.load()
+    return ds
+
+class LandwaterAR6(Component):
+    def __init__(self):
+        self.lw_ds = load_landwater_projection()
+
+    def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
+        """Project land water storage contribution to GMSLR.
+        This follows the IPCC AR6 methodology as closely as possible.
+        Projections are relative to 1996-2014 baseline.
+
+        Returns
+        -------
+        np.ndarray
+            Land water storage contribution to GMSLR.
+        """
+
+        lw_ds = self.lw_ds
+
+        # Interpolate to annual projections
+        interp_ds = lw_ds.interp(
+            years=np.arange(2005, state.end_yr, 1), method="linear"
+        ).squeeze()
+        lw = interp_ds["sea_level_change"].values * 1e-3  # mm to m
+
+        del interp_ds
+
+        # Make a Monte Carlo ensemble of projections
+        full_repeats = (state.nt * state.nm) // lw.shape[0]
+        remainder = (state.nt * state.nm) % lw.shape[0]
+        lw = np.vstack([np.tile(lw, (full_repeats, 1)), lw[:remainder]])
+        lw = lw.reshape(state.nt * state.nm, lw.shape[1])
+        lw = lw[:, 1:]  # Start at 2006, end at end_yr
+        
+        return lw
+
+    

From eff4c7a795ba16ebe4db334787d91798ca505726 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Tue, 21 Apr 2026 10:01:41 +0100
Subject: [PATCH 114/276] Fix landwater end year

---
 profsea/emulator/gmslr.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/profsea/emulator/gmslr.py b/profsea/emulator/gmslr.py
index 5a07f92..543be14 100644
--- a/profsea/emulator/gmslr.py
+++ b/profsea/emulator/gmslr.py
@@ -362,6 +362,7 @@ def project(
             raise RuntimeError(
                 "No active components were evaluated. Cannot compute GMSLR."
             )
+
         self.gmslr = np.sum(components_to_sum, axis=0)
 
         # Output percentiles
@@ -958,12 +959,14 @@ def project_landwater_ar6(self) -> np.ndarray:
 
         del interp_ds
 
-        # Go from shape (20000, 294) to (101000, 294)
+        # Go from shape (20000, 296) to (101000, 296)
         full_repeats = (self.nt * self.nm) // lw.shape[0]
         remainder = (self.nt * self.nm) % lw.shape[0]
         lw = np.vstack([np.tile(lw, (full_repeats, 1)), lw[:remainder]])
         lw = lw.reshape(self.nt * self.nm, lw.shape[1])
-        lw = lw[:, 1:]  # Start at 2006, end at 2299
+
+        lw = lw[:, 1:self.nyr+1]  # Start at 2006, end at final year
+
         return lw
 
     def project_landwater_ar5(self, rng: np.random.Generator) -> np.ndarray:

From 6408238fd9bf7dfcc2d6caf6bcea9562c5a4960c Mon Sep 17 00:00:00 2001
From: Hemant Khatri 
Date: Tue, 21 Apr 2026 10:49:46 +0100
Subject: [PATCH 115/276] fix landwater time end

---
 profsea/components/global_/greenland.py | 10 ++++++----
 profsea/components/global_/landwater.py |  4 ++--
 2 files changed, 8 insertions(+), 6 deletions(-)

diff --git a/profsea/components/global_/greenland.py b/profsea/components/global_/greenland.py
index a1b4bec..a2e2498 100644
--- a/profsea/components/global_/greenland.py
+++ b/profsea/components/global_/greenland.py
@@ -90,9 +90,11 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
         sle_ens += trend
 
         # Persist 2100 rate of change
-        rate = np.diff(sle_ens, axis=2)[:, :, 94]
-        sle_ens[:, :, 95:] = sle_ens[:, :, 94:95] + (
-            rate[:, :, None] * time_delta[None, None, 1 : state.nyr - 94]
-        )
+        if(state.end_yr >= 2100):
+            rate = np.diff(sle_ens, axis=2)[:, :, 94]
+            sle_ens[:, :, 95:] = sle_ens[:, :, 94:95] + (
+                rate[:, :, None] * time_delta[None, None, 1 : state.nyr - 94]
+            )
+        
         sle_ens = sle_ens.reshape((state.nm * state.nt, state.nyr))
         return sle_ens
diff --git a/profsea/components/global_/landwater.py b/profsea/components/global_/landwater.py
index 0378ec1..996a274 100644
--- a/profsea/components/global_/landwater.py
+++ b/profsea/components/global_/landwater.py
@@ -36,7 +36,7 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
 
         # Interpolate to annual projections
         interp_ds = lw_ds.interp(
-            years=np.arange(2005, state.end_yr, 1), method="linear"
+            years=np.arange(2005, state.end_yr+1, 1), method="linear"
         ).squeeze()
         lw = interp_ds["sea_level_change"].values * 1e-3  # mm to m
 
@@ -47,7 +47,7 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
         remainder = (state.nt * state.nm) % lw.shape[0]
         lw = np.vstack([np.tile(lw, (full_repeats, 1)), lw[:remainder]])
         lw = lw.reshape(state.nt * state.nm, lw.shape[1])
-        lw = lw[:, 1:]  # Start at 2006, end at end_yr
+        lw = lw[:, 1:state.nyr+1]  # Start at 2006, end at end_yr
         
         return lw
 

From 762feccb3a4bbf7dd7ddbffa00652f8f4c5c67ab Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Tue, 21 Apr 2026 10:52:33 +0100
Subject: [PATCH 116/276] Add AR5 dyn and SMB AIS comps

---
 profsea/components/core/global_model.py  |   3 +
 profsea/components/core/state.py         |   1 +
 profsea/components/global_/antarctica.py | 121 +++++++++++++++++++++++
 test_build.py                            |   9 +-
 4 files changed, 133 insertions(+), 1 deletion(-)

diff --git a/profsea/components/core/global_model.py b/profsea/components/core/global_model.py
index 4521114..53014f4 100644
--- a/profsea/components/core/global_model.py
+++ b/profsea/components/core/global_model.py
@@ -22,6 +22,7 @@ def __init__(
         parallel: bool = True,
         input_ensemble: bool = True,
         output_percentiles: list | np.ndarray = None,
+        palmer_method: bool = True,
         random_sample: bool = False,
     ):
         self.components = components
@@ -32,6 +33,7 @@ def __init__(
         self.parallel = parallel
         self.input_ensemble = input_ensemble
         self.output_percentiles = output_percentiles
+        self.palmer_method = palmer_method
         self.random_sample = random_sample
 
         self.endofhistory = 2006
@@ -103,6 +105,7 @@ def run(
             T_int_med=T_int_med,
             therm_ens=therm_ens,
             fraction=fraction,
+            palmer_method=self.palmer_method,
             endofAR5=self.endofAR5,
             endofhistory=self.endofhistory,
             end_yr=self.end_yr,
diff --git a/profsea/components/core/state.py b/profsea/components/core/state.py
index 63090c2..09c0589 100644
--- a/profsea/components/core/state.py
+++ b/profsea/components/core/state.py
@@ -10,6 +10,7 @@ class ClimateState:
     T_int_med: np.ndarray
     therm_ens: np.ndarray
     fraction: np.ndarray  # shared correlation array
+    palmer_method: bool # whether to use Palmer method for time projection
     endofAR5: int
     endofhistory: int
     end_yr: int
diff --git a/profsea/components/global_/antarctica.py b/profsea/components/global_/antarctica.py
index 16dcb63..d26d7f2 100644
--- a/profsea/components/global_/antarctica.py
+++ b/profsea/components/global_/antarctica.py
@@ -2,8 +2,13 @@
 from rich.progress import track
 import numpy as np
 from scipy.signal import fftconvolve
+from scipy.stats import norm
 import xarray as xr
 
+from profsea.components.core.base import Component
+from profsea.components.core.global_model import ClimateState
+from profsea.components.core.time_projection import time_projection
+
 
 class AntarcticaISMIP6:
     """
@@ -152,3 +157,119 @@ def predict(
             all_preds[model, :, :] = term_fast + term_slow + term_drift
 
         return all_preds
+
+
+class AntarcticaDynAR5(Component):
+    """
+    AR5 Antarctic ice-dynamics response to warming, as a function of
+    cumulative emissions or scenario. Following the implementation
+    as given by Jonathan Gregory's ar5gmslr (https://github.com/JonathanGregory/ar5gmslr).
+
+    Requires cumulative emissions total to be specified for a given scenario,
+    or will default to scenario-based regression, based on rcp scenarios.
+    """
+
+    def __init__(
+        self, d_ant: float = (2.37 + 0.13) * 1e-3, cum_emissions_total: float = None
+    ):
+        self.d_ant = d_ant
+        self.cum_emissions_total = cum_emissions_total
+
+    def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
+        """Project Antarctic rapid ice-sheet dynamics contribution to GMSLR.
+
+        Parameters
+            ----------
+            fraction: np.ndarray
+            Random numbers for the dynamic contribution.
+
+        Returns
+        -------
+        np.ndarray
+            Antarctic rapid ice-sheet dynamics contribution to GMSLR.
+        """
+        # This is a naive solution to calculating the AntDyn contribution
+        # for any given scenario. Basically linear regressions through existing data
+        # to find rough relationship between cumulative emissions and AntDyn contribution.
+        if self.cum_emissions_total:
+            upper = (0.000110 * self.cum_emissions_total) + 0.375  # in metres
+            lower = (1.363e-05 * self.cum_emissions_total) + 0.0392  # in metres
+            final = [lower, upper]
+        else:
+            lcoeff = dict(
+                rcp26=[-2.881, 0.923, 0.000],
+                rcp45=[-2.676, 0.850, 0.000],
+                rcp60=[-2.660, 0.870, 0.000],
+                rcp85=[-2.399, 0.860, 0.000],
+            )
+            lcoeff = lcoeff[state.scenario]
+
+            ascale = norm.ppf(state.fraction)
+            final = np.exp(lcoeff[2] * ascale**2 + lcoeff[1] * ascale + lcoeff[0])
+            final = final.reshape(state.nm, state.nt)
+        return (
+            time_projection(state, 0.41, 0.20, final, rng, fraction=state.fraction)
+            + self.d_ant
+        )
+
+
+class AntarcticaSMBAR5(Component):
+    """
+    AR5 Antarctic SMB contribution to GMSLR, as a function of global
+    mean surface temperature change.
+
+    Following the implementation as given by Jonathan Gregory's ar5gmslr
+    (https://github.com/JonathanGregory/ar5gmslr).
+    """
+
+    def __init__(self):
+        # Conversion factor for Gt to m SLE
+        self.mSLEoGt = 1e12 / 3.61e14 * 1e-3
+
+    def project(
+        self,
+        state: ClimateState,
+        rng: np.random.Generator,
+    ) -> np.ndarray:
+        """Project Antarctic SMB contribution to GMSLR.
+
+        Parameters
+        ----------
+        T_int_ens: np.ndarray
+            Ensemble of time-integral temperature anomaly timeseries.
+        fraction: np.ndarray
+            Random numbers for the SMB-dynamic feedback.
+
+        Returns
+        -------
+        antsmb: np.ndarray
+            Antarctic SMB contribution to GMSLR.
+        """
+        # The following are [mean,SD]
+        pcoK = [5.1, 1.5]  # % change in Ant SMB per K of warming from G&H06
+        KoKg = [1.1, 0.2]  # ratio of Antarctic warming to global warming from G&H06
+
+        # Generate a distribution of products of the above two factors
+        pcoKg = (pcoK[0] + rng.standard_normal([state.nm, state.nt]) * pcoK[1]) * (
+            KoKg[0] + rng.standard_normal([state.nm, state.nt]) * KoKg[1]
+        )
+        meansmb = 1923  # model-mean time-mean 1979-2010 Gt yr-1 from 13.3.3.2
+        moaoKg = (
+            -pcoKg * 1e-2 * meansmb * self.mSLEoGt
+        )  # m yr-1 of SLE per K of global warming
+
+        if state.fraction is None:
+            fraction = rng.random((state.nm, state.nt))
+        elif state.fraction.size != state.nm * state.nt:
+            raise ValueError("fraction is the wrong size")
+        else:
+            fraction = state.fraction.reshape((state.nm, state.nt))
+
+        smax = 0.35  # max value of S in 13.SM.1.5
+        ainterfactor = 1 - fraction * smax
+
+        z = moaoKg * ainterfactor
+        z = z[:, :, np.newaxis]
+        antsmb = z * state.T_int_ens
+        antsmb = antsmb.reshape(antsmb.shape[0] * antsmb.shape[1], antsmb.shape[2])
+        return antsmb
diff --git a/test_build.py b/test_build.py
index e6b9187..ea800da 100644
--- a/test_build.py
+++ b/test_build.py
@@ -1,11 +1,15 @@
 import numpy as np
+import matplotlib.pyplot as plt
 
 from profsea.components.core.global_model import Global
 from profsea.components.global_.greenland import GreenlandAR6
+from profsea.components.global_.antarctica import AntarcticaDynAR5, AntarcticaSMBAR5
 
 
 slr_components = {
     "greenland": GreenlandAR6(),
+    "antdyn": AntarcticaDynAR5(cum_emissions_total=1000),
+    "antsmb": AntarcticaSMBAR5(),
 }
 
 model = Global(
@@ -13,5 +17,8 @@
     end_yr=2301
 )
 
-projections = model.run(scenario="test", T_change=np.arange(295).reshape(1, -1), OHC_change=np.arange(295).reshape(1, -1), member_seed=42)
+projections = model.run(scenario="test", T_change=np.linspace(0, 5, 295).reshape(1, -1), OHC_change=np.linspace(0, 5, 295).reshape(1, -1)*1e24, member_seed=42)
 gmslr = model.sum_components(projections)
+
+plt.plot(np.arange(2006, 2301), gmslr[0], label="GMSLR")
+plt.show()
\ No newline at end of file

From 6e71bd1fc333bfcf3e7515460ec0f8d0aae286da Mon Sep 17 00:00:00 2001
From: Hemant Khatri 
Date: Tue, 21 Apr 2026 11:00:25 +0100
Subject: [PATCH 117/276] change to 2301 in interpolation

---
 profsea/components/global_/landwater.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/profsea/components/global_/landwater.py b/profsea/components/global_/landwater.py
index 996a274..c158ea2 100644
--- a/profsea/components/global_/landwater.py
+++ b/profsea/components/global_/landwater.py
@@ -36,7 +36,7 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
 
         # Interpolate to annual projections
         interp_ds = lw_ds.interp(
-            years=np.arange(2005, state.end_yr+1, 1), method="linear"
+            years=np.arange(2005, 2301, 1), method="linear"
         ).squeeze()
         lw = interp_ds["sea_level_change"].values * 1e-3  # mm to m
 

From 3205e1f60f708545ac5c24233e9b5b0ce3c7cfa9 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Tue, 21 Apr 2026 11:20:38 +0100
Subject: [PATCH 118/276] Add Global model save method back in

---
 profsea/components/core/global_model.py | 40 +++++++++++++++++++++++++
 test_build.py                           |  1 +
 2 files changed, 41 insertions(+)

diff --git a/profsea/components/core/global_model.py b/profsea/components/core/global_model.py
index 4521114..6c5d4f4 100644
--- a/profsea/components/core/global_model.py
+++ b/profsea/components/core/global_model.py
@@ -1,8 +1,11 @@
 import concurrent.futures
+from pathlib import Path
+import os
 from typing import Dict
 
 import numpy as np
 from rich.console import Console
+import xarray as xr
 
 from .state import ClimateState
 from .base import Component
@@ -150,6 +153,8 @@ def run(
             for comp_name, data in results.items():
                 results[comp_name] = sample_members_2D(data, self.output_percentiles)
 
+        self.results = results
+
         return results
 
     def sum_components(self, components: Dict[str, np.ndarray]) -> np.ndarray:
@@ -157,6 +162,41 @@ def sum_components(self, components: Dict[str, np.ndarray]) -> np.ndarray:
         components["gmslr"] = np.sum(list(components.values()), axis=0)
         return components["gmslr"]
 
+    def save_components(self, output_dir: str, scenario_name: str) -> None:
+        """Save all SLR components as .npy files to a directory.
+
+        Parameters
+        ----------
+        output_directory: str
+            Directory to save components to.
+        scenario_name: str
+            Name of the scenario you've run the emulator for.
+
+        Returns
+        -------
+        None
+        """
+        # Create directory if it doesn't exist
+        Path(output_dir).mkdir(parents=True, exist_ok=True)
+
+        # Save data in netcdf format
+        ds = xr.Dataset()
+        member_dim = "percentile" if self.output_percentiles is not None else "member"
+        for name, component in self.results.items():
+            xr_dataArray = xr.DataArray(
+                component,
+                dims=[member_dim, "time"],
+                coords = {
+                    member_dim: self.output_percentiles
+                    if self.output_percentiles is not None
+                    else np.arange(component.shape[0]), # handle if no output percentiles
+                    "time": np.arange(2006, component.shape[1] + 2006),
+                }
+            )
+            xr_dataArray.attrs["units"] = "m"
+            ds[name] = xr_dataArray
+        ds.to_netcdf(os.path.join(output_dir, f"{scenario_name}_global.nc"))
+
     def _calculate_drivers(
         self, T_change: np.ndarray, OHC_change: np.ndarray, rng: np.random.Generator
     ) -> tuple:
diff --git a/test_build.py b/test_build.py
index e6b9187..2687827 100644
--- a/test_build.py
+++ b/test_build.py
@@ -15,3 +15,4 @@
 
 projections = model.run(scenario="test", T_change=np.arange(295).reshape(1, -1), OHC_change=np.arange(295).reshape(1, -1), member_seed=42)
 gmslr = model.sum_components(projections)
+model.save_components("test_output", "test_projection")

From 730d99b89c986b881aca8402f00f02d995061734 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Tue, 21 Apr 2026 14:23:34 +0100
Subject: [PATCH 119/276] Add time_projection.py

---
 profsea/components/core/time_projection.py | 92 ++++++++++++++++++++++
 1 file changed, 92 insertions(+)
 create mode 100644 profsea/components/core/time_projection.py

diff --git a/profsea/components/core/time_projection.py b/profsea/components/core/time_projection.py
new file mode 100644
index 0000000..57a2c6e
--- /dev/null
+++ b/profsea/components/core/time_projection.py
@@ -0,0 +1,92 @@
+from collections.abc import Sequence
+
+import numpy as np
+
+from profsea.components.core.global_model import ClimateState
+
+def time_projection(
+        state: ClimateState,
+        startratemean: float,
+        startratepm: float,
+        final,
+        rng: np.random.Generator,
+        nfinal: int = 1,
+        fraction: np.ndarray = None,
+    ) -> np.ndarray:
+        """Project a quantity which is a quadratic function of time.
+
+        Parameters
+        ----------
+        startratemean: float
+            Rate of GMSLR at the start in mm yr-1.
+        startratepm: float
+            Start rate error in mm yr-1.
+        final: list | np.ndarray
+            Likely range in m for GMSLR at the end of AR5.
+        nfinal: int
+            Number of years at the end over which final is a time-mean.
+        fraction: np.ndarray
+            Random numbers in the range 0 to 1.
+
+        Returns
+        -------
+        np.ndarray
+            Projection of the quantity.
+        """
+        # Create a field of elapsed time since start in years
+        timeendofAR5 = state.endofAR5 - state.endofhistory + 1
+        time = np.arange(state.end_yr - state.endofhistory) + 1
+
+        if fraction is None:
+            fraction = rng.random((state.nm, state.nt))
+        elif fraction.size != state.nm * state.nt:
+            raise ValueError("fraction is the wrong size")
+
+        fraction = fraction.reshape(state.nm, state.nt)
+
+        # Convert inputs to startrate (m yr-1) and afinal (m), where both are
+        # arrays with the size of fraction
+        startrate = (
+            startratemean + startratepm * np.array([-1, 1], dtype=float)
+        ) * 1e-3  # convert mm yr-1 to m yr-1
+        finalisrange = isinstance(final, Sequence)
+
+        if finalisrange:
+            if len(final) != 2:
+                raise ValueError("final range is the wrong size")
+            afinal = (1 - fraction) * final[0] + fraction * final[1]
+        else:
+            if final.shape != fraction.shape:
+                raise ValueError("final array is the wrong shape")
+            afinal = final
+
+        startrate = (1 - fraction) * startrate[0] + fraction * startrate[1]
+
+        # For terms where the rate increases linearly in time t, we can write GMSLR as
+        #   S(t) = a*t**2 + b*t
+        # where a is 0.5*acceleration and b is start rate. Hence
+        #   a = S/t**2-b/t = (S-b*t)/t**2
+        # If nfinal=1, the following two lines are equivalent to
+        # halfacc=(final-startyr*nyr)/nyr**2
+        finalyr = np.arange(nfinal) - nfinal + 94 + 1  # last element ==nyr
+        halfacc = (afinal - startrate * finalyr.mean()) / (finalyr**2).mean()
+        quadratic = halfacc[:, :, np.newaxis] * (time**2)
+        linear = startrate[:, :, np.newaxis] * time
+
+        # If acceleration ceases for t>t0, the rate is 2*a*t0+b thereafter, so
+        #   S(t) = a*t0**2 + b*t0 + (2*a*t0+b)*(t-t0)
+        #        = a*t0*(2*t - t0) + b*t
+        # i.e. the quadratic term is replaced, the linear term unaffected
+        # The quadratic also = a*t**2-a*(t-t0)**2
+
+        if state.palmer_method:
+            y = halfacc[:, :, np.newaxis] * timeendofAR5 * ((2 * time) - timeendofAR5)
+            quadratic[:, :, 95:] = y[:, :, 95:]
+
+        quadratic += linear
+
+        quadratic = quadratic.reshape(
+            quadratic.shape[0] * quadratic.shape[1], quadratic.shape[2]
+        )
+
+        return quadratic
\ No newline at end of file

From a8fcdb7dd2c4690365a8a39da3186694a873d5de Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Tue, 21 Apr 2026 14:32:31 +0100
Subject: [PATCH 120/276] Provide components as arg

---
 profsea/components/core/global_model.py | 14 +++++++++-----
 1 file changed, 9 insertions(+), 5 deletions(-)

diff --git a/profsea/components/core/global_model.py b/profsea/components/core/global_model.py
index 6c5d4f4..5a379a6 100644
--- a/profsea/components/core/global_model.py
+++ b/profsea/components/core/global_model.py
@@ -162,7 +162,9 @@ def sum_components(self, components: Dict[str, np.ndarray]) -> np.ndarray:
         components["gmslr"] = np.sum(list(components.values()), axis=0)
         return components["gmslr"]
 
-    def save_components(self, output_dir: str, scenario_name: str) -> None:
+    def save_components(
+        self, components: Dict[str, np.ndarray], output_dir: str, scenario_name: str
+    ) -> None:
         """Save all SLR components as .npy files to a directory.
 
         Parameters
@@ -182,16 +184,18 @@ def save_components(self, output_dir: str, scenario_name: str) -> None:
         # Save data in netcdf format
         ds = xr.Dataset()
         member_dim = "percentile" if self.output_percentiles is not None else "member"
-        for name, component in self.results.items():
+        for name, component in components.items():
             xr_dataArray = xr.DataArray(
                 component,
                 dims=[member_dim, "time"],
-                coords = {
+                coords={
                     member_dim: self.output_percentiles
                     if self.output_percentiles is not None
-                    else np.arange(component.shape[0]), # handle if no output percentiles
+                    else np.arange(
+                        component.shape[0]
+                    ),  # handle if no output percentiles
                     "time": np.arange(2006, component.shape[1] + 2006),
-                }
+                },
             )
             xr_dataArray.attrs["units"] = "m"
             ds[name] = xr_dataArray

From ee783bf005e8c5063b3a4221ad02b4fdcb576b2a Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Tue, 21 Apr 2026 14:33:12 +0100
Subject: [PATCH 121/276] Update docstring

---
 profsea/components/core/global_model.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/profsea/components/core/global_model.py b/profsea/components/core/global_model.py
index 5a379a6..49be4a6 100644
--- a/profsea/components/core/global_model.py
+++ b/profsea/components/core/global_model.py
@@ -169,6 +169,8 @@ def save_components(
 
         Parameters
         ----------
+        components: Dict[str, np.ndarray]
+            Dictionary of component names and their corresponding arrays.
         output_directory: str
             Directory to save components to.
         scenario_name: str

From ebca8f49a8e86778833b2c1cd0e7744ea46406d8 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Tue, 21 Apr 2026 15:27:00 +0100
Subject: [PATCH 122/276] Add AR5 comps

---
 profsea/components/global_/greenland.py | 121 +++++++++++++++++++++++-
 profsea/components/global_/landwater.py |  16 ++--
 2 files changed, 128 insertions(+), 9 deletions(-)

diff --git a/profsea/components/global_/greenland.py b/profsea/components/global_/greenland.py
index a2e2498..9afb445 100644
--- a/profsea/components/global_/greenland.py
+++ b/profsea/components/global_/greenland.py
@@ -8,6 +8,7 @@
 from profsea.components.core.base import Component
 from profsea.components.core.global_model import ClimateState
 
+
 @functools.lru_cache(maxsize=1)
 def load_greenland_calibration():
     """Loads the CSV once and keeps it in memory."""
@@ -90,11 +91,127 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
         sle_ens += trend
 
         # Persist 2100 rate of change
-        if(state.end_yr >= 2100):
+        if state.end_yr >= 2100:
             rate = np.diff(sle_ens, axis=2)[:, :, 94]
             sle_ens[:, :, 95:] = sle_ens[:, :, 94:95] + (
                 rate[:, :, None] * time_delta[None, None, 1 : state.nyr - 94]
             )
-        
+
         sle_ens = sle_ens.reshape((state.nm * state.nt, state.nyr))
         return sle_ens
+
+
+class GreenlandSMBAR5(Component):
+    def __init__(self):
+        self.fgreendyn = 0.5
+        self.dgreen = (3.21 - 0.30) * 1e-3
+        self.mSLEoGt = 1e12 / 3.61e14 * 1e-3
+
+    def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
+        """Project Greenland SMB contribution to GMSLR.
+
+        Parameters
+        ----------
+        state: ClimateState
+            State object containing relevant information for the projection.
+        rng: np.random.Generator
+            Random number generator.
+
+        Returns
+        -------
+        greensmb: np.ndarray
+            Greenland SMB contribution to GMSLR.
+
+        """
+        dtgreen = -0.146  # Delta_T of Greenland ref period wrt AR5 ref period
+        fnlogsd = 0.4  # random methodological error of the log factor
+        febound = [1, 1.15]  # bounds of uniform pdf of SMB elevation feedback factor
+
+        # random log-normal factor
+        fn = np.exp(rng.standard_normal(state.nm) * fnlogsd)
+        # elevation feedback factor
+        fe = rng.random(state.nm) * (febound[1] - febound[0]) + febound[0]
+        ff = fn * fe
+
+        ztgreen = state.T_ens - dtgreen
+
+        greensmb = ff[:, np.newaxis, np.newaxis] * self._fettweis(ztgreen)
+
+        if self.palmer_method and state.end_yr > state.endofAR5:
+            greensmb[:, :, 95:] = greensmb[:, :, 94:95]
+
+        greensmb = np.cumsum(greensmb, axis=-1)
+
+        greensmb += (1 - self.fgreendyn) * self.dgreen
+
+        greensmb = greensmb.reshape(
+            greensmb.shape[0] * greensmb.shape[1], greensmb.shape[2]
+        )
+        return greensmb
+
+    def _fettweis(self, ztgreen: np.ndarray) -> np.ndarray:
+        """Calculate Greenland SMB in m yr-1 SLE from global mean temperature
+        anomaly, using Eq 2 of Fettweis et al. (2013).
+
+        Parameters
+        ----------
+        ztgreen: np.ndarray
+            Global mean temperature anomaly.
+
+        Returns
+        -------
+        np.ndarray
+            Greenland SMB in m yr-1 SLE.
+        """
+        return (
+            71.5 * ztgreen + 20.4 * (ztgreen**2) + 2.8 * (ztgreen**3)
+        ) * self.mSLEoGt
+
+
+class GreenlandDynAR5(Component):
+    """
+    AR5 Greenland ice-sheet dynamics contribution to GMSLR.
+
+    NOTE: This is not scenario independent. It will run with either rcp85/ssp585 related projections,
+    or will default to a temperature independent projection based on AR5.
+
+    This is based on Jonathan Gregory's AR5 implmentation,
+    which can be found at https://github.com/JonathanGregory/ar5gmslr
+
+    """
+
+    def __init__(
+        self,
+    ):
+        self.fgreendyn = 0.5
+        self.dgreen = (3.21 - 0.30) * 1e-3
+
+    def project_greendyn_AR5(
+        self, state: ClimateState, rng: np.random.Generator
+    ) -> np.ndarray:
+        """Project Greenland rapid ice-sheet dynamics contribution to GMSLR.
+
+        Parameters
+        ----------
+        state: ClimateState
+            State object containing relevant information for the projection.
+        rng: np.random.Generator
+            Random number generator.
+
+        Returns
+        -------
+        np.ndarray
+            Greenland rapid ice-sheet dynamics contribution to GMSLR.
+        """
+        # For SMB+dyn during 2005-2010 Table 4.6 gives 0.63+-0.17 mm yr-1 (5-95% range)
+        # For dyn at 2100 Chapter 13 gives [20,85] mm for rcp85, [14,63] mm otherwise
+        if state.scenario in ["rcp85", "ssp585"]:
+            finalrange = [0.020, 0.085]
+        else:
+            finalrange = [0.014, 0.063]
+        return (
+            self.time_projection(
+                0.63 * self.fgreendyn, 0.17 * self.fgreendyn, finalrange, rng
+            )
+            + self.fgreendyn * self.dgreen
+        )
diff --git a/profsea/components/global_/landwater.py b/profsea/components/global_/landwater.py
index c158ea2..4abcd96 100644
--- a/profsea/components/global_/landwater.py
+++ b/profsea/components/global_/landwater.py
@@ -2,21 +2,25 @@
 
 from pathlib import Path
 import numpy as np
-import pandas as pd
 import xarray as xr
-from scipy.stats import truncnorm
 
 from profsea.components.core.base import Component
 from profsea.components.core.global_model import ClimateState
 
+
 @functools.lru_cache(maxsize=1)
 def load_landwater_projection():
     """Loads the NetCDF once and keeps the VALUES in memory."""
-    path = Path(__file__).parent.parent/ "aux_data" / "ssp_global_landwater_projections.nc"
+    path = (
+        Path(__file__).parent.parent
+        / "aux_data"
+        / "ssp_global_landwater_projections.nc"
+    )
     with xr.open_dataset(path) as ds:
         ds.load()
     return ds
 
+
 class LandwaterAR6(Component):
     def __init__(self):
         self.lw_ds = load_landwater_projection()
@@ -47,8 +51,6 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
         remainder = (state.nt * state.nm) % lw.shape[0]
         lw = np.vstack([np.tile(lw, (full_repeats, 1)), lw[:remainder]])
         lw = lw.reshape(state.nt * state.nm, lw.shape[1])
-        lw = lw[:, 1:state.nyr+1]  # Start at 2006, end at end_yr
-        
-        return lw
+        lw = lw[:, 1 : state.nyr + 1]  # Start at 2006, end at end_yr
 
-    
+        return lw

From 61e7731f148cf83b7470f1fe359b97f62ab5c366 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Tue, 21 Apr 2026 15:36:17 +0100
Subject: [PATCH 123/276] Add time_projection part

---
 profsea/components/global_/greenland.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/profsea/components/global_/greenland.py b/profsea/components/global_/greenland.py
index 9afb445..20084a1 100644
--- a/profsea/components/global_/greenland.py
+++ b/profsea/components/global_/greenland.py
@@ -7,6 +7,7 @@
 
 from profsea.components.core.base import Component
 from profsea.components.core.global_model import ClimateState
+from profsea.components.core.time_projection import time_projection
 
 
 @functools.lru_cache(maxsize=1)
@@ -186,7 +187,7 @@ def __init__(
         self.fgreendyn = 0.5
         self.dgreen = (3.21 - 0.30) * 1e-3
 
-    def project_greendyn_AR5(
+    def project(
         self, state: ClimateState, rng: np.random.Generator
     ) -> np.ndarray:
         """Project Greenland rapid ice-sheet dynamics contribution to GMSLR.
@@ -210,7 +211,7 @@ def project_greendyn_AR5(
         else:
             finalrange = [0.014, 0.063]
         return (
-            self.time_projection(
+            time_projection(
                 0.63 * self.fgreendyn, 0.17 * self.fgreendyn, finalrange, rng
             )
             + self.fgreendyn * self.dgreen

From 0032870afca9426dc277b1337f32c75fcc9f2c4c Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Tue, 21 Apr 2026 15:43:04 +0100
Subject: [PATCH 124/276] Merge with latest, fix smol bug

---
 profsea/components/global_/greenland.py | 8 +++-----
 1 file changed, 3 insertions(+), 5 deletions(-)

diff --git a/profsea/components/global_/greenland.py b/profsea/components/global_/greenland.py
index 20084a1..5602dc1 100644
--- a/profsea/components/global_/greenland.py
+++ b/profsea/components/global_/greenland.py
@@ -138,7 +138,7 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
 
         greensmb = ff[:, np.newaxis, np.newaxis] * self._fettweis(ztgreen)
 
-        if self.palmer_method and state.end_yr > state.endofAR5:
+        if state.palmer_method and state.end_yr > state.endofAR5:
             greensmb[:, :, 95:] = greensmb[:, :, 94:95]
 
         greensmb = np.cumsum(greensmb, axis=-1)
@@ -187,9 +187,7 @@ def __init__(
         self.fgreendyn = 0.5
         self.dgreen = (3.21 - 0.30) * 1e-3
 
-    def project(
-        self, state: ClimateState, rng: np.random.Generator
-    ) -> np.ndarray:
+    def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
         """Project Greenland rapid ice-sheet dynamics contribution to GMSLR.
 
         Parameters
@@ -212,7 +210,7 @@ def project(
             finalrange = [0.014, 0.063]
         return (
             time_projection(
-                0.63 * self.fgreendyn, 0.17 * self.fgreendyn, finalrange, rng
+                state, 0.63 * self.fgreendyn, 0.17 * self.fgreendyn, finalrange, rng
             )
             + self.fgreendyn * self.dgreen
         )

From 837eff80d27b061a01accca6c23c120d9e939e92 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Tue, 21 Apr 2026 15:44:52 +0100
Subject: [PATCH 125/276] Add docstring

---
 profsea/components/global_/greenland.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/profsea/components/global_/greenland.py b/profsea/components/global_/greenland.py
index 5602dc1..9bdc1a6 100644
--- a/profsea/components/global_/greenland.py
+++ b/profsea/components/global_/greenland.py
@@ -103,6 +103,10 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
 
 
 class GreenlandSMBAR5(Component):
+    """
+    AR5 Greenland SMB contribution to GMSLR.
+    """
+
     def __init__(self):
         self.fgreendyn = 0.5
         self.dgreen = (3.21 - 0.30) * 1e-3

From 973d9144bbfcd849cae69918bcc0d6c9fd5240a7 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Tue, 21 Apr 2026 16:02:02 +0100
Subject: [PATCH 126/276] Isolate expansion component

---
 profsea/components/core/global_model.py | 63 ++-----------------------
 profsea/components/core/state.py        |  1 -
 profsea/components/global_/expansion.py | 28 +++++++++++
 profsea/utils/utils.py                  | 25 ++++++++++
 4 files changed, 58 insertions(+), 59 deletions(-)
 create mode 100644 profsea/components/global_/expansion.py

diff --git a/profsea/components/core/global_model.py b/profsea/components/core/global_model.py
index d049c61..72a3782 100644
--- a/profsea/components/core/global_model.py
+++ b/profsea/components/core/global_model.py
@@ -9,7 +9,7 @@
 
 from .state import ClimateState
 from .base import Component
-from profsea.utils import sample_members_2D
+from profsea.utils import sample_members_2D, check_shapes
 
 console = Console()
 
@@ -43,59 +43,23 @@ def __init__(
         self.endofAR5 = 2100
         self.nyr = self.end_yr - self.endofhistory
 
-    def _check_shapes(
-        self, T_change: np.ndarray, OHC_change: np.ndarray, n_time: int
-    ) -> None:
-        """Check that the input arrays have the correct shape.
-
-        Parameters
-        ----------
-        T_change: np.ndarray
-            Array of surface temperature changes.
-        OHC_change: np.ndarray
-            Array of ocean heat content changes.
-        n_time: int
-            Expected number of time steps.
-
-        Returns
-        -------
-        None
-        """
-        if T_change.ndim == 1:
-            T_change = T_change[np.newaxis, :]
-        if OHC_change.ndim == 1:
-            OHC_change = OHC_change[np.newaxis, :]
-
-        if T_change.shape[1] != n_time:
-            # Split over lines for readability
-            raise ValueError(
-                f"T_change should have shape (realisation, time) with time \
-                dimension of length {n_time}. Got {T_change.shape}."
-            )
-        if OHC_change.shape[1] != n_time:
-            raise ValueError(
-                f"OHC_change should have shape (realisation, time) with time \
-                dimension of length {n_time}. Got {OHC_change.shape}."
-            )
-
     def run(
         self,
         scenario: str,
         T_change: np.ndarray,
-        OHC_change: np.ndarray,
         member_seed: int = 42,
     ) -> Dict[str, np.ndarray]:
         """Run the emulator to project GMSLR components for a specific state."""
         seed_seq = np.random.SeedSequence(member_seed)
         run_rng = np.random.default_rng(seed_seq)
 
-        self._check_shapes(T_change, OHC_change, self.nyr)
+        check_shapes(T_change, self.nyr)
 
         if self.input_ensemble:
             self.nt = T_change.shape[0]
 
         T_ens, therm_ens, T_int_ens, T_int_med = self._calculate_drivers(
-            T_change, OHC_change, run_rng
+            T_change, run_rng
         )
 
         # Shared physical correlation state
@@ -106,7 +70,6 @@ def run(
             T_ens=T_ens,
             T_int_ens=T_int_ens,
             T_int_med=T_int_med,
-            therm_ens=therm_ens,
             fraction=fraction,
             palmer_method=self.palmer_method,
             endofAR5=self.endofAR5,
@@ -207,7 +170,7 @@ def save_components(
         ds.to_netcdf(os.path.join(output_dir, f"{scenario_name}_global.nc"))
 
     def _calculate_drivers(
-        self, T_change: np.ndarray, OHC_change: np.ndarray, rng: np.random.Generator
+        self, T_change: np.ndarray, rng: np.random.Generator
     ) -> tuple:
         """Calculate the drivers of GMSLR: temperature change and
         thermosteric sea level rise.
@@ -223,29 +186,14 @@ def _calculate_drivers(
         T_int_med: np.ndarray
             Median of time-integral temperature anomalies.
         """
-        # Sensitivity of thermosteric SLR to ocean heat content change
-        # From Turner et al. (2023)
-        exp_efficiency = (
-            rng.normal(loc=0.113, scale=0.013, size=self.nt)[:, None] * 1e-24
-        )  # m/YJ
-
         if self.input_ensemble:
             T_med = sample_members_2D(T_change, [50])
             T_std = np.std(T_change, axis=0)
-
-            therm_med = sample_members_2D(OHC_change, [50]) * exp_efficiency
-            therm_std = np.std(OHC_change, axis=0) * exp_efficiency
-
         else:
             if self.T_percentile_95 is not None:
                 T_med = T_change
-                therm_med = OHC_change * exp_efficiency
 
                 T_std = (self.T_percentile_95 - T_change) / 1.645
-                therm_std = (
-                    (self.OHC_percentile_95 - OHC_change) * exp_efficiency / 1.645
-                )
-
             else:
                 raise ValueError(
                     "If input_ensemble is False, and T_change and OHC_change "
@@ -265,6 +213,5 @@ def _calculate_drivers(
         # For each quantity, mean + standard deviation * normal random number
         # reshape to [realisation,time]
         T_ens = z[:, np.newaxis] * T_std + T_med
-        therm_ens = z[:, np.newaxis] * therm_std + therm_med
         T_int_ens = z[:, np.newaxis] * T_int_std + T_int_med
-        return T_ens, therm_ens, T_int_ens, T_int_med
+        return T_ens, T_int_ens, T_int_med
diff --git a/profsea/components/core/state.py b/profsea/components/core/state.py
index 09c0589..8c45299 100644
--- a/profsea/components/core/state.py
+++ b/profsea/components/core/state.py
@@ -8,7 +8,6 @@ class ClimateState:
     T_ens: np.ndarray
     T_int_ens: np.ndarray
     T_int_med: np.ndarray
-    therm_ens: np.ndarray
     fraction: np.ndarray  # shared correlation array
     palmer_method: bool # whether to use Palmer method for time projection
     endofAR5: int
diff --git a/profsea/components/global_/expansion.py b/profsea/components/global_/expansion.py
new file mode 100644
index 0000000..8395226
--- /dev/null
+++ b/profsea/components/global_/expansion.py
@@ -0,0 +1,28 @@
+import numpy as np
+
+from profsea.components.core.base import Component
+from profsea.components.core.state import ClimateState
+from profsea.utils import check_shapes, sample_members_2D
+
+
+class ThermalExpansion(Component):
+    def __init__(self, OHC_change: np.ndarray):
+        self.OHC_change = OHC_change
+
+        # check the shape here
+        check_shapes(self.OHC_change, self.nyr)
+
+    def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
+        # Sensitivity of thermosteric SLR to ocean heat content change
+        # From Turner et al. (2023)
+        exp_efficiency = (
+            rng.normal(loc=0.113, scale=0.013, size=self.nt)[:, None] * 1e-24
+        )  # m/YJ
+        z = rng.standard_normal(self.nt) * self.tcv
+
+        therm_med = sample_members_2D(self.OHC_change, [50]) * exp_efficiency
+        therm_std = np.std(self.OHC_change, axis=0) * exp_efficiency
+
+        therm_ens = z[:, np.newaxis] * therm_std + therm_med
+        expansion = np.tile(therm_ens, (state.nm, 1))
+        return expansion.reshape(state.nm * state.nt, state.nyr)
diff --git a/profsea/utils/utils.py b/profsea/utils/utils.py
index a195020..93d9789 100644
--- a/profsea/utils/utils.py
+++ b/profsea/utils/utils.py
@@ -29,3 +29,28 @@ def interpolate(data: da.array, lats: int, lons: int) -> np.ndarray:
         lat=target_lat, lon=target_lon, method="linear").data
 
     return data_interp
+
+
+def check_shapes(self, array: np.ndarray, n_time: int) -> None:
+        """Check that the input arrays have the correct shape.
+
+        Parameters
+        ----------
+        array: np.ndarray
+            Input array of some kind.
+        n_time: int
+            Expected number of time steps.
+
+        Returns
+        -------
+        None
+        """
+        if array.ndim == 1:
+            array = array[np.newaxis, :]
+
+        if array.shape[1] != n_time:
+            # Split over lines for readability
+            raise ValueError(
+                f"Array should have shape (realisation, time) with time \
+                dimension of length {n_time}. Got {array.shape}."
+            )

From 0c5e784c8587f16b5f680ba04913bf82224b8a66 Mon Sep 17 00:00:00 2001
From: Hemant Khatri 
Date: Wed, 22 Apr 2026 09:55:45 +0100
Subject: [PATCH 127/276] glacier component

---
 profsea/components/core/global_model.py |   5 +-
 profsea/components/core/state.py        |   1 +
 profsea/components/global_/glacier.py   | 131 ++++++++++++++++++++++++
 3 files changed, 136 insertions(+), 1 deletion(-)
 create mode 100644 profsea/components/global_/glacier.py

diff --git a/profsea/components/core/global_model.py b/profsea/components/core/global_model.py
index d049c61..6a06784 100644
--- a/profsea/components/core/global_model.py
+++ b/profsea/components/core/global_model.py
@@ -22,6 +22,7 @@ def __init__(
         nt: int = 100,
         nm: int = 1000,
         tcv: float = 1.0,
+        glaciermip: bool | int = 2,
         parallel: bool = True,
         input_ensemble: bool = True,
         output_percentiles: list | np.ndarray = None,
@@ -34,6 +35,7 @@ def __init__(
         self.nm = nm
         self.tcv = tcv
         self.parallel = parallel
+        self.glaciermip = glaciermip
         self.input_ensemble = input_ensemble
         self.output_percentiles = output_percentiles
         self.palmer_method = palmer_method
@@ -115,6 +117,7 @@ def run(
             nyr=self.nyr,
             nt=self.nt,
             nm=self.nm,
+            glaciermip=self.glaciermip,
         )
 
         # Child RNGs for each component
@@ -168,7 +171,7 @@ def sum_components(self, components: Dict[str, np.ndarray]) -> np.ndarray:
     def save_components(
         self, components: Dict[str, np.ndarray], output_dir: str, scenario_name: str
     ) -> None:
-        """Save all SLR components as .npy files to a directory.
+        """Save SLR components as nc files to a directory.
 
         Parameters
         ----------
diff --git a/profsea/components/core/state.py b/profsea/components/core/state.py
index 09c0589..864897a 100644
--- a/profsea/components/core/state.py
+++ b/profsea/components/core/state.py
@@ -17,3 +17,4 @@ class ClimateState:
     nyr: int
     nt: int
     nm: int
+    glaciermip: bool | int
diff --git a/profsea/components/global_/glacier.py b/profsea/components/global_/glacier.py
new file mode 100644
index 0000000..b583271
--- /dev/null
+++ b/profsea/components/global_/glacier.py
@@ -0,0 +1,131 @@
+import functools
+
+from pathlib import Path
+import numpy as np
+import pandas as pd
+import xarray as xr
+from scipy.stats import truncnorm
+
+from profsea.components.core.base import Component
+from profsea.components.core.global_model import ClimateState
+
+class Glacier(Component):
+    
+    def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
+        """Project glacier contribution to GMSLR.
+
+        Returns
+        -------
+        glacier: np.ndarray
+            Glacier contribution to GMSLR.
+        """
+
+        # glaciermip -- False => AR5 parameters, 1 => fit to Hock et al. (2019),
+        #   2 => fit to Marzeion et al. (2020)
+        dmzdtref = 0.95  # mm yr-1 in Marzeion's CMIP5 ensemble mean for AR5 ref period
+        dmz = (
+            dmzdtref * (state.endofhistory - 1996) * 1e-3
+        )  # m from glacier at start wrt AR5 ref period
+        glmass = 412.0 - 96.3  # initial glacier mass, used to set a limit, from Tab 4.2
+        glmass = 1e-3 * glmass  # m SLE
+
+        if state.glaciermip == 1:
+            glparm = [
+                dict(name="SLA2012", factor=3.39, exponent=0.722, cvgl=0.15),
+                dict(name="MAR2012", factor=4.35, exponent=0.658, cvgl=0.13),
+                dict(name="GIE2013", factor=3.57, exponent=0.665, cvgl=0.13),
+                dict(name="RAD2014", factor=6.21, exponent=0.648, cvgl=0.17),
+                dict(name="GloGEM", factor=2.88, exponent=0.753, cvgl=0.13),
+            ]
+        elif state.glaciermip == 2:
+            glparm = [
+                dict(name="GLIMB",   factor=3.70, exponent=0.662, cvgl=0.206),
+                dict(name="GloGEM",  factor=4.08, exponent=0.716, cvgl=0.161),
+                dict(name="JULES",   factor=5.50, exponent=0.564, cvgl=0.188),
+                dict(name="Mar-12",  factor=4.89, exponent=0.651, cvgl=0.141),
+                dict(name="OGGM",    factor=4.26, exponent=0.715, cvgl=0.164),
+                dict(name="RAD2014", factor=5.18, exponent=0.709, cvgl=0.135),
+                dict(name="WAL2001", factor=2.66, exponent=0.730, cvgl=0.206),
+            ]
+        elif not state.glaciermip:
+            glparm = [
+                dict(name="Marzeion", factor=4.96, exponent=0.685, cvgl=0.20),
+                dict(name="Radic",    factor=5.45, exponent=0.676, cvgl=0.20),
+                dict(name="Slangen",  factor=3.44, exponent=0.742, cvgl=0.20),
+                dict(name="Giesen",   factor=3.02, exponent=0.733, cvgl=0.20),
+            ]
+        else:
+            raise KeyError("glaciermip must be False (AR5 parameters), 1 (Hock et al., 2019), or 2 (Marzeion et al., 2020)")
+
+        ngl = len(glparm)
+        r = rng.standard_normal(state.nm)[:, np.newaxis, np.newaxis]
+        glacier = np.full((state.nm, state.nt, state.nyr), np.nan)
+
+        r_per_model = state.nm // ngl
+        r_remainder = state.nm % ngl
+
+        # Precompute mgl and zgl for all glacier methods
+        mgl_all = np.array(
+            [
+                self._project_glacier1(
+                    state.T_int_med, glparm[igl]["factor"], glparm[igl]["exponent"]
+                )
+                for igl in range(ngl)
+            ]
+        )
+        zgl_all = np.array(
+            [
+                self._project_glacier1(
+                    state.T_int_ens, glparm[igl]["factor"], glparm[igl]["exponent"]
+                )
+                for igl in range(ngl)
+            ]
+        )
+        cvgl_all = np.array(
+            [glparm[igl]["cvgl"] if state.glaciermip else cvgl for igl in range(ngl)]
+        )
+
+        # Make an ensemble of projections for each method
+        current_ensemble_idx = 0
+        for igl in range(ngl):
+            mgl = mgl_all[igl]
+            zgl = zgl_all[igl]
+            cvgl = cvgl_all[igl]
+
+            num_reals_for_model_i = (
+                r_per_model + 1 if igl < r_remainder else r_per_model
+            )
+            ifirst = current_ensemble_idx
+            ilast = current_ensemble_idx + num_reals_for_model_i
+
+            glacier[ifirst:ilast, ...] = zgl + (mgl * r[ifirst:ilast] * cvgl)
+            current_ensemble_idx = ilast
+
+        glacier += dmz
+        np.clip(glacier, None, glmass, out=glacier)
+
+        glacier = glacier.reshape(glacier.shape[0] * glacier.shape[1], glacier.shape[2])
+        
+        return glacier
+
+    def _project_glacier1(self, T_int: np.ndarray, factor: float, 
+                          exponent: float) -> np.ndarray:
+        """Project glacier contribution by one glacier method.
+
+        Parameters
+        ----------
+        T_int: np.ndarray
+            Time-integral temperature anomaly timeseries.
+        factor: float
+            Factor for the glacier method.
+        exponent: float
+            Exponent for the glacier method.
+
+        Returns
+        -------
+        np.ndarray
+            Projection of glacier contribution.
+        """
+        scale = 1e-3  # mm to m
+        
+        return scale * factor * (np.where(T_int < 0, 0, T_int) ** exponent)

From 9af28565e65a733c72a2dbd740295de324796c0e Mon Sep 17 00:00:00 2001
From: Hemant Khatri 
Date: Wed, 22 Apr 2026 10:59:43 +0100
Subject: [PATCH 128/276] landwater ar5

---
 profsea/components/global_/landwater.py | 20 ++++++++++++++++++++
 1 file changed, 20 insertions(+)

diff --git a/profsea/components/global_/landwater.py b/profsea/components/global_/landwater.py
index 4abcd96..64e308b 100644
--- a/profsea/components/global_/landwater.py
+++ b/profsea/components/global_/landwater.py
@@ -6,6 +6,7 @@
 
 from profsea.components.core.base import Component
 from profsea.components.core.global_model import ClimateState
+from profsea.components.core.time_projection import time_projection
 
 
 @functools.lru_cache(maxsize=1)
@@ -54,3 +55,22 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
         lw = lw[:, 1 : state.nyr + 1]  # Start at 2006, end at end_yr
 
         return lw
+
+class LandwaterAR5(Component):
+
+    def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
+        """Old projection function. Project land water storage
+        contribution to GMSLR.
+
+        Returns
+        -------
+        np.ndarray
+            Land water storage contribution to GMSLR.
+        """
+        
+        # The rate at start is the one for 1993-2010 from the budget table.
+        # The final amount is the mean for 2081-2100.
+        nyr = state.endofAR5 - 2081 + 1  # number of years of the time-mean of the final amount
+        final = [-0.01, 0.09]  # AR5
+        
+        return self.time_projection(0.38, 0.49 - 0.38, final, rng, nfinal=nyr)

From 691034cd8f6a81bb845cbadeaa528bfdd7cb9ccb Mon Sep 17 00:00:00 2001
From: Hemant Khatri 
Date: Wed, 22 Apr 2026 11:22:03 +0100
Subject: [PATCH 129/276] Use glaicermip within class, rather than state
 variable

---
 profsea/components/core/global_model.py |  3 ---
 profsea/components/core/state.py        |  1 -
 profsea/components/global_/glacier.py   | 11 +++++++----
 3 files changed, 7 insertions(+), 8 deletions(-)

diff --git a/profsea/components/core/global_model.py b/profsea/components/core/global_model.py
index 6a06784..2f5d218 100644
--- a/profsea/components/core/global_model.py
+++ b/profsea/components/core/global_model.py
@@ -22,7 +22,6 @@ def __init__(
         nt: int = 100,
         nm: int = 1000,
         tcv: float = 1.0,
-        glaciermip: bool | int = 2,
         parallel: bool = True,
         input_ensemble: bool = True,
         output_percentiles: list | np.ndarray = None,
@@ -35,7 +34,6 @@ def __init__(
         self.nm = nm
         self.tcv = tcv
         self.parallel = parallel
-        self.glaciermip = glaciermip
         self.input_ensemble = input_ensemble
         self.output_percentiles = output_percentiles
         self.palmer_method = palmer_method
@@ -117,7 +115,6 @@ def run(
             nyr=self.nyr,
             nt=self.nt,
             nm=self.nm,
-            glaciermip=self.glaciermip,
         )
 
         # Child RNGs for each component
diff --git a/profsea/components/core/state.py b/profsea/components/core/state.py
index 864897a..09c0589 100644
--- a/profsea/components/core/state.py
+++ b/profsea/components/core/state.py
@@ -17,4 +17,3 @@ class ClimateState:
     nyr: int
     nt: int
     nm: int
-    glaciermip: bool | int
diff --git a/profsea/components/global_/glacier.py b/profsea/components/global_/glacier.py
index b583271..0c09832 100644
--- a/profsea/components/global_/glacier.py
+++ b/profsea/components/global_/glacier.py
@@ -10,6 +10,9 @@
 from profsea.components.core.global_model import ClimateState
 
 class Glacier(Component):
+
+    def __init__(self, glaciermip: int=2):
+        self.glaciermip = glaciermip
     
     def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
         """Project glacier contribution to GMSLR.
@@ -29,7 +32,7 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
         glmass = 412.0 - 96.3  # initial glacier mass, used to set a limit, from Tab 4.2
         glmass = 1e-3 * glmass  # m SLE
 
-        if state.glaciermip == 1:
+        if self.glaciermip == 1:
             glparm = [
                 dict(name="SLA2012", factor=3.39, exponent=0.722, cvgl=0.15),
                 dict(name="MAR2012", factor=4.35, exponent=0.658, cvgl=0.13),
@@ -37,7 +40,7 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
                 dict(name="RAD2014", factor=6.21, exponent=0.648, cvgl=0.17),
                 dict(name="GloGEM", factor=2.88, exponent=0.753, cvgl=0.13),
             ]
-        elif state.glaciermip == 2:
+        elif self.glaciermip == 2:
             glparm = [
                 dict(name="GLIMB",   factor=3.70, exponent=0.662, cvgl=0.206),
                 dict(name="GloGEM",  factor=4.08, exponent=0.716, cvgl=0.161),
@@ -47,7 +50,7 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
                 dict(name="RAD2014", factor=5.18, exponent=0.709, cvgl=0.135),
                 dict(name="WAL2001", factor=2.66, exponent=0.730, cvgl=0.206),
             ]
-        elif not state.glaciermip:
+        elif not self.glaciermip:
             glparm = [
                 dict(name="Marzeion", factor=4.96, exponent=0.685, cvgl=0.20),
                 dict(name="Radic",    factor=5.45, exponent=0.676, cvgl=0.20),
@@ -82,7 +85,7 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
             ]
         )
         cvgl_all = np.array(
-            [glparm[igl]["cvgl"] if state.glaciermip else cvgl for igl in range(ngl)]
+            [glparm[igl]["cvgl"] if self.glaciermip else cvgl for igl in range(ngl)]
         )
 
         # Make an ensemble of projections for each method

From 920a4e9d9f539b8d8762d319fadaeacdfc8cff14 Mon Sep 17 00:00:00 2001
From: Hemant Khatri 
Date: Wed, 22 Apr 2026 11:44:20 +0100
Subject: [PATCH 130/276] landwater AR5

---
 profsea/components/global_/landwater.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/profsea/components/global_/landwater.py b/profsea/components/global_/landwater.py
index 64e308b..614fdd6 100644
--- a/profsea/components/global_/landwater.py
+++ b/profsea/components/global_/landwater.py
@@ -58,6 +58,10 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
 
 class LandwaterAR5(Component):
 
+    def __init__(self):
+        self.startratemean = 0.38
+        self.startratepm = 0.49 - 0.38
+
     def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
         """Old projection function. Project land water storage
         contribution to GMSLR.
@@ -73,4 +77,4 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
         nyr = state.endofAR5 - 2081 + 1  # number of years of the time-mean of the final amount
         final = [-0.01, 0.09]  # AR5
         
-        return self.time_projection(0.38, 0.49 - 0.38, final, rng, nfinal=nyr)
+        return time_projection(state, self.startratemean, self.startratepm, final, rng, nfinal=nyr)

From 8166dfe35f627be96c715783eae89d911982bd58 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Wed, 22 Apr 2026 16:41:11 +0100
Subject: [PATCH 131/276] More changes

---
 profsea/components/core/global_model.py |  4 +---
 profsea/components/global_/expansion.py | 12 ++++++------
 profsea/utils/utils.py                  |  2 +-
 3 files changed, 8 insertions(+), 10 deletions(-)

diff --git a/profsea/components/core/global_model.py b/profsea/components/core/global_model.py
index 72a3782..e7d4456 100644
--- a/profsea/components/core/global_model.py
+++ b/profsea/components/core/global_model.py
@@ -58,9 +58,7 @@ def run(
         if self.input_ensemble:
             self.nt = T_change.shape[0]
 
-        T_ens, therm_ens, T_int_ens, T_int_med = self._calculate_drivers(
-            T_change, run_rng
-        )
+        T_ens, T_int_ens, T_int_med = self._calculate_drivers(T_change, run_rng)
 
         # Shared physical correlation state
         fraction = run_rng.random(self.nm * self.nt)
diff --git a/profsea/components/global_/expansion.py b/profsea/components/global_/expansion.py
index 8395226..fe192e4 100644
--- a/profsea/components/global_/expansion.py
+++ b/profsea/components/global_/expansion.py
@@ -6,19 +6,19 @@
 
 
 class ThermalExpansion(Component):
-    def __init__(self, OHC_change: np.ndarray):
+    def __init__(self, OHC_change: np.ndarray, distribution_scaler: float = 1.0):
         self.OHC_change = OHC_change
-
-        # check the shape here
-        check_shapes(self.OHC_change, self.nyr)
+        self.distribution_scaler = distribution_scaler
 
     def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
+        # check the shape here
+        check_shapes(self.OHC_change, state.nyr)
         # Sensitivity of thermosteric SLR to ocean heat content change
         # From Turner et al. (2023)
         exp_efficiency = (
-            rng.normal(loc=0.113, scale=0.013, size=self.nt)[:, None] * 1e-24
+            rng.normal(loc=0.113, scale=0.013, size=state.nt)[:, None] * 1e-24
         )  # m/YJ
-        z = rng.standard_normal(self.nt) * self.tcv
+        z = rng.standard_normal(state.nt) * self.distribution_scaler
 
         therm_med = sample_members_2D(self.OHC_change, [50]) * exp_efficiency
         therm_std = np.std(self.OHC_change, axis=0) * exp_efficiency
diff --git a/profsea/utils/utils.py b/profsea/utils/utils.py
index 93d9789..958a4ad 100644
--- a/profsea/utils/utils.py
+++ b/profsea/utils/utils.py
@@ -31,7 +31,7 @@ def interpolate(data: da.array, lats: int, lons: int) -> np.ndarray:
     return data_interp
 
 
-def check_shapes(self, array: np.ndarray, n_time: int) -> None:
+def check_shapes(array: np.ndarray, n_time: int) -> None:
         """Check that the input arrays have the correct shape.
 
         Parameters

From 8545653eec2e7c41cf1995149fb850d8bfa553a3 Mon Sep 17 00:00:00 2001
From: Hemant Khatri 
Date: Fri, 24 Apr 2026 14:23:13 +0100
Subject: [PATCH 132/276] single member treatment

---
 profsea/components/core/global_model.py | 89 +++++++++++++++++++++----
 profsea/components/global_/expansion.py | 17 ++++-
 2 files changed, 90 insertions(+), 16 deletions(-)

diff --git a/profsea/components/core/global_model.py b/profsea/components/core/global_model.py
index ecd14fb..c12f180 100644
--- a/profsea/components/core/global_model.py
+++ b/profsea/components/core/global_model.py
@@ -15,6 +15,51 @@
 
 
 class Global:
+    """Global sea level rise component emulator.
+
+    Parameters
+    ----------
+    components: Dict
+        List of SLR components for projections
+    end_yr: int
+        End year of the projections.
+    nt: int
+        Number of realisations of the input timeseries
+    nm: int
+        Number of realisations of for each component.
+        Must be a multiple of the number of glacier methods.
+    tcv: float
+        Multiplier for the standard deviation in the input fields.
+    parallel: bool
+        If True, project SLR components in parallel.
+    input_ensemble: bool
+        If True, use an input ensemble of temperature and
+        ocean heat content change.
+    input_single: bool
+        If True, use a single timeseries of temperature and
+        ocean heat content change.
+    output_percentiles: list|np.ndarray
+        If not None, calculate percentiles from a 1D list/array for each
+        component
+    palmer_method: bool
+        If True, allow integration to end in any year up to 2300,
+        with the contributions to GMLSR from ice-sheet dynamics,
+        Greenland SMB and land water storage held at the 2100 rate
+        beyond 2100.
+    random_sample: bool
+        If True, randomly sample a single ensemble member across all
+        components
+
+    Attributes
+    ----------
+    endofhistory: int
+        First year of AR5 projections.
+    endofAR5: int
+        Last year of AR5 projections.
+    nyr: int
+        Length of projections.
+    """
+    
     def __init__(
         self,
         components: Dict[str, Component],
@@ -24,6 +69,7 @@ def __init__(
         tcv: float = 1.0,
         parallel: bool = True,
         input_ensemble: bool = True,
+        input_single: bool = False,
         output_percentiles: list | np.ndarray = None,
         palmer_method: bool = True,
         random_sample: bool = False,
@@ -35,6 +81,7 @@ def __init__(
         self.tcv = tcv
         self.parallel = parallel
         self.input_ensemble = input_ensemble
+        self.input_single = input_single
         self.output_percentiles = output_percentiles
         self.palmer_method = palmer_method
         self.random_sample = random_sample
@@ -49,7 +96,18 @@ def run(
         T_change: np.ndarray,
         member_seed: int = 42,
     ) -> Dict[str, np.ndarray]:
-        """Run the emulator to project GMSLR components for a specific state."""
+
+        """Run the emulator to project GMSLR components for a specific state.
+        Parameters
+        ----------
+        scenario: str
+            Name of the scenario.
+        T_change: np.ndarray
+            Array of temperature change values.
+        member_seed: int
+            Seed for numpy.random.
+        """
+        
         seed_seq = np.random.SeedSequence(member_seed)
         run_rng = np.random.default_rng(seed_seq)
 
@@ -58,6 +116,9 @@ def run(
         if self.input_ensemble:
             self.nt = T_change.shape[0]
 
+        if self.input_single:
+            self.nt = 1
+
         T_ens, T_int_ens, T_int_med = self._calculate_drivers(T_change, run_rng)
 
         # Shared physical correlation state
@@ -184,22 +245,22 @@ def _calculate_drivers(
         T_int_med: np.ndarray
             Median of time-integral temperature anomalies.
         """
+        
+        if self.input_ensemble and self.input_single:
+            raise ValueError(
+                "input_ensemble and input_single cannot both be True or False. "
+                "Choose only one option to be True and set the other to False."
+                "If input_ensemble is set to True, then T_change and OHC_change must be 2D arrays."
+                "If input_single is set to True, then T_change and OHC_change must be 1D arrays."
+            )
+                             
         if self.input_ensemble:
             T_med = sample_members_2D(T_change, [50])
             T_std = np.std(T_change, axis=0)
-        else:
-            if self.T_percentile_95 is not None:
-                T_med = T_change
-
-                T_std = (self.T_percentile_95 - T_change) / 1.645
-            else:
-                raise ValueError(
-                    "If input_ensemble is False, and T_change and OHC_change "
-                    "are not 2D arrays, you must provide a 95th percentile "
-                    "timeseries for T_change and OHC_change. Add this using "
-                    "T_percentile_95 and OHC_percentile_95 keyword arguments."
-                )
-
+        elif self.input_single:
+            T_med = T_change
+            T_std = 0. * T_med # dummy variable
+    
         # Time-integral of temperature anomaly
         T_int_med = np.cumsum(T_med)
         T_int_std = np.cumsum(T_std)
diff --git a/profsea/components/global_/expansion.py b/profsea/components/global_/expansion.py
index fe192e4..c49be19 100644
--- a/profsea/components/global_/expansion.py
+++ b/profsea/components/global_/expansion.py
@@ -6,6 +6,14 @@
 
 
 class ThermalExpansion(Component):
+    """
+    Parameters & Attributes
+    ----------
+    OHC_change: np.ndarray
+        Array of ocean heat content change values.
+    exp_efficiency: float
+        Sensitivity of thermosteric SLR to ocean heat content change.
+    """
     def __init__(self, OHC_change: np.ndarray, distribution_scaler: float = 1.0):
         self.OHC_change = OHC_change
         self.distribution_scaler = distribution_scaler
@@ -20,9 +28,14 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
         )  # m/YJ
         z = rng.standard_normal(state.nt) * self.distribution_scaler
 
-        therm_med = sample_members_2D(self.OHC_change, [50]) * exp_efficiency
-        therm_std = np.std(self.OHC_change, axis=0) * exp_efficiency
+        if state.nt > 1: # when input_ensemble = True
+            therm_med = sample_members_2D(self.OHC_change, [50]) * exp_efficiency
+            therm_std = np.std(self.OHC_change, axis=0) * exp_efficiency
+        else:
+            therm_med = self.OHC_change * exp_efficiency
+            therm_std = therm_med * 0. # dummary variable
 
         therm_ens = z[:, np.newaxis] * therm_std + therm_med
         expansion = np.tile(therm_ens, (state.nm, 1))
+        
         return expansion.reshape(state.nm * state.nt, state.nyr)

From f6b50728a594e37d5cef84656ac9bc3010aa3ab5 Mon Sep 17 00:00:00 2001
From: Hemant Khatri 
Date: Fri, 24 Apr 2026 14:30:39 +0100
Subject: [PATCH 133/276] add error message

---
 profsea/components/core/global_model.py | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/profsea/components/core/global_model.py b/profsea/components/core/global_model.py
index c12f180..1c428d4 100644
--- a/profsea/components/core/global_model.py
+++ b/profsea/components/core/global_model.py
@@ -248,7 +248,7 @@ def _calculate_drivers(
         
         if self.input_ensemble and self.input_single:
             raise ValueError(
-                "input_ensemble and input_single cannot both be True or False. "
+                "input_ensemble and input_single cannot both be True or False."
                 "Choose only one option to be True and set the other to False."
                 "If input_ensemble is set to True, then T_change and OHC_change must be 2D arrays."
                 "If input_single is set to True, then T_change and OHC_change must be 1D arrays."
@@ -260,6 +260,12 @@ def _calculate_drivers(
         elif self.input_single:
             T_med = T_change
             T_std = 0. * T_med # dummy variable
+        else:
+            raise ValueError(
+                "Provide valid values for input_ensemble and input_single."
+                "input_ensemble and input_single cannot both be True or False."
+                "Choose only one option to be True and set the other to False."
+            )
     
         # Time-integral of temperature anomaly
         T_int_med = np.cumsum(T_med)

From ab82d73e7574bf7e734c59e123f90c9094db10c5 Mon Sep 17 00:00:00 2001
From: Hemant Khatri 
Date: Fri, 24 Apr 2026 15:34:13 +0100
Subject: [PATCH 134/276] remove input_single argument.

---
 profsea/components/core/global_model.py | 29 +++++--------------------
 1 file changed, 5 insertions(+), 24 deletions(-)

diff --git a/profsea/components/core/global_model.py b/profsea/components/core/global_model.py
index 1c428d4..1419b45 100644
--- a/profsea/components/core/global_model.py
+++ b/profsea/components/core/global_model.py
@@ -34,10 +34,8 @@ class Global:
         If True, project SLR components in parallel.
     input_ensemble: bool
         If True, use an input ensemble of temperature and
-        ocean heat content change.
-    input_single: bool
-        If True, use a single timeseries of temperature and
-        ocean heat content change.
+        ocean heat content change. if False, use a single timeseries of 
+        temperature and ocean heat content change.
     output_percentiles: list|np.ndarray
         If not None, calculate percentiles from a 1D list/array for each
         component
@@ -69,7 +67,6 @@ def __init__(
         tcv: float = 1.0,
         parallel: bool = True,
         input_ensemble: bool = True,
-        input_single: bool = False,
         output_percentiles: list | np.ndarray = None,
         palmer_method: bool = True,
         random_sample: bool = False,
@@ -81,7 +78,6 @@ def __init__(
         self.tcv = tcv
         self.parallel = parallel
         self.input_ensemble = input_ensemble
-        self.input_single = input_single
         self.output_percentiles = output_percentiles
         self.palmer_method = palmer_method
         self.random_sample = random_sample
@@ -115,8 +111,7 @@ def run(
 
         if self.input_ensemble:
             self.nt = T_change.shape[0]
-
-        if self.input_single:
+        else:
             self.nt = 1
 
         T_ens, T_int_ens, T_int_med = self._calculate_drivers(T_change, run_rng)
@@ -245,27 +240,13 @@ def _calculate_drivers(
         T_int_med: np.ndarray
             Median of time-integral temperature anomalies.
         """
-        
-        if self.input_ensemble and self.input_single:
-            raise ValueError(
-                "input_ensemble and input_single cannot both be True or False."
-                "Choose only one option to be True and set the other to False."
-                "If input_ensemble is set to True, then T_change and OHC_change must be 2D arrays."
-                "If input_single is set to True, then T_change and OHC_change must be 1D arrays."
-            )
-                             
+               
         if self.input_ensemble:
             T_med = sample_members_2D(T_change, [50])
             T_std = np.std(T_change, axis=0)
-        elif self.input_single:
+        else:
             T_med = T_change
             T_std = 0. * T_med # dummy variable
-        else:
-            raise ValueError(
-                "Provide valid values for input_ensemble and input_single."
-                "input_ensemble and input_single cannot both be True or False."
-                "Choose only one option to be True and set the other to False."
-            )
     
         # Time-integral of temperature anomaly
         T_int_med = np.cumsum(T_med)

From 78c5d498316d4f6dd4ddd991561fee3e13acc260 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Wed, 20 May 2026 15:03:29 +0100
Subject: [PATCH 135/276] Update spatial model and add sterodynamic module

---
 profsea/components/core/state.py | 15 +++++-
 profsea/utils/utils.py           | 93 +++++++++++++++++++++-----------
 2 files changed, 76 insertions(+), 32 deletions(-)

diff --git a/profsea/components/core/state.py b/profsea/components/core/state.py
index 8c45299..87daf12 100644
--- a/profsea/components/core/state.py
+++ b/profsea/components/core/state.py
@@ -1,18 +1,31 @@
 from dataclasses import dataclass
 import numpy as np
 
+
 @dataclass
 class ClimateState:
     """ClimateState context object to hold relevant state information for SLR projections."""
+
     scenario: str
     T_ens: np.ndarray
     T_int_ens: np.ndarray
     T_int_med: np.ndarray
     fraction: np.ndarray  # shared correlation array
-    palmer_method: bool # whether to use Palmer method for time projection
+    palmer_method: bool  # whether to use Palmer method for time projection
     endofAR5: int
     endofhistory: int
     end_yr: int
     nyr: int
     nt: int
     nm: int
+
+
+@dataclass
+class SpatialState:
+    """SpatialState context object to hold relevant state information for spatial SLR projections."""
+
+    scenario: str
+    grid_lats: np.ndarray
+    grid_lons: np.ndarray
+    n_years: int
+    n_members: int
diff --git a/profsea/utils/utils.py b/profsea/utils/utils.py
index 958a4ad..6bed420 100644
--- a/profsea/utils/utils.py
+++ b/profsea/utils/utils.py
@@ -3,9 +3,10 @@
 from scipy.spatial.distance import cdist
 import xarray as xr
 
-def sample_members_2D(array: np.ndarray, percentiles: list|np.ndarray) -> np.ndarray:
+
+def sample_members_2D(array: np.ndarray, percentiles: list | np.ndarray) -> np.ndarray:
     """Sample real ensemble members from a 2D numpy array."""
-    # Caculate statistical timeseries, then match with closest real timeseries 
+    # Caculate statistical timeseries, then match with closest real timeseries
     array_percentiles = np.percentile(array, percentiles, axis=0)
     distances = cdist(array_percentiles, array)
     mem_indices = np.argmin(distances, axis=1)
@@ -13,44 +14,74 @@ def sample_members_2D(array: np.ndarray, percentiles: list|np.ndarray) -> np.nda
 
 
 def interpolate(data: da.array, lats: int, lons: int) -> np.ndarray:
-    """
-    """
+    """ """
     original_da = xr.DataArray(
         data.data,
-        coords=[
-            ("lat", data[data.dims[0]].values), 
-            ("lon", data[data.dims[1]].values)
-        ],
-        name="v")
+        coords=[("lat", data[data.dims[0]].values), ("lon", data[data.dims[1]].values)],
+        name="v",
+    )
 
     target_lat = np.linspace(-90, 90, lats, endpoint=False) + 0.5
     target_lon = np.linspace(-180, 180, lons, endpoint=False) + 0.5
     data_interp = original_da.interp(
-        lat=target_lat, lon=target_lon, method="linear").data
+        lat=target_lat, lon=target_lon, method="linear"
+    ).data
 
     return data_interp
 
 
+def interpolate_to_grid(
+    data: da.array, target_lats: np.ndarray, target_lons: np.ndarray
+) -> da.array:
+    """
+    Interpolate a dask array to a target grid defined by target_lats and target_lons.
+    Assumes the input data has dimensions (lat, lon) and coordinates named 'lat' and 'lon'.
+
+    Parameters
+    ----------
+    data: da.array
+        Input dask array with dimensions (lat, lon) and coordinates 'lat' and 'lon'.
+    target_lats: np.ndarray
+        1D array of target latitudes to interpolate to.
+    target_lons: np.ndarray
+        1D array of target longitudes to interpolate to.
+    Returns
+    -------
+    da.array        Interpolated dask array on the target grid.
+    """
+    original_da = xr.DataArray(
+        data.data,
+        coords=[("lat", data[data.dims[0]].values), ("lon", data[data.dims[1]].values)],
+        name="v",
+    )
+
+    data_interp = original_da.interp(
+        lat=target_lats, lon=target_lons, method="linear"
+    ).data
+
+    return da.from_array(data_interp, chunks=data.chunks)
+
+
 def check_shapes(array: np.ndarray, n_time: int) -> None:
-        """Check that the input arrays have the correct shape.
-
-        Parameters
-        ----------
-        array: np.ndarray
-            Input array of some kind.
-        n_time: int
-            Expected number of time steps.
-
-        Returns
-        -------
-        None
-        """
-        if array.ndim == 1:
-            array = array[np.newaxis, :]
-
-        if array.shape[1] != n_time:
-            # Split over lines for readability
-            raise ValueError(
-                f"Array should have shape (realisation, time) with time \
+    """Check that the input arrays have the correct shape.
+
+    Parameters
+    ----------
+    array: np.ndarray
+        Input array of some kind.
+    n_time: int
+        Expected number of time steps.
+
+    Returns
+    -------
+    None
+    """
+    if array.ndim == 1:
+        array = array[np.newaxis, :]
+
+    if array.shape[1] != n_time:
+        # Split over lines for readability
+        raise ValueError(
+            f"Array should have shape (realisation, time) with time \
                 dimension of length {n_time}. Got {array.shape}."
-            )
+        )

From 3020804a76af90c6558a332ce1288f6366f0acd4 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Wed, 20 May 2026 15:35:08 +0100
Subject: [PATCH 136/276] Enforce Spatial attribute

---
 profsea/components/core/base.py  | 11 ++++++++++-
 profsea/components/core/state.py |  2 ++
 profsea/utils/utils.py           |  7 +++++--
 3 files changed, 17 insertions(+), 3 deletions(-)

diff --git a/profsea/components/core/base.py b/profsea/components/core/base.py
index 3375e6c..1baa743 100644
--- a/profsea/components/core/base.py
+++ b/profsea/components/core/base.py
@@ -3,8 +3,17 @@
 
 from .state import ClimateState
 
+
 class Component(ABC):
     @abstractmethod
     def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
         """Calculate and return the SLR projection for this component"""
-        pass
\ No newline at end of file
+        pass
+
+
+class SpatialComponent(Component):
+    @property
+    @abstractmethod
+    def global_projection(self):
+        """Spatial component must define a global_projection attribute or property"""
+        pass
diff --git a/profsea/components/core/state.py b/profsea/components/core/state.py
index 87daf12..aa366fb 100644
--- a/profsea/components/core/state.py
+++ b/profsea/components/core/state.py
@@ -29,3 +29,5 @@ class SpatialState:
     grid_lons: np.ndarray
     n_years: int
     n_members: int
+    grid_interpolation: str
+    output_percentiles: list[int] | np.ndarray
diff --git a/profsea/utils/utils.py b/profsea/utils/utils.py
index 6bed420..90f005c 100644
--- a/profsea/utils/utils.py
+++ b/profsea/utils/utils.py
@@ -31,7 +31,10 @@ def interpolate(data: da.array, lats: int, lons: int) -> np.ndarray:
 
 
 def interpolate_to_grid(
-    data: da.array, target_lats: np.ndarray, target_lons: np.ndarray
+    data: da.array,
+    target_lats: np.ndarray,
+    target_lons: np.ndarray,
+    grid_interpolation: str = "linear",
 ) -> da.array:
     """
     Interpolate a dask array to a target grid defined by target_lats and target_lons.
@@ -56,7 +59,7 @@ def interpolate_to_grid(
     )
 
     data_interp = original_da.interp(
-        lat=target_lats, lon=target_lons, method="linear"
+        lat=target_lats, lon=target_lons, method=grid_interpolation
     ).data
 
     return da.from_array(data_interp, chunks=data.chunks)

From 1f39cf968c0ac52d373e216046bd178dedbe130e Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Fri, 22 May 2026 11:52:38 +0100
Subject: [PATCH 137/276] Add spatial model, sterodynamic component

---
 profsea/components/core/spatial_model.py   | 203 +++++++++++++++++++++
 profsea/components/spatial/sterodynamic.py | 128 +++++++++++++
 profsea/utils/utils.py                     |  40 ++--
 3 files changed, 345 insertions(+), 26 deletions(-)
 create mode 100644 profsea/components/core/spatial_model.py
 create mode 100644 profsea/components/spatial/sterodynamic.py

diff --git a/profsea/components/core/spatial_model.py b/profsea/components/core/spatial_model.py
new file mode 100644
index 0000000..e8b7ba9
--- /dev/null
+++ b/profsea/components/core/spatial_model.py
@@ -0,0 +1,203 @@
+import os
+from typing import Dict
+import warnings
+
+import dask.array as da
+import numpy as np
+from rich.console import Console
+from rich.progress import track
+import xarray as xr
+
+from .state import SpatialState
+from .base import Component
+
+console = Console()
+warnings.filterwarnings("ignore")
+
+
+class Spatial:
+    """Spatial sea level rise component emulator."""
+
+    def __init__(
+        self,
+        components: Dict[str, Component],
+        grid_config: dict = {
+            "start_lon": -179.5,
+            "end_lon": 179.5,
+            "step_lon": 1.0,
+            "start_lat": -89.5,
+            "end_lat": 89.5,
+            "step_lat": 1.0,
+        },
+        grid_interpolation: str = "linear",
+        end_year: int = 2301,
+        baseline_yrs: tuple = (1986, 2005),
+        output_percentiles: list | np.ndarray = [5, 17, 50, 83, 95],
+    ):
+        """
+        Parameters
+        ----------
+        scenario: str
+            Name of the scenario.
+        expansion_patterns_dir: str
+            Directory path of regression patterns of thermal expansion component from cmip models.
+        fingerprint_dir: str
+            Directory path of GRD (Gravitational, Rotational, Deformational) fingerprint data
+        gia_dir: str
+            Directory path of GIA (Glacial Isostatic Adjustment) data
+        components_path: str
+            Path to global sea-level rise components (output of ProFSea's gmslr module)
+        component_list: list
+            Namelist of components for spatial projections
+        end_year: int
+            End year of the projections.
+        output_percentiles: int
+            List of percentiles for output
+        output_dir: str
+            Path to output directory for saving spatial projections.
+        random_seed: bool
+            Seed for numpy.random.
+        """
+
+        self.components = components
+        self.end_year = end_year
+        self.baseline_yrs = baseline_yrs
+        self.output_percentiles = output_percentiles
+        self.start_year = 2006
+        self.n_years = self.end_year - self.start_year
+
+        if output_percentiles:
+            self.num_members = len(output_percentiles)
+        else:
+            self.num_members = next(iter(self.components.values())).global_projection.shape[0]
+
+        # Define the grid coordinates
+        self.grid_lons = np.arange(
+            grid_config["start_lon"],
+            grid_config["end_lon"] + grid_config["step_lon"],
+            grid_config["step_lon"],
+        )
+        self.grid_lats = np.arange(
+            grid_config["start_lat"],
+            grid_config["end_lat"] + grid_config["step_lat"],
+            grid_config["step_lat"],
+        )
+
+        console.log(
+            f"Baseline period = {self.baseline_yrs[0]} to {self.baseline_yrs[1]}"
+        )
+
+        # Log the size of each component and provide an estimate of their memory usage
+        for name, comp in self.components.items():
+            if not output_percentiles:
+                comp_size = comp.global_projection.nbytes / 1e9
+                future_size = (
+                    comp_size
+                    * self.num_members
+                    * len(self.grid_lats)
+                    * len(self.grid_lons)
+                )
+            else:
+                comp_size = (
+                    comp.global_projection[: len(output_percentiles)].nbytes
+                    / 1e9
+                )
+                future_size = comp_size * len(self.grid_lats) * len(self.grid_lons)
+
+            # Warn if memory usage is going to be large
+            if future_size > 20:
+                console.log(
+                    f"[bold red]Warning: the output array for component "
+                    f"[bold blue]'{name}'[/bold blue] requires a large amount "
+                    f"of memory [bold blue]({future_size:.2f} GB)[/bold blue]. "
+                    f"Consider reducing the number of members, the grid "
+                    f"resolution or take percentiles.[/bold red]"
+                )
+
+    def _calc_baseline_period(self) -> float:
+        """
+        Baseline years used for IPCC AR5 and Palmer et al 2020 -- 1986-2005
+        :param yrs: years of the projections
+        :return: baseline years
+        """
+        midyr = (
+            self.baseline_yrs[1] - self.baseline_yrs[0] + 1
+        ) * 0.5 + self.baseline_yrs[0]
+        return self.start_year - midyr
+
+    def run(self, scenario: str, member_seed: int = 42) -> None:
+        """
+        Calculates global and regional component part contributions to sea level
+        change.
+        :param mcdir: location of Monte Carlo time series for new projections
+        :param components: sea level components
+        :param scenario: emission scenario
+        :param yrs: years of the projections
+        :param array_dims: Array of nesm, nsmps and nyrs
+            nesm --> Number of ensemble members in time series
+            nsmps --> Determine the number of samples you wish to make
+            nyrs --> Number of years in each projection time series
+        :return: montecarlo_G (global contribution to sea level rise) and
+            montecarlo_R (regional contribution to sea level change)
+        """
+        seed_seq = np.random.SeedSequence(member_seed)
+
+        console.log(
+            f"Simulating {len(self.components)} sea-level components...: {', '.join(self.components.keys())}"
+        )
+
+        state = SpatialState(
+            scenario=scenario,
+            n_years=self.n_years,
+            n_members=self.num_members,
+            grid_lats=self.grid_lats,
+            grid_lons=self.grid_lons,
+            grid_interpolation="linear",
+            output_percentiles=self.output_percentiles,
+        )
+
+        child_seeds = seed_seq.spawn(len(self.components))
+        comp_rngs = {
+            name: np.random.default_rng(s)
+            for name, s in zip(self.components.keys(), child_seeds)
+        }
+
+        spatial_projections = {}
+        for name, comp in track(
+            self.components.items(), description="Spatialising components..."
+        ):
+            spatial_projections[name] = comp.project(state, comp_rngs[name])
+
+        self.results = spatial_projections
+        return spatial_projections
+
+    def _save_projections(self, montecarlo_R: da.array, component: str) -> None:
+        """
+        Save the regional sea level projections to a file.
+        :param montecarlo_R: regional sea level projections
+        :param component: sea level component
+        :param scenario: emission scenario
+        :param percentile: percentiles used for spatial projections
+        """
+        # Save data in netcdf format (Assuming first dimension is percentile, but can be more general percentile/ensemble)
+        xr_dataArray = xr.DataArray(
+            montecarlo_R,
+            dims=["percentile", "time", "lat", "lon"],
+            coords={
+                "percentile": self.output_percentiles,
+                "time": np.arange(2006, montecarlo_R.shape[1] + 2006),
+                "lat": np.arange(-90, 90) + 0.5,
+                "lon": np.arange(0, 360) + 0.5,
+            },
+        )
+        xr_dataArray.attrs["units"] = "m"
+        xr_dataArray.attrs["long_name"] = f"Regional {component} sea-level projections"
+        xr_dataArray.attrs["source"] = "ProFSea-Climate v0.1"
+        ds = xr_dataArray.to_dataset(name=component)
+
+        file_header = f"{component}_{self.scenario}_projection_{self.end_year}"
+        R_file = "_".join([file_header, "regional"]) + ".nc"
+        encoding = {component: {"zlib": True, "complevel": 5, "dtype": "float32"}}
+        ds.to_netcdf(
+            os.path.join(self.output_dir, R_file), encoding=encoding, compute=True
+        )
diff --git a/profsea/components/spatial/sterodynamic.py b/profsea/components/spatial/sterodynamic.py
new file mode 100644
index 0000000..a3d793b
--- /dev/null
+++ b/profsea/components/spatial/sterodynamic.py
@@ -0,0 +1,128 @@
+from pathlib import Path
+
+import dask.array as da
+import numpy as np
+import xarray as xr
+
+from profsea.components.core.base import SpatialComponent
+from profsea.components.core.state import ClimateState
+from profsea.utils import interpolate_to_grid, sample_members_2D
+
+
+class SterodynamicCMIP6(SpatialComponent):
+    """
+    Parameters & Attributes
+    ----------
+    global_projection: np.ndarray
+        Array of global sea level rise projections to use as input for sterodynamic projection.
+    """
+
+    def __init__(
+        self,
+        global_projection: np.ndarray,
+        patterns_dir: str = None,
+        sample_spatial: bool = False,
+    ):
+        # Convert to dask array for cheap as possible compute
+        self._global_projection = da.from_array(global_projection, chunks="auto")
+        self.sample_spatial = sample_spatial
+
+        if patterns_dir is None:
+            raise FileNotFoundError(
+                "Please specify the path to the CMIP6 sterodynamic "
+                "patterns using the 'patterns_dir' argument."
+            )
+        else:
+            self.patterns_dir = Path(patterns_dir)
+
+    @property
+    def global_projection(self):
+        return self._global_projection
+
+    def _load_CMIP6_slopes(self) -> da.Array:
+        """
+        Load in the CMIP6 slope coefficients.
+        :return: 3D Dask array of regression coefficients (model, lat, lon)
+        """
+        slope_files = list(Path(self.patterns_dir).glob("*/zos_regression_ssp585_*.nc"))
+
+        if not slope_files:
+            raise FileNotFoundError(
+                f"No NetCDF slope files found in {self.patterns_dir}"
+            )
+
+        # Lazily load all files keeping metadata intact
+        datasets = [
+            xr.open_dataarray(f, chunks={"lat": 45, "lon": 45}) for f in slope_files
+        ]
+
+        # Concatenate along a new dimension (representing the ensemble/models)
+        slopes_stack = xr.concat(datasets, dim="model")
+
+        return slopes_stack
+
+    def _calc_expansion_contribution(
+        self, rng: np.random.Generator, state: ClimateState
+    ) -> da.Array:
+        """
+        Calculate the thermal expansion contribution to the regional component of
+        sea level rise.
+        :param scenario: emission scenario
+        :param nsmps: determine the number of samples
+        :return: expansion estimates converted to mm/yr
+        """
+        # Select slope coefficients based on the MIP
+        coeffs_da = self._load_CMIP6_slopes()
+
+        # Align the grid coordinates + interpolate if necessary
+        interp_da = interpolate_to_grid(coeffs_da, state.grid_lats, state.grid_lons)
+        coeffs = interp_da.data
+
+        if self.sample_spatial:
+            rand_samples = rng.choice(
+                coeffs.shape[0], size=state.n_members, replace=True
+            )
+            return coeffs[rand_samples, :, :]
+        else:
+            # Calc pattern ensemble mean
+            mean_coeff = da.nanmean(coeffs, axis=0)
+            return da.broadcast_to(
+                mean_coeff,
+                (state.n_members, state.grid_lats.shape[0], state.grid_lons.shape[0]),
+            )
+
+    def project(self, state: ClimateState, rng) -> np.ndarray:
+        # Calculate percentiles locally without mutating self
+        if state.output_percentiles is not None:
+            current_projection = sample_members_2D(
+                self.global_projection, state.output_percentiles
+            )
+        else:
+            current_projection = self.global_projection
+
+        expansion_contribution = self._calc_expansion_contribution(rng, state)
+
+        sterodynamic_projection = (
+            current_projection[:, :, None, None] * expansion_contribution[:, None, :, :]
+        )
+        return sterodynamic_projection.compute()
+
+
+class SterodynamicCMIP5(SpatialComponent):
+    """
+    Placeholder for a sterodynamic SLR component based on CMIP5 projections.
+    """
+
+    def __init__(self):
+        pass
+
+    def project(self, state: ClimateState, rng) -> np.ndarray:
+        # For now, just return zeros as a placeholder
+        return np.zeros(
+            (
+                state.n_members,
+                state.n_years,
+                state.grid_lats.shape[0],
+                state.grid_lons.shape[0],
+            )
+        )
diff --git a/profsea/utils/utils.py b/profsea/utils/utils.py
index 90f005c..8657eda 100644
--- a/profsea/utils/utils.py
+++ b/profsea/utils/utils.py
@@ -26,43 +26,31 @@ def interpolate(data: da.array, lats: int, lons: int) -> np.ndarray:
     data_interp = original_da.interp(
         lat=target_lat, lon=target_lon, method="linear"
     ).data
-
     return data_interp
 
 
 def interpolate_to_grid(
-    data: da.array,
+    data: xr.DataArray,
     target_lats: np.ndarray,
     target_lons: np.ndarray,
     grid_interpolation: str = "linear",
-) -> da.array:
+) -> xr.DataArray:
     """
-    Interpolate a dask array to a target grid defined by target_lats and target_lons.
-    Assumes the input data has dimensions (lat, lon) and coordinates named 'lat' and 'lon'.
-
-    Parameters
-    ----------
-    data: da.array
-        Input dask array with dimensions (lat, lon) and coordinates 'lat' and 'lon'.
-    target_lats: np.ndarray
-        1D array of target latitudes to interpolate to.
-    target_lons: np.ndarray
-        1D array of target longitudes to interpolate to.
-    Returns
-    -------
-    da.array        Interpolated dask array on the target grid.
+    Interpolate an xarray DataArray to a target grid defined by target_lats and target_lons.
+    Safely handles longitude wrapping mismatches (e.g., [0, 360) vs [-180, 180)).
     """
-    original_da = xr.DataArray(
-        data.data,
-        coords=[("lat", data[data.dims[0]].values), ("lon", data[data.dims[1]].values)],
-        name="v",
-    )
+    # Normalize source longitudes to [-180, 180) and sort monotonically
+    data = data.assign_coords(lon=(((data.lon + 180) % 360) - 180))
+    data = data.sortby("lon")
 
-    data_interp = original_da.interp(
-        lat=target_lats, lon=target_lons, method=grid_interpolation
-    ).data
+    # Normalize target longitudes to [-180, 180) and sort
+    target_lons_norm = np.sort(((target_lons + 180) % 360) - 180)
 
-    return da.from_array(data_interp, chunks=data.chunks)
+    # Interpolate!
+    data_interp = data.interp(
+        lat=target_lats, lon=target_lons_norm, method=grid_interpolation
+    )
+    return data_interp
 
 
 def check_shapes(array: np.ndarray, n_time: int) -> None:

From cf5459a0243353b3d89bae87c3cebd54b99e8235 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Fri, 22 May 2026 12:09:40 +0100
Subject: [PATCH 138/276] Some fixes

---
 profsea/components/core/spatial_model.py   | 67 +++++++++++-----------
 profsea/components/spatial/sterodynamic.py |  2 +-
 2 files changed, 34 insertions(+), 35 deletions(-)

diff --git a/profsea/components/core/spatial_model.py b/profsea/components/core/spatial_model.py
index e8b7ba9..55b4440 100644
--- a/profsea/components/core/spatial_model.py
+++ b/profsea/components/core/spatial_model.py
@@ -21,14 +21,7 @@ class Spatial:
     def __init__(
         self,
         components: Dict[str, Component],
-        grid_config: dict = {
-            "start_lon": -179.5,
-            "end_lon": 179.5,
-            "step_lon": 1.0,
-            "start_lat": -89.5,
-            "end_lat": 89.5,
-            "step_lat": 1.0,
-        },
+        grid_config: dict = None,
         grid_interpolation: str = "linear",
         end_year: int = 2301,
         baseline_yrs: tuple = (1986, 2005),
@@ -37,26 +30,19 @@ def __init__(
         """
         Parameters
         ----------
-        scenario: str
-            Name of the scenario.
-        expansion_patterns_dir: str
-            Directory path of regression patterns of thermal expansion component from cmip models.
-        fingerprint_dir: str
-            Directory path of GRD (Gravitational, Rotational, Deformational) fingerprint data
-        gia_dir: str
-            Directory path of GIA (Glacial Isostatic Adjustment) data
-        components_path: str
-            Path to global sea-level rise components (output of ProFSea's gmslr module)
-        component_list: list
-            Namelist of components for spatial projections
-        end_year: int
-            End year of the projections.
-        output_percentiles: int
-            List of percentiles for output
-        output_dir: str
-            Path to output directory for saving spatial projections.
-        random_seed: bool
-            Seed for numpy.random.
+        components: dict
+            Dictionary of spatial components to include in the model. Keys should be the component names and values should be instances of Component subclasses.
+        grid_config: dict, optional
+            Dictionary defining the grid configuration with keys 'start_lon', 'end_lon', 'step_lon
+            'start_lat', 'end_lat', 'step_lat'. If None, defaults to a 1-degree global grid.
+        grid_interpolation: str, optional
+            Interpolation method to use when interpolating patterns to the target grid. Default is 'linear'.
+        end_year: int, optional
+            The final year of the projections. Default is 2301.
+        baseline_yrs: tuple, optional
+            Tuple defining the start and end years of the baseline period for calculating anomalies. Default is (1986, 2005).
+        output_percentiles: list or np.ndarray, optional
+            List or array of percentiles to sample from the ensemble for output. If None, outputs all members. Default is [5, 17, 50, 83, 95].
         """
 
         self.components = components
@@ -69,7 +55,19 @@ def __init__(
         if output_percentiles:
             self.num_members = len(output_percentiles)
         else:
-            self.num_members = next(iter(self.components.values())).global_projection.shape[0]
+            self.num_members = next(
+                iter(self.components.values())
+            ).global_projection.shape[0]
+
+        if grid_config is None:
+            grid_config: dict = {
+                "start_lon": -179.5,
+                "end_lon": 179.5,
+                "step_lon": 1.0,
+                "start_lat": -89.5,
+                "end_lat": 89.5,
+                "step_lat": 1.0,
+            }
 
         # Define the grid coordinates
         self.grid_lons = np.arange(
@@ -99,8 +97,7 @@ def __init__(
                 )
             else:
                 comp_size = (
-                    comp.global_projection[: len(output_percentiles)].nbytes
-                    / 1e9
+                    comp.global_projection[: len(output_percentiles)].nbytes / 1e9
                 )
                 future_size = comp_size * len(self.grid_lats) * len(self.grid_lons)
 
@@ -114,7 +111,9 @@ def __init__(
                     f"resolution or take percentiles.[/bold red]"
                 )
 
-    def _calc_baseline_period(self) -> float:
+    def _calc_baseline_period(
+        self,
+    ) -> float:  # TODO: move this to the GIA componenent once made.
         """
         Baseline years used for IPCC AR5 and Palmer et al 2020 -- 1986-2005
         :param yrs: years of the projections
@@ -186,8 +185,8 @@ def _save_projections(self, montecarlo_R: da.array, component: str) -> None:
             coords={
                 "percentile": self.output_percentiles,
                 "time": np.arange(2006, montecarlo_R.shape[1] + 2006),
-                "lat": np.arange(-90, 90) + 0.5,
-                "lon": np.arange(0, 360) + 0.5,
+                "lat": self.grid_lats,
+                "lon": self.grid_lons,
             },
         )
         xr_dataArray.attrs["units"] = "m"
diff --git a/profsea/components/spatial/sterodynamic.py b/profsea/components/spatial/sterodynamic.py
index a3d793b..205b26f 100644
--- a/profsea/components/spatial/sterodynamic.py
+++ b/profsea/components/spatial/sterodynamic.py
@@ -105,7 +105,7 @@ def project(self, state: ClimateState, rng) -> np.ndarray:
         sterodynamic_projection = (
             current_projection[:, :, None, None] * expansion_contribution[:, None, :, :]
         )
-        return sterodynamic_projection.compute()
+        return sterodynamic_projection
 
 
 class SterodynamicCMIP5(SpatialComponent):

From 1d654eab03690362b710f6d04643a753866af195 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Fri, 22 May 2026 12:30:41 +0100
Subject: [PATCH 139/276] Add Zarr saving

---
 profsea/components/core/spatial_model.py | 136 ++++++++++++++++++-----
 1 file changed, 108 insertions(+), 28 deletions(-)

diff --git a/profsea/components/core/spatial_model.py b/profsea/components/core/spatial_model.py
index 55b4440..9f3899c 100644
--- a/profsea/components/core/spatial_model.py
+++ b/profsea/components/core/spatial_model.py
@@ -1,4 +1,5 @@
 import os
+from pathlib import Path
 from typing import Dict
 import warnings
 
@@ -165,38 +166,117 @@ def run(self, scenario: str, member_seed: int = 42) -> None:
         for name, comp in track(
             self.components.items(), description="Spatialising components..."
         ):
-            spatial_projections[name] = comp.project(state, comp_rngs[name])
+            lazy_projection = comp.project(state, comp_rngs[name])
+
+            # Rechunk before saving to optimize memory during writing
+            lazy_projection = lazy_projection.rechunk({0: -1, 1: -1, 2: 10, 3: 10})
+            spatial_projections[name] = lazy_projection
 
         self.results = spatial_projections
         return spatial_projections
 
-    def _save_projections(self, montecarlo_R: da.array, component: str) -> None:
+    def save_components(
+        self,
+        components: Dict[str, da.Array],
+        scenario_name: str,
+        output_dir: str = ".",
+        output_format: str = "netcdf",
+    ) -> None:
         """
-        Save the regional sea level projections to a file.
-        :param montecarlo_R: regional sea level projections
-        :param component: sea level component
-        :param scenario: emission scenario
-        :param percentile: percentiles used for spatial projections
+        Stream all regional sea level projections to disk in a single file/store.
+
+        Parameters
+        ----------
+        components: Dict[str, da.Array]
+            Dictionary of component names and their corresponding Dask arrays.
+        output_format: str
+            Format to save the output in. Must be either 'netcdf' or 'zarr'.
+        output_dir: str
+            Directory to save components to.
+        scenario_name: str
+            Name of the scenario you've run the emulator for.
         """
-        # Save data in netcdf format (Assuming first dimension is percentile, but can be more general percentile/ensemble)
-        xr_dataArray = xr.DataArray(
-            montecarlo_R,
-            dims=["percentile", "time", "lat", "lon"],
-            coords={
-                "percentile": self.output_percentiles,
-                "time": np.arange(2006, montecarlo_R.shape[1] + 2006),
-                "lat": self.grid_lats,
-                "lon": self.grid_lons,
-            },
-        )
-        xr_dataArray.attrs["units"] = "m"
-        xr_dataArray.attrs["long_name"] = f"Regional {component} sea-level projections"
-        xr_dataArray.attrs["source"] = "ProFSea-Climate v0.1"
-        ds = xr_dataArray.to_dataset(name=component)
-
-        file_header = f"{component}_{self.scenario}_projection_{self.end_year}"
-        R_file = "_".join([file_header, "regional"]) + ".nc"
-        encoding = {component: {"zlib": True, "complevel": 5, "dtype": "float32"}}
-        ds.to_netcdf(
-            os.path.join(self.output_dir, R_file), encoding=encoding, compute=True
+        output_format = output_format.lower()
+        if output_format not in ["netcdf", "zarr"]:
+            raise ValueError("output_format must be either 'netcdf' or 'zarr'.")
+
+        # Create directory if it doesn't exist
+        Path(output_dir).mkdir(parents=True, exist_ok=True)
+
+        ds = xr.Dataset()
+        member_dim = "percentile" if self.output_percentiles is not None else "member"
+
+        # Build the shared coordinates once to ensure alignment
+        # Extracting time dynamically based on the shape of the first component
+        sample_shape = next(iter(components.values())).shape
+
+        coords = {
+            member_dim: self.output_percentiles
+            if self.output_percentiles is not None
+            else np.arange(sample_shape[0]),
+            "time": np.arange(2006, sample_shape[1] + 2006),
+            "lat": self.grid_lats,
+            "lon": self.grid_lons,
+        }
+
+        encoding = {}
+        if output_format == "zarr":
+            import numcodecs
+
+            compressor = numcodecs.Blosc(
+                cname="zstd", clevel=5, shuffle=numcodecs.Blosc.BITSHUFFLE
+            )
+
+        # Loop through the isDask arrays and add them to the single Dataset
+        for name, component in components.items():
+            xr_dataArray = xr.DataArray(
+                component,
+                dims=[member_dim, "time", "lat", "lon"],
+                coords=coords,
+            )
+            xr_dataArray.attrs["units"] = "m"
+            xr_dataArray.attrs["long_name"] = f"Regional {name} sea-level projections"
+            xr_dataArray.attrs["source"] = "ProFSea-Climate v0.1"
+
+            ds[name] = xr_dataArray
+
+            # Populate the encoding dictionary variable-by-variable
+            if output_format == "netcdf":
+                encoding[name] = {"zlib": True, "complevel": 5, "dtype": "float32"}
+            elif output_format == "zarr":
+                encoding[name] = {"compressor": compressor, "dtype": "float32"}
+
+        # Define output paths
+        file_header = f"{scenario_name}_spatial_projection"
+
+        # Stream the computation and write to disk
+        if output_format == "netcdf":
+            out_path = os.path.join(output_dir, f"{file_header}.nc")
+
+            # The spinner will animate while to_netcdf is blocking
+            with console.status(
+                "[bold cyan]Streaming computation and saving NetCDF...[/bold cyan]",
+                spinner="dots",
+            ):
+                ds.to_netcdf(out_path, encoding=encoding, compute=True)
+
+            console.log(
+                f"[bold green]✓ Successfully saved NetCDF:[/bold green] {out_path}"
+            )
+
+        elif output_format == "zarr":
+            out_path = os.path.join(output_dir, f"{file_header}.zarr")
+
+            with console.status(
+                "[bold cyan]Streaming computation and saving Zarr...[/bold cyan]",
+                spinner="dots",
+            ):
+                ds.to_zarr(out_path, encoding=encoding, mode="w", compute=True)
+
+            console.log(
+                f"[bold green]✓ Successfully saved Zarr:[/bold green] {out_path}"
+            )
+
+        console.log(
+            "Output shape was " + str(ds[name].shape) + " (members, time, lat, lon)"
         )

From e469d25c4a790d281d5e3dbbee6989c584f747bc Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Fri, 22 May 2026 12:33:03 +0100
Subject: [PATCH 140/276] Update test_build.py

---
 test_build.py | 40 +++++++++++++++++++++++++++++++---------
 1 file changed, 31 insertions(+), 9 deletions(-)

diff --git a/test_build.py b/test_build.py
index 8f45066..e8cae04 100644
--- a/test_build.py
+++ b/test_build.py
@@ -3,22 +3,44 @@
 
 from profsea.components.core.global_model import Global
 from profsea.components.global_.greenland import GreenlandAR6
+from profsea.components.global_.expansion import ThermalExpansion
 from profsea.components.global_.antarctica import AntarcticaDynAR5, AntarcticaSMBAR5
+from profsea.components.core.spatial_model import Spatial
+from profsea.components.spatial.sterodynamic import SterodynamicCMIP6
 
 
 slr_components = {
-    "greenland": GreenlandAR6(),
-    "antdyn": AntarcticaDynAR5(cum_emissions_total=1000),
-    "antsmb": AntarcticaSMBAR5(),
+    "expansion": ThermalExpansion(
+        OHC_change=np.linspace(1, 5, 295).reshape(1, -1) * 1e24
+    ),
 }
 
-model = Global(
-    components=slr_components,
-    end_yr=2301
+model = Global(components=slr_components, end_yr=2301)
+
+projections = model.run(
+    scenario="test",
+    T_change=np.linspace(1, 5, 295).reshape(1, -1),
+    member_seed=42,
 )
 
-projections = model.run(scenario="test", T_change=np.linspace(0, 5, 295).reshape(1, -1), OHC_change=np.linspace(0, 5, 295).reshape(1, -1)*1e24, member_seed=42)
-gmslr = model.sum_components(projections)
 
-plt.plot(np.arange(2006, 2301), gmslr[0], label="GMSLR")
+spatial_components = {
+    "sterodynamic": SterodynamicCMIP6(
+        projections["expansion"],
+        patterns_dir="", # Update this!
+    ),
+}
+
+# Now pass to the spatial model
+model = Spatial(components=spatial_components)
+model.run(scenario="test", member_seed=42)
+
+model.save_components(
+    model.results, 
+    scenario_name="test", 
+    output_format="zarr"
+)
+
+plt.pcolormesh(model.grid_lons, model.grid_lats, model.results["sterodynamic"][3, -1, :, :])
+plt.colorbar(label="Sterodynamic SLR contribution (mm/yr)")
 plt.show()

From 4639e7dbadf384f9da573c743b0bc430159ad4a2 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Fri, 22 May 2026 13:51:55 +0100
Subject: [PATCH 141/276] Add total regional sea-level

---
 profsea/components/core/spatial_model.py | 22 +++++++++++++++++++---
 test_build.py                            |  4 +++-
 2 files changed, 22 insertions(+), 4 deletions(-)

diff --git a/profsea/components/core/spatial_model.py b/profsea/components/core/spatial_model.py
index 9f3899c..be15bf1 100644
--- a/profsea/components/core/spatial_model.py
+++ b/profsea/components/core/spatial_model.py
@@ -116,9 +116,7 @@ def _calc_baseline_period(
         self,
     ) -> float:  # TODO: move this to the GIA componenent once made.
         """
-        Baseline years used for IPCC AR5 and Palmer et al 2020 -- 1986-2005
-        :param yrs: years of the projections
-        :return: baseline years
+        Calculate the baseline period for anomalies based on the start year and the baseline years.
         """
         midyr = (
             self.baseline_yrs[1] - self.baseline_yrs[0] + 1
@@ -175,6 +173,24 @@ def run(self, scenario: str, member_seed: int = 42) -> None:
         self.results = spatial_projections
         return spatial_projections
 
+    def sum_components(self, components: Dict[str, da.Array]) -> da.Array:
+        """
+        Sum the spatial components to get total sea-level change.
+
+        Parameters
+        ----------
+        components: Dict[str, da.Array]
+            Dictionary of spatial component Dask arrays.
+
+        Returns
+        -------
+        da.Array
+            Dask array of the summed spatial projections.
+        """
+        total_rsl = da.sum(da.stack(list(components.values()), axis=0), axis=0)
+        components["total_rsl"] = total_rsl
+        return total_rsl
+
     def save_components(
         self,
         components: Dict[str, da.Array],
diff --git a/test_build.py b/test_build.py
index e8cae04..1063604 100644
--- a/test_build.py
+++ b/test_build.py
@@ -27,7 +27,7 @@
 spatial_components = {
     "sterodynamic": SterodynamicCMIP6(
         projections["expansion"],
-        patterns_dir="", # Update this!
+        patterns_dir="/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/data/cmip6",
     ),
 }
 
@@ -35,6 +35,8 @@
 model = Spatial(components=spatial_components)
 model.run(scenario="test", member_seed=42)
 
+total_rsl = model.sum_components(model.results)
+model.results["total_rsl"] = total_rsl
 model.save_components(
     model.results, 
     scenario_name="test", 

From 4813021d71656c5bebbd887d0f6838a2c60d7816 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Fri, 22 May 2026 13:54:05 +0100
Subject: [PATCH 142/276] Small fix

---
 test_build.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/test_build.py b/test_build.py
index 1063604..ef4dfee 100644
--- a/test_build.py
+++ b/test_build.py
@@ -35,8 +35,7 @@
 model = Spatial(components=spatial_components)
 model.run(scenario="test", member_seed=42)
 
-total_rsl = model.sum_components(model.results)
-model.results["total_rsl"] = total_rsl
+model.sum_components(model.results)
 model.save_components(
     model.results, 
     scenario_name="test", 

From 717ae2cddbc571ac1f4fe1e52944e640ca48a444 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Fri, 22 May 2026 14:30:16 +0100
Subject: [PATCH 143/276] Add spatial components

---
 example_script.py                         | 68 ++++++++++++++++
 profsea/components/core/spatial_model.py  | 11 ---
 profsea/components/global_/__init__.py    |  6 ++
 profsea/components/spatial/__init__.py    |  3 +
 profsea/components/spatial/fingerprint.py | 96 +++++++++++++++++++++++
 profsea/components/spatial/gia.py         | 55 +++++++++++++
 test_build.py                             | 47 -----------
 7 files changed, 228 insertions(+), 58 deletions(-)
 create mode 100644 example_script.py
 create mode 100644 profsea/components/global_/__init__.py
 create mode 100644 profsea/components/spatial/__init__.py
 create mode 100644 profsea/components/spatial/fingerprint.py
 create mode 100644 profsea/components/spatial/gia.py
 delete mode 100644 test_build.py

diff --git a/example_script.py b/example_script.py
new file mode 100644
index 0000000..15170cb
--- /dev/null
+++ b/example_script.py
@@ -0,0 +1,68 @@
+import cartopy.crs as ccrs
+import numpy as np
+import matplotlib.pyplot as plt
+
+from profsea.components.core.global_model import Global
+from profsea.components.global_.greenland import GreenlandAR6
+from profsea.components.global_.expansion import ThermalExpansion
+from profsea.components.global_.antarctica import AntarcticaDynAR5, AntarcticaSMBAR5
+from profsea.components.core.spatial_model import Spatial
+from profsea.components.spatial import SterodynamicCMIP6, Fingerprint
+
+### Global projections first ###
+slr_components = {
+    "expansion": ThermalExpansion(
+        OHC_change=np.linspace(1, 5, 295).reshape(1, -1) * 1e24
+    ),
+    "greenland": GreenlandAR6(),
+}
+
+# Pass to the global model
+model = Global(components=slr_components, end_yr=2301)
+projections = model.run(
+    scenario="test",
+    T_change=np.linspace(1, 5, 295).reshape(1, -1),
+    member_seed=42,
+)
+
+### Now spatial projections ###
+spatial_components = {
+    "sterodynamic": SterodynamicCMIP6(
+        projections["expansion"],
+        patterns_dir="/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/data/cmip6",
+    ),
+    "greenland": Fingerprint(
+        projections["greenland"],
+        fingerprint_paths="/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/data/grd_fingerprints/greenland_ar6.nc",
+        scaling_factor=1e3,  # convert from m to mm
+    ),
+}
+
+# Pass to the spatial model
+model = Spatial(components=spatial_components)
+model.run(scenario="test", member_seed=42)
+
+model.sum_components(model.results)
+model.save_components(model.results, scenario_name="test", output_format="zarr")
+
+
+### Plot example ###
+fig = plt.figure(figsize=(10, 5))
+
+total_rsl = model.results["total_rsl"][3, -1, :, :]
+vmax = np.nanmax(np.abs(total_rsl))
+vmin = -vmax
+
+ax = fig.add_subplot(111, projection=ccrs.PlateCarree())
+ax.pcolormesh(
+    model.grid_lons,
+    model.grid_lats,
+    model.results["total_rsl"][3, -1, :, :],
+    transform=ccrs.PlateCarree(),
+    cmap="PuOr_r",
+    vmin=vmin,
+    vmax=vmax,
+)
+ax.coastlines()
+fig.colorbar(mappable=ax.collections[0], label="Sterodynamic SLR contribution (mm/yr)")
+plt.show()
diff --git a/profsea/components/core/spatial_model.py b/profsea/components/core/spatial_model.py
index be15bf1..dad2758 100644
--- a/profsea/components/core/spatial_model.py
+++ b/profsea/components/core/spatial_model.py
@@ -112,17 +112,6 @@ def __init__(
                     f"resolution or take percentiles.[/bold red]"
                 )
 
-    def _calc_baseline_period(
-        self,
-    ) -> float:  # TODO: move this to the GIA componenent once made.
-        """
-        Calculate the baseline period for anomalies based on the start year and the baseline years.
-        """
-        midyr = (
-            self.baseline_yrs[1] - self.baseline_yrs[0] + 1
-        ) * 0.5 + self.baseline_yrs[0]
-        return self.start_year - midyr
-
     def run(self, scenario: str, member_seed: int = 42) -> None:
         """
         Calculates global and regional component part contributions to sea level
diff --git a/profsea/components/global_/__init__.py b/profsea/components/global_/__init__.py
new file mode 100644
index 0000000..7691994
--- /dev/null
+++ b/profsea/components/global_/__init__.py
@@ -0,0 +1,6 @@
+from .antarctica import AntarcticaDynAR5, AntarcticaSMBAR5, AntarcticaISMIP6
+from .expansion import ThermalExpansion
+from .expansion import ThermalExpansion
+from .glacier import Glacier
+from .greenland import GreenlandAR6, GreenlandDynAR5, GreenlandSMBAR5
+from .landwater import LandwaterAR5, LandwaterAR6
diff --git a/profsea/components/spatial/__init__.py b/profsea/components/spatial/__init__.py
new file mode 100644
index 0000000..6a5a0cc
--- /dev/null
+++ b/profsea/components/spatial/__init__.py
@@ -0,0 +1,3 @@
+from .sterodynamic import SterodynamicCMIP5, SterodynamicCMIP6
+from .fingerprint import Fingerprint
+from .gia import GIA
\ No newline at end of file
diff --git a/profsea/components/spatial/fingerprint.py b/profsea/components/spatial/fingerprint.py
new file mode 100644
index 0000000..5b0debc
--- /dev/null
+++ b/profsea/components/spatial/fingerprint.py
@@ -0,0 +1,96 @@
+from pathlib import Path
+
+import dask.array as da
+import numpy as np
+import xarray as xr
+
+from profsea.components.core.base import SpatialComponent
+from profsea.components.core.state import SpatialState
+from profsea.utils import interpolate_to_grid, sample_members_2D
+
+
+class Fingerprint(SpatialComponent):
+    """
+    Spatial fingerprint component that applies spatial patterns to global projections.
+    """
+
+    def __init__(
+        self,
+        global_projection: np.ndarray,
+        fingerprint_paths: str | Path | list[str | Path],
+        scaling_factor: float = 1.0,
+        sample_spatial: bool = False,
+    ):
+        """
+        Parameters
+        ----------
+        global_projection: 2D array (members x years) of global projections to apply the fingerprints to.
+        fingerprint_paths: Path(s) to NetCDF files containing the spatial fingerprint patterns. Each file should contain a DataArray with dimensions (lat, lon).
+        scaling_factor: Optional multiplier to apply to the fingerprint patterns (e.g., to convert from m to mm).
+        sample_spatial: If True, randomly sample a different fingerprint pattern for each member. If False, use the mean of all provided fingerprints for all members (storyline mode).
+        """
+        self._global_projection = da.from_array(global_projection, chunks="auto")
+        self.scaling_factor = scaling_factor
+        self.sample_spatial = sample_spatial
+
+        # Normalize the input to always be a list of Path objects
+        if isinstance(fingerprint_paths, (str, Path)):
+            self.fp_paths = [Path(fingerprint_paths)]
+        else:
+            self.fp_paths = [Path(p) for p in fingerprint_paths]
+
+        for p in self.fp_paths:
+            if not p.exists():
+                raise FileNotFoundError(f"Missing fingerprint file: {p}")
+
+    @property
+    def global_projection(self):
+        return self._global_projection
+
+    def _load_and_interpolate(self, state: SpatialState) -> da.Array:
+        """Lazily load and regrid all provided fingerprints."""
+        grids = []
+        for path in self.fp_paths:
+            fp_da = xr.open_dataarray(path, chunks={"lat": 45, "lon": 45})
+            fp_interp = interpolate_to_grid(fp_da, state.grid_lats, state.grid_lons)
+            grids.append(fp_interp.data * self.scaling_factor)
+
+        # Stack them into a 3D array: (n_fingerprints, lat, lon)
+        return da.stack(grids, axis=0)
+
+    def project(self, state: SpatialState, rng: np.random.Generator) -> da.Array:
+        fps = self._load_and_interpolate(state)  # Shape: (n_fps, lat, lon)
+
+        # Handle the global projection
+        if state.output_percentiles is not None:
+            global_proj = sample_members_2D(
+                self.global_projection, state.output_percentiles
+            )
+        else:
+            global_proj = self.global_projection
+
+        # Determine the spatial fingerprint for each member
+        n_fps = fps.shape[0]
+
+        if n_fps == 1:
+            # Only one fingerprint available
+            selected_fps = da.broadcast_to(
+                fps[0],
+                (state.n_members, state.grid_lats.shape[0], state.grid_lons.shape[0]),
+            )
+        elif self.sample_spatial:
+            # Probabilistic mode: pick a random fingerprint per member
+            fp_indices = rng.integers(0, n_fps, size=state.n_members)
+            selected_fps = fps[fp_indices, :, :]
+        else:
+            # Storyline mode: take the mean of the available fingerprints
+            mean_fp = da.nanmean(fps, axis=0)
+            selected_fps = da.broadcast_to(
+                mean_fp,
+                (state.n_members, state.grid_lats.shape[0], state.grid_lons.shape[0]),
+            )
+
+        # Broadcast and multiply: (members, years) * (members, lat, lon)
+        spatial_projection = global_proj[:, :, None, None] * selected_fps[:, None, :, :]
+
+        return spatial_projection
diff --git a/profsea/components/spatial/gia.py b/profsea/components/spatial/gia.py
new file mode 100644
index 0000000..e7dbad2
--- /dev/null
+++ b/profsea/components/spatial/gia.py
@@ -0,0 +1,55 @@
+from pathlib import Path
+import dask.array as da
+import xarray as xr
+import numpy as np
+
+from profsea.components.core.base import SpatialComponent
+from profsea.components.core.state import SpatialState
+from profsea.utils import interpolate_to_grid
+
+class GIA(SpatialComponent):
+    """
+    Handles Glacial Isostatic Adjustment.
+    GIA accumulates linearly over time and has no global warming driver input.
+    """
+    def __init__(
+        self, 
+        gia_path: str, 
+        baseline_yrs: tuple = (1986, 2005)
+    ):
+        self.gia_path = Path(gia_path)
+        self.baseline_yrs = baseline_yrs
+        
+        if not self.gia_path.exists():
+            raise FileNotFoundError(f"Clean GIA NetCDF missing: {self.gia_path}")
+            
+        # Dummy property required by the base Spatial architecture
+        self._global_projection = da.zeros((1, 1))
+
+    @property
+    def global_projection(self):
+        return self._global_projection
+
+    def project(self, state: SpatialState, rng: np.random.Generator) -> da.Array:
+        # 1. Load the NetCDF lazily and interpolate to the target user grid
+        gia_da = xr.open_dataarray(self.gia_path, chunks={"lat": 45, "lon": 45})
+        interp_da = interpolate_to_grid(gia_da, state.grid_lats, state.grid_lons)
+        
+        # Shape is now (n_models, target_lat, target_lon)
+        gia_rates = interp_da.data
+        n_models = gia_rates.shape[0]
+
+        # 2. Calculate the accumulation time vector (mm/yr to m/yr)
+        midyr = (self.baseline_yrs[1] - self.baseline_yrs[0] + 1) * 0.5 + self.baseline_yrs[0]
+        Tdelta = 2006 - midyr
+        unit_series = (np.arange(state.n_years) + Tdelta) * 0.001
+        
+        # 3. Randomly sample the GIA models for each Monte Carlo member
+        rgiai = rng.integers(n_models, size=state.n_members)
+        selected_gia = gia_rates[rgiai, :, :] # Shape: (members, lat, lon)
+        
+        # 4. Multiply accumulation time by spatial rates
+        # (years) * (members, lat, lon) -> broadcasts to (members, years, lat, lon)
+        spatial_projection = unit_series[None, :, None, None] * selected_gia[:, None, :, :]
+        
+        return spatial_projection
\ No newline at end of file
diff --git a/test_build.py b/test_build.py
deleted file mode 100644
index ef4dfee..0000000
--- a/test_build.py
+++ /dev/null
@@ -1,47 +0,0 @@
-import numpy as np
-import matplotlib.pyplot as plt
-
-from profsea.components.core.global_model import Global
-from profsea.components.global_.greenland import GreenlandAR6
-from profsea.components.global_.expansion import ThermalExpansion
-from profsea.components.global_.antarctica import AntarcticaDynAR5, AntarcticaSMBAR5
-from profsea.components.core.spatial_model import Spatial
-from profsea.components.spatial.sterodynamic import SterodynamicCMIP6
-
-
-slr_components = {
-    "expansion": ThermalExpansion(
-        OHC_change=np.linspace(1, 5, 295).reshape(1, -1) * 1e24
-    ),
-}
-
-model = Global(components=slr_components, end_yr=2301)
-
-projections = model.run(
-    scenario="test",
-    T_change=np.linspace(1, 5, 295).reshape(1, -1),
-    member_seed=42,
-)
-
-
-spatial_components = {
-    "sterodynamic": SterodynamicCMIP6(
-        projections["expansion"],
-        patterns_dir="/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/data/cmip6",
-    ),
-}
-
-# Now pass to the spatial model
-model = Spatial(components=spatial_components)
-model.run(scenario="test", member_seed=42)
-
-model.sum_components(model.results)
-model.save_components(
-    model.results, 
-    scenario_name="test", 
-    output_format="zarr"
-)
-
-plt.pcolormesh(model.grid_lons, model.grid_lats, model.results["sterodynamic"][3, -1, :, :])
-plt.colorbar(label="Sterodynamic SLR contribution (mm/yr)")
-plt.show()

From a00c60228003348c65772cdbc4e20b73e4c97bf6 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Fri, 22 May 2026 15:27:42 +0100
Subject: [PATCH 144/276] Update ukcp18 test script

---
 example_script.py                         |  68 -----------
 profsea/components/spatial/fingerprint.py |   1 -
 profsea/components/spatial/gia.py         | 117 ++++++++++++++-----
 ukcp18_example_script.py                  | 136 ++++++++++++++++++++++
 4 files changed, 225 insertions(+), 97 deletions(-)
 delete mode 100644 example_script.py
 create mode 100644 ukcp18_example_script.py

diff --git a/example_script.py b/example_script.py
deleted file mode 100644
index 15170cb..0000000
--- a/example_script.py
+++ /dev/null
@@ -1,68 +0,0 @@
-import cartopy.crs as ccrs
-import numpy as np
-import matplotlib.pyplot as plt
-
-from profsea.components.core.global_model import Global
-from profsea.components.global_.greenland import GreenlandAR6
-from profsea.components.global_.expansion import ThermalExpansion
-from profsea.components.global_.antarctica import AntarcticaDynAR5, AntarcticaSMBAR5
-from profsea.components.core.spatial_model import Spatial
-from profsea.components.spatial import SterodynamicCMIP6, Fingerprint
-
-### Global projections first ###
-slr_components = {
-    "expansion": ThermalExpansion(
-        OHC_change=np.linspace(1, 5, 295).reshape(1, -1) * 1e24
-    ),
-    "greenland": GreenlandAR6(),
-}
-
-# Pass to the global model
-model = Global(components=slr_components, end_yr=2301)
-projections = model.run(
-    scenario="test",
-    T_change=np.linspace(1, 5, 295).reshape(1, -1),
-    member_seed=42,
-)
-
-### Now spatial projections ###
-spatial_components = {
-    "sterodynamic": SterodynamicCMIP6(
-        projections["expansion"],
-        patterns_dir="/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/data/cmip6",
-    ),
-    "greenland": Fingerprint(
-        projections["greenland"],
-        fingerprint_paths="/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/data/grd_fingerprints/greenland_ar6.nc",
-        scaling_factor=1e3,  # convert from m to mm
-    ),
-}
-
-# Pass to the spatial model
-model = Spatial(components=spatial_components)
-model.run(scenario="test", member_seed=42)
-
-model.sum_components(model.results)
-model.save_components(model.results, scenario_name="test", output_format="zarr")
-
-
-### Plot example ###
-fig = plt.figure(figsize=(10, 5))
-
-total_rsl = model.results["total_rsl"][3, -1, :, :]
-vmax = np.nanmax(np.abs(total_rsl))
-vmin = -vmax
-
-ax = fig.add_subplot(111, projection=ccrs.PlateCarree())
-ax.pcolormesh(
-    model.grid_lons,
-    model.grid_lats,
-    model.results["total_rsl"][3, -1, :, :],
-    transform=ccrs.PlateCarree(),
-    cmap="PuOr_r",
-    vmin=vmin,
-    vmax=vmax,
-)
-ax.coastlines()
-fig.colorbar(mappable=ax.collections[0], label="Sterodynamic SLR contribution (mm/yr)")
-plt.show()
diff --git a/profsea/components/spatial/fingerprint.py b/profsea/components/spatial/fingerprint.py
index 5b0debc..0ef52f8 100644
--- a/profsea/components/spatial/fingerprint.py
+++ b/profsea/components/spatial/fingerprint.py
@@ -92,5 +92,4 @@ def project(self, state: SpatialState, rng: np.random.Generator) -> da.Array:
 
         # Broadcast and multiply: (members, years) * (members, lat, lon)
         spatial_projection = global_proj[:, :, None, None] * selected_fps[:, None, :, :]
-
         return spatial_projection
diff --git a/profsea/components/spatial/gia.py b/profsea/components/spatial/gia.py
index e7dbad2..f681729 100644
--- a/profsea/components/spatial/gia.py
+++ b/profsea/components/spatial/gia.py
@@ -1,28 +1,48 @@
 from pathlib import Path
+
 import dask.array as da
-import xarray as xr
 import numpy as np
+import xarray as xr
 
 from profsea.components.core.base import SpatialComponent
 from profsea.components.core.state import SpatialState
 from profsea.utils import interpolate_to_grid
 
+
 class GIA(SpatialComponent):
     """
     Handles Glacial Isostatic Adjustment.
     GIA accumulates linearly over time and has no global warming driver input.
+    Supports loading single files, lists of files, or directories of files.
     """
+
     def __init__(
-        self, 
-        gia_path: str, 
-        baseline_yrs: tuple = (1986, 2005)
+        self,
+        gia_paths: str | Path | list[str | Path],
+        baseline_yrs: tuple = (1995, 2014),
+        sample_spatial: bool = False,
     ):
-        self.gia_path = Path(gia_path)
         self.baseline_yrs = baseline_yrs
-        
-        if not self.gia_path.exists():
-            raise FileNotFoundError(f"Clean GIA NetCDF missing: {self.gia_path}")
-            
+        self.sample_spatial = sample_spatial
+
+        if isinstance(gia_paths, (str, Path)):
+            path_obj = Path(gia_paths)
+            if path_obj.is_dir():
+                # If it's a folder, grab all NetCDF files inside
+                self.gia_paths = list(path_obj.glob("*.nc"))
+            else:
+                self.gia_paths = [path_obj]
+        else:
+            self.gia_paths = [Path(p) for p in gia_paths]
+
+        # Validation
+        if not self.gia_paths:
+            raise FileNotFoundError(f"No GIA NetCDF files found for input: {gia_paths}")
+
+        for p in self.gia_paths:
+            if not p.exists():
+                raise FileNotFoundError(f"Missing GIA file: {p}")
+
         # Dummy property required by the base Spatial architecture
         self._global_projection = da.zeros((1, 1))
 
@@ -30,26 +50,67 @@ def __init__(
     def global_projection(self):
         return self._global_projection
 
+    def _load_and_interpolate_rates(self, state: SpatialState) -> da.Array:
+        """
+        Lazily loads all GIA files, regrids them, and stacks them into a
+        single 3D array of shape (total_models, lat, lon).
+        """
+        grids = []
+        for path in self.gia_paths:
+            gia_da = xr.open_dataarray(path, chunks={"lat": 45, "lon": 45})
+            interp_da = interpolate_to_grid(gia_da, state.grid_lats, state.grid_lons)
+
+            data = interp_da.data
+
+            # Normalize dimensions: if a file is purely 2D (lat, lon),
+            # we expand it to 3D (1, lat, lon) so it can be concatenated safely.
+            if data.ndim == 2:
+                data = data[None, :, :]
+
+            grids.append(data)
+
+        # Concatenate along the model axis (axis 0)
+        return da.concatenate(grids, axis=0)
+
     def project(self, state: SpatialState, rng: np.random.Generator) -> da.Array:
-        # 1. Load the NetCDF lazily and interpolate to the target user grid
-        gia_da = xr.open_dataarray(self.gia_path, chunks={"lat": 45, "lon": 45})
-        interp_da = interpolate_to_grid(gia_da, state.grid_lats, state.grid_lons)
-        
-        # Shape is now (n_models, target_lat, target_lon)
-        gia_rates = interp_da.data
-        n_models = gia_rates.shape[0]
-
-        # 2. Calculate the accumulation time vector (mm/yr to m/yr)
-        midyr = (self.baseline_yrs[1] - self.baseline_yrs[0] + 1) * 0.5 + self.baseline_yrs[0]
+        gia_rates = self._load_and_interpolate_rates(state)
+        n_patterns = gia_rates.shape[0]
+
+        # Calculate the accumulation time vector (mm/yr to m/yr)
+        midyr = (
+            self.baseline_yrs[1] - self.baseline_yrs[0] + 1
+        ) * 0.5 + self.baseline_yrs[0]
         Tdelta = 2006 - midyr
         unit_series = (np.arange(state.n_years) + Tdelta) * 0.001
-        
-        # 3. Randomly sample the GIA models for each Monte Carlo member
-        rgiai = rng.integers(n_models, size=state.n_members)
-        selected_gia = gia_rates[rgiai, :, :] # Shape: (members, lat, lon)
-        
-        # 4. Multiply accumulation time by spatial rates
+
+        # Handle sampling if required
+        if n_patterns == 1:
+            # Only one pattern available; broadcast it to all members
+            selected_gia = da.broadcast_to(
+                gia_rates[0],
+                (state.n_members, state.grid_lats.shape[0], state.grid_lons.shape[0]),
+            )
+        else:
+            if self.sample_spatial:
+                # Probabilistic mode: pick random GIA models for each member
+                rgiai = rng.integers(n_patterns, size=state.n_members)
+                selected_gia = gia_rates[rgiai, :, :]  # Shape: (members, lat, lon)
+            else:
+                # Storyline mode: use the mean of all GIA patterns for all members
+                selected_gia = da.mean(gia_rates, axis=0)  # Shape: (lat, lon)
+                selected_gia = da.broadcast_to(
+                    selected_gia,
+                    (
+                        state.n_members,
+                        state.grid_lats.shape[0],
+                        state.grid_lons.shape[0],
+                    ),
+                )
+
+        # Multiply accumulation time by spatial rates
         # (years) * (members, lat, lon) -> broadcasts to (members, years, lat, lon)
-        spatial_projection = unit_series[None, :, None, None] * selected_gia[:, None, :, :]
-        
-        return spatial_projection
\ No newline at end of file
+        spatial_projection = (
+            unit_series[None, :, None, None] * selected_gia[:, None, :, :]
+        )
+
+        return spatial_projection
diff --git a/ukcp18_example_script.py b/ukcp18_example_script.py
new file mode 100644
index 0000000..b2c7342
--- /dev/null
+++ b/ukcp18_example_script.py
@@ -0,0 +1,136 @@
+import cartopy.crs as ccrs
+import numpy as np
+import matplotlib.pyplot as plt
+
+from profsea.components.core.global_model import Global
+from profsea.components.global_ import (
+    LandwaterAR5,
+    GreenlandDynAR5,
+    GreenlandSMBAR5,
+    ThermalExpansion,
+    AntarcticaDynAR5,
+    AntarcticaSMBAR5,
+    Glacier,
+)
+from profsea.components.core.spatial_model import Spatial
+from profsea.components.spatial import SterodynamicCMIP6, Fingerprint, GIA
+
+### Global projections first ###
+slr_components = {
+    "expansion": ThermalExpansion(
+        OHC_change=np.linspace(1, 5, 295).reshape(1, -1) * 1e24
+    ),
+    "greenland_dyn": GreenlandDynAR5(),
+    "greenland_smb": GreenlandSMBAR5(),
+    "landwater": LandwaterAR5(),
+    "antarctica_dyn": AntarcticaDynAR5(),
+    "antarctica_smb": AntarcticaSMBAR5(),
+    "glacier": Glacier()
+}
+
+# Pass to the global model
+global_model = Global(components=slr_components, end_yr=2301)
+projections = global_model.run(
+    scenario="rcp85",
+    T_change=np.linspace(1, 5, 295).reshape(1, -1),
+    member_seed=42,
+)
+global_model.sum_components(projections)
+gmslr = global_model.results["gmslr"]
+
+### Now spatial projections ###
+spatial_components = {
+    "sterodynamic": SterodynamicCMIP6(
+        projections["expansion"],
+        patterns_dir="/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/data/cmip6",
+    ),
+    "greenland_dyn": Fingerprint(
+        projections["greenland_dyn"],
+        fingerprint_paths=
+        ["/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/data/grd_fingerprints/greendyn_klemann_nomask.nc",
+         "/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/data/grd_fingerprints/greendyn_slangen_nomask.nc",
+         "/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/data/grd_fingerprints/greendyn_spada_nomask.nc"],
+    ),
+    "greenland_smb": Fingerprint(
+        projections["greenland_smb"],
+        fingerprint_paths=
+        ["/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/data/grd_fingerprints/greensmb_klemann_nomask.nc",
+         "/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/data/grd_fingerprints/greensmb_slangen_nomask.nc",
+         "/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/data/grd_fingerprints/greensmb_spada_nomask.nc"],
+    ),
+    "landwater": Fingerprint(
+        projections["landwater"],
+        fingerprint_paths="/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/data/grd_fingerprints/landwater_slangen_nomask.nc",
+    ),
+    "antarctica_dyn": Fingerprint(
+        projections["antarctica_dyn"],
+        fingerprint_paths=
+        ["/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/data/grd_fingerprints/antdyn_klemann_nomask.nc",
+         "/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/data/grd_fingerprints/antdyn_slangen_nomask.nc",
+         "/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/data/grd_fingerprints/antdyn_spada_nomask.nc"],
+    ),
+    "antarctica_smb": Fingerprint(
+        projections["antarctica_smb"],
+        fingerprint_paths=
+        ["/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/data/grd_fingerprints/antsmb_klemann_nomask.nc",
+         "/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/data/grd_fingerprints/antsmb_slangen_nomask.nc",
+         "/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/data/grd_fingerprints/antsmb_spada_nomask.nc"],
+    ),
+    "glacier": Fingerprint(
+        projections["glacier"],
+        fingerprint_paths=
+        ["/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/data/grd_fingerprints/glacier_klemann_nomask.nc",
+         "/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/data/grd_fingerprints/glacier_slangen_nomask.nc",
+         "/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/data/grd_fingerprints/glacier_spada_nomask.nc"],
+    ),
+    "gia": GIA(
+        gia_paths="/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/data/gia_clean/global_gia.nc",
+        sample_spatial=False,
+    )
+}
+
+# Pass to the spatial model
+spatial_model = Spatial(components=spatial_components)
+spatial_model.run(scenario="rcp85", member_seed=42)
+
+spatial_model.sum_components(spatial_model.results)
+spatial_model.save_components(spatial_model.results, scenario_name="rcp85", output_format="zarr")
+
+
+### Plot example ###
+fig = plt.figure(figsize=(10, 4), layout="constrained")
+
+total_rsl = spatial_model.results["total_rsl"][2, -1, :, :]
+vmax = np.nanmax(np.abs(total_rsl))
+vmin = -vmax
+
+ax = fig.add_subplot(121)
+
+# Global projections
+yrs = np.arange(2006, 2301)
+ax.plot(yrs, np.median(global_model.results["gmslr"], axis=0), label="Global Projection", color="royalblue")
+# fill between 1 and 4 members
+ax.fill_between(
+    yrs,
+    np.percentile(global_model.results["gmslr"], 17, axis=0),
+    np.percentile(global_model.results["gmslr"], 83, axis=0),
+    alpha=0.3,
+    color="skyblue",
+)
+ax.set_xlabel("Year", fontsize=14)
+ax.set_ylabel("Global Mean Sea Level Rise (m)")
+
+ax = fig.add_subplot(122, projection=ccrs.PlateCarree())
+ax.set_title("50th Percentile, Year 2300")
+ax.pcolormesh(
+    spatial_model.grid_lons,
+    spatial_model.grid_lats,
+    total_rsl,
+    transform=ccrs.PlateCarree(),
+    cmap="PuOr_r",
+    vmin=vmin,
+    vmax=vmax,
+)
+ax.coastlines()
+fig.colorbar(mappable=ax.collections[0], label="Relative Sea Level (m)", orientation="horizontal", pad=0.02)
+plt.show()

From 42e8459aa5aff8b40355475cf3595c4fdfd62ed9 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Fri, 22 May 2026 15:31:05 +0100
Subject: [PATCH 145/276] Update paths, default output is now zarr

---
 profsea/components/core/spatial_model.py |  2 +-
 ukcp18_example_script.py                 | 36 ++++++++++++------------
 2 files changed, 19 insertions(+), 19 deletions(-)

diff --git a/profsea/components/core/spatial_model.py b/profsea/components/core/spatial_model.py
index dad2758..44b48f2 100644
--- a/profsea/components/core/spatial_model.py
+++ b/profsea/components/core/spatial_model.py
@@ -185,7 +185,7 @@ def save_components(
         components: Dict[str, da.Array],
         scenario_name: str,
         output_dir: str = ".",
-        output_format: str = "netcdf",
+        output_format: str = "zarr",
     ) -> None:
         """
         Stream all regional sea level projections to disk in a single file/store.
diff --git a/ukcp18_example_script.py b/ukcp18_example_script.py
index b2c7342..98a7b01 100644
--- a/ukcp18_example_script.py
+++ b/ukcp18_example_script.py
@@ -42,49 +42,49 @@
 spatial_components = {
     "sterodynamic": SterodynamicCMIP6(
         projections["expansion"],
-        patterns_dir="/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/data/cmip6",
+        patterns_dir="data-minimal/cmip6",
     ),
     "greenland_dyn": Fingerprint(
         projections["greenland_dyn"],
         fingerprint_paths=
-        ["/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/data/grd_fingerprints/greendyn_klemann_nomask.nc",
-         "/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/data/grd_fingerprints/greendyn_slangen_nomask.nc",
-         "/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/data/grd_fingerprints/greendyn_spada_nomask.nc"],
+        ["data-minimal/grd_fingerprints/greendyn_klemann_nomask.nc",
+         "data-minimal/grd_fingerprints/greendyn_slangen_nomask.nc",
+         "data-minimal/grd_fingerprints/greendyn_spada_nomask.nc"],
     ),
     "greenland_smb": Fingerprint(
         projections["greenland_smb"],
         fingerprint_paths=
-        ["/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/data/grd_fingerprints/greensmb_klemann_nomask.nc",
-         "/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/data/grd_fingerprints/greensmb_slangen_nomask.nc",
-         "/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/data/grd_fingerprints/greensmb_spada_nomask.nc"],
+        ["data-minimal/grd_fingerprints/greensmb_klemann_nomask.nc",
+         "data-minimal/grd_fingerprints/greensmb_slangen_nomask.nc",
+         "data-minimal/grd_fingerprints/greensmb_spada_nomask.nc"],
     ),
     "landwater": Fingerprint(
         projections["landwater"],
-        fingerprint_paths="/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/data/grd_fingerprints/landwater_slangen_nomask.nc",
+        fingerprint_paths="data-minimal/grd_fingerprints/landwater_slangen_nomask.nc",
     ),
     "antarctica_dyn": Fingerprint(
         projections["antarctica_dyn"],
         fingerprint_paths=
-        ["/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/data/grd_fingerprints/antdyn_klemann_nomask.nc",
-         "/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/data/grd_fingerprints/antdyn_slangen_nomask.nc",
-         "/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/data/grd_fingerprints/antdyn_spada_nomask.nc"],
+        ["data-minimal/grd_fingerprints/antdyn_klemann_nomask.nc",
+         "data-minimal/grd_fingerprints/antdyn_slangen_nomask.nc",
+         "data-minimal/grd_fingerprints/antdyn_spada_nomask.nc"],
     ),
     "antarctica_smb": Fingerprint(
         projections["antarctica_smb"],
         fingerprint_paths=
-        ["/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/data/grd_fingerprints/antsmb_klemann_nomask.nc",
-         "/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/data/grd_fingerprints/antsmb_slangen_nomask.nc",
-         "/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/data/grd_fingerprints/antsmb_spada_nomask.nc"],
+        ["data-minimal/grd_fingerprints/antsmb_klemann_nomask.nc",
+         "data-minimal/grd_fingerprints/antsmb_slangen_nomask.nc",
+         "data-minimal/grd_fingerprints/antsmb_spada_nomask.nc"],
     ),
     "glacier": Fingerprint(
         projections["glacier"],
         fingerprint_paths=
-        ["/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/data/grd_fingerprints/glacier_klemann_nomask.nc",
-         "/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/data/grd_fingerprints/glacier_slangen_nomask.nc",
-         "/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/data/grd_fingerprints/glacier_spada_nomask.nc"],
+        ["data-minimal/grd_fingerprints/glacier_klemann_nomask.nc",
+         "data-minimal/grd_fingerprints/glacier_slangen_nomask.nc",
+         "data-minimal/grd_fingerprints/glacier_spada_nomask.nc"],
     ),
     "gia": GIA(
-        gia_paths="/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/data/gia_clean/global_gia.nc",
+        gia_paths="data-minimal/gia_clean/global_gia.nc",
         sample_spatial=False,
     )
 }

From a277f18277e797450f5744905e558686ca3ce172 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Tue, 26 May 2026 10:35:06 +0100
Subject: [PATCH 146/276] remove cvgl bug

---
 profsea/components/global_/glacier.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/profsea/components/global_/glacier.py b/profsea/components/global_/glacier.py
index 0c09832..09459f9 100644
--- a/profsea/components/global_/glacier.py
+++ b/profsea/components/global_/glacier.py
@@ -85,7 +85,7 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
             ]
         )
         cvgl_all = np.array(
-            [glparm[igl]["cvgl"] if self.glaciermip else cvgl for igl in range(ngl)]
+            [glparm[igl]["cvgl"] for igl in range(ngl)]
         )
 
         # Make an ensemble of projections for each method

From 8c797321f965a5785a816d92d3c3ce192ab8109c Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Tue, 26 May 2026 10:46:12 +0100
Subject: [PATCH 147/276] Add __init__ to core components for easier imports

---
 profsea/components/core/__init__.py | 2 ++
 1 file changed, 2 insertions(+)
 create mode 100644 profsea/components/core/__init__.py

diff --git a/profsea/components/core/__init__.py b/profsea/components/core/__init__.py
new file mode 100644
index 0000000..0528ab9
--- /dev/null
+++ b/profsea/components/core/__init__.py
@@ -0,0 +1,2 @@
+from .global_model import Global
+from .spatial_model import Spatial

From 5dcef0719a30cb33eabbe5d2e28225079436c895 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Tue, 26 May 2026 11:02:06 +0100
Subject: [PATCH 148/276] Update GreenlandAR6 projection

---
 profsea/components/global_/greenland.py | 40 ++++++++++++-------------
 1 file changed, 19 insertions(+), 21 deletions(-)

diff --git a/profsea/components/global_/greenland.py b/profsea/components/global_/greenland.py
index 9bdc1a6..5814099 100644
--- a/profsea/components/global_/greenland.py
+++ b/profsea/components/global_/greenland.py
@@ -48,8 +48,10 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
         trend_std = 0.1
 
         # Calculate trend contribution distribution
+        a_bound = (0.0 - trend_mean) / trend_std
+        b_bound = (99999.9 - trend_mean) / trend_std # Or just np.inf
         trend = truncnorm.ppf(
-            rng.random(state.nm), a=0.0, b=99999.9, loc=trend_mean, scale=trend_std
+            rng.random(state.nm), a=a_bound, b=b_bound, loc=trend_mean, scale=trend_std
         )
         trend = trend[:, None] * time_delta[None, :]
         trend = trend[:, None, :]
@@ -69,26 +71,22 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
         sle = np.cumsum(dsle, axis=2)  # mm SLE per K of global warming
         sle = sle * 1e-3  # convert mm to m SLE
 
-        # Make a Monte Carlo ensemble of projections for each model in the calibration
-        sle_ens = np.zeros((state.nm, state.nt, state.nyr))
-        r_per_model = state.nm // sle.shape[1]
-        r_remainder = state.nm % sle.shape[1]
-
-        # We want to distribute the remainder evenly across the models
-        unc = rng.normal(scale=sigma)
-        current_ensemble_idx = 0
-        for i in range(sle.shape[1]):
-            num_reals_for_model_i = r_per_model + 1 if i < r_remainder else r_per_model
-            ifirst = current_ensemble_idx
-            ilast = current_ensemble_idx + num_reals_for_model_i
-            model_term = sle[:, i, :]  # Shape (nt, nyr)
-            uncertainty_term = (
-                model_term * unc[None, i, None]
-            )  # Shape (num_reals_for_model_i, nt, nyr_param)
-
-            sle_ens[ifirst:ilast, :, :] = model_term[None, :, :] + uncertainty_term
-            current_ensemble_idx = ilast
-
+        # Vectorized distribution of nm samples across the models
+        n_models = sle.shape[1]
+        r_per_model = state.nm // n_models
+        r_remainder = state.nm % n_models
+        
+        # Calculate exactly how many realizations each model should get
+        counts = [r_per_model + 1 if i < r_remainder else r_per_model for i in range(n_models)]
+        
+        # Create an array of indices and expand sle
+        model_indices = np.repeat(np.arange(n_models), counts)
+        sle_ens = sle[:, model_indices, :]  # Shape: (nt, nm, nyr)
+        
+        # Transpose to match the intended (nm, nt, nyr) shape
+        sle_ens = sle_ens.transpose(1, 0, 2)
+
+        # Add the trend uncertainty
         sle_ens += trend
 
         # Persist 2100 rate of change

From f39fee44f2eadd14ec19859830878c731f2ca31e Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Tue, 26 May 2026 11:03:17 +0100
Subject: [PATCH 149/276] Format

---
 profsea/components/global_/greenland.py | 14 ++++++++------
 1 file changed, 8 insertions(+), 6 deletions(-)

diff --git a/profsea/components/global_/greenland.py b/profsea/components/global_/greenland.py
index 5814099..82c781d 100644
--- a/profsea/components/global_/greenland.py
+++ b/profsea/components/global_/greenland.py
@@ -49,7 +49,7 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
 
         # Calculate trend contribution distribution
         a_bound = (0.0 - trend_mean) / trend_std
-        b_bound = (99999.9 - trend_mean) / trend_std # Or just np.inf
+        b_bound = (99999.9 - trend_mean) / trend_std  # Or just np.inf
         trend = truncnorm.ppf(
             rng.random(state.nm), a=a_bound, b=b_bound, loc=trend_mean, scale=trend_std
         )
@@ -75,21 +75,23 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
         n_models = sle.shape[1]
         r_per_model = state.nm // n_models
         r_remainder = state.nm % n_models
-        
+
         # Calculate exactly how many realizations each model should get
-        counts = [r_per_model + 1 if i < r_remainder else r_per_model for i in range(n_models)]
-        
+        counts = [
+            r_per_model + 1 if i < r_remainder else r_per_model for i in range(n_models)
+        ]
+
         # Create an array of indices and expand sle
         model_indices = np.repeat(np.arange(n_models), counts)
         sle_ens = sle[:, model_indices, :]  # Shape: (nt, nm, nyr)
-        
+
         # Transpose to match the intended (nm, nt, nyr) shape
         sle_ens = sle_ens.transpose(1, 0, 2)
 
         # Add the trend uncertainty
         sle_ens += trend
 
-        # Persist 2100 rate of change
+        # Persist 2100 rate of changeg
         if state.end_yr >= 2100:
             rate = np.diff(sle_ens, axis=2)[:, :, 94]
             sle_ens[:, :, 95:] = sle_ens[:, :, 94:95] + (

From ce673393e96627c4cf30dbd876b5ad8bef93cb13 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Tue, 26 May 2026 11:04:22 +0100
Subject: [PATCH 150/276] Update scaler for consistency

---
 profsea/components/global_/greenland.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/profsea/components/global_/greenland.py b/profsea/components/global_/greenland.py
index 82c781d..122a9a0 100644
--- a/profsea/components/global_/greenland.py
+++ b/profsea/components/global_/greenland.py
@@ -55,7 +55,7 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
         )
         trend = trend[:, None] * time_delta[None, :]
         trend = trend[:, None, :]
-        trend /= 1e3  # convert mm to m SLE
+        trend *= 1e-3  # convert mm to m SLE
 
         # Calculate GIS contribution rate
         dsle = (

From 78985e98668f0ab2efef083e14710f5fa33c5777 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Tue, 26 May 2026 13:07:47 +0100
Subject: [PATCH 151/276] Update to vectorized joint sampling of input GMST,
 fix bugs

---
 profsea/components/core/global_model.py  |  57 +--
 profsea/components/global_/antarctica.py | 147 +++----
 profsea/components/global_/expansion.py  |  24 +-
 profsea/components/global_/glacier.py    | 122 +++---
 profsea/components/global_/greenland.py  |  76 ++--
 profsea/components/global_/landwater.py  |  32 +-
 profsea/updated_wrapper.py               | 486 +++++++++++++++++++++++
 7 files changed, 668 insertions(+), 276 deletions(-)
 create mode 100644 profsea/updated_wrapper.py

diff --git a/profsea/components/core/global_model.py b/profsea/components/core/global_model.py
index 1419b45..1993e26 100644
--- a/profsea/components/core/global_model.py
+++ b/profsea/components/core/global_model.py
@@ -34,7 +34,7 @@ class Global:
         If True, project SLR components in parallel.
     input_ensemble: bool
         If True, use an input ensemble of temperature and
-        ocean heat content change. if False, use a single timeseries of 
+        ocean heat content change. if False, use a single timeseries of
         temperature and ocean heat content change.
     output_percentiles: list|np.ndarray
         If not None, calculate percentiles from a 1D list/array for each
@@ -57,12 +57,11 @@ class Global:
     nyr: int
         Length of projections.
     """
-    
+
     def __init__(
         self,
         components: Dict[str, Component],
         end_yr: int,
-        nt: int = 100,
         nm: int = 1000,
         tcv: float = 1.0,
         parallel: bool = True,
@@ -73,7 +72,6 @@ def __init__(
     ):
         self.components = components
         self.end_yr = end_yr
-        self.nt = nt
         self.nm = nm
         self.tcv = tcv
         self.parallel = parallel
@@ -92,7 +90,6 @@ def run(
         T_change: np.ndarray,
         member_seed: int = 42,
     ) -> Dict[str, np.ndarray]:
-
         """Run the emulator to project GMSLR components for a specific state.
         Parameters
         ----------
@@ -103,18 +100,21 @@ def run(
         member_seed: int
             Seed for numpy.random.
         """
-        
+
         seed_seq = np.random.SeedSequence(member_seed)
         run_rng = np.random.default_rng(seed_seq)
 
         check_shapes(T_change, self.nyr)
 
-        if self.input_ensemble:
-            self.nt = T_change.shape[0]
-        else:
-            self.nt = 1
+        # Standardize T_change shape to (nt, nyr)
+        if T_change.ndim > 2:
+            T_change = np.squeeze(T_change)
+        if T_change.ndim == 1:
+            T_change = np.expand_dims(T_change, axis=0)
+
+        self.nt = T_change.shape[0]
 
-        T_ens, T_int_ens, T_int_med = self._calculate_drivers(T_change, run_rng)
+        T_ens, T_int_ens, T_int_med = self._calculate_drivers(T_change)
 
         # Shared physical correlation state
         fraction = run_rng.random(self.nm * self.nt)
@@ -160,7 +160,7 @@ def run(
 
         # Random Sampling
         if self.random_sample:
-            random_idx = run_rng.integers(low=0, high=self.nm)
+            random_idx = run_rng.integers(low=0, high=self.nt * self.nm)
             for comp_name, data in results.items():
                 if data.ndim > 1:
                     results[comp_name] = data[random_idx][None, :]
@@ -179,7 +179,9 @@ def run(
 
     def sum_components(self, components: Dict[str, np.ndarray]) -> np.ndarray:
         """Sum the components to get total GMSLR."""
-        components["gmslr"] = np.sum(list(components.values()), axis=0)
+        components["gmslr"] = np.sum(
+            [np.atleast_2d(c) for c in components.values()], axis=0
+        )
         return components["gmslr"]
 
     def save_components(
@@ -223,9 +225,7 @@ def save_components(
             ds[name] = xr_dataArray
         ds.to_netcdf(os.path.join(output_dir, f"{scenario_name}_global.nc"))
 
-    def _calculate_drivers(
-        self, T_change: np.ndarray, rng: np.random.Generator
-    ) -> tuple:
+    def _calculate_drivers(self, T_change: np.ndarray) -> tuple:
         """Calculate the drivers of GMSLR: temperature change and
         thermosteric sea level rise.
 
@@ -233,31 +233,14 @@ def _calculate_drivers(
         -------
         T_ens: np.ndarray
             Ensemble of temperature changes.
-        therm_ens: np.ndarray
-            Ensemble of thermosteric sea level rise.
         T_int_ens: np.ndarray
             Ensemble of time-integral temperature anomalies.
         T_int_med: np.ndarray
             Median of time-integral temperature anomalies.
         """
-               
-        if self.input_ensemble:
-            T_med = sample_members_2D(T_change, [50])
-            T_std = np.std(T_change, axis=0)
-        else:
-            T_med = T_change
-            T_std = 0. * T_med # dummy variable
-    
-        # Time-integral of temperature anomaly
-        T_int_med = np.cumsum(T_med)
-        T_int_std = np.cumsum(T_std)
+        T_ens = T_change.copy()
 
-        # Generate a sample of perfectly correlated timeseries fields of temperature,
-        # time-integral temperature and expansion, each of them [realisation,time]
-        z = rng.standard_normal(self.nt) * self.tcv
-
-        # For each quantity, mean + standard deviation * normal random number
-        # reshape to [realisation,time]
-        T_ens = z[:, np.newaxis] * T_std + T_med
-        T_int_ens = z[:, np.newaxis] * T_int_std + T_int_med
+        # Time-integral of temperature anomaly
+        T_int_ens = np.cumsum(T_ens, axis=1)
+        T_int_med = np.cumsum(np.median(T_ens, axis=0))
         return T_ens, T_int_ens, T_int_med
diff --git a/profsea/components/global_/antarctica.py b/profsea/components/global_/antarctica.py
index d26d7f2..3058b1a 100644
--- a/profsea/components/global_/antarctica.py
+++ b/profsea/components/global_/antarctica.py
@@ -27,136 +27,93 @@ class AntarcticaISMIP6:
     different response characteristics to warming.
     """
 
-    def __init__(self, params_path: Path, samples: int = 1000):
+    def __init__(self, params_path: Path | str):
         self.param_ds = xr.load_dataset(params_path)
-        self.n_samples = samples
         self.n_models = self.param_ds.coords["model"].shape[0]
 
     def _impulse_response_term(
         self,
-        tas: np.ndarray,
+        tas_1d: np.ndarray,
         tau1: float,
         tau2: float,
         gamma: float,
-        params: np.ndarray,
+        params_1d: np.ndarray,
         dt: float,
     ) -> np.ndarray:
-        """Computes the slow response term using convolution with two exponential decay kernels.
-        Parameters
-        ----------
-        tas : np.ndarray
-            Time series of global mean surface air temperature anomalies (shape: n_time).
-        tau1 : float
-            Timescale of the first response component (in years).
-        tau2 : float
-            Timescale of the second response component (in years).
-        gamma : float
-            Exponent for the temperature forcing term.
-        params : np.ndarray
-            Array of shape (n_samples, 4) containing [alpha1, alpha2, beta, drift] parameters for each sample.
-        dt : float
-            Time step in years.
-        Returns
-        -------
-        np.ndarray
-            Slow response term for each sample (shape: n_samples x n_time).
-        """
-        n_time = tas.shape[0]
-
-        # Two slow coeffs
-        alphas1 = params[:, 0]
-        alphas2 = params[:, 1]
+        """Computes the slow response term for a single trajectory."""
+        n_time = tas_1d.shape[0]
+        alpha1, alpha2, _ = params_1d
 
-        # Base forcing term to power of gamma, but preserving sign
-        forcing_base = np.sign(tas) * (np.abs(tas) ** gamma)
+        forcing_base = np.sign(tas_1d) * (np.abs(tas_1d) ** gamma)
 
-        # Impulse response function for timescale 1
-        decay_factors1 = np.exp(-np.arange(n_time) * dt / tau1) * (dt / tau1)
+        # decay_factors1 = np.exp(-np.arange(n_time) * dt / tau1) * (dt / tau1)
+        t_arr = np.arange(n_time)
+        decay_factors1 = (t_arr * dt / tau1**2) * np.exp(-t_arr*dt/tau1) * dt
         rate_delayed1 = fftconvolve(forcing_base, decay_factors1, mode="full")[:n_time]
-        t_conv1 = np.cumsum(rate_delayed1, axis=0) * dt
-        term_slow1 = alphas1[:, None] * t_conv1[None, :]
+        term_slow1 = alpha1 * (np.cumsum(rate_delayed1) * dt)
 
-        # Impulse response function for timescale 2
-        decay_factors2 = np.exp(-np.arange(n_time) * dt / tau2) * (dt / tau2)
+        # decay_factors2 = np.exp(-np.arange(n_time) * dt / tau2) * (dt / tau2)
+        decay_factors2 = (t_arr * dt / tau2**2) * np.exp(-t_arr*dt/tau2) * dt
         rate_delayed2 = fftconvolve(forcing_base, decay_factors2, mode="full")[:n_time]
-        t_conv2 = np.cumsum(rate_delayed2, axis=0) * dt
-        term_slow2 = alphas2[:, None] * t_conv2[None, :]
+        term_slow2 = alpha2 * (np.cumsum(rate_delayed2) * dt)
 
-        return term_slow1 + term_slow2  # Slow terms are linearly combined
+        return term_slow1 + term_slow2
 
-    def predict(
-        self, tas: np.ndarray, dt: float = 1.0, display_progress=True, seed=None
-    ) -> np.ndarray:
+    def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
         """
-        Projects AIS response using empirical additive bootstrapping.
-        Parameters
-        ----------
-        tas : np.ndarray
-            Time series of global mean surface air temperature anomalies (shape: n_time).
-        dt : float, optional
-            Time step in years (default: 1.0).
-        display_progress : bool, optional
-            Whether to show a progress bar during prediction (default: True).
-        seed : int or None, optional
-            Random seed for reproducibility (default: None).
-        Returns
-        -------
-        np.ndarray
-            Projected AIS contributions (shape: n_models x n_samples x n_time).
+        Projects AIS response using empirical additive bootstrapping aligned
+        to the ClimateState ensemble size.
         """
-        tas = np.squeeze(tas)
-        n_time = len(tas)
+        tas = state.T_ens
+        if tas.ndim > 2:
+            tas = np.squeeze(tas)
+        if tas.ndim == 1:
+            tas = np.expand_dims(tas, axis=0)
+
+        dt = 1.0
+        nm = tas.shape[0]
+        n_time = tas.shape[1]
+
+        preds = np.zeros((nm, n_time))
+        tas_int = np.cumsum(tas, axis=1) * dt
         physical_time = np.arange(n_time) * dt
 
-        # RNG
-        rng = np.random.default_rng(seed)
-
-        # Integrated temperature term
-        tas_int = np.cumsum(tas) * dt
-
-        # Output shape: (n_models, n_samples, n_time)
-        all_preds = np.zeros((self.n_models, self.n_samples, n_time))
-
-        # Shape assumed: (n_models, n_training_scenarios, 4)
+        # Randomly assign an ISMIP6 model and residual draw to each ensemble member
+        model_indices = rng.integers(0, self.n_models, size=nm)
         all_residuals = self.param_ds.param_residuals.values
         n_train_scenarios = all_residuals.shape[1]
+        residual_indices = rng.integers(0, n_train_scenarios, size=nm)
 
-        for model in track(
-            range(self.n_models),
-            description="Projecting AIS response... ",
-            disable=not display_progress,
-        ):
-            # Unpack model-specific parameters
-            tau1 = float(self.param_ds.tau1[model].values)
-            tau2 = float(self.param_ds.tau2[model].values)
-            gamma = float(self.param_ds.gamma[model].values)
-
-            # Unpack general parameters [alpha1, alpha2, beta, drift]
-            general_p = self.param_ds.general_params[model].values
+        for i in range(nm):
+            m_idx = model_indices[i]
+            r_idx = residual_indices[i]
 
-            # Sample random parameter residuals for this model
-            rand_indices = rng.integers(0, n_train_scenarios, size=self.n_samples)
-            sampled_residuals = all_residuals[model, rand_indices, :]
+            # Extract assigned model parameters
+            tau1 = float(self.param_ds.tau1[m_idx].values)
+            tau2 = float(self.param_ds.tau2[m_idx].values)
+            gamma = float(self.param_ds.gamma[m_idx].values)
 
-            # Add sampled parameter residuals to general parameters
+            general_p = self.param_ds.general_params[m_idx].values
+            sampled_residuals = all_residuals[m_idx, r_idx, :]
             total_params = general_p + sampled_residuals
 
-            # Slow response term
+            # Slow response
             term_slow = self._impulse_response_term(
-                tas, tau1, tau2, gamma, total_params, dt
+                tas[i], tau1, tau2, gamma, total_params, dt
             )
 
-            # Fast response term
-            betas = total_params[:, 2]
-            term_fast = betas[:, None] * tas_int[None, :]
+            # Fast response
+            beta = total_params[2]
+            term_fast = beta * tas_int[i]
 
             # Drift term
-            drift_coeffs = total_params[:, 3]
-            term_drift = drift_coeffs[:, None] * physical_time[None, :]
+            # drift_coeff = total_params[3]
+            # term_drift = drift_coeff * physical_time
 
-            all_preds[model, :, :] = term_fast + term_slow + term_drift
+            # Combine
+            preds[i, :] = term_fast + term_slow
 
-        return all_preds
+        return preds
 
 
 class AntarcticaDynAR5(Component):
diff --git a/profsea/components/global_/expansion.py b/profsea/components/global_/expansion.py
index c49be19..ca75cf8 100644
--- a/profsea/components/global_/expansion.py
+++ b/profsea/components/global_/expansion.py
@@ -14,6 +14,7 @@ class ThermalExpansion(Component):
     exp_efficiency: float
         Sensitivity of thermosteric SLR to ocean heat content change.
     """
+
     def __init__(self, OHC_change: np.ndarray, distribution_scaler: float = 1.0):
         self.OHC_change = OHC_change
         self.distribution_scaler = distribution_scaler
@@ -23,19 +24,16 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
         check_shapes(self.OHC_change, state.nyr)
         # Sensitivity of thermosteric SLR to ocean heat content change
         # From Turner et al. (2023)
+        mean_eff = 0.113
+        std_eff = 0.013 * self.distribution_scaler
+
         exp_efficiency = (
-            rng.normal(loc=0.113, scale=0.013, size=state.nt)[:, None] * 1e-24
+            rng.normal(loc=mean_eff, scale=std_eff, size=(state.nt, state.nm)) * 1e-24
         )  # m/YJ
-        z = rng.standard_normal(state.nt) * self.distribution_scaler
-
-        if state.nt > 1: # when input_ensemble = True
-            therm_med = sample_members_2D(self.OHC_change, [50]) * exp_efficiency
-            therm_std = np.std(self.OHC_change, axis=0) * exp_efficiency
-        else:
-            therm_med = self.OHC_change * exp_efficiency
-            therm_std = therm_med * 0. # dummary variable
-
-        therm_ens = z[:, np.newaxis] * therm_std + therm_med
-        expansion = np.tile(therm_ens, (state.nm, 1))
-        
+
+        ohc_3d = self.OHC_change[:, None, :]
+        # Efficiency shape: (nt, nm, 1)
+        exp_efficiency_3d = exp_efficiency[:, :, None]
+
+        expansion = ohc_3d * exp_efficiency_3d
         return expansion.reshape(state.nm * state.nt, state.nyr)
diff --git a/profsea/components/global_/glacier.py b/profsea/components/global_/glacier.py
index 09459f9..27e48e2 100644
--- a/profsea/components/global_/glacier.py
+++ b/profsea/components/global_/glacier.py
@@ -9,11 +9,11 @@
 from profsea.components.core.base import Component
 from profsea.components.core.global_model import ClimateState
 
-class Glacier(Component):
 
-    def __init__(self, glaciermip: int=2):
+class Glacier(Component):
+    def __init__(self, glaciermip: int = 2):
         self.glaciermip = glaciermip
-    
+
     def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
         """Project glacier contribution to GMSLR.
 
@@ -22,6 +22,11 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
         glacier: np.ndarray
             Glacier contribution to GMSLR.
         """
+        tas = state.T_ens
+        if tas.ndim > 2:
+            tas = np.squeeze(tas)
+        if tas.ndim == 1:
+            tas = np.expand_dims(tas, axis=0)
 
         # glaciermip -- False => AR5 parameters, 1 => fit to Hock et al. (2019),
         #   2 => fit to Marzeion et al. (2020)
@@ -42,93 +47,60 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
             ]
         elif self.glaciermip == 2:
             glparm = [
-                dict(name="GLIMB",   factor=3.70, exponent=0.662, cvgl=0.206),
-                dict(name="GloGEM",  factor=4.08, exponent=0.716, cvgl=0.161),
-                dict(name="JULES",   factor=5.50, exponent=0.564, cvgl=0.188),
-                dict(name="Mar-12",  factor=4.89, exponent=0.651, cvgl=0.141),
-                dict(name="OGGM",    factor=4.26, exponent=0.715, cvgl=0.164),
+                dict(name="GLIMB", factor=3.70, exponent=0.662, cvgl=0.206),
+                dict(name="GloGEM", factor=4.08, exponent=0.716, cvgl=0.161),
+                dict(name="JULES", factor=5.50, exponent=0.564, cvgl=0.188),
+                dict(name="Mar-12", factor=4.89, exponent=0.651, cvgl=0.141),
+                dict(name="OGGM", factor=4.26, exponent=0.715, cvgl=0.164),
                 dict(name="RAD2014", factor=5.18, exponent=0.709, cvgl=0.135),
                 dict(name="WAL2001", factor=2.66, exponent=0.730, cvgl=0.206),
             ]
         elif not self.glaciermip:
             glparm = [
                 dict(name="Marzeion", factor=4.96, exponent=0.685, cvgl=0.20),
-                dict(name="Radic",    factor=5.45, exponent=0.676, cvgl=0.20),
-                dict(name="Slangen",  factor=3.44, exponent=0.742, cvgl=0.20),
-                dict(name="Giesen",   factor=3.02, exponent=0.733, cvgl=0.20),
+                dict(name="Radic", factor=5.45, exponent=0.676, cvgl=0.20),
+                dict(name="Slangen", factor=3.44, exponent=0.742, cvgl=0.20),
+                dict(name="Giesen", factor=3.02, exponent=0.733, cvgl=0.20),
             ]
         else:
-            raise KeyError("glaciermip must be False (AR5 parameters), 1 (Hock et al., 2019), or 2 (Marzeion et al., 2020)")
+            raise KeyError(
+                "glaciermip must be False (AR5 parameters), 1 (Hock et al., 2019), or 2 (Marzeion et al., 2020)"
+            )
 
         ngl = len(glparm)
-        r = rng.standard_normal(state.nm)[:, np.newaxis, np.newaxis]
-        glacier = np.full((state.nm, state.nt, state.nyr), np.nan)
-
-        r_per_model = state.nm // ngl
-        r_remainder = state.nm % ngl
-
-        # Precompute mgl and zgl for all glacier methods
-        mgl_all = np.array(
-            [
-                self._project_glacier1(
-                    state.T_int_med, glparm[igl]["factor"], glparm[igl]["exponent"]
-                )
-                for igl in range(ngl)
-            ]
-        )
-        zgl_all = np.array(
-            [
-                self._project_glacier1(
-                    state.T_int_ens, glparm[igl]["factor"], glparm[igl]["exponent"]
-                )
-                for igl in range(ngl)
-            ]
-        )
-        cvgl_all = np.array(
-            [glparm[igl]["cvgl"] for igl in range(ngl)]
-        )
-
-        # Make an ensemble of projections for each method
-        current_ensemble_idx = 0
-        for igl in range(ngl):
-            mgl = mgl_all[igl]
-            zgl = zgl_all[igl]
-            cvgl = cvgl_all[igl]
-
-            num_reals_for_model_i = (
-                r_per_model + 1 if igl < r_remainder else r_per_model
-            )
-            ifirst = current_ensemble_idx
-            ilast = current_ensemble_idx + num_reals_for_model_i
+        model_indices = rng.integers(0, ngl, size=(state.nt, state.nm))
 
-            glacier[ifirst:ilast, ...] = zgl + (mgl * r[ifirst:ilast] * cvgl)
-            current_ensemble_idx = ilast
+        base_factors = np.array([p["factor"] for p in glparm])
+        base_exponents = np.array([p["exponent"] for p in glparm])
+        base_cvgls = np.array([p["cvgl"] for p in glparm])
 
-        glacier += dmz
-        np.clip(glacier, None, glmass, out=glacier)
+        factors = base_factors[model_indices][:, :, None]
+        exponents = base_exponents[model_indices][:, :, None]
+        cvgls = base_cvgls[model_indices][:, :, None]
 
-        glacier = glacier.reshape(glacier.shape[0] * glacier.shape[1], glacier.shape[2])
-        
-        return glacier
+        r = rng.standard_normal((state.nt, state.nm))[:, :, None]
 
-    def _project_glacier1(self, T_int: np.ndarray, factor: float, 
-                          exponent: float) -> np.ndarray:
-        """Project glacier contribution by one glacier method.
+        T_int_ens_3d = state.T_int_ens[:, None, :]
+        # Median shape: (1, 1, nyr)
+        T_int_med_3d = state.T_int_med[None, None, :]
 
-        Parameters
-        ----------
-        T_int: np.ndarray
-            Time-integral temperature anomaly timeseries.
-        factor: float
-            Factor for the glacier method.
-        exponent: float
-            Exponent for the glacier method.
+        zgl = self._project_glacier1(T_int_ens_3d, factors, exponents)
 
-        Returns
-        -------
-        np.ndarray
-            Projection of glacier contribution.
-        """
+        # Passes (1, 1, nyr) + (nt, nm, 1) -> Returns (nt, nm, nyr)
+        mgl = self._project_glacier1(T_int_med_3d, factors, exponents)
+
+        # 6. Apply variance and clip using 3D matrix math
+        glacier = zgl + (mgl * r * cvgls)
+        glacier += dmz
+        np.clip(glacier, None, glmass, out=glacier)
+
+        # 7. Flatten to standard 2D output for easy summation
+        return glacier.reshape(state.nt * state.nm, state.nyr)
+
+    def _project_glacier1(
+        self, T_int: np.ndarray, factor: np.ndarray, exponent: np.ndarray
+    ) -> np.ndarray:
+        """Project glacier contribution by one glacier method."""
         scale = 1e-3  # mm to m
-        
+        # np.where works perfectly with n-dimensional broadcasting
         return scale * factor * (np.where(T_int < 0, 0, T_int) ** exponent)
diff --git a/profsea/components/global_/greenland.py b/profsea/components/global_/greenland.py
index 122a9a0..bb32851 100644
--- a/profsea/components/global_/greenland.py
+++ b/profsea/components/global_/greenland.py
@@ -33,16 +33,28 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
         np.ndarray
             Total GIS contribution to GMSLR.
         """
-        df = self.df
-        b0 = df["b0"].values[None, :, None]
-        b1 = df["b1"].values[None, :, None]
-        b2 = df["b2"].values[None, :, None]
-        b3 = df["b3"].values[None, :, None]
-        b4 = df["b4"].values[None, :, None]
-        b5 = df["b5"].values[None, :, None]
-        sigma = df["sigma"].values
+        tas = state.T_ens
+        if tas.ndim > 2:
+            tas = np.squeeze(tas)
+        if tas.ndim == 1:
+            tas = np.expand_dims(tas, axis=0)
+
+        nt = tas.shape[0]
         time_delta = np.arange(state.nyr)
 
+        df = self.df
+        n_models = len(df)
+
+        model_indices = rng.integers(0, n_models, size=(nt, state.nm))
+
+        # Extract parameters and reshape to 3D: (nt, nm, 1)
+        b0 = df["b0"].values[model_indices][:, :, None]
+        b1 = df["b1"].values[model_indices][:, :, None]
+        b2 = df["b2"].values[model_indices][:, :, None]
+        b3 = df["b3"].values[model_indices][:, :, None]
+        b4 = df["b4"].values[model_indices][:, :, None]
+        b5 = df["b5"].values[model_indices][:, :, None]
+
         # GIS trend values taken from FACTS GitHub repo
         trend_mean = 0.19
         trend_std = 0.1
@@ -51,55 +63,35 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
         a_bound = (0.0 - trend_mean) / trend_std
         b_bound = (99999.9 - trend_mean) / trend_std  # Or just np.inf
         trend = truncnorm.ppf(
-            rng.random(state.nm), a=a_bound, b=b_bound, loc=trend_mean, scale=trend_std
+            rng.random((nt, state.nm)), a=a_bound, b=b_bound, loc=trend_mean, scale=trend_std
         )
-        trend = trend[:, None] * time_delta[None, :]
-        trend = trend[:, None, :]
-        trend *= 1e-3  # convert mm to m SLE
+        trend_sle = (trend[:, :, None] * time_delta[None, None, :]) * 1e-3
+
+        tas_3d = tas[:, None, :] 
 
         # Calculate GIS contribution rate
         dsle = (
             b0
-            + (b1 * state.T_ens[:, None, :])
-            + (b2 * state.T_ens[:, None, :] ** 2)
-            + (b3 * state.T_ens[:, None, :] ** 3)
+            + (b1 * tas_3d)
+            + (b2 * tas_3d**2)
+            + (b3 * tas_3d**3)
             + (b4 * time_delta[None, None, :])
             + (b5 * time_delta[None, None, :] ** 2)
         )
 
         # Now integrate
-        sle = np.cumsum(dsle, axis=2)  # mm SLE per K of global warming
-        sle = sle * 1e-3  # convert mm to m SLE
-
-        # Vectorized distribution of nm samples across the models
-        n_models = sle.shape[1]
-        r_per_model = state.nm // n_models
-        r_remainder = state.nm % n_models
-
-        # Calculate exactly how many realizations each model should get
-        counts = [
-            r_per_model + 1 if i < r_remainder else r_per_model for i in range(n_models)
-        ]
+        sle_ens = np.cumsum(dsle, axis=2) * 1e-3  # convert from mm to m
 
-        # Create an array of indices and expand sle
-        model_indices = np.repeat(np.arange(n_models), counts)
-        sle_ens = sle[:, model_indices, :]  # Shape: (nt, nm, nyr)
-
-        # Transpose to match the intended (nm, nt, nyr) shape
-        sle_ens = sle_ens.transpose(1, 0, 2)
-
-        # Add the trend uncertainty
-        sle_ens += trend
+        sle_ens += trend_sle
 
         # Persist 2100 rate of changeg
         if state.end_yr >= 2100:
-            rate = np.diff(sle_ens, axis=2)[:, :, 94]
-            sle_ens[:, :, 95:] = sle_ens[:, :, 94:95] + (
-                rate[:, :, None] * time_delta[None, None, 1 : state.nyr - 94]
+            idx_2100 = 94
+            rate = np.diff(sle_ens, axis=2)[:, :, idx_2100]
+            sle_ens[:, :, idx_2100 + 1 :] = sle_ens[:, :, idx_2100 : idx_2100 + 1] + (
+                rate[:, :, None] * time_delta[None, None, 1 : state.nyr - idx_2100]
             )
-
-        sle_ens = sle_ens.reshape((state.nm * state.nt, state.nyr))
-        return sle_ens
+        return sle_ens.reshape(nt * state.nm, state.nyr)
 
 
 class GreenlandSMBAR5(Component):
diff --git a/profsea/components/global_/landwater.py b/profsea/components/global_/landwater.py
index 614fdd6..4e42862 100644
--- a/profsea/components/global_/landwater.py
+++ b/profsea/components/global_/landwater.py
@@ -43,21 +43,21 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
         interp_ds = lw_ds.interp(
             years=np.arange(2005, 2301, 1), method="linear"
         ).squeeze()
-        lw = interp_ds["sea_level_change"].values * 1e-3  # mm to m
+        interp_ds["sea_level_change"].values * 1e-3  # mm to m
 
-        del interp_ds
+        # Base array: Shape (n_samples_in_nc, 296)
+        lw_base = interp_ds["sea_level_change"].values * 1e-3
+        n_samples_nc = lw_base.shape[0]
 
-        # Make a Monte Carlo ensemble of projections
-        full_repeats = (state.nt * state.nm) // lw.shape[0]
-        remainder = (state.nt * state.nm) % lw.shape[0]
-        lw = np.vstack([np.tile(lw, (full_repeats, 1)), lw[:remainder]])
-        lw = lw.reshape(state.nt * state.nm, lw.shape[1])
-        lw = lw[:, 1 : state.nyr + 1]  # Start at 2006, end at end_yr
+        # Sample!
+        sample_indices = rng.integers(0, n_samples_nc, size=(state.nt, state.nm))
 
-        return lw
+        # Resulting shape: (nt, nm, nyr)
+        lw_ens = lw_base[sample_indices, 1 : state.nyr + 1]
+        return lw_ens.reshape(state.nt * state.nm, state.nyr)
 
-class LandwaterAR5(Component):
 
+class LandwaterAR5(Component):
     def __init__(self):
         self.startratemean = 0.38
         self.startratepm = 0.49 - 0.38
@@ -71,10 +71,14 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
         np.ndarray
             Land water storage contribution to GMSLR.
         """
-        
+
         # The rate at start is the one for 1993-2010 from the budget table.
         # The final amount is the mean for 2081-2100.
-        nyr = state.endofAR5 - 2081 + 1  # number of years of the time-mean of the final amount
+        nyr = (
+            state.endofAR5 - 2081 + 1
+        )  # number of years of the time-mean of the final amount
         final = [-0.01, 0.09]  # AR5
-        
-        return time_projection(state, self.startratemean, self.startratepm, final, rng, nfinal=nyr)
+
+        return time_projection(
+            state, self.startratemean, self.startratepm, final, rng, nfinal=nyr
+        )
diff --git a/profsea/updated_wrapper.py b/profsea/updated_wrapper.py
new file mode 100644
index 0000000..cc39927
--- /dev/null
+++ b/profsea/updated_wrapper.py
@@ -0,0 +1,486 @@
+import argparse
+import concurrent.futures
+import json
+from pathlib import Path
+
+from fair import FAIR
+from fair.io import read_properties
+from fair.interface import initialise
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+from rich_argparse import RichHelpFormatter
+from rich.console import Console
+from rich.progress import Progress
+import xarray as xr
+
+# --- NEW IMPORTS ---
+from profsea.components.core.global_model import Global
+from profsea.components.global_ import (
+    AntarcticaISMIP6,
+    LandwaterAR6,
+    GreenlandAR6,
+    ThermalExpansion,
+    Glacier,
+)
+from profsea.utils import sample_members_2D
+
+console = Console()
+
+worker_tas = None
+worker_ohc = None
+worker_emissions = None
+worker_scenarios = None
+
+def init_worker(tas, ohc, emissions, scenarios):
+    """Initialize worker with read-only shared data."""
+    global worker_tas, worker_ohc, worker_emissions, worker_scenarios
+    worker_tas = tas.copy()
+    worker_ohc = ohc.copy()
+    worker_emissions = emissions.copy()
+    worker_scenarios = scenarios.copy()
+
+def index_df(df: pd.DataFrame, baseline_start: int, baseline_end: int) -> pd.DataFrame:
+    meta_cols = ['ensemble_member', 'scenario', 'region', 'variable', 'unit', 'climate_model']
+    existing_meta = [c for c in meta_cols if c in df.columns]
+    df_indexed = df.set_index(existing_meta)
+
+    years = df_indexed.columns.astype(str).str[:4].astype(int)
+    df_indexed.columns = years
+
+    mask_full = (years >= 1750) & (years <= 2300)
+    df_indexed = df_indexed.loc[:, mask_full]
+
+    years_sliced = df_indexed.columns
+    baseline_mask = (years_sliced >= baseline_start) & (years_sliced <= baseline_end)
+    
+    if not baseline_mask.any():
+        raise ValueError(f"No years found in baseline range {baseline_start}-{baseline_end}")
+
+    baseline_means = df_indexed.loc[:, baseline_mask].mean(axis=1)
+    df_anom = df_indexed.sub(baseline_means, axis=0)
+    years_final = df_anom.columns
+    mask_final = (years_final >= 2006) & (years_final <= 2300)
+    
+    df_final = df_anom.loc[:, mask_final].reset_index()
+    return df_final
+
+
+def df_to_arr(df, scenario_order):
+    meta_cols = ['ensemble_member', 'scenario', 'region', 'variable', 'unit', 'climate_model']
+    existing_meta = [c for c in meta_cols if c in df.columns]
+    
+    df = df.set_index(existing_meta)
+    array = []
+    for scenario in scenario_order:
+        try:
+            mask = df.index.get_level_values('scenario') == scenario
+            group_df = df.loc[mask]
+        except KeyError:
+             raise ValueError(f"Scenario '{scenario}' not found in the input DataFrame.")
+
+        if group_df.empty:
+             raise ValueError(f"No data found for scenario '{scenario}'")
+
+        group_sorted = group_df.sort_index(level='ensemble_member')
+        scenario_data = group_sorted.values
+        array.append(scenario_data)
+
+    array = np.stack(array, axis=0)
+    array = array.transpose(2, 0, 1)  # (n_scenarios, n_members, n_time)
+    return array
+
+
+def load_magicc_forcing(
+        input_path: str, scenarios: list,
+        baseline_start: int, baseline_end: int) -> tuple[np.ndarray]:
+    df = pd.read_csv(input_path, index_col=0)
+    
+    tas_condition = (
+        (df["variable"] == "Surface Air Temperature Change") & 
+        (df["scenario"].isin(scenarios))
+    )
+    ohc_condition = (
+        (df["variable"] == "Heat Content|Ocean") & 
+        (df["scenario"].isin(scenarios))
+    )
+    tas_df = df.loc[tas_condition]
+    ohc_df = df.loc[ohc_condition]
+
+    tas_df = index_df(tas_df, baseline_start, baseline_end)
+    ohc_df = index_df(ohc_df, baseline_start, baseline_end)
+    tas_arr = df_to_arr(tas_df, scenarios) 
+    ohc_arr = df_to_arr(ohc_df, scenarios) * 1e21  
+
+    # Trim between 2006 and 2300 just to be safe
+    # The years are after the 6th column, so we can slice by column names
+    tas_arr = tas_arr[:, :, (tas_df.columns[6] >= 2006) & (tas_df.columns[6] <= 2300)]
+    ohc_arr = ohc_arr[:, :, (ohc_df.columns[6] >= 2006) & (ohc_df.columns[6] <= 2300)]
+
+    # These are now of shape (time, scenario, 1, member) - we want (scenario, member, time)
+    tas_arr = tas_arr.squeeze().transpose(1, 2, 0)
+    ohc_arr = ohc_arr.squeeze().transpose(1, 2, 0)
+    return tas_arr, ohc_arr
+
+
+def load_fair_forcing(
+        input_path: str, scenarios: list,
+        baseline_start: int, baseline_end: int) -> tuple[np.ndarray]:
+    tas_path = Path(input_path) / "tas.nc"
+    ohc_path = Path(input_path) / "ohc.nc"
+
+    tas = xr.load_dataarray(tas_path)
+    ohc= xr.load_dataarray(ohc_path)
+
+    tas_baseline = tas.loc[dict(timebounds=np.arange(baseline_start, baseline_end+1))].mean("timebounds")
+    ohc_baseline = ohc.loc[dict(timebounds=np.arange(baseline_start, baseline_end+1))].mean(["timebounds"])
+
+    tas = tas.loc[dict(timebounds=np.arange(2006, 2301))] - tas_baseline  
+    ohc = ohc.loc[dict(timebounds=np.arange(2006, 2301))] - ohc_baseline
+
+    tas = tas.sel(scenario=scenarios).transpose("scenario", "config", "timebounds")
+    ohc = ohc.sel(scenario=scenarios).transpose("scenario", "config", "timebounds")
+    return tas.values, ohc.values
+
+def run_fair(
+        baseline_start: int, 
+        baseline_end: int, 
+        scenarios: list, 
+        args: argparse.Namespace
+    ) -> tuple[np.ndarray]:
+    f = FAIR()
+    f.define_time(1750, 2300, 1)
+    f.define_scenarios(scenarios)
+    species, properties = read_properties('../data/fair/fair-parameters/species_configs_properties_1.4.1.csv')
+    f.define_species(species, properties)
+    f.ch4_method='Thornhill2021'
+    df_configs = pd.read_csv('../data/fair/fair-parameters/calibrated_constrained_parameters_1.4.1.csv', index_col=0)
+    f.define_configs(df_configs.index)
+    f.allocate()
+
+    if args.emissions_file:
+        f.fill_from_csv(
+            emissions_file=args.emissions_file,
+            forcing_file=args.forcing_file
+        )
+    else:
+        f.fill_from_rcmip()
+
+    f.fill_species_configs('../data/fair/fair-parameters/species_configs_properties_1.4.1.csv')
+    f.override_defaults('../data/fair/fair-parameters/calibrated_constrained_parameters_1.4.1.csv')
+    initialise(f.concentration, f.species_configs["baseline_concentration"])
+    initialise(f.forcing, 0)
+    initialise(f.temperature, 0)
+    initialise(f.cumulative_emissions, 0)
+    initialise(f.airborne_emissions, 0)
+    initialise(f.ocean_heat_content_change, 0)
+
+    f.run()
+
+    tas_baseline = f.temperature.loc[dict(layer=0, timebounds=np.arange(baseline_start, baseline_end+1))].mean()
+    ohc_baseline = f.ocean_heat_content_change.loc[dict(timebounds=np.arange(baseline_start, baseline_end+1))].mean()
+
+    tas = f.temperature.loc[dict(layer=0, timebounds=np.arange(2006, 2301))] - tas_baseline  
+    ohc = f.ocean_heat_content_change.loc[dict(timebounds=np.arange(2006, 2301))] - ohc_baseline
+
+    tas = tas.sel(scenario=scenarios).transpose("scenario", "config", "timebounds")
+    ohc = ohc.sel(scenario=scenarios).transpose("scenario", "config", "timebounds")
+    return tas.values, ohc.values
+
+
+def simulation_task(random_idx: int, iteration_number: int) -> dict:
+    """Runs the global emulator components for a single ensemble member across scenarios."""
+    tas_sampled = worker_tas[random_idx, : :]
+    ohc_sampled = worker_ohc[random_idx, : :]
+    results = {scenario: {} for scenario in worker_scenarios}
+
+    for idx, scenario in enumerate(worker_scenarios):
+        # The new API requires reinstantiating components or updating their state per run.
+        # ThermalExpansion requires OHC_change in the constructor.
+        slr_components = {
+            "expansion": ThermalExpansion(
+                OHC_change=np.expand_dims(ohc_sampled[idx, :], axis=0)
+            ),
+            "greenland": GreenlandAR6(),
+            "landwater": LandwaterAR6(),
+            "wais": AntarcticaISMIP6(params_path="/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/aux_data/wais_params.nc"),
+            "eais": AntarcticaISMIP6(params_path="/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/aux_data/eais_params.nc"),
+            "pen": AntarcticaISMIP6(params_path="/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/aux_data/aispen_params.nc"),
+            "glacier": Glacier()
+        }
+
+        global_model = Global(components=slr_components, end_yr=2301)
+        projections = global_model.run(
+            scenario=scenario,
+            T_change=np.expand_dims(tas_sampled[idx, :], axis=0),
+            member_seed=iteration_number,
+        )
+
+        # Print the shame of all components
+        for comp_name, comp_data in projections.items():
+            console.log(f"Scenario: {scenario}, Component: {comp_name}, Shape: {comp_data.shape}")
+
+        global_model.sum_components(projections)
+
+        # Aggregate dyn/smb components to match original data structures 
+        results[scenario]["gmslr"] = global_model.results["gmslr"]
+        results[scenario]["expansion"] = projections["expansion"]
+        results[scenario]["antarctica"] = projections["wais"] + projections["eais"] + projections["pen"]
+        results[scenario]["greenland"] = projections["greenland"]
+        results[scenario]["glacier"] = projections["glacier"]
+        results[scenario]["landwater"] = projections["landwater"]
+        
+    return results, random_idx
+
+
+def plot_samples(tas: np.ndarray, ohc: np.ndarray) -> None:
+    fig = plt.figure(figsize=(12, 6), layout="constrained")
+
+    ax = fig.add_subplot(121)
+    ax.plot(tas.T, color='seagreen', alpha=0.05)
+    ax.set_xlabel("Simulation years")
+    ax.set_ylabel("GMST ($\degree$C)")
+    ax.plot(np.arange(tas.shape[1]), np.median(tas, axis=0), color="black")
+
+    ax = fig.add_subplot(122)
+    ax.plot(ohc.T, color='seagreen', alpha=0.05)
+    ax.set_xlabel("Simulation years")
+    ax.set_ylabel("OHC (J)")
+
+    fig.savefig("forcing.png", dpi=200)
+    plt.show()
+    plt.close()
+
+
+def process_global_ensemble(components: list, percentiles: list, scenario: str) -> None:
+    for comp, data in components.items():
+        sampled_ensemble = sample_members_2D(data, percentiles)
+        components[comp] = sampled_ensemble
+    return components
+
+
+def save_to_netcdf(components: dict, filename: str) -> None:
+    scenarios = list(components.keys())
+    comp_names = list(components[scenarios[0]].keys())
+    sample = components[scenarios[0]][comp_names[0]]
+    n_member, n_time = sample.shape
+    
+    years = np.arange(2006, 2006 + n_time)
+    members = np.arange(n_member)
+    data_vars = {}
+    for comp in comp_names:
+        stacked_data = np.stack([components[s][comp] for s in scenarios], axis=0)
+        data_vars[comp] = xr.DataArray(
+            data=stacked_data,
+            dims=["scenario", "member", "year"],
+            coords={
+                "scenario": scenarios,
+                "member": members,
+                "year": years
+            },
+            attrs={
+                "units": "m", 
+                "description": f"Sea level contribution from {comp}"
+            }
+        )
+        
+    ds = xr.Dataset(data_vars)
+    ds.attrs = {
+        "title": "ProFSea GMSLR Projections",
+        "source": "FAIR v2.2 + ProFSea Emulator",
+    }
+    
+    encoding = {var: {"zlib": True, "complevel": 5} for var in data_vars}
+    ds.to_netcdf(filename, encoding=encoding)
+    console.log(f"Successfully saved full ensemble to {filename}")
+
+
+def plot_component(
+        ax: plt.Axes, component_dict: dict, component: str, 
+        scenarios: list, plot_legend: bool=False) -> None:
+    time = np.arange(2006, 2301)
+    scenario_colors = {
+        scenarios[0]: '#800000',
+        scenarios[1]: '#ff0000',
+        scenarios[2]: '#fc7b03',
+        scenarios[3]: '#d3a640',
+        scenarios[4]: '#098740',
+        scenarios[5]: '#0080d0',
+        # scenarios[6]: '#100060',
+    }
+    for scenario in reversed(scenarios):
+        ax.fill_between(
+            time, 
+            component_dict[scenario][component][1], 
+            component_dict[scenario][component][3],  
+            color=scenario_colors[scenario], 
+            edgecolor='none',
+            alpha=0.3)
+        ax.plot(
+            time, 
+            component_dict[scenario][component][2], 
+            label=f"{scenario}", 
+            color=scenario_colors[scenario])
+
+    ax.set_xlabel("Year")
+    ax.set_ylabel("SLE (m)")
+    ax.set_title(component)
+    if plot_legend:
+        ax.legend(frameon=False, loc="upper left")
+
+
+def main(args):
+    percentiles = [0, 5, 17, 50, 83, 95, 100]
+    if args.emissions_file:
+        scen_df = pd.read_csv(args.emissions_file)
+        scenarios = scen_df["scenario"].unique().tolist()
+    else:
+        # Defaulting to an SSP list to avoid UnboundLocalError
+        scenarios = ["ssp119", "ssp126", "ssp245", "ssp370", "ssp534-over", "ssp585"]
+
+    console.log(f"Using scenarios: {scenarios}")
+
+    if "ssp" in scenarios[0].lower():
+        emissions_path = args.cumulative_emissions_file
+        with open(emissions_path) as f:
+            cumulative_emissions = json.load(f)
+
+    baseline_start = 1995
+    baseline_end = 2014 
+    
+    # Updated keys to align with the grouped components
+    components = {}
+    for scenario in scenarios:
+        components[scenario] = {
+            "gmslr": [], "expansion": [], "antarctica": [],
+            "greenland": [], "glacier": [], "landwater": []
+        }
+
+    if args.input.lower() == "magicc":
+        tas, ohc = load_magicc_forcing(args.input_path, scenarios, baseline_start, baseline_end)
+    elif args.input.lower() == "fair":
+        tas, ohc = load_fair_forcing(args.input_path, scenarios, baseline_start, baseline_end)
+    elif args.input.lower() == "run_fair":
+        tas, ohc = run_fair(baseline_start, baseline_end, scenarios, args)
+    else:
+        raise ValueError("Input source not recognised.")
+
+    # n_iterations = 1000
+    # rng = np.random.default_rng()
+    # random_indices = rng.integers(0, high=tas.shape[1], size=n_iterations)
+
+    # sampled_tas = []
+    # sampled_ohc = []
+
+    # max_workers = 12
+    # with concurrent.futures.ProcessPoolExecutor(
+    #     max_workers=max_workers,
+    #     initializer=init_worker,
+    #     initargs=(tas, ohc, cumulative_emissions, scenarios)
+    # ) as exectutor:
+    #     futures = [exectutor.submit(simulation_task, idx, i) for i, idx in enumerate(random_indices)]
+
+    #     with Progress() as progress:
+    #         task = progress.add_task("[orange1]Simulating and sampling...", total=n_iterations)
+    #         for future in concurrent.futures.as_completed(futures):
+    #             result_dict, idx_returned = future.result()
+    #             sampled_tas.append(tas[:, idx_returned, :])
+    #             sampled_ohc.append(ohc[:, idx_returned, :])
+
+    #             for scenario in scenarios:
+    #                 for key in components[scenario].keys():
+    #                     components[scenario][key].append(result_dict[scenario][key])
+
+    #             progress.update(task, advance=1)
+
+    # sampled_tas = np.asarray(sampled_tas) 
+    # sampled_ohc = np.asarray(sampled_ohc)
+
+    # plot_samples(sampled_tas[:, -1, :], sampled_ohc[:, -1, :])
+
+    # for scenario, data in components.items():
+    #     for key in data:
+    #         data[key] = np.array(data[key]).squeeze() 
+
+    # sampled_components = {}
+    # for scenario in scenarios:
+    #     sampled_components[scenario] = process_global_ensemble(
+    #         components[scenario], percentiles, scenario)
+
+    # 1. Get your 1000 random climate members
+    n_iterations = 1000
+    rng = np.random.default_rng()
+    random_indices = rng.integers(0, high=tas.shape[1], size=n_iterations)
+    
+    tas_matrix = tas[:, random_indices, :] # Shape: (n_scenarios, 1000, 295)
+    ohc_matrix = ohc[:, random_indices, :] # Shape: (n_scenarios, 1000, 295)
+
+    sampled_components = {}
+
+    # 2. Loop only over scenarios (e.g., SSPs)
+    for idx, scenario in enumerate(scenarios):
+        console.log(f"Projecting {scenario}...")
+        
+        # Sliced matrices: shape (1000, 295)
+        tas_scen = tas_matrix[idx, :, :] 
+        ohc_scen = ohc_matrix[idx, :, :]
+
+        slr_components = {
+            "expansion": ThermalExpansion(
+                OHC_change=ohc_scen
+            ),
+            "greenland": GreenlandAR6(),
+            "landwater": LandwaterAR6(),
+            "wais": AntarcticaISMIP6(params_path="/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/aux_data/wais_params_APR_nodrift.nc"),
+            "eais": AntarcticaISMIP6(params_path="/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/aux_data/eais_params_APR_nodrift.nc"),
+            "pen": AntarcticaISMIP6(params_path="/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/aux_data/pen_params_APR_nodrift.nc"),
+            "glacier": Glacier()
+        }
+
+        # 3. Pass the 1000-member array in one shot
+        global_model = Global(components=slr_components, end_yr=2301, nm=1)
+        projections = global_model.run(
+            scenario=scenario,
+            T_change=tas_scen, 
+            member_seed=42 # Only need one seed for the whole matrix operation
+        )
+        global_model.sum_components(projections)
+
+        projections["antarctica"] = projections.pop("wais") + projections.pop("eais") + projections.pop("pen")
+
+        # Process the 1000x295 outputs for saving
+        sampled_components[scenario] = process_global_ensemble(
+            projections, percentiles, scenario
+        )
+
+    output_dir = Path(args.output_dir) / args.output_filename
+    save_to_netcdf(sampled_components, output_dir)
+    
+    fig = plt.figure(figsize=(16, 8), layout="constrained")
+    ax = fig.add_subplot(231)
+    plot_component(ax, sampled_components, "gmslr", scenarios, plot_legend=True)
+    ax = fig.add_subplot(232)
+    plot_component(ax, sampled_components, "expansion", scenarios)
+    ax = fig.add_subplot(233)
+    plot_component(ax, sampled_components, "glacier", scenarios)
+    ax = fig.add_subplot(234)
+    plot_component(ax, sampled_components, "antarctica", scenarios)
+    ax = fig.add_subplot(235)
+    plot_component(ax, sampled_components, "greenland", scenarios)
+    ax = fig.add_subplot(236)
+    plot_component(ax, sampled_components, "landwater", scenarios)
+
+    fig.savefig(f"{args.output_dir}{args.output_filename.replace('.nc', '_components.png')}", dpi=300)
+    plt.show()
+
+
+if __name__ == "__main__":
+    p = argparse.ArgumentParser(formatter_class=RichHelpFormatter)
+    p.add_argument("--input", default="run_fair", required=False, help="Input climate forcing source (default: FaIR)", type=str)
+    p.add_argument("--input_path", required=False, help="Path to input climate forcing", type=str)
+    p.add_argument("--emissions_file", required=False, help="Path to CSV file containing emissions scenarios (optional)", type=str)
+    p.add_argument("--forcing_file", required=False, help="Path to CSV file containing climate forcing time series (optional)", type=str)
+    p.add_argument("--output_dir", default="", help="Directory to save outputs (default: current directory)", type=str)
+    p.add_argument("--output_filename", default="gmslr_projections.nc", help="Filename for output NetCDF (default: gmslr_projections.nc)", type=str)
+    p.add_argument("--cumulative_emissions_file", default="cumulative_cmip6_emissions.json", help="Path to JSON file containing cumulative emissions for SSP scenarios (default: cumulative_cmip6_emissions.json)", type=str)
+    main(p.parse_args())
\ No newline at end of file

From ef957c5b5ad4333ef834e29737da155ae27aa246 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Tue, 26 May 2026 13:23:07 +0100
Subject: [PATCH 152/276] Update the wrapper

---
 profsea/updated_wrapper.py | 407 ++++++++++++++++++-------------------
 1 file changed, 194 insertions(+), 213 deletions(-)

diff --git a/profsea/updated_wrapper.py b/profsea/updated_wrapper.py
index cc39927..a480481 100644
--- a/profsea/updated_wrapper.py
+++ b/profsea/updated_wrapper.py
@@ -1,5 +1,4 @@
 import argparse
-import concurrent.futures
 import json
 from pathlib import Path
 
@@ -11,7 +10,7 @@
 import pandas as pd
 from rich_argparse import RichHelpFormatter
 from rich.console import Console
-from rich.progress import Progress
+from rich.progress import track
 import xarray as xr
 
 # --- NEW IMPORTS ---
@@ -27,21 +26,16 @@
 
 console = Console()
 
-worker_tas = None
-worker_ohc = None
-worker_emissions = None
-worker_scenarios = None
-
-def init_worker(tas, ohc, emissions, scenarios):
-    """Initialize worker with read-only shared data."""
-    global worker_tas, worker_ohc, worker_emissions, worker_scenarios
-    worker_tas = tas.copy()
-    worker_ohc = ohc.copy()
-    worker_emissions = emissions.copy()
-    worker_scenarios = scenarios.copy()
 
 def index_df(df: pd.DataFrame, baseline_start: int, baseline_end: int) -> pd.DataFrame:
-    meta_cols = ['ensemble_member', 'scenario', 'region', 'variable', 'unit', 'climate_model']
+    meta_cols = [
+        "ensemble_member",
+        "scenario",
+        "region",
+        "variable",
+        "unit",
+        "climate_model",
+    ]
     existing_meta = [c for c in meta_cols if c in df.columns]
     df_indexed = df.set_index(existing_meta)
 
@@ -53,36 +47,45 @@ def index_df(df: pd.DataFrame, baseline_start: int, baseline_end: int) -> pd.Dat
 
     years_sliced = df_indexed.columns
     baseline_mask = (years_sliced >= baseline_start) & (years_sliced <= baseline_end)
-    
+
     if not baseline_mask.any():
-        raise ValueError(f"No years found in baseline range {baseline_start}-{baseline_end}")
+        raise ValueError(
+            f"No years found in baseline range {baseline_start}-{baseline_end}"
+        )
 
     baseline_means = df_indexed.loc[:, baseline_mask].mean(axis=1)
     df_anom = df_indexed.sub(baseline_means, axis=0)
     years_final = df_anom.columns
     mask_final = (years_final >= 2006) & (years_final <= 2300)
-    
+
     df_final = df_anom.loc[:, mask_final].reset_index()
     return df_final
 
 
 def df_to_arr(df, scenario_order):
-    meta_cols = ['ensemble_member', 'scenario', 'region', 'variable', 'unit', 'climate_model']
+    meta_cols = [
+        "ensemble_member",
+        "scenario",
+        "region",
+        "variable",
+        "unit",
+        "climate_model",
+    ]
     existing_meta = [c for c in meta_cols if c in df.columns]
-    
+
     df = df.set_index(existing_meta)
     array = []
     for scenario in scenario_order:
         try:
-            mask = df.index.get_level_values('scenario') == scenario
+            mask = df.index.get_level_values("scenario") == scenario
             group_df = df.loc[mask]
         except KeyError:
-             raise ValueError(f"Scenario '{scenario}' not found in the input DataFrame.")
+            raise ValueError(f"Scenario '{scenario}' not found in the input DataFrame.")
 
         if group_df.empty:
-             raise ValueError(f"No data found for scenario '{scenario}'")
+            raise ValueError(f"No data found for scenario '{scenario}'")
 
-        group_sorted = group_df.sort_index(level='ensemble_member')
+        group_sorted = group_df.sort_index(level="ensemble_member")
         scenario_data = group_sorted.values
         array.append(scenario_data)
 
@@ -92,25 +95,23 @@ def df_to_arr(df, scenario_order):
 
 
 def load_magicc_forcing(
-        input_path: str, scenarios: list,
-        baseline_start: int, baseline_end: int) -> tuple[np.ndarray]:
+    input_path: str, scenarios: list, baseline_start: int, baseline_end: int
+) -> tuple[np.ndarray]:
     df = pd.read_csv(input_path, index_col=0)
-    
-    tas_condition = (
-        (df["variable"] == "Surface Air Temperature Change") & 
-        (df["scenario"].isin(scenarios))
+
+    tas_condition = (df["variable"] == "Surface Air Temperature Change") & (
+        df["scenario"].isin(scenarios)
     )
-    ohc_condition = (
-        (df["variable"] == "Heat Content|Ocean") & 
-        (df["scenario"].isin(scenarios))
+    ohc_condition = (df["variable"] == "Heat Content|Ocean") & (
+        df["scenario"].isin(scenarios)
     )
     tas_df = df.loc[tas_condition]
     ohc_df = df.loc[ohc_condition]
 
     tas_df = index_df(tas_df, baseline_start, baseline_end)
     ohc_df = index_df(ohc_df, baseline_start, baseline_end)
-    tas_arr = df_to_arr(tas_df, scenarios) 
-    ohc_arr = df_to_arr(ohc_df, scenarios) * 1e21  
+    tas_arr = df_to_arr(tas_df, scenarios)
+    ohc_arr = df_to_arr(ohc_df, scenarios) * 1e21
 
     # Trim between 2006 and 2300 just to be safe
     # The years are after the 6th column, so we can slice by column names
@@ -124,50 +125,60 @@ def load_magicc_forcing(
 
 
 def load_fair_forcing(
-        input_path: str, scenarios: list,
-        baseline_start: int, baseline_end: int) -> tuple[np.ndarray]:
+    input_path: str, scenarios: list, baseline_start: int, baseline_end: int
+) -> tuple[np.ndarray]:
     tas_path = Path(input_path) / "tas.nc"
     ohc_path = Path(input_path) / "ohc.nc"
 
     tas = xr.load_dataarray(tas_path)
-    ohc= xr.load_dataarray(ohc_path)
+    ohc = xr.load_dataarray(ohc_path)
 
-    tas_baseline = tas.loc[dict(timebounds=np.arange(baseline_start, baseline_end+1))].mean("timebounds")
-    ohc_baseline = ohc.loc[dict(timebounds=np.arange(baseline_start, baseline_end+1))].mean(["timebounds"])
+    tas_baseline = tas.loc[
+        dict(timebounds=np.arange(baseline_start, baseline_end + 1))
+    ].mean("timebounds")
+    ohc_baseline = ohc.loc[
+        dict(timebounds=np.arange(baseline_start, baseline_end + 1))
+    ].mean(["timebounds"])
 
-    tas = tas.loc[dict(timebounds=np.arange(2006, 2301))] - tas_baseline  
+    tas = tas.loc[dict(timebounds=np.arange(2006, 2301))] - tas_baseline
     ohc = ohc.loc[dict(timebounds=np.arange(2006, 2301))] - ohc_baseline
 
     tas = tas.sel(scenario=scenarios).transpose("scenario", "config", "timebounds")
     ohc = ohc.sel(scenario=scenarios).transpose("scenario", "config", "timebounds")
     return tas.values, ohc.values
 
+
 def run_fair(
-        baseline_start: int, 
-        baseline_end: int, 
-        scenarios: list, 
-        args: argparse.Namespace
-    ) -> tuple[np.ndarray]:
+    baseline_start: int, baseline_end: int, scenarios: list, args: argparse.Namespace
+) -> tuple[np.ndarray]:
     f = FAIR()
     f.define_time(1750, 2300, 1)
     f.define_scenarios(scenarios)
-    species, properties = read_properties('../data/fair/fair-parameters/species_configs_properties_1.4.1.csv')
+    species, properties = read_properties(
+        "../data/fair/fair-parameters/species_configs_properties_1.4.1.csv"
+    )
     f.define_species(species, properties)
-    f.ch4_method='Thornhill2021'
-    df_configs = pd.read_csv('../data/fair/fair-parameters/calibrated_constrained_parameters_1.4.1.csv', index_col=0)
+    f.ch4_method = "Thornhill2021"
+    df_configs = pd.read_csv(
+        "../data/fair/fair-parameters/calibrated_constrained_parameters_1.4.1.csv",
+        index_col=0,
+    )
     f.define_configs(df_configs.index)
     f.allocate()
 
     if args.emissions_file:
         f.fill_from_csv(
-            emissions_file=args.emissions_file,
-            forcing_file=args.forcing_file
+            emissions_file=args.emissions_file, forcing_file=args.forcing_file
         )
     else:
         f.fill_from_rcmip()
 
-    f.fill_species_configs('../data/fair/fair-parameters/species_configs_properties_1.4.1.csv')
-    f.override_defaults('../data/fair/fair-parameters/calibrated_constrained_parameters_1.4.1.csv')
+    f.fill_species_configs(
+        "../data/fair/fair-parameters/species_configs_properties_1.4.1.csv"
+    )
+    f.override_defaults(
+        "../data/fair/fair-parameters/calibrated_constrained_parameters_1.4.1.csv"
+    )
     initialise(f.concentration, f.species_configs["baseline_concentration"])
     initialise(f.forcing, 0)
     initialise(f.temperature, 0)
@@ -177,73 +188,38 @@ def run_fair(
 
     f.run()
 
-    tas_baseline = f.temperature.loc[dict(layer=0, timebounds=np.arange(baseline_start, baseline_end+1))].mean()
-    ohc_baseline = f.ocean_heat_content_change.loc[dict(timebounds=np.arange(baseline_start, baseline_end+1))].mean()
+    tas_baseline = f.temperature.loc[
+        dict(layer=0, timebounds=np.arange(baseline_start, baseline_end + 1))
+    ].mean()
+    ohc_baseline = f.ocean_heat_content_change.loc[
+        dict(timebounds=np.arange(baseline_start, baseline_end + 1))
+    ].mean()
 
-    tas = f.temperature.loc[dict(layer=0, timebounds=np.arange(2006, 2301))] - tas_baseline  
-    ohc = f.ocean_heat_content_change.loc[dict(timebounds=np.arange(2006, 2301))] - ohc_baseline
+    tas = (
+        f.temperature.loc[dict(layer=0, timebounds=np.arange(2006, 2301))]
+        - tas_baseline
+    )
+    ohc = (
+        f.ocean_heat_content_change.loc[dict(timebounds=np.arange(2006, 2301))]
+        - ohc_baseline
+    )
 
     tas = tas.sel(scenario=scenarios).transpose("scenario", "config", "timebounds")
     ohc = ohc.sel(scenario=scenarios).transpose("scenario", "config", "timebounds")
     return tas.values, ohc.values
 
 
-def simulation_task(random_idx: int, iteration_number: int) -> dict:
-    """Runs the global emulator components for a single ensemble member across scenarios."""
-    tas_sampled = worker_tas[random_idx, : :]
-    ohc_sampled = worker_ohc[random_idx, : :]
-    results = {scenario: {} for scenario in worker_scenarios}
-
-    for idx, scenario in enumerate(worker_scenarios):
-        # The new API requires reinstantiating components or updating their state per run.
-        # ThermalExpansion requires OHC_change in the constructor.
-        slr_components = {
-            "expansion": ThermalExpansion(
-                OHC_change=np.expand_dims(ohc_sampled[idx, :], axis=0)
-            ),
-            "greenland": GreenlandAR6(),
-            "landwater": LandwaterAR6(),
-            "wais": AntarcticaISMIP6(params_path="/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/aux_data/wais_params.nc"),
-            "eais": AntarcticaISMIP6(params_path="/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/aux_data/eais_params.nc"),
-            "pen": AntarcticaISMIP6(params_path="/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/aux_data/aispen_params.nc"),
-            "glacier": Glacier()
-        }
-
-        global_model = Global(components=slr_components, end_yr=2301)
-        projections = global_model.run(
-            scenario=scenario,
-            T_change=np.expand_dims(tas_sampled[idx, :], axis=0),
-            member_seed=iteration_number,
-        )
-
-        # Print the shame of all components
-        for comp_name, comp_data in projections.items():
-            console.log(f"Scenario: {scenario}, Component: {comp_name}, Shape: {comp_data.shape}")
-
-        global_model.sum_components(projections)
-
-        # Aggregate dyn/smb components to match original data structures 
-        results[scenario]["gmslr"] = global_model.results["gmslr"]
-        results[scenario]["expansion"] = projections["expansion"]
-        results[scenario]["antarctica"] = projections["wais"] + projections["eais"] + projections["pen"]
-        results[scenario]["greenland"] = projections["greenland"]
-        results[scenario]["glacier"] = projections["glacier"]
-        results[scenario]["landwater"] = projections["landwater"]
-        
-    return results, random_idx
-
-
 def plot_samples(tas: np.ndarray, ohc: np.ndarray) -> None:
     fig = plt.figure(figsize=(12, 6), layout="constrained")
 
     ax = fig.add_subplot(121)
-    ax.plot(tas.T, color='seagreen', alpha=0.05)
+    ax.plot(tas.T, color="seagreen", alpha=0.05)
     ax.set_xlabel("Simulation years")
     ax.set_ylabel("GMST ($\degree$C)")
     ax.plot(np.arange(tas.shape[1]), np.median(tas, axis=0), color="black")
 
     ax = fig.add_subplot(122)
-    ax.plot(ohc.T, color='seagreen', alpha=0.05)
+    ax.plot(ohc.T, color="seagreen", alpha=0.05)
     ax.set_xlabel("Simulation years")
     ax.set_ylabel("OHC (J)")
 
@@ -264,7 +240,7 @@ def save_to_netcdf(components: dict, filename: str) -> None:
     comp_names = list(components[scenarios[0]].keys())
     sample = components[scenarios[0]][comp_names[0]]
     n_member, n_time = sample.shape
-    
+
     years = np.arange(2006, 2006 + n_time)
     members = np.arange(n_member)
     data_vars = {}
@@ -273,54 +249,53 @@ def save_to_netcdf(components: dict, filename: str) -> None:
         data_vars[comp] = xr.DataArray(
             data=stacked_data,
             dims=["scenario", "member", "year"],
-            coords={
-                "scenario": scenarios,
-                "member": members,
-                "year": years
-            },
-            attrs={
-                "units": "m", 
-                "description": f"Sea level contribution from {comp}"
-            }
+            coords={"scenario": scenarios, "member": members, "year": years},
+            attrs={"units": "m", "description": f"Sea level contribution from {comp}"},
         )
-        
+
     ds = xr.Dataset(data_vars)
     ds.attrs = {
         "title": "ProFSea GMSLR Projections",
         "source": "FAIR v2.2 + ProFSea Emulator",
     }
-    
+
     encoding = {var: {"zlib": True, "complevel": 5} for var in data_vars}
     ds.to_netcdf(filename, encoding=encoding)
     console.log(f"Successfully saved full ensemble to {filename}")
 
 
 def plot_component(
-        ax: plt.Axes, component_dict: dict, component: str, 
-        scenarios: list, plot_legend: bool=False) -> None:
+    ax: plt.Axes,
+    component_dict: dict,
+    component: str,
+    scenarios: list,
+    plot_legend: bool = False,
+) -> None:
     time = np.arange(2006, 2301)
     scenario_colors = {
-        scenarios[0]: '#800000',
-        scenarios[1]: '#ff0000',
-        scenarios[2]: '#fc7b03',
-        scenarios[3]: '#d3a640',
-        scenarios[4]: '#098740',
-        scenarios[5]: '#0080d0',
+        scenarios[0]: "#800000",
+        scenarios[1]: "#ff0000",
+        scenarios[2]: "#fc7b03",
+        scenarios[3]: "#d3a640",
+        scenarios[4]: "#098740",
+        scenarios[5]: "#0080d0",
         # scenarios[6]: '#100060',
     }
     for scenario in reversed(scenarios):
         ax.fill_between(
-            time, 
-            component_dict[scenario][component][1], 
-            component_dict[scenario][component][3],  
-            color=scenario_colors[scenario], 
-            edgecolor='none',
-            alpha=0.3)
+            time,
+            component_dict[scenario][component][1],
+            component_dict[scenario][component][3],
+            color=scenario_colors[scenario],
+            edgecolor="none",
+            alpha=0.3,
+        )
         ax.plot(
-            time, 
-            component_dict[scenario][component][2], 
-            label=f"{scenario}", 
-            color=scenario_colors[scenario])
+            time,
+            component_dict[scenario][component][2],
+            label=f"{scenario}",
+            color=scenario_colors[scenario],
+        )
 
     ax.set_xlabel("Year")
     ax.set_ylabel("SLE (m)")
@@ -346,107 +321,77 @@ def main(args):
             cumulative_emissions = json.load(f)
 
     baseline_start = 1995
-    baseline_end = 2014 
-    
+    baseline_end = 2014
+
     # Updated keys to align with the grouped components
     components = {}
     for scenario in scenarios:
         components[scenario] = {
-            "gmslr": [], "expansion": [], "antarctica": [],
-            "greenland": [], "glacier": [], "landwater": []
+            "gmslr": [],
+            "expansion": [],
+            "antarctica": [],
+            "greenland": [],
+            "glacier": [],
+            "landwater": [],
         }
 
     if args.input.lower() == "magicc":
-        tas, ohc = load_magicc_forcing(args.input_path, scenarios, baseline_start, baseline_end)
+        tas, ohc = load_magicc_forcing(
+            args.input_path, scenarios, baseline_start, baseline_end
+        )
     elif args.input.lower() == "fair":
-        tas, ohc = load_fair_forcing(args.input_path, scenarios, baseline_start, baseline_end)
+        tas, ohc = load_fair_forcing(
+            args.input_path, scenarios, baseline_start, baseline_end
+        )
     elif args.input.lower() == "run_fair":
         tas, ohc = run_fair(baseline_start, baseline_end, scenarios, args)
     else:
         raise ValueError("Input source not recognised.")
 
-    # n_iterations = 1000
-    # rng = np.random.default_rng()
-    # random_indices = rng.integers(0, high=tas.shape[1], size=n_iterations)
-
-    # sampled_tas = []
-    # sampled_ohc = []
-
-    # max_workers = 12
-    # with concurrent.futures.ProcessPoolExecutor(
-    #     max_workers=max_workers,
-    #     initializer=init_worker,
-    #     initargs=(tas, ohc, cumulative_emissions, scenarios)
-    # ) as exectutor:
-    #     futures = [exectutor.submit(simulation_task, idx, i) for i, idx in enumerate(random_indices)]
-
-    #     with Progress() as progress:
-    #         task = progress.add_task("[orange1]Simulating and sampling...", total=n_iterations)
-    #         for future in concurrent.futures.as_completed(futures):
-    #             result_dict, idx_returned = future.result()
-    #             sampled_tas.append(tas[:, idx_returned, :])
-    #             sampled_ohc.append(ohc[:, idx_returned, :])
-
-    #             for scenario in scenarios:
-    #                 for key in components[scenario].keys():
-    #                     components[scenario][key].append(result_dict[scenario][key])
-
-    #             progress.update(task, advance=1)
-
-    # sampled_tas = np.asarray(sampled_tas) 
-    # sampled_ohc = np.asarray(sampled_ohc)
-
-    # plot_samples(sampled_tas[:, -1, :], sampled_ohc[:, -1, :])
-
-    # for scenario, data in components.items():
-    #     for key in data:
-    #         data[key] = np.array(data[key]).squeeze() 
-
-    # sampled_components = {}
-    # for scenario in scenarios:
-    #     sampled_components[scenario] = process_global_ensemble(
-    #         components[scenario], percentiles, scenario)
-
-    # 1. Get your 1000 random climate members
     n_iterations = 1000
     rng = np.random.default_rng()
     random_indices = rng.integers(0, high=tas.shape[1], size=n_iterations)
-    
-    tas_matrix = tas[:, random_indices, :] # Shape: (n_scenarios, 1000, 295)
-    ohc_matrix = ohc[:, random_indices, :] # Shape: (n_scenarios, 1000, 295)
+
+    tas_matrix = tas[:, random_indices, :]  # Shape: (n_scenarios, 1000, 295)
+    ohc_matrix = ohc[:, random_indices, :]  # Shape: (n_scenarios, 1000, 295)
 
     sampled_components = {}
 
-    # 2. Loop only over scenarios (e.g., SSPs)
-    for idx, scenario in enumerate(scenarios):
-        console.log(f"Projecting {scenario}...")
-        
-        # Sliced matrices: shape (1000, 295)
-        tas_scen = tas_matrix[idx, :, :] 
+    for idx, scenario in track(
+        enumerate(scenarios),
+        total=len(scenarios),
+        description="Projecting scenarios...",
+    ):
+        tas_scen = tas_matrix[idx, :, :]
         ohc_scen = ohc_matrix[idx, :, :]
 
         slr_components = {
-            "expansion": ThermalExpansion(
-                OHC_change=ohc_scen
-            ),
+            "expansion": ThermalExpansion(OHC_change=ohc_scen),
             "greenland": GreenlandAR6(),
             "landwater": LandwaterAR6(),
-            "wais": AntarcticaISMIP6(params_path="/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/aux_data/wais_params_APR_nodrift.nc"),
-            "eais": AntarcticaISMIP6(params_path="/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/aux_data/eais_params_APR_nodrift.nc"),
-            "pen": AntarcticaISMIP6(params_path="/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/aux_data/pen_params_APR_nodrift.nc"),
-            "glacier": Glacier()
+            "wais": AntarcticaISMIP6(
+                params_path="/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/aux_data/wais_params_APR_nodrift.nc"
+            ),
+            "eais": AntarcticaISMIP6(
+                params_path="/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/aux_data/eais_params_APR_nodrift.nc"
+            ),
+            "pen": AntarcticaISMIP6(
+                params_path="/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/aux_data/pen_params_APR_nodrift.nc"
+            ),
+            "glacier": Glacier(),
         }
 
-        # 3. Pass the 1000-member array in one shot
         global_model = Global(components=slr_components, end_yr=2301, nm=1)
         projections = global_model.run(
             scenario=scenario,
-            T_change=tas_scen, 
-            member_seed=42 # Only need one seed for the whole matrix operation
+            T_change=tas_scen,
+            member_seed=42,  # Only need one seed for the whole matrix operation
         )
         global_model.sum_components(projections)
 
-        projections["antarctica"] = projections.pop("wais") + projections.pop("eais") + projections.pop("pen")
+        projections["antarctica"] = (
+            projections.pop("wais") + projections.pop("eais") + projections.pop("pen")
+        )
 
         # Process the 1000x295 outputs for saving
         sampled_components[scenario] = process_global_ensemble(
@@ -455,7 +400,7 @@ def main(args):
 
     output_dir = Path(args.output_dir) / args.output_filename
     save_to_netcdf(sampled_components, output_dir)
-    
+
     fig = plt.figure(figsize=(16, 8), layout="constrained")
     ax = fig.add_subplot(231)
     plot_component(ax, sampled_components, "gmslr", scenarios, plot_legend=True)
@@ -470,17 +415,53 @@ def main(args):
     ax = fig.add_subplot(236)
     plot_component(ax, sampled_components, "landwater", scenarios)
 
-    fig.savefig(f"{args.output_dir}{args.output_filename.replace('.nc', '_components.png')}", dpi=300)
+    fig.savefig(
+        f"{args.output_dir}{args.output_filename.replace('.nc', '_components.png')}",
+        dpi=300,
+    )
     plt.show()
 
 
 if __name__ == "__main__":
     p = argparse.ArgumentParser(formatter_class=RichHelpFormatter)
-    p.add_argument("--input", default="run_fair", required=False, help="Input climate forcing source (default: FaIR)", type=str)
-    p.add_argument("--input_path", required=False, help="Path to input climate forcing", type=str)
-    p.add_argument("--emissions_file", required=False, help="Path to CSV file containing emissions scenarios (optional)", type=str)
-    p.add_argument("--forcing_file", required=False, help="Path to CSV file containing climate forcing time series (optional)", type=str)
-    p.add_argument("--output_dir", default="", help="Directory to save outputs (default: current directory)", type=str)
-    p.add_argument("--output_filename", default="gmslr_projections.nc", help="Filename for output NetCDF (default: gmslr_projections.nc)", type=str)
-    p.add_argument("--cumulative_emissions_file", default="cumulative_cmip6_emissions.json", help="Path to JSON file containing cumulative emissions for SSP scenarios (default: cumulative_cmip6_emissions.json)", type=str)
-    main(p.parse_args())
\ No newline at end of file
+    p.add_argument(
+        "--input",
+        default="run_fair",
+        required=False,
+        help="Input climate forcing source (default: FaIR)",
+        type=str,
+    )
+    p.add_argument(
+        "--input_path", required=False, help="Path to input climate forcing", type=str
+    )
+    p.add_argument(
+        "--emissions_file",
+        required=False,
+        help="Path to CSV file containing emissions scenarios (optional)",
+        type=str,
+    )
+    p.add_argument(
+        "--forcing_file",
+        required=False,
+        help="Path to CSV file containing climate forcing time series (optional)",
+        type=str,
+    )
+    p.add_argument(
+        "--output_dir",
+        default="",
+        help="Directory to save outputs (default: current directory)",
+        type=str,
+    )
+    p.add_argument(
+        "--output_filename",
+        default="gmslr_projections.nc",
+        help="Filename for output NetCDF (default: gmslr_projections.nc)",
+        type=str,
+    )
+    p.add_argument(
+        "--cumulative_emissions_file",
+        default="cumulative_cmip6_emissions.json",
+        help="Path to JSON file containing cumulative emissions for SSP scenarios (default: cumulative_cmip6_emissions.json)",
+        type=str,
+    )
+    main(p.parse_args())

From 86dc0703cea45bc4f2b25361d65dadde393f392d Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Tue, 26 May 2026 13:28:55 +0100
Subject: [PATCH 153/276] Add reduced params, update paths

---
 .../components/aux_data/eais_params_reduced.nc  | Bin 0 -> 27795 bytes
 .../components/aux_data/pen_params_reduced.nc   | Bin 0 -> 27700 bytes
 .../components/aux_data/wais_params_reduced.nc  | Bin 0 -> 27724 bytes
 profsea/updated_wrapper.py                      |  10 ++++++----
 4 files changed, 6 insertions(+), 4 deletions(-)
 create mode 100644 profsea/components/aux_data/eais_params_reduced.nc
 create mode 100644 profsea/components/aux_data/pen_params_reduced.nc
 create mode 100644 profsea/components/aux_data/wais_params_reduced.nc

diff --git a/profsea/components/aux_data/eais_params_reduced.nc b/profsea/components/aux_data/eais_params_reduced.nc
new file mode 100644
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HcmV?d00001

diff --git a/profsea/updated_wrapper.py b/profsea/updated_wrapper.py
index a480481..55f93a3 100644
--- a/profsea/updated_wrapper.py
+++ b/profsea/updated_wrapper.py
@@ -355,8 +355,10 @@ def main(args):
     tas_matrix = tas[:, random_indices, :]  # Shape: (n_scenarios, 1000, 295)
     ohc_matrix = ohc[:, random_indices, :]  # Shape: (n_scenarios, 1000, 295)
 
+    wais_params_path = Path("components") / "aux_data" / "wais_params_reduced.nc"
+    eais_params_path = Path("components") / "aux_data" / "eais_params_reduced.nc"
+    pen_params_path = Path("components") / "aux_data" / "pen_params_reduced.nc"
     sampled_components = {}
-
     for idx, scenario in track(
         enumerate(scenarios),
         total=len(scenarios),
@@ -370,13 +372,13 @@ def main(args):
             "greenland": GreenlandAR6(),
             "landwater": LandwaterAR6(),
             "wais": AntarcticaISMIP6(
-                params_path="/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/aux_data/wais_params_APR_nodrift.nc"
+                params_path=wais_params_path
             ),
             "eais": AntarcticaISMIP6(
-                params_path="/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/aux_data/eais_params_APR_nodrift.nc"
+                params_path=eais_params_path
             ),
             "pen": AntarcticaISMIP6(
-                params_path="/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/aux_data/pen_params_APR_nodrift.nc"
+                params_path=pen_params_path
             ),
             "glacier": Glacier(),
         }

From 38b4f8e2d2ef3bca7256b7436cb317e6fac87397 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Tue, 26 May 2026 13:29:51 +0100
Subject: [PATCH 154/276] Format

---
 profsea/updated_wrapper.py | 12 +++---------
 1 file changed, 3 insertions(+), 9 deletions(-)

diff --git a/profsea/updated_wrapper.py b/profsea/updated_wrapper.py
index 55f93a3..8776154 100644
--- a/profsea/updated_wrapper.py
+++ b/profsea/updated_wrapper.py
@@ -371,15 +371,9 @@ def main(args):
             "expansion": ThermalExpansion(OHC_change=ohc_scen),
             "greenland": GreenlandAR6(),
             "landwater": LandwaterAR6(),
-            "wais": AntarcticaISMIP6(
-                params_path=wais_params_path
-            ),
-            "eais": AntarcticaISMIP6(
-                params_path=eais_params_path
-            ),
-            "pen": AntarcticaISMIP6(
-                params_path=pen_params_path
-            ),
+            "wais": AntarcticaISMIP6(params_path=wais_params_path),
+            "eais": AntarcticaISMIP6(params_path=eais_params_path),
+            "pen": AntarcticaISMIP6(params_path=pen_params_path),
             "glacier": Glacier(),
         }
 

From 60f4857517052366255e417f0ab1afbc24aed625 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Wed, 27 May 2026 10:09:13 +0100
Subject: [PATCH 155/276] Greenland fix, update wrapper

---
 profsea/components/global_/greenland.py |  2 +-
 profsea/updated_wrapper.py              | 19 ++++++++++++++-----
 2 files changed, 15 insertions(+), 6 deletions(-)

diff --git a/profsea/components/global_/greenland.py b/profsea/components/global_/greenland.py
index bb32851..b17b02b 100644
--- a/profsea/components/global_/greenland.py
+++ b/profsea/components/global_/greenland.py
@@ -87,7 +87,7 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
         # Persist 2100 rate of changeg
         if state.end_yr >= 2100:
             idx_2100 = 94
-            rate = np.diff(sle_ens, axis=2)[:, :, idx_2100]
+            rate = np.diff(sle_ens, axis=2)[:, :, idx_2100 - 1]
             sle_ens[:, :, idx_2100 + 1 :] = sle_ens[:, :, idx_2100 : idx_2100 + 1] + (
                 rate[:, :, None] * time_delta[None, None, 1 : state.nyr - idx_2100]
             )
diff --git a/profsea/updated_wrapper.py b/profsea/updated_wrapper.py
index 8776154..4669560 100644
--- a/profsea/updated_wrapper.py
+++ b/profsea/updated_wrapper.py
@@ -285,14 +285,14 @@ def plot_component(
         ax.fill_between(
             time,
             component_dict[scenario][component][1],
-            component_dict[scenario][component][3],
+            component_dict[scenario][component][5],
             color=scenario_colors[scenario],
             edgecolor="none",
             alpha=0.3,
         )
         ax.plot(
             time,
-            component_dict[scenario][component][2],
+            component_dict[scenario][component][3],
             label=f"{scenario}",
             color=scenario_colors[scenario],
         )
@@ -312,6 +312,15 @@ def main(args):
     else:
         # Defaulting to an SSP list to avoid UnboundLocalError
         scenarios = ["ssp119", "ssp126", "ssp245", "ssp370", "ssp534-over", "ssp585"]
+        # scenarios = [
+        #     "Very Low - SSP1 (Marker)",
+        #     "Low-to-Negative - SSP2 (Marker)",
+        #     "Low - SSP2 (Marker)",
+        #     "Medium-to-Low - SSP2 (Marker)",
+        #     "Medium - SSP2 (Marker)",
+        #     "High-to-Low - SSP5 (Marker)",
+        #     "High - SSP3 (Marker)",
+        # ]
 
     console.log(f"Using scenarios: {scenarios}")
 
@@ -355,9 +364,9 @@ def main(args):
     tas_matrix = tas[:, random_indices, :]  # Shape: (n_scenarios, 1000, 295)
     ohc_matrix = ohc[:, random_indices, :]  # Shape: (n_scenarios, 1000, 295)
 
-    wais_params_path = Path("components") / "aux_data" / "wais_params_reduced.nc"
-    eais_params_path = Path("components") / "aux_data" / "eais_params_reduced.nc"
-    pen_params_path = Path("components") / "aux_data" / "pen_params_reduced.nc"
+    wais_params_path = Path("components") / "aux_data" / "wais_params_expanded.nc"
+    eais_params_path = Path("components") / "aux_data" / "eais_params_expanded.nc"
+    pen_params_path = Path("components") / "aux_data" / "pen_params_expanded.nc"
     sampled_components = {}
     for idx, scenario in track(
         enumerate(scenarios),

From d32ee3ab9a05d614ac4bd27040565d095cfa2aba Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Wed, 27 May 2026 10:19:10 +0100
Subject: [PATCH 156/276] Review updates

---
 profsea/components/core/spatial_model.py | 38 +++++++++++-------------
 profsea/components/core/state.py         |  1 -
 2 files changed, 18 insertions(+), 21 deletions(-)

diff --git a/profsea/components/core/spatial_model.py b/profsea/components/core/spatial_model.py
index 44b48f2..6ac650b 100644
--- a/profsea/components/core/spatial_model.py
+++ b/profsea/components/core/spatial_model.py
@@ -53,8 +53,8 @@ def __init__(
         self.start_year = 2006
         self.n_years = self.end_year - self.start_year
 
-        if output_percentiles:
-            self.num_members = len(output_percentiles)
+        if self.output_percentiles is not None and len(self.output_percentiles) > 0:
+            self.num_members = len(self.output_percentiles)
         else:
             self.num_members = next(
                 iter(self.components.values())
@@ -88,7 +88,7 @@ def __init__(
 
         # Log the size of each component and provide an estimate of their memory usage
         for name, comp in self.components.items():
-            if not output_percentiles:
+            if not self.output_percentiles:
                 comp_size = comp.global_projection.nbytes / 1e9
                 future_size = (
                     comp_size
@@ -98,7 +98,7 @@ def __init__(
                 )
             else:
                 comp_size = (
-                    comp.global_projection[: len(output_percentiles)].nbytes / 1e9
+                    comp.global_projection[: len(self.output_percentiles)].nbytes / 1e9
                 )
                 future_size = comp_size * len(self.grid_lats) * len(self.grid_lons)
 
@@ -112,20 +112,18 @@ def __init__(
                     f"resolution or take percentiles.[/bold red]"
                 )
 
-    def run(self, scenario: str, member_seed: int = 42) -> None:
+    def run(self, member_seed: int = 42) -> None:
         """
-        Calculates global and regional component part contributions to sea level
-        change.
-        :param mcdir: location of Monte Carlo time series for new projections
-        :param components: sea level components
-        :param scenario: emission scenario
-        :param yrs: years of the projections
-        :param array_dims: Array of nesm, nsmps and nyrs
-            nesm --> Number of ensemble members in time series
-            nsmps --> Determine the number of samples you wish to make
-            nyrs --> Number of years in each projection time series
-        :return: montecarlo_G (global contribution to sea level rise) and
-            montecarlo_R (regional contribution to sea level change)
+        Run the spatial model to generate regional sea level projections for each component.
+        Parameters
+        ----------
+        member_seed: int, optional
+            Seed for random number generation to ensure reproducibility of member sampling. Default is 42.
+
+        Returns
+        -------
+        Dict[str, da.Array]
+            Dictionary of spatial projections for each component, where keys are component names and values are Dask arrays of shape (n_members, n_years, n_lats, n_lons).
         """
         seed_seq = np.random.SeedSequence(member_seed)
 
@@ -134,7 +132,6 @@ def run(self, scenario: str, member_seed: int = 42) -> None:
         )
 
         state = SpatialState(
-            scenario=scenario,
             n_years=self.n_years,
             n_members=self.num_members,
             grid_lats=self.grid_lats,
@@ -260,10 +257,11 @@ def save_components(
 
             # The spinner will animate while to_netcdf is blocking
             with console.status(
-                "[bold cyan]Streaming computation and saving NetCDF...[/bold cyan]",
+                "[bold cyan]Computing and saving NetCDF...[/bold cyan]",
                 spinner="dots",
             ):
-                ds.to_netcdf(out_path, encoding=encoding, compute=True)
+                ds.compute()  # Compute before saving, for speed
+                ds.to_netcdf(out_path, encoding=encoding)
 
             console.log(
                 f"[bold green]✓ Successfully saved NetCDF:[/bold green] {out_path}"
diff --git a/profsea/components/core/state.py b/profsea/components/core/state.py
index aa366fb..a9cf5c3 100644
--- a/profsea/components/core/state.py
+++ b/profsea/components/core/state.py
@@ -24,7 +24,6 @@ class ClimateState:
 class SpatialState:
     """SpatialState context object to hold relevant state information for spatial SLR projections."""
 
-    scenario: str
     grid_lats: np.ndarray
     grid_lons: np.ndarray
     n_years: int

From 986b89ab707a60669dc1e9329ee805355e67449d Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Wed, 27 May 2026 10:21:39 +0100
Subject: [PATCH 157/276] Update example script

---
 ukcp18_example_script.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ukcp18_example_script.py b/ukcp18_example_script.py
index 98a7b01..8caced8 100644
--- a/ukcp18_example_script.py
+++ b/ukcp18_example_script.py
@@ -91,7 +91,7 @@
 
 # Pass to the spatial model
 spatial_model = Spatial(components=spatial_components)
-spatial_model.run(scenario="rcp85", member_seed=42)
+spatial_model.run(member_seed=42)
 
 spatial_model.sum_components(spatial_model.results)
 spatial_model.save_components(spatial_model.results, scenario_name="rcp85", output_format="zarr")

From e908a0f6feb7d303a961dd57e20beab90d2fc9d6 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Wed, 27 May 2026 10:57:01 +0100
Subject: [PATCH 158/276] Review updates

---
 profsea/components/core/spatial_model.py  |  7 ++++---
 profsea/components/core/state.py          |  1 +
 profsea/components/spatial/fingerprint.py | 12 ++++++++----
 profsea/components/spatial/gia.py         | 14 ++++++++++----
 4 files changed, 23 insertions(+), 11 deletions(-)

diff --git a/profsea/components/core/spatial_model.py b/profsea/components/core/spatial_model.py
index 6ac650b..2728895 100644
--- a/profsea/components/core/spatial_model.py
+++ b/profsea/components/core/spatial_model.py
@@ -25,7 +25,7 @@ def __init__(
         grid_config: dict = None,
         grid_interpolation: str = "linear",
         end_year: int = 2301,
-        baseline_yrs: tuple = (1986, 2005),
+        baseline_yrs: tuple = (1995, 2014),
         output_percentiles: list | np.ndarray = [5, 17, 50, 83, 95],
     ):
         """
@@ -41,7 +41,7 @@ def __init__(
         end_year: int, optional
             The final year of the projections. Default is 2301.
         baseline_yrs: tuple, optional
-            Tuple defining the start and end years of the baseline period for calculating anomalies. Default is (1986, 2005).
+            Tuple defining the start and end years of the baseline period for calculating anomalies. Default is (1995, 2014).
         output_percentiles: list or np.ndarray, optional
             List or array of percentiles to sample from the ensemble for output. If None, outputs all members. Default is [5, 17, 50, 83, 95].
         """
@@ -88,7 +88,7 @@ def __init__(
 
         # Log the size of each component and provide an estimate of their memory usage
         for name, comp in self.components.items():
-            if not self.output_percentiles:
+            if self.output_percentiles is not None and len(self.output_percentiles) > 0:
                 comp_size = comp.global_projection.nbytes / 1e9
                 future_size = (
                     comp_size
@@ -138,6 +138,7 @@ def run(self, member_seed: int = 42) -> None:
             grid_lons=self.grid_lons,
             grid_interpolation="linear",
             output_percentiles=self.output_percentiles,
+            baseline_yrs=self.baseline_yrs,
         )
 
         child_seeds = seed_seq.spawn(len(self.components))
diff --git a/profsea/components/core/state.py b/profsea/components/core/state.py
index a9cf5c3..ead9910 100644
--- a/profsea/components/core/state.py
+++ b/profsea/components/core/state.py
@@ -30,3 +30,4 @@ class SpatialState:
     n_members: int
     grid_interpolation: str
     output_percentiles: list[int] | np.ndarray
+    baseline_yrs: tuple[int, int]
diff --git a/profsea/components/spatial/fingerprint.py b/profsea/components/spatial/fingerprint.py
index 0ef52f8..e0a3eeb 100644
--- a/profsea/components/spatial/fingerprint.py
+++ b/profsea/components/spatial/fingerprint.py
@@ -24,10 +24,14 @@ def __init__(
         """
         Parameters
         ----------
-        global_projection: 2D array (members x years) of global projections to apply the fingerprints to.
-        fingerprint_paths: Path(s) to NetCDF files containing the spatial fingerprint patterns. Each file should contain a DataArray with dimensions (lat, lon).
-        scaling_factor: Optional multiplier to apply to the fingerprint patterns (e.g., to convert from m to mm).
-        sample_spatial: If True, randomly sample a different fingerprint pattern for each member. If False, use the mean of all provided fingerprints for all members (storyline mode).
+        global_projection: np.ndarray
+             A 2D array (members x years) of global projections to apply the fingerprints to.
+        fingerprint_paths: str, Path, or list of str/Path
+             Path(s) to NetCDF files containing the spatial fingerprint patterns. Each file should contain a DataArray with dimensions (lat, lon).
+        scaling_factor: float, optional
+             Optional multiplier to apply to the fingerprint patterns (e.g., to convert from m to mm). Default is 1.0 (no scaling).
+        sample_spatial: bool, optional
+             If True, randomly sample a different fingerprint pattern for each member. If False, use the mean of all provided fingerprints for all members (storyline mode). Default is False.
         """
         self._global_projection = da.from_array(global_projection, chunks="auto")
         self.scaling_factor = scaling_factor
diff --git a/profsea/components/spatial/gia.py b/profsea/components/spatial/gia.py
index f681729..b2c53e2 100644
--- a/profsea/components/spatial/gia.py
+++ b/profsea/components/spatial/gia.py
@@ -19,10 +19,16 @@ class GIA(SpatialComponent):
     def __init__(
         self,
         gia_paths: str | Path | list[str | Path],
-        baseline_yrs: tuple = (1995, 2014),
         sample_spatial: bool = False,
     ):
-        self.baseline_yrs = baseline_yrs
+        """
+        Parameters
+        ----------
+        gia_paths: str, Path, or list of str/Path
+            Path to a single GIA file, a list of GIA files, or a directory containing GIA files.
+        sample_spatial: bool, optional
+            Whether to sample spatial patterns probabilistically. Default is False.
+        """
         self.sample_spatial = sample_spatial
 
         if isinstance(gia_paths, (str, Path)):
@@ -78,8 +84,8 @@ def project(self, state: SpatialState, rng: np.random.Generator) -> da.Array:
 
         # Calculate the accumulation time vector (mm/yr to m/yr)
         midyr = (
-            self.baseline_yrs[1] - self.baseline_yrs[0] + 1
-        ) * 0.5 + self.baseline_yrs[0]
+            state.baseline_yrs[1] - state.baseline_yrs[0] + 1
+        ) * 0.5 + state.baseline_yrs[0]
         Tdelta = 2006 - midyr
         unit_series = (np.arange(state.n_years) + Tdelta) * 0.001
 

From 336558c921628b019497a1ae93d70d2beb0b8316 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Wed, 27 May 2026 11:22:54 +0100
Subject: [PATCH 159/276] Add docstrings

---
 profsea/components/core/spatial_model.py   |  7 ++-
 profsea/components/spatial/fingerprint.py  | 30 ++++++++++++-
 profsea/components/spatial/gia.py          | 27 ++++++++++-
 profsea/components/spatial/sterodynamic.py | 52 +++++++++++++++++++---
 4 files changed, 107 insertions(+), 9 deletions(-)

diff --git a/profsea/components/core/spatial_model.py b/profsea/components/core/spatial_model.py
index 2728895..edc4e89 100644
--- a/profsea/components/core/spatial_model.py
+++ b/profsea/components/core/spatial_model.py
@@ -27,7 +27,7 @@ def __init__(
         end_year: int = 2301,
         baseline_yrs: tuple = (1995, 2014),
         output_percentiles: list | np.ndarray = [5, 17, 50, 83, 95],
-    ):
+    ) -> None:
         """
         Parameters
         ----------
@@ -115,6 +115,7 @@ def __init__(
     def run(self, member_seed: int = 42) -> None:
         """
         Run the spatial model to generate regional sea level projections for each component.
+
         Parameters
         ----------
         member_seed: int, optional
@@ -198,6 +199,10 @@ def save_components(
             Directory to save components to.
         scenario_name: str
             Name of the scenario you've run the emulator for.
+
+        Returns
+        -------
+        None
         """
         output_format = output_format.lower()
         if output_format not in ["netcdf", "zarr"]:
diff --git a/profsea/components/spatial/fingerprint.py b/profsea/components/spatial/fingerprint.py
index e0a3eeb..659f2a5 100644
--- a/profsea/components/spatial/fingerprint.py
+++ b/profsea/components/spatial/fingerprint.py
@@ -20,7 +20,7 @@ def __init__(
         fingerprint_paths: str | Path | list[str | Path],
         scaling_factor: float = 1.0,
         sample_spatial: bool = False,
-    ):
+    ) -> None:
         """
         Parameters
         ----------
@@ -52,7 +52,18 @@ def global_projection(self):
         return self._global_projection
 
     def _load_and_interpolate(self, state: SpatialState) -> da.Array:
-        """Lazily load and regrid all provided fingerprints."""
+        """
+        Lazily load and regrid all provided fingerprints.
+
+        Parameters
+        ----------
+        state: SpatialState
+            The state object containing the target grid information.
+
+        Returns
+        -------
+        da.Array
+        """
         grids = []
         for path in self.fp_paths:
             fp_da = xr.open_dataarray(path, chunks={"lat": 45, "lon": 45})
@@ -63,6 +74,21 @@ def _load_and_interpolate(self, state: SpatialState) -> da.Array:
         return da.stack(grids, axis=0)
 
     def project(self, state: SpatialState, rng: np.random.Generator) -> da.Array:
+        """
+        Calculate the spatial projection by applying the fingerprints to the global projection.
+
+        Parameters
+        ----------
+        state: SpatialState
+            The state object containing the target grid information and number of members.
+        rng: np.random.Generator
+            Random number generator for sampling fingerprints if sample_spatial is True.
+
+        Returns
+        -------
+        da.Array
+            A 4D array of shape (members, years, lat, lon) containing the spatial projections for each member and year.
+        """
         fps = self._load_and_interpolate(state)  # Shape: (n_fps, lat, lon)
 
         # Handle the global projection
diff --git a/profsea/components/spatial/gia.py b/profsea/components/spatial/gia.py
index b2c53e2..156d8f7 100644
--- a/profsea/components/spatial/gia.py
+++ b/profsea/components/spatial/gia.py
@@ -20,7 +20,7 @@ def __init__(
         self,
         gia_paths: str | Path | list[str | Path],
         sample_spatial: bool = False,
-    ):
+    ) -> None:
         """
         Parameters
         ----------
@@ -60,6 +60,16 @@ def _load_and_interpolate_rates(self, state: SpatialState) -> da.Array:
         """
         Lazily loads all GIA files, regrids them, and stacks them into a
         single 3D array of shape (total_models, lat, lon).
+
+        Parameters
+        ----------
+        state: SpatialState
+            The spatial state containing the target grid information.
+
+        Returns
+        -------
+        da.Array
+            A Dask array of shape (total_models, lat, lon) containing the regridded GIA rates.
         """
         grids = []
         for path in self.gia_paths:
@@ -79,6 +89,21 @@ def _load_and_interpolate_rates(self, state: SpatialState) -> da.Array:
         return da.concatenate(grids, axis=0)
 
     def project(self, state: SpatialState, rng: np.random.Generator) -> da.Array:
+        """
+        Project the GIA component by multiplying the accumulation time vector with the spatial rates.
+
+        Parameters
+        ----------
+        state: SpatialState
+            The spatial state containing the target grid information.
+        rng: np.random.Generator
+            Random number generator for sampling GIA models if sample_spatial is True.
+
+        Returns
+        -------
+        da.Array
+            A 4D array of shape (members, years, lat, lon) containing the spatial projections for each member and year.
+        """
         gia_rates = self._load_and_interpolate_rates(state)
         n_patterns = gia_rates.shape[0]
 
diff --git a/profsea/components/spatial/sterodynamic.py b/profsea/components/spatial/sterodynamic.py
index 205b26f..6c85980 100644
--- a/profsea/components/spatial/sterodynamic.py
+++ b/profsea/components/spatial/sterodynamic.py
@@ -22,7 +22,17 @@ def __init__(
         global_projection: np.ndarray,
         patterns_dir: str = None,
         sample_spatial: bool = False,
-    ):
+    ) -> None:
+        """
+        Parameters
+        ----------
+        global_projection: np.ndarray
+             A 2D array (members x years) of global projections to apply the fingerprints to.
+        patterns_dir: str, optional
+             Path to directory containing CMIP6 sterodynamic patterns.
+        sample_spatial: bool, optional
+             If True, randomly sample a different fingerprint pattern for each member. If False, use the mean of all provided fingerprints for all members (storyline mode). Default is False.
+        """
         # Convert to dask array for cheap as possible compute
         self._global_projection = da.from_array(global_projection, chunks="auto")
         self.sample_spatial = sample_spatial
@@ -42,7 +52,15 @@ def global_projection(self):
     def _load_CMIP6_slopes(self) -> da.Array:
         """
         Load in the CMIP6 slope coefficients.
-        :return: 3D Dask array of regression coefficients (model, lat, lon)
+
+        Parameters
+        ----------
+        None
+
+        Returns
+        -------
+        da.Array
+            A dask array of shape (n_models, n_lats, n_lons) containing the sterodynamic fingerprint patterns (i.e., regression coefficients) for each CMIP6 model.
         """
         slope_files = list(Path(self.patterns_dir).glob("*/zos_regression_ssp585_*.nc"))
 
@@ -67,9 +85,18 @@ def _calc_expansion_contribution(
         """
         Calculate the thermal expansion contribution to the regional component of
         sea level rise.
-        :param scenario: emission scenario
-        :param nsmps: determine the number of samples
-        :return: expansion estimates converted to mm/yr
+
+        Parameters
+        ----------
+        rng: np.random.Generator
+            Random number generator for sampling spatial patterns if needed.
+        state: ClimateState
+            The state object containing the target grid information and number of members.
+
+        Returns
+        -------
+        da.Array
+            A dask array of shape (members, years, lat, lon) containing the thermal expansion contribution to the sterodynamic component for each member and year.
         """
         # Select slope coefficients based on the MIP
         coeffs_da = self._load_CMIP6_slopes()
@@ -92,6 +119,21 @@ def _calc_expansion_contribution(
             )
 
     def project(self, state: ClimateState, rng) -> np.ndarray:
+        """
+        Project the sterodynamic component by applying the CMIP6 patterns to the global expansion projection.
+
+        Parameters
+        ----------
+        state: ClimateState
+            The state object containing the target grid information and number of members.
+        rng: np.random.Generator
+            Random number generator for sampling spatial patterns if needed.
+
+        Returns
+        -------
+        np.ndarray
+            A numpy array of shape (members, years, lat, lon) containing the sterodynamic component for each member and year.
+        """
         # Calculate percentiles locally without mutating self
         if state.output_percentiles is not None:
             current_projection = sample_members_2D(

From 00313fd4fec35d2e6278b6759639f5b2dbd7c431 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Wed, 27 May 2026 13:35:16 +0100
Subject: [PATCH 160/276] Fix Zarr saving bug

---
 ProFSea-emu-env.yml => profsea-env.yml   | 6 ++++--
 profsea/components/core/spatial_model.py | 5 ++---
 2 files changed, 6 insertions(+), 5 deletions(-)
 rename ProFSea-emu-env.yml => profsea-env.yml (77%)

diff --git a/ProFSea-emu-env.yml b/profsea-env.yml
similarity index 77%
rename from ProFSea-emu-env.yml
rename to profsea-env.yml
index e4bd032..381ba79 100644
--- a/ProFSea-emu-env.yml
+++ b/profsea-env.yml
@@ -1,4 +1,4 @@
-name: profsea-emu
+name: profsea
 channels:
   - conda-forge
 dependencies:
@@ -7,10 +7,12 @@ dependencies:
   - iris
   - matplotlib
   - netcdf4
+  - numcodecs
   - numpy
   - pandas
   - python=3.12
   - pyyaml
   - rich
   - scipy
-  - xarray
\ No newline at end of file
+  - xarray
+  - zarr
\ No newline at end of file
diff --git a/profsea/components/core/spatial_model.py b/profsea/components/core/spatial_model.py
index edc4e89..29dc71d 100644
--- a/profsea/components/core/spatial_model.py
+++ b/profsea/components/core/spatial_model.py
@@ -230,10 +230,9 @@ def save_components(
         encoding = {}
         if output_format == "zarr":
             import numcodecs
+            from numcodecs.zarr3 import Blosc
 
-            compressor = numcodecs.Blosc(
-                cname="zstd", clevel=5, shuffle=numcodecs.Blosc.BITSHUFFLE
-            )
+            compressor = Blosc(cname="zstd", clevel=5, shuffle=numcodecs.Blosc.BITSHUFFLE)
 
         # Loop through the isDask arrays and add them to the single Dataset
         for name, component in components.items():

From f426153ab721d7f808f73fb919c52ca8154f0e7a Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Wed, 27 May 2026 16:05:48 +0100
Subject: [PATCH 161/276] Return data in dataarrays

---
 profsea/components/core/spatial_model.py | 108 ++++++++++++++---------
 1 file changed, 68 insertions(+), 40 deletions(-)

diff --git a/profsea/components/core/spatial_model.py b/profsea/components/core/spatial_model.py
index 29dc71d..dcb74f1 100644
--- a/profsea/components/core/spatial_model.py
+++ b/profsea/components/core/spatial_model.py
@@ -112,6 +112,44 @@ def __init__(
                     f"resolution or take percentiles.[/bold red]"
                 )
 
+    def _arr_to_xr(self, arr_dict: Dict[str, da.Array]) -> Dict[str, xr.DataArray]:
+        """
+        Convert a dictionary of Dask arrays to a dictionary of xarray DataArrays with appropriate coordinates and metadata.
+
+        Parameters
+        ----------
+        arr_dict: Dict[str, da.Array]
+            Dictionary where keys are component names and values are Dask arrays of shape (n_members, n_years, n_lats, n_lons).
+
+        Returns
+        -------
+        Dict[str, xr.DataArray]
+            Dictionary where keys are component names and values are xarray DataArrays with dimensions (member, time, lat, lon) and appropriate coordinates.
+        """
+        xr_dict = {}
+        member_dim = "percentile" if self.output_percentiles is not None else "member"
+
+        for name, arr in arr_dict.items():
+            xr_dict[name] = xr.DataArray(
+                arr,
+                dims=[member_dim, "time", "lat", "lon"],
+                coords={
+                    member_dim: self.output_percentiles
+                    if self.output_percentiles is not None
+                    else np.arange(arr.shape[0]),
+                    "time": np.arange(self.start_year, self.start_year + arr.shape[1]),
+                    "lat": self.grid_lats,
+                    "lon": self.grid_lons,
+                },
+                attrs={
+                    "units": "m",
+                    "long_name": f"Regional {name} sea-level projections",
+                    "source": "ProFSea-Climate v0.1",
+                },
+            )
+
+        return xr_dict
+
     def run(self, member_seed: int = 42) -> None:
         """
         Run the spatial model to generate regional sea level projections for each component.
@@ -158,30 +196,42 @@ def run(self, member_seed: int = 42) -> None:
             lazy_projection = lazy_projection.rechunk({0: -1, 1: -1, 2: 10, 3: 10})
             spatial_projections[name] = lazy_projection
 
-        self.results = spatial_projections
-        return spatial_projections
+        # Put into xarray datasets for easier saving and metadata handling
+        spatial_projections_xr = self._arr_to_xr(spatial_projections)
+        self.results = spatial_projections_xr
+        return self.results
 
-    def sum_components(self, components: Dict[str, da.Array]) -> da.Array:
+    def sum_components(self, components: Dict[str, xr.DataArray]) -> xr.DataArray:
         """
         Sum the spatial components to get total sea-level change.
 
         Parameters
         ----------
-        components: Dict[str, da.Array]
-            Dictionary of spatial component Dask arrays.
+        components: Dict[str, xr.DataArray]
+            Dictionary of spatial component DataArrays.
 
         Returns
         -------
-        da.Array
-            Dask array of the summed spatial projections.
+        xr.DataArray
+            DataArray of the summed spatial projections.
         """
-        total_rsl = da.sum(da.stack(list(components.values()), axis=0), axis=0)
+        # Using xr.concat preserves all dimensions and coordinates, and summing
+        # along the new dimension handles the underlying dask arrays cleanly.
+        total_rsl = xr.concat(components.values(), dim="component").sum(dim="component")
+
+        # Optionally, apply attributes so it matches the other DataArrays
+        total_rsl.attrs = {
+            "units": "m",
+            "long_name": "Regional total sea-level projections",
+            "source": "ProFSea-Climate v0.1",
+        }
+
         components["total_rsl"] = total_rsl
         return total_rsl
 
     def save_components(
         self,
-        components: Dict[str, da.Array],
+        components: Dict[str, xr.DataArray],
         scenario_name: str,
         output_dir: str = ".",
         output_format: str = "zarr",
@@ -191,8 +241,8 @@ def save_components(
 
         Parameters
         ----------
-        components: Dict[str, da.Array]
-            Dictionary of component names and their corresponding Dask arrays.
+        components: Dict[str, xr.DataArray]
+            Dictionary of component names and their corresponding Xarray DataArrays.
         output_format: str
             Format to save the output in. Must be either 'netcdf' or 'zarr'.
         output_dir: str
@@ -204,6 +254,9 @@ def save_components(
         -------
         None
         """
+        # Wrap dataarrays in a single Dataset for saving
+        ds = xr.Dataset(components)
+
         output_format = output_format.lower()
         if output_format not in ["netcdf", "zarr"]:
             raise ValueError("output_format must be either 'netcdf' or 'zarr'.")
@@ -211,42 +264,17 @@ def save_components(
         # Create directory if it doesn't exist
         Path(output_dir).mkdir(parents=True, exist_ok=True)
 
-        ds = xr.Dataset()
-        member_dim = "percentile" if self.output_percentiles is not None else "member"
-
-        # Build the shared coordinates once to ensure alignment
-        # Extracting time dynamically based on the shape of the first component
-        sample_shape = next(iter(components.values())).shape
-
-        coords = {
-            member_dim: self.output_percentiles
-            if self.output_percentiles is not None
-            else np.arange(sample_shape[0]),
-            "time": np.arange(2006, sample_shape[1] + 2006),
-            "lat": self.grid_lats,
-            "lon": self.grid_lons,
-        }
-
         encoding = {}
         if output_format == "zarr":
             import numcodecs
             from numcodecs.zarr3 import Blosc
 
-            compressor = Blosc(cname="zstd", clevel=5, shuffle=numcodecs.Blosc.BITSHUFFLE)
-
-        # Loop through the isDask arrays and add them to the single Dataset
-        for name, component in components.items():
-            xr_dataArray = xr.DataArray(
-                component,
-                dims=[member_dim, "time", "lat", "lon"],
-                coords=coords,
+            compressor = Blosc(
+                cname="zstd", clevel=5, shuffle=numcodecs.Blosc.BITSHUFFLE
             )
-            xr_dataArray.attrs["units"] = "m"
-            xr_dataArray.attrs["long_name"] = f"Regional {name} sea-level projections"
-            xr_dataArray.attrs["source"] = "ProFSea-Climate v0.1"
-
-            ds[name] = xr_dataArray
 
+        # Loop through the xrDataarrays and add them to the single Dataset
+        for name, component in components.items():
             # Populate the encoding dictionary variable-by-variable
             if output_format == "netcdf":
                 encoding[name] = {"zlib": True, "complevel": 5, "dtype": "float32"}

From 0c842b35d71d7fcdc260dce5e28adac97f5ef288 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Wed, 27 May 2026 16:52:25 +0100
Subject: [PATCH 162/276] Cleanup!

---
 .../aux_data/eais_params_expanded.nc          |  Bin 0 -> 30177 bytes
 .../aux_data/pen_params_expanded.nc           |  Bin 0 -> 30177 bytes
 .../aux_data/wais_params_expanded.nc          |  Bin 0 -> 30177 bytes
 profsea/components/global_/gmslr.py           | 1058 -----------------
 profsea/components/spatial/spatial.py         |  456 -------
 profsea/components/spatial/sterodynamic.py    |    2 +-
 profsea/config.py                             |   30 -
 profsea/directories.py                        |   54 -
 .../eval_plots/rcp_eval_ar5_antarctica.png    |  Bin 258936 -> 0 bytes
 .../eval_plots/rcp_eval_ar5_ar6_greenland.png |  Bin 426679 -> 0 bytes
 .../eval_plots/rcp_eval_ar5_ar6_landwater.png |  Bin 203527 -> 0 bytes
 .../eval_plots/rcp_eval_ar5_expansion.png     |  Bin 364344 -> 0 bytes
 .../eval_plots/rcp_eval_ar5_glaciers.png      |  Bin 312904 -> 0 bytes
 .../eval_plots/rcp_eval_ar5_gmslr.png         |  Bin 312691 -> 0 bytes
 .../eval_plots/rcp_eval_ar5_greenland.png     |  Bin 347737 -> 0 bytes
 .../eval_plots/rcp_eval_ar5_landwater.png     |  Bin 177730 -> 0 bytes
 profsea/notebooks/worked_example.ipynb        |  177 ---
 profsea/slr_pkg/__init__.py                   |  342 ------
 profsea/slr_pkg/cmip.py                       |  233 ----
 profsea/slr_pkg/cubedata.py                   |  194 ---
 profsea/slr_pkg/cubeplot.py                   |  207 ----
 profsea/slr_pkg/cubeutils.py                  |   27 -
 profsea/slr_pkg/models.py                     |   63 -
 profsea/slr_pkg/process.py                    |  213 ----
 profsea/slr_pkg/whichbox.py                   |  112 --
 profsea/spatial_projections.py                |  556 ---------
 profsea/user-settings-emu.yml                 |   81 --
 profsea/utils/tide_gauge_locations.py         |  271 -----
 28 files changed, 1 insertion(+), 4075 deletions(-)
 create mode 100644 profsea/components/aux_data/eais_params_expanded.nc
 create mode 100644 profsea/components/aux_data/pen_params_expanded.nc
 create mode 100644 profsea/components/aux_data/wais_params_expanded.nc
 delete mode 100644 profsea/components/global_/gmslr.py
 delete mode 100644 profsea/components/spatial/spatial.py
 delete mode 100644 profsea/config.py
 delete mode 100644 profsea/directories.py
 delete mode 100644 profsea/notebooks/eval_plots/rcp_eval_ar5_antarctica.png
 delete mode 100644 profsea/notebooks/eval_plots/rcp_eval_ar5_ar6_greenland.png
 delete mode 100644 profsea/notebooks/eval_plots/rcp_eval_ar5_ar6_landwater.png
 delete mode 100644 profsea/notebooks/eval_plots/rcp_eval_ar5_expansion.png
 delete mode 100644 profsea/notebooks/eval_plots/rcp_eval_ar5_glaciers.png
 delete mode 100644 profsea/notebooks/eval_plots/rcp_eval_ar5_gmslr.png
 delete mode 100644 profsea/notebooks/eval_plots/rcp_eval_ar5_greenland.png
 delete mode 100644 profsea/notebooks/eval_plots/rcp_eval_ar5_landwater.png
 delete mode 100644 profsea/notebooks/worked_example.ipynb
 delete mode 100644 profsea/slr_pkg/__init__.py
 delete mode 100644 profsea/slr_pkg/cmip.py
 delete mode 100644 profsea/slr_pkg/cubedata.py
 delete mode 100644 profsea/slr_pkg/cubeplot.py
 delete mode 100644 profsea/slr_pkg/cubeutils.py
 delete mode 100644 profsea/slr_pkg/models.py
 delete mode 100644 profsea/slr_pkg/process.py
 delete mode 100644 profsea/slr_pkg/whichbox.py
 delete mode 100644 profsea/spatial_projections.py
 delete mode 100644 profsea/user-settings-emu.yml
 delete mode 100644 profsea/utils/tide_gauge_locations.py

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HcmV?d00001

diff --git a/profsea/components/global_/gmslr.py b/profsea/components/global_/gmslr.py
deleted file mode 100644
index 106e380..0000000
--- a/profsea/components/global_/gmslr.py
+++ /dev/null
@@ -1,1058 +0,0 @@
-"""
-Copyright (c) 2023, Met Office
-All rights reserved.
-"""
-
-# Calculate Monte Carlo projections of GMSLR using methods
-# from Jonathon Gregory and AR5. Staying close to JG's original
-# code where possible.
-import os
-import concurrent
-from collections.abc import Sequence
-import functools
-import atexit
-from pathlib import Path
-
-import numpy as np
-import pandas as pd
-from rich.console import Console
-from scipy.stats import norm, truncnorm
-import xarray as xr
-
-from profsea.utils import sample_members_2D
-from .antarctica import AntarcticaISMIP6
-
-console = Console()
-
-
-@functools.lru_cache(maxsize=1)
-def load_greenland_calibration():
-    """Loads the CSV once and keeps it in memory."""
-    path = Path(__file__).parent / "aux_data" / "ISMIP_GIS_calibration.csv"
-    return pd.read_csv(path)
-
-
-@functools.lru_cache(maxsize=1)
-def load_landwater_projection():
-    """Loads the NetCDF once and keeps the VALUES in memory."""
-    path = Path(__file__).parent / "aux_data" / "ssp_global_landwater_projections.nc"
-    with xr.open_dataset(path) as ds:
-        ds.load()
-    return ds
-
-
-_SHARED_EXECUTOR = None
-
-
-def get_shared_executor():
-    """Returns a singleton ThreadPoolExecutor."""
-    global _SHARED_EXECUTOR
-    if _SHARED_EXECUTOR is None:
-        max_workers = min(8, os.cpu_count() or 1)
-        _SHARED_EXECUTOR = concurrent.futures.ThreadPoolExecutor(
-            max_workers=max_workers
-        )
-    return _SHARED_EXECUTOR
-
-
-@atexit.register
-def shutdown_executor():
-    """Ensures the executor is cleaned up when the script exits."""
-    global _SHARED_EXECUTOR
-    if _SHARED_EXECUTOR:
-        _SHARED_EXECUTOR.shutdown(wait=True)
-
-
-class Global:
-    """Global sea level rise component emulator.
-
-    Parameters
-    ----------
-    T_change: np.ndarray
-        Array of temperature change values.
-    OHC_change: np.ndarray
-        Array of ocean heat content change values.
-    scenario: str
-        Name of the scenario.
-    end_yr: int
-        End year of the projections.
-    seed: int
-        Seed for numpy.random.
-    nt: int
-        Number of realisations of the input timeseries
-    nm: int
-        Number of realisations of for each component.
-        Must be a multiple of the number of glacier methods.
-    tcv: float
-        Multiplier for the standard deviation in the input fields.
-    glaciermip: bool | int
-        If False, use AR5 parameters. If 1, use GlacierMIP
-        (Hock et al., 2019). If 2, use GlacierMIP2 (Marzeion et al., 2020).
-    parallel: bool
-        If True, project SLR components in parallel.
-    input_ensemble: bool
-        If True, use an input ensemble of temperature and
-        ocean heat content change.
-    output_percentiles: list|np.ndarray
-        If not None, calculate percentiles from a 1D list/array for each
-        component
-    random_sample: bool
-        If True, randomly sample a single ensemble member across all
-        components
-    T_percentile_95: np.ndarray
-        95th percentile of temperature change timeseries.
-    OHC_percentile_95: np.ndarray
-        95th percentile of ocean heat content change timeseries.
-    cum_emissions_total: float
-        Total cumulative emissions from 2015 to 2100.
-    palmer_method: bool
-        If True, allow integration to end in any year up to 2300,
-        with the contributions to GMLSR from ice-sheet dynamics,
-        Greenland SMB and land water storage held at the 2100 rate
-        beyond 2100.
-
-    Attributes
-    ----------
-    endofhistory: int
-        First year of AR5 projections.
-    endofAR5: int
-        Last year of AR5 projections.
-    nyr: int
-        Length of projections.
-    fgreendyn: float
-        Fraction of SLE from Greenland during 1996 to 2005 assumed
-        to result from rapid dynamical change.
-    dgreen: float
-        m SLE from Greenland during 1996 to 2005 according to AR5 chapter 4.
-    dant: float
-        m SLE from Antarctica during 1996 to 2005 according to AR5 chapter 4.
-    mSLEoGt: float
-        Conversion factor for Gt to m SLE.
-    exp_efficiency: float
-        Sensitivity of thermosteric SLR to ocean heat content change.
-    """
-
-    def __init__(
-        self,
-        end_yr: int,
-        seed: int = 1234,
-        nt: int = 100,
-        nm: int = 1000,
-        tcv: float = 1.0,
-        glaciermip: bool | int = 2,
-        parallel: bool = True,
-        input_ensemble: bool = True,
-        output_percentiles: list | np.ndarray = None,
-        random_sample: bool = False,
-        T_percentile_95: np.ndarray = None,
-        OHC_percentile_95: np.ndarray = None,
-        cum_emissions_total: float = None,
-        palmer_method: bool = True,
-        active_components: list[str] = None,
-    ) -> None:
-
-        self.end_yr = end_yr
-        self.nt = nt
-        self.nm = nm
-        self.tcv = tcv
-        self.glaciermip = glaciermip
-        self.parallel = parallel
-        self.input_ensemble = input_ensemble
-        self.output_percentiles = output_percentiles
-        self.random_sample = random_sample
-        self.T_percentile_95 = T_percentile_95
-        self.OHC_percentile_95 = OHC_percentile_95
-        self.cum_emissions_total = cum_emissions_total
-        self.palmer_method = palmer_method
-
-        # First year of AR5 projections
-        self.endofhistory = 2006
-
-        # Last year of AR5 projections
-        self.endofAR5 = 2100
-
-        # Length of projections
-        self.nyr = self.end_yr - self.endofhistory
-
-        # Fraction of SLE from Greenland during 1996 to 2005 assumed to result from
-        # rapid dynamical change, with the remainder assumed to result from SMB change
-        self.fgreendyn = 0.5
-
-        # m SLE from Greenland during 1996 to 2005 according to AR5 chapter 4
-        self.dgreen = (3.21 - 0.30) * 1e-3
-
-        # m SLE from Antarctica during 1996 to 2005 according to AR5 chapter 4
-        self.dant = (2.37 + 0.13) * 1e-3
-
-        # Conversion factor for Gt to m SLE
-        self.mSLEoGt = 1e12 / 3.61e14 * 1e-3
-
-        self.seed_seq = np.random.SeedSequence(seed if seed is not None else 1234)
-        self.rng = np.random.default_rng(self.seed_seq)
-        self._stochastic_components = [
-            "glacier",
-            "antsmb",
-            "greenland_ar6",
-            "greendyn",
-            "greensmb",
-            "antdyn",
-            "antarctica",
-        ]
-        child_seeds = self.seed_seq.spawn(len(self._stochastic_components))
-        self.component_rngs = {
-            comp: np.random.default_rng(s)
-            for comp, s in zip(self._stochastic_components, child_seeds)
-        }
-
-        self.active_components = active_components or [
-            "expansion",
-            "glacier",
-            "greenland_ar6",
-            "antarctica",
-            "landwater",
-        ]
-
-        # Setup AIS emulator, if needed
-        if "antarctica" in self.active_components:
-            wais_path = Path(__file__).parent / "aux_data" / "wais_params.nc"
-            eais_path = Path(__file__).parent / "aux_data" / "eais_params.nc"
-            aispen_path = Path(__file__).parent / "aux_data" / "aispen_params.nc"
-            self.wais_model = AntarcticaISMIP6(wais_path)
-            self.eais_model = AntarcticaISMIP6(eais_path)
-            self.aispen_model = AntarcticaISMIP6(aispen_path)
-
-    def get_components(self) -> dict:
-        """Get all GMSLR components as a dictionary."""
-        components_dict = {}
-        # Explicitly check for sub-components alongside active ones
-        export_list = self.active_components + ["gmslr", "wais", "eais", "aispen"]
-
-        for comp in export_list:
-            if hasattr(self, comp):
-                components_dict[comp] = getattr(self, comp)
-        return components_dict
-
-    def list_components(self) -> list:
-        """List the available SLR components."""
-        component_dict = self.get_components()
-        print(list(component_dict.keys()))
-
-    def save_components(self, output_dir: str, scenario_name: str) -> None:
-        """Save all SLR components as .npy files to a directory.
-
-        Parameters
-        ----------
-        output_directory: str
-            Directory to save components to.
-        scenario_name: str
-            Name of the scenario you've run the emulator for.
-
-        Returns
-        -------
-        None
-        """
-        # Create directory if it doesn't exist
-        Path(output_dir).mkdir(parents=True, exist_ok=True)
-
-        # Save data in netcdf format (store all components in an xarray dataset)
-        # Assumes first dimension is percentile, but can be more general (percentile/ensemble)
-        ds = xr.Dataset()
-        for name, component in self.get_components().items():
-            xr_dataArray = xr.DataArray(
-                component,
-                dims=["percentile", "time"],
-                coords={
-                    "percentile": self.output_percentiles,
-                    "time": np.arange(2006, component.shape[1] + 2006),
-                },
-            )
-            xr_dataArray.attrs["units"] = "m"
-            ds[name] = xr_dataArray
-        ds.to_netcdf(os.path.join(output_dir, f"{scenario_name}_global.nc"))
-
-    def check_shapes(
-        self, T_change: np.ndarray, OHC_change: np.ndarray, n_time: int
-    ) -> None:
-        """Check that the input arrays have the correct shape.
-
-        Parameters
-        ----------
-        T_change: np.ndarray
-            Array of surface temperature changes.
-        OHC_change: np.ndarray
-            Array of ocean heat content changes.
-        n_time: int
-            Expected number of time steps.
-
-        Returns
-        -------
-        None
-        """
-        if T_change.ndim == 1:
-            T_change = T_change[np.newaxis, :]
-        if OHC_change.ndim == 1:
-            OHC_change = OHC_change[np.newaxis, :]
-
-        if T_change.shape[1] != n_time:
-            # Split over lines for readability
-            raise ValueError(
-                f"T_change should have shape (realisation, time) with time \
-                dimension of length {n_time}. Got {T_change.shape}."
-            )
-        if OHC_change.shape[1] != n_time:
-            raise ValueError(
-                f"OHC_change should have shape (realisation, time) with time \
-                dimension of length {n_time}. Got {OHC_change.shape}."
-            )
-
-    def project(
-        self,
-        scenario: str,
-        T_change: np.ndarray,
-        OHC_change: np.ndarray,
-        cum_emissions_total: int = None,
-    ) -> None:
-        """Run the emulator to project GMSLR components."""
-        self.scenario = scenario
-        self.T_change = np.asarray(T_change)
-        self.OHC_change = np.asarray(OHC_change)
-        self.cum_emissions_total = cum_emissions_total
-
-        # Check input shapes are correct
-        self.check_shapes(self.T_change, self.OHC_change, self.nyr)
-
-        if self.input_ensemble:
-            self.nt = self.T_change.shape[0]
-
-        T_ens, Exp_ens, T_int_ens, T_int_med = self.calculate_drivers()
-        fraction = self.rng.random(
-            self.nm * self.nt
-        )  # correlation between antsmb and antdyn
-
-        # Evaluate Inline Components
-        if "expansion" in self.active_components:
-            self.expansion = np.tile(Exp_ens, (self.nm, 1))
-
-        # Evaluate Standard Components
-        if self.parallel:
-            self.run_parallel_projections(T_int_med, T_int_ens, T_ens, fraction)
-        else:
-            self.run_serial_projections(T_int_med, T_int_ens, T_ens, fraction)
-
-        # Handle Random Sampling
-        if self.random_sample:
-            random_idx = self.rng.integers(low=0, high=self.nm)
-
-            for comp in self.active_components + ["wais", "eais", "aispen"]:
-                if hasattr(self, comp):
-                    comp_data = getattr(self, comp)
-                    if comp_data.ndim > 1:
-                        setattr(self, comp, comp_data[random_idx][None, :])
-
-        # Calculate GMSLR
-        components_to_sum = [
-            getattr(self, comp)
-            for comp in self.active_components
-            if hasattr(self, comp)
-        ]
-        if not components_to_sum:
-            raise RuntimeError(
-                "No active components were evaluated. Cannot compute GMSLR."
-            )
-        self.gmslr = np.sum(components_to_sum, axis=0)
-
-        # Output percentiles
-        if self.output_percentiles is not None:
-            console.log(
-                f"Sampling {len(self.output_percentiles)} members per component..."
-            )
-            self.gmslr = sample_members_2D(self.gmslr, self.output_percentiles)
-
-            for comp in self.active_components + ["wais", "eais", "aispen"]:
-                if hasattr(self, comp):
-                    sampled_data = sample_members_2D(
-                        getattr(self, comp), self.output_percentiles
-                    )
-                    setattr(self, comp, sampled_data)
-
-    def _build_task_registry(
-        self,
-        T_int_med: np.ndarray,
-        T_int_ens: np.ndarray,
-        T_ens: np.ndarray,
-        fraction: np.ndarray,
-    ) -> dict:
-        """Helper to map active component names to their functions and arguments."""
-        registry = {}
-        for comp in self.active_components:
-            rng = self.component_rngs.get(comp)
-
-            if comp == "glacier":
-                registry[comp] = (self.project_glacier, (T_int_med, T_int_ens, rng))
-            elif comp == "antsmb":
-                registry[comp] = (self.project_antsmb, (T_int_ens, rng, fraction))
-            elif comp == "greenland_ar6":
-                registry[comp] = (self.project_greenland_AR6, (T_ens, rng))
-            elif comp == "greendyn":
-                registry[comp] = (self.project_greendyn_AR5, (rng,))
-            elif comp == "greensmb":
-                registry[comp] = (self.project_greensmb_AR5, (T_ens, rng))
-            elif comp == "antdyn":
-                registry[comp] = (self.project_antdyn, (rng, fraction))
-            elif comp == "landwater":
-                registry[comp] = (self.project_landwater_ar6, ())
-            elif comp == "antarctica":
-                registry[comp] = (self.project_antarctica_ismip6, (T_ens, rng))
-
-        return registry
-
-    def run_serial_projections(
-        self,
-        T_int_med: np.ndarray,
-        T_int_ens: np.ndarray,
-        T_ens: np.ndarray,
-        fraction: np.ndarray,
-    ) -> None:
-        """Run components of the emulator sequentially."""
-        registry = self._build_task_registry(T_int_med, T_int_ens, T_ens, fraction)
-
-        for comp in self.active_components:
-            if comp in registry:
-                func, args = registry[comp]
-                setattr(self, comp, func(*args))
-
-        # Handle AR5 greenland combination if both are active
-        if (
-            "greendyn" in self.active_components
-            and "greensmb" in self.active_components
-        ):
-            self.greenland_ar5 = self.greendyn + self.greensmb
-
-    def run_parallel_projections(
-        self,
-        T_int_med: np.ndarray,
-        T_int_ens: np.ndarray,
-        T_ens: np.ndarray,
-        fraction: np.ndarray,
-    ) -> None:
-        """Run components of the emulator in parallel."""
-        registry = self._build_task_registry(T_int_med, T_int_ens, T_ens, fraction)
-
-        with concurrent.futures.ThreadPoolExecutor() as executor:
-            futures = {}
-            for comp in self.active_components:
-                if comp in registry:
-                    func, args = registry[comp]
-                    futures[executor.submit(func, *args)] = comp
-
-            for future in concurrent.futures.as_completed(futures):
-                comp_name = futures[future]
-                try:
-                    setattr(self, comp_name, future.result())
-                except Exception as e:
-                    raise RuntimeError(
-                        f"Component '{comp_name}' failed during projection."
-                    ) from e
-
-        # Handle AR5 greenland combination if both are active
-        if (
-            "greendyn" in self.active_components
-            and "greensmb" in self.active_components
-        ):
-            self.greenland_ar5 = self.greendyn + self.greensmb
-
-    def calculate_drivers(self) -> tuple:
-        """Calculate the drivers of GMSLR: temperature change and
-        thermosteric sea level rise.
-
-        Returns
-        -------
-        T_ens: np.ndarray
-            Ensemble of temperature changes.
-        therm_ens: np.ndarray
-            Ensemble of thermosteric sea level rise.
-        T_int_ens: np.ndarray
-            Ensemble of time-integral temperature anomalies.
-        T_int_med: np.ndarray
-            Median of time-integral temperature anomalies.
-        """
-        # Sensitivity of thermosteric SLR to ocean heat content change
-        # From Turner et al. (2023)
-        exp_efficiency = (
-            self.rng.normal(loc=0.113, scale=0.013, size=self.nt)[:, None] * 1e-24
-        )  # m/YJ
-
-        if self.input_ensemble:
-            # Check if dimensions are the right way around
-            if self.T_change.shape[1] != self.nyr:
-                self.T_change = self.T_change.T
-            if self.OHC_change.shape[1] != self.nyr:
-                self.OHC_change = self.OHC_change.T
-
-            T_med = np.percentile(self.T_change, 50, axis=0)
-            T_std = np.std(self.T_change, axis=0)
-
-            therm_med = np.percentile(self.OHC_change, 50, axis=0) * exp_efficiency
-            therm_std = np.std(self.OHC_change * exp_efficiency, axis=0)
-
-        else:
-            if self.T_percentile_95 is not None:
-                T_med = self.T_change
-                therm_med = self.OHC_change * exp_efficiency
-
-                T_std = (self.T_percentile_95 - self.T_change) / 1.645
-                therm_std = (
-                    (self.OHC_percentile_95 - self.OHC_change) * exp_efficiency / 1.645
-                )
-
-            else:
-                raise ValueError(
-                    "If input_ensemble is False, and T_change and OHC_change "
-                    "are not 2D arrays, you must provide a 95th percentile "
-                    "timeseries for T_change and OHC_change. Add this using "
-                    "T_percentile_95 and OHC_percentile_95 keyword arguments."
-                )
-
-        # Time-integral of temperature anomaly
-        T_int_med = np.cumsum(T_med)
-        T_int_std = np.cumsum(T_std)
-
-        # Generate a sample of perfectly correlated timeseries fields of temperature,
-        # time-integral temperature and expansion, each of them [realisation,time]
-        z = self.rng.standard_normal(self.nt) * self.tcv
-
-        # For each quantity, mean + standard deviation * normal random number
-        # reshape to [realisation,time]
-        T_ens = z[:, np.newaxis] * T_std + T_med
-        therm_ens = z[:, np.newaxis] * therm_std + therm_med
-        T_int_ens = z[:, np.newaxis] * T_int_std + T_int_med
-        return T_ens, therm_ens, T_int_ens, T_int_med
-
-    def project_antarctica_ismip6(self, T_ens: np.ndarray, rng) -> np.ndarray:
-        wais_raw = self.wais_model.predict(T_ens.squeeze(), display_progress=False)
-        eais_raw = self.eais_model.predict(T_ens.squeeze(), display_progress=False)
-        aispen_raw = self.aispen_model.predict(T_ens.squeeze(), display_progress=False)
-
-        random_ais_idx = rng.integers(low=0, high=43)
-
-        # Match the correct output shape
-        self.wais = np.repeat(wais_raw[random_ais_idx, :, :], self.nt, axis=0)
-        self.eais = np.repeat(eais_raw[random_ais_idx, :, :], self.nt, axis=0)
-        self.aispen = np.repeat(aispen_raw[random_ais_idx, :, :], self.nt, axis=0)
-        return self.wais + self.eais + self.aispen
-
-    def project_glacier(
-        self, T_int_med: np.ndarray, T_int_ens: np.ndarray, rng: np.random.Generator
-    ) -> np.ndarray:
-        """Project glacier contribution to GMSLR.
-
-        Parameters
-        ----------
-        T_int_med: np.ndarray
-            Time-integral of median temperature anomaly timeseries.
-        T_int_ens: np.ndarray
-            Ensemble of time-integral temperature anomaly timeseries.
-
-        Returns
-        -------
-        glacier: np.ndarray
-            Glacier contribution to GMSLR.
-        """
-        # glaciermip -- False => AR5 parameters, 1 => fit to Hock et al. (2019),
-        #   2 => fit to Marzeion et al. (2020)
-        dmzdtref = 0.95  # mm yr-1 in Marzeion's CMIP5 ensemble mean for AR5 ref period
-        dmz = (
-            dmzdtref * (self.endofhistory - 1996) * 1e-3
-        )  # m from glacier at start wrt AR5 ref period
-        glmass = 412.0 - 96.3  # initial glacier mass, used to set a limit, from Tab 4.2
-        glmass = 1e-3 * glmass  # m SLE
-
-        glacier = np.full((self.nm, self.nt, self.nyr), np.nan)
-
-        if self.glaciermip:
-            if self.glaciermip == 1:
-                glparm = [
-                    dict(name="SLA2012", factor=3.39, exponent=0.722, cvgl=0.15),
-                    dict(name="MAR2012", factor=4.35, exponent=0.658, cvgl=0.13),
-                    dict(name="GIE2013", factor=3.57, exponent=0.665, cvgl=0.13),
-                    dict(name="RAD2014", factor=6.21, exponent=0.648, cvgl=0.17),
-                    dict(name="GloGEM", factor=2.88, exponent=0.753, cvgl=0.13),
-                ]
-                cvgl = 0.15  # unnecessary default
-            elif self.glaciermip == 2:
-                glparm = [
-                    dict(name="GLIMB", factor=3.70, exponent=0.662, cvgl=0.206),
-                    dict(name="GloGEM", factor=4.08, exponent=0.716, cvgl=0.161),
-                    dict(name="JULES", factor=5.50, exponent=0.564, cvgl=0.188),
-                    dict(name="Mar-12", factor=4.89, exponent=0.651, cvgl=0.141),
-                    dict(name="OGGM", factor=4.26, exponent=0.715, cvgl=0.164),
-                    dict(name="RAD2014", factor=5.18, exponent=0.709, cvgl=0.135),
-                    dict(name="WAL2001", factor=2.66, exponent=0.730, cvgl=0.206),
-                ]
-                cvgl = 0.20  # unnecessary default
-            else:
-                raise KeyError("glaciermip must be 1 or 2")
-        else:
-            glparm = [
-                dict(name="Marzeion", factor=4.96, exponent=0.685),
-                dict(name="Radic", factor=5.45, exponent=0.676),
-                dict(name="Slangen", factor=3.44, exponent=0.742),
-                dict(name="Giesen", factor=3.02, exponent=0.733),
-            ]
-            cvgl = 0.20  # random methodological error
-
-        ngl = len(glparm)  # number of glacier methods
-
-        r_per_model = self.nm // ngl
-        r_remainder = self.nm % ngl
-        r = self.rng.standard_normal(self.nm)
-        r = r[:, np.newaxis, np.newaxis]
-
-        # Precompute mgl and zgl for all glacier methods
-        mgl_all = np.array(
-            [
-                self._project_glacier1(
-                    T_int_med, glparm[igl]["factor"], glparm[igl]["exponent"]
-                )
-                for igl in range(ngl)
-            ]
-        )
-        zgl_all = np.array(
-            [
-                self._project_glacier1(
-                    T_int_ens, glparm[igl]["factor"], glparm[igl]["exponent"]
-                )
-                for igl in range(ngl)
-            ]
-        )
-        cvgl_all = np.array(
-            [glparm[igl]["cvgl"] if self.glaciermip else cvgl for igl in range(ngl)]
-        )
-
-        # Make an ensemble of projections for each method
-        current_ensemble_idx = 0
-        for igl in range(ngl):
-            mgl = mgl_all[igl]
-            zgl = zgl_all[igl]
-            cvgl = cvgl_all[igl]
-
-            num_reals_for_model_i = (
-                r_per_model + 1 if igl < r_remainder else r_per_model
-            )
-            ifirst = current_ensemble_idx
-            ilast = current_ensemble_idx + num_reals_for_model_i
-
-            glacier[ifirst:ilast, ...] = zgl + (mgl * r[ifirst:ilast] * cvgl)
-            current_ensemble_idx = ilast
-
-        glacier += dmz
-        np.clip(glacier, None, glmass, out=glacier)
-
-        glacier = glacier.reshape(glacier.shape[0] * glacier.shape[1], glacier.shape[2])
-        return glacier
-
-    def _project_glacier1(
-        self, T_int: np.ndarray, factor: float, exponent: float
-    ) -> np.ndarray:
-        """Project glacier contribution by one glacier method.
-
-        Parameters
-        ----------
-        T_int: np.ndarray
-            Time-integral temperature anomaly timeseries.
-        factor: float
-            Factor for the glacier method.
-        exponent: float
-            Exponent for the glacier method.
-
-        Returns
-        -------
-        np.ndarray
-            Projection of glacier contribution.
-        """
-        scale = 1e-3  # mm to m
-        return scale * factor * (np.where(T_int < 0, 0, T_int) ** exponent)
-
-    def project_greensmb_AR5(
-        self, T_ens: np.ndarray, rng: np.random.Generator
-    ) -> np.ndarray:
-        """Project Greenland SMB contribution to GMSLR.
-
-        Parameters
-        ----------
-        T_ens: np.ndarray
-            Ensemble of temperature anomaly timeseries.
-
-        Returns
-        -------
-        greensmb: np.ndarray
-            Greenland SMB contribution to GMSLR.
-
-        """
-        dtgreen = -0.146  # Delta_T of Greenland ref period wrt AR5 ref period
-        fnlogsd = 0.4  # random methodological error of the log factor
-        febound = [1, 1.15]  # bounds of uniform pdf of SMB elevation feedback factor
-
-        # random log-normal factor
-        fn = np.exp(self.rng.standard_normal(self.nm) * fnlogsd)
-        # elevation feedback factor
-        fe = self.rng.random(self.nm) * (febound[1] - febound[0]) + febound[0]
-        ff = fn * fe
-
-        ztgreen = T_ens - dtgreen
-
-        greensmb = ff[:, np.newaxis, np.newaxis] * self._fettweis(ztgreen)
-
-        if self.palmer_method and self.end_yr > self.endofAR5:
-            greensmb[:, :, 95:] = greensmb[:, :, 94:95]
-
-        greensmb = np.cumsum(greensmb, axis=-1)
-
-        greensmb += (1 - self.fgreendyn) * self.dgreen
-
-        greensmb = greensmb.reshape(
-            greensmb.shape[0] * greensmb.shape[1], greensmb.shape[2]
-        )
-        return greensmb
-
-    def _fettweis(self, ztgreen: np.ndarray) -> np.ndarray:
-        """Calculate Greenland SMB in m yr-1 SLE from global mean temperature
-        anomaly, using Eq 2 of Fettweis et al. (2013).
-
-        Parameters
-        ----------
-        ztgreen: np.ndarray
-            Global mean temperature anomaly.
-
-        Returns
-        -------
-        np.ndarray
-            Greenland SMB in m yr-1 SLE.
-        """
-        return (
-            71.5 * ztgreen + 20.4 * (ztgreen**2) + 2.8 * (ztgreen**3)
-        ) * self.mSLEoGt
-
-    def project_antsmb(
-        self,
-        T_int_ens: np.ndarray,
-        rng: np.random.Generator,
-        fraction: np.ndarray = None,
-    ) -> np.ndarray:
-        """Project Antarctic SMB contribution to GMSLR.
-
-        Parameters
-        ----------
-        T_int_ens: np.ndarray
-            Ensemble of time-integral temperature anomaly timeseries.
-        fraction: np.ndarray
-            Random numbers for the SMB-dynamic feedback.
-
-        Returns
-        -------
-        antsmb: np.ndarray
-            Antarctic SMB contribution to GMSLR.
-        """
-        # The following are [mean,SD]
-        pcoK = [5.1, 1.5]  # % change in Ant SMB per K of warming from G&H06
-        KoKg = [1.1, 0.2]  # ratio of Antarctic warming to global warming from G&H06
-
-        # Generate a distribution of products of the above two factors
-        pcoKg = (pcoK[0] + self.rng.standard_normal([self.nm, self.nt]) * pcoK[1]) * (
-            KoKg[0] + self.rng.standard_normal([self.nm, self.nt]) * KoKg[1]
-        )
-        meansmb = 1923  # model-mean time-mean 1979-2010 Gt yr-1 from 13.3.3.2
-        moaoKg = (
-            -pcoKg * 1e-2 * meansmb * self.mSLEoGt
-        )  # m yr-1 of SLE per K of global warming
-
-        if fraction is None:
-            fraction = self.rng.random((self.nm, self.nt))
-        elif fraction.size != self.nm * self.nt:
-            raise ValueError("fraction is the wrong size")
-        else:
-            fraction = fraction.reshape((self.nm, self.nt))
-
-        smax = 0.35  # max value of S in 13.SM.1.5
-        ainterfactor = 1 - fraction * smax
-
-        z = moaoKg * ainterfactor
-        z = z[:, :, np.newaxis]
-        antsmb = z * T_int_ens
-        antsmb = antsmb.reshape(antsmb.shape[0] * antsmb.shape[1], antsmb.shape[2])
-        return antsmb
-
-    def project_greenland_AR6(
-        self, T_ens: np.ndarray, rng: np.random.Generator
-    ) -> np.ndarray:
-        """Project Greenland ice-sheet contribution to GMSLR.
-        This follows the IPCC AR6 methodology as closely as possible.
-        Projections are relative to 1996-2014 baseline.
-
-        Returns
-        -------
-        np.ndarray
-            Total GIS contribution to GMSLR.
-        """
-        df = load_greenland_calibration()
-        b0 = df["b0"].values[None, :, None]
-        b1 = df["b1"].values[None, :, None]
-        b2 = df["b2"].values[None, :, None]
-        b3 = df["b3"].values[None, :, None]
-        b4 = df["b4"].values[None, :, None]
-        b5 = df["b5"].values[None, :, None]
-        sigma = df["sigma"].values
-        time_delta = np.arange(self.nyr)
-
-        # GIS trend values taken from FACTS GitHub repo
-        trend_mean = 0.19
-        trend_std = 0.1
-
-        # Calculate trend contribution distribution
-        trend = truncnorm.ppf(
-            self.rng.random(self.nm), a=0.0, b=99999.9, loc=trend_mean, scale=trend_std
-        )
-        trend = trend[:, None] * time_delta[None, :]
-        trend = trend[:, None, :]
-        trend /= 1e3  # convert mm to m SLE
-
-        # Calculate GIS contribution rate
-        dsle = (
-            b0
-            + (b1 * T_ens[:, None, :])
-            + (b2 * T_ens[:, None, :] ** 2)
-            + (b3 * T_ens[:, None, :] ** 3)
-            + (b4 * time_delta[None, None, :])
-            + (b5 * time_delta[None, None, :] ** 2)
-        )
-
-        # Now integrate
-        sle = np.cumsum(dsle, axis=2)  # mm SLE per K of global warming
-        sle = sle * 1e-3  # convert mm to m SLE
-
-        # Make a Monte Carlo ensemble of projections for each model in the calibration
-        sle_ens = np.zeros((self.nm, self.nt, self.nyr))
-        r_per_model = self.nm // sle.shape[1]
-        r_remainder = self.nm % sle.shape[1]
-
-        # We want to distribute the remainder evenly across the models
-        unc = self.rng.normal(scale=sigma)
-        current_ensemble_idx = 0
-        for i in range(sle.shape[1]):
-            num_reals_for_model_i = r_per_model + 1 if i < r_remainder else r_per_model
-            ifirst = current_ensemble_idx
-            ilast = current_ensemble_idx + num_reals_for_model_i
-            model_term = sle[:, i, :]  # Shape (nt, nyr)
-            uncertainty_term = (
-                model_term * unc[None, i, None]
-            )  # Shape (num_reals_for_model_i, nt, nyr_param)
-
-            sle_ens[ifirst:ilast, :, :] = model_term[None, :, :] + uncertainty_term
-            current_ensemble_idx = ilast
-
-        sle_ens += trend
-
-        # Persist 2100 rate of change
-        rate = np.diff(sle_ens, axis=2)[:, :, 94]
-        sle_ens[:, :, 95:] = sle_ens[:, :, 94:95] + (
-            rate[:, :, None] * time_delta[None, None, 1 : self.nyr - 94]
-        )
-        sle_ens = sle_ens.reshape((self.nm * self.nt, self.nyr))
-        return sle_ens
-
-    def project_greendyn_AR5(self, rng: np.random.Generator) -> np.ndarray:
-        """Project Greenland rapid ice-sheet dynamics contribution to GMSLR.
-
-        Returns
-        -------
-        np.ndarray
-            Greenland rapid ice-sheet dynamics contribution to GMSLR.
-        """
-        # For SMB+dyn during 2005-2010 Table 4.6 gives 0.63+-0.17 mm yr-1 (5-95% range)
-        # For dyn at 2100 Chapter 13 gives [20,85] mm for rcp85, [14,63] mm otherwise
-        if self.scenario in ["rcp85", "ssp585"]:
-            finalrange = [0.020, 0.085]
-        else:
-            finalrange = [0.014, 0.063]
-        return (
-            self.time_projection(
-                0.63 * self.fgreendyn, 0.17 * self.fgreendyn, finalrange, rng
-            )
-            + self.fgreendyn * self.dgreen
-        )
-
-    def project_antdyn(
-        self, rng: np.random.Generator, fraction: np.ndarray = None
-    ) -> np.ndarray:
-        """Project Antarctic rapid ice-sheet dynamics contribution to GMSLR.
-
-        Parameters
-        ----------
-        fraction: np.ndarray
-            Random numbers for the dynamic contribution.
-
-        Returns
-        -------
-        np.ndarray
-            Antarctic rapid ice-sheet dynamics contribution to GMSLR.
-        """
-        # This is a naive solution to calculating the AntDyn contribution
-        # for any given scenario. Basically linear regressions through existing data
-        # to find rough relationship between cumulative emissions and AntDyn contribution.
-        if self.cum_emissions_total:
-            upper = (0.000110 * self.cum_emissions_total) + 0.375  # in metres
-            lower = (1.363e-05 * self.cum_emissions_total) + 0.0392  # in metres
-            final = [lower, upper]
-            # print(final)
-            # final=[-0.020, 0.185]
-        else:
-            lcoeff = dict(
-                rcp26=[-2.881, 0.923, 0.000],
-                rcp45=[-2.676, 0.850, 0.000],
-                rcp60=[-2.660, 0.870, 0.000],
-                rcp85=[-2.399, 0.860, 0.000],
-            )
-            lcoeff = lcoeff[self.scenario]
-
-            ascale = norm.ppf(fraction)
-            final = np.exp(lcoeff[2] * ascale**2 + lcoeff[1] * ascale + lcoeff[0])
-            final = final.reshape(self.nm, self.nt)
-
-            # final=[-0.020, 0.185]
-            # final = [0.06, 0.49] # AR6, SSP2-4.5
-
-            # For SMB+dyn during 2005-2010 Table 4.6 gives 0.41+-0.24 mm yr-1 (5-95% range)
-            # For dyn at 2100 Chapter 13 gives [-20,185] mm for all scenarios
-        return (
-            self.time_projection(0.41, 0.20, final, rng, fraction=fraction) + self.dant
-        )
-
-    def project_landwater_ar6(self) -> np.ndarray:
-        """Project land water storage contribution to GMSLR.
-
-        Returns
-        -------
-        np.ndarray
-            Land water storage contribution to GMSLR.
-        """
-        # Read in AR6 landwater contributions
-        lw_ds = load_landwater_projection()
-
-        # Interpolate to annual projections
-        interp_ds = lw_ds.interp(
-            years=np.arange(2005, 2301, 1), method="linear"
-        ).squeeze()
-        lw = interp_ds["sea_level_change"].values * 1e-3  # mm to m
-
-        del interp_ds
-
-        # Go from shape (20000, 294) to (101000, 294)
-        full_repeats = (self.nt * self.nm) // lw.shape[0]
-        remainder = (self.nt * self.nm) % lw.shape[0]
-        lw = np.vstack([np.tile(lw, (full_repeats, 1)), lw[:remainder]])
-        lw = lw.reshape(self.nt * self.nm, lw.shape[1])
-        lw = lw[:, 1:]  # Start at 2006, end at 2299
-        return lw
-
-    def project_landwater_ar5(self, rng: np.random.Generator) -> np.ndarray:
-        """Old projection function. Project land water storage
-        contribution to GMSLR.
-
-        Returns
-        -------
-        np.ndarray
-            Land water storage contribution to GMSLR.
-        """
-        # The rate at start is the one for 1993-2010 from the budget table.
-        # The final amount is the mean for 2081-2100.
-        nyr = 2100 - 2081 + 1  # number of years of the time-mean of the final amount
-        final = [-0.01, 0.09]  # AR5
-        # final = [0.01, 0.04] # AR6
-        return self.time_projection(0.38, 0.49 - 0.38, final, rng, nfinal=nyr)
-
-    def time_projection(
-        self,
-        startratemean: float,
-        startratepm: float,
-        final,
-        rng: np.random.Generator,
-        nfinal: int = 1,
-        fraction: np.ndarray = None,
-    ) -> np.ndarray:
-        """Project a quantity which is a quadratic function of time.
-
-        Parameters
-        ----------
-        startratemean: float
-            Rate of GMSLR at the start in mm yr-1.
-        startratepm: float
-            Start rate error in mm yr-1.
-        final: list | np.ndarray
-            Likely range in m for GMSLR at the end of AR5.
-        nfinal: int
-            Number of years at the end over which final is a time-mean.
-        fraction: np.ndarray
-            Random numbers in the range 0 to 1.
-
-        Returns
-        -------
-        np.ndarray
-            Projection of the quantity.
-        """
-        # Create a field of elapsed time since start in years
-        timeendofAR5 = self.endofAR5 - self.endofhistory + 1
-        time = np.arange(self.end_yr - self.endofhistory) + 1
-
-        if fraction is None:
-            fraction = self.rng.random((self.nm, self.nt))
-        elif fraction.size != self.nm * self.nt:
-            raise ValueError("fraction is the wrong size")
-
-        fraction = fraction.reshape(self.nm, self.nt)
-
-        # Convert inputs to startrate (m yr-1) and afinal (m), where both are
-        # arrays with the size of fraction
-        startrate = (
-            startratemean + startratepm * np.array([-1, 1], dtype=float)
-        ) * 1e-3  # convert mm yr-1 to m yr-1
-        finalisrange = isinstance(final, Sequence)
-
-        if finalisrange:
-            if len(final) != 2:
-                raise ValueError("final range is the wrong size")
-            afinal = (1 - fraction) * final[0] + fraction * final[1]
-        else:
-            if final.shape != fraction.shape:
-                raise ValueError("final array is the wrong shape")
-            afinal = final
-
-        startrate = (1 - fraction) * startrate[0] + fraction * startrate[1]
-
-        # For terms where the rate increases linearly in time t, we can write GMSLR as
-        #   S(t) = a*t**2 + b*t
-        # where a is 0.5*acceleration and b is start rate. Hence
-        #   a = S/t**2-b/t = (S-b*t)/t**2
-        # If nfinal=1, the following two lines are equivalent to
-        # halfacc=(final-startyr*nyr)/nyr**2
-        finalyr = np.arange(nfinal) - nfinal + 94 + 1  # last element ==nyr
-        halfacc = (afinal - startrate * finalyr.mean()) / (finalyr**2).mean()
-        quadratic = halfacc[:, :, np.newaxis] * (time**2)
-        linear = startrate[:, :, np.newaxis] * time
-
-        # If acceleration ceases for t>t0, the rate is 2*a*t0+b thereafter, so
-        #   S(t) = a*t0**2 + b*t0 + (2*a*t0+b)*(t-t0)
-        #        = a*t0*(2*t - t0) + b*t
-        # i.e. the quadratic term is replaced, the linear term unaffected
-        # The quadratic also = a*t**2-a*(t-t0)**2
-
-        if self.palmer_method:
-            y = halfacc[:, :, np.newaxis] * timeendofAR5 * ((2 * time) - timeendofAR5)
-            quadratic[:, :, 95:] = y[:, :, 95:]
-
-        quadratic += linear
-
-        quadratic = quadratic.reshape(
-            quadratic.shape[0] * quadratic.shape[1], quadratic.shape[2]
-        )
-
-        return quadratic
diff --git a/profsea/components/spatial/spatial.py b/profsea/components/spatial/spatial.py
deleted file mode 100644
index 4f9aca4..0000000
--- a/profsea/components/spatial/spatial.py
+++ /dev/null
@@ -1,456 +0,0 @@
-"""
-Copyright (c) 2023, Met Office
-All rights reserved.
-"""
-import pickle
-import os
-from pathlib import Path
-import warnings
-
-import dask.array as da
-import numpy as np
-from rich.console import Console
-from rich.progress import track
-import xarray as xr
-
-from profsea.utils import interpolate
-
-console = Console()
-warnings.filterwarnings("ignore")
-
-class Spatial:
-    """ Spatial sea level rise component emulator."""
-
-    def __init__(
-            self, 
-            scenario: str,
-            expansion_patterns_dir: str|Path,
-            fingerprint_dir: str|Path,
-            gia_dir: str|Path,
-            components_path: str|Path=None, 
-            component_list: list=None, 
-            end_year: int=2301, 
-            baseline_yrs: tuple=(1986, 2005),
-            output_percentiles: list|np.ndarray=[5, 17, 50, 83, 95],
-            output_dir: str|Path=None,
-            cmip5_patterns: bool=False,
-            random_seed: int=None,
-            sample_spatial: bool=False
-        ):
-        """
-        Parameters
-        ----------
-        scenario: str
-            Name of the scenario.
-        expansion_patterns_dir: str
-            Direcotry path of regression patterns of thermal expansion component from cmip models.
-        fingerprint_dir: str
-            Direcotry path of GRD (Gravitational, Rotational, Deformational) fingerprint data
-        gia_dir: str
-            Direcotry path of GIA (Glacial Isostatic Adjustment) data
-        components_path: str
-            Path to global sea-level rise components (output of ProFSea's gmslr module)
-        component_list: list
-            Namelist of components for spatial projections
-        end_year: int
-            End year of the projections.
-        output_percentiles: int
-            List of percentiles for output
-        output_dir: str
-            Path to output directory for saving spatial projections.
-        random_seed: bool
-            Seed for numpy.random.
-        """
-
-        # Start off with some error handling
-        if not components_path:
-            raise ValueError(
-                "Provide an input directory")
-
-        if not component_list:
-            raise ValueError(
-                "Provide namelist of global projection components. "
-                "Currently allowed components are expansion, antdyn, "
-                "antsmb, wais, eais, aispen, glacier, greenland and, "
-                "landwater. antdyn and antsmb are the AR5-related dynamic "
-                "and SMB contributions to Antarctic sea-level rise. wais, "
-                "eais and aispen are the ISMIP6-related contributions to "
-                "sea-level rise from the WAIS, EAIS and AIS Peninsula regions "
-                "respectively.")
-
-        self.components = {}
-        component_path = Path(components_path)
-        ds_component = xr.load_dataset(component_path).sel(scenario=scenario)
-        for comp in component_list:
-            self.components[comp] = ds_component[comp].data
-  
-        if not output_dir:
-            output_dir = Path.cwd()
-
-        # Assign attributes
-        self.scenario = scenario
-        self.expansion_patterns_dir = expansion_patterns_dir
-        self.fingerprint_dir = fingerprint_dir
-        self.gia_dir = gia_dir
-        self.components_path = components_path
-        self.end_year = end_year
-        self.baseline_yrs = baseline_yrs
-        self.output_percentiles = output_percentiles
-        self.output_dir = output_dir
-        self.start_year = 2006
-        self.n_years = self.end_year - self.start_year
-        self.sample_spatial = sample_spatial
-        self.rng = np.random.default_rng(seed=random_seed)
-
-        if self.sample_spatial:
-            # Get full ensemble
-            self.n_samples = self.components["expansion"].shape[0]
-        else:
-            # Use global trajectories
-            self.n_samples = len(self.output_percentiles)
-
-        # Get lat/lon sizes
-        sample_pattern_filepath = Path(expansion_patterns_dir) \
-            / "ACCESS-CM2/zos_regression_ssp245_ACCESS-CM2.npy"
-        try:
-            sample_pattern = np.load(sample_pattern_filepath, mmap_mode="r")
-        except FileNotFoundError:
-            raise FileNotFoundError(
-                "expansion_patterns_dir expects the " 
-                "patterns' parent directory")
-        self.nlat, self.nlon = sample_pattern.shape[0], sample_pattern.shape[1]
-        console.log("INFO: Spatial() expects sterodynamic patterns on half-integer grids:\n"
-                    "\tlat: (-89.5, ..., 89.5)\n"
-                    "\tlon: (-179.5, ..., 179.5)")
-        console.log(f"Baseline period = {self.baseline_yrs[0]} to {self.baseline_yrs[1]}")
-
-    def _calc_baseline_period(self) -> float:
-        """
-        Baseline years used for IPCC AR5 and Palmer et al 2020 -- 1986-2005
-        :param yrs: years of the projections
-        :return: baseline years
-        """
-        midyr = (self.baseline_yrs[1] - self.baseline_yrs[0] + 1) * 0.5 + self.baseline_yrs[0]
-        return self.start_year - midyr
-
-    def _calc_gia_contribution(self) -> None:
-        """
-        Calculate the glacial isostatic adjustment (GIA) contribution to the
-        regional component of sea level rise.
-        Option to use the generic global GIA estimates or use the GIA estimates
-        developed for UK as part of UKCP18.
-        :return: GIA estimates converted to mm/yr
-        """
-        console.log('Calculating GIA contribution...')
-        nGIA, GIA_vals = self._read_gia_estimates()
-        Tdelta = self._calc_baseline_period()
-
-        # Unit series of mm/yr expressed as m/yr
-        unit_series = (np.arange(self.n_years) + Tdelta) * 0.001
-        GIA_unit_series = np.ones([self.n_samples, self.n_years]) * unit_series
-
-        # rgiai is an array of random GIA indices the size of the sample years
-        rgiai = self.rng.integers(nGIA, size=self.n_samples)
-
-        GIA_T = da.from_array(GIA_unit_series)
-        GIA_vals = da.from_array(GIA_vals)
-        GIA_series = GIA_T[:, :, None, None] * GIA_vals[rgiai, None, :, :]
-        GIA_series_out = da.percentile(GIA_series, self.output_percentiles, axis=0)
-
-        # Save data in netcdf format (Assuming first dimension is percentile, but can be more general percentile/ensemble)
-        xr_dataArray = xr.DataArray(
-            GIA_series_out, 
-            dims=["percentile", "time", "lat", "lon"], 
-            coords={
-                "percentile": self.output_percentiles,
-                "time": np.arange(self.start_year, self.end_year),
-                "lat": np.arange(-90, 90) + 0.5, 
-                "lon": np.arange(0, 360) + 0.5})
-        xr_dataArray.attrs["units"] = "m"
-        xr_dataArray.attrs["long_name"] = "Regional GIA sea-level projections"
-        xr_dataArray.attrs["source"] = "ProFSea-Climate v0.1"
-        ds = xr_dataArray.to_dataset(name='gia')
-
-        file_header = f"gia_{self.scenario}_projection_{self.end_year}"
-        R_file = '_'.join([file_header, 'regional']) + '.nc'
-        encoding = {'gia': {"zlib": True, "complevel": 5, "dtype": "float32"}}
-        ds.to_netcdf(os.path.join(self.output_dir, R_file), encoding=encoding, compute=True)
-        return GIA_series
-
-    def project(self) -> None:
-        """
-        Calculates global and regional component part contributions to sea level
-        change.
-        :param mcdir: location of Monte Carlo time series for new projections
-        :param components: sea level components
-        :param scenario: emission scenario
-        :param yrs: years of the projections
-        :param array_dims: Array of nesm, nsmps and nyrs
-            nesm --> Number of ensemble members in time series
-            nsmps --> Determine the number of samples you wish to make
-            nyrs --> Number of years in each projection time series
-        :return: montecarlo_G (global contribution to sea level rise) and
-            montecarlo_R (regional contribution to sea level change)
-        """  
-        console.log(f"Running in {'PROBABILISTIC' if self.sample_spatial else 'STORYLINE'} mode.")
-        console.log(f"Processing {self.n_samples} input trajectories...")
-
-        if self.sample_spatial:
-            resamples = self.rng.choice(self.n_samples, size=self.n_samples)
-        else:
-            resamples = np.arange(self.n_samples)
-
-        # Calculate GIA contribution. 
-        gia_raw_samples = self._calc_gia_contribution() 
-        
-        nFPs, FPlist = self._load_fingerprints()
-
-        if self.sample_spatial:
-            rfpi = self.rng.integers(nFPs, size=self.n_samples)
-        
-        # Initialise the Total array
-        total_montecarlo_R = gia_raw_samples
-
-        for comp in track(list(self.components.keys()), description="Calculating components..."):
-            mc_timeseries = self.components[comp] 
-            sampled_mc = mc_timeseries[resamples, :self.n_years]
-            montecarlo_G = da.from_array(sampled_mc[:, :, None, None], chunks="auto")
-
-            # Apply regional scaling/fingerprints (Fixes the slice assignment bug)
-            if comp == "expansion":
-                sampled_coeffs = self._calc_expansion_contribution()
-                montecarlo_R = montecarlo_G * sampled_coeffs[:, None, :, :]
-            elif comp == "landwater":
-                landwater_vals = self._calc_landwater_contribution(FPlist[0]["landwater"])
-                montecarlo_R = montecarlo_G * landwater_vals[None, None, :, :]
-            elif comp == "greenland":
-                greenland_fp = self._calc_greenland_fingerprint_ar6()
-                montecarlo_R = montecarlo_G * greenland_fp[None, None, :, :]
-            elif comp == "wais":
-                wais_fp = self._calc_antarctic_fingerprint(comp)
-                montecarlo_R = montecarlo_G * wais_fp[None, None, :, :]
-            elif comp == "eais":
-                eais_fp = self._calc_antarctic_fingerprint(comp)
-                montecarlo_R = montecarlo_G * eais_fp[None, None, :, :]
-            else:
-                if self.sample_spatial:
-                    fp_vals = self._calc_fingerprint_contributions(FPlist, comp)
-                    montecarlo_R = montecarlo_G * fp_vals[rfpi][:, None, :, :]
-                else:
-                    fp_vals = self._calc_fingerprint_contributions(FPlist, comp)
-                    fp_mean = da.nanmean(fp_vals, axis=0)
-                    montecarlo_R = montecarlo_G * fp_mean[None, None, :, :]
-
-            # Accumulate overall pattern
-            if total_montecarlo_R is None:
-                total_montecarlo_R = montecarlo_R
-            else:
-                total_montecarlo_R = total_montecarlo_R + montecarlo_R
-
-            # Calculate and save percentiles for the individual component
-            if self.sample_spatial:
-                comp_percentiles = da.percentile(montecarlo_R, self.output_percentiles, axis=0)
-            else:
-                comp_percentiles = montecarlo_R
-
-            self._save_projections(comp_percentiles.astype(np.float32), comp)
-
-        console.log("Processing total sea-level rise grids...")
-        if self.sample_spatial:
-            total_out = da.percentile(total_montecarlo_R, self.output_percentiles, axis=0)
-        else:
-            total_out = total_montecarlo_R
-
-        self._save_projections(total_out.astype(np.float32), "total")
-
-    def _save_projections(self, montecarlo_R: da.array, component: str) -> None:
-        """
-        Save the regional sea level projections to a file.
-        :param montecarlo_R: regional sea level projections
-        :param component: sea level component
-        :param scenario: emission scenario
-        :param percentile: percentiles used for spatial projections
-        """
-        # Save data in netcdf format (Assuming first dimension is percentile, but can be more general percentile/ensemble)
-        xr_dataArray = xr.DataArray(montecarlo_R, dims=["percentile", "time", "lat", "lon"], 
-                                    coords={"percentile": self.output_percentiles, 
-                                            "time": np.arange(2006, montecarlo_R.shape[1] + 2006),
-                                            "lat": np.arange(-90, 90) + 0.5, "lon": np.arange(0, 360) + 0.5})
-        xr_dataArray.attrs["units"] = "m"
-        xr_dataArray.attrs["long_name"] = f"Regional {component} sea-level projections"
-        xr_dataArray.attrs["source"] = "ProFSea-Climate v0.1"
-        ds = xr_dataArray.to_dataset(name=component)
-
-        file_header = f"{component}_{self.scenario}_projection_{self.end_year}"
-        R_file = '_'.join([file_header, 'regional']) + '.nc'
-        encoding = {component: {"zlib": True, "complevel": 5, "dtype": "float32"}}
-        ds.to_netcdf(os.path.join(self.output_dir, R_file), encoding=encoding, compute=True)
-
-    def _read_gia_estimates(self) -> tuple:
-        """
-        Read in pre-processed interpolator objects of GIA estimates (Lambeck,
-        ICE5G)
-        :param: none
-        :return: length of GIA_vals and numpy array of pre-processed interpolator
-        objects of GIA estimates
-        """
-        gia_path = next(Path(self.gia_dir).glob("global*"))
-        with open(gia_path, "rb") as ifp:
-            GIA_dict = pickle.load(ifp, encoding='latin1') # Interp objects
-
-        GIA_vals = []
-        for key in list(GIA_dict.keys()):
-            val = GIA_dict[key].values
-            GIA_vals.append(val)
-
-        nGIA = len(GIA_vals)
-        GIA_vals = np.array(GIA_vals)
-
-        # Sort out the crazy values in the 0th GIA array
-        GIA_vals[0][GIA_vals[0] < -99999] = 0
-
-        # AND shift them from -180, 180 to 0, 360
-        GIA_vals = np.roll(GIA_vals, 180, axis=2)
-        return nGIA, GIA_vals
-
-    def _calc_expansion_contribution(self) -> da.array:
-        """
-        Calculate the thermal expansion contribution to the regional component of
-        sea level rise.
-        :param scenario: emission scenario
-        :param nsmps: determine the number of samples
-        :return: expansion estimates converted to mm/yr
-        """
-        # Select slope coefficients based on the MIP
-        coeffs = self._load_CMIP6_slopes()
-        coeffs = da.roll(coeffs, 180, axis=2)
-
-        if self.sample_spatial:
-            rand_samples = self.rng.choice(
-                coeffs.shape[0], size=self.n_samples, replace=True)               
-            return coeffs[rand_samples, :, :]
-        else:
-            # Calc pattern ensemble mean
-            mean_coeff = da.nanmean(coeffs, axis=0)
-            return da.broadcast_to(mean_coeff, (self.n_samples, self.nlat, self.nlon))
-
-    def _calc_landwater_contribution(self, data: xr.DataArray) -> da.array:
-        """
-        Calculate the regional landwater contribution to sea level rise.
-        :param interpolator: dictionary of interpolator objects
-        :return: numpy array of landwater values
-        """
-        landwater_vals = interpolate(data, self.nlat, self.nlon)
-        landwater_vals = da.roll(landwater_vals, 180, axis=1) # change lons from (-180,180) to (0,360)
-        return landwater_vals
-
-    def _calc_fingerprint_contributions(self, FPlist: list, comp: str) -> da.array:
-        # Initiate an empty list for fingerprint values
-        fp_vals = []
-        for FP_dict in FPlist:
-            # Interpolate values to target lat/lon
-            val = FP_dict[comp]
-            val = interpolate(val, self.nlat, self.nlon)
-            val = da.roll(val, 180, axis=1) # change lons from (-180,180) to (0,360)
-            fp_vals.append(val)
-
-        fp_vals = da.stack(fp_vals, axis=0)
-        return fp_vals
-
-    def _calc_greenland_fingerprint_ar6(self) -> da.array:
-        """Load and prepare the GIS fingerprint.
-
-        This fingerprint was/is used by FACTS for AR6 projections, and was 
-        originally calculated by Mitrovica et al., (2011).
-        
-        :return dask array containing GIS fingerprint
-        """
-        # Load in the fingerprint
-        fp_path = Path(self.fingerprint_dir) / "greenland_ar6.nc"
-        fp_ds = xr.open_dataset(fp_path, chunks={})
-
-        # Interpolate to (180, 360) grid
-        fp_vals = fp_ds.fp.interp(
-            lat=np.linspace(-90, 90, self.nlat, endpoint=False) + 0.5, 
-            lon=np.linspace(0, 360, self.nlon, endpoint=False) + 0.5, 
-            method="linear").data * 1000  # convert mm to m SLE per m GMSLR
-        return fp_vals
-
-    def _calc_antarctic_fingerprint(self, fname: str) -> da.array:
-        """Load and prepare the WAIS fingerprint.
-
-        Fingerprints from Kopp, R. E. (2022). Framework for Assessing Changes 
-        To Sea-level (FACTS) Module Data (1.0) [Data set]. Zenodo.
-        
-        :return dask array containing Antarctic fingerprint
-        """
-        fname += ".nc"
-
-        # Load in the fingerprint
-        fp_path = Path(self.fingerprint_dir) / fname
-        fp_ds = xr.open_dataset(fp_path, chunks={})
-
-        # Interpolate to (180, 360) grid
-        fp_vals = fp_ds.fp.interp(
-            lat=np.linspace(-90, 90, self.nlat, endpoint=False) + 0.5, 
-            lon=np.linspace(0, 360, self.nlon, endpoint=False) + 0.5, 
-            method="linear").data * 1000  # convert mm to m SLE per m GMSLR
-        return fp_vals
-
-    def _load_CMIP6_slopes(self) -> np.ndarray:
-        """
-        Load in the CMIP6 slope coefficients.
-        :param site_loc: name of the site location
-        :param scenario: emissions scenario
-        :return: 1D array of regression coefficients
-        """
-        # Read in the sea level regressions
-        slope_files = list(Path(
-            self.expansion_patterns_dir
-        ).glob("*/zos_regression_ssp585_*.npy"))
-
-        def _load_one_slope(f):
-            return np.load(f, mmap_mode='r')
-
-        # Create a list of lazy dask arrays
-        lazy_slopes = [
-            da.from_array(_load_one_slope(f), chunks=(45, 45)) 
-            for f in slope_files]
-        slopes_stack = da.stack(lazy_slopes, axis=0)
-
-        return slopes_stack
-
-    def _load_fingerprints(self) -> tuple:
-        """
-        Create 2D Interpolator objects for the Slangen, Spada and Klemann
-        fingerprints
-        :param components: list of sea level components
-        :return nFPs: length of FPlist and interpolator objects of all sea level
-        components
-        """
-        # Create empty dictionaries for the Slangen, Spada and Klemann fingerprints
-        # interpolator objects.
-        slangen_FPs = {}
-        spada_FPs = {}
-        klemann_FPs = {}
-
-        # Only 1 fingerprint for Landwater
-        comp = "landwater"
-        landwater_path = Path(self.fingerprint_dir) / f"{comp}_slangen_nomask.nc"
-        slangen_FPs[comp] = xr.open_dataarray(landwater_path, chunks={})
-
-        # Other FPs have multiple components
-        components_todo = [
-            c for c in list(self.components.keys()) 
-            if c not in ["expansion", "landwater", "greenland", "wais", "eais"]]
-        for comp in components_todo:
-            slangen_path = Path(self.fingerprint_dir) / f"{comp}_slangen_nomask.nc"
-            spada_path = Path(self.fingerprint_dir) / f"{comp}_spada_nomask.nc"
-            klemann_path = Path(self.fingerprint_dir) / f"{comp}_klemann_nomask.nc"
-            slangen_FPs[comp] = xr.open_dataarray(slangen_path, chunks={"lat": 45, "lon": 45})
-            spada_FPs[comp] = xr.open_dataarray(spada_path, chunks={"lat": 45, "lon": 45})
-            klemann_FPs[comp] = xr.open_dataarray(klemann_path, chunks={"lat": 45, "lon": 45})
-
-        FPlist = [slangen_FPs, spada_FPs, klemann_FPs]
-        nFPs = len(FPlist)
-        return nFPs, FPlist
diff --git a/profsea/components/spatial/sterodynamic.py b/profsea/components/spatial/sterodynamic.py
index 6c85980..0091094 100644
--- a/profsea/components/spatial/sterodynamic.py
+++ b/profsea/components/spatial/sterodynamic.py
@@ -112,7 +112,7 @@ def _calc_expansion_contribution(
             return coeffs[rand_samples, :, :]
         else:
             # Calc pattern ensemble mean
-            mean_coeff = da.nanmean(coeffs, axis=0)
+            mean_coeff = da.mean(coeffs, axis=0)
             return da.broadcast_to(
                 mean_coeff,
                 (state.n_members, state.grid_lats.shape[0], state.grid_lons.shape[0]),
diff --git a/profsea/config.py b/profsea/config.py
deleted file mode 100644
index 2c2d1cf..0000000
--- a/profsea/config.py
+++ /dev/null
@@ -1,30 +0,0 @@
-"""
-Copyright (c) 2023, Met Office
-All rights reserved.
-"""
-
-import os
-import pathlib
-import yaml
-import argparse
-
-
-# Create argument parser
-parser = argparse.ArgumentParser(description="Run script with optional custom path")
-parser.add_argument("--path", type=str, help="Supply the directory path (optional)")
-
-# Parse arguments
-args = parser.parse_args()
-
-
-# Use user-supplied path if given, else fallback to script's directory
-if args.path:
-    path = pathlib.Path(args.path).as_posix()
-else:
-    path = pathlib.Path(__file__).parents[0].as_posix()
-
-
-with open(os.path.join(path, "user-settings-emu.yml"), "r") as f:
-    settings = yaml.load(f, Loader=yaml.SafeLoader)
-
-print(f"Using path: {path}")
diff --git a/profsea/directories.py b/profsea/directories.py
deleted file mode 100644
index 45dd50a..0000000
--- a/profsea/directories.py
+++ /dev/null
@@ -1,54 +0,0 @@
-"""
-Copyright (c) 2023, Met Office
-All rights reserved.
-"""
-
-import os
-
-from profsea.config import settings
-
-
-def makefolder(directory):
-    """
-    Check if subfolder exists, if not then creates the subfolder
-    :param directory: directory of subfolder
-    """
-    if not os.path.isdir(directory):
-        os.makedirs(directory)
-
-
-def read_dir():
-    """
-    Creates multiple directories from user settings
-    :return: file paths required to run ProFSea tool
-    """
-    root_dir = settings["baseoutdir"]
-    data_region = settings["siteinfo"]["region"]
-
-    # output data directory - '' adds last /
-    cmipdir = os.path.join(root_dir, data_region, 'data', 'cmip5', '')
-
-    # output map directory
-    mapdir = os.path.join(root_dir, data_region, 'figures', 'maps', '')
-
-    # output zos data directory
-    zosddir = os.path.join(root_dir, data_region, 'data', 'zos_regression', '')
-
-    # output zos figure directory
-    zosfdir = os.path.join(root_dir, data_region, 'figures',
-                           'zos_regression', '')
-
-    # output sea level projections data directory
-    sealev_ddir = os.path.join(root_dir, settings['experiment_name'], 'data',
-                               'sea_level_projections', '')
-
-    # output sea level projections figure directory
-    sealev_fdir = os.path.join(root_dir, data_region, 'figures',
-                               'sea_level_projections', '')
-
-    # output baseline sea level figure directory
-    base_fdir = os.path.join(root_dir, data_region, 'figures',
-                             'baseline_sea_level', '')
-
-    return cmipdir, mapdir, zosddir, zosfdir, sealev_ddir, sealev_fdir, \
-        base_fdir
diff --git a/profsea/notebooks/eval_plots/rcp_eval_ar5_antarctica.png b/profsea/notebooks/eval_plots/rcp_eval_ar5_antarctica.png
deleted file mode 100644
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F{6A<+@0 If you want to use the CMIP6 patterns sterodynamic patterns instead of CMIP5, you need to run [this recipe](https://github.com/ESMValGroup/ESMValTool/blob/steric_patterns/esmvaltool/recipes/recipe_steric_patterns.yml) in ESMValTool. I recommend running with as much memory and processors as possible. \n",
-    "\n",
-    "> However, if you prefer not to go via ESMValTool feel free to reach out at gregory.munday\\@dtc.ox.ac.uk and I can just send them over (they're not large in terms of data size).\n",
-    "---\n",
-    "All other data required for the tool can be [downloaded here](https://catalogue.ceda.ac.uk/uuid/47c849b907414f3ab5e56ba9cf83e779/).\n",
-    "\n",
-    "I like keeping all my data in a `data/` folder next to the `profsea/` folder, which looks like this:\n",
-    "\n",
-    "  `data/` \\\n",
-    "   `cmip5/` \\\n",
-    "   `cmip6/` \\\n",
-    "   `gia_estimates/` \\\n",
-    "   `grd_fingerprints/` \\\n",
-    "   `monte_carlo_timeseries/` \\\n",
-    "   `uk_cmip_slope_coefficients/`\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ef69532c",
-   "metadata": {},
-   "source": [
-    "#### Setting up the environment\n",
-    "The next step is to set up the conda environment. You should use the `ProFSea-emu-env.yml` environment file, building it with \n",
-    "\n",
-    "```bash\n",
-    "conda env create -f ProFSea-emu-env.yml\n",
-    "```\n",
-    "\n",
-    "and then activating with `conda activate profsea-emu`.\n",
-    "\n",
-    "---\n",
-    "\n",
-    "One final step here is to install ProFSea as an editable package, using\n",
-    "\n",
-    "```bash \n",
-    "pip install -e . \n",
-    "```\n",
-    "\n",
-    "while inside the highest level directory."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5616586f",
-   "metadata": {},
-   "source": [
-    "#### Setup FaIR output\n",
-    "Run FaIR for the scenarios you want to simulate and save out the `global surface air temperature` and `ocean heat content`. You'll also need to calculate the 2015-2100 cumulative CO2 emissions and save it to a `.json` file. The CMIP6 cumulative emissions are included in the repo: \n",
-    "\n",
-    "```profsea/emulator/aux_data/cumulative_cmip6_emissions.json```\n",
-    "\n",
-    "Here's some sample code to calculate it from scratch in FaIR. Perhaps this could be included in this branch at some point.\n",
-    "\n",
-    "```python\n",
-    "# Output cumulative emissions from 2015 to 2100\n",
-    "cum_emissions_dict = {}\n",
-    "for scenario in hist_gcam_ext.scenario.unique():  # loop over your scenarios\n",
-    "        emissions = hist_gcam_ext.loc[\n",
-    "            (hist_gcam_ext['scenario'] == scenario) & (hist_gcam_ext['variable'] == 'CO2 FFI'), \n",
-    "            '2015':'2100'\n",
-    "        ].to_numpy()[0]  # select anthro emissions\n",
-    "        cum_emissions = np.cumsum(emissions)\n",
-    "        cum_emissions = cum_emissions / 1000  # Convert to PgC\n",
-    "        print(f'Total cumulative carbon emissions from 2015 to 2100 for {scenario}: ', cum_emissions[-1])\n",
-    "        cum_emissions_dict[scenario] = cum_emissions[-1]\n",
-    "        # Save to json\n",
-    "        with open('ngfs_data/cumulative_scenario_emissions.json', 'w') as f:\n",
-    "            json.dump(cum_emissions_dict, f)\n",
-    "```\n",
-    "\n",
-    "You need to do this because the current Antarctic Ice-Sheet dynamics component uses the relationship between cumulative emissions and sea-level contribution to calculate the lower and upper bounds of the projection. This was implemented to remove scenario-dependence from the GMSLR emulator, and should be redundant when the component is updated.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6cef2ba4",
-   "metadata": {},
-   "source": [
-    "#### Running global → local ProFSea projections\n",
-    "Point your paths in the `user-settings-emu.yml` file to the right places, fill in your scenario names and you should be ready to go!\n",
-    "\n",
-    "Run: \n",
-    "\n",
-    "```bash\n",
-    "python spatial_projections.py\n",
-    "```\n",
-    "\n",
-    "The global projections will be saved to `data/runs/gmslr_output/`, while the spatialised projections should be saved to `data/runs//`\n",
-    "\n",
-    "The way this is accomplished (so that it runs on a laptop) is by taking and saving 101 percentiles of the GMSLR module output for each component. This can be updated via the `output_percentiles` keyword in the `GMSLR` class, currently line 458 in `spatial_projections.py`."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "84c2ef51",
-   "metadata": {},
-   "source": [
-    "You can also run the GMSLR module by itself:\n",
-    "\n",
-    "```python\n",
-    "from profsea.emulator import GMSLREmulator\n",
-    "\n",
-    "emulator = GMSLREmulator(\n",
-    "    T_change,  # your T_change anom from FaIR\n",
-    "    OHC_change,  # your OHC_change anom from FaIR\n",
-    "    'low-overshoot',  # your scenario name (str)\n",
-    "    2300,  #  projection end year\n",
-    "    palmer_method=palmer_method,  # recommended for runs beyond 2100\n",
-    "    input_ensemble=True,  # must be true if providing a complete FaIR ensemble\n",
-    "    output_percentiles=np.arange(101),  # list/array of percentiles to sample\n",
-    "    cum_emissions_total=1450)  # int, cumulative emissions in scenario from 2015-2100\n",
-    "\n",
-    "#  run the projections (auto-parallel)\n",
-    "emulator.project()\n",
-    "\n",
-    "# Save to desired folder with the name of the scenario\n",
-    "emulator.save_components(\n",
-    "    os.path.join(settings[\"baseoutdir\"], 'gmslr_output'),  # output dir\n",
-    "    scenario)  # scenario name\n",
-    "\n",
-    "# Lists the available components\n",
-    "emulator.list_components()\n",
-    "\n",
-    "# You can also access the data from each component directly:\n",
-    "emulator.greenland  # returns greenland projection ensemble\n",
-    "\n",
-    "```"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "cmip7",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/profsea/slr_pkg/__init__.py b/profsea/slr_pkg/__init__.py
deleted file mode 100644
index b2cb7c8..0000000
--- a/profsea/slr_pkg/__init__.py
+++ /dev/null
@@ -1,342 +0,0 @@
-"""
-Copyright (c) 2023, Met Office
-All rights reserved.
-"""
-
-import os
-import matplotlib
-import matplotlib.pyplot as plt
-import numpy as np
-import pandas as pd
-import cartopy.crs as ccrs
-
-from profsea.config import settings
-from profsea.slr_pkg import cmip, cubeplot, cubeutils, cubedata, process
-from profsea.directories import makefolder, read_dir
-
-
-def abbreviate_location_name(name, chars_not_needed=' ,()'):
-    """
-    Returns an abbreviated version of the location name, by removing the
-    specified characters.
-    Check for a comma in the name of the location; then remove the part after
-    the comma.
-    :param name: site location
-    :param chars_not_needed: characters in location name to remove
-    :return: the name after removing spaces and brackets
-    """
-    if ',' in name:
-        abbrev_name = name.split(',')[0]
-    else:
-        abbrev_name = name[:]
-
-    return ''.join([c for c in abbrev_name if c not in chars_not_needed])
-
-
-def extract_dyn_steric_regression(models, df, scenarios):
-    """
-    Calculate, plot, and save regression between the global mean (
-    thermosteric) and local (sterodynamic) sea level change from CMIP models.
-    :param models: list of model names to be extracted
-    :param df: DataFrame of all site location's metadata
-    :param scenarios: list of RCP scenarios
-    """
-    # Base directory for CMIP "zos" and "zostoga" data
-    datadir = settings["cmipinfo"]["sealevelbasedir"]
-    # Dictionary of CMIP models and experiments
-    zos_dict = cmip.zos_dictionary()
-
-    mstyle_dict = {'rcp26': 'bo',
-                   'rcp45': 'co',
-                   'rcp60': 'yo',
-                   'rcp85': 'ro'}
-
-    # Update as required for regression plots
-    xmin = -0.05
-    xmax = 0.6
-    ymin = -0.05
-    ymax = 0.6
-
-    matplotlib.rcParams['font.size'] = 10
-
-    # For indices of dataframe which will hold the slopes of the sea level
-    # regressions
-    iterables = [models, scenarios]
-    mi = pd.MultiIndex.from_product(iterables, names=['Model', 'Scenario'])
-
-    for loc_name in df.index.values:
-        # Abbreviate the site location name
-        loc_abbrev = abbreviate_location_name(loc_name)
-
-        # Empty list used to construct the output dataframe
-        result = []
-
-        # Read in the grid indices of the ocean points
-        in_cmipdir = read_dir()[0]
-        df = read_ij_1x1_coord(in_cmipdir, loc_abbrev)
-
-        for model in models:
-            i = df.at[model, 'i']
-            j = df.at[model, 'j']
-            print(f'Calculating regression for {model} at grid box indices: '
-                  f'i = {i} and j = {j}')
-            plt.figure(figsize=(5, 4.5))
-            plt.plot([-1, 3.0], [-1, 3.0], 'k:', linewidth=3)
-
-            for scenario in scenarios:
-                print(f'Scenario: {scenario}')
-                try:
-                    # dynamic sea level (zos)
-                    zos_date = zos_dict[model][scenario]['driftcorr']
-                    zos_file = f'{datadir}normalized_zos_Omon_{model}_' \
-                               f'{scenario}_{zos_date}_driftcorr.nc'
-                    zos = cubedata.read_zos_cube(zos_file)[0][:, j, i]
-                    # --------------------------------------------------------
-                    # global mean thermosteric (zostoga)
-                    # Extract, and drift-correct CMIP "zostoga" data
-                    # Normal (concatenated)
-                    zostoga_date = zos_dict[model][scenario]['zostoga']
-                    zostoga_file = f'{datadir}zostoga_Omon_{model}_' \
-                                   f'{scenario}_{zostoga_date}.nc'
-                    zostoga_raw = cubedata.read_zos_cube(zostoga_file)[0]
-                    # piControl (concatenated)
-                    piControl_date = zos_dict[model][scenario]['piControl']
-                    zostoga_pic_file = f'{datadir}zostoga_Omon_' \
-                                       f'{model}_piControl_{piControl_date}.nc'
-                    zostoga_pic = cubedata.read_zos_cube(zostoga_pic_file)[0]
-
-                    regr = process.Regress('linear')
-                    cube_drift, _ = regr.regress_t_scalar(zostoga_pic)
-                    zostoga, _ = regr.detrend_scalar(zostoga_raw, cube_drift)
-
-                    # --------------------------------------------------------
-
-                    plotlon = zos.coord('longitude').points[0]
-                    plotlat = zos.coord('latitude').points[0]
-
-                    if plotlon > 180:
-                        plotlon -= 360
-
-                    yrs = get_cube_years(zos)
-
-                    if model == 'bcc-csm1-1':
-                        index = np.where(yrs >= 2100)
-                        zostoga.data[index] = zostoga.data[index] +  \
-                            zostoga.data[index[0][0] - 1]
-
-                    # Calculate regression coefficients for periods 2005-2100
-                    # and 2050-2100
-                    idx1 = ((2005 <= yrs) & (yrs <= 2100))
-                    idx2 = ((2050 <= yrs) & (yrs <= 2100))
-
-                    zostoga.data = zostoga.data - zostoga.data[0:10].mean()
-                    zos.data = zos.data - zos.data[0:10].mean()
-
-                    # Calculate the slope and intercepts of linear fits of the
-                    # global and local sea level projections
-                    slope1, _ = np.polyfit(zostoga.data[idx1], zostoga.data[
-                        idx1] + zos.data[idx1], 1)
-                    slope2, _ = np.polyfit(zostoga.data[idx2], zostoga.data[
-                        idx2] + zos.data[idx2], 1)
-
-                    result.append([i, j, plotlon, plotlat, slope1, slope2])
-
-                    # Add the data points to the figure
-                    leglabel = scenario.upper()
-                    plt.plot(zostoga.data[idx1],
-                             zostoga.data[idx1] + zos.data[idx1],
-                             mstyle_dict[scenario],
-                             markersize=8, markeredgecolor='None',
-                             label=leglabel)
-
-                except IOError:
-                    result.append([i, j, -99, -99, np.nan, np.nan])
-                    continue
-
-            plt.xlabel('Global thermal expansion (m)')
-            plt.ylabel('Local sea level (m)')
-            plt.title(model)
-            plt.xlim([xmin, xmax])
-            plt.ylim([ymin, ymax])
-            plt.legend(loc='upper left', numpoints=1, frameon=False)
-            plt.tight_layout()
-
-            # Create the output figure file directory and filename
-            out_zosfdir = read_dir()[3]
-            makefolder(out_zosfdir)
-            outfigfile = f'{out_zosfdir}{loc_abbrev}_{model}' + \
-                '_zos_regression_2005-2100.png'
-
-            # Save the sea level regression figures to file
-            print(f'SAVING: {loc_abbrev}_{model}_zos_regression_2005-2100.png')
-            plt.savefig(outfigfile, dpi=200)
-            plt.close()
-
-        # Save the regression slopes for all models and scenarios
-        df_out = pd.DataFrame(result, columns=['i', 'j', 'lon', 'lat',
-                                               'slope_05_00', 'slope_50_00'],
-                              index=mi)
-        df_out['slope_05_00'] = df_out['slope_05_00'].apply(
-            lambda x: round(x, 4))
-        df_out['slope_50_00'] = df_out['slope_50_00'].apply(
-            lambda x: round(x, 4))
-
-        # Create the output data file directory and filename
-        out_zosddir = read_dir()[2]
-        makefolder(out_zosddir)
-        outdatafile = f'{out_zosddir}{loc_abbrev}_zos_regression.csv'
-
-        # Save the sea level regressions data to file
-        df_out.to_csv(outdatafile, na_rep='NA')
-
-
-def get_cube_years(cube):
-    """
-    Extract a numpy array of years from Iris cube
-    :param cube: iris cube containing local sea level (zos)
-    :return: array of years in cube
-    """
-    time = cube.coord('time')
-    dates = time.units.num2date(time.points)
-    years = [date.year for date in dates]
-
-    return np.array(years, dtype=int)
-
-
-def plot_ij(cube, model, location, idx, lat, lon, save_map=True, rad=5):
-    """
-    Plots the location of the requested site (based on lat, lon - red
-    cross), the location of the nearest  model grid box (based on model indices
-    i,j - black circle) and the sea surface height above the geoid in meters
-    (re-gridded onto a 1x1m grid) for each CMIP model.
-    :param cube: data cube containing the zos field for the CMIP models
-    :param model: CMIP model name
-    :param location: site location
-    :param idx: latitude and longitude of the closest ocean grid point
-    :param lat: latitude of site
-    :param lon: longitude of site
-    :param save_map: determine is maps are saved to file, default is True
-    :param rad: plotting radius, default is 5
-    """
-    i, j = idx
-
-    # Define region for plotting
-    minlon, maxlon = lon - rad * 1.5, lon + rad * 1.5
-    minlat, maxlat = lat - rad, lat + rad
-
-    # For sites that cross 180deg. e.g. minlon = 175, maxlon = 185
-    if maxlon > 180 or minlon > 180:
-        maxlon -= 360
-        minlon -= 360
-
-    region = [minlon, minlat, maxlon, maxlat]
-
-    # plotlat, plotlon are the coordinate of the nearest ocean point
-    plotlat = cube.coord('latitude').points[j]
-    plotlon = cube.coord('longitude').points[i]
-
-    # targetlat, targetlon are the coordinate of the site
-    targetlat = lat
-    targetlon = lon
-
-    if plotlon > 180:
-        plotlon -= 360
-    if targetlon > 180:
-        targetlon -= 360
-
-    ax = cubeplot.block(cube, land=False, region=region, cmin=-1, cmax=1,
-                        plotcbar=True, nlevels=25, cent_lon=targetlon,
-                        title='{} (1x1 grid) - SSH above geoid'.format(model))
-
-    # Transform the points onto the projection used by the map. May not be
-    # necessary, but is done to avoid possible position errors (i.e. the points
-    # plotted in the wrong place).
-    # Set up projections of the map, and the tide gauge and ocean points.
-    MAP_CRS = ccrs.PlateCarree(central_longitude=targetlon)
-    SRC_CRS = ccrs.PlateCarree()
-
-    orig_lons = np.array([plotlon, targetlon])
-    orig_lats = np.array([plotlat, targetlat])
-
-    # Transform the points onto the same projection used by the map.
-    # The function returns x, y and z-coordinates.
-    # The z-coords are not used, hence the underscore.
-    new_lons, new_lats, _ = MAP_CRS.transform_points(
-        SRC_CRS, orig_lons, orig_lats).T
-
-    # Plot symbols showing the ocean point and the tide gauge
-    ax.plot(new_lons[0], new_lats[0], 'ok')
-    ax.plot(new_lons[1], new_lats[1], 'xr')
-
-    if save_map:
-        # Create the output file directory location
-        out_mapdir = read_dir()[1]
-        makefolder(out_mapdir)
-
-        # Abbreviate the site location name suitable to use as a filename
-        loc_abbrev = abbreviate_location_name(location)
-        figfile = os.path.join(out_mapdir,
-                               f'{loc_abbrev}_{model}_ij_figure.png')
-
-        # Save the CMIP grid box selection map to file
-        plt.savefig(figfile)
-        plt.close()
-    else:
-        plt.show()
-
-
-def read_ar5_component(datadir, rcp, var, value='mid'):
-    """
-    Return yrs and data for specified global mean sea level component loaded
-    from file provided as part of supplementary material for AR5 Ch13.
-    :param datadir: directory containing datafiles provided as supplementary
-        material for CH13 of AR5.
-    :param rcp: emissions scenario
-    :param var: sea level component name e.g. 'greendyn'
-    :param value: 'mid', 'upper', or 'lower', default is 'mid'
-    :return: array of yrs and data
-    """
-    f = '%s%s_%s%s.txt' % (datadir, rcp, var, value)
-    arr = np.loadtxt(f)
-
-    yrs = arr[:, 0]
-    dat = arr[:, 1]
-
-    return yrs, dat
-
-
-def read_ij_1x1_coord(datadir, location):
-    """
-    Read ij coordinate pair for specified model.
-    :param datadir: directory in which the CMIP model grid box indices for
-    each site location are stored
-    :param location: site location
-    :return: DataFrame of CMIP model grid box indices
-    """
-    filename = '{}{}_ij_1x1_coords.csv'.format(datadir, location)
-
-    df = pd.read_csv(filename, skiprows=3, header=0, index_col='Model')
-
-    return df
-
-
-def choose_montecarlo_dir():
-    """
-    Choose the Monte Carlo directory based on the projection end year.
-    """
-    if settings["emulator_settings"]["emulator_mode"]:
-        return os.path.join(settings["emulator_settings"]["gmslr_output_dir"])
-    
-    end_yr = settings["projection_end_year"]
-    if (end_yr >= 2050) & (end_yr <= 2100):
-        mcdir = settings["short_montecarlodir"]
-    elif (end_yr > 2100) & (end_yr <= 2300):
-        mcdir = settings["long_montecarlodir"]
-    else:
-        raise ValueError('Projection end year must be between 2050 and 2300')
-    
-    return mcdir
-
-
-if __name__ == '__main__':
-    pass
diff --git a/profsea/slr_pkg/cmip.py b/profsea/slr_pkg/cmip.py
deleted file mode 100644
index 5d580a1..0000000
--- a/profsea/slr_pkg/cmip.py
+++ /dev/null
@@ -1,233 +0,0 @@
-# """
-# Copyright (c) 2023, Met Office
-# All rights reserved.
-# """
-
-
-def model_dictionary():
-    """
-    Define a dictionary that contains metadata of CMIP model experiment data
-    ranges, used in filename convention
-    :return: model_dict
-    """
-    model_dict = {'ACCESS1-0': {'historical': '185001-200512'},
-                  'bcc-csm1-1': {'historical': '185001-201212'},
-                  'CanESM2': {'historical': '185001-200512'},
-                  'CNRM-CM5': {'historical': '185001-185912'},
-                  'CSIRO-Mk3-6-0': {'historical': '185001-200512'},
-                  'GISS-E2-R': {'historical': '185001-200512'},
-                  'GFDL-ESM2G': {'historical': '186101-186512'},
-                  'GFDL-ESM2M': {'historical': '186101-186512'},
-                  'HadGEM2-CC': {'historical': '186001-186012'},
-                  'HadGEM2-ES': {'historical': '186001-186012'},
-                  'inmcm4': {'historical': '185001-200512'},
-                  'IPSL-CM5A-LR': {'historical': '185001-200512'},
-                  'IPSL-CM5A-MR': {'historical': '185001-200512'},
-                  'MIROC-ESM': {'historical': '185001-200512'},
-                  'MIROC-ESM-CHEM': {'historical': '185001-200512'},
-                  'MIROC5': {'historical': '185001-201212'},
-                  'MPI-ESM-LR': {'historical': '185001-200512'},
-                  'MPI-ESM-MR': {'historical': '185001-189912'},
-                  'MRI-CGCM3': {'historical': '185001-200512'},
-                  'NorESM1-M': {'historical': '185001-200512'},
-                  'NorESM1-ME': {'historical': '185001-200512'}}
-    return model_dict
-
-
-def zos_dictionary():
-    """
-    Define a dictionary that contains metadata of CMIP model experiment data
-    ranges, used in filename convention. Where a model has not been run for
-    a specific RCP then 'xxxxxx-xxxxxx' is included.
-    :return: zos_dict
-    """
-    zos_dict = {'ACCESS1-0': {'rcp26': {'driftcorr': 'xxxxxx-xxxxxx',
-                                        'zostoga': 'xxxxxx-xxxxxx',
-                                        'piControl': 'xxxxxx-xxxxxx'},
-                              'rcp45': {'driftcorr': '200601-210101',
-                                        'zostoga': '200601-210101',
-                                        'piControl': '030001-080001'},
-                              'rcp85': {'driftcorr': '200601-210101',
-                                        'zostoga': '200601-210101',
-                                        'piControl': '030001-080001'}},
-                'bcc-csm1-1': {'rcp26': {'driftcorr': '200601-230101',
-                                         'zostoga': '200601-230101',
-                                         'piControl': '000101-050101'},
-                               'rcp45': {'driftcorr': '200601-230101',
-                                         'zostoga': '200601-230101',
-                                         'piControl': '000101-050101'},
-                               'rcp85': {'driftcorr': '200601-230101',
-                                         'zostoga': '200601-230101',
-                                         'piControl': '000101-050101'}},
-                'CanESM2': {'rcp26': {'driftcorr': '200601-230101',
-                                      'zostoga': '200601-230101',
-                                      'piControl': '201501-301101'},
-                            'rcp45': {'driftcorr': '200601-230101',
-                                      'zostoga': '200601-230101',
-                                      'piControl': '201501-301101'},
-                            'rcp85': {'driftcorr': '200601-210101',
-                                      'zostoga': '200601-210101',
-                                      'piControl': '201501-301101'}},
-                'CNRM-CM5': {'rcp26': {'driftcorr': '200601-210101',
-                                       'zostoga': '200601-210101',
-                                       'piControl': '185001-269912'},
-                             'rcp45': {'driftcorr': '200601-230101',
-                                       'zostoga': '200601-230101',
-                                       'piControl': '185001-269912'},
-                             'rcp85': {'driftcorr': '200601-230101',
-                                       'zostoga': '200601-230101',
-                                       'piControl': '185001-269912'}},
-                'CSIRO-Mk3-6-0': {'rcp26': {'driftcorr': '200601-210101',
-                                            'zostoga': '200601-210101',
-                                            'piControl': '000101-050101'},
-                                  'rcp45': {'driftcorr': '200601-210101',
-                                            'zostoga': '200601-210101',
-                                            'piControl': '000101-050101'},
-                                  'rcp85': {'driftcorr': '200601-210101',
-                                            'zostoga': '200601-210101',
-                                            'piControl': '000101-050101'}},
-                'GISS-E2-R': {'rcp26': {'driftcorr': '200601-230101',
-                                        'zostoga': '200601-230101',
-                                        'piControl': '333101-453101'},
-                              'rcp45': {'driftcorr': '200601-230101',
-                                        'zostoga': '200601-230101',
-                                        'piControl': '333101-453101'},
-                              'rcp85': {'driftcorr': '200601-230101',
-                                        'zostoga': '200601-230101',
-                                        'piControl': '333101-453101'}},
-                'GFDL-ESM2G': {'rcp26': {'driftcorr': '200601-210101',
-                                         'zostoga': '200601-210101',
-                                         'piControl': '186101-210001'},
-                               'rcp45': {'driftcorr': '200601-210101',
-                                         'zostoga': '200601-210101',
-                                         'piControl': '186101-210001'},
-                               'rcp85': {'driftcorr': '200601-210101',
-                                         'zostoga': '200601-210101',
-                                         'piControl': '186101-210001'}},
-                'GFDL-ESM2M': {'rcp26': {'driftcorr': '200601-210101',
-                                         'zostoga': '200601-210101',
-                                         'piControl': '186101-210001'},
-                               'rcp45': {'driftcorr': '200601-210101',
-                                         'zostoga': '200601-210101',
-                                         'piControl': '186101-210001'},
-                               'rcp85': {'driftcorr': '200601-210101',
-                                         'zostoga': '200601-210101',
-                                         'piControl': '186101-210001'}},
-                'HadGEM2-CC': {'rcp26': {'driftcorr': 'xxxxxx-xxxxxx',
-                                         'zostoga': 'xxxxxx-xxxxxx',
-                                         'piControl': 'xxxxxx-xxxxxx'},
-                               'rcp45': {'driftcorr': '200512-210101',
-                                         'zostoga': '200512-210101',
-                                         'piControl': '185912-210001'},
-                               'rcp85': {'driftcorr': '200512-210101',
-                                         'zostoga': '200512-210101',
-                                         'piControl': '185912-210001'}},
-                'HadGEM2-ES': {'rcp26': {'driftcorr': '200512-230001',
-                                         'zostoga': '200512-230001',
-                                         'piControl': '185912-243606'},
-                               'rcp45': {'driftcorr': '200512-230001',
-                                         'zostoga': '200512-230001',
-                                         'piControl': '185912-243606'},
-                               'rcp85': {'driftcorr': '200512-230001',
-                                         'zostoga': '200512-230001',
-                                         'piControl': '185912-243606'}},
-                'inmcm4': {'rcp26': {'driftcorr': 'xxxxxx-xxxxxx',
-                                     'zostoga': 'xxxxxx-xxxxxx',
-                                     'piControl': 'xxxxxx-xxxxxx'},
-                           'rcp45': {'driftcorr': '200601-210101',
-                                     'zostoga': '200601-210101',
-                                     'piControl': '185001-235001'},
-                           'rcp85': {'driftcorr': '200601-210101',
-                                     'zostoga': '200601-210101',
-                                     'piControl': '185001-235001'}},
-                'IPSL-CM5A-LR': {'rcp26': {'driftcorr': '200601-230101',
-                                           'zostoga': '200601-230101',
-                                           'piControl': '180001-280001'},
-                                 'rcp45': {'driftcorr': '200601-230101',
-                                           'zostoga': '200601-230101',
-                                           'piControl': '180001-280001'},
-                                 'rcp85': {'driftcorr': '200601-230101',
-                                           'zostoga': '200601-230101',
-                                           'piControl': '180001-280001'}},
-                'IPSL-CM5A-MR': {'rcp26': {'driftcorr': '200601-210101',
-                                           'zostoga': '200601-210101',
-                                           'piControl': '180001-210001'},
-                                 'rcp45': {'driftcorr': '200601-230101',
-                                           'zostoga': '200601-230101',
-                                           'piControl': '180001-210001'},
-                                 'rcp85': {'driftcorr': '200601-210101',
-                                           'zostoga': '200601-210101',
-                                           'piControl': '180001-210001'}},
-                'MIROC-ESM': {'rcp26': {'driftcorr': '200601-210101',
-                                        'zostoga': '200601-210101',
-                                        'piControl': '180001-248001'},
-                              'rcp45': {'driftcorr': '200601-230101',
-                                        'zostoga': '200601-230101',
-                                        'piControl': '180001-248001'},
-                              'rcp85': {'driftcorr': '200601-210101',
-                                        'zostoga': '200601-210101',
-                                        'piControl': '180001-248001'}},
-                'MIROC-ESM-CHEM': {'rcp26': {'driftcorr': '200601-210101',
-                                             'zostoga': '200601-210101',
-                                             'piControl': '184601-210101'},
-                                   'rcp45': {'driftcorr': '200601-210101',
-                                             'zostoga': '200601-210101',
-                                             'piControl': '184601-210101'},
-                                   'rcp85': {'driftcorr': '200601-210101',
-                                             'zostoga': '200601-210101',
-                                             'piControl': '184601-210101'}},
-                'MIROC5': {'rcp26': {'driftcorr': '200601-210101',
-                                     'zostoga': '200601-210101',
-                                     'piControl': '200001-267001'},
-                           'rcp45': {'driftcorr': '200601-210101',
-                                     'zostoga': '200601-210101',
-                                     'piControl': '200001-267001'},
-                           'rcp85': {'driftcorr': '200601-210101',
-                                     'zostoga': '200601-210101',
-                                     'piControl': '200001-267001'}},
-                'MPI-ESM-LR': {'rcp26': {'driftcorr': '200601-230101',
-                                         'zostoga': '200601-230101',
-                                         'piControl': '185001-285001'},
-                               'rcp45': {'driftcorr': '200601-230101',
-                                         'zostoga': '200601-230101',
-                                         'piControl': '185001-285001'},
-                               'rcp85': {'driftcorr': '200601-230101',
-                                         'zostoga': '200601-230101',
-                                         'piControl': '185001-285001'}},
-                'MPI-ESM-MR': {'rcp26': {'driftcorr': '200601-210101',
-                                         'zostoga': '200601-210101',
-                                         'piControl': '185001-285001'},
-                               'rcp45': {'driftcorr': '200601-210101',
-                                         'zostoga': '200601-210101',
-                                         'piControl': '185001-285001'},
-                               'rcp85': {'driftcorr': '200601-210101',
-                                         'zostoga': '200601-210101',
-                                         'piControl': '185001-285001'}},
-                'MRI-CGCM3': {'rcp26': {'driftcorr': '200601-210101',
-                                        'zostoga': '200601-210101',
-                                        'piControl': '185101-235101'},
-                              'rcp45': {'driftcorr': '200601-210101',
-                                        'zostoga': '200601-210101',
-                                        'piControl': '185101-235101'},
-                              'rcp85': {'driftcorr': '200601-210101',
-                                        'zostoga': '200601-210101',
-                                        'piControl': '185101-235101'}},
-                'NorESM1-M': {'rcp26': {'driftcorr': '200601-210101',
-                                        'zostoga': '200601-210101',
-                                        'piControl': '070001-120101'},
-                              'rcp45': {'driftcorr': '200601-230101',
-                                        'zostoga': '200601-230101',
-                                        'piControl': '070001-120101'},
-                              'rcp85': {'driftcorr': '200601-210101',
-                                        'zostoga': '200601-210101',
-                                        'piControl': '070001-120101'}},
-                'NorESM1-ME': {'rcp26': {'driftcorr': '200601-210201',
-                                         'zostoga': '200601-210201',
-                                         'piControl': '090101-115301'},
-                               'rcp45': {'driftcorr': '200601-210301',
-                                         'zostoga': '200601-210301',
-                                         'piControl': '090101-115301'},
-                               'rcp85': {'driftcorr': '200601-210101',
-                                         'zostoga': '200601-210101',
-                                         'piControl': '090101-115301'}}}
-    return zos_dict
diff --git a/profsea/slr_pkg/cubedata.py b/profsea/slr_pkg/cubedata.py
deleted file mode 100644
index a7cb154..0000000
--- a/profsea/slr_pkg/cubedata.py
+++ /dev/null
@@ -1,194 +0,0 @@
-"""
-Copyright (c) 2023, Met Office
-All rights reserved.
-"""
-
-from datetime import datetime
-import inspect
-import os
-import cf_units
-import iris
-
-from profsea.slr_pkg import process
-
-
-def _derived(cube, cube_src, var_name=None, derived_type='unknown',
-             derived_long=None):
-    """
-    Write metadata attributes to a cube describing the derived variable
-    contained within it.
-    NOTES:
-    * Much of this metadata is probably unnecessary. However, derived_type
-    is currently required in slr.component.Dynamic to recognise when a
-    scaling map is provided as an input argument.
-    * Currently the units of the derived variable are unset for
-    compatibility purposes.
-    :param cube: cube to which metadata will be written
-    :param cube_src: list or cubelist of the cubes used to generate the
-    derived variable
-    :param var_name: new variable name. If None, the existing variable name
-    is used.
-    :param derived_type: short name describing the process for creating the
-    derived variable
-    :param derived_long: new long_name attribute. Can either be given as a
-    regular string or as a formattable string corresponding to the cubes in
-    cube_src. In the latter case, the derived_var cube attribute will be
-    used to format the string if present, else the var_name attribute will
-    be used
-        i.e. "Product of %s and %s"%(cube1.var_name,cube2.var_name)
-    If None is given for this keyword, the existing long_name is used
-    """
-
-    # Ensure cube_src is an iterable
-    if type(cube_src) == iris.cube.Cube:
-        cube_src = [cube_src]
-
-    # New var_name, if applicable
-    if var_name is None:
-        var_name = cube.var_name
-
-    # Coordinate information, describing lat/lon coordinate if sliced to scalar
-    cube_coord = ''
-    for i_cube in cube_src:
-        coord_name = [i.name() for i in i_cube.coords()]
-        if ('longitude' in coord_name and 'latitude' in coord_name) and \
-                (len(i_cube.coord('longitude').points) == 1 and
-                 len(i_cube.coord('latitude').points) == 1):
-            if i_cube.coord('longitude').points[0] < 0:
-                cube_lon = ('%sW' % i_cube.coord(
-                    'longitude').points[0]).replace('-', '')
-            else:
-                cube_lon = '%sE' % i_cube.coord('longitude').points[0]
-            if i_cube.coord('latitude').points[0] < 0:
-                cube_lat = ('%sS' % i_cube.coord(
-                    'latitude').points[0]).replace('-', '')
-            else:
-                cube_lat = '%sN' % i_cube.coord('latitude').points[0]
-            cube_coord = '%s %s' % (cube_lat, cube_lon)
-            break
-
-    # Derived history, describing derived variable in a "function(args)"
-    # format and inheriting previous values set by this function (can get
-    # rather long..)
-    derived_hist = []
-    for i_cube in cube_src:
-        if 'derived_hist' in list(i_cube.attributes.keys()):
-            derived_hist += [i_cube.attributes['derived_hist']]
-        else:
-            derived_hist += [i_cube.var_name]
-
-    # Derived variable, describing derived variable as for derived_hist but
-    # not inheriting any previous values
-    derived_var = [i_cube.var_name for i_cube in cube_src]
-
-    # Derived period, describing data periods comprising the derived variable
-    # in a form corresponding to derived_hist such that
-    # "function_1(args,function_2(args))" corresponds to
-    # "(dates_function_1,(dates_function_2))" (can also get rather long..)
-    derived_period = []
-    for i_cube in cube_src:
-        if 'derived_period' in list(i_cube.attributes.keys()):
-            derived_period += [i_cube.attributes['derived_period']]
-        elif 'period' in list(i_cube.attributes.keys()):
-            derived_period += [i_cube.attributes['period']]
-        else:
-            try:
-                period_min = i_cube.coord('time').units.num2date(
-                    i_cube.coord('time').bounds[:, 0].min())
-                period_max = i_cube.coord('time').units.num2date(
-                    i_cube.coord('time').bounds[:, 1].max())
-                derived_period += ['-'.join(['%04d/%02d' % (i.year, i.month)
-                                             for i in [period_min,
-                                                       period_max]])]
-            except Exception:
-                derived_period += ['--']
-
-    # Check derived_long keyword: if not set then use a generic string, check
-    # string has sufficient
-    # string formatting statements if a formattable string is being used
-    # (contains %s, %i etc)
-    if derived_long is None:
-        derived_long = 'Unspecified derived variable based on ' + \
-                       ', '.join(['%s'] * len(derived_hist))
-    elif derived_long.count('%') != len(cube_src) and \
-            derived_long.count('%') > 0:
-        raise TypeError('The number of string formatting statements in ' +
-                        f'derived_long ({derived_long.count("%")} does not'
-                        f' match the number of source cubes ({len(cube_src)})')
-
-    # History attribute entry; add the calling function and arguments
-    try:
-        cube_history = str(cube.attributes['history'])
-    except KeyError:
-        cube_history = ''
-    history_callfunc = os.sep.join([inspect.stack()[1][1],
-                                    inspect.stack()[1][3]])
-    history = '%s: %s(%s)\n' % (datetime.now().ctime(), history_callfunc,
-                                ','.join(derived_var))
-
-    # Drift correction, a record of the drift-corrected variables
-    drift_corr = []
-    for i_cube in cube_src:
-        if 'drift_correction' in list(i_cube.attributes.keys()):
-            drift_corr += [i_cube.attributes['drift_correction']]
-
-    # Apply string formatting where required
-    if derived_long.count('%') > 0:
-        derived_long = derived_long % tuple(derived_hist)
-    derived_hist = '%s(%s)' % (derived_type, ','.join(derived_hist))
-    derived_var = '%s(%s)' % (derived_type, ','.join(derived_var))
-    derived_period = '(%s)' % (','.join(derived_period))
-
-    # If a function of several drift-corrected variables, write as "func(a,b)".
-    if len(drift_corr) > 1:
-        drift_corr = '%s(%s)' % (var_name, ', '.join(drift_corr))
-
-    # Otherwise if a function of one drift-corrected variable, i.e. "func(a)",
-    # write "new_var: old_var" if the derived variable has a different name
-    elif len(drift_corr) == 1:
-        parse_result_1 = drift_corr[0].split(': ')[0]
-        parse_result_2 = drift_corr[0].split('(')[0]
-        if parse_result_1 != var_name and parse_result_2 != var_name:
-            drift_corr = '%s(%s)' % (var_name, drift_corr[0])
-        else:
-            drift_corr = drift_corr[0]
-
-    # Write attributes
-    cube.long_name = derived_long
-    cube.var_name = var_name
-    if len(cube_coord) > 0:
-        cube.attributes['coordinate'] = cube_coord
-    # REPLACE WITH SPECIFIC CUBE.ADD_HISTORY CALL
-    cube.attributes['history'] = history + cube_history
-    cube.attributes['derived_hist'] = derived_hist
-    cube.attributes['derived_var'] = derived_var
-    cube.attributes['derived_type'] = derived_type
-    cube.attributes['derived_period'] = derived_period
-    if len(drift_corr) > 0:
-        cube.attributes['drift_correction'] = drift_corr
-    cube.units = cf_units.Unit(None)
-
-
-def read_zos_cube(cmip_file):
-    """
-    Read cube of CMIP sea level data, applying appropriate time bounds.
-    Could be: dynamic sea level (zos), global mean thermosteric (zostoga) or
-    piControl (concatenated)
-    :param cmip_file: filepath and filename as string
-    :return: cube of sea level data
-    """
-    cube = iris.load_cube(cmip_file, iris.Constraint())
-
-    # Guess time bounds if they are not present
-    if 'time' in [i.name() for i in cube.coords()] and \
-            cube.coord('time').bounds is None:
-        cube.coord('time').guess_bounds()
-
-    # Reject cube if it has an AuxCoord time coordinate
-    cube = process._reject_auxcoord(cube)
-    if len(cube) == 0:
-        raise Exception(
-            f'The file {cmip_file} did not have a CF-compliant time '
-            f'coordinate')
-
-    return cube
diff --git a/profsea/slr_pkg/cubeplot.py b/profsea/slr_pkg/cubeplot.py
deleted file mode 100644
index 2c546e3..0000000
--- a/profsea/slr_pkg/cubeplot.py
+++ /dev/null
@@ -1,207 +0,0 @@
-"""
-Copyright (c) 2023, Met Office
-All rights reserved.
-"""
-
-import cartopy
-import cartopy.crs as ccrs
-from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
-import matplotlib.pyplot as plt
-import matplotlib.ticker as mticker
-from matplotlib.colors import BoundaryNorm
-import numpy as np
-import iris
-import iris.plot as iplt
-
-
-def block(cube, **kwargs):
-    """
-    Draw a block color plot for a given cube. Invokes contourf() with
-    pcolormesh=True.
-    :param cube: iris.cube.Cube
-    :param kwargs: See contourf() for other valid keyword arguments
-    :return: a block color plot for a given cube
-    """
-
-    return contourf(cube, pcolormesh=True, **kwargs)
-
-
-def contourf(cube, anom=False, subplot=111, cent_lon=0, land=True,
-             coast=True, title=None, region=None, plotcbar=True,
-             charsize=12, cmin=None, cmax=None, nlevels=11, levels=None,
-             cbarlabel=None, cbar_orien='horizontal', pcolormesh=False,
-             reproject=False, nx=400, ny=200, proj=None, xticks=None,
-             yticks=None, cmap=None, **kwargs):
-    """
-    Draw a filled contour plot for a given cube.
-    :param cube: iris.cube.Cube
-    :param anom: boolean, use blue/red colormap for anomalies
-        (def=False)
-    :param subplot: 3-digit integer specifying subplot position
-        (def=111)
-    :param cent_lon: central longitude of the map projection
-        (def=0)
-    :param land: boolean, if True, filled land drawn
-        (def=True)
-    :param coast: boolean, if True, coastlines drawn
-        (def=True)
-    :param title: map title
-        (def=None)
-    :param region: 4-element list [E,S,W,N] defining sub-region to plot
-        (def=None)
-    :param plotcbar: boolean, if True, colorbar plotted
-        (def=True)
-    :param charsize: font size to use
-        (def=12)
-    :param cmin: minimum value for colormap/bar
-        (def=None)
-    :param cmax: maximum value for colormap/bar
-        (def=None)
-    :param nlevels: set number of bands within colormap
-        (def=11)
-    :param levels: set minimum, maximum and number of bands within colormap
-        (def=None)
-    :param cbarlabel: colorbar label
-        (def=None)
-    :param cbar_orien: orientation for colorbar
-        (def='horizontal')
-    :param pcolormesh: boolean:, if True, iris.plot.pcolormesh() used for
-    plotting
-        (def=False)
-    :param reproject: boolean, if True, reproject cube to PlateCarree using a
-    specified number of sample points (nx & ny) before plotting. Used for
-    fields with 2D latitude/longitude fields
-        (def=False)
-    :param nx: number of sample points in x-direction used for cube
-    re-projection
-        (def=400)
-    :param ny: number of sample points in y-direction used for cube
-    re-projection
-        (def=200)
-    :param proj: If not defined, use ccrs.PlateCarree()
-        (def=None)
-    :param xticks: plot xticks on colormap
-        (def=None)
-    :param yticks: plot yticks on colormap
-        (def=None)
-    :param cmap: set colormap to blue/red
-        (def=None)
-    :param kwargs: defined in iplt.pcolormesh() and iplt.contourf()
-    :return: filled contour plot
-    """
-    # Map projection
-    if reproject:
-        cube, extent = iris.analysis.cartography.project(
-            cube, ccrs.PlateCarree(), nx=nx, ny=ny)
-
-    if proj is not None:
-        map_proj = proj
-    else:
-        map_proj = ccrs.PlateCarree(central_longitude=cent_lon)
-
-    # Color map
-    if cmap is None:
-        cmap = plt.cm.RdBu_r if anom else plt.cm.jet
-
-    # Create subplot, if specified.
-    if isinstance(subplot, list):
-        if len(subplot) != 3:
-            raise ValueError('"subplot" needs to be defined as a'
-                             ' 3-element list or a 3-digit integer')
-
-        ax = plt.subplot(subplot[0], subplot[1], subplot[2],
-                         projection=map_proj)
-    else:
-        ax = plt.subplot(subplot, projection=map_proj)
-    ax.set_global()
-
-    # Color levels
-    if levels is None:
-        if cmin is None:
-            cmin = cube.data.min()
-        if cmax is None:
-            cmax = cube.data.max()
-        levels = np.linspace(cmin, cmax, nlevels)
-
-    # Plot using pcolormesh or contourf
-    if pcolormesh:
-        norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
-        plot_handle = iplt.pcolormesh(cube, cmap=cmap, norm=norm,
-                                      rasterized=True, **kwargs)
-    else:
-        plot_handle = iplt.contourf(cube, cmap=cmap, levels=levels,
-                                    rasterized=True, **kwargs)
-
-    # Land and coastlines
-    if land:
-        ax.add_feature(cartopy.feature.LAND)
-    if coast:
-        ax.coastlines(resolution='50m')
-
-    # Extract region
-    if region:
-        if len(region) != 4:
-            raise ValueError('"Region" needs to be defined as a'
-                             ' 4-element list')
-        ax.set_ylim([region[1], region[3]])
-        x0 = (cent_lon - region[0]) * -1
-        x1 = (region[2] - cent_lon)
-        ax.set_xlim([x0, x1])
-
-    # Color bar
-    if title is None:
-        title = cube.name()
-    plt.title(title, fontsize=charsize)
-
-    # Color bar
-    if plotcbar:
-        cbar = plt.colorbar(plot_handle, orientation=cbar_orien)
-        cbar.ax.tick_params(labelsize=charsize * 0.85)
-        if cbarlabel is None:
-            cbarlabel = cube.units
-
-        cbar.set_label(cbarlabel, fontsize=charsize * 0.85)
-
-    #  Tick labels and grid lines
-    if (xticks is not None) or (yticks is not None):
-        ax = plt.gca()
-
-        # First do labels
-        gl = ax.gridlines(draw_labels=True, linewidth=1, color='gray',
-                          linestyle='--')
-        gl.xlabels_top = False
-        gl.ylabels_right = False
-        gl.xlines = False
-        gl.ylines = False
-
-        if xticks is not None:
-            gl.xlocator = mticker.FixedLocator(xticks)
-            gl.xformatter = LONGITUDE_FORMATTER
-            gl.xlabel_style = {'size': charsize * 0.8}
-
-        else:
-            gl.xlocator = mticker.FixedLocator([-1e6])
-
-        if yticks is not None:
-            gl.ylocator = mticker.FixedLocator(yticks)
-            gl.yformatter = LATITUDE_FORMATTER
-            gl.ylabel_style = {'size': charsize * 0.8}
-
-        else:
-            gl.ylocator = mticker.FixedLocator([-1e6])
-
-        # Now do grid lines
-        if xticks is not None:
-            # xticks += xticks
-            gl_x = ax.gridlines(linewidth=1, color='gray',
-                                linestyle=':')
-            gl_x.xlocator = mticker.FixedLocator(xticks)
-            gl_x.ylines = False
-
-        if yticks is not None:
-            gl_y = ax.gridlines(linewidth=1, color='gray',
-                                linestyle=':')
-            gl_y.ylocator = mticker.FixedLocator(yticks)
-            gl_y.xlines = False
-
-    return ax
diff --git a/profsea/slr_pkg/cubeutils.py b/profsea/slr_pkg/cubeutils.py
deleted file mode 100644
index d80ad90..0000000
--- a/profsea/slr_pkg/cubeutils.py
+++ /dev/null
@@ -1,27 +0,0 @@
-"""
-Copyright (c) 2023, Met Office
-All rights reserved.
-"""
-
-import iris
-
-
-def loadcube(files, ncvar=None, *args, **kwargs):
-    """
-    Load data using iris.load() with optional constraint by netcdf variable
-    name.
-    :param files: any sequence of file types accepted by iris.load()
-    :param ncvar: netcdf variable name
-    :param args: See iris.load() for other valid arguments
-    :param kwargs: See iris.load() for other valid keyword arguments
-    :return: an iris.cube.CubeList object
-    """
-
-    cubes = iris.load(files, *args, **kwargs)
-
-    if ncvar:
-        var_constraint = iris.Constraint(cube_func=lambda c:
-                                         c.var_name == ncvar)
-        cubes = cubes.extract(var_constraint)
-
-    return cubes
diff --git a/profsea/slr_pkg/models.py b/profsea/slr_pkg/models.py
deleted file mode 100644
index fc91f2f..0000000
--- a/profsea/slr_pkg/models.py
+++ /dev/null
@@ -1,63 +0,0 @@
-"""
-Copyright (c) 2023, Met Office
-All rights reserved.
-"""
-
-
-def cmip5_names():
-    """
-    List of all available CMIP model names
-    Ref: Slangen et al. (2014)
-    :return: List of all CMIP5 models
-    """
-    print(f'Getting data for all CMIP5 models')
-
-    models = ['ACCESS1-0',
-              'bcc-csm1-1',
-              'CNRM-CM5',
-              'CSIRO-Mk3-6-0',
-              'CanESM2',
-              'GFDL-ESM2G',
-              'GFDL-ESM2M',
-              'GISS-E2-R',
-              'HadGEM2-CC',
-              'HadGEM2-ES',
-              'inmcm4',
-              'IPSL-CM5A-LR',
-              'IPSL-CM5A-MR',
-              'MIROC-ESM',
-              'MIROC-ESM-CHEM',
-              'MIROC5',
-              'MPI-ESM-LR',
-              'MPI-ESM-MR',
-              'MRI-CGCM3',
-              'NorESM1-M',
-              'NorESM1-ME'
-              ]
-
-    return models
-
-
-def cmip5_names_marginal():
-    """
-    List of all available CMIP model names that are appropriate for use in
-    marginal seas e.g. Mediterranean. Marginal sea is where the native grid of
-    the CMIP model has the 'marginal sea' disconnected from the wider ocean
-    basin. AR5 recommends excluding these from analysis as values are
-    unrealistic.
-    Ref: https://www.ipcc.ch/site/assets/uploads/2018/07/WGI_AR5.Chap_.13_SM.
-    1.16.14.pdf
-    :return: List of CMIP5 models used in AR5 for marginal seas
-    """
-
-    print(f'Getting data for subset of CMIP models - marginal selected')
-    models = ['bcc-csm1-1',
-              'CanESM2',
-              'GFDL-ESM2M',
-              'HadGEM2-CC',
-              'HadGEM2-ES',
-              'MIROC-ESM',
-              'MIROC-ESM-CHEM',
-              'MIROC5'
-              ]
-    return models
diff --git a/profsea/slr_pkg/process.py b/profsea/slr_pkg/process.py
deleted file mode 100644
index e219be5..0000000
--- a/profsea/slr_pkg/process.py
+++ /dev/null
@@ -1,213 +0,0 @@
-"""
-Copyright (c) 2023, Met Office
-All rights reserved.
-"""
-
-import iris.analysis.cartography
-import numpy
-import scipy.stats
-
-from profsea.slr_pkg import cubedata
-
-
-class Regress:
-    """
-    Class to act as an interface to various regression functions.
-    """
-    def __init__(self, method='linear'):
-        """
-        Return an instance linking the appropriate regression functions to
-        be used as a set of generically-named regression functions, given an
-        initialization method.
-        :param method: regression method to be used
-        """
-        self.method = method
-
-        # Associate generic functions with method-specific functions
-        if self.method == 'linear':
-            self.detrend_scalar = self._linreg_detrend_scalar
-            self.regress_t_scalar = self._linreg_regress_t_scalar
-        # OTHER METHODS
-        else:
-            raise Exception(
-                f'Given regression method is not implemented: {method}')
-
-    def _linreg_detrend_scalar(self, cube_t, cube_slope):
-        """
-        Remove a linear trend from data in one cube with dimensions of (time)
-        given by the slope in another cube.
-        :param cube_t: iris.cube.Cube with a variable of dimensions (time)
-        :param cube_slope: iris.cube.Cube with a dimensionless regression slope
-        :return: iris.cube.Cube containing the detrended timeseries,
-        and a numpy.ndarray containing the magnitude of the trend relative to
-        the starting time corresponding to the times of cube_t
-        """
-        # Require cubes to have expected dimensions
-        cube_t_coords = [i.name() for i in cube_t.dim_coords]
-        cube_slope_coords = [i.name() for i in cube_slope.dim_coords]
-        if not ('time' in cube_t_coords and len(cube_t_coords) == 1):
-            raise Exception('cube_t does not have dimensions of (time)')
-        if not len(cube_slope_coords) == 0:
-            raise Exception('cube_slope is not dimensionless')
-
-        # Output cube is based on input cube
-        cube_detrend = cube_t.copy()
-
-        # Convert times to the units of the slope cube
-        if cube_detrend.coord('time').units != cube_slope.coord('time').units:
-            convert_time_units(cube_detrend.coord('time'),
-                               cube_slope.coord('time'))
-
-        # Calculate the trend as a function of time
-        nparr_trend = cube_detrend.coord('time').points * cube_slope.data
-        nparr_trend -= nparr_trend[0]
-
-        # Remove trend from the data
-        cube_detrend.data -= nparr_trend
-
-        # Value of the scalar evolving under the removed trend only
-        nparr_trend += cube_t.data[0]
-
-        # Metadata
-        cubedata._derived(
-            cube_detrend, [cube_t, cube_slope],
-            derived_type='detrended',
-            derived_long='%s, minus a linear trend as calculated from %s')
-        cube_detrend.attributes['drift_correction'] = \
-            f'{cube_detrend.var_name}: linear'
-
-        return cube_detrend, nparr_trend
-
-    def _linreg_regress_t_scalar(self, cube_t):
-        """
-        Regress (using a linear regression) data in a cube with dimensions
-        of (time) against its time coordinate.
-        :param cube_t: iris.cube.Cube with a variable of dimensions (time)
-        :return: iris.cube.Cube containing the regression slope, and an
-        iris.cube.Cube containing the correlation coefficient R^2
-        """
-        # Require cube to have expected dimensions
-        cube_t_coords = [i.name() for i in cube_t.dim_coords]
-        if not (cube_t.ndim == 1 and 'time' in cube_t_coords):
-            raise Exception('cube_t does not have dimensions of (time)')
-        if not len(cube_t.coord('time').points) > 1:
-            raise Exception('The input cube must have at least 2 time ' +
-                            'coordinates')
-
-        # Output cube is based on cube_t
-        cube_slope = cube_t.collapsed('time', iris.analysis.MEAN)
-        cube_corr = cube_slope.copy()
-
-        # Call scipy.stats.linregress on the cube data and its time coordinate
-        nparr_regr = scipy.stats.linregress(cube_t.coord('time').points,
-                                            cube_t.data)
-        cube_slope.data = nparr_regr[0]
-        cube_corr.data = numpy.square(nparr_regr[2])
-
-        # Metadata
-        cubedata._derived(
-            cube_slope, [cube_t], var_name='linregslope',
-            derived_type='linear_regression',
-            derived_long='Slope of a linear regression of time (x) vs %s (y)')
-        cubedata._derived(
-            cube_corr, [cube_t], var_name='linregr2',
-            derived_type='linear_correlation',
-            derived_long='Coefficient of determination of time (x) vs %s (y)')
-
-        return cube_slope, cube_corr
-
-
-def convert_time_units(time_in, time_ref):
-    """
-    Convert one time coordinate to the units of another time coordinate.
-    **METHOD:
-    - First attempt the iris.coords.Coord.convert_units method.
-    - If this fails (which it will do for certain units even if the resulting
-    time coordinates would be valid), use the slightly more forgiving
-    netCDF4 num2date/date2num method of converting. This will still fail if
-    the attempted time coordinate conversion results in an illegal date.
-    :param time_in: iris.coords.Coord (time) coordinate to be converted
-    :param time_ref: iris.coords.Coord (time) coordinate containing the target
-              units
-    """
-    # Check that the input coordinates are time coordinates
-    if not ((type(time_in) is iris.coords.DimCoord or type(time_in) is
-             iris.coords.AuxCoord) and
-            (type(time_ref) is iris.coords.DimCoord or type(time_ref) is
-             iris.coords.AuxCoord)):
-        raise Exception('The provided inputs are not both iris coordinates')
-    if not (time_in.standard_name == 'time' and
-            time_ref.standard_name == 'time'):
-        raise Exception('The coordinates provided do not have "time" as ' +
-                        'the standard_name')
-
-    # Time coordinate conversion
-    try:
-        # First attempt the iris.coords.Coord.convert_units method
-        time_in.convert_units(time_ref)
-    except Exception:
-
-        # Otherwise attempt the slightly more forgiving netCDF4
-        # num2date/date2num method
-        date_points_in = time_in.units.num2date(time_in.points)
-        date_bounds_in = time_in.units.num2date(time_in.bounds)
-
-        num_points_out = time_ref.units.date2num(date_points_in)
-        num_bounds_out = time_ref.units.date2num(date_bounds_in)
-
-        time_in.points = num_points_out
-        time_in.bounds = num_bounds_out
-        time_in.units = time_ref.units
-
-        date_points_out = time_in.units.num2date(time_in.points)
-        date_bounds_out = time_in.units.num2date(time_in.bounds)
-
-    # Check dates of converted times against those of original times
-    if not ((date_points_in == date_points_out).all() and
-            (date_bounds_in == date_bounds_out).all()):
-        raise Exception('Unit conversion did not conserve original dates')
-
-
-def _reject_auxcoord(cube_list):
-    """
-    Remove all cubes with AuxCoord time coordinates in a cube list.
-    :param cube_list: iris.cube.CubeList
-    :return: iris.cube.CubeList
-    """
-    # Require a cube or cubelist
-    if not (type(cube_list) == iris.cube.CubeList or
-            type(cube_list) == iris.cube.Cube):
-        raise Exception('Input must be a cube or cubelist')
-
-    # Convert cube to cubelist if necessary
-    if isinstance(cube_list, iris.cube.Cube):
-        cube_list = iris.cube.CubeList([cube_list])
-
-    cube_list_out = iris.cube.CubeList(cube_list)  # Is this needed?
-
-    for i_cube in cube_list:
-        # Skip cube if no time coordinate
-        coord_names = [i.name() for i in i_cube.coords()]
-        if 'time' not in coord_names:
-            continue
-
-        # If time coordinate is an AuxCoord, print some info
-        dc = i_cube.coord('time')
-        if type(dc) == iris.coords.AuxCoord:
-            time_min = dc.units.num2date(dc.points.min())
-            time_max = dc.units.num2date(dc.points.max())
-            print("    * slr.process.reject_auxcoord: Found a cube with an " +
-                  "AuxCoord time coordinate (dates " +
-                  f"{time_min.year}{time_min.month}{time_min.day}-" +
-                  f"{time_max.year}{time_max.month}{time_max.day}),\n" +
-                  "      which suggests a problem with the coordinate data. " +
-                  "Skipping this cube..")
-
-            # Remove cube from cubelist
-            cube_list_out.remove(i_cube)
-
-        # TEMPORARY: Guess time bounds if they are not present
-        elif dc.bounds is None:
-            dc.guess_bounds()
-
-    return cube_list_out
diff --git a/profsea/slr_pkg/whichbox.py b/profsea/slr_pkg/whichbox.py
deleted file mode 100644
index e8419d0..0000000
--- a/profsea/slr_pkg/whichbox.py
+++ /dev/null
@@ -1,112 +0,0 @@
-"""
-Copyright (c) 2023, Met Office
-All rights reserved.
-"""
-
-import numpy as np
-
-
-def find_gridbox_indicies(cube, points, xcoord_name='longitude',
-                          ycoord_name='latitude', drop=False):
-    """
-    Find the grid box indices containing the specified points.
-    **NOTES:
-    - Points just west of zero degrees longitude can lie outside the
-    coordinate bounds for some models.
-    - If this is the case, subtract 360 from the points, so they lie within
-    the bounds of the model longitude coordinate.
-    - Similarly, add 360 to any points smaller than the lower limit.
-    :param cube: iris.cube.Cube containing zos field from CMIP models (
-    should be 2D)
-    :param points: a 2D numpy array of pairs of points (longitude, latitude)
-    with dimensions (number of points, 2)
-    :param xcoord_name: name of x-coordinate in the cube
-    :param ycoord_name: name of y-coordinate in the cube
-    :param drop: If True, return indices for the points which lie within the
-    cube boundaries;
-    If False, return indices of -99 for points which lie outside the
-    boundaries.
-    :return: grid box indices of location
-    """
-    # Get the grid box coordinates. Add bounds if not present.
-    grid_lons = cube.coord(xcoord_name)
-    grid_lats = cube.coord(ycoord_name)
-
-    if not grid_lons.has_bounds():
-        grid_lons.guess_bounds()
-    if not grid_lats.has_bounds():
-        grid_lats.guess_bounds()
-
-    # Find the minimums and maximums of the cube's coordinate boundaries
-    lon_lims = [grid_lons.bounds.min(), grid_lons.bounds.max()]
-    lat_lims = [grid_lats.bounds.min(), grid_lats.bounds.max()]
-
-    # Get the lons and lats of the points of interest (as copies)
-    if isinstance(points, list):
-        points = np.array(points)
-    pt_lons, pt_lats = points.T
-
-    if iscoordglobal(grid_lons):
-        widx, = np.where(pt_lons > lon_lims[1])
-        if len(widx) > 0:
-            pt_lons[widx] -= 360
-        widx, = np.where(pt_lons < lon_lims[0])
-        if len(widx) > 0:
-            pt_lons[widx] += 360
-
-    # Find all points which lie within or on the boundaries of the model grid
-    # boxes
-    valid_lats = (pt_lats >= lat_lims[0]) & (pt_lats <= lat_lims[1])
-    valid_lons = (pt_lons >= lon_lims[0]) & (pt_lons <= lon_lims[1])
-    valid_pts = valid_lats & valid_lons
-
-    if np.sum(valid_pts) == 0:
-        raise ValueError('find_gridbox_indicies: None of the points lie '
-                         + 'within the range of the coordinates')
-    else:
-        # Find the indices of the grid boxes containing the points
-        if drop:
-            idx_lon = [grid_lons.nearest_neighbour_index(x)
-                       for x in pt_lons[valid_pts]]
-            idx_lat = [grid_lats.nearest_neighbour_index(y)
-                       for y in pt_lats[valid_pts]]
-        else:
-            idx_lon = [grid_lons.nearest_neighbour_index(x)
-                       if l else -99 for x, l in zip(pt_lons, valid_pts)]
-            idx_lat = [grid_lats.nearest_neighbour_index(y)
-                       if l else -99 for y, l in zip(pt_lats, valid_pts)]
-        idx = np.array([idx_lon, idx_lat]).T
-
-    return idx
-
-
-def iscoordglobal(coord, tol=0.01):
-    """
-    Tests if the longitude coordinate is global.
-    :param coord: iris.cube.Cube coordinate to be tested
-    :param tol: decide whether floating point numbers are tolerably equal
-    :return: True if longitude coordinate is global, otherwise false.
-    """
-
-    if not ((0 <= coord.points.min() <= 360) and
-            (0 <= coord.points.max() <= 360)):
-        raise ValueError('All points must lie within 0 to 360')
-
-    if not coord.has_bounds():
-        coord.guess_bounds()
-    diff = coord.bounds.max() - coord.bounds.min()
-
-    return (diff > 360.0 - tol) & (diff < 360.0 + tol)
-
-
-def wraplongitude(longitude):
-    """
-    Converts input longitude to values within a range 0 - 360 plus a
-    specified base.
-    :param longitude: site location's longitude value
-    :return: converted longitude
-    """
-    wrapped_lons = ((longitude + 720.0) % 360)
-    print(f' Converted Longitude from {longitude} to {wrapped_lons}')
-
-    return wrapped_lons
\ No newline at end of file
diff --git a/profsea/spatial_projections.py b/profsea/spatial_projections.py
deleted file mode 100644
index 0c45077..0000000
--- a/profsea/spatial_projections.py
+++ /dev/null
@@ -1,556 +0,0 @@
-"""
-Copyright (c) 2023, Met Office
-All rights reserved.
-"""
-import glob
-import pickle
-import json
-import os
-from pathlib import Path
-import warnings
-
-import dask.array as da
-from netCDF4 import Dataset
-import numpy as np
-from rich.console import Console
-from rich.progress import track
-import xarray as xr
-
-from profsea.config import settings
-from profsea.directories import read_dir
-from profsea.emulator import Global
-from profsea.utils import sample_members_2D, interpolate
-from profsea.slr_pkg import choose_montecarlo_dir
-
-console = Console()
-warnings.filterwarnings("ignore")
-
-def calc_baseline_period(yrs: np.array) -> float:
-    """
-    Baseline years used for IPCC AR5 and Palmer et al 2020 -- 1986-2005
-    :param yrs: years of the projections
-    :return: baseline years
-    """
-    byr1 = 1986.
-    byr2 = 2005.
-
-    console.log("Baseline period = ", byr1, "to", byr2)
-    midyr = (byr2 - byr1 + 1) * 0.5 + byr1
-    return yrs[0] - midyr
-
-
-def calc_future_sea_level(scenario: str) -> None:
-    """
-    Calculates future sea level at the given site and write to file.
-    :param df: Data frame of all metadata (tide gauge or site specific) for
-               each(all) location(s)
-    :param site_loc: name of the site location
-    :param scenario: emission scenario
-    """
-    # Set the UKCP*18* random seed so results are reproducible
-    np.random.seed(18)
-
-    # Directory of Monte Carlo time series for new projections
-    mcdir = choose_montecarlo_dir()
-
-    # Specify the sea level components to include. The GIA contribution is
-    # calculated separately.
-    components = ['expansion', 'antdyn', 'antsmb', 'greenland',
-                  'glacier', 'landwater']
-
-    # Select dimensions from sample file, [time, realisation]
-    sample = np.load(os.path.join(settings["baseoutdir"],settings["experiment_name"],
-                                  settings['emulator_settings']['gmslr_output_dir'], f'{scenario}_expansion.npy'))
-    
-    nesm = sample.shape[0] # also number of samples to make
-    nyrs = sample.shape[1]
-    
-    yrs = np.arange(2006, 2006 + nyrs)
-    console.log(f"Running with {nesm} ensemble members")
-
-    grid_path = os.path.join(
-        settings["cmipinfo"]["sealevelbasedir"], 
-        "ACCESS-CM2/zos_regression_ssp245_ACCESS-CM2.npy")
-    grid_sample = np.load(grid_path)
-    array_dims = [nesm, nesm, nyrs, grid_sample.shape[0], grid_sample.shape[1]]
-
-    console.log(
-        "INFO: This module expects sterodynamic patterns on half-integer grids:\n"
-        "\tlat: (-89.5, ..., 89.5)\n"
-        "\tlon: (-179.5, ..., 179.5)")
-
-    # Get random samples of global and regional sea level components
-    calculate_sl_components(mcdir, components, scenario, yrs, array_dims)
-
-
-def calc_gia_contribution(
-        yrs: np.array, nyrs: int, nsmps: int, 
-        scenario: str) -> None:
-    """
-    Calculate the glacial isostatic adjustment (GIA) contribution to the
-    regional component of sea level rise.
-    Option to use the generic global GIA estimates or use the GIA estimates
-    developed for UK as part of UKCP18.
-    :param yrs: years of the projections
-    :param nyrs: number of years in each projection time series
-    :param nsmps: determine the number of samples
-    :param coords: coordinates of location of interest
-    :return: GIA estimates converted to mm/yr
-    """
-    console.log('Calculating GIA contribution...')
-    nGIA, GIA_vals = read_gia_estimates()
-    Tdelta = calc_baseline_period(yrs)
-
-    # Unit series of mm/yr expressed as m/yr
-    unit_series = (np.arange(nyrs) + Tdelta) * 0.001
-    GIA_unit_series = np.ones([nsmps, nyrs]) * unit_series
-
-    # rgiai is an array of random GIA indices the size of the sample years
-    rgiai = np.random.randint(nGIA, size=nsmps)
-
-    GIA_T = da.from_array(GIA_unit_series)
-    GIA_vals = da.from_array(GIA_vals)
-    GIA_series = GIA_T[:, :, None, None] * GIA_vals[rgiai, None, :, :]
-
-    file_header = '_'.join(['gia', scenario, "projection", 
-                    f"{settings['projection_end_year']}"])
-    sealev_ddir = os.path.join(settings["baseoutdir"],settings["experiment_name"],
-                               settings['emulator_settings']['spatial_output_dir'])
-
-    # Save data in netcdf format (Assuming first dimension is percentile, but can be more general percentile/ensemble)
-    xr_dataArray = xr.DataArray(
-        GIA_series, 
-        dims=["samples", "time", "lat", "lon"], 
-        coords={
-            "samples": np.arange(nsmps),
-            "time": np.arange(2006, GIA_series.shape[1] + 2006),
-            "lat": np.arange(-90, 90) + 0.5, 
-            "lon": np.arange(0, 360) + 0.5})
-    xr_dataArray.attrs["units"] = "m"
-    xr_dataArray.attrs["long_name"] = "Regional GIA sea-level projections"
-    ds = xr_dataArray.to_dataset(name='gia')
-
-    ds.attrs["source"] = "ProFSea-Climate v0.1"
-
-    R_file = '_'.join([file_header, 'regional']) + '.nc'
-    encoding = {'gia': {"zlib": True, "complevel": 5, "dtype": "float32"}}
-    ds.to_netcdf(os.path.join(sealev_ddir, R_file), encoding=encoding, compute=True)
-    
-    del GIA_series
-
-
-def calc_expansion_contribution(
-        scenario: str, nsmps: int) -> da.array:
-    """
-    Calculate the thermal expansion contribution to the regional component of
-    sea level rise.
-    :param scenario: emission scenario
-    :param nsmps: determine the number of samples
-    :return: expansion estimates converted to mm/yr
-    """
-    # Select slope coefficients based on the MIP
-    if settings["emulator_settings"]["emulator_mode"]:
-        if settings["cmipinfo"]["mip"].lower() == "cmip6":
-            coeffs = load_CMIP6_slopes('ssp585')
-            coeffs = da.roll(coeffs, 180, axis=2)
-        else:
-            coeffs = load_CMIP5_slope_coeffs('rcp85')
-    else:
-        coeffs = load_CMIP5_slope_coeffs(scenario)
-
-    rand_samples = np.random.choice(
-        coeffs.shape[0], size=nsmps, replace=True)               
-    rand_coeffs = coeffs[rand_samples, :, :]
-    return rand_coeffs
-
-
-def calc_landwater_contribution(data: dict, lats: int, lons: int) -> da.array:
-    """
-    Calculate the regional landwater contribution to sea level rise.
-    :param interpolator: dictionary of interpolator objects
-    :return: numpy array of landwater values
-    """
-    landwater_vals = interpolate(data, lats, lons)
-    landwater_vals = da.roll(landwater_vals, 180, axis=1)
-    return landwater_vals
-
-
-def calc_fingerprint_contributions(
-    FPlist: list, comp: str, lats: int, lons: int) -> da.array:
-    # Initiate an empty list for fingerprint values
-    fp_vals = []
-    for FP_dict in FPlist:
-        # Interpolate values to target lat/lon
-        val = FP_dict[comp]
-        val = interpolate(val, lats, lons)
-        fp_vals.append(val)
-
-    fp_vals = da.stack(fp_vals, axis=0)
-    return fp_vals
-
-
-def calc_greenland_fingerprint_ar6(lats: int, lons: int) -> da.array:
-    """Load and prepare the GIS fingerprint.
-
-    This fingerprint was/is used by FACTS for AR6 projections, and was 
-    originally calculated by Mitrovica et al., (2011).
-    
-    :return dask array containing GIS fingerprint
-    """
-    # Load in the fingerprint
-    fp_path = Path(settings["fingerprints"]) / "greenland_ar6.nc"
-    fp_ds = xr.open_dataset(fp_path, chunks={})
-
-    # Interpolate to (180, 360) grid
-    fp_vals = fp_ds.fp.interp(
-        lat=np.linspace(-90, 90, lats, endpoint=False) + 0.5, 
-        lon=np.linspace(0, 360, lons, endpoint=False) + 0.5, 
-        method="linear").data * 1000  # convert mm to m SLE per m GMSLR
-
-    # Flip vertically and roll by 180 degrees
-    fp_vals = da.flip(fp_vals)
-    fp_vals = da.roll(fp_vals, 180, axis=1)
-    return fp_vals
-
-
-def save_projections(
-        montecarlo_R: da.array, component: str, scenario: str, percentile: da.array) -> None:
-    """
-    Save the regional sea level projections to a file.
-    :param montecarlo_R: regional sea level projections
-    :param component: sea level component
-    :param scenario: emission scenario
-    :param percentile: percentiles used for spatial projections
-    """
-    sealev_ddir = os.path.join(settings["baseoutdir"],settings["experiment_name"],
-                               settings['emulator_settings']['spatial_output_dir'])
-    file_header = '_'.join([component, scenario, "projection", 
-                            f"{settings['projection_end_year']}"])
-
-    # Save data in netcdf format (Assuming first dimension is percentile, but can be more general percentile/ensemble)
-    xr_dataArray = xr.DataArray(montecarlo_R, dims=["percentile", "time", "lat", "lon"], 
-                                coords={"percentile": percentile, 
-                                        "time": np.arange(2006, montecarlo_R.shape[1] + 2006),
-                                        "lat": np.arange(-90, 90) + 0.5, "lon": np.arange(0, 360) + 0.5})
-    xr_dataArray.attrs["units"] = "m"
-    xr_dataArray.attrs["long_name"] = f"Regional {component} sea-level projections"
-    ds = xr_dataArray.to_dataset(name=component)
-
-    ds.attrs["source"] = "ProFSea-Climate v0.1"
-
-    R_file = '_'.join([file_header, 'regional']) + '.nc'
-    encoding = {component: {"zlib": True, "complevel": 5, "dtype": "float32"}}
-    ds.to_netcdf(os.path.join(sealev_ddir, R_file), encoding=encoding, compute=True)
-
-
-def calculate_sl_components(
-        mcdir: str, components: list, scenario: str, 
-        yrs: np.array, array_dims: list) -> None:
-    """
-    Calculates global and regional component part contributions to sea level
-    change.
-    :param mcdir: location of Monte Carlo time series for new projections
-    :param components: sea level components
-    :param scenario: emission scenario
-    :param yrs: years of the projections
-    :param array_dims: Array of nesm, nsmps and nyrs
-        nesm --> Number of ensemble members in time series
-        nsmps --> Determine the number of samples you wish to make
-        nyrs --> Number of years in each projection time series
-    :return: montecarlo_G (global contribution to sea level rise) and
-        montecarlo_R (regional contribution to sea level change)
-    """  
-    # Numbers of ensemble members, samples, years
-    nesm, nsmps, nyrs, lats, lons = array_dims
-    nFPs, FPlist = load_fingerprints(components)
-    resamples = np.random.choice(nesm, nsmps) # Preserve correlations across comps
-    rfpi = np.random.randint(nFPs, size=nsmps)
-    # Take the 0th, 25th, 50th, 75th and 100th percentiles
-    output_percentiles = np.array([0, 25, 50, 75, 100])
-
-    # Calculate GIA contribution and save it out
-    calc_gia_contribution(yrs, nyrs, len(output_percentiles), scenario)
-
-    for comp in track(components, description="Calculating components..."):
-        montecarlo_R = da.zeros((nsmps, nyrs, lats, lons), dtype=np.float32) # (FPs applied) + GIA
-        montecarlo_G = da.zeros((nsmps, nyrs, lats, lons), dtype=np.float32) # (no FPs applied)
-
-        # Load global projections in for the component
-        #mc_timeseries = np.load(os.path.join(mcdir, f'{scenario}_{comp}.npy'))
-        mc_timeseries = np.load(os.path.join(settings["baseoutdir"],settings["experiment_name"],
-                                             settings['emulator_settings']['gmslr_output_dir'],f'{scenario}_{comp}.npy'))
-        sampled_mc = mc_timeseries[resamples, :nyrs]
-        montecarlo_G[:, :] = da.from_array(sampled_mc[:, :, None, None], chunks="auto")
-
-        if comp == "expansion":
-            sampled_coeffs = calc_expansion_contribution(scenario, nsmps)
-            montecarlo_R = montecarlo_G * sampled_coeffs[:, None, :, :]
-            del sampled_coeffs
-
-        elif comp == "landwater":
-            landwater_vals = calc_landwater_contribution(FPlist[0]["landwater"], lats, lons)
-            montecarlo_R[:, :, :, :] = montecarlo_G[:, :, :, :] * landwater_vals[None, None, :, :]
-            del landwater_vals
-
-        elif comp == "greenland":
-            greenland_fp = calc_greenland_fingerprint_ar6(lats, lons)
-            montecarlo_R[:, :, :, :] = montecarlo_G[:, :, :, :] * greenland_fp[None, None, :, :]
-
-        else:
-            fp_vals = calc_fingerprint_contributions(FPlist, comp, lats, lons)
-            montecarlo_R[:, :, :, :] = montecarlo_G[:, :, :, :] * fp_vals[rfpi][:, None, :, :]
-            del fp_vals
-
-        montecarlo_R = da.percentile(montecarlo_R, output_percentiles, axis=0)
-        montecarlo_R = montecarlo_R.astype(np.float32)
-
-        # Create the output sea level projections file directory and filename
-        save_projections(montecarlo_R, comp, scenario, output_percentiles)
-
-
-def get_projection_info(indir: str, scenario: str) -> tuple:
-    """
-    Read in the dimensions of the Monte-Carlo data. These files are all
-    relative to midnight on 1st January 2007
-    :param indir: directory of Monte Carlo time series for new projections
-    :param scenario: emission scenarios to be considered
-    :return: Number of ensemble members in time series, number of years in
-    each projection time series and the years of the projections
-    """
-    sample_file = f'{scenario}_exp.nc'
-    f = Dataset(f'{indir}{sample_file}', 'r')
-    nesm = f.dimensions['realization'].size
-    t = f.variables['time']
-    nyrs = t.size
-    unit_str = t.units
-    first_year = int(unit_str.split(' ')[2][:4])
-    f.close()
-
-    yrs = first_year + np.arange(nyrs)
-    return nesm, nyrs, yrs
-
-
-def load_CMIP5_slope_coeffs(scenario: str) -> np.ndarray:
-    """
-    Loads in the CMIP slope coefficients based on linear regression of
-    'zos+zostoga' against 'zostoga' for the period 2005 to 2100.
-    Some models are missing regression slopes for RCP2.6. If so, use RCP4.5
-    values instead.
-    :param site_loc: name of the site location
-    :param scenario: emissions scenario
-    :return: 1D array of regression coefficients
-    """
-    # Read in the sea level regressions
-    in_zosddir = read_dir()[2]
-    filename = os.path.join(in_zosddir, 'zos_regression.npy')
-    coeffs = np.load(filename)
-    scenario_index = ['rcp26', 'rcp45', 'rcp85'].index(scenario)
-    coeffs = coeffs[:, scenario_index, :, :]
-    coeffs[np.isnan(coeffs)] = 0
-    coeffs[coeffs > 999] = 0
-    coeffs[coeffs < -999] = 0
-    return coeffs
-
-
-def load_CMIP6_slopes(scenario: str) -> np.ndarray:
-    """
-    Load in the CMIP6 slope coefficients.
-    :param site_loc: name of the site location
-    :param scenario: emissions scenario
-    :return: 1D array of regression coefficients
-    """
-    # Read in the sea level regressions
-    cmip6_dir = settings["cmipinfo"]["sealevelbasedir"]
-    slope_files = glob.glob(cmip6_dir + f'/*/zos_regression_{scenario}_*.npy')
-
-    def load_one_slope(f):
-        return np.load(f, mmap_mode='r')
-
-    # Create a list of lazy dask arrays
-    lazy_slopes = [
-        da.from_array(load_one_slope(f), chunks=(180, 360)) 
-        for f in slope_files]
-    slopes_stack = da.stack(lazy_slopes, axis=0)
-
-    return slopes_stack
-
-
-def read_gia_estimates() -> tuple:
-    """
-    Read in pre-processed interpolator objects of GIA estimates (Lambeck,
-    ICE5G)
-    :param: none
-    :return: length of GIA_vals and numpy array of pre-processed interpolator
-    objects of GIA estimates
-    """
-    gia_file = settings["giaestimates"]["global"]
-    with open(gia_file, "rb") as ifp:
-        GIA_dict = pickle.load(ifp, encoding='latin1') # Interp objects
-
-    GIA_vals = []
-    for key in list(GIA_dict.keys()):
-        val = GIA_dict[key].values
-        GIA_vals.append(val)
-
-    nGIA = len(GIA_vals)
-    GIA_vals = np.array(GIA_vals)
-
-     # Sort out the crazy values in the 0th GIA array
-    GIA_vals[0][GIA_vals[0] < -99999] = 0
-
-    # AND shift them from -180, 180 to 0, 360
-    GIA_vals = np.roll(GIA_vals, 180, axis=2)
-    return nGIA, GIA_vals
-
-
-def load_fingerprints(components: list) -> tuple:
-    """
-    Create 2D Interpolator objects for the Slangen, Spada and Klemann
-    fingerprints
-    :param components: list of sea level components
-    :return nFPs: length of FPlist and interpolator objects of all sea level
-    components
-    """
-    # Create empty dictionaries for the Slangen, Spada and Klemann fingerprints
-    # interpolator objects.
-    slangen_FPs = {}
-    spada_FPs = {}
-    klemann_FPs = {}
-
-    # Only 1 fingerprint for Landwater
-    comp = "landwater"
-    slangen_FPs[comp] = xr.open_dataarray(
-        os.path.join(settings["fingerprints"],
-        comp + "_slangen_nomask.nc"), chunks={})
-
-    # Other FPs have multiple components
-    components_todo = [
-        c for c in components 
-        if c not in ["expansion", "landwater", "greenland"]]
-    for comp in components_todo:
-        slangen_FPs[comp] = xr.open_dataarray(
-            os.path.join(settings["fingerprints"],
-            comp + "_slangen_nomask.nc"), chunks={})
-        spada_FPs[comp] = xr.open_dataarray(
-            os.path.join(settings["fingerprints"],
-            comp + "_spada_nomask.nc"), chunks={})
-        klemann_FPs[comp] = xr.open_dataarray(
-            os.path.join(settings["fingerprints"],
-            comp + "_klemann_nomask.nc"), chunks={})
-
-    FPlist = [slangen_FPs, spada_FPs, klemann_FPs]
-    nFPs = len(FPlist)
-    return nFPs, FPlist
-
-
-def calculate_global_components(scenario: str, palmer_method: bool) -> None:
-    """
-    Calculate the global contributions for each of the sea-level components
-    using the GMSLR module.
-    :param scenario: string representing the scenario being simulated
-    :param palmer_method: boolean to determine whether to use the palmer_method
-    """
-    # Check inputs are correctly set up
-    if not (os.path.exists(settings["scm_data"]["temperature"]) and 
-            os.path.exists(settings["scm_data"]["ocean_heat_content"])):
-        raise Exception(
-            'SCM data paths (temperature and ocean heat content) must be '
-            'correctly configured in user-settings.yml')
-
-    if not os.path.exists(settings["scm_data"]["cumulative_emissions"]):
-        raise Exception('Cumulative emissions path must be correctly configured '
-                        'in user-settings.yml')
-
-    if (settings["scm_data"]["temperature"].split(".")[-1] != 'nc' or
-        settings["scm_data"]["ocean_heat_content"].split(".")[-1] != 'nc'):
-        raise Exception('SCM data must be saved in NetCDF format.')
-
-    percentiles = np.arange(101)
-    # percentiles are hard coded. We could make it an user input in future updates.
-
-    # Now run the simulations
-    console.log(f'Projecting global components for {scenario} scenario...')
-    T_change = xr.load_dataarray(settings["scm_data"]["temperature"])
-    OHC_change = xr.load_dataarray(settings["scm_data"]["ocean_heat_content"])
-    with open(settings["scm_data"]["cumulative_emissions"]) as f:
-        cumulative_emissions = json.load(f)
-
-    T_change = T_change.sel(scenario=scenario).data.T # (member, time)
-    OHC_change = OHC_change.sel(scenario=scenario).data.T
-
-    T_change = sample_members_2D(T_change, percentiles)
-    OHC_change = sample_members_2D(OHC_change, percentiles)
-
-    gmslr = Global(
-        T_change,
-        OHC_change,
-        scenario,
-        settings["projection_end_year"],
-        palmer_method=palmer_method,
-        input_ensemble=settings["emulator_settings"]["use_input_ensemble"],
-        output_percentiles=percentiles,
-        cum_emissions_total=cumulative_emissions[scenario])
-    gmslr.project()
-
-    console.log('Saving global components...')
-    gmslr.save_components(
-        os.path.join(settings["baseoutdir"],settings["experiment_name"],
-                     settings['emulator_settings']['gmslr_output_dir']),
-        scenario)
-
-
-    console.log('Saved!\n')
-
-
-
-def main():
-    """
-    Reads in and calculates global and local (regional) sea level change
-    (sum total), based on the different contributing factors e.g. thermal
-    expansion, GIA and mass balance. Writes out the selected emissions scenario
-    estimates of the various components and their sums.
-    """
-    console.log(f'\nProjecting out to: {settings["projection_end_year"]}\n')
-
-    # Sort out paths
-    Path(
-        os.path.join(
-            settings["baseoutdir"], 
-            settings["experiment_name"])
-    ).mkdir(parents=True, exist_ok=True)
-
-    Path(
-        os.path.join(
-            settings["baseoutdir"],
-            settings["experiment_name"],
-            settings['emulator_settings']['gmslr_output_dir'])
-    ).mkdir(parents=True, exist_ok=True)
-
-    Path(
-        os.path.join(
-            settings["baseoutdir"],
-            settings["experiment_name"],
-            settings['emulator_settings']['spatial_output_dir'])
-    ).mkdir(parents=True, exist_ok=True)
-
-    # Extract site data from station list (e.g. tide gauge location) or
-    # construct based on user input
-    if settings["emulator_settings"]["emulator_mode"]:
-        console.log('\nInitiating ProFSea emulator')
-        if settings["projection_end_year"] > 2100:
-            palmer_method = True
-        else:
-            palmer_method = False
-
-        # Get the metadata of either the site location or tide gauge location
-        for scenario in settings["emulator_settings"]["emulator_scenario"]:
-            calculate_global_components(scenario, palmer_method)
-            calc_future_sea_level(scenario)
-    else:
-        scenarios = ['rcp26', 'rcp45', 'rcp85']
-        for scenario in scenarios:
-            calc_future_sea_level(scenario)
-
-
-if __name__ == '__main__':
-    main()
diff --git a/profsea/user-settings-emu.yml b/profsea/user-settings-emu.yml
deleted file mode 100644
index 4815ea8..0000000
--- a/profsea/user-settings-emu.yml
+++ /dev/null
@@ -1,81 +0,0 @@
-# Site(s) information
-# region: region of the world
-#   N.B. used to create output folder structure
-# sitename: tide gauge name or other site name
-#   N.B. if site_name has an apostrophe use triple quotes
-#       # sitename: '''site'x'''
-# sitelatlon: site location's latitude and longitude
-#   N.B. if tide gauge name: latlon taken from TG metadata
-        # sitelatlon: [[]])
-#   N.B. if other site name: latlon needs to be specified
-        # sitelatlon: [[-51.69, -57.82]])
-siteinfo:
-    region: 'cmip7_example'   # str
-    sitename: ['Stanley II']     # list
-    sitelatlon: [[]]             # list
-
-# Base output directory
-#   N.B. the code will add region to the output directory
-experiment_name: 'exp_name' # str
-baseoutdir: '/add/your/directory/' # all output will be saved inside baseoutdir/exp_name/
-
-# Set the end year of the projection(s)
-projection_end_year: 2300 # int between 2050 and 2300
-
-# Emulator settings
-emulator_settings:
-    emulator_mode: true # bool
-    use_input_ensemble: true # bool
-    gmslr_output_dir: 'gmslr' # str # directory name to save global projections
-    spatial_output_dir: 'sea_level_projections' # str # directory name to save spatial projections
-    emulator_scenario: ['high-extension', 'high-overshoot', 'medium-extension', 'medium-overshoot', 'low', 'verylow', 'verylow-overshoot'] # str
-    
-scm_data:
-    temperature: '/path/to/temperature.nc' # str # global-mean surface temperature file
-    ocean_heat_content: '/path/to/ohc.nc' # str # ocean heat content file 
-    cumulative_emissions: '/path/to/emissions.json' # str # cumulative emissions file
-
-# Science method
-#   UKCP18 --> sciencemethod: 'UK'
-#   Palmer et al (2020) --> sciencemethod: 'global
-#       N.B. Used to identify which GIA estimates to use
-sciencemethod: 'global'  # str
-
-# Tide gauge information (recommend default below)
-# source: source of tide gauge information
-#   N.B. Also used to identify which TG directory to use
-# datafq: temporal scale of tide gauge data
-#   N.B. not set up monthly files for PSMSL
-# psmsldir: TG base directory
-tidegaugeinfo:
-    source: 'PSMSL'    # str
-    datafq: ['annual'] # list
-    psmsldir: '/home/user/data/PSMSL/'
-
-# CMIP information
-cmipinfo:
-    mip: 'CMIP6'  # str
-# cmip_sea: for non-UK locations, specify if site is within marginal sea
-#   Not in a marginal sea --> cmip_sea: 'all'
-#   Marginal sea --> cmip_sea: 'marginal'
-    cmip_sea: 'all'     # str
-# sealevelbasedir: Base directory for CMIP "zos" and "zostoga" data
-    sealevelbasedir: '/data/cmip6/'
-# slopecoeffsuk: CMIP5 slope coefficients developed for UKCP18
-    slopecoeffsuk: '/data/uk_cmip_slope_coefficients/'
-
-
-# Directories for the GIA estimates (independent of scenario)
-#   UKCP18 --> UK specific ones
-#   Global locations --> Lambeck, ICE5G
-giaestimates:
-    global: '/data/gia_estimates/global_GIA_interpolators.pickle'
-    uk: '‘/data/gia_estimates/Bradley_GIA_interpolator.pickle'
-
-# Directories for all the fingerprints
-fingerprints: '/data/grd_fingerprints/'
-
-# Directory of Monte Carlo time series for new projections
-#   N.B. Originally developed by Jonathan Gregory
-short_montecarlodir: '/path/to/2100/montecarlo/simulations' # Required for projections up to 2100
-long_montecarlodir: '/data/runs/emulator_output' # Required for projections from 2100 up to 2300
diff --git a/profsea/utils/tide_gauge_locations.py b/profsea/utils/tide_gauge_locations.py
deleted file mode 100644
index ced9dd4..0000000
--- a/profsea/utils/tide_gauge_locations.py
+++ /dev/null
@@ -1,271 +0,0 @@
-"""
-Copyright (c) 2023, Met Office
-All rights reserved.
-"""
-
-from math import cos, asin, sqrt
-import pandas as pd
-import os
-
-from profsea.config import settings
-
-
-def closest(data, v):
-    """
-    :param data: all tide gauges latitude and longitude
-    :param v: site latitude and longitude
-    :return: DataFrame row of the closest TG to site lat and lon
-    """
-    return min(data, key=lambda p: distance(v['lat'], v['lon'],
-                                            p['lat'], p['lon']))
-
-
-def distance(lat1, lon1, lat2, lon2):
-    """
-    The haversine formula determines the great-circle distance between two
-    points on a sphere given their longitudes and latitudes.
-    Ref: https://en.wikipedia.org/wiki/Haversine_formula
-
-    :param lat1: site latitude
-    :param lon1: site longitude
-    :param lat2: TG latitude
-    :param lon2: TG longitude
-    :return: distance between points on a sphere
-    """
-    p = 0.017453292519943295
-    a = 0.5 - cos((lat2 - lat1) * p) / 2 + cos(lat1 * p) * cos(lat2 * p) * \
-        (1 - cos((lon2 - lon1) * p)) / 2
-    return 12742 * asin(sqrt(a))
-
-
-def extract_site_info(data_source, data_type, region, site_name, latlon):
-    """
-    Extract all available tide gauges from station list
-    Check if site_name is in this list, if so return metadata
-    If not construct a dataframe in the same format that contains user defined
-    metadata.
-    :param data_source: source of tide gauge information
-    :param data_type: temporal scale of tide gauge data
-    :param region: region of the world to assess sea level rise
-    :param site_name: site specific location
-    :param latlon: site location's latitude and longitude ([] if using tide
-                   gauge)
-    :return: Data frame of all metadata (tide gauge or site specific) for each
-    location requested
-    """
-    print('running function extract_site_info')
-
-    # Get a list of all available tide gauges from data_source
-    df = tide_gauge_locations(region=region, source=data_source,
-                              type=data_type)
-
-    # Create an empty pandas DataFrame to hold site information
-    dfObj = pd.DataFrame()
-    for i, location in enumerate(site_name):
-        # Re-format user defined site name
-        if data_source == 'PSMSL':
-            if location == 'Aberdeen':
-                print(f'CAUTION - There are two gauges available for ' +
-                      'Aberdeen, using ABERDEEN I (1931-2019)')
-                site_to_check = 'ABERDEEN I'
-            else:
-                site_to_check = location.upper()
-        else:
-            raise ValueError("data source not recognised")
-
-        # --------------------------------------------------------------------
-
-        if site_to_check in df.index:
-            print(f'{site_to_check} - Site metadata taken from {data_source}')
-            site_data = df.loc[site_to_check, :]
-
-            if data_source == 'PSMSL':
-                if 'NEWPORT' in site_to_check:
-                    print(f'CAUTION - There are two Newport gauges listed in' +
-                          'the file list from PSMSL')
-                    user_input = input('Location of Newport required, UK or ' +
-                                       'US?')
-                    if user_input == 'UK':
-                        print('Newport in the UK selected')
-                        site_data = site_data[~site_data.index.duplicated(
-                            keep='last')].transpose()
-                    else:
-                        print('Newport in the US selected')
-                        site_data = site_data[~site_data.index.duplicated(
-                            keep='first')].transpose()
-
-            df_temp = pd.DataFrame(site_data).transpose()
-            dfObj = dfObj.append(df_temp)
-
-        elif latlon != [[]]:
-            print(f'{site_to_check} - Site metadata taken from user input')
-            data_type = 'user defined'
-            station_id = 'NA'
-            latitude = latlon[i][0]
-            longitude = latlon[i][1]
-            site_data = [[site_to_check, data_type, station_id, latitude,
-                        longitude]]
-
-            df_temp = pd.DataFrame(site_data, columns=[
-                'Location', 'Dataset type', 'Station ID', 'Latitude',
-                'Longitude'])
-            df_temp = df_temp.set_index('Location')
-            dfObj = dfObj.append(df_temp)
-
-        else:
-            raise IndexError(f'No site metadata for this site: {site_to_check}, have you spelled it correctly?')            
-
-    return dfObj
-
-
-def find_nearest_station_id(root_dir, data_source, data_type, data_region,
-                            site_lat, site_lon):
-    """
-    Extracts all available tide gauges from data sources, then calculates
-    the closest tide gauge to the site based on latitude and longitude using
-    the Haversine formula
-      ** N.B. Testing only completed on PSMSL station list **
-
-    :param root_dir: base directory for all input and output data
-    :param data_source: source of tide gauge information
-    :param data_type: temporal scale of tide gauge data
-    :param data_region: region of the world to assess sea level rise
-    :param site_lat: site latitude
-    :param site_lon: site longitude
-    :return: Nearest tide gauge name and station ID
-    """
-    # Get a list of all available tide gauges from data_source, extract
-    # latitude and longitude
-    df = tide_gauge_locations(region=data_region, source=data_source,
-                              type=data_type, in_dir=root_dir)
-    latitude = df.loc[:, 'Latitude'].tolist()
-    longitude = df.loc[:, 'Longitude'].tolist()
-
-    # Re-format latitude and longitude
-    temp_data_list = []
-    for i in range(len(latitude)):
-        temp_data_list.append({'lat': latitude[i], 'lon': longitude[i]})
-
-    # Find the closest latitude and longitude in list to site location
-    site_location = {'lat': site_lat, 'lon': site_lon}
-    closest_latlon = closest(temp_data_list, site_location)
-
-    # Extract a dataframe based on the closest latitude and longitude
-    df_nearest_tg = df.loc[(df['Latitude'] == closest_latlon['lat']) & (
-            df['Longitude'] == closest_latlon['lon'])]
-    tg_station_name = df_nearest_tg.index.values.astype(str)[0]
-    tg_station_id = df_nearest_tg.loc[tg_station_name, 'Station ID']
-
-    return tg_station_id, tg_station_name
-
-
-def get_psmsl_gauges(data_type):
-    """
-    Search for available tide gauges from PSMSL, extract metadata using
-    function read_psmsl_list_of_gauges, re-order into correct format for
-    future code.
-    :param data_type: temporal scale of tide gauge data
-    :return: Re-formatted dataframe of available tide gauges from PSMSL
-    """
-    print('running function get_psmsl_gauges')
-
-    columns_needed = ['Location', 'ID', 'Lat', 'Lon']
-    ids = []
-    tide_gauges = []
-
-    for d in data_type:
-        # Read in the PSMSL station list of all gauges worldwide
-        psmsl_gauges = read_psmsl_list_of_gauges(d)
-
-        psmsl_basedir = settings["tidegaugeinfo"]["psmsldir"]
-
-        for row in psmsl_gauges.index.values:
-            idn = int(psmsl_gauges.at[row, 'ID'])
-            if any(j == idn for j in ids):
-                continue
-            ids.append(idn)
-            tide_gauge_file = f'{idn}.rlrdata'
-            tide_gauge_file_full = os.path.join(
-                f'{psmsl_basedir}rlr_{d}', 'data', f'{tide_gauge_file}')
-
-            if os.path.isfile(tide_gauge_file_full):
-                l = psmsl_gauges.loc[row, columns_needed].tolist()
-                l.insert(1, f'PSMSL - {data_type[0]}')
-                tide_gauges.append(l)
-
-    return tide_gauges
-
-
-def read_psmsl_list_of_gauges(data_type):
-    """
-    Read in a list of all gauges available from PSMSL.
-    :param data_type: temporal scale of tide gauge data, for PSMSL can be
-                      annual, monthly or hourly
-    :return: Pandas data frame of all available tide gauges from PSMSL
-    """
-    print('running function read_psmsl_list_of_gauges')
-
-    psmsl_basedir = settings["tidegaugeinfo"]["psmsldir"]
-
-    if data_type == 'annual':
-        station_list_file = 'filelist.txt'
-        filename = f'{psmsl_basedir}rlr_annual/{station_list_file}'
-        df = pd.read_csv(filename, sep="; ", header=None, names=[
-            'ID', 'Lat', 'Lon', 'Location', 'num', 'num2', 'flag'],
-            engine='python')
-        df.drop(columns=['num', 'num2', 'flag'], inplace=True)
-        df['Location'] = df['Location'].str.strip()
-        df = df[['Location', 'ID', 'Lat', 'Lon']]
-    elif data_type == 'monthly' or 'hourly':
-        raise ValueError("Monthly or Hourly tide gauge data not available " +
-                         "for PSMSL gauges")
-
-    return df
-
-
-def tide_gauge_locations(**args):
-    """
-    Wrapper to read in a list of all available tide gauges found on PSMSL.
-    :param args: Argument list
-        region --> region of the world to assess sea level rise
-        source --> source of tide gauge information
-        type --> temporal scale of tide gauge data
-    :return: Data frame of all available tide gauges from relevant source,
-        indexed by Location
-    """
-    print('running function tide_gauge_location')
-
-    # Name of columns in dataframe.
-    columns = ['Location', 'Dataset type', 'Station ID', 'Latitude',
-               'Longitude']
-
-    # Update parameters passed to this function
-    data_region = None
-    data_source = None
-    data_type = None
-
-    if 'region' in args:
-        data_region = args['region']
-    if 'source' in args:
-        data_source = args['source']
-    if 'type' in args:
-        data_type = args['type']
-
-    if data_region is None or data_source is None:
-        raise ValueError("tide_gauge_locations: must specify names of " +
-                         "region and source")
-
-    if data_source == 'PSMSL':
-        if 'type' not in args:
-            raise ValueError(
-                "tide_gauge_locations: must specify data_type when " +
-                "specifying PSMSL as source (hourly or monthly)")
-
-    if data_source == 'PSMSL':
-        tide_gauges = get_psmsl_gauges(data_type)
-    else:
-        raise ValueError(f'Source {data_source} not known')
-
-    df = pd.DataFrame(tide_gauges, columns=columns)
-
-    return df.set_index('Location')

From 057d89ff4773fd631a38b0c526b9cb49582931d8 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Wed, 27 May 2026 16:58:24 +0100
Subject: [PATCH 163/276] Add tutorial notebooks folder

---
 .../tutorial_notebooks/worked_example.ipynb   | 177 ++++++++++++++++++
 1 file changed, 177 insertions(+)
 create mode 100644 profsea/tutorial_notebooks/worked_example.ipynb

diff --git a/profsea/tutorial_notebooks/worked_example.ipynb b/profsea/tutorial_notebooks/worked_example.ipynb
new file mode 100644
index 0000000..45c04da
--- /dev/null
+++ b/profsea/tutorial_notebooks/worked_example.ipynb
@@ -0,0 +1,177 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "46a1a49f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from profsea.emulator import GMSLREmulator"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "52cdd115",
+   "metadata": {},
+   "source": [
+    "#### Introduction\n",
+    "The aim of this notebook is to break down the steps required to run the emulator version of ProFSea.\n",
+    "\n",
+    "All of this can't run in Jupyter Notebook but the steps should be clear enough to follow along!\n",
+    "\n",
+    "> If you want to use the CMIP6 patterns sterodynamic patterns instead of CMIP5, you need to run [this recipe](https://github.com/ESMValGroup/ESMValTool/blob/steric_patterns/esmvaltool/recipes/recipe_steric_patterns.yml) in ESMValTool. I recommend running with as much memory and processors as possible. \n",
+    "\n",
+    "> However, if you prefer not to go via ESMValTool feel free to reach out at gregory.munday\\@dtc.ox.ac.uk and I can just send them over (they're not large in terms of data size).\n",
+    "---\n",
+    "All other data required for the tool can be [downloaded here](https://catalogue.ceda.ac.uk/uuid/47c849b907414f3ab5e56ba9cf83e779/).\n",
+    "\n",
+    "I like keeping all my data in a `data/` folder next to the `profsea/` folder, which looks like this:\n",
+    "\n",
+    "  `data/` \\\n",
+    "   `cmip5/` \\\n",
+    "   `cmip6/` \\\n",
+    "   `gia_estimates/` \\\n",
+    "   `grd_fingerprints/` \\\n",
+    "   `monte_carlo_timeseries/` \\\n",
+    "   `uk_cmip_slope_coefficients/`\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ef69532c",
+   "metadata": {},
+   "source": [
+    "#### Setting up the environment\n",
+    "The next step is to set up the conda environment. You should use the `ProFSea-emu-env.yml` environment file, building it with \n",
+    "\n",
+    "```bash\n",
+    "conda env create -f ProFSea-emu-env.yml\n",
+    "```\n",
+    "\n",
+    "and then activating with `conda activate profsea-emu`.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "One final step here is to install ProFSea as an editable package, using\n",
+    "\n",
+    "```bash \n",
+    "pip install -e . \n",
+    "```\n",
+    "\n",
+    "while inside the highest level directory."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5616586f",
+   "metadata": {},
+   "source": [
+    "#### Setup FaIR output\n",
+    "Run FaIR for the scenarios you want to simulate and save out the `global surface air temperature` and `ocean heat content`. You'll also need to calculate the 2015-2100 cumulative CO2 emissions and save it to a `.json` file. The CMIP6 cumulative emissions are included in the repo: \n",
+    "\n",
+    "```profsea/emulator/aux_data/cumulative_cmip6_emissions.json```\n",
+    "\n",
+    "Here's some sample code to calculate it from scratch in FaIR. Perhaps this could be included in this branch at some point.\n",
+    "\n",
+    "```python\n",
+    "# Output cumulative emissions from 2015 to 2100\n",
+    "cum_emissions_dict = {}\n",
+    "for scenario in hist_gcam_ext.scenario.unique():  # loop over your scenarios\n",
+    "        emissions = hist_gcam_ext.loc[\n",
+    "            (hist_gcam_ext['scenario'] == scenario) & (hist_gcam_ext['variable'] == 'CO2 FFI'), \n",
+    "            '2015':'2100'\n",
+    "        ].to_numpy()[0]  # select anthro emissions\n",
+    "        cum_emissions = np.cumsum(emissions)\n",
+    "        cum_emissions = cum_emissions / 1000  # Convert to PgC\n",
+    "        print(f'Total cumulative carbon emissions from 2015 to 2100 for {scenario}: ', cum_emissions[-1])\n",
+    "        cum_emissions_dict[scenario] = cum_emissions[-1]\n",
+    "        # Save to json\n",
+    "        with open('ngfs_data/cumulative_scenario_emissions.json', 'w') as f:\n",
+    "            json.dump(cum_emissions_dict, f)\n",
+    "```\n",
+    "\n",
+    "You need to do this because the current Antarctic Ice-Sheet dynamics component uses the relationship between cumulative emissions and sea-level contribution to calculate the lower and upper bounds of the projection. This was implemented to remove scenario-dependence from the GMSLR emulator, and should be redundant when the component is updated.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6cef2ba4",
+   "metadata": {},
+   "source": [
+    "#### Running global → local ProFSea projections\n",
+    "Point your paths in the `user-settings-emu.yml` file to the right places, fill in your scenario names and you should be ready to go!\n",
+    "\n",
+    "Run: \n",
+    "\n",
+    "```bash\n",
+    "python spatial_projections.py\n",
+    "```\n",
+    "\n",
+    "The global projections will be saved to `data/runs/gmslr_output/`, while the spatialised projections should be saved to `data/runs//`\n",
+    "\n",
+    "The way this is accomplished (so that it runs on a laptop) is by taking and saving 101 percentiles of the GMSLR module output for each component. This can be updated via the `output_percentiles` keyword in the `GMSLR` class, currently line 458 in `spatial_projections.py`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "84c2ef51",
+   "metadata": {},
+   "source": [
+    "You can also run the GMSLR module by itself:\n",
+    "\n",
+    "```python\n",
+    "from profsea.emulator import GMSLREmulator\n",
+    "\n",
+    "emulator = GMSLREmulator(\n",
+    "    T_change,  # your T_change anom from FaIR\n",
+    "    OHC_change,  # your OHC_change anom from FaIR\n",
+    "    'low-overshoot',  # your scenario name (str)\n",
+    "    2300,  #  projection end year\n",
+    "    palmer_method=palmer_method,  # recommended for runs beyond 2100\n",
+    "    input_ensemble=True,  # must be true if providing a complete FaIR ensemble\n",
+    "    output_percentiles=np.arange(101),  # list/array of percentiles to sample\n",
+    "    cum_emissions_total=1450)  # int, cumulative emissions in scenario from 2015-2100\n",
+    "\n",
+    "#  run the projections (auto-parallel)\n",
+    "emulator.project()\n",
+    "\n",
+    "# Save to desired folder with the name of the scenario\n",
+    "emulator.save_components(\n",
+    "    os.path.join(settings[\"baseoutdir\"], 'gmslr_output'),  # output dir\n",
+    "    scenario)  # scenario name\n",
+    "\n",
+    "# Lists the available components\n",
+    "emulator.list_components()\n",
+    "\n",
+    "# You can also access the data from each component directly:\n",
+    "emulator.greenland  # returns greenland projection ensemble\n",
+    "\n",
+    "```"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "cmip7",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From c1b36ec14420279c82127307e21e62be8f7f4b87 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Wed, 27 May 2026 17:05:00 +0100
Subject: [PATCH 164/276] Update lat --> latitude

---
 profsea/components/core/spatial_model.py   |  6 +++---
 profsea/components/spatial/fingerprint.py  |  2 +-
 profsea/components/spatial/gia.py          |  2 +-
 profsea/components/spatial/sterodynamic.py |  2 +-
 profsea/utils/utils.py                     | 10 +++++-----
 5 files changed, 11 insertions(+), 11 deletions(-)

diff --git a/profsea/components/core/spatial_model.py b/profsea/components/core/spatial_model.py
index dcb74f1..3a07532 100644
--- a/profsea/components/core/spatial_model.py
+++ b/profsea/components/core/spatial_model.py
@@ -132,14 +132,14 @@ def _arr_to_xr(self, arr_dict: Dict[str, da.Array]) -> Dict[str, xr.DataArray]:
         for name, arr in arr_dict.items():
             xr_dict[name] = xr.DataArray(
                 arr,
-                dims=[member_dim, "time", "lat", "lon"],
+                dims=[member_dim, "time", "latitude", "longitude"],
                 coords={
                     member_dim: self.output_percentiles
                     if self.output_percentiles is not None
                     else np.arange(arr.shape[0]),
                     "time": np.arange(self.start_year, self.start_year + arr.shape[1]),
-                    "lat": self.grid_lats,
-                    "lon": self.grid_lons,
+                    "latitude": self.grid_lats,
+                    "longitude": self.grid_lons,
                 },
                 attrs={
                     "units": "m",
diff --git a/profsea/components/spatial/fingerprint.py b/profsea/components/spatial/fingerprint.py
index 659f2a5..6507c35 100644
--- a/profsea/components/spatial/fingerprint.py
+++ b/profsea/components/spatial/fingerprint.py
@@ -66,7 +66,7 @@ def _load_and_interpolate(self, state: SpatialState) -> da.Array:
         """
         grids = []
         for path in self.fp_paths:
-            fp_da = xr.open_dataarray(path, chunks={"lat": 45, "lon": 45})
+            fp_da = xr.open_dataarray(path, chunks={"latitude": 45, "longitude": 45})
             fp_interp = interpolate_to_grid(fp_da, state.grid_lats, state.grid_lons)
             grids.append(fp_interp.data * self.scaling_factor)
 
diff --git a/profsea/components/spatial/gia.py b/profsea/components/spatial/gia.py
index 156d8f7..294fbd5 100644
--- a/profsea/components/spatial/gia.py
+++ b/profsea/components/spatial/gia.py
@@ -73,7 +73,7 @@ def _load_and_interpolate_rates(self, state: SpatialState) -> da.Array:
         """
         grids = []
         for path in self.gia_paths:
-            gia_da = xr.open_dataarray(path, chunks={"lat": 45, "lon": 45})
+            gia_da = xr.open_dataarray(path, chunks={"latitude": 45, "longitude": 45})
             interp_da = interpolate_to_grid(gia_da, state.grid_lats, state.grid_lons)
 
             data = interp_da.data
diff --git a/profsea/components/spatial/sterodynamic.py b/profsea/components/spatial/sterodynamic.py
index 0091094..270b1f7 100644
--- a/profsea/components/spatial/sterodynamic.py
+++ b/profsea/components/spatial/sterodynamic.py
@@ -71,7 +71,7 @@ def _load_CMIP6_slopes(self) -> da.Array:
 
         # Lazily load all files keeping metadata intact
         datasets = [
-            xr.open_dataarray(f, chunks={"lat": 45, "lon": 45}) for f in slope_files
+            xr.open_dataarray(f, chunks={"latitude": 45, "longitude": 45}) for f in slope_files
         ]
 
         # Concatenate along a new dimension (representing the ensemble/models)
diff --git a/profsea/utils/utils.py b/profsea/utils/utils.py
index 8657eda..d1119f7 100644
--- a/profsea/utils/utils.py
+++ b/profsea/utils/utils.py
@@ -17,14 +17,14 @@ def interpolate(data: da.array, lats: int, lons: int) -> np.ndarray:
     """ """
     original_da = xr.DataArray(
         data.data,
-        coords=[("lat", data[data.dims[0]].values), ("lon", data[data.dims[1]].values)],
+        coords=[("latitude", data[data.dims[0]].values), ("longitude", data[data.dims[1]].values)],
         name="v",
     )
 
     target_lat = np.linspace(-90, 90, lats, endpoint=False) + 0.5
     target_lon = np.linspace(-180, 180, lons, endpoint=False) + 0.5
     data_interp = original_da.interp(
-        lat=target_lat, lon=target_lon, method="linear"
+        latitude=target_lat, longitude=target_lon, method="linear"
     ).data
     return data_interp
 
@@ -40,15 +40,15 @@ def interpolate_to_grid(
     Safely handles longitude wrapping mismatches (e.g., [0, 360) vs [-180, 180)).
     """
     # Normalize source longitudes to [-180, 180) and sort monotonically
-    data = data.assign_coords(lon=(((data.lon + 180) % 360) - 180))
-    data = data.sortby("lon")
+    data = data.assign_coords(longitude=(((data.longitude + 180) % 360) - 180))
+    data = data.sortby("longitude")
 
     # Normalize target longitudes to [-180, 180) and sort
     target_lons_norm = np.sort(((target_lons + 180) % 360) - 180)
 
     # Interpolate!
     data_interp = data.interp(
-        lat=target_lats, lon=target_lons_norm, method=grid_interpolation
+        latitude=target_lats, longitude=target_lons_norm, method=grid_interpolation
     )
     return data_interp
 

From 5f03cd11c7338fc0b95fa4ff12c8916b2bc9f162 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Wed, 27 May 2026 17:43:16 +0100
Subject: [PATCH 165/276] Add ipykernel to env

---
 profsea-env.yml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/profsea-env.yml b/profsea-env.yml
index 381ba79..4ded4a4 100644
--- a/profsea-env.yml
+++ b/profsea-env.yml
@@ -4,6 +4,7 @@ channels:
 dependencies:
   - cartopy
   - dask
+  - ipykernel
   - iris
   - matplotlib
   - netcdf4

From 161bc7d2f8c83b305d82046a08a00aa9e0a6c7e7 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Thu, 28 May 2026 11:40:38 +0100
Subject: [PATCH 166/276] Back to lat/lons

---
 profsea/components/core/spatial_model.py   |  6 +++---
 profsea/components/spatial/fingerprint.py  |  2 +-
 profsea/components/spatial/gia.py          |  2 +-
 profsea/components/spatial/sterodynamic.py |  2 +-
 profsea/utils/utils.py                     | 10 +++++-----
 5 files changed, 11 insertions(+), 11 deletions(-)

diff --git a/profsea/components/core/spatial_model.py b/profsea/components/core/spatial_model.py
index 3a07532..dcb74f1 100644
--- a/profsea/components/core/spatial_model.py
+++ b/profsea/components/core/spatial_model.py
@@ -132,14 +132,14 @@ def _arr_to_xr(self, arr_dict: Dict[str, da.Array]) -> Dict[str, xr.DataArray]:
         for name, arr in arr_dict.items():
             xr_dict[name] = xr.DataArray(
                 arr,
-                dims=[member_dim, "time", "latitude", "longitude"],
+                dims=[member_dim, "time", "lat", "lon"],
                 coords={
                     member_dim: self.output_percentiles
                     if self.output_percentiles is not None
                     else np.arange(arr.shape[0]),
                     "time": np.arange(self.start_year, self.start_year + arr.shape[1]),
-                    "latitude": self.grid_lats,
-                    "longitude": self.grid_lons,
+                    "lat": self.grid_lats,
+                    "lon": self.grid_lons,
                 },
                 attrs={
                     "units": "m",
diff --git a/profsea/components/spatial/fingerprint.py b/profsea/components/spatial/fingerprint.py
index 6507c35..659f2a5 100644
--- a/profsea/components/spatial/fingerprint.py
+++ b/profsea/components/spatial/fingerprint.py
@@ -66,7 +66,7 @@ def _load_and_interpolate(self, state: SpatialState) -> da.Array:
         """
         grids = []
         for path in self.fp_paths:
-            fp_da = xr.open_dataarray(path, chunks={"latitude": 45, "longitude": 45})
+            fp_da = xr.open_dataarray(path, chunks={"lat": 45, "lon": 45})
             fp_interp = interpolate_to_grid(fp_da, state.grid_lats, state.grid_lons)
             grids.append(fp_interp.data * self.scaling_factor)
 
diff --git a/profsea/components/spatial/gia.py b/profsea/components/spatial/gia.py
index 294fbd5..156d8f7 100644
--- a/profsea/components/spatial/gia.py
+++ b/profsea/components/spatial/gia.py
@@ -73,7 +73,7 @@ def _load_and_interpolate_rates(self, state: SpatialState) -> da.Array:
         """
         grids = []
         for path in self.gia_paths:
-            gia_da = xr.open_dataarray(path, chunks={"latitude": 45, "longitude": 45})
+            gia_da = xr.open_dataarray(path, chunks={"lat": 45, "lon": 45})
             interp_da = interpolate_to_grid(gia_da, state.grid_lats, state.grid_lons)
 
             data = interp_da.data
diff --git a/profsea/components/spatial/sterodynamic.py b/profsea/components/spatial/sterodynamic.py
index 270b1f7..d9910a7 100644
--- a/profsea/components/spatial/sterodynamic.py
+++ b/profsea/components/spatial/sterodynamic.py
@@ -71,7 +71,7 @@ def _load_CMIP6_slopes(self) -> da.Array:
 
         # Lazily load all files keeping metadata intact
         datasets = [
-            xr.open_dataarray(f, chunks={"latitude": 45, "longitude": 45}) for f in slope_files
+            xr.open_dataset(f, chunks={"lat": 45, "lon": 45})["zos_zostoga_regression_slope"] for f in slope_files
         ]
 
         # Concatenate along a new dimension (representing the ensemble/models)
diff --git a/profsea/utils/utils.py b/profsea/utils/utils.py
index d1119f7..8657eda 100644
--- a/profsea/utils/utils.py
+++ b/profsea/utils/utils.py
@@ -17,14 +17,14 @@ def interpolate(data: da.array, lats: int, lons: int) -> np.ndarray:
     """ """
     original_da = xr.DataArray(
         data.data,
-        coords=[("latitude", data[data.dims[0]].values), ("longitude", data[data.dims[1]].values)],
+        coords=[("lat", data[data.dims[0]].values), ("lon", data[data.dims[1]].values)],
         name="v",
     )
 
     target_lat = np.linspace(-90, 90, lats, endpoint=False) + 0.5
     target_lon = np.linspace(-180, 180, lons, endpoint=False) + 0.5
     data_interp = original_da.interp(
-        latitude=target_lat, longitude=target_lon, method="linear"
+        lat=target_lat, lon=target_lon, method="linear"
     ).data
     return data_interp
 
@@ -40,15 +40,15 @@ def interpolate_to_grid(
     Safely handles longitude wrapping mismatches (e.g., [0, 360) vs [-180, 180)).
     """
     # Normalize source longitudes to [-180, 180) and sort monotonically
-    data = data.assign_coords(longitude=(((data.longitude + 180) % 360) - 180))
-    data = data.sortby("longitude")
+    data = data.assign_coords(lon=(((data.lon + 180) % 360) - 180))
+    data = data.sortby("lon")
 
     # Normalize target longitudes to [-180, 180) and sort
     target_lons_norm = np.sort(((target_lons + 180) % 360) - 180)
 
     # Interpolate!
     data_interp = data.interp(
-        latitude=target_lats, longitude=target_lons_norm, method=grid_interpolation
+        lat=target_lats, lon=target_lons_norm, method=grid_interpolation
     )
     return data_interp
 

From 87908bcaeef5f94d74076b46f2af26bd36cd66d6 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Thu, 28 May 2026 17:02:44 +0100
Subject: [PATCH 167/276] Add zenodo retrieval code

---
 profsea/components/core/spatial_model.py   | 131 ++++++++++++++++++---
 profsea/components/spatial/fingerprint.py  |  56 ++++++++-
 profsea/components/spatial/gia.py          |  15 ++-
 profsea/components/spatial/sterodynamic.py |   5 +-
 ukcp18_example_script.py                   |  52 ++++----
 5 files changed, 200 insertions(+), 59 deletions(-)

diff --git a/profsea/components/core/spatial_model.py b/profsea/components/core/spatial_model.py
index dcb74f1..3e3be6c 100644
--- a/profsea/components/core/spatial_model.py
+++ b/profsea/components/core/spatial_model.py
@@ -2,11 +2,21 @@
 from pathlib import Path
 from typing import Dict
 import warnings
+import zipfile
 
 import dask.array as da
 import numpy as np
+import requests
 from rich.console import Console
-from rich.progress import track
+from rich.progress import (
+    Progress,
+    TextColumn,
+    BarColumn,
+    DownloadColumn,
+    TransferSpeedColumn,
+    TimeRemainingColumn,
+    track,
+)
 import xarray as xr
 
 from .state import SpatialState
@@ -15,6 +25,10 @@
 console = Console()
 warnings.filterwarnings("ignore")
 
+PROFSEA_DIR = Path(__file__).resolve().parent.parent.parent
+ZENODO_DOWNLOAD_LINK = (
+    "https://zenodo.org/records/20427061/files/profsea-assets.zip?download=1"
+)
 
 class Spatial:
     """Spatial sea level rise component emulator."""
@@ -45,6 +59,13 @@ def __init__(
         output_percentiles: list or np.ndarray, optional
             List or array of percentiles to sample from the ensemble for output. If None, outputs all members. Default is [5, 17, 50, 83, 95].
         """
+        # Define the path where the data should live
+
+        fetch_zenodo_fingerprints(
+            zenodo_url=ZENODO_DOWNLOAD_LINK,
+            data_dir=PROFSEA_DIR,
+            expected_folder_name="profsea-assets",
+        )
 
         self.components = components
         self.end_year = end_year
@@ -87,21 +108,18 @@ def __init__(
         )
 
         # Log the size of each component and provide an estimate of their memory usage
-        for name, comp in self.components.items():
-            if self.output_percentiles is not None and len(self.output_percentiles) > 0:
-                comp_size = comp.global_projection.nbytes / 1e9
-                future_size = (
-                    comp_size
-                    * self.num_members
-                    * len(self.grid_lats)
-                    * len(self.grid_lons)
-                )
-            else:
-                comp_size = (
-                    comp.global_projection[: len(self.output_percentiles)].nbytes / 1e9
-                )
-                future_size = comp_size * len(self.grid_lats) * len(self.grid_lons)
+        # Output shape will be (num_members, n_years, n_lats, n_lons)
+        bytes_per_element = 8  # Assuming float64. Use 4 if strictly float32.
+        
+        future_size = (
+            self.num_members
+            * self.n_years
+            * len(self.grid_lats)
+            * len(self.grid_lons)
+            * bytes_per_element
+        ) / 1e9
 
+        for name, comp in self.components.items():
             # Warn if memory usage is going to be large
             if future_size > 20:
                 console.log(
@@ -316,3 +334,86 @@ def save_components(
         console.log(
             "Output shape was " + str(ds[name].shape) + " (members, time, lat, lon)"
         )
+
+
+def fetch_zenodo_fingerprints(
+    zenodo_url: str, data_dir: Path, expected_folder_name: str
+) -> None:
+    """
+    Downloads and extracts the ProFSea fingerprint dataset from Zenodo if it doesn't already exist locally.
+
+    Parameters
+    ----------
+    zenodo_url: str
+        The direct download URL for the fingerprint dataset on Zenodo.
+    data_dir: Path
+        The base directory where the dataset should be stored.
+    expected_folder_name: str
+        The name of the folder that should be created when the dataset is extracted. Used to check if the data already exists.
+    """
+    target_dir = data_dir / expected_folder_name
+
+    # 1. Check if data already exists
+    if target_dir.exists() and any(target_dir.iterdir()):
+        console.log(
+            f"[bold green]✓ Fingerprint data already found locally at {target_dir}[/bold green]"
+        )
+        return
+
+    # Create the base directory if it doesn't exist
+    data_dir.mkdir(parents=True, exist_ok=True)
+    zip_path = data_dir / "temp_fingerprints.zip"
+
+    console.log(f"Initiating download from {zenodo_url}...")
+
+    # 2. Stream the download with a rich progress bar
+    try:
+        response = requests.get(zenodo_url, stream=True)
+        response.raise_for_status()  # Raise an error for bad status codes
+
+        total_size = int(response.headers.get("content-length", 0))
+
+        with Progress(
+            TextColumn("[bold cyan]{task.description}"),
+            BarColumn(),
+            DownloadColumn(),
+            TransferSpeedColumn(),
+            TimeRemainingColumn(),
+            console=console,
+        ) as progress:
+            download_task = progress.add_task(
+                "Downloading dataset...", total=total_size
+            )
+
+            with open(zip_path, "wb") as file:
+                for chunk in response.iter_content(chunk_size=8192):
+                    if chunk:
+                        file.write(chunk)
+                        progress.update(download_task, advance=len(chunk))
+
+    except requests.exceptions.RequestException as e:
+        console.log(f"[bold red]Failed to download data: {e}[/bold red]")
+        if zip_path.exists():
+            zip_path.unlink()  # Clean up partial downloads
+        raise
+
+    console.log("Extracting data...")
+    try:
+        with zipfile.ZipFile(zip_path, "r") as zip_ref:
+            # Filter out the __MACOSX directory and its contents
+            valid_members = [
+                member for member in zip_ref.namelist() 
+                if not member.startswith("__MACOSX/") and not member.startswith("._")
+            ]
+            zip_ref.extractall(data_dir, members=valid_members)
+            
+        console.log(f"[bold green]✓ Successfully extracted data to {data_dir}[/bold green]")
+    except zipfile.BadZipFile:
+        console.log(
+            "[bold red]Error: Downloaded file is not a valid zip archive.[/bold red]"
+        )
+        raise
+    finally:
+        # 4. Clean up the zip file
+        if zip_path.exists():
+            zip_path.unlink()
diff --git a/profsea/components/spatial/fingerprint.py b/profsea/components/spatial/fingerprint.py
index 659f2a5..2f7e81c 100644
--- a/profsea/components/spatial/fingerprint.py
+++ b/profsea/components/spatial/fingerprint.py
@@ -8,6 +8,38 @@
 from profsea.components.core.state import SpatialState
 from profsea.utils import interpolate_to_grid, sample_members_2D
 
+PROFSEA_DIR = Path(__file__).resolve().parents[2]
+FP_DIR = PROFSEA_DIR / "profsea-assets" / "grd-fingerprints"
+
+FP_PATH_MAP = {
+    "greendyn": [
+        FP_DIR / "greendyn_klemann.nc",
+        FP_DIR / "greendyn_slangen.nc",
+        FP_DIR / "greendyn_spada.nc",
+    ],
+    "greensmb": [
+        FP_DIR / "greensmb_klemann.nc",
+        FP_DIR / "greensmb_slangen.nc",
+        FP_DIR / "greensmb_spada.nc",
+    ],
+    "landwater": [FP_DIR / "landwater_slangen.nc"],
+    "antdyn": [
+        FP_DIR / "antdyn_klemann.nc",
+        FP_DIR / "antdyn_slangen.nc",
+        FP_DIR / "antdyn_spada.nc",
+    ],
+    "antsmb": [
+        FP_DIR / "antsmb_klemann.nc",
+        FP_DIR / "antsmb_slangen.nc",
+        FP_DIR / "antsmb_spada.nc",
+    ],
+    "glacier": [
+        FP_DIR / "glacier_klemann.nc",
+        FP_DIR / "glacier_slangen.nc",
+        FP_DIR / "glacier_spada.nc",
+    ],
+}
+
 
 class Fingerprint(SpatialComponent):
     """
@@ -17,7 +49,8 @@ class Fingerprint(SpatialComponent):
     def __init__(
         self,
         global_projection: np.ndarray,
-        fingerprint_paths: str | Path | list[str | Path],
+        fingerprint_component: str,
+        fingerprint_paths: str | Path | list[str | Path] = None,
         scaling_factor: float = 1.0,
         sample_spatial: bool = False,
     ) -> None:
@@ -36,16 +69,27 @@ def __init__(
         self._global_projection = da.from_array(global_projection, chunks="auto")
         self.scaling_factor = scaling_factor
         self.sample_spatial = sample_spatial
-
+        self.fingerprint_component = fingerprint_component
+
+        # Default paths if not provided (can be overridden by user input)
+        if fingerprint_paths is None:
+            # Determine default paths based on the component type
+            try:
+                self.fp_paths = [Path(p) for p in FP_PATH_MAP[fingerprint_component]]
+            except KeyError:
+                raise ValueError(
+                    f"No default fingerprint paths found for fingerprint component '{fingerprint_component}'. Please provide explicit paths."
+                )
         # Normalize the input to always be a list of Path objects
-        if isinstance(fingerprint_paths, (str, Path)):
+        elif isinstance(fingerprint_paths, (str, Path)):
             self.fp_paths = [Path(fingerprint_paths)]
         else:
             self.fp_paths = [Path(p) for p in fingerprint_paths]
 
-        for p in self.fp_paths:
-            if not p.exists():
-                raise FileNotFoundError(f"Missing fingerprint file: {p}")
+        if fingerprint_paths is not None:
+            for p in self.fp_paths:
+                if not p.exists():
+                    raise FileNotFoundError(f"Missing fingerprint file: {p}")
 
     @property
     def global_projection(self):
diff --git a/profsea/components/spatial/gia.py b/profsea/components/spatial/gia.py
index 156d8f7..98b09cd 100644
--- a/profsea/components/spatial/gia.py
+++ b/profsea/components/spatial/gia.py
@@ -8,6 +8,9 @@
 from profsea.components.core.state import SpatialState
 from profsea.utils import interpolate_to_grid
 
+PROFSEA_DIR = Path(__file__).resolve().parents[2]
+GIA_DIR = PROFSEA_DIR / "profsea-assets" / "gia"
+
 
 class GIA(SpatialComponent):
     """
@@ -18,7 +21,7 @@ class GIA(SpatialComponent):
 
     def __init__(
         self,
-        gia_paths: str | Path | list[str | Path],
+        gia_paths: str | Path | list[str | Path] = None,
         sample_spatial: bool = False,
     ) -> None:
         """
@@ -31,19 +34,21 @@ def __init__(
         """
         self.sample_spatial = sample_spatial
 
-        if isinstance(gia_paths, (str, Path)):
+        if gia_paths is None:
+            self.gia_paths = list(GIA_DIR.glob("*.nc"))
+        elif isinstance(gia_paths, (str, Path)):
             path_obj = Path(gia_paths)
             if path_obj.is_dir():
-                # If it's a folder, grab all NetCDF files inside
                 self.gia_paths = list(path_obj.glob("*.nc"))
             else:
                 self.gia_paths = [path_obj]
         else:
             self.gia_paths = [Path(p) for p in gia_paths]
 
-        # Validation
         if not self.gia_paths:
-            raise FileNotFoundError(f"No GIA NetCDF files found for input: {gia_paths}")
+            raise FileNotFoundError(
+                f"No GIA NetCDF files found. Looked in: {GIA_DIR if gia_paths is None else gia_paths}"
+            )
 
         for p in self.gia_paths:
             if not p.exists():
diff --git a/profsea/components/spatial/sterodynamic.py b/profsea/components/spatial/sterodynamic.py
index d9910a7..e798cea 100644
--- a/profsea/components/spatial/sterodynamic.py
+++ b/profsea/components/spatial/sterodynamic.py
@@ -38,10 +38,7 @@ def __init__(
         self.sample_spatial = sample_spatial
 
         if patterns_dir is None:
-            raise FileNotFoundError(
-                "Please specify the path to the CMIP6 sterodynamic "
-                "patterns using the 'patterns_dir' argument."
-            )
+            self.patterns_dir = Path("./profsea/profsea-assets/cmip6-patterns")
         else:
             self.patterns_dir = Path(patterns_dir)
 
diff --git a/ukcp18_example_script.py b/ukcp18_example_script.py
index 8caced8..a43c50f 100644
--- a/ukcp18_example_script.py
+++ b/ukcp18_example_script.py
@@ -25,7 +25,7 @@
     "landwater": LandwaterAR5(),
     "antarctica_dyn": AntarcticaDynAR5(),
     "antarctica_smb": AntarcticaSMBAR5(),
-    "glacier": Glacier()
+    "glacier": Glacier(),
 }
 
 # Pass to the global model
@@ -42,51 +42,33 @@
 spatial_components = {
     "sterodynamic": SterodynamicCMIP6(
         projections["expansion"],
-        patterns_dir="data-minimal/cmip6",
     ),
     "greenland_dyn": Fingerprint(
-        projections["greenland_dyn"],
-        fingerprint_paths=
-        ["data-minimal/grd_fingerprints/greendyn_klemann_nomask.nc",
-         "data-minimal/grd_fingerprints/greendyn_slangen_nomask.nc",
-         "data-minimal/grd_fingerprints/greendyn_spada_nomask.nc"],
+        projections["greenland_dyn"], fingerprint_component="greendyn"
     ),
     "greenland_smb": Fingerprint(
         projections["greenland_smb"],
-        fingerprint_paths=
-        ["data-minimal/grd_fingerprints/greensmb_klemann_nomask.nc",
-         "data-minimal/grd_fingerprints/greensmb_slangen_nomask.nc",
-         "data-minimal/grd_fingerprints/greensmb_spada_nomask.nc"],
+        fingerprint_component="greensmb",
     ),
     "landwater": Fingerprint(
         projections["landwater"],
-        fingerprint_paths="data-minimal/grd_fingerprints/landwater_slangen_nomask.nc",
+        fingerprint_component="landwater",
     ),
     "antarctica_dyn": Fingerprint(
         projections["antarctica_dyn"],
-        fingerprint_paths=
-        ["data-minimal/grd_fingerprints/antdyn_klemann_nomask.nc",
-         "data-minimal/grd_fingerprints/antdyn_slangen_nomask.nc",
-         "data-minimal/grd_fingerprints/antdyn_spada_nomask.nc"],
+        fingerprint_component="antdyn",
     ),
     "antarctica_smb": Fingerprint(
         projections["antarctica_smb"],
-        fingerprint_paths=
-        ["data-minimal/grd_fingerprints/antsmb_klemann_nomask.nc",
-         "data-minimal/grd_fingerprints/antsmb_slangen_nomask.nc",
-         "data-minimal/grd_fingerprints/antsmb_spada_nomask.nc"],
+        fingerprint_component="antsmb",
     ),
     "glacier": Fingerprint(
         projections["glacier"],
-        fingerprint_paths=
-        ["data-minimal/grd_fingerprints/glacier_klemann_nomask.nc",
-         "data-minimal/grd_fingerprints/glacier_slangen_nomask.nc",
-         "data-minimal/grd_fingerprints/glacier_spada_nomask.nc"],
+        fingerprint_component="glacier",
     ),
     "gia": GIA(
-        gia_paths="data-minimal/gia_clean/global_gia.nc",
         sample_spatial=False,
-    )
+    ),
 }
 
 # Pass to the spatial model
@@ -94,7 +76,9 @@
 spatial_model.run(member_seed=42)
 
 spatial_model.sum_components(spatial_model.results)
-spatial_model.save_components(spatial_model.results, scenario_name="rcp85", output_format="zarr")
+spatial_model.save_components(
+    spatial_model.results, scenario_name="rcp85", output_format="zarr"
+)
 
 
 ### Plot example ###
@@ -108,7 +92,12 @@
 
 # Global projections
 yrs = np.arange(2006, 2301)
-ax.plot(yrs, np.median(global_model.results["gmslr"], axis=0), label="Global Projection", color="royalblue")
+ax.plot(
+    yrs,
+    np.median(global_model.results["gmslr"], axis=0),
+    label="Global Projection",
+    color="royalblue",
+)
 # fill between 1 and 4 members
 ax.fill_between(
     yrs,
@@ -132,5 +121,10 @@
     vmax=vmax,
 )
 ax.coastlines()
-fig.colorbar(mappable=ax.collections[0], label="Relative Sea Level (m)", orientation="horizontal", pad=0.02)
+fig.colorbar(
+    mappable=ax.collections[0],
+    label="Relative Sea Level (m)",
+    orientation="horizontal",
+    pad=0.02,
+)
 plt.show()

From de5b85af24bb732f088dca04ea51be756d1f9714 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Thu, 28 May 2026 17:04:37 +0100
Subject: [PATCH 168/276] Update env.yml

---
 profsea-env.yml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/profsea-env.yml b/profsea-env.yml
index 4ded4a4..19bf5a5 100644
--- a/profsea-env.yml
+++ b/profsea-env.yml
@@ -13,6 +13,7 @@ dependencies:
   - pandas
   - python=3.12
   - pyyaml
+  - requests
   - rich
   - scipy
   - xarray

From 92e02ba1ce93f856a8ae1823ee2118292e020a8e Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Thu, 28 May 2026 17:20:34 +0100
Subject: [PATCH 169/276] Fix expansion OHC shapes

---
 profsea/components/global_/expansion.py | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/profsea/components/global_/expansion.py b/profsea/components/global_/expansion.py
index ca75cf8..351e160 100644
--- a/profsea/components/global_/expansion.py
+++ b/profsea/components/global_/expansion.py
@@ -22,6 +22,13 @@ def __init__(self, OHC_change: np.ndarray, distribution_scaler: float = 1.0):
     def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
         # check the shape here
         check_shapes(self.OHC_change, state.nyr)
+
+        # Ensure OHC_change is 2D
+        if self.OHC_change.ndim > 2:
+            self.OHC_change = np.squeeze(self.OHC_change)
+        if self.OHC_change.ndim == 1:
+            self.OHC_change = np.expand_dims(self.OHC_change, axis=0)
+
         # Sensitivity of thermosteric SLR to ocean heat content change
         # From Turner et al. (2023)
         mean_eff = 0.113

From 69b090ece08427336c509a2a6ba91c2f0ecbb6fb Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Thu, 28 May 2026 17:26:20 +0100
Subject: [PATCH 170/276] Patterns are run-directory agnostic

---
 profsea/components/spatial/sterodynamic.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/profsea/components/spatial/sterodynamic.py b/profsea/components/spatial/sterodynamic.py
index e798cea..f2aa914 100644
--- a/profsea/components/spatial/sterodynamic.py
+++ b/profsea/components/spatial/sterodynamic.py
@@ -8,6 +8,9 @@
 from profsea.components.core.state import ClimateState
 from profsea.utils import interpolate_to_grid, sample_members_2D
 
+PROFSEA_DIR = Path(__file__).resolve().parents[2]
+PATTERNS_DIR = PROFSEA_DIR / "profsea-assets" / "cmip6-patterns"
+
 
 class SterodynamicCMIP6(SpatialComponent):
     """
@@ -38,7 +41,7 @@ def __init__(
         self.sample_spatial = sample_spatial
 
         if patterns_dir is None:
-            self.patterns_dir = Path("./profsea/profsea-assets/cmip6-patterns")
+            self.patterns_dir = PATTERNS_DIR
         else:
             self.patterns_dir = Path(patterns_dir)
 

From 987093977b3ee7a3bb69abac38998b6bd65bda61 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Thu, 28 May 2026 23:15:19 +0100
Subject: [PATCH 171/276] Readability tweaks

---
 .../aux_data/ISMIP_GIS_calibration.csv        |    0
 .../aux_data/aispen_params.nc                 |  Bin
 .../aux_data/cumulative_cmip6_emissions.json  |    0
 .../{components => }/aux_data/eais_params.nc  |  Bin
 .../aux_data/eais_params_expanded.nc          |  Bin
 profsea/aux_data/ismip6_emu_evaluation.ipynb  | 3261 +++++++++++++++++
 .../aux_data/pen_params_expanded.nc           |  Bin
 .../ssp_global_landwater_projections.nc       |  Bin
 .../{components => }/aux_data/wais_params.nc  |  Bin
 .../aux_data/wais_params_expanded.nc          |  Bin
 profsea/components/core/global_model.py       |   12 +-
 profsea/components/core/state.py              |    2 +-
 profsea/components/core/time_projection.py    |    6 +-
 profsea/components/global_/antarctica.py      |   12 +-
 profsea/components/global_/expansion.py       |    4 +-
 profsea/components/global_/glacier.py         |    8 +-
 profsea/components/global_/greenland.py       |   26 +-
 profsea/components/global_/landwater.py       |   24 +-
 18 files changed, 3310 insertions(+), 45 deletions(-)
 rename profsea/{components => }/aux_data/ISMIP_GIS_calibration.csv (100%)
 rename profsea/{components => }/aux_data/aispen_params.nc (100%)
 rename profsea/{components => }/aux_data/cumulative_cmip6_emissions.json (100%)
 rename profsea/{components => }/aux_data/eais_params.nc (100%)
 rename profsea/{components => }/aux_data/eais_params_expanded.nc (100%)
 create mode 100644 profsea/aux_data/ismip6_emu_evaluation.ipynb
 rename profsea/{components => }/aux_data/pen_params_expanded.nc (100%)
 rename profsea/{components => }/aux_data/ssp_global_landwater_projections.nc (100%)
 rename profsea/{components => }/aux_data/wais_params.nc (100%)
 rename profsea/{components => }/aux_data/wais_params_expanded.nc (100%)

diff --git a/profsea/components/aux_data/ISMIP_GIS_calibration.csv b/profsea/aux_data/ISMIP_GIS_calibration.csv
similarity index 100%
rename from profsea/components/aux_data/ISMIP_GIS_calibration.csv
rename to profsea/aux_data/ISMIP_GIS_calibration.csv
diff --git a/profsea/components/aux_data/aispen_params.nc b/profsea/aux_data/aispen_params.nc
similarity index 100%
rename from profsea/components/aux_data/aispen_params.nc
rename to profsea/aux_data/aispen_params.nc
diff --git a/profsea/components/aux_data/cumulative_cmip6_emissions.json b/profsea/aux_data/cumulative_cmip6_emissions.json
similarity index 100%
rename from profsea/components/aux_data/cumulative_cmip6_emissions.json
rename to profsea/aux_data/cumulative_cmip6_emissions.json
diff --git a/profsea/components/aux_data/eais_params.nc b/profsea/aux_data/eais_params.nc
similarity index 100%
rename from profsea/components/aux_data/eais_params.nc
rename to profsea/aux_data/eais_params.nc
diff --git a/profsea/components/aux_data/eais_params_expanded.nc b/profsea/aux_data/eais_params_expanded.nc
similarity index 100%
rename from profsea/components/aux_data/eais_params_expanded.nc
rename to profsea/aux_data/eais_params_expanded.nc
diff --git a/profsea/aux_data/ismip6_emu_evaluation.ipynb b/profsea/aux_data/ismip6_emu_evaluation.ipynb
new file mode 100644
index 0000000..cb7ad43
--- /dev/null
+++ b/profsea/aux_data/ismip6_emu_evaluation.ipynb
@@ -0,0 +1,3261 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "5c0efc4e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import glob \n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import xarray as xr\n",
+    "from scipy.spatial.distance import cdist\n",
+    "from profsea.emulator import AntarcticaISMIP6"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "d46b1a0e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading Global Temperatures...\n",
+      "Loading Regional SLE Data...\n",
+      "  Processing Region 1...\n",
+      "  Processing Region 2...\n",
+      "  Processing Region 3...\n",
+      "Data loading complete.\n"
+     ]
+    }
+   ],
+   "source": [
+    "def sample_members_2D(array: np.ndarray, percentiles: list | np.ndarray) -> np.ndarray:\n",
+    "    \"\"\"Sample real ensemble members from a 2D numpy array.\"\"\"\n",
+    "    array_percentiles = np.percentile(array, percentiles, axis=0)\n",
+    "    distances = cdist(array_percentiles, array)\n",
+    "    mem_indices = np.argmin(distances, axis=1)\n",
+    "    return array[mem_indices]\n",
+    "\n",
+    "def load_global_temperature(path: str) -> np.array:\n",
+    "    ds = xr.open_mfdataset(path).tas\n",
+    "    ds = ds.groupby('time.year').mean('time')\n",
+    "    global_weights = np.cos(np.deg2rad(ds.lat))\n",
+    "    ds = ds.weighted(global_weights).mean(dim=['lat', 'lon']).compute()\n",
+    "    tas = ds.sel(year=slice(2015, 2299)).values\n",
+    "    tas -= tas[0]\n",
+    "    return tas\n",
+    "\n",
+    "def load_stable_temperature(path: str) -> np.array:\n",
+    "    tas = xr.open_mfdataset(path).tas.values\n",
+    "    tas -= tas[0]\n",
+    "    return tas\n",
+    "\n",
+    "def load_var(path: str, variable: str, subtract_control=True) -> np.array:\n",
+    "    control_files = sorted(glob.glob(\"../data/AIS/ctrlAE/sle_goelzer/*.nc\"))\n",
+    "    files = sorted(glob.glob(path))\n",
+    "    anoms = []\n",
+    "    \n",
+    "    for f_idx, file in enumerate(files):\n",
+    "        ds = xr.open_dataset(file, decode_times=False)[f'{variable}']\n",
+    "        ds = ds.sel(time=slice(2015, 2299)).values\n",
+    "        ds -= ds[0]\n",
+    "        \n",
+    "        if subtract_control:\n",
+    "            # Ensure we don't go out of bounds if fewer controls than files\n",
+    "            if f_idx < len(control_files):\n",
+    "                ctrl = xr.open_dataset(control_files[f_idx], decode_times=False)[f\"{variable}\"]\n",
+    "                ctrl = ctrl.sel(time=slice(2015, 2299)).values\n",
+    "                ctrl -= ctrl[0]\n",
+    "                ds -= ctrl\n",
+    "        \n",
+    "        anoms.append(ds)\n",
+    "    \n",
+    "    # Return array of shape (n_members, n_timesteps)\n",
+    "    return np.array(anoms)\n",
+    "\n",
+    "def get_model_name(path: str):\n",
+    "    return path.split(\"/\")[-1].split(\"sle_AIS_\")[-1].split(\"_exp\")[0]\n",
+    "\n",
+    "def get_common_models(ais_path: str, variable: str, model_pool: list) -> np.array:\n",
+    "    exp_paths = sorted(glob.glob(os.path.join(ais_path, \"expAE*\")))\n",
+    "\n",
+    "    for idx, exp in enumerate(exp_paths):\n",
+    "        models_path = os.path.join(exp, \"sle/*.nc\")\n",
+    "        model_exps = sorted(glob.glob(models_path))\n",
+    "        \n",
+    "        model_names = [get_model_name(path) for path in model_exps]\n",
+    "\n",
+    "        # check if name in common_models, if not, remove from common models\n",
+    "        for model in model_pool: \n",
+    "            if model not in model_names:\n",
+    "                model_pool.remove(model)\n",
+    "    return model_pool\n",
+    "\n",
+    "SCENARIOS_TRAIN = [\n",
+    "    (load_global_temperature, '../data/AIS/forcing/global/CESM2-WACCM/*.nc', 'expAE04'),       # High (585)\n",
+    "    (load_global_temperature, '../data/AIS/forcing/global/CCSM4/*.nc', 'expAE02'),             # High (85)\n",
+    "    (load_global_temperature, '../data/AIS/forcing/global/HadGEM2-ES/*.nc', 'expAE03'),        # High (85)\n",
+    "    (load_global_temperature, '../data/AIS/forcing/global/UKESM1-0-LL/*.nc', 'expAE05'),       # High (585)\n",
+    "    (load_stable_temperature, '../data/AIS/forcing/global/NorESM1-M_STABLE/tas_Amon_NorESM1-M_rcp26_stabilised.nc', 'expAE01'), # Low (26)\n",
+    "    (load_stable_temperature, '../data/AIS/forcing/global/UKESM1-0-LL_STABLE/*.nc', 'expAE06'), # Stable (585)\n",
+    "]\n",
+    "\n",
+    "SCENARIOS_TEST = [\n",
+    "    (load_stable_temperature, '../data/AIS/forcing/global/NorESM1-M_STABLE/tas_Amon_NorESM1-M_rcp85_stabilised.nc', 'expAE07'), # Stable (85)\n",
+    "    (load_stable_temperature, '../data/AIS/forcing/global/HadGEM2-ES_STABLE/*.nc', 'expAE08'), # Stable (85)\n",
+    "    (load_stable_temperature, '../data/AIS/forcing/global/CESM2-WACCM_STABLE/*.nc', 'expAE09'),# Stable (585)\n",
+    "    (load_global_temperature, '../data/AIS/forcing/global/UKESM1-0-LL_126/*.nc', 'expAE10'),   # Low (26)\n",
+    "]\n",
+    "\n",
+    "# 1. Load Temperature (Global - same for all regions)\n",
+    "print(\"Loading Global Temperatures...\")\n",
+    "tas_train_list = [loader(path) for loader, path, _ in SCENARIOS_TRAIN]\n",
+    "tas_test_list  = [loader(path) for loader, path, _ in SCENARIOS_TEST]\n",
+    "\n",
+    "# Calculate Integrated Temperature\n",
+    "tint_train_list = [np.cumsum(t) for t in tas_train_list]\n",
+    "tint_test_list  = [np.cumsum(t) for t in tas_test_list]\n",
+    "\n",
+    "# Convert TAS to numpy arrays (Shape: [n_scenarios, time])\n",
+    "tas_train_arr = np.array(tas_train_list)\n",
+    "tas_test_arr  = np.array(tas_test_list)\n",
+    "tint_train_arr = np.array(tint_train_list)\n",
+    "tint_test_arr  = np.array(tint_test_list)\n",
+    "\n",
+    "# 2. Load SLE per Region and Build Dictionaries\n",
+    "regions = [1, 2, 3]\n",
+    "train = {}\n",
+    "test = {}\n",
+    "\n",
+    "print(\"Loading Regional SLE Data...\")\n",
+    "for region in regions:\n",
+    "    var_name = f'sle_goelzer_region_{region}'\n",
+    "    print(f\"  Processing Region {region}...\")\n",
+    "\n",
+    "    # Load Train SLE\n",
+    "    sle_train_list = []\n",
+    "    for _, _, exp_id in SCENARIOS_TRAIN:\n",
+    "        path = f'../data/AIS/{exp_id}/sle_goelzer/*.nc'\n",
+    "        sle_data = load_var(path, variable=var_name)\n",
+    "        sle_train_list.append(sle_data)\n",
+    "    \n",
+    "    # Load Test SLE\n",
+    "    sle_test_list = []\n",
+    "    for _, _, exp_id in SCENARIOS_TEST:\n",
+    "        path = f'../data/AIS/{exp_id}/sle_goelzer/*.nc'\n",
+    "        sle_data = load_var(path, variable=var_name)\n",
+    "        sle_test_list.append(sle_data)\n",
+    "\n",
+    "    # Convert to object array or structured array depending on member count consistency\n",
+    "    # (Using np.array with dtype=object allows varying member counts per model if necessary)\n",
+    "    # If all models have same member count, you can remove dtype=object\n",
+    "    sle_train_arr = np.array(sle_train_list, dtype=object) \n",
+    "    sle_test_arr = np.array(sle_test_list, dtype=object)\n",
+    "\n",
+    "    # Populate Dictionaries\n",
+    "    train[region] = {\n",
+    "        'tas': tas_train_arr,\n",
+    "        't_int': tint_train_arr,\n",
+    "        'sle': sle_train_arr\n",
+    "    }\n",
+    "\n",
+    "    test[region] = {\n",
+    "        'tas': tas_test_arr,\n",
+    "        't_int': tint_test_arr,\n",
+    "        'sle': sle_test_arr\n",
+    "    }\n",
+    "\n",
+    "model_list = sorted(glob.glob('../data/AIS/expAE04/sle_goelzer/*.nc'))\n",
+    "model_names = [item.split(\"/\")[-1].split(\"sle_goelzer_AIS_\")[-1].split(\"_exp\")[0] for item in model_list]\n",
+    "common_models = get_common_models(\"../data/AIS/\", \"sle\", model_names.copy())\n",
+    "common_idx = [model_names.index(item) for item in common_models if item in model_names]\n",
+    "\n",
+    "print(\"Data loading complete.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "2ad4bbea",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+      "(1000,)\n",
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+      "(1, 285)\n",
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+      "(1, 285)\n",
+      "(1000,)\n",
+      "(1, 285)\n",
+      "(1000,)\n",
+      "(1, 285)\n",
+      "(1000,)\n",
+      "(1, 285)\n",
+      "(1000,)\n",
+      "(1, 285)\n",
+      "(1000,)\n",
+      "(1, 285)\n",
+      "(1000,)\n",
+      "(1, 285)\n",
+      "(1000,)\n",
+      "(1, 285)\n",
+      "(1000,)\n",
+      "(1, 285)\n",
+      "(1000,)\n",
+      "(1, 285)\n",
+      "(1000,)\n",
+      "(1, 285)\n",
+      "(1000,)\n",
+      "(1, 285)\n",
+      "(1000,)\n",
+      "(1, 285)\n",
+      "(1000,)\n",
+      "(1, 285)\n",
+      "(1000,)\n",
+      "(1, 285)\n",
+      "(1000,)\n",
+      "(1, 285)\n",
+      "(1000,)\n",
+      "(1, 285)\n",
+      "(1000,)\n",
+      "(1, 285)\n",
+      "(1000,)\n",
+      "(1, 285)\n",
+      "(1000,)\n",
+      "(1, 285)\n",
+      "(1000,)\n",
+      "(1, 285)\n"
+     ]
+    }
+   ],
+   "source": [
+    "wa_ais = AntarcticaISMIP6(\"wa_params_JAN.nc\")\n",
+    "ea_ais = AntarcticaISMIP6(\"ea_params_JAN.nc\")\n",
+    "pen_ais = AntarcticaISMIP6(\"pen_params_JAN.nc\")\n",
+    "\n",
+    "# The region number doesn't matter for tas and t_int\n",
+    "wa_train_preds = [wa_ais.predict(train[1][\"tas\"][scen], train[1][\"t_int\"][scen], display_progress=False) for scen in range(train[1][\"tas\"].shape[0])]\n",
+    "ea_train_preds = [ea_ais.predict(train[1][\"tas\"][scen], train[1][\"t_int\"][scen], display_progress=False) for scen in range(train[1][\"tas\"].shape[0])]\n",
+    "pen_train_preds = [pen_ais.predict(train[1][\"tas\"][scen], train[1][\"t_int\"][scen], display_progress=False) for scen in range(train[1][\"tas\"].shape[0])]\n",
+    "\n",
+    "wa_test_preds = [wa_ais.predict(test[1][\"tas\"][scen], test[1][\"t_int\"][scen], display_progress=False) for scen in range(test[1][\"tas\"].shape[0])]\n",
+    "ea_test_preds = [ea_ais.predict(test[1][\"tas\"][scen], test[1][\"t_int\"][scen], display_progress=False) for scen in range(test[1][\"tas\"].shape[0])]\n",
+    "pen_test_preds = [pen_ais.predict(test[1][\"tas\"][scen], test[1][\"t_int\"][scen], display_progress=False) for scen in range(test[1][\"tas\"].shape[0])]\n",
+    "\n",
+    "wa_test_preds = [wa_test_preds[scen][common_idx] for scen in range(test[1][\"tas\"].shape[0])]\n",
+    "ea_test_preds = [ea_test_preds[scen][common_idx] for scen in range(test[1][\"tas\"].shape[0])]\n",
+    "pen_test_preds = [pen_test_preds[scen][common_idx] for scen in range(test[1][\"tas\"].shape[0])]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b4588b5",
+   "metadata": {},
+   "source": [
+    "Probably want a plot that shows predictions vs training for each scenario, for each region, and added up. Then same for testing.\n",
+    "\n",
+    "We also want to see the same for the ice-sheet collapse simulations. \n",
+    "\n",
+    "Something numerical? RMSE?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e7a8caf0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
        "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(16, 6), layout=\"constrained\")\n",
+    "fig.suptitle(\"ISMIP6 emulator | WAIS | Training fits\")\n",
+    "for scen, _ in enumerate(wa_train_preds):\n",
+    "    ax = fig.add_subplot(2, 3, scen+1)\n",
+    "    p5 = np.percentile(wa_train_preds[scen], 5, axis=(0, 1))\n",
+    "    p17 = np.percentile(wa_train_preds[scen], 17, axis=(0, 1))\n",
+    "    median = np.median(wa_train_preds[scen], axis=(0, 1))\n",
+    "    p83 = np.percentile(wa_train_preds[scen], 83, axis=(0, 1))\n",
+    "    p95 = np.percentile(wa_train_preds[scen], 95, axis=(0, 1))\n",
+    "\n",
+    "    true_median = np.percentile(train[1][\"sle\"][scen], 50, axis=0)\n",
+    "\n",
+    "    time_axis = np.arange(len(median))\n",
+    "\n",
+    "    # Plot Emulator Uncertainty\n",
+    "    ax.fill_between(time_axis, p5, p95, color='skyblue', alpha=0.2, label='5-95% Confidence')\n",
+    "    ax.fill_between(time_axis, p17, p83, color='skyblue', alpha=0.4, label='17-83% Confidence')\n",
+    "\n",
+    "\n",
+    "    ax.plot(np.tile(time_axis, (train[1][\"sle\"][scen].shape[0], 1)).T, train[1][\"sle\"][scen].T, '--', lw=1, color=\"black\", alpha=0.4, label=\"ISMIP6 models\")\n",
+    "    ax.plot(time_axis, true_median, lw=2, color='black', label=\"ISMIP6 median\")\n",
+    "    ax.plot(time_axis, median, color='skyblue', lw=3, label='Emulator Median')\n",
+    "\n",
+    "    ax.set_xlabel(\"Time (years)\")\n",
+    "    ax.set_ylabel(\"Sea Level Equivalent (m)\")\n",
+    "\n",
+    "    if scen == 0:\n",
+    "        handles, labels = ax.get_legend_handles_labels()\n",
+    "        unique_labels = set(labels)\n",
+    "        handles = [handles[labels.index(label)] for label in unique_labels if label in labels]\n",
+    "        ax.legend(handles, unique_labels, frameon=False, loc='upper left')\n",
+    "\n",
+    "    ax.grid(True, alpha=0.3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "7eae8edd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
        "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(16, 6), layout=\"constrained\")\n",
+    "fig.suptitle(\"ISMIP6 emulator | EAIS | Training fits\")\n",
+    "for scen, _ in enumerate(ea_train_preds):\n",
+    "    ax = fig.add_subplot(2, 3, scen+1)\n",
+    "    p5 = np.percentile(ea_train_preds[scen], 5, axis=(0, 1))\n",
+    "    p17 = np.percentile(ea_train_preds[scen], 17, axis=(0, 1))\n",
+    "    median = np.median(ea_train_preds[scen], axis=(0, 1))\n",
+    "    p83 = np.percentile(ea_train_preds[scen], 83, axis=(0, 1))\n",
+    "    p95 = np.percentile(ea_train_preds[scen], 95, axis=(0, 1))\n",
+    "\n",
+    "    true_median = np.percentile(train[2][\"sle\"][scen], 50, axis=0)\n",
+    "\n",
+    "    time_axis = np.arange(len(median))\n",
+    "\n",
+    "    # Plot Emulator Uncertainty\n",
+    "    ax.fill_between(time_axis, p5, p95, color='skyblue', alpha=0.2, label='5-95% Confidence')\n",
+    "    ax.fill_between(time_axis, p17, p83, color='skyblue', alpha=0.4, label='17-83% Confidence')\n",
+    "\n",
+    "\n",
+    "    ax.plot(np.tile(time_axis, (train[2][\"sle\"][scen].shape[0], 1)).T, train[2][\"sle\"][scen].T, '--', lw=1, color=\"black\", alpha=0.4, label=\"ISMIP6 models\")\n",
+    "    ax.plot(time_axis, true_median, lw=2, color='black', label=\"ISMIP6 median\")\n",
+    "    ax.plot(time_axis, median, color='skyblue', lw=3, label='Emulator Median')\n",
+    "\n",
+    "    ax.set_xlabel(\"Time (years)\")\n",
+    "    ax.set_ylabel(\"Sea Level Equivalent (m)\")\n",
+    "\n",
+    "    if scen == 0:\n",
+    "        handles, labels = ax.get_legend_handles_labels()\n",
+    "        unique_labels = set(labels)\n",
+    "        handles = [handles[labels.index(label)] for label in unique_labels if label in labels]\n",
+    "        ax.legend(handles, unique_labels, frameon=False, loc='upper left')\n",
+    "\n",
+    "    ax.grid(True, alpha=0.3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "3995353f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
        "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(16, 6), layout=\"constrained\")\n",
+    "fig.suptitle(\"ISMIP6 emulator | Peninsula | Training fits\")\n",
+    "for scen, _ in enumerate(pen_train_preds):\n",
+    "    ax = fig.add_subplot(2, 3, scen+1)\n",
+    "    p5 = np.percentile(pen_train_preds[scen], 5, axis=(0, 1))\n",
+    "    p17 = np.percentile(pen_train_preds[scen], 17, axis=(0, 1))\n",
+    "    median = np.median(pen_train_preds[scen], axis=(0, 1))\n",
+    "    p83 = np.percentile(pen_train_preds[scen], 83, axis=(0, 1))\n",
+    "    p95 = np.percentile(pen_train_preds[scen], 95, axis=(0, 1))\n",
+    "\n",
+    "    true_median = np.percentile(train[3][\"sle\"][scen], 50, axis=0)\n",
+    "\n",
+    "    time_axis = np.arange(len(median))\n",
+    "\n",
+    "    # Plot Emulator Uncertainty\n",
+    "    ax.fill_between(time_axis, p5, p95, color='skyblue', alpha=0.2, label='5-95% Confidence')\n",
+    "    ax.fill_between(time_axis, p17, p83, color='skyblue', alpha=0.4, label='17-83% Confidence')\n",
+    "\n",
+    "\n",
+    "    ax.plot(np.tile(time_axis, (train[3][\"sle\"][scen].shape[0], 1)).T, train[3][\"sle\"][scen].T, '--', lw=1, color=\"black\", alpha=0.4, label=\"ISMIP6 models\")\n",
+    "    ax.plot(time_axis, true_median, lw=2, color='black', label=\"ISMIP6 median\")\n",
+    "    ax.plot(time_axis, median, color='skyblue', lw=3, label='Emulator Median')\n",
+    "\n",
+    "    ax.set_xlabel(\"Time (years)\")\n",
+    "    ax.set_ylabel(\"Sea Level Equivalent (m)\")\n",
+    "\n",
+    "    if scen == 0:\n",
+    "        handles, labels = ax.get_legend_handles_labels()\n",
+    "        unique_labels = set(labels)\n",
+    "        handles = [handles[labels.index(label)] for label in unique_labels if label in labels]\n",
+    "        ax.legend(handles, unique_labels, frameon=False, loc='upper left')\n",
+    "\n",
+    "    ax.grid(True, alpha=0.3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "b9d3da32",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
        "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(10, 6), layout=\"constrained\")\n",
+    "fig.suptitle(\"ISMIP6 emulator | WAIS | Test fits\")\n",
+    "for scen, _ in enumerate(wa_test_preds):\n",
+    "    ax = fig.add_subplot(2, 2, scen+1)\n",
+    "    p5 = np.percentile(wa_test_preds[scen], 5, axis=(0, 1))\n",
+    "    p17 = np.percentile(wa_test_preds[scen], 17, axis=(0, 1))\n",
+    "    median = np.median(wa_test_preds[scen], axis=(0, 1))\n",
+    "    p83 = np.percentile(wa_test_preds[scen], 83, axis=(0, 1))\n",
+    "    p95 = np.percentile(wa_test_preds[scen], 95, axis=(0, 1))\n",
+    "\n",
+    "    true_median = np.percentile(test[1][\"sle\"][scen], 50, axis=0)\n",
+    "\n",
+    "    time_axis = np.arange(len(median))\n",
+    "\n",
+    "    # Plot Emulator Uncertainty\n",
+    "    ax.fill_between(time_axis, p5, p95, color='skyblue', alpha=0.2, label='5-95% Confidence')\n",
+    "    ax.fill_between(time_axis, p17, p83, color='skyblue', alpha=0.4, label='17-83% Confidence')\n",
+    "\n",
+    "\n",
+    "    ax.plot(np.tile(time_axis, (test[1][\"sle\"][scen].shape[0], 1)).T, test[1][\"sle\"][scen].T, '--', lw=1, color=\"black\", alpha=0.4, label=\"ISMIP6 models\")\n",
+    "    ax.plot(time_axis, true_median, lw=2, color='black', label=\"ISMIP6 median\")\n",
+    "    ax.plot(time_axis, median, color='skyblue', lw=3, label='Emulator Median')\n",
+    "\n",
+    "    ax.set_xlabel(\"Time (years)\")\n",
+    "    ax.set_ylabel(\"Sea Level Equivalent (m)\")\n",
+    "\n",
+    "    if scen == 0:\n",
+    "        handles, labels = ax.get_legend_handles_labels()\n",
+    "        unique_labels = set(labels)\n",
+    "        handles = [handles[labels.index(label)] for label in unique_labels if label in labels]\n",
+    "        ax.legend(handles, unique_labels, frameon=False, loc='upper left')\n",
+    "\n",
+    "    ax.grid(True, alpha=0.3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "df7e0815",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
        "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(12, 8), layout=\"constrained\")\n",
+    "fig.suptitle(\"ISMIP6 emulator | EAIS | Test fits\")\n",
+    "for scen, _ in enumerate(ea_test_preds):\n",
+    "    ax = fig.add_subplot(2, 2, scen+1)\n",
+    "    p5 = np.percentile(ea_test_preds[scen], 5, axis=(0, 1))\n",
+    "    p17 = np.percentile(ea_test_preds[scen], 17, axis=(0, 1))\n",
+    "    median = np.median(ea_test_preds[scen], axis=(0, 1))\n",
+    "    p83 = np.percentile(ea_test_preds[scen], 83, axis=(0, 1))\n",
+    "    p95 = np.percentile(ea_test_preds[scen], 95, axis=(0, 1))\n",
+    "\n",
+    "    true_median = np.percentile(test[2][\"sle\"][scen], 50, axis=0)\n",
+    "\n",
+    "    time_axis = np.arange(len(median))\n",
+    "\n",
+    "    # Plot Emulator Uncertainty\n",
+    "    ax.fill_between(time_axis, p5, p95, color='skyblue', alpha=0.2, label='5-95% Confidence')\n",
+    "    ax.fill_between(time_axis, p17, p83, color='skyblue', alpha=0.4, label='17-83% Confidence')\n",
+    "\n",
+    "\n",
+    "    ax.plot(np.tile(time_axis, (test[2][\"sle\"][scen].shape[0], 1)).T, test[2][\"sle\"][scen].T, '--', lw=1, color=\"black\", alpha=0.4, label=\"ISMIP6 models\")\n",
+    "    ax.plot(time_axis, true_median, lw=2, color='black', label=\"ISMIP6 median\")\n",
+    "    ax.plot(time_axis, median, color='skyblue', lw=3, label='Emulator Median')\n",
+    "\n",
+    "    ax.set_xlabel(\"Time (years)\")\n",
+    "    ax.set_ylabel(\"Sea Level Equivalent (m)\")\n",
+    "\n",
+    "    if scen == 0:\n",
+    "        handles, labels = ax.get_legend_handles_labels()\n",
+    "        unique_labels = set(labels)\n",
+    "        handles = [handles[labels.index(label)] for label in unique_labels if label in labels]\n",
+    "        ax.legend(handles, unique_labels, frameon=False, loc='upper left')\n",
+    "\n",
+    "    ax.grid(True, alpha=0.3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "65be2301",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
        "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(12, 8), layout=\"constrained\")\n",
+    "fig.suptitle(\"ISMIP6 emulator | Peninsula | Test fits\")\n",
+    "for scen, _ in enumerate(pen_test_preds):\n",
+    "    ax = fig.add_subplot(2, 2, scen+1)\n",
+    "    p5 = np.percentile(pen_test_preds[scen], 0, axis=(0, 1))\n",
+    "    p17 = np.percentile(pen_test_preds[scen], 17, axis=(0, 1))\n",
+    "    median = np.median(pen_test_preds[scen], axis=(0, 1))\n",
+    "    p83 = np.percentile(pen_test_preds[scen], 83, axis=(0, 1))\n",
+    "    p95 = np.percentile(pen_test_preds[scen], 95, axis=(0, 1))\n",
+    "\n",
+    "    true_median = np.percentile(test[3][\"sle\"][scen], 50, axis=0)\n",
+    "\n",
+    "    time_axis = np.arange(len(median))\n",
+    "\n",
+    "    # Plot Emulator Uncertainty\n",
+    "    ax.fill_between(time_axis, p5, p95, color='darkgoldenrod', alpha=0.2, label='5-95% Confidence')\n",
+    "    ax.fill_between(time_axis, p17, p83, color='darkgoldenrod', alpha=0.4, label='17-83% Confidence')\n",
+    "\n",
+    "\n",
+    "    ax.plot(np.tile(time_axis, (test[3][\"sle\"][scen].shape[0], 1)).T, test[3][\"sle\"][scen].T, '--', lw=1, color=\"black\", alpha=0.4, label=\"ISMIP6 models\")\n",
+    "    ax.plot(time_axis, true_median, lw=2, color='black', label=\"ISMIP6 median\")\n",
+    "    ax.plot(time_axis, median, color='darkgoldenrod', lw=3, label='Emulator Median')\n",
+    "\n",
+    "    ax.set_xlabel(\"Time (years)\")\n",
+    "    ax.set_ylabel(\"Sea Level Equivalent (m)\")\n",
+    "\n",
+    "    if scen == 0:\n",
+    "        handles, labels = ax.get_legend_handles_labels()\n",
+    "        unique_labels = sorted(set(labels))\n",
+    "        handles = [handles[labels.index(label)] for label in unique_labels if label in labels]\n",
+    "        ax.legend(handles, unique_labels, frameon=False, loc='lower left')\n",
+    "\n",
+    "    ax.grid(True, alpha=0.3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "381e03ba",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
        "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    " # Now all together\n",
+    "fig = plt.figure(figsize=(16, 6), layout=\"constrained\")\n",
+    "fig.suptitle(\"ISMIP6 emulator | Antarctica | Training fits\")\n",
+    "for scen, _ in enumerate(pen_train_preds):\n",
+    "    sum_train_preds = wa_train_preds[scen] + ea_train_preds[scen] + pen_train_preds[scen]\n",
+    "    sum_train_ismip = train[1][\"sle\"][scen] + train[2][\"sle\"][scen] + train[3][\"sle\"][scen]\n",
+    "    \n",
+    "    ax = fig.add_subplot(2, 3, scen+1)\n",
+    "    p5 = np.percentile(sum_train_preds, 0, axis=(0, 1))\n",
+    "    p17 = np.percentile(sum_train_preds, 17, axis=(0, 1))\n",
+    "    median = np.median(sum_train_preds, axis=(0, 1))\n",
+    "    p83 = np.percentile(sum_train_preds, 83, axis=(0, 1))\n",
+    "    p95 = np.percentile(sum_train_preds, 100, axis=(0, 1))\n",
+    "\n",
+    "    true_median = np.percentile(sum_train_ismip, 50, axis=0)\n",
+    "\n",
+    "    time_axis = np.arange(len(median))\n",
+    "\n",
+    "    # Plot Emulator Uncertainty\n",
+    "    ax.fill_between(time_axis, p5, p95, color='darkgoldenrod', alpha=0.2, label='5-95% Confidence')\n",
+    "    ax.fill_between(time_axis, p17, p83, color='darkgoldenrod', alpha=0.4, label='17-83% Confidence')\n",
+    "\n",
+    "\n",
+    "    ax.plot(np.tile(time_axis, (train[3][\"sle\"][scen].shape[0], 1)).T, sum_train_ismip.T, '--', lw=1, color=\"black\", alpha=0.4, label=\"ISMIP6 models\")\n",
+    "    ax.plot(time_axis, true_median, lw=2, color='black', label=\"ISMIP6 median\")\n",
+    "    ax.plot(time_axis, median, color='darkgoldenrod', lw=3, label='Emulator Median')\n",
+    "\n",
+    "    ax.set_xlabel(\"Time (years)\")\n",
+    "    ax.set_ylabel(\"Sea Level Equivalent (m)\")\n",
+    "\n",
+    "    if scen == 0:\n",
+    "        handles, labels = ax.get_legend_handles_labels()\n",
+    "        unique_labels = set(labels)\n",
+    "        handles = [handles[labels.index(label)] for label in unique_labels if label in labels]\n",
+    "        ax.legend(handles, unique_labels, frameon=False, loc='upper left')\n",
+    "\n",
+    "    ax.grid(True, alpha=0.3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "a61a6847",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
        "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    " # Now all together\n",
+    "fig = plt.figure(figsize=(10, 8), layout=\"constrained\")\n",
+    "fig.suptitle(\"ISMIP6 emulator | Antarctica | Test fits\")\n",
+    "for scen, _ in enumerate(pen_test_preds):\n",
+    "    sum_test_preds = wa_test_preds[scen] + ea_test_preds[scen] + pen_test_preds[scen]\n",
+    "    sum_test_ismip = test[1][\"sle\"][scen] + test[2][\"sle\"][scen] + test[3][\"sle\"][scen]\n",
+    "\n",
+    "    ax = fig.add_subplot(2, 2, scen+1)\n",
+    "    p5 = np.percentile(sum_test_preds, 0, axis=(0, 1))\n",
+    "    p17 = np.percentile(sum_test_preds, 17, axis=(0, 1))\n",
+    "    median = np.median(sum_test_preds, axis=(0, 1))\n",
+    "    p83 = np.percentile(sum_test_preds, 83, axis=(0, 1))\n",
+    "    p95 = np.percentile(sum_test_preds, 100, axis=(0, 1))\n",
+    "\n",
+    "    true_median = np.percentile(sum_test_ismip, 50, axis=0)\n",
+    "\n",
+    "    time_axis = np.arange(len(median))\n",
+    "\n",
+    "    # Plot Emulator Uncertainty\n",
+    "    ax.fill_between(time_axis, p5, p95, color='darkgoldenrod', alpha=0.2, label='5-95% Confidence')\n",
+    "    ax.fill_between(time_axis, p17, p83, color='darkgoldenrod', alpha=0.4, label='17-83% Confidence')\n",
+    "\n",
+    "\n",
+    "    ax.plot(np.tile(time_axis, (test[3][\"sle\"][scen].shape[0], 1)).T, sum_test_ismip.T, '--', lw=1, color=\"black\", alpha=0.4, label=\"ISMIP6 models\")\n",
+    "    ax.plot(time_axis, true_median, lw=2, color='black', label=\"ISMIP6 median\")\n",
+    "    ax.plot(time_axis, median, color='darkgoldenrod', lw=3, label='Emulator Median')\n",
+    "\n",
+    "    ax.set_xlabel(\"Time (years)\")\n",
+    "    ax.set_ylabel(\"Sea Level Equivalent (m)\")\n",
+    "\n",
+    "    if scen == 0:\n",
+    "        handles, labels = ax.get_legend_handles_labels()\n",
+    "        unique_labels = set(labels)\n",
+    "        handles = [handles[labels.index(label)] for label in unique_labels if label in labels]\n",
+    "        ax.legend(handles, unique_labels, frameon=False, loc='upper left')\n",
+    "\n",
+    "    ax.grid(True, alpha=0.3)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "cmip7",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/profsea/components/aux_data/pen_params_expanded.nc b/profsea/aux_data/pen_params_expanded.nc
similarity index 100%
rename from profsea/components/aux_data/pen_params_expanded.nc
rename to profsea/aux_data/pen_params_expanded.nc
diff --git a/profsea/components/aux_data/ssp_global_landwater_projections.nc b/profsea/aux_data/ssp_global_landwater_projections.nc
similarity index 100%
rename from profsea/components/aux_data/ssp_global_landwater_projections.nc
rename to profsea/aux_data/ssp_global_landwater_projections.nc
diff --git a/profsea/components/aux_data/wais_params.nc b/profsea/aux_data/wais_params.nc
similarity index 100%
rename from profsea/components/aux_data/wais_params.nc
rename to profsea/aux_data/wais_params.nc
diff --git a/profsea/components/aux_data/wais_params_expanded.nc b/profsea/aux_data/wais_params_expanded.nc
similarity index 100%
rename from profsea/components/aux_data/wais_params_expanded.nc
rename to profsea/aux_data/wais_params_expanded.nc
diff --git a/profsea/components/core/global_model.py b/profsea/components/core/global_model.py
index 1419b45..2311e32 100644
--- a/profsea/components/core/global_model.py
+++ b/profsea/components/core/global_model.py
@@ -25,7 +25,7 @@ class Global:
         End year of the projections.
     nt: int
         Number of realisations of the input timeseries
-    nm: int
+    num_members: int
         Number of realisations of for each component.
         Must be a multiple of the number of glacier methods.
     tcv: float
@@ -63,7 +63,7 @@ def __init__(
         components: Dict[str, Component],
         end_yr: int,
         nt: int = 100,
-        nm: int = 1000,
+        num_members: int = 1000,
         tcv: float = 1.0,
         parallel: bool = True,
         input_ensemble: bool = True,
@@ -74,7 +74,7 @@ def __init__(
         self.components = components
         self.end_yr = end_yr
         self.nt = nt
-        self.nm = nm
+        self.num_members = num_members
         self.tcv = tcv
         self.parallel = parallel
         self.input_ensemble = input_ensemble
@@ -117,7 +117,7 @@ def run(
         T_ens, T_int_ens, T_int_med = self._calculate_drivers(T_change, run_rng)
 
         # Shared physical correlation state
-        fraction = run_rng.random(self.nm * self.nt)
+        fraction = run_rng.random(self.num_members * self.nt)
 
         state = ClimateState(
             scenario=scenario,
@@ -131,7 +131,7 @@ def run(
             end_yr=self.end_yr,
             nyr=self.nyr,
             nt=self.nt,
-            nm=self.nm,
+            num_members=self.num_members,
         )
 
         # Child RNGs for each component
@@ -160,7 +160,7 @@ def run(
 
         # Random Sampling
         if self.random_sample:
-            random_idx = run_rng.integers(low=0, high=self.nm)
+            random_idx = run_rng.integers(low=0, high=self.num_members)
             for comp_name, data in results.items():
                 if data.ndim > 1:
                     results[comp_name] = data[random_idx][None, :]
diff --git a/profsea/components/core/state.py b/profsea/components/core/state.py
index ead9910..f9cc0d8 100644
--- a/profsea/components/core/state.py
+++ b/profsea/components/core/state.py
@@ -17,7 +17,7 @@ class ClimateState:
     end_yr: int
     nyr: int
     nt: int
-    nm: int
+    num_members: int
 
 
 @dataclass
diff --git a/profsea/components/core/time_projection.py b/profsea/components/core/time_projection.py
index 57a2c6e..be1a3f0 100644
--- a/profsea/components/core/time_projection.py
+++ b/profsea/components/core/time_projection.py
@@ -38,11 +38,11 @@ def time_projection(
         time = np.arange(state.end_yr - state.endofhistory) + 1
 
         if fraction is None:
-            fraction = rng.random((state.nm, state.nt))
-        elif fraction.size != state.nm * state.nt:
+            fraction = rng.random((state.num_members, state.nt))
+        elif fraction.size != state.num_members * state.nt:
             raise ValueError("fraction is the wrong size")
 
-        fraction = fraction.reshape(state.nm, state.nt)
+        fraction = fraction.reshape(state.num_members, state.nt)
 
         # Convert inputs to startrate (m yr-1) and afinal (m), where both are
         # arrays with the size of fraction
diff --git a/profsea/components/global_/antarctica.py b/profsea/components/global_/antarctica.py
index d26d7f2..a2266e8 100644
--- a/profsea/components/global_/antarctica.py
+++ b/profsea/components/global_/antarctica.py
@@ -206,7 +206,7 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
 
             ascale = norm.ppf(state.fraction)
             final = np.exp(lcoeff[2] * ascale**2 + lcoeff[1] * ascale + lcoeff[0])
-            final = final.reshape(state.nm, state.nt)
+            final = final.reshape(state.num_members, state.nt)
         return (
             time_projection(state, 0.41, 0.20, final, rng, fraction=state.fraction)
             + self.d_ant
@@ -250,8 +250,8 @@ def project(
         KoKg = [1.1, 0.2]  # ratio of Antarctic warming to global warming from G&H06
 
         # Generate a distribution of products of the above two factors
-        pcoKg = (pcoK[0] + rng.standard_normal([state.nm, state.nt]) * pcoK[1]) * (
-            KoKg[0] + rng.standard_normal([state.nm, state.nt]) * KoKg[1]
+        pcoKg = (pcoK[0] + rng.standard_normal([state.num_members, state.nt]) * pcoK[1]) * (
+            KoKg[0] + rng.standard_normal([state.num_members, state.nt]) * KoKg[1]
         )
         meansmb = 1923  # model-mean time-mean 1979-2010 Gt yr-1 from 13.3.3.2
         moaoKg = (
@@ -259,11 +259,11 @@ def project(
         )  # m yr-1 of SLE per K of global warming
 
         if state.fraction is None:
-            fraction = rng.random((state.nm, state.nt))
-        elif state.fraction.size != state.nm * state.nt:
+            fraction = rng.random((state.num_members, state.nt))
+        elif state.fraction.size != state.num_members * state.nt:
             raise ValueError("fraction is the wrong size")
         else:
-            fraction = state.fraction.reshape((state.nm, state.nt))
+            fraction = state.fraction.reshape((state.num_members, state.nt))
 
         smax = 0.35  # max value of S in 13.SM.1.5
         ainterfactor = 1 - fraction * smax
diff --git a/profsea/components/global_/expansion.py b/profsea/components/global_/expansion.py
index c49be19..728d4bc 100644
--- a/profsea/components/global_/expansion.py
+++ b/profsea/components/global_/expansion.py
@@ -36,6 +36,6 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
             therm_std = therm_med * 0. # dummary variable
 
         therm_ens = z[:, np.newaxis] * therm_std + therm_med
-        expansion = np.tile(therm_ens, (state.nm, 1))
+        expansion = np.tile(therm_ens, (state.num_members, 1))
         
-        return expansion.reshape(state.nm * state.nt, state.nyr)
+        return expansion.reshape(state.num_members * state.nt, state.nyr)
diff --git a/profsea/components/global_/glacier.py b/profsea/components/global_/glacier.py
index 09459f9..687c543 100644
--- a/profsea/components/global_/glacier.py
+++ b/profsea/components/global_/glacier.py
@@ -61,11 +61,11 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
             raise KeyError("glaciermip must be False (AR5 parameters), 1 (Hock et al., 2019), or 2 (Marzeion et al., 2020)")
 
         ngl = len(glparm)
-        r = rng.standard_normal(state.nm)[:, np.newaxis, np.newaxis]
-        glacier = np.full((state.nm, state.nt, state.nyr), np.nan)
+        r = rng.standard_normal(state.num_members)[:, np.newaxis, np.newaxis]
+        glacier = np.full((state.num_members, state.nt, state.nyr), np.nan)
 
-        r_per_model = state.nm // ngl
-        r_remainder = state.nm % ngl
+        r_per_model = state.num_members // ngl
+        r_remainder = state.num_members % ngl
 
         # Precompute mgl and zgl for all glacier methods
         mgl_all = np.array(
diff --git a/profsea/components/global_/greenland.py b/profsea/components/global_/greenland.py
index 122a9a0..38d8412 100644
--- a/profsea/components/global_/greenland.py
+++ b/profsea/components/global_/greenland.py
@@ -13,9 +13,7 @@
 @functools.lru_cache(maxsize=1)
 def load_greenland_calibration():
     """Loads the CSV once and keeps it in memory."""
-    # path = Path(__file__).parent / "aux_data" / "ISMIP_GIS_calibration.csv"
-    # Path is actually relative to the project root, not the component file
-    path = Path(__file__).parent.parent / "aux_data" / "ISMIP_GIS_calibration.csv"
+    path = Path(__file__).parents[3] / "aux_data" / "ISMIP_GIS_calibration.csv"
     return pd.read_csv(path)
 
 
@@ -51,7 +49,11 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
         a_bound = (0.0 - trend_mean) / trend_std
         b_bound = (99999.9 - trend_mean) / trend_std  # Or just np.inf
         trend = truncnorm.ppf(
-            rng.random(state.nm), a=a_bound, b=b_bound, loc=trend_mean, scale=trend_std
+            rng.random(state.num_members),
+            a=a_bound,
+            b=b_bound,
+            loc=trend_mean,
+            scale=trend_std,
         )
         trend = trend[:, None] * time_delta[None, :]
         trend = trend[:, None, :]
@@ -71,10 +73,10 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
         sle = np.cumsum(dsle, axis=2)  # mm SLE per K of global warming
         sle = sle * 1e-3  # convert mm to m SLE
 
-        # Vectorized distribution of nm samples across the models
+        # Vectorized distribution of num_members samples across the models
         n_models = sle.shape[1]
-        r_per_model = state.nm // n_models
-        r_remainder = state.nm % n_models
+        r_per_model = state.num_members // n_models
+        r_remainder = state.num_members % n_models
 
         # Calculate exactly how many realizations each model should get
         counts = [
@@ -83,9 +85,9 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
 
         # Create an array of indices and expand sle
         model_indices = np.repeat(np.arange(n_models), counts)
-        sle_ens = sle[:, model_indices, :]  # Shape: (nt, nm, nyr)
+        sle_ens = sle[:, model_indices, :]  # Shape: (nt, num_members, nyr)
 
-        # Transpose to match the intended (nm, nt, nyr) shape
+        # Transpose to match the intended (num_members, nt, nyr) shape
         sle_ens = sle_ens.transpose(1, 0, 2)
 
         # Add the trend uncertainty
@@ -98,7 +100,7 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
                 rate[:, :, None] * time_delta[None, None, 1 : state.nyr - 94]
             )
 
-        sle_ens = sle_ens.reshape((state.nm * state.nt, state.nyr))
+        sle_ens = sle_ens.reshape((state.num_members * state.nt, state.nyr))
         return sle_ens
 
 
@@ -133,9 +135,9 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
         febound = [1, 1.15]  # bounds of uniform pdf of SMB elevation feedback factor
 
         # random log-normal factor
-        fn = np.exp(rng.standard_normal(state.nm) * fnlogsd)
+        fn = np.exp(rng.standard_normal(state.num_members) * fnlogsd)
         # elevation feedback factor
-        fe = rng.random(state.nm) * (febound[1] - febound[0]) + febound[0]
+        fe = rng.random(state.num_members) * (febound[1] - febound[0]) + febound[0]
         ff = fn * fe
 
         ztgreen = state.T_ens - dtgreen
diff --git a/profsea/components/global_/landwater.py b/profsea/components/global_/landwater.py
index 614fdd6..f935c94 100644
--- a/profsea/components/global_/landwater.py
+++ b/profsea/components/global_/landwater.py
@@ -13,9 +13,7 @@
 def load_landwater_projection():
     """Loads the NetCDF once and keeps the VALUES in memory."""
     path = (
-        Path(__file__).parent.parent
-        / "aux_data"
-        / "ssp_global_landwater_projections.nc"
+        Path(__file__).parents[3] / "aux_data" / "ssp_global_landwater_projections.nc"
     )
     with xr.open_dataset(path) as ds:
         ds.load()
@@ -48,16 +46,16 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
         del interp_ds
 
         # Make a Monte Carlo ensemble of projections
-        full_repeats = (state.nt * state.nm) // lw.shape[0]
-        remainder = (state.nt * state.nm) % lw.shape[0]
+        full_repeats = (state.nt * state.num_members) // lw.shape[0]
+        remainder = (state.nt * state.num_members) % lw.shape[0]
         lw = np.vstack([np.tile(lw, (full_repeats, 1)), lw[:remainder]])
-        lw = lw.reshape(state.nt * state.nm, lw.shape[1])
+        lw = lw.reshape(state.nt * state.num_members, lw.shape[1])
         lw = lw[:, 1 : state.nyr + 1]  # Start at 2006, end at end_yr
 
         return lw
 
-class LandwaterAR5(Component):
 
+class LandwaterAR5(Component):
     def __init__(self):
         self.startratemean = 0.38
         self.startratepm = 0.49 - 0.38
@@ -71,10 +69,14 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
         np.ndarray
             Land water storage contribution to GMSLR.
         """
-        
+
         # The rate at start is the one for 1993-2010 from the budget table.
         # The final amount is the mean for 2081-2100.
-        nyr = state.endofAR5 - 2081 + 1  # number of years of the time-mean of the final amount
+        nyr = (
+            state.endofAR5 - 2081 + 1
+        )  # number of years of the time-mean of the final amount
         final = [-0.01, 0.09]  # AR5
-        
-        return time_projection(state, self.startratemean, self.startratepm, final, rng, nfinal=nyr)
+
+        return time_projection(
+            state, self.startratemean, self.startratepm, final, rng, nfinal=nyr
+        )

From b64e2783a32b7616852fa0fd123106b7bc658f62 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Thu, 28 May 2026 23:44:14 +0100
Subject: [PATCH 172/276] Fix expansion

---
 profsea/components/global_/expansion.py | 7 -------
 1 file changed, 7 deletions(-)

diff --git a/profsea/components/global_/expansion.py b/profsea/components/global_/expansion.py
index 423f80c..351e160 100644
--- a/profsea/components/global_/expansion.py
+++ b/profsea/components/global_/expansion.py
@@ -42,12 +42,5 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
         # Efficiency shape: (nt, nm, 1)
         exp_efficiency_3d = exp_efficiency[:, :, None]
 
-<<<<<<< HEAD
         expansion = ohc_3d * exp_efficiency_3d
         return expansion.reshape(state.nm * state.nt, state.nyr)
-=======
-        therm_ens = z[:, np.newaxis] * therm_std + therm_med
-        expansion = np.tile(therm_ens, (state.num_members, 1))
-        
-        return expansion.reshape(state.num_members * state.nt, state.nyr)
->>>>>>> profsea-climate-v2

From 812cd13ddba952350babff6f872f9a88133bbbf0 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Thu, 28 May 2026 23:45:39 +0100
Subject: [PATCH 173/276] more fixes

---
 profsea/components/global_/glacier.py   |  8 -------
 profsea/components/global_/greenland.py | 30 -------------------------
 2 files changed, 38 deletions(-)

diff --git a/profsea/components/global_/glacier.py b/profsea/components/global_/glacier.py
index d3f9073..27e48e2 100644
--- a/profsea/components/global_/glacier.py
+++ b/profsea/components/global_/glacier.py
@@ -68,19 +68,11 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
             )
 
         ngl = len(glparm)
-<<<<<<< HEAD
         model_indices = rng.integers(0, ngl, size=(state.nt, state.nm))
 
         base_factors = np.array([p["factor"] for p in glparm])
         base_exponents = np.array([p["exponent"] for p in glparm])
         base_cvgls = np.array([p["cvgl"] for p in glparm])
-=======
-        r = rng.standard_normal(state.num_members)[:, np.newaxis, np.newaxis]
-        glacier = np.full((state.num_members, state.nt, state.nyr), np.nan)
-
-        r_per_model = state.num_members // ngl
-        r_remainder = state.num_members % ngl
->>>>>>> profsea-climate-v2
 
         factors = base_factors[model_indices][:, :, None]
         exponents = base_exponents[model_indices][:, :, None]
diff --git a/profsea/components/global_/greenland.py b/profsea/components/global_/greenland.py
index 343cfaa..767d4ec 100644
--- a/profsea/components/global_/greenland.py
+++ b/profsea/components/global_/greenland.py
@@ -61,15 +61,11 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
         a_bound = (0.0 - trend_mean) / trend_std
         b_bound = (99999.9 - trend_mean) / trend_std  # Or just np.inf
         trend = truncnorm.ppf(
-<<<<<<< HEAD
-            rng.random((nt, state.nm)), a=a_bound, b=b_bound, loc=trend_mean, scale=trend_std
-=======
             rng.random(state.num_members),
             a=a_bound,
             b=b_bound,
             loc=trend_mean,
             scale=trend_std,
->>>>>>> profsea-climate-v2
         )
         trend_sle = (trend[:, :, None] * time_delta[None, None, :]) * 1e-3
 
@@ -88,29 +84,7 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
         # Now integrate
         sle_ens = np.cumsum(dsle, axis=2) * 1e-3  # convert from mm to m
 
-<<<<<<< HEAD
         sle_ens += trend_sle
-=======
-        # Vectorized distribution of num_members samples across the models
-        n_models = sle.shape[1]
-        r_per_model = state.num_members // n_models
-        r_remainder = state.num_members % n_models
-
-        # Calculate exactly how many realizations each model should get
-        counts = [
-            r_per_model + 1 if i < r_remainder else r_per_model for i in range(n_models)
-        ]
-
-        # Create an array of indices and expand sle
-        model_indices = np.repeat(np.arange(n_models), counts)
-        sle_ens = sle[:, model_indices, :]  # Shape: (nt, num_members, nyr)
-
-        # Transpose to match the intended (num_members, nt, nyr) shape
-        sle_ens = sle_ens.transpose(1, 0, 2)
-
-        # Add the trend uncertainty
-        sle_ens += trend
->>>>>>> profsea-climate-v2
 
         # Persist 2100 rate of changeg
         if state.end_yr >= 2100:
@@ -119,13 +93,9 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
             sle_ens[:, :, idx_2100 + 1 :] = sle_ens[:, :, idx_2100 : idx_2100 + 1] + (
                 rate[:, :, None] * time_delta[None, None, 1 : state.nyr - idx_2100]
             )
-<<<<<<< HEAD
-        return sle_ens.reshape(nt * state.nm, state.nyr)
-=======
 
         sle_ens = sle_ens.reshape((state.num_members * state.nt, state.nyr))
         return sle_ens
->>>>>>> profsea-climate-v2
 
 
 class GreenlandSMBAR5(Component):

From 848c4416849868d4c440e398b93ebef742ca1a88 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Thu, 28 May 2026 23:47:00 +0100
Subject: [PATCH 174/276] landwater fixes

---
 profsea/components/global_/landwater.py | 17 ++---------------
 1 file changed, 2 insertions(+), 15 deletions(-)

diff --git a/profsea/components/global_/landwater.py b/profsea/components/global_/landwater.py
index 7674d2d..0203cf5 100644
--- a/profsea/components/global_/landwater.py
+++ b/profsea/components/global_/landwater.py
@@ -47,27 +47,14 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
         lw_base = interp_ds["sea_level_change"].values * 1e-3
         n_samples_nc = lw_base.shape[0]
 
-<<<<<<< HEAD
         # Sample!
-        sample_indices = rng.integers(0, n_samples_nc, size=(state.nt, state.nm))
+        sample_indices = rng.integers(0, n_samples_nc, size=(state.nt, state.num_members))
 
         # Resulting shape: (nt, nm, nyr)
         lw_ens = lw_base[sample_indices, 1 : state.nyr + 1]
-        return lw_ens.reshape(state.nt * state.nm, state.nyr)
-=======
-        # Make a Monte Carlo ensemble of projections
-        full_repeats = (state.nt * state.num_members) // lw.shape[0]
-        remainder = (state.nt * state.num_members) % lw.shape[0]
-        lw = np.vstack([np.tile(lw, (full_repeats, 1)), lw[:remainder]])
-        lw = lw.reshape(state.nt * state.num_members, lw.shape[1])
-        lw = lw[:, 1 : state.nyr + 1]  # Start at 2006, end at end_yr
->>>>>>> profsea-climate-v2
+        return lw_ens.reshape(state.nt * state.num_members, state.nyr)
 
 
-<<<<<<< HEAD
-=======
-
->>>>>>> profsea-climate-v2
 class LandwaterAR5(Component):
     def __init__(self):
         self.startratemean = 0.38

From 265a6238929d3d39127db25427125309bdb470c8 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Thu, 28 May 2026 23:48:48 +0100
Subject: [PATCH 175/276] fixes

---
 profsea/components/global_/expansion.py | 8 ++++----
 profsea/components/global_/glacier.py   | 6 +++---
 2 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/profsea/components/global_/expansion.py b/profsea/components/global_/expansion.py
index 351e160..20c0638 100644
--- a/profsea/components/global_/expansion.py
+++ b/profsea/components/global_/expansion.py
@@ -2,7 +2,7 @@
 
 from profsea.components.core.base import Component
 from profsea.components.core.state import ClimateState
-from profsea.utils import check_shapes, sample_members_2D
+from profsea.utils import check_shapes
 
 
 class ThermalExpansion(Component):
@@ -35,12 +35,12 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
         std_eff = 0.013 * self.distribution_scaler
 
         exp_efficiency = (
-            rng.normal(loc=mean_eff, scale=std_eff, size=(state.nt, state.nm)) * 1e-24
+            rng.normal(loc=mean_eff, scale=std_eff, size=(state.nt, state.num_members)) * 1e-24
         )  # m/YJ
 
         ohc_3d = self.OHC_change[:, None, :]
-        # Efficiency shape: (nt, nm, 1)
+        # Efficiency shape: (nt, num_members, 1)
         exp_efficiency_3d = exp_efficiency[:, :, None]
 
         expansion = ohc_3d * exp_efficiency_3d
-        return expansion.reshape(state.nm * state.nt, state.nyr)
+        return expansion.reshape(state.num_members * state.nt, state.nyr)
diff --git a/profsea/components/global_/glacier.py b/profsea/components/global_/glacier.py
index 27e48e2..ce6b387 100644
--- a/profsea/components/global_/glacier.py
+++ b/profsea/components/global_/glacier.py
@@ -68,7 +68,7 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
             )
 
         ngl = len(glparm)
-        model_indices = rng.integers(0, ngl, size=(state.nt, state.nm))
+        model_indices = rng.integers(0, ngl, size=(state.nt, state.num_members))
 
         base_factors = np.array([p["factor"] for p in glparm])
         base_exponents = np.array([p["exponent"] for p in glparm])
@@ -78,7 +78,7 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
         exponents = base_exponents[model_indices][:, :, None]
         cvgls = base_cvgls[model_indices][:, :, None]
 
-        r = rng.standard_normal((state.nt, state.nm))[:, :, None]
+        r = rng.standard_normal((state.nt, state.num_members))[:, :, None]
 
         T_int_ens_3d = state.T_int_ens[:, None, :]
         # Median shape: (1, 1, nyr)
@@ -95,7 +95,7 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
         np.clip(glacier, None, glmass, out=glacier)
 
         # 7. Flatten to standard 2D output for easy summation
-        return glacier.reshape(state.nt * state.nm, state.nyr)
+        return glacier.reshape(state.nt * state.num_members, state.nyr)
 
     def _project_glacier1(
         self, T_int: np.ndarray, factor: np.ndarray, exponent: np.ndarray

From 72d155d5c612634528ea176861a9d907431404a0 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Thu, 28 May 2026 23:53:08 +0100
Subject: [PATCH 176/276] Updates

---
 profsea/components/core/spatial_model.py | 16 +++++++++-------
 profsea/components/global_/antarctica.py |  6 ------
 profsea/components/global_/glacier.py    |  6 ------
 3 files changed, 9 insertions(+), 19 deletions(-)

diff --git a/profsea/components/core/spatial_model.py b/profsea/components/core/spatial_model.py
index 3e3be6c..5533684 100644
--- a/profsea/components/core/spatial_model.py
+++ b/profsea/components/core/spatial_model.py
@@ -30,6 +30,7 @@
     "https://zenodo.org/records/20427061/files/profsea-assets.zip?download=1"
 )
 
+
 class Spatial:
     """Spatial sea level rise component emulator."""
 
@@ -110,7 +111,7 @@ def __init__(
         # Log the size of each component and provide an estimate of their memory usage
         # Output shape will be (num_members, n_years, n_lats, n_lons)
         bytes_per_element = 8  # Assuming float64. Use 4 if strictly float32.
-        
+
         future_size = (
             self.num_members
             * self.n_years
@@ -355,9 +356,7 @@ def fetch_zenodo_fingerprints(
 
     # 1. Check if data already exists
     if target_dir.exists() and any(target_dir.iterdir()):
-        console.log(
-            f"[bold green]✓ Fingerprint data already found locally at {target_dir}[/bold green]"
-        )
+        console.log("[bold green]✓ ProFSea assets found locally![/bold green]")
         return
 
     # Create the base directory if it doesn't exist
@@ -402,12 +401,15 @@ def fetch_zenodo_fingerprints(
         with zipfile.ZipFile(zip_path, "r") as zip_ref:
             # Filter out the __MACOSX directory and its contents
             valid_members = [
-                member for member in zip_ref.namelist() 
+                member
+                for member in zip_ref.namelist()
                 if not member.startswith("__MACOSX/") and not member.startswith("._")
             ]
             zip_ref.extractall(data_dir, members=valid_members)
-            
-        console.log(f"[bold green]✓ Successfully extracted data to {data_dir}[/bold green]")
+
+        console.log(
+            f"[bold green]✓ Successfully extracted data to {data_dir}[/bold green]"
+        )
     except zipfile.BadZipFile:
         console.log(
             "[bold red]Error: Downloaded file is not a valid zip archive.[/bold red]"
diff --git a/profsea/components/global_/antarctica.py b/profsea/components/global_/antarctica.py
index 60946a5..7f748d3 100644
--- a/profsea/components/global_/antarctica.py
+++ b/profsea/components/global_/antarctica.py
@@ -1,5 +1,4 @@
 from pathlib import Path
-from rich.progress import track
 import numpy as np
 from scipy.signal import fftconvolve
 from scipy.stats import norm
@@ -76,7 +75,6 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
 
         preds = np.zeros((nm, n_time))
         tas_int = np.cumsum(tas, axis=1) * dt
-        physical_time = np.arange(n_time) * dt
 
         # Randomly assign an ISMIP6 model and residual draw to each ensemble member
         model_indices = rng.integers(0, self.n_models, size=nm)
@@ -106,10 +104,6 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
             beta = total_params[2]
             term_fast = beta * tas_int[i]
 
-            # Drift term
-            # drift_coeff = total_params[3]
-            # term_drift = drift_coeff * physical_time
-
             # Combine
             preds[i, :] = term_fast + term_slow
 
diff --git a/profsea/components/global_/glacier.py b/profsea/components/global_/glacier.py
index ce6b387..a4640c8 100644
--- a/profsea/components/global_/glacier.py
+++ b/profsea/components/global_/glacier.py
@@ -1,10 +1,4 @@
-import functools
-
-from pathlib import Path
 import numpy as np
-import pandas as pd
-import xarray as xr
-from scipy.stats import truncnorm
 
 from profsea.components.core.base import Component
 from profsea.components.core.global_model import ClimateState

From cb7fcca915988e9ae39fa1055fcd7cc6435e1294 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Fri, 29 May 2026 00:13:14 +0100
Subject: [PATCH 177/276] Update notebook

---
 .../tutorial_notebooks/worked_example.ipynb   | 239 ++++++++++--------
 1 file changed, 130 insertions(+), 109 deletions(-)

diff --git a/profsea/tutorial_notebooks/worked_example.ipynb b/profsea/tutorial_notebooks/worked_example.ipynb
index 45c04da..ea63a1f 100644
--- a/profsea/tutorial_notebooks/worked_example.ipynb
+++ b/profsea/tutorial_notebooks/worked_example.ipynb
@@ -1,56 +1,18 @@
 {
  "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "46a1a49f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from profsea.emulator import GMSLREmulator"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "52cdd115",
-   "metadata": {},
-   "source": [
-    "#### Introduction\n",
-    "The aim of this notebook is to break down the steps required to run the emulator version of ProFSea.\n",
-    "\n",
-    "All of this can't run in Jupyter Notebook but the steps should be clear enough to follow along!\n",
-    "\n",
-    "> If you want to use the CMIP6 patterns sterodynamic patterns instead of CMIP5, you need to run [this recipe](https://github.com/ESMValGroup/ESMValTool/blob/steric_patterns/esmvaltool/recipes/recipe_steric_patterns.yml) in ESMValTool. I recommend running with as much memory and processors as possible. \n",
-    "\n",
-    "> However, if you prefer not to go via ESMValTool feel free to reach out at gregory.munday\\@dtc.ox.ac.uk and I can just send them over (they're not large in terms of data size).\n",
-    "---\n",
-    "All other data required for the tool can be [downloaded here](https://catalogue.ceda.ac.uk/uuid/47c849b907414f3ab5e56ba9cf83e779/).\n",
-    "\n",
-    "I like keeping all my data in a `data/` folder next to the `profsea/` folder, which looks like this:\n",
-    "\n",
-    "  `data/` \\\n",
-    "   `cmip5/` \\\n",
-    "   `cmip6/` \\\n",
-    "   `gia_estimates/` \\\n",
-    "   `grd_fingerprints/` \\\n",
-    "   `monte_carlo_timeseries/` \\\n",
-    "   `uk_cmip_slope_coefficients/`\n",
-    "\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "ef69532c",
    "metadata": {},
    "source": [
     "#### Setting up the environment\n",
-    "The next step is to set up the conda environment. You should use the `ProFSea-emu-env.yml` environment file, building it with \n",
+    "The first step is to set up the conda environment. You should use the `profsea-env.yml` environment file, building it with \n",
     "\n",
     "```bash\n",
-    "conda env create -f ProFSea-emu-env.yml\n",
+    "conda env create -f profsea-env.yml\n",
     "```\n",
     "\n",
-    "and then activating with `conda activate profsea-emu`.\n",
+    "and then activating with `conda activate profsea`.\n",
     "\n",
     "---\n",
     "\n",
@@ -65,97 +27,156 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5616586f",
+   "id": "36f6320e",
    "metadata": {},
    "source": [
-    "#### Setup FaIR output\n",
-    "Run FaIR for the scenarios you want to simulate and save out the `global surface air temperature` and `ocean heat content`. You'll also need to calculate the 2015-2100 cumulative CO2 emissions and save it to a `.json` file. The CMIP6 cumulative emissions are included in the repo: \n",
-    "\n",
-    "```profsea/emulator/aux_data/cumulative_cmip6_emissions.json```\n",
+    "#### Importing components\n",
     "\n",
-    "Here's some sample code to calculate it from scratch in FaIR. Perhaps this could be included in this branch at some point.\n",
+    "The next step is to import all the model components you want to run simulations with - a full list is available in the documention [here](). This can be any combination of available `global` and `spatial` components, how you build your model is up to you!\n",
     "\n",
-    "```python\n",
-    "# Output cumulative emissions from 2015 to 2100\n",
-    "cum_emissions_dict = {}\n",
-    "for scenario in hist_gcam_ext.scenario.unique():  # loop over your scenarios\n",
-    "        emissions = hist_gcam_ext.loc[\n",
-    "            (hist_gcam_ext['scenario'] == scenario) & (hist_gcam_ext['variable'] == 'CO2 FFI'), \n",
-    "            '2015':'2100'\n",
-    "        ].to_numpy()[0]  # select anthro emissions\n",
-    "        cum_emissions = np.cumsum(emissions)\n",
-    "        cum_emissions = cum_emissions / 1000  # Convert to PgC\n",
-    "        print(f'Total cumulative carbon emissions from 2015 to 2100 for {scenario}: ', cum_emissions[-1])\n",
-    "        cum_emissions_dict[scenario] = cum_emissions[-1]\n",
-    "        # Save to json\n",
-    "        with open('ngfs_data/cumulative_scenario_emissions.json', 'w') as f:\n",
-    "            json.dump(cum_emissions_dict, f)\n",
-    "```\n",
+    "To generate a comprehensive set of sea-level projections, we'll run with all the components that contribute to sea-level change: \n",
+    "- thermal expansion\n",
+    "- glaciers\n",
+    "- greenland ice-sheet\n",
+    "- antarctic ice-sheet\n",
+    "- landwater \n",
     "\n",
-    "You need to do this because the current Antarctic Ice-Sheet dynamics component uses the relationship between cumulative emissions and sea-level contribution to calculate the lower and upper bounds of the projection. This was implemented to remove scenario-dependence from the GMSLR emulator, and should be redundant when the component is updated.\n",
-    "\n"
+    "In the example below, we'll run with the configuration being used for SLEIP and Munday et al. (2026), *in prep*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "868f2127",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from profsea.components.core.global_model import Global\n",
+    "from profsea.components.global_ import (\n",
+    "    LandwaterAR6,\n",
+    "    GreenlandAR6,\n",
+    "    ThermalExpansion,\n",
+    "    AntarcticaISMIP6,\n",
+    "    Glacier,\n",
+    ")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "6cef2ba4",
+   "id": "eced6e8d",
    "metadata": {},
    "source": [
-    "#### Running global → local ProFSea projections\n",
-    "Point your paths in the `user-settings-emu.yml` file to the right places, fill in your scenario names and you should be ready to go!\n",
-    "\n",
-    "Run: \n",
-    "\n",
-    "```bash\n",
-    "python spatial_projections.py\n",
-    "```\n",
+    "`ProFSea` can run with a single or an ensemble of temperature and ocean heat content forcing trajectories. The trajectories need to be anomalies, relative to some baseline period - `ProFSea` assumes by default this is `1996-2014`\n",
     "\n",
-    "The global projections will be saved to `data/runs/gmslr_output/`, while the spatialised projections should be saved to `data/runs//`\n",
-    "\n",
-    "The way this is accomplished (so that it runs on a laptop) is by taking and saving 101 percentiles of the GMSLR module output for each component. This can be updated via the `output_percentiles` keyword in the `GMSLR` class, currently line 458 in `spatial_projections.py`."
+    "Here we'll just run with an idealised temperature ramp up and stabilisation, and corresponding ocean heat content change, running from `2006` to `2300`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "7c4b26cd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
        "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "t_change = np.concatenate(\n",
+    "    [np.linspace(0.01, 8, 150), np.linspace(8, 8, 145)]\n",
+    ")\n",
+    "t_change = t_change[None, :] * np.random.normal(loc=1.0, scale=0.1, size=(100, 1)) # add some variability across members\n",
+    "ohc_change = t_change * 1e24 # in Joules\n",
+    "years = np.arange(2006, 2301)\n",
+    "years = np.broadcast_to(years, t_change.shape)\n",
+    "\n",
+    "plt.figure(figsize=(4, 2))\n",
+    "plt.plot(years, t_change, color=\"royalblue\", alpha=0.1)\n",
+    "plt.plot(years.mean(axis=0), t_change.mean(axis=0), color=\"royalblue\", label=\"GMST change\")\n",
+    "plt.xlabel(\"Year\")\n",
+    "plt.ylabel(\"GMST change (°C)\")\n",
+    "plt.show()\n",
+    "plt.close()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "84c2ef51",
+   "id": "c0375155",
    "metadata": {},
    "source": [
-    "You can also run the GMSLR module by itself:\n",
-    "\n",
-    "```python\n",
-    "from profsea.emulator import GMSLREmulator\n",
+    "#### Building the model\n",
     "\n",
-    "emulator = GMSLREmulator(\n",
-    "    T_change,  # your T_change anom from FaIR\n",
-    "    OHC_change,  # your OHC_change anom from FaIR\n",
-    "    'low-overshoot',  # your scenario name (str)\n",
-    "    2300,  #  projection end year\n",
-    "    palmer_method=palmer_method,  # recommended for runs beyond 2100\n",
-    "    input_ensemble=True,  # must be true if providing a complete FaIR ensemble\n",
-    "    output_percentiles=np.arange(101),  # list/array of percentiles to sample\n",
-    "    cum_emissions_total=1450)  # int, cumulative emissions in scenario from 2015-2100\n",
-    "\n",
-    "#  run the projections (auto-parallel)\n",
-    "emulator.project()\n",
-    "\n",
-    "# Save to desired folder with the name of the scenario\n",
-    "emulator.save_components(\n",
-    "    os.path.join(settings[\"baseoutdir\"], 'gmslr_output'),  # output dir\n",
-    "    scenario)  # scenario name\n",
-    "\n",
-    "# Lists the available components\n",
-    "emulator.list_components()\n",
-    "\n",
-    "# You can also access the data from each component directly:\n",
-    "emulator.greenland  # returns greenland projection ensemble\n",
-    "\n",
-    "```"
+    "To build your model, simply add your components to a dictionary, with whatever names you like, and pass it to the `Global()` class"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "a1c9b208",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "global_components = {\n",
+    "    \"landwater\": LandwaterAR6(),\n",
+    "    \"greenland\": GreenlandAR6(),\n",
+    "    \"expansion\": ThermalExpansion(ohc_change), # only the thermal expansion needs OHC change, so we'll pass it in here\n",
+    "    \"wais\": AntarcticaISMIP6(params_path=\"../aux_data/wais_params_expanded.nc\"),\n",
+    "    \"eais\": AntarcticaISMIP6(params_path=\"../aux_data/eais_params_expanded.nc\"),\n",
+    "    \"peninsula\": AntarcticaISMIP6(params_path=\"../aux_data/pen_params_expanded.nc\"),\n",
+    "    \"glacier\": Glacier(),\n",
+    "}\n",
+    "global_model = Global(components=global_components, end_yr=2301, num_members=1000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f9cf711",
+   "metadata": {},
+   "source": [
+    "now we're ready to run out simulation!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "84894d79",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'AntarcticaISMIP6' object has no attribute 'project'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mAttributeError\u001b[39m                            Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[10]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m projections = global_model.run(\n\u001b[32m      2\u001b[39m     T_change=t_change,\n\u001b[32m      3\u001b[39m     scenario=\u001b[33m\"stabilisation\"\u001b[39m,\n\u001b[32m      4\u001b[39m     member_seed=\u001b[32m42\u001b[39m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/global_model.py:148\u001b[39m, in \u001b[36mGlobal.run\u001b[39m\u001b[34m(self, scenario, T_change, member_seed)\u001b[39m\n\u001b[32m    145\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.parallel:\n\u001b[32m    146\u001b[39m     \u001b[38;5;28;01mwith\u001b[39;00m concurrent.futures.ThreadPoolExecutor() \u001b[38;5;28;01mas\u001b[39;00m executor:\n\u001b[32m    147\u001b[39m         futures = {\n\u001b[32m--> \u001b[39m\u001b[32m148\u001b[39m             executor.submit(\u001b[30;43mcomp\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mproject\u001b[39;49m, state, comp_rngs[name]): name\n\u001b[32m    149\u001b[39m             \u001b[38;5;28;01mfor\u001b[39;00m name, comp \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m.components.items()\n\u001b[32m    150\u001b[39m         }\n\u001b[32m    151\u001b[39m         \u001b[38;5;28;01mfor\u001b[39;00m future \u001b[38;5;129;01min\u001b[39;00m concurrent.futures.as_completed(futures):\n\u001b[32m    152\u001b[39m             comp_name = futures[future]\n",
+      "\u001b[31mAttributeError\u001b[39m: 'AntarcticaISMIP6' object has no attribute 'project'"
+     ]
+    }
+   ],
+   "source": [
+    "projections = global_model.run(\n",
+    "    T_change=t_change,\n",
+    "    scenario=\"stabilisation\",\n",
+    "    member_seed=42\n",
+    ")"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "cmip7",
+   "display_name": "profsea",
    "language": "python",
    "name": "python3"
   },
@@ -169,7 +190,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.12.13"
   }
  },
  "nbformat": 4,

From 03c925797c2d6b4ac0e9723ec9907e9960a17a1f Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Fri, 29 May 2026 00:19:25 +0100
Subject: [PATCH 178/276] Some more fixes

---
 profsea/components/global_/greenland.py       |  6 ++---
 profsea/components/global_/landwater.py       |  2 +-
 .../tutorial_notebooks/worked_example.ipynb   | 23 ++++---------------
 3 files changed, 9 insertions(+), 22 deletions(-)

diff --git a/profsea/components/global_/greenland.py b/profsea/components/global_/greenland.py
index 767d4ec..3bba81f 100644
--- a/profsea/components/global_/greenland.py
+++ b/profsea/components/global_/greenland.py
@@ -13,7 +13,7 @@
 @functools.lru_cache(maxsize=1)
 def load_greenland_calibration():
     """Loads the CSV once and keeps it in memory."""
-    path = Path(__file__).parents[3] / "aux_data" / "ISMIP_GIS_calibration.csv"
+    path = Path(__file__).parents[2] / "aux_data" / "ISMIP_GIS_calibration.csv"
     return pd.read_csv(path)
 
 
@@ -43,7 +43,7 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
         df = self.df
         n_models = len(df)
 
-        model_indices = rng.integers(0, n_models, size=(nt, state.nm))
+        model_indices = rng.integers(0, n_models, size=(nt, state.num_members))
 
         # Extract parameters and reshape to 3D: (nt, nm, 1)
         b0 = df["b0"].values[model_indices][:, :, None]
@@ -61,7 +61,7 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
         a_bound = (0.0 - trend_mean) / trend_std
         b_bound = (99999.9 - trend_mean) / trend_std  # Or just np.inf
         trend = truncnorm.ppf(
-            rng.random(state.num_members),
+            rng.random((nt, state.num_members)),
             a=a_bound,
             b=b_bound,
             loc=trend_mean,
diff --git a/profsea/components/global_/landwater.py b/profsea/components/global_/landwater.py
index 0203cf5..425829c 100644
--- a/profsea/components/global_/landwater.py
+++ b/profsea/components/global_/landwater.py
@@ -13,7 +13,7 @@
 def load_landwater_projection():
     """Loads the NetCDF once and keeps the VALUES in memory."""
     path = (
-        Path(__file__).parents[3] / "aux_data" / "ssp_global_landwater_projections.nc"
+        Path(__file__).parents[2] / "aux_data" / "ssp_global_landwater_projections.nc"
     )
     with xr.open_dataset(path) as ds:
         ds.load()
diff --git a/profsea/tutorial_notebooks/worked_example.ipynb b/profsea/tutorial_notebooks/worked_example.ipynb
index ea63a1f..1d9b60a 100644
--- a/profsea/tutorial_notebooks/worked_example.ipynb
+++ b/profsea/tutorial_notebooks/worked_example.ipynb
@@ -73,13 +73,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 2,
    "id": "7c4b26cd",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
        "
       ]
@@ -121,7 +121,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 3,
    "id": "a1c9b208",
    "metadata": {},
    "outputs": [],
@@ -148,23 +148,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "84894d79",
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "AttributeError",
-     "evalue": "'AntarcticaISMIP6' object has no attribute 'project'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
-      "\u001b[31mAttributeError\u001b[39m                            Traceback (most recent call last)",
-      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[10]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m projections = global_model.run(\n\u001b[32m      2\u001b[39m     T_change=t_change,\n\u001b[32m      3\u001b[39m     scenario=\u001b[33m\"stabilisation\"\u001b[39m,\n\u001b[32m      4\u001b[39m     member_seed=\u001b[32m42\u001b[39m\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/global_model.py:148\u001b[39m, in \u001b[36mGlobal.run\u001b[39m\u001b[34m(self, scenario, T_change, member_seed)\u001b[39m\n\u001b[32m    145\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.parallel:\n\u001b[32m    146\u001b[39m     \u001b[38;5;28;01mwith\u001b[39;00m concurrent.futures.ThreadPoolExecutor() \u001b[38;5;28;01mas\u001b[39;00m executor:\n\u001b[32m    147\u001b[39m         futures = {\n\u001b[32m--> \u001b[39m\u001b[32m148\u001b[39m             executor.submit(\u001b[30;43mcomp\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mproject\u001b[39;49m, state, comp_rngs[name]): name\n\u001b[32m    149\u001b[39m             \u001b[38;5;28;01mfor\u001b[39;00m name, comp \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m.components.items()\n\u001b[32m    150\u001b[39m         }\n\u001b[32m    151\u001b[39m         \u001b[38;5;28;01mfor\u001b[39;00m future \u001b[38;5;129;01min\u001b[39;00m concurrent.futures.as_completed(futures):\n\u001b[32m    152\u001b[39m             comp_name = futures[future]\n",
-      "\u001b[31mAttributeError\u001b[39m: 'AntarcticaISMIP6' object has no attribute 'project'"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "projections = global_model.run(\n",
     "    T_change=t_change,\n",

From 0279bd55ce2ebebf6dd4228199a07644488595ef Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Fri, 29 May 2026 00:58:26 +0100
Subject: [PATCH 179/276] Update fp paths, optimize AISISMIP6

---
 profsea/components/global_/antarctica.py      | 153 +++++++++++++-----
 profsea/components/spatial/fingerprint.py     |   3 +
 .../tutorial_notebooks/worked_example.ipynb   | 118 +++++++++++++-
 3 files changed, 235 insertions(+), 39 deletions(-)

diff --git a/profsea/components/global_/antarctica.py b/profsea/components/global_/antarctica.py
index 7f748d3..12880c6 100644
--- a/profsea/components/global_/antarctica.py
+++ b/profsea/components/global_/antarctica.py
@@ -9,7 +9,7 @@
 from profsea.components.core.time_projection import time_projection
 
 
-class AntarcticaISMIP6:
+class AntarcticaISMIP6(Component):
     """
     ISMIP6 2300 Antarctic ice-sheet emulator with two-timescale response.
 
@@ -58,56 +58,135 @@ def _impulse_response_term(
 
         return term_slow1 + term_slow2
 
+    def _precompute_delayed_rates(self, tas: np.ndarray, dt: float) -> tuple[np.ndarray, np.ndarray]:
+        """
+        Precomputes the cumulative delayed rates for all models across all trajectories.
+        Returns two arrays of shape (n_models, n_traj, n_time).
+        """
+        n_traj, n_time = tas.shape
+        cum_rate1 = np.zeros((self.n_models, n_traj, n_time))
+        cum_rate2 = np.zeros((self.n_models, n_traj, n_time))
+        t_arr = np.arange(n_time)
+
+        for m_idx in range(self.n_models):
+            tau1 = float(self.param_ds.tau1[m_idx].values)
+            tau2 = float(self.param_ds.tau2[m_idx].values)
+            gamma = float(self.param_ds.gamma[m_idx].values)
+
+            # Forcing base is model-specific due to gamma. Shape: (n_traj, n_time)
+            forcing_base = np.sign(tas) * (np.abs(tas) ** gamma)
+
+            # Decay factors broadcasted to 2D: (1, n_time)
+            df1 = ((t_arr * dt / tau1**2) * np.exp(-t_arr * dt / tau1) * dt)[np.newaxis, :]
+            df2 = ((t_arr * dt / tau2**2) * np.exp(-t_arr * dt / tau2) * dt)[np.newaxis, :]
+
+            # Vectorized convolution across all trajectories (axes=1)
+            rate_delayed1 = fftconvolve(forcing_base, df1, mode="full", axes=1)[:, :n_time]
+            cum_rate1[m_idx] = np.cumsum(rate_delayed1, axis=1) * dt
+
+            rate_delayed2 = fftconvolve(forcing_base, df2, mode="full", axes=1)[:, :n_time]
+            cum_rate2[m_idx] = np.cumsum(rate_delayed2, axis=1) * dt
+
+        return cum_rate1, cum_rate2
+
     def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
         """
         Projects AIS response using empirical additive bootstrapping aligned
         to the ClimateState ensemble size.
         """
-        tas = state.T_ens
-        if tas.ndim > 2:
-            tas = np.squeeze(tas)
-        if tas.ndim == 1:
-            tas = np.expand_dims(tas, axis=0)
-
+        tas = np.atleast_2d(np.squeeze(state.T_ens))
+        n_traj, n_time = tas.shape
         dt = 1.0
-        nm = tas.shape[0]
-        n_time = tas.shape[1]
+        nm = state.nt * state.num_members
 
-        preds = np.zeros((nm, n_time))
+        # 1. Precompute expensive convolutions for unique (Model x Trajectory) combos
+        cum_rate1, cum_rate2 = self._precompute_delayed_rates(tas, dt)
         tas_int = np.cumsum(tas, axis=1) * dt
 
-        # Randomly assign an ISMIP6 model and residual draw to each ensemble member
+        # 2. Generate random model/residual assignments for all members
         model_indices = rng.integers(0, self.n_models, size=nm)
         all_residuals = self.param_ds.param_residuals.values
         n_train_scenarios = all_residuals.shape[1]
         residual_indices = rng.integers(0, n_train_scenarios, size=nm)
 
-        for i in range(nm):
-            m_idx = model_indices[i]
-            r_idx = residual_indices[i]
-
-            # Extract assigned model parameters
-            tau1 = float(self.param_ds.tau1[m_idx].values)
-            tau2 = float(self.param_ds.tau2[m_idx].values)
-            gamma = float(self.param_ds.gamma[m_idx].values)
-
-            general_p = self.param_ds.general_params[m_idx].values
-            sampled_residuals = all_residuals[m_idx, r_idx, :]
-            total_params = general_p + sampled_residuals
-
-            # Slow response
-            term_slow = self._impulse_response_term(
-                tas[i], tau1, tau2, gamma, total_params, dt
-            )
-
-            # Fast response
-            beta = total_params[2]
-            term_fast = beta * tas_int[i]
-
-            # Combine
-            preds[i, :] = term_fast + term_slow
-
-        return preds
+        # 3. Create flat mapping array to match the (Trajectory x Member) layout
+        # This groups by trajectory: [Traj0_Mem0...Traj0_Mem999, Traj1_Mem0...]
+        t_indices = np.repeat(np.arange(n_traj), state.num_members) 
+        
+        # NOTE: If your required grouping is interleaved [Traj0_Mem0, Traj1_Mem0...]
+        # uncomment the line below instead:
+        # t_indices = np.tile(np.arange(n_traj), state.num_members)
+
+        # 4. Vectorized Parameter Extraction
+        general_p = self.param_ds.general_params.values[model_indices] 
+        sampled_residuals = all_residuals[model_indices, residual_indices, :]
+        total_params = general_p + sampled_residuals
+
+        # Slice with [:, 0:1] to maintain a 2D shape (nm, 1) for broadcasting against time
+        alpha1 = total_params[:, 0:1] 
+        alpha2 = total_params[:, 1:2]
+        beta = total_params[:, 2:3]
+
+        # 5. Final Vectorized Assembly
+        term_slow = (
+            alpha1 * cum_rate1[model_indices, t_indices] + 
+            alpha2 * cum_rate2[model_indices, t_indices]
+        )
+        term_fast = beta * tas_int[t_indices]
+
+        return term_slow + term_fast
+
+    # def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
+    #     """
+    #     Projects AIS response using empirical additive bootstrapping aligned
+    #     to the ClimateState ensemble size.
+    #     """
+    #     tas = state.T_ens
+    #     if tas.ndim > 2:
+    #         tas = np.squeeze(tas)
+    #     if tas.ndim == 1:
+    #         tas = np.expand_dims(tas, axis=0)
+
+    #     dt = 1.0
+    #     nm = state.nt * state.num_members
+    #     n_time = tas.shape[1]
+
+    #     preds = np.zeros((nm, n_time))
+    #     tas_int = np.cumsum(tas, axis=1) * dt
+
+    #     # Randomly assign an ISMIP6 model and residual draw to each ensemble member
+    #     model_indices = rng.integers(0, self.n_models, size=nm)
+    #     all_residuals = self.param_ds.param_residuals.values
+    #     n_train_scenarios = all_residuals.shape[1]
+    #     residual_indices = rng.integers(0, n_train_scenarios, size=nm)
+
+    #     for i in range(nm):
+    #         t_idx = i // state.num_members
+    #         m_idx = model_indices[i]
+    #         r_idx = residual_indices[i]
+
+    #         # Extract assigned model parameters
+    #         tau1 = float(self.param_ds.tau1[m_idx].values)
+    #         tau2 = float(self.param_ds.tau2[m_idx].values)
+    #         gamma = float(self.param_ds.gamma[m_idx].values)
+
+    #         general_p = self.param_ds.general_params[m_idx].values
+    #         sampled_residuals = all_residuals[m_idx, r_idx, :]
+    #         total_params = general_p + sampled_residuals
+
+    #         # Slow response
+    #         term_slow = self._impulse_response_term(
+    #             tas[t_idx], tau1, tau2, gamma, total_params, dt
+    #         )
+
+    #         # Fast response
+    #         beta = total_params[2]
+    #         term_fast = beta * tas_int[t_idx]
+
+    #         # Combine
+    #         preds[i, :] = term_fast + term_slow
+
+    #     return preds
 
 
 class AntarcticaDynAR5(Component):
diff --git a/profsea/components/spatial/fingerprint.py b/profsea/components/spatial/fingerprint.py
index 2f7e81c..634223d 100644
--- a/profsea/components/spatial/fingerprint.py
+++ b/profsea/components/spatial/fingerprint.py
@@ -22,7 +22,10 @@
         FP_DIR / "greensmb_slangen.nc",
         FP_DIR / "greensmb_spada.nc",
     ],
+    "greenland": [FP_DIR / "greenland_ar6.nc"],
     "landwater": [FP_DIR / "landwater_slangen.nc"],
+    "wais": [FP_DIR / "wais.nc"],
+    "eais": [FP_DIR / "eais.nc"],
     "antdyn": [
         FP_DIR / "antdyn_klemann.nc",
         FP_DIR / "antdyn_slangen.nc",
diff --git a/profsea/tutorial_notebooks/worked_example.ipynb b/profsea/tutorial_notebooks/worked_example.ipynb
index 1d9b60a..dfbe188 100644
--- a/profsea/tutorial_notebooks/worked_example.ipynb
+++ b/profsea/tutorial_notebooks/worked_example.ipynb
@@ -79,7 +79,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
        "
       ]
@@ -148,7 +148,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "84894d79",
    "metadata": {},
    "outputs": [],
@@ -157,8 +157,122 @@
     "    T_change=t_change,\n",
     "    scenario=\"stabilisation\",\n",
     "    member_seed=42\n",
+    ")\n",
+    "global_model.sum_components(projections)\n",
+    "gmslr = global_model.results[\"gmslr\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "578b6a8c",
+   "metadata": {},
+   "source": [
+    "visualise the global simulations!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "5b63dfb5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
        "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(4, 3))\n",
+    "lower = np.percentile(gmslr, 5, axis=0)\n",
+    "upper = np.percentile(gmslr, 95, axis=0)\n",
+    "plt.fill_between(years[0], lower, upper, color=\"lightcoral\", alpha=0.5, label=\"90% range\")\n",
+    "plt.plot(years[0], gmslr.mean(axis=0), color=\"darkred\")\n",
+    "plt.xlabel(\"Year\")\n",
+    "plt.ylabel(\"GMSLR (m)\")\n",
+    "plt.show()\n",
+    "plt.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "af51e3f9",
+   "metadata": {},
+   "source": [
+    "#### Building the `Spatial` model\n",
+    "\n",
+    "Start with the imports!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "47694def",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from profsea.components.core import Spatial\n",
+    "from profsea.components.spatial import (\n",
+    "    SterodynamicCMIP6,\n",
+    "    Fingerprint,\n",
+    "    GIA,\n",
     ")"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0a19cc97",
+   "metadata": {},
+   "source": [
+    "Now we'll build the model in the same way as the global model, but we'll pass the individual components' global projections to each spatial module. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "09f1fff4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "spatial_components = {\n",
+    "    \"sterodynamic\": SterodynamicCMIP6(\n",
+    "        projections[\"expansion\"], # passing in the global thermal expansion projection\n",
+    "    ),\n",
+    "    \"greenland\": Fingerprint(\n",
+    "        projections[\"greenland\"], fingerprint_component=\"greenland\"\n",
+    "    ),\n",
+    "    \"landwater\": Fingerprint(\n",
+    "        projections[\"landwater\"],\n",
+    "        fingerprint_component=\"landwater\",\n",
+    "    ),\n",
+    "    \"wais\": Fingerprint(\n",
+    "        projections[\"wais\"],\n",
+    "        fingerprint_component=\"wais\",\n",
+    "    ),\n",
+    "    \"eais\": Fingerprint(\n",
+    "        projections[\"eais\"],\n",
+    "        fingerprint_component=\"eais\",\n",
+    "    ),\n",
+    "    \"glacier\": Fingerprint(\n",
+    "        projections[\"glacier\"],\n",
+    "        fingerprint_component=\"glacier\",\n",
+    "    ),\n",
+    "    \"gia\": GIA(\n",
+    "        sample_spatial=False,\n",
+    "    ),\n",
+    "}\n",
+    "\n",
+    "# Pass to the spatial model\n",
+    "spatial_model = Spatial(components=spatial_components)\n",
+    "spatial_model.run(member_seed=42)\n",
+    "\n",
+    "spatial_model.sum_components(spatial_model.results)"
+   ]
   }
  ],
  "metadata": {

From 5fb15a1307dde5b832e599b86bb0676dfe4ecc64 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Fri, 29 May 2026 11:28:21 +0100
Subject: [PATCH 180/276] profsea/tutorial_notebooks/worked_example.ipynb

---
 profsea/components/spatial/gia.py | 34 +++++++++++--------------------
 1 file changed, 12 insertions(+), 22 deletions(-)

diff --git a/profsea/components/spatial/gia.py b/profsea/components/spatial/gia.py
index 98b09cd..fac7a32 100644
--- a/profsea/components/spatial/gia.py
+++ b/profsea/components/spatial/gia.py
@@ -21,38 +21,23 @@ class GIA(SpatialComponent):
 
     def __init__(
         self,
-        gia_paths: str | Path | list[str | Path] = None,
+        gia_dir: str | Path = None,
         sample_spatial: bool = False,
     ) -> None:
         """
         Parameters
         ----------
-        gia_paths: str, Path, or list of str/Path
-            Path to a single GIA file, a list of GIA files, or a directory containing GIA files.
+        gia_dir: str, Path, or list of str/Path
+            Path to a directory containing GIA files.
         sample_spatial: bool, optional
             Whether to sample spatial patterns probabilistically. Default is False.
         """
         self.sample_spatial = sample_spatial
 
-        if gia_paths is None:
-            self.gia_paths = list(GIA_DIR.glob("*.nc"))
-        elif isinstance(gia_paths, (str, Path)):
-            path_obj = Path(gia_paths)
-            if path_obj.is_dir():
-                self.gia_paths = list(path_obj.glob("*.nc"))
-            else:
-                self.gia_paths = [path_obj]
+        if gia_dir is None:
+            self.gia_dir = GIA_DIR
         else:
-            self.gia_paths = [Path(p) for p in gia_paths]
-
-        if not self.gia_paths:
-            raise FileNotFoundError(
-                f"No GIA NetCDF files found. Looked in: {GIA_DIR if gia_paths is None else gia_paths}"
-            )
-
-        for p in self.gia_paths:
-            if not p.exists():
-                raise FileNotFoundError(f"Missing GIA file: {p}")
+            self.gia_dir = Path(gia_dir)
 
         # Dummy property required by the base Spatial architecture
         self._global_projection = da.zeros((1, 1))
@@ -76,8 +61,13 @@ def _load_and_interpolate_rates(self, state: SpatialState) -> da.Array:
         da.Array
             A Dask array of shape (total_models, lat, lon) containing the regridded GIA rates.
         """
+        gia_paths = list(self.gia_dir.glob("*.nc"))
+
+        if not gia_paths:
+            raise FileNotFoundError(f"No GIA NetCDF files found in {self.gia_dir}")
+
         grids = []
-        for path in self.gia_paths:
+        for path in gia_paths:
             gia_da = xr.open_dataarray(path, chunks={"lat": 45, "lon": 45})
             interp_da = interpolate_to_grid(gia_da, state.grid_lats, state.grid_lons)
 

From c087e2338797e70c805b0523078baa0a34194fdf Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Fri, 29 May 2026 11:29:29 +0100
Subject: [PATCH 181/276] Fix GIA paths, update notebook

---
 .../tutorial_notebooks/worked_example.ipynb   | 212 +++++++++++++++++-
 1 file changed, 204 insertions(+), 8 deletions(-)

diff --git a/profsea/tutorial_notebooks/worked_example.ipynb b/profsea/tutorial_notebooks/worked_example.ipynb
index dfbe188..c3fc57b 100644
--- a/profsea/tutorial_notebooks/worked_example.ipynb
+++ b/profsea/tutorial_notebooks/worked_example.ipynb
@@ -79,7 +79,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
        "
       ]
@@ -148,7 +148,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 4,
    "id": "84894d79",
    "metadata": {},
    "outputs": [],
@@ -172,13 +172,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 5,
    "id": "5b63dfb5",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
        "
       ]
@@ -211,7 +211,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 6,
    "id": "47694def",
    "metadata": {},
    "outputs": [],
@@ -234,10 +234,102 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "09f1fff4",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
        [11:26:35] Initiating download from                                                            spatial_model.py:366\n",
+       "           https://zenodo.org/records/20427061/files/profsea-assets.zip?download=1...                              \n",
+       "
        \n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m[11:26:35]\u001b[0m\u001b[2;36m \u001b[0mInitiating download from                                                            \u001b]8;id=3056143;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/spatial_model.py\u001b\\\u001b[2mspatial_model.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=3056144;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/spatial_model.py#366\u001b\\\u001b[2m366\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[2;36m           \u001b[0m\u001b[4;94mhttps://zenodo.org/records/20427061/files/profsea-assets.zip?\u001b[0m\u001b[4;94mdownload\u001b[0m\u001b[4;94m=\u001b[0m\u001b[4;94m1\u001b[0m\u001b[4;94m...\u001b[0m          \u001b[2m                    \u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
        \n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
        [11:26:44] Extracting data...                                                                  spatial_model.py:399\n",
+       "
        \n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m[11:26:44]\u001b[0m\u001b[2;36m \u001b[0mExtracting data\u001b[33m...\u001b[0m                                                                  \u001b]8;id=3056150;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/spatial_model.py\u001b\\\u001b[2mspatial_model.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=3056151;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/spatial_model.py#399\u001b\\\u001b[2m399\u001b[0m\u001b]8;;\u001b\\\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
                   ✓ Successfully extracted data to                                                    spatial_model.py:410\n",
+       "           /Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea                                      \n",
+       "
        \n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[1;32m✓ Successfully extracted data to \u001b[0m                                                   \u001b]8;id=3056157;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/spatial_model.py\u001b\\\u001b[2mspatial_model.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=3056158;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/spatial_model.py#410\u001b\\\u001b[2m410\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[2;36m           \u001b[0m\u001b[1;32m/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/\u001b[0m\u001b[1;32mprofsea\u001b[0m                  \u001b[2m                    \u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
                   Baseline period = 1995 to 2014                                                      spatial_model.py:107\n",
+       "
        \n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mBaseline period = \u001b[1;36m1995\u001b[0m to \u001b[1;36m2014\u001b[0m                                                      \u001b]8;id=3056164;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/spatial_model.py\u001b\\\u001b[2mspatial_model.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=3056165;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/spatial_model.py#107\u001b\\\u001b[2m107\u001b[0m\u001b]8;;\u001b\\\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
                   Simulating 7 sea-level components...: sterodynamic, greenland, landwater, wais,     spatial_model.py:188\n",
+       "           eais, glacier, gia                                                                                      \n",
+       "
        \n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mSimulating \u001b[1;36m7\u001b[0m sea-level components\u001b[33m...\u001b[0m: sterodynamic, greenland, landwater, wais,     \u001b]8;id=3056171;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/spatial_model.py\u001b\\\u001b[2mspatial_model.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=3056172;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/spatial_model.py#188\u001b\\\u001b[2m188\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[2;36m           \u001b[0meais, glacier, gia                                                                  \u001b[2m                    \u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
        \n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "spatial_components = {\n",
     "    \"sterodynamic\": SterodynamicCMIP6(\n",
@@ -271,7 +363,111 @@
     "spatial_model = Spatial(components=spatial_components)\n",
     "spatial_model.run(member_seed=42)\n",
     "\n",
-    "spatial_model.sum_components(spatial_model.results)"
+    "total_rsl = spatial_model.sum_components(spatial_model.results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "79d407cb",
+   "metadata": {},
+   "source": [
+    "Saving the spatial projections is easy + quick using Zarr format: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "60d81a5b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
        \n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
        [11:27:28] ✓ Successfully saved Zarr: ./stabilisation_spatial_projection.zarr                  spatial_model.py:331\n",
+       "
        \n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m[11:27:28]\u001b[0m\u001b[2;36m \u001b[0m\u001b[1;32m✓ Successfully saved Zarr:\u001b[0m .\u001b[35m/\u001b[0m\u001b[95mstabilisation_spatial_projection.zarr\u001b[0m                  \u001b]8;id=3056178;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/spatial_model.py\u001b\\\u001b[2mspatial_model.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=3056179;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/spatial_model.py#331\u001b\\\u001b[2m331\u001b[0m\u001b]8;;\u001b\\\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
                   Output shape was (5, 295, 180, 360) (members, time, lat, lon)                       spatial_model.py:335\n",
+       "
        \n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mOutput shape was \u001b[1m(\u001b[0m\u001b[1;36m5\u001b[0m, \u001b[1;36m295\u001b[0m, \u001b[1;36m180\u001b[0m, \u001b[1;36m360\u001b[0m\u001b[1m)\u001b[0m \u001b[1m(\u001b[0mmembers, time, lat, lon\u001b[1m)\u001b[0m                       \u001b]8;id=3056185;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/spatial_model.py\u001b\\\u001b[2mspatial_model.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=3056186;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/spatial_model.py#335\u001b\\\u001b[2m335\u001b[0m\u001b]8;;\u001b\\\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "spatial_model.save_components(\n",
+    "    spatial_model.results, scenario_name=\"stabilisation\", output_format=\"zarr\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "e62d28d9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "
        "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import cartopy.crs as ccrs\n",
+    "\n",
+    "fig = plt.figure(figsize=(6, 4), layout=\"constrained\")\n",
+    "\n",
+    "total_rsl_med = total_rsl[2, -1, :, :]\n",
+    "vmax = np.nanmax(np.abs(total_rsl))\n",
+    "vmin = -vmax\n",
+    "\n",
+    "ax = fig.add_subplot(111, projection=ccrs.PlateCarree())\n",
+    "ax.set_title(\"50th Percentile, Year 2300\")\n",
+    "ax.pcolormesh(\n",
+    "    spatial_model.grid_lons,\n",
+    "    spatial_model.grid_lats,\n",
+    "    total_rsl_med,\n",
+    "    transform=ccrs.PlateCarree(),\n",
+    "    cmap=\"PuOr_r\",\n",
+    "    vmin=vmin,\n",
+    "    vmax=vmax,\n",
+    ")\n",
+    "ax.coastlines()\n",
+    "fig.colorbar(\n",
+    "    mappable=ax.collections[0],\n",
+    "    label=\"Relative Sea Level (m)\",\n",
+    "    orientation=\"horizontal\",\n",
+    "    pad=0.02,\n",
+    ")\n",
+    "plt.show()"
    ]
   }
  ],

From 94cd4f7c535da088919da6cd8afbaeadc5fffb5f Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Fri, 29 May 2026 12:40:16 +0100
Subject: [PATCH 182/276] Updated README.md

---
 README.md | 24 +++++++++++++++---------
 1 file changed, 15 insertions(+), 9 deletions(-)

diff --git a/README.md b/README.md
index 731e75a..12f5831 100644
--- a/README.md
+++ b/README.md
@@ -1,22 +1,28 @@
-# ProFSea-Climate
+# ProFSea 
 
 [![DOI](https://zenodo.org/badge/713453340.svg)](https://zenodo.org/doi/10.5281/zenodo.10255467)
 
-🚧   🚧   🚧   🚧   🚧   🚧   🚧   🚧   🚧   🚧   🚧   🚧   🚧   🚧   🚧   🚧   🚧 
        
-**Projections created using this branch are currently not fit for any use other than in the development of this repository.** 
        
-🚧   🚧   🚧   🚧   🚧   🚧   🚧   🚧   🚧   🚧   🚧   🚧   🚧   🚧   🚧   🚧   🚧
+ProFSea is sea-level rise simulator based on statistical emulations of physical modelling experiments and lines of evidence from the IPCC and across the literature. It's modular, easy to setup and self-contained - all you need is global mean surface temperature and ocean heat content forcing anomalies, using any baseline period, and you're good to go.
 
-## About
-This is the experimental science branch of ProFSea, dedicated to extending the capabilities of the existing ProFSea-tool. These extensions have not yet been published or peer-reviewed.
+We simulate sea-level change contributions from:
+- Antartic Ice Sheet 🇦🇶
+- Greenland Ice Sheet 🧊
+- Glacier melt 🏔️
+- Thermal expansion 🌡️
+- Landwater ⛰️
+
+ProFSea can calculate projections of both global mean sea-level rise and spatially-resolved sea-level change fields.
+
+## Developments
 
 Ongoing developments include:
 
 - [x] 🛠️ Full spatial field projections of regional sea-level change
 - [x] 🛠️ Use any input climate forcing to produce ProFSea projections
-- [ ] 🛠️ Updates to sea-level components based on the latest evidence in the literature
+- [x] 🛠️ Updates to sea-level components based on the latest evidence in the literature
 - [ ] 🛠️ Observational constraints to projection ensembles
-- [ ] 🛠️ Structural enhancements such as updated workflows, GitHub Actions and general reformatting
-- [ ] 🛠️ Full code documentation
+- [x] 🛠️ Structural enhancements such as updated workflows, GitHub Actions and general reformatting
+- [x] 🛠️ Full code documentation
 
 ## Contributors
 Several people have contributed to the development of the ProFSea tool and User Guide documentation, namely: Rachel Perks, Jacob Cheung, Benjamin Harrison, Katie Hodge, Mathew Palmer, Michael Sanderson, Hamish Steptoe, Jennifer Weeks and Gregory Munday.

From 8398ec85041332b8103192342a5ccba027d0ef96 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Fri, 29 May 2026 13:09:27 +0100
Subject: [PATCH 183/276] Add logo

---
 README.md |   6 ++++--
 logo.png  | Bin 0 -> 1843342 bytes
 2 files changed, 4 insertions(+), 2 deletions(-)
 create mode 100644 logo.png

diff --git a/README.md b/README.md
index 12f5831..e65db88 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,4 @@
-# ProFSea 
+# ProFSea 
 
 [![DOI](https://zenodo.org/badge/713453340.svg)](https://zenodo.org/doi/10.5281/zenodo.10255467)
 
@@ -11,7 +11,9 @@ We simulate sea-level change contributions from:
 - Thermal expansion 🌡️
 - Landwater ⛰️
 
-ProFSea can calculate projections of both global mean sea-level rise and spatially-resolved sea-level change fields.
+ProFSea can calculate projections of both global mean sea-level rise and spatially-resolved sea-level change fields:
+
+
 
 ## Developments
 
diff --git a/logo.png b/logo.png
new file mode 100644
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From 45d9032eab2017d591be1fbeb8c6ef71de1b9185 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Fri, 29 May 2026 13:24:54 +0100
Subject: [PATCH 184/276] Update readme

---
 README.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/README.md b/README.md
index e65db88..e4d2546 100644
--- a/README.md
+++ b/README.md
@@ -5,6 +5,7 @@
 ProFSea is sea-level rise simulator based on statistical emulations of physical modelling experiments and lines of evidence from the IPCC and across the literature. It's modular, easy to setup and self-contained - all you need is global mean surface temperature and ocean heat content forcing anomalies, using any baseline period, and you're good to go.
 
 We simulate sea-level change contributions from:
+
 - Antartic Ice Sheet 🇦🇶
 - Greenland Ice Sheet 🧊
 - Glacier melt 🏔️

From 9813b99745ba3e3ef115db68593ad1b2aaf27071 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Fri, 29 May 2026 13:47:28 +0100
Subject: [PATCH 185/276] Update Antarctica path loading

---
 profsea-logo.png                         | Bin 0 -> 679505 bytes
 profsea/components/global_/antarctica.py |  63 +++++++++++++++++------
 2 files changed, 46 insertions(+), 17 deletions(-)
 create mode 100644 profsea-logo.png

diff --git a/profsea-logo.png b/profsea-logo.png
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diff --git a/profsea/components/global_/antarctica.py b/profsea/components/global_/antarctica.py
index 12880c6..50dcfec 100644
--- a/profsea/components/global_/antarctica.py
+++ b/profsea/components/global_/antarctica.py
@@ -8,6 +8,15 @@
 from profsea.components.core.global_model import ClimateState
 from profsea.components.core.time_projection import time_projection
 
+PROFSEA_DIR = Path(__file__).resolve().parents[2]
+AUX_DATA_DIR = PROFSEA_DIR / "aux_data"
+
+PARAMS_MAP = {
+    "wais": AUX_DATA_DIR / "wais_params_expanded.nc",
+    "eais": AUX_DATA_DIR / "eais_params_expanded.nc",
+    "peninsula": AUX_DATA_DIR / "pen_params_expanded.nc",
+}
+
 
 class AntarcticaISMIP6(Component):
     """
@@ -26,7 +35,17 @@ class AntarcticaISMIP6(Component):
     different response characteristics to warming.
     """
 
-    def __init__(self, params_path: Path | str):
+    def __init__(self, ais_region: str):
+        """
+        Parameters
+        ----------
+        ais_region: str
+            The Antarctic region to model. Must be one of "wais", "eais", or "peninsula".
+        """
+        params_path = PARAMS_MAP.get(ais_region.lower())
+        if not params_path:
+            raise ValueError(f"Invalid region calibration: {ais_region}")
+
         self.param_ds = xr.load_dataset(params_path)
         self.n_models = self.param_ds.coords["model"].shape[0]
 
@@ -47,18 +66,20 @@ def _impulse_response_term(
 
         # decay_factors1 = np.exp(-np.arange(n_time) * dt / tau1) * (dt / tau1)
         t_arr = np.arange(n_time)
-        decay_factors1 = (t_arr * dt / tau1**2) * np.exp(-t_arr*dt/tau1) * dt
+        decay_factors1 = (t_arr * dt / tau1**2) * np.exp(-t_arr * dt / tau1) * dt
         rate_delayed1 = fftconvolve(forcing_base, decay_factors1, mode="full")[:n_time]
         term_slow1 = alpha1 * (np.cumsum(rate_delayed1) * dt)
 
         # decay_factors2 = np.exp(-np.arange(n_time) * dt / tau2) * (dt / tau2)
-        decay_factors2 = (t_arr * dt / tau2**2) * np.exp(-t_arr*dt/tau2) * dt
+        decay_factors2 = (t_arr * dt / tau2**2) * np.exp(-t_arr * dt / tau2) * dt
         rate_delayed2 = fftconvolve(forcing_base, decay_factors2, mode="full")[:n_time]
         term_slow2 = alpha2 * (np.cumsum(rate_delayed2) * dt)
 
         return term_slow1 + term_slow2
 
-    def _precompute_delayed_rates(self, tas: np.ndarray, dt: float) -> tuple[np.ndarray, np.ndarray]:
+    def _precompute_delayed_rates(
+        self, tas: np.ndarray, dt: float
+    ) -> tuple[np.ndarray, np.ndarray]:
         """
         Precomputes the cumulative delayed rates for all models across all trajectories.
         Returns two arrays of shape (n_models, n_traj, n_time).
@@ -77,14 +98,22 @@ def _precompute_delayed_rates(self, tas: np.ndarray, dt: float) -> tuple[np.ndar
             forcing_base = np.sign(tas) * (np.abs(tas) ** gamma)
 
             # Decay factors broadcasted to 2D: (1, n_time)
-            df1 = ((t_arr * dt / tau1**2) * np.exp(-t_arr * dt / tau1) * dt)[np.newaxis, :]
-            df2 = ((t_arr * dt / tau2**2) * np.exp(-t_arr * dt / tau2) * dt)[np.newaxis, :]
+            df1 = ((t_arr * dt / tau1**2) * np.exp(-t_arr * dt / tau1) * dt)[
+                np.newaxis, :
+            ]
+            df2 = ((t_arr * dt / tau2**2) * np.exp(-t_arr * dt / tau2) * dt)[
+                np.newaxis, :
+            ]
 
             # Vectorized convolution across all trajectories (axes=1)
-            rate_delayed1 = fftconvolve(forcing_base, df1, mode="full", axes=1)[:, :n_time]
+            rate_delayed1 = fftconvolve(forcing_base, df1, mode="full", axes=1)[
+                :, :n_time
+            ]
             cum_rate1[m_idx] = np.cumsum(rate_delayed1, axis=1) * dt
 
-            rate_delayed2 = fftconvolve(forcing_base, df2, mode="full", axes=1)[:, :n_time]
+            rate_delayed2 = fftconvolve(forcing_base, df2, mode="full", axes=1)[
+                :, :n_time
+            ]
             cum_rate2[m_idx] = np.cumsum(rate_delayed2, axis=1) * dt
 
         return cum_rate1, cum_rate2
@@ -111,26 +140,26 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
 
         # 3. Create flat mapping array to match the (Trajectory x Member) layout
         # This groups by trajectory: [Traj0_Mem0...Traj0_Mem999, Traj1_Mem0...]
-        t_indices = np.repeat(np.arange(n_traj), state.num_members) 
-        
+        t_indices = np.repeat(np.arange(n_traj), state.num_members)
+
         # NOTE: If your required grouping is interleaved [Traj0_Mem0, Traj1_Mem0...]
         # uncomment the line below instead:
         # t_indices = np.tile(np.arange(n_traj), state.num_members)
 
         # 4. Vectorized Parameter Extraction
-        general_p = self.param_ds.general_params.values[model_indices] 
+        general_p = self.param_ds.general_params.values[model_indices]
         sampled_residuals = all_residuals[model_indices, residual_indices, :]
         total_params = general_p + sampled_residuals
 
         # Slice with [:, 0:1] to maintain a 2D shape (nm, 1) for broadcasting against time
-        alpha1 = total_params[:, 0:1] 
+        alpha1 = total_params[:, 0:1]
         alpha2 = total_params[:, 1:2]
         beta = total_params[:, 2:3]
 
         # 5. Final Vectorized Assembly
         term_slow = (
-            alpha1 * cum_rate1[model_indices, t_indices] + 
-            alpha2 * cum_rate2[model_indices, t_indices]
+            alpha1 * cum_rate1[model_indices, t_indices]
+            + alpha2 * cum_rate2[model_indices, t_indices]
         )
         term_fast = beta * tas_int[t_indices]
 
@@ -280,9 +309,9 @@ def project(
         KoKg = [1.1, 0.2]  # ratio of Antarctic warming to global warming from G&H06
 
         # Generate a distribution of products of the above two factors
-        pcoKg = (pcoK[0] + rng.standard_normal([state.num_members, state.nt]) * pcoK[1]) * (
-            KoKg[0] + rng.standard_normal([state.num_members, state.nt]) * KoKg[1]
-        )
+        pcoKg = (
+            pcoK[0] + rng.standard_normal([state.num_members, state.nt]) * pcoK[1]
+        ) * (KoKg[0] + rng.standard_normal([state.num_members, state.nt]) * KoKg[1])
         meansmb = 1923  # model-mean time-mean 1979-2010 Gt yr-1 from 13.3.3.2
         moaoKg = (
             -pcoKg * 1e-2 * meansmb * self.mSLEoGt

From eac721306febfb88011001f7979b422737a8fec2 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Fri, 29 May 2026 13:50:02 +0100
Subject: [PATCH 186/276] Add updated workbook

---
 .../tutorial_notebooks/worked_example.ipynb   | 78 ++++++-------------
 1 file changed, 23 insertions(+), 55 deletions(-)

diff --git a/profsea/tutorial_notebooks/worked_example.ipynb b/profsea/tutorial_notebooks/worked_example.ipynb
index c3fc57b..bba35b8 100644
--- a/profsea/tutorial_notebooks/worked_example.ipynb
+++ b/profsea/tutorial_notebooks/worked_example.ipynb
@@ -79,7 +79,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
        "
       ]
@@ -130,9 +130,9 @@
     "    \"landwater\": LandwaterAR6(),\n",
     "    \"greenland\": GreenlandAR6(),\n",
     "    \"expansion\": ThermalExpansion(ohc_change), # only the thermal expansion needs OHC change, so we'll pass it in here\n",
-    "    \"wais\": AntarcticaISMIP6(params_path=\"../aux_data/wais_params_expanded.nc\"),\n",
-    "    \"eais\": AntarcticaISMIP6(params_path=\"../aux_data/eais_params_expanded.nc\"),\n",
-    "    \"peninsula\": AntarcticaISMIP6(params_path=\"../aux_data/pen_params_expanded.nc\"),\n",
+    "    \"wais\": AntarcticaISMIP6(ais_region=\"wais\"),\n",
+    "    \"eais\": AntarcticaISMIP6(ais_region=\"eais\"),\n",
+    "    \"peninsula\": AntarcticaISMIP6(ais_region=\"peninsula\"),\n",
     "    \"glacier\": Glacier(),\n",
     "}\n",
     "global_model = Global(components=global_components, end_yr=2301, num_members=1000)"
@@ -143,7 +143,7 @@
    "id": "7f9cf711",
    "metadata": {},
    "source": [
-    "now we're ready to run out simulation!"
+    "now we're ready to run our simulation..."
    ]
   },
   {
@@ -178,7 +178,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
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@@ -206,7 +206,7 @@
    "source": [
     "#### Building the `Spatial` model\n",
     "\n",
-    "Start with the imports!"
+    "Start with the imports:"
    ]
   },
   {
@@ -241,51 +241,11 @@
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      "data": {
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        [11:26:35] Initiating download from                                                            spatial_model.py:366\n",
-       "           https://zenodo.org/records/20427061/files/profsea-assets.zip?download=1...                              \n",
+       "
        [13:46:57] ✓ ProFSea assets found locally!                                                     spatial_model.py:359\n",
        "
        \n"
       ],
       "text/plain": [
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-       "\u001b[2;36m           \u001b[0m\u001b[4;94mhttps://zenodo.org/records/20427061/files/profsea-assets.zip?\u001b[0m\u001b[4;94mdownload\u001b[0m\u001b[4;94m=\u001b[0m\u001b[4;94m1\u001b[0m\u001b[4;94m...\u001b[0m          \u001b[2m                    \u001b[0m\n"
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-     "metadata": {},
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-     "data": {
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-       "
        [11:26:44] Extracting data...                                                                  spatial_model.py:399\n",
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-      ],
-      "text/plain": [
-       "\u001b[2;36m[11:26:44]\u001b[0m\u001b[2;36m \u001b[0mExtracting data\u001b[33m...\u001b[0m                                                                  \u001b]8;id=3056150;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/spatial_model.py\u001b\\\u001b[2mspatial_model.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=3056151;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/spatial_model.py#399\u001b\\\u001b[2m399\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "
                   ✓ Successfully extracted data to                                                    spatial_model.py:410\n",
-       "           /Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea                                      \n",
-       "
        \n"
-      ],
-      "text/plain": [
-       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[1;32m✓ Successfully extracted data to \u001b[0m                                                   \u001b]8;id=3056157;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/spatial_model.py\u001b\\\u001b[2mspatial_model.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=3056158;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/spatial_model.py#410\u001b\\\u001b[2m410\u001b[0m\u001b]8;;\u001b\\\n",
-       "\u001b[2;36m           \u001b[0m\u001b[1;32m/Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/\u001b[0m\u001b[1;32mprofsea\u001b[0m                  \u001b[2m                    \u001b[0m\n"
+       "\u001b[2;36m[13:46:57]\u001b[0m\u001b[2;36m \u001b[0m\u001b[1;32m✓ ProFSea assets found locally!\u001b[0m                                                     \u001b]8;id=9379629;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/spatial_model.py\u001b\\\u001b[2mspatial_model.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=9379630;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/spatial_model.py#359\u001b\\\u001b[2m359\u001b[0m\u001b]8;;\u001b\\\n"
       ]
      },
      "metadata": {},
@@ -298,7 +258,7 @@
        "
        \n"
       ],
       "text/plain": [
-       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mBaseline period = \u001b[1;36m1995\u001b[0m to \u001b[1;36m2014\u001b[0m                                                      \u001b]8;id=3056164;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/spatial_model.py\u001b\\\u001b[2mspatial_model.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=3056165;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/spatial_model.py#107\u001b\\\u001b[2m107\u001b[0m\u001b]8;;\u001b\\\n"
+       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mBaseline period = \u001b[1;36m1995\u001b[0m to \u001b[1;36m2014\u001b[0m                                                      \u001b]8;id=9379636;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/spatial_model.py\u001b\\\u001b[2mspatial_model.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=9379637;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/spatial_model.py#107\u001b\\\u001b[2m107\u001b[0m\u001b]8;;\u001b\\\n"
       ]
      },
      "metadata": {},
@@ -312,7 +272,7 @@
        "\n"
       ],
       "text/plain": [
-       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mSimulating \u001b[1;36m7\u001b[0m sea-level components\u001b[33m...\u001b[0m: sterodynamic, greenland, landwater, wais,     \u001b]8;id=3056171;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/spatial_model.py\u001b\\\u001b[2mspatial_model.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=3056172;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/spatial_model.py#188\u001b\\\u001b[2m188\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mSimulating \u001b[1;36m7\u001b[0m sea-level components\u001b[33m...\u001b[0m: sterodynamic, greenland, landwater, wais,     \u001b]8;id=9379643;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/spatial_model.py\u001b\\\u001b[2mspatial_model.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=9379644;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/spatial_model.py#188\u001b\\\u001b[2m188\u001b[0m\u001b]8;;\u001b\\\n",
        "\u001b[2;36m           \u001b[0meais, glacier, gia                                                                  \u001b[2m                    \u001b[0m\n"
       ]
      },
@@ -393,11 +353,11 @@
     {
      "data": {
       "text/html": [
-       "
        [11:27:28] ✓ Successfully saved Zarr: ./stabilisation_spatial_projection.zarr                  spatial_model.py:331\n",
+       "
        [13:47:42] ✓ Successfully saved Zarr: ./stabilisation_spatial_projection.zarr                  spatial_model.py:331\n",
        "
        \n"
       ],
       "text/plain": [
-       "\u001b[2;36m[11:27:28]\u001b[0m\u001b[2;36m \u001b[0m\u001b[1;32m✓ Successfully saved Zarr:\u001b[0m .\u001b[35m/\u001b[0m\u001b[95mstabilisation_spatial_projection.zarr\u001b[0m                  \u001b]8;id=3056178;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/spatial_model.py\u001b\\\u001b[2mspatial_model.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=3056179;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/spatial_model.py#331\u001b\\\u001b[2m331\u001b[0m\u001b]8;;\u001b\\\n"
+       "\u001b[2;36m[13:47:42]\u001b[0m\u001b[2;36m \u001b[0m\u001b[1;32m✓ Successfully saved Zarr:\u001b[0m .\u001b[35m/\u001b[0m\u001b[95mstabilisation_spatial_projection.zarr\u001b[0m                  \u001b]8;id=9379650;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/spatial_model.py\u001b\\\u001b[2mspatial_model.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=9379651;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/spatial_model.py#331\u001b\\\u001b[2m331\u001b[0m\u001b]8;;\u001b\\\n"
       ]
      },
      "metadata": {},
@@ -410,7 +370,7 @@
        "
        \n"
       ],
       "text/plain": [
-       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mOutput shape was \u001b[1m(\u001b[0m\u001b[1;36m5\u001b[0m, \u001b[1;36m295\u001b[0m, \u001b[1;36m180\u001b[0m, \u001b[1;36m360\u001b[0m\u001b[1m)\u001b[0m \u001b[1m(\u001b[0mmembers, time, lat, lon\u001b[1m)\u001b[0m                       \u001b]8;id=3056185;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/spatial_model.py\u001b\\\u001b[2mspatial_model.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=3056186;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/spatial_model.py#335\u001b\\\u001b[2m335\u001b[0m\u001b]8;;\u001b\\\n"
+       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mOutput shape was \u001b[1m(\u001b[0m\u001b[1;36m5\u001b[0m, \u001b[1;36m295\u001b[0m, \u001b[1;36m180\u001b[0m, \u001b[1;36m360\u001b[0m\u001b[1m)\u001b[0m \u001b[1m(\u001b[0mmembers, time, lat, lon\u001b[1m)\u001b[0m                       \u001b]8;id=9379657;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/spatial_model.py\u001b\\\u001b[2mspatial_model.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=9379658;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/spatial_model.py#335\u001b\\\u001b[2m335\u001b[0m\u001b]8;;\u001b\\\n"
       ]
      },
      "metadata": {},
@@ -423,6 +383,14 @@
     ")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "d8e3a1e6",
+   "metadata": {},
+   "source": [
+    "Plot the output! This takes about 90 seconds, since our data goes from lazy → in memory"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 9,
@@ -431,7 +399,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
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From e4cb0e5997e315c31ed4d68463ce763f3e1d2820 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Fri, 29 May 2026 13:56:02 +0100
Subject: [PATCH 187/276] Remove input_ensemble

---
 profsea/components/core/global_model.py       |   6 -
 profsea/probabilistic_wrapper.py              | 437 ------------------
 ..._wrapper.py => scm_profsea_experiments.py} |   0
 3 files changed, 443 deletions(-)
 delete mode 100644 profsea/probabilistic_wrapper.py
 rename profsea/{updated_wrapper.py => scm_profsea_experiments.py} (100%)

diff --git a/profsea/components/core/global_model.py b/profsea/components/core/global_model.py
index 07ed1c2..ded3fe2 100644
--- a/profsea/components/core/global_model.py
+++ b/profsea/components/core/global_model.py
@@ -32,10 +32,6 @@ class Global:
         Multiplier for the standard deviation in the input fields.
     parallel: bool
         If True, project SLR components in parallel.
-    input_ensemble: bool
-        If True, use an input ensemble of temperature and
-        ocean heat content change. if False, use a single timeseries of
-        temperature and ocean heat content change.
     output_percentiles: list|np.ndarray
         If not None, calculate percentiles from a 1D list/array for each
         component
@@ -66,7 +62,6 @@ def __init__(
         num_members: int = 1000,
         tcv: float = 1.0,
         parallel: bool = True,
-        input_ensemble: bool = True,
         output_percentiles: list | np.ndarray = None,
         palmer_method: bool = True,
         random_sample: bool = False,
@@ -77,7 +72,6 @@ def __init__(
         self.num_members = num_members
         self.tcv = tcv
         self.parallel = parallel
-        self.input_ensemble = input_ensemble
         self.output_percentiles = output_percentiles
         self.palmer_method = palmer_method
         self.random_sample = random_sample
diff --git a/profsea/probabilistic_wrapper.py b/profsea/probabilistic_wrapper.py
deleted file mode 100644
index d0b13f1..0000000
--- a/profsea/probabilistic_wrapper.py
+++ /dev/null
@@ -1,437 +0,0 @@
-import argparse
-import concurrent.futures
-import json
-from pathlib import Path
-
-from fair import FAIR
-from fair.io import read_properties
-from fair.interface import initialise
-import matplotlib.pyplot as plt
-import numpy as np
-import pandas as pd
-from rich_argparse import RichHelpFormatter
-from rich.console import Console
-from rich.progress import Progress
-import xarray as xr
-
-from profsea.emulator import Global
-from profsea.utils import sample_members_2D
-
-console = Console()
-
-worker_tas = None
-worker_ohc = None
-worker_emissions = None
-worker_scenarios = None
-worker_emulator = None
-
-def init_worker(tas, ohc, emissions, scenarios):
-    """Initialize worker with read-only shared data."""
-    global worker_tas, worker_ohc, worker_emissions, worker_scenarios, worker_emulator
-    worker_tas = tas.copy()
-    worker_ohc = ohc.copy()
-    worker_emissions = emissions.copy()
-    worker_scenarios = scenarios.copy()
-
-    worker_emulator = Global(
-        2301,
-        input_ensemble=True,
-        random_sample=True,
-        parallel=False 
-    )
-
-def index_df(df: pd.DataFrame, baseline_start: int, baseline_end: int) -> pd.DataFrame:
-    meta_cols = ['ensemble_member', 'scenario', 'region', 'variable', 'unit', 'climate_model']
-    existing_meta = [c for c in meta_cols if c in df.columns]
-    df_indexed = df.set_index(existing_meta)
-
-    years = df_indexed.columns.astype(str).str[:4].astype(int)
-    df_indexed.columns = years
-
-    mask_full = (years >= 1750) & (years <= 2300)
-    df_indexed = df_indexed.loc[:, mask_full]
-
-    years_sliced = df_indexed.columns
-    baseline_mask = (years_sliced >= baseline_start) & (years_sliced <= baseline_end)
-    
-    if not baseline_mask.any():
-        raise ValueError(f"No years found in baseline range {baseline_start}-{baseline_end}")
-
-    baseline_means = df_indexed.loc[:, baseline_mask].mean(axis=1)
-    df_anom = df_indexed.sub(baseline_means, axis=0)
-    years_final = df_anom.columns
-    mask_final = (years_final >= 2006) & (years_final <= 2300)
-    
-    df_final = df_anom.loc[:, mask_final].reset_index()
-    return df_final
-
-
-def df_to_arr(df, scenario_order):
-    meta_cols = ['ensemble_member', 'scenario', 'region', 'variable', 'unit', 'climate_model']
-    existing_meta = [c for c in meta_cols if c in df.columns]
-    
-    # Set the index (this creates a MultiIndex including 'scenario')
-    df = df.set_index(existing_meta)
-    array = []
-    for scenario in scenario_order:
-        try:
-            mask = df.index.get_level_values('scenario') == scenario
-            group_df = df.loc[mask]
-        except KeyError:
-             raise ValueError(f"Scenario '{scenario}' not found in the input DataFrame.")
-
-        if group_df.empty:
-             raise ValueError(f"No data found for scenario '{scenario}'")
-
-        # Sort by member to ensure internal alignment
-        group_sorted = group_df.sort_index(level='ensemble_member')
-        scenario_data = group_sorted.values
-        array.append(scenario_data)
-
-    array = np.stack(array, axis=0)
-    array = array.transpose(2, 0, 1)  # (n_scenarios, n_members, n_time)
-    return array
-
-
-def load_magicc_forcing(
-        input_path: str, scenarios: list,
-        baseline_start: int, baseline_end: int) -> tuple[np.ndarray]:
-    df = pd.read_csv(input_path, index_col=0)
-    
-    tas_condition = (
-        (df["variable"] == "Surface Air Temperature Change") & 
-        (df["scenario"].isin(scenarios))
-    )
-    ohc_condition = (
-        (df["variable"] == "Heat Content|Ocean") & 
-        (df["scenario"].isin(scenarios))
-    )
-    tas_df = df.loc[tas_condition]
-    ohc_df = df.loc[ohc_condition]
-
-    tas_df = index_df(tas_df, baseline_start, baseline_end)
-    ohc_df = index_df(ohc_df, baseline_start, baseline_end)
-    tas_arr = df_to_arr(tas_df, scenarios)  # shape (scenario, mem, time)
-    ohc_arr = df_to_arr(ohc_df, scenarios) * 1e21  # convert to J from ZJ
-    return tas_arr, ohc_arr
-
-
-def load_fair_forcing(
-        input_path: str, scenarios: list,
-        baseline_start: int, baseline_end: int) -> tuple[np.ndarray]:
-    tas_path = Path(input_path) / "tas.nc"
-    ohc_path = Path(input_path) / "ohc.nc"
-
-    tas = xr.load_dataarray(tas_path)
-    ohc= xr.load_dataarray(ohc_path)
-
-    tas_baseline = tas.loc[dict(timebounds=np.arange(baseline_start, baseline_end+1))].mean("timebounds")
-    ohc_baseline = ohc.loc[dict(timebounds=np.arange(baseline_start, baseline_end+1))].mean(["timebounds"])
-
-    tas = tas.loc[dict(timebounds=np.arange(2006, 2301))] - tas_baseline  # shape (294, 7, 841)
-    ohc = ohc.loc[dict(timebounds=np.arange(2006, 2301))] - ohc_baseline
-
-    tas = tas.sel(scenario=scenarios).transpose("scenario", "config", "timebounds")
-    ohc = ohc.sel(scenario=scenarios).transpose("scenario", "config", "timebounds")
-    return tas.values, ohc.values
-
-
-def run_fair(
-        baseline_start: int, 
-        baseline_end: int, 
-        scenarios: list, 
-        args: argparse.Namespace
-    ) -> tuple[np.ndarray]:
-    f = FAIR()
-    f.define_time(1750, 2300, 1)
-    f.define_scenarios(scenarios)
-    species, properties = read_properties('../data/fair/fair-parameters/species_configs_properties_1.4.1.csv')
-    f.define_species(species, properties)
-    f.ch4_method='Thornhill2021'
-    df_configs = pd.read_csv('../data/fair/fair-parameters/calibrated_constrained_parameters_1.4.1.csv', index_col=0)
-    f.define_configs(df_configs.index)
-    f.allocate()
-
-    if args.emissions_file:
-        f.fill_from_csv(
-            emissions_file=args.emissions_file,
-            forcing_file=args.forcing_file
-        )
-    else:
-        f.fill_from_rcmip()
-
-    f.fill_species_configs('../data/fair/fair-parameters/species_configs_properties_1.4.1.csv')
-    f.override_defaults('../data/fair/fair-parameters/calibrated_constrained_parameters_1.4.1.csv')
-    initialise(f.concentration, f.species_configs["baseline_concentration"])
-    initialise(f.forcing, 0)
-    initialise(f.temperature, 0)
-    initialise(f.cumulative_emissions, 0)
-    initialise(f.airborne_emissions, 0)
-    initialise(f.ocean_heat_content_change, 0)
-
-    f.run()
-
-    # Make anomaly with respect to baseline
-    tas_baseline = f.temperature.loc[dict(layer=0, timebounds=np.arange(baseline_start, baseline_end+1))].mean()
-    ohc_baseline = f.ocean_heat_content_change.loc[dict(timebounds=np.arange(baseline_start, baseline_end+1))].mean()
-
-    tas = f.temperature.loc[dict(layer=0, timebounds=np.arange(2006, 2301))] - tas_baseline  # shape (294, 7, 841)
-    ohc = f.ocean_heat_content_change.loc[dict(timebounds=np.arange(2006, 2301))] - ohc_baseline
-
-    tas = tas.sel(scenario=scenarios).transpose("scenario", "config", "timebounds")
-    ohc = ohc.sel(scenario=scenarios).transpose("scenario", "config", "timebounds")
-    return tas.values, ohc.values
-
-
-def simulation_task(random_idx: int):
-    """Runs the emulator for a single ensemble member across all scenarios."""
-    # Access data from global scope (initialized via init_worker)
-    tas_sampled = worker_tas[:, random_idx, :]
-    ohc_sampled = worker_ohc[:, random_idx, :]
-    results = {scenario: {} for scenario in worker_scenarios}
-
-    for idx, scenario in enumerate(worker_scenarios):
-        worker_emulator.project(
-            scenario,
-            np.expand_dims(tas_sampled[idx, :], axis=0),
-            np.expand_dims(ohc_sampled[idx, :], axis=0),
-        )
-
-        results[scenario]["gmslr"] = worker_emulator.gmslr
-        results[scenario]["expansion"] = worker_emulator.expansion
-        results[scenario]["antarctica"] = worker_emulator.antarctica
-        results[scenario]["wais"] = worker_emulator.wais
-        results[scenario]["eais"] = worker_emulator.eais
-        results[scenario]["greenland"] = worker_emulator.greenland_ar6
-        results[scenario]["glacier"] = worker_emulator.glacier
-        results[scenario]["landwater"] = worker_emulator.landwater
-    return results, random_idx
-
-
-def plot_samples(tas: np.ndarray, ohc: np.ndarray) -> None:
-    fig = plt.figure(figsize=(12, 6), layout="constrained")
-
-    ax = fig.add_subplot(121)
-    ax.plot(tas.T, color='seagreen', alpha=0.05)
-    ax.set_xlabel("Simulation years")
-    ax.set_ylabel("GMST ($\degree$C)")
-    ax.plot(np.arange(tas.shape[1]), np.median(tas, axis=0), color="black")
-
-    ax = fig.add_subplot(122)
-    ax.plot(ohc.T, color='seagreen', alpha=0.05)
-    ax.set_xlabel("Simulation years")
-    ax.set_ylabel("OHC (J)")
-
-    fig.savefig("forcing.png", dpi=200)
-    plt.show()
-    plt.close()
-
-
-def process_global_ensemble(components: list, percentiles: list, scenario: str) -> None:
-    for comp, data in components.items():
-        # Since the spatial projections won't change the ensemble order
-        # we can take percentiles here to pass to spatialise.py
-        sampled_ensemble = sample_members_2D(data, percentiles)
-        components[comp] = sampled_ensemble
-    return components
-
-
-def save_to_netcdf(components: dict, filename: str) -> None:
-    """
-    Saves the full ensemble results to a single NetCDF file.
-    
-    Structure:
-        Dimensions: (scenario, member, year)
-        Variables: gmslr, expansion, glaciers, etc.
-    """
-    scenarios = list(components.keys())
-    comp_names = list(components[scenarios[0]].keys())
-    sample = components[scenarios[0]][comp_names[0]]
-    n_member, n_time = sample.shape
-    
-    # Create coords
-    years = np.arange(2006, 2006 + n_time)
-    members = np.arange(n_member)
-    data_vars = {}
-    for comp in comp_names:
-        # Stack data across scenarios
-        # Resulting shape: (n_scenario, n_member, n_time)
-        stacked_data = np.stack([components[s][comp] for s in scenarios], axis=0)
-        data_vars[comp] = xr.DataArray(
-            data=stacked_data,
-            dims=["scenario", "member", "year"],
-            coords={
-                "scenario": scenarios,
-                "member": members,
-                "year": years
-            },
-            attrs={
-                "units": "m", 
-                "description": f"Sea level contribution from {comp}"
-            }
-        )
-        
-    ds = xr.Dataset(data_vars)
-    ds.attrs = {
-        "title": "ProFSea GMSLR Projections",
-        "source": "FAIR v2.2 + ProFSea Emulator",
-    }
-    
-    # Save with compression
-    encoding = {var: {"zlib": True, "complevel": 5} for var in data_vars}
-    ds.to_netcdf(filename, encoding=encoding)
-    console.log(f"Successfully saved full ensemble to {filename}")
-
-
-def plot_component(
-        ax: plt.Axes, component_dict: dict, component: str, 
-        scenarios: list, plot_legend: bool=False) -> None:
-    time = np.arange(2006, 2301)
-    scenario_colors = {
-        scenarios[0]: '#800000',
-        scenarios[1]: '#ff0000',
-        scenarios[2]: '#fc7b03',
-        scenarios[3]: '#d3a640',
-        scenarios[4]: '#098740',
-        scenarios[5]: '#0080d0',
-        scenarios[6]: '#100060',
-    }
-    for scenario in reversed(scenarios):
-        ax.fill_between(
-            time, 
-            component_dict[scenario][component][1], 
-            component_dict[scenario][component][3],  
-            color=scenario_colors[scenario], 
-            edgecolor='none',
-            alpha=0.3)
-        ax.plot(
-            time, 
-            component_dict[scenario][component][2], 
-            label=f"{scenario}", 
-            color=scenario_colors[scenario])
-
-    ax.set_xlabel("Year")
-    ax.set_ylabel("SLE (m)")
-    ax.set_title(component)
-    if plot_legend:
-        ax.legend(frameon=False, loc="upper left")
-
-
-def main(args):
-    # Set up for main loop
-    percentiles = [0, 5, 17, 50, 83, 95, 100]
-    # scenarios = ["ssp119", "ssp126", "ssp245", "ssp370", "ssp534-over", "ssp585"]
-    # scenarios = [
-    #     'RESCUE-Tier1-Extension-CB1700_2110_2.0',
-    #     'RESCUE-Tier1-Extension-CB1700_2150_1.5',
-    #     'RESCUE-Tier1-Extension-CB500st_2110_1.5']
-    if args.emissions_file:
-        scen_df = pd.read_csv(args.emissions_file)
-        scenarios = scen_df["scenario"].unique().tolist()
-
-    console.log(f"Using scenarios: {scenarios}")
-
-    if "ssp" in scenarios[0].lower():
-        emissions_path = args.cumulative_emissions_file
-        with open(emissions_path) as f:
-            cumulative_emissions = json.load(f)
-
-    baseline_start = 1995
-    baseline_end = 2014 # inclusive
-    
-    # Set up components data struct
-    components = {}
-    for scenario in scenarios:
-        components[scenario] = {
-            "gmslr": [], "expansion": [], "antarctica": [],
-            "wais": [], "eais": [], "greenland": [], "glacier": [], 
-            "landwater": []
-        }
-
-    # Enter 1000x loop
-    if args.input.lower() == "magicc":
-        tas, ohc = load_magicc_forcing(args.input_path, scenarios, baseline_start, baseline_end)
-    elif args.input.lower() == "fair":
-        tas, ohc = load_fair_forcing(args.input_path, scenarios, baseline_start, baseline_end)
-    elif args.input.lower() == "run_fair":
-        tas, ohc = run_fair(baseline_start, baseline_end, scenarios, args)
-    else:
-        raise ValueError("Input source not recognised.")
-
-    # Pre-generate the random ensemble member indices
-    # Shape is # (scenario, ens, time)
-    n_iterations = 1000
-    rng = np.random.default_rng()
-    random_indices = rng.integers(0, high=tas.shape[1], size=n_iterations)
-
-    sampled_tas = []
-    sampled_ohc = []
-
-    max_workers = 12
-    with concurrent.futures.ProcessPoolExecutor(
-        max_workers=max_workers,
-        initializer=init_worker,
-        initargs=(tas, ohc, cumulative_emissions, scenarios)
-    ) as exectutor:
-        futures = [exectutor.submit(simulation_task, idx) for idx in random_indices]
-
-        with Progress() as progress:
-            task = progress.add_task("[orange1]Simulating and sampling...", total=n_iterations)
-            for future in concurrent.futures.as_completed(futures):
-                result_dict, idx_returned = future.result()
-                sampled_tas.append(tas[:, idx_returned, :])
-                sampled_ohc.append(ohc[:, idx_returned, :])
-
-                # Now add the results into the components dictionary
-                for scenario in scenarios:
-                    for key in components[scenario].keys():
-                        components[scenario][key].append(result_dict[scenario][key])
-
-                progress.update(task, advance=1)
-
-    sampled_tas = np.asarray(sampled_tas) # shape (nens, time, nscen)
-    sampled_ohc = np.asarray(sampled_ohc)
-
-    plot_samples(sampled_tas[:, -1, :], sampled_ohc[:, -1, :])
-
-    # Convert to Numpy arrays
-    for scenario, data in components.items():
-        for key in data:
-            data[key] = np.array(data[key]).squeeze() # Now shape (nens, time)
-
-    sampled_components = {}
-    for scenario in scenarios:
-        sampled_components[scenario] = process_global_ensemble(
-            components[scenario], percentiles, scenario)
-
-    output_dir = Path(args.output_dir) / args.output_filename
-    save_to_netcdf(sampled_components, output_dir)
-    
-    fig = plt.figure(figsize=(16, 8), layout="constrained")
-    ax = fig.add_subplot(231)
-    plot_component(ax, sampled_components, "gmslr", scenarios, plot_legend=True)
-    ax = fig.add_subplot(232)
-    plot_component(ax, sampled_components, "expansion", scenarios)
-    ax = fig.add_subplot(233)
-    plot_component(ax, sampled_components, "glacier", scenarios)
-    ax = fig.add_subplot(234)
-    plot_component(ax, sampled_components, "antarctica", scenarios)
-    ax = fig.add_subplot(235)
-    plot_component(ax, sampled_components, "greenland", scenarios)
-    ax = fig.add_subplot(236)
-    plot_component(ax, sampled_components, "landwater", scenarios)
-
-    fig.savefig(f"{args.output_dir}{args.output_filename.replace('.nc', '_components.png')}", dpi=300)
-    plt.show()
-
-
-if __name__ == "__main__":
-    p = argparse.ArgumentParser(formatter_class=RichHelpFormatter)
-    p.add_argument("--input", default="run_fair", required=False, help="Input climate forcing source (default: FaIR)", type=str)
-    p.add_argument("--input_path", required=False, help="Path to input climate forcing", type=str)
-    p.add_argument("--emissions_file", required=False, help="Path to CSV file containing emissions scenarios (optional)", type=str)
-    p.add_argument("--forcing_file", required=False, help="Path to CSV file containing climate forcing time series (optional)", type=str)
-    p.add_argument("--output_dir", default="", help="Directory to save outputs (default: current directory)", type=str)
-    p.add_argument("--output_filename", default="gmslr_projections.nc", help="Filename for output NetCDF (default: gmslr_projections.nc)", type=str)
-    p.add_argument("--cumulative_emissions_file", default="cumulative_cmip6_emissions.json", help="Path to JSON file containing cumulative emissions for SSP scenarios (default: cumulative_cmip6_emissions.json)", type=str)
-    main(p.parse_args())
\ No newline at end of file
diff --git a/profsea/updated_wrapper.py b/profsea/scm_profsea_experiments.py
similarity index 100%
rename from profsea/updated_wrapper.py
rename to profsea/scm_profsea_experiments.py

From deca1cf84f7617c0984250699406918968d4fcf1 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Fri, 29 May 2026 13:59:00 +0100
Subject: [PATCH 188/276] Improve NetCDF saving

---
 profsea/components/core/spatial_model.py | 11 ++++-------
 1 file changed, 4 insertions(+), 7 deletions(-)

diff --git a/profsea/components/core/spatial_model.py b/profsea/components/core/spatial_model.py
index 5533684..572559d 100644
--- a/profsea/components/core/spatial_model.py
+++ b/profsea/components/core/spatial_model.py
@@ -284,6 +284,7 @@ def save_components(
         Path(output_dir).mkdir(parents=True, exist_ok=True)
 
         encoding = {}
+        # Sort out Zarr encoding
         if output_format == "zarr":
             import numcodecs
             from numcodecs.zarr3 import Blosc
@@ -292,28 +293,24 @@ def save_components(
                 cname="zstd", clevel=5, shuffle=numcodecs.Blosc.BITSHUFFLE
             )
 
-        # Loop through the xrDataarrays and add them to the single Dataset
+        # Set the encoding/compression for each variable based on the output format
         for name, component in components.items():
-            # Populate the encoding dictionary variable-by-variable
             if output_format == "netcdf":
-                encoding[name] = {"zlib": True, "complevel": 5, "dtype": "float32"}
+                encoding[name] = {"zlib": True, "complevel": 1, "dtype": "float32"}
             elif output_format == "zarr":
                 encoding[name] = {"compressor": compressor, "dtype": "float32"}
 
-        # Define output paths
         file_header = f"{scenario_name}_spatial_projection"
 
         # Stream the computation and write to disk
         if output_format == "netcdf":
             out_path = os.path.join(output_dir, f"{file_header}.nc")
 
-            # The spinner will animate while to_netcdf is blocking
             with console.status(
                 "[bold cyan]Computing and saving NetCDF...[/bold cyan]",
                 spinner="dots",
             ):
-                ds.compute()  # Compute before saving, for speed
-                ds.to_netcdf(out_path, encoding=encoding)
+                ds.compute().to_netcdf(out_path, encoding=encoding)
 
             console.log(
                 f"[bold green]✓ Successfully saved NetCDF:[/bold green] {out_path}"

From e4c5e74b5c6dc4572301bfe1fa58fa4aa187cec8 Mon Sep 17 00:00:00 2001
From: Greg Munday <100290135+gregrmunday@users.noreply.github.com>
Date: Fri, 29 May 2026 16:23:06 +0100
Subject: [PATCH 189/276] Update README with data source link

Added link to data for regionalisation module in README.
---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index e4d2546..fe64bdc 100644
--- a/README.md
+++ b/README.md
@@ -14,7 +14,7 @@ We simulate sea-level change contributions from:
 
 ProFSea can calculate projections of both global mean sea-level rise and spatially-resolved sea-level change fields:
 
-
+All the data required to run the regionalisation module can be found here: [![DOI](https://doi.org/badg/10.5281/20427061.svg)](https://doi.org/10.5281/zenodo.20427061)
 
 ## Developments
 

From 44b618b41f45a2b769b69cbfc5182b385a57b04f Mon Sep 17 00:00:00 2001
From: Greg Munday <100290135+gregrmunday@users.noreply.github.com>
Date: Mon, 1 Jun 2026 09:43:00 +0100
Subject: [PATCH 190/276] Fix DOI badge link in README.md

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index fe64bdc..88a27a1 100644
--- a/README.md
+++ b/README.md
@@ -14,7 +14,7 @@ We simulate sea-level change contributions from:
 
 ProFSea can calculate projections of both global mean sea-level rise and spatially-resolved sea-level change fields:
 
-All the data required to run the regionalisation module can be found here: [![DOI](https://doi.org/badg/10.5281/20427061.svg)](https://doi.org/10.5281/zenodo.20427061)
+All the data required to run the regionalisation module can be found here: [![DOI](https://doi.org/badge/10.5281/20427061.svg)](https://doi.org/10.5281/zenodo.20427061)
 
 ## Developments
 

From 92a32d35be35615cfbd0617fb2c50346a1a99ef7 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Mon, 1 Jun 2026 09:50:07 +0100
Subject: [PATCH 191/276] Remove old plotting script

---
 profsea/components/plotting.py | 174 ---------------------------------
 1 file changed, 174 deletions(-)
 delete mode 100644 profsea/components/plotting.py

diff --git a/profsea/components/plotting.py b/profsea/components/plotting.py
deleted file mode 100644
index b1e6931..0000000
--- a/profsea/components/plotting.py
+++ /dev/null
@@ -1,174 +0,0 @@
-"""
-Copyright (c) 2023, Met Office
-All rights reserved.
-"""
-import matplotlib.pyplot as plt
-import matplotlib
-import pandas as pd
-
-from profsea.plotting_libraries import (
-    calc_xlim, calc_ylim, plot_zeroline, multi_index_values,
-    extract_comp_sl, plot_tg_data, location_string, ukcp18_labels,
-    get_emulator_colors)
-
-
-def plot_slr(r_df_list, tg_name, nflag, flag, tg_years, 
-             non_missing, tg_amsl, site_name, scenarios, fig_dir):
-    """
-    Local sea level projections for specified RCPs - sum total, and annual
-    mean tide gauge observations.
-    :param r_df_list: regional sea level projections
-    :param tg_name: tide gauge name
-    :param nflag: number of flagged years
-    :param flag: flagged data
-    :param tg_years: tide gauge years
-    :param non_missing: boolean to indicate NaN values
-    :param tg_amsl: annual mean sea level data
-    :param site_name: site location
-    :param scenarios: emissions scenario
-    :param fig_dir:figure directory
-    """
-    # Get the values of the multi-index: years and percentile values
-    years, percentiles = multi_index_values(r_df_list)    
-
-    fig = plt.figure(figsize=(5, 4.5))
-    matplotlib.rcParams['font.size'] = 10
-    
-    scenario_colours = get_emulator_colors(len(scenarios))
-
-    # Draw regional sea level projections and uncertainties under each scenario
-    ax = fig.add_subplot(1, 1, 1)
-
-    for i, scen in enumerate(scenarios):
-        scen_col = next(scenario_colours)
-        # Get the sums of all components of local sea level projections
-        r_df = r_df_list[i]
-        rlow, rmid, rupp = extract_comp_sl(r_df, percentiles, 'sum')
-        ax.fill_between(years, rlow, rupp, alpha=0.3,
-                        color=scen_col, linewidth=0)
-        ax.plot(years, rmid, color=scen_col, label=scen)
-
-    # Calculate sensible x-axis range and y-axis range based on tide gauge data
-    # and projections
-    xlim = calc_xlim('tide', tg_years, years)
-    df_tmp = pd.DataFrame([rlow, rmid, rupp])
-    ylim = calc_ylim('tide', tg_amsl, df_tmp)
-
-    plot_zeroline(ax, xlim)
-
-    # Plot the annual mean sea levels from the tide gauge data
-    plot_tg_data(ax, nflag, flag, tg_years, non_missing, tg_amsl, tg_name)
-
-    ax.set_xlim(xlim)
-    ax.set_ylim(ylim)
-    ax.yaxis.set_ticks_position('both')
-    ax.yaxis.set_label_position('left')
-    ax.set_title(f'Local sea level - {location_string(site_name)}')
-    ax.set_ylabel('Sea Level Change (m)')
-    ax.set_xlabel('Year')
-    ax.legend(loc='upper left', frameon=False)
-
-    # Save the figure
-    fig.tight_layout()
-    ffile = f'{fig_dir}emulator_scenarios_SLR_{site_name}.png'
-    plt.savefig(ffile, dpi=300, format='png')
-    plt.close()
-
-
-def plot_slr_components(g_df_list, r_df_list, site_name, scenarios, fig_dir):
-    """
-    Subplot 1 - Global sea level projections for RCP8.5 - total and component
-    parts, including uncertainty range.
-    Subplot 2 - Local sea level projections for RCP8.5 - total and component
-    parts, including uncertainty range.
-    Same style as Figure 4 Howard and Palmer (2019).
-    :param g_df_list: global sea level projections
-    :param r_df_list: regional sea level projections
-    :param site_name: site location
-    :param scenarios: emission scenario
-    :param fig_dir: figure directory
-    """
-    # Get the values of the multi-index: years and percentile values
-    _, percentiles = multi_index_values(r_df_list)
-
-    # Calculate sensible x-axis range and y-axis range
-    xlim = ([-0.4, 7.4])
-    r_ylim = calc_ylim('proj', [], r_df_list)
-    g_ylim = calc_ylim('proj', [], g_df_list)
-    ylim = [min(r_ylim[0], g_ylim[0]), max(r_ylim[1], g_ylim[1])]
-
-    # -------------------------------------------------------------------------
-    # Global sea level projections, uncertainties and component parts
-    fig = plt.figure(figsize=(7.68*len(scenarios), 4.5*len(scenarios)))
-    matplotlib.rcParams['font.size'] = 9
-
-    for i, scen in enumerate(scenarios):
-        scenario_colors = get_emulator_colors(len(scenarios), get_all=True)
-        r_df = r_df_list[i]
-        g_df = g_df_list[i]
-        
-        ax_labels = []
-        ax = fig.add_subplot(len(scenarios), 2, (i*2)+1, label=f'Scenario_{i + 1}_Plot_1')
-        for cc, comp in enumerate(['sum', 'ocean', 'antnet', 'greennet', 'glacier',
-                                'landwater']):
-            # Get the components of regional sea level projections
-            clow_G, cmid_G, cupp_G = extract_comp_sl(g_df,
-                                                            percentiles, comp)
-
-            colour = next(scenario_colors)
-            label = ukcp18_labels()[comp]
-            
-            ax_labels.append(label)
-            xpts = [cc - 0.4, cc + 0.4]
-
-            ax.fill_between(xpts, clow_G[-1], cupp_G[-1],
-                            facecolor=colour, alpha=0.85, label=label)
-            ax.plot(xpts, [cmid_G[-1], cmid_G[-1]], linewidth=2.0,
-                    color=colour)
-
-        plot_zeroline(ax, xlim)
-        ax.set_xticks([0, 1, 2, 3, 4, 5])
-        ax.set_xticklabels(ax_labels, rotation=90)
-        ax.yaxis.set_ticks_position('both')
-        ax.yaxis.set_label_position('left')
-        ax.set_ylim(ylim)
-        ax.set_ylabel('Sea Level Change (m)')
-        ax.set_title(f'Global sea level - {scen}')
-
-        # -------------------------------------------------------------------------
-        # Second figure, regional sea level projections and uncertainties under
-        # RCP 8.5 and component parts
-        
-        scenario_colors = get_emulator_colors(len(scenarios), get_all=True)
-        bx = fig.add_subplot(len(scenarios), 2, (i*2)+2, label=f'Scenario_{i + 1}_Plot_2')
-        bx_labels = []
-        for cc, comp in enumerate(['sum', 'ocean', 'antnet', 'greennet', 'glacier',
-                                'landwater', 'gia']):
-            # Get the components of local sea level projections
-            clow_R, cmid_R, cupp_R = extract_comp_sl(r_df,
-                                                            percentiles, comp)
-
-            colour = next(scenario_colors)
-            label = ukcp18_labels()[comp]
-            
-            bx_labels.append(label)
-            xpts = [cc - 0.4, cc + 0.4]
-
-            bx.fill_between(xpts, clow_R[-1], cupp_R[-1], facecolor=colour,
-                            alpha=0.85, label=label)
-            bx.plot(xpts, [cmid_R[-1], cmid_R[-1]], linewidth=2.0,
-                    color=colour)
-
-        plot_zeroline(bx, xlim)
-        bx.set_xticks([0, 1, 2, 3, 4, 5, 6])
-        bx.set_xticklabels(bx_labels, rotation=90)
-        bx.yaxis.set_ticks_position('both')
-        bx.yaxis.set_label_position('left')
-        bx.set_ylim(ylim)
-        bx.set_title(f'Local sea level - {location_string(site_name)} - {scen}')
-
-    # Save the figure
-    fig.tight_layout()
-    outfile = f'{fig_dir}emulator_components_{site_name}.png'
-    plt.savefig(outfile, dpi=300, format='png')
-    plt.close()
\ No newline at end of file

From 6a425d65ce1a35aaf42ea1a0aa8fd68e2ac52825 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Mon, 1 Jun 2026 11:44:32 +0100
Subject: [PATCH 192/276] Remove old params

---
 profsea/aux_data/aispen_params.nc            |  Bin 33392 -> 0 bytes
 profsea/aux_data/eais_params.nc              |  Bin 33471 -> 0 bytes
 profsea/aux_data/ismip6_emu_evaluation.ipynb | 3261 ------------------
 profsea/aux_data/wais_params.nc              |  Bin 33445 -> 0 bytes
 4 files changed, 3261 deletions(-)
 delete mode 100644 profsea/aux_data/aispen_params.nc
 delete mode 100644 profsea/aux_data/eais_params.nc
 delete mode 100644 profsea/aux_data/ismip6_emu_evaluation.ipynb
 delete mode 100644 profsea/aux_data/wais_params.nc

diff --git a/profsea/aux_data/aispen_params.nc b/profsea/aux_data/aispen_params.nc
deleted file mode 100644
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diff --git a/profsea/aux_data/ismip6_emu_evaluation.ipynb b/profsea/aux_data/ismip6_emu_evaluation.ipynb
deleted file mode 100644
index cb7ad43..0000000
--- a/profsea/aux_data/ismip6_emu_evaluation.ipynb
+++ /dev/null
@@ -1,3261 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "5c0efc4e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "import glob \n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import xarray as xr\n",
-    "from scipy.spatial.distance import cdist\n",
-    "from profsea.emulator import AntarcticaISMIP6"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "d46b1a0e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loading Global Temperatures...\n",
-      "Loading Regional SLE Data...\n",
-      "  Processing Region 1...\n",
-      "  Processing Region 2...\n",
-      "  Processing Region 3...\n",
-      "Data loading complete.\n"
-     ]
-    }
-   ],
-   "source": [
-    "def sample_members_2D(array: np.ndarray, percentiles: list | np.ndarray) -> np.ndarray:\n",
-    "    \"\"\"Sample real ensemble members from a 2D numpy array.\"\"\"\n",
-    "    array_percentiles = np.percentile(array, percentiles, axis=0)\n",
-    "    distances = cdist(array_percentiles, array)\n",
-    "    mem_indices = np.argmin(distances, axis=1)\n",
-    "    return array[mem_indices]\n",
-    "\n",
-    "def load_global_temperature(path: str) -> np.array:\n",
-    "    ds = xr.open_mfdataset(path).tas\n",
-    "    ds = ds.groupby('time.year').mean('time')\n",
-    "    global_weights = np.cos(np.deg2rad(ds.lat))\n",
-    "    ds = ds.weighted(global_weights).mean(dim=['lat', 'lon']).compute()\n",
-    "    tas = ds.sel(year=slice(2015, 2299)).values\n",
-    "    tas -= tas[0]\n",
-    "    return tas\n",
-    "\n",
-    "def load_stable_temperature(path: str) -> np.array:\n",
-    "    tas = xr.open_mfdataset(path).tas.values\n",
-    "    tas -= tas[0]\n",
-    "    return tas\n",
-    "\n",
-    "def load_var(path: str, variable: str, subtract_control=True) -> np.array:\n",
-    "    control_files = sorted(glob.glob(\"../data/AIS/ctrlAE/sle_goelzer/*.nc\"))\n",
-    "    files = sorted(glob.glob(path))\n",
-    "    anoms = []\n",
-    "    \n",
-    "    for f_idx, file in enumerate(files):\n",
-    "        ds = xr.open_dataset(file, decode_times=False)[f'{variable}']\n",
-    "        ds = ds.sel(time=slice(2015, 2299)).values\n",
-    "        ds -= ds[0]\n",
-    "        \n",
-    "        if subtract_control:\n",
-    "            # Ensure we don't go out of bounds if fewer controls than files\n",
-    "            if f_idx < len(control_files):\n",
-    "                ctrl = xr.open_dataset(control_files[f_idx], decode_times=False)[f\"{variable}\"]\n",
-    "                ctrl = ctrl.sel(time=slice(2015, 2299)).values\n",
-    "                ctrl -= ctrl[0]\n",
-    "                ds -= ctrl\n",
-    "        \n",
-    "        anoms.append(ds)\n",
-    "    \n",
-    "    # Return array of shape (n_members, n_timesteps)\n",
-    "    return np.array(anoms)\n",
-    "\n",
-    "def get_model_name(path: str):\n",
-    "    return path.split(\"/\")[-1].split(\"sle_AIS_\")[-1].split(\"_exp\")[0]\n",
-    "\n",
-    "def get_common_models(ais_path: str, variable: str, model_pool: list) -> np.array:\n",
-    "    exp_paths = sorted(glob.glob(os.path.join(ais_path, \"expAE*\")))\n",
-    "\n",
-    "    for idx, exp in enumerate(exp_paths):\n",
-    "        models_path = os.path.join(exp, \"sle/*.nc\")\n",
-    "        model_exps = sorted(glob.glob(models_path))\n",
-    "        \n",
-    "        model_names = [get_model_name(path) for path in model_exps]\n",
-    "\n",
-    "        # check if name in common_models, if not, remove from common models\n",
-    "        for model in model_pool: \n",
-    "            if model not in model_names:\n",
-    "                model_pool.remove(model)\n",
-    "    return model_pool\n",
-    "\n",
-    "SCENARIOS_TRAIN = [\n",
-    "    (load_global_temperature, '../data/AIS/forcing/global/CESM2-WACCM/*.nc', 'expAE04'),       # High (585)\n",
-    "    (load_global_temperature, '../data/AIS/forcing/global/CCSM4/*.nc', 'expAE02'),             # High (85)\n",
-    "    (load_global_temperature, '../data/AIS/forcing/global/HadGEM2-ES/*.nc', 'expAE03'),        # High (85)\n",
-    "    (load_global_temperature, '../data/AIS/forcing/global/UKESM1-0-LL/*.nc', 'expAE05'),       # High (585)\n",
-    "    (load_stable_temperature, '../data/AIS/forcing/global/NorESM1-M_STABLE/tas_Amon_NorESM1-M_rcp26_stabilised.nc', 'expAE01'), # Low (26)\n",
-    "    (load_stable_temperature, '../data/AIS/forcing/global/UKESM1-0-LL_STABLE/*.nc', 'expAE06'), # Stable (585)\n",
-    "]\n",
-    "\n",
-    "SCENARIOS_TEST = [\n",
-    "    (load_stable_temperature, '../data/AIS/forcing/global/NorESM1-M_STABLE/tas_Amon_NorESM1-M_rcp85_stabilised.nc', 'expAE07'), # Stable (85)\n",
-    "    (load_stable_temperature, '../data/AIS/forcing/global/HadGEM2-ES_STABLE/*.nc', 'expAE08'), # Stable (85)\n",
-    "    (load_stable_temperature, '../data/AIS/forcing/global/CESM2-WACCM_STABLE/*.nc', 'expAE09'),# Stable (585)\n",
-    "    (load_global_temperature, '../data/AIS/forcing/global/UKESM1-0-LL_126/*.nc', 'expAE10'),   # Low (26)\n",
-    "]\n",
-    "\n",
-    "# 1. Load Temperature (Global - same for all regions)\n",
-    "print(\"Loading Global Temperatures...\")\n",
-    "tas_train_list = [loader(path) for loader, path, _ in SCENARIOS_TRAIN]\n",
-    "tas_test_list  = [loader(path) for loader, path, _ in SCENARIOS_TEST]\n",
-    "\n",
-    "# Calculate Integrated Temperature\n",
-    "tint_train_list = [np.cumsum(t) for t in tas_train_list]\n",
-    "tint_test_list  = [np.cumsum(t) for t in tas_test_list]\n",
-    "\n",
-    "# Convert TAS to numpy arrays (Shape: [n_scenarios, time])\n",
-    "tas_train_arr = np.array(tas_train_list)\n",
-    "tas_test_arr  = np.array(tas_test_list)\n",
-    "tint_train_arr = np.array(tint_train_list)\n",
-    "tint_test_arr  = np.array(tint_test_list)\n",
-    "\n",
-    "# 2. Load SLE per Region and Build Dictionaries\n",
-    "regions = [1, 2, 3]\n",
-    "train = {}\n",
-    "test = {}\n",
-    "\n",
-    "print(\"Loading Regional SLE Data...\")\n",
-    "for region in regions:\n",
-    "    var_name = f'sle_goelzer_region_{region}'\n",
-    "    print(f\"  Processing Region {region}...\")\n",
-    "\n",
-    "    # Load Train SLE\n",
-    "    sle_train_list = []\n",
-    "    for _, _, exp_id in SCENARIOS_TRAIN:\n",
-    "        path = f'../data/AIS/{exp_id}/sle_goelzer/*.nc'\n",
-    "        sle_data = load_var(path, variable=var_name)\n",
-    "        sle_train_list.append(sle_data)\n",
-    "    \n",
-    "    # Load Test SLE\n",
-    "    sle_test_list = []\n",
-    "    for _, _, exp_id in SCENARIOS_TEST:\n",
-    "        path = f'../data/AIS/{exp_id}/sle_goelzer/*.nc'\n",
-    "        sle_data = load_var(path, variable=var_name)\n",
-    "        sle_test_list.append(sle_data)\n",
-    "\n",
-    "    # Convert to object array or structured array depending on member count consistency\n",
-    "    # (Using np.array with dtype=object allows varying member counts per model if necessary)\n",
-    "    # If all models have same member count, you can remove dtype=object\n",
-    "    sle_train_arr = np.array(sle_train_list, dtype=object) \n",
-    "    sle_test_arr = np.array(sle_test_list, dtype=object)\n",
-    "\n",
-    "    # Populate Dictionaries\n",
-    "    train[region] = {\n",
-    "        'tas': tas_train_arr,\n",
-    "        't_int': tint_train_arr,\n",
-    "        'sle': sle_train_arr\n",
-    "    }\n",
-    "\n",
-    "    test[region] = {\n",
-    "        'tas': tas_test_arr,\n",
-    "        't_int': tint_test_arr,\n",
-    "        'sle': sle_test_arr\n",
-    "    }\n",
-    "\n",
-    "model_list = sorted(glob.glob('../data/AIS/expAE04/sle_goelzer/*.nc'))\n",
-    "model_names = [item.split(\"/\")[-1].split(\"sle_goelzer_AIS_\")[-1].split(\"_exp\")[0] for item in model_list]\n",
-    "common_models = get_common_models(\"../data/AIS/\", \"sle\", model_names.copy())\n",
-    "common_idx = [model_names.index(item) for item in common_models if item in model_names]\n",
-    "\n",
-    "print(\"Data loading complete.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "2ad4bbea",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
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-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
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-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
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-      "(1, 285)\n",
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-      "(1, 285)\n",
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-      "(1, 285)\n",
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-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
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-      "(1, 285)\n",
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-      "(1, 285)\n",
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-      "(1000,)\n",
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-      "(1, 285)\n",
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-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
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-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n",
-      "(1000,)\n",
-      "(1, 285)\n"
-     ]
-    }
-   ],
-   "source": [
-    "wa_ais = AntarcticaISMIP6(\"wa_params_JAN.nc\")\n",
-    "ea_ais = AntarcticaISMIP6(\"ea_params_JAN.nc\")\n",
-    "pen_ais = AntarcticaISMIP6(\"pen_params_JAN.nc\")\n",
-    "\n",
-    "# The region number doesn't matter for tas and t_int\n",
-    "wa_train_preds = [wa_ais.predict(train[1][\"tas\"][scen], train[1][\"t_int\"][scen], display_progress=False) for scen in range(train[1][\"tas\"].shape[0])]\n",
-    "ea_train_preds = [ea_ais.predict(train[1][\"tas\"][scen], train[1][\"t_int\"][scen], display_progress=False) for scen in range(train[1][\"tas\"].shape[0])]\n",
-    "pen_train_preds = [pen_ais.predict(train[1][\"tas\"][scen], train[1][\"t_int\"][scen], display_progress=False) for scen in range(train[1][\"tas\"].shape[0])]\n",
-    "\n",
-    "wa_test_preds = [wa_ais.predict(test[1][\"tas\"][scen], test[1][\"t_int\"][scen], display_progress=False) for scen in range(test[1][\"tas\"].shape[0])]\n",
-    "ea_test_preds = [ea_ais.predict(test[1][\"tas\"][scen], test[1][\"t_int\"][scen], display_progress=False) for scen in range(test[1][\"tas\"].shape[0])]\n",
-    "pen_test_preds = [pen_ais.predict(test[1][\"tas\"][scen], test[1][\"t_int\"][scen], display_progress=False) for scen in range(test[1][\"tas\"].shape[0])]\n",
-    "\n",
-    "wa_test_preds = [wa_test_preds[scen][common_idx] for scen in range(test[1][\"tas\"].shape[0])]\n",
-    "ea_test_preds = [ea_test_preds[scen][common_idx] for scen in range(test[1][\"tas\"].shape[0])]\n",
-    "pen_test_preds = [pen_test_preds[scen][common_idx] for scen in range(test[1][\"tas\"].shape[0])]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5b4588b5",
-   "metadata": {},
-   "source": [
-    "Probably want a plot that shows predictions vs training for each scenario, for each region, and added up. Then same for testing.\n",
-    "\n",
-    "We also want to see the same for the ice-sheet collapse simulations. \n",
-    "\n",
-    "Something numerical? RMSE?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "e7a8caf0",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "
        "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig = plt.figure(figsize=(16, 6), layout=\"constrained\")\n",
-    "fig.suptitle(\"ISMIP6 emulator | WAIS | Training fits\")\n",
-    "for scen, _ in enumerate(wa_train_preds):\n",
-    "    ax = fig.add_subplot(2, 3, scen+1)\n",
-    "    p5 = np.percentile(wa_train_preds[scen], 5, axis=(0, 1))\n",
-    "    p17 = np.percentile(wa_train_preds[scen], 17, axis=(0, 1))\n",
-    "    median = np.median(wa_train_preds[scen], axis=(0, 1))\n",
-    "    p83 = np.percentile(wa_train_preds[scen], 83, axis=(0, 1))\n",
-    "    p95 = np.percentile(wa_train_preds[scen], 95, axis=(0, 1))\n",
-    "\n",
-    "    true_median = np.percentile(train[1][\"sle\"][scen], 50, axis=0)\n",
-    "\n",
-    "    time_axis = np.arange(len(median))\n",
-    "\n",
-    "    # Plot Emulator Uncertainty\n",
-    "    ax.fill_between(time_axis, p5, p95, color='skyblue', alpha=0.2, label='5-95% Confidence')\n",
-    "    ax.fill_between(time_axis, p17, p83, color='skyblue', alpha=0.4, label='17-83% Confidence')\n",
-    "\n",
-    "\n",
-    "    ax.plot(np.tile(time_axis, (train[1][\"sle\"][scen].shape[0], 1)).T, train[1][\"sle\"][scen].T, '--', lw=1, color=\"black\", alpha=0.4, label=\"ISMIP6 models\")\n",
-    "    ax.plot(time_axis, true_median, lw=2, color='black', label=\"ISMIP6 median\")\n",
-    "    ax.plot(time_axis, median, color='skyblue', lw=3, label='Emulator Median')\n",
-    "\n",
-    "    ax.set_xlabel(\"Time (years)\")\n",
-    "    ax.set_ylabel(\"Sea Level Equivalent (m)\")\n",
-    "\n",
-    "    if scen == 0:\n",
-    "        handles, labels = ax.get_legend_handles_labels()\n",
-    "        unique_labels = set(labels)\n",
-    "        handles = [handles[labels.index(label)] for label in unique_labels if label in labels]\n",
-    "        ax.legend(handles, unique_labels, frameon=False, loc='upper left')\n",
-    "\n",
-    "    ax.grid(True, alpha=0.3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "7eae8edd",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "
        "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig = plt.figure(figsize=(16, 6), layout=\"constrained\")\n",
-    "fig.suptitle(\"ISMIP6 emulator | EAIS | Training fits\")\n",
-    "for scen, _ in enumerate(ea_train_preds):\n",
-    "    ax = fig.add_subplot(2, 3, scen+1)\n",
-    "    p5 = np.percentile(ea_train_preds[scen], 5, axis=(0, 1))\n",
-    "    p17 = np.percentile(ea_train_preds[scen], 17, axis=(0, 1))\n",
-    "    median = np.median(ea_train_preds[scen], axis=(0, 1))\n",
-    "    p83 = np.percentile(ea_train_preds[scen], 83, axis=(0, 1))\n",
-    "    p95 = np.percentile(ea_train_preds[scen], 95, axis=(0, 1))\n",
-    "\n",
-    "    true_median = np.percentile(train[2][\"sle\"][scen], 50, axis=0)\n",
-    "\n",
-    "    time_axis = np.arange(len(median))\n",
-    "\n",
-    "    # Plot Emulator Uncertainty\n",
-    "    ax.fill_between(time_axis, p5, p95, color='skyblue', alpha=0.2, label='5-95% Confidence')\n",
-    "    ax.fill_between(time_axis, p17, p83, color='skyblue', alpha=0.4, label='17-83% Confidence')\n",
-    "\n",
-    "\n",
-    "    ax.plot(np.tile(time_axis, (train[2][\"sle\"][scen].shape[0], 1)).T, train[2][\"sle\"][scen].T, '--', lw=1, color=\"black\", alpha=0.4, label=\"ISMIP6 models\")\n",
-    "    ax.plot(time_axis, true_median, lw=2, color='black', label=\"ISMIP6 median\")\n",
-    "    ax.plot(time_axis, median, color='skyblue', lw=3, label='Emulator Median')\n",
-    "\n",
-    "    ax.set_xlabel(\"Time (years)\")\n",
-    "    ax.set_ylabel(\"Sea Level Equivalent (m)\")\n",
-    "\n",
-    "    if scen == 0:\n",
-    "        handles, labels = ax.get_legend_handles_labels()\n",
-    "        unique_labels = set(labels)\n",
-    "        handles = [handles[labels.index(label)] for label in unique_labels if label in labels]\n",
-    "        ax.legend(handles, unique_labels, frameon=False, loc='upper left')\n",
-    "\n",
-    "    ax.grid(True, alpha=0.3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "3995353f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "
        "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig = plt.figure(figsize=(16, 6), layout=\"constrained\")\n",
-    "fig.suptitle(\"ISMIP6 emulator | Peninsula | Training fits\")\n",
-    "for scen, _ in enumerate(pen_train_preds):\n",
-    "    ax = fig.add_subplot(2, 3, scen+1)\n",
-    "    p5 = np.percentile(pen_train_preds[scen], 5, axis=(0, 1))\n",
-    "    p17 = np.percentile(pen_train_preds[scen], 17, axis=(0, 1))\n",
-    "    median = np.median(pen_train_preds[scen], axis=(0, 1))\n",
-    "    p83 = np.percentile(pen_train_preds[scen], 83, axis=(0, 1))\n",
-    "    p95 = np.percentile(pen_train_preds[scen], 95, axis=(0, 1))\n",
-    "\n",
-    "    true_median = np.percentile(train[3][\"sle\"][scen], 50, axis=0)\n",
-    "\n",
-    "    time_axis = np.arange(len(median))\n",
-    "\n",
-    "    # Plot Emulator Uncertainty\n",
-    "    ax.fill_between(time_axis, p5, p95, color='skyblue', alpha=0.2, label='5-95% Confidence')\n",
-    "    ax.fill_between(time_axis, p17, p83, color='skyblue', alpha=0.4, label='17-83% Confidence')\n",
-    "\n",
-    "\n",
-    "    ax.plot(np.tile(time_axis, (train[3][\"sle\"][scen].shape[0], 1)).T, train[3][\"sle\"][scen].T, '--', lw=1, color=\"black\", alpha=0.4, label=\"ISMIP6 models\")\n",
-    "    ax.plot(time_axis, true_median, lw=2, color='black', label=\"ISMIP6 median\")\n",
-    "    ax.plot(time_axis, median, color='skyblue', lw=3, label='Emulator Median')\n",
-    "\n",
-    "    ax.set_xlabel(\"Time (years)\")\n",
-    "    ax.set_ylabel(\"Sea Level Equivalent (m)\")\n",
-    "\n",
-    "    if scen == 0:\n",
-    "        handles, labels = ax.get_legend_handles_labels()\n",
-    "        unique_labels = set(labels)\n",
-    "        handles = [handles[labels.index(label)] for label in unique_labels if label in labels]\n",
-    "        ax.legend(handles, unique_labels, frameon=False, loc='upper left')\n",
-    "\n",
-    "    ax.grid(True, alpha=0.3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "b9d3da32",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "
        "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig = plt.figure(figsize=(10, 6), layout=\"constrained\")\n",
-    "fig.suptitle(\"ISMIP6 emulator | WAIS | Test fits\")\n",
-    "for scen, _ in enumerate(wa_test_preds):\n",
-    "    ax = fig.add_subplot(2, 2, scen+1)\n",
-    "    p5 = np.percentile(wa_test_preds[scen], 5, axis=(0, 1))\n",
-    "    p17 = np.percentile(wa_test_preds[scen], 17, axis=(0, 1))\n",
-    "    median = np.median(wa_test_preds[scen], axis=(0, 1))\n",
-    "    p83 = np.percentile(wa_test_preds[scen], 83, axis=(0, 1))\n",
-    "    p95 = np.percentile(wa_test_preds[scen], 95, axis=(0, 1))\n",
-    "\n",
-    "    true_median = np.percentile(test[1][\"sle\"][scen], 50, axis=0)\n",
-    "\n",
-    "    time_axis = np.arange(len(median))\n",
-    "\n",
-    "    # Plot Emulator Uncertainty\n",
-    "    ax.fill_between(time_axis, p5, p95, color='skyblue', alpha=0.2, label='5-95% Confidence')\n",
-    "    ax.fill_between(time_axis, p17, p83, color='skyblue', alpha=0.4, label='17-83% Confidence')\n",
-    "\n",
-    "\n",
-    "    ax.plot(np.tile(time_axis, (test[1][\"sle\"][scen].shape[0], 1)).T, test[1][\"sle\"][scen].T, '--', lw=1, color=\"black\", alpha=0.4, label=\"ISMIP6 models\")\n",
-    "    ax.plot(time_axis, true_median, lw=2, color='black', label=\"ISMIP6 median\")\n",
-    "    ax.plot(time_axis, median, color='skyblue', lw=3, label='Emulator Median')\n",
-    "\n",
-    "    ax.set_xlabel(\"Time (years)\")\n",
-    "    ax.set_ylabel(\"Sea Level Equivalent (m)\")\n",
-    "\n",
-    "    if scen == 0:\n",
-    "        handles, labels = ax.get_legend_handles_labels()\n",
-    "        unique_labels = set(labels)\n",
-    "        handles = [handles[labels.index(label)] for label in unique_labels if label in labels]\n",
-    "        ax.legend(handles, unique_labels, frameon=False, loc='upper left')\n",
-    "\n",
-    "    ax.grid(True, alpha=0.3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "df7e0815",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "
        "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig = plt.figure(figsize=(12, 8), layout=\"constrained\")\n",
-    "fig.suptitle(\"ISMIP6 emulator | EAIS | Test fits\")\n",
-    "for scen, _ in enumerate(ea_test_preds):\n",
-    "    ax = fig.add_subplot(2, 2, scen+1)\n",
-    "    p5 = np.percentile(ea_test_preds[scen], 5, axis=(0, 1))\n",
-    "    p17 = np.percentile(ea_test_preds[scen], 17, axis=(0, 1))\n",
-    "    median = np.median(ea_test_preds[scen], axis=(0, 1))\n",
-    "    p83 = np.percentile(ea_test_preds[scen], 83, axis=(0, 1))\n",
-    "    p95 = np.percentile(ea_test_preds[scen], 95, axis=(0, 1))\n",
-    "\n",
-    "    true_median = np.percentile(test[2][\"sle\"][scen], 50, axis=0)\n",
-    "\n",
-    "    time_axis = np.arange(len(median))\n",
-    "\n",
-    "    # Plot Emulator Uncertainty\n",
-    "    ax.fill_between(time_axis, p5, p95, color='skyblue', alpha=0.2, label='5-95% Confidence')\n",
-    "    ax.fill_between(time_axis, p17, p83, color='skyblue', alpha=0.4, label='17-83% Confidence')\n",
-    "\n",
-    "\n",
-    "    ax.plot(np.tile(time_axis, (test[2][\"sle\"][scen].shape[0], 1)).T, test[2][\"sle\"][scen].T, '--', lw=1, color=\"black\", alpha=0.4, label=\"ISMIP6 models\")\n",
-    "    ax.plot(time_axis, true_median, lw=2, color='black', label=\"ISMIP6 median\")\n",
-    "    ax.plot(time_axis, median, color='skyblue', lw=3, label='Emulator Median')\n",
-    "\n",
-    "    ax.set_xlabel(\"Time (years)\")\n",
-    "    ax.set_ylabel(\"Sea Level Equivalent (m)\")\n",
-    "\n",
-    "    if scen == 0:\n",
-    "        handles, labels = ax.get_legend_handles_labels()\n",
-    "        unique_labels = set(labels)\n",
-    "        handles = [handles[labels.index(label)] for label in unique_labels if label in labels]\n",
-    "        ax.legend(handles, unique_labels, frameon=False, loc='upper left')\n",
-    "\n",
-    "    ax.grid(True, alpha=0.3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "65be2301",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "
        "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig = plt.figure(figsize=(12, 8), layout=\"constrained\")\n",
-    "fig.suptitle(\"ISMIP6 emulator | Peninsula | Test fits\")\n",
-    "for scen, _ in enumerate(pen_test_preds):\n",
-    "    ax = fig.add_subplot(2, 2, scen+1)\n",
-    "    p5 = np.percentile(pen_test_preds[scen], 0, axis=(0, 1))\n",
-    "    p17 = np.percentile(pen_test_preds[scen], 17, axis=(0, 1))\n",
-    "    median = np.median(pen_test_preds[scen], axis=(0, 1))\n",
-    "    p83 = np.percentile(pen_test_preds[scen], 83, axis=(0, 1))\n",
-    "    p95 = np.percentile(pen_test_preds[scen], 95, axis=(0, 1))\n",
-    "\n",
-    "    true_median = np.percentile(test[3][\"sle\"][scen], 50, axis=0)\n",
-    "\n",
-    "    time_axis = np.arange(len(median))\n",
-    "\n",
-    "    # Plot Emulator Uncertainty\n",
-    "    ax.fill_between(time_axis, p5, p95, color='darkgoldenrod', alpha=0.2, label='5-95% Confidence')\n",
-    "    ax.fill_between(time_axis, p17, p83, color='darkgoldenrod', alpha=0.4, label='17-83% Confidence')\n",
-    "\n",
-    "\n",
-    "    ax.plot(np.tile(time_axis, (test[3][\"sle\"][scen].shape[0], 1)).T, test[3][\"sle\"][scen].T, '--', lw=1, color=\"black\", alpha=0.4, label=\"ISMIP6 models\")\n",
-    "    ax.plot(time_axis, true_median, lw=2, color='black', label=\"ISMIP6 median\")\n",
-    "    ax.plot(time_axis, median, color='darkgoldenrod', lw=3, label='Emulator Median')\n",
-    "\n",
-    "    ax.set_xlabel(\"Time (years)\")\n",
-    "    ax.set_ylabel(\"Sea Level Equivalent (m)\")\n",
-    "\n",
-    "    if scen == 0:\n",
-    "        handles, labels = ax.get_legend_handles_labels()\n",
-    "        unique_labels = sorted(set(labels))\n",
-    "        handles = [handles[labels.index(label)] for label in unique_labels if label in labels]\n",
-    "        ax.legend(handles, unique_labels, frameon=False, loc='lower left')\n",
-    "\n",
-    "    ax.grid(True, alpha=0.3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "381e03ba",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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YN24cn3zyCRUVFQ3Obyq2er2+1bFdunQpDzzwAPPmzeOLL75gx44d7Nq1i4EDBzYZtzPfd3FxMYBLNxgvLi5usb2dO3cyffp0AN544w1++eUXdu3axYoVK4DWfc9dqanPgyv6WP/zW0+v17f52vrrf3ttaWkpERERjc5r6lh7efHFF1m+fDnr169n0qRJBAcHM2/ePI4ePXre+iCEEEIIIdqf7FkihBBCCNEF+fj4cP/99/Pvf//bsW/Dmby9vbnmmmt4/PHH8ff3b9PeHVqtlmHDhp31nJSUFAoKChodV/+7wfTZ9kipf9r8pZdecmwcfyZXTarWT/ifuZl6SUlJi9euX78eo9HIp59+Snx8vOP4vn37Wn3/kJAQduzYgaqqDRImRUVF2Gy2Bk/eAy5bRfFblZWVfPLJJwAMHz68yXM++OAD7rjjDpfds35Pmscee6zB8ZKSEgIDAxudf+b7rt+j4uTJky7rU1hYWIvtffjhh3h4ePDll182SBatX7/eZf1wRlOfh87Wx6aEhIQ4kqK/1dTfGe3Fx8eHlStXsnLlSgoLCx2rTObMmcORI0fOWz+EEEIIIUT7kpUlQgghhBAXufz8/CaP15cw6tatW7PX3n777cyZM4cHH3yw0eoAV6kvm/TNN980OP7111+j1WqbnZQHGDt2LIGBgRw+fJhhw4Y1+aelDeJbq74E2YEDBxoc//zzz1u8tn6iuv6pe6hLBr3xxhuNzj3zyfp6U6ZMobq6utFE9jvvvON4vb198MEH1NTUsGrVKn766adGf0JDQ5tdqXQ2Z1uNoNFoGsQN4KuvvuLUqVOtanvMmDEEBATw2muvORJwzvbhTLNmzSIzM5Mff/yx2XM0Gg3u7u4NSlTV1NTw7rvvtqrf58OF0McJEybw448/NkhKKorCRx995PJ7Nfez91sREREsWrSIP/zhD2RkZGAymVzeDyGEEEII0TFkZYkQQgghxEWuX79+TJkyhVmzZpGYmIjZbGbHjh38/e9/JyIi4qzltQYNGtTuT5nfeOON/POf/+SOO+6gpKSEvn378v333/OPf/yDO+64o8FKjDP5+vry0ksvsXDhQsrKypg/fz7h4eEUFxezf/9+iouLefXVV13Sz+HDh9OrVy/uu+8+bDYbQUFBfPbZZ2zdurXFa6dNm4ZOp+MPf/gDf/nLXzCbzbz66quUl5c3OnfAgAF8+umnvPrqqwwdOtSxOueGG27gH//4BwsXLiQ3N5cBAwawdetWHnvsMS699NIW989whTVr1hAUFMR9993XZPLshhtu4Nlnn2X//v0MHDiw1e36+fkRHx/P//t//48pU6YQHBxMaGgoCQkJXHbZZaxdu5bevXuTkpJCamoqTz/9dKvLavn6+vL3v/+dm2++malTp7JkyRIiIiLIyspi//79vPzyy0Bd3AGefPJJZs2ahZubGykpKU0m2+655x7+/e9/c/nll/PXv/6VESNGUFNTw+bNm7nsssuYNGkSs2fP5tlnn+Xaa6/llltuobS0lGeeeaZR4qcjXQh9XLFiBV988QVTpkxhxYoVeHl58dprrzn2IjrbyjNnDRgwgE2bNvHFF18QFRWFn58fvXr1YuTIkVx22WWkpKQQFBREeno67777LqNHj8bb29tl9xdCCCGEEB1LVpYIIYQQQlzknnjiCRRF4dFHH+XSSy9l7ty5vPPOO1x77bXs3r273fa2aC0PDw82btzINddc45j4/+yzz3jiiSd44YUXWrz+uuuu46effqK6uppbb72VqVOncvfdd7Nnzx6XrrZwc3Pjiy++oHfv3tx2223ccMMN6PV6x2T72fTu3ZtPPvmE8vJyrrzySu666y4GDRrEiy++2Ojcu+++m/nz5/O3v/2NUaNGOVbWeHp68tNPP7FgwQKefvppZs2axdq1a7nvvvv49NNPXfY+m3PgwAFSU1NZuHBhs6uMbrnlFuB/+5o4Y82aNXh7ezN37lyGDx/Oww8/DMALL7zAddddx+OPP86cOXP4/PPP+fTTT0lMTGx12zfddBNff/01drudm2++mcsuu4znn3++webi1157LTfffDOvvPIKo0ePZvjw4Zw+fbrJ9vz8/Ni6dSs33XQTr7/+OrNnz2bJkiVkZGQ4VmpNnjyZt956i7S0NObMmcOKFSuYP38+f/3rX52OTXu5EPo4cOBANm7ciJeXFzfccAO33HIL/fr1c5R6CwgIcNm9XnjhBXr27Mk111zD8OHDufXWW4G6OH3++efceOONTJ8+naeeeoobbriBL774wmX3FkIIIYQQHU+jnm0tuhBCCCGEEOKit2nTJiZNmkROTo6j3JjoeBqNhrfffptFixZ1dFc6nenTp5Obm0tmZmZHd0UIIYQQQlwkpAyXEEIIIYQQQohOa+nSpQwePJjY2FjKysp4//332bhxY5tWMAkhhBBCCNEcSZYIIYQQQgghhOi07HY7Dz74IAUFBWg0Gvr27cu7777Ldddd19FdE0IIIYQQFxFJlgghhBBCCCGE6LReeOGFVu1fJIQQQgghxLmQPUuEEEIIIYQQQgghhBBCCNGlaTu6A0IIIYQQQgghhBBCCCGEEB1JkiVCCCGEEEIIIYQQQgghhOjSJFkihBBCCCGEEEIIIYQQQoguTZIlQgghhBBCCCGEEEIIIYTo0iRZIoQQQgghhBBCCCGEEEKILk2SJUIIIYQQQgghhBBCCCGE6NIkWSKEEEIIIYQQQgghhBBCiC5NkiVCCCGEEEIIIYQQQgghhOjSOjxZcurUKa677jpCQkLw9vZm0KBBpKamdnS3hBBCCCGEEEIIIYQQQgjRRbh35M3Ly8sZO3YskyZN4ptvviE8PJzs7GwCAwM7sltCCCGEEEIIIYQQQgghhOhCNKqqqh1187/+9a/88ssv/Pzzzx3VBSGEEEIIIYQQQgghhBBCdHEdmizp27cvM2bM4OTJk2zevJno6GjuuOMOlixZ0uT5FosFi8Xi+FpRFMrKyggJCUGj0ZyvbgshhBBdjqqqGAwGunXrhlbb4VU8Oy1FUTh9+jR+fn4yNhFCCCHa0cUwNpFxgxBCCHF+tHbc0KHJEk9PTwCWLl3KVVddxc6dO7nnnnv45z//yQ033NDo/IcffpiVK1ee724KIYQQ4r9OnDhBTExMR3ej0zp58iSxsbEd3Q0hhBCiy7iQxyYybhBCCCHOr5bGDR2aLNHpdAwbNoxff/3VcexPf/oTu3btYtu2bY3OP3NlSWVlJXFxcRw/fhx/f3+X9UtRFEpKSggNDb1gn1A5nyRezpOYOUfi5RyJl/MkZi2rqqoiPj6eiooKAgICOro7nVZlZSWBgYGcOHHC5WOT4uJiwsLC5DPaChIv50i8nCcxc47EyzkSr9apqqoiNjb2gh6btMe4QT4/zpOYOUfi5RyJl/MkZs6ReLVOa8cNHbrBe1RUFH379m1wrE+fPnzyySdNnq/X69Hr9Y2OBwYGunxCora2lsDAQPmQtYLEy3kSM+dIvJwj8XKexKxl9XGREhFnVx8ff39/l49NzGYz/v7+8hltBYmXcyRezpOYOUfi5RyJl3Mu5LFJe4wb5PPjPImZcyRezpF4OU9i5hyJl3NaGjd0aATHjh1LRkZGg2OZmZnEx8d3UI+EEEIIIYQQQgghhBBCCNHVdGiy5M9//jPbt2/nscceIysriw8++IDXX3+dO++8syO7JYQQQgghhBBCCCGEEEKILqRDkyXDhw/ns88+41//+hf9+/dn1apVPP/88yxYsKAjuyWEEEIIIYQQQgghhBBCiC6kQ/csAbjsssu47LLLOrobQgghhBBCCCGEEEIIIYToomTXFyGEEEIIIYQQQgghhBBCdGmSLBFCCCGEEEIIIYQQQgghRJcmyRIhhBBCCCGEEEIIIYQQQnRpkiwRQgghLiKKYsNYloXFWNjRXRFCCCGEwGYxYChOx241dXRXhBBCCNFJnThxgtzc3I7uRsdv8C6EEEII17DVVlNdkoG5Mg//yEHg09E9EkIIIURXpaoqluoCjCUZ2G0m9L4RuHl4d3S3hBBCCNGJFBYWsnPnTvLz80lKSiIhIaFD+9MlkyWKvRZVsTX/uqKg2GqwW02o2nNbfKPRuqN1051TG0IIIURLLMYijCVHsFoqQaPp6O4IIYQQogtT7LWYyo5hqjiGRuuBqiod3SUhhBBCdCJms5ktW7aQm5tLcHAwM2fOJC4urqO71fWSJYq9lsrTu7HVGps/R1GorqzArSYQ7TkmS9x1PgR0GyYJk3akqiq33norH3/8MeXl5ezdu5d77rmHQYMG8fzzzzd7XUJCAvfccw/33HPPeeurEEK4mqoqmCqOYyrNREVF7xuNxXCqo7slhBBCiC7Kaq7AWJqBuboAnVcoWndPLNX5Hd0tIYQQQnQiOp0ORVGYPHkyiYmJaDrJQ59dbs8SVbFhqzWiddPhpvNt8o+73hete91/mzunNX+0bjpstcazrmJpysMPP4xGo2nwJzIyssXrsrOzueKKKwgLC8Pf35+rr76awsKGNesTEhIatf3Xv/7V8XpZWRlz5szB19eXIUOGsH///gbX33HHHfz9739v1fsoKCjgrrvuokePHuj1emJjY5kzZw4//PBDq65vrQ0bNrB27Vq+/PJL8vPz6d+/P59++imrVq1y6X2EEKKzsdvMGIoOUl2UhtbdC71PRKcZYAghhBCia1FVhZrKE1Tm76bWWIKnbzcpuyWEEEKIBg4ePEh2djZarZaZM2eSlJTUqeYxutzKknoaNx1u7p5NvqaqClp3PW7unmg0bc8n2QHstW26tl+/fnz//feOr93c3M56vtFoZPr06QwcOJAff/wRgAceeIA5c+awffv2BitkHnnkEZYsWeL42tfX1/H/jz76KAaDgT179vDqq69y8803s2vXLgC2bdvGzp07eemll1rsf25uLmPHjiUwMJCnnnqKlJQUrFYr3377LXfeeSdHjhxpXSBaITs7m6ioKMaMGeM4Fhwc7LL2hRCiM7LWlFNdcoRaUzE673C07vqO7pIQQgghuii7zYyxLIua8lzcdN7o/bp1dJeEEEII0cnU1taya9cu+vXrR2JiYrPnKTZLh81xdLmVJRcKd3d3IiMjHX/CwsLOev4vv/xCbm4ua9euZcCAAQwYMIC3336bXbt2OZIn9fz8/Bq0/dtkSXp6Otdccw3JycnccsstHD58GACr1crtt9/Oa6+91mLiBupWoGg0Gnbu3Mn8+fNJTk6mX79+LF26lO3btzvOy8vL4/LLL8fX17fJ1TAPP/wwgwYN4t133yUhIYGAgACuueYaDAYDAIsWLeKuu+4iLy8PjUbj2ARo4sSJDcprFRUVMWfOHLy8vOjevTvvv/9+oz5XVlZyyy23EB4ejr+/P5MnT26wsqalvkBdCbcnn3ySpKQk9Ho9cXFxPProo47XT506xe9//3tCQkLo27cv8+bNIzc3t8V4CiFEPVVVqak6SWVBKlZzOXq/aEmUCCGEEKLD1JpKqMrfQ035MXTeoXh4BnV0l4QQQgjRCR0+fBi73U7//v2bfF1RbBjLsqjM34Nis5zn3tWRZEkndfToUbp160b37t255pprOHbs2FnPt1gsaDQa9Pr/TZh5enqi1WrZunVrg3OffPJJQkJCGDRoEI8++ii1tf9b/VK/MsVms/Htt9+SkpLiuGbixIkMGzasxb6XlZWxYcMG7rzzTnx8fBq9HhgYCNRN+M2bN4+ysjI2b97Mxo0byc7O5ve//32D87Ozs1m/fj1ffvklX375JZs3b+aJJ54A4IUXXuCRRx4hJiaG/Px8xyqYMy1atIjc3Fx+/PFHPv74Y1555RWKioocr6uqyuzZsykoKODrr78mNTWVIUOGMGXKFMrKylrVF4D777+fJ598kgceeIDDhw/zwQcfEBERAYDJZGLSpEn4+vqyadMm1q9fj6+vLzNnzmzwPRBCiOYodivGkgwMBftA1aL3jTqnFZBCCCGEEG2lKnaM5TlU5u/Baq6UBziEEEII0SRVVdmyZQu7d++mV69eeHs3LtNpq63GUJiGoeggdqsRVVU6oKdduAxXZzZy5EjeeecdkpOTKSwsZPXq1YwZM4ZDhw4REhLS5DWjRo3Cx8eH5cuX89hjj6GqKsuXL0dRFPLz/7eZ3t13382QIUMICgpi586d3H///eTk5PDmm28C8Ne//pXbb7+dxMREEhISWLNmDUePHuWdd95h27Zt3HbbbXz33XcMGzaMN954g4CAgEZ9ycrKQlVVevfufdb3+f3333PgwAFycnKIjY0F4N1336Vfv37s2rWL4cOHA3WrNdauXYufnx8A119/PT/88AOPPvooAQEB+Pn54ebm1uy+LpmZmXzzzTds376dkSNHArBmzRr69OnjOOenn34iLS2NoqIiR8LpmWeeYf369Xz88cfccsstLfbFYDDwwgsv8PLLL7Nw4UIAEhMTGTduHAAffvghWq2WN998E1VVKSoq4q233iI4OJhNmzYxffr0s8ZLCNG12SwGqkuOYK4+jc4rDDcPr47ukhBCCCG6KFutEVPZUUyVeXjoA3D39u/oLgkhhBCik6moqCAgIACNRoOqqgwfPpx+/fo1Os9SXUh16RFslio8PINRlY57qFySJZ3QrFmzHP8/YMAARo8eTWJiIuvWrWPp0qU89thjPPbYY45zDh8+TFxcHB999BG33347L774Ilqtlj/84Q8MGTKkQdmsP//5z47/T0lJISgoiPnz5ztWmwQEBPDBBx806M/kyZN5+umnef/99zl27BgZGRksWbKERx55pMnN3lVVBWhxc5709HRiY2MdiRKAvn37EhgYSHp6uiNZkpCQ4EhOAERFRTVYFdKS9PR03N3dG6yK6d27t2OFC0BqairV1dWNklE1NTVkZ2c7vj5bX9LT07FYLEyZMqXJfqSmppKVleW4XlVVNBoNZrO5wT2EEOJMluoCqouPYKutxtO3Gxqt/PoWQgghRMewVBdiLM3Aaq5A7xOB1k3X0V0SQgghRCdSUlLC3r17ycnJYcaMGcTHxzNhwoRG5ymKDVP5MUxl2Wg0buh9o1FsZkmWiLPz8fFhwIABHD16FIDbbruNq6++2vF6t251m+dNnz6d7OxsSkpKcHd3JzAwkMjISLp3795s26NGjQLqVoM0tWrlrbfeIjAwkMsvv5wrr7ySefPm4eHhwVVXXcWDDz7YZJs9e/ZEo9GQnp7OvHnzmr13fbKgpeMeHh4NXtdoNChK65ditSZ5oygKUVFRbNq0qdFrv02qnK0vXl5nf8pbURSGDh3K+++/j6IolJaWEhISglarbXFPGiFE16QoNmoqcjGWHkWjdUfv163FRLQQQgghRHtQ7FZMFTn/m9Dwi5FxiRBCCCEciouL2b17NydOnMDf35/x48c3eEj+t2wWA9WlmVgMJ3H3DMFd13grh44ghc4vABaLhfT0dKKiogAIDg4mKSnJ8cfdvWHOKzQ0lMDAQH788UeKioqYO3dus23v3bsXwNH2bxUXF7Nq1SpeeuklAOx2O1arFajb8N1utzfZZnBwMDNmzOAf//gHRqOx0esVFRVA3SqSvLw8Tpw44Xjt8OHDVFZWNiiRda769OmDzWZj9+7djmMZGRmOfgAMGTKEgoIC3N3dG8Q2KSmJ0NDQVt2nZ8+eeHl58cMPPzT5+pAhQzh69Cjh4eEkJSXRvXt3xz2aKmcmhOja7FYThqKDVBcfxk3ni847VCYkhBBCCNEhbJYqqgr2YSxOx03nh84nrMuPSx5++GE0Gk2DP82VhhZCCCEuZvVzxIWFhRgMBiZPnszVV19N79690Wobph9UVcVSXUDl6VQshtPofLt1mkQJdOGVJaq9lqan+kFVFRSbBbvNfE4b56r2ti0Zuu+++5gzZw5xcXEUFRWxevVqqqqqHPtgNOftt9+mT58+hIWFsW3bNu6++27+/Oc/06tXLwC2bdvG9u3bmTRpEgEBAezatYs///nPzJ07l7i4uEbt3X333dx7771ER0cDMHbsWN59912mT5/O66+/ztixY5vtyyuvvMKYMWMYMWIEjzzyCCkpKdhsNjZu3Mirr75Keno6U6dOJSUlhQULFvD8889js9m44447mDBhQqs2km+tXr16MXPmTJYsWcLrr7+Ou7s799xzT4OVIFOnTmX06NHMmzePJ598kl69enH69Gm+/vpr5s2b16r+eHp6snz5cv7yl7+g0+kYO3YsxcXFHDp0iJtuuokFCxbw9NNPc/nll/Pwww/j5eVFeno669evZ9myZcTExLjsPQshLmy1pmKqizOoNZeh94lE6+bR8kVCCCGEEC6mqioWw2mMpRnYrDV1q1ylHKhDv379+P777x1f/7YEthBCCHGxy8vLY8+ePYSGhjJu3Dj69u1Lv379mn2gom6V6n/Lbmk90PtFd7qHL7rcKEejdcdd54Ot1gjNJDMURUGxVWOzuDfKfjnLXefj9GDy5MmT/OEPf6CkpISwsDBGjRrF9u3biY+PP+t1GRkZ3H///ZSVlZGQkMCKFSsa7FGi1+v597//zcqVK7FYLMTHx7NkyRL+8pe/NGrr22+/JTs7m/fee89x7I9//CO7d+9m5MiRjBgxgoceeqjZvnTv3p09e/bw6KOPcu+995Kfn09YWBhDhw7l1VdfBepKWK1fv5677rqL8ePHo9VqmTlzpmMliyu9/fbb3HzzzUyYMIGIiAhWr17NAw884Hhdo9Hw9ddfs2LFChYvXkxxcTGRkZGMHz+eiIiIVt/ngQcewN3dnQcffJDTp08TFRXFbbfdBoC3tzdbtmxh+fLlzJ8/H4PBQHR0NFOmTMHfXzZEFELUJetrKvIwlmWiKgqeUt5CCCGEEB3EbjNjLMvGXJGDxt0LT79uHd2lTsfd3V1WkwghhOhyioqK+PnnnyktLSUyMpKEhASAs86j2yxVVJdkYDGcxsM7FDcP7/PUW+do1PoNHS5AVVVVBAQEUFlZ6dRks2KvRVVszb+uKBQXFxMWFnbOyRKN1v2i3/BOURSKiooIDw8/53h1FRIz50i8nCPxcl5niJndZsZYepSailzc9f6469ueRDVXncQvvD/eQc3vWeWstv7O7WraK06d4TN6IZF4OUfi5TyJmXMkXs7pDPGqNZViLM2g1lSCh3cYbu6ebW5LVRUs1fkExY5B5xXssj529Njk4Ycf5umnnyYgIAC9Xs/IkSN57LHH6NGjR7PXWCwWLBaL4+uqqipiY2MpLy932Xtw5XxGVyExc47EyzkSL+dJzJxzvuOlqiofffQRWq2WsWPHNrm1w5nnW6oLMJVmYKs1oveNgLMsLFCsNSg2E4ExY3DzOPv+0M6oqqoiKCioxXFDl1tZAtQlL86SwNAoClp3L9w8vOWHUgghRLuz1pRTXXKEWlMxOu9wtO76ju6SEEIIIbogVbFjqszDVJaFYrf+tzyG/Ju4KSNHjuSdd94hOTmZwsJCVq9ezZgxYzh06BAhISFNXvP444+zcuXKRseLi4sxm80u6ZeiKFRWVqKqqsxntJLEzDkSL+dIvJwnMXNOR8SrZ8+eeHp64ubmRlFR0Vn6ZsViKMRiLECjccddH4yxygpYm7/GXotqt2MtLnHp3IjBYGjVeV0yWSKEEEJ0BqqqYjacwlh6BMVmQe/bDY227bWuFUXh8JEsQnzt+IW7sKNCCCGEuOjZao0Yy45SU5mHhz4AnXdoR3epU5s1a5bj/wcMGMDo0aNJTExk3bp1LF26tMlr7r///gav1a8sCQsLc+nKEo1GI09kO0Fi5hyJl3MkXs6TmDnnfMdLVVXCw1uecLCaKzGVHUejFuAfEoK2lWW3FCsoNhuBYaEuXVni6dm6VbKSLBFCCCE6gGKvxVSWjak8G627N3rfc6sDXlBYzPad+6isMjCwdyRxyS7qaBf2+OOP87e//Y27776b559/vqO7I4QQQrQLVVWpNRZiLM3Eaq5A7xNx0ZeSbg8+Pj4MGDCAo0ePNnuOXq9Hr2/8lKxWq3XpBJdGo3F5mxc7iZlzJF7OkXg5T2LmnPMRr4KCAlJTU1FVlSlTpuDl1XQi438PhWagWM14+XVzaj9vVaNpl/fT2rYkWSKEEEKcZ3Ubmx3BXJ2PzivsnJ6WqKkxszP1ADm5JwgLDeGymZPw8TC6sLdd065du3j99ddJSUnp6K4IIYQQ7Uax12KqyMFUlo1G64HeLwaNRtPR3bogWSwW0tPTueSSSzq6K0IIIYTL1CdJTp06RXBwMEOGDGk2UaLYLFSXZWGuyKl7KNTv3B4K7QiSLBFCCCHOk/qNzYwlR7BZTXj6OveERXNtlpSUM3bUUJIS49FoNJirJFlyLqqrq1mwYAFvvPEGq1ev7ujuCCGEEO3Caq7AWJqBuboAnVcobq0sjyHq3HfffcyZM4e4uDiKiopYvXo1VVVVLFy4sKO7JoQQQriEzWbju+++w9vbm2nTppGQkNDsQxXWmnKqSzOwVBei8wnHzb11Za86G0mWCCGEEOeBotgwlR/DVJaFRuuB5zk8YVFlqGbvvkOMGjEYb28vrrx8ujwF6kJ33nkns2fPZurUqS0mSywWCxaLxfF1VVUVUFc3VlEUl/VJURRUVXVpmxcziZdzJF7Ok5g5R+LlnPaOl6oqmKtOYyrPQLFa0PtEgdYdRVXb5X6o6n/fj2vfU0d/nk6ePMkf/vAHSkpKCAsLY9SoUWzfvp34+PgO7ZcQQghxLvLz89m3bx8TJkzA29ubefPm4efn1+ycQ9244iTG0kwUey2eftHntBdrR5NkiRBCCNHObLXVVJdkYDGcxOMcn9xUFIVvN/6MRqPBZKpBr9dJosSFPvzwQ/bs2cOuXbtadf7jjz/OypUrGx0vLi7GbDa7rF+KolBZWYmqqlK3txUkXs6ReDlPYuYciZdz2jNeis2CuTqfWmMxWnc9bh5BUGUFrC69z2+pqNhqwFpShofedfcxGAwua6stPvzwww69vxBCCOFKRUVF7Ny5k9OnTxMSEkJNTQ3e3t74+/s3e43dZsZYepSayuO4efig941q8/0VRSHvZD4n8vIYNbTjNmF1OlmSm5vLzz//TG5uLiaTibCwMAYPHszo0aNbvat8vYcffrjRBENERAQFBQXOdksIIYTolCzGQqpLjmAzV6Lz7Yb2HMtunThVgNFkYu6lUwgKCnBRLy9srhqbnDhxgrvvvpvvvvuu1dfdf//9LF261PF1VVUVsbGxhIWFnXVQ6SxFUdBoNISFhclEYytIvJwj8XKexMw5Ei/ntFe8LMZiTKV5aNQyAsPC0bg33mi8XagKFjcIDA1G5xXksmadnX+o58o5DSGEEOJisG/fPnbu3ElwcDDTp08nPj6+xYcya00lGEszqDWVovMOR3uO44r0jGx2pR4gONAXs6X2nNo6F62esfnggw948cUX2blzJ+Hh4URHR+Pl5UVZWRnZ2dl4enqyYMECli9f7tSy0379+vH99987vnZzu3CX6QghhBD1VMWOqSIXY+lRNBqtyzZMzTyaQ2hIMMHBgefeyQucq8cmqampFBUVMXToUMcxu93Oli1bePnll7FYLI3GKXq9Hr2+8aBQq9W6fEJQo9G0S7sXK4mXcyRezpOYOUfi5RxXxkuxW6mpzMVYmg0a8AqIQaM5f98HFc1/34/Gpd9/Z9tqrzkNIYQQ4kJkNpsxmUwEBwcTFxeHt7c3PXv2bHHeQlXsmCqPYyrLQlXs6P2i2zSuqKio4nBGFl6engwe2JekHvFERYQR4KdHsZna+rbOWauSJUOGDEGr1bJo0SL+85//EBcX1+B1i8XCtm3b+PDDDxk2bBivvPIKV111Ves64O5OZGSk8z0X4jc2bdrEpEmTKC8vJzAwkLVr13LPPfdQUVHR0V0TQnRBdqsJY2kmpso8PDyDcNf5uqRdm81GRUUVAwf0dkl7F7L2GJtMmTKFtLS0BsduvPFGevfuzfLly+WBDiGEEBccq7kSY2kmlurTuHsGu2xMcqFpzzkNIYQQ4kKjKArr16/H09OTefPmERwcTHBwcIvX2WqNmMqOUlN5And9AO7efk7f22iqYeuvu8kvKMLLy5MB/XoBoNfr0Ot12K01TrfpSq1KlqxatYrZs2c3+7per2fixIlMnDiR1atXk5OT0+oOHD16lG7duqHX6xk5ciSPPfYYPXr0aPLcrrKJ6o033khFRQWfffYZUFcz7sEHH2TDhg0UFhYSFBRESkoKDz30EKNHjwagR48eHD9+nPfff59rrrmmQXsDBgzg8OHDrFmzhkWLFjnOv/vuu7n77rsbXA/g5eVFjx49uPPOO7n11lsd7VgsFlatWsX7779PQUEBMTEx3H///SxatKjDN2ysv3f9Z+Gqq65i5syZHb7pX3M6+jN2oZF4OUfi5TxXxqzWVIKxJINacxl6n0g0bh4u2TDVVFODt5cXV1w+HeCsbaqqiuLiz0Bn+zy1x9jEz8+P/v37Nzjm4+NDSEhIo+NCCCFEZ6aqCmbDaYylGditZpeUAr2QteechhBCCHGhyc3NpaqqiqlTp7b6GouxEGNJJlZzOTqfCLRuujbdO3XvQcorKhk/djgJ8TGdbtVxq0ZLZxtUnCk0NJTQ0NBWnTty5EjeeecdkpOTKSwsZPXq1YwZM4ZDhw4REhLS6Pyusomq2WzGYrFQVFQEwOWXX47NZuO5554jLi6OkpISfv75Z3JyckhMTATqyoR069aN119/ncmTJzvaSk1NJT8/H29vbwwGg6NNu93e6Otly5Zx3XXXYTQa+fe//80dd9yBVqvl8ssvB2DRokUUFxfz9NNP0717d0pKSrDZbBQVFXX4ho31K0iKi4uprf1fXbv699fZdPRn7EIj8XKOxMt5roiZqihYTMVYDKdRUXDXh2Iy2ADbOfXNYrGQujeNwqISZs+cgk7n0eI1VhNY3A1U17ru78CO3kT1TO01NhFCCCEudHZrDcbybGrKc3Hz8MbTr1tHd6nDybhBCCGE+J8DBw7QrVu3Vv2+UxQbNRW5GEuzQKM55xLjYaHBREdF0KN7XMsnd4A2P1pSVFREUVFRoydNU1JSWt3GrFmzHP8/YMAARo8eTWJiIuvWrWuwWWq9rrKJqqenJ2azmfDwcCoqKti5cyc//vgjEyZMcJwzY8aMBte4ubmxYMECnn/+eSwWC7GxsQCsX7+eBQsW8O677+Ln50d4eLjj/DO/joqKcjw5O3LkSL7++mt++uknlixZwoYNG9i+fTtZWVmNlmU1F69NmzYxZcoUvv76a/72t79x5MgRRo8ezQcffEBqair33Xcfp06d4tJLL+XNN9/E29sbqHsi+plnnuGf//wn+fn5JCcns2LFCubPn+9o++uvv2bp0qWcOHGCUaNGcf311wMQFhbmKMO1dOlSysrKAMjOzubee+9lx44dGI1G+vTpw6OPPtogg9qjRw+WLFlCVlYWH3/8MUFBQfztb3/jlltuOYfvZtM6+jN2oZF4OUfi5bxzjVndpEQWGlsuvoGBuOmdX4ralNzjJ9m+cx+qqjLpkmFEhrfud51FC77BfngHhrukH9D2TVTPJ1eMTc60adOmc+yVEEIIcf5YjEUYSzOx1pSj8w47581WL2btMW4QQgghOrv6339nzi03xWYxUF2aicVw8pzLeRpNNWg1Gvr0SmxzG+eD08mS1NRUFi5cSHp6Oup/S4BoNBpUVUWj0WC329vcGR8fHwYMGMDRo0ebfN0Vm6gOGzaMgoKCFs9TFMVlk4yRkZHs3r271edrNBrHhn7+/v74+vry+eefM2bMmCbf/2/vM2PGDN59913+7//+D5PJxH/+8x82b97Mu+++2yhO9fdo7mtPT09sNhtarZYvv/ySYcOG8cwzz/Duu+/i4+PD3LlzWbVqFXq9vskNCOv//5FHHuHll1/G29ubq6++mmuuuQa9Xs8HH3xAdXU1V1xxBf/4xz9Yvnw5ACtWrODTTz/l1VdfpWfPnmzZsoUbbriBiIgIJkyYwIkTJ5g/fz633XYbt99+O7t37+bee+913PO3/aj/r8lkYvbs2Tz66KN4enqybt06Lr/8cjIyMhrUq3322WdZtWoVK1as4OOPP+bOO+9k4sSJ9O7t+v0BZJNL50i8nCPxcl5bY1ZrKsVYegSLsRS9T4TLJiVS9x4k7VAG8bHRjBoxCC+v1icrNBoNWk3HbqJ6PrXn2EQIIYS4EDie+izLAkDv1+28buJ+IZFxgxBCiK7IYDDg5+dHcHAwPXv2bLR/12+pqoqlugBjSQa2WsM5lfMsLCoh/Ug2x0+con/fZIYO7twlrp1+lzfeeCPJycmsWbOGiIiIc1p2cyaLxUJ6ejqXXHKJy9o8U0FBAadOnWq39l3N3d2dtWvXsmTJEl577TWGDBnChAkTuOaaa5p84mXx4sXce++9jsn+xMREBg0a5NQ9bTYb7733Hmlpadx+++0AHDt2jK1bt+Lp6clnn31GSUkJd9xxB2VlZbz55ptnbW/16tWMHTsWgJtuuon777+f7Oxsx9408+fP56effmL58uUYjUaeffZZfvzxxwb7sWzdupV//vOfTJgwgVdffZUePXrw3HPPodFo6NWrF2lpaTz55JPN9mHgwIEMHDiwQZ8+++wzPv/8c/74xz86jl966aXccccdACxfvpznnnuOTZs2tUuyRAhxYVNVBXPVSapLMlDsVjz9o10yKVFba0Wn8yA+LpqQ4EAS4mNc0NuLW3uOTYQQQojOTjZxd46MG4QQQnQl5eXl7Nixg1OnTnHNNdfg4+PDpEmTmj1fsddiKjuGsSIbrVaH3i+6Tb8ri4pL2bF7P6Wl5fj7+TJ8aApJPeLP5a2cF04nS3Jycvj0009JSko655vfd999zJkzh7i4OIqKili9ejVVVVUsXLjwnNtuTmRkZKvOc/XKknPxu9/9jtmzZ/Pzzz+zbds2NmzYwFNPPcWbb77p2LC93uzZs7n11lvZsmULb731FosXL271fZYvX87//d//YbFY0Ol0LFu2zLHBe32Jmvfff5+AgACgbhXG/Pnzeemll87a7m+TOhEREXh7ezsSJfXHdu7cCcDhw4cxm81MmzatQRu1tbUMHjwYgPT0dEaNGtXgB7U+sdIco9HIypUr+fLLLzl9+jQ2m42amhry8vKa7atGoyEyMrLT7nsihOg4dpsZY1kWNeU5uOn88PQ+97rWNTVmtu3Yi7GmhstmTiI0JIjQkCAX9Pbi58qxiRBCCHGhkE3c20bGDUIIIboCk8nE7t27ycjIwM/Pj0mTJuHj43PWa6zmCoylGZirC9B5heLm4e3UPWtrrRiNJoKCAnDTatF5eDBl4hhioiMvmIcTnB5JTZkyhf3797tkYHHy5En+8Ic/UFJSQlhYGKNGjWL79u3Ex7dflqk15bAURaGoqIjw8PBOU3bE09OTadOmMW3aNB588EFuvvlmHnrooUbJEnd3d66//noeeughduzYwWeffdbqeyxbtoxFixbh7e1NVFRUgw9xVFQU0dHRjkQJQJ8+fVBVlZMnTzY4fiYPj/9tRqzRaBp8XX+svk5s/X+/+uoroqOjG5xXX4Ksfqm0M5YtW8a3337LM888Q1JSEl5eXsyfP7/BZvBn9vXMvgkhBEBtTRnGkgxqTcXovMNdUnYrN+8U23bsBWD0iEEXzCCis3Dl2EQIIYS4ENitNXUPblQcl03cnSTjBiGEEF3Btm3bOHXqFKNHj6Zv375nneOuq5xxCmNZBorVgqdvNzROPIBRZagm/Ug2mVk5BAb6M2fWZEJCgpgx1bnqUSZTDRkZmfTv3XEVNpxOlrz55pssXLiQgwcP0r9//0aTy3Pnzm11Wx9++KGztxf/1bdvX9avX9/ka4sXL+aZZ57h97//PUFBrX8qOTQ0tNkB49ixY/noo4+orq7G17duWXdmZiZarZaYmBgMBoPT76Epffv2Ra/Xk5eX12BD+zPPOfO9b9++/azt/vzzzyxatIgrrrgCgOrqanJzc13RZSFEF+EYPJRmoNhr/7sU9dwT6tt37uNIZjZxMd0YM2oInp6yEauzXDk2EUIIITo7i7EIY0kmtTVl6H1c8+BGVyLjBiGEEBej2tpaDh06RHBwMN27d2f06NG4u7uj0+nOet3/Kmfk4qbzRu/EAxj1FTLyTp5Gr9fTv28yvXp2b/N72LUnjZMnTxIfHURH1dlwOlny66+/snXrVr755ptGr8lmaK5XWlrKVVddxeLFi0lJScHPz4/du3fz1FNPcfnllzd5TZ8+fSgpKcHb27mlUmdz7bXXsmrVKm688UZWrlxJSUkJy5YtY/HixXh5ebksWeLn58d9993Hn//8ZxRFYdy4cVRVVfHrr7/i6+vLwoULue222/j73//O0qVLufXWW0lNTWXt2rVnbTcpKYlPP/2UOXPmoNFoeOCBB2TFiBCi1RSbhWpH2S0f9L5RLms7PCyY0JAgkhI7f+3OzkrGJkIIIboCxW7FVJGDqewYaHDZfmldjYwbhBBCXExsNhsHDx4kLy8PVVUZPnw4QKvmhWtNJRhLM6k1lbSpcoZer6PWamXMyCH06B6Lu7vz5UBLS8uptdqIigxj+JABDBuYjM69434XOz2y+tOf/sT1119Pfn4+iqI0+CODCtfz9fVl5MiRPPfcc4wfP57+/fvzwAMPsGTJEl5++eVmrwsJCcHLy8ul/di4cSMVFRUMGzaMBQsWMGfOHF588UWX3aPeqlWrePDBB3n88cfp06cPM2bM4IsvvqB797rMZFxcHJ988glffPEFAwcO5LXXXuOxxx47a5vPPfccQUFBjBkzhjlz5jBjxgyGDBni8r4LIS4+1ppyKgv2YirLwsM7BA/PwHNus6LSwC/bU1FVlR7d4yRRco5kbCKEEOJiZzVXUFWwF2NxOm46X/Q+EZIoaSMZNwghhLhYlJeX8+GHHzrKS/7hD39osB9zc1TFjrE8h8r8PVjNlej9oludKFFVlS+/+Ync4yfRarXMnDae5J7dnU6U1NZa2bFrP19u+IlD6ZkAeHt7dXi1DY3q5AYQfn5+7Nu3j8TExPbqU6tVVVUREBBAZWUl/v7+Lmu3M+5Z0plJvJwnMXOOxMs5Ei/nNRUzVVUxG05hLD2CYrOg845Ao3Vzyf127NpPzvETXH3lpe3yPTJXncQvvD/eQW1f/nqm9vqd6woyNhFnkng5R+LlPImZcyRezvltvDSaut/rxtLMuvGIT7hTNcQ7A1VVsFTnExQ7Bp1XsMvabevv3It93CA/b86TmDlH4uUciZfzJGatpygKv/76KxERESQmJrYqXrZaI6ayo5gq8/DQB+Cud+73z7GcPLb8ssuxL0lb5OSeYGfqAWprrQwe2Je+vZMcfbdba1BsJoJix+Lm4bqFAK39nev0J+7KK6/kp59+OqfOCSGEEJ2ZYrNQXZxOVf4+QIvet5vLEiU2m42sY8fpmZggAz8XkbGJEEKIi5Gt1oih8ABVBftB44bez7nNVkXTZNwghBDiYmAymdBqtYwZMwY/P79WXWOpLqQqP5Wayjz0PhFOJ0pqa60cOJhBdLfINidKFEVh7/7DhIUEc8Xc6fTvm9yp5kacHmklJydz//33s3XrVgYMGNBoM7Q//elPLuucEEIIcb5ZzRVUl2RgqS5A5xOOm7unS9vPPX4Kq9VKz6QEl7bblcnYRAghxMVEVVWsNeUYzBnYLJXofCLQup19c1bRejJuEEIIcaEzGo3861//YurUqcTFxbV4/v/2PctGo3FD7xeDRqNx6p4VFVV8ueEnFEVl3JhhTl1rt9tJO5RBQnwsgQF+zJ45Cb2+4dimttbK/rR0Tp06xawpzrXvSk4nS9588018fX3ZvHkzmzdvbvCaRqORgYUQQogLkqqq1FSdoqY8A8VmwdMv2mWrSeopikLqvoPEREfi7+fr0ra7MhmbCCGEuFgoNgvGsmMYy7PQ++vbNJkhzk7GDUIIIS5kiqKQmpqKm5sb0dHRLZ5vNVdiLM3EYjiNu1cI7jqfVt+rvLySU/mF9O+bTECAHyn9e5PUIw5v79aXxzp1upDtO/dhNJrw9fEhMMDPkSix2WwUFZfRLSocd3c3Tp4qICI0GJut4/YQczpZkpOT0x79EEIIITqMYrNgrjqJtqoANw8f9L7dXNp+aWk5vr4+6PU6pkwcQ2BA65bIitaRsYkQQoiLQa2pFGPZUcyGQtw8/PDwDpRESTuQcYMQQogL1YkTJ/j111+pqqpi1KhReHh4oChKk+f+bx/WDOzWGqfKeZaVVbDvQDp5J0/j4+1NclJ3dDoPUvr3anVfa2ut7Ni9n+xjx4mMCGPKpP/NhVRUGsjIPEbWsePY7QrXzJ+NTufBFXOnO/Ys6ShS8FQIIUSXZjVXYCg+gtlQiH94GO4u3EDMbrdz4GAGBw4eYUC/XgwZ1I/QNtb1FEIIIcTFSVFs1FTmYSrLQrHb8PTrhqmqtqO7JYQQQohOJj8/H19fX6ZNm0ZwcHCz59ltZoxl2dSU5+Dm4Y2nX8srUOr9/Otuso8dx9/Pl3Gjh9Gje2yb9hSptVrJLyhizMghJPfs7jiee/wkm37egV6vp3dyD5J71iViOotWvdMnnngCk6l1GZ0dO3bw1VdfnVOnhBBCiPZWV3brJJX5u7HWlODhFYzWhfuTnDpdyP/78nvSDmYwcEAfBqX0cVnbQsYmQgghLg42SxVVBfsxFKWhdfPE0y8KXFwGVMi4QQghxIVJURQOHDhAWloaAMOGDWP27NlnTZTUmkqoyt9DTXk2Ht4heHi1/MBmtdGEzWYDIMDfjzEjhzBvzjSSEuOdSpSYzRZ27NpPba0VXx9v5s+b2SBRAlBaVkFsTBRXXzmLoYP74+fbsCxY2qHMVt+vPbRqZcnhw4eJi4vjqquuYu7cuQwbNoywsDCgrrbY4cOH2bp1K++99x75+fm888477dppIYQQ4lzU1QPPpqbiGFp3b3S+3dBU1LisfUO1ke9/+oWI8FAmTxhNYKC/y9pujtVqpaLSQFho84Omi4mMTYQQQlzIVFXBbDiNqTQTW60JvW83tK0sjSGcJ+MGIYQQF5qSkhK2bNlCSUkJAwcOBDhr4kJV7BgrczGVZaEqdvR+0Wg0Z0901NSYOXAwg4zMYwwZ3I/+fZOdKrX1W7l5p9i+cy92u0L3hBjCw0Ia9VdVVYYO7o+qqg1KjeYXFOPv74uPtxfe3l6Yasx0VE2OVo3G3nnnHQ4cOMA//vEPFixYQGVlJW5ubuj1esfTGYMHD+aWW25h4cKF6PX6du20EEII0VbWmnKqSzOxVBeg8wnHzd0TRVXPuV1VVck9fpL4uGj8fH2YPXPSeSm5paoqWdnH2bP/EFqNlt/Nm9Hu9+wMZGwihBDiQmW3mjCWZVFTkVdXGsO/9aUxRNvIuEEIIcSFQlEUdu7cSVpaGsHBwcybN4/w8PCzXmO31lBVlIbFcBIPfSDu3i3vk3oo/Sh79h1Cq9WSMqA3vc5YAdJaZrOF7Tv3kZt3kriYbowaMajBBvCqqnLiVD5HMo4REhzI0MH9HYmSEyfzOXAwg+KSUoYM6k9K/14kdo+9MPYsSUlJ4Z///CevvfYaBw4cIDc3l5qaGkJDQxk0aBChoaHt2U8hhBDinKiqgrnqFMayDBSbBU+/aDQuKnNRUVHFrzv2UlRcwrTJ44juFnFeEiUFhcXsTD1AWVkF3RNiGTq4f5tqiV6oZGwihBDiQqKqKrXGIoylmdSaK9B7h6F1l0n580XGDUIIIS4EWq0Wg8HAiBEjGDBgwNlXk6gqlupCjGVZuHnWoPeJROvW8v4fhmoju1IP0DMpgWGDB6DX69rc34rKKvILixk/djg9usc5jlsstWQczSEj8xhGk4mQkCBCggMBKC4pY9vOvZSVVRAaEsyUiWOIjYlqcx9cyel1vhqNhoEDBzqW/4iLy8SJExk0aBDPP/98R3flvFq0aBEVFRWsX78e6LpxEOJipdgsVJdl1W1upvNG79vNJe3Wb+CedjADX19vZkwdT1RkmEvabo2dqQfQarRcOmMi4WEh5+2+nY2MTYQQQnR2is2CqTwHU8UxNBr3uoc2flN+Qpw/Mm4QQgjR2ZhMJn799VeSk5OJi4tj2rRpLV6j2GsxlR2jujwLVXFD7xvd6ocn/Xx9mDV9AqEhQbi5Of8QaU2NmSOZxxiU0ofIiDCuumIm7u51aQaLpRa9Xkd1tZF9B9LpnhBDn16JDR4odXdzQ+fhcd7nUFpDiqJ2QosWLWLdunWNjs+YMYMNGzZ0QI+at2nTJiZNmsSRI0daXBLWVvX/iNi2bRujRo1yHLdYLHTr1o2ysjJ++uknJk6c6LJ7fvrpp3h4tJyJFUJ0frU1ZRhLMrAYixxlt1zlxMl80g5mMKB/L1L692rTIMNZdrud2lorXl6eTJs0Fk9PvUy2CCGEEJ1YrakUY2kmFlMROq9Q3Dy8O7pLQgghhOgEVFXlyJEj7NixA61WS1JSUquuc5QXNxbg4RmCu00DrZwXsNlsuLu7ExHethWVp04X8vOvu1BVSOwRh7+fLxqNhqzs46RnZqPYFS6/bCohIUFcM382Op0HdrudjKPHOHWqkPHjhhMUFMDMaePbdP/21mWSJaqqYK0pbdW5iqJiM5dSa9Ki1bpmAsrDK6TFTXV+a+bMmbz99tsNjl3MdVNVVcVutzuykGeKjY3l7bffbpAs+eyzz/D19aWsrMzl/QkO7hobJAtxMasru3USY2kmir3WpWW36iXExxAcHIi/n69L2z2b7GN57Ni9n6uumIWXV+PET02NmZoaCy1XKBVCCCFEe1IUGzUVxzGVZ6PYbXj6un4sIoQQQogL16FDh/j111/p1asXI0eOxNPz7A93qqqCufIExrKjKHYLnr7RqBotUNPiveoSFjnsTztCWGgQUyeNdaqviqKw70A6Bw4eoVtUBJeMGYaHhzv7045wJDObmhoz3aIi6NMr8Tf9VdmfdoT0jCzMZgsJcTGYLbX4NjP/2xl03p65mLWmlJ9eaNtGNa4w6e4cdN6tX1ak1+uJjIxs9nWNRsNrr73GF198wY8//kh8fDxvvfUWYWFh3HzzzezatYuUlBTee+89EhPrPqRnlpoCuOeee9i3bx+bNm1q8j7vvfcezz//PBkZGfj4+DB58mSef/55wsPDyc3NZdKkSQD07t0bgIULF7J27VosFgvLli3jww8/pKqqimHDhvHcc88xfPhw4H8rUjZs2MCKFSs4cOAA3377raO9My1cuJAXX3yR559/Hi+vuk2C3nrrLRYuXMiqVasanHvq1CmWLl3Kd999h1arZdy4cbzwwgskJCQAdX85LFu2jLfeegs3Nzduuukm1DM2dz6zDNfZ4vDb9/P999+zfPlyDh8+zKBBg3j77bfp1atXs99HIUT7sNvMGEuPUlN5HDcPH/S+rq19abHUkp6RTZ9eiec1UaKqKofSjxIdFYGnZ9MJdDc3NzKz8oiIH3re+iWEEEKIhqzmSoxlR7EYTuOuD0TXio1WhRBCCNG1nDhxgqSkJCZMmNDiuXarCWNpJjWVJ3DT+TrKi585p3kmVVXJPpbH3v2HMdXUkNgjnkEpfZzua+7xk6QdymDIoP4M6JeMRqNBURSyso8TGxNF3949CQxoON7Z+OMvlJVXkpQYR/++yed1/qStus4usBehVatWccMNN7Bv3z569+7Ntddey6233sr999/P7t27AfjjH/94Tveora1l1apV7N+/n/Xr15OTk8OiRYuAutUen3zyCQBbt27l1KlTvPDCCwD85S9/4ZNPPmHdunXs2bOHpKQkZsyY0WgVyF/+8hcef/xx0tPTSUlJabYfQ4cOpXv37o77nThxgi1btnD99dc3OM9kMjFp0iR8fX3ZsmULW7duxdfXl5kzZ1JbWwvA3//+d9566y3WrFnD1q1bKSsr47PPPmtzHH5rxYoV/P3vf2f37t24u7uzePHis7YrhHC9WlMpVfl7qKnIRecViodnoMvvkZ2Tx4G0I9jtdpe33Ryr1cqu1ANUVhkY0L9hEtZQbeTHTdswmmrQ6TwYNKDneeuXEEIIIf5HVRVqKvOozN+FpboAnW8U7npJlHQFjz/+OBqNhnvuuaejuyKEEOICMXPmTMaNG9fieZbqQipPp2KqPIHOO8ypeQ5DtZFft+8hNCSIy2dPZdzoofj6tL4kaEWlAYAe3eOYe+kUUvr34udfd3PiZD5arZYr5k5jzMghBAb4UVJazqYtOygrqwBg9IhBXHXFTMaMHHJBJEqgDcmSxYsXYzAYGh03Go0yMexCX375Jb6+vg3+nLmC4sYbb+Tqq68mOTmZ5cuXk5uby4IFC5gxYwZ9+vTh7rvvbnbFSGstXryYWbNm0aNHD0aNGsWLL77IN998Q3V1NW5ubo5yVaGhoURGRhIQEIDRaOTVV1/l6aefZtasWfTt25c33ngDLy8v1qxZ06D9Rx55hGnTppGYmEhIyNk3J77xxht56623AHj77be59NJLCQtruFrnww8/RKvV8uabbzJgwAD69OnD22+/TV5eniMWzz//PPfffz+/+93v6NOnD6+99hoBAQFtjsNvPfroo0yYMIG+ffvy17/+lV9//RWz2dxinIUQ505V7BjLc6jM34PVXInerxtad9eXL6yoNHA4PYu42G54e3u5vP3m7N5zkCOZOQwe2I+w0Lq/e1VVJT0jm/VfbKS0rAKTqeWltxcjGZsIIYToDGy11RgKD1BVsB/Q4ukXjVbbZYo5NElV1RafeD3f2mPcsGvXLl5//fWzPgAohBBC1LPb7VRUVKDRaNDpdM2ep9itVJdmUJm/B7u1pm5s0Yp5joLCYjb/vANFUfD38+XKeTOYNGEUgYH+re6joijs3L2f9V98R3FJ3cPvQUEB5BcUcywnz3GeRqPhxMl8vvluM19+8yOlZRWYLXUPrIeEBDVZPrwzczpZsm7dOmpqGk/G1NTU8M4777ikUwImTZrEvn37Gvy58847G5zz24FYREQEAAMGDGhwzGw2U1VV1eZ+7N27l8svv5z4+Hj8/Pwcm6jn5eU1e012djZWq5WxY/9X+87Dw4MRI0aQnp7e4Nxhw4a1ui/XXXcd27Zt49ixY6xdu7bJgWxqaipZWVn4+fk5kkzBwcGYzWays7OprKwkPz+f0aNHO65xd3dvsR+tjcNvvydRUXVlf4qKilr9HoUQbWO3mjAUpVFdlIbWTYfeN9KpfaJa60jmMT7/6ge0Wg2DBvZ1eftnOp1fRN7J0wAMHNCbKy+fzsABdWUPDdVGNmzcwo5d+0jqEc/ll011JFG6GhmbCCGE6EiqqmI2nKby1G5qKvPQ+YTj4RnU0d3qUKqqkpN7gv/35ffk5J7s6O404OpxQ3V1NQsWLOCNN94gKKhrf9+FEEKcnaqqZGdn85///IePPvqIgoKCZs+1miuoKtiLsfgI7np/dD5haFrYxL2wqIQNG7ewYeMWKg3VmGrqHuB2ZiUJQLXRxFffbuJIxjFGDBvYYK5h34HDBAcHEhtTN+954GAGP2z6FbtdYeL4UVx5+XS6RYU7db/OpNWPuVRVVTmeCjEYDA02nLHb7Xz99deO/Rs6Iw+vECbdndOqcxVFpaSkmNDQMJdu8O4MHx8fkpKSzt6mh4fj/+t/WJo6pigKAFqtttFTPVartdn2jUYj06dPZ/r06bz33nuEhYWRl5fHjBkzHCWtmlJ/jzN/gFVVbXTMx8en2XbOFBISwmWXXcZNN92E2Wxm1qxZjZ4IUhSFoUOH8v777ze6/sxVKK3lTBzOFn8hRPuwGIswlmRSay5H7xOB1q35pzLOlbubG72TuzNkUD/c23FDsvLySnbvPcip0wUkxMUQF9N4FYvdZsdstjBj6niiItv299uF7kIfmwghhLjw2a01GMuPYa7IQeOmR+8X0+JExsVMVVWOnzjN/gPplFdUEt0tkoAAP1qz8Wx7a69xw5133sns2bOZOnUqq1evPuu5FosFi8XSoE9Q929GV/27UVEUVFWVf4c6QWLmHImXcyRezrtYY3b69Gl27NhBcXEx8fHxTJ8+naCgoEbvU1UVzFWnMJVnolgt6HyjQOuO0sxKzfrfbdt27CXjaA5Bgf5MHD+KuNi6PU2au645ZWUVfPfDVjw83Jk5YwKhIUEoqkq10cT3P/5CRaWBxB6xpGceo1fP7vToEUd4eAgR4aF1/aHlfVTOpv79KIqCxoWfgdZ+nlo90xMYGIhGo0Gj0ZCcnNzodY1Gw8qVK1vfw/NMo9G2eoN1RVFw91TQeYei1V4827qEhYVx8ODBBsf27dvXYIL/t44cOUJJSQlPPPEEsbGxAI69UOrVLxX7bd3+pKQkdDodW7du5dprrwXqkjK7d+8+5/qtixcv5tJLL2X58uW4ubk1en3IkCH8+9//Jjw8HH//ppeWRUVFsX37dsaPHw+AzWYjNTWVIUOGNHl+a+IghDj/FMVGTcVxjGVHAfD0i3b55ISqqhzJPEZFRRWjRw4mKTEeiHfpPX7LarWyM/UAWdnH8fXxYeL4USTERTter6g0cODgEcaOGkJgoD/z5kzr0hMyF/rYRAghxIVLVVVqTUUYS45iNZfj4R2Km/uFVWaiPeQeP8nmrTvpFhXBmFFDCAsNRlUVLNUdnyxpj3HDhx9+yJ49e9i1a1erzn/88cebvEdxcbHLyjcrikJlZSWqql5U8xntSWLmHImXcyRezrtYY7Z3716qqqoYPXo0ERERWK3WRtVoFJsFc3U+tdVFaD08cfMIgior0PTD7qWl5XjoPEDjgZ9/EIMH+RMTHYVGo6Gsom2/e612N4KCQ0np3xurDfbsP0pCfAyqCu4ennh5KaQdzKZXLwgLiwTAQ+fT5vudSbHXotrtWItLXFpavakSnE1pdbLkp59+QlVVJk+ezCeffOLYqwLqJszj4+Pp1q2b8z0VTbJYLI2WYrm7uxMaGtrmNidPnszTTz/NO++8w+jRo3nvvfc4ePAggwcPbvL8uLg4dDodL730ErfddhsHDx5stG9KfHw8Go2G77//npCQEHx8fPD19eX2229n2bJlBAcHExcXx1NPPYXJZOKmm25qc/+hbuOj4uLiZhMhCxYs4Omnn+byyy/nkUceISYmhry8PD799FOWLVtGTEwMd999N0888QQ9e/akT58+PPvss1RUVDR7z9bEQQhxftlqqzGWHqWmKg8PfVC7bJxqqDbyy7ZUCgqL6dWzR5Or41xFURS0Wi3u7u4YDEaGD02hd3IPx8BQURQOHj7KvgPp+Pp4YTTV4O/n26UTJSBjEyGEEB1DsVkwledgqsgBjRa9X7d2Kf95oTCbLWRm5ZLSvxfxcdFcOmMi4WHOVVY4H1w9bjhx4gR333033333XYNVKmdz//33s3TpUsfXVVVVxMbGEhYW1uy/cZ2lKAoajYawsLCLapKxPUnMnCPxco7Ey3kXS8yMRiOpqakEBwfTv39/ZsyYcdYKFZbqIkxlx9FQQWB4GJoWEgXFJWX8sm07fXsnkpjYk/jo+DbPEdjtdnbtSaNv7ySCA/0YObQvh49kkZ2Th0ajoXfPGA6lZ1NZWY6iKEyZNJJePbu36V4tUayg2GwEhoXi5uG6fWJb+7u61cmSCRMmAJCTk0NsbOwF/WG9EGzYsMGx50W9Xr16ceTIkTa3OWPGDB544AH+8pe/YDabWbx4MTfccANpaWlNnh8WFsbatWv529/+xosvvsiQIUN45plnmDt3ruOc6OhoHn74YR599FHuuecebrjhBtauXcsTTzyBoihcf/31GAwGhg0bxrfffnvONVw1Gs1ZE0be3t5s2bKF5cuXc+WVV2IwGIiOjmbKlCmOwee9995Lfn4+ixYtQqvVsnjxYq644goqKyvbHAchxPmhqiq1xiKqS49gs1Sh94lC69b06rhzkXk0h52pB9DrdEyfckm71dtUFIWj2bnsO5DOtEljCQ4OZOa08Q3OKS+v5Odtuykvr6Rfn54MSulz1gFWeXklthoTfl2g+pSMTYQQQpxvtaYSjKVHsZiK0HmF4ubhXA3wi9H+tCNkHTtO/7490Wq1nTJRAq4fN6SmplJUVMTQoUMdx+x2O1u2bOHll1/GYrE0qoag1+vR6xtPfmm1WpeOYzQajcvbvNhJzJwj8XKOxMt5F3LMrFYr+/btIy0tDXd3d6KiotBqtc1u5K7YazFV5GAqO4ZG44aXf+tKeh48lEFQgB/Dh6ZQXmmui1kbkiVGUw0/bd5OWXklMd0i2LPnIHknT+Pl5UnPxASGDuqHXq/DWG2kb69E+vRKbFQm3JXU/64CdfX3v7VtadQ2FBGrqKhg586dFBUVNar3dcMNNzjbXJtVVVUREBBAZWWly57CgLrJq6KiIsLDwy/IH8rzTeLlPImZcyRezrlY46XYrZgqjtUNILTueHiFuGxlhaKqlFXUEBzohVajYefu/VhtNoYPSUGnc30yBuDkqQJ270mjorKK7gmxDB3cv8lN146fOM2+/YcZO3oooSHNJ5xtNhs7Uw9wNCuX+Cgfpl36e7yDXPekR3v9znUVGZuI35J4OUfi5TyJmXMuhngpdis1lbmYyo6hqgo67zA02sZlgV1yrzPGJZ1ZRaWB//flRoYM6s+Afo1LWwH/LcOVT1DsGHRewU2e0xbn8jvXFeMGg8HA8ePHGxy78cYb6d27N8uXL6d///4tttEe44aL4eftfJOYOUfi5RyJl/Mu5JgZDAbWr19PbW0tKSkpDBw4sNkkCYC1ppzq0kwsxgKnHsIoK6vg869/4JIxw+nePbbN44ai4lK+/+kXSksruOqKWURGhpF2KJOaGjOVVQZOnS5o1wdI69lsNvJOnOZY7gmiwgNJ7hFJUOxYl64sae3vXKd3p/3iiy9YsGABRqMRPz+/BhNVGo3mvE5ICCGE6BpsliqqSzKwGE7X1QRvh6c4j+XkUeTjQd9eiYwYNtDl7f9W/YAkMiKMOWMmE3JGEqSktJyc3BMMH5pCfGw3YqMjWxwkurm5UVVVzfChKSREtt8m952RjE2EEEK0J2tNOcayTCyGAty9gnHX+XZ0lzqF3LxTpO45iI+3N317J3Z0d1rNVeMGPz+/RgkRHx8fQkJCWpUoEUIIcfEoLS0lJCQEPz8/+vXrR3JyMr6+zY8XVMWOueokxrKjKHYLnr7d0GhbP01/4GAGvj4+dE+IaXOfDYZq3n73k7oHOONjqLVZKSgs5tTpAgoKiwnw92Pc6GFERrR9S4iWVBtNpB/JIjMrF6vVSlhoCN5e7bdqpTWcTpbce++9LF68mMceewxvb9dNVj3++OP87W9/4+677+b55593WbtCCCEuXKqqYjGcxliagc1aU1cT3IkBRGsYTTX8sn0PmUfzGDKol0vbbo6PjzeDUvoycEDvBv9At9ls7DuQzqH0owQFBVBba0Wn82g2UVJQWMyO3QcYOSyFsvJKBg/sS0R4KOaqk+flfXQW7TU2EUII0bUpio2ayjxqyo+h2C3tMg650NhsNlRVxcPDg9LScgICfBk2eECjclOdmYwbhBBCuNLOnTvZt28f8+bNIzw8nCFDhpz1fFttNaayLGoq83DT+aP3bbl8paqqHD9xGh9vL8JCg+nXtyfubm5otVoUJ4tGKYpCbW0tGzb+TIC/L+NGD6VXcg9CQ4I4cDADq83GpAmjiYuJard9Ui2WWvR6HaVlFRzNPk6vnt1J7tkdfz9f7NYaFJupXe7bGk6P9E6dOsWf/vQnlw4qdu3axeuvv05KSorL2hRCCHFhq9s89Rim8mw07l54+rl+o+4TJ/PZ8ssu3NzcuGTcSAb0bZ8Nys7k4+3FoJQ+DY4VFpXwy7ZUqo01DB7Yz1H3uylGUw2796SReTQXs8VCcXEpHh7uDB7Yj4jw9nvqo7Nqj7GJEEKIrs1qrsRYloXZcBIPfSB6r865D8f5YjZbOJJ5jPSMbPr0SmRQSh+GDOrXbpMo7ak9xw2bNm1yeZtCCCE6r5qaGtLS0hg0aBDh4WcvVaWqKpbqgrqHQS1V6Hwi0LqdvSpEfZJk/4F0yisq6d83mbDQYMJC21bWsqbGzE9bdmCz27HarIwcPojc4yfR6TwIDQmif9+epPRv34dI9+4/TGlZOVMnjSUuJoroK2eddV/W883pnsyYMYPdu3fTo0cPl3SgurqaBQsW8MYbb7B69WqXtCmEEOLCVltThrE0E0t1ITrvMJfWqfytjKPHCAkOZPy4EZjMSssXnKPc4ydJO5xJ315JJPaIa/BafkExer2eyRPHEBjg12wblVUGvv52M5WVBoymGkJDgkhOSqBf3574+3XNsiCuHpsIIYToulTFjtlwCmNZJnarBb1vN7RdeDVJtdHEocNHyczKAaBnYgI9uscCXJCJEpBxgxBCCNdJT09Ho9EwcODZS3k7HgatyEGj9UDv1/Im7uXllWz5ZRflFZVERYYza/qEc3o4sqS0nB83b0NVVYYM6seBtCPsT0snNiaKhPi6cl7tvUfM8ROn2Z+WzqCUvkDdWKIzJUqgDcmS2bNns2zZMg4fPsyAAQPw8Gi48e3cuXOdau/OO+9k9uzZTJ06tcVkicViwWKxOL6uqqoC6pYPnbkp27lQFAVVVV3a5sVM4uU8iZlzJF7OuZDjpaoK5qqTmMqOothr0ft2A62b08tKW2vyxDHYbDbc3Nww1tTUxa0d7lNQWEzq3oMUl5QTEx1JSEggiqpyOr+Iisoq+vZOon+/ZAb074VGoznr+3VzdyMiPJRZMyaQlX2cPr0S8fLyBHBcp6oqios/A5358+Sqscmrr77Kq6++Sm5uLgD9+vXjwQcfZNasWa7ushBCiE7IZjHUrSapOoGbzq9dVrVeKGw2G+7u7hgMRo7lnmBAv2R6Jyfi6anv6K6dM1fPaQghhOiaFEXhyJEjJCcno9c3//ux1lRS9zCosbjFh0FVVaWiooqgoAC8vb3w8/Vh1IhB51xBIvtYHj9s2obRaCIiMpQdu/bj6+PN3EunEBwceE5tt0ZhUQl79x+moLCYuJhujSptdCZOJ0uWLFkCwCOPPNLoNY1Gg91ub3VbH374IXv27GHXrl2tOv/xxx9n5cqVjY4XFxdjNptbfd+WKIpCZWUlqqq2e0btYiDxcp7EzDkSL+dcqPFSbBbMhtPUGovRenjh5hEIVbXtdr+qKgP+/nUrOFSsGIy1oNHg6mckc4+fYPuOPQQHBzJyxHAiwkMxma38unMbx44dJzIynIiIbmd9qsRut5OekUV8bDQlpWUEBYdis7uRkNCDGotKjaWmwflWE1jcDVTXFrnsfRgMBpe15WquGpvExMTwxBNPkJSUBMC6deu4/PLL2bt3L/369XNdh4UQQnQqdQ9rnMJUdhRbrRGdTyRaN4+WL7wInc4v4uDhTGw2O5fOmEBUZBhXXTGz0z31eS5cOachhBCi66l/QNXNzY3evXuTmJjYzHk2aiqOYyrLQlUVPP1j0GianqNRFIWc3JMcOHgEo6mG3//uUvR6HZMnjj7n/hYUFvP1tz9x8HAWY0cPpVdSd7y9PElKjG/0wIArVRtNWGutBAUFYLXaUBSV8WOHO1axdFZOj3hc9WTpiRMnuPvuu/nuu+/w9PRs1TX3338/S5cudXxdVVVFbGwsYWFh+Pv7u6RfUPceNRoNYWFhF9REY0eReDlPYuYciZdzLsR4WYxFmEqPo9FUEBgehsa9/Z5aVBSFQ4ePkrrvEFMnjSEmOhJVVUFVCQ7wdElJCVNNDSUl5cTFdsPPpzvBgd7Ex0Wj0Wg4eSqfbTv2Umu1MWXSSHr1PPs+KadOF7J91z6MRhOhQb5kZmbSPSGG4MCmB2QAFi34BvvhHXj2mqnOaO3v6o7gqrHJnDlzGnz96KOP8uqrr7J9+3ZJlgghxEXqf5usnsDNwwdP/879D/j2UlFRxZZfd1FWVkFwcCAD+iY7XruYEiXQuVfLCiGE6NxOnDjB9u3bSUxMZMiQIc1u5m41V/63tPhp3PVBuOubLrWtqipZ2cc5cDADQ3U1MdGRjBsz7JyTGIqiYLPZyco+zudf/0BhUQnJSQnMmDquXfc5VVWVk6cKyDh6jJOnCoiNiWLKxLp5l5joyHa7ryud06jHbDa3efIkNTWVoqIihg4d6jhmt9vZsmULL7/8MhaLBTc3twbX6PX6Jpc1abVal08IajSadmn3YiXxcp7EzDkSL+dcKPGqe9IiB2NpNmg0ePm3XLfzXJSWlvPLjj2Ul1eS0i+Z2OjIurJX1MVMo9GgPYf722w2Dh7OJO1QJp56PXExUeh1OnokxDrOOXbsBMFBgYweORhfn+Y3FjWZatiVeoCc4ycJCQ4iMjyUtEMZAAwa0Oes/ax/H678/nf2z1K9cxmb/Jbdbuejjz7CaDQyenTzT/NIidDOSeLlHImX8yRmzumM8VJVBYuhAFNZJjZrNTrvcDRuunYr/elc31THn/MVsV927KG21sbUyePoFlX3sIXLYlH/XpTOVyLUVeMGIYQQF7fy8nK2b9/OiRMniIqKIi4ursnz6lerGssysFvN6FrY+8xiqeV0QRHBQQFMvGQEISFB59RPk6mGjKM57DuQzunThZwuKGLIoH7Mmj6B3sk90OvPvqH8uSgtLefHzdsxmkwEBwcyZuQQuidceA+hOJ0ssdvtPPbYY7z22msUFhaSmZlJjx49eOCBB0hISOCmm25qVTtTpkwhLS2twbEbb7yR3r17s3z58kaJEiGEEBcXm6WK6tKjWAwncfcMwV3n0673Kygs5tvvfyYwwJ/ZMycReo6DkDNlZR8ndd9BLOZa+vZJIqV/b0eCIff4SbRuWuJiujFuzNBWPaGpqCrFJeVcMmY4lVUGDh4+SnJSAv37JePt3T4b3l+oXDU2AUhLS2P06NGYzWZ8fX357LPP6Nu3b7PnS4nQzkni5RyJl/MkZs7pbPGyW81YjAXUVhejcdfhpgtBY7ADNS1eez6o0G7lQZvTp3cfbHY7nl5+lFW4Ng4qKrYasJaU4aG3uqzdtpYIdeW4QQghxMWvvLycjz/+GD8/P6ZNm0b37k1Xh7DVGjGVHa1brarzwdMvusW2PT31jB87/JwfGi0rr+Dw4UPs35+OxWqlpsZMgJ8Pi677HYMH9mm3VaL5BcUYDNUk9+yOn58v3bqF06tnD5fPt5xPTkfq0UcfZd26dTz11FOOWp8AAwYM4Lnnnmv1wMLPz4/+/fs3OObj40NISEij40K0ZNOmTUyaNIny8nICAwNbdU1CQgL33HMP99xzT7v2TQjRkKqqWAynMZZmYKs1tfikxbmqNprw9fEmPCyEkcMHkZyU0C4TNbl5J4kIC2XokP74+dYlfkymGnbs2s/xE6dI7tmduJhuZx2kFBaVkHYog5R+vcnMziW5Z3cSe8RhsdQ22MhdNOSqsQlAr1692LdvHxUVFXzyyScsXLiQzZs3N5swkRKhnZPEyzkSL+dJzJzTWeKlqgqW6kJMphzcqCIwPLxdS3+2lavLgzbHZrOReTSH2NhuJMS5rnRnI6qCxQ0CQ4PReblu8qStK0JcOW4QQghxcVIUhZycHBITEwkKCmLy5MkkJCQ0+XC/qqpYqgswlmViM1ei8w5H24rxxdGsXFRVJbmF0twtUVWVHTv3cjg9HbtdwctTz9jRQ5kwbkS7rSQ5eaqAXakHqKwyEBISRHLP7uh0HowdNbTlizs5p2en3nnnHV5//XWmTJnCbbfd5jiekpLCkSNHXNq5rmrRokVUVFSwfv16AIqKinjggQf45ptvKCwsJCgoiIEDB/Lwww87SoMkJCRw/Phx/vWvf3HNNdc0aK9fv34cPnyYt99+m0WLFjnO/22ioP56AC8vL3r06MFdd93Frbfe6mjHYrHwyCOP8N5771FQUEBMTAwrVqxwtCmEEC2x28wYy7IxV+SgcffC07/lJy3aymq1krr3EJlHc5hz6WSCggLondzDZe3X1JjZtmMviYnxxMd2Y/KE0Q0mgLKP5bFj9340Gg0TLxl51k3MamrM7N57kD37DmEwVJOVfZygoABCB/QBaNelshcDV45NdDqdY4P3YcOGsWvXLl544QX++c9/Nnm+lAjtvCRezpF4OU9i5pyOjpet1oipPJuaijy07p54+se2ayLiXLiqPGhzbDYbGUdzOHg4E7PZgqdeT4Cfr8vvY7fbOZVf6Ch7qtV2jhKhMqchhBDibHJzc9m+fTsGg4Hg4GCCgoKa3cTdbjNTU56DqSIHjdYDvV/LpcVtNhvpGcfYn5ZO94SYNidLTp0u5Gh2LqVlFZSWlTN29FD6JCdiqK5uUOnC1QzVRjb9vIPQkCBGjxxMZERYu9ynozidLDl16pRjEuG3FEXBaj23JbWbNm06p+svVr/73e+wWq2sW7eOHj16UFhYyA8//EBZWVmD82JjY3n77bcbJEu2b99OQUEBPj4tl7d55JFHWLJkCdXV1axdu5bbbruNwMBAfv/73wNw9dVXU1hYyJo1a0hKSqKoqAibzebaNyuEuGjVmkrqNjgzFqPzCcfNvf1WSeQeP8nO1ANYLLUMGzqAwEDXPeFfb8/+QxQUldAzKQFo+A92RVE4eDiT6G4RjBw2EE/P5p8qyck9wa879lJdbaKmxkz/vsmk9O9FQnyMTMC1UnuOTVRVbbAniRBCiAtL3dOe+RhLM7FaqtC38mnPi9XJUwX8sj0Vs9lCYo94Uvr3wt/FiZL6eukZR49hNluYd9lUPDtRle32HDcIIYS4cJWUlLBt2zby8/OJjY1lxowZBAU1vyKy1lT83zmOEnTeYbh5tFwuO+1QJgcPZ2KttdIzKYEhg/q1qa+qqrJ7Txp2ux0vL09Cg4OYOH4kvt7N74vqKtnH8tDrdEyeMBqd7tw2ou+MnJ6F6devHz///HOj4x999BGDBw92SafE/1RUVLB161aefPJJJk2aRHx8PCNGjOD+++9n9uzZDc5dsGABmzdv5sSJE45jb731FgsWLGhVbTo/Pz8iIyNJSkpi9erV9OzZ07G6ZcOGDWzevJmvv/6aqVOnkpCQwIgRIxgzZkyz7W3atAmNRsO3337L4MGD8fLyYvLkyRQVFfHNN9/Qp08f/P39+cMf/oDJZHJcZ7FY+NOf/kR4eDienp6MGzeOXbt2NWj766+/Jjk5GS8vLyZNmkRubm6j+//666+MHz8eLy8vYmNj+dOf/oTRaGy2vw8//DBxcXHo9Xq6devGn/70pxZjJoRomarYMZYfozJ/D1ZzJZ7+Me2aKEk7lFn3lENwEPPmTKNv7ySXPzlabTSRnZ1HSv9exMZEOY5nHs2hvLwSrVbLrOkTmDBuRLOJEpvNxrGcPA4cyiAhPpqFC65g4YIrmTdnGj26x7U5UVJlqKa0vKpN116oXDU2+dvf/sbPP/9Mbm4uaWlprFixgk2bNrFgwQJXdlcIIcR5Yqs1YihKoyp/D4rdhqdfTJdMlNhsNioq6sYGvr4+RHeL5Iq50xk3eqjLEyU7du3n4882cCj9KAlxMcybM52AAD+X3uNcyZyGEEKIpuTl5WE2m5k1axazZs1qNlGi2K1Ul2ZScToVq9lQN8dxlkSJ2WxBURQAqgwGEuKiueLy6YweObhNVSRO5xfx3of/j6LiUlL698ZoNFFjNmMyuW7PzDNVGapJz8gGYFBKH2bPnHhRJkqgDStLHnroIa6//npOnTqFoih8+umnZGRk8M477/Dll1+2Rx9dzmQyNZich7oyGn5+ftjtdkpLSyktLW2wTDw0NBSoS16cuZrCz88PvV6P2Wymurq6wWve3t54n0NWz9fXF19fX9avX8+oUaOaLPVRLyIighkzZrBu3Tr+7//+D5PJxL///W82b97MO++84/S9PT09HU/WfP755wwbNoynnnqKd999Fx8fH+bOncuqVavO2ieoS0K8/PLLeHt7c/XVV3P11Vej1+v54IMPqK6u5oorruCll15i+fLlAPzlL3/hk08+Yd26dcTHx/PUU08xY8YMsrKyCA4O5sSJE1x55ZXcdttt3H777ezevZt77723wT3T0tKYMWMGq1atYs2aNRQXF/PHP/6RP/7xj7z99tuN+vjxxx/z3HPP8eGHH9KvXz8KCgrYv3+/0zETQjRkq63GWHqUmqoTeOgDcfdun38sW61WKioNhIUGk9QjjsAAvwZJDFfbdyAdD52Ho6yXodrIL9tSKSgsZsig/gQFBZx14HAkI5t/ffQl3RNi6BYVwYihKXh4eBAV2bblq6Wl5RiqjSTEx6DX6ThyupiEXm1q6oLkqrFJYWEh119/Pfn5+QQEBJCSksKGDRuYNm1aO/ZeCCGEq9XtTVLw39UkBvTeYV0ySWK1WjmSmcOh9Ex0Hh5cefkMAgP8GDfatfXEbTYbbm5uaDQa/Px8GDqkPz0TExxjIVVVXHq/c3UxzGkIIYRwHVVV0Wg0DBw4kEGDBp31wcXamjJMZUexGApw9wrBXdd8JR+TqYZD6Uc5knmMUcMH0TMp4Zz29MgvKGbPvoP8uGk7Kgr9+/Vi+659+Ph4M23qhHbZVL2wqIRDh4+Sd/I0np56eiTEotfr8PZueRXNhcrpZMmcOXP497//zWOPPYZGo+HBBx9kyJAhfPHFFxfMZEJ6ejqpqakNjiUlJTF58mSMRiOffvopBoMBPz8/xw/ILbfcAtStligqKmpw7aRJk+jZsyfZ2dn88ssvDV4bOnQoQ4e2/QfB3d2dtWvXsmTJEl577TWGDBnChAkTuOaaa0hJSWl0/uLFi7n33ntZsWIFH3/8MYmJiQwaNMipe9psNt577z3S0tK4/fbbATh27Bhbt27F09OTzz77jJKSEu644w7Kysp48803z9re6tWrGTt2LAA33XQT999/P9nZ2fToUTfJOH/+fH766SeWL1+O0Wjk1VdfZe3atcyaNQuAN954g40bN7JmzRqWLVvGq6++So8ePXjuuefQaDT06tWLtLQ0nnzyScc9n376aa699lrHniw9e/bkxRdfZMKECbz66quNNgPMy8sjMjKSqVOn4uHhQVxcHCNGjHAqbkKI/3FscFaSgc1qQO8TidatfZ46OH7iNNt37kWr0fK7eTPw8vJs10QJ1JVpGNAvGXd3d45kHmNX6gE89XqmTR5HdLeIs157LCePDz/+CoDZMycRFhrcpj7U1JjJzskj+1ge5RWVBPj71SVL9DpS+jVdT/Vi5aqxyZo1a9qxl0IIIc6H+r1JzJV5aNw88fSL7rR7k7QXm83GwcOZHD6Sjc1qIzExjgH92u8pil+278FuszN54mj69m5c3qqzuRjmNIQQQrjOpk2b8PLyYtSoUc2eoyg2aipPUFOejWK3oPfrhkbb9LR6tdHEwUOZZGbl4uampV+fnuc0R2G321n/xfdUVhkIDQ1i5vTxJMRFs23nPrpFhjN2zFAMRtdvk7Bh4xYKCosJ8PdjzMghJPaIa3KD+4uN08kSgBkzZjBjxgxX9+W86dOnD/Hx8Q2O1a+O8PHx4corr6S4uJiwsLBG2cSJEyc2ubIEIDExkYiIhpNk57KqpN7vfvc7Zs+ezc8//8y2bdvYsGEDTz31FG+++WajzdVnz57NrbfeypYtW3jrrbdYvHhxq++zfPly/u///g+LxYJOp2PZsmWODd4VRUGj0fD+++8TEBAAwLPPPsv8+fN56aWXztrub5M6EREReHt7OxIl9cd27twJQHZ2Nlar1ZFcAfDw8GDEiBGkp6cDdcmuUaNGNfhHT/1G9/VSU1PJysri/fffdxxTVRVFUcjJyaFPnz4Nzr/qqqt4/vnn6dGjBzNnzuTSSy9lzpw5rSpfJoRoSLFZMJUf+98GZ77tN0lRZahm05btRHeLYNSIwe22x0eVoZo9ew8R3S2CnkkJjBk5GHd3d2pqzOzZd4jEHnEMHzIAD4/mE0IVlQZ27t5P7vGT2Gw25l8xq82JEpOpho8/2wBAXGw3hgzq12KS5mJ3oY9NhBBCnBtVVbAY8jGWHsVa2zX3JrHZbLi7u6PVajmed5rE7nH075eMTzs+/Xn8xGlyck8wbvSwZs+prDTgpnSuvS5l3CCEEALAaDSSnZ191kSJzVJFdelRzIZTeOgD0HuFnLXNgoJico6fZOCA3vTpldjmclUFhcVkHM0hv6CI4pIyRo8YwqCU3o5KSMFBAQQHB6LW9bJN9/it2lormVk5JCd1R6fzICE+hv59k4nuFnFeHzyxWGrx6MCcTJecCT5baSw3NzdCQ0NRFIXQ0NBGE2+BgYHNtuvp6dloxYKreHp6Mm3aNKZNm8aDDz7IzTffzEMPPdQoWeLu7s7111/PQw89xI4dO/jss89afY9ly5axaNEivL29iYqKavCDEBUVRXR0tCNRAnVJJ1VVOXnyZIPjZ/rt5KFGo2k0majRaBy1+1RVdRz7rfolcb8952wUReHWW29tct+RuLi4RsdiY2PJyMhg48aNfP/999xxxx08/fTTbN68+ayTn0KIhmpryuo2OKsubPUGZ+fiwMEMPD31TLxkZLskN2tqzOxPO0Lm0Rw8vfQkxEcDkHv8FLExUXh5eXLFnGl4ebX8d39JSRllZRXMmj4BT089Qeew6by3txfjxw0nKjK8TTVOhRBCiIuJrbYaU/kxairy0Lp74ukX0+VWk6iqytffbWb8mOEEBvozd/aUdo1BXSmQQxSXlNItKoKkxIYPI5aUlpNfUMyAfsn4+/tSdLqg3foihBBCtEVJSQlbtmzBzc2N5OTkRq+rqoK56hTGskzstTXofaPQNrGaxGiqYd+BwyiKyiVjhtGjeywJ8dFtnqOoMlSz/ouN/LpjLyHBgcyeOZFZ0yfg4e7ONxu3EBEWwohhAwn5b9mt1syTno2h2sjh9CyOZuei2BUCA/yJiY50lB1vb4ZqI0ezciktq+DkqXyKi4q5+7b55+XeTWnVdy0oKKjVA62ysrJz6pBonb59+zo2Xz/T4sWLeeaZZ/j973/f7GZETQkNDSUpqell02PHjuWjjz6iuroaX9+6TQAzMzPRarXExMRgMBicfg9NSUpKQqfTsXXrVq699lqgrtbu7t27HSW1mnrv27dvb/D1kCFDOHToULPvpyleXl7MnTuXuXPncuedd9K7d2/S0tIYMmTIOb0nIboCVbFjqszDVJaFqtjqSl5o2/dRAEVRKCuvoH/f5HZJlJSXV/LVt5vQaDQMGtiXvr0TsdRa2fjjL5w6XcCYkUNI7tm92USJoigcyTxGaWk5ISFBHDp8lLCwkDYtv7VYask5fpKj2blER0UwZFA/EuJjzvUtXtBkbCKEEOK3q0lstVXouuBqknpZ2ccpK6ug9r97TrZHoqTaaMJmtREY6I/dbsfNTcvE8aOI++/YRlVVTp4q4ODhTAqLSvD386VPrx64uWkJDHDtRvLOknGDEEKI3zIYDHz22WcEBwcze/ZsdLqGDyHaaqsxlWVRU3kCN50Pnv7Rzbb1w6ZfMRprSOlfV/Lyt3tgO6OwqIS0Q5mcPl2IobqaGVPGMX3KOHx8vCkpLWfDxi1oNBoSuzd+CLyt9qcdYd+Bw3h4eNC3dxK9k3u0234kVquVouIyCgqLKSouJSoyjEEpfam11JKekUVlpYHSskrCQurGGR2lVbNLzz//fDt3QzSntLSUq666isWLF5OSkoKfnx+7d+/mqaee4vLLL2/ymj59+lBSUuKSEmD1rr32WlatWsWNN97IypUrKSkpYdmyZSxevBgvLy+XJUt8fHy4/fbbWbZsGcHBwcTFxfHUU09hMpm46aabALjtttv4+9//ztKlS7n11ltJTU1l7dq1DdpZvnw5o0aN4s4772TJkiX4+PiQnp7Oxo0bmywbtnbtWux2OyNHjsTb25t3330XLy+vRuXahBCNNdzEPQB379D2vZ/NRnlFFWGhwcyZNfmcn6L4LUVRKCwuIyoyjMBAf1L69yY5KQFPTz1Z2cfZsXs/7u5uTJk45qxJj7yTp9m+Yx+ZWbkoikJsTBQ9useS4mS98PLySvanHSHvxGlUVKK7RbS5dNfFRsYmQgjRtdksBkzl2dRUnUTr7oW+C64mqVdlqGbP/kN0T4glPOzspUHa4nR+EUcysjlxKp+Y6EimTBxDTHQkMdGRjnNUVeXLDT9RWlpOaEgwE8ePIj62GxqNplNs8C7jBiGEEFC3Z3FsbCx+fn7MnDmT6OjoBokNVVX+u/9qZt2DGD4RaN2ar+Zw8lQBZWUVzJg6nqjIsDb1Kb+giEPpR8k9forikjIumzWZPr16OB4KPZaTx9ZtewgOCmDyhFEuTWZEhIcwcvggknrEufwh1Nraugc4dDoPjmQeY8eufaiqiqennoiwUAL867a1qKg04O7ujp+fL2NGDaVvciwa1eLSvjijVVFYuHBhe/dDNMPX15eRI0fy3HPPOfbziI2NZcmSJfztb39r9rqQENcOkn19fdm4cSN33XUXw4YNIyQkhKuvvprVq1e79D4ATzzxBIqicP3112MwGBg2bBjffvutY5VMXFwcn3zyCX/+85955ZVXGDFiBI899liD/VlSUlLYvHkzK1as4JJLLkFVVRITE/n973/f5D0DAwN54oknWLp0KXa7nQEDBvDFF1+4PI5CXEzO5ybuULep2ZHMY6QdykBV4eorZ+Hm5uayiZFTpwvYvOUo1dVGfjdvJr4+3o4nQyoqqvhleyrdE2IZOWxgs6WvVFVl44+/cDq/kKCgQMLDghnQrxf9+yXj5+vTqn5UVFRRa7USHhaCzW6nssrAkMH9SOwe16pyX12FjE2EEKJrUhU7ZsNpTGVHsVmN6LzCuuxqEqjbM2TTlu14euoZOri/S9suK6tg89adVFYZCAoMYOTwQSR2j21wTv3elhqNhuSkBAKHphAR3r4PzrSFjBuEEKJrMxqN/Pzzz+Tl5XHppZcSExNDbGzD32l2qwljWTbmyuNo3Dxb9SDGgYMZhIYEtzlRsunnHXyyfgOXjBnOxEtG0j0hptEm6qcLikmIj2bsqCEu2WDdarWyM/UAAwf0ITIijMiItvW9KaWl5Zw4VcDJUwWUlpUzdHB/+vdNJiKsLikTGRFGYIBfg2tOni4gIiyUYUMH4Ovjjd1aQ0dudaZRz+GR3JqaGqz/XeZbz9+/7TXYnVVVVUVAQACVlZUuva+iKBQVFREeHt5umwVfTCRezpOYOUfi5ZzzES/HJu7lx9C46fDwCmm3pzlVVeVoVi770tKpqTGT2COegQN6tzr50JLikjJ2ph4gJzefpB7RDB8ywFH7MzfvlOOpyIqKKgKb2WfEYqnFarVyJPMYv2zbw9zZk+meEOvYaLUl9WW2srKPU1JaRreoCKZPGdem91NTY0a1FBEQmYJ3UPc2tdGU9vqd62oyNhEg8XKWxMt5EjPnuCpeNktV3SRG1UncPHzw8Gp9yeELiaKqlFXUEBzohbaJ8VWVoZryiiriY7thsdSSdew4vXp2d8kToeXllZRXVNKjexy1tVa27dxLr57dG02kqKpK3sl89h1Ip1tkGMOHpjTbZt1TuvkExY5B5+W6FbKu+J17MY4b5O8n50nMnCPxco7Ey3mujtmRI0fYvn077u7uXHLJJY2qyKiqSq2xEGNpJrU1Feh9Wi7rqSgKWq2W4ydO46nXtelBgdP5RTz17OskJMRw6+JrGjwcabVaKS2rIDIizHGvZvvSwrihnqHaSEbmMTKzcrHb7cyZNbnZOY7WslqtaDQa3N3d2bFrP+kZWXh4eBAdFUFUVBjR3SLx9WlY+chms7HvQDrBQQH06B7X6P3VJUtMBMWOdek+uK39nev0aMpoNLJ8+XL+85//UFpa2uj1jqwpJoQQov3VmkrqNnE3Fp+XTdw1Gg3ZOXlEhIUwaGBfx1JNV8nKPo7VamPC+NH06x2PVqPBUG3k1+17yC8oYuqkscRERzY5iFAUhQNpR/j6u81oNBpiY6IYP264oyxFayYtior/P3vvHd7Yed5p3+iFAEmQBAiw9945vRfNaNQsWa6Jba0dx8k6tteJy3524iTORrGdKHbiXa8Ve10iJ3ac4irbklWmanrhDMlh7x0ESfQOnPP9gRl4qCFnSA7H0kjnvi5dl4A55z0vXhA4D97neX6/eZ5/4XhSZmvPri3k3yBrsVJmHfN09w4yOjrJxoZcGqzLb1q80ZBiEwkJCYk3NqIQJ+SZwO/sJx4Jok6x3FIS442K2+OlvbOXoeExUo0GCvJsaDRqaqvL73hsl8vDqbNtzDrmSEs1UlyUj1qtYveOTYuOE0WR0bFJrnT04HS5sWab7zkfNSlukJCQkHjzMDQ0xPHjx6msrGTLli1oNIuTIPFYiKBzGL9rCLlMhTb11t0kHq+Py+3dBAJBDh3YRWF+zqrnFI1Gae/s5WpXP/ft3cbunZsWJUq8Pj+Hj54mGArx9scOrUsxxNXufs5fbEelUlFRVkRlRQmpxrX5iXl9fsYnppmYnGFmxsG2LS2UlRZSUVZEQX4O2ZbMZZM7o+NTnDt/hWAozIaWREfsjccGAkEmJycoynvt5MdXvdr/83/+T44cOcLXv/51nnjiCf7v//2/TE5O8o1vfIMvfelLd2OOEhISEhKvAwQhRtA1mjBxF4VrQcTdqYyZsTu4cKmT1uY6bFYzB/fvWJd206XY2FqPTC7H6Q4hiiLdfUNcuNSBVqPhwL4d5OZkL3me0+nm2CvnOHHqAkZDCocO7KK5sWZZia4bcXu89PQOsXljI5kZ6bS21FFcmLcm7dGxiSnarnTjdLowGgy0NNeSY7l7cmivR6TYREJCQuKNSzTkSnSTeCdRqo1oU++tjfn1IB6Pc+LkBUbGJtDptGxsbaCirGjdunrj8ThHT5xFEAT27NxMwbWu2qWYX3Bx9MRZcmzZbNnU9LqU3LodUtwgISEh8cZGFEXsdjtWq5Xi4mLe8pa3YLVabzomEpjFP9dPNLSASpd1y0JQnz/AlY5uBgZH0Wo1NNZXI4rimu7FPn+Ql46cYvOGRnZsa12UDJmxOzhy/CxqlYpD9+1ac6IkFArTPzhKaqqBwvwcbNlmtm1uoaQ4f9VjXhelkslknD7bRm//EHKZnOzshHSW9ZoEmcmUtuwY4XCEV05fYHximtwcK/dvbFyUrBkYHGVweIzpmVlkYhzrIzvX8KrXh1Wv+LPPPsv3vvc99uzZw+/93u+xc+dOysrKKCws5Pvf/z7vec977sY8JSQkJCReQ6IhN/6FAcLeSZSadJSa9e3uuI7T6ebi5U4mJmfIyEhHqUwkSO5GouTchSukpOiprS5HuHbzH5+Y5uz5y1SWl9DaXIdavXTSIRgM8euXT6DTavnIH7yHHFs2Wu3K9NIjkSiHj55OVk8oFIpVV4R6fX4AjIYUwqEIKXotrU3byc3JRiaTEfJMrGq8ex0pNpGQkJB44yHEowQ94wSdQwixMBqDDbl8fY1HX+/MzTuxZGWgUChQqpRs3dxMWUnhusdF/YOjeDw+Hnlw300bHaIoMjQ8zvjENHt2bSYr08RjDx+4Y9mO1xIpbpCQkJB44+LxeDh27BgzMzO8613vIjU19aZESUJWfJiAaxhkcjTG3FsWggqCwLO/OgzAhpb6NUlfTs84uNjWSVamiYGhUSzmLHJzsheNMzI6wfFXzmOxZLJn5+YV7zHciH12jp6+IUZHJwFoqK+iMD+HjIx0MjLSVzxOPB5nesbB2MQU4xPTbN/SSl6ulaLCPGw2CzlWy7L7JUuhUikRRZE9u7ZQVJCLIAiMTUyRn2tDJpMxNDKOKIpUlBejUcrQrqAI9W6x6mhzYWGB4uKEBnpqaioLCwsA7Nixgw9/+MPrOzsJCQkJidcUURQIeSbxL/QlZC/u4kbFyNgkR4+fwWgwsGfnZgoLcu+KD0osFqO7d4ju3kE2tNQjiiKTU3Z0+lQK8nN45IF9Sc+SVyMIQmKMeJzZ2Xne+fiDySqK2+HzBxgYHGFgcIxwJMJDh/auaf6BQJBnf3WYosJctm1uobysiPKyojWN9UZBik0kJCQk3lhEAvMEnAOEfTMoNelojJmv9ZR+q0xMznDqbAeBgJcHDuzCZjWzY2vrul7D6XQzOW2nrqaCyvJiMjPSFyVKRFFkeGScKx09uD1e8vNsRCJR1GrVPZ0oASlukJCQkHij0tHRwfnz59HpdDz00ENL+lKE/bMEFgauyYpnoVDplxgpIZV1tbuf6soyNBo1+3ZvIcOUhkq1OhUHj9fHxUudXG7vZmpmloryYloaa6ipKlskvQWQlZVBbU05zY01a/JqGRufSu6ptDTXUlZSuKaES9uVLq529xOLxTAaDJQU5WO45hm7GiP76RkH5y5cYcfWVjIzTdy3dzsAk1N2jr1yjkgkwq4dm4hFY+zbvQWlUslzLxzD6Vygqsy26nmvF6ve8SopKWFkZITCwkJqamr4j//4DzZt2sSzzz5Lenr6XZiihISEhMRrQTwawL8wQNA9hkKpR5uae1evl2O1sHVzM+WlRXfN+K6nb4jL7V1EwlEqyoqxWrL45fNHccwvsGfXTjLSdcsmSsYmprjUdpXKihLs9jkslkyysm5tLBsOR4hEoxgNKczNLdDZ1U9hQS61VWWkp62tO+eV0xdRKOQ0N9Qs+e8ul4d4MMjd6f15fSLFJhISEhJvDIR4hIBrhKBzGFGMozHkIHsTdZPE43F+/dIrzMzOodOlsH/PVqzZ6ydzFY1GGR6ZoG9ghLn5BTQaTXIjxZy1WBv81y+dYMbuIC/Xys7tG8laJj66F5HiBgkJCYk3HsPDw5w+fZq6ujo2bdp0U+eHEAsTcI8QWBgGWFZWXBAE+gZGaLvSRTQaIzPDRH6ebU2yk1e7+jh64hxmcwYH9+8gEAxSX1u5KIERDke40NbBhuZ6DCl6WpvrVjx+LBbj3MV24nGB6uoacnOyObh/JzarecWFp5FIlLHxKYZHJ2htqiUjIx2jIYW6mgoK83NuKa21HMFgiPOXOhgaHsOclYnihvfC6/Pz01+8iCgImNLTOP7KOWQyGeasDEymNPbu2oJSHkeMB1d93fVi1ZHnBz7wAa5cucLu3bv57Gc/y0MPPcT/+T//h1gsxle+8pW7MUcJCQkJid8ioigS8c/in+8lEnKj0ZuRK1dfjbBSjp88T15ONiXFBVSWl6z7+Ne7QeRyOW63l7xcG/W1FYyOTfHL549iMOg5dGAXKnXKkufbZ+e42HYV++wcMuDs+SuAyI6tG5ZsvRUEgfHJGQYHR5mYnKEgP4c9uxL63+/Ota5Zc9Qxt8CFSx3YZ+e4b+/2m6pQzl9sZ3hkHNfcBPkmN3v0avSm4jVd615Dik0kJCQk7m0SuuGORKVnwIFKm4FSvTbT0XuZ8YlpHHPz3Ld3G/qUNDLSdevWZSuKIj999iUCwSC5Odns2bWFgjxbskDlunG7xZyJXq+juqqM1ua6m5IobwSkuEFCQkLijUdOTg579+6lvPxmietIwIF/vp9wwIFat3w3yYzdwemzbbg9XkpLCmlurMGQsvSxyxGJRPEHAkzPODhx+gKBYJDHHr4PtfpmWSmX28vLR04RiUapKi9Zkf/pjZw8c4mx8Ska6qqAhMR3js2yonPHJ6bpHxhhYsqOIMTJtmQRv7Z3UlZauKp53Mj0jIPDxxKy49u3tFJWWojX56d/YITSkgJeeOkVxsenqautoKgoj1xbNjm27KSkl1arIR4NIq55BnfOqnds/uRP/iT5/3v37qWnp4cLFy5QWlpKY2Pjuk5OQkJCQuK3y2+0O4eQyZRojXdHCus60zMOhobHKMjPWfexr2tsX27vprqqlJqqMjZvTNyn2jt7abtylbqaChrrq5ArFCy4bq5cmJ5x8PyLx8nISOfAvu2cv9hOWqqR5saaJSsspmccHD1xlnA4TEZGOhta6ykuTBjRyuXyVXfMLCy4iAsC5qwM4vE4ogh7d28lx2ZhfGKasYkpNrU2oFQqiQYdGGWjFOS5MWq8xKOBNazavYkUm0hISEjcu8SjQYKuEQKuEZDJ0BpykcnX36vsXqCoMI/09DRSUw1LxiWrIRQKMzg8xsDQGPfv34FWq2HblhbS0ow3bfyIosiJUxcYGh5j88YmqitLKbwLsdnrBSlukJCQkHjjodFobkqUXO9YDTiHQBRvG2MoFApSUvTs3rFpVf4e8Bu575cOv8K80015aSF1NZU01lctmSgZHZ/ixMnzGFL0PLR/zyKz89vh8wfovNrH8Mg4e3ZupqAg97ZxgyAITE7Zyco0odNpmZqexR8M0tJUQ3FRPin65c3tV0IsFkOpVJKeZqSkOJ9cWza9/UP87BcvMW2fpb6mitycbCrKi9mxrXVNnTq/LVadLBkZGaGoqCj5uKCggIKCgvWck4SEhITEa0AkMJeotvDbUevNy1ZbrAeCINDbP8yVjm4yM00UFayvxNfI2CSXr3ThcnsoyMvBlm1GEAQWnG6yMk3UVJWSY7MkJSWuG7xDwhNkYnKGkuJ8FpwuQqEwu7ZtwGRKI9uSuUijNB6PMzo2STQWo7K8JBEYFOVTUVa0pnZVSGxuDI2M0z84itPpoiAvh317tmLNNtNQV8nw6ASvnLpANBol1WhgbmYYVWSUXO0AOXkx1CmlBOb77mwB7zGk2ERCQkLi3kMUBcI+O4GFAaIhJypd5l2NPV6vuFweunoHUCoUbNrQSHqacVFcslqmpmfp6x9mbHwKgIL8HKKxGFo05OZk33R8MBji3IUrjIxNsmv7RkqK3/j3TylukJCQkHjjEI/HOXHiBOnp6TQ1NSWfjwTm8C8MEPbbUWkzUS6jJAHg9njR67SYsxJyWatlZHSCcxfbCQZDxAWRnds2sGVTE2mpS4tje7w+jh4/Q0FeDju2ta7IB0UQBCKRKFqthrm5BQaGRmlpqqOoMG/ZuEEURWbscwyNjDE6NkUkEmH7llbKy4rY2Fq/LvLn8Xicjqu99PQOsWPbBnJsFuQyOf/wtX8mEo2Qbc5k+9ZWDu7bgV6vo7624rZjRiJRlHdHmX1FrMmzZNu2bbzvfe/jHe94BxkZ915bbiziQ4iFlv13QRCJhRaIBBTI5XdWUS1Xat+ULeQSEhL3DoIQI+gaJbAwgCDE0Rrz7npFZ3tnL5fbuygtKaSlqXZdx56csnP0+BlybNns2LaBrEwTTqebXzx/BK/Xzzve+gBqteom7e1oNEbblS46OntwzDmxZmchCAK1NeUoVYnb5Y1BzPjENCdOXSASiVBUkEdleQk6nTbZvbIWZuwOXnjpFQDy82w0N1Qjk8kQRRGZTEZnVx/BYIiaqjJsmWqUkRECs4cJxcJo9BYUtwgA38i8EWITCQkJiTcTsYiPgHOIkHsMmUKDxph3VztZX2+IosjklJ2ungGmpu3odFpqq2+WDVkLPb2DeLw+WlvqKC0uuKWxaywW46e/eIl4PM6u7RspLspflzm83pHiBgkJCYk3BvPz8xw5cgSXy8WePXuA1XeTQEIaXKfVJA3IV0MkEuW7//IjGuoqeeyRAxhS9CgUS18vHI6gVqtINRp48P49K5K6DASC9A2M0DcwTFaGiX17tt5W4vv6/sGps5foHxjBkJJCZXkxJUX5yaLO9UiUTE7N8OxzR+juGUQQ4gSCIX73nY/g9nh5+IG9VFWUYLOaV3ytaDTK5fZuevv6efjgZl4rt7RVJ0suXLjAv/3bv/Hkk0/y8Y9/nPvvv5/3vve9vOUtb0GjuXua9utFLOJjvO07RIPOWxwl4PcHCKTogTv741HpTOQ3/56UMHmNCQQCvO997+PFF1/E6/XidDppamrij//4j/njP/7jZc+TyWT85Cc/4bHHHvutzVVC4rdJNORKdJP4plBqTKg1d88WfHLKTjAYoqy0MCnvsNbui1djn51janqW5saEqdlDh/ZizspAEIREYuZKF0ZjCvfftzOphXkjA4OjHD/VhkqZaL3NNKVTUpxPfW3lku2w8/NOjp44i81qZmNrw7IVI7fD5fYyMDiCIAhs2tBIVqaJDa31pBoNTEzOcPLMJcLhMA8c3E22JYt9u7dCzI3X3oHf3k8wGkCdYkFjeHPfY+712ERCQkLizYIoxAl6JwgsDBCL+FDrLXfVF+31itfn56UjJ8nISGfX9o0UFebd8aZFNBpFpVKxY9uGJWOd61w3ri0tzkelUrHrmnn7anXSV0MgGOL1JKy2XnHD008/zdNPP83IyAgAtbW1/MVf/AUPPPDAXZq5hISEhAQkkgFtbW1cunQJk8nE448/TkZGxqq6Sa4zNDzG/LyTA/tW11HidLpRqpREwhFsVjMNdVW33BdwzC1w5PgZaqrKqKupuG2ixOvzc6mtk9GxKWRyGSXF+VRd83hdSuLb7w8wPj7C0NB4UlqruqKUirLidfUfux5vnDh5nn/42neJRGPk5VopLy3igQO70Go1a+rOgUTzwsjoJLVVZWjvYlxyO1adLGlpaaGlpYW/+7u/4+jRo/zgBz/gD//wD/n93/993va2t/Gd73znbsxz3RBiIaJBJwqlFrlqaT02URRRxNQotYY7qnASokGiQWeii2UVyZLjx4/z1FNPcfHiRaanp5fcrF9uXn/3d3/Hpz/96WXH7uvr49Of/jQnT54kEolQX1/Pk08+yd69e4FEVvY973kP7e3tzM/PY7FYePTRR/nCF75AamoqkGhbfuKJJ7h06RKtra388z//Mzrdb9byoYce4vd+7/d429vedtvXOjAwwN/8zd/w4osv4nA4yMnJYcuWLXzyk59kw4YNtz1/pTzzzDOcOHGCU6dOkZWVRVpaGufPnycl5c1ZhS0hIQpxQt5J/At9xKMh1IYc5PK1GY/fDsfcAhfbOpmxO8jLtVJWWohGo16XH+WiKHLsxDlGxiYwmdKpqylHpVIlg4HzFzvo6Ruktrqc5saaJSs8QqEwZ861EY/FeefjD6CUyxFEcUk973g8jlKpZHxymvT0VPbs3Lwmw/ZQKMyR42ewz86hVqupKCsCQKlUMjPj4NyFK+j1OspLCykpyicjI52wz47PcZWAczC5waQxWFe/aG9A7vXYREJCQuLNQCzsw2OfIOKbRqFOQZv65uhigIS2eG/fEFMzszx8aC+pRgOPPXyA9PTUOxpXFEVmHfMMj0wwNDLOfXu3YTFnLnv8yNgkFy914vX50Om0FObnLCnNdad4fX5mZhzMzM4xM+MgEAzytgfX77fdnbJecUNeXh5f+tKXKCsrAxK/OR999FHa2tqorV3fzmkJCQkJid8gk8mYn5+nsbGR1tZWEGP45vtW1U3i9fk5e/4yE5Mz5OVaV3w/dLm9XGnvYnh0gurKMnx+PxZzJqUly8s59vQNce78FTIzTbfs4vT5A7jdXnJzslEqFLjcXja01lNWUrhsIcTE5AwdV3sZHJ4iNVVHcWFeMmmzWt+V5YjFYoyOTdE/OIJ9do73vOstDI2M01hfzb49W6mvqVhTTCMIAgODo3T3DnJw/w50Oi1ve+x+xHgYIfbaebCueWdMJpOxd+9e9u7dy4c//GE++MEP8swzz9wzGxJylW7Zbg9RFFGoRJTqO0uWxID4LeS+lsPv99PY2MgHPvCBZRMO09PTix4/99xzfPCDH7xtguKhhx6ioqKCw4cPo9Pp+Md//EcefvhhBgcHsVqtyOVyHn30UZ588knMZjMDAwN85CMfYWFhgR/84AcAfPKTnyQ3N5dvf/vbfO5zn+PTn/40X/va1wD44Q9/iEKhWFGi5MKFC+zfv5+6ujq+8Y1vUFVVhdfr5Wc/+xmf/OQnOXbs2EqWa0UMDg5SXV1NXV1d8jmz2bxu40tI3EvEIj78CwOE3GMo1Ea0xvX1C0leJxbjxMkLjI5PYkpPY/+ebeTn2db1GsMj44yMTbB9SytlpYXIZDIEQcDnD5BqNFBTXUZxUd6SGwf22TnaO3qwO+YRgeqqcgwpeuSv+t6fn3cyPDrB8MgEZaWFNDfWUFleQlVF6ZoSJQCvnL6Ay+1lz87NqFRK+gdGcbm9pKcZKSsrpLKihBybBZlMRiS4wPzwEfwL/cSjAdT6LPQp67+x8UbgXo9NJCQkJN6ICPEIftcY/oV+VPo46pRs5Irba3O/EZicstPbN8T45DRKpZKKsqJk4cWdJkqudPTQ2z9EIBBEr9dRUVZE5jKbIvPzTs6cv4Jjbp7cHCv7dm9Zt+5e+E3SJtuSRSQS5cc/+zWiKJKRkU5RYS4WSyZyubBu11sv7jRueOSRRxY9/pu/+Ruefvppzpw5s2yyJBwOEw6Hk489Hg+Q2DAShPVZI0EQEEVx3cZ7MyCt2eqQ1mt1SOu1epZbs66uLgwGAwUFBezbty/xezkwR2BhgHDAgUqbgUKdgkji3rQU1yWqBFHE4/Wza8emW/p+XMfnD3CprZOhkQkMKTo2b2xiwelmdHyands2wLUxbyQej3PqzCUGh8eprixN+oTceJwoioyOTdI/MMLk9CxGg57HH70fjVbDIw/t/82aXDtHFEWmpmcxpOhJSzPi9QcQgU0bm2ioLU0mVe7E/yx5TUHg3IUrDA6PJxI5Ljc6nY64IPDoIwdQq1RJuc/VXE8QBAaGRmnv6MHnD1JYkEM0HkcjinBNhvz6+y9bx8/NSj+Da06WjI+P82//9m/84Ac/oKOjg61btyY3zCXujAceeOC2rbtW6+Jq4p/97Gfs3buXkpKSZc+Zm5tjYGCA73znOzQ0NADwpS99ia9//etcvXoVq9WKyWTiwx/+cPKcwsJC/uiP/oinnnoq+Vx3dzdf+cpXKC8v5/3vfz+f+tSnAHC5XHzuc5/j8OHDt32Noijy/ve/n/Lyck6cOLGofaypqYmPf/zjyccdHR18/OMf5/Tp0+j1et72trfxla98BcM12Zn3v//9uFwuduzYwZe//GUikQjvfve7+cd//EdUKhV79uxJJl5kMhm7d+/m6NGjFBUVLZLh6u/v54Mf/CDnzp2jpKSEr371qzfNe3Jykk984hO88MILyOVyduzYwVe/+tWkQeDt5gKJAPlzn/scP/jBD5ibm6OgoIDPfOYzfPCDHwQSX/6f+tSnOH78OCkpKRw8eJB/+Id/ICsr67brKiFxK0RRIOydxj/fRyzqu7ZZsf6tjaFQGK1Wg1KpRKFUsGPrBkpLCtZdizwWi3GhrZPC/FzKr3VmOOYWOHXmEvF4nLe+5SBGQwpGw+IOMlEU6R8Y4ZUzF+ntG+KBA7vYuKGRYHjxzX18YprzF9vxeH1oNBqKCnLJy0189+p02jXPWalUUlyYh0at4UpnL06ni7RU47UfzkYK8nKAhESab64Xn6OTWNiLWm++bSfJjMNNZ88U+el+rDVrmuI9jRSbSEhISLx+EEWRSMBBYGGQsG8WmTwFtTH7pqKENyqiKHLm3GWUSgWbNzYlZa/WysKC61oVayl6vQ5RFCnIy0kWhSwVZ13fEAqFI8TicQ7u30mOzXInL+smIpEox0+ewzHn5Hfe8TBqtYqD+3eSYUpLdhGLokDYN32bkX77rGfcEI/H+c///E/8fj9bt25d9rgvfvGL/NVf/dVNzzscDkKh1RdaLoUgCLjdbkRRXBdN+jcD0pqtDmm9Voe0Xqvn1WsWDAY5ffo0k5OT1NXVodVqEeJRwn47Yb8dRFBqMpAF5BAILjlmLBajr3+YkbFxDu7fhVKpZNfOhFzUgmvpcyDx/a5QKPD6AgyPzlBeVk5FeTEKhYLuvjHKSstIN2UtOYYoini8YRrq6ykqzMPlCd90zJlzlxgZGSczK4O62lry83Jwum++H4RCYYaGxxgcGsHvD1BTXUFDfTVms5UssxWvL4w3EEMWiK10mZd9vZNTMxTkJ4pqZ+c8uFwBhkbGCAaDVJSX4PXHAAWxuEAgtPzaLcfZ820MD49RUJBLa0sLaWmpRKKJ9yEWi+FyOTGlaok65tZVrtXr9a7ouFUnS775zW/y/e9/n5MnT1JZWcl73vMefvrTnyY3iyV++9jtdn75y1/yzDPP3PK4zMxMqqur+d73vkdLSwsajYZvfOMbZGdnJ9rWlmBqaoof//jH7N69O/lcY2MjL730EgcPHuSFF16gvr4egE9/+tN89KMfpaBg+daz61y+fJmrV6/ygx/8YMmbRXp6OpDwGjl06BBbtmzh/PnzzM7O8vu///t89KMf5Z//+Z+Txx85cgSbzcaRI0cYGBjgXe96F01NTXzoQx/ixz/+MZ/5zGfo7Ozkxz/+MWr1zZvDgiDw+OOPk5WVxZkzZ/B4PDd5mQQCAfbu3cvOnTs5fvw4SqWSJ598kkOHDtHe3p4c91ZzAXjiiSc4ffo0f/3Xf82uXbsYHR1lbm4OSHQM7d69mw996EN85StfIRgM8v/9f/8f73znO1eUhJKQWI54NIB/YZCgaxS5UovGkLvuyYtIJEpnVx9Xu/vZv2cbOTYLu7ZvXNdrQOLzer0qs6m+GqvVTDQape1KF109A2RkpLN766YlX9/U9CwX2jpYWHAhCiL1tZVs29KKUqXEsTDP5MQYaakGigrz0Go1mM2ZbN7YtCpTslcTDIYYGhlncHgMrSah3zm/4GJweJS8XCsbmncsavmNRwN4HVfxznYQC7lRaTPQpRff8v2acbjp7J3C7vBwsXOMIotAy541TfeeRIpNJCQkJF5fxKMBgq5RAq4RQIbGmIPCE3mtp/VboatngKxMExZzJg8c3IVev7T080pwu72MjE4wPDKOx+tDrVZjs1rQ63U0NVQve14wGOJyezden5+D+xNxxvWO1fWi42of4xPTzM07USjk7N6xKflvNuvru4N/PeOG60mWUCiEwWDgJz/5CTU1y1esfPazn+UTn/hE8rHH4yE/Px+z2ZyUvb5TBEFAJpNhNq89fn2zIa3Z6pDWa3VI67V6blyzsbExjh8/jkwm4x3veAcFBQWE/Q6CzjFkwhxGU6Kb5FZMTE5z9vwV/IEgleUlpKdqb+nvBYlOkvaOHqbtDh57+D5SDSqaGsq52tVPcWE2ZpuFxx7es+z1ZDL5NT/V5f07BEEgzajl/vu23VLGa2h4nFdOX0Amk1FanEdFefEi9QxRFEEUyUjTrvle7/F46e0fYWBwhHAkSn5uJiqVistX2pmcslNUkMve3QcoLysiI311sY0gCAwNj6PTacnNyWbbpnq2bqxLdtnGYjEmp+wMj0wwMTWDgjhvf8suTOYsFMtYaKwFrXZlRa+rTpb89V//Ne9+97v56le/SlNT02pPl7gLPPPMMxiNRh5//PFbHieTyXjxxRd59NFHMRqNyOVysrOzef7555PJiev8zu/8Dj/72c8IBoM88sgjfOtb30r+29///d/zh3/4hxQVFdHQ0MDTTz/N6dOnaW9v56mnnuKd73wnFy5c4ODBg/zv//2/l0xO9Pf3A1BVVXXLOX//+98nGAzyve99L+kv8rWvfY1HHnmEv/3bvyU7O7HBaDKZ+NrXvoZCoaCqqoqHHnqIl19+mQ996ENkZGSg1+tRq9U3deRc56WXXqK7u5uRkRHy8vIA+MIXvrCow+eHP/whcrmcb33rW8kvn+9+97ukp6dz9OhRDh48eNu59PX18R//8R/8+te/pqGhAYvFktS4hYRJYEtLC1/4wheSz33nO98hPz+fvr4+KioqbrleEhKvRhRFIn47/vk+IkEXmpS7Y6Q6PePg6ImzxGIxaqvLyco0rfs1YrEY/YOjdHUPkJdrZfPGRirKiwF47oVjzM072dBST01V2bJBaP/gCJFIlAfv34M5K4O5BScDQ6MMjYwzMjpDaqqOhtrE58yclXFHRmger49zF64wOWVHhgyVWklWZmK8mupyKitKFhnACbEw/oUBPPbLRPyzKLUmdOkltwx24nGBw6d6abs6hiDCex7bxKamInqvtq153vciUmwiISEh8fog2cXqHCAW8qDSZaBQ6ddFCuJeYNYxz/mL7TTWV2MxZ64pUeL1+ZMdGW1XupiemaUgP4dNGxrJsVluudEWjUa52t1Px9U+5HI5DXVVye6StW6eiKLIwoKLyelZZuwO9uzcjFqtSvieaDW0NtdSkJ9zUyfv65n1jBsqKyu5fPkyLpeLH/3oR/y3//bfOHbs2LIJE41Gs6SJ/FJmvXeCTCZb9zHf6Ehrtjqk9Vod0nqtnuv3rQsXLmCz2di1axdqlYzAfN+1YgzQGW/vTXLuwhW6egbIsWVzYN8O0tOWN2EH8AeCtHf00D8wgkqtoq66nN6+ITq7+oiEo1RWlJBpSluyU1YURS63d3Olo5vSkkLyc2/eg3S5vQwMjqBWq2moq0xIeL2KUCjMwNAoapWKivJisi2ZbGptoLS4YEnvV+HaeslksjV18L5y+iL9A8OEwhG0Gg3Zlkwy0tOQy+Vs2tBIri2bxvpb798uhSiKDI+Mc7m9G4/XR11NBfm5Vkw3SJEGgyH+48fP4XS6iMbjVJWXsGNLA3L5+n9mVjrWqpMlY2Nj61aR8vTTT/P0008zMjICQG1tLX/xF39xWwkqicV85zvf4T3vec+iDNl//+//nX/9139NPvb5fIiiyB/90R9hsVg4ceIEOp2Ob33rWzz88MOcP38em+03XgL/8A//wF/+5V/S29vLn/7pn/KJT3yCr3/96wDk5ubyi1/8InlsMBjks5/9LP/6r//Kk08+idFopLe3l0OHDvGNb3yDj33sYzfN+bp24O3+lrq7u2lsbFxkxL59+3YEQaC3tzeZLKmtrV1k3Gyz2ejo6FjR+l2/TkFBQTJRAtzUPn3x4kUGBgYwGhd/sYZCIQYHB5OPbzWXy5cvo1Ao2L17N06n86Z5XLx4kSNHjiQlxm5kcHBQSpZIrIp4LETYPYrfNYRcrkabmrfu3SSQqBI4c66NVKOBvbs231EF5VKEQmF6+gbp7h0iEolQVJBLWUkBoVCYuCCQotexoaUejUZNqnHxZ8fr89N2pYtcWzYpKToi4Qg+n59IJIpMJmNhwcWFix1YbRa2bG6lvqYIzRIJ3pVin53D6/VTVlqIVqMhGo1RVVGCfXaeBacLUUxoZN5oHn89SeKd7SDityNX6q91kix/I3cseNFp1fQOzvDj59sYGpsn06Tnicc3UV6cjVnz5jJ+X8/YREJCQkJibURDbgLOIULeSeQKLRrj+nexvl4ZGh5jYGiM6ZlZsjIzaKirXNM4LpeHZ587zPatraSmZbJpYwM6jWbRb4vlEEWRX71wHLfbS01VKfW1lUtuqKyGE6cuMDE5QzgcRqFQYLOaCUciqNUqtm1uuaOxX0vWM25Qq9XJ4rcNGzZw/vx5vvrVr/KNb3xjXcaXkJCQeDNit9vxeDxYLBbe8pa3oNFoiARm8UwPEPbPodZnoVDplz0/Ho8TDkfQ63Xk5+VgMWdSVJi37PE38vLRU/h8AZoaa6iuLKG3f5iLbZ2UFBfQ1FC9bHFAKBTm+MnzTM/M0tJUR33tb/bvIpEow6Pj9A+MMje/gFqtprqy9KYx3B4vXd0DDAyNIgoi1VWJY4yGFGqqym46fq0IgsDY+BRZWRkoFQpmZ+fx+QKkpOhQKRWMjU3xyumL7N6xiQcP7r79gEvg9ng5cuwMLreHvFwru3dswmRKY3R8ipHRCTxeH1s2NtHV3Y9jbp4UvY68rEzKy4pINaa8/g3e29vbqaurQy6X33YD+roXxkrIy8vjS1/6UjK4eOaZZ3j00Udpa2tb1hBNYjEnTpygt7eXf//3f1/0/P/6X/8r6SVyncOHD/OLX/wCp9OZbPH9+te/zosvvsgzzzzDZz7zmeSxVqsVq9VKVVUVmZmZ7Ny5kz//8z9flFC5zhe+8AV2795NS0sLf/AHf8CTTz6JSqXi8ccf5/Dhw0smS65v+nd3d9+ymud6JdRS3Pj8q/V/r5s8r5SljJ9efV1BEGhtbeX73//+TcfeaBZ/q7nodLfeRBYEIdk182qWWnsJieWIhlx4ZwaIBhdQ6da3dfHVxONxbFYL5aWF65ooua4NGgyG6LjaR0VZMTXVZRgNKQwNj/HC4ZPYss3s2bX5pg6QUChMx9VeunsG8QUC9PYPoVIqSU9LZdvmFozGFGKxGJXlJZQU5aNQKllwBdekJR4MhhgYGqN/YBiP14cpPY2y0kIAUlL0dPcOkpZq5MC+xXJbQjxKwDmI136FkHcahUqPNjUfmXz5W/OCy8+V7glmHB7GphY4f3mUUDhKXWUOv/voBmyWdAAUijd+xdLdik0kJCQkJFaHEI8S9IwTdA4Rj4ZQp5jviifa64l4PM7ElJ38XCtyuZzB4XHi8TibNzZRUpS/pirIWCzG0RNnMRpSyLFl4wvE0Ot0K64QlclktDTWYDQabls1uxQ+f4CrXf045hZ46NAeZDIZSoWCyvJicmwWLObMVb+uhQUXYxPTjI1PsmNjMevfd7xyfltxgyiKiwzcJSQkJCRWjiAIXLp0iYsXL5KdnU15eTkqhYh/roeAaxhkimtFoMvfjyan7Jw9fxmDIYWD+3esSBrSMbeAVqvBaEhh59YNLLhcRKNxVCoVFWXF5OVYk5JRy3H85HnmF1wc2LeDHJsFURTxB4Kk6HV4vT7OnLtMji2bPTs3k59nu6kQYn7eybPPHUaj0VBfW0llefGavVKXIxAI0jcwTG//MMFgiE0bGikvLUQURTZtaEQul9E/OEp6euqKk0uvxuXykJ6eSopeh8mUxvatraSlGrnY1snw6ASRSIT0tFSKi/Lx+wO4vT7u27udwvwcTKY0AOLR1fugrCcrSpY0NTUxMzODxWKhqakJ2TVn+utcfyyTyYjH4yu++COPPLLo8d/8zd/w9NNPc+bMGSlZskK+/e1v09raSmNj46LnLRYLFsti875AIJGVe3WQK5fLb5lYuP5eLxX0dXd388Mf/pDnn38eSPxwiEajQKINfLm/h6amJmpqavjyl7/Mu971rpvm5HK5SE9Pp6amhmeeeQa/35/sLjl58iRyuXxduyxqamoYGxtjamqKnJyEufLp06cXHdPS0sK///u/Y7FY1qwnW19fjyAIHDt2bMkgvKWlhR/96EcUFRWhVK668UtCAiEWxucaxr8wiNoov1bVefc2zufnnWRmmtiyqWndxnS5PFzp6Mbl9vKWh/ZjMqXx7rc/hEqlwuvz88LLrzA1bae4MI9NGxpvOn9hwcXPf3UYURRobqxFFEUccwuUlRQSjUUZHB7j1NlL7Ny2kdKSAlQq1ZrlQYLBEP/54+dAJqOwIIetm1vIuHaDV6mUhEJhtmxqpqKsKPk9Jwgxgs5hPPYrhL1TyBRqtGkFyG+RJPEHw7R3TzI2uUCKXkNtuY3DJ3vINKXw9geb2b2lArXqzfWdcbdiEwkJCQmJlZE0cHcOJeQjNWloUzNvf+I9jGNugYHBhHxnNBrl0IFdWLPN7N+z9Y5kIto7e+npGyQcjvDIA/sSOuorMGj1+QNc6ehGEER2bttAft7aiqsWFly8eOQkoiiSn2cjGo2hVqvYurl5VeMIgkAkEkWr1TA2McXho6dRqVTk2izE4ysvZLsb3I244U//9E954IEHyM/Px+v18sMf/pCjR48mfxtLSEhISKyOixcvcvnyZVpbW7HZbIR9doLOAaIh522LQGOxGCdPX2R4dIJsSxYbW+pve73rMlEnz1yiqDCPspJCLrZ1Mje/QH6ejaqKEtRq1S39TSKRKGq1ik0bGlEqFQiCQNuVLgYGR1GrVTz68H1kZpp45+MPLkp+XO/usM/Os3ljI5mZJnbv2ERBfs6KOkpXS0/fEOfOX0Eml1FclIfX48eclYFKpeJtj93PsRNnGR6doKggjy2bmtBqVyffPj3j4NLlq8zPO3n80YN4fQEyM9IxZ2UgiiLzC65EAkirZWhknMryYrRazZqTMneTFe2sDA8PJyvnh4eH78pE4vE4//mf/4nf779J/ug64XB40Ya9x+MBEn9gK+0iEAQREIhHAkt2E0DiwxKL+IiGby8TdctrRYOAgCCIq+py8Pl8DAwMJB8PDQ1x6dIlMjIyFpmnezwe/vM//5OnnnpqReNv3rwZk8nEE088wZ//+Z8nZbiGh4d54IEHEASBX/3qV9jtdjZu3IjBYKCrq4vPfOYzbN++nYKCgkXXEUWRP/iDP+Dv//7v0ev1CILAtm3b+OY3v0lZWRnf+973ePe7373s3L797W9z8OBBdu3axWc+8xmqqqrw+Xz84he/4MUXX+TIkSP8zu/8Dn/5l3/JE088wV/+5V/icDj42Mc+xnvf+17MZjOCICCKIqIo3jQ3IPncqx/feJwgCOzbt4/KykqeeOIJnnrqKTweD3/2Z3+WPEcQBH7nd36Hp556ikcffZTPf/7z5OXlMTY2xk9+8hM+9alPkZeXd9u5FBQU8MQTT/D7v//7fP7zn2fnzp2Mj48zOzvLO9/5Tj784Q/z//7f/+Pd7343n/rUp8jKymJgYIB///d/55vf/OZd+cK8F7j+Pq/mc/RmJBKYJ7DQT8jvQK40oNKnIyJb9rvuTnA63Vy41MHk9CxveXAfGRnp6zLmlY4eRsYmMaToqKutJC4IyOVyFEolkWiUZ391GKVSwb49W8nLTWwICNc+d7OOeZRKBV3dA0xMzbBr+yYarulqtl3p4vip84iiSI7VwrYtreTmWpNJkuufXVEUWclf2fUf0xqthh3bN2CzJkxTO6728uKRkzxwcBeZGSbu27c9eU48GiLgGsY7e5WwbxqZTInakItcobo25vLXGxqd42LHGJPTLkoKs9i/vYpP//f7yc4ykmrQJc+PRGNEovFr7/vN33l3wuvt8/fbiE0kJCQkJJYmHg3gdw4Tco8BMjSGnNtqht/rHDl2htHxSfR6HVUVJZQWFySrTFebKPF4fQwNj1NVUYJWqyEWi5Gfa6OyvJj09NTbFnH4/AHaO3sYGBhFpVatWvYrHo8jiiJKpTKprW4ypXFg7/Y1VbFGIlHOX2pndGyKHKuFPbs2k2O1cGBfoqJXJoOwb3rV464ndyNusNvtvO9972N6epq0tDQaGhp4/vnnOXDgwLqMLyEhIfFmIhaL0dXVRX19PQ11lUyMdOHxOZArNWiMt5cU7+kbYnRsKlkUeTtm7A7OX+pgft5JYX4u4VCYX790nMxMEwf37yTHZrnl+dFolFNn23B7vDx8aC8qlZJjJ84x65hDpVJRXJRIvlzn+v01Go0mvVh9fj/WbDOxWAylUklxUf4KVmplRKNRBofHEwmJglzMmSYKC3KRyWXXJDYjRKO/KcxIT09jd37OqufgmFvg0uWrTE7ZkSvkmNJSefa5I4TDYQwpKVRVlDC/4CLbksno2CQer4+8XOstE1CvNStKlhQWFi75/+tBR0cHW7duJRQKYTAY+MlPfrKsGdoXv/hF/uqv/uqm5x0OB6FQaEXXi0f9hGJq4v75ZY+53jobD7vuWMtUoUln3ulB4Vt5VeupU6d429velnz8yU9+EoB3vvOdfPWrX00+/y//8i8IgsB9993H7Ozsisb+13/9V770pS+xf/9+otEolZWVfPe738VmszE7O0s4HOaf/umf+MQnPkEkEiEnJ4cHH3yQj370ozdd43vf+x5paWls3LgRl8uFKIp85CMf4Y/+6I/YsmULe/fu5e1vf/uycysqKuK5557jq1/9Kh/60IdYWFjAYrGwceNGPve5zyXP+/73v8+f//mfs3nzZnQ6HQ8++CB/8Rd/kfz3UChEOBxedJ1AIEAkEkk+9+rHkPiR4PV6k89985vf5BOf+ARbtmwhPz+fv/7rv+Z3f/d3cbvdyWP+67/+iyeffJLHH38cv9+P1Wplx44dyeuvZC6f//znUavVfOYzn8HtdpObm8v/+B//g9nZWZRKJT/96U958sknOXToEOFwmLy8PPbu3cvc3NybRvf51QiCgNvtRhRFyRBtCQQhStg3S8RvT/zwVWfgD0WRu0Os919MQhKrh6HhMYyGFFpbmkGuYcF1Z22Soijy7C+PIpPJqK2pofiahIXLE8bl8mAw6FEqlTQ0NJCZYUKlUiavOT0zy4mT57ja1UtJcQFpqalUV1bhmPfQ3TdOtiULlUpHRUUFBXm5yQoJXyCWrNoUAa8/AjLZbdcsEolyuf0qAJs2NKFPSeP8pR56egeIC3EqykqIxOTJ+YnxGCHfFEHXCLGwG5lciVJXjUyuJCYCSxSOiqLI6PgsXn8Qx7yHto5B2rtGCAQjaPVGQqRjy0tU7864Q8zMOpmZdTK34MFqNtFYlos7oEC5wnvDSvB6ves21npwN2MTCQkJCYmlEYU4Id80gfkBomHPNc3wuyf1+VpxvdpzYGiU1qY6TKaExGbFNUmqtcTkr9YrVyqVWMyZ5NgstDStXFUhEonyk5+/gFKppLmplurKktt2pF+v6JyanmV6Zhb77Dzbt7RQWlJAjs2CXqelpDh/TZ3tgiBw9MRZHHMLVFeWUpif6NJXKpVJ+dHrnm2vJXcjbvj2t7+9LuNISEhISCR+b2q1GkrzTXimLhD2eUmzZKFcYZyRmmqgsaF6RYmSWCzGkeNnMRj0HDqwi6xME5cuX2V7QSvlZUW3Pd/l8nDk+Bn8gSDbt7Qgl8vRaTXodVp2bd9IQX7OkvdUQRD46bMvEQgGKSrMY++uzWRmrq9IZTAYoqtngJ6+ISKRKJXlRRQV5LLgdDM8Ok6KXk9pcQG5OVYGhkaZm3fSUFe5ZgP3s+evEI/H2bVzE6fPXCIWi5Ofa0WlUrGxtR65XM7l9u6kd8nG1gZyc7Jf13t7MnGVJcff+973bvnvTzzxxKomEIlEGBsbw+Vy8aMf/YhvfetbHDt2bMmEyVKdJfn5+Ys8OFZCLOJDiC2vIyoIAvPzc2RmZt3xmydXalCqbzbrfiMhCAIOhwOz2fy6/mN/PSGt2eqQ1mt5IoEFAs4BIj47Sp0JhdqAKIosuIJkpOvWPcF2sa2Tvv5hGhuqqaooWdf3w+32YjSmJMeMx+Nc6eihs6uP5oYa6peomuwbGObUmTZS9Fp8Pj+ZmRn4/QHCkSjpaUY2tNQlO1Buxe3WTBRFpqZnGRgcZWxiCkEQ2LyxiaqKEi62dXK1u5+KsiIa6qvQX/cmEmIEnCN47FcIeSdRKPWo9Zm39CRxeQK090xyqWMMW3YaOq2KV84PMj7lJD1VywN7ajmwswadNqEFPz61wKlLQ8hlMiyZRnKy07FlpyH6+rBWP05O3btXsvQrwuPxYDKZcLvda5YivFusd2xyJ3g8HtLS0tZ9nQRBYHZ2FovFIn0PrgBpvVaHtF6r5824ZpHgAgHnMGHvFHKVHpXWtOI4Q7jhPrtSD47XkmOvnGN4ZJyszAw2bWjAYl6bvNj1n9oymYwjx84wNjFFbk42pcUF5OfZlk1OvHq9fP4A3T0DtDTVolAomJyyYzFn3NJrzeX2YkjRoVQqk9IaSqUSa3YWNquFgvycZU1qV8P5i+109wxy377ty1bhiqJA2DeNKX8bal3GkseshbXec9/occOb8fvpTpHWbHVI67U6pPVaGbGwl4BzkKB7HJlCiyeiJ3OFccOt/I6vEwyGaO/spaGuEp1Oi8frw2hIWfWeyfDIOK+cvkiq0cDO7Rvp6RskN8eaLBZ4NYFAkO7eQepqKtBo1IxPTGMypWFIWd6gfrVcjxuEeIhfPX8Mj9eLXqdFrpBTVVHCts0thMMRvD4/WZkmJiZnOHnmIrFYnK2bmigpvn2C6Tout5fjJ84hiAKRaJRAIMh73vUWpmYcDA2P4XJ5cHu8yGQyHj60l8xME6FQGI1GvaK1djtnGB84j0njomznn6HWr5/E60rvuasuHfn4xz++6HE0GiUQCKBWq9Hr9asOLNRqddLgfcOGDZw/f56vfvWrfOMb37jpWI1Gg0Zzs2aaXC5f1ReOWnvrIEQQBFQBEa1B2phdKTKZbNXvw5sdac1Wh7ReixHiUYLuUQLOIUQhjjY1Nyl/IZBYL5lMdscbEqIoMjQ8TiyeMENvrKuiobYSjWZ9jFsXFlycOH2R/Xu2YrrBMG3G7uDUmUv4fAGa6qupqym/6bVMTtn52bMvsbG1nr27tiT1vivKiigtLliVNNhyaxYIBNHrdYQjUY4cO0OqMYXmhhrkcjlqpRK5TEZ9TQWV5cWkGhOJcVGIEXSP47W3E/SMIZMr0aXlL+tJEovHudo7zfi0k4sdYwRDEWzZaezdUs7YtJNYNM7WlmIeua+ekgIzEzNOZgdnaKkrINtsZPuGUqzmVFTK38ifeH0iMtnqZUFuxev5s7fesYmEhISExG+Ix0IE3WMEXSOI8SjqlOykhOQbkYHBUYZHxtm1feOqNg9uxO3xMjA4yuDwGJs3NlGYn0NLcy2bNzai16+8E8fnD3D1ah/9AyOo1CpKivLJzDQlOzZezYzdQf/gKNMzswQCQe7bu528XCs11eVUVZZizsq4o/u50+lmbGKa8YlpWppqE8bvliwslqzbypW8npDiBgkJCYnXD9NTkxw7/Eu2NuegVQqo9RZQqJFFbq9eIYoiV7v7GRoZ56H79ywpXR+Lxejs6qezqw+5XE5+ng2dTkuq0UAsFqO9sxeVSkV97cp8kUVRJDPDRIYplRdePkEoFCbHevM90OcP0Hm1j76BEeRyGTm2bGxW85r9xZbD5fYyNTOLxWLD5/Mz63BgSk8nw5RGcVE+RQW5AGg0apRKBSfPXKR/YIQcWzbbt7aSssK4xOP1cenyVX767IvEY3HyC3KwWrJ44ODuhKznlS5isTg2m5nmplps2ebkvtHt/E9EUeTi+VN0dVxgzj5KPOLlsQM1t2x0uJusOlnidDpveq6/v58Pf/jDfPrTn77jCV2XwJKQkJCQWJpIcIHAQj9hnx2lJh2l3nhXrnOjhmdZaRGV5aybrmQ4HKGnb4ju3gG0mkS76nVcLg/Pv3gcc1Ym+3ZvTeqBQyJ5Mb/gIjcnG6/Xx9S0nemZ7IR8V3UZ9bUVd7yp7/MHGBoeZ3hkHJ8/wLvf/hAajZrHHrkPl9tD2+UunC43ZaVFlJUWotVq0Go1SeN2n+MqQc8EyEQ0KVbkypsDg3hcYGDUQXmRmWm7m5k5D9lZRppr83B5gkSjMZRKBRvqCynJNyOXy/D4gpy+NMT4lJN8mwlBENCoVeTbFrftXvcteTNxt2MTCQkJiTcjiW4AO4GFQSLBeVS6DJR682s9rbvCjR0gQyPjlJYUrjlRMjo+xZFjCWPzkqJ80q4VU6Slri5e6+kdYGBwAI1aTVNjDdWVJbfsJOnqGeDchSukp6VSXJiHzWrBmp0FgDnrzro5rnb3090ziM/vT8hr2bJRqRJbCctV0t6I2+1FI19/D721IsUNEhISEq890WiU068c5tL545gMIIq5aFMTfhm38+6CRAL/lTMXmZ93UlNVtqRX6+SUnVdOXyAcilBdVUpDXRUajRpBEBgYHKWtvYtwKJL0Ol2OhQUXo+NTNDfWoNPpmHXM4fZ4KSnKp7y08KZCzavd/Vy81Jn0FaupKlt3j465eSdtl7s4fvIcJlMav/vut5NhSuPAvh0UFeRiMqXddI5CoSAaibFtcwsV5cW3vUY4HGF4dIKrXX34fAG0Og17d2+hrLSQK+3dKBQKUo0GZDIZDxzcveLXGAyG6OkbYnJymi1NOQj+ERxDF0iRBShvKSbLpEOFf9Vrsl6sXpR0CcrLy/nSl77Ee9/7Xnp6elZ83p/+6Z/ywAMPkJ+fj9fr5Yc//CFHjx7l+eefX49pSUhISLyhSHSTjBFwDiIKMTQG2y0lndZKLBbj+CvnGZuYIjPTxAMHd5NtyVq38a+3rYoilJUW0FBXhVwuZ8buwJptJj09lQP7dizSBE94pfRytXuA6ZlZ9Ck67PY5tFoN1ZWJ7sS16GzfSDwe5/kXjjPrmEMuV1CYb6O5qQaZTEYoFOb4K+eZm1/Amm3moUN7kxsP15Mk3tkOQp4JZHIlmpTsJZMk1zl7eZgr3ZP0DdoJhCI0VOcybfcwND7P+OQCImBKH+DxQ01kmlL46QtXCIejmNL01FflUFVqXdTC6g+GmZpxM2V3cXVgBqvBy/sa72g57nnWGptISEhISEA05CboGibonkCu1KBNzUMme/12GK4VfyBI/8AwfQMjbNvcQl6ulU0bGjGkrM2HJRAIcvL0RQrycti9c9OSFa63Ih6PE4vFUalVpBoNNDfUUFNVelOSxOvz43DMM+tYwGazUJifQ1qqkYa6Kpoba+5IhtUfCDI5OcPE5AzNjTWYTGnI5XJyc7IpyM/Bmp1129flcnuRyRIJouGRcY4cP819W/Ix5b1+izmkuEFCQkLit8f01ATP//K/8CxM0FRTREPzJhSr6Frt6hngwsUOUlMNi36bXyccjqDRqNFq1FgtWbQ01yWlJyORKL98/ghuj5fionxammqXlaVM+HKd4+XDJ9Gl6KitLifbksne3VvJz7UuKtQMhcKEwhHS04xkZZpoaa6lsrz4loUOa0EURV4+eorxiWnGxqdJT0vl4P4dKJVKUlN1NDcutrWIRqNcuNRJfp6NvFwre3Ztvu34k1N2uroH6OzuIzMjHZvVQmlpIcWFeYyNT9PVNYBOq+WBg7uTXSMrSZSEwxHGJ6Y5f/EyId8c6RoP071XMKaoaKwtQ6FOvA/RkItY6B5PlkAiOzU1NbWqc+x2O+973/uYnp4mLS2NhoYGnn/+eQ4cOLBe05KQkJB4Q/CbbpIZlBrTXekmiUajqFQqlEolKrWKXds3UlyUvy6+J3PzTsLhCLk52ZhM6dTVVFBVUYJOpyUQCHLk2BlGxyc5sG8HuTnZNxiCipy/2M6pM22YzRk01lfRcbUXYzhCQ20ljQ3V1FSVrXlejrkF+gdH2LKpGYVCgcmURkVZEQX5OajVKnz+AHK5HK1Wg8mUSnNjTXJuQjxCwDmEz9FJyDOVSJIYc5ArlpYoEwQB+5yX8+0jnLs8Qm52Oulpera0FvPckat09U/j9ATQa9Xk2dIJhSNEIjHUaiXbWkswpmiSXiWiKOIPhknRaQhHovzgp+cIh2NUlduIR2MEgq/fzYjfJmuJTSQkJCTezAjxCEH3OEHXMPFoCLU+65bJ/3uV6RkHXT39TEzOIJfLKSnOx3BtoyQ9bfUxViAQRKlUotNpaairpKykcNWJkkAgyNET51Ao5BzYv4OcHCsZ6TpEQUAQBORyOZ1dfXR29REKJZQY0lKNZF6rZr0xfloL0zMOzl1sx+l0IZPJMGdlEo3FAKiuLL3luaIo0j8wwvTMLNN2B6FQmPKyIjY1lZAit9Nc4CEyf46QdyualNdvd5IUN0hISEjcfSKBOYLzXajFeR59cD+mLOuKz43H4ygUCgwGPfV1lTTUVS663y4suDh/qYNQKMxbHtpPZqaJ3TsTyYHpGQc2qxm1WkVxUR75ubZlzdVDoTAXLnXw65dOYJ+do6K8mEP37UwmBG7sqozFYnT1DNJxtZesTBP337eTbEvWuhacAoxPTJNjs6BQKFApVeh1Ogryc9i/dyt5uTYWXDfLls3YHZw8fYlAMHjbLlOvz4/RkEIkEuXf/+uXTE7bycrKoLmxlsyMdHJzspmecXC5vYscm4XNG5tuK68VDkeYdcyTmZGOXq/j4qVLtF++QJYhSHO5Cq1WhUafKDSNxwWm7C5m5704F+bZUndzZ8xvi1UnS37+858veiyKItPT03zta19j+/btqxrr29/+9movLyEhIfGm4jfdJEOIQhSNIeeudJOIoshzLx6ntamO3Jxsdm7bcMdjxmIxhkcm6OkfYn7eSY4t8SM+Pc1Ic2MNoijS1z/M+UsdKBRy9uzc/JtEhCAwPjHN8VfO89yLx4hG43z9Hz+POSsDa7aZ1FTDHRuieX1+Xnj5FfQ6LcFgCIDNGxuRy2QEAkHOX2pnYHCUQwd2kW3JYvuW1mtzixFYGMBrv0LYN4NMoV42SRKNxQmHo+h1aiZmnJy+OEwwHKWq1EquNZ36qhzSU/Xk2UwcO9uP1ZJKU3UeZUUWcixpKJWJShVL5m82biLRGEdP9+FyB6mttHHp6jgj4wts31TK/m2VNNfkoQwN3tHa3GusZ2wiISEh8WZEFEUifjsB5yAR/xxKrQlt6voZar4e8PkDyGUy9Hodc/NO/IEgWzY1UVKUv6aqz0gkyvjENMOj40xO2dnQUk9tdTl1NSvTPL+O1+enq3uAvoFhlEolu7ZvYGJyhoGhKYJBL/PzLvbu2kx+ng2j0UBleQlZmSbMWRm33aRYDROT06iUSnZt30hujnVV/nQ9fUOcu3CFzAwTZSUFZBgEUmQOZrraiEV8ZBg0xEJ+REFYt/neCVLcICEhIfHbZ2iwl8sXjrO1KYcUjYyHHnrLivc2fP4A5y+0I4gC+/dsoyAvh4K8xTKQg0NjnDh1nrRUIy3Ndcmiz/l5J+cvdTBjd3DowC6s2WaaGmpuukYoFMbp8mCzmpHJZJw+14ZGq+Gj//19NNQtLdM1PjHNmXOXCQZDVFaU0HgbOa+14PZ4OXPuMv2DI+zfs43qylKMxhRmHfNs39JCQV7OTbJlsViMS5ev0tUzgMWcxYH925MeqzcSiUQZHh2nf2CUufkFysuK6ekdxBcIUFSQi16v49LlTsrLisjNycaancXvvvORWxaEjIxNMjk1g8OxgMvtAWBzSznW1CBZsnZ2VgUxGA0oNZk4fVHmHAHybRpi8Tgnzg2g06oxGeTE4q9dzLDqHbfHHnts0WOZTIbZbGbfvn18+ctfXq95SUhISLzpiQTmCTgHbugmWd/KhBvpHxhhYcGV1J++U1xuL798/gjRaJTcHCv79mwlP3exkdnQ8Dinzl6irLSIjS31yR/lQ8Nj/Plf/yMAmZkm9u3eyqYNDUkD+PUwEHW5PBw/dR6NWs2D9+9BpVYRDAcTVSHdA3Rc7UWhULCxtSFZgSEKcYLu0YRxu3sskSRJvdm4PRKJMTHjYnLGxfSsG0EQSNFrMKXp2bu1gqlZN5evTvCj59r42QtX+PyfPMyuzWUU5JrIzkzFaNAuNWUgkUQ60zbMgjvA5LSTroFptBolrfUF7NxQhkwmI9NkwGu/826ge4n1ik2++MUv8uMf/5ienh50Oh3btm3jb//2b6msrFznGUtISEi8foiFPQScQ4Q8kyBXojHmIpOvrivi9YooikxMztDbP8TklJ2aqjI2tjZQV1O+YiPXpejs6uPS5S4EIY7FnMXmjU0UF+atehxBEPjFc0cQRZH62gqqK8v49csnmJt3ERdklBTZ2NBSl9QdL8zPWZFHyEoIBIJMTtnx+QM0N9awsbUhWbF7O0KhMHbHPG63l4a6SirLizHqIE0bJLAwSMg9QVCIodRloNObgTix0M0+Ia8V0p6GhISExG+PhYUFThx5jqG+K1gytYiyUrTGpTs6Xk0sFqOnd4grHd2o1So2ttQvedz4xDSvnL5AWWkR2zY3I5fL8Xh9tF3pYnhknPS0VPbv2YY1e+nuRo/Xxy+fP4ooCuzZuZkcWzaf+NjvEYlE0emW/n0ejUZ55fTFRDfJgZ1LJiPuhHg8zulzlzl24hwej5fs7Cyi0UTHZ0Nd5W1lN6dnHMlCjqWOO322jf7BUeLxOGmpRpobavD6AsmO1ZKifHJzssnJyU4WqspksmScIAgC8wsu7LNzzM7Os21LC1qthtGxSVwuD2ZzJuWF6aQo5lGEzuHyeNFp00lNL2dsysmF9m5isThpqTrybSY0ahUP7a9HFES0ytBrGjeseldMeJ1Ug0hISEi8UUl0k4xe6yaJ3bVukusEAkEuXblKcVE+FvPaqkhFUWR0fAqn001zYw1pqQZqq8spKc5fFDQIgoB9dh6b1UxxUR4Gg56sTBNtV7o4cuwM5WVFuNweykuL2LK5if27t66bxmckEkWtVhEMhvjpL15ErVZz//4dCYO3a5UYV7v76ejoXWT+JooCQdcInmtJEmSyZTtJ7A4Px8/1IwgiMlnClF2lVFCYm4nRoOX4+QFGxudo754iFhdoqMwhGIqSnqqnrPDWSaDpWTe/OtJJik6D0aAhRa+hvjqXxqo8ss2pyePCkSjhyJtLhmu9YpNjx47xkY98hI0bNxKLxfizP/szDh48SFdXFykpS+vYSkhISNyrLJbcCqLWm99QklsDg6NcunKVQCBIRkZ6sosEWJXEaDweZ2LKzvDwOMXF+RTm55CZYaKlqYaiwrxVdbqKosj45DQ9vUPs2r4RrVbDhuY6fIEgFnMmGo2a3Ts2IQgicVFJRroO+TrIoV4nHI7Q3TvA+OQM8/NOZDIZ2ZYsRFFctAGy3LkXL3cyOzufrBTVa5UUZMUIe4YRfdPMRXzIFRpUejMK5W82l1bg0/tbRdrTkJCQkPjtcP7cSU6feAGNPMjubfWUlNes2ANNEASe/dVh/L4AVZUlNDXULOuLoVQqKczPZfuWluQ9fmx8ihm7g22bWygrLVzkL3IjkUiUl4+cIh6Po9GoOXzsDO946wNoNGp0usX3RUEQ6OkbSnZdPPzA3mX9Tu6UF14+yU+ffZGCfBuHDu6ivLQoWTi6lF9rPB7n0uWrVJQVkWo08MiD+xa9ZrfHy+DQGLXV5Wg0alJS9EQiEbp7BzFnZWA2Z7Bz2waEa/Kft/KEDQZDvHz0NHPzCygUCsxZGYTCEbRaDTu3tRL2TuKd7SToHkWIhZHpMtGlJzp2hsbmOH9lhILcDCpLszGl6nF5AoxNLjA25WTB5aeqyEh96Z0pidwJd2/3TUJCQkJi1UQCc/gXBon4ZlBq7443yY3MzTt57oVjKJVKWpvr1jzOmXOX6e0fwmLOorE+oavd1FB907VOnr6I2+3l7W89hF6vI9uSxS+fP8IP/+uXeL3+pKG8zbo+etbBYIjh0QkGh8cIBIK88/EH0ekSRmRZmaaENvX0LF6fn8ysbKoryygtLiDVaEAURUKeiUSSxDWEiIgmxZrcSBIEgdHJBcannWjVKjY1FWFK19NUk0+mKYWXT/ZQUpBFcUEWudnpON0BfvDTMfpHHWSk6XnibZvZ3FyMcomNiVg8zvSsh8kZFwqFDJVSwfGzA7i9Qd7+YDOWTCNbmovJNBkWndM/PEv3wAxmnZPCpnVZwjcVzz///KLH3/3ud7FYLFy8eJFdu3a9RrOSkJCQWF9EUSDin71Bciv9DSe5BWCxZFJUkEtpccGymuS3YtYxT0/fEGPjU8RiMUym9OS/2azmVcUqsViMweExrnb14/H6kMvlvHT4JL5AgFAonNAfb6whx2YhLdWIIIpLao+vlmAwxNT0LOFIhJqqMuRyGT19Q1gtZmoqy8jNyV5Sysvl9jI7O8fM7BwymYyd2zagUilxzDkxZ5moKEonVe2D4CjOkReRKVSotCZ0esu6eN1JSEhISNy7CIJAJBxCjMyjCI1SV5ZBQ+MGVJqVbX5PTtkxmzOSewpZmSbSUm/eFxkaHmNsfJrdOzdhs5rJtmRy6fJVRFFkQ0s91ZWlVFWU3HLTf2p6lpePnsIxt0BKih69TssDB3YtKUU5PePg7PnLuD1eNGo1pSUF65YoCYcjjI5NcubCFQx6HY8/ej+bWuvJy7NSfZvXADC/4OTXL51CiEfISE8l1WhALpcTiUQZGZ1gYGiMWcccKpUKm9VCWqqBCxc7OHfhClWVpRzct4Oa6oQXrFwuXzKxFI/HEQQBlUrFhbZO/IEAB/btwGY1I5fLEYUYAecQ3tlOQp5xRDEOqkyc/jizUx4qS3TodWoWXH5KCrLY0FCIKIr8+ngXbk8QtUpJni2dSCTG2JSL0pz1KZpdC6tOlnziE59Y8bFf+cpXVju8hISExJsSIR4h4Boh6BxGEOJ3VQLjxu6ODFMajfXVVFWULFulcTsmp+z09g+xaUPjkmbr1/Uyu3sHMZnSeOjQHvR6HeFwhLb2Ll48fJJtW1rYs2MTVZWl6/IjOxKJcuyVc0xN25EhIy/XSl11ebJyMtuShcvl4fylDianZrBZLWRmZaNWq9Bq1IT9dryznfjn+xCFKGp9NgqVbtE1zl4eYWxygawMA7nWdALBCL1DduqrcgiFY1iyUrnYMcqxM/28/cEW6qty2bmpnKbaPB7eX49ed/PmxLzTR2fvFLPzPgRBYMEVYHrWRVwQMaXpeXBvHdVltpvOA+jqn6F3cIbSQjMFr50X2mvC3YpN3G43ABkZy5vhhcNhwuFw8rHHk6i2vV6Rs14IgoAoilI17AqR1mt1SOu1eu7VNYuG3ATdI4SvSW6pDTkgV9ykdb3eiKKY/O9ur5jH40Wn02IwpLChtQFgTa9vbsGFY26B2ppyigrySLtm/L7asTweL88+d4SrXf20Ntdx6OBuFpxuhkfGKSstItuShTU7C4XiN+/DnaxXIBikt2+YyakZ5uZdANiys6iqLEWhVPKOxx9cFGvd+HoccwscPnqaYCiMTAYZpoShqyDEifgd7G7OIOAcIOKfIxqIoFCno00rWlQl/OrliccFBkZnsRpY98/MWseS9jQkJCQk7h6/evZHxIIOtjbnUZBnpaSsasW/8ds7e7l0uZMtm5vJyrJSXJR/U4elzx/gzLk2JiZnKCrIIxaLoVKpuNo9QGdXH431iaLNpbolPV4fg0NjyGQymhqqycxIR6fVkqLXUVtdzuYNDTcpW1z3Sxkdn8SclcnDh/auqQhjOfoHRnj+xeOMjE2ikMtpaqwhFouRmWm67XVisRiX27vp6OpDrdbz0MF9ZJjSkvser5y6wNjEFDm2bHbv2IQ5K4NoLM5//fR5rvb08+jD9/HQob1Lji2KIk6nm6mZWaamZ7HPztHcWENdTQWbNzQSiUYxpOgRhXgiSWLvIOhJKHEMTApMOwK4PPOIoohepybPlo5ep6a6zEr/yCyiKCKXy8nONOLyBJEpYMbhIRCKUF+ehV53DyVL2trauHTpErFYLKnh3dfXh0KhoKWlJXmcVFEiISEhsTIiAQf++QHCgVlU2kzU6rsn9zM8Ms6ly134/YFkd0dD3dr9GGKxGMdeOUeOLXvJRAnApctX6e0fprW5jsKCXLxePwD22Tm++Z0fYjFn8s63PnBHAYcgCExO2Zl1zNPaXIdarUKjUbNlUxOF+bmLqiYFQeDM+cv0D4xgSElhz64tFOTnsOAKEg3M43MkkiTxaAB1igWl+mbt0YGRWcYmF9jWWoLVkkbP4Ay/PNyBSqnA7Q1yoX2U2TkvcrmMUDjG2SvDVJdbeWBv7aJ5zLv8TNvdpKRoKC0wExcEJmfclBebyUjXc+R0P5asVDY2FFJXlUPKqxIsEzNOotE4xflZVJVkU5KfSSgcI7hgX/Na3ovcjdhEFEU+8YlPsGPHDurqlu+6+uIXv8hf/dVf3fS8w+EgFAqt4lXcGkEQcLvdyaBS4tZI67U6pPVaPffamgnxKJHAHBG/HSEWRalJRaZQQjTyW7m+CHj9EZDJuBu/EuPxOOMT0wwMjjA3N09WVib7925f9W9SURSZdcyTbcnCbLZisSQKFOIiq+r08Hi8PP/iMfyBADqNhmAoTEV5GZWVlShVeiwWfXJsALd38ftwJ+vl9we42NaD1Wqmvq4Wa7YFnU57y/mHw2E0Gg2xuJzsbBtmcyaZGSYURAj7ZxnsPEIs7EGIR5ArNSg0pcgVauLAcsqfoigyMTXH1d4xgqEwG6rSyHCHCCtmV/mKlsfr9a7pPGlPQ0JCQmL9EeIR+rvO0tt5kp1b6lGnWJErVr7hHQyGaO/soaqilIqy4iXvW739Q5y70I5Go2bfnq1Jk/dQKEx7Zw8VZcU3KVxEo1GGR37TXaFUKqksLyYWi6HRqHnw/t3MOuYpWsZ7LBqJMjfvZOe2jZSWFKxiRW6Py+3lu//yI2RyGVs2NbF9S+uqPFpD4Qj9g6O0NNZiTDUxMjbJ4aOn2bypkYK8HFqa66ipLscxt0B7Zy8ut4dDB3bRWF/NYw8fIDV18V5HLBZLynKeu9BOd+8AcrkCa3YWzY015OclYhe1WoVSLuCa6WSk7wLjY8PMuQLs39mKRqsnPDpKqlFHWZEFS5YRtVLBxIyLrv4ZZuc8CKJIjiWdeZePX5/oxunyU1Vmpbk2n+xIKp29Y1hN2eu61qth1cmSRx55BKPRyDPPPIPJlNjYcjqdfOADH2Dnzp188pOfXPdJSkhISLwRicdCBJ0jBFwjAGgNd6+bZHrGwYW2DubnneTlWtm3ewt6ve72J74KQRAYm5hmbHyKnds2oFQqaairpLR4cdAQCoVxuT1Ys82UlxUhVygYG5/mwqUOfL4ARYW5zM072dBcR11t1R0lStqudNHTN0Q4HCY9LZX62krUahW7tm9c8ni5XE40Ek225srlcsJ+B17HKAHvFeJRD2q9BY3BmjwnEo0xM+vBkKIhIz0FhUJOdbkNpyfAT164gmPex86NpezZWsEvX+5EFEW0OhUBf4TC3Az2bK5YJLfl8gQ4drafUCiKRq0k15qOyx2kd3AGrz9MWqqOPJuJzc1FlBdZ0GoWB5mOBS9XuiaZd/rIzzFRnJ8FwMmLQ5y6OEiROUbV5jUv6T3H3YhNPvrRj9Le3s4rr7xyy+M++9nPLqpQ9Xg85OfnYzabSU1NvcWZq0MQhKQB7b2wMftaI63X6pDWa/XcK2smigJhn52gcxhZ3IkhLR2FZuU/wtdvHiKIIhlp2nXfgJ51zHP46GlC4Qi27Cw2NG+nIN92W8mKG1lYcDE0Ms7I6AQ+f5A9Ozctu2lyK+LxOJfbu+i42k8o5Ke0KJcNLXVkW7JW5cG22vXy+QN0dvVRW11Ofm4mH/xvb73leV6fH7t9Dvs1qS2fz8/B/TuxWc1Ys+oIeacILJwn6BolFvWiVujRa9NQqK5LqISu/bc0Hl+QM5eGcXoC5Gan01BVhDw8SmaaljTL+v39abVLm+7eDmlPQ0JCQmL9EEWRSGAW//wAJw7/gmxLNpU1Lbc/8VW0d/YmOz6WQxBEykoKkwWS1zl74QqiKNLcWJOckz8QxJCixx8IcfpcGzarhV3bN5KXa6WrZ5Cf/PxFHnlwH3q9btE9XxRF+gdGGBoZ5+D+HZhMabztsfvXLd6Lx+P0DYxQkGcjPc3Inl2bKSspoLgof0X3/EgkSsfVXhrqKjGk6Nm8oYHuviGGT7WRnqZP+MYaEkmQS22djIxNMmN34PX5ectD+0lPSyXbkrVovMmpGUbHphifnGbrpmbKSgspLy2kID8HizljUZdOLOwh4Brm5PEX6e4dRkRBiiEdqyWXuJiIvTbUFxKJxFCpFMhkMo6c7mV4fB6ZTIZcBtGYwHNHO9Hr1GxvLaG63EaaUYdKqSAciWLQxjDqX7v4etXJki9/+cu88MILyaACwGQy8eSTT3Lw4EEpsJCQkJC4DaIoXtMKHyDsn0Otz7rhx+f6IggJ/5DB4TEA7r9v15r8QFwuD30DwwwOjxMOh8nKzCAYDKHX66irqVh07NDwGKfPXUan1XBg/w5+/suXiUYTFQqzjjmGhicIBIO87dFDlBSvLCBYjqvd/Vzp6Ka6sozy0kIyMtKXPM4fCHL67CUqy0vIz7Oxe2cikxALe3DPduKd7SIQNZCqVaNLKUEmkxGORJmYcTE548Lu8CAIIjUVNjLSU8i1pnGla5JLneNEIjH2ba2gqsyKWqWkuCCTix2jqFRK3v5gM5uaihYFVpFIjONnB9CqVezYUIrLE+T42X7mXX7kcjnF+ZmUFyU0v+srcxe9jmgszplLw0zZXZjS9OzZUkG2ORVBEHjl/CCXOsYoys3Elrq2Sst7lfWOTT72sY/x85//nOPHj5OXd+vNMo1Gg0Zzs6Taclqvd4JMJrsr475RkdZrdUjrtXpe72sWCS4QcA4T9k4jV2rQpeat2FR1vRFIrFfiR/KdJUtCoTDDoxPE43HqaipITzVSWlxAZUUJ6Wmr93o79so5hkfG0Wg0FBXkUlKcv2gT4XaIosj8gouh4XGee/EYXq+P9777UeprK29pmH4rVrJeM3YHo2NTTE3bcXu8yOUKCvNySDMa4FXnuD1e5uedlFwrbnnxpVfw+f2Y0tPIz7GSbcnElBLDPXGGgHOQaMgJoohKl4E6JWvFsVo0FkelVKDVKNGolezfXok5w4goxvCHxeRnZr1Y61jSnoaEhITE+hCL+Am4Rgi5Rzl26gq+kILde5cuWrwVoVCY3r4hGhuq0Wo1SYnIeDzOlat9RKNRNm1opLqy9DfXjsWAhNl5qtHAlo1NhMIRrnb3Mzg8hlKh4PFH7yc9zZj0LvX5A7x05DSOuXka66tukiKfnnFw7sIVnC43JcUFxGJx1Oo7j/Xi8TgTU3b6+4c5fa6NiUk7jz96kPvv28nB/TtWPM74xDSnz7Wx4HSTlmakrKSQUDiCQi6nsrIMg17J1NRsco/GYslifHIaa7aZ++/bSWN91aJikrPnr9DbN4QgCmRmmmisryY7OxEDvXpvJRKYxz/fi2++h2jQTTDgp6G+hsK8LFINiULccCTK4JiDiSknMw4P+67FAeFIDKUisYblxRZs5jQcTi/VZTZ0GhVX+6Z45fwgD+ypQaNWUV6URSzkvJMlvyNWnSzxeDzY7XZqa2sXPT87O7vmNlgJCQmJNwvxaAC/c5iQexRkCrR3YeNCEARGRic48cooNVWlVJaXLKm9eTui0WjynMPHThOORCktzqeirJj09Jsr5ucWnDz7y8N09fRjSk/jTz76AXQ6Lfl5Njqu9mNKM7KhpYGNrY3s3rFx1fO5kXg8jkKhwGBIoamhmqaGmmWPvZ68USoVySAnFvbim+/DN9tBNOREqc1Co7ESiTqIh6PotGqGx+a50j2BOdNIQ3UeebZ0YjGB4+f6mVvwEY3GuW9nFQq5jLbOcY6f6+fgzhpqK3IQRJGaUhtabeI1+vwhpmbdFOZmoFGryMxIIc2gJS1Vx8XOcYKhKBXF2dSUWynKz7xpUyIciaJRq1ApFahUCra2lJCfY2J4fI7/+71jpOjUqNUKPviu7ViyjHjt7Wte23uR9YpNRFHkYx/7GD/5yU84evQoxcXF6z1VCQkJibtOPBog6Boj6B5FFGKoU8zIFTcbld5LXJfbHBgcZXxiGhExufGv1WrYvLFxReN4vD5GRicZGZtgy8YmLOZMykoKKS0uIMdmWfFmSCwWQ6lUMjll50c/fZ7UVANyuYIUvY5HHth3y7hkLYRCYeyOeWZn56goLyYt1cj4xDTjE9Pk5FhobqzBZrUkDWmvx4JTMw6mp2fxBwLIZDJybAlD9727NmMwpKCUxwh5p/DPdzDXP0Y8GkKpMaIx5KxOOiUU4WrfNBPTTh7cW4dGrWLP1orbn/gaIe1pSEhISNwZohAn5J3CPtaOKARJy8ilorKO2joVFnPmisaIRKL09A1RW12GVqvh4H07FhUrOBzzHDvehd8XoKG+Kvm8IAj0DYwkiyYb6ippbqxhaHiMnz77Amq1muLCPMpKC5Pn6HRaxiemOX7yPGqVikMHdt1UGHHm3GV6+gbJyszgoUN7MWct71u5EmKxWLKo5uWjpzh7/gout5esTBPv+51H2bKpacVjhUJhTp29xPmLHUk/E5fLQzwex+v143J7mZufJDPDSEF+QiprcspO25Wr6HRadm3fiDX7N0Wz1+OY0pIC9HodxUV5GFJuLuAVhRhBzyTjQ5cZHrjK9Mwc1uxsmhur2L5l8Z7FhfZR+kdmcXmCCc8UIBZLeIvlWNJxe4OIIjRU55Ki01CUn4l9zsPxM/34g2FqK3JQKe+O0spqWXWy5K1vfSsf+MAH+PKXv8yWLVsAOHPmDJ/+9Kd5/PHH132CEhISEm8EEjIYMwQWBogEXde6SVYvg3U7BgZHaevoxm53Ul6WT4YpHWDFiQlRFJmcsjM4NMro+PS1Nk0j+/duw2hIWXYTob2zl+/94CeEw2Fam+uxmDOYnLKj1qiYnJolFArx2HvfSmrq6is+rxMIBBkdn2JkdIJQKMxb33KQwvwcCvNzljw+Eoly6swlRsYmKC7KZ8vGJuRiENfkWXyO7kSSRJNGVJnDyISLgYl2Ar45asqsNFTnUVKQdS1xAcNjc/znLy8xOrHAhoYCNjYUolDK6R920NkzydDYHMhklBWaaa0vpKW2gAWXn75hO5N2Nx5vELlcRjQWx+MJ0d0/Q11lDkqFgow0PQ/tryPfZrppfSORGN0DM/QNz7JrcxnZWalsaS7G7Qnwt0+/QNvVcfR6Ne96qJUtLcWkp96dDqXXO+sVm3zkIx/hBz/4AT/72c8wGo3MzMwAkJaWhk63/p9XCQkJifVEEGKEvVMEFoaIRTwotRko76IP2m+D6z/mPR4fLx89hSk9jdaWOkqK8tHpVi7B1Nc/THffEE6nC4VCQX6uDcW1e25uzu01sUOhMDN2B5PTdqanHSiVCjIy0hkYHMXrC/DWRw+Sa8te9y6jKx3djI1N4XS5AUjR68nNsZKWaqS1uY6N1wzsr+Nye5PdNafPXcaQoqewIAeb1YI1OyEFJgpxUtRB/PZOAguDRMMeZHIZKm0mGsPSMdVyhMJRugdmGBhxoFTIqS63olC8PjutbkTa05CQkJBYO5HgAi57HxfPnaBncIba2no2W7Qrlq+MxWL09A3RcbWPWCyGNTsLizkzuZkviiJnzrVx6XIvRYVW9u/eSnp6KqIoMjwyTtuVbrw+HyXFBRQX5SXP6eoZpLgonx1bW5fs7NRo1OTlZLNlU3OyuMDj9SGXyzGk6LHZLFjMGclijLUQi8WYmLIzOjbJ+MQ0e3ZuJi/XSq7Nitk8yaEDu6ivrVzkqboSOq728dNnXyI/18qmDY1UlBeTn2tFLpeTkpKQEWtsyKCiLOERm2o0oNVoqKupoLa6HJVKydy8k4nJacYmphEFkUcfvo+sTBNZS0iiC7EwQfco3e2vcOZ8O6FwFJ0+FWt2HubsRHHn5IyLgZFZWuoKMBq0TNndLDj9pBp1WDKN5FjTiMXjHD7Vi2PeS1aGIZkoAejqn6ajZ5KsDAM7NpUmu1NeD6w6WfJP//RPfOpTn+K9730v0Wg0MYhSyQc/+EGeeuqpdZ+ghISExL1OLOIj4Bwi5B5DptBc6yZZf8NIx9wCr5y+QF6ejcaGBsqKbauSuujtH+JyezfBYIj0tFSaG2vQXgsi0pZJcszPO4nHBYZHxtmxtRW9TsvM7Dyjo5MMDo1hNKZgs1p4+NCeNSdKAoEgR0+cY9Yxh1wmx2azUFdTmKhWuMXrk8tl+PwBdu/YRJ41Ff9cGz7HVaIhN0ptOrr0Ejp6JukeGEGhkJOemUNDeTE52WkAxOICpy4O4pj30d4zSYpOzYaGAu7fVYMgivzoV220d0/i9YfItaXz0L46skwG4nEBhULO1b5p5p0+bNlpVJZmc7VviuNnB3C5AxgNWsoKE8HghobCm+YuiiJD43O0d00SFwSqyrJJM+qYmHHi9Yb54bPnmZnzct+OKt79lg0YUxZvGIUi8YTe+ZuE9YpNnn76aQD27Nmz6Pnvfve7vP/971+v6UpISEisKwmtcEdCcstvR6kyoDHenVjjt4ljboFTZ9t49KH9pKen8tjDB5bsbF2OgcFRsrOzMBpSCARDpKcaaKyvIi8n+7Z+JoIgEIlE0Wo1jIxNcvT4GQBSjQYmJmcQEYnF4mza0EBZSeGqNz2WIxAI8vKx0+zbvRWAWCxOVpaJ2ury5Gu5zo2JGVEUae/s5XJ7F48+dB/p6am8460PJOVFRCFOJODAPTdNwDlAxO9AiEdQaU3oUvPX7Jl3tm2YOaef6jIrFSUW1KpVby+8Jkh7GhISEhKrR4iF8TmHaTt/jPaOXlDoaWlppa6mfMVjDA2Pce5iO+FwhLLSQpoaakh5lZdqQrJRQWtLA5taq5PFDTP2OY6fPE9+no19u7eQlmZkYmqG+QUXRQW5HDqwEyCZKBFFkb6BYUbHpjiwbzsWc2ay68Xl8tB+tZfhkXEqyorZurl52SLMldJ2pYvOrj7i8ThGQwo6nZbOrl7ycq3U1pRTUrzyQo/wNTmxqWk7Dz+wj/KyIt777rdQXVWGKIqMT0xzpaOH5sYa6moqEESR/qEpnnvhGC6Xh3e89QE0GjXNjTV4vD5+8uwLBIMhVCoVeblW8nOtN13T6/MzNNDFUF8nJp2HIosMpRCjrKSIvFwzmekpiCJMzbr4+YtX6B2aRRRFSgrNGA1aasqtVJZmk2tNx6BPxEXnr4wQicbYuamMnOz0xGu7ppphtaSi0SgpyV+53Odvi1VHM3q9nq9//es89dRTDA4OIooiZWVlpKTc21VLEhISEuvN9dbUwEI/sagftc6MXLk+P6aXwpyVwf4928jNtbLgCq76fL8/iNWSRW1NxZLVBdeJRKIMj05w5NgZ/IEAG1rqqa0u55XTF4jHBZoaqhkdmyAWi7OhpZ78PNuq52KfncM+O09DXSU6nZbUVAMVZUXk59mSVSBLEY1GuXCpk5rqMtJSjRzYVUtgYYCZnh5iIXdiUyC9JHkzriqzYkrTk2kyMOaQYZ+bZNruprWhgIERB1qNii0txWzfUILVkkYwFEUul6GUK1CrlYiIbG4upigvg76hWbrjM0kfkdKiLNKMWhqq8xifWmBiyoVSIWNLczE1FTayMgzLvo7+kVnaOscpys+kutTK6NQC//GLi2iuXbMoP4uPfWAvWaabxzh8qpf/9Q8v8uf/M58PN7xn1Wt/L7JescmbKcEkISHxxiBhsjlCyDMByNEabMjk98aG9a0IhcIcPX4WnU6b9F9bTaJkZGySV05fYNf2jRivSXaulEgkyuFjp0lLNbJ1czM6jYb0tFT27t5CWqqRq939pBoN5OVa1/XHvcvl4cjxM0RjMeKCAMhoba67ZeHL4NAYA0OjOOYWiMViNDVUJ9dJpZQT9s1ci0UHiATnEWNh5Co9Kn0WCuXqzdEj0Rj9w7OYM41YMo201BWgVivQqNcurfpaIO1pSEhISKychEqGncDCIPaZUdo6hqisrKWpvhq9/vbdALFYjEgkil6vQ65QkJdro6GuklTj4t+yLpcHp8tNcVE+mzY0sOAKMjllx253sLG1AZvVzFse3I9coWBgcISBoVFCoTBFBXkUFeQuKoTweH2cOnOJGbuD8rIi4vF4olPV6+PipU5GxyfR63VsbG2goqxoXdbJaEihqqKEUDjC+MQ0sWiMbEtWUkJ8JYmSWcc83T0DnLvYzvDIBHl5Vnbv3IxGrUKhUPDykVO43B7kMnlynyUQCHKhrZP2zgEyM4wUFuRy4tR5AO7bux1Dip6ykkJyc7KxmDNv6oIdHh7l7JnjzE4NIURcZKQqsBbnoEktwpauxGITEBGRy+X8168ucbFjDKVSTmmBmdoKG6bUxN9AWZEFfzBMZ+8UWSYDpYVmmmrzUSrkyGQyfP4QF9rHCEdiHNxVTUZaChlpr8/77poj6ZSUFBoaFrf9zs7OYrFY7nhSEhISEvc60ZAL/8IgYe8kCrUBrXFlLalrYXrGgWNugYa6SvLzbEkztJXi8wfQ67S0NNXe8rjJKTtdPQO0d/QwNDKe0BXdv4Od2zZc0xKfobSkkLxcK+WlhWg06lXJUYRCYQaGRukfGMHt8WI0GKiuLEGlUrFja+ttz5+csnP6bBvBUAhzupLovIPAwgDxaCCZJHF5gkyMTTE542JLczGRaJyBUQenLw0TjGnJMAoIgsAvXu5AEER2bCzFak4jGovT3T/NifMDqJQK3v/2rRzaU4NWrcQXCBMKx6gpt2E1G3G6g1w+1sW8y49Oq6K82IIvEKa+Koea8oRB/FIEQxEWXAFyremUFGRhStMzN+/jez8+y8jEPOlGHX/wnp0Y9Bq0GuVNazsx4+Rv/s/zHD7VC8BfPfWvvOcP/4bU1JVvLt3rSLGJhITEm4V4LETIM07QNUo8GkStv7sFGb8t3B4vl9u7GZ+YRqFQsGfX5lXFEj5/gKHhca50dFNUkLcqKQ2Xy8PE1AwDg6P4/AHMWZm8ePgkk1MzqNVqotGEkWxt9coraJdDFEWi0RhqtYrpGQfHT54jGAyh02k5dGAXhhR9svBFFEVcLg+OuQVm5xZwOObZtqWFbEsW4UgEpVJBQ11VUsYkEpgn5BnDP99PJDiPEAuhUKWg0mWuKUECCVnQ3mE7/cOzxOMizWollkwjRsPtx/MFwiw4PaQpX38FCVLcICEhIXFrYmEPI33naW87y/YtLdjyq3jX24tXnCTp7R+ms6sPc2YG+/Zspaggl6KC3JuOnZqe5ZXTF9BoNBTk5zA2Mc2psx2EwwEsWRlEIlHUahVxQeDnv3oZtVpNaXEB5aWFNxmR9/QNce5CO3qdloP7d5JjsxAMhpLJFJfbw7bNLZSVFt6RfKYoiszY55iYnKa5sYaS4nz+66dXCYcjVJQVU1tTvqQPyHJMzzj46bMvMD3jQKVSsnPHRprqqjEaUgiFwvQPjpCbY6WpsYZcmyUptX76XBvDIxOEwmGCQRXDw+NYLJkUXOuUkcvltDbX3XS9eDRAwDmEa/Q0ysAwrZUGcnNb0OkT8lkT027aeybpHbRzaE/CmzXPmo7VkprY17gh0RGOROnqn2FgZBaVKhEjAKiUCgRBoHtghqt90+i0KlrrC153nSSvZsXJEr1ez+joKGZzQjbk0KFDfPe738VmS2Sy7HY7OTk5xOPxuzNTCQkJiXsAIR4l6B4j6BomHguhTrGuyiBzpcRiMYaGx5P625mZJupqyld8sxdFkYnJGXr7h5iYnGHPri03BS0er4+x8SmyLVmYszIIBkOEQmFE4OEH9tLaXMfU9Cw9vYMMj04yP+/EfK2tdTU64pCQu/jxz18gFo1RWJDD5o1N2KzmFd1EQ6EwZy9cYXhkHLNJx6aqFGT+M3idftQpFjQGK1390wyNdeIPhFGpFNgsachkMtJTdRgNWvKsJrzRFEaH+4nF4pQWmqkus6JRKxkcdXCmbZgLHWP4/CFqy3MIBCOkp+nZuqEErVqFXpfodvnVkU5cngCIierL6rJsdFo11WXLd9fE4vFEV8rADGqVApVSjlarwusLc6ZtBPuchyxTChsaipJBx41EIjG+8x+n+Pq/HCcciSWfLy/JxePxvKGTJVJsIiEh8WZDEGKEfTMEnUNEgk5UWhPa1JUZqb4eicfjTE7bicfiFBflo1AocLk81NVUUFZauKJNhuu+JqIo8qtfHyUUilBUmMu2zc23PC8SiTK/4MJmNSMIAr/89VHicQGb1YxapaLjag8ZGels39JKcVHebaW7bvc65+adzNjnmHXMM+uYp7S4gC2bmjAaUygtLsCabcaanYUgJPzjdPrE/fu5F44z65hLxC3paVizzWjUibijpqqMmqoyhFiYkHcCx+AFQu4xYhHfHSdIrmOf8/DK+UT3RWmhmarSRGyzFNe7gDy+IG1XJ3C6/Hh8Iexzbt62J4M7TzPdGVLcICEhIbEyhHiUmYkuTh17npGRMSzWfOKKVGQy+W0TJTcmSUKhMKUlhTTUVS55bCgU5vylDgaHRsm2ZLFrxyaef/EEdsc8BkMqNVU1+Hx+jp88x317t5OVaWLv7q3k5WQv6UsCoJDLqa4sobmxhhn7HL98/iiBQJC3PXY/qUYDb33LwTtaG/vsHMMjE4yOT7Kw4MLl9lKYn4vFksmOrRvIMKWtSJ4zFArTNzCCx+tjx9ZWUlNT0Gg1iKJIeloq4VCYkbEJqqtK0Wo1vOttDyX3R0ZGJ1Cr1SgUcjZvbGJjayPHTrZRU1lAfq4tKcP5agQhzvR4H0N97Qz2d7K5No1MgxHr1tbkvtXxs/2cujTEgsuPVq0k15pOmjHxnm9pKblpTLc3yEuv9ABQU26joiR7kVH7sbP9OOZ9VJZkU1tpQ7nM+/Z6YsURXygUWiRPcfLkSYLBxTIvknyFhITEm5lIwIF/YYiI345Sk47WeHc2L+LxOD/62a8JBkPk5VrZ0LxjReak1xkYHOVyezc+v5+MjHS2bW4h15aooHO5vQyPjDM2njATlcsVtDRWMzk1Q2V5CcVFeZQU5TM5bedXvz6Kx+MjEAyRY8vmgft3Y87KWPXrEcVES+fuHZvIzEhfte53LB5nanyY+iI1lpRpwgsunAEtdqecuko1SiAuCFgtqdgsqSDClN1NKBwl1aClriIHjVrFiB1i2enUVtqSpmPTsy5+8LPzjE7Mo9er2dhQSI4lnTOXh9mxoRS3J8il0XG2thajUijQ69S4vUGUSgUVpdmUF9/6fRmdnKe9e5JAKJI0NDt8uo+q0mzc3iCRaJSyQgu1lTYaq2/uTjp9cYjP/+MvGJlYSD5nzjTwJ++p4X0f/B/k5d29jqbXA1JsIiEh8Wbhui9J0DVC2DeDXKm/5oH2+jfTfjWJblQ7I2OTjI1PEY1Gyc2xUlyUjyFFz6MP33fbMQKBIOOT04yOTTFjn+Ntj91Pil7Hvt1bSUs1JKstX33d6RkHM3YH0zMO5hecyJDxu+96BKVSmfBe02sxZ2XimFsgGo2RY1tbh0EkEsU+O0dqquGadNcAly53olQqsZgzqaupIO9a7GZI0bOhpR5I+LS88PIrhCNR9u7ZRUa6joa6ShSKGrIy0xe9LvF64sw9RsA5RDS4ADIZKm0GOr3ljqo2Q+EoCy4/OdnpmNL0lBeZqSjJRqtZevPFPufhQvso5gwjm5qKUMjlCIJAWZGF2QUvWo0CpeK1ryKV4gYJCQmJW5OIN2Y59tLP6ey4QorRxL79Bykpzl/xfSUSidJ2pYuiwrwl5bZu5EJbJ2PjU2zb3EJ5WREymYyykny0Og3jEw7arnSRZjRQfk0qSyaT3eQtEovFuNLRQzgSSY4zPePghZdPMuuYw5yVyZZNTXfURTI/7yTzmlT5mXOXmZ93EonGiMXjlBTnI5K4d6wkbnC5PHT1DjAwOAZAVkYaV7v7yc2xolIq2bFtQ8IUPsdKetpviiVlMhnTM7McPnaGwaFR9Ho9lqwMHn/sfgwGPVs3t5CRrltSvjMW8XP45ecY7r+KzzOPUiGQn2dFl17IpMPLlVOdbKgvJD8nA6fbT3amkf3bK6kozk4mSm5EEATsc15sljRSDVpqy20UF2QmZTkj0RiiKKJRq6gsyaapJh9T2sq7bF5r1lXQ9vXeRiMhISFxN4hHgwRdowRcI4CIxpCzZqPM5ZixO+gfHGXb5mYUCgUbmusxmzNuGXjciH12Dq1WQ1qqEVEUyc7OYnfFppuSG6Njk3T1DJCXa6WxoZq4EOfSpau4vT5MpnTS04ycv9SOxZzFts0tXOnsQafVsn1LCyZT2qpeUyQSpbOrj7l5J/ft3baqhI/H6+Py5U4aqzKJeIbYWuZiYmqWMwMw64wTj7sxpmgJBCPotGqyTAaGxuY4fXGYeFzAkKJBJpPRdnUclVLBvm1VZGWmUpRdhNPtZ2DEQWmhGXOmkSxTCm5vkLqKHDLSU7BaUlGrFRw/N4DXF8JqTiUWEwiHY8wt+Kgus1JZsnzV5Y2MTCwgk4EcGRPTTnyBMDs2lNJYnccLJ7qxZKayubkoaYZ2ndl5L1/6+q/55eHO5HNymYz3vW0z/+P9exB9fdI9+RrSOkhISNzrRENugu7Ra74kMtQGG/J7zJdEEAT8/gAZ6TrmF1y8fPQUaalGaqvLKCrIW5UfyXVpLJlMRrYli42t9aiudX3c6LkmCAKOuQWCwRBFhXnEYnFeOnISjUaNzWqhoqyInJxsZh0LdPX0MzE5Q2F+Lnt3Z66p+OM6/QMjnD7bhiAKtDbXU19rpLSkgFzb/8/ef0fZdaX32eBzc8656lbOEQWgkCMDmJrNJjuxW91SS7Y0sqYlL0ufxzNeXt/MtP1pNFr67PGyl0ce21LL+pRbUudu5oBAgEQOlXPVvXVzzumc+eMCRRZRAAok2CS777MW1yIL9+yzz75g7ffs931/PydWq3nLfSmfL7DqC3Dx8g3MJiOHDuyiKtSfyfseE1ZRqFLKhSlm1ikkFijnIwi1KnKVAZXR+6H/XhSKZaYXQiysRFAoZHz2ESNKhZzRLQo2ajUBXzBBNJ5jYSWCw6bH7TAyvRBkYSVCa7OV4b4mKtUalUqZWmb+Q83tZ0UjbmjQoMEvKtVylnxiiWJ6DadVzZ59Bxns77nvzkqtVsOXP//UHbsbUukM+XwRj9vB7rEhdu0YRKvVEI0lsFpMdHa0cunqFHa7ld1jfXhcjjvea3nVz/kL1ygUSxvdK6Iocu6dy8gVck48fH+Fpe8lGkuwtLzG8oqfXD7P5z7zKBaLCafDRjyRxGG3MjzUQ3dn2x27XN5PtVrlhz99DaVSwdBAN2u+dX76yikO7d9Ff28nX3v+mduSOvl8Pal/7p0rfO9HLyOTyejpamdooJue7g70Ou1tUuyJRArfeojA+gp7h6zkopMkfDdosmlp3bEDk9nMpeur/Jc/P0k4lkGjUtDd5qSlCT776OhdE0tr63GuTfvJ5cs89fAweq2K/u53Y5W1QIJLN1bxOEzsHbv9LOPTwKcrym7QoEGDTxDvGp3NUykm6lIHigeXLd+Q2ppZIJFMYTIayGTzmE31l+57US5XWFpeY2Z2kWQqzchQH7t3DtPT3b5RmXGLW5USg/1djAz1ki8UOXn6HS5fnaRSrdLd2Uar18PKqh+NRsOBvWNYLCZaW5ruqxNEFEUCwQhzC8usrK4jigKjw/3brgbS+NYAAQAASURBVPIolytcvXadq5fPIylHMJU1mAxKFBob4WyeYqXEYI8Tg15NNldEJquPm0oXKBTLDPZ40KjkLPniN1/sDewYqMuPZbJ5Tl+Z4NyVRWYWwgz1uvlf//ln+KdfOcTSWgyX3YDZqOXGzDoTs+sYdGpami0Ui9WNaotnToyiVNx5a83mS1yf8uOwGTDoVRwe7yIQSfGT126gkMsY6WuitdmKRCJh51ALWo0Svfbd9a3WavzV987zH//0NXL58sbPdwx6+dbvfmZD7iuT3fZX0qBBgwYNPqHUKgUK6TWKqVVqlcIHNuX+OCkUily6OsHyih+pTMnzX3gch93Ks0+f2FaCJJXOsLTsw+cP8sSJI8jlctwuBx1tXrzN7jvGIPl8gRdfOUUqncFo0NPe5kWpVPDcM49tFJokEilefeMsiUQSi9m0Ibf1QSmXK1y7Mc2NyVl6utsZGXq3mlan1aB7j2xJuVwhEIqg1ahx2K2EwlHePn8Fb7Obo4f2IJPLN3mWVAoxCqk18vFZyvkYglBGJteh1LoeiFeNIAhcnvCxuBpFJpPQ3+Wmt8OJVCpFFEXS2SKJVJ54MgfAruFWJBJ458oKGrWCtmYrtZrAG+dmUanktDZZyefLvHpmmky2SLFc5thI4+ihQYMGDT6JCLUKhbSPQnKRSjGDSu+i4y4y0nciFktwfWKWPeOjm/a8W1QqFa7dmGFicg673YrHfQyNRk02l+fcO1e4cPE64+Oj7N+zgy99/kmS6RJW89aSX6Io8srrb+FfD9bVNjrbWFhcIZFIYbGYePzRI9vyVbkTP3nxTcKRKCqVirbWJlQqJcVSCYCe7nbcLjttrc33PMeoVqssLK0yN7/C448eRqFQML5rhPmFZf7uH35CMp1hfOcwX3z2iY2ES7lcIRiOsL4eZj0QIpPN8cVnn6BULvOl556kr7cTh916W3K/Wq1y7p3L+P0hUskw1UIYkzJDSG1Eq7cwNrYThUKGTqPir79/nssTa7Q2WfnSU7sY7H1XXeNOzxSKprk25SeezOFxmTg03rXprCKbK3J5wsd6KEmzu67Y8Wll2xGLRCLZ9EW8/78bNGjQ4BeJailDPrFAMe1DIlOhMjQ/cCmM1948x3ogRIvXw57do/clBbG65ufGxASiINDqbWLv+I7bri8WS6ys+Vla9hEMRTaqLorFEi+/eprX3jxHZ0cLYyP9KJVKvvuDl0hnsjR53q3M2G6iJJ8voNVqEEWRk2feQaVUMjY6QHdn67aDmPnZSd5841XyaT8dHiX9ww5uLJYw5hUM9erpaRfwBZMsrkbJ5UvIZFK0GiVmo5b+bjf93W6qtRo/fPk6Go2CI3u7N6oczlxc4Ds/mSAYClMqV/E4TRzY3Um5XGUtkMAXSCCTSjAbtbidRiLxLLFklnywTHe7g2qthlwmu2OipFSuMDUf5NqUn2gii06rosVjYddwC2fOL+D1WBjq8xCOZJhbCuOyG2/zJ7kyucb/4z/8iOmF0MbPTAYN/5ffPMEXnvxwbcWfVhqxSYMGDX4eEWoVStkA+fgilVLqU+tLUqvVePWNs2SyOfp6OzGZ332G7SRKgqEIL71yGqlMSkuzm1K5glwuZ2So967XrQfCvH3+CpVqlaceP76pS0SpULAeCNPkcaLTaTHotOzZNXLfclv5fGHDe0QikbBn9yhSqYTZ+eWN4pT3k0ikWFrxsR4IE4snEEWRwf5uHHYrLV4PX/3SZzeqcAVRRKiVycd85BPzFNM+apUcMoUOpdbxQBIkALlCCZ1GhVQqvem15sZp16NSKlAq5UTiGd48N0etJgCg16k2xScnjvajkMs4f3WFl05N4bDo+dpze3HaDEzNBxBEEbfDiE4rRyUNPJA5fxgacUODBg0avMstic98YolyLkS2KOHyDR/g48TDh7Y9Tjye5Mq1KVZ965iMBmRbvJcuLq1y/tJ1SqUKoyP9DA/2kM3luXZjmstXJoklkijk8g1pxDu929ZqNaRSKRKJBKfDhtViJhqL8+aptzEa9JTK9YLC+02UFApFXn79DI8eP4hWq6G/t4ORoV6KxRITU3MkU2nKpQoetxO7zbKpk3UrcvkC0zMLzMwtkcvl0ajVpDJZ7FYL8USSK9en8bgdfP2XnmWgt5NEMo3DbqVWq/G3//BjarUaogiJZIonThxFrVbx5GPHtrzPjZsJKplMStg/j1GSoN2bw6KTkS66ODeVZnr+OuuhJE8+NMRDB/o4srebI3u78Hq230m7HkohkcDxA7247JvjuHK5ygtvTqJUyDk43kWL5+7r80ln28kSURTp7e3dCCay2Sw7d+7c+Avc0PZs0KDBLwKCUKWY8pFPLlCr5FFqHtwLK9QPBtQqFWazkV1jQ+zfO7Ztqa1qtUomk8NkNmKxmBkZ6qWvu2PLQOGdC1eZmlkAwO1ycPjAONlcnjdOvs2RQ+OYjAZ+6zd+ia6OViam5rh2Y5oWr4cjh/ZsW5qiXK6wvOJjfnGVSDTGl557Eq1Ww2efemTLSpOtEEWBTGyFSmaZ1Mo19Pjp63eSq6g4eyNHLJHjgLs+H38ohS+QpMltotllxmkzIJNJyWSLTM0H2THYjEqp4JHDfRh0aoqlKhNz6wx2e0AUSaSyeBxGjh/sY3yklaW1GN9/+SqiCC67Ab2u/j2fv7pCNleiu/3uJqe3iCWyvHJmhsWVCGqVAq/bgt2q58jebmRSCft3daBSyLl0Y41coczukc1dQ4lUnn//31/hOz++tOnnX/7MLn7vNx79VGl/PmgasUmDBg1+nhBFgXIuTD6xSDkfRabQozZuXyP8k8bE1DzxRIonHzuKzWbZ6JTYDoVCkTdOvY3TaePEw4fuKG9RqVSIRBMEgmF6ezow6HX4/AGkMhlPPHRwUxfJ5Mw8i0trKBTyDXmQh48f2NZ8arUaMpmMSDTOydPnyWTrLZx6nW5D3kMul/PVLz298X0lEin8gRBulwO7zUIwHGVmbokmt4Pe7nbcbsfG/G5JnIhCjVI2QC6xRCKWIldZRCKRotBYUendW8zsg5HNFZmcC7Lsi3F4Txcepwm9TsXUfJCJWYE2r439Ozvq3m59TVhMWnQaJYVShVAkzY0ZPwurUZZWo3icJuRyGV/97G7cTtNGXDLwnqpkUaySi4Yf2Pw/KI24oUGDBg3qVMtZ8slliqlVcrkSk/NhFpZ9aDUa9oyPbnucy1cnuXp9CoNez+ED43R2tNyW6BAEgesTszjtNsZ3j6DVqJHJZIRW1/nH77+ExWyku6OVvt5ORoa2NoKHuq/XmbMX6eluZ2igB5lMyuWrE1gsZo4d3kt7m/cDxUylUpmXXz9DoVCkWqsBYDGbePWNs2RzObzNbg7s24nLad/2mD/6yWvML66g0ajRajRUqlWuXpumraWJvbtH2b9nDJlMRj5f4IWX652wv/TlzyKTyThycJyVVT/Lq366Ottocju3jIOyuTwvvXKKcrlIX4ucZDjADm8YQaih1rr47iuzvHNlGZlMSmuTlc88PMyu4RYAvNtIZuQKJa5Pr2M2aujvcjPa37yh3HELXzBBk9OEUinn4O5OnHbDp8LA/V5sO1ny7W9/+6OcR4MGDRp84inno+QTixSzAeRKI2rDgzXPLhZLvHn6HRw2Kw8fP3DPaoX3c/HyBPOLK3zh2Scw6HW0ee1IJRIqlQprvgBLKz52jg5itZpx2K0YjQbaWpoQRZEXXjnJCy+d5MihvaTTWaxWM3KZDJVKSX9vJx1t3vvyJDlz7iKLS2vUajWaPC6OHBxHpaonFbaTKBFqFfzL1zlz6lUyiRA7+m00NXtxuLx876WrqJRyLGYdSqWMWCJPuxdG+pvYOdSyMUauUGJyNsDSWgyVSk5n1o7KqsCo1zCzEOQHr1wjX6jw298wMtLXzJF9QxzZXfcbWVuPUyhW2DHoxWU34g8mMejr0ie7R1ox6FT3TJIkUjkqVQGbWcdgjxu7RUelUiOVLZLOFoF6xUw4mmFxNYrdqufQnq4NSa9qrcbf/vAi//FPXtv4PEB/l4v/5+8+velZf1FpxCYNGjT4eaAusxQnn1ymnA2AVPGR+J99lFQqFXz+IKtr6zQ3uenuasPtsnPsSN0f7f1a2luRzeWJJ5K0eptQKOSolEqOHd675QHBtRszrKz5iceTiKKIWq3C7XJg0OsY3zWycVBTq9V45fW3CATDaDRqRof76evpuGc3ZrlcYWlljWAoSigcpcnj4vCB3ei0GpqbXLhdQzgdttsKUiQSCcFQhJNnzpPPF5DJZOwdH8Vus9Db3U5/b+dtBzm3ZLaKaR+5+DzlXIiaUEVUdKI2tCB9gIcO6WyBybkgq/44SqWMHYNezCYtJ9+eIxhJ3+wsMaJS1o8J5DIZoUiamcUQmWyRtfUEyXSe0QEvfZ0u+jpd6HUqHFb9hqnrJ5lG3NCgQYNfdN4ruVUr55AoLfz0jfPIZFL27B6lr6fjnv4biUQKiUSC2WzE7XKg12np6mzdtLeWSmWuXJuip6sNq9XMk48dI55I8cOfvEYikeKbv/l12tua+exTD9Pd2UaTx3nHREe5XOHSlQmmZxeQ3DzfAOhsb8FsMm7y9rof0pksb5+/SiAQRiqT8uhDh8hkchgNegwGHR6Pg4He/Vit5nuOtbq2zquvv8UjDx2itcWDTqehva2ZFm8TapWSVX+ANd86ZpMBhULB5PQ8a74A4UgMlUrJow8dRCKREInGOX/xOsVSiV1jwwwNdN8Wsyyv+LgxOUvAv4yCDHv6Nbz12iWuLdbwrYd54vgIe3YYGOhx09JkZXSgGe02/FRvUS5XmZwPMLcURqGQ47LXO0rfmyhJpvNcvL5KNJ7l0J4uvG7Lp9Kb5E5sO1nyjW9846OcR4MGDRp8YtkwcE8tgyii1jchecDGqqFwlEtXJqjVBPbvHbuva2/pYE7NzLNn92hdwiFfxecPsrCwwpo/iCDUcNhtVKpVADra6wftc/PLvPTqaeYWVhjfOUJvdzs/fvENBEFkdLguc6HVau7ZxloqlZmaWWB4sG4Ap1IqGR3up7urbdtdJFCXN4sGpnnt1Ze4dG2OclnAarMhLlRp6zSRL5RpcpvI5csEwylkMilejxlgUwXD/HK4bt6ukLNj0EtXmx25TMaNmXV+8voEa+tx4uk8Jp2aMxcWAAkKhZzMzaRES5MVt9PE/HKY187MUK3VMOhU6DSq2+Sx3k84mubl09PMLATp6XDxzIlRFDIZqUwBQRAZ7KnrgMuk9WDQoFezZ0c7HS22jQDx7StL/G//+afMLr5bhanVKPndf/owv/Tsnp+Lao0HQSM2adCgwaedailNPrVKMeUDaii0DqSy7b/QftwEghEmpmZZXw8jiAJWq5mWm/ub03Fv6bB8vsDK2jpLyz7CkShKpZKvfNGNXC7nmc88QrFUZmFxlVAkSjgc25CiKBaLGA16ervbcTkdmE3v7s1SqXRD/lMmk2ExG+ntbt+WtjjUu1pu+Z3YrJab/ij1LgmtVnPXOC0WS/Dya2dw2K0cPjCOy2nbOHR6/+FTtZyjmF4jF5+nlFm/KbOlRal1IpGpEKoGJNL0Pee7HWo1gVS2wMVrq0zMBjAbNVjMZvo6XfVkTVXA5TCyFkgwNR/EoFfz1EPDiIjki2U6W+3otEpuTK/T0+Giq81+V4+291IqV1jxRXHpPv6ujUbc0KBBg19U6pJbYfKJJTIJH7OLUcZ27kKhUPDwsQM47JZ7GrmHwlGuT8zg8wfp7Gjl6KE9eNwOYLMB+9LyGufOX6VWq+F0WFn1BXj95Dlm55dQyBXsGOlHEARkMhnHDu+96z0z2RwvvPQm4XAMuUKOWqUkFk8C2zuneC/pTJaV1XVqtRpjowOoVSoEQWBkqJdcocirb7wFwPNfeAq5XM6h/bvvOWYgGOHc+Su8+voZFAoFu3eN0Nri4bFHjgAir75xlrffuQIScDntDA/Wz1hSqQxyuYwdIwP0dLWh0dQLM1UqJRaLkb3jOzAa9BSLJfzrIXzrQXq62nFYFKQj05RjV+i1V7g+E+Df/484lZoMpVpPf6cDl73etTrc27zttblFNlfkpVNTiCIM9njo7XShkL8bv5QrVW7MrDO/HEGvU3Fsfw9ux/aLaj8tNFzWGjRo0OAO1A3cgzcN3JMP3MD9FpeuTHDtxjRGg55jh/fe14YficZ58ZVTVKtV2lu9mIwG8jd1PtcDYTK5HDt3DNLR7kWve3fuoihSqVR5+bXTBMMRdo8NIYgC84srDPR1MdjfvbFh3wtBEHj1jbPEE0mam1w47FbGd41s+xlEUaSYWSe6PomQX+U7P7rA9bkkLc1O+nrqL+NuZ10TUyKBVLqIw6anr9OF22Hc6PAolSsUihXMRi1Ws46BbjdGg4ZQJE00nkWrUfLG2Vl8wTiVWg2X3YDVqKVcrdHebGPU3U6bo/4i7w8mOX91mUq1Rmerg4FuN1rN3Q+vcoUSP371OpcnfMjlMvaMtnFgVycmg4a5pTDtXhuDPR4KxQqn3pnH5ah3tPR3vVsJ4w8m+cM/fpEXT05tGvuzj47wr/7ZY/dM1DRo0KBBg08Hm8zbqwUUahsyxQc3Iv1Zkc3lWV1bx2ox4XY5KBaLVCpVdu8aprWlCYNed88xBEFAKpVSKpX5++++AEBTk5MjB/fQ4vVsJDSWV/ycOXsRQRQwGQ24XHZqQt07Y+/4ji3HLhZLTM8ucGNyjkP7d9HR3nLHz76XarVKLJ7E5bQjiCJKpZLPPX1iUxJmq2si0QTBUIRiscSBfTsxGvUM9HUxNjqw5aFTrZKnlA1RzPjIxxeolJJIpAoUassmma0PowZ1y5A9nsxRLteIJrLkC2XiyRyiCD0dDvRa1UbHbLUmkCuUKJWqdLTYGOj2IJdJuDyxxvJajGpNoK/ThVIhp7PFcY+718nmihRKFRxWA6VylSuTPvb3fzrl5O7GH/zBH/CP//iPTE9Po9FoOHjwIH/4h39IX9+dZWQaNGjQ4GdNtZQmn1wmHVtiem6N2cUIggje1iQet+NmwuPOpNIZ3jp3iVA4islo2JDb2orFpVVOnjmPy+Xg2KE9CILA//6f/gSDQc/TTzzEoQO7MZvu7VtWKNSLGBVyOZFIHKVSQXOTm7HRgfvqJCkUiszMLbGytk4ikUQqldHRXlcIUSjkmE1Grk/OIZVK6OvpYGig555Jo1tcuzHNj194nVQmx8hQH1/98tPUqgKCIKBQyPnOd3/Kxcs3sJpN9PZ04HTaqNZqqIAD+3YC9T17dn6JmbklnnrsGEaDnkcfOsT8wgpvnn6HWCyBUKtiUFfRC/OsZINcnVxhZKCZjrZOYjkVotzC7pFWHJ5ODIoM96tEJooi4WgGl8OIXqdmsMdDu9eGWnV7x2gokmZpLcboQDO9Hc6fW9/URrKkQYMGDbagUkzVJbfSfqRyFSrDB9O/3IpEIsX07CJOh42uzlY6O1pxu+pByr3ukc3lmV9YoVqtMr5rBIvZyPBgD50drUQicV55/Qz79+3Ebnczvmt4UwdCrVZjfnGVN06+TX9fJ8cO72VooJcvPPsEToeNazdm6Ovp2LZpO9Q7Si5cvk40GueJx45u288E6rrcxYyf1bkL/N13X8ZqUvC5x3ezf99utCY/tapAoVhBeM+BgUat5OlHNidiqrUas4thpheCmAwaBrrdLKxECUXT1GoCNUHEZTfidmgwGtUINZGje7o5caSfQCTDYI8bmVRGsqQllYliNmrQaZV4mywMdLvRae6+HrlCCYVcxrUpP+F4lgO7Oulus7Pki5HOFnHYDIyPtlGp1rgxs87cUhiDXo3b8W6QWCiW+e9/fYb//tenKVdqGz8f6vXwv/7zp7YtuSUIAv5gEnIlHpyyeYMGDRo0eFAItTKlTIB8YolqOY1cZUZt+GSbtxeLJeYWVlhZ9RONxZFKpOzaOYTb5aCjvWWjW/Vu1H3M1rh0OUoqmeaLzz2BSqXk+LF9OO021GoVoiiSTKapVpVotRqC4QgdHS3s2TVyz9gklc5wY3KWhcU1JBLo6Wq/62GKKIoEQ1GCoQiBYIRoNI4gCnz580+h02p46vHbTVRvkc5kOXXmQv0AQxRQKpU0uesG8QqF4raCkUoxRTkXpJBao5heo1rKACJylRmNqR2J5MEcNNRqApdurLLij5POFlkPpVApZQz2ePC6zchlUiq1GplsiWyuRK5QN8FVyGV0tNhpbbJgMmh489wcoWgalVJOZ6t9210k6WyBtfUEvkCSWDKLUiFnpL8ZURT53GOjVFJzD+Q5P0m8+eabfPOb32TPnj1Uq1X+zb/5Nzz22GNMTk6i0907cdigQYMGHyVCtVSX3EotsTC/wKUbfqqC5KZ5ed9diyNFUSSVymA2G1EplUgkEh46doBWr+eOZxaiKHLx8g2WVnzcmJzjyIHd6PU6/vW//Gd4m7b3dlqr1bg+McOFSzc4cGA/A70tjO0YpK2liRav557XC4JAOBKjVK7Q1tJETRC4MTlLS7ObHSP9eJtcZHMFRFFEIpFQrVUZHe5joK9rQzb8ThSLJS5cukapXOGR4wdxOe1otVp2jA4ik8n47g9eJhZP0uxx8dCx/ewY6efgvl047NYtx04m07z19mXWAyHUGjWvvPYW47tHsNsslCsVtEpo7lKTiS0wObXI6ZNxkhkBlUqN3iLS3a3h0Hj3zbWHbPX+z6tiiSyXbqwRT+Z4/NggZqN2UzEnQDyVYz2YYriviZYmKw6bYctEys8TjWRJgwYNGrwHoVamkFqjkFyiVimi1D0YOQxBEFhZ9TM1s0g4EkWjUWO7qX1pNhnuWbm4vOJnfnGFYCiCTCaju7MNqBuC9nR3sLLq58LF63R1ttHb3UE8WdjI8icSKeYWlnnr7cvMzi+j1ajxeBz84CevEo8nGejvQiKRsGOkf9vPIpVKEQSBv//eC1SrVQ7s3bktuQ24qZOaWiEbnmBpcYo//+51QokKJ44Mo1BbcNoKWIxaPE4jbocJs1GzZUAmCAKLq1EuXF8hGE6zc7iV/TvbWfHHKVfq1ZGhSIa55TA/eOUqHS12SqUqu0db+ern9iCRSHDYjFRrNWYWglyZS2PSVHjy+BBmo5bxkba7PkcqU+DU+XkCoRSH93Sxc6gFr9vM5FyQ6zPreFwmbJb6i3KxVOGlk1OUK1VG+5vp7axXYYiiyE9fn+AP/+tLBCPvSm1YTFr+5f/pUT7/xNi2qjWy+RKLKxEW12Ik03nabXm29202aNCgQYOfBaJQo5QLkY8vUinGkCkND7QQ46NkenaRa9en8Ta7OXpoD95mT13ycxvk8gXeOX+VVX+ATKZIR7uH4aHeDfkNo17P/OIK4XCMYDhKuVxm984RRoZ6ObB35z33wGq1ilwuJxiK4PMH2THSv2Xhx60ukEwmS29PB6Io8uobbyGTyXA77ewZH8XttG86OKpWq4Qj8Y2Eik6r4fjRfWg1anRaDZ0dLbiddsxm46bv8ZYHTSkbpJBcppQNUK3kkEgkyFUm1MaWB+ZHU63VKBYr6HVqApEUs0thtBoly74YKoWcHQNeHj3cTzJTwB9MYTFpafFYsJi0mI31juNsrohEAka9GolEgt2qp7PVTrPbfJuR6/spV6ooFXJiiSyvnJ6mWKqSyhRQKmQYDRquTflpdpvo67RTeSBP/MnihRde2PTf3/72t3E6nVy8eJGjR49+TLNq0KDBLzp1hYwQ6cgM2UQQk82D0dZOR4eC0ZH+e8pkl8sVfvrSm2RzeZ7/wlOo1SqeOHHn32mhcBSdTotep0UmkxEMRTm0f9dGN+h2EyXrgTAnT7/D9OwiUpkUoVa//vCBe8thAQRDEV4/+TalUgmH3UZbSxN6nZZf+vJnkUqlRKJx3jz9Dmu+AMeP7qe9tfmeUluZbI7ZuSUuXrnBjYk5isUiA33dG8mSo4f2ML+wQjgSQ0REqVRgt1nQaNR3PR+ZnJ7nwsXrqNUqlEoFQrUGknrskU8HqCZv4NUGUUoKnLru48ZihtYmFw8fqStT3NrDPyiFYplr036W12JYTFoePtR325ilcoXr0+ssrEQwGjT0djpRKuQfeaKkXKkSCKVxfozqXo1kSYMGDRpwU8MzFyafmKeciyJXW1AbP3yl562KhdW1dd48/Q5ul4PjR/bR2tJ01wMAURTJZOvmYuVyhTPnLuJy2jl8YJy21iYKxRJQTxh874cv35ThaubQ/l1AfZO9Nc7Lr5/hytVJlEolO4b70Go0xONJmpvcnHj48La6QarVKr71EMvLPgKhCF967gnkcjlHD+3BZjVvSzpMqJbIJ5fJRq6TTfh484KfU5dCJNNVdo200d/tAsBk0PDwwTvLF9xaU1GEty4tsh5K0dpkpd1rRaVU0NZsJZ0pMjEbYHoxyMxCCJfdyI4BL7tHWnHaDEgkEgRBYG45wtR8kFKpiqeplV39924JTqbzvH1liXeurFAoltk93EZ3u5NoPMtbFxexW/U8fKgPh9VAKFpPgKhVCno7nLQ0WzY6VabmA/xv//mnXLi2ujG2TCblG1/Yx//5l49tyGNsh/NXl1lbTyAIIiDe83CjQYMGDRr8bBBFgXI+cvPQPIxUrkalb/5UmLcXiyWUSgVjowP093beV+fpLdQqJRqNml07htBoDSgVdV+PQrGEXqdlamaBuYUVnA4rg/1duJwOHHYLwB3jpFy+wMLiKguLKzgcNg4f2E13ZxvdnW2bfEGKxRJTMwsEQxEikXrniEajpqe7HalUyueefhS9TrtlwmppeY1TZy4giAIqlQqPy7HRqSKXyzl+dN+mz4tClXI+Silb7xoq56PUKjmkMhVylQmN9t7dw9shmyuy4o+TTBdIpQtkckWMBjU97U6uzawDUK3WGOqtS2h43RakUilWk46nHh7eGEcQBPyhFIsrEYKRNEqFHK+7nkQZ7mu66xwSqTzzKxFm5oOUq1Xamm1YTFqO7O1Gp1ExuxTCatZhMWsxGzQ3i0OqH/rZPw2kUikArNY7x9alUolSqbTx3+l0PVYUhLp0y4NAEAREUXxg4/0i0Fiz+6OxXvfHz3K9KoUk+dQygZUpXj99Bburmcce7cXtNuC+Kbcl3EPr8e0LV8lkc5x45DBSmeyOny8WS1y8fIO5hRWam5xks3neOneJ4aE+vvKlp5HL5fe81y2mZxb4znd/SjaXp6ernb3jo9jtlvq6beP6QDDCa2+exW41s2vnAew2y8a9Q5EYV65OEghFMRn1HNi3C2+ze8u53fIIUamUeJvdRGMJ/uwv/xGZVIa32UVHewtSqZRiqYxSqSBfKFIolZDIpLQ2u9m9cxi3687rLAgCEokEg0FPR0cLgWAYuVzOI8f2Eg0u8ON//BNuTMwSiWc4uq+Xzz62l8891crjpcqmZMb7hxbFd//ZDnPLEdZDKXaPtNHZat84X7nF/HKYa9N+AMaGWuhuc9zcz7c3/v1yqyC3VhP4ux9d4trkMv3tBv7vxx/s/zfbHauRLGnQoMEvPNVShnxyiWJqDaRyVIYPd4ghiiLrgTDTswvIpDKOH60nR559+gRm890P46vVKtduzDC/uIIoinzpuSfRajV84XOPk0pnWfMFuHx1kkKhyFe//DQKhYJHHzqI2WREpVISjsSYnJ5nanaFLz93AovZxL7xMbo6WmnyuIjGEmSyWQb7e+7azfLeZzl99iIrq36q1SpWq5nhwd6bh/JsqxW2VsmTjswyP3WW5eVVbBYDTlcz0yuL6LUahnu9PH586J5+HOVKlYWVCHNLER4+2Ic/lESlkHNgVyeHx7sAWAskOHN+gdfPzaBVK9GoFTz10DDPPb4Dq7ludFat1bh1/DK/HKbJaWKg2w0qJ3r5nY1Uy+UqUpmEyzfWOHN+kY5WGwd3dYJEgkIuw+0wcnx/Ly6HkWg8y6tnponGsxuJk/7u+iFLPJXjP/7Ja3znR5c2BVBH9nbzr7/5OF2td9eMrdZqrPjizC6F2TXUgsthRKNSIJdLMRu19HW5sCnW7/W1NGjQoEGDj5Bb3QWF1CqlzDpIZCj1bqTST/brV6VSYXUtwOLyGuuBEA8fO0CL13NfiZJkMs2aP4hapaSzowVRFJlbWGY9EEOrVaJRq3C7HOh1WnaNDbFvz45tdVHG40nOX7pOIBhGJpPR2tJEV0crcLt5OtSTLTNzi7gcdsZ3j+BxOTZ1gWzlr3LLGL7F69noNnl/58gt6ma5EQqpVfLxWSqFJIJQRipTo1CZUOpcHzhBks0V8cUrlLIhkqk8bqeR/i43mXyJyxNryOVSBEEkFMmw4ouRyZZwWPU0u0zsGm7dZMa6FafemScYSWOz6Nk71k5Lk2WTdOt7EQSBZLpAqVzl8sQaCysR/MEUZqOGJpcJjVqBzaKjyWUGYHy0jVyhvHHwUSiWOXd5geGWn++EiSiK/N7v/R6HDx9meHj4jp/7gz/4A771rW/d9vNIJEKxWHwgcxEEgVQqhSiKP7d68g+axprdH431uj9+FuslVEuUchHK+Qi1apnXzi2A3ERf/yDxZGFbY4iiyMLiClevz7FnfAyZXHPHa+fml3j95FmEWo1HHz5Ck8fF3/z9D7BZbTzy0FHS2Qrco6dQFEWSqTQWswl/II5UquSJEwfo7+9GqVSSyZaIp4pstZOKokg0GsdoMqBSKrk2sYBCoWHnzp3I5HISqSK1Wg2ZTMbyaph4Ms/OsR14m+syYsl0adN45XKFs29fIBAMk0ymUcjl/NNf+ypanQm7zY5Wq0Gj0VCpgt1mZmk1TLlcpq+vn/aOTsLhKC3eerHB+9esXK6wtLLG2XMXiMYS7NuzkyOH9tLV1U08kWCwy8H//LP/waWrM6iUcpqbnDy9/wA9nU3kajqQgVwL2btso6IIxVo9mbJV6FGt1phfCiCRSujraqalTYe3tQeFQk6udvvnY7kkDlcLg30tqJQK8gJsK2t1H4iiSDSexh+IseqPcHjvIMVSjR+9Ps21yWW+XxN4+PPnGd25/4HdM5PJbOtz24rWf+/3fm/bN/4P/+E/bPuzDUO0Bg0afJwItUpdwzO5QK1SQKlxIJXff9Xk+3nr7UvMzS9jMZsY6Ksf4kul0nsmSurXXmZl1U93Zxtdna1IpVKq1So/+MlrlMtldFotrTf1Om8dDLicdqDedvriK6fQ6XTYbVb+6u9+iFKpRKVUMDY6uC3zNlEUCQQj+PwB9uweRSKRIJFIGB7spb3Nu60Eyy3KhTir85e4eultVnzrJNI1IqkaX3m6HYfdwqHxTmo1gd5O110TJcVSmUsTa1yZ8FEsVji6rweZTMLsYgivx8KukVZ+8voNFlYi2Cx6rGYtLW4LTruBJ44P0dpUr+7LFUrMLoZZWovy2JEB9Do1TxwbQiaT3tT4vP3egiDgCySZXggil8s4vr+HSrXGc0/sQBRFrk37UasUdLbaUchlaDVKzlxYwBdIYDZqOba/B4e1/myVao2//v55/tO3XyeTezc4a2my8G9++wmO7++966FKuVxlbjnM3HKEUqmCyaghX6zrjfd0OPE2WWh2mZFIJGRCgW1/T59WPqrYpEGDBg0+LJVikkJqlWJ6Haih0NgeSHzxUXP56iQ3Jmep1Wo4HXb27RnbthdZMplmenYRnz9INpdDEEV6u9vp6W5Hr9fhEkVaWlrp6WzaZOx6L33wcCRGsVSi1duEXCGnVhM4tH83ba3Nt0mBFYulDcms3u52bDYLX/ni03cdv1KpEAxFCQTDBIIREskUn/vMo1gs78Zw76daylDM+MnF5yllfNQqBWRKPUrtg4kj37myzNxymFRehkpWRC6TYjbWO3iz2dJGImRhJYJMJmWox8Mjh/vRa7e+d7VWwx9MsrRaN2S1mnUM9nrYMei9o4xHJJ5haTXKzGKIeDKP22Gkr8uNzaKjp92BUiHHYTOg1dS/v3S2wNlLi6QyBTLZEoIg4HYYOba/F4VChkIuo1b7iMpRt8lHHTf89m//NteuXeP06dN3/dy//tf/etNc0uk0LS0tOBwOjMZ7vytsh1uVyw6Ho3GQvU0aa3Z/NNbr/vgo10sQqpQyAQr5RaRiGp3JzPXpKNVqmc888RB2m+WeY9xKKoiiyKl1HyNDXezZdbuosyAIBIIRJqfn+f4PX0ar1dTPJprteNxWfvVrn0MUxW3FDpFonO/+4CXSmRz//Ld+hSMHx9i/d3hDIky82SZhNak33pEFQSAUjrGy6mN1LUC+UOTwgd14uto4dmgnSqUCqVSKzx/k2o1pzCYjB/fvYu/uAfaND95xLtVqlR/95DTn3rlMoVikWCxjMupRKUV0Wj1f+eKT6PU6HHYLlUq9uPXtd97BaNAzPNCORKLB8x7NqFqtRqVSRa1WMb+wzF/8zfcJhqIY9Fp2jQ1jN6s4e+onXLp4nt19RrQ2PQcHlAx27KSvs/k9ChM14M7FnO/lVg2mXp7elCypVGvML4eZWQxRrQr0dbnQyw23ZQOy+RJXJ31YTFoGezzsGzLf/JPCzX8eLDMLQWaXwkQTWVLpAtl8iZmZeb730lVK5XcPZv7Hn/wZf/N3zzyw+6rV21Pv2Fay5PLly9sa7H4rZxqGaA0aNPg4qFcChsnHFynnw8iUJtQG7wMZe2Vtnbn5Zfbv3Ul/b+d9XXtLUqKjvYVCocibp97hC88+jlwuZ+/uUWxWMxbL1sKNpVKZU2cuoNfrKJUrnDx1FqNRy/jOYXbvHKaj/d7Pd2NylhuTsxSLJQx6PYMDPeh12m1rhEK9amJxfgKx4EMvCXL1yjznJ+LkihIS6SJatQKVqr71HNh19/Uplipcm/Jz6vw8oWgal93IUI+HXcP11teHD/aRK5R5460ZXj09TbFc5YtPNvHokQH2jXVgMWmRyaTEUzlm5kOsBRIo5DK6250oFPWDhjvJVVVrNeaXI8wthYnEs2SyRZ58aAipVEpnm50b0+tUawKDPR76ulwb1Zj+YJJ4MsfesXbavbaNffGtiwv8/n9+gfmVyMY9NGoF3/yVY3zjC/tRKu+9HS+sRpicC+J2GCgUKyRSefzBJB0tdmwW/b2/nJ8zPqrYpEGDBg0+KNVShmLaRyHtQ6gWUWhsyBT3lqn8OCgUiqz61lldC7BrxyA2mwWjQc+OkQE6O1rQ6+6uhZ3N5fH5g2i1alq9TQRCEd4+fwW1WoVUKkUqlWI01IsFRoZ6EUSReLKA0bi99UimMly5Osnyqg+3y0GrtwmjQb+l8frE1BzzCyskknUJJKNBj7fZzVZiqoIgEE+kNg6PfvDj18hks+i0WjweJyNDveje9+yiKFItJilm1ykkV+oeJKUMEpkCpdqKSn932aq7IYoi2VyJRCqPIIq0e21kc3UDdqXKiE6lQq9VYTCoWVqLMrcU5sCuTjwuE+lMAb1OheoO/jGxRJbF1Sir6wmq1RoOm2GjM/hWIcctUpkCS2tR2r02cvkyL52aYn45jF6noqvVwcMH+zbiKoB8oUwmWySTK+KyGykUK8STOVwOI52tdgw6NUZD/UBCLpNxaLyTXHT6A6/Tg+CjjBt+53d+hx/84AecPHkSr/fuMbdKpUKluj2xdev/mweFRCJ54GP+vNNYs/ujsV73x4Ner40zjcQypVwImVyH2thCsVhicmqesZEBnPdIWhQKRaZnF5meXeTxRw5jtZr57JMPoVC8u6+Ioki+UEQmlXL27UssLvuQSGCgrwulUoHBoKeQLyCVSO55P6ifF/z4xTd48+TbIIFHHz6E+mbhhPo9BRQC9TWrVqso5HKkUimnz11icWkVnVZLV0cLba3NOOxWJBIJGrWKVV+AazemicUS2G1W2lubkUokW7ZaZHN5/OtBerraEQWBH73wGkJNYGS4j907hxns70avrUt19vV0IAgC125Mc2NyDolEwq4dQwz2dyG7+X1mc3mWln0EgmFC4SidHS3s3zPG2+evYjIaePLRo+QyUd46+xanX1uhXCpgMamJJtWojW30WD7834tbj3rrcQvFMi+8MUm1VqOj1c5At3tDEvwWtZrA9EKQybkgSqWMtmbLlp0pH5ZsvsTaepzWZis6jYpwLE0oksIXTHJjZp2JuWA9QXYTtUrOLz09wL/733//gf6O2e5Y20qWvP766x9qMneiYYjWoEGDnzUbkltpH0ikD0w3fKMaQxDo6mzbdqIkGkuQTmfo7GilyeNErVazvOLDbrPS39e1IWHQ3bW12Xg4EtswDtNo1QQCESRSGN89ylOPH6bJ7dzWPGbnlrhw6To93e309XRuqwLlvcTjCc6dPcnM5BVCAR97Rxzs3dVPvmZGpiihkwrsGmnj2P6e2zboW2RzRdbDKZKp/IbBaDKd5+B4J163GY/TRL5QZmYxxNp6grevLCOXSclkiyhVcpo9Fjra6l02NotuY7NdWI4QS+bYOdRCR6vtjjITUJf6UinliCJcmfCRL5SQSCT0drqwmetJ/HSmiMdlYrS/GYVCxuximJogMNLXTE+Hk+4Ox8Y91gIJ/t//3xd55fTmA4JnH9/B//Ibj961oyaeyjGzEEKnUTI64KX1phfL6noctUrB3rF22ppvD0hFUaRaShNdfJXmkV+6y7f26eajik0aNGjQ4H6pVfIU0j6K6VWq5QIKtQWl1v5xT2tL5hdWmJ1fJhyJIpFIcDntGwasXZ2td702mcqwsLjC/MIKa/4A2WyO40f30+ptIpXKYDGbsFrNOOxW7DYLLtf9r0GxWOKFl0+STKVRq1UcPbSHjvYWBEEgkUgRjsYJR2KEwzFOPHIYs8lArSZsyIS63Y5NxrWiKBKLJ1kPhAmGIoTCUWq1Gs9/4TNoNGoO7t+FVqvGZNy8H9dl1GLvepDkgtQqeZAqUKhMaMw2JJL7f3mv1mrIZTJC0TTnry4TimbIZIsUihX6uly0e22MDDTR3mJDoXWik2dZXY9xbdJPuVLF7TBi0KtRyGVbFkrkCiXkMikqpQJfIEkomqGv00W714pet7mactkXwx9MEolnyeVLaNVKzCYtb19aoslp4vB4F2ajBr1OhUatJBBOcWNmnXS2SLVa1+xw2g247EZcdiNPHh/6RB+afhRxgyiK/M7v/A7f/e53eeONN+jo6Hjg92jQoEGD91MpJikklylm1ikUKyyuJVleneSZzzyCRqPmC88+flcT93Qmy8TkHHMLK0gk0Nvdgeqm3OatREk0lmBhcZXF5VXiiRRmkxFRFGlp9rAeDOFtdjPQ3013Zyty+fYkRsvlCv/hP/8py6s+do8N8+zTj+LYwgQ9ncmy6gswPbtKNpvi4WP1WGOov5uBvq4tO1cy2RxvnDyH02HjxMOHaW5y3faZ5VU/q2t+ZmaXWVhcYc23zr/9X3+Xrs5Wvvqlp+npaqe9bXPC+9Z5jFQqJRyJ09fTwchQ3yZ50mQqw09fepNqtYrTbsVqMdHe6sW3HkIQqzyyv4MmS5CzcxfIRJfYPdjEYH8nzR7rAzdLL5errAXidLc70aiVDPV5aHabtzyDKZUrvHp6hlyhRF+ni4Eezz1lPO+HfKHMWiDB2nqcWCKHRCJBr1OjkMk4c2GRl09N4Q+lNl2jUsr52rN7+MZzI5jUJazW+zuXelB8YNHc+fl5FhYWOHr0KBqNZsNw98NwL0O0n4UZ2q3xGmZV26exXvdPY83ujwexXkKtTCHjp5hcpFYpoNDYkcrViLApg32/81rzBZidX6ZQKPLMZx6htbWZ1tbmuxqZZbI5FpdWWVxaI5XOYjToaWvzEorEKBRLHNy/k97ud1+23j9WKpVhcXmNxaVVMtk8XZ2tCDWBSqXKgf076elqI52tYDVr7jqPSqVCLlfAbDaiN+gYGuxl987hLe95J0ShSjwwy5/9H3/N6soqGrUMu91G/8AIcpWOjpYy+UKFXcMttNyUw3r/0KlMgbMXFwlF0wQjaWo1gaHeJmwWPSeOvNsu++a5WfyhJEqFHLfDSFebnSsTPqxmHaMDzezf2YFRr2FhJcrsYoieDiddbQ5GB7wo5LKNPWqrRwvHMlybC5BKBPnsIyMEIilEEexWA+1eK4lUnkgsi9WsZ+dQ/TBp2Rfj2pSPUrlKb6cLUXy3UqFUrvJn3znLf/nzNze1kY72N/NvfudJdgx4t5yLKIoEwilmFkOEYxl0GhX2Lj2iCAq5nEg8y0D35m6WW2NUigly0SlysWkqhRj4z9J56P+Kztq9re/yXnwafl99FLFJgwYNGtwJoVYml1yinFqhWs4iV5nRGLfqZ/h4qdVqSKVSJBIJqXQGlUrBof27t+1Fcut36etvnuXkmfNotRosZiMDfd0bUlU7dwyyZ/fIlt4hd6NYLLG86icaS3D4wG7UahXNTS52jg3R7HFuHMB874cvk0pnkEqkWK1m2tuaN7ocRofvLOEsiiIvvHwSURRxuxyMjQ7S5HZsPPd75UlFofYek/bFmybteaTyukm7Qnt/HiSFYpn1YBJfMIk/mCIUTXNkbxfjo+1cnljj6pQfnUaJyaihv9vNSF8zADazDotRR0HUMz25hC+QoKPVRler4z3SHO9SrdXwBZIsrUUJRzOMDjQz0O1hqM/D6EAz+WKZWDzH3HKEWCLLofEuNGolwXCKZV+MYrGMQiHns4+OsLIex2LSUq0KXJ5YQxTFjfEUchkmg4YWjwWDXo3JoN6Q4YLtV2t+kviwccM3v/lN/uqv/orvf//7GAwGgsEgACaTCY3mk9lV1qBBg08vtUqBQnqVQnKVRDzG3EqCxZUgEgn09XRQqwnI5dwxUXLrd9z0zAIra352jPTT39t5myTmyto6r795llK5QjQaJ5cvYDIa+MLnHkcqlRKJxmlu2v6euOYLoFarcNit7BvfwRefe5LuOxRonL94jYmpOZBI0OtNjO8awX7zwNz2noJOQRBYXFpjacXHI8cPYDTo+dzTJzZkw6vVKqFwjEAowvjOYQRB4E//53dYD4Qolso3C1LbN2KJEw8fvm2tlld8XLoyyf69YzQ3uTjx8KGNZ67VasQTKRx2KyqlgiaPE4/byetvnmVqZpFXX3uDQi5BtZgitmTg84/1Mz7Wze5dIw80IXGLUrnCxNwqvtUlQMRpN2DUa+jtuD1plCuU0GnqnaneJgttzVZMhgezZxVLFeRyKXKZjOszflb9cQw6NTqdikg0w19+921+8Mp1YoncpuvsVj1fe3YPzz+9G5tFT6WYpFos3eEuHz33nSyJxWJ8+ctf5vXXX0cikTA3N0dnZye//uu/jtls5t//+3//gSayHUO0n4UZGjTMqu6XxnrdP401uz8+zHrVpRNSNyUT0kgVWmQKG2RFPqj2Yrlc4cbENCurPkqlMjabhc6ONmKJ/B0Dhg2Ty0KR7//wRaQyKUajEb3eiNvtIpEqIkqUdLR3YLe772ikdubsedbW1pHJZJTKZRaXVnnj5DvsGR9jfNcoLlcT6VyVTK4MEsltZmjFYgn/ehCfP0AwFMFg0PHU4w+jVOnp6NBv3/ytViUaWkJSDhEO+VkLJOgfGmWorx21SsFKOIdSb8TVbMDmakehkN/RE+TVtxfIZEVSBRltbe0M9LZgtegJhBJcfmWBw/sGUSkVZMsq5teyeFwWHnt0jObWDFKFibHhThw2EwurQRaX5ymWKjS5rCi0TrJVA0igvIVpGYAvEGN+cZ1YMotCZaDV3UpeNKEz6ensklMslbk6F0GtVuLx2slWjdRqAqffmSQWz9DscbG3rxW9Tr3xfJeuLfAH/+nvWVoNbdzHatbzz3/9szz16G6kUukdzdlS6RyvnvNjNesZGmolXyhzaTqE2d6KWq3k6JGDSCQSiiJQhVo5TTl+g1L8GtW8/7bxFi78T9xjv7Ot7/RebNcM7ePgo4pNGjRo0GArhFqZQnqdbHQOiSqPQmVEZfB+IpOzoihy8vR5pDIpxw7v3SiK2C43JmdZWvbxxIkjtLY08Utf/ixOpx2n3YpG8+7B/b28R96LIAgsr/jqJvLrYUREmptcVKtV5HI5e3aPUqvVOPvOFYYHezGbDOzaOYxapcRmNd+zgrVUKnPl2hT9fZ2YjAaefuIhjEb9ljGkIFQpZ4MUMwEKyUXKhThCtYRUrkah/uAm7aIo8u2/O8vSWhSFQo5Oq8Rh1W/IX+0ZbWP3SCt6bb1rA+qHF9dn/CyuRhns9uD2mhnpb2LXcMsdZUMXViNcmfBRrdZw2g3sHm3dOPCQy2T89I0J0pl6bKfXqbCadRRLdaN2fzCJTCalyW2ms8WOVCqhUKigUilw2NQY9WoMejXmm+PZrXrs1p8P2c8HFTf88R//MQDHjx/f9PNvf/vb/Oqv/uoDnnWDBg1+URFqlXoiP75ItZxGrrZwcSJIJptlbHSAvp6OO+7DoiiysrbOxOQcrS1NjAz1MjY6yK6xoS33U1EUuXp9CrvVQrlSYXFpjY42L309HRv7vrfZva15R2MJvvv9l7h45QbHjuzjq196moeObW3WfUupw9vsQafT0tXZSiZXxWrW1KW03vO5+cUVrt+YJZvL0eL1UC5XUKtV6HUazl+8zsTULMvLfpLpNBKJhKH+bjQaNV0dLWi1Gjo7Wtk7PkpbS9OWe/x6IMyFy9eJx5N4m90b8pzJZBp/IEQgGCEYilCr1fjc0ydYXFollcqwsLDIytI0qegaHquMkT4D/T1jtLS0oFBsP066X6bmA0zMBinW1Ax3Oejvcm3ZsVIsVbgxu87iSpSj+7pxO+pqGR+WQrGMP5hkLZAgEsuyZ0cbHS12rGYdiWSOmcUQ71xZ5sqkj2ptcwHmcG8Tv/ql/Tx+bBCl4gP3czxw7nsmv/u7v4tCoWB1dZWBgYGNnz///PP87u/+7gc+kNiOIdrPwgwNGmZV90tjve6fxprdHx90vSrFFIXUMqWyH6VajtLSBB9QcqtQKBKOxGhrbUYQVOTzGUaHe+jubL2jj4ggCPj8QRaWVkkk0jz3zAkkYpkmj418vkC5lEeKErVKitWswWrW0OZ9V7KiWq2yuhZgYWmVfXt2YDToaXbbGOhrI5nM8Jd/+31sVhNfe/4z7Nk9unHd+83QbiVqYvEEL73yOhIJuBx2jh3aSUtLEwb99qsIQv5FZiYvMj1xhXA0ymOHejHobBza1cG+XR3MLYa5fj2IXqdipEuDXC6D9+3T5UqVYDhNTahRrYocGnOg1SiJxLJUqjVWfMtMT+dRyGU0u80E/Qu8dWGRZV8MqVQCFQVqSZIOl5yWE93I5TLW1n0sLy7T6bXR29mCUa8BRLYyRKvVBKTSunF9JLCMTingaNOxHCwSjyYxKizkq2X8viUEQWT3gJveDhc1QUApT4McWh0yxvtbcDmMQBkos7Ye54/+28u8dHJq415SiYSvP7eXf/5rD92Uv8humkuuUGJuKUw8kePhQ/3orfDMQx3k8mUuT8xTKJbpbnNgUGZQyesLWasUyMdnycWmKKbX7vp9ZVd+iuPEv30gB3jbNUP7OPioYpMGDRo0eC9CrUIpF6KQWKJciIOoQW1oRvoA5DwfJIIgsB4I4/MHWVnzUyyW7ng48X5EUcTnD3L1+jRTMwssLa8xOtKPXC6/70TLe6lUKoTCUazmFgDevnANo0HPnvFROtq8qNUqItE4q2vrRKJxItE4oggdbV7MJgNtLXf3BYnGEqys+lkPhonFEkglUtxuByajAbN583virQRJIe2nkFygUogj1KrIFLq6z4x8e/tdrSYQiKRIZwoEImnC0QzxZJ59Y+2Mj7ZxZF83J47047QbMehUm2LY90poxRJZJueCBMIpZDIpbV4rTns9qaJSKjZph8cSWVb8cZx2A163BaNeTUeLDYNeTTZX4vrUOtVajc8/sROZTEpfpwuVUo5MJiGZLtDf5cYXTDA5F6hrvKsUpDNFiqUqUqmU4b4P7r/yaeJBxQ0ftDu9QYMGDbaDKAqUcxFy8QXmZq8zMx9i7759eIx6jh7eg0atumNHpyAITM8uMjk1TzaXw+W0Y715bqFUbvYkCQQjLC6v0XyzO0Kv07K87Een1/C155+ht7t9W52ot4jHk7zwyineOncJqVTCk48d29JzLJ3JsrpW90+r1qo889QjeNwOPG7HTaWL2ysMX37tDMFQhPZWLw8f24/Vat74s0Qizd9850coFHI8Hiejo/0givjXg3R3tfPsM49RKpU3ZMy3Yn5hhdNnL+Cw2zhyaA9KhQKzyYAgCPzkpTep1QQcdgtOR/1M56/+5nvMzM4gVHL86ud6ON5rJpLU0dzkRaH+6AoMiqUKEkk9TpDLZPS0O2hu68GmLdzmOVKp1phdDDG9EEIiqatdvN+77INyZXKNmYUQEokEp81AT4cTk1FDrSbw1sVF/v4nl5hdDG+6RiaV8NjRQb7xxf2MDX4yi43uO1ny0ksv8eKLL95mXNbT08PKysoHmsR2DdF+VmZo0DCrul8a63X/NNbs/rif9apVixTTaxSSK9QqBZRaB1L59jf3jXFqNdb8QeYXlvGvh5BKpTR7XCiVCp59+tE7XpfN5bl+Y4alFR/JVJqaINDb1Q6iSLVaRQL093bS4vXgdNhu2xzWA2EWllZZWfVTrVYxGQ1cvzFDOBwjk80y0N/DzMwCX3/+GXbuGLytIkSg3kEyNbPO6uo6MpmUxx89gs1i5ujBcbzN7vsKdkRRoJjx873v/gNXr02STmdRKNW0tbhpae9CLpMhVyh57cw0+UKZwR4PA93uTVWQuUIJfzDJejDF0loUfyiJTqOir8vFntE29FolRr2dd64so9epGOz10OQ04Q8m+ZO/PUOpVMVh0+N2GOnpcBKOpllcjSKXyzg03oXXY8ZpH7mr5melWmNxNcr0QpDdI6143RZ6O51cn15n2RfF7vTS67Ujk0kx6NUMdrtpb7GhUspZXItyfcrPjkEvHS12RgfercDI5or88V+c4n/+/Tkq1XdbWIZ6Pfzb3/vslgcP0XiW2aUQvkASuVxKV6sDQRCQyaT4g0kmZtdxO4wc29+NUa+pS71EJ8lGpyiklkG8tySWQm3B0fMUErGKVHb/f//fzyf5d9VHEZs0aNCgwS3qh+uhegFGPoZMrkVlaEYmlOAD+FZ8FGSyORLJFK3e+p7zxqm3USmVtLd66epsvaMXWbVaJRpLUKlU8Ta7uTE5x5/9xT+QyxVo8bp59OGDPPHo0Q/8IhuLJbh2Y4ZVf4BsrkxXuxulQs7xI3tJpjKEIzFsVjNqtYpgKMr84goOm5Wx0UFavJ4NOY33ks8XCEdihMIxerrasFrN+PwB5haW8bid9Pd00tTk2uxdItQo5cIU0z7yiXkqhTiiUEGm0KPUuZHKtq74LJUrxBI50tki2XyJ5E0j9n1jHWg1Sn786g2W1qKolHJUSjlWs65e2AEbslpbUSiWqVYFDHo1+WKFfKHM7pFWWputKOQyRJGNLtRMtsjSWt2oPZnOUxMEOgt2vG4LVpOON87OIggiKpWCzlY7Lc0WEqkc0USO2aUwc4shIvEsgz0eOlpsZLMl2r12zEYNJoMGs0nzwOQ3Pi004oYGDRp80ikX4mSiC0xev8Dk7AqFkhSPx7Xx7q+/2e1wJ3zrQc5fvEZ7m5fjR/fdFgckk2lm5pZYXvVRKBTRabVksznOvnMFqVTCwf076eluvy95zVtFmsur61y+Osme8VE+95lHMJs2Fy1kc3leff0tEskUUqmM5iYnvS3tW45ZrVaZnVvC2+TGbDYyNjqAWr0Ts8lAoVDk3PkrTEzOcuLhw7S2NPEvfvtXKVeqLK+sseYLoFQqN7oZ3u9PdotEIlWPoVqaqFar6PU68vkCp86cRyqV8bXnP4tEIuGpx45hNOr53g9f5kc/eYl0OkFvi442q5ShLjMOixqVtol260dXRFMolpleCDG/HKGnw8HYYAs9Hc6bcYOCrdRTFlYiTM4F6G53MtjjRqX8YD4pxVKF1fU4K744I/1NuB0m3A4jOq2KarUuCXp92s+LJyd59fQ0q+uJTdcbdCqe/+w4X3t2D00u8weaw8+K+06W5HI5tNrb/6eMRqNbJjLuRsMQrUGDBg8SUahRygbrOtPFOAqVBfUH1A4XBIG//94LFApFbDYL+/aM0dHm3VSFcSfKpRLXJ2ZJpTNoNWpMBgN2mwWZTIbLaee5Zx67632XVtaIRGIMD/YSCkcJhaL410NYLCaeOHEUu83CQF8nRsPtlQrpTJYzZy+ysLSOXq/C2+Sis71exXk3o/j3E4slmJufY3riCvuGDBQzfqYmZ9Bqtewc6cHrsdDsNr9rZL6eQK1ScGRv/XBfFEViiSxqtQKdRsWKL86NmXWC0XQ9WdTlptltplqt8dpbM+zZ0U5nq529Y+0EoykuXFvFoFPh9Zjp7XQhCCItTRYMOjXBSIrltRhWs472FtvGs6lVWx9YpbMFZpfCrPji1GoCbV7rhqTEtam6dNX4SBtz/hJvXfJtyFT0d7uJJ3OcPj9PLJGjzWvD7Xg30KvVBP7+J5f4//zJayRS+Y2f2yw6/sU/eZgvPLlzU8LolqmrIAicubiAQiZj13ALbV4bpVKFZDqPzaKntdmCyajB6zKQTy4Rnp0mn5hHFO6g3fUepHINeZqIZ6Wc+Mz/gnf069v6vj/tPMjYpEGDBg1uceuAvZBcppyPIJVrUOubkEhl2/b2+qgQBIFQOIbPH8DnD5JKZ5DL5Xz1S08jk8l47pnH7qhZHgxFWF7xMT27xNLyGvF4EpfLzm/+06/S0e7lN//JV/E2u9DexRx2O5x75wrTswuoVSrGRgcwme1cunKD+YVVBKG24T9SqdT3t6GBbkaGeu843rUbMywsrpBK12UhDXo9TR4nVquZkaE+xkYHN32+btIep5jxkY/NUcqFEYQyMoUOpc51xwRJuVylXKmi16kJRzP8+PUbJFOFuoceoNeqsFv07Bpu5Yuf2Uk8kcdi0mIyau6qQy4IAuvhFEurMQLhFF6PmYO7u2jxWGjxbD7EyuaKZCoK9Oa6R9oLb06iUsrRapQY9GrSmboEtUwm5cjebgx6NUqFfOP+L745ycl35pBJpTR7zOwaaWV8pBWVUkF/t5v+7u1JqPy80ogbGjRo8EmlWs5STK1RSK1y6cokU4thOtvbGRrsvWPhw1a0epv44nNPbooFEokUEqkUs8lANJZgedVHR5sXlUrJzNwS127M4LBZeP6Ln9l2DCCKIqu+AG+fv0oymeIrX3qa0eFeBvv/BWq1inK5wvKqn/VAiFpN4MjBcbQaNXa7hR2jAzR7nBum8u+lVqsxO7/I6soK5VIZlVKJ2WzE7XLw4xff4PKVSXz+IIJQw2wysnd8R30+iJw68w5mk5GD+3bR2dFyR/nOZCrDlauTLK/6sJhNNDe5eefCNUxmIx6XHZezLlF56swFLl+bZHigC7O2yuLUGWTVEMfHLOzf2YmnqRmF8qMtOigUy0zOBVlcjSKTSRjodtPb4dzys4IgsLgapVKtMdDtobvdQUuTZUuT9+0QCKeYXQoTiqSRSMDjNG3EG6VylWtTfqLxDDdmApw6P0+hWNl0fVuzlV/90gGefWzHJq+zTzL3nSw5evQof/7nf86/+3f/DmBD4uWP/uiPeOihh+5rrIYhWoMGDR4U5XyMfHKJUiaAVK5GbfAi+RDVnlKplD27RrBazLfJN9yJQqFIpVolnclhNhsYGerF2+zG43bcU18b6hIVcrmcJreTfeM7kMvlnD57kZogoFbXdbpdTjsSiWTLRAlQb8WVy9m7Zycjgx1o7qODBOD8xeucPnOK1eVF0okQzQ4luzuGcbpbeOYJOy6HcUNLMhRNE45laGu2MTboRRBFIrEMMwsh1sMpisUKgz0edFoVHV4b3e0O5pcjhGMZIrEMq/66iej4aBsdLTbiySw/fWOSa1N+RKCrxY7ZqGX/zg7MRg06rYofv3aDJpeJ/Ts7NklYvB9BEKhWBZRKOaFIBn8wSW+nk9YmKyu+GKVyFb0O9o11ML8S5uL1VWQqM4fGu7CadfXni6R549wsJqOGhw/1bWpVPXtxkd//Ly8wt/RuS6lCIePXvnSA3/ylwzclt+pE4hkWV6P4AkmeOD6ITqPikUP96DRKwtEM5y4tsR5K4rQbOL6/B0U1hDo3zeqFWYTavU3NylUpS1EdMkM3jz7yMBKJlIT/yof6+/9p40HGJg0aNGhQN/qOkE+uUM5HkEgVqPQeJNKPV0s5ny+QSmfxuB1UKlVeevUUarUKb7ObnWNDNLmdGxWg7z0cEQSBK9em8HiceFwOVtfW+e4PX0Emk+JxOTh8cJyBvi6sFhMymYzenvb7nlu1WsUfCLPmC7BvfBSFQoFMJsVo0JNMZWhp9iCgwG6zYjIacNit2KzmTRWr7+9gzObynDx9niOHxjHodYiiiMtlZ2x0AJfTvukg59Y4oihQKcRvmrQvUc4FqZZzyORaFFr7lhJbpXKFUCRDPJUjlS4QSWSJRLM898QO3E4TB3d3Eo1nsZh0WExazEYNxptG6zazHpv53jIbkXiGMxcWKZUqWM06dg230Npsfd/zFrkxE+DSjVXWwylaW1v5jS+N0ea1sXu4BZtVj9Wkw2rWotepSWcLpNIF0tki8ysRpuaCmAxqPvfYDg7v7WKoz4PFpP3AhyMPglK5gj+YwmpW3f/Bw0dII25o0KDBJ426OoafQmqJajmPUmNj38FjDIxk79gR8X6CoQjTM4toNGr27dmBTqshk82xtOxjaXmNRDJFb0/HRhKhs6OFbC7PS6+c4ur1aZo9TlpaPNtSoRBFkcWlNS5ducHE9Dy5bIHurlZyuQJGg55cvsBrb54jEo0hiiImo4EWrweo7/eH9u++49ihcJQ3T79DMJyse5Hqtbz46inMZiMOu5X1QBiNRsVnn3qY3u52srk82Wy9eNHb5ObxR4/icTvuOL4gCJw+e5Gp6XnK5QoOh5UTDx9CpVLylS89jVKp4NKVCb7z3Z+yHghTKuYx66X8ZPYczXY53a0WvnDiMbR680cuIbXhfVuqsLYeZ6i3nvzYyt+jVhOY94WZXgiSL5TpbK1LvMtlMuSa7Xe7FIpl1kMpHDY9Rr2GZDpPtVpj13ALbqeJUDRNvljBUKly4doqf/vDC1yb9vP+OqJD45184wv7ObK3+xOtUrEV9x2z/NEf/RHHjx/nwoULlMtl/tW/+ldMTEwQj8c5c+bMfY3VMERr0KDBh6VazpJPrtS9G0ThZqXgB2srrFQqLK/4SSRT7N45TGdH6z2vqdVqLK74uHDhGjNzi/T2dPD5Zx7nS889ua2NM58vsLzqZ3FpjXA0hlajoVgscvzIPkLhGIuLq+h1WsZ3DdNxs0Pk/ZRKZZZXfbgcdsxmI48+dJB4snB/Zqu1ChPXzvGPf/eP5LMJ2j1a+vcN0dbqxWatJ4tamuqHEuVKlWvTfhaWI3hcJtqabchkUq5NrDG7GEKvU+FxmiiVq0wtBInEMrR6rIzvaGOwx4PVrMVtN+L1mDeSCi+fmuK1t2YQRQG304TNrCMczxA6myGZzvP0oyPIZTKeOTF616rNXKHEwkqUxdUoXo+Z8ZE2OlvtdLTYWPbFeP3sLJVKDb1Ojc2iZy2QYNkXZ3SgmaaWboqZANPzQfq73Thsevbv6qDFY9nY3JfWovzhf32J19+a3XTfJ44N8i9/88SmytD55TCzS2Ey2SI6rYqBbjfyW50mosgLb06SzhQwGdWMD2gwStdYu/gKtUqeeyGIUpYiGlYTZsyuHnbv7aTZbd5IkMjln65g5MPyIGOTBg0a/OJST5JE63Jb2QgSmbweV3zMSRKANV+A1988h0qt5PkvfAaVSsmznz1x1wMUQRCIxZO8ff4qJ0+/Q3OTi1/7lS8yvmuE1pYm1Gr1ljJX98PCYl021B8IUavVMBnr1aqlUpl8oUg6k2XnjkG0Wg3ZfJXurrZNRq13mnc4EuPUmQtA/eUfYMdI/x2vqRQTFFJr5OPzlPMRapUCUrkKucqERls3aRdFkUy2SCKVJ57KYbPoafFYiCVynDo/T7VW73bJF8sYDWpkMikKuYzR/jvLRN+JQrHMij8O1LtpDTo1rU0WOlrsWEz1jgZRFCmVK6iUCt6+ssQPXrpGpVbDZTNwcFcXHd095Atl8oUSLc1W0pkiC6sR3M4uAK5M+lhYiZBMF8jlSygVctq8NsrlGga9+mNLkpQrVXyBJKv+OOFYBlEU2TXcjOcT5A3fiBsaNGjwSUEQqpQyAQrJJcrFBDKFkbVQFYMhh8etvWeipFKpML+4yszsIslUGpPRQJOn3nFwy39DJpPR6vWwc2yQZo+LdCbLpcsTSKQSVtfWkclkPPv0o/T1bq1esRXxeJIf/uRVYvEkHreTg0/vYniwZ+PPNWo1apWS/XvHaPK4MOh1dx2vvkfnMBr0KBQKLl2ZJJnKcv1GBYVcjsftIJPN4bBb+Y1f/TLJZJrrk7OcffsyEqmEvp66UpFUKr1joiSby6PXabl8dZJTZ84jk8lwOqxo1GquXp8mGIrS0uJhfOcwoiiQTcfoblbQ12yk2S4lkTVisbmxbLOY9sOQzRWZmg+SSOU5cWQAq0nHZx8d3aRc8V7KlSovvXEZoZKirdnKYI/nviQ2c4USvkASXyBBNJ5FIpGwe7QVo17DQLcHh9XAsi/G1Sk/oUiahdUob11cIJ7cfHahUMj43IlRvvHF/fR2uD7UGnyc3HfkPzg4yLVr1/jjP/5jZDIZuVyOz3/+83zzm9/E4/Hc11gNQ7QGDRp8UIRamULaRyF5q/LCikxxd93OrbhlaDa/uMLKqp9arUZzk/umf8Tds+8XLl3nH77/IvF4Ep1OS0ebl8cePrJtP5A3T73N8qqfcqlCVahRLpVRyOU8cvwgLV4Pq74AYzsGGRrovm0upVKZ1bV1llf9BAJhBFGgu6udwwfuXKGxFelUlBuXzpBNLNHbLOWZo05c7p3o9OYtP+8PJrl4fZVypcqu4Va62uwUSxXUKgU9HU662uysh1KcPj9PKJpBpZDjtBvQvKfdsq5taaJSrRFP5rCadeg0SprdZkxGNblcuW6aGkpht+rZOdyycbByp0RJKlPg+rSf9VAKuVxKW3O9kwUglS1w9uIi2VyJNq8Nj9OIINT3n74uF52tNmLJPCfPTZJLh7GatPR2OpFK6+MAJFJ5/vgvTvIX331n49AGYLiviX/9zccZH2nbmIdOq0Quk5FI5bEYtewebsVpNyAIIolUHpVVgVajxGGsMOxOQH6JaiT1Puv32xFEyNVsdPTvJZq34raLHO9yb0ih/SLzIGOTBg0a/OLxbpJkhVI2XE+S6D8ZSZJyuYLPH+D02Ut4m90ces8+//4DlEw2h1QqRafVsLzi49RbF8hkc6yurtPe5uWJE0fQatRIpVLcrjtXXN6NbC7Pmi9AX08HUqmU2fklUqkMJqOBgb4uerrbWVxa5eSZ8+h1Og7s20lfT2ddtix/ZzlJURSRSCT410O8ceptKpUKZpORE48c3lJOTBQFKvkYpVyw3gGUDVItZ5HK1SjUJlR6D+lsAQly5BIJU/MBJmYD1GoCNUFERMRm1qGUy3DaDewfa+fC9VXUagUdLTYGez1YTXc/2Hk/pXKFFX8cXyC5cdDQ0VqPI9QqBSP9zcQTOc5dXmJmIcSSL8pwbxNffGoX7S02ju7rprvdiSAIdLe7SJf1/OilNwERqVSKQa/CqFdTKFaQy6TsHmmlVhNIZ4t0tNjpbLWj1348CZJaTUAQRRRyGZNzAWYWQjjtBnaPtNLsNqNSSshFYx/L3LaiETc0aNDg46Zu3h4mn1iilI+ARMVyoMyNibPk8nk62rw4HdY7nklUq1Xkcjm5fJHzF67R4vWwb8/YpkSBx+Pk6KE9tHg9KBQKUqkMf/ePP+X8xWvs2T3K3t2j9Pd2YrOat6WEkc5kmZtfZrC/G5vNwheee5KVVT9jowMUCkWuXp9mbmGZh47ux+W08/DxA9tai1XfOpevTJJMpfna889gtZjo6W4jl6+wZ1f9POS9SZxSqcwPfvIaarWSnWND9PV0bCmXns3lCQYjLK6sceHSdZx2GyceOYxMJuXEw4eQSqX41oNMzSzwyutnEEU4sGeIgVYldvkCw94sy2sxap52zM5uLK6PviAxnS0wNR9kxRdHqZTT3+XaiJHenyi5FXf0tDtRKuR0d3joavJuO0lSrdX9VuUyGTMLIRZWIrgcRvbsaKfZ/a7UViCc4tUz0yysRLkyucbkXPC2sZpcJr70mV08//TuuyqAfFr4QG8Abrebb33rWw96Lg0aNGhwT0RRoJQNUUguUs5FkalMaIz3X/GXzxfQajUIgsDrJ8+hUasYHe6nq7N1S7O0UqnM/OIK1ydm8LidHDu8l1A4RldHK889fYL+/q67VmEUCkVW1tZZWfVz7PBeVColRqOBA3t3olAoeOvtSxg8WjKZunEnwNFDezaNUSyWqNZq6HVaVtfWOXPuIi6nnT3jo7S1NG1bVzSbzfP6669y5cpFVpYWKBTyHNjZTl/nXjq77z7GzGIIvU5Js9tJPJVj8tUA1arAcK+HYrlCu9eOTquiyWWm5WYFpddt2aRNKQgClyfWeOHNSRxWA7/+lYM4bAZcdiN9nU5C0QwGvZreDueGJNZWlMoVcvkyVrMOiQRy+c3mqLlCXcJKp1FiMWnZPdLKsi/OuUtLOGwGOlrsyKQSXnl7nlSmgN7k5PB4N16PeeMemWyRb3/nLN/+zlnyhfLGzx02Pf/yNx7lmROjAKz648wuhYklsuwda6ejxc6eHe0AxJM5Lt1YY9UfRypmOTgApeQctlKU6r1VtghlNEyv60hWnBw/OIre7sXwEbf7fhppxCYNGjS4X96bJCnnwiCRo9Q5P3CH6gOZkygSjSXQajXotBompua4en2K5iY3x47s3XRoEonG8fmDRGOJm90cJTraW9gxMoDFYsLltJNMphga7OHJx47dl9b5e8nm8swvrLDqWyceTyKVSCmVSgRDUcKReP3Ap1yhUKxvai1eD1/+/FN3jUuq1SqRaIL1QAjfegiPy87e8R2YTAaGBrppbnJjt1k2dene8iApZdfJxeYo5cMIlSJSmfJmB4lj4/NrgQTnLi0xMtBEd5sDq1mHxaglmsgCInKplEy2RCyZw+Uw0uq14XaZ7rsbI5srUixVsVv1FEtVrk76cNqN7B5txWbWkcoUyOaK6HVqzlxY4JXT01SrNUwGDf1dbkb6mimWKiBCR6ud6YUgogjd7S6kUinH9vWg1ynRqBWEohlWfHFeOjm10fl6YFcnSoXsY5G3qNUEwrEMq+v1BNFQr4f+Ljd9nS56O1ybYj9RvLfv2s+aRtzQoEGDj4P6Xha7megPgFRBBSM/fek0xWKJ9jYvO4YP3VEKfD0QZmpmnlQqw3PPPIbZZODLX3gKlUpJKBzl7NuX8a+HOHJoHJfTTmdHK6FwlOuTs3zv+y8hiLBnfITPPH4M4zblvcrlClevT3Hx8gTrwRAT0/N87cufpaXZTSwW50c/fY1isYRKpaK1xYPVYtrWuKtrAV4/eY6l5TUKxRKFQhGVsi6F9Wu//EXiyQJWswbpzWKKmdlFjh7eg0ql5PFHD+OwW++4/33/R6+wHgzj94fI5wvYbGZGhnoplyo47Daam1y8/NoZXnvjHE6HmeP7e9jRo0NWi3Dyxf+DmeUUKrWBA/t20dlq/5lIXIuiyJvn5hBEkR2DXrra7FsWRibTeeaWwiz76h2sTpsBk0FLT2cTenn6rvcQhPrevbaeYC2QYLivid4OF4M9Hkb6mwHwBRK8dXERhVyGzaLjH356he+9eIVMbvPhhVwu5bEjA3zpM7vYv7PjgcYiH3dzxX0nSzo6Ovj617/O17/+dfr6+j6KOTVo0KDBbYiiSDkfp5heuelLokJlaEYi3X5VfalUZmnFx/zCCvFEiudvBhXPfOaRLdtCa7UaV65Ncf7SdeYXVkinM+h0Op48cZRqtcpnnjh+z3vOzi2xuLxGKBwFwGoxc+HyDYLBCHq9lidOHCUciWHQ64gnknib3ZhN7wZGxWKJlTU/yyt+gqEIXZ1tHD6wm/a2Zpqbtme+WigUmZiaR6VS0N2i5bvf+TteO3kBq1HBnpFmhoe6aW2ybql7CbCwGkEmldLutXFgVwc/fu0GkVgWg16NXCplPZriz64uY7PqefaxHXS3O28zKoX6dzizGOKlk1OsrcexmHRIEPnpGxNkcyWO7O2myWXeqJy4E/FkjrnlMKv+BHqdiiePD2HUa3j8WN3YNZbIcm3aTyKV5+lHRlAq5FhMWk6fX0AukzI25EUU6y/5MpmUnnYHZqMWleHd4CJXKPEX//gOf/K3Z0jdNFEFUCnl/PpXDvHrXzmEVqNkbT3OlUkf+UIZp93Awd2dNLnMG8/7yulpMqk4Tn2MYWsUeS1GNnDPrwylzkm06OS1y2UcTjdHjnfQ0Wr/1Gl9/qxoxCYNGjS4H7ZKkii0H1+SJJcv4PcHWQ+EWQ+GKZfL7N45wshQL309HXR1tmLQ61haXiMQijDY143FYmJ1bZ3rEzNAPWYplyvMzi0hlUo5cnCcY4f3MjTQg9Nh3dI8dbt874cvs7i0ilyh4JFjB9i5Y5DlFR9KhYJdY0O4nLZN/iMKheK2+xUKRUqlEqBhfmGFt85dQhAF1GoVzU1uPO66ZIhep73NqL1ciFNK++oeJPkQ1XL+ZgeJGZn+3QKXdLbA/HKEWCKHP5QklS5QqVYx6tQ0ucwUOyoY9GqsZi1Wiw6zQbOxryrksrvKfL6XdLbA2noCXyBJMp3HatZx4sgAJoOG4wd68QeTXJ/2E4ykSaYK7NvZwUMHevG6zewaasFpr2uAjw7Ui32++8IVypUqUqkEu1XPntG2jXu5HEbmlkNMzgYolasYDRqG+zzYLfW4Va36eP7Ozi6FuDblp1YTMOjV9HW68LrNAGjUn3zz1kbc0KBBg4+DSjFFKbNGMe2nUqkQTtTo7GxGBfR0tdPV2bql7JYgCMwtLDM1vUAylcZiNjE82Lvx3jw9u8jM3CKFQhGdVkt7WzM6nZZqtcrC0ipz8yuUSmU+8+RD7N87hsW8vWQG1KW83nr7EkvLPgRRrHehyGTk8nVvErlcTldHK60tTTgdtru+x1erVcKR+Eb3y3/9H3/JwtIaer0Op8NKR7uX8fHRjc+LorgR68RiCew2K4ViCYNejstpv23sC5duMDY6gFqtoq+3E51Og06jRq/XE08m+Yfvv0QymWJsdJB/9uvPs2/UQ6djH8pakFopCuUY6YKShaCUgYE+Bns8dzwjeVAEIymm50OM72hDr1VxZG83Bp36jnJb71xZZmktilqtYLjPQ2erHZVScZtfyFYsrEa4PuW/6d+qorvduXF2URMErkz68AUSpDIFFlejXLy+ytLa7V2h7V4rz392nGcf23HX4tb7oVAsE41nCceyROIZtMoaewY+nFTsh+G+v/Xf+Z3f4a//+q/5/d//fXbu3Mkv//Iv8/zzzzfaVRs0aPCRUS1lKKTWkGZiSCTifVd9iqLIqbcusLziRxQFmptcHD00juLmxrdVoiQWS2CzWVhZWycYjDAy1MPusRF679Di+V5C4Sh2mwWZTMZ6IIxUKmVsZIBQOEowFCWZStPe5qW/t5M1X4BX33gLi8V8mxHZ4tIqp96q63W7XQ727RmjraUJ2Pog4r0kkynOnjvH5NQ8yyurFPMpDo25MO61MOitsuefncBitd/x+nKlysJKlDffnmPVF8PtNPHb3ziGRq3kwK5OypUqr701w8xC3aPk4HgXDx3s21L+4VYQl82V+M6PLiGVgsNqIJLIUipX6G53MD7ahsteTxLdKcAqFMucPr9APJlDq1Ey3Oeho+XdZ0ik8kzMruMPJjEaNOwb69gIbiLxLG1eGyqljKm5IJVqDatZh9NmoLvdiShCtgrFUoW//eEF/utfniKReld/UyaT8qWndvJbv3wUlVJBpVrb+DOHzUBfpwuLSUu5XGXZF8Pr0lJOLdCpvYpUHgJEePeSLclXNCzFjAjqdp7aewhNtsiv9kk2tM0b3JlGbNKgQYPtsMm4PRdBIv14kiTVapVgqB4rqNUqrl2fZnZ+CZvVwkBfF00eJw573fz7VhfsqbcusLi0ikqlAhH6ezsZGx1Aq9XwzoWruJwOPG4HbpcDp6N+rUqlpLnp/vSiE4kUK2vrrPrWObhvF7VajVyuQE93By1eD92drSiVCnp7Oui9qQ9+N1bW1rl46TrJdJauri48LjN2m4U946O4HDYsFtNt+/67Ju1B8sklytnAzQRJ3YNEoXKQyRaJ+bJEE0FsFh097U4qlRqTcwFyhTKiINLT4WR0oBn7TTmItmbbhrzm/VKuVFEq5ETiGV47M4NUKsWoU9HsMiFXyDa6R65O+blwdZlyRUCvVTLY68Fi0mwkRAAC4TSF4rudFof2dKFRKdBplXUT12KZqfkABosUvQ2UCjntLfW5fxwxwa0q1BV/vN457LFgMWkZ6vXgcZowGz99cUojbmjQoMHPko3zjGycUiHPgi/F9NwalXIFp8uJXqdl19jQHa+XSCTcmJjDYjayb88YcrmMpeU1isXSRvFke6uXjnYvDrsViUTCT158k8XlNQx6LccO76Wttfm+zMhvGYtXazUmpuaplCvYbGYMeh1mk5FyuQLA8GDvXcdZD4QJhiKsB0JMTM4xPbfIs0+f4HNPP8o/+ZUvcfnaFD1dbbQ0u7FaNxumn3v7EpFoGI/LzmOPHNnwYrlFpVIhEIrg94fw+YPEE0nWA2HsNgsjQ730dLXxgx+/yk9fehODXkd/XydDvW46PEqCk39POR9FKtaI5mSshWvsHetEY5LwzIl6AuKjQhAEfIEkU/PBjaKLeoygum1PrVRrLK/F8DiN6HVqPE4jLodhk6/qVoiiWO8gCSRocppocpnRaZR0ttrxNlmwmur3TKYL6LUqREHkzXOzXJvyc3nSt0l6HECpkPHEsSG+/PQuxkfbPrSxfblSpVYT0KjrBahvXVwEQK9T4bAasJskgHD3QT5CJOIH7G2ZnZ3lL//yL/mbv/kbFhcXeeihh/j617/Or/zKrzzoOd6RdDqNyWQilUphND44gx1BEAiHwzidzkYV7zZorNf901iz7VGrFimm/eSTi8TieRwOO3Ll9l7IRFFkcnqegb4upFIpb5+/ilarobuzFY1Gfcfr0pksP3nxDQKBCL/5T7+CVquhVqvd07/kFslUhh+/8DqHD47jctiIROO0eD1UKhVefOU0nR0t2Kxm0uksPd3t1Go1Vlb9dLS3IJFIWFpeI5nKsHPHINlcHv96kLaW5m35oAiCwJo/SIvXw//vT/+eK5cv4bYrabML9LSo8LotqPQOZPI7Pz/Uuyr++1+dYT2UxGTQcGBXB93tTkLRNKvrCQZ73LQ127hwfQWdRkV/t2tL2QpBEFjxx7k8scaBXZ2ks0Wm5uqVkYlUns5WO4f3dG+SaXg/mWyRUDR9M6Ehcv7qCl6PGY/z9sOV18/OkC+UGezxYNCpmJgL0t3moNltZmktytVJH5VqjY5WOwPd7k1zLpWq/OWPpvjTv3qRSPxd9xCpVMLnTozyz752FCTcrFjN0tn6rswWQDiWYWFpnVxiAasyhEUZZzubu1ShYz6sZzlmJl/V43SY2L+z/QMf5mysW+ga7sEv0DzySx9qnPfyUe25D5JGbNLgFo31uj9+3tdLEKqUcxEKqVXK+ZtJEo3tQ3mSCKK4SR5iO8RiCS5cvkEoFEUQBY4e2kNnRyu5fAG5TIZKpbw5X4FKpYpKpSQSjfPGybeJRuMgkaDRqJBIJIzvGmFooGfjsOJehRz34sbkLDOzSySSKZBAT2cb3mY3Z85ewum08cjxA/fVnZLN5bl2Y5rZuSW8zW462ltQqvQ0eyxbrpdQK1PKhShlghSSS1SKcWqVItmCSLqowOVyYtCpmZ4Pcu7KEvl8mWKpglQqQatV8sihfrpaHayHksyvRLCZdfR1uT6Ur1cska2bnQYTKOVyThwdQBAEfvDKdUqlCiJQLFao1QQO7O5gsKeJiTk/r5yqd/sMdns4cXSAcqXK/HIEo0GNSa/ZSIq8F1EUCYRTLK5GWQ+lkEgkDAwOMtSu5uNS3kym8yysRFkLJCiVKhh0aob6PB8oRhHFKrnoNJ2H/m+Ym+7PX+9ufNg99+c1bvh5/53+UdBYs/ujsV7bo1bJU8z4ySeXicYLhMIxJmdXqVVr9HS3MzLct6UEOLAhF97f20Vzkwv/ehCfP8Tq2jq5fB61WsXxI/tu8yDL5Qu8+PJJXnj5JAf37+bzn3ts26btAMlkmrfevsT1iRm+8bXP43Y5ePHlkwiiSE9XO95m9z3PJhaXVunsaAXgf/7FP3J9cpZIJE6xVMRht/HU48d46vHjWz7z4tIaFrMRi9XMzJwPs0mN5z3PmEik0GjUqNUq3rlwlcnp+XrhYy7PeiBELJagXK7w0LH9fONrnycciREKrNLqlFJILFLKhRCqBWRKPWVBy/XZMGvrCSwmLUf2dv9MuiOvTK4xsxDC7TDS3+3eKBp9L5lskfmVCEtrUapVgfHRNjpb71zsWi/+NEIpzMJqhBV/nGKxglajZKS/mXav7ebn6kmUFV+c1fU48VSecDTDD1+5RjJduG3c0f5mnntijKceGvpQxRG1mkA0kSUUzRCOpokn83S22RkfaaNQLBOJZXHY9GjUSkRRpJCNI6ml6Xv4/4Xa2PSB7/t+trvnfuA3hN7eXr71rW/xrW99i3PnzvFbv/Vb/Nqv/drPNLBo0KDBzyeCUKWUCVBILlEuJpArzSi0WqSK7XlyAFy7McPlqxO4nXZsNgv79uy442fj8SRzCyv4/EH8gRALiysc3LdrIwjYTqIkm8uzsupncmoeURSZX1jhzZNvI1fIef4LT6FQKDh8cJyr16d458JVDHo9XZ2tyGSyjUDCvx7i1JkL9HS3A3Upir6ezm09ryAI/Lc//RvUKjXPP3uQfcM2Hh7sQCnJIVcaUGhsd5Usi6dyXJ30cXB3JyqlHLNJw4Fd7TS5zZx+Z4E33p6jUCjj9VjYu6MNrUbJ0b09W44liiIr/jjXpnxcmfQRjWcJhtM4bHqanGbUagUjfU0olVtvQYIgsB5OMb8UIRRNo1LKaW2uy4TtHWt/d87JHBOzAXo6HLgdJvbv7CCbKzG1ECQQSqFWKyhV6odIcrkMr8fCQM/mJEmxVOHvfnSR//bXp4nENlusP/XQEL/9q8fRa1ScvbRIqVzF7TByaE8XTc5667Io1Lh29R0qyVmcqhhS/T3aR4CKICdZcTE8dhhfXEk04GNg0EJbc918vvHC8cFpxCYNGjR4L0KtQjkXrstt5WNIZEqUuo/HuD0WS/DCK6fQ67SM7x6hyePCbKrLC8ikUoLhKJFIjHAkTiyeoKO9hcMHdqPTavC4HcSTKTxuBztHB3E57RvJkQ+SJBEEgUAwwuraOl2drSSSqXoSJxhBqVQw0N/NsSP76tJelSo9XW13NH7N5wtE4wlisSTRWAKrxcTuncPUajV8/iC7xoYZHe7bSC69l2opQykbpJjxU0yvUSmmEIUqMoWOsqBlZq3ClUkf2XyRtuY4Tx4fwukwYNSrqdUEmj1mrCYdJqNmo+OiyWXekJT4oMRTOV47M0MwnKJUrqJUylGrFJRv/rvbYWTFFyOZziOV1KWzKlWB7710lVKpQpvXxt4dbbgd9ZdwpULOYM/dOxYm5wLcmFnHZNSwc6iF1mYrZYkVuLv2+IMmmc4jk0ox6NXEkjl8wQTtXiutzdb7NryHelfwWiCB2ajik9h/0ogbGjRo8FEgVEsUMusUk8tk0lFUGitKrQ1Bkqa7s43hoV5095DTvjE5x+z8MgN93QDMza8QikRp9TbR3ubF7bJvKh6s1WosLq1x8sx5Lly+zqEDu/nG1z6/7TkXCkWuXJvirbcvcvnKFEhgbn4Zt8vB4yeO3vN6URSJx5NcuzHDm6ff4cnHjrJvzxgD/V1MzsyzY6Sfwwd2s3vX8G3nK7Vare4Pe2OWbC7HrrFhLFYzDocNlbIuBRYIRQgEw+Tzhfq4fV3093XR3Ozmv//p33L1+jQqlZL21mZGR/o5vHeQTGQCMbGIuRogvlzvUFWoLcgUTUzPB7k+M4dSKWPvWDvt3rtLiH0YYoks8ysRnDd9U7taHXfdV2eXQly+sYZKKae7zUFXu+OunmrZfKkuw6Wsd68urcZo81ppa7ZuMlsvV6q8+OYkyXSBZV+MyxNrTMzerhHutOl59vExnn18B12tjtv+fDuIokgilUelkqPTqDbkO1VKOS67kY5W+0aSSH2zu/byxBqzi2GWfFF2DTh5bL/zHnf56PhQbwrvvPMOf/VXf8Xf/u3fkkql+OIXv/ig5tWgQYNfQERRoJyLkE8uUcqFkSl0qA1eRCRQuD3LvRXVapXlFT9Xrk0yNjqAbQsz02Qqg389iNVixuN2kEylmZ1fYn09hFwh59CB3Tz9xEP3PLQulysolQrK5Qr/8L0XkCChVKlLPySTaXbtHKK7sw2pVMrJM+dZWl5Dq9FwYN9OerraN8a/PjHL7NwSmWwWj9t518TOe6lUKnXj12vTTM3MEQ6t89yJHsIzP0AlGtGq1Sg0rjtu+oIgEIlnCUXSnL+2QiyRo7/bRS5X5tFD/ZhNWv7+xxdZW08y3Oth31gHbd57VxNOLwR5/a1ZlnwxcvkSBr0am0XH04+M3LNSQxRFXnhzkky2iM2iZ9/OuoHpezU7Y4ksE3MBAqEUBv27XTLRRI63Liwgl0vRa1Xki2VCkQydLQ5aPJZNPir5Qpm/+eEF/sffnCGWyG2aw8MHe/nqM3tobbbS7v3/s/ffQZad6Xkn+Lvee+/Se1veF8oAVbANoB2txF4OpR1pNEbUrmakiPlHmpiQRtTuKnZjYlaUWQ4VbJJNNrub7eFRKADlbVZmVqU313vvz9k/btYFEmVQBRTQ3eT9RSC6q5B5zndvJu73nfd9n+exUa7U6PbbGOh2oNepiMQzXL18AYc2jlhcxdiswMPFOjQFGYmKjXjZQbxoZGrUj9bcRa++SY/f/sDGUYfHp3M26dChQ0ulEKOSXaNWSiGVqVDp3Ui+xCaJIAgkkmkq1Spdfi8SiQSX08aRg3va04/NRgObzcLy6gYXLrUUsDqdFrvVQrFQ4r0PLnH00B4OH9xNqVzh2JF9bfXJZ+HOwgrXbs6xsLiGRqOiy+9FoVRwa/YOFrOJoYEeHDYrDkdrr5fJZIwO97e/v1qtkUimMRh0GA165u8sc+7CVQBUKhUOu6VtbWoyGvi1r71wzxoalQy1YpRyZrXVJCllyRfq5Gty3C43ZouB+cUIl2fuEI5mUSnleJ1mjHo1tXoDq0nHc8fGkculT8RH/K7F1GY4Q6PZ5MDOPtQqBRuhNAa9mt4tL3CJBBZWYyRSBe6stKZQTUYNvQEbr55undtkUikmgxqn3fBQ+45Gs0kokmU1mMRpNTAy4KY3YMftMLaLGqIItS8pE71QqrIRSrEWTJHNlenvcbBnsptev42+gP2xi0eVap3NcCs4NpbII5VKmBh2o310i/wvlc65oUOHDk8KoVmnWthSSJbTNCUa3nh/gaGBXnz+LnZOj32qGjWXLzAze4dbswtkszlqtRoAhw7sQqGQb/tMFkWRmzO3CYajKJUKJEjoCng4fmw/fd2BR153tVrjP/6f3+Hi5ZsIgoDX4+S3f/1lpqdGH/g9H88ZvXx1hvk7y5RKZTaCYXRqDZVKa92T48Po9TqGB3vvO4Qaiyd5573zlEplerv9HDuyj0KxSLXa+v7LV26yuraJxWKmp6uVxTJ3e4n/9Eff4fChPXz1K6c4eewAXQEve3YM0+1WUi+sU06/SyKSQypTIleb0WhbtqSNpoAMUKnkjAy4GOl3P3Jm2ePQaDbZCKVZXI23bcTvNgc+XsOAVm1iLZhEq1HS7bPhdhjZt6OHLq/1gdklpXJrGGEjlCKZLjLQ42RoxEFvl42+bhtymYxarcHiaoxYMs/+Hb1cvrHOj9+a4cMrK1Sq9W3XUynlnDo6ylefnebgrr4H3vdhFEpVIrEskXiOWDJPvd5kfMjLxLCXHr8Nj9OEyaBBIpFQqzeQb93jh2/e5P2LS4iiiNWsY2LIy3Cv+bHv/yR57BPmXanqt7/9bVZXVzlx4gT/+l//a772ta9hMPziwlc6dOjwq4soitTLKUqZVWqFMEjlqPWedlHjUd0CBUHgL7//MyqVKt0BH9OTH23u8USKxS31SLFUQiKR4HE7CIWj7NoxTm9PgPMXr9MV8OJxOx74YJjJ5llbD7K2EaRUKvNrX3uBeCLFyWMHcTntfHDuCgaDjh1ToxRL5dahRSJBrVKyf+8Oerp8RGIJ3j93hZ3TY+h1WprNJh6Pg32+KXxe1wObNPV6nfk7K2SyOXZMjRKNJvjuX/016WQEn13C0WMmxntEZEo9KrUbhTx3j3VDNl9Go1agVMi5MrPB9dlNIokcjUYTo17Dn37/Ek1B5NljY/g9Fg7s6uObL1keOslwV0kSS+RpCgKFYhWZTEJvwIYoiHT7bewY9z+wURJL5lleT7B7sguFXMbYoAejQX3fSYv1YIoPryxj0Ks5sKsXnUbZDmDXa5XI5VLqDQG5XGB8yEt/93apaqFU5dvfv8B/+vMP7pGZHt0/xldOjiCVQjjWsiHp8dvQqJUM9DhYX71NKXEboyyMVVaj+SnDnoIoRW/tp6Hq5uzNKiIyDHo1+wZt9PhbfvJfxMHsbyOds0mHDh2gZd9ZK0QpZ9epV9JI5RpUes9DlZVPkmq1xtp6kFA4RjAcpV6vYzGb6PJ7sVrNeNxO/uJ7P6XZbCKVSNm9cxyNRk1fTwCDXsfZDy8Tj7eCNE1GA0Zjq3AukUg4/fSRz7ymer3OO+9d4IPzVyiXK3g9Tg4d2MWBvTtoNpvsmBx5oHIEPrLoyhdaCsyd0+NMT47g9Tg5fnQ/drv1gTYid/NHMok1lleCeHQxhEaBG3dSRNJNKnUpgtCys+oJNNm/sxev28RaUIdUImH/zt57bCceZt/5qBSKFS7PrHN7KUYqU6TeaGLUq9k71YNGpaCv287CSoyltTguu4EunxWTQYNMJmX/zl5OP9UKdDfo1O1z28Tww20iMrkSd5ZjbITTNBpNbBYdOq2y/ZqexOt6XJbW41y6voZMJsXnMjM57MPjbBVzHkfp2mg2EZoiSqWctWCK67ObuOwG9k734HebUSigmEh/US/jsemcGzp06PAkEYTG1vljlVopiVShQ2X088H5q1SrNbq7fTQ+3YAAgA8vXGVhYRVBEOnr6cK5NcDwcSVpqVTmnTPnOXvuMolEisMHdz92LkmtVufSlZtYzEZGRwbYv2eaYCjKoQO72b9n6p4AdWg1csKRGOFwnHA0zumTh7HZLBgNevKFIsViCQkSFColyVQaURTRaNSMjQxsu06z2SSTyWGztYYsPG4nk+PDqFVKXn/7fZLJNCePH0KrM7FzxzgH9u4gncnx7//zn3FzZp5sroDDbkWlUCA06+yddDPZVaOUvkpqJYNEAnKVBY25B4lEiiiKbIbT3LwdwmbRsX9H77bs0yfJ3SZSOJrlwrVV3E4TR/cN3GMjXq3V2QilWQ+liSfzSKVSxgbdABj1Goz6e5VHzaaATCZt791SqaTlsLGrF4/TTJXW4EY4lmV1M0komiEcy7G4FuN/+tffJ5bI33PNLq+F3351H68+O/3YNlvlSo1YMo/DakCrUbKwEmNxNYbVrGOo14XLYcC2FQCvVikoV+ucv7bC7aUoq5spjh0Y4PiBYaaGfViMGjwuE41Ga4hlfjlOl/NXSFkyMjLCnj17+Ef/6B/xG7/xG7jd7i9iXR06dPhbQr2SpZzboJLdBJootA6ksk9/WBRFkXAkzsraBqFQjK++fAq5XM6BvTswm02YjHoWFlexWc3YbBbiiRShcAyTyYBKpSSbyxMKx6jXmwiCgEwm48C+HQ+8X61W56evvUs6k0Umk2E2GdFpNXznuz+hUq3y9PFDKJUKjj+1n1y+wAfnr7K8ss7RQ3vo6+3CZt2aHL14HUEUsJhNlMsV9DotOx4ysZFMpvnw4jVmZxdY2whRr9fxe124zAI6aZxvHtNi0HQjVxlRqC1IpPKWBHNrGrFSrRNN5IjEc0TjecqVGvt39tLjtyGIIhKpBK/TxGqwZSfhtBkYHnC3N+qR/gd/xouiyMJKjHfO3WFlI4lcJuXg7j76umwEI628k7EhDwPdjnsmE2q1BmvBFItrcXL5Mga9mmKpitmobftpQivQbGUjgdAUGRlw43YaObS7D41awa2FCKFoBpVCTm/AhlqlwKjXMNDjuGcKI1co88ffPc8f/eU58oVK++8lEjh9dJRvfeMAt5YL1OpVun1WBnocGPRqCtkI9ewiqfBNtEIB7af8aooiFAUbNXkXGusg/cPdZPNl+ntidPutOKydB/Avgid5Njlz5gx/8Ad/wOXLlwmHw3zve9/j1VdffXKL7dChwxOnWS9RKUSoZNZpVLNIlXpUBh8SyRdra9hsNgmGokgAv89NqVTmwwtXsVktjI8O4PW4sFnNQMu/+/zFa1jMJjQaNdVajctXbxGLpzhx7AAup52RoT4cdmurAPAZFSSCIBBPpNgMRlhdD1IoFAn4PJTLFf7et76Jy+XYZv3xaVajc7eXuHTlJv193eycHm0XRQCMBv19fdCFZp1qMUIlF+b61QvMzC9TLlepY+L0oS78/l5sTj2byU2qtRpCU0CtlpNMF0lnS/jdFg7v6QeJBL320zPbHoVyucbtlSiVap0DO/toNAXeODuP2ailt8uGzaRDIpWwGkywtJpgeS2BRq3g2P5BhvtddPusn8kmM5MrIQitSclqtUE8lWeoz0mPz3bPZOkXjSiKRBM5VjaSmI0aRgc8uB1G9u/sxec2f6YBjmS6wMpGkvVQit6AnZ3jAXoDraGQj6trRPFLksk8Ip2aRocOHZ4EotCkVoq3hj6Lsa0hDS/1hsDV63MsLK5ycP9OjAb9PVaUdwmFY8zfXqK/vxufx0koHEOjUWO3WTh6aE87wB1aNYnbCytcunKTC5euMzTYwze++jw7JkceaY+6O9Rx89Ztzrx/ibX1TXp7A/yb/+V/ZN+eafbt2e5wUalU29bkP3v9DJFonEajiUTSeu790c/e5rd+7Sv093VxfWaeYrFEd5ePndNjjI8O3tO4qddb6781t4AgiPz6119Ao1GzY2qUhcVVVlY3qdXrHD20h0wmx7mLM7gcJo4d2cef/PkPuHFzjuHhPl567gTTI14apSCRub+gVkohig3kKhMaY2DbkMxmJM3M7RDZXBm308Rgz5MvwN/NHVtciyOXSTm0ux+f28yLJyfQ6z7a62u1BrV6A71OTSJV5MrMBi67gX07evC5zfdVzGZyJdZDLQVJt8/GxLAXl+3e78nmy0i3bvXehQWuzwW5fHOdtWDqnmvqtEqePz7Oy6em2DvV/Vjnm81ImnA0SzxZIF9s1VXu1pfGBt1MDHvb54lardG+9l/97BpXbq7TaApbZxAXPlfL+cPrNnH11gahaBapVIrdqqM3YKUpfKaI9SfCYzdL5ufnGRoa+iLW0qFDh79FNGpFKvlNKtkNmo0yCrUN2SNkkoiiyIVLN1hZ26BSqbazP4StD9Kebj8A5y9eZ+72Igf27cRo1DM63M/QQA9/+hc/QqlUMDYyQH9fd9sv/JNkc3mWltdJZ7LtRojP62LH9Bgrqxusrm2iUqno7fYz0N+N3WYhly9w/eY8yyvrNJtNLGYTHndrMw5FYjSbTfbsnsTvcz8wZK1Wq3Pm/YtIJRIOHdhFKBLn8pUZlCoFJ47soN+vxqnNUC+cpw7Y7Hbkyo+u1Wg2iScLqI06kMOF66uEo1lMRg1dPgtKhZxgJMOd5ShSqZTjB4a4PLOG32Nh71QPAz2OTy1MNJsChVKVDy4vs7QWY2UjicmgxmLScerIKAqFDJ1Ghctu3GYvJQgCEokEiUTCuWsrRGI5/B4zuyYC94SalSu11mTCWpxGQ6A30GqgyGVSVjaTrAdTlMo15DIZGlXrYVyjVnLq6PbGUzyV54+/e55v/+AihWK1/fdSiYSDu3s5dXSU33x5L6IIakOVPpeEZCLC5p13kVXX0MpbU7SfdnxQGbzkml4qEh+JnEAyXsAtaUmHTQYNe6a6P+UKT4ZCsfLpX/Q3kCd5NikWi0xPT/O7v/u7fP3rX38i1+zQocMXQ6Oap1IIU8lt0KgWkKuMqIyBL8xzGlrTnKvrQTaCEZZXQ6iUMgI+N36fG4vFxKtfOUUmmycWS3Du4jWMej3Hn9pPd5eP3h4/a+shBEHE4bAyPNiHe2tyU6lUsHN67DOtKZ3OIpNJCUfjvPH2BywsrVGr1vD73Hz91WcJ+DwolYqHqkfyhSKJRIpYPEU0nsBmNXP4wG4G+rrQ6TR0+R+smhAEgWwmSSKyQjK6htdSQahlOXdtnaWNEnKlGrnSQrOu5L2rQb7hcjLY66RSq1OrNTEbNVjMWswGTfuh+uPFhc9KPJnj7KVlVtaTRBNZBEHE7TQx0O0kmsgx1OsklipwfXYTm1lHb5edcrmBRq3kt7+6D6ftsw04ZPPltg1VNlfG5zZzZO8ATruBF09Ofu7X9bgUSlWWVlthr+VKDYO+ZRUGoNOo0PkfvyEVTeS4fHOdfKGypcB10rs17PIk7NG+aDo1jQ4dOnwe7tqHl7NrVIsxJFLFlpK19fm3vrHJzOwCUxMjDA/2IXzCKaNWq3NncYVzF66xsraJ0BTo6wsgl8t56tBebDZLu07RaDQ4f/E6iWSaUrnCrulxdkyN8upXTmExPzigurVOkWQqQ7PZxOW0E4sl+aM/+Ss2NsPIpFJOPHWQZ08dvWdwIpPN894HF0km03zzay+g02oYGughnki11Q0Wixmv24EgiCgUUvQ6LU8d3ttSiHwiAF4QBG7M3Gbu9hLVSg27w4LFbGJ5ZYOB/m5q1Rq3F1bQ67TU6w3ePXuRSDTO0mqQF55t5aW8+pVTfO2lozgMDUqpRVJLl2nWSy1XDb37vgO3hWKFDy4t47DpOXl4+IkPLtbqDZbXEiyuxSmWqlhMWgZ7W/UfqVSKXteyEA1Fs6yHUkTjOdyOu0oTI6+cnnqgbWcomuH6XJBcvoxCIcPvtrSVn3qdGr1OTb5Q4c5yjPVgilSuhN4Y4d2zl3n9vVnqDWHb9WQyKU/tH+CVU9OcODiEWvXpmXe5Qpl4skAsmWfXRACVUsFmONMasnUYmBj2tkPZAVRKBelsiaW1GHOLEdY2U7z49CQ7xwOMDbgx6FQ47QYEQSSeLDC/FGGw14lSIWdi2IvJqMFm1iGTSalXMjQqvzhF6mOfZIaGhshkMvzlX/4lS0tL/NN/+k+xWq1cuXIFl8uFz+f7ItbZoUOHvyG0ws6CVDKrNGpF5Gozas3DczDS6Sy35lc5enAaiURCuVyhrydAb08Ah9360bUFgcWlNdY3Q2wGI/R0+9kMhrl8dYZf+9rzKBQKXnruBGaz8aGFlEwmx49//g4SiQSvx8Xyyjora5tMT45it1mQSaX09gQI+NzbuvDRaIJgKIJUKqXRaFIslckXimg0ao4e2vOp7025XOF//8M/YWl5jbGRASYnhhkZ7OL//g9epJxZopxZo1Er0KxpUek99xwIUpkiZ84vUKk12LvHgNktZceYn71T3eSLVX7w2nVmF8LUG026vFa+/vwO+rrsWM1ajHr1p04UROM5Lt9ap1ppBZ03Gk0Meg1DfS5EQUSvUxFP5fG6zAS8H/1cUtkiqxutBseBXb24HSZ2jPlRTMnua81VrtT40ZszSKUS+rvtDHQ7SOfKCEJrww9HspTKNSymlgplqO9e67K1YJL/9Ocf8Fc/u0a9/pHmWSqVsH9HD3unuvG4THT5rDSbAkK9iKIww9LGDXTyHAb41B2yKhqR6fvpGdpDsabk/ffmEcV8W/3i99ybl/NF0Gg2CUYyW8WgHP3dDoZdX8qtf2l4kmeT559/nueff/4LXG2HDh0+D6Io0qhkqBTCVPMhGrUSCrUJ9RfcJLlLPJnm8tUZHHYrk+Mj9AScmLeKFWsbId5+90MAdFotRpMBJC0v7907Jzjx1EEy2fwDBzUeBUEQ2AxGmL+9xOLKOvVaA5NJz+T4CDOztzEZjZw+eZi+3gAOm/W+2W2VSpVEMo1er8NsMnB7YZkPz7eyRwx6PQ6HFZ+ntZEoFIp2o6RarZHN5anXG/i8LmrlDN/9q++TSgSplVI0GxUkwJE9AwS6/PT26QimlrCYNDisBhRaBx5Ls638nBx+cs+NgiCQSBdYWIkhk0o5sKuPUDTHm2fnMerVmE1a9k/3YtCrWdlIEIxkKFcaaFUKRvb0M9Lvpjdg+0zqEWjtxXKZjI1weis/TYbXZWJi2IvX2Qrr+DJ+P++SL1SoN5pYzTrKlRorGwn8Xgu9ftu2sNdHpdkUCEUzSKQS/G4LKqUci0nbHnj5Ml/bk6BT0+jQocNnQRQFaqUE5cwatWIUJHKUOhe5XIkrMzcRRIHDB3bT1xvA63FuU4VAa69CIuG7P/g5739wCYNBz+BANzunx9v7bn9fV/vrz5y9wA9+/Cb5fIGpyVFOnzxMf1/Xp6pCATY2w5z98DLVahWvx8Xpp49QqVap1epMTYxw6uQhJsaGtg1SZDI51jfD3Lx1m2q1htGo42evv8fXXj5FX28XmWwevU6L2+1gMxjhxsw8XQEvLqedZ585es9eUCiW0Ou0SKVS5m8vkUpl0GjUJBIpMpkcQrNJo9l6Vv+Nb7xIMpXhP/7RdwiHYxTLZYwGIz6nhWJyAZO4TCW3STKRRyJToVCbUenvVQXmCmXurMTYNR5Ar1Pz7LExTIZPH8p9VERRpFiqotepEQSRWwthfG4zB3f13rO/RuJZ3ruwhCAI2Cx6psf87RxVqVSKSvnRmaNaqxOMZNGoFXicJhQKGWajhqkRH26H8R6njrc/vE0klmM9nGZpNc6lm2sUS7V71js14uPV09M8f3L8gYHyn+T8tRXC0SzVWgOJRILFpKVcqaNSKti/o2fbz7n+MX+5b//gYqveVG9iMqrpDdhRKlu/qwGflZk74a3QdwVuhxGP09i2LbvbZKrVG4RiWWLRCGM9X64C9+M8drPkxo0bPP3005jNZlZXV/n7f//vY7Va+d73vsfa2hp//Md//EWss0OHDr/ifDzsrFZJI1eatiwyHvxwlUimef/DyyTTWRoNmJ7ox2w0cPyp/cBH9hfpTJaJsSGkUinXbs6h02oJ+Dysrm1is1nYvXOifR+L5eHpkplsntffeh9oWWpsBiOsrm1gs1loNBrtv4fWFOaNmXlEEY4c3E1XwEskGmd5dYOjh/Y88kEmXyjyxtsfcOnyTbK5HN/6O19j10QXlew68Tvz1EpxQEShtqLR3punUq3V2YxkuD67iVGv5tiBIfI1OTfm1pHJJGjUSt45d4drs5vYLDomhr2M9LvbxfxP86YMR7P88M0brG4kQQIvPT3J2JCHM+fuUC7X0GqUeF0mpsf826Y1VjeTzC9FyObKqNUKegK2tmrlkx6c6WyJtWCS6dFWtsm+Hd04rHo2Ixl+fmaWcCzHN1/chctuxO0yMTHiZaDHcc8kxuxCmD/89ll+/u7stgkeuUzKq6en6euyE/Ba6O9x4LYpiG/cZOPGGcRKBAXwacOQdXRItH1IdX1kCwrq1SaDajMmlciOMT9+j/lTQ+yfJMFIhvPXVqjXm9itevbv7MXvMVNOZL+0Nfwy8Is8m1SrVarVj1RLuVwrzEYQhHaD70kgCAKiKD7Ra/5NpvN+PR6/Cu/X3eyLSj5ItRBBbNaRqy2ojK3mvMijZ5w9Lrl8gc3NMEODvTgdNvbtnSYWS7K4tMqdO3cYGe7jwL6d2Gxm9u2ZZm0jSD5fJBiKAmAy6pkYH0KhUGA06u+ZMH0U8oUic/NL3F5Y5vrNeQqFIk6HjanJEQ7t34XdZmZyYuge9cjde62ubbK+ESKeTJPPFwGYnhxh5/QYHo+Lk8cPYrdZt02DCqJIpVLl2o1ZVteCVCoVmvUSalmVUwe91ApRdM0Vcs0yco0aAQP1hsC5W2nsbj/dfhtGgxqjvqUYKTSM6Lfy1D7vj+ruZOt6KMW75+6wHkqTzbcsToZ6najVSuYXI4wOuKk3BOqNJmuhFA6rnj1T3eyZ6tkWTnuXx1lXOltkI9RSkNgseg7s7MVpM3B4z8A9hY3Hfb2i+NE/j0qpXGsHtadzJdx2I8cODGEz6/nKM1PtRtDjXDObL7O8nmAtmKRaa9Djt+FzWTAZtBzY2bdtvZ/6epA88c+Zz3qtTk2jQ4cOj0MrYzVBKbPeapIgQa5xsBlOMH/uQ8KRGGq1itHhVjaHVCptN0qazSaXrs7w7tkrHD4wzeEDrT37N3/tK4wO92Pdsuv8JOFIjL/+8Zt4PU5e/nu/xWD/ozsVLK+sc/aDy3i9TmwWM6vrQRLJNAP93fzTf/z3MBlb1uTpdJZwNI7P68ZsMrC6vsnNW3daw4SCgAQpfq+Ler2BUqlo2WUtrfKz18+0Blh7u9BtZZZ9fD9dXdvkzNmLzN1Z4pWXnuHA3h0cPbyXm7duYzIZKRZLFApFllbWWV7dIODzMDLUx/lL11ld26S/18fhPQO4HAaUlVniixmQSlGoLChM9vvWkARB4NZCmLmFCFqNkmJvS0n5pBol+UKF1WCS1Y0kzabAy6emUKsUvHJ6qh2mvrqZZDOcRqtRsmuiC4tJy+SIl4D3/hmw5UqNzXCGYCRDLJlHFEWG+lx4nCYcVkO7tlIoVlgLpghGMhw/OMTcQoSfvDXDmQuL9+SwApiNGr767A6+8cJOBh5gOyYIAqlMiXgqTzxVIJ0t8ZWnJ7eaOHL6uh04bXpsFv02m06JREIskePWnTB3VmJsRtL89qv7GOl3M9znRKdVYDZqEYXW0GwwnGF80Iteq2LnuB+HzXDfn0kyXeDqrU1SmSKiKKJTCwz6Pl398kXx2M2S3//93+d3f/d3+Tf/5t9sCz97/vnn+a3f+q0nurgOHTr86iMIDWrFWKtJshV2pjb4P9VHvFgq8+Y7H6DRqHnmxEHUGiNGg45Go8HC0hrBUIRwJE6z2USn1TIy1IdcLufrrzxLsVTm+z98g8GBHg4f2P3Q+zSbTULhGFKpFJ/XRbVaRSqV4ve5iUTjDA/23mPXlcsXuHnrNktL64iI9PUEAKg3GqysbnJw306GBnvvuVe9XicWTxEMRZm/s0S5XOHIoT0MDbS68w67mVee3UGfLUF07hKNWh6ZQo9K70Uq+2ijKJarxBJ5lAo5PreZfKHKpetr2Kx6LEYN711cJF2QoVNW6euysxaMIIgip58aZedYAL/H/NDJyVq9wUa4JXms15pcm90knSsxPuJFpZDh91gwGTRMjPhQqRSMDXnwucwIgshGOI3FqGlLTo16NdOjflx2w33vGUvmmVuMEIll0WlVDPY60WlUVGsN/vQHlwjHW9YiHqcJ3Vaj5dj+wW3XEEWR89dW+Q/fPsvZS0vb/p1KKWffdA//83//PD1+G8VCjkxsnkL4ItHNGI8yCNlEjcU9jtI8yGJQZC2UplrNYdCr6fa1inQfn4b4IilXaqxtppDLpQz0ODEZNQz0OOkL2J6IZcmvKr/Is8m/+lf/in/xL/7FPX8fj8epVJ6cLZogCGSzWURR/MyTz3+b6Lxfj8cv8/slCE2a1RzVUoJGNQuIyBR6pDIjlIDS/T3APy+hcJRINE44EiWfKyCVyZArtITCUWbn7mAwGhCRojOYWNuME46+zbGjB3A4PczMrWCx2hkasm3lj6jIFxu0A8UeAVEUSaUzLC2vEQpHqJSr7N+3i4H+AUaHR3C5nduyRwqlJtAE6tuucbfAEYqkCUVS2G1Wenv62qGqLQ91CWqNkUgsSy5fIJcrIJNJGR7qJ5fNcvXqdbQqEKoZ6rUSxVqV7/0sxsmndjG6s4/gmeuoVQoMBu1WKKmWhlRPoSFFoTNSFkFsQKV5t6DyeD+LQrFMIpUnmysST2YJhlP097oZGwxwYyHPpZkoRoMWvcHCjvEeNBoVMrWF0TErmWwrvN1pN+Gwmdp7ZeFzRGgkkjku3VikVKqiVMrxuty4vHYKDSNIwGS3Uv5YftxnQRQf7/2KJTKcPb/QCnt1WpieHsTtNFNofGxo5zH7ColkjjPnllEq5XT7u+gJuDDoNZ/pvRPFJlWJl2S2QlUWe/wLPIB8/t6w2kehU9Po0KHDo3C3SVLOblIthAGQqSzIFWpKpTLvnjmP1WrmqcN76en233OGKhZL/Ov/5x8SicYxGExYtwY3T508ct/75fIFZucW8fvczMze4fhT+3nh2eOfejbL5QsEQ1GGB3uRSqUsLK1hsZiIxhL88CdvoVDI6e3xY7dZKJcrzM0vEY7Gt2ofMjRqFWaTgfHRQVQqJecvXmfXjnF27Rjfdp+Z2QWuXJuht9vP9NTYPUrZjc0wZ85e5P1zl1EqFIyNDqBSKrm9sIxCLue5U0+RTmf52Rvv4XbZUauU3JxbQCqRIggN9ow56bbuxaxMU6/MUc46USpBbezalkPySeKpPFdubpDNlxkb9DA64L5HifFZqTeavHvuDsl0EYVCRsBjoSdgazdscoUKN2aDxFN5BEHEZtHh2VKUqpSKezJg84UKIiJGvYZYMs+12Q2cNgO7JgL43NsHLxdWYqwFkyRSBaKJPKubSf63/+M1NiOZe9apUSk4dniSl08McXTfwDY7dGgpYIulGiaDhkazyfd/fr09eOKw6hnodtAURKRS2DEW2Pa9tVoDuVyKVCrl//gv77K8nkAqleCyG5kc8bXfa7/HwuJqnFgtj92qZ7jPhdvxkVXc3cZNqVwjHM8SieUwGzWMD3lRKuRo1Ap2T3bhchhRSko0q/e+zi+Lx26WXLp0iT/8wz+85+99Ph+RSOSJLKpDhw6/+rR8PGOUMmtbYWdqVHrvQzc5aBVspFIpyVQauUzO8SP7yOQKLK+sY7eOIpFIuHLtFjarmZ3TY/g8LsxmI9FYglyuwNBgL1qNGrfLzoG9O+57j3q9TjAUZW3Lc7xer2MyGXDYbRj0Or72yul2vsYnDyaVSpX/8u3vky8UMeh1qNUqYvFWaJZep+XXv/FiO5S1VqvTaDTQajVsbIb5q79+jdW1TWq1OkaDDo/HSb3eQKznOLnHRqEnQa10i0LiXhVJKlNsbTzJPMVSa4q9r8uOz21Gq1Hw1Wd3UCzXeOfDO1u+5Sl8DgUOq56pUR+CID7UQ1oQBCLxHGubrdB1qVTCQI8Dk0GDw6ZHp1W2Jh4qdQz6CF1eKz1+G70BO6lsK5xsPZiiVm+wYzzAcJ+aoV4XQ70P9oM6e3GxFQRv1HBgVy+Wj8lCP7yyQjJTZMdYgNEBNwGP5Z4NXxAE3nz/Nv/+T97j5u3Qtn+n0yg5sKuX4weGmBqyY5JuEJl7h3JmFRBQAjyk+NBEiUzfh807SUPmwGrWUW80CcVm6fa1XrvF9HBFzpPibmDc8nqCUDS71ZhxAKDXqpga6VhF/CLPJv/8n/9z/sk/+SftP+dyOQKBAA6HA6Px4T7Cj8PdzySHw/FLV8z+ZaTzfj0ev4zvl9CoUi3GqeY3qNVSKBVyFEY7kvt4Un/ue22FokeicSbHh5FKpZy/sEaxWMLtMNMTcKHXaRka8DHY76W328WZsxcoFKrIJHWcDhsetxOrudW8+Marz3ymdeRyrQft2flF3nzngy1ligSv28Gvf/MlBj5FsZrL5QlF4qTTWVLpDOlMjp4uH0cO7eHQ/kkO7Z+kVquTyxfIZvM0G1IcdiubwTBvvXuulf8mCsioYzcrEO0xFPkgOwNR3r+8gUajwWI2Y/E6MRs1mDQNIM9Xn+67z2oK2/50V3lwV1nyIERRJF+skM6WcFgNlCs13j4zx8Wba1QrdRpNgZEBF2qJgYU7cxSyeXp9emr1BjJZg0wqisFrocdpRa/TAB/fq2tb/zw6oiiSSBfYCLUmRUf63UiMVXrdCvxuJw6b/mP/zeQe69oPv2/rf+/3flVrdULRLJvhNAq5jAO7+lA7RI7ucn0iILb4GPcTiSXyrGwmEQSBQ7v70ToETu5v2Yi1XmOdjzfjHu/1NJCIIWwmNSbnkxsuUas/26BIp6bRoUOHh9FqkiQpZzfaTZJCRcb84gbh8HW++vIptFoNr7586oFZpIIg8If/+c9Ip7P8d//wd/B4/Ngs939+DIYivH3mPNdvzjPY343DbmV6chSL2fjAc9nGZphgKEowFCVfKCCVSHE5bFitZgRB4O0z5yjki/T1dTE23E9/X0uZEo0lKRRLDG/VTUrlKnO3l6k3GgwP9mG3WTmwbycjQ32IosjK6ga1eoORoT6GB3sJbGW03aXRaFCt1tDptIQiUa7PzDMxPsTocD/JZIbrN+eQSCQM9HfT19uFeqsx8/aZc0QjccxGFcM+GZHZv6BWSqAXBUTRiNrgoylYUTzk3HBXIVqp1JHJpDxzZASr+dGsph5EsykQjmeJxvPsnuxCIZdhNesY7nPhcZmoVFquHvlCazBVvtUo2DkewOsyo9VsP6eKokgqUyQYyWx9X4WegI39O3rxuc28cmq6Xeeo1RusbCTo8beaMdfnNzl/dZUL11aJxO89YyjkMp7aP8BLT09y/MAQTbm9fW64m2UbT+aJJQuks0WUSjmvnJpGLpOxa6ILs1GD2ai553dMFEXWQ2nmFsIsrMQIxrL8d79zDJ/HwvSYny6fFaNeTb5QJV+sEIpmGexxYrfoOXZgELtVj/w+59VwLMu12U1y+TISiQSrWYd7K4fFoFcz1OckEs9x6foi9fwaU911gjf+C/1H/qfP9TP9LDx2s0StVrctJj7O7du3cTgcT2RRHTp0+NXl4z6e1UIUiUyOUu9GKn3wx02j0WB9I8TSygaFQpGvvnyaLr+XRr3BD378JvVGE5lcyd5dI8hkMn7jGy8ikUiIRBPM3VlifSNEpVLFaNAzONCDXC7n9NPbpzUqlSq1eh2jQU8oEued986j02mRyWQ0G01yuQKI4LBbkUgk7WKEKIosLa8zd3uJF549RqlUpt5o4Pe6CAS8BHxufN6PpgVUKiWra5u8feY86xshdDotz506Sl9PgL27pxjs72awvweH3YRSTFNMLRCdP0ujVmirSBqChEgyTyyxgcNmIOCxUK7USWWLeF0m7FY9EgnEkwV++MYNpFIJL56cZCOcZjOSZi2YQqM10RvwYrNsbVT3qa2Ioki5UkerUZLJlfn5u7PkihUkIhza08+eqW6W1+PcXopS2Aos2zHuZ2zQA7TUFLfuhJi5HUKtVtDXZacnYHug1LVWb7C8nsDvNqPXqen22xjocSCVSnjn3AK3boc49dQoR/YMcProKDqt8h67LmhNNvz1Gzf4D396ltXN1LZ/5/eYefrwCCf3B/AaszTyc5CMkEl+uueERKZGZx1AYx2mJNoIRXPMXE1TraV4+dQUSoX8Sw1mvds4TKQLvHdhEbNRy87xAN0+6z2No7/t/CLPJiqVCpXqXlm1VCp94kXnuw3cX5Zi9i87nffr8fhleb8atQLVQpRKdoNGLYdUrkGtd7dDU58Uoihy/eY8kWicWDyFIDRRKpX093YhlUpRKhQURJFwpDUFbzEb8bideD1O/F4Xu3dOoFDp6O9x3/eB8GGUyxUSyTSr60FWVzdY2wihUChwOW0cO7yPZCrDxNgQx4/ubxdNFArFtu9PpbMkUxlSqQyDAz34vC7CkTiXLt3AZDJgtZrp6wngctqRSiRcvjrD0so6pY8pcaYnR3E5bJgNGqaG7FSKSUqZEIVciuhqlr9aFHnx5DS9vYMEuoY+994jkXz0D3yU8wEwvxhhaT1OMJIhlSlRrdU5vKefdLZEIlPAbtHhsBnQaZR0+axUqw0O7e5DpZRzZWYdpUKOy2F44AP645IvVNr2EpVKHbVawWCPE4mkNaiwZ+rR7VA+K598v9LZEldm1kmmWxYVdqsen8eMRNIqmPR12R/7HtVandtLUVY3t8LfdWp6u2xIJK0w2MATyF8TRZFoIoee+w8ifR4+67U6NY0OHTrcj3ubJCKCxMCZD68RjsTQajUMD/a1bT8/2ShpNBrk80XUahWlUplGU+Dv/uYrTIwNbSk5t5NIpnnr3Q95691zqJQKJseHeeHZ43g99zaV0+kskViC0eF+AK5cu9XOEPP5JrGazZQrrXuYTUbUKhV+nxu71YJcoaBWrYFex/690ywsrnL1xiylUhm5XI7X7USvazUZHHYrdpuF1fUg167Pksnm6O0JMDLUh0qlRKVSUqvVWd8Ice7iNa5en8XrdvJ3fvMVugM+/u5vvorVYuT8xev4fW48Hidup51ypTXwGQpF+Osf/RSPXc2rT1kZ9CuQSbM0ao1tuayiyAMVkZVqnZnbIWr1Bod29xPwWrdlpj4udwcGNiJpNoJpavUGFpOWWq2BUilnuN/F6kaS2YUImVwJqVTKQE9rrzDqNRw/OLTteoIg0GgKKBVyltbiXL65jkopx+MyMTXqa6st5DIZxVqVtZUUoWhmazC2xtviHV5/b44rMxv3rFUqkXBgVy8vnpzg9FOj7XpJsymwmcxTlORwO4yUyjXOnF9ArW4N0Pb4A9itH/2+fvLMUNqyVxcEgf/Hf3iDWCKPTC7D5zSxc8xPfStbxu+2sBFKI0GC02FgcsSL095SGMlkUtyOViOtUKoSiWUJxbJ4HKatIHcZNrOO8UEPFrOWTLZEPFWgWq0i1hKEFq4irUXokmeRGARKKYjM/eWvRrPklVde4V/+y3/Jd77zHaD1YLW+vs4/+2f/jK9//etPfIEdOnT41eDeg4UEpc65zT7qk9Rqdc5fus7aepBGo4HDbmN0ZABBEMhkcrx/7go+j4sd02MIKBBFkUwmh9lspFyu8Nqb76HX6ejv7aKnuyUr/bh/ZalUZn0zzNp6kEg0Tpffy8H9O9FqVHz91eeoVKq89uZZhgZ76e/rwun4KGg+mUxz9sPLXLpyk2gsgcfj4sSxA1itZn7zmy/hsFvv+4C2th7kf//3fwISEZ/XzfBQH2aTEa1Ww6H9O1uNpOwGxc0PyZQSAFsqEicSiYSltThXZtYRBBGdVoXJ2Nr8fG4zPreZQqnKG+/NUa01UKsVuOwGPA4Tq5tJfvbOLYqlKq+e3sHE5BQmVfG+UxiJVIH1UIqNcBqtWsnuqS5uzgeRSCT0dznwukwY9a1JPYfNgEIhY//OXsYG3DQFgcXVOPlChYEeJ11eK1az7oE2W9CyDVtYibG0lkAQBNQqBXqdGlEU+eEbN1lYiaFUyJga9TMx1AqRvStd/TjZfJk//+El/vivzhNPbp9YddoN/Le/vYfTe7UUEneol27STD5UPAKAiAyZvgerewLRuAONLM9P375FuZJCrVbgc5vpC9gfqsp5knw8rL0pCDx9eASH1cDpp8YeSclSqdYJRjPIKxXujbv7m0vnbNKhw682oihSr6SpFaJU8kGa9RIypWEr2+zJNm+q1RoqlbL1ObERotFoYNBrEQQBl9OO0aCnVqtTrzfQajTotBrq9SbZbIHX3nyPF587gcNuZWx0kFSm/FjF2kw2TygcpVqtcf3mHFevz6JUKPF6nIwM9zM5PoTbZae3N/DAa7x79gIrq62HZ7lcjtVibuc2tAYyuilXqmSzeVLpLNVqS0nh3QqONZuMyORSKsUk6fgK59++hs9aR1FI8NZ7S0jlSixmM1Z7y2tbrm6drZSfs/8giiKRWJp6KUYqUySWzJNMF3n12WmCkQw/fPMmm6E0EgloNSq+8swkY4MeVEo5Wo2S9y4sksoUEQSRTLaE22FELpcik0nZO93z+Ra3tb67fuFuh6kVMBrN0OW1EvBYsFl0X2qIuSAIxBIZ7iQ2UavkjA95UavkqJRydk914XWaPnNGWrVWJ5Mrt4PZVzaT+Nxmeny2bYWUz0uj2WRxJc7iWpxCqcz+IQn9T+zqn4/OuaFDhw4f5167LRGF2oZUruLsh5dJJNMcP7qfroD3vs4Tm8EIN2fvcOPmPMVSmZeeP8FTh/fy9771TSwW07assnQmx8rKBlKZlGazSbMp8NWvPMPB/bvuab6k01lmby8SDEUplcrIZDK6Al50Wg3PnXoKlUpJtVrljbc/4LU338fltPI//v7/FavVxMhQH3v3TGE1m4jGk1y6OsPIcD/dAS8qtZLugA+/z43bZd+mWq3V6vz09TOk0xm8HheHD+7GZjVTLJXRaTWEwjH+4x/9OZvBCLV6S13bFAR++tq7dPm9nDx+EICvveKmUCxx9fosf/LnP6CYy/BP/v4pNOUl/i8v2HCYZMiVBhRqM1L5vYNn90MQBO6sxJhdaKl9xrdqB5+FZlMgnS21971z11aQSiT0d9vp9ttoNJpkC2UcVgPFUo35pShel4mxQTdup2lbjge09rxIPMdmOEM4mqUnYGPneAC/x4JBr8Zh/UiJmsmV2rmx711YJJbIE43nmF2McPH62rbQdGgNTezf0csLJ8Y5dXS0rZ4plqvMLYaJJVq5I8WaCq9NitthxKjX8MKJCQz6ByswM7kScwsRrs8HCUcz/A//1UmsZh07xv0USzW0aiXZfBlBEEmmS/T4weMy8dIzk/fNYAHYCKW4eTtEvlBBKpVgt+pRq1q1FJtFTzpbYj2Y5MbNmxhkSSyaHKFrGRDqGOGeLkUhMUetlECpffyBkM/DY1d//u2//be88MILOJ1OyuUyx44dIxKJcODAAf7X//V//SLW2KFDh19iWgeLFJXcBpV8ywpJobY+cMNLJNMkkmlGhvpQKOQUiyUmxgbp6+1CpVRSbzSQSqVksjlMJgOHD+4iEkty49Yy2UwSuVzGr3/9RTQaNa9+5fQ9Ppl3icYS/PyNs4iigNNhI+D3Uq5U+M53f4LVaual509g0Ov4jW+8eI+lRTqd5Q/+3X8kly8wONDDi8+fYGp8uB3S5nJu/6DOF4ooFQpUKiXvfXARlVrJP/x7v4XP2ypMNOslisnbFFNLVHKbNOtFZMqPskjuykfjqTyXbqzR3+NgpN+NUi4jGM1w9uIijYbA8YND6DTKVq6HVkksWeD2UhSZVMrYkIenD48w0OtAJpVt96jeIlco8+65BUrlGgqlDJfNyEi/C6EpUqk22L+jh2SmyPuXlihV6vz+753EoFPzjRd2EY3nuHB9lUKxismoocvbmjQ06NUP3YDvTlLI5VIGexz0b3lhAiyuxIgm8px+aoxDu3vR3mfDbTYFzl9b4Qev3+Dn78xSrm63f9g7ZuF3XnDT7ywibVwgs976+4eVMyQSGTJdF0bnGLmmg1CswK2ZIkcOy5DLZIwOuLGYtF9qYaRSrXNjLshmJN0Oa+8NfPR79rBGiSAILKzG2QynSaZblht9jieXlfGrwJM8mxQKBRYXF9t/XllZ4dq1a1itVrq6up700jt0+FuNIDSol5JU8iGqhSiiUEeuNqPW2D79mx+TUqlMKBzj0tWb7N01hVqtIpcv0Gg0kMlk6HU6cvki5y9eZ//eaV549hjf/f7PEUURh8PK6Eg/Lodtm/XEpyGKIitrmywsrjI7v0g6nWVkuJ+XnjvOQH83r37lNHqd5p5Q9rvfmy8UyWRzpFJZAj43NpuF/t4uugNeDAYdoiiSzRZw2FvTlDdmbjNz6w6C2GqeKBQKpiZG8HlduBwmsolV3n7jLYqZCM16EblUIOA147P0Y3N28+JzXkx6zef2+RZFkWKpSiZfJl+o4LQZWNlI8mc/nqFQyFKvNdFplQz1ucgXKgQjGaZGfOweD6DVKhEEEUEQuXprg+eOtfzSzUYt3T4rLrvxoeeOxyWbL7O2mWwrK/weC26HCZulFYr+ZZPJlZhdCBOO5ShUlVh0zfb0p0at5Mjegc90XUEQCMdyrG4mCUUzKOSytmr25Wemnuh5J1coY9RrkEokLK3Hcdj0HNjZhbp574TsL4pOTaNDhw7QcsVoDXze2ySp1eoogV3TYwz0deF2faQ6azabyGQy4okUf/aXP2ZxaRWZTIbX4+TYkb1MT44AtM8MjUaDW3N3uH79BnPzi/h9Hk4eO8De3ZPs2jH+wMDyt949hyiK9HT58HnduJy29pmhXKnwX/7sB7z1zodkMjkcDivPPXMEuVyO2+VgdHSA6zfnqFSqyGQyPG4Hiq3v7fJ76fJvbzREYwlcTjtKpQKv28HYcD/VWo1rN+aIRONYLSb27Zkmm8uj1Wp5+sQhstk8Xo8Lv8+Fz+vGbmvVCCqVCv/lT7/PzK1bFHJpbEYJu0ZM5ILvIVPoCAS6kckfby8XBIHX3psjl6/Q3+1gYtiDSvl4AeB3GxoboTThWJZ6vckrp6dRqxQ8fWiEQqlCMJLl3XMLlCs1vC4zjn0GHFY9r56efuD5aGUjwaUb6wiCgNGgYaDX0VZmqlUKlAoZsWSeYCRLKJqhVK4xOeLj4vU1fn5mlmuzmzSb98poBnscvHJ6mpefmcJh05PJlYklWrbsAa+VUrnG7EIEu1XP+JAXrdGD3/ZRo+WT56WPK3q//f0LLKzGqFRbKpr+LgflSg3Q0e2zcXM+hEatpCdgw2U3tt1DFHIZCrmMSrVOPFUgmS6QSBUY6HHS47ehUMhwWPWMDrhBArlchcW1GE6zSDW/SSU8g1tM4DN9zBb1UzLVUmtncI9+7dN+vE+Ux26WGI1Gzp49y1tvvcWVK1cQBIFdu3bxzDOfzZu3Q4cOv5rcnQKtZNep5CMgNlFobPc0SURRJJ5IsbYeZHUtSLFUQqVS0dcTQKlUcPjgbjY2w3x4/irRaILuLi/HtqY21CoVf/G9n1GrN5ArVIyPDNDX7W8fJu7XKLkribWYTUyMDeL1OHn7zHmq1QQWi5nduybagexAu1GSTGc4+/4lDu7bgcNh46XnTzA6MoDNar7nHtlcnvWNEPO3l1hYWiMaSzA5PszvfeubnDp5hG+8+hwqpZxKbpNSZoVSeolGJQMSOQqNFZR2Upki8fUo8VSBRkPg2WNjOKwGju4bwGTUcOHaKvFkAVFshYT5tzbbdK5EJJZldiFCLFXA4zQyNuhBp1ExMuDeeg9a68wXKmyEU1RrDXZNdKHTqLCadeh1KtLZEuVKDZtFjyAImAxq3nh/nkS6gN2i58iePqrVBkqFnGK5ys3bIQJeC/t39D506rBUrrGykUCvU9Hts2Gz6Ng5HsBi0nLp5hp//fpNvG4Tv/vNgxze28/xg0P39cicW4zw16/f4Idv3CCR3u65PeSX8mtPO9g9IKKRFYH1Tw1RFZGh0HdhcY+hNPVw6WaY0OUszeYaRoMGv8dC8+5k7pcQ1N5oNglHsxTLNUb63SjkMjK5EoO9Tnr9nx7WnsmV2ocSqVTK0locvU7F7qkufC4z9czcF/4afpl4kmeTS5cuceLEifaf7+aRfOtb3+KP/uiPntSSO3T4W02zUaFWjFPJrVMrpUEiRal58KDF52FldYMbM7dJZ7IA6HRafF4XTUFgdLifzVCEUqlMNpfbChgVaDQayOVyXn7x5Db7q08jny+QSGVo1BvodFreee88771/ERERh83K4YN7OHXy0LYhDVEUyebyZLJ5XA4barWKq9dnuXnrDoLQeuBVKBQIgoDNZmFpZZ1YLEmxVGpf49TJI/i8LtwuBxqNGqNBj06noVHNEQ2tcP3cLezaApVYFK8uiy1gw2ofwWAwbSvQWE2Pp1QQRZFiudYqDug15IsV3v7wDuFollSmSK3eQK9V8Y0Xd5FIF3FYjQx0GfE4jPg9Frp8lrbFZb5Q4afv3CJfrKJSylsWU25ze6Bk9+STb1ZH4zneOXcHpUJOwGd54sqKR0EQBIJbQa13LUSKpRrDfS5MNj9+m/BIAe8Po1Zv8JO3ZqjWGpiNWqZG/XT7PlJIP4lGSb3RZD2UYmk1Tjpb4ukjI9jMOp4/Ps78UpRLN9fZ3df89At9SXRqGh06/O2mbR2e3aBajCBBgkJtRSJTEgrHmL+zRCSa4JtffR6tVoNWqyGdzrK2EWJldYN8ocjBfTvp6w1w5OBuhgd7GR9t1R4++Zl6Y+Y2Fy7f4L33L+Nx23j26aMcP3bgvrWMYqlMMBjBaNTjdjl4+sQhtJrWc2E8kWJm9g7dAR8Wi4n3z13m+z98Hb/XxTe++hyTY0O43Q5qtTomowGbxUR+y7nD6bA+MPMsHIlz9fossXiC508fw+W00xXw8rPXz9BoCtRrNTY2IxSKJeKJFP193fwP/83vIJFIqNcbaDRqms0mG8EI16/PcGTfALnUOndunqHbpmDnIQc9fgcqrRWZ4vEzPzO5Ega9CrlMxnCfC6tZ90Db7/tRq7dqGqIo8qM3blKtNTAaNAz1unDZDe0aQDpb5IPLy2g1SvyelquHY+tM0LJp38qSzRYJbTU9Al4LowMerGYdkyNefC5zu0HRaH605/3s3VmyuTKJdIHNcJrrc0FWNpL3Xa/FpOXlU1O8enqa0QE3sUSe2ytR3r+8RL3eRCaTMtTrJOC1Yrfo+eqz00ilUkQRCg0DUulHFpPNpkAiXSAUzbayRyIZ/uvfPopWo6Rab+C0G3Ha9DQaIiCSL3yUjTvQvT3HsFqrI5fJkMmkXJlZZ2GlZVOr1ShbFqjy1tfarXpuzofY3AxhUKSwqnP4FSlC11sWcTr4dPsPQBBBobbgm/h19I7RT/+GJ8xn9hU5efIkJ0+ebP95bm6OF198keXl5SeysA4dOvxy0m6S5Dap5EKIYgOFxvbAyYBsrsBb734IQHfAR3eXD5vVjFKpYGl5nfc+uIhUIsXtdrBv7zQBfysPQy6X4/W6OHxgF2aziaYox2puTal9kkqlSigcJRiOEQxFOHxgF41Gk107xmk2mwwN9NDXE7hnGjQciTN/e4kLl2+wvLKBVCrB53XhdNo5cmgP0JoaSaWzRGMJfB4XFouJH/7kLd57/yJarQaP28Hxp/YzOT6CKIpYDTLKmXnSydvUSnGEZpOmVIcgdWEy6sjkSrx25jqiKKJSKXDa9Dishnbzwusytzv+uye78LpM1BtNyuV6y/M5luPyzAZGvZoXTowxNerfJoGs1RssryW4vbFKuZBAIZfR5bVQrtS4emuDYCSDXC6lr8uO12VCFEWkUik3b4cQRTi+fxC9Xk0iVeTa7CZH9w3gdZp4+dTkAyc37k4rLq3HicRySKWSdq5JqVzjnXN3WFiNI5G0piOOHxhEIpHcc71kusAP37jJd396lTtbmy+0ZKcTPVKOTys5NqVEr27yyeDY+yFK5KiMvZicIxQEB/F0hYDjozD08SEPPrcZo16zdbj4/B7nD6PRbLIZzrAZThOJ52g2BWyWVlicTCbl9FNjD34tokgyXWQzkiYYyVAoVlEoZHR5W/klzx8fRxBEIvEc1+c2MUqLuB98ub+xPImzyfHjx9tN1w4dOjw5RFGkUc1RLUap5oI0anmkcg1KveuhuWaPQzqdJRiOEgrHGB7qozvgpVypUqnWMBr0CKJIKpXhzNkLnH7mKLt2jBNPpPB5XHjcTpyO7dkgn9YoSWeyLC8vcuPmPKvrQRLJFE67jfGxQZ4/fYyxkX52TI3SHfCi020vEJy7cI1oPEk2m283RZ4+foiA34PDbmVkqBepVIaISKPeYGpiGACDXodep8VkNGAyGbbCWStkc3ncTjP1Uox3Xn+TVHyDeq2A0KxiNWp4+sg4Lm8PnsDjWzc1mwKCKKKQy9iMpLk5HySZLpLKlNBqFHhdZgZ6HFy4usK75xeRSiUo5DICPiuHdvfjcZro8trYvVtNKRMins4TT+VZXI1hs+h55shIq+E/2YXdqseoVz9xZeddP/Ll9ZYF6sHdfThseg7v7f9YgPmXQ63WIBjNEI5licRz1OtNegI2Al4rZqOWU0dHt84leh43NP7jgfTpbImnD4+gVMgZG/LgtBnath9Pkks317h4bZVCqYparcCgVfPG2Xle3LL/kEokWE1aRPJP/N6fl05No0OHv12IQnOrSbJOrRhDRIJya+Dz1twC87eXyRcKWMwm9uyaQCqVkMsXeOOt91nbCJFKZxEFAYvFRDgaZ3Cgh53T93/oEgSB5ZUNugIeGs0mY2Nj7NkxhPITZ4tkMs3y6gbBUJRMtvWZPz05itvlIBKNMze/SDaXRxBE1taD1Ot1/tF//Xd5+tgh9uycRK1WEYnGCUViXLl+C6NBz/Onj9HX20Vf74MHDaKxBFeu3SIaS2A2G5maGCGeSOFy2rFaTAwO9DB3e4kr125htZg5engPRw7uxumwtffMZCrDe+9/yLlz59jc3EROGa9iEo1Kwj/49Sk0eutjK0juUqnWuTq7TDi4xs4JP0O9rm3OCw8jX6i0n5szuTKvPtsKNd8z3Y1CLiVfqLIZzjC3GMbvtnBwdx8el+mh1tdrwSTXZ4OUKzUUChkeh6n9tSaDBqNeTTpXYm4xTCSeI5EqcGh3HxdvrPP6mTku3lgjm783twZaluunjoxwaE8fHqeJRKqAXqtCIpHQaAoITZGRfjdOmwGrWbtt2OGT56VKtY5GraDZFPjPf/EB0ViOUqVlqaXRKMjly2g1SnZPdnF7KYrZqMXnMuP3WtBrW3UluUxGtVYnnsoSTxaIJnJkc+VWbchlxu0wtodZSuUasWSedDqDRZmgkl2jV7mEzPp45xcAmcrKSlyJXOfH6fRhs8vpPfBPUBs/u93aZ+WJmbDXajXW1tae1OU6dOjwS8a2Jkk+hCg0UKityBQPCPOu1ZFKJZhNBl589jipdJaNYIQz71+gtzvA/r3TrUbD0f14PS6USkU7KGx1fROH3cb05Ai9PYFWceNjgWh3g68Bzrx/keWV9bbfpyAI/OS1M+h1WjxuJ2q1it07J1rF5mSaaDzJ2EjLwuCvf/wGN2Zu43LaeOUrT3No/y7MptYH/9Xrs9y4Oc/aRohMNke5XOHp4wf55tde4IXTx9i/Zwqv141Oq6FRK1LNh0itvkk5s0q5lCOWFsiUZCQyVXL5JA6bgZOHhjHq1eye6sJq0pErlInEc8wuhqlU6igUMl48OYFKqeDwnj6CkQznrq4QS+SxmnU8c2SEtVCKg7v6mBzxth94K9U62XzLe1oURWbuhDFZ3UwN9KBWK/A4TDSbAuVKnZ3jARx2PbN3wvzpDy5y4tAIUyM+nj02xuydMLFkgWSm1M7qgFZ4pkp5bxHh7s8hHMtx9uIiFpOW3ZNdKBUy8sXK1toaFEtVnjkyzIEdvRg/MQVSqzd499wC3/3pVc5cWNwmP+3zSDkxLefUbhVm3d2/f/hUoihRorcNorcNUZe7WAvluHItSam8gk6rYrjXhVIp59DuL8c1O50tUSxX8bstNJsCF66tYjVrmRjy4veYH6ogqTeaZHIlHFYDoihy5sICUqkUv9vMrgkzTpsBmUyKKIqcu7pCKJql0Wi2rNGsnWI/dM4mHTr8MiAIDerlJNV8mGohitCsIleZUBn8T6wgPjN7h1tzCxQKJcqVClqthvHRQQBm5xYpl8vI9DrUKiWiKJLLF9t72HOnnnrk+4iiyNkPL9NsNjGbjeTyNZaXl1he2SDg9/DU4b2MDvfj97mRSCTsmPqogJLJ5rl05QZHDu5BrW49AJtNemxWM1KpFLVK2R4Yef/cZcrl1j4qk8kwmQzE4im8Hie7doyzvLLO8uoG6UyObDpOo5an36thrEdBLhlHK8bx9pkwWwNYrVbUqsdrkJQrNdLZEtl8mVSmRCSeY3LESzia5ebtEIursZb/uijB725ZVtmteg7u7qfLb8NlM+B1m0GEeKpAKlPEZTeRzZW4dHUZo06Nw6ZnuM+Fw9aarJVIJPR3P/lg7VqtwcJqjJWNJMVSFYNe3b5Pa0/9/AHmj0I2X6YpCFhNOtK50tZ5QMdQrwu/x/y5mxiNZpNrs5sEIxkqlToadWsy9u4AzlCv63O/hmZTIJMrsRFOM7cQweUwcPLQCEq5DBHo73bgshsxGzWYjBpUWx7lIwNuRLFBMTH/udfwRdM5N3To8DcTQWhQK8YpZ9epl+IgkSPX2Iglcrj1rT0yHIlht1s4cmj3NttthVyOdMt+acfkCIMDvfT1BtDr7v+5XalUOfvhZT44fwW3085XXniaHVOjpDKtMPVMNk8wFMHltGO3WYhvNUt8XjfTkyM4HR9ZbRUKRcxmI4IgcP7idTLZHH6/h1q1hlqtIpFM89PX3kUikWCztqw6Pe6HuyS0n+Ejce4srmKxmMhk8qysXiCXK3Dtxhw9XT42ghGMej3//T/8FmOjA+36iyAINKo5CulN/uAP/gOpZBybSc7hCTuToyOYbW5kn0MpXG80ubMcZX4pSlXQsGvMz8Ajng+aTYE3zs6TyZWQyaR4nCb6uhzUa03kGhlCU+SdiwtIpRKcNgM7xgJ43a2BWrlM1m5+lCs1QrEs4WiWgNdCt8+GWqXYpjiRSqXtbBFRFPnJ2zNkcmWCkQzBaIbbS1H+53/7wweudXLEy6kjo5w8NIxUKuH2cpRILEc0nsdq1rZVpXezax9ENl8mnswTTxZY2CxRyCb5+791BLmstT6rWUdA21KuqlVy8sUqbmC4z9Wyft/KZi2Wq6wFkwQ8FqRSKe9fWiaezKPVKLGatFiMWqrVlp2HVqNkeTWCWZPHocvjNqWQNdPEbrdqEI86glqoKigKNnTmHsYmdiFT6rjb3qtXMjQq6Ue80pPny0ms7dChw68sj9MkqdXqbAbDrKxtEgzFcLvs+LwuLl+9hSA0MRkNDPR109PtB0Cr1dDT7SeeSHFjZp5gMIogCjgddowG3bZrl0plkoko4XCMYDjKi8+dwGTU4/e6cDvtfHjhKjKZiN1mpbfHT2+3H6VSwfydZcLhGOFonGq1Rjabo9loMjkxzNe/+jxPHz9EX29g20RhrVbn0pWbrG0Ecdht7NoxztBAD96tDBKr1YzZrKeaD5FcXaacWaFRzYJEikJtJV2Vc+X2Cga9GqfNwNigG6tZRyyZp1Kp09/loFZv8MbZOQx6NT0+G26HEbtVj0wmpVCq8ub781QqddRqBVKJhB3jraLSyUPDKBVyypUai6sxNsJp4skCSqWcV05NoVIqeO7YGLOrFa7PrlJvNHnl1BRKpZy9091curnO91+7RiZbxmTUkM62LK5cdgNLa3H29HUT8FgeGGheqzfYDGdY3UyiVMg4sncAj9PIqadGCYbT/OTtW2yEU7hsBnr8dvq67Pzj33v6nt+pucUIf/Wza/zwjRtkch81woZ8Uo5MyDi5S43dcLdB8nATS1GiRmPpx+QaRaHzoVAoW56mr99AEEW6vFa6/VYc1vvn2zxpUtkim6E0m+EM+WIFg16N321BpVTwyumph3qr5guV9sEsnmpNYH71uR3IZTJOHRlFr1O1FSTXZjfZNRFoyYKlUkb6Xfg9FkwGDfnojS/ltXbo0KHDg2jWS9RKCSq5DerlNCISFGoLSsXntzpMp7O89+El9u2ewmoxc2dhhVA4hkwqRa/XolQosNssiKLIiaf2s7iyxsLiGoVCEavFzKmnDz+WkiCWSHL9+hxXrs+ytLzG+Ngg05OjjI6OcfjAJNKPTfYJgtD+/zdv3SEYipDJ5qhUqhgNeirVGuVyhcXlNer1j3K4DHo9u3a08jn27Z5CqVRiNOrR67TUanUuX5tBoZBjM+vIpTYpJBcxSLK47BX0ahGDvoEgmLC7unB6Hy3XotFskstXyOTKZHIlhvtdiCK8d2GB2YUI+UKFUqVGX8BOvlChr9uB2ajBbNTgdhhx2gxYTFosJi16nRqrqWX1ubQWZ345SqVSRyKRMNjrxGU3YbcaeeXUNBr143mMPy53X5fVrENE5M5yDJ/bTO/Oni/tLCAIAvFkgWA0QyiapViq4nGZeGrfIA6rvu2R/llpNgViyTyZXInRAQ9ymYxsrky3z4rf/WQC6UvlGvVGE5NBQzJT4C9+dIVYskBh62zjdhkBmBr1MzF8b+jxx6nWGh3VZocOHb50hGadWjFGObtGrZxCIpVTxcDycpDFpasUikWefeYpPG4HTx8/BEAqleHajTmCoShTE0ME/F5efekUyWQam+3BDfbNYIS3z5zjwqUb1Go1err9nDh2AI/bQTAc4/rMIoV8hlKpjFQqY9+eKew2S0t9qtUQiyeZu73Mex9cZnS4j727p9iza5I//P/9OW+98wFGg57hoT5sVjNrGyF6ewO4nDaeOXEYp8OG8iHPmK3MlEXOfnAJtVrF733rm0xNDLO2HqRYLLMWjFAqV6hWqwT8HgYHepiaHMGgb+0loiiSim9y9uxZLly4yG+/2I9GUWPPsBKvZze9PQGUysdXrt6PZLrA7EKY/m4nXb1D2LTlB9pR3m3gJ1IFjh8YQiaT4veYGex10BQEIrE8V29tkAnY2DXR1crQ2tWLx2m6b71jI5zm9lKEZLrYakBZdO2cEpfdiMOqJ5kpcmshTCSWI5svs3MiwIeXl/n5u3PcmN+kUr2/P7hWo+TQrl727ehhqM9JpdpgdMCN22FiPZjCYdUzOezFZTeiVN6/FiMIAplcmVKlht9toVZv8Bc/vkI6W6LeaFIXlKjkdQqFKhazlt3jXQSjGZw2A26HEbfD2B7WVCkVRBM5wrEsoWiWfKE1pGM6pmnnxImiSLlSZzOSQYIAtRhm6QKV7Bp7HEHYysj7lHnWNlKFFqnGS7Zqxh0YocfufuBZRRB+sWeGTrOkQ4cO92V7cHukZbf1ECXJ+maId85cQBCa2G1Wdu0Yo6fbj1wmQxRFugJejIaW52Oj0WBldQOlUtnyDW82KZer7N41QU+3H512+z1ef/MsC0ubaLVK7FYzdpuZC5euk85k+forzyKXy1GpVVgtJgz67U2WmVt3UKmUaNQqyuUKRqOe1JZvuVGvo1atMTO7QDyRZHUtSHeXj+NH9/Ff/c43qNXq7VB3aMl2q4UI5dwmpdQitVKitUHIDaSrFhLpEpPDBnr8EtwOI1KJhPVQio1Qmks31ltT/zo1XT5rK1Bzq7lx1xpidjHM5LAPnUaJx2Ekm6+QyhS3+V4qFXJK5Ro/fOMGEokEl93A7sku/B4zEomEC9dWWd1MUhO1jHSbGepztv0jr9zc4LV35zAZ1PR02dCqFZQrrUKNSqngxMHhB/4+FEpVbs4H2QxnEAQBt8NI15a3dipT4j9/5wPiyTwOm4GXTk6yZ6oLrWb7gSmRKvCjN7fbbEkkMNol5ci4nKem5DhMdzfLhzdI5GoLOusAWusgosJONFHg6mKGcGyGl56eRK1ScOzAEEa9+nMH1T4KlWodtUpBvlDh9TNzKBVyfG4zOycCuOwfFWY+2ShpNgXyxQpmo5ZavcFP37mFRCLBadMzPebH6zS1/xsqlKrMLoYJRjLU602MBs2WzFbJvh09X/hr7NChQ4dPoxWUmqZWjFEphGjWikjlGhS6J2e1FYsn+e4Pfk46neXIgd3I5TJkcjl7dk6gVLZsB0rlMn/5/Z9x8thBPG4H+UIRtUqNx+3AbrM8sKgriiLJVIalpTXKlSoOhxWdVsPP33yPq9dmsdssfOu3v8aBfTuQSKWkMmXyhSKpZJpEMk08kSaVzvCNV59Do1FTqVRQKZUM9vdQqVTIF0oY9FqaTYHJ8WGMBh0GvQ6jUY9cLt9ap5LengDLK+t8cO4OmWyOQi4FzSKa+hJVQwlzM890oIFMpkGuciBVaD+1MF4q18gVWirUYqnKz8/MEormWs2bSoPBHgcuu4H5pSg354OEYzn0OhVOm4FuvxW7VU/AYyHgsTA9FkAQBFKZEvFUnpXNJL0BOwGPhWq1QblSp9dvw2EzYLPotrzCQSaTopZ/cY2SRKrAykaCjXAaqUTSPme9cnrqS7HZqtUa1JtNdBoVm+EMH15ZRqNW4nWb8LlailBgS0n0+OtpNgU2wmlC0QzhWI5Go4lep2Kgx4lCLuPpwyOfa/3FcsuWJJkukEwXKZVrOO0GThwc5sZciFq9wdSoj8lhL10+Kwr5R7OjH39/K9U6qUwRnVaFyaBhdTPJuatLPDXRKT106NDhy6GVjRajnFmjVkkjk2tQ6Vycu3iT2wvLyGQyensCDPbvaatIrt+c5/bCMsViiXyhBGLL9urg/p3snB67p1FSLldYXllHIpHgdjlYWFylVCpzaP9Onjq8F5/P3f7aaCxBPJFkqD+AxWQEiYjH3VJL3F5Y5tqNOTQaNU67jV3TY4SiMa5en2Xn9FgrbH1kgJ3T43jcDtwuB06HDWjZhfo/dp9PUiiW+KsfvMbNmdtkslnUahVdAR9//eM36Qp4OHn8IJcu32A9GMLndjI6OsCuHeMY9LqWZVkxxq2bl3nrnbPML6xTrVbxOg3UmzIs9h5OHOv73D+rRrPJ0lqCVKbIwV19uB2mred5JYWGAthuXSUIArMLEdaDKfLFCgqFDJ/bTL3RqpVo1Eou3VhDFEXsVj1jg248zpZ6RKNW0u1rvXfNpkA81Qpd93vMuOwtBU/r2dqB12VCpVRQqzfa6/zh6zfJFyush9IEw2luLYSJJh5sMTk64Oap/QMc3TuATCZlZSNJo9EkGMnitBvae2eXz0qXz3rfa9TqjXYgfTSRb61HhN98ZS9SqYTKli2YRq1AlOqwGaFYqWJBy87xAHunu9v3KVdqrAdT7XtdvrlOsVRDq1ZgNKgRBJFoIo/ZqEWvU6GS5vFYs2isCSS1CKJQJ7Px6D/belNGWbSiNnbhDoxgtHiQSCQEHvI9V2fWmV2MEIkm+Aff/Hznms9D58TSoUOHbbQKHUnK2U2qhQiIAnLNdq/JcrnCxmaYtY0QZpOBvbunsFstTIwNolaryGbz3L6zgt/nQa/TMjE2RLlc4fbCMhubYcKROM1mk+HBvnYg6UvPn9i2jkqlilwuQy6X093tw2Z3kc+lCEfipNI1TEYDw4O97Y5zd8BLuVxhdW2TUCTG0EAvdpuF088c4cc/e4dyuYLdasbhsHFw/04AXnvzLNdvzlEollobo0ZNUxDYOTWKzWZpy1/r5TTl3DrF5B1qxThCs4pUoWMxDIl0lUQqiSCIaNRKAl4zQlPEZtFTKFW5MrOBzaJjpN+Fx/mRr6UoimSyZTYiobZlgsmoYaTfTSSWZS2YQqVUMDHiRSaVcmMuSLXW4IUTE62phN19OO0GFHIZoVi2/cAql0uZHPHh9PRRKYT5yVszqNVKXj09zeSIj9vLUYx6NV63mYHu1iHgQSRSBUrl2tYDsZRcvsLEsAe/28LKRoLrs5u4HUa0GiX9PQ5eODHO+JB3W8EmX6jw+tk5fvjGTc5dWUEQRaRbGSRHJ+QcmZBjMz7a5KNUZcXoHENnHUKhsSKRSDh/bYX14AyCIGIxaZkc8SGVtq73IL/RJ0UqU2QjlGYjnEYmk/L88XEMejUnDg1jt+geWJgplWvtCY5oIodMKuXVZ6dRKuScODSExaRFLpO1C1F6Xcue5MrMOhJaE7pdXusDg+1a4cQPbzZ16NChw5Ok2ahQLyWp5IPUSklEoYlcZXwiVlu1Wh2lUkEimeYvv/8zEskUqVSWZ04cQiqVIpVKeeXFp/nZ62dYWllHKpXhdFgZHx1Er2/tA3cVrdvW3GwikUiQSqUsLq1x/tJ1rt2YI5fLI4oi3V1+Th47QH9vFy8+e4K/8+uvoNNpSaezLC6vMTjQiyiK/PAnb9FsNDDo9dhtZnq6fVtBm2IrAL1UZmMzjCAK+H1u8oUiZpORqYlhbszcZnllg1y+sGUN1uTk8YP43FZK2SC13DIWSRa/o4pFD1plDUQjKr0HqezhTYdGs8nyeoJcvkIkniMYyWAxaXnpmUl+/NYMy+txiuUacpmsvecUyzX2THUzNtjyV7db9GjUrcGHWr3Rtu64MR/k9lIUQRCQyaQ4rHpkW3vvwx74vyhq9QZvvDdPvlhBq1Ey2Ouk1/+Rp/oX2SjJFypt9UgiVSDgtXBwV8v7/NRTo1hNuk+/yEPIFcrk8uV2+PuVmXWMejWjA268LtNntu8qV2ok0kWS6QJ2qx6/20ImW+bGXBCdVkm90aRaa9DX1SoiTo/52DvV9UD70PVgirVgilS2SGVrEGd00MPUiA8RcUtx9ZmW2qFDhw6PTKNWpFqMUsms06jlkMq1qPVeRCRIpFJcThs2qxmTyUA0luTKtVsc2LsDi8WEWq1CLpdRKlcx6HXYbBZ6u/30932U+5HJ5lnfCHH95jzztxdJpbOMjvTzzInDHD28hxPHDrRUGKkM12/OIwgCO6fHsFpMWC0W1taD3K4sAXBo/y5MRgNDA7309Xah02q4dOUm3/7OX7O8usHO6XF2To/x7DNHOf300YcqRz6OIAjkC0VMRgPVSpX5O0uYzHqcTht2mwWFQt56fVYLl6/MEI4mOHXiMCPD/aiVEmqFKKtz55HWIoj1LH/25+fJFRrsGA+wf9cwAY/lidio1htNllZbStRarUG330qz2TpXaNRKPi5GrFTrRBM5un2tvT0Sz2K36hkecFGvNQhGsyytxRkd8OCw6tk1EcDnNrfPMB8nHMuyvJ4gEm8NHWg1ynaQe7fPht9tIZEqMLcYIRLPUarUGexx8P7FJV57b475xQjNB6gerGYth3b3MzboxucyUarUObZ/EJtFz1owiUatwGHdnjtyv/cllsgjCAIBr5V6o8mF66vIpBIEETK5MvV6g3K1jkalYGTATblSx2U3YrD66HNJ2jaYcrmU9VCaRLpAIlUgky1RrtR59blpLEYdDquefCFBoVRFr1PhMEvRCmvEFi5Sya7howQtwQmPovMQkaIx+pHrfCgNXRitfiSS+7/ORrNJJJZjYTXG6maSl5+ZwmbRc3slxtpmCo9dQ7X+iJKVL4BHbpZYLA//D6LRuL/UqEOHDr8aiKJArRSnnN2kVowCIgp1K+zsLrF4kktXZognkgDYbRYs5lax/dyFa2wEw4iiiNGgx+txIpVK2sFPkWiccxeu4XTY2DE1RnfXR0qTu9TrdcLROKFQjPXNEHqtFqfTxs4d46SzFWZns4wM9dHb7d8W1j5/Z7kdfAatcPh4PMVXXjjJzVu3iSdSKORy1jZCzMwtkk5neeG54+zfO029Xken0+J2OXC77O2J02a9RDm7QSm9TCy02AphLYjU0XD8YMuDPBybRa1WMNjrRBRF8sUqb569jUat4IUTE+i1Kr763I5tk3eFYgW9rtWUOXtpCaVCRrfPitNuQCmXo5DLcNgMDPU4iSbzzMyHkEoluBxG+rrs7ffTZTeyvJ5gYTVGqVzj2IFB3A4TO8cDLK0l+M5fn2V5ZZVqtUG3rzUFY7PoOHFoCK/T9MCH3XS2xEao9cBbKtcwGTV0+ayolAqmRr2cu7rKd350mUKxisth5PDefqxmHV9/bmf7GtVanXc+XOCHb97g3XML1OpNFDLYNSjj0JiMA6MyrIZHK1zIVSZ0tmF0tlGKDR2hWJbYRoKj+8zIZTJUSjnTY366vNbPZWfxOBRKVd7+4Dalcg2VUo5/a9L2LncnR+8iCALlah2dRrVNFWSz6Bgf8uBxmtr7q92iJ54qsB5KsRnOUK3WefHkBHqdmqcPjzz0NcaSee4sR4km8nRb8/invpjX/8tE52zSocMvDlEUaVQyVEsxqrkQjVoeiUyFQmNFKvvsVgyFYolQKMrC8hqLS6uMDPXz7DNHOXfhKrFYArPJyOTYENlcnu//6HVefekUZrORyfFhpidHcTqsyGQPdksuFEt8/4evs7i0RneXjx1Toxj0OkwmA2MjA/T1Bhge7MXltLc/X/w+N7VanbnbS1y8dAONRs1Afw/NZpPpyRGajSbFUpl0JksqnW3npaQzOQwGHX29AUxGA7fvLPODH73B3/3NV5FKpWSyORrNJk67mW6vHq2iijR/lXAijr6WZ9LXRCrXIlPakSnutVWq1RukMyWyhTLZXJlsvoxapWD/zl5WNhL85K1bCKJANldGqVTQ47OACMcPDOF2mMgXy9gsemwWHXaLHrNRg1Qqbdk5FiqEYzlSmSKJdIFsrszxA0O4HEasZi2TI96twHDNlxqODq0H7FCkNXCwd7oHpUJOwGfBaTXgtBueeDj8xxEEgUZTQKmQs7ye4OL1VaRSKS5HS+l7dwhFIZd9pkbJXfuuzUiGlXCdRjWNakutKpNJeenpyQdapT7smtBqGi2uxphbjFAq14CWLchdJXCjKWA2akhlishkUob7Xe3BDKtJ11KeRFrB8alMiXS2xOE9fTisBorlKoVSBdmWXUuuUGlbekTjOa7PBpnq+fIDWj/JF3FuOHPmDH/wB3/A5cuXCYfDfO973+PVV1/9HKvs0KHD49Ko5qgUwlRymzSqBeQqAyqDj0ajybWbt4lE4zz7zFEy2TwLS6uUyxVkMhkqpZJzl64xOjzAyFAfZpOBSDRBT7cfs+le28YPzl3h+swc2UyegN/DV18+zejIADqthkKxxOVzVwiFY1SrVeRyOQGfm0ajQSgSI5fLM9gfwLOlDFFt5YlptRo2wxH+b//sX7EZDKPX65kaH2JspJ9CsfTAbJSPs7S8vqVwTTG/sMzq6iaH9u9kx/QYv//f/i4A6xshGo0m4WiM7i4vAb+nlZsmVmhW4uQ23+OdK1e4eH2ZlY00v/GVnUxPDPA7v3YKo0H9UCvpx0UURV47M0upXKM3YGdkwN0OF79LtVZneSNCMrZBIlVAIgGH1YBWo2Ri2MvN+RArG4l2rcSob+1XBr267cxRbzRJpgvEkgUCHgsWk5ZMrkS5UmsPHZi23BqgNUjw47dmyObKrIVSrAfT3LoTIre1n30SuUzKrskuju4b4OjeflKZEpvhVs6GXC5juM+Kest69K6i5X6UyjXWgknCsRzJdAFBEHHaDAS8LUeSbK68ZbUloNcqsVv1lEpVNFtuGgq5jHqjyWZSxvJGhGqtzo6xltX8u+cWKFdqIJEgQUSnVZHJlrEYdfQFjDh1adRijHphg3olTSMOj7oTiiIIchs6SzcmRz9qg++Bwzwfzx3+P//yHEtrccqVGnJ5KysmlS1is+j52rM7kEgl0Mj/amSW/Lt/9+++wGV06NDhF4UoNLeaJOvUinFEJCg11naTpF6vk8sVWkoLmYx6vYbDZkUE0plsW/bp9brw+9x4va72hr6wuMq5C9d4+vhBAn4Pv/GNl9qHAtgqtDQaKBQK1jZCvHvmPIIoABIQRdKZHPlikcHBXkDK9NQo8ViSW/OLRKMJDuzbgc/rQiGX43Y5CAS8JBNpwtHYludmjVyuQL3eoFKuoNVp6Ap4UKmV1Gp1HHYrX335dHs9QqNKJbdGJbdJKbVEOhXnzfMbiBINMrkKi0mL1aKjUq1TqdY5/dQY2XyZn71zC6lUgt2qZ2LIS0/A1n4QU8hlFEpV1oMpVjeTFEs1Xjk9hVIh57njY6iVCpbW47z9wR2y+TK7J7vYv6OXqTE/F66vMtTnwuva7qk5vxhh5k6oZW/mszLY68SyNVkYiWf5f/2nN6k1ZHidGqZGfPR326nVGygV8vsGfNYbzdY6ixVeOzP7UQPAa8GgUyEIAoVSlf/05x9QrtQZG/Cwf2cPAz0f+c43mk3OXVnhR2/O8Np7sy05pwoOjco4OKZi77AMrerRihdytQWdbQiddRiF1sGF62tE5kNUaw3kchkuu4HaVkjbjrGHiTg/P6IokkgXWo2LWoMDO3vRaZQEvBY8TlM73O2TVGt1IvEcoWiWSCyHSinnhZMtVdCRvQM4rPq2F+ndIgbAT9+5Rb7Qmozt8Vvp8lrbja2PN0qaTYFEqkA4nsVpM+B1manVG1SqDQIeM2Zl7Qt9X35Z6JxNOnT48tmuIkkhinXkSiMqg++B02MPo1yuUG800Ou0/OTn7/LOe+coFsuo1SrMJiM9XT4A9u2ZZuf0OPl8gQuXb+J0WJmaGMGwlXHm8z48wLpYKnPl2i1+8KM3qFSqDA324ve5sdssBPweBvq7t31tKBxrX/Nnr58hEo0jiiJajYaxkX4kEgnFYolLV2aQy2SYzQYsZhNWi6mthnn2maPcmLnN6lqQVDqDRCLh8IHdWwMqaXYNG6kWwlRyKzSqeYRSjYZUgVypR6X33vPAmcoWiScLmA0a1GoFV29tcP7qCvVGE6lEgs2sZ9/OHgCu3dqkXKlSbwgYdGq6Aza0WjWNZhOXw7itqVCrN0hliswvRRkdcLeVm8l0EZNRg20riNxkbBUivqxQ9I8jiiLBaKYVnLplR2m36qnVGiiVciaHfV/YveuNZludE45m6QnY2DkewOM0cmTvAC6HAflDGnSfRqPZJFeoYDXpaDQE3j2/gFqlwOXw0uex4XJ8ZNXxKI2STK5EMlMknS2RzpTI5Moc2NVLwGPZUkFbsJl16LRKUpkSLnsre6RUrqJUyNi3owe7VU8uX2EznGmrV85eWGpl3UkkKOQtNVKhUMFhbeXefXB5mXq9iUwmw2LWtjNKdk92s2vCRy2z8JnfoyfFF3FuKBaLTE9P87u/+7t8/etff+LX79Chw/1p24bnQ1QLEYRGBbnKhMrgJxiKsrxyh/XNMPV6gx1TI201qsXcUpEUCiVK5XJrUHIrqLs1QPlRmHiz2eSHP32LYrHMxNgQRw/v4cih3eTzrUyLUDjKnYUVdk6PoZDLyeUL9PcGUKlU1Ov1LbWKnP17phkaHMZq1tCoNwhH4wSDETaDEb75tedBaDkJ/MY3XuLY0X143M5tdZO7lEplEqk0iUSaYqnM0UN7ALhy7RbLqxvM31mmVCphtZhRq1VYzC2b8vk7yywur1EuV/B6XK1ci/gspcwq1UKY18/c4sPrYcpVsNvNnDiyi4GBfmRyNQ7b/YcsH5dKtc7iapyhXidKpZyd4wFMRg06zUdNkrt1CVEU+dk7s+QqcrqcCob6WsOpqWwRrUaJRCJBp1Uy1Oe8b/7IejDF8nqCWLKlFlapFFiMGiwmLaMDHnxuM9F4npnbIeLJAkjAZNBw7uoK73x4h7Vg6oGvI+CxcGh3H2ODblxb1unPHBlBr1WxspHA4zThcRrvq2qB1u9tOlcilsijUSvo9tmoVOvMLkSwGLWYjVpSmSJzixGOHRhEIZdhNKhxbw3PuhxG7BY94pbWo1qt886Hd0imi6QKEoR6HoVcxuiAG5VSQY/fSiZfxm7RYzOrsagLyBuzhG6ut5xkECk9xs9RpraiM/egNnWjMQa2DVd/nHK5xuJanKX1OOvBFLFkgX/+3zzbsug0qtk5EaC/y06P34bxY44Zd2sk9V/wzOMjN0u+9a1vfZHr6NChw5fMPWFnEjkKrWPbg3mtVuetdz8kly/wtZdP8+7ZC2RzeaRSGS6njenJ0faD9shQy69yYzPMzK07BENR8oUCQ4O9SKVSJBIJcnlrgw9thbSHwjH6egLs3zuN3WZh754pllc3iMeTKJQKJieGmZ4cQSqT8YMfv0s6nUQqAYvZhN/nRqtpbdxWi4n3P7zM4vIazaaAyWRg7+4J1GoVz58+hslkwGwy4nbasVhM2ybKBKFBtRCmnFmlnF4hl4kTjhcZ7A9gcw+wY6o1pVBvNEhlSsSSeX7w2nWsZh2njo5iMmg4um8Ah82wTUHSurbAO+cWiCfzW2FjFnZNBJDLWvYcS6tx3j2/QCSew6hX0+2zIQhiWz2yf0cv0NpQw7EsOq0So16DVqNkdMBNf7eDWq3BB5eXmVuM8I9/7yRuh4mxQQ8ej5+JfgPdfst9p0BK5Vo7T6XeaLaUMDo1xw+2cj7mFyP88I0bhKNZfu83DuN1mfn1l/ZsqUzk7XVdn9vkR2/c5Cfv3CKZLmI1SDg2IePwmIqpPhkK+aM1SBQaG1rrIDV5gFhGxlKwzLGAs2WRIpHQ1+3A7TA+1N7qSVKu1Lh1J8xmpKXuUKsVbfWIRCK5b5OmWqujUirI5sv8/N1ZRLFlDTbQ68Dj+EgJ5XObW5My6wmCkQzxVL49KTo57EWrUWI13z+YNRzLsrgaJ5rI0WwKqNUKDFvNlEKxSrlSa02j2Mr3fO/fRDpnkw4dvhxEUaBeyVArxqnmQ9RreaQyJQq1+YEPSZ/Ghcs3+MnP3yGXK/DKS89w7Mg+BEFgz64pBvq6CPg9qNUqksk0sXgSp8PG8so65y5eozvg46kje++rIBEEgXg8xZ3FVZaW1xAEgf37dmA06Ll+Yw6P28E3v/o8XYGPptzr9TrXbsyRzuRIpjJUq1UAfuMbLyGVSlEpldhtVkrlCqVSiVK5NWFoMOh55cWnyWSyZHMF0pkss3OLXLx8g1//+otoNGpqtRpajQqXzUPApUEhbhKd+5BGNUezUUIikSJTGFBqHfd9L++Gl16+uc7t5Sg+l5njW5lc0XgOk1GDXCZFaIqIiCyuxpgc8fKNF3ayvJ5AoZBhs+jvmdgURZFLN9ZIZopkc609Q6mQ0+23otOo2L+jF5VK/tgqhidJpVonmS7ic5uBVvaaQiFjsNdJj8/Wnhz9IlnZSHDpxjqCIGA0aOjvcbTPAxq1Ep/7s6mo7jZgNkNpgtEMcpmUl09NoVTKef74OHqdmkLDiF6ee6B9VaPZbE+bprMldk4EkMtkXJ8LEo3nMOjVWM1auv3WtjWpyaAmX6hwezlKMl1EKpUwNepnWK9msNdJOlti5naIUrlGoykgCCI9gZZ6t1CqsLqZolqrtzPvRgc9AHhdZk4dHSXgteB3W7YNeNwtfP0yjHF8EeeG559/nueff/6JX7dDhw73pzXsmaCc26RWjCGKAnKViWJVgUVlQBRFPrxwlUajgclkpNFoEI0lSSbT7Jga5c7CCjOzdxge7KW7y4fDvt06slarsxkMc31mnplbCwRDUfbunsJk1NOoN7h0dYZItGUrrtGo6d46T+QLRSRImL+9jCAKqFQqAn7Plg2hhFwuzwcffkgikSYcibG0so7QFHn6+EH8Pjf/3//3v3zgc26+UORnr52hWCpRrzeo1mrIJFJSqQyT40NYzEaUCjmjw/0cPrCLHdNjaNQqpFIpC4ur3Lx1m26fhV6vA6UQI7p4hZ9eX2Z8yE2X349MbWd8RMfeHd30BexP9Hm7UKwwvxxlZT2JRNJyu/A4TXhd5q33u8FmJMNGqFVQf/nUJCqlgvEhD5tJkXwmRjSR28rlaO25Lrux3egXhFb2SDiWo6/Ljl6rIpMrIZHArolA++vqzVZDLJku8JO3b7EZbuWALK7FWV5Pthv8n0SrUXJgZw9H9w1ydG8/C6tx4smWo0m93qTXb2vv070B+wPfh2gix/xihES6SKPRRCqVMtDjoNtnQ6NWEk/luT67SblSQ6dV4XGayBermAwavvnCbnKFMvFUgaW1OB9eXkarUXL6qTHUagULKzEEUUSmNGK36rGZdVRrDZRyKXtG1NQKSSrZeaqJECXx8boQErkBraUbrbkHjbELmfJe1Wyj2bIOS6SLTI34qNUa/C//n59QrzdRqxR43WYO7+lvW3m9enrHY63hF8EvNLOkI1nt0OHL556wM5kalc6F5GPBq3cWVlhZ2yQaTZAvFHn1K6eQy+WMjgyg12lxu+zI5fK2H+fK2ma7WXLx8g2azZYvt883ic/jol5voFQqmL+zzLkLVwGwWs24nXbyhSLf/f7POX50H6PD/W1Vitlk4L0PLpNMZXA4bPT3dqEZ7UGlVFIolgiGoiwurzE5PszFKzdZXd9sTaGaTTSbDeZuLzMyNIDH7eDQ/l3b3gNRaLSC2rPrlDIr3JpbIpEukClKqDcVyGQy5PoGZkOR0QEP0USOsxdXWz6OVgMj/e5tVkt3N/pmUyAUzRCMZtm3FaTlsOnp77bjc5sRBJGVjQTZfIXhPhfRRB5RhGefGmOoz4nTZth2MKk3mqxtJrmzEiNfqDA25GFy2EeXz8pGOMVf/PgKF66ukC1UMJu0JDNF7BY9v/frh6hLbfd9wK5U63xweZl48v/P3n8HyZGn993gpzLLe1/VVe19N7w3AwzGYLzbnXVcLkUjka9EnfQqjhTfC75xp5N4itOFFEdF6NVR7/vG+4rSUhS5JJfrubM7O97PYOCBBtBo39Vd3mbZdPdHFmoGOzO7wCxmDdnfCCxMT2Vl/So3f08+z9fUEAQTfVEf08kYuq6jahpvnl7izKU16s0OIb+LfTuHCPqNDXFixFCSzC9l+fbzF/jWs+dJZSr0h03cv8PMkVk7M4O3zqy0OiM4Q5PY/eOcm6+zcbmMLOexWc3Eo75eSNtPI7xcUVVSmwUEpcTkaBSzWSRXlBjuD9IfDxAKfHB4oahGo2MzU2EzW8XpsHLy2DRet50Du4aIRz7IKNF1nRffvEauIL0XOjfe1/v5DU9yMAq/fKnOZqZCIu4jEvTQaHXoyAqJmL/rhd/G32X63gi3i0e8OJTlT26xtrCFLfydgSo36HRVJHKzhK4pmG1e7B9TRQKGDdYLL7/Jt7/7PIP9CU7ee5QDe3cA8ORj97OZzrG4vMqrr79LTZIA2Ll9mmgkxEB/H59/+lGcTuO+p+s61ZqEKIjYbBbmri7ynWdeYHllHV3XjWyQHVN4PW6SiRj/6De/iK7rnD57mTPnLmOzWXn4gbupN5qcOn0BURQRRRGTYGJ0eAC73Ua1JrGylsJms+HzehgbGaDRbPHKa++wbdt2/D4v3/3+S1gsZoIBPyPDhnWH2qlQb6wyFq7Ssq0jtyo0NhqACcHiwmzzYnXFMJlMtDsymWKddC5DtiChKBo7Z5IIJhMvvHGNueubiKLAxHCU/j4fHVlhMBmkL+bjG98/h9dtJ+Bz9n4J3SyW8eFoTzWyvFagUK4jywonj80gCAKN7l4/ORIjHHThcdl7e91PYxDxwzAaHhLpbJV0rtptdph46kEjqP2hEzN31Abkh1GpNY06Ll0mGfczM95H0O9ix3SC/rj/Iy1MbxWKqmIWRZqtDt9+7gKapuP3Opmd6GOgL9CrAT1u+01e7e2OjFRvoygasYjXYNy+dJlqzRhymUwmvB47rZaM2yVyYOcQVqvYU7uUKg2sXULP+Sspzl5ex2GzYLdbMOkmFldzTI3GMIsiC6s56o0OjWaHZkvGYhap1zs47VZK5SaRkJt4xEtf1Ec88l6zau/2QbZwa2i3272BLEC1WgWM6//9auOfBJqmoev6HTve3wVsrdnt4WexXprSpt3I066u0mkUMQkiUktkaS3H0vJZalKDzz39CJJUp93udLOXSrhcTlbXNvD7fRwK+hkbG2J8fLh3XFXTKJbKqKqG0+mgUqnxrWdeZGFhhWQixhOP3Ud/Is7gYJJKtUalWiPgNwiYjVYLTdfRdB2L1YLb7WR0dJBYJITf76VcrnL+0lV2bJvCZrdRqUqsrG2Qzebpi0e55/ghvD4Pmq6DyWT83kU6k+Pa/BL79mzHarUwNJQktZEmnSngdDqRZRmH047D6WBsbIipqTESfVHW1jd5+dW3cTltHNg5SNCW5/h0C7l5mddfWOHclQLr2SaCaCEUHWZk1MtDJ7bftNYfMTe4bcxd3+T8lRR2q4XZiT7GhyJYrebe8V995zqb2QqarhPyu4hHvJQqTWJhC4IgkM2XGO3zkoz7iYY8iKLQe+3aRpHVjRKZfBVZUbFZzUSCblwOG1NjcbL5GpvZCpfn0xTKEvVGB03TefvsMmcur31kxqfJZASz798xxPRYjEDATalc75Eb2x2VscEIsYjnpprkxnlpmka52uy6TJQZ7g8xPhxF76qHkjE/xXKddK7Ky2/Ns3t2ALvNgmgyLL2mRmOEAy5abQVZVtF1WEkVeOYlw71DEEyIggmf14mm6YiCyN2HJrBazFhdUSK2DWRpjfrqtynWUuiafFvfmUm04/AN4vANYfcNYbb5b+qD3PiclVqT59+4SmqzTDZfo9k2aoVtE31YLGaefGAXiaif/r4Pf/2PgmEz28RrN6Fpd/Y+c6vH+pkOS7Ykq1vYwk8PSkeiLWVoVdZQ2hUEi6sXdpbNF1lb32TXjmksFgupzSzliuGz3ZFlMtk8yUSM6clRNE1jYXGVjXS258cpiiLDg0nsdhuPPnQPdrvBXmy3O/zN919iINnH7p0z9MUj3H3XAfKFMiurKYrFMjabjcGBPsSubLVSrZHO5Ehn8lgs5l7o6OmzF9HUDpVKjc10lnK1Sl88SjQS5ujhvei6xmY6h9/nJRIJMjzYT1/8PQmtpspkNhZJrV1jY/kK5XKe43v7MFs9ZCtmzLYICY8ZVdOp1VucvbjGUH+IWNiQOT5+csdNEtH3I5OvspoqsrZZQpZVgn5XL6NiajTGaqrIMy9e5sLVFMVyg6P7RpgajXHy2DQPHJ/50GOm0mXeOruEomj09/k5sGuIUHdooes6/+4/PUux2mCgL8BD92xj97Z+gl3WoM1q6ckWZUUllS5TrTXZOdOPzWrGYbNwcPcwsbCXtc0ip86v4nLZ8HsMn+qZ8Tj7dw4xMvBeOGoqXeY7L1zkW8+eZ3E1y45hgcf2mTk07SAZvvWGmc2TwOYbo6pEyTYs7Ok3FBqyfJ2JkSjJuJ+A1/mJ+o3fwI21Wd8skc5VqXds9IdFJkejWMwij9yz7QOvuaH6KZQknn/9Gppm2JsMJAI9n3KTycTIQNjwVM+U2cgaoa8PHjeaU5Ggh8FkkGTM/6EZJBuZMsvrBdK5KrKsYrNZ8PuMxmC93qYmtcgXJWxWM7GIF1E01n9s8L3rvZb56frHb2ELW/jbA01TkJtF2lKGTiOD2q4jmB23nUWiqirlcpV0Js/C0ioDAwn27Jzh9TdP8/1nX2Hv7u089dj95Asl3nrnHNNTY/TFI5TKFfL5Ev3JOLFYmHg03KsrLBYLFouFYrHMd599mcWlVYqlCslEjE89/gDhUIC7juzlxLEDTE2MEgrdbBc1v7DMhYtXuTq/hNPp4MBeI9xpYzOLw2HH4bDj9bjxeNz0dW04PG4Xv/z5J9nYzLCytsGFS1cJBv1Ewob3tMlk4vNPP4pAm049Q7u2SbN6geJ1CVU2mv2CxYnZ5kGxhrm+nCWbL+H1NNkxneT8XIrnX79KrlDDZDJht5mZGo2hyApOp42A30E05MHltOJx2UA3Ye42vi1mkacf3t3bq1VVo1w18iTCQXfPKhQM1UgoaLA5b+xlJw5P/gRXyp1BTWpRkZo9a69X3r6O2SwSj3iZHo8RC3t7zYhPalCytlHk/JUUUr2NKAr0Rd8LTvd5HL3cjtuFruuUKg0jAD5dQdU0Hr13Ow67lX07hoiG3L0BTEdWyBZqmM0CQZ+LUqXOy6eXUFpl1C4b1maz8KkHd2EymRhKBrHZzAR8TnxuR68WALDbzGS6OWbzSzkKJYmDu4a59+gU/XE/5+ZSKJpOo2kQMArles92NLVZQVZUnHYr/X0BhpJBIiHDcvQ3Pn/kpve51TVQOxKdRpZGeZmVd/4j/qf++GOt598W/Jt/82/4V//qX33g33O5HK3Wh/vi3y40TaNSqaDr+k89U+gXFVtrdnv4aa6XKjcNdWsjjyY3MIlWRKuf73//FVaW12g0W5gtZqYmRml3QFFF4n0JRFGk1Wyxkc5isTpIJgcolo0hc7vTIZ3OkdrY5Or8Iul0FpPJxL49O7j/3mN86olHqTebNBst3n7nLI2mQrJ/kCtXl0lnSmAyEfD7iISDeLzB7nEFtm/fgSwrbGSKnL1wnesLywahMxBB00XK1SZSvcVdRw9x+OA+/H4vldp7urvFpVVy+QKbmxnWU2k6nQ5nL8xz/73HGB0dx+H04nJlSGdyNBodbHY3FqsLTDYWl1b4wQtvUS3n8LpEhuN2Fi+vIssdbA4X33wxx9nLBWIRH/fcvYtd20fweVxId9juKJ0tYTaLhINeXD4Ts7N+BpJhdE3n+maJzUye/bvGEUUBzezD7bOhqhrpUh1Na2N2qLj8XkIxN/dEtuEwNzGZICe12MwUGR6IYrGYWdjMItXNDA6NEQl50XQdRRCRFDcX5pb56795g410kfXNAplc+Uee8+hQjP27xjm4d4rd24Z5/Z0rtNoyDcWEXXYwNNKHJHuxmszEkl2lCoZVlCwrmAQTZlFkfnGDy9fWUFXNsA+PBFDEAJLipWMy8dKp0xRKNdAhGHAzPBCj0nYhiiL33nOU1fUcZ6+VyRfWqDdajAzGePTkfhxeM6J1ncmBAEGfG5/Xhc/jpCa70TtFou4KcnWRTn6JrHqbDhMmEYt7GIt3FKt3DNHZ1yNENTWNaqlJPl9hbSPP2mYBn8fJUw8fotyCUxezREM+dmxPMtAfYTARoqG5QINt23cBUH9fRvsN9Y7JZCJfqFIs12g0291fHUYGY4wNx9nMFnn9rTSP3zdFvljB2vr4Vqc/jFqtdkv/3c90WLIlWd3CFj5Z6LqO0q7Qrm3SqqVQ5Qai1YPNO8DKaoqVtQVSGxna7TYWi5mRoX7qzSbrqU1UVUVRVKYmR4nHwlyam2fbzAQmk4lTZy7idjuZmhjpDizeC1O1221UqjU20zmuXF2gJtWJRcK88NKbHNi/k9GRQaR6k8GBBF6PC1XTiEXD+H0ers0vcfrsZSLhAIm+GNVqDafTCA4dHRni5VdfY219E7vdxtjwIMlkHJ/Xjdvl5OjhfdisVqzve5AulyvYxTql7BJf/frf0G5WQVfx+bxEI1FUawJVhwfujrCRKfPK29d72SO7ZwfoixrNb1EUPjAoqUrNXojYmUvGhjgxEmWgL4CsqJQqDVwOG2sbJf706+9gMkEi5uPx+3YwM2HkvPzwQCBbqNGRFfrjAXxeB2ODEcZHIiiyyvNvXOWVtxf45791P4l4gMdP7iAS8jA9FvuAekHTNFKbBQrZVTYyFTRNIxLy9EK1YlEP75xb5upilmqticdt5/A+wy7t1z57uFfwFst1vvviJb71g/OsrKY4MCnyhaMie37Vecv5I5hEHL4hHP4x8s0A89k22ZUamlYkFHD1fEmPHxy/xav6J4NUb1FvdoiFvciyyltnlrpB6wn84QHivpuZF5qmUao0SOcMlqvdZuGu/WP4vA52ziRJRH0fYN9qmsZrpxZ7Vlkup41EzIeiaFitAtun3rN+UVVDNpzOVRnuD+H3OilVGjSanW7wu2GvFQq4AaNRMjoUIRH1fajaZQtb2MIWPg50XUfpSNRLErK0idyqYBJMiFYvNm/wx95rFEVBqjfx+wz7i69961nOX7hCpVqj0TDyR/bsmmXH7AQH9+9i5/YpLl6+xjPPvowgiETCgR7Ta3Z6nNnpD+4JsiwjCAKFYpn/5T99mWpNYmgwyfbZKaYmRkj0GQ/PqqpSLFW4ODdPpWooOJ967H7KlRp/872XKBTLDPb3MTk+wviYwYafGBtidLifcsVgjFYqNRaX17i+uMJDJ49jtVp4852zANx1eB8T48PIrSq5fI5qs0C7tk6nkUftGEoY0eLEZHaj4sfjtlOVmnz3haucubRGo9HBZDJxeO+IkTnmd7J7tp+a1MYkQLtjdC0K5QYjgxHuOzLFxHCUoM+F3+f4QDZGudZkcTXfzaZoGKGgYQ/3HpnC47JxcPcwoYCrV7P8rKGoKpm8sadmclWkehuLRSTxoA9BEHj4nm0fsAy7k++dK0hk8lWy+Rrjw1FGB8NYu+SDPdt8xMLe2x4IfBikRpvnXrtCqyVjtZiJR70kY34UVcWEidHBMJvZCufmUpQqDeoNQ2kwOhgmuMuF1WomFPAQ9jhwu2y4nNab1mV24j1VarPdYWmxQDLux+dx8FffPcNbpxfRAIfNit/rQOoe32wWkertLltVx2IRGRuMoGk6Xredpx7cSdDvIuBzfsCC7Ueti65rKK0KnWYBufur0yggt4ro6nuNwHp+jpkH/h1W50dblfxtx+///u/zO7/zO72/V6tVBgYGiEQieL3eO/IeN2x/IpHIVuP/FrG1ZreHT3q9jDySEq16Grmziag2qNZbXL62wX33HmU9lWZ9fQ2rTWRocNhwtUjEaDaq9Cfj9CeCfPUb30NTVWanhpidmaDRaILWJhj0s7iU5/U33mR5NYXb5WR6cpjtsxOMjw0RCrr43rPvspHO0Wi2KJfKzE4PEfQ72Ll9jOHBGJFwAIvF6Du0Wm0KxXIv5+yvvvZdpHqzqwDQabfrrK4sMTMzy4P3HcJhv5u+eJR8oURqfZWaVO+pR9bWVrh2fZlGo4HX4+HAvlkmx0dIJmK02x02N1Osp9IE/F5OHNtrOH6YNEq5NBdO/YCIt8P0iIlsscqpUxILazXuPjTFfXdN8+CxEU7eNUQydoNIogLVO/J9KarKaqrEtaUMlVqT8aEIw9Eh3BFYbEpcOLdOOldF03U8Thtyw40v4CbmVTmbyhAJepjYFiIZ83VJBFV0ERbTVdYLKTL5ClWphSgIJIIagbCXfdNeNjI62XyGV67McX3FsMdaWiuwuJr/kec70Bfg0J4RZsbjJON+YzDS7PDosX4EQWP/bBCP20bI/34LcCPZoya1yBUlCiWJQqlORWpydO8oA4kgiYCGOuhEUVXyxTpX5q4wPy/wj750N46AxlDczrE9fURDHhRVI52vUimsMpgIcSm9wlunrmIWRVxOKyMJF2NJG25zFXcA/sFnd2EWBZR2hVZ1gVZ1lUpqDbVza83/98PqiveUIzZPAkEwruWq1CSfXsblsBENezh1boVvPX+eZtOwJfe67Ywm7LjNVVxhnX/5P97fs6LXdR1ZaaMqzV5/aj1dIl+Uev2NRrPDg3fP4vM4mEsvs7ZZwumw4nJYCUdtxH0KbnOVoSgEjidwkCEc9GH3Rj/ys9wu7PZbUwn/TIclt4ufhmT1xvG2JJi3jq31un180mum61o37CxFR8qgaR0abTObuSYz00l04J3TF8nmCpi7AY0z0+MEgn4sdSu7d84Sj4cJBvxcX1jhe8+9itvtYmpyFEEQ+MynHsJsfu/2IdUbVKsSib4osizztW89S7PZQlU1rBYLF+fmiYQDNFstCsUy+UKJdCZHq23ICPfv3UEoFEBqNAgFfRSKZTqyQi5f4PTZy/zGr36GmelxKpUCe3dtY3JihHA4gNdjNJA1XcfVte/SVIWWlOGVV15hc+0a9+yLoOkdpoY8+APDaJgplOpk8lW+8/zFbkDXGOGgh+MHJwgH3e+74d+8rjdyPpbXC1RqTR66exa/18nhPSMUSg3SuQrPLV6l0ezg9zpIxgLUmx2O7htleixOIubrbbY3jt2RFdY2SlxfyVGuNoiHvSRjAVwOG1aLwB99+WUuXt2g1Zbpi/koVpv0xQI8cHz2fd+3UaCUK03CQTeqpnPq3HXCXp3tUwkSMT/lSoNW2whI/9oz56jWWkyMRNk128/kcLQnh83mJV544yrPvjpHIbPC/kmB3zghMtnvvOXrzyRacfrHEFzD1LUwsf44mqbxwg8u4PXY2TUzQDLux+mwfug630nouk6xXCeVLrORqVCRmnhc9h6z86kHd2GzWtB1kBQHui6jqhqiKJDJV3nt1EJvoBMNeUjG/eg6iILI5IhREJcqDdbTZUrlOscPTmAyCdisZrZNJkjEfDc1qG581pVUoRt0VkNRNRw2C5GgB5/HSUdWaTRl8qU6ZlEgFvaiKBq6Tu89f/h4H/jcmND1W5eZ3gq27vFb2MLfPiidOnKrRKuaQirkERsqFqsbm/tme84Pg67rnLswx9zVRdZTm3TaMifuPsSxI/sY7O8jnc6xb+92xkeGSCZjWMxmGs0Wfp+HK10F6b0njjCQjH+g0dJqtdlIZ7k2v8TC0irr62ncbie/8sVPYTGbObBvJ9OTo5hMJkrlCoIo4HQ6mL++zFe/8QyKYoRM68DundMA5PNFwqEAs9PjDA4kaLXaXL22SKFYZtvMBNWqxDPPvozJZMLjduHzeQgG3lOmPPnQUUxqlXYjT3ruNJ1mGUlx09I3MZksZCsaV5Yk8sUGhZJEudpk+2Qfn3l0L++cW+HaYpZk3M+28QR2m5lqvUWzKTM2GEGWVeaXsgR8zl6jOug39l2H3crkSAyp3mIjXaFYqVMo1RlKBhkfjtJuK2TzNUIBF8P9IYJ+Z08VIQjCj/TP/mmg01HIFmt0OiqRPi+aqvPaOwu4XTbiER9927xE3mdDeicHJTf2LUEQuHh1g7nrm2iajt1uIRb24nYZ7/V+7/OPg2arw0a2wkbaIKjcfWgCl8PKSH+IjqKia1BvtDk3t84bpxc5un+Mgb4AzZZskGT6/Pi9hoWap3tOLoeN3dsjN1mqvt9P/e1zy1xbzLK2WSKTrdCWFZ58YCeP3LOdvoiPoN9l2GxhwmQy4ew2LhrNDkP9Qfoivt51FvA5EUUBURSYeZ816IevqYLSLNJpFn9oKFICXf2Rr72BeuHq3+lhic1mw2b74HV+I3z6TsHUtePbavzfOrbW7PbwSayXpsp0GnlaVYOEcCOPJF9R+U9//Bck+qLcdXQ/oWCAxx66h4Dfh1Svs7a+yetvnkHTVD776Udwu5w89uAJnE4Hmqbx0qtvc/rsJVxOJ9tmJzh8YBdf+sITnDp9EYvFTL3eZP76Mgf27qBcqrC2vokkNXC6HExNjjI7PY5gMuF1u3A57Kyspkhn82QyeSpVo1n9xc89gc1mZc+ubSwur7KxkSGXL6CoKpPjRs3Sn4jzwktv8tzzr1OuVJHqTXRd5+q1RZ56/CSffuIB1jfSyB2FeDyCy+mg05GxWi102h3qUp17jx/EbOpw7vSrPJ9e48S+IGq7yn07NV4/X+b/fGGTjqLjcTuYmUgyPhLtETc/CeSKNV55+zqyrNIX8zE9FkNVtd7etbSWp91R8LjtqKqG1GizkS4TCboZHQwzPBDquS1UpSbXljKMDxlDuPmlDeRGib6ol5nxOCaTCZvVTEeWefaVyzzz0hwr6wVWN0ofmTliFgWmx+Ps2TbAtok4B3ePEAm5+eazhn18VWoSj3jZNtGHyWTYcI0NGXuUoqpkC1UKpbph1S2KnJ1bYzNTwed1EA66SMS8mM0CJhM0Wm2+9/IlalIbh91C0O+kL+Q1rmOzQDzi5ezldRqtDopi/JvdYmYoGWJ2Mo7VKhIKuAj5Xbhd9i75uUSrsoZUWqFVXQO1ftvfkcUZxuEdxO4bxO4dQDS/NzDYyJS5dG2D5bUC+VIdqd7m8L5Rnrh/B4m4l7v2jzEyECLgdWAymejIKuVqg4DPiVRr8e75VeqNNp0uCcNht/LkA4Zy+8zFNcDIqQn6nbgcNuw2MyYT7N85+JF26zarGb/XhtJqIAimO3qPudVj/UINS34aklXYkmDeLrbW6/bxSa2Zpiko7Sqdeg6lYwwTRYubhaU8b7x5CtFsxuH0srGZYTNTQBTN+P0BYtEwwVC0Kx810ZfoZ3UtxUuvniGdzjI+Nsy+vTspV41hpSwrpDbSZHPplsrfAAEAAElEQVR5stk8klRHEESOHN5LNBrm8MGDXJ1fJLWRxulyYzKZmJyYwiTYuXb9Olfnl7FaLJgEE4qiEQrFKJabpLMViuUmuXyFlZV1SuUKQ4P95Et1dMHF4cOHucFtVVR6MlodHaVd5fzZd9lcWyRfyCPLMru2DZOSQjgcTkamnSyvZjh9YRGn00Y0FGNk1Ec45EVSjAdITzBAG2h/iBT1zVNX2cgUEQQTsUiQoZCdXM2K2emlrph59cwSZrNAu22mI+tMxAeRFC/Dox5GxoyzbmjA+/rNUr3Jc6/MoWkasYif/fumcbtsFOpmbDYL//Xrz7CWyjE7NcL9x3YyMZrAajX3pLK6rpPtSiI30kV0XefxBw4gCAJ3HTlCq57j4pVl/vKZOaq1Jr/y2ROMDYd56rF7Cfo9WCxmZFnhjYvLvPnuNc6cvYxbzHBoSuSfPGgmcBt+5YLFg9U/g2IbIV31sJGqUK7UMZvLPBoZwyyKnDhxtMeI1eCOS35vQFHVrm+3nXK1zvOvrGC1molHI4yOB4lGfEhKl5krQKOpkMlXWM+kqJSyREJe9u4cQ7TbGRyCSNhHwPceq0RSjPe4cm2dVLpIvdHCYhGJRwKUWy7MZpGZbUaB0Gp3uL5Rp1JrUKnWmZ0cxO2ykyoUqbZtBEIedKDZbOPwJpAUM23dQzjmIhr2Ew6+x3C9nfVqCUkqDRFzNnvH1vVWJatb2MIWfr6hKW3kVol2PUOnnkPt1EG0YTa7sXs9CLeoWHv51Xf4zjMvYLNZcbtdDA709Zjo+/fuYGpylNRGhmyuwOUr16lJEkMDSe49cZjpyVGSiRhul5OaVKdUrpLPlwgEvAwPJjl7fo6vfuMZ6o0W4VCAsdFBZmfGiYQCWK0WXG4H//0vvkWj2UQwCXzqiZMArKU2CQYDhIN+PB43Pq+b4aF+AEZHBhgcSPDG22d4652z2O1GBskN1Yzf7+VTTzyI12Pc75WOhNwoUNk8Tau6RqeRp9OscWWpwGKqTqGi0FRdHNkVZ9+OOK889y5z85t4vQ6CfsPuanQojKqqHNw1jKrplMp11tMGiy7gcyKIxntPjcaYHov31rbebJMrSkSCbmxWC6cvrjK/ZNzPnQ4roYCrRzjoi/p6StifF0iNNvNLWbL5GuWqwcQM+l1E+kaxWs08cXJn7/zvJHRdp1xtkslXyeRr5Ao1Du8doT8eIBx0sXt2gFjEc0dUNrquI9XbvH56kfmlLJ2OgtVqxmG3kCtKfOqhXeyc6eflt+epNzp4XDaGksFe6DoYKpLRwQ8fGmiaxvJqBq1doFSps5GrsrpW4B/9vbvxe5384NUrLK8VcDosuN02IjYPfRHjOmjLMgG/C4fdQsDrJOB3MTVqsDLHhiKMD/94hqamtLsqkfcNRZoFlFYF+MlYLlL+KoGBu36iY2xhC1v42wWlI9Gu52hX15FbZUyCBYs9QKuj8fIbZ3n+xTfQVJWnHrsfQRDwepz4vG7+/K++Q6fTIRoJsW/PNgb6+3oZqHa7jStXF3jr1DnOnLtMPGYMH3xew3Hhz/7y21SrNVptGbMoMDkxAoDFamF8dIhwOEgkHCQSDrCZzjF3dYGZqTEAXnvzNB63i3gswq4d00Sj793Lw0E/3/3ei3RkGb/PS0eWyReKZLJ5pif68fk8LK+msFgsTI7H6OuL0heL4HY5EQSBwX7DhWBjM8urr5+iXm/wqSdO4jC3mBww89pzf8b6+jpKpw2Cif/1isbxw9Mc3D1BvM/O4f0udk4nGUz8eGXwx/quVJWVVJFOR2FmvA+fx9FTI1ZqTd4+u4yO0SAP+FwkYj4uXNno2VzumE706har1cx6usRmpkI6V6XR7CAIAtGQB7/XybbJARoVM6lMiVffWeD6So5M3rCZvKHE/WGIgomdM/0c2TvCjukEkZCHaq1FrlBDFAWScT8Ae7YPEPK7egST9+Pc5XUy+SrlarOnwkzEDIvOydEYAa+TXEniwtUU+WKdWNjLr332MH0xH/Gwl4kRO4qiUSzXubqYYTlVYHQggtks4HJZmR6PEe+SFsIBw2rd63awZ9sASquMVJxjdXEZpbGBSWvc9ndksQew+4Zw3BiOWJzUm22K5QbFazlS6Qo7Z5L09wV498Iqpy+u4XHbGEwEiIY9hINumq0OyXiAqtTi4tUNZPk9QsToYJgDu4axWswGSTjux2Y1Y7WIWK3vjRkevmf2AyrVG/hxfVBV/dkSNT/WsGR9fZ1vfvObrK6u0ul0bvrZH/7hH96RE/sw/DQkq7AlwbxdbK3X7eNOr5nSqdNu5OhU1xDaFWw2KxZflKrUYu7qdV594zSSVOepx08yMhQjFHQxPBgjFg1h7maFbKZznD9/nu3bJgmHAmxu6HhcVmbvOcjwYJLNdBZdN5oNUr3B+QsXMHdZaB63HVlROHP2HPfefYjJ8SThoIvFZT9vnzqPzWbF57Xh81hJbazjdFhw2G0EAl6sVguZzAYTY0M8fPIIX/7vX2PuylUG+vv43NMPceTQHsAYjAR99p69Rj5fZHNjhUJmjbt3+5GbObKL17EINkaSXhRNZGkljbKYYmo0RiIwwGS/heHY2A8FdLa6vwz8sBLhxOEJHHYriRBYTBZ0XadQSlPMq7QkLxNJM3W1SsCt0mg2iQftjA0lGe53YzXfLGvtdBSWUwUq1SYHdg3j9sH+2SADfQEW13J87Vs/4Nxciqcf3sOnH9rNP/17h/G47Xh65/veRqmoKn/zwkWabRmPy86uCSOI3N19z7948QLXry9hMYsM9Qd5/J4xto3YMYtVClqZv/neu7zy9jzra8vsHNE5OGXmM78sYBZvfUBidfXhDIwiOAfwBPpRVI2vf+8sglAgEfOxZ7KPeNSHxdxlP3yC4/lmq8NmtsJGpkImX8XjsvPg3bO4g/Do8aGbLKs0zVBzWC1mltcLRkGn65jtAYbjFvrjXbmrG8IzXkBH06rkihLlSoOpsTi6qFMtbTIYddLf108sbATzVWtZ3N1smR+8OkehbHx2i1nE73Fgw4Pb7AK5REuqIlWMMLp42IuVEi6zjUPb/O/7ZNLHWg9dS+FzqkSjP33J6s8KP6vaZAtb+EWApikorRKdeoG2tInSkcAkYLb5sHmD6IBJ/qDHcavVpliqUCiWKZbKFItl7j5+kKDfh6qpzEyPc+LYAQIBH5LUIJcvsrGZJdEXJZPJ8/Y75wgEffQn40QjQSKRUO/Y1+aXePWNdymXK9RqdVrtDuNjQ3zxc4+zbXaCeCyM2+Wi3emQ2szgdjlxOh2cv3iFr33jWcZGBjlyaA8Bv5fts0b2xrEj+zlycE/PTqtarXHuwhWCAR/79myn3Wmytr7J8aMHGBu9OZBaVxpY9TzV9FVa1XWqpTSFQpFStUk8GiTgD/DdN4u8fXYVp9NGKOAiFgnQ3+fHajHzxMkd7NsxQKXWolg2mHnXFrMM94cJ+Jzsmk6ioxPwOT+QvWEymbiykCaTq1KqNHoNgLsOjNEfDzA8ECIe8RpqgQ/JuvpZ4UZGSqFsqF18HgezE33omk4qXSYScjM5GiUS8uBy2HpD/zs1KNE0jWK5QdBvNJheO7VAKl3uNlrcbJvs6zVB4hEf74uyuyXcyHap1JosrxdIpcusbRRpthV2zSQ5fnAcl8OKYIKh/pDBBHXb8L4voP3ugxMfeXxFValUm1RqLSq1JqVKHanR4cmTOzGZTPzv/+17tBoSalchY7db2MxW8XsN67ZY2GMwU0UBu93CYDIIwM7pfrZPJvB7nR+4Xm4OWdXRlAadRhG5mUduFns2Wjcs5e4YBAuC6CA4eARnYOTOHvtj4k7VDZIkcf369d7fl5aWOHv2LMFgkMHBreD7LWzho9Bzw5AydKRNlE4d3eRgeb2O1WZnfMzGN7/2bV554xSRUJB7Txzh3TMXkaQGX/jsY9hsVu47cRif14PdbqPZbBmh6NeXGB7sJxwKcObcZc5fvEooGKDZbNHudHpWoM1GC7/Px0B/H5FwkP5kHLPZjMdt5r57jnTPUeetd85x5doCAb+PmakxRFHkC595DKvVgqqqLK+kePHlt5BlmU898QCapqGoKj6vB6neoFSqoCgK6UyJ/sQTHD+6n8nx4R5p4+Y10VlZTXH+0lXyuRweh85Yv4vNS39Bu1nitVevsZySKFQVNF3EYhGJBD09x4092wbYs23gE/m+ytUG11dyrKaKBgnAbWNm3CDKVKWWYatpFhEEE4qqk8nXCPhc9PcF8HocxCNeREGgVGmwuJrvkUQuX9tEUTWScT9etx1N0wGdi9c2+Otnr/LumSusbhRptj46qHx8KMJd+0fZPp1k53SC4f4w5WqD7710mUKpTtDvYnQoQjTk7r1mKBmkVGlwdTFDsVyn3mhz8piRJ1uVWng9DiPnDZ12W+m6hzjJ5qp8/fvnaHcUTCYTZlEgXzJIhQ6b1bBHXcvjdFjxuh1Mj8Vx2Iy658ShSe47Ot07B13XadQyVNNpWtV1asUlBP293tStjrpEiwu7bwiTexp/IIouOozPHTCUu9/6wXnml7LU6m2aLcMOVkOnvy/AXQfGUFSNjqyCrhsDlXKDWNiLw26Qe2bG47hddlxOKw6bBVt3IOJ22jiwa/gjz+ujBiXwXlZbo9mh1ZJptGQ0TePovjF0Xedbz8/x+PHER77+k8Ztt66ee+45nnzySUZGRrh69Srbt29nedloNO3du/eTOMceflqSVdiSYN4uttbr9vGTrpmu68itEh0pQ0tKobTr1Bo6mUITv9/MQL+VbHadN98+i9yROXnvUbbPTCCYTPi9HvxeD2fPzzF/fZl6wwggDYeCqIqKYDKxY9skO7ZNksnm+do3v48sy4TDQUzA0GCCX/7c47z6xinSmTwDyThen4e+eIRkX4yVtQ1+8Pxr5AtFFEVjZmqUeJdt8dD9x1lcXqVSkUinc2QyeTK5Avfdc4THH76Xpx69jycfvY9gwA9AuVKj2Wphsbpot1r8+Ve+SqdZRmkX8NjaOG06y8tBQuEYD95/nCsLac5dXsdmNTM5amSI+L0OTCaw2czYbB992zt7eY2VVJFWy2CXOB02MoUaI/0hwkEXl65tEAq4mJ2I43HZcDqsmEyGtDPoc3Jo1zCxyM2DW03TSOeqvYdsgETMj64b+SGrqQJ/9CcvGnkYVgt7tw+wezaJyUSP9aCqxjHW0yVKlQYP3T2LxSwyO2EwOTqKwpXraV588xpPnNzBYCJEOOhj6v7t7JhKYBYF3jyzxL/+D3/DW6cXCDhqHJwW+dW7RBKhW29YmEQrDt8wzsAYsjnORq7DuetFWu08n3owidUicv9dU/i9zjvi9/2joOs6HVnBZrWQL0o899oVAEIBozmSjPt70t9IyE1VavZyR3IFiYmRKDunk0SCLvbvGiQW9qJbbra7UFSVlfUim9kKmXwNRVFxOqyMDUewmEVOHpsmlS6TzlW4dG2j5xX/6Yd3Y7WYGR+O0N8OoCgqUqNNqdLoXYsup5Vtk3FiEe8dD7TP5Kuo9TYm063LTG8FP8/3959lbbKFLfy8QtdU5FYZuVWkXUujtCvomDBb3dg8iV54I9xgyTeQaiXa7TZTE6Pous5ffu27VKsSjabBrFMUlVy+yOc+/Qj3nTjCxcvXOHt+jmrNaKza7TZcLgcQZXgoyfBQErPZjKIolMtV5ueXwWRiz64ZBEFAVVVi0QgH9u3C63Ux2J8gHArw/ede4+1T5ygUy7TbbUwmE089fpLZ6XEajRaPP3IvRw/tpdVuUypXOH/xKiPD/cSiYS5fWeHtU+cwmUx4PW68XnfPqtPtcvL5px/FYhGRWyWatRzrq0tUi+tE/Sr5fJHn31xhZVOi1QFFFQj4nXzmkQEidi8Ws5kvPX2QsYEwhXKD9TwsrxW6tYaTN04vYTEbzMn32ygAxCJepEabbEGiVK5TqjQoVZs8fv92zKJIpdbEJJgYH44S8BnWSDeGCkGfC34OxCM1qYXZLOCwW1lay3Pq/AqapiMIpt45A3jcdh6/f8dNr71TlpuZvGGHUSjVyRVryLLKyWPThAJuJkaiTIxECQfct12HpHMVckUJSWojNdpI9TYzE3GSMT8vvnmNt8+tYBEFfF4HI/0hJkYMO45jB8Y5snf0I99P0zTqjQ61egup0aYmtQn6nYwMhFleK/DHf/kmNalJu63QUVQcDitP3L8DTdMZHepD1Bs4HRasVjNmUWR82Jj4OOwWxoejhPwuwkEXQZ+rx+aMhn64+WbkicitYlchUuwpRjTlzrkzAMiahabqpKW4aKpOAuEk22anaSoi9fw1xo79z/gT++7oe34c3Mm64dSpU9x77729v98gd/7ar/0a/+W//Jc7edpb2MLfCqhKq2u1lUJuFiiVq5y7vM7c/BoLS2t02m2eevwBxseGOLh/J3cfO0i+WOL0mYsMD/ZzYN9OLN0mrN/n5ez5OdZTmywur1Eslul0FHbvmuFLX3iSg/t3YhIE3nz7DKFggN27DCvrkeEBfuPvfYZAwPeRzziX5ua5vrBCuVLlyKE9TE2M9n4mCCbOnLvM3NXrFIplVFVD7nQoFsuEQgHisQinz17CYjazf+8OpqfHsNvdeNwGqS4WvVlVeCNbtNMs89rL38eCxIi/w/Jqmi+/mMXpdnDy2HYeefBuXn93kYWVHLPjfYwMhoi+z8ryTqMjK1gtZhrNDt/4/jmabRmLWcQsCpQrDWpSE4/bQSToMXooZkMVEg17SHTVI26njWK5wTvnVsjkqrQ7hjX4QMKwHp8ajbGeLvPG6UWuLGRYXiuwni7TaHY+8rwSMR9H9o6yZ1s/iZgfVdPIFyWazQ7Xl/MM94fxeRzcc2SSUMDVJXc0u4MYg2j5rR9c6Dm9BHxO/F5H7/NGQm6uLWYpVxuUqg0ajQ5jQxE+//g+Qn4XUvdZW1V1XE4riqJRq7fwuOx89pE9CIKA3+fA47Lf9JyvaQrrK4s0KmvorTQWLY9ZeE8hc6vfomBx4vAOYPcOYPcOYnEEubqYJbWmkzt1ldVUkUqtya986iAzE300mh3W02W8bjuxsBeX04oktdA0jaDPZRBddB23y4bbacPtsvUcQQyyyY8vQlVVo9WRabUMMq/VamYzWyGVLtNsyUYuTKtDfzzAvh2DNJodXntnAU3XDfKGqhMMGHXkZrZCvtT8mapLbntY8vu///v87u/+Ln/wB3+Ax+Phq1/9KtFolC996Us8/PDDn8Q5bmELW3gfNFVGbhZoVlN06jnS6QwLayUy+RqlkpH1cc/xQyQTMd45fYFgwMfIngFGhvo5d2GOzXSOe08cxutxI4oigwMJ+voixKORm8LRAU6fvcSZ83N02m3C4SD5fJFX8kXc7hPY7TaCAT+CSSC1meHC5asc2LeTZF+Mc+fnKJUq+P0+wqEAbo+Lr379ezz91IP0xSOcPX+Z5dV1arU6Tqedh04eY/vsFACB7pBElmVef/M0i0urOG06Rw7MIreuMh7apFarIjV0qi0L5RJsliR2TPvw+2EgETA8EX2uH/mgrGka2UKN9XSZ3bP9mEUj8FJVNaxWM7KsGg32bIWR/hCRoJvHT+4gm6+xtJbnwpUa8YiXE4cnCQfdHAveHEZbb7ZxOWxous6bZ5ZwOqzsmE7icdk5O7fG1aUsM2NxqlILq1nkS08d5IHj0zjfFySvqCrvnFthI1NBUVQ8bjv9fQFUTcMsil1PzMtkclVj2BV0Y7dZ0DSNeNTP6dPn+KMvv8Taeop94wIHp0W+8JsiDtutKwTM9gDOwBhWzzDuwCCKBn/zwkVarWUsFpFk3M9A33ue7jeCyO80dF2nUmuSydfI5mvkijXCQTd3H5wg4HNycPcwiZivx9Ztd+SeJcalaxtcvLqBIBhrdCPIDcDtsnf9QKHcVtksVmg024wPRzFh4tzcOj6Pg8nRKA67BUXWuLaYYdukwXJ46+xS1xLFzchACLfLhsUsoqgq56+kaLVkTCajiWQEu2pYBYHdsx+f8aPrOvVGm0qtZYSwFSXSuSqP3Lsdt9PGc69dwUmBiQM/8bL/wmCrNtnCFgz0QpZbJTrdoHZdVxEtLqyuD+aQtNsdXnvjXTbSWcqVBna7CJhQVY3Z6XHuO3GEH7zwOm6Xk1AoQCwaIhIO9h7KvV430WiY3TtniERCPVstXdcxm828e+YiL7/6NulsnlqtjqIojI4MMjrST6PRRO7IrOSKnL0wh9Vi4de+9GkGBxJUKjVCIT87d0zRn+gjHgsTjxmNhcMHd3Px8jW+/q1n0XStNxS58fPR4QESfbGenRYYtVO7nkVuFinl1/hvX3mGjXSeUqWOpukMJoL80pMHEB1x3L4msz6jCR0NefD7nHjddqwWMw/cPcOzL89x9XoGHTDbAiTDYq/J8fCJWUwmE7quU6u3KFUaZAo1xgYjhiL0+YvoupGZEfS5GB8ygrUR4dDunw+2/ftxY38plOsUS3U6ssKO6SSzE30E/S52zfb3LCzuNEniRhZbsVKnWmuxf+cQAGcurdFodgj4nEyNxohHvL0hzY/LHWl3ZMqVpjGoqhgNkOMHxvG47axvlkllythtFlotGZfLSjTsMSwzHFZ++cn9JOP+D7XrMJmMQdL7ByJjQ2H8XicvvTnPa+8uUKu3aTQ6dGSFiZEo//x/eACX00a7bTQOvF47TruVeMSLruu0OjJBvxubKOKwW/C5HURCbkTBaLrce2TqpnPQdR1VbiC3SsjNUvf37lDkNvJEbhVt1UZLddLBQ7J/mFAkSblpoVOHkNOKw26h3ZbJlxr84M01NjIlVleW+az1TZ7+4s9+WHIn64Z77rnnI73yt7CFLRjQdR2lVabdyCIVVllaWsDt8RJPDPG1777A2fOXSfTFOHZkL7t2TJPoi1FvNBkdGWTu6gKnz1xkZmqMwQHD8WIznWXv7m1YLGZW1lJcvHQNq9VCXzyKw2nnkQdOkC+U+NO/+BbFUoWZqXG+8NnH8PuMYbLL6cDlNCwZFUUhmyt2j5vj7mMH8HrcyLJCIODjwL6dJPqi6LpOJpMnHo+gKCpf/cb3KBXLVGoS1aqExWLG6XTym7/+eZ5+8kGOHt6Ly+kgEg6i6XrPQvz9qNclLp4/zeVLFzi+O4RNkJgI53juzVX+/GKeVkfD5bTRbzGjaCJWi5m79o9x4tDEJ2KxBUZuyPpmmdVUgVKlyRefOoDdZu5lcKmqjqapOB0WCuUGHreDbZN9TI/FCPic6LpOvlQnlakwNWpHEAQuXElhs5rpi/oQRYFWW+HaYoZzcym+99JlLl3b/EhbLQCf18HumX62TfYx1B9k+1SC8aEomVyV195dIOhzMTUaIxLyEOq6PBTLda4sZChXG7S6qpSAz0lf1IfDbmX7VB+qahAwS5UGS2sFBvqCxCJesvka75xfpia16chKN9PWuM8n4v4eMWN0KEws7LlJyTk8EKbVlqnUmmxmijTK69goELDXaNU2QFfodWNusXS6MRzBFqfc8bFZgOy8RLEs8bnH7QSdJv7062+xuFbFataw28z4PA5WNorMTPTxxAM72TGdxOmwGoHqXYXIjTp5ajT2ke+tqsYgqN1WaLaNYYisquyYSgLw0pvXKJTrN9l0HT84TiLmR6q3KZbr2O0WfB4HPo+dSND4frL5GlarmXZbRhAELGYT/X2GQjYcdPPEfdOYTB89MPukcdvDkrm5Of7sz/7MeLHZTLPZxO128wd/8Ac89dRT/PZv//YtH2tLsrqFLdw6lE6demWDlflzrK4u0Z+M05cYZm6pzLtnLyGYBJwOB8GAj53bpxAEgYdOHsfjdnHm3GWee/F1XE4nfd2NHmDHNsO2oibVWV3bIJPLk80WesOUTkemVq3h9xtsykg4yO4dMwT8Pr7x7R/w7pmLCIKAzWbF43Gxc5shKZycGCHRF6VUrpLaSGO32ehPxslmC8RiYY4e3ku1JnHk4B4mxodxu5woynub4/Mvvc7ayjy1cpaIR4Zag29+Y47jOwOMjw5waaGM0qrRn3D2BiOebr6Gy2HD5fjocNC1zRLrmyU2sxWqtRaKquFz25kYiTGUDJIt1IgE3UbY5/t8rUuVBi+9NY8sq0RCHg7uHmYgEbjp2FKjzdpGkeX1IlK9zVMP7sRqMXNw9zBz82n+8junWVzN02jK3H1ojJmxOJ95ZA9PP7wbQRCQFZWVVIFiucGebQOYRRFV1ZgeixEJuqk12qxtlCiUDF/Mc5dTKKrGicMTeN125pdz/Ov/8F3ePrdM3Nfm4JTI3z8hMp68DW9uk4jDO4DNN4ykRcmUILNYRdNqPHFSxCLA1EgMv89gkHySCpKqZBSUXreD9c0Sr7+7iCAIhIOuXoMEQBQFBhNB8kWJTD5Dumtjsmf7AJMjMQYSAQI+p9H06DIkbqDTUVhcy5POVlnLKVhMDexWC/GoD7fTxomDE7x7cZUr19Nd9qxALOLpve/9x6YpV5pkCzUuzW8imEw8cXInZlFkaiSGz+sgFHD9SAnqj0Kno1CuNalUDWbF9HgcVdP4z3/xOpVai46sIAoCDruF1GaJqbE4Jw5N0ijM/QQr/4uHO1mbbGELv2i4eUCSRm5V0DQF0eLE4ggjiB9t26SoqjHc6P5dVTVsVgtnz88xMTZEMhHjwfvvolqtU65UyWQLzF1dJBYN8dDJ4wwk+5CkBqnNDG+eOsf6eppKtcah/bt4/JF7UVWVUqVGKOBnqD9BLBbm+NEDKLLCi6+8harpTIwPE42EiMciPS/wX/rcY6iqSqVSo1ypUiiWWFxeY2S4n8H+BC6ngx3bpxgaSODzeRDfd2+3222YBY2NtWusrSyyvrpAan0N9DZP3TvM6bkcxXKFkaEYh8JB4lFfTxkC8Mg9TtY2jb12eb1AY36TUMDNyWPTOO1Wg80fMELYZSGEVSvS7ig47FbK1Sanzq9Qqb3HhvN7nYwNRjCLIvccmcTjsuGw3/m8jp8EHVmhXDWGCMVyne2TCTxuO6sbRVZTRYIBFxMj0Z5aBug+9P7k2R9gEFhurGGno/Dim9eo1Jq9fTfge4/tec/hSWxW80c2iTTNCI+tSi1qUot2R+kRFJ556TKtlowoGizSeMSLSTDRkRUCPif1Zodsvoqm6URCbkPRA5w8NtM77kambAw+mp2e1cmffeMdVjdLSN1/lxWVTz24i5PHZqg12qRzhnXWxEgEv9fJUL9hR2cywX13TSPV2112qwmPy2gsuRw27ju2g5hPxmp57/rW1A5tqdRVibz3u9IqoantO/J93ICum2hrDlTBQyI5hMUZotK0YbYFsZvM6NUGlYLEq5crbGSvks5WjaD7TJlsvmZYevwQ+sbe5ekv3tHT/FjYqhu2sIWfDjS1Y6hIahs0qmn+63//FpevraNqAo8+dA+DIzM8+dj9fOZTD9Nqt9jYyHJ57jpvvn2WHdum2LdnO4MDCew2GxcuX+XUmYvU68Yz4tp6mgdPHusSyZoUimXW1jcZGx3EbBaxWu08fPJuwiE//cm+HjFUlmUsFuPPL778FqtrG2i6hs1mIxGP9NQHu3calkzFYon/8t++yrkLV5DqDf7xb32JPbtmiUXDLCyuEAz4eejkcXZtnyYej2CxmBEEgeHB5IeuidKRyG4scP78Ga7MzVGpVrBb4a83LJw4PEOp6SGV04mG/eya7WdyJEZ/33vDeqNxf+fRast8+7kLbKTLSI1ON6tDoNHq4LRbScb8lKtNwgEX8YiXeNTXy9twOa0srua5PL/Zc2SwWc2MDoaxmEX6ol4uXNng2VfmWFjNs7xeoN746Ea4z+Pg0O5hdu+aIRHqqm8xnA7cThtCVx0dCbm5/64pSpUmxXKd83MpEjEfsxN9CIIRuj46GMZuNaNqei+E3GQysZExLLzbHZlmU6ZcbSGYTHzm0b34PXaKZcMVYjgYJBhwMRD39177G58/glkUDRJGtcnqRhGfx0Ek4GBl+SoL1y7hs5QIOCT8gnE9tW6n7y866QhhSi0vlbaPk/ccoiK1+P/8/75HqXIVRdVB13E4rDx8Ypagz8UDx2Yo1s0MxcwMJIzr5Ubvw2m3MjvR95FvV642yBclGs0O9WaHRrNDNOxhx1SSWr3F91663PtvLRYRh93aG5bEoz6iYQ92mwWH3YLNasHjNvpxgmDC53V0M2MkFEXt5dW4XTZGBkL4vU58HoO82rsmdPjBa/N84aHh21i0O4vb7t64XC7abaMQSyQSLCwssG3bNgDy+fxtHWtLsrqFLfxovN/H89RbL/Pyq+9Qrbdxu/0kBiYRzDZy+SL7dm8nFg0Ti4aJhAPIsoKmaUTCQa4vrHDt+hJHD+3thZbVG0aBoWkaX/vms5TKZSpVCU3V0HSdXL7Ip598kMMHdxMI+Pjrb3yPS5fnsdltTE0YVgOqqnLviUOEQ0FsNhuapnJp7hp7dm1jenKU5198g43NDAMDfYgmkavzS6Q20jz91EO4XU6OHdlPsVTh3dMXKBTLNJtNnnpoN3I9TX3zVbLLq3RkmY7kxO8PEI0lcfjciBYHO6ddt7yGrbZMKlNmdCCMyWTinXPLPT9rh92Cz2NHVoyGRjLuJxn3IwgCzVaHlVSR5bUCO2f68XkdTI7EGO4P/lDmibGOz79+jUJJMkLDYn7iES+NRgerz8xzr17hey/P4XXb2T6d4N7DE0yP9XW/Y521zRJrGyU2s1VDCul3ISsqlq489fpyjjfPLFEo1ZEVlaFkkFzRCCjbzJT52jNn6bRq7JsQ2Tsh8vfvFvG7br2BIVhcOAOjmF1D+CJjNFoa333xMpqWw+OyE48YG+CN4mB6PP7jD/ox0Gh22MxVyOZrZAs1Wi2Z4YEQh3aPEAt7uefIZM9a44ZP+Q31yOmLqyyu5rHZLMTCHiZGor2N2Ot29IZe7Y5MOldFUTXGBiPo6Lxzbhl0E5rowiSaaHdkLl7d4PCeEZxOw6dzZCDcG3rIivHwX5WaPPuyMZQI+JwMJ0Mfe51usJAFkwm3y04mX+WZly5RLDVotju0WgpWi8jv/aMHsFktzE4YypZo2EPA68DndeB22tA0DatVRHD+fDXiPmncydpkC1v4RcAHByRVdF1GEB1YHEEE8eZ7gCzL5Atl8oUShUKJfKHEXUf20ReP8MSj9/GN7zyH3x8iEQ9gFkVkWWYjnWNoIEFHVnjj7dN4PW5CoQAjw4YPuKZpKIrKcy++zvkLV8BkQhRFRof68fs8LCyu8uIrb9NoNCgUSgiCic1MjkRfjCOH9vBPf/tX8bhdtFptalIdSapz7foyM1Nj2GxWXnntFMur64AxAPF6PGjdAcTIsNGkbrc7rKxtsLa6zPraMkN9biYHbLz4yjt887lLaJqKzWLB4bSzb/sIVu8wB/cPcfCAYYd6YzgwN58mGvEwNhhBarSZu54m4HMymAgS9DsJdpsBJpOJvqiXVLrM/FKWdGkZTa4wOhDm4O5hrFYRr8fOYDKI32Pcm9+fGfHD9kg/bSiqSlVqUa+3GUgY7L0X3rhKNm94bb9/MAGwcybJ3u13nsDWastsZiuUq0Zzo1Rp4HHZeejELFarmWDAxehgmGDAhd/juMle5P3reUN9oqMTCXqo1Jp8/+XLvUaXxSLi8zh6+/LhPSM47BY8LjtSvU27o+B22ihXG5w6v0I44GJ8OILP40BWNBZX84wOhqk32vznv3iNateiq9HsoCga/+//6Sl8Hgdr6TKpdBmX00Yi5ifkd/YUrDumk0RDhj1JVWrRbHV6Ae92m4V4xItvzAjH9bkdiKLQtdCr4NBbNLIpqq1iTzGiync4SwTQdAFd9OINxDHbg9TadnSzj1rLQrbQJLVR5ptnymxkLrK2USKVqVCTPp5913oqfYfP/uNhq27YwhY+ObynIsnRrm6gdGpgMvNf//wHnL+S4sRdh9i1c4aA30uj0SSZiPHumYtcuHQVj9tNX1+EvXu2E+vmnLmcDi5dmefFl99CUVUajRaRUICdT0yxurbBpcvzJPqijAwPMDU+QjIZ7ylIDu7fCRjW3u+cPk8uV6RSrfHFzz2B1WohEgkSjYZIxKP4/R9UJ/7Jn32dZ559mXq9QSgUIOD3cvnKdfbsmuWJR+7l/nuOMDM19iNVHrquITcKNCt5MunrKK0cb52Z553zm0gtnXpTJ19uEg642bfbxPapJCMDYcyi8IG+w52CkcNaZ3EtR75Y59F7tyMIJi5e28CEqWvDJOD12KlJLZx2K8cPjGM2C9htForlumETvlli7/ZBBJOJU+dWEAQTFosxmLh8fZPVjRKX5zc5dWH1R+4bXo+d2fE+hgdCxMIegj4nB3ePEOkbpVXdoFJrEAy48LrslKoN3E6jGX9pfpPL1zYBo/ke9LvwuIyf+TyGKng1VaRWb9Nqy8iyQjToJh71sZEp89Kb83RkFVUzFDyhrg3Utqkkv/XFuwj6XAT9TiwWkXqXDGG1mLm+nOPaYpZWo4xNL+KyVOkPdmiYKqBrjPlv7/tQcOALD9PQgvzVDzbZKKi0OyqqXqIvonLimIbNYiYW8Xat5oP0Rb2Eg+5ez+P4wQkkxdvNsNVptWWkers3ZJtfylKRmjSbMo3uQOTQnmESMT+b2QoXrmzgsFt66pMba+xx2bn/rmnsdgt2m/km8qmqajjtFqpSi3S2SkVqUpPafPrhXYBhndpodvB5HAz0BbBYRRzdOk7XdU5fXKNQqlOuGn0dt8vOv/hnhl2uquo3qVV+2rjtYcnhw4d57bXXmJ2d5bHHHuN3f/d3uXDhAn/913/N4cOHb+tYW5LVLWzhw6HKTTrNAq3qOp1mgZXVTf7qGy9isztJJgaYmhztBZN+6QtP0unIpLN51tY3eef0BUqlMttmJjiwbyfxeIT77zmKVG/wwktvks7mUVWV++85Ql88yvTUKG+8fQavx0Mw4KOvL0I0HMLn9bC+vsmX//RrtFpt7r5rP+Pjw4yODCAIAr/0ucd58eW3uHZ9ufew4Xa5mBwfwWr1sW12go10luXlFIJgwu/z9tgVrXaHZ59/FVVR0BWJTqtEo5rmf/uPz/HI8UF2Twbxut2YLVYGE0EsFvP7bvw/Hh1ZIZUus7iaZ2ElR7XW4tMP72JsKMrsRByXw0Ys4iEW9hIJuXs3fEXVWE0VWdsskStImEwmBpOGesQsimyfSnTPX2Zto0Q6V+Wu/aOG2iDswWYTyeRqPPvqHOubJQ7uHuY3f+kYj927nZHBMNu6thU3iovRQcMy5MzFNdwuGzumE3hcNkqVJs1WB4vbwYUrKeYWMogmE7Kiki/W+cf/9z9nfSPP9mFjOPL//KLISPyDthA/CjZ3HzgGqCkRshULuXkJjwseiFtxu2D3bD/xiLen2Pkk0Gh2yOSrXY9QJ+vpEmcvrRvDiX5j8HDD1svaZZFeXcyQL0k9ZsLhvSMMJUNMjcWYGIl+qD1GvdlmYTnHRqZMKlMxhlhWM+4HbcQiXg7tHuH8lQ0cbh/JsImQ30mgy5y9Yd2VK0hcW8og1dsE/S4eOD6D1+3g2IFxIkF3zyf8dpDKlLhy3VDCFEoS9aZMf9zH33v6MC6njWKpgcdtY2IkQjziI+B7L9vk5LFpQzJcbpAvSRTKDSZHophFge88f5G4u8b4/p/gy/kFw52sTbawhZ9XGCHtFeRWmU7dGJBomoJodmBxBHoDElU1ckXyhRLTk6OYTCa+94NXyeWLdDqdnk3Us8+/xmMPnSAUCjAxPswbb18gl8sgmMDpdODs2lMk+6IcP3qAzUyO1dUUb79zjkw2z6EDu/D7fdRqdUTRjNVqwWq1IIgCI8MDhIJ+jh3ZR6VSJRaLEAz48Hk9eDzG/dVqsfDnf/WdXg0BxlBkIBnHZrOyY9skM9NjBPw+rFbDYrJaqxv5KGqDL/+3v+TdMxeMQFhNxmqGB+4aZMAzTCLq4/NPHCYeDeDzOLDbLdS7Vps2q6Vnzwj0VAZC9/4aC3t58O5ZqrUm1VqLjWyFy/NpdkwnSMT8FMp1VlIGg3AgGSYRCPUerl0O28+FnVazZTTzPW477Y7Mm6eXqEqtm/y/Px3xYrWYGe4PMTJghNB7XLabBhM/rMa8HciKilS/kf/Rolxp4vTF2TnmpFZv8fbZZdwum2FTGU8QCrxHhNm/Y6j3Z03TqDfbmEWhl1M2v5ylXG1Sk1rout61RfXgdtrYs20Ar8eB122/abCi6zqiILC4mmdxNU+uIGG3WXj8/u2EAm6mxmK89Oa8YaVVb9NoyYQDLv7FP3sMi0Xg8nwGs9lgtA4mAgT9rt5z7Gcf2W00NTSdmtSiKrV6lmA1qUWxXMfrtjM8EMLrthMOutF1DZe1w8yAjtxKIZdK5DaMoYjSKgOfwDOyYMfmCoHZR112UO/YKUoWVrMy62nDV3wjs876Zhmp8fFVKm6nUV8l4z4SMT/JmI941EPAVuaux//ZHfxAHx9bdcMWtnDnoSot5EaBVm2DTrOA3G6QzrcYGhljbT3NynqGE3cdIBYNc+bcZWRZ5uD+XcxOjzM9NcboyIDR18jkOXd+jmvXl9m7exv333MEq8XCemoTq9VKIhHj0IFdDA0aOZ//4vf/CWbzRz+LLS2v8dqbp7FZrSSTMbbPTvYyKrfNTNBqtSkUy6yub7KwsMz5S9f4zFMPsn3bFNlcAZfTwT3HDqGoKqCza4fhpjE81P+R76m0q3QaOdpShmphidPn5nnrUhm72OLph/cyPbONNy/W2MjlcDnt3LVvjAO7h5gcjWExix/6THsn0GrJvHpqgSsLaTL5KjWpjdNh5cHj01gtZnbN9AM6fVF/L3vkRtPcJJh498KqQWpsK7Q7MgGvk+1TCea6A5H1zRKpdJl8qf4jz8NuszA5EuWRe7dx7MAY2XyNcrVJyO8i4DfcQ/xeJy0dzGYBRdG4cGWDfNHoAdxwkBhKBnE5regahuVTxbDeGkgYlrFvnV1iI1NGkto02wrNVge3y84XnzzA+FCMTL7G2FCEieEoAb8TX7f3IYoCoiiwsJrj9CWD6CB3Wty9J4jfXkfLzTNuy+Fw/1Do/C1u3bW2g3zdRbbmYK1oZ2J8hE8d2cXFqymaaplk3IrXYyfgNwiZN+qZ3/6VuxEEE/UucaPe6JDKlOmPB2g0O7zw1nl0uUK7o/SIIp991MhQSeeMwYXDYSEUdDHoCODpDuMmRqJMjcY+NPtGR0cQTRRKkmEDXmv23FMA3ji9hNUi4nHbe3VdoynjdYu4nTauLWUpVTYoV5o0mm12zfbzK58+hMkElWqTSMjN7EScSMjTcw4xmUz86tN7UVqlW1vQTwC33d35wz/8QyTJYLT8y3/5L5Ekia985SuMj4/z7//9v7/jJ7iFLfxdgaEiKZFJXWdl4SLr62uYBAsPPvgA9U6JaCzGE4/cx+jIAK1Wm6XlNUKhAF6PmyvXFjh7fg6P200sFmbb9DjxuBEAabdZuXj5GgtLq0aYazeg9ZlnX+Ezn3qI2elxRJNAKpMhncnz8mvvcPTQXsbHhnjj7bPY7VaeePQ+TCYTC0urnLtwhS9+7nFMJhMOh53pyVHCoQA2u42NjQwX5+Y5fnQ/0UiIRF+UtbVNdIzg2I1MDu/CPH1BMyd2u/ibZ16k2ZRwWAWG4kFi8TE8oRhWq5npifevzY9fP1XVepZQf/bNUyyv5TGZBIJ+J6ND4Z4N0vRYX0/VAUZDoVhuEA15UFWNM5fWiIY87Ns5yEA80GuC67rO1YUMG9kK+aKEyWSoCapSC7/XyfkrKV5+y2AmGA3uGLMThrIgGDB8yZfWCrx6aoFWS8ZmNTPcb2zidx+cIJUts7CSI5OrUq23ePie7Vy5nuHVdxa4cHWDxdU8AxHYNy7yD+4T2T7ixGa5dZ9Sk2jD7OzH7B4ikpwhX1F48Y1rCEKLcFBkajRGNPwe43ViJHrLx74dZHJV1jZLZPJVpLrxIL5rth+/18lwf4jh/hCCYLB9C8U615dzHNozgsUscmUhTb4odUPI4kRDnp5P+Q3liKZpFMp1NjMVOrLCnm2DaKrO869fpdVW8LhtBAOGD72sGkyFsaEI48NR6qoP2llyRYma1DLYppUmb59dxud1GAqbaTeR9zGDbzBHPwqr6wVSWYOBWao0yBUlDuwa5tDuYdZSJV56cx6nw4LP62Qi5n8vS8Vp4x//6t29DBZV1ZBlBbMokCvWePaVOVZTJaNwU1Q8LjuKMsP+nUPs2z5ILXftTn5tP/fYqk228LcVmtrphrSX6EhZ5E4VNBXB4rpJQaJpGq+/dZp8oUS5VEXTDQWGzWZldHiAg/t38uLLbxlZIqJIKBTA5XJQrkrdWsJFf7KPUMCFKAgUSxXePnWOYMCHoqj897/4JmfOzyGYTNhsNrbNjDM4kGRmegypXmdmeoxQMIDf58Xv9xAJB7HbbRw5tIeNzSxSvU6hWGZ5JUVVkjh5z1GcTgfHj+5H1TS8bhdutxOLxYLeDXoMBHycv3iVF156g83NdXKZDPV6hYeODrJj3EPCtYF3r5tEbIh4PEIwEETsNksmvLCaKpLJV7lyPUNVMiydbgzYY2EvdpsFm80MOtTqLbKFGgMJQ1lz6twKmXwVs1nE57ET9DuxdeuByZEYkyMxdJ0ekeMTsg3/0deGZqiBzaJIsVJnYTlHpWZYT3VkhXDQzf13TRtBrGaRoWQQj9uO123H081gARgZCP+Yd/poNFudXhj6jd+nx+IEfE7m5jeZu26oCCwWEZ/bgctvLFTI7+LpR/b0rER0XUeqt3vZL1cXM2xmKj0Vh67r7N42wNRoDFlRaTQ7REJuJkeivVBWMJob48NROh2FerPD2maJaq3FzpkkxXKd/+1PXyFXkNDR0TQdHbBYRX75yQM9RZHNasbjsjHSH+ypb8yiyD/99RNGTdi1iag338fWXM5TrTVx2K143XaiISPrRNMUpgftjEft3f8fL6FUy5SyJXJtg/l5x2ESMNv8dDQntY6NastGtiywuCmzuC6RSpdJpZep/4jg3B8FUTARDXvp7/OTjPnpi/noi3i7v/voi3o/lAmt6wr1/JVettDPGlt1wxa2cGeg6xqdRolOPUerlmJzc50Lc6tcvb7J4soGgYCX3/qNLxCPRXjo5HFW1zZottpsm5kgHgv3mrMup4PvfPd5Ls7NGxlnqmoMyPNFVlY3eOX1d5iZHucf/9aXPhCOfgOtVrtHFsnlixw+uBuvx006k2egv4+jh/YgywqFYrn3bzWpzn/9068xd+U6mWyBVruN3WZlcnyY7dum+NIXnuSlV96i2Woz1Jdk5/Yp4rHIB95bVVp06lk6jTzNyipyI0+1WuZ7ry3x7qU8FUnBZHYQ8VupNk14RJ1EzM9d+8fYNdOPy/nRNuI/CTRNY2E1T7Fc59DuEdodmW98/xxms4DXbWd8OEIk5KHVUXGbzTx273bMZsF4Fi/XuTCXwmYzs3f7IKqicvl6mnq9TSpTJluosZGp8P/4/37rQ20X3w+fx0E05GaoP8T4UJiJ0RgBr5Nds/24nTbGhyIIguEeUao0yBUk3E47iHB1McP6ZolI0M30WAyb1Yy3awOqqjpvn12hJrUolOtUqi3K1To7pvqIR43MjGxeIhRwMTYcZXQgxN7thjr5wO4hYlGP8ZxebbC6UaTRlPn0Q7swm0Xmri7RljYIOiT6XHXCoRZ6FkqAA+CjXW5vgq5DXfHgC48Qio/xxkWJ8/N5BMGEpuu43Bb6osaQYDAZ5PH7tmO1GgoOTdeoNzoUShKhgJvVVJFzc+u9YwuCicFkkP54ALNZwOd1EXBZcNot2O0WnHZrj2x5/OD4h54fGHWOoqqUbgxEpCbhoJv+eIB0rspr7yx08whFbBYRm814/hBFAafdwka2wrWlLM2WTLstYzGL7N0+SCZfI1+QjKFPf4hIyMNwv1FbJWIB/q+/ef+tLeLPALc9LBkdHe392el08kd/9Ed39IS2sIW/a+i0JNROmY2Vy3z9698mmyvQ7OiIZjv3nTiMIFowm83cfewAmVye8xevGKxK4PDBPXg9bibHR5gYH8HpsFMuV8nmC5w5d5mp8RGi0RDBgJ+V1RTObpCZIArs3j6DzWrlj/73P+X8xSuACZfLQV88wlBXAXLviUMk+qJcubaAx+0mGg0xNTGCpmmIosi+PdtYWFrlwqVrrK6laLU79MUjxgO8prGwuEqj0SIcdFKvZllMX2DhXIXHjw8gCHB4dwJfIILHfeu2Wu9HvdFmYSXHlYUM11dy/OrTh+jvCzA5EqU/7mcoGSQe+eBDW1VqspGukMqUyRclnA4rT5zcicNu5akHd2G1mFFVIwA+X5LYMZXEZDKxulGk2ZbpKArFUp3XTy3SaHZ48oFd7J7pJ1eosWdbP0PJMBaL2GWfgKrpvHZqAafDylAySDTkQet6ZtptAhfnNzh7aY1sQSKTr7KZqfCfv/IGNrPCnnGRJ/eJ7P2CnbDv9vJBRFuAtthHsRUkVbQiZ3QSMR/xYSfhgMZ9d00R9Lk+sdyRdkcmW5DI5ms9W4ts0bDYikW87Jw22Co3NnCLWeT5169SLBuBu6IoEAq4eqFqh/eMfGT2R65Y47lXrrCWLnUbRWo3dD1MKODmM4/sodVWCPqdHwiizZckri/nWM+p6EoVE/RsvCIhN596aFdvaPFhqNVbrG+We1k4+aLEr3/+CEGfix+8fpVrixkjV8RhJehzIorG5923c4jJsRhup633uW5Yn9SklmEZlymTLUiUynX8XidffOoAPo8dt9NGIubD5bCg6dDuKIwNGYX7S2/Nk/B9MEDwbzO2apMt/G2C0ql3GYl55EYepWOw80SrE6sziiB88D4oCALVrp2m2+NC7sg0W21efvVtwqEA0UiIHdunSG2kURSVUrlKLl+gWpMYHe4nXyjzre88S6lcoi41UDWNbTMTjI0Mouk6DqedndunsNts6LqOx+Mi3CVsPHzybjbTWWpSg2pNYmMzw1VxiQfuuwuTycQLL7+Jqqq4nE7cbiehoB+ty4Lw+72cOXuJdCZvWISVSsjtBr/6+XvxOeGrf/4VcvkcbruA32uhv9/OcNyMYHZw9MgBwES1y9xfndugVKlz96EJrBYz6+kSNalNwOegL+pFNAs9JUDQ7+Slt+ZRuraKN4YinY6K2SGyd8cAoij8yAy0nzbSuQrpXLWXxVFvdNgxnWBmvI9Oxwgn9bjtJGI+vB47vi6JQBAE7to/9rHeU1WNpkW9+R6DsdWWOXbAeOB+6a15KlVjv7HbLbidtl7tMzoYJhn343bZsFktaJpOqekCGgiCwLWlNFKjTbnSpFxtoKoaj967HY/bjiyrWCwiA30BXE4rLqetNxDpi/roi/rodBRqdUPFUao2GBs0Anf/wx+/QCZfpVRtItUNRcr//E8eZnIkSjzspVCSEAQRv8dBKOhmpJsfsnfbAP19fqwWM4ps5JPcGJA1WjIXrhhKJJPJhNNhMD5lRcGkNjk0a0fQNHS5itJeR26VKV+pUPgELLNuwGTxUO/YqbVtFGoiGwWda2tN5pYkUuk0jZb84w/yIbCYhd4wpD8eMCxqY76eVe2H5b/9ImKrbtjCFn4yKB2JtpRlrXCeRjVPMOBhfjnPl7/yXSSpQV9fjBPHDzI8mETXdZxOB4cO7MbpcGC320hncrx16jzFYpETxw8xNjJIvdlmdW2zl2U6kIyzf+8OopEgTz12kh3bJrHZjH1ZlmXKlRqRsNF4/c4zL5LLFwCw2WxEwgHU7h5/cP9Ors4v8adf+Sa5fBFJaiCKIo88eDcH9u3E7XJgMYvcdXgPu3fNsmN2qvesaLfZiIRD7NwxTTj0Xl6prinIzRLtepZWLUW7tkGlUmQzV+X6So37j++gqYc4feU8Olb6E37MVhcTA26GkiEsFpEvPrn/Q5n8PymqUot3zi9zfSnL/FKOqmQM8w/sHMLndXLv0SnCARexiJdoyNPb6wByRYlX3rmOVG/TbHWMIUjXLun83DpvnlmiUvvRNoyiYCIW8TKYCDI9FuOXP3WAwUSQTL7as3f64c89v5xjM1MhX5J6JFi/z4nLD2NDYVwOC/lSnbnr6V422D/4wl143Xa+99Llnp21zWomFHBRKjeIR/38g186avQGOirlWpNSuc7Fa5sk4wFUVeMvvv0uUr2NYJIJ2JuE3E2Wzl7DLlQ5EKnAB+ditwATDdVNXnKRq3soNj10VJFHh7bhCkYIBleIhNrYrCJWqxmLWaRUbqBpGl63g2LFyA8Bg2zichjP/QCJuA+329bN6bXe5G5htZjZu3Pslkg89WabSrVJKODCZrVw/kqKuflNOrJCq60giiZGByP0xw31SbnWpNWSjZ+3ZBRV4+5D49htFjKFGs2WTKzrChIOuhkbMgaaJ49N88DxmVteOU0zyF7G4Oxn60L1sRJny+Uyf/VXf8XCwgK/93u/RzAY5PTp08RiMZLJDw8x2sIWtmBAVVXW11Z5+61XWV+Zpz/u4u5DM7z80juspcv4/SEmk3FisQjbZwx5xc7tU7zx1hk2NrL0J+Ps3b2NaCTUCzR1Oh08+/yrLCytUSqVqdbqKIrKyFA/f/9XP0t/MsbzL73OlWuL1OsNXC4ng/0Jhof72bl9il07ZhgbNSwzypUa6UyedrRDOBTkkjLP/r072D472Tv/bK5IXzxCsVTmj//kq90gSgcWq4XUZpbTZy6wfTLC4R0h3njzDextjVhQJzjiJBjejt3hwySI9Ptvb+0UVe2xEP+PP3+V68s5VNXwlxwZDGMSjF3hyN7Rm16nqlpPclmVmnz3hUuIokAs7OXg7mGSMeNENE1jbaPERqZCJl81wllNMDoQxuW0sZmt8NbZZVptBbtVJBRw9/JOJkajiGaBfFHi3YurKIqK22UziiGzyJG9o5SrTTL5KnPX01SqTWYn+9hIV/j6989yZSGDLMtMDwgcnRD5p4+aGU9YEYTboKsKVlRzHLtvCHf8AJVyhXPnl7ssDA+RkLsXzCqKApHgJ+OdfuV6mpVUkXK1ARj+ocm4D5/Hwex4nGTcT6FYZz1d5txcCllW+fTDuzGZTERCHgYTQULBD/qUWy1mOh2F1c0iiyt5VlNFHA4rX/rUQQSTiXShSijgZu+OQYYSwZ7XOcBAIoiiqhS6RVauWGNsMMJgMkinY2z6g/0xBsJRoqH3bLUEQcBmFeh0FLKFGtlClWxeoi2rPPXATlRV49/+p+/TastGs83rIBr00GkbRf5nHt6NphtrYDGL6LreyzzRdZ3V9SKZQpVMrkauKBH0Ofm1zx5mbbPEG2eWsJhFIkE3YwNhPB47fp8DXYfF1Tz5okS7oyAIAk6HtTdoeejELHp98RP5bn+esVWbbOEXFbqmonRqKO0q7XoWpVVGkRuYTCJmqwebO45JuLk52bOMWNvA5/UwOzPOww/czZ//1bdRFRWn04HDYcdkMpFO5/B63HjcLjLZAk6n0RRoNnXOn7/CtukJLl+5jtVmJRT0Mz46hNNhJxwKcHD/LiMQVdcpV2r4/V78Pi8+rxtf1w+8WpN4+bV3sNlsuN1OvG4XPt97vt+feuIB5I5MrV4nly+RyxV47vnXePLxk2xupPirv/4GcruB22HCbddIhiy0Nl9CcIg8fV8Mj2cSp8sLgg2pYTy8m21GPtQ3vn+Odse499lsFsNHu1QnHvUxNRrj7XMrrKSKvQcuj9vOcL/BZN29rR+Xw4rHbf/AUOSGWvGnjVS6TL1pKDRqUotKrcWJwxP4PA5S6Qqb2Qpej51k3I/HZScSMqwq4xEf8YjvY72nqr4XiF6tNalKLcMadCpJqyPz3GtXAGMPvjG4uNHIOLBzCFEUcLtsmDDRaHYQuoQATde5vpLr+WLXGx0Eq5/PPGAMbhZWcoiigM/joL/PT8DrxOEwiAnbJvtotmSaLeO1+aJER1YYSoZY3yjyl989Q6XapNHqINU7CCb4j/+vXyKVKXPqwiqKquJ1GYOjoN/ZDU8XuP/YFNumEiTjfuw2C1K91cujyeRrvH12ubcuVouZ/j4/I0kvFr3M3TstmGmAUkPtVFFaFVKnvoWuf3J+2h3VQq1tpdK0ki2bWEorXFlpcGmxjtT40RYnHwWLRaQv6iMZ89HfZwxDEt1hSH/cTyTouaMkmo6sfCTR5WeNrbphC1u4PWhKm3ajwLVL73Lu3GnePXeNcrlMKBzht3/rVxgdn+RXf9mBrmvUpAapjTSX5uZZXdtkaCCJxWzm0pV55ueXqUoSlUqNUChApVylUCyzvr7ByFA/Q4MJxkYGGR8bJhYNIQgC+/fuYDOdY2FplUKhRLlSRdd1fvnzT2K1WpgcH2Z2eoxQKIDH7aJSqdFstZBlmWpV4hvf/gH1RhO/z0MoFEBTtZ57xWMP38tjD9+L3W5j7sp1nvnBy3jcLp56/CR+v5f77jnSVdAUkJt52vUsjfIqaqeGprR4+0KGK8sSS6kq6ZyE025h394d+D1Oju4bpVxp4vc56UsOsX3E2XvO/FE5J7cKVdXIFmvML2bQgROHJimWJf7LX7yB3WYlFHCxc6afmfF47/M+dGKGdK5q2EwvZsgVJaZGo+zZNsjb55Z49uU5ipU6mXyNwo+x0wIjj+3QnmG2TfYR8LnYOZMgEvDgclpvepaPR3woqkquIJEt1sgXJY4fHDcUsuU6JgEGE0FE0YQsqzRbMi6gUG7wJ197m5rUpiY1aTRlLBaBLz11ELvdwr1HJxFNJvxeJxazSLXRolQziBytlsz/+ZU3aLdlNM34zv1uM4dm7AhKgbtG1vDbG3js71NbyhWU2/gOZFWk3HJRbLo4eOAQ/sgQX/7rd1nbLGECLBYVUdS4upRldCjC9qkk80s5LGYzNut7KhBF1bAKAnu2D2DChMtp/cD++f4s1ltBuyP3SJ/nr6TI5qvkihKS1KLdUbjn6CTbJ5PIsspGpoyOoYRpd5QeMUIUTEh1g0CSCPoJBVxEgu8N2b70qYOYTCArGrKsoqhG3goY1/i1pQztjmL8TNGQFZWd00k8bjuXrm2wsJJHVtQegWl8OMq+HYMUy3X8n4wb3S3htiuX8+fPc/LkSXw+H8vLy/zWb/0WwWCQr33ta6ysrPDlL3/5kzjPLWzhbwVeeuH7fP3rX6WY20SqG9ZPh/c8jc3Tz/5DIoeO3EUo6Kcm1clk85w5dxmPx8WBfTs5sG8H/ck+KtUqyysp3jp1jmKxwkMnjzE6Mkgw4OfdM5dwu1xsn50iHgsTi4aQFYX/+qdfo1SusH/PDoaHkoyNDvbko8fvOsDps5c4ffYyhWIZTVMxm83EY2GikRAnjh9idX2D737/Ja5cXWRxeQ2vz8Pv/bN/gN1uZ++uWZZXNxBNGjaxjcsqkbr6DBHNjVuVefBgEIvdh2h1GzZgtwFVVVlZL3JlIcPcSo18NsP/9I8eIOBz9SybpsZi9EV9H2C5VaUmm9kq6VyVbL6Gx23j4RPb8LodnDg8QTjoRhQECqU6qxtFxoejmEwmzs2laDQ7SPUWxUqdbF4ywsUPTzIxEiWdqzI9Fmd4IITdasHVDdKWFZXzcylCAcMiymm3GiqU7kPilYU0r51aINVVtGxmKzSbHYbjAjuGBZ78rMiuMSdO2+0UTSZMtijljp901UOl7UIUzWz3JfDZAiTjJgYSuz8RxgoYm2+uKJEvGhkiR/eP4nLYkFUVv8/B5Khhj9Hssg8AWh2FZ1+eQxBMXZ9yP+GAq2e9sXPaeDjVNI1KrcVqqojTYWFiJMaFKyn+5K8NGxlRNJiPsYgXRTVUJP/sN+67qejsyEqvKDw/t87VxQyapmO1mImE3FitxjUzkAjS3xek3HbRrGxweX6TTKFG0OfkwK5hFlZz/K9/8nLvuGazSCTk7jWLPvf4XnweJ30RL1aroUrSu9YaiqqxvF5EahiNr0JJYqg/xD2HJzl1fpW/eeEiVouI3WYhFHCxYzqJrtP1f+0jm5dIZcrMXU/j9di57+iU4c8e9TE5GqM/7icR8+P1vGepMjEcpZb5+QhQ/WlhqzbZwi8aVLmJ0qka+SNStpu70TEsE61u7PZgL1ukJtWxmM04HHaWV9Z5590L1BsGE79eb9BstQkGfcRjEYYH+7k2v0SlWgNM2G1W1tbTjI0OYrNaOHdhjqXldVqtNrqus3O74bv9S599jGqthdNhJuDz4vd58Pm8qKqKyWRlz+5tveMuLK4gSQ3cbidPPX6SSDjI559+lGqtTjabJ5srcOXaArWaxPG7DrC6muI//R9/RrvVRNdkLGZIxFzsGihiVWv8X54OYzWbwCQgmp0IFgeixYnJJKDb2szNpylXi5QqTWRFQVU1vvDEfqwWM0G/i2yhhqbrtNuG9H89XSYe9WGzWYiGPIwNhfG67XjdDpyO94LvxwY/Fl3wtqHrOqqmYRZFmq0Oy+sF6t3hwQ2LqUfv3Q7A6YurtNoKLqdh5zTcH8RsNvbwfTs+ftC6ohr5IbW6kR9Sk9oMJgPEIz6W1wucOr8CGAMCr8eOr0s0cNqtPHj3rFHr6CB1FSY39r9SpcHyegGp0aHdNpQMEyNR9m4fROvmdzgdVgI+J067DZMt1Dung7uGKVUalKsNFtfyFEp1ThyaYDAR5C++/S6vnVqgIyvdLBCNnVNJ/vk/fABZ1bl4ZQOTyTCxMgHJeABN00jE/Pze/3ASh8OKw27BYhZRNY14Nz+k3uiQK9RY3zQ8sE0mE7tn+/BYO3jNRfaPt7AJTUS9jtaporQrrLzz8YLMbxUdxUShJrCe11hIyaxlVdbzGqmChvQxRKJWi9hVg/h7apDE+5QhkaD7J64LDeapTLtLNjGbjQFMR1Z4u0sqandkmi0ZVdX49MO7+Xmbl2zVDVvYwq1B11TkVpFOvUBb2uTM2bN8+SvPYhIsuDx+7jp2lKGBJI1mi2gkxPBQkr/+xvfw+7wMDSRwOBx0Oh2uzS8Ri4XJZPPMLywjimKXoDHIwf27CIUCjP/OP8RiMX/oEKHTkXn+pTdwOuzEYmFmpsaIhI1sU4BYLMz5C1d59Y3TrK6lKJeryIrKr//K0+zeOcMTj9zLtYVlJMlg8IdDQQb6DWtuj9vF62+eZi21icViYWZqjKnJUcM+sVmgXc8hFZfY3Nwkky2SzjfIFDr8ymeOYrKEefbtC+TyEh63jcN7R5gcjpCI+dB1CPnd7NsxRH88QFP333IG64/DwkqW7754maXu/tnpqPQn/Jw4NMlgIsg//NJx4hEvoYAbi1kgW5CoN9t43Q7OXVrnxbfmKVcaFEp1ipU6/63WYjNb+bFsfpvVzMhAiJ3TSfZuH+DArmGScf+HfmeaZpAx3E5Dlfx+Bwmb1Yy/2yfwuEQCXgevv7vEZs5waag32vRFvPzz/3GESMDF8loBl9NGPOrF5zGyRVY3CkyOxgn5Xbz4xjVUTeu5U0yPxTm6bwwTCpMJjYhbIeRuEXA0sIsNqksGsXDAf3vrrutgdUUwO2J8/808KzmBUsOKWRSx28wMTbsJxq0c2DVEOOgm6HfidFhx2K34PIbbidVi5jOP7vlIlWbQ9/FcV2RF5frSBm0p1814rSE1OvzmL92Fw27hwpUUq6kiomjChAndZKLeMIZEgmBCEEXsXStSv9dJOORCUVVcThtPntxJrW4MWNpthVyxxtXFDNNjcSrVJi++ebMF+A3nFjCC5dsdxeiOdP9nZtywq6/UmtQb7d53pygqN66kWr2D3/kz8Lnt4rbLlt/5nd/h13/91/m3//bf4vG8x0p+5JFH+OVf/uU7enJb2MLfBsjtBpupBVyWFhdOPYvPLnPgvoNE+kaZHEvidDhotzsM9idYXlnnL7/2XVRVRRRFIuEg7q5FlSQ1+P7zr5LLFdB1HU3TsNltvPH2WZKJOGazmf5EjFK5xlvvnKVUqfL5px9lcCDJ0089iN/npd3pkM+XOH/xKi+9+jaf+/QjCIKAVDeaHsNDSWLRMMGAr/cgNXd1gX//v/wxzVYTq9VKwOfF53Fz8fI1Du4a4d4DCS7YM7jMTbwOBYfNjGgNY7b5el7qtwpd1ymW64QCbnRd51//x2eQpJaxFtEYxw+M987r5LGb5Xyt7mDC63aQK9Z4/rWrCIKJSNDDjulELyxKUVVabYV3zq2QyVVptmXaLZnBZBCrxczSqmHrpeoaTruVSNDdy9bYPduP22WjWmuxuJKnIysE/S76+wK4HDb2bB8gX5S4spAhtVliaa2Ax23n+nKOc3PraJrKRFJgz7DIr94tMDvkxO24vQ1AF1woljie0BjR5BTLqRqljSJDo14iQQ9Bv9FgkhRDPXInfdRbbbkXLvbca1d68lCnw0ok5EHvFleDiSDXFjNcWchQ7bI6bnheuhw27r9rGr/P0SsQFFXtMSluDJXSWcNqRNM0Du4eZmIkRizi4aF7ZhlMBBjuD/dURjdQqhr+poWSRKFUp9HscPehia6dlgeH3Uok5MbjsrGRrTC/lEUH+iI+vv/KZb7/6hImvYUJY+32bDMKwHjYy+P37yQSchMLGzkp7280REMeckWJjXSZcs0InN2zbYDx4QjXlrOcubjWY1FYzSKDSUPC7fPaObp3hHqz07WRabCSKrB/5xDNtszbZ1eIhjxMDEXpi/sY6DNeJwgCv/TE36H09lvAVm2yhZ9X6JqKpsnoagdN7aDKDTr1HHK7hCobTVjR4rwpoH1tfZPUxjWKpQrFUhlFUdi3Zwc7tk3idruw263UG01kRcVut1FvNMnmisRjEfx+D5lcnly+RL5QpFKpEYtFmJ4a5cy5ywiCyMhwP+FQEI/Hia7R2/MPH9qLiIJUr1OpSGykc8iyzIF9O5E7MssrKXw+D5FwiFgsgqoq5AslYtEwX/nqd3jjrTPG5+k+MB49MIOU9yC0lji2w4rPacHnEnBYweWwIKgVBLMDl89PtSFTLNUpluvkSxmcDiv3HJ5EMJl49+IquqZjEkwIXQukfFEiEfMz1B/EZjPjddtxu+x4XDbcLkMl4nbafqIBw+2g2epgsYiYRZHNbIX1zVIv16LR7DAyEGb/ziHaHYUr143P53RYiYU9N/mUP3Ri9mOz8BVVpdmUqdVb1OptavUWO6eTWC1mXn93kc1MBTCUBR6XHVkxlCh9UR/3Hp3C57FjwkSt3qLZHXyYTCbOXFqjWK4batsuThyeIB7xYTaLeN1GppfLaUUUBRzdOkHTdMrVJsvrRSq1BlWpRb0t8u/+b48C8Af/4W8olCQ0zVB9iKKJZNTHYCL4/2fvv6Mkuc/7bvRTuatzmp4cdzZnYBOwyJEACAaQpgItWTIlJ8mSr8NrOUqW7devL20fXdtHMq3XkhUZREqkKOYAAgRAAItdbM6zOzl3jpXvH7+aXoAARQDM8j7nDBo7oae6qqZ/z+/5JlIJk2w6Rippkk5GiUf17hroui6HbxkjYmgM9Wbo702STcWQZRlZFtanC8sV6k0Ly3KQ8Lj38DBxwyUhL7G9t4qhCEAkcGp4Kw3mV0T/IgNO+PHdrHIjYHHdZ7Hos1gKWCz6LBUDlko+9TcJiMSiOv09KQZeEZ4+1JdhoE8AIrl07DsGQ0rVJvVGJ1T3OHQ6DuMjOfp6UkzNrHXBtY0q5BMhcUnG8wMScYMePR76pWuhSvoHa6fxzXWzb7hZN+tbVxD4uJ0qdruEVV+iUV+jXKrR2z/Env238VNqFttxWC/VaTXrXLx0lfX1EmMjgyQTcfbs2sb1mXmefuYYxXKFRqPF5k2jPPTAnVgdm/c98ShbNo8zNNhHLpvuvmfpukanY7FeLLNeLFMqVVgvlXno/jtJpxI8+tDdABRLFYqlCmfOX0bXNe696wie6/GNF05QrlTJZNIcOrCXVCpBNpsGwA8g8EWg+8TYMKYZYXllrft7Xc/jwN7NDPeZ+FaZ5QufpFJaIZcS4eIf+dxVqk0PyxbrjKrKOL6CoUrsnOwndyiOqggiZr1lI0sSsiJzz21bwnMKb0quEFaj2WFlvcb5q8ucv7LM5Gie9zxyC+vFBsfPzJLPxrnt1gl2bRlgYkRYH8myzNhwjun5Ik8+f5kr11ZZXq+RSpjUGx0uXF2mUvv2i4+iyIwP5di9bYD9u4bZt2OYTaP5bznkt2yHucUylXqbSrVFudpGU2Xe9bBwkIhGdNScjO24LCxXOH52lnbb4ZF7d3Ly/Bx/8tnjBIHoUyKhaqHR7NCXjnPf7VuYW6qITLKWRbNlUaoIF4tEzGCgL000olBI2qQjLQazy8yf/B38dol7J9/6+mO7EssVncVKhNVGFFfJ8Ys/8wCGrtG3coHhzTL5TJxcJkYsanSJtDu3DLBzy8C3fN63amfZatusrNeo1NsUSw1Wiw0MXeU9j+xHkuBPPv0cqmRhaCqyLKEoMtV6GzOikU6aLK0oBICiSKiKQr0h9iJbJgpcub4CgURAEPaSHSbHCmRTMdodm+W1GhFDQ9dkEloEMyL6PUWVGOhL4XsBnh/geR6JuACGXM+j2bK7Nri+H+B4XlfB6no+jicAEgkJRVG6PWgiphMEby1f7btRb7obP3bsGB/60Ide8/nBwUGWl//PYrLerJv1etVsNjl//hyXLpzi6qXzzM5cY6A3zc//zLt5/LGH0PQoy6vrXL22yNe+vkClUmXPrm3csm8nmUyK/Xt3kEoKe4lSucLVqWliUZOR4QHuveMQH//U57uoawS46+hBfN/nc196isXFVTKZJMNDAxw5vJ8tk2MADA708aef/AJ+4BOJGORzGbZunsDzPGRZ5rZD+yiWKpTKVY4dP82Fy9eoVev8g1/8GSYnRjh8cC/tToeRgTypOCQjLgntIssXTuC7LSZ6VFQjhaonX2MV8u2qWGlwaWqFy9dXmZkv0erY/Po/fDuGrvHA0a3d3Albyr7Kf9G2XVbWa6wU66yu16k3OvT1JLn7yBZy6Rh3HpqkkE8gSxLr5SblaqsbxPm15y93LS42wry3TfZx5JYJbtkzgmnqbNvURzIRQQJyoUVDx3a5NLVCNh1jbCiLoio4jovtuFy+tsofffJFzl9ZYnahRKvjYGiwY0Rm55jCew9pbBs23lQoO4Aka7hKgbVWktVGko5nYugae3sGUdQIm0Yj3ayKjQq+S/vReqPDaqnOWlGoR9odm3e/bR+qojAykGVsOIeqyKFFhrDV2rapD88TIesbIWw92fircmMURWZqZp219TqzSyWWVmvcd9sW9u8aYXahRLnaYnwkz+igAEV6e8QmtpBLcv/tSYIgoNmyWF6tUq612b11QAx0zs5RqrTIpqMM9WfQdYVyVSi4+gspPvG5E1yZXqNSFYxsSZZ490P76O9JMT6U456jMUYLKv29ya5dGUAsKmzEWm2by9dWabYt6g2LB+/cRjwWYXq+yPW5omBoBAG6rtJfSIqcm/kS0YiOrEi4jk/bcuiEDcC1mSJPv3AZXVdJJ016columOz4cI5//0/e+W2vkWU73WDdiKHRm09SqbWYX6zTt+O7cx/8KNTN3uRmfb8q8D0C38X3XQLfIfDdV/zbxfccfM8icC18z8L3bILAxffE1wkCJEVH0WIY8UxXcXn85bNsmhglnUqwsLjC0vIq6XSSoWgfvu+zsLBMf18P+VyGfC7L9ZkFVFXB9wMajSZf/uozbN08ztPPHOPFY6fxA5/eQp7J/aPccdut5LJp7rnrMJ1Oh0azhSzJZLIpdE1DCYHnWrXG7Nws6VSCeCxKPN6LJEtYlk0ulyGfz3Dy1Hlq9SYdy6Jeb/Dedz9CPhvn4N4xJodM0vGAmFJHlVoE7irrU18gAty2O4usmnRchXrTZaHaJhOY9BVSXLy6zF985QyW4yLLYvOWiEe469AkZkTnlp3DOK7XBUOS8Ug32HN0MMfoYO5bXa7vWlm2Q7Ntd9l+Zy4tUCq3KDYUPKuC7/vccXCSwb40zZZFudoSa0cqTcw0yIY+AulklHe/bd+3/D1/GVDSsRzWinVar7Cn0jWVA3tGAfizz5/sMkJlWSYRN7BtoW7dOtHLyEBGKC28gFbnxubT932haOk4XUszWZZ576OCKSo2qwEdy6FtObTbdvf7Tp6f4+kXrnattizbZcdkP7/6/3mMcq3Jxz97Ak0VntzRiI4RTeB5Hqqq8Pj9u/H9gGwmRipukkqaZFLiuh7eP87oUJZSRbBeGy0LOdxYp5NRhvszqIpMvdmiemmVeMTjtj0FXLuO3pyjV2oxlLBQE21kLBpTsJEeoiPG9m9hZvUty3IClssByyX/xmMpYLksHt9IlnoiHqE3n2AgDErv60mSzBToz0ikUiaZZJTefOJ1w9O/XbmeR6fj4AcBybiJ63mcu7REqyMyaDZAkcfv342uq5y9tMjSSlUMrAwNM6LheeLe6s0nuP3WidD2TiWia6+yLr378ObXPYYg+G6e8e+8bvYNN+tmvbqCIMCz6zjtMs3qPJcunGdlbZ35pRIz80VKpSr333sbjz96P2Ojg/zF57+Gomjkc0kUWabd7jC3sMTwYD+NZouvfu0bwgJRkTEjEe46eogd2yb5l//076FpWvd3lstVKtUaE+OC3PDnn/0KrZYgaeZzGTaNj6CGfcq16Tm++tQ3KFdquI6Lqipks2l2bJ1k08QIf/vnfoLllTVKpSrTs/MsLq3QW8gzMtTPrh2b2b1zC8sr65w+d4mZmQUIHNLRW5HcGtt6lrl08RlOPVNmtdig0vDxJY2/+1P3AhJuMI9heESjwla5N58gEYvg+T6ppEml2iKVNLll1zAjg9m3BFpv5IRtOAZ8/LPH+fIzl6jW2928rHRSrAG37h3lP0/0omkK5UqTi1Mr/MGfvkAyEWFxucqzL02xUqzjfJvg9Y2SZYkt4wV2bxtk99YBdm0dYPN44XX7knbHplhphiSXFumUyb4dwziuz8vn5jAjOoamEI1olGtNllcq9PWm+dIzF/jGiWtYlovn+0iSsDB/5N6dHN4/wVMvXA3zzyJoqkzE0AkCQdToK6SIGDqKIoXKFIWBXEB99QyjsRkGdq8gexUkQmKH/eaJD54vUW5FkIw8W7ftpOMnefF8hdxQgsN7E+Sz8VflrTz0JrI43kxV623KVXFuSxVBJNo0Kqyprs2u8+FPHSMADF1BkWXisQh+qGAu5FI0G6XQnlsi8ANmF0v09STp70lxdXoNMyLW9UQs0gUtohG9a08eBBKSFEB4z4Gw2PL9gHK11bXL2rCCrTcspqZFYL0kSUiSUIWAAIUURabRtPB9QR4NgoD1coNUwsT3AxaWKvhBgO8H+J6PLCOy9iTpB5pb8qbBkkgkQq32WunYpUuX6On5/sjZb9bN+mGoTqdDqVRiZWWFqakptm7dwrbJYT7zyY/yp3/6SWTJp783x/492zly5CCp3AiSJHH85bOcOnsJRdGZGOtny6ZRYjGxiU4lE3z58nNcm56lWq3TandQVZW19RKHD+7jjz/2aVZX14lEDOKxGPl8Fs/zMM0Iv/R3fhrP8+hYNpVKjUq1xplzl8lm0sRjUR647yiJRCwEYaqUy1XWixX6+3qYuj7H7/7+nzC/uEK7Y6HIMn29ec6du8SBvSO89+EttKtzOK0lPLshbJCkKKqRQI71vim/zWq9TbHcYGKkh2bb4v/7P75E4Adk01G2beply3gBJVyAjh4QAaJBAPWWQ3m1RNTUyWfjrBTrPHf8GolYhEI+wa4tA903bNcTQ+kXXp5mZb3W9b3+wI8dRVUULlxdZmmliixL5NLCNisSIuO7twwQ0bQQJKiHNigwNpQnnTCZHO1hYbnC505OMz1fZHq+yOpanbblEDdh56jCT94js2s8wuSAjKq8eWmHFaSo2hnGN++lp3+CqdkSWr3N3ok4+UzsLW2Uv105rke52sKynG6+x+efOid8PZMiHNeMaFiWixoVDeuJM7P4foCqKuTSsa5vZTYd48E7tlNttKnW2lydWaNaa7Nn+xCZVJQvP3OBs5eXkBCqlHTK7HqG33NkCw/eub17T/m+uJaqqeD7Pl97/gqVWgvLcnFcwUpIxAzB2t09yp9/6RSnL5apVNu0O4LF8E//7sPks/Gu//j+ncMM9KYY6c9ghg1AX0+K1ZpKtV5mea1Ks2VjGGrXGuXs5SUUWTQMsiLT35tCUWRqjTazC+XuPRYEAYFP1zal3rSYnl8n8MEwVAxdJcwt4/AtY+zfNUQ+E+8qdjbqlX9TjuuJENt6h55cnJhpcPn6CmcvLeI4Ho7jYbseg71pHr57B2ZEo9X54RpKfK/rZm9ys95KBUEg8gZ8jyDwxUcIerzyw/NsfLeN57Tw3Q5B4BH4N76XV5KmJRlJkpEkBUlWkWQFWTFRNBVJ1l6zXjZbbS5dvsaZc5dIhfZXRw7t4+lnXuTs+csCmOhYSJKEqqk8cO/tDPQXWF8v0Wp3KBbLrKyu88B9R/E8j7vvPMS2LRO4nket1qBSrXFteo7DB/cRj0XZsX0z1WqdIMwgWVxa4aUTZ7jnriNs2TzB+voq09MLlCtVOh0Ly7b5h3+/h6HBPq5Pz1GtlknFdcb7k+QzeYZi0yydPU/CaREzbSRfQpZNOq5Ks23Sk0+jqSrPHLvK8TPnabaFXdOGX/GPv+MgA30phgYy9OYTpJNREjGDxCvWuYN7x76n98EGENJuO2iaQiGXoN2xeeHlaVohKOF5PpIk8d5H9yPLMp2OgyxL9PakySdixKMG+axYxybHCkyOFb7t7w2CQNgYWU53w7qwXGG1WKfVtml3bJptm81jBXZs7qdSa/Hc8WvIsoShqSiq/Cpbsf6eJJbjic0qErbj0myLvLb1UoPTFxYEgy/cqE6M5Bnuz9DqOFy4vETLcrAscTye7/PXHrsFgP/9J8+zVmoIIoYkvKt7cglGBwVhIhrRKeQSJOMR0mmza282Npjjv//6j5OIG+GwRaLhJlEU8V790F07RDB9rcV6ucHZSwvs3jZIb17jxNlpTp+bImUGRA2PhOGitZZYv3YFu11jSF7DkC3UuN0l0axdFY8JxHF+N8v3A9Zr3woQCSg3gm9JVEklIhR6omTTUfKZONl0rKvE6OtJ0ldI0teTfE1uThBAw03+pUGtli0ALnEfCUusXDZGNhVjcaXC6YsLtNtON9Msn41z/9FtyJLEwnIFwxAgVjoZFQzR8Pcc3jeGLMuvUfACxGOR70kf+v2um33DzbpZYh1q1Ysszk0xP3ORdrPMLbvHkVWTZ49dZHpuBU1VSCbjTIyPYpomvu/Tk8/Sk8tw5do8S8vL1Gt1HMelUq3z+CP3cvzEWXxfOGWkkgl6e/Lor9jrvHzqPKtrRdbWS7iuiyzJDA/1o2kad99xCMMQ1o8rq0WuTc8yM7vIIw/dxe6dW5mZW0SWZAYHChQKeXpyGfH9wJWr05w5d4lo1GTT+Ahjo0PdAHhJkvjEJz/H+uoS+E3iuk1EbvLcl86yfVOWWgu+8o0ZUDRAvDf2ZGJIkky745BMRDB0lXw2/ioAWVUUNo30kM+KYOs3W8trwu3g/JVlrs2tUSw1efCuHfzY22+ltyfJ2FCWrZt62TrRR39PkpnFEl997hJTM2t88enzLK5UqdTaXcvrN1LppNm1M98yXmDX1gG2bep7zX4UhJKhXG2RiBsk4yZTM2s8//J1HNdDkiQcW/TBm8cLxKMG3zh+janZdVptC8v28P2Aaq3DL/yNe8imosRMg+H+DLlMjFTCZHxYqGEGelPcdXgzuqYiSWLddT2fSEQnCBxyCY+EXCZlNIgqdQypirfosR4e55vXacjosR4aToJK20QxC8SSBfoTYg2NmjoJ4LG+0Tf9zG+kZhaKrJcblMotytUm5Wqbdzy4m/5Cmi88dZ6XTs8gSaDIMn4gbERv3T1COmWiaYKw22jZBH5Aq21TqbXJpGKkUjEkXwBYmVSUXDrKQEEoiSdGRcav7/u4nk/HcomGMzDP95mZLwHifWEDvNi1ZYCIruE4LtV6GwJhNu77AStrNbaM95JJmjiuJ5xebFfciwFdy9bZhRIr6zVc1+/2M7u3DrJppAff9+lYov/WVAVZv2HDl06YBI71PTn/b6TeNFjyzne+k1//9V/nYx/7GCDedGZnZ/mVX/kV3vOe93zXD/Bm3awftvI8j9/93d/l8uXLVKtVmo0qhiazMjtBwbidPZMJtv3Dn2VgaIRG02JtvUSxVCG4ep2tmyfYND4CksTicpnllTVOnblAo9HmXY8/wNbN4/QWcpw8c4FY1CSVSqAqKvfedYSR4QHe8ej9JBNxevLCiqBWb9CxBGprmhH+8COfwvME0puIx8mkkziui2HoTF2fZWZ2gU6nQ73eolytous6f+cDP8HYyCD5fI54PM7WyUEmhlOkTJeoNMXyhZfwXQtZ0VGMBJHk8JtSj1i2w5XpVa5eX+Pa7Dpr5Qa6qvJv/uHbiZkGP/H4AUaGsq/rzVgsN5ieL7G6Xme1CobSZutEL/lsnP5Cknc8uAczouN6HuthdsbwQBbP8/niU+dptCzWy02KYRD2kVvG2bVlkEfv2Um10WGoL4MiS7Qtp+vP7XkBjZbFQG8KTVHwg4C1Up0vff0CL52e4Ytfv8DSapUggIGcxI4RhXcekNk+YjLe99ZsD1QjTdVKsFxPULUzaEacof40sWwvkqy+oaHLW6lGs8P5K8sUK82uXVbU1BkeyKIqCtsn+2i1bepNi+tzRTzPR9fE8eQyMfbvHO76oNabIhx2o7749Qssr1VpNC1czycIAnZtFVLUwb4MIwNZCvnEa1QnfhAIMGu1ymqxQancQJLh7fftYWQwS6PV4cyFebwAZEnC830WV6r8k7/9ILGYzlqpQTIRYXw4Rz4TJxU3u5Yst+waYXGlQqNlMT1X5PzlJbZu6g2ZMB6r61WyMb9rrZYIf65SazE6mKVcbbG8WqXetEglIhzZP45k2Vy8uowXetJLSBi6gmW7RAydPdsG2L9zqGsnkoxHurLbQvaG7YPtuDRbNo2W1bUaOXZqmqXVWheEsR2PA3tG2bG5H1mSKJYb2LZN4PvIks/1mRrVYhxdDUjGvjc5NT+sdbM3uVkCqAwBjsDDcx0cq4bVAAkvBDhC5YfbwXMtfK8Dvk+AJ5r/wBcgSJcBHU4/AwlJUZEkFVnRkJUIkqqArAhQ5NsQBnzfp1ZvUq8X0XWN3kKeYrHM57/8dRzHwXFcHMfh43/2Of7G+5+gv6+HhcUVrk3Ph7L1gEazzTPfeIkH7r2dSrXO1PVZbNshkYhx+223cuTgPqJRk82bxnjxpdMi0D0RJ51K4AdQrlS7WWUvHDuJLMtIkkSt3mRhcYW77zxMEAScOX8ZGcikk+TSBfJpHdOfZ+3aGe7Y1kDdFkcKbIKghUSLwG5BJIYeK/DVb0wxs1CiWG7QbAmVwXse2c/BvWNomoKuKwz2FSjkExRyCTKh2jMZN79n1oKNZodmyxaKiI5Nu+MwOpgll4lzdXqVl8/NvYq11t+bopBLoKrieFPJFGZEIx4ziJp691of3Dv2LYfZvu8LpUVoX9Tq2Ph+wI7Nwhv9U188SbVhhey9AIKAtz+4h/7Q4ujy9RVURRG2XqpMqy02ihFd69octS0HLDHE2KhWx6HW6KAoMp7nUW9YjA/n6c0nuTi1wjdOTOF6YoPs2D4razXuvW0rzVaH42fnkBUZQ1NE6H3C7G5yn3hkPxCQS8fpycZJJU3MiBhMvf3+Pbz9/j2ve+4VRSGbjtLqOFTrDSq1NovrS9y+y8R3m3zpayeoVUtosoWpusRNn8oVDWvaZVxuM7H7tehDfUU8xl87z/mOq97+JiCkHAgwpOSzWgnYIOcm4xExgMjEGByNsXtvjGw6Rjb8XDYVI5OOkk3HyKSirws4vLI2QLNKrYVlu9iOh6bK9OZTeJ7PsXPTOK6H7bjYtnh86K7tGLrGiydnWFypdJ9LliX27Rgmm4oRCTN7zIhGxNCIRvQuuCbLMo/et+tbHtNGGOxbLcf1sGyXZtOiXGsx2JcmHv3hCi252TfcrP9TKwgCPKfJ6tI0X/jcpymuLVOv1+jYLopq4ssx7rz9AD/9/vfyyb/4stiD2A6WZXF1apqJ8WHyuQytToczZy8CPhKQTifZs2sr2Wyaw4f2sW3LBGNjQwz0F0glb+x5FEXh6tQM2WyKPbu2kcumUFWVdsdC0zTaHYv/+bsfpVSq0Gq1MQyDnnyG2w7vp7+vhyfe8RCe57G4tMr8wjIvHDvJrh1b2LFtks2TYwwN9pFMxMWc5fR5Ll++yGP37SSmtvArx1mdXUCVA+Y7PpKs09+f59ZbJ1CjLqMjVXRNIQjE3sw0xLpvRjQmR3uwbJe1UgPP88ll4nQsBzOisy3MX/h2571Wb7FcXeXi1Ap3H54kmTD5fz/8LGcvLxLRNTLpKHt2DDE52sPUzBr1hoXteHz8My8zs1ASlphvgmmfSpiMDmbZPF5g55Z+towX2DTaQzb92rmL47rdMPCLV5d5+dwc5WqLZtvCtj1GBjP81BNHMCMqz798nWK5QbXWpm2JXubxB/YQMw3Wy81QNRonl46SS8fYu3MIgEfv201PLgGSUNIqikQ6EQ3vC5l226FYrBHVO2SjHXKRNuvnrlAP1hmiA28tyoMgkLBJ4MoZJKMHPVZgcGicaPR7lx7ebFmcu7JEqdykXGtRrrSwHZdf+tl7kWWZ//nHz1CptYVVliyhqjKXr/fSX0iTy0QJCHDdgI7noMgylXBWk06YDPSmMHSVmGkQjxskYgYRQ6yxt+7ZRKsW6/afy2t18uHMod7ocPnaChFD9JMbwAkI0C+XiVELLTgdx6Vtuayu18imY7Qth+uzIojdcQVZs9m2ufPQZizb5eylxfC1yMiKhKFr4m9I0RnoS4l9gKagqgIUyYRE2aH+LDs29+N5Pp4fIEkwPJAJr9sP1r7zTXct/+k//SceffRRCoUC7Xabu+++m+XlZW677Tb+/b//99+LY7xZN+sHWkEQUKvVqFar5HI5omaEXds3US0ucMuOXkb6k0LRIBvYgUn/SD/TM/N85BOf7wIX6VQS13XZMjmOYeg8+fTzzMwuQ+DhBz7xWJTFxRUy6SRf+do3WFpeRZFlDEMnm0lj2Q6KorB50yhPPfMiLx5vdN88MukU27ZMIEkSd91xiHjUJBYzqTdaFEsV6vUm8ViULZNjvHDsJAuLK7TbHWRZJpNJ0WzUiOtt/taPH8SqL2A3l3GtWYJWgKdH0SJZFM18Q+fK9TyK5SaLy0JKd+vuUaq1Nr//8ReImjr9vSn27hhi66ZegiAQwZo7hwFhq7VaEpZaQ/0ZCrmEGE6vVenJJhgd72e0IHV9IC3b5drsugBSinWKpQaeH/BLP3svhq5y6vwClXqbZDzCYH+GHZv7u4DMyGCW81eWhC8jwtJhY2iTSprUGh2eeekql6ZWmJ4XtlCGBpsHZe7YqrDjIYNtIwrp2JunLgYBNN0oDSdNzUlx5MgRUuk89uwaY35AX0/yu87Ya7YtiqWmYC9UmmRSMW7dPYIkS6yXG+iaSm8+AZIULnwWMdOg2bZZLzXIpmIM9qWJmnrXWz0eNTh1YZ6zlxaxbBfPD/ADn/HhHJIksRwGxPVk4+QycZEVEnpX3n7rBOVqk+W1Gi+emmZ5rY7nebzzwb34QcAf/tmLFMsN4WEuS+i6yvmrS4wMZtk20cfsQplkPCIsO5JR+goij0ZVFO4+soVKrUWjZbGwXGGBCoWeBBFDo97oUK62iMcMchkho92wRomaOru3j+K2hU3X5eVVJoZzDA9kWV6r8ekvn+7KmAMJmi3xc6qq0NuTJJuOMtCXJp+Jk06aXVb0oX3jWLZDqy0GdeVKC01VujLXLz97kY7l4DgefhDQanV45wM7SSd1KpUS16eXsB0Hy7IJCHDaJcZ7OuRMl3q1CJKCoWvomoYRMbClHLneQbamNhHNbPqu3kc/zHWzN/mrVWKA7L/K3kqAIG7XBivwXXzXFlZXvi3yP0KgROSEuDTrPlrnm0jmkowkK0iSGj4qyLIGSMIS6w0CIK9X7XaH9WKZbDZNLGpy5eo0p85cpNlqddfs8dEhegt54vEYuVyG4nqJ1bUS5y9dRZYkXnr5DI89fA+u53L58nUChIVFxNA5dOtubNvBcRze/753YEYjWJbD0vIq12fmOXRgD6qqsn3bJq5cnWF5ZY1Gs4XvBzSbTR5/9H76e3uo1Ru4rrDhDHyPXTvGsesLBFabn33XLnTq+E4V3y3juR1qizNIsoaCxomLJdZKHdZKTdbLTZoti3/2C28jruhMzxep1Tv09aTIZ0XG0wZj8PC+cQ7vG//Obw7EINbzfCKGhuN6TM2sdXMVWh0by3J52z07kGWZF0/NsFasA8ID24zo9IZh39lMjL07hkR+SETvDpUBNFXhwO5RbNfDdT1cVwAgUUMjEtG5OLXMtdl1yk0Fz6rSbjts3VTgntu2cnVmjT/4xAvdsMogEAP2DbBkcbVGp+N0wRBNVbrrSzp1A4ggANf1qYe5aZ4fML9c6YZrWo6D6/q899FbkCSJrz1/mYWVCo4tcsAkSaKQT7B5vICqyui6Ri4eEQqQpMnkmADPhvuz/Nt//DjZVIxYTH+Nj/b9R7d9y2th2Q7laotqpUajXqZeq2KoNjs2pWk3K7x8+jIR1SGqe8QjHqNGwMJJ8bO78kD+m5+xjed814UhADQ7AevVgLXqK4CQsk+pLuNKUUxT9Cu5TIxcPsa+yZgAP9IbH1HSyW8NfgRBIJSejosdDhIUWbySucUSlXq7C3bYtsumsR6G+jJcnyty7NT0q54rl4nTm08hyxKVWhtDV4joGom4YDZvvD/t3NrPtsledE0lYqghI1d8beO4v5PaAGks20VVZJJxE8t2uDq9RsdyabQ61BsWlu3wnkeEEum//96TrK7XQ0axGMK8/92H2Lu9/zs6lu923ewbbtb/SSUstho4nQrN6gKBU8dr1Qi8FtWGjRnNEolJqKrK9PQ8d9x2K6YZwXNdiqUKkizTaAgLzk9/9qu86/EHmJldQNVUCj1p8llhl5XLZZBlmdsP7weg3miysrLO2fOXWVlZ5647DpHPZThyaB9Xr81y/OWzLC6uUGs0yKRT/ONf/gCZdJKJsWH27NzKpokRBvt7SaeT3fe2cxeucOLkuTAXIc7o8CC9PcKOM2oofOTDn+LS5avUamXw2sRMibx6lUN7BunriXFlOo6kquQTGqmESSEvBskd28HzApqOjRnRGSikUEMLKkmSKFXFHm7nln6G+zPfdr++MesIgoCPfvolzl9ZZnG9TashiIDJuMHdR7bw7rftY/e2QYplkXv62a+e5UN/9HVc940rRbLpKKMDWTaN9bBrywDbN/exaaSnu/feKNt2u7ZLZy4tcPnaKmulOqVKk2qtzSP37OSe27ayuFrhhZPTeJ6H7Xi02hZrpQY/9cQRAC5cWULXxboz0Jsml4113TTe/66DLK/VhJsCEPgBcnjtIobK4mqVdscWfY/v0Jt22d5XAqfMoYFpInIDRX7FkNyBN34mBM3Jl5JIRg/5vlGMeD96tAdZ+e6wLGzHRZYkVFXh0tQyL5+fZ71Yp1RtUam1GexL849+/gFKlSb/9XeeFBbdmoKqyOiGSqnSJJ9NMDKQCXOIRW6bqsivWr9HB3PEYzqmIfrTDVvyqKlTyCexbWFlVq0JJ4+92wUg1Wx2aDYtNE0mHmaobKzF6WQUx/Wo1Fp0LFf0zpYjrDV1jeNnZplfroicQEnMOfZuH+yeWGHlGRG5JbrKyKBQbSUSJvfcthlVUcKMMgnP97quIz3ZxKtiy1RV6SpaUokIEyP5rrWnoavEw3mTpim43o9QwHsymeSZZ57hq1/9KidOnMD3fW655RYeeOCB78Xx3ayb9X0r3/ep1Wqk02kAnn/+ea5du8bs7HUqpXWq1TLphMnP//RjTBRUjCPDLCxXuT5f5sS5eTzPC7NH0iSTcQrhot1stVhYXOHl0+cxoyYSUCxWWFpexTAUYqZJrGCydcsE2UyKRx8WwWWZdBJVVWm1O90FxjQjDPT3smP7JJl0inRK5De4rouqqnQ6HZ79xkssLq1SqzcpFcsYpsGv/rO/T6Enh67rbBofYstEL5uGEyT1Flbla6ysNvA9R6hH9DeuHtloAq7PF/naNy6xtCLsizzfZ3Qwy627Rynkk/ydn7qTvnzqVXYRG3V1epWpmXUqNRHQFYsa5DNCwrpptIfJsQKu6zO3LnNtbpFoRGPTaA/VWpuP/vlLVOptavVOGAwlhvT9vWl+9n23oagSEV2jY7msFuvdpkPXVBKxCKmEieN4XJ1Z46kXLhMzDY6fneX8lSVySYkdIzI/dqfCjpEIE/1vzVLLD8BM9BNJDnH8skXdSRGLJcnkooynTCJRIYvcsK34TqtjORTLTSRDI56mG8QZgFikNQVNFW/9MVP4mdcbHWRZIhGPkIhFsG2XmGmwOVS01BsdltaqOI5HLhPjgTu2i+CvRgc/EIMg3/dJJcyur+nRA5vQdRVdV2m1bFbWq7zw8jR3Hppken6d//w/v0Kj2cH1fDRVIZ00uefIVob60zx+/y7WSsLH0jR1FEkiH1qsjQxmObxvrGs5Ua23WS83mBztQZZlfN8nFjUo5BNhg2CQTgqwb9tkH5PjPdTqHcHwqLa61m1PP3+FF86s4DlNbFfkCrQ7Nnu2D6FrKrqmkEoIlUpfT7Kr/lAVhZ//iaO4nk+90aHW6LCyVhNDqlyCqdk1nnr+ClbHotW26Fg20YjKz7xnDxENLl+ZYmmtjuf6dBwPRZbpScHb7tpKJmlQqjnE4zH6BgZIZTL0FfLkNx1BVnV+On0XihYhkUgRi6eIx2Oo4bXt1OYxYt8bRdIPY93sTX5464biYyPfw+uCHq+f82Hj+44AS7p2V+JnCIJwirrxXiwjyRuWV0qo/DAE6CHJqK6DkTC7a+j3qnzf59r1OZ4/dhLXdbnjtgNMhhabuWyaRCJGo96gVm9y7MRZBvp72Tw5xvLyOl/88tO0Oxa1eoNEIsbqahFZlomaJg/cfzuD/X309GSJRAxiURNd1zhwy24+/mefZ2lllU7HxrYdIhGduYUlxkeHKZWqHDt+CtfzBcNLFgwvp1NF9kr0pCSyqShRzSEdCxjtW2Xp4idpU0DqzHNuoUmx6lCsupRrFqap8zffdzvNtsVnn3weTVPJZaKMDeUo5BPdgfDP/fgdb/kc2rbbzXSyw/f3Qj5BKmGyuFLh4tRKN1vBdT36CqluNsK5y0uYEQ1DV9FUBTMEUQxdZtNonr58AlmVCfwAx/XDjZzYwE/PFYWNomXT7oiB8E89cRiA//q/n6TRtLosusAP+Fvvv4PNY72cubTIy2fnUPQYKTPANLVu/kw+HePA3lF0TQmZk/KrlCdbxvKsFBu02kJNWCw3KJcbjAxkmZpe44WXr2NvDN0djy3jBaEAaVu8dGYWJWTu6ZqCYQhrBF3X2L1tQAxHEhFy6Sj5TILRIbGRfccDe3jng3tf99yrqsLwQAbLdqnVO3Qsh0a9zlCviYzNuQvXWF1fx7PbBF4bCZv+nEEmodBqVFHoENEhC2RDJ6nStHjc9n2aj3fsgLVqwFolYL3mU6xB09bwJRNZS2CYSdLpND25OD2bEmzOxsilY+Qy8df0pkLJecPaKh4VqqJiucGZiwtdRqXteKQSJrfuHsF2XP7s8ydfc1zvfGgvEUNmfrnCeqnRtZnYYHUC9PYkOHpwExFd3MO6rnR7KUmSQmvS13/dr6fKfr3yfR/LFvalybjoieYWS10F2Mbr3T7ZRy4T5+LUMqcuzHdzbGzbZXwkz2P37Wa91ORjnxHrrCLLAojTVHzfR5Zl+gopMqkY0YiGYWjoukJ/aAHyw1Q3+4ab9Ve9gsDHteqsr8ywMHuZleU51tfXWVgqs23bVu6/9y7uuy/HH330z7FdB9/zsW0HRZFZXSvSW8gzPj7M1PU5oWQPAqIxk/17d9Df28N73vkwU9eX2LZlmL5CD6qqUC5Xu7//C1/+OkvLq4Agd/YW8jihJeDKWpFPf/YrRKMmmXSKrVsmGB8bwvcDUskE7/+xdwBQLJaZmVvkmeePs2v7ZsbHhslm0kyMjaAoMkvLSzz91JP88R8v8Ld//BBJo4lVOUNMrjMwHEHVenADhXh6CDM1wqBmsbctQGXbcilWmsJaCEjEIowNZwGJZtNifrkSqkL7iJkGD965/S9VCjqux1qxztXpVc5dWWJ+qcK/+qVHiBg6py8uUKm1GRnqZ6x3mGbb4evHrvIHf/YiJ8/NdUkRf1lJkkQuzOU8sGeErRN9jA1nmRwtoOsK7bZDq21jO243A/PYqWlKlRbrpTrFSouO5fBTTxymv5Di2WNTTM2uh4pVn07HYb0k0r1W12tcnV7FD4k+iiJ3FYcDvRnedvdOHM8nGlHxfGHhtLBSJZdJYOga1+eKXYWhBJQqDQ7t6kH3FtmSXyRjdkhH2piaeN2NxWkAYm9BgKgaKfRYL2q0l2hyACPWi6wa3/4HX1G+72M7HtV6m1JZEEwP7xtH11V+56PPCUvxeotm06ZjOzzxyH7e99itvHxunk9+6RSRcOYRjWiESzvppMnRAxPi3IbqmyAIMEPLuJ5cHD8AXVPEPkWCTDizyGeF+wYQEmuUru2bLMv0ZIWqyfM8/ABcV7isxKIRvMDn2KnpcO7g0rEdBnvTbJ/sx/U8nj9xXRygJBHRVUxT69qIbhrpIZeJi3467FeG+tIAjA3nqNbb3cw6oEswUhUZVVEwdPVVoIcfBMjAvp1DENywJH8lKac3n+ySmH7Y6i3rYe+77z7uu+++7+ax3Kyb9X0t13U5efIk5XKZcrlMpVKhVi3z4AN3s3VyFKe5xPHnv4wsCS/prbsHSaWSKIqCZmYoN9eYXaqhaSrRqEkQBCwsrLB96yYcx+ULX36GxcXl7kxnoL+XaMRgYKCPvXu2sXPHNiYnhoiaIiRKUWQ0TWNibLhrzQEgywrpdILRkUFMM0Ium2ZtvcT16XmWllcplirs2b2Nh+67A1mWeeGl03TabZBk0qkEuUwKq7GGbM3zxL19SM4arnUdv2rRlFVUPY4e60VWXgtkvOp8eR5rpQbTc0VmFkosrlTYNtHLo/ftxnM91ooNRodyjA1mGR7IkM3c8OycGO7BdtyuF/dasc6BPaNk0zGRiZEy2TIhrDle6dm8Vmpw/vIS88tlri00KRWLxGIG//4fv4NMMsr8SgVDU9k02sPm8R62ThRIhJtAVVW4Oi0atKipYxpaN9DUclw++hfHmVsU+RT4HSYHFCYHZN59QOZX3mXSk35rNkaOJ7FWN2l6aQK9l0higLt2iQyO2/IdzIjeZXR8t2pmocjcYplSpUW7Y+O6PsOjDv37e4hHdYTLR4CHT9vzkaR2F+iaGBXgge/7NFoWiysV0kmTTCqG5wsAQNdVcukYrud3F+6O5VJvWiiKTNTUiESiRHSNy9dW2DLRS71p8ZEPPyssUNrCT3NoIMMdBzeRTIjrnUvHKOQS6IaKLElETeHnX+hJslJssLx2w0u6bTmMDuaQJATL0lBJxA0KoWJlYzHe8Lb3fcHG3QjABfjE504wu1CmYwl/esfxeO9j+zm4ZwzPD3Ach2hEJRuJEo3oXdbtYF+K97/7EDFTMG5tx6PeaLO+XiaT0vni0+d54dQs7ZZNy7LxPZ/h/gS//FO3YgRtXj59Cd8XDBRNU2m0VNqOQa4wyI6dLpn1JslEgp6eHD09PYyNjNA/0E//bp2H36t0AZBvrp1pwdLuhINWxxGg6bXrszitVXYVvrXFxl/VutmbfH8rCHwCzxEAh2eHwIdN4Dt4bgfPab5K8eGHj/gBSK+gGAWSADtk9TWZH7IaZn+En3+j5QcB0puOdnz9ajRbNBpN2u0OrXaHdrvD2OgQ+VyGi5ev8cKxk2KIEDWJRaM89cyLTIwPM9Bf4L/8t//F1LUZXNfr5hUlEjE2T44x0J/HcT1UTWXz5BiPP3ovt+wTf7ePPHQ3Tz3zIitrRRYWl+lYNq7rMjYySCwm5Pnr6yUs22F2fhnbskglE4wM5MilFNJxhXw2TiETIR2DiFJh4cxHIHDYP1pjcbXBWsnlSt3ji1+32bl1mAMHe7k8t8qffPGM2PDoGumUSSEn2Gwx0+Cf/cLDxGORv1R588phMwibLcf1uHxthY7ldnMWOpbLQ3duR1FkvvbCZZZWqkKl6PkEBBzZP8HubYO0OjYrazUUVQ5Vt2pXqSMGzyrNlkWx3MT1hApky0QvRlbj3OUlzl1exPOEx3IQBOzfOUR/IUW52uLY6RkUWeoqGVPJG0rarRO9uJ6PqgrGn6rKZMLh9ME9I+RSJstlCatVot7sUKo0Aai3Onz12UvdIbRtewTAHQcmURSZT3z+lCA1SIT9n8T+HSPs3Sk2kvFYhKipEY8Ka4WRMMB+oJDi7/31O4mZBpGIhq6pAvgJr8U9R7ZSCoM3XdejZdnUGm0SpszM/CJnzl7Dtps4VhPXaaPJDvmUhoSFYzWJGQFxExImaIrEypo4D1kgm/nmq1wHG2J/eev4HZfniTyQUj2g2pJodGRsT8eTIkhKDNVIEo2lSaVT5IcS7NktPONTicirwnU3Bvme57O8VqNjOzRagilr2W43o+OZY1dZXKm+yvrhwJ5RNo320GrbrKzXw8B6BTOiddXOmqqEAJmKripouoKuCssJgNtumfiWrzFmGq/JKwFelYESBMK7fUPlFIloaKpCpdaiWGlih7kllu2SS8eYHCtQb3R48huXsR0XL/Syl2WJv/bYrQCcubRIqSLuwyAQwUvDAxlymTjLq1Wm50piuBPSQTcCXc2IxnB/umu1ISF1gR+ATDLKeqmB7XhhRo6Kbf/w5qjd7Btu1l+Vcl2X9fVVVhZnWF+dZ/+OAVy7zmc+8yRXry8TSCqg0LEsKi+d4647biOTFnZ/7ZZwnZAlCcf1eOrrL/C+9zyGLMnCjqcjhtqtdoep67PcdfQgY6NDNFsuK6tFzp2/wnqxjOd5vPfdjxCPRRka6EVRFaqVGjOzCzzzjePEYyb/4v/6exzYv4uefJZ0KvEqi66NunBpitNnL1IuV7Edh1g0yhk/YHggS1Sp8alP/gnLy0uokksiKpOKaxRXr5IcHmDH1knOyatomiLUgqEtIkCn4zA9V8T3AxIxYan4SsB8YamC6/kU8gn27hhiqC/T/forgZIgCATQUmuxabRAs2Xxzz/4KUrlJq7nY+gq2XSU2YUKtUabaETn4tQKL5ycodHs8O0qk4pyZP84k2M9mBGNseEc40NZVEWl1baJRYVtdq3R5otPn+/2W7YjhvI/9cQRHMfj9MUFypUmtuvjez6e73d7hvmlMpeureDYHgFC4RCPGbz3sVvZPtlPLhPrrmeRiI6uKtTqbZIJk0QiwtxCiUaDLiGk0RL3SCIqM94XkIm6pM02SaNFXJtj7sRxAHZ8B1w+STEx4v1EEn0Y8X6MeB+K9lorrQ0Fp6LImBGdUrXJpasr1JodKtUW1XqHWNTg/e86SLHc5J9/8FM0Wxau6+F5QlUx+WsF+gsp1kp1Wh2LTCrKUF8Gw1AZD4koO7b0Ua42sUJbVcf1yaTErETXNRotG0WRiBg3CD2O6wIGY0M5FFlB02QkJBzPp90R+5ZUIiLIQ47olZstAYLt2zlMNKLzzLGrTM+L+9jzgxCIgdGhPKurFVbWa0QMrUsg3bB0N3SVuw5vRtMVlDCXNwgCtDBrNZsR8x9JkrrAhhICG6mEKXprQyWiC0BkI/NX11Qef+D1LVnhjRM7ftjqDYMlL7zwAqVSiUceeaT7ud///d/nV3/1V2k2m7zrXe/iv/23/4ZhvDkU72bdrO9nBUHA3NwcIyMjEHicevlFVleXcawWnXYdXQn4ymeX6HniHrYMa7z37XfQ6ni4gUyl2uD6/BJ6dJ47b++jWmvw4kunKZertNptbNthYmKYyclROh2LiKGRy2cwdB3TjJBMxIjFY6RTCQ7s383zx85y6co1gnB6Mj42TF9vD/F4jN07t5JOJ0inkuiaRqUqQBkQzcOJk+dww4BOz3M5eeoC9999G+OjQ+zYsolCT5SBvEk27hGVK7QXv0jDaSPJMooWRzNzKOq3lo4GQUCp0mJmocjIQJZ8Ns5nvnqWZ45NIUsi3LGvJ0lPKFvdNNrDP/nbD3aHJ7bjCrujUEL39Revdj2VhXQw0WV2bh4vEAQB5VqLuQUBXmTTMfZsH+LazBof+qOnhRcmCjFTYUCRsWwH09T5tV9+DMsR4delcpOpmSJD/VniMQNDU7DDIKprs0UWV4TkMx7xUbwSm/plbrtNZvOgTOE7sClodGQaboodO/eiRfu5OOcwMpF6Xbbid2KvtWF1Uaq0wscmRw9uCkM8RTCcJEsEPsiKRMcSi20mHSOVFEMDCTFA7FgiUDWbiuF5PuVqk2RcXNOIod4ARDoOK+t1KrUWtu2BBKl4hJ5cQlz/TJznT16jVrdohuyF+45uZctEL7W68OAcG8mTT8dEKFyYC5NNCeuTpZUqxXC4JMtSd5OeiptMjvZgmlrXGiUa2pIYusaDd27vnhffF2G1qqLQats8c+wqK2s1ipUWrY5Fp+Pyz/7ew5imzsJKlbVSoyvvNF7B3ty9fYCOZxLXW2gqyFJApVykuKYSjyg889w5Llxdo9l2aLTFoO+Ruzfz7gd3IMsB1bpNzDQZ7ukhnUoxNNBLbvxucpLO3/u7e0km02QyWZKpNNFoAjP0SP2pybe98XvAsrsBhmfOXWZhcZlypUq93sSyLHZu38K9dx/BMAzKa+23fK/9KNXN3uR7W0Hg47uWsLoKARGR99HBs1v4bluoRDxXqEEIujJrEW5+40OWDJQNi6s3kXv1/axGs8WpMxeo1wUw8o7H7kdRFJ557iWWw8mxoihETZEpls9l6O3J4boel69c5/S5S7TbHSTgyMF97NyxGVVR8X2IRqMYuoamaxR6xCYrEolwx+23oGsaqqZy+swl5heW+dt/8yeIRAy+/txLOI6L53t4rrC0OXf+Crfu20y9ss7JkycBn2wqyuH9vfTqUyyeXSePxd6RBtVGkZU5i4t1l44V8M6H9zI8MMDxZ9c5fWENTZXRdRUzohOLivVp83gvv/g37ukqDV8L7kusrNdotCwajQ61psWmkR4G+9Kcv7LEsy9NhWw3H8/z6S+k+Ml3HcL3Az79lTPIklBFSDJISNx+6wSphIkfqj5URUbRVQgES3Oj1ssN/AACX3gaJ2IG9xzZAsCFq8t0LKeropEkwpyPOLqmhiQFMdSFAC3c+MViOslYBNfzsGyPdsem1rjB7nz6+Suslhq4rovjirXmZ957hHc+tI+PfeY4Tz53hQAFRRYby81jPbz7bftQZIX1ciMEnFRS8Qi6ruK4Loqic/TABGtFYYFpGCqKLHetDDaP9TK/VOmev2K5Sa3RIWZqVOsNvvjkSWTJQZE9NNnDUD22jaeJaB71eg1NsolFIB4RwEenCbMz4pXveY3l1TfX98/qwHGh3paotWWalkLHVbE9Q2S2DQ0gazFOXiyi6lHMiI6Z1DEzMk/cuxNVUXj+xDVW1uvhdXEpVdtsGiuwebzA8TMzfO35K7iuj+cJVdBgb5qf+4k7cD2f3/7wMwBoqiyyaTSVnZv7SSZMOpYAuDRVZMYY+g1gLhYT4JW4xyRkWeqCAJIkidwx3xLgA6KX3jxWIBY1mFkosrJexw+t2Xw/oLcnweaxXkqVBs8dvxaqiYTFnKrKvO8xkePzkT8/RqXe7irkJBkevnMHo0M5ZhZKnL20gKFrYrCh3bCvkGUJwxD3uhUI1ZbzCluXa7NrrJWa3XtNArZP9jE6mMPQVcyIsNkwdIV41KAvVIdETZ3x4TyyIiEjoSgCaPS8AFmGob606HdDJaDr+kjyD85G45vrZt9ws/6q1AbxrdOu8/GP/TGl9RXsdpl6vUrg+ziNWzl8+CD33f8wV37nI+JnfAG6tsoVZuaXGBnsZ2Son6efPRZaCYoB811HxfvP0vIasiwx0FdgdHSIibEhCoUbi8nzL75MLKoRNYVCxHEdZmYW2LljM47n8Sef+CyRiEEmk2LHtkl2bd/cnReMDossy1arzdWpGS5enmLz5Bj79+4kCHwuXprCUAMcu8XCdJUrfotdhWvIQZu+ZJV8LIGmRZAVHUlWkPUeNDPD2LBJ1DRpWw5rRWFt1deTZKA3TTxudAkg9aawbJZliT3bB1EVhfuObiUeNV4Ftm9UsdzgqeevcG1unbnFMtVaG01X+ND//ZNdJ4NoRKdWb7O0VuPi1WU+++T5b5u7kE4KUsroUJaJkTyTowVSCZNdW/vJZeKcv7LEmYsLrBeF6kPTFIb7swwPZMOBuljnmm0b1/VIJ0wkSULTFC5cWWK93KDZdGi0O9i2x7ZNffzY4wdIp0xUWcJMGKiKTNQ0usrDLRO9bJ/sR5bF82yQSjbmN/l0DFnyyMUcEkaHqNqkN3iBuRNfIWtVuWvsrd7VN0rRE+jRAr4xSDKVIRLvY60qFCB128VadaldW2L/jmGips4Xnj7P+ctL1BrtbmbcA3ds4/EH9vDkc5f5+GdPICFIKqoqMz4siCiGrjAykMbQVDRdQZMVVE0hkxJ79eH+DI7jYTlifuI4Hivr4lp4XsDSai0kPcvoqkqzbREEQRcwK5WbNGyLWl04alyZXuPg3hjNlt1VEruhQmRkIMuhfeNIksSJs3NifQ3XWEWRu3PD0SEx79J1FUMTCs6dW4SUd9N4P7ibQRIB674fkA1fi6apoeI/QDVUYqFNl+cHKAoc2juKqiqvsvvcqO+GteePWr1hsOTXfu3XuOeee7qNxZkzZ/jABz7Az/zMz7B9+3Y++MEPMjAwwK/92q99r471Zt2st1Rzc3Osry5RLK6wurLE5UuX2L5lkAfu3M8jd4zyqc9OEc2YGJEMimrgo1Jtmwz0F3DlBi+ceJEAMai8fOU6K6tF9u/dQbVap9PuICsSfb09GIZOX2+e8dEhdF2j1W7TaLRQVQVFVmi22rih9FTXNXK5DMMDOVLJBKp6YzNmGDrVap2XT59jba1Erd6g3eqwa+cW/sb7n+DuOw8xM7uAbTvIskQsFmWwL0Wneh27tcbbD0t49gq+24YO+FoslCf2vS4TdKPRAnjm2FUuTolw7XpDvNG/46E93JndzC27RhgZzDI6kCWdNF/VRARBwKVrK5SrYpDfCKWkb39gNzHTYGQww1B/mp5coruJ2/BOPH9liS9+/QIz80XWSg2aLYuRgSz/v197H5vHC2ye6GViOM/I+ASjPTKO43UH3Fem10L1QUDHdmm3bU5fXGCg0OCTXzrFCy+dJxvrsKlf4uAtMpMDMvmUDLxV0EKiEyS4PB/gKnkK/ZsojPSzNZcgFS4et6Tf4lO/oppti3K1Rb3RYfukWPg+/9R5VtZroQe2DEgsrlTJpmL09iSYnisKloAsQwD1hhiW65pK4EOt2caM6BiGSjYVpdm0xM/mE1ydWWVxqsJqsUG13iafifF//Z2HiMUMrs+tU6t3wjD1gDm/zKH94/QXUpy9vMRqsUHM1Bnuz5KIG4z0Cfrp0QObxHArDKELArpDCEPXGBvMMdSXIRYVOSjRiNa9p/JZwQzdqA0/TxBh69fniqyX6iyv11kvNkjGDX72fbcjyxJf+Pp5HMdHlkBRFVRZZnm1zMhgivGBBHa7haKCIjmois25c+fY3O8QUzzOnLtMp1XF8Xxs28f2AvZsH8ZIFJhdu0DDiRFPxxgaT9PXm+euOw/Tt2Mr79zkcd/jFul0iljUfM3f2T33bX3T94Dv+1y8fI1KtUa1WmdtvUSlWuPtb7uPifFhGo0GLx0/AxJEIgZmJML1mXkOtzsMDvSiU/32v+SvQN3sTb47FfgentvGdzv4roXrtHCtKp7dJPAdfE9YaG1YYHXzPmQNSTZQ1BiSor0p5ccPonzfp9OxaLc7dCwby7KYGB8B4PjLZzl/8Sq6rpFNp4iaJgtLK4wMDTA01MfzL56kWqtRLtcolqp4vsen/+RDZDIpPvaJz1IsVYjHo+SyGTLpJFpoV7B16wS9hTypVJxoNIqmqezYNgnApvERXjh2iuWVdebml5AlGdM0sNpNZMmhVS+zsLSCFPiAh6FJnH/pz+jXRhiKLzJWsNBVlYjRYX11nrU1hW2bDqHqCU5emaLesoTsXdfRDRXNiCHJKkduGWfPtkEihoqsyCGLTrxvaarCqQvztFoOzY5FqyWCyv/+z95DRNf4w0++0GVlKmEIuXQXDPalsSyHcrUpPq/esKACwWZLJcyQ5R7ghwObRrNDKmGyXm5y5foKnud3bY0jEZVbdo1gdURG2caAWpKgWr9BRmi2LKr1Np7ri4wRT9hBjAzmuHRtmWdfmgoVKx6eHzA7WeaBO7ezslrjC1+/IDbP4XOrqtzti2zXQ1Fk4rEY0RC8H+rPYNkOh/aOYWgaDhEiio2hq+SyMc5dXsT1fB69Zyeu72PbHpZl47gOX/zaCQLPori6gt1q4uBiyS6a7HHiuZc5/1KA79qkcTD1ADMK0R6IRSRinRP0RyS23ZjvvqJWXudzP5jhtOsruIGOrMaQtShOYCArUVQjgaLF6JBnsDdGIpGkVBHZZY7rdbM8UglTqInaNoulc1i2h227FMtNke3lB6DA1Zk1Fleq3d5AUaRu2H3U1OnJxkNFkMiFKYThprqmcM+RG8M6NwQKlJBVmUxEqDXbeJ6P43i0Ow61kAVcqbY4fXFBgB2BUEDFogZ7Qp/wzz15lmbbxveC0KI04AM/fpTJ0R5eeHmal07P4Pu+YHISsH/nMJvHeilXW3z9xasEAd2gV8O44a0+s1ikVrcIfL9rfXrLzjqjQzkuX1/hhZenXxW4KoJ8B1heq/L/fvg5cX5kCUWV0VWFn37iMKqq0GwJe1BVUVAUAWLaYXaO6/oUy2IYJAAeWF2vc3jfOI7r8WefPxmehxu5PJsnesln4vzp509ybW5dIEaSIOk8cvdO3vPIt2adfj/rZt9ws36Uy/ddAs/myuWLvHziJe65bRea0kZ1V6gWZ6m3HPxAwbJ9vvDUCY7cfpRcziBqmqysrmPbwi5bkRUuXZyiv7eHa9NzuI6LGTXJpJPEYtGuGu0dj93Hrft3oGk6tXqDlZU1Tp+5xLsef4BUOkkqleDa9eu0mi3qDUGAW1ktsnPHZnZum+Qf/fIHRA8UqkdE7pqLoig89/wJvvTks6ysrON5HvFohFpllcl+iHuzSM2rrDcaeK6Drqtomk7Hhmx2iD27NUqVFrkwT0qS6OZqrpUaHDs9g6oqJBMRculYNwtMVWRWiw0ScYOB3hS9eZE9u2ELZEZ0VtbqzC6VmFsqMzNXoicf56efOMJ6scGH//yYyGvQRP9jOx4f/NCXmFsq843j17vqim9ViiIslAZ6U4wMZPnFn7mHob40X3n2UpjXKaEoEn7g44bXIGbqDPalcRyPWrNDudLEyglS5Np6g88+eZZ606LTsbEcD8dxefzBPSTjJuevLlOptkJijFALb6gEH7xrBwQSRkQoiSO6Sn+YA6ppCj3ZOJbjYig+8UiHuNamvvgMDbfKnswKSrr5aotIG96KhjBAQtazxFJ9OFKKa0s+dStKdS2g0bbQjDg//c4t2I7L7338y1RDu1DL9vB8ny1jBaKmzkunZlhYrnRB/kTCIJUQM5+xwSy37B4WKmM/wPH8LoCg6ypzi2Vs28MNVRWyRNgrD9GyHOaXK6iKLIAjRWZxtQKIIPK2ZaPIMh1LQpZsytUWHdvBNHQ6lkOl0UF9BdjU7VtUmVTSDNdmYeM1GhJnVEXm6IEJFEUJz7EEQYCuid6gvycleoZXXIDu/RKNkE3HME0N09CIRDSSYXaNpiq859H9r8mm26jvdm7uj3q9YbDk5MmT/Nt/+2+7//7IRz7C4cOH+e3f/m0AhoeH+dVf/dWbjcXN+r7XRgB7qVSiVFylVFylXi3ztgfvxLUbfOUzn2R6Zg4QzEzP81ldlvH8/ZiJHgr941y6cp1qdZVOKDHVdZ2B/gJnzl7iq0+/QLPRFJsBAnLZDJ7n8fij99JqtdB1HU1TUVThN9hqd7rDyytXp7vHGYtGu4Hv2WyKarXG6soSxVKFVrtNu23xN97/brZMjlNvNLg6NYOh60SjJslErNtkSLgUsgY9mTi5hETSaKIEs6xduUQAKJrYlMqx3tcFR5bXqswulllcqbC8WmO1WOcXf+YesqkY1+eK1BsWWyb6GBnIMDGcIx9uMIf7M2RSUSq1FgsrFcrVFkEAdx/ejCzLTM2sEzV1BnrTZFJRsuloVw0wMpClXGsxM1/k6swaZy8ucMuuYR65dzfHTk3z5a9fwDA0+noS7N0+xP6dYuOZSUV5x4N7WC81uT69wtXLwjohHjMY6s/QbFlcvLpEs23RbFRJGU3aqy9SLAQ8skXm/Qdk4K0zw1qWxGJFp2olGBzZwuFDB/FRGLvFuxHA+h2W43poqoJtuzx3/FrXO7XdsXE9n6G+DIl4BDn0VpVCZoGmKhRDX9GeTIJm26ZTEQCaqigESsDKeo2+niS9PXGePb5Go9Gh3rRotiwO7Bnlp99zhIvXVvjsV8+hawqxqIFpqHi+kOkKVoHwId3g9+qa2vWnv+e2SfLZWJd9YBgquiGWFV1X2b9zuBsKH4u+OjR2oC+F4wg7CdtxqVZbQtYbj3Dqwjwnz81TqjSp1FrUGh32bBvkZ993O1Mza/zn3/4Kvu8R+CBJAamEweP3jBKNyMR0j5rdIWZomCZEdIn5+Wn6c8NsHUswu7iO5UC1FdDsuHhqmvz4fbQ70PYXSebzpNMpctks2VyWyVvfRjRq8q9/7R5UVSVi6K+xxUpFIPUW7Lg7HYvllTUq1TqVao219RKu6/HY2+4hEY/x9DPHxKAUwQQxIxFOnj7PxPgwtx2+BVVVicWixONRErEYsVgUPRzQJuKvlST/VaybvcmbL9+z8ZwWntPGc5q4nSquXe8qRwQbWEKWdSRFR1ZNFCOFLL9l59bva1WqdWZmF2i1O6yuVxkb7mXv7m3U6g3+9FNf6H5fEHr8Dg32oes6i8urnDx9gatXp6nWG7TbFtu2jPN7v/1BOm2LT33myyiygq5rGLpOLB6l0WyRTMS57fB+Wq0WgwO95Hty4N/Y0GydHOczn/8aU9OzdNoWzWaTrz4Z57/8u18irlX54he/jGXbaIpEOmFgNQKOfeV/MtIfY7KvgWxbJGI60UiEAJWI5iPLOoXeflSjRLNjs95wCQIHQwdJjSMrwh7H80TOhuM4eH7AtZk1+ntSnLmwwJ994VQITIhN20Bvmn/6y6MgwZ9/6TRK6D+sKjKqJlMsNRjsy6ApSghaCGVr27KZX6wA0GjbXLm+ih/QZc+nEhHe++gt1Jsdnj9xjY7lCHshSbDxs+kofT0pjp+aYWah1D0e3xe2Q/lMnNn5EtPzRQg2rLRAVWWmZtZotm1OnV+g0ergh18MgE9+8RQvnZ7h+JlZVosNAaArwsZrfqnCf/wfXwzzuXRkSSIgCIdDHv/43/4xnutQLlfxPRu35dOWfaqyz+/87ikMHSKaWF8iOvi6hGRItFtwaU0iqsOQIWGaEM1KmDpdNibAntdYWn1zSfygwI5vLklSQIngSwbI4kNSTDQjSiKRQlJMKi0JWTHxJBPVV8D3mBwtIEkSF6eWqTc6WE0Xy/aodSwS8SjJpMR6ucnZS4uv+n0bAzpNE7keiVika1X7SnHCwb1j1Brt8NoBAV1f7VwmzmBfOlSW+Diehx0OhyRJEE0cVwBnG6zLW3YNEzMNypUmcwtlJEkKbdKkLgFIHIOErIjA3kCWX2XLMtCb7qp6Pd/H94KuDVdPNsZAIYUfWp34fkA6KdZpQ9eIRHSC8Hg8P+hacYhzEtDp2N17PyDovh4jHLDEogaaKqNpKmNDgi2bS8e557YtaKocnqPQriN83smxAhFDWM54vv8qBYjIIVEA8bcRBAHmhhWNIjMylBVKLkkKf69CNAR4Du4bY3w4hxyeH02T2bH5hyfc/WbfcLN+2Mv3HEFe8SysThNF8nGdJieOH2dpcZFSuUKpVGKtWKZRus4T73qUWw8c4ZOffzEcyIp8CUWRmZldYGx0iM2bxwQpQ1HwfJ9KtUY2kyYWNXnHo/cxdX2OXDZNxIyghJaFAKqqcvzlc5TKFTptG2RYXl7jzPnL3HH7Afr7e5menmbL5nHGR4cY6C/Q2ytsjDVNIxGPc/nKdZaW17g2Pcfy8hqPPHQ3dx29BbtTxVRsdm/OoARNyqVV1ueWWL9eIwgkWh0LM5ogl03Sk02QTUcxzBSSrDI2mENTFVaLdS5dW8F1PTaN9XBg9yjppElPLk6741AqCwDHC0maqqLwzof2IMuSyLCstnj+5euMDmYZHczx5186zZ9+/mXaHQffFyq/Qi7Jgd2jXLiyRKvjML9cpd7odNWv3QyI16lCLsHdRzazaSTPwmqV4cFh+rJylyzYXxDB9dsne2m2M3Qsl6XVKtNzRYb7M/Tmk3z12Yt8+smztFs3wJBcOsYdByeJR4W9lySJPXrEUElm4pQrLZJxk59690HWS02iUYOILhQIu7eJwO64qROLGTiOi2M7KEEHq16jtmxht4rs771GVG1hqDfWo866eFThLbUpsmLQ8hKsVjUqnQjllknDNrh1zzi3TW7i5Pl5njxxGssuizXR98mKJQ3P8ylVWt3rqEWVrhoKIGpqdGyHaqOFZfk4rkPM0Lnr8BaKlSZPPnsZWRZruKqIfLtf+Om70TWVVDJKq2VhRnR0XRB+NsgD2zb1Ui43UVW520f29YiNv64pbBnvDUkDQTiHEeAHwJbxXgq5BLIkFNWyJFEInVl6snE2jxfEuuz7eF6AGhI3NE3p7lNkWdh4mRENz/fRUJgY7WGoP0PEuGGHZYZrcG9Pmk39W75l1tm3Akpu1mvrDe98y+Uyvb293X8/9dRTvO1tN+xDDh48yNzc3Hf36G7WzXqdCnyPpcVZVleW2LZlnHq1xB9/5KN4ThvXsURbH3h8yV3ktoN7uf+uA3zi0w0cx0fTdNaKZc5dnme11CYRl/nSV5/l8pXroaWVH/rzwp23H2B8dIixkQEkSSYWjRBPxMhns13gIhaLUqmKXAVZkonFol3AZWigDyQJRZJwPJe5uSW+9NVn6e8vYBg6c/OLNJt1TMMgk0qxdXOu25js3b2dRrNNs9nCdzsYmkcmUmX18l9gt9Y5PN7E9xxhqxVEUbQEWvQGOBIEAdV6m4XlStd+6h0P7iUIAv7r7z6J44hhf08uzrZNfd1z+/53HeyGZNcaooFYLdbpL6RYLzX4yrMXATAMjUzSJJu5IcV7+/27u//vuh4LK1VURSFq6vzWHz3Nc8euUa23ReCsIqPrCo/cu5uH797J+EiewUIKy/FYWq1SqbXDBUfm6RevsrpWw3I1VMnCcxy+8cJxEkabzvoiR0bqDOZ8MvGNFeGtLQC2C4GaId87xnJVpdKJM7JlnLvyyW6gGYDMW19kWm2bxZVKN5S+WG6g6yrvfHAvmqbw4qlp6s0OgR/gB4IJeOrCPHccnKQ3n+ClMzNIoeWBJAlbkjsOThKL6iiyFDIaRYhns1NjdU+Bvp4kT784xaWpldCSQSMVghIAEyN5tkwUaLcd2pZDq+OgqAqO4xPRNQYKabKpGKlklHQ8ghnVu97a48P5LmtTQihPIiFYEgRClrrhH7/Benzgjm1EIzqf+tJprlxfpRqei1bb5qfffSt3HRrn2RfO8/Sx68iyGE7oKlRKCq3KdQwqjA9G0BSFVNIkm46TiBmY0RixRJqH7ruN5fU29ZZDvWHTsV30/AH6t9/JTPM8ZbuJYUQY7Esz0N/D+NgwicJuYkHAr/6rX2F4IBsqd15d6dRbCx6zbYdKtUapVGF5dZ3VtSKpZIJ77zpCuVLjDz78ScrVOookY0R04rEoV6dmuGXfTn7sPY9y/tJVkokEyWScVCJOInHjb+7grT8cLM0fZN3sTf7yCoIgBEYauFYDp13Esxt4rkXgO4CErBjIqoGqJ3+o1CG27WA7Dq7j4rgu2UwKRVFYWl6jUq2JzZ3jYNsOw0P9DA32sbC4wme/8BTVWh0/CKg32ly8aLB96wRRM8Kly9e5NjNHq9nuqkvq9Tr/8Jc+wPkLU3z2C1/DNE3isSiZTJKxsWEA8vksb3/kPgxdQ1VVZFnGjBhIgYfntCjkYpyYn+PkqbN0rA6O43Dm1Iv8s196L+dPPslnP/cFfN8LWeUBuiazfGGAwHeR/Bq+7WAkdDQFCBSuzVeYGOunaemculTFCnMvPM9nZCjLvXceoNle5U8+cxwkKdyvCtbZz7znMH2FNF977jILK0JhJksSkgy6qnD0wCQLKxWuz611h6YEEvWGYM9HdI1rc+uvYMeD53m87a4dqIrCl565wPRcSTDrAx8CuHp9len5IifPz3P+ypL4OZ9u3sGOB379W17nk+fmkSTQFNBV0FQpfIRLzTV+4+pFdBUMFXRVQlPD71Mkfut/fAhNgV0Dr/h893suoHUkHtgO2i7QNAldEc+rqQ10dQVNhbdvl8LPiecX1fimo5TDjx/t8gMJx9dwPBXb13B8lcnxYRTV5OTFNUp1j7at0HFkWpbMfXfuZefWYb763CWefuFK167J9Ww2jSb5O3/9VtaKdT74u58VIFUgVAQE8J//1XswIzqf+uJpFlfKKIoiACM5Qia6maH+NMVyg8vXVpFC4AxZot2xuTu0V3vx5WkCKYBA6gJh2yf7yGcTvHR6hqnZNRRZDC6Q4LZ94/T1pFheq/LiyekusCAB6VSU+24XKs+FlWpX+bHxR9CxxLCl1RF9kBwekyxLWCFw4XkBzbbdPV5JAtW5cV+0Ojb1hrDZEIZvAR1LqF1sx6PR3gBdJBRZwQtEz6/ralcJI9TBAdIr+pCdm/spVZpCqR7acQwU0gBs39xPo22Fai2RBdUbWszohkosqt8YmISvdUPJHjE0wWrdAKNkqWvDOjac567DQTekVQ+VYQCRiM4v/PTdAghRldcQsx44uk3cbyEAYzsumvbDM5y52TfcrB9kBUFAsGFr6jtd9a7vWnRaFS5eOE+5VKJSqVCpVrEsm59874Moqs7y8iLXphdpWTZLyyWWltZo2zLve1+cQiHO8GAfjWaLIAhoty0kSWI6BEtm55ZED0KAqqrs2b2NXD4NwPDQAJevznDpynU6HZHN5roeE2NDYrZh2Vy+Mo3jurTbHVKpRJcAun3rJDu3jdFsNimVqszMLnLy9AXuuuMQ+VyGp77+AidOniPwbSKaRDYu015/mcWzlzGay1y/KoAJSVJIxKPksyn0xAiqovCTT2TRVIWAgEq1LRj7lks8BgvLFU6cm8XQNFRNALMb70SGrtHpuPTmk2wZNzEjWqhgdNF1lb/48hm+8fJ1ytUmlWqLju2xZ3s/j96zm3OXF1hcruAHYjjfthzOXlriK89e+rbX1tBVMqkoY0NZtm/u48CuETaNFRgfFrZl1XqbpbJKs7rEeqnBtdk1tk4UUE2FD37oS1y+vkq7bQsw3xOuHf/Pr7yb1VKDpeUqaghMJ+JiHwswOpznPY/uIxEz0BQF1w/wPA9FEWfDMAyW1pYF+BAEKJKH11lnOFNHbS8yGr1AJuqQidoYmliTiiH2k/kORAZBAB3PpOlEqdtRalaU++66nUQ6x8c/d4Kp6XVsx8VxPBy3xvCA6LvWi3WuXl/FCe3mXS9gYbUFHMXQFS5OrYSkG9GjSgGcv7LMQF8GxxVB6oaukYxrxGM6o6HV1m23TlCptUWYuizC1G80wXD/7duo1ETejO8Jsk4unHPl0/GuVeqGPWd/FyxRxdfCdXUjP8QPfBRkEXLuB/h4bDBPN0C2WGh7FjFUkWliqKQ2sncVhUfu3dnND/zmGu5/fdbNt3F8+5EoYRMWZl263z7f53tZbxgs6e3t5fr16wwPD2PbNidOnODf/Jt/0/16vV5H0157IW/WzXqzJeSlThgSa+O7djco1rEazMzM8PQzx4jGdHRnN7lMkr1b+zl28hIBCm3bQ5Z0Lk4Vuf1ojmhEIpBUZhdmqVRqXLs+Rzqd5tjxM7z9kXs5cmg/KyvrRMwI8WiMeMJkdHgAWZY5dGAP5y9e7W6KbMshFjPDRUhh+7ZJKpUanufRaLYoFsu8fOo8A/0FZEXm43/6Waq1Bp7nETEjjA4NUK83icWiPP7Yg1jtBo7jUK83WSuWqNfKdOqL+O15Iv4C/TmLXAKiukcQlGjXDBQt1g1kD4KAeqPD0nwVTW0xMdLDzHyR//Wx52iHdgRC7hmqUiSJv/nXbiebjZFOCCutjuV0GY8LyxUuXF2mWm93JYITI3n6CynSKZM7D02SSUW7qopXenA++dwlzlxa5NrsGktrNVotiw/82FHe9fA+PNcnElHZsWWCrRMFRgay9IUyT0NXeOHlaWqNNo2WTeD7yJLM7beMUVxbIuLNM5GukYk59KZcBrKgKOEmJr3x298ctUGSNZbKCis1HV/NkskPMzY2wdCAYKl8W1vvv6Qc16NWb1OptSmWmxTLDZIJk6MHNrGyXuO3/uApmm2n2yAA7Ns+xOhQDlVVWF2voWkapqESqArTc0XuODjJxGiedCLatQixbLc71O9YLosrVUrVZnivSvjobMQCDPVnWFypimBQ22WtbTEzXwRAkWXK1RamoZFLx4iZejdrRVFkJsd6WAwHbvWWRb1lMdArTny9YXHh6jIAqipAk41gUcf1KJbrWJZFvdGmVm/heR733pqj4wScOHmBat1CU2RMTSaf0MglXVyrxubxDLqmYEQi6HqEiBllYmyY3NhOksMy1eAsHSeg2uhQLDWYq9mYg4/QN9BL46U/ZbF0HSOSpG8wSS6XYeu2nejRHIcPH2bf/v3EY9HX9aKNx6J/aWjxt6ogCFgvlllYWGZlrcjaeonxsWGOHrmFU2cu8Acf/iSdjoVhGJhmhJ5chlv27aTQk+U973wbzVaLeDxGLGaSiMWIh6qQTCbF0SO3vunj2Tim/xPqe9Wb/OZv/iYf/OAHWVpaYufOnfzGb/wGd95553fz0L8n5XsudmsNu1XEbq1g1ZdwNpQjrgUEwvBeJBgReG7YkG7kjrgEvrDcCnxPfD4ciIMfsvaDsBsPh43f4t8bd6BQj4dM+VcM9rss6VcM8DRdsKtnF5bpdOxuGKXr+UxuGiWZSHBlaprFpVVhqeQJAGEqEeP2Q/solioUzz/fVVRoQYAtwekvXieditMXPEsi10YpSOGAUGJc/xqXvjrF/uwyqXeCLHXw/ZbY6LhznPvcFYLAo9A5i9/0RTB9AIEU8NXf/zCjA2mi64vcknM4kJeQZfFyo5FnmPr6BTYbFr/0WItXUrwkyWPt+lMoisJP3d0M14I2hPZ5KbvK6qXLbEkUKdzREqdO/AddW2bx7IcxPJ9/8WNB+DWhsgNozX2KxVWNX3i0SbvTPbkEBMSiV7jwjQV2Z9sMv9vZcMkR5ATJ5sxX/gNB4PGP3mm9YsMlhuCd+U/x0jz89J0brwFuAAgN4CR77wLuEn9vihx+KNKN/5cRILgsyAAbAIeq/HCoKH6UykfBR8NHJxpNoEdiNNpQbvhdQMTxVHL5PDu3jVNrBXzmyQtCTeEKtYXvBxy5/x4ALn39KVaLza7VE0DHFnZkbcuhYztIgYQkS6iquI4g+kpBoBCBqLIkdVVDAMmEQa0Z6Q4lHE/urk+249FoWQSB31WHRPRwaxoENDv2xv92P+d54h/VeptiqSnYwuHX10YFm7nVdlhcrSIL/BDfFyDHRk3PrdGxvPDNSYAxlXor7JUqXLm20n0NQSCsXR4EGk2L85eXNv5oCALQ9RsgwJVrq9QabfHeEjJQq3Wx0feDANt2UVUFkZ4T4IevZUP54YVDGlmWIARXASxbeKVbjte1B+mE56bVsWm1HVRFQlZkJGTk0PpOkWUSsQgRQw19zUVGzsa1Obh3DN/3Rd6JvhHkKn62N58glTBxXBfLsrFth8AXqkTf97h4aUH0lGEmjOu6HNjVj2moHD+3yOxCJQyxFa9xx0SO7ZsyIP3gQZObM42b9b0uf2OOsQGKeBae28azGriOsDcNPJf1Uoljx89hGDr33HELrgcvnTiFbphIyLiBSaPj8Zknz/LX3v0oR28/yh9+7FchCGh3LKJRk0gkwvTsArGoiaapNBotFEUmEtGJmib9vSLY+dCtuzEMjd4eESpdrlQ5cfI8WybHMQydC5euUqs3cWwHP/BxHJeXT53njtsPsH3rJiqVKv19BbKZFNFolI2/bUmSeOrrL1AuC2AnCAKsTpP9O/owvHlkawbVnqHdabPc7HC941BZS7Nz7HZ6+3rZtmWC0YEs/b1pkvFI2I+I96iZ+RIzCzdCrBVZIhGPCKvmsC9yvZB5H1G7SoCoqdPqWDz5jUUWliqsrNcp11rceWiSqKnzua+eZbXUCMF/oS68cn2VT3z21Bu+xsJNI8WmkR6OHpjg8P5xenMJitUmzaZNqdJgtdjg5XNzjA/nqdRa/Pw//SNqTRvL6mBZwkJq01gPR2+dZHahRKncRNUUVFUhFtW6WZ8P3bmdxZUKmqoI61RfkAA23CnW1htcuLwsHEbkgJTpsTCpkpQTDJlXSGxdJxV1SRgOEc0DZlm9dBqA7d+h6C8IoG5pFBsaDdvkvruOIOlp/vH/8xXqLSdcH6o4bonB8QYHM3meP3GdMxcX8LukGnE7vfOhvbiex8p6HVmR0MLZwsaeXVEU7jg4gSRJpOImibhBIm6ya6vIvnnibfvYu2Mo3DsEOI7XBZUIg8wt+4ZR2CtB/ERMzNU2+GISEqp6I98uk4ri+wG249Fq21RDu3NNVahUW12HjY3g84052qauAuQGIBIJwY+B3nR3pvJ6tZEf86NYQeCH+0gPwsfuvvJV//a6uFV3JyBJ3XzLIAhQDUHm+0HUGwZL3va2t/Erv/Ir/Mf/+B/55Cc/STQafdXg4PTp02zatOl7cpA3669OCaTQ6dp9bIAintvBd1q4dr0bFFur1ajV6gz0CUT4yWdOsbxaptHqUKo0iZomX/Ov8v4fewcjE3E+/MmnaLXa2I5Hu91GVVWOHNrH6PAgp09f4OyFK0JGHokQj0VotlrousaObZuYujYrsh3Cd2tZkVFC9cBAfy++7xOPmVRrDeYXlnnpxFkOH9xLuVzhDz/y54IpauhEdJ1sJsXDD9xBOpXgsYfvQdN1ctm0IH36fncQuri4xNTlc8jYJGMS6UgHvT7FykUJ33fZMyIL+xMthodBqdIkrkeIRCKcODvDMy9do1Ru0mqLxmTLRC8TIz3kMjFu3TVCXyHFUF+K3p7kq5QQpqkzu1DidLVNpd7GshwO7Bll02gPWhhmNTacI5OKkkqY3XwQRZaxbJfjZ2aZWSgxPVdkfrnCf/6XTxA1DX7nY89RqrSIGBqZlMmuzf1snRDMrfc+up/NYwUWV6pcurbCN05cJxGN8K9/+WE0qYFszTMYs0hkbZKmQzbuUb98jZgq8cgNwQpvRe9pObBYVqm0TVw5y/333k42P8Ao0hsajAdB0FVF6JqKpirUGx1WS/Vu0Hq50mJyrIdbd4/ypa+f56OfPk4rDFlzvYBsOsbh/WMUsglWiw083ydi6KSTUeJRnVq44B7aMxIOkGQkWQwUNhQgjYbN4mqFTseh3RGBv4am0rFszIjOeqnBaqnetXfwUZmZK7Fry2Do0+8QNTUyySixmMHmMTFUGCikeODotq4H5wZTcEMGumm0h4HetGCzqMLXOm6qeHaTpOlyaGeGRrPNerlOsdTA0aO0KwoLa3VeOH4BxxW+9rIio+s6ZrIPw0ywY2dVgCWahqxoaJpBeuQg/Tt3sNdYYOGLz2K7PpYfsFayWW2UeeCxvQC8ePrzeJ6HaUZIJOKMjY1ihmqXd779AZAkkonYawARw9C7AelvtmzboVSusLy8xup6kWKpyuEDe5gYH+H3//iTPPuNlwCQZJlEXPzuo0duYeuWCX7yfY+TTidJJhLEYiaxqNm18tqxffINH0MQBFiWTSQiXuv5i1ep15u0Wm2arTbNVosH7z1KNpvuqtT+qtf3ojf56Ec/yj/4B/+A3/zN3+To0aN86EMf4pFHHuH8+fOMjIx8t1/Ct63Fsx+hNPsMnt2g1awwJ3n4bhvPbeHZzVA5Ij6EWuRHv6LhB0r4oUF74TJtIAkkX4Nor7N6eQaAe3a+9vnc9WdYX4fDr3srTFOenSYJ7Bt97VfrKycBOLgZXmuP1MCqN9g2tHGwrywfu7WGIcH2kdeuNW5rCRcQ7jTf/PUWnXqL/hT0p775ax5WfR6AzQPwKmocgL2KZcNA+vVeawfoEElAIfHKz2/8DmFvMJD+0VdR/DCX58u0bbBcGcuRsFyZeCzO+NggK8UWL5xaom1Bx5FoO8IS7x//3ceQFYNf/vVPUqk7+L4AHCVJ4pd+9gi3b53gw//rK7xwclooHxDAxq6tHvv27WN1bpGvPnc5VBmJXIxX9oalcptGoyOG/KG39gZIkY6bZFMxYV8R/nwuI26giKEy1J/u2mEpIWNz464UCgipGyhq+QbxmFjDcukYg32prhLJD24wOTVNJZuKdYGVjdD0jUFGTzZOuSru141cs0T4vJkwLHcje0aRpa5fN8CubUPdEGNVltF1lVxaKCruObKZ8eH8Bh6Cj89Iv2CUDvWneNfDewWbNTz3G7akAPffsZVqo43vBjiuj+t55MM8u2QiQiGXFGpbT6htN/zFbcdjbb0O0AWqNkBKEFkpxVITSRZgs6wIopPvuzi2RafTDsNfBQjs2hpOu4xlWVQrRUqeH1qSubiez+b+ADOi8PkvXWB6sYrnhfZcfsCdB4e4+8AwL51b4i+evCb65BDsLuSi/OJfPwzIfPnZyzdAIVkM9/bs2EQ6lsGVmviKUMygKBBIqOlRMiObca0aRvyGov0HUTdnGjfrzZYY/LndjxvEEo/Ac/A8G9/rCOssRyh3X/k9nu9hOz6xaBwvkDl1dopGq8PC4hqarqOaCdYqEsPDA0xMbOZ3//AT1BstXNfF932SyQTvfvwhkskEg/29lCs1AVhIkIhFURWFdCrB5MSYsOTVRA6kETEoVwQJY+uWCS5cmmJ6doFGo8nS8hrRqMmjD99NMhEnn8vQanVI9yQo9GRJJUV2CcC2LRNcmZqmWqtTrdWRJZlozGTPrq14ThOrscba4jTlcplKpYRldcgqV7n74CCStcryWpVCPs3E6BBDg1nGh3Lo0TQ6cGD3KOVqi0tTK9Qagqz58N07SCejoRWiDIjht+v5lEMbpmQiQrHcYLVcZ2W1znqpQa0pbJuXVmucvrDQZfBv1PW54pu+9rIskcvEOLR3jJ1b+kknxXpoGCrlSouV9Tr5bIyxoRxPfuMi//33nhbWk7aDZQtV3RNv208iZjA9v47nC8KBpiiYhkG1ImYAD9+9k5dOT2MYmrA7laGQE+tHJh0lQMJxfSTPI2r4FFIBzbXzBF6dx2+tokttYoZNVHNCMss3KF6DHh16Cm/ptn9VeT4U6zKenGTbtm2cm2ryx5+9wkIJbAfAJ5Xwecd7dgCwuN7Ac/1umHrE0EKalLBnb3UcohGtCyIc3CMa8P07R/j5nzyKoavdHsWTbzSsu7YOdpXQAJbtvqoLdhxvg5slbMLDNVrTFBIxAz+40TO8cr5jOx6VWqv7PJqm4Nji/tE1VYSph4QDQ1dJhNkeiiLzrof3omuvDUQHboA1P0IVBMErQI4Q3PA98T74SvAj8CDwQ/fmDT0tCPKMLGxcZQU5fJQkFUWNIasGshJB0SJi1qkaSLKOrKhIsooka8iKgaRoBL4HgYeqx//yg/4e1RsGS/7dv/t3PPHEE9x9993E43F+7/d+D12/MXT6nd/5HR566KHvyUHerB/+CoJAACCu96pmQjQUDr4jlCGe08b3LQLPxfNs6o0mQRCQSiToOB7PH7tAo9mh0ezgI2HbHu9918Pk81lyhSInL8zTbrWZnpmn2WozNLjK+554hIXFFa7PLFCt1lAU4SmeiMW4MjXNnl3bePyx+4nHY6iqimVZJBJx+kI/zf6+Aps3jRKJGASBj2U7tNsWK6vr9BbyyLLE08+8RL3exHEdMpkUoyPC63H/3h0oikIqmSAWi5JIxF7FWu/pyTE7v8jUteu0GjU8p8Wtu4cZ7pFJyy0OjFdIRUGSJQJJp9aW0GM9yIrGF54+z9TMTBj0bYmclAf2cNfhzSKwVVPZs22Q3kKSgUKKgV4hB4zHItx5eDK0eWpyfa5Ird7hntu2EDV15pbKFMsNUgmTTaN50sko+XBz2ptPEjMNqo0284tlnl24iuV4/NjbD1Cptvkn//cn6FgugS/8jKOmTrnWImoa/K2fvJNiRQST1RsWa8UGU3PrbB3PsrRwnbOnXqInGbA955AacsjEPK49fx5ZgicOfvMd9dZYpm0bVqo6e/bswYj38czJMn39g+zfm0dRRDi8qipIkoxti8BYOwzEFMwHjzsOiuH10y9eYb3UwLJd2m2LRsvmsft2sWm0wH///a9x/MwsHcvBcQQz5Lb9E9y6exTb9lhdr6PrYvFPGRrJhMgcicUM9u8aplQRCpANL85KTSz6K8U6Zy8tYrsuriOsC7ZO9PLOh/ZiOy6zC+Wuj7SmqUSjOp4vGslNYz2MhiBXNhXFiOa5ZZtQ77z7oX08es8uEXaqCg/6jQVdliUeuGMLQRjgLBZFl8Bap9120Pw2tmNTrdisV9oUyx327ehndDDHsdNzfO2FGRxPQtV0FEVn00Sat296GLXHYnBcxveF57mkaCiKQnzwAdLpJIWhFsWLV/E8CTwJr+Mys1jn4KEM0Wgdx3HRNJVIJMLI8AD9fTc6vV/+hZ8hYuivyujYqHT6rdllARSLZdbXl1lfL7O2VqTdsXj4/jswzQj/9bd+j2vXhaJJDgN/t24eB+DobbewbcsE/f0FentyrwJkkok4Rw7tf0O/f0OhJoeAS63e4NSZizSbLZqhLZ8R0fmx9zwGwJWr0/i+TywWJZ1OimB344Yq6P+E+l70Jv/lv/wXPvCBD/BzP/dzAPzGb/wGX/jCF/it3/ot/sN/+A+v+X7LsrCsG4GOtZqwZhShuN85aFWafZaFU//7O36em3WzbtabL88XagjXk3A8CcOIkMmkKFVtZpdqWI6E7QpihqwYvP3BWwhQ+a0/eo5WJ6DjSAL0sOAf/PxD7Nwyxr/44J9z7spyV3AlSxJHD45w8L4HOLNyic+/vN4dxktALKaiRwX5pOMoSLKPIgWA8MD2PI8gEACDYajIktRl4m4M/R3Xx3JcAj/ohoWqmoLvC0BhabVCqdoOLaCEmnojm256ocSFK8sggyLJSLIkesEAHMfn0tSKsIcKLaIURcayXCK6xlq5ycJyhXAfjUuEeiPbVXw0WnYXgJFDJccGSJBKRAgCuhZRoncRIM5AryAFqIoI2UWSGOzLEASQSQvSkCTdsNqIRLTu824d78V23O6/Xc/vEqYcN6DWeLXtg2V74ffKr1KogAhh3XgeWZLRZBU1KqMqEqoiYRoKrtNhuBBD2lFAlhH5ObJMIqFhNYtEFYf3PDiBRIBMALJQ6HXqM1iSxV23pKk3TTwvwHVF9kvCqGPVlzDVDrmkgusFuG6A64k9TICMImuIw1VA0kCNgARGapJEOkWixyZll0RGFcLKLDu4i/zkLfR2rtF3JYJHgOeKXjWaTtO7/X0gyZQ6F+l0bKGGCQI8z+G92aP0bJ3k1Md+g2PHZ7oZARISDTfFA4/sx2osISmR78rauFFv9rluzjRulv9Ns4oNxrPnuljNEq1Kk8Cz8F0L323jew4EXpiXKL63+4cvgef6tDoumq4Ti8Uolhpcmpqj2bJoNjs0Wy168jkee9s9BEHAzMLzWJbN1WtznLt4mXarw22H9vGv//kvkc9maDbbmBEdRTbRNI10OkmxVBGgRj6LETFwHIdYNIqmazSaTYa0PsZGB1lYXKbWFlaknY5FpVLlyKF9RAydF46dxLIdPNcjm00zOjJIpVIlmYhz19GDnD13Bcu2abZaFEtlkMSswzB0enJp2pGA0voayyvLLCwusSU3h27mOfb8F7kyXSaiK8RjJv09aXL5AaLpCe64fYwDt+6n3rAo11qUqy0uX1ulP7QSfPnsHB3LFc4JBDSaFqVKk1TC5Mr1Vb72/BWabYtG06LZEu+/UVNnfrnc/fcra3ah/IbvA1mWMHSR85FJRXnozh305OK8cGoaVZaJxwwMXaXetHj/Ow6yZ8cQ//KDf87zL1+nY9v/f/bePE6Os77zf9fZd/fcl2Y0Go2O0X1blnwbn9jY2BzmDCEBFghkCeQiJIFkNz/YZMPCLwQS8mN3E0ICIQHCDTa+OW1ZtmVblmXdx4zmnr67zt8fT3V1t2bG1tiyZVvPW69Rd1dVd1c/XV31fb7H54ttC2nKZUs6eNVFqzh4dIIDR8aCHmkapqETj5phBcilF6ygUPGJGF4gG6/QnBHVA5l0DF3TUHFImBVSMY+MdpypYz9Fz03y2k0jpGIumZiLoYtjb+rwCQB6n/vUdxaWA3qkhVRzJ/c9NMZjB4uM5TSm8ho+GpduX87Oay4hOnWYaCrH6jadqKmj6xpN6VolxE2vWkc5qOIwAt9DV3sTABdvGxTVQQG6ptKUEb6oRNzE84TNIuwTFcu3w+SHdCIaNqgHcf2u9goDKBRr8zHD0HCCaiND12jKxEP5cF0XclpVdZXNaxfj4xMxRWJsfcJlZ1uazrb5B3kumaxzie97QQ/KakDDrwU3Gqo6vLCyAxqrOxRVA0XIDqPqKIqGqhqoWgJVD4IcRlQENVQDRdVQg0CHouoomo4a3Fc1MwiAiMcLwbVLeE7x2Td8gTjjvW1vb+e+++5jZmaGZDIZZt1X+frXv04yeW4iPpIXhlqJVBD0cO1adNG18Tw7kMcqkpvIYpSUupKrwIgI66pU8YPRdBQ1QqEEd967m2wuT3dXB9deNYThupSsJ8kVHfIFi5lcDsuy+fdv/ZD3vustrFm1nC986V8ZH5tkcnqGtpYmiqUSh44cZ+XyAW687jL2HzgankwVoK21BVVVaW9rJRaNEomadHa0YZg6o6PjOI5DOpXksb1PcfzECG7YAFKlr6eTzo422ttaWD20jObmDL2Lulg9tCx0hkYiETZvXMPk1AwTk9McOnKcsbFxLtuxmohW4cSh3UydOklLzGJpt0oi6uEU91CcSmMYXdz7q3EmZypMzRSZzpVwbJePffB6mtIGU9NikjrY3057S5KO1iT9QdPGtUOL6O5sEkZY0WJ4dIaRsSw7Ni8F4O6fP4Vtu8RjJulklO4gkAKwfeMSFEWhUKowky0xkyvhuh5Lelt54NHDfOmrP2M6KxqMe55PcybObTduJZOOsnxJB82ZBOlkBF3XRImj5eHaBUZPPs2x40dpijm0JF0Wd7q06h5HHvghzcDrLji7x6dmprH8JPftnmCyEKVMhmS6jd7uZpzkMlrb0nQvOs5De47ywKPHgooLj4G+Vq67fC3ZQpkf3vs4risag5UrIntn55alWLbLF758L5PTBcqWgxtUiCxb3MZgfwcHj45zaiwrdCsjBomYiR44p3dsWcrdv3iq5qxWQAkm2KlElFjUxHXzuJ7QI/WCPjnVjRPxCC2RJBFTwzR11q4Q9bFtrUluuW4jyXiEVCJCMhElk4qG2ZIfeMfl4nfre3iuQ95OEVfGsUtT4NmYvotvuThlBzvQ8AYF1/PIFR2KZZ/pnM3kTJmZvM3N124jFk/x1Tt+zuFjE1i2h6pqqLrBktVruHDNZSzKPUrz0fuDjALhKFAjzSTbhjDTNh4/oWxXUBQf36pgmgYjp8ZoakrT1dHGqdFx4rEYra1NtLY0MdDfC0D/4h4+8N5fI5moVWDU093VvqBjpXpOqFQsDh89Tj5fZHxiitGxSXRd481vuJGjx07yhS/+E4ViXjicTDMMqMZiUS7esZWrLr+Irq522ttaGgIiy5b2s2zpHCnpdQgHh025VKZQKtHe2kIsFmX/04fZt/8Q+UIh7He0fNkSLrpwC77vMzOTExq+rc2imXui1rj95huvmvf9nouc2MuRs22bWJbFrl27+MM//MOG5ddccw0/+9nP5nzOJz/5yQYJjypjY2OUy89fa7XiPPs2EskrEc8HVTPwPIWZXAXLBcchvF22tBczEmXXo4cplh1sB2xPwXagp7udCzat4pcPH+DxfSdxPHBccFyFWCzG+955E64Hf/bX/y6WewqOq+C48LvvfwODg4v5w7/4Fw4cHQsai4v+FTu2DfGhd9/E/T95kC//4G48NDTFQ1UgnU5w81uuBGDPsUexHCeQXBLBgOlSmoLXTLqplVgsKyoqEBn9ZVsn76Qpu3EqVpBE4wlnge055J00rusxnbMCZ3hgXvswURDPPT5aYnhUNLPVgwrp6bxP3kljeSk830DTxOdQFAVN05ipiESieDJDyVJQFTVQV1OoeDHyThr0FHokJvqpBY3Ly7ZG3kkzmrMZn7FqE2xFVDjk7ASak+TocJHRqQqaookMT9ViqmSQd9KM53SyRcIAgqKAEVXEewLjWdFLUFFqzvCZUoxoKs2JcY/9xwoiuILYJ5s4iwdSnBjP8tOHT4i5QHD9j8cMNm1cC77Hzx85QaFYDrInhYxXW1sLK/UoTxwYZ/djJ1CqdpKikC9YDPR3MDw2wyN7hXOqOr1RVYUrdq4C4PH9w2Hz2arMYDSexIyneXT/DD994ID4oEFl8+KeNt50yyryTpmv/vhh8BV8RRG3wO//9tWoRhN3/fAQJ0aKQeNmDUUx0NtWMdC7jdzY4xyY+Dl44CHe1I23E118C6bncyp/OOg144QVp+XoetLpNqZK+zgxNlb7wSkKg/kIZbUHR8syU1LQdQPTNEmYBolMM9mysH127LgQRVEwdB3DMNB1jVg8xeR0idfdciNXXHYJ0WiESMTEMAySiTgTM2WcEtjjkxiRs1cFmcvlFrS99Gm8cvFOD4AEsqKeG/gsrDyuXRASpDj4Xp1UTHA+LuUh79RkYJTAUaioBpoeqy1XVGzb5uFH97L3yQN4vsemDWvYsK4XX3U4OTxBoVgimy+Qncnx2ONPEYmYXHXFTmzL4f9++T+oWDbpdEJUhAdTQd3QWTrQhxfITkWjUdpam0klE8RiUbo727EsC0VRyObyVCoVDh0+ztCKQTRVZV/Qh9X3RWV/sVimXK4QjUbYtnU9ExMzRCMm8biQR5ycyrK4bxHJZIJoLEJrazPlUp7p6XHK2ZPMnHyQwvQJ/v7z/8JUtgi+h6pCPGpy/EQny4b6eM01O8jlK3S0pYhFDPLFCpFAUjFXqPC9n+wJJAMtIUloi+REH59djx3l4NFxEQwpWZQrNv/8zV+RK1QolmYHQ84U0U+JMJBvaCorBzt515suZnwyx3/8YDfxmEkkYqCp4CsKv/OuVwHw/bsfZ2IyT7FsYwV+gKW9Laxf3cuBo2NMTOWFJKUmEhijEXEOuXjrUu64/0mhzhAkBBi6huuKYEk04uNYWZojNumYSzrmoeV/wcgTu1nfNMaG64pEjcZq4enjopJ44CxUh9SjmUn0SIZde2eYyOvkKlHylRi2H+Gdb7yIjp4WIkcfJZk9RaIVlgZ2w7IlorS7sy1Nd0dGVH36hJWNVVqakxRLlvCPBAkC1crJdCJKOhVFU9VQCrZapSmkr4RsJYhracVVsR2XiFlNFPWIRg2igYRkc0bMjft6muloS80Z8NA0lZ1b5q8YTCWfR4OWF4D6vh31lRy+5zVUe+C7+HgEYr14KNhKFxYTqJoSVneISg8dLRJH1aKoQaBDM2J1VR1zBTeCW81EXWCg45XAgj9xJpOZc3lLS8vz3hnJC8fp2nCzS0kdPLciKkCcCp5bgbC0SjhfG7IoAFCCqKMWzBhiaFrVoKiVolUqFqVyhaaUKKG7/+e7OHrsJLlcgVQyzsFDx/jZL3ezc/smkskEX/7qt0Smv2Vh2w7pdIp3vv31TE7NMDUxTbFUZml/L93dnWiaSjwaJRIxicdFtDgaNYlFo5iGKSZBCOerqmvMzOTZ//RhFEUhlUpyw/VX0pRJsWPbJuxNDm1tzXS0tdLe1hJK3WzZtJYtm9YCwuE5NZ0lP1xgYEkfrl3kq//2LfK5aXy3jKmW8ZwCT2iP0NsWgdwUTz55imzBIVd0KVU8OlpTfOQ9V2PbKfYeGCeTitLb1cyG1b10tKaIRkR0+prLVjE5VaBQtCiUKpwaz6FpGquXx5jJlbj3l/tRVYVY1CQRNxt0Da/YsTKUjyqVbSEJFTRiu+vnT3HvL/czPpVnJiv6hGxa28uf/vYNWJZDNl8mnYzS1ZHG0FVMXeGnv3wYq5SlxRxFLRRRPI900qM9DaVDv+DocdjcKf7ONmVb58BJl+EplYm8TsGKE02187HfvhmAxyYehpkiliMa4B46NsG6oUV0tqV56uApUalhO6KCxHIpli2uu3wtu/cc4zt37MGy3UCewMM0NP74tz0ips6p8SyuJ+7H0zES8Qh6YPi98/UX8tMHDxAxDTRdxTR0uoMeLM2ZOBdtHcT1hOaq67ooqjCmACKGViczITItFnU3ATDY34ZlO8SiJqlEhHQySmdrEs+pkIp6vPbKQVRVXDyFnnWF4vShcKyUsB+BgatqeKqFqqlUvAijUw5TWYvpvMdMziKVSnLrjZdRtn0+/cl/pGI5QgtcVfE9uOm2HbT3dmMmjuGpFuiOkI8oexwfzaNH0mQyLYyOTqCqKoZpYBoG2rTQlzRNg+WD/RRKJTLpJJ0d7aRTSbq7hbW388LN7LxwM/H4bD1OXddpyqRmLZ8L27ZFBlZMnAdEldlxisUS09NZJianWTU0yOWXbGfX7sf4t298H8/zMQ0dM2Kyfo34rSQSMa688iKW9nfR1tJMOpVs2LeLd26ddx9c12VqOkuhWKIY/FmWzY7toqLkW9+5nemZbMNzrrhsB/19PRiGTlNTmt5FnSQTCRKJOOm0mKRn0iluvP6KMxqH852zZZuMj4/jum5D81cQGucjIyNzPuejH/0oH/7wh8PH2WyWvr4+2tvbSaeff7pXNt3K+PN4fsWGUsWnYvtUbEQGsisy5lFUKpaH5fi4LnUOZZG17jg+jucHzZXFpb6axOv7Na3hajZ49T51t9W4XTXJu9Zjo9bPRDl9uzm2PVtUJ3b1+1v9TNUm0t7p6/1nfk44FtUxCsarvTWBZTtMzVRm7Udfd4ruthS/2nNy1jpD17jxiiHu+NnTZPONz/WB//rrl2LoGv/z/7ur9rmCdVfsWMGl21fwP77wo4ZsO99XaEpF+Z13vYr/vP1Rdj9+rLpCmHCKwh988PVEtDL/7bPfE83bq28IXLh5gOsuW8vn/vEuJqbyELiTFURG5Id+81X86uFD/ODux8W4eOAGjbk/9Qe3gqLyB5/8JqWyg+cruMGxdtH2Fbz7TZfxZ5/9Pg/uOSECHo44FttbU3z7Sx/g0X1Hed+n/jX8/sTnVfiPL95Ad3uG93/680xMFfDxqaoiXXtpO6+5ZTtP/TjLD3YNhxUWAM0Z+OP2XizL5rFD1d4Vdceab5OO2lQqJRyr0tAzxi5mSepZcHJYlsjgd4NgSaWkiHVAuVLEsTzxeqpImvCcKZJ6Fs/K49iVsMeHoijolEnqWdozHumkyHasBlliEUO8rg7L+puZzpaC/REHXFvCI6lnWdaX5tSpVCAxIQItmTgk9Sy6MoPvVbCdqlSnT0TXSWg5DF1jdHSMmVyJavaDoih49gxJPUu5MEG5WEBVRYDF0CCquyT1LG0Ji9a0CYoILgidboW4kiWpe+CVsMpFPC/oP+RplGaGiXodnDz2FAcOHA7GVxxN3R1JjPIAuUKZ/fufwvdFfEFVRT8gJ9eClsgwfPRxho9PAgqqFkjFTjvoJQMnP0E5P46mKKiaCE5ovknMPQGKim9l8WxbaJJrKqqmkIy6NCUgk9BJxQ3C3k6KSlNzCz2LloA2TSq1H6Xq4EHIlS0e3CwkOJsPU3FnwnQUFIXOvrUsXbWBA6eiNB0q4wdj63vQ3NXH8g3XMjU9Q7r5cdGPJNhfVVHoW7qesuXT0d1L0VLC4IyqKHR0dtHZ3UP38AQtTU1B3zgdTdfo6mwP7cztW9cRiZhEoxFi0SjxeJTFvW3E4zF+/W03Y1k2pmkQMU00XSNimpimwSU7NnDxhetnBRKqvP1NN+K4bpBM5OA4Lq0tTei6jmM3E4tqOLYjep6UKzRnorRmolQ0aGprwYw1z/m6z4Vo9Lk5uaRP46VPVf7Kc+0g8GELn0S1KbpbCZQtRF8QPHd2EKRqvKCgaEYt21mNoehVR6Imznm+j+GWiKRiqEGSVb5QJF8shBXefb3ddHa08fSBI9z/8wcp5EuMT05SKJT5xa8e4c//5L/S3dXOvT99gD2P7xO9iVRxjkok4lx1xU56e7tob2tBNww62ltpaUqzbLAf3/fp6emkp6uDmVwO3wPLtjhxcoSnDxxh/bohkskEJ4dHKRRLqIpKU1OKw0dEEHfliqUsX7YE0zSIRiMoKHieRzabJxqNsGrFIHv3HSAWjWBGTDTFpVyYoDh1gJHDB/j6v32T8fFJyuWyODfoCpsXX4VuRFmzvININEZ7WxOZZJRIRKe/rw1VjxIxTR45dpJH9x4nWyiTzZVpb03xX956CUeOT/Cv335ABELKjujZ6bh85/ZHmZgq4Hr+Mx0Cc6JrKp3tacYn84FUlxIERDTe+9aLecONW/nCl+/l4b3HiEVMTENUAC7rb+eSC5bx4KNCuePY8DTlih1WKniekI06cHiUcsURr6uqGKZe7c3NFTtWBP3YtFDScmigFbs0RcrMsabPIhHxSEY9MnHxd+qxf8J3C7zrkrmCPyVKMxA7y35o19eJJVso2hGeOlIkV9HJVUwKZQNfS/E77xbVc1++70eUyrYIzmmil1+1r0csaoqKSVNIdeuaihn08WhuirNysDOwYQAUPN8LExRTySi2I3qjKT5B0F7YlaqmUixaGIaovqn2zAIwDY0Nq3tFICSiYxo6jtqCoQu5ss1r55dDFv23XnpVHrXqtTp/bFXGL/DLQq2qoyqxWgvM1s5Tqmag6kk0PYZmxIMqDyFrpWoGqAbZok5zxkDVzOB8p4XyVudLMuXZ4vwLD72CCCOOgcHgexa+5wb3HTxHNBMT5aNWGPioD5w0/irV4Aep1/0wdVRFRTP1MIuiHs/zyBWKTE5PoesGrS1Jpmdy7Nq9h0KxRD5fxLIsYrFoKB0zcmqMR/c8ye5Hn6CQL2I7Djsu2MjO7ZvwPI/xsSkM0yCVTNDRnqSnu4NSqUxXZxtvesONHDpynPGJKVpbMqiKykw2Ty+weFEPjz3+FJOTM5TKo1i2zdETJ7nysp2kkgnK5QrJZJz16y7isosvoK21OYw4X3n5jlnj6zgOuq5TLBb51QO7GBsdYXhkhOzMFIXcDL/x+o1EDZuTTz/EgSDzvmL5KKqG4y5j2eBK9Kk4RXuK1tYMA/0xkskIzUGzLkVReN31m8gVyqIPRcXm6IlJ+nqaiUYMjp+cYt/BU8RjJolYhEwqFka9M8kYO7cuxfegVBHBkHqnyHduf5SjJyeZzpbI5osUija/8aaLeNXOlfzswQP84qEDJOM6mQT0t3kY1jG++q9fplLKcsWqPJm4T2sKWjMqTQkFvIMQgSUXnt1juEqhrDA85TM6rTCeVZgu6vQtXsw7bruOR54a5Z77HsRTI+BWcByX/LQVGjQ/vm+vkIoImmW6nse6lT2sGOhkz1Mn2bPvRChH4boe0aAZZzQqml22Nolm960tQju7epH/33/1dkplO+jfIUo2m4Lvbmh5N5l0HCuoSMkXK7Q2iayGmWyJk6dmQomHqqFV7f3Skolj6ArJuEE8qok/vUglN8xAh8tAe3NdwzEPRSlilSxUVUNTdVB09EgczYiDGsWIptCMKAeOjHP0xASTM0WmpwsMjz/FFZds5rqrr+Ce+x/k77/yNRxHZBY6jk1zcxNvf8d7MCybE6emKZXKiGxHH1VROXZ8mL7ebiIRg1K5TDwWJZFOE0/EaG8VE8llg4t50+tvJJ1JEo/FRNAyFg0vxG+57SaMeTQ85wqSVHEch1OjE1QqFRHosmwcx2HzRtGM4Oe/3M2p0XHRp6NQxHFdrr/6UgaW9HHfzx5k776ncV0vNHCr+9vS0sQbb3110D8kSTqdDJu6t7Y0s3P7VlqaYmFjQdcNmqmqKhMTUwyfGqNQKFEqlckXinR3tbNl01py+SLf/cGdgKiuicdjJBKx8FjasG4Iz/NDh0U8FiUWE7/lJf29LAkqaiQvHU4/Zqvf5VxEIhEikcis5SIL+PnLoTX37sDd+j5UPU654pFubkczEmHGj8gCMgJ5GlFqrVSd9ngoXpB15HnCHamqKATXczXQlFWqzj9vVrRDZCw1Us1a9L2qrj9h3wF8Hy/oUaAoauBrD6RqFAXfF05IHyV4i+p9P3AyKti2jW07uIGD1QvkdiIRk0rFYmo6i+s5gYSig+JDb28Xvg9P7jtAsViiYlWYmi6iaB7rVi2nqSnN7oef4OmDR3CC17cdh57udl5z/eUcPz7MP/7Lt7FtB8exsV0H34X/9akPoyrw4Y/9LyYmxPv6Hri+y+teczkfef8b+L0//Tx33bcbz/OpaiG3t6W46/Of4tEnDvCGd/1lEITxgiaT8PW//zVWL+/mM6/580D2xw+HfdPqTi7YeQVfvSvHXbuCgLgPvgIxU+OTvcvwfZ8f7WrU41aAbRekMaMZfrVfY3S8UnfceqxYEkGPZhieiXLnw15d4EE0qtZTg0SUCe5/XMV2a30ZANZu7CKWWcz+0SSPP1UKJnKACisHMiRaBvEiHr/Yty+MdCmIbMN48wCgcGwyIRpeK7WeFtFEB5FEG0a0mYo7jqKAboLmQ1MmiaJALBIJG2L6df9HIobQpY7qYSPO6nEbjYl1pqGiVT+oKhz5UVMPgmYemq7WqiKCAETEUIOMVOG8VuqkQXVdrBPZfb7oX6H6uEHPjepbObaLE0TUVE8FXCFhpYDlBsdt2IiDoHk3WLZDsVgJpLCEY9xz3fB184UKpbKFSjD+ioLrWfiejaGDrvqBtJfQPI9GwbXztKY0utsSqEq1+lrFVzzcyiQ4CisWpxmZUMVcAVGRkYlWqOSOk4pYpGLgVqVqbA/fzVGaOcT4qSzZ7HQt4ORDJKJhF49RUqOYSol0zEfVNDTNwNcStDVp+E6R5YszrFneDoFsmO8rtLemiCTbwfTo6enCsd0gICnOE539O2jtasXWTuIbflAJo+IpCm19F9A99CqeGH0I35zARcELKmgyHR30rHsTlYpFqn2MhO9jGCamaWIYOmu23yJkead60dLHiJgmkWiEaMSkr7eb5t4VGM1Fbn5tOjj2xfgrqkqybTkAN954E8WSCGRVt1m2dDGRRDPbtl1Az6L+8HmqqpHJpFAVheZMmt/54DsDeRANXdPQNI14PErFLvOuX3+j6A+iabOuQxdsWc8FW9YzH7/xa6/HcUQww3YcHNshHhPSsKqiYFs2xWIpOO859HR30NXZzvjkNI/seRLLssPzcTIZ5/prLgPg37/xg1pANeDmG66iuTnDgYNH2P/04VAW2dB1EvFYOCZq0BPnbHE2X0vywlPfv7SqVOF7Tq33h1MSQRCnEmZWC+eicDgCtWwK1NBnUb2dKwjybExMTIleHoUip8ZmuOnVlxGNmNz3swc4fkIkyliWTalYprWtmbe96Wba21v42S928/iT+ymXymi6jqFp7H3yAB3trXR3BhX08RiZdIpkIsaFF2wEoKO9lQu2baBcrpDN5hifmOLkyChvfN2rSSbinBqd5Ojx4yLQEgTrn9j3NOvXDdHW2oTtOKRTCfr7FtHW1kI0qHavVp6Mjk8wM5MT5zcNJsdPkIoWOX7oMX7x018yk52hkC9QLJUZXJzhD35zG6XJPLnpUTqaY7S1tNLb1UIqnSKSWoJhaCxbmuOJp4c5uucI45MFJmcKrF7WxYoVK7j7vgf45cOHcRzRMN11PXzgb//pnrDR9emUK/l5vw9FEX0tIhGdYyen0DQllE/KpCJ850sfQNNU/svHvoJVdjEMFfCpVBy6OjPEYyYjYzMcPDqOYwd2pOdz9OQUv/Puq5iZKTI8mhWvq2ukElGiEQPbdjFNhat3LqVQzBOPQNTwiOoOa7smmTxyD2vbD9Ozc4ZkzCcV9UnGfEz9FMcffgKAt14y+/N41plLg50prgeqkSaeauXAiRIHT1TIlg1yZYNsSWPdqqW8eec2Dh4b44F7fxlKcGuaSjJWU0dYOdiJZQUyaB64vh/6LLo70oxP5fFdYesISUxxvjV0jXLZRlUVDEPHNFSiRgTXE/b6QG8r3e1pocJhiqBHVaZroK+Vgb7WOc/dqqqyYqCWsOb7kHdMFOX5V+o/Fxoaldcp6AgbsF7WyqHeQhVzKj84B2mnnadqvpz6gIeiGkLKSjNFoCPo26GG0lbGs1Z3eL5PiRJmoubPkDx3ZLDkJYwIfFTCbArPtfFdC9ct41byOHYRPLtW+dHQ2FWkZoY/LlUDVUSHlSAowhkaEHPum++zb/8hdu1+jIplUyxabFy/got3bEFVFY4cPcn4xBS5fJ58tsD41DTlYpl3vP11ZLN5vvfje4gYOkv6e0mnknQG/Qgu2bmVt7/1ZizLoVyuUCyWKZWFbE1TUxrLthgeGaNiVZieyaIqKp2dj7Fm9XKam9Ooqkpfbzdtbc20t7XS0y1Otqqq8mcf++15P082l+fAgQMcOXKYY0ePc3J4hGhE4f1v3UExP87f/L/fwrJtwCcSNPk+caKD5UsX0d3TTdmPE48axGMm8ajJwOI2FEWhqz3N0LIuymXR5Gt6pki+UKHaomN0PIfjusSiJu2JKGYQYQdRShiLmpTKFrlCmcmZIq7n0dfdTKli849f/wUzuSLZvKg8qVQc/vdfdhDRHXbtfhCcEpkEdLX5pONQOvYD7v/eD7l6ucXrNvhEzfrshjJQLWM/+5MOH5jIwqkpn5Epn5FJGM3CzdddzBWXbOVP/tcPefypk8QiBpGIgaoopNpTaEaMRCzCxHQBX3EwNRdNFQ27XM9HVUXvloip4zguri+qOeJRYQT0djYx0NdGczpOR1uKzvY0a1f0AHDR1kG+87/f39DbwXW98LGiKGH/kamZItPZEpdsX0YyEeWxJ09wzy+fDj6cj6bBsv4W1i3LYCplmhIeqWSERFQnHlGJxQzykwfRNYXLNqcxjVZ0wwwj/apmohkJEq1JPCWK7SrMFBzGJvLYrsIlO7fio/PZv/tXxiez5PJFCvkCpXKFj//Rb7N29XL+6dN/wQO79gjtZgU8V2SWXn/t1ZTKFY4dGw6yLzUM06BSFgEn0zTYuH4VnuuRyaRpacnQ1tzM4IDI3Hjj627gVVdcFPYIScRjoaM/nUpy/bWXzfu91/cTsSybYqlMLJBlODU6zuEjJyiVyhRLZYrFEp2dbVyycyvlisXtd96P63rYtsh2icejbFg3RDab5+E9e8ll88Ig0zWamtLhb72ro43eRV1hMCSTSpIJqlSGViydtT+nRidCWa/HHn8SyxLBkGKxRKVSCStATo6M8cievSTiMTHxyaTIpFPBOCR4zfVXkkjEw4q0egaW9J3hL0Vyrmlra0PTtFlVJKOjo7OqTV4s2pddS/uya/E8j9HRUTo6Op7VOSQyLi1818LzrMB+sBuTKBwL33dqlaP4KH61rENozxJo0NY/PlPnw7li3ZoVgJgwTE6XGgKgOy7YPO/ztm2FW15747zr7/jev8y77h/+7jPiPYM+NUI6SME0DXa2r+PRX16OFVTLWraDbdkMrVyKpmn8x1d7GR2boFgqUypWKFcqbNqwikXrhvjI761j9e33YJUrVCyLSqVCS0uGrtVvxHVsrrj4EVzXFRneQXb3lVe/ifalPey44BB7nzoqbERfSD3t2L6alr6LWLXWp2fXRCABKQI8yXgEM95KRNVobU1SsexQrhGgt7sJFOHMbkrnqIsf0Nkcw7XymKoVZOUT6kCbuoZVGAV8mlI6umqG/SlUTaEz41CaPkRPq0JfZ6wuQ1Rl6eIkxelDGE6OdSua0bWqo1VBRUErH6fkG1y2qZOxqZQIbigiQeGClUlK04fYsiKGW1lCNS6hqSqdrTFK04ewbZebr1giKi18X0hjej5N0SzFqUNctKGNRW1GWDnkej4bVyQpTh2it9Vh3fJmbD+C4omMx+Z0hFJQ7bl8cZKK7QVSWuDhkzKylKYP0ZLwaGsywiFUVehIu5SmD2F6E7Q3G/XDSzSqUpo+hI9PzLDxTC+sMlAUBd3LYhVGMPwi8agYn2pwMm4qeE4JxymjqT4KQvNKQdgDmm6gajpGJEIsVksQ0FSVWLqTeMsgfQMwMCrkMTRdOL97e9poWXwxZkuZyy/X8T1fVE0AhmbQu+rVxOIxBldrtE7n8YJqrELZYf2F19G9ZiXayM9p702AohAxI0SjEVauGKBr6Epmsjmuvc7EMHWikQimYRCNRVkytI5oNMKHP7yIbK4gGiC7Lj7Q1dFONN3K9h1xunsHwfdxHBfHc0mnUuhmAtvVuPyyHVQqQcA0CAS0tYpKh7GxqTARo+riWxTYF6dGJ5icmsGMGERNEzNikk6JCg7f92nKpGltaSISMUX1iO+HDZJjsSjtbSJxww+at8cDW8rzPEqlcsP5w/M81q1dCcChI8cp5Isi8z1YP7Ckl472Vk4Oj7J339NBsMPFdV2amtJcdvEFuK7LP3/1P+ukXgWvv+V6kok4e588wOGjx1EVFd3QRVAjEaOrs1041VSVdDqJaRiYpkGiLsnlissuDBr3BsEdXSeVFJ91xwWb2Ll98xxJB2evT4nkpY3ve0G/j7Ko/HCF7SH8F4Uwq9rz7FqZpgKgznYsqhE0XQ/nTM9vv3xKJSFL1dycQVEUHtnzJLsfeVxcg8sWExMzxGM6N153BU1NaT77t//ITC5PqVQW/aE8n5tveBWZdIoTJ0dQFYX1a1fR1tZMPBalpyeooN+xhUg0gut6lMoVCvkijzy6l+uvuYxVKwf575/6HIViCd/zSSQSaLrKkWMnWbqkj77eTqZnZoI+VJHgnCLsvpUrlrKouwNFUcgXihSKRRzHojizBcW3ePKJh4L5VZFSqYRVsXCnd3P9JYs5/vR+jhw+JK6xhkFTKo7t6tz+QJZsvsypSYv9R7PkC8coV2ws22VJbyuu53HwyDiO2/gb/tXDR4BfPqfvIhEzWdLXSjZfplCooBsqEV303dq2YQmf+J0befiJY/zPv78d2/FwXFc08w4qIAAOHBxjOieawlfPq0sfOsQ1l6xCV23aUh7phEo6rpOKK/R1aUyf+AUr2mb4vTfGiZoeMdMnZnrEzDInH/48eBa/eelce5xj5iR0J8XfC02holK0dPoX96GZKe785XFmiiol26RoR7B9k9tu3EbX0k72Tu1jhmH0hEprSqVdV8Nets3pOIsXtaAqhAmgUEsCS8YjTFouCipmRMPQtTBps701xarBLkxDxzCFjyoRD6ToTZ1br9+Eoc9dfTjYP79k9osd4K4GPAh6dNQe1wViq8o7QdpWNeBBUOEh5kGq8J+qQYWHlkDVY6hGFE2PCpk+Va8lsamiukMJKzyCgIdW+z1LXtrIYMk5ptqARxgTIovCtYrYlSyeUwxKT08LhIT9P4wwAqlF9OftwLAsm1K5TDIRR9M0hkfGGB2boFwWk/dyuUL/4kUMrVjK8MgY3/3+nRw8dJR8scT4xAzf+Pb3+ez/+Bjr1g7x/R/exeNPPi0CM4rQCWxrbeYdQH9fD2tWLmPRog40TcfzPCqBVn88HuPBXY+RzRfwPE9M5FSVp546xKLuTgzDFH1OOtvZtmU93d3tLFkssrJXLB/g43/0wXk/38ipUfY/tY/DR45x8uQJhkdGWTfUy02vGuL2ux7k7//lp2ga6JqCqau0NkcpTnWh6lG2ru/HQyUWixExNCKGRntnH3okSWdHCxPTZTzPp1i0KBQrpJJRVi7tRFNVbMsNG4j5gOd6oSRWNl/i2PAUhUKF4fEc0zMFNq/qYGiwnUcfP8j+Q8cxdZ+o7hE1fZqTOvZ4N6VinsWxCRLNEI8opGLQlFQYf+wLAHyowd9TPSbsOZadPTxfYWTSY3jSZ3jS59S0z0RO4zP/7Tcwok185L3/H4qq0JqJ0ZRO0NIdobN3AFWPcPXFq+hqTwttbsfFshyW9In+LKqq0JSOY7k6eGVs28N2vfDinExG0DSV5qY4rc0JWpoSLAuEPd/+ugt515suQlVVPM+jWLbDrFPLdnlwzxFmsiWmsyVmsiWKpQr/9dcvQVV9vvWDBzk5OoMRHBO6Bid7oDudoy1RYKjfDDQxdSKmIWTQfNFse/vmIRxPQ9WjKHoUH514dz9NTc088dQJnth3jFyxQrFUoVDMsX7tKm69+Vruvf8B/udn/z/KFSuoAnGJxaK86qrrRBbxT35GuWKLcFbgiDo1Osba1ctpb22mva0Z0zRIJhLokSjbgmzDrVvW8fsfeTcdrc20t7eRyaSIRiOhsfKJP5o/kNje1hJO8Gd9555HNlegUqlQrliUy2UqFZv1wQT/p7/YxalTogLEDY75Sy/axtKBxWSzeTHJCDRMFVWhPXBWHDh4NExurwYfViwfQNM0NF1j1YpBEomYCN4k4iTjsbCHyKUXbxMTnlKZQrHEqbEJsvkC/X09FIolfnzHfRSKJRyn1gji195yCygKxVIZXYP2tmbisR6SiTitLU0ArF29PHTCno6qqrS2nj1JCcm5wzRNtmzZwu23384tt9wSLr/99tu5+eabz+GeLQxhxBtAYt5tvCCTM8zwrJO6cJ0ynl3Gc8vBugqOU6rp59Y730R6e825Ua1UCRMzVJGZr2pB5r76kg62PB+q1UT1rZZE37T5ZV02rl8977rt2zaxfdumedd/9ct/G1bIVCVvWlua0DSNT/zpHzE9kw0zxm3boXdRF5meLm64eTFtPWupVCqUyhUqlQrJZIJMzzZammK87S2TVCpCW7wqn3T5ja9nUXc7N9/awbKhp4LggYtjO2xYu4LuNVeyxjzMFU8b+I4I4PgqxMwIXatfB77P6nWnmJrKYjt26BBePHQlnSvX09SpkmxywmoN1/MwkkvoWH4Def04OWtvUH0gMkTxfTpXXIOmaRwa28vIqZr8A8C6TX20DlzKyK9+wq/2Hqh9H4pCf1+Md/ZfRj5f4qH9v0JThBST+P4UUj3baWlrIueMMVN20ILlmq6RaB2ipX8rLTMH6Ohx8YgQNz10Q6O1OU3zYuFl2bqtDL5ooq4o4vmD6zfT0tLEpVd00b98NLDEhJ73isFe2gZXsjwxwVXTnbieK6plXZ9o1KRjxasBhW3bVfKFciBdIqSZhrZeSddAH0uHf8m4/Ti+JyoeVEVl+apldK+5BqVtnBVrAgkoP3AFqCp9G38NgP6VLvHxqbCaSFEVepZfTvvgWppO/IJEi43runiuR9F1sdV2Mt1b8KazHB3+EZ7nNTjlI+l+4skEFcfg5KhIxvFRsB2Xk2N5NkVSmGaMU2NTwnGBcN6UKxVuvP5KLMvmh7ffi+O5+EF2dSwWY/XKQaLRCJ//4lc4OTIWOHZE34ybb7iKjo5Wjhw9yQ9+fC+GrqEFQYCW5gxrVy8nFosyNZ0NqmlrTExO09XZTk93O6NjE9iuHQYgTo0JIcRSscSDD+0RvzdHSMk0Z1JsWr+acrnCZ/72/1AsisCZbhgYusYH3/trLBvs5657f8Huh58IZE40DF1n1dAgN15/JYViiXvu/xVm0PvDMHQ0VWP1qmUAnDo1ztjYJH5dZm9Hewu0t4b7H4sKh7Kh62GCiKZpXHjBRnRNRzd09KDSIxbYVRfv3MKlF2+b03HV3tYyZ9V9lb7e7nnXyUqP84dQCssp4zkVHLuIa+VwKrmabeFXKyDr/RfVAEiQ0HkWKJdFImW5XKFYKhMxTQaXLsaybH5w+72USiUqFQvXdSkWy7zu5mvp7u7g9jvv55vfvp1cvoDrutiOy67dj3LjdVcQi0YYOTVGJGLS0pwhk0rR0SGCBwDXX3sZBw4eZSab5+TwKUqlCj/88b0sf98SLMvhjjt/Kq5XQYVb+rjwsgvJPQ0zYtLV0U4iEUdRfMbGJli6pI+BJX3sf/owrudRLBbJzkxzyPQpzazFLYyzf9/jonesbYmgSMXmh/0z4Hn8/Oe7GR4rBJKrHhXL48jJGf7uqw8zPDqDZbuzxu7ff/DovOO69+m5JWifDV1TGVrWxaKuJnY9egQ/SGj0FCG5+F/efBG/9vqdvP+P/5UH9xzBKXsUfRs157PniUM4Vp7pyVPMTA0TNSAZha5mhVRMYXj/PUQMn1+/2sNQNZIxhUQUElHIJPZx+Bd7ec/lwOXVc1E1yJNl6uh9AFy4co6d9p57j5SFUKyo5Cs6RUsnnmxm5YqlTOXgzl+dwHIjIglD1YlGDC645mIAogcfxS1WSNfZz/EgcNHb3SzUMAwtCIiodLSJ60AyEWXnlqVC8ioIhNQHOK7YsbIhcbSe9pYU7S1zy2KLnlVn57d7JpwuZ8Vpajmi2sMJengI6dMgNSHwR54W8FDErWZGRXWHERPSVnq0FuDQzKCao5rg2tjHQ/LKRwZLXgTC7Ao3+HMsXKdYl11h4TmiBLWaVaFWy6/0OFrEeN6BEN/3w8my7Thk0kLT+Be/epjjJ0ZEtoTn4TgO1119KX293ex98ml+8OO7KZUrFAtlCqUiiqrwr//nM6RTCb7ytf8UzkVdGPWJZJzR8UkArrx8J21trUEWuYFmGKxc1g/Apo1rQIHHntiPoeuB3qNKqVQWmVftrUGvkiSZdIqmTIrW1iYArr7yIrZv3cCS/kWhMV4qiSaNnlvhjrvu5djRE5waHWN8bJzxqRne86aLWL44wV987vs89NjxoBJAIWpqxBijuMlgcZfJmuVd6IaOqlYj7mAme1BVlZm8w8hYNswUsx2XWNzgsgsGefzJw9z78ydRFAcFF11xeOoJg2MH2igWCxw+egpT94mYPjEToobCd752F6auoGOzKgWJdkgMKcRMUNVRAJZtBhqSYBXABY5DEpZ3vHgXKABFNRibdhmZdJjMw1QORqchkWrl9z/4Bh7cM8rnb7+PdDJCS1OSlkUJBjMxzFgriqLw1tduY3g0Kxq2FSvkcmXyOTFZferQKX6+6yC26+EHRmXJsrn1uk1BdY2NETFIxWPE4wbxWISKZRMxDa67bDVjk3kqlkOpaDM2keP48CSdrTF++qt9fPcnj1OpWKJRu+fS05bk4x+8mFy+yBf+751BFqsSNGgzOHnyKF3tGRZ1xLEdB8MwMMwoum4QySynZfEGsuoEpx79Oc6M0P63HY94XOf6294Cis6ffOFjZHN5bMvGdoQW6ic+9l/ZOTDIv3zjazzw0J5a1oKqUC7b3HrztSiKkH6KRiMYegJNE1q0IIySHds343kemVSSdCZJJpVi3WrhwH/3b76Jd//Gm2huSqNqWphRDdDe2sJNr37VM36/nudRqVhhj6FMWvTsGDk1xqHDx8UYVipUKhbt7S3s3L6ZUrnCt77z44bXiUQirB4aRNd1UskkoszFw0M4qRb1dAFw4NBR8vliKOOgKiqplJhEtLU2s2nDapJBMCQej4YapOlUklVDg8zM5CiWyszMnKJYKrF6aDndXe088eTTPPjQnnB/VEWlv38R/X09REyDnu5OEomYkBVLxIkFsmE+cMHWjQ1Z6A3H/yvUuSuZzYc//GHe/va3s3XrVnbs2MEXv/hFjh49ynvf+95zvWtnFVEpogPzS+JBMEFx7brGqfZpshiO0Av3RPZodVs8D88VAWoxkfHxqQ+0nCbNEMqBVnuf1TdWnVsK9HxHC+R6osyuaOvp7qCne+6OoN1dHbz2NVc1LHM9j6kZcU1+x1tvxbLtUILHsh062lpQVI31a1fR3t4WXN8cLNtmcGAxeiRNc0sn69etrb2m6wk7MCEy87dv306lUkHTNKFzrSqs37SDWFMbl155FYuWLEdVNVEx4Sv0Le4m0bKMxXon7/6NCrYTyLMFwfemLpEQcMOrb2B0fDKssvFcj60XXkaqY4iN22xylSig4CuiAc2iRd2kuzai5vPsuHAnvicCP15w27F4C5lMmvbuR8iWzTCw4zgu0Uw/me7NVB4vcmrSoVwpi+awvk9Hu0qmWwS37v7lF3Fdp3aYK3D5Va8h3TnIL/fcze6H94q2GMH6spvk8quGmDywm58+eABN04OGqRqZTJp4s6iMHJt2KVdcdF0P5Zr0WBuRZCeRWDOpdGuQGSnknhLJDLqZQDMKJFM1ewLfxzBqk/14LEYyUQ6qaETgo1r5YBgG6VQirDpRFYXFfT3BOp0N64ZEdYquomsaqqaFiQ6bN66lf/EioZ3vexSKNkuDytW+vh52BBK8XiC319YmEg+am9KsX7cKz3OF1Jjj4DoeWhCJ7F3URaViBVVVHpZlBxXgcHJ4lMNHj4fBM01VWbF8AIDRsQkefGiPkIgyRHPfRKJWMZsvlGhvayEeixJPxEkmYvQtEnbLqqFB/uDD75nTHjBNgw++9+0US5UwcaRiVcJxyqRTDK1cGlSziGN42VIxLxo5NRbaOK7r4nkevYu6MAwD161QKpVpac6gVWVhNT20pVzXFdsGjdYNQycd2FKu67J0SR+6Prccqq5LN4Dk2fF9D9euyXp7TgXHyuNaOeHfCIMiXpAkYaBqEVQ9hh7JPO9qkCrVpE7LsqlULCzLIpNO0drazOGjJ/jJXT9jYnKamZkshUIJ34dPfOyDNDWl+cZ//pCnDx6lXBJVmq7rsv/pw3z2f/4JkUiEXL5AR3srTZk0hmmwYlCcowb6+9i8eS35bJ5yxaJQKLJv/wy7Hn6cV122gwMHj3L/zx4kkYiHAcnhUyK4umLZEjKZtLjeqaqY73g++XyeZDJJT1c7Tx88zKlTIziuTblUJmG6eMWj3P6De7j9nodFJartYNkumqbwwC/uZipb5uG9o2GPtCq/8dHZ/c+qTGefn4SRaWgYuoKqesQMBUMH01RpS0e44eotjA6fZNfjhzB1iOgQMSAaUfj996zH82z++RsHMTSfqKGIdSZ0Jh7kxCP7eeelE7zzEpWoIZ5n6ApQ5tiuL9Cvwf/7PnPW/pTHf0UZuGgVzE4APXcVbIqqky0qlGyNsq1TdgwsN8raNcvp6upm78Esew45RAwH09DQoxqt3a00LVqEUaqwqtAeVnbUV3gA7Ny8FB8/YXvhXgABAABJREFU7M0i+n2I9csHOlg+Twd4Q9cYGuyad5/nC5ScbXzfF4GM01sC1PdUrrtPYMd4KFh0oCljgbyo1qCSo6omWiSGZiZQjTiaEQuqOIxa0/KGgIdeJ3dlyHmF5BmRVtJZxvdcXLsY/jmVLHZlRkhhBL1EgIbqEFWLoZmZZ9Wgm4/jJ0Y4fmIE23HCyWtXZzsb1g0xPZPjuz+4M8ykEDIOFr/x9tfR0tLMN7/zY3714KNUAokHy3b48Z338/Uv/w2O5/HN79whdjfIiIhETHL5POlUirWrV6LrKpFIlFy+iOs4nBoVwZJ4LMq+/QcDo19IPDz11EF+7S23kk4lKOSLrFw+wMrlS8mkU6TSCSEfBLznnbeRy+bIF0tMTowxNTHO2PAB8t0ax/bv50tf/k+KhQLFYpFKpYTqu/zVH70a3y3z95/9Pq7rYGhgGGBqCvsemqRNWcyyljEiK0qYeq1SoDl+gokTD1OZzrKi+Ti65tdVEvg8es9BFDwu7Cmh9SJeVwdTB0M9zPEH4cJOuPC1p38rFSDQ4hya6yRcvZCf+xN0seKTLUL/4l40I8G37tjHRNZjKq8wUxB/b3ztJdz66u187e9+zEOPHSViiKZbqgrtzd0Y0QwtrWVc12NsssDIWC5s4vWO1+9AURT+7Xu7mZouiOyaoNro0PEJLty8lHQqRjoVEzJcUQPTUFnc04Ln2iQiCpmkTqHiMj1TYGxcyJhMjw2QTup89Vv3cXQ415DorPl51g0YTE9PMjGVQ9cUNN0gGokQS6VJta0i0xVl6bIjIMQ8As1shab+a+ge6Cd311c4NrEvaEhWwPN9Mh0ar+newugTP+XBR4+GE3zfh9aWJlRNGHS5bB7bERke0WhEZKPGhENy1apBKraFoWsoiMboFwaNwPsXL+KqKy4imUqQTiVoaW6it6cm/fPxj36gwblRT7Uvh+M4FEslZmay6JpLUzqFZdk8ffAIlmWJSYZlY1t2mDX4w9vvZeTUWMPrXbJzG4NLF1MolBgdmwiag0ZobsqEchWxaIRXXb4T13GxXdFbJBGPoes62VyePY/vw7ZrFU2GYYRNz3t7OmltbSZiGmia0I+3g2qPluYMDz+6N8gOq1WlvP3Nr0XTNHY/8kSQ6RUJgh41Z+/ivh6aMulQLqwqhwHCMbB924Y5x+90mQrJ+cttt93GxMQEf/7nf87w8DBr167l+9//Pv39/ed6184JiqKi6BHUORzy81HtnRJq+c5RAo/vB7deOEGqJpa41V5rnoPnV8JATcOJvq6aZS65sJfC9fXlRL0ztbl57sbHAMsG+1k2OPdvoae7g7e88SbKlQq2ZVOuWA3n1uWD/RRLZSzLolKxsSwrdBKXimVyuULD63V2tgFg2w4VyyJimsTjMSHJFKk5UJYvW0J/f6+Q4NIanfmDA31ETKOhAqclqBhUVY01q5Zj2aIvVnVfk0lRkbVyxdIwS7+6/fq1Q8F79nPrzddSKjukU1FUVaWlSYyb7/t88L1vJ3RlBTJeiwOn+/VXX86aoRVhsMmxbdasWh6MfZo1q1Zg28Imt2ybVCDlBJAvFMgXimGVsuO45HPC3nzqwCEe2bM33FYBDF2M7+TkNLsfeSKUlQAR6Kjy+JP7mZyYFj2NFJHcMDE5DcCpU2M8/fSRoPpGExXjQUZpuVRh774DoVQZCCmz17/2OnQddj/yBMeOD6OqonlyxXIZ6O9m1YqljI1Psu+pg+i6FkizCTkoqCZwVIQqYDUL2zCCWhro6e4UzWS9oGG954UyUINLF7Nx7SqRKOYKGa7qd55IxDFNE6tiky3ncKbcUJonk05xz/2/4ukDRwLHr6jEecMt13PLTddw+50/5ZvfuV0EUuIx0skES/p7ue31N+A4LocOH8cwTVLJOB3tqVCCC+DiHVvCuRQIW636m+td1M3VVyawHUfYU44TBps8z8c0TTzPxSrZodRWlf1PH+bo8UYn6QVbN7B6aBlHj53knvt/BYjAqmHotLe18KrLdwLwk7t/hqZq6IbQkzdNk6EVS4lGI0zP5HAdB8M0wuCmYegywPIMfP7zn+ev/uqvGB4eZs2aNXzmM5/hkkvmaGDwEkb4MQq4dqnmxyhPh1JaojGZWqefb6CbMRTNbLiGhI5Rzw2qU+uco6cnSgQ4jsOex5+iUChj2YFspWVz2cXbSKdT/PyXD/HQI3spFArM5PLk8wVaW5v50z/4LZzSBF/8h38Qv426/mAPX7eVi3ZuxSlNgjVFzNRIxURgMRm1sQpj9HfF6WrRcK1JxkfGcF2HU8eeoPTBW1F8nxMHdlMoFsW5CFHPt+v+77FykUJcHaEzMY2izoDjojhw+LETfPOfK8xMz+BOPYSLF/SKEqIAn/jI6zB0GB6eJoGQlNYUUCMKR/c8xT898U1UBS5bLbbXVKGyoCo+mjqC2gEXrzAb16nB/XBZ9X5tnaGL5t34bvB6tW1bm+JoKszkisHrKOE6U1cRp+W5vjcfeBC64abNs23EqWOiiuM12+dK8KxgFUcRBRAvzYQ01wPL1ag4OkYkSUd7O2Vb49DJAooeQ9VioMUxIgm2bRpC1Uz2Pj2M7kNrIK1umhotmQSmqbN2rcvAUJpMJB9KlVZJxCLs3DI47760tbwI2l+ncXqAI+x/7J9236va9m5wjgDCtFDCBI5qRYdIhBK3KCqaGUXVoyLQoUVED4/g/IJqki0bNKcMND0iqj3q+necrWCsRDIX5/zoejkbFp5rY1emsYuT2OUprOI4VnEsqBjJ15WK+XXOgCAzs+ogCIwHz3MoFot4roPnufjBbSoZAzxy2RyWVcbzhC6169jEY1FSqQTDp8Y5evREqLnseR5HFYXI6DZ836ew7wEsy6HakNUA7vvaT1g1NMiW1ifo2zIRnLDFpCARP8i+Oz9Gt2XxB2+oORvESbDIvjs+Sjqd4qrlj1MollAVBb85aBA6coS9P76TXneEd1w8gqaCpvqoCuj6GA9/8634nsu7LhlBUfahVu5AGffRJuChUwYRQ6NULguDQINmRaEZ4AQ8dkLsx9u2zfoqGH3yPwD4/dcDnH5BHmHm5AjbB2F7eA2qXvDLWNN7SQAXDokxqKEARQDS0ZfmRXw+bBem8jBTgGwRsiWFWCzFDVdfwJ6nJvi/33yUoqVRtDR8NJKJCP/8mbcAsO9rXydfCprCxiAd9eloF9UhqaTIwq/YDqWgofzktHBu6JoYv1jEwDR0NE1MuF3Hwldh8+puJqbzaIqCqvpoqkJPs0dp5igRsiheiWy2gD0pJD/scg6r0I9dsjhyfBJfM4nqPrquEY8nMJLdpNrbWb9+nJb2KUzDwIiYGEaE7Rdtpmv1djbb+zk0fid+kJnge9DW1kzrwJXitxD9Ma7niq65imgoKxrNNtHa1kZX5yRVoXNFVehbJGQHerrb2bl9M2bEwNRFNVJTnUPlNTdcGQb/QDjpexeJoMflF29nzarlxKLRMADRFDhYFvf18DsffCe2XQto2HVyUUeOniSXL4gKEMvCtm3WrVlJZ0cbj+/dz67dj+N5ohlqsWixamgJr7psB47j8OBDjxGJGIH2tMimdF0XTdNYuXyApUv6iAT7E4mYoTZ1d1c7yWScQqEoeqUUi6TTwmA7vYrD96F3USdL+nuJx6K0tTYHTdJFA13X8zg5PErvoi5UTePxJ54Kn6soCksWL2LJ4kUiMzKdpLOjlWhUaJXXN4q/4tILMQx9TqmHdCoZZlVKJM+V97///bz//e8/17vxskU4GauVK88N0QQ2qGTxnCBgInTOq01ha5ro1f5uVbkwoYVuF6GiBtJMp8uFBU0WQ6kwpS5bTfKcqPZpqe9XVc+WTWvnXA6wfdsGNm1Y3SApFqlrWrt29Yrg2mhhWzZK3fn/xMlTzMzkGhpO9wSZ96KnwwEiEZOIaYbZ99X9Xb5sCaZpEDFNdF1DVdTQob1101pxLa7bp2qyQDzoQTE1UyRiqjiOG16jHMdheGS0wakNhK+bzxfIFwqi34iuE49GaWluAqCluYmLd2zBCGQ+TdMgHqslBLzvXW8RFqzvB/aJT//iRQDceN0VbFy/WvThcFwc26Y3sFva21u4+sqLA/koUdmga7VjfXDJYlqaMmHQw/c9WluCzxqP0dycEU1yg+odI3CaO66Dqqj4CqGDUtOUhmxVLZhjCAedF45TuVwhly8EVWfifSOm+M5d1+Xg4WOzEhksS9ieT+47wL79h8JqF0VV6OsVAbLJySkef3J/zXnr+2EgLhGPMT2TxXNrwRBFUSgHcsB9vV2Mjk0EiTIiGDU5NU1wwDAyMhoGLBzXo7WlidtefwPTM1k+9ekvYllW2JBZ13X++pMfZeP6VXz8Lz7LE3ufRtf14FiMcOlFW3nH227l5PAoX/36dzANI7DDhH349re8FsPQcX3RJ6EpnSSeiKPrOuVyGdM02LB+FYt6OkUwxXZxHDscw0wmRU9XB5ZlU7aE3Tg2NoXnic++/+nDTE/nsF0H13axHZu3vfm1rFo5yA9/fA8//+VuMa8MglJr16zk/e9+C0eOnuD/+asvoChqGJyMx2P88R/8FqlUgr/+my8xPDKGEmS+Kwpcd9WlXHn5hbxS+drXvsaHPvQhPv/5z3PRRRfx93//91x//fU88cQTLF68+EXfH6s4hlXOUp46wYx7HLyKCIA4JTy7JKQ2nRJOJY9TmQmTO51KDtcpiubqYaKnHfou5s0IP+0W3332nZyHePBX5ekfitsIsKMJaGrc/qF/E4mdn33XHC92/C946N/gg3MW19/FQ1+/iw7gz2+bvfaRb74VgD9+/VzPvYfj99/Da1bCa2bJORXB+T7dSRh6o8Zsv0QxuJ1dMfHiMJeNIypP2jNzJZm8vJPJPE/B9jRcXzjeW1tbUPUo41MWaBF0I4puxNCMKC3NLUSiccqOiucbaEYklLXS1JqE7OI187/fqmXzSxQauobOC9f3r8Hn6HtBwpLXmJg0x33qbCe/7p6ouqjJ6grpqqqklVDC0bSIaE6umWGwQ1GNoIpDCwIa1R7KQT+PsNrDeNbqDs/3qUyXiGZkw3LJi885DZa81AyL7MjDHPrFZ3CdMuXiDEdUP9DgLOE55dCwcO1ykF3x3A2BM6VyqvGxgvjSdMC1YHpGCGisnOO8PH3sfgA2zJkAeJzJI8fpb4b+WVL7NlNH7wVg3RxfgzvzCFMzsHLOir5xZk6OkwSGFs1eW545CkBn01zPFU3sIwa8VDMMXkx8X1R9FCtQqigULbBshZ3bVqHqEb72vUfJFX3KtkLZVsiXVV53ww4u3bGB//a523nosWOBNrKKoWtsXN1LumsTrflhlMgpUjFIQjCxrV2kTFPHL1ooniekMFRwKiWcSpauFoNFHQl8PFTExLg9A6Xpw+hujs5mDdfzcVwL1xWX2+LMMNFYhGKxyORUAc8X2ZmGoVOmmVTHWjr6IrR1ehiGjmmYqLpGf+8iulbdSpvrc821OiUbYhE9mMhD14rraW5Ks2qdTSR1VGQXBk1sE03dRBIdJNKTNLe0BuMpJuMdgXa8oiisGhoMJ+IiIdInHlQp9PZ0Y1l2KHmgaULOCaCzs52tm9cSMU0iUeGAqUo4gMgg9Dw/kPjS8H0/XB+Px9CnNYqlktDHdVw6Oko0ZVJMz+T49nfvaHD4KIrCO956KwD7Dxwmly8QMc3QuVM1uDo72sJ9MkydQtGlp7vm6Pi1t7y24fjyPA9VVcNsWsu2mZ7JUiyKhuvXXS2C1rff+VOOHDuJ4zioioJuGODXepkI34gIxKqqwomTp3AcJ5R9yOULRKNRYtEIiWQ8dFJVK0Bi0cicFSCX7Nw6728jEjlXEwyJRPJioSgKimbAGeoCV/WMq1W8rmNjj4+Tbk6h4NbkwtwKbuAIqm1flynn1dl2Nb3EMJgipMJqk0VFabz/Su7J8kKiqirRaGROSbFEPBb2wpqLm28UkmL1iQbJoMqgd1EXkYgpZJosEWypNjgtlyvs23+QSsUWck+ICsi33nYTAPf/fBfZXKEh0aDax8rzRPNuTY/S3poiEjFJBxUpuq7z5jfcGFxjRaWAbdeqBdauWcGS/kWhxIrjOGEFi+t6WLZNvlAMgh4O6VQylJH6+a92z/r8XV0dxGJRxsYnGR+fDJt1x6JRMkFyQzQaZdOG1SKYZYgeFrFozW55x1tvCQq3RBDGdd1QhuuiHVtYt2YlrufhBsGWZEJ81nQqyetuuQ4nqIKxbSF5Wq1AWD7YTzqdDGW28kWLzg5hlzVlUvQv7sFzqw3rVZYsFgEP34fVQ8tCyTAU4c+JB4kc/Yt7QFHCHjae59PeLuy75qYMS5YsQlO1UD63paU22RlY3Ct66gSVML7vE42I76aro52B/l6RRBZ4+vuCKqUVS/vZuX1T0Kzex3UdosEYdna0ceEFG5mazmJbVtjvJLQxPVG94gT9fBzXDRtBP7DrUb79vZ9Q7dfj+z6pZIK3v+W15PIFPvWXf0cuLzLbRU8knd/7nd/kpldfxb987dv8+Cc/RddFLxNFUVgztJw/+5P/imU5/ONXvlELKCkiYHXTDVcSj8fI54scPzkSBowATpwYYdXKQdpam7ED+7Da6DabFb1nwmPVc3FcB8/zKFdEJXAqlWB6KsupkbHg+xERk2Lp+ckAvdT59Kc/zW/+5m/yrncJj/1nPvMZfvSjH/GFL3yBT37yky/6/jz41VvInXr4RX9fieRsI87SGigi2KGoEaLxJLoRw1dMND2CZsTQjRiqLqoSNE1UKqh64LifwyabW7RK8GLMMmvXH6/O9vQCe/T0gEY1ECmWKcFJuXpurtZvNAY3an06qrapqkVQA5k8RRNjJ1RuIoE0lRYEL2r9hBoCHKEkbk0eVyJ5JXNOgyULNSwqFdGEsko2mwUIpXCeL+X8KUb2/vvzfh3J+Yvr+ZQtKFvQ2dGKqpnseuwkJau2vGzBto3LGFy2gq988z6Oj+Qp20oYEFmyuJv/5/ffwPfu3sv/+tJdoa63qirEoyY3vfkGfN/jp/uOkC+J30P1glsoq7iORSKiYGoKKB54YuKdnZmmlB2mlD3F9MwM+B4KCr4iSnGL04fxgYnJCSanS8EFV1RcTM/k8FybiekSR0/OCF3WoJFk0UmS6tqEm3QwE8dEg0nDEOsNk9ZlryGZSrP9ohSnxmeCkn4d3/dZtXkbzf1r6Z9uZ/CEH+qN40M804aR6ER1XWxPxbZs9KAxZlXOoVpJ5bgeKkqg3+ph245oGi5mlkHzVjWUcvCCyauQNhBBg6rOtut6eL5Pe3szpXI5bMapqSrRWDR83XyxxPRMLpDBcDANg1VDohnnQ4/sJRvIYlS56ood9MaiTGdzHD95qiEIU60Ki8UibN26PnDKGEH2oRHu77VXXzrncef5Pi0tTbS0NIlG9qUyxdJU2Dz95PApHn70CWayBfL5Arl8gXQyybve+UZcz+Of/vU/qVSsIDimogJLB3pZsXwpbW0tHD0xQiqVFNrouoZl23i+T3NzhsHBxUSjQg4rGmRDoojv56orL5p3f+PxWOj0AMKs0nNFtfLO9/1zqHZ79vDrfh9ni7P5WhLJ2UZR1LAhI4DmeRhRh2iqY96Gw2HflbDy9/QsWSeoUgkybMPeLLZImPFskXXtiwkuVCe5QdZ/fcOK6vktDKooogGlEjS9p676JbglnOArdZP92nLlDBJL/FAKyq/bn5rdUN0voUtv45TLwjbwvXC9T71sWvA8pf4zNbx08NmE8ILYRqkLLAWfXdFOu1WDz6nWHle3P0NE1YgBidqyjvZWOuoaYdfT3JzhttfdAIhqEOu0as6NG1ZTLJaCQItFxbLDiorpmSxPHzjM5HSBiKmhKrB0YDGXXrSNbC7PN7/9Y1RVIxIRiQ2RiMl1wTV8cmoGy7KJRoRkk2EYZNLCAd3e1syVl104r9zRG259da1fTNBTJhkkeVSrgIW0V7WCRJy3c7kCjz3xFLZth9faTDrFLTddA8C3vntHg2ympmm8+prLaG1t5tjxYY6fGBFVnkGwpVrtomka/X09ogrUrH3WKrfcdA2eJ+wyy3EYG8/R0yUCF0MrB+lob20IKFWltDRNY/PGNSJgFHwW26oFnNLpFO1tjQ749qDfiaZrNGdEJYzoY6/QHDj4Pc8jnU7iA5oqsoU1XSMWF0GPtrYWlg32o4YBBpXeoEopnoixKgjgKIqKT60SBmDLxjVCIq2u2X21b9AtN13D8mVLGuTKqgGwyy7axujYRPi7cz2PdLJWIXvZpduZnp6hVKlQLlVwXY/lAyITbnRsEtPQ8HzAF31qWtuaADh67DiKpmHqwmbXDZ1kPIZpGnieh2EYdLa3Eo1FRWWVobEokIpb3NfDNVdehGmaooraMMN+MulUkt//nfeIRvSaFgZbqlVXv/c778a2ascS0GDvvdKwLItdu3bxh3/4hw3Lr7nmGn72s5/N+ZwX2qeBFn32bSSS54mPEjrZXU8JHeuaZqLqBppuBuuNBum2Wh+JatVBrXF2bRu95rh/ngkoZzK9rNlENTvOD22k4JxeZw/V1tfWVW1AYT+J2+qe+8F4lelAZVRISoZ2oFZnH9Ul4GgammqKIIcRFbfVxuN1lRqNvf6qfzWZqmpwg7MY3Hix5u2vtPn5C80rbbzCz+J5KOfAp3HOgiXPxbD45Cc/yZ/92Z/NWj42Nka5/PwzVvK50vN+DcmLj+v6+IHDoVBysR0f2wXbAdvx8dEYHOjh8aeGyRfthnWOC7fesINsocK/f28XtgOW4wfr4Yart7BxzSAf/+tvkCvaOMFyx4Oezhb++0ffyuf+70/48X1PULEVbFtkw8ZiJt//8m8C8N//+H/gesLBruCjKgodywYZyFzIQ4ce4unDBRRFXGxURSHTqjBd8CiWXRzHC/wi4lLreT6T0yVQFWbyFcplG/HRhdZzvmySd1KUbINCxQucL6KSwyKFGx9Cy7SgRR4PG4NWqzy0jmvRdJ3lq6aZni5gGIZoJqlp9K25jEjfRjbuHKSsP4znC2PedT0WdXfip7egkCPVvCiUh/B9n4rrk3fT+FacY8MzHDp6LNTLVoDOrm4GBgY5fGyUJ548SFWbWlEUypbL5HQJ3/c5evwUtu0SMfUwO+7U6Aw+BqfGZxg5NYmqqmFgYzpbZHK6RMVWwFchKB0V1Q66GENA1Uxcz8b3VTw08BSyeQvdKGHZCpYNquujaaLKJpurMDldolhyaWluC8dHDyai1dfdsEE0na2uq2pFT06X6OzsobOzZ9ZxXH1uR0etTMzzPAolG8cT606eFIGuXL5AoVAkXyhy6UUX0NSU4bs/uINH9+ylWCzjuA7lis2O7Zu47XWv4diJCX7wo/sDJ4dwJjkOjE3k0TSN7RdspVyuEInUKlYMM8HkdIn+/gE6O3vCdVUZker+rl49uxZ5OluZteyljg/kClZ4bL7csYtQ0XPkrdGz9pq5XO6svZZE8lKgOql8LtQaUla1nBsz/6rBhQZtZ8/F951QSsz3XDzfCbIKqz0z/EAWoTr59sN/4o2Dm3CSWh8MoS7AAtUq3QZHg1K3nKr8aBDtUITjQ9P0oIJGQyEYI1VDVfSwP0ytyqZemqGaSd4o1+J5DgRBJyEz64AfVHMETW893CA2U++oEAkfz/ANNgRaZgddQGRZVoNP9feVWoBKUdFUiEVNYkqEamPwJYvnKJEOWD20jKGVg0xOl2hKR4Q0WDDOsWiUSy/aRjloQlyp2DhurUfFsePDjI1PCsmmgCsu20F/Xw979x1k1+49oUSXaRgs7uth+7YNWJbNgw/tCRMqjOC2GgxsasrQ1JQJdNKNhoDLop5O3vLG1wAiMCQqQGpje+lF23CDZunVxJVqz42IKSpoLdumUCxh2zbRaJS+3m4mp2b4yd2N87ZYLBoGoe6466e4rhc443XKlkdLc5xoxBQOdUUhFouSCT5PtRdNJGJy2cUXzDv+1151ScP+up4XBi76+xZxw3VJLMuiHPRsjNdV/27ZtFYk2gRBGsd2wsoS8IPAmRNWG7luHwDlisXk1Ez4OroueoBUaW7K0NLShGkY6LqwsVNBtdGini4hEaooYYVIJiUCOK2tzVx/zaWBvJcXvi9AKpng+msuo5Av4npivW07tASBieuuvoTurnZsxw57PAytWBrsn0FbS3MoX1coFIlFIyiKQjab5/G9TzE9ncX1apJkWzauCb63n3HP/b8KfwuqqrJm9TJWDy1jYmKKv/irz6OqKkZg4+q6xn/7098hlUzw79/4IYePHg/HQNc1Lr3oAi68YMO83+fLmfHxcVzXpbOzs2F5Z2cnIyMjcz7nhfZpuJ7s2XW28fzaZbkqQed6iAoylGB9VdI8BorCdLYcJPIp4W1TOoFpGkxlS5TLjlgXXIcT8SjNTUnKFZfxyTxUKwJUFV3T6epoBkUR82OgJh+qkkzEMU2DiuXiuD6apgdzYx1V14NKO1F5UH1dIU1a691JQ/JCXTKHIoIaqHpYdYBqACplL0FULTSaHnNR51iv2jOeX1vSsMYHHF/8hQuqwYngsV99lbrED9+rLfNPs5uUmm5JfUWGErxyQ8JKYC80PA5eoJbAESR2BEF1gooLVTPC4ES9rUQwpo5tYEZpXKeqwr6qft+KBurcySIez9DC3gfc4G/Ws6xZm78ceKXNz19oXmnj5bkWvutij42j6mfeO/PZOFOfxjkLljwXw+KjH/0oH/7wh8PH2WyWvr4+2tvbSafTz3ufIk43B57na7geWLZw1ls2WI7oH6GoOpbtkSu6OKIPEp7v43qQiJqgqkxnKziuWOZ5okrBNHSSCZOZnEW+5ITLPV9Mfhd1is99fDgw3usuBB0tCeIxkxOjM8LpTm0OHYsYdHWkmZwqMn1akEhRFAaXdKIqCk8dHK67kInrUHd7hs6OJvbuP0k56FtR3SRqGmxau4TjI1McODIW7G+wz57Cm2+5CE3V+d9fuxfL8XBdH88TwYfN65dw22t28Fd//yMOH5vA8cB1wXF90sk4X/vCe7jnp/v5o//1XRy3+rpin+7+5/fR3Bznojf+DcWCDYq46PnAJVv6+eyrX81ffv0/+emuI+FnVFBIJU3e856NeBN5/uP+B+vGUPRYeOOt3TQ1t3JsKs7IWL5ujKC3L0UqBk3pOK5noOui+TuKSjKhk9SzKKpKPGrgBVUc1clIV3uSVDJGe2uaU+M5kdEfrOvs6GDR4hUs6ivT3PQQSnUdCrF4lCUrLwRFobP7+xQK5UBDU0XRVJat3sqydZcwuHKEo6NC01sNEjuXLB1iYPWlWMYhEsk76jJfhfHWu2QFADO5MsOjE+FJVlEUKpUyba1pxifGeeyJvVR7UCiqgqb6tDTFULGZnp4S+sWqFtyqpBIGTZkYra0p8oWmsKLCMHQWL+qgpSnGquV9nNq2vmGC397WQkuTyER78+uvp1zxaGlOBpMvlSWLO4hGI9zymivJ5wui+iOQHUunkqTTMZLxJSzpe4MICgXrNFULsx5/4+2vnfe3fOG2NVy4bW5R0pamGL09LcFvwg8n7NXX1ZSgaavj4roVKrZLJtVKLBZldGyC0dEJHNcN9bBbW5pYNtjP8PAp/vN7d1IoFCmWSpTKFaIRkz/96AexbZv//U//QqEgtG5VVSUWjbBz+zpamrpY0teJY5VJpRJCHkM1GFreS0tTjG2bh1i29AMiQzBwrtQ7z6591TPpSL9yswHrqWYFtWSizzuDaZ43EE7BQPKjqg/rh8tqGdsNza/nyuwWz5ydKlXNZPeBlEKyNU0880yF5gsjGpWZihJJlWog4Wzjn3YeCJaeluUI4bmkun72Hor/56tIOS1o4Pk+9ugYLZ2d81binG1q+tpeeBtKTpyms10LPtX3/PNqwadqk1EvcHL7VWd3ffDJwcMLhrN+nBH3qRvz+vNrnRRUWFQD+J6PXQJbFfYQKJQt4X7paVWBaPAnxr2cE424L9k2AAwgJDAdbMsmEnEo507S0QTbN/ZjOy62bWNZDqmoRTl3kmKpwszksKi0sF0sx8GxXQYWZVA1lfvu+zmjY1PBPouqiYt2bGbpQB/Hj4/w5P7DGKYZ2FoRWpqF7eF5HpZlo+ka0WgksNO0sPfM4r4e+hf3BAk2jcdGZ0crb7z11WEvtXLFahi7TDpFoVDCsm1mcnmmpgtsWCsaCB44dJQnnny64fVWLl/Kju2bmJqa4Qe33xtWqkQiQu704h1bADh0+BiO44ZNx3Vdx2wW+6vrGu1tLXP2zlFV9Rl75+zcvpmd2zcD1MYlkKrt6mjjuqsvpWLZWMHnrUqLgpClsso203YO27KxbJtlg6IC5OmDRzh46GjDe61fO0R7ewvTM1l2P/IEhm6EEmpVGTkQv+FUKhFUbIv11d5s69cNMbRiafi8+ibsmzeuYfPGmh1bzc7UNI1EIsaf/fGHqFgWlUoFqyL2d9OG1eJ7WDFAe1sLlm1RKlWwbZvLLtkOwJFjJ1kxuETYsZ7oS9nW2kwiHqNcrjCTzYl+OD5hAOd86CV3uu1YDTTNxQvt0ziRbA47Y/gIx7avGKDogaSRHiQaip4avqJRlewh6DcmmisbYRZ71bmrajUHvVp1tgeV+6qiBvPWwEmvqCjVXlC6qEAQ50M1nPeG1yaluqwqexk48FEDx3F1m/oAvRrM27Xw+lbtGVT3zVS/oPDxLCd0mJigMFNUaEoodX2xgu3rkhdq9nrtfr19P3ubOvu/ej0PbX0Xz3chSB7wggSLFeE29U21xfZdDdfLOWyB0/e1obLUrztFe7VYBPWvcZrHvfqE6hTErW3p0I7BeOB1OY05j3+lbpUyx+PgVqkmQCi14wDqjgO1VkWh1CSjVNUIAkx1/TDm6LmhVF8zqHBVAtnGcH3Yz65aAdtY+VFdvxB8XyS9tjTFXpi55iuQF3x+/grjlTZeng2e49DU3oZmnD2f1Jn6NM55g/eFGBaRSIRIZHZESVXVszKxi2X6WLrzd1G0KMWSQ6a5LWhUFEXR6srYgpOkrwCej+87weRJFc3ZPSuYcImGeA3Za9Sd+KkZBeHJtu5iFka5a6N12oV+juw4qF1A6gyJs6GnXc3C0nUxWSoUS5RKZWzHYXyyQCphkE4maGlpolgscTCYyLiBU1hRFLZtERn3TSt2kc8XhHZvkOF28c4tdHd18HttV/PArkfxvKDsyvdZNthP34bLeHXfFMdLi2Zlk6299F0oisLvfshheHgskMUA33O5/rorWLRuC+/5L4vp/8l94IkJjef79C3qYtG6N5HJF7jh+uOBTqSY6Pr47LzuXbS3tXDrLQWePngsHF/wueqKnSxa/2qumlnKcDaFqqr44lulrb2F3g1vA+CKK/aJJp9V402BjRffQqa1i0uvuJbmjn2AIuS2FIXt29aT6d7EqnUJLrnkZBBkUVFUSMTjxJvFxOvii3ZQyBdD6SQFhSUDg+hmgtWrhyhbXoOE1IrlA6Kqpa2Zm294VShnpaoigFBtmvXW224iny+EUlq6obFy2VJUReH6qy5l84Y1aJqCoYsmqclEHFVRaG5K87ef/kQgGSYaPmqaFvai+MB/efus46lqKK9cPsBAf2+o1VyVxqru0/q1Q0xlyzSlI+FvImIaqIpCMhHD0LWG46E6nrZtMzY+GUoieJ6PaRph1t3uR57Atu1ABkvoXm9YN0QqmWD/04c5evwkrusFGtMOA/19rFuzgvGJKX50x324jhv2F4lEIrz5DTcCcOfdPyeXzwcZgEIm4+Ybr2ZxXw8P7trDg7sfw3Ec3OD437F9EyuWLSGfLzI6Oo5pGjQ3ZViciNPe0YqqKERMk/f+5ptJxGOk00lSyUTDee/yS7ZzeTCR9eqMMTWYwLS31rIfz1dmTVjqAha+7+G5Fp4tSqYbAhWzghaN68UFAECpTSjqAxfVx0pNdqeatSQMcxGgrWUpBZnCpzWhDiePqqgIazinh69Xk7DRzdRZdXq+WA5UieR8pmrbLUR+6qy8r+c9J9vweb1nkMn6QgSdqswKPoUSGfMFpk93ftUFU+odXfh4rkdlcoZUSwpVUWYFs2bvw+nrZjuXEq3QM1B9VHMkgUfC87n5lhWiaavn4lNttCyqES7avpFSpYJdsajYoqqlOW3iWnl8t4jilynlcswEzb6zrU30tmuUKxZ3/uTO2lsGu/bGW68iYur85J5dnBwZBx/UIBFm68ZVrFjWz4mRMXY/8lRdwopGJpNmUUcCFMhnJ1FUhaihETMjmKZJRPdx7SKtzXGGli8O+s2JBKr21jSea+O4Ns1NKdELxbIoFovkcrVp689/uZtsLi/sSFVUV1939aX09Xbz+N79PPzoXhRFCZNElvQvYsumtRSLJR7Y9Si6oQeVtiK4sHa1SBgaHZsQElW6jhkRFTWGIYIu0WiErmj7vMfaqy7fOe+6C7asZ8O6oVAezbYdUkEAIR6PMbRiENd1Q8kx06gFekZOjVEpW0ESjoPrutx4/ZXEYlEef2I/Tz7VmOo3tGKQCy/YyOTkND+5++fBPFnY5qZpcP01l2EYBoePHKdSsYS0blABks0VaMqk2LhuFdMzuXAd+CSDKpkVywdoac7geaLfnWXZZNJJ0aPHdVk1NBgGlKqScKtXLZvzeH8l0NbWhqZps5I9R0dHZyWFVnmhfRobbv4Svg/jk1k6O7vmfE3f94Mm7haeZ+O7du3WqYhm8E4plJ0UdrIT+D3q7F6Uuj4GjTYroe368nDaeb5PRSsRb3p5NZNuSLiCMEFg3oQs8azTgj00JgnUtmp4VH/X932mCh7NybqqzTpm5baflrxR9YuEx1GDr6y+SlSt2Qsvo+/ldDwIfR8vp+PrXCLHbGG80sbLr36Ws3RtrHKmr3XOgiXPxbB4oYmmelh+2SfwPI/R0VE6OubXuT4d0SDUDm9FZpsTNA8VhojvVPDcSmB0iAwBL9DKniUzEJTgNUbD65uJ1mdUvDjNRIWRXRuPRDxGIh7D830MMxE6ZkEY/tWJx1xUM8PmYuvmdWzdvC7MunddN/we0ukUv/62W4UD23VxAz3m6ue/5lWXkM8XQ+e263oMrViKqhmsGlouqlGqGf2uS2dHG6oewYzCRTtEQ2nRKFI415ub29B0kzWrV5FKZYSWrC+ktFYsX4qq6vR0d7P9gk0oCiKDRqnp9gLsuHBT4EtVgsQZlY62VjwfNqwboqkpLcYtqBZaGTjyW1qaWb92VUM5fiJeyzQb6O+ldFrDxJag8WhrSzPdXY0TumpDRlVRw+am1aqIerLZPFPTMw3LlizuBWBsYpKHHn6sYd3i3h6uvHwHpVKZb3339lnf51tvuwnDMPjRHfcxPNIoCXThBZsYWrGUQ4ePc//PH2xY19HexquvvQzf9/n29++kWLSIx03U4FB/3WuvI5VM8PAjT3DoyPGG525cv5qN61cxM5PjgV17xHcTnGTTqWQYLDl+cgTXcRsm264TyC4Ex4BpGGGmZVVL29B1lizuxXFFxpxj20QDiQfRM6SEbTk4nhs0rY2F49/e1sLWTWuJx6Jh345Fgab14NLF/NHvvW/ObEgg3O+XK9VM4Dmzrhoe1wc0Gp1Ts4z96m0QmwjvA7Pm5fUBi6pRrtS0x4UcDHUZbNrsoEVdqbXIpquWUdcZ9A2BC6hvuFe/XXUSUCvRrnsskUgkkufNCxl88jyPSMUkljnz+cILSUtDVnN91rFHc5/Pmm3+HNdgYeu877cvxHFEcodtWdiuQ1tLEwpw4aVdFAt5Id3l2jiWTVdXG9FMhnQlQk+fE9jjNq7rYURiqLqJ7/sUSmXRwN0X0q3Zgo1jD+C5MDIyzOEjJ4W9FfRqiEc87O4kuakxjh89RH1gKZWMU84Ke8+1s2hU8BwPJ3iuUxqjnHM5dfIAp4aP4gZN5X3fw3dmWDPYxMjIOLffcQf1OtiaqvEnv/frgMI/ffk/mJrONSQbvfqanawZWsrjew/x+L5DxGMRYrEIiXiUzo42BvpFk/qR0QnMiEnUFLKltSpeYXXEdMBQAJHNr+Bgl6aI6bBmeddpfkYFpzyNaztcc9mmhnleNejmVLKsXtHDQF8Lli0ScCzbIZVM4Fh5VMVmoL8z7Ivnez6apuG5FqDgujblSgmn4IUSYH293UCKo8eHeWTP3objq9qTx/d97v3pAwChlK+uaSzp7yWZiNOUSZMvFMikkkG/E4Pp6SxNTamzebi/ZDBNky1btnD77bdzyy23hMtvv/12br755nOyT7qZDJLS8vNuoygKih55VmkTvyoTGfo1qn6O4NatBMGVMp5TEZKLriUSEKtVf3WVeVRL84LqFAU1vF+zlesqSeorT+oTjSQhNdnJ08MVLyye72M4JczEyyu4JJFIJGfCOQuWvBQNi+eDqhmgGWhz+zgb8H2/0egIjI2w0WiwTARYTm8m6jQ0E/Vxa+WVc12jfKivSKlvrPlSz/7Qgp4a9ei6Tlfn/FldK5fP70xe0t/Lkv7eOddFoxHe/ubXBkGWoBrGcUOn9Y7tm1i/bggvCOA4rktXRxsgAhPr1qzAsR2cICssnRbZYp7noWs6juPguLWmoZqm4TlQLluUTwt4VCdBigLpVCLsi6GqaqgfDUIWwXFctDBjTAsbbnZ2tgsda00Ne5NUtZETiThXXLbjtPeqfedbN68Lm3xW11eDP72LukPd52rySbVpo2kaXBwEnOqpfodrVi1noG78FUWhPWi62tXVzpWX70ANdFkVRSESjL2iKNx4/RVMZ8s0Z6JogZFc3Y8tm9exfu0QWqCNrKlqKIvQu6iLX3vLa2ftU5XXXH8lrutSqVhYtkOlUiGVSgSfK0pLcxOlcplSqcz0TDmszpianmH/gUPh65imSWcgwaWqKquHlmGaZtD0XDQ/r+pWb9+2Yd79ma+h67mivvy7Wvo9l2yKcLxUGxz7jQGM+mqLQBe3VuZcX1VRl02k1je600VJf1U3X1HCKr/G4PE8DYLnzFCqXyf2w/PBHp+gqb2jptf/EjkXSiQSiUTybNQqdRbOM7lLlzb1z7su3QGD86ta8ZbfuJhqwoPruSIZrb0NVVG4tu/ieZMmmhZ5rNx0Ha7r4rkurufie15QjeFzyxsHcT0P13Xwgsrirs52IpEI2y9dzIq1U2G1secKudN0Vw9qIs8tr0vXEqccB1VVSLavBnzWbbyQqekpSqUK5VKJUrlMqqWfWGYxE/lD7Ds4Kmz64G/likGGVm9kdGyCz/79N4K5i6jo1VSVj3/0t9B1na9/84dMT88Qi8eIRU3i0Qjr165kUU8Hk5NTjE1ME4tGAqktLbAdI6B4ocwRc9js8VhUbFelKqfj2SRiGhvW9NcljQv7zC5PgQ/b1i8WY19fEeVXKGePs2ogzYr+rdi2G1S8uBiGRjl3HMWy2bG5H9tysEMFAZ9KfhgAz57GKRcoOWJOVLFsWtIaicjZkwV9qfHhD3+Yt7/97WzdupUdO3bwxS9+kaNHj/Le9773XO/a80ZIPZugmWe0vVfn4wj9G74TBE9Ekmi1b5foY2UHz7HDSrlGycTaPAR8sU3d76FeFuml7tuQSCQSycuHc+qZeyUbFs+EoigoQXBlIdQ3y6zeVitUGp2Zpzs4vbB6xcfDd21ct4LvWGHly6zsj4YdrlW21Cpc6u6/qDkMLxzV4MxcE8b2thbmC9G0t7U0NHisR1XVUJrJ9/1gcuViRkymZspsXL+KlcsHsB2RlVftXwEQjUTo7urAccVy13FR1dpYj45OUK5UQikz3/dZtnQxAMeOn+Sp/Yca9mXNquV0dLQyPZPlnvt+Ffb30FSNWCxKf59oOr7/6cOUK5Uw8KCqKk2ZNCQgl8szNj4VlvOrqooeBCY8z8cOJp2aqjVsA4RyXNXqHN/3wwoXx3aolK1Qds33fRKJGC0tTTiOw4mTI0zPlJmZNlAQQaiq5vSRoyeYnJoR1UBBVdDyZUvo6+3m5PAojz72ZFgt5PkeTZk0l160Dc/z+MrXvj2rsubWm68lnUpy7PgwJ0+OEotFicUiQo85CFZ1dbZzw3VXiHXRyKyg3jPpYZ9tar/3Wkap77m4joVbcXCpZY7ScE4IAhvhX+0mJNRpVWqTDaUauKg2/DNRVU3owqp6ICE1VwWG1jihOS14MTuQ8eKieB6qaojGfC+BDGGJRCKRSF7u1GRVhEytquqomnlGlTjP5JrtXTJ/4lR/qpv+wbnXxTJwZe/qeZ/7xrcum7WsKhF9462DXH7NG0N5VcuySCQSZLq7MTJF3vZOhUKhQLFYpFgsUi6XaV8ipLnK/i8YncniTOTDQEvv8h2s7dvJ3Q9+k//4jx+gBPK1mqYxNDTERz7yEWYOHeJ//O3fEY/HicViJBIJ4vE4b3nLm4lGozz88MPk83lisRixaJRYLEZbexvJRALbtiiXSxi6HkooN1bxniZRx+mBK+aVqGvrm13tW32diy8bCG3TUAooSKpR0FC1s9ek9aXCbbfdxsTEBH/+53/O8PAwa9eu5fvf/z79/fMHGl+pqKroefJc8ANfxuykrPr+HSIA43lOGHDxquodTmV+30Y4hVZP82moDT6N8PcgAywSiURyXnNOgyXSsFgYYTPRsyjtXK1mqWWB2CLg4lYzPxw8x8JzKyLA4lrBdqL0vSolZhc9Kmo1qw3wlVp5rVILtDQ0ZjvPsj0URcEwDAzDwAsCUplMiuamuRv5NTWln7EK4eYbr2p4XC9XtmHdECsGFwspBUfIIqRTCTynQjxmsmXTKjxXBC1c129w9icSMRRVEZl6nottOXiBTFy+UGTk1JgIPAQBiMWWTX9fD8VSmQd27WmQDVMVNazm+fmvdjM5Od2wz5detI3kwGJODJ/igV2Phs9BUehb1MXi3h5c12PfU4colGzSyUgYwNm4XjSpLBSK5HIF0bsl0DSs6qtqmkosFg31mhVFIZUUVT+qqrJ187pAl9oQTURNk2QQELnowvml4qLRSNDMPZhAes5pWZG1CeZsiSkRvJg9CT3DXhj1hMGLuh5IqlabwPpBg0LFQFP1oNeFETbBE4GNxt9lWL2hBPJTYeVGXZCjoTmeRCKRSCQSySuTqq0TjUbnbQoaj8d51ateNe9r/PZv/za+7zcEWuKBtO4VV1zBqlWrKJVKlEolisUimUwGEMlBvb294brJyUnK5TJve9vbUBSVb3/7Ozz99NNhJTjAm970Jm688UZ+es99/Nu//ZvoPxj8DQwM8Fu/9Vt4nsdnP/vZsHdGNBolEolw/fXXk0gk2LdvH7lcjkgkEj63ra2N5uZmLMuiUCiEzz09Yeh85f3vfz/vf//7z/VuvKwRWvvPzz0127fRqObhuTaeW2mUR/dsPL+C5zo4FYdKfroWWwn9GrXm8/U+jrPZH/Z8RCTzuYFqSjXpt+pLqFciOE2GLUy6k+MskTwTfoMPaa7+RH79mrp1Z9jLaI5efbPfe77X8ufeLkjY8Dxb+KrOEedc80UaFueWasmq+owF+I2EcmFBMMVxbGxzjHRzGkWp6Zp6rtWQ8eF7bmiMVC+GfvViWK9lKvas0RBBmUPLtCa/0/D4FcJsuSO38b5XMyzwPVBqJx/Nh1RUR4kbKEokdNg7lRkMxWdZX4bTS+/L2ROgwLrlLXW6suJWUQqUcwUGuiMM9AzV1gVO/HL2OFFV4Y03bgWq1SNiklfOnQTg4q1L8X3CihNFUTB0nUr+FIO9KQZ7L5pl8FTypwB47bVbmMrXN5Dz8a0JKhZsGOoEOmddCMq5k2RisH1Db3VAqR5gVc3rge5I8Bk9UCpAGSufnfX5G4/NgHppu1BCqvY4DBxSC2YQ9KyBugbdQRBUUbVn74Uxl5TUHH2MQkmpjk40TX9F/S4kEolEIpFIXm6EDedNk0QiES5vamqiqalp1vae55FKpfjgBz84byXO7/7u71IqlSiXy2FApadHVIovWbKEa665pmF9e7uoyLFtm9HRUSzLwvO8UJbsyiuvJJFI8N3vfpfHHnss6HuhoOs6N9xwA7fccguPPPIIX/nKV9B1HU3TME2Tnp4e3ve+9wGE66LRKPF4nEgkwpYtWxo+s0TyQrFQ30bopPccXMfCGhujqSUT+DSqEuluTSLdrfaoFQmkwrdRk0gX1fxezSnY0FOxTjqYmjT6XPLAs/u1cE4DBKFCQdhTUqgWeK6FZ/t4ULf+dNnmoGKoqmYQzq1rc2fltKQ8gtfzfTvwV9BQwSaSDOskBKvUD41fdWQ09qoM5+YNygbVJ9cpHITrqw1h1FrT+vpm9NU3Pn17yUueBid9NbFVrKhbVpfMGj5ndpXm6RWY4ePq/dOeJ44Zv+78UFtc28G6+/UR3LmSaIN9s4sKFdWvO95Po+rDangjpfE2vKnbRjltG5j7WFca7yinv/asBkvKafta/X2CpkRR9ZhQZToHnPNgieTlR9UIqaIaHkbUJpp65gaX9cZI6OyvNrj3a6WyYWlttdLFtUVfF7duW9+m2pyxWl4bXjQb37Rux2kwVqDO0V3v1J71o6+/KM774ap3GpaFJ8hwVbVU3Q+qcRRm+7GDE2BoOFWDRjWHuKqZKIaJqptCzkAzg4oBPfirVQ6I16g/QXtnVHo//8mfxmXh569dQMLm2wS3nk+iGfzq4/rnzjFujcOh4Pk+plcikomHDeTm+67CE7Jaf0KuGi612+prz230nG7w1G0fPm7ctr4/xqyAxmnbvNBGVCgppcpAiUQikUgkEskrkUQiMW8QYunSpSxdOncvx0gkwic/+Uk8zwurXWzbDita3vrWt5LL5cLl5XKZ3l6RfNTe3s62bdvCKphSqdRg1z744IM4jhMGYFzXpb+/XwZLJC9JRE8WFTQDRYugmyXMeOsZSQX6p/kvqJdL90WfJSGFLhIcvTrfhqh0ccGv3lal6+bq1wLg1SkHnO5BPU2J4IynmTWfQ23+P+cghbfVPpOKouCj4LsenuugKhpUA0CqUDRQNF0k/2lC5lhVDRp6yVQVSIJelKEPIwggzeqPWXfbsCx0RteW1z8O5eir34vvie+k2m+zWtkS+kVqgZjqv5rL43TfCAgHe50fqjqmpzm+fd/DLkJFq/oxOC0ps25Z/bjP8kfUf/fhFrO/r9O/67m+0+fCnI76+Q6e+Z87py/Ir92v+vrskkdFC8YgVN6oCxiEga7gudU4RP1ePUvya80nVJcAO1cALPQZ1nyIIhFWbFNrV1ANiFZVO9TwmAc1qJaokyOfFYirfaZGv9Hpx0DjA98He2KKptaWOvn+2cfGMwVBFGX+580dKJntK53vs8x6/2f8rOceGSyRvGjUGyPPlQYtU++0Sov5tE3DAMFpPVzCHi/Vfi5VA8QLnfq+VwskBDtQ/4HqPx3hj7x6chQPGk+CwYnSQ6GsFki2ZtDU08t2a5kmNVmk2n3lPHSAe55H2R8l3f7MATmJRCKRSCQSiUTy7KiqGspp1dPV1UVXV9ecz1myZAlLliyZ9zX/4i/+okFuzLZtFi1adDZ3WyJ5SRD6Nnj+Wc+zAgANt2eS5Aj1Tua6V55v74PPMH+2d239HImAiorn+TjjkzR1tKNpep2v4+zM1c+Fv6NWuTJHhUCYEPpsVQUecyWgep6HZUyTaknXcjrnqEioJZ42SnSHgSCxo7X9re084TFAbbnwZ1Wp237uAZi9bF4H9lxO9fqn1TvPT/su66p3Ghzn9UmngO8rlJQC8ZYkaigBXhc8mivxtf51Z21f28na8fUMybL1j+urvV7kRNgzxfM8jLx3xgFfyTMjgyWSlxUNWqYvY4lcz/MoOqPEm6TzXyKRSCQSiUQikbz8qfZikUgkZ87LUU7c8zxUvYCmR18x/oyqJNcL4fr2PA+zpBFLS//PmeJ5HkVvlGSLHDPJi4884iQSiUQikUgkEolEIpFIJBKJRCKRnNfIYIlEIpFIJBKJRCKRSCQSiUQikUgkkvMaGSyRSCQSiUQikUgkEolEIpFIJBKJRHJeI4MlEolEIpFIJBKJRCKRSCQSiUQikUjOa2SwRCKRSCQSiUQikUgkEolEIpFIJBLJeY0MlkgkEolEIpFIJBKJRCKRSCQSiUQiOa+RwRKJRCKRSCQSiUQikUgkEolEIpFIJOc1+rnegeeD7/sAZLPZs/q6nueRy+WIRqOoqownPRtyvBaOHLOFIcdrYcjxWjhyzJ6d6rW2eu2VzI20TV4ayPFaGHK8Fo4cs4Uhx2thyPE6M14JtskLYTfI42fhyDFbGHK8FoYcr4Ujx2xhyPE6M87UbnhZB0tyuRwAfX1953hPJBKJRCI5P8jlcmQymXO9Gy9ZpG0ikUgkEsmLy8vZNpF2g0QikUgkLy7PZjco/ss4DcPzPE6ePEkqlUJRlLP2utlslr6+Po4dO0Y6nT5rr/tKRY7XwpFjtjDkeC0MOV4LR47Zs+P7Prlcjp6eHpmt8gxI2+SlgRyvhSHHa+HIMVsYcrwWhhyvM+OVYJu8EHaDPH4WjhyzhSHHa2HI8Vo4cswWhhyvM+NM7YaXdWWJqqr09va+YK+fTqflQbYA5HgtHDlmC0OO18KQ47Vw5Jg9My/XrM0XE2mbvLSQ47Uw5HgtHDlmC0OO18KQ4/XsvNxtkxfSbpDHz8KRY7Yw5HgtDDleC0eO2cKQ4/XsnInd8PJMv5BIJBKJRCKRSCQSiUQikUgkEolEIjlLyGCJRCKRSCQSiUQikUgkEolEIpFIJJLzGhksmYNIJMLHP/5xIpHIud6VlwVyvBaOHLOFIcdrYcjxWjhyzCQvdeQxujDkeC0MOV4LR47ZwpDjtTDkeEmeD/L4WThyzBaGHK+FIcdr4cgxWxhyvM4uL+sG7xKJRCKRSCQSiUQikUgkEolEIpFIJM8XWVkikUgkEolEIpFIJBKJRCKRSCQSieS8RgZLJBKJRCKRSCQSiUQikUgkEolEIpGc18hgiUQikUgkEolEIpFIJBKJRCKRSCSS8xoZLJFIJBKJRCKRSCQSiUQikUgkEolEcl4jgyUSiUQikUgkEolEIpFIJBKJRCKRSM5rZLDkND7/+c8zMDBANBply5Yt3Hfffed6l14SfOITn0BRlIa/rq6ucL3v+3ziE5+gp6eHWCzG5ZdfzuOPP34O9/jF59577+U1r3kNPT09KIrCt771rYb1ZzJGlUqFD37wg7S1tZFIJLjppps4fvz4i/gpXjyebbx+/dd/fdYxd+GFFzZscz6N1yc/+Um2bdtGKpWio6OD1772tezbt69hG3mM1TiT8ZLHmOTlgrRN5kbaJs+MtEsWjrRNFoa0TRaGtE0kLxbSbpgbaTc8M9JuWDjSbjhzpM2wcKTdcO6QwZI6vva1r/GhD32Ij33sY+zevZtLLrmE66+/nqNHj57rXXtJsGbNGoaHh8O/PXv2hOv+8i//kk9/+tN87nOf44EHHqCrq4urr76aXC53Dvf4xaVQKLBhwwY+97nPzbn+TMboQx/6EN/85jf56le/yv33308+n+fGG2/Edd0X62O8aDzbeAFcd911Dcfc97///Yb159N43XPPPfzWb/0Wv/jFL7j99ttxHIdrrrmGQqEQbiOPsRpnMl4gjzHJSx9pmzwz0jaZH2mXLBxpmywMaZssDGmbSF4MpN3wzEi7YX6k3bBwpN1w5kibYeFIu+Ec4ktCLrjgAv+9731vw7KhoSH/D//wD8/RHr10+PjHP+5v2LBhznWe5/ldXV3+pz71qXBZuVz2M5mM/3d/93cv0h6+tAD8b37zm+HjMxmj6elp3zAM/6tf/Wq4zYkTJ3xVVf0f/vCHL9q+nwtOHy/f9/13vOMd/s033zzvc87n8fJ93x8dHfUB/5577vF9Xx5jz8bp4+X78hiTvDyQtsn8SNvkzJF2ycKRtsnCkbbJwpC2ieSFQNoN8yPthjNH2g0LR9oNC0PaDAtH2g0vHrKyJMCyLHbt2sU111zTsPyaa67hZz/72Tnaq5cW+/fvp6enh4GBAd70pjdx8OBBAA4dOsTIyEjD2EUiES677DI5dgFnMka7du3Ctu2GbXp6eli7du15O4533303HR0drFixgne/+92Mjo6G68738ZqZmQGgpaUFkMfYs3H6eFWRx5jkpYy0TZ4daZs8N+Q147kjrxvzI22ThSFtE8nZRtoNz460G54b8nz+3JHn9LmRNsPCkXbDi4cMlgSMj4/jui6dnZ0Nyzs7OxkZGTlHe/XSYfv27fzTP/0TP/rRj/iHf/gHRkZG2LlzJxMTE+H4yLGbnzMZo5GREUzTpLm5ed5tzieuv/56vvKVr3DnnXfy13/91zzwwANceeWVVCoV4PweL9/3+fCHP8zFF1/M2rVrAXmMPRNzjRfIY0zy0kfaJs+MtE2eO/Ka8dyQ1435kbbJwpC2ieSFQNoNz4y0G5478nz+3JDn9LmRNsPCkXbDi4t+rnfgpYaiKA2Pfd+ftex85Prrrw/vr1u3jh07djA4OMg//uM/hs2D5Ng9O89ljM7XcbztttvC+2vXrmXr1q309/fzve99j1tvvXXe550P4/WBD3yARx99lPvvv3/WOnmMzWa+8ZLHmOTlgry+zo20TZ4/8pqxMOR1Y36kbbIwpG0ieSGR1765kXbD80eezxeGPKfPjbQZFo60G15cZGVJQFtbG5qmzYqsjY6OzopsSiCRSLBu3Tr2799PV1cXgBy7Z+BMxqirqwvLspiampp3m/OZ7u5u+vv72b9/P3D+jtcHP/hBvv3tb3PXXXfR29sbLpfH2NzMN15zIY8xyUsNaZssDGmbnDnymnF2kNcNgbRNFoa0TSQvFNJuWBjSbjhz5Pn87CDP6dJmeC5Iu+HFRwZLAkzTZMuWLdx+++0Ny2+//XZ27tx5jvbqpUulUmHv3r10d3czMDBAV1dXw9hZlsU999wjxy7gTMZoy5YtGIbRsM3w8DCPPfaYHEdgYmKCY8eO0d3dDZx/4+X7Ph/4wAf4xje+wZ133snAwEDDenmMNfJs4zUX5/sxJnnpIW2ThSFtkzNHXjPODuf7dUPaJgtD2iaSFxppNywMaTecOfJ8fnY4n8/p0mZYONJuOIe8sP3jX1589atf9Q3D8L/0pS/5TzzxhP+hD33ITyQS/uHDh8/1rp1zPvKRj/h33323f/DgQf8Xv/iFf+ONN/qpVCocm0996lN+JpPxv/GNb/h79uzx3/zmN/vd3d1+Nps9x3v+4pHL5fzdu3f7u3fv9gH/05/+tL97927/yJEjvu+f2Ri9973v9Xt7e/077rjDf+ihh/wrr7zS37Bhg+84zrn6WC8YzzReuVzO/8hHPuL/7Gc/8w8dOuTfdddd/o4dO/xFixadt+P1vve9z89kMv7dd9/tDw8Ph3/FYjHcRh5jNZ5tvOQxJnm5IG2T+ZG2yTMj7ZKFI22ThSFtk4UhbRPJi4G0G+ZH2g3PjLQbFo60G84caTMsHGk3nDtksOQ0/vZv/9bv7+/3TdP0N2/e7N9zzz3nepdeEtx2221+d3e3bxiG39PT4996663+448/Hq73PM//+Mc/7nd1dfmRSMS/9NJL/T179pzDPX7xueuuu3xg1t873vEO3/fPbIxKpZL/gQ98wG9pafFjsZh/4403+kePHj0Hn+aF55nGq1gs+tdcc43f3t7uG4bhL1682H/HO94xayzOp/Gaa6wA///8n/8TbiOPsRrPNl7yGJO8nJC2ydxI2+SZkXbJwpG2ycKQtsnCkLaJ5MVC2g1zI+2GZ0baDQtH2g1njrQZFo60G84diu/7/nOvS5FIJBKJRCKRSCQSiUQikUgkEolEInl5I3uWSCQSiUQikUgkEolEIpFIJBKJRCI5r5HBEolEIpFIJBKJRCKRSCQSiUQikUgk5zUyWCKRSCQSiUQikUgkEolEIpFIJBKJ5LxGBkskEolEIpFIJBKJRCKRSCQSiUQikZzXyGCJRCKRSCQSiUQikUgkEolEIpFIJJLzGhkskUgkEolEIpFIJBKJRCKRSCQSiURyXiODJRKJRCKRSCQSiUQikUgkEolEIpFIzmtksEQiOY/4xCc+wcaNG8/Z+//Jn/wJ73nPe87Z+58pn/vc57jpppvO9W5IJBKJRPKKR9omZ4a0TSQSiURyviNthjND2gwSyfND8X3fP9c7IZFInj+Kojzj+ne84x187nOfo1Kp0Nra+iLtVY1Tp06xfPlyHn30UZYsWfKiv/9CqFQqLFmyhK9//etcfPHF53p3JBKJRCJ5WSJtk7OHtE0kEolE8kpG2gxnD2kzSCTPD/1c74BEIjk7DA8Ph/e/9rWv8ad/+qfs27cvXBaLxUgmkySTyXOxe3zpS19ix44d59ywcF0XRVFQ1fkL6yKRCG95y1v4m7/5G2lcSCQSiUTyHJG2yZkhbROJRCKRnO9Im+HMkDaDRPLCI2W4JJJXCF1dXeFfJpP5/9m77zi57vLQ/58zfWZnZutsL9JKWvUuWZYt925jcHBIM2AggRC4FxLflOv87s2NE8Dk3hsuSagOBHASMOTlQDCh2Ilt2cayem/bV9vb9HbmtN8fKy0WajvSrLY979fLLzyzZ84857uD95nz/X6fB0VRLnjul7etfuADH+CRRx7hM5/5DFVVVZSUlPDUU0+h6zp/9Ed/RFlZGfX19fzDP/zDee/V39/Pr//6r1NaWkp5eTnvete76O7uvmx8zz333HlbQZ999lnKy8tRVfW84x599FHe//73Tz5+4YUX2Lx5Mx6Ph+bm5sn4zvnc5z7H2rVrKSoqoqGhgY997GMkk8nJn3/zm9+kpKSEH/3oR6xatQq3201PTw+vvvoqN9xwA0VFRZSUlHDzzTfT09Mz+bp3vvOd/OAHPyCTyUxp/IUQQghxPslNJDcRQgghpkJyBskZhJgtZLJEiAXu5ZdfZmBggNdee43Pfe5z/Pmf/znveMc7KC0tZffu3Xz0ox/lox/9KL29vQCk02nuuOMO/H4/r732Gm+88QZ+v5/777+fXC530feIRCIcO3aMLVu2TD73nve8B8Mw+OEPfzj53NjYGD/60Y/44Ac/CMDPfvYz3vve9/KJT3yCEydO8NWvfpVvfvObfPrTn558jc1m42//9m85duwY3/rWt3j55Zf54z/+4/PeP51O8/TTT/O1r32N48ePU1ZWxiOPPMJtt93GkSNH2LVrFx/5yEfO2/q7ZcsWNE1jz5491z7IQgghhJgyyU0kNxFCCCGmQnIGyRmEKDhLCDHvfOMb37CKi4sveP5//a//Za1fv37y8eOPP241NTVZhmFMPrd8+XLrlltumXys67pVVFRkfec737Esy7K+/vWvW8uXL7dM05w8RlVVy+v1Wj/72c8uGs/BgwctwDpz5sx5z//e7/2e9cADD0w+/vznP281NzdPnvuWW26xPvOZz5z3mn/8x3+0ampqLnnt3/ve96zy8vLJx9/4xjcswDp06NDkc+Pj4xZgvfrqq5c8j2VZVmlpqfXNb37zsscIIYQQ4sokN5HcRAghhJgKyRkkZxBiJknPEiEWuNWrV59X77Kqqoo1a9ZMPrbb7ZSXlzMyMgLA/v37aW9vJxAInHeebDZLR0fHRd/j3NZPj8dz3vMf/vCH2bp1K/39/dTV1fGNb3yDD3zgA5MrIvbv38/evXvPW3lhGAbZbJZ0Oo3P5+OVV17hM5/5DCdOnCAej6PrOtlsllQqRVFREQAul4t169ZNnqOsrIwPfOAD3Hfffdxzzz3cfffd/Nqv/Ro1NTXnxef1ekmn01MbSCGEEEIUhOQmkpsIIYQQUyE5g+QMQhSalOESYoFzOp3nPVYU5aLPmaYJgGmabN68mUOHDp33T2trK7/1W7910feoqKgAJravvt3GjRtZv349zz77LAcOHODo0aN84AMfmPy5aZo89dRT573P0aNHaWtrw+Px0NPTw4MPPsiaNWt4/vnn2b9/P1/84hcB0DRt8jxer/e8LakA3/jGN9i1axc33XQT3/3ud2lpaeGtt94675hwOEwoFLrSEAohhBCigCQ3kdxECCGEmArJGSRnEKLQZGeJECIvmzZt4rvf/S6VlZUEg8EpvWbJkiUEg0FOnDhBS0vLeT/7nd/5Hf7f//t/9Pf3c/fdd9PQ0HDee50+fZqlS5de9Lz79u1D13X++q//enI1yfe+970pX8vGjRvZuHEjTz75JNu3b+fb3/42N954IwAdHR1ks1k2btw45fMJIYQQ4vqT3EQIIYQQUyE5gxDiSmRniRAiL4899hgVFRW8613v4vXXX6erq4udO3fyyU9+kr6+vou+xmazcffdd/PGG29c9Hz9/f38/d//PR/60IfO+9mf/dmf8eyzz/Lnf/7nHD9+nJMnT/Ld736X//E//gcwkbTous7f/d3f0dnZyT/+4z/yla985YrX0NXVxZNPPsmuXbvo6enhxRdfpLW1lZUrV04e8/rrr9Pc3MySJUvyGR4hhBBCXGeSmwghhBBiKiRnEEJciUyWCCHy4vP5eO2112hsbOTd7343K1eu5EMf+hCZTOayKzM+8pGP8Nxzz01ufz0nGAzy6KOP4vf7eeSRR8772X333cePfvQjXnrpJbZu3cqNN97I5z73OZqamgDYsGEDn/vc5/irv/or1qxZwz//8z/z9NNPT+kaTp06xaOPPkpLSwsf+chH+C//5b/wu7/7u5PHfOc73+HDH/5wHiMjhBBCiJkguYkQQgghpkJyBiHElSiWZVkzHYQQYv6zLIsbb7yR3//93+c3f/M3z/vZPffcw8qVK/nbv/3bGYrufMeOHeOuu+6itbWV4uLimQ5HCCGEENNAchMhhBBCTIXkDEIsHLKzRAhxXSiKwjPPPIOu65PPhcNhnnvuOV5++WU+/vGPz2B05xsYGODZZ5+VxEIIIYSYxyQ3EUIIIcRUSM4gxMIhO0uEEDNm0aJFRCIR/uf//J/84R/+4UyHI4QQQogFTnITIYQQQkyF5AxCzE8yWSKEEEIIIYQQQgghhBBCiAVNynAJIYQQQgghhBBCCCGEEGJBk8kSIYQQQgghhBBCCCGEEEIsaDJZIoQQQgghhBBCCCGEEEKIBU0mS4QQQgghhBBCCCGEEEIIsaDJZIkQQgghhBBCCCGEEEIIIRY0mSwRQgghhBBCCCGEEEIIIcSC5pjpAK6FaZoMDAwQCARQFGWmwxFCCCHmLcuySCQS1NbWYrPJWotLkdxECCGEuD7mQ24ieYMQQghxfUw1b5jTkyUDAwM0NDTMdBhCCCHEgtHb20t9ff1MhzFrSW4ihBBCXF9zOTeRvEEIIYS4vq6UN8zpyZJAIABMXGQwGCzYeU3TZHR0lFAoNGdXqFxPMl75kzHLj4xXfmS88idjdmXxeJyGhobJv73i4iQ3mR1kvPIj45U/GbP8yHjlR8ZrauZDbjIdeYN8fvInY5YfGa/8yHjlT8YsPzJeUzPVvGFOT5ac26YaDAYLfkMim80SDAblQzYFMl75kzHLj4xXfmS88idjNnVSIuLyJDeZHWS88iPjlT8Zs/zIeOVHxis/czk3mY68QT4/+ZMxy4+MV35kvPInY5YfGa/8XClvkBEUQgghhBBCCCGEEEIIIcSCJpMlQgghhBBCCCGEEEIIIYRY0GSyRAghhBBCCCGEEEIIIYQQC5pMlgghhBBCCCGEEEIIIYQQYkGTyRIhhBBCCCGEEEIIIYQQQixoMlkihBBCCCGEEEIIIYQQQogFbcYnS/r7+3nve99LeXk5Pp+PDRs2sH///pkOSwghhBBCCCGEEEIIIYQQC4RjJt88Eolw8803c8cdd/CTn/yEyspKOjo6KCkpmcmwhBBCiDktlwljs7txuIpmOhQhhBBCLHCGlkZX4zh9FdhsM3oLQgghhBCznKFn0bMxXEWVKIpy3d9/RjOVv/qrv6KhoYFvfOMbk88tWrRo5gISQggh5jDT1MlEe0hHOigqW4bDtXimQxJCCCHEAmSaOlomjJoaJZcaxDItims34/KWzXRoQgghhJiFLNNATQ2TDndgmQYl7iB2p/e6xzGjkyU//OEPue+++3jPe97Dzp07qaur42Mf+xgf/vCHL3q8qqqoqjr5OB6PA2CaJqZpFiwu0zSxLKug55zPZLzyJ2OWHxmv/Mh45W8+jJmuJkiF28km+rAMHbPA1zOXx0YIIYQQ08+yLIxcAjU9hhofQFejgILdFcAw4jMdnhBCCCFmqVx6nHS0GzU5CCjYHe4Zi2VGJ0s6Ozv58pe/zBNPPMGf/umfsmfPHj7xiU/gdrt5//vff8HxTz/9NE899dQFz4+OjpLNZgsWl2maxGIxLMvCZpvxti6znoxX/mTM8iPjlR8Zr/zN5TGzLAstGyEb78fUMjg8pejZKFo4QTI3UrD3SSQSBTuXEEIIIeYPU1fJZcbJJgbRMuOYuorN6cNVVIVic2BZJnpOJkuEEEIIcT5DS09Ux4j1gGXg8lVimTqmnp6xmGZ0ssQ0TbZs2cJnPvMZADZu3Mjx48f58pe/fNHJkieffJInnnhi8nE8HqehoYFQKEQwGCxoXIqiEAqF5txNs5kg45U/GbP8yHjlR8Yrf3N1zEwjRzrSSUbrwlPkxOmtA0VBjUfxlwXwlVQW7L08Hk/BziWEEEKIuc0yDbRshFxqDDU5iJZLYLO5cLiDuHyhmQ5PCCGEELOYaeqoiQHS4Q40NYHLV47d6QPAMPUZjW1GJ0tqampYtWrVec+tXLmS559//qLHu91u3O4Lt+HYbLaC39xSFGVazjtfyXjlT8YsPzJe+ZHxyt9cG7NcJkxqvA01OYjLV3leLU9FUbCdvZ5CmSvjIoQQQojpo6sJtEyYbKKfXDYCpoXDHcATqENRJFcQQgghxKVZlkUuPUo60oWaGsLhCuAJ1s9II/dLmdHJkptvvpnTp0+f91xraytNTU0zFJEQQggxu1mWSTbWSyrchmnk8ATqUWz2mQ5LCCGEEPOUaeQmmrUnhlDToxhaGrvTh8sbwmZ3znR4QgghhJgDdDVOOtpNNt4H2PD4a1FsMzo1cVEzGtEf/MEfcNNNN/GZz3yGX/u1X2PPnj0888wzPPPMMzMZlhBCCDErGVqa1HgrmVgvdlcAt798pkMSQgghxDxkWSZaJkIuM4YaH5zoOaI4cHqKcfkqZjo8IYQQQswRpq6SifeRiXVhaBmc3grsjtlb5ntGJ0u2bt3K97//fZ588kn+4i/+gsWLF/P5z3+exx57bCbDEkIIIWYdNTVMaqyVXDaKu6gSm9010yEJIYQQYp55e5ktLRvFMo2JBRpSZksIIYQQebAsEzU5PNGXJDOOw1OCJzD7F3zO+F6Xd7zjHbzjHe+Y6TCEEEKIWck0NNLRLtLhTlCUszXBZ089TyGEEELMbZcqs+X0VkiZLSGEEELkTctEzpbc6sfmcOMO1s+ZRRczPlkihBBCiIvTsjFS462oiQEc3nIcrqKZDkkIIYQQ88CFZbYSKDYHDndQymwJIYQQ4qoYWoZM7AyZWA+WoeEqCs25qhgyWSKEEELMMpZlkk0MkBo/jaFlcAdmZ+MzIYQQQswtei45UWYr3vdLZbZq58yKTyGEEELMLqapoyYGSUc60NU4Dk8ZDt/cXOwpd16EEEKIWcTQs6TC7WQi3didPjyBupkOSQghhBBz2GSZreQQufQYupbG7vDi9JbPudWeQgghhJg9LMtCy4yRjnShJoexOX24A/VzunS4TJYIIYQQs0QuPUpqvJVcehyXrxKbwz3TIQkhhBBiDrIsEy0bJZcenSyzhWLH6SnG6539zVWFEEIIMbvpuSTpaA/Z2Bksy8Llr8Y2DypiyD5bIYQQYoaZpk4q3EFs4ABaNoE7UCcTJQXw2muv8fDDD1NbW4uiKPzgBz+44mt27tzJ5s2b8Xg8NDc385WvfGX6AxVCCCEKRM8lycTOEO3fQ7RvN6nxNizAHajFE6jB7vTNdIhCCCGEmMNMQyMV6SLWv4d0pAOHuxhPoGZeTJSA7CwRQgghZpSuJkiOt6Im+nG4S3C4AzMd0ryRSqVYv349H/zgB3n00UeveHxXVxcPPvggH/7wh/mnf/onfv7zn/Oxj32MUCg0pdcLIYQQM2GyzFZiiFzm7WW2yqTMlhBCCCEKwrIscqkR0pEOculR7K4g3mDDTIdVcDJZIoQQQswAy7JQk4Okxk6j55K4/PNnJcZs8cADD/DAAw9M+fivfOUrNDY28vnPfx6AlStXsm/fPv7v//2/MlkihBBiVrEsEy0TIZcZkzJbQgghhJhWWjZGJtpFNt4PNjtufx2KzT7TYU0LuSsjhBBCXGemrpKOdJKOdKLY3bgDdXO6Adp8sWvXLu69997znrvvvvv4+te/jqZpOJ3OC16jqiqqqk4+jsfjAJimiWmaBYvNNE0syyroOeczGa/8yHjlT8YsPzJe+bnceOlqAi0bQU30o2WjWKaB3RXA5a8BZaLKtmlZ1zvky7Oss9dT2M+AfJ6EEEKI6WXqKpl4L5loN4aWweULzfuS4TJZIoQQQlxHufQ4qfFW1NQILl8Iu9M70yGJs4aGhqiqqjrvuaqqKnRdZ2xsjJqamgte8/TTT/PUU09d8Pzo6CjZbLZgsZmmSSwWw7IsbDZpOXclMl75kfHKn4xZfmS88vPL42UaGnougZaJoKtxTCOHze7C7gxMrOrMAln1iuedKRYWega0sTBOt1aw8yYSiYKdSwghhBC/YJkGamqYdLiDXDaM012KJ7gwdq7KZIkQQghxHVimQTp2hnS4HdPQ8ATm77bVueyXd/hYZ1fnXmrnz5NPPskTTzwx+Tgej9PQ0EAoFCIYDBYsLtM0URSFUCgkNxqnQMYrPzJe+ZMxy4+MV34mdkxYlPid6LlxculBbLk4bocTR1EQ21xr0m6ZqHYoqSjD5S0t2Gk9Hk/BziWEEEKICblMmHSkCzU5iM3uxhOoR1EWTv4mkyVCCCHENNNzSVLjbWTivTjdxbh8FTMdkriI6upqhoaGzntuZGQEh8NBefnFV9G43W7c7gu3IdtstoLfEFQUZVrOO1/JeOVHxit/Mmb5kfGaGj2XRE2NkR7vwp5Mg2VidwXwBOfujQoL5ezvXyno718+S0IIIUThGFqaTPQMmVgPlqXj8lVis19Yinq+k8kSIYQQYppMNHEfIjV+Gj2XwF1UvSCTjbli+/btvPDCC+c99+KLL7Jly5aL9isRQgghCsHU1Yk+JMkh1NQoupbG0F04SyqwO1wzHZ4QQggh5jHT1FETA6TDnei5BE5vGfa5tou1gPKeLOnu7ub111+nu7ubdDpNKBRi48aNbN++XbbBCiGEEGeZRo50uJNUtAObzYXbvzCbuM9k3pBMJmlvb5983NXVxaFDhygrK6OxsZEnn3yS/v5+nn32WQA++tGP8oUvfIEnnniCD3/4w+zatYuvf/3rfOc735nWOIUQQiw8lmWiZSLkMmOo8UH0XAIUO05PMQ5vOWkzgyILLKaF3NMQQgghJhZ35tKjEyW3UsM4nH7cgYV53+LtpjxZ8u1vf5u//du/Zc+ePVRWVlJXV4fX6yUcDtPR0YHH4+Gxxx7jT/7kT2hqaprOmIUQQohZLZcJkxpvQ00N4fJWLMhVGbMhb9i3bx933HHH5ONzvUUef/xxvvnNbzI4OMiZM2cmf7548WJ+/OMf8wd/8Ad88YtfpLa2lr/927/l0UcfnZb4hBBCLDy6mkDLhMkm+tGyUSzTwO4K4A7UTpbZMs/2yxKFNd25ydNPP82f/umf8slPfpLPf/7zhb8AIYQQokB0NUE62k023gsoePw1KDYpQAVTnCzZtGkTNpuND3zgA3zve9+jsbHxvJ+rqsquXbt47rnn2LJlC1/60pd4z3veMy0BCyGEELOVZRpkYr2kI+2YRg6Pf2E2cZ8tecPtt98+2aD9Yr75zW9e8Nxtt93GgQMHCh6LEEKIhcvUVXKZMGpyiFx6DENLY3f6cHorpDzndTLducnevXt55plnWLduXaFDF0IIIQrGNHJk4n1kop0YuTROXwi7Q3ZVvt2UJkv+8i//koceeuiSP3e73dx+++3cfvvtfOpTn6Krq6tgAQohhBBzgZ5LkQ63kYmdwe4K4vZfvCH4QiB5gxBCiIXOMg20bJRcahQ1OYieS6LYHDjcQVy+ipkOb8GZztwkmUzy2GOP8fd///d86lOfuuyxqqqiqurk43g8DoBpmpimOeX3vBzTNLEsq2DnWwhkzPIj45UfGa/8yZjlZyrjZVkmudQomWgHudQ4Dk8xrkD9xOtn2Y5Wy7Imr0cp4Gdgqp+nKU2WXC6p+GUVFRVUVEjyJ4QQYmGwLItcapjUeCtaNoqrqAqbfWE3Y5W8QQghxEL19jJbuWwETAuH+/wyW+L6m87c5OMf/zgPPfQQd9999xUnS55++mmeeuqpC54fHR0lm81O+T0vxzRNYrEYlmVhs8lnbipkzPIj45UfGa/8yZjl50rjpatJ1NQwWiaMYnNgd5WhZGyQycxAtFdmGjksw0AbHcPmcBfsvIlEYkrHXXUxspGREUZGRi6YlZFtp0IIIRaKc03c09FOFJsTd6B+wTdDuxTJG4QQQsxXlyqz5fKGpMzWLFaI3OS5557jwIED7N27d0rHP/nkk5M91GBiZ0lDQwOhUIhgMDjl970c0zRRFIVQKCQ3GadIxiw/Ml75kfHKn4xZfi41XoaWIZPoJZvswWHP4aoMocyBhZ2mBqauUxKqwO70Fuy8Hs/Uyo3lPVmyf/9+Hn/8cU6ePDlZB1xRFCzLQlEUDMPI95RCCCHEnKNlIiTHWxd0E/epkLxBCCHEfGRZJlomQi4zhhofRM8lpMzWHFGo3KS3t5dPfvKTvPjii1O+AeN2u3G7L1wla7PZCnpDUFGUgp9zvpMxy4+MV35kvPInY5aft4+XaeqoiUHSkQ50NY7DU4a7qHKmQ5wyS1Gm5fc/1XPlPVnywQ9+kJaWFr7+9a9TVVUlK2iFEEIsKJZpkI33kQq3YRrqgm3iPlWSNwghhJhP9FwSLT1ONtGPlo1imQZ2l5TZmksKlZvs37+fkZERNm/ePPmcYRi89tprfOELX0BVVex2yRGFEEJcH5ZloaZGyES7yCaHsTuLpPrFVch7sqSrq4t//dd/ZenSpdMRjxBCCDFrSRP3/EneIIQQYq4zjRza2TJbamp0ssyW01shZbbmoELlJnfddRdHjx4977kPfvCDrFixgj/5kz+RiRIhhBDXjaFlSIweJ5foB8Dtr8Fmu+ruGwta3qN21113cfjwYbnpIYQQYsGQJu5XT/IGIYQQc9HFymyh2HF6iqXM1hxXqNwkEAiwZs2a854rKiqivLz8gueFEEKI6WAaOVKxXlLhNuxuFVdRJXbH1EpDiovLe7Lka1/7Go8//jjHjh1jzZo1OJ3nr6R55zvfWbDghBBCiJkmTdyvjeQNQggh5hI9l0TLhMnG+9GyESmzNQ9JbiKEEGKusywTNTlMJtqJmhwDinAHK7HJvYprlvdkyZtvvskbb7zBT37ykwt+lm+j1j//8z/nqaeeOu+5qqoqhoaG8g1LCCGEKLhcJkxqvE2auF+DQuYNQgghxHR4e5mtXHoMXUthd/hwestlJ+k8NJ25yauvvnoNkQkhhBBXpmUipKPdqIl+FJsLd7AOe0yd6bDmjbyXxnziE5/gfe97H4ODg5imed4/V5NUrF69msHBwcl/frnmpxBCCHG9WaZBOtJNfPAAWmYcj79OJkquUqHzBiGEEKIQLMsklwmTHD9N5MybRPv3kokPYHN48QYbcfkqZKJknpLcRAghxFxkaBmS46eJDuwlm+jH6QvhKgqB7HwtqLx3loyPj/MHf/AHVFVVFSYAh4Pq6uopHauqKqr6i5myeDwOMJnYFIppmliWVdBzzmcyXvmTMcuPjFd+ZLzy9/Yx03NJ0pEOsrFe7K4AzqJqLCb6lswVlmVhFvgzcLXnKnTeIIQQQlyLyTJbiQG0TATL0rE7pczWQiK5iRBCiLnENHXUxCDpSAe6GsfhKcXlC810WPNW3pMl7373u3nllVdYsmRJQQJoa2ujtrYWt9vNtm3b+MxnPkNzc/NFj3366acvKNsFMDo6SjabLUg8MHFDKBaLYVkWNpskzFci45U/GbP8yHjlR8Yrf6ZpEo1GyaUjqMl+TC2Nw1OCYjogm5np8PKmpUF1JEjmRgp2zkQicVWvK3TeIIQQQuTr0mW2ymT3yAIkuYkQQoi5wLIstMwY6UgXanIYm9M3b3uomqZJR+cZamur8DivfPx0ynuypKWlhSeffJI33niDtWvXXtAM7ROf+MSUz7Vt2zaeffZZWlpaGB4e5lOf+hQ33XQTx48fp7y8/ILjn3zySZ544onJx/F4nIaGBkKhEMFgMN9LuSTTNFEUhVAoJDcap0DGK38yZvmR8cqPjFf+9FyGbLwPlz6Mt8iN01sPczgBUW3gLwvgK6ks2Dk9Hs9Vva6QeYMQQggxVZZpoGWj5DJjqPFB9FwCFDtOTzFe74XfNcXCIbmJEEKI2U5XE6RjPWRjvViWhctfjc2W9238OaGnd4ADB48Riye48YaNLFtcM6Px5D3KX/va1/D7/ezcuZOdO3ee9zNFUfJKLB544IHJf1+7di3bt29nyZIlfOtb3zpvUuQct9uN2+2+4HmbzVbwG4KKokzLeecrGa/8yZjlR8YrPzJeU5dLj5McO42aHKU4VIHDNfd7kyiKgu3sZ6BQrvZchcwbhBBCiCvR1QS5zDhqop9cNgqmid0lZbbEL0huIoQQYrYyjRyZeB+ZaCdGLo3TW4Hd6Z3psKbF+HiEXXsOMTYepramiltu3kpFeSmGNrPVPfKeLOnq6pqOOAAoKipi7dq1tLW1Tdt7CCGEEHC2iXush3S4A0PPTZTimKdJyEyazrxBCCGEADD07Hlltkw9i83hw+UNYbPPcC0HMetIbiKEEGK2sSwTNTlMJtpJLjWGw1OCJ9gw02FNK7vdDsC9d91CbU3hqmJcq1m1f0dVVU6ePMktt9wy06EIIYSYx3Q1QSrcTibei9NdgsNbTio693qTCCGEEAvVRJmtMLnUGGpyEC2XxGZz4XAHpempEEIIIeaMXCY80ZckMYjN4cYdrJ/Xu2Hb2rupqqqgpCTIOx64Y6bDucCURv6zn/0s6XR6SifcvXs3//7v/z6lY//wD/+QnTt30tXVxe7du/nVX/1V4vE4jz/++JReL4QQQuTDsiyyiQFiA/vIxvtwF1XjcAdmOqx5Z7ryBiGEEAubZVlo2RipSCeR3l1E+naTinSC4sATqMPtr5q3pSrEtZHcRAghxGyj51IkR08S699LLjmEqyiEy1cxrydKorEEb+4+QF//0EyHcklT2lly4sQJGhsbec973sM73/lOtmzZQig0sVpH13VOnDjBG2+8wT/90z8xODjIs88+O6U37+vr4zd/8zcZGxsjFApx44038tZbb9HU1HT1VySEEEJchKFnSYU7yEa7UOwe3IE6lDncxH02m668QQghxMJk6Fm09DjZ5BBaegzTyGFz+nAVVc3bZqeisCQ3EUIIMVuYhoaaGCAd6URTE7h85didc7936pVomsa+A0fweb0sX7Z4psO5pCllls8++yxHjhzhi1/8Io899hixWAy73Y7b7Z5cnbFx40Y+8pGP8Pjjj1+0CfvFPPfcc1cfuRBCCDFFufQoqfFWculxnL4QdodnpkOa16YrbxBCCLFwmKaOno2gpkbJJYfQc0kUuwuHuxiX/B0XeZLcRAghxEyzLItcaoR0pJNcehS7y48nWL8gFnH2Dwzzxq59qKrGrTu2TvYrmY2mvAxn3bp1fPWrX+UrX/kKR44cobu7m0wmQ0VFBRs2bKCiomI64xRCCCHyZhoamVgPqXA7WJzdTTJ/t7TOJpI3CCGEyJdlWehqjFx6HDUxgK7GsACHK4g7sDBuJojpI7mJEEKImaJlo2Si3WTj/WCz4/bXothm74RBoWSzKh6PG5/XQ3VViM0b1+Avmt27aPLes6woCuvXr2f9+vXTEY8QQghREFo2Rmq8FTU5gMNThsPln+mQFiTJG4QQQlyJoaXJZcKoiQFymTCmoeFwFuEqqkKRMluiwCQ3EUIIcb0YepZM7AzZ2BkMLYvLV4HNMf93L46NR9h/8BjpdIZ3veNuSkuLuW3HDTMd1pRI5imEEGJesSyTbLyfVPj0RDLir5V65kIIIcQsYxo5tEwENTVMLjWCoaVR7G6c7pIFcRNBCCGEEPOXZRpkk4NkIp3kMhGcnlI8wfKZDmvaRWMJDh46Tk9vPyXFQTZvWoPNNreqe8jdIyGEEPOGoaVJhdvJxM5gd/rwBOpmOiQhhBBCnGWZBrlshFxmDDU+iK4lAQWHuxi3p0zKbAkhhBBiTrMsCy0zRjrShZocxubw4gk2LIgcx7Is/vOVNzFNkx3bt7CkuXFOXrdMlgghhJjzzjVKS42fJpeN4faFZFWqEEIIMQtM9CGJoyaHieU6JvqQmAZ2VwB3UfWCqNcthBBCiPlPVxOkYz1kY71YloXLXz3vq1xksypHj59meUszwYCfu27fTiBQNKsbuF/J/P6NCSGEmPdMI0c63Ek62omiOPAE6ubk6gUhhBBiPjG0DFo2QjYxSC49RiaaxVviw+mtwGZ3znR4QgghhBAFYRo5MvE+MtFOjFwap7cCu9M702FNK13XOX6ynWMnWrEsi1ConGDAT0lJ8JrOaxgGu/YcYuuGpQWKNH95Fw370Ic+RCKRuOD5VCrFhz70oYIEJYQQQkxFLhMmNniAZPg0DncxrqKQTJTMMpI3CCHEwmGaOmpqhPjIcSJ9bxIb2EcuNYrd6cfpq8DpK5eJEjHjJDcRQghRCJZlkk0MEu3bQ3L4KIrixBNsmPcTJQCvvbGXw0dOsrS5iUffdR+LGq+tBHo2q2JZFna7HZfLRTqjFijS/OU9WfKtb32LTCZzwfOZTIZnn322IEEJIYQQl2OaOqlIJ7GB/WiZMB5/HXanb6bDEhcheYMQQsxvlmWiZSKkwu1Ee98k1r+HbLQbRXHiDtTj9ldhWwA3DcTcIbmJEEKIazWxcPMgsYH9GFoad7Aeh/vadlXMFdmsyuh4mB03bWbb1vV4vZ6rPpeu6xw5dprn/+1ndHX3ArB5wyr8RTOXO065DFc8HseyLCzLIpFI4PH8YiAMw+DHP/4xlZWV0xKkEEIIcY6uxkmOt5FN9ON0l+DwVcx0SOIiJG8QQoj5Tc8l0TJhsokBtEwUy8xhd/lxFVWhzPP63GJuktxECCHEtdJzKbKxM2RiZ7BMHVdRCJvdNdNhXVcej5tH33XfNfUlsSyLru5e9h88TiaTZXlLM7U1VQWM8upNOYstKSlBURQURaGlpeWCnyuKwlNPPVXQ4IQQQohzJra4DpAeb0XPpXAXVUspj1+SSmcYHQtTXTLTkUjeIIQQ85GhZ9EyYdTkMLn0GIaexu7w4fSUYHO4Zzo8IS5LchMhhBBXyzQ01MQA6UgnmprA5StfUNUtDMOgtb2bU6c7eODe2/B4ri3v6+ru5bWf76WpoY5NG1dTHAwUKNJrN+XJkldeeQXLsrjzzjt5/vnnKSsrm/yZy+WiqamJ2traaQlSCCHEwmZoGVLhdjLRHuxOH55g/UyHNKtomsaxE20cO9GKx+3mwTtWzXRIkjcIIcQ8YRoaWjaMmhojlxpCz6Ww2Zw43EFcsrtTzCGSmwghhMiXZZnkUqOkIx3k0qPYXQE8wfoF0yv13CTJkWOnyGZVmhc3YprmNZ93UVM9fn8RlaHyAkRZWFOeLLntttsA6OrqoqGhAZst73YnQgghRF4syyKXHiE11kouE8FdVCkrV3/J6bZODh4+QS6ns3rlUtauXo6ZHZ7psCRvEEKIOexcH5JcZhw1MYCuJlEUZeIGQWDh3CAQ84vkJkIIIfKhZSKkYz2o8X6wOXD761BsV196ai56Y9d+unv6aF7cyLo1y695B0g0GmdoZIyWpYtm5UQJ5DFZck5TUxPRaJQ9e/YwMjJywWzS+9///oIFJ4QQYuEyjRzpaBfpcCeKYl9Qqzfy0d8/TG1NFZs2rMZfNLENOJud4aDeRvIGIYSYGyzLwsglyGXCqIkBtGwUyzKxO4tw+6sX3M0BMX9JbiKEEOJyDC1DJn6GTPQMppHD5S1fMIs2czmNU62dlJeVUFdbxfq1K9mwbmVBymSpao49+48QjydpWbro2oOdJnlPlrzwwgs89thjpFIpAoHAeTeuFEWRxEIIIcQ1y6XHSYXbUFPDuLwVC6oW6FTsfH03TY11LGqq5/Zbt83qlZGSNwghxOxmaOmJCZLkEFp6HNNQsTl8OL0V0htMzEuSmwghhLgY09RRE4Okwx3ouTgOT+mCKTmazaqcONXOydMdGLrBpo2rqautoqT42idJDMPg5OkOjhw7jWEY3HbLDbP6HkbekyX/7b/9Nz70oQ/xmc98Bp9Pbl4JIYQoHNPUycTOkA53YJk6ngW4zfVKRsfCdPX0UVtTBTCrkwyQvEEIIWYj08ihZSKoqWFy6RGMXArF7sbhLsbl8Mx0eEJMK8lNhBBCvN1E+e9R0pEu1NTwxK7aBVR2dGh4lJde/jkAK1qaWb1yGT6ft2Dn7+ruY//BY7QsXcyGdSvxemd3rpn3ZEl/fz+f+MQnJKkQQghRUFo2Rircjprox+EuwbFAVnDk6/iJNgJ+P0uXNM10KFMieYMQQswOlmmgZSPkUmOoySH0XAIUGw53EHegbMHcEBBCchMhhBDnaNkYmVgP2XgfYMPjr0Gx5X27fE7q6x+ivq6aivJS1q1ZwfJli/F4ClNubGBwhLHxCOvWLKd5cQOhUNllS3lFIjEOHz3FhvWrCPhmdvzzfvf77ruPffv20dzcPB3xCCGEWGAsyyQb7ycVbsXIZXD5a7AtkOQkX9FYgp7efrZt3TBnbmpJ3iCEEDPHskz0bOxsH5JBNDUKFthdftyBWhRldu9OFGI6SG4ihBDC0LNk471koj0YWgaXL7Rg+pIA9A8M8/LOt/jVR+7D5/Oyfu2Kgpw3Eomx7+Ax+geGqAxVsGbVMmw22yUnSqKxBIePnKCrpw9/URGJRILwWIaG2pKCxHM18r4b9dBDD/FHf/RHnDhxgrVr1+J0nl/H9p3vfGfBghNCCDG/6bkU6XAbmXgfdqcPT7BupkOa1XrO9FPk87G0ufGSx1iWdR0jujLJG4QQ4vrT1QRaNkI2MYCWiWKaGnanD1dRlSxIEAue5CZCCLFwWaZBNjlIJtJJLhPB6SnFEyyf6bCuK9M02XfgKBXlpQUtt/XGrv20d3QTDPi5/dYbWdR4+fs7rW1d7NpzEJ/Xy/ZtG3G5XLz+5n5y2TSPPHQzpQWLLD95Z8of/vCHAfiLv/iLC36mKAqGYVx7VEIIIeY1y7JQk0Okwq3o2RguX+WCWsWRj86uM6QzWdasamH1yqWsWrEEh+PCP9+maXLiVDsdXb3ctX3pDER6cZI3CCHE9WFoGbRM+GwfknEMPYPd4cXpKZG/sUK8jeQmQgix8FiWhZYZm+hLkhzC5vDhCdYvuF22o2NhDh05SSQa4x0P3HnN59N1HZvNhs1mw+txc+MNG2lZuuiyvVWzWRWPx01NTSXr1qygpjpEdVWIaDTOksWNrFhWj28GU9e8J0tM05yOOIQQQiwQhp4lFe4gG+1GsbsWVOO0fERjCd7ac5Ch4VEWN9UDXHSSBCAcjvLz3QcYH4+wcvnsmSgByRuEEGI6/aJR+wi59DBGLo1ic+FwB3BJ7y8hLkpyEyGEWFh0NU46eq4vCQuy9LdlWSiKQntHD8lkittv2UZF+bXt3ejq7mXvgaOsXb2clcuXsHnjmsser+s6Bw+foL3zDHfcdiPtHT10dp6hf2CYdzxwByUlQbZtXY+hZTD19DXFdi2u6ZORzWbxeGZ3B3shhBCzh5oaIR1uI5cex+mtwO4s3JbP+cKyLPYfPMaJk+0UFfm4+46bqa+rvuTxx0+2sf/AMYJBPw/dfwehirLJJHC2kbxBCCGunWnq6NkIudT4RKN2LQko0qhdiKtwLbnJl7/8Zb785S/T3d0NwOrVq/mzP/szHnjggQJGKIQQ4mqZukom3ksm2o2hZybuQTgWzvdRy7I40zfI0WOnaV7cwKoVS9myaQ0Oh+Oa88Xdew9z8nQ7jfW11NVWXTGO9o4eDhw+TjKZxqYo/PTFnXi9HjZtXM3yZYuvKZZCy3uvkWEY/OVf/iV1dXX4/X46OzsB+J//83/y9a9/veABCiGEmPtMI0dy/DSxgQNoagJ3oE4mSi5BURTSmSzr1q7gkYfvvuREyblVkcGAn3VrV/DOh+4iVFF2PUOdEskbhBDi2lmWiZaJkAp3EO3dRaRvN6lwOxbg9tfgCdTicPllokSIKShUblJfX89nP/tZ9u3bx759+7jzzjt517vexfHjx6crdCGEEFNgmQbZeD/R/j0kRo+j2Fx4AvULZqLENE06u87ww3//T17ZuQu73UZZaTEATqfzmvPFSCTGqdYONm1Yw523bycY8F/2+Dd3H+A/Xn2T6soKHn3kPurqqrlp2yZ+9ZH7WbOq5YLeYTMt78mST3/603zzm9/kf//v/43L5Zp8fu3atXzta1+76kCefvppFEXh93//96/6HEIIIWafXHqc2OBBkmOnsLv8uIuqFlxd0CtJpTO8/Oouurp7Abj15q1sWLcSu91+wbG5nMabuw/w8s5dADTU17Bh3crL1gSdSdOVNwghxEKgqwkysTNE+/cQ6dtNYvQEpqHhKqrCE6zD6SmWv6lC5KlQucnDDz/Mgw8+SEtLCy0tLXz605/G7/fz1ltvTUfYQgghrsCyLHLpUWKDB4gNHsDQs3gC9TjcgZkO7brqHxjmtZ/vxefz8sC9t/HAvbdRXRUq2Pl7evsJ+ItYs2rZJY+JRGKMj0cIh6OMjIxjGAYb1q+ipDjIvXftoGXZ4ove75gN8i7D9eyzz/LMM89w11138dGPfnTy+XXr1nHq1KmrCmLv3r0888wzrFu37qpeL4QQYvYxTZ1MtId0pBPL1PD461Bss/OP4UyxLIuTpzs4cOg4TqeDpUubLnt8T+8Ab+05iKbpbNm09jpFeW2mI28QQoj5zNAyaNkIanJIGrULMQ2mIzcxDIN/+Zd/IZVKsX379ksep6oqqqpOPo7H48DEKuBC9VIxTRPLsqQ3Sx5kzPIj45UfGa/8Xc2Y6WqCdPwMaqwXy7JwFVWh2BxYTHzvns80TePU6XYsM8fN2zdTW1vFww/eRenZ3SRmAa7fNE0GBoepr6th3dqVLG9ZAopywbnTmQyHDp/k6LHTqLkcwWCAYMDPA/feht9fNKVY3v77Vwr4/5upfp7ynizp7+9n6dILm8eapommafmejmQyyWOPPcbf//3f86lPfSrv1wshhJh9tGyMVLgNNTGAw12CQ5rMXiCVzvDyzl2Mj0dYvqyZzRvX4HJdevvpzjf20NXdS0N9Ddu2bsBf5LuO0V69QucNQggxH5lG7uwEyQi59AhGLnW2UXtQGrULUWCFzE2OHj3K9u3byWaz+P1+vv/977Nq1apLHv/000/z1FNPXfD86Ogo2Ww2r/e+FNM0icViWJY1a3cezzYyZvmR8cqPjFf+8hkz09DIpcfIpYcx9Rx2VwCb3UUqrgHz+/umZVmcPNXOqdZ24okMK1oWMRZOTYyZ4iIczVzze5imSXdPHydOtpJMprjn7lspL5toDJ/K/OL8uq5z6nQ7J0+347DbsTlcFLm8rF61gsWLGrDZbERil/47F45EGRsL07KsGUPPYeoamnusoAuFEonElI7Le7Jk9erVvP766zQ1nb/69V/+5V/YuHFjvqfj4x//OA899BB33333FSdLrscqjHPnk1nfqZPxyp+MWX5kvPIzk+NlmQbZxADpSCuGlsVVVI1icxRkJcN0sixr8p/rNWput4sin4+tm9dRGSoHLr7iwzRNbDYbFRVlNNTXsKip/pLHnmNZFmaBPwNXe65C5w1CCDFfWKaBlo2Qy4yjJgbRc0kURcHuCuIOlEr/ESGmSSFzk+XLl3Po0CGi0SjPP/88jz/+ODt37rzkhMmTTz7JE088Mfk4Ho/T0NBAKBQiGAzmfzEXYZomiqIQCoXkxuwUyZjlR8YrPzJe+ZvKmFmmgZoaIRPpQjEi+IPF2N2V1znSmXW6rYv2jnZWr1hMbV0DjXXlBc0f29q7OXz0JMlUhsaGGtav3TE5UfLLhoZHefW1n7NmdQvvuP8OTNPE4bDjcFx66kHXdbp7+jnd1snoWAR/kZctG5djs8DUDUpCFQXtdevxTK1nTd6TJf/rf/0v3ve+99Hf349pmvzrv/4rp0+f5tlnn+VHP/pRXud67rnnOHDgAHv37p3S8ddjFQbIrG++ZLzyJ2OWHxmv/MzUeBlahmxiAC09hs3pxe4smzOrOSwgkcqBonA9bk0ZhoHdbp8sP3mxFR+JRJK9+w9TXlbK+nWrqKqqveSxv0xLg+pIkMyNFCzmqa7C+GWFzBuEEGKusywLXY2Ry4RR4wNouRiYJnZXAHdRtZSrFOI6KGRu4nK5JnepbNmyhb179/I3f/M3fPWrX73o8W63G7f7wlWyNputoHm7oigFP+d8J2OWHxmv/Mh45e9SY2ZZFlpmnHSkCzU5hM3hwRusX5A93Jx2O6tXLGXrlnWEo5mJMbvGyZJ0OoPNZsPjcaNpGjXVlaxZuWyypNfb9fUP8frP91JcHGBkdJxVy5ewZcNqPG7XRc58PtM0+cELL5FOZ6itqeKu27fTUFeNzWbD0PRp+f/MVM+V92TJww8/zHe/+10+85nPoCgKf/Znf8amTZt44YUXuOeee6Z8nt7eXj75yU/y4osvTnlm53qswgCZ9c2XjFf+ZMzyI+OVn+s9XpZlkk0Mkkl3YbclcFVVodiv/MdxNrEsCyyLsmLPtK/kVdUc3//hf3Lz9s001Ndc8HPTNDl+oo1DR0/i83pYvqyBspL8VlOoNvCXBfCVFG5lzVT/Vv+yQuUNQggxl+m5JFomQjYxiJYNYxoadqcPl68Smy3vr2RCiGswnbmJZVnnVcMQQghROLqaIB3rIRvrA0xcRVXY7JcuZT1fjY9HKCrysXRJE0uXNBWkkkc0luD4yVY6Os6wetUyNm9cw5pVLRc91rIs3ti1nwOHjtPbN8DG9au55aatLF5UP+V7UDabjRs2r6OsrIRgwH/N8RfSVWXm9913H/fdd981vfH+/fsZGRlh8+bNk88ZhsFrr73GF77wBVRVxW4/f2XV9VqFATLrmy8Zr/zJmOVHxis/12u89FyKdKSDTPQMNqcXb3HjtL7fdDGZGLNCrMS4HMuyOHb8NLpuUFlRdsF7aZrGT156jUgkxqoVS9m4ftVlt61ezPh4BCObJlilzMgqjIspRN4ghBBzjaFn0TJh1OQwWmYcXUtht3txuqVRuxAzrRC5yZ/+6Z/ywAMP0NDQQCKR4LnnnuPVV1/lpz/9aYGiFEIIAWDqKpl4H5lYF0YujdNb2PJMc8XA4AjHTrQyMDjM5o1rWbv64pMZ+YhEYhw4fJzevkG8Xg8bN6xm+bLFlzw+mUrz7z99hWxW5cF7b8PldtFYXzPlRae9fYO0d/Rwy81bJkuMzzYztozprrvu4ujRo+c998EPfpAVK1bwJ3/yJxdMlAghhJg9LMtCTQ6RCreiZeO4fSG58XMF0ViCN986wMjoGBvWrcLr/cVOjXNluZxOJw11Ndy0bRMV5RevBXopR4+38qOfvExFeRnLF5dQt2RboS9hTvrSl77E//k//4fBwUFWr17N5z//eW655ZaLHvvqq69yxx13XPD8yZMnWbFixXSHKoSYB0xDQ8uGUVNj5FJD6LkUNpsThzuI11s+0+EJIQpoeHiY973vfQwODlJcXMy6dev46U9/KjtnhRCiQCzTIJsaIh3uJJcZw+kpxRNsmOmwrruh4VH27D9COBylrKyE23bcQFNjXUHObZomiUSKm2/cTPPihkvej1fVHEePn+bQkVMcOXaKD3/w11i2dNGU3ycSiXHidDudXb1UV4XyXhR6PU0pstLSqTcYDIfDUzouEAiwZs2a854rKiqivLz8gueFEELMHoaWIRXpJBvtRrG78ATqpAntFBw70Uomk+W+u2+lpjo0+fzA4Ai7dh9k04ZVLF7UwMb1F28IeinpdIZ//t4P2bvvCNXVIbbdsJ6ldTO7ymY68oar8d3vfpff//3f50tf+hI333wzX/3qV3nggQc4ceIEjY2X3gV1+vTp88p7hkKhSx4rhBAXNGpXkyg2BbszgCdQL38jhZgFpiM3+frXv34tIQkhhLgEy7LQ1Djx4V5yqWFsdjeeYMOC6kui6zrpTJZgwI+iKLhdLu696xZqawpTantwaJTyshLKy0t55OHLT/Lv3neEn7z4Kk0NdWzeuJpfe/cDBINTL5315u4DtLZ14fV6WLt6OatWLL3W8KfVlCZLPv/5z09zGEIIIWY7y7LIpUZIjbehZcM4fSHsjqvrY7FQDA6NoukajfW13LB5HTabMrmCQlVz7D1wlPaObqoqKygrK8n73INDI5w83YFpWPzWrz3MbbdsQ1EUsvG+abiaqZstecPnPvc5fvu3f5vf+Z3fASbi+tnPfsaXv/xlnn766Uu+rrKykpKSkim9h6qq59Umj8fjwMQKHdM0rz74X2KaJpZlFfSc85mMV35kvPJnGAaamiARSaInBs9r1O4qqoKzjdotzvbFWuAsy5r8Rz5lVzbvxuvctZiF/e9MPueaLbmJEEKIy9PVBMloN6nxM7j8TPR3W0B9SbJZlVOtnZw83UEw4Oeh+2+nqrKC++6+eHWEqxGNJfjPV99k+bLFbN287pJxgMWp1k5e/I/XcTmd3H37dhoaaq94/mQqTVt7N00NtZSVldBQV0NtdSWNDbVzorT+lCZLHn/88emOA5gofyGEEGL2MfQsmUgX6WgXKHbcgboFtaojX9FYgv0Hj9LbN0h9XTWN9bW4XL9I8KLROD/9j9cwDJObtm1i2dJFea08fnnnLs70DhCqKGPxono2b1iD2+2ajku5Ktcrb7icXC7H/v37+e///b+f9/y9997Lm2++ednXbty4kWw2y6pVq/gf/+N/XLQ01zlPP/00Tz311AXPj46Oks1mry74izBNk1gshmVZcyLBnGkyXvmR8Zoa09Aw9AymliWXCROLJ0i4TewOD3Zn8cTfxSyQzc10qLOOBSRSOVAUZJ/Nlc238bKw0DOgjYVxurWCnTeRSEz52NmQmwghhLi0t/cl0dUUdmcQl794WnuKziaqmuPQkZO0tncBsGzJIlavWlbQ9xgeGePkqQ56evvxeb2sW3NhqelsVuXYiVbe3H0QXdepKCvlwftuY92a5Tidl560siyLvv4hTrd10j8wjN1upzjon5gsqa+5YmyZTJb+gWH6B4cZGx3h4ftuvKZrvRbXVCAsk8mgaecnO28vWyGEEGLuU1MjpMNtqKkxXL4K7E7fTIc0a+m6zt79R2lt76LI5+P2W7ad17Qsl9NwuZwEg36WLG5k9cpl+HxTL5mVy2n89KXX2Pn6Hj7y27/O8mXN03EZ0+Z65g1jY2MYhkFVVdV5z1dVVTE0NHTR19TU1PDMM8+wefNmVFXlH//xH7nrrrt49dVXufXWWy/6mieffJInnnhi8nE8HqehoYFQKFTQazNNE0VRCIVCcjN7CmS88iPjdSHLsjD1DHouhaGl0DLjGGocXc+AaeLxuMEKUF4ekDJbU2BZFlgWZcUeGa8pmHfjZZmodiipKMPlza8n2+V4PNe+w1nuaQghxMyyTAM1OUQ60kkuG8bpLsEdbCAVzcx0aNeV3W5jYHCYNataWLl8yUSuWQCWZaEoCqZp8sprb+FyOtm2dQNLmxvP6xuSy2kcP9nG0eOt2GwKK1ua0Q2Dm7dvJhi4csmtw0dPcejICcrKSrjxhg00L2q47OSKaZoMj4zj9/sI+Is43dY1+fqG+hp03SjI9V+NvCdLUqkUf/Inf8L3vvc9xsfHL/i5YczcxQghhCgcU1dJx7pJhydWNniC9bKb5BLOJSB2u514IsnmjWtYuXzJZHM00zQ5dqKNo8dP8+C9t1FaWnzJ7a6X0tF5hr0HjrDvwDFali2mZeni6biUgpvpvOGXbzKd+11dzPLly1m+fPnk4+3bt9Pb28v//b//95KTJW63G7f7wkTWZrMV/KazoijTct75SsYrPwt9vCzLxMgl0XMp9FwcLT2OoaUxtCwoFjabG5vTi8cdRLE5MC0Lm5aZGLf5cDN7mplMfMZkvKZmvo2XxdlrsSkF/W/M1Z5rpnMTIYQQZ/uSZMZIR7pQk8PYHJ6zvd5smAuohOlbew7R1FhHTXWIRx6+p2CLJFLpDK1tXbS2d/HwA3fi83l58L7bCfiLLvoeZ3oH+MELL1FWVsLvfug3rjhZE4nEOHj4BJWV5axZ1cKypYuor6umovzyiyIsy+L4yTYOHTmJruts2rCGdWuWs6KlmeXLFuP1ejC0DKaevqbrvxZ5T5b88R//Ma+88gpf+tKXeP/7388Xv/hF+vv7+epXv8pnP/vZ6YhRCCHEdZZLj5Iab0dNj+Dyym6Sy+ntG2TfgaPsuGkLoYqyC2qJDg2P8taeQ8TiCVatWIrfn/9YvrLzLXp6+6mpqmTLprXctuOGSyZRmUyWbFYlcFVXU3gzlTdUVFRgt9sv2EUyMjJywW6Ty7nxxhv5p3/6p0KHJ4SYYaahTUyOaCm0bAw9E8bQ0phmDlCw2T3YnV4cnlJZKCDEPCP3NIQQYmbpapx0tJtsvA/LApe/GpvtmoofzUk9vQOcau2g5mzT9kJMlIyMjnPyVDs9Zwaw2W0sW7Jo8mdv3yGi6zqn27oYHQtTGSrn4OETLG6qZ+uWdVcs8Z3LafzHK29is9lY1FQHQJHPS9EVqmaMj0fYve8II6NjrFy+lKXNjZO9Wwu1k6YQ8v4kvvDCCzz77LPcfvvtfOhDH+KWW25h6dKlNDU18c///M889thj0xGnEEKI68A0cqSj3aQjnWBZePx1KGeb1IrzpdMZ3ti1n4HBYWqqKy+6xbS1rYs3dx+goryMhx+4M68m7rquY5oWhmEQTyTZsG4VG9atvOTOiHPbZo+daKWx2sfdjZuv5fIKZqbyBpfLxebNm3nppZf4lV/5lcnnX3rpJd71rndN+TwHDx6kpubKNVaFELOboWcxckmMXAotE0HLRjH0DJapgWLH7vThcBdjc8yeL2pCiOkh9zSEEGJmGFqGTLyXbOwMhpbB6avA7rj2kopzjaZptLZ3c+TYaepqq2maQtP0yzEMA9M0ATh5qp2x8ShbNq9laXPTeb1TYaLqRWt7N4ePniSbVRkbj1BaUsyK5c1sWr96SpMWu/cdRs3leOThe/AXXXwxqGEYDA2P0T8whN9fxKoVS7E7HNjtNu65cwd1tVNfwHi95T1ZEg6HWbx4ovRHMBgkHA4DsGPHDn7v936vsNEJIYS4bnLpcVLhdnLJIRzeMhyuK9elXMj27j/CeDjKnbdvp7H+F8mNZVkkkimCAT8N9TXcRP4N3Hv7BnnjzX1kVBWnw4HNZiPgLwIuvtpkcGiUV157C103aF7cwKLq2XOzbybzhieeeIL3ve99bNmyhe3bt/PMM89w5swZPvrRjwIT/Ub6+/t59tlnAfj85z/PokWLWL16Nblcjn/6p3/i+eef5/nnn5/WOIUQhWVZ5kQJrVwKPZcglw5jaPGJklpYKDYndocPp69iQa5iFGKhk3saQghxfZmmjpoYJB3tRMtEcXpK8QTLZzqsGfPW3sN0dfWyeHEDmzesvurzmKbJkWOnOXm6g2XLWqgoW8b2bZtwOh0XvW9gWRYv/OQVIpEoixc1sHH9KiLROH6fl/IrlM86p7unj47OHm6+cfNFJ0pGx8IcOXaKgcERDMPA5/NO9lotKQ5cUIljNsr720FzczPd3d00NTWxatUqvve973HDDTfwwgsvUFJSMg0hCiGEmE6moZGJ9ZAOd2CaBu6A7Ca5EsMwSKYybN6w+ryJkkgkxpu7D5JIJvnVR+7H6/XQsmzqvUWSqTR79h7myLFTDI2MsWRxIxvXrWLViqUXbIU1TZNYLEFpaTFer5uS4gC337INm83Gwf1vsnhlwS73msxk3vDrv/7rjI+P8xd/8RcMDg6yZs0afvzjH9PU1ATA4OAgZ86cmTw+l8vxh3/4h/T39+P1elm9ejX//u//zoMPPjitcQohro1p6hO7RrQ0ejZOLj2GoacxdRVQsDnc2Bxe3EUl8vdNCCH3NIQQ4jqxLItceoR0pBs1NYzdWYQn2FCwvhxzRTQa59jJNupqKlm8qIEN61ayacPqK5atupITp9o5dOQELS3NlJ+tYvHLO0lgYjFmVWUFLpeTtauWEY8nSWez+It8l23eblkW/QPDtLZ14XK72LF9M/V11dx/z61UV4WwLIvRsTB9/UOUFAdoXtyIaZqoqsb6tStpqKumtLT4mq5xJuQ9WfLBD36Qw4cPc9ttt/Hkk0/y0EMP8Xd/93fous7nPve56YhRCCHENPnl3SQu2U1yRbmchsvl5KH7b8c623hO13UOHz3F8RNtBAJF3H7LNhyO/P7EJpMpnv32DygvL+GBe28jlc6wasXSi26DPdcnZXhkjOUtS+jtG8TpdOBy2cklemn09xIfPoKvdOabwM903vCxj32Mj33sYxf92Te/+c3zHv/xH/8xf/zHfzztMQkhro2pq+i5JIaWQstG0DIRDC2LZeYmSmo5vNhdAZzeigX3ZVwIcWUznZsIIcRCoGWjZKLdZBMDgA2PvwZlge3oHRoe5diJVvr6h/B6PdRWhwAmq0Zci2QqzcHDJ1i5fClbt6wjHM1ccExv3yCHjp5kfDzC9m0bqSgr5VRrFyOjYzQ11GGaJjbbhb35slmVU60dtLX3kEqnKS0pZmVdNQAOh4PqqhBHjp3m+Mk2VFXF7XazZlULAFWVFTx4323XfH0zKe9P6R/8wR9M/vsdd9zBqVOn2LdvH0uWLGH9+vUFDU4IIcT0mNxNEunEMnXcgdoFl7jkK5XOcOx4K63tXbzj/jsoLS2evAn32ht76RsYZv26laxd3XLRhONSRkbHGRuPcOTYKQzD4J0P3oXXe+m6re0dPbz6+m76+oeoqCglHI6ybtViassMRlt/iJocRE0NU1y39ZqvuRAkbxBCXAvLsjD1zMTkSC5FLjOOocbR9QyY5kRJLacXp7cMm/3ClXRCCPHLJDcRQojpY2hpMrFeMrEeTD2Hy1exIHvCdZ/p59XX3qK0pJgd27fQvLghr/sEVzIwMIzb7WLj+lUX/Gx4ZIzd+w4TDkepDFVwz5076OsfYtfug5QUBy/aM8Q0TeKJFCXFAXTD4PjJdhY11dGydDGhirLz33twhAOHjrGiZQnNixsIVZTNqwVKed8Z6+7uZtGiRZOPGxsbaWxsLGRMQgghplEuEyYdbkNNDuNwl+DwBWY6pFnt3CTJ6bYuHA4769asoKjIRyaTJadpFAcDbFy/is2b1lAcnPpYZrMq//bv/8Ebb+6nqbGWbVs38ND9d1x2oqSj8wxv7NrHqpVL2bRhNaESF35nlPT4UVKDY9jsHtz+GvRsrBCXXhCSNwgh8mFZJkYuNbFrRI2jZcJnS2xlQbGw2dzYnF7cvqCU1BJCXBXJTYQQovBMQ0NNDJCOdKLnEjg8Zbh8oZkO67rRdZ22jh7S6QybN66hoa66oI3M44kkp053YFmwbet6mhc3UBkqx+VyYp6teKFpGm6XC9O0cDoc3H3HzVRXVeBwOIhEY2zbuoHlyxafN2kTTyRpa++mraMbRVH4tXc/iL/Ix2/86kPY7XZM02RoeJSBwRGi0Th33r6d2ppK3vHAnVRMsc/JXHNVPUtuuukm3ve+9/Ge97yHsrKyK79ICCHEjJvYTXKGdKRjYjfJAtwGezWOHD1FV08f69euYOXyJTidDlrbu9h34BihijLuvWtHXnU4LcvidFsXBw4d5+jxVrZsXss7H7jzkucwDINEMk1JcYDyshLqaqvZuLKadLiVdLidaC6Jw+XHG2yalTcOJW8QQlzOZL+RXApdjZNLj/+i34iiYLN7sDu9ODylKErhVuMJIRYuyU2EEKJwLMsklxohHekklx7F7grgDtTPq50GV9Le0cPeA0fJ5XIsXtQAgN1uL8hEycDgCCdPt9PbN4jb7Wbl8olm6Q6Hg5KSIADj4Qg73ziIx23jHfffQU11CE1fxu69h1m8qJ5NG1ZPlsk6x7Isdr6+h+4zfTidTpY2N9GydNHk703XDV59fTeDQ6Pouo7b7aa2phJd13E4HPN2ogSuYrJk3759fOc73+FTn/oUn/zkJ7nvvvt473vfyzvf+U7c7oW3rUoIIeYC2U0ydel0hqPHWykuDrCipXli18jGNbhcTiKRGD9/6wBj42GWLlnElo1r8j5/T+8Ab761n+UtzbzrobsoKvJd8tjunj72HTiGYlPYtH4lRw/tpafrBCECFPsUnN4yvCWhWZ2ISt4ghHg708hNltSa6DcSPr/fiNOLwxXE5pP/PgghpofkJkIIURi59DjpaDdqYhDF7sTtr5uVC/imUyye4M3dB2msr2HzpjUF6UdyTjKV5qWX36CkOMhN2zbRvLjhvN6oqXSGfQeO0tHVi93hYvOGjUQiMfbsP8Lg0Ai1NVU0n528+WW6rgNw07ZNNDbUMDIW5lRrJ9msyh233YjL5URBYd2aFdTVVFJWVjKr7zsUUt6TJZs2bWLTpk387//9v3n11Vf59re/ze/+7u/yO7/zOzz66KP8wz/8w3TEKYQQ4ipc0JtEdpNckqqq7N3fTmtbJzabjc1nJ0LONVg3DIOf/efreNxu7r/nVqqrpr6lOJtV6e0bZNnSRVSFyjENi6aGuktOlIyMjrN3/1FGx8YxDR1djfLC87sIutNsWlJMZagKu6twSdh0krxBiIXN0DIYWhI9l0LLhNGzMXQ9DaaBYnNic3hxekux2V0zHaoQYoGQ3EQIIa6NnkuSjvaQjfeCZeAqCi3YXG5waJQin5cdN20+byLjaiRTaU6d7qB/YJiHH7wTf5GPdz5410WrUJimyY9/+iqGaXDTjRspK6vE5TD5tx/9BwF/EXfevp3G+toLXpdKZwiHozTU17Bl81refOsAb+05hGmZBPx+6s82clcUhTtv335N1zNXXfVvUVEU7rjjDu644w5+7/d+j9/+7d/mW9/6liQWQggxS+TSYTLRDnLJIRyeUtlNchnRaJwf/vtL+IvcrFnVwuqVy3C5JhoFn+kboLKiHI/Hzb137qCkJDjlxmyWZdHa3sX+g8exLIviYIAjx0/h8bovuW3VNE1+8MJLBPxedmyqY/jMUUaGelmyoZSKqpXYnd6CXff1JHmDEPOfZVkYWmqipFYuMVFSS0tgalksy0Kxu7A7vLh9lTJxL4SYcZKbCCFEfkxdJRPvIxPrRs+lcHnLsTsvXSlhvjIMg67uPpoXN7CipZmlzY3XNFEyNDzKyVMdnOkbwOFw0LJ0EYZhYLPZLpgoGR0LE/AX4fG4uXXHVoqDAfoHh7HZbASDRdx2yzYa62vOu2ehaRo9Zwbo6DrD4NAIiqLwvt98BK/Hjc2msGXzWupqq/LqwTqfXfVvsre3l+985zt8+9vf5ujRo2zfvp0vfOELhYxNCCHEVTCNHNnEAPHECGDhDtTKTalLyGSyeL0eiosDrFuzko3rluE722A9mUqzZ+9hzvQNsHXzOlavXEZZWcmUzz02HmHXnoOMj0dY1NSAoev85KWdeDxudmzfPLljBSASibF732H8RT76BwbIxAdYUmbHnuihvtxBc8N6bI65XRZC8gYh5h/LNDC0FHouiZ6Nk8uMYWgZTD0LKNgcbuxOHw639BsRQsw+kpsIIcTUWKaBmhya6EuSDeN0l+ANXry803ymqjlOtXZy8nQ72axKIFBEVWXFNU2UZDJZdu87gmkYbNu64ZITL5lMlgOHj9PW3s2GdavYsG4lXq+Hn7y4k2g8wc03baesxMuixrrzXpfLaXznX14gHI5is9lwuVw4nQ6SqTTBgJ+777j5qmOfr/L+bT7zzDP88z//Mz//+c9Zvnw5jz32GD/4wQ9YtGjRNIQnhBAiH7n0GMnxdrKxYfwVpTjdsjLgUjKZLM//28+4cesGmpsbWd6yBI/HjWmanDjVzsHDJ3C5nNx+yzYWNdXnff7OrjOkUmkeuPc2qior2PnGHrZuXsfyZYux2yfquIbDUQ4dOcne/Ufo6++nqdbPslqLHSstQhWlOL0V2OzOQl/6dSV5gxDzh2loGLkkupZCy0bR0uMYegbLyKEodmwOL3aXH6e3fMHUNBZCzD2SmwghxNRYlkUuPUom2o2aHMLm8OIJ1C/IRTCHj57iyLFTWBYsXdLI6pXLCrITw+v18NB9t11ywsU0TU6e7uDQkZMAbNu6gRUtzcTiCX72H6/jdDh454N3YSkTZdAikRhtHd10dvXx67/6IC6Xk3g8idfnpby0hNraSmqrK/F6ZudiTE3TGBsNU1HqmbEY8p4s+cu//Et+4zd+g7/5m79hw4YN0xCSEEKIfJlGjnS0m0ykC8PQcXjL5kxPi5ly6MhJFEWhob7mvOej0TgHDh5n+dnm7ufKcV2JZVm0d/RgWibVVSHSmSyZjIquGwDctuOG84798c92cuJkGw01Abat8rNlcZCGkILHF8DlC82bxniSNwgxdxl6FuNcM/ZMBC0bxdDTmKaOojiwO7043SVzfuebEGJhkdxECCGuTMtGyUS7ySYGAAWXvwbbAqpYYZomPWf6KS0toaQ4QMDvY+3q5Sxfthiv99pu5GuaRld3H2f6Bti8Yc1Fe5KcE4nE2H/gGMuWLmLj+lV4PG7C4Sg/+umrBPw+7rv7FlxuF/+5cy9tbe309g2QyaiUlgRJJtMEAkW8+1334ff7ZmWZLcuy6OkdYGRkjKGRMSKRGKah8Ru/cseMxZT3p/zMmTOyUkwIIWaRXHqU1HgbanoUp6cch89HKpqZ6bBmLVXNcfJ0O63tXWzeuAa320Umq3LsxGl23LiOsrISHv2V+ynyTb03yPh4hF17DtE/MAQoeDwuPB43N96wgeqqivOOnUiMztDZdowqf5ptzRqYOZw1FTg8JfNulY7kDULMDZZloedSWEYaXU2iZcbRcwkMLQNYKDYndocPpy+0oL4oCyHmH8lNhBDi0gwtTSbeSyZ6BlNXcfkqFtTCmGQqTWtbF63tXWSzKls2raWkOEDz4sZrPvfoWJjW9i66uvvQdZ3amiqUi/RDHR+P0NrezY03bKC8vJRHf+V+vB43/QPDNNTXUFpazJZNa1iyuBG320VO0/i3H75IcbGP1ataWLuqhfq6avz+iX4ydbVV1xx7ISVTado7etiwbiWKovDWnoPYbXaqqipYsayZitIiHI6ZWzw6pW86R44cYc2aNdhsNo4ePXrZY9etW1eQwIQQQlyeqauko92kI10AePx1KDY7pmXNcGSzW1dPH0eOtbJy+RJWrVhKZ9cZdu87QiyRYc2KRVSUl055osQwDPbuP8rptk5KioO0LFvM0PAY69euOK/cFkzsWDl89ARtp4+gJgaoCuS4YV0NLl/ZvGuKJ3mDELPfL/qNpMipMVJjgzjSOqahgmVhc3iwOby4i4rnzU43IcTCJbmJEEJcnmloqIkB0pFO9FwCh6cUl6/iyi+cR9rau3lz9wEcDgdLFjeyoqWZkpLgNZ0zm1VxOh3Y7XZOnGxjZDTMmlXLWLpkEf6i8+8DJJIpDhw6Tld3L8XBAKl0Bn+Rj9GxMPsPHCOZSvHud92Hy+nE43bx/774TR6+/3ZWrlzGJz7+IZoXVeFyzu4y3tFonBdffgOAdWuWY7PZ+JWH78Xtdk0eM9EDMT1TIU5tsmTDhg0MDQ1RWVnJhg0bUBQF62034849VhQFwzCmLVghhBDn6oaOkA63o6bGcPkq5t3N9kJKpzMcP9mGZVncsGU9y5Y00dRQS07TeOnlnzM4NEJjQx07bm7Jq4E7gGGYHDl2ivLyUh5+8E5M08SyLJxvS1BGx8IcPnKc9tYjGJkhYuFR3B4XW269Ea/fX+CrnR0kbxBi9jFNfaLfSC6Jlo2hZ8IYWnpicgQbhuHE5pB+I0KI+UlyEyGEuDjLMlGTw2SineTSY9hdAdyB+gWZD1ZVVXDjDRtoXtRw3nf6fFmWxeDQKG3tXfScGWDHTZtpXtzIjTdsxOVyXnRsjx5v5eCh47g9Lm7atomlS5oYG4+w8/U9jI6NU1dbTXVViFdf383Q0CiDQyMkkilsZ3dgVFSUXVOj+elgmiaKoqAoCqdaOznd1kUkEqW0pJh77rwZ29ldNW+fKJkNpjSKXV1dhEKhyX8XQggxMwwtQybaTTraBYoNT3BhNlebikQyxdHjp2lv78HusLN65TIA7HY7Xq+d/oFhEokUd99xM7W1VYSnWLosHI7y1t7D1NZUcuJUO16vh+ZFDdhstsk/9ucYepaXfvoDujtOcPPaYhrqqjnYGmTp4mqKrnGiJKfpJJJZYoksiWSWeDJLS3MlVRXXtvKlECRvEGLmmbqKnktiaGf7jWQiE83YTQ0UO3anF4e7GJvDjWlZOIwMNqd3QX4xFkLMf5KbCCHE+SzLQsuMk472oCYGsdlduM9Wq1hITNPkP1/dxeqVy6itqSQYuLbv6e0dPRw6cpJkKkVxMMCmjauprZkog/XLkwKmaaLrBi6XE5/Xw7q1K1izahkOh4NoNM4rO9+itb2b9//WIzQ11vHmWwdo7+hB03Rqqip56P7VrGxZMmuqi2QyWUbGwoyMjDEyGmY8HOW+u3dQVTmxQ6mstJjVK5bS1Fh7TZNR021KkyVNTU0X/XchhBDXh2VZ5FLDpMbb0LIRnN4K7M6p99RYaDKZLN//txdxupxsWL+KFS3NuFxOBs+uwNi0YTVLlzSxqKkOh8MxpeQil9M4ePgEp1o7CPiLOHLsFGWlJdx524343la2q7dvkL37DhD0ZBjuPcGZ3gH8Pi+LmlvweL28rc/7lCXTKtF4mkgszeplNdhsNn6+r4ORsQQART43Ab8HZkeOJHmDENeZZVmYenaix0guRS4zjqHG0fUMmOZEvxGnF6e3HJt99n4xEUKI6SK5iRBC/IKuxknHzpCN9QEmrqLKBZcjnmvgfvJ0J6Nj42xav+qqzzU6FsZht1NaWozNplBdHaJl6VYqQ+WXfM3g0Chv7TlIRUUZt9y0hSXNjWSzKt/8538lHk9SVlqMZcGS5kY6unppbKjlphs3YbfbKS4OsKixDo9n5nrJWJZFNBpnbDzCsqWLAHjx5Z8TiUQp8vmoDJWxqKmOorOlxla0NLOipXnG4s1H3vtznn322cv+/P3vf/9VByOEEOJCei5FOtpFNtqDYnct2C2xV3KuCdq2revxej3ccduN1FSHcDgcpNIZdu0+QFdPH5WhCgzDwG63T3mb6vh4hJde+TmaprN54xr8RT5efX03N96wYXKiZGR0nD1793Gm8wSnT52gLACLGkPcccsWGurKcdivvELHsiwyWQ2f14Vpmry2u51ILE1O0wFwu50sri/HX+Rh/cp6UCDo90zp3DNF8gYhCs+yLAwthZFLoecS5NJjGLkkhpYFTBS7G7vTh9sXQJFm7EIIcR7JTYQQC5WhZ8nGe8lEezD0DE5P+YJchBmNxvnpf7xGNqtSVVnBnbdtp7y89KrOlUylefE/36Bl6SK2bl5H8+LGyzaDz2ZV9h44SkdnD5WhCspKivnRT16mob6GYyfa6OsfYlFjHVWVFURjcVQ1RyQSm+xfsm3r+qu97ILRNI2Xd77F4NAIiqJQU1OJv8jHjhs34fa4L+jFMtfk/e3pk5/85HmPNU0jnU7jcrnw+XySWAghRIFYlomaGJzYTZJLTPQmcXhmOqxZZ3QszKEjJ+kfGCLg95NMpQkG/DTU1wBw7EQrBw+fwOl0sGP7xIqNqU42qWoOt9tFMOinqaGOZcsWU1ocwG638w5/ERVnE6oDBw7y3He/y9JaGxuafWxaspKyihqKg0WXPb9hmIxHU4yMJRgNJxiPpHC7HDx89zpsNht+v5tQuZ/SYh+lxT68nl9s2y0rufy5ZwvJG4S4dpZpTJbU0rNxcpmxiX4jugoo2BwTkyMOT6mUZhRCiCuQ3EQIsdCYpo6aGCQd7UTLRHF6SvEELr3rYb6xLIu+/iEi0Tjr1iwnGPSztLmJpc1N19TAPZtVee2NvbicTtatWXHF4zVN4wc/egnTtNiwdiUo8Nqbe+nvH6Kvf4iN61fz6Lvu48c/e5VINM7ipnqWNDdedofK9TYyOs6efUeIxuLcfuuN1NdWTS5CvdoJp9km78mSSCRywXNtbW383u/9Hn/0R3+U17m+/OUv8+Uvf5nu7m4AVq9ezZ/92Z/xwAMP5BuWEELMK7qaIB3pIBPrxebw4gnUyW6Si4gnkvzkxdcIBoq49eatLGqqn+wbcq5JZzarsnzZYjasW4XLNbWtxbmcxqEjJ2ht7+aRh+9BURRsNhs/+dmrbNm0lpXLl+Av8rHrrV0M952m/dRRimwxtq5dT01twyVvVuqGwXgkhWVZVIeKSaZVXnnzNC6ng1C5nzUttZSW/GIVxpa1Vy4ToeY0BoZj9A9FqSj1s3xJFaY5S+pxUdi8QYiFwLIsLFOb2DUy2W8kPNFvxMih2OzY7F7srgBOb4X8bRBCiDxJbiKEWCgsyySXGiUd7UJNjWB3FuEJNiyY/DGZStPW3k1bRzfpdIaK8jLWrFqGzWZjy6a113TuYydaOXDwOCgK992947JNyiORGH6/D5vNxg2b1zEwOMKhoydRFIUVLUuw22xYlsXylmY8Hjd33XETAX/RBT1RryfDMBgbjzAyGmZkdIylSxbR1FBLIpHCME3uu/sWQhVlMxbfdCrIvvxly5bx2c9+lve+972cOnVqyq+rr6/ns5/9LEuXLgXgW9/6Fu9617s4ePAgq1evLkRoQggxp1imQTbRTyrchpFL4fJVYnPMXB3K2S4eT1Ic9PPQ/bdPrmaIJ5Ls3X+EylAFa1e35J0EdXadYe+Bo+RyGkuamzh0+ASdXb04nA7Wrm6heVED4dE+vvb1f+DkqdNsW1vBtg2NLGnegf0i5bCSqSxdfeOMjCUIR1OYpkVlRYDqUDHFAS/33rqKkmD+TZWHx+Icbx1kLJzEsizcbgeJVJbWrhGWVaaozets19fV5g1CzFXnJkAs08CyTCxTwzRymEYOTAPLMjD13ERpLS2DaaqYuopp6iiKY6LfiKcUm/3SX8KEEEJcPclNhBDzTS4TJhPtQU0MgM2Ox1+zoMqzaprGv/7bi9hsCs2LG1i+dPFV73wwTZOBwRE6uyd6hyxqrCNUUcbWLetY1FiH13vxCiDhSIz/fOVN9h86RnlZCe9594M0L25EsdkYHBph9cpltLZ3U15WytrVLfjOnqc4GLjq675auq5js9mw2WzsO3CUEyfbMS0Tu91OqKIM29n7FUuaG1nSfOkyY/NBwf5fYrfbGRgYyOs1Dz/88HmPP/3pT/PlL3+Zt956SyZLhBALjpaNkgp3oCYGsLsmVnyIi9N1nVxOo76umrraKhRFQdd1jh5v5ejxVrweN0uXLMr7vAcOHefY8dPU1VSz/caNjI1H2L33EBs3rGb5skVo6VH2v/E8hw4eQMklefTetWzZtHJyosOyLCLxNEMjcQJFbhpqy0hlcnR0jxIqD7B+VT2V5QGKA7+oC1tafPX1PB0OG9WVQcLRNKqqgQUNtWUEfbmrPuf1cjV5gxCzkWWZGNrErg/T1LBMfWIiRMti6BlMQ8U0climBpZ1duJExzJ1UAAUsCxQbNhsThS7A8XmxukLYltAX2iFEGKmSW4ihJgP9FySbKyXTOwMlqnj9JYviAWYhmFw+OgpzvQO8PCDd+J0Orn7jpsIVZTidF5d8/pIJEZrezddPb1ksyrFwQANddUAVFVWUFVZcd7xpmmiKAqKovAv3/8Jr76+G8Mw2bB2Bdu3bSQY8E/cczjRhsvl4NjJNrLZHO988M5rKgd2tcbHI/T0DjA0PMbYWJh77tpBTXWIyspyfD4vVaHys03rF1aZ37y/gf3whz8877FlWQwODvKFL3yBm2+++aoDMQyDf/mXfyGVSrF9+/aLHqOqKqqqTj6Ox+PAxIfRNM2rfu9fZpomlmUV9JzzmYxX/mTM8jPfx8s0NDLxXrLRLgw9i8tXiWJ3YlpXV0rJOnczzrKYbyOWzaqcau3k1OkOqqtC3H7rNgBS6TQ//umrpDNZ1q5eztrVLTgcjimNoaZpE1tJLQd2uw3LAmzg83lp8Hqor61Cz4wRO7OTn+96k5NtQ9TXVXP/XdvweSdWeY+OJ2nvHmFoLI6a03HY7SxfUkW9BaGyAO+8Z/0FO0fy/fUmkln6h6N09Y7RUFtKXVUJt2xdRlfvGE6Hg6a6MqoqAui6SXwkjGVR8L+NV2O68gYhrpdzu0JMQzs7yXF2QkRX0dQ4WjYyMVFi5LBM4+wECIANxeZAUewT/2tzoyi2iTJ9NvvE8wukBIIQQswmkpsIIeYjU1fJxPvIxLrRc2lc3jLszrndaDsfBw+f4MTJdlqWLUbXDex2O7U1lXmfJxpLYLMpBAN+RsfCdJ/pY8niRpoXNVxyZ8rwyBinTnfQNzDMXbdvxzBM+vqH2HHjFu675xbKSouJRGL89D9eZ2w8wk3bNrJuzXJeeW03i9fVz8hESU/vADtf243T5aSmKsQNW9dTHPQD0Fg/M3UqLMtEy4TJxM6QS49SXLsVu9N75RcWWN6TJY888sh5jxVFIRQKceedd/LXf/3XeQdw9OhRtm/fTjabxe/38/3vf59Vq1Zd9Ninn36ap5566oLnR0dHyWazeb/3pZimSSwWw7KsBTd7djVkvPInY5af+TxemhpHTQyiZ6PYnEXYnWWkEjqgX/U5LSCRyoGiMF9uw2WzKsdPnKaz+wwASxY30bxkCb394xQVTSSAoVA1ixY1EPAXEU9qgHbF8/ac6Wfv/sOEIxECgWKwDKqrQlRX1zEeSZGKDXDiyH4cSgqXw2IsHcR028gpJXQNmxT5oKIsyHhKYzimUF3bRHWolLJSPzabjeTV/xqBiS/vJ9v66BsYo3dgjGgshc2mMBIF016KsyhIRXUAmydJ72iU/Sd7icSSLK72ULLMjmNk5NoCeJtEInFVryt03iBEoVmmcXb3h/aLiRBDw9AzGGoCXUuBqWOaxtkyWvrkbKdic2BzeLA5fDjcxQuqtIEQQkzVqdZO6kNXt6p3OkhuIoSYT0xTR00OkYl0kcuGcbiK8QbrZzqs62p0LMzxk21sXL+adWuWX9U52tq7OdnaQTgcZfmyZrZv28jSJU0sW7rosguc+vqHePnVXRQXB6iuqqCoyEfAX8Rjv/5OKkPlhMNRIpEYbR3d9Jzpp7QkSF1tFU6nk3vuvPm6Lp5KpzPkcholJUEsy6KpqY5bbtoyo/fZLFMnlx4jmxggHW5Hy4bRsnHsTg9GLoXTU3zdY8r7G12hV3YvX76cQ4cOEY1Gef7553n88cfZuXPnRSdMnnzySZ544onJx/F4nIaGBkKhEMFg4Wbhzm2bCoVC8+7G7HSQ8cqfjFl+5uN4GXqWTOwMmVwXTqeJu6QGCnSTzbIssCzKij1zftVyNqvi8bhJZyAaHWfbljWsaGnGZrNx+OgpTp5u5+47bqa2ppLbdmyc8nljsQRv7TnI4PAYjfUhHHaLmppatm1eRWmxj+hIO4de/xHHTraRTOXQTCfFxcUYpknAYycRG+XYsVFWLa1hUSUsb3CyvGHx294heVXXqxsGQ6NxRseTbFzdgGmaZBMjaJkYbrvK9vXVLGkKUVriI51W8TviWJbFfx48jGVBVUWAVYsr8dNHsc+gsjL/lTSX4vFcvA7rlczXHWFi7rFMY7IniKFn0HNJjFwSU8tiWTqmMbFr5BcUbHYXit2JYnNN9EWSHSFCCHFRiWSKcCRGJBIlHI4RTyR51zvuRlEUunv6KfZWUris5NpIbiKEmA8syySXHiUd6SaXGsHm8OIJ1E/sYl5gdu05SGlpMWtWLbuq1/f0DvDzt/bTUF/DujUrqK+tArji/adoLMHLr+6ivKwEp8tBb98gG9auBH8RhmHyo5+8wptvHaCpsZZAwM9tO25gSXMjlaFygGn/TpHLaQwMjTA4OMLQ8CixeIKqygoeuPc2FjXWsaixblrf/1IMPYuaGEBNDJKJn0HLRjH1LHZnEQ53CTaHDz0bmZHYoIA9S66Wy+WabPC+ZcsW9u7dy9/8zd/w1a9+9YJj3W43bveFdfbONaApJEVRpuW885WMV/5kzPIzX8bLsixyqWFS4+3kMuO4fBUF3xprwmSdTNscvaE3MDjCsROtjIejvOdX7sfv8/Fr734QgI7OM+w7eBRN09m0fjU1VRV5XadlWbz62luk0llu33EDzYsbyGka0Vgal95L2759/Ou/v0U6a3LD5hU0N1XR0TOGAnT3jVMS8NFUV0ZDXSlF3muv/WqaJp1nxugfjjEyliCnaWRVnb7BMEuaQtx24zJSaZX+oQiJVI7WzmESqSw2m8IjlRtwOuzcs2MFRT73ZLKVGB5EUa6c3OVjrv9/TywMpjHRON3QVLRMlGxcxzJVDDWBpsYw9exEU3UFFMWBYnNis7uw2X3Y3U6ZCBFCiCvQNI1oLEEkEsNms7F0SROZTJbnf/BTYOI7e3lZCfV11RiGgcPh4P57bkFNDs5w5IX39NNP86//+q+cOnUKr9fLTTfdxF/91V+xfPnVrWoWQoipOq95u2LH7a9eMDucLcuit3+Q4yfa2H7DRkpKgmy/YSMBf9FVfWe1LIsDB4/RWF/LnbdfvC0EgKrm6O0bpKe3H8uyuPuOmykO+qmoKGU8HMXpdHDLzTfQ1dPL1771LwQCRTTU1/Cuh++huqqChrrqaf9ObZomo+MR7HY7FeWlDI2M8uprbxEM+KmuDrFh3coL+qxcL1o2ipocJBsfIJvoQ8/GAAu7swint5ycbkOx27A7HXT3jlBbNiNhAlcxWfL2nR1X8rnPfS7f02NZ1nl9SYQQYr7QcynS0S6y0R4UmxNPcGGu+rict299LSsr4cat6ycTCkVRONXayVt7DrK4qZ7Nm9biL5r6RFN3Tx9lZSUEA35uuXkru3Yf5HRbJ00NIWKDxzl06Dj2XDedfUn6xgyKirz0DcWxO5zcuHExuZzOiqXV5zVnv1qGYdLVO8bSRZXYbDZOdw6f7YtgYZoWQb8Hn8+Npk+sfHS7HRw+OYDX46Q6FGTtyjqqKgI4HXYA/EUTuz5yms54JIWZzFF9zVEWxnTnDWJhsSwLy8hNNE7Xs5Ols3QtfXaXSAbT1DEMlVTcxJkDBQXF7sRm9+BwFy+IBpdCCFEIpmmi6wYul5PBoVHe2nOQWHyiLKeiKNTWVLF0SRNer4d77txBaUkQn+/8PMkyDQxDRc+lJnY/zwKFyk127tzJxz/+cbZu3Yqu6/x//9//x7333suJEycoKioqRKhCCHEeXU2Qjp1BjfctqObtALqu0955hhMn24gnkoQqytENA4BQRX531i3Lon9gGLvdTk11iHvvvgX7JSYy4okkb751gOGRMSzLIlRRzqKmiR0Zbe3djIyOU1FRhtfjZtfuA5w63YHNbufeu3awcf3F20wUUjKVpqd3gNNtvaRScQxdp3lxI7fevJXa6kp+9VceyOu+SaEYehYtPUYuPTbRgyQ1jJ5LodjsOFwBMlYZY9EM4+Ek49FhUmmVLeuaWNIUwud1ohuFa7eRr7wnSw4ePMiBAwfQdX1yxURrayt2u51NmzZNHjeVVXl/+qd/ygMPPEBDQwOJRILnnnuOV199lZ/+9Kf5hiWEELOWZRpkk4Okx9vQcglcvhB2x9WVNJrvTrd14fVMfOGuO7v1NZtVGR0L01Bfw9LmRkqKA1RXhaZ8zlg8we69hxkYHKZ5cSNOh4PO7l5yapqta0K89P0vcuxkJ4faMyyuC5JMa1SHSmioLaWxtozGuokmbi6XA5fr2lfrZLI5fr6vk0gsTUWZH8uCB25fzVgkxVsHuigu9mIaJuPhJMmUyvqVdTjsdh66c81kQ/lf1t03TlvXCJFYGsuyWFSeouWaIy2MQuYNYmGxLGuil8hk2awEWjqMoacx9dxE7xAAlLNN1Cd2idhdHhxKCU49hzvgnbM77IQQ4npLpzMMj4wxOhZmdCzCeDjKyuXNbNm0Bo9TIVTuZ8WSKooDHgJFLuw2SI23YZkaAVsONdxFaiiBpasTu/z0LJY5UeLQ0DMEQitx+8pn+jILlpv88n2Lb3zjG1RWVrJ//35uvfXWwgcuhFiwDD1L9mzzdiOXxuktX1DN22Gigfvxk200NdSx46Ytk6Ws8pFMpWnv6KatvYdUOs2S5iZqqkMUvW2iPxpLcKZ3ANM02LBuFV6PG4fDzratG2isr8Hn8xKNTSwcaF7cgN1u54WfvIxNUbh1xw3cf8+tlJeVXLfvtwMDw+zdfwSfL8Da1S3U11ZTXlYCgMPhwO+4PjuOTENDy4yTS4+SPVtiS88lsSwdm92NwxXE66ucHJfTJzvpG4xQWlxEfU0J5SVFhMoDAFSUFqFnc9cl7ovJe8QefvhhAoEA3/rWtygtnbiBFIlE+OAHP8gtt9zCf/tv/23K5xoeHuZ973sfg4ODFBcXs27dOn76059yzz335BuWEELMSlo2SircgZoYwOb0na0hKjfu3u7IsdP4i7w0L27kgXtvxW6f2C1hWRan27o4cOg4druNX625H4fDMeWJEl3XOXLsNIePnCIQKOLO27dz5Ohp4rFR6ssgYB/n+8+/jGE5WLq4gd/a0Eh9mcXwWJym+vKC7CB5u/buETp6xojEUqSzGqHyAD/beYKqiiC3b2+hJOAlp+moOY2aymJWt9RSU1U8+Xl5+0TJyfZBzvRHWLeyjprKiYZn/iI3zU0VVJYFINVa0NivRSHzBjF/maaOoSYmmqpraYxcCj2XwDSyExMjpg6KDZvdg83hwekLYrtMqQFzlqxeFkKI2cQ0cpi6imlqqJk0o2OjjI6OUVddQtDv5sjxdg4fa8fnsVMadNNU4qBIjdB/5BBYJk0+AyuroaUNxi0DyzJRmMhTLCxAQbE5zpY0tKPYbGdLHjow1DimoV02vutlunKTWCwGQFnZpVc4q6p6XiWNeDwOTOziKVQvFdM0JxYcSG+WKZMxy4+MV36uZbxMQ5to3h7tRMvGcXpKcAUmmrfP53zXsixisTgnT56gvLyUFS3NrFixlGXLFhMM+IH8r39gcISXXn4Du91O86IGli1dRKiiDNOysCyLY8dbae/sIRZP4nDYWdRYh2lZ2B0O7rz9JgDSmQwvvfJzBgZHeOThu+nq7qe1rZPKUDnbt22k6WwPEOvsNUyXdCZDLJakpjrEokX1NDbWkUhplJV4URRl2t//HCOXRE0Mko33kU30o6tJTDOHYnPicPpxFdWg2BzEk1nGh1KMR8YpDnppWVzFxtUNbF23CLv9/B09Ey14FSwUTLOw/52Z6rnyniz567/+a1588cXJpAKgtLSUT33qU9x77715JRZf//rX8317IYSYE0wjRybWSybahaFncRVVYbM7ZzqsWWd8PMLBw8fZsG5ie+q5iZLhkTHe2nuYSCTKsqWL2LR+9eTPpmpoZIx//9mrOOx27rtnB+VBhfWLDcxUHDUT499e6eXnh8bYsLoet8dNS3MdfkecqlDwmq4pp+kMjyUYHU8wMp5gy9omKsomEjq7w0YipeL1OHE57RRXFk8mZy6Xg63rm6iqCOJxX/qzsv/oGdq7R2iqL8d9dqfLovpyFtX/YmVNInVNl1BQhcwbxPww0VMkjalnMfQseiaKlo1i6Jmzu0UmbrbZbC5sDi92V/FlJ0aEEGKhsUwDy9TP7tzQJksSWqZ+tneTOlmy0NDSZ//JYBk5TrQNcmYgQiyRwbJMnA4bm1dVU18doNyhcecGO16P5+xEhw1FMcFSQLFjs7tQbIGzEyH2KZeTtSwdNTHNg5KH6chNLMviiSeeYMeOHaxZs+aSxz399NM89dRTFzw/OjpKNluYkiOmaRKLxbAsS3rOTZGMWX5kvPJzNeNlWSZ6NoaaHEJT49gcHuyuCpQMkMlMb8AzbHhkjFOn2+ns7qe0xM9adxHh6Llrtr/t3y8vkUzR2dmDYZps2rAGh9PLqpWraGyow+mc+G5x7lzxeIJde49SX1fDpiVLqa4K4XA4znuv7p5e9h84imGalJYG+fNPf4lwOMKdd9zM7bdsp6jIN+XYrsbYWJjBoREGBoeJRKIA3LpjG7W11VhAIpUD5dwShulhGiq6mkDLRsmlxzFyCUw9PbGozVGC3V2NZigoCjidDnp6RzhyogNNM1AUCPh9OLxFJPUg2ECzAP3C9zFMOwYuxsIxXNn87gNdTiIxtWQk72+e8Xic4eFhVq9efd7zIyMjU35TIYSYryzLIpceJR3pJJccxuEpxROY+XIDs008keR0ayet7d2UFAdZu/r8olFHj5/GZlN46P478qo/Gk8kefE/38DtcjIejrKooZYlDQH08X0M9ffR3TtEPOOivTdF65kkm9Y28hsPb6GyPEjyIn+k87XnUDc9/eOYpoW/yE1J0EvvQJjhsTirW2pZ1FBOkceJaVqMhpNEY2mqQ0HUnI7H7aSp7vzPimVZkz9LplX+4/WTqDl9spbnXCB5w8JmWRZGLomhpdDUOFomiqklMXR1soyWYnNid3hx+ipkUkQIsWBM9Co7O8FxtmyVaahnS1ZpZFIW8UwWrInJj/MmPUwdLGNiosQ0wDKx3rbTIxpXiSWyRJI5YgmNeFLj7h0tBPw+bA4PlZWVrFoZpKIsSNDvndzFWsg9tbphoGkGXo+LRDKLrhkFPPu1mY7c5L/8l//CkSNHeOONNy573JNPPnlez5R4PE5DQwOhUIhg8NoW7JxjmiaKohAKheRG9hTJmOVHxis/+YyXZVlomXEysT5UdRCXx4mrtBZshbthPJt1dfeye89eSosD3LZjCxvXLctr0aRhGJzpHaCtvZuBoVFcTgfLli6mrGTiL1xlxYrJY+OJJGfODNDYUMOixkre/5sP4/FcvP/L4aMn2bP3EINDwzTW15LNpFi2pIFtv/4O1qxehtNZ+IWxmUyWkdHxyd0qr+48jqqq1NZUccPmVdTVVk3Ga01sx6Cs2FPYSiaWiZYJo6ZGyMb6UJOD6FoSLBOXzYPd5SNlOhgdjzMeHWQ8kiKRyrJxdQMti6uoKTFQlhVTXlpEWUnRZM9ViF/2bTU9hk6EirJiPMHKgl2OxzO1cvh5fyP9lV/5FT74wQ/y13/919x4440AvPXWW/zRH/0R7373u/M9nRBCzBuGliYT7SEd7QYU3IE6lAWS1EyVaZrYbDZOne6gvfMMK1qaWbl8ycS21xOtFAcDNNTXcOvNN+B0Oqb8h358PEJbRw+tbV0Mj4yxqLGaG9aEKHWNk4mfZM/uAY52pCgvKyGjxonG06xZXsMDt6+hOODlanaopjIqp9qHGRyJcfuNy/AXeago81Mc9KJgMTyWZGA4hmVBVWii9qbDbieayJLJ5ljcUMHSphAB/y/+YJumSTSeYeTsrpSxcJLigJe7bl5BkdfF0kWVVIeCkztV5gLJGxYWy7LOltFKoGWi5DJjE4/1LIpiw2Z3ny2jFZCJESHEvGCZBqaRO2+Hx3mPjRymqWHqKrqWwtQyE/2Y9OzEDhHLOLtTRMOyzk58YCNDFTrDKFig2M7u5nBMlrnC5sCwnMTSGtFEFjUH61ctAuBnew6jqjqBIh8lpV6aFvnw+stxelysXVUyLeMQT2boH4oSjWWIxNMkklnqqkvYsXUpbreT5NjsmSwpdG7yX//rf+WHP/whr732GvX19Zc91u1243ZfeDPOZrMV9KazoigFP+d8J2OWHxmv/ExlvLRslEzsDNl4P2Dh9i+86hRLFjdSVlJMcUmQcDSD3W7Pq/9gWs3xxpv7CFWUc+tNW1jUVIfjbT07IpEY3Wf6OdM7QCQaw2azE/D7KCkO4vOefyNd13Xa2rs5eboTRYFsNktZSTE3bFnP8mWLLzmxcrUsy2J0LEz/wDC9/YOEw1EA3vPuBynyebn/nlso8nkveo/EZOIzpijKNfdr1NU4ufQoanKYTOwMuhrF0LIoNgcOdxBvoBYU+2Qcbae76e4bpyToozoUZM3yGkJlARQFykuLKC8tyjsGRbFQsLDZlIL+N2aq58r7W+pXvvIV/vAP/5D3vve9aNpEzVGHw8Fv//Zv83/+z//J93RCCDHnWZaJmhgkFWlHz8ZxessWXLO1y9F1nY6uM5w81cGS5ibWrm5h/dqVbNqwGofDQU/vAPv2HyWZSrFx/Woa6mtwuaaWFJqmyfM/+Bmv/Xwv1VUV3H/XTbzjjmay4ZPEwq3s703wo9d6GRxJsnRRiEe3t5DL6eR0g6a6Mhx5lvY6J57MsPOtNgzToqGmFBSFXE6nubGC/5+9/wyT5Lyvu+Ffhc65pyfntLM5BywWOQdSIEWKlGkrU5HmY5GPLUuyLcn2a8t6aFG0bIuybIu6JNkSqUAJFCkSJEAARMYusDlNzqlzrFzvh+rumcEG7AILYQn0ua7d7unqSndVV931P/c5p1zR+LsnTxEJe2lrDqPrFivJArlChUjIx5H9A/h9bmRJwrIsVtMFJFEkHg2wnCzw7MujSJJIIhZkZKCVloRDtAiCwPaRjre0ve8mGv2G9z5s20JXHCstrbRSVZJUHHJE9iG5Q7j93x9KqAYaaOD9jTWyY92rqWIaKpZRcV71EqapYhsalqmsKeUss6ryMNeRHo4dRi3PQxRdVaJDrJMeouhCcFVJkKq1lW2DaYTxyT5qNQ9F1dF0k5DfS7Gk8MyLoxRLTvaFKIrEIj5s20YQBO4+vAm/z71uBOeNQamiks1VyBcr5IsK+YLCcH8LfV1NLK3keeHYBH6fG7/PTSwaqNuKul0SPu/NQ5DfqL6Jbdt8+tOf5qtf/SpPP/00/f3979QmN9BAA+9hGFoJJTdLJT+LZSi4/Akk+dpGwL9XsJpMUyqV6evtIhaLXHceiWmaCIJAMODnox9+uB7YblkWS8urRCNhvF4P5y9OMDE1S3dXO7t2bqGro3UDmQIwv7DMmXOjPPO9lzl3YZye7g5279zCIw/exdBAzyXff7uoDSgFePLpF7Ftm872FraODNHZ0YqvSuIEA+9MjclQC2iVJFo5hZKbRa+kMPQSAiC6/MieKO6AQ9IYpslKusTo5AqRsI8dI53s2NzJnu3db7m2cjPiuo+w3+/n937v9/jc5z7H+Pg4tm0zNDREIHD9TFEDDTTQwPc7dCVHOTOBUphHlLyOmqQR4A5AsVTm3PkxLo5NYRgG3Z3ttLY4NlMej5tyucJ3vvsCS8urdHa0ce9dh4lGr89+4NSZi8wvLrNn5ybuu6UHWT9LbmaFpbTBs6+tspIqoagWu7Z28sjd22lNvDV7A9u2MUwLlyyxsJzl+aPjBANe7jowSDJd5OXXJymWVT547w78Pjc9nTHml3JkcxWaYgF2b+3GVy0YhIM+KorG80cnWEkWsCyLno44h/cN0NwU5N4jm4lH/W95BIWmGxTLN0eAKjT6De9VOPZaBfRKBqUwj67ksC3D8VN2BZC98ca1sIEGGnjX4GR3aFWFh7FmcWXWiBCtrggx9QqmXsLQinVbLGzTCQG1DWzLUUTUyjbr8zpqag9RdCNIEpIocb15Hm+EY9ciks0VGV2apVBSyOUrVBSNtuYwd96yCa/XRUdrlFjETzTsIxz0bug3REJvz1BL0wyS2RKpTJFkusjB3X0EfB6efWmUCxPL2Da43RIuSSIY8NDX1UQw4CHg9yBU57dtcMk354jzG9U3+dSnPsX//b//l7/9278lFAqxtLQEQCQSwee7kaZmDTTQwHsRlqFSKcyjZKfQ1SIuXxy3P/Fub9Y/GCzLYnpmnjPnxkim0oSCQeLxaD3A/VqRSmU4fuocum7w0P134PW4mZlbYGZ2kdm5RVRV5cgt+5ws1N3bOHRg1xWftU3T5Ct/9Q2mZ+fp7GjlJ3/0Y+zYvomervZ35NkmXyjy5Hdf4MjhfbQ0N/HoQ3cRCgbe0ecoQy2glZNo5SRKfha9ksbUS4CNKPmQ3EF8/gSCINaJnJVUgeNnZsnmK07eqkumr9up7Vwtb/VaUaqoFIqKMxCjqBD2mXQn3r1nybdMhwUCAXbu3Lnhs5WVFVpabpyXWAMNNNDAzYoNAe66gjvQjCi53+3NuilgGAayLJPLFRgdn2ZkuJ/NI4P1kRC16R6PG1mWuO/uI3R1tl3z8nVd5+TpC4xNzFAu5dk7EqYnXuDCiW9x4mKWaCzB+EwS24aRgVZGBlrp72nCc41qlRqKJYXlZIHlZJ7l1QI9nXH27eghGvazY6STbL7Ct793rurH7QS2q5qBz+smHPQRHPDS2xnfYLMFDpnxzMujqJrBjs0dtDSFiIadB2pZkt6SxZajUCnSmgjjkiVW0hWGrnsp7ywa/Ybvf5h6BUMroCs59PIqRlVBIrn8uHzxxjWwgQYauKGohZhbloFtaVWVh45laWukh21gm7oTZF69JtVCz23bBMvAssy63ZWj8mBd+KkAooRYs7cSZUTJiyBKyIIENfLjBhYtTNNCFB2rjMWVHMlMkXJFqxcJdox0MtTXQrmisbSaIxTw0tcVJxbxE4s6xXxZktizrftN12WYJqZpYZgWsiTicbsoVVTml7LoukmprJHJlzFNi0fudkLJ/8N/+3ty1WKIIAj4fG76upro73ay2Npbo4QCHrweF16PTEdrBICWRIjH7t+JyyVddnTpzUicvN2+yRe/+EUA7rrrrg2ff+lLX+LHf/zHb8QmNtBAA+9BWJaBWlyikplEr6SRPBG84a731UAjy7L46uPfplAs0tbazD13Haa789oJCdM0GZ+Y4fzoBOl0Fr/fx8F9zvX8uReOMjk9RyQcYtNQHz3dHSSaYoAzaPNyeP3EGaZmFjh7dpTJmTkevO92fuCRe6/4/bcDwzBYXFplZm6B6ZkFvB43oaBzf79eouia1qeV0MurqOUkSm6mSo6UsW0LyeVDcgVx+ZpAEMnmKyQXiiTTqyQzRXq7mti5uRO3SyIS9jHY20xTLEAkdHk7sGvF4kqOeNSPx+3i5Pl5zo0uYpgWqmYgCAIRP3Qnrr1GdKNxzWSJ3+9nenqa5mbHRuGhhx7iS1/6Eu3t7QAsLy/T0dGBad48XqQNNNBAAzcaToD7CuX0BFppxQlwDzcC3G3bZmZukTNnRxFFgYfuv4POjlY+9oMP12Wqpmly5twYp89e5NGH7iISDnHf3Ueuaz1nzo3y6rGT2KZCQC6hZ6ZImTZPPJHh7ESWUMDLPUfa+NADu/C4XbQ1h6/5Jq6oGi5Lx+txcXFymddPzyIIAvFogKH+ZloTYWYXM3S3x9g81MZTL5yvj6KoKDo2UCyr+Lxutg63b1h2saSwtJpnoCeBKArEIn42D7a97ZGfqqYzOZNidGqFckXj0Xt3EPR7GOi6MQGhbweNfsN7A4ZWQlcyaKUVdCWNqVUAAdHVsNdqoIEG3hyOqsPJ8DDUCloxj207+R12VfVh2yaWqWPqZSxDwTTK1cBzfS24vEqcrIWYAwjY2PX3guRCEGpWVpKj9pD9yPWsjxtnD2FU712yJKFqOvmiUiclNM1EkgR6O53+4fNHx1E1A003UDUDRdF59J7tBANeZhbSLKzk8Lll/H7PhkEW8ViQ4JYuLMuqFxBWUwWCfscj/ejJaTTdwDAtdN3EMCyO7B8gGPBy9NQ041OrAFiWjaobjAy0cHjvIKupAl/5u9fQDQPLtJEkkYDfXSdLbtnTj0uWiEZ8xCJOIaM2qOPwvkEOX6FNZOnyJMnNhBvdN7HfSuhdAw008L6FbVuoxVXK2UnU0gqS7McT7nrLSsTvN2RzBS6OTtTtuHduH6EpHiUej17zMkrlCgG/Yzt59PXTtDTH2X3XYdpaEpTLCgA7t29m186tRCOhN11eKpXhwugkX/3atwmHg+zesYUP/cD9bNsy/FZ387JQFBXLsvD7fYxNzPDSK68TCgYZHuxl25bhutXWjYBlGejlVFU5ModSnMdUi4DlWCS7griqypFsvozHlpFFqU5a1OoV3R0x2lucukI07OfQ7rduNbmczLOSKpDLV8jmHSvPwZ4Eh/b009UW5fiZWWzbxu91IUkiXo/T93m3cM1kiaIoGzoDzz//PJVKZcN3Gp2FBhpo4L0MQytRzk6i5GYAsRHgvg5HXzvFmXOjtDQn2LJ5TdNQI0qmpuc4+tppyuUKI5sG8F4m2PJqWFxa5f9+5XHOn79Af6efH7yrFbdY4fmCzDPHkxRLOh2tUR68YysHd/dd83Kz+TJTcykWV/KsZGHflhjbRzroaIkQPOghEQ9SLKtMTCf53itjGIZZL3D4vG5WU0W62mMM9iZoTWwkZqbnU6wknaD2YklFFAUS8eDb7mjU8PqZWcaqhZCezhib+lvrBZSbAY1+w/cvLFNHr6RQi0to5SSmVkKQPUiuIJ5Q7H3zQNdAAw04sG27GlJeU3ho2KaBZWlYhoZtqlhV+yqzGl5u6hUso1xVf5jYpkXJiqPYy4AOl7v+1+2rZARRRBBkqGd6SPWMjxvV97IsC8OwcLtlLMtici6FphnouolmmOi6yb7tPbjdMkdPTjG7mEVVdFTdwDQt9m3vYde2bqbmUnznufNYpo1pWViWTSIe5Ec/0oRlWZy6MO/sHgKiKCBJIqbp7L9pWmiqgaYa5ApOkcfvc9OaCJPJFnnt9bG6CkYUBaJhP/3djkVLoeQQNAhOc9q2jaIaBAMgSyKmZaGqDkEjigLligZAW3OEu24ZJhL2EQn6CIe8hAJrRZp7j2y+YW2rmyY+j4ubZbB0o2/SQAMNvBuwbRtdzZNfmkMvLYEo4w22I4g3T5bTO4mFxRXOnh9lbn4Jr9fDYH8PTU0xhof6rml+wzCYmp7n/MUJ0pkcH/vBh/F6PfzQhx/CsmymZ+Z54snnKZZKfPRDDxGLRa5puV/92rdZWl7lwugEuWyBT//8j9DTdeMyQvOFIrNzi8zMLrCymmLzpkEOHdhFX08nbS2J67YgvxJsy0Avp1HyWdLZBdTCHIZWwDJVBEFG9kTwhrsv6T9NziY5enKa3Vu7Ge5vob+ribbmMPGo/y0NfihVVPIFxzo0V6yQy1c4vHeAUNDL7EKGuaUs0ZAzEKNc0VhczQMQjwY4uLsPn9dFNOwnFPBgankMJXND2uet4Ib+Mt9PkrEGGmjg/QPbMlEKC5TTY+haEbevCcnV8CGuIZnKcPb8GHt2bWPXjksfsI+fPMvxk+fo6mzjvnuOXNMIjxpm5xZ58eXXyedWsZV59gzayEKWQiVBc3MPilmmt7OJW/b00dfVdF05H/NLWV44NoHbLdGaCNPT10F/q3MfCwa8BANennnpIkurebxeFy1NQSwL8kWFYMDL1uF2dox0EAx4sW2bTK7MSrLA5iFHLnpubAnbhtbmMLu2hmlpCuF2vfXbrmVZzC/naE04y3G7JLZtamewN1G3GKtZZny/4PtpW98PMPUKamnZCfZTc4CI7AnjCTfyRxpo4L0A27aq9lXaujwPfUOuh21qmIZSVXYoVdKjUrexurzCYy3AfC2vY434EEU3SBKyFcIt24jSW8/zePN9tNF0g4qio6oGFVUnGvYRDftZTuY5fX6BQlmlXFEpVzQSsRAfenAXpmXzd985VS/q67qJYVpsH+nA7ZYZm04yv5RFkgQkUUSSROzqd0VRJOD3IEsibreMxyXT3BSsT9sy1I4siciyWCdMXC6nCNHZFsXndVFTyVimVVethkI+Oluj2LaFadqYll1XtIBDtKQypQ37XygpJOJBmuMh+ro0gn5PtU/jIRRwBlR4PS5uO3B1s07TtDAtq/pq45Yl3G6ZiqKRzJTQdKOuZvF6ZIb6WrAsi689eQpFddreMC0s0+LDD+8hHrl5BnO8GRr3uwYaaOBGQldylLJTlFJzuIPg9r+/7Lufff5VJiZniMWi3HZ4P/19XUjXUYh/9dgpxsan0HWdjvZW7rjtAG63C9u2eea5V1hYWMGyLdpam7njyMErhq+vJtNMTc8xN7/ELQd3c/7iBMdPnKWpKcatB/eyf++Ot02U2LaNZVlIksTZ82O8cvQEoijR0d7M4YN76tbjXq8Hr/et3xdt28ZQc2ilVZTSkmOtpRQpWxF0YRXZFcDtSyDKl66jVFG5ML5MOlsilSkx0JNgsNcZhBEKei+xEL8aFldyrKaL7NzcCcB3njuPouhIkkg07JAiiqoTCnrZu70b07JYXi1QUTSCAQ+37husL2vz4JrllmlapLNlwjdObHPdeH/QmA000EADbxFaOUU5M4FaXEJyBfA2AtwvgapqdLS3smPbpvpn5XKFYqlMS3MTQ4N9NCea6OxovablLa8kKRRKKKrKi89/j2x6lv4WG03IoGoews3tLKZMjp45w8x8mkN7+hnouboVkG3b5AoVVlNFRElgsKe56rmZYNeWLkRRpGiEMZQVjp+dZctQGx63i5ZECAQoFFUWlnME/B50wylUhINellbznB9fZn45i6LouF0yvV1xfF4399+2BUl6+8UgTTOYmEnWrbZu2dtPb2cT2zatdeYyuTKjkyvkChXuv33L215nA+8vmHoZpbCIkptG1wqOgiTQ1lDONdDATQrbturERp30MPW6umON9KhUyQ6lam+lVgPLDSzbhCoBgm1hI6zL8xDWsjrEmqJDRpTcTqj5W1B42DYIhgtRemsqg9pgANO0WFjJUSgqFIoKxbKKouo8cMdWXLLEX3z9GFNzaafIX7XFuufWEe44NMzMfJqXT0zikqW1XI3qtggC9HTGcbsk3C6ZoN9DIOCpEhlw9+FNqKqBaVkYholuWMSjThZba1OITf2tTvGiul5VM+rbbhgmyXQR07TqqoX2lgh+n5tSWePixMqGfVVVk76uBKIgkM2VkSSHnBFFAZcs1cNWm5tCBHxuhKpaxSVLRMPONrndEh63C1UzKCsFllZzhIM+to90oBsmT7940SE8DNOxCFNNHntwF+Ggl7954jjj06sYhoVh2hiGyYGdvTxyz3bOjS/xx3/5MrphYlVVND6viy/8+scQRZFvP3uWfEmtnk1O844MtHLbwbevqG2ggQYa+H6CoZVQ8nNUcjPVjL8w7mAY8X1QS7gwOkE8FqU5EWfTUB/Dg320t12bdW8t9L2723nW1TSNzZsG6O5qJ53JMTU1R293B4IgEAoG2b+vlb6eTvz+yw9mffXYSaam5ymVy5TLFTTNYDWZZnZukUDAz6H9O9m3Z/sVSZZrxZlzo5w+e5GR4QF279xCV2cbfr+PzvYWXK63H4BuGgp6OYlaWqaSnUSvZKqh7CKSO4DLG8UttOKT/QgCVBSNVDJDJlcmnSkRi/jZuaUL27KreSEBhvpa6Ou6dkt527aZX86SzVVYTuZJpotEwj52jDjH45Y9/WSyZVRNJ5uvML+UZXI2xUce3oMkifg8Lvq64kTDftpawvWBpKWKSjJdBKC3swlF03nqpQk+cHv71TbnHcU1nw2CIGwoEL7x7wYaaKCB9xJMQ6GSm6aSmcK2DdzBNsT3iUz2zWAYBmMTM4xPzPDAvUfo7GitEyGGYXDm3BgnT58nHA7x2KP3Egz46+HuV4Km6YxPznBxdJKJyWmWFmfoSgh0NVm0d1qMz+v09Q4SCXl57fQsZy4ucNuBIR69Z/tV1RrpbInTFxdIpovouokoCnR3xBnsaSbo97B3e49z01/Kcmpijlx6CbcskYgF6WqP4ZKlutXWgV29tDSF6ve+iqLz2ukZbBt6OuJ0tEZojgfr6pYbQZRcnFzm5Ll5bNumpzPOcH8L8YgT/mbbNnNLWUYnV1hNFfB53Qz3N2NZ756353o0+g03P0y9glJcRMlOo6t5R6Id6m4cpwYaeAdh23ZVzaGDbVatrar/alZXNaWHZWBbet3ayjJULKNSJT3Wh5+bYJtQTfCo0x6CuEHpQVXpIbm8SHUyRH7Hf/OmaVFWdHJKiWDc+WxxJUdZ0dB1E71qedXX3UQ8EuDi5DKvnpymWFIplTUqikZLIsRP//BtqJrBf/3Sdx3SQRCQZRGXJHHrvgFikQDhkI/Otih+n7uqqvAw1OcUaDYPttFSVWfWCBOP2+lDyNVllCsaiqpTUXRKJZViSSUa9rOSKnD24mLdZsuybPq7m2hrjmBaNnOLGYceEMCycdq/ioqqo+kG61u5dq82LQvbBsu2nONg11Q6DsGUzJQwzGobGY6S45Y9/QT8Hl47NcPkXArLssEGy7a549AQD925jbHJVb7y9WN1wsg0LcJBL5/7Vx9BEgX+7PFXUVS9uj5nczcPtXBgVz/jU6u8emLaOZuq926PW+aRe7bjdcvk8mXEGoEjCJiW7ZzXtk1LUxi3uwyCgCCALEqEQjePqqTRN2mggQbeaZiGgpKfp5KbwlBLuHwxZF8TpWzlzWd+D2A1mealV46zd/d2mhNx2lqvL9/wuRePMTE5wwP33Y7LHSAejzIzM8+pMxcQBIG21mYURcXn83LowK5L5l9ZTTEzu8C+PdsRBIFSuUJ3VzvdXe08872XCQb97NyxBcs0CYWCNCfib3ufT56+wGvHTzM81Ed3VT0SDgXfVli7ZahoFSd3RC3MoxaXMbQitm0gyU52pMvfgiAIDgGULYG7QjACo5MrvHZ6BgCPx0U86idQVZcGA14evWfHVdetGybZfLmeL2KYTt9DEARePT6NIEAs4mfH5k4UVefkuXl2be2iKRbgmZdG8XpcxCJ+hvpaiEbWSKydW7rq79O5EufHpllNF1EUHYD21gi9nU0EfB7uPzKEJJbfcvu9XVxz5c+2bTZt2lTvTBSLRfbs2VMvCjW8PRtooIH3ApzQtSXK6TG0SgaXrwnZHXi3N+umQKFY4vyFcS6OTWEYBj1dHWi6UR8pMTU9x6vHTlGpKGzZPMiuHdemcMjmCvzd3z+FrqsE3TorMycRrQJNwRYO7duDiYvhEY2l1TxPPX+BZKbIzi2d7N3eXb8HmaZFKltiNVVgNVWktTnElqF2p2hh2owMtNLcFLqs/+bRk9OMzyQR5DChgBdFddbV1R6jr7upOtJ07XZZrmhoukE07OfBO7fe0DBTy3JGzPo8LppiQSJBH1uH2xnoSdRtOQzTRJYkbNvm9dOzBPxu9u/qRdNMVtMFZ79vAjT6DTcvLEOlUph3SBItj+wOOz62jYJRAw1cFk4h2FwjNSwDy9DQKxqKYAEmtukQG7ZtOf/eSHToCpapVC2v1ik6bBPbcubBMrEFNhTVbXBIDUFcZ28lgygjSV7kWnC5IL1jv2HLsuqEhmaYBP0e3C6ZdK5EOlOqFvKdkPFoxMdgTzPFssrTL16kVFapqDqmYVE23Pzsx/fidkk8/eJFZhfXvKht2+b2g0Pcum8QRTWYX8zidkmEg146WiO0NTsWnl6PzM984jb8fjchvwe32yE+aveWh+7cVld4lCsapbJa5y10w+Ts6CKlsla14dKxgZ//J3cA8OWvHSOXLyOIjmWWJIq0JEJEw36yuTKpTMkhZ2QJWZZwV620TNMimSmg605Gh8Ba8LvH7WI1VWBxOYdp2ZimhWXbtLVE6O6Ik84WeeXEZFXFYWEaFol4kCP7h/B63Dx/dBzTsmAdgTK3mGFksI1socLsYsahxwSn8D+74LSp1ydTrmiIokMGuGQJSXLOD9Oy6WyLVosTQt1aLFJVpfT1NFFWNWxr7fhvqVqM1rbdxsa2nL8DXldd+ZMrlClVNLAc0seybMoV/Z04Ld8SGn2TBhpo4J2CZeqoxUXK2Ul0JYvsjuCLdDvT3ifXFkVRee6Fo8TjUbZtubrd4+UwUR082d/bRVtrM+lshYujkwQDfo7cso/urvar2ledvzjBS6+8jt/vo6e7g4XFFVZX0zz84J2USmUEUaC1pZm+ns63s5sYhlFXonz18SfI5Qvs2rGFPbu2vuVl2raFVk6ilVZRiwsohQVMrYhl6giSC9kVxBNsR5Q2qlQmZpIcOzWNadkMDgm0RcK0t4S5df+gQ5L4rj5gQTdM0tkSkiiSiAdJ50p8+9lzgJOVFgp664M1AR69ZzsAU/MpTpydw+tx0d7qZMTIksSHHtiF272RaqhZlifTRdxuid7OJiRRpFTW6O9qoikWJBEP1K3FASIhL4by7hGM10yWfOlLX3ont6OBBhpo4F2HrmQpZyZRCvOIkgdvuKsRZLwOz71wlEw2z8hwP5tHBjeoRSzL4vjJc8TjEfbvvY1I+NpzSWy9gMtcpTtaYnxyBq9Loauzl/7+bkxcnD6/wIuvTdCSCLF7WzcjAy00xdZGaZw8P8+F8SUsy8blkkjEg/VOQTwS4K7DmzasL5kuMja9Sld7lK62GImmIPNLWXIVHbdoMNjbXA9PlSUJS7BYTTuFjoWVHLl8hZZEiLsPj9wwouSNVlsjg600xYK0NodpbXaC3/LFChcnV5ieS/PQXVsJ+Dzcum+AmYU0x8/MYVkWTbEAZ0cX8VsKbW+yzncajX7DzQeHDF6mnB5Hr6SQPOGGkqSB9xwc5YaxjoywqsqLGjFhvoH4MOu5HZalO3ZVemWdxZWKZRo41d81YsOyLMp2EwpL1KrKa/kd1PysHJLjEnVHNbRcFKskh/MdRwly43+PqqZTqmj1jAndMAn43TTHQ5QqKufGlpzPdbOeiXH34REA/u7JU6Sq6gYnw8Lm7sOb2DzYxujkCs+8PIpgO0oWG+hoiTDY04xblrg4sVzP55BlCUmUKJZU4lE/m4faiEZ8uF1yNc9DorMtCsBQXzM/+pFDiKJYH6BQu9+KosjSap5SRUNVDScfQ9N58M5ttDSF+Oq3jvPK8Sk0zbHMsi2b3du6+ImPHSGdK/PEs+eRJWebJFHEu67Q73XLqD432E5Qu6YZZHLOqEZFNZiaS2GYVj3PZHouxaE9A/h9bo6fmasfdxuwLYtUpkhHa4xUtsTMQgZBcPJKBFFgtWo3EfB5seuEhYztArdrrXAfDfvQDANswSFibBBlZ7pgg8clIwqAIGDbdt02LJ9X0HXTITWonqLrLMfmFjOUy1pdBWNbkEwV2NTfyvmxZV47ObuOtROwgcce2IVp2FyYWMZZpUPEuF0iRjU/paTo5PIVp8AS8OLxSMzMp5El6E+sWZO9W2j0TRpooIEbDdsy69ZIWjmF6Aq8L/vX4xMzPP/Sa4iiwAceuvu6skQBXjl6grPnx0lnsrhdLsoVp1D+wUfuuaZn7oXFFV559QQdba34/V6++e3vIQjQ19PJa8fPMDE5Q6IpzsimgeveN8uySKYyzC8sM7+wTCab4x/90AeQZZltW4YJBgN0tLdc/3INFbW8glpcppKZQFPSWIbihLK7g7gDrVfMtylXNE6cm2NmPs1AT8LJ/PC0APl6/urVYJgmU7MpTp6fR9dNOtui3BYfIhz0cmBXH7GIn0jIi6abZHJlxqdXGextxu2W+ZtvHUfVDIb6WtizrWvDsV5PlKymC5wbW9rg8tHfnaC3s4lIyHdT24dfM1nyYz/2Y+/kdjTQQAMNvGuwDJVKfoZyZgrL1N53oWuXg2EYjE/OcO78OIcO7Ka9rZnbbt2Pz+upj6Iolyu8duIs2zYPEYtFeOTBu3C7r82P0zINLp57ne89+11K+WU0pcwEMr3dHTx6/04sy+Kl1yd5/YyIbTuSzQO7euvqCtO0KJZVIiEfw33NeD0yzfEQ0bDvsh1TwzSZnk8zPrVKJldGlkS8HtkhS6JBwiEfI1sG2NQpIUkiqqZTLKsE/R7ml3O8cHQcj1umvTXC1qF22qoExo3A0mqO514dv6zVVm36hfFlllbzeDwuRgZakSUR27Z57ugYpmkx3N+KrpuMTa2QypQYbFZv2Pa9VTT6DTcXtHKKSm4GJT+PKHvwNMjgBm4S1MkNS6+Hh9eyN+pB4rVQccvEttdIDsvUqkHk6ppNlalTJzZs2xmKT5U0qf69ltNBPVnBphpWLlTVG4IEouj8TgTJGb0vuhGk6mdI6HYcr+xGFK+/IFJTa5iGjdcjI4gipYpKpaJvsE4KBj3EIwHKFY2ZhXS10ahbJNUCMV88NkGpoqKts7W685ZhmuMhTl9Y5NT5+bXlWhZ9XU08cvd2TMPildenABvLBttyiusHd/cR8HnQdZNcoVwvjAtAruAUMFoTIbraooiiiFy1nqwFg8qySG9XHBCwbBtdN1H0tUfPheUs58eW0KrWUoZhsmdbN72dTUzNJvnDL7+IYTqfG5aFxyXzX37jYwD85TdeI19Q6oV+y7LpbItxz60jFEoVUpkigiggCs6/TM7Z3mjQh6JodSutGgpFhXDIx1Iyz9RcymlfAURgcnaV/Tt7MUyTTL7skASiY3WmaHp1XwVESQS7ZlslAk4gOzhES6miYZomhmljmTYrq3kARieXODe6uO4HAfGY0wfQDYMLE0uYpu3kkojOcisl5x6fypVZTuY3nFcLy9nqeWBQLCvOeSsJ+DxuJFFkai6FqhkEfG4kSUKuhrD6fW6CQS+abnBodx/hoBdZEnG5ZfweFx1tERaWsyDYPHDHFkzDQpQE2lui2Ng88ew5SmWVztYo0ZDTF9u2uQNJEPD73OQKCmbs3R9Z3eibNNBAAzcKjhJglUp2GrW0gii68QQ73jeZf7VsEVGS6O3uoKkpxu6dW9g01HdN4eWKojI+OcPQQC8ejxtNNzAMg5bmJh5+8E78Ph+KWrkq6aLrOi6Xi0pF4Yknv0dbazOyS2JhaYXdO7cw0NfFE08+R7FUYf/eHWzbMnzNJJaiqHi9HgzD4Ct//fdomobb7aajrYVNQ331720avvZMLtu2MJQcamkJJT+PUpjHUAvYloHkCuDyxJCCG7NXTNMinSuRSpdYTRfx+Vzs39GLLInkCwp7tndXc9OgeJkxCZZlkS8q5AsKbrdEW3OETK7ME8+eBWCgJ8FwfwuhgNMHcLtk2prDHD01TSZXrltkedwy/d1NiKLI4b3OQJFan88wTVLpEivpAsl0ka62GMP9LdjVfuXVXD5uVjQM+BtooIH3LdaPstaUFC5PDLc/8W5v1ruKYqlct9rSNI2ero66HDIUdB7eTdOs55JI1c5RLBa5JqIkk05y9uRLPPnUU7x2cgLDMPnUj97Ojm3DrKYLpDIlXj05zfEzs0iSyJ2Hhji0px+vx0U2X2FqLsXyap7VdBFZEnnsgV34vG429V8+PL42WnRqNsXRUzN4XDJut4yq6qwkC9X98nLXLZuYS0mcG5tjaTVHKlOipyPO4X0DtDWHue+2zcSjgRsyQsiyLOaXsmi6yWBvM/FI4BKrLd0wEQUnsHV6Po2qGezf2YNp2ZwbXcKybbL5MvmiwsFdfWzqb2F2MYuqGYwMtuJWx9/2djbw3oCpV6hkpyjnpsE2cQcaZHADbx0OeWFVSYua0mJ9dob+BuWG7qgy7CqxUcvpMDVMQ8E2VceWqq7+MDcun7VA7Fr4eA31+OiaNVVNnSHWLKvcTnZHvXhdfRXEda9vfk13CAYT3bTwuVyIokg2X6ZUrpDXsrjtHJblhH03xYJk82XGplYdIqRKeHi9Lg7tdh6mn3j2LKWyo5awbRvLtnnwjm00xQKcOjfPxcmVuo2UZdlsGmjhzkObyOZKfOvZs1imhWE4ygdZEhnqa0aWJE5dWGAlmceqLtMyLdqaIzTHQyiKxvRcqk4gCKKAp2oh5fW6yBYqWKYNVR2CIAhoqkHA56mSDyWH1Kq2SSbrqC1WUgVeOTGNaTgkgG3bhENeHrxjqxP2/dx5iiUVy3SWa1gSI31+juwf4vz4Ei+9PlW/T4OjePjQg7uxbZhdSIPgKDxFUaAkqJimhSSJ2EBZ1RFscKLpIZMtASAJEvlSlSQQbARbZGk1B4AsCWTyZagmu1jVfaqoOuGQj9VUkXS2XLVdA7DJ5hQA5pezzC1mq6QbVXspDYBCUWVqNrV2XlZJGl03ndDSVJFyRUUSHQsvj18mHPIyv5TF63HT29WEx+VkqMQifuLRAIsrOTJlD3t39mAZFqZlE48GHOsvQeDE2TlamoIM9TajaAYel0Qw4KWrPcbffecUK+kCkbAfoapaGe5rwed1cebiAhVFrxNjqqlj2z5M02JsapVzo0ucODfHxEwS27bx+z1Egj76UnGWVwukMkW+VbXnkCWRRFMQtyzzEx87TF93E+fGlhwbLhtePjYBwEce2cudhwYoJc+/+UWmgQYaaOAmh23b6JU0ldwManEREBwFwPsk37RcrnBxbIoLoxNUKgrDQ330dncQjYSIRkauOq9t2ywurTI6PsX0zAK6rhMOBfF6PYyNT+H1erjt1v00J+JXtC6rVBSmZuaZmJylWCrx2KP3MTE1y5aRQQ7scwZd2rZdtwrfNNxPV2c70cibO18sLq0yNT3HwuIKuqHzwx911CP792wnFouQaIpddz3AMnW00gpKYY5KdhpDzWJoZQRRRnaHLrHWUjUdy7Lxed3ML2V54dg4lmUjSSKJWJBwlaBwu2UevHOj7VelouL2GHg8MrMLaU5fXKRYUuoDRHo647Q1RwgFPVX1iI+F5RzHTs2QzVdIxIPceWgYt9vpI/Z3J4iFfcSiAYL+NQKsdd3A0XNji5y+sIBl2bhdMs1NQXw+Z39amkK0NF2748jNhPfHr7mBBhpo4A3QlSyV7BRKfg5BdOMNNUZZgyOfvTA6yaahPjaPDNYJkhpSqQxPf+8VSqUym0cG2LVjCx7P1QuvtRyYzPIo/+fLj5NMplnNqnS0Jbjr1s1s3zqAS5Y4emIaw7To6YjT3BSkryuO3+vG53VTLKs88exZJEmkOR5k+0gHrYnwZTsrtm0zv5xlfGqVgN/D/p29JJqCuGQRTTeIRwPs2tJFT2esPs/kbJLnXl8h4NZobwlzYFcz7S1OJ8AlSxtsv94qShWV8ekkEzNJVFWnvTVSl7JuHXZyRopllbGpFSZmkuze2s1AT4JNA61MzSY5eW6B8elVVM0gV6jQ3RGjLRFmYnqV0xcWME0nHLatOUzzzZOl2sC7hPX5S7qSxeVrQnL533zGBr4vUcvUoE5UVF+rORh1IqNObqyRGbZtYpkmxbIEmRy2rTtWU+vUHZZlrFNqWNhYUHtf/VcnOqp+P2+8OjvZGyIgIohineCokRui5FkjMaqfXW8Oh23bKKqO1+NCEATmlzJksmUqqo6iGqiaTl93E0O9LSyu5DgzuoBh2Bi6iWE5WRz33bYFy7L4X3/+AorqqDxqpMaPffQwzfEg333xIufHl9BMD7LgFOYP7Orh/tu3ksoUefL5C2BbmLaNadrIklgnS54/Ou4oM2xHyWHZNt3tMW4/OMzUXIpnXrqIadn19ltYyXHnoU143C5ePzVbD/92lC5CPYA8nS2ytJpHrBMikC9ULaQ0g5V00cnLsGxM2yKXd6aJosC50UU03cS0bCzLKcyPTq9wMNrPhfFlTp6fr9IL1I/9Q3dtQ9UNxiZXqsfXxrZspHUjBldWCxTKCoItVGkYkZn5NEf2g64a5IsVnMwM5wRJV0kYG4tcUXFOsZo6SBBQdQO/5GYlWSCdKVUVIE5bFcuO2mJ+OUs6UyVEBEcBEo06ozSTuRLlilYljURE0Sn4hwJrhYdQ0IMoirgkEbdbprM1wuxCmnDQx46RDmRZwuuRaUuE6O6Mc/rCAmVF48E7ttbzSgZ6mnG7JOYWM7x6Ypp41A8450Is6qc5HiAW8fPcq2PML2Upl1UKllXdJicr5dUTU6SLEhfHVzAMC0GARLyCz+NicTWPYZispouksiVcLomAzwmzjwR97NvZw+xChqXlnGMFh0i+pKDqJj9fDXWdX8qQKyjrfjvQ1hyhsy2CbpgoqoEkOsHuPq+L5niIH7h/J2VFxzAc0kqWJWRZxO2S2bapHY/bxT1HRsjmK44qxeUoV4b7ri/ct4EGGmjgZoWu5KjkZ1Hyc2CbuLxNiPL758ErmcrwjW8+jSAKDA70sGXTILFY5Jrnf+7FY7x+4gyaqiPJEgN93XR3tWPbNvfedSsd7S0b+hHroWk6T3/vZRaXVhAQCIUCiILIV/76G2DDtq3DiKJIMpXhuReOsn3rJjYN97N966bLLu+NOHdhnJdfPU4oGKSjvYWOjtb6gI7rUY8AGGoBtbSMWlysEySWaSC5A8iuEC5/64b+bb5Y4fzYMsl0kUJJYbi/hb3be4hGfOza2kUiHiQa8l1WZTO/lGV0coV0rkxBcXHrrmaG+1twux3iYrivmUjYRyToq1tkyZJEX1ecp18aJZUp0tkWpaM1Uq95yJLEHQeHN7a/bpDKVDNi00UGe5vp62oiEQ+yZ1s3zU0hwkHve8Z+rkGWNNBAA+8rOJZbs1SyU5i6gtufeF91cK6EmoR16+ZBtm4erI/EqKEWYubzeYlFw9x7961vOjojm17m9IlXuHDuOH0tAhcmV5hfKtHb3cntt7ayebCVYMBLNl/mleNTpDIldmzuAAEKBYUXjk4QDvl4+K5tBP0e7jkyQjwSQJIuT2ppmsH4zCpjU6uUKxp+35o1SiToY6CnmZ6OWL0TsLSaY24py77tPXS0RrnjcDM9CeuKy387KFVUvv7kaSRJpK+ricHeBNHwWuE6nStxbnSJ+aUssizS3RGjomi8cGycF45OEI342Lejl307usnmK/T3JAgFvJy+sECuUGGgN0BTzCm+yJJEYXnhhu9DA98/MLQS5cw4Sm4GQfLgCXW9Zzqu7xYcMqAasIxDEtTsnOpWUevIgw1WT7ZVfXVG7q8tr6aicBQUzjxWdb7asteRH5aJZWlVQsTaQGTYllldxhvXV7OeqhEZl1dp2AgotGELq4iCgGnZ2LaAolsYhkU8GgRBYHGlSFkxnFHpNgQCHlpiYXx+L6lshXROqRMLtmUT9Hvo7WpC0w3OjC5iGha6oWGYFrIkcWBnL7IscfL8PJlc2bFcMi1M02TrcDt9XQlGp5Y5dnLGyYowTEzDoikW5MMP7UbVDD73P76Nrhv1kHFsm1/9pw8RiwT42pOnGJ9aBUFAlkQkSeQeUWSot4X5pSxPPX+xqgRwivKRsI/7btuCKIqspgqouoEkCFhVImA1XaA5HsSs5jOohoJoaZi2xbkxN/ffvhW3S+b8+CKWta51BaF+X7Jtm2JZq6ZAOP+nc44qIuj3ILskXDjKBcO0KFUtlwJ+D7rptI9dIzVMi4WlDNGwn/nlPBcnlh2LKdtRMcqSxCP37GB+OcuJMw7RUhuoWcu2kESR2cUMum5WC+tOFsVy1SYqV6iQyytreRc49k8ApaJKoaRQS6UXBIH1zhuWXQ3wEByqRRQkAgGPYzemGbjcMlXND6IkEI/4yRcrZLMKQb+naj3lFOz9fjfFokIqXaS9OUIk5NhEBQIegn4vI4OtnBtbpKMlzKE9fU7IukuirSlMd0eMZ18ZJZevsHmwra6SGehJ4HG7OHlujrmlLD6vi0jQi2VBIh6kKRZAEAVeODbB9HyaxZUclu0oVMqKTr6kIksSiqrzyvEpZ38FGJ1cxe2W+Pl/ciebBlqZWUg75IMgkC9U0DSDjvYYjz2wi7GpFRRVdzJUJIcoCQe9PHTnNspWBLdQoqLqCIAki8iSxLbhdrraY/R3Jzh1fn7DtSoW9VdHjXqZmks5y5TE+vlvWRaiKHLnLSMUigqiuJY9MtTXTHM8hO92F7urHuQC1eyUkB+f141twz23bXauUdV1ypJYVyHblo1LlhzVimpQtqy6araBBhpo4PsVhlZCyc1Syc9iGUp1EJLvzWf8PodpmkxOzZHN5dm/dwfxWISDB3bR39v1pgMmDcNgZnaB0fFptmweJOj3c/HiBCDQ3d1OT1cHvdWgdUEQ6O5qv2T+6Zl5xsbK3HJgF263C5/Pyy0Hd+P1ePjusy/h9/vYuX0zm4b6cLtdvHrsJGfOjZJoitPScnXHEMuySGdyBAN+PB438wtLbNsyzIF9O6+7nSzLQCutopVXUHKzqKVlDK3o3LvdIdyBtisq+9O5Es+8OIosi7S3Rtg20k5z3KmzBHyeuoOGbphkckVyBYXW5hABn4dTF+Y5e3GRRDzIyEArLn8zXQnn7tyaCNMcD5IvKmRyZWYXMuQKFec+fusIoihiGCZ3Hd5UX996lCsabreELEmcODvHhYllbNvG43HR0hTEV723N8dDl53/+x0NsqSBBhp4X+ByllvecNO7vVk3BbK5At9+8jl27dh8yagJVdU4fvIcUzNzfPiDD+D3+7jnrsNXXJZt20yOnebE6y8yOXaeVDrLckphqaeNrSP97NkTpi0Rwe9zfElfOT7F1586xfaRDvZs6+b8+BKhoJfW5jA7NjvqkRqudBOujSJWNYMzFxcJBTzIspd8QcE0nYBWt1tmz7ZuiiWF8elVlpN55pdytDaHsG0br8dFIh5GFPOXXcf1oqJoTM6mWE0VuPOWTU4Y+/4BWhNhXLIzWsasFv68Hhf5gkKuUGH31i5Gp1b4+pOnScSDbB5s5a7Dw8QiAUzLYn4px9xSBsu22b21m+0jHRvWq+kGK9kCLtO63GY18B6HbduoxUVKyYsYWh63v+WmJIPXsifWF/bXkQ7VMG6oEhNWTcmwRizUiQfbrr+vLauWYVBW3MgVFYEq8WCuZWFYpvNan68KoZpdgWU52Rj1QO81oqO23vXr3EhM1KY5n1EtGFfryVVlgFAnLIQ3UBfV2nJVieHYSPEGOynTtCmWdTTDAhw1hmOJE0JEZHIujapZqIaFoVsousWOkU7i0SAnzs1xfmyJsqKhqAaaZjDY18ydt+8klazw+3/6rGMfVWVEREnkt375wwD8+TeOkqwGU9fwQx/Yx8FdfRw7NcqTz5+vEzK2DYN9CX7hR+6iVFb5k798aS0jRHCCundu6USWJZ5+4QKzSxmqrAW2XRv1lmBhybEHcPbfaaBE1XLJJYsOOYBToHXJMoIAyXSRWCRAU8zP9JyEZVlUFA3dMDkzusA9t47g87uYWUxjWQ4RUhs9WLN6ml3MsJoq1PcFwebsxUW2DrWTyZWYmk1h2hKSaIAN7uq13SXL5PIVbJwHccuqPZAX8HnjTM6lmFnIYFVVHpZl0ZoI8dj9uzk9usBrp2qkhrM9kaAXRdUpKxpjU6tYtqOkEKvqiNmlLLFokGQqR7GiIghOtoUsO5ZPo5MrVGoDCKRaILdMNOxjcSVHuaKSiAcxTBPRFvB6XEiySCjo5aXXJ5FliZZEqK4gCAY87N/Rw4vHJlhJFejrbHIGJrzLpT4AAQAASURBVAjQ3uIU6Z97dYxkpkRLIoimOfkV3e1R/MEYAZ+Hv/vOKRAEWhMhsCHgc9PdEaO3u4m//+4ZcsUKwaCnfmy62mJIssipC4sk0wVKFZVsoYJt2UR0J3B9diHDkpxnbDrJ6QsLdQuvuXCG5VSBe4+MEPC5GZ9JIoogIpLJlZFEkUO7e9mxuZMT5+YoKzqCKJDMFMkVFbo7Y3z4od28dmqaZLrgBNVXyQev28X9tztKpNmlLJZhIYhUra9EmmIOcXFkXz+JWKBKSjhKj45EBFEU8HpdtCTCVd2N4LS934PbLaNoAl6vG5dLxLSc345p2UiiiG6YjM2sMLOQxrIssBxyyuWSKJYURqeS9ZwavWqT5vPI3H5gCFkW+ZO/eoliScW0LYfkseAfPbafg7v7+etvvM6Lx6fAdnJXTMtioKeJf/aT9zI5k+Q/ffEJRxmFcw5LgsC/++c/QEUx+MIfPkU6U8LGaX/btvnu8xe4+8gwbcEC/qEk0Y1dlwYaaKCBmxamoaDk56nkpjDUEi7f+8e2+/jJc5y7MI6qqnR2tNXJ9s3XEJB+/OR5nn3+FZZXkjTFo+zYNkI4HGTnjs30dHfQ0tx0xYFcuXyB4yfPMT27QD5fobenlVy+wOTULIIgMDI8gG3b3H3nYbo72xBFkVK5wuNff5JiqcK+PTvYvvXy2SSpVIb5xRWWV5IsryQxDIPbDu9naLCXe+48fM2h9LZtY6hZtNIqSnEJJT+LoeSwTA1RciN7wvgiTZc4l1iWRbZQIZUuoekG2zZ1EPC56WyLsntbF26XjG6YFEoKfp9Drhw9OV3tr2n15dy6f5CAz0Nna5SWphCtiTC6YbKQlplbXMbnc9HVFiOTK/Od5xwLzFDASyTsIxZZG7D5wB1rNl65QqWuGkmmi5QrGrcfHKKjNUpbc5hg0ENLPFTPKXmvo0GWNNBAA+956JUM5exUPdi4YblVtapaWObs+TEWFpfx+310dLRumH5xbJLXjp/FNE12bt98VcWFqVdQ8jMUUxf53refp1Kp4Pf6OZm0iYQjHNo7xI7NXQAsr+Y5cW6Oc2NLvH5mFq/bxX23bXbC2gdaCPjevLhrWRbT82kuTqygGyaP3rOdUNBLKOAhm68QCfvYv6uX1kSI1bQjLQV4+qVRyhWNeDTAlqE2Ng+1IopifaTt28VyMs/49CrzS1kEQaCnI45umLhkia42x/aromiMTye5OLmMLEm0t4SZnk/R3hrl/PgSU3MpmuIB7jzktNnY1ApHT04jiiKxiJ/NQ21sG14beXNubJFUpkSuUKFYHYF8cJNF143ZpQa+T2DqFUqZcZTsFILkfcfUJLZtORkT1fyJtfcalqVhV0kI01CwDMUhJUxtXWC3fgUrp/XEg0N41AkEu0Yv2OusgNZlVkB9ihPSLWDZAhWhFYMlRNaREoJYLbpL9TyLGgzTYm4pj6oZSJLEcG8TCALHzy6j6mZVMeGMnN65pZ1wyMfEVJqZxRySJNLWEsHv9eD3uYlG/BiGzeRsilS2TLmiUSyrGIbJUF8LB3f18ewrozz94kXKiuYIVbBpigb4kY8coqLofPnxo05uAM412et2cdetm3jozq387VMneeLZs84IL7dMKOAh4PfyH37pMVRN50t/9Tr5glIfQS+JIkG/l4Dfw9nRRV6uFsElUajmJARQNZ1iUcEwneMi2BaCKBH0eyiVVVbTRURRwLAsh8yxbWRZIplySGZflQQ3LbtafLWZX8wCEA76yFeXbTreU5i2zdjkCru2dpMrVpiaTVfPA2ebj52a4YE7tiLKokMSVLMpbBsmq17Koihy5uIi+UJlg6Jl63A7w/2tvHpihpdem3TOW7sWTq7w6R+DclFlcmYtZ0IQQRRECiWFaNjPSjpfz+awq3ZZY9MrTM2lODu6yOxiBssWEAUblyxiGBYvHptgOZlnej5dP+fcbseq6NjpOV47M0+hqGAaJoIoEAi48XvdRENeHv/2ScoVnZZEsB6Y3tUepbMlxjefOUuhqNDTFcc2nf1sbQrh9shIosizL48SCQfoMByCJRh0k4gG2bW1i+NnZ8nky4RDPqcgLwh0t8dpivl5/cws+aKCLDn5LaIgEI368XlkTMOiZKqEgl4kydk/WXKWr6gGiqYjIFJSnHwKQYDlZIFsvkJ7a4RIyIdtOapPURSZXcwiiGX2bI5z5MAgq+lC3cLLMEy0acfC6tF7t7OaKvG9V8YQqyRgNl9ClEQCfhdeT4yQ34OiOoGjtmVRKmssLGfrylG3S6qTdrphsbSS55tPn6Vc0VAUDVESEQVwqRKCAF/+2lGKZY3xqVXyVW9vWZaQJYF0psjLr00xPZ9ias45Ry3LaQeXS+LoqWlUzeDs6OKG0Hgb+MrXjyEgsJLKO6qnddctr8dRy5Qr2oYCiJOtA5//309hWZDJFR2ylTVF2L//3W9ctd/yK7/1t1ec9pd/f/yK056vZoxcDq+dnuUvv3HleX/o5//XFactrOT4+2ecINlPTbXz3/7Hg1f8bgMNNNDAzQDL1Bw728wEuprD5Ynii3S/25v1D4ZzF8Y5fvIsmzcNsnlk8JryPmqYnpnnj/70LwmFgmzdPMj2rSN0tLcAvKlqw7Ztnn72ZTRNZ+f2EXRTpJDL8Ldf+w6CKDA00FsfTNLb3eEMFgD8Pi/tbS1s2TxU31bTNEmmMiwurbJ18xBut4sTp86zsLRCS3MTO7dvpq01QaLJeUZ/M6LEUAto5SRqaQUlP4OhZJzsEUFEcgdx+5svGaRW29Z8scKxUzOkMiVM00IUhXqWh8ftor01wkuvT5IvKJSqtqIffnA3breM3+empyNOJOwjHPISDnrrIenxaIB0tsQTz54lk6ugmF58ssJAb4KuthjRsJ97jowQCflwuy4t/6cyxXrfydm+IrGIn+6OGIl4kER1WmtzmMsnxL53cU1kyWc/+9lrXuDnP//5t7wxDTTQQAM3EqahUMnNUMlOY5lqI9h4HZaWk3znu88Tj0e5/dYD9Pd1beggPPfiMcYnphkc6GXf7m34/ZeXGeuVDKX0Rb771HeIBw1kl4eSJiN5EmiawSN3b2ffzh5kScQwnbDbx79zgnxRxSWL9HbG2b6pg4DP8Qh/M6LEsizGple5ML5MuaIRi/rrKhWP28VAb7MTXGpZjE6ucPTENACPPbALr8fFbQcG8fvcl+0svFUYplkfPfnK8SkkSWTX1i76upo2rKdYUjh5fp75pSyzi1lM06QpFkSWRcplnW8+fYZQ0EsiFiTgc+OSnXk726LEIn6iYT+SJLK8mufJ5y9w/+1bAFheLdS/Fw37iYZ9CKXRG7Z/bwWNfsM/LNTSCqXkRXQljcvfjCRf34gf27axjAqmXsHUS+iVDIZawDTKmGoRQy9gamVMvYxlKtiWhlVVaWDqjg1U1VZqzaoKh5QA6v491c9qVT+hqohwOIs3Bm/XArkFoBrEXf+sukxBqC6jprxg7W9bAGsVW9axBAHDsFA1E8OyMQzHvsjlctEUC2KYFqOTqyyu5skXFSwTJFlCxGSgtwVDV3j9zAymUVWEmTYXxhf46KN78XpFzo3OMj2foVTW0U0Tv9fDPUe2cOu+Ab759EleODbpjJCvSks6WqMc3NWH3+fm3NgyumHUmyedKbGwlCUWCaCoOoWSWlXgCKhug7mFNKIg4Pe5KCs6YJPJV1hNOw87qqrzN0+cYGxq1SEQqsuNRfxMzqyiaSavnpjixLn5+nHyeVxkchUO37KfV09O88rrU460Bad4Hgp6mF/KcObiEs8fnWAlWagrH7weGa/XxUBvC8+/OsYrJ6arJBTIokAw6GElVWB+KcuFiWV0w6zamDkPpRcnl9m6qYOxqRWm55KAgC2AJMBpr5t0tsT0XIrZpYxDhtk2ougoJ8amVlhYyTG3mKFS0UBwLJtkWWRmIc1f/f3rzMynKZZVRMdbiqDPgyQ64dgzi1lnedVT0i1LyLLM6NQKyXQJVTXQTRPbBrdLJuzzEPC5efn1SaRqzoWNiEuGSMhHV3sEt1smEvLR2Ratqwh8HheSJLJ1uI18USXg91BRnEK/k+kgEQr66O9pYmJmtaqmcM7R2fkspbLGRx7ZTbGs8uXHX8WwLWwLJisaggD5A4MM9CR48vnzFIoKNo739eJSDtOy+aFH97KSzFMoK9TK7ecnlnC7ZDYPtWOaJqmMM8LRsmyWkjlEBCwbPG6Zk+fmSWVLde5SFAXGp1d5+fUpMrkSM/OZur2XLDm/5//n17+CouosJwtVAqFGc8KJs5MIOKHqTh7M2uXhuy9e5H/++QtXvFY98eyVA8Kffumt3/deqfYVLofp+Qxw5elvVFqtx1yVLLwUNqWK5gShX2aabV9+uXVt2w0a4PFuwH4XN77RN2mggQbeDJZloBWXKWcm0ZUUkiuEN9T9vrOzFUXhui2pjGp/9vS5UQb7e/jExz5IU1PsTeZag6KoeL0e7jhyANkl43K5+MM/+RtaEhH279vB0EAvbvearePi0iovvPQatx/ZT0tzE4cP7cEwDI6fPMfS8iorq2ksy8TlctHV2UaiKcatt+zF7Xa9KTFiWya6mkOvpNHKKdT8HLqSwdTL2LaF5PIjuYP43pA9UigqJDOOMiOZKRGL+LllTz9ul4xLlti+qYNIxIdLkuoDJXTD5NjJaUJBL93tMcIhL5GQD1l2tnHrcPsl26eoOhVFJxbx18Pf9+/swR1opT1q1Od18l7XiK50rsRqytm+1XQRVdW56/AmWhNhDu7uw+uR60TM+x2CfQ09lrvvvvvaFiYIPPXUU297o64V+XyeSCRCLpcjHA6/+QzXCMuyWFlZoaWl5ZplWO9nNNrr+tFos+vD9baXbZnVkSDjaJUMLl8c2f32A7q/X2DZNulshXjUh1i9eReKJc5fGKdYLHP3nbcAsJpM05yI1+crlhy/+GgkRCrlWD2tn16DbZkoxQXKqVHKmQnGp+b5zstL+AMhgn4voaCXgN/Nnq3dpLIlXjs9w4XxZfZs76Zc0Tk3tsgte/rpaovRkggRi/jftAO6npD45jNncckiNpDJltF0g82Dbezf2YtlWfzNEyeQJYnWZkeS2poI4fO+WQg9FI0wQTnPtfaFk+ki4zOrzC5keKiaq1LzpK9B0w2WVvNUFJ3JmSRlRWP7SAcT00mefukih3b38eGH9nB+fInFlRztzRFiUT+xiH8D0XJx0gl8y+TKFEsqLYkQdxwcvqLap7B8kratH6FzxyeubWeuAddzz71Z+w3/EPiH7JtYlkElO0UpPQaA29+8QTVnWQZGJYOuZNCVLGppGa20glZOolfS1RFRBYcEMVQsU61aUzXwzsPJqjBNm5pzl2U7JWW3S0aWJSqqgaY5xXoLx2FMliXi0SCKZpBMl+rZIZbjE0VPRxOGaTO3lEHTqxZQ1VHp0bCfrrYYU3MpUpnKWlB41a5ox9Y+sDSOn5ldp+Rx1BE9nTFikQBnRxdRVWO9IRl+r2OhtJIskM6Wq0oYZ7mCILB7azfFssrFiZXqdjr7X7NmkmWJ+eUciqLX7cpkSSTo99DaHCGbK7OYzNcrxU4YvMCerd2UKhrnx5cwDSc+XKgSZw557GN6Pu2EWFfbVxDA63HR0RqlVFaZX87W218UnWMy3NeCaVpMzqbRDXPDMYtFfDQ3hVhYzpErKGv7Y1PNpEqg6gaTs2mwwaxZsAFej4zHLTvWR7WDUm1jURRxuyQ0zcRcCzu5ZrzZ09y1lKerfJLzXtj495ut743Lv9r2vNm8b/zgjQTB+vnXNf+brndtnit/qXpI6ufwG/9m3ecb/q6+t+xLt6/+p1072lA7/2vFDF2v2Q5WpwqC028RRBRFXVOsVAPrgwEP4aCPckWlWNLq89o2eNwSiaYQhmGxuOL8bizWGqolEUaSnEweTa+e37ZDUkZDXsIhH4WSSqb6W67B5ZLo6WjCtm3Gp1er1nRr7dPXlcDndTM1n6ZQVNfaBIGmSIDuzhiGWuCeh/4R/+Rn/+0N8/p/L/RN3ol+Q+NZ8/rRaLPrw3utvWzbQiutUM5OoZVWEGUfLm8MQbwxhePLPZ/fbFhcWmV6Zp5bDu6+5nls2+b8xQlefOV1dN3g3jsP4/a4CPh810SU1Oy1JqfmEEWRO44c4NXXTnHnbQdxuV1MTi/T19OCtO4c03Wdo6+d5sLoBK0tCY4c3kc4FKxvz/deOIqhG7S2JmhrSRCPR6+J7DL1MkphAbW46NhqqUUso4yNgCT7kFwBJHfgig4l58YWOVkdjBQJ+0jEgjQ3BentbELVdF475Sh9iyW1rjj58EO7cckSFUW7ar2iVFGZmU+TzpZJZ0uUKxqhoJdH7t6+7lhsrGeYpkUqWyKdLbF5sA2Abz1zlnxRIR710xwP0poI05II3ZRkoK5kMZQMI/f8R7zhG+ffea333GsaWvvd7373hm1YAw000MA7Ca2copydRC0sIso+vOH3t+XW0vIqZ8+NMTu/iCzLjAz312/ONSLEMAxOn73IydMX6exo4d67br1s58bQilSy05RSF1CLi+QLZaaWTf7629OkMkVGBmQ+eN8uErEATz5/gf/+x8+QzjrBpB63TDJVZMeWTh69Z/s1h31WFI0LE8tMzCS594hj1dXRGuH82BL+qsf51FyauaUse7d3I4oiD9+17U3JkbeDiZkko5MrZPNlggEP2zd11L3qa0RJJlfmyefPc+r8PLphsntrF4lYEJ83wHOvjnN+bInmphCbBhxB6+bBNkYGWimUFJLpUj2A7Z5bRxAEgYWlHJZt09EaJREP0tV2bZ2+dws3U7/h937v9/jc5z7H4uIi27Zt4wtf+AK33377Fb//zDPP8NnPfpYzZ87Q0dHBL/3SL/FzP/dz/4BbfG3Q1QK5hWMUVk5iGSqGmnPIkOIyamkFtbiEXklxbWXSBv7h4VhKyZc8g9uAjm3qeGXwXtJTNzDULDLQdsll2sbWkkhAb/Pl1lnGqpTpaYKepqoaZ/16y5MA7B683D0zB2qObT0Ab9xoE0gSaoXB1ssUFYx5Ym44tPkKywVaBwDeOF0FVkh4Yaj1jdNssGbweeC2rVT3Zf3+ZIEsiQHe8DmAASQhDtu7LlcESQKwqQUu3VcVUNnUfLlpAGkADvRf6RHL5OqPXw2H5AZqsLny+WIAhSvMZ+Kc/+ux/jdQuMLnAEr135WW69imcevlpievMJ9dXWd1vYXfIzv/CE19d13h++8cbqa+SQMNNHBzwLZttPIqlew0anEZQZLxBNsRxPfH/dg0TSYmZzl7foxMNkcsGqFSUfD5rq5SX15J8vyLx3jpleNkc3kCAT/333OEtrZmggH/VecFZ5DmU8+8SDqdRRQl/H4vqqrx+DeeJBoJo2oaLreLSCS84Zk3nc7y5NMvoqgqhw7spr2tmYXFFY69dpq2tma2jAxyx5ED17TvtmWgVVJO7kh+FrW4iK4WEbCRXAFkTxgx0HrFZ25NN0hnS5TKGoO9zXS3xygUVQQRSiWNucUMhZJCb2eTQ4ioOi2JECMDrUTDPiJhX13FUatdaLpBJld2/mXLxKJ+Ng+2oWoG58aWiEX89HTEiUf9xKKBS7bJNC1Ojs6RzBRJZ0tYlpNl1tsZx+d1c+TAYF3x3MDV8ZavAGNjY4yPj3PHHXfg8/nqxbcGGmiggXcDhlZCyc1Qzk2DbeEOtCJK11aQf6+h7tOt63znuy8Q8Ps4dGA3QwM9yPLGy/7U9ByvHjtFpaKwbeswO7ePXLIsrbRCOT1KKT2KVsli4WYxI3J2vMKpcwv4vS523zpC0O/B7ZIQRZGVZJ6WRIiH796Gqhq4XBJbhtqumcQoVVTOjS0xOZNEkkTi0QBlRauTJX6vG7dL4tjpGTpawtx+cKg+qumdIEoyuTKhoAdZkkhligT8bnZt7aQ14XTgNN1gcjbJ7EKaYlkjky0xu5glHg2wf0cPB/f0Mz2f4g/+z3NEQj7uOryJu27ZhKI5ViyabvB3T55Cr47yDId8NEUD6IaJ2yVz1+FN17SdhmGSSc2hrFyknPkt2rd+9Kaxnns3+g1f/vKX+cVf/EV+7/d+jyNHjvA//sf/4OGHH+bs2bP09PRc8v3JyUkeeeQRfvqnf5o//dM/5fnnn+cXfuEXaG5u5iMf+cg7uq1XQnbhKKXkecq5GbIrY8xqSZSck8Fkmeq7sk0NNNBAAw008FZxMw1iatQ0Gmjg/QnbttEraSq5GZTiAtgC7kDL+65+8PVvPU06naWrs40D+3bWs0XeCE3TmZ1bxDBNmptizMwusLySZGigh4MHdrNz+8hVFUaKojI1M0+xWGL/3h34fV5i0Qg7t40wNTPP1PQcTU0xdg9uZXiwF0mS1qmR1xAI+GlpaSIWDXPqzAVefvU4oiDS1BSrW1hfDYZaQC0uoZacUHZdyWEZKoLkQnaH8IW7r6omqigapy8skMyUyBcqqJpT5+jvbiJbqDA5myQY8BAJ+Rjqa6Ep5hAaoihyz60jV1wuOE4Sr5+eBRy1cizip1lyFDOxsJ8PP7j7kvuTYZpMz6dZSRa4Zc8AkiSyuJonHPSwa2sXiXiQWHjNxSPof/Ns2AYcXDdZkkql+NjHPsZ3v/tdBEFgdHSUgYEBPvnJTxKNRvnt3/7td2I7G2iggQYuC8vUUQsL1fC1Am5/E5LrzUczvFdx8vQFLoxOcOuth4lHfXzg4XuIhIOXffDTNJ0XXn6d1pYmDuy7vS5fBWekhZKfp5g8RyU3hVIuMb1i8tKpVWwLIiEvuaLC4X39DPQm+PqTpzl1YYF4NEA8FiDg93BoTz9DfZfvcL0Zzo8tMzufobU5TKWisbyax+OSiAR9NMdDlMoaL70+SSIe5PC+gXdE/m2YJrMLGcanV0llShzc3Ud/d4IDu/rq31lO5jlzcZEzFxdpaXKyDxZX8gT8blqbQ6QyJU6en+fQ3n56OuJ89qfvpTke5OLkCl9/6hQBn4dH7tmO2yWzfaSDSNBHLOq/pkwVVdMxTAu/101qeZLpsdfxs4hXqjjbD6Snv0di4N4b3jbXg3ez3/D5z3+en/qpn+KTn/wkAF/4whf41re+xRe/+EV+8zd/85Lv//7v/z49PT184QtfAGDLli0cPXqU//yf//O7RpZMPP9brI79/buy7hp0w0Y3QTfW3hsmqLpd/Wztc7OW2W6v2T9VM9yxcWwIas8+NZsfURAQxdr76qu49l6oZlgIglAN4V77V58urr0XBcdSacPy3vheXFv3G9f3xu8K1eU10EADDTRwA3ATkCWNmkYDDbx/oStZKrlZlPw82CZuX9MlwdzvVawm05y/OMHunVsIBQPs272dQMB/2QD3SkVhZm6Bs+fHOXXmAssrSXq7O7j3rlvZu3vbm+aZmKbJ1PQ8k9OzLCysYGPT1ppgbHya0fEptm4Zpre7g0g4xM5tI8Tj0UuWoWoaz71whpdfOU53Vzt7d2/jztsOsri0iqKotLe10NaawOW6PMllWwZ6JYNaXkHJzaAUFjC1AiAguYK4fE2XzXrUNINUtkQqUyKVKeLxuLhlTz/5osLRkzP15wKPR6azNYooinS1xfjwQ7uvORtV0w0y2TKyLNIUC9LSFOLg7j7i0QDhoHdD/Wb9e90wuTixzEqq4FjyWhadbdH6gNkH79h6zbbiDVwZ102WfOYzn8HlcjEzM8OWLVvqn3/84x/nM5/5zHV1LH7zN3+Tv/7rv+b8+fP4fD5uvfVWfuu3fouRkaszbg000EADdcVDZgKtvIrkDlYtt96/d4ax8WleO36aTcP92FV/6zd2fBRF5cSp8+zasRmv18OHPnDfhvB201CoZCYoJs+iFJYolFSmlixOjSY5fXEBTTN56M6t3HnLMMWSytxilmdfHmMpmefQ7j5W00Xml7P0dTXR3hq55m0vlhTOjS0RDfsZ7m+hpzPGaqrAzFwaGwj43cwuZhElkUO7++lsi/LI3dsJBa8vyPpaMTa1wqnzC2i6QVtzmCMHBuloidS31ed1s5LK8wf/9zmm5lJs6m/hE4/tp1zRODu6SK6oUC5rDPU109sRrwcEj08neeX4NBVFY8tQGwPrvHI29bde07YVSwovHBvDKs/THSsSklcwtSLxywyCWb7wN+86WXIj+w3XA03TOHbsGL/8y7+84fMHHniAF164fIDwiy++yAMPPLDhswcffJD//b//N7quX7Yjrqoqqrqm8Mjn84Dj5Wy9heyBN8IT6nzbyzAtm3wZskWbXMkmW3JecyWbQtmmWLEpKVBSbIqKTakCFa1KhKxltn/fQhActVYuX7ns9I7WCNl8mXJFv2TaD9y3E7db4i+/8folZIvPI/HIPdv5+pMnsSx7AwEjCvDzP3IH41OLPPXC6AZCRxTgo4/sYs+2Xn7jd772BkIIAn4Xv/2vP8Jv/M7XyGRL9aDw2vyf/zcfwbZsfvm3/noDoSMJArcf7OfRe3by659/vDpCem26zyvz//70ffyfv36JhZVcfR9rD1f/7GcewyMq/Nbvf4t1EQcADPcm+Mij+/id//mdeo5H7X4rywKf+cl7+ebTpzkzurThAU0Q4Bd/6h6w4Xf/6CmqISh185+25hAffWQvX/qLF6koWv1zoZpb8skfvo1zYwt875Wx+ue17/zgw3uIRfz80V+8sOEcFQSIRnx86P5dPP6dE2RzlQ3rFCWBT/zAAYqlCo9/59SGdSLAwV199Hc38dVvHscwzbVpQNDv5qE7t/P80TGWk/kN2ySIAj9w304sy+Ib3z2NgNP2NfT3NDEy0MZTL1xA051soNpkj8fFXbds4vz4ItPz6Q3noCgI3H+7c+389vfOXfJ77GqPsHW4gxeOjVMqrwsMF8Drlrn94BALy1nOjS7xRtx96yZEQeTply46YetQz6BoSYTZtbWboyemnID3de3kKB5HKBQrvPTa5IZ1CsDB3X2Egz6+9+oomrY+B0kgEfOza2s358YWWVp3HoKTyXP7gSFMy6oe8407u3WojeZEmFdPTDoZN+uOTTjkY+fmTqbnU8wtZjbMJ4kit+ztx7bhpdcmsWwbQVjLD+ntjNHRGuX0hXlKZdU5LtV98flcbN/UQTJdYGYhU19n7djt2tIJ2Jy9uLguk8T5Tizip605zOxCmlJF25ADI0si/T1NVCo688vZjech0N0eRZJdzC0kMS17w7x+n4tELEgqU6Si6PWNEXCI3bbmMLZts7Sa37CtCBAN+/C4ZFLZYj0LpfYdWRbrWSiqZmyYVxAEwkEPNk4/pLa+Wju53TKyJKJpOrB27bkc0Wzb3JD7I7z15bxbfZMGGmjg3YOhFlDyc1Tys1iG6hTKb1B+0s0MwzCYnJrj3MVx0ukswUCAUqlMKBigs+PKz5/fe+Eox0+eY3U1RWtrgh/60MPs2LbpmkPbTdPi+ZdeI9EUY2RkAKWiMrewxOLSKm2tzbirz1Wx2OVrBk898yJfffxJNE2hp6udlpYmolEnY6K9rZn2tst6zmJoRbTSCmppmUp2qqoeURBEF7I3jDfSd1WF49jUKk8+f45yRUc3TCTJyd8D0HST3q440bCPaNhff63hzYiSfLHC2dEl0tkShaJzL/X73Dx459bq8jYO+jVNqx7KrukGu7d2I4kCEzNJYhE/u7Z20tESIRjwft8/r91suG6y5IknnuBb3/oWXV1dGz4fHh5menr6upb1zDPP8KlPfYoDBw5gGAb/6l/9Kx544AHOnj1LIHCp/1oDDTTQAFRHg2QdGxpE6X3lK3ol5AtFXj56gsGBXm45uId0dmNR0LIszl+c4PUTZwHo7mqno72lTpToSpZyZpJi8iyV/DK67cIfSLBazDE2O4dl2YT9Htr6Iwz3txAO+sjmKoxOrpDLV9gy2EZF0enrbmLbcDvBwLWRGMWSwtnRJabmUrhdErLshLhHQj6KZZWKqhMJ+YhFnGDizrYoAC5ZwhW8MYF7tfaZW0zRGtaIRwN4vS4GehIM9iYIBrxkcmXOjS2xsJwjnS0hiCInzswiiAIfvG8nh/cO0BQLEgoafO+VMfJFhUjYh2lYTM+n2bPdsXzyuGV6O+N0tEVojl86gudq0NQSo+deQ81NMuBKIkVMsMDUrjzPysW/Y+uDX7hh4YRvBTey33A9SCaTmKZJa+vGh4DW1laWli4tGgIsLS1d9vuGYZBMJmlvb79knt/8zd/k3/7bf3vJ56urqyjKlbzfrx2GGL3qdMu2SRdgJWOxkrNZzVqsZG1Wczaq4SFfkZhcKPDGepLbLdMcD5PMFFBVY12BW0ASRTrbmzAtk4WldL1YZlerqG0tMQIBLxPTS2vBw7XluiS2bOphcnqJfHHjdUgAPvzoYVyyxJf/9rkNnwMc3r+Ju2/bxX/731+nUCyvS5e2STRF+P/+9Y/zlcef5ZvffX3DcmVR4ltf+XfIksh9H/s1TNOsB30LwIc/cIh/8fMf4Uc+9duMT6/Ul4ltk4iHefxP/g2Pf/NF/tN//Wq98OcUAUV+7Zd+HIBnXhqjVNloe3bbkZ189p9+gunFP+C1U+OOpX91/lg4xGOPPcbo1AJfe+a/rYV5VhUy+2+5j+GBDkTfqyRTOYRq4LsN3LZ5M/6W/bR0TfPa6OsbAqIjET+e5lvQDYMzs9/Awsa2bIcYweaRD92OEN/OVOZl5heT1fxlZ907tvYiNd+D1Czw3aeexhZsBMspJPsDHoTE/YhSmddnXkXTDex158ze2+5Bat5P0pzlzMWZeli1DQwNtCO13o/UEuM7f/W3WJaNZdtYtoUoiPzzX30QgG+99jwVRduwPz/w0FZc7Y/w+twkJ89NO8SNXQuk9/Hzn32U1PhJ/uTJCxtPJOCxH70Pd3OCP3v2ZSqVjb+1A7v7+NhPfIhnLizz6onRDdOCPg8/8akfJJ/M8AffWHcu2c6Chw/cyZaeLfzRk6fI5deOuSAIbB1p4kM/+jGe/4s/5xtPHaOecI1zff/4J/8RumHwhb8+sRaqXd3oz/zMIfYM3M4f/+ZvMbeYWrcrAgN9ER75xD/hxCtP8od/9UR9P23bKWA/9mM/AsAX/vmvomnmunltPv6hrex/8IP8+e/+F86NL1BvQARaE0Ee/OEfZWLmNf7TV/68GhC+tj8P/vAncLvd/O6/+nVKFWNtGgIP3DnA4Uc/xtf/z5d44ZULGwrjsaiHRz7xj0nPr/L5r36+urFre/vFww/SNtDD//oP/4nVVK52uAGBQ7vbOfyBH+Hpb32Vx7/1ylrzY+P3uXjwh38Uy7L4z//y1xxSY90x/9VPH6FvcC9//F//K1MzS/X5sGHzcBNf/MBPcPrss/z+X3yr3raCIOB2SXzjYz8JwO/8xr9F1Qxq1weAn/mRg2y67Xb+6s/+kJOnp+vtDzDQ28IXP/AznHr1PP/hy3+2/lRCEAS++ec/D8Bv//ZvkcuXN0z/6Af28dN3PMjj3/4Kzzx/ZsO0ttYYf/S7/4zs/DK/+rnfuyRe6g//y08QT3Tz//3+/4/l1dwGwuPe23fwS5/6Qf78j/6ev/nmy7UGBGyikQB//gf/AoAf/vivV6c5Vxfbhv/wqx/l4K4RPvvPvsDMXLLWggDs2trD7/z7T/I3f/YEf/IXz2zYHq9H5u//7F87y/2hX6sTibXFf/qn7ucjHzjCT3/mvzI6ubBhd/q7m/nD//Jp1PwcrZs/hOYeYGVlhRuBQuFK2S1Xx7vVN2mggQb+4WHqZSr5OZT8DIZWweWN4fZfvtD+XsQrx05ycXSSrs429tx1K12dbVcdYJovFPF6PNxz5y3s37udXK5AT3cHknRtz5MTkzO0tCRwu1x84OG7iccinLswztT0HJs3DTA81LfB0WI9cvkChm6wvJri1JmL9PV189ijd7NpsPeK67NMDa2cRCsnUfJzqMUlTK2IXc0ecXljVyXFTNMilSmRK5QZ7m9lYTmDYVq0NYdpawkTCfno6XCyXrvbY3S3X50ssm2bQkkhlSmRzpbJ5Eok4sE64VIoKrQmwmwZaqMpFiAU8F5yPApFhVdPTpPKOMoRl0uiNeEQRaIo8sH7rq7qaeDt47qri6VSCb//UoubZDKJx3N90rVvfvObG/7+0pe+REtLC8eOHeOOO+643k1roIEG3uMw9Qrl4jxKbgZTV3D7E+8byeyb4envvYzb5eLQ/l2XTFtNpnn+xWNkc3k2Dfezd9c2vF4Ptm2hFhYppUcpZ8Yp5FJMLmpMLKhUFINgIM3icpaSoqFrFm0tUWIRPwvLWcJBL51tUXZt7aSi6OzY3IlhWtflg5nOlXjyufPIsoTLLTG/lOX4uXksy2bfjh4evnsby6sFOtsieNzvjH9ssawyMZNkYjpJriKzZ3OUeDRAe3OEzlYnRP35o2NcmFihUFLYubmT7SMdPPXCBTYNtnLXLcNcnFzh7548xYcf3I3P58LGpqcjTiziJxpxRpvUPLD377xyRw8c0iZbqNSD3Yq5JEFphc5IHiU/h9+28F9LBInooan3dgaP/NK6gvO7gxvZb3greGPn8838yC/3/ct9XsOv/Mqv8NnPfrb+dz6fp7u7m+bmZsLh8Fvd7LX1d++iML0Vb7gLW44TinUiyR4EQcBGdEbFSS4sS8fQSphqHkMrYpkatqlj2QZYBrbtFLaqA/udV0Gq/hNBENdecSQMzt/VV9b+dr5Tfb1C+5qmhW6YmJaFZTqFc8G2CQY9YNv8wj/aW21bp7CObROP+nHJAo8c+UkqFbVewrNMk4DPRThgsuXnD/BzP7QZ27Lq84kSRIUxbMvmy7/zQUzDwkZAIY6HNJ3NfqTSSb7wy7eRypQxLbuq/IFY2INUOslDBz10/9p91CqWdrWhpNJJAD7/L++gWNHrJIFp2Yz0xZFKJ/mln9jKxal2BGzsagG2KR5ALp9jKGby65+6jVrRtlaB3dRSxKVO8Ks/c5hktgy2UD0mAlsGW/FaC/z4h7eyYzhSPWYCtu2MVA+IGfCIfPaT91zi53zH3haCcp5f+rl7mZ5PbZi2dbidoJznsbuHiPo3zhfwefBKZYJynp/9xK3VYvIa7j7QQVDO81MfO8DFyY15P0O9zQTlPPce7MRU794wTZYkgrKjwPiNzzxKvjqKrnba3LKnn6Cc5998+gHOjy0hiiKyJCKKAq2JEEE5zz2H2vjNX37MyXTHdkgNG4ZaZUQxz3/+1Q+RKyqYlu2oIwyLvdt7CMp5/sXP3Mmpi5twyxIel4QgCMQiPoJyHm8C/tMvfwjLcggnvXrO3rozQVDO86//6UMsrmYRbAFREhBFkV1bugjKeX7uEwfYt60FxXTjkTRsyyIccpZriRa/8gsPYmHjkiVndL0kctehYYJynt/4xYdZXM4654NgIyAy1Oes8+MPjTDQ4cW2nfPTsCzc8lob/r+fvIdSRcO2wLAcr7tH7tlKUM7zkz90kDOji1iWjWFZmKbF1qE2gnKe3UMRfuC+HXWyThDA63YR9zuB3h99ZFf92NSOz4ce3OGcL/duob3Ju+7sXjvm8YDKY/fvXLO0Ex1iYtdAEI+c5+Mf2EMqU0CUnOuLbnu475ZugnKeB2/rJ+S1EURHiWXZNu3NEYJyHkXV+ccf2o9cDSCtnS/3HXHm/ceP7WJhceP9dN+OXoJyntv2tCEYt2PbNmaVbIlF/ATlPIZp8smPH8Y0rXXkg8AjdwwQlPN84tHd3L63Z4NSY6i/laCcZ8uAn//nJ+4EQUDCKVb4fK76sfmXP3sPmu4QtaLokM5bhp15f+6HD/ADd29y7AFx9jUcdM6X/haJ//jPP4AoCpgmdZKtr1lAE8v8i0/eRaZQcc7Tqq/hthHnt/xDD29m+3C02n5gWxah6nlomhb/8ufuxbar7Wc56qf9W6PV38bdLK3mHbIVG8uG4T7nuP7gfSN0JNx1NYuAgM/rru/rr/3iQ2iq6WxT9Rp++wHnN/fZT97J5JwT9l67PPV2xAm5CogskYiHibS2caPg9b41hfG73TdpoIEG3nmYhoJSWEDJTmNoBWRPFF+46d3erH8wFEtlggE/O7aNsH3rpisSFLZts7ySZHpmgamZORaXVonHIjxw7220tTYTj0XfdF2WZbGwuMLk9BwXRydxV5/dhwZ6OXxoD5uG+hgZ7r+sdXaxVGZxcYX5xWUmJmdpa23m8MHd3H/PEVpa2knELx1Ib6h51NIKSsGpDRlqHsvUESU3kieEN9JzVfVIMlPkleNTzC5kWE7mKZVVhvtb6e1sYu+OXvbv7MPvu77cz9oz0IXxZU6cmwMgFPQSjwZoijltHw766mrhGjTNYGI2yexChmDAw+G9A3g8Mh63zK6tnZfkjjTwD4PrJkvuuOMO/viP/5h//+//PeB0uC3L4nOf+xx33333m8x9deRyzsijeDx+2envtNVFDZZlYdv2DV3mexmN9rp+NNrs+mAYGkpxhWz5LIZawOWN4g4514nLBX+911Hz/zx3YYxbD+0lHo9y66G9BAJ+ZJeMbdtr55coggBut5tHH76bpngMUy2QX75IKTWKWlzGsjVsMcyzJxUuTq6QyZZobgqxa2s33e1RTl1YoLczjmFY5EsKiyt5cnkFURSoqDoP3bkNj9uFhze361FUnYXlLAM9zURDfuKxABcnVxifWqU5HuLIvgHaWyLYNvi9Hvq7nQfWd+IwT8+neOn1SVyyRG9nE7taekBP8fzRcSZnU/R2xalUdI6dmmY5WSAa9tHT0URfVxxVNzh5bp4v/un3sEyLzUNteDwu/F4PH3l472XJnTfug2GaZHMOMRLwu+lojbKcLPDq0deJe1Zp8qZokpwRk0ruksVdAtkbp2S3cXHJT0tUZNvWjxHpvKWaH/H2rzVv9Xr1TvYbroZEIoEkSZeoSFZWVi5Rj9TQ1tZ22e/LskxT0+Ufrjwez2ULK6Io3pAsnfYtH6Z9y4exLIuVlRVaWloQRdGRmJeTKLk5DDXrFPDcYSRXoNrGBpahYBlqlThxXp1/OralYeoV5zv1zzVsywQssC1sSwfbwrItwKoW6mrvqzqI2ol9ud+oABLOv5rHkV5yXqO+WjWyqgARAKuIpQuEfQLhgKdKyAiAhCCKCIJE2BcmEm9DFGUQJQRRRhRdiKILQXLR1CMBDumTr7iI+O26PX7zgFBfX32d69C7TXB+K7ZVHa1e03rAXf1V4sxev+/ObyLeDbsPOfPapkNM2Zbh/LNNfuDRQbBMZ7ptYFvVddgWB3b3g23Wl11rW9so05mQ6Liz5w1tb6MW58C2+OBtcUfdYVWzYCwbyVigkhPY1m2yqSPiEGNV5sfl0lDys0Tc8OhtrWALmJaFKEmIyOh6AcPIcv/hbue6IQhOWyLg88rYls6WwWYGepzfQm2dHreMIEAw4OGOg0OOqsS0MavXjFo7D/c3byRhbKqezNAUDTDU14xpWnXSIxjwIAjgckmX2BLUlutYbvkBwVlvVWXj9bvqNkjN1XDN2qnqqm6vKAp1uwTLtnFV53W5RGfeqJ+yotUffkVRwOt1VS3NXLhcEqboxiUa2LaI2yU5dmiSiNsjYxhmtWAPpmVS4xpdsohhWnXSQsDZZ0FwVCSabtT3w7bBFMU1G7CA95LrisfjbFNHe5RsYaOaq6Mt6rRvU4DNQ21Y1bY1LRtJWvsNDPY0k86W6yoNGwh43QgCdLVFWe1OAGAYDqEUCfsRBMcOqqnevna9eF6zpPN6ZQRRdNZpmui2iGk6++pxy9VlmlAlW2rWcYIApmWhaBut8WzLQhBA00yKlaqsssr+6qaJIEBF0VlYyVbbt0pOVOcDWE4W6jZote02bGfe1UyeidnkumnO+SUIUCmrXBhfrhOmlm0T8Ll55O7tAHzvlXEqykapZ0drhNZEhKOnZjh6crq6uY48Z+eWTrYMt5PMFPjO8xuVOx6PizsODiHY8MqJabLrFCu2bdPbFUcQYHRymRePTdT3TRAEBnubuf3AELlimRNn59emAZIk8cAdWxAEOH1hgaWVfH2iAPR0xBAEWFzNMT69CrZDWduWsy9O+2qcG11yCKfqRkuigP92Z7kzC2lm5zPVfCrnnKjNK1D7Ld24zJK3uqx3q2/SQAMNvPOwTA21uEQ5O4muZJHdYTyh95dd98XRSV569QQf+uB9VyRJwKkr/OXffJNUOkshX8S0LAJ+H03xGM2Jy9dl34jllSRPPfMSpVKZckVB0zTaWpvZvnUTm4b6ADaoUjRNp1xRiEZCZDI5/vbr3yGXK5JMZ4hFwvzwRx/F7/cRiYbrbhmWoaKVV1GKSyi5GfRKGlN3LEIldxh3oBVRupTcMAyT2YU0U/POvamvJ8EdB4e4ML7EC8cmaI4F2TLcRm9HnKG+FtxuGbf72svktef58ZlV3C6ZPdu66Wp3Bpi+WQ5pJlfmtdMzpKtWp51tUTqqNuZul8yR/YPXvB0N3HhcN1nyuc99jrvuuoujR4+iaRq/9Eu/xJkzZ0in0zz//PNveUNs2+azn/0st912G9u3b7/sd95pq4saLMsil8th2/Y7Ehr8XkOjva4fjTa7Nti2jaHmUArLZLNZlKAH2ZNAqACVy3vPv5dRKpWZmJxhbGIKVVFpa2shnauA6AHRQ6liUqpU0HSDo6+doVjMcd/dtyGKXg4cPIChZJkZPYZaXMDQytWRF4NoBpw4PcWJC2nmFrJYtk1vT4zB4a1YlkU00c2LRy/g87pBcqOqCpYm0d2Z4MBAJ5LPR9G4+rYbhsnFiQVGJxawbZtwUzdej5uS6iOVs3jw3ls5tHdTvRP7Zst7K1AUjam5FWRJZKi/g0DEy7atfpqawrz46nm+8dyLhAIigz2tNDV38NKJaYolBb8vyMF9fQz1t9PS0U1eN3nyxVlWkjkO7d3BLfs3EQkF1rZbdAKvr4bZ+VVeOzWBaVqIImzuFhCKp1EyZ9kRuwZmpArL1UZSbcEb28rA8DbcFZW9vQZua5VcWUK+QTYX8NatLt6pfsObwe12s2/fPr797W/z4Q9/uP75t7/9bR577LHLznP48GG+9rWvbfjsiSeeYP/+/VcMDny3ILuDyNWsJr2SRi0uo5WXUQsZBMmN7A4ju4PgvvID0uXgFO1NbMusFv2dV2zb+ds2q8V+s0os1AiF2gPo+vdVVKufAusVLOCUDYU1tYogIAhS9f06xYsoXXV02OVg2TZ6tkIw6luzwLpJUW/zKoFiW2adGLHXESl1Iqd2bOrHpUa+2ICFbVl1wsb53KwSZCbYJpZl1N876zMwDQ3DkEAAj0vAxoIqcWbbFqZaxKweb0+dLKsSITpUss6+RKu84ZrySEQpLCAIAh1xCUGQ1xRJgoiAjqEZxIICsVC0evxrZJaIbdt43C7uODh8xfar2RpcDh2tUTpao5edJkkiD9yx9YrzHt47cNXl/uBDeygaYYJy/hLi7SMP7wEur2Tbu72Hvds3qnNqaIoF+cGH9mz4rNbWAA/euXUDkWJj10mHTf0t9HTEHRu0qvKqNi3g93DPrWuZkG/cpiP7B+tZNM46HRs0gN6uJkchWbd8A6/XuR76fZ46WbAeUlURcvfhTZRqpIYNZSNIV8Kub28ktNEWo1ag8HpcPHb/pRYTPo+z3jsPDbNnW/eGjJB4xLkPD/e38LEP7KvvBzgkFDgWnj/40O4N+woQDTlk3G0Hhtg23FHdXGfmGlHX0xnnYx/Yu2G5668tH/vgXsoVDcsCy7QwbYvmJsduc+fmTqJhH6ZpYVnOOdHW4igPE9Eg9xzetG7wj0M+1FQ8tx0YolhWEAQBqRoiUpt3uL8Fy3IsRMBR6TRFnXYIBbwc2tNXP1/W8kmq6rb+ViIhnzOtSmwEA55qWzrWp+I6JVKsulxJFOlojWAYtWsUdes9gKZYwMkfqpFVAoQCN59S493qmzTQQAPvHCzLQCsuU85MoitpRFcAb6jruvuQ36/QdZ3xyVkujE6SyWQZHOjdQJTous7c/BIzswskU1kefsBx89m1Ywurqylm55cY6OtmcKDnqkTJymqKyak5ZFli357tuGSZTUN9tDQ38d1nX6a3u519e3cQDDj3T9M0WVlNs7i0wsLiCql0hkRTnEcfugvZJePz+jAMg6HB3dxyYBd+vw/bMtDKKZR8llR6DrU4h6EWsC0DSfYhuYO4/IlLjq1t25QVjYDPw99++wQvHpus2/M2xYIM9Tv2a3u29XBgV981h7E7++Godt1umWS6yNFT0+QLCrZtI0ki+3Y4fbtgwLvBklw3TFKZIiupIqupArGIn73be/C4ZbweV5VgieH13FzPmu93CPb6Hvg1YmlpiS9+8YscO3YMy7LYu3cvn/rUpy7r5X2t+NSnPsXXv/51nnvuuUu8Q2u4nLKku7ubTCZzQ6wuarAsi9XVVZqbmxuF7GtAo72uH402e3PoSo5KbgqlMA+2SEEP0hQLvK9GhEBtpKaFJEm8euwUF8cmGezvZsvIEJE3hLebpsmFixOcOH2eXK7CoQPb2bFlAL20SDF5HiU/i2kouDwxZG+E5WSek+cXyOZK5PIVzowuMjzQyub+FlxuGbcsccvegXoAWU9nnAsTyyiqzqb+1muWpk7MrHLy/DyKomMDyXSRjtYID921Db/XTa5QIRLyvWPHdnk1z/jMKvNLWURRoL0lQjTsZ2Y+jSgKZPJlVlNFkAIMdfvZvbULr8fFK8enmFvK4PW4MAyLbL7C/bdvZstQOxfGl0jEQ/URtW8GTTPq+SsVReNr3znOcLtBzLWKVZlGtNU3XwiAIOEKdpHVmplM+impEk1RZ8RwV1us/rXCyinatvwgHdt/+K002WWRz+eJxWLkcrnrvue+E/2Ga8GXv/xlfuRHfoTf//3f5/Dhw/zBH/wB//N//k/OnDlDb28vv/Irv8L8/Dx//Md/DMDk5CTbt2/nZ3/2Z/npn/5pXnzxRX7u536OP/uzP+MjH/nINa0zn88TiUTeUjtdDW9UllwOpl5Gr2RQikvolTSWUakSJyFE+Z37jd2MsGybdLZC/PuALLkZUG+vsKtOsNjriJkNJI1VU8/UyDRjnXKm9rmxTsWkO5ZwlrZG2mCtI4BqeQprxI/z9zqVz7p8iRo2JIIIIvBGwk28AhG3RtqtfXb954htc0WypIFL0Wiv68N7rb1s26CUPM/AkV8m2rHvhi337dxz362+yRvxTvQbrqXP0MBGNNrs+nAztZdtmailFSrZKbRyElH24vLG3tXMxjfineyXWpaFKIocP3mOE6fO0d3Zzqbhfjo7WhEEAcMw+O6zL7O4uIJuGPX6gizL7Ni2iQP7dmIYxlVV8aVyhXPnx5icmqNULiNJEj6fBwER0zL56IceQhRFNE3H7XZRKJbQVI2mphhz80t857vP4/F46Ghrpr2thY6OVgJ+H199/Al0w2D/nh30dkZRi8soxUWU/Cy6UqRkRvCLyfogscupR2zbplhSWUkV+OYzZ9i5uZPbDw7z+pkZlpNF+rvi9HQ24fNeHxlRKCosJ/NkcmXS2TL5YoWBnmb27eihWFI4P75MLOp3rLdDvnrbqZruDCzxuJicTfLqienq4B+Z5qYQXe1RejtvvB3ce63foCtZDCXDyD3/EW+444Yt91rvuW8pEbmtre2yCo+3ik9/+tM8/vjjPPvss1ckSuCdt7pYj5pE+N2+8H+/oNFe149Gm10eTgDbLJXsDJah4vEnQHJTylacNnsvXPmvAeVyhYtjU4yOTTGyaYCd20fYtX2Evbu2XHGE+99/5zmSqTSDA710HGymyZslNfY4aslRF7h9CSRfC2cvLvLcq8c5N7ZER1uUjzy8h7/8xmvs3NxBJOwnlS0RDnlxh3w8+/JFllbz7NjciSgKbBm6dp/pWsctkysxOZMknSuj6QatCScozVFVCMQil9qrvF3URvOmcyWeefki4ZCPSMiHqhvMLKRZXMnhccssL+dpb42gqDpjs1kq5QztLWEGehKIokB/d4J0toSiVujqiOKSHZuVzW/SDqWKysx8ut65KpVVomEXh0YkcisXOJCYQdAN0B2Tm6tC9OCNDuAK9hFv28RSssz4yWn6upro7Ypf3p4Gu2oz8+5bXcCN7zdcKz7+8Y+TSqX4d//u37G4uMj27dv5xje+QW+v43W/uLjIzMxM/fv9/f184xvf4DOf+Qz//b//dzo6Ovjd3/3dayZK3m1ILj+Sy48n1IGpFdGVLGppGUPJoitpQER2B5FcfgTxLXUBG3ivQ5Tf8fvsmoLG2ECYOBZlNXKGDVZm9jobM6rz1j+zTCebx9SxasRMzVaupoTCwra0qurGqipnHHIHy7pEDLVhe+ENBIvzHiQMBHQj61wf30jAiBJrWTUNNNDAzYZ3q2/SQAMN3BjYtoVWXqWSnUYtrSCILjzBtvdFH9f4/7P3X0Fypfl5N/g7Lr235b0BUPCu0d6O6RkOOeSQopZGJCOk/T6SywtRoVjxkoxgUCEqFNrYiO9CFysGdzWUKFJDUkMzprunfQNoeI/yPqvS+8xj9+JUJYAG0A30oBsN9PlFVJdB5sk338zO8z/v8/6fR9eZX1jh2vQ8vT1pDuzb1QlP9/vsrs18vkg8HkWWZTweN+l0nOWVDLIskYgnGRsdZGigFwBZvn3OypUq9XqTnu4Uuq4zPbtIb3eabD5Prdag0WgzNNjLxNhQ5zrR5VLI5Yv84Mdv09OV4sXnj9GVTvCtV18iFrPzQJdW1jANDbVe5dDOMG6xjtl4j/VLNQytBohILj+KN4qbFF7Zd8vif6OpdjZtvnX8OldmNiiWG1TrLTxupdN9eWDqzl28d51Tw+Dy9XV6uyLEowFWMkUuXF0jHPQSi/gYHUx0OkYDfs8tWaSVWpP5pTxrm2Uq1Sa7JrrZM9lLIhrg0N4BkrEAocDdQ+Ydvnjc96fI8PAwv/Zrv8av/dqvMTk5+cl3+Bgsy+L3fu/3+N73vsdPfvIThoeHf6rjOTg4PLqYhka7ukajOI+uVpA9UVw+2yP7y5RL0my2OH7yHEvLawiiwPBQP73dKQA8ntvF4oWlVRLxKAG/j727R5H0KLK+QSG3Ql5fQFa8uAM9iJLCRrbC3/zwXS7PZCiVG3g9Ln75Zw4yOpjit3/tOd54/zo96TDRsI+5pRxLa0WiYR/HDg7T3x297bHvRiZb5u0TMwiCwM99dR8BvwcL2DXWxdEDwwz2xlDkB7/Tx7IsNnNV5pZyVOst9u3qYzVT4tjBIWRJ5t1Ts+TyNYIBD7/0zYNs5qr83Y/P89YHM/h9bkYG+3l6f4rJETvP4vDeQX741mUCfjcHpvoZ6ot3rEVuptXW2MxV2chViIR8jA+naDY1rsxkSIRFUp4MHtcafilPcX7LBuMTnougBPFGxtCVXhazMqszFdJJN8/1uuhOKXzr5T2PjND6IOuGT8Pv/M7v8Du/8zt3/Lc/+7M/u+1vzz//PKdPn/6MR/XZIggCsjuI7A7iDfejq3X0dhm1kUdr5Gk3NsE0ESQFSfZtiSdfnN13Do83HcHhc1rMsLYt5LbFl47QotsZM6Z+I0uGGx00pqHbXTGmjmkZWLqKZWpbGUAahtFG0Oz/b7aFGDAxb7I4szMfbnTGdKqZrdBtW4iRtqzmbAsyW4yRbliVdX6WfupuGAcHB5uHXZs4ODh8eizLQmvmaJSWUGsZEEQ7s+JLIJJUqjWuXJ1lZm4RTdPo7ekilbS7FLbXCkzTZG5+mTffOUEiHmHn5BjPPnWYYrFMOpVkZLj/Y3NMANYzWX702jtEoiFeCT/FxkaOX/7ONxBFkTfe/ICdk2OMDg/gdt/o9FhZzbCe2eT6zAKRcIinjtnWlbIsEw37KGzM8f4Hx1mYn2NywMPEgA/TUGmJIpLsQ1R8t1hrWRbobYPlzQKlSoN8qc7KeolytcnTh0Y5sLufSNiPYZoM9cUY7IszOZru2HLeK6qmUyw1OHdlhXK11QljHx1MMj6cQpZuvUayLItytYnHreBxK1yZWef8lVVcikxfd4Rd410kY7aoEgx4CAY8tz2mwxef+/40+b3f+z3+4i/+gj/+4z/mwIED/Pqv/zq//Mu//KnaVX/3d3+X7373u/zt3/4twWCwE6oaDofxeh3VzcHhy4Blmaj1TRrFOdRGDkkJfOkC2G4mly+SzRU4cngvo8MDuO4QFA6wtr7JqbMXyeWK7NvVy3AaxPx12q0ibUFCUEZo6SnmZrMUytO4FZnLM+s0WxpDfQnos+jvjuD3eWi2VKJhH88dHSMRC5Av1skX6xzZN9g50d8Ll2fWeefEDNMLWdqqRn93jFq9zdhQkv/Hbzx/x9DzB4FuGEzPbzK3lGN1o0S91gYBCqU6oihy4mwdQbCDjBEEGi2VS9fWmBzt4ui+IV56cpKh/gQNI0x+Y573T8/x5MERomEfzx8bJ50I3fH9uJGtcPbySid4NRjwYOgmWjNHqzzHwVQOq521W0fuQdfQxTCh5CSR9A5qqp/3Ts3RaNYJh7zs3dnHUJ/tHbvtA/6o8CDrBodPh+zyI7v8eII9mIaG3q5gqDXUZh69VaJdzwAWouRBcgWQZKeod3h8sD8z5QcuzpiWRb7UIBZybeX83BBjTEO7Y/ZPR6TZyqUxDQ3TaNtfWsvujDFVTMMADDA1TEu9YY1mbnfiGB8ZzZYUY4El3KEThptyYrbtybjx861CzU0CjYPDY4pTmzg4PJqozQKt8hKt6jpgofgSd7Rmepxotdo0my2i0TCtVpv5xeVOF8nNokcuX+TS5eusrG3QbqsUS2Xcbhe6YdcM0WiYaDT8iY+3mc3z+pvvo7hkvG43//N7/4goivT1duHzeXnx+WO2rXyuwGa2wJ6pCQBOnb1Eu91meKiPg3vGMFsZKqUizfIaFy5d4uLVZWTRYv/OLgYHEsiKH1G+fTPo+maZYrnBzrFuTMPiJx9Ms5GrYJgmbkUm4HeTL9WxLIu9O3qZGu++44bGO2EYJsVyA0WRCAe9rGZKvHNyBrDz3V5+ZrIjttycZ7KZr5LJVigU6+RLdXTd4MDufiaG0/SkIwQDHrqT4Xseh8MXn0+VWQJw/fp1/tt/+2/89//+35mbm+PFF1/k137t1/gX/+Jf3PuD36UI/6//9b/ym7/5m594/4fpC+5wA2e+7h9nzrZ3hORplBZpV9cRJReKN3bH3c1fBu95VdUwTROPx41hGEjSnXd5l8pVjp88y+raOmGfwUSvQMhVwtRayO4QlhTi/LV1TlzIsbCwSDZfZedYF/un+nG5JDayFVwumcHeGKIosrRWQBJFAj43LVXja8/t+lQLJOVqk//X/+d1Gk2VUMBLTzrE/qn+jw3e/WkpV5v4vC7ml3K8c3IGSRKZXsxSq7UYGUjwq98+SqnS5J/evARAo6HRVjXCIR9PHhzutM6qms71uU0uztVAr9DXFeHYweHbdpFso+kGiiyRL9a4NL1Of3eEUnaednmOsJLFI7XuafwWoAlxfLExEt27yFZEWqqdB6NqOuevrjLcF+/sbrkfqhvn6dr1HXr3/Mp93/du/LTn3AdRNzwKPGq1iam30dUqWquM2siit6uYRgsQkRQvkuJ/JC9CvwznjQeJM1/3z2c5Z9YtGTF2p4ppaDesym7pktE7nTBYBqap26KL3u7Yktm327IpM+1MGDoZMYbdfdOxJtvqsLFMtvsgt59dJ4L8ZjGGLVsyAftncVtsuVWAAZG6GSMgVxFF5z32STxu3uNfxMwS+GLUJk5myRcDZ87uj897vrRWiWZlhVZlBcvUcXkTd1xo/6LyaWuGi5evc/rMJRKJGN/42vP2sbasrkvlKssr64TDAQb6elhd2+DU2YtEQkHK5SqFUpmvvvws3V3JTx7f1jHPXbjKmXOXyGSyJBIxksk4k+PDjAz1I8sSl6/OkNnIktnIoes6iqLw89/6Ci5Jp1peR9QrtKprqM0chloHy6TaNHnjxBpjI73s2TFwx1B1wzApVRqsrJc4eX4RwzDZu6OX3Xv3I6hZTl1YJBELkIwHiUf89yVKbOQqLK8XKRTrlKtNTNNidCjJ4T2DNFsqmWyFeNRP0O/BsixK1Sb5gi2K7N3Ri8/r4uS5BVY3yiSifuJRP/FogFjEd9f1gofB41Y3POzMkk8tltzMBx98wG//9m9z/vx5DOOjO50+Ox61BYnHFWe+7p8v+5zp7QqN8hKt8gpgongTiNLduw4e10Ucy7JYz2SZnl1gcWmNHRPDHD2874633RZQsplFfvzjHzLWZZIMqSBsZZEofjTd4IdvXeat4zOIig+MJlMTPRzZN8DkSBdtVePkuUVqjXbH19M0LVyKTDIW4Oj+IdKJe/8srdVbnL2yws7RLsIhL9/93kkURWKwL8beHb0E/A9+d7phmMwsZvngzDwr60WG+uLMLGxSb6h4PApej4tw0MOTB0bYP9VHo6WyvlHm/dPzxCN+RoeSDPfFcbnsIs2yLP7xjUvUmypdPQPsGwsQCt4Yt2nau0/ypXqn40ZVdY4d6GNl/hK94TJ6bWlrcfmTMS0ByddLrHsnvugY1abI/HKOxZUCqqbTnQ7z3NHxn3qevohiyc08rLrh8+BRrk0sy8LQ6ujtKnqrhNrIoWt1LEMFQe5konzc5/UXhcf1vPFZ4czX/fOoztl2Zgw3d7uY+tbfbxZiDG5kxmg3iTQGpql+JBtm+/bGja4ay+6eAVuUMQ2LBgm81joCt15+2kZlFhbCHTthbu542c6D6ViSdX5+vOrpx23R44sqltzM47Sm8WW/1vw0OHN2f3xe86W3K7QqqzQry5h6G8UbR1IePReaT1MzrK1v8sPX3mbHxCj79+7E43FTKJSYnl1kZTVDtVZDkiT2TE0yPNSHx+3G7XbxwYmzzC+ucOTgHsZGB+96/Fy+yOLSKguLq4yNDhKNhnC7XOi6QblSRTcMXIpCtVrjyKG9APz13/wAn89DOuEnERIJuFXU6ipaq4ihNbAsE1H2YFhuZlZq7J7sR5JE2qrWcZkwTZNStUm9oXasvr/3T2dptjVK5QaarjM50sVQX5zewYn7Og+qqs7VuQ3yxRq7xrtJJ0Jcm9tgbilHLOIjHvETi/iJhLy3vW8/vLDI0moBTTM6uaoH9wwQC/vRDeMLJYzcicetbnjYYslP1Y9+4sQJvvvd7/I//sf/oFwu84u/+Is/zeEcHBwec+zw9hVa5SUMvYnieTSLnQfBymqG94+fod5oEA4FObBvF6PDt3dh1OoNPvzwNOsrM7x4OIFWX+fYWA3JFUDx9HQWLeuNNuvZMrpu8ju//jyhRD/5jQVW1oqcOLOIIAhMDKfZP9XP6YtL7BhNc/byCuGgl7GhFL3p8D0VuvVmm9nFLGcuLnN1ZoOWqvHSU5O8+uIUh/YO0JUMfapOiE/CMEyuzW1wfX6T0xcWabRUupJhDkz1c3DPAIVSnWQsQDjoRdMMlteL/OMbl/j6C7sYG0oRjwY6QfK6YXBtboPB3hget8LBPf2EAl4MOQHtTRZX84iCQH9PjEZL48fvXLXnxqgTlDZJe/I0514jKVio5U8eu25KNEnhjYzSM7gbt9fehVJrtPnhWxdwuxVGBhIMD8Qf++A3p274YiMIArIrgOwKQLAbyzTQ1dqWZVcBrZlHa+btYG5B2hJPvI9k54mDw5eVzzozxrKsTtbLzd0xpqlRrOiEvQawZVfWEWU0sKyt3+1uGHM7L8bQsAzVFmhMuxPG1NXOsbfzYSzL3EqGsWWXLQXmIzkvEoK4JbxwNwuybXHGsSL7suDUJg4OXzx0tUarukqrvIyhNVC8cVy+T+6QeFxYz2R5850TJBNx4rEI1Vodj8dNsVRhaXmNvt4uYrFxGs02SytrnD1/mSeO7Gfn5CgH909x9PDeu17bzy8s8+Hpi9QbDQzDBAFOnjqPLEscO3qAHRMjaJrGW++cBCCRiKG1a+jNHC8djtKuLKG1rqPnG5QBSbY70RVvHEGUWFjJc/bSMqZl0d+TIBbxY5oWH15YpFhqUKo0ME0LwzB55sgoA71xjuwf5L0P54iGfQwPJDi6bxBBEKnp9zZfumGQL9Q5eX4RVdNJJ0KdLtbJkXQnk9SyLEqVJrOLOXLFGoVSna8+twtFllAkifHhFN1JO8P15u6VL7pQ8riQyZYpVZqUq00q5SLP7Lv33NwHzX1Xydutqt/97ndZWFjgxRdf5N//+3/PL/zCLxAM3ru3vYODw5cH01BpVdZolubR1SqyO4InGH/Yw/pc0XWdxaU1ZEVmsL8Hn9dDT0+K8dGhTijbzTTqdU59+AEXz5/CUvNM9HtpFGu4/TG8vhSCINhWTZcWePODaWYWs3Qlw7z41ATxqJ/j52a5dOkKtUabWMTHh+cX2chWyWTLvPribgI+Nz3pyB3bYG9muzXVpcj0dkW4NrvB935wFkWWSCYChANe9k/1I0sSUxMPTvEHu5iZWdgkX2xwdS7DSqZItdqm0VSRZRFBgO6UXcyYpsmVmQwnzy1Sb7QJ+j0MD8QxLQsROreZW8pxaXqddlvH61YY2Aqb//D8Iqv5eQSjimVZBP1uavUWxfwaT4yWUfRV1PrGvQ9e8hNMTGB5+nEFevH5vKysFzlxYY1GU+WbL+0h4HPz0tOTxCP+x3oHm1M3PLoIooTiCaN4wnhCvZimjqHW0NUaWrO4JZ4UsUzVFk9kL6LsQZQ9ziKjg8OXFEEQECQXfERENS0LRWvi+Sk6cba7W+4kqtzofNFu/LtpYBhtTL21ZUumbgktGpZl2bkxlg7mDesx6xYrMms7DaZjRdbJhUHY6mqR7E6XbQsy7M6XG+KMdKsI4/CFwKlNHBy+mOhq3RZJKkvo7QaKN4rH+/ivG2w7ThiGQX9fN16vh2QiSqlU5d0PTrF/7y6SiRjDQ32Mjgxw5txl3j9+BlGUGOjrZu/uHfT12ILAzZmnpmmysZlnYXGFnp40g/09uN1u+nq7qDeaLC2v4fG4GR0eYHx0kFgswtXrc7z/wYf0dwU5sDOJ0dwgc+UvMdo1LMu0N0u5Anh9yVvOa9Vaiw8vzLCRrSAKIoGAm+vzmxw7MIwkiWxkK9QbKoZpousGkiTy/ul50skQfV1Rnj82jtslEwn5tubkznO13ZlSLDUIBT0kY0HWMmXeP22LLS88OUHAZ1u0qZpur4eE/Zimyd/+8Dyqpnc6R3rSEUzTBCT27er7bF5ch1vQDYNKtUWx0qBcaaLpBk/sHwbggzML6LpBOOjF73XZYt5D4r7Fkh07dnD48GF+93d/l3/+z/85XV1dn8W4HBwcHgNMU0etbdAozqO1CkiuL194ezZXYHp2gfmFFTRNY2J8mMH+HmKxCE8fu92GQGsVaZaX+d73/o5yKcf4QIgdExN4A/FOMVKrt3jr+AzvfDjDwkoBlyKxe7KHX/n2EXpSEX701hV+8N4cA2kfY0MJNN32IK032xzcPYDHbX/0300oqdSazC/lWc+WKVeaWMBIf4LerghgMTaYRBQFggEvU+Pd9KY/OSjuXjBNk2q9TaFU5x9ev8gHZ+fRdZOvPbeTZDzI+kaF0aEEgz1xRgcT9HZFO+HqoiiytlEmFQ8yfGDotmD6jWyFE+cWaDRVBvviTI13oSjbVlz21+hgF9mNNoKaISrkETZydEstrDKo9zD+uh6gKXThj40zMDRGwOdB1XROXVhiNVPCMExSiaAdVrflC/vRcT6OOHXD44MoyoieCIongjfUtyWe1G3rrlbF9ifW6mitAmAhiK4tAcXtdJ84ODj81AiiLT6I/HQ+9bZQstWR0umA+agN2c1WZB/5m2ViGRqG3sLUm5jb+TCmtnVMdUt02cqc2RZibsqB4aafLG7PgbEQ0bFQxaK9O7bT8SLcEGicDphPjVObODh8sTC0Bq3qGs3yIoZaR3ZH8IYff5Gk0WgyPbvIzOwi1VqN/r5u+nq7KBSK1BstNF3n1a8+T6VS44evvcPkxAiD/T30dKcIBQP093XfIo7czNr6Jm++c4J2u43f50MQBFZW1+ntTvPkEwfYzOYJBf2IokQmkyEdlVD0ZSqr5+jyrDAa9lNZu4YoupBcQTyh/jvmy25TrDRYzZQI+D1Uay2klshmrsrxs/M8sX+Yb7y4m++/doFQwEMs6icRDRCP+Dv22HeyA7csC9M0kSTbunp2MUux3MQ0TQRBYNd4N8lYkK5kiK88txO/10UmW+HqbIZ8wc4okWWRn//afkRRZN+uPoIBN9HwFytv5HFFNwyKZXu9JhkLUqo0+OFbV+zNKIJAwO8muiWOAXztuZ143AqCIHRsuB4W9y2WXL16lYmJic9iLA4ODo8JlmWi1rM0Sguo9U1E2YM72Pul21G3urbBj15/B7/Px64dY4yODBAK3m5RZZk6tcIS506/S9RTwefS2D8RIBQdwecLkC/WOHl8ho18lZH+BIosohkGTx0a4dtf3c/kaJpqvYWq6pimyeRoF/GufnYNurl4bQ1NMxgbTn7swrxlWWiagcslU6m1mF/J050KEY/4yWQrHYElFPASDHiYHEkzMZJGkR9MkVGqNPjeP53l0vV1MtkKtUYLr8fN04dG+OVvHcbrcfHNl/YA9q6V2aUsP3jzEm1V55sv7Sbg9/CVZ3feckzdMGg2NYIBD6Zl0WipJONBKtUm//TmFdqqTl9XmEKxzIsHfDQr75PwXUXwavc2aEHEE+qnJXRTMZIMDQ7i87pY3ygzt5Rn745eXIqMqupMTXQz0BvD7310gggfFE7d8Phiiyd25wnBnq3MkwaGVsNQG2itInqrgtYqbtnsCIiSa6v7xBFQHBwcHg52HopsixUP+HOo0/libNmIbee9bAsuxs3iy41OGdPU7O6XrcwXw9BQVS8iVcC2GzNNDSzzJmHH/rstt9xcY1udVJhb7cVuWI7dbE3WEWC2b7stxDzGPMja5K233uJP//RPOXXqFOvr63zve9/j29/+9gM5toPD446hNWlV12iVF9HaVRRP5EuzuTKfL/L9f3rD3kCXiNHbm+bYkf0AnD1/BVmR6evt4gc/ehvTMulKJzsL/OlUgnTq7seuVGu8/ub7hEMB+nu7yOYKXLk2S8DvoyuV5IMTp7ly+SLtZhnRbBDzaxQUL1ZIIuVX6Il2I7kCd80pbKsaxXKDKzMZNnNVfvlbh+nvjuJ2KVRrLXTdQNcN/D5XZ8yCIPCtV/Z+4rxUay3ml3PkinUyRYvnD3VtbdoEv89Nf0+MWMRnW2WJIqVKg3ZbJ50MUW+2+eD0PMGAh0Q0sLUGEui8n0YGEvf46jh8WvLFGrOLOQrlOpVqC8uy6EqFef6JIAG/m0N7B4gEvYRD3tsEK6/ni3NteN9iycTEBKVSib/6q79idnaWf/tv/y2xWIzTp0+TTqfp7e39LMbp4ODwCGBZFlozT6O0iFrLgCjhCnQhfka+2F9ETNNkenaBibFhuruSfOWlZ+jpTt2x4NPbVerFeS6de48z56/RamkcPThBfHQYXWnx4YVlTl9cJpOtYpgmfq+LfKFGIhbg6cOj9KQjFMp1Tl9cYnGlQDZfJREP4PW42L17N26XwuG9dw9120bTDc5cXKZQrvPVZ3fSm47gPiRz/uoqmzn7sbc7OEYGEvT3RDsBbZ8GTTcoVRosrhS4Opshm6sSi/oRRIGhvjh7dvRwdP8wE8OpW7xCAd47NcvyWhGXIjM8EGdkINEJktcNg0qtRTZf4/L0GldnNohF/Pwfv/os4aAXSRQxDJONbBVDq9AVLBM3KgxEi5QW7BbPTyrLTRRKapxCK8bg2F6GR/sxDJOVTJELV1dZ36xgmiaxiB9jvBtJEnn+2OcrFKiazvELm3S3rvHzez7Xh74jTt3w5cHOPPEju/zgBxjudJ+YehNdraO3Sujtqi2gGJqdKSAqiJLb/pLdj/0inYODw+OLIMpIogyy56c6jmlZKFthwMKWMLItkGx3udywINMwDX2rW+bm2xh21ouhYehtLEPdup+6JcqYmOb2/aytDJgtIWbLguxmrK3/dHJfOgLLzWKL/d2+jXST/dhd/FQeEg+yNqnX6+zbt4/f+q3f4jvf+c5nOGoHh8cHQ2/ZIklpEa1dQXaH7c6Fx1gkqdcbLC0t0Gg0efrYQQzTJBQMoOsGmY0sm9kC+3bvwOv18LPffJl8ocQ//vBNdk6OsXtqAr/v47MtS+Uqq2sZdk6OEgz42bdnJ81Gizfe/gCXS0ZC5+UnBwnIi2zkz9LnLxHvUewuC3cK2RW840amtqqh6SYBn5tSpcHbJ2YoV5ssrhao1lr0dUdYz5bpSoT45ku7effDGSRJ4unDI/e9ZrC+adtpiaJIPOJnYjRNKGAfY7g/wXB/gmZLZXGlwJWZDLlCDU0zCAY8fOPF3fi9br79tX0/1VqFw73RVjVyhTr5Yo1socZQf5zRgSSttk6p0iAe8TM+nCIW9hMO2jWRLEmMDjwa2UP3vYJ5/vx5Xn75ZSKRCAsLC/yrf/WviMVifO9732NxcZE///M//yzG6eDg8AVHa5VolpdoVVYBC8WX+NLtGrYsixMfnuf69DypRJxoNEzvlndo5zamTqu6RqM0z/L8BY6fmqbWhOHBPvbuGkSRJdY3y/i8LjYLNRRF4tDuflRNx+WSGR5IsHOsC0kUeefkDGcvr1CttTAME003mBhJc3D3AKF4Aqh97FgvXV9nI1ehUKpjWXB0/xCiKFKuNnnt3au0NQNREHC75I7KL4oibte9L2Sqmk6x1CAe8yNLEq+/d5Wl1SLNlsaFa7YYoygS3/7qPn7xGwdvE0fqzTZzSzkmhlO4XQqpeJCedIT+7iiSJKLpBqpqz83MQpa/f/0i2XwVWRbpTkXo747w3qlZcoUarz7dhVqZJ++6jGSW7vk5yO4QLbGb6YwXXUoyPJDkyf5Ex0jVwuLD80uEgx727Oihrzva8Un9vChVGrx1fIbX37vG2ydmqDXavPRsjZ//jT/8XMdxJ5y64cvNdvcJhDvGOaapY2oNDK2JoTXQ2xW0dhlTb9gWXpZlL7BJLrsTRXIjSIojojg4OHwpEUQZ4QFvPLphMWbc1PnyESHmo10wWzZlpmFnv5hGC9PQtsQaW7ixO2BU0Le7X7bsxywD4QuUZ/Uga5NXX32VV1999TMcrYPD44MtkqzbIolaRnaFHnuRJLOR5fyl61y9vogkWhzYtxPTNPnxG+8hCAL9fd3093XT2223iiwsrjDQ30MqGeeFZ59gaPDuWRqZjSzLK+ssr6xTqdao11usrW3g9bpxSRofnjqDT6wR9+rEAlBb/QmmR6G/K4js6kKU73zNWm+2uXB1jVyhRr3Rpq87ytOHR/F5XCiKxPpmBVkS2bujF0WReOuDab7+whThoJe9O/tIRAO3XdffjGVZVOstiuUGhVKDZDxAX1cU3TBJxYMc3T+EIsuU2n7q1XUWVwv4vC5GBhK02joXr6+RiAaYHEmTiAWIR/2dYztCyWdDtdbC41FQZIlzl1e4OpsBwONRSMYC+LbWi3q7Ip1OoEeZ+666/vW//tf81m/9Fv/hP/yHW8LPXn31VX7lV37lgQ7OwcHhi4/ertKqrNCsLGPqKoovjvRT7qZ71Gg0mlybnmd61t4p8sSR/USjt+Z46O0KzdICtdxVSoU1PC4Bn8dHLNXPc5O9xMJ+VtaLfPdvTzI5kuabL+/m689PEfC52chVWF4rsnO8C7ci02ipKLLEZr6KIAhMjKTYzNc4MNXHwd0DKLJMTb+1OClVGmzkqpQqDZ7YP4wgCGzmq3jcCgem+gkHvWQLtrji97kwTAu3IjHQG2Nqoodw8ON3stzMzMIm2UKNYqlBpdakWG4QDHgI+N1s5qpUak0ym1VCATeTI2mO7h/i8N7BTkFlWRbrm2VmFrNkNitIkkgqFiSdVBgbsovIRlOlUK5z4eoaXo/C80+MEwv76e0K89zRMRZXC5iGjqSt42uXmQpusnnZ7o65F+MwU45TbMcJJMYZmtxJvakS7G3h97lZXivy1vFp6g2Vn/vKXlwumW+9vKfjt/p5sbRa4PX3rvHjd69y6sISpnnrrs13j1+iVqsRCNxu/fZ54tQNDh9FFGVEdwjZfcOb2LJMTL2FoTUx9Ra6VkdvlzHUOrpawTRUW6AUhK1OFFtIESTXl6p70cHBweFBsJ3/8iC5VYDZsiPbFl8MDbW+gTv4xegmfZi1Sbvdpt1ud36vVCqA3Z1uBw3/9Jim2fH6d7g3nDm7P+53vgy9RauWoV1aRFMryK4A7kAfCAIWW1lSjyGqqvFX3/snypUahgm93QkO7t+NIIp842vPEwwGME2T1bUN3n7vFMur6+i6wddeeZburiQDA72YN82NqmpkNrIM9PcA8P6Js9RqdXStTb1awjKaTF9aYaRHxiMbDEfrjA1GcbnjyK4Aouy9RZjaPrRlWaiajtulUG+2+cGbl5Elkb6uKKIoYFkWpy4scXD3AH1dUS5fX2dsKE1XMkwi6iceDRDwubEsSMVDtx27Wm/h9bhQZIkrM+tcns6gGwYAAZ+boN+DZUFfV5S+riiZbJmL19ZZL5i4hAYul8zoQALLgnDQ28kguZnH9C10z2znsD6oedjMV8kXa+SLdfLFOi1V4+lDo/R1R+lOhwkFvSRigVs2iT7I18CyBCwETPPBfi7f67Hu++ryww8/5L/8l/9y2997e3vJZDL3ezgHB4dHFF2t06qu0CovY2gNFG8cl+/RaKl7EJimSbPVxu/zUipXuXRlmpHhfibGhknEo4BdFLarqzRKizRL82xsbHB5vkpTc/OtV/bjk0Qmhj3MzGe5NneBxdUipmkSj/r4/msXCQc9vPjkJKm4fRH3o7eucvriIulkiNGBJPGIn595aQ+KItFWdTxuexfF9klK0w1OXVhkM1+l1dK2AsUDaLqBIku89NQkqqZzdSbD6YvLlCpN+roihIJeju4bIhkPELkpcOtuGIbJ7FKW8SHbbmxxtcDCSgHLMllaLVKqNLEsk2eOjPFzX92HYZjMr+TZOdpFLOK/7Xgnzy0yv5wjGvZxaO8A6USI7ZquUKrz9skZWi2NRktlI1dBVQ07V8Ul8wtf2UG7skDcnEZor4O1lT/ySSduQQLPALlGkOViAAOP3cESTyIIAj6PwslzC2zmqkiSSG9XhEO7B1AUe6Hh8xBKNN3g9MUlfvLBNG9+cJ3Zxdwdb+f1KByZSvAr/7dfQpYf/iKyUzc43AuCICIpPiTl1s8cyzQwdFtAMfU2ht5Eb1fR1Sqm0cRsV+zdzAKAiCgqdheKqGA5Cx4ODg4OnxsfJ8BYlokoyXf1v/+8eZi1yZ/8yZ/wh394e+dvNpul1Wo9kMcwTZNyuYxlWbctKDrcGWfO7o97nS/TUFGbRdT6JobWQFK8iEoCoQU8oPf7FxVd1ykWy7Q1k77+fpLJFBNjA9QaOmZNRRRliuUWr73xDtlsnnA4xODgEAP9vbg9fgqlJgC1eoO1tQyraxk2s3ks0+TFZ59A1+tMjafIbazyN99/B0kwkSQYG04zNDyGKHkQJJcdlg1oALY+QaPZZiNbolypUyrXKVcbJGIhnj66E100CMe6aLZULi/U0HWTXKFMKOhjdHwXgyM7+K3B8dvyJhomsFV6b+ZKrGUKlCoNypU6hmHy5OFJutMhFJ/B0EiAaNhPJBRANwwKxSrvnssTjQQY6E3SskBwNZnckaInrhAO2QH1Nf2mB3TK/FuwLGgZ9nXU/TRq6YZBqVQnX6pSKtc5vG8MSRI5fW2FYqlGJOSnqzdBPBrEHwlS02W8oRDerT1vt7wmDxDDlDBwkSuUcbUe3OaOarV6T7e771UUj8fT2f1wM9euXSOZ/PIslDo4fFnZDmFrlhcw1MaWv2j8YQ/rc6NcqTI9s8DM3CKRcIivf+U5uruS/PJ3voGiKFimYXfalBaoF2fRW2XKtTZXF1tkChqK7CEVtwULgMXVAmcuLVOptPB5FEJBD+ubFYb64uwc76atavzPvz/N5ekMuWKVSNCLS5Hx+9yMDSU7i/Qet4JhmFy6vka13mbvvgMosoSq6gz1xkkngyRigVuKmpmFTc5eXmEjZ58wIiEvzbZGKOhlfPhjEuOASq1JvlinUGqwka2wkilyZSbDc0fHCYe8JGMBXC4JWRKpVFvEowEGemOkE/ZZtScdAeydJplshbmlHIN9Mfq6onQlQ3ZQtGlyZTrDh+cWGeiN8eTBEXxehXjUz/pmmetzm4gC7Bnzsa8vj48Mq2d+BHxy9giAKHmwPH0owWG6+nexkJWpLcywf0+MnnSYfLHO4mqBZDxg+6ZGAwz3J+jtijywYPtPolJr8tbxGV579xpvHZ+m1mjf8XbRsI+dY13snujh8N4BeoIF9j/3BB7Pw+/ycuoGh58GQZSQXQFw3dohZVkWptG2RRSj3RFSjHYNXatjGk10VaVdKSGIAiAgCDKCpNiCimgv2j1oaxsHBwcHhy8+D7M2+YM/+AN+//d/v/N7pVKhv7+fZDJJKBT6mHveO6ZpIggCyWTSWfi/R5w5uz8+ab4MrUmrnqFdWkLQK/iDAWR33/2t4j6CtFptFpZWmZ1bRBAEerrT7JwY4isvP02h1KTVrHL58iWWltf42leeJR6L8tzTB/C43YTDwTse88evvUGr1STohVSwSX5zjb/7nycI+0R2joY5dWmT7riX7nSa7lSUdDJExC8AbVStTrHUoFSx3R5602H6e2IUqwWuXJ4nFPCQCPsY74sSjwbwCiUEBZ4/lOLkuUUiXhermRL9aTc7R5OE3Vs2359QPp+cn6bRVElE/Iz3RomEfcQiAqJQYigNck+QuaUs77x7gWbb3twY8LlJhlIEZDeBFAwmu6jpIQJyBUG4twXuLzPbG2bt+br77bY3zpqmyRvvX6dQqmNaFrIk2fk1Zh6/281Lh7tQFOkjFnmNz/Q53DrOMjpFErEwntDHr03dD/e6PnLfV4g/93M/xx/90R/xl3/5l4Ad5rm0tMS/+3f/zgk0c3B4jDH1Ns1OCFsZxRPBHex9rP1Fb6ZSrfHeB6fJbGRRFIWxkUEmxoYA+3MQo061uEI9d5V2fQPT0JE9ITyhPv7XT06TK9YI+j2YpsXCSp6FlQJH9g5yeM8gUxM9VGstjp+ZpysZQpZF2qqBz6Nw4uwC2XyVfbv6sCyLXePdjA4m8Htv9RdVVZ33Ts2RLdQY7o93Wpk/Gi5uGCaGaeJSZFY3SqxmSsSjfsaGUuwa7yIUuNVuy7IsavU2hbItjOzZ0YMsSZy5tML6ZhlZEmm0NMrVJs2WRr3Rxu9zMzqQ4NL1dSJhPwd3DzDcnyAYuHFiqtVbzC3lmF/JU640KVeb+Lwu+rqiaIbB8nqRaNhHX3cEWZJQNZ2fvH+dRkslEnSR8lf4Z0+rRFw5MOrQurfNJbI7jOUZINuMsbApoesW48MxeiQXqUSIsLuHhdU856+s0FZ1IiEfzbaG3+tm747PxzqiWmvx2rtX+Yc3LvHuh7Poxp2f2UBPlF/65kFefnoHiiwSCnoJB+3W6urG+c9lrPeCUzc4fBYIgoAke+5o+2hZJrrWoi2vE44GwNK2LL4aGGrN9trXG+htbSvQeOvqQpAQRRlBkhG2BZXPICvAwcHBweHh8jBrE7fbjdt9e06AKIoPdJFeEIQHfszHHWfO7o87zZehNexMkvLSlt1WEO9jnkkCti332++e5OLlaQqlEvFYhF/42a8xNNiHsdvg9LnLnD57DUURiIaDTO0cx+f1IgoC3enbBdp2u4XeLGLpZaYGNFYXF/j7H5+nVm/h8bjYv6ufr7+wD0FSSHSNkoj6b3vfXp3NcP7KKpZlIUkikZAXC9uxobcrwne+sR+AfKHOZqHK+asrFEp1njs6TiIWQNN0MtkK3akQB3b337ZOoBsGpXKzs05QKNUZH0oxPpzi2aNjKLJEs6WSK9RY3yxz8doqxXKDw3sHGe5PEPC5Ge6PE4/amSPbLhk3Iwg3vhw+mY/O17YFWrZQI7f1pRsmP/eVfbbVeTzAUF+MeDRAJHSrRZvb/XCvfwTBQsBCFIUH+pl8r8e672f/H//jf+Qb3/gGqVSKZrPJ888/TyaT4dixY/zxH//xfQ/UwcHhi41pqLRrGZqlBbRmEckdfOxD2LYplSoUiiVGhgfwetxIksSzTx1hcKAHWZYxtCb1wgyN4hyt8hK6WkWUvdRUP28cX+CFJxMkvTLJWADDMKnUWrgUieGBOP3dUaqNFj948zIvPzNJdyrMN17azclzi0zPZtA1k818hVpd5ee+uo+RgVuLKFXVkWW7OP3wwiJzizkkSeS5J8ZIxUPU9FtfH9M0WVgpcOHqKl3JEE8cGGaoL47HrbBrrLsjZLTaGh63gmVZvHV8mnypjqbZXTCKIuHxKIwOJDi0Z4DTFxZZ36xQKNrtubGIG/dWfsd2J0Y6EeyckHTDQNdNPG6FlfUSZy+vEAzYYZ9DfXGScXvn+EB3jJH+BM2Wxo/euUIuX6NULnJgBCbjdSRtHcu1Za9lfPLr6A50442M4o+Ps5qHE2cXCAY87BiNMdgbw7gp6+PkuUU03WC4P8FgX+yebMgeBBu5Cm+8d53X3r3K+2fmO3N+y/Nwyewa7+KJA8O8cGyC8aEkAf/D7xz5JJy6weHzRhBEJNmD7PLj9t++49E0dSxDtUOKDRXL0DAN1bb70poYWhPL1DD0FvqW3z4CW3Z+wlbYstQJXbZFFeWB+/87ODg4OHw2OLWJg8ODRVfrtGtbIkm7iuwO4Qk+3msG2VyBYrFMOp3g3fdP8fqbHxCLRTiwb4rx0aFOMLskSXjcbibGR9g7NUI8FrntWKahUcwuMz93jfn5GZaXFlHbLXoSHpJxH1fmawwN9DA52kNvV4RELIBLsZdzU3HbYaJQrlMsN/C4FPs20QBTE930dUcJ+t2IooimG2Sy5S2ba5Efv3OVfLGG2yWTjAfZM9lLMOBBkuyNeGNDqU5Yd7XWIles0ZMO43YpnLm4zNxSDlEUiYa9pBJBggEPm/kq8S2r7dMXl1lZL+L3uYlH/Qz0xkjG7Gv+dDJEOvlguukcbAzDJFupYpomXckw9Uabf3zjEoIgEAl56UqGScYCWJaFIAjs3dn3sIf8heW+xZJQKMQ777zD66+/zunTpzFNk4MHD/LKK698FuNzcHB4SJiGdkMkaRUQlQDuUB+C8PjvtDFNkzffPsHi8ioBv5+hwT4UReErLz2NZeq0axkqpXkaxVn0VgUEAcUTRXHHOXd5hb/94Yc02xqKLHJk3xAvP72DXKHGcqZIbypCpd7k6uwG7bbOYF8M07SoN9tcuLrKD966TLXWIhr2sWdnL2ODSSIhH6qqky3U2MxX2dwKan/p6UmSsSBdyRDRsI/uZBif13VbsNbyepFzl1eYX87RVg38WyFcg71xBnttC7VytcnxM/M0mirf/tp+BEEgGPDQauuoso6mGWiawcmtTpdarU0g4GJ0MInf56JQbhAOehkZSHSK4u6UHXKfzVc5e3mZq7MbjA0lefWF3fh8CoosoWkGg70xelJhMpsVLlxdQ5FFXn56B+XiOgH9GgPpAuGBqr1Don0P8SOigjcyhCs4TL4Z4fJ6g6Dh4Yn+BH0ug3DQiySJLK0VeOvEDNVai68+twvZF+K5J8bxeZXPvLC3LItrsxu89t41Xn/vGhevrd3xdtGwj2+9soeXn95xx86fRwGnbnD4oiGKMojybfko21iWtRVIrGKZGpapYRoalqFhGCqm3sTQWphGC7ZEFc1UYSsjRRBlO3h+O4Re/Ow/UxwcHBwc7p0HWZvUajVmZmY6v8/Pz3P27FlisRgDAwMPctgODl84dLWGWs/QqixjqHUkV+ix3lhZrdU5eeoCZ85eolavs3vXJAP9PbhcCv/yN/8Z/X3d+H1eWq0216fnKVeqHDqwm6ld4xRKTaIR+1rONDTajTyNahbaWeZmrvDmB1ep1JqYpkC9beLxeNkR72NiRw9TuyXcLrvzQlVvhEQsrxW4NL1OtdbCNO0F8JGBREdQScQCrG+WWVjOky1UKZYbmKbF157fRSTkY/+uPhRFIhTwsJIpce7yCn6fC583xoGpfq7Pb/DWiWkKxTrtrcd99ugYPekIk6NpxoZSVGtNclsh4PNLOUzT4pkjY/R2Rdi3s5eDu/vxelyf/4v1JaFaazG7lCVXqLNeMFGEBomon65kmIDfwwtPThCL+D83G/HHhU/dV/PSSy/x0ksvdX6/cuUK3/zmN5mbm3sgA3NwcHg4mIZGu75Bs7glksg+3MEvh0iyzakzF1laWeOZJw8zMtyPIGALR+UlGsVZtGYB09RQ3BG7GBQlltYKfP+1d1jbKGMYJlMT3VgWNJoqkiR2dk68f3qO5bUig30xBntiuNwyhmGytlnmx+9cpdlSObRngK8+sxPDsjqdDa+/f41yxbaqSiWCTIykCG11g/R1Re/6XMrVJt9/7QLVWotELMDhvV1MTXR3/v3itTWyhSq5Qh1dN4iEvPzo7cu8/PQODu4e4K0T07hc9ol1faOCIAisrBUZGUwy1Bfng9NzRCN+9k/105MKo2oGhmkiSxKnLy7x43euks1XkSSJnnSYrqQtoMSjAb72wi4kQeS1966yvlkmFHTTHW4SVnIsfPgW6BVGIvf2msnuEL7oKN7oKLqY4tL0BiszJSwrR1cq1NkRo8gSF66ukslWUBSJ3q4I+3f1EQp4aJjg87o+kzZf0zSZXczx4YVFjp9Z4IMz8xTLd/b8jIa8PHdsgm9/dR/HDgzdc6uoZVmUq002clVcWouuB/kEHgBO3eDwqCAIgp1t8glBxLaoYgsp27kppt5C1+oY7Qqm3kLT6liGthWkJCCISieE3hFSHBwcHB4uD6I2+fDDD3nxxRc7v2/nkfzGb/wGf/Znf/bAxurg8EVCa5Vpllco14sYWnPLorvvsa1pKtUap05f5K//5p/QDYOuriSHD+zhm19/AUEQeOXFp9F1ndn5JRYWV8lsZLEsi57uNLpuIAkGWrNAvrbJ4vx1FhdmWVvPkY65GB+OEwpGGBzo44OzyzRbKuFggFQiyJ4dvdTrbTa3hI5ypUmjqXJ47yCjg0lcikw84mdsMEk07CMc8t4WvP7h+UVMyyIVCzLUFyeVCHY24CViAQqlOq+/d41coUZXKkw4dGNzXr5QxzItxoZSRMJeBMteX1hYyXPswDCiKHLm0jKtlkYs6mewL0Y84icetbtHHgUXhEeJSq1JrlAnV6gRj/oZHUzSUjWWVoskYgF270gzmLKzaLfZzox1uD8emAmZqqosLi4+qMM5ODh8zmyLJK3yIu1GHkn24g70fOlsRdbWN7l0ZZqjh/cx2Bumlr1Io3Cddj2LabSRlAAuXwrNFFnLVlDVPOPDKVyKRL3RZqgvjiKLPHNkbCs7xA5SD/o9pJMhRgZs66tMtsLJswvopklXIkRb1TkwNcDYYJKltQI/OT6NZVl886XdBPwejuwdxO1WCPhu9zi+GcuyWFkvcX0lw4uH08iyiCwJjA4m6EqGabd13nj/escqq1JrIksiAZ+LYqWBBYSDPtazZbL5GoO9MQZ742SyFUqVJu22BoLAgak+3C6Fb7y0G5cic31+g++/dpFao83eHb3snuwh6HMTCXp5+sgoQ31xWm2NzVyVv/3ROSJBL88fm0BtVtjZVSGk5FGrS9DSoHVvr5U70IMvNoovOkrT8NNsacQjYerNNuVakz07ekjEAuQKNa7OZIhH/Hg9LgZ6Y4wOJelOhpEkcWveuLfQk3vEMEzOXF7m5LlFTl1Y4tzlFSq1uz+xnWNdvPTUJC89NcnURPd9X2icubTM4kqetqojiiKjyTsHwX+RcOoGh0cdW1Sxu0fAf9u/m4baCaA3toQUQ62jq1UsQ90SUnS2++U6HSnitpAif+nOwQ4ODg4Pk09Tm7zwwgudrEAHh8cZy7LQWyU7x7SySrvaJJiI4vUlHvbQPlMWl9cIBf2UK1W++epL7N09QU932s6JrNUpFMsM9vcgCAKnzlwiHotw5MAuuhIuFOqUl9+gVc1wdbHFufPnAAFEF7UmrOVbZMo1Xn1xiGxxnb07eunrjpJOBomFfUiSvdFvM18lEvIx2GvbRCfuYmXVVjUy2QrrG2VGB5PEIn6++tzOTlfKR8lky7zx/nVkWWKwL4YgCLx9YobRwSQjAwmePDSCYZi8/t41rsysY5oWsiwRi/hoqzpej4vnnxjvXFM7PDgMw8TCDl6fXcpy4cpqp7snEvIRi9ibapOxID/7lb1YFtT00CcGvH/RqTfbnL+ySqlY4IVD8Yc2Diex0sHhS45paKj1TZrlhY5I4vkSiSSb2Tyzc0sIgsCxo/uJRzwc2JEgqcyRufwTdLWGJHtRPDGaqsDcSoFM1u4OWV4v4fXI/PK3DtPXFeHf/KtXePvEDOlkiMG+GDOLWa7ObFBvtNm70w4H/8kH06xtlKk325gWHN47wNS4nRli6CZvn5wh4HNzeO8gqXigsxtje3fG3TAM0w4mv7zK0lqBWkvkiakoLsVu2dV1k0KpTjTsY6gvzmqmRDoR4qlDo5w8t8DaRpnB3jguRWJ1o8z8cg63W8HrcXH64hILK3k0zaA7FWZkMNlp46w12ly+vrDlVyrQamvMLmYZ6ouRToX4v//qs6xkirxzYgZBEIiGPYymDcLKIqvn3kNtZPEAavOTX6ttey1fdBRfdARL9LC8VuTUmRy5wiKRkI+u58P4vW7Gh1LML+c5d3kFURTpSYcxtoLSh/sfbEFvWRaZbIXL0+tcmclw+fo6py4uUarc/Ul5PQqH9gzw0pOTvPjUBD3pyD09lqYbbOaqbOQqbOSqvPjkBB63gkuRGBlIkE6GiEf9NHOXHtCzc3Bw+LSIHSHlVjodKVtCiqmrmEYbXa1jqFVMvY2uVjBNDaxtFVfc6kiREUTXVhC98qXq+nRwcHBwcHD4/LEsC62Zp1VdpVVdxzJ0ZE8ExedHcj16FsH3Sr5Q4r3jp1nPbPLMk4f59re+gmVZ5PJFzpy7zPLKOsVSGUVR6P7Z5zHaJb7+VDdGK0u7Pkt1sU6hVCNfUpkcGyQYSRKOdnP+6grlShVZlhjuT/Dy0zvwehSePjRCqdqkUKqzsJJnuD/O1EQPR/cNfaIYMbOwyfRClkrVvv70eV3EowFiEf9tQkm52mR6fpNd492k4kFCAQ/VWoul1QIuRUYQBK5MZ5ie3+Rbr+xFkkSS8UAnjP2jQeCOUPJgaKsauUKdfLFGtlCjUGpwcE8/owNJQgEPo0NJkjH7Nd3Oq3lUUTWdcrVJudKkVG1SLDUI+N08eXAERbI3IQf8LnTjAe5mvU8e7Rl2cHD41Nypk+TLIpK0Wm2uXp9jdm6Jaq2G1+NmqMdHfvEtmqU5QkaFeh4MIUi2FsLrUegKeqmVyly8tkat0SJXqOH3uYiEvLx/ao4Xn5okFQ/y1OFRqrUWf/n902zkKsiSSG86TKutI0oCR/cPsbha4NrsBrIk4vO48HldxMJ+FlbyhINenj06dt8nwDfev875qyssrxUxTYtoLIGqGQT8br72/C7abY1CuUE2X2Nto4xlWTxxYAhZlkhE/YwOJanX25y/ukp/dwRBENi3sw9JEjlxdp7uVJhwwENL1bk+t0G7rTE2lKJSbXL87AIuWcTtVvD73Ki6zt+/fpHBvjjHDgwTD8s8tRMUY5V2eQGz3kb/5KcEgOQK4IuO4YuO4gkP2FkD2CLND986j6YZpBMhDu0ZwLQsdMNAliQ281XcLpljB4fpSUceuEdnudrk3ZOz/OT4NG+fmKZQurOl1jbRsI8DU/3s39XHk4dG2DXedVuL9Cfx5vFpNnMVTNPC73OTTgQxtjISdo13M7ecY3m9yPEz83QFSnTt+tRPz8HB4TPk1o6U2zFN3bb0MtqdIHpDb2KodQy1gWm20bX6lphibVl8SYiSYtt8bX0XRPmxtcNwcHBwcHBw+GyxLBO1kaVZXkGtb4BlIXuiSIoX07KAe9jt9ohRKJQ4f/Eab717ktW1DAhw7Mh+urtSALTbKn//j68jChrdCR/D4x6ivgYbV/8KQ6tjWSai6EI1XGTLMh9eqrOxWWE5L7F/v50LunOsm0jQSzIeZGQwQcDn5uzlZa7NbiBJItGwj96uCMl4ELhVjKjUmmQLNXKFGtl8jaP7h0jFg4iiSCLqZ+dYF4lY4DY3itmlLGuZMtdmN5hdzmIYJv/ynz1Nb0+EZ4+OUam2+ODs/JZLgUAiFrhlg+H+Xf2fw+x/eTBNk1KlSa5YY6g3jsslc/riMkurBTwehWQswL5dvaS33gPJWJBkLPiQR/3pUFWdYrlBsdwgFvWTjAVYzZR479QclVoTRZZxuWT8NRdvvH+NF5+c5JVndvIPPz6JJH78huHPEkcscXD4kmEaKu3aBs3SImqr8KURSVRVo1KtkYhH0Q2Di5ev0ZPys3s4SlDaRG0sUM2YyO4Qir+X42cXWc3MAzA+nCIW8ZOKB0nEAswtZQkGPAz3Jxjsi9HfHcXncbG2UaI7FebKbIb55Rwet4wpCGQLdSRZQpIELlxdZWW9hMtl+4tu5qqk4vbJr787ykBP9BMzKkzTJJuvcnk6w5F9gwT8Hi5dX2N5rUh/T5SDewYIRXqYXtjk0J5+0okQbx6fplRpEAv76E6HaLd1Pjy/hGGYTI6mGR5IIgoCXckQ1+Y2KJWbpOIB+rpjiJLI/HIWgKDfQzDgplRtUqk1iUcCPLF/CFUz2MhVSEQDJGN+EkENLxnWLr5Pu7oGWPcskLj8XfiiI/hiY7h8KQRBwDRNVtZLZAs1Du0ZIOCzu0cQBArFGqcvLgN2S2oqHuTJgyOf9q1y1zm/PrfJWyem+ckH05y5tIxp3t1ywaVI7J7s4bknxnn56UnGh1L3vGhZq7fIZO3OkVyxxs+8tAdJsgvgrkQQr0dB1QyK5QZnLi7zzJExVE3n7354nnpTRZYElGFngdTB4VFFFGVEl8ydrL1udKW0bZsvw+5KMbUmulazxRSjialWscwbFl8gdqy9blh8yU5nioODg4ODg8MtmKaOWs/SLC+hNbJYiLi8MUT54+2gH0XqjSbzC8uoqkYw4KdWbzC3sIzP5+GXfuFVDh/Yw8rqOt//+3/g21/dh9rIcni4SMCtg7mJhYWlepGUAIonhiBKvH1ihpnFZTLZMstrJQI+Fwd3D3BtZhVRFNB1g1yxRqFcJxrxEfC5GdvKAw0FPLesBbTaGh633Rny5gfXyWTtDNFIyEtPOozbZS/pjgwkGBm41T1hfjlHX3cURZa4NrvB5evrNJpt+rui9HZHOH9tlYXVPF99bhehgJepeotYxE886r/vTX0O98al62ts5Krki3VM00QUhc76xe6JHvbs6P1E2/UvMvV6m7amE/C7mVvKcerCEivrRTTNQDdMUokgB3cPcHCqn+ePjfPOiRncLgWv13Y08XkULMtCEAQO7e7lnv3ZPwPuWSyJRqMfu9Cj6/e6DObg4PAwMPW2HdxeWkRrFRG/BCKJaZqsrm0wO7fE0so6Xo+bn//m01j1RV7craO3ZzGbLXTFjzvQhSi5sCyLt07MkM1XmZrswTBMLl1b49zlFX7+6/s5dmCInWNp/D43kZCPVlvj+twG0wtZREHg5WcmKRTr7J/qo1Bs4PO4QLB48uAwXreL6flNpia6Gey1w9USsUCn6+FOLaz2SVSk3mxz8doaG9kK1+c22cxXEQSB7nSYHaNdHN47yHB/HE03yGxWWMpopCMCrZaGopgcmOojFPAyPb/J6YtLxKN+pia66euKEgx4+OD0HG8en6ZaaxGN2Ir/9iLa6ECS/u4orZbG6UvLnP9wlUqtyc6xbqYmunnmyBjtdo1mUUOrLdAsLaBVamj3+DoJkgtfZBhvZARfZBjJdWOBUFV15pZyTC9s0miqxKJ+NN1AkSWyhRrZfJVUIsihPQP0dUfu6sd6v7RVjUvX1zlzaZkPzy/y4fmlu2aOuBSJqYlujuwbYrA3RlcyxOE9g3g89zaW7dfYMEz+6c1L1OrtLcsyH4logM18le5UmL7uKD948zKttka13sayLAI+N08fHkWRJaYmekglAiRjQbzGwgOZh58Gp25wcHjwfFJXimWZmIba6UjphM9rLXStiqE1t8QU/YaYYoGFiN4W0Vtt2z7MsflycHB4DHFqEweHu2OvF2zSqiyhNgoIkoLiSyFKD+b66otCoVBiZm6J02cvMje/TLlSY2x0gGNHD/Dk0f3s2z2O3i6TzSxy9vj/5uzZC4z1+8nNbSAgEPb7kZQYBgobuSrrG2XWNjc4sm+IdCJIsVJnZn6TtqoRi/j4jV88xthgig+vVAh5QsTGu4iEfISDN4SROwWhl6tN3vxgmuePjRMOehkfTjExkiYevbMNk2VZlKtN8sU665tlltaKjA5UGeqPE4/6GRlMUCw1iEX8xML29X4yfmPn/s6x7s9u0r9kVGstsoUa+WKNYrnBK8/ssPNiqy3cLpm9O3tJRP1EQr7OGlAwcPt74ItEW9VoNDWaLZVWW2dkIMFmvsrbx6dZXi9SLDdoNFUmRtJ87bldeD0KkZAXy7JIxgPEIwHbUSXiw+WS6U1H+MVvHryrMBeL+NBbDy+H9Z7Fkv/8n//zZzgMBweHzwpDb6HWNmiWt0US/5ciuL1Wb/APP/gJjUaTkF9hx5CPrrDK+uX/id6uIMpeFE8E3aWwma+RW9xg/64+BEHAMi3cLpl3Ts6wlimjyCJPHBjG61FwuxQCfg+WZXHm0jKXrq9RKDXQdIOxwSSrmRITwykuXlujXG1wbXaDoN/NU4dGCQY8/NI3D33suJstlemFLMVyg1KlQSoW5MlDI0iiyLXZDTLZCn6viz07exGBlfUiIwMJnj48ypsfXEc3TKYmejDkGLXiKifOLZIr1BgbSnJw9wCDvTF6usJYpsX0VhEXxEOh3CAa9vGNF6dIxoMEfG6qHVuuKLOLWU6cXSCbrzE2nOTlpybpjWu4jEXWLrxFu5bhxg7mT8blS+GNDuOLjOAOdN/x/WhZFv/05mXaqkY07CPgd1MoNShXmiRiAQ5M9eNxy3g9d140vB+qtRbvnlvhwsXLnLqwxKXra2j63T0yu1NhRgYSjA8lOXZwmN2TPffVGluttVjbLLO+UaZab/EzL9/oHtkumksV27NW1XS6U2FUTadaayGIAvGtXT9dqZC9eCoIfPW5nRiGSbHcoF02fuo5+Wlx6gYHh88fQRCRZA/Id77guiGmbIkoW6KKptapGQUE0cQy2+h6A9NQ2RZTEAQEQe50pIiSgiBIjqDi4ODwSOHUJg4Ot2NoDVq1DK3yUuc62R3oQhAfHyMaXdcxDJNms8XV6TkuXLzO0so6u3aOsW/vJCO9EQSjSn7hTfRmhivXFjh7ZRVZFBkb7uLg3jFE+UZWxzsnZ5hbzrGWKVGqNKnV2xRLDZ44MIwoCIwMxpFlCZcsdy6Rd4z3EZBDHxu+Xa21WM3Ybgqb+Sp+nwuXYl8nfzTjsq1q5It1wiEvfq+bi9fXOH95hVpDpVprspGrUSjVMS2Lo/uGOLxnkGq9RTjodWxaHyCGYdJoqgQDHnTD4Ps/vtAJYg+HvCRiAXTdxOUSefLQg3W/eFBoukG13qK5JYY0mio+r4uxoRSNpsrf/egc1XqbRlOl0dIY6Y8z0Bvl/JUVVjfK+HxuhvoTpOJBBnqipOJBXC6Zwd67B7QLgvCF7mC650+/3/iN3/gsx+Hg4PCAMbQGreo6rfIyeruM5AriDvZ9aRY13JLOQFIk4jHxy1l0tYbZEJHdEdyhYS5cW2Uzt0CxbGdNBP0eJkfSeD0KB3f38w9vXMQwTJ47OsahvYNEwz7Azsrwe11cn9/ktXevkslWkUQIBrzohoHP6yKTrZDJVnApMq88M2nbYgU+OfyuWG7w5vFpTNO0fUL74giCwNpGiZ50hJ95ZQ8/fvsq69kSy2tFAj43FrCaKTHUF+fIvkHcLoW5pRzvnTuHV27RlQpxaO8AfV0RSpUGl66vc2V6neX1Iqpm8PXnd/H8sSAvPjlBW9XJZCucu7xKrlgjV6zT1xUmGvISi/j55guTpIJV1MoCjeIZ2isN7lnrF2R8kUG80VF8kRFk952FhWyhyvT8Jof2DOB2KfSmw6xvlskX63g9LiZH0vh9tjiy/Zp8GorlBqcuLHLi3CLHz8xzfW5zy3v3zoQCHvZP9fHskTF2jKVZy5QZGUgwOpTE7733Vtm2qvGjt6+SzVdptjRcbhlFEpme32RiJE1/T4xCyQ6l93ldqKreaa8OB70c2N1PVzJEIhbApchUay3yxRrxaIBmS+X7r13ANC0G4zXGPvXsPBicusHB4YvHrWLKjc9h0zRpmptEk0kES8c0tZs6VGxhxdAaGFoLU7e7UyzDwDQ1LMu0L7ot4VYxZdvqS1Sci3IHB4cvBE5t4uBwA61Vpl3P0Koso7fryO4g7mDvY7deYFkWf/7dv2F5eZ1DB3fz8nOH2b8jRa20ianmWZh7gx9+mGVto8jkcIypyUEG+vuJJe3rLqBjef3SM5N4XAoXrq5y4eoarbaGz+fm8N5BXnp6klKlgaaZjA2miEb8pOIBupJhPnqZaVkW1XqLQqlBoVSnNx0hnQyRLdS4eH2NRDTA5EiaieEULteNZdvltQJrm2XyhTqVWpNmS2OwN8bURA9DvXFK5Qavv3sNVTMYH0nxlWd23JI9Egl9+utnBxtNt+3Hc4UauUKdYrmO26Xws1/ZiyxJ7BzvJhz0fCGC2LctrQA2chUq1ZYthrRsUWRyJE1POsLCcp7TF5cAME0LURIZG0yi6QavvXeVzXwVlyITCfkYHw7w4pMTyJLEM0fGePnpHQ+szjcMs9NpM7dcYCD58K4fHh+p2MHBAQC9XaVVW98qemrI7hDuUP9jv1BhGAbLS4tcvXyWgFJjMKmRdlUxdZF8zUW2olCvqzx5KAxAodQgtNXO6nErlCtN3j01i9etcHTfEKlEiOePTdCdCtNsqZy/ssLJ84sYhsWxA0NIksjuiW68HoV0MoRLloiG/fR3R7l0bZ3J0TT7dvXd1m2g6Uana6RcaVKqNPF6FJ45MkbA76avK8LURDebuSqnLiyxulHC53Xx6z//BD6Pi4WVPIoicWRLwDEMk+X1IucurzAxkmbXeDepeJCd4y7C3ja1epuuZAi3S+Fvf3Sek2cXSMZDHNyyrXJJMsfPzrNzzG4Hnp63Lb5qjTaxsJdje5MEhHnk9iytyjKFzbt3W3wUQQkSiI3Y4eyhgbu2cJumyWqmxLW5DfLFOoIoMNQXpycdwe1WCId8HNwzQFcy9Il5LncjX6xx8vwiJ88tcvzsAtPzmx97+6H+OHsme+jviRIL+5Flkf7uKM8cGcOyLMzd1h1t025GVXVWN0rMLmbJ5mv8/Nf343ErVGstVM0gGvERCfmIReyuGQBRFGi2NTTNQFEk0okQvV0RAFyKzMRImsWVPDOLWfIFu+skHPLy9een8HpcjA+n0DWTmOvhd5Y4ODg8egiCgCi5Ebm7CGxZ5lZnimqLJds/dwSVxlamyu12X3aHinSTkCIjigqCKD1WO1gdHBwcHBy+iFiWidbM06qu065lMPUWsjuCJxR7LNcLdF3nBz98nXfffZdXnt3Jnt4i+em/xtDqXJ/b5MJMAQQX0WiYXTt3MtAbw+Xz4fJBGHvx9gdvXeb7r19AFiVKlSahkBfdMBkdShCPBBjqizM12U0yFqQ3HWHvjr7bxlGrtzDkAMhw/uoq0/Ob6Lp9vRb02wvrAIO9MYb6Yp1r3tmlLLMLWZ4+MorP42IjV2VuMUetYVsye9wK+VKdzYKdg7q8XmTfVD9H9g52riEdfjrqzTabuSqSKDLQG6PZUnn35Cw+r4tENMBAb5RE7Iad2eRI+nMZl2maNFoalmkRDHjQdIPzlxdAK9JsbdtlaXz7a/tRZImrMxk281W8Hhdej4LP40KSRLtbpNEmHPLSbKm02zpBn5tDewYAGOqLs3dHL9Gwj6Dfc8vnxHaezv2gGwayJNFWNWYWstQa9ppVrdFG102+8+oBAFbWy/TGQg9krj4NzlWJg8NjgtYq0a6u06quoqsNFE8Yz5dAJMlurnHuzIfMTl+hUd0k4DGZGIqjRns5fV1nM1fDMExcikwqEeyo1S89NUmhVOe9U3OUq002c1VUTefbX9uHyyXz0lOTXJ3N8O6Hs1y4ukomW8EChvpi5Et1piZ6uDq7QTzixzQtCqUGI4MJRFHkxacm8LgV6o02y+tFSpUG8YifnnSEjVyFd0/OIooC4aC305oJoMgSI4MJ/t//9SfkijV8PhcuWaLeVKk22sTCPn73XzyPz+vi6myG81dWAYhF/IwMJkklgrx9YoaNXJW66gK9Sq3ewjRNjuwb4pWnd/Dik5OsrBdZWiswM5/tBMS1VZ3zV1aYW9qkK9xkYLiOjwzq0tsU7vG1MC0RXUoRSo6R6J5E9ny8L/Q2H55fYmYxCwJIgoBpmJSrTXrSEXZP9nyq98VGrsLJc4ucOLfA8bMLLCznP/b248MpDu6d4Ml93RzdP0izrXVep3QyRH93lN6t1mdBEJCk25/X9om/2VL5i7/9kOX1Io2misejEAl6yRfr9HZF+PoLU7RUDU3VyRXrZLIVFFmiJx0hGPAwMZwmnQgiSSLFSoONbBVNMxgbStFqaVyb2yAeDTA+nMLlkjpes9Pzm1yb3cC0YDT58MLQHBwcHm8EQUSQ3R8b9Hprdopmh9Jvfde1BqbexNBaYGroegvL1LDMbZHXAkG2c1Pu9PWY1zUODg4ODg4PGtPQUBs5WpVl2o0sAiC7I7h8yYc9tAeKZVkYag21meP48ZP88MfvsLS6yY7hEEFridfenGFsuJvxkX56B6N4wnV6UuE7ZoasrBf5mx+e5fX3riGKIj/7yl4O7u5HUST27ejd2vB26/0+upmuVm/x9slZytUmTxw5TDhtuyPsGu8iFvYTjfhu6T6QJJG2qlEsV5leyHLm4hIuRaZQbvD04VEO7x0kGQ+wvFbsZJeoms7OsW4sy+LZI2P0dd/bNbjD3SmU6/Ym0lyVRlMFYKAnxkBvjFDAy8+8sue+3CXuF1XTO4JHs2VbkkdCPtY2Sly8tkajpdFu2wmxyXiQl56aRBIFNrIlon6DYMBNKhHE51E6tm+H9w5SrbVsq+9ynWjERzoRolCuM7OUIxLyku5LEA3bmzm32TPZe19jtywLy7IQRZF8scbyerEjhtTqbXrSYZ46NIppWkwvZAn43Ph9LlIJ2wp+O8/1uaPD6K3iA5vT+8URSxwcHmEsy9raGbJGu7qOaarI7gje8N29AR8HNtaXadcy+MQSC/NXmLkwR39PiKF9E8QTKQRRQtMNZKnE7skeUokgbkUmk63w3qk5fF4Xh/YM4HLJtNoabVWntzvC1Hg3o4NJDMNENwxq9TaZbIW1jTKRsI9E1M/UZA/PHR1HNwx2jKZpNFWW1gqEgh5iYXtHyMJKnsvTmc5uEY9H6RRBqXiQr78wRdDv7uwYaasal6fXGRlIEA35GBtKMjaUpNGyT8zRkI8r0+vkCjX27OhldDBJOhniqFshFPDQaKpEI34CPjfpRBDDNFHzBpW6hiAIzC5lWc2UePXFKdwuhc1chaHeONGQl3KtRTIi4NbmiOgzHE2ugKVCC+4l4tKUQkRSEyiBAdzBXtzujw8m03TDFmtWC4wM2sHxHreMYRi4FJnudJjRgSTdqXvfRWAYJueurPDh+UUuXFvjwtVV1jcrd729KAjsHO/iyN5BjuwbYv+uPsrVFlcXmyQjJvFogLaqcXT/EL1dkbu2zzaaKhu5CldnN7g+t0Gt0eb/+dtfxe2SESWB3Tt6GOlP0JMOEw37kLeK53K1yclzi5imic/rojsVJhkPoOkGfq8br0fhrRMz6LqBINii2rblmNslsWeyh2yhxszCJi1VZ3QgwZF92/klwE3ttg4ODg4Pg0/KToGti6ltEWW7S8XUMQ3V7k7Rmhh6E8vQMfUGpqFjWVtdKtjfBFH5SJfK9s9fXA9kBwcHBweHzwNDa6K3y2jtMmptA61VQpBcuLwJROmnz3z8ImCZBlqrxGZmietXLnHq7HmCHpgaDSGYJgd3Rnjh6ADZksZirk1POk4kmkSUXMSjLuLRAIZhsrFlo33h2iput8LhPQO8c3KWD07PE/J7+T9+9VmOHRy+52usUqXB4kqB+eUcLkXm2SNjeEM+oEF/d9Qeu2VRq7fZyFUpVRrEwj66kmFmFrL8/esXKZTqDPTE2DneTTziJxy0a6qBnhguReb6/CaZzXInW0KRJfp7Yp/VVD+WqJpOsWxboeWLdbqSIcaGUui6SbnSpK87SjIeIBkL4Hbd6KL4aYQS0zQplBq32GE1WxqH9w6iyBLvnJxhNVO65T4HdvcTCflwu2RiET+9HgWvx4XPqxDwbTtUiHzl+f14hRKVetPOeY0GkCW7q+TclRUAZFm6ZW0iGvLxC1/f/6nXD5bXi+SLNWr1NtW6LYgc3jvAcH+CylYGT8DnJhGzu7C21zW8Hhff/uq+TzmLnz0PVSx56623+NM//VNOnTrF+vo63/ve9/j2t7/9MIfk4PBIYJkGaiNHs7KCWt8Ay0L2RHEpn5yL8ahSLheZufgO169dIJddpTum8OTBPrqTEb716nPkSw3WclXOXr/O5Fb2w1OHRskVanx4bpFiuYEgCMSjfnrSthXX+SsrWJbFgal+xoaS5Ao1/vofzzA9v7m1AD1ENOJj/1QfY0MpetMRulMhJEmkWm+xvF6kXGky1BenrzuCshW+lowFGR8ySCWChIPeWwLIXYqMS5FZ3yyzvFbk0vU1phc20XWT3/jFY0xN9JCMB1jfrCDLEppmUGu0EUSBkYEE8aifq7MZVrfC5LYFmUN7BujviRIKejh1cYlr8xWSEYlUPEgqHiQRC3S8UruTfpbmL7O2sUBIKdAsNmje4+tgWQIVLUJZT+KPjjA0NEz8prbTu5Er1Jhe2GQ1U0LTbBEgGvHZHRtdERDs57d9sv84NrIVTl1Y4sL1NS5fX+fi9TVq9bunp4iiwNREN0/sH+bovkEO7h4gGPBQrbW4PLPOTz64jm6YhKNdpOK2bZrbpdzi72rv8GmgqgYDvTGq9Rb/15+/RbFcR5ZEulNhnjw4vPV4Ir/5i09iGCbZQpW1jTKnzi8x1B9n13g3sYiPydE0LkWi2dLI5qvMLeU4tGeA0cEk4aDX3m0U8SOJAoVyoyMeTS9mOX1hCUkUEUURy7RYWitwZN8QQb+HnePd+LwufMbSJ86jg4ODw8NEEAQEybW1YOO/6+1MU8fqdKeoHUFlW1TZtv3aDqa3TB3LMsGyQAAQbZsvQe7Yfd3oUpEccdnBwcHB4bHB0JporRJqYxO1kcVQmwiCiKh4cQd6HvnNBKahoTULqM0caj3L6dOnOXtxgfmVHLW6RijkZ9/OPkKxfrrSUXYCb52YplRt8+KTEx1Hh+3d6xeurvI3PzzL/HKBfLGG3+dm744eupJB/D4Xv/6dY+yZ7LktXH2bbYvtQrFOvlRnoCdKf0+MSrXF4mqB7nSYA7v6MS2LtWKdiMeuf06cXeDy9DrlapNao01b1elOhfnOqwcYGUjw81/bhyiK9HVHblmktyyLH719hWK5QTTs4+j+IQZ7Y5/aqvrLhGVZFCsN/F4XbpfCpetrXLy2BtgCQiziQ5bt/z9S8SBffW7XfR2/rdobVV2KTLnaZHmtaAshbY1mU8Pvc/HMETtV9LV3rwJ2J5F3S/jQdQNFlhgbSjLYG8Oz9XevW+l0LMWjAeLRG2sv284WYOfqzK4t0K7nsbYWfo7sGyIY8NCVCuH1Dt/RSuuT6mDTNJlfyVPfEkOq9Ra1eptvvbIHt0thea1Asdwg4HOTigcZHbTXrACG+xO3rKk8SjxUsaRer7Nv3z5+67d+i+985zsPcygODo8EpqHSrm/SKi+jNvIIooTiiX2sFcajjKG3aFdXmb5yhn/8yQXcVoHedIBnDo/Q29ODLMucv7rKtdk5TNPE7ZJJJ0K43QptVcPtUnC7ZIIBD5OjadLxIGublU5A+J7JXgZ6Y5y9vMxf/8NpljMlWi2NWNjH04dH6UmHeeWpHcRj/s5JyDTtzI5avc3SaoFY1M/iaoGFlTxdyRDPH7OFiZt9K8vVJtl8lWy+xv6pPrweF29+MM3lmXV8HhcHpvqRZZGV9SIzi1lGB+xuhI1clUy2giSJ1BvtTpj48noRr0chFPSQy9dYXCvwdz86z2BfnJ95eQ9fe26K/fsVUkGVaNiLIAiojU2audOsb0xjtjKEBYvwxzeB3EDyEIiNock9zKzLDI51sbsr0pmTu5Ev1nAp9vyXKg1W1ksI4tYCmQCGYZ/EP3rS/yiZbJnjZxc4eW6RD07Ps7z+8e2YXrfCrolujuwb5Oi+IfZP9XV2fxRKderNNsGABwuLfKHOrvFuBnpjWEqSgFzpFM+VWpMLV9e27qNSb7SRJFsIcbtkBnujvPzMJBPDKUIBb6flFGBmYZOzl1dsCziXTNDvplxt0lY1QgEvjabKlek8Pq/dcjo6lKQ7aYt49WabQqnBtdkNStUmjYbKgd39PHlwhMHeOFdnMnjdLmJRP4mon3jU3wlv2zPZS1vVqG6s3OOL6+Dg4PDFRhRlEGXg7htCOjkq2xkqN303jTam1sLQ25iGbf1l6C0s07A7VbZFFYubhJSbxJVOxsqjvcDk4ODg4PB4Yugt9FaJdt0WSHS1jiDIW4Htj3YWiWUaaM0C7cYmteIqczNXWV7N0Jf2Egp4MQ2dcDjCpDfCvqle+rujlCstljMlkokwkiTi87p44sAQ9Wabax9ucHUmQ39PlEjIyw/eusyFq2u4XDKH9wzy3BNjPHVotLMJ8mZM06RUbRL0e1BkibOXl7k+t4llWZ3F9m3Po/6eqL3BrtbizePTrGcrFGsCT+xOcuzg8NZGRoug316n6EmFScQCREJeXIrM+PCN3Itao83cYpZdE93IksToYJJQ0HNbNqrD7RTKdTazVTYLVXKFGppmcHT/EMP9CbqSIXxeF/Go/zYB4WYMw7ylC6R3ax3kysw6axvljl2WaVrs29nHjrEuavU2s0tZvG5b8IjH/ESCdh0riiKvvjiFx6Xgct2+JN+1tSZwJzbzVYrlRuerWmvxjZd2dzacRkJ+eoZsG61wyNtZr4mEbBuvu1FvtlnLlG0hZMsuy+NWeOmpSQRB4MzFZXtdze8hEQsw3J/ozNdTh0bv7cV4xPhUYsnKygp/93d/x9LSEqqq3vJv/+k//ad7Ps6rr77Kq6+++mmG4ODwpUJX67TrG7QrK6itIpLkxRVI2wsIjxmG1qRdy9CsLDM/fYFE0MBrmewc6yXo7aZYbnDqch6PN0I6GSIW8bFnRw/pRIig302+VGdlvcg7J/N89dmdBAMejh0YZmW9yA/fvspqpkg04mfnaJpg0EMo4GVlrcjMYo5kzM/eHT30pCI8dWhkywPUYGm1SLZQ5eK1NQTg179zDHErhDwa9hGL+omFb7TGbnPi7AJrGyXaqo4gCPh9LupNFa/HxcRIih1jadY2Shw/s0CjqTIxkubIvkH6e2K88d41dMMkEvISCfnwe13kC3Vy1JgcSXNlJsNf/u9TqJpOIhZgcrSLA1N9yJJINOxDcomItetsXJ+jUVpAtG5kWIj3UC8bUoR8M0a2ESGRHmZ4bASAnqGPv1+tbu+iWVwpUK23mBxNs39XP263jGmaeFwKE0MphvsTBAO3qzWWZbG8VtwKY1/g5PlFVtZLH/uYyXiAg1P9PHt0nANTfQz3Jzq7L0zTJFuocXVmg9VMiWZLJREL0JUMEwp4eeWZHeRLdRZXCqzlCzTrWbqTIY7sG8KyYCVTRNNM2m0NWRY7woRLkfnnP3vEbtvOVbg6s8HaRqljkxYN+3ApEoYkousG+WKdYrnJ6GCSVFxh90Q3e3b0oOsmm7kqG9kKPSm7MJpbzHHpur3Lxe1WiIS9HREmHvHzrZf3dubOsizqjXanWPnxO1fIF+uMJOsM7v/k1/nz4kHVDQ4ODg53opOj8jHB9LBt/aXflKFiB9Db4oqKoTUx9Aam1u50sFiGgbktqnSEFQEECb0Neqt1S0i9La44woqDwxcdpzZxeJTZtthq17NoWwIJgojsDuEJPrqZFaahobdKW90xWZrlJbRWCV1t8qMPlphZrqPrIulUhANTMQ7s7WffHouV9SLL60UuXVvHMExCQXtzWjDgYf+uPv7q78/w/dcvUK40iEb87N3Ry3NPjPEzr+zlN3/pSXrTkbt2Z1RrLT68sEiuUMc0TZ49OkZPOkL31vVkLOIjHNzaoKjqnY13V2cz/Pidq1SqLSIhLx5XiI1chVZbY2TAXqz3uOW7Pu5GrsL0/CZrG2UUWaK3K0I8GmB08PHKmXlQmKZJsdxgM19ldDCJS5G5OpNhbaNMMhZgciRNKhHsWKfHowEiIV8nwL3RUmk0VSRRZMdYF5Zl8bc/Ot/JBtnm6y9MEQ56EUURv89NMhbA63Hh8Sgdi6nerojtnnEXQoG7bwBSNZ1KtUW5Zltp6brJ0f1DALz34SyabhINe0nGA0yMpHBtdcNMTfRQ00ME5Ap3+t+/UK5TKNapN1Sq9RbVepuhvhg7x7qp1lqcvbyM3+cm4HfTlQwRDtljFASBX/j6/s+9e0nTDR7mp9h9r7S+9tpr/OzP/izDw8Ncu3aN3bt3s7CwgGVZHDx48LMYY4d2u027fcNqpVKxPelN0+zs9n4QmKaJZVkP9JiPM8583T/3MmeWZaG3yrZIUltBbzeQXQHcgV4QthaCt32VHnGMdpV2LUOrskKzvEyxmOPafIHlzTbffPkg52bWmc/k8UotErEAE8NpfF4XlgW9advz8/jZeVbWi+iGvSA/NpjC53VTqbb4p59c4vLsOppqYFoWpy4u8/0fnWdsOMUf/O7X6UpG+Je//DQ9XfaOjljYjySJnDy3wNxyjmKpQa5Yw6VIHN5rL6J3pyJ0pyK3PZdytUnAZ+eRuF0yIwNJ/H4XaxtlLl1bp9WexqXI7BxLc+n6OldmMgQDHg7vHUSWJYb6EvYi/rM70TSDKzMZMtkKtXqbZlujKxGiKxlmfinPC09OsGeyl3QyhGnqtKurlFYu0iwtoDU2O2O6l9OaKHvxhgdpCSlOXtdB9DHQE+PYnjjx6A0Lr49jZmGTUxeXEAUBr8eF263QbGlYlr1D4tmj43QlQ53iffuYq5kSb5+c4eS5BU6cWyCbr931MWRJZM+OXp7YP8S+XX1MTfR0rLO20Q2DRr1NwOcmV6zzxvvX8XldJGIB/D4XoiCQK9SIRwPMr+Q5c2kZRZJwBxKdnUKWBbJkf0/EAnQnQ3SlwiSifkDAsuDa3Abnr6xSqTWxLAu3S+bkuUWG+uLEIgHSyTCWZRGP2N0f20WVZcHl6QzL60VyhVpH7JAkkaP7htm7sw8L+3GTsQCxiB3eZ1n2cyuWG8wu5SiU6hTLDdqqzre/ug+vx4Us29Ze2pYDzYM+N34aHmbd4ODg4HAztvWXApKCpHz8bc0tIcXaElW2M1UsQ8MwVHStgaRXEEXAMjB1datbxbgRVi9s/0fc6pCR7O9bgopjBebg8HBwahOHRw3LMtHbVfR2BbWRQ2vlMdSmLZC4griDPQjCo2fFZBoqaiOP1szTrmVo19bR1TqWYa/7iYoPxRPl0oLGh5crjA+n2DXWTXcqhLF1bSIIAtfmNjBNi13j3fR1Rwj43DRa9iL33FKOv/i7k/h9Ll56cpKnDo+yb1cfHvcnFALYVtJvn5jB7ZLZu9MOdd9eEE8nQ6xvlllZL3H6wjIrGfvabmqyh689twtFkRBFgWePjtm2SsFuksF2ZyHb5717bsyJswvML+cIBb0c2jPAYF/sE10dvqxcn99gfbNCrlBD1w1ESSTgc9PbFeHQngGG+uxr5lZbZ3p+k2ZLY6gvzuhgkky2wjsnZzrHcrtkYlE/O+hCEAR2jnXhUiR8Hhder90lomyJE5Mj6bsN6Z5RNZ18sY4oCp2w9R+9dQWw39cBn5tI+Iaw8sqzO/F5lLsKFytrObRmlnqjTb3RptZQee6JMWJhP4srBabnN/H7XB3LrO33cioe5DuvHrjrcR+0ULLtilEsN8gWqjSbGo2mLeKkEyH27eqj2dLwiA9vvfO+xZI/+IM/4N/8m3/DH/3RHxEMBvnrv/5rUqkUv/qrv8rXv/71z2KMHf7kT/6EP/zDP7zt79lsllardYd7fDpM06RcLmNZluP9dw8483X/fNycWaaJrlbsoqFdAtNAlH2ISgKhDbTvns3wKGGoddRWHrWWRWsVMPUmK5kyc8tlynUNgQA7d+xA8vXQ3ecl1uOlN6kgiQLZfIVzM0U2c2u88NRuFEVGcscZGkmQTkYIB32sZYrMrptEIhGauoemKjE7n8UwTcIhH3v3TPIL3zyGKkR5+aVn0TSdcrXBar5OTRNIxkN094+SKQk01ApPjI2xZ+cgAb+XNtDeSj+vN1rki1UKxSqFUo1ypc7OiX7GhrtJ945wbWaV//XjC+TzFfw+N/mKyfNP7Ub2Jri2ME0okiAc8tMyZDDgxx+skk5F2T81TEttsVHK0Gq7qDR0JFnhiSeOoAoyLzz/JFZrA7Uyy+qlObTaIlj3Esm+hSAi+/tR5T4y1TC6kODg8DiKYbBTKdDddaMgq93hsLphsJ4psLyWIx4LMTnai+IFxVOl2VZp1kxikRCBSJqabudtBGNRarrFaqbA+UvznLu8wJkLs8wtbtx1mIois2fnIAf3jHBor/0aeDy3FpY1HVRVJ5MtsrZeYCNXwudx8fJz+/AEg8QTNQqlKtPLTaBpt1W74riDIUIxhZFRD5Vqk9XNJjlDo9xUGJ0IgQKvvPQsbpdie5yWarx2YpVYNMjkaC8oGouZGYIBL4GAl2g4QDwWpNwOoCgyu6b2Uq01yRUqnLleJptb5eXn9uJxu1jY0JhdqhHweUj3JEnFw0TiSWp6CFcgxJEj3Xard7nO5YUqlgXjIz1oms7//skZNN1AFARAQBQFri1rjA0nGByaQPbkifoblBsS8ubmHef101CtVj/V/R5m3eDg4ODwaenYf90lpN40TVpsEk2lALMjqtiZKbodSL8lsBh6C1NvY+rNrY6WNqZu3moF1kG43f5rW1zpfHfqbQeHnwanNnF4FDANDb1dQmuVUesbaO0qptFGlNxISuCRtNgyTR2tkUdt5mhXM7Rra+jtCqahIYgyksuP4o0hiC7K1RYhjwdJEtnIVRkZSHB4zwD5Yp0PzsxjmhaxiJ9QwMvzx8ap1u0OgfdPz3NtdoN8scZv/bOnuDq7wZ7JHv7PX3+Ovq7oHcfVaKrkizWyhRr5Yp3h/jhjQ6mOi8PeHb1UGy0y2QpXZjIc3D1AwOfm5NkFTl1cQpbtBfVIyIsii5imyehAkv/zV5/tbJar6W7gzms5+WKNuaUcg31xUvEgwwNxBntjpJOhz/DVeHQwTZNmW6NcsbNA1rNl9kzajg7rG2XOX13F41bwuGUUS+K9U3N87fldREI+1jbKrKwX7QwQt4Lf5+4IZYmYnxefmsS3lQ+y7U6xzYMQRD5KoVRnbilHrlijXLGTY/u6o6QTIUIBD8cODhMKeAkFPLeNx6VILK8Xqdbs7JB6o42qGXzjxd0AXL6+jGTWCAbcdlZJMox7y+prz44e9u3sveOa7YNax9V0g7aqo2p6p4Pn6myGUqVpW5k1VRpNjacPj9CTjpDJlrl4bQ2f14XX47JdWyK2gBP0u9FbjQcyrk/DfYslV65c4S/+4i/sO8syzWaTQCDAH/3RH/FzP/dz/PZv//YDH+Q2f/AHf8Dv//7vd36vVCr09/eTTCYJhR7ch4hpmgiCQDKZdBb/7wFnvu6fO82ZobdQ6zna1WXEdgFFElHiUcS7XKQ/cpg6aiNHu75Js7SIWt+kVi+zttmiqytFPJ4km68gCxq9MZmmqlHMrhLYGWasR6Kme7h44SxrG2V0w8DndTGQCuMRSnhlF4d32p8Ba5sF/umHJ/nBW5cI+Nwc3jfIL339AFfGgpy6EGD/rj5GBu3sD49bYmV9kXNXVqg1tnavCAKxiJ9kYIi+mAfPnji6EaErGUZVmxSKOfLFOkP9cfxeN9eXlphe2CTo9+BTJDxRgWJ2hR/PzTA5kuInb5/DMk2iQYlmq4nWNtg94sPjVvmX/2w/2UKNS9fXUVVbkYhH/AwmBXxiiQuzC2jNIrIgsHc8wkiPG7nyHo3yIq3KEqZ+fyKx7A7jjQwj+fpZLnpYWK3QaKqEAhYjAxCQKyBDZNAN1O94jFKlwdVZ29JKNwxCAQ+yaRKQg4ghFYU6oyMxhvrihINeVE3n8vVLnLm0zOmLy5y+uES+eOdjg503sn+qn6P7Bjmyb4g9O3puCbWD1tbXjWC+zXyV//WPZ6htZYq4FAnd58FobhAN+xnpUUhH/URDPsJBDy3VtlYJyBU0GszNThMN+5kc7mGkK0k86kMU7c7F/EaeN84tsriap1hu2KLFcIpDk0Em+xVcX50kHPQS2WpTbTRVAt4GpmnyvR+cJVewM1Isy0JAILsWZOd4Ny8f7eHgZIRUPNix1LKfT4Viuc6Zi8ts5qtU6y1abR2XS2bHgIuA18We8QjZfJVo2N7ZFAn5SMZl/HKFQBqG0nGqm2uEfQapVOq+3iMfh8fz6T6LHmbd4ODg4PB5cCNX5ZOxLHPL4utWccUyjU7OiqG3sPS2nbdiqHcWV4StoBUERMmD5PIjSu5HbuHMweFh4NQmDl9ELMvCUGvoatXeONnIY2g1e4Ol4kNxRx65rFLLMtFbZdRGlnZ9g2Z5yRZH9DaCKCG5Arj8aUTJRbOlspqtkNlcY2YxSyZbpjsV5mde3sOTB0c4fmaey9MZknH7mj7gd9Nq64QC0Ghq/H//+jjX5zcpVZpoms7URDcet0wyGuB3f+MFurcsj7ezRwI+Ny5F5sylZa7P2Zv3An438WiAoN++7ulJRyhWGvz/vnecar2FqpkIArTbOl95didffX4XA30xYmE/sYiPgP/W66WPWx9TVZ2F1TxzSznKlSY+r4uurTE+7nkkpmmiagattkbQb4sCaxsl8qU67bZOq63RauskuwbYM+LlzeMz/OSD6zS21mx8PjeiIDI6mOT5YxP4fW4kScTjlrdEE6XTuXN47yCH9w7ecRxul0Iq/skdRp8GwzAplOu2lXqxRn9PlMHeOI2WSrZQ61iDJaKBznqALEmEg142clXml3PUGyr1ZptkLMihPQO02zofnJ7H7VYI+m1BJOj3bFl2C7z87D7C7todbbh+ms6kbbFq+7VpNFU8HoW+rii1eovX37tOW9UwTXvzjyAI/NI3DyIIAvlinWZLw++1xRCf10VoK79lciTNjtEuDNOkreq0tlxJto/xMGva+xZL/H5/xwqrp6eH2dlZpqamAMjlcg92dB/B7Xbjdt9+chBF8YEv0guC8Jkc93HFma/7Z/t/fkOt0K5v0q6soqtVRNmLK9D1WOSRmKaOWtugVV2jUZxFaxYoV2qs51U2Syb5koZhmvjCIu5Gm4vX1jAME8MwSCWC9KQivHNyhqcOjSAIdlvkzrE0vV2R2wKqMtkyf/Y/3+fC1TWq/3/2/jtMkvO87oZ/lTt3T0/OO5tzwgJYBCIRAAGCOUgkJVFUtC3pk2XJek3Z1idKsqRX0fosW7ZMWZRoJYoURVKMIgkCJJGxC2zOO7OTZzrHylXfH09P7w52F8SCiGSf6+qZnq6p6urq7qrnuc99zmlYeH7I+vEeGg2b2YUivh8wMdrD9HyJyZkCu7aOsHndANGI1tpelETMYGG5ypmpZQ4eneH2/Rvo7U60Cv3T1Bvi3KdrqvCmNDS2bhhgx6YhHnzsFJPTeSbnCriuz0+872bWDHezaW0/i/kqruOxdqyHVDLCVx4+xtaNg+zYNIyqyriuT3dXnHhUZylfY3RI+MwaqsuetS4prYRTe5rmVIVr4dYlWSOaHieaWYMaH8Xyo2QzcZqmw7lnjzM61MXEaPfzhqsHQUCuUEeSJfq6kziOR6FUJ5uO4vkBlZro7ti9dZR4TOfW69fx7PFZvvbtExw4Ms3RU/M4rn/V7cuSxLaNg9x1yyZuuW4dWzcOtqWtz0UYhswvlTl5bomzU8uAxL/6oTeQzcSIGCr9vUn6e1JtEiGZiCBJMNiXYn6pzPR8kaV8Fdf1GehNMdAnMm/ece8uFEVmcjHk5PkpZuaKrJ/o5cbdExTLDY6fmWe4P8PubaOsH++hJ5tsD0CG+tPML1U4fmaBqdkCpuXwo+/ZLwgeCWQZJka7GexL09edZKAvhSRBVzqKJAsv2mNn5skX66STUW67cQO6rnL87AJhEBKPGwwNpOnOxHE9n5gEN+2duOox8nyfwyfmSEoNBqWXVjL7Yrf1ao4bOuiggw5ea5AkGUnRkZWr229ciovh9d4l5Iqw+lpRrQi/+jquVwBCJFlDVnVkJYKsXj20tIMOvl/RGZt08FpAGIb4bhPfqeHaVdxmHs+pi4Y4SUXV4+jxfqTXUV3gUnLEaSxj1eZwrQqBZwKSIEeiPciqge8H5Et1+ltF7YceP8Op84s0Gk5rbpdiw0QvjuvR151i+6Yhci0L42ePz5Er1ECCt9+zk2ePz3L01Byu5zMykGH7pkHuu30bvd1JBnrTWLbLkVNz5FvKEd8PuPm6tYwOZRkb6qK3O0F3Jk7E0KjUTC7MFpBkiZ6uOEdPzmPaLts2DjHYnyabFhbLABFDY/fW0Rd8fIIgwA8CdE3l3HSOo6fmGR7IsHvrCP09qdf99dpxPSo1E8v2sFuEh6qIDBCArzx8HNNysJ2L1hUrCpDFXJW5xTKyLNG0HGzbZ3RcR5IkLMtl384xRge7GBvOkmnZW6/gamTIKwmrlXGqKgpHT81z4uwCQRCiKK3c09b/jQx04fsiX2V+qcKZyWUaTYd9u8YZHexiKV/jyMk5YlGdRNygN5ukr1vUaxJxg3fetxtdu/ycEIZcpkS5GsIwxHV9HNdrE3xCsWK1iCrx3m3bOEh/T4rT55c5dGK2vb4kSYyPZBkZ6MIwNCbGuonoGoauYugqsiK3rbY2r+unXDNxHA/LFrZjUUMjETNYytd45Olz+P5Fu+9kItJWyryauOaz7v79+3nkkUfYunUrDzzwAL/0S7/EkSNH+PSnP83+/ftfjn3soIPvOQSBh2uWqS7N4zVzBL6NaqQxkiOv+wuk7zZF/khtDrMyjd0osrBcobsnSzSW5ehUncOncmiKjKzIrB/vZd1YL0EQsH3TEH09SbpSMcpVk4ceO000qoliuwJ7t48hSVCtm5w8t8iZ88ucn81zz61bKJTqzC9WsBxXDIr6krzrTXvZsn6gJf1rkk5FGR3qIpOKtv0Ze7IJ0qkop84t8cyxGYIgpCeboFhu4LgeuqaiKDKDfWm6u+IoskytYfHsiVlqdYsH7trOV751nCeemaRStUgmDDau6RO5EZ7PyGAGTZMxLZe+7iTdXQm6u+L0rlzwYgbZTIxzF3IsLRdIahXiLoTmAtlW7sgLJ0gkjMQAkdQ4JLeRTqXIl5ocny0wuzCNrqm89e4dxKI6b79351WL30EQsJSvMTNfagfUjw1n2yoIx/VoNG2ymRjdXQmW8lX+4+99loNHp5mcKTzvHsZjBru3jnDdjjH2bhtl59Zh4tErd0i5no9luSQTEWbmi/yfTzwq8j1kicG+NOvHe/H9AE1V+OC79yPLEpWaSanSZG6pjON6jA93U62bHDw6QzYTY9Pafvp7UsiyhOv5aKrCNx49xSMHzlM3JXTFJZOKMTQgunr2bh9j55bhVZ0YtiP8bxumzV984lHKVRNJEu9lKhGlVG4y0JfmTW/YiiQLr1HH8ciX6gS+GCadOLfIN584S9O8GCiaTka4Zd86EjGD+27fhq4rZNNxEvHVncKyJFEo1anULKqt4LcwhDtu2oiqKBRKDbTE1QmqVxqdcUMHHXTQwYvHCwmvD7PrRcHNbRC4Jq5dwbOq+G4D1yyA1FKfaFGhPukE0HfwfY7O2KSDVwuBZ+M5NTy7ht1Yxndq+K4pztNqFEVPosdeRwHegY9jl3GaeZxmDqs6h2dX8d2maKjVYqhGEjnejyRJ1Js2k9NlFpcrLOSqlKtNNq/r58bdE9y4Zw19PQkcR8zRTMthZr6MaXr03ZQiCEO++u0TFEoN8sU6vh+wZf0AINHfm+L9b7+eTWv7Gb6C1dbThy+wXKjR151k+8YhurPxVUHfB49e4OHHzzAzX6JUES4Bd+zfyANv3MG779+D4/rPmzHyfAjDkOVCnVPTeQq5WdaN9bBzywjrxnqZGO15QdkprwZcz8e2XdHxb3v09STRVIXJmTyLuSq27YlljsvGiT62rB+kUGrwzSfOAKKgbhgqmVSsTZYMDaRRZBnDUNvF9URcEGeGrhKNaBTLTcJQ1GR6u1NAk/vv3PYqHokro1o3yRXq5Et18sU6lZrJjXsmWDfWSzoVIRrR0HUVQqjWLZ44OMnIQAZVUbgwV6RWt4hHDbrSMUaHukgnBWmxYU3vVe2/JEm6jChZseeWZZlqrUmunsdxPZwWGZJJRlm/po+GafPQY6dxHPH4Ct77wF5kWebM5DLVmonRsjOLRjSUVr2ovyfJzi0jwgZcCgn8UFibA5qqsJyvtZ/PdX18P+DNd24nmYhw9kKOqZkChq6i60L547cUKMlEhJ1bhtski6GL534t4Jr34o/+6I+o10X47kc+8hHq9Tqf+MQnWL9+Pf/1v/7Xa9pWvV7n7NmLYTqTk5M8++yzZLNZxsbGrnXXOujgNQ/PaeA28zTL0zQKZbSkjB7tQtdeOqucVwOeXcOuz2NWprGqs3i2yDbwpQhffmyZUsXmrlsGiXoOi/kqa0a66e9JtgKshcRUlmU2rxtgMVfha98+SbHcIJuJc/uNG1BVBc+HRsPm459+nPMzeRaXK1TrFol4BN8PeMc9u/ihd93A5HQex/PRVYVMOkosqrN3+5XPJys2TmEQcvDYDFFDI5WMkCvUyGbiEIoOgfXjvZybzvGpLz5DqdLEcTy6u+K8/d5dBH7IqXNLpBIR+rqT1BoOZ6aWiUQ0BnvT3Hr9ehzHQ1aky6SPge/yxJOPEzTnyUaqDGUrSITYz883rIIW6SKSHieaHieSHkNRI4Qh5BsRvvDQk5imQzIeYdvGQcaHu9tF9+cSJZ7v47o+0YjOzEKJxw9OEo0I+Wo0qrc7DI6fEaH056fzrXDz57cCGx7IsG/HGHu2j7J3+xjrx3uv2vFgOy6LuSoLyxXOTeeZWyjTk03w4z9wM9lMnK0bB9m4po+N6/qJRXTqDQs/CFAUmTNTy21lEogLbzoh5J193Ul2bh5mdrHEk4emmF+qUK2ZvOv+Pdy4e4KRwS5u2DVO/+A4W9ZESCVWy6dd12dhqcLMfIlTk0vUGzY/+8HbiUcNhvszbF47wPBghu4u0WW08j43TJvZhTLzSxUWlis0TYeNa/t44K4dDPdn0FSZdeM9F0m0TLytWNkwIc4JpuWwsFyhXDVJxg1Gh7IUKw0efOQUILpL0kmhGFrBPW/YQm3p8Hf87LxSeCnHDR100EEHHVwOSZJQ9TiqLq4FUUR3r++a+E4d1y4LOxeviWuVgBBCkNUIihZrqU866vAOvn/QGZt08EogDEMCz8Rz6vhuE9cs4lllPLcJYdg+B6uR10/2SOC7uFYJp1miXqphz57Gd4StFkjIWhTVSApFzHNek+f7fO3bJ1lYrhAEAUEQkogZmKbIohjoS+M4Hg8/LortsahOOhnFdl2qdZMDR6ZZWKpQrdv0dMXZsKaf/XsnGOxLMTyQWfVc9YbFwnIVQ1cZG85y3Y4xPF88Z75U59ipeWYWStx721b6e1Kcu1BgarbAyGAXN+9by/rxXvp6RJ1CVRXUq6j6vxNmF0scODKNabkoepoNoz2MDWcBRCH9FYTvB4SEqIpC03RYylexHU9YKzkumqq06yaf/vIzuM9xh7j3tq10pWM0TUeEbxsqiYRBRNfoyYpG0J5sgvvv3EZE19A05bLPwI5Nw9QbFoVyg8VcFdfz2b9nAoCp2QJd6Rhrx3oZ7EsRMXTq3mujcG47HvNLZeaXyuzcMkIyHuHBR05x7MwCuqaiqTKGoXH+Qo51Y70M9WV49tgsqqIQj+kMRTPEYxfJtttu2HDV55JlGc/32+ockQHis2akG4DDJ+coVZrYLTss2/G45fp1DPd3sZQrc/r0FJqqoGsKuqa2yRVdUxnsTYOIPYUQgiDED0JkWdRtNFXBcT1My6VSs+hKx+jJJmhYDocvUZYoikxPV4L1a0TNIp0wkOUAXQVdDdFVkLw8ZiVky4jPtpEkhF5LJW0TBmVKMycIfY9s6BGaHmHDxQk8rMClFHgi68+3Kc8+zm0/e/wVH6de8ydv7dq17fuxWIw//dM/fdFP/vTTT3PnnXe2/17JI/nRH/1R/vIv//JFb7eDDl5LCAMf1ypi1Zdx6gt4bhNZjaFGMxjJeCuk+fWFMAxxzSJ2fRGzPIXdWMR36iI03EijxoeZX67y2MFJjpyY565bNrFurBfP93n7PTvbUr9602ZxucKx0/MM9qVZ1yqkJ2IG69f0omkKf/e5p6g3He594630pQLmcmUuzBbxfJ9oRGPHpkHWj/cxv1whX6zT35sinYwy1J9moDd92b6vPOezJ2Y5fyHPT7zvZrozCTav7WchV0FTFVLJCJMzeU6eW6K/J8ldN2/i3FSOwf402zYMEoQhs4uiCD461MV9t2/lwcdOky/U6O9Ncc+tm9m7fbQ9qFoZCNXqNeanT1MtXGAg1cQzlxgggNhlu3lVyGqMaEaQI9H0OKqRIgxDCqUGZ07nqNVtbr1+PRFDZ81IN0N96fbg5bkQoehC7jq3VGZ4IMNNe9fSlY61ZJFVpmaFddXsYpmp2QKeF1xxWwCaKrNt4xB7to2yd/sou7eN0td9Zb9Vy3bJl+oUy00yyShjw1nOTxf4h88/jeeHxKM6/b1Jtm8aAoQi5bYb1rOcr/HkM1MUKw1c12f/3gnGh7sFmbJhEFmRME2HxVyNJ56ZZLAvRSIe4WuPnGR+qUImFWVitJvxoW7WjYuurR2bh9m+aZi6lyKhVqnUTOoNi+GBLorlBn/6f79Jw7RRJIlUMsrYUBdNyyURM3jX/XvwfJ9CscFSvsbBo9Ps2jLCUH+GE+cW+fYT59B1hWTcYKA31SY1MqkYP/zOG9udRGEYUm/YuJ6Pocucm85x5OQ8ti1ULJqmsH68l9GhLF3pGPfctkXkxXwX3qOvFF7KcUMHHXTQQQcvDJIktwkUI9FPmA0JPAvfbRL4Nr5r4jRzgkxpESgSsuhs1mKvO1/8Djq4FnTGJh281FghRnzXxPdMfLuOaxXE364FBEiKgaJGMeIDrxuFnyB5SrhmAauxhF1bECpG38Wkn4RqohpdyPHvbPmoKgp33byRZ47NYJquKKYjuu4nZwsM9KXpSsdQFJmmabOUq1Kpiea4eMxgqD/NW+4WTWcjA5nL8kHK1SbnLuRZylWpNSxkWWLdeC9jw1k8L+BTXzrI4nKVWsNGVWSSiQhL+Sr9PSnedd9u4IXbGF0NxUqDmbkS6VSUNSPdxKI6o4NdjA5miaSGSKjVK+ZJvFisWFqtEB6247FmpBtdUzl5dpHZxTK2IyyVPM9n28Yhtm8aolo3efLZKTRNaXf0r2RwAuzaOoKqyEL90coBWQkK37ZxiG0br7w/mqqgJS5ux/NF4Hc8alCpmXzjsdPt+W0yHllVp3jgrh2rtrWSXfFyw3ZcTEvcmpaDabpsXt+PIsv83eeeEpkyNQtCYakVjYhm3F1bR0inoiK7NqoTjWjtpktFkXnr3TsBQVKthJ4HfgiKCHlvk1Ut5U53Js72TUPUGxZfePDoZfs5NtSFLAsLd1kSTZNd6RiaphDRVwLrUyjrBnB9H8fxcBwf0xJOFqoic2ZqEUXyWzcPRfKo9DSJ6EAjRyKoo+ohaiRAkQOi1jTLp0HxHG4ad5BCDwmfMHAJfY8LT36ZIPDoDlcTayGQW3xp3h/fqRF4Fop2DUWzlwAviqYrl8t86lOf4ty5c/zyL/8y2WyWgwcP0t/fz/Dw8Avezh133NEKoumgg+89+G4Tp5nHqs7iWCWkEBQjRTTaTRCGSLb5au/iNSEIPNxmHru+QLM0idPM47sNZMVANVLUiWKaHkOpNE8fvsAXvyFO8Ldcv47b928QwXSSRCIe4fx0nmOn52maDpIk0d0Vb2cw9GaT9HQlOHchxx//n6+zlK8xPtzNY0+fRPLr2KZHOhmlKx1ly/pBbrl+LRsn+sX2n6OWqDcsyjWTkZYU97NfPcSpc0vki3V0XWXDRB+m6VKUGmzfNIiuK3z5oeOUq03iMYN9O8ZYP9FLJhXlrXfv5BNfOMAjB85jWQ6xmM5AbwrPC1BVhd1bRti0Tlg8tY+ZZ2PVZlmeP4NZmUWnjC6F9KjgvVBvLUlFT46Q6Jogmh5Hi/W0B6K24/L0M5MsLlewHQ/D0BgZzBAEgtDYsWn4ssGY5/uoisLMfJHHDk4ShCGqIqPIEucv5JicKXDw6DSPHjj/vEHsAOlklL3bR9nbstTavmnoijLiIAjaHpqTM3mOnJwX4eV1C8tx2bl5mLHhLD3ZOLfdsIGBvhS6pmLZLoVL7NDOTuUolBtk03EmRruRkDFNMdiKRTQ+97XDlMpCNm0YKl3pOLWW+uiH33lDS9p7uXy6VG7w9JEZzs01KeSWqNYtMqkov/Kz95FJRdm7bZTeniQDLTVUxNDa78G/fPM4R0/PU6tb2I5PLCIC5Yb6M+zbMU42JcL+utKXB/7lCjWWCjXKFZNKzcTzfPbtGmfdWC/JeIR14z1kUrE2ebUCVVHasvHXC16qcUMHHXTQQQcvDpIkoWhRFO1iESPWtbZFoDQIfEeECjdyeHaFoGmDpLbWib3gjJUOOni9oDM26eDF4iIx0sB3TVy7imeWCXwL37MQpUJZKEfUKGqk63Wj3ltxwhCWWrM4VhHfaRIGHpKsougJtGg3shLB91LoavSKxX/H9ShVmuSLdaZmCkwvFLnv9m1MjPYwPpzl0QPnaTYdZFlCUWQaTZu920dRFJlK3eT8dL7VpBfS25VgoDfF+vGLbhy24zKzUGI5X6M3m2BsOEvTdFjMVejrTrJ5fT+6rrKYq7YdKyZGe0jEDNaO9TI8kKa7K9GuQXw3JEm1tb9zi2XqDRtDV9u2U9m0sPsKQ6h7z7+dMAxxXFE811SFaESnVre4MFfEctx25oOhq9yybx0An//60fbcH4Qqoa87ia6pqKpMMmHQqycwDGFztNK419ed5D1v3nvV171u7MVZwZmWw1K+RqHUoFCqU66aDPSluO2GDcRjOmvHeuhpuTEY+itjP9Y0HfLFuiBCLBfLcjEMlb3bx/A8n0/884E2SdK0HAhhYuwu4lEDVVXYsKaPgb4UA70p0skYibiYl6+omVaUH4WyaJ5cyYN9+PHT5Fp2cStYycnJl+qcOreErquiFqMINQmIRtuh/jRhGCDjI+EiBS6NyiyqHCLbs9jlMrLk4baIj5ITJSxpuHUHqgWickBC9pGlAMX2ufBUQBi43NBz+YewduEZaoDeunHJv3jWqj9fNfhu87VPlhw+fJi7776bdDrN1NQUP/VTP0U2m+Wf/umfuHDhAh//+Mdfjv3soIPXBVapSBoL+E5TBLZHe5GV16YX5fPBtSo4jWXsxiJmZRrPqhD4DooaBTXB1ELI6fNLnJ8+Ra1hcd2OMYYHMjSaDnfevImhvjSarvDtJ89RKDe4bscYa0a6iUY0RgZFkNrKxTxfrOP5Poos8yd/+RAPP3Ea2/ZYv6aXsZEsILFr6wjX7xpnqC9NJKKt8mu8tJMlDEOOn1ng6Kl5AN51/x40VaHesFAViY0TfSiqzMJShb/5zJNsXj/A3bduZnK6wJqRLPH4IKl4hFKlyTNHZ5kY7eGJZ6bIF2psnOgllYiKHI+hLtLJKOlklM3rBvBdk0bxDFZlhlrpAqEtglYBIi+wgyQIJXylh67+dSSya4gkhtqdR9W6yfnzS1i2y+6to2iqQqNps3a8l+H+NNlMHEmSRBdG65q8kj8yu1BiYbnK2HAXu7eOEoQwNVdg8kKeC3PFVtfJ818K14x0i6yRlqXWxGj3FTuImqZDoVSnUG6QLzYolhvs3THK+vE+lnJVzkwtE4toDPSlGBnoYqhfKIB6uhKYtsvhE3OAkIpmMzFsR5Ala8a6mV0sc2BmmlK5gWm5JOIRPvxv7iUW1dm3fYx43GB0qIvebGLVACyTimFaDmemllpZLFWGB9LcedMm8qU6X/32cdKZbibGehgb6mLNiJBHy7LM/XduI1+qc/5CnkcOnGdmrsT7334dI4NZQkRWyfZNQ4wNZelKxcikRSEqGY+wbdOgCG9brlCqLFCqNLlj/0YihsbcUoVSpUkmFWVkICOC6TNiENDXnbyqKuf1hs64oYMOOujgtYkrEShhdgO+U8ezazhWCbdZwDVLBIGDJKkoWgRFjSEp+uvGNqaDDp6LztikgxeKMPDxPbOtGnGtSmtObLUUIyFICooaQVajqEbmdaMaCcMAz67imkVcq4RVncMx88IxIgyQlAiKHsdIDF5Wy7i059nzfUqVJp4XMNiXxnZc/vqfnmRxuUrDtIkaGslEhGNn5pkY7SEZj1CuiM7BWtPBNG1iUYOzUzkR+l6s02g6pOIRMqko6ydEIxkIq6aT5xapVEXjaTymY+jieA/1ZzhxdoFnjs2SL9aoNWz8IOD+27dx582buGP/VeQQ1wjP91nK1TB0lZ5sgmrdYmq2yHB/mut2jNHXnbysiTNfrFNseqhBqaUkcNm2cRBD1zh0fJapuQK27bWbybduHGTHpmGapsO56RwR/WKOxKWW0W+4YR2a2lKHGFqb/AFYv6aP9Vd5DVfLLn2hCMOQRtOmXDMpV0x6snEGetPkCnWeeGaSZCJCd1ecdeO9bfWIqijs3PzdE9FhGGI7Hk3LaQe9T80WWM7XMG0Xs0V8bNkwwOZ1AxTKDR47eF6si8jbWCGNLNul0bQIQ2H71tudIBmL4LfcNK7bPsbJc4sUS00Wl6sAjA1nuWnvWmzb5elnz6FrYGhgaCGKFNAsSYBP1lgi1mUhhR4yPuDhF5fJWyp6vcGaWBVay+TARyuHTB+QCH2XIc9BllYLC/Inxe8MkHlumcCEugkykLq0yh/Sstv6rg/7qwpBQr+yuGay5Bd/8Rf50Ic+xO/93u+RTF58h+6//34+8IEPvKQ710EHrxd4dg3XLGLVZnHNMiBUJEby9eM/ChAGHo5ZwKkv0SxP4TSW8d06ICGpcQp1A1VPkYgY/PNXD3PgyDQRQ2NkqItdW4fb3ot33LQRz/c5M7nMgSPT9PUk2bphgO7WRWmgN0W52uSpQxc4fX6ZcxeWsR2PO2/axJrRbkzLIZOKocoSiixRKjXZvbOfidEEyfjlthAN0yYWEZP2Q8dnuTBXpNG0UVSZpVyVBx89ydb1g+zYPILlXODM+SUy6RiDfSn6e1OsG+8lHjV4+707+ccvPUuz6RD4AUEYsnXjILIkMdSfplLto9awCIHtm4ZYMxSlnj+JVZ3Fqs7gmvkXcdQlLDLY9NLVt5aR0Y1o+sXXaFoOJ87OsbBcod6wkWWZwT6hXpFlmTfesvmqW56ZL/L0kQs4jkfDdJhbLPPlh4+xsFzh5LklguDqyj5DV9m2cZB9O8fZs22U3dtGrqhm8P2AcrVJvtRgYlRIfg8cucCp88t4nk8Yhvh+SKMh5J9rx3twPKFu8X2fSs0kV6wxMdqNLMusGc5Sqjap1CwKpTpnppZZWK7wvrddjyLJnL+QpycbZ93OcUYGMgwNZFBVGVmWufsNWwAxgM0V68wtlBgdytLfk+IrDx/n64+cJAxDJEkimxEdLQDrxnv5jV98G66cJa5UyBVqLOaqDPVnkGWZP/4/D7KwXAFEFspwf6YdSnbvG7YgSVJ7klAoNcgV6237sM999bCQysoymVSUnmyifdxX/Fm/19EZN3TQQQcdvH4gSTKqkUI1UkRSw4SBj+fURUHNruCZJbwVf3oJJElFUnTkS24ddPBaR2ds0sFzEYYhge/gWhUIbDynIfJFnBqB77bOeSGSpCKrhrDVfh0pRoD2udyzKy0HjDl8p4rnmkiEbXIkkhz9joRPudrk2TPLNCvLLOYr1BoWQRDy5ju3s3ndAHu2jnJYmSUWMQgQdsNnzueo32hTKDco1yzmFsuEYYgiS8QiBsm4QTYT5/47thH4IQN9KXqyCVRFaasnQkJs2yWZMAgCyJfqXJgrMTzQRVc6RrVm0WharBvvZXy4m/GRLL3dV7akvhbYjsvcYoWZhVaToe0xMpBh/94JhvrS3HbDes5MLXPq/DKHT8xhOx7RiMbdt4r56cNPnKbhGBiKSaRFbGxY04eha2QzMVRVbhMeEUNtuwr096Z4+z27rrpfV7Idf6nhuB6Vmkk6GUXXVI6dnufkuSU8TyghDF1FVQcY6E0zNJDmHW/a9aJVI57vtxUepuXS9Gy2jBuEYcjXHzmFaTlYttueT6+EiOeLNRZzlXZOiqrKeK7IyDCbNSrlHK5jE/ou4BNaGo2CSui7bB6oEAYemiKspzQlpDg1hWXIyM0GE1ETOeoj4SERoDg+U08IK6p9PZe/hqUWqZFo3bikHBjWoCbifUlfoRrvi5IJ8uunhHhVSLLaumlIsor8nL8lWUVWtKv+X+C7BL7FyK4fQ49mX/H9v2ay5KmnnuLP/uzPLnt8eHiYxcWXyJSsgw5eBwh8p53bYTdyLWlYHC3ehyy/NoKoXgg8p4HTWMSuLWBWp3GtCoFnIys6qpFG0rN88gsHOX72CKoic/N1a7nnDVvYsXmYG3ZPMDrUhaYq2I7LUqGKIssM9qWp1iwOn5hj8/oBFFXm9Pkljp2e5y1v3MkjT53l459+gsVcFU1TiEcN9u0coysdpb8nyaa1QjaryDK3XL+OXZtHMMMMCVWw+dNzRUqVJqVKk3K1ie143H/nNlKJKKoqU642efLQFMVSA0PXMC0Rcr5v5zgjgxlOnl2iaTqUKk0qVZNjpxfYsKYPSZJYN97DcqFGpWoiyRKut1LojhHVHbYMBySUReza0ywdLl7z8Q6RaHgpqk6adRu30ze0ASRVBM2HIaVqk6XpIkEQsm3jEIois7Bcob8nxZ5tafp6kpflVPh+QKHcYDlfY7kgpMh1t8TTBw7x8BNnmJopUGvYz7tf3V1xrtt+UTWyZcPAKuXOc3Hk1BxLuVqri8jHtF1SiQiDfWn8IEDXReeLoavtoDCAVCLK3EKZkFBsPwTb9Tg9uczmdQPMLJb52rdOtAiNGIN9GcZbYWaDfWl+5Wff1CYgHcejUjfbnU2ff/AIJ88uUio328/3lrt30t+TYsNEL4ahMjbUxchg16rX5rg+X/j6ESYXLUqFHI7jIUkSH/6ZN5HNxNm/ZwJFkZgY62kTfm5rcFhrWDx64DzVmiUG+4pMVzrWJkv275kgGtFJJyPfdQfP6xWdcUMHHXTQwesXkqygRdJokTRRRlvB8c22/Yxn19p+0q7bIAzcTvZJB695dMYm358Iw4DAdwg8mzBw8F0L32uKfBGnTr1ioTQCwtBFQkJSdBQ10raeel01QYYBnlXBMYU60KrN4ZoiQyUMPCRJRtZiKFqCaKzvBZM+puUQjeiEYciZ8/NUSjkURUJVFDJpg8npApvXDbBr6wjfePwUc1YZ0/YgDAmCkLMXlglDMDSF0cEMvdkkY8NdDPZlyGbiJOIRdm4eoWHa5Ap1njk6Q65YR5Yk3njrZgZ703x+/ghN0yEMIRrRSMQNiuUGXekYD7xxB7IsveBcxyAI8LygnTF6Ya6AZXtYlkOh1MD1Am67cT3zSxU++YWDWLZLJhUlk4pRqjS5MFdkx6ZhgiCkVreJGMLqyjBU4tGL17973rAFR+qiO2YiP6cSPjr0yheCvxNOnl0kV6xTqZk0mqKOcNuNGxhs5cps2zBIJi0cNi61uVYV5XmPfanSpFo3MU0by7awbYfBnigDvQmWlkscPj6FhA+BTxC4aHqEYaOXMHCJOtNEQhfZ8JElHyn0yJ85RU2TiNVqjCoWShigSAGaEaIWQy48ARpwzxUkNsunhdpkzRW4tLAOjbq4H71s4SuXp/JyQzS7GO1ml5XmF0nWBGmhaK37Wuu+Ku63lon7GpJyyeMrv5/nfLliDR8EAXNLFWzbxTK9tt3cvp3jSH4dzyrRM3HXK27BBS+CLIlEIlSr1cseP3XqFL29L87XroMOXi8IwwDXKuM089jVeTynBpKYROqxK9DKr0GI7JECTnNZKCJqC3hOHYkQRRMDQUWNtP434GOffIxT55bYv3eCLesH2DAhSIVtG4coVZocOjFLrlCnWhNS2NGhLgb70mRSUfZsG+WP/8+DzCyUCMOQ0aEuQASt/fQHbqVat9BVlSAMmVkoYdrCbmndml6u2znOYG+K+aUKR07NUW7muGNfPwDPHJ8hDEJUVcEPApqWw9e+fZKdm4cpVZs8dWgKy/G485ZNrB3tISQkGtHRNZWIHlCqCN/SseEuujNxMilx8j07lePM5LLo7DAUQreOXTxB7uxRrOoMa5QKFKF+DcdbkhSMxCCz5Qj5ehw1NsDwYC87BjOkWuFntbrF0dPzLOVEyJeiyK1jJayonht2FgRBK2tE4fTkEt9+6hwXZgtMz5eYWSixsFx53iB2SYKNE/3s2T7aJkdGBjKXXdCCIBBqiXKDQrFBuWZy3+1bkSSJ2fky5WoTf2WAqYkgPoC+nhRTMwXCQKLRtAnCkEK5weZ1AyQTEfwg4MzkMpbtASG6rjI8kGbzugGu2z7GxEg3Y8PZdv5Ju5MoDPn2U+eYWyyxlK9RrjYxLZdf+ul76M0mcF2fnq4E2zcOMdifZmSwi2xavLdjw1kMXWN6vsDBI9Ms5KrIsszPfvB2ZFni9OQysWSWG3ZPMDYkCJoVJc3ubSMsF2rMLpQ5fGKOUqXJQG+KW69fT8TQ6M7E2TDRRzYdv4wUGerPXMOn5XsTnXFDBx100MH3DkRwfAJVX11dEAVIq9WNLbqWPbtCYDpIyKAYBL7KFcoOHXTwiqMzNvnexAoZEvoOgW+3OpMdAtfCc+v4bpMwcAg8jzAQuYdIMrKsQavIp0XjKOrrTyEXBJ6w0zKLrbyROTy7SuCZhIQoShRFj6NGuq6psdO0HKp1i2rNYmG5zCNPT3L3rZvZt3OcvTvW8vWHlnC9kCAQNszT8yXeeMsmdF2lKxVjyanR3RVHkSXqDYuuVIzxkW6G+tLIiiTyPAgpVZqYtrBYLlYafOHrRzAtF9cLsGyXpumwZ/so/T0p7rhpI/WGTU9Xgu5svG3FBGLu7LgelabZCj4X4ebZTIzurgS5Yq2t/LBtEbqdTkW57/ZtNE2HLz54lErNwmqFkI8NZ/EDUcf4wDuuJwhCIoaKrqsi/LxFsvRkE9x969UdH1KJKHVPR5JeeTuhK8H3A5byVap1i1rdolIzqdUt3vrGrUj4lEs5ZN9hrE8jGYsRjyjElGXq+QWSkkci4eLZDtW6Q9GxCQKPqC4RBB6FYgXfdyFwCQMP8EnGVGR8HMeB0MOQQ1aoJH8O5oQLN1szl+9rcUr8HrrS8MEqYVsQlSH6PT68kFqEhSxrF4kNWVtFcHhSAkMLBflxyf+3SY9LyJGX0k7V830s28NuuthOo/2dy6RiLOWrnDy7KEjIFhmSzcTbDimPPn0OWZba36mIoeL7wYsLWH8Jcc3P//a3v53f+I3f4B/+4R8A4XU7PT3Nhz/8Yd797ne/5DvYQQevNsIwxHdqFzsyrDJh6KNoCYzE4OvCk9Sza9iNJez6gsgesYV9gqRoqHqKaOqi1HYpX6VUqbBuvIcTZxbIFeq854E97Ng0TK5Q59ljs/R1JxkbzmJaDsu5Gr3dCbasHyAZN2i2pJGnzy/xP//6m0zNFLlhzzg7Ng+zdrSHTCrK8ECm7c/51OEL1OoWiiLxhuvXsW5cTFDqDYsHHz1FsdxAlhUCOUGpkiARNxjqz/DYgfPMLZbbJ+b+niQb1/azZ+sogR8yv1xBQhAR2a54274rFtXb5EO52uTsVA7DqLB9wwCZaI3B2CxxpUxSq6DJQgdZz13DwZZVArWXip1isRrj1pv3k0ol0Ip1tkbFgCpfrHN+Ok9E19i8fgBJlqg3RPbIQG+Knq74qoL7CmmxXKhxdirHwaMzOK7H4nKVo6fnKbc8W6+GqKGxa+sI1+0YE5ZaW0dIJiKX/Z/ZkrtmM3Ec1+Oz/3JYdNv4AYosISsytuOJgHMZqg2LMAgxLQfPDzl0YpZ1430M9qaZWSjTtByCICQMQgxdpdawSCYirBvroa87SX93ku7uBJlktB1+nkpGMC2H42cXmFsos5Sr4vkB//qHbyMIQh56/DSSJNGTjbNryyhD/an2e/vON+3G831qdZulfJVnj83QlY6xd/sYZyZz/MUnHkGSJBJxg76eJGuGhWJF11Q+/DP3kW9E8M1lanWL89N5rD6Xof4MS/kaTzwzSSJukE3HGR7ItPNEdE3l+l1rruED8v2Hzrihgw466OB7HytdiaqRguSgUKA4DWH74lRxGjkCr4ZdqSCrulCeqJGO8qSDVwWdscnrE23ywxd2OkHgEniWULs5DUGUhC6B7wm7nXYdUGnbvchKFEW7vPM5CENky0R6HeSMivpEHdcq4Vol7PoSTmMJz6kT+g4goWgxVCOJHO//jgVR3w9omDb1hk21buF6Pts2DCLLMp/64jNMzxdpmg6u59M0HQ4em2HfznF6e9LUmzayJBMxVIy4iqFrLOYqDA90YegqluNRKDeQEPPwlfdE0xXOX8jzxMEplvJVmqZDKhnlR9+zn1Q8wvmZPKbpIkkS8ZhONhOjUjXp70mxfeMQp84v0TBt8mfrWI6L7Xi84Yb1xKMGB45MMz130QFCliW2bxqiu0tYesVjBt1dcWRJot506O8R87qp2QKZdIwNE33096bo71k9Lx97FRUgYRgShj6hLwiIMPAIgpX7lzzmu+2/XdfBtk0cy8J1LTzXRpUDEjEZ33Oo1xvIUkCXFNCt+EjpgJmnvwZA/8oT18Wtgbg9H1ruUkRAvM9K6waELviAIrHKlup7DcKadIWYuPLvi/fVi4+1VRp6W6WxmhDRvqP6Kwyh7qVIqFW+Ww4kCAJsxyMIQ+JRgyAIODOVw7bdVuaOh+243HbDBnRd5bEDk8wvlVdtY/e2UTKpGLIsCTeZuNEmQxKt+o0sy7zzTbvb9mmXwrWa392L+C5xzWTJH/zBH/DmN7+Zvr4+TNPk9ttvZ3FxkZtuuonf+q3fejn2sYMOXnEEviv8PJ0aTmMZ1yrjuyaKFkOLdr/m/ZgD38Vp5nGay5jlCziNZTxXDFIUPYEe7WlPTouVBmdPTXNmMsfkTJ5K1WTNaDdd6b2MDHXxvrft4+xUjs999TAgMhu6MjGCIECWZfww4OuPnOLs1BJLhTqJmMF9t29l55YRbrl+HT/6nv1s3TBIrlhndqHMuQs5HNcnm4kzu1hCU2U2retvq05WMiUePXCek+cW8X0R4FWqL1EpL3HPrVsYG+pibjFLrWFRqZrEozqxqE4QhMRjBnt3jLGm1CCbibWL8Cvw/YC5xTLnpmaxa/PElBLRWJMLpSph6DF6jQo/SdGJJIeJpEY5MQMzyxKSrNDfm2LT1gyRqHh+SYKnD11guVAnCAIiEY21Y0KNlIgZ3NPK21jZx3yxTioZYSlX5ROfP8CTz05xYa74HYkRgOGBDDu3rmXf9gH2bh9l49q+K8phLdtler5IodSgUGrQaNrEojpvvXsnmqogy+C6opunXDFpWg5HR+bZt2uceEzn+OkFwhBUVUaShGWZ7wekkxHWjGRJJqIM9qUY7E2RTERJJ8WxuH73Gmp1m2rdpFRpcub8EqlklOt2jDO/VObP/+4R/ECogeIxnZHBDACKIvNzP3pHy6M2pFa3KFaaWLZLxND45hNneOjx05iWi+f56JraUs+MMTqY4UfevZ+xoS4ihtbunnEcD11XeerQFCemGhiKiSJLIpSuZbk1PJD5rrxXXwmEYdgeJIeBRxj6r/YutdEZN3TQQQcdfP9BZJ8kUY0kMIifWY8pTZNMqvh2WXRA22XCptMqHBhty5vXQyNSB69vdMYmrz2sVoVcerPxnSaBZxJ4ooM9CJyLicWS1C5Qys9Dhrye4TkNPLsiyBGziF1bwLOr+F5TVEglFUWPrXKIeD5UaibVmsnoUJZSpcnnvnqYRtPGbKkwXNfDfqOwwlk33sPhk7MQgucF+F5AsVTH8310LUJvd5JKyxa72vDQVEXYaSsyiiIzNtTF6GAXa0a7iUWNdp18bqHMP37pIK4X4PsBfhCgKQqzCyVGBru4fucapmYLJOKGmJdKEudn8mxcK8ifU+eX0BSFSEQjamikk1Hk1vu9Zf0A69f0CuWHoa6yXw7DkFhUZylXpVRpEoYhPVkx51s/3sv6Nb3Pa0V9KVbPv9wWgeGtnpMFrnjM97A8FY86hK3/9Z+77mryQyy/+Nh3A611wwe7xWpEvg8vtavJjEtzMp5jM3U1+6lLlBrPVWyIc85r23a7Vrdomk6bYLQdj4HeFL3ZJPNLZZ49PovTehygKx3n3ttERuszx2bQNQVNVVBVGV1VcTxRSxkZzJCI6yiKgqbKKLJMpuXwEYvqRAwNzwtE/a4WkivW23k7B45OU2/YbccS1/W56boJup8bYv8K45rJklQqxbe//W0efPBBDh48SBAE7N27l7vvvvvl2L8OOnhFILrf6nh2Dccs4jYLeF6DMAhQFAPFSKLHXruSbBHMXsRp5ESGSn1eqEd8F1kVoezRWA+uF1AsN1icWqYrHWNsKMvJs4t8/ZFTyLLExGgPG9f2s2V9PxFDQ1UUmqZD07TZsmGAUqXJ/FKFg0emyabifPOJM3zua4colpp0ZWJsWTfAnm2jXLdznPXjPezZNsqJswt8+aHjAGTT0VbYWpb5pQrnpwtk0zFqdYsjJ+eoVE1qDZPtm4aJx3SqdQtFlkmnYvT2pUkYHrGo8CU9fyGH74fs2znG9k1DDPSlySSF9jIRM9qhaCDIo3pljtDKUSvNUS/OsEa14JI8tBfqOykrEYzkMJ7aR9FMMpuXeOPuLRi6Rp+XY2hUJpuOU6w0WMxVCfxQWJfJEkgSO7cMM9CbIp1crRNdzFVYWK5w5OQ8B4/OcG46R65Qp1R5fkY9k4qyed0AOzYPsXurUI10dyUu6yqoN22KpQbFSoNEzGD9mj4s2+WJg1NIshj8Niwba8mlXG2SScWYWyxz4uwirhcgSaIr5/DJOfbtGme4P8PYcJZk3KA3m2SgL0lfdwpFEQOEH3nXfjzfp1oXhNaF2QLDAxl6u5M8fnCSbz91Fsv2xMBYVdi2cYjrdozTm03y1rt30t+boisdIxrRqNWtNjl36Pgsp84vUig1MC0Xy3Z5zwN7uXH3BKlkhE1r+8lmYnSlYsTjRnuwm4hHKJUbnL+Qa1/8ZVkik4rRk02wZrSbVHaYwS6PZNxYpezR1EvaYl5BhGFwcZDtX0qErPwOVjXmXAxHU9GiXSha/BXf5yuhM27ooIMOOuhAkiQULUo01YcsjxIGPr7baI29C/hOHd9rYjfKEIZIioaiRpHVyGu+SamD1x86Y5NXHkIVYrfyQjxBjAQu/iXKkLYqJBBWvQLyKp98RY+gydr3JKnaVuTZFTy7imuVsesLuHYF32mKZigJFDWGogpy5FqPQ7HS4J++/Cyu5/MDb7mOZDxCEPicvZAjCEPCICAI4FtPnmbfznG2bhjkbz/7lFBktJQhpuXguj6u69NsOhBCOhmlJxunvzfVnn+99Z6dHDg8zfxShWOnFzhxdgFZlvijX30vGyb6CEKxrXQiSjoVJRE3yBXrjAx2cdPetXSlY0QMQYZEo5pQprSwEnq+mrCwcc0GMcUlwKNRb5BfaFCvNxjsjaNrEgtzOWqVOv1xnXVrNOJRFVU6TmHyqFBuBAGXqTWeQ4i8FpvTvicgrf6uS7JGKBkoitRSr15KTGhtgvTyzAz1YubGc4mN1ziZsQLfv0giit/C/m0lj3cpX23XQkzLI2Io7Nk2juf7/N0Xn8J2WqRnSwXyU++/hUwqxucfPMKZyWXCMAQkFEXmluvWcufNm6g1bU6fXxbHW5ZRlYvHSpIkIrra2u7Fz71leSRigoA9fX551f+vX9NLX3cS3w9YLtRQFaW9be2S9yEeE4SoJAkFiqbKrVreq2tZ96JtwO666y7uuuuul3JfOujgFYXvNvGcGq5VEcqLFdmqpKLqcYxYH9JrOKjdc+qCHGksYpYn28HskqwI9Ui8vz25bJg2Dz14jMnZAuVqk6bpsn5NL++4dxd7t4+xc/MIISGFUoNcocbDj59BkiTefOd24lGDbz5xlv/76ScwLdHR0JtNEI8ZvPnObdx18yYqtaYo0DdtFpYqnDy7iK4prBnpFpJmWaJcNTlyco7urjh7t4+xdqyHvp4EM/MlqnWLet1GkiX8IGBkMMuGiX4WczUs24FQoli3cUwLXRdhae972z76ey4W51cQhgFuM49dX6RamsGqLiB5ZSTpIhsSvYa3VVKiRNOjRFKjRFMjHDrTYPZCBcf1iERgeCCN74ttRwyNY6fn2wRHJhWjp0t4emfTcW6/cQO+H1CpmZydWubCXBFVkTlyap4vP3SMqdkC7vNkjaiqzPaNQ1y3Y4y928fYsWmIvp7kZV1Tlu3ieB6oIqTumWOzNFvSast2yaRjrBntRpYkjpyaI1+s4wchvi9IkRN7F7lp71r6ulMsLFdJJyNk03EG+tJsXi9EuaNDWX7iB28mlYgQhGHby3aF1PjSQ0c5fX4Z03IxW3Zcb7tnJ73dSTLpGBsn+unvTZLNxEkloqRaqpNYVMcwVM5dyLGwXKFQatA0HX7mg7eRTsaYWShiWi5DAxnSqQipeITerGg7GBvKcmZymWK5SbEs3oNoRG9bu3V3JejJJkglIqSS0VWkSG82STT10shWr4bLu438FvHhi0lh6LenhRKITrn2QE/IdBUjhaLHxU2NIqtGy3fUIEBFN2JIik65MEuqf+fL80JeJDrjhg466KCDDlYgyQqqkUI1UkRSwwCiaOrU8Zw6rllqd1KL8bmMLOvIagRFi31PFko7eOXRGZu89Ah8l8Az8T2LwLPxvSauWSFwhU1WELiIhOTWCpJySRE0iqK1CpzfI6qQq2GlFuHZNTy7KoLYmzl8p4nvWYShh4QkLAu1GGoijfwdLMI83xfFUaulEPF8xoezSJLEP375GWbmiizla8wulNA1lfHhLPffsZ2B3jRPHppGliUUWUJRJOqmmPfrmkp/T4p6w6bWEBZdE6PdVGsWXV09PHDXdp48NEmlZnFmcpmnDk1z6Pgcv/yv7uHsVI7f+7N/IQhCFAkyKYNtG3txmgWiEYV/8wObkUIXQwNVCVClAMIFSjPTBL7DhowrbKU8h6Di4hZdZmccYcHmu4SB0yLWnh8JoDYj7seAWAThCVWDZu3q63UAQSjhhzJBIIiMeDxGgEK+ZBKECiGy+C0prF8ziKxoLOYbuL6Equqomrhl0kmi0SigoKj6akJDWVGCPbeu89LZSn03WCEuPD9oW5SvZN4u5apYjksQhARBiB8EDPSmSCWi5Io1ZuZL7cdd1yeZiLBrywiW7fL5rx/B9Xxcz8dxxO8PvWc/0YjOJ794kKmZAkEY4PuiTnPnTRu5ff9GDh6d4TNfeXbVPg70pdmzbRxFlpmeK6JpCoamEtFVUpfYr2/dMEhfd5KIoaFrCrIsM9iXAmC4P8Obbt+CLMsosoQkS+jqxbHW/r0T4rusCOWIrEgkWy4u2zYOsWX9AIoiI0vSqsbTVEJkAl0NOzcPX/Fx13qdkCVPPPEExWKR+++/v/3Yxz/+cX7t136NRqPBO97xDv7kT/4Ew+j4znbw2kMYhgSe2RqQ1HGaBXyniu9ZEIKsRdGMzGvaN9n3LNxmHqeZx6xM4zRzogOHEFWLo0WyKNqVU62eOTbDgaPT9HTF2bNtlHXjvcISSRIF/qLV4KvfPAFAOhVloDdNImbQNB0eO3iefKnOUH+GidFutm8eYmKkh/6eJIl4hEQc5pcrHDx6giAISSYiDA9kSCejLCyX+fQXn2G5WMf3AyK6CHN/6LFT3HbjBtaO9jA1nSeiq3SPJohHNSKG6BpJxAwmRruZWyxTq1tk0gnWDfe0FSND/RmCwMOuL+M0lrEbSzjNZZxGbpVMVYZr8sVUI92o8QFiqREafoZcWeJsrsGd6zeKC4M6zZrRbqIRFcfxWS7UWMhVWDfWi6rKpJJRNq7tp79HXIRWwuEsy+HP/vZbHDk5z8JylVyxRrVuPa+iJRrR2Lt9lH07x9m3Y5ydW4bboeeXYmG5wvxSmaV8jeWC2O7ExFrects4ruvzyNPnqFSbuC1Zo64pvOtNu0klo4SEpJOikyeditKVirbD5e95w2Zu2beWZCLSvhCardejKjJHT82zlK+ynK+1FEgOP/ehO+hKx2maDrquMjrYRX9fiu5MnJGBDAAb1vQSj+pUaibLhTonziziBQE/8MB1+EHAV755AtvxUBUZTVXIZuJtNcjubaOcOLNIGIY4tk/ebpBKRFk71kMqGWH31lFSiQixqE40qq2yH9u+aeiFfxC+A8IwhNC/hPzwLyo+gsuVHyGhkP3K6iXkh4qsJlFahR9Zi6NokVXBa7JiIKsGISpNW3RxpFNJLMvm4KFjNJtVGg2TeqOJ53n8yPvfgSzL1OoWgy/Zq31x6IwbOuiggw46uBYoagRFjaDHeiCzhjDw8RwRyuy7DWGLa9dwGouEhK3CagxZjXzPF1Y7eGnQGZu8dAjDUChFXBPfM/HdJq5ZxHca+L7dagQKBdHZGtOqRvp11d393UIo6AQpIojgRiuEPY/nNghcizAQGZnImshx0mJo0ezzNm16vs+ZyWXyxQZdmSjbNw5TKNX56888iWmKMPRq3aTedPjVn7+P8eEeDh+f5cipecIQFFkiCELOX8gThiGpmIJr11HVAFkGWQ3BqlFYPIVMwGCigKM7GLqMIgd47hkunKwxPjZEfvIUUbNAVwyMVIihQSqxzOyhWbo8hz/8cRdVDpAlHzCBMqXTZyitHKPWox28OISo+KEMkgKohJKCqupEY1GCUKZUdfBDBT9U8QOZAIUtG8eQZZ1nTyxQbXiECNLDDxX2bFvD8GAPF+YrnJspoes6uqZg6CrdXXFGhrvx/QCtUGs/rutqWxkAkB1/8a/nUjWF5wVUbIVI2kdTFWGj3bDaxIWw/47S35OiYdqcnczhBwFBGBL4omF3306xM08dmqLetFeRGtftGKM3m+T05BKnzi21lFUhYShsuG/YvYZa3eJzXzvUJiz8ICQIAn7q/bciSRIPPnaKheUqfotICcOQe2/b2spMXeYbj50mDEKCULxb40NZdm0ZIQxDzl3IIctSi1wQBIPni8ZZ0XAbomsqhi5ua0ZF3uq2TUP0dSeJRkTdLBoRubggFB3/4d+86ark0q4tI1c99omYwZb1V68grNhmXQkrThxhKI6v43pCKaIqBEFAvWkThog8W8Rx7m41FRfLDRzXa70v4vh2Z+IYr3JPzAsmSz7ykY9wxx13tAcWR44c4Sd+4if40Ic+xJYtW/j93/99hoaG+MhHPvJy7WsHHbxghGHQkvPWce0KbrPQmmiZIAkrJUWLoUayr9nJVeA7OM0CrpnHqs5i10VoG4GHpBqoehItlb2ss873A8rVJvlSg0qtyfo1fdy4e4KxwS4apkO5YnLq/BKW5dLXk+TOmzaRSUbZvK6f8zMFnnhmijNTy5imw7vfvIc33rKZG3evQVUVYlGdXKHGwnKVE2cX2bdTXGAIQ1LJKJoqU23YPH34AsMDaaKtgrhg0YUlkq4rnDq/xP69a7l+15pWSLtg57vSMSIRFV0VJ9q5xTIAu7aO0ts3QIIZzNwzVBrL2I1lXLPAxbakF3GMQ5lA7SaSGiHdPU68axRJ1nn4iTPkz9QJgmUiEY2udAzTdkjEImia0i7WG4ZGf3eSRFRMqLKZOE3L4dzUMp/8wkFOnV9kcbmKJEtMTuefVzUC0N0V57rtY+xpESRb1g+0LjwijH5yOs9SoUYuXyNXanD3LZvYuLafQ8dn+daTZ1FVMfFomi5uMA+3jWM5HvNLZXRNJWqoDLaIi0hEkC4/9t6bAIlUMkIqESERu6i2iEQ05pYqXJgrUqo0yRXrqIrMB9+9nzAMefyZSfF/hsZgX5qh/nSbzLnv9m2YlkOtYVOpmSzla/h+wLaNQ+QKdT71hYM4npCGhgHEYkIFpSoKAz0p6k2LeNQgEtFQZBnbFmTJUF8aQ1eF1VrcIB412uoiVVHYMNF3zZ8DcVH3CXwHP2gI0iO8hARpyawvEh8ryg9FSH4vIUFkPYmiCeWHqsWQVQNJFqG3QgVykQiRFB1JEjk9TdOi2TSpNJpksxkSyQRT03McOXaERqOJZdkAjAwPcPedtyDLMrl8iUQ8Sn9fDxOxKIn4RbJ0sL/7mo/DS43OuKGDDjrooIPvBpKsoEXSaJGLE/TAd1qKk2or0LghxoOS1B7fd8iTDq6GztjkxUHMrU2hGHFbDYhWCd8zCbxWd78kt9XOWqTr+0IdskIY+U4D32sSuE1BGlkVPKuCa1cJfbulFPHb8we5RQyr8RSSrOEHAYosMiDL1SaNZh3H9XBcH8dx6e+J0Z0yWFwu8OyxKYrFCrMLeYLAJRlTGc9cj+TaaOZx1MAlGwvRU6BrIUsnP4dRTfGWnTnuWNdAV0FXQyIaRPQiU48/xRDwn959+eurTn4OgDduuXwZVDEXTrMmA2syz13m4jYFBaJ/n4kAgwC8QML1JTxfIpWMYxgGpapDzQwEUSipSLJCKpmgr7cLx4Wloomm6aiqhm5EUFWd7mwaSdYoVW38QMIPFLxQwvVgeKCXZDLG1GyBycnldv6E7weMj3Szf+sE9YbFtx882rI2EvZGmqaS6N2ILMv0VrMkbFdkUGgKqiLTlU2g6FH6ehVCWSPwBbEQBGG7kK8oMnOL5fbjYav7c8+2UaIRnZPnFlku1FokQYjvh0yMdbNurJd8sc7Bo9OE4UViJBrRuPtW8SH73FcP47hi3h8Cth/lgdvG6ckmOHchx5nJ5fYySRI5M/09KUzL5dTkkngTQkFOaKrSJksW81Vqdbu9jDCk3rTpzSapNyxyxXprmSj2RyKiVB4QUqmayK3vpywL6yrfD1BVkVMbMTQ0TSGiq6iKwlC/GLNsnOgnETNQ1Ys5H/FW42/E0PhXP/SG1jK5ZVF1kUS+6+aN+EGLvCFsKzoAYYfXnWgTP9W6hSxL9HQJt43ZhRKe7xOEQsEXhiEjgyK3dSlfpVRuCkJqhZjoSjA62EW9YXHoxNwqQkNRZG69fj0Ajzx9jlrDaj9vEITcuHsN/b0pTpxd4MjJ+fZnAYTrx03XraVpuXzpG8cu+66894G9yLLMM8dmyBfrq5bdsHsNI72vrsvPC372Z599lt/8zd9s//33f//33HjjjXz0ox8FYHR0lF/7tV/rDCw6eFUQ+I4YpLhNIWNtFvA9IWGVJLk1eYq+pskRoRwp4JiFVu7IAr7TIAhE8KWqJzASg5fJb11PMO3FSoMnnplier5IudKkUreIGRq+F3LTdWtRVYUjJ+dJJyNkklHCZJRG3abUytb4oz//OrOLZXRVpbcnwRtuWM++HWP0dQubp6ePXOD0+SXqDcEKr1wYLMflsYOTnL2QI1esU6k2CQKIRjU++K79/Osfvo0vPHiMaEQlGtHpSsfIpKLtovo9t20hCELmlypcmC1QqjQY6dVxgxI3risT2AWc2mM0ClUa38XxDUMJSc+Q7Bol1HvwlCx2kKBcMpnKNTBnTN52j1BPGIbasnYKaVouC0sVSqM9JGIR+rqTxCI6yYRBrW4xOVPgiw8dJREzOHh0hgcfPUWlZq26UFwJsiSxdqyH63YKS63rto8SjWjUmzbL+RoXZgscOj7DO9+0m0Q8wsc++Rjzi2VkWUJrSSpzpTob6UdVJeqmhWl6mKYDEmgRwdRvmujjbXfvZLA/TV822SZFlBYhMjaUJV9qUCw3mZopUK1Z9HYnuH7XGkrlJl/55vFWoItENKKRbQV1ybLMO+7dhaYK70nb9anVLUzbJRrROXshx6MHzlOtNrEcH8f1WDfey7aNQ8iyRLHSJB4zyKRiRCM63Zk4juuhayp33LQB3w9JxEX2TDRycbLV3ZVodyFc+X0OW36zHgR++3646r5HGIarOi5CScWXBvHlBrKiXEJ8xFC0KLIabak8WmTHiv9pWwGiX7FTLgxDKtU6pmnRLJk0mjVM0+L663YgSRLfePhxZmYXCMKLRNpNN+4hlUxg6DrZrjSjw4PEY1Hi8RjJpMgh0XWNtz/wxuc9Dq82Xu1xQ6lU4ud//uf53OfERO9tb3sbf/Inf0Imk7nqOh/60If4q7/6q1WP3XjjjTz++OMvyz520EEHHXRwbZAVHT3Wix7rJda1Ft9ttJqjqrjNfIc86eB58WqPTV4PWHFkEN+tJq5dxrUqhL7TcmUIRKC4GkFWo6h6+jVtXf1isZKrIqzELALfEhZZTg3XKuOaJVy7LAgS3ybwrFaTlQ+EopKL3GqMkggCH1kKCEOfRqOJaVrUanU81yYMPQZ6YmhKSKlcxXMdVCVAV0ISKjRKtOfBW1NAClhzcV/z574MwP17rvRKCpjlAj1x6LkszvDVny+8HAgBz5fwW6SF60sgqQz2ZUFWOXpqET8ALwDfB8+HvTvWkErGefrINMtFkyCQRWMlMmvHB7hhz3pmF2s8+ewMiq5j6Dp6JEImleDmfZuQZJVDJ+chFMSDoshEJIm+oS4SsQjubIH5WRvFqwrbJc9DiWToXjNOoVTnwMFDItza83HdBtDg3/3kXgD+9suPUCg1WvNcQVbcf4fG9lScYrnB+Zk8iiwLCyRZot4Q9kVqq+lyJV9mBZ4XoOsyi/kai8uVVcduz/ZRkokohXKDpw9dEJZMilA/ZLvirBsT9talSrNNHhCGBAjVwMrzmZYj6i+tS6/viecPWiHeXhCsakRcQTSqIcsSos4fEnhqe7tCfSEJEiAQ9YmVKa8iy4St/1s5Tp538TWrrQwORZba+73SlJpOxhjqzyAiZiVkRaa3S3xZIrrGnu1jLYs6ub0PKxgfzlJNWfhB0CaHVuAHAaVKs00YhWFIKinqSQDffOIswYoSpkU+vPnObSTiER5/ZpKp2WL79YBwydg5KMi3AAB8XUlEQVS1ZYSlXJVvPHa6dewEIZKIG7zzTeIE8K0nz2I77kWnQwnefOd2IobGhdkip84vtV+Logg1y+hgF47nky/WxDlLEm+MpiiCePFDGqaNaTqE7X2SyBVr+EFAsdxsETQgtTpL86U6R07NYZoutuu1nDnAD8Tvhx4/DUjMLZbbtu20GkkfefocmuzhOXXeO36e7XteOoeQF4oXfFUrlUr09/e3/3744Ye577772n9ff/31zMzMvLR710EHV8CKnNV3Gy1P42IrFNIW9kuSgqJFUfQkWrTnNTs58l0Tq5zDs0vY9YW2ciQMvBY5EkeP960KtXQcj3y+TKHcoFhqkCvVSSWi3LBrHENXRVaF7TE2nKU3G8fQtfbFRVVl8sUa3376LMuFGuWKSToZxYiorF/Tx7vfvIdkLML6Nb00TIelfI3DJ+fp6U5SKjc5cmKOWsMmDMWJslCqs268h40TfZw8t8D56QLRiMZwf4b+3lT7IrBuvJd3vGknyYQo0l9qixT4LoeePUSztkxcrTMWs9jQV6V44qHv+vh6xKg6CSSjj2hyEDXaSyKRoKcvTb1h8YUHjwIFkZGhq0SjGq7ro2kKruOzlK+SSkbp606yfk0PaquD4OTZRf76n55kdrFMqdKkWjPFCf95IMsSa0d72Lx+gIHeFKODIui8aTmEQcjb7tlFvWnzRx/9GrWGjQQYhkY6GcFtXeSzmRj5Yp1Gw6FUMTEth4nRbm65bh2KrEAoMTooQtfHhrvp7R8DQNNUHrhrO5WaRa1uMbtQptaw2L5piP6eFCfOLfL4gUn8IECShCR7ZQCQSUXZt2NMfE4MFVmSRZdBGCJJEnNLFRaXK9QbNoVyg6blcPetm7l133qm54ucOLuIoSvEowbZTJz+HvGZGOrP8JPvv4VE1CAe04lF9VW+luPDq1URYeDj+y5Xs7siCMSFVVy6hd2VoiJLilBeSSIMUlGjyFoMVY9fovIQ5AeyRqWp0J2JoajGZcSHGHSFrQGej+f7yJJEJBalWCxz9MRh6vUGjUaTer1JNBblfe95ANf1+PO//ASW5RCGAYqiYBg6w0P9jAwPEI9HUTUVQ9cxDA1d02g2xQC3rzfLs4ePUy7XhF+p5+O4Lm+9/y6i0Qh//6nPMz09j2U7OI6DZTu89533c+P1u9odOa8mXu1xwwc+8AFmZ2f58pfFBPKnf/qn+ZEf+RH++Z//+XnXu++++/jYxz7W/lvXO8HCHXTQQQevRUiShKonWg1FA4TZDVcnT6CVdxLvkCffx3i5xiZ/+qd/yu///u+zsLDAtm3b+OM//mPe8IY3vCT7/HIj8F1hE+U2cZpFnPoSnlPBs0XgOqEEsoSELKpuKza0rbF5ELgi+LoVzC6KdyEQiF9hcPE+AYQrneiXzp+eM5e67M/nzrXC1nYCCIPW75CQ1t+BICVEo5Qv9tl326Hyoe+29ztYeR2+i+8HXAjttqp8pdlqpQHrpYQGZHWgNcwMrSoOENdaC7/nIbIwTDvE8aT2TdMjrFsziBvIPHVoFteXsF0J2w2xXZl3P7CPZDLJ3372GaYXqtiOhO2BH8Ab9m/hB95yA19/9DSf+uKzwt5IkZAl6OtJ8Z/f+GYAPvbgF5EkkceiqAqaIhMfuo6e3hTSzDHKpWVCeaUrH8LoIImeLWjNHBeK+ZYiwsb3TAyjzm03XwfA579+glp9dc7CB9+9nx2bh3n2+CzfOjBLRPHapIZleezbMU4YQrlqoqsKqiZj6Ea7kA+iIN+dibeaFAUpks2IYn5vd5JNa/sv8nOSRCYl3AYUWWKgL4X0HE/yle9TVypG4AvrKD8U31G1NSdXZJloRGsrSlzfp96w29uYWyy3rZNWyImdm4eIxwwWliucOrfUJjzCQFh4b1w7QL3pcPT0XGuZsEyPRQ3uaSlLpmYKNJo2QWs929e5bkuGvp4kuUKdyZk8l3qsG4bKdTvGaDQtzkwti+22zjGSLLOwXMEPAk6cXaRYaRAEF5fLskS1ZnHw2AzPHJ1uK07CEAZ6kjiuT8O0+dQXnhEWXa1lYRhy/53bUBWZbz15dhWRFYYh4yNZhge6mFsscWZyuaXUEMsiEY2HHjuNH4QcPDJ98Ri11n/0wDkURWF+SVjRB5dsNxE3SCUiNJoO+VK9be+1Upv5q089jutLVKo1An/1WfOv/+lJVEXGtD0cxxOqoJat2Apc12+rh1bhNy9/6JXE2M4DbN9z6yv+vC+YLOnv72dycpLR0VEcx+HgwYP8+q//ent5rVZD074vzuodvIIIA7/tgRq4prDUMksEnoXv263Qs4iQ+8aSyK/RrpbAd4VlgFnEMQtYlTkqpkbTnwZ8ZFlH0eNXVI5cisnZAo88fY5Gy/MvDAMqMZNMMsINuye459bNfP2RU9iOR77YwA9CVEWiVB3gwOFpHjswiWm79GQT3Lhngn3bx1g33sNQfxdrRrr52rdP8OiB88iyUBEk4gaEkO2KCeXBXAHP9XE8nzCEUrmJoWvcsm8D2zYOs268h+5MnHQqSjYtLuCSJDE21IVjFsnPnqVcmMNp5snEbHy7LHIVkq0X+Nxx8wtAGIIdxGh4CZpegg2bNtM3MEG+4lGeK1IsN6nkTcJwge6uBIN9aRLxCF3pGK7r07RE1oYsyzRMm4wWY6A3RanS4PCJWc5MLjO3VMFxPIrlBk3Lfd79kSQY6E0xNpRlsC9NTzbBzddNcMu+9Zy7sMw/feUQ0/Mlzs8UUGSpLdOMtqysEg2Las2kXDWZXShRqjTpSsdZXK6Sy9fIdsUZG80y0p9h9zbhOXnbjRvYu30My3GpN2yqdYv5pSJbxkXI1qMHzjOzUMJxRYE/CEOG+9P09wh1UE93kq50lFQigqYqpJNigBWJCALp3IUcli38b+sNm8HeFF3pOCfOLHDi7CIgOlfiUb09GNu/e4J14730ZROkElF0XW2RDWIitWYg1pqEmHhmbVXQeRiGIHHRjzSQCFFJJ2NIsspi3sQPFUIpTihpBJLOuolRkskUF2aLLCxXCCUVUECWGRkeZsvmDVRrdR574hl83yUIbIIwQJZk3nL/nQRhyLe/8WBLJi88SR3H4647bmR8dJhvfPNxHn3iILbtYFsOpmVz475d/PD73878Yo6/+fvPCk9XXUXXdVKJBJ7noesaYyND1JtNIoaOpmrCj9QTZEZ/fy+LS3lcz6PRMCl5NVRVnMeKxTInTp3DcT1cx8H1POLRWJtYyhdKIEEmkyIWjRCJRhjo7wFYNcB+tfBqjhtOnDjBl7/8ZR5//HFuvPFGAD760Y9y0003cerUKTZt2nTVdQ3DYGBg4GXZrw466KCDDl4+XJk8abayAmo4zTyeXWuTJ5KstuYReotA+f7IUfh+xssxNvnEJz7BL/zCL/Cnf/qn3HLLLfzZn/0Z999/P8ePH2dsbOylfgnfEQvHP4lZmaFazlHWZQJvxT5LWFL7Th3PrbdcGRotey2rTSR00IEfSPihgqbpaLpO0/JpNN3W40KxETEMBgYH8F2PZ44vYLpgOSGmHWK58KH3vgHdiPIH//sbzOeaLbJDwnJD3vLGPfz4+27jo3//bT731cMokoSsKqiyzPBAmj9+09uxbJdP/unHCRGh8ysKh7cbE8SzXVS9KQpNX3THqzKqBIpmIMkqyXgUQ1doxUQgyxL+JUXgSs0SVtCtgjxAodygvzfFUr7GzFyxTWhIkkS+LAKwA1/kf0pSS6lgyET0i/Wn3myCaERHlkBWxJxYaf3uzsQY6E0jY7VyMYKLLgchNJsOtZbKAEQB3PM8VFXl5NlFZhfLlzTvhciyyLU4fGKWLz10XCgUWrZK/b3CdrvesPg/f/+IsHMKwxZREGA7t5BMRPmbzzzB3GKlRWSKovz2jYNcv3sNx08v8OjB84IwahXzoxGd2cUygR/w2a8evkiUtNQN52fyJOMRjp6ep1CsXyzrhPDtp87y2a8eplo3mZoptl/LSqH/kQPn8P2gbdEeti2xJL7yjaeRJYmG6eA4nsi+uGT9//A7n7nqZ/lv/unJqy77p68cet7vwcc+dXVngRVlx5XwyIHzz7vdxw5MXnXZSm3lxWDhOSqhS1GqNF/0dl9t+FcicF4BvODK8n333ceHP/xhfvd3f5fPfOYzxGKxVd0Shw8fZt26dS/LTnbwvY8g8Ag8uzWYEyy9Z1XwnFpb/ooEkiQmNaqRRn8th7G7TVyzdIml1qIgfHyhGpCUGJIyQiQ2gqw8f1EzCAJqDZt4TGfNSJZqzeTkuSU0VXT3BEFAoSSEuYl4hLppUyw1KVcb5Ip1qjULw9B4853b2bC2H9dx8fyQfKnOUr7G/FKFt969E0mCYqXJ3GKFWsOkVrfxg4Dx4SwbJvqIxww0RRZdBlGdnq44g32i0P+eNwvJXxiG+E4dp5nDqZwltyAC6e1mHglxkjMAQwHfvuLLff5jEUo03DiDwxMYiT6OnLepWhE0PYJiKIR6SNXNMKjFkGXx2lRVJhk38IMAy3ba2zJ0lSAIUBSZQrnG5Eyek2cXqTdtnjw0JQruL4C4SSejjAxmuP3GDdx83Vpsx+P8dB6/JTF1PaFWEccHYhGdpmXj2z7lukWxLN47RZH55pNncByfVDJCT1eCidEedE2cpv/VD70By3JxPZ9G06Fu2u3Qcz8I+PLDx1q+th5IQllj7eomHjNwPR9dU0nGRfB5xFCJtXJW+ntTFEoNmk2HC3NFTNNBURQ2ru1HliRBzpkOvt/qNlFl6g2brnScrRsGGOhN0NMVZagvQSquEQYedn2RpOZR8UvkFpZYCEJ8TxAf68a6iUYjTM1VWSw08X3RVRSgsm5imK2b1pIvO3zjkWOEktZSh6gkkkneu/9+ZEXn25/5ajvDQ1EUNE1l0871JHqyKMXzeJKHLF8c4K7k+siyTMTQQZLwfeEBvEI8BEFALl8g8F0sy8Z1PYIwQG9NmM2mRTwao7+nm0QyQSadZP1a4YG6eeMEv//bH0Zt7YuiXHw+gDfccj2maWJatrDjMi26u7sAWFzMUSyVV32mEglhdaZpGrt3bCEajRCNRohFI8RiUQxDtKD9m5/8QHvAfLFDRl713K8mXs1xw2OPPUY6nW4TJQD79+8nnU7z6KOPPi9Z8tBDD9HX10cmk+H222/nt37rt+jru3oWjm3b2PbFE1q1WgVoTVpeusHdSmjgS7nN72V0jte1oXO8rh2dY3ZteLWOl6wKK00t1kskPXFJcdjGtcsid6EVTB2GoVCf6AlkNfKK7udzcWkh6XviE3ZJke+lvjZeC16Osckf/dEf8RM/8RP85E/+JAB//Md/zFe+8hX+5//8n/zO7/zONW3rpcDkE/+d2uKBV/x5O3jl4LjgtOylXA+isRiGHmEx36RYtbFdsF2wXNA1g/UTQywVGjx9ZB7LBdsJsRxBatx1yzZMO+QL3zhJpe5iORe71ft7U0yMpjl9fplCubFKnxCL6rz57rXUq0W+/M1ca1nrpwSesUg8pvPVp8o0Tbf9OMDnv3GCqbkKx88ssLBUIUR4/oQhTM8X+cn/8Nd4ns+xMwvt+bgoysOHfvHjKKrMUq6K4/pcOmF/+PEz/MnHHsa0RQPhyv5KLXulbz55ljAIWS7U29ZGK2s//swkmqZQb9ht+yZJkkCCL37jGP/9rx7Gbm13hYRBLOYzXz2EoshtK+5L7aX+/nMH0HWVRuPi3F2sL5QNv/0/vkIQBOK1rOxv68cXv3EMCbBsoQRYyZ8IgX/++tHn/Yx88gvPXHXZz/1//+Gqy06cXeSTX7z6ukdOzl912fR86arL5pcrHD29cNXlS/naVZc1Xr91/lcMsiShqjKyrBAE/mXqWV1XUWSprR651FYsYmhkUlGhcKo1kS9ZV5JEc68iyywXani+IPlkSdRZBnpTdKVjFMuN1jlCan/fUskIm9b24/kBx07Nt5VPkiTaa2++bi2GofHM0WmRJ9P+4oRsWjfAhvEU+CY37d/3ih3HS/GCyZL/8l/+C+9617u4/fbbSSQS/NVf/dUqa4q/+Iu/4N57731ZdrKD7x0I4sMSvp+e1fJBrRK4TYLAIfAdccGTJGRZBCCregopqr9m5fKB7+JZZVyrjGMWsesLuFYR32kShj6SrKJocbRoFlkxWr6l4HpRJPnK3Tsnzy2ylKsyv1RhqVCjXGmya8sw77xvD0P9GZ45NkPZ8VBkCdNxmZotsH/vWoIg4JmjM5y/kCcIA4zWiU9TZHRNZd1YDx//x8cplpu4vo9texi6gu14qKrMs8dmaTRtJFlCavlhhqEIzk6nIkyM9TA6lGXHxkFG+nR8p0Rl4el21orTzBNegQV5Me+cUAv1cnLapWrHadKHrovu+fH+9aSSUbpqC8yenIOGIBwMQyVZF5PbaETHsl1CSxSQFVnCtB0OHp1mbrHMP3zhIJPTeap1qz1w+U4YHsiwfeMg8ZhBb3eSkcEMUUPD9wPecON6ujMJvvTQUWYWSti221JjuBDCDbsmqDUsvvHYaWQZIoZOMm4w0Jdqb/+XfupuIppKKEGj6dBoCsIKoFqzeOzgeVzPF5ZZSHSl4+zYNIwiyzSaDooiE4/pgEQgR9oyyqH+NL4f0rRsZhaKmKbYt7HhLKVSg8985Vls28VxPXzfJ2KovOeeCVQVFGyiikssqZGIasRjGoXlSbpjZYa6bKanizRqEhdmZCRJIZ2O8+a79qBoMY4+eZgABUXRUDUdXTfITNxENpulrM5gRYvoRhRV09FUlaGhPrIjQ0R6TO6IrkFTVRRVRVNVdF1DNYQE6R1vuQdVVVAU5bLzwpqxYfp6sjSaZpuYSKfEepVKjaVcAdO8mCnTlRGEnyzL9HRn6ckmScSjbXKiOytIjfvuvY033fOGVpePT61+MUGnXm/ypX/5JtVajUbTotFooigy//ZnP0Q8FuUfPv1F8vkisixjGAa6rpFKxNm9ayvpdAIQOTSaqqJpKsWS6ArJZFJUanVmFxbxXB/P8/B8n7e9+Y2MDA/wzUee4uCzx8SgueWRunXLBt56/50v7Ev2MuPVHDcsLi5ekeDo6+tjcfHqHTv3338/733vexkfH2dycpJf/dVf5a677uLAgQMYxpUJ+t/5nd9Z1ZW6glwuh2VZV1jjxSEIAiqVyipSrIOro3O8rg2d43Xt6Byza8Nr73jpQB+h0UPou/iqJYKrrQp+rULo50CSkdq5ZPqLGs++WIRAreGIwsIr+LwvF0JCPBPcfBHNeOnUC7Xa1QtsV8JLPTZxHIcDBw7w4Q9/eNXj9957L48++ugV13m5mywk5dUl+r4f4XghrgeuJ+47rftu63HHu/g/Tutxx2uRFi5YDthuiOUIdYbdUmlcJDbAai13vFX8QAvf6XuQu+qSg6evTqzNzJeYuUoB3HFN/vbTD1913T//+0euuuz4mUWOn7nyeLxpisyFq+7TwtUL8k3LfV4XiNmF8lWXVetXGrOLA+15woniKosplq9ezS+75lWX+UGI611huy1canv1/QSpxRatTPPjUZ14zKDWsERj6CXLIhGNtaM9WLaoiYn1W9dNSeIN169HUWQef+Y8tuMJF4wWyTUx2s22jUMcPTXP9Hxx1XMnYgb33rYV03b48kPHLpIASITAu+/fQ082wae+ePAy1caOzUO8/d7dfPOJ03z7qXMtKlDscDKh85FfeCuyLPHvf+sf2zkrkiy+1/+fH72D63aO85v/7Yucv5C/qM6RYM+2YX7j372NL379KP/rb7/ZtuiSkDAMhS9//N9S91K8+8d+E8t2Lz2Y/PyH7uA9b76OX/yNT3Lg2HRLMUSLmOjjo//vj/DwE6f4z7//z0Ld1Dq+CvC3/+0nAHjrj/8PTMsBVuzP4cY9a/iZH7mDX/ujzzE5W+TihkWT8K/+/AOcnVrmx/79x9vHZ4Wm/IEH9jHYn+bd//rPWMpXW8dWsJBbNgzy3vt34dkl1q1d+6o0WbxgsqS3t5dvfetbVCoVEolEu3N2BZ/85CdJJK4eutvB9w/CMGgTIitBcL7bxLPKLfssh9Bf+fLK7cwAVU9dMSD5tYQw8IR83y7jWWXs+hJ2Y1moRjwLJAlFiSBrsee11PJ8n8XlEp6Zo1o3WcrVKJYbvO2enWQzcQ6fmOPY6QUURULXFJLxCKVKA98Xg+hT55coV5vUmw6u45NMRLgwV2B4IMP4SBZdU4lFNTRNxVAVpudLYr1zSzx5aArb9pBkCU2VySRiSBIk4xGG+lIUKyaqKqFIEqmoR5Qc1aVlbl5fQqUJ3izu8kPML1z9wn6tqDs6pYZBqRmhYkXx5C5+7P33IEkSh/PHQHGRgxhhUKdet1jKV0kno8JqSJIIwwDfD2iUHUzLZc+2EfLFGs8en+HCXIl8qU6x3GyF039nuYgiS/RkE6wZ7WbtaDdrRntYM5rltus3IMsyf/vZJ7kwW+SpZ6eoNWws2yWTinLHTZs4fyHHM0dn0HW1bWUmCAzYvG6AH//B/STjUeQWs99oOtSbNomYgWV7nDq31GLrZZAgnYwwPtxNEAQsF2ttFYFtexQqjbY6pmk5FCtNbNvBshwatsRw1uPmvSM8/ewpvv7ouZaEURTWc7lF3nhdEtmtUiqVSMQMersiZNMxersTBKhokSTve+ftlOseuhFHNww0zWB4qJ/+vj5S4z496+voegRVj6DrUYxIjExakBM/9lN3oqrKFYsiu3b3sOs5j628N9FohNHhwZb0Vvhpep7XXn7oyAmKpSpNUxAipmnxwH13MDTYz9e/8SjPHjkhBg9BiK5r7Ni2iQfuu4NYNEK93kTTVXRVR9PEvlmWjW7oZLsyLOeWmZxsUG+YNJpN7rztRvbfsIevfeNRPvPPX8VxXBzHwfN91k2M819+7d8RBAFf/JeHkJCIRHQiEYNoJEKj0SQei9Lbk6VSEWFprufiui6Ly4XWi4bJqZlLuptCMukU991zGwCnz0xi2w6SLIL1JFnG90Xnket4BK2sGUmRUZGI6K8dO8yXY9zwkY985IrExKV46qmnAK5IsK9Iva+GH/zBH2zf3759O/v27WN8fJwvfOELvOtd77riOr/yK7/CL/7iL7b/rlarjI6O0tvbSyqVuuI6LwYr73Vvb+9rpND42kbneF0bOsfr2tE5ZteG18vxWlFJe04Dz63iNQt4ToPAq7VC4w1x06Iva6i1CEINyaa/R/JVwgBbgUxPFj3a9ZJtNhK5NmLgpR6b5PN5fN9flYMCwu7ras0ZL3eThRe89N8vkVsAbisM2/Of87cX4voiRtBfyXlYdWupFbj4GFyp6H9lR+Yr/V/Q2k4QXHK/bTUkHrv4uAjydv2wHejt+uD7Yr/9QJAbftD6u/06LyU/LiU7Lj7u+Vfevw5eHqiqQjRqUKtdTlBIQG9vF8VSdVW49wqyXUkkJAql6mXLIoZGtivBwlLpiu9nX0+KesO6ImkyPNCF7wcs5i63QdJUha5W9mgQrN6wJMHEaA+1ukWuWL9s3aH+NKqiMDNfvOx7EY1obFrbz+nJpbZyZwWyLPHGWzZj2x7ffPLMZdu9+bq1rB3v4xP//JSwFWvvEGRSMT703pv53Fef5fx0fvV2JYnf/OV3oioy/+F3PrXqOEnAA3fv4t1vvp5/92t/Q6W+miTqySb58z/4KT72dw/x2a8evGy73/rsb6LIMre+/VdFzaIVMA5w6w2b+fVf/kHe/ZN/yNxCkUvPErKk8NE/+nk+/cXH+L3//pnVOwT8xq/8GIqicNs7fgXHXt0gm0ql+aWf+wA/9Yt/QqPprFoPZH7mJ9/DmXOzbZXOpVfiG6/fza7ta/nff/cI1Zp5cakENTPglpv38y+PnKdcvfQ4SDiOz+atOwiCgFLlry+bmwZynO7+CXJFk8olBJ4kSeSKDhh9lJoSddNdReAgydS9FJYfw2udi8NL9rjh6mK5J+P7lxJKMkGoUfdS+FIKVdXaL2VFPVL3xHxWNyLols+lHxhFTVH3UhjRFBHDaKuiQCKeiIvtKjbRSKT9+AqRZQVJ6l6KZCpFvdkiwWSxPJHqphlk8dHJFyvo1ktnMf5CmyyueYSXTqev+Hg2m73WTXXwOkUYBm1rrDBw8FwHu16grpYJnUYrpNzF920R6BaGIKntQGVNjYnurNf4wD8MPFy72lKNVHAaSzjNXNvXNSQQWSNabJVq5Grw/YBSpUk2E8N2PL7w1Weo10qtADqQZImnDl3gTbdv5Y6bNjI5U0CSwLIdCqUGM4slqnWTXLFOvWFBCD1dcTLJGJlUhLnFMru3jvIj77qR//0338JvjVRtx0XTxcklFjNIxqN0dylEdJVENCCbCGnkT0DV4a6teRyzjCGbGIotiOj8CQqt6+R3S4/YnkbVjlK1Ikh6lltvvp6KpfM3f/s4kYhGNKKRSkSJKBKW7RKNiIL27GIZLwxQaWI5Pou5GuPDWfLFGo88fY5iqUGp2qRat6g3bH7z//fF7xi6voJYVGditJuNE/1sXt/P9TvHGepP89ShC0zNFZidL/P04Qs89Nhpbt6zFl2XefCRU+SKdQxdWFt1pWPIipiY3HPbVoYHulAUGdcLsG237VUajejMzJdpmMsiHzEEKYQNE71smOinUm1y4uwirutjOS6O47KcK7F9fRehX+fU2Rk8x8cPA2QJVEWiXjiPqiksLs6zlG8QIiFJMgEGliUuzkP9GTavHyKVTNDf20V/fx8b1o7Qs3YdPes1/vH2D6JqwrNbkldCz0XRfXDr6uN1qd2T7nl4chnHEQRAw2zgF6ttsuTYiTOUShUsx8F1HBzXY//1exgdGeDAs0d58unD+J4v8kFchzXjI7z3nfdzYXqO3/uv/1vkg7SOg6aq/NVHfw9VVfnox/6BcrWKqgh1iaLIrJ0YZWiwH1mWWM4XMVr5Ib7vs7AkuqkUReb4ybO4rrAz8zwRSnfbLdejGzqf+NTnyOVFlswKObFt6wbxuoMAWVFIpyMYuoZhGIyNDQGQzWb4gXe9mUjEuEh6hCGJuLDT2rl9EwN9PcIarBXKNzYi1h0a6uedb7u3/TraVmEt/NAPvg1JklphfgqqopBMijygO267kdtuvb7l4SvWfS2eV1/KccPP/dzP8b73ve95/2fNmjUcPnyYpaWly5blcrnLChrPh8HBQcbHxzlz5vKJxgoMw7ii6kRYwb20BQtJkl6W7X6vonO8rg2d43Xt6Byza8Pr5Xgp0TR6NA0MCfLEbeI7NREab5YJ3AaeWSAMXCTFQNHiKGqkbfv5UiCgVcyQpFWWGK9XiPGphNzKHnip8GK39VLXNJ47/nq+5oyXu8mitvYWKpEIXiATjWdQtCjIGpKkgaQSSgoBELaa8ATZIBGEUotYkAhQCAKZUFJb90FDJgKtEGRhBe231DBhEOIHQSsb4RIbOSSRz0CLJEFa9Tto5VWKop7IVSSUWs1wtCxbWmSN54s6RCiyGXw/aI+1q7Uaruu3G+jCMCSZiBOLRalUa+TyxVbzlYvrB8QjEdatG8f1PJ546hCe6+H5HvWmjSEF3HXbjUSjUf7l64+wnCvgeh6+7+H7AZs2rOG2m/fx2JPP8sgTzxB4Ab4v5meRSIRf/NkfBkJ+7bf/F34QtOf6QRjwA2+/m317t/Lbf/gXFEoVWDkGhPR0Z/h3P/MB/vGzD/L0sycu/TQhSRK/8DM/jO/7/Lf/9XeXNf9tXL+Gt9x3B3/2F5+g0VxdpI5GI/z2r/8SX3/wUT7/5W9c9nn57D/8L3Rd523v/Wlcd3Ux+eab9vB/P/qH3PO2D3L+/OyqZZGIwV999L9y9PhRfvN3/vSy7f7n/+dn2L5tIz/607/cUgNcxLq1Y/zln/0u//aXf5ODzx4Xvfqt70skYnDs6S8jyRLrtl1Uy698n/7g//0VPvj+d7J5z5soFi+qTMIQ1k2M8vjD/8i//rn/zD9+5svtknqIIFnOHn0IgL7xG1rWkBetwd7/g2/jD3/nP7Jp1z3k88X2egDZrjQnn/06/89/+l3+/K8+ueq1SBIcevxzYrtrb7+sa/3Gfdv59F//IXtvfT+zi6sVPrFohEe/9nH+5pNf4pf+059ctt3P/s0fMDLcx/jOd+O63orzlziG48N84R9+n3vf/e85furCqnU1VeFj/+MjXJhe4Ja3/MJl6sQffMd9vOttt/LZr/wEnrf685JNx/nZH38LR04tcH46v2pdRZF571v2E4Yh//F3//EyAmftcJr9uwaJRTVqTVHol2gFm+sSa/tD+jISstw2bENC5LskWIAgQFcl3EuIB0mS6E2HRPxZ+jMq+YJ8cRnQnVbRrNP0RYsY+sVr8ErRXreOAZCKytRDuf1+h0gMZn2UxmEGukLOGnL76IYSdCVllMZhokGJmCFfulUA0soMcqNOJi7j2Jdc+yXoTQYojSNk400iunRxu0AiJqGapwiCkIgur/4uS5DWaujOJFEtQFOkVQxNVHPRnUmUoIS8KuxXnDcNdwY/zLbyWVtkYYsUMagTDZcgMGkH9UiSyPTBIcoyoVsgDLz2vviI701MKrT+10Gi1agpieeN6hZxpYIm24ieTfF8kiQRMyQSapWIVCcWU9pjGUHUSGTiNgm1SndCwUrryFyswfR3ycTkIh4lerJpIqmrW2JfK15ok8VrMw27g1cFwsrFFeFuvksQuCJs2XfxfacdDBd4FmHoEXgeYeASEmLWwPBllJZMXVZjqEbmJZ00vJwIfAfPquDaFTy7gl1fxmnmxGv2LPGFlzVkLYpqpJDjfS9IAVOrWzRMm3rD5tCJWQ4dn+WH3nkDGycGWDvez7OHCri+YPM9O+DwyTnedPtWujNxytUmDdPGdX3KVZMwCJmcybNn2xgfePsNPHrgPBDiB1A3HXIF0Y1QKDao1sV6vm8TUx1iUkhl/gBxp8KP3F5Dk5qoYQMJcRJ1lk7gAFEgql/t1bxw+KFC3YkRTfYxOLSGc/MuX3pkAdtTLlpD9SV5Y2KA3njIyGBXa7AdUq6Z+J7P3GKZZCLC8TMLPPnsFNWmj9ls0mjafOwfxGD2WiBJEsP9abZuHGT7xiFGBruQZYlCqc70XImFXIXz03k+9J6bWFyu8Acf/RqEIpQtYmhEDI25pQoToz3csX8DU/NFNEXB831sx2cxJzpUdFXhW0+dRW5NbkMJNEXinfduQyagUCxTLDfaEwzf85mbjjLc1aRcmKOQyxEQosgyiqoQ+jaBbxHRVeLRKGpSR1YUZFXDMCL0TNyOqkXYvgtGKg2isRjZbBeJZJbbbt7NwMAA79vg8XbLRlEUXFdkcgRhSKJXFOyPHj+NaVZwPQ/X9XAdl727t5HNZjj47DEOHz2J7TjYtott22zdvJ4H7ruThcUcH/3YJ/B8H88VXqqGrvPbv/5LAHzpXx4mXyhdMoEKWLdmjNGRAQ4fPcUTTx7Ca008wjBAaZ0vxIQkIJVKCpVGNEIqEW93/73/vW+hUqsTBAGe7xP4QTs/JNudYcPacRxXkCy27RCPiguiadrEYhFkKYaiKsSiURLxKPG4CLR/59vvw9Ck9uQqEY+1s0XuvP0mbrx+N5Iso6oKEUNvB7EDvPXNd131s7d960bYeuVlqWSC3Tu3XHXdkeGrB43rryEVySuFnp4eenp6vuP/3XTTTVQqFZ588kluuOEGAJ544gkqlQo333zzC36+QqHAzMwMg4ODL3qfO+iggw46eP1ChMbHUfU4RkJckwPfbYfG241lPLuM0xDhu5Kio6hRlJdZedLBawM9PT0oinKZimR5efmqzRkvd5PFhtv+s1CkLy/T19f3HbcZ+G473F3UArx2TUCEwlsEvgWBRxD4oj4Q+KzSgEgykqwgSa2brIjHJGHTu3Jf/H5tkX8/+oF3AkKVUiybZDPRdlHvh9//zquu98Effu/zbvdDH/zAVZf94A+8B8/32zlOvu9j6DqxWJQf/ZEPsJTLi2Y0x8VxXSRJbs8X7rj9VpoNU8zbPA/P87n91usZGuzntltv5MixU9iug+96OK7Ppo0T/MC73sxtt1zP4GAfXmue5bki5/L2W8U4+QM/8FZyuQJeIAinwPf58R/9AbqzGT74vnfx+S89eHH+6gesmxjjur07mRjr428/8fmLOYpBSEjAj3/wXWSzWXZu38L8whKsGPeEcN89t7F960buuPUG5uaX2wHhgjTK0pVJYds22Wzm4ses1UG/ZeM6ZEliw8QYF826BI2we+cWZEli+/ZNPPztJ9tLAJKJWPt9HejrxfO8FlMiyKhb91+HLEncdOMenjl0HLmVrwASe3dvQ5Zlbr/tRh58+HGQL3b0G7qGqotGtr27tratmkX96P/f3p3HyVHXif9/VVXf03Pfk2NynxNCLiDhhnAEI6crXgjrCiKi8sM9dF1XdL8rrn5l/Sq63iheoKuyCiobl0uuBSGBhHAEcpJMMpn76LOqPr8/PtXV3ZOZZBpCJsm8nw/CzHT1Uf2Z6u7P1Pvzfr8N3vvuywnFarj8irdx/7pHCFiWFwSAeXNmEok3sfKUM2hb8EcMTBQuLgpcmDx9AdFolNNWLWfvPr16NVcm6t3vXEtZzUze+VeX8p+/+YO/TWEwZVIj8brZTA42sLhtrhes9Mo9YXDq2edTXj+V8887k1df2+7vrwIuOPc06qadwaWX9bC/z/XTpgygqrqS+pmrATjxxJ/g2DYFjSZYc8n7aZ6/lPPO28SzG17IN4d3FStPOpHJi9/HGRdO5pHner371c3ho+EQLW3vRCmXufMeZHAoieEdD7YDK89cS0vbBZy4Yi99Ga9snNcbZuG86TQvuIJZyY00N78GytVBVy+A0DT3EhQuLS2P05nrE6r0s52zYBn1M1YzbVYnL2xL6CJQ3gHTMqmeuhnnkgztp7LqMS/oo5+uCTRMX0FNUy1NzU9i0+UHZwxg2oyZVLUso3V6H/V1XeRzPAzqamLEqiYDBo31lV5gPdeJwKW6tgwrqHsVp3N9bLz7nTGlBisQZEpTJS1NFf7IG4ZBLBLEMBWG6zK5sZxEKutlqOr7rixzcTJ91FcGqaoI5YO4GMQjCjvdjekMEggCbq68l8IEsom9gMLJZsjaGb98l1Iw2N9JemAHXV376R8Y8n41CgODzggk+7bRs7+Lzq7+/EvVe70mel4jomK8ur2dzt580E6hq2tkT6/CtILjtshCZnATgHIdHfhwMriOFwBxbVw3i5tN4dg6U8J1MiilJz/+JMhPfzMwrIBusG4FMa0oVjCIYQZQQNBJEi6PHvUrn5RycTJD2GndPF5njHSQ9Zo76n4bhhcY0QGfUNno6e/pTJb93YMkkhmGEhn6BpKkMzZnr5xDKBjgJ/c8xevtPQwOpUlnbVzb5bnNrzNnehON9dXs7xn0V8Y4jktvf5LBwRQuitf39rC/axBvUQ3N9ZV0dg9hGAaJZIq+3m7iYZtYyCYas2mO9LH/1V7MgR4uXrSP8qhLJJhf2dC7qwOAGIyc3/wGuAQIx2oJl9Xz2p4sr+xKs78/SGe/wnYUy0+YytxlSwkN7mEw1U5ZNEB5mQUYJFM60yAUDNDVO8ir2/czMJSirz9F30CKn/7X0/QPlJ6KHouGaJ1Uw5TmaspiIYIBC2VAKmXT1TNI29wWPvSe0/mPHz/MT37zlC6pYOg3zZoqvTqpqaGSWdPqcV0IBy1MA6yARf9AAtfJUFkeIJNK41q6ZFc0pDDdAZJ9O1HpPqpjLhnbIZN1MVyFMiycTA/KsFDK0Vk/hoVlhQiGAsRqZlMzZTHN0yuo3hUkGAwRCAaxLIuGaVNoXvguqtIOk/6cImgFiMdjRCMRQqEgZvkcyivKWbnKZs/eDgKBALbj0NuXoH1/Py2TptDV3ckvfv17PwMkk8kSjoT5zD98BIDf/G4dg4ND3iow/YE7dUoLNTVV7N6zj+07d3sNwHR2Q25JRiBg0dxYj2F5zdQxik7gl5frAIflZVQYhkF9nV6xt+zEhTQ11lMWixCLxYhFIzTW1wIwvXUyX7jlEySSKb/Ulm3nG5UNJZP09xenT+YCF+XxOK2tk4iEw37fkQbvfuvqqvnbj3+QaCRMMFgcaHCVYsWyE4v+QCoUDof8xuri6Dd//nwuvPBCrr32Wr797W8DcN1117F27dqi5u7z5s3j1ltv5bLLLmNwcJBbbrmFK664gubmZrZv384//uM/UldXx2WXjf7HshBCiInFtIKY0WqC0WqilVN1yeHMAHZmiGyiSy+8SnSA6+qSXYGI/jdKeV5x7AqFQixbtox169YVzRXWrVvHJZdcMo57Nnb6b/uDH5v5RZVZP6iiXMc/t+C6uuqE66S96hMOuFlcpRdEKdcF9PcU1MPXf+iaGGYAwwxgmkEddPF+PprLc78Z4XCIkTvh6UyQaVMnj3rbs884ZdRtZ51xMmedcfKI22ZOn8qX//UfRr3t//vyZ0bddtON13DTjdcUXZYLLs2aNZ1nHr3HD/y43gLI3N9N//nTr5NKpcl6vTGztk2dtxjt+mvfy0VrztaZPbZeBFce13+PB4NB/v2L/4jrKGzH8W6bZeG8WQDceMNVbN++m6ydxbYdbNth+dI2AE5ftYyOjk5sx8V1bLK2S1VVub/v55y1kmQy5S3WdLBtl3lzZgCwYN5Menv7vb+L9Yn3mdOnAFBTVUldXQ3KzWc4Ff7tGw6HSHn9iFzXRQGhoB4HfU5O4Rq6QbaLoctuA45j4yrlr9q3DBPDwv97VVc1CPknzQ3DJBrTi/0i4SCxWNRf7W8YBpWV+Qy1yspy/2/33Mr9WEwffU31tQwODuXLPRsG01r1sdfS1MiM6VMwDRMDsEyT+vp85t3CebNRysXwjgWA5ma9+v+EhXP1YkjvpLgBLF2ySJ9jmNzM8hPbvDCJPpkfj8f83qTLlyxmYHBI3y/oslXzFhAIl7N86Ymk0k4+uGbA4rZ5hOONTJ1mc+rK5Qc812iVXlC5evWZdHhlsJVSuChOWXUW8foFnHzK6fQWVUFTTJs2hfL6hTRa3Zxy8kn4XTpcvd/T5pxORUU5K05aSf2O13UWqldjcPGyM6iesopZ8xPM2rjfKweoj5fGxjoaZr8N13VpaPov7z3S66OhFLOXvpNJs6dTN2kn7f2vYLjgGroaScuMU5h0wvuob3+IeGVBGW8FFZVxWha9m+7eJPHqewmmM15pd8A0mLV4Lc0L25g+N0V73/O6KoVhYFoWJy5ZQPP8v2KBtY0l/5vAsgxM72ALBAI0zX8HSrmcc04v3d19GKbpH2ur15xD49x5XHJJIxW1G8iVyEfBrGlN1M+8AKq6WLM6gaIg+KagZdYZRCMh3n5RL7v3dOlgPLqU4jlnnkB5w3SUcrC8YOSRJsGSY5SedHhBDyeLcjPFq0DsFMrvG2KjsL3tdn6CMmxyoleBhLECAX+SMrZ9OfqKdCrX0X1S0v3YmQEdGEl0kkl242Z1tohyba/WfxgrECUYrT1kKa2BwRSPP7uV8rIwq5bNpH8wxU9+8xQDgykyWZtMWn/Q1dfEWdo2lVg0yJ59eqVZxnZxbJcXX9WlYdKZLDt3d+uG3bb+YIzHLPbs2UVLQ4QLVsRJpRR15SZVcQMnO0Cz9Sg7n3mYueEh5p0zfNz7GNzfDkD94SuTj0uQSFk1gXAluzsdugZMOvsN9nQb9A7Cu96+goWzWvivp//M4xu2oUh45ZFMdntN1KY0V5NK2/QO6GBQMqUbn7fvH2DPvl42vbyHTPbAuqKjicdCzGytZ2pLjc66wcW2XTJZm1TK5p8+uoZlJ7Ry1U138MrWDr0CwCtlVFsVQymXpQtbWP9CE+XRIOVx3bg8aJmkBnajXJeTFlaRSGSxvPJa2ayDaXeTSVgYbppkKkUm6+C44LgGlhWnvKGNdCjD9o4XMS2deWCaJiiLcNMF1NXVUtM8yICzL3+cKXDCU6lsWU7jZIUR3IoVChEMBQmHQrhKr1YpD0F9TQ2Oo1fBJFNpkqk0gwNDVFaU09nVw0uvbNW1eh2HoWSW+rpyYKFfCqA8Xka8LEZ5RZya6nz5gbUXno3rugSDQf1Bbzs0N9UDMGf2NCLhEBnb9lc3tTTpiZBpmkRjOmsjGAxSFotSUZ6v83zR+WdhWaYOWsSiGOjUaoCmxnpcV5FMpujr66d9bwfpdIba2mr6+wf53R8eAPSHcCQSJl6W/5CcN1tPZnPBkHAkTMzLHlkwbxYLvEn0cJZlFe2fOL799Kc/5WMf+5jfqPXiiy/m9ttvL7rOyy+/TF9fH6CPj40bN3LnnXfS29tLc3MzZ599NnfffTfl5eUH3L8QQggBYAUiWIEIoVg9VE3TGeuZQZz0AOnEfpz0INmkLm+RyzyR4Mnx4+abb+aqq65i+fLlrFy5ku985zvs3LmT66+/frx37bDRfzPrUr1joc9VOCjleOcfcj+7KOXo1d9eBQvXTnuLN5O61LeTwc0c/LyFWXT+InDMVLM4numywdYBfYAqK8qprBh5Hl1fV+MvpBvONE3Wrhk9c3/NeWeOum3J4oUsWbxw1O1f+7+jB4Y++YnrCzJ+dNZPbiX6imWL+NF3/g3bcXFs26+YkXPr5/+OVCpFNuuQTqdJZzMsPUHvx6VvP4+WlsmEgrqEnW3btE7RVR4aGmq55G3n+gEl28vsyS0GbFs4l5raDl1KztElrCc160zHysoKqqsq/d62jusQ88oM2Y4ik7W9kmO5rB/l/472d/ewv1P3AMmd0uv0Spvt3dfBtm27gPz5vq7ePv+5bnh+c77kmFfqqae3n2mtsGnzFja/tAXDMP0gzKQWff6gs7OHbTtf9y83DIPygr/PO/Z3MTiU0AEc0ySTcUkm9QLazp5eenv7vCw1fcwNDOlshnQmw+BgEgylM9pQmIH8sZhIJkmm05iG6QcQclHbYDBANBqmsJ9Ghde7yjIDVFeW6+saygsSWP7vprmpnmxW9wPO9Z2d3KJ/N5MnNbJg/mzwyswZGDQ26kWcpmlyykmL9WMWlKeq9c7RnLxiMZWVFbhe4MFxXU5YNNe732aWndjmlTrUv5vKinL//XHhgjmkvUoiul+UoqGxhWCkmoamFiZP7vJKHupjoraugVCslmC4m1Aoovs9eeUVA2aQcFxnSQ4kXAZTLgZuPigVqiFaOZXB9LP0DtpeRpVeSGubFcTr5tGf7SBLzMue1EFC0zSobz2FQCBAvOoFKjNlOnhjWhgmTJl1CjWt83DtxJg/dw43CZYchfyVG7nVGV5/kOKJRBaUV7czFwQhP5kwhk0gTDOKFTy+VmjkskSc7JBXS3gQOz1AJtmNnfaaydtpPU6AaQb1HyZej5HRgkG6f4Ku1ReNhEils6x/YRe9/Qk2vPA6r+3YTzgcYFlbKzWVZXT3JtizrwfHydVmhac3bGPx3DoaK6EsMEgk4BAKOESCLs2xDN07HqI1kuWGi2zKwi5lEZeykEMwoKDrHvZ3wanTR37eTiZfW/NwcJWBY8SoqKxHWXFe253CJsZAOkRnP3T2ZrjpA6sJBi2+9J+/YvuuDhzbxfGyEFon17BwbgvlZVF6+5Le5MHBUYp9Hf2kMzY793Tz1IYdJFKZosZmr+7YP+p+RcJBliycTFN9BTt2d6MIYGDj2C6BkMXP/t8HUK7DWVd+lYxtY6DrIIeCJu3tr5OaHmDVCXXUVxpUVUSpiAeJhALMmFpFqn8nU+td2mZXMZSwSaQc2jsT2LZBsGwK4UiMTds2sLu9Vz8fR9fcbZi2nNMvugL72fvY3bWbYDBCIBDAClhkgw3UTjuLWDJFvOI+8CaMgYCFZVq4ZoxAuIKGhgYGhlKEgiGCwQDhUMg/gT95chOL2+b7Kw9c5VJdlQ9q1NToiZBhgGlahMNBol6QYNbMVuLlZYS9QEsq7TJzui4d1NRYx3uvvBjHcUmndZClsJ5qV3cPfX2DJJJJv3n4pEmNlJfHSSbTDA4liMWilNeUEYtG/EltbU0Vl19yAaFggHQmSyKRIp3Jd7bZ39lNV3cviYTODnGVy+qzT2XypCba9+7npVdeIxaLEotGqKmu9HudVFaW8/Y15xCNRohGD8zsmjN7lBfHccK2bX8CNpRIkknrhvK2rVdVVVVVEC+L0ds3wJ72fd5EXqfUl5XF/JVRE11NTQ0/+clPDnqdwmB/NBrl/vvvf6t3SwghxHHOtEKEojUQrSFa1Zov25XuJ5PoxE73kU12o9wMGAGsYAQrEMMMjLbWXBzNrrzySrq6uvj85z9Pe3s7bW1t/P73v6e1tXW8d23cGIaJYZlAaQHBfEWMXGkwO/+zncZxUrjZhBdUSaKcXECmsCIGwwIpgXyJMNMCwzrqSoGJo0tx4Cd/DIdCIZqbRu+dcELb3FG3LVm8kNbWGSNWMWhsqOMfbv7QqLf99N/fMOq2d71jLe96x9oRt9XWVPHHe34A4Gf9OI7rZ8N88XN/x1AyQSZj42SzZGzbr8hw7tmrmDq5hayjS73ZjkM8FvPv+4Zr36tLunmZOY7jMGWyPvdw9lmnMGlSE47r4GT1bU9om6f3qa6aBfNm6gWetg7ulJXl77eiohzLsgr6IWUJeRk2ru2QzmR1A3jvOaWSOpMnkUyyb3+nn6HhugqrIFjy2taddOwvDAwp9nXo0mYvb9nOhudfRAcuAGWQSukAzcDAIOuf34yfzOL96nI9Qf6yfiM7d+0hF/QwDGid2sKpK5fx6ms7+cv6jTpo5FXhmDxZB1Js2+b5jS/nszQwMSy9IBZg247dvLZtpxdc0IGjnh5dyiqZTNLd0+vtjw7wBAqClJlMhkw26wdocuXZQJfxjkRCoHTmDeAFivTxXV1TjYWBZRm4mMQi+UBF69QWqiorvKCRgVKKigq9oLW2tpKpUyahMwgBVxUtzi2Px3SPJjd/zjQXhEwkkvQPDHpl/HSpv0EvCDaeJFgyjlwn65W/0oEQ2zvh72SGcN00ytGTg/wr08I0A+B/2AcxzQBWyJsIHGcf+kq5OuCRTXi9UnTPlGx6ADvVi53uz4+fm8VA94cwzbBuJh8sIxitPSAo0jeQZNe2DoaSaWZOraeuJs7Gl3fzxLNb6R9IMZhIkU4lqa+OcOP7TyU5NMh//fYPBEyboOkwt9EgFlZs3fg76qrCnL9gN/bsFLGQIhaBWBiioafY+ZenmFsGcw/IwB6iv/1pAOYcoTL4aSdIMFJFeWUdPYMWr+1JMZQO0ZcM0DMA1VVlfPBdp9HXl+Cb3/w5A4PtJBMZUhmbrO0Qj0Upj4fZ+OLrdPcmALwMBsV//ORRbv/RIyRT2REfe/Ore0e8vFB5PEwoYBGNBAkFLSwT2uY08LmPnc09/7OZh598GReDgKmbmpfFQiR6t2IYFksX1pOxFfFYiHA4iOsaVFfXE62aRk1TN90vDrC3L4PjpMnaDpN3W/zV+y4ns7uTex95SKeWeo2xDcPArVhO7aQmyqseIjagUw8DwQABy6KucQrhsgamz5zNjOmvgqFPtlqmRUOdnlhEImHmzplJOBwkGAwSCgUJBXV6LOiVIfF4GcFggGAgSDAU8PtS1FRVsmTxAkKhIIFAgFAoHwwBuGTtaj/V1bEdUumMP7mIRiOEQzr1t7evn87uQWJRi8b6Wnr7BvjDfz/s349pWlRWxHUvDaC6qpKqygqi0QhlsSjRaMQP0sya2UptTRXJZIpkKk0qlSbhNQ4cSiS59w8Pks3q330uEHbN+y73VoJkMS2T2poqQmHdFD032Zk9s5WK8jIdFPJSmXPvYZZl0dXd66+ccbxJ2vx5syiLRXl5y1Z2vd7uN+UzDIMpk5qZM3s6/QODrN/wgi6v5v1eQ8EAJy1fDMBzG18ilUr7twOYPWs6FRVx2vd2sGVLL6ZXN1QpRW1tFXNmTSeRSPLsc3oFjZ11SKXTZDIZLn37eQD8+Ge/Yd/+Li8IpQN35519KotPmM/jTz7LI4897T1P/XynTm7hmvddTl//AF/8yrf9yWtuBci//NPHicfL+NZ3f8rW7fmGigrF2gvO5qILz2LDc5u5+1f3+YFCTJOWpnr+8e8+fMjXnBBCCCGOjKKyXbngSTbhlwS2k93Y6T7cRFr/vWeFMMwgyj2+/rY7nt1www3ccMPoJzTF2BimXmRG4OANeHNlw/1AiltQatyvsqH7PuZ7srj+7Sh6aeX7qtgZhZ3KYFoFwRavH4vQcmPoOBmcbIpsYkgvTvUbQ+sToHr1v169X9yvxswv3jW8bX5Db31yF++2ue+Pt/Nc4yHXE6mg5SY1NZXUUDni9Rsb6mhsGL1P5JXveNuo2y6+6NxRty1um8fif/3kqNsLs34K+wgBfOyGq/nYDVfrbV4wJeeEtnnc9aOv+hkRufMLOV/+10/qvq1ez1PbcZjqBXcuedu5LJw/S1fR8Mq65cqDV1TGecdla3AcB9fxAkOu4zcJP2nZCbQ0Neo+ql7/oRnTpgK6BHjr5BYc5QV+FNTX6kWnjuPoCh0qX7LNdXXfWoBUKkUymfLKwel/CS+A0z+QoKu7V9/WyxCxnXyVlp272v3zM7kATtoLwuzes48dO/boImhe0KixXv+ee3r72bJlW67vOygIR/KLODZuepme3n7v9ajv94zTdNm/na/v5cWXtvinrgECQX2wpTMZXtu2y+/7YhScawHYt7/LL5GWe60PDuZ7mIwXCZa8xfQJ/7R/Ut+xU15adr+XOZLRH+7KxTQsMHVPEF0OqwzDCh43mSCFlGvj5MbF1k3jnGwK107qslnpAbKpXpxs0t+uU3d1EymlDNJZF8fV9RNj0TCuY7O/q1fXfHRtspkMtm0zqTGOZcLO1/fTNzBEIpFAORmClmJHV5hUbZTg4CArG9OEJ0Ow4FWxe8PLAHzoghGeRPZVBvfDzNEXF7zlXAXJTICBlMVgymL+3OnEK2p47Nl2tu9N0Z802d/j0t2foW1uMyuXzuQPD27iwSdeJZN1dAkw18U0DO57YBM9fQnaO/oPeJyv/uCB0XdijH3WDQOCAZNgwMQydWDiktWz+OA7lvDpf3+Y517uIDNo68bmlkl7Z4JQrJZFCxZQV/sSlhUmEDB0QzrbJT75PMrLK2nveYy9+7q9eo/6BHnLjKVcftUauhKv8PreAQKWSTAQxAoEMK0IkYrJNFHFpJYWAgGToBeYCIdDRLz6qnNmzyQQ1KWwQsEAoXCI5gZdmmrG9Cmctmo5ZWUx4vEYoWCQivIy73kavO3Cs3Fdxw/AmAUpybNntlIeLyOdyWBns6QzWbK2nlw6rsvuPXvJZm0y2SzZrI1lWVzzvssB+PHP7qHP69WR+yB5x2VrmDNrGtt3vM5zG1/yAy0ZWzE4qANb8bIYUyY1AQa2qzMU0qk0juNgWRavbt3Jnj37SKXTpNMZUuk0l649j1NOOpHHnniGP/z3wzooFAgQDFjMmD6VObOnEwmH2blrjx/4CYX0OA4lkpTHy7Adm9272/MBEdfFdVwaGmrZuWsP/3Xfn/z0Zsd1qawo5/+78a8BuPPnvyGZTPupp66r+Oj172PWzGk8/sSzPLN+k9esTk88TlpxAnNmT2fHzt389O7f6m2u3h4Oh/xgybe+9zN6evv9cmWu6/LRD7+fM08/mXt//ycee+IpvRrFm5QsbpvHF//l79i1ey+f/8LXvYCQVyTV0I3dLcvisSefpX3vfh208MyYPpXFJ8zn1de288RT6wH8lOOBAV0Y1QB27NztT2Bz/V3SmQxxygiGggSDupybritqEgjpN6mqqnIaG+r8wI9hGLoRohBCCCGOWrpHRCXBSCXRisletvwgdmYQO9WPnR3ATg9iZ4ZI9/dimAGsYBQrEMOwQnLiUEx4umS4hTlq54+8fGDF9nqt2PkMFq+kueukcbIprOSAfn25WVyV1rdRDrhucYDFW8iqF9yN3My+KEBgmEftOR3dBNopKJPm6rJpyvYW7+oyav4pUNPUvWUMC8MIEIxWEAjFMK2wd38uuA4Kr2y8csEff9u/f+8POdyCPgZ6X9yChu8FfW68Mkj5Hafgd2IUnIUt2OiXN8oFX0z/sgMDNAWBHTjg95grNXU0/y4nktzfzoU/l8dH722RW5w6kpkzpjJzxtQRt1VWVPChD7xr1Nt+8JorR922+qxVrD5rlf9zrhwa6CyOr3/ls14QJldCzfX7+Xz42vcylEh65ysUjutQV6O3rVjaRl3tDX5wxnWVX+oc4NoPXImT1f2BMpks6WyG1qmTADhp2SIqyuP50mwoFi+aD0BjQy1nnnYSTm6bKr7fJYsX0DcwqINRSgelJnl9apqbGpgze7peiOy9tObMmqafazDInFnTvGwUrwIahn9+bMb0KZSVxVDeeRZXcdBA3ZEy7sGSb37zm3z5y1+mvb2dhQsX8tWvfpXTTz99vHerZPksiKT3YZvATvVhZwZRblYHBpwMuk2R4aV/mvoN27QwMHCVA9kMTib3YeUMf5ADH3ekTt3eBwu5Gnbege59IhVc7nqRv9wHUMH1/J9d//kp/wPOKWgCrycYQ0MJBoOunlh4tUbzDeUz/iQk10her+5wihrK6/10vA/EsfevAMglaZneP8CfOg3pFiFUWVBVBVQV3jJBNpkgbEE4WtJDvqUMw8ImTFefQ3/SoD8BPYOKtB1izbknMZi2+JdvPEzPoINtK2w7Q9Z2aJvTBUYX6zftIpnK+s22AJ54dhvf/fnjoz7m5i2HzgAZScAyaJ1UiW277No74E0ivOcBfO9LVzG9tYW3vf/fSWdsMrbym01ljEbmrHg3VQ07MLb06CZnBtgOdA+YNM2/nC620L7/x6AGdWkrr37lYLac+orJhMNRwhGdsaADIpZf63Fx2zyee/4lrzG5SSgUZMpkXRu0vLyMRQtme71BgoTCISKh/B+gK5a1UVVZ3N+i3AuIxKJRMpkMyWSSvXv3YdsO0WiE5UsXAfDgI08wMDCkMyNsfUL+2g+8i7mzp7Pugcd44qkNXrqlnnStWHYCC+fPprevn0cee5pAQNfBDAQsf9UC6KyVRDKpJ+ag61d6qwgMw2BgcNBLk3UZHEoRDpqctnIZA0ND/Oqe+1G5x9Sd4Vi75mwsy2LHrt3sbd+PaRkErIB3sl5HwcrjZdTWVmMaRkHtVh3csSyTrq4ebO852q7OBFlz/pmUx8v4yzMb2fTiFu/9Q0+Ao7EoJ69YTMf+bp7b9LLfPAwMqqvzzXY69neRSev6n5ZlEDADZDJ6hUQoGCQYCvrjYBgGoZB+xZdFo9RWV4FpYpk6iyYazb+4lyxeQP/AkG5q5h0Xk1p0Hc7ly06gsb4K0zSwTN13Z5KXKju5pZGPXP9eAoaFYekAmxWw/AnPtddcSWd3j/59ew0BF8zXvVPOOXsVLS2NWGZ+ZVQuIygeL+Oyt5/vpTG7/sQlYOmP53mzZxAOhXVAyXFxlUPEe646uBcm69goV+E4LkOJxEFfr0IIIYQ4uhiGSSBcQSBcAeV6nmrbGVLWLioqwjjZfrKJbuxMP46dwjQtzEBUSncJMQZjDay4rkva6qC6vk43c84FU1zbDya4rg3eV5U7v+FmvOwWG1wXpbJ+c/B84MEtOImf2zHvq/J+8E/WG/7PRsEJf4q+97srjMg/R6QKz/9454dyJcv861BUpiwXHDCtEFa4EjMYxQpEMaygDpJYQR0YMQJkO7upbGwsOml9KLmAiP6+YHxQwy5TBdvyQRP/q3/OioLnWHgeS/mBLj9w4zqgHH8xnn/OTDkF+5W7bPjvkfx++GOb+2UU1IDLDW1u+bz/ezPJphXZoUEMU2fT5Hpv+Nk2BzsGDNP/XoI1x6bC14lhGMQLyo8NV19XQ/0o26qqKlhSteCAy3Pn/s49a9UBpd5yzj5zJWefudIPhrhuvrrHpJZGPvTBd/uXu64qKv9/1bsv9TJo8rfNBXdWnbyEObOm6XNRto1Syl/EWV1VweUXn6/PI3kBnkJLFi+kr38g31dHKVqntow6NkfKuAZL7r77bm666Sa++c1vcuqpp/Ltb3+bNWvWsHnzZqZOHTmy91ZK9e+ma/uD2Nkk/b2dDG0NopxcrxCd3eDaaZxs0u+TkSsN5WS9clBO2k8F1R+WXtCjIAVMTCyOa5DKGiTSBok0DKX0mx+BcjZs3s3r+1L0DLn0DCp6+l0isUpOWjqbvzy3kxdfbc83WPI+f7/2y9+O+liP/uW1N7yfAUv39ujqTZDrpZer/TpvZiMfePfZfPEb99HVM1B0u2gsxmPrfspdv/4fPvHpr/qrLgyvLuN5F19LWVkZ0bLvYLtDfkDENAzMYJxIeQsVlbVYVkBnlXiBgjovRXFySyOzZkzFMi1iZRGCXlms3Anwiy44m9e27fD7gwQsi3lz9EnqeXNncOqqpfrEtqVPbOf6gxiGQUNDDal0Fsdx/PJSiUSS2tpqtu/YzYsvb/UmRwaucomXlbFsSRud3T1s3PyKH+xQSvlltkDXXczaWd0APRLBtAw/wFBRHqe6sjzfcMtxitIkM9msvn3WIevYfmqmaZo8+vjT7Ovo0ifGXQfHVTQ11TF/7kwee/IZ7v3DQ/5EMeso2tvb+eurriCbyfDsc5t1NB+lS9YB3d19NDXVs2HDZl7bvgvX8Samhs5GOfO0k9n84iv87r7/8TMX9LjVcsO178M0TZ7Z8AK5BvK5fx0dXbQ0N9I3MEhXd69fZ9M0Dfp69fETDod16qzh1eE0DBw7/8EZL4sx4CbydUGVq2tvAoZlMDRUHBTo69MZUYFggK6e3qJtRsHE5PU9+9i7t6No+6KFc5k1cxodHV088dT6oj89Zs+cxpmnnoRtO9z3h4cYbsXSRZSXx/mfh59g67adQD6l1DQtZk5v5dVXt/OLX/2+6HbNTQ2sWHYChmHwyONPYxmmXzfUMAzOX306lZXl7N3XydbtO/WJFEsHW3Jl0EzDIJPJeCXHdAZTrsyZEEIIIY5dphkgECojUtGAaeayT4awM4NkUz1kE126dFcyo+uhB8qwgtFxa4QqxPFC9xcwwSqx34p/cj+fpZHrMatch6IT/v521z8Zr5QNrio4f6T8+3BVfnGr/zX/yCM9C68cluk1nvb6tpgBTCukgx6F/VyKerwU9Hc5iMKTrKXQmTaWt5dHV3mz4swWd9jvsWAxsRfcKVxoXBzMKbgP5aIcG9vNEnQGCMZCGIYXtFKu7n2RC9K4OqiVy6w54P7JP442LNvGfyK5zYVBmYKSZ34QxruPwgAN5K9bEKChKFBjvKHfvTh6+BVQCs6TWJZFWWz0FeS1XmBkJPV1NX5f2+Fisahf5WMkZ5y6YsTLnez4luIa12DJbbfdxt/8zd/wwQ9+EICvfvWr3H///fzHf/wHt9566xHfn4GOTWy67/oj/rji6OYok6Gky2BSMZSGREoxlAJHWQTDMfZ0JOjoTjOYVAwkFYPev4EkDCYVGXukez2w1FVeJ5u2dB725xEMWnzoqvP5y3NbefKZlw/YvvulP2IFwrTMWe3XPMx9aM6e38a1N/w99/zpFZ7Z8AKWaerG5QGLmdOnEilv5uSTT6G1dZIOWniBi2Aw6NeSvOSic9i3r4tQJEQ4GCIUDnDxRasB+Jv3v4P5s6cTjUUJe9sbGnSdyMrKCv75H2+kfzBDRVkIAwPHdf2yVycunk91dQXZrE06kyadShMJ65VDyUSKffs6yWSyZLxyV4UN0zc8/xLJVArXyfeL6LioiylTWnjp5dd47vkXvbJu4CgXy7J4x2UXsnv3Xp56eoPXf0JHwMPh/IT6/j89ysDgEMrrUaFcxaSWRhbMm81/3fcnHn38L0WLUebOns7fXPNO9rTv48GH/1d/cBnoNGfDwHEcTNNk774u9nd2YwVMP/Mh5dWfrK6uorKy3CvjZKEUtLTorIiqygoWzp+tA1GWRSAYIOQFnQAuuuBMdr7eTiwWJeSVfVp2YhsAq885jXh5nIAV8D5QDcq9gJNpmvzDzdehXBfDKyFlmAbTp08B4Jr3Xk7Hhd2YuaZmhuGnVM6bM52bb/yAH1gARbDgRP97r7yEdCrjzfUNlKuYOlmnj646eSnTWif7x6iBbmAHUFtTyfvfc7kORCld9quwXuma886gf2BIZ/KhG5PN8PZ3zuzplMfDoJR+TKWblQGEw0EuWH0GppUvl2UYhp+aevYZp7C4bS5WwPJXes2a2ap/v3NmcMUlF/r7oZTSzdG8/V9z3pnYXnZIbhVHbqIye9Y0TMv0VnDoYFZjox7DeDzO1GGrLuJlo6cfCyGEEOLYpLNPygmEy4mUN5Mv3TVENtVDZqiDbKoH18lgYOr+jYEIphXGLPGkrxCidLkggIHFURYDEGOU/x16DuPv0XVdUqqDyoaGAzJxirNoCgMiBw/C4AdVcoE3p+j6hVVc8lVdvMdw7WFVZUBn1aCDON73wzN5/Ko0I2XWFMRw8jG8YSXOijJj9PeGmStfZxWVQQMJyIjxM27BkkwmwzPPPMMnP1nc3Of888/n8cdHLhWUTqdJp/MNEvr79Qnn4c193ihDVuIcExxXkbXx/imyDv7PmaKfdaBC/+zdxoF0VpFMQzKjSOW+ZvTXZBpSGUUyA8m0IqUThA5i8Eg9bd9lF62isrKcH/78/gO2LV+6gLvuuI0VZ11JT08fgB/1j8fjfP5f/g/f+8EveOa5r3ir0U1MU/d9Ccb0CdgpU1sYHBwiEAgSDFiEAkFWnbQUVykuftu5BIMBopEokWiYcDjEvDkzcJVi0qQmLlh9unei31ttYBooDFylOGHRAnZW7wYFtuPiOg5W0MJVis7uXp574WWyXkAjm7Vpaqzj9FNXkE6n+eKXv006a2MauneFq1y+/Y3/w9yZ0/nRT37NCy+96k0K9GdxZ08/p526nJdf3c5jT6wHQ/knsCu9rA6A7Tt2YzuOLt3mZcIMDSZxlWIokWIokfQj7oapy065SlFZVU5dbQ2BYEA3cQ8GiZfH/Pu96IKzSKbSRLym5pFomDNOOxlXKf6/G/+ayy8+n0g0QjQcJhaLUF1ThasUK5adwB/+6wdeJozpBzasQABXKX73q+/4TcsLuUrxwWveyQeveSegJ1y5ZmiuUsTKyvj27f8H23b8/iCu4xCPl+EqxdvWnEMimfJqXur306rKClylqKqupG3BHC+Y5GLbDuFQyH+u0WiEdCaLY9uks1mvJJfu+dHbP8COHbuLGpsrBVMmN5NMp9n04pai5xEJhzhxsU4p3bp9FwODxdkjU6a0UKUq6Orp46VXthVtmzl9CjNnTiNrO7zy2vYDXhu5/e3s7mF/Z0/Rttkzp6GUIpu16ezs9iZx+rUTjYb1bQ2TVDqdPx4MsKwAWdvBCgTA0I+hbMf//SjvcXVZYxOLXMND/N9p7piEfO1V09Q1PPXrqpF4vAzDzGfn1HrHS3V1JWedcYoXiNKBtWDQwnV0PeDcmB8uh/O+hBBCCPHGFZbuipQ349bOwc0mvMbxg2QS3bjZQbKZIZSbAcPySumEdCDFHPdq3EIIIRgWaDvCChcuFgVFvOo4wwM3BwRyCjJfisvOFWbDOCjX9UrbFZZC8zKnHNvvE1SYweO6NtmES9rMjVFR7Tp05lRhJpTpB1/8/kFmQLJgxBsybrOkzs5OHMehsbGx6PLGxkb27h25d8Ktt97K5z73uQMu379/P6lU6k3v01D/+Kb55DiuwnEPdZK+OJg7nOsFe3OlMV3ve+Wl8+WCwaro8mHfq9zJPrwT3Hq/HFf3lLBdcByF7VB0ueMobO971/uadQoCHI4XxCgIdmSyw6/jBT7sAwMfx8r5uiUnzCUWi/LYkxsO2LZg3gzOP+9cvvGtO8hmi1NP4vEY3/nGl7jzZ//J/f/9MIC/ij0YCvLlL38JgN/991PYWcfvMxEImLztogshEOftF63mqWc2EAzoZt9By2LB/Dl09yZZtKiNU1etwDTwmtLpE7Pdvfr4P23lyd4JY29RgAG1dfV6uxnCCoRIpTMMJlI4rg2Gvm17ewePP7ne62uhy0QppXjvu66gzjb55T338/ruPfmDUhm4mCxc0Mb6DS/x7IbNAH6wJZFM092bxHEcBoeSuAoCAb2iP2AF6e1O0F2bpLK6ivq6Wv8Es2la1Nfr/a2prmXWzGl67LzARnV1lf9c11xwNo6jCAZ1f5BQMEhNfQPdvUnevvYCVqxYQsAKEA4FCQaDNDTU0t2bZOqU6fzD396IUvjZAoZh+Pd7ztlnkEjq75X3ojKssDeGeqVfOqNIpZN09yVJpFzi8WrSmQzPbXzVryGZmyyctuokLMti/XOb6O7u9V6b+kU6b+4spkxuYfeevTy/8UWUlxKcSGRoaqrj9FUrcF2XX/zn7w44Dt/+tvMoK4vx2BPr2bVrT9G2trZ5tC2Yy572Th57YgOWaWFaevwrKuK0TNLZGD19SVzXJRCwMM0gkUiE/sEMjkoSjsSZNHkKpneMGYZBZWWl/r26FicsWuSlgOYDBbkxPOGEE9A1dA0/MyUYitLdm2TSpCk0NeUzKlxXYVr6tq5rcu7ZZx7wXHP3e+LiE/2yYaapJ13BQIDuvhSNTS3MnDkNa9hqn9xtLzhfZ0Kp3P+VYihpM5TIMm1qK9Omtno1gnMZJLB/fzeWabG4bZ5/eW6VTsc+3VBp5rQWb5WOf8DQ39tFf48iaEBdlZcO683z7NQQ+9qHAIOwlfsg0Jkyjp1lX8c+IEC6e5DBTHHJsTdjYGDg0FcSQgghxBFnmgFML3gSBspqwHUyXrnoBHaqn0yyE9dJk03047qO7kkQiGIFoxgSPBFCiAknH0gw/PM/2pHLSCzuj5zLdnFxbJtseD+VNVWYJv7f0Dr4Yus2CNkUjp1GuZl8IMbOePfplbfTD1LQRwYwLL1owOsfXdw7xlucanjBF8PUwSwJukwo4z4rGn7A5U5ijeRTn/oUN998s/9zf38/U6ZMob6+noqKihFvU4pBcxp7G04EM4jtmoTCZRhWEAiAGQAslGGilLdaPtfYxvtZKQPXNbzAhInj6v4AyjW925m4mChM7zZW/jIjoO/X1b0EXFfhel/1Y0CunqAep1w9QTMfFCFf7z7/NmcUV7TMRYvRTcGUUtiOoxvpkD9BmytjNDA4iOvoVeL6eg6VlRXEgkG6e3rp7x8ka9sMJdMELZOKeIypk5roHxjgpZe3YjuO/pfVvRfOOuMklHL547pHSQ4mcAIutmOTDTicdOIC5s2dzgMP/y8vbdlBWLkEsy62cqirqeSqK9ey8/Xd/OSXf/T3KdcA6NN/+wEsK8AX//0HDCVS/uUKxcK5M3nfu9byvTt/w5bcinNXn7aMREJ8/tMfo7e3jy985bve+OUGCz70N+9i2dJF/MNnvkRf36A+qas7c7P8xDZ+9L3/yxe/8m1+9ovf6hOsXkmgeFkZ//OHnwEw+4TVXrqjt1od+Pubr+fUVStZv/55Nr+0RT+Y9ztbtrSNC1evpKenkw0bNvm3A4NoJESNd+K0tqaSoUTS+01qNVVl1FRFydoZBgeGCu5WUVUV97YlePW1/Kp8w3vSufvd8Nwmurp7vJUF+vNkyaK51FSdy/Yd23lly2t+ZqVhGAQDFjVVURJDUVKplF+mKBwMYBkmVRVRaqqiTGquI5NOYXolvEzDZOa0SdRURVm0cCbPbZxGMBgiGLSwLIuWlgZ/n05dtRTbVpTFwn5j7mmtjdRURTnz1OW0NNZ6zbotTNNg6YnzqamKsnDBDM46fUVRz4p4WdS/3xnTJpFIFgdaa6pi1FRFiceCpFMJ0sDQkH6ulRURaqqi9PZm2blzR1GfDitgcfpKXYtxaKif/v5BPzvBMAyiYZOaqig93QaOncrXiTQMIt62dNoiGrH8AELuOtWVEQKBAA21FYQCFD1uQ10FNVVRHLuSxIxJ/vvnYCLDpOZa/7med84pfuP4XGCivq6SQCDAuWetwLYdLMvSZbxyvyPTpKZqGm3zpzGaiy86Y9RtNVWj3w6iNDdWjX7bynB+1Qp4K0+8MIXS78f6e0W+J1QWpVyqYmY+bdhvXjiIwiUegPxKGApWxiiyFpQHjHz5Vsi/hlQueuhNtHK1Xoc138unDoOBrhXspx8bBgamPh79iVgu7djUk7Vhl/npyujGgPnHymUY5R53+HYDK1R2WFeORiKRw3ZfQgghhHhrmVYI0woRjFRBeYv+28jJYGeHsNP9ZBKdOOl+0okOcB0MM4QZiGAFY1K6SwghxBFhGKau8jOs0o8ZdAmGM4TL6g8oXTaSXH8gv2+QH4Bx8EuWubZeSGAncbNJ3Wta6eoa+JkxWe88bi7DRpctK9hj7+9vq+jv/3w2S66cWL7MmCxIOPYYKp93dURlMhlisRi//OUvueyyy/zLP/7xj7NhwwYefvjhQ95Hf38/lZWV9PX1HZZgSY7runR0dNAwQj3BkeReiG7uxejauh6gY3uXZ3Ft3SjedTO4TgZcB0UuHc1F4eRTOIbX+IP8i9B/UZp+ulm+IVdBjb8jyC0o+WMe5dFW5ZcC0r0mAEKhIEopevv6sbOODt5ks2Rth9YpLQQCAV7buoOevn5cR/mliiZPbqJ1yiTa2zvYtPkVHNfre+E6lMVinHPWSgD+854/YtuO17/CxXXhgtWnYwYiPPf8c7z66vaC/YKFC2Zz1ukns2PHbu65bx1g+OVvotEI1//NuwH4xnd+QmIolV+RbsBlF5/P7JnT+NNDj7N+w2YMDPR5WZN5s6ez5oKz6Ozs5q5f3eef9DUwMEyDG659LwC/uXcd/b0DWAGLYCCAaVmctGwRrVMnsX3762zduYtQMEgoFCIcDFJRWc701sk4jsOWrTu8gJKJ6TWsbp3SgmVZdOzvIplM+SfzTcukqqKceLyMbDZLMpX2X2+5huC5fhCpdIaevhS11VE/yyZ33XwGRl4ukFCYoVEYhC287fG4QmAsr8l8muywuqfk0nFVUSDBS3vw/stv8+/Lr1OqDri9LxeFUAXvc4WX+dcreN/zgw0MCxKQDwwUBC1GCkiYIwUkMMHM1UY1cDHo6hqgrrYS07L8gENukpPbzwMb4+VXnlDQHC/3fn3g98fu8fZWfeYeb46WuclEJ+NVGhmv0smYlUbGqzRHarxcJ4OdGcTJNY5P9uBkUyg3g2GGdOAkEHnTwROlXNKD7VRPWUUoOnIT2DfieJibvBXPQV5vpZMxK42MV2lkvEp3pMdMFZQfy5Ui0+d285kvrpPVX+00rrJRTlaf+3UVKL0d5fjn6AqDNwcyMKwAhhEYVk4sd/6gOMPlUI6l87Jj4WSTuHaC6imnYgVHbzxfqrF+5o5beCsUCrFs2TLWrVtXFCxZt24dl1xyyXjt1huiAxYWJuExXV8p5Uc4/Yin0iljOlXMa8zkOgVpZvnrKUcHXFwnq2v7OWmvRr2dPwnpL4suruM3/OtESyfLlZwa6fLCxt/DzZzROuq25uYGmpsbRt3+jksvPOCy3BvZ2Wecwrlnrhzxdq2tk/j4DdeMer8fue59o25bfdYqVp+1asRtdXU13Pihq0a97WVrzxt127Rpk5k2bfKI2yzLYt7sGaPetqG+dtRtwWCwqLn3cLrpuK2zIoYdr7nAyEgOti23/Wh1QDDDb+aWy6QY1vCN/MoH13XIJhRpU+mgQVEwyYtSGIUfwmZxQKAoAEH+QxodAMPwaoKaXjDDD0R41zEs3QPGyK+qKMx4GDmAUJghMYaAg2Hm76/w+zcYkHBdl8F0B9FKmcAKIYQQYmIwrZAOXkRriFZOxXVt3Tg+PUA20UU21Us22e31PTG9hvFhzGBU+p4IIYQ4buhzR2/ucy2X3ZJbjOoHXNysd87W8c/zuq6te43ZSX9BvetkCsqNFfaAgdw5nKLFqLlvvVJhdhrsVAYrEJqw53wPl3Gd4dx8881cddVVLF++nJUrV/Kd73yHnTt3cv3114/nbr3lDMPwynu9+RU6qiCbxfWjmvmUM9fO4DppHDuN66TBu8yPcBaVoiF/sjEXUPFfYKZ/4lNecGIiKApKQH6FwfCMC5XrT1HQ4MwLbBRev3C7n0E2PKMipyBDIp/amQsSFAQozACmaYGhvxpmAMMMoAyLlJkkXluJZRaUdDILU0ULUkT9YElx0GHE8lJCCCGEEOK4ZJoBzEgVwUgV0copuE4WJzvkN47PJntxs0NkE/txXRvDaxxvBqOYVlj+PhRCCDFh6ZJipZ83yS2oLwyy5L93/YX1uXNQuUWyuQW0rp3Gziax0v0YhovrJL0qRk7+uoWVPfwgS26xqeWd/tHnjkwzULCodmIGXcY1WHLllVfS1dXF5z//edrb22lra+P3v/89ra2jr+IXeaPV9huNKkwlK/pXkLVSEGBx3QyundaRUTeLq9Lei80tesEpV5FNQtrMZ9kU19wfVn/fr8s/gU++5t747DQOblHwKv8m6fWW8U9cg7/qv6gRV+4y72fyX4v71+Q2FV8nf72xGOl6h67kd2C1v1wfCXXAZfnrFpR9Ugo7Y5NNJPK7n+tfUXC94pJRjPChMLz807DnNLwUVFGvCIYFLfRt8wENw/8AMbwPF9Pw6lOa+Q+ZXBBy5MyOwiyKwstH/jraa8h1XRJOB7EqyZIQQgghhBBvjGkFMS0dPMlxnQxOZgg7O4STHiCT7MLJDJCx92Pksk8CEaxA1CspIoQQQojR5BfUv3Gu65IyOqiur8NQDq6b9c/3Qq7hvZPPWHF1LxfXzXpVixwvAyZXxcjGzZ2rLAq6DCurnitN7pc4L24Xcaye/x333NkbbriBG264Ybx3Y0Lw08pKTJnOZ7AUBlUcL4vFxXFs0p09xGvKwMniupl8qTDXBtdrkqSUdzvl9WjRTc/9E9v+SXDvh8I+LRxYjqewvE9x6Z3Ck9yMGBzgYMGB4SfbGaVHQ0Ekt7gUUmG63LA+NN5XBdhpEzsc8Poj6DcUy4p6f2CEvV4N6PHG9cYxd5+6/wnDSjIdECgoCiRQ8H1hoIGi/1Nw1TfkgKEdVjaL4b8Po+BLPnvCv7b3+7WsLIFwxHsTHlb+aXjGRUF/n6ISTwct25QrOeUdV0UZFSMfd8XZGAW9NIQQQgghhDgOmVYIMxoiGK0G9N9FTjah+56kB7y+JwOkh/pAKQwrhBWIjPNeCyGEEMc/vWghMOY2ESMpLCdWGHTJnQP2K6rkenQ7WX0O2F9wb+M6qRECLRSVDitaCGx6pd69y5RrH4bReOPGPVgijn75DJaRt7uuSzgVHHEVe755dP5Fkn+x5DNUchkrxbX9RgrO6N4urut6ZY7yAQnXD2hAcYPpfFBg5ABAUZqB96Q5IIOjOJsgl22QK1lmeiWQLAwz6JVHCuYvK0pfM1FY2F29VDXU63qCXtT1cJ1sPzBoUjgGhZkbDLus4HoH3unoDzjCfhsHjKlRtLXwsuLAVmFWjOa6LplQB1XSEE0IIYQQQoijhmGYBEJxAqE44XgTgC4JkhnAzgySSXTipAcIhOIH/n0ghBBCiKNKYTmxNxJ0yZ3LLQq0eD/nslr8bd6Ce7+qES7KzaJcFysYHbeMFAmWiLeUX5oIa9Rgyxs1PKNjxH4SBQGTwuDAgWWhhu3zsJP3B2Yn5LMM3siL13VdAqEsgVD8LTn5X/wcir4IIYQQQgghxFvGDIQJBcKEYnXEqqbhOllcJ40VLBvvXRNCCCHEWyjXnqHUQEvxYnsbpRRm4I1nyLwZEiwRxyydnoUOxAghhBBCCCGEOOro3idvrh67EEIIIY5fxYvtx9ab+60i9WyEEEIIIYQQQgghhBBCCDGhSbBECCGEEEIIIYQQQgghhBATmgRLhBBCCCGEEEIIIYQQQggxoUmwRAghhBBCCCGEEEIIIYQQE5oES4QQQgghhBBCCCGEEEIIMaFJsEQIIYQQQgghhBBCCCGEEBOaBEuEEEIIIYQQQgghhBBCCDGhBcZ7B94MpRQA/f39h/V+XddlYGCASCSCaUo86VBkvEonY1YaGa/SyHiVTsbs0HKftbnPXjEymZscHWS8SiPjVToZs9LIeJVGxmtsjoe5yVsxb5Djp3QyZqWR8SqNjFfpZMxKI+M1NmOdNxzTwZKBgQEApkyZMs57IoQQQkwMAwMDVFZWjvduHLVkbiKEEEIcWcfy3ETmDUIIIcSRdah5g6GO4WUYruuyZ88eysvLMQzjsN1vf38/U6ZMYdeuXVRUVBy2+z1eyXiVTsasNDJepZHxKp2M2aEppRgYGKClpUVWqxyEzE2ODjJepZHxKp2MWWlkvEoj4zU2x8Pc5K2YN8jxUzoZs9LIeJVGxqt0MmalkfEam7HOG47pzBLTNJk8efJbdv8VFRVykJVAxqt0MmalkfEqjYxX6WTMDu5YXbV5JMnc5Ogi41UaGa/SyZiVRsarNDJeh3asz03eynmDHD+lkzErjYxXaWS8SidjVhoZr0Mby7zh2Fx+IYQQQgghhBBCCCGEEEIIcZhIsEQIIYQQQgghhBBCCCGEEBOaBEtGEA6H+exnP0s4HB7vXTkmyHiVTsasNDJepZHxKp2MmTjayTFaGhmv0sh4lU7GrDQyXqWR8RJvhhw/pZMxK42MV2lkvEonY1YaGa/D65hu8C6EEEIIIYQQQgghhBBCCPFmSWaJEEIIIYQQQgghhBBCCCEmNAmWCCGEEEIIIYQQQgghhBBiQpNgiRBCCCGEEEIIIYQQQgghJjQJlgghhBBCCCGEEEIIIYQQYkKTYMkw3/zmN5k+fTqRSIRly5bx5z//ebx36ahwyy23YBhG0b+mpiZ/u1KKW265hZaWFqLRKGeddRYvvPDCOO7xkffII4/w9re/nZaWFgzD4J577inaPpYxSqfTfPSjH6Wuro6ysjIuvvhiXn/99SP4LI6cQ43XNddcc8Axd8oppxRdZyKN16233sqKFSsoLy+noaGBSy+9lJdffrnoOnKM5Y1lvOQYE8cKmZuMTOYmByfzktLJ3KQ0MjcpjcxNxJEi84aRybzh4GTeUDqZN4ydzBlKJ/OG8SPBkgJ33303N910E5/+9KdZv349p59+OmvWrGHnzp3jvWtHhYULF9Le3u7/27hxo7/tS1/6Erfddhu33347Tz/9NE1NTZx33nkMDAyM4x4fWUNDQyxevJjbb799xO1jGaObbrqJ3/zmN9x11108+uijDA4OsnbtWhzHOVJP44g51HgBXHjhhUXH3O9///ui7RNpvB5++GE+8pGP8OSTT7Ju3Tps2+b8889naGjIv44cY3ljGS+QY0wc/WRucnAyNxmdzEtKJ3OT0sjcpDQyNxFHgswbDk7mDaOTeUPpZN4wdjJnKJ3MG8aREr6TTjpJXX/99UWXzZs3T33yk58cpz06enz2s59VixcvHnGb67qqqalJffGLX/QvS6VSqrKyUn3rW986Qnt4dAHUb37zG//nsYxRb2+vCgaD6q677vKvs3v3bmWapvrjH/94xPZ9PAwfL6WUuvrqq9Ull1wy6m0m8ngppVRHR4cC1MMPP6yUkmPsUIaPl1JyjIljg8xNRidzk7GTeUnpZG5SOpmblEbmJuKtIPOG0cm8Yexk3lA6mTeURuYMpZN5w5EjmSWeTCbDM888w/nnn190+fnnn8/jjz8+Tnt1dNmyZQstLS1Mnz6dd73rXWzduhWAbdu2sXfv3qKxC4fDnHnmmTJ2nrGM0TPPPEM2my26TktLC21tbRN2HB966CEaGhqYM2cO1157LR0dHf62iT5efX19ANTU1AByjB3K8PHKkWNMHM1kbnJoMjd5Y+Qz442Tz43RydykNDI3EYebzBsOTeYNb4y8n79x8p4+MpkzlE7mDUeOBEs8nZ2dOI5DY2Nj0eWNjY3s3bt3nPbq6HHyySdz5513cv/99/Pd736XvXv3smrVKrq6uvzxkbEb3VjGaO/evYRCIaqrq0e9zkSyZs0afvrTn/LAAw/wla98haeffppzzjmHdDoNTOzxUkpx8803c9ppp9HW1gbIMXYwI40XyDEmjn4yNzk4mZu8cfKZ8cbI58boZG5SGpmbiLeCzBsOTuYNb5y8n78x8p4+MpkzlE7mDUdWYLx34GhjGEbRz0qpAy6biNasWeN/v2jRIlauXMnMmTP50Y9+5DcPkrE7tDcyRhN1HK+88kr/+7a2NpYvX05rayv33Xcfl19++ai3mwjjdeONN/L888/z6KOPHrBNjrEDjTZecoyJY4V8vo5M5iZvnnxmlEY+N0Ync5PSyNxEvJXks29kMm948+T9vDTynj4ymTOUTuYNR5Zklnjq6uqwLOuAyFpHR8cBkU0BZWVlLFq0iC1bttDU1AQgY3cQYxmjpqYmMpkMPT09o15nImtubqa1tZUtW7YAE3e8PvrRj/Lb3/6WBx98kMmTJ/uXyzE2stHGayRyjImjjcxNSiNzk7GTz4zDQz43NJmblEbmJuKtIvOG0si8Yezk/fzwkPd0mTO8ETJvOPIkWOIJhUIsW7aMdevWFV2+bt06Vq1aNU57dfRKp9O8+OKLNDc3M336dJqamorGLpPJ8PDDD8vYecYyRsuWLSMYDBZdp729nU2bNsk4Al1dXezatYvm5mZg4o2XUoobb7yRX//61zzwwANMnz69aLscY8UONV4jmejHmDj6yNykNDI3GTv5zDg8JvrnhsxNSiNzE/FWk3lDaWTeMHbyfn54TOT3dJkzlE7mDePore0ff2y56667VDAYVN///vfV5s2b1U033aTKysrU9u3bx3vXxt0nPvEJ9dBDD6mtW7eqJ598Uq1du1aVl5f7Y/PFL35RVVZWql//+tdq48aN6t3vfrdqbm5W/f3947znR87AwIBav369Wr9+vQLUbbfdptavX6927NihlBrbGF1//fVq8uTJ6k9/+pN69tln1TnnnKMWL16sbNser6f1ljnYeA0MDKhPfOIT6vHHH1fbtm1TDz74oFq5cqWaNGnShB2vD3/4w6qyslI99NBDqr293f+XSCT868gxlneo8ZJjTBwrZG4yOpmbHJzMS0onc5PSyNykNDI3EUeCzBtGJ/OGg5N5Q+lk3jB2Mmconcwbxo8ES4b5xje+oVpbW1UoFFJLly5VDz/88Hjv0lHhyiuvVM3NzSoYDKqWlhZ1+eWXqxdeeMHf7rqu+uxnP6uamppUOBxWZ5xxhtq4ceM47vGR9+CDDyrggH9XX321UmpsY5RMJtWNN96oampqVDQaVWvXrlU7d+4ch2fz1jvYeCUSCXX++eer+vp6FQwG1dSpU9XVV199wFhMpPEaaawAdccdd/jXkWMs71DjJceYOJbI3GRkMjc5OJmXlE7mJqWRuUlpZG4ijhSZN4xM5g0HJ/OG0sm8YexkzlA6mTeMH0Mppd54XooQQgghhBBCCCGEEEIIIcSxTXqWCCGEEEIIIYQQQgghhBBiQpNgiRBCCCGEEEIIIYQQQgghJjQJlgghhBBCCCGEEEIIIYQQYkKTYIkQQgghhBBCCCGEEEIIISY0CZYIIYQQQgghhBBCCCGEEGJCk2CJEEIIIYQQQgghhBBCCCEmNAmWCCGEEEIIIYQQQgghhBBiQpNgiRBCCCGEEEIIIYQQQgghJjQJlggxgdxyyy2ceOKJ4/b4n/nMZ7juuuvG7fHH6vbbb+fiiy8e790QQgghjnsyNxkbmZsIIYSY6GTOMDYyZxDizTGUUmq8d0II8eYZhnHQ7VdffTW333476XSa2traI7RXefv27WP27Nk8//zzTJs27Yg/finS6TTTpk3jl7/8Jaeddtp4744QQghxTJK5yeEjcxMhhBDHM5kzHD4yZxDizQmM9w4IIQ6P9vZ2//u7776bf/7nf+bll1/2L4tGo8TjceLx+HjsHt///vdZuXLluE8sHMfBMAxMc/TEunA4zHve8x6+/vWvy+RCCCGEeINkbjI2MjcRQggx0cmcYWxkziDEW0/KcAlxnGhqavL/VVZWYhjGAZcNT1u95ppruPTSS/nCF75AY2MjVVVVfO5zn8O2bf7u7/6OmpoaJk+ezA9+8IOix9q9ezdXXnkl1dXV1NbWcskll7B9+/aD7t9dd91VlAp65513UltbSzqdLrreFVdcwfvf/37/59/97ncsW7aMSCTCjBkz/P3Lue2221i0aBFlZWVMmTKFG264gcHBQX/7D3/4Q6qqqrj33ntZsGAB4XCYHTt28NBDD3HSSSdRVlZGVVUVp556Kjt27PBvd/HFF3PPPfeQTCbHNP5CCCGEKCZzE5mbCCGEEGMhcwaZMwhxtJBgiRAT3AMPPMCePXt45JFHuO2227jllltYu3Yt1dXV/O///i/XX389119/Pbt27QIgkUhw9tlnE4/HeeSRR3j00UeJx+NceOGFZDKZER+jp6eHTZs2sXz5cv+yv/qrv8JxHH7729/6l3V2dnLvvffy13/91wDcf//9vO997+NjH/sYmzdv5tvf/jY//OEP+dd//Vf/NqZp8rWvfY1Nmzbxox/9iAceeIC///u/L3r8RCLBrbfeyve+9z1eeOEFampquPTSSznzzDN5/vnneeKJJ7juuuuKUn+XL19ONpvlqaeeevODLIQQQogxk7mJzE2EEEKIsZA5g8wZhDjslBDiuHPHHXeoysrKAy7/7Gc/qxYvXuz/fPXVV6vW1lblOI5/2dy5c9Xpp5/u/2zbtiorK1M///nPlVJKff/731dz585Vruv610mn0yoajar7779/xP1Zv369AtTOnTuLLv/whz+s1qxZ4//81a9+Vc2YMcO/79NPP1194QtfKLrNj3/8Y9Xc3Dzqc//FL36hamtr/Z/vuOMOBagNGzb4l3V1dSlAPfTQQ6Pej1JKVVdXqx/+8IcHvY4QQgghDk3mJjI3EUIIIcZC5gwyZxBiPEnPEiEmuIULFxbVu2xsbKStrc3/2bIsamtr6ejoAOCZZ57h1Vdfpby8vOh+UqkUr7322oiPkUv9jEQiRZdfe+21rFixgt27dzNp0iTuuOMOrrnmGn9FxDPPPMPTTz9dtPLCcRxSqRSJRIJYLMaDDz7IF77wBTZv3kx/fz+2bZNKpRgaGqKsrAyAUCjECSec4N9HTU0N11xzDRdccAHnnXceq1ev5p3vfCfNzc1F+xeNRkkkEmMbSCGEEEIcFjI3kbmJEEIIMRYyZ5A5gxCHm5ThEmKCCwaDRT8bhjHiZa7rAuC6LsuWLWPDhg1F/1555RXe8573jPgYdXV1gE5fLbRkyRIWL17MnXfeybPPPsvGjRu55ppr/O2u6/K5z32u6HE2btzIli1biEQi7Nixg4suuoi2tjZ+9atf8cwzz/CNb3wDgGw2699PNBotSkkFuOOOO3jiiSdYtWoVd999N3PmzOHJJ58suk53dzf19fWHGkIhhBBCHEYyN5G5iRBCCDEWMmeQOYMQh5tklgghSrJ06VLuvvtuGhoaqKioGNNtZs6cSUVFBZs3b2bOnDlF2z74wQ/y7//+7+zevZvVq1czZcqUosd6+eWXmTVr1oj3+5e//AXbtvnKV77iryb5xS9+MebnsmTJEpYsWcKnPvUpVq5cyc9+9jNOOeUUAF577TVSqRRLliwZ8/0JIYQQ4siTuYkQQgghxkLmDEKIQ5HMEiFESd773vdSV1fHJZdcwp///Ge2bdvGww8/zMc//nFef/31EW9jmiarV6/m0UcfHfH+du/ezXe/+10+8IEPFG3753/+Z+68805uueUWXnjhBV588UXuvvtu/umf/gnQkxbbtvn617/O1q1b+fGPf8y3vvWtQz6Hbdu28alPfYonnniCHTt28N///d+88sorzJ8/37/On//8Z2bMmMHMmTNLGR4hhBBCHGEyNxFCCCHEWMicQQhxKBIsEUKUJBaL8cgjjzB16lQuv/xy5s+fzwc+8AGSyeRBV2Zcd9113HXXXX76a05FRQVXXHEF8XicSy+9tGjbBRdcwL333su6detYsWIFp5xyCrfddhutra0AnHjiidx2223827/9G21tbfz0pz/l1ltvHdNzeOmll7jiiiuYM2cO1113HTfeeCMf+tCH/Ov8/Oc/59prry1hZIQQQggxHmRuIoQQQoixkDmDEOJQDKWUGu+dEEIc/5RSnHLKKdx00028+93vLtp23nnnMX/+fL72ta+N094V27RpE+eeey6vvPIKlZWV4707QgghhHgLyNxECCGEEGMhcwYhJg7JLBFCHBGGYfCd73wH27b9y7q7u7nrrrt44IEH+MhHPjKOe1dsz5493HnnnTKxEEIIIY5jMjcRQgghxFjInEGIiUMyS4QQ42batGn09PTwmc98hr/9278d790RQgghxAQncxMhhBBCjIXMGYQ4PkmwRAghhBBCCCGEEEIIIYQQE5qU4RJCCCGEEEIIIYQQQgghxIQmwRIhhBBCCCGEEEIIIYQQQkxoEiwRQgghhBBCCCGEEEIIIcSEJsESIYQQQgghhBBCCCGEEEJMaBIsEUIIIYQQQgghhBBCCCHEhCbBEiGEEEIIIYQQQgghhBBCTGgSLBFCCCGEEEIIIYQQQgghxIQmwRIhhBBCCCGEEEIIIYQQQkxo/z/1LUJU8axLJAAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "
        "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    " # Now all together\n",
-    "fig = plt.figure(figsize=(16, 6), layout=\"constrained\")\n",
-    "fig.suptitle(\"ISMIP6 emulator | Antarctica | Training fits\")\n",
-    "for scen, _ in enumerate(pen_train_preds):\n",
-    "    sum_train_preds = wa_train_preds[scen] + ea_train_preds[scen] + pen_train_preds[scen]\n",
-    "    sum_train_ismip = train[1][\"sle\"][scen] + train[2][\"sle\"][scen] + train[3][\"sle\"][scen]\n",
-    "    \n",
-    "    ax = fig.add_subplot(2, 3, scen+1)\n",
-    "    p5 = np.percentile(sum_train_preds, 0, axis=(0, 1))\n",
-    "    p17 = np.percentile(sum_train_preds, 17, axis=(0, 1))\n",
-    "    median = np.median(sum_train_preds, axis=(0, 1))\n",
-    "    p83 = np.percentile(sum_train_preds, 83, axis=(0, 1))\n",
-    "    p95 = np.percentile(sum_train_preds, 100, axis=(0, 1))\n",
-    "\n",
-    "    true_median = np.percentile(sum_train_ismip, 50, axis=0)\n",
-    "\n",
-    "    time_axis = np.arange(len(median))\n",
-    "\n",
-    "    # Plot Emulator Uncertainty\n",
-    "    ax.fill_between(time_axis, p5, p95, color='darkgoldenrod', alpha=0.2, label='5-95% Confidence')\n",
-    "    ax.fill_between(time_axis, p17, p83, color='darkgoldenrod', alpha=0.4, label='17-83% Confidence')\n",
-    "\n",
-    "\n",
-    "    ax.plot(np.tile(time_axis, (train[3][\"sle\"][scen].shape[0], 1)).T, sum_train_ismip.T, '--', lw=1, color=\"black\", alpha=0.4, label=\"ISMIP6 models\")\n",
-    "    ax.plot(time_axis, true_median, lw=2, color='black', label=\"ISMIP6 median\")\n",
-    "    ax.plot(time_axis, median, color='darkgoldenrod', lw=3, label='Emulator Median')\n",
-    "\n",
-    "    ax.set_xlabel(\"Time (years)\")\n",
-    "    ax.set_ylabel(\"Sea Level Equivalent (m)\")\n",
-    "\n",
-    "    if scen == 0:\n",
-    "        handles, labels = ax.get_legend_handles_labels()\n",
-    "        unique_labels = set(labels)\n",
-    "        handles = [handles[labels.index(label)] for label in unique_labels if label in labels]\n",
-    "        ax.legend(handles, unique_labels, frameon=False, loc='upper left')\n",
-    "\n",
-    "    ax.grid(True, alpha=0.3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "a61a6847",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "
        "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    " # Now all together\n",
-    "fig = plt.figure(figsize=(10, 8), layout=\"constrained\")\n",
-    "fig.suptitle(\"ISMIP6 emulator | Antarctica | Test fits\")\n",
-    "for scen, _ in enumerate(pen_test_preds):\n",
-    "    sum_test_preds = wa_test_preds[scen] + ea_test_preds[scen] + pen_test_preds[scen]\n",
-    "    sum_test_ismip = test[1][\"sle\"][scen] + test[2][\"sle\"][scen] + test[3][\"sle\"][scen]\n",
-    "\n",
-    "    ax = fig.add_subplot(2, 2, scen+1)\n",
-    "    p5 = np.percentile(sum_test_preds, 0, axis=(0, 1))\n",
-    "    p17 = np.percentile(sum_test_preds, 17, axis=(0, 1))\n",
-    "    median = np.median(sum_test_preds, axis=(0, 1))\n",
-    "    p83 = np.percentile(sum_test_preds, 83, axis=(0, 1))\n",
-    "    p95 = np.percentile(sum_test_preds, 100, axis=(0, 1))\n",
-    "\n",
-    "    true_median = np.percentile(sum_test_ismip, 50, axis=0)\n",
-    "\n",
-    "    time_axis = np.arange(len(median))\n",
-    "\n",
-    "    # Plot Emulator Uncertainty\n",
-    "    ax.fill_between(time_axis, p5, p95, color='darkgoldenrod', alpha=0.2, label='5-95% Confidence')\n",
-    "    ax.fill_between(time_axis, p17, p83, color='darkgoldenrod', alpha=0.4, label='17-83% Confidence')\n",
-    "\n",
-    "\n",
-    "    ax.plot(np.tile(time_axis, (test[3][\"sle\"][scen].shape[0], 1)).T, sum_test_ismip.T, '--', lw=1, color=\"black\", alpha=0.4, label=\"ISMIP6 models\")\n",
-    "    ax.plot(time_axis, true_median, lw=2, color='black', label=\"ISMIP6 median\")\n",
-    "    ax.plot(time_axis, median, color='darkgoldenrod', lw=3, label='Emulator Median')\n",
-    "\n",
-    "    ax.set_xlabel(\"Time (years)\")\n",
-    "    ax.set_ylabel(\"Sea Level Equivalent (m)\")\n",
-    "\n",
-    "    if scen == 0:\n",
-    "        handles, labels = ax.get_legend_handles_labels()\n",
-    "        unique_labels = set(labels)\n",
-    "        handles = [handles[labels.index(label)] for label in unique_labels if label in labels]\n",
-    "        ax.legend(handles, unique_labels, frameon=False, loc='upper left')\n",
-    "\n",
-    "    ax.grid(True, alpha=0.3)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "cmip7",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/profsea/aux_data/wais_params.nc b/profsea/aux_data/wais_params.nc
deleted file mode 100644
index 58c10ef0d214c55fc66e3d100bfea765d60f55ae..0000000000000000000000000000000000000000
GIT binary patch
literal 0
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From 471178c929247fe06044b0e80df6c58dff7f9253 Mon Sep 17 00:00:00 2001
From: isabellaascione 
Date: Fri, 29 May 2026 16:41:47 +0100
Subject: [PATCH 193/276] Add docs setup, workflow, env file, and fix autodoc
 warnings

---
 .github/workflows/deploy-docs.yml          | 68 ++++++++++++++++++
 .gitignore                                 |  2 +
 docs-environment.yml                       |  8 +++
 docs/Makefile                              | 20 ++++++
 docs/make.bat                              | 35 +++++++++
 docs/source/_static/.gitkeep               |  0
 docs/source/api_reference.rst              | 53 ++++++++++++++
 docs/source/conf.py                        | 72 +++++++++++++++++++
 docs/source/development_guidelines.rst     | 63 +++++++++++++++++
 docs/source/documentation.rst              | 82 ++++++++++++++++++++++
 docs/source/index.rst                      | 48 +++++++++++++
 docs/source/references.rst                 |  4 ++
 docs/source/user_guide.rst                 |  4 ++
 profsea/components/core/global_model.py    |  2 +
 profsea/components/core/spatial_model.py   |  2 +
 profsea/components/core/state.py           |  2 +
 profsea/components/global_/antarctica.py   |  4 +-
 profsea/components/global_/expansion.py    |  4 +-
 profsea/components/spatial/sterodynamic.py |  4 +-
 19 files changed, 471 insertions(+), 6 deletions(-)
 create mode 100644 .github/workflows/deploy-docs.yml
 create mode 100644 docs-environment.yml
 create mode 100644 docs/Makefile
 create mode 100644 docs/make.bat
 create mode 100644 docs/source/_static/.gitkeep
 create mode 100644 docs/source/api_reference.rst
 create mode 100644 docs/source/conf.py
 create mode 100644 docs/source/development_guidelines.rst
 create mode 100644 docs/source/documentation.rst
 create mode 100644 docs/source/index.rst
 create mode 100644 docs/source/references.rst
 create mode 100644 docs/source/user_guide.rst

diff --git a/.github/workflows/deploy-docs.yml b/.github/workflows/deploy-docs.yml
new file mode 100644
index 0000000..f76cd2f
--- /dev/null
+++ b/.github/workflows/deploy-docs.yml
@@ -0,0 +1,68 @@
+# (C) Crown Copyright 2025, Met Office.
+# The LICENSE file contains full licensing details.
+name: Build and deploy the documentation
+
+on:
+  # Runs on pushes targeting the default branch.
+  push:
+    branches: ["profsea-climate-v2"]
+
+  # Allows you to run this workflow manually from the Actions tab.
+  workflow_dispatch:
+
+# Use a login shell so the Conda environment is activated.
+defaults:
+  run:
+    shell: bash -leo pipefail {0}
+
+# Sets permissions of the GITHUB_TOKEN to allow deployment to GitHub Pages.
+permissions:
+  contents: read
+  pages: write
+  id-token: write
+
+# Allow only one concurrent deployment,
+# skipping runs queued between the run in-progress and latest queued.
+# However, do NOT cancel in-progress runs
+# as we want to allow these production deployments to complete.
+concurrency:
+  group: "pages"
+  cancel-in-progress: false
+
+jobs:
+  # Build job
+  build:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v6
+
+      - name: Setup GitHub Pages
+        uses: actions/configure-pages@v6
+
+      - uses: mamba-org/setup-micromamba@v3
+        with:
+          environment-file: docs-environment.yml
+          environment-name: profsea-climate-docs
+          cache-environment: true
+          cache-environment-key: env-${{ runner.os }}-${{ runner.arch }}-${{ hashFiles('docs-environment.yml') }}
+
+      - name: Build with Sphinx
+        run:  make --directory=docs html
+
+      - name: Upload artifact
+        uses: actions/upload-pages-artifact@v4
+        with:
+          path: docs/build/html
+
+  # Deployment job
+  deploy:
+    environment:
+      name: github-pages
+      url: ${{ steps.deployment.outputs.page_url }}
+    runs-on: ubuntu-latest
+    needs: build
+    steps:
+      - name: Deploy to GitHub Pages
+        id: deployment
+        uses: actions/deploy-pages@v5
diff --git a/.gitignore b/.gitignore
index 38fdc3d..8a9f4dc 100644
--- a/.gitignore
+++ b/.gitignore
@@ -2,5 +2,7 @@
 *__pycache__*
 profsea.egg-info
 data/
+# Ignore the documentation build directory
+docs/build
 *.DS_Store
 .ipynb_checkpoints/
diff --git a/docs-environment.yml b/docs-environment.yml
new file mode 100644
index 0000000..1d4b3f9
--- /dev/null
+++ b/docs-environment.yml
@@ -0,0 +1,8 @@
+name: profsea-climate-docs
+
+
+
+dependencies:
+  - pre-commit
+  - pydata-sphinx-theme
+  - sphinx
diff --git a/docs/Makefile b/docs/Makefile
new file mode 100644
index 0000000..d0c3cbf
--- /dev/null
+++ b/docs/Makefile
@@ -0,0 +1,20 @@
+# Minimal makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line, and also
+# from the environment for the first two.
+SPHINXOPTS    ?=
+SPHINXBUILD   ?= sphinx-build
+SOURCEDIR     = source
+BUILDDIR      = build
+
+# Put it first so that "make" without argument is like "make help".
+help:
+	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+.PHONY: help Makefile
+
+# Catch-all target: route all unknown targets to Sphinx using the new
+# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
+%: Makefile
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
diff --git a/docs/make.bat b/docs/make.bat
new file mode 100644
index 0000000..747ffb7
--- /dev/null
+++ b/docs/make.bat
@@ -0,0 +1,35 @@
+@ECHO OFF
+
+pushd %~dp0
+
+REM Command file for Sphinx documentation
+
+if "%SPHINXBUILD%" == "" (
+	set SPHINXBUILD=sphinx-build
+)
+set SOURCEDIR=source
+set BUILDDIR=build
+
+%SPHINXBUILD% >NUL 2>NUL
+if errorlevel 9009 (
+	echo.
+	echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
+	echo.installed, then set the SPHINXBUILD environment variable to point
+	echo.to the full path of the 'sphinx-build' executable. Alternatively you
+	echo.may add the Sphinx directory to PATH.
+	echo.
+	echo.If you don't have Sphinx installed, grab it from
+	echo.https://www.sphinx-doc.org/
+	exit /b 1
+)
+
+if "%1" == "" goto help
+
+%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+goto end
+
+:help
+%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+
+:end
+popd
diff --git a/docs/source/_static/.gitkeep b/docs/source/_static/.gitkeep
new file mode 100644
index 0000000..e69de29
diff --git a/docs/source/api_reference.rst b/docs/source/api_reference.rst
new file mode 100644
index 0000000..8eb6a97
--- /dev/null
+++ b/docs/source/api_reference.rst
@@ -0,0 +1,53 @@
+API Reference
+=============
+
+This page exposes selected Python API docs generated from in-code docstrings.
+
+Core Models
+-----------
+
+.. automodule:: profsea.components.core.global_model
+   :members: Global
+   :undoc-members:
+   :show-inheritance:
+
+.. automodule:: profsea.components.core.spatial_model
+   :members: Spatial, fetch_zenodo_fingerprints
+   :undoc-members:
+   :show-inheritance:
+
+.. automodule:: profsea.components.core.state
+   :members: ClimateState, SpatialState
+
+.. automodule:: profsea.components.core.time_projection
+   :members: time_projection
+
+Global Components
+-----------------
+
+.. automodule:: profsea.components.global_.antarctica
+   :members: AntarcticaISMIP6, AntarcticaDynAR5, AntarcticaSMBAR5
+
+.. automodule:: profsea.components.global_.greenland
+   :members: GreenlandAR6, GreenlandSMBAR5, GreenlandDynAR5, load_greenland_calibration
+
+.. automodule:: profsea.components.global_.glacier
+   :members: Glacier
+
+.. automodule:: profsea.components.global_.expansion
+   :members: ThermalExpansion
+
+.. automodule:: profsea.components.global_.landwater
+   :members: LandwaterAR6, LandwaterAR5, load_landwater_projection
+
+Spatial Components
+------------------
+
+.. automodule:: profsea.components.spatial.fingerprint
+   :members: Fingerprint
+
+.. automodule:: profsea.components.spatial.sterodynamic
+   :members: SterodynamicCMIP6, SterodynamicCMIP5
+
+.. automodule:: profsea.components.spatial.gia
+   :members: GIA
diff --git a/docs/source/conf.py b/docs/source/conf.py
new file mode 100644
index 0000000..418bca9
--- /dev/null
+++ b/docs/source/conf.py
@@ -0,0 +1,72 @@
+# Configuration file for the Sphinx documentation builder.
+#
+# For the full list of built-in configuration values, see the documentation:
+# https://www.sphinx-doc.org/en/master/usage/configuration.html
+
+import os
+import sys
+
+sys.path.insert(0, os.path.abspath("../.."))
+
+# -- Project information -----------------------------------------------------
+# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
+
+project = 'profsea-climate'
+copyright = '2026, MetOffice'
+author = 'isabellaascione'
+release = '1.0.0'
+
+# -- General configuration ---------------------------------------------------
+# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
+
+extensions = [
+    "sphinx.ext.autodoc",
+    "sphinx.ext.napoleon",
+]
+
+napoleon_google_docstring = False
+napoleon_numpy_docstring = True
+napoleon_include_init_with_doc = True
+napoleon_include_private_with_doc = False
+napoleon_include_special_with_doc = True
+napoleon_use_admonition_for_examples = False
+napoleon_use_admonition_for_notes = False
+napoleon_use_admonition_for_references = False
+napoleon_use_ivar = False
+napoleon_use_param = True
+napoleon_use_rtype = True
+
+autodoc_mock_imports = [
+    "cartopy",
+    "dask",
+    "fair",
+    "matplotlib",
+    "numcodecs",
+    "numpy",
+    "pandas",
+    "profsea.emulator",
+    "profsea.plotting_libraries",
+    "requests",
+    "rich",
+    "rich_argparse",
+    "scipy",
+    "xarray",
+]
+
+templates_path = ['_templates']
+exclude_patterns = []
+
+
+
+# -- Options for HTML output -------------------------------------------------
+# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output
+
+html_theme = 'pydata_sphinx_theme'
+html_theme_options = {
+    'show_nav_level': 2,
+    'secondary_sidebar_items': [],
+}
+html_sidebars = {
+    "**": ["search-field", "sidebar-nav-bs", "page-toc"],
+}
+html_static_path = ['_static']
diff --git a/docs/source/development_guidelines.rst b/docs/source/development_guidelines.rst
new file mode 100644
index 0000000..a60e751
--- /dev/null
+++ b/docs/source/development_guidelines.rst
@@ -0,0 +1,63 @@
+.. _development_guidelines:
+
+Development Guidelines
+======================
+
+This is the Development Guidelines page.
+
+.. _open-an-issue:
+
+Open an Issue
+-------------
+Before forking the repository, open an issue in the main repository to describe the changes you plan to make. This helps maintainers track contributions and provide feedback early.
+
+.. _fork-the-repository:
+
+Fork the Repository
+-------------------
+First, create a fork of the repository in your GitHub account:
+
+1. Go to the repository page on GitHub.
+2. Click the **Fork** button in the top-right corner.
+3. Select your GitHub account as the destination for the fork.
+
+.. _clone-your-fork:
+
+Clone Your Fork
+---------------
+Clone your forked repository and switch to the `profsea-climate` branch:
+
+.. code-block:: bash
+
+   git clone git@github.com:/ProFSea-tool.git
+   cd ProFSea-tool
+   git checkout profsea-climate
+
+Replace ```` with your GitHub username.
+
+.. _commit-changes-to-your-fork:
+
+Commit Changes to Your Fork
+---------------------------
+Once you are satisfied with your changes, commit and push them to your fork:
+
+.. code-block:: bash
+
+   git add .
+   git commit -m "Adding feature: "
+   git push origin profsea-climate
+
+Replace ```` with a short summary of your updates.
+
+.. _create-a-pull-request:
+
+Create a Pull Request
+---------------------
+To contribute your changes to the main repository:
+
+1. Go to your forked repository on GitHub.
+2. Click the **Pull Request** button.
+3. Select the ``profsea-climate`` branch of the main repository as the base branch.
+4. Select the ``profsea-climate`` branch of your fork as the compare branch.
+5. Add a title and description for your pull request.
+6. Click **Create Pull Request**.
diff --git a/docs/source/documentation.rst b/docs/source/documentation.rst
new file mode 100644
index 0000000..69dcc11
--- /dev/null
+++ b/docs/source/documentation.rst
@@ -0,0 +1,82 @@
+How to update the documentation
+========================================
+
+This is the page describing how to update the documentation.
+
+If you want to update the documentation, follow these steps to ensure that your changes are tested locally before contributing:
+
+1. Initial Steps
+-----------------------------------
+First of all you need to open an issue, fork and clone the repository. For detailed instructions, refer to the following sections in the :ref:`Development Guidelines ` page:
+
+- :ref:`Open an Issue `
+- :ref:`Fork the Repository `
+- :ref:`Clone Your Fork `
+
+2. Set Up the Environment
+-------------------------
+Ensure you have the required dependencies installed to build the documentation.
+
+2.1 Create a Conda Environment
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+If you are using Conda, create the environment from the ``environment.yml`` file:
+
+.. code-block:: bash
+
+   conda env create -f environment.yml
+   conda activate profsea-climate-docs
+
+2.2 Install Dependencies with Pip (Optional)
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+If you are not using Conda, install the dependencies with ``pip``:
+
+.. code-block:: bash
+
+   pip install sphinx pydata-sphinx-theme
+
+3. Make Changes to the Documentation
+-------------------------------------
+Edit or add ``.rst`` files in the ``docs/source`` directory. For example:
+
+- Update ``index.rst`` to include new pages.
+- Modify existing ``.rst`` files to update content.
+- Add new ``.rst`` files for additional sections.
+
+4. Build the Documentation Locally
+-----------------------------------
+Before committing your changes, build the documentation locally to ensure there are no errors:
+
+.. code-block:: bash
+
+   cd docs
+   make html
+
+This will generate the HTML files in the ``docs/build/html`` directory. Open the ``index.html`` file in your browser to preview the changes:
+
+.. code-block:: bash
+
+   xdg-open build/html/index.html
+
+5. Test the Documentation
+--------------------------
+- Verify that all links work correctly.
+- Ensure the side menu and table of contents are updated.
+- Check for any warnings or errors in the terminal output during the build process.
+
+6. Commit Changes and Create a Pull Request
+---------------------------------------------
+Once you are satisfied with your changes, commit and push them to your fork. For detailed instructions,
+refer to the following sections in the :ref:`Development Guidelines ` page:
+- :ref:`Commit Changes to Your Fork `
+- :ref:`Create a Pull Request `
+
+7. Verify Deployment
+---------------------
+After your pull request is merged, the GitHub Actions workflow will automatically build and deploy the updated documentation. Verify that the changes are live on GitHub Pages:
+
+1. Go to the **Actions** tab in the main repository.
+2. Check the logs for the ``deploy-docs.yml`` workflow to ensure it completed successfully.
+3. Visit the GitHub Pages URL to confirm the updates.
+
+Notes
+-----
diff --git a/docs/source/index.rst b/docs/source/index.rst
new file mode 100644
index 0000000..3a86631
--- /dev/null
+++ b/docs/source/index.rst
@@ -0,0 +1,48 @@
+.. profsea-climate documentation master file, created by
+   sphinx-quickstart on Wed Apr  8 11:10:23 2026.
+   You can adapt this file completely to your liking, but it should at least
+   contain the root `toctree` directive.
+
+profsea-climate documentation
+=============================
+
+Add your content using ``reStructuredText`` syntax. See the
+`reStructuredText `_
+documentation for details.
+
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Contents:
+
+   user_guide
+   development_guidelines
+   documentation
+   api_reference
+   references
+
+User Guide
+----------
+
+See :doc:`user_guide`.
+
+Development Guidelines
+----------------------
+
+See :doc:`development_guidelines`.
+
+Documentation Guide
+-------------------
+
+See :doc:`documentation`.
+
+API Reference
+-------------
+
+See :doc:`api_reference`.
+
+References
+----------
+
+See :doc:`references`.
+
diff --git a/docs/source/references.rst b/docs/source/references.rst
new file mode 100644
index 0000000..6392322
--- /dev/null
+++ b/docs/source/references.rst
@@ -0,0 +1,4 @@
+References
+==========
+
+This is the References page.
diff --git a/docs/source/user_guide.rst b/docs/source/user_guide.rst
new file mode 100644
index 0000000..f267896
--- /dev/null
+++ b/docs/source/user_guide.rst
@@ -0,0 +1,4 @@
+User Guide
+==========
+
+This is the User Guide page
diff --git a/profsea/components/core/global_model.py b/profsea/components/core/global_model.py
index ded3fe2..14c640e 100644
--- a/profsea/components/core/global_model.py
+++ b/profsea/components/core/global_model.py
@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import concurrent.futures
 from pathlib import Path
 import os
diff --git a/profsea/components/core/spatial_model.py b/profsea/components/core/spatial_model.py
index 572559d..a6eb281 100644
--- a/profsea/components/core/spatial_model.py
+++ b/profsea/components/core/spatial_model.py
@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import os
 from pathlib import Path
 from typing import Dict
diff --git a/profsea/components/core/state.py b/profsea/components/core/state.py
index f9cc0d8..5fb71ec 100644
--- a/profsea/components/core/state.py
+++ b/profsea/components/core/state.py
@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 from dataclasses import dataclass
 import numpy as np
 
diff --git a/profsea/components/global_/antarctica.py b/profsea/components/global_/antarctica.py
index 50dcfec..2c2c366 100644
--- a/profsea/components/global_/antarctica.py
+++ b/profsea/components/global_/antarctica.py
@@ -238,8 +238,8 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
         """Project Antarctic rapid ice-sheet dynamics contribution to GMSLR.
 
         Parameters
-            ----------
-            fraction: np.ndarray
+        ----------
+        fraction: np.ndarray
             Random numbers for the dynamic contribution.
 
         Returns
diff --git a/profsea/components/global_/expansion.py b/profsea/components/global_/expansion.py
index 20c0638..3fba025 100644
--- a/profsea/components/global_/expansion.py
+++ b/profsea/components/global_/expansion.py
@@ -7,8 +7,8 @@
 
 class ThermalExpansion(Component):
     """
-    Parameters & Attributes
-    ----------
+    Parameters and Attributes
+    -------------------------
     OHC_change: np.ndarray
         Array of ocean heat content change values.
     exp_efficiency: float
diff --git a/profsea/components/spatial/sterodynamic.py b/profsea/components/spatial/sterodynamic.py
index f2aa914..03308db 100644
--- a/profsea/components/spatial/sterodynamic.py
+++ b/profsea/components/spatial/sterodynamic.py
@@ -14,8 +14,8 @@
 
 class SterodynamicCMIP6(SpatialComponent):
     """
-    Parameters & Attributes
-    ----------
+    Parameters and Attributes
+    -------------------------
     global_projection: np.ndarray
         Array of global sea level rise projections to use as input for sterodynamic projection.
     """

From 9d23229ee34ec40ba78e5a5ed3e8d343ac790979 Mon Sep 17 00:00:00 2001
From: isabellaascione 
Date: Tue, 2 Jun 2026 12:07:57 +0100
Subject: [PATCH 194/276] update docs to include rendering of notebooks in the
 sphinx pages; updated names of branches to refer to profsea and not to
 profsea-climate; build docs from profsea-v3

---
 .github/workflows/deploy-docs.yml      |  2 +-
 docs-environment.yml                   |  4 +++-
 docs/source/conf.py                    |  5 +++++
 docs/source/development_guidelines.rst | 15 +++++++++------
 docs/source/documentation.rst          | 10 +++++-----
 docs/source/index.rst                  |  5 +++--
 docs/source/user_guide.rst             | 10 ++++++++++
 docs/source/worked_example.ipynb       |  1 +
 8 files changed, 37 insertions(+), 15 deletions(-)
 create mode 120000 docs/source/worked_example.ipynb

diff --git a/.github/workflows/deploy-docs.yml b/.github/workflows/deploy-docs.yml
index f76cd2f..9cb6e1d 100644
--- a/.github/workflows/deploy-docs.yml
+++ b/.github/workflows/deploy-docs.yml
@@ -5,7 +5,7 @@ name: Build and deploy the documentation
 on:
   # Runs on pushes targeting the default branch.
   push:
-    branches: ["profsea-climate-v2"]
+    branches: ["profsea-v3"]
 
   # Allows you to run this workflow manually from the Actions tab.
   workflow_dispatch:
diff --git a/docs-environment.yml b/docs-environment.yml
index 1d4b3f9..9673356 100644
--- a/docs-environment.yml
+++ b/docs-environment.yml
@@ -1,4 +1,4 @@
-name: profsea-climate-docs
+name: profsea-sphinx-docs
 
 
 
@@ -6,3 +6,5 @@ dependencies:
   - pre-commit
   - pydata-sphinx-theme
   - sphinx
+  - nbsphinx
+  - pandoc
diff --git a/docs/source/conf.py b/docs/source/conf.py
index 418bca9..ca52c83 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -22,6 +22,7 @@
 extensions = [
     "sphinx.ext.autodoc",
     "sphinx.ext.napoleon",
+    "nbsphinx",
 ]
 
 napoleon_google_docstring = False
@@ -56,6 +57,10 @@
 templates_path = ['_templates']
 exclude_patterns = []
 
+nbsphinx_prolog = ""
+# Allow nbsphinx to find notebooks outside the docs/source directory
+nbsphinx_allow_errors = False
+
 
 
 # -- Options for HTML output -------------------------------------------------
diff --git a/docs/source/development_guidelines.rst b/docs/source/development_guidelines.rst
index a60e751..f3a6a2d 100644
--- a/docs/source/development_guidelines.rst
+++ b/docs/source/development_guidelines.rst
@@ -9,7 +9,7 @@ This is the Development Guidelines page.
 
 Open an Issue
 -------------
-Before forking the repository, open an issue in the main repository to describe the changes you plan to make. This helps maintainers track contributions and provide feedback early.
+Before forking the repository, open an issue in the main repository to describe the changes you plan to make. This helps maintainers track contributions and provide feedback early. Check with the repository owners what is the name of the development branch you need to use. In this guide, the name used for the development branch is generically indicated as "development_branch_name"
 
 .. _fork-the-repository:
 
@@ -25,16 +25,18 @@ First, create a fork of the repository in your GitHub account:
 
 Clone Your Fork
 ---------------
-Clone your forked repository and switch to the `profsea-climate` branch:
+Clone your forked repository and switch to the `development_branch_name` branch:
 
 .. code-block:: bash
 
    git clone git@github.com:/ProFSea-tool.git
    cd ProFSea-tool
-   git checkout profsea-climate
+   git checkout 
 
 Replace ```` with your GitHub username.
 
+Replace ```` with the development branch name provided by the repository owners
+
 .. _commit-changes-to-your-fork:
 
 Commit Changes to Your Fork
@@ -45,7 +47,7 @@ Once you are satisfied with your changes, commit and push them to your fork:
 
    git add .
    git commit -m "Adding feature: "
-   git push origin profsea-climate
+   git push origin development_branch_name
 
 Replace ```` with a short summary of your updates.
 
@@ -57,7 +59,8 @@ To contribute your changes to the main repository:
 
 1. Go to your forked repository on GitHub.
 2. Click the **Pull Request** button.
-3. Select the ``profsea-climate`` branch of the main repository as the base branch.
-4. Select the ``profsea-climate`` branch of your fork as the compare branch.
+3. Select the ``development_branch_name`` branch of the main repository as the base branch.
+4. Select the ``development_branch_name`` branch of your fork as the compare branch.
 5. Add a title and description for your pull request.
 6. Click **Create Pull Request**.
+7. Assign reviewers to the pull request
diff --git a/docs/source/documentation.rst b/docs/source/documentation.rst
index 69dcc11..348d309 100644
--- a/docs/source/documentation.rst
+++ b/docs/source/documentation.rst
@@ -1,7 +1,7 @@
 How to update the documentation
 ========================================
 
-This is the page describing how to update the documentation.
+This is the page describing how to update the sphinx documentation.
 
 If you want to update the documentation, follow these steps to ensure that your changes are tested locally before contributing:
 
@@ -19,12 +19,12 @@ Ensure you have the required dependencies installed to build the documentation.
 
 2.1 Create a Conda Environment
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-If you are using Conda, create the environment from the ``environment.yml`` file:
+If you are using Conda, create the environment from the ``docs-environment.yml`` file:
 
 .. code-block:: bash
 
-   conda env create -f environment.yml
-   conda activate profsea-climate-docs
+   conda env create -f docs-environment.yml
+   conda activate profsea-sphinx-docs
 
 2.2 Install Dependencies with Pip (Optional)
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -72,7 +72,7 @@ refer to the following sections in the :ref:`Development Guidelines 
Date: Tue, 9 Jun 2026 12:37:24 +0100
Subject: [PATCH 195/276] Add projection GIF

---
 README.md | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/README.md b/README.md
index 88a27a1..c9443f7 100644
--- a/README.md
+++ b/README.md
@@ -14,6 +14,10 @@ We simulate sea-level change contributions from:
 
 ProFSea can calculate projections of both global mean sea-level rise and spatially-resolved sea-level change fields:
 
+
        
+  output
+
        
+
 All the data required to run the regionalisation module can be found here: [![DOI](https://doi.org/badge/10.5281/20427061.svg)](https://doi.org/10.5281/zenodo.20427061)
 
 ## Developments

From 6ebc65bfb37754b2c04047fe60b1d69901948d6b Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Tue, 9 Jun 2026 23:20:18 +0100
Subject: [PATCH 196/276] Update spatial components for local site computing

---
 profsea/components/core/base.py            |  65 +++++++++
 profsea/components/core/local_model.py     | 146 +++++++++++++++++++++
 profsea/components/core/spatial_model.py   | 101 +-------------
 profsea/components/core/state.py           |  13 ++
 profsea/components/spatial/fingerprint.py  |  10 +-
 profsea/components/spatial/gia.py          |  45 +++----
 profsea/components/spatial/sterodynamic.py |  41 +++---
 profsea/utils/utils.py                     |  99 ++++++++++++++
 8 files changed, 372 insertions(+), 148 deletions(-)
 create mode 100644 profsea/components/core/local_model.py

diff --git a/profsea/components/core/base.py b/profsea/components/core/base.py
index 1baa743..c0cebcb 100644
--- a/profsea/components/core/base.py
+++ b/profsea/components/core/base.py
@@ -1,5 +1,7 @@
 from abc import ABC, abstractmethod
+import dask.array as da
 import numpy as np
+import xarray as xr
 
 from .state import ClimateState
 
@@ -12,6 +14,69 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
 
 
 class SpatialComponent(Component):
+    def extract_spatial(self, da_input: xr.DataArray, state) -> xr.DataArray:
+        """
+        Spatial extractor for both Grid (2D) and Site (1D) states.
+
+        Parameters
+        ----------
+        da_input: xarray.DataArray
+            The input data array containing spatial coordinates (lat/lon).
+        state: SpatialState
+            The state object which may contain either target_lats/lons for point extraction or grid_lats/lons for grid interpolation.
+        """
+        if "lon" in da_input.coords and da_input.lon.max() > 180:
+            da_input = da_input.assign_coords(
+                lon=(((da_input.lon + 180) % 360) - 180)
+            ).sortby("lon")
+
+        if hasattr(state, "target_lats"):
+            # Point extraction for LocalState
+            lats = xr.DataArray(state.target_lats, dims="site")
+            lons = xr.DataArray(state.target_lons, dims="site")
+
+            return da_input.interp(
+                lat=lats,
+                lon=lons,
+                method=getattr(state, "interpolation_method", "linear"),
+            )
+        elif hasattr(state, "grid_lats"):
+            from profsea.utils import interpolate_to_grid
+
+            return interpolate_to_grid(da_input, state.grid_lats, state.grid_lons)
+        else:
+            raise ValueError(
+                "State object is missing required spatial attributes "
+                "(needs either target_lats/lons or grid_lats/lons)."
+            )
+
+    def broadcast_spatiotemporal(
+        self, temporal_array: da.Array, spatial_array: da.Array
+    ) -> da.Array:
+        """
+        Dynamically multiplies a temporal series by a spatial pattern.
+
+        Parameters
+        ----------
+        temporal_array: dask.array.Array
+            2D array of shape (members, years)
+        spatial_array: dask.array.Array
+            Either 2D (members, site) or 3D (members, lat,
+        """
+        # Determine how many spatial dimensions we are dealing with
+        spatial_dims = spatial_array.ndim - 1
+
+        if spatial_dims == 1:
+            # 3D Output: (members, years, site)
+            return temporal_array[:, :, None] * spatial_array[:, None, :]
+        elif spatial_dims == 2:
+            # 4D Output: (members, years, lat, lon)
+            return temporal_array[:, :, None, None] * spatial_array[:, None, :, :]
+        else:
+            raise ValueError(
+                f"Unexpected spatial dimensions. Expected 1 or 2, got {spatial_dims}."
+            )
+
     @property
     @abstractmethod
     def global_projection(self):
diff --git a/profsea/components/core/local_model.py b/profsea/components/core/local_model.py
new file mode 100644
index 0000000..968fed7
--- /dev/null
+++ b/profsea/components/core/local_model.py
@@ -0,0 +1,146 @@
+from typing import Dict, Tuple
+
+import dask.array as da
+import numpy as np
+from rich.console import Console
+from rich.progress import track
+import xarray as xr
+
+from .base import SpatialComponent
+from .state import LocalState
+
+console = Console()
+
+# Assuming fetch_zenodo_fingerprints, LocalState, and Component are imported here
+console = Console()
+
+
+class Local:
+    """Site-specific (Local) sea level rise component emulator."""
+
+    def __init__(
+        self,
+        components: Dict[str, SpatialComponent],
+        locations: Dict[str, Tuple[float, float]],
+        interpolation_method: str = "linear",
+        end_year: int = 2301,
+        baseline_yrs: tuple = (1995, 2014),
+        output_percentiles: list | np.ndarray = [5, 17, 50, 83, 95],
+    ) -> None:
+        """
+        Parameters
+        ----------
+        components: dict
+            Dictionary of spatial components to include in the model.
+        locations: dict
+            Dictionary mapping site names to (latitude, longitude) tuples.
+            Example: {"Aberdeen": (57.144, -2.080)}
+        interpolation_method: str, optional
+            Interpolation method to use when interpolating patterns to the target sites. Default is 'linear'.
+        end_year: int, optional
+            The final year of the projections. Default is 2301.
+        baseline_yrs: tuple, optional
+            Tuple defining the start and end years of the baseline period. Default is (1995, 2014).
+        output_percentiles: list or np.ndarray, optional
+            List or array of percentiles to sample from the ensemble for output.
+        """
+
+        self.components = components
+        self.end_year = end_year
+        self.baseline_yrs = baseline_yrs
+        self.output_percentiles = output_percentiles
+        self.start_year = 2006
+        self.n_years = self.end_year - self.start_year
+        self.interpolation_method = interpolation_method
+
+        # Parse the locations dictionary
+        self.site_names = list(locations.keys())
+        self.target_lats = [coords[0] for coords in locations.values()]
+        self.target_lons = [coords[1] for coords in locations.values()]
+
+        if self.output_percentiles is not None and len(self.output_percentiles) > 0:
+            self.num_members = len(self.output_percentiles)
+        else:
+            self.num_members = next(
+                iter(self.components.values())
+            ).global_projection.shape[0]
+
+        console.log(
+            f"Baseline period = {self.baseline_yrs[0]} to {self.baseline_yrs[1]}"
+        )
+        console.log(f"Configured for {len(self.site_names)} specific target locations.")
+
+    def _arr_to_xr(self, arr_dict: Dict[str, da.Array]) -> Dict[str, xr.DataArray]:
+        """Convert Dask arrays to xarray DataArrays with site coordinates."""
+        xr_dict = {}
+        member_dim = "percentile" if self.output_percentiles is not None else "member"
+
+        for name, arr in arr_dict.items():
+            xr_dict[name] = xr.DataArray(
+                arr,
+                dims=[member_dim, "time", "site"],
+                coords={
+                    member_dim: self.output_percentiles
+                    if self.output_percentiles is not None
+                    else np.arange(arr.shape[0]),
+                    "time": np.arange(self.start_year, self.start_year + arr.shape[1]),
+                    "site": self.site_names,
+                    "lat": ("site", self.target_lats),
+                    "lon": ("site", self.target_lons),
+                },
+                attrs={
+                    "units": "m",
+                    "long_name": f"Local {name} sea-level projections",
+                    "source": "ProFSea-Climate v0.1",
+                },
+            )
+        return xr_dict
+
+    def run(self, member_seed: int = 42) -> Dict[str, xr.DataArray]:
+        """Run the local model to generate site-specific projections."""
+        seed_seq = np.random.SeedSequence(member_seed)
+
+        console.log(
+            f"Simulating {len(self.components)} sea-level components for {len(self.site_names)} sites..."
+        )
+
+        state = LocalState(
+            n_years=self.n_years,
+            n_members=self.num_members,
+            target_lats=self.target_lats,
+            target_lons=self.target_lons,
+            interpolation_method=self.interpolation_method,
+            output_percentiles=self.output_percentiles,
+            baseline_yrs=self.baseline_yrs,
+        )
+
+        child_seeds = seed_seq.spawn(len(self.components))
+        comp_rngs = {
+            name: np.random.default_rng(s)
+            for name, s in zip(self.components.keys(), child_seeds)
+        }
+
+        local_projections = {}
+        for name, comp in track(
+            self.components.items(), description="Localising components..."
+        ):
+            # Project method should return array of shape (members, time, sites)
+            lazy_projection = comp.project(state, comp_rngs[name])
+
+            # Rechunk to optimize for reading full time-series per site
+            lazy_projection = lazy_projection.rechunk({0: -1, 1: -1, 2: -1})
+            local_projections[name] = lazy_projection
+
+        self.results = self._arr_to_xr(local_projections)
+        return self.results
+
+    def sum_components(self, components: Dict[str, xr.DataArray]) -> xr.DataArray:
+        """Sum the local components to get total sea-level change."""
+        total_rsl = xr.concat(components.values(), dim="component").sum(dim="component")
+        total_rsl.attrs = {
+            "units": "m",
+            "long_name": "Local total sea-level projections",
+            "source": "ProFSea-Climate v0.1",
+        }
+        components["total_rsl"] = total_rsl
+        return total_rsl
diff --git a/profsea/components/core/spatial_model.py b/profsea/components/core/spatial_model.py
index 572559d..32c20ea 100644
--- a/profsea/components/core/spatial_model.py
+++ b/profsea/components/core/spatial_model.py
@@ -2,25 +2,16 @@
 from pathlib import Path
 from typing import Dict
 import warnings
-import zipfile
 
 import dask.array as da
 import numpy as np
-import requests
 from rich.console import Console
-from rich.progress import (
-    Progress,
-    TextColumn,
-    BarColumn,
-    DownloadColumn,
-    TransferSpeedColumn,
-    TimeRemainingColumn,
-    track,
-)
+from rich.progress import track
 import xarray as xr
 
 from .state import SpatialState
-from .base import Component
+from .base import SpatialComponent
+from profsea.utils import fetch_zenodo_fingerprints
 
 console = Console()
 warnings.filterwarnings("ignore")
@@ -36,7 +27,7 @@ class Spatial:
 
     def __init__(
         self,
-        components: Dict[str, Component],
+        components: Dict[str, SpatialComponent],
         grid_config: dict = None,
         grid_interpolation: str = "linear",
         end_year: int = 2301,
@@ -332,87 +323,3 @@ def save_components(
         console.log(
             "Output shape was " + str(ds[name].shape) + " (members, time, lat, lon)"
         )
-
-
-def fetch_zenodo_fingerprints(
-    zenodo_url: str, data_dir: Path, expected_folder_name: str
-) -> None:
-    """
-    Downloads and extracts the ProFSea fingerprint dataset from Zenodo if it doesn't already exist locally.
-
-    Parameters
-    ----------
-    zenodo_url: str
-        The direct download URL for the fingerprint dataset on Zenodo.
-    data_dir: Path
-        The base directory where the dataset should be stored.
-    expected_folder_name: str
-        The name of the folder that should be created when the dataset is extracted. Used to check if the data already exists.
-    """
-    target_dir = data_dir / expected_folder_name
-
-    # 1. Check if data already exists
-    if target_dir.exists() and any(target_dir.iterdir()):
-        console.log("[bold green]✓ ProFSea assets found locally![/bold green]")
-        return
-
-    # Create the base directory if it doesn't exist
-    data_dir.mkdir(parents=True, exist_ok=True)
-    zip_path = data_dir / "temp_fingerprints.zip"
-
-    console.log(f"Initiating download from {zenodo_url}...")
-
-    # 2. Stream the download with a rich progress bar
-    try:
-        response = requests.get(zenodo_url, stream=True)
-        response.raise_for_status()  # Raise an error for bad status codes
-
-        total_size = int(response.headers.get("content-length", 0))
-
-        with Progress(
-            TextColumn("[bold cyan]{task.description}"),
-            BarColumn(),
-            DownloadColumn(),
-            TransferSpeedColumn(),
-            TimeRemainingColumn(),
-            console=console,
-        ) as progress:
-            download_task = progress.add_task(
-                "Downloading dataset...", total=total_size
-            )
-
-            with open(zip_path, "wb") as file:
-                for chunk in response.iter_content(chunk_size=8192):
-                    if chunk:
-                        file.write(chunk)
-                        progress.update(download_task, advance=len(chunk))
-
-    except requests.exceptions.RequestException as e:
-        console.log(f"[bold red]Failed to download data: {e}[/bold red]")
-        if zip_path.exists():
-            zip_path.unlink()  # Clean up partial downloads
-        raise
-
-    console.log("Extracting data...")
-    try:
-        with zipfile.ZipFile(zip_path, "r") as zip_ref:
-            # Filter out the __MACOSX directory and its contents
-            valid_members = [
-                member
-                for member in zip_ref.namelist()
-                if not member.startswith("__MACOSX/") and not member.startswith("._")
-            ]
-            zip_ref.extractall(data_dir, members=valid_members)
-
-        console.log(
-            f"[bold green]✓ Successfully extracted data to {data_dir}[/bold green]"
-        )
-    except zipfile.BadZipFile:
-        console.log(
-            "[bold red]Error: Downloaded file is not a valid zip archive.[/bold red]"
-        )
-        raise
-    finally:
-        # 4. Clean up the zip file
-        if zip_path.exists():
-            zip_path.unlink()
diff --git a/profsea/components/core/state.py b/profsea/components/core/state.py
index f9cc0d8..b47030d 100644
--- a/profsea/components/core/state.py
+++ b/profsea/components/core/state.py
@@ -31,3 +31,16 @@ class SpatialState:
     grid_interpolation: str
     output_percentiles: list[int] | np.ndarray
     baseline_yrs: tuple[int, int]
+
+
+@dataclass
+class LocalState:
+    """LocalState context object to hold relevant state information for local SLR projections."""
+
+    target_lats: list[float]
+    target_lons: list[float]
+    n_years: int
+    n_members: int
+    interpolation_method: str
+    output_percentiles: list[int] | np.ndarray
+    baseline_yrs: tuple[int, int]
diff --git a/profsea/components/spatial/fingerprint.py b/profsea/components/spatial/fingerprint.py
index 634223d..b260849 100644
--- a/profsea/components/spatial/fingerprint.py
+++ b/profsea/components/spatial/fingerprint.py
@@ -114,7 +114,7 @@ def _load_and_interpolate(self, state: SpatialState) -> da.Array:
         grids = []
         for path in self.fp_paths:
             fp_da = xr.open_dataarray(path, chunks={"lat": 45, "lon": 45})
-            fp_interp = interpolate_to_grid(fp_da, state.grid_lats, state.grid_lons)
+            fp_interp = self.extract_spatial(fp_da, state)
             grids.append(fp_interp.data * self.scaling_factor)
 
         # Stack them into a 3D array: (n_fingerprints, lat, lon)
@@ -137,6 +137,7 @@ def project(self, state: SpatialState, rng: np.random.Generator) -> da.Array:
             A 4D array of shape (members, years, lat, lon) containing the spatial projections for each member and year.
         """
         fps = self._load_and_interpolate(state)  # Shape: (n_fps, lat, lon)
+        spatial_shape = fps.shape[1:]
 
         # Handle the global projection
         if state.output_percentiles is not None:
@@ -153,7 +154,7 @@ def project(self, state: SpatialState, rng: np.random.Generator) -> da.Array:
             # Only one fingerprint available
             selected_fps = da.broadcast_to(
                 fps[0],
-                (state.n_members, state.grid_lats.shape[0], state.grid_lons.shape[0]),
+                (state.n_members, *spatial_shape),
             )
         elif self.sample_spatial:
             # Probabilistic mode: pick a random fingerprint per member
@@ -164,9 +165,8 @@ def project(self, state: SpatialState, rng: np.random.Generator) -> da.Array:
             mean_fp = da.nanmean(fps, axis=0)
             selected_fps = da.broadcast_to(
                 mean_fp,
-                (state.n_members, state.grid_lats.shape[0], state.grid_lons.shape[0]),
+                (state.n_members, *spatial_shape),
             )
 
         # Broadcast and multiply: (members, years) * (members, lat, lon)
-        spatial_projection = global_proj[:, :, None, None] * selected_fps[:, None, :, :]
-        return spatial_projection
+        return self.broadcast_spatiotemporal(global_proj, selected_fps)
diff --git a/profsea/components/spatial/gia.py b/profsea/components/spatial/gia.py
index fac7a32..1b146b4 100644
--- a/profsea/components/spatial/gia.py
+++ b/profsea/components/spatial/gia.py
@@ -69,14 +69,15 @@ def _load_and_interpolate_rates(self, state: SpatialState) -> da.Array:
         grids = []
         for path in gia_paths:
             gia_da = xr.open_dataarray(path, chunks={"lat": 45, "lon": 45})
-            interp_da = interpolate_to_grid(gia_da, state.grid_lats, state.grid_lons)
-
+            interp_da = self.extract_spatial(gia_da, state)
             data = interp_da.data
 
-            # Normalize dimensions: if a file is purely 2D (lat, lon),
-            # we expand it to 3D (1, lat, lon) so it can be concatenated safely.
-            if data.ndim == 2:
-                data = data[None, :, :]
+            # Determine expected spatial dims based on state
+            spatial_dims = 2 if hasattr(state, "grid_lats") else 1
+
+            # If the raw file lacks a 'model' dimension, prepend it
+            if data.ndim == spatial_dims:
+                data = data[None, ...]
 
             grids.append(data)
 
@@ -102,6 +103,9 @@ def project(self, state: SpatialState, rng: np.random.Generator) -> da.Array:
         gia_rates = self._load_and_interpolate_rates(state)
         n_patterns = gia_rates.shape[0]
 
+        # Dynamically capture spatial shape (site,) or (lat, lon)
+        spatial_shape = gia_rates.shape[1:]
+
         # Calculate the accumulation time vector (mm/yr to m/yr)
         midyr = (
             state.baseline_yrs[1] - state.baseline_yrs[0] + 1
@@ -109,34 +113,25 @@ def project(self, state: SpatialState, rng: np.random.Generator) -> da.Array:
         Tdelta = 2006 - midyr
         unit_series = (np.arange(state.n_years) + Tdelta) * 0.001
 
+        # Broadcast 1D time series to match expected (members, years) signature
+        temporal_array = da.broadcast_to(unit_series, (state.n_members, state.n_years))
+
         # Handle sampling if required
         if n_patterns == 1:
-            # Only one pattern available; broadcast it to all members
             selected_gia = da.broadcast_to(
                 gia_rates[0],
-                (state.n_members, state.grid_lats.shape[0], state.grid_lons.shape[0]),
+                (state.n_members, *spatial_shape),
             )
         else:
             if self.sample_spatial:
-                # Probabilistic mode: pick random GIA models for each member
                 rgiai = rng.integers(n_patterns, size=state.n_members)
-                selected_gia = gia_rates[rgiai, :, :]  # Shape: (members, lat, lon)
+                selected_gia = gia_rates[rgiai, ...]
             else:
-                # Storyline mode: use the mean of all GIA patterns for all members
-                selected_gia = da.mean(gia_rates, axis=0)  # Shape: (lat, lon)
+                mean_gia = da.mean(gia_rates, axis=0)
                 selected_gia = da.broadcast_to(
-                    selected_gia,
-                    (
-                        state.n_members,
-                        state.grid_lats.shape[0],
-                        state.grid_lons.shape[0],
-                    ),
+                    mean_gia,
+                    (state.n_members, *spatial_shape),
                 )
 
-        # Multiply accumulation time by spatial rates
-        # (years) * (members, lat, lon) -> broadcasts to (members, years, lat, lon)
-        spatial_projection = (
-            unit_series[None, :, None, None] * selected_gia[:, None, :, :]
-        )
-
-        return spatial_projection
+        # Delegate dimensional multiplication to the base class
+        return self.broadcast_spatiotemporal(temporal_array, selected_gia)
diff --git a/profsea/components/spatial/sterodynamic.py b/profsea/components/spatial/sterodynamic.py
index f2aa914..b1fa661 100644
--- a/profsea/components/spatial/sterodynamic.py
+++ b/profsea/components/spatial/sterodynamic.py
@@ -49,7 +49,7 @@ def __init__(
     def global_projection(self):
         return self._global_projection
 
-    def _load_CMIP6_slopes(self) -> da.Array:
+    def _load_CMIP6_slopes(self) -> xr.DataArray:
         """
         Load in the CMIP6 slope coefficients.
 
@@ -59,8 +59,8 @@ def _load_CMIP6_slopes(self) -> da.Array:
 
         Returns
         -------
-        da.Array
-            A dask array of shape (n_models, n_lats, n_lons) containing the sterodynamic fingerprint patterns (i.e., regression coefficients) for each CMIP6 model.
+        xr.DataArray
+            A xarray DataArray of shape (n_models, n_lats, n_lons) containing the sterodynamic fingerprint patterns (i.e., regression coefficients) for each CMIP6 model.
         """
         slope_files = list(Path(self.patterns_dir).glob("*/zos_regression_ssp585_*.nc"))
 
@@ -71,7 +71,10 @@ def _load_CMIP6_slopes(self) -> da.Array:
 
         # Lazily load all files keeping metadata intact
         datasets = [
-            xr.open_dataset(f, chunks={"lat": 45, "lon": 45})["zos_zostoga_regression_slope"] for f in slope_files
+            xr.open_dataset(f, chunks={"lat": 45, "lon": 45})[
+                "zos_zostoga_regression_slope"
+            ]
+            for f in slope_files
         ]
 
         # Concatenate along a new dimension (representing the ensemble/models)
@@ -101,9 +104,10 @@ def _calc_expansion_contribution(
         # Select slope coefficients based on the MIP
         coeffs_da = self._load_CMIP6_slopes()
 
-        # Align the grid coordinates + interpolate if necessary
-        interp_da = interpolate_to_grid(coeffs_da, state.grid_lats, state.grid_lons)
+        # Get the data either at sites or on a grid
+        interp_da = self.extract_spatial(coeffs_da, state)
         coeffs = interp_da.data
+        spatial_shape = coeffs.shape[1:]  # either (lat, lon) or (site,)
 
         if self.sample_spatial:
             rand_samples = rng.choice(
@@ -115,7 +119,7 @@ def _calc_expansion_contribution(
             mean_coeff = da.mean(coeffs, axis=0)
             return da.broadcast_to(
                 mean_coeff,
-                (state.n_members, state.grid_lats.shape[0], state.grid_lons.shape[0]),
+                (state.n_members, *spatial_shape),
             )
 
     def project(self, state: ClimateState, rng) -> np.ndarray:
@@ -144,10 +148,7 @@ def project(self, state: ClimateState, rng) -> np.ndarray:
 
         expansion_contribution = self._calc_expansion_contribution(rng, state)
 
-        sterodynamic_projection = (
-            current_projection[:, :, None, None] * expansion_contribution[:, None, :, :]
-        )
-        return sterodynamic_projection
+        return self.broadcast_spatiotemporal(current_projection, expansion_contribution)
 
 
 class SterodynamicCMIP5(SpatialComponent):
@@ -158,13 +159,11 @@ class SterodynamicCMIP5(SpatialComponent):
     def __init__(self):
         pass
 
-    def project(self, state: ClimateState, rng) -> np.ndarray:
-        # For now, just return zeros as a placeholder
-        return np.zeros(
-            (
-                state.n_members,
-                state.n_years,
-                state.grid_lats.shape[0],
-                state.grid_lons.shape[0],
-            )
-        )
+    def project(self, state, rng) -> da.Array:
+        # Dynamically determine the spatial shape to prevent crashes when using LocalState
+        if hasattr(state, "target_lats"):
+            spatial_shape = (len(state.target_lats),)
+        else:
+            spatial_shape = (state.grid_lats.shape[0], state.grid_lons.shape[0])
+
+        return da.zeros((state.n_members, state.n_years, *spatial_shape))
diff --git a/profsea/utils/utils.py b/profsea/utils/utils.py
index 8657eda..d05a12a 100644
--- a/profsea/utils/utils.py
+++ b/profsea/utils/utils.py
@@ -1,8 +1,23 @@
+from pathlib import Path
+import zipfile
+
 import dask.array as da
 import numpy as np
+from rich.console import Console
+from rich.progress import (
+    Progress,
+    TextColumn,
+    BarColumn,
+    DownloadColumn,
+    TransferSpeedColumn,
+    TimeRemainingColumn,
+)
+import requests
 from scipy.spatial.distance import cdist
 import xarray as xr
 
+console = Console()
+
 
 def sample_members_2D(array: np.ndarray, percentiles: list | np.ndarray) -> np.ndarray:
     """Sample real ensemble members from a 2D numpy array."""
@@ -76,3 +91,87 @@ def check_shapes(array: np.ndarray, n_time: int) -> None:
             f"Array should have shape (realisation, time) with time \
                 dimension of length {n_time}. Got {array.shape}."
         )
+
+
+def fetch_zenodo_fingerprints(
+    zenodo_url: str, data_dir: Path, expected_folder_name: str
+) -> None:
+    """
+    Downloads and extracts the ProFSea fingerprint dataset from Zenodo if it doesn't already exist locally.
+
+    Parameters
+    ----------
+    zenodo_url: str
+        The direct download URL for the fingerprint dataset on Zenodo.
+    data_dir: Path
+        The base directory where the dataset should be stored.
+    expected_folder_name: str
+        The name of the folder that should be created when the dataset is extracted. Used to check if the data already exists.
+    """
+    target_dir = data_dir / expected_folder_name
+
+    # 1. Check if data already exists
+    if target_dir.exists() and any(target_dir.iterdir()):
+        console.log("[bold green]✓ ProFSea assets found locally![/bold green]")
+        return
+
+    # Create the base directory if it doesn't exist
+    data_dir.mkdir(parents=True, exist_ok=True)
+    zip_path = data_dir / "temp_fingerprints.zip"
+
+    console.log(f"Initiating download from {zenodo_url}...")
+
+    # 2. Stream the download with a rich progress bar
+    try:
+        response = requests.get(zenodo_url, stream=True)
+        response.raise_for_status()  # Raise an error for bad status codes
+
+        total_size = int(response.headers.get("content-length", 0))
+
+        with Progress(
+            TextColumn("[bold cyan]{task.description}"),
+            BarColumn(),
+            DownloadColumn(),
+            TransferSpeedColumn(),
+            TimeRemainingColumn(),
+            console=console,
+        ) as progress:
+            download_task = progress.add_task(
+                "Downloading dataset...", total=total_size
+            )
+
+            with open(zip_path, "wb") as file:
+                for chunk in response.iter_content(chunk_size=8192):
+                    if chunk:
+                        file.write(chunk)
+                        progress.update(download_task, advance=len(chunk))
+
+    except requests.exceptions.RequestException as e:
+        console.log(f"[bold red]Failed to download data: {e}[/bold red]")
+        if zip_path.exists():
+            zip_path.unlink()  # Clean up partial downloads
+        raise
+
+    console.log("Extracting data...")
+    try:
+        with zipfile.ZipFile(zip_path, "r") as zip_ref:
+            # Filter out the __MACOSX directory and its contents
+            valid_members = [
+                member
+                for member in zip_ref.namelist()
+                if not member.startswith("__MACOSX/") and not member.startswith("._")
+            ]
+            zip_ref.extractall(data_dir, members=valid_members)
+
+        console.log(
+            f"[bold green]✓ Successfully extracted data to {data_dir}[/bold green]"
+        )
+    except zipfile.BadZipFile:
+        console.log(
+            "[bold red]Error: Downloaded file is not a valid zip archive.[/bold red]"
+        )
+        raise
+    finally:
+        # 4. Clean up the zip file
+        if zip_path.exists():
+            zip_path.unlink()

From 8d6b26877a78f876773580ec65f13e3d6c704cd2 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Tue, 9 Jun 2026 23:56:20 +0100
Subject: [PATCH 197/276] Update params, some fixes and save component

---
 profsea/aux_data/eais_params_expanded.nc      | Bin 30177 -> 29894 bytes
 profsea/components/core/local_model.py        |  18 +-
 profsea/components/core/spatial_model.py      |  91 +-----
 .../tutorial_notebooks/worked_example.ipynb   | 289 +++++++++++++++---
 profsea/utils/utils.py                        |  77 +++++
 5 files changed, 338 insertions(+), 137 deletions(-)

diff --git a/profsea/aux_data/eais_params_expanded.nc b/profsea/aux_data/eais_params_expanded.nc
index b69ad5845220ac08c62801ba523b10bf6e373d93..370df7cc9d5622bc346a5e0f4264e145bd8ba15e 100644
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zv~;ODX@}^d#$);md)E~xY!%+cILNnNXUx{~K|e*miv?HH&C^qUY&KXrY39@$Bc}&C
z=0X)MFj(((+bfzX_&2@*R`YPH0uKPO}$)3cF~Fgw63u)=`;eQ
z@H3rS=$j6dE5e@tn?hfoYWCoq4&ll*wcLFO`giV!^yF8BXG#$Yqw?u8Rvc2+vS;D=
zr!`azwp}P@^f~U0_%{1y&TQXEU&EWYO6WS8FZ`ASrI43y4spi`+QPjWpwJ=QGWULv
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eQ32l`$LV?d(et_|BrdTdvD9N%jP3j5e*Xs&YGS Dict[str, xr.DataArray]:
         """Convert Dask arrays to xarray DataArrays with site coordinates."""
         xr_dict = {}
diff --git a/profsea/components/core/spatial_model.py b/profsea/components/core/spatial_model.py
index 32c20ea..bc459ee 100644
--- a/profsea/components/core/spatial_model.py
+++ b/profsea/components/core/spatial_model.py
@@ -1,4 +1,3 @@
-import os
 from pathlib import Path
 from typing import Dict
 import warnings
@@ -11,7 +10,7 @@
 
 from .state import SpatialState
 from .base import SpatialComponent
-from profsea.utils import fetch_zenodo_fingerprints
+from profsea.utils import fetch_zenodo_fingerprints, save_components
 
 console = Console()
 warnings.filterwarnings("ignore")
@@ -122,6 +121,9 @@ def __init__(
                     f"resolution or take percentiles.[/bold red]"
                 )
 
+    # Instance method!
+    save_components = save_components
+
     def _arr_to_xr(self, arr_dict: Dict[str, da.Array]) -> Dict[str, xr.DataArray]:
         """
         Convert a dictionary of Dask arrays to a dictionary of xarray DataArrays with appropriate coordinates and metadata.
@@ -238,88 +240,3 @@ def sum_components(self, components: Dict[str, xr.DataArray]) -> xr.DataArray:
 
         components["total_rsl"] = total_rsl
         return total_rsl
-
-    def save_components(
-        self,
-        components: Dict[str, xr.DataArray],
-        scenario_name: str,
-        output_dir: str = ".",
-        output_format: str = "zarr",
-    ) -> None:
-        """
-        Stream all regional sea level projections to disk in a single file/store.
-
-        Parameters
-        ----------
-        components: Dict[str, xr.DataArray]
-            Dictionary of component names and their corresponding Xarray DataArrays.
-        output_format: str
-            Format to save the output in. Must be either 'netcdf' or 'zarr'.
-        output_dir: str
-            Directory to save components to.
-        scenario_name: str
-            Name of the scenario you've run the emulator for.
-
-        Returns
-        -------
-        None
-        """
-        # Wrap dataarrays in a single Dataset for saving
-        ds = xr.Dataset(components)
-
-        output_format = output_format.lower()
-        if output_format not in ["netcdf", "zarr"]:
-            raise ValueError("output_format must be either 'netcdf' or 'zarr'.")
-
-        # Create directory if it doesn't exist
-        Path(output_dir).mkdir(parents=True, exist_ok=True)
-
-        encoding = {}
-        # Sort out Zarr encoding
-        if output_format == "zarr":
-            import numcodecs
-            from numcodecs.zarr3 import Blosc
-
-            compressor = Blosc(
-                cname="zstd", clevel=5, shuffle=numcodecs.Blosc.BITSHUFFLE
-            )
-
-        # Set the encoding/compression for each variable based on the output format
-        for name, component in components.items():
-            if output_format == "netcdf":
-                encoding[name] = {"zlib": True, "complevel": 1, "dtype": "float32"}
-            elif output_format == "zarr":
-                encoding[name] = {"compressor": compressor, "dtype": "float32"}
-
-        file_header = f"{scenario_name}_spatial_projection"
-
-        # Stream the computation and write to disk
-        if output_format == "netcdf":
-            out_path = os.path.join(output_dir, f"{file_header}.nc")
-
-            with console.status(
-                "[bold cyan]Computing and saving NetCDF...[/bold cyan]",
-                spinner="dots",
-            ):
-                ds.compute().to_netcdf(out_path, encoding=encoding)
-
-            console.log(
-                f"[bold green]✓ Successfully saved NetCDF:[/bold green] {out_path}"
-            )
-
-        elif output_format == "zarr":
-            out_path = os.path.join(output_dir, f"{file_header}.zarr")
-
-            with console.status(
-                "[bold cyan]Streaming computation and saving Zarr...[/bold cyan]",
-                spinner="dots",
-            ):
-                ds.to_zarr(out_path, encoding=encoding, mode="w", compute=True)
-
-            console.log(
-                f"[bold green]✓ Successfully saved Zarr:[/bold green] {out_path}"
-            )
-
-        console.log(
-            "Output shape was " + str(ds[name].shape) + " (members, time, lat, lon)"
-        )
diff --git a/profsea/tutorial_notebooks/worked_example.ipynb b/profsea/tutorial_notebooks/worked_example.ipynb
index bba35b8..541b2bc 100644
--- a/profsea/tutorial_notebooks/worked_example.ipynb
+++ b/profsea/tutorial_notebooks/worked_example.ipynb
@@ -79,7 +79,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
        "
       ]
@@ -178,7 +178,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
        "
       ]
@@ -211,7 +211,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "47694def",
    "metadata": {},
    "outputs": [],
@@ -234,18 +234,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 24,
    "id": "09f1fff4",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "
        [13:46:57] ✓ ProFSea assets found locally!                                                     spatial_model.py:359\n",
+       "
        [22:56:08] ✓ ProFSea assets found locally!                                                             utils.py:115\n",
        "
        \n"
       ],
       "text/plain": [
-       "\u001b[2;36m[13:46:57]\u001b[0m\u001b[2;36m \u001b[0m\u001b[1;32m✓ ProFSea assets found locally!\u001b[0m                                                     \u001b]8;id=9379629;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/spatial_model.py\u001b\\\u001b[2mspatial_model.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=9379630;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/spatial_model.py#359\u001b\\\u001b[2m359\u001b[0m\u001b]8;;\u001b\\\n"
+       "\u001b[2;36m[22:56:08]\u001b[0m\u001b[2;36m \u001b[0m\u001b[1;32m✓ ProFSea assets found locally!\u001b[0m                                                             \u001b]8;id=6536702;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/utils/utils.py\u001b\\\u001b[2mutils.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=6536703;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/utils/utils.py#115\u001b\\\u001b[2m115\u001b[0m\u001b]8;;\u001b\\\n"
       ]
      },
      "metadata": {},
@@ -254,11 +254,11 @@
     {
      "data": {
       "text/html": [
-       "
                   Baseline period = 1995 to 2014                                                      spatial_model.py:107\n",
+       "
        [22:56:08] Baseline period = 1995 to 2014                                                       spatial_model.py:98\n",
        "
        \n"
       ],
       "text/plain": [
-       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mBaseline period = \u001b[1;36m1995\u001b[0m to \u001b[1;36m2014\u001b[0m                                                      \u001b]8;id=9379636;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/spatial_model.py\u001b\\\u001b[2mspatial_model.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=9379637;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/spatial_model.py#107\u001b\\\u001b[2m107\u001b[0m\u001b]8;;\u001b\\\n"
+       "\u001b[2;36m[22:56:08]\u001b[0m\u001b[2;36m \u001b[0mBaseline period = \u001b[1;36m1995\u001b[0m to \u001b[1;36m2014\u001b[0m                                                       \u001b]8;id=6536710;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/spatial_model.py\u001b\\\u001b[2mspatial_model.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=6536711;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/spatial_model.py#98\u001b\\\u001b[2m98\u001b[0m\u001b]8;;\u001b\\\n"
       ]
      },
      "metadata": {},
@@ -267,18 +267,32 @@
     {
      "data": {
       "text/html": [
-       "
                   Simulating 7 sea-level components...: sterodynamic, greenland, landwater, wais,     spatial_model.py:188\n",
+       "
                   Simulating 7 sea-level components...: sterodynamic, greenland, landwater, wais,     spatial_model.py:179\n",
        "           eais, glacier, gia                                                                                      \n",
        "
        \n"
       ],
       "text/plain": [
-       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mSimulating \u001b[1;36m7\u001b[0m sea-level components\u001b[33m...\u001b[0m: sterodynamic, greenland, landwater, wais,     \u001b]8;id=9379643;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/spatial_model.py\u001b\\\u001b[2mspatial_model.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=9379644;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/spatial_model.py#188\u001b\\\u001b[2m188\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mSimulating \u001b[1;36m7\u001b[0m sea-level components\u001b[33m...\u001b[0m: sterodynamic, greenland, landwater, wais,     \u001b]8;id=6536717;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/spatial_model.py\u001b\\\u001b[2mspatial_model.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=6536718;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/spatial_model.py#179\u001b\\\u001b[2m179\u001b[0m\u001b]8;;\u001b\\\n",
        "\u001b[2;36m           \u001b[0meais, glacier, gia                                                                  \u001b[2m                    \u001b[0m\n"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "999ac2ede7234db7ad763b918b23c257",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
     {
      "data": {
       "text/html": [
@@ -336,45 +350,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "id": "60d81a5b",
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "
        \n"
-      ],
-      "text/plain": []
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "
        [13:47:42] ✓ Successfully saved Zarr: ./stabilisation_spatial_projection.zarr                  spatial_model.py:331\n",
-       "
        \n"
-      ],
-      "text/plain": [
-       "\u001b[2;36m[13:47:42]\u001b[0m\u001b[2;36m \u001b[0m\u001b[1;32m✓ Successfully saved Zarr:\u001b[0m .\u001b[35m/\u001b[0m\u001b[95mstabilisation_spatial_projection.zarr\u001b[0m                  \u001b]8;id=9379650;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/spatial_model.py\u001b\\\u001b[2mspatial_model.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=9379651;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/spatial_model.py#331\u001b\\\u001b[2m331\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "
                   Output shape was (5, 295, 180, 360) (members, time, lat, lon)                       spatial_model.py:335\n",
-       "
        \n"
-      ],
-      "text/plain": [
-       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mOutput shape was \u001b[1m(\u001b[0m\u001b[1;36m5\u001b[0m, \u001b[1;36m295\u001b[0m, \u001b[1;36m180\u001b[0m, \u001b[1;36m360\u001b[0m\u001b[1m)\u001b[0m \u001b[1m(\u001b[0mmembers, time, lat, lon\u001b[1m)\u001b[0m                       \u001b]8;id=9379657;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/spatial_model.py\u001b\\\u001b[2mspatial_model.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=9379658;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/spatial_model.py#335\u001b\\\u001b[2m335\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[2K/Users/gregorymunday/anaconda3/envs/profsea/lib/python3.12/site-packages/zarr/co\n",
+      "re/group.py:2662: ZarrUserWarning: The `compressor` argument is deprecated. Use \n",
+      "`compressors` instead.\n",
+      "  compressors = _parse_deprecated_compressor(\n",
+      "\u001b[2K/Users/gregorymunday/anaconda3/envs/profsea/lib/python3.12/site-packages/zarr/ap\n",
+      "i/asynchronous.py:231: ZarrUserWarning: Consolidated metadata is currently not \n",
+      "part in the Zarr format 3 specification. It may not be supported by other zarr \n",
+      "implementations and may change in the future.\n",
+      "  warnings.warn(\n",
+      "\u001b[2K\u001b[32m⠸\u001b[0m \u001b[1;36mStreaming computation and saving Zarr...\u001b[0m\n",
+      "\u001b[1A\u001b[2K\u001b[2;36m[15:28:57]\u001b[0m\u001b[2;36m \u001b[0m\u001b[1;32m✓ Successfully saved Zarr:\u001b[0m                       \u001b]8;id=625604;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/spatial_model.py\u001b\\\u001b[2mspatial_model.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=625605;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/spatial_model.py#319\u001b\\\u001b[2m319\u001b[0m\u001b]8;;\u001b\\\n",
+      "\u001b[2;36m           \u001b[0m.\u001b[35m/\u001b[0m\u001b[95mstabilisation_spatial_projection.zarr\u001b[0m          \u001b[2m                    \u001b[0m\n",
+      "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mOutput shape was \u001b[1m(\u001b[0m\u001b[1;36m5\u001b[0m, \u001b[1;36m295\u001b[0m, \u001b[1;36m180\u001b[0m, \u001b[1;36m360\u001b[0m\u001b[1m)\u001b[0m \u001b[1m(\u001b[0mmembers,    \u001b]8;id=625611;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/spatial_model.py\u001b\\\u001b[2mspatial_model.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=625612;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/spatial_model.py#323\u001b\\\u001b[2m323\u001b[0m\u001b]8;;\u001b\\\n",
+      "\u001b[2;36m           \u001b[0mtime, lat, lon\u001b[1m)\u001b[0m                                  \u001b[2m                    \u001b[0m\n"
+     ]
     }
    ],
    "source": [
@@ -393,13 +391,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 53,
+   "id": "8bd17c34",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Add a land-sea mask to results.total_rsl (set vals to NaN over land) \n",
+    "# Mask is in here: profsea/profsea-assets/cmip6-patterns/ACCESS-CM2/zos_mask_ssp245_ACCESS-CM2.nc\n",
+    "import xarray as xr\n",
+    "mask = xr.open_dataset(\"../../profsea/profsea-assets/cmip6-patterns/ACCESS-CM2/zos_mask_ssp245_ACCESS-CM2.nc\").zos_mask\n",
+    "total_rsl_masked = total_rsl.where(mask == 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
    "id": "e62d28d9",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
        "
       ]
@@ -413,7 +425,7 @@
     "\n",
     "fig = plt.figure(figsize=(6, 4), layout=\"constrained\")\n",
     "\n",
-    "total_rsl_med = total_rsl[2, -1, :, :]\n",
+    "total_rsl_med = total_rsl_masked[2, -1, :, :]\n",
     "vmax = np.nanmax(np.abs(total_rsl))\n",
     "vmin = -vmax\n",
     "\n",
@@ -437,6 +449,187 @@
     ")\n",
     "plt.show()"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "69021097",
+   "metadata": {},
+   "source": [
+    "Next, we'll drill down on a few grid-boxes of interest. To do this we can use the `Local` model.\n",
+    "\n",
+    "We give the spatial components to our `Local` model, and a dictionary of locations with associated lat/lons: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "5512d606",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
        [23:54:17] ✓ ProFSea assets found locally!                                                             utils.py:117\n",
+       "
        \n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m[23:54:17]\u001b[0m\u001b[2;36m \u001b[0m\u001b[1;32m✓ ProFSea assets found locally!\u001b[0m                                                             \u001b]8;id=14796535;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/utils/utils.py\u001b\\\u001b[2mutils.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=14796536;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/utils/utils.py#117\u001b\\\u001b[2m117\u001b[0m\u001b]8;;\u001b\\\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
        [23:54:17] Baseline period = 1995 to 2014                                                         local_model.py:79\n",
+       "
        \n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m[23:54:17]\u001b[0m\u001b[2;36m \u001b[0mBaseline period = \u001b[1;36m1995\u001b[0m to \u001b[1;36m2014\u001b[0m                                                         \u001b]8;id=14796543;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/local_model.py\u001b\\\u001b[2mlocal_model.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=14796544;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/local_model.py#79\u001b\\\u001b[2m79\u001b[0m\u001b]8;;\u001b\\\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
                   Configured for 3 specific target locations.                                            local_model.py:82\n",
+       "
        \n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mConfigured for \u001b[1;36m3\u001b[0m specific target locations.                                            \u001b]8;id=14796550;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/local_model.py\u001b\\\u001b[2mlocal_model.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=14796551;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/local_model.py#82\u001b\\\u001b[2m82\u001b[0m\u001b]8;;\u001b\\\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
                   Simulating 7 sea-level components for 3 sites...                                      local_model.py:117\n",
+       "
        \n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mSimulating \u001b[1;36m7\u001b[0m sea-level components for \u001b[1;36m3\u001b[0m sites\u001b[33m...\u001b[0m                                      \u001b]8;id=14796557;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/local_model.py\u001b\\\u001b[2mlocal_model.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=14796558;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/local_model.py#117\u001b\\\u001b[2m117\u001b[0m\u001b]8;;\u001b\\\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5acb0a39044549d287f2d3a3f15aff6e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
        \n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from profsea.components.core.local_model import Local\n",
+    "\n",
+    "from profsea.components.core import Spatial\n",
+    "from profsea.components.spatial import (\n",
+    "    SterodynamicCMIP6,\n",
+    "    Fingerprint,\n",
+    "    GIA,\n",
+    ")\n",
+    "\n",
+    "locations = {\n",
+    "    \"Aberdeen\": (57.144, -2.080),\n",
+    "    \"Holyhead\": (53.314, -4.620),\n",
+    "    \"Llandudno\": (53.330, -3.830),\n",
+    "}\n",
+    "\n",
+    "local_components = {\n",
+    "    \"sterodynamic\": SterodynamicCMIP6(\n",
+    "        projections[\"expansion\"],  # passing in the global thermal expansion projection\n",
+    "    ),\n",
+    "    \"greenland\": Fingerprint(\n",
+    "        projections[\"greenland\"], \n",
+    "        fingerprint_component=\"greenland\"\n",
+    "    ),\n",
+    "    \"landwater\": Fingerprint(\n",
+    "        projections[\"landwater\"],\n",
+    "        fingerprint_component=\"landwater\",\n",
+    "    ),\n",
+    "    \"wais\": Fingerprint(\n",
+    "        projections[\"wais\"],\n",
+    "        fingerprint_component=\"wais\",\n",
+    "    ),\n",
+    "    \"eais\": Fingerprint(\n",
+    "        projections[\"eais\"],\n",
+    "        fingerprint_component=\"eais\",\n",
+    "    ),\n",
+    "    \"glacier\": Fingerprint(\n",
+    "        projections[\"glacier\"],\n",
+    "        fingerprint_component=\"glacier\",\n",
+    "    ),\n",
+    "    \"gia\": GIA(\n",
+    "        sample_spatial=False,\n",
+    "    ),\n",
+    "}\n",
+    "\n",
+    "local_model = Local(components=local_components, locations=locations)\n",
+    "local_timeseries = local_model.run(member_seed=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "061f92d5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Relative Sea Level (m)')"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
        "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "loc_ts = local_timeseries[\"wais\"].sel(site=[\"Aberdeen\", \"Holyhead\", \"Llandudno\"], percentile=[17, 50, 83])\n",
+    "fig = plt.figure(figsize=(6, 4), layout=\"constrained\")\n",
+    "ax = fig.add_subplot(111)\n",
+    "for loc in loc_ts.site.values:\n",
+    "    loc_data = loc_ts.sel(site=loc)\n",
+    "    ax.fill_between(loc_data.time, loc_data.sel(percentile=17), loc_data.sel(percentile=83), alpha=0.3, label=f\"{loc} 66% range\")\n",
+    "    ax.plot(loc_data.time, loc_data.sel(percentile=50), label=loc)\n",
+    "ax.set_xlabel(\"Year\")\n",
+    "ax.set_ylabel(\"Relative Sea Level (m)\")"
+   ]
   }
  ],
  "metadata": {
diff --git a/profsea/utils/utils.py b/profsea/utils/utils.py
index d05a12a..31d2144 100644
--- a/profsea/utils/utils.py
+++ b/profsea/utils/utils.py
@@ -1,4 +1,6 @@
+import os
 from pathlib import Path
+from typing import Dict
 import zipfile
 
 import dask.array as da
@@ -175,3 +177,78 @@ def fetch_zenodo_fingerprints(
         # 4. Clean up the zip file
         if zip_path.exists():
             zip_path.unlink()
+
+
+def save_components(
+    self,
+    components: Dict[str, xr.DataArray],
+    scenario_name: str,
+    output_prefix: str = "projection",
+    output_dir: str = ".",
+    output_format: str = "zarr",
+) -> None:
+    """
+    Stream all regional sea level projections to disk in a single file/store.
+
+    Parameters
+    ----------
+    components: Dict[str, xr.DataArray]
+        Dictionary of component names and their corresponding Xarray DataArrays.
+    output_format: str
+        Format to save the output in. Must be either 'netcdf' or 'zarr'.
+    output_dir: str
+        Directory to save components to.
+    scenario_name: str
+        Name of the scenario you've run the emulator for.
+    output_prefix: str
+        Prefix for the output file name (e.g., 'projection' will result in 'ssp
+
+    Returns
+    -------
+    None
+    """
+    ds = xr.Dataset(components)
+
+    output_format = output_format.lower()
+    if output_format not in ["netcdf", "zarr"]:
+        raise ValueError("output_format must be either 'netcdf' or 'zarr'.")
+
+    Path(output_dir).mkdir(parents=True, exist_ok=True)
+    encoding = {}
+
+    # Sort out Zarr encoding
+    if output_format == "zarr":
+        import numcodecs
+        from numcodecs.zarr3 import Blosc
+
+        compressor = Blosc(cname="zstd", clevel=5, shuffle=numcodecs.Blosc.BITSHUFFLE)
+
+    # Set the encoding/compression for each variable based on the output format
+    for name, component in components.items():
+        if output_format == "netcdf":
+            encoding[name] = {"zlib": True, "complevel": 1, "dtype": "float32"}
+        elif output_format == "zarr":
+            encoding[name] = {"compressor": compressor, "dtype": "float32"}
+
+    file_name = f"{scenario_name}_{output_prefix}"
+
+    # Stream the computation and write to disk
+    if output_format == "netcdf":
+        out_path = os.path.join(output_dir, f"{file_name}.nc")
+        with console.status(
+            "[bold cyan]Computing and saving NetCDF...[/bold cyan]", spinner="dots"
+        ):
+            ds.compute().to_netcdf(out_path, encoding=encoding)
+        console.log(f"[bold green]✓ Successfully saved NetCDF:[/bold green] {out_path}")
+
+    elif output_format == "zarr":
+        out_path = os.path.join(output_dir, f"{file_name}.zarr")
+        with console.status(
+            "[bold cyan]Streaming computation and saving Zarr...[/bold cyan]",
+            spinner="dots",
+        ):
+            ds.to_zarr(out_path, encoding=encoding, mode="w", compute=True)
+        console.log(f"[bold green]✓ Successfully saved Zarr:[/bold green] {out_path}")
+
+    dims_str = ", ".join(ds[name].dims)
+    console.log(f"Output shape was {ds[name].shape} ({dims_str})")

From 383c3853b57c3626884cebabcccbcf314d805887 Mon Sep 17 00:00:00 2001
From: Hemant Khatri 
Date: Wed, 10 Jun 2026 16:17:07 +0100
Subject: [PATCH 198/276] mask issue

---
 profsea/components/spatial/sterodynamic.py | 24 +++++++++++++--
 profsea/utils/utils.py                     | 34 ++++++++++++++++++++--
 2 files changed, 53 insertions(+), 5 deletions(-)

diff --git a/profsea/components/spatial/sterodynamic.py b/profsea/components/spatial/sterodynamic.py
index f2aa914..7e9c2fc 100644
--- a/profsea/components/spatial/sterodynamic.py
+++ b/profsea/components/spatial/sterodynamic.py
@@ -77,7 +77,24 @@ def _load_CMIP6_slopes(self) -> da.Array:
         # Concatenate along a new dimension (representing the ensemble/models)
         slopes_stack = xr.concat(datasets, dim="model")
 
-        return slopes_stack
+        # Read land mask if present
+        mask_files = list(Path(self.patterns_dir).glob("*/zos_mask_ssp585_*.nc"))
+        
+        if mask_files:
+            self.land_mask_present = True
+
+            datasets_mask = [
+                xr.open_dataset(f, chunks={"lat": 45, "lon": 45})["zos_mask"] for f in mask_files
+            ]
+            mask_stack = xr.concat(datasets_mask, dim="model")
+            
+        else:
+            self.land_mask_present = False
+            mask_stack = xr.Dataset() # dummy dataset to return 
+
+        print(self.land_mask_present)
+        
+        return slopes_stack, mask_stack
 
     def _calc_expansion_contribution(
         self, rng: np.random.Generator, state: ClimateState
@@ -99,7 +116,10 @@ def _calc_expansion_contribution(
             A dask array of shape (members, years, lat, lon) containing the thermal expansion contribution to the sterodynamic component for each member and year.
         """
         # Select slope coefficients based on the MIP
-        coeffs_da = self._load_CMIP6_slopes()
+        coeffs_da, mask_da = self._load_CMIP6_slopes()
+
+        if self.land_mask_present: # apply land mask
+            coeffs_da = coeffs_da.where(mask_da == 0.)
 
         # Align the grid coordinates + interpolate if necessary
         interp_da = interpolate_to_grid(coeffs_da, state.grid_lats, state.grid_lons)
diff --git a/profsea/utils/utils.py b/profsea/utils/utils.py
index 8657eda..2a239b1 100644
--- a/profsea/utils/utils.py
+++ b/profsea/utils/utils.py
@@ -7,7 +7,7 @@
 def sample_members_2D(array: np.ndarray, percentiles: list | np.ndarray) -> np.ndarray:
     """Sample real ensemble members from a 2D numpy array."""
     # Caculate statistical timeseries, then match with closest real timeseries
-    array_percentiles = np.percentile(array, percentiles, axis=0)
+    array_percentiles = np.nanpercentile(array, percentiles, axis=0)
     distances = cdist(array_percentiles, array)
     mem_indices = np.argmin(distances, axis=1)
     return array[mem_indices]
@@ -46,10 +46,38 @@ def interpolate_to_grid(
     # Normalize target longitudes to [-180, 180) and sort
     target_lons_norm = np.sort(((target_lons + 180) % 360) - 180)
 
-    # Interpolate!
-    data_interp = data.interp(
+    # Pad longitude with one points from each end to handle periodicity 
+    data_padded = data.pad(lon=1, mode='wrap') # need more padding for higher-order interpolation
+
+    # Fix the longitude coordinate after padding
+    lon = data.lon.values
+    lon_padded = np.concatenate([[lon[-1] - 360], lon, [lon[0] + 360]])
+    data_padded['lon'] = lon_padded
+
+    # Now interpolate (maybe add land_mask if condition here)
+    for dim in ["lat", "lon"]:
+        data_padded = data_padded.interpolate_na(
+            dim=dim, method=grid_interpolation,
+            fill_value="extrapolate"
+        ) # this to handle nan values or land mask 
+        
+    data_interp = data_padded.interp(
         lat=target_lats, lon=target_lons_norm, method=grid_interpolation
     )
+
+    data_interp = data_interp.where
+
+    
+
+    # Acount for land mask (1 where NaN, 0 elsewhere)
+    #if mask_present:
+    #    nan_mask = data.isnull().astype(float).interp(
+    #        lat=target_lats, lon=target_lons_norm, method=grid_interpolation
+    #    )
+
+    #   # Mask out any grid point that had NaN influence
+    #   data_interp = data_interp.where(nan_mask == 0)
+    
     return data_interp
 
 

From 9c598c0fd71607a0b2583e9b750f8193deb1b4ce Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Wed, 10 Jun 2026 17:13:55 +0100
Subject: [PATCH 199/276] Update env with docs packages

---
 profsea-env.yml | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/profsea-env.yml b/profsea-env.yml
index 19bf5a5..3053b16 100644
--- a/profsea-env.yml
+++ b/profsea-env.yml
@@ -7,14 +7,20 @@ dependencies:
   - ipykernel
   - iris
   - matplotlib
+  - nbsphinx
   - netcdf4
   - numcodecs
   - numpy
   - pandas
+  - pandoc
+  - pre-commit
+  - pydata-sphinx-theme
   - python=3.12
   - pyyaml
   - requests
   - rich
   - scipy
+  - sphinx
   - xarray
-  - zarr
\ No newline at end of file
+  - zarr
+  
\ No newline at end of file

From ab3713eb2f76842cda9d0a387f028db242df10c5 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Wed, 10 Jun 2026 18:16:57 +0100
Subject: [PATCH 200/276] Update docs, fix type hint-related warnings

---
 logo.png => docs/source/_static/favicon.png   | Bin
 docs/source/_static/logo.png                  | Bin 0 -> 1843342 bytes
 .../source/_static/profsea-logo.png           | Bin
 docs/source/conf.py                           |  31 +++-
 ...t_guidelines.rst => development_guide.rst} |   6 +-
 docs/source/documentation.rst                 |   4 +-
 docs/source/index.rst                         |  80 ++++----
 docs/source/user_guide.rst                    |   2 +
 profsea-env.yml                               |   1 +
 profsea/components/core/base.py               |   2 +
 profsea/components/core/time_projection.py    | 173 +++++++++---------
 profsea/components/global_/antarctica.py      |   2 +
 profsea/components/global_/expansion.py       |   2 +
 profsea/components/global_/glacier.py         |   2 +
 profsea/components/global_/greenland.py       |   2 +
 profsea/components/global_/landwater.py       |   2 +
 profsea/components/spatial/fingerprint.py     |   2 +
 profsea/components/spatial/gia.py             |   2 +
 profsea/components/spatial/sterodynamic.py    |   2 +
 profsea/utils/utils.py                        |   2 +
 20 files changed, 181 insertions(+), 136 deletions(-)
 rename logo.png => docs/source/_static/favicon.png (100%)
 create mode 100644 docs/source/_static/logo.png
 rename profsea-logo.png => docs/source/_static/profsea-logo.png (100%)
 rename docs/source/{development_guidelines.rst => development_guide.rst} (95%)

diff --git a/logo.png b/docs/source/_static/favicon.png
similarity index 100%
rename from logo.png
rename to docs/source/_static/favicon.png
diff --git a/docs/source/_static/logo.png b/docs/source/_static/logo.png
new file mode 100644
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literal 0
HcmV?d00001

diff --git a/profsea-logo.png b/docs/source/_static/profsea-logo.png
similarity index 100%
rename from profsea-logo.png
rename to docs/source/_static/profsea-logo.png
diff --git a/docs/source/conf.py b/docs/source/conf.py
index ca52c83..5e6762f 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -11,10 +11,10 @@
 # -- Project information -----------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
 
-project = 'profsea-climate'
-copyright = '2026, MetOffice'
-author = 'isabellaascione'
-release = '1.0.0'
+project = "ProFSea"
+copyright = "2026, MetOffice"
+author = "isabellaascione"
+release = "3.0.0"
 
 # -- General configuration ---------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
@@ -23,6 +23,7 @@
     "sphinx.ext.autodoc",
     "sphinx.ext.napoleon",
     "nbsphinx",
+    "sphinx_design",
 ]
 
 napoleon_google_docstring = False
@@ -54,7 +55,7 @@
     "xarray",
 ]
 
-templates_path = ['_templates']
+templates_path = ["_templates"]
 exclude_patterns = []
 
 nbsphinx_prolog = ""
@@ -62,16 +63,26 @@
 nbsphinx_allow_errors = False
 
 
-
 # -- Options for HTML output -------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output
 
-html_theme = 'pydata_sphinx_theme'
+html_theme = "pydata_sphinx_theme"
 html_theme_options = {
-    'show_nav_level': 2,
-    'secondary_sidebar_items': [],
+    "show_nav_level": 2,
+    "header_links_before_dropdown": 4,
+    "secondary_sidebar_items": [],
+    "icon_links": [
+        {
+            "name": "GitHub",
+            "url": "https://github.com/MetOffice/ProFSea-tool",
+            "icon": "fa-brands fa-square-github",
+            "type": "fontawesome",
+        }
+    ],
 }
 html_sidebars = {
     "**": ["search-field", "sidebar-nav-bs", "page-toc"],
 }
-html_static_path = ['_static']
+html_static_path = ["_static"]
+html_logo = "_static/profsea-logo.png"
+html_favicon = 'static/favicon.png'
diff --git a/docs/source/development_guidelines.rst b/docs/source/development_guide.rst
similarity index 95%
rename from docs/source/development_guidelines.rst
rename to docs/source/development_guide.rst
index f3a6a2d..783f77c 100644
--- a/docs/source/development_guidelines.rst
+++ b/docs/source/development_guide.rst
@@ -1,9 +1,9 @@
-.. _development_guidelines:
+.. _development_guide:
 
-Development Guidelines
+Development Guide
 ======================
 
-This is the Development Guidelines page.
+This is the Development Guide page.
 
 .. _open-an-issue:
 
diff --git a/docs/source/documentation.rst b/docs/source/documentation.rst
index 348d309..e6b536a 100644
--- a/docs/source/documentation.rst
+++ b/docs/source/documentation.rst
@@ -7,7 +7,7 @@ If you want to update the documentation, follow these steps to ensure that your
 
 1. Initial Steps
 -----------------------------------
-First of all you need to open an issue, fork and clone the repository. For detailed instructions, refer to the following sections in the :ref:`Development Guidelines ` page:
+First of all you need to open an issue, fork and clone the repository. For detailed instructions, refer to the following sections in the :ref:`Development Guide ` page:
 
 - :ref:`Open an Issue `
 - :ref:`Fork the Repository `
@@ -66,7 +66,7 @@ This will generate the HTML files in the ``docs/build/html`` directory. Open the
 6. Commit Changes and Create a Pull Request
 ---------------------------------------------
 Once you are satisfied with your changes, commit and push them to your fork. For detailed instructions,
-refer to the following sections in the :ref:`Development Guidelines ` page:
+refer to the following sections in the :ref:`Development Guide ` page:
 - :ref:`Commit Changes to Your Fork `
 - :ref:`Create a Pull Request `
 
diff --git a/docs/source/index.rst b/docs/source/index.rst
index 51a1574..80fae2f 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -1,49 +1,57 @@
-.. profsea documentation master file, created by
-   sphinx-quickstart on Wed Apr  8 11:10:23 2026.
-   You can adapt this file completely to your liking, but it should at least
-   contain the root `toctree` directive.
+ProFSea
+====
 
-profsea documentation
-=============================
+**ProFSea is sea-level rise simulator based on statistical emulations of physical modelling experiments and lines of evidence from the IPCC and across the literature. It's modular, easy to setup and self-contained - all you need is global mean surface temperature and ocean heat content forcing anomalies, using any baseline period, and you're good to go.**
 
-Add your content using ``reStructuredText`` syntax. See the
-`reStructuredText `_
-documentation for details.
 
+.. grid:: 1 1 2 2
+   :gutter: 2
 
-.. toctree::
-   :maxdepth: 2
-   :caption: Contents:
-
-   user_guide
-   development_guidelines
-   documentation
-   api_reference
-   references
-
-User Guide
-----------
-
-See :doc:`user_guide`.
-The worked tutorial notebook is listed on that page.
+   .. grid-item-card:: User Guide
+       :link: user_guide
+       :link-type: doc
+       :class-card: sd-rounded-3
+       
+       Learn how to use ProFSea and run the tutorial notebook.
 
-Development Guidelines
-----------------------
+   .. grid-item-card:: API Reference
+       :link: api_reference
+       :link-type: doc
+       :class-card: sd-rounded-3
+       
+       Detailed descriptions of ProFSea functionality by module.
 
-See :doc:`development_guidelines`.
+   .. grid-item-card:: Development & Docs
+       :link: documentation
+       :link-type: doc
+       :class-card: sd-rounded-3
+       
+       Guidelines for contributing code and updating this documentation.
 
-Documentation Guide
--------------------
+   .. grid-item-card:: References
+       :link: references
+       :link-type: doc
+       :class-card: sd-rounded-3
+       
+       Academic and software references for the project.
 
-See :doc:`documentation`.
 
-API Reference
--------------
+.. toctree::
+   :caption: User Guide
+   :maxdepth: 2
+   :hidden:
 
-See :doc:`api_reference`.
+   user_guide
 
-References
-----------
+.. toctree::
+   :caption: Development
+   :maxdepth: 1
+   :hidden:
 
-See :doc:`references`.
+.. toctree::
+   :caption: Reference
+   :maxdepth: 1
+   :hidden:
 
+   api_reference
+   references
\ No newline at end of file
diff --git a/docs/source/user_guide.rst b/docs/source/user_guide.rst
index c24e8ce..bb57aed 100644
--- a/docs/source/user_guide.rst
+++ b/docs/source/user_guide.rst
@@ -12,3 +12,5 @@ The worked tutorial notebook is available below:
 	:maxdepth: 1
 
 	worked_example
+	development_guide
+	documentation
diff --git a/profsea-env.yml b/profsea-env.yml
index 3053b16..bd29167 100644
--- a/profsea-env.yml
+++ b/profsea-env.yml
@@ -21,6 +21,7 @@ dependencies:
   - rich
   - scipy
   - sphinx
+  - sphinx-design
   - xarray
   - zarr
   
\ No newline at end of file
diff --git a/profsea/components/core/base.py b/profsea/components/core/base.py
index 1baa743..2663bb9 100644
--- a/profsea/components/core/base.py
+++ b/profsea/components/core/base.py
@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 from abc import ABC, abstractmethod
 import numpy as np
 
diff --git a/profsea/components/core/time_projection.py b/profsea/components/core/time_projection.py
index be1a3f0..bb38d1c 100644
--- a/profsea/components/core/time_projection.py
+++ b/profsea/components/core/time_projection.py
@@ -1,92 +1,95 @@
+from __future__ import annotations
+
 from collections.abc import Sequence
 
 import numpy as np
 
 from profsea.components.core.global_model import ClimateState
 
+
 def time_projection(
-        state: ClimateState,
-        startratemean: float,
-        startratepm: float,
-        final,
-        rng: np.random.Generator,
-        nfinal: int = 1,
-        fraction: np.ndarray = None,
-    ) -> np.ndarray:
-        """Project a quantity which is a quadratic function of time.
-
-        Parameters
-        ----------
-        startratemean: float
-            Rate of GMSLR at the start in mm yr-1.
-        startratepm: float
-            Start rate error in mm yr-1.
-        final: list | np.ndarray
-            Likely range in m for GMSLR at the end of AR5.
-        nfinal: int
-            Number of years at the end over which final is a time-mean.
-        fraction: np.ndarray
-            Random numbers in the range 0 to 1.
-
-        Returns
-        -------
-        np.ndarray
-            Projection of the quantity.
-        """
-        # Create a field of elapsed time since start in years
-        timeendofAR5 = state.endofAR5 - state.endofhistory + 1
-        time = np.arange(state.end_yr - state.endofhistory) + 1
-
-        if fraction is None:
-            fraction = rng.random((state.num_members, state.nt))
-        elif fraction.size != state.num_members * state.nt:
-            raise ValueError("fraction is the wrong size")
-
-        fraction = fraction.reshape(state.num_members, state.nt)
-
-        # Convert inputs to startrate (m yr-1) and afinal (m), where both are
-        # arrays with the size of fraction
-        startrate = (
-            startratemean + startratepm * np.array([-1, 1], dtype=float)
-        ) * 1e-3  # convert mm yr-1 to m yr-1
-        finalisrange = isinstance(final, Sequence)
-
-        if finalisrange:
-            if len(final) != 2:
-                raise ValueError("final range is the wrong size")
-            afinal = (1 - fraction) * final[0] + fraction * final[1]
-        else:
-            if final.shape != fraction.shape:
-                raise ValueError("final array is the wrong shape")
-            afinal = final
-
-        startrate = (1 - fraction) * startrate[0] + fraction * startrate[1]
-
-        # For terms where the rate increases linearly in time t, we can write GMSLR as
-        #   S(t) = a*t**2 + b*t
-        # where a is 0.5*acceleration and b is start rate. Hence
-        #   a = S/t**2-b/t = (S-b*t)/t**2
-        # If nfinal=1, the following two lines are equivalent to
-        # halfacc=(final-startyr*nyr)/nyr**2
-        finalyr = np.arange(nfinal) - nfinal + 94 + 1  # last element ==nyr
-        halfacc = (afinal - startrate * finalyr.mean()) / (finalyr**2).mean()
-        quadratic = halfacc[:, :, np.newaxis] * (time**2)
-        linear = startrate[:, :, np.newaxis] * time
-
-        # If acceleration ceases for t>t0, the rate is 2*a*t0+b thereafter, so
-        #   S(t) = a*t0**2 + b*t0 + (2*a*t0+b)*(t-t0)
-        #        = a*t0*(2*t - t0) + b*t
-        # i.e. the quadratic term is replaced, the linear term unaffected
-        # The quadratic also = a*t**2-a*(t-t0)**2
-
-        if state.palmer_method:
-            y = halfacc[:, :, np.newaxis] * timeendofAR5 * ((2 * time) - timeendofAR5)
-            quadratic[:, :, 95:] = y[:, :, 95:]
-
-        quadratic += linear
-
-        quadratic = quadratic.reshape(
-            quadratic.shape[0] * quadratic.shape[1], quadratic.shape[2]
-        )
-
-        return quadratic
\ No newline at end of file
+    state: ClimateState,
+    startratemean: float,
+    startratepm: float,
+    final,
+    rng: np.random.Generator,
+    nfinal: int = 1,
+    fraction: np.ndarray = None,
+) -> np.ndarray:
+    """Project a quantity which is a quadratic function of time.
+
+    Parameters
+    ----------
+    startratemean: float
+        Rate of GMSLR at the start in mm yr-1.
+    startratepm: float
+        Start rate error in mm yr-1.
+    final: list | np.ndarray
+        Likely range in m for GMSLR at the end of AR5.
+    nfinal: int
+        Number of years at the end over which final is a time-mean.
+    fraction: np.ndarray
+        Random numbers in the range 0 to 1.
+
+    Returns
+    -------
+    np.ndarray
+        Projection of the quantity.
+    """
+    # Create a field of elapsed time since start in years
+    timeendofAR5 = state.endofAR5 - state.endofhistory + 1
+    time = np.arange(state.end_yr - state.endofhistory) + 1
+
+    if fraction is None:
+        fraction = rng.random((state.num_members, state.nt))
+    elif fraction.size != state.num_members * state.nt:
+        raise ValueError("fraction is the wrong size")
+
+    fraction = fraction.reshape(state.num_members, state.nt)
+
+    # Convert inputs to startrate (m yr-1) and afinal (m), where both are
+    # arrays with the size of fraction
+    startrate = (
+        startratemean + startratepm * np.array([-1, 1], dtype=float)
+    ) * 1e-3  # convert mm yr-1 to m yr-1
+    finalisrange = isinstance(final, Sequence)
+
+    if finalisrange:
+        if len(final) != 2:
+            raise ValueError("final range is the wrong size")
+        afinal = (1 - fraction) * final[0] + fraction * final[1]
+    else:
+        if final.shape != fraction.shape:
+            raise ValueError("final array is the wrong shape")
+        afinal = final
+
+    startrate = (1 - fraction) * startrate[0] + fraction * startrate[1]
+
+    # For terms where the rate increases linearly in time t, we can write GMSLR as
+    #   S(t) = a*t**2 + b*t
+    # where a is 0.5*acceleration and b is start rate. Hence
+    #   a = S/t**2-b/t = (S-b*t)/t**2
+    # If nfinal=1, the following two lines are equivalent to
+    # halfacc=(final-startyr*nyr)/nyr**2
+    finalyr = np.arange(nfinal) - nfinal + 94 + 1  # last element ==nyr
+    halfacc = (afinal - startrate * finalyr.mean()) / (finalyr**2).mean()
+    quadratic = halfacc[:, :, np.newaxis] * (time**2)
+    linear = startrate[:, :, np.newaxis] * time
+
+    # If acceleration ceases for t>t0, the rate is 2*a*t0+b thereafter, so
+    #   S(t) = a*t0**2 + b*t0 + (2*a*t0+b)*(t-t0)
+    #        = a*t0*(2*t - t0) + b*t
+    # i.e. the quadratic term is replaced, the linear term unaffected
+    # The quadratic also = a*t**2-a*(t-t0)**2
+
+    if state.palmer_method:
+        y = halfacc[:, :, np.newaxis] * timeendofAR5 * ((2 * time) - timeendofAR5)
+        quadratic[:, :, 95:] = y[:, :, 95:]
+
+    quadratic += linear
+
+    quadratic = quadratic.reshape(
+        quadratic.shape[0] * quadratic.shape[1], quadratic.shape[2]
+    )
+
+    return quadratic
diff --git a/profsea/components/global_/antarctica.py b/profsea/components/global_/antarctica.py
index 2c2c366..78c2a59 100644
--- a/profsea/components/global_/antarctica.py
+++ b/profsea/components/global_/antarctica.py
@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 from pathlib import Path
 import numpy as np
 from scipy.signal import fftconvolve
diff --git a/profsea/components/global_/expansion.py b/profsea/components/global_/expansion.py
index 3fba025..3aec835 100644
--- a/profsea/components/global_/expansion.py
+++ b/profsea/components/global_/expansion.py
@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import numpy as np
 
 from profsea.components.core.base import Component
diff --git a/profsea/components/global_/glacier.py b/profsea/components/global_/glacier.py
index a4640c8..e525044 100644
--- a/profsea/components/global_/glacier.py
+++ b/profsea/components/global_/glacier.py
@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import numpy as np
 
 from profsea.components.core.base import Component
diff --git a/profsea/components/global_/greenland.py b/profsea/components/global_/greenland.py
index 3bba81f..173e261 100644
--- a/profsea/components/global_/greenland.py
+++ b/profsea/components/global_/greenland.py
@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import functools
 
 from pathlib import Path
diff --git a/profsea/components/global_/landwater.py b/profsea/components/global_/landwater.py
index 425829c..9eddb9a 100644
--- a/profsea/components/global_/landwater.py
+++ b/profsea/components/global_/landwater.py
@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import functools
 
 from pathlib import Path
diff --git a/profsea/components/spatial/fingerprint.py b/profsea/components/spatial/fingerprint.py
index 634223d..228fe3c 100644
--- a/profsea/components/spatial/fingerprint.py
+++ b/profsea/components/spatial/fingerprint.py
@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 from pathlib import Path
 
 import dask.array as da
diff --git a/profsea/components/spatial/gia.py b/profsea/components/spatial/gia.py
index fac7a32..8923654 100644
--- a/profsea/components/spatial/gia.py
+++ b/profsea/components/spatial/gia.py
@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 from pathlib import Path
 
 import dask.array as da
diff --git a/profsea/components/spatial/sterodynamic.py b/profsea/components/spatial/sterodynamic.py
index 03308db..d7aab24 100644
--- a/profsea/components/spatial/sterodynamic.py
+++ b/profsea/components/spatial/sterodynamic.py
@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 from pathlib import Path
 
 import dask.array as da
diff --git a/profsea/utils/utils.py b/profsea/utils/utils.py
index 8657eda..c504437 100644
--- a/profsea/utils/utils.py
+++ b/profsea/utils/utils.py
@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import dask.array as da
 import numpy as np
 from scipy.spatial.distance import cdist

From f10bc22556e116d6469dfdc13277dff6c33a05f6 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Wed, 10 Jun 2026 18:25:29 +0100
Subject: [PATCH 201/276] Small tweaks

---
 docs/source/conf.py   | 2 +-
 docs/source/index.rst | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/source/conf.py b/docs/source/conf.py
index 5e6762f..cde8514 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -85,4 +85,4 @@
 }
 html_static_path = ["_static"]
 html_logo = "_static/profsea-logo.png"
-html_favicon = 'static/favicon.png'
+html_favicon = '_static/favicon.png'
diff --git a/docs/source/index.rst b/docs/source/index.rst
index 80fae2f..07ca9ff 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -1,5 +1,5 @@
 ProFSea
-====
+=======
 
 **ProFSea is sea-level rise simulator based on statistical emulations of physical modelling experiments and lines of evidence from the IPCC and across the literature. It's modular, easy to setup and self-contained - all you need is global mean surface temperature and ocean heat content forcing anomalies, using any baseline period, and you're good to go.**
 

From 2db91427cffe1bf69bcc600969efcd504f97fddd Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Wed, 10 Jun 2026 18:31:16 +0100
Subject: [PATCH 202/276] Update docs-env

---
 docs-environment.yml | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/docs-environment.yml b/docs-environment.yml
index 9673356..2589e29 100644
--- a/docs-environment.yml
+++ b/docs-environment.yml
@@ -1,10 +1,9 @@
 name: profsea-sphinx-docs
 
-
-
 dependencies:
   - pre-commit
   - pydata-sphinx-theme
   - sphinx
+  - sphinx-design
   - nbsphinx
   - pandoc

From 2d9bc275c954620584600effd5a693a3661ef1e2 Mon Sep 17 00:00:00 2001
From: Hemant Khatri 
Date: Thu, 11 Jun 2026 10:50:16 +0100
Subject: [PATCH 203/276] add land mask to sterodynamic patters

---
 .gitignore                                 |  2 ++
 profsea/components/spatial/sterodynamic.py |  7 ++---
 profsea/utils/utils.py                     | 36 ++++++++++------------
 3 files changed, 22 insertions(+), 23 deletions(-)

diff --git a/.gitignore b/.gitignore
index 8a9f4dc..5b04ab0 100644
--- a/.gitignore
+++ b/.gitignore
@@ -2,6 +2,8 @@
 *__pycache__*
 profsea.egg-info
 data/
+profsea/profsea-assets/
+
 # Ignore the documentation build directory
 docs/build
 *.DS_Store
diff --git a/profsea/components/spatial/sterodynamic.py b/profsea/components/spatial/sterodynamic.py
index a1f781b..74b969f 100644
--- a/profsea/components/spatial/sterodynamic.py
+++ b/profsea/components/spatial/sterodynamic.py
@@ -87,12 +87,11 @@ def _load_CMIP6_slopes(self) -> da.Array:
                 xr.open_dataset(f, chunks={"lat": 45, "lon": 45})["zos_mask"] for f in mask_files
             ]
             mask_stack = xr.concat(datasets_mask, dim="model")
+            mask_stack = mask_stack.sum(dim='model', skipna=True)
             
         else:
             self.land_mask_present = False
-            mask_stack = xr.Dataset() # dummy dataset to return 
-
-        print(self.land_mask_present)
+            mask_stack = None
         
         return slopes_stack, mask_stack
 
@@ -132,7 +131,7 @@ def _calc_expansion_contribution(
             return coeffs[rand_samples, :, :]
         else:
             # Calc pattern ensemble mean
-            mean_coeff = da.mean(coeffs, axis=0)
+            mean_coeff = da.nanmean(coeffs, axis=0)
             return da.broadcast_to(
                 mean_coeff,
                 (state.n_members, state.grid_lats.shape[0], state.grid_lons.shape[0]),
diff --git a/profsea/utils/utils.py b/profsea/utils/utils.py
index 2a239b1..d682f22 100644
--- a/profsea/utils/utils.py
+++ b/profsea/utils/utils.py
@@ -28,7 +28,6 @@ def interpolate(data: da.array, lats: int, lons: int) -> np.ndarray:
     ).data
     return data_interp
 
-
 def interpolate_to_grid(
     data: xr.DataArray,
     target_lats: np.ndarray,
@@ -41,42 +40,41 @@ def interpolate_to_grid(
     """
     # Normalize source longitudes to [-180, 180) and sort monotonically
     data = data.assign_coords(lon=(((data.lon + 180) % 360) - 180))
-    data = data.sortby("lon")
+    data = data.sortby(["lat", "lon"])
 
     # Normalize target longitudes to [-180, 180) and sort
     target_lons_norm = np.sort(((target_lons + 180) % 360) - 180)
 
-    # Pad longitude with one points from each end to handle periodicity 
+    # Pad longitude with one points from each end to handle periodicity in zonal direction 
     data_padded = data.pad(lon=1, mode='wrap') # need more padding for higher-order interpolation
-
-    # Fix the longitude coordinate after padding
     lon = data.lon.values
     lon_padded = np.concatenate([[lon[-1] - 360], lon, [lon[0] + 360]])
     data_padded['lon'] = lon_padded
+    data_padded = data_padded.sortby(["lat", "lon"])
 
-    # Now interpolate (maybe add land_mask if condition here)
+    # Now interpolate
+    data_padded = data_padded.chunk({"lat": -1, "lon": -1})
     for dim in ["lat", "lon"]:
         data_padded = data_padded.interpolate_na(
-            dim=dim, method=grid_interpolation,
-            fill_value="extrapolate"
+            dim=dim, method="nearest",
         ) # this to handle nan values or land mask 
         
     data_interp = data_padded.interp(
         lat=target_lats, lon=target_lons_norm, method=grid_interpolation
     )
 
-    data_interp = data_interp.where
-
+    # Account for land mask (1 where ocean, 0 where land (NaN))
+    ocean_mask = (~data.isnull()).astype(float)
+    ocean_mask_padded = ocean_mask.pad(lon=1, mode='wrap')
+    ocean_mask_padded['lon'] = lon_padded
+    ocean_mask_padded = ocean_mask_padded.sortby(["lat", "lon"])
+    ocean_mask_padded = ocean_mask_padded.chunk({"lat": -1, "lon": -1})
+    ocean_mask_interp = ocean_mask_padded.interp(
+        lat=target_lats, lon=target_lons_norm, method="nearest"
+        )
     
-
-    # Acount for land mask (1 where NaN, 0 elsewhere)
-    #if mask_present:
-    #    nan_mask = data.isnull().astype(float).interp(
-    #        lat=target_lats, lon=target_lons_norm, method=grid_interpolation
-    #    )
-
-    #   # Mask out any grid point that had NaN influence
-    #   data_interp = data_interp.where(nan_mask == 0)
+    data_interp = data_interp.where(ocean_mask_interp == 1)
+    data_interp = data_interp.chunk("auto")
     
     return data_interp
 

From d1deb45597ccaaaf29eeae2fe11e1a7bc1160ad4 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Sat, 13 Jun 2026 15:06:49 +0100
Subject: [PATCH 204/276] Update toml

---
 pyproject.toml | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 0e5bcaf..c2f7882 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,4 +4,8 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "profsea"
-version = "1.0"
\ No newline at end of file
+version = "3.0"
+
+[tool.setuptools.packages.find]
+include = ["profsea*"]
+exclude = ["docs*", "tests*"]

From 8433a6655862da799f897b7514e19cf9e2d1694f Mon Sep 17 00:00:00 2001
From: Greg Munday <100290135+gregrmunday@users.noreply.github.com>
Date: Sat, 13 Jun 2026 15:11:59 +0100
Subject: [PATCH 205/276] Fix logo

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index c9443f7..a606665 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,4 @@
-# ProFSea 
+# ProFSea logo
 
 [![DOI](https://zenodo.org/badge/713453340.svg)](https://zenodo.org/doi/10.5281/zenodo.10255467)
 

From 7421fbdcbf8e4ec16ce0120956d565fcc62069a7 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Sat, 13 Jun 2026 15:22:45 +0100
Subject: [PATCH 206/276] Add test infrastructure

---
 profsea-env.yml        | 1 +
 profsea/utils/utils.py | 5 ++---
 pyproject.toml         | 6 ++++++
 3 files changed, 9 insertions(+), 3 deletions(-)

diff --git a/profsea-env.yml b/profsea-env.yml
index bd29167..8cd5695 100644
--- a/profsea-env.yml
+++ b/profsea-env.yml
@@ -15,6 +15,7 @@ dependencies:
   - pandoc
   - pre-commit
   - pydata-sphinx-theme
+  - pytest
   - python=3.12
   - pyyaml
   - requests
diff --git a/profsea/utils/utils.py b/profsea/utils/utils.py
index c504437..fed3597 100644
--- a/profsea/utils/utils.py
+++ b/profsea/utils/utils.py
@@ -73,8 +73,7 @@ def check_shapes(array: np.ndarray, n_time: int) -> None:
         array = array[np.newaxis, :]
 
     if array.shape[1] != n_time:
-        # Split over lines for readability
         raise ValueError(
-            f"Array should have shape (realisation, time) with time \
-                dimension of length {n_time}. Got {array.shape}."
+            f"Array should have shape (realisation, time) with time "
+            f"dimension of length {n_time}. Got {array.shape}."
         )
diff --git a/pyproject.toml b/pyproject.toml
index c2f7882..492e40b 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -9,3 +9,9 @@ version = "3.0"
 [tool.setuptools.packages.find]
 include = ["profsea*"]
 exclude = ["docs*", "tests*"]
+
+[project.optional-dependencies]
+test = [
+    "pytest>=7.0",
+    "pytest-cov",  # Useful for checking test coverage later
+]

From 6a003edfaab1c1758b192aafa71be43dd7c2d548 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Sat, 13 Jun 2026 15:45:02 +0100
Subject: [PATCH 207/276] Add tests folder

---
 tests/__init__.py         |  0
 tests/utils/__init__.py   |  0
 tests/utils/test_utils.py | 69 +++++++++++++++++++++++++++++++++++++++
 3 files changed, 69 insertions(+)
 create mode 100644 tests/__init__.py
 create mode 100644 tests/utils/__init__.py
 create mode 100644 tests/utils/test_utils.py

diff --git a/tests/__init__.py b/tests/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/utils/__init__.py b/tests/utils/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/utils/test_utils.py b/tests/utils/test_utils.py
new file mode 100644
index 0000000..a1fcd4e
--- /dev/null
+++ b/tests/utils/test_utils.py
@@ -0,0 +1,69 @@
+import pytest
+import numpy as np
+import xarray as xr
+
+from profsea.utils import check_shapes, interpolate_to_grid, sample_members_2D
+
+
+def test_check_shapes_valid_2d():
+    # 5 realisations, 10 time steps
+    arr = np.zeros((5, 10))
+    # Should not raise an exception
+    check_shapes(arr, n_time=10)
+
+
+def test_check_shapes_promotes_1d():
+    # 1 realisation, 10 time steps
+    arr = np.zeros(10)
+    # The function should silently promote this to (1, 10)
+    check_shapes(arr, n_time=10)
+
+
+def test_check_shapes_raises_value_error():
+    arr = np.zeros((5, 10))
+    # Passing the wrong time dimension length
+    with pytest.raises(
+        ValueError, match="Array should have shape .* time dimension of length 12"
+    ):
+        check_shapes(arr, n_time=12)
+
+
+def test_interpolate_to_grid_longitude_wrapping():
+    # Create mock data with 0-360 longitude format
+    lats = np.array([-45, 0, 45])
+    lons_360 = np.array([0, 180, 350])
+
+    data = xr.DataArray(np.random.rand(3, 3), coords=[("lat", lats), ("lon", lons_360)])
+
+    # Target grid using -180 to 180 format (includes 180, which should wrap)
+    target_lats = np.array([-45, 0, 45])
+    target_lons = np.array([-10, 0, 180])
+
+    result = interpolate_to_grid(data, target_lats, target_lons)
+
+    # Check that output dimensions match target dimensions
+    assert result.shape == (3, 3)
+
+    # Check that coordinates were successfully wrapped to [-180, 180) and sorted
+    expected_lons = np.array([-180, -10, 0])
+    np.testing.assert_array_almost_equal(result.lon.values, expected_lons)
+
+
+def test_sample_members_2D_extracts_real_member():
+    # 5 ensemble members over 3 time steps
+    arr = np.array(
+        [
+            [1.0, 1.0, 1.0],  # Min
+            [2.0, 2.0, 2.0],
+            [3.0, 3.0, 3.0],  # Median
+            [4.0, 4.0, 4.0],
+            [5.0, 5.0, 5.0],  # Max
+        ]
+    )
+
+    # Request the 50th percentile (median)
+    result = sample_members_2D(arr, percentiles=[50])
+
+    # The closest real member to the median is [3., 3., 3.]
+    assert result.shape == (1, 3)
+    np.testing.assert_array_equal(result[0], [3.0, 3.0, 3.0])

From 0ebca6b35a4cdd5c1a58fa9db8b2418a97d35af4 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Sat, 13 Jun 2026 16:25:00 +0100
Subject: [PATCH 208/276] Add pytest workflow

---
 .github/workflows/pytest.yml | 37 ++++++++++++++++++++++++++++++++++++
 1 file changed, 37 insertions(+)
 create mode 100644 .github/workflows/pytest.yml

diff --git a/.github/workflows/pytest.yml b/.github/workflows/pytest.yml
new file mode 100644
index 0000000..f979dd7
--- /dev/null
+++ b/.github/workflows/pytest.yml
@@ -0,0 +1,37 @@
+name: Pytest Suite
+
+on:
+  push:
+    branches:
+      - 'profsea-v3'
+
+  pull_request:
+    branches:
+      - profsea-v3
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    
+    steps:
+      # 1. Grab the repository code
+      - name: Checkout Repository
+        uses: actions/checkout@v4
+
+      # 2. Set up the Python environment
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.12" # Adjust this to match your project's target version
+          cache: 'pip'           # Speeds up subsequent runs
+
+      # 3. Install the package and test dependencies
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install -e ".[test]"
+
+      # 4. Execute the test suite
+      - name: Run Pytest
+        run: |
+          pytest tests/ -v
\ No newline at end of file

From 7049df4f42501ba128d510ffeae152cd8dc67e59 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Sat, 13 Jun 2026 16:29:23 +0100
Subject: [PATCH 209/276] Add some dependencies to toml

---
 pyproject.toml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/pyproject.toml b/pyproject.toml
index 492e40b..c2a79aa 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -5,6 +5,7 @@ build-backend = "setuptools.build_meta"
 [project]
 name = "profsea"
 version = "3.0"
+dependencies = ["numpy", "xarray", "scipy", "dask"]
 
 [tool.setuptools.packages.find]
 include = ["profsea*"]

From a6e100fcdb758cfdc64c7fead766f77281a17ab4 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Sat, 13 Jun 2026 18:18:02 +0100
Subject: [PATCH 210/276] Update gitignore

---
 .gitignore | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/.gitignore b/.gitignore
index 8a9f4dc..98b8bf8 100644
--- a/.gitignore
+++ b/.gitignore
@@ -2,7 +2,9 @@
 *__pycache__*
 profsea.egg-info
 data/
+.pytest_cache/
+.ipynb_checkpoints/
+
 # Ignore the documentation build directory
 docs/build
 *.DS_Store
-.ipynb_checkpoints/

From a3fb7ae9f0fb12085fee17da3b83a1f42c5461f3 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Sat, 13 Jun 2026 18:43:29 +0100
Subject: [PATCH 211/276] Add tests for global, spatial models and some comps

---
 tests/components/__init__.py          |   0
 tests/components/test_glacier.py      |  54 +++++++++++++
 tests/components/test_sterodynamic.py | 108 ++++++++++++++++++++++++++
 tests/core/__init__.py                |   0
 tests/core/test_global_model.py       |  64 +++++++++++++++
 tests/core/test_spatial_model.py      |  47 +++++++++++
 6 files changed, 273 insertions(+)
 create mode 100644 tests/components/__init__.py
 create mode 100644 tests/components/test_glacier.py
 create mode 100644 tests/components/test_sterodynamic.py
 create mode 100644 tests/core/__init__.py
 create mode 100644 tests/core/test_global_model.py
 create mode 100644 tests/core/test_spatial_model.py

diff --git a/tests/components/__init__.py b/tests/components/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/components/test_glacier.py b/tests/components/test_glacier.py
new file mode 100644
index 0000000..31aa93f
--- /dev/null
+++ b/tests/components/test_glacier.py
@@ -0,0 +1,54 @@
+import pytest
+import numpy as np
+
+from profsea.components.global_.glacier import Glacier
+from profsea.components.core.state import ClimateState
+
+
+def get_dummy_state(T_change_val: float) -> ClimateState:
+    """Helper to generate a state object with a constant temperature."""
+    T_ens = np.ones((2, 4)) * T_change_val
+    T_int_ens = np.cumsum(T_ens, axis=1)
+    T_int_med = np.cumsum(np.median(T_ens, axis=0))
+
+    return ClimateState(
+        scenario="test",
+        T_ens=T_ens,
+        T_int_ens=T_int_ens,
+        T_int_med=T_int_med,
+        fraction=np.random.rand(10),
+        palmer_method=True,
+        endofAR5=2100,
+        endofhistory=2006,
+        end_yr=2010,
+        nyr=4,
+        nt=2,
+        num_members=2,
+    )
+
+
+def test_glacier_parameter_validation():
+    # Attempting to initialize with an invalid MIP index
+    glacier = Glacier(glaciermip=3)
+    state = get_dummy_state(1.0)
+    rng = np.random.default_rng(42)
+
+    with pytest.raises(KeyError, match="glaciermip must be False"):
+        glacier.project(state, rng)
+
+
+def test_glacier_mass_limit():
+    glacier = Glacier(glaciermip=2)
+
+    # Force a massive, physically impossible temperature spike (1000 degrees)
+    # This ensures the mathematical scaling tries to exceed the physical ice volume
+    extreme_state = get_dummy_state(1000.0)
+    rng = np.random.default_rng(42)
+
+    projection = glacier.project(extreme_state, rng)
+
+    # Calculate the hard limit:
+    max_sle = (412.0 - 96.3) * 1e-3
+
+    # Assert no value in the projection exceeds the hard mass limit
+    assert np.all(projection <= max_sle)
diff --git a/tests/components/test_sterodynamic.py b/tests/components/test_sterodynamic.py
new file mode 100644
index 0000000..dc185df
--- /dev/null
+++ b/tests/components/test_sterodynamic.py
@@ -0,0 +1,108 @@
+import numpy as np
+from unittest.mock import patch
+import xarray as xr
+
+from profsea.components.core.state import SpatialState
+from profsea.components.spatial.sterodynamic import SterodynamicCMIP6
+
+
+@patch("profsea.components.spatial.sterodynamic.xr.open_dataset")
+@patch("profsea.components.spatial.sterodynamic.Path.glob")
+def test_load_cmip6_slopes(mock_glob, mock_open_dataset):
+    # 1. Setup mock file paths
+    mock_glob.return_value = ["dummy_path_1.nc", "dummy_path_2.nc"]
+
+    # 2. Setup a mock xarray dataset that has the required variable
+    mock_data = xr.DataArray(
+        np.random.rand(5, 5),  # 5 lats, 5 lons
+        dims=["lat", "lon"],
+    )
+    mock_dataset = {"zos_zostoga_regression_slope": mock_data}
+    mock_open_dataset.return_value = mock_dataset
+
+    # 3. Initialize component and call the method
+    # global_projection shape: (members, years)
+    dummy_global = np.zeros((10, 100))
+    stero = SterodynamicCMIP6(global_projection=dummy_global)
+
+    slopes = stero._load_CMIP6_slopes()
+
+    # 4. Assertions
+    # We expect 2 models (because we mocked 2 paths), and 5x5 spatial dimensions
+    assert slopes.shape == (2, 5, 5)
+    assert "model" in slopes.dims
+
+
+@patch.object(SterodynamicCMIP6, "_load_CMIP6_slopes")
+def test_expansion_contribution_storyline_mode(mock_load):
+    # 1. Mock the slope data (3 models, 2 lats, 2 lons)
+    # Model 1 is all 1s, Model 2 is all 2s, Model 3 is all 3s
+    mock_coeffs = xr.DataArray(
+        np.array([np.ones((2, 2)), np.ones((2, 2)) * 2, np.ones((2, 2)) * 3]),
+        coords=[("model", [0, 1, 2]), ("lat", [0, 1]), ("lon", [0, 1])],
+    )
+    mock_load.return_value = mock_coeffs
+
+    # 2. Create the state object
+    state = SpatialState(
+        grid_lats=np.array([0, 1]),
+        grid_lons=np.array([0, 1]),
+        n_years=100,
+        n_members=5,
+        grid_interpolation="nearest",
+        output_percentiles=None,
+        baseline_yrs=(1995, 2014),
+    )
+
+    # 3. Test Storyline Mode (sample_spatial = False)
+    stero_storyline = SterodynamicCMIP6(
+        global_projection=np.zeros((5, 100)), sample_spatial=False
+    )
+
+    rng = np.random.default_rng(42)
+    result_storyline = stero_storyline._calc_expansion_contribution(rng, state)
+
+    # In storyline mode, it calculates the ensemble mean of the models: (1+2+3)/3 = 2
+    # Therefore, every member should receive a pattern of 2s.
+    assert result_storyline.shape == (5, 2, 2)
+
+    # Convert Dask array to NumPy for assertion
+    computed_result = result_storyline.compute()
+    np.testing.assert_array_equal(computed_result, np.ones((5, 2, 2)) * 2)
+
+
+@patch.object(SterodynamicCMIP6, "_load_CMIP6_slopes")
+def test_expansion_contribution_sampled_mode(mock_load):
+    # Use the same setup as above
+    mock_coeffs = xr.DataArray(
+        np.array([np.ones((2, 2)), np.ones((2, 2)) * 2, np.ones((2, 2)) * 3]),
+        coords=[("model", [0, 1, 2]), ("lat", [0, 1]), ("lon", [0, 1])],
+    )
+    mock_load.return_value = mock_coeffs
+
+    state = SpatialState(
+        grid_lats=np.array([0, 1]),
+        grid_lons=np.array([0, 1]),
+        n_years=100,
+        n_members=5,
+        grid_interpolation="nearest",
+        output_percentiles=None,
+        baseline_yrs=(1995, 2014),
+    )
+
+    # Test Sampled Mode (sample_spatial = True)
+    stero_sampled = SterodynamicCMIP6(
+        global_projection=np.zeros((5, 100)), sample_spatial=True
+    )
+
+    rng = np.random.default_rng(42)  # Seeded for determinism
+
+    result_sampled = np.asarray(stero_sampled._calc_expansion_contribution(rng, state))
+
+    # In sampled mode, the array should NOT be uniform across the 5 members.
+    assert result_sampled.shape == (5, 2, 2)
+
+    # Check that member 0 and member 1 are not guaranteed to be identical
+    # (Though with a seed of 42 and 3 choices, they might be, so we check the unique values)
+    unique_values = np.unique(result_sampled)
+    assert len(unique_values) > 1  # Proves it didn't just take the mean
diff --git a/tests/core/__init__.py b/tests/core/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/core/test_global_model.py b/tests/core/test_global_model.py
new file mode 100644
index 0000000..7655c8d
--- /dev/null
+++ b/tests/core/test_global_model.py
@@ -0,0 +1,64 @@
+import numpy as np
+import xarray as xr
+
+from profsea.components.core.global_model import Global
+from profsea.components.core.base import Component
+from profsea.components.core.state import ClimateState
+
+
+class MockGlobalComponent(Component):
+    def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
+        # Just return an array of 1s with the correct shape
+        return np.ones((state.nt * state.num_members, state.nyr))
+
+
+def test_calculate_drivers_math():
+    # 2 members, 3 time steps
+    T_change = np.array([[1.0, 2.0, 3.0], [3.0, 4.0, 5.0]])
+
+    global_model = Global(components={}, end_yr=2008, nt=2)
+    T_ens, T_int_ens, T_int_med = global_model._calculate_drivers(T_change)
+
+    # Assert time integrals (cumulative sum over axis 1)
+    np.testing.assert_array_equal(T_int_ens, [[1.0, 3.0, 6.0], [3.0, 7.0, 12.0]])
+
+    # Median of T_change across members is [2.0, 3.0, 4.0]
+    # Cumsum of that median is [2.0, 5.0, 9.0]
+    np.testing.assert_array_equal(T_int_med, [2.0, 5.0, 9.0])
+
+
+def test_run_orchestration():
+    components = {"mock1": MockGlobalComponent(), "mock2": MockGlobalComponent()}
+
+    # 2 time series, 3 members each -> output shape should be (6, nyr)
+    global_model = Global(components=components, end_yr=2010, nt=2, num_members=3)
+
+    # Shape: (nt=2, nyr=4)
+    T_change = np.zeros((2, 4))
+    results = global_model.run(scenario="ssp119", T_change=T_change)
+
+    assert "mock1" in results
+    assert "mock2" in results
+    assert results["mock1"].shape == (6, 4)
+
+
+def test_save_components(tmp_path):
+    # tmp_path is a built-in pytest fixture that creates a temporary directory
+    global_model = Global(components={}, end_yr=2010)
+
+    # Create dummy result: 5 members, 4 years
+    components = {"mock_comp": np.random.rand(5, 4)}
+
+    # Save the output to the temporary directory
+    global_model.save_components(
+        components, output_dir=str(tmp_path), scenario_name="test"
+    )
+
+    expected_file = tmp_path / "test_global.nc"
+    assert expected_file.exists()
+
+    # Verify the contents of the NetCDF
+    ds = xr.open_dataset(expected_file)
+    assert "mock_comp" in ds.data_vars
+    assert ds["mock_comp"].shape == (5, 4)
+    assert list(ds.dims) == ["member", "time"]
diff --git a/tests/core/test_spatial_model.py b/tests/core/test_spatial_model.py
new file mode 100644
index 0000000..25e9ded
--- /dev/null
+++ b/tests/core/test_spatial_model.py
@@ -0,0 +1,47 @@
+import dask.array as da
+import numpy as np
+import xarray as xr
+from unittest.mock import patch
+
+from profsea.components.core.spatial_model import Spatial
+
+
+@patch("profsea.components.core.spatial_model.fetch_zenodo_fingerprints")
+def test_spatial_init_blocks_download(mock_fetch):
+    spatial = Spatial(components={}, end_year=2050)
+    mock_fetch.assert_called_once()
+    assert spatial.end_year == 2050
+
+
+@patch("profsea.components.core.spatial_model.fetch_zenodo_fingerprints")
+def test_arr_to_xr_metadata(mock_fetch):
+    # Initializes with default output_percentiles=[5, 17, 50, 83, 95] (Length 5)
+    spatial = Spatial(components={}, end_year=2010)
+
+    arr = da.zeros((5, 4, 180, 360))
+    arr_dict = {"mock_spatial": arr}
+
+    xr_dict = spatial._arr_to_xr(arr_dict)
+
+    assert "mock_spatial" in xr_dict
+    da_out = xr_dict["mock_spatial"]
+
+    assert isinstance(da_out, xr.DataArray)
+    assert list(da_out.dims) == ["percentile", "time", "lat", "lon"]
+    np.testing.assert_array_equal(da_out.time.values, [2006, 2007, 2008, 2009])
+
+
+@patch("profsea.components.core.spatial_model.fetch_zenodo_fingerprints")
+def test_sum_spatial_components(mock_fetch):
+    spatial = Spatial(components={}, end_year=2010)
+
+    # Two identical arrays of 1s
+    da1 = xr.DataArray(np.ones((2, 2)), dims=["lat", "lon"])
+    da2 = xr.DataArray(np.ones((2, 2)), dims=["lat", "lon"])
+
+    components = {"comp1": da1, "comp2": da2}
+    total = spatial.sum_components(components)
+
+    # 1 + 1 = 2 everywhere
+    np.testing.assert_array_equal(total.values, np.ones((2, 2)) * 2)
+    assert "total_rsl" in components

From 2938b01f96ac6a51b9a281de7196a12e729c18e6 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Sat, 13 Jun 2026 18:48:34 +0100
Subject: [PATCH 212/276] Add Ruff formatter workflow

---
 .github/workflows/ruff.yml | 30 ++++++++++++++++++++++++++++++
 pyproject.toml             | 20 +++++++++++++++++++-
 2 files changed, 49 insertions(+), 1 deletion(-)
 create mode 100644 .github/workflows/ruff.yml

diff --git a/.github/workflows/ruff.yml b/.github/workflows/ruff.yml
new file mode 100644
index 0000000..58f80b2
--- /dev/null
+++ b/.github/workflows/ruff.yml
@@ -0,0 +1,30 @@
+name: Ruff Linter and Formatter
+
+on:
+  push:
+    branches:
+      - 'profsea-v3'
+
+  pull_request:
+    branches:
+      - profsea-v3
+
+jobs:
+  ruff:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+
+      # The official Ruff action is pre-compiled and runs instantly
+      - name: Run Ruff Linter
+        uses: astral-sh/ruff-action@v1
+        with:
+          # Checks for linting errors (unused imports, syntax errors, etc.)
+          args: "check --output-format=github ."
+
+      - name: Run Ruff Formatter
+        uses: astral-sh/ruff-action@v1
+        with:
+          # Checks if the code is properly formatted
+          args: "format --check ."
\ No newline at end of file
diff --git a/pyproject.toml b/pyproject.toml
index c2a79aa..54c8751 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -5,7 +5,7 @@ build-backend = "setuptools.build_meta"
 [project]
 name = "profsea"
 version = "3.0"
-dependencies = ["numpy", "xarray", "scipy", "dask"]
+dependencies = ["numpy", "xarray", "scipy", "dask", "rich"]
 
 [tool.setuptools.packages.find]
 include = ["profsea*"]
@@ -16,3 +16,21 @@ test = [
     "pytest>=7.0",
     "pytest-cov",  # Useful for checking test coverage later
 ]
+
+# Settings for Ruff formatting
+[tool.ruff]
+line-length = 88
+target-version = "py312"
+
+[tool.ruff.lint]
+# Pyflakes (F), pycodestyle (E, W), isort (I), and pyupgrade (UP)
+select = ["E", "F", "W", "I", "UP"]
+ignore = [
+    "E501", # Line too long (let the formatter handle this, ignore in linter)
+]
+
+[tool.ruff.format]
+# Use double quotes for strings (standard Python convention)
+quote-style = "double"
+# Indent with spaces
+indent-style = "space"

From 54daa567ffda41121c712d4ef8829ca8222dd5c9 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Sat, 13 Jun 2026 18:51:23 +0100
Subject: [PATCH 213/276] Update toml

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 54c8751..a65659c 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -5,7 +5,7 @@ build-backend = "setuptools.build_meta"
 [project]
 name = "profsea"
 version = "3.0"
-dependencies = ["numpy", "xarray", "scipy", "dask", "rich"]
+dependencies = ["numpy", "xarray", "scipy", "dask", "rich", "requests"]
 
 [tool.setuptools.packages.find]
 include = ["profsea*"]

From 4e9d80e397232126d17953d227633d2258c1470f Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Sat, 13 Jun 2026 18:53:35 +0100
Subject: [PATCH 214/276] Update Ruff workflow

---
 .github/workflows/ruff.yml | 24 ++++++++++++++----------
 1 file changed, 14 insertions(+), 10 deletions(-)

diff --git a/.github/workflows/ruff.yml b/.github/workflows/ruff.yml
index 58f80b2..1043e53 100644
--- a/.github/workflows/ruff.yml
+++ b/.github/workflows/ruff.yml
@@ -4,7 +4,6 @@ on:
   push:
     branches:
       - 'profsea-v3'
-
   pull_request:
     branches:
       - profsea-v3
@@ -12,19 +11,24 @@ on:
 jobs:
   ruff:
     runs-on: ubuntu-latest
+    
     steps:
       - name: Checkout repository
         uses: actions/checkout@v4
 
-      # The official Ruff action is pre-compiled and runs instantly
-      - name: Run Ruff Linter
-        uses: astral-sh/ruff-action@v1
+      - name: Set up Python
+        uses: actions/setup-python@v5
         with:
-          # Checks for linting errors (unused imports, syntax errors, etc.)
-          args: "check --output-format=github ."
+          python-version: "3.10"
+          cache: 'pip'
+
+      - name: Install Ruff
+        run: |
+          python -m pip install --upgrade pip
+          pip install ruff
+
+      - name: Run Ruff Linter
+        run: ruff check --output-format=github .
 
       - name: Run Ruff Formatter
-        uses: astral-sh/ruff-action@v1
-        with:
-          # Checks if the code is properly formatted
-          args: "format --check ."
\ No newline at end of file
+        run: ruff format --check .
\ No newline at end of file

From aaa8e87ae8017515242625c20273d1ef08f14aa3 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Sat, 13 Jun 2026 19:02:39 +0100
Subject: [PATCH 215/276] Fix Ruff errors

---
 docs/source/conf.py                           |  2 +-
 profsea-env.yml                               |  1 +
 profsea/components/core/__init__.py           |  2 +
 profsea/components/core/base.py               |  1 +
 profsea/components/core/global_model.py       | 18 ++++-----
 profsea/components/core/spatial_model.py      | 21 +++++-----
 profsea/components/core/state.py              |  1 +
 profsea/components/global_/__init__.py        | 16 +++++++-
 profsea/components/global_/antarctica.py      |  3 +-
 profsea/components/global_/expansion.py       |  3 +-
 profsea/components/global_/greenland.py       |  4 +-
 profsea/components/global_/landwater.py       |  6 ++-
 profsea/components/spatial/__init__.py        |  6 ++-
 profsea/components/spatial/sterodynamic.py    |  5 ++-
 profsea/scm_profsea_experiments.py            | 25 ++++++------
 .../tutorial_notebooks/worked_example.ipynb   | 38 ++++++++++---------
 profsea/utils/utils.py                        |  2 +-
 pyproject.toml                                |  1 +
 tests/components/test_glacier.py              |  4 +-
 tests/components/test_sterodynamic.py         |  3 +-
 tests/core/test_global_model.py               |  2 +-
 tests/core/test_spatial_model.py              |  3 +-
 tests/utils/test_utils.py                     |  2 +-
 ukcp18_example_script.py                      | 14 +++----
 24 files changed, 107 insertions(+), 76 deletions(-)

diff --git a/docs/source/conf.py b/docs/source/conf.py
index cde8514..3109f1b 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -85,4 +85,4 @@
 }
 html_static_path = ["_static"]
 html_logo = "_static/profsea-logo.png"
-html_favicon = '_static/favicon.png'
+html_favicon = "_static/favicon.png"
diff --git a/profsea-env.yml b/profsea-env.yml
index 8cd5695..4e095ae 100644
--- a/profsea-env.yml
+++ b/profsea-env.yml
@@ -20,6 +20,7 @@ dependencies:
   - pyyaml
   - requests
   - rich
+  - ruff
   - scipy
   - sphinx
   - sphinx-design
diff --git a/profsea/components/core/__init__.py b/profsea/components/core/__init__.py
index 0528ab9..e19121e 100644
--- a/profsea/components/core/__init__.py
+++ b/profsea/components/core/__init__.py
@@ -1,2 +1,4 @@
 from .global_model import Global
 from .spatial_model import Spatial
+
+__all__ = ["Global", "Spatial"]
diff --git a/profsea/components/core/base.py b/profsea/components/core/base.py
index 2663bb9..0e571ac 100644
--- a/profsea/components/core/base.py
+++ b/profsea/components/core/base.py
@@ -1,6 +1,7 @@
 from __future__ import annotations
 
 from abc import ABC, abstractmethod
+
 import numpy as np
 
 from .state import ClimateState
diff --git a/profsea/components/core/global_model.py b/profsea/components/core/global_model.py
index 14c640e..2a3e879 100644
--- a/profsea/components/core/global_model.py
+++ b/profsea/components/core/global_model.py
@@ -1,17 +1,17 @@
 from __future__ import annotations
 
 import concurrent.futures
-from pathlib import Path
 import os
-from typing import Dict
+from pathlib import Path
 
 import numpy as np
-from rich.console import Console
 import xarray as xr
+from rich.console import Console
+
+from profsea.utils import check_shapes, sample_members_2D
 
-from .state import ClimateState
 from .base import Component
-from profsea.utils import sample_members_2D, check_shapes
+from .state import ClimateState
 
 console = Console()
 
@@ -58,7 +58,7 @@ class Global:
 
     def __init__(
         self,
-        components: Dict[str, Component],
+        components: dict[str, Component],
         end_yr: int,
         nt: int = 100,
         num_members: int = 1000,
@@ -87,7 +87,7 @@ def run(
         scenario: str,
         T_change: np.ndarray,
         member_seed: int = 42,
-    ) -> Dict[str, np.ndarray]:
+    ) -> dict[str, np.ndarray]:
         """Run the emulator to project GMSLR components for a specific state.
         Parameters
         ----------
@@ -175,7 +175,7 @@ def run(
 
         return results
 
-    def sum_components(self, components: Dict[str, np.ndarray]) -> np.ndarray:
+    def sum_components(self, components: dict[str, np.ndarray]) -> np.ndarray:
         """Sum the components to get total GMSLR."""
         components["gmslr"] = np.sum(
             [np.atleast_2d(c) for c in components.values()], axis=0
@@ -183,7 +183,7 @@ def sum_components(self, components: Dict[str, np.ndarray]) -> np.ndarray:
         return components["gmslr"]
 
     def save_components(
-        self, components: Dict[str, np.ndarray], output_dir: str, scenario_name: str
+        self, components: dict[str, np.ndarray], output_dir: str, scenario_name: str
     ) -> None:
         """Save SLR components as nc files to a directory.
 
diff --git a/profsea/components/core/spatial_model.py b/profsea/components/core/spatial_model.py
index a6eb281..cd1312f 100644
--- a/profsea/components/core/spatial_model.py
+++ b/profsea/components/core/spatial_model.py
@@ -1,28 +1,27 @@
 from __future__ import annotations
 
 import os
-from pathlib import Path
-from typing import Dict
 import warnings
 import zipfile
+from pathlib import Path
 
 import dask.array as da
 import numpy as np
 import requests
+import xarray as xr
 from rich.console import Console
 from rich.progress import (
-    Progress,
-    TextColumn,
     BarColumn,
     DownloadColumn,
-    TransferSpeedColumn,
+    Progress,
+    TextColumn,
     TimeRemainingColumn,
+    TransferSpeedColumn,
     track,
 )
-import xarray as xr
 
-from .state import SpatialState
 from .base import Component
+from .state import SpatialState
 
 console = Console()
 warnings.filterwarnings("ignore")
@@ -38,7 +37,7 @@ class Spatial:
 
     def __init__(
         self,
-        components: Dict[str, Component],
+        components: dict[str, Component],
         grid_config: dict = None,
         grid_interpolation: str = "linear",
         end_year: int = 2301,
@@ -133,7 +132,7 @@ def __init__(
                     f"resolution or take percentiles.[/bold red]"
                 )
 
-    def _arr_to_xr(self, arr_dict: Dict[str, da.Array]) -> Dict[str, xr.DataArray]:
+    def _arr_to_xr(self, arr_dict: dict[str, da.Array]) -> dict[str, xr.DataArray]:
         """
         Convert a dictionary of Dask arrays to a dictionary of xarray DataArrays with appropriate coordinates and metadata.
 
@@ -222,7 +221,7 @@ def run(self, member_seed: int = 42) -> None:
         self.results = spatial_projections_xr
         return self.results
 
-    def sum_components(self, components: Dict[str, xr.DataArray]) -> xr.DataArray:
+    def sum_components(self, components: dict[str, xr.DataArray]) -> xr.DataArray:
         """
         Sum the spatial components to get total sea-level change.
 
@@ -252,7 +251,7 @@ def sum_components(self, components: Dict[str, xr.DataArray]) -> xr.DataArray:
 
     def save_components(
         self,
-        components: Dict[str, xr.DataArray],
+        components: dict[str, xr.DataArray],
         scenario_name: str,
         output_dir: str = ".",
         output_format: str = "zarr",
diff --git a/profsea/components/core/state.py b/profsea/components/core/state.py
index 5fb71ec..9f4c3fb 100644
--- a/profsea/components/core/state.py
+++ b/profsea/components/core/state.py
@@ -1,6 +1,7 @@
 from __future__ import annotations
 
 from dataclasses import dataclass
+
 import numpy as np
 
 
diff --git a/profsea/components/global_/__init__.py b/profsea/components/global_/__init__.py
index 7691994..fd27983 100644
--- a/profsea/components/global_/__init__.py
+++ b/profsea/components/global_/__init__.py
@@ -1,6 +1,18 @@
-from .antarctica import AntarcticaDynAR5, AntarcticaSMBAR5, AntarcticaISMIP6
-from .expansion import ThermalExpansion
+from .antarctica import AntarcticaDynAR5, AntarcticaISMIP6, AntarcticaSMBAR5
 from .expansion import ThermalExpansion
 from .glacier import Glacier
 from .greenland import GreenlandAR6, GreenlandDynAR5, GreenlandSMBAR5
 from .landwater import LandwaterAR5, LandwaterAR6
+
+__all__ = [
+    "AntarcticaDynAR5",
+    "AntarcticaISMIP6",
+    "AntarcticaSMBAR5",
+    "ThermalExpansion",
+    "Glacier",
+    "GreenlandAR6",
+    "GreenlandDynAR5",
+    "GreenlandSMBAR5",
+    "LandwaterAR5",
+    "LandwaterAR6",
+]
diff --git a/profsea/components/global_/antarctica.py b/profsea/components/global_/antarctica.py
index 78c2a59..bb01118 100644
--- a/profsea/components/global_/antarctica.py
+++ b/profsea/components/global_/antarctica.py
@@ -1,10 +1,11 @@
 from __future__ import annotations
 
 from pathlib import Path
+
 import numpy as np
+import xarray as xr
 from scipy.signal import fftconvolve
 from scipy.stats import norm
-import xarray as xr
 
 from profsea.components.core.base import Component
 from profsea.components.core.global_model import ClimateState
diff --git a/profsea/components/global_/expansion.py b/profsea/components/global_/expansion.py
index 3aec835..1bf5b05 100644
--- a/profsea/components/global_/expansion.py
+++ b/profsea/components/global_/expansion.py
@@ -37,7 +37,8 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
         std_eff = 0.013 * self.distribution_scaler
 
         exp_efficiency = (
-            rng.normal(loc=mean_eff, scale=std_eff, size=(state.nt, state.num_members)) * 1e-24
+            rng.normal(loc=mean_eff, scale=std_eff, size=(state.nt, state.num_members))
+            * 1e-24
         )  # m/YJ
 
         ohc_3d = self.OHC_change[:, None, :]
diff --git a/profsea/components/global_/greenland.py b/profsea/components/global_/greenland.py
index 173e261..8910dac 100644
--- a/profsea/components/global_/greenland.py
+++ b/profsea/components/global_/greenland.py
@@ -1,8 +1,8 @@
 from __future__ import annotations
 
 import functools
-
 from pathlib import Path
+
 import numpy as np
 import pandas as pd
 from scipy.stats import truncnorm
@@ -71,7 +71,7 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
         )
         trend_sle = (trend[:, :, None] * time_delta[None, None, :]) * 1e-3
 
-        tas_3d = tas[:, None, :] 
+        tas_3d = tas[:, None, :]
 
         # Calculate GIS contribution rate
         dsle = (
diff --git a/profsea/components/global_/landwater.py b/profsea/components/global_/landwater.py
index 9eddb9a..d1f262b 100644
--- a/profsea/components/global_/landwater.py
+++ b/profsea/components/global_/landwater.py
@@ -1,8 +1,8 @@
 from __future__ import annotations
 
 import functools
-
 from pathlib import Path
+
 import numpy as np
 import xarray as xr
 
@@ -50,7 +50,9 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
         n_samples_nc = lw_base.shape[0]
 
         # Sample!
-        sample_indices = rng.integers(0, n_samples_nc, size=(state.nt, state.num_members))
+        sample_indices = rng.integers(
+            0, n_samples_nc, size=(state.nt, state.num_members)
+        )
 
         # Resulting shape: (nt, nm, nyr)
         lw_ens = lw_base[sample_indices, 1 : state.nyr + 1]
diff --git a/profsea/components/spatial/__init__.py b/profsea/components/spatial/__init__.py
index 6a5a0cc..f08d4f2 100644
--- a/profsea/components/spatial/__init__.py
+++ b/profsea/components/spatial/__init__.py
@@ -1,3 +1,5 @@
-from .sterodynamic import SterodynamicCMIP5, SterodynamicCMIP6
 from .fingerprint import Fingerprint
-from .gia import GIA
\ No newline at end of file
+from .gia import GIA
+from .sterodynamic import SterodynamicCMIP5, SterodynamicCMIP6
+
+__all__ = ["Fingerprint", "GIA", "SterodynamicCMIP5", "SterodynamicCMIP6"]
diff --git a/profsea/components/spatial/sterodynamic.py b/profsea/components/spatial/sterodynamic.py
index d7aab24..1f4dfc6 100644
--- a/profsea/components/spatial/sterodynamic.py
+++ b/profsea/components/spatial/sterodynamic.py
@@ -73,7 +73,10 @@ def _load_CMIP6_slopes(self) -> da.Array:
 
         # Lazily load all files keeping metadata intact
         datasets = [
-            xr.open_dataset(f, chunks={"lat": 45, "lon": 45})["zos_zostoga_regression_slope"] for f in slope_files
+            xr.open_dataset(f, chunks={"lat": 45, "lon": 45})[
+                "zos_zostoga_regression_slope"
+            ]
+            for f in slope_files
         ]
 
         # Concatenate along a new dimension (representing the ensemble/models)
diff --git a/profsea/scm_profsea_experiments.py b/profsea/scm_profsea_experiments.py
index 4669560..f0ebcaa 100644
--- a/profsea/scm_profsea_experiments.py
+++ b/profsea/scm_profsea_experiments.py
@@ -1,26 +1,25 @@
 import argparse
-import json
 from pathlib import Path
 
-from fair import FAIR
-from fair.io import read_properties
-from fair.interface import initialise
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
-from rich_argparse import RichHelpFormatter
+import xarray as xr
+from fair import FAIR
+from fair.interface import initialise
+from fair.io import read_properties
 from rich.console import Console
 from rich.progress import track
-import xarray as xr
+from rich_argparse import RichHelpFormatter
 
 # --- NEW IMPORTS ---
 from profsea.components.core.global_model import Global
 from profsea.components.global_ import (
     AntarcticaISMIP6,
-    LandwaterAR6,
+    Glacier,
     GreenlandAR6,
+    LandwaterAR6,
     ThermalExpansion,
-    Glacier,
 )
 from profsea.utils import sample_members_2D
 
@@ -215,7 +214,7 @@ def plot_samples(tas: np.ndarray, ohc: np.ndarray) -> None:
     ax = fig.add_subplot(121)
     ax.plot(tas.T, color="seagreen", alpha=0.05)
     ax.set_xlabel("Simulation years")
-    ax.set_ylabel("GMST ($\degree$C)")
+    ax.set_ylabel(r"GMST ($\degree$C)")
     ax.plot(np.arange(tas.shape[1]), np.median(tas, axis=0), color="black")
 
     ax = fig.add_subplot(122)
@@ -324,10 +323,10 @@ def main(args):
 
     console.log(f"Using scenarios: {scenarios}")
 
-    if "ssp" in scenarios[0].lower():
-        emissions_path = args.cumulative_emissions_file
-        with open(emissions_path) as f:
-            cumulative_emissions = json.load(f)
+    # if "ssp" in scenarios[0].lower():
+    #     emissions_path = args.cumulative_emissions_file
+    #     with open(emissions_path) as f:
+    #         cumulative_emissions = json.load(f)
 
     baseline_start = 1995
     baseline_end = 2014
diff --git a/profsea/tutorial_notebooks/worked_example.ipynb b/profsea/tutorial_notebooks/worked_example.ipynb
index bba35b8..8c10e56 100644
--- a/profsea/tutorial_notebooks/worked_example.ipynb
+++ b/profsea/tutorial_notebooks/worked_example.ipynb
@@ -53,11 +53,11 @@
    "source": [
     "from profsea.components.core.global_model import Global\n",
     "from profsea.components.global_ import (\n",
-    "    LandwaterAR6,\n",
-    "    GreenlandAR6,\n",
-    "    ThermalExpansion,\n",
     "    AntarcticaISMIP6,\n",
     "    Glacier,\n",
+    "    GreenlandAR6,\n",
+    "    LandwaterAR6,\n",
+    "    ThermalExpansion,\n",
     ")"
    ]
   },
@@ -92,17 +92,19 @@
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "\n",
-    "t_change = np.concatenate(\n",
-    "    [np.linspace(0.01, 8, 150), np.linspace(8, 8, 145)]\n",
-    ")\n",
-    "t_change = t_change[None, :] * np.random.normal(loc=1.0, scale=0.1, size=(100, 1)) # add some variability across members\n",
-    "ohc_change = t_change * 1e24 # in Joules\n",
+    "t_change = np.concatenate([np.linspace(0.01, 8, 150), np.linspace(8, 8, 145)])\n",
+    "t_change = t_change[None, :] * np.random.normal(\n",
+    "    loc=1.0, scale=0.1, size=(100, 1)\n",
+    ")  # add some variability across members\n",
+    "ohc_change = t_change * 1e24  # in Joules\n",
     "years = np.arange(2006, 2301)\n",
     "years = np.broadcast_to(years, t_change.shape)\n",
     "\n",
     "plt.figure(figsize=(4, 2))\n",
     "plt.plot(years, t_change, color=\"royalblue\", alpha=0.1)\n",
-    "plt.plot(years.mean(axis=0), t_change.mean(axis=0), color=\"royalblue\", label=\"GMST change\")\n",
+    "plt.plot(\n",
+    "    years.mean(axis=0), t_change.mean(axis=0), color=\"royalblue\", label=\"GMST change\"\n",
+    ")\n",
     "plt.xlabel(\"Year\")\n",
     "plt.ylabel(\"GMST change (°C)\")\n",
     "plt.show()\n",
@@ -129,7 +131,9 @@
     "global_components = {\n",
     "    \"landwater\": LandwaterAR6(),\n",
     "    \"greenland\": GreenlandAR6(),\n",
-    "    \"expansion\": ThermalExpansion(ohc_change), # only the thermal expansion needs OHC change, so we'll pass it in here\n",
+    "    \"expansion\": ThermalExpansion(\n",
+    "        ohc_change\n",
+    "    ),  # only the thermal expansion needs OHC change, so we'll pass it in here\n",
     "    \"wais\": AntarcticaISMIP6(ais_region=\"wais\"),\n",
     "    \"eais\": AntarcticaISMIP6(ais_region=\"eais\"),\n",
     "    \"peninsula\": AntarcticaISMIP6(ais_region=\"peninsula\"),\n",
@@ -154,9 +158,7 @@
    "outputs": [],
    "source": [
     "projections = global_model.run(\n",
-    "    T_change=t_change,\n",
-    "    scenario=\"stabilisation\",\n",
-    "    member_seed=42\n",
+    "    T_change=t_change, scenario=\"stabilisation\", member_seed=42\n",
     ")\n",
     "global_model.sum_components(projections)\n",
     "gmslr = global_model.results[\"gmslr\"]"
@@ -191,7 +193,9 @@
     "plt.figure(figsize=(4, 3))\n",
     "lower = np.percentile(gmslr, 5, axis=0)\n",
     "upper = np.percentile(gmslr, 95, axis=0)\n",
-    "plt.fill_between(years[0], lower, upper, color=\"lightcoral\", alpha=0.5, label=\"90% range\")\n",
+    "plt.fill_between(\n",
+    "    years[0], lower, upper, color=\"lightcoral\", alpha=0.5, label=\"90% range\"\n",
+    ")\n",
     "plt.plot(years[0], gmslr.mean(axis=0), color=\"darkred\")\n",
     "plt.xlabel(\"Year\")\n",
     "plt.ylabel(\"GMSLR (m)\")\n",
@@ -218,9 +222,9 @@
    "source": [
     "from profsea.components.core import Spatial\n",
     "from profsea.components.spatial import (\n",
-    "    SterodynamicCMIP6,\n",
-    "    Fingerprint,\n",
     "    GIA,\n",
+    "    Fingerprint,\n",
+    "    SterodynamicCMIP6,\n",
     ")"
    ]
   },
@@ -293,7 +297,7 @@
    "source": [
     "spatial_components = {\n",
     "    \"sterodynamic\": SterodynamicCMIP6(\n",
-    "        projections[\"expansion\"], # passing in the global thermal expansion projection\n",
+    "        projections[\"expansion\"],  # passing in the global thermal expansion projection\n",
     "    ),\n",
     "    \"greenland\": Fingerprint(\n",
     "        projections[\"greenland\"], fingerprint_component=\"greenland\"\n",
diff --git a/profsea/utils/utils.py b/profsea/utils/utils.py
index fed3597..7e5b1f6 100644
--- a/profsea/utils/utils.py
+++ b/profsea/utils/utils.py
@@ -2,8 +2,8 @@
 
 import dask.array as da
 import numpy as np
-from scipy.spatial.distance import cdist
 import xarray as xr
+from scipy.spatial.distance import cdist
 
 
 def sample_members_2D(array: np.ndarray, percentiles: list | np.ndarray) -> np.ndarray:
diff --git a/pyproject.toml b/pyproject.toml
index a65659c..7c580e4 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -21,6 +21,7 @@ test = [
 [tool.ruff]
 line-length = 88
 target-version = "py312"
+extend-exclude = ["*.ipynb"]
 
 [tool.ruff.lint]
 # Pyflakes (F), pycodestyle (E, W), isort (I), and pyupgrade (UP)
diff --git a/tests/components/test_glacier.py b/tests/components/test_glacier.py
index 31aa93f..300e362 100644
--- a/tests/components/test_glacier.py
+++ b/tests/components/test_glacier.py
@@ -1,8 +1,8 @@
-import pytest
 import numpy as np
+import pytest
 
-from profsea.components.global_.glacier import Glacier
 from profsea.components.core.state import ClimateState
+from profsea.components.global_.glacier import Glacier
 
 
 def get_dummy_state(T_change_val: float) -> ClimateState:
diff --git a/tests/components/test_sterodynamic.py b/tests/components/test_sterodynamic.py
index dc185df..27b0f42 100644
--- a/tests/components/test_sterodynamic.py
+++ b/tests/components/test_sterodynamic.py
@@ -1,5 +1,6 @@
-import numpy as np
 from unittest.mock import patch
+
+import numpy as np
 import xarray as xr
 
 from profsea.components.core.state import SpatialState
diff --git a/tests/core/test_global_model.py b/tests/core/test_global_model.py
index 7655c8d..19dabe8 100644
--- a/tests/core/test_global_model.py
+++ b/tests/core/test_global_model.py
@@ -1,8 +1,8 @@
 import numpy as np
 import xarray as xr
 
-from profsea.components.core.global_model import Global
 from profsea.components.core.base import Component
+from profsea.components.core.global_model import Global
 from profsea.components.core.state import ClimateState
 
 
diff --git a/tests/core/test_spatial_model.py b/tests/core/test_spatial_model.py
index 25e9ded..71d22c8 100644
--- a/tests/core/test_spatial_model.py
+++ b/tests/core/test_spatial_model.py
@@ -1,7 +1,8 @@
+from unittest.mock import patch
+
 import dask.array as da
 import numpy as np
 import xarray as xr
-from unittest.mock import patch
 
 from profsea.components.core.spatial_model import Spatial
 
diff --git a/tests/utils/test_utils.py b/tests/utils/test_utils.py
index a1fcd4e..724af11 100644
--- a/tests/utils/test_utils.py
+++ b/tests/utils/test_utils.py
@@ -1,5 +1,5 @@
-import pytest
 import numpy as np
+import pytest
 import xarray as xr
 
 from profsea.utils import check_shapes, interpolate_to_grid, sample_members_2D
diff --git a/ukcp18_example_script.py b/ukcp18_example_script.py
index a43c50f..c05a065 100644
--- a/ukcp18_example_script.py
+++ b/ukcp18_example_script.py
@@ -1,19 +1,19 @@
 import cartopy.crs as ccrs
-import numpy as np
 import matplotlib.pyplot as plt
+import numpy as np
 
 from profsea.components.core.global_model import Global
+from profsea.components.core.spatial_model import Spatial
 from profsea.components.global_ import (
-    LandwaterAR5,
-    GreenlandDynAR5,
-    GreenlandSMBAR5,
-    ThermalExpansion,
     AntarcticaDynAR5,
     AntarcticaSMBAR5,
     Glacier,
+    GreenlandDynAR5,
+    GreenlandSMBAR5,
+    LandwaterAR5,
+    ThermalExpansion,
 )
-from profsea.components.core.spatial_model import Spatial
-from profsea.components.spatial import SterodynamicCMIP6, Fingerprint, GIA
+from profsea.components.spatial import GIA, Fingerprint, SterodynamicCMIP6
 
 ### Global projections first ###
 slr_components = {

From f0a77970ba7a486e8ac366baa9d126ffe4089001 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Sat, 13 Jun 2026 19:11:45 +0100
Subject: [PATCH 216/276] Update Ruff workflow

---
 .github/workflows/ruff.yml | 1 +
 pyproject.toml             | 1 +
 2 files changed, 2 insertions(+)

diff --git a/.github/workflows/ruff.yml b/.github/workflows/ruff.yml
index 1043e53..173be00 100644
--- a/.github/workflows/ruff.yml
+++ b/.github/workflows/ruff.yml
@@ -10,6 +10,7 @@ on:
 
 jobs:
   ruff:
+    name: Code Quality (Ruff)
     runs-on: ubuntu-latest
     
     steps:
diff --git a/pyproject.toml b/pyproject.toml
index 7c580e4..2ba1c61 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -15,6 +15,7 @@ exclude = ["docs*", "tests*"]
 test = [
     "pytest>=7.0",
     "pytest-cov",  # Useful for checking test coverage later
+    "pytest-github-actions-annotate-failures"
 ]
 
 # Settings for Ruff formatting

From 7e3994fd210cd3c2c119c9fb8e5c96bea2ae9745 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Sat, 13 Jun 2026 19:12:47 +0100
Subject: [PATCH 217/276] Update pytest name

---
 .github/workflows/pytest.yml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.github/workflows/pytest.yml b/.github/workflows/pytest.yml
index f979dd7..a1ca01e 100644
--- a/.github/workflows/pytest.yml
+++ b/.github/workflows/pytest.yml
@@ -11,6 +11,7 @@ on:
 
 jobs:
   test:
+    name: Run Unit Tests
     runs-on: ubuntu-latest
     
     steps:

From 1124ceab480197b442b5d6242e6cbf1de48fa3d6 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Sat, 13 Jun 2026 19:18:46 +0100
Subject: [PATCH 218/276] Add badges

---
 .github/workflows/ruff.yml | 2 +-
 README.md                  | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/ruff.yml b/.github/workflows/ruff.yml
index 173be00..c4caf3d 100644
--- a/.github/workflows/ruff.yml
+++ b/.github/workflows/ruff.yml
@@ -1,4 +1,4 @@
-name: Ruff Linter and Formatter
+name: Ruff Linting
 
 on:
   push:
diff --git a/README.md b/README.md
index a606665..4a6b1a7 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,6 @@
 # ProFSea logo
 
-[![DOI](https://zenodo.org/badge/713453340.svg)](https://zenodo.org/doi/10.5281/zenodo.10255467)
+[![DOI](https://zenodo.org/badge/713453340.svg)](https://zenodo.org/doi/10.5281/zenodo.10255467) [![Tests](https://github.com/MetOffice/ProFSea-tool/actions/workflows/pytest.yml/badge.svg?branch=profsea-v3)](https://github.com/MetOffice/ProFSea-tool/actions/workflows/pytest.yml)  [![Tests](https://github.com/MetOffice/ProFSea-tool/actions/workflows/ruff.yml/badge.svg?branch=profsea-v3)](https://github.com/MetOffice/ProFSea-tool/actions/workflows/ruff.yml)
 
 ProFSea is sea-level rise simulator based on statistical emulations of physical modelling experiments and lines of evidence from the IPCC and across the literature. It's modular, easy to setup and self-contained - all you need is global mean surface temperature and ocean heat content forcing anomalies, using any baseline period, and you're good to go.
 

From 4304af8759ac3f25657e368e1ff1c5f23128f293 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Sat, 13 Jun 2026 19:19:57 +0100
Subject: [PATCH 219/276] Add docs badge

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 4a6b1a7..1624bfe 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,6 @@
 # ProFSea logo
 
-[![DOI](https://zenodo.org/badge/713453340.svg)](https://zenodo.org/doi/10.5281/zenodo.10255467) [![Tests](https://github.com/MetOffice/ProFSea-tool/actions/workflows/pytest.yml/badge.svg?branch=profsea-v3)](https://github.com/MetOffice/ProFSea-tool/actions/workflows/pytest.yml)  [![Tests](https://github.com/MetOffice/ProFSea-tool/actions/workflows/ruff.yml/badge.svg?branch=profsea-v3)](https://github.com/MetOffice/ProFSea-tool/actions/workflows/ruff.yml)
+[![DOI](https://zenodo.org/badge/713453340.svg)](https://zenodo.org/doi/10.5281/zenodo.10255467) [![Tests](https://github.com/MetOffice/ProFSea-tool/actions/workflows/pytest.yml/badge.svg?branch=profsea-v3)](https://github.com/MetOffice/ProFSea-tool/actions/workflows/pytest.yml)  [![Tests](https://github.com/MetOffice/ProFSea-tool/actions/workflows/ruff.yml/badge.svg?branch=profsea-v3)](https://github.com/MetOffice/ProFSea-tool/actions/workflows/ruff.yml) [![Docs](https://github.com/MetOffice/ProFSea-tool/actions/workflows/deploy-docs.yml/badge.svg?branch=profsea-v3)](https://github.com/MetOffice/ProFSea-tool/actions/workflows/deploy-docs.yml)
 
 ProFSea is sea-level rise simulator based on statistical emulations of physical modelling experiments and lines of evidence from the IPCC and across the literature. It's modular, easy to setup and self-contained - all you need is global mean surface temperature and ocean heat content forcing anomalies, using any baseline period, and you're good to go.
 

From 64141e35e0071fc7f539bfe4b4257634c32fd9a9 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Sat, 13 Jun 2026 19:24:26 +0100
Subject: [PATCH 220/276] Update readme

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 1624bfe..c1fd957 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,6 @@
 # ProFSea logo
 
-[![DOI](https://zenodo.org/badge/713453340.svg)](https://zenodo.org/doi/10.5281/zenodo.10255467) [![Tests](https://github.com/MetOffice/ProFSea-tool/actions/workflows/pytest.yml/badge.svg?branch=profsea-v3)](https://github.com/MetOffice/ProFSea-tool/actions/workflows/pytest.yml)  [![Tests](https://github.com/MetOffice/ProFSea-tool/actions/workflows/ruff.yml/badge.svg?branch=profsea-v3)](https://github.com/MetOffice/ProFSea-tool/actions/workflows/ruff.yml) [![Docs](https://github.com/MetOffice/ProFSea-tool/actions/workflows/deploy-docs.yml/badge.svg?branch=profsea-v3)](https://github.com/MetOffice/ProFSea-tool/actions/workflows/deploy-docs.yml)
+[![DOI](https://zenodo.org/badge/713453340.svg)](https://zenodo.org/doi/10.5281/zenodo.10255467) [![Tests](https://github.com/MetOffice/ProFSea-tool/actions/workflows/pytest.yml/badge.svg?branch=profsea-v3)](https://github.com/MetOffice/ProFSea-tool/actions/workflows/pytest.yml)  [![Lint](https://github.com/MetOffice/ProFSea-tool/actions/workflows/ruff.yml/badge.svg?branch=profsea-v3)](https://github.com/MetOffice/ProFSea-tool/actions/workflows/ruff.yml) [![Docs](https://github.com/MetOffice/ProFSea-tool/actions/workflows/deploy-docs.yml/badge.svg?branch=profsea-v3)](https://github.com/MetOffice/ProFSea-tool/actions/workflows/deploy-docs.yml)
 
 ProFSea is sea-level rise simulator based on statistical emulations of physical modelling experiments and lines of evidence from the IPCC and across the literature. It's modular, easy to setup and self-contained - all you need is global mean surface temperature and ocean heat content forcing anomalies, using any baseline period, and you're good to go.
 

From 78f45a14c25b3f2571ddb6daa6c518f39d378b47 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Sat, 13 Jun 2026 19:29:22 +0100
Subject: [PATCH 221/276] Update gitignore

---
 .gitignore | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.gitignore b/.gitignore
index 98b8bf8..d643cab 100644
--- a/.gitignore
+++ b/.gitignore
@@ -4,6 +4,7 @@ profsea.egg-info
 data/
 .pytest_cache/
 .ipynb_checkpoints/
+.ruff_cache/
 
 # Ignore the documentation build directory
 docs/build

From fe6da469f4923d805803ed0863191e50aecf90af Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Mon, 15 Jun 2026 16:03:11 +0100
Subject: [PATCH 222/276] Update docs deployment

---
 .github/workflows/deploy-docs.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/deploy-docs.yml b/.github/workflows/deploy-docs.yml
index 9cb6e1d..bdbb38b 100644
--- a/.github/workflows/deploy-docs.yml
+++ b/.github/workflows/deploy-docs.yml
@@ -65,4 +65,4 @@ jobs:
     steps:
       - name: Deploy to GitHub Pages
         id: deployment
-        uses: actions/deploy-pages@v5
+        uses: actions/deploy-pages@v4

From 93506ca81a315ccac923db27c7d9bccf795dd60f Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Mon, 15 Jun 2026 16:21:05 +0100
Subject: [PATCH 223/276] Add dependabot workflow

---
 .github/dependabot.yml | 23 +++++++++++++++++++++++
 1 file changed, 23 insertions(+)
 create mode 100644 .github/dependabot.yml

diff --git a/.github/dependabot.yml b/.github/dependabot.yml
new file mode 100644
index 0000000..856f418
--- /dev/null
+++ b/.github/dependabot.yml
@@ -0,0 +1,23 @@
+version: 2
+updates:
+  - package-ecosystem: "github-actions"
+    directory: "/"
+    target-branch: "profsea-v3"
+    schedule:
+      interval: "weekly"
+      day: "monday"
+    groups:
+      actions-updates:
+        patterns:
+          - "*"
+
+  - package-ecosystem: "pip"
+    directory: "/"
+    target-branch: "profsea-v3"
+    schedule:
+      interval: "weekly"
+      day: "monday"
+    groups:
+      python-dependencies:
+        patterns:
+          - "*"
\ No newline at end of file

From 57c8cb2ef5c84b9c8f0576bf9ab7670426cb272c Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Mon, 15 Jun 2026 16:25:22 +0100
Subject: [PATCH 224/276] Add Ruff badge

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index c1fd957..4ae2cea 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,6 @@
 # ProFSea logo
 
-[![DOI](https://zenodo.org/badge/713453340.svg)](https://zenodo.org/doi/10.5281/zenodo.10255467) [![Tests](https://github.com/MetOffice/ProFSea-tool/actions/workflows/pytest.yml/badge.svg?branch=profsea-v3)](https://github.com/MetOffice/ProFSea-tool/actions/workflows/pytest.yml)  [![Lint](https://github.com/MetOffice/ProFSea-tool/actions/workflows/ruff.yml/badge.svg?branch=profsea-v3)](https://github.com/MetOffice/ProFSea-tool/actions/workflows/ruff.yml) [![Docs](https://github.com/MetOffice/ProFSea-tool/actions/workflows/deploy-docs.yml/badge.svg?branch=profsea-v3)](https://github.com/MetOffice/ProFSea-tool/actions/workflows/deploy-docs.yml)
+[![DOI](https://zenodo.org/badge/713453340.svg)](https://zenodo.org/doi/10.5281/zenodo.10255467) [![Tests](https://github.com/MetOffice/ProFSea-tool/actions/workflows/pytest.yml/badge.svg?branch=profsea-v3)](https://github.com/MetOffice/ProFSea-tool/actions/workflows/pytest.yml)  [![Lint](https://github.com/MetOffice/ProFSea-tool/actions/workflows/ruff.yml/badge.svg?branch=profsea-v3)](https://github.com/MetOffice/ProFSea-tool/actions/workflows/ruff.yml) [![Docs](https://github.com/MetOffice/ProFSea-tool/actions/workflows/deploy-docs.yml/badge.svg?branch=profsea-v3)](https://github.com/MetOffice/ProFSea-tool/actions/workflows/deploy-docs.yml)[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)
 
 ProFSea is sea-level rise simulator based on statistical emulations of physical modelling experiments and lines of evidence from the IPCC and across the literature. It's modular, easy to setup and self-contained - all you need is global mean surface temperature and ocean heat content forcing anomalies, using any baseline period, and you're good to go.
 

From 115dab4e67c1c5fbe1b5a91ba80c7bdebe169523 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Mon, 15 Jun 2026 16:26:23 +0100
Subject: [PATCH 225/276] Fix

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 4ae2cea..45b25c6 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,6 @@
 # ProFSea logo
 
-[![DOI](https://zenodo.org/badge/713453340.svg)](https://zenodo.org/doi/10.5281/zenodo.10255467) [![Tests](https://github.com/MetOffice/ProFSea-tool/actions/workflows/pytest.yml/badge.svg?branch=profsea-v3)](https://github.com/MetOffice/ProFSea-tool/actions/workflows/pytest.yml)  [![Lint](https://github.com/MetOffice/ProFSea-tool/actions/workflows/ruff.yml/badge.svg?branch=profsea-v3)](https://github.com/MetOffice/ProFSea-tool/actions/workflows/ruff.yml) [![Docs](https://github.com/MetOffice/ProFSea-tool/actions/workflows/deploy-docs.yml/badge.svg?branch=profsea-v3)](https://github.com/MetOffice/ProFSea-tool/actions/workflows/deploy-docs.yml)[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)
+[![DOI](https://zenodo.org/badge/713453340.svg)](https://zenodo.org/doi/10.5281/zenodo.10255467) [![Tests](https://github.com/MetOffice/ProFSea-tool/actions/workflows/pytest.yml/badge.svg?branch=profsea-v3)](https://github.com/MetOffice/ProFSea-tool/actions/workflows/pytest.yml) [![Lint](https://github.com/MetOffice/ProFSea-tool/actions/workflows/ruff.yml/badge.svg?branch=profsea-v3)](https://github.com/MetOffice/ProFSea-tool/actions/workflows/ruff.yml) [![Docs](https://github.com/MetOffice/ProFSea-tool/actions/workflows/deploy-docs.yml/badge.svg?branch=profsea-v3)](https://github.com/MetOffice/ProFSea-tool/actions/workflows/deploy-docs.yml) [![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)
 
 ProFSea is sea-level rise simulator based on statistical emulations of physical modelling experiments and lines of evidence from the IPCC and across the literature. It's modular, easy to setup and self-contained - all you need is global mean surface temperature and ocean heat content forcing anomalies, using any baseline period, and you're good to go.
 

From d83a24b2186cedc02341224f79b6a2cc05edfcd9 Mon Sep 17 00:00:00 2001
From: Hemant Khatri 
Date: Mon, 15 Jun 2026 17:23:39 +0100
Subject: [PATCH 226/276] update to land mask scheme

---
 profsea-env.yml                            |  3 +-
 profsea/components/spatial/sterodynamic.py | 37 ++++++++++++++++------
 profsea/utils/utils.py                     | 26 ++++++++-------
 3 files changed, 44 insertions(+), 22 deletions(-)

diff --git a/profsea-env.yml b/profsea-env.yml
index 19bf5a5..12a70bf 100644
--- a/profsea-env.yml
+++ b/profsea-env.yml
@@ -13,8 +13,9 @@ dependencies:
   - pandas
   - python=3.12
   - pyyaml
+  - regionmask
   - requests
   - rich
   - scipy
   - xarray
-  - zarr
\ No newline at end of file
+  - zarr
diff --git a/profsea/components/spatial/sterodynamic.py b/profsea/components/spatial/sterodynamic.py
index 74b969f..3949dc0 100644
--- a/profsea/components/spatial/sterodynamic.py
+++ b/profsea/components/spatial/sterodynamic.py
@@ -3,6 +3,8 @@
 import dask.array as da
 import numpy as np
 import xarray as xr
+import logging
+logging.basicConfig(level=logging.WARNING)
 
 from profsea.components.core.base import SpatialComponent
 from profsea.components.core.state import ClimateState
@@ -60,9 +62,13 @@ def _load_CMIP6_slopes(self) -> da.Array:
         Returns
         -------
         da.Array
-            A dask array of shape (n_models, n_lats, n_lons) containing the sterodynamic fingerprint patterns (i.e., regression coefficients) for each CMIP6 model.
+            A dask array of shape (n_models, n_lats, n_lons) containing the sterodynamic 
+            fingerprint patterns (i.e., regression coefficients) for each CMIP6 model.
         """
-        slope_files = list(Path(self.patterns_dir).glob("*/zos_regression_ssp585_*.nc"))
+        slope_files = sorted(
+            Path(self.patterns_dir).glob("*/zos_regression_ssp585_*.nc"),
+            key=lambda p: p.name
+        )
 
         if not slope_files:
             raise FileNotFoundError(
@@ -78,16 +84,27 @@ def _load_CMIP6_slopes(self) -> da.Array:
         slopes_stack = xr.concat(datasets, dim="model")
 
         # Read land mask if present
-        mask_files = list(Path(self.patterns_dir).glob("*/zos_mask_ssp585_*.nc"))
+        mask_files = sorted(
+            Path(self.patterns_dir).glob("*/zos_mask_ssp585_*.nc"),
+            key=lambda p: p.name
+        )
+
         
         if mask_files:
-            self.land_mask_present = True
-
-            datasets_mask = [
-                xr.open_dataset(f, chunks={"lat": 45, "lon": 45})["zos_mask"] for f in mask_files
-            ]
-            mask_stack = xr.concat(datasets_mask, dim="model")
-            mask_stack = mask_stack.sum(dim='model', skipna=True)
+            if(len(slope_files) == len(mask_files)):
+                self.land_mask_present = True
+    
+                datasets_mask = [
+                    xr.open_dataset(f, chunks={"lat": 45, "lon": 45})["zos_mask"] for f in mask_files
+                ]
+                mask_stack = xr.concat(datasets_mask, dim="model")
+                #mask_stack = mask_stack.sum(dim='model', skipna=True)
+            
+            else:
+                logging.warning("There is a mismatch between number of slope files and mask files. "
+                                "Ignoring mask files.")
+                self.land_mask_present = False
+                mask_stack = None
             
         else:
             self.land_mask_present = False
diff --git a/profsea/utils/utils.py b/profsea/utils/utils.py
index d682f22..f604d2d 100644
--- a/profsea/utils/utils.py
+++ b/profsea/utils/utils.py
@@ -2,6 +2,7 @@
 import numpy as np
 from scipy.spatial.distance import cdist
 import xarray as xr
+import regionmask
 
 
 def sample_members_2D(array: np.ndarray, percentiles: list | np.ndarray) -> np.ndarray:
@@ -63,18 +64,21 @@ def interpolate_to_grid(
         lat=target_lats, lon=target_lons_norm, method=grid_interpolation
     )
 
-    # Account for land mask (1 where ocean, 0 where land (NaN))
-    ocean_mask = (~data.isnull()).astype(float)
-    ocean_mask_padded = ocean_mask.pad(lon=1, mode='wrap')
-    ocean_mask_padded['lon'] = lon_padded
-    ocean_mask_padded = ocean_mask_padded.sortby(["lat", "lon"])
-    ocean_mask_padded = ocean_mask_padded.chunk({"lat": -1, "lon": -1})
-    ocean_mask_interp = ocean_mask_padded.interp(
-        lat=target_lats, lon=target_lons_norm, method="nearest"
-        )
+    # Account for land mask (using regionmask library)
+    land = regionmask.defined_regions.natural_earth_v5_0_0.land_110
+    land_mask = land.mask_3D(data_interp)
+    is_land = land_mask.squeeze("region", drop=True)
+    data_interp = data_interp.where(~is_land)
     
-    data_interp = data_interp.where(ocean_mask_interp == 1)
-    data_interp = data_interp.chunk("auto")
+    #ocean_mask = (~data.isnull()).astype(float)
+    #ocean_mask_padded = ocean_mask.pad(lon=1, mode='wrap')
+    #ocean_mask_padded['lon'] = lon_padded
+    #ocean_mask_padded = ocean_mask_padded.sortby(["lat", "lon"])
+    #ocean_mask_padded = ocean_mask_padded.chunk({"lat": -1, "lon": -1})
+    #ocean_mask_interp = ocean_mask_padded.interp(
+    #    lat=target_lats, lon=target_lons_norm, method="nearest"
+    #    )
+    #data_interp = data_interp.where(ocean_mask_interp == 1)
     
     return data_interp
 

From e7bce7519deeafa1f28341d6ed091b6ebfbc5aa8 Mon Sep 17 00:00:00 2001
From: Hemant Khatri 
Date: Mon, 15 Jun 2026 18:07:47 +0100
Subject: [PATCH 227/276] remove commented code

---
 profsea/utils/utils.py | 10 ----------
 1 file changed, 10 deletions(-)

diff --git a/profsea/utils/utils.py b/profsea/utils/utils.py
index f0a5b87..a0aa637 100644
--- a/profsea/utils/utils.py
+++ b/profsea/utils/utils.py
@@ -72,16 +72,6 @@ def interpolate_to_grid(
     is_land = land_mask.squeeze("region", drop=True)
     data_interp = data_interp.where(~is_land)
     
-    #ocean_mask = (~data.isnull()).astype(float)
-    #ocean_mask_padded = ocean_mask.pad(lon=1, mode='wrap')
-    #ocean_mask_padded['lon'] = lon_padded
-    #ocean_mask_padded = ocean_mask_padded.sortby(["lat", "lon"])
-    #ocean_mask_padded = ocean_mask_padded.chunk({"lat": -1, "lon": -1})
-    #ocean_mask_interp = ocean_mask_padded.interp(
-    #    lat=target_lats, lon=target_lons_norm, method="nearest"
-    #    )
-    #data_interp = data_interp.where(ocean_mask_interp == 1)
-    
     return data_interp
 
 

From b230f251b0acfac6cfcd9bc7fb4933aa969627ba Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Mon, 15 Jun 2026 23:42:54 +0100
Subject: [PATCH 228/276] Fix console logging

---
 profsea/__init__.py                      | 17 ++++++++++++++
 profsea/components/core/global_model.py  |  6 ++---
 profsea/components/core/spatial_model.py | 28 +++++++++++++-----------
 3 files changed, 35 insertions(+), 16 deletions(-)
 create mode 100644 profsea/__init__.py

diff --git a/profsea/__init__.py b/profsea/__init__.py
new file mode 100644
index 0000000..95261cd
--- /dev/null
+++ b/profsea/__init__.py
@@ -0,0 +1,17 @@
+import logging
+
+from rich.logging import RichHandler
+
+# Tie logger to the "profsea" namespace
+logger = logging.getLogger("profsea")
+
+# Print by default
+logger.setLevel(logging.INFO)
+
+# Use Rich!
+rich_handler = RichHandler(rich_tracebacks=True, show_time=True, show_path=False)
+
+formatter = logging.Formatter("%(message)s")
+rich_handler.setFormatter(formatter)
+logger.addHandler(rich_handler)
+logger.propagate = False
diff --git a/profsea/components/core/global_model.py b/profsea/components/core/global_model.py
index 2a3e879..2fc3424 100644
--- a/profsea/components/core/global_model.py
+++ b/profsea/components/core/global_model.py
@@ -1,19 +1,19 @@
 from __future__ import annotations
 
 import concurrent.futures
+import logging
 import os
 from pathlib import Path
 
 import numpy as np
 import xarray as xr
-from rich.console import Console
 
 from profsea.utils import check_shapes, sample_members_2D
 
 from .base import Component
 from .state import ClimateState
 
-console = Console()
+logger = logging.getLogger(__name__)
 
 
 class Global:
@@ -165,7 +165,7 @@ def run(
 
         # Output percentiles
         if self.output_percentiles is not None:
-            console.log(
+            logger.info(
                 f"Sampling {len(self.output_percentiles)} members per component..."
             )
             for comp_name, data in results.items():
diff --git a/profsea/components/core/spatial_model.py b/profsea/components/core/spatial_model.py
index cd1312f..1c0fece 100644
--- a/profsea/components/core/spatial_model.py
+++ b/profsea/components/core/spatial_model.py
@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import logging
 import os
 import warnings
 import zipfile
@@ -23,6 +24,7 @@
 from .base import Component
 from .state import SpatialState
 
+logger = logging.getLogger(__name__)
 console = Console()
 warnings.filterwarnings("ignore")
 
@@ -105,7 +107,7 @@ def __init__(
             grid_config["step_lat"],
         )
 
-        console.log(
+        logger.info(
             f"Baseline period = {self.baseline_yrs[0]} to {self.baseline_yrs[1]}"
         )
 
@@ -124,8 +126,8 @@ def __init__(
         for name, comp in self.components.items():
             # Warn if memory usage is going to be large
             if future_size > 20:
-                console.log(
-                    f"[bold red]Warning: the output array for component "
+                logger.warning(
+                    f"[bold red]The output array for component "
                     f"[bold blue]'{name}'[/bold blue] requires a large amount "
                     f"of memory [bold blue]({future_size:.2f} GB)[/bold blue]. "
                     f"Consider reducing the number of members, the grid "
@@ -186,7 +188,7 @@ def run(self, member_seed: int = 42) -> None:
         """
         seed_seq = np.random.SeedSequence(member_seed)
 
-        console.log(
+        logger.info(
             f"Simulating {len(self.components)} sea-level components...: {', '.join(self.components.keys())}"
         )
 
@@ -313,7 +315,7 @@ def save_components(
             ):
                 ds.compute().to_netcdf(out_path, encoding=encoding)
 
-            console.log(
+            logger.info(
                 f"[bold green]✓ Successfully saved NetCDF:[/bold green] {out_path}"
             )
 
@@ -326,11 +328,11 @@ def save_components(
             ):
                 ds.to_zarr(out_path, encoding=encoding, mode="w", compute=True)
 
-            console.log(
+            logger.info(
                 f"[bold green]✓ Successfully saved Zarr:[/bold green] {out_path}"
             )
 
-        console.log(
+        logger.info(
             "Output shape was " + str(ds[name].shape) + " (members, time, lat, lon)"
         )
 
@@ -354,14 +356,14 @@ def fetch_zenodo_fingerprints(
 
     # 1. Check if data already exists
     if target_dir.exists() and any(target_dir.iterdir()):
-        console.log("[bold green]✓ ProFSea assets found locally![/bold green]")
+        logger.info("[bold green]✓ ProFSea assets found locally![/bold green]")
         return
 
     # Create the base directory if it doesn't exist
     data_dir.mkdir(parents=True, exist_ok=True)
     zip_path = data_dir / "temp_fingerprints.zip"
 
-    console.log(f"Initiating download from {zenodo_url}...")
+    logger.info(f"Initiating download from {zenodo_url}...")
 
     # 2. Stream the download with a rich progress bar
     try:
@@ -389,12 +391,12 @@ def fetch_zenodo_fingerprints(
                         progress.update(download_task, advance=len(chunk))
 
     except requests.exceptions.RequestException as e:
-        console.log(f"[bold red]Failed to download data: {e}[/bold red]")
+        logger.error(f"[bold red]Failed to download data: {e}[/bold red]")
         if zip_path.exists():
             zip_path.unlink()  # Clean up partial downloads
         raise
 
-    console.log("Extracting data...")
+    logger.info("Extracting data...")
     try:
         with zipfile.ZipFile(zip_path, "r") as zip_ref:
             # Filter out the __MACOSX directory and its contents
@@ -405,11 +407,11 @@ def fetch_zenodo_fingerprints(
             ]
             zip_ref.extractall(data_dir, members=valid_members)
 
-        console.log(
+        logger.info(
             f"[bold green]✓ Successfully extracted data to {data_dir}[/bold green]"
         )
     except zipfile.BadZipFile:
-        console.log(
+        logger.error(
             "[bold red]Error: Downloaded file is not a valid zip archive.[/bold red]"
         )
         raise

From 4d0eb99bffcb7df3efa44103f748e2f5f8436391 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Mon, 15 Jun 2026 23:49:02 +0100
Subject: [PATCH 229/276] Fix markups

---
 profsea/__init__.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/profsea/__init__.py b/profsea/__init__.py
index 95261cd..21484bb 100644
--- a/profsea/__init__.py
+++ b/profsea/__init__.py
@@ -9,7 +9,9 @@
 logger.setLevel(logging.INFO)
 
 # Use Rich!
-rich_handler = RichHandler(rich_tracebacks=True, show_time=True, show_path=False)
+rich_handler = RichHandler(
+    rich_tracebacks=True, show_time=True, show_path=False, markup=True
+)
 
 formatter = logging.Formatter("%(message)s")
 rich_handler.setFormatter(formatter)

From a300ab79f772bfb0b3afa3a258b0fbb1f4583032 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Mon, 15 Jun 2026 23:56:30 +0100
Subject: [PATCH 230/276] Fix timestamp

---
 profsea/__init__.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/profsea/__init__.py b/profsea/__init__.py
index 21484bb..e7a317c 100644
--- a/profsea/__init__.py
+++ b/profsea/__init__.py
@@ -10,7 +10,11 @@
 
 # Use Rich!
 rich_handler = RichHandler(
-    rich_tracebacks=True, show_time=True, show_path=False, markup=True
+    rich_tracebacks=True,
+    show_time=True,
+    show_path=False,
+    markup=True,
+    log_time_format="[%H:%M:%S]",
 )
 
 formatter = logging.Formatter("%(message)s")

From 6d21581a35176bda188ce8dd97b24829426dc19c Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Tue, 16 Jun 2026 00:02:52 +0100
Subject: [PATCH 231/276] Readability...

---
 profsea/components/core/spatial_model.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/profsea/components/core/spatial_model.py b/profsea/components/core/spatial_model.py
index 1c0fece..61c85b5 100644
--- a/profsea/components/core/spatial_model.py
+++ b/profsea/components/core/spatial_model.py
@@ -189,7 +189,7 @@ def run(self, member_seed: int = 42) -> None:
         seed_seq = np.random.SeedSequence(member_seed)
 
         logger.info(
-            f"Simulating {len(self.components)} sea-level components...: {', '.join(self.components.keys())}"
+            f"Simulating {len(self.components)} sea-level components: {', '.join(self.components.keys())}"
         )
 
         state = SpatialState(
@@ -209,6 +209,7 @@ def run(self, member_seed: int = 42) -> None:
         }
 
         spatial_projections = {}
+        console.print()  # Add a blank line for better readability in the console output
         for name, comp in track(
             self.components.items(), description="Spatialising components..."
         ):
@@ -217,6 +218,7 @@ def run(self, member_seed: int = 42) -> None:
             # Rechunk before saving to optimize memory during writing
             lazy_projection = lazy_projection.rechunk({0: -1, 1: -1, 2: 10, 3: 10})
             spatial_projections[name] = lazy_projection
+        console.print()
 
         # Put into xarray datasets for easier saving and metadata handling
         spatial_projections_xr = self._arr_to_xr(spatial_projections)

From aaf7985b9c453e1f38bee7bf18c0e0a82d767c81 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Tue, 16 Jun 2026 01:21:12 +0100
Subject: [PATCH 232/276] Update CLI UI

---
 profsea/components/core/global_model.py  |  3 +
 profsea/components/core/spatial_model.py |  3 +
 profsea/utils/ui.py                      | 88 ++++++++++++++++++++++++
 ukcp18_example_script.py                 |  4 +-
 4 files changed, 96 insertions(+), 2 deletions(-)
 create mode 100644 profsea/utils/ui.py

diff --git a/profsea/components/core/global_model.py b/profsea/components/core/global_model.py
index 2fc3424..397f494 100644
--- a/profsea/components/core/global_model.py
+++ b/profsea/components/core/global_model.py
@@ -9,6 +9,7 @@
 import xarray as xr
 
 from profsea.utils import check_shapes, sample_members_2D
+from profsea.utils.ui import print_global_preflight
 
 from .base import Component
 from .state import ClimateState
@@ -112,6 +113,8 @@ def run(
 
         self.nt = T_change.shape[0]
 
+        print_global_preflight(self, scenario)
+
         T_ens, T_int_ens, T_int_med = self._calculate_drivers(T_change)
 
         # Shared physical correlation state
diff --git a/profsea/components/core/spatial_model.py b/profsea/components/core/spatial_model.py
index 61c85b5..e0f8a01 100644
--- a/profsea/components/core/spatial_model.py
+++ b/profsea/components/core/spatial_model.py
@@ -21,6 +21,8 @@
     track,
 )
 
+from profsea.utils.ui import print_spatial_preflight
+
 from .base import Component
 from .state import SpatialState
 
@@ -186,6 +188,7 @@ def run(self, member_seed: int = 42) -> None:
         Dict[str, da.Array]
             Dictionary of spatial projections for each component, where keys are component names and values are Dask arrays of shape (n_members, n_years, n_lats, n_lons).
         """
+        print_spatial_preflight(self)
         seed_seq = np.random.SeedSequence(member_seed)
 
         logger.info(
diff --git a/profsea/utils/ui.py b/profsea/utils/ui.py
new file mode 100644
index 0000000..75568b5
--- /dev/null
+++ b/profsea/utils/ui.py
@@ -0,0 +1,88 @@
+from rich.console import Console
+from rich.panel import Panel
+from rich.table import Table
+
+# Central console for package-generated UI elements (not logs)
+ui_console = Console()
+
+
+def print_global_preflight(model, scenario: str) -> None:
+    """Renders the pre-flight summary for the Global model."""
+    table = Table(show_header=False, box=None)
+    table.add_column("Property", style="cyan", justify="right")
+    table.add_column("Value", style="magenta")
+
+    components_list = ", ".join(model.components.keys())
+    ensemble_size = model.nt * model.num_members
+
+    if model.output_percentiles is not None:
+        output_str = f"Percentiles: {model.output_percentiles}"
+    else:
+        output_str = f"Full Distribution ({ensemble_size} members)"
+
+    table.add_row("Scenario", scenario)
+    table.add_row("Components", components_list)
+    table.add_row("Timeframe", f"{model.endofhistory} -> {model.end_yr}")
+    table.add_row(
+        "Ensemble Size",
+        f"{model.nt} inputs × {model.num_members} draws (= {ensemble_size})",
+    )
+    table.add_row("Output", output_str)
+    table.add_row(
+        "Compute Engine", "Parallel Threading" if model.parallel else "Sequential"
+    )
+
+    panel = Panel(
+        table,
+        title="[bold green]ProFSea Global Run Configuration",
+        expand=False,
+        border_style="green",
+    )
+    ui_console.print(panel)
+    ui_console.print()
+
+
+def print_spatial_preflight(model) -> None:
+    """Renders the pre-flight summary for the Spatial model."""
+    table = Table(show_header=False, box=None)
+    table.add_column("Property", style="cyan", justify="right")
+    table.add_column("Value", style="magenta")
+
+    components_list = ", ".join(model.components.keys())
+
+    # Calculate the mathematical grid size: $N_{lat} \times N_{lon}$
+    n_lat = len(model.grid_lats)
+    n_lon = len(model.grid_lons)
+    grid_str = f"{n_lat} × {n_lon} cells"
+
+    # Memory estimation
+    bytes_per_element = 8
+    future_size_gb = (
+        model.num_members * model.n_years * n_lat * n_lon * bytes_per_element
+    ) / 1e9
+
+    if model.output_percentiles is not None:
+        output_str = f"Percentiles: {model.output_percentiles}"
+    else:
+        output_str = f"Full Distribution ({model.num_members} members)"
+
+    table.add_row("Components", components_list)
+    table.add_row("Timeframe", f"{model.start_year} -> {model.end_year}")
+    table.add_row("Baseline", f"{model.baseline_yrs[0]} - {model.baseline_yrs[1]}")
+    table.add_row("Grid Resolution", grid_str)
+    table.add_row("Output", output_str)
+
+    # Color-code the memory warning
+    mem_style = "bold red" if future_size_gb > 20 else "magenta"
+    table.add_row(
+        "Est. Output Size", f"[{mem_style}]~{future_size_gb:.2f} GB[/{mem_style}]"
+    )
+
+    panel = Panel(
+        table,
+        title="[bold blue]ProFSea Spatial Run Configuration",
+        expand=False,
+        border_style="blue",
+    )
+    ui_console.print(panel)
+    ui_console.print()
diff --git a/ukcp18_example_script.py b/ukcp18_example_script.py
index c05a065..ea2cfcf 100644
--- a/ukcp18_example_script.py
+++ b/ukcp18_example_script.py
@@ -16,7 +16,7 @@
 from profsea.components.spatial import GIA, Fingerprint, SterodynamicCMIP6
 
 ### Global projections first ###
-slr_components = {
+global_components = {
     "expansion": ThermalExpansion(
         OHC_change=np.linspace(1, 5, 295).reshape(1, -1) * 1e24
     ),
@@ -29,7 +29,7 @@
 }
 
 # Pass to the global model
-global_model = Global(components=slr_components, end_yr=2301)
+global_model = Global(components=global_components, end_yr=2301)
 projections = global_model.run(
     scenario="rcp85",
     T_change=np.linspace(1, 5, 295).reshape(1, -1),

From d75071ebd7acc8326e6b8d66530f2fcd78b1c74d Mon Sep 17 00:00:00 2001
From: Hemant Khatri 
Date: Tue, 16 Jun 2026 09:27:46 +0100
Subject: [PATCH 233/276] Refactor logging setup and improve docstring
 formatting

Removed redundant white spaces.
---
 profsea/components/spatial/sterodynamic.py | 12 +++++-------
 1 file changed, 5 insertions(+), 7 deletions(-)

diff --git a/profsea/components/spatial/sterodynamic.py b/profsea/components/spatial/sterodynamic.py
index 2bb77ff..bdc392e 100644
--- a/profsea/components/spatial/sterodynamic.py
+++ b/profsea/components/spatial/sterodynamic.py
@@ -6,12 +6,13 @@
 import numpy as np
 import xarray as xr
 import logging
-logging.basicConfig(level=logging.WARNING)
 
 from profsea.components.core.base import SpatialComponent
 from profsea.components.core.state import ClimateState
 from profsea.utils import interpolate_to_grid, sample_members_2D
 
+logging.basicConfig(level=logging.WARNING)
+
 PROFSEA_DIR = Path(__file__).resolve().parents[2]
 PATTERNS_DIR = PROFSEA_DIR / "profsea-assets" / "cmip6-patterns"
 
@@ -64,7 +65,7 @@ def _load_CMIP6_slopes(self) -> da.Array:
         Returns
         -------
         da.Array
-            A dask array of shape (n_models, n_lats, n_lons) containing the sterodynamic 
+            A dask array of shape (n_models, n_lats, n_lons) containing the sterodynamic
             fingerprint patterns (i.e., regression coefficients) for each CMIP6 model.
         """
         slope_files = sorted(
@@ -94,27 +95,24 @@ def _load_CMIP6_slopes(self) -> da.Array:
             key=lambda p: p.name
         )
 
-        
         if mask_files:
             if(len(slope_files) == len(mask_files)):
                 self.land_mask_present = True
-    
+
                 datasets_mask = [
                     xr.open_dataset(f, chunks={"lat": 45, "lon": 45})["zos_mask"] for f in mask_files
                 ]
                 mask_stack = xr.concat(datasets_mask, dim="model")
                 #mask_stack = mask_stack.sum(dim='model', skipna=True)
-            
             else:
                 logging.warning("There is a mismatch between number of slope files and mask files. "
                                 "Ignoring mask files.")
                 self.land_mask_present = False
                 mask_stack = None
-            
         else:
             self.land_mask_present = False
             mask_stack = None
-        
+
         return slopes_stack, mask_stack
 
     def _calc_expansion_contribution(

From 8efa5534d799ef0a0f9feb6f6438cd9d56da958a Mon Sep 17 00:00:00 2001
From: Hemant Khatri 
Date: Tue, 16 Jun 2026 09:37:06 +0100
Subject: [PATCH 234/276] Remove trailing whitespaces and clean up comments

---
 profsea/utils/utils.py | 11 +++++------
 1 file changed, 5 insertions(+), 6 deletions(-)

diff --git a/profsea/utils/utils.py b/profsea/utils/utils.py
index d010d10..ba61858 100644
--- a/profsea/utils/utils.py
+++ b/profsea/utils/utils.py
@@ -2,10 +2,9 @@
 
 import dask.array as da
 import numpy as np
+import regionmask
 import xarray as xr
 from scipy.spatial.distance import cdist
-import regionmask
-
 
 def sample_members_2D(array: np.ndarray, percentiles: list | np.ndarray) -> np.ndarray:
     """Sample real ensemble members from a 2D numpy array."""
@@ -48,7 +47,7 @@ def interpolate_to_grid(
     # Normalize target longitudes to [-180, 180) and sort
     target_lons_norm = np.sort(((target_lons + 180) % 360) - 180)
 
-    # Pad longitude with one points from each end to handle periodicity in zonal direction 
+    # Pad longitude with one points from each end to handle periodicity in zonal direction
     data_padded = data.pad(lon=1, mode='wrap') # need more padding for higher-order interpolation
     lon = data.lon.values
     lon_padded = np.concatenate([[lon[-1] - 360], lon, [lon[0] + 360]])
@@ -60,8 +59,8 @@ def interpolate_to_grid(
     for dim in ["lat", "lon"]:
         data_padded = data_padded.interpolate_na(
             dim=dim, method="nearest",
-        ) # this to handle nan values or land mask 
-        
+        ) # this to handle nan values or land mask
+
     data_interp = data_padded.interp(
         lat=target_lats, lon=target_lons_norm, method=grid_interpolation
     )
@@ -71,7 +70,7 @@ def interpolate_to_grid(
     land_mask = land.mask_3D(data_interp)
     is_land = land_mask.squeeze("region", drop=True)
     data_interp = data_interp.where(~is_land)
-    
+
     return data_interp
 
 

From 1ad61afadb60dbb7ca51b440fdeb2fa3c70f538c Mon Sep 17 00:00:00 2001
From: Hemant Khatri 
Date: Tue, 16 Jun 2026 09:40:31 +0100
Subject: [PATCH 235/276] Reorganize import statements in sterodynamic.py

Moved logging import to the top of the file.
---
 profsea/components/spatial/sterodynamic.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/profsea/components/spatial/sterodynamic.py b/profsea/components/spatial/sterodynamic.py
index bdc392e..507fd0d 100644
--- a/profsea/components/spatial/sterodynamic.py
+++ b/profsea/components/spatial/sterodynamic.py
@@ -1,11 +1,11 @@
 from __future__ import annotations
 
+import logging
 from pathlib import Path
 
 import dask.array as da
 import numpy as np
 import xarray as xr
-import logging
 
 from profsea.components.core.base import SpatialComponent
 from profsea.components.core.state import ClimateState

From 2e3062ef43e7b25f7e52bf336de3c7e8a843edd3 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Tue, 16 Jun 2026 11:50:18 +0100
Subject: [PATCH 236/276] Add regionmask to deps, fix linting

---
 profsea/utils/utils.py | 13 +++++++++----
 pyproject.toml         |  2 +-
 2 files changed, 10 insertions(+), 5 deletions(-)

diff --git a/profsea/utils/utils.py b/profsea/utils/utils.py
index ba61858..bb98f41 100644
--- a/profsea/utils/utils.py
+++ b/profsea/utils/utils.py
@@ -6,6 +6,7 @@
 import xarray as xr
 from scipy.spatial.distance import cdist
 
+
 def sample_members_2D(array: np.ndarray, percentiles: list | np.ndarray) -> np.ndarray:
     """Sample real ensemble members from a 2D numpy array."""
     # Caculate statistical timeseries, then match with closest real timeseries
@@ -30,6 +31,7 @@ def interpolate(data: da.array, lats: int, lons: int) -> np.ndarray:
     ).data
     return data_interp
 
+
 def interpolate_to_grid(
     data: xr.DataArray,
     target_lats: np.ndarray,
@@ -48,18 +50,21 @@ def interpolate_to_grid(
     target_lons_norm = np.sort(((target_lons + 180) % 360) - 180)
 
     # Pad longitude with one points from each end to handle periodicity in zonal direction
-    data_padded = data.pad(lon=1, mode='wrap') # need more padding for higher-order interpolation
+    data_padded = data.pad(
+        lon=1, mode="wrap"
+    )  # need more padding for higher-order interpolation
     lon = data.lon.values
     lon_padded = np.concatenate([[lon[-1] - 360], lon, [lon[0] + 360]])
-    data_padded['lon'] = lon_padded
+    data_padded["lon"] = lon_padded
     data_padded = data_padded.sortby(["lat", "lon"])
 
     # Now interpolate
     data_padded = data_padded.chunk({"lat": -1, "lon": -1})
     for dim in ["lat", "lon"]:
         data_padded = data_padded.interpolate_na(
-            dim=dim, method="nearest",
-        ) # this to handle nan values or land mask
+            dim=dim,
+            method="nearest",
+        )  # this to handle nan values or land mask
 
     data_interp = data_padded.interp(
         lat=target_lats, lon=target_lons_norm, method=grid_interpolation
diff --git a/pyproject.toml b/pyproject.toml
index 2ba1c61..5bc38a1 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -5,7 +5,7 @@ build-backend = "setuptools.build_meta"
 [project]
 name = "profsea"
 version = "3.0"
-dependencies = ["numpy", "xarray", "scipy", "dask", "rich", "requests"]
+dependencies = ["numpy", "xarray", "scipy", "dask", "rich", "requests", "regionmask"]
 
 [tool.setuptools.packages.find]
 include = ["profsea*"]

From 06f47f065ab39100a45bf57e842663d26eae2416 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Tue, 16 Jun 2026 11:52:02 +0100
Subject: [PATCH 237/276] Fix linting

---
 profsea/components/spatial/sterodynamic.py | 22 ++++++++++++----------
 1 file changed, 12 insertions(+), 10 deletions(-)

diff --git a/profsea/components/spatial/sterodynamic.py b/profsea/components/spatial/sterodynamic.py
index 507fd0d..055d58c 100644
--- a/profsea/components/spatial/sterodynamic.py
+++ b/profsea/components/spatial/sterodynamic.py
@@ -70,7 +70,7 @@ def _load_CMIP6_slopes(self) -> da.Array:
         """
         slope_files = sorted(
             Path(self.patterns_dir).glob("*/zos_regression_ssp585_*.nc"),
-            key=lambda p: p.name
+            key=lambda p: p.name,
         )
 
         if not slope_files:
@@ -91,22 +91,24 @@ def _load_CMIP6_slopes(self) -> da.Array:
 
         # Read land mask if present
         mask_files = sorted(
-            Path(self.patterns_dir).glob("*/zos_mask_ssp585_*.nc"),
-            key=lambda p: p.name
+            Path(self.patterns_dir).glob("*/zos_mask_ssp585_*.nc"), key=lambda p: p.name
         )
 
         if mask_files:
-            if(len(slope_files) == len(mask_files)):
+            if len(slope_files) == len(mask_files):
                 self.land_mask_present = True
 
                 datasets_mask = [
-                    xr.open_dataset(f, chunks={"lat": 45, "lon": 45})["zos_mask"] for f in mask_files
+                    xr.open_dataset(f, chunks={"lat": 45, "lon": 45})["zos_mask"]
+                    for f in mask_files
                 ]
                 mask_stack = xr.concat(datasets_mask, dim="model")
-                #mask_stack = mask_stack.sum(dim='model', skipna=True)
+                # mask_stack = mask_stack.sum(dim='model', skipna=True)
             else:
-                logging.warning("There is a mismatch between number of slope files and mask files. "
-                                "Ignoring mask files.")
+                logging.warning(
+                    "There is a mismatch between number of slope files and mask files. "
+                    "Ignoring mask files."
+                )
                 self.land_mask_present = False
                 mask_stack = None
         else:
@@ -137,8 +139,8 @@ def _calc_expansion_contribution(
         # Select slope coefficients based on the MIP
         coeffs_da, mask_da = self._load_CMIP6_slopes()
 
-        if self.land_mask_present: # apply land mask
-            coeffs_da = coeffs_da.where(mask_da == 0.)
+        if self.land_mask_present:  # apply land mask
+            coeffs_da = coeffs_da.where(mask_da == 0.0)
 
         # Align the grid coordinates + interpolate if necessary
         interp_da = interpolate_to_grid(coeffs_da, state.grid_lats, state.grid_lons)

From 98fc9023a61c8e9ba828714225778d6ce9bdf30a Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Tue, 16 Jun 2026 11:58:29 +0100
Subject: [PATCH 238/276] Update tests

---
 tests/components/test_sterodynamic.py | 88 ++++++++++++++++-----------
 tests/utils/test_utils.py             | 41 +++++++------
 2 files changed, 74 insertions(+), 55 deletions(-)

diff --git a/tests/components/test_sterodynamic.py b/tests/components/test_sterodynamic.py
index 27b0f42..761c8ca 100644
--- a/tests/components/test_sterodynamic.py
+++ b/tests/components/test_sterodynamic.py
@@ -1,4 +1,5 @@
-from unittest.mock import patch
+from pathlib import Path
+from unittest.mock import patch, MagicMock
 
 import numpy as np
 import xarray as xr
@@ -10,41 +11,58 @@
 @patch("profsea.components.spatial.sterodynamic.xr.open_dataset")
 @patch("profsea.components.spatial.sterodynamic.Path.glob")
 def test_load_cmip6_slopes(mock_glob, mock_open_dataset):
-    # 1. Setup mock file paths
-    mock_glob.return_value = ["dummy_path_1.nc", "dummy_path_2.nc"]
-
-    # 2. Setup a mock xarray dataset that has the required variable
-    mock_data = xr.DataArray(
-        np.random.rand(5, 5),  # 5 lats, 5 lons
-        dims=["lat", "lon"],
-    )
-    mock_dataset = {"zos_zostoga_regression_slope": mock_data}
-    mock_open_dataset.return_value = mock_dataset
+    # 1. Setup mock file paths (glob is called twice: once for slopes, once for masks)
+    mock_glob.side_effect = [
+        [
+            Path("dummy/zos_regression_ssp585_1.nc"),
+            Path("dummy/zos_regression_ssp585_2.nc"),
+        ],
+        [Path("dummy/zos_mask_ssp585_1.nc"), Path("dummy/zos_mask_ssp585_2.nc")],
+    ]
+
+    # 2. Setup mock xarray datasets
+    mock_data = xr.DataArray(np.random.rand(5, 5), dims=["lat", "lon"])
+    mock_mask_data = xr.DataArray(np.zeros((5, 5)), dims=["lat", "lon"])
+
+    def mock_open(f, **kwargs):
+        if "regression" in str(f):
+            return {"zos_zostoga_regression_slope": mock_data}
+        elif "mask" in str(f):
+            return {"zos_mask": mock_mask_data}
+
+    mock_open_dataset.side_effect = mock_open
 
     # 3. Initialize component and call the method
-    # global_projection shape: (members, years)
     dummy_global = np.zeros((10, 100))
     stero = SterodynamicCMIP6(global_projection=dummy_global)
 
-    slopes = stero._load_CMIP6_slopes()
+    slopes, masks = stero._load_CMIP6_slopes()
 
     # 4. Assertions
-    # We expect 2 models (because we mocked 2 paths), and 5x5 spatial dimensions
     assert slopes.shape == (2, 5, 5)
     assert "model" in slopes.dims
+    assert masks is not None
+    assert masks.shape == (2, 5, 5)
+    assert stero.land_mask_present is True
 
 
+@patch("profsea.components.spatial.sterodynamic.interpolate_to_grid")
 @patch.object(SterodynamicCMIP6, "_load_CMIP6_slopes")
-def test_expansion_contribution_storyline_mode(mock_load):
-    # 1. Mock the slope data (3 models, 2 lats, 2 lons)
-    # Model 1 is all 1s, Model 2 is all 2s, Model 3 is all 3s
+def test_expansion_contribution_storyline_mode(mock_load, mock_interp):
+    # 1. Mock the loaded data and masks
     mock_coeffs = xr.DataArray(
         np.array([np.ones((2, 2)), np.ones((2, 2)) * 2, np.ones((2, 2)) * 3]),
         coords=[("model", [0, 1, 2]), ("lat", [0, 1]), ("lon", [0, 1])],
     )
-    mock_load.return_value = mock_coeffs
+    mock_masks = xr.DataArray(
+        np.zeros((3, 2, 2)),
+        coords=[("model", [0, 1, 2]), ("lat", [0, 1]), ("lon", [0, 1])],
+    )
+    mock_load.return_value = (mock_coeffs, mock_masks)
+
+    # Let the mocked interpolator just return the array passed into it
+    mock_interp.return_value = mock_coeffs
 
-    # 2. Create the state object
     state = SpatialState(
         grid_lats=np.array([0, 1]),
         grid_lons=np.array([0, 1]),
@@ -55,31 +73,36 @@ def test_expansion_contribution_storyline_mode(mock_load):
         baseline_yrs=(1995, 2014),
     )
 
-    # 3. Test Storyline Mode (sample_spatial = False)
+    # 2. Test Storyline Mode
     stero_storyline = SterodynamicCMIP6(
         global_projection=np.zeros((5, 100)), sample_spatial=False
     )
+    stero_storyline.land_mask_present = (
+        True  # Simulate the flag being set during the load phase
+    )
 
     rng = np.random.default_rng(42)
     result_storyline = stero_storyline._calc_expansion_contribution(rng, state)
 
-    # In storyline mode, it calculates the ensemble mean of the models: (1+2+3)/3 = 2
-    # Therefore, every member should receive a pattern of 2s.
+    # In storyline mode, it takes the ensemble mean: (1+2+3)/3 = 2
     assert result_storyline.shape == (5, 2, 2)
-
-    # Convert Dask array to NumPy for assertion
     computed_result = result_storyline.compute()
     np.testing.assert_array_equal(computed_result, np.ones((5, 2, 2)) * 2)
 
 
+@patch("profsea.components.spatial.sterodynamic.interpolate_to_grid")
 @patch.object(SterodynamicCMIP6, "_load_CMIP6_slopes")
-def test_expansion_contribution_sampled_mode(mock_load):
-    # Use the same setup as above
+def test_expansion_contribution_sampled_mode(mock_load, mock_interp):
     mock_coeffs = xr.DataArray(
         np.array([np.ones((2, 2)), np.ones((2, 2)) * 2, np.ones((2, 2)) * 3]),
         coords=[("model", [0, 1, 2]), ("lat", [0, 1]), ("lon", [0, 1])],
     )
-    mock_load.return_value = mock_coeffs
+    mock_masks = xr.DataArray(
+        np.zeros((3, 2, 2)),
+        coords=[("model", [0, 1, 2]), ("lat", [0, 1]), ("lon", [0, 1])],
+    )
+    mock_load.return_value = (mock_coeffs, mock_masks)
+    mock_interp.return_value = mock_coeffs
 
     state = SpatialState(
         grid_lats=np.array([0, 1]),
@@ -91,19 +114,14 @@ def test_expansion_contribution_sampled_mode(mock_load):
         baseline_yrs=(1995, 2014),
     )
 
-    # Test Sampled Mode (sample_spatial = True)
     stero_sampled = SterodynamicCMIP6(
         global_projection=np.zeros((5, 100)), sample_spatial=True
     )
+    stero_sampled.land_mask_present = True
 
-    rng = np.random.default_rng(42)  # Seeded for determinism
-
+    rng = np.random.default_rng(42)
     result_sampled = np.asarray(stero_sampled._calc_expansion_contribution(rng, state))
 
-    # In sampled mode, the array should NOT be uniform across the 5 members.
     assert result_sampled.shape == (5, 2, 2)
-
-    # Check that member 0 and member 1 are not guaranteed to be identical
-    # (Though with a seed of 42 and 3 choices, they might be, so we check the unique values)
     unique_values = np.unique(result_sampled)
-    assert len(unique_values) > 1  # Proves it didn't just take the mean
+    assert len(unique_values) > 1
diff --git a/tests/utils/test_utils.py b/tests/utils/test_utils.py
index 724af11..6460538 100644
--- a/tests/utils/test_utils.py
+++ b/tests/utils/test_utils.py
@@ -1,56 +1,60 @@
+from unittest.mock import MagicMock, patch
+
 import numpy as np
 import pytest
 import xarray as xr
 
-from profsea.utils import check_shapes, interpolate_to_grid, sample_members_2D
+from profsea.utils.utils import check_shapes, interpolate_to_grid, sample_members_2D
 
 
 def test_check_shapes_valid_2d():
-    # 5 realisations, 10 time steps
     arr = np.zeros((5, 10))
-    # Should not raise an exception
     check_shapes(arr, n_time=10)
 
 
 def test_check_shapes_promotes_1d():
-    # 1 realisation, 10 time steps
     arr = np.zeros(10)
-    # The function should silently promote this to (1, 10)
     check_shapes(arr, n_time=10)
 
 
 def test_check_shapes_raises_value_error():
     arr = np.zeros((5, 10))
-    # Passing the wrong time dimension length
     with pytest.raises(
         ValueError, match="Array should have shape .* time dimension of length 12"
     ):
         check_shapes(arr, n_time=12)
 
 
-def test_interpolate_to_grid_longitude_wrapping():
-    # Create mock data with 0-360 longitude format
-    lats = np.array([-45, 0, 45])
-    lons_360 = np.array([0, 180, 350])
-
-    data = xr.DataArray(np.random.rand(3, 3), coords=[("lat", lats), ("lon", lons_360)])
-
+@patch("profsea.utils.utils.regionmask")
+def test_interpolate_to_grid_longitude_wrapping(mock_regionmask):
     # Target grid using -180 to 180 format (includes 180, which should wrap)
     target_lats = np.array([-45, 0, 45])
     target_lons = np.array([-10, 0, 180])
 
+    # 1. Setup mock regionmask to act as if there is no land
+    # mask_3D needs to return an xarray DataArray with a "region" dimension
+    mock_land = MagicMock()
+    mock_mask_da = xr.DataArray(
+        np.zeros((1, 3, 3), dtype=bool),
+        dims=["region", "lat", "lon"],
+        coords={"region": [0], "lat": target_lats, "lon": [-180, -10, 0]},
+    )
+    mock_land.mask_3D.return_value = mock_mask_da
+    mock_regionmask.defined_regions.natural_earth_v5_0_0.land_110 = mock_land
+
+    # 2. Setup mock input data
+    lats = np.array([-45, 0, 45])
+    lons_360 = np.array([0, 180, 350])
+    data = xr.DataArray(np.random.rand(3, 3), coords=[("lat", lats), ("lon", lons_360)])
+
     result = interpolate_to_grid(data, target_lats, target_lons)
 
-    # Check that output dimensions match target dimensions
     assert result.shape == (3, 3)
-
-    # Check that coordinates were successfully wrapped to [-180, 180) and sorted
     expected_lons = np.array([-180, -10, 0])
     np.testing.assert_array_almost_equal(result.lon.values, expected_lons)
 
 
 def test_sample_members_2D_extracts_real_member():
-    # 5 ensemble members over 3 time steps
     arr = np.array(
         [
             [1.0, 1.0, 1.0],  # Min
@@ -61,9 +65,6 @@ def test_sample_members_2D_extracts_real_member():
         ]
     )
 
-    # Request the 50th percentile (median)
     result = sample_members_2D(arr, percentiles=[50])
-
-    # The closest real member to the median is [3., 3., 3.]
     assert result.shape == (1, 3)
     np.testing.assert_array_equal(result[0], [3.0, 3.0, 3.0])

From 75d372a9b9dd6a96294293139adb3ca781ac8625 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Tue, 16 Jun 2026 12:01:01 +0100
Subject: [PATCH 239/276] Fix linting

---
 tests/components/test_sterodynamic.py | 12 +++++-------
 1 file changed, 5 insertions(+), 7 deletions(-)

diff --git a/tests/components/test_sterodynamic.py b/tests/components/test_sterodynamic.py
index 761c8ca..f3dbc5f 100644
--- a/tests/components/test_sterodynamic.py
+++ b/tests/components/test_sterodynamic.py
@@ -1,5 +1,5 @@
 from pathlib import Path
-from unittest.mock import patch, MagicMock
+from unittest.mock import patch
 
 import numpy as np
 import xarray as xr
@@ -11,7 +11,7 @@
 @patch("profsea.components.spatial.sterodynamic.xr.open_dataset")
 @patch("profsea.components.spatial.sterodynamic.Path.glob")
 def test_load_cmip6_slopes(mock_glob, mock_open_dataset):
-    # 1. Setup mock file paths (glob is called twice: once for slopes, once for masks)
+    # Mock file paths (glob is called twice: once for slopes, once for masks)
     mock_glob.side_effect = [
         [
             Path("dummy/zos_regression_ssp585_1.nc"),
@@ -20,7 +20,7 @@ def test_load_cmip6_slopes(mock_glob, mock_open_dataset):
         [Path("dummy/zos_mask_ssp585_1.nc"), Path("dummy/zos_mask_ssp585_2.nc")],
     ]
 
-    # 2. Setup mock xarray datasets
+    # Mock datasets
     mock_data = xr.DataArray(np.random.rand(5, 5), dims=["lat", "lon"])
     mock_mask_data = xr.DataArray(np.zeros((5, 5)), dims=["lat", "lon"])
 
@@ -32,13 +32,11 @@ def mock_open(f, **kwargs):
 
     mock_open_dataset.side_effect = mock_open
 
-    # 3. Initialize component and call the method
     dummy_global = np.zeros((10, 100))
     stero = SterodynamicCMIP6(global_projection=dummy_global)
 
     slopes, masks = stero._load_CMIP6_slopes()
 
-    # 4. Assertions
     assert slopes.shape == (2, 5, 5)
     assert "model" in slopes.dims
     assert masks is not None
@@ -49,7 +47,7 @@ def mock_open(f, **kwargs):
 @patch("profsea.components.spatial.sterodynamic.interpolate_to_grid")
 @patch.object(SterodynamicCMIP6, "_load_CMIP6_slopes")
 def test_expansion_contribution_storyline_mode(mock_load, mock_interp):
-    # 1. Mock the loaded data and masks
+    # Mock the loaded data and masks
     mock_coeffs = xr.DataArray(
         np.array([np.ones((2, 2)), np.ones((2, 2)) * 2, np.ones((2, 2)) * 3]),
         coords=[("model", [0, 1, 2]), ("lat", [0, 1]), ("lon", [0, 1])],
@@ -73,7 +71,7 @@ def test_expansion_contribution_storyline_mode(mock_load, mock_interp):
         baseline_yrs=(1995, 2014),
     )
 
-    # 2. Test Storyline Mode
+    # Test Storyline Mode
     stero_storyline = SterodynamicCMIP6(
         global_projection=np.zeros((5, 100)), sample_spatial=False
     )

From 661aa8914beb0b27b75fc4650bfcfe02e2ea6796 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Tue, 16 Jun 2026 12:18:26 +0100
Subject: [PATCH 240/276] Add docstrings to utils

---
 profsea/utils/utils.py | 52 +++++++++++++++++++++++++++++++++++++++---
 1 file changed, 49 insertions(+), 3 deletions(-)

diff --git a/profsea/utils/utils.py b/profsea/utils/utils.py
index bb98f41..cdf85ad 100644
--- a/profsea/utils/utils.py
+++ b/profsea/utils/utils.py
@@ -8,7 +8,21 @@
 
 
 def sample_members_2D(array: np.ndarray, percentiles: list | np.ndarray) -> np.ndarray:
-    """Sample real ensemble members from a 2D numpy array."""
+    """
+    Sample real ensemble members from a 2D numpy array.
+
+    Parameters
+    ----------
+    array: np.ndarray
+        Input 2D array of shape (realisation, time).
+    percentiles: list | np.ndarray
+        List of percentiles to sample from the input array.
+
+    Returns
+    -------
+    np.ndarray
+        Sampled array of shape (len(percentiles), time) corresponding to the closest real ensemble members to the specified percentiles.
+    """
     # Caculate statistical timeseries, then match with closest real timeseries
     array_percentiles = np.nanpercentile(array, percentiles, axis=0)
     distances = cdist(array_percentiles, array)
@@ -16,8 +30,24 @@ def sample_members_2D(array: np.ndarray, percentiles: list | np.ndarray) -> np.n
     return array[mem_indices]
 
 
-def interpolate(data: da.array, lats: int, lons: int) -> np.ndarray:
-    """ """
+def interpolate(data: da.array, lats: int, lons: int) -> da.array:
+    """
+    Interpolate a 2D dask array to a target grid defined by lats and lons.
+
+    Parameters
+    ----------
+    data: da.array
+        Input 2D dask array to be interpolated.
+    lats: int
+        Number of latitude points in the target grid.
+    lons: int
+        Number of longitude points in the target grid.
+
+    Returns
+    -------
+    da.array
+        Interpolated 2D dask array on the target grid.
+    """
     original_da = xr.DataArray(
         data.data,
         coords=[("lat", data[data.dims[0]].values), ("lon", data[data.dims[1]].values)],
@@ -41,6 +71,22 @@ def interpolate_to_grid(
     """
     Interpolate an xarray DataArray to a target grid defined by target_lats and target_lons.
     Safely handles longitude wrapping mismatches (e.g., [0, 360) vs [-180, 180)).
+
+    Parameters
+    ----------
+    data: xr.DataArray
+        Input xarray DataArray to be interpolated. Must have 'lat' and 'lon' dimensions.
+    target_lats: np.ndarray
+        1D array of target latitude values.
+    target_lons: np.ndarray
+        1D array of target longitude values.
+    grid_interpolation: str, optional
+        Interpolation method to use. Default is 'linear'. Other options include 'nearest', 'cubic', etc.
+
+    Returns
+    -------
+    xr.DataArray
+        Interpolated xarray DataArray on the target grid.
     """
     # Normalize source longitudes to [-180, 180) and sort monotonically
     data = data.assign_coords(lon=(((data.lon + 180) % 360) - 180))

From f56ec0c5fa8e32f32261b076adc858cad7e47958 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Tue, 16 Jun 2026 12:20:09 +0100
Subject: [PATCH 241/276] Update badge

---
 .github/workflows/deploy-docs.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/deploy-docs.yml b/.github/workflows/deploy-docs.yml
index bdbb38b..f8bedbe 100644
--- a/.github/workflows/deploy-docs.yml
+++ b/.github/workflows/deploy-docs.yml
@@ -1,6 +1,6 @@
 # (C) Crown Copyright 2025, Met Office.
 # The LICENSE file contains full licensing details.
-name: Build and deploy the documentation
+name: Documentation
 
 on:
   # Runs on pushes targeting the default branch.

From 83081ccdf692e11b4e092799ed1648aaa9154f0a Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Tue, 16 Jun 2026 13:59:42 +0100
Subject: [PATCH 242/276] Update worked example

---
 .../tutorial_notebooks/worked_example.ipynb   | 139 +++++++++++++++---
 1 file changed, 122 insertions(+), 17 deletions(-)

diff --git a/profsea/tutorial_notebooks/worked_example.ipynb b/profsea/tutorial_notebooks/worked_example.ipynb
index 8c10e56..a67a908 100644
--- a/profsea/tutorial_notebooks/worked_example.ipynb
+++ b/profsea/tutorial_notebooks/worked_example.ipynb
@@ -32,7 +32,7 @@
    "source": [
     "#### Importing components\n",
     "\n",
-    "The next step is to import all the model components you want to run simulations with - a full list is available in the documention [here](). This can be any combination of available `global` and `spatial` components, how you build your model is up to you!\n",
+    "The next step is to import all the model components you want to run simulations with - a full list is available in the documention [here](https://metoffice.github.io/ProFSea-tool/). This can be any combination of available `global` and `spatial` components, how you build your model is up to you!\n",
     "\n",
     "To generate a comprehensive set of sea-level projections, we'll run with all the components that contribute to sea-level change: \n",
     "- thermal expansion\n",
@@ -79,7 +79,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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",
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",
       "text/plain": [
        "
        "
       ]
@@ -155,7 +155,48 @@
    "execution_count": 4,
    "id": "84894d79",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
        ╭──────────────────────── ProFSea Global Run Configuration ─────────────────────────╮\n",
+       "│        Scenario  stabilisation                                                    │\n",
+       "│      Components  landwater, greenland, expansion, wais, eais, peninsula, glacier  │\n",
+       "│       Timeframe  2006 -> 2301                                                     │\n",
+       "│   Ensemble Size  100 inputs × 1000 draws (= 100000)                               │\n",
+       "│          Output  Full Distribution (100000 members)                               │\n",
+       "│  Compute Engine  Parallel Threading                                               │\n",
+       "╰───────────────────────────────────────────────────────────────────────────────────╯\n",
+       "
        \n"
+      ],
+      "text/plain": [
+       "\u001b[32m╭─\u001b[0m\u001b[32m───────────────────────\u001b[0m\u001b[32m \u001b[0m\u001b[1;32mProFSea Global Run Configuration\u001b[0m\u001b[32m \u001b[0m\u001b[32m────────────────────────\u001b[0m\u001b[32m─╮\u001b[0m\n",
+       "\u001b[32m│\u001b[0m \u001b[36m \u001b[0m\u001b[36m      Scenario\u001b[0m\u001b[36m \u001b[0m\u001b[35m \u001b[0m\u001b[35mstabilisation                                                  \u001b[0m\u001b[35m \u001b[0m \u001b[32m│\u001b[0m\n",
+       "\u001b[32m│\u001b[0m \u001b[36m \u001b[0m\u001b[36m    Components\u001b[0m\u001b[36m \u001b[0m\u001b[35m \u001b[0m\u001b[35mlandwater, greenland, expansion, wais, eais, peninsula, glacier\u001b[0m\u001b[35m \u001b[0m \u001b[32m│\u001b[0m\n",
+       "\u001b[32m│\u001b[0m \u001b[36m \u001b[0m\u001b[36m     Timeframe\u001b[0m\u001b[36m \u001b[0m\u001b[35m \u001b[0m\u001b[35m2006 -> 2301                                                   \u001b[0m\u001b[35m \u001b[0m \u001b[32m│\u001b[0m\n",
+       "\u001b[32m│\u001b[0m \u001b[36m \u001b[0m\u001b[36m Ensemble Size\u001b[0m\u001b[36m \u001b[0m\u001b[35m \u001b[0m\u001b[35m100 inputs × 1000 draws (= 100000)                             \u001b[0m\u001b[35m \u001b[0m \u001b[32m│\u001b[0m\n",
+       "\u001b[32m│\u001b[0m \u001b[36m \u001b[0m\u001b[36m        Output\u001b[0m\u001b[36m \u001b[0m\u001b[35m \u001b[0m\u001b[35mFull Distribution (100000 members)                             \u001b[0m\u001b[35m \u001b[0m \u001b[32m│\u001b[0m\n",
+       "\u001b[32m│\u001b[0m \u001b[36m \u001b[0m\u001b[36mCompute Engine\u001b[0m\u001b[36m \u001b[0m\u001b[35m \u001b[0m\u001b[35mParallel Threading                                             \u001b[0m\u001b[35m \u001b[0m \u001b[32m│\u001b[0m\n",
+       "\u001b[32m╰───────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
        \n",
+       "
        \n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "projections = global_model.run(\n",
     "    T_change=t_change, scenario=\"stabilisation\", member_seed=42\n",
@@ -180,7 +221,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
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        "
        "
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@@ -245,11 +286,11 @@
     {
      "data": {
       "text/html": [
-       "
        [13:46:57] ✓ ProFSea assets found locally!                                                     spatial_model.py:359\n",
+       "
        [13:58:11] INFO     ✓ ProFSea assets found locally!                                                                \n",
        "
        \n"
       ],
       "text/plain": [
-       "\u001b[2;36m[13:46:57]\u001b[0m\u001b[2;36m \u001b[0m\u001b[1;32m✓ ProFSea assets found locally!\u001b[0m                                                     \u001b]8;id=9379629;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/spatial_model.py\u001b\\\u001b[2mspatial_model.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=9379630;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/spatial_model.py#359\u001b\\\u001b[2m359\u001b[0m\u001b]8;;\u001b\\\n"
+       "\u001b[2;36m[13:58:11]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m \u001b[1;32m✓ ProFSea assets found locally!\u001b[0m                                                                \n"
       ]
      },
      "metadata": {},
@@ -258,11 +299,11 @@
     {
      "data": {
       "text/html": [
-       "
                   Baseline period = 1995 to 2014                                                      spatial_model.py:107\n",
+       "
                   INFO     Baseline period = 1995 to 2014                                                                 \n",
        "
        \n"
       ],
       "text/plain": [
-       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mBaseline period = \u001b[1;36m1995\u001b[0m to \u001b[1;36m2014\u001b[0m                                                      \u001b]8;id=9379636;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/spatial_model.py\u001b\\\u001b[2mspatial_model.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=9379637;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/spatial_model.py#107\u001b\\\u001b[2m107\u001b[0m\u001b]8;;\u001b\\\n"
+       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Baseline period = \u001b[1;36m1995\u001b[0m to \u001b[1;36m2014\u001b[0m                                                                 \n"
       ]
      },
      "metadata": {},
@@ -271,13 +312,64 @@
     {
      "data": {
       "text/html": [
-       "
                   Simulating 7 sea-level components...: sterodynamic, greenland, landwater, wais,     spatial_model.py:188\n",
-       "           eais, glacier, gia                                                                                      \n",
+       "
        ╭─────────────────────── ProFSea Spatial Run Configuration ────────────────────────╮\n",
+       "│        Components  sterodynamic, greenland, landwater, wais, eais, glacier, gia  │\n",
+       "│         Timeframe  2006 -> 2301                                                  │\n",
+       "│          Baseline  1995 - 2014                                                   │\n",
+       "│   Grid Resolution  180 × 360 cells                                               │\n",
+       "│            Output  Percentiles: [5, 17, 50, 83, 95]                              │\n",
+       "│  Est. Output Size  ~0.76 GB                                                      │\n",
+       "╰──────────────────────────────────────────────────────────────────────────────────╯\n",
        "
        \n"
       ],
       "text/plain": [
-       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mSimulating \u001b[1;36m7\u001b[0m sea-level components\u001b[33m...\u001b[0m: sterodynamic, greenland, landwater, wais,     \u001b]8;id=9379643;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/spatial_model.py\u001b\\\u001b[2mspatial_model.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=9379644;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/spatial_model.py#188\u001b\\\u001b[2m188\u001b[0m\u001b]8;;\u001b\\\n",
-       "\u001b[2;36m           \u001b[0meais, glacier, gia                                                                  \u001b[2m                    \u001b[0m\n"
+       "\u001b[34m╭─\u001b[0m\u001b[34m──────────────────────\u001b[0m\u001b[34m \u001b[0m\u001b[1;34mProFSea Spatial Run Configuration\u001b[0m\u001b[34m \u001b[0m\u001b[34m───────────────────────\u001b[0m\u001b[34m─╮\u001b[0m\n",
+       "\u001b[34m│\u001b[0m \u001b[36m \u001b[0m\u001b[36m      Components\u001b[0m\u001b[36m \u001b[0m\u001b[35m \u001b[0m\u001b[35msterodynamic, greenland, landwater, wais, eais, glacier, gia\u001b[0m\u001b[35m \u001b[0m \u001b[34m│\u001b[0m\n",
+       "\u001b[34m│\u001b[0m \u001b[36m \u001b[0m\u001b[36m       Timeframe\u001b[0m\u001b[36m \u001b[0m\u001b[35m \u001b[0m\u001b[35m2006 -> 2301                                                \u001b[0m\u001b[35m \u001b[0m \u001b[34m│\u001b[0m\n",
+       "\u001b[34m│\u001b[0m \u001b[36m \u001b[0m\u001b[36m        Baseline\u001b[0m\u001b[36m \u001b[0m\u001b[35m \u001b[0m\u001b[35m1995 - 2014                                                 \u001b[0m\u001b[35m \u001b[0m \u001b[34m│\u001b[0m\n",
+       "\u001b[34m│\u001b[0m \u001b[36m \u001b[0m\u001b[36m Grid Resolution\u001b[0m\u001b[36m \u001b[0m\u001b[35m \u001b[0m\u001b[35m180 × 360 cells                                             \u001b[0m\u001b[35m \u001b[0m \u001b[34m│\u001b[0m\n",
+       "\u001b[34m│\u001b[0m \u001b[36m \u001b[0m\u001b[36m          Output\u001b[0m\u001b[36m \u001b[0m\u001b[35m \u001b[0m\u001b[35mPercentiles: [5, 17, 50, 83, 95]                            \u001b[0m\u001b[35m \u001b[0m \u001b[34m│\u001b[0m\n",
+       "\u001b[34m│\u001b[0m \u001b[36m \u001b[0m\u001b[36mEst. Output Size\u001b[0m\u001b[36m \u001b[0m\u001b[35m \u001b[0m\u001b[35m~0.76 GB\u001b[0m\u001b[35m                                                    \u001b[0m\u001b[35m \u001b[0m \u001b[34m│\u001b[0m\n",
+       "\u001b[34m╰──────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
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+      "text/plain": [
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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
                   INFO     Simulating 7 sea-level components: sterodynamic, greenland, landwater, wais, eais, glacier, gia\n",
+       "
        \n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Simulating \u001b[1;36m7\u001b[0m sea-level components: sterodynamic, greenland, landwater, wais, eais, glacier, gia\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
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      "metadata": {},
@@ -292,6 +384,19 @@
      },
      "metadata": {},
      "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
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+      "text/plain": [
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     }
    ],
    "source": [
@@ -357,11 +462,11 @@
     {
      "data": {
       "text/html": [
-       "
        [13:47:42] ✓ Successfully saved Zarr: ./stabilisation_spatial_projection.zarr                  spatial_model.py:331\n",
+       "
        [13:58:26] INFO     ✓ Successfully saved Zarr: ./stabilisation_spatial_projection.zarr                             \n",
        "
        \n"
       ],
       "text/plain": [
-       "\u001b[2;36m[13:47:42]\u001b[0m\u001b[2;36m \u001b[0m\u001b[1;32m✓ Successfully saved Zarr:\u001b[0m .\u001b[35m/\u001b[0m\u001b[95mstabilisation_spatial_projection.zarr\u001b[0m                  \u001b]8;id=9379650;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/spatial_model.py\u001b\\\u001b[2mspatial_model.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=9379651;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/spatial_model.py#331\u001b\\\u001b[2m331\u001b[0m\u001b]8;;\u001b\\\n"
+       "\u001b[2;36m[13:58:26]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m \u001b[1;32m✓ Successfully saved Zarr:\u001b[0m .\u001b[35m/\u001b[0m\u001b[95mstabilisation_spatial_projection.zarr\u001b[0m                             \n"
       ]
      },
      "metadata": {},
@@ -370,11 +475,11 @@
     {
      "data": {
       "text/html": [
-       "
                   Output shape was (5, 295, 180, 360) (members, time, lat, lon)                       spatial_model.py:335\n",
+       "
                   INFO     Output shape was (5, 295, 180, 360) (members, time, lat, lon)                                  \n",
        "
        \n"
       ],
       "text/plain": [
-       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mOutput shape was \u001b[1m(\u001b[0m\u001b[1;36m5\u001b[0m, \u001b[1;36m295\u001b[0m, \u001b[1;36m180\u001b[0m, \u001b[1;36m360\u001b[0m\u001b[1m)\u001b[0m \u001b[1m(\u001b[0mmembers, time, lat, lon\u001b[1m)\u001b[0m                       \u001b]8;id=9379657;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/spatial_model.py\u001b\\\u001b[2mspatial_model.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=9379658;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/spatial_model.py#335\u001b\\\u001b[2m335\u001b[0m\u001b]8;;\u001b\\\n"
+       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Output shape was \u001b[1m(\u001b[0m\u001b[1;36m5\u001b[0m, \u001b[1;36m295\u001b[0m, \u001b[1;36m180\u001b[0m, \u001b[1;36m360\u001b[0m\u001b[1m)\u001b[0m \u001b[1m(\u001b[0mmembers, time, lat, lon\u001b[1m)\u001b[0m                                  \n"
       ]
      },
      "metadata": {},
@@ -403,7 +508,7 @@
    "outputs": [
     {
      "data": {
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",
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",
       "text/plain": [
        "
        "
       ]

From e11d58a57ede2720c007d87e698d741b98c583d4 Mon Sep 17 00:00:00 2001
From: Greg Munday <100290135+gregrmunday@users.noreply.github.com>
Date: Tue, 16 Jun 2026 15:11:10 +0100
Subject: [PATCH 243/276] Fix badge links in README.md

Updated badge links in README for tests, linting, and documentation.
---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 45b25c6..77c5dc9 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,6 @@
 # ProFSea logo
 
-[![DOI](https://zenodo.org/badge/713453340.svg)](https://zenodo.org/doi/10.5281/zenodo.10255467) [![Tests](https://github.com/MetOffice/ProFSea-tool/actions/workflows/pytest.yml/badge.svg?branch=profsea-v3)](https://github.com/MetOffice/ProFSea-tool/actions/workflows/pytest.yml) [![Lint](https://github.com/MetOffice/ProFSea-tool/actions/workflows/ruff.yml/badge.svg?branch=profsea-v3)](https://github.com/MetOffice/ProFSea-tool/actions/workflows/ruff.yml) [![Docs](https://github.com/MetOffice/ProFSea-tool/actions/workflows/deploy-docs.yml/badge.svg?branch=profsea-v3)](https://github.com/MetOffice/ProFSea-tool/actions/workflows/deploy-docs.yml) [![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)
+[![DOI](https://zenodo.org/badge/713453340.svg)](https://zenodo.org/doi/10.5281/zenodo.10255467) [![tests](https://img.shields.io/github/actions/workflow/status/MetOffice/ProFSea-tool/pytest.yml?branch=profsea-v3&label=Tests&logo=github&logoColor=white)](https://github.com/MetOffice/ProFSea-tool/actions/workflows/pytest.yml) [![lint](https://img.shields.io/github/actions/workflow/status/MetOffice/ProFSea-tool/ruff.yml?branch=profsea-v3&label=Linting)](https://github.com/MetOffice/ProFSea-tool/actions/workflows/ruff.yml) [![docs](https://img.shields.io/github/actions/workflow/status/MetOffice/ProFSea-tool/deploy-docs.yml?branch=profsea-v3&label=Documentation)](https://github.com/MetOffice/ProFSea-tool/actions/workflows/deploy-docs.yml) [![ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json&label=Code%20style)](https://github.com/astral-sh/ruff)
 
 ProFSea is sea-level rise simulator based on statistical emulations of physical modelling experiments and lines of evidence from the IPCC and across the literature. It's modular, easy to setup and self-contained - all you need is global mean surface temperature and ocean heat content forcing anomalies, using any baseline period, and you're good to go.
 

From d40efe11e7c40a52befbe430e3a91d063772fc28 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Tue, 16 Jun 2026 15:52:44 +0100
Subject: [PATCH 244/276] Fix some merge artefacts

---
 profsea/components/core/local_model.py    | 18 +++++++++---------
 profsea/components/spatial/fingerprint.py |  2 +-
 profsea/components/spatial/gia.py         |  1 -
 3 files changed, 10 insertions(+), 11 deletions(-)

diff --git a/profsea/components/core/local_model.py b/profsea/components/core/local_model.py
index 664ecf4..9e062c6 100644
--- a/profsea/components/core/local_model.py
+++ b/profsea/components/core/local_model.py
@@ -1,16 +1,16 @@
-from pathlib import Path
-from typing import Dict, Tuple
 import warnings
+from pathlib import Path
 
 import dask.array as da
 import numpy as np
+import xarray as xr
 from rich.console import Console
 from rich.progress import track
-import xarray as xr
+
+from profsea.utils import fetch_zenodo_fingerprints, save_components
 
 from .base import SpatialComponent
 from .state import LocalState
-from profsea.utils import fetch_zenodo_fingerprints, save_components
 
 console = Console()
 warnings.filterwarnings("ignore")
@@ -26,8 +26,8 @@ class Local:
 
     def __init__(
         self,
-        components: Dict[str, SpatialComponent],
-        locations: Dict[str, Tuple[float, float]],
+        components: dict[str, SpatialComponent],
+        locations: dict[str, tuple[float, float]],
         interpolation_method: str = "linear",
         end_year: int = 2301,
         baseline_yrs: tuple = (1995, 2014),
@@ -84,7 +84,7 @@ def __init__(
     # Instance method!
     save_components = save_components
 
-    def _arr_to_xr(self, arr_dict: Dict[str, da.Array]) -> Dict[str, xr.DataArray]:
+    def _arr_to_xr(self, arr_dict: dict[str, da.Array]) -> dict[str, xr.DataArray]:
         """Convert Dask arrays to xarray DataArrays with site coordinates."""
         xr_dict = {}
         member_dim = "percentile" if self.output_percentiles is not None else "member"
@@ -110,7 +110,7 @@ def _arr_to_xr(self, arr_dict: Dict[str, da.Array]) -> Dict[str, xr.DataArray]:
             )
         return xr_dict
 
-    def run(self, member_seed: int = 42) -> Dict[str, xr.DataArray]:
+    def run(self, member_seed: int = 42) -> dict[str, xr.DataArray]:
         """Run the local model to generate site-specific projections."""
         seed_seq = np.random.SeedSequence(member_seed)
 
@@ -148,7 +148,7 @@ def run(self, member_seed: int = 42) -> Dict[str, xr.DataArray]:
         self.results = self._arr_to_xr(local_projections)
         return self.results
 
-    def sum_components(self, components: Dict[str, xr.DataArray]) -> xr.DataArray:
+    def sum_components(self, components: dict[str, xr.DataArray]) -> xr.DataArray:
         """Sum the local components to get total sea-level change."""
         total_rsl = xr.concat(components.values(), dim="component").sum(dim="component")
         total_rsl.attrs = {
diff --git a/profsea/components/spatial/fingerprint.py b/profsea/components/spatial/fingerprint.py
index da3f3ae..6e26afc 100644
--- a/profsea/components/spatial/fingerprint.py
+++ b/profsea/components/spatial/fingerprint.py
@@ -8,7 +8,7 @@
 
 from profsea.components.core.base import SpatialComponent
 from profsea.components.core.state import SpatialState
-from profsea.utils import interpolate_to_grid, sample_members_2D
+from profsea.utils import sample_members_2D
 
 PROFSEA_DIR = Path(__file__).resolve().parents[2]
 FP_DIR = PROFSEA_DIR / "profsea-assets" / "grd-fingerprints"
diff --git a/profsea/components/spatial/gia.py b/profsea/components/spatial/gia.py
index cec0c6b..5821b1d 100644
--- a/profsea/components/spatial/gia.py
+++ b/profsea/components/spatial/gia.py
@@ -8,7 +8,6 @@
 
 from profsea.components.core.base import SpatialComponent
 from profsea.components.core.state import SpatialState
-from profsea.utils import interpolate_to_grid
 
 PROFSEA_DIR = Path(__file__).resolve().parents[2]
 GIA_DIR = PROFSEA_DIR / "profsea-assets" / "gia"

From 4cd4746cd5a04f64866e91ff6653a76cdf3800ec Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Tue, 16 Jun 2026 15:56:54 +0100
Subject: [PATCH 245/276] Update post merge

---
 profsea/components/core/spatial_model.py | 173 +----------------------
 1 file changed, 4 insertions(+), 169 deletions(-)

diff --git a/profsea/components/core/spatial_model.py b/profsea/components/core/spatial_model.py
index e0f8a01..685bf60 100644
--- a/profsea/components/core/spatial_model.py
+++ b/profsea/components/core/spatial_model.py
@@ -21,6 +21,7 @@
     track,
 )
 
+from profsea.utils import fetch_zenodo_fingerprints, save_components
 from profsea.utils.ui import print_spatial_preflight
 
 from .base import Component
@@ -136,6 +137,9 @@ def __init__(
                     f"resolution or take percentiles.[/bold red]"
                 )
 
+    # Instance method!
+    save_components = save_components
+
     def _arr_to_xr(self, arr_dict: dict[str, da.Array]) -> dict[str, xr.DataArray]:
         """
         Convert a dictionary of Dask arrays to a dictionary of xarray DataArrays with appropriate coordinates and metadata.
@@ -255,172 +259,3 @@ def sum_components(self, components: dict[str, xr.DataArray]) -> xr.DataArray:
 
         components["total_rsl"] = total_rsl
         return total_rsl
-
-    def save_components(
-        self,
-        components: dict[str, xr.DataArray],
-        scenario_name: str,
-        output_dir: str = ".",
-        output_format: str = "zarr",
-    ) -> None:
-        """
-        Stream all regional sea level projections to disk in a single file/store.
-
-        Parameters
-        ----------
-        components: Dict[str, xr.DataArray]
-            Dictionary of component names and their corresponding Xarray DataArrays.
-        output_format: str
-            Format to save the output in. Must be either 'netcdf' or 'zarr'.
-        output_dir: str
-            Directory to save components to.
-        scenario_name: str
-            Name of the scenario you've run the emulator for.
-
-        Returns
-        -------
-        None
-        """
-        # Wrap dataarrays in a single Dataset for saving
-        ds = xr.Dataset(components)
-
-        output_format = output_format.lower()
-        if output_format not in ["netcdf", "zarr"]:
-            raise ValueError("output_format must be either 'netcdf' or 'zarr'.")
-
-        # Create directory if it doesn't exist
-        Path(output_dir).mkdir(parents=True, exist_ok=True)
-
-        encoding = {}
-        # Sort out Zarr encoding
-        if output_format == "zarr":
-            import numcodecs
-            from numcodecs.zarr3 import Blosc
-
-            compressor = Blosc(
-                cname="zstd", clevel=5, shuffle=numcodecs.Blosc.BITSHUFFLE
-            )
-
-        # Set the encoding/compression for each variable based on the output format
-        for name, component in components.items():
-            if output_format == "netcdf":
-                encoding[name] = {"zlib": True, "complevel": 1, "dtype": "float32"}
-            elif output_format == "zarr":
-                encoding[name] = {"compressor": compressor, "dtype": "float32"}
-
-        file_header = f"{scenario_name}_spatial_projection"
-
-        # Stream the computation and write to disk
-        if output_format == "netcdf":
-            out_path = os.path.join(output_dir, f"{file_header}.nc")
-
-            with console.status(
-                "[bold cyan]Computing and saving NetCDF...[/bold cyan]",
-                spinner="dots",
-            ):
-                ds.compute().to_netcdf(out_path, encoding=encoding)
-
-            logger.info(
-                f"[bold green]✓ Successfully saved NetCDF:[/bold green] {out_path}"
-            )
-
-        elif output_format == "zarr":
-            out_path = os.path.join(output_dir, f"{file_header}.zarr")
-
-            with console.status(
-                "[bold cyan]Streaming computation and saving Zarr...[/bold cyan]",
-                spinner="dots",
-            ):
-                ds.to_zarr(out_path, encoding=encoding, mode="w", compute=True)
-
-            logger.info(
-                f"[bold green]✓ Successfully saved Zarr:[/bold green] {out_path}"
-            )
-
-        logger.info(
-            "Output shape was " + str(ds[name].shape) + " (members, time, lat, lon)"
-        )
-
-
-def fetch_zenodo_fingerprints(
-    zenodo_url: str, data_dir: Path, expected_folder_name: str
-) -> None:
-    """
-    Downloads and extracts the ProFSea fingerprint dataset from Zenodo if it doesn't already exist locally.
-
-    Parameters
-    ----------
-    zenodo_url: str
-        The direct download URL for the fingerprint dataset on Zenodo.
-    data_dir: Path
-        The base directory where the dataset should be stored.
-    expected_folder_name: str
-        The name of the folder that should be created when the dataset is extracted. Used to check if the data already exists.
-    """
-    target_dir = data_dir / expected_folder_name
-
-    # 1. Check if data already exists
-    if target_dir.exists() and any(target_dir.iterdir()):
-        logger.info("[bold green]✓ ProFSea assets found locally![/bold green]")
-        return
-
-    # Create the base directory if it doesn't exist
-    data_dir.mkdir(parents=True, exist_ok=True)
-    zip_path = data_dir / "temp_fingerprints.zip"
-
-    logger.info(f"Initiating download from {zenodo_url}...")
-
-    # 2. Stream the download with a rich progress bar
-    try:
-        response = requests.get(zenodo_url, stream=True)
-        response.raise_for_status()  # Raise an error for bad status codes
-
-        total_size = int(response.headers.get("content-length", 0))
-
-        with Progress(
-            TextColumn("[bold cyan]{task.description}"),
-            BarColumn(),
-            DownloadColumn(),
-            TransferSpeedColumn(),
-            TimeRemainingColumn(),
-            console=console,
-        ) as progress:
-            download_task = progress.add_task(
-                "Downloading dataset...", total=total_size
-            )
-
-            with open(zip_path, "wb") as file:
-                for chunk in response.iter_content(chunk_size=8192):
-                    if chunk:
-                        file.write(chunk)
-                        progress.update(download_task, advance=len(chunk))
-
-    except requests.exceptions.RequestException as e:
-        logger.error(f"[bold red]Failed to download data: {e}[/bold red]")
-        if zip_path.exists():
-            zip_path.unlink()  # Clean up partial downloads
-        raise
-
-    logger.info("Extracting data...")
-    try:
-        with zipfile.ZipFile(zip_path, "r") as zip_ref:
-            # Filter out the __MACOSX directory and its contents
-            valid_members = [
-                member
-                for member in zip_ref.namelist()
-                if not member.startswith("__MACOSX/") and not member.startswith("._")
-            ]
-            zip_ref.extractall(data_dir, members=valid_members)
-
-        logger.info(
-            f"[bold green]✓ Successfully extracted data to {data_dir}[/bold green]"
-        )
-    except zipfile.BadZipFile:
-        logger.error(
-            "[bold red]Error: Downloaded file is not a valid zip archive.[/bold red]"
-        )
-        raise
-    finally:
-        # 4. Clean up the zip file
-        if zip_path.exists():
-            zip_path.unlink()

From a52d6d897555854692ccf0ed34e8817023df2f0b Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Tue, 16 Jun 2026 16:03:26 +0100
Subject: [PATCH 246/276] Fix regionmask test

---
 profsea/utils/utils.py    | 3 ++-
 tests/utils/test_utils.py | 9 +++++----
 2 files changed, 7 insertions(+), 5 deletions(-)

diff --git a/profsea/utils/utils.py b/profsea/utils/utils.py
index cdf85ad..4b54230 100644
--- a/profsea/utils/utils.py
+++ b/profsea/utils/utils.py
@@ -2,7 +2,6 @@
 
 import dask.array as da
 import numpy as np
-import regionmask
 import xarray as xr
 from scipy.spatial.distance import cdist
 
@@ -88,6 +87,8 @@ def interpolate_to_grid(
     xr.DataArray
         Interpolated xarray DataArray on the target grid.
     """
+    import regionmask
+
     # Normalize source longitudes to [-180, 180) and sort monotonically
     data = data.assign_coords(lon=(((data.lon + 180) % 360) - 180))
     data = data.sortby(["lat", "lon"])
diff --git a/tests/utils/test_utils.py b/tests/utils/test_utils.py
index 6460538..7ef5f68 100644
--- a/tests/utils/test_utils.py
+++ b/tests/utils/test_utils.py
@@ -25,14 +25,13 @@ def test_check_shapes_raises_value_error():
         check_shapes(arr, n_time=12)
 
 
-@patch("profsea.utils.utils.regionmask")
-def test_interpolate_to_grid_longitude_wrapping(mock_regionmask):
+def test_interpolate_to_grid_longitude_wrapping():
     # Target grid using -180 to 180 format (includes 180, which should wrap)
     target_lats = np.array([-45, 0, 45])
     target_lons = np.array([-10, 0, 180])
 
     # 1. Setup mock regionmask to act as if there is no land
-    # mask_3D needs to return an xarray DataArray with a "region" dimension
+    mock_regionmask = MagicMock()
     mock_land = MagicMock()
     mock_mask_da = xr.DataArray(
         np.zeros((1, 3, 3), dtype=bool),
@@ -47,7 +46,9 @@ def test_interpolate_to_grid_longitude_wrapping(mock_regionmask):
     lons_360 = np.array([0, 180, 350])
     data = xr.DataArray(np.random.rand(3, 3), coords=[("lat", lats), ("lon", lons_360)])
 
-    result = interpolate_to_grid(data, target_lats, target_lons)
+    # 3. Intercept the local import using sys.modules
+    with patch.dict("sys.modules", {"regionmask": mock_regionmask}):
+        result = interpolate_to_grid(data, target_lats, target_lons)
 
     assert result.shape == (3, 3)
     expected_lons = np.array([-180, -10, 0])

From 99846f0e5edbefba0f8dff8cc45886f960dfaedb Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Tue, 16 Jun 2026 16:15:48 +0100
Subject: [PATCH 247/276] Fix tests

---
 profsea/components/core/spatial_model.py | 13 +------------
 tests/components/test_sterodynamic.py    |  4 ++--
 2 files changed, 3 insertions(+), 14 deletions(-)

diff --git a/profsea/components/core/spatial_model.py b/profsea/components/core/spatial_model.py
index 685bf60..1082a62 100644
--- a/profsea/components/core/spatial_model.py
+++ b/profsea/components/core/spatial_model.py
@@ -1,25 +1,14 @@
 from __future__ import annotations
 
 import logging
-import os
 import warnings
-import zipfile
 from pathlib import Path
 
 import dask.array as da
 import numpy as np
-import requests
 import xarray as xr
 from rich.console import Console
-from rich.progress import (
-    BarColumn,
-    DownloadColumn,
-    Progress,
-    TextColumn,
-    TimeRemainingColumn,
-    TransferSpeedColumn,
-    track,
-)
+from rich.progress import track
 
 from profsea.utils import fetch_zenodo_fingerprints, save_components
 from profsea.utils.ui import print_spatial_preflight
diff --git a/tests/components/test_sterodynamic.py b/tests/components/test_sterodynamic.py
index f3dbc5f..466c281 100644
--- a/tests/components/test_sterodynamic.py
+++ b/tests/components/test_sterodynamic.py
@@ -44,7 +44,7 @@ def mock_open(f, **kwargs):
     assert stero.land_mask_present is True
 
 
-@patch("profsea.components.spatial.sterodynamic.interpolate_to_grid")
+@patch("profsea.utils.interpolate_to_grid")
 @patch.object(SterodynamicCMIP6, "_load_CMIP6_slopes")
 def test_expansion_contribution_storyline_mode(mock_load, mock_interp):
     # Mock the loaded data and masks
@@ -88,7 +88,7 @@ def test_expansion_contribution_storyline_mode(mock_load, mock_interp):
     np.testing.assert_array_equal(computed_result, np.ones((5, 2, 2)) * 2)
 
 
-@patch("profsea.components.spatial.sterodynamic.interpolate_to_grid")
+@patch("profsea.utils.interpolate_to_grid")
 @patch.object(SterodynamicCMIP6, "_load_CMIP6_slopes")
 def test_expansion_contribution_sampled_mode(mock_load, mock_interp):
     mock_coeffs = xr.DataArray(

From af2f5adabae4358e036a65fd6a7ed165877284ff Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Tue, 16 Jun 2026 17:04:30 +0100
Subject: [PATCH 248/276] Update local model with land mask

---
 profsea/components/core/base.py               |   2 +-
 profsea/components/core/local_model.py        | 101 +++++++++++--
 profsea/components/core/spatial_model.py      |   2 +-
 .../tutorial_notebooks/worked_example.ipynb   | 142 +++++++-----------
 4 files changed, 150 insertions(+), 97 deletions(-)

diff --git a/profsea/components/core/base.py b/profsea/components/core/base.py
index b55f984..e4e3b12 100644
--- a/profsea/components/core/base.py
+++ b/profsea/components/core/base.py
@@ -41,7 +41,7 @@ def extract_spatial(self, da_input: xr.DataArray, state) -> xr.DataArray:
             return da_input.interp(
                 lat=lats,
                 lon=lons,
-                method=getattr(state, "interpolation_method", "linear"),
+                method=getattr(state, "interpolation_method", "nearest"),
             )
         elif hasattr(state, "grid_lats"):
             from profsea.utils import interpolate_to_grid
diff --git a/profsea/components/core/local_model.py b/profsea/components/core/local_model.py
index 9e062c6..a765d4d 100644
--- a/profsea/components/core/local_model.py
+++ b/profsea/components/core/local_model.py
@@ -1,3 +1,4 @@
+import logging
 import warnings
 from pathlib import Path
 
@@ -12,6 +13,7 @@
 from .base import SpatialComponent
 from .state import LocalState
 
+logger = logging.getLogger(__name__)
 console = Console()
 warnings.filterwarnings("ignore")
 
@@ -28,7 +30,7 @@ def __init__(
         self,
         components: dict[str, SpatialComponent],
         locations: dict[str, tuple[float, float]],
-        interpolation_method: str = "linear",
+        interpolation_method: str = "nearest",
         end_year: int = 2301,
         baseline_yrs: tuple = (1995, 2014),
         output_percentiles: list | np.ndarray = [5, 17, 50, 83, 95],
@@ -42,7 +44,7 @@ def __init__(
             Dictionary mapping site names to (latitude, longitude) tuples.
             Example: {"Aberdeen": (57.144, -2.080)}
         interpolation_method: str, optional
-            Interpolation method to use when interpolating patterns to the target sites. Default is 'linear'.
+            Interpolation method to use when interpolating patterns to the target sites. Default is 'nearest'.
         end_year: int, optional
             The final year of the projections. Default is 2301.
         baseline_yrs: tuple, optional
@@ -76,16 +78,28 @@ def __init__(
                 iter(self.components.values())
             ).global_projection.shape[0]
 
-        console.log(
+        logger.info(
             f"Baseline period = {self.baseline_yrs[0]} to {self.baseline_yrs[1]}"
         )
-        console.log(f"Configured for {len(self.site_names)} specific target locations.")
+        logger.info(f"Configured for {len(self.site_names)} specific target locations.")
 
     # Instance method!
     save_components = save_components
 
     def _arr_to_xr(self, arr_dict: dict[str, da.Array]) -> dict[str, xr.DataArray]:
-        """Convert Dask arrays to xarray DataArrays with site coordinates."""
+        """
+        Convert Dask arrays to xarray DataArrays with site coordinates.
+
+        Parameters
+        ----------
+        arr_dict: dict
+            Dictionary of Dask arrays to convert. Keys should be the component names and values should be Dask arrays of shape (members, time, sites).
+
+        Returns
+        -------
+        dict
+            Dictionary of xarray DataArrays with site coordinates. Keys are the same as in arr_dict.
+        """
         xr_dict = {}
         member_dim = "percentile" if self.output_percentiles is not None else "member"
 
@@ -110,11 +124,66 @@ def _arr_to_xr(self, arr_dict: dict[str, da.Array]) -> dict[str, xr.DataArray]:
             )
         return xr_dict
 
+    def _apply_universal_mask(self) -> None:
+        """
+        Identify locations that evaluate to NaN in ANY component (typically driven
+        by the sterodynamic land mask) and propagate that NaN to ALL components.
+        This ensures physical consistency: a site over land has no valid components.
+
+        Parameters
+        ----------
+        None
+
+        Returns
+        -------
+        None
+        """
+        if not hasattr(self, "results") or not self.results:
+            return
+
+        member_dim = "percentile" if self.output_percentiles is not None else "member"
+        spatial_slices = [
+            comp.isel({member_dim: 0, "time": 0}) for comp in self.results.values()
+        ]
+
+        # Site is valid if it is non-NaN in ALL components
+        stacked_slices = xr.concat(spatial_slices, dim="component")
+        valid_site_mask = stacked_slices.notnull().all(dim="component").compute()
+
+        # Inform user of land mask
+        masked_sites = [
+            site
+            for site, is_valid in zip(self.site_names, valid_site_mask.values)
+            if not is_valid
+        ]
+        if masked_sites:
+            logger.warning(
+                f"The following site(s) may be over land: {', '.join(masked_sites)}. "
+                "Expect NaN values for all components. "
+                "Either try increasing your grid resolution or check your site coordinates."
+            )
+
+        # Apply the mask uniformly
+        for name in self.results.keys():
+            self.results[name] = self.results[name].where(valid_site_mask)
+
     def run(self, member_seed: int = 42) -> dict[str, xr.DataArray]:
-        """Run the local model to generate site-specific projections."""
+        """
+        Run the local model to generate site-specific projections.
+
+        Parameters
+        ----------
+        member_seed: int, optional
+            Seed for random number generation to ensure reproducibility of member sampling. Default is 42.
+
+        Returns
+        -------
+        dict
+            Dictionary of local projections for each component, where keys are component names and values are xarray DataArrays of shape (n_members, n_years, n_sites).
+        """
         seed_seq = np.random.SeedSequence(member_seed)
 
-        console.log(
+        logger.info(
             f"Simulating {len(self.components)} sea-level components for {len(self.site_names)} sites..."
         )
 
@@ -135,6 +204,7 @@ def run(self, member_seed: int = 42) -> dict[str, xr.DataArray]:
         }
 
         local_projections = {}
+        logger.info("Starting localisation of components...")
         for name, comp in track(
             self.components.items(), description="Localising components..."
         ):
@@ -146,11 +216,24 @@ def run(self, member_seed: int = 42) -> dict[str, xr.DataArray]:
             local_projections[name] = lazy_projection
 
         self.results = self._arr_to_xr(local_projections)
+        self._apply_universal_mask()
         return self.results
 
     def sum_components(self, components: dict[str, xr.DataArray]) -> xr.DataArray:
-        """Sum the local components to get total sea-level change."""
-        total_rsl = xr.concat(components.values(), dim="component").sum(dim="component")
+        """
+        Sum the local components to get total sea-level change.
+
+        Parameters
+        ----------
+        components: dict
+            Dictionary of xarray DataArrays representing individual components.
+
+        Returns
+        -------
+        xr.DataArray
+            A single xarray DataArray representing the total sea-level projections, with appropriate attributes.
+        """
+        total_rsl = xr.concat(components.values(), dim="component").sum(dim="component", skipna=False)
         total_rsl.attrs = {
             "units": "m",
             "long_name": "Local total sea-level projections",
diff --git a/profsea/components/core/spatial_model.py b/profsea/components/core/spatial_model.py
index 1082a62..b8a5afd 100644
--- a/profsea/components/core/spatial_model.py
+++ b/profsea/components/core/spatial_model.py
@@ -237,7 +237,7 @@ def sum_components(self, components: dict[str, xr.DataArray]) -> xr.DataArray:
         """
         # Using xr.concat preserves all dimensions and coordinates, and summing
         # along the new dimension handles the underlying dask arrays cleanly.
-        total_rsl = xr.concat(components.values(), dim="component").sum(dim="component")
+        total_rsl = xr.concat(components.values(), dim="component").sum(dim="component", skipna=False)
 
         # Optionally, apply attributes so it matches the other DataArrays
         total_rsl.attrs = {
diff --git a/profsea/tutorial_notebooks/worked_example.ipynb b/profsea/tutorial_notebooks/worked_example.ipynb
index a0b75de..d9d6761 100644
--- a/profsea/tutorial_notebooks/worked_example.ipynb
+++ b/profsea/tutorial_notebooks/worked_example.ipynb
@@ -79,7 +79,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
        "
       ]
@@ -221,7 +221,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
        "
       ]
@@ -256,7 +256,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "id": "47694def",
    "metadata": {},
    "outputs": [],
@@ -279,18 +279,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 7,
    "id": "09f1fff4",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "
        [13:58:11] INFO     ✓ ProFSea assets found locally!                                                                \n",
+       "
        [17:01:32] ✓ ProFSea assets found locally!                                                             utils.py:188\n",
        "
        \n"
       ],
       "text/plain": [
-       "\u001b[2;36m[13:58:11]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m \u001b[1;32m✓ ProFSea assets found locally!\u001b[0m                                                                \n"
+       "\u001b[2;36m[17:01:32]\u001b[0m\u001b[2;36m \u001b[0m\u001b[1;32m✓ ProFSea assets found locally!\u001b[0m                                                             \u001b]8;id=1422858;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/utils/utils.py\u001b\\\u001b[2mutils.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=1422859;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/utils/utils.py#188\u001b\\\u001b[2m188\u001b[0m\u001b]8;;\u001b\\\n"
       ]
      },
      "metadata": {},
@@ -299,11 +299,11 @@
     {
      "data": {
       "text/html": [
-       "
                   INFO     Baseline period = 1995 to 2014                                                                 \n",
+       "
        [17:01:32] INFO     Baseline period = 1995 to 2014                                                                 \n",
        "
        \n"
       ],
       "text/plain": [
-       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Baseline period = \u001b[1;36m1995\u001b[0m to \u001b[1;36m2014\u001b[0m                                                                 \n"
+       "\u001b[2;36m[17:01:32]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Baseline period = \u001b[1;36m1995\u001b[0m to \u001b[1;36m2014\u001b[0m                                                                 \n"
       ]
      },
      "metadata": {},
@@ -375,20 +375,6 @@
      "metadata": {},
      "output_type": "display_data"
     },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "999ac2ede7234db7ad763b918b23c257",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Output()"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
     {
      "data": {
       "text/html": [
@@ -459,7 +445,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "id": "60d81a5b",
    "metadata": {},
    "outputs": [
@@ -476,11 +462,11 @@
     {
      "data": {
       "text/html": [
-       "
        [13:58:26] INFO     ✓ Successfully saved Zarr: ./stabilisation_spatial_projection.zarr                             \n",
+       "
        [17:01:47] ✓ Successfully saved Zarr: ./stabilisation_projection.zarr                                  utils.py:322\n",
        "
        \n"
       ],
       "text/plain": [
-       "\u001b[2;36m[13:58:26]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m \u001b[1;32m✓ Successfully saved Zarr:\u001b[0m .\u001b[35m/\u001b[0m\u001b[95mstabilisation_spatial_projection.zarr\u001b[0m                             \n"
+       "\u001b[2;36m[17:01:47]\u001b[0m\u001b[2;36m \u001b[0m\u001b[1;32m✓ Successfully saved Zarr:\u001b[0m .\u001b[35m/\u001b[0m\u001b[95mstabilisation_projection.zarr\u001b[0m                                  \u001b]8;id=1422865;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/utils/utils.py\u001b\\\u001b[2mutils.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=1422866;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/utils/utils.py#322\u001b\\\u001b[2m322\u001b[0m\u001b]8;;\u001b\\\n"
       ]
      },
      "metadata": {},
@@ -489,11 +475,11 @@
     {
      "data": {
       "text/html": [
-       "
                   INFO     Output shape was (5, 295, 180, 360) (members, time, lat, lon)                                  \n",
+       "
                   Output shape was (5, 295, 180, 360) (percentile, time, lat, lon)                            utils.py:325\n",
        "
        \n"
       ],
       "text/plain": [
-       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Output shape was \u001b[1m(\u001b[0m\u001b[1;36m5\u001b[0m, \u001b[1;36m295\u001b[0m, \u001b[1;36m180\u001b[0m, \u001b[1;36m360\u001b[0m\u001b[1m)\u001b[0m \u001b[1m(\u001b[0mmembers, time, lat, lon\u001b[1m)\u001b[0m                                  \n"
+       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mOutput shape was \u001b[1m(\u001b[0m\u001b[1;36m5\u001b[0m, \u001b[1;36m295\u001b[0m, \u001b[1;36m180\u001b[0m, \u001b[1;36m360\u001b[0m\u001b[1m)\u001b[0m \u001b[1m(\u001b[0mpercentile, time, lat, lon\u001b[1m)\u001b[0m                            \u001b]8;id=1422872;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/utils/utils.py\u001b\\\u001b[2mutils.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=1422873;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/utils/utils.py#325\u001b\\\u001b[2m325\u001b[0m\u001b]8;;\u001b\\\n"
       ]
      },
      "metadata": {},
@@ -516,27 +502,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
-   "id": "8bd17c34",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Add a land-sea mask to results.total_rsl (set vals to NaN over land) \n",
-    "# Mask is in here: profsea/profsea-assets/cmip6-patterns/ACCESS-CM2/zos_mask_ssp245_ACCESS-CM2.nc\n",
-    "import xarray as xr\n",
-    "mask = xr.open_dataset(\"../../profsea/profsea-assets/cmip6-patterns/ACCESS-CM2/zos_mask_ssp245_ACCESS-CM2.nc\").zos_mask\n",
-    "total_rsl_masked = total_rsl.where(mask == 0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 9,
    "id": "e62d28d9",
    "metadata": {},
    "outputs": [
     {
      "data": {
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AAA8ePEiWkClExsgiRkTsQehBaGnmVWUbBfwhUIuNUcl+YtXU02W/6YJYGfUBZWkkyyrerKah/L5TQkr7VPC6p9c1T/zbKnssWd8XIECAgD8ZH6M/w9S+LvOoNJMxMbSzf4FWdhkTjwDFIZ6E5U2sqZmk/3TEqPic1TVUu+/YX4pvq5ZNtftWdn9pvSapuXbS3xGQ9ZAGEi9AgIC0QSP7F4W2U2qkp5Xr37R6zRSrTHIDY0YRMVWTH2XISka053c6XNvUtDExSVH1eSY+hir2ryixku7Hv1PzfSVJpjKET0XkIyuMdeIxitqSpa3IAgT8AfdTVj7vf/PqCBAgQIAAAQIEZBGoyAfyZyKtMS6qa4iKLSZpsZBkhpVLVeefHpantLgVFWmPvP0pajmSdQzpfSprgZLeX3LWKOnfTNoSJas9svaT3HWU1WZF95u4bbLaqCCykiUqK7VFgIC/oQ/HZjFLXdZqjQABAgQIECBAwD+Gf9oylhq2nGXYfaosUL8y1+KU3jFxyVly0noOil476e3SdEwlg+elrUmqCvpXxhqlLFQdM5aOQepZbQWdnkiP8U0RD4R0TFyW8VgI+KuhJqNPZea9nk3ZxkufwN8+SKl8AFCGjCg7kStDtDLCnZceSMuEqyHLVabMddBQ/jpqZEu7m1LW59JEJiMyGVO67ikSpXQM1k/JbaoCpDcRyOxxVJXnp+i5yNouue/K+0wgaQJUifToT4ruU7CMCRDwlyA2Nha/fv3G9x+/8P3HT378jolBNg0NZMumgWwa6glfZ9OAmiyNNAECBAgQkKH4p6UtUoLKzOWqtoilZAVLaxC9spYFWZYMZaw3yRxP+jcgsvH67Qe8fR+ND9Gf8TH6C37++sUE5Nfv3/gdA6irq6GYkQG+fvuBD9FfeLsP0Z/w88dP5MyZAzlzZOdHLnqdM+45R3bkzq7O/+fUAHLlUIdWvjzIp/EN6upxx8+eR/ScLY9y1yGNiHz9HlduPcTt+88Qi1hoqKtDQyMbEykNDXW8inqHg0GXcD78Dr58+cLXSBnQ+WXLli3BI2+u7DA2MkCxooYwKWoI06IGKGakD1NjQxgZ6iF79myK92mypv/+luS3/pfGkSRQ5v5Kz+skdgv+wZplsapuT0o6kKrYdzpAsBCmP1I7Zin6PcEyJiDL4P2HTzh58Squ3X6EXDmzQ1dbC5WcbHHywhXMXOqH+09e4Pv3n0rvlwgHEa5v338oRVbIapRfMzf0dLRgXqwwzIsZom51J9Sp7IC0IvrTFyaQ2vk1k7w/YYEvrtx8gCs3H+HV63f8PhEvOo/fv2MQExO/ICAyWcPFDqNGjYKWliZy5syJHDlyiohnzpzQ0NDA79+/8UtMWvn5F37/Fj3HP0Sf/fz5E1/fPMCjJy/w4PFzBJ25jOcvX8u8bpbmJji9eynya+VL8/UQIECAgH8ZAhlT1QpD0VVPSlar5KxeKX43HQPtZa0UpS1BieOCNHIkWRmcvXwdEbce4fHTV0w6vn77zlYusnZFvn6Huw+fMdHInz8/kwKy9Ijh7u6OTt0romhRI+jqFkT+/FrQ0tJC9uzZmXCQRYee6XuPHj1C3rx5+XPaF70mYiVy4/3iWqvfv3/nZypRIf7/+/cf+Pr1C168eIH79+/j7t17CA8Px82bN3H74Utux3/HwzBlRBcmKO/ef8THT58R/ekroj9/4WciWOKSSWICY25qhBqVHPAq6i0uht3EpfBbuHnvCX+uV1AbFmZFUaqEMYoYFMSEuesl59ygQX20bNkKNjbWKF68OJ+feL9EsOghPndFQedWsmQpvg5pxY07j/Dlpzq01ONLfFCb3r2PRtSbN3j99j3U1QDtfDlQIH8+aGvmQe482USu0SxmYVE5lFlFp9O1iJWRLK+GGNWfQ3r9lqq0JCk6rqdnv0xHwW/xGCtYyFSPjLLiq8UqYCr4+PEjT2rvrv+X/rUps0A5oHRzRaaWiCX3vfS4XokHjcSkKxl3XKx6diYLT168wY+fv/Hj5y/8/BWLAlp5Ubx8M8l21tbWXKdLR0cbBQsWQsGCBWFpaYGqVauiWLFiPGG/e/cOwcHByJcvH2rWrKn68xS3OTYWnp6eOHjwkMzPjQsXwut30fjyNd7dlidPHujo6EBTU5PbR89E+nLkEJFQar+YAIaEhDC5I+Joa2uLcuXKomzZssiVKzdu376FW7du49atm4iMjMTLl6+SHD88PAwlS5ZUybm+efMGRkZF07yfLu2aIlseXTx//pz3+fr1a36mR0pDSo0aNbBr1TjkyZOL+69SBEGFxCRVUHSyljWAS48rsj5XkggoPEnI2a/Gl6eiF+/uSsaY2NfX+Tnm13f8/v6VX//6Eff8LRohJy/y629fRdvnyaOB3LlF+8+ZQx1WNRrza/WcIouvmr49YnStU3YBpqcLL639K87NrhbzUypc4iPefYiGmYkh1LPHLUY0csWfS0Zkf2bwXPknE73YjCBUcu4zqk2pXbohPnz4wAYCeRAsYwJUBnIhbtx1BN2Hz0/yGbnTjIvoI/LNB7ZEkWWmSZMmsLe3Y4JSoEABHuTEFjF6JitZnTp1ki2umhYQkQgLC8O1a9eSELEcObKjRLHCuPPgGR4/j5K8v3WrH6pUqcKLE2WC3589e8aEk1yHKSE6Ohrnz5/Hpk0+ePv2LX9PVdDV1WXrX0r49OkTnj59iidPnjCJLFWqFCwsLNmSSFjn68/vGRkVgYmJCRwcHFCoUEHeP7VXW1ubidnVq1dx+vQZnD59mn9TwrFjx7D7YFW09HTF3wKyqpKVtKBOAe4X1L9v3n2E/Jr5YKCnK4qzy2Q8fxmJ6zfvIerNW7Za5oiJhhrUYJj3Oz59/orQm8/w7WMkiunnQ6ki+eBqq/PPJ3jQGBRx7S6OngrFuctXEXL1Hp5HvsGPH6KFc37NvKhS3pZDBZo0qoPCBqq7VwX8W8galjEVMfwMYe5pdUcq64ZM6XhpLeacnPtR7HpMYBnTiF9lSH339KVr6DF8Lq7ffggLCwt27U2ePBkVK1bkeKfjx49j9eo1TEpSC0NDQ9y/fw9pBRGw2bPnICAggAmgZr68+B3zG1++iFbA5PqztLSEg4M97O3t+TURDH19fZUSoz8NFy5cwN27d1GxYiV2F1MM26lTp9GjRw8m2PR4//496tSpjfDwCCa7BEP9grC2LAFrCzNYW5SAtaU5HGzMRRM93bOJ+riqLGUyrWHS/V16tZzoPvj6/SevaPUK6Yj6PNRx/+ETfP7yFa8iX6PPsMl48OgpShQvhsjXb3nVS6C+VNK8OO4/fMqWXTGo3xjq68KipBlsSpeEqYkRmnvWV2psS/XqPu7c9M0c8e69qJ2K4LFPcxTWzYNfXz/x/z+/fUJ4cDC/dpoWnmT7e3McUbJGqwQJL2wZK1AqqWVM1riWFSwvUtfYvf0Q7D0sOl+xRZfGAgoZKFzYkK3hFy9eRFDQCZw9exYxMb/h4VYDPTt4obJzmbj+LcPym9KYnt5JYn+5FSxDLWQKWLMFy5iADMXS9QFMxBo3boyhQ4cwcSKiQxM2vXZycsKQIUPw+PFjyePhw4e4Fn4RJ89cxNt3KU8S7dq1TfZzskaQVe3r169MDOiZCMGJEyfw6tUrdqVRzFRISChMTU2xeO5U1KtVFSvWbsLshSsSxD3R5Dlv3jx2PQoQgX5Dekhj8eLFuHcvIUEODDyKTp06cpyfg0lO6OoUiJ8wxINXFghHkMbpC2FYs8kfD548x4NHz/DsRSS/7+5WAwb6hbB7/zG8eBVvIRXjzr0HktfOzs6I+fkNz1+8ZFd0gwYNEB39iRcQ9+7dx93bN7E9YD8/CE3d68Vn62YAtm1YgmPBZ3Hrzn347zmY4DMDAwPUr+/Gz3p6eqiQ/TQTsX8ZlPQjBo0Do0ePRoUK5RNYCyl8YsSIEUzG/dbMw7J121DDswfsrUsicMcyTgAiqylZ2gUZGQGqs4zd2KecZUwV7DoVg3a6svrUWKoyIxZMatL7+fMXrtx8iHz58sCkSCHkzJ1XpvwExUQ5NuyPG3efsItOTzc/jAvrobixPooXLQQzYwPYWpVE/oJG/BWSW3j+Kgq5cuZEvnx5sWqjPzb7H8T7D9EJmrJt21Y0bNhQblODgoJQr55bgvdGjBiOcePGSfzsKU1aFLRPbjRZMNDXg3HRIihI8Wm6OqjgXBb2tlZ48uwFHj56gvsPH+HYiTO4dSeeVNDA+fbtm3Rzkf5N2LVrFzp37sJjw8KZY+DRoE7Ce1C6L8e9lnwuK75GVURNlhVMhmWX4hxnLvTG2MmzUbRoUbRq1ZKtH0SkfH192FVVooQ56tWry65Z6htkEdm6dStbAIn0Kwrqx7VqVEM911ro1rk9smnEWQcTI53JKg37d+8/xOfPn1GimDE0NZXNiJX+TeX81vLGYxn9Idntk9lW5ZBh6Xj74RMate6Lc5ciYGCgj/LlK8DKqjQvMgsXLszPhQoV4n5AluGFCxfB398fI/p3hbfvTryMfI18efPA1KQIypezhUs5W5Qva4USpkWhJhYWSSl2Ut65KzLX/SmxZbFKtjMNyRapspCl5nhq6iLLmGX9FGPGMtdNmYZOovQPntYOmZrg+8wMvI9zLY5bdwlTpkxNsunD0+thbGSYoFNSRqOhQ3yQvSyQaX7qiK7o2d4ThnbubG3iw6mrc0A7medbeLrhYtg1zFnszZ/NnTuXB6wCBfJzsDuRwy9fPnM80trVq3A5NKnL4+TJYJQrV07hUydrVr9+/eDtvY7/J/I3uGcbaGpq4dDRE7gQdhOPHj3Ew4ePEriPcufOzQkDxYqZoFgxU35taloM1apV43MVkDxu3boFOzt7eDWuh9ULJ0Mzb+5kJ16ZE3Myk7WiLktlXJJEvqQ/p/9joIFufUdi45adsC9jg959+3FMY0oxfps2bcLgwUM4zk8MiqGrXbsWvn79hjatmqOQrg7y5M2DPLlzQosSPXLnzNwJNLmxM4XjJh13UyZlyZKxrErKEoGsW4EnL+DMhQicuRiOuw8es8aftMyMNGi+pHmzW7dusLMrgwcPHuDcufOckCTG0D4dMH1M3/hziksQkNn30+razKxQoKxiAVfTSB+SpkI3pUDG/kAyRvyZ1NXVSYQXsRwU/yLyPaxLFeMBn/Au+iu8es5E0PmrSb5PgqaPzmxgeYjEnW/Y1NWYu3IbvxZbo2QNOLPH9cWFGy+xfft2XhVSoDkRIkKtahVgWdKM5R5u3LqH85eSki0xdLS18VaKHBE8PDywYMF8dpekBJKkmDRpMgIDj+DGjZuSIPGdO3egYXU7nLsQjip1vSTbU8xH9erVeYC0sbHhLEUiZIILIXU4e/Ycx9KEnt4Hq1LFU7SCZFUyhrgs4EPHz2HJirU4HBjEyQjkbsybNx8mT57E1jIxKHZu3779GDNmDCeZ1KpVmy3A586d48/JgiKdGdu3dw/MnjFFdFhF7n2BjGUpMpYEMT85LOLV6w94/uo1L2Tz5MmNV1Fv0HPoDER/+syZ1ZR1TZOwLJvHf5sXo14NF9E/AhlLXwhkLAsFGqbmuJmhCZa40/BNKnI1Bl+6iV2Bl7D76EU8fZmQwBAmD2qF0b2bsvvxv6MX0LjrZJmHaOtVG+vnDxMdK26SilWTmqCkAm05K+zeM4Rcu8fE6u37Dzh7PgTD+nZACw9XeG/ejX2Bp3Dv4TPcvJ00uN572Sy0bNoYH6M/4f2Hj/ycI3t2fPkWg8io1+g3ZDQePX7CMVy0ehSDkgBCQ0PkXr7169dj4sSJkgmvdbNGcHKwgUMZK34WE8no6I+Yv2wDQq/cwJXrd/DoiSioXB4GDBiA6dOnJbuNgHhQJmq5co684ls9fzw8G9SQG3gu33WpYjelvCD9RJ9LuyslBC3u+ebth5i/dA3WbfLj/0lyZffuXWwpe/ToMSd20L1BcVYbN6zjjFNPzyY4EJeVS/+LFwaEihVdcPTQXtnnm06QS/qSu74pWM2StYwp8H1u1+9vkv38/B3DloMC+TVlhiLI7TPKtDvBDtMhRk+qv12/dR/TFq5jDwCd08uot7h6/Raev3iFyKg3Sb5a1s4KvitnoYSJQbxlTHydaCGS+JzlJT+IoUzfyqwQoKxAplVJ0FLYT9a1jAlkLPnrkOiHfRn5Bst8D2Kl31FEvf2Iooa6aFzDHqYO9Ti7jQLSX758idu3b2PK4FawtSiGooUNWBuLLFoXr9xjIpXfuCxK6n7BxbDb+O/IGbYEWZUyxdzxfaCZL49cMkaIevsJ6jly4+nzVzh7MQxXrlxjodHDQWfR0rMeyjnYoFGrPjJPp5R5cckgq6NdAM0868OzUV0UtaiQYDuyKpC7i0BWLrK2kWWM3D/iJACKzxB35uLFzfjcxZg1aTgG9myXbH979uIVipWpnWz3JJLXvHnyrtp/HdTXKlWqnMA1J0bkrRPQLqD1x5MxKgwf/eUHmrTujOMnTvE7ZOUwNjZm1zyVowoLvczjohgk+0FCwZSYQrGIZFkrY20Jc/MSImFe8Xn9A2SMppVv377z4osfH6Px/sN7XLtxBxcvhSIk4gZbkWiiIlDFiFP7N8LOutQfS8YoNizi+h0qWga1bNkxY95KBJ+5CEcHW1SqUA6WZZxRunRpTJ06FQcOJEygaN3EDW/fvecqIZQewLrIamrInSsHHKzN4WRvCSc7S9ZlFMiYivBHk7Fru5QiYxmeHqsqGYgE+/yVBhmLtAUkXr56D44ewyX/16pVi7OdQkNDcf36dYnpu4BWHqipqePdB1EKuhhEskyK6KGSozWaNqzGwqXNe0zkoHTK9Dpw4ACTthbutTlINW+e3GjbrCHMTYuImp8tL7bvOohWXQdL9imWfRBrKdHEbG9bmsNQb9y6iy9fvsrNwqPjUeZdZRcnhIRdxWcphX1FQVlNlCzw6pUo241QwbEMVs4Zg9IlTURvJDOAk1bQxm37sX3vMVaKNzIyYo0sKg9EcSFiNX6Rwr2oTBC9pmyonDlzsUuTSKKjoyOfE7k7/7Ugf4p7cXWtK/m/jHUpzJ00BLramrCxNFc+4DiZ+0TspiQ3pCyXZbJirsqSMunvSAX60zGu3LiDw0eD8erVG+TMlRMejdzgYGeT9DjJHS8lZFRVAmVJTXLxX6xJ9xkRV28g7Mo1hIZfQ2jEVbaSizXpFMWkEb0xcmAX2QRMUVKmItBvTuMB6a9RuMWJMyEIDL7AVUPED5LsKV/WhhfMp86H4e6DJwn2QWMGJXnUrRt/r4hBYwzFrlLAP8ns/Pfff3BxqQAtrfw8rtNCmp5pwUOi0RQGQnC0s8TIvq3R2LVSXEPT0TOk5Dy+7+h5HAq+jGcvXiPyzXu+Ptk0NJA9uwby5MqF4iaGPLeUNC0C82IGKKyvq3h4SEw6/OaK1hJOI2ljMmblrloy9v6qf/pYxlQ1CKmqJJGqyFgaC22/iHyLIuXbS1KsDQppw1BPB1alTFDJ0QbOdqWwbvsRzFm5HS4uLhg2bChKlCjB7r47d+7gzp27HGB99OhR/j6t1EmIk1DKzBgDhozAzJmzeFAgyxNpgzVrXAe+q+fEtUEDvtv/Q4deIyTNatu2DVatWiX5v3///gjw98edsJOYtWA5gs+FcOwWreCcy9rhclgE3rx9jwcP7nPGY/PmLbhNNFCT0CuRK3L9EKHJoaGGD58+IzIyirO85KGIoR68GtTE0D7tYVgoXjZBZnyRnD5BA+2x06EIPHkJV289QM4c2eKKhudAjuzZWKSTBhKygKirqXFBcio+ToPw42evEHLlDq9i6XNy0/n5bWEdsn8FNFlQFiwlh5DG0qOQvVzOSenC82mZWOXtVwVlfGQStBQJnPRxNRRbWaf1HFSBlCZb6bbExuDR46c4c/4STp+9gDPnL0sWhhQjRVmGZcqUwY8fP3kMIqs9WbATl9+ieqbmpsYoXsyIH67VXVDJySZeey65wH4V6tDRQuvFqzf4/uMHS/OcOHMZx0+H4PqdRwnczARaeNE9TpUzaFFGYxTFCBoaGsDFpSKPwWXLOiB79hx8f9CikcbcFNsRGyuRAZL3OS1i9+8/gOHDRYvzT3cOIbesRJB0dlPKw5Hgy6jXdiTMzMxQqhTF4eZhV7249u2nT9Es70JWY3EMMo39tVxsMHVYB/QbvwKGetooqJMf+gUL8DxXWF+Hx5SihgXZsJAhRE1RgpYcEt3T7Ia39hTI2J9MxpL/XAMXw2/BuVE/hQ9DwepkyRKD0vRpAKWUbFJcp4FzwrAeGD2kl+QYRKTmr9jIxGTqnGV8g1GtRHV1De5cVOZn1JC+OBp0igdo8X7ppqPVn/eKBejUYwCXGyILFLl5yBpFOmNUAogGQ7Km0c368W0ktHT0OKORMjNpYKbPaTVBgxppiAUHn8Ty5cv5OFdP7oCFmVGqyFjS7ZQb4Gmyibj5AH67j2H+qm3cvh49uqNPnz5MMv8FNGzYCIGBgfz63IH1KFfGIv5DgYz9lWTswKEjcG/Wnl9blDKHS8VKbCW2tCyNyMhXOHLkCPbu3ccEjMYKWhyWMjPicAWLEsVQorgJzIsZQreAZlKriIJWMFWQMSI4FK4xbNJS3JGyaFGVkGou9rCrUJsNEOJSZ6Q7SCXcMirRh4ggVa8gCZVLly7h0qXLvFimdvfu4IFFU/rLObHMIWO9Ri3ESp+9PHZTAhaN9bT4Jo09koih60Zkk4gZjf2UAEPWvp5t6+P37xis2nwg2f3fObGWS0/JxD9Jxq7sgJZmKoQAU7pYqrgAyh4zNaQrNftV4py/fPuFys2GI/TafViWKIpPX76xmZyeqbZjEf2CMC1qgNx5ciF7tmxsBqeblhTO9QvpIE/uXBwcnytbDFr0mSHzsLTysDQvBh1tLeTLkwd58+Zh9yQNOuXsSsOtTtUErhrpyeTOvYfYte8o6xPRzVUgvxaMChuia8eWWLpqE2YtWMHxIdKr4MH9uuPGrdsIv3ID7959wJdEmkxkFaNji03zdD7JWcXEqFnFGT7LpqKQblznllJyl5kSnlLafSpcynRdqN0Bh87Af/9JHtyNTUxZd4sU6v9WkJAuZQ9KJ1wsnTEM3duK6hLKhbyahMpaxuURFUX2o+jvnGhfSQiVDGtZitulSLTSqTZlapDIxXv9xi34bt2J+YuW8wLJ29sbLVu2YKv62LFjsXXrNl7QmRoXRsM6ldC4TgVUdLRBtpz54uPwxNc+5mcC93OSQ6dlUZWS1Ynu29hYrNu6H10Hz+B5bc2a1TwGkSSJuC5uZoHa1qFDR2zbJspoJ1iUMIaTfWmYGBmga+tGKGKgkzHq/bKSyeSAE8zOhWHH/pMIOHgaUW8+MFcoYVIY2vnz8dhO/YbYBr2OiY1F7Ur2GD+oPaI/fcGRkyG4EH4LF8Ju4dHTV+zm/PL1O3seBnR2x4zhHZPXmkwPN6aK8DH6CwqUaa5iMha+FVr5U6G9RBdKTD6kL1p6kDBF26NqkqYopC1eia7Dt28/oGvXnF1hhJEjR3BaPREmWkVQKaHHj5+wxYlIC7n6KKaA3AEEqoG3ecVkVCpXGsdPh+JyxE1oZMuG3Lly8qqUStLoFZS6kWkwV9PAw8fPMWvNHl7B0L7Nimija/tmMDWOW4moafDNscJ7C9Zt3sXbWFlZcdDt27fv2IQ+ffwQFlP1aN2TzdEEshYRMRPXmxSjft1a6NqhJa7duIXgc+GIiori86CVFJ1Tr67tUdhAj2vovXr7ma1sZLWrVrEctPNrYc+hE7zqFsWP5eO+WaO8JRq71YRRYYN4UkbZSXGDk1rszwSTQbJkTNkbO+73o6zTeu3H8MAUsGcf19z8W0ADKK1oCbRC9/DwlHzWrFFNtPJ0RYPaFeO/oAzBUrBotirIR4rxLwpOTDLbwtnJqWh3eseKpXjd5JDJuHZNm70QE6fO5tfdu3dnUVyKl6Q4p359+7Ce2oDendHYrTqszYsymZFcZ74HZWQEpkHGJCUEn4/AlPnr8PjlB9YypDhQahONGZRIQWOM9IKRvAVZZfFE07G1tQ17HMQQW5WIzDjYlEQp67JseaSqIvS+2Hones6HJrXKoH4tlyTX8/6j52jVfw4nopCljx4UHiJ2Mdey10PVis6SOqoyyzgpkGxDcbYfoj9zOMfgmVs50YyuOV37ChUqoG6FEujT0Z3nJck5xqazRS8TyZpAxv5AMkaFttsOnMOriylTpmDw4EFyd0Ms+/DhI9i82TdJkWtChbLW6N3JC/cfvYB2fk00aVidNb32HArmm9LDrRomz1uLA0fP4MPHTxwTJQ2Sili/ZAoTqcNB5zBg5DQ8fPxMQhIpLowGBxqQCcWLFUU5hzI4ezEcGzduhKNjOQ72Dw8Px+XLl3Ho0GHs2bMHdrbWWDxvGuYuWIZdew8ykSJXBsWYHTkicnlVq1wBQSfP8mtya1paWnAcxt69e6GlmY+tbwSKT0hciqe4iRGunPIXlR/JYDJGk8yL19Fo2GEkbj94gdWrV3FJoL8BFGRMFRESY8aYXhjSs3XSLwhk7I8nY6SdRZprM+cu5v8rV66Mw4cP8YKQSpvRvV+/bg0smTMJRWgRRBav36L6rsqSMSIE2/YcwaHj55l8GOrpctFtWmCShY2eWQcu6AJv71rNSa4Fa8LcdZi5ZBN69OjJFRW0tDR5HBM9fvHYkD9/AdZZpGQccqllRZ1BMoJQfC3F5FHbKVN34cKF3F6SAhLHqJJ8Dy2kyTIpKjX3ELaWJZAnV07kyp2TSQ9lZb6KfIujpy6zG5ESBygrmBbW4mPRglengBa6t/PApGFdoa6WyE6jZLWMToNnY8P2wzI/WzixNxMyMdQEMpYKy1hq3JTykFmWsYzMtEyBgIk+1+BBokXfGdh/7AI2bFjPNR5lgW5MmhQpNVocCEkrPspwJNV4Eh6cMWOmZHuyTomLGJNcBK2oyDUovglpZUWB/ydOBHN8Aq0Y7ctY4eDOtdDJr4ljwefg6tU5QRs+f/4kMRkT2SCiRaC4EKrpFxR0nGUoJowehlXrfHlbM1MT9OnZBV07tUOPvoPht30XlixZjKZNm0pWZ1RSZ8uWLdy+pUuX8L6la0OeOXOWi1KTJTBP7txJXJ5iTBk7GK2aucPIsKCIkImtZOLXRMwSDy7SZXokpO23/N9aVkxfnOuSXMuurUfgXMh1Fq8la8KfCsrkosLgiXHz9HYUMSiE3LnlZJKKJ3W6nmkIqJdrgVIUyii7p/A9eceWG+wvZ/vMgMJxa3Hb0eRevWFbTgLq2LEjevbswYsmHx8f9O8/gAPw50wdjWZuFQGNHHEH+S2536Tvq8QWFmnXpPizpet2ot+YBShTxpazlsWB/0RCyHV4O3g93n/4BD2bBrz9lOFdMaBrc1wMv4FrNx+wN4GC8H/8isGlsBvYf/QMQkIu87j4t4DGSPIe0PVIbq6eM2cOexvIYimq0/uV43LpNY3LW7f6JSGftO+wsDBs2uTDMbkbF49Da8/aKZMx8ftSIA9Pu5HeTNblZdTSmE9zk65WDp5ndLW1WBJHp4D4tSYH8btWdmBXpewLkgZrVwZayhS1jMlgCfJx5vINOFibQa/gvxGgnJEYMnU1Ag6cRN26rqz0TVYnsixJY+7ceUzEKLidbh4XJzvMnLuIBSjF21LnpzJCRYoU4dUTkWhaAe3YsYPdS23atOEBauzYcXzTEaErUEAbx44dZYLWpk1bnDl9kk3NBJvSJVG3VmUcDDwpaceIESPRr19fDsSn+KgrV66gTeuW0CtUEFFv3nN2IYFuokF9umL8iL48acdq5OEMxI2+2zB69Ci0bRtf+JsGh7Vr16BWrZowNzfnoODEoNRv0gCjDD4iYuQmLe9oz7plOoYlcPbUUVy4GIqJMxZh7JR5qFS+LOZPHQ6Tooa8mtfRyoOiRdI/45FiHoiIEQoV0sOTJ0+zjBtEWdBvmxhEkKmunoC/E1ev34ZDJVGtWC8vL8ydG5ddDWD37t08oZ8L3M4WJ7UfH8TVFVMFihdatt4fE+etQ8+ePTFv3lzJZzR5kTucsnVX+/6HA8dE1Q0IY2au5u/QIpbGGbL+iLLO1fD67XvexsGhLMLCQjmh6G8AjZHJETECTfaTJk1K1b5pHtm8eQv/f/ehyAsixqGg89i+5xgqO9vAtaoj1NXVcPPuY9x79BwGhQrAsoQxjIvo4VLEbdy694T7CSUy0cKcnim28M2bt3j37i2H3FB4C71+//QqSwy9/RCNR88i8fZ9NN68/4gPH0Vxw+XtLbFgQg+Usy2ZYn3irIov30TW4pSglGVMGuvnDES7JskLaKp6VUgE5fGzKOTNkwuh1+5i639BOHYmnFNhzUwKw626E+rXdOIflG5OqpOXbEpsaqGCgqaJV9Prtx3EqBmr8ebdRyZCdL1HjRqFggV1ERFxhSfFY8eOoUqVKjh//hxLMyTGwoULuBaaoteyffsOTNQ2btwgKT0UGRkJaysrdGjdBDMmDmOrG+Hb958IOnkO+w4dxc49h7lP0MBAsWBPnz7lbQwN9PAq8jXHkxCxo31RnFvVSuWxafUi6BuIiFClWo3ZgkaB4OT62L9/n0gUUwGQeZ1cnyT6WqRIYZk3KA3itCobPnyERJ+HQOcyqE9n9OzgxTFp6jHfZVjLYuJX94mqIMhFIsvYYu8ADJiwLMEmFStWRMeOHdjiSbEdfwIaNWrErmNv77U8UJMFdP78eazxdvnIJpSxitMUS84ypACUtn7J+0wFZYYUdZek2Gap1/cePMapc5eRLXsOXkDQvfvsxUs8ey6K+XFxtIOLsz2KGCazUFA4K1iBhIgUxiMSRza1qcavaWFIWYSzZs2CiYkxL+AGDRqMqhUd0btLazR0rYLs6rHy2ykncYZI1KylPliw2g+fv3xj69uMGTMSaPaRpXzz5s2S/0l+h+rPjhw5UvLeuXNnuX3SC1dy7fn4+LKF6NChgzxmClC8vBl5JH78+IYurRqiTlUnrtvarOtIFCwo8qzIowwUa0a/qzS2b9/GmpbK4tevX1zLk7wKFD9nUEgH9Wo4wa2GE5zLlORygJzc9vkbPn8VJ7p9ZUJeSKcACunm52S3fOJauRms+k9GjpnLt7P0VEnTwth//JJqA/il4btwGFq611S4cQoF3yYaJEj35eqth7jxMpZXR/TjSKt+k9nczc0NHz68x82bt3D+/Hke3KRrKVL9QXKF5fn5Aq9ev2MTqLmpEUqZFUUxIwPkypUjock2vX+0uHN88uI1AvYHo3SpYqhRiVYaouvz+esPXAy7Ad+dh+C9RRSPZWpcBLZW5myl6tCyMbLnzIN+I6Zi9z5RjJUYVhYlYGZqzAGrugYmvHKln3fo0KFsxVIU48ePx6xZs/k3r127NupUc0b1KhVhXFQkBktq2pv8drDeENXHrFC+HN59+oXuPXqxphlZsAj0O+zfvx99+/ZjsrXTdw3s7Gyx/1AgPJrFq+UTGaPYDmqjKgVUKSWc3K8Ud0YPineZM2cuWw/p3KjkUukSheHuVhNu1R1F/YDiXqSIGUEUe5aoX8hLRIn7fe88isS0tUewYcPGJO2i5IcLF0R9NauC3OHly5eXDK40KVOsyfOHN7Bl5350aNEQa+aNFm2chpioNLkhUyoNk+BAKr6v5WiPff78hesS5sqdh11Dsxeuwsp1W5LobJG1gKzX5DoSB2uTdhW56BdMH4VWTRuKdp2S2GlqzksBonshJAIh4dc5g3pbwAE+J+0C+VmLj4pkS+PbkzNxFQYUz4K8EHIFLg17cJkpymaUNT6Ri7xdu/YcB0WB6+dDrmHMmNGc1OTr68sxo1u2bM6S8V5/MmiBTcR4yxY/yUKbBMep3i8RiqCgE/x7k54YjQuUSEBeF3JpU3yvnZ0d61nSwpleU3JAavHz50+cOXOGw3IOHTrEx1EUtOjt1asnJnerJklKSIB0nOvJVZvHQhQTl18zLyc0qJSM7Vo1BiVMDGFhbiKaSJRYBStDxsKu3cXQyctZlFM8cJUv78yievTj0gBGkyuJC0rfiCdOnOBJhNxwRNomT57CEy9ZaH58/wr9gtqIevuBTeNikEowWc8oyJ2sa6QJ8vhZJArqaMHe2hx2VmYobmzImYUJmHYy+lO/Y2LYMkcT2dmQ6zh4/CLCb9zD7XtPOSuxU4t6OHvpOi6GizoWDTRTRnRjIdapCzci/NodnNyzBifPh+Jy+C3o6BRgS05ll3LIlzcPoJ4DX779QPcBY+G3M67eXTJo1aoVuwAVBXUJGggPHjzINwG9pvdMihphyfxpqFOzKk80C5augo+fP+zLWCNXHi34+G5mEzmlvEsPrhSLUKGCC1um3jy7zROOr58/OnVPWEKJfkuqMECfU6YPddxChQrC0LAwzMyKc8wb7YNucjJzE9F78+Y1WrZsxWSuatUq/L3kQNY4Siigm5qSECJCL+DazXtwtLfCno3zUEgnn0rIGJXXKe85nO8T0h77+FE0iFFh9axedomspUWLGie7zdbV0+FVvzr+dTL24lUUduwJxOXwawgJv4Zbdx4kWAzm19LE4CFDWYOOrDfirDJp6yjFR5FFW5zAQpgydhDXLaxe0THe6pOBZEwat+4+wJ79R/Hl6zeeqPn56zdcDrsK93rVMWFoV4lY64tXrzlJSFc7P0vuFDHQ5WB8sghK49vXr7Cr1QE6eoXZlSWPUPn7B6B169bwWToBbXpPwODBgzFliuyauwJUCxrzaaFA9WeJjFEWZmbjwYMHuHHjBodKiLPpiZyTwC79T4YIipcj4XCKvabFcMThlbAuVYzPhyQ3KFTm12+Kf/vNnIA8bfJAXqoBk1bizKXr0NMtAP1CZHUrwN44rXx52EIn2l8MV7OxMjdBKbMiLDN1+codhF2/j8J6OqjfcXzmlkNK2VyecCCm1WPgycvoO2Yh69NQfFTNmjX42Kpa/dDpEpOnIHBKe6YMFApWJSZPmXkFCuRnIkEELjQ0jDsi/cBi0GRvVaII6teqgAZ1qsK8eFEemNZv2YM9h0/h9KXrvD11DGoz7V9UPqcczM1L8j5WrlzJK4qZM2fwqmHcuPGs5EzbU4cnnS2K9xKvSsRK0EREnJ2dUdamBEqZl4BFKTMEn7qACdPn8+dk6aFBr2TJUqyETf+TaT+51QkNrkRMqAPTqp7iQWjFo62tzZ+TJYl0hMSYMXUiPn7+hmnTpvP/ZC2hwH9qK7Uvltx8auro27cvRowYzinXXbt248BfAsW79e3bB25uDTilnILDyZX46tVLPHj4CAH+23H06LEkFs7EIIVn85IlYVTECIcPH+S+Q9du+PBhGDZsmFL94eTJk2jbth2TvcAdy1CoQBzhlnZhSl4no0smVUKHtJVqNhuEkCu3ULNWHe5X4eERXMqKjkkLjBcvki9anlmg9u3atZsti1QnlNzk0vjPdxHq1ayYun2nlnypSkMspW3kirAm1Ny7cfse5q3cxtYDum/JAu/gYM/uXLrfqS4j3bcUA6mICjvdP+SCE9csFCfZkOvSo6ErjIsWRiFdbegV0uVJwcK8OHKRNICqCFoy5ywTMo7x/vVLFLKqJ3NzGofKO1jCxbEM6teujJJmJvDqOBi7DwSxW4zOvUKF8km+t2zZcg7XsLEsjo9ffrN1m66vYA0TkBL27duHJk2assWZ5ltKfqD5ODGIX1CfovuNPidjj1u1spgwqC175lr2mc4kqnr16nFEL5KfiReRJ4fuVVowiavbUN8kA45t6eJwq+GMauVtYV65vWrJWBnL4ujYoi66taqfZKWTmgH04dNXaN9/Gp48j2QG+vETmfi/oHTJYvDx25klMmGIEBB5e/jwEetdPXr0EJfPBCIw+BIzYroOGurqnMlD6dZVaruzNScq6jUPxtWqVeVUZGmXFO1T+n/6CcgKRQMyBcwS+aGSG1RgmFaFROxohUIuwGPHjuP61XDcf/hEQlaoDfQr0qpbmsRQjFKNGjVF6vYf6fEBHz585GeyLFFw67Nnz2XGABDpJCsVrUIovoywYulCeHk0wpKV6zBxovwg0a4d22D1Oh/07t0bc+bM5v5DEztZAObPX8BuB+r01E5yNZNLtHbtOvgdE4vp0yax1YiuNV0/uvZkzaJzX7PGm78XHnGLbzDp60lq/tWridyjzZs3R/Xq1TgejW7C5LB48RJ2dYSFhfN12LN5KbssVUHGbt57ji27jiDw7A18+fKZ+7ja7++4dusel5GaMUNEaLMCxP08IiKCpVKoP9JvQANN7dq14OnqzIsPnfxpi3f7k8kYE/dzIZi7xBv7DwcxwaA+3qVL5yRhHKnFqVOnOfuN3HdkUd68fiUCg07j5avX+CQlhkxZ07WqVkD9OlXgVqsKDPRll6Ii92LgiXMoWsQARQsboFBBbYXc47S4Cb16B6fOXsapcyHYc/A4PBvUwrolU9n69fv3T97mxu37OHTsDFuX2zVxhX9gCMe2UgLOihXLOcaIHrdu3caF08dxIfQqE9XSJYvj85ev+PbzN8cg0mQ2dOgQHvuk20dxn1R+TV0NvOgljBo1MsECUYAAWaD7leYdysKnSgDFipmyh4UW7WSdFo15othlIli0uBELkHuvXs4esqrONgg6F8H7u3nzBgsDywORLQqXou1uXQ7EhdCbOHP5OqqWt0XQ2XDVkjFifLQ5PbdsXB292jfm1Y46YhUIaE36ebmGA9j6RKCVpZeXJ+rUqcOWnKy+8iGLErlFHzx4yK8pLo0CXDMKxOLJkkfWpUePHjPrp99mxYoVkm2ow4ljVUg4ljoaWcg08+Vj6939Bw/5M7KG0eqdWD65JOmZfgOy0NF2Ojq6WL58KZdTIp5HbkI6vqtrHYngLIEm7rIO9rC0KIm16zYiIMA/SZFcCsgkq8vo0aNRqVJFNG3aDEWKGOFI4DGEhYbBza0OW9sojkQadENRSSVq6+HA05IBW8yN3r9/B/fGrqyxQ31HLN1Bwb3NmjXFwIEDZSYJkNWO5DSIcOpoF0BzrwawtjBDCVNj2JQ0hl4hHeD3N9muSxmQJmOxaqLkhxeR71DNowfuPXzK15biHFetWplAtiMjQP3j9OnT2L17DxN0cqmLhIQf80JAbIE1MTKEW62KqFvTBdVdyiGPtBlfnop+RshUqKLGpaK1caXe+/k7Bjv2HMHCZd7smitdujQGDOjPpD8t8TDKgsYZWpG/ePESp06dYjc9xcnSpFLO3hoNXKuhmbsrzIvHTxhUMaNpx4GS/0lji0hZ326t0KdTvJucZBDOh1zFqfOhOHkuFOcuRTBZov4q7RkokF8T7z/Ex+0SyAJfrJgJJ3hQHyMrw4IFC+Dh4S7zHKiE1p49/3H7KcuOQi8+ff7C1/L27VsJarzSAo4ImbQifVZ28Qv4O/Djxw/WyySpDwuLUmwsIH6iLC8hkkfja+XKVVRLxp49e8pZapSpQpM/uflogu7oWRltvWrDUF+OOZ6VqePLUYhBbNFnx0FcCr+BKzfucSmNeQuXyKxyLyBlkLutTh1Xyf/kahSXF0r8M9NnNBAS9u3bixo1avBrWsVOnz4dO3f6M3kjSxS5EYlo0SBJg6O0dgxZTZYuXYbQ0BAuAk4gN6+1tRV27tyZZBVObaHJg9yXhMKFi8DA0BCtWrbBjBlTOQ39yJHDSeITKFW6RYuWqFOnNrzXieKuCOLzGjVqMHbu8EPTpk0kQfPzFyzFqZMnsGvXTlYNly5wnrh/U9FrmiSOHw9iKyS1kc6ZJDiGdKqHHOpiEhY3MUnrkUlD3L+JjEGdV/PVmw/j1RfFMFCmKe03o0HEnaREaPFjVFgPhvqFuKQWvSa5D+MiBjA2MkTJ4sYoWcI0waCTZuX7FMhOmqBil5zkXOPGrKfPXqBZx4G8wqZ7pH//fpzUklUWixTfd/jwYezbt5/rQhJxGj+sN4YP6MrXhu6PMpXdceP2A6xbtw5du3aVLNB2rpvD1tr5awM4jpLGCnKfk7uQwgcoA5hcr5SVSPG3pAFGSR2UEUwizGTNoiQEY2ORzAllfFOwdYsWLRSyFNLxaHFAxIxCJSZMGM/3hzQo9rd37z7w9/fH+MGdMW7WKtZTPHBgfzpdUQECVAuaX/T1DVRLxiiuR7wzmqxoAtu4cRN27wpglx1pjeTXyssB8RTcVlhfF11b1YeVhZlMMiYt/keEbPCExTh9IZwHgYYNG6Jt2zYcYyRAcVCMG5FketBATbFV4uBGehB5vn79BiZPnsyEiCxG7u6N5U4u9DtTwVoKPqdVeeHChtyxKPGBBnIarCm+jUCxatSdyGWoaKYgWbw6derM7t9OnUTp7UQCpdtDq4sdO3Zy7AgRvRPBl+P7UFz3HTa0P7Zu9ZEUo6V06hUrN7JL1qp0cX7/5csXCslKENmk1Yy39zpWvLYsWRzlHUpzUGYVZ2t4ulVRmIzNX+mHoZMWZ1rgMf1+RKzJnUZketn0gajmUjb530eR0j//ABk7fT4ELTr0QbbsOTnmkZKIMhrkNqYsNSI9pUtbJjuYExEbMGAAL0bePbiAfHlFYspFSleDRvac2LNnN5ycnCXWT88GNbFuy25YWFqx9bhSpUqc6ZtVsnyJAJP8Dt2LHZq7wW/XERgZ6mHBkhXsxhUg4J8lYxS3o6dXiON8pDFv3nx2OxHat28XF5/0kTMbydLiXrcyRvRtC0e7+BiwmFg1RFy/i1MXwnE+5AabyMmNkxjHjx/PlEFQgPIgqxfFcik7mNPqlyw3NBHQqpfi7Sjmi2JLaCCmBAEiZzVq1kafvoPgYO8oIWHi3vv6dRQCArax1al48RKoVKkq7t27jbZtmrBrhLJJ5VlciRBSWSkuB6Kjw4K4YpFWCrifNm0au4bIhVvNxQFHti1KEEeWAJIi6+rspiQBSse6nVBQpwDOXBBlB6cn6LqQNZPaGhAQgD27/DnGhzIfV88dKTsBR26cVDwx2X3oFPYdOYGXr96w4G4zj3po1bQxx1kEn76Io8FnUbJEMVhZlMLJsxfhv+cQQiOuIVfOnHj/4SPePbqMfMlkLamEeClCzOQQwwQWMQCr1vthwPBJbAny9fWR6PApi8SWYGVA/b5KlYSkg7KGiZSVLm0V91ya3c0Up0I1AMmtQosRRwcbRL1+i4K62lwibNuug+jcuRO0tXWwbNkyvifoXqF7jtz0YutWVgHpi3Xv3iOBJAjF65CuGFnvBAj4p8mYGCdOBLGVhKwU5Fai+JzF00fwZ2NmLOf4GxLmpMlt+fIV7P4hhBxex8H5wyYvw859QXj2Mor1P+xtLFDewRq25WuiS5euCY5NFrJt27am5VoI+ANAq/rOnTsjIGAXK/ATaAKkPmZn74SGDT1QIC7Dk41SiciYCLH8/tevX/DxwwdUrlyW+2ZKhYDr1q3H7hLKfKOgacqupcUFadTQhEXp1FTnLjg4GDvXTkfjulUUJmM0EetYuHJtvQ0+WziZQ1WgfZNbiKyhJNNBFkxyFdH9Kp64PetVgmf9GnApaynftZYCGVvjuxs9hkwV7ZMCwYsY4uzFMC5fQmWpnj5/CV2dAngTp3xOCSW1q1dGlYqO+Pr1O8ZPm48WXg2wYdn0tFldMoCMff32EwNGTMI6nx1cemvWrJkS4WNFQQvIxYsXswWawjno++TOb9++PQvpKgrqy4sXzMWipSs40YYIHUlgXL9+TbJveXBxsucKFI+fvcB/B45BT9+Ax23KGKb+QYk88kSTswLIpUqZlE8eP0TD2pXQd9gEJo6Jq5IIEPBPkzFpkIuKVHYp81F8Y2/atAndusXX4yP5h75dWqB7O098/f4L5eu2xc07D3myJcFPusnEoPRQspRQsLU444FuQOmgTgF/F4i8N2jQEKdPn8HPn/HxaFRKyN2jCRq7e6FMGXuoxbm1pbssLQratPbEzZvXZUphUGzc8+cJS3skBrl2Vq5chbJly6JlMy/OMt28dSffODVr1sSBAwdQSCc/Vs0fh5outlKFx2VkViYiY4TwGw/QecAkjotcs2YNWrRojrSCLM6kUE4TPy1ozE2LwtqyBMpYlWQxYzNTI5S1tUhSFzDx/4nDBbj96qJ2iwlKp34TsGnrbskmRC4oRnDtWm8m0c2aNYOzsxMndhBJsLW1kViC6DchUkMWm8oujjiwc13SoHdF60cmR7YUdVfKEWulcyaB05Yd++LOvQfsnpYu16UoSGePasNSKR7K5i1VygJRUZEYN06U/RcRES5ZbCgK6u8kvRIRHoqrl0/h/fsPmLNgKZ4/fwF9PV0mvjdv38HAEZPY1UgZzKTBKIY4ZvRPqfwgQMDfAjJIUVZ69eo1VEvGbpzbywHJMbFgvZsbN++geefB+PAxGlqa+eDZsA4aN23DA++hgwcQfjKAs2+08uWOX5WrafDku2v/UYyZsgjPX0XCtnQplDI3xS/1vHjz8jEKFNDC92/fcSToDGfZEC5fvsQmeQF/F8ia06//YJw7ewq167jh5o0rrPRMekskteDn58cEnVb0lSpXx5gxk6CnF0/MWzRvjNOngzmblaxgNPFQ/6J4RgIVQSdrAsXcpJT8QDUv6ZkWAVUru8DOphQOBQajkrM9po3qCc18eTmzUq7cReIAcAmpyc4CwDWb9kF+XQMORk4rOrZugqPB5+C9cAJqVXFC9mzqSYueE/FS0FokIWRSyTYkXEugFG8zh/jEEAIlWdDEnxzIFUZxP+vWrODC8FRH9MjuTQllcVIiYqqwhvFOZQvzEkiwcYOvP4aNncbJJOSypixcZUEuNUpIITmZdes3cl/csN4bS5Ys5kGZ5B7Iyk+SMcpi5syZmDdvHgL81mPuwuVcxYJIrTiZhmJByQJMqvRXryatJypAgID0BykbUHYxwbmcHS6GRKCiswNOnlVxOaQ3984w6ZIMbLG/WcfmUuhVTodeu2kHnr+M5I9aeLph6pj+rIdFujn2Npa8ipMeFF+/eYd1vgG4dusubt19iOy5NFlwk2qjvXgZBWtLcxw+fpq3ffLkcYrq6gKyPmjCGD9+As6ePYNPnz7zhEUxXs2at8aM6RM5HpGsB0Teyf02ZswY1r2iTC4STSWQ1MaKlRtQoUJFzJ83E6tWLWWpBjHEEixiKw5NkpQU4unpwcRM2tVBJbYosJ/cR2TZJamHLRu9MWHqLIwb0R8jB/dibTC1OBKWWjJGGDtrDRav8cPp06d48kwtaAJ2LucAOxsL+CybHE9K0omM0Xt3Hr3C3oNHMWzsDMnXqKgzuZHFD7KCUZwTWTjJfUrxdpQxZ6Cvh4ljhqF9y0asF5UAmUjGfkMDO3Yf4MLyd+4+QOvmHpi3aHmyA6YsUP/avn0HZyGT1Av175+/fmHZUlG/bN26FQYNGpSm35y0u1q2bMnxuGKMHDkCnTp1Yvc5PTQ1tVjyoVy5cqk+jgABAlIP8upJGwtWzJ+EmlVdYO5QS8Vk7P55JmPysqto4KUyBDQoU1CppbNbkm1qV3PBphUzOMZEGp8+fcHGbf8xCStsqIcNWwJwOeya5HNyHVF8moA/FzRplSxpwaSnSdNWyK+Vn4vPOjqVR4XyIvcfgTIhyRpG9UiLGpsgv1YBXL0anmBfFKDvt3U38wYSVH3+7Bm7xuhB7hg6BhljKZvywIF96Nunh0TnjOKAxK4yXd2CbMERi+SShY0sPpMmTWZX1f0rp2Ggm1cxMiZVpJnvEanMSsKHzz9Rwa0T3r3/iJWr5ScUyAPdqqRtRzIDly5dRNDeTXCyNZcqZv4zqSsyDWQsgQVJTQPv3n9AgxY9EXHtJgz19ThWTKxNJo0ihQ1QsbwTKlZwRKUK5TibmkMYZGSg8rVLLxV5GQQsBmq4FHoNu/YdQcDew7h77yHq1avL9VilXXuKgMZFylxcunQpx29R7CHJOpCbkIpVk4wEucCTi1dUBmTxpZhAqlBB7unp06exPqMAAQKyDmicXrBgIWf/r5g3EU0au6KgWfmMJWOiHYomAsqEGz59Da/YSM+KLA5iPL16XGQli8P37z/QvtdI7D5wHAZ6BdkyZmtVCj06teDSOg2adlSopIiArAkSqBw4cBBn1xJRX7p0LRo28pR8Tl3w+PEjyJkzFy5fOsnuGAqmp3qWm7f44c7tW7wduRpJ7bx4cSsUMy3OQZGaWpp4+uQxgoOPs8XM3LwUq+6TRYwC+W/dugFra1vcu3sb7dq1ZP89ETZKjadSW+Q6GjRoMGeiiUFCl+QWfffuLe5fOQPtfNlUQsZi1XPi5ZvP6NJ/LA4ePYVevXph6tQpChVHp3gsKu1BiQZ2dmUwfXQf1KrmEt+uDCBjktf8mTp+x2ogMuo1nj1/iafPnvNv62BvzwXlxW0QXZu4NmQiGXvzLhrT5q2A/39HJAkHDerWQqe2zeBUrYHSuybdO4qLJSLfpEkT9OvXF8WKmaFyZSoTpQZf301KkzsBAgSkP4gUBQYexdu3b2BtbSOzDFdqF0uUAEaaeRTjTmEvJMq8ccVMtOsxXPVkTFMJE760u4FckpGv38CypFmCrC5ya7o168bBs+vWrWdXEg1wNEFl1UwfAYp3zhEjRmLJkiWoUKECGjQUqe27VKwiUcOX7gvkwqLuSJaufPk0MWf2VCxaNEfyeefOPTBx0gwsmD8Lc+ZMY7eMj+92eHq4SUo28X7U1dniFh39keuMkVxF8+Yt0btPP35v7949OH78GC6cP8/WOpLjINefeB+6OjqoVaMqqldyQqd2LaD2+4viZExOaSSJu1JDpP202HsnRkycAw8PT6xfvy7Fa0laa1TbtKlHA3gvnQ71WFHKfwIypqybUpbmn6yqGTKkH2S+Jw2p48q8PrI+V5W2WSKL2IWQCDRp14+1EFu0bMW6ei4uLjIrMigCSlqgQdzCwpJlIqhPE4jo29hYYcqUKRg0KF71XoAAARmLr1+/shGIKrZIQ1QvtrZk8e3mVo/FyVMD2hfd8xRnTNZqilMmgfTEWLVoKrr1G50iGVNqNFJ2gJTenvRu6JEYg0ZNx70HTzgmgrLZCKquDk+lXkiegLKbBGQMyErSoUMP+PtvwchRk9GuvShWSxwzFPNbtAZgA1Lce/QWkbPceUT1werWa4jPnz8hb958aNGyLYoWNeb3yQpGIOFZQ0MDJlFUXoiU0Skm7e7duxwwnSdPXpQt68DJAJQqv2bNKgwYMAhdu3VHz559EBPzG5cvXWCX0r17d1lcMyjoONq0ao6Z0ycz0eFWxmYHYkSB0mqSiT6G5PhFL+Vk/olpZqyYtKnHGc8A9OvcBAUL5EG73mN5ZUaxbMmpupOljpTx371/L9ouJmXLEREsRYTiE2RTytwg/lziz/WnzBJnyUFmhmQK2yo15iQmhXGE0dvHH1DPhkuXzyYZnFODNWvWcv/av/8Ay4eIYWZWjN3c3t7eAhkTICCDE8EWLVqEe7dv4OHjp3j+4hW/TxqRAwcO4PmI6u0unDebq010btccazduZa+Isjh79hwWzJ2OfQeP8dxDi39HB1v06NSSvRX2tlaIjIrCk6fkLYiFfZnSTMZSQuqWhiqEZSkzBOwL5JI63t5rVbpvEhIly4O4fiLJaFDldWKovr6bER4exlYRqo9IE6JgiVMdKIh5587NmDFzCdw9UldHzsrKhh+JScr6DX44fGgf7OwcYGxcDMWLmyEkJJSlCChTrUqVKgm2JxJOwc7TZ8zG7Nkz+UGgVUpExFVeKanHsUR1NVEx74xAS896OHEuHAMGDMSGDRtgaioqak79sG5dVw7YFvdJKkN29fotzJqc8k0tICGMihjEZTqmnYgR7t69w1Y1ihNLLFNBq2WqeEHHS87yRtuROC+5zFNroRMg4F/Hr1+/MHfuXEydOo0XRuXL2aBa5QooXqwozoRQia0JHK5C8z2VuaMazYUK6sBnawC8vLxQsaKL3PuTStiJC4OT54RqKpMlnFyPpczNMGvySJQuZYay9jYokD+hxcs0jxFMTUQW84/RnxQ6F6XclK8fXBRlU6YBtOKlQ/YcPIGzL+1sLPEq6jXevn2Plq1aYdSo0TyY0UBHAftEllIDyuQi7Sqx2bBNmzaYO3cOD36UJj5x4iTYWluyZs/jp8/Z8kCuNHJfUGFQEv78/v0bt4GCZDOjnuCfisePn7A0QI+eg9Cr12CoSaXQSRRO4l7QR8l9Lt5G+j16UtcQf18Nw4cNxOHD+3Djxo1k46+o39GqiDS6KOtv9uw5CI+4xjcc7YegoaHGhIxEVMeOGYMe3bugTlUndlXyscVuSsqwFNeplHZXJlMiSV4c2bHToVi61g9fvn5nS9ynz19x7mIo63VRGSM677lz56GIvjYO+q8TFSkXuyKlXYCy3IHJIS0lidJisVIASrspkxFz3bJjL9r3GJqgnFtaQG5KCtSnwtht27TGvDkzRbpqsb/RtkNnbN8RAAMDfS5obWhoyK6Ma9euc9+jB8XPihMfiCDSb9ynT+8MLTouQMCfjmvXrrFHISwsHEP6d8PYYX0TzNMUt+5UrRHu3n+EMjal8fjJM0S9fsP3ZI8e3Tlml5K9KM6LKpbUq1cPJ04EcwF7qvdKRhvKwm/SxAszZ85ia3i1yuXRp1s7NKhbQ2HjzcePn1DQ1EG1MWOvH4RASytfmoNrybRnZFlJothNaNrYFSfOXOISHuImkZbZrt17YG9vr/C+KUh82qSx8N9zULIfKipLWZ5+PutwMSSc49cIoaf3cQzb6XOXceT4KZw5f5l1Qb5+jZtk40DB4NalS8GhjDXqNfKCm5tblikUnBWxdu1a9O3bD+cv3ON6mGKrkzzSJS13IL2NeLvE15o2kexTTQ379u1G927tuX6gl1d8YkBKZN3FpSJ27zkAF5dK0BCHd2moITTkIv/G/L+6OgIP7oGDtagEmJiUqf/6Ek/CpEiZzPio5GogUg3LOOkLaaJ2+lIEhk+Yj3OXRHIeFHC+z28pypYpnZD0KSMDIX2MBO8lGlSScyfKErhN9ngKDFjJ1cJME2EUkbELl8NRybUFzpw5LXcsoSQTE5NiCmc+0oq8S8f22LojAHt2+sK1VnVR23//QEjYFQwfMxknz8QnhSQWyjYzKczyFydOnef39u79jwWGBQj4V0Cx4RQLKy4XpqWlJSFTFG6yce1yTg6ipD7jEqVZxkX8OekX1qxZiyV1Nq2cDceyspNlyFU4ed5abN68hUnY+PHjuMSZs5MTYmJjUVBHG4+eJBQFt7MtjfquNVDCrBiWrtqISyERrJG4bP5UWJdWQJ4m0fipKBnLFPs4xQ69uHkKF0Ku4HjwOZgYF0aTRq4cYLs14AA0NfMxIerabzQzVSqndPlyCGe/keuGxEDphzx+PIjVzKXrxjVv3oJjhkYM7gVbKwu06tQP9eqJJlYX57Lo1bUdEyvLUiVQqkQxfp+UwekhNke+inrDFsDs2XPg2o3buBQagcuh15isrd3ox9lsVC6nfv36AimTAbIqkuvlwIEAGBubwtm5Yrpdp+PHAzGgf084OTlxPUtFYWtry+r8q1Yu44m1WtWq3MY7d25z3A+VLfLz28Ju7gaNm+LALh+UsYmvFJHecHG0Q/De9azjlztXTqVL8vytoAWWsn3JysKcLaY0XiQmYzRWDBs2jOMK9fX12B2cklwExRhSsO7uvQfQukVT1KkZ3++obWXtbbF3+wZ07TsM2/z/k7xPbhSadCh+lR4EyuodPnwY12QVIOBvBZVHpBrWVGuYCBg9yE2fGDlz5uR75PXr19DRLoDSFuY4e/4SHj1Zy0Yh8mDR/E9kjOZqcj3K855duBSG9t0H4enzVxgyZDCGDBnCYz15yWg8HTGoB15GvkZZO2uYmxVDeMQN1KxeCUZFDCX7oPq75y+GwtLCHPm1lK8vqwxSZxkTI40WsuQCeiPfvGfrGTWPihGTyZGQO3euBJar//7bg1q1avFr+nGoRMyOHTvYn9uzSxtmthQz4lS2DIoZGylVfkXWCv3E6QuYNGMRr3rJFefq6soDPLWRCAhZgijQ3MTEGP8qyMRLtRzJrEu/35ix09GmbRf+LPE8KsvqlRiyrGViNPGsi5CQi1i2bCk6dOig1ERN2lJU/44GhYYN3fHjx3cEBh5maQzKtiQ5FcrIIXf3gwf3sXfXdjjaiCxkiPkmso7xaykLWUouy+Rcl9LZi7KyG6Wh7L2X+BjJIfG9kIKVL/njpnA86YxOGdmZpy+Eo1nHAYj+9IXvLZL4mDlhCPp1b6vYdYjbV5MO/XH2Qih69urN7ghK6gkKOoGVK1eisIE+xo8eggVLVuFXDHDhwnm5NRBpUilfvgJ/7lqzKvzWL+N7XwTpbNEYDhDuOXAsy6dMnjyJiReBLAFULYImHiFOVcDfDLpfSbOR7jOz4iasSq9XqCD0C+lCT4+eC/K99DH6E1fyiY57NjM1QeP6tfneunL9FspWqs/7o/GdEgFJPqeQrg60tfNj3vSx0NWJTw6kEICZ81dg6uwlvKimWHQSE6fQJ4/GDfH02Qv4rl2EurVF92O6QGoMTV83ZQaQMRqYz5wPYZdiZZdyCL9yEw+fPEfQyXNwLGvHddiaezXE2g2bcfHiJezatYvdVBQjRqSL3Fh2tlZYuWBqwjankYxxzcFYdZw4dQ6rfQI4rZVIhzRogN23b+8/v9olckxaTEFBwTh05Dxy5aKyWKolY5GRLzFp4ijs37cbffv2ZUFXZUDdf9u27ejTpy/fsB07tkfz5s0T1GKlAaVxw4a4c+8ejv7nx5ZVgYylPxm7cfs+KtVrA5vSJVGpWi2O2yB0auPFsabtW7gjT+4U4qzi9vXgyQtMmrkYAXsDueoDgSyjvXv3Qt8uzXHi1AWEhEVgxpxFnJkrry7lokWLmcQ/uXUJ+TXzJvpUNK5QNtfuvYdgZ1MaW7bvhrfPDl6Nr169Kvm2ChDwl4DG1S1btrDw6efPXzB2WD/06d421Rb+yKg3yJZNg61TshZKZKzp0mc4Iq7ewP2Hj/Hr129O2ho+fLjkmKNHj2ENyw6tm2DYwB4oUVzkGfs7yJgqBRoVAWkGXQpHn6HjERZxnWsYkmXj6NGjnCHx/kM0s2JyW5IZlOC7dgGaurulvuxKCvEsdPnevv+Mn79juP7gpy/f0KPvEHyI/sKELDV16P4mkBvIwaEca4vlzpUHb95EwbK0DcqWdUbZcs7Q00v5+oi7qCyrl5inrd+wClMmjeIi9RRwqWr3FxEysoJGRUbi2JH9KG5UMMWg/mTjx5KzCElbyxRtvzx5iiRyDynJWCgYgyYv9iwl+YrEx5ejYUboO2wy9hwK5uobVJR85MiREukbClM4fWgLnBxslRqDPn3+hssRN2BarChCwm/g1NmL2LP/KB48esKfezV2w9kLl3Hh4iWZQtOtWrXmseX4HiltOKljr1jri37Dp3BfIsvX5FF9MWLiPCxcuACdO3dWqI0CBPzpgfX9+w9ggWrSRaQM8CKFC1GAktRWquMMsbGxmL9kLUaMnwFX1zqoU8cVlStXShJucOrUaYwbN46TaGrXqISAzYkXR6mJT02pXFuMwmRMNaqqaQmyVRD7DwehRuNOqOTalIkYka927dpxWZBNq+fh8fXTsLcVFRKnwdLM1BgbV86BV8PaIp2k2JgkD1EGWtxDDOn3ZDwok02yj5ifLL6pWyA3DAoVQNHCBWFZwgQLZ01EVOQr2NnZYd2qpfiXQdamCRPG4+GDO/j+PRpFixriZPAR9O/XGZVcrNG5owcOH/oPlJjCgflxD+kbjbgYPUizhR6/f8VIXv/6LXq0adOF3UGkjJ4apOTeLFCgAFteX7x8hTXrNyM2W15APRc/YknIVYNeZ0cs/a8ufp3wIVK3j3vIgqSfxYjcneKsycQPWe2XVt2XvClLjDUmhUfyx0nQfiaNUg9570m+m8jSnMy4QQSN6tJSnUWKIaFsQ9KCo6oIVSqU5YQGB7rfpduawr1Lj3x5c6GqSzlcv34bTdv1hv/eo7C0ii8KPnJwD3YhUpaWeBEg6oOxnCV86NBB1KxoJxoHxI+48eDJ48cYNHoGunXriqdPn7A7dOi42ZwlSfIpAgT8zSCLM6khkBs/KvIFDvhvgO/axVweLb2IGGHSjIVMxLp27YKAgAD06tVTZtxnpUoVcezYUR5TEkpRUNvSn8OkhCwrcEMWLj//fQgNv477Dx5j/5ETfIFJMTc4+CT7iPs1awzPRnU5+I5wYOc6bAvYhxKmxuzazKx4DHJPhF8Iwoixk9Gr/1CUsnaAi0sF/KsgNfLEiuQkL0FCv5s2+aBP745sOatevQ7KlXWGtY0d0wtlcCUilIX/SJ4kvUCZuoSjRwNha1USzRvVEmJ+0hEdWnth3NQFbP0mgkMyEGSZOnj0JBrWrR5HllKXGBJ44gwH1N+6dZODhY2NRXpCU2cvRWFDfRZ03bt3LxMq0iOiRCIiVfk186F/j3Yy90kSORQgTCnzVPVh82ZfnqDIQiZoiQn4m0GJL4MHD0FUVBTGDu+PQX3iMx/TE79+/cKaDX5cc3jBgvnJbkuGm969+3ASQYVyWc9KrRo3pSyk0nX58PEz7N53BMu8t0kyjhI0WE0NtapVhM+a+dAukF++pUCRIOS0uldllYYR1+6DBn5DA2ZWjjAwLMKZHFSGRZDESIo9e/ZwYVXSeiGrBNWXrFatNivv58yVC5qa+WFY2AjOTpU4XoAsYmK8f/8G3t7LsGH9Cq4FeOTI4VRr06UECrzet28fVqxYyUSygrMjhg7qB6uSJjApWgQasd8SlE2SWKvigvoZifXBUtLWklWeKPH74u8nNnSrKvg/pX2mpnRRYomPuPdocD1zIQIfP33imA4bF1HdyO7du2HBggXo2bMnB9FHRFzBoF4dMG1s//jvJxOKkPg4Y6YuxLot/3H2LAlB371zh2MRP3yMz/CiWqikQUcij+OH9mApiob1asDexlLmKZEsjkvdNggI8Fe6CLwAAX8iHj16jCED+2DvgUDUrVUNC2ZNRPFilLwmvhdljUGqs4wdDz4LV/e2CA4+wZ6R5MbucuUckTunBpq4u6G5ZwMUNYqvnpF6qM5NmWUsYyfPXETHXsPx+OmLBO+TeGKjutVQsXw5nqhrVK0AY6kSJFkZZJlbuWQum1FbtWoFT09P+Pr6ZHazshwaNWrED1Y53h2IrVt9cODAHhbX/PbtKz59iubtNmzcBScnkWLyVr8NWLt2MZ4/f4ocOXJizJjRGDBgQLquxuj3bNiwIT9Il6pP797wbC4K9Cah4k6tPTF1RA/O+BWgPM5cCEPf4VMRcU1UGL5Afk0mQ5QFtXLlKq4BSQG5xYoV4zR5kpcpWkQfLb3ckihgpwSvRnUwb9lGLF++ghNL8uXNgy7tmmDq3JWsg0iWLTpO/vxaWL1gIto2q88DeujVO5i5cA1OnL6EPLlzwaFMadSqVgG9h05G2JWbvO937+L1EwUI+Fvh6+uLfv36c/ymZ6N68FmzKMMtwJ/iknFI8kLuNp8+saWbRMFJwmpIv27IisgyljGHKo1x9fptfk3ZD16NXNGpTRN2N8pLM082KzM9LGFyGxAf/yJegfvu2IejQWdgoK/HYnNUuHTMxBnYvXsX6tSpkz7t+EtBisheXk1wJPAijIxM8PPHL1SpUpqD6sliQoHd+vr6Gd4umpyfPn3Gri4KVp0/fwHGDumOEQO6ilTyxYH9SlrIUrSSybGQpVhjMpn4MkW/K+u4aRV4ffoiCiMnzYef/wFORZ81axYKFzZE69atERoahn492rMw8+17j5hsk5wN6cS1b9+BZWwoLCHsxHbkzKmcgn2ZKl4wK1YUNeu5c0kqgkhfMBsLUluVMkP7lo1QxqoU/AIO4kDgKdYlItVuij95+/adpOAwxbDNnjOXK3iIS6gIEPA3Y/bs2fDx8WU3P6kY9OjcBv16dsaCpWsQFnGVF0id27eAR0NXznZs2KwTgk9fwIdnV5ErV+L6079T1Ya3797DsIQjVq5cITMDmtT1SRyWCCO1Z+TgXhjYp4sKY8RUZxlLPzImt3G/4wdlKXL07MUr3Ln7kGtVkvJ+Su68ZIsOZyQRS+JKEr22r+rJtQRJbFJHOz9CzhxBi3Y9EHTyDFo288La9RsFl6WCuHLlCpycnLF6jR8qV6nJAfxv375Gu7aNYW9vm2Wsjf369YO39zpYWZXG0ulDUMEhzp0l0R6jgPyEGZYpFc+WS8pkZCXKIlYyA/tTgELELKWknZRcruJjqanDs90AHA46y4V+SQKCLJA0LNnbO6CQdj5sWjkLS9f44vv3H1ju7Yf27dthyZIl/H1ybVeuXAWzJw5Gv26t5JLm85ev4OBRIlNvcPxMGJckIksslTujbE3SHSNxaNKVo9qmlJV17Nhx+Pv7s+uUElEaNGiAevXqclyiOF2eyBh9x9KyNIyN/wyLvQABqgbVlqZQHALpAXp4eHAZMPIgULkvr3oVUaOB6P70WbMQzTxFumGqCOx3rNoQNmXKypSOGTp0GPx3bsfRvVtZu0z1SLnNHz9Go2Ax+z+HjCmLrE7G5i3fiOHjRJpXbq41EeDnzZPcwuXeGD5qPAtLpqT0LUAE6qLW1jYoamyGFSt9EfNb1GVXr1qIVasW4MmTxyx3kNmg1dfmzZuxbt06RL9/gxund4g+EMhYsmRs8479aN97NE6eDOZMJ/G1NDMrgS5tPaGhroH5y9dDV7sAPn3+gmXLVySQL6HBfueO7ejbtRWsLUugcb3qEnfJk2cvUb1xJzx68oKtV1RaxcikOEqUMMfHjx8wderUZF0cN2/exJs3bzkBR4j3FCBAPigpi5TxraysWDibxm0KAxgxYgRiY2P4fqbqFfVqV8PurWtURsaGjp6Knf8dwe3bt/gepTi2gQMHcD1YCtbv2aUtFs6alE4/nerIWManG4pdekqQI5myFNJQIPU/XSHjmIN6tsOaJTP49f5DRzF7/mK2UrRr6cmZoM7O5Tnd/d69exnf3j8MdINNmzYVJ4KOYP++nciWXZ0fFStX40n73LlzyAogQtilSxcuyfXsZWRCKQvJQyz7IPo/pYLYMvu8WP5CjiQF9TPxI1mpBxlQ2F0pb3+JJS2kz13OMX/EFc2mwZwG8IiICLRt4YkfP76hWWNXDoz/8eMn1m/chMioqCQ6ckSoKlepipUbA9CiyzBcCL0qOfaQKavx/WcMjhw5gkePn3BpMxKInjNnNlatWpUsESNYWFigYkUXgYgJEJACKByH4mmJiPHtp6bGMhMdO3ZkEVaSOJoyZQqCTp2TIyehkSr3Ye0aldmyLc52pyzm4OBgNPOoizVLZ2HGxOF/xG+XOdoP/wiIeJEGGmHfwWP8rKOtjWshJzF10liOMyKBPAEpo3HjxmjQwANDBvfC5EkjEB39Edu3bWL9L2UKyacnqGYaiQqSQruamjrHHQlI4ZpFvkHfEdM5GJ7S421ty/BC5ezFMGxeNRthV2/i2ElRMW2KD6PU+cSgPrBx40ZJjdqSxUXuCP//jrCbcdq0aRzjlVLsqQABAlQLcuFTzC/h9+8Ytlir2sJcpaIzL4QpSP/EiRNYvXoN6taqimnjh/Ec/KckVCnnpnwYCq0kJUBSCQUtWCnF1Ci8r/S2mCVROpehsM6yF6L3X0S9Q2mHymjduhWXaBAmipRBWW4UmzBx4kSO96FYnpkzZ6Jfv77ILJw+fQarV6/m2KHEUizb186GR/0a8YH7LD78U67SfYoVIWT1KRmfJYs0Bv8nv+/kY9n4nBK1M/rLN0yavQJ37j3Ci8jXsLYwR9PGtVG9khNy5MjOwf2mdnUSrL7v3xdZk2noevPmDfcLGqPKlLHj7zT3qIdTZy/j/qOnqFvXlQmZ4GIUICDj0alTZxw6dAhFC+uz1NO4cWPRrFlznD26G2Xt46pnJMFvpY9DBcH37KdSZ19QqVIlrFs6leWGRMigYP0/JmYslWQozWQso92WMrPMkhaFrlSnGS5cDuPXJCZJmko1a9dhywplawmQD5I6oNismjVrsraMKidaUlmnouDR0dE82VMwOfV9KmtFRIBWYHRTUT3S1cvmY9nqTShtYc5Cv9t37UeBAvkRFfUaHvVrYuvqGSJRWHEfZLV2BciYIn1WTvyVIttJFySXScZkKvf/Vm7fyW2TeNdyXLV0/SkruWOfsfy/mZkZy9vo6Wohe7ZsuHnnARcVFoN+J/ptbG1tULFiJdSuXYtLWAnFuAUIyBxQEg6N0/VrOMLNqwPOnDnNYRwVne2xcdU8lemQ3XvwCC079kWX9i34kfCelzcGZQA3yJLZlAIZS0DG6ri3R9DJs0kuE9XDmzRpIqfppraoqoDUgQLvSTeHLG7yQGZ28ed58uTG5DGD0b1TK9So3xKRbz5wBhEhV66cuHZyJwz0dJEzR9xgIJAxpcjYxdBrLKIqDVNTU5S1McerqNcoW8YKzuVs8ejJc5w+H4rzIdf5/rl8+ZJgCRMgIJNB1IIqW/Tu3Qs1ylujar1mCAm5jDNnzqBPn744fWQnHMs6ZABJ0vgjyFj6KrRlRjB9ZkP6nMVWAbHlgy0QIqxfPgtTZi9F4PFTePTkGerXd8P16zfY1UUlGyjOZcyYMWjWrFmWyBT8W0EWMCrJtG3bNlZaJzdXx9ZeiImNZW24bQEHEmxfu5oL2rVw56y8UuamXCN1/rJ1HGS+ffs2zux79SqSdXVKV/Lisl4ujraYOKwbqpYvk7QBcXVTE0BWsHuM1P/qMj5X05CtNi+dvczPSYkPW8VSlKpI5vPUKPBLfz3R+Yvb72BrgbmTh+Ld+w/o2Mod33/+RhEDPY4ByV/MGafOhUIvoBBfbxKHJlFlChgWXJICBGQ+7ty5wxpk9pYmiHz9ht+jIt0U0kHj7JDRUxF0YGsG3K+/kaFQxJuX4ZYxFZCxP85NmUIcmWSijIuvIbfLkvV7sHz5cmbO9EgcuO7ntyUDG/3vgALuGzZsxDpmYtDAoF1AixXZqRpEOTtrWNrYY+vWrRynZmNVEiFBAbwt3Tq9h07C6g3buF7hjPkrJARv67oFOBfxGMWLm2Ly5Cn8/vPwvdDXjbt/pFySCokWJyZjysSTSX8uI2ZMITKWHJIjY6nYb+L237n/CEPHzcPVm3fx7EUkTIwM8fxVFPLl0+TKFrSQIbFVIe5SgICsgyVLlnJllJd3QzF60gzsP3KSNcgo85HGSNL/u3xqH2xKl8JfhUTjeca7KVVEehQiX8ocNyuRsUTvSROzFy8jMXPBSvj4BeBjdHx9PMLdu3e4Tp4A1YEskGXLlmPrF8Ha2hqdOnVEixYtoK2tzRmRs2bNRtNGtbF55XQmadGfPiN3rviiz9du3kOZas349Zcvn2Wu8O7fvw8rK2t+PX/SAPTr5J5wA7EsRWq08xQhY0kWBPHES2acWBqtXHKPqySkz2HLzgNo12sUEy5anDx//pxrSpJO34QJE1TQWAECBKgSpM9HIsr2NhasKTZwxCQsXbWRPzvgvwHO5exw98EjlLG2/DMt2bGK85R0clOqVsdLaeIlDUXbkchVmO5IaRKSaoe4CxIbNtTXxYIZY1mDbPLMRahVqxYCAwP583PnzsPLyzM9W/1P4dSp06hdu7bk/0OHDqJy5coJBoXg4JOo6FQGm1dMEel1xcRAMw+V24mVZEcGnRJJLhDKWFtAXU0dX75+Q0xsDHQKGsDWvDB8/Q+hSZMmXGdztc+ueDKWUt9Xtn+nlGEpRfwlJExeEfJMJGGS3Ui5XMvZl0Z+rXwcaxIRHoaWrVph/fr10NTUxL8OWihTeIOurg7Mzc0zuzkCBLBOYIP6btArVBCrFou0Ngvq6kiuDLko8+XTgl0C0fPfGXvlUhp/xeNhWjiKksgyhcIFiNCne3vMWrCCM1Bo5T9//nwOFBegGvj4+KBr126S7LzAwCOcKZkYVA5n9qxZ6D54KtzrV0e96uUlZG37nkAsWO2H/zbOxddv3/HuQzQH9f+OiUHe3LkxdYE3nr2Ikrg/ybJJMWSawu+YKpgXN8GL68cQcuU2jhw/g7nLNiIo6AQOHjyAwoUL428BOSkuXryIe/fuo2LFiknKK5Fb58yZs+zmOXXqJNau9ZZ8RmMFVfVIy7HJihsQsAtbtmzh/lyhQnnWhqpbt66wGBSgEMhqXbduPRQzLoz9O9ezcYHw+s1bjut8+fIV6nm2x6XgA7AoaSZc1YwmY2mygMlCaqxcifWOVI3UWASkAvv5XwDa+TXh4uTAQY4bNqyHh4c7yzcIUA0+fRK5gEnvZtCgQVx4WhYocWLixEnw3rKHH2L07tQUS7238+vTF0IxpGfLJHUg9XQ1MWOJDxZPHYS9R05j046D/P4u72mqW2klKkwv/V78/0ljwWTGhyW3D0XuGRVZw5IDZRY7l7XlR3PPeqjj2YXdIGTVlEWm/0TMmTMH48aN59fUL6mag42NNZo3b85u9RbNm+P2nbv8uZ5U5QAKIZk8OfXlXki5vHFjdyZ5lAXs3qAu8uTNg9CwELx+/ZYtkoJlXkBKoMXC2LHjEBvzm12ROtoFJOMdkX0iYgSqpnE8+CQsShZT/UWNjcn4/aTIJxTVVFUqZuwitDTzZSzxkgVVkSlV7Setk1EiV1OvQeNxKeIWzp5NKn0hIP1BLkUK1qfCt76+m3mQkYZYz4rw5HIAChsUlN2f4u6FDx8/ISYmFtoFNJN3WyenM5ZSTJiiYq4pCbNmMcgjm3fvP4ZjzWbo268fa/OpEvTbUqmk3bt2IfrTJ+zZswcmJulRZDihxbZ79x7o36cHyjnYYd3GzQg8FsSf9ezZE46O5VhAs1f3zrC1sYKDXRkMGjGeawGeO3eWC54ri+PHj7Pl/dLFizA00MeMiSNR3rkc8ufXQmTUa9y+cx8B/x3A5m278OzZ03Q4awF/C6gsXaf2rfDf/kCsXDgN7VslDKtZ4b0Z/YZOwPQJw7B5+x6sWjQdZe1TWZs5Nh05RjoYaigGvKCpYyZLWwhIFYjwkmqvgIzD+/fvUalS5SS1Qrt37w5jY2MWmb179y5nv9JNRQRsVL928UQsGVC8kwDVokRxY06kyJNHRRVBpDBt2nQsWrRI8r+FhSVatmyJtWvXqDzYmOpxLlq0GAsWLECLpp6YNmksC1a+//BBQsaKFi2KMmVEsijLVq5N8H1y1aaGiAUFBcHT04sTV7p1aoVuHdqgqFFhJr+rvH3Qd/BoybbVqlVL83kK+HtBfbhJk6a4desmdvquQH3XGkkI08Ejor788lUkLp7YIwgxp5WMySzSnRlQstB4svtJjFSqn6sSD588S/eVuIB4q+/evXsxf+4smUXbqdzOVr/NeP8hGoV0tVGzXhXUqeqEZg2rI3funEn7TFoyIhVV15cTmK+Qmr4iLskMREqF0uW1lWLw3n/4yHEoqQUVD6dwgAYNGkjIDuHDh/coWFAXwSeOo1LlqiyZQXFU1atXYzFmVWLhwkVYuHAhHOzLYN6sqZKKDW1aeCFH9mzo2msgRo0ahUmTJjH5LGpUhB/Bp87w96m0DMU3Llu2TKnKHd26dWcL8PHda5EzNxFaDUS9fgP7Sm6IjIxEsWLFWDfv2bNnKFmypErPWcDfg6tXr8LTvTHHyx7btwX2tlYypR0OBgZzHPSiFRtw7cYdDB3QgyuXrPPZho5tmkFfT86iVhXjaTojJU6kKGcSLGNZEA8ePoGDY/nMbsZfC3I7rlmzBv/9txcnT55ka1fVio7Y7bsMBwJPYOuuw+jcuTNWrlzJrpwubdzR0r0ObCzNoK4WmyA+TIDq8PbdB6zfspsJiXlxYy4eTtDVKYCSJYrz6+8/fnAppHOXwvn/1ATwUxD8ihUrsH37diYwU6ZMRYkSJbBrVwAndVSrVh0bNmzE8BGjcPjQQVhYlEI+zQJMYJycnFJliZIFihCh/ufRuAG2bExodcuVKxfatmrGgrb7Dh5BvTq10KljWxQqKJq0KMB+1LjJ2Lx1B7Zv38F9edWqlWjatKlCxyaZENLOO3n2ImrVqCaJyyMiRmjXri1bzeghQIAsUFHupk2boXgxIwRsXgWjIoYyt7scdoXH3NmzZ3Hs4+iRw+HqHr+oyZ4tOwb36/rPX2SlYsbe3D+fYsxYpiCLMORUQ2rV/zLqLYqXqcEdl1xkAlSPYcOGY8mSJahVtQJca1aEZ4PaKFpYFBC9cMUmDBlPNdOALq3dMWNsXxTQypukjzEZS1ZsOEY1fTWl+o+yLGIK1qOUZ51KT+u3LPkNKvQ9efYKLF7ty2RLTU2drTYpgTIO9+/fx7VdE+Ps2XMslkxVFR4+fMSuPiqlRISHYsCMjIpg5PAhaFjfDTsDdmHy1Jno3LkTW6BoSPTz28paczSJhIdcwOs3b+Du2RS3b9/hQsQjRgxH9erV03QtBgwYgJUrV8Fv01omZKntG37b/NG9zwB8//4Dw4YNZa08CwuLZL9KMZFUv5MsYP4bF0hKxrz7GA2nym4wMy+JadOmwtbW9s/UgRKgMnz+/JnHy6tXr/G8RCEd9D9VLqla0Ql+6xZBUwYvCL96A+27D8GTp895/Hnx4jlbmen+mjt3Hi90T58+BfcGdbCJ61TKQDL3RJbw0ikYM6Zb3Fm1oq9v7p2RTcZSylTMaHdIViFnqThvmpCGjp2JJ08es/ioANVr4Dg7l8esCQMxqEebJH2GMoFeRb3Fz5+/ULSIvtz+lKJlTF4fVGQAkee6k0WkUuGSVNg1mA6DniwydvXmfdhXEemvPXz4AIUKFeI4FLJYEhGgLCwSeSXLTY4cObnEFA1bpLwvb3ArV84Rb9+8Qa1a1WFeogSePn2G+w8eIDIyCl27dELnju1w9+49uLo15visZk09sWv3f3HuSBHJojqjVOi4rmttrFqxjNuywz8AGzb6sPafs7MzunTpzJm3sghhShmMJDo8Z8Zk9O3VLc0Zq5QlPHbiNGzw2cLXbdmsUWjbc2Sy3yEB3Tt37uLUoe0oVFBX1AT17Ni1Zz869xqIT58+o3rVSth34JBAyP5B0EJk69ZtGD1yBF6/fQctzbx48/Y9f2aor4eeXVpjUJ/OyJFdxrgUG4vyNZvgy/ffqF+/PnsZRo8ejY4dO/A9NmjQYOTX0oSttQV6dG6Npo1dJd9Nk/h1ViVjZi4CGfvTyNj+wJNo3LIHTpwIYpeIANXi/PkLkoDkTcumoKVnvYQ3uSKliQQylmrIFKZV00DDFj3wIzY79u3bm/ZjxMbC0LAwBg/sh2FDBsrdrl4DDxwPOsGvjwcewLQZs3H0WBCGDx+GkSNHMvnbtWsXuydJVLVK5cqoWaM6mjX1wu49/2H12vU4evQoCwZv2rQR+vqKx6+FhISwZWrj2uVo3tQzST+jQOeDh4/i9NnzePkqivW+iAzWrlUdbVs24/bIAhGxjh074tzlK7h9936ygdIUI0lxPIUN9HB4ty/ya2kxGRNbzrxadcLhwCAmp2RJpAxTAf8GyF1NCR5US7JShXJYu3QmcufKhQNHgliyol7tqvELEBnjI/UfzSJ2WLBgPu+LqpkQ6DuUedm3e3vMnjIynuTHysgq/8fImGpixlJTkig9rWWpDcxP7zYoANeaIiV4EgwVyJhqVndkDifXFK30Hz9+LPlswQoftHSvlar+kUQyIjFkWJ/YmpbKfiFTokLe8ZI5hrJWMfF3VO4SSNRGig97F/UxTQTsyZOnuHnzBpYuWcwDnznFmSXTblvr0kzGKDDe2qo0Anb4YfbcBZgybSZ27dqNbt26smXs/PlzGDZsGDZu8uEHWeaaNvGEe+NGCDx6DA0aeWD27DmYM0c04SgCBwcHtqy169wT3ht90bZlcxQqVBAF8mvhv/0HsXjZaiZWZcrYwsSkGLt3vn79gjHjp2DcxGnw8PBAw7o1UK1qJZ4kHz95ity5c+P+oyd48PwtPn35ztckOVB83H///Yc6dVzRoGkH7Pf3RT5NERnLkTMXrCwtmIxdu3qF76Fy5cqhW7c4K56AvxpDhw7D/Xt3sWLBFHg0dIV2gfw8RnZsLSVZkaiMm/TYQqTLqLABBgwYyP2wsosjfFbPx+oNfkzGpo0bzOOhmlg2KPYPK2uYHodRiZsyVUf+y12XqTy/r99/QtukLK8oSPRRQOpBGkyVK1dJ8J6trQ2qOFrCqLA+urd1h2a+vBnWP9IS9J+ZZIx3qSIyJl2PNX7nGlizaTt6DpqAp0+fQFdX5DJTBlOnTuVAfEJhQ0MsX7oArrVryt3+WFAw3BqKJhbNfPlw6cJpmMQp3odHXMGwkeNw6tQpJvNEQHr27IF3796jR4/uuHXrNgfSjx41Aq1aNoeeYVGsWbMarVu3jj/P2FiOrQkMPIqlS5dyViIlC1DCwdixY5m47du3X2bbqOJGv3790LdvX+joJLSAkZWBYnVIaoVcnbJgZWXFIrD16sVZfVMAWT9cXV1R37UWJo8fiWImRbk/3b33AFYOFZkIktXs7bt32LrVD40aNVJovwIyF2/fvmX34ObNW9CuXTsMHTpEoe/duHEDHh6eKGNljh2blsV/IGeMlEXGCJ16j4DP1t08xn76/AV3w4NQlIL8FdVW/EvIWMa6KVOLLJBar/IfOY3ndPZSOKrUa8WZXTRACkg9cufOE/+zqKmhZ3sPTB/dC3mlyhLJIkgyrV4pBdInB5Wp7idzPGViwhKfiwL3gCrImHx5Dg3MWrgaU+auZDKWJ0/876YItm3bjk6dOqFO7ZosUWJsVDiJe05NHBMYd9yLl0JQuXodnqA2btyIxg3rY9F8KcuWmjonFkyfOQer16zj1P2VK1dAW1sHNWrUkGxWu1ZNHAk8ysRpxozpkuPOn7+AJSnEcLC3h4ODPdZIlTCSxq1bt6CursYTKBG2gnFZk8mBFPMpo41A1rPv378hV67ccHGpoHSMFwnnknWPEHruOEpblsKryCgYm4skP/T19ZiE0XaKtE1A5oGmdIrLGjFiJAff586VE2/evuNgeZJwob6mqamFokWNkpAwHx9fzjTXK6iNBTPHoU6NykksYIre50aWleDh2YQXBqamxTG4T0eMGdpbtB9JWIii6vQxyl0EBduYbpAa3zLWTfknIysQQilYWZSElaU5Bg4chGvXrgqBs6mAt7c3LC1LcyC4RfHCCPJfItetmGripUy/UaJ4vKLfUWgwUWHfTs+Myw8fo7Fhyy5Uq1ZVaSJGLo/Bgwczmdq0fjVbceLbGH9dxSRMDItSoqLalpaWHI+VN/FxY2Ogoa6GMSOHolPHdmjavA2aN28hiQurVrUKgk4Ec9bnkEH9MWfeQujoaGPEiBH8+efPorJbYvTr2xstmjfFkydPcOjwEVSoUIGJILlzSCrDyEg0MYqfFQFtK22NSwvUY37ws3tDNz4PwpcvXxOIxFLmpYCsC4rTIimU1cvmYvaClWjm2QCN3GqjTZf+3M8pU/j0qdP49Pkzu+ZJN2/y5MlM0G7fvg0nJ2cWqG7u6Ybp44dBSzM3j03KkjAxihjq48eP79DU1EStWrVw4tR5jB3SUzTeKUnwVI2UjptmsiYee5QYgwUylsWgqZkXE0b2Q9N2fTmbi9LxBSiGnTv92b1D1gXJe6snCJcvi8PPfz/uP3qK9Zt8UyU6+fr1a3i4N0pAxFICxVfR9lRiiJC4KLc0yO157Mh+GBUzZ3cjQUtTE9ciLkO7QAEmL3fu3MOCBQs5Q5EmPtKpmzp1Gm/78V2UJNhZS0tUEmvAgP4KuxHTGzTO+PptRzOvxtjkvVzyvmkxY4wZMQhTZszjmpnr168TlNOzKMhl3ahRY0n/nDlpBAb26YLajVpLrF60cBg5uCccHWxx7mIoxk2dz3GARMaCg4NZu66FV0PMmTpKdC+lwWtEMiuv37zj13Rcitnt01U1C4e/Felsq/tzLFJZCZXKl4WBfiG0atFUUgNRQPIgpfw2bdowEaPgaMKKWUNR0dGWX3OwaNxDVjaf5EHZZFxgW130WvyI+5yyzfihph7/EL+nzCOl70t/LmO7JO2W9UhHSLcrrfj67RtPBCdPnlL6u+L7I+jESRlt1JA8EoMsA6uWL0YDt7pYt2Y52rRqHv+92FicOHkKZ86eR8SVqzhyJBA+m/0gDuggIte8mRfMiptKrEgrly+GUZHCcHf3wLt379iCRvGJhHXrN0j23TbOkiVtdcpsrFmzFk+fvcD4MUmlMIYM6I3OHdtix44dbHkRkDWxfPlyJmKL50zGpZN7mYiRddfC3Iw/Xzp3EkKDd2Fo/26oVrk8vn0X/ZZExgikTTdo0CAsW+ODIaNFiwhFIWscWL52M569eMXu+7Zt23EB+uae9eO+ILK2/Sk6YalCKsbgzCNjAuRCV0cb+3Z4sxJ/lSpVOatKQMqre2nZALIotvUSYu7+BHRq7cWu+eHDh3PQu6KgwHMS8CU1+RnTJil93NYtm2HHVh+0aNaEXSliBJ86DVc3d9SoUx9OLtXQ0KMZevcl3S2R6/Ho4X3w8hTpoolRoEB+7Ni2mbN1qbwWxY7p6opiq/oPHMIZmIRrcUH3165dQ1ZBiRJm3N7bd+5yxrE0iHjqFyoEfb1CXBVAQNYEuccprq/vkLEYNGISxzsuWrEeIeFXMXnMINhaWXAyihhHjokWL15eTfgeovCAqVOnYN600Vi6ehP27A9MVTuo//QZMgHDxs1E1/bN+Jh0PyyfNwnOZUULYwGZTcYycNX+J0O8YrCxLIH1y2eyL3/nzp2Z3awsj7Jly3LSgxj+qyfyaixxfBjHiCVnBZNl/ZJnkVLPIXooYqVKYoFLxXfFSM13srCVjJKCxg/rza99fTdj1KjRmDVrltxsQcKcOXO5sPvz588wcdyoBGSKEB0djYePHuPqteu4cPEyHj99jiXLVqFDl55o16k7lq9ci3UbfZlo5cmvh8Im5ujdbzCOHj+BX7/i3TN6enqslE+ZkOWdHRF8/DAc7O1ktsm0mAnKly/PQdAUY0VyEATXOrUlLtQB/ftiyqTxmDNnDg4cOKDwNSLLYXpZpqiEEi1ePJq1gZlVOWzavAOPnjzHr9+xePYiEg8eP2HdM2WIsoCMRZEiRXDx4kW4u7sj+PQFFLOqiKFjpkNLuxDGTpmHynWboeegsZLtt29ciuqVy8O6dCksXrwYEydO5Pe79x/JtVr7DBkvWUDIurel7/lbdx7Ayrkexk5dgHLVPOCz7T8sXLgA85euZVJIKFHcGH8CYtNi7U/juJsx2ZQC+VIa4g7h1bYPQiJuIiwsVKlCwP8iSGdKXNT417PTCT/kmyShrILc7L5k3kurWy7TTPPKxH+oIMNY1nnKvHZx13itz070GCSaELQLaHFQfbZsGrh+4yZrYc2cNgUfoj9BQ12dMxujXr/FiEHdMWbcZCY6x4JOYPvO3ayMT+4aImOyXJOOjo7shjx37hy/R1Y10vNbsIBKAoHV9nft3IqoqNfYsdMfO/x3MZmjiWnIwH6YMmlcMietjv/27kfTFm24gDlVDqCSS+PHjkp4HWJj0aCxF06fOceyGqUsLDB27BiEhYVj3ry5yJ49B2rUqM7B1ZQVSVa0Th074WN0NGfHUeUBchuqEjRp3rx5E9OmTYe/v3+Sz6l258WLFwTrWBYHkfbdu/cgKOg4k+zFi5ewZYpQsXxZHN+3JcH2S1dtxMCRU5jM3b17h98LCwtDhQouOLxrI7s0kxsPuC+36sdi2sQTKDGFZF6KFxfVkqVFFfWpe2FHoa8bl0koIykgVmpsyKwxUqmxXQlOk7WkLQQyluqO8fDxM9i6NODAYG/vtUIAbQqgCWzViuV4eWVf0j4okLEsS8ZoGHr7/hPXpsyvmQ9vPkTDrEy8hIRXwzqwtaHMx99MvkyKFoGZaVGcvHSL48UOHznKIqaUtWVsbMzyECSimi+fJltIqexPxYouMDQ0lMQYkgtOnL3p6emJAwcO4vL5U7CyKh3XYNE5PHv+Alv8tqNd25bQK1QomZMWnR8Rsumz5iIkJBTTp05E29atUDCu3JAYL1+9wsZNm9mNs2vPXiZi4nqbFZwdsXrtugTxotSmJp4emDhZpKVGArDpAfod7ty5wwWdidRSRjLplpmYmCiVICEgc0GSJ2PGjGXx8BkThqKCkwOsS5dMUraL3JFN2vbCnj27Ubt2bYmWHUml+PuuQIO6NRKMB7QooTnpzr2H3FeoPFKXvqOwbdtW2NjYcA1Y6X5C99WHDx8RtGct1GJEljaBjGVGNqVAwlJ/6eImgmLGRbB26Qy07ToI+fLl5QKtAuSDBos37z7gx4+fyJEjzvUY1xflCY5mpP6MqvefLqvI5O5bBYma0vUv1dSgqx3nalTTQO7cuXjhIY5zuX3/IWdcGuoXgn0ZK2zfdQD7Dh1nazEVxfb19YWHh7tcKRjKGJNGYmHZKVOmMBkjYichY3HnUKSwIYYM6qfwuTRs4MaP9+8/wMDIFCNHj5fEldF7X6PfwEBfX1KqiZ7puPnza7ELlM6hTu0a8N2yFcePn2B1/WvXrmPZkkXY7LdVafkPZUDHJuuy2MIs4M/D9u07WOS1rJ019u/wZvV7eZILbnWqIWfOHIi4GMxkjDIfT5wI5s9y5YwP84iMeoM2XQfj1LnLbH2TBi2AyLWZ+N6jclvXr9+ApaVFAm2xBGOW+D2k39irsv2mM58RpC3+ADR1d8ObN+/Qb9hEVKtWHZUrV1KqDt7fDlqhBQTs4vTpbdu2YXDPViIiJuCPhbfvTiZiR44cwZcvn7Fl42rky5sHj588x5qN21jVe/369WjatIlKrMVkDSCQNU2VsCptiWvXb/BrImKEseMncdyYGNT+GtWrJvjetBlzWMdMDAuLUnByLMduzdJW1ipto4C/ayykrEhabKxaPB02pUslu/3zl5FxMhRvOZB/9erVTMgonqx6lfK8v0OBwRg0ciqr6E+YMAE/f/6AhkY2vHjxgq3RzZqRft5TrFvnjY8fo/Ht21cmYRQKQDGXohCAvzhzUkVIXzelYBlTGaunLBWHKu54+Pgp/0+Bmp07d+JVyb9e8ogGkI0bN8HBphS8GtTE0N5tRBM0B+fHucHEFjJ5MWOp6bdZqGhtmixkqT0PVZy/DCvlseBzaNZpENzdG2PFihVIb9CkYm5eEpYWpXDi2CHkzRtXIksMZa6tjD5FrtX9Bw5xzBpZv5ydHONdObL2rabO8WQDBg1lF2u7tm3QoV1r3Lv/CM4ulVkugFTNBQiQZxmjSgnkZg49tRclS5jKvmfVNJiI6Zk54uvXr7zA6dujAxq4VoedbWnetlbj9jhzPkQig0HxZNKWsf79SS+vLtq0aYvfv37C0ECPFf9JmqmZhxvc61Xn8l4JjptCzFiWgAq5S+bHjAlETGUQd9SvX7/h1t0HOHbiLGbMX4n3Hz6iefPmWL58Gce//GsQ1yOkCWvJtKHo0KKB6IO46yXJfkyuXFBG9NMMIm0qd1kq2u7UnJ8MEnYw8CRmL16L4DMXObB+x47t0NYW6XilJ2hyKVmyFJOySRPGYcjggSIyL+u8krvGqZlQ5O1Pxr6atGjH4p0XLpxPV1elgD8fRK5KlDBH27ZtMXucKFOZIaNPUwwYkbKiRQyYOBEl2LR1N7w3/4czZ85ItqMFhDg7kkDeh+zZs+Pbt++oWtERvmvmsSyTzLEoERlLAjWNjB9PM4ijCGTsL4KsVcO377/gvWkbRk6cw/76jRvjhSX/dlCK/aRJk1nocHCvNpgyoheyZ5MmWAIZ+9PI2MIVGzFk7Ew4lyuDQUNHomHDhhlaCoysCFQuhiysFM9VpUplrhVpbVUarnVq8TO3Jxkyduv2Xc6+jIyMgpmZKcrY2qJK5YrJn4eCZIzEZ50qVMHSpUu4DqcAASmBrGOUTXk5eA/MzYopfK+Scn4Ri4ocjD979iwEB5/kYvdikAuUxLVXLJiEW3cfIVeO7BgztBdbfiXdVyBjEghk7B8hZ4tX+WDE+Fm4c+f2PxFHFh4eDk9PL3z88B5jBnXGgK7N4weBOFdkgkB9RWUsMgKZ6NZMVzemMueV+NqraeDWvUewLu/Gel7Tpk3NtHqsZBE4ffoM9u3bx+VhyDpGliiSkyhpbo45s6aiTm3ZYQErVq7B0BGj2cVJmZyUjUjWCS+PxujcqQNKliwBoyJFUt22EaPGwW/bdtYdJGuEAAGK1G11dHSCtlYeBO7xQd68eRS+Z/sOnYiV6/zQoUN71KxZiy1sbnWqIiziBo7v8+X4zUIFdWR+N0EhcAHIWDImuCQzHGJy8e79BxQvUwN9+vTh4MotW/ywYulCVK/lCg8PjyRZZH8yHj9+wqn/JoULYqf3LBgV1ov/ME6klSHlmkxOTiFd8AcNQgoTtHQmY1OWbMXMmbPw9OmTpPFamQwSWj127BhmzZqNR48esfSFtnaBBNvcu/8ANnaO6NKlM2bOnMlaXJR8MH36dKxcuQpRUVG8XbVq1fh+pOy1yi5OqFTRRRJe8ObNW+zZfxivXr2CfiEdNG7YQFRqSU2DyaC5hRVPiv+SBVxA2hEaGorateugYb0a2Lhybor3rHhMoKz0Os368IIif97s+P7jJ47v9VFszPiDxsCMgEDG/nJIk4wRE2Zj5bqtCA8P4+BeStGnmBJaGW3YsIGzXf4GDBgwELt370L40U3Q1U60whDI2B9LxkztXVGnTu0ErpCsBkrTL1u2HJMzG2sr1HWtgwb168KxXFlW+re0tmd5DU9PjyTxaLdu3WJrGynzk9Xvw4f3ePUqkokYZVGWKmkOH18/RL1+zS4gKnxOKG1pAScnR0REXEFIaBgOHTqIKlWqZNIVEPAnghYFPXr0QHDQcdwJE1WEUISMEcrXbYerV69yOb5Nq+ZwbUmBjKUfGVNO2kIoZZRlIL4piJSNHNQDm/x2Ydy48awmfub0aTy/cwFmtlWwe/fuLEnGKAvy8OHDePtWVFSZJqnnz5+zxlPLli2ZTNIgkFNK64bK4zg7l4eujpRlQtoNmShYX26GTqKMon91pSe+Pql2Yablukj9RuRm1tSUP0hlBVAKPy12SEyTdJjWrtuAWXPmcRmuwoYGvI24aLg06NxINJUew4YN4/fIGUHuz4MHD7E6+roNPvDy8sTIkSPZxXnq1Cn07z+Atb6uXL2ObNlzYOfOHelGxChejso3ubq6cttI6FXA34ElS5Zi0yYfeDWul+K2iceBLSuncYkjyopsVI/EX2P+rrFRTSNLHUc5N+X986lT4BeQ7hPq6vVb0XvIBAwdOgSzZ8/B9k3L8ejxU4wYPxPnz59D6dJxQpYZAOpSVMSZAj+JYNGERSKeRKzq1q3L1oUCBUQTV6lSpfDy5Ut+TaTs7t27sLS0hLm5OeuGUXmN7du3cVbdwIGD2MJw79z2eG2p1JAxafzDZEyMFMmYvPNPS+B+3HMM1FDLszvHQe3bJyrb8ieAssoCAwMxb958PH78CFMmTYBX0+Yq2/+uXbvw6NFj9O/fT3JPkaVb1W7c+vUbsBtWjIIFCyIiIjxDslgFpD8oBpLkj0j4dfeWVSwcLu/eTdWi7E8eG9UyMJuyuLOKY8YEMpblICYcNDm41GmOazdus/o8rchXLJiCuYvXcAry2fOXMiQwmjIdO3TogEOHDnOQp34hXdx7+ETyObmjqCgxxdJQ/NeJnYsSfD/s6i00bjsAefPkRpNGtbBi/Q6ULGWJ/fv34fLlEFaJPrRtCWpWdhJ9QVpHTBkSJkD+4KvIAKuCDEpKpx83YzHmLfHmwsLdunUTfpU42Nvb4+bNWwgJucyLk+bNW7Al+d27tyq9Rrq6BZnkHdqzFbfv3seIsVPQtWtXTJ8+Tfgt/hLMmTMXY8eOxYMrwShS2CDJ/StzHKDPU7tQFWLIU0XGhFnrLwFpwBzy90afbm0lsSpd+oxAcVNjhF+5gbVr16br8SnImAQ6KziXw/lzZ7F93VxE3grCzfN78DjiMJ5fO4od6+bh5bNHOLR/L4qbGKFhTakyHXGwsy6FhyH7cf20PyYN74W9vovYpSkiby6wsyuDEZMW43dMfIC+QL6UAw2+ShMxel/6odQBNZI8Hjx5AceaTbBg2XrMmDFDIGKJQNeE4ObmxothshKTMrqqQRUMjAobolKFcujWsRUG9+2CZcuWctFwAX8HSIZCX68gChvqp+7+VfLeFpA6CJaxPxyJicjNO/fh2aY3vn77yTFXVEqmZZNGWLtxK7p374ZevXqpvO7c06dP0ahRY067d3ergSmj+6OEqWJp/IqYxs0rNEGtWjWxaNEi7N+/H15eTRAetA1WFmYpy1gIUO6aq9IlKTlg3O8CdZZi2RqwHzdu3ePU+K3bd8LaWijvIyvwOm9eUUgIWZJJKoMQFhbKrn1VSsWUL18BPmuXoKlnQ3z7Eg3n6o2RM3dermxBv01mSY0ISBvI6dWlS1cuEdehtReWzZusuHtSUcuYgBQhWMayOMQWHUUeysCjVU/cvfeQSyWRa+/Ll6+IzZYH40cOwLZt27lshaoxcOBARH94i5CgHfBbOxclihsr/N2UzpUGlDdv3iB79hz8PyUoUHzRoePxytACFEeyFrH0ImJxjylzV2DwmBkwMimBgYMGISj4lEDE5IBiIk+dOsmvxUTM2NgYrVu3ZreiqkBSGzVr1kSP/sOxI2Avx3ZuWDUPjx89gpOTMypWcOJMUmmQhS4kJASfPn1SWTsEqB7kDdm8eTPGDuuLmROHK2chF4hYhkMwIfxlWDhzLD+TQv3w4SMwevQoeHuv40LjVMj1ypUr6N27tyR9XhW4dOkyOrR0R+lSZlA1aFXes2cPHlhoUqAAY9JP8968i2OOiljVhKaxM7Lr2WLH7kMqP74A1SD86k0u4TV8+DD4+vpw5qCQtZc8KPHFy9OdX797/RIB/jtw//4DLgStSpB2GYUXdOwxEEtWboC9rRUe3ziDPVvXIjr6E1xcKmLMmLFslSY9Q0PDwqhYsRJKlbLgAtECsh7IWzFy5Ch06tQRIwf3hKaQeJflIZAxFVmvlH0oA2X2U6dGJdwND0KH1k1w/PhxLF26jAu8Llm1ATq5fmL5/Cnw8/NDAzdXqApkqfr67btKVlayznFUD3cY6OmwBY4sZeQ6iXrzHpPnrUHk67f49v0Hf7dlF9VOUrJWkck9siKSbWNKMWAqig9bs2kHKtRpATOzEhg+POkKXYB8DB8xkp+3bd8JK8tSWDBvDjZs2MhSFKqCjo4O/Lbt5EznoWOmYevOvdi+az/q1q6Kc8d2oWWTBvDZtIHDA/7bs1tSeJpikWgsEZD1MHjwEOTLmxszx/ZJcC9n9fHqX8Y/EzP2z8URqWngzdt36DVoHHbvO8K6Xe71a8F72Sy06jwAx06cwaHDh1XiJiJl8J/fonHmkJ+c4spK6HrJmfz3HQ6Ge9v+6NCyMTTy6OJ08HEE/bcBoVduwdjIEFYVGqBWNRcc2L46Tb91WgeprNDPUi1VkdrtZP2mce9Fvf0Ac4facHd3x6JFC4UC16kAhRaQvlnopfMoWFAXVarVxMtXkThxIgiGhoZQpSbV0KFDJf8H7d8Kl/Jl+TVNE3fuPcTlsGto320Av2dqUhQvI6NgUdIMJUsUR1nnyrCxseaYNgMDA7ZqU9zqu3fvOKHo9+8YvH//jguyk5wNFYNXdfyqACBgy2q06tQfPmvmo5lHfdx/8BCmJkb8ewgkLOMhxIwJgK6ONob268rBwM2bN4PP1l0IPn0Bfbq1h2a+vFy3bM2aNWm6UtHR0YiIiEDtai7pesXr16mC6WP7Y/2W3Vi71huN3Wqw+Gv27NlgV8Ud08cPxrZ1C9K1DQKUx/WbdzjTlvSyaEEgQHnMmzeXy9OMGTeeJ9RNG9exhMz8+art7x4eIpeoGES0xKDjHj4ajA7dB0JLU5PfW7FwGoYP7AmHMtZ4/OQZF6auV88NxYubsSuzSBEj1hM0NS0Oc/OSsLCw4GQBDw9P9OzZC1WqVMXFixdVeg7/OiiJqnu/USzy2qRxPYyeNAcW5eqgbbchmd00ASlAOQX+PwhZwUKRqYizatAESAMpCbASGrXoiksn9uD6xcMYOHIqBg0ajBw5cqBdu3apOgwVVKZA3nYtEg7kqXZZJt5WyjozuE8nFDUqzK7JLm2b8bYr12/Dz5+/MGHGYvTv2UHp3/1PXykq3f60lC5S4jv0Ozx78Qr9hk9mVXlVZgD+a9DT08OMGdOZwFB5pM4dO6BWzeoICAhA69atVFZ/tkiRIhxjOnWqSGPs5JkL+PT5C3LmyIHwq9cxZ+EqLuY+ePAgWFvbYO3m/7B+ySTWNCSQ9ev+wye4cesubt6+y+8ZGuhDRzs/smlkg4aGOrQL5IehgR7y5MkH9xad0axZMy4XRWOQgLTh1KnT6NKlC0tYrFo4lTUmZy9cxb/P1Ru35X5PmTHzTx8vszL+WjflP0/GxFDTQGj4NfQbOQPnz5/nt4yNCuPU4e1cvqXvyFlYt2491y+jFbiyaexz587D7FkzEXX3rOi7qlZklufijHsdEnEDzjWb8OvH14JhoK9cKRdVDy4Z3e/SlYylBnG/y5dvP1Cpbgu8e/cBm3y3oHx55/Q97j8Ax3KOuHHzJh7cvcXxWvUbeXCFiwUL5qtUp40KmxsbmyR5f9y4sZx4QdiyxY8n/qJFDOHsaA/PRnX5oTg0mLSVqVAbzs7OnNhRr17KJXsEJAUthidOnMS1Xelablg2lbNxbSvUQ4dWXti8Yy/6dmuDccP7yBwvBDKWvvir3JTpHSD/t8O+jBWC/luPQX278P+Pnz7HuKnzkSN7NqycMwpL5kxkwdbJkycrve/o6I8okF8zXbSIqKrA4aALmDJvDfqNnAWv9gPQoc8YDBw9C8FnL+N3rOjc5k0bhRZeDVBQN2kJl4wMuk/vfpdlkwZkCD6u892JazfuwH/XboGIqQhu9d04dktLSxMWFqVw63oEOnfqgGHDhnP2nKpAWa4PHz7ApUsXWfE/MvIVXrx4LiFihJYtWyA4+ATcGjTCvUcv0KJDH6zbtE1UvzClB+M3LEuZYtvGFciZDfD09MKAAQPTRdg2NaA4t1evXvEYlFVBrmt//wDY2dljzZrVmDlpOI7vWcexfAcOB+Hbt++wsi/PJICSMAiqSiYT8AdbxlTxAz549JQDEQWkEmoaqFK3Oc5dDMWm1fPQ3FN0gxLInE3xBaRLkzh2JDn07NkTYZcv4OKxHYpZYJJzfUl95879p2jUsgfuPnicYBPKDKXgX5p8qlatCkuzwihmbIS+3dtyVmeSw6UjYcmIQUnl7U+rZSwF16X4mjRs3g1v3r7HyTMia6yAtOPkyZNo0KAhytjawNdnA4yLFuWx2dK6DJo1a86W7cwCyeVs3LgJU8YOxqA+nRX/Ik3usbFYsXYz+g8bj6YeDbBx8zZkJigOVk9Pn187lbXD4aPHua5uViBfU6dORVhYGK5cucqJEHTtGtStiXnTR/M4KMaho8Fo2KwLCwYX1tfB8b0+mdp2eeNYViB2aum8qP2rLGOEbQH7UapsbezZfxRRr9+ymKkA5eHvuxzXLx5JQMQIQ/p1hZtrdQ7CVYCfS0DxWj9+/lTqO7IgvQK9GHoVpcu7SYiYnY2l5DMaiHLnzs1ClZRhttV/P0ZNmotq9dtwvIqAzMfGLQEIDDoD9wa1M7spfxUqV66Mo0cD8SoyCtWq12bSQIO7s5MTFyvPTMydO5djxvYePKr0d8mq3syzIb/eHpCxxeJp3Hr27BmOHj3KhJJi4fT1RfUbm7jXR9iV61i8eEmGtOX+/fssR0QEWxqkCbly5UpefM6cOYvr/lJfoNrDwQe38pguTcQINau6wN7ejgWDh/YTeUQEZG0oRcYySldLFpzLlUHt6hWRM2cOmNnVQAFjB3i27omLIRGcLShAkR/wNwrq5EcJU6MkGlI0IPbv0QF3795l65iioADi6zfvYueewwq7sc5dCke/4VNQrroX8puUQ6ESFZCnsB1GTl7A23z+InJV6BXSxZLZ43DqwGZ8ehqKPX4rMW/aSFiaG0t0lj58jMaSJYvx+l00nKp7Yvi4WThx6kL8KauoX/41rnBZteSUeSQD8TVZsmoTatasgf7DJmTQSf07IMvwgQP78fzFC+zbf5CvuVb+/Hj1KjLT2kSEZs6cOfx6zNA+Sn5Z5LrULpCPF4PieDTC48dPMGHCBEycOBHphVq1aqNECXO2OJI4NglL07hDRMxnzSK4N3DF7t27kd4uUYrZtbKyhptbfSaDrq51OWykmIkxihY1Zt0wg4IFELjHB7dCjmLTssno2KYpypezTVo3NvY3smmoYeOyaZgxYSjq1a6KzEZWDieKTUc9UWXOUyk35esHF7NEAP+iFRuwYPl6PH32kv+vXrk8DgWsy+xm/ZmQmmBJR8jKqU6CQF1FULlyFZgY6sDPe36Kgff/HTwGz7Z9WRusRpUKKG1hhl8/f3GpnBKmxrgc5C/3ONKd+v2Hj7hwOQI9Bk1kF4KDgwNePn2Ak2cv8edmpsaYOXEYGtarkWw8m6Im6swaOLJMXJgS18jWpQF+xVAG7wloayeN4xOQNvz48QP58xfgeDFaiGzdth0dOnTC1q1+aNSoUYZeXpItmTlzJmbPnoMxw/pg3PB+qduRmjpb1noOGIMNm3ew/hgtDGl6omD0V69eIm/evCptOy3iiYi5ONlj8tihLPdTUFcHGhrx49XSVesxdMxUjp9Lj4xgcj23bduW47umjB2Eyi5OKFu5AXsK6LwpKaKhWy3UrFqRSWJ8zF0GJeQIUImbsqCp49/jppRGvx7tcT/8OK6dP8BaU2fOX4Z7q564lyi+SIBymLNoFT+TC1AZtGjRAjv/Owyf7VIuBjnWlMAT51C8WFHcuHUHy9f6oO/QidAubM5u577dkq+bKR20XiC/Flcb2Lt1OQrpaGL79u1MxCZNmsiDGPWFJu36YNai1dh36Dj+O3AMl0OvYqv/PhwMDOZSSoSsnhCSFdogC8ldo5ULJiPy1UtMm6A4oRegxLWPIyilSpbk+6F5E080aFAf7dq1VzrMIK1o1aoVE7EaNWpwDcT4Ria11iRb8SE2hiUYVi2eDu/ls1GrqjMWz5mMQ7t8mZiQ/IUqcefOHbRv34HjTzu0boYSxYtCX4+IGH36W/Lo1LYpipsao4mnh8rbcPXqVTRp0pTLSoWf2Y/unVqzu7FL+xYSstjYrRZaejWAXkFtgYj95chaI7ySMDcrBu+lM/D9x0/sPxyEhs26ZuhA9LehW4eW/Lxzp79SWUS9evXkQbnvsMn48OlLkslZ/H8M1BB67T4sSlvzZCLGmjVrYWZmhg6tPJS2FpUuVQIV41TCxTU5aRAjaxgR9a9fvsGjdS94te2NCrWbonOfUWjUojv0SpRHi04DcO3mHfwpyOjQgNTuu4KTPYcV3H8oLI7SA2QJLlasGB4+EsWJUV9fv3YVBg3sz8Ro3LjxyCir2JEjgRg5uBf8N8yPl7ZRxFojaztabCEWbZp7YMHM8ejaoTl0dESWBFVlNbImYrv2KFPGDqdOnoD38rlwrVUpQRuksz6pcPqerWvw/sMHzlrculU1yQU0T1E7TE2KIGDTIhQ2KMSWk+r1W2Clty/09UUJBGMmJ5OUkU7hBgIyB380GSMUk8quLFRIV4gfSwPK2ttg9pSRWLRoEZycnBWuf0eD8MSJE9jN0K7bECbGX7+K4r54VXvnAdZs2Ibajdvj7Nmz6NChQwKXS2RkJEqVSn1ZlDFDeuF++DF8fhHBj8h75/HpeTj8fZahnL0NcuQQZVmuWrUSL1++YJcDxd747zkE+0qNsHD5eh4cP336zCWksnI6+5+CYiZFoFdQJ7Ob8deCYvL2HzgoWXzmy5cP48eOxsgRw7BkyZIkQeCqBmUz9+8/gI9Poq558uROl+NkjxOU/fLlS5r3RbIZTZs2w8GDB7F07hTcDj2J1s1SXgAWL2aMsDMH+TUVS1cFnj59hhs3bqBvj46SIt5BJ8+xJiQlE/j4iLIfe3Vtk6bjUKF3GmMFZH38EWQsOW2lUiVMMW3cYKxeNJXTd6X9/QKg9Aq1f8+OOHVoO3Ty50Hjxu6YNGkSLly4wJlbycHIyAje3t4IvXqbXcYkxFrZrTU0i5aFTQU39Bk6Ed9/q2P37l1o2FCUOUUoVEgPT548gWe9ykr9XOK+4L/7ALRNyqJ4mRqwdHTFnMVrkF9LE4+fvkBVt1bwbNMLP3785O8MHDiICxu3aNGSM5fEGDp2JrsxdYqVg2FJF+TWt8aGzfJj1/4UZETQqTwY6uvhQGCwys5FQEJ4enri4cNHaNGqDSKlyhZ1aVWfF0VEyNILVG/Sy8sLgYFH0KNTK7g3qJP6+CU5FjKxZYq8H/ny5cXFi6JY0LRYxCic4vz5c9jttwZdOjRHrlxE9H7L0EBL2g5OJpo7Cdu2bUNgYCDSCkNDA5QtWxbT5yxhwkQQE1o3NzeO/bOyLMnjsdKge1dNHcvW+EC3mAMKFS+HHLolefEZv00K86QyFjbB6vZ3k7HkhC2lPyN315B+XdC+lWcC15eqjp3Rj0yD1KDoVK4MDvqvZ/XmZUuXomrVaihuWhz+m1cl6wYmfbJ79+7i1KmTyJMvPwrqG2HmzBnYu/c/Fo0MCjqOOnXiBu44iFdtSskgSA3eX799l7wmAjZ60jx2PRYqqIPyjnZJ3Cq9evXmwOCihfW4fJJHQ1eMG9GP3RHSePj4meLtEZAE+fLm5cBVAemDatWqwdfXF6fPnEOb9p0k75MiPul8UVD9li1bVH7cfj07cb3JiIgrWDBjDBbNGgsTIwPJPZnq8SyZODLnsnY4e/ZMqttMFqia1avi9OnT8PddiUoVysonYDLbJtqua4eWsLMrgw0bNiKtoPPauHEDXkW9Rb9RsyWJaCTA3aVdMx6bTuz3S35OiyNdSR5xWLPJH9WrV0ePHj35/8io14m+rwI3pqxt05qxrfZvulyzbDZlZma5ZSYpyvRA7USdm4QGQ8KuYcaCFdh38BgcHR1Z6NXdvTHrfaUVJK7YuY0XZk0ervh1p4Fbqp1hV25gy8590NYuAMuSZqjvWo0tpNS1KVA/Vy6RYCMVSZ+5YBUXOKZJSxoxv0XnSYNfjpzZYWVhni5VBf4V9Bo0nhMqwiKuZnZT/mrs27ePg8APHdiLqlUqQ/3bK87Ma91lACeuEBExNi6a5vJIa9euZc0qEnZt0aQh2rXwRK3qFZOQqMT3sFLjmcyJXR3jp87H2k3buSqAsvckuft69+4DE+Mi8Fu/DDalzeMalooxXk2dy8cdO3YcERHhkgXenj172FKYmvqaRKi7dOmaRIRb0fYkZ73UNLTCypUrOFuzUgVnrlCyd/ta/HWIzdphJX91NqU0UrKc/VHWKQXbnq5IlPFEqvbOjnYI8F3BN3K+XBro1KkTuxerVKmKXbt2JYixotcREREIDQ1NMX6PNH3I/VnctGiaiCiJws6cMAQj+ndB43rVkU1dTXSt1NQkRIxQpaIT9m1fw1axxNldRMLKOdjAwc4K1pYlBSKWBpCbzGfbHjRopFhChoDUg1xatrY2WL5ipeQ96vPL5k3myXfcqMGp3ndAwC6ULVuO61SSpe3yxfNo07wxls+dgFrVyvN9k9LYpNS4Jcdl6VTWluNKHz9WPCGExh6KvaLi6jZWFrh04j8REZO2hiWX3SkHV69ek0hc0BhHY2CnTp2xbNlypAaU+NS0aVN07z8Gq9Ztke95SMYCJgsbt4jCLHR1dfmZ4vpInPuvhNrfYUX748mYgIxDnRqVcWjXRly9cASLZ09k3Z+WLVuhSBEjtG3bDrNmzYK9vQOcncvDxaUip2wnJyBL7k/CCu8taNq+L1tTSjjUhl3lxrBxaYhh42cLrq4/EF+/fmf3c5EihTO7KX89aMFBVpkjgcdw/8EDyfv6egXRpHE9XLgUlqr9ksWnffv2XKdy46p5uBN6HKGn9nL2et68eZCRcHSw5WdFY7VIKFYs5Fq6tCVWLJyeJAwhNaCF5vnz5/Hw4UN07doNlSpV5kVc/fpunPSU2t9v+fJlaNWqJfoMGY8R42dCFeg9aCw/0+/XqlVrBAad5ixVAVkXWZaMKRNQnJUsWxmBTLGSSa0gS5qZoEv7Zji8cxXOBu7EgF4dcP1qOJMxq1KmOBSwAYd3bUQFR1t07txFrvaRuAZmrjyaeB75AedCrsO5QiU4VaiEcs4uWLNpJ6wrNMDBwJOJLoCGQivaf6lPZCVMmb2UY2Lq11fS7SIgVWjXrh0MDQ1RpVot7N53RPI+ZTVHf/qslDUpJCSE6x9aWVlxiMKciQPQwrMe9AtpK2QJUwkS3dsiRfx6GD9+AlvIkgO5UqlkkI2lOU4c2IGLx3ejjHUpqRgx5a1hYlDoA2mg6evmx5lTJzBl3BCcP+qPVp71uE4kKemnBrSoXbx4MVsf5y/1xuFjp5SygjESxb9RZjz1CQsLC/ZezJg4Au1aeaWqfX8V1LKutSzLxoylBGGCzXqxZtSVEsd0zF+6FsPHzUTPzq0xa8FynqQVBQ2spUtboZy9Nc4e3pqiuv8fc63+YgwePQ2LV25iqZNhw4ZldnP+GVD9QnLJ7d27F+1aemLx7AnoPXgcfLbuwvv371IsdO27dhHGTp6HF68ieYxvXL8WendphbJlrFTWxlTdd3H3d2TUG9hVasgB6RT4Lg8kW+Hh4YlbocEwNRbVmFSIeCk6jsg4h9CIm3Cu1gDHjh1DhQrlkVrQ+EmSQnbW5vBeNjvRh8oR31ETZ2POotWS/1cvngGX8uVQwrSoEIKRwfFn/0zMmICsA1nBtQN7d8ayeZOwar0fF7odP348CydSgGlKMDU15ewlKlMi4M+Afdzk/fXr18xuyj+FggULYtv/7Z0FWFPvF8e/gC0WFgYqBhYWBrbY3d3dnX/rZ3d3d3d3d7eiYoCKKGJ3wv7PecfGNraxDjif5xljN9773nrvuSe3bBa59LbuOoBGrbujS7vmYh5FEaqDCmRTupcGDRqgQ4/BKFPKG0d3rcbrRxewfN5EkwpixkLascGDB2Pnzp1ac6hlyyZ10H/q/8xifSMf00TOzjh79ozR4ydFyZ49H1Fb11CGDeiu9Ltjz8HIXbgCvErVxJoNOzg5ug3CwpidYzOlcrSUPqHyHsf3rkNS53jYtGG9SPqaI0dOtGnTVghm5H9x8uRJtW/7t27dRpN61U3aVTZfmo8WjWqjd5fWmDt3HidgtjD0MKfIue3bt+PUuSuYOmeJyPe2Z89e+TI7duxEjx49hACWL18+rFu7Fu/eBIkSVmsWTUGZkkVE0I6S5cEI057RKGy3YvFcIjjk2jXNOceoMgGZEwPCqxMYsh3ty0VOhxE7tiN8ShcVBc4pOe3z58/RokVLkc/w1i39fPZKly6F54FBePbipcbt6QL5yOXKER45qoDvg0fo0HOwSC7L2Ba624wYxgiKexfEoZ1S88LNO/exdvsxHDx4EJs3b5aHoP/48V1Ju/bhwwfxnTlTRJUFYyCfGHLCpYcNYz4SJ04kjrGp8v4x+kGmvHXr1gphgK73o0ePCuF4x44duHTpkogGdHNzw8BendC5XVMkT6bZdGJLZM+WWeSvu379hqiFqekep4obX7/q7itnCkYO6YNCpWoIQZiOOX3L6N27t87mS1dXqWn18xftSbajgsbR+wql3vr06Y1Zs2bLf0+auRD9enRAkYL5+D61EexytIyp/mK2VMBaL1Q0ZQXyZMeM0b1w//IBPLt3VkRpEmQyUXRh/Pjxkzw6TwkDnC+fB74SEZoumb1x4swlzF2yDucuXjfVHjIK5MyeBZ8+fdJbK8CYjho1amDnTml6A6o2MWTIELx/+wbTJwzDnXM7cWDTXAzt3zmSIBYY9BrL12zBq2DtjvKGYKzTPwn35coUw6pVqzSW+CHNH5Viq1BOod6kBTRkWd0ziu9YsWKLvGyknaPUIpQHjgTHOnXq6FTSSfYCmsJFTWFwPYWxyyd2yn/3bFcf34LvY8zwfuL3rn1HUbpKY8xZtMrgbUQbJFbU/CpgJ09zZexGCNGCpcvT2BwUmYUwpHVNIfKXzZ48AosXLxZFjmUC2cmTJ8R3udKGOcUGv3mLuw+fYPbitchXqg4ePX0mEmL2/N849Bs2EZXqt8fcxWvwIVzoY0xDxbIlRAWEpo0bCu0nYx3u338gvsuXKY5Pgbfhe+UwenZqoaR9/vjpMzbv2I+5qw+gSadhKFCqDrr2G4k5S6S1Ec2BQQJZ+INy9LC+IrXErMmj1S525sxpkVcsb+4ckbZp7pd4Mg3ev30J+/btFy+Yrx5fxbfX97BhxWwcO3Ycy5ZFnXD16tWr4jt5siQG9+P79x+4fe+B8N/cvWkJ6tSojCx5S6NBm76IEzsOdm1cIl928qylMdOdQGIbApgibKZkbIKuHVqI0kaUZyck5I0QzOiNPm7cOIgdW7/L9In/c3TqM0JkgCfo4dOhQ3ssXbpM/H70JCIf05DR07Bw+QYc37NGCBAhbz8gRfKkIps2mRse+z/H928/8PPXL7x9/wH//oXigd9TUbIkbZpUJj4K0QOqDbp7wyK07zkE9erVR/v27TBjxgyDMpQzhlO2rDSP3/nL18ULB+Ueu+v7EP7PXuDhI39s33MYd339xMsPRTl7eXmhc+dOmDp1GpInS2qTh54qYwzo1RFjp8xFyw49RPoGReieTWCCnGL6QnUlvQvlx6ate/DyVTDWL58t1+ZRvrfDx85g+vTpaNiwQaQ+E2/evMF///2HtWvXoXvHlkrJqvWlSr02uHztlhAEq1b0gd/jAOzadxiHDh3GmdOn8ePnL/my79+/h99jf+TMntXg7TExPLUFE40INzeS6cE5jSeSJUuG69eviVIulKtq9aKpaNqghk5vtnQ5ZytcXfgsNWhQX6TG8PTMjZw5c6Jgfk/c9/PHgAED0KVLZ6RLl04IfLlzeyq14Z4xPcYM64P1W3ZHznEWDtXl69ZBGq3GaD4XS1ZuQq//jcWcObPRoUMHPlQWhkxmVMJMEhYqNCDv3ktzYSVIkEBk7y9XriwqVqwo7gV6aQkODoa7e2YsnDEaHVo2MEkqGW0YovH/9OU7MuQqgeHD/0P//lKzG/Ho0SPky5df+EGdO7pdOdO+Ieiyr+H9p8ACZ9dciBcvHmLHckLggwtKAtWr129QpGwdvH33QUSIL1q0CHnzSpPZktl14MCBiB0rFiaN/h/atgg/7gZwx/chCpWuJf3/0mHk8MiC1eu3iWhKdZQp6S0Kp4si5THU/Qdm1o7pmtqCNWOMzUCaExqoKIIyc+Ys2Lhxg8gu3rHXUDgnjI9aVcrKl73j64fL12/DI0smlPD2kucv838WKKKZduzYjqpVq8qXp+LgJIgR3759Ew8fInPmzDh48AAeP34sBDgSBKdPmYiWnQaI313aNUWbTr3QtnULoRGT4ZoqhQWPjH1CD3dyEN977CL695ceT8rqzlgOysBOZYGWzJsOZ+eE8KlUS7yYUJkcdalotm/fIe7DerUq2+xpSpokMWpWLY+tW7fIhTHSiNG1lSpVKvz+8xenz11CmRJFLNYn8hEjAZfGljbNG0TSbKVNkxqXju/EsVPnMH85pfnxwd27d0RetJ49e4l1Jo0ZDBfSSBooFJG/n0wQIx81j6zu4v9qlcuieuWyolapjCoVyuDQsdPInzcXO/DbCKwZs9qRt53MvzphCdu6gxPuP3yCFh374t59P3ntNhroDh8+jDLFvNC+ZQOsXL8dO/cflzvxpnVNhdULJyFt2jQoVqGheBN5+PABMmaUOtUSr169Etmo//79h3bt2mL+/PlRahRkDzOZdnj16tU4fPiIeLiNGtILQ/t3NePBiD78+PET/YZOEOftwIH9IpcSY5vQy8+3b99xbMdiiwRKGZoIdseew2jStifu3bsrXqhmzJiJ4cOHo1TxIjh74YoYM7aumY8aVcqZVzMmlnPEA78nyFesivjpmjolxgzrKwQsdYyZPAfT5ixD48aNsHr1GvTo1EoEVuhbBF2Rew8ewauktOJFcW8vnNy/EQ6OEf3/b9x0TJ4hrZ+5aNZ45M+XG1XqthGBNuRbdunELun2Y6p2TB/0vJ501YzpKYzdQOLEzuZ9MNubkMKox4gBkC5JetPtNWg0lq3ejIkTJ2LhwoXyQsHp06fH718/8fbdezGAkCr+y7efohwJhezTsupCySdNmoSjR49h+vRpyJ8/v8FnrlGjxti7dy96dGqJYQO6IjlFPjFaofNZoXZrhCK2PDCDsS1evnwp8v9NGzcYPTo0s+i29RXKSMDP6FkG+Qt4CT+sHj16InMmN2TNkgnPX4Ygc2Z33Lh+DfcuHxY+jHqPR/o8hxwcsXDZWvQeNBozZkzHrl27cfPGdQTcPSt9XqpAVRHadRsk/GGnjx+Gjm2aGCWIUfRokvRSkycFDmxfvyhSxQUyo9Zu0gHXb91HixYtsHHDOmEypWTBlM/R/+5ZpE+XhoUxM/DlCwljXpyBn7E/aGAis+PA3p2EcEVh+SSIOTs7Cz8XemiQIHbm0BYhuNFb6eLFi9CvX18cPXpEY04fyuB9/PgxowQxomfPHmjatCnWbNyJnIWr4ODR00a1FxMgTUX1SmSauSsGJcb2WLx4iXB+b9lEWjPWliEfp50bFonoQxLEqlcph4fXj+P5iyD4+JQRtR4pUnTJyo0W6c9Tf2mS2X79+mPChPGiJujuAxF1QhVp1rAWxg7vh7OHt6BT26ZGlycKev1GfFNgw94ty9SWvmrffRCOnjgnUmdQUfMUyV3g63tPRJ8SijnJGOvgaLJim6b6xCRUi8Ga82PxfTPw/CqEG7tndMOj60ewdM5E7Nq4GClckuLAgQNyvxHvwgVE4kJK8kgmyWHDhsnNiuakVKlSWLFiOe7eu4fiJUqgUZteIpEto53G9asL/5SGDRshMDA8wzhjE5BWmSKYKUrYGkFahlTFKFnUC0d2rkD/nu2xaqG0lmOypElEagfyCS1evAQOHL+oZ0f0fA6Fj63tWjUW1zb5RXbu3EUEAe07dEJt2SFa7n99uyB/nlwwBbStnyEPMGHkQLWC3eWrN7Fx6x6laa3bdhBRnevXrxe/H78MT+9jC8+OGAofacbmNSqtm9dHtUplcefiQTSoUxX58uXFwlnjxcAzqG8X1KhSHtWqVcfXr8ZlrdYXchbesGEDwsIk8C5XXyllBhMZt3RpRG1BPz8/EUk2cOAgYb5krA/5UJIPZt/ubWFPUOTkxJEDpKZIABnc0opgHIICFSh1gyWg0kO/3j3C2cNb4evri2RJk2Ln3sMoWLoWNm3fZ7btvgwKRtZ8PkifoxguXb2pdhn/54Him8bNgX06i/+/fv2CXr16Y/z4CeI3V8uwPob5jNkrLOVHxpYcNg0JoVdwnt21aycqV7Z8FNjGjRsxbtx4fPn8EeOG9xXlgJ4GvBC+HEUK5kXJYoU4JYyKQ+usBaswccYiVK9eHfPnzxO+K8Zqdnbv3o3jx0+IhyGlaJD5HlI2+oULF6g13zAQWkp6UHdt1wyTR/W3+iExJsH1+i170Lbb/8Q5p0CbunXrYtXcUVH7jBlqlVHT154DRmDpqk0YO3Yszp07K/J7DerTWTj1G2uSVIQSWNdt1QfXr18XFoGQkBA8vnkcSZMmjeQvRpGTFcuWFEJX5jylkDdffvz+8QVnwouSBz68JPLQ2f0zxI59xuxDGGMhynxEgxuJiup6FCiHBQvmo21b67zZ0xt4q1atceJEhHM6DZAUlUmpAiifU99OTVDWwGoC0ZE9B46jY69hcIoVG6tXr9JYb5DSBZBwlTBhwkgJM8+fv4AFCxZg3769IiFvYa88KJgvN9KnTS0efJTgcsqcZSJf3fr15ssqb8+0bNkKZ8+cgu/lg0jinMCuhTHKpdagVU8EvQpGtUo+GNCzPRImTGBSAYwerkNGT4WjowPc0qdFRrf04oUrXVpXeeWPDDmLCRNgnTq1RQASvayRD+z4EQNgCihvWesuA3D52m2R4oN8bGvUqCnGGkr7MWXsEJFOQ904v33PIRFsEPJWqjU8uGM1yvuUgM08Zxzs1GCnYR91FcY4zxhj95AGSmY2tBaUt2n//n34/v27SD5J4fZ04z19+lS8GW/atAlV6rfD2OF9MbBXB5O+IdsrtaqVh3ehfGjXezRq1aqNESP+Q9euXUWgxo0bN7BixQocOHBQCGIyMmTIIPyByEn72rVrIjo2V65cGDe8H5rWqyrSCqiSKUM6tO85TCxfqFAhecZzSpdCAiBF58ZUNm3ajG3btmHFgklS7a2dv5yR5mfHOu1pa4zlRdArLF21Ue67+unzF1G949TBLciWJZMQCKkfdO/TN/myxo0bDyNGjEC9mpVRsEAeo7ZP5s9OvYaKXGb79u1DiRLFxXRKlJ0/fwFs2bEPNatWQOP60lQXqtSvVQVVK5bFmo3b4ZkrO0oWK2xUfxjTYB+aMWthKgldNsBRe5r+V1yO0Qty2E2WIT+mTZuG7t272eTRowF63LhxmDhxEtq3bIh500YKf7iYBDlnq9N6kBll2JgZmLtkLeLHi4tkLslF9Gy6NKnRpEENeOb0QPq0rgh59x7Xb93D46fPcfrcZRTImwutmtRGi0a1tPq8/P37F8UrNxWagGIlSgnzFb2l0tBHJYDIlBQThWOqcFGqVGnUrlYOq+ZPtOoxsHjtXSOCxcj0XbV+W9y4dQ+HDh9G6tSpUbVqNfz9/RNH9qxDRrcM6NRzIM5duoa79+7Lr3G39OnRr0d7DO5neH7CoyfPoXaTTqhZs6Yw71OSatn4Mnr0aEyZMlXcK7cvHkIiY7Sc9qqdshUUnuXRy0zJMJpwcETA80BkL1AWVatWwY4dO2z6WPXp00ekEFgwfTQ6tG4Ee8DcyT9lD2LKIL5i7TahaahSoTTKlymG2E6meShQvcC+QycKgcwrX27kzZ0d377/EAXja1T2wYatu0UkXEyB/It8SpcUKSLOH9yg2ZRnZuxJCFPk69dvQiB7/PQZlq9YIcquValSBb9+/cKOdYtw/6EfOvUaItwWvL2LCME/VcpU+F/fziKS0hAePnoKn+rNUbCglxjnZFVHiOULpqFH/xHo3K4ZRg7uJVJXWBV7FOYkYfrvkw5jI5spmRjD1et3xHeDBobXdLMUs2bNEnnS1m7eZTfCmCWjLUcO7qk80USCIGkLtq6ardy0RAKXZEnQrsdQLFu2TJhIYwI/fvxAgwYNhXn/6O7VVhPE7JlEiZxFTq+23QahXr36Injo1KmTIm2LT7VGWD5/CooWLiDyET5+/EhobhMkiIfvP37qtR2qhrBz3xHcvO2LHXsOCZ9JqgSiKIiRpm7Y6KmoW7My5k4bwxYWO8UOxVeGiXiYNmjTDy069Ea9evVE6SR7gCK8Ll65idfBIbAVFPM8qX7sbtuyXHWaPrLtOjigecOacEvnihcvpOH/0R3az/I+pXD/vi92rp+PjOmkTueW1obJPhbDDHksKafZzg1LUMGnBPr06im0YpR0miI5O/QYhPq1q+H169fo2LGjKDtE1QKWrNosggt0pVbznmjf/X84cuoSSpb2we7du+SmSRnk7lClajUcOXFOaM9sArp37e0TFYasowcsjDF2y7FT57Fnzx507twJy5Ythb1ABczpzbbnoDF4E/LO2t2J0VD28ueBr1CgQAFE9xeXrVu3oXjx4iIz/an961Eov6e1u2X3kEA/d/oY4bNVuHARnDlzRiSE9i5UAP2HjhXLbNmyVTjWU9QqLTd3yRqN7dH8rn2Ho3iF+qKG5fnz50WJpdu3b4mIYwpeUQctQ8FDK9du0bmc1IXL15XSZMRJ7oE6zTrLoywZy8LCGGOXvH7zTjiyEmReih8/PuwFFxcXTJ06BXsPnhDpHayFJbVfFkFF86XLcvsOnxLfFStWQHSF0qs0bdoMrVq1gk/Jwrh0dAvy5/awuBbM4towGeas7CIJQxb3jLh+bj+88uVCvz69xIvWzj37sXPnDixfvkyYwj9//iTS7pCG7MDhkxqbm7t4NZav2YJYcRNi9sLVwg+2SZMmUXaDxr8cOXJg0Yr1wsmfINNm/RZdMXnmIpy7eFXkPqQksRev3EDKzIXgU60pzl64KpY9c0Gab4z6VrJKU5MdHkZ3OLUFYxq0PQRNPBhSaHfjNj1FcsPJkyche/bssDcoIIa0FUmSJrH4tqON8GWCwvTkgB2duX37Nho1qCfyrW1aPgP1a1kmKbJVhC7CSiX1qALAhJGDUKJifRGtW6lSJeHQT5QoURLe3t5InSo5smXOJFK6aOL0uSvixYA0/vrg7++Pjx8/CD/Aes27YOf6RajWoJ2Yt/eg5hc+z1xSofzR4wBh7iStPaXLID+0mBbtbW1YM8bYHafOXhLfDx7cF2/79hjJ1rFjJ3lU1r5DJ0UEIWN5KKIyS5YskfxwogOUq61ChYpI7pIMV45vtZggFlMp5JUX2bK6Y8aMmcJ/TEbGjBmwePEivAgMwl3fh6hTo5LGNj58/AgXl+R6b3vy5CmIHz8BVi2ahlP7N8oFMU3Ejx8PN8/vF35vRL48OYUARoIYJY5VJ4jpkHiBMQLWjDGW01qY4K2VwoSXr92CQYMGRir7YS9QZv527dpi27bt6NZvhBjkvAvlR57c0gSMlcoWR4rkycyutbB7DZmB2jDFsky79h9Dq9b2VY9RFyjhaJ3atZAre2Yc2b6cIybNSfh95ODgiNmTR6Je886oX7+BiLCUpUupXbu2MDfu3bsPcePE0dhU5kwZcPv+fTEm6JP37dWrVyhWrBiaNawl8p/JuHxiJ5xix8ZDv6e4cPma8BMb2r+bCDpwdk4oX66QQiLaDctnyf/3f/YCt+48EOWUqMYmpYTxKVUUrZvVFwluGdPBmjHGrmjarhf+/PmLdu20v/nZMjTIzpkzB69eBeHLl88oXaIILl+7hYNHTqFNlwFwy1UKdZp2wdZdB4VjLWMeyF/s/YdPaNOmdbQ6xPQgp9JclB1+14ZFLIhZkAplS2LP5mU4efKkqCChyIcPH0XZNHLM//37j9r1mzSoibt374pSSrqe6wULForqEjLtLo0bBKXT6NR7KIqUqSUizhcsXYtbd+7DI2tmJUFMpinr17OD+F+mpb9+8y5yFKyAJm17Yvf+o+jZswcyZkiH2QtXonaTjqwpMzEsjDGmDR3X9jEB33/8gJdXAWTMmBHRAXL2rVO/MYYMGQy/x0/g7/9UREa9+/wTzTv0Q5nqzfH9x2+z+MJYzaHaRvDKm0OYY0hbEZ2c9bt06SJMlHOm/GdWDas2rBYYoiaFieW2LU134FOqGJo3roMJ48fh69ev8tlUG7Vbt26YMW85zl+6praJSuVKoX7tqsKNYdeuXVFukmqz9u/fX1T1mDRMmifv+NkraNGiBbZv3yaqVVCEpsyvbdr4YciZI5vatiaOHAjfK0fQskld8Tt3Tg9kzuQm/v/7L1TUd121bovIc/bE/zmCQ96LRKgSOGD/kVMoW70ZPItWQcXaLbFu8y78+aNe4GTUwxn4GbsiT9Eq8HvsDw8PDxw5Ii1FEl25evUqSpcuIx6oJUqWRkHPzKhYtiQK5MsdUf7HRA8duzRZmmDf+w2fjOXrdoiUBLlz54Y9c+/ePTRr1hzv3oZg/H/90LGVbSVBtqrgb0nHfgdHvHj5Cp5FKgqhasqUyfJZ5JdFKTBISKtcuRKmjugZSUtF2i6PAuXQoFETjBsnTY+hCRLYKFI2+MkVuCRLKqpYkDaLtkn5zUhDRwl+06VJhbNHtiGlDsI59ZH6QC+KVEqMtGld+g7HXV8/peUObF8ltPr1W/fB4cNHULJkSVH79c6dO6LyAKXhoFJb3oULCLOoLttWx+Yd+xA/XjxRAN0ey5ZxBn4mWtK/ZwdRJJeKcV+5ckXUaIuuyISDd++l5o0ps45hxPiZwvyUyDkhfv/5I4pgjx3WF2VLF7V2d+2SsUN64uS5q2jUqDGOHTsqMpzbK/37D8Djx49x4cgWFPLKw5nYrUiG9GkxakhfDBk1GaVKlZSPU6SJJQ3Z9OkzRJH2vz8+Ydm8SUrrksDhlj4NNm3ahICAACRIkAB9+vTGpUuX4ObmJiI1ZQQEPBNpLWg8IOYuWiV+16pVC9mz50BQUBAaN26MzZs34+DR02jVpI7StsgUeercZcSK5YR8njlRpV4buSbNK58nWjSugx6dW+Hr1+/ydeLFiycCFEaMnyHSepw4cRJbtmwWyW5lwhLVPSVXjOOnzgnzKPnO1a5eAe1aNIJPKW+lCgLaePHyFVp27Cf+p9JdF45uR64cWREd0U8z9uwmEidOZLqi2DHYRKIRe9RQWBAaPCrWaSU0Y6dOnYr29QQ/fvwoisvSIE5vqZcvXxGFrckEQFFPBw8eEkJp9SrlsHDGWLimTmmUxsguNWSEEfsc8PwlytZqi4QJ4uPA4aMaE2vaMqSNoKLf4//riz5dbDvC2GZM4+bUljk4Cg1T8/a9sf/wSezfvx8lShRXWoRMlhfOncXdy4cjjfuHj5/B+i27ERLyDtdv3cPnL1+FoEOPaxoPOnXqhKJFvYXWq1gRL5w+uEmst2HrHuF3mitXLty/Ly1SvmbNGvTs0R2Z3TMIh35FKA+ZLPUF1Wy9cds30q5Q25SculHrHihatCgSJkyIrFmz4ty5c/D19cXs2bNEfzRBL5Lr12/AypUr8fDhQ6HBq1a5rCjfVKNy2cjaLgfl62PXviNo1KqbQn+2oJi3F+yFL1++IkWmAiYuFM7CmPmx14ehBSA/Ba9SNZAnVw5s3LLNLh+apoZuX4rK/N/AAYgTJzb2bF6KnB7uBrcXE4Ux4umzIFSs2xaJkrjg9OlTcHZ2hj1BD8MzZ87i7vndiB/Xtl9QYoowRvz+/RuV63fEixcvsGPHduTJExG16ONTFu5uqbFmyQyt4z69gD4JeC78ya5cv409Ry9h6dJl8vld2jXDnKmjxP///v1DtcbdxIuqDNJCUT4xMjdeOLpNpOCQQZGXvYZMQrJkSdG9XSPhvE+yEUV8Xrn7DNOmTcO08UPRq0sbDBg2AXMWrVLovw8qVqyI+vXri/QduoxV169fx76dG7H3wDHcf/hYvESuXDBFaPtVj50iYybNFlo/EkqJTy/vCk2ZPWA9YYyxHvb6INWRIaOmYsXaLXjy9KlQ3TPKNQfr1q6BV6/fYHC/riju7YX0adMgfdqUMUcwM1Ige/DIH8UqN0f16tWxatVKu/FPoXI7rVu3xrj/+mFQ746RzxsdFyslQ0VMF8zC/cdqNmwnEgz37dsH//33n9DoZ86cBe1bNsCIwb31GrvJjLhq/TYUyJubbJrIntVdSTDZc+AYGrSUapIoQnLG3GVKUZOfX97ReVuk3aP7gHxUSVBbuW4rqlcuhyf+z9Dnf2Px7MVLsVz58uXRo0d3YUKV+7NGAWkLO7Rvj/Tp0uD0wc2i+LoiZAnYvucQypcpjrLVm+LRY3/5PP+7Z/Hr928haGbKkB7RQRizkTuBMQk0sOnysVPcM6YXAwK9ZTLKZMjghuMnT6Na9RoYOWEWSldpjGwFymLH3mMGP2jUlbKJ6mNVjIzezemRGfOnDMOWLVtEqgB7YeHChajgUxwDe3VQFsSsFVVojyW5tBSUN67dMGRI54orJ3dh2MAemDVrNlq0aCncDOgTL15cvZskYaddy0YikKdA3lyRNETHT50X3yTwpUmdEiMG95LPi6VnVn1yj5AJV6S96tu9PTyyuuNR4GdMnjoND64dw8qFU/Hp/RvUrVsPXvnzIjBQKqBFBb30HD5yBPfu+wnNl6wm5oBRc9BlwHg0at0drTr2RToPb7kg5l04P7auXSgEuFyFKsAjvw9eB4cgOsCasZiItQc+A6EoSoqmHDt2LAYM6G/t7tgspMl++fKlcOj+8/MLTh3YZLGHstUfqooYsM+kCciQpzzq1m+AWbMikl/aKvTgI//JNYunoUn96pGFMRk2qhlTxOrCvCqmPmYOjth/+AQat+4hT/tAUYJUuzKHRxb06toG5UoXR9y4mpPC6gL5gJHfGAlpaxZPR9nSxTBz2U5MmTIFowb3ROd2zYxqn8ymxSrUF///ef9IfJOB7dLVm6JMXanSPlizZrXO7fXt2w/Lly8XbVSp6IN9B4+pXY5MvBuXzxDCIFGtXmscO3Uehb3y4cjutTabT4/NlIxh2NLDVMMgQNFClEOnbNmy1u6STbN06VIx0L15chWJE9mmf4XZhTcDBLKaTbvhzMUbCAjw12pWsAVI6M6WzQNNG9TE4lljlf3F1O27JgFDdVkrCm82J5SZ+JjcvHMf6zfvEv5XhQsXFrUoDx06LHLDlSlTBtXKF0XCBAkwbc5SYZKkjPf0qV2tQiRTnraXCtJomcPUvmj5evQaNBru7u7wu3ZYad7iFRvQc+AoBAa+QIoUKXRu8/Hjx8ibN5/4n4TSF4GvcODISZHgO2uWTBg5uBca16uhZNm5ev22qAVKUBkqiv6cOYly67nAlmAzJRPtKFggD57dO4sSJUqgVq3aOHr0qLW7ZNOUK1dODMqnz1+2dlfsiunjBomH2Lx582DrpE+fHhMmTMDGbXtFaSfG9imQz1Pue025BKk+7bx5czFz5gxR2H3clHlCoCF/LNKY3bzji3bdBoloRlpWV/OiuXwey/uUgKenJ5YsWRJpXq7whLIXLlyIsp3v37/Ls/h/+/ZNns6nYe1q2LJ6Hl4/uozHt07i7sWDUkFMhcIF82HhzHFyDePm7XuRNlsRrN24A+OnzhNCkD1ho68gjNWwcX+ztGlSY8/6OeKNcuzYcfKcOExkXFykb4i2XITc7H5nBviPeWTJhBqVSmPPnj2wB1xdpYmPvQtJNQt6+0ap06DpupwZsAkfMnWY0I/sv0E98P7ZDVE0fNGiRShevAT+97/B+PTpk8hoX61aVSxatBCz5i3C5as3sHXrFhw/fQHJM3khdZbCwkH/2g1lR/yLV26I8mmyMZFexH7+lBYsp2mkSdI0FuhTBJxqUt44vQNlvJSroFDwUO0mnZA8eXI8efJU5IJUHZ9pO0eOHBHZ/FOlSi1yoQ0ePAQpUqREwfyeQhv92P+ZWJa0gBnd0ikXLQ+vciCz4LRv3QRfX/vi8sldaNNcmuR45fqtGD1xlnCaP3g0IqrU1mGfMcY0WHjwPHHmEqrVb4PBg/8nopOYyBw4cEAULH54/ZgoQCw9T7bt0K2KyR/KOu7/ll2H0LzTIJEXSZewfWtCGoo+ffri++s7iO2kIMxa6lxbwKRpc6ZLE+4zCUw3bt/D1Rt3kCxJEgSHvBW5ycgHi6CUNcmSJhG5vlQZPbQvypUuht5DJwkzp8xxf0j/rhgzaY7QjqVP6yoy8xPTJwxDz86tRW3MBcvW4vK127hw5SaCg9/gf327YOxwaYJVQxg6eqowrZLv1vfvP8Q08mfcvXsXMmWSFhUnQax27TrixaFJ/RrCD3jLzgPIktUDBw7sR+/efUSC2kvHtyGfZw6DjjtlvK/bvDPOXrgqfhfMn0doF0n4NatfmYZrVFczpW5pcBnGxihXprgI2549ew66d+8u1wIxysJYWtdUcM8orS/H6M79h0/FQ80e0ltky5ZNaBwOHDmN2lXZj9LeoHQTJYoWEh8ZJBhRdCEJaQHPAvH+4ycxPTj4LbbtOSzKKVEEbZmSRUQh76DXb5RSQuzcI/XloutCJogRJbwLYs3GHUJoevjoqVoTo6FUq+QjfLxIePTMlV2Uces9eILw7S1fvgJSpkyJz5+l+7Fw5hh45vQQ/zesUxXla7USbgELFy7AzeuXUa95NzRrVAtjh/XRux+JEzvj+N71orj56Emz8Sr4jdDQzV28WqT9sVVsrzalrb0BMTarLaPBKmv+siKyctiwYWbdlr0hyz01afT/0K9H+4gZdqYZM7mGTMf97zV4ArbvO4GnT5/oXLrFWtAQXqlSZfHAObVnecSxYs2Y6bGBqFRpWoy/8pqWJGTcvO2L/HlzCcGue78RYjoJRW1bNIR3ofzIlycncmTLLLL6Uzk5GeQc36pJXZEqI1XK5CbvK2nz+g0dh6BXwXj+IggfP38RJZkmjx4k+ipj7JT5GEu+cj17onHjRihZspSYnjKFCwLunBTVRvQ9F1SGqc9g5dqeP0MeKJs9bag2pe0JY6aChboYIaD1HTIOG7fugd+jR3aXNd2cgli7du2EGWDFgilS7Y6dCmFmMVfqcCxu3X2IwuUbYfXq1WjUqCFsnZ07d6FZs2Yi8ev/eikI3/qi73ViZuHEauZJU+2Xhe+7b9++o3zt1kI4I5wTJsD75zflGt7sXuVE+S9i/IgBGNCro8W0v+TDRjUtEyaIEMIUmb90HQYMn4SkSRIhSeJE8H8WKKaTAHPhyCZ5Sgtd8XvyHPWad8XjpwFC63jl+i2cPrABhQpEVEGwxLXBwhgLYzFCGHsRFIxchcpjyJAhGDx4MGI6W7duQ5s2bdC0aVMsmzk84i2QhbEIdDwWddoOFrVAyanYHrRjXbt2FQlr3z25qF6ToFNDLIzZszAmcHDC/YdPcOzkGVHWqHP7FvJZU2Ytxt37fiKLfuN61XUWxKgUHWXdL128iEFliMgvrlyNZuI+OrV/nUbt1NUbd3D05Hk8ehIgUoA88JOaUi+f2C4S3BpyDuneGDd1HsZOnit+H9y+XGT1t3NhjPIVOduEqjZmC2f6HP/QaC+cUc00KtNx/8EDvXLbRDdCQkKQJ09eVK1YGqsWTlWJQtIj55S2dWKKqdLBSaSKaNimN54/f4ZUqVLB1lm3bh06duyEb69uC2FM1+NF2ie1y2o7TmZ8BlhFG2au/bGxe8iQ/SUTo3ue0vLfqxZNE35e5FMpq4f548cvZauZbL/Dt0OJaKkoOXFk5yr4lPKWzlZz3UnCzz+Z3W/ffYBUqVIgXRppxLChUB8XLt+ApwEv0L9ne7ilSwNL8eUrCWOFuRwSE/0hZ1di8uQpiMmMGjVKJHqcNek/i/tFREfo7Tx27Fh2ExxiaJoXSndQs0kXEQDAMIqQhm32wpVInSriJZey+yd1yw93z1JC25UgdS7kKFReREZqYuuuA0iTJg3ixYuHeUvX6nSQHR0dRcknYwUxgjRyPTu3wqxJwy0qiOmDYa8gVsg9Ez1xMvBjjm1YCRPkMSMnz4F9OosafZQ0MaZBvhjTpk3HypWrMGFEPyR3SaZ5YX1qN6rWejSy9qOxmCwPmQ79v3brHsbPWILmzZvbvIlSRoYM0hQcz14EaV1ONa/bnEVrcOj4WTTtOAAvg99GHGMLn3ubqG9qKqLBs/HE6QsoVKYW1m3Zi9AwiEoBt27dxPnz50Q6oZKlfeAYOwFat26Fd+8/Yu2mndIVFfc7/Dhs2rYXVapURrdu3XDkxDlh9rT5eqUWxrhRxpbNlTZ5U9vw8dK5b6GWPW863pD9enTA5u370LVzB5w5d9FuHqCmgNJ70ODYrUMLdGjdxHL3qro27fwBRPv09dt31G/ZU2QDnz59OuyFZMmSyX1UCJlgQw81TUIOFVk+fvqiyAf19u1bzF+6HhNGDgT5rpjzYWgTQpep7wl7uPZ13GeK2KxSrw1cXV3x8OEDxI2rXNDcy8tLfN+/fx91atUUfmTtWqoJdHFwkhfyzpkzF9q1ayvy4u3cdwQDe3WMuruSMNu5XsxM9N9DJkZAPjJNG9YSUUQfPnxATGLz5k2oWLakqMvGGM+qDTvx9v0H4YOVIIFtFh9Wh+yB+efvX51Tw2T0LCMyt8+YMR21a9fCwaNsqowpkFl7x57DaNmpH3IWqoDBIyfLk7WOGD9TfA8ZMjiSIKbIuXPnRB6z5fMnRySWVuFpgFQL5uNTBgkTJkTSpEkR8OwlTp29rFfm/+iOnsKYdc0UGrHBsj02ZQo0KRY2d+pxTiuVlzqZ7twZri6PIdAAmtczp20kKLUnc5YG89uWXQdRuXJlZMhgX8lyKciKePEyIsknoelYnb5wTXz369cP5cuXR44cORHwPFDvB6S6klZRfSyODZjZrYrKPpPmq3mHviJhLFkUSpQqgwXL1gttGEUzzpi3HMOHD0OnTp00Nkn1JGfNmo2SxQqhXs3KyttSQOY2QQFGBJk7l63Zgkp122DirKUIQ9TjlqLpUtePvixZuQkXr0irHpgMcSx0u9ZsQWphGJOQP08ukeSQSsOMHj0av3//jhFHlrQ3P3/+tHY3oo1ge/veQ3nSSXuCNBgU4fb23Xuty5GwNWriHDRv30fURhw3TpoYk0rF/PjxE6fOXrJQjxlrQOd/6Ohp2LXvCKpUKI27d+9g8eLFOHr0CO4/fIzqDdqK5eLH164VptqTAQEBosKHtuCRpEmk6Rzev5daLIYOHSqSu1L6nZETZmPfoZOwhWPSY+BolKnWTJ7fzNLYvzDGWjAbw4zaMh20ZAtnjceooX2FQ3uRIt44edL6N7olHsI/wgsC2xxm1ESYQ9Oy68BxkROpVq2asDfmzp0LR0cHkT9K3cPmw8dPWLBsA0pXbYYJ0xdizJgxWL95h9CoBgcHo2fPXmLZx0+lhZrVHVub0HLZarCJrWrfFPpB18HM+SswZ9EqTJ48GTv3HkLWrFnFvEKFCon0QBcunMfOnTvQpk1rrc0WKFAAgwYNxLrNu5C7SCUsWr4+QihT2P8b4QloPTyk5ZbSp0+PKVMmY+bMGeL3py/qi5cbvdt6aMsUrQqHjp4xwcYj9v+XjkoBG7mT7Inoan60s2OmwSxN4dBDB3THlVN7kNIlMapVq44WTerDz88P0ZXAwBdIa4Lwb3vGVELB+cs3kDtnNri765ft2xbqkM6YMRO9u7ZRW9ambuv+cM1WDP2Gjkd856RYvnwZBg4cIO4X1YdRjSrllB/gtmZetFWhx5j+m3K/NLRBQlNhnzrCN4wEou7du0ValfI0kpBVpUqVKFO60DVDFohz586isHdx9Bo0GtUad8Pbd8o+uwePnBL1U/Pnzx/JrJ4lSxZcuRNgsWtLm2D25rFUI9xnyDi8ei01qRqT12zxig0oULouylSPSLqrDRbGmGiJZy4PnNi/EcvmT8GVa7fg5VUQbdu2w+PHjxGdePPmDd68CUGe3Nmt3ZVowavXb5DBRvMQqUtpsmfPHlStWg316zcQD9ARg7pHWo4ejkePHkPVqlXw+PEjHDx4QJROUkQW9DJv2ugYL9hHRyjpartug3Dn3gPxe/78eSZrmyIrV61aKa4rSi2ULntRERRAGmb6bNm5H9WqVVO7bsWKFbF8+Qrc93sCa5MsWRLUrl3baG0daQePnjiHngNHwdfXF0+fKhdkN5EwppJfTN3H0sSwXCTRB/NrGOnNrVXTerh/7RhmTxmJE8ePomjRonj+XHOOG3uDQssJz1w5TNdoVPe4oR8zYwrtTbq0rgh6/Qa2zqlTp5A7tycaN26CX98/YdXCKdi0ZLzaMkizF60WfoXkF5Q2bVq17a1ftRiJnBOikRoTp8W0QPak8bIUuh6TKOaPnDQfJUuWFP/nz58P5cqVM3lXfXx8cOnSRYwbNw5bdx5A7qLVREDA5y9f0bFjB7XrjBjxn4iwXLPB8kFX6kyYW5dPwp93D5Aru9R0qy+U4T9bgQqo3bQTEiVKhNFDeuH+lUM6rcuaMSZG+FR1btcc54/tQCwnJ5QoUVLUcIwOrFmzVqrud1cfVs7oR5rUKfA88JVNp0fZv38/ateuI0ypF49uxZmDG9GsYU2NufXIZELmp5QpU2ps8/mLl/DMlV3ubM1EH6jCgr+/P8qXL4e3b0Nw4cIFs+VhJPNn//79cOLECZQrVxZx4sTGro2LhTlSU2488lV7FO6naO8cO3lepPogvn79isb1a6h1G7CMMGaNt2KSai2mIbODxH5mCEW3jN+Ief3wMrqlw93LR1AwXy7069sb9g6ZqbZv347/9e2MWE5qzktU96GtaLdNjDHXavOGteDk5IgWLVrir475uix5vgcPHiK0YdUq+WDv+tkoWMAzSp+YLJkyCG3wp0+f1Lb769cvHD52GhXKSjUnGmEtlm2ghwaRrhnSTpHGtEaNGnB2drZIChxv7yJCE3vo6ElUqq3dZ4ryjvk99sebdx+tGhBiihQZNauWR9MGNVGmRBFMHz8EmTPpnh7HcntuiUFfJpSZXTALVfnYF8bmZrGMoGYeM2Ya11Ro1ayBKN9hy9oPXQgKeiUEhtw5PZRnaLu/opHQpQv6XpfuGdNj84qZIgp3167dsBUop1OX9i1FxOSI//XAhmXTETduHJ3u32qVfcT3hg0b1c4/fPiwqDpAxZ+VYJOhfRIe1PTx81cMGTUVN27dw44dO+DpqSy42woUSBIc8h7ZvCjx7FR8+/HLdqJ09Xxepk2TCqsXTcHR3atFLUx9sJ09ZhgLkTVzRvFNOXLsGcqaTmaAPLnYed+U+JT0hnehfFi9ejVshb59+4louBULpmBIvy56mZnSuqYSvmIvX75UO3/jxk2iIHMOD/WmJMY++P37D/YdOoFy1ZvC07sS0mQtLFJYTJo0CcWKFYWtki9fPlFyqV/3dli4YgMK+dQV2fnNCfmxDRwxBbMWrRYJcG0B6xbw0/Z2bqzzprq3RLNJ24r7Yd9Op5rernV9U1G3nOlq3Kk7tvpreJIkSSTXNtgrGzduxOLFSzB/+hikTxue1iKGaLsMQbFOoy50bdcUbboNFgESuXLlgrV58OABaleviGYNa+l9njfvOIAXL14InyFVPn/+jIMHD2Lcf/1M2FvGUjx8HIBFy9fB75E/7j3ww5uQd8I/q0qVquiTO5eIViQ/LlvHxcUFw8dOQ5PWXVC4cBGRnT/g7imkd9Xs52gMZBmZtVD6srV2025sXTVbL5OiqZ6XirBmjIlxJAyvN2jPwtjw4f+hQYMG6NC6sbW7Ei1pWLuy0CaNHDkKtgAloSWth+9D/VKzPPF/jrbd/ici6ajkkbr8ZKQZaFBbfeoBxjahczZ55iLkK1ZFZNL/++8vqlcuh/Pnz+HmzRuYNm0q2rZtaxeCmCJZs2bF8ePHxP9DRk0z23Yo4Gnw4P+J/+/4+sG7QiNRBsqa6CWMmaMelEbMESKv6FOm+jGbP1n00FYYc87NGxCgv08ZRZcRsoSX9gZpa169eoXalUtGOOOyVkwndL3eyOF5xtgB2LdvH1auXAlr06dPHyRJ7Iz+Q6Sli7SheF/NmLcCKVIkx8iRI9Quu2PHTngXyo/06VxN3mfG9Fy7cQdxknsgVZbC+G/cDOFvdf/BQxw+dgrzl6wSOb+oJJY94+Xlhblz52DT9n3YeeB41LUrDRz7xvRriaH9OssjTqs16oSpc5bBFBgiF5n8aWQRYc0c+YwsJqBFP4w956YT1nQTyvyfSX1nMmfOrEfbtsH27TtEmacM6dPCp2QRa3fHLtH1uqpXsxLaNq+Pfv36W732JwmHLslT4sQZ5bqRUd0vlPeoWLHi8hxTinz//h3Hjh1DXcUiz4xNU6ORNF9XWJgE165dFRnwKXVPdKNJkybiu1HrXkiXowReBr+NfM8qPu8NkAFEBYEhvbB9zVy4pkohpg0dO1OYMK2BfaoGGMYInvo/FzdipkyZbPo4Ulg6+fMMGTIUpUqVRubMWdCxY0f4lCyKe5cPI3X4AMKYB7pGWjetI1I/UJ4ma9OqVSu980NlypAO9+7dU1vI+cyZM2Lfqlcua8JeMuYkYYL44ptMkblz5462B9vZ2Vlk9G9Ut5oQjk6asXh9rarl8ODyAcybOgIHtixBiuTJECMc+PVJn2AwmiRjfYMCNPXVKNNa9HH2N+ScG3JeVdeJ+hqSHVf118HTZy/gli6Nzb5RUtHmU6dOY/78+bh27RrSpUmNUiW8UbV8cSEgUCHoePHC+87mSbM69cuOsy1EXN2+fkGY2KnYs665olo0ro2V67fj7NmzKFOmjNK8Y8eOI0OGDPDIal91OGMq7z98xJev3zB06BCbf5E0VUZ/Hx8fXM6REyvXbkOtquWROJGzbjKEnjKAs3NCdG7TWHs7Zq4IYd1oSi1EdcANEtZMFb2p2jeDhTN1/YneAprsvBojbCuuq/06cVJ7jP38g5DVw4Tlg0wEmcLIYXzevHnigVuwQB6c2LceJYoWivzwjSFCmD6uDYZeU9qup2ThGenfvn0La0JpKa7fvKu3NrRE0YLCN/LJkydKwhgJdTu3b0OdGhUtkgSUMRIHR+zad1SkZCDH/JjE4sWLRKLj0tVbY/f6OciUPo3yM1ufsVCdcKXr+mYWzNhMycQ4Hj16BA8PlUSpViYw8KXw7VmyZAnGjRiAwIeXcPH4TpQsVpgfllaEksC6JEuCq1evWmX7ly5dRtOmzZAjR058+PgZy+dN1Ot6IEGMIsfIUZ80rgQJZhSN+/pNCNq0aGjG3jOmJHlyF/FtCyZzS1KmTBmcPHkCP378QKU6bfDylfQ6jm7YrTBm8iABYwIBTOr8H/0iMdVhqoAOfZ38SSPw9OlTeHhkg61ANczq16+PXz++4cqp3RjYuzNSp3RRvo6iWckic2CKcSCSM7yjE4oUzCuEIkvy+/dvNGvWHGXLloXv3duYOXEo/O+cRKVyUZQsUsPEkQNw+fIlZM+eHRUqVETevPmwbNlSdG3fHAXyhudQ4+vL5ilUII/47t9/gFofwOhMzpw5RaWIUIkj2nYfqr8WXFsJKRspYG+3whjDGELQ6zfC/0dT4VprMHnyFDx79gw7Ny5FzuxZrd0dRoVK5Urh9OnTGms7moMrV65g586daNG4Du5eOogu7ZoJfxlDqFWtPALunMSowb1E2oMFM8bg9aPLmD1lpMn7zZiPyTMXim8KyLh40XwO7bZKhgxuIkXLqXOXo6V2LFoKY9FLWxY985aZGs1vSsrpLt68kfr+pEkT7ndgA1y7fA4VfIrDM1e46VSdRiyGYkwuOlONAxRtSDVAL1y4AEtRoEAB8Z00SSI4OkiMbi9pksQY0KsDjmxbiPatGkcEgGgiBl9ztgqls5CRMmXMjKT28vIS389fBGlf0A7rqkZLYUwdJst1ZkoBzWDUCWg8eOpCcIhUGEudOryEkA2QxjU1Xrx8pXxdxHAhzFwYIqBReggS3ilbvaV4/15axL6wV17TN27KRNqmJKr8kbbUV0sjCcOEkQPkJjtb83m1pEsHQTV55agzOdohMUYYYxji1p37iBcvHpInT24zB+Tb9+9wSZrE2t1gNEAO8926dcPq1Wtw4cJFixync+fOiu9qlZTTUTAxlySJE2H5gqmiTunHj9ZJTGptPDw8REDNirXbEN2IUcKYacrvqGvYRFn/jUaTxsx23yZNWUJL+/l1wrdv37Fw2Tq0bt0aTk628fb0/v17nL94DQUVNSAx9e3fhq/B3m1roniRAqhTpw7u3Llj9j6dO3lQ5MKjB7DVMKcmyigLQwzUoNG45uCIksUKCa1Qjy7tERNJkiQJWrVuiwPHzpml/aiqwejyMZQYJYwxMZvjpy+IxIk9enSHLUAh6iVLlhKal46tpeU/GNuEfKx2bVgoCi9TeSTKA2cOKHy/Q4eOWLV+B/r1aGeWbTD2i3tGN4we2k8UB6cUPTGRnz9/CKEsuuFoaanRElKmeQpSR7VRE7yNmaUupgzb1ZiZvMi8BhI5S7UMtqAVo4d5ly5d4SAJxaWTu+CWPq3SfLPVdbVTzH3/6nKMKUv3lFF9cf78eezdu9cs10Tr1m2wc+cOrFgwCd07ttC/EXP4yxg7tllLa2XvGrRwbZhqUvFuHVsiZQoXLF++AjENiUSC/Xv3wKd4AYWJUZ9Dc2u1otqWXWfgl2F2IcpWUXwwmPUY6DIYOdlF2aSocHJOJb4/f/4Ma7NmzRpRoubQzjXI5JY2XAiXngtNQoGp8rJF5/JF5q4QQXm+KpYtIeqFVqpUSfgfmoqtW7dh37592LZmnkhHobmTTuYTyNQ92Axpz5aFHHPts6mha1HNdRg/fjykTJEc375JndljEnfu3BFpLerWqITohv2OzAyjJ/v27Ufq1Kng6elp1WNHSWfpYd68eXOUK1Pcqn1h9INMytMnDMWLFy8wceJEkx0+Pz8/9OrVS/ikaRXEGAZAudLFsGfPXnl0YUwhJCREfGd2d0N0g4Uxe8CkGf7tw8RpDtMcZd9PkiQpYsWynkKYknmWLVsOKVMkw9RRvSM0YpJQi5gjTVq1IoaSI1tm/DeoO6ZNmy7Op7HcunULlSpVRlrXFFg6Y6hVsn/LMWZ79mD60xVbMWdqGO97d2uHr1+/YMGCBSb3Y128eLEowUXjVOHCReDpmQeDBw/BixeBImG2Nblz5y4SJIgP1xQu6s+LhnOl63hnzXGRhTEmxkAlkGiwoQSe1oDqTpYvXwHu7u44dWATUoTXmmPsj0G9OyJOnDg4duyYUe2cOHFCCGJubm44vmetdaMnGbuBfExrV6+EQ4cOG9zGt2/fMH/+AvTp01cEjVCZrNy5PTFgwEC8ffsWWbNmRcmSJVG+fDmsWLFClNNKlSo1evbsiYCAAFiaf//+YdmyZahesUzUSYvtEJv3GbM7ZFK5ud9gVaV2q/gCRfW2aPwxoLcTff2cNPkWOcf+J25oGoSSJUsGS0KDG71dUnmbedPHIE7sWHKtmC1optT1wZb8y2zhGCly7NQF/Pr1CwkTOhtce3L27NkYM2Ysypcpho3LZyFRooQRC2g79jZ2LKKFJsyY/VQd66M6Hgb54EX2IaYi8IZw48YNIYQdPHhQjIVpXVPBNXVKlCpaABNG9EPZUkWRWOWlYMz/OuPM+SvwffAYoyfOxLJly+Hrew+ZM2eWL0MC2oEDB9G9ezeYwzzZpUsXPH/+HFtXTIt8vNWdAy3HWdfxJKrlTDlGsjBmLqK4GEy/PQ0XjVUfqOoGJSeTO1XryvFzN5A3bx6LC2I04HXq1AlOTo6YOPp/QqOCsL+Rb3bFQdwGHIhtTQCyJfLmzq7TA5HqWSZOnFj8//PnTxG0cebMWWzasA4h7z6gb/e2GDO0t9R0LjvnUV3nhtwHfC5tRxg1wX1+8/Y9nDp3BUWLFtW6HBUUpxdQMeYAwszYqlVrhP37g45tGqNz22ZwS+eqpo/K975L0sSoU6OS+HgXyodq9dugaeOGOHbiFBIlSiRMmLly5RbLNmvW1ORjbN+enXH1yhXsWDsX+T09Ipsj1SlBjFGM6ChQmzLojIUxJkZApskjR46IhK+mpn37DkiXLi0GDx6M+PHjCydvxVDsNm3a4Ny589i4Yg6Su1hWEGTMQxpXaWTuoEGD0KVLZ1GAm4Stu3fvCtPl+vUb8ObNG3z//h0JEyZEokTOCA5+I9ZxcXFB47pV0LldU+TiwvCMAfQeNBrBwcFo376d2lx1ffv2FcXEX758KcY+MjFSFDl9SKN75tAWFCyQxyBBvULZkrh6Zi/KVWuC/v37o1y58mJ7ilpfU3L9+nXs3HsE86eNQo3KZa2uiZXmGJQojfOmwEGiQ/bCL1++iCRr7wKuInEiw9TyuncozGZNJQZjA1oOvbD4Mdfv+OhzTciup83b96Jlx164evWKyaMp48dPIP+f/CxatWop/s+YMZPwcSBtyI4NS1GjCg0k0v44hGvGBKYYXOztGrMjNF1vA4dPwuxFq4VmoHPnzli5cqWoqJAwYQI0qF0F2bO6I11aV7x6/QZv331A5kzpET9eXLRoUld5INdVI2aSnTGDtjOmmCktjZZ7unmHPti68wC8vApg6tRpwh+WtGATJkzA4sVLhJN7h1aNkT5dGuFfde/+IyRNmhhJEydCiaIFUayIl4F9irhG23cfhINHz+D3r18oUjAfjp8+L6bXrFkTW7ZshqmoVq063oe8wqWjmxE7lqNu15uZxsOfP3/By6cBngW+wqOrB+CWPl2U48WXr9+Qwr2wEIRlWnK1XWZhzALY24MyGgpjpSrVQ3znJDh40PTFnnfu3IVmzZqJ/zNlyiTeRklDRmHnNFiOGNQD1SqVk5ptWRizO7Rdb9du3MWEOWtw+vRp8RDq2bYucufMptbBWKO5nYUxRs/nBulQzl26jn7DJuH27YjyXPRi0KV9MzRtUBueuTxML4wrXLu+Dx6hz9DJuH3rJn78/KUUafn9+zeDfdpU0wBRYMGCaSPRvmUD3SNbzfTMJYE3lUcpfP7yVexf66Z1sXD6SKVE4jYjjFnTz8QutGj2JpjpgkmPu5PJhbGbd3zh7VMDmzdvQq1atWAOnj17hpw5c2HutLHo1La5uGlfvHyFTBnSw8Eh/J5Ql9jV3rQKtnr9GnscddwvfccYTeOhvB3F7eratrYxNqo2TD0+29v1a++oXKfkD3by7CV8//4DP3/9RunihYU21qBzp8+9HX6dkfhAfRg5YSbu3H2AE2cuit8ZM2bEkiWLUbp0aRhDlQo+Isnr1RPbkMg5IRD2RzfrmRnHqcvX76Bk1YjqGKf2rUUJby/rCmP24OBr0wKarT7YTIHJjruT0ee17/9GYseeg3j8+LHZcozRPZIvXz78+/sXN84dgmtqhbQVCoKX3Qph5rrONWU/t9Xjo+89G4WzttJ1LJuva9SepbHCObGHZ0xUY5OpApB0viaMOU/6XmMq+/Q8MAjnLl7F6vXbce3mHVE/01Bn/sDAl/Dw8MDqhZPRrH7VyO4dVhTItu89hibt+4n/k7skxeghvdCueT3hO6raL12FMRuWVBjGeLbt3IcFS1aJJIbmTPZKN9mMGTPx7v0HBL4MMtt2GIZhbJWMbunQvFEdrFkyW2jrqLyXoVy+fFl8V/SxvSol9WtVxujBPcX/7z98Qo+BYzB/+Qaj2tRLM/be/zISJU5sl28rNq8ls4U3X3Nj8PHX4jsRRZslKzXA1atX8eDBfeHPZc48Yt7eRZHVPT2O7F4n9SFQ0YKJ+8ZWNT4yzNE/S13Xljy2uu6TLlouXbQbehxDk2kJLHg87fWZEu2eWYZcJxr6WKx8Xfz6E4Z9+/bC1TWy6fTBgwfCnSN3bmlKDJk2bPPmzfjw4QO2bNmC169f43vQTcRyDNPN7G/MfuiDg5PwkctbohaePgsUk9wzpkffbm2Ek/+aTbtQu1p5oTEjzVjyzN5RasY4tQUTrXFxSYYaNaqbTRB79+4dtm7diqVLlwlfibVLZyk5czIMw8REls6djGoN2iJbNg8UL14cefLkQalSJYUf2X///Yfly1eI5YKCXgohher1Lly4EPHixUOaNGmQPn16LJkxQmrRCLNuGSZ1UO62bFkyIUfuPBgxYgRGDBmA3oPHh6e+gHgOkDCmK7Fi0huMrWcZt3iiWEujJou0bsje0CMfm6j8MXJly4RN23aJNzBjonso83OjRo2QIEFCJE2aROT4efXqtcgMTYNF5QplsHzuRKRNk1q6grnvE02+VlEtYyRR3v9qEi1KDOmjqX1hFJs2wbmRGLs9xetW9Zip20899t1B9dgJHzzj/Zbseey3V7Qdc7M8u0xYQYaiOa+e2i38dalixaED+zB//nwxj3yrEidKhC9fv4rAp0yZMsLX9z7GDO+Hru2awdk5odQ/TKFur61pD7fvOYyLV2+hQ8dOyJs3L3btPyIi6U+ePIn/DRqIyuVK6tVsjNeMRcu8ZtEWzUKZJrJlzYzXwSEICnoFN7f0hm85NFQUqaW3nSoVy6Jw/txIW7UcMrilExE/KVMkV0pdofjwNIvTvi6DpZGO8FofvkrZrzULG6IdhchBnYUYEwuSRgkS6goPG7m+2nb07aPGdv8qXSMSB4Vr0VTFxm3d3G4uVAVmK74863pNG/RM00cxIOuHmu2kTpUCXTu0FB8at58GPMeR46dR2CsfCnnlE2Pz1FkLcfPOPZzYtxHFKCpRQQhTWwhcW78sdD4oUl7mwN+iRXNp1yQSkRONAsWSJU2Mbh2k6Y7M5jNm7qSv1sbqwlh01owpYtRxjiIqTTZNIoFH3hLImSMbdu7Zb1TGZF9fXxQqVBj7d6xDhbKlNAyEFhTGdMVawphSQwrCmJXuL1MLY3rdp9rOgTH3u44lWyJFbLIwFi2EMV0x+J4zMrIyMlG1Fyr9kpWKkwllqmOoSr/URiSbkX+hEuEX1rzTQBw6fhapUqUUfsnkQ5Y1azYM7NkeQ/t1Fto9gn3GLFiYmtHnADta7Ly9fBWCFy+DMGPWLL0FMX9/f2zcuBFlyvjA0zM3Tp48Jd9O5Ad7qO2adYwwXaotuK5NCNOhbQddBlATCq0mF8J0madxHRNdD2o1keoExnCnZyWBmO4TI4VLTdszFbYs4KjT1FgCI46Jwe45+gqcUbqhKB+rPv8bLSIRfUp6o0ypoqLe5ovAV3B0ALp1aI54sdWM2YruD5YSwhTa3rrrELoNGI0OrRqgV+eW6NWlFao26IANGzaKMmipUqVCn66t5YKYXpthzZhmrCqU2fKAZKPJYVXP1/7DJ1GvSRv4+fkhQwY3vVovXbqMeNtRxDNXduzYsEwkclU7yKgmdbVkvh+DhAM9/JC0CV6ahDX5ymoSmypqbaLKr6VPFJUO65gEY86tar+MSfaq0g8HqHnwUgYjnYuQa7+vLPmCYTMvxfY6FpspkbE+bUcs7xjJahE3eTbxP/nyhoWFyX3JRB3hnatQtoRXZM2Ypn6b+hypae9VcAjc85WX95WgIuuBQcEoWbIkzp07hwNblqBi2RJK67FmjInx3LnrK47BjRvXkTJlClGiSJ9ImXI+JZHDI6soa9SlXQt45c/DkZIMwzBGQpaKlk3rYc+B46JE3atXr1CgQAEx7rq5ZcDHz59t6hj/+vVbaMCoCsDlK1cwZ85c7NixHd++/xDzL168iDmTh0cSxMznwK9LXShj89nY0FuIVU2W5vIzsSTWOnbhx+fq9VvimxK+UnmOhw8f6NxEnTp1MHDgQJw4dU78njpuuBpBTI1jt67+YRZ4kzOd+SE0sslSMThAdp5pniEmR3UO8jpqYLRF09I0Y6KwtPbBmHHOYB8eheOsaROa8njrqslTY2pyMGepKa3aUBt5LmiLRLXlsVjH86bOgSPK556+50aNNnja+GHYtG2vSO7apUsXMXnixIlCU5ZRTQFujX0z5TlQpxmWSDBs3Ew8evocFy6cF88Sqjm8YMECTBzRD43rVYNzwgRIlsTZqEhoG9EDM4xpoRvoyrUb8t/6aMUIb+8i8v/LlCljkqK3DMMwjJRkSZMI8x5pmd6/f49x48ZhzJixKJjfE175IxLBWhOKmqzfqifmLF6LCRMmiFxpK1euRJcuXdG2bRv079EObunSiH0xFqM0Y4pvjnb31mDPqS+Mje6KIWkusmfLim/fv4vIlzJldC9YS8lba9ashSIF82Ptsjlwz6ifv5lWDZSBfhyG+ExpQu+YUgUtmETBv8tB31Qaavqq636Zcv91XUfdclGOeVqWN1nuNG2aSFOii2+bLulT1N0HkRzho8pfp2PErqHocG7UapCM22rU589KKUSEVjKqWqr69lHp/EvP54Kpw1C4TG2kTy8dYzOkT4MBPdpqf+6a65pXaNf34ROs27JbCGEuLsmxadNGVK5cGWvWrEG3bt2RNEliLJjQVxoYFlWgj47Hx6A8Y+oGRrm5QD5B0YwRxcHTwYHXVrDZaMvonjBWTz5++oRrN28hUaLEQhgrVaq0XgleqXTF6GH9tQhi6q9V+bUhTGRaQrD1RK91o7gO5AKVfIIO950aM6VEbQ4g3YQoffdNp/1Xs1+mvlf1bS9K53drmDtNgboXDFMVODdVMIrWfFSOJjKnR5FqwYA2LUWES4UGM6yaklxy07Gugpq29gBkyZQOl45vxdUbd5HSJQkqly+lOceYhu0ajUKblJ5i4IipWLB8A5ydndGvXz/07dtX5JnM65kLL1+9EcvVrFJWWhnAhOcvxid9ZaIn12/eEU6XzZrVEFmRy5cvF+U6AQEBmD17joi+pBstT+4cFukrwzBMTMUjq7v4WDsV0Lv3H1GvZQ9cu3UPM2ZMR9u2bUVpJmLmzFkIDnmHm2d2IXeOrGbZvl7CmDzHkhFFbLVvwHY1OzapDWM0cv3GbZGoeN68eTrlGKMSFg0aNESSxM5wz5gBwwf1QqqUKbSsoeZadVA2IUXSQEX55mjiwUjXa1aU5NE1m7d+Zkq9zDjmCGowdXoRXbX9arZrkEnZVNo0S6DueOvaV03HRmcndC0lpxRdo+XXrw6l2Qy8HyMFPBh4viydUiTqUmcKViE1Dvz6BAKoLxEWqttxM5m5XyFnmUSCdj2G4nHASxw9ekzJZ/jjx4+YNm0aWjauHSGImToHIWvGmOjIqTPnMXXWfFSuXEXnZK9Tp05D9uweOLZnAxIl1M/Zn2EYhrFf3oS8w8FjZ4QmbPv27YgfPx48PT2xf/9+DB06TOQ+Gz24p1n7oL+ZUucQaQVbuonD/FlLZSdoqVlmLr58+YpaDVvh9+/fIgLy0qXLKFrUO8r1aFkqVLtk+RrUr1MdGd3SGVA+SdWPJlTLMur8IazpA2SII3zk+9vwglMGaJt0Wd5aKURMtF2jHcQtjM7nX9f6ghI16TyUrreoNUa0jFw7pk9aFx1Sieg1X897zMEG/ajV+YzJr1HFeTJtma4aPYkBQQDapuspc7imTokhfTvh3KUb2L51M+bOnSufV8GnOLatnC6WidS2SauF6FOb8j9SvqYAAEhtSURBVMlZJHFOoFPDBuUCMcQpMKZjqyYLdZjlXCqXx6DLecXazRgwZBR+/PiJwYP/h5EjR0bZCglv9AZEuWOIEYP7ClOl+VC8iaPOX2YYBgiEhhCFycGYXF/q0DWi0do+KFrR1TlZtpw+g765xgQrCAZqS3FFVfFBa4NqqkEYgjmuLW390WRWNbSagylRF8AQyUVDwzVpySolOkJasANHz4hSTe4Z04lyTVFGTSqispzIwJ+1lAgKS5w4scbV2IGfiVbQTRMU9Bp///7D9OnT0bWrNJlgVMSNGxfTp0+Dj08ZNGrUWOSOYRiGYWIWsWPHRu1q5S2+Xf2EMQpnd4yttV6URVJA2JM2SN/Mxcaqa20ZkRfJvG9upOFavGIdOnTogG7duuq8HiUdpHUOHTqMnNmzoX7tajpqsfTLTaVbO4ac21A92jLkbVRLO1Gc00iBDFERxT1gUJ4wbTmujMQgDZyWY6K2coCx97sp9l+nTOuGtU11//YcOoVC+XMjRzZ3kdFcNKeYUiWSKcqAPGumGn/U5tsyUlumzZypTRum2oaltWPq8rGppsswh8uARM9KIsaiqxuFrlpBFVgzxkQrPn76jHfv3qFsWR+91hs7diwuXryE1UtmoWGd6tIcMgzDWIRqjbrB1++p/PeGJZPRsHYlPvpMjEFvzZhaxz01b4ca30oNyUCub4oAU2oGVN9WjH370TUBbnTFzE79LsmSiu9Pnz7prEnbvHkzli1bjvEjBqFpg9pGZf5XVyvRMn5NUV035vD3MVxbphVdtQD6tqU6zUTH3qxO9nrUD9W5DUOS/pqZN+/eK/1OnCihZm2hMWOkOcbXqJLwWitljbEY6o+moAVT8u/U9xp00OPat/BzU33FIQ2pSMyqGVPraK9jdIwhjv+Rdjzq9rSbMvTI36J2IDPB4GVOB1Bbdlw2M3HixEHixInw7PH9KJf98OEDKpYvh/sPH6FmtYro1rG1Hluia8CIyDp5eSHTDKxRC3XGOu1rCwSIapoRyI+dsfecmnaMPfa6Cgd6CjumNnvqtK6pBDIDq6nMHDcI9x48QcIE8eGRNSMqlyuhw7NB9/Nn1mAOg6ooqMsfZ8D1aAohRGPlBNMKfRoFM1MUI5dY2GQZBbq+jCvCthgm2tG4QR3MmLsI7Tr3QLp06TQut2PHDiGIXTixF4Xye+q1jUdPAnDnnh+yZnHHpm27UalcaZQrE/kBwjBM1DSpV5UPExOj0UsYE6kDlMyHZlCXqjVJ6ln7Sy9NnCEqe/3r7+n8Vq6tLX0yquuLpbRpFlCx58+TC8v//EVYmPb3r4IFC4rvNyFvlR2FtRAWFoaGrbph74FjStNnzF2CmZNGonunNrp10lRvcFo0bLoHDphbW6Yb2s25MBEmDI7Q1Ylb27k2IlO/SbU9pjCHqmvPnCZQXd1fqEap+Xqhv3VFTQ1XtctZCiuYAyMdM0kUNT51NcNHVZTeiP6bO80WJ/Fioh3JkiZBvHhxsXbtWnz79k3tMlevXsXo0WPE/3WbtBO1LHWBcpepCmIyvPLlMaLXDMMwTEzFNGZKY2tFqVtfLoU66Z5A1hSSqx51+pS0a1r2N+ItQAcna2sluTWl07Rqe2YlwqFedpzr1KyC12/eoP/g0Zg6dSoGDhyIQYMGigjJN2/eoHr1GvD19VVqJejVaxQskFfDNiLOm7NzQiycOQ6P/Z+jUrkyuHH7Lgrk9URhr3zCV82sqPMJ1JYiIarmNF6XxmgztAc4mCRNhZHor1HSQ5toQb9NqxwTfTVoZkwpoti+/Fio6Z9DmBm0PAr7ovd5sNdE5qaqraqUwV6zJtlB13q5atbVeK0ZoSVTF5xlSvTKwP8u4CoSJ3JWWNvUzrqKO6lwk+kaTWmuPCZ6raMtB4xyVnLVk2q5jOFGDIqa+mj1AUb53Ae9Coa7ZzH5bxcXF7Rq1Qo/f/7E4sWL4eKSDEuWLEHSpMlQt25d1K9dFYvnTjXpOdD2gDAsSinMbNe1QaZ2rajfZ60DmYkejsZg2PnXVjTYCsE0FoiONOg4RdUvfU2glPdS9R5T91JJRe0V/tfYni5YOvLU0hnqTVy6UGNOUh3Pg8TAZ6ROChwTFHBXh+qznTLwp3AvzBn4mZhJurSuuHPlBMpXb4y3b9+KyMlZs2bJ50+ePBnVq1cP/38SunXrjmfPA9G3R0eULVVcmDkZhmEYxhLoqRm7gcSJnU24dXUSpha1s7bcY8Zk/FbtlqnffqJSy6pZzi60ZDZH5PP+8ctXjBo/HQuXrpZPGzJkMIYNGwYnp/BrTSLB7t270bRpM2krTk4IDQ3Frk3LUa1SORPkn9E1BYYZtYs6XneiG9YysVnLgdkoTaEJtGTaAgHMkendGAwYG/U+ljpqZ5SsJtqOkw7jqi6mJ437oU+ON10w5PljzhQXpsJM7Tvoc071zHmq8brQw/yuq2bMNMKYOj8rgx4sCgll9RDG9Ba+ouqb1jJPRl5Q6taPyq5uAqIeEKO3UBYQGIQ5C5Zj1brN+P79h8hH5lOqGOo1bAJXV1ecOnYAs+YtVVpn+/olqFm1omE+AkaXrzHAdKlroWETCye6R3Lq6G9hDaFMJ59PQ4gG95Ux+2/uaEoF3+LI6PgSbKH8cIrXvjHXlCl8lgzevi7HxMR590x6PWkRxox+QdTgj/blCwljXiyMqT8hLIxFy4eGHM2+B58+f8alKzfw6Ik/tmzfg6vXb8kz9zeqXwtpXVPhz5+/KFggD6pXlhaLZWGMhTHDiQb3FQtjOsHCmOxAsDAGswtjz26aMGIsCmc9TRox1XnGmCUNiJyMsklDnFT1MCGZ/23InA8PJwtvX/v26Hr7+fMXQt6+QxrXVEJbpoSNZHO2OtYol2PpY2/EPlrOpcBQTHj+bGFftbi3qMeA8neWRmMWfBPnfzMSc13rEn20htr2VZ8ABNVIXNX5Jso4oKswxhn4mRhN/PjxkDFDemt3g2EYhonBGCiMmfatVWfJNAptlN4aMhNK+Vo1YhpznuiwfRO+GemWZd7a2iAbyEKti++ALm+ihvoCmlMrZEieHWuknDCk1pwxPnNGZIzXtRC8qXyG9EeH/Ia6oq+/kkF1NjVtw9DrUJ9rCNZBn+Oqb5UHEyJ/xppaE+cQhQ+40rilW7WUqLanNf1FlFV+9DhfDg7mEMaos6Y56To7y+lVDFazUGbyCElNRGmmNNMgrEd0h74+UNY2w5itDIUBkbpalzHVNaaPqt3UzrAmzx1oIeFa18FT39I1Rh5jTdeuMde06e9HxX029UNWR/9ckwtghmLNl1Fd86wZMR6a6toxNkpUX3Oggxq3Ji3r6hRApGv+Ugth7UydDMMwDMMwMZpYVtVSGKkJ09ispTQUOrdjYc2SsdoIlf02d4FUk2NIXi9TaHCiKIsVVcoIvY6zuTS9+pSJMfV1oe99Yqnr0lK5onRtWo9UIvqjR+knU2BvY4tZMWQMCrW9460xn54BlUgcDOuvTi5Lkdp2MvK4a1pft3b5TmAYhmEYhrEiloum1NkRPSofAgs6QUel6dC5HRuTeQ3xm4myTfM5dJoctdm6oyhsrEt/VCst6JnZXudCtJbKlB1lviBTJHo2M7aQisEC6BacY2850qwdTMTohbHXnkRdYIK2WramHm9CjUy/FGrUdauXMKaXGcVoU5kOQpnW9VUOmMG5yAyIorN1zFFixRZycqnul6X3SVV4V4k8ovtHl4elUdnrjcWoyMkw4wZInZ3sLVxY3dTH0cwmTGsH3BiPOccSfcxL9oCs71Z8LpnzepPoIgdEjyAPG3yVZRiGYRiGiTnopRmjNy766PSGrk94vtZ2dMnFpWd/bMEMZ2uYqq6hOdFX62IiTa6SM2gUx8khiuOmeu9YRYth1jxmFtgfW7kebWjs0O86svWxzFTaKx2Ltlsrt5itYcr7ylTXvIOTDjV4dTUv6oIh/TbNtjkDP8MYwYcPH7Fm3Xo89HuEvJ650aFNc8SNG5ePKcMwDGNeYUxnR2NThdCb2vHPkOze5sScCURNgSHnWeubjI2gLpGgOi2YQrbmf6EOuHHjBq5du4YTJ07gzJnT+Pr1q3yV5MlToHGj+oDECQ5hf8yTNsRWrlvGYvetcRpUGxpLDMaMaYaM9XU0aZUBE25bnQ+mPg7x+u6XOa59iRrfb7WBQ1FtW9cx03pjq1GaMaOFMnMKRbpEetmKwGMr/TAllhbA9BXIVVeXXYMOjpA4xpZOC789Hj16jOPHj+Px40dYtGih2vVTpEiJRo0aom7dOhHXc6TgD+PKd0QrouM1b8smRV0e2jEBRQHFIKfwUNPc08aMBUZV3bAzE7+DkxF5zRTPr7ZjZhtjK5spGUYDr169Qp8+vbFv3z6l6fHjx8ew4SOQwc0N/v5P4eXlhbJlyyJWLEc4hP3l48kwTLTnacALJIgfD2lcU1m7K9EC2xDGtEn65jBlxqQ3weiAMW+fWrLsC21Y+HxFbRj5gW3cuAHz58/H79+/sGTJYixcuBg/fvzArl37kDp1ajg6OcAxvACsrA5sWBjgGN6OA6UZoz+WrItqTqLDPmjBeukgrFTLVuM4qFCwWSLB88Ag+D16ipdBr/Dp8xc4J0qEVCmTI5NbOuTLkwuOjo7W3T9Dx3KxnragHU3tqhaX1tC8wn5dvnIVw8dNx58/f1GxXEk0b1wX7hnS6dZPWVociQRBr9/gyvU7yJUjK7JndYdDVAWo9Q1a08eSEBaKUpUb4t37j1g+fzJaNqlrume1g4lS1+isDTaViTM6CGMMYwOQsDVj5lzMmTMbP3/+RMWKldGufQe8fxeMmzdviGWeP38mhDGGia7sP3wcG7fuxskzF/D23XsxjR78iZyd8e37d4TRWweAdGlcsWnVAngXLgB75tbd+xg6ciJGDO6LokW8TNbujVt3MWfRCmzYsgv58+ZC1swZMWPucoyfOh9tmtfHwN6dkDlTBrXr0jG+fO0W1m3aia27DuLzl69CIJNRsmhBjBnWB/k8c+Jf6D8kS5pEa1/8n72A74PH+PHzJzJndINb+rRImiQx/J8F4t37D3B0dECqVKmQLk1qJEgQHwHPA/HA7wme+j/Hx0+f8eu31P/196/feBX8Bq9evxGCmJOTE4aPmY7yZYojbRoeF43BQaJ4hjXw5csXJEmSBO+e3UHixIksX7MwuvjLWFIjZ4rs6JoS7tmT34GCE74MmRZMOOaH/3/s2Al07NgBHz58QNeuXdG2bRusWbMW8+bNw58/f5AoUWLkzu2JlavWCf8wJ0cI7Rjh5Cj9pgHN0UH6lkXmSrnJUvYtCdVbA6PxHjPlPWFFrZftJCi10jHQtP/6nhOD6olK16EH64kzF8X/p89dwur1m5E7d27UqFEd3t7eyJUrF9KlS4dYsWIJIYHukYcPH6JZs+b4/v0b+nTvhA5tWyBd2jRq+6/+HIdaoDapkxBgXga9xt+wMCRPlgxv338QWr5Xr4IRO05cfP36DROnzsbzwJeIHz8ehgzojYJe+ZAyeXJ8+vwZ7z98QNKkyZA4kfS5F/TqNeLGiwuPrFngnikDQt59wK+fP/Hn7x8EBLxAaFioWHfpijVYtW4T3NzcMHDgALRr104ILvTCt2TJUsyYPg0fPn5ClYplkDK5i5j39+8/BL8JwYuXr4RG8ufPX0ibNi1atmwh2nFxSY7ChQuJAKLRo8fg/v378j3N65kDlcuXQmGvPMjolg7JXZLi16/fePz0GVas2459h04oCXPaiBMnjhjzZC4ZLi4uiBcvnhDIY8eOjTRp0sDV1RUFChQQPrI+pUsLIa90iaKIFy8uvn77hn9//+Hfv3/48vUbvv/4Idqia4emOTg6Im6cOHBOmBAuyZKiSKH86NS2CVIkT6njiTfBtaMJnZ+Tuo+/X758RYpMefH582ckTpzYPMKYrphFaIsuAhpjPjM2XXfh84VJUmaKDP+mF/zQUAk+fvyIrFndkSePJ9atWyd+N2zYCJ8+fUaXrj3QvXtvJEyYUC5wyQQwVWHMyUkfYUx/1bg1C7bbjtCkL6HWjYjTp11TFJJXdz8orPcm5AOOnTqPx08DhIb31Jnz2HvgiHhIEvSgbdGiOUaPHh2lGezx48eYPXs2Nm/egm/fvol7JEvmTGjWpCEa1a8jhDNJ2D/cun0Xs+Ythn/AMxQuWAAe2bIgo1taZHRLD1fXVEJDI72VDHeylh+b8P0nbc+k6XOxY9c+IUBpo0yZMpg1ayamTJmKvXv3in3RBRJUSIOujhQpUuC//4ajffv2QtBShYSyVatWY/fu3fj165cQVGg5EnJI8M2UKZMQdooXL6bWFEzLnz59Gm/fvkNo6D/s338A58+fR3BwcKRlSbDu3r0bqlatKvrs7++PoKAgMc7Rduich4aG4s2bN2L6u3fv4eGRTaxHwmBUpujXr19j/vwFuH79uuiXs3NCxIkTV+xPokTOSJAgobiWZMIciRy0z3Sc379/h4MHDyF1qpR45ndT87Zk94FGId+aApr2a5OFMSb6YGZhjAaGTJnchEZs3LixaN68BXbs2IHLV+4gvVsGubBFsDBmb9i/MEaCEmml9BXG/v79K8xQ9/2e4vqtuzh28hxu3Lon5rmmTomQt++RM0c2tG3XEU2bNhEPapkGRB8+ffqEI0eOioCXG1cvYfe+g0KzkiJ5cvGQ//jpEzJmSI+iRQri2o3beP7ipVz4k3fbwQGVypXC9vWLhGbGEGHswqWr2HvwGE6duYDbd32FUNC6RROU8qkg9u39+w9ImTKFEHboQ30g4SFZsmTydmhaYGAg3r17JxQQJFSRRoPS19CyJJzQvt28eRNPnjxF5szucHZ2FueHBBtHRye8fPlSvNjRdEtDghGdh/fv34tzScJdtmzZ9D6nlqRhw4bYt28/kiVNisKFCqCYdyEUKeSFbFmzIH26NFJhloUx02jGbOLt3uhamaEWrvNnQw7TJguiMOE+adMAaBDASPgKCQnB5ctXhHmlZq26cHCIJfzAyvoUR8uWLTF9+jRs3boNrVq1Qq5cnqhRsy5q166PjBkziXZixYowScaO7ahZMxb6OyLPWLhmTPomF6rfQ1qNWclU95XN5a4ytfaNjomubVry/tZhW3RuyMw0cMQULFm9BS7JkqBM8cKYPWkYUqVOpbYPX758w82794Xma++BY/B77C+EISJlypQi4rdy5UooX7680IrRPHVaG2Mh4Yy0HSSUkNamcOHCKF26tNCKELTdV69e48WLF0Ib8+nTR6E9GDFihDB3zpoyNoqDoxyUQ8JRi7adsXvvAaHlIU1XyZIlULNmTeEHxdg2pCm7dOkyTp06icuXL4vxma4hgq6Z9OnSIoNbOmTOlFFoXskU+vDRExw8fAwlinmjcf1aqFaxjJprWccKDMagcdx1MreZ8qYWYUy3m9qaphZTYZKktwZsz5yYpQi8qQVKhVxgqtM0RUaGhkqP3b9QCS5dPIPGjZuI65koWrQ4xo2fjsaNaiJp0iQ4duyoeJMkLly4iJkzZ+LkyZPCpNC8eRuMHTcVceJKY15iOTkidrhgRiktjBLG1B2nKMxNkTH2oWrAuTLkurSllwx9UCn6HuVyuqKaBFgiwfsPn3Dv+VdMnDgJFy9eFD5HNJ1MQfSQIv8huoZz5MiB/t3bonQJb5w+dwFtuw7A9+8/hI9P9erVhL9XlixZkDNnTiGU2LJ2hBg6dJi4594HPRZaLU3QLU3athcvXuLRs9dYu3Ytzpw5g6VLl6JhwwY2v5+MdsLCwvD06VNhTqVPYOBLoa18+vQJnj17jt+/fwvzZ506dXDp0iXcvn1HvGyQwJ8hQwahzUyXQmoyL1wwPxyhTsQxg1lTw/gsFcYKsDBmDlgYs09hrHatavj27Ss2bdqMgAB/VKtWHZ6eefHgga+40ekmVuX79+9YsWIlBg8ejIGDhqNX735iOgtj9imMkVbm6MnzWLp6C/LnySnyJH368hWd2jSGWzqpAzppWmiZk2cuiXnOCROgVAlv/PjxExcuXUPQ62B8+fpdaK7owU9v6pTuwTVVCvHmW6RgPtSsWj7KvpAZkfyZnj1/iaOnzuPM+Su4e/+R2A5BD5aFCxegXLly4jdpdY8cOQI/v0fipZj8hEiTIKNU8cKYNXchPDw8hNnMHiAhkxIqL1iwQGjTMmfOjPs3zkYSqNZu2CIc7QNfBsmdy2WQcLpgwXxUqFDBwr1nbIEbN25g165dOH/+gtC0vn37Vv7CXa92NVSrVA5lSnoLH8XoI4wF3EDixM5656thTIU5H2R6mMAsETyhSeCSzZNNkwleEgeEhUkv5X/hAljoP4kQwmTTGtavhowZM2LFiuViGjnrd+zYSfy/ZctmYdbQxJAhQ0XesQkTpqNJ05bCRBk7XCMmN1c6OcAJEU77kTVjf3XwfTBBNGVUWk5bKHMSBb//hCJ27Fg65LHSDOXFCngWKFIEXL91D08DnuPu/cdiUPT09MSzZ8/EciS4hP77i//+1xOJEzlj1frtuHT1pnjIk48QRQ+SszqRL19eMZ0i7OLGlfo2UdQbPQSkJrdPwjxXsWxJ5MyeFY+eBODTt98iXx0NtfT59y9UOC9LzXjSY0svAmQ+zJ8/PzJlyigEMfo/qv339fUV/ku0LEU9GnO8rAE5zDdq1Fj8n8czN7Zt2YCMGdzk8+l4LV22Er36DkC9evVQokQJJEyYQDyP6DyQ5i9p0qRW3APGFvn+/bvw+502bToePXok7otqVSqgQL48cBUm+n9iGt3v5GZCvoIUTZssSWKRxDZDelfxkqXWgqHn84/cB1K4e7EwFr1gYUxXYYxU3WdOnxW1I+/f94WjkxOOHT2EUaNGoW/fPvIj2qZtF2zbul7ciKR1oAgmddDbeJ8+fbBy5Sps3LRL+N+wMGYclL9o++7DCA55K8LcK1cohZweWbBg2XoM/G+yeBB75vLAf4N6IlWK5Hj9JkTkP6JUDMEh70QbSRIlQruWDUUI/alzl+H74JHIDP4iMEhMk/md5MmVHR7Z3JHD0wulS5dB0aLeon3SwJBw9t/gvli2erMYoEmImjVnPkqUKC7m03Lbtm2Hp2duYfKLip07d2H61In4/PkLsntkgUvKtMKZWiYoxYoVWziUZ8yYQbwcpE+fXmizzOG/Zes8f/4cBQsWEg9P4te3D/J512/cRM/e/XHj5i20bt0KCxcuZBMkozd0f2/dsBqbtu7Ak6cBCHn7TvoCFhoqz5mnCt33NA54F8qHAnlzC81aTg93WxLGrgpJUn1LTlG8nVtroLFTHxVj0KlWl+6pFA4dO43hoyaKN9a0adMguYsLnBM5I0VyF/lbLIVYlytbWkRQ6dVVTdeNXBumIHhJpKYLumRlgldo+Dddxf/+Sf+ni75s2RIIfPFc+M8UKlRQmH7oBty+fRsSJEgg39zGjZtEDqBsHjmFU7/fQ1+NJh7abrZsHmjatCkGDPwvkjBGvmOOEqk2zCH0V8SblBbNmNCK6ap50jIImMp/0VT+iWr7o9D/C5evo07Tzvj67buIcCPBhd5OyVRICSrJH6RYsWKYNGmSCMGXQekTSHghHygSbqQ+JYFiHvmN5MmTB1mzZkGGDFLtEgk8NI2En6ggzRYNlrosy5gGeiAWKeItcmYtXDAPbdq0Fj5BR48eQ/sOnUS0Il0D5A/EvmCMKaHxnF4CZGlFKE0JjTUUjRoQEIBr167hypWrePDggRibWjSugxULpmhpUP04Ti+FKdwLm1YYe+9/GYmd1QxUigk1tZSfkU6zohrdHnMlmfR46ZC7Ktz3ii7Q6zfvIlWqlGjRrqu4KHUhIEC3DPWUz8bPz09omBQHWdnlKLsqpaYd6f9h4f+EhUqEE6/4P1wYo29KU0HQRZ83T2Yx0OfJmx9BQS/x4b1Uk1K0aCm4Z86Kx48e4uXL5wgJCUbBQkXF9LlzJos+ZVAwk6gLw753zxenTl9GkiRSJ2OZUEaKDSGEhecZkwth8nqVoSrO/Co3sDHmQHOajqNoW+09ryYKlDRhR87fxZYtW4R5qnjx4li3bq2IgKP0IsuWLRcDIV17pL2k64gGS/KVom8StlQd0Wls2rlzJ/LmzStMdfzAti/o/Hl65hF+PqTBpDQTlFaCrgGK/CTn/EThCVcZxhocPXoUtWrVhk/p4ji8e6NOgViKL7UkjCXP7M3CmK0LYyRs3L3vJzSOlDlZ9WHy4dMXJEwQH3HjxrWoMOb3+AnyFiodMTncXCODBk76UKShLDM3fcgJl0LL6e2WfHJI6CKNA/nEFCpUSKz74MF9kcvrzZtgEfHl41NWaDu+fv0izIHkQ0KaDU3C2NkzZ/Hn7z+ULEn9c4gkjNH/fg/vY8PGNfj+7TvSpnPDksWzRV8TJUqC+AkSiGzWlJjw398/IvHgmzevUb1GPWzbui5KZ9ESJUpi9pyFaNWqpfRYsDCmfP2Ib0cEv3mLzdv34sZtX5Hn6tFjf7nfFeV0a9asmTzdARNzoZcmilp+8uSJ8M+jiOacOXMJUzIL14w1oeuyXr364jm1csEUpEvraiPC2NMLUjOl6hu8Wg2YalFmx+ibfd8IjUbd5t2w//BJ+W96ONWrVRlzpo4WhWXdchQV08m3JJNbGqRKmQJbd+4T084d2YEihQooHV86nfd8H+LshcuoUqkcMruH27kJLY74qs7x1E7GzNkQEvIWixcvEpFfJHRRNmUSmp4+9ce5c+dw9epVYbqjhIeUrJG0GKTdePUqSKM9nvDy8kL//v2xYsUK4YRMmaPpQqV1SF3s5pZBCKCJEydB6tSuyJgpE7Jnz4nz589i86b1og1X19QiySJd5GnSpoeXV2E0bdZalC768OE9Hj7wxa2b13H58nkEBUlNWTLIFEWJH0kIJI1KpUoV4ePjE+XgT8elcOEiSJcuPbZt26GU2sIB/0RKC/E/Oe+rasEM0IzpYzbU+R6L6v5x0F1oV8ZJvtz23QfRrE0X4SuVL18+5M+fT+SbKlmylDAdMgzD2CJ37twRKTPoGbdo0SL4lC6JrRtWIl6ciPQ2SmO6tqAsSahUGMtSnIUxS7zVkRbprq8fHj15hnjx4ojs1rGcYglHwU+fv4riqxkzpENschoMC8WHj5+FE/Kf8AzZm7YfkDuwymhUtzp6dmmDUpUbatw2OQe/fh2CFMmTIUf2bPj1+zf8Hj1B0CtpSQyyg0+dMBKd2rXE9Zt3hCaoQP48EQKHFmGMOHbiFOrUrS+0WhS5RMLL6NGjhLOxjIsXL2HRoiVCQPv9+4+o20hmp+zZs4hIJ1qPTEsiO/eNm+GmiKQoWrSokm+OLAElHYeNGzfi0eMAoV379PEj3oQEw//pUwQGvhCC2eDBg5A3bz7s379fRLyQwHb//mOcOHEUL1++kLdJwmP27LlQuEgx+JQpFh4dl0aYu2TljXSBTGiUoJKi3+bOnYtDhw5jzpz5aNOmTfh2WBhTFcZKVqiNq9dvCbOTr+89pSznDMMwtqYBO3nypBDEDh8+Ip4d9LyrUqk8pk4YJbVMaSprZzVhTMFnTK+6afpqs/RY3lRaN9WM1HRoSFgi4eZfaKjQVJEgRXXPHj7yx70Hj3DH10+eF4iEDjKzybIHk6M4HXzSFqlqiWgenWBaVtcCrjJImCItA0X+kYBB/hZk+iOtVdasWVGqVEmhVh01arSQ7BULv9IFVriQlxBISNtUrFgJuLtnEiY8cl4kn6+XL4PEcSDBiTRcJ06cEKY9inx6GRiIYcOGYvjw4bA0dB2SAKfJtEVOlmfPnhU3Ej38SRg0xhmbzsvBgwfRtWs3cQ4J0thNnDgFNWrWELnGFEskCc2Yop9YJAd+NeptxRtbR/8xo7VlkYImVHw+1QZVqNF0q/qHOTjh3fv32Ll7P+YuWAK/R4/F8X/9+pWJzOwMwzCmg555EyZMxLx580Qam9y5cqFmzRpo16alNJCLxuHw8dZBNibTtx5jumU1Yzo8IPQWmHQwferdtpr1SUPVufdQ3Ln/RJjKqlWriuTJk4tMvxRFQYKOKmR6oXpfVEiVHIcLFMgvIrZoPYIEG2nBVOeI5I5BQXJhj/Li0PEkoYqm0UkioYw+NI0eXBQpRvNIwJB908UhqzdGUYK6QInwSOVasmRJketo79598PN7KPaLaqupK4or6zdppqjvqpDT/YED+xGdoNuAfNtIA+bvH4BLly7i5MnTePTID2XLVUCf3v3EzZoxU0YhDJJyUVYGSS6MiWK4ksjCmOLNrDJNY/Fw1ftJx7xkGvdPW2CNirClWnQ5YhnlaT9//4Xv/Qe4fuM2rly7jstXrol8XHTtVqxYQeSFqlWrFueBYhjGpvj37x/++2+EqNpADBs2DH369I6wGqmLdpeN4+SCom38VliH5lnWZ8yOhbEho6Zi+tylaNeuLbJmzSZMZCQMkQ9UtmxZUaRIEaFtIkGIPiQE0dt+dHAspQuS1LIkhJLZji4U2m9FkxIVyKX5lD6AHPIp63jbtm3sPvyfEgGSuZEE0wcPHorQerpZZJCwXbRoCVSrVgPlK1SKELycZIJX9BXGfv/9J5zvg16H4NPnz/jz+w9+/v6Nt+8+IPhNCPwDnuOh3yM8evxU/pJACTuLeFNUalGUKVNamKoZhmFskd69e2P58hUYMGAgunbtHDkDgK0LY7I8Y5EeBqbK0K2jsHX3wROkTO4ifLP0yWlG7ZCJ8fzFKzh45ARu3LqHu74PhBaLsrJT/ijGPqFEm+RDRtcpXdI/fnzHt2/fhTYwduw4wlRLwhKZa+l6JtNrnLhxkTVLVmTLll1Eb9F3BpGEU5phXYai3C0Twkn+krveyYUxmh7xv2xZ+XIKwprWm12duVKHe071vozSROngKISpfQeOYMfu/fAPeIbngUFyk6wqJIDToEXBJDlyZBcZ3yn4Qdc8XgzDMNYmJCQEGTNmwuTJk9GrV0+t46q68TmSyVJd7kgFwUzXpK/2UcBMga9fv6NgKWnZmhJFCyJD+nT4/OWrKH3y9v0HvHv3AS7JkqJvj45ImiQJPn/5gpdBr/HA7wnu3n+Ip/7SEige2bKgZHFvEXFISUyrVKli5T1jjGHPnj3YtGlTpOlUr460eSSEkdBNWk8qYzNt2lTUq99SWvJCheig9dQEmbyl0baXcO7CFZy7cAlv372HV4F88MyVE1WqVRd+hWQKp0hT0pJKj1lcoT2NzseGYZjoz4cP0ioP5JIiq8JhC+gnjDnEBRzjQaJvkc0ozIgSx3hKy5GvEjnIyZ1+Fd7mEyZ1RoIE8UXkXhKXVAgM/oAkSRIjU5Y0KOSdQjjRU7qFHv2lDubkv0LTqIxJ5SrVhON7sWJFxds9E32YOXOGELKuXr0mfO8oepM4duwYjp+4CA+PHPJlFXXBf/7oZ/JTpyWT/3aM0Iwpmy8jlg+fpKBVc8Lz54E4e/YMgl6+FAXLKXrT3d1dpAypWLGiEIoUNW3K/VGj2A5/QyPzK90LDx4+xONHj/HU318EaND9RZpCyvvWrn174ddFqUYYhmGiO9mzZ8eAAQMwbdo04U7RokVz4X4kAsNkskb4GErWBfmoqzBPNuo6KE2XpZeiZ0rsiGUdlQvbm8RM+SY4SKjZdPFZMaTAMZW72bt3Nxo3biJ+07Yo6s/LqyBq166N6tVriAM2duwYTJ48Sfj1UHFd8lGpUaOGUhkbkn5pWXJGtxXJlzEPlEB2//4DwiSpjm3bDqBgYWm+NkUUL31td4EkPMu/JmRmSm0mTdlvRcEsOPg1xo8bgZ07t4ppqVOnQpYsWUWk7dOnT0X0KmnzqDjyjBlzxfWubpuq2yWBcMaMaRg1aqT4TX6AHh7ZRNuZM2cWSVfppYSCQRiGYWIi//vfYMyZM0dp2sKFi9CyZSv5b02igz4vxiQ/pXZNZ1qfMUsIY/PmzcWgQYPkpWfIR+XMmTPC0Zw0BGvXrkOhQoVx/vx5rF+/VnxTfTqSdsl3JVEiZzg7J4KLSzIh7VKm9YQJpWkk4saNJx549ECSFe1l7BtKEEvnmFi5ahPSp8+Ab9++4uuXL3BNkwYeHjnFjaNOnrKmMHbq1HH06N5eFI2eMGGcyEavWhOT7rt9+/Zh/PgJ4po+fvy0PPVKVMJY6dIlRH64JUsWC/Mi+YaRxpBKzdCLCqVjIdPtv39/hYYsR44IzSHDMEx0e04sWrRYWEwoQ8Dfv3/E/xTEpQjVs71//6HtC2PPnkmFsUjb09BCVA9AdeuTJmDnzm0YM/o/oTmgdBFubulx69ZtMb9UqVLYuk2agV7GrVs3sGjhPLx791akaqCCz5Q/5OPHD8JHRhXaB3I8podQkSKFUb16dY3FoRnbhkxuVGiYoiNJCzpr1mKULV9ZLmyr1roU/ytcmKrXaFTXJ11P69cvx83rV1C1Wh2ULVdZbd4zVbMirfc2JBhPnvph+7aN2L9vJ6pUqSwqEESVEJWCDUgrfPTYGeTJky+8fWUTqOI0okP71ti9W1ohICroReby5UucC4xhmGjHs2fPULt2HWFpcHfPjNixY4kxmxKIUy5KcuYnpQ9Z4bJmy4aUKVPJ19UlBWhUL+skP2XKZIfCmOJD9vr1q7h86bR4myctF0W4lSxZAh7Z80fVZfm2vnz5LHxw6EOagFdBL+HndwfXr98Q5Xzev3+P7t27C4duxj6h80w54Vq2bIP79+8hZcrUokB4smQuSJIkKZInT4GMGTMjc+asyJjJHXHjxNN4jQa/foXSpfLCM09+pEyRSmiu4sdPIJLeUlLdC+dPw8/PF5kzZ4O//2P06DkIPXoO1CiM7du3A9OnjhUFyWU529zds6B//z5o165dpCLpdK0HBAQI4dLP7xHu3buLEydOimt369bdKFXaRydhjHLdnTp5TP4y4uDgKIS+5MldxHGR3dfr16/B/Plzxe8jRw6Llx2GYZjowN+/f4Uby+XLl0Xd5OzZc2i1isjqH6ubZ25hTC910M9foXCK9S9yZxS1Dgo/jBHGAEfkzectPvLlwhv89u2vMNO8ffsGSxfPQ+Ys2YSUS9Lu33//8OXzJ3z+/FHsfGrXNChUsAjSu2WAv/9TBL95h5dB7xAUFCxPeKpOe8bYDySEUJqFS5fO4+LFi+Kme/z4EYJeBuDe3Y8imausKgKZ+Tyy50KB/AWRI1ceZMuWQ3yoniVxz/eO9PvuLY3bo2AQ0iaRMFaiuI/6m9FRWlh9xbIF8rqYlK+OUkJQsmAKrx42bLjoG31ev34t3twUy2JR/UsKPOjatTe8vArBu2gp/PsnCQ8UUD0G9ALzSwiTr+jzKki8eAS9einyxFFZqc9fPuPzp0+ibie9nKjy9u07A88AwzCMbfFFCEHuwkQ5btx4kbooLHysJqFLJnfIxm9loQxahTZtLi6KAh15dJHcpAt6acZu3w2As3PiSA8fxZ/qzELSTkXejOzAGCJtEsuXz8XMmePVzqNoMcp0T5nmVXeRfM8oOSV9qlatInzImOgNaZwePXqM+/d9ce3aNVy5clVon2SCOPlV0TVO1ww5toeGko/VNyHlSBPiJkGSxEnx4+cPIeyTxrV27Ubo3KWPfBvq/LiePXuKI4f348qV80JQIl8F0nKR4BSLAkwSOiNR4iTijcklWXKkSJkKGTJkFAMH1fkkjTB9k9n1588f2LB+Na5cvSSERRIwyawv+6gKWKQJo2ud6nGSNoz2jeqCkqaQzP8pU0qjj0lI5DxhDMNEJ4KDg1GggJf8Rbxzl64YMmS4GOfVCWH0U1V+od/qhC15ajE182maojBHdZvz5XE3rZnS1oQx2tajxw9w69ZVnD93EidOHJLPy5s3D9asWSNCVym8n8oReXhkF5oJeigxDAkvJJDdvy8te/Xp00d8+iQtTUXaqoCAFwgODhImP4IEogwZ3EXxcdKu0XexYqWF+VKdMEba2Ud+9/Ho0QMEBj4XGrJXQYF48eKZuEF1hV4sXNOkFab7jx/eo2DBwvD2Liz8HKhP9CHtYOLEiYTwRXnCqHSTrKwVwzBMTOX9+/dYtXodRo8aIfxiqapKufIVULJEKTFO2qUwdutOAJwTJlLr9KxN+CKhS60wpmXTisurPuRonuo0gnba7+E9nDt3EitWzEPfvn0xYYJ6zRnD6AJd2yScybRq9+75wtfXF3fv3hVRiaRhat2mK5q3aIerVy7g+vUr8PO7L/zK3gS/lgtTlF6CInUyZqRPJmTP7gEPDw8hUJHpVCZUkY8DlaCie+7Ll694+zYEL1++RGDgSxEl2qtXL6HJYhiGYXSHoicpYIoi1G/flrqjpEufHoULFRHps0iLliUrWSRSyNeRqBHQNAljsuUUhTrCLMLYPd9nwrdG1Z4aVaSaJj8xXbRfoh0ty8kSa8qgEP4a1YuJ75MnT4pITIYxNXRNU0qVuXPnYfny5cLcSdNIK0Va2dy5PUWqlTx5PEV+MI7WZRiGsQ3evHkj/IsvX76CK1eu4NatW3ILCFkUMmd2h6dnHowcOQ4uycPLLkK9AKYopIlpYcqyEQljuXNlNK0wtmDhSlG3j6RHijB78MAXt29dF5FlBbwKiwfOI7+HOH36GN69DcGXr19EvqevXz8LE0uu3HlRpEhxeBX0RrJkyc0ijN2/fxf165UX/1OfScqlT4UK5dG6dRukSePKD0bGpJAP2vXr11GuXDmlxKwMwzCM7fPv3z/4+fnhyZMn8PcPQECAP3bt2i18cTNkyIRkLi5I7pJcfOfJkxc1atQRFg9Kp0XBWPQhV5EMGTMhXVo3uYxhNmFMvpKDg3AOltV4ImgjyVOkRID/U5FBnGyxNI3Wow+ZYsh3i+pBEZTfI2euvEidOo1wWk6ZIiWSJ08pfGPIgVnmh6NJq6YpGRsdvAsXzuD1qyCRZ4wix16/foVjRw8KB2iCtHuUJoOcmlOmpDIIscLNRFJzEfWVHK07dOiAypUrR3V4GIZhGIaJRjx9+hSrVq0WAtf79yRLfBC+xeRnTIIYuZSok09IfkiX3k2UtMuUKbMwf/bp3d20wtju3btFniLymSGhytvbGyVLlhS/d+0+IKIX6tWtIUoUyetKqkDh+xcvXhKqwes3bosdFVq0L5+VliMhjaRMivwi4Ymi2eiTKFEiJEqUBGFhoQh5+0as+zb84+jgKPJBJUyQEAkSJpR/k2AXJ25chLwJFpnMqW/kz/Pw4X08fvxAOPipY/Dg/2HkSGlJGYZhGIZhYjYBAQHYt28/nJ0Timh08vtNlSq1kEnIdYU+D/2e4llAAJ49C0CevPmwaeM6E5dDehOstTFjIDNmSMhbBAYGinxgJJVSUWPSbFGEG+0ICWz0vyyEn5ynXV1dww+GNGuuNAP/d/FNNmD6JodoRS0eqRBJLamIrC2KvqRvcrTu2bOnkkaQYRiGYRhGV0Q5pNSupk36ak5IW0XO9vQpXryY1mVJGCNzIqkKdYUEM9LKUWkE0uqRho3SAEjzMKXloskMwzAMw1gFmxHG9IGScuoL+bFR5nT6MAzDMAzD2ArSasoMwzAMwzCMVWBhjGEYhmEYxoqwMMYwDMMwDGNFWBhjGIZhGIaxFwd+CtFkGIZhGIZhTCc36SSMUQoJyr2VLZuHTo0yDMMwDMMwEPJTVKm4dEr6Ksvt9efPHz6uDMMwDMMwOkKCWFQpuXQWxhiGYRiGYRjTww78DMMwDMMwVoSFMYZhGIZhGCvCwhjDMAzDMIwVYWGMYRiGYRjGHvKMcTQlwzAMwzCM6aMpY+kqiCWKnxT/8FvPLjAMwzAMw8RcKM9YQECAVoFMJ2GM8ouRIJYD5RDLISJxmYODk/h2dHRU+q08Lfxb5bd0GSelabJ1lNp2ULee8jRZO+q2ozRPZTvy3wptq+6To5NDxDwn5WkOivNUlldcT3V5x/B2pNtRnhaxTOTtal8/cp8i+qJmX2THQLaMrB2FcyBfX7Zv4csoHjPV7Uvb1rKe6naVzqv69RT3N6Jt5W+16ynui8rysnaU2nCI3KZ8e+HzVLcvnSY7JprbVrceZNOg+3pKy2jtU/g82e/wZRV2U95vhVny5SIOiZr15NtV7ofaZdS0HdEnRO6vg4FtOoRn6JGESn9LwiIWCp+G8Cw+DrLfSvPCl1eYF9GG8vqK0+TLqGkz0jzF9WXLQN36YVr2Jfz/MFmb/yLmyabJ+itfRqHtMG3z/ilPC4vYrkQ+TWUZdcur65N8GZV21O5TRNuS8OVk25co9il8eUmobJ7ietL/w8LUzZO2IQnvp0TdMvI2wzS2Lf+WaJkX3o66vqjbbliY9BqR7aXaUx9+HSkcCkhk60mU21GcJl9GcZ7K+qrbUNyOvG+Kl7FKm4rz5NuRaGlTPg+R+hv5O+LGl19qeqxPhMqmqbSjri2121Wdp7C+6jxZe79DgZnXgoUcZbQwJsMRseDkEFuLwKQgjKkIUY7h85SEKpVp2oQi7UKcZiFQvTCmgxCogzCmfV5koUatMOUUtTAWeV7U66ubp7W/+gpjWgQmTUKV4rQIIVA/YUyXtlX7rbyeFsFHmzCmRajSJLCZYz29hTE1QpVc0FJZxlLCmIrsa0FhLMxIYSxym/oJY2EmEMZUhSkdBB91y+gkjIVqFsYU5kXenpMOwpiTlvUVhRtHzQKTbF6obJmI+132f1iYunmytmS/HZS+pW06qAh1Ct0NU/+tPC1cuJE9/RWEE4mGb8VlVAUoxT6oEwQkWuZFtKV5nly4UWlHabtq5qm2qW49rW0aKYyFGSqM6SBMGSuMRWxXt1Su7MDPMAzDMAxjRVgYYxiGYRiGsSIsjDEMwzAMw1gRFsYYhmEYhmGsCAtjDMMwDMMwVoSFMYZhGIZhGCvCwhjDMAzDMIwVYWGMYRiGYRjGirAwxjAMwzAMY0VYGGMYhmEYhrEiLIwxDMMwDMNYERbGGIZhGIZhrAgLYwzDMAzDMFaEhTGGYRiGYRgrwsIYwzAMwzCMFWFhjGEYhmEYxoqwMMYwDMMwDGNFWBhjGIZhGIaxIiyMMQzDMAzDWBEWxhiGYRiGYawIC2MMwzAMwzBWhIUxhmEYhmEYK8LCGMMwDMMwjBVhYYxhGIZhGMaKsDDGMAzDMAxjRVgYYxiGYRiGsSKx9Fk4DP8QKomQ3xwQJr4l4dNkv5WnSb/DJKFKvwlHiZPSNEeltqXzHMPC23FQWA/K0xzDl1VsS+08+TSVZRS36+CkNM0RDpHalk1zUJwnW17ioPSt+L+DfJ7CvoQ5KO+n/LdC25HmKawfGj7NKXwb4d+K0xydwvumMM/BMbxN2TKOsm+FtuXzHJWWEf/Ljp1sPaW2taynul3F86phPVn/ldtW/la7nuK+qCwva0epDYfIbcq3Fz5PdfvSabJjorltdetBNg26r6e0jNY+ya5R2a4pzHNQ7rfCLPlyEYdEzXry7Sr3Q+0yatqO6BMi99fBwDYdJNIJsnFGEjEWyaZBIl3GQfZbaV748grzItpQXl9xmnwZNW1Gmqe4vnw8VLd+mJZ9Cf8/TNbmv4h5smmy/sqXUWg7TNu8f8rTwhTH81D1y6hbXl2f5MuotKN2nyLaloQvJ9u+RLFP4ctLQmXzFNeT/h8Wpm6e7LkV3ra6ZeRthmlsW/6t1F+V9UIj+hsmb0v9t3QZ6TUim6L21IdfRwqrQSJbT6LcjuI0+TKK81TWV92G4nbkfVO8jFXaVJwn345ES5vyeYjUX03fiutL9FifCJVNU2lHXVs69S1M8zxZe78VbhWjhbE4ceLA1dUVD4NPyA+sdGuyXui2MYZhGIZhmJiEq6urkKO04SCRKL3uaeTXr1/48+ePqfoW7fny5Qvc3NwQGBiIxIkTW7s7jA7wObNP+LzZH3zO7BM+b4ZBgli8ePFMY6akhqJqjIkMCWIsjNkXfM7sEz5v9gefM/uEz5vpYQd+hmEYhmEYK8LCGMMwDMMwjBVhYcxMxI0bFyNHjhTfjH3A58w+4fNmf/A5s0/4vJkPnR34GYZhGIZhGNPDmjGGYRiGYRgrwsIYwzAMwzCMFWFhjGEYhmEYxoqwMMYwDMMwDGNFWBgzA+PHj0fx4sWRIEECJE2aVO0yL168QM2aNZEwYUKkSJECvXr14goHNkamTJlE7UPFz+DBg63dLUaBBQsWwN3dXSSkLliwIM6ePcvHx4YZNWpUpHuKSsUwtsOZM2fEsylt2rTi/OzatUtpPsX80Xmk+fHjx4ePjw98fX2t1t/oAgtjZoDKRjVs2BBdu3ZVOz80NBTVq1fH9+/fce7cOWzatAnbt29H//79zdEdxgjGjBmD169fyz/Dhw/n42kjbN68GX369MGwYcNw8+ZNlCpVClWrVhUvOoztkjt3bqV76u7du9buEqMAPZfy5cuHefPmqT0uU6ZMwYwZM8T8q1evCmG6YsWK+Pr1Kx9HY6DUFox5WLlypSRJkiSRph84cEDi6OgoCQoKkk/buHGjJG7cuJLPnz/z6bARMmbMKJk5c6a1u8FooEiRIpIuXbooTcuRI4dk8ODBfMxslJEjR0ry5ctn7W4wOkIiws6dO+W/w8LCJK6urpJJkybJp/369Us85xYtWsTH1QhYM2YFLl68CE9PT6HmlVG5cmX8/v0b169ft0aXGA1MnjwZyZMnR/78+YX5mbSejPWh80D3SqVKlZSm0+8LFy5YrV9M1Dx+/FiMfWRebtKkCfz9/fmw2QkBAQEIDg5Wuu8oEWyZMmX4vjMSnQuFM6aDLubUqVMrTUuWLJmo7E7zGNugd+/e8PLyEufmypUrGDJkiBiMli1bZu2uxXjevXsnzP2q9xH95nvIdvH29saaNWvg4eGBN2/eYNy4ccK/lnyO6KWHsW1k95a6++758+dW6lX0gDVjRjieqn6uXbum84Gn5VUhrbC66Yx1zmPfvn3FG1/evHnRoUMHLFq0CMuXL8f79+/5lNgIqvcL30O2Dfn01a9fH3ny5EGFChWwf/9+MX316tXW7hqjB3zfmR7WjOlIjx49hEo9qug7XSCHx8uXLytN+/jxI/7+/RvpjYOxnfNYtGhR8f3kyRN+i7cyFIHs5OQUSQsWEhLC95AdQdHkJJiR6ZKxfWSRr3TfpUmTRj6d7zvjYWFMj8GfPqagWLFiwv+IIolkF/SRI0eE7Z3C8xnbPI8UsUcoDkKMdSCTPt0rR48eRd26deXT6Xft2rX5tNgJ5Cf74MEDEQnL2D7k50cCGd1nBQoUkPtvnj59WvjXMobDwpgZoND6Dx8+iG/ya7l165aYnjVrVjg7Owvnx1y5cqFly5aYOnWqWHbAgAHo2LEjEidObI4uMQYEWVy6dAlly5ZFkiRJRAg3mS1r1aqFDBky8PG0Afr16yfuoUKFCokXnCVLloh7rkuXLtbuGqMBGucohxXdQ6RNIZ+xL1++oHXr1nzMbIRv374J7b8M8pOlZ5iLi4s4b5ROZsKECciWLZv40P+UU7NZs2ZW7bfdY0woJqOe1q1bi5Bg1c/Jkyflyzx//lxSvXp1Sfz48SUuLi6SHj16iBBhxja4fv26xNvbW4Rsx4sXT5I9e3YRlv/9+3drd41RYP78+SIFSZw4cSReXl6S06dP8/GxYRo3bixJkyaNJHbs2JK0adNK6tWrJ/H19bV2txgF6Dml7vlFzzVZegsaCynFBaVjKl26tOTu3bt8DI3Egf5YWyBkGIZhGIaJqXA0JcMwDMMwjBVhYYxhGIZhGMaKsDDGMAzDMAxjRVgYYxiGYRiGsSIsjDEMwzAMw1gRFsYYhmEYhmGsCAtjDMMwDMMwVoSFMYZhGIZhGCvCwhjDREOePXsGBwcHeSkua7fDGHd8/fz8RE3Ar1+/GnUoCxcujB07dvDpYBgbg4UxhrEx2rRpIx7Q9IkVK5aoB9e1a1d8/PjR7NutU6eO0jQ3NzdR0N7T09Os2z558qSoA0r176jOHdW8o3qF//79M+t2fXx8RK09W2fYsGHo3r07EiVKZFQ7//33HwYPHoywsDCT9Y1hGONhYYxhbJAqVaoIIYg0J8uWLcPevXvRrVs3i/fDyclJaGRIKDQXvr6+qFq1qtDanDlzBnfv3sXcuXMRO3ZsFhoAvHz5Env27EHbtm2NPtbVq1fH58+fcfjwYVOcOoZhTAQLYwxjg8SNG1cIQenTp0elSpXQuHFjHDlyRGmZlStXImfOnIgXLx5y5MiBBQsWaGwvNDQU7du3h7u7O+LHj4/s2bNj9uzZ8vmjRo3C6tWrsXv3brlW7tSpU0pmNNKmUH8WLVqk1PaNGzfEMv7+/uI3Pew7deqEVKlSIXHixChXrhxu376tsW9Hjx5FmjRpMGXKFKGBy5IlixBGSQiNEyeOfLkLFy6gdOnSov+ksevVqxe+f/8un79u3ToUKlRIaI/o2DVr1gwhISEwBm3bHDJkCIoWLRppnbx582LkyJEGnSd1bNmyBfny5RPHXsaqVauQNGlS7Nu3T5xL0iY2aNBA9I3OY6ZMmZAsWTL07NlTnHtF4bpatWrYuHGjgUeEYRhzwMIYw9g4JOQcOnRIaIpkLF26VJiuxo8fjwcPHmDChAnCBEUPYnXIBCl6sN+/fx8jRozA0KFDxW9iwIABaNSokVwjR5/ixYsrteHo6IgmTZpg/fr1StM3bNiAYsWKIXPmzJBIJEL7EhwcjAMHDuD69evw8vJC+fLl8eHDB7V9I8GJtkdaMU2Qtqxy5cqoV68e7ty5g82bN+PcuXPo0aOHfJk/f/5g7NixQvDbtWsXAgIChOnVUKLaZvPmzXH58mU8ffpUSctH69E8Q86TOui4kJCpyo8fPzBnzhxs2rRJXB8kPFNf6bjTZ+3atViyZAm2bdumtF6RIkVw9uxZg48LwzBmQMIwjE3RunVriZOTkyRhwoSSePHiSeg2pc+MGTPky7i5uUk2bNigtN7YsWMlxYoVE/8HBASIdW7evKlxO926dZPUr19fabu1a9dWWka1nRs3bkgcHBwkz549E79DQ0Ml6dKlk8yfP1/8Pn78uCRx4sSSX79+KbWTJUsWyeLFi9X249+/f5I2bdqI7bi6ukrq1KkjmTt3ruTz58/yZVq2bCnp1KmT0npnz56VODo6Sn7+/Km23StXrog2v379qvEYlClTRtK7d2+183TZZt68eSVjxoyRzx8yZIikcOHCJj1P+fLlU9oGsXLlSrHekydP5NM6d+4sSZAggdL+Vq5cWUxXZPfu3WIf6NwxDGMbsGaMYWwQcmYn0yBpXsjURBoa+ibevn2LwMBAYXZ0dnaWf8aNG6ekpVGFzIukYUmZMqVYnrQ2L1680KtfBQoUEKY2mZnr9OnTwhRIWjWCNGHfvn1D8uTJlfpGWipNfSPTGZnyyDeKTJVp06YVmqTcuXMLjZmsXTLNKbZJx4Q0ftQ2cfPmTdSuXRsZM2YUpkpyzif03UcZumyTNGAyTSFpBem4yLRihp4nVX7+/ClMnKqQaZJMujJSp04tzJO0DcVpqqZaMrnSPvz+/duAo8IwjDkwn1cuwzAGkzBhQmTNmlX8T6YoEs5Gjx4tzHCySDgSpry9vSMJNuogc2Tfvn0xffp0YVIkYWXq1KlC2NMXEjbINElRefRNAkqKFCnEPOob+X+RyUwV8nHSRrp06dCyZUvxIYHFw8NDCJC039Ru586dhc+WKhRtSr5S5FtHH/IdI4GThDDqG5kvDSGqbRLkl0bHgfzmSGgi4YtMubL19T1P6qBjqy6SVtFsTZDfnrppqpGTZC4mQY6EMoZhbAMWxhjGDiCHcIo4pBQXpDkiwYV8yWRamKggHyHyAVOMyFTVzpCzvKKztyZIABk+fLjQHJE/0sKFC+XzyD+M/MUo+pK0NIZCzuck1Mmc5ald8seSCaiqkJ/Wu3fvMGnSJOFoT1y7ds3g7euyTYL88MjBn7RjJIxVqFBBaKMI+tb3PGnSRpKfn6m4d++e2DeGYWwHFsYYxg4gkxuZ7cgBfN68eSL6kTQ2FK1IQhqZnEj4IA1Kv379Iq1PAsWaNWtESgOKqCTn7qtXr4r/ZZDwRPMpwSiZGZMkSaK2L7QOCXZkfqM8YGQalEHCCGneKF/Z5MmTRaTfq1evhEM5TVPniL548WJhkq1bt64wu/369Uv0lQQhSnFB/O9//xORi5Rrq2PHjkJzSA7xFIlJy5CmioRJ+r9Lly5C4CAtoi6QOVE16SoFFUS1TRkkaNH5IA3czJkzldrR9zypg7R7HTp0EIKyPho1bYI5aRAZhrEhrO20xjCMMuoc6Yn169dL4sSJI3nx4oX8d/78+cW0ZMmSSUqXLi3ZsWOHWsdwcqgnJ/kkSZJIkiZNKunatatk8ODBwjlcRkhIiKRixYoSZ2dnse7Jkyc1OpiTwz5Nb9WqVaR+fvnyRdKzZ09J2rRpJbFjxxZO7M2bN5f3WxUKCmjRooXE3d1dEjduXEny5MnFvuzZsyeSQ76sfxTcQM7z48ePl88nR/lMmTKJNshBntaPyjmeHPhlARKKn5EjR+q0TeLjx49im6rO84rnTdfzpCnAgYIkDh06pOTAT+dSEeqz4vlUdy29fPlSnJPAwECN22MYxvI40B9rC4QMwzCMZig3GeWAMzZZ68CBA0UeOEp5wTCM7cBmSoZhGBuHkuiSaZNqUxpTEokS8VJOOYZhbAvWjDEMwzAMw1gRzjPGMAzDMAxjRVgYYxiGYRiGsSIsjDEMwzAMw1gRFsYYhmEYhmGsCAtjDMMwDMMwVoSFMYZhGIZhGCvCwhjDMAzDMIwVYWGMYRiGYRjGirAwxjAMwzAMA+vxf9bLrhjqBCJPAAAAAElFTkSuQmCC",
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",
       "text/plain": [
        "
        "
       ]
@@ -550,7 +522,7 @@
     "\n",
     "fig = plt.figure(figsize=(6, 4), layout=\"constrained\")\n",
     "\n",
-    "total_rsl_med = total_rsl_masked[2, -1, :, :]\n",
+    "total_rsl_med = total_rsl[2, -1, :, :]\n",
     "vmax = np.nanmax(np.abs(total_rsl))\n",
     "vmin = -vmax\n",
     "\n",
@@ -587,18 +559,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 10,
    "id": "5512d606",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "
        [23:54:17] ✓ ProFSea assets found locally!                                                             utils.py:117\n",
+       "
        [17:02:02] ✓ ProFSea assets found locally!                                                             utils.py:188\n",
        "
        \n"
       ],
       "text/plain": [
-       "\u001b[2;36m[23:54:17]\u001b[0m\u001b[2;36m \u001b[0m\u001b[1;32m✓ ProFSea assets found locally!\u001b[0m                                                             \u001b]8;id=14796535;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/utils/utils.py\u001b\\\u001b[2mutils.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=14796536;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/utils/utils.py#117\u001b\\\u001b[2m117\u001b[0m\u001b]8;;\u001b\\\n"
+       "\u001b[2;36m[17:02:02]\u001b[0m\u001b[2;36m \u001b[0m\u001b[1;32m✓ ProFSea assets found locally!\u001b[0m                                                             \u001b]8;id=1422878;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/utils/utils.py\u001b\\\u001b[2mutils.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=1422879;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/utils/utils.py#188\u001b\\\u001b[2m188\u001b[0m\u001b]8;;\u001b\\\n"
       ]
      },
      "metadata": {},
@@ -607,11 +579,11 @@
     {
      "data": {
       "text/html": [
-       "
        [23:54:17] Baseline period = 1995 to 2014                                                         local_model.py:79\n",
+       "
        [17:02:02] INFO     Baseline period = 1995 to 2014                                                                 \n",
        "
        \n"
       ],
       "text/plain": [
-       "\u001b[2;36m[23:54:17]\u001b[0m\u001b[2;36m \u001b[0mBaseline period = \u001b[1;36m1995\u001b[0m to \u001b[1;36m2014\u001b[0m                                                         \u001b]8;id=14796543;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/local_model.py\u001b\\\u001b[2mlocal_model.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=14796544;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/local_model.py#79\u001b\\\u001b[2m79\u001b[0m\u001b]8;;\u001b\\\n"
+       "\u001b[2;36m[17:02:02]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Baseline period = \u001b[1;36m1995\u001b[0m to \u001b[1;36m2014\u001b[0m                                                                 \n"
       ]
      },
      "metadata": {},
@@ -620,11 +592,11 @@
     {
      "data": {
       "text/html": [
-       "
                   Configured for 3 specific target locations.                                            local_model.py:82\n",
+       "
                   INFO     Configured for 4 specific target locations.                                                    \n",
        "
        \n"
       ],
       "text/plain": [
-       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mConfigured for \u001b[1;36m3\u001b[0m specific target locations.                                            \u001b]8;id=14796550;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/local_model.py\u001b\\\u001b[2mlocal_model.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=14796551;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/local_model.py#82\u001b\\\u001b[2m82\u001b[0m\u001b]8;;\u001b\\\n"
+       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Configured for \u001b[1;36m4\u001b[0m specific target locations.                                                    \n"
       ]
      },
      "metadata": {},
@@ -633,11 +605,11 @@
     {
      "data": {
       "text/html": [
-       "
                   Simulating 7 sea-level components for 3 sites...                                      local_model.py:117\n",
+       "
                   INFO     Simulating 7 sea-level components for 4 sites...                                               \n",
        "
        \n"
       ],
       "text/plain": [
-       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mSimulating \u001b[1;36m7\u001b[0m sea-level components for \u001b[1;36m3\u001b[0m sites\u001b[33m...\u001b[0m                                      \u001b]8;id=14796557;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/local_model.py\u001b\\\u001b[2mlocal_model.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=14796558;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/components/core/local_model.py#117\u001b\\\u001b[2m117\u001b[0m\u001b]8;;\u001b\\\n"
+       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Simulating \u001b[1;36m7\u001b[0m sea-level components for \u001b[1;36m4\u001b[0m sites\u001b[33m...\u001b[0m                                               \n"
       ]
      },
      "metadata": {},
@@ -645,13 +617,12 @@
     },
     {
      "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5acb0a39044549d287f2d3a3f15aff6e",
-       "version_major": 2,
-       "version_minor": 0
-      },
+      "text/html": [
+       "
                   INFO     Starting localisation of components...                                                         \n",
+       "
        \n"
+      ],
       "text/plain": [
-       "Output()"
+       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Starting localisation of components\u001b[33m...\u001b[0m                                                         \n"
       ]
      },
      "metadata": {},
@@ -666,6 +637,21 @@
      },
      "metadata": {},
      "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
        [17:02:06] WARNING  The following site(s) may be over land: Land point. Expect NaN values for all components.      \n",
+       "                    Either try increasing your grid resolution or check your site coordinates.                     \n",
+       "
        \n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m[17:02:06]\u001b[0m\u001b[2;36m \u001b[0m\u001b[33mWARNING \u001b[0m The following \u001b[1;35msite\u001b[0m\u001b[1m(\u001b[0ms\u001b[1m)\u001b[0m may be over land: Land point. Expect NaN values for all components.      \n",
+       "\u001b[2;36m           \u001b[0m         Either try increasing your grid resolution or check your site coordinates.                     \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -682,6 +668,7 @@
     "    \"Aberdeen\": (57.144, -2.080),\n",
     "    \"Holyhead\": (53.314, -4.620),\n",
     "    \"Llandudno\": (53.330, -3.830),\n",
+    "    \"Land point\": (-34.6037, -58.3816),\n",
     "}\n",
     "\n",
     "local_components = {\n",
@@ -714,46 +701,29 @@
     "}\n",
     "\n",
     "local_model = Local(components=local_components, locations=locations)\n",
-    "local_timeseries = local_model.run(member_seed=42)"
+    "local_timeseries = local_model.run(member_seed=42)\n",
+    "total_rsl = local_model.sum_components(local_model.results)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "061f92d5",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0, 0.5, 'Relative Sea Level (m)')"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "
        "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "loc_ts = local_timeseries[\"wais\"].sel(site=[\"Aberdeen\", \"Holyhead\", \"Llandudno\"], percentile=[17, 50, 83])\n",
+    "loc_ts = local_timeseries[\"total_rsl\"].sel(site=[\"Aberdeen\", \"Holyhead\", \"Llandudno\", \"Land point\"], percentile=[17, 50, 83])\n",
     "fig = plt.figure(figsize=(6, 4), layout=\"constrained\")\n",
     "ax = fig.add_subplot(111)\n",
     "for loc in loc_ts.site.values:\n",
     "    loc_data = loc_ts.sel(site=loc)\n",
-    "    ax.fill_between(loc_data.time, loc_data.sel(percentile=17), loc_data.sel(percentile=83), alpha=0.3, label=f\"{loc} 66% range\")\n",
+    "    ax.fill_between(loc_data.time, loc_data.sel(percentile=17), loc_data.sel(percentile=83), alpha=0.3)\n",
     "    ax.plot(loc_data.time, loc_data.sel(percentile=50), label=loc)\n",
     "ax.set_xlabel(\"Year\")\n",
-    "ax.set_ylabel(\"Relative Sea Level (m)\")"
+    "ax.set_ylabel(\"Relative Sea Level (m)\")\n",
+    "ax.legend(frameon=False, loc=\"upper left\", fontsize=8)\n",
+    "\n",
+    "plt.show()"
    ]
   }
  ],

From 3e309ca4ab56f099afca3db92d6b3d5910cb0ddd Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Tue, 16 Jun 2026 17:04:54 +0100
Subject: [PATCH 249/276] Update workbook

---
 .../tutorial_notebooks/worked_example.ipynb   | 47 ++++++++++++-------
 1 file changed, 29 insertions(+), 18 deletions(-)

diff --git a/profsea/tutorial_notebooks/worked_example.ipynb b/profsea/tutorial_notebooks/worked_example.ipynb
index d9d6761..e24f95c 100644
--- a/profsea/tutorial_notebooks/worked_example.ipynb
+++ b/profsea/tutorial_notebooks/worked_example.ipynb
@@ -79,7 +79,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
        "
       ]
@@ -221,7 +221,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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        [17:01:32] ✓ ProFSea assets found locally!                                                             utils.py:188\n",
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        [17:03:25] ✓ ProFSea assets found locally!                                                             utils.py:188\n",
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+       "\u001b[2;36m[17:03:25]\u001b[0m\u001b[2;36m \u001b[0m\u001b[1;32m✓ ProFSea assets found locally!\u001b[0m                                                             \u001b]8;id=1099534;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/utils/utils.py\u001b\\\u001b[2mutils.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=1099535;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/utils/utils.py#188\u001b\\\u001b[2m188\u001b[0m\u001b]8;;\u001b\\\n"
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        [17:01:32] INFO     Baseline period = 1995 to 2014                                                                 \n",
+       "
        [17:03:25] INFO     Baseline period = 1995 to 2014                                                                 \n",
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       "text/html": [
-       "
        [17:01:47] ✓ Successfully saved Zarr: ./stabilisation_projection.zarr                                  utils.py:322\n",
+       "
        [17:03:41] ✓ Successfully saved Zarr: ./stabilisation_projection.zarr                                  utils.py:322\n",
        "
        \n"
       ],
       "text/plain": [
-       "\u001b[2;36m[17:01:47]\u001b[0m\u001b[2;36m \u001b[0m\u001b[1;32m✓ Successfully saved Zarr:\u001b[0m .\u001b[35m/\u001b[0m\u001b[95mstabilisation_projection.zarr\u001b[0m                                  \u001b]8;id=1422865;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/utils/utils.py\u001b\\\u001b[2mutils.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=1422866;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/utils/utils.py#322\u001b\\\u001b[2m322\u001b[0m\u001b]8;;\u001b\\\n"
+       "\u001b[2;36m[17:03:41]\u001b[0m\u001b[2;36m \u001b[0m\u001b[1;32m✓ Successfully saved Zarr:\u001b[0m .\u001b[35m/\u001b[0m\u001b[95mstabilisation_projection.zarr\u001b[0m                                  \u001b]8;id=1099541;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/utils/utils.py\u001b\\\u001b[2mutils.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=1099542;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/utils/utils.py#322\u001b\\\u001b[2m322\u001b[0m\u001b]8;;\u001b\\\n"
       ]
      },
      "metadata": {},
@@ -479,7 +479,7 @@
        "
        \n"
       ],
       "text/plain": [
-       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mOutput shape was \u001b[1m(\u001b[0m\u001b[1;36m5\u001b[0m, \u001b[1;36m295\u001b[0m, \u001b[1;36m180\u001b[0m, \u001b[1;36m360\u001b[0m\u001b[1m)\u001b[0m \u001b[1m(\u001b[0mpercentile, time, lat, lon\u001b[1m)\u001b[0m                            \u001b]8;id=1422872;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/utils/utils.py\u001b\\\u001b[2mutils.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=1422873;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/utils/utils.py#325\u001b\\\u001b[2m325\u001b[0m\u001b]8;;\u001b\\\n"
+       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mOutput shape was \u001b[1m(\u001b[0m\u001b[1;36m5\u001b[0m, \u001b[1;36m295\u001b[0m, \u001b[1;36m180\u001b[0m, \u001b[1;36m360\u001b[0m\u001b[1m)\u001b[0m \u001b[1m(\u001b[0mpercentile, time, lat, lon\u001b[1m)\u001b[0m                            \u001b]8;id=1099548;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/utils/utils.py\u001b\\\u001b[2mutils.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=1099549;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/utils/utils.py#325\u001b\\\u001b[2m325\u001b[0m\u001b]8;;\u001b\\\n"
       ]
      },
      "metadata": {},
@@ -508,7 +508,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
        "
       ]
@@ -566,11 +566,11 @@
     {
      "data": {
       "text/html": [
-       "
        [17:02:02] ✓ ProFSea assets found locally!                                                             utils.py:188\n",
+       "
        [17:03:55] ✓ ProFSea assets found locally!                                                             utils.py:188\n",
        "
        \n"
       ],
       "text/plain": [
-       "\u001b[2;36m[17:02:02]\u001b[0m\u001b[2;36m \u001b[0m\u001b[1;32m✓ ProFSea assets found locally!\u001b[0m                                                             \u001b]8;id=1422878;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/utils/utils.py\u001b\\\u001b[2mutils.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=1422879;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/utils/utils.py#188\u001b\\\u001b[2m188\u001b[0m\u001b]8;;\u001b\\\n"
+       "\u001b[2;36m[17:03:55]\u001b[0m\u001b[2;36m \u001b[0m\u001b[1;32m✓ ProFSea assets found locally!\u001b[0m                                                             \u001b]8;id=1099554;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/utils/utils.py\u001b\\\u001b[2mutils.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=1099555;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/utils/utils.py#188\u001b\\\u001b[2m188\u001b[0m\u001b]8;;\u001b\\\n"
       ]
      },
      "metadata": {},
@@ -579,11 +579,11 @@
     {
      "data": {
       "text/html": [
-       "
        [17:02:02] INFO     Baseline period = 1995 to 2014                                                                 \n",
+       "
        [17:03:55] INFO     Baseline period = 1995 to 2014                                                                 \n",
        "
        \n"
       ],
       "text/plain": [
-       "\u001b[2;36m[17:02:02]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Baseline period = \u001b[1;36m1995\u001b[0m to \u001b[1;36m2014\u001b[0m                                                                 \n"
+       "\u001b[2;36m[17:03:55]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Baseline period = \u001b[1;36m1995\u001b[0m to \u001b[1;36m2014\u001b[0m                                                                 \n"
       ]
      },
      "metadata": {},
@@ -641,12 +641,12 @@
     {
      "data": {
       "text/html": [
-       "
        [17:02:06] WARNING  The following site(s) may be over land: Land point. Expect NaN values for all components.      \n",
+       "
        [17:03:59] WARNING  The following site(s) may be over land: Land point. Expect NaN values for all components.      \n",
        "                    Either try increasing your grid resolution or check your site coordinates.                     \n",
        "
        \n"
       ],
       "text/plain": [
-       "\u001b[2;36m[17:02:06]\u001b[0m\u001b[2;36m \u001b[0m\u001b[33mWARNING \u001b[0m The following \u001b[1;35msite\u001b[0m\u001b[1m(\u001b[0ms\u001b[1m)\u001b[0m may be over land: Land point. Expect NaN values for all components.      \n",
+       "\u001b[2;36m[17:03:59]\u001b[0m\u001b[2;36m \u001b[0m\u001b[33mWARNING \u001b[0m The following \u001b[1;35msite\u001b[0m\u001b[1m(\u001b[0ms\u001b[1m)\u001b[0m may be over land: Land point. Expect NaN values for all components.      \n",
        "\u001b[2;36m           \u001b[0m         Either try increasing your grid resolution or check your site coordinates.                     \n"
       ]
      },
@@ -707,10 +707,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "061f92d5",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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IBCeddJKMdIUjRJ5KpZIdFITYEuJM8Omnn8qOC4KsrCzZ05KtmUgsEdHdfRUtNHUlhIw4GBmLgEFtkFGrobjOQBCi6+KLL5aRKBFpCprLie1arTYUGQsiRJLH4+k1PSpEm4ieBQlPRXbXNSFIpOt1d2xv5yKkLxyuMdNLjBAyImFkLAJCJIj04WA/BipGhEhyu90YP368XH/44YejOk6kC+12OzZt2iTX33rrLekOHETUewVrz7755hsZSRMI92ARgRPpzeBre/fujeqa69atC9WJ1dXVyXTlmjVr+vTzEtIdxfVWlDfaeYMIISMSRsZGAELIiGhVkGBDdSGOfve738m6qwkTJuCCCy6I6nw6nU42Vr/55ptlOlGkK8XxQf7whz/ImjDRgF0U6s+ZMyf0mmjYft111+Evf/mLfE0U80fDQw89JNOo8+fPh8/nwy9/+UvWi5GYUN3iwLFaC+8mIWTEwt6UhJARS5PVhZ1lTbSwIIT0C/amJISQAWBxCi+xZgoxQsiIhzVjhJARh8PtlV5iHu/AvfoIISTeUIwRQkYUHq9PeokJQUYIIaMBijFCyIjB5/NjT0ULLI52yxRCCBnpUIwRQkYMB6tb0WhpNzUmhJDRAMUYIWREUFhnQVVzuwExIYSMFugzRggZ9lQ021FUZ8VwwOf3Y/PRetSYHdCpVdCpldCqlfI5uB5c1mqCy4F91Ep+/yWEdIVibJgzadIkvP/++9L5fjAQXQDMZjMSEhJicr4f/ehH0jD21FNP7XG/YDPxM844IybXJaOXeosTh6paMVxq1p7/qhhbjjf063i1UtFRuLWJNbFN3ybk9JrAdrEul8W+beuRtmtVSrYWI2SEQzEWAdG70W8f/NYqCoNh1P0RfeaZZ6LaT4gxi8VCMUZ6pNXhxt7yFvTSTnVI8Pr8eHZzEb4pboRSAZxakCm3Oz1eOD0+uNw++SzWXfK5fd3XNn6Pzw+PywubS8wEdcdkXOJPSGchF1zucXsXgdcuEDUqRvAIGUooxiIghNjhxUsG/ebP2LEdCqOxX8f+5Cc/kYJG9KYUzcGFCCooKEBxcTGWLl0qWx198MEHsuekaEV0zjnnyOPeeecd/OIXv0BqampoWyR+85vf4MCBA1IwlZaWYsqUKXjxxRflcWLb7bffLntTCi699FL8+te/lsuitdI999yD8847D9deey2MRiOOHDkizyGie3/961/leZ944gnZFunTTz/FRRddhF/96lf9ug9k9GJ3BbzEhAiKNx6fD09vKsL20iaolArceOoULJmYGvWXOyHCpGBrE2fBZWHPES7cxHpQwDnc7evdPQfOD9jdXvmAPTYCT/yMUrgFRVpbJC4YkQsIusD2oIjrst7pWZyTEBIZirERys9+9jP8+c9/lstC4Nx1110ynSloaGjAkiVLZN/Kjz76CHfccYcUXrW1tbjhhhvw1VdfYcaMGfif//mfHq/xxRdfYNeuXcjOzpbiTvSTfOyxx/D73/9ephj37NkjG46vWLECs2fPlqKsM+L4zz77DFqtFitXrsTbb7+Nyy+/XPapFKLu/vvvH6Q7REYybq9PtjkSQmU4jOXJTcelt5lIM960aioWjk+J+ngR/daoxEMJ6GJbuxYUclKcCfEmRVyboOu03mG729v2Wrs4FNvcbSa6QgDbQhG82CDuQWeRFozMddkeTMN2Wg9/Fqld5SjLLJCxC8VYN+lDEbUaiuv0l48//hgPP/ywrPcSEabW1vaaGpPJhO985ztyefny5SgsLJTLX3/9tWzuLYSY4MYbb5SirjtEdEsIseC+3/ve9+SyiGY9+OCDUCqV8lpXX3213BZJjImol2hGLhANzYNjIaSnuqzdZc2wOb3DQog9trEQeytapBC7dfU0zB2XjOGAECIyQqVRIdmgick5hQhzhou0kKDzdd0uBV3Y9jDR5wgTiN62HLMQem6vBxYnYkZwckQHsRYUeay/IyMIirFuvsn2N304FIiUXzBNKNKHIkK1Zs2a0Ot6vT60rFKp4PV6Q+mSgRCsbxPn6Vzr1l3tW+exeDw06yTdI95b+ypb0GyLTbptIAiR8eiGQhyoapVF8retmYZZuUkYzYhUolGrhlEb244JITEXJtJCqdgw0dfT9lCEz+MN1RAG07utMTIB7q3+Lryurrt6u1AKt227mvV3JAooxkYgog5MpP1ycnLkh9cjjzwS1XEiSnb99dfLGq7p06f3Wmwvas5EajMrKwvPPvss1q1bJ7effvrpePrpp3HyySfDZrPhlVdewb333tunnyEpKQkVFRV9OoaMfo7VWlDbGsPQyQAERFCIiQ/j29cUYEZOYryHNSIRYiRBPGL0cSP+5rm8gXRreySus6DrtD1MyHVYD9XoDW79nRBnwShm53RsUMx1qMdr269jjR7F3WiGYmwEIESQWt3+qxLpRpESnDNnDiZMmCDFUTQIUfXUU0/h/PPPR3p6Oi655JIe91+7dq0Ub0VFRaECfsF//ud/4rbbbsO8efPkuhhLb+fqzHe/+128/PLLWLhwIQv4iaSs0YaSBlvc74b4sH9xS0lIiN25rgAFWRRiwwURhQ/4ualiXn8XSr2Gp1w7pWijfQ6vv7O6vPIRC0S6vIuoC9qdBAVe523h4q8tchd8jd53wwOFf6C5qxGGqK0Ssw9FdElEZ0j3sylZYE+GijqzE3vKm4eFhcW7Oyvw/t4qaV8hImLDpUaMjCzEDNwOUbpOwq5zRK/Ltrb9g5MwguIu1oSLuw7p2DDRFo3AC0bwRtqs2RUFGXLc8dYcjIwRQuLuJbavYnh4iW06UieFmODqkyZRiJF+IyJOap0SphjNoA2fXBEu7oJirbMVSvhEikhCUNitCMSzxRm7iRVi1mxnL7ugaDOECTuDNij8VG3LSui1HfcZS1E7ijHSbWSMkMFGfCiImZPDwUtMzJh8ZWuJXD5/fq78xkzIaJ1cISdWtKVkI4m6zrNiexN9wf/DwVmzZnhiJ+w0QZHWTWq2h7q78P2VwzhqRzFGCIkL4sNgZ2mz/EMfb4obrHji80LplH/y1HRcsCAv3kMiZGgmVujUMbOB6U60iQkRHaJ5bZMkguv2Tq+LCRodhF2MZssGW5G1+9spkfOVHv990XyMT4uvgwLFGCEkLl5ieypaYHV6hkXvy4c+Oyo/NGbnJuHq5RNHXZsyQgYbYWqsidGsWa/PHxJnAaHWfWo23AolFMUL2y/c605M0hCP8KhdYZ11WETmKcYIIUPOoWozGi2uuN95USvzwGdHpU/V+FQDfrxq6piqUyFkuKZkTTq1fAyUYDuycPEWbmsyMd2IrKQYtsboJxRjhJAhpajeispme9zvukirPLrhGKpbHEg1anD72gJZSEwIGT0owtqRJbZ7kA/ZbMpo4VdAQsiQIYRPYa0l7ndc+Eo9u7kIR2stsjD4zrXTkRpL23lCCOkDFGOEkCGhyerCgaqWYXG3395ejm0lTTIdcvNpUzEutf99YgkhZKBQjBFCBh1RqL+7vBm++E+cxPpDtfj3gRq5fO3Jk0Z9v0lCyPCHYowQMqiI2Uu7yprhGSQH8b6ws7QJr39TKpe/u2gclk9Jj/eQCCGEYowQMniIKeMiImaPUV++gVBYZ8FTXxyHkIQrCzJwztyceA+JEEIknE1JCBkUxJTy/ZUtaLG5436Ha1odeHj9MWkiOX9cMq5cNjAvMY/PDbvXAgUinaPrtsiXirBflNs6nrun1xVRn6fH6yrEWnBL+1Jge/s2+rMR0j8oxgghg8KxWgtqW2PU8G4AmB1u6SUmPMWEp9CNK6cMqJmx02tHifUgXL74/2zDkXARF5JwEW531EK20zYh+ALbwoSgouN66NptQjW4f/dX6XCBXsbQdrbQNTsJ3kjCNSRaw7ZF/Fki/zwdf47I9yV8fL3/SB0kdGgsUf88bSelEI8dFGOEkJhT1mhDSYMt7ndWGDyKiFid2YmMBC1uX1MwIE8hq6cVpdZD8Prjn3YdrvhlIrjTcizLBeNfekh6oJNEC2nDdtHZWYh2OipKYR2SjJ0EbfD4zmPpLCqD2z2+FGEzG/ffKcUYISSmCOFzpMY8LFouPf1FEY7XW2HUqnDH2gIkGzT9Pl+zqx4VtmMdxAYhpCPB/x+9CvFh8t/I65+N4QDFGCEkZrQ63NhX0YK2VnBxrVf767dlchanWqnAbaunITe5/15idY4K1DgCszAJISTWUIwRQmJmYbG7rHlYNN39+EAN1h+ulcmIH62YjILsxH6Luip7ERpdAV8yQggZDCjGCCEDRoiWfZUtcLrj7+r6TVEj/ra9XC5fujQfSyel9es8oi6s3HoEZk9zjEdICCEdoRgjhMSk+XejxRX3Oylq1Z77skgur52ZhdNnZffrPG6fSxbq273WGI+QEEK6QjFGCBkQzTaXFGPxprLZjkc2HIPH58eiCSn4/tLx/fK9cnhtKLEchNsff3FJCBkbUIwRQvqN2+vDvorWuBfsC0H44GdHYXN5MTXThBtWTIGyH15iVk8LSq2HaV1BCBlSKMYIIf3mUJUZDnd8PbfE9R9afwwNVheyE3W4dfU0aNV9b7tL6wpCSLygGCOE9IuKZrtsMxRPxMzNJzYVorTRhkS9GnesK0Civu9eYrSuIITEE4oxQkifsTo9OFJtjvsMzle+LpFpUq1KidvWTENWor7P56i2F6PBVd2PAcR/5ujQEXAwJ4QMDhRjhJA+O9vvrWiJu5/YB3ur8MWxeqkRRL/JKRkJfTreJ60rjqLV09Sn4xQ+DwyWMqjd8e8yEHfCWs8E8Ye2tW+P3TulvS9ipPXQdTq8roix2Xuka3YeV6d95RjCe3SGbe9wSOf72fW88jwx+zGCP0OksUVYVrT9HF1eDjtP23poucvPF+l9o+h4fNh5I20bjV8MKMYIIX3iWJ0FFocnrnftq8J6vLurUi5fccIELBwv+stFj8fnltYVNq+lT8cpvU4YzcXymcjQYheppYigfEbfRycZdig6fQHosB7pi0GbqPOsAmBEvKEYI4T0qe9kaZwbgB+obMWLX5XI5bPm5GD1zKw+He/02qUQc/r6Vu+mdplhtJSMsfQkISPzi4Gim3Bo1y8G8e8YIqAYI4REhdPjxYGq1rjerbImGx77/Bi8fj9OnJSGixaP69PxNo9ZCjGPv2+RPZ29DjpbVR9HSwgh0UExRgiJiv2VrXB74hcVarS68NBnR+Fw+zA9OwHXnTIJyj7UjrS6G2WNmA99+Bn8PhisFdA4+1ZXRgghfYFijBDSK8Vxbndkc3mkqWuTzY28ZD1uOW0aNKrovcQandWoshfD34eUhMLrgdFaDJU7vmlZQsjoh2KMENIjLXY3jtf3rdA9lni8Pjy+sVD6miUbNLhjbQFMuuj/dNXYS1DnDBT7R4vSY4fJXAyFz92PERNCyBCKMafTCZ1ON5BTEEKGMUII7a9ogS9O2UnhA/bilhIcrDZDp1bijjUFSE+I7m+Oz+9Dha0QLe76Pl1T42yGwVrWVhBMCCGDT596hvz73//Gtddei6lTp0Kj0cBoNCIxMRGrVq3CH/7wB1RW9u3bJyFkeHOo2iz7PcaL93ZVYsvxBog2kz9eNRUT0qObgu71eWSz774KMZ2tBgZLKYUYIWT4ibF3330XM2bMwDXXXAOlUomf/OQneOedd6Q4e/bZZ6UY+/TTTzFlyhTcdNNNqKurG/yRE0IGlaoWO6pb4tfuaNOROry/NzCD8eqTJmHuuOSojnP5nCiy7IPV29q3Qn1LKXT2mv4OlxBCBjdN+cc//hH3338/zj33XCnGOvO9731PPldUVODBBx/ESy+9hP/v//v/+j8qQkhcEQXzIioWL4TD/ytbA15i58/PxYqCjKiOs3ssKLUehtsf/WQDpdcNo6VY1omRgSFtNpWBb/kKhQJKpUJGNcWsV/maeG5bFpMpRDcHkQwWz6Khg88vngPLzBKTsURUYuybb76J6mTjxo3D//zP/wx0TISQeLc7Km+B1xufmqniBiue+LxQfiCfPDUdFyzIi+o4s7tJWld4EX1aVem2w2QZ24X6QiwpgoJJCKjwdfG6FFSK0LbAA522Bc4T6ct6f5ECTUg2f6AhvHgOCrWgaBP+u8Fl4T0n9/EFag294lixHLMRETJ4cDYlIaQDhXUWmOPU7qje4pReYk6PD7NyE3H1SROlQOiNRmcNquxFfbKuGImF+uGRJ6VCGVjuRTgFltsiVW37yG2IrXiKNfJnafNL16j6fx6fz9cm4LpG3qSI6yD02l+XUbuQ+Ivdz0VITMSYeHO+9dZb2LBhA2pra+UbPRxRSxYtmzZtwp///Gds374dVVVV+Pvf/44LL7yw2/03btyI1atXd9l+8OBBzJw5s48/CSGkMw0WJ0ri1O7I4vTggc+OotXhQX6qATevmgZ1FF5i/bGu0NmqobPXYqgJjyAFnoMCKbCuahNTQYEV3CcgngJpP9LHey7u7QBvWkCUBUVbQLgFBVxIyMm0a6C7TihyF4rwBbpoMWJHYibG7rjjDjz11FNSFGVnZ0f1rbU7rFYrFixYgOuuuw4XX3xx1McdPnwYSUlJofXMzMx+j4EQ0t7uSLjsxwO314dHNxyTEwZSjQEvMYNWFXvrCp9w1C+FxjWwn1MElHQqZUAohYkpsT0omoKCSggsiqmRjficUw3gsy7aiF242GPEbmzRZzH2yiuvyOjXOeecM+CLn3322fLRV7KyspCSkjLg6xNC2hFCzBWHdkfig+fZzUU4WmuBQaOSQizVqO3dusJ6CDavecgL9UWwLidJD91AcmdkzBPriJ03GIET23wdo3WBtKtY42SJUSPGkpOTpYVFPFm0aBEcDgdmz56N//f//l/E1GW4Ma14BGltjW+jY0KGIyUN8Wt39Pb2cmwraZIRpJtPm4r81J69xFxehxRiTl/0okq0NDJaSgZcqK9RKpCTou9TKyZChiJip1bFZrJEx5mtnaJ28rWA6AvuJ1OvbZMpWFo3hGLsN7/5DX7729/iueeeg8FgwFCSm5srU6RLliyRAuvll1/G2rVrZS3ZypUrIx5z3333yfESQiLT6nDLov14sP5QLf59IODtde3JkzArt738oDvrCiHEPP7oRZVo8m2wlA14rBqVArnJ+qjq2AgZSYRPlsBARV0kERdcDxN6QRHXYbIEArNhxyIKv7gDfcBms+Giiy7Cl19+iUmTJkkn/nB27NjRv4EoFL0W8Efi/PPPl8f+4x//iDoyNn78eLS0tHSoOyNkLCJSG1uPN8TFZX9naRMe21gov01/d9E4nDsvt8f9W92N0rrCJ/9kR4fOVgWdfeAm1KIVU3ayDuq22Ydunwc2nyswuzH4L+ilJWrG5HLgX8hjq20fQkiMUq9+X4RoXt986i5efSuSE9IG7VciNIfIKPamOfocGRPtkMTsxx/84AcDLuCPBSeddJKsY+sO0TuT/TMJiczhOLU7EpG4p744LoXYyoIMnDM3p8f9G5xVqLaXRG9d4fPBaC2FeoCF+gKDRonsJF3IBmJ7axHeqPkK9n6mPAMSDd0INqVMOwW3CfsKOSEAYctBr6/AXoH9FRH2Ef/aztV+TiVUiLx/YL+2s0Y8X6TzDu3+8f68IaMv9aoaJjOU+yzGPvjgA9kGacWKFRgO7Ny5U6YvCSF9o6bVgcpme1yu+/D6Y3B7/Zg/LhlXLuveS0x8I652lEgxFi1KrwtGczGU3oG3cjLqVMhKEEJMIaNh79R9i83Nh+VrAfkUcJLvC2J/WUgd/tWdxTZREZCfncRbm6CLh4Dtbv/w11Xhx4Udo2wTmx23Bfdv30dev9dt7SKegrVvqVfNMPHa67MYEym+WKX3LBYLjh07FlovKirCrl27kJaWhgkTJuDee++VLZZEeyXBAw88IFOjc+bMgcvlkhGxt99+Wz4IIdFjd3lxsGroJ7OYHW7pJSY8xSamG3HjyindfjP1+b0otx2T6cloUbmtbYX6AzetTdCpkJmokx9uda5WPFe5EeXOwFjOSJuPczIWyg/TwFgDEkv+a/OXku1+wrbL1Ek3+8hv8GH7e0X6JfiaXyRm29Izcl9hidDxOLl/MIUT3L/TesjjKvwcnc8Z9f6d9wu/ftf9u/xMPVy/J+R55LlCG0gnugjBkDhUtgvAMHGoEmvyueM2ZR9EYVDgBl+LdM7AeAL7Bc7ftizHo4zwWuARHHMwStphDGGvB8X4SKXPYux///d/8dOf/hRPPPGEFEYDYdu2bR1mQt59993yWTQkf+GFF6QRbGlpaeh1IcDuueceKdDE5AEhykSkLhY2G4SMFcSH5r7KFniGuN2R8DETEbE6sxMZCVrcvqYA+m7sITw+N0qldUX0Ews0jkYYrOUxGWuSQSPHKNhpLsZr1V/C4XPDpNLh6txTMduU32H/wIdA2wfByP08GBYExVt3ojJcvAVEXu/7dxWDkfYPE5hdBG7bNUP7BYWwr60NU/dj7LxdCNf2c/g6bgu9Ftg/OGZvp209idbgdcTSWBOrCilGI4nFzinv9n0Sqr/AQ2sfQX5i/sgq4E9NTZVF/B6PB0ajsUsBf2Nj9N9i40G0xXSEjFaO1VpQXG8d0muKmo3HPi/ErrJmGLUq3Hv2TOQmR56N7fTapRBz+qJMM/r90NuqoHX0wfy1B1KMGqSZtHD7vHi37ltsaj4kt08xZOHa3FVI1Zhich1CBkIHMRcSe2Gir9M2ud5BJAaEZPg5QsIvTBS2C8V2wRqMmIaLyIAobjtnJ1EavLY37HVvaHtgW+BcnZfbxxg+/lhrzPe/+z4mJk3EiCrgF6lCQsjIpMnqkp5iQ4n4o/3Xb8ukEFMrFbht9bRuhZjV04oy62F4/NGlGUU60mAphdodG2uOtAQNUgxaWDwOPFnxGYodgZmY69Lm4ryMxaG0JCHxJhDZUY3JSKwvLNoZEpwRBGbwNW+30VE/Zk49C1nGrHj/SH0XYyKFSAgZeQh3fZGeHOq+2B8fqMH6w7XyM+NHKyajIDsxJtYVSo8jUKjvG7hZrRhbRqIOiXo16l1mPFb+CercrTAqtbgq91TMTRg/4GsQQmKDsq0sIBZfjk7KOQE69dB6pvZbjIkekiZT9KH5vu5PCBl8RMG+0z20jorfFDXib9sDdVyXLs3H0kmR/XwandWoshdHPTNR7TLLQn1p+z1AxN91UaifoFOj0tmER8s+RqvXjjS1CT/OPx05OrZeI4QMLlHJymnTpuGPf/wjKisre0xFfPLJJ7LX5EMPPRTLMRJCBkhZo00Wzg8lR2rMeO7LIrm8dmYWTp+VHXG/GnspKu1FUQsxYeJqNBfFRIiJiZzCQ0wIsWJ7HR4s/VAKsVxtCu6eeC6FGCFk+ETGRLsh0QNStBVauHAhli5diry8POj1ejQ1NeHAgQPYsmWLLOYXdhQ33njj4I+cEBIVwkbiaG30DbVjgfAve2TDMXh8fiyakILvLx3fxf9I1HJU2grR7I6y8N7vk7MlNc7mmIxR2AtlJ+llc/JD1ko8XbEeLr8Hk/SZuCl/nZw5SQghw0aMzZgxA3/7299QXl4unzdt2oSvvvoKdrsdGRkZsnH3008/LS0mgi7VhJD4I9qJ7C1vGdJ+b802Fx787Kh09p+aacINK6ZIA8YO4/J7ZaG+xdMS1TmVXjcMlmKoPLExqRXtJXOS9NBpVNhlLsGLVZ/D4/dhpjEPPxq3Gjplx1ninScNjCvdBoOtCX5RbaYQDgJBWwtF27Y2Q9i22pbAs9whtK39uLZtYcf6FcqwY7sui+fg/oFlcS1l1+PbtkVcDl0veM7wfdrOH7xW23HhYwn+jISQOFhbjHRobUHGWp1YRdPQuew73F78z78Po7TRhuxEHX5+9kwk6jsKG4/PJZt9273RzepUuW1tRq79az/UGbUQYskGaNVKfN1yFK9VfyVTpAsTJuLq3JXQKLu36zaaazF7z9+RYA40Nx/LSNPaTkItJODQvZgLicVw4RhaVvR4zpBo7ZPw7G7/KM7ZWQBHHFcnQdt2zo6iVwm/Mnj+9vO1nytsv9BxHYU3GRyWL/wRdPrkQTr7IFpbEEJGBrVmx5AKMRGFe2JToRRiYlbiHesKuggx4SFWYj0Ily+6+jWNswkGS1nMxqhRKZCbrIdapcT6xv34e923cvvy5AJclr1cmkRGxO/HuNJvMeXwp1D5PHBpjagat6j9Q9IvP/oDEsUvP7JDnYoDy23PbdvlUfJ1v/hG3PYsNvrkukLUw7WdM7Tctj14brkcOrZtOdIx0vyzh/0jnDMw1p6RUiVYtzemvtLHB18nIRsQd2HbOoi88O3hQrPjPpGEX/Dc7aKy877hUdKgQO3h2kEhqoj0aNtPGeE1parLvh3G2kG4jvyoLcUYIaMQEaE6UDl07Y5EgP2Vr0uwr6IVWpUSt62ZhqxEff89xPx+6OzVslg/VohIWE6yTjpyv1+/A/9u2CO3r02dg+9kLu22p5/GacHMff9Ael2gdVtDxlQcnvcduHQJGNUERWGbYOwo+DqJuTbH98B+YntA4IWWgwIvuNxh/3Dh2b3Q7DyWnoVn+D7hY+08ju7PGf3P2mkcHcbU6To+cV86/dxyH7G9Z5Ryf7HkDWxoeyJdCRdrkYRpeLmA9usXgKv/AaRNRjyhGCNklCGE0f4hbnf0wd4qfHGsXn4hFf0mp2Qk9NtDTOHzthm5xm7SgU6jRE5SoM/kW7VbQ676wsj1jLR53QqxtNojUohpXTb4lCoUzjgdFRNOGJHfvPuM/OAKpGwZ+BoCIojUdlHYvtxB4AVFb5jI67pvZzEZFMc+eVy74O1GJHbe3ll0d75Oh/F3N55OD18ft/f21pXj9Eb3xnW0xGRm9kChGCNklFFUb0WTNTb1VdHwVWE93t0VsL254oQJWDi+oy9Xo7MGVVFaVyi9LmlbofTGzobDoFUiO1EPv8KPl6u+wDbzcfnH/NKsk3Bq6syIx4gPgamHP0F+yVa5bknMxsH534U1Mf5O3WSUEl5zFu+xjHjh6u+6rUsUNbB9XsH50CblxfsnohgjZDQhZjIKMTZUiFToi1+VyOWz5uRg9cysLh5idc6KqM6lclthtBTLyFisMOlUyEzQwQMvnq/YiH3WctkcWLjqL02a0s04HJiz+22k1RfK9bKJy1A0fS18Kn53JWS0CVf/uCWAZoQ48O/ZE6itiIb58+cPZDyEkH7i9vpkzdZQzY8ua7Lhsc+Pyb5vJ05Kw0WLx3VIlVbaC9Hkiq7mS+NshMEScOqPFQn6gBBz+Nx4quIzHLPXQKNQ4Yd5p3Xb3kjraMX87a/L2ZJelQYH512I+pxZMR0XIYT0S4wJo1dRU9GdC0bwNfHs9bKqkJB42ViIwv2hoNHqwkOfHYXD7cP07ARcd8qktn5xffcQ09mqYlqoL0g2aJCeoIXZ48Dj5Z+gzNkAvVKD/xi3DtOMkTsBGC11mL/tVegdrXDqErB38WWwJMc/fUEIGf1EJcaKigItTQghw5PyJhtqW4em3ZHN5ZGmrk02N/KS9bjltGnQCBfVvnqI+XwwWkuhdsV21meKUYM0kxZNbiseLf8YNa4WJKj0uDn/dIzXp0c8JrmxBHN3vAGNxwGbKR17llwJh5E9KQkhw0iMTZw4cfBHQgjpF2aHW/aBHAo8Xh8e31iIima7jD7dsbYAJp06JMSKLPvh9DmictQPFOr3vm9fENEwMa5aVwseKfsYTR4rUtUm3DL+DGRrIxs7ZlQfxOw970Dp86IlJV9GxDxaY0zHRQghPdGv3kUvv/wyTjnlFNmfsqQkULz7wAMP4L333uvP6QghAzBaFXViQ9HuSJQivLilBAerzdCplbh9zTSkJwT6N3p8bhRbDkQlxISjvqn1WEyFmEiQZibqpBArdzTggdIPpRDL0iThzglndyvExpV8gzm7/iaFWF3WDOw+4SoKMULI8Bdjjz/+OO6++27Zh7K5uTlUI5aSkiIFGSFk6DhcbYbVGYWJagwQ9hVbjjdAtJn88aqpmJhuCjX8LrUegsPXu9u/aPJtMhfGrLWRQJSqZSXppOt/oa0GD5Z9BLPXgXxdmhRiaZoI5qx+P6Yc/gQFBz+SQq5iwlLsX3QpfKrue1ISQsiwEWMPP/ywbAr+y1/+EipVew+3pUuXYu/evbEeHyGkG2paHahsHpp2R58fqZPGroKrTpqIueOSQ9GycttR2LyWXs+hs9VIM9dYTvcUwjA7SSdTpfst5bJGTMyenGbIxu3jz0KiuuuUdWGdMWvPu5hQtEWuHy9Yg6OzzpZtVQghJB702ThHFPMvWrSoy3adTgerdej8jQgZy9hdXhyoGpp2R3vKm/Hq1kA5wnnzc3FqQWbotWpHiXTX7xG/DwZruYyKxRLRPi8nSQ+9RoXtrUV4qWoTfPBjjilf2ldolV3/vKk8Tszd+SZSG4pkr7/Dc89HzbgFMR0XIYQMuhibPHkydu3a1aWo/8MPP8Ts2bP7PABCSN/wiTqxyhZ4h6DdUXGDFU9uOg6fHzh5ajq+s6Dd6qHeUYkGZyBa1h0KrwdGSxFUnthG8FRtQkynUWFz82G8WbNFmj8uSZwsDV1VEaJcWocZ87e/Jj3EPCqtTEs2ZUyN6bgIIWRIxNhPfvIT3HLLLXA4HDJF8c033+D111/Hfffdh2eeeaZfgyCERE9hnQUttsFvd1RndkovMafHh1m5ibj6pImhHo4trgbUOEp7PF7pscNkFo76sR2rRqlATope2ml83LAH/6zfIbevSJkhWxwF/c66eoi9Br2jBS6tCXuWXAFLcm5Mx0UIIUMmxq677jp4PB789Kc/hc1mwxVXXIFx48bhwQcfxGWXXdbvgRBCeqfB4kRJg23Qb5XFGfASa3V4kJ9qwM2rpkHd5iVm9bTKOrGeek1qXK1t9WGxneapUSmQm6yHSqnAe3Xb8GnjPrn9zLT5ODdjUcSG38lNpZi746/QuB2wGdOxZ+kVcBhTYzouQggZCP1qtnbDDTfIR319PXw+H7Ky2DyXkMHG6fFif2XrkLRVenTDMVS3OpBqDHiJGbSByTpOr1266/ckxISbvnDVjzU6jVIW64ui/b/WbMFXLUfk9gszl2Jt2tyIx2TUHMKs3e9A5fOgJXkc9i25HG56iBFChhl9nj7029/+FoWFgQa6GRkZFGKEDAGiJED4ibk8g2so5vP78ezmIhyttcCgUUkhlmrUhrnrH4TH342Vht8v+0sOhhAzaJTITdIBCj9eqNokhZgCClyRc0q3Qkx6iO18Uwqx+qzp2H3i1RRihJDRIcbefvttTJ8+HSeddBIeeeQR1NXFtqccIaQrxQ02NFldg35r/ra9HNtKmmQa8ObTpiI/NeBE7/N7UWI9DJfP2b0Qs5bJht+xxqhTITtJDze8eKr8M+w0F0MFJa7LW4XlyQURxzLl8KftHmLjl2D/wu/RQ4wQMnrE2J49e+RjzZo1+L//+z9ZLyYMYF977TVZQ0YIiS3NNheO1/Xu4zVQPjlQIx+CH548CbNyk0JRuTLrUdh78BIzWCtibl0hSBBCLFEHh9+FR8s+wUFbJbQKNW7KX4dFiZMieojN3Cs8xL6S68cLVuPo7HPgFz4YZFjRtbqPkLGLwi/+0g6AL7/8Ugqxv/3tb3KGZWvr0Hgf9RcxvuTkZLS0tCApKfBhQ8hwRdRvbT3eCIc70OlisNhW0ognPz8uK8EuXjwOZ89tn2lYaTuORldApEVCZ6uGzl4b8zElGdTISNCh1WPDo+WfoNLZBINSix/nr8NkQ1ZEDzGRlkxrKIJfocDhOeejOn9hzMdFIqOEAhqlGjqFWnq8yYdCDZ1SDU3bNo1CBY1SBbWi3TBcdHDwt6XIAb/0ihPLoTU/5DZ/aHv7c8dldLO9/Xyh49vO2/n14LJ4HRGuK/dpWxa0L5GRyvKFP4JOH7ld2lBqjn4V8IdjMplgMBig1WphNg9Ns2JCxgoHKlsHXYgdrTHjmS+K5IfK6hmZOGtOTui1OkdFj0JMa68fFCGWYtQgzaRFg9uMR8s+Rp3bjCSVQTb8ztOlRvQQm7f9dSSaq+FVabB/4aVozJwW83GNVZElhJROqYFWoYJWqZECSwitwPbAshBi/Tp/myecagSHyjoKtDaRJlfbRV67kBsckdlRyPZdZApRLAhIYzLU9Ot/j3DhF9GwV199FUeOHMHKlSvxm9/8BpdeemnsR0jIGKWs0Sa9vgaTqhY7Ht5wDB6fHwvHp+DyEyaE7CGaXfU9eolpnE3Q2ypjPqa0BA1SDFpUOZvxWPnHaPbYkK5JwC35ZyBT2/WbpdFSj/nbXg3zELscluR2c1oSWWCJ6FRnMRUQWKq2Z00okkV6RvyfEf86bhy5dy08WtlV2Pm6iLzgszy27fXAsr/PQhJdoqNjQ0j2WYwtX75cGr3OmzdPeo4FfcYIIbHD7HDjaO3gRppb7G488OlR2FxeTMkw4YZTJ0MpfCOEz5i7BRW2Y90eq3aZYbCUxXQ84soZiYGG3yX2ejxe8QmsXidytCm4Jf90pGgCjcnDSWoqxbwdb0DjtsNmTMOepVeOaQ8xIbB0QWGlFMuaUHowFlEsMjYYydFKXy9p784iT6PUYDjQ5/+Rq1evlk77c+bMGZwRETLG8fr82FvRAt8guliI1KcwdW2wupCVqMNta6ZBpw5EQBxeG8ps3XuJqdxWGC3FMR2PCMaJcYiG30dsVXLWpNPvwUR9hqwRM6n0PXqItSaPw95R6iEmPg+DNVfh6cFAPZZGRq6CKcTghyghYxVlX4XkMPk/02cx9sc//lE+u1wuma6cOnUq1Gp+yyIkVhyqboXNOXh1Yh6fD098XojSRpuMQt25rgCJ+sC3Q7fwErMchNfv7aHFUZG0j4gVIhiXlaSDUavGHnMpnq/aCI/fh+nGXNwwbg30Eb655pV+i4IDwrrCj/rM6Tiw8OIRaV0h0oVCSAUeAbElolnB9aDoitRZgBAyeuizirLb7bj11lvx4osvynVRMzZlyhTcfvvtyMvLw89//vPBGCchYwJRw1XV7Bi084sC4le+LsW+ylZoVUoZEctKDESdhAATQsztj+xnpvS6ZK/JWLY4UrY1/NZrVNjacgyvVX8p0wrzEybg2tyVXdNpfj8mH12Pice/lKuV+YuHrXVFV6GlkSlE8SwEppxlyHQhIaQ/YkyIrd27d2Pjxo0466yzQtvXrVuHX//61xRjhPQTm8uDQ9WDWyf2/p4qbD5WL9OCN66cgikZCaE6C9HmyOGL7BWo8HpgbD0e06bfqjYhptOo8HnTQbxVu1VuX5Y0DZfnnAxVp/SB8BCbse+fyKncI9eLCk5DyZRTAznOOAgtg0rbFsVSh0WygoKLQosQMohi7N1338Ubb7whHfjDQ+ezZ88OtUkihPQNn8+PPeUt8HoHbzaQEGHv7Q7MfvzBsoly9mSQSlshLJ6WiMcJEWSyFEHpc8W04XdOsh5qpQIf1u/Cvxp2ye2npc7GdzNPgLKTwAp4iP0NaQ3H4+4hJrzOZpvGIVFtiMv1Ry4KQKSS1TpARgTF71iYffnaHoGy6tCyjMBKM7Cw5cFtB0bIiBFjov1RpMbgVquVdQ2E9BPRC9Li6KbnYwzYV9GCl7YEiu7PmZeDVdMzQ6/V2svQ7K6PfKDfB6OlRNaKxQqRHs1J1smZm+/UfYONTQfl9nPTF+HM9Pld/o5onRbM2/4aEluDHmKXoDEzQhukISBTk4jpxjzaPYQjIpgqLaDWA+rgs3joAs/yNV3gORZRTH8kwRa+HPZaULxFej3ivsFz9XDu4PmCrwU2tG9rW+329V7H3WlsI9iugQyiGDvhhBPwwQcf4LbbbpPrwT+cTz/9tLS9IIT0jVqzQ3qKDRYlDVY8/nkhfH5g+ZR0fHdhuxVNk7MGtc7yHhp/l0Lljl0rJp1GiewknfxMfrV6M75pDUTTL8lahlWps7rsbxAeYttfg8HeLD3ExIxJcxw8xERaUrj+j9enY+ygCIgrla6juOrw3PYY0mEpgDAH/1FPJIEWSXiGi7uIwq+nYxG9wI0oWHuKbAb8wRjdjLEYu++++2St2IEDB+DxePDggw9i//792LJlCz7//PO+no6QMY2wmBAu+4NFvcWJh9Yfg9Pjw6ycRFyzfGLoC5TZ3YRKe1G3xxqs5dC4Yjc2Q5sQ88KH5yo/x15LmRQ5P8hZgROSp3bZP6mpDPN2/DXMQ+wKOIxpGGpEsf1sUz6SRktaUqQIZaQqTEyFIlvhy9p4j5SExGcwojhKRGh3Ys7fi/jrbd+e9u+cEg/uO0xMjfssxk4++WTZj/L++++XthYff/wxFi9eLMWYMIIlhEQ/s1GkDz2DVCdmcXrwwGdHpbnruBQDfnzaVKhF1bz0ErOi3Hq0Wy8xna1KOuzHCpNOhcwEHVzw4OmK9dJLTK1Q4od5qzEvYXwvHmJ5bR5iXU1fB5sMTSJmjJS0pKzH0oeJrLC0YbjYGgk/CxkjAnP4zYIesY3Cg9TU1ODJJ5/Er371Kwxn2CicDBeO1ZpRXG8btAbj//fJEVmLlmrU4N6zZ8lejwKXz4ki875uLSx09jopxmJFghBiiTrYfE48Vv4pSh31csbhf+SvRYGxvSF5kLzSbSg48GGbh1gBDiy4GL4hjtKIj4kphuzhkZYMiqxQ5CpcbAUjW0Jk8YONkJGqOWImxoTdhYiQeb2D29R4oFCMkeFAg8WJnaXNg3Ju0eLjyU3Hsb2kCQaNCj8/aybGpQZSbF6fB0WWfXD4Ihfka5yNMFi6qSHrB0kGDTIStGh2W/Fo+SeodjXDpNLh5vzTMUGfEcFDbAMmHt8cVw8xYbI6JyEfyeohcPMXESuNoU1UGQBNUHSFPSiyCBmxRKs5aJ1PyBDj9HixfxDrxP62rVwKMZVSgVtWTw0JMeElViq9xLoTYi0xFWIiIpdq0qLO1YpHyj9Go9uCFLVRNvzO0bXbarR7iL2PnMrdcr1o2iqUTF055B5iqWqTtK2IiRmr6BwQLq6k6Ap7ptAihLRBMUbIECIC0UKIuTyD45f08YFqfHKwRi7/8ORJmJnT/k2swlYIqzeyCFS7LTBYSmI2jvQELZINGlQ4GvFo+ccwex3SFuLW8WciTRMwmu3gIbbrLaTVF7Z5iJ2H6vxFGEqE5Juoz5S9MKNqPSTsHEQNm7otqhUe3QpZOvDPKyEkOvjXgpAhpLjBhkZL7MxTw9lW3CijYoKLF4/Dsint9U419hK0dOMlJjzEjKLNUQwQMiYjUSd7XhbZa/F4+aew+1wYp0vFzflndJmRGPAQex2JrVVx8xATacmZprwuIrH7AxKA3AWAvvuUAyGEDIoYu/vuu3s1gyWEdE+zzYXjdbHz7ArnSI0Zz2wuknMjV8/IxFlzckKvNTqrUecMOO9H7jcpGn8PPFInAkpZiTqYdGoctFbgmYoNcPk9mGLIwn+MWwujKDIPw2BtwPxtr7Z5iBnbPMTaPdCGAlEXJtKSop1RVKRMADJnsY6LEBIfMbZz585e91m5cuVAx0PIqESkJfdWtLQbcseQymY7HtlwDB6fX7Y4uvyECaFUW6u7EVX24l76TQ7c+V/UmGcn6mHQqrDTXIwXKzdJP7FZpnH4Ud5qaDvVYCU1l8uImPAQsxtTsXvplUPuITZely6FYlRpSTGjMXsekJg9FEMjhIwxohZjGzZsGNyREDKKOVDVCqfbNyjRtgc/Owqby4spGSbccOpk2WZIYPOYu/USEwLMZD4ek36TyraG33qNCl81H8Ffa7bIay5KnISrc0+FupNbenrtYcze9Xa7h9jiy+HWDZ2HmBjPTGMeMrSJ0R1gzABy5gWK8QkhZBBgzRghg0xpgw31ZueguPcLd/0GqwvZiTrctmYadOqA8HF67Si1HoIPvu77TXodAx6Dqk2I6TQqfNq4D+/VbZPbT06eju9nnwSlKHQPI7d0O6Yf+Jf0EGvInIb9Cy4ZUg+xBJUec0z5MAhLid4QY88oANKmDMXQCCFjGIoxQgYR4X5/rM4c8/N6fD488XkhShttslj+jnUFSNQH6p48PrcUYh5/hPSj3w+j7DdpHfAYNEoFclL0UCsV+EfddnzSuFduPz1tHs7PWNwx/ef3Y9KxjZhU+IVcrcxfhKOzzx1SD7FcbSoKjNldBGJENEYgbyGgTx6KoRFCxjgUY4QMEsIFX7Q78vlib4/xytel2FfZCq1aidvXFCArMZBC8/m9Uog5fZGjXgZrGdQx6DepUSmQm6yXKco3arbgy5Yjcvt3MpZgXfq8Lh5i0/d/gNyKXXHxEFNBiemmXGRroxRWSeOA7DlsG0QIGTIoxggZJA5VmWF3xb4jxT/3VGHzsXqpZf5j5RRMzjCFRJqoEbN5I8/Y1FtFv8mBu/7r1EpkJ+tE/w68UPWFLNhXQIHLspfj5JTpHfZVeVyYvetvSG/zEDsy+1xUjV+MocKk1Ek3/c4zOSMiJhkIEZaUNxRDI4SQEBRjhAwC5U021LQOvCarM5uP1uMfuwM2FT9YNhEL8tud7KvsRWj1RG7urbPVQusYuP2MXqNETpIObnjxbMVGaWEhIk/X5K2UBfvhaJwWzA/3EFtwMRqzOoq1wUREwqYbc6GKJi2pTwl4h2mHoAUSIYTESozZbDaUlpbC5eo4G2v+/Pn9PSUhowKzwy19v2KNSHm+9HXApuLceblYNT0z9FqdoxyNroDzfmc0jkbo7NUDvr5Bq5T2FQ6/C0+Uf4YiR600TP3RuNXSwqJbDzFNm4dYytB4iCmhQIExB7m61OgOEAX6GdOHvPUSIYT0W4wJc9frrrsOH374YcTXh3ujcEIGE6/PL/3EYl0nVtJgxeOfF8LnB5ZPSceFC9tTac2uOtQ4yrrvN2kdeL/JBJ0KGQk6WHx22fC70tkEg1KLH+evw2RDVod9E6WH2F+hddukh9ieJVfCbhoaDzExJjFbMkG0I+oN0bYoZz5g6tSwnBBChpg+T2W688470dTUhK+//hoGgwEfffQRXnzxRRQUFOAf//jH4IySkBHCwapW2Jyx/UJSb3FKLzGnx4dZuYm4ZvnE0ExFi7tZ9pwczH6TYrZmZqIOjR4L/lL6oRRiSSoD7pxwdhchll57BAu/eUkKsdakPOxY9sMhE2Ki9+WSpMnRCTFTFjBxBYUYIWRkRsbWr1+P9957DyeccAKUSiUmTpyI008/HUlJSbjvvvtw7rnnDs5ICRnmCCf86pbY1olZnB488OlRtDo8yE814OZV06AW5l4A7B4LyqxHIpq6Kt2x6Tcpmn2Lpt9VziYZEWvx2JCuScAt+WcgU9uxN2Nu2XZM39/mIZYxDQcWXgLvEHiIibSkEIXj9e29OLtF1I9lzgBSO9a3EULIiBJjVqsVWVmBb8NpaWkybTl9+nTMmzcPO3bsGIwxEjLssTo9OFxtjrk1xiPrj6G61YFUowZ3rC2Q7YYELq8DJdZD8KJrFE7pdcJkGXi/yVSTBqlGLYrtdbLht83nRK42BbeMP0P2dOzoIfY5JhVukqtV4xbiyBzhIdbReX8wED0lRW/JDuPpDq0JyBXeYWzwTQgZ4WJsxowZOHz4MCZNmoSFCxfiySeflMtPPPEEcnNzB2eUhAzzOrE95S3yOVb4/H4880URjtVZYNCocOfa6VIYBU1dS6wH4fG7uxyn9LphikG/SRENE1GxQ9ZKPF2xXjb8nqTPxE3562AKs4no7CFWPHUliqetGpJi+DR1gpw4oIlG9CXnA1mz6R1GCBkdYkzUjFVVVcnlX//61zjzzDPx6quvQqvV4oUXXhiMMRIyrBERMREZiyVvbivD9tIm6W5/y+qpGJdqCJm6lnRj6iobf5uFEOsq0qJFSKiMRJ2sE9tlLsGLVZ/D4/fJXo5i1qSIREX0EIMCR+acg6rxS/p97b6MUQjDiYb22aTdIsYrvcP4RZEQMnxR+IVT5AAQFheHDh3ChAkTkJEx/Gcltba2Ijk5GS0tLbLOjZCBIGrEhOVELPn4QDXe3BaYASkafy+bHKiFEv9VS62HYY7gJSYiVLLxt8fe7+uKYFZWog4mnRpbWo7i9eqvZD3awoSJuDp3ZYcIVGcPsQMLLkbDEHiICSsNkZZM0UTRWNyQGvAO0wSELCGEDFfN0W+fMeEvVlRUhKlTp2Lx4qFz1CZkuGBzeXCweuCthcLZVtwYEmKXLM4PCTFBpf14RCEWavw9ACGmFEIsSQejVo31jfvx97pv5fblyQX4fvbyDsapBks95m9/LeAhpjVi7+Kh8RBLUZukENMKp/weUQDpU4H0afQOI4SMTmsLEQm7/vrrYTQaMWfOHGn8Krj99tvx3//934MxRkKGHT7hJybqxLz+mKY7n9lcJJfXzMjCmXOyQ6/V2svQ5KrtepDfD4Ns/B25BVI0iP6SOcl6WZv2ft2OkBBbmzoHl2ef3EGIJTWVYfHW56UQEx5iO5f9cNCFmEhLTtRnYEHChN6FmPAOG38ikFFAIUYIGb1i7N5778Xu3buxceNG6PXtfj7r1q3DG2+8EevxETIsEYX1Zkfs6sQqmux4ZMMxeHx+LJ6QgstOGB/yEmt01qDWWd5t42/NABp/C5eM3CS9bDj+Zu3X+HfjHrn9/IzF+E7m0tAYBOk1h7Hg25ehcdvRmjw0HmIahQrzEiZI64rwsUQkIQuYdCpgHBpfM0IIiZsYe/fdd/HII49gxYoVHf44zp49G4WFkc0nu2PTpk04//zzkZeXJ88lzt0bn3/+OZYsWSKF4JQpU+QsTkKGkjqzE6UNtpidr9HqwgOfHYHd7UVBVgJ+tGIKlCJvKOoN3I2y52Qk9NbKATX+VgshlmyAWq3AS1WbsLn5sIxCibTkGenzO/z/zivdhrk734TK50F9ZgF2nXA13Loo6rYGgLCrWJo0BWmahJ53VKgCRfrjlgCq9gkGhBAyasWY8BUL+ox19h/r9ZtrhGMWLFggxV00iBq1c845B6eeeip27tyJX/ziFzI9+vbbb/fpuoT0F4fbi/2VLTGtOxNCrMnmRm6yHresniajVAKrp7VbU1edrQZaR32/r6tRKZCbYgCUPjxVsR7bzUXSPPWa3FVYkTKjfUe/H5MPf4bpBwJmrpX5i7F/0ffhG2Qz1/G6dDlxIHz2ZkR0icDE5UDKhEEdDyGEDCZ9LuAXzvsffPABbrvtNrkeFGBPP/00li9f3qdznX322fIRLSIKJmZtPvDAA3J91qxZ2LZtG+6//35cfPHFfbo2IX1FzGYUMyc9MaoTk6auG46hstmBFIMGd64tQIIu8F/S4bWh1HooohDT2uuhs0duCh4NWpUSOck6uODGk+Wf4bi9VqYDhXXFbFN+hxmaM/b9EzmVgdRl0bTTUDL11EGtxVIrVNJGI0Ob2PvOKROBzJmBojdCCBlLYky0PDrrrLNw4MABeDwePPjgg9i/fz+2bNkiU4iDibjGGWec0WGb8Dl79tln4Xa7odF0/RbtdDrlI3yaKSH9obDOimZb/z28Opu6Pru5CEdqAqaud6wrQHpCwEzV7XOhxHIQXn9Xd32Nswl6W2W/r6vTKJGdpJNu+o+Xf4JyZyMMSg1uGrcOU4ztEwZUHifm7HwTaQ1F8CsUODznfFTnL8RgkqgyyNmSBlUvUTeRihQNvkWNGCGEjAL6/JXy5JNPxpdffilnVQpbi48//hjZ2dlSKIlarsGkurpaXiscsS5EYX19fbfiUXh8BB/jx48f1DGS0UmDxYmSBmvMImzC1HVbSRNUSgVuPm0qxqcG2vl4fR6UWA7A7Xd1OU7jbIHBUtbv6xo0SuQm6dDqteGB0n9JIZao0uP28Wd3EGJahxmLtr4ghZjwEBPWFYMtxMbp0rAocWLvQsyYHmjwTSFGCBlF9MtnTPShfPHFFxEPOtelBT1ru6tXE7M/77777g6RMQoy0hecHlEn1irKp2LCxwdq8OnBgE3FD0+ehFm5ASNAn98nTV0dvq5+YWq3GQZLSb+vadAqkZ2oR427BY+Vf4xmjw1pahNuGX8mssIafhstdZi/7TXoHS1waU3Ys+RyWJLzMFiooMQMUy6ytMm97KkI2FWkTaFlBSFk7Ioxn88nH2p1+yE1NTWyjksU4l9wwQVyhuVgkpOTI6Nj4dTW1soxpae3m2OGo9Pp5IOQ/iDEvhBiLs/Amm4H2VrUgL9tD9hUXLokH8umtLvrl9uOwurtmkZXuW0wmvsvxEw6FTITdChzNuDxik9g9TqRIxp+55/ewck+ubEEc3e8AY3HAZsxHXuWXgGHMbXf1+11XCod5pjGw9hbNEw46IsG34aUQRsLIYSMCDEmjF5FTdZTTz0l181msyzmdzgcskH4X/7yF7z33ntytuNgISYI/POf/+ywTaRJly5dGrFejJCBUtxgQ6Ola8qwPxysasVzXxbL5XWzsnDG7PbUoLCvEDYWnRGu+qLNkXDZ7w8J+oAQO2qvxlPln8Hp92CCPgM3y4bf7T6BGdUHMXvPO1D6vGhJyce+xZfBrQ2kTgeDXG0qCozZUIYZykYkKQ/ImgOo+t0shBBCRk/NmKgTu+SSS0LrL730kqzVOnr0qDSBFanAP//5z326uMViwa5du+QjaF0hloOu/iLFePXVV4f2v+mmm1BSUiKvdfDgQTz33HOyeP+ee+7p03UJiYZmmwvH6/rvbB9OWaMNj20shNfnx9KJqfje0nZT1zpHORpdXWdHKr1OmMzF/RZiyQYNshL12Gspk8X6QohNN+bitvFndhBi44q3Ys6uv0khVpc1A7tPuGrQhJiwz5hlGidTkz0KMeG0L4r0RW9JCjFCyCgn6q+bFRUVKCgoCK1/9tln0k5CFMULrrnmGjz//PN9uriwpVi9enVoPVjbJc71wgsvoKqqKiTMBJMnT8a//vUv3HXXXXj00UelWexDDz1EWwsSc0Racm9FS0zqxETx/wOfHZWmrtOzE3D9islQtgmxJmcNahxdi/KVXjeMrceh8PVv9maKUYM0kxZbW47hteov4YMf8xMm4FrZ8Lvtv73fj6mHP8H44q/lasWEpTg66yygt2hVPzEpdZiTkA+jqpeyAX1KQIQNYmSOEEJGpBgTjvd2e3th8ddff90hEiZeF5GuvnDaaaeFCvAjIQRZZ1atWoUdO3b06TqE9JUDVa1wugdeJ2ZxClPXo2ixu5GXosctp02DRvQgEql+dxMqI7jrK7weGM3HoeynEEtL0CDFoMXGpgN4u/YbuW1Z0jRcntPeZ1Lh82DmnveQXb1frhdOX4uyyScPWnG8qFErMOZ06HPZFUWgQJ99JQkhY4yovwILp/yXX35ZLn/xxReyeH/NmjWh10UrJBGpImSkIyws6s3t3nQDia49sv4YqlocSDUKU9fpMLWZuto85oju+sJo1WQpkinKviJkVGaiDsl6DT6o3xkSYqtTZ+OKnFNCQkjtdmD+tlelEPMplDg4/0KUTTllUISYSEsKE9eZpryehZho8J1/ApA5nbMlCSFjjqgjY//5n/8pi/PffPNNmT689tprZeF+kL///e845ZRTBmuchAwJLTY3CmNQJ+bz+fHM5uOyobg0dV1bINOGAqfXLt31fegUefP5YLQUy6L9viJ0lCjUN+pUeKt2KzY1H5Lbz8tYhDPS2vtM6uwtmLf9dSRYauFRabF/0ffQlDEFg4FRqZVpyfD6tIgIz7DsecAgt1gihJARL8ZEbdf27dvxySefSIuJSy+9tMPrCxcuxIknnjgYYyRkSBDtifZVtghNNCBE6v31b0uxo7QZaqUCt6yeivw2U1fhrl9sOQCP39PpICHESqBy991YVvQUz0rSQasJNPwWfSaF9LokaxlWps4K7Wcy10gPMZ3TDKcuAXuXXAFLUg4GA+FdJiYLiPZG3SJey5rJvpKEkDGPwt9T0dYoRJi+ikkHLS0tSEpqN7skZE95M2pbB56e/HBfFd7eUSGX/2PlFJwwKS3krl9k2dfV1NXvh8FSCo2r7w3IRVtGYeaqVPvwbMUGHLRVytTgVbmnYmlSe8QrpaEIc3e+CbXHCaspQ3qIOQfBt0tce5oxB3m61N4bfIsiffFMCCFjXHPQvIeQNuuJWAixLYUNISH2/aXjQ0JMuOuXWA9FdNc3WMv7JcTEPICcJD08Sg8eK/sUxY46aBVqXD/utA4Nv7Oq9slifaXfi+bUCdi36PvwaA2INQaRljTlI0HdS1qSDb4JIaQDFGNkzNPqcONorXnA92F/ZQte+Cpg6ioMXU9vM3WV7vrWI7B5u15Db62Szb/7ikapQHayHja/HY+VfoIqV7Os0bopfx0mG9oaaPv9GF+8BVMPfypXa7Nn4dD878I3CL5dmZpETDfmQaPsIS3JBt+EEBIRijEypvGIOrHygdeJlTa0mbr6/ThxUhouWZLf0V3f01Vw6Ww10Drq+nwtjUqB3GQ9Gr1mPFr2MRo9ViSrjbK9UW4wPej3Ydqhj5FfEphRWT5xGY7NPCPmMxXF2aYYsjFeH7kdWQhjBpA7PzBrkhBCSAcoxsiY5mCVGTaXd0DnqDM78eD6o3B6fJiZk4jrTpkUMnWt7cZdX2evg87edXtvaNVK5CTrUOVqwmPln8DidSBTk4Rbxp+OdE2g/krp9WDWnr8js+agXD8243SUT14+oJ8x4lgUajlbUgjBbhF2FsEG34QQQmInxpqbm/HWW29Jb7Gf/OQnSEtLk0as2dnZGDduXH9OSUhc6sRqWh0DOofZ4cYDnx2Rpq7jUgy4+bSpIVPXRmcNaiO462ucjdDZqvp8Lb1GiewkHQodNXi6Yj0cPjfydWm4Of90JKoDNWBqlx1zd/4VKU1l8ClUODT/O6jNnYtYk6o2YbZpXLubfyS0pkCRvj7QpYMQQkiMxNiePXuwbt06OTuguLgYN9xwgxRjwmdM9I0UPSsJGe6YY1An5vR48ciGY6hpdSLNqMWd6wpg1Ab+S7W4GmR6sjMaZzMMlvI+X8ugVcpZk3utpXih6nN4/D4UGHJww7g1MKgC/lw6e7O0rjBZ6+FR62ShfnP6JMQSEe+bqM/ERH1GyLssIsn5QNZsoKcaMkIIIZI+N6ET/SOF4atoEC5aIAU5++yzsWnTpr6ejpC41IntHWCdmGj4/fSmIhTWWWHUqqQQSzUGRJHV04Jy29Eu7vpql1laWPSVBJ1KCrGt5mN4tnKjFGKiz+SP89eFhFhCSxUWf/2cFGJOXSJ2Lrsu5kJMo1BhXsIETDJkdi/ElBogbxGQM49CjBBCBisy9u233+LJJ5/ssl2kJ6urq/t6OkJGXJ2YmB352jel2FUeMHW9dfU05KUE0oQOrxWl1sNdhZjbIt31+0qSXo30BC0+a9yH9+q3y23Lkwvw/ezlofZCaXVHMWfXW1B53bAkZGGv8BDTx9ZDT9SFibSkToit7jCkBtKSmtjbZhBCyGimz2JMRMOEiVlnDh8+jMzMzFiNi5BhWyf2wd4qfH6kTqbsbjh1CqZnBwrnXV4HSiyH4PV3FHoqtw1Gc7G0mugLKUaN7Gn5Xt02fNYUaOi9Lm0uLshYEopM5ZbtwPQDHwj3ZjSmT8b+hZfCq+nF56uPjNelY4ohq4e0pAJInwqkT2NfSUIIGYo05Xe+8x387ne/g9vtluviD3RpaSl+/vOf4+KLL+7PGAgZMX5iXx6rx7u7KuXy5SdOwJKJASsJj8+NEutBuP2uDvuLPpMm83FpNdEX0kwaJBvVeK36y5AQuzBzKb6TuTQgivx+TD6yHjP2vy+FWHXeAtneKJZCTLQymmsaj6nG7O6FmDB4HX9iYMbkIDQaJ4SQsUCfxdj999+Puro6ZGVlwW63Y9WqVZg2bRoSExPxhz/8YXBGScgw8BPbW9GCF7cEUo1nzcnBmpkBc1URCRNCzOnrGHFTep0wmYv6JMSEnMlM1MGoV+LZyg34uvWYbDF0Zc4pWJsWmBWp8Hkxc++7mHh8s1wvnroSh+ZdAH8Mi+UTVHosSZyMDG1izw2+J60AjIEuA4QQQoYoTSl6K23evBnr16+XdhY+nw+LFy+WMywJGa11YsX1VjzxeSF8fuCkKWm4aPG4UJujMuth2L0dG3wrvS6YWo9D4evUELwHRGApK1En+0w+Xv4ZjtlroFYocV3uaZifOEHuo3Y7MGfnm0htLIZfocDhOeehOn8RYonoKznNkA1lW01a14GywTchhMRVjAk7i0mTJmHNmjXyQchorxOrNTtCpq6zchNx7fJ2U9cKWyEsno59JRVeD4xSiAVS+dGgFEIsSQeP0o1Hyj5BubMReqUGN45biwJjjtxHZ2/B/O2vwWSpg0elxf6Fl6ApcxpihQpKTDflIlvbgy+YNgHIW8gG34QQEs805ZQpU7BixQo5o7KxsTGWYyFk2NWJSVPXT4/C7PBgfKoBN6+aBnWbqWuVvRgt7voO+4tImKgRU/o61o71hFI0/BZ9JmHHA6X/kkIsUaXH7ePPCgmxhNbqgHWFpQ5OXQJ2Lbs2pkLMpNRhSdLknoVYygRg4ikUYoQQEm8xtm3bNixfvhz/9V//hby8PFnQ/7e//Q1OpzPWYyNkQLgH6CfmdHvx0PpjqDU7kW7S4o61BTBoA3VZ9Y5KNDg7uuiLWi5RI6b0Rh+FUyuBvGQDGn2t+Evpv1DnNiNNk4A7J5wT6veYWncMC7e+AJ3TDGtCJnacdD0sSQGRFguytElYnDQZRpWuB++wxUD2nIByJIQQElMUfmGa1A/EYRs3bsRrr72Gt99+G16vV86mfO655zCcEbYcontAS0uLrH8jo5c95c2obXX229T10Y3HsKe8BSatCj8/eyZykwP+Wc2uOpTbjnU8wOeDyVIElbtj7VhvDb9FRKzMVY8nyj+F3edCrjYFt4w/I9TvMad8Z2jGZFPaROxf9H14YjRjUkwMmGrIxjh9DwX4hrQ277DY2mUQQshYoDVKzdFvMRaOKOS//vrrZaskIcqGMxRjY6dO7HB1/9KT4r/Ei1tKsPlYvRRMd58+HQVZgVmFZndTV1NXvw9GcwnU7uivp1MrkZ2sw2FbpZw16fZ7MVmfhZvy1wYiVH4/Jh37HJMKA10tanLn4dC88+HvqRdkHxDmrXNM+Uhq62kZ2TtsWsA/jJYVhBAyqJqj33/Zy8rK8Prrr8vI2N69e2Xq8pFHHunv6QiJGaJp90DqxN7bXSmFmNAgN546JSTE7B4LyqxHOgkxv2xx1BchZmhr+L3DUoyXq76AD37pbn993mpolWqZ7pyx733kVO6W+5dMWYGigtUxE0Vp6gTMkk2+Vd17h4loGC0rCCFkSOizGHvqqafw6quv4ssvv8SMGTNw5ZVX4t1335UzLAkZDnVi+yr6Xye24XAt3t8TqAX7wbKJWDQhYOrq9Nqll5gPHU9ssJZB4+rakaI7TDoVMhN0+KLlEN6q3Sq3LU2cgh/krpDtjVQep7SuSGsoktYVR2afg6rxSxDLJt+it2S3JOYA2XMBVQ9tjwghhMRXjP3+97/HZZddhgcffBALFy6M7WgIGSAHKlth76ef2PaSJry2NdDI+/z5uVg1PSBa3D4Xii0H4PF39AzTWyugcTZHff4EvQoZJi0+bNyFjxoCUa9VKbNwUdaJ0ipD62jF/O2vI8FcA69KI60rGjMLEKsm3yIaJiYHdO8dNgtIGR+T6xFCCBlEMSZaH3Xfo46Q+FHaYEOduX8F+0dqzHj6i+MyAbmyIAMXLMiT270+D0osXdsc6WxV0Doaoj5/ikGDFJMaf6vdis3Nh+W2c9MX4cz0+fL/k8lcg3nbX4fe0QqX1oQ9Sy6HJTkwhoGSpDJgTkJ+902+dYmBtKR4JoQQMjzFmCjMnzt3LpRKpawP64n58+fHamyERE2Lrf91YhVNdjyy4Rg8Pj8W5qfgymUTpUAS7vol1kNw+Gwd9tfZaqGz10V9/rQEjYyKvVi1CTvMxTJdeGnWSTg1daZ8PaXhOObu/BvUHiespgzZY9JhTEEsyNelySbf3brpJ48HsmbTsoIQQoa7GBPpyOrqatmPUiyLD6rwSZjBdfE83GdTktGHy+OTfSP7My+4weLEA58dka2SpmaacOPKKVApA+/ncusR2LwdBZ6Ihuns1VGdW4iujEQdtFo/niz/DIdsldLl/urcU6WvlyC7Yjdm7PsnlH4fmlMnYN9iYV3R3QzH6BHXmWnKQ6a2m9k7IkqWMzdQI0YIIWT4i7GioiJkZmaGlgkZTuyvbIHD3fcvARanBw98dhRNNjdyk/W4bU0BtMKFFUCl/ThaPU0d9tc4m2SdWF/6TELtwSNln6LYUQ+tQo0bxq2RIkkox4nHv8Dkoxvl/jU5c3Bo/ndiYl1hUukwxzQeRpU28g6G1DbvsIGLPkIIIQMnqr/8EydODC2XlJTg5JNPhlrd8VCPx4Ovvvqqw76EDDaigXeDJfrWQ+HRtIfXH0VViwOpRg3uXFuABF3gPV1jL0WTq7bD/hpnCwyWsqjOLUzqsxP1sCvseKz0E9S4WmBU6vDj/HVyJqOwrig48C/kle+U+5dOPhnHp6+NiXVFjjZFtlASMzO7ogDSpgAZBfQOI4SQYUSfv4avXr0aVVVVMmUZjjA0E68xTUmGiiarC4V1ln656z+16TgK66wwaFS4c+10pCcEWgGJFkd1zo7RL7XLDIOlJKpzi7aVOUl62d7osbJP0OyxIVVtws35pyNHlyKtK2bvegvp9YXwQ4Gjs89C5YQTEAs3fSHCcnUBK44uqHVAzgLAFGixRAghZASLsWBtWGcaGhpgMpliNS5CesTp8WJfZd/rxMT799WtJdhV3gy1UoHb1kzDuNRAuq7FVY9qe0fRJdobGS3FfW5v9GT5p7D5XDJSJYRYqsYErcMsZ0wmmquldcWBBRehIWvGgH/TBqVWuuknCLPWSJgygZz5gLqbtCUhhJCRIcYuuugi+SyE2LXXXgudrr2psIiGiRmXIn1JyGAjBNX+ylY43X13dv3H7kpsOtrmrr9yCqZnB+wcLO5m2W8y3F1f6bbLxt/RKD5Ra5aTpMMhewWeq9zY1t4oE/+Rv07WcBnNtdJDTO9okdYVe5dcBnPyOAyUTE0SZphyoRY+YZ0RqcqM6UBaYLIAIYSQES7GRG+l4AdhYmIiDIb24l+tVouTTjoJN9xww+CMkpAwjtdb0diPOrHPj9Thn23u+leeOAGL29z1I7U5UnrsMFmOy76TvaFva2+0zXwcr1V/KdsbiUjVD/NOk+2NUhqKMHfnm9K6wmZMx56lwrqim3RiH9KSUwzZyO+uybfGCOQtBPSB/7eEEEJGgRh7/vnn5bNoe3TPPfcwJUnigrCiEEX7fWVnaRNe2RpIQZ43Pxenzcjq0ObIi/bZmEqvU0bERKF9bxi0SmQl6LChaT/eq98ut52YNBVX5Jwii+izKvdi5t73pHVFS0o+9i6+DB6tEQNBr9Rgdk9NvpPygKw5gCo2TcUJIYQMLn3+a/3rX/96cEZCSC8I+wqRnuxrndjRGjOeEu76fuDUaRn4Tpu7fqQ2R0qvC6bW41D4OrY+ikSCToU0kxbvNWyXYkywNnUuvpO5RHqMTSjcjClH18vttdmzcGj+hfANsOdjhiYRM4x5kZt8C1sM0dIoOX9A1yCEEDK09Our81tvvYU333xTtkZyuTqmi3bs2BGrsRHSqU6sRVpS9IWKZjse3nAMbq8fC/KT8YOTAu76gTZHBzq0OVJ4PTBKIebu9bxJBjVSjGq8VrMZ37Yel9suzFyKtWlzu1hXlE06CYUzTh+QnYRIS042ZGG8vpvZkLqkQFpSy0k0hBAy0uimR0r3PPTQQ7juuuuktcXOnTtx4oknIj09HcePH8fZZ589OKMkYx5hYdFk7V0khdNodeGBT7u66/v8XpmadPjsoX1FJMxkLoTS13stWopRg0SjEk9VrpdCTAilq3JOlUJMWFeIGZNCiEnrillnoXDmGQMSYqKn5ILEid0LsdTJwITlFGKEEDJWImOPPfYYnnrqKVx++eV48cUX8dOf/hRTpkzBr371KzQ2Ng7OKMmYRjT/Lq7v2B+yN6zCXf/TI9JdX9hN3La6ADq1SvabLLUehs3b7k8mIlmiRkzUivVGeoIWaq035KqvUahwfd7qQCNue4sUYgmW2phZV6SpEzDLNC5yWlI47AsnfVPGgK5BCCFkhEXGRGoyaGEhZlSazYHefVdddRVef/312I+QjGkCdWItfTpGpDJF4+/KFgdSDBrcJdz19WqZ6qywHYPFE3Y+nw9GS5GcPdkTIq6VmaiDV+3EX0o/lEJMuOrfNv5MKcQSWqux+OvnpBBz6hKw88RrBiTExPVEg+/5iRMiCzFTFjDpVAoxQggZi2IsJydHGrwKROujr7/+OtSzMrx5OCEDxefzY095Czxef5/c9Z/+4jiO1loC7vrrCkLu+lX2IrS4A+9diV8IsWKo3Lbe+0wm6WBRmPGX0n/J9kYpaiPumnC2rONKqzuKhVtfgM5phjUhEztO+iEsyYFJAv1B9LBcmDgJE/QZkb3DRJF+/hKauBJCyFhNU65Zswb//Oc/sXjxYlx//fW46667ZEH/tm3bQsawhMQCIaha7e4+u+vvLAu469+6ehryUwM2ErX2MjS6asJ29sFoLoHabYmqz2SVtyHMVT8ZN+efIV3180q3oeDAh1DAj6a0Sdi/6HvwaLpxwo8C0TpptkxLRvivqU0IpCX1Sf0+PyGEkOGHwt/HcJbP55OPYKNwMaty8+bNmDZtGm666SZpADucaW1tlQa2opdmUhI/1IYrNa0O7C3vW3ryn7sr8d7uSpniu2nVVCyZmBrqN1llD2tp5PfLXpMaV2tUfSaPOitDrvqT9Jm4KX8tTEodphz5FBOKtsh9q8YtwJE558EfKaUYBWLM4twTDZmRd0iZAGTOBPp5fkIIIcNXc/RZjI10KMaGPzaXB1uLGuHtQ3py05E6vPR1Schdf/XMgKlrs6tOtjnqIMSsZdA4m6PqM7nD0u6qL4xWf5i3CgY/MHPPu8iqOSj3LZp2GkqmntrvGZMiLSmiYSmaCLYUwpcsex6QmN2vcxNCCBn+miOqNKXoOxkt8+fPj3pfQiLVfIk6sb4IMeGu/3Kbu/6583JDQszsbkKFrbDDvgZrea9CTKtSIidZh40tB/Be3bYOrvp6tx1zd7yB5OZy+BRKHJ53AWry5g9OWtKYHmjwPYC0JyGEkOFPVGJs4cKF0iiztyCaNNP09t5ChpDuOFTdCoujd/f7IEdr2931V0zLwIULA4XzVk+LtLAI7zept1RA42zq8Xw6jRJZiVr8s2E71odc9efggsylMNkaMX/bazDYm+BW67Fv8ffQkjapX79MEUMTxf/dFumnTwPSp/br3IQQQkahGBMzJQkZbCqb7ahqdvRp/4fXB9z15+cn46o2d32H14pSSychZq2C1hk2k7KbPpMZCVq8XvtlF1f95MYS2exb47bDbkjB3iWXw5bQTX1XFCauIhqWrDZGbvAtivQNKf06NyGEkFEqxoSFBSGDidnhxuHqgGdd9O76R6W7/pQME/6jzV1fNP4utnRs/K2zVUHrqOu1z6Rw1X+maj0OWCukq75ISy5Lnoasyn1tzb69aE3Ow97Fl8Ot61/boXRNAmYauzFxFT0ls2azSJ8QQsYYffYZE7z88ss45ZRTkJeXh5KSQK3OAw88gPfeey/W4yNjAI/XJ2dOinqxaN31H/zsKBptLjnb8bY106S7fnvj73Y7DJ2tBjp7z0IsSa+G0ejHYxUfSyEmXPVvHLcWy5Kmymbfs/e8I4VYXfZM7Drxmn4JMSHuphqyMS8hgomrUgPkLgRy5lGIEULIGKTPYuzxxx/H3XffjXPOOQfNzc2hGrGUlBQpyAjpKwerzDLC1Rd3fdEAPFm4668rQKJeA4/PLYVYeONvIcJ09jBvsW76TCr1bjxQ9lGbq75WuurPNeZixv73MeXo+lCz7/0LL4FPzG7sI3qlRpq4RuwtKYr0J60AknL7fF5CCCFjVIw9/PDDePrpp/HLX/4SKlX7N/ylS5di7969sR4fGeWUNtikp1i0jvxPb+7qru+Vjb8PwRnW+Ftrr5fpyd76TDrVtg6u+ndOOAfTNMmyx2RuW7PvI7PObmv23fdAcqYmCUuTpiBJbej4gjhX5gxg/ImcLUkIIWOcPjvwi2L+RYsWddmu0+lgtVpjNS4yBmixuXGszhy9u/43pdhZGnDXv2X1VIxPNcrG32XWw7CHNf7WOBqht1X2OJMxI1GHen8jnijt6Kqf4/Fg3rfPhzX7vhgNWdP7nZYcp0/r+iKd9AkhhAxEjE2ePBm7du3qUtT/4YcfYvbs2X09HRmjiHTj3ooW0ac7Kt7fW4XPj9RJIfWjFZMxMydJCrRy65EOjb+FdYXwEusOpSLQ8LvIXdXFVT/b2oR52/8qe0yKZt97F1/Wrx6TItUpDGIT1BH8weikTwghZKBi7Cc/+QluueUWOBwO+WH4zTff4PXXX8d9992HZ555pq+nI2MQ8b7ZV9kChzu6OjEhwt7bFYh0XX7iBCydFIg2VdiOodXT1FGIWcp67jOZpMcu23H8tfqrNlf9cfhh3mnIbSjGnF1vQeV1y2bfe5ZcDmc/7CWytckoMOZArehUpK/SBgxc+2mHQQghZPTSZzF23XXXwePx4Kc//SlsNhuuuOIKjBs3Dg8++CAuu+yywRklGVUU1VvRaGkvtO+J7SVNeKXNXf+ceTlY0+auX2UrQrO7PrSfcNXvSYiJPpPZiTp83roP79fvlNtOSJqKK3NOwfiyHe3NvtMnY9/CS+Hto+u9SEtON+YiRxdBwJmyAjMl1cO7byshhJD4MKDelPX19bJpeFZW4AOyoqJCCrPhDHtTxpcGixO7ypqlY35vCN+xv3x6BB6fH6dOy8DVywOmrrX2MtQ621ORouG3wRzWCDxCn8msJC3ea9iGL5oPyW2np83D+emLMPXIZ5hQHGz2vRBH5pzb52bfJpUOc0z5MKp0HV8Q0bGsmYHUJCGEkDFHayx7U3ZHRkaglUt1dTX+8Ic/yDSl3d4+o42QcERacn9la1RCrKzRJi0shBBbND4FP2hz129wVnUQYmqXuUchplUrkZ6gxss1X2C3pUTWnF2UdSLWJBVg5u6325t9F5yGkil9b/adq03BNGMOVJ1nWuqTA0762v6ZwxJCCBk7RD1XX3iKXXnllcjMzJRmrw899JCMiv3qV7/ClClT8PXXX+O5554b3NGSkV0nVtEiC/d7o87sxAOfHYXd7UVBVgJuODXgrt/krEWVvV14qd1mGM3dt+rSa5RITlDgqapPpRBTK5S4NncVTjdNxIJvXpJCzKdQ4cD876Jk6so+CTEVlJhlGocZprxOQkwBpE0Fxp9EIUYIISQqoo6M/eIXv8CmTZtwzTXX4KOPPsJdd90ln0Uhv5hJuWrVqmhPRcYgwhus2dbujN8drXa3TE222N0Yl2KQ7voiutXqbkSlPdAvUqB2W2DsISJm1Kmg1XnwcPmnqHI1S+PVG8atwQKfBvO3PBdo9q3RY9+ivjf7DqQlx8MoivLD0RgCRfrGCHYWhBBCyEDF2AcffIDnn38e69atw80334xp06Zh+vTpdN0nvVLb6pDmrtGkMUVErNbslA27hbu+UauGxd2CMuuRUONvldsaEGLd5DtFn0mP1o4Hyj9Fk8eKJJUBP84/HXOtrZiz62Vo3I62Zt9XwJYQSLVHS642FQXGbCg7pyWT8oCsOYBqQJl/QgghY5CoPzkqKytDPmIiLanX6/GjH/1oMMdGRgE2lwf7q1p73c/t9eHRDcdQ2mhDgk6NO9dNR4pRC5vHLE1d24WYDSaRmvRHTneKFkktyiY8WfaZNHPN0ibh5vzTMbv2OGbs+yeUfh9aksdh3+LL+tRjUqQlp5typXVFl76S2XPYzogQQsjgizFRH6bRtPflE62QTCYWJ5PuEY2/94gG4F5/r22Ont1chIPVZujUSty5tkA2AHd4bSixHoQXAT8ypdsOk/l4t0Is1aRBmbcaL5R/3mbmmoH/GLcW845vwcTjm+U+tTmzcWjed/rUY7LbtKQhDcidH0hPEkIIIYMtxkQB9rXXXivbHglErdhNN93URZC98847/R0LGWUcqm6FxeHp9X31+rel2FbSJIv0bzltGiZlmOD02mXjb9F3UqD02JFgLowoxBRtfSb3Oo/jjZqvZRRtrikfP8xegQX7PkBW9X65X8mUFSgqWN2nQv08XSqmGTqlJcVy+jQgbUqfZ18SQggh/RZjonA/nB/84AfRHkrGIBXNdlQ1994A/IO9VdhwONDm6PpTJmN2XhLcPpeMiHn87pAQ6y4iJrRQZoIWmyz78WHDLrlteXIBfpA6Hwu2v4bk5nL4FEocmXMeqvMX9iktOcOUi6zOaUmNMWBZ0Q93fkIIIWRAYkwU7xMSDa0ONw5Xt0bV5ujdtjZHl50wHidOToPH55YRMZfPKbcrPQ5ZI6bweSO2N0pP0OD9pm/xZcsRue2s9AW4RDcO879+HgZ7M9xqPfYvuhTN6ZOj/uUlqPSyt2SXtGRyPpA1G+ijKSwhhBDSE5z6RWKKKMTfW957A/DwNkfnzsvF2lnZMiUpImJOX8A4WOl1yoiYwueJ2N5ImLm+Vv8F9lrKZGTt0qyTcL5Pi7lbn4fa44TdkCp7TNr7MGNynC4NUw1ZHdOSokg/Zy6QmBP1eQghhJCYm74OFo899hgmT54sZ2cuWbIEX3zxRbf7bty4Ubqwd34cOhRocUPij3DYt7u8vbY5evqL49KZYmVBBi5cmAefEGKWg7B7re1CrDWyEBPtjZITlXiq+jMpxISZ6w/zVuNSqx3zt70mhVhLSj52LP9h1EJMNPYWLY1Ek+8OQsyYDkxaQSFGCCFkdEbG3njjDdx5551SkJ1yyil48skncfbZZ+PAgQOYMKH7fn6HDx/u0ONJdAUgw6MBeL05kF6Mts3RlcsmyoJ74SNm85rlPkqvq02IdTWJFQawOqMbj1Z8hmpXCwxKLW7MW4PTy/djYtGXcp+a3Lk4PPcC+KL0/EpUGTDbNA6G8LSkEGQZBYEifUIIIWS4NgofKMuWLcPixYvx+OOPh7bNmjULF154Ie67776IkbHVq1ejqakJKSn9K6Bmo/DBodHqws7Sph77Too2R//90SHpri/aHN21brqMcpXZjkiH/XYhVhhRiBk0Snh0NjxZ+RlaPDakqI24JXc11hzeGOoxWTx1JYqnrYp6lmO+Lg1TOqclRT9JUaQv+ksSQggh/SRazRG3NKXL5cL27dtxxhlndNgu1r/66qsej120aBFyc3Oxdu1abNiwocd9nU6nvBnhDxJbhHO+6DvZkxALb3OUn9re5qjSXthBiBm7EWImnQpmdTMeLv9ICrEcbQp+mnMaztn9XluPSSUOzvsOigtOi0qIibTkXNN42eS7gxBLmQBMPIVCjBBCyOhPU9bX18Pr9SI7O7vDdrFeXV0d8RghwJ566ilZWyZE1ssvvywFmYiYrVy5MuIxIsL229/+dlB+BhIwbO2tAXjnNkfC1FW0ORJNv5tcdXIfpdcNY+txKCMIsSS9GmX+KrxUuQkev09Gsu5Ino+Tv30dekcL3BpDW4/JiVH9SpLVRtnkW/SrDCFMYEVfyYQs/loJIYSMrdmUogA/HJE17bwtyIwZM+QjyPLly1FWVob777+/WzF277334u677w6ti8jY+PHjYzb+sc6xup4bgIe3OUrUq2VqUrQ5qrGXosFZFSbECqH0ubocn2rUYK+rEG/VbpUNkeYnTMCd2nws+vZVWahvM6Zhr5gxaUqParwT9OmYrM/q+B4zZQI58wB1wNCYEEIIGRNiLCMjQ7ZU6hwFq62t7RIt64mTTjoJr7zySrevi44Bwa4BJLbU9NIAvHObozvWFiA7SY96RyXqnBU9CjEhldJMGmyy7sPHjXvkthXJM3CbS4NZO94QxY5oTp0gI2IerbHXsWoUKhkNS9MkhF1ECWTOAFIn9f8mEEIIIQMkbjVjWq1Wphs/+eSTDtvF+sknnxz1eXbu3CnTl2RosTo9ONBDA/CIbY7STWh01qDaEfAXU3g9MJqPdxViCiA9UYP3W74JCbFz0xfi581mzD7wLynEqvPmYfcJP4hKiIlC/6VJUzoKMV0iMPFkCjFCCCFjO00p0odXXXUVli5dKlOOoh6stLRU9rwMphgrKirw0ksvyfUHHngAkyZNwpw5c+QEABERe/vtt+WDDB0erw+7y5t7bAAeqc1Rs6seVfaikBAzmQuln1hnV/1Ukwqv1m/CAWs5FFDg8swT8aOSvcisCfjJFU1bhZKpK3st1BevTtBnYJI+s2NaMnUykDE9cDFCCCFkLIux73//+2hoaMDvfvc7VFVVYe7cufjXv/6FiRMDhdhimxBnQYQAu+eee6RAMxgMUpR98MEHOOecc+L4U4w9RETM5vRG1ebo8hMnyDZHYsZkhe2Y9BTrToiplYDoO/90zacocdTL1OINGSfiykNfIKm1Ej6FCofmXYDavHm9jlGrUMu0ZKomrJG9qAnLWQBEWV9GCCGEjHqfsXhAn7GBUVxvxbFaS49tjp7YVChtLs6bl4sLF42Dxd2MEuuhHoWY8BtTG914uvJT1LnNMCp1uDNtEb679yPoHa2BGZOLv4+W1O7NgIOkqk3SxFWjDPuukZAdKNIXsyYJIYSQYaQ54j6bkowsY9fCOkvUbY6+szAPVk8rSq2Hw4TY8S5CTKdRwqE147Hy9bB4HbK2617jdKzb8S7UXhdsxvS2GZNpPY5PJCInG7JkajKEEGRZswJNvgkhhJBhCMUYiQrhFba3B2PXSG2OHF4rSi2H4IMvTIg5Ohxn0CpRp6jDi+Wb4PJ7MF6Xjl8rMnDirveggB9NaROxf6GYMWnocXw6pUZGw4SHWAh9SsBJP4oif0IIISReUIyRXhEWFXvKW+DuxthVtDkSpq52txfTsxNw48opcPvtKLEehBde2ew7khBL0KlwxFuCN2u/lpGzWcY8/N7ixYySwAzb6rwFODz3PPiVqh7Hl6FJxAxjHjSh/cR0zGlA+tSo2yIRQggh8YJijPTKoWqzbGcUiZZObY5uXT0NPjhRbDkAj98TEGKtXYWYcNXf6jyAjxp2y/XliVPwu6oK5NQdlevHC1ajdMqKHsWUEgpMMWQjXx+WvtQYgdz5gCGVv1lCCCEjAoox0iPlTTZUNtu79RoTQiy8zZFa7UWRWQgxd1tErKiLEEs2qvBh67fY2npMrp+XNBO/OL4bieYa+JQqHJx3Iepy5/Q4LqNSK2dLJqrD0peiLixzFqDi25oQQsjIgZ9apFtabG4cqTFHfM3p9uKh9UdR3mRHskGDu0+fjgQ9UGQ5ALff1S7EPO1CTsS4Ek1KvNGwCQdtlTKydXXiTNx2cDO0LitcWpOcMdma0nOxvWgSXmDMgSrY4FvMkMyeByRG37mBEEIIGS5QjJGIOD1e7Klohs8Xud/kYxsLUVhnhVGrwl3rCpBmUqHYsh9OnyOyEFMABoMPz9WuR5mzQfqA3amfjCv2fQaVzwNLYjb2Lr4MTkNyt78RFZSYbspFtjZsH2NGwLJCo+dvkhBCyIiEYox0QVjP7atogdPti1jM/8zmIuyvag31m8xN1qLYegAOnz2iEBNG9wq9E4/XrEej24IElR6/9afhjAOfydfrMwtwcMFF8PbQqDtRZZBpSaNKG9igUAGZ09nOiBBCyIiHYox04WitBU1Wd0SR9tLXJdLYVd3Wb3JyhgHFloOwe60RhZhw1bdpW/F85UbYfE5kahLxvxY/FlV+LV8vm3QSCmesCzTt7gZhdzHZkAllcB99MpAzH9CF9ZokhBBCRigUY6QD1S0OlDbYIgqxt7aXY/OxeplyvOHUKZiZm4BS6yHYvOaIQky46teqavFq1Rdw+72YrE3DQ7X1mNRUDp9CiaOzz0HV+MXd/gZEKnOmKS+swbewrJgasK2gZQUhhJBRAsUYCWF2uHGwqjXiHflwXzX+faBGLl9z0iQsmpCMMuthWDwtEYWYcNU/4ivC36u/gfCJXajLwsPFR5HiaIFbrcf+RZeiOX1yjy2NRFpSG2xpRMsKQgghoxSKMRIqyhfGrl5fV4v9DYdr8c7OCrn8vaX5OGVauhRiZk9zRCGm1yjwjWsfPmvaL9fXaLPxp6O7oZetjdLaWhtFbtYtZliKlkbj9WGvp0wAMmcCvZi/EkIIISMRijESKti3u7xd7sbWoga8trVULovG36fPyka57ShaPU0RWxzptcDH1m+x3Vwk169UZeCnh7dBKVsbTcL+hZd229rI0OYdlhT0DhMF/aI2zBTWa5IQQggZZVCMEWlR0WBxdbkTe8qb8dzmYplmXD0jExcsyEW57Rha3A0RhZhW58dbLZtx1FYtI1x3exNwTdEO+Vpl/iJZI9ZdayNhVyG8w9RilqQgMQfInhvwECOEEEJGMRRjY5xaswPF9dYu24XZ6+OfF8Lr92PZ5DRcdsJ4VDmOo8VdH1GIKXVuvNzwOSpdTdAp1PiDxYcza/dLIVc443SUTzopYtG98A4TIixHl9J2ImHgOhtIyhvcH5wQQggZJlCMjWFEO6P9lV0L9ksarHh4/TG4vX7Mz0/GdadMQrWjCE2uujYhVgil1yn3FfLKo7Pj+br1aPbYkKzU4ZH6ZixsqYNXpcGBBRehIWtGdN5hNHAlhBAyBqEYG6N4vD7sLmuG1+vvYm3xwGdHYXd7MT07ATetnIpaRzGaXLVQet0wthZC6QukNEWgy6xuwis1n8PucyNXZcTT5WWY6LDCoU+SjvrWpJzevcNo4EoIIWQMQzE2RhERMVungv0GixP/98kRmB0eTEw34rbVBWhwlaDRVQOl19UmxNwhV/0qZRXerP0KHr8PM5UmPFV0BKleL1qT87Bv8WVwRTBlFd5hM0x5SA96h9HAlRBCyBiHYmwMcrzOgjpzIM0YpNXuxv99egSNNhdykvW4c20Bmr1laHBVy5SkqfU4FG1CTK0A9vqO4qP6XXL9FOjx4PGD0PmB2pzZODTvO/BFKLxPkd5hedCJujBp4DotYOJKA1dCCCFjGIqxMYYQYcfrOhbs21wemZqsaXUizaTF3eumw+qvRIOzCkqPQ/qIBYWYSuXHRsdOfGsulOvfc6vxi/IjEHMgi6euRPG0VV3ElVibpM/EBH0GFOI1rSlgWWFoK9onhBBCxjAUY2MIIbr2V7Z02Ob0eGWxfmmjDYl6Ne4+fTrcyirUOyulkWtAiHkCO6vdeNe8BcfsNdK64v+zuHF1XSl8ShUOzL0AtXnzulxTRMFmm8YhWW0MbKCBKyGEENIBirExVLC/q6wZnrCCfbFN2FeIxuAGjQp3rZsOhaYWdUKIue0wWURqMlBX5lbb8WbTJtS6W6FXqPDn+mac1toEl9aEfYu+h9bU8V2uKZqCTzfmQSO8xYSBa/Y8ICFzSH9uQgghZLhDMTaWCvad7QX7Pp8fz31ZjH0VrdCqlLh97TToDQ2odVZA5bbKiBj8Prlvq6oZf23YBKvXiXSFBk+Wl2OGywlLQpYs1HcYO6YbRdRsmjEHebrUwIaE7ICBq7rNwoIQQgghISjGxgBF9dYOBfui/dErW0vwTXEjVEoFbj5tKpKTWlDrKIfabYHRXCyFmCjvqlBU4u36LXLGZIFfgydLi5Dp9aEuawYOzb8QXhHxCsOk1GF2wjiYVHpANPnOmg0kj4vDT00IIYSMDCjGRjn1FlGwb+kgxN7YVoZNR+ul2PrRisnITjejxlEGtcsMo0UIMT8UCj/2eY/i4+bd8rhT3cD9Fcdh9Pu7LdQXkbBphuyAd5ghDcidD2gi96EkhBBCSACKsVFesC8agPvDfF3f21WJTw/WyuVrl0/CpBybFGIaVysMIiImzVx92OTciW2W43L9SqsTP6mtkX0i9827EPU5szpcR/STnGHMRaY2CRBCLKMASJ1MywpCCCEkCijGRrXDfkuHgv1/7a3C+3ur5PIVJ07A9HxnQIg5m2GwlMrtXqUbH1i2oNAhZkwC9za24LKWFtgNKdi3+PuwJmZ3uI6YJSlaGumFd5guMWBZoU8a4p+WEEIIGblQjI3ign3RezLI+kO1eGdnhVy+ePE4zJvkRo2jFBpnEwyWMrndrrTinZbNcsakAQr8pboGp9gdaEqfjP0LLoZHa+zgHSZ8w4R/mEJEw9ImA+kFAWt+QgghhEQNxdgYcNjffKwer30TiHydNz8XS6f5pBDTOhqgtwYEWpOiEX9r3Ayrz4ksnwKPV1ZiutuNsonLcHzG6fCHiSwRBZsV9A4TNWEiGmZMi8NPSgghhIx8KMZGucP+N0WNeHFLoBbs9FnZOGUGpBDT2eugswVSlmWowLuNX8sZk7PcPjxaVYl0nwKH5l6A6vyFHc6frU1GgTFH1okhOR/InAWo+DYihBBC+gs/RUcRIi25L8xhX5i8Pru5SBbwryzIwJq5StQ4S6Gz1UBnrxFzK7HXewSftuyR+6+2O/DfNXVQaU3YJYxcU/JD59IoVJgeLNKXBq5zgYSsuPychBBCyGiCYmyU4JYF+83wthXsH6hsxROfF8Lr92PZ5DSctUAjhZjeWgWtow4+hRebnbuwvW3G5DXNrbi7qRmW5HFSiLn0iaFzp6pNmBls8J00DsgS0bCujcAJIYQQ0ncoxkYBwjtMOuy7Ag77R2vMeGTjMXh8fiyakIILluhQ6yqF3lIBrbMBbrjwL+vXOC5mTPqBXzY04ntmC6rzFuDInHPha0s7Cif9qYZsjNOnsZ0RIYQQMkhQjI0CCuusqG8r2C+ut+Kh9cfg8vgwNy8JF52gR52zBAZrwMLCAiveNW9GnbtVGrj+X00dTnY4cXTmmaiYeGLIGyxRZcAsUx6MKh1rwwghhJBBhGJshFPb6pACTFDeZMNfPj0Cu9uL6dkJuPQkIxpcJdJDTJi6Vvtr8W7LFth9LuR4vHi0uhaTocbupVeiOX1KV8sKOVNyHmDKiPNPSQghhIxeKMZGMGaHW6YnBdWtDvzfJ0dgdXkxJcOEK042oslVAqOlBCpXKw76ivBxyw744McChxMP1NRBb8zA9sXfh8MYaOhtUGplbZi0rEgeD2TO5ExJQgghZJChGBuhiDTknvIWeH1+2X/yfz8+jFaHB+NTDfjBChNa3KUwiT6TLjO+dO3Gt9Zj8rgLzBb8ur4RLdkzsXOeaPStldtztamYZsyWMymRLaJh6XH+CQkhhJCxAcXYCC3Y31vRArvLi2abC//7yRE02dzISdLjqlMTYHaXwGQpgsvdjH/btuK4swYKvx93NjXj2hYLiqavRdnkk2V9mFahlpYVGdpEIGUikDkDUKri/SMSQgghYwaKsRHI0VoLmqwumaYUqUlh9JqRoMU1KxPg8JbAZD6OFncd/mn5Cg0eM4w+H/67rgEr3MDepVegKWOqPE+GJlE2+NbokgK1YXTRJ4QQQoYcirERRmWzHaUNNthcHvzl06OobHEg1ajBNSsT4faXwNhaiAp3Gd43fw2Hz408twcP19Qh15CBbSdcCqchBSoopYt+ji4VSJ0EZIiekoyGEUIIIfGAYmwE0WJz41B1KxxuLx787ChKG21I1KulEPOjCMaWIuxzHcJ68y744cdihwN/qamHM3ceds4+Bz6VBilqI2aaxkGvTwVy5gKGQPE+IYQQQuIDxdgIQQiw3eXNsk7sofVHpbeYUavCNacmQqUogq71GD63bccue8BR/7tmC37Z0IySWWehcvwSKBVKTDVkYbw+A0ibAqRPA8KafxNCCCEkPlCMjQB8Pr+cOSl6Tz66oRBHaizQa5SyWF+vPg5Fy2H8w/oVSly1UPr9uLuxGd93AvuXXSv7S5pUOsw2jYPJmAnkzAf0SfH+kQghhBDSBsXYCOBAVSsaLU48vrFQLuvUSvzglAQkaY7D2rQb71u+RJPXCpPPh/+prcd8fRZ2nHwJ3LqEgIGrIQvKjOmBiFibwz4hhBBChgcUY8OckgYrKprseOqL49hT0QKNSoErTjYhVXcMNU3f4F/mr+H0e5DvduORmjpo85Zg94x10KsNmCMMXBPyAjMldQnx/lEIIYQQEgGKsWGMMHM9Um3Gs5uLsKO0GWqlApctNyJDexgH6zdik20P/ACW2h34c30zamdfgLK8ucjTpWKqMQ8q4RkmZksyGkYIIYQMWyjGhimiPkwU7L/wVTG+KW6ESgFcssyAbN0BfNnwEfY6iuR+F7dacLfdj8PLroM7aRzmm/KQljwRyJ4LaI3x/jEIIYQQ0gsUY8MQt9eHXaVNeOmrEnxZ2CADWxedaECWbjv+Vfc+ytwNslD/p41NOFOXjT3LL0KaKRsFpgnQZM8GUsbH+0cghBBCSJRQjA3DVkd7ypvx/FfF+PxIHUS5/YVLdEjRfo6/132IZp8diV4f/lxXj3F5J+JwwWrMMI1DZto0IGsOoNHH+0cghBBCSB+gGBtmHK4245kvivDpwVq5ft4iDbSaf+Kd+o1wwYcJbjceqG+BY/aFMI9bjBMSJ0Er7CqScuM9dEIIIYT0A4qxYURFs13aV3y4r1qunzlPAbvyRWxs3iPXT7Lb8RurApUnXI/xmXOQkzkbyJoNqDRxHjkhhBBC+gvF2DBBNP5+4JPDeG93pVxfM8eFOsUTOGoLrF/V0oof6PJRf8r3MS9tBvR5i4GEzDiPmhBCCCEDhWJsGCBaHN3/8WH8bXuFXF8+sw4leAK1bis0fj9+Vd+EORNWwjf9PMzPXQQIA1cVf3WEEELIaICf6HHG4/Xhfz8+jFe3lsr1BdMP47jiJdh8XmR6PPifZgcSF1yDcZPXwDhuCRt7E0IIIaMMirE4z5x8aP0xaeoK+DFj2noUKz+BD8A8hxO/8ZhgPPVOjJu8Ggo29iaEEEJGJRRjceSZL47j4fVH4Vd4MHXqq6jUHJTbzzdbcGPKAmQuuxWmCcsAXWI8h0kIIYSQQYRiLE68+nUJ7vvwEKBqxaRJT6BW0yiNXO9qseKsmZch+4QboWArI0IIIWTUQzEWB97ZUY5fvbcfSl0JsiY8jQaVB0leL35vBZat/V+YZpwNaAzxGBohhBBChhiKsSHm/d2V+Mnf9iAhaTNUuf+EWQFMdbnwR+1kzLzicSjTJw/1kAghhBASRyjGhpB/76vCHW9sR27Wq2hJ2y8L9U+zOfCr6T9A5mm/oHkrIYQQMgahGBsi1h+swS1vbMLE8Q+jztgit11v9eDWs5+DevKpQzUMQgghhAwzKMaGgI2Ha3HPW68hd+ILqNP4YPT58FtlLs667i3AkDIUQyCEEELIMIVibJDZdKQOf3jv18D4L9CsUGCS24O/TLsS01b9ElAoBvvyhBBCCBnmUIwNIhsOleOhj69DdbZo/K3AapcffzzreSSMP2kwL0sIIYSQEYQy3gN47LHHMHnyZOj1eixZsgRffPFFj/t//vnncj+x/5QpU/DEE09gOPLRji3438/Px7Hkaij8ftyqzsMDV31JIUYIIYSQ4SPG3njjDdx555345S9/iZ07d+LUU0/F2WefjdLSQJ/GzhQVFeGcc86R+4n9f/GLX+D222/H22+/jeHEm589iv/a9SOU6IV/mA8PT7gI/3HFR1Dqk+M9NEIIIYQMMxR+0SAxTixbtgyLFy/G448/Hto2a9YsXHjhhbjvvvu67P+zn/0M//jHP3DwYKBtkOCmm27C7t27sWXLlqiu2draiuTkZLS0tCApKQmx5u8bHsVvSx6HV6HAFJcffzntL5hScHrMr0MIIYSQ4U20miNukTGXy4Xt27fjjDPO6LBdrH/11VcRjxGCq/P+Z555JrZt2wa32x3xGKfTKW9G+GMwWXXC1ZjuUuFEeyJevPRTCjFCCCGEDM8C/vr6eni9XmRnZ3fYLtarq0XBe1fE9kj7ezweeb7c3Nwux4gI229/+1sMFWkJiXjkko+QaMqAQacZsusSQgghZGQS9wJ+RSd7B5E17bytt/0jbQ9y7733yvBg8FFWVobBJistl0KMEEIIIcM7MpaRkQGVStUlClZbW9sl+hUkJycn4v5qtRrp6ekRj9HpdPJBCCGEEDIciVtkTKvVSouKTz75pMN2sX7yySdHPGb58uVd9v/444+xdOlSaDRMCRJCCCFk5BHXNOXdd9+NZ555Bs8995ycIXnXXXdJWwsxQzKYYrz66qtD+4vtJSUl8jixvzju2WefxT333BPHn4IQQgghZIQ68H//+99HQ0MDfve736Gqqgpz587Fv/71L0ycOFG+LraFe44Jc1jxuhBtjz76KPLy8vDQQw/h4osvjuNPQQghhBAyQn3G4sFg+4wRQgghhIwInzFCCCGEEEIxRgghhBASVxgZI4QQQgiJIxRjhBBCCCFxhGKMEEIIISSOUIwRQgghhMQRijFCCCGEkLFq+hoPgrZqwvuDEEIIIWSwCGqN3ixdx5wYM5vN8nn8+PHxHgohhBBCxoj2EOav3THmHPh9Ph8qKyuRmJgIhULRZ4UrRFxZWRnd+wcI72Vs4f3k/Ryu8L3J+zmW35t+v18KMdG+UansvjJszEXGxM3Iz88f0DnEL42tlGID72Vs4f3k/Ryu8L3J+zlW35vJPUTEgrCAnxBCCCEkjlCMEUIIIYTEEYqxPqDT6fDrX/9aPpOBwXsZW3g/eT+HK3xv8n4OV3TD6DN9zBXwE0IIIYQMJxgZI4QQQgiJIxRjhBBCCCFxhGKMEEIIISSOUIwRQgghhMSRMSXG7rvvPpxwwgnSfT8rKwsXXnghDh8+3GEfMZ/hN7/5jXTLNRgMOO2007B///4O+zidTtx2223IyMiAyWTCBRdcgPLy8g77NDU14aqrrpJmb+IhlpubmzGaiNX9FNtEN4Twx2WXXTam7mc09/Kdd97BmWeeKd934h7t2rWry3n43ozt/eR7s/d76Xa78bOf/Qzz5s2Tfw/F//Wrr75adjrhe3Pw7iffm4jq/7n4/Jk5c6a8l6mpqVi3bh22bt06/N6b/jHEmWee6X/++ef9+/bt8+/atct/7rnn+idMmOC3WCyhff77v//bn5iY6H/77bf9e/fu9X//+9/35+bm+ltbW0P73HTTTf5x48b5P/nkE/+OHTv8q1ev9i9YsMDv8XhC+5x11ln+uXPn+r/66iv5EMvnnXeefzQRq/u5atUq/w033OCvqqoKPZqbmztca7Tfz2ju5UsvveT/7W9/63/66afFDGj/zp07u5yH783Y3k++N3u/l+L/6rp16/xvvPGG/9ChQ/4tW7b4ly1b5l+yZAnfm4N4P/ne9Ef1//zVV1+Vn9WFhYVyv+uvv96flJTkr62tHVZ/N8eUGOuM+GWIP8Kff/65XPf5fP6cnBwpIII4HA5/cnKy/4knngj9R9FoNP6//vWvoX0qKir8SqXS/9FHH8n1AwcOyPN+/fXXoX3EfyixTfznGq30534G/6jccccd3Z53LN7PzvcynKKioojige/N2N5PAd+bfbuXQb755hu5T0lJCd+bg3A/+d7s/71saWmR+3z66afD6u/mmEpTdqalpUU+p6WlyeeioiJUV1fjjDPOCO0jzOBWrVqFr776Sq5v375dhpHD9xFh5Llz54b22bJliwxjLlu2LLTPSSedJLcF9xmN9Od+Bnn11VdliHjOnDm45557ZGPVIGPxfna+l9HA92Zs72cQvjf7fi/FPiL1m5KSwvfmINxPvjf7dy9dLheeeuop+dmxYMGCYfV3c8w1Cg8iooJ33303VqxYIW+6QAgHQXZ2dod9xXpJSUloH61WK3PPnfcJHi+eRf66M2JbcJ/RRn/vp+DKK6/E5MmTkZOTg3379uHee+/F7t278cknn4zJ+xnpXkYD35uxvZ8Cvjf7fi8dDgd+/vOf44orrgg1X+Z7M7b3k+/Nvt3L999/X9Yh22w25Obmys8W8eV/OL03x6wYu/XWW7Fnzx5s3ry5y2viG0jnX3LnbZ3pvE+k/aM5z1i8nzfccENoWfwnKigowNKlS7Fjxw4sXrw44jkinWcs3Mv+wPdm/+8n35t9e2+KCIP40PP5fHjsscf43hzE+8n3ZvT3cvXq1XKCTn19PZ5++ml873vfk0X8kQRWvP5ujsk0pZg18Y9//AMbNmxAfn5+aLuIzAg6K93a2tpQdEfsI0KdYmZFT/vU1NR0uW5dXV2XKNFYv5+REAJMo9Hg6NGjY+5+dncvo4Hvzdjez0jwvdn9vRTCQXzIifIEEXkIj+LwvRnb+8n3Zt/upZghOW3aNJlafPbZZ6FWq+XzsHpv+scQoqD8lltu8efl5fmPHDkS8XVRcP6nP/0ptM3pdEYs4BczXYJUVlZGLPbbunVraB9R+DfaCs5jcT8jIWZdhhdhjoX72du97EsBP9+bsbmfkeB7M/K9dLlc/gsvvNA/Z86cDrPU+N4cnPvJ92bf/5+HM3XqVP+vf/3rYfV3c0yJsR//+MdSCGzcuLGDjYLNZgvtI2b+iX3eeecd+Yf38ssvj2htkZ+fL2djiGmwa9asiTgNdv78+XLGhXjMmzdvVFkxxOp+Hjt2TNoLfPvtt/JD8YMPPvDPnDnTv2jRojF1P6O5lw0NDVIwiHsk/giI2T9iXewXhO/N2N1Pvjeju5dut9t/wQUXyL+Jwl4gfB/x5YvvzdjfT743o7uXwuLi3nvvlZ8ZxcXF/u3bt0trC51OJ20uhtPfzTElxsQf3EgP4VMSrrSFYhYRHfELW7lypRQR4djtdv+tt97qT0tL8xsMBvkLKS0t7bCP+EN/5ZVXSo8t8RDLTU1N/tFELO6nuG9im7iXWq1WfmO5/fbb5f0bS/czmnspliPtE/yGJ+B7M3b3k+/N6O5lMLIY6bFhwwa+NwfhfvK9Gd29FH8Pv/vd78rImfh8EYEAIXSFVUg4w+HvpqLtByKEEEIIIXFgTBbwE0IIIYQMFyjGCCGEEELiCMUYIYQQQkgcoRgjhBBCCIkjFGOEEEIIIXGEYowQQgghJI5QjBFCCCGExBGKMUIIIYSQOEIxRgghhBASRyjGCCFjCtF0ZN26dTjzzDO7vPbYY48hOTkZpaWlcRkbIWRsQjFGCBlTKBQKPP/889i6dSuefPLJ0PaioiL87Gc/w4MPPogJEybE9Jputzum5yOEjC4oxgghY47x48dL0XXPPfdIESaiZddffz3Wrl2LE088Eeeccw4SEhKQnZ2Nq666CvX19aFjP/roI6xYsQIpKSlIT0/Heeedh8LCwtDrxcXFUvC9+eabOO2006DX6/HKK6/E6SclhIwE2CicEDJmufDCC9Hc3IyLL74Yv//97/Htt99i6dKluOGGG3D11VfDbrfLaJnH48H69evlMW+//bYUW/PmzYPVasWvfvUrKcB27doFpVIplydPnoxJkybhf//3f7Fo0SLodDrk5eXF+8clhAxTKMYIIWOW2tpazJ07Fw0NDXjrrbewc+dOmb7897//HdqnvLxcRtIOHz6M6dOndzlHXV0dsrKysHfvXnmuoBh74IEHcMcddwzxT0QIGYkwTUkIGbMIEXXjjTdi1qxZ+O53v4vt27djw4YNMkUZfMycOVPuG0xFiucrrrgCU6ZMQVJSkhRegs5F/yLCRggh0aCOai9CCBmlqNVq+RD4fD6cf/75+NOf/tRlv9zcXPksXheRsqefflqmHsUxIiLmcrk67G8ymYboJyCEjHQoxgghpI3FixfLmjBR7xUUaOGIdObBgwflLMxTTz1Vbtu8eTPvHyFkQDBNSQghbdxyyy1obGzE5Zdfjm+++QbHjx/Hxx9/jB/+8Ifwer1ITU2VMyifeuopHDt2TBb133333bx/hJABQTFGCCFtiLTjl19+KYWXMIUV6UdRhC+MYMVMSfH461//KmvLxGt33XUX/vznP/P+EUIGBGdTEkIIIYTEEUbGCCGEEELiCMUYIYQQQkgcoRgjhBBCCIkjFGOEEEIIIXGEYowQQgghJI5QjBFCCCGExBGKMUIIIYSQOEIxRgghhBASRyjGCCGEEELiCMUYIYQQQkgcoRgjhBBCCEH8+P8BxuefJvJBiGEAAAAASUVORK5CYII=",
+      "text/plain": [
+       "
        "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "loc_ts = local_timeseries[\"total_rsl\"].sel(site=[\"Aberdeen\", \"Holyhead\", \"Llandudno\", \"Land point\"], percentile=[17, 50, 83])\n",
     "fig = plt.figure(figsize=(6, 4), layout=\"constrained\")\n",

From cb9dc4d9c7a2cc748fec6ebd711db05c2bbaf88e Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Tue, 16 Jun 2026 17:05:46 +0100
Subject: [PATCH 250/276] Linting

---
 profsea/components/core/local_model.py   | 4 +++-
 profsea/components/core/spatial_model.py | 4 +++-
 2 files changed, 6 insertions(+), 2 deletions(-)

diff --git a/profsea/components/core/local_model.py b/profsea/components/core/local_model.py
index a765d4d..7a8778e 100644
--- a/profsea/components/core/local_model.py
+++ b/profsea/components/core/local_model.py
@@ -233,7 +233,9 @@ def sum_components(self, components: dict[str, xr.DataArray]) -> xr.DataArray:
         xr.DataArray
             A single xarray DataArray representing the total sea-level projections, with appropriate attributes.
         """
-        total_rsl = xr.concat(components.values(), dim="component").sum(dim="component", skipna=False)
+        total_rsl = xr.concat(components.values(), dim="component").sum(
+            dim="component", skipna=False
+        )
         total_rsl.attrs = {
             "units": "m",
             "long_name": "Local total sea-level projections",
diff --git a/profsea/components/core/spatial_model.py b/profsea/components/core/spatial_model.py
index b8a5afd..6c22662 100644
--- a/profsea/components/core/spatial_model.py
+++ b/profsea/components/core/spatial_model.py
@@ -237,7 +237,9 @@ def sum_components(self, components: dict[str, xr.DataArray]) -> xr.DataArray:
         """
         # Using xr.concat preserves all dimensions and coordinates, and summing
         # along the new dimension handles the underlying dask arrays cleanly.
-        total_rsl = xr.concat(components.values(), dim="component").sum(dim="component", skipna=False)
+        total_rsl = xr.concat(components.values(), dim="component").sum(
+            dim="component", skipna=False
+        )
 
         # Optionally, apply attributes so it matches the other DataArrays
         total_rsl.attrs = {

From 802ae15140609ad17e11c8f40f493cbce3a70fbd Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Tue, 16 Jun 2026 17:07:38 +0100
Subject: [PATCH 251/276] Readability

---
 profsea/components/core/local_model.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/profsea/components/core/local_model.py b/profsea/components/core/local_model.py
index 7a8778e..1efb777 100644
--- a/profsea/components/core/local_model.py
+++ b/profsea/components/core/local_model.py
@@ -204,7 +204,8 @@ def run(self, member_seed: int = 42) -> dict[str, xr.DataArray]:
         }
 
         local_projections = {}
-        logger.info("Starting localisation of components...")
+
+        console.print()
         for name, comp in track(
             self.components.items(), description="Localising components..."
         ):
@@ -214,6 +215,7 @@ def run(self, member_seed: int = 42) -> dict[str, xr.DataArray]:
             # Rechunk to optimize for reading full time-series per site
             lazy_projection = lazy_projection.rechunk({0: -1, 1: -1, 2: -1})
             local_projections[name] = lazy_projection
+        console.print()
 
         self.results = self._arr_to_xr(local_projections)
         self._apply_universal_mask()

From b4fe2143e337ab266e70b1facf7bafebc843d3d3 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Wed, 17 Jun 2026 15:08:05 +0100
Subject: [PATCH 252/276] Updated interpolation, switch to xr outputs

---
 profsea/components/core/base.py               | 171 +++++++++++++++---
 profsea/components/core/global_model.py       |  84 +++++----
 profsea/components/spatial/fingerprint.py     |   6 +-
 profsea/components/spatial/sterodynamic.py    |   6 +-
 .../tutorial_notebooks/worked_example.ipynb   |  53 ++++--
 tests/components/test_sterodynamic.py         |   4 +-
 tests/core/test_global_model.py               |   4 +-
 7 files changed, 239 insertions(+), 89 deletions(-)

diff --git a/profsea/components/core/base.py b/profsea/components/core/base.py
index e4e3b12..7bf462a 100644
--- a/profsea/components/core/base.py
+++ b/profsea/components/core/base.py
@@ -18,40 +18,155 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
 
 class SpatialComponent(Component):
     def extract_spatial(self, da_input: xr.DataArray, state) -> xr.DataArray:
-        """
-        Spatial extractor for both Grid (2D) and Site (1D) states.
-
-        Parameters
-        ----------
-        da_input: xarray.DataArray
-            The input data array containing spatial coordinates (lat/lon).
-        state: SpatialState
-            The state object which may contain either target_lats/lons for point extraction or grid_lats/lons for grid interpolation.
-        """
-        if "lon" in da_input.coords and da_input.lon.max() > 180:
-            da_input = da_input.assign_coords(
-                lon=(((da_input.lon + 180) % 360) - 180)
-            ).sortby("lon")
-
         if hasattr(state, "target_lats"):
-            # Point extraction for LocalState
-            lats = xr.DataArray(state.target_lats, dims="site")
-            lons = xr.DataArray(state.target_lons, dims="site")
-
-            return da_input.interp(
-                lat=lats,
-                lon=lons,
-                method=getattr(state, "interpolation_method", "nearest"),
+            return self._extract_points(
+                da_input, state.target_lats, state.target_lons
             )
+
         elif hasattr(state, "grid_lats"):
-            from profsea.utils import interpolate_to_grid
+            from profsea.utils.utils import interpolate_to_grid
 
             return interpolate_to_grid(da_input, state.grid_lats, state.grid_lons)
+
         else:
-            raise ValueError(
-                "State object is missing required spatial attributes "
-                "(needs either target_lats/lons or grid_lats/lons)."
-            )
+            raise ValueError("State object is missing required spatial attributes.")
+
+    def _extract_points(
+        self, da_input: xr.DataArray, target_lats: np.ndarray, target_lons: np.ndarray
+    ) -> xr.DataArray:
+        target_lats = np.asarray(target_lats)
+        target_lons = np.asarray(target_lons)
+
+        # Normalize longitudes to [-180, 180) lazy metadata update
+        if "lon" in da_input.coords:
+            da_input = da_input.assign_coords(lon=(((da_input.lon + 180) % 360) - 180))
+        target_lons = ((target_lons + 180) % 360) - 180
+
+        # Extract 1D coordinates eagerly
+        lats = da_input.lat.values
+        lons = da_input.lon.values
+
+        # Handle decreasing latitudes
+        is_descending_lat = False
+        if lats[0] > lats[-1]:
+            is_descending_lat = True
+            lats = lats[::-1]
+
+        # Sort lons
+        lon_sort_idx = np.argsort(lons)
+        lons = lons[lon_sort_idx]
+
+        # Sort the lazy array to match the coordinate math
+        da_input = da_input.isel(lon=lon_sort_idx)
+        if is_descending_lat:
+            da_input = da_input.isel(lat=slice(None, None, -1))
+
+        # Calculate bounded indices locally
+        lat_idx_hi = np.clip(np.searchsorted(lats, target_lats), 1, len(lats) - 1)
+        lat_idx_lo = lat_idx_hi - 1
+
+        # Handle longitude wrapping for global datasets
+        idx = np.searchsorted(lons, target_lons)
+        wrap_lo = idx == 0
+        wrap_hi = idx == len(lons)
+
+        # Modulo arithmetic perfectly wraps indices over the array bounds
+        lon_idx_hi = idx % len(lons)
+        lon_idx_lo = (idx - 1) % len(lons)
+
+        # Extract physical coordinates
+        lat_lo, lat_hi = lats[lat_idx_lo], lats[lat_idx_hi]
+        lon_lo, lon_hi = lons[lon_idx_lo], lons[lon_idx_hi]
+
+        # Fix coordinates for wrapped bounds so distance calculations work
+        lon_lo = np.where(wrap_lo, lons[-1] - 360, lon_lo)
+        lon_hi = np.where(wrap_hi, lons[0] + 360, lon_hi)
+
+        dlat = np.where(lat_hi == lat_lo, 1.0, lat_hi - lat_lo)
+        dlon = np.where(lon_hi == lon_lo, 1.0, lon_hi - lon_lo)
+
+        # Calculate weights locally
+        w_lat_lo = (lat_hi - target_lats) / dlat
+        w_lat_hi = (target_lats - lat_lo) / dlat
+        w_lon_lo = (lon_hi - target_lons) / dlon
+        w_lon_hi = (target_lons - lon_lo) / dlon
+
+        # Build DataArrays for the 4 corners so they broadcast properly with the lazy data
+        sites = xr.DataArray(np.arange(len(target_lats)), dims=["site"])
+
+        w_lo_lo = xr.DataArray(
+            w_lat_lo * w_lon_lo, dims=["site"], coords={"site": sites}
+        )
+        w_hi_lo = xr.DataArray(
+            w_lat_hi * w_lon_lo, dims=["site"], coords={"site": sites}
+        )
+        w_lo_hi = xr.DataArray(
+            w_lat_lo * w_lon_hi, dims=["site"], coords={"site": sites}
+        )
+        w_hi_hi = xr.DataArray(
+            w_lat_hi * w_lon_hi, dims=["site"], coords={"site": sites}
+        )
+
+        # Pull the 4 corners
+        da_lo_lo = da_input.isel(
+            lat=xr.DataArray(lat_idx_lo, dims="site"),
+            lon=xr.DataArray(lon_idx_lo, dims="site"),
+        )
+        da_hi_lo = da_input.isel(
+            lat=xr.DataArray(lat_idx_hi, dims="site"),
+            lon=xr.DataArray(lon_idx_lo, dims="site"),
+        )
+        da_lo_hi = da_input.isel(
+            lat=xr.DataArray(lat_idx_lo, dims="site"),
+            lon=xr.DataArray(lon_idx_hi, dims="site"),
+        )
+        da_hi_hi = da_input.isel(
+            lat=xr.DataArray(lat_idx_hi, dims="site"),
+            lon=xr.DataArray(lon_idx_hi, dims="site"),
+        )
+
+        # Handle NaNs and renormalization within the Xarray/Dask graph
+        # Create a boolean mask of valid data
+        valid_lo_lo = da_lo_lo.notnull()
+        valid_hi_lo = da_hi_lo.notnull()
+        valid_lo_hi = da_lo_hi.notnull()
+        valid_hi_hi = da_hi_hi.notnull()
+
+        # Zero out weights where data is NaN
+        w_lo_lo_masked = w_lo_lo.where(valid_lo_lo, 0.0)
+        w_hi_lo_masked = w_hi_lo.where(valid_hi_lo, 0.0)
+        w_lo_hi_masked = w_lo_hi.where(valid_lo_hi, 0.0)
+        w_hi_hi_masked = w_hi_hi.where(valid_hi_hi, 0.0)
+
+        # Sum the active weights
+        w_sum = w_lo_lo_masked + w_hi_lo_masked + w_lo_hi_masked + w_hi_hi_masked
+
+        # Prevent division-by-zero warnings, but we will restore NaNs later where all 4 corners were NaN
+        w_sum_safe = w_sum.where(w_sum != 0, 1.0)
+
+        # Renormalize weights using the safe denominator
+        w_lo_lo_norm = w_lo_lo_masked / w_sum_safe
+        w_hi_lo_norm = w_hi_lo_masked / w_sum_safe
+        w_lo_hi_norm = w_lo_hi_masked / w_sum_safe
+        w_hi_hi_norm = w_hi_hi_masked / w_sum_safe
+
+        # Calculate final weighted sum
+        result = (
+            (da_lo_lo.fillna(0) * w_lo_lo_norm)
+            + (da_hi_lo.fillna(0) * w_hi_lo_norm)
+            + (da_lo_hi.fillna(0) * w_lo_hi_norm)
+            + (da_hi_hi.fillna(0) * w_hi_hi_norm)
+        )
+
+        # Restore pure NaNs where all 4 corners were originally NaN (where w_sum was 0)
+        result = result.where(w_sum != 0, np.nan)
+
+        # Re-attach target coordinate metadata
+        result = result.assign_coords(
+            lat=("site", target_lats), lon=("site", target_lons)
+        )
+
+        return result
 
     def broadcast_spatiotemporal(
         self, temporal_array: da.Array, spatial_array: da.Array
diff --git a/profsea/components/core/global_model.py b/profsea/components/core/global_model.py
index 397f494..aacd519 100644
--- a/profsea/components/core/global_model.py
+++ b/profsea/components/core/global_model.py
@@ -83,6 +83,43 @@ def __init__(
         self.endofAR5 = 2100
         self.nyr = self.end_yr - self.endofhistory
 
+    def _arr_to_xr(self, arr_dict: dict[str, np.ndarray]) -> dict[str, xr.DataArray]:
+        """Convert a dictionary of numpy/dask arrays to xarray DataArrays.
+
+        Parameters
+        ----------
+        arr_dict: dict
+            Dictionary of arrays, where keys are component names and values are arrays.
+
+        Returns
+        -------
+        dict
+            Dictionary of xarray DataArrays, where keys are component names and values are xarray DataArrays.
+        """
+        xr_dict = {}
+        member_dim = "percentile" if self.output_percentiles is not None else "member"
+
+        for name, arr in arr_dict.items():
+            member_coords = (
+                self.output_percentiles
+                if self.output_percentiles is not None
+                else np.arange(arr.shape[0])
+            )
+
+            xr_dict[name] = xr.DataArray(
+                arr,
+                dims=[member_dim, "time"],
+                coords={
+                    member_dim: member_coords,
+                    "time": np.arange(
+                        self.endofhistory, self.endofhistory + arr.shape[1]
+                    ),
+                },
+            )
+            xr_dict[name].attrs["units"] = "m"
+
+        return xr_dict
+
     def run(
         self,
         scenario: str,
@@ -174,27 +211,29 @@ def run(
             for comp_name, data in results.items():
                 results[comp_name] = sample_members_2D(data, self.output_percentiles)
 
-        self.results = results
-
-        return results
+        self.results = self._arr_to_xr(results)
+        return self.results
 
-    def sum_components(self, components: dict[str, np.ndarray]) -> np.ndarray:
+    def sum_components(self, components: dict[str, xr.DataArray]) -> xr.DataArray:
         """Sum the components to get total GMSLR."""
-        components["gmslr"] = np.sum(
-            [np.atleast_2d(c) for c in components.values()], axis=0
-        )
-        return components["gmslr"]
+        gmslr = xr.concat(
+            [components[name] for name in components.keys()], dim="component"
+        ).sum(dim="component")
+        gmslr.attrs["units"] = "m"
+        gmslr.attrs["description"] = "Total global mean sea level rise"
+        components["total_gmslr"] = gmslr
+        return gmslr
 
     def save_components(
-        self, components: dict[str, np.ndarray], output_dir: str, scenario_name: str
+        self, components: dict[str, xr.DataArray], output_dir: str, scenario_name: str
     ) -> None:
         """Save SLR components as nc files to a directory.
 
         Parameters
         ----------
-        components: Dict[str, np.ndarray]
-            Dictionary of component names and their corresponding arrays.
-        output_directory: str
+        components: Dict[str, xr.DataArray]
+            Dictionary of component names and their corresponding xarray DataArrays.
+        output_dir: str
             Directory to save components to.
         scenario_name: str
             Name of the scenario you've run the emulator for.
@@ -203,27 +242,8 @@ def save_components(
         -------
         None
         """
-        # Create directory if it doesn't exist
         Path(output_dir).mkdir(parents=True, exist_ok=True)
-
-        # Save data in netcdf format
-        ds = xr.Dataset()
-        member_dim = "percentile" if self.output_percentiles is not None else "member"
-        for name, component in components.items():
-            xr_dataArray = xr.DataArray(
-                component,
-                dims=[member_dim, "time"],
-                coords={
-                    member_dim: self.output_percentiles
-                    if self.output_percentiles is not None
-                    else np.arange(
-                        component.shape[0]
-                    ),  # handle if no output percentiles
-                    "time": np.arange(2006, component.shape[1] + 2006),
-                },
-            )
-            xr_dataArray.attrs["units"] = "m"
-            ds[name] = xr_dataArray
+        ds = xr.Dataset(components)
         ds.to_netcdf(os.path.join(output_dir, f"{scenario_name}_global.nc"))
 
     def _calculate_drivers(self, T_change: np.ndarray) -> tuple:
diff --git a/profsea/components/spatial/fingerprint.py b/profsea/components/spatial/fingerprint.py
index 6e26afc..8875b15 100644
--- a/profsea/components/spatial/fingerprint.py
+++ b/profsea/components/spatial/fingerprint.py
@@ -53,7 +53,7 @@ class Fingerprint(SpatialComponent):
 
     def __init__(
         self,
-        global_projection: np.ndarray,
+        global_projection: xr.DataArray,
         fingerprint_component: str,
         fingerprint_paths: str | Path | list[str | Path] = None,
         scaling_factor: float = 1.0,
@@ -62,7 +62,7 @@ def __init__(
         """
         Parameters
         ----------
-        global_projection: np.ndarray
+        global_projection: xr.DataArray
              A 2D array (members x years) of global projections to apply the fingerprints to.
         fingerprint_paths: str, Path, or list of str/Path
              Path(s) to NetCDF files containing the spatial fingerprint patterns. Each file should contain a DataArray with dimensions (lat, lon).
@@ -71,7 +71,7 @@ def __init__(
         sample_spatial: bool, optional
              If True, randomly sample a different fingerprint pattern for each member. If False, use the mean of all provided fingerprints for all members (storyline mode). Default is False.
         """
-        self._global_projection = da.from_array(global_projection, chunks="auto")
+        self._global_projection = da.from_array(global_projection.data, chunks="auto")
         self.scaling_factor = scaling_factor
         self.sample_spatial = sample_spatial
         self.fingerprint_component = fingerprint_component
diff --git a/profsea/components/spatial/sterodynamic.py b/profsea/components/spatial/sterodynamic.py
index 4b078e4..84c4e1f 100644
--- a/profsea/components/spatial/sterodynamic.py
+++ b/profsea/components/spatial/sterodynamic.py
@@ -27,14 +27,14 @@ class SterodynamicCMIP6(SpatialComponent):
 
     def __init__(
         self,
-        global_projection: np.ndarray,
+        global_projection: xr.DataArray,
         patterns_dir: str = None,
         sample_spatial: bool = False,
     ) -> None:
         """
         Parameters
         ----------
-        global_projection: np.ndarray
+        global_projection: xr.DataArray
              A 2D array (members x years) of global projections to apply the fingerprints to.
         patterns_dir: str, optional
              Path to directory containing CMIP6 sterodynamic patterns.
@@ -42,7 +42,7 @@ def __init__(
              If True, randomly sample a different fingerprint pattern for each member. If False, use the mean of all provided fingerprints for all members (storyline mode). Default is False.
         """
         # Convert to dask array for cheap as possible compute
-        self._global_projection = da.from_array(global_projection, chunks="auto")
+        self._global_projection = da.from_array(global_projection.data, chunks="auto")
         self.sample_spatial = sample_spatial
 
         if patterns_dir is None:
diff --git a/profsea/tutorial_notebooks/worked_example.ipynb b/profsea/tutorial_notebooks/worked_example.ipynb
index e24f95c..2c1792a 100644
--- a/profsea/tutorial_notebooks/worked_example.ipynb
+++ b/profsea/tutorial_notebooks/worked_example.ipynb
@@ -79,7 +79,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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",
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iYeAF1K/h7grlvSOca4d7/VhdW+f6PakuYzVgkWjVqhXt3LmTWrRoUeb5r776ijp16hTKvQlCxAnE8bM2+mRdARUUKZzeOu6aZLqyRWDV05ooAIO+1HoI9kaDG4n2GcpgCO0NK5xrh3v9WF1b54oT6CNVJDDrGn2TiouL1ZS/LVvogw8+4FqD119/PTy7FIQgGvWVT3HlSXIIUrtqIIC/8Qf8rsO1hApqvLVemp4mD02hurUDa9fMf9c69Q9csx6kfEKIZAIWiTvvvJPsdju7dAoLC+m2227j2RHPP/883XLLLeHZpSCEQCAwdhSiEKj1gNoJ9F7ad1RNb+3UykRjBibzCMlAxUFzJ2iuKVUsPM4Lxt3k+clTB1914GvUxNrhXj9W19ZFursJYLYEDnSCReYRUlMFIZIEokKKqzM4gUB665K1+Zy9hBv6sL6J1LuDJcDsJR3p4FYiBV+5/cxVFQdBqA6qVExXr1690O1EEIJo9Y1Dq2vwFAgeHOQsTXEFgQrEvqNWLpCz2tXurTcMSaGmDQL9k1FdSrAeFEWNUsPP7I5DsGCoD/i8YD6RKnoqtqn/PQkmHVkCsHBqdO1wrx+jaxsNOj5MHOGOQJFAZpO3T1F4Dq3E27Zty72Vhg4dGqo9CoJ/rb5d7iUErCEQwQaoYXVk7iqmHT9b+XHzhkaaOCSZUgJKby0NSCM4jbbjcHdBBDRXgRaX0BtcsYkgg5Emo+pKU7/W8fcLFeFcO9zrx+raenZXqkdEigSm06GgrmvXrtS3b18O6G3bto127drF4oApdcOHD+f2Gddff314di3ENeEUiIIiJ63cXEgnz6lFov06W2hor0DTW0tTW/E+TRQ0KwGWA/7w+Y9cpyPMufeslQgUFh4txmEIbf58ONcO9/qxurbOY5haRIoE4hB/+MMf6M9//nOZ55988klu0bFq1SqaPXs2zZ07V0RCCDn4lA+BuJDnoJREg9pOwyPFVZskx1XUAWYwncq204rMQiooVvjTHNJbO7Y0B209eKa24sAO8IcNqwJ//BzAdgchJSghxEgXWHQRxKhSuJU8OXToEPXq1Ys7DGJUaJ8+fSgvL4+ikWC7JQrh5/Q5Kx09Y2NRSEnQc68lT4Hg6XKOwKwHtob3ldA3W4t4jTq19DRmYBK1aGSqsvWgfQgstjp5n3jOYtZRaqKhjKC4VwnChQArSuscio6hEKFQEc61w71+rK5tMOi4AyzSrxGbiLgusIg7ZGZmVhAJPIfXADKepKuqEA43U7YriwluJXUwkK5KKa5Ib/0ys4B+OqKmt7ZrZqJreycGkN7q3XrwDEDDrWQx6VkoICAJJr3L/aT5mIITh9Jv4FGh61GtGxLCuXa414/xtXVUPQQsEg888ADdd999bE3AWoCZjII6FNI99thjfM7KlSu9tu4QhFCkuUIg4GKyOfBnEnyKa84lNb01+6Ka3np19wTq3s4cgOuncuvBMziNvzSDQ6e6ngwugXCJQ5lU2GAujkfrBqwdUs9VONcO9/oxurbOXbVPkSkSTzzxBLfmePHFF7llOMD0NzT9Q2EdgIjcf//9od+tQPEuEOjmyjEIV6or/pqCEYgDx6z0OdJbbUTJiWp6a1qqPmTWg5rFpAanEYQ0oF4DX7NLSRUI3ABKVxSEGKqTmDJlCh++QPdVQQhHoRxcSrAkAL7WcW2EKgn+BKghKOt+KKLNe9Turc0aGmlSRjKlJOkrTATzTmkrDc+2GqXZSeQ1cwmeMbYoXFlOmkCIOAgxW0yHltxZWVkcf/CkefPmodiXIFQQCAT0SqyqDHCaq5PI7Iot+yMQSG/9dH0BHTujdm/t28lCQ3sn+pnjXrn1UL7uwTNzSVtfp1fchXXad9QK6fjroOMSKK7S8upDnTwfzrXDvX7srq3nOokITYHFMB/MdUCg2hN8msMfiDaESBBCLRAcj7C7AtSu1Fet6vRyAoG23p+sy6e8QjW9dezVydSplb/prcFZD+54BL42elRZ82ulf+D10gKbVSwI1UnAv5komMN0uOXLl/NIUMnvFqpNIJDRhPiDq3mfRmUCgQ8v2/eX0GqktzqJ6tZWu7fixhwK68FXUZwWmAZwLTnLNGxTXxBxEGJSJDBLAplNHTp0CM+OhLjGWxYTjFN3q28HHrtkQalcIJDe+tWmQtp7WG2v0aGFicZek+xXnx0te8TTevAVmGahKOdaKh+YhjjgAHVrG6lBulgOQnQQ8G8qBguh6loQqlsg7B4CURqD8H7DR1X2krUFdO6Cg2/eqH1ADOJyli+/GoT14Ola8pbWCvdSWqqBi6BEIISYFomnn36aZ0k89dRT3L/JZCpblSoVykIoBEKrnPZs9Y1hQf5kMf183EqfbyigEqS3Jui4OZ8/1dOaQGieIk/rQRMCrn5lq6D0tfKuJc/1JPYgxJ1IoHkfGDZsWJnnJXAtBMvZHBudzysrEDabWvegtfr2pw8ThOTbHcWUubuYHzdtYKBJQ1K4tUEwMx9KG/Jpw+f9cy2JOAhxLRJr164Nz06EuAOzIM5hHgRiC66Z1HAnccGcgopq//swFRQ76bP1BXT0tJre2qejha7t4196q+aCQrahU1FdTEajn1lLIg5CjBOwSGRkZIRnJ0JcupfUTCVX59YgBeLUOTu319DSWzFatHNrc8CprRyDcFksPAPCl/XgxbWkBaWBZC0JsUTQKRaYb338+HEuqvOkW7duodiXEKcprnYuklPYxXS5+ANcnD/sL6FVWwr5/em11PTW+nUMflsPmiuJv8ZsYYf6WJv+pQ0J8se1JEFpIRYJuFTw3LlzNG7cOEpNTaXOnTtzIz/PI5T89a9/LTNgA0ejRo0qfc/69eu5ZTk60rZu3ZpeeeWVkO5JCL1AwJLQBIIzmPwQCJtdodVbimjFZlUg2jc30Z3javkhEOrvEQeh2VJQhQCH0ajjA2KBiWBGbsRXaj2gIM6znQbXPLgEAtaDZC0JsUjAIvHQQw/RhQsXaPPmzdyjacWKFfT2229Tu3btaNmyZSHfIITo9OnT7mP37t0+zz1y5AiNGTOGBg0aRDt27OCutDNmzKAlS5aEfF9C6ASCezK5BMKfDKZL+Q766Jt82n/M5k5vnTw0mec0VIZntbTWtdUzOI3qbX7OoCMzWxFqMPty4tChpUUqpoWYJWB305o1a+izzz7jNuF6vZ5atGhBI0aM4NTXefPm0dixY0O7QaPxstaDBqwG9I6aP38+P+7YsSOPVn322Wdp8uTJId2XEJ4aCLqMQBw5ZaNV3xdy99ZEi44mDUmmlo1NAbfV8Fb3oGfxcvVdQsGdl4I4DYk7CPFCwJZEQUEBNWjQgL9OT09n9xNAzcQPP/wQ8g2iV1STJk24Pfktt9xChw8f9nnupk2baOTIkWWeGzVqFAuFDTmVQsTUQOAmD4Hgw+YqknP6Fgite+vyjapANKproFtGpFxWIMpbD1pqK2cwGXTsVkJDPlgRJriX1CBDhU6tBo+4g1gOQjwRsEhgdsSBAwf466uuuopeffVVOnnyJH+KRy+nUNKvXz965513eIgR5lWcOXOGBg4cSDk5OV7Px+sNGzYs8xwe2+32SqvES0pKeLSf5yGETyC0Ggh/M5gKi5304ep8ytyl1j90a2umG4ao7b0DjT1o86Y9M5eQ2sqxB7ibePB8aadWiTsI8Y4xmJgEYgNg9uzZ/El90aJFZDab6a233grp5q677jr317BUBgwYQG3atOEYyMyZM72+p3zbBW2Ed2XtGOAmmzNnTsj2LfgnEP4EqE9l22np2gLKLXByIBnxhytbmAPKXPLs2up2LXHKa9m0Vj0K6Vy/JrAytHXEtSTEMwGLhOewIWQzHT16lPbv38+xgHr16lE4SU5OZrGAC8obiF3AmvAEMy8Q16hbt67PdWfNmlVGdGBJNGvWLIQ7jz+qKhAQ950HrbRqcyEHtuuk6mnytSkchwgm9oDnkLlUWgNRsSgOsRGIh/YdRBwEoQp1EhpJSUnUs2fParmWcAvt27ePs5e8AUvj888/L/PcqlWrqHfv3hV6THlisVj4EEID4gsQiPOXHJSUgM/nxALhbw0E0ltXbi6kXYfUGpz2zUw0blAyJZh1PqbHVWzpXZn1oHVwLR+YNun1pCMHN+JD3EEQhCBEAkOF4Fb65ptvvE6mQ/ZTqHjkkUdo/PjxbKXgez355JP8Kf93v/ud2wJAPARxC222NmZvwyq45557OJD9xhtv0AcffBCyPQmX50y2jQUCwWYEpRVFR3b0YfIjg+lCnoPdS2fPq91bM3ok0oCulXVv9W49qM/pyGDw7lpijdBXLIhrfYVZ6h0EoSoi8eCDD7JIINW1S5cuYR06dOLECbr11ls56Fy/fn3q378/12cg7RYgNoKqbw1kQH355Zf08MMP00svvcRZUS+88IKkv1Zzs75sdjPBraTdiJ3uuojKAtSHTtho2bcFVGxV2K00MSOZWjUxVdqxVbMK/HUtcRtvL+IAxL0kCBXRKVpk108Qd8AndxStxSqwVmrXrk2XLl2S1ud+gl+jszl2djMVW510IdfJPn6zUcevVeZegqBs/LGYD9CknoFuGJpCtZIrZi/B3eTZ0rtWstHlSgrMtSQ1D0K8kRvkfS1gSwJZTG3btg30bUIMA5dj1nmHO1BdUKQWyOHmX1SicCW0L4FAeuuyDQV0+KTavbVnBwsN75PI2UXeUNNa1XUgBMh4clsMBtU6KO25JOIgCNVeJ/GHP/yBnn/+eXdqqRDfnLtgowPHrW6BUIvjVIHgfkxakZwXgTidbaeFn+exQOBmP35QEo3un+RTICABpS4lLd7gEgXus+TRlI9bapRaD57jQ6WVhiBQaC2JG264oUJw+quvvuK+SuWzhpYuXRrAtxdiMc3VwUFqVSiAN4HY+XMJZzBp6a03DE2mhj7nPpcGpyEEWBY6YjK5GvKVcy35ijtIl1ZBCJNIwI/lyaRJk4L4VkKsCoR7BoRVFQirXU11BVqgWsOO9NbvC+nHg2p6a7tmJhp/TRIlWPR+Fcbp9QrpFVenVlcTPrTTENeSINSgSCxcuDBM316IdoGw2lwN+tCLyel0T5cr62JSb/QXkd66roDO5Dj4cUaPBBrYLcFHhlzZ1FYtOG026Xh9d+8lCIRWG6HNnJaMJUEIGQEHrtGOG72Q0BrcE1RBw/XUsmXL0O1OiDqBgIvJWwziF6S3bijgQDbSW68fnEytrzD53VZDTWPVUYKZqKgEAkEcEEdzPhEHQYigwPUdd9xBmZmZFZ7//vvv+TUhfgQCdRCaQKBozptAIMFh449F3KAPAtG4roHuGp/qQyDKNuVDwz0eBuQxBMiox6wH1/Q4oys4LUFpQYgcSwLDfK6++uoKz6PQbfr06aHalxCBRXLn88oKBEZ9agLhrQ4CRXFff19IR0+r6a092ptpRD/v2UuaOACtY6u3qml8B6vDVSgHMZGgtCBElkjgjzkvL6/C8yjQQMsOITYFAhYEsp5LbArZ7aoglDicPvswnbvgoC8z0b1VYQsAqa3d2ln8jj34qntQcJ5VcdVISJdWQQg3Abub0FwPrbU9BQFf47lrrrkm1PsTIkQgYEGwQCDF1amwQNh9CMS+I1b6aE0+C0StZB1NHZPqVSBw09daZsC1pLqT1OC0Ol9aRxb+Wo09YIwou6BcnVrr1jbKACBBiDRL4p///CcNHjyYhw9p3Vg3bNjAJd+hbO4nREgfpkseRXJWdHJ1kt2jD5OnOKBwDqNFd/6spre2aGykkf2SqH6aRz8MH9aDFpj2dC25i+JcmUtaG290aZU+S4IQoZZEp06daNeuXXTTTTdxZ1a4nqZOncozJdDwT4g9gUDdgyYQiEV4E4hL+U5696s8t0D062xR6x/MustaDzw61Fj6tdlIpdaDoaz1INXSghDhDf7igXhu8OfZqE8TCLiYNIHwlsF05JSNPl1fmt46om8itfCYPZ3KY0a9Ww8sBq7nIA7lK6YlMC0IUdbgT4iPRn2IN5TYK89ggqBg7vT6HWr31kZ1DTx7Gp/8fWUuaWmtnq4lWBBcEyE1D4IQcYhICEEJRHGJkz7fWEgHf7Xx46vamTn+gNoFbXqcZ0tvzXpAYBpCoRXKla+YlvGhghBZiEgIFQXCpgqC1eHkUaJAa9YHzp638/S4C3lOtgpG9U+iq9qXzV7SXEvlrQdYDRAMLXtJcy2JOAhClIsEJsA1a9YsrJPohJoVCAiC1dXmuwRN+rwEqHcfKqGvNhVyC47ayWr31sb1jF5nPmjFcNyIz1XzgAC1L9eSdGkVhCgWCYwGxbjQBg0ahHdHQrXOgnCnuF5GIJDeunpLEf1woIQft77CSBMGJVNSgr6CQJgMak8ng15PCRa1z5LmWoI48HlwO0kjPkGIHZGQJKjYAiIAgci56KCkRD1XUdvs3msgcguctHRtPp3KVgsor+mewAdu/KWoMQetQlp7zJ1ajWpLb3EtCUL0ITGJOOVMto0FAhZCSQk++SteU1w901tR8zBhcDK1bWqqNHspUa+jEpuTRSLRpC/TiE+TFSmGE4QYFInXX3+dUlJSKj1nxowZVd2TUA2FcmcvOLiKGhYEVIGzlzwC1LAcN+0uofU7irhnU8N0A8cf6qSWVk9zTYM6Rrq0Y6tBp44udap1Dwaz2s1VxEEQYryYTq/XU9OmTcmACKSvxXQ6Onz4MEU7sVxMp1VSYwBQQbHa3hvqAJeQO73VqtDyDQX0syu9tVtbM2cw4Zzy1gMHpzneoGYsAYhFoVVdC6ICV5MEpQUhDorptm3bJoHrGBAIxByKS1SrATOm8djo+k3IuuCgJWvy3emtI5He2s7szmorbz141j1oj9HsxaB2B+fnxbUkCHHQu6kmUl/RWbZPnz6UmprK4jRx4kQ6cOBApe9Zt26d60ZW9kBvqXimjEBgFrXTyQKBmASym8CeX0roreW5LBC1kvV0+5hU6tHe4vrZlx0IhDgDOrSqPZfUr9G91cDP6VlEYEV0bJVA9dIk9CUI0UpEZzetX7+epk2bxkKBkamPP/44jRw5kn766SdKTk6u9L0QE0+Tqn79+hSPePZi0grlYEFY7WrLbwSp7Q4nrdxcQtv3q+mtrZoYebyolt5aPjDNdQ+uoUAsEi43lGfsoW0zs4iDIMSTSMyePfuyQetQs2LFijKPFy5cyBbF9u3buV15ZeC8tLQ0ime8FcrBckDAGgIBscgvdHJx3JkcNb316m4JNOgqLb21tCkfT4tzdWnVXEtaSw2tV5PWpVUsB0GIQ3fTAw88QOfPny/z3N69e+nOO+/ktuHvv/8+hRsEXEB6evplz+3Rowc1btyYhg0bRmvXrqV4FggMC9IEAl1d4XKCQJzIstPir/NZIOAuunFYMmX0TGRB0Fp6c8aSUcfpr5ZyriU96h9c1kPdWgYZACQI8WxJwO2Dm+6//vUvfoxZEhg61KRJE2rTpg3dcccdPKHu9ttvD5vbZObMmTz9rrK5FdjjggULqFevXlRSUkLvvvsuCwViFb6sD5yHwzMLIBYE4txFhzoLAu2+Xf8i/uBUnOxa2rS7mNNb66Xp6cZrU6hOLUMF64GL4QyVu5bEehCE2MVvkdi8eTO7ezTeeecd/kS/c+dOMhqN9Oyzz9JLL70UNpGYPn06DzvauHFjpedhYh4OjQEDBtCvv/7K+/MlEgiQz5kzh2IBT4FAKivcShAIiAXiD8VWJy3/roAOHFPTWzu0MNGQXoksEJ5prRABs9ZnSe+a9SCuJUGIO/x2N505c4b7N2lgVOmkSZNYIMCECRPo4MGDYdkkXF3Lli1jtxFqNQKlf//+le5t1qxZ7MrSDohKNOJwOOlsjm+ByLpgp4XLc1kgUA09pGcCDe+bqIqBvlQQEi16di3B1QTXksWsI6NJ77YeMI60Q0uLxB4EIQ7w25JAptDFixepRYsW/HjLli109913u1/Hp1BPl02oXEwQiE8++YTdRZ4iFQg7duxgN5QvLBYLH9GM3a5aENmX7FTCKa5qgNrmEog9h0voy8wCstkxKU5HowckUaO6xlLXEruT9BVcS9xKw9WTSdxKghB/+C0Sffv2pRdeeIFee+01Wrp0Kc+2vvbaa92v//zzz9xKPJQgDoKA+Geffca1ErBmAKoGExMT3VbAyZMn2f0F5s+fTy1btqTOnTuT1Wql9957j5YsWcJHrAKBQPA555KdA9Q2LYOJRcJJa7YW0dZ9qoC3bGyk6zOSWTi0mz+Sk2A9aAVy5V1LCEo3SJdaB0GIR/z+y587dy4NHz6cb7qoWXjssceoTp067tcXL15MGRkZId3cf/7zH/53yJAhZZ5HbASBcoD25Zh1oQFheOSRR1g4ICQQiy+++ILGjBlDsUjWeRu7l9itVE4gLuU76JN1+XQiS01vHdg1gQb3UNNb0bAP/ThQGGdGfyW9aj1wDyZJaRUEIdDeTeDcuXOUmZlJjRo1on79+pV5DTfiTp06Be0SiiSipXeTVkWNmEOJVc1c4hRXu0JHz1jp03UF3J/JYiIaPyiZ2jc3u1t4YyQpQO0D0lvTUo0iDoIQw+QGeV8LSCTihWgQCQiEluJqtSnkRMtvm2pBfL+3mNZsV7u31q9joMlDk7nBXvnspRKr2p8prZaREsyqu0niDoIQm+SGu8Gf5vO/HFOnTvX7mwtVEwhkMHHvJfRjsqFpn0LLN+bTfld6a5fWZg5Qm016th4gEEaDnmMOcDXpCM+rqa7IWJJKaUEQqtQqHG05kPLq6y34pFq+KjsaiWRLAgKBTq2wGpC5BMsB1kTWeTstWZtPOZfUYT/D+yRSrw4W/rmpqa1EFrOeg9QcnDbpKK/QSWmpBrqyubnclDlBEGKN3HBbEh07dqSzZ8/Sb3/7W7rrrruoW7duwe5VqEKjPlgQ5QUC3Vu/+K40vXXSkBRq1sDkmgZHZDbqXXOmiYPURr2eX0OXVriXRCAEQahyMR36NCE4XVRUxJXLvXv35uyjaG9hES1V1BAIFMPBxcRtNhwKFVmdtGpzAY8XhUC0aGSku8bXouaNTO5mfEkWPZlNsCJ0lJRgUNt466UgThCEMAauIRQfffQRp6KiqA5zHt58882oL0iLRHeT1mbj7Hm7Gph2ZTBduOSgT9bn069n1ek+/btYaGjPJDK4K6X1aoM+A5rzqcIAJPYgCPFJbk1kN3377bfcQhz/Zmdnl6mbiGYiRSTQZgMCAQvCUyCOnLDSknX5VFCksJUw/ppk6tjK4gpMq4FotiRMpa4lKYgThPgmtzrGlwIUqb399ttsRRQUFHCMAm6nWBGISAGtvFFFDQuCU1wVohKbkzbtKqI124r4Mbq3/mZoKtWrY2DXEhfDIe6gTYcT60EQhCrit0j897//ZWHAtLhRo0bRc889R2PHjiUDPrIKIaeo2EmnztnpUoGdi93yixT6fEM+7Tuqprd2amWicdek8GssChgGpEd7DYOIgyAINZMC27x5c5oyZQo1bNjQ53kzZsygaKem3U3oxfTzcSudyrZxFTWqqj9dn0/ZF53sUhreJ4n6dbG4GvKhEZ8rvdXVTkPiDoIgVHtMAk3zUAdR6WI6HR0+fJiinZoUCa1Z34HjJZziuu+o1d29NSVRR5OvTaFWTUzu2IPJpAqFlrEkBXGCINRITOLo0aN+LyoELxCnsu0ch7DZnNxaY5ure2vzhkb6zbUplF7bwO27TcZS1xJabjSULq2CIIQB6f8cQQJx8pxaC3Exz0kffF2a3tqzg5nGXZ3CA4AQnIZrSRsjKtaDIAgRIRKojfjmm29o3Lhx7jkOnkOGEMBGO/GEhITw7DTGBeJElioQR07Z6P2VuZRXqLC1MKpfEnVqbVanxZnRd0lcS4IgVB8BNfhbvny5WyRefPFFntWgDf/Zv38/NWnShB5++OHw7TZG50HAgrDanPTdrmL68rsCTm9Nr6WnSRnJ3MUVcYfUJNW1JJaDIAgRKRKLFi2qIACYGte6dWv+GsOIXnrpJRGJAHsxnThnp+ISJ328Jp92HVIts44tzXT94GROaUUyQFoKJsNJUFoQhAgWCYwnbd++vfsx3EpIi/Ucb4pxo4L/rTZOnLNxJtN7X+VyZ1ckjw3vm0SDuidw9hKmxzVMN9GVLc08QU4QBCFiRQJpU2gT7jmlrvyNzzNGIfhutXH2vINOnrPR7kMl9NE3+dxyIzlRRzcPT6W2TU1kNOq5SK52qjrnQQRCEISIF4mmTZvSnj176Morr/T6+q5du/gcofIANQTi+FkrrdxcSN/uKHKnt946MpXSUvVksRg4i6lebQPVryPJZ4Ig1Cx+34XGjBlDf/nLX7gVR/kMJmQ+zZkzh18TfAvE6Ww7HfzVSu+vzKPDp9T2GgO7JdAYTI8zGyg5QU91a+tZHC5XuCgIglAd+F1xjYFDV111FZnNZpo+fTrHJ3AjQ1YTMp3sdjvt2LGj0pYd8VpxzUVy5+y05aciem9FHuUWOHmEKKqnu7dL4PTWhumq5SDiIAhCVFZc4+afmZlJ999/P/3pT39yjzDFTW3EiBH08ssvx4RAhBoIxLEzNp49vXxjAbf7RvfW345OpaYNzNSkvpEa1MF0OL/nPwmCIFQbATm9W7VqRStWrOA51ocOHeLn2rZtS+np6eHaX1Rjsztp/zErvfX5JdrxsxrU79LaTDcOT6W6tYwsFmI9CIIQyQQVGYUoIOVV8A2K4zbtKaJXl17kNFdksI4ekEQj+yVTo7pGEQdBEKKCqPBxwJUFKwYB8169etGGDRsqPR8zL3Aezkex3yuvvELVLRCfrMujp98+zwKB7q33TqpNv72uNnVpY6EG6SaJPQiCEBVEvEh8+OGH9NBDD9Hjjz/OgfFBgwbRddddR8ePH/d6/pEjRzgTC+fh/Mcee4xnXCxZsqRa9mu1O2n+4gv06ieXqNiqUItGRpr3+/o0KSOFO7VK7EEQhGiiSjOuq4N+/fpRz549eUSqRseOHWnixIk0b968Cuf/8Y9/pGXLltG+ffvcz9133330448/0qZNm8KaBXAxz0Fz38h2xx9G9k2i6TelUXKiQSwHQRBqlGDvaxFtSVitVtq+fTuNHDmyzPN4jEwrb0AIyp+Pcavbtm0jm02tTSgPKsVxAT2PQHE4FZo5P4sFAsVw029Mo0d+m04pSZLWKghC9BLRIpGdnU0Oh6NCai0enzlzxut78Ly381HHgfW8AYsECqsdzZo1C3ivaJ1xx7ja1LSBkV56tAFNGpLK7TUEQRCimai4i5UvMIOHrLKiM2/ne3teA7MxYIJpx6+//hrUPgf3SKI3nmhMra+wiHtJEISYIKKbA9WrV4+HGZW3GrKysnwW7jVq1Mjr+WhOWLduXa/vsVgsfIQCTI4TBEGIFSLakkALEKSyfv3112Wex+OBAwd6fc+AAQMqnL9q1Srq3bs3mUymsO5XEAQh1ohokQAzZ86k119/nd58803OWMLgI6S/ImNJcxVNnTrVfT6eP3bsGL8P5+N9b7zxBj3yyCM1+F8hCIIQnUS0uwncfPPNlJOTQ3/729/o9OnT1KVLF/ryyy+pRYsW/Dqe86yZQNEdXoeYYFIeRqq+8MILNHny5Br8rxAEQYhOIr5OoiZA8DotLY0D2KHoAisIglDTILUfmZsXL17kLM6YsSRqgry8PP43mFRYQRCESL+/BSISYkl4AaNYT506RampqT5TWTVVjlZrI5r3H817B7J/uf418fsDpxEEAi74QNoDiSXhBVxAf0ex4ocUjTeqWNh/NO8dyP7l+lf3708gFkTUZDcJgiAINYeIhCAIguATEYkgQYX27NmzQ1apXd1E8/6jee9A9i/XP5p+fyRwLQiCIPhELAlBEATBJyISgiAIgk9EJARBEASfiEgIgiAIPolbkcA0uj59+nBVdYMGDXhm9oEDBypUKP71r3/lCsXExEQaMmQI7d27t8Lo0wceeIBnXyQnJ9OECRPoxIkTZc65cOEC3X777e7Jd/ga/VMiYf94DlXlnsctt9wSEftfunQpj57FtcW+du7cWWGdSL7+/uw/Uq8/Rv1iXnzXrl35uuJ3CN2W0YkgGq6/v/uvies/z4/fHfzddujQgfdep04dGj58OH3//fc1c+2VOGXUqFHKwoULlT179ig7d+5Uxo4dqzRv3lzJz893n/OPf/xDSU1NVZYsWaLs3r1bufnmm5XGjRsrubm57nPuu+8+5YorrlC+/vpr5YcfflCGDh2qdO/eXbHb7e5zRo8erXTp0kXJzMzkA1+PGzcuIvafkZGh3HPPPcrp06fdx8WLF8t8r5ra/zvvvKPMmTNHee2119CEUtmxY0eFdSL5+vuz/0i9/tjD8OHDlQ8//FDZv3+/smnTJqVfv35Kr169ouL6+7v/mrj+o/z43Vm0aBFf019++YXPu/vuu5VatWopWVlZ1X7t41YkyoOLjz/k9evX82On06k0atSIb7QaxcXFSu3atZVXXnmFH+OXyWQyKYsXL3afc/LkSUWv1ysrVqzgxz/99BOvu3nzZvc5+IXFc/jlrcn9a38kDz74oM91a2r/nhw5csTrTTaSr78/+4+W66+xZcsWPufYsWNRdf197T9Srn+WH3u/dOkSn7N69epqv/Zx627y1h4cpKen879HjhzhMagjR450n4PilYyMDMrMzOTH27dvZ7PW8xyYtZh5oZ2zadMmNvP69evnPqd///78nHZOTe1fY9GiRWyydu7cmYczaV1wa3L//hDJ1z8QouX64xy4Y9BGPxqvf/n9R8r1v3SZvVutVlqwYAF/z+7du1f7tZcGfy7fPSbZXXPNNXyRgTYnu/wsbTzG5DvtHIxYhc+w/Dna+/Ev/I7lwXPlZ3FX9/7BlClTeFATZoPv2bOHJ/39+OOP7hGwNbV/f4jk6+8v0XL9i4uL6U9/+hPddttt7qZy0XT9ve0/Eq6/Usnely9fzvGRwsJCaty4Me8JYlbd115EgoimT59Ou3btoo0bN1a4QOVbheOH6qt9uK9zvJ3vzzrVsf977rnH/TV+Sdu1a8fzwH/44Qfq2bNnje8/GCLp+l+OaLj++MSKmxVa6L/88stRd/0r239NX//plex96NChnOyQnZ1Nr732Gt10000cvPZ24/e1r1DsPe7dTcgOWLZsGa1du7ZMe3B8sgDlFTcrK8v96RznwBREBkFl55w9e7bChT937lyFT/nVvX9v4A/DZDLRwYMHa3T//hDJ1z9YIu364waLmxPcl/gk6/kpPBquf2X7r+nr/8Bl9o6MpbZt27KL6I033iCj0cj/Vvu1V+IUBHanTZumNGnSRPn555+9vo7A79NPP+1+rqSkxGvgGhkUGqdOnfIaPPr+++/d5yCQVNXAVyj27w1kQXkG0Wpq/4EEriPx+vuz/0i//larVZk4caLSuXPnMlk10XL9L7f/mrr+zgB+dzxp06aNMnv27Gq/9nErEvfffz/fMNetW1cm/a2wsNB9DjKDcM7SpUv5l+fWW2/1mgLbtGlTzjpAGtq1117rNQ2tW7dunFmAo2vXrlVOAQzF/g8dOsQpmlu3buUb2RdffKF06NBB6dGjR0TsPycnh2+s2Bd+sZHJgcc4Lxqu/+X2H8nX32azKRMmTOBrizRNz3PwYSPSr78/+6+p63//ZfaOVNhZs2bx9zp69Kiyfft2ToG1WCycDlvd1z5uRQJ/tN4O5C97Kj6UG5/I8QMaPHgw32w9KSoqUqZPn66kp6criYmJ/AM4fvx4mXNws5gyZQrXLODA1xcuXKjx/WOfeA57N5vN/EllxowZvN9I2D++9naO9mkq0q//5fYfyddfs368HWvXro346+/P/mvq+tNl9o5rOmnSJLY0sC98sIPgIYXXk+q69tIqXBAEQfBJ3AeuBUEQBN+ISAiCIAg+EZEQBEEQfCIiIQiCIPhEREIQBEHwiYiEIAiC4BMRCUEQBMEnIhKCIAiCT0QkBCFEoJgWYyYxsrQ86D6KPv7Hjx+X6y1EFSISghAi0H554cKF3M751VdfdT+PDqSYt/z8889T8+bNQ3q90eVUEMKJiIQghJBmzZqxGGDCGcQB1sXdd99Nw4YNo759+9KYMWMoJSWFWzVjKD1mBWisWLGCh89gclrdunVp3Lhx9Msvv7hfP3r0KAvRf//7XxoyZAglJCTQe++9Jz8/IaxI7yZBCAMTJ06kixcv0uTJk2nu3Lm0detWHmaDITdTp06loqIiti7sdjutWbOG37NkyRIWga5du1JBQQH95S9/YWHA4Bm9Xs9fY4pay5Yt6bnnnqMePXrwSFqMrRSEcCEiIQhhAMNfMOksJyeHPv74Y9qxYwe7oVauXOk+58SJE2x5HDhwgNq3b+91OAymkO3evZvX0kRi/vz59OCDD8rPTagWxN0kCGEAN/d7772XOnbsSJMmTeLB9ZhABleTdnTo0IHP1VxK+BczmFu3bs0T1CAIoHywGxaJIFQXMuNaEML1x2U08gEwX3n8+PH09NNPVzgPQ+4BXodlgXnGcCHhPbAgMKay/FhLQaguRCQEoRrA7GTEHBBP0ITDE7il9u3bx1lRgwYN4uc2btwoPxuhxhF3kyBUA9OmTaPz58/TrbfeSlu2bKHDhw/TqlWr6K677iKHw0F16tThjKYFCxbQoUOHOJg9c+ZM+dkINY6IhCBUA3AffffddywIKLaDGwnBZxTYIXMJx+LFizl2gdcefvhheuaZZ+RnI9Q4kt0kCIIg+EQsCUEQBMEnIhKCIAiCT0QkBEEQBJ+ISAiCIAg+EZEQBEEQfCIiIQiCIPhEREIQBEHwiYiEIAiC4BMRCUEQBMEnIhKCIAiCT0QkBEEQBJ+ISAiCIAjki/8HBrnxbdLb144AAAAASUVORK5CYII=",
       "text/plain": [
        "
        "
       ]
@@ -202,7 +202,7 @@
     "    T_change=t_change, scenario=\"stabilisation\", member_seed=42\n",
     ")\n",
     "global_model.sum_components(projections)\n",
-    "gmslr = global_model.results[\"gmslr\"]"
+    "gmslr = global_model.results[\"total_gmslr\"]"
    ]
   },
   {
@@ -221,7 +221,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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nTpIL9wTPFMVWs/DpLOw8UKKurk6mCo0bN84t04VQzQLUislgMDscXrxodjjkMW4e1l7eXspyckTALx48aB3JxsI99oEHaMS99yp+U9Ok9GoWzkWNHTtWbgAAx2MoLTVH4wUFYlHr2865AZ4EB4u5Fy6I6VXWqVPW45xC4VTK4NmzSeOHuo2WwFUBQEmzOC9fJn16unn6D0fjXlKNwfnw9O+/FxG3eIgz/e64g8Y98ABFTpjgNW9obQViDoASjLEKC8Xh0FhS4lXTf2pKSylp715K2reP6srL5Zhfhw7iWjjmZz+jsKgody/RY4CYA+DuckNuxeeh5tyK360b+Xioa589FGdkiP0sT/SxOBd27tGDxqxYId2abfUQ92Yg5gC4qVZaNjhTU8lYUeEVzT88jo3z4HG7djWrD+81cqRUpfBsTU+1n1WdmO/atYseeOABRz4kAKqDXQ2l+Sc/n3z8/FRfbqitqZFxbPF79lBlfr4c4/8ve4eziKvJudBjxJwbhNLS0sQpkd0TLezdu5dee+01Sk1NhZgDcLsNzowM0l+6JE6HssEZEKDa61WRl0cJX30lQs6CzgQGB0t9+Oj77nOp6ZU3YLOYc5fnkiVL6GpjK/Hy5ctp8+bN9NBDD8mA51/84he0f/9+Z64VAM/t4MzPN9eMs7shb3ByNK7CDU5OH2WfP08Je/bQ1TNnrEMgukRFSVXK0PnzxcUQuFHMX3nlFbG//dOf/kTbtm2jHTt2UFJSEj3++OMi4sHYsADgJoyVlSLi+pwcua/WDc6G6mpK/fZbicQrcnOtx/tOmSKbmv2mTFF1KsmjxPzs2bN04MABmjBhAk2fPl3E/De/+Q09++yzzl0hAB4Id//prlwhw6VLZKypMfupqLCDszQrSybbp373nXUoMrfac2khp1K6REa6e4leg81iXlRURH369JHvu3TpIg6KM2fOdObaAPDcmnFOqRQXS4WK2lIqlqoUTqVwt6YFnt4zesUKGrpgAQUglaJcMecXo2+Tj0n8/Y0j4wAgbzfF4pQKm2LxwIiuXaVaRU2GVxcPHKDEvXup6to1OcapkwF33implD7jx6vqTcvT8LMn4uAKFssvq7q6WpwSmwo8U1pa6vhVAqBgTHq9NP1wG76YYnHNuIosarnBhw2v0n/4QWZrMoEhITRy8WIatWwZhfTq5e4lAnvEfOvWrbhgANyYUikpMZtiXbsmOXG1pFRYtC8fOyZReEFiovV49yFDJJUSPWcO+alwD8ArxJx9ym+HTqeTwRQAeE0bfloaGbKyZLPTNzxcFSmVyoICSvr6a6kNt3ilcFfmoJkzJZXC3ZpqeLNSIw579XEdOle6GAwGRz0kAMqsGc/KEiHnNnyf4GDS2OFVrdQNTa4JZ7Orq2fPWmvDeSAyN/iwV0pnN8wrAPbh+aEEAC5siNElJ5M+LY1IozE7G3pw7TQ7FvI0++T9+6m6qMh6nGdojl6+nPpPmwavFA8CYg6AjZuc2oQE0mdkkE/nzuRrw3BzpX6yyIuLE9vZK8ePS1TOdAgJoeGLFtHIpUupS2MJMvAsIOYA2OCpoo2NlYoVT61Uqa+qkg5NTqWUN3ajMpwD5yicc+J+KvaJ8QZsFnOe+3k72IALALXBhljaCxekHd/TjLE4Ci9KTRUBv3T4MOkbGuQ4e6OwR8qopUu9fqK9V4o5D23mXeyW5j9bjmOXG6itYkV7/ryYZEkDkIc0ybFPCteEcz68+NIl6/GuAwdKFB49bx4FeGiaCDhAzDMzM209FQBVGGRpY2LIUFRkNsdSeNkhB1OFSUmU/M03lHHkiDUK1/j7i284N/egrFDd2PwK7devn3NXAoBCMJSWSkRuLC9XvMsh14KzyVXKgQNU1mhPbfFJGbFkiaRTgkJD3bpGoDAx5zb92tpaimzigpacnEx/+MMfqKamhu677z567LHHnLVOAFwCR+IckUtbPgu5AksPuUQyNzZWSgqlIqVxhiYPQh4ya5aIOE/vQdrTu7BZzFevXk29e/emP/7xj1YXxRkzZlBERAQNGjSInnrqKWkYeuKJJ5y5XgCchj43V6pWTA0N5hpyhXU6VpeUUOrBgxKFc6emhe7R0eKTEj13rtjPAu/EZjE/ffp0M3+WTz/9lMLDwykuLo78/PwkQv/ggw8g5sBjuzq1PGTYaDRvdipEyC3dmZwLv3r6tETlDIv20HnzpDuT/VKAsjAZDFIJpUgxLywslElDFn788UdasWKFCDmzbNky2rhxo11Pzufv3r1bZocGBQXRnXfeSW+99RYNHTrUrscBoD1Czq353Nkpw5XDwxVxMTny5gj84sGDVFNSYj3ee/RoicK5LtzfA+vd1YiJ32AbGqQfgW9ih+DrK8ZrGv6E5yJvd5vFPCQkhMrLy60boTx56JlnnrH+nCOZhsYddFs5evSopG8mT54sw6L/67/+ixYsWCA+L53wcRG4IHrSJSWRLj3dPESic2e3OxVeOXFCRDzn/Hnr8Q6hoTRs4UIR8bC+fd26Rm/HxEKt00kqjm9kMJj3VQIC5DWkiYyUqVK+wcHkGxLi0gYzm8V8ypQpMv/zo48+kmi6qqqK5syZY/15eno6RUVF2fXkBw8ebHaf0zg9evSgmJgYuvvuu+16LADsgZ0OtXFxpM/MlD88V0VPLVGWnS1pFO7QrK+oaOaRwgI+4K67pMQQuCld0tBAVF8vrxmGG8dYpDU9ekgjmbx+WLg7dnRres5mMf/d735H8+bNo88++0yi6FdffZXCmrjFbd++vd1j5CoaX8ici28JjvybRv+VlZXtej7gnTTr6nTTbE5dfT1dPnpURLypXzg7FbJHyohFiyikd2+Xr4u8PV2i1Zqjbk6X8H2NxuxTHx4um+LWiJsFXGElqz6mllo6b0FxcTGdPHmSevXqRVOnTm32s2+++YZGjBjRLK9uD7yM5cuXU1lZGR07dqzFc9544w3asGFDi28CnAYCwJbRbtLVee2a2YPcxREvd2RyZ2ba99+TtqZGjvHH9P533CElhTzFnv3DgXMxsew15rgl8m7cWGbh5qhbxJuj7pAQuREfd1HUzUFqaGio3bpml5g7E86d8xvC8ePHm9WytxaZc2oHYg5sgZuAGrgZ6Pp1lzYDsWhzez1H4cXp6dbjHHmPuPdeGnbPPfALdyImlji93hxtc9TN6RIfH3kjF+Fm0W4Ubom4O3Vya7qkrWJuc5qFSxFt4ec//znZy/PPP0/79u2jn3766ZZCzgQGBsoNAHsxFBebm4EqK13iQy7t9SkplLJ/P13i9vrGMjVff38aNH26lBRG8gBkBTYlqSHPTU3TJSzmfn7mdEm3blJ6yu6Xkuvu3Flx6ZK2YnNkzoObO3fuLKWIt/on/G5mz0BnfhwW8j179tCRI0doiJ31sm19BwPe1wyki4sjY12dOSJ3YtRVX1lJad99R8kHDlBpEz+jMG6v5yh84UK01zsjXdLQWF3C9znq5rRIx44i3Bq2LbaItwe4Xjo9Mh8+fDhdu3aNHn/8cXr66adpzJgx5IjUyueff0579+6l4OBgqWVn+D/CdecAtPcPXX/lCul4g5GbgZwk5Pw8+fHxkkbhTU1DY9UDDzxmkyuuSOk1apRiGpFUky5ptDGQdElQEGl697aWBUp1SVCQV11zu3LmZ86cob/97W+0Y8cOGjx4sNSZr1y5ss1R8a0uNJcosj1AayAyB7cd8ZaaSvqLF4n8/c2bWA6mtqxMygm5LrzpwIdugweb2+vnzaNAN9euq6YZxxJ1W9IlvDHJ6ZLGDUofFaVLXLoBWldXR1988YWILjcPsckWi7yr89kQc3DLEW+JieYRb/xR24ENaCwwOTExEoVnnjhhNbnigQ/sjcIVKT2io70qInRWM450UTY24/CnKg2XkXpQusSjqll4w/L111+XryUlJc3qzl0BxBzcCAuBjHi7etXhI96K0tPph7feoutXrliP9Rw2TCbYD54zhwKQGrS/GcdSXcKwnQJH3VxZ0hh1K6EZR3U5cwt5eXn0ySefSFTO1recQ9+8ebPLhRyAGzHW1JgHShQUOHTEm0Gvp5ht2+j8P/4hxldicrVggaRSug0ahF+EPc04lnSJpRmHc9xcYWJJl3DUrfBBIErF5qu2c+dOEXD2U1m4cCG98847tHjxYtKoJE8FVFRDzq6HDhKE0qws+v7NN6moccYtG1zNeuklVKTYsEkproGcLrG0wHfsSJqoKGtZoHxyQqmxe0oT+/btKxuePXv2vOV5L7zwArkKpFmAswZKcAQe/+WXdPqvf5XqlMDgYJq5di0NmTPHqz7ytynqtmxShoaShn8flqibm3FQV+/+nHn//v1bfRHzz680ySc6G4i5dyM+5Dk5YpjFkaCjfMgr8vMlN56fkCD3+02dSrN//Wuv79K0Rt1NNymbRN3etEnp0TnzrKystq4NAOf4kKenkz4lRe47Qsj5MZO//ppObN4sRlhcoTJ99Wpp9vHGaPx2UbemZ0/S8DVvWhrohddISWCnAXisD7n+0iUxQOIosL1UFxfTD7//vdVHPGLsWJq3bp3XOBdaSwMtNd1cctmkNJBz3RJ1W8QbUbfnijnXlv/www+0ZMkSub9+/fpmple8Eco2uR0w/QQ4ERYbqw95Y5dfux7PZKL077+no++9J4ZYmoAAmvbsszT2Zz9TdX73dqWB0knJjpJNc92IuhWPXUZb+/fvt4r5+++/TyNHjrS23fPoNx7u/NJLLzlvtcCrkdLDCxfIkJ/vEB9y7uA88n//R1caLZd7DBtG89evV900n1s25KA00DvFfNu2bTcJNfuqDBw4UL7noRU80BliDpyBsayMGrhixUGlh5ePHaMjf/wj1ZWXi3/45CefpImPPaYKL/FmUTf7mFg2KTnq5rFmljb40FCX+nQD52LzXwSPhYuOjrbe53QKlys2HSvHxlkAOBoeJMERuZQettO+tr6qio79+c+UduiQ3A8fMIDmv/oqdR88mFQbdXN1iUKn4wA3iDmXybD9bdOpQ00xGo12D3QGoNXSw6tXSZuQYC49ZCFvRxR59exZ+vHtt2XaPb8hjH/kEZr65JOSJ/fYqJuvB+e6eSZlnz7mCTks3hx1d+iAqNuLsFnMeWhEUlISDR06tMWfJyQk3HawBAB2ux6mpZEuJUWiyfaUHmrr6qTckMsOmdDISJr3yivUe+RIRf9SkOsGThHze++9l1577TVp4b+xYoUrXXg2J/8MAIe6HrJjXjtsZPPi46UBqLKgQO6PWbFCqlW4hlzxdd0MR91cGsiT4DnqtsykRNQN2toByoMpxo0bRwEBAbRmzRrJn3OkxFUsXNmi1+spNjb2tq3+jgYdoOqD/TzE9TA7u12uh/qGBjr98ccUt2uXNLsE9+xJc3/7W4qcMIGU9KbVrDywqeVr9+7/7qbk64Bct9dQ6ewOUBbpkydP0nPPPUevvPKKdXQcC/r8+fNp06ZNLhVyoD54g1NcDwsLzWkVf/82Pc611FQxxyq7elXuD1+0iKb/53+6dVCE/L1wvru+/t8GVBbnQBZu9jCxbFSirhu0AbvquwYMGEAHDx6UOZ8ZGRlyjCcOhYeHt+W5AbBiKCkxV6yUlZk3OtsQibIhFtvUnt+2TVIWHcPDxVNlwLRp7s13s3hzCqWxFd6PNyq5PJCrTeAcCBxEm4p1Wby5FBEAR6DPyyNdbKx54HIbSw9LrlyhH958k4q5xZ+IhsyeTXevXesyq1qrCZUl8mbx5nF1nO/u1+/fE+FZvNv4iQOA2wFvFuDRA5fZqjZ25046s3UrGdmqNiSEZjVa1bqkTJDFu67OnDaxbFb27282oeLIm9MmGLYAXADEHLiv9DAlhfSpqUQBAZJysJfy3FzJjRcmJ8v9/tOmSVqlkxPTfiLgVVVS4y1vPB06kF9EhHyikHFnvGkJ8QZuAGIOXA5Xbmjj4yUqZ+tU344d7fv3RiMl7t1LJ7dskaoVHuM2Y80aGrZwodOaZETEKyqkAoVF22/IENJwqSAbUmFaDlAAEHPgUjglIaWHOTltMsuqLCykH3//e8qNjZX7kePH09x166T00GkiXllp7kANC6MAFvHISOS9geKAmAOXYaysNLsetqH0kPPrFw8epGPvv0+62lryCwykO//jP2j08uVOsarl6N8q4l26kP+YMeTXty9EHCgWiDlQfOlhzfXrdPiddyjr1Cm532vkSGnH7+IE+wgRcc6J19ebRXzUKLOIe5B/C/BOIOZA0aWHlw4fpiPvvksNlZXk6+9Pd6xaReMeesjhVrUc+YuI8xpDQsh/+HDy69cP+XDgMUDMgSJLD+sqKuin994TMWe6DxlC89avp64DBjh8jabqajLV1ooHjN/YseTPw8sxMQt4GBBz4Lw5nVx6mJZmd+lh5smTklapLS2VKH7S44/LTePAZhsR8ZoaEXJun5d0yoABdlfWAKAUIObAOXM6ufQwM9Ou0sOG6mo6/sEHstHJhPXrJ9F4z1vYLrdZxGtrJaXi07Ej+Y8YQX4DB7bLmREAJQAxBw5F5nTGxpIhL8+u0sPcCxfoh9//nqquXZOBC+MefJDuePppqVpxmIjX1ZlFPCjInBNnEQ8OdsjjA+BuIObAYRhKS80VK3bM6dTV19OpDz+khD175H5IRATNW7eOIsaMcZxnSn29lEVyHtwvOpr8WcRd5NkCgKuAmAPHVazEx5OxutrmipWC5GRpx6/IzZX7I5cupbuee44CHDQ4gssLjRUVUlboN2gQ+Q8aJI0/AKgRiDlov8fKpUukT0mR722Z02nQaunMJ59Q7Pbt8m86detGc37zG+rnICdOtp1lEWfjK7/+/clv8GBz2z2m0AMVAzEH7fNY4fFuly9LCkNjQ/65OCODvt+4ka5fuSL3h86fTzOef546OCB3zesxlpdLzp09w9k/pb1DoAHwFCDmoE1wOkUbF2fe6LRhvBtb1V745z/p7CefkFGvp6AuXWjWSy/RoLvvbvdvgM2vWMR9TCbS9OxJ/izivXpBxIFX4XhTCzv46aefaOnSpRQRESF/eF999ZU7lwNsxFBURA0nT5qFnNMXrQh5WXY2fblmjczkZCEfOGMGPfq3v7VbyLmW3VhaKjf2Dw+YNo0Cp08nTe/eEHLgdbg1Mq+pqaGxY8fSqlWr6P7773fnUoA9HZ1JSWYDqlY2OjkfHr97N5366CPJk7NV7cy1ayl63rx2ia34p7AdLa8hPJwCoqPNToYYegy8GLeK+aJFi+QGPKQRKCmJDJzr5nForbTms1UtV6rkx8fL/ahJk2jub39Lnbt3d5gJVsDYsaSJioKTIQDuFnN7aWhokJuFyspKt67HW+DKEMmPs3VtK/lxFtzk/fvpxF/+Qrq6OvLv0EHKDbnssK3ReDP/lOBgmGAB4OlivnHjRtqwYYO7l+E1sIgacnPFKMtYVdVqI1B5Xh4d/sMfKC8uTu73Hj1aGoBC+/Rpd+u9mGCNHm32T3FQHToAasKjxHz9+vX08ssvN4vMo6Ki3LomtcJlfrqLF6WGnCNq3x49bhlZc6VK3K5ddOZvf5PcuF+HDjTtmWdo9IoVbbKqbdZ636GDORLnhh/4pwCgDjEPDAyUG3BBWiU+ngwFBeQTHHxbo6zrmZniqVLEg5l5jNuECTT7//0/Co2IaNNzs4jzpwDp2hw8mPy54Qet9wCoS8yBC9Iq2dmkS05uNa3CEfj5zz+nmG3bpNyQK1WmP/ccDb/33jblxi1dmzxKztq1GRaGEkMAPEHMq6urKSMjw3o/MzOT4uLiKDw8nPr27evOpXkdLKba5GRztYpGc9u0Sk5MDB197z0qz8mR+wPuuotmvvgide7Wzf7n1WrNXZsaDflFRZlF3I4hFgAABYj5+fPnafbs2db7lnz4k08+SX//+9/duDLvwlBcTNqEBDIWF5urVW6xwVhTWkonNm2i9B9+kPsdw8NpxurVNHj2bLvF19J6z3XqmogIc9fmbd5AAAAKFvNZs2aZLUqB+6YBsUlWWtq/m4Ba2LDkDc6kffukg1NbUyMCPGr5cvEbD7RzU/Km1nuOxLn13o65oACAm0HO3Ethf28uOdTn5srEnVulNq6lpdGRP/6RitPT5X6PoUPFU4W/2i3i7GRoNJKmWzcxweKIHCIOgGOAmHsZ3NQjm5wpKeZNTvZWaWG2Jg9UPrt1KyXu28c7o7LBOe0Xv5DmH3vKDcU/hXPibI/btatE4po+fdB6D4CDgZh7EcbaWvFVYTG/1SanJaVyZutWaqiqkmPspcJdnJ3Cw+0ScfFPMRikKoVz4iLiNkwfAgDYD/6yvKXkkCcBcclhefktZ3Nylcqx99+n0qwsud914ECasWYNRY4fb7+I6/Ui4gEcibN/CkQcAKcCMVc53ISjTUkhfaNAt+R0WJGXR8c3b6bMEyfkfoeQELrjmWdoxOLFNqdUWLxNlZXNRZydDFtI4QAAHA/EXM3ReH6+ORovLTVH4zcYZGlra+n8Z59JK75RpxORH7NiBU1+8kmbJ/9YNzY5ncJ2tIMGQcQBcAMQc7Xmxi9e/Hc0zrnxJtG4Qa+nlP37ZepPHW9ONlrUcs14eP/+tteJs4ibTOaNTRZx5MQBcBsQczW6HHKlSlnZTdE4//zK8eMyLMLSvRkaGSlt+P2nTbOpYUfa7isr5VwpMWQR5xJDDIYAwK1AzFU0k5NFXCpVfH1visYLkpLEY7wwOVnu8wzOKU8+SSOWLCFNK5uT0thVXy+ljFwFw2PZ/AYOJA2afQBQDBBzNdSNZ2WRLjXVXDd+Q6VKWU6OROJXjh2T+2xPO/7BB2n8I49QwG3cEJv5iVdXy2P69etn9hPHxHsAFAfE3IPhVApH4/q8PLGMbVo3zj4q5//xD0r++mupHecoffiiRTTlqadaNcSS8Ww82aeuTnxa/KKjRcjhYgiAcoGYeyDso6K7fJn0ly6J4Dbt4uTOzdjt2ylhzx7SN47Y63/nnXTns8+2urkp5YU8Y5N9WoKDzZN9oqLkewCAsoGYexCc9jAWFkrduLGkxOyp0hiNN1RXS4lh3BdfkK62Vs7vNXIk3fGLX1DkuHGtb2pyPpwrU7p0If+BA8mPK1NuM+sTAKAsIOYegrGmxlxuePWqWXTZGEujkaHJCV99RRe2b6eGxgHX3YcMoalPP039pk69ZYWKNR9eU0Pk5yebmTwUQjY10a0JgMcBMVc43B7P9eJsU8slgRa/cZ70k/TVVzLpp7asTM4N69ePpq5aRYNmzLilG6G023MqpaHBnA8fPJj8+vY1TxWClzgAHgvEXMkpleJiicYN166ZNzh79pQRban799O5f/yDqouK5NyQiAgpM4yeO/eW7ffWVAq7F4aGkt+wYeQXGYkhyQCoBIi5UlMq6elScsidlrzBaTQaKXnfPor5/HOriHfq1o0mP/GEzN1sqVZcUik1NeZUir+/DIPgqhSuE4dnCgDqAmKuILiaRFIq6enmLsuQEDJ17EgJe/dKTrympETO69i1K0185BEauWwZ+QUE3Pw4Op05lcJ+Kx07mksLuSoFqRQAVAvEXCkplWvXpPFHUiqBgWQICaHk/fulzNCSE+/cvTtNePRRcTO8UcQlCq+rMzf4+PqSD1el8IZmRAT5ttIcBADwfCDmbobNqnRpaaRnrxSjkfQdO5pFfOdOqwlWcM+eNPGxx2j4PfeQ5kYR59pwbvCprzdvaPbvb47CuWQRfikAeA0QczfBG5LS+JORYfYc9/WlxAMHKGH3bqpvLDHkjc1JK1fS0AULmuXErVE4D1cmIp/QUPIfPtwchaPBBwCvBGLuYrg0UGZwcl68vJxq6uoo/sABSvn6a9LV11udDCc//riMa2tanSK5cI7CtVpp6BGvFK5I4SgcteEAeDUQcxd3b3JKxVBUROVFRRR34AClHTok5YZMt0GDJJ0yaOZMq4g3a+5hN0SOwhsrUhCFAwAsQMxdIeKlpVKhosvNpWvp6ZTw7bd0+fhx6eRkIsaMoYkrV1LfyZOtjTtSF15dTaTXmytSBg2SFntxLEQuHABwAxBzJ8LlhbqMDNJlZtLlY8dExK+lplp/zgZYEx99lHqPGmVNwXCNOefDpS68a1dzd2avXqhIAQDcFoi5k8a26S9fpprEREo5cIASv/uOqouL5We+/v40dN48Gvfgg9R1wABz5N7oGU4+PtKRyZ7hspnJbohosQcA2ADE3NEVKpmZVHriBMXv2kWpR4+KEZZlss+o5ctp9LJlFBQWRqTVkuH6dXMahUsKOQ/ep490aaI7EwBgLxBzB8BVJrqrVyl7924R8czz5635cPYQH/fAAxQ9fz5pfHxkI9NYVCReK5rwcNL07SsCzhE5AAC0FYh5O+CGnbqLFynpww8p8auvqCw31/qzvlOmSColcuxYIk6jlJWR0c+PfENCyG/oULOAh4UhjQIAcAgQ8zaKeNGRIxT35z9T6qFD1lQKz9fkfPiYFSsorHt380ZmRQX5cB6cByCzgLM/CqpRAAAOBmJuB4b6ekr/+98pbssWyouLsx7vEhVFo5Yto6HTp1MA+4j7+Ihga4YMIT+uB+dBEmjqAQA4EYi5DZRnZFDin/9MSZ9/bnUuZDOr/tOm0aiFC6lPdLTcZ0Mr3969ZVqPhgW8BUdDAABwBhDzW8DDkDN276aETZso+8QJ64YmV6WMmD+fRsyaRcHcwGMR8J49ScP3IeAAAG8U802bNtHbb79NBQUFNHLkSHr33XdpxowZblvP9YsXKWHLFkr59FOqa7SeZXgjc/js2TRg8mTyDwuTRh4/zoEjAgcAeLuY79ixg1588UUR9Lvuuou2bNlCixYtopSUFOrbt6/L1lFbXExpX3whAl5w5oz1eMcuXWj4rFk0bP586jJokPihaHr0MG9i+vu7bH0AANAaPiZuQXQTU6dOpQkTJtDmzZutx4YPH0733Xcfbdy4sdV/X1lZSaGhoVRRUUEhISF2Pbe2poYu791LKdu20dXvvrOaXXHuu9/48TRi4ULqN3Mm+UdFSf5bujFRhQIAcDJt1TW3ReZarZZiYmLolVdeaXZ8wYIFdPLkSac9L4v2wVWr6NKePaRjJ8JGug8cSNGzZ1P0kiUUMnq0+KKwTzja6QEAnoDbxLykpIQMBgP17Nmz2XG+X1hY2OK/aWhokFvTdzB78fXzo4qsLBHykN69RcCHLlxI3efOlY5Mbq0HAABPw+0boDdGvpz1uVU0zKmXDRs2tPs5Z3AKp66Oeo0ZY65A4dpwAADwYNymYt26dSONRnNTFF5UVHRTtG5h/fr1kkey3HJ4bmYbiJw+nSLnz5dqFAg5AEANuE3MAwICaOLEiXTo0KFmx/n+nXfe2eK/CQwMlA2BpjcAAABuTrO8/PLL9MQTT9CkSZNo2rRp9OGHH1J2djb96le/wu8GAAA8Rcwffvhhun79Ov3P//yPNA2NGjWKDhw4QP369XPnsgAAwONwa515e2lPnTkAAKhJ11DGAQAAKgBiDgAAKgBiDgAAKsDtTUPtwZLub0snKAAAKBGLntm7nenRYl5VVSVfo6Ki3L0UAABwuL7xRqhXVLMYjUbKz8+n4ODgFi0A+B2OhZ47RT2x2gXrx/XH68f7/n5NJpMIeUREBPnaYTXi0ZE5/0cjIyNbPc/Tu0Wxflx/vH686+831I6I3AI2QAEAQAVAzAEAQAWoWszZmOv111+Xr54I1o/rj9cP/n69YgMUAACAF0TmAADgLUDMAQBABUDMAQBABUDMAQBABShezHmI8+TJk6XLs0ePHnTfffdRWlpas3N4D/eNN96QjqmgoCCaNWsWJScnNzunoaGBnn/+eZk92qlTJ1q2bBnl5uY2O6esrEwmH3HBPt/4+/LyckWsn49xl2vT2yOPPKKI9e/evZsWLlwo15bXFRcXd9PjKPn627J+d1z/1tau0+lo3bp1NHr0aLmm/Pr5+c9/Ll3RnnDtbV2/kl/7b7zxBg0bNkzWHxYWRvPmzaMzZ8645/qbFM7ChQtNW7duNSUlJZni4uJMixcvNvXt29dUXV1tPefNN980BQcHm7788ktTYmKi6eGHHzb17t3bVFlZaT3nV7/6lalPnz6mQ4cOmS5cuGCaPXu2aezYsSa9Xm8955577jGNGjXKdPLkSbnx90uWLFHE+mfOnGl69tlnTQUFBdZbeXl5s+dy1/o//fRT04YNG0wfffQRV0aZYmNjb3ocJV9/W9bvjuvf2tr5+efNm2fasWOHKTU11XTq1CnT1KlTTRMnTvSIa2/r+pX82t+2bZtc18uXL8t5zzzzjCkkJMRUVFTk8uuveDG/Eb5I/Ad39OhRuW80Gk29evUSQbRQX19vCg0NNf3lL3+R+/yL9/f3N23fvt16Tl5ensnX19d08OBBuZ+SkiKPe/r0aes5/OLiY/xCc+f6LS/otWvX3vJx3bX+pmRmZrYohkq+/rasXynX/3Zrt3D27Fk55+rVqx517W+1fqVce1vXX1FRIed8//33Lr/+ik+z3AiPUmLCw8Pla2ZmJhUWFtKCBQuaNdvMnDmTTp48KfdjYmLkI13Tc/gjHc8ctZxz6tQp+XgzdepU6zl33HGHHLOc4671W9i2bZt8VBs5ciT9+te/trpGunP9tqDk628P7r7+tqydz+E0RJcuXTzy2t+4fqVce1vWr9VqZSg9P+/YsWNdfv09ymiLP0m8/PLLNH36dLkYDAsh07Nnz2bn8v2rV69azwkICJCc1o3nWP49f+W82I3wMcs57lo/s3LlShowYAD16tWLkpKSaP369RQfH0+HDh1y6/ptQcnX31bcff1tWXt9fT298sor9Nhjj1mNnTzp2re0fiVc+9bWv3//fsnh19bWUu/evWVd/Mbj6uvvUWK+Zs0aSkhIoOPHj9/0sxstcPnit2SLe7tzWjrflsdxxfqfffZZ6/f8YhoyZAhNmjSJLly4QBMmTHD7+tuCkq5/a7j7+re2do7+WFDYFnrTpk0ed+1vt353X/vW1j979mzZNC8pKaGPPvqIHnroIdkEbUmgnXn9PSbNwrvB+/bto8OHDzezveV3a+bGd7CioiJrtMvn8Ecg3jG+3TnXrl276XmLi4tvippdvf6W4Bexv78/Xbp0ya3rtwUlX/+24srr39raWQhZQDhlx1Fh06jWE6797dbvCa/9Tp060eDBgyU18vHHH5Ofn598dfn1Nykc3iBcvXq1KSIiwpSent7iz3kD8a233rIea2hoaHEDlHfNLeTn57e4CXHmzBnrObwh0d5NFEesvyW46qXpZoy71m/PBqgSr78t63fX9bdl7Vqt1nTfffeZRo4c2ayCwlOufWvr95TXflMGDRpkev31111+/RUv5s8995wI25EjR5qVJtXW1lrP4UoQPmf37t3yi3700UdbLE2MjIyUXWYuD5ozZ06L5UFjxoyRnWS+jR49ut3lTY5Yf0ZGhpTOnTt3TgTnm2++MQ0bNsw0fvx4Raz/+vXrIoC8Ln4B8s493+fzPOH6t7Z+d13/1tau0+lMy5Ytk+vKpXNNz+GAQOnX3pb1K/m1X11dbVq/fr08X1ZWlikmJkZKEwMDA6VM0dXXX/Fizn9cLd24/rPpOyi/E3KEyxfy7rvvFlFsSl1dnWnNmjWm8PBwU1BQkFyo7OzsZufwH/XKlSul5ptv/H1ZWZnb18/r5GO89oCAAHnnf+GFF2S9Slg/f9/SOZboROnXv7X1u+v6t7Z2yyeJlm6HDx9W/LW3Zf1Kfu3X1dWZVqxYIZE7r40DMH5z4vLKprjq+sMCFwAAVIDHbIACAAC4NRBzAABQARBzAABQARBzAABQARBzAABQARBzAABQARBzAABQARBzAABQARBz4FVwYx+P9uIxcTfCbn3sIZ2dne2WtQHQHiDmwKtgS9GtW7eKRemWLVusx9mxj+dRvvfee9S3b1+HPie7AgLgbCDmwOuIiooS0eaJNSziHK0/88wzNHfuXJoyZQrde++91LlzZ7Ef5cG67FNt4eDBgzKggCfhdO3alZYsWUKXL1+2/jwrK0veMHbu3CmDiDt06ECfffaZm/6nwJuANwvwWnjaOk9Av//+++l3v/sdnTt3ToYe8DAEnhJfV1cn0bper6cff/xR/s2XX34pYs0T5Wtqaui1114TAefhBL6+vvI9T8Xp378/vfPOOzR+/HgZA8ijwgBwJhBz4LXwgACeXHP9+nXatWsXxcbGSvrl22+/tZ6Tm5srkXxaWhpFR0e3OECAJ8okJibKY1nE/N1336W1a9e6+H8EvBmkWYDXwiL8y1/+koYPH04rVqyQ4bs8TYZTLJbbsGHD5FxLKoW/8ozKgQMHykQcFm7mxk1TjvABcCUeNQMUAEfDI774xvD8yaVLl9Jbb71103k8qJfhn3OkzrMeOXXC/4Yjch4NduMoMQBcCcQcgCazJTknzvlui8A3hdMxFy9elCqYGTNmyDFHDbcGoL0gzQJAI6tXr6bS0lJ69NFH6ezZs3TlyhX67rvv6OmnnyaDwUBhYWFSwfLhhx9SRkaGbIq+/PLLuH5AEUDMAWiE0yYnTpwQ4eamIk6f8CYmNxJxpQrftm/fLrl1/tlLL71Eb7/9Nq4fUASoZgEAABWAyBwAAFQAxBwAAFQAxBwAAFQAxBwAAFQAxBwAAFQAxBwAAFQAxBwAAFQAxBwAAFQAxBwAAFQAxBwAAFQAxBwAAFQAxBwAAMjz+f+Eh9AKWgJ2RQAAAABJRU5ErkJggg==",
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0wsDwcBqxYAGNWryYQrt1I2/AJjHnxh5On4TYMMPhyJEjMtPclrG5PN8FAGAbLN6cUuEuTuTGiSoLCyUK52jc0DyhMKJnTxq9ZIlUpgR4mbGGTWKekZEh7kLsHsQdnrxRyd6fjF6vl+/zhugHH3wgUfbGjRudvW4AvGqminRxZmebuzi9uG6c9wlKU1MlH855cQsxCQlSXjhgyhTFpxeqWsxZnFNSUuj1118Xn08umfH19ZXKEjZ1ZtjU+aGHHpLSRVScAOAY4TJWVJij8dJS8gkK8tq6cd7UzNu/X0T8fFaW+aCPD/WfNInG3H039Rw50iuvS6fqzPl0FnYejtXQ0CCuQjfccIMi7kKwjQOeiqmhgXSnTklEzgbLMlPFT9EtLkXQNjRIu/3JrVutvpps/pBw++3Sbq/mmSkmtdeZ87vf6NGj5QYAcEK5YXGxeYPz4kXyCQ83Gyt7WdTJnZq8ocnemk3N1mtBERE0cvFiGrlwIYW4wO3e3fC+t3oA1DwYKzNTqlVMPj7mlIpKjA9cBadQeFMzd98+qxEEW7JxKoUt2fyDgpReomqBmAOgMCaD4fIGZ20tabp0IY0XNf9Y8uHsq9myyYc7NTmV0u/mm712U9MeIOYAKLnBWV5ujsbPnfO6MbVNtbWUvnOnONyzv6ZlhvjgGTNkemHMkCFKL9GtgJgDoACmxkbzBmdenmyUSbnhFXOOPJXKoiJK2b6dsnbtsk4u5PGz3OTD42dDo6KUXqJb4lAx37p1Ky1ZssSRDwmA525wVlaST1iYV2xw8u9dnJQk88MLDh2yHufJhRyFD5k1i/y8tHZeETHnBqHs7GyZlMjTEy18+umn4t2ZlZUFMQfgGvBoWqkZLy42b3CyobKHb3Dqm5ro1J49dGLbNrrYYq4T58E5H84GyZ7+RqY6Mecuz/nz59OZM2fkPnt1ssXb3XffLQbPPN/8iy++cOZaAXBLOI2iy8sjw+nT5g3Orl09foOztryc0j/7jFI/+8zq5MOVKAlz58q8lK7x8Uov0XvF/JlnnqH+/fvTq6++Sh9++CFt3rxZTJ3vvfdeEfFwN7RZAsDpG5znz5OWR9ReuEA+wcEePd3Q0mqfsmOHVKdYSgvDe/SQ+vDhd9zhlnZsHifmR48epS+//JLGjh1LkydPFjH//e9/Tw8++KBzVwiAu9q3ZWWRnkfUGgykiY4mHw8tr+NNzFPffitVKeW5udbjvUaNkijcm+elqFLMy8rKqHfv3vJ1ly5dZILi1KlTnbk2ANyzZrygwFwzzvZtvLnpodP7uL2eTZEzvvzS2qXJHpq8mTlq0SKKbnYQAyoTc/5oqGmxWcNfX2kZB4C30moo1vnzUmboiSkV/j2Ljh+XKDyfq1KaRzuxKfLIRYto+Ny50nYPVCzm/J/IFSyWF2dtba1MSmwp8MzFixcdv0oA1F4znp19eSiWB9aMa+vrZW4458Orioqsx+PHj5dUSt+JE5FKcRcxf++995y7EgDc0fWHa8azsjx2KBYbQKT8+98i5Lrmcdf+ISE07LbbJBJX89RCb8NmMec55ddDp9OJMQUA3gA3/LCIi+sPe3B60FAsrkLhxh7Ohxf99JP1OAs3C3jCbbdRgA2uY8BNO0C5Dp0rXQzN5UgAeKrrj7Th5+TIzHFPcv3hsbO8mZn+xRdUV17eygCCUylxY8d61KcOTwOzWQCwo/lHe+KEzFPxCQ31iKFYnCoqSkqitM8+o/wff5T7llkp7KPJ81IiYmOVXiawAYg5ADZgamoibVKS1I3ziFqecOjONFRXSx6cRbyaU0UtasNZwAdOmUK+HvKJw1uAmAPQDsb6etIeP06Gs2fN9m1uWqnCFWk8L5wFPGfvXqujfUBoqBg/jLjzTurWv7/SywTOFnP2/bwePIALAE/s5NQeOya149LF6YY+nFxWyMOueEOzIi/Perz74MEycpbnhwd4aGOTN2HzK5NNmzk/2Jb/s+W4u+cPAWiJsbqamo4dk2YgmXDoZi3p3FrPUXj27t2ka2iQY5w6YfEeuWABxSQk4G/WG8U8v8X4SgA8HUNFhaRWjFVVbjWqlueksH8mGyG3tGDrEh8vuXAuK8SwKy8X8759+zp3JQCoBENZGWl/+sk8rpaF3A0+cV44fVpKCnnglbauTo7xcKsBkydLKqV38ydr4LnYLObcpl9fX09xcXHWY+np6fTnP/+Z6urqaNGiRXTPPfc4a50AuAR9SQnpkpPJ2NioeiFnD83T330nIs5ibiGiZ08ZNzvsjjtgweZF2Czmq1evpp49e9Jf/vIX6xTFKVOmUK9evWjgwIH0i1/8QhqG7rvvPmeuFwDn2bkVFJD25Ekio5E03bqpUsgtFSlshMwVKfpmD02Nvz8NnDyZhs+bR3Hs3uMmaSGggJgfPny41XyWjRs3UlRUFJ04cYL8/PwkQn/99dch5sDtYIHkrk5derpscvpwV6fKhJzrwrO/+YYydu6ki81uX0zXvn0pcd48KS0MjoxUdI3ATcT83Llz4jRk4bvvvqPFixeLkDMLFiyg9evXO2eVADhx/jiPreX549wIpFGREw53YxYnJ4uA5x44QMbmunC/oCAaPG2aROGxiYmqe+MBKhfziIgIqqqqsm6EsvPQqlWrrN/nF1RTU5NzVgmAk+ascFpFn5tLPmFhpFHJ8Cj2z8zatYsyvvqKas6etR7vPmSIROFcWhgYFqboGkHbyDgErVY6hnk0sis7hW0W8wkTJoj/54YNG2j79u106dIlmjFjhvX7p06doniYtAJ3bM/nsbVBQYpPKjxz5IhE4QWHD1tnpEh35qxZEoVzkw9QDybuudHpzMLNgWzz/xkPXvMJCSHfuDjy5Y5hF5l12Czmf/zjH2nWrFn0wQcfkF6vpz/84Q/UtWtX6/c3bdpkt40cp2X4jSErK4uCg4Np0qRJ9OKLL9LQoUPt+y0AsANjXV3r9nwFZ5Cw9RpPKszctevypEIi6jlypEThA6dOFVd7oI6UnIlFm8W7OeVFfn6kCQ4m3+7dzRM0eaZ9RIQMYnN1+suuDtDMzEw6ePAgxcbG0sSJE1t9f9myZTR8+HC7nnzfvn1SJXPjjTfKG8R//ud/0pw5c2ScbmhoqF2PBYAtcBNQEzcDlZcr1p5v0Gop78cfJQpnCzYLQZGR0tTDIg7TB2UxcZTdHHGze5TY4/HmOEfdERHkFx0tA9dEuPmmgu5gH1Nb/fkKceHCBYqJiRGRv/XWW9s9v6amhiIjI6m6ulpy+gBcD8OFC+auTjZaZiF3cfkeV6GwgGd98w01VlebD/r4UPy4cSLg/W+5hXzddIiXO2NiCbTkuTny5vs+Pmbh5lHH3bqJaMuNN8gDA50adXdU12wOS7gU0Rbuv/9+6ii8eIZLHtuCN1hbbrLyLw2AXc1AbCjhwmYgbq/nenAW8dK0NOvx0OhoMT/mxh7MC1cgz601izfp9ZeFOyiIfGNjJV3Cos0pE859u0u1kM2RORs3h4WFSSnitX6Ef+mOGjrzYy5cuJAqKytp//79bZ7z/PPP07p16646jsgcXO91ZcjPJy1P/TQaXVZD3lZ7PX8S6HfzzRKF95kwAQbIrsxza7XmdAnj52cuQ42MJF9uDmuOuLmiSQ3NVh2NzG0W88TERDp//jzde++99Mtf/pJGjRpFjoRz5zt37qQDBw60GhnQXmTOFTQQc3CtvKcuO1vqyDk3zn+8zoRFm8X7qvb6Xr3M7fW3306h3brhP8tV1SUGg/izsnBzusS3ZZ6bxVul44ydnmbhOSxHjhyhd999V/LZgwYNkjrzFStWdDpf/dvf/pY+++wz+uGHH64p5ExgYKDcALCphjw1Vbw6fYKDSeOkumxpr8/IoIwvvqDTaK9XNuo2mYj8/UnD6ZK4OKlUEuFuLj11l3SJSzdAGxoa6JNPPpH2fm4e4iFbLPL2Ci0/NQv5jh07aO/evTTYzjpabICCa9aQs1dnQYH5j9kJxgvSXr97t7m9vqDAepyrULgmnKtS0F7vhGYcS66b718ZdUdGmnsGVFJdoto0S1twJP3cc8/Jv+Xl5a3qzm3hN7/5DX300Uf06aeftqot51+E687bA2IOrmnxVlLilBry85mZdGLrVsrbv99qu+YXGEiDpk2TXHjsiBEeHwG6tLqEI25LusTf3/wpiyPulmWBHhZ117hKzEtKSuj999+XqJxH31py6AkJCXYv+lr/AfzYPIWxPSDmoCVcctjEc8gvXDBPPXRgTlTf1ESH33lHhFw+zhNR9KBBIuBDZs1Ce30HEfnR6y/nuVtWl/AmJVeWWJpxOM/NzTgq2KR065z5li1bRGS5Bvy2226jl19+mebNm0e+nfg4o6ISd+DmGMrLpT3fWFlpriF34Mdsjsb3vPACVRYWyn0W79FLllDMkCEeFRE6G/l7b6uLkvPcgYHk27On+dOURbhVUl3iLthVmtinTx/Z8OzRo8c1z3vsscfIVSAyBwy35Wu5hry+3hyRO0gAuFPz6MaNlPTxx5KzDYmKohlPPinlhcC2DcpW6RKmuYtSGnAsG5SWFniVVpd4XJqlX79+7UYh/P28Fu7fzgZi7t1YDSVSUqR6RaI6B0XKF3JyaM/69VY3+yEzZ9Ktjz1GQeg07tAGJb/J+vLmpCXqRlWacmmWghY79gCorobcQULO0wuPf/QRHXv/ffma56VMW7OGBtk5RM5rpwXy0KlevcyVJR66QalW8LkGuB3OqiHnEkPOjZdlZ8v9AVOmiJCH2Fml5ZEblJwmseS5WZj5DZTruXv3NqdLONpm4eb2d+S51S3mXFv+7bff0vz58+X+2rVrW3Vj8kYoj8kNwrhO4GY15ByBc5XKkXfekXJDNn7glApvdHpbRHnVmFcWc0v7Ow+cajF0SjYokedWDXYN2vriiy+sYv7aa69Ji7+lHpxnkrO585o1a5y3WuDVOGMOeVVJCX37wgvWIVh9J06k6f/xHxTWvTt5RdTdvDl5VZ7bMua1ZZ5bwbnvwIFi/uGHH14l1NzwM2DAAPmaTSvY0BliDpw2h5xryCsqHDKHnHPuqZ9+Sgffflsc7v2Dg2ny6tUyQ8UTo/HrTgu05Lm5EceSLkGe2+2w+S+CbeGGDBlivc/pFC5XbGkrx8OyAHA0hrIycw15dbV5fG0nc7I1587Rd3/6k5glM73HjKGZTz3lUaNoW6VLON/dnOfmqNuPx7w225lZXXGQ5/YeMecyGR5/29JIoiVGoxGGzsDxpYeFhWK6zIKkiYnpVNTMj8cWbQfeeIN09fXShj/p17+mkQsXurWYtVkWaKnn5qoStjSzCLeKpwWCzmHz/ypPM0xLS7umP2dKSsp1Jx4CYK/wWksPNRpzM1AnhLz2wgX6/uWXxTSZiU1MpFnPPENd3Ow1e932d06XcBclt8A3i7ezXXGAG4r5HXfcQc8++6y08F9ZscKVLmwawd8DwBEpAl1aGulOnZLcrVh1dfSxTCaZbrj/b3+jptpasWW76Ve/otF33eUW5hDWqLulpZklXdKjh/lNDukSYE8HKBtTsKlzQEAAPfroo5I/53d8rmLhyhY2ZE5OTr5uq7+jQQeo58GpAik9zM/vdOlh/cWL9P1f/kL5P/4o92MSEiQaj+rbl9xidglH3ZZmnJAQ2fj15aFTFvGGX6hHUuPsDlAW6YMHD9IjjzxCzzzzjHVIFgv67Nmz6Y033nCpkAMPHV+blESG4mLzpLxOtHyf/v572vfKK9TI5s1+fjRh5Uoau3y5qqJxa2mgJepmmksDOc/NM7pFuLk8kDcpkS4B18GunZD+/fvTrl27xOczJydHjrHj0LUMmAHo0PjaTpQesmkEizibKDPRAwdKNM7jalVTYdLYeFXU7RsfT75dulyOulHTDeykQ38xLN5cigiAIzBcuGAuPayq6tT42rwDB2jvX/5C9ZWVsmk6fsUKGn/ffZInVwIWbBMLd2PjZYMF3gPg9neuMLE446A0EDgA1CgBRdEXF5PuxAkyNjR0uPSQNzZ/+NvfKPubb+Q+58Rnrl1LPa5ReeUy8ebywKAg8uOGnJa2Zhh5AZwAxBwoli/W5+WRLjVV6qIlIu+AkJ85epS+e+klqisvlxK9sUuX0oQHHiA/F6QpJG3C4t3Q0Fq8efiUxQmeUyfYqAQuAGIOlCk9zMggPU8n5MaWLl3sfgxtfb00/7ChMhMZF0eznn6aeo4YQU6Pvi9dMndVNqdN/Li2m9MmXN8N8QYKATEHLoUn8XFHJ0flPHVPExJi92NwG/63L75Il86fl/ujfvYzuvnBB8nfiekLFnEeJ8AROOe8OXXiy7nvTlbdAOAoIObAZXBeXJeUJHlyiWDtFEFdQwMd3LCBUnfskPvhsbE08+mnKe6GG5z65iMibjKJiPsPGiQzvNESD9QGxBy4rPRQxteWlZmHPNmZRy5NTaU9L75I1SUlcj/xzjvplocfpoAORPb2ijjXe/sNHCiTBSHiQK1AzIHTMVRUiJAbKyvtLj3Ua7V0+J136MQnn4iw8pzxGb//PfW58UbnpVOqqi6LOEfiLOIqajYCoC0g5sCpsJGENjlZjCXsHV97PjNTbNwqCwvlfsLtt9OU1avFCchpOXGjESIO3BKIOXDe+Nr8fPHqZKEUIbex9JCt245t3CjGyjxoKiQqStx/+k+a5NyNzejoyzlxROLAzYCYA4fDAqzLyiJdZqbkmCVHbqOQX8jJoT3r11NFXp7cHzxjhvhxBkdGOl/EkRMHbgzEHDgUKT1MTSV9To60qWtCQ236OYNeT0kffSQRORssB0VG0rQ1a2jQ1KmOb/SprjZ/WuDqlMGDUZ0CPAKIOXBs6WFyMumLiuxqW79YUCC58TJuIiKiAVOmiJCHdO3qWBGvqZE3G64ND+BIPD4e1SnAY4CYA4fAKYsmrljhqYc2lh5yBM5VKkfefVfy5LyxySmVIbNmOWzcaysR79LlsoijxR54GBBz0GkM586ZK1YuXbK59LCquFii8XPp6XK/78SJNP3JJyksOtpheXsR8aYmEXHOifuxiGO0LPBQIOag88Oy0tLMka8NFSsssqmffkoH33qL9E1N5B8SQlN+8xsadscdDonGRcR5dkpjo6R6/EeMIL8+fSDiwOOBmIOOb3Smp5OBTUp4WJYNhss1587Rt3/6E5UkJ8v9uDFjaMZTT1FEbKxD3lhExHmUbkQE+Q8bRn59+2JuCvAaIObAbrgBiGeQy4wVG3w6WWgzvvxSphzq6uvJLyiIJj30EI1cuNCuJqJrPbaptpZM3JQUHk5+o0eTf79+mBkOvA5FxfyHH36gl156iY4fP06lpaW0Y8cOWrRokZJLAra4ArGZxMWLNm101paX0/d//jOdOXJE7scmJoqNW5e4uM6LeF2dWcRDQ8lv5Ejy69+/Q1MYAfAEFBXzuro6Gj16ND3wwAN01113KbkUYGtHJ+fHeVOxndZ8Pv/Unj30w6uvihMQW7dNXLWKbliypFOmyiLi9fWSUmHvTEmnDBhAGie0+APgTigq5nPnzpUbUDdsxMAibuCuTH//dl2B6i9epL2vvEJ5+/fL/ZihQyUaj+rXr3Mi3tBgFnE2hEhIIH8W8YiIDj8mAJ4EcubguvAEQTaT4PLD9hqBWHCzd++m/a+/Tk01NRKB37hyJY1dvpx8/Tr2UuPHZDd7HqHL88/9Bg8m/4EDZS0AADcV86amJrlZqKmpUXQ9Hp9WKSqSskOpH+dqlesIMleq7P2//6PCo0flfvTAgWIc0X3w4I6vgUW8ulrKCjmVwrXi3L0JAHBzMV+/fj2tW7dO6WV4RVqFh2TpeL6KRkOamJhrplUsdeOHNmwQJyDOjXM0Pmbp0o5H401NZhH39ye/fv1kprg9w7oA8EbcSszXrl1LTzzxRKvIPD4+XtE1eRpsIKFNSTGnVdopO+Q549+99BKVpqXJfTZTZuOIrn36dFzE+dOWr690a3JKxZb6dQCAm4l5YGCg3IDj4QjbcOYM6dLTzUYS10mrcOfm8Y8/lnnjRp2O/IODpW58xIIFHaob508CMo5WoxGjZInEr/NpAACgMjGvra2lHO4gbCY/P59OnDhBUVFR1KeD0R2wH64SkW7OggIinj9+HSHlenEuN6w+e1bu95kwgaY/8QSF9+jRYZ9Nfibf2FhzTjw2FiIOgLuJ+U8//UTTp0+33rekUFauXEn//Oc/FVyZ92xyGsvKJK1irKiQgVTXqla5VFZGB15/nXJ/+EHuh0ZH05RHH6WBt95qt/i2EvHu3SWdwmLe2W5QALwZRcV82rRp5tIz4HJYUHWnTpH+1CkZEyvReBtiyqYRJ7dto2P//CfpGhvlnNFLltCElSspwM5uy1ZmySziFncfiDgA3pUzBw7c5ExNJUNpqbgB+V6j3K/k5Ena98orYh5h2eCcumYNRQ8YYNfzwfEeAOcDMfciOALX5+dL2aFMF7zGJifXjB/8+98pZ98+uc8Wbrc8/DAlzJljVxTd0vGenwtmyQA4D4i5l8Alfzr25iwpkbx4W7PHtQ0N4sOZvHmzOP+wcCfOn083rVpFQXa0zcPxHgDXAzH3hpLDggLSZWSYSw7bmHTI53Ab/qF//IPqysuts8Ynr14tnZwddrwfOBBmyQC4CIi5p0fj6elisMwt8W2VHJamp9OB116j81lZcj+iVy+a/Mgj1P+WW2yuUrFUp8jGZnQ0+bGI88ZmBztAAQD2g782T82NFxSQPiuLjLW1Ms/kSu/L6pISOvzOO3T6++/lPtu33XjvvTT6rrvI10afTEuzD4t+KxHvxIhbAEDHgJh7GFz6J9H42bNtRuMNVVV0bONGSvv8czLq9UQ+PjR87lyZNR4aFdXu40spaYu2e64P5yFYvj17osQQAAWBmHsInK/mwVhSN86VKlfkxnkI1olPPqGkzZvFus3SvTnpwQcpetAg200h6urMA7B4dgrPE2/HpAIA4Bog5p7QxVleLhucPByLB2O1jMaNBgNl7NxJR99/X0wjmO5DhtAtv/41xY0da5vbPduz1debTSEGDpRJhphiCIC6gJi7MTzvW7o48/Ikf92ybpxFPu/AARlNW1VUZN3cvHnVKho0bVq70TTn3U01NebHDQsjv+HDxe0ezj4AqBOIubsaRxQXS/MPd3P6hIWZXYB8fOR7bBBx5L33qCw729r0c+P999OIO++UeePtbmpyPpwbfbp0If8RI8g3Lo401xmFCwBQHoi5O5YbZmaKC5DJx6dVzro4KYkOv/sunUtPl/v+QUE0+uc/p7FLl1JAaGj7/pq1teZNTZ6b0r+/eVOzHfEHAKgDiLk7DcbKzSX96dOSv+aoWdM82/1saiodefddKjlxQu5zaeGoxYtp7LJlFNyly/Xz4bW15nx4cLAIuKRS2LAZm5oAuBUQc3fY4CwtJS2nVMrLW21wcqMPi3jhsWNyrsbfn0bMn0/jVqyg0G7drp9KuXTJ3KnJ+fBRo8iPUylwugfAbYGYqz2lkpUlHZzcXSkRs68vXcjJoaPvvUf5Bw/KeRpfXxo2dy6Nv+8+Co+Jabe0UFIpUVFSlSJNPnBvAsDtgZir1VA5L48MOTnmeSq8uRkUROczM+nYBx9QQbOIcypk6OzZsrkZ2atX24+l15tTKU1N5lTKgAFSI476cAA8C4i52qpUSkokGmfnH0tKpTQ1lY79619U9NNPVhHn8kI2iGjLPFm6NBsbpZWf4TcD/2HDJArXhIe7/PcCADgfiLlK4BJDFnEWc/Ze8unWTcwhuPX+bErK5Uh8zhwad8891DU+vu3acI7CGxrMDT69e5Nvnz5mSzYMvQLAo4GYKwwLr7Thc+MP27JFRlJRcrKkU85nZMg5Gj8/yYmPW76cInr2vHYUznn1iAjx1GSXe34sONwD4B1AzBWCc9mGwkLp4OThWKbgYMrPyKCkjz+mC6dPW0sME+fNo7HLl1NY9+7XzoUHBpqj8Ph4cxSO2nAAvA6IuRJ58dJS0mdnk+HCBTIQ0amjRyl5yxYZS2tp9hmxcCHdcPfdrSYZtmru4YYhjsKHDiU/bu5BFA6AVwMxd2W9eEWFTDVkMW+sraWM/fspZft2qq+slHMCw8Np1KJFNOquuyg4MvLyz+p05ihcqzXnwvv2lRZ73x49kAsHAAgQc1fVi+fkkOHMGbpUWkppe/dS+pdfkpZrvokoLCaGbvj5z2n4vHkU0DwDxTqtsKFB6sJlTgpvZvbsKY0+AADQEoi5EzHW18vGJt9KU1Iodfduyj1wQISaierXT1ruB8+cSb5+ftY0inUzMzSUfHkzs3dvtNgDAK4LxNwJ8KakrqCAtNnZlLNnD6V+8w2db55gyPQeM0Yi8X4TJ0q5oXhoVlaa0yi8mdmrlzmNwpuZ6M4EANgAxNyBsCjrCwupNjmZ0rdvFxGvq6iwzk0ZOnOmeGyys4+lJtzIaRQ/P3NjD6dROA8eEYGSQgCAXUDMHVVmWFxM5/fsoZNbttCpfftIr9XK90K6dpXKFJ4lzhMMeT6KoazMXI0SGkp+3JnJeXA2lsCkQgBAB4GYdwKOrrVnztCp99+n1G3b6GzzHHGGo+8bliyhQVOnkoaj8Pp689TDkBCzi31sLPny9EPUhAMAHADEvIMiXpWURCl/+xulf/451VdVyXGOrPvfcguN+tnPqOfgwURsnMyuPTzgilMonAtnM4mgIEf83wEAgBWIuR0YdToq2LqVTrz5JuX/+KO1KiUkKoqGz59Pw2fOpNCQELFc8zEYzJuYnEKJiSENHwcAACcBMbeB2pISSnv9dUr917+ourjYerz36NGUOHcu9Rs1inx9fSXi9o2Ovizg17FqAwAARwIxvwZGvZ7ydu6k1DffpLzdu61RuH9ICCXMmEHDZ8ygqN69zQLevbtZwLt3h4ADABQBYn4FlTk5lLphA6W//z7VnT9vPd5jyBBKmDaNBt9yCwVERcnmJW9isvsPUigAAPJ2MX/jjTfopZdeotLSUkpMTKRXXnmFpkyZ4tI1NFy8SKe2bqWMf/2LSg4csB4PCgujoVOmUMLs2RQ9dChpuAKlRw/y5TJCbGICAFSEomK+efNm+t3vfieCfsstt9Bbb71Fc+fOpYyMDOrThoOOI+G5KHmff04ZH31EBbt2yeam4OND8SNH0rA5c2jA9OkUwFUonD6JikIZIQBAtfiYxN1AGSZOnEhjx46lN99803ps2LBhtGjRIlq/fn27P19TU0ORkZFUXV1NETY6y3MufNcDD9DpHTtI1zzoionu148GT51KQ+bPpy6jR0sTDw+3QiMPAMCVdETXFI3MtVotHT9+nJ555plWx+fMmUMHmw2Lr6SpqUluLX9pe2HXnpozZ0TI2bVnyLRpNGT2bIqZPVuib+S/AQDuiGJiXl5eTgaDgXr06NHqON8/d+5cmz/D0fq6des6/dyT//d/yaexkXqMHGlu4kEbPQDAzdEovYArPSo563Mt38q1a9fKRw/LraioqEPPGTd5MvWeNYv8eKgVhBwA4AEoFplHc3ONr+9VUXhZWdlV0bqFwMBAuQEAAFBJZB4QEEDjxo2j3bt3tzrO9ydNmqTUsgAAwC1RtDTxiSeeoPvuu4/Gjx9PN998M7399ttUWFhIDz/8sJLLAgAAt0NRMV+6dClVVFTQf//3f0vT0IgRI+jLL7+kvn37KrksAABwOxStM1eqHhMAADxN1xSvZgEAANB5IOYAAOABKD5oqzNYMkQd6QQFAAA1YtEzezPgbi3mly5dkn/j4+OVXgoAADhc3zh37hUboEajkc6ePUvh4eFtdo3yOxwLPXeKuuMGKdaP64/Xj/f9/ZpMJhHyXr16kcaODnW3jsz5F42Li2v3PL6Q7vhisID14/rj9eNdf7+RdkTkFrABCgAAHgDEHAAAPACPFnMeyvXcc8+57XAurB/XH68f/P16xQYoAAAAL4jMAQDAW4CYAwCABwAxBwAADwBiDgAAHoDqxZxNnG+88Ubp8oyJiaFFixZRdnZ2q3N4D/f555+Xjqng4GCaNm0apaentzqnqamJfvvb34pdXWhoKC1YsICKi4tbnVNZWSlmGVywzzf+uqqqShXr52Pc5drytmzZMlWsf/v27XTbbbfJteV1nThx4qrHUfP1t2X9Slz/9tau0+no6aefppEjR8o15dfP/fffL13R7nDtbV2/ml/7zz//PCUkJMj6u3btSrNmzaIjR44oc/1NKue2224zvffee6a0tDTTiRMnTPPmzTP16dPHVFtbaz3nhRdeMIWHh5u2bdtmSk1NNS1dutTUs2dPU01NjfWchx9+2NS7d2/T7t27TUlJSabp06ebRo8ebdLr9dZzbr/9dtOIESNMBw8elBt/PX/+fFWsf+rUqaYHH3zQVFpaar1VVVW1ei6l1r9x40bTunXrTBs2bODKKFNycvJVj6Pm62/L+pW4/u2tnZ9/1qxZps2bN5uysrJMhw4dMk2cONE0btw4t7j2tq5fza/9Dz/8UK5rbm6unLdq1SpTRESEqayszOXXX/VifiV8kfgPbt++fXLfaDSaYmNjRRAtNDY2miIjI01///vf5T7/x/v7+5s2bdpkPaekpMSk0WhMu3btkvsZGRnyuIcPH7aewy8uPsYvNCXXb3lBP/7449d8XKXW35L8/Pw2xVDN19+W9avl+l9v7RaOHj0q55w5c8atrv211q+Wa2/r+qurq+WcPXv2uPz6qz7NciXsvsFERUXJv/n5+XTu3DmaM2dOq2abqVOn0sGDB+X+8ePH5SNdy3P4Ix3b1FnOOXTokHy8mThxovWcm266SY5ZzlFq/RY+/PBD+aiWmJhITz75pHVqpJLrtwU1X397UPr627J2PofTEF26dHHLa3/l+tVy7W1Zv1arFR9jft7Ro0e7/Pq71aAt/iTBJtCTJ0+Wi8GwEDI9evRodS7fP3PmjPWcgIAAyWldeY7l5/lfzotdCR+znKPU+pkVK1ZQ//79KTY2ltLS0mjt2rV08uRJ2r17t6LrtwU1X39bUfr627L2xsZGeuaZZ+iee+6xDnZyp2vf1vrVcO3bW/8XX3whOfz6+nrq2bOnrIvfeFx9/d1KzB999FFKSUmhAwcOXPW9K0fg8sVvayzu9c5p63xbHscV63/wwQetX/OLafDgwTR+/HhKSkqisWPHKr7+jqCm698eSl//9tbO0R8LCo+FfuONN9zu2l9v/Upf+/bWP336dNk0Ly8vpw0bNtDdd98tm6BtCbQzr7/bpFl4N/izzz6j77//vtXYW363Zq58BysrK7NGu3wOfwTiHePrnXP+/PmrnvfChQtXRc2uXn9b8IvY39+fTp8+rej6bUHN17+juPL6t7d2FkIWEE7ZcVTYMqp1h2t/vfW7w2s/NDSUBg0aJKmRd955h/z8/ORfl19/k8rhDcLVq1ebevXqZTp16lSb3+cNxBdffNF6rKmpqc0NUN41t3D27Nk2NyGOHDliPYc3JDq7ieKI9bcFV7203IxRav32bICq8frbsn6lrr8ta9dqtaZFixaZEhMTW1VQuMu1b2/97vLab8nAgQNNzz33nMuvv+rF/JFHHhFh27t3b6vSpPr6eus5XAnC52zfvl3+o5cvX95maWJcXJzsMnN50IwZM9osDxo1apTsJPNt5MiRnS5vcsT6c3JypHTu2LFjIjg7d+40JSQkmMaMGaOK9VdUVIgA8rr4Bcg793yfz3OH69/e+pW6/u2tXafTmRYsWCDXlUvnWp7DAYHar70t61fza7+2tta0du1aeb6CggLT8ePHpTQxMDBQyhRdff1VL+b8x9XWjes/W76D8jshR7h8IW+99VYRxZY0NDSYHn30UVNUVJQpODhYLlRhYWGrc/iPesWKFVLzzTf+urKyUvH18zr5GK89ICBA3vkfe+wxWa8a1s9ft3WOJTpR+/Vvb/1KXf/21m75JNHW7fvvv1f9tbdl/Wp+7Tc0NJgWL14skTuvjQMwfnPi8sqWuOr6YwQuAAB4AG6zAQoAAODaQMwBAMADgJgDAIAHADEHAAAPAGIOAAAeAMQcAAA8AIg5AAB4ABBzAADwACDmwKvgxj629mKbuCvhaX08Q7qwsFCRtQHQGSDmwKvgkaLvvfeejCh96623rMd5Yh/7Uf71r3+lPn36OPQ5eSogAM4GYg68jvj4eBFtdqxhEedofdWqVTRz5kyaMGEC3XHHHRQWFibjR9lYl+dUW9i1a5cYFLATTrdu3Wj+/PmUm5tr/X5BQYG8YWzZskWMiIOCguiDDz5Q6DcF3gRmswCvhd3W2QH9rrvuoj/+8Y907NgxMT1gMwR2iW9oaJBoXa/X03fffSc/s23bNhFrdpSvq6ujZ599VgSczQk0Go18za44/fr1o5dffpnGjBkjNoBsFQaAM4GYA6+FDQLYuaaiooK2bt1KycnJkn75+uuvrecUFxdLJJ+dnU1Dhgxp00CAHWVSU1PlsSxi/sorr9Djjz/u4t8IeDNIswCvhUX4oYceomHDhtHixYvFfJfdZDjFYrklJCTIuZZUCv/LHpUDBgwQRxwWbubKTVOO8AFwJW7lAQqAo2GLL74x7D9555130osvvnjVeWzUy/D3OVJnr0dOnfDPcETO1mBXWokB4Eog5gC08JbknDjnuy0C3xJOx2RmZkoVzJQpU+SYo8ytAegsSLMA0Mzq1avp4sWLtHz5cjp69Cjl5eXRN998Q7/85S/JYDBQ165dpYLl7bffppycHNkUfeKJJ3D9gCqAmAPQDKdNfvzxRxFubiri9AlvYnIjEVeq8G3Tpk2SW+fvrVmzhl566SVcP6AKUM0CAAAeACJzAADwACDmAADgAUDMAQDAA4CYAwCABwAxBwAADwBiDgAAHgDEHAAAPACIOQAAeAAQcwAA8AAg5gAA4AFAzAEAwAOAmAMAALk//w9PIQ+v15AzrQAAAABJRU5ErkJggg==",
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@@ -286,11 +286,11 @@
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      "data": {
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-       "
        [17:03:25] ✓ ProFSea assets found locally!                                                             utils.py:188\n",
+       "
        [14:29:54] ✓ ProFSea assets found locally!                                                             utils.py:188\n",
        "
        \n"
       ],
       "text/plain": [
-       "\u001b[2;36m[17:03:25]\u001b[0m\u001b[2;36m \u001b[0m\u001b[1;32m✓ ProFSea assets found locally!\u001b[0m                                                             \u001b]8;id=1099534;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/utils/utils.py\u001b\\\u001b[2mutils.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=1099535;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/utils/utils.py#188\u001b\\\u001b[2m188\u001b[0m\u001b]8;;\u001b\\\n"
+       "\u001b[2;36m[14:29:54]\u001b[0m\u001b[2;36m \u001b[0m\u001b[1;32m✓ ProFSea assets found locally!\u001b[0m                                                             \u001b]8;id=3664210;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/utils/utils.py\u001b\\\u001b[2mutils.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=3664211;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/utils/utils.py#188\u001b\\\u001b[2m188\u001b[0m\u001b]8;;\u001b\\\n"
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@@ -299,11 +299,11 @@
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      "data": {
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-       "
        [17:03:25] INFO     Baseline period = 1995 to 2014                                                                 \n",
+       "
        [14:29:54] INFO     Baseline period = 1995 to 2014                                                                 \n",
        "
        \n"
       ],
       "text/plain": [
-       "\u001b[2;36m[17:03:25]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Baseline period = \u001b[1;36m1995\u001b[0m to \u001b[1;36m2014\u001b[0m                                                                 \n"
+       "\u001b[2;36m[14:29:54]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Baseline period = \u001b[1;36m1995\u001b[0m to \u001b[1;36m2014\u001b[0m                                                                 \n"
       ]
      },
      "metadata": {},
@@ -462,11 +462,11 @@
     {
      "data": {
       "text/html": [
-       "
        [17:03:41] ✓ Successfully saved Zarr: ./stabilisation_projection.zarr                                  utils.py:322\n",
+       "
        [14:30:10] ✓ Successfully saved Zarr: ./stabilisation_projection.zarr                                  utils.py:322\n",
        "
        \n"
       ],
       "text/plain": [
-       "\u001b[2;36m[17:03:41]\u001b[0m\u001b[2;36m \u001b[0m\u001b[1;32m✓ Successfully saved Zarr:\u001b[0m .\u001b[35m/\u001b[0m\u001b[95mstabilisation_projection.zarr\u001b[0m                                  \u001b]8;id=1099541;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/utils/utils.py\u001b\\\u001b[2mutils.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=1099542;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/utils/utils.py#322\u001b\\\u001b[2m322\u001b[0m\u001b]8;;\u001b\\\n"
+       "\u001b[2;36m[14:30:10]\u001b[0m\u001b[2;36m \u001b[0m\u001b[1;32m✓ Successfully saved Zarr:\u001b[0m .\u001b[35m/\u001b[0m\u001b[95mstabilisation_projection.zarr\u001b[0m                                  \u001b]8;id=3664217;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/utils/utils.py\u001b\\\u001b[2mutils.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=3664218;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/utils/utils.py#322\u001b\\\u001b[2m322\u001b[0m\u001b]8;;\u001b\\\n"
       ]
      },
      "metadata": {},
@@ -479,7 +479,7 @@
        "
        \n"
       ],
       "text/plain": [
-       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mOutput shape was \u001b[1m(\u001b[0m\u001b[1;36m5\u001b[0m, \u001b[1;36m295\u001b[0m, \u001b[1;36m180\u001b[0m, \u001b[1;36m360\u001b[0m\u001b[1m)\u001b[0m \u001b[1m(\u001b[0mpercentile, time, lat, lon\u001b[1m)\u001b[0m                            \u001b]8;id=1099548;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/utils/utils.py\u001b\\\u001b[2mutils.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=1099549;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/utils/utils.py#325\u001b\\\u001b[2m325\u001b[0m\u001b]8;;\u001b\\\n"
+       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mOutput shape was \u001b[1m(\u001b[0m\u001b[1;36m5\u001b[0m, \u001b[1;36m295\u001b[0m, \u001b[1;36m180\u001b[0m, \u001b[1;36m360\u001b[0m\u001b[1m)\u001b[0m \u001b[1m(\u001b[0mpercentile, time, lat, lon\u001b[1m)\u001b[0m                            \u001b]8;id=3664224;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/utils/utils.py\u001b\\\u001b[2mutils.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=3664225;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/utils/utils.py#325\u001b\\\u001b[2m325\u001b[0m\u001b]8;;\u001b\\\n"
       ]
      },
      "metadata": {},
@@ -508,7 +508,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
        "
       ]
@@ -566,11 +566,11 @@
     {
      "data": {
       "text/html": [
-       "
        [17:03:55] ✓ ProFSea assets found locally!                                                             utils.py:188\n",
+       "
        [14:30:23] ✓ ProFSea assets found locally!                                                             utils.py:188\n",
        "
        \n"
       ],
       "text/plain": [
-       "\u001b[2;36m[17:03:55]\u001b[0m\u001b[2;36m \u001b[0m\u001b[1;32m✓ ProFSea assets found locally!\u001b[0m                                                             \u001b]8;id=1099554;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/utils/utils.py\u001b\\\u001b[2mutils.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=1099555;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/utils/utils.py#188\u001b\\\u001b[2m188\u001b[0m\u001b]8;;\u001b\\\n"
+       "\u001b[2;36m[14:30:23]\u001b[0m\u001b[2;36m \u001b[0m\u001b[1;32m✓ ProFSea assets found locally!\u001b[0m                                                             \u001b]8;id=3664230;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/utils/utils.py\u001b\\\u001b[2mutils.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=3664231;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/utils/utils.py#188\u001b\\\u001b[2m188\u001b[0m\u001b]8;;\u001b\\\n"
       ]
      },
      "metadata": {},
@@ -579,11 +579,11 @@
     {
      "data": {
       "text/html": [
-       "
        [17:03:55] INFO     Baseline period = 1995 to 2014                                                                 \n",
+       "
        [14:30:23] INFO     Baseline period = 1995 to 2014                                                                 \n",
        "
        \n"
       ],
       "text/plain": [
-       "\u001b[2;36m[17:03:55]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Baseline period = \u001b[1;36m1995\u001b[0m to \u001b[1;36m2014\u001b[0m                                                                 \n"
+       "\u001b[2;36m[14:30:23]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Baseline period = \u001b[1;36m1995\u001b[0m to \u001b[1;36m2014\u001b[0m                                                                 \n"
       ]
      },
      "metadata": {},
@@ -618,11 +618,11 @@
     {
      "data": {
       "text/html": [
-       "
                   INFO     Starting localisation of components...                                                         \n",
+       "
        \n",
        "
        \n"
       ],
       "text/plain": [
-       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Starting localisation of components\u001b[33m...\u001b[0m                                                         \n"
+       "\n"
       ]
      },
      "metadata": {},
@@ -641,12 +641,25 @@
     {
      "data": {
       "text/html": [
-       "
        [17:03:59] WARNING  The following site(s) may be over land: Land point. Expect NaN values for all components.      \n",
+       "
        \n",
+       "
        \n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
        [14:30:27] WARNING  The following site(s) may be over land: Land point. Expect NaN values for all components.      \n",
        "                    Either try increasing your grid resolution or check your site coordinates.                     \n",
        "
        \n"
       ],
       "text/plain": [
-       "\u001b[2;36m[17:03:59]\u001b[0m\u001b[2;36m \u001b[0m\u001b[33mWARNING \u001b[0m The following \u001b[1;35msite\u001b[0m\u001b[1m(\u001b[0ms\u001b[1m)\u001b[0m may be over land: Land point. Expect NaN values for all components.      \n",
+       "\u001b[2;36m[14:30:27]\u001b[0m\u001b[2;36m \u001b[0m\u001b[33mWARNING \u001b[0m The following \u001b[1;35msite\u001b[0m\u001b[1m(\u001b[0ms\u001b[1m)\u001b[0m may be over land: Land point. Expect NaN values for all components.      \n",
        "\u001b[2;36m           \u001b[0m         Either try increasing your grid resolution or check your site coordinates.                     \n"
       ]
      },
@@ -713,7 +726,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
        "
       ]
diff --git a/tests/components/test_sterodynamic.py b/tests/components/test_sterodynamic.py
index 466c281..3401649 100644
--- a/tests/components/test_sterodynamic.py
+++ b/tests/components/test_sterodynamic.py
@@ -44,7 +44,7 @@ def mock_open(f, **kwargs):
     assert stero.land_mask_present is True
 
 
-@patch("profsea.utils.interpolate_to_grid")
+@patch("profsea.utils.utils.interpolate_to_grid")
 @patch.object(SterodynamicCMIP6, "_load_CMIP6_slopes")
 def test_expansion_contribution_storyline_mode(mock_load, mock_interp):
     # Mock the loaded data and masks
@@ -88,7 +88,7 @@ def test_expansion_contribution_storyline_mode(mock_load, mock_interp):
     np.testing.assert_array_equal(computed_result, np.ones((5, 2, 2)) * 2)
 
 
-@patch("profsea.utils.interpolate_to_grid")
+@patch("profsea.utils.utils.interpolate_to_grid")
 @patch.object(SterodynamicCMIP6, "_load_CMIP6_slopes")
 def test_expansion_contribution_sampled_mode(mock_load, mock_interp):
     mock_coeffs = xr.DataArray(
diff --git a/tests/core/test_global_model.py b/tests/core/test_global_model.py
index 19dabe8..6309c09 100644
--- a/tests/core/test_global_model.py
+++ b/tests/core/test_global_model.py
@@ -47,7 +47,9 @@ def test_save_components(tmp_path):
     global_model = Global(components={}, end_yr=2010)
 
     # Create dummy result: 5 members, 4 years
-    components = {"mock_comp": np.random.rand(5, 4)}
+    components = {
+        "mock_comp": xr.DataArray(np.random.rand(5, 4), dims=["member", "time"])
+    }
 
     # Save the output to the temporary directory
     global_model.save_components(

From 609477b77370675cd62a61013ef267a2f93f2b77 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Wed, 17 Jun 2026 15:10:15 +0100
Subject: [PATCH 253/276] Ruff update

---
 profsea/components/core/base.py               |  4 +--
 .../tutorial_notebooks/worked_example.ipynb   | 34 +++++++++----------
 2 files changed, 18 insertions(+), 20 deletions(-)

diff --git a/profsea/components/core/base.py b/profsea/components/core/base.py
index 7bf462a..a997ba1 100644
--- a/profsea/components/core/base.py
+++ b/profsea/components/core/base.py
@@ -19,9 +19,7 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
 class SpatialComponent(Component):
     def extract_spatial(self, da_input: xr.DataArray, state) -> xr.DataArray:
         if hasattr(state, "target_lats"):
-            return self._extract_points(
-                da_input, state.target_lats, state.target_lons
-            )
+            return self._extract_points(da_input, state.target_lats, state.target_lons)
 
         elif hasattr(state, "grid_lats"):
             from profsea.utils.utils import interpolate_to_grid
diff --git a/profsea/tutorial_notebooks/worked_example.ipynb b/profsea/tutorial_notebooks/worked_example.ipynb
index 2c1792a..1fd20ae 100644
--- a/profsea/tutorial_notebooks/worked_example.ipynb
+++ b/profsea/tutorial_notebooks/worked_example.ipynb
@@ -79,7 +79,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
        "
       ]
@@ -221,7 +221,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
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-       "
        [14:29:54] ✓ ProFSea assets found locally!                                                             utils.py:188\n",
+       "
        [14:59:35] ✓ ProFSea assets found locally!                                                             utils.py:188\n",
        "
        \n"
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-       "\u001b[2;36m[14:29:54]\u001b[0m\u001b[2;36m \u001b[0m\u001b[1;32m✓ ProFSea assets found locally!\u001b[0m                                                             \u001b]8;id=3664210;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/utils/utils.py\u001b\\\u001b[2mutils.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=3664211;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/utils/utils.py#188\u001b\\\u001b[2m188\u001b[0m\u001b]8;;\u001b\\\n"
+       "\u001b[2;36m[14:59:35]\u001b[0m\u001b[2;36m \u001b[0m\u001b[1;32m✓ ProFSea assets found locally!\u001b[0m                                                             \u001b]8;id=4042414;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/utils/utils.py\u001b\\\u001b[2mutils.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=4042415;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/utils/utils.py#188\u001b\\\u001b[2m188\u001b[0m\u001b]8;;\u001b\\\n"
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@@ -299,11 +299,11 @@
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        [14:29:54] INFO     Baseline period = 1995 to 2014                                                                 \n",
+       "
        [14:59:35] INFO     Baseline period = 1995 to 2014                                                                 \n",
        "
        \n"
       ],
       "text/plain": [
-       "\u001b[2;36m[14:29:54]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Baseline period = \u001b[1;36m1995\u001b[0m to \u001b[1;36m2014\u001b[0m                                                                 \n"
+       "\u001b[2;36m[14:59:35]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Baseline period = \u001b[1;36m1995\u001b[0m to \u001b[1;36m2014\u001b[0m                                                                 \n"
       ]
      },
      "metadata": {},
@@ -462,11 +462,11 @@
     {
      "data": {
       "text/html": [
-       "
        [14:30:10] ✓ Successfully saved Zarr: ./stabilisation_projection.zarr                                  utils.py:322\n",
+       "
        [14:59:50] ✓ Successfully saved Zarr: ./stabilisation_projection.zarr                                  utils.py:322\n",
        "
        \n"
       ],
       "text/plain": [
-       "\u001b[2;36m[14:30:10]\u001b[0m\u001b[2;36m \u001b[0m\u001b[1;32m✓ Successfully saved Zarr:\u001b[0m .\u001b[35m/\u001b[0m\u001b[95mstabilisation_projection.zarr\u001b[0m                                  \u001b]8;id=3664217;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/utils/utils.py\u001b\\\u001b[2mutils.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=3664218;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/utils/utils.py#322\u001b\\\u001b[2m322\u001b[0m\u001b]8;;\u001b\\\n"
+       "\u001b[2;36m[14:59:50]\u001b[0m\u001b[2;36m \u001b[0m\u001b[1;32m✓ Successfully saved Zarr:\u001b[0m .\u001b[35m/\u001b[0m\u001b[95mstabilisation_projection.zarr\u001b[0m                                  \u001b]8;id=4042421;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/utils/utils.py\u001b\\\u001b[2mutils.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=4042422;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/utils/utils.py#322\u001b\\\u001b[2m322\u001b[0m\u001b]8;;\u001b\\\n"
       ]
      },
      "metadata": {},
@@ -479,7 +479,7 @@
        "
        \n"
       ],
       "text/plain": [
-       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mOutput shape was \u001b[1m(\u001b[0m\u001b[1;36m5\u001b[0m, \u001b[1;36m295\u001b[0m, \u001b[1;36m180\u001b[0m, \u001b[1;36m360\u001b[0m\u001b[1m)\u001b[0m \u001b[1m(\u001b[0mpercentile, time, lat, lon\u001b[1m)\u001b[0m                            \u001b]8;id=3664224;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/utils/utils.py\u001b\\\u001b[2mutils.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=3664225;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/utils/utils.py#325\u001b\\\u001b[2m325\u001b[0m\u001b]8;;\u001b\\\n"
+       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mOutput shape was \u001b[1m(\u001b[0m\u001b[1;36m5\u001b[0m, \u001b[1;36m295\u001b[0m, \u001b[1;36m180\u001b[0m, \u001b[1;36m360\u001b[0m\u001b[1m)\u001b[0m \u001b[1m(\u001b[0mpercentile, time, lat, lon\u001b[1m)\u001b[0m                            \u001b]8;id=4042428;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/utils/utils.py\u001b\\\u001b[2mutils.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=4042429;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/utils/utils.py#325\u001b\\\u001b[2m325\u001b[0m\u001b]8;;\u001b\\\n"
       ]
      },
      "metadata": {},
@@ -508,7 +508,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
        "
       ]
@@ -566,11 +566,11 @@
     {
      "data": {
       "text/html": [
-       "
        [14:30:23] ✓ ProFSea assets found locally!                                                             utils.py:188\n",
+       "
        [15:00:02] ✓ ProFSea assets found locally!                                                             utils.py:188\n",
        "
        \n"
       ],
       "text/plain": [
-       "\u001b[2;36m[14:30:23]\u001b[0m\u001b[2;36m \u001b[0m\u001b[1;32m✓ ProFSea assets found locally!\u001b[0m                                                             \u001b]8;id=3664230;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/utils/utils.py\u001b\\\u001b[2mutils.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=3664231;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/utils/utils.py#188\u001b\\\u001b[2m188\u001b[0m\u001b]8;;\u001b\\\n"
+       "\u001b[2;36m[15:00:02]\u001b[0m\u001b[2;36m \u001b[0m\u001b[1;32m✓ ProFSea assets found locally!\u001b[0m                                                             \u001b]8;id=4042434;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/utils/utils.py\u001b\\\u001b[2mutils.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=4042435;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/utils/utils.py#188\u001b\\\u001b[2m188\u001b[0m\u001b]8;;\u001b\\\n"
       ]
      },
      "metadata": {},
@@ -579,11 +579,11 @@
     {
      "data": {
       "text/html": [
-       "
        [14:30:23] INFO     Baseline period = 1995 to 2014                                                                 \n",
+       "
        [15:00:02] INFO     Baseline period = 1995 to 2014                                                                 \n",
        "
        \n"
       ],
       "text/plain": [
-       "\u001b[2;36m[14:30:23]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Baseline period = \u001b[1;36m1995\u001b[0m to \u001b[1;36m2014\u001b[0m                                                                 \n"
+       "\u001b[2;36m[15:00:02]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Baseline period = \u001b[1;36m1995\u001b[0m to \u001b[1;36m2014\u001b[0m                                                                 \n"
       ]
      },
      "metadata": {},
@@ -654,12 +654,12 @@
     {
      "data": {
       "text/html": [
-       "
        [14:30:27] WARNING  The following site(s) may be over land: Land point. Expect NaN values for all components.      \n",
+       "
        [15:00:05] WARNING  The following site(s) may be over land: Land point. Expect NaN values for all components.      \n",
        "                    Either try increasing your grid resolution or check your site coordinates.                     \n",
        "
        \n"
       ],
       "text/plain": [
-       "\u001b[2;36m[14:30:27]\u001b[0m\u001b[2;36m \u001b[0m\u001b[33mWARNING \u001b[0m The following \u001b[1;35msite\u001b[0m\u001b[1m(\u001b[0ms\u001b[1m)\u001b[0m may be over land: Land point. Expect NaN values for all components.      \n",
+       "\u001b[2;36m[15:00:05]\u001b[0m\u001b[2;36m \u001b[0m\u001b[33mWARNING \u001b[0m The following \u001b[1;35msite\u001b[0m\u001b[1m(\u001b[0ms\u001b[1m)\u001b[0m may be over land: Land point. Expect NaN values for all components.      \n",
        "\u001b[2;36m           \u001b[0m         Either try increasing your grid resolution or check your site coordinates.                     \n"
       ]
      },
@@ -726,7 +726,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
        "
       ]

From 3c9de4ebea162b0bdf98ee20f982409e7e23bd5d Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Wed, 17 Jun 2026 15:11:44 +0100
Subject: [PATCH 254/276] Add local model tests

---
 tests/core/test_local_model.py | 105 +++++++++++++++++++++++++++++++++
 1 file changed, 105 insertions(+)
 create mode 100644 tests/core/test_local_model.py

diff --git a/tests/core/test_local_model.py b/tests/core/test_local_model.py
new file mode 100644
index 0000000..17b1861
--- /dev/null
+++ b/tests/core/test_local_model.py
@@ -0,0 +1,105 @@
+import dask.array as da
+import numpy as np
+import pytest
+import xarray as xr
+
+from profsea.components.core.base import SpatialComponent
+
+
+class DummyState:
+    """Mock state object to hold target coordinates."""
+
+    def __init__(self, lats, lons):
+        self.target_lats = np.atleast_1d(lats)
+        self.target_lons = np.atleast_1d(lons)
+
+
+class MockLocalComponent(SpatialComponent):
+    @property
+    def global_projection(self):
+        # Return a dummy array to satisfy the abstract base class requirement
+        return np.zeros((1, 1))
+
+    def project(self, state, rng):
+        # Return a dummy array of shape (n_members, n_years, n_locations)
+        return np.ones((state.num_members, state.n_years, len(state.target_lats)))
+
+
+class TestLazyLocalExtraction:
+    @pytest.fixture
+    def lazy_grid(self) -> xr.DataArray:
+        """
+        Creates a predictable 3x4 global grid with known values and NaNs.
+        Latitudes: -10, 0, 10
+        Longitudes: -170, -90, 0, 90
+        """
+        lats = np.array([-10.0, 0.0, 10.0])
+        lons = np.array([-170.0, -90.0, 0.0, 90.0])
+
+        # Predictable values 1 through 12
+        data = np.arange(1, 13, dtype=float).reshape(3, 4)
+
+        # Inject NaNs to test coastal weighting
+        data[0, 2] = np.nan  # lat=-10, lon=0
+        data[2, 3] = np.nan  # lat=10,  lon=90
+
+        dask_data = da.from_array(data, chunks=(3, 2))
+
+        return xr.DataArray(
+            dask_data, dims=["lat", "lon"], coords={"lat": lats, "lon": lons}
+        )
+
+    @pytest.fixture
+    def component(self):
+        return MockLocalComponent()
+
+    def test_preserves_lazy_evaluation(self, lazy_grid, component):
+        """Ensure the extraction operations do not eagerly compute the Dask array."""
+        state = DummyState(lats=[5.0], lons=[-45.0])
+        result = component.extract_spatial(lazy_grid, state)
+        assert isinstance(result.data, da.Array), (
+            "Extraction triggered eager evaluation!"
+        )
+
+    def test_standard_bilinear_interpolation(self, lazy_grid, component):
+        """Test a clean extraction in the center of 4 valid nodes."""
+        state = DummyState(lats=[5.0], lons=[-45.0])
+        result = component.extract_spatial(lazy_grid, state).compute()
+        np.testing.assert_allclose(result.values, [8.5])
+
+    def test_coastal_nan_renormalization(self, lazy_grid, component):
+        """Test extraction where one corner of the bounding box is NaN."""
+        state = DummyState(lats=[-5.0], lons=[-45.0])
+        result = component.extract_spatial(lazy_grid, state).compute()
+        np.testing.assert_allclose(result.values, [5.0])
+
+    def test_exact_node_strike(self, lazy_grid, component):
+        """Ensure zero-division safeguards work when hitting a coordinate exactly."""
+        state = DummyState(lats=[0.0], lons=[-90.0])
+        result = component.extract_spatial(lazy_grid, state).compute()
+        np.testing.assert_allclose(result.values, [6.0])
+
+    def test_zonal_periodicity_wrapping(self, lazy_grid, component):
+        """Test extraction across the -180/180 antimeridian line."""
+        state = DummyState(lats=[0.0], lons=[180.0])
+        result = component.extract_spatial(lazy_grid, state).compute()
+        np.testing.assert_allclose(result.values, [5.3])
+
+    def test_all_nan_handling(self, lazy_grid, component):
+        """Ensure regions completely surrounded by NaNs yield NaN."""
+        lazy_grid *= np.nan  # Make the entire grid NaN
+        state = DummyState(lats=[5.0], lons=[45.0])
+        result = component.extract_spatial(lazy_grid, state).compute()
+        assert np.isnan(result.values[0])
+
+    def test_missing_spatial_attributes(self, lazy_grid, component):
+        """Ensure a ValueError is raised if the state lacks valid spatial targets."""
+
+        class DummyStateEmpty:
+            pass  # A truly empty state object
+
+        state = DummyStateEmpty()
+        with pytest.raises(
+            ValueError, match="State object is missing required spatial attributes"
+        ):
+            component.extract_spatial(lazy_grid, state)

From 5ad96fc4d2fffe660b7b1090dd9aa049a7984af3 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Wed, 17 Jun 2026 15:37:11 +0100
Subject: [PATCH 255/276] Update example script

---
 ukcp18_example_script.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/ukcp18_example_script.py b/ukcp18_example_script.py
index ea2cfcf..a0d05f3 100644
--- a/ukcp18_example_script.py
+++ b/ukcp18_example_script.py
@@ -36,7 +36,7 @@
     member_seed=42,
 )
 global_model.sum_components(projections)
-gmslr = global_model.results["gmslr"]
+gmslr = global_model.results["total_gmslr"]
 
 ### Now spatial projections ###
 spatial_components = {
@@ -94,15 +94,15 @@
 yrs = np.arange(2006, 2301)
 ax.plot(
     yrs,
-    np.median(global_model.results["gmslr"], axis=0),
+    np.median(gmslr, axis=0),
     label="Global Projection",
     color="royalblue",
 )
 # fill between 1 and 4 members
 ax.fill_between(
     yrs,
-    np.percentile(global_model.results["gmslr"], 17, axis=0),
-    np.percentile(global_model.results["gmslr"], 83, axis=0),
+    np.percentile(gmslr, 17, axis=0),
+    np.percentile(gmslr, 83, axis=0),
     alpha=0.3,
     color="skyblue",
 )

From d92b21811cdcb981bca4293bc8eae23c9132f2dc Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Wed, 17 Jun 2026 15:47:42 +0100
Subject: [PATCH 256/276] ais_region to region

---
 profsea/components/global_/antarctica.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/profsea/components/global_/antarctica.py b/profsea/components/global_/antarctica.py
index bb01118..453e224 100644
--- a/profsea/components/global_/antarctica.py
+++ b/profsea/components/global_/antarctica.py
@@ -38,16 +38,16 @@ class AntarcticaISMIP6(Component):
     different response characteristics to warming.
     """
 
-    def __init__(self, ais_region: str):
+    def __init__(self, region: str):
         """
         Parameters
         ----------
-        ais_region: str
+        region: str
             The Antarctic region to model. Must be one of "wais", "eais", or "peninsula".
         """
-        params_path = PARAMS_MAP.get(ais_region.lower())
+        params_path = PARAMS_MAP.get(region.lower())
         if not params_path:
-            raise ValueError(f"Invalid region calibration: {ais_region}")
+            raise ValueError(f"Invalid region calibration: {region}")
 
         self.param_ds = xr.load_dataset(params_path)
         self.n_models = self.param_ds.coords["model"].shape[0]

From 760dc38fcb628378522daa112338958666deacd1 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Wed, 17 Jun 2026 15:58:25 +0100
Subject: [PATCH 257/276] Update scm_profsea_experiments.py

---
 profsea/scm_profsea_experiments.py | 39 ++++++++++++++----------------
 1 file changed, 18 insertions(+), 21 deletions(-)

diff --git a/profsea/scm_profsea_experiments.py b/profsea/scm_profsea_experiments.py
index f0ebcaa..8c196df 100644
--- a/profsea/scm_profsea_experiments.py
+++ b/profsea/scm_profsea_experiments.py
@@ -96,7 +96,7 @@ def df_to_arr(df, scenario_order):
 def load_magicc_forcing(
     input_path: str, scenarios: list, baseline_start: int, baseline_end: int
 ) -> tuple[np.ndarray]:
-    df = pd.read_csv(input_path, index_col=0)
+    df = pd.read_csv(input_path)
 
     tas_condition = (df["variable"] == "Surface Air Temperature Change") & (
         df["scenario"].isin(scenarios)
@@ -278,7 +278,7 @@ def plot_component(
         scenarios[3]: "#d3a640",
         scenarios[4]: "#098740",
         scenarios[5]: "#0080d0",
-        # scenarios[6]: '#100060',
+        scenarios[6]: '#100060',
     }
     for scenario in reversed(scenarios):
         ax.fill_between(
@@ -310,16 +310,16 @@ def main(args):
         scenarios = scen_df["scenario"].unique().tolist()
     else:
         # Defaulting to an SSP list to avoid UnboundLocalError
-        scenarios = ["ssp119", "ssp126", "ssp245", "ssp370", "ssp534-over", "ssp585"]
-        # scenarios = [
-        #     "Very Low - SSP1 (Marker)",
-        #     "Low-to-Negative - SSP2 (Marker)",
-        #     "Low - SSP2 (Marker)",
-        #     "Medium-to-Low - SSP2 (Marker)",
-        #     "Medium - SSP2 (Marker)",
-        #     "High-to-Low - SSP5 (Marker)",
-        #     "High - SSP3 (Marker)",
-        # ]
+        # scenarios = ["ssp119", "ssp126", "ssp245", "ssp370", "ssp534-over", "ssp585"]
+        scenarios = [
+            "Very Low - SSP1 (Marker)",
+            "Low-to-Negative - SSP2 (Marker)",
+            "Low - SSP2 (Marker)",
+            "Medium-to-Low - SSP2 (Marker)",
+            "Medium - SSP2 (Marker)",
+            "High-to-Low - SSP5 (Marker)",
+            "High - SSP3 (Marker)",
+        ]
 
     console.log(f"Using scenarios: {scenarios}")
 
@@ -335,7 +335,7 @@ def main(args):
     components = {}
     for scenario in scenarios:
         components[scenario] = {
-            "gmslr": [],
+            "total_gmslr": [],
             "expansion": [],
             "antarctica": [],
             "greenland": [],
@@ -363,9 +363,6 @@ def main(args):
     tas_matrix = tas[:, random_indices, :]  # Shape: (n_scenarios, 1000, 295)
     ohc_matrix = ohc[:, random_indices, :]  # Shape: (n_scenarios, 1000, 295)
 
-    wais_params_path = Path("components") / "aux_data" / "wais_params_expanded.nc"
-    eais_params_path = Path("components") / "aux_data" / "eais_params_expanded.nc"
-    pen_params_path = Path("components") / "aux_data" / "pen_params_expanded.nc"
     sampled_components = {}
     for idx, scenario in track(
         enumerate(scenarios),
@@ -379,13 +376,13 @@ def main(args):
             "expansion": ThermalExpansion(OHC_change=ohc_scen),
             "greenland": GreenlandAR6(),
             "landwater": LandwaterAR6(),
-            "wais": AntarcticaISMIP6(params_path=wais_params_path),
-            "eais": AntarcticaISMIP6(params_path=eais_params_path),
-            "pen": AntarcticaISMIP6(params_path=pen_params_path),
+            "wais": AntarcticaISMIP6(region="wais"),
+            "eais": AntarcticaISMIP6(region="eais"),
+            "pen": AntarcticaISMIP6(region="peninsula"),
             "glacier": Glacier(),
         }
 
-        global_model = Global(components=slr_components, end_yr=2301, nm=1)
+        global_model = Global(components=slr_components, end_yr=2301, num_members=1)
         projections = global_model.run(
             scenario=scenario,
             T_change=tas_scen,
@@ -407,7 +404,7 @@ def main(args):
 
     fig = plt.figure(figsize=(16, 8), layout="constrained")
     ax = fig.add_subplot(231)
-    plot_component(ax, sampled_components, "gmslr", scenarios, plot_legend=True)
+    plot_component(ax, sampled_components, "total_gmslr", scenarios, plot_legend=True)
     ax = fig.add_subplot(232)
     plot_component(ax, sampled_components, "expansion", scenarios)
     ax = fig.add_subplot(233)

From 572b793eada5f87d516873e891abed49518cfdcd Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Wed, 17 Jun 2026 15:59:37 +0100
Subject: [PATCH 258/276] Ruff fix

---
 profsea/scm_profsea_experiments.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/profsea/scm_profsea_experiments.py b/profsea/scm_profsea_experiments.py
index 8c196df..e62b592 100644
--- a/profsea/scm_profsea_experiments.py
+++ b/profsea/scm_profsea_experiments.py
@@ -278,7 +278,7 @@ def plot_component(
         scenarios[3]: "#d3a640",
         scenarios[4]: "#098740",
         scenarios[5]: "#0080d0",
-        scenarios[6]: '#100060',
+        scenarios[6]: "#100060",
     }
     for scenario in reversed(scenarios):
         ax.fill_between(

From 75dc6081c5dd7301351cfc4ce749c10603961d4b Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Wed, 17 Jun 2026 17:54:23 +0100
Subject: [PATCH 259/276] Add new global model save options, dtype setting,
 update wrapper

---
 profsea/components/core/global_model.py    | 117 +++++++++++++++++----
 profsea/components/core/state.py           |   3 +
 profsea/components/core/time_projection.py |   2 +-
 profsea/components/global_/antarctica.py   |  54 +---------
 profsea/components/global_/greenland.py    |   8 +-
 profsea/scm_profsea_experiments.py         |   8 +-
 profsea/utils/utils.py                     |  51 ++++++---
 7 files changed, 153 insertions(+), 90 deletions(-)

diff --git a/profsea/components/core/global_model.py b/profsea/components/core/global_model.py
index aacd519..35d71e8 100644
--- a/profsea/components/core/global_model.py
+++ b/profsea/components/core/global_model.py
@@ -7,6 +7,8 @@
 
 import numpy as np
 import xarray as xr
+from rich.console import Console
+from rich.progress import track
 
 from profsea.utils import check_shapes, sample_members_2D
 from profsea.utils.ui import print_global_preflight
@@ -14,6 +16,7 @@
 from .base import Component
 from .state import ClimateState
 
+console = Console()
 logger = logging.getLogger(__name__)
 
 
@@ -68,6 +71,7 @@ def __init__(
         output_percentiles: list | np.ndarray = None,
         palmer_method: bool = True,
         random_sample: bool = False,
+        dtype: np.dtype | str = np.float32,
     ):
         self.components = components
         self.end_yr = end_yr
@@ -78,6 +82,7 @@ def __init__(
         self.output_percentiles = output_percentiles
         self.palmer_method = palmer_method
         self.random_sample = random_sample
+        self.dtype = np.dtype(dtype)
 
         self.endofhistory = 2006
         self.endofAR5 = 2100
@@ -152,10 +157,11 @@ def run(
 
         print_global_preflight(self, scenario)
 
+        T_change = T_change.astype(self.dtype)
         T_ens, T_int_ens, T_int_med = self._calculate_drivers(T_change)
 
         # Shared physical correlation state
-        fraction = run_rng.random(self.num_members * self.nt)
+        fraction = run_rng.random(self.num_members * self.nt).astype(self.dtype)
 
         state = ClimateState(
             scenario=scenario,
@@ -170,6 +176,7 @@ def run(
             nyr=self.nyr,
             nt=self.nt,
             num_members=self.num_members,
+            dtype=self.dtype,
         )
 
         # Child RNGs for each component
@@ -193,7 +200,9 @@ def run(
                     except Exception as e:
                         raise RuntimeError(f"Component '{comp_name}' failed.") from e
         else:
-            for name, comp in self.components.items():
+            for name, comp in track(
+                self.components.items(), description="Projecting components..."
+            ):
                 results[name] = comp.project(state, comp_rngs[name])
 
         # Random Sampling
@@ -214,37 +223,105 @@ def run(
         self.results = self._arr_to_xr(results)
         return self.results
 
-    def sum_components(self, components: dict[str, xr.DataArray]) -> xr.DataArray:
-        """Sum the components to get total GMSLR."""
-        gmslr = xr.concat(
-            [components[name] for name in components.keys()], dim="component"
-        ).sum(dim="component")
-        gmslr.attrs["units"] = "m"
-        gmslr.attrs["description"] = "Total global mean sea level rise"
-        components["total_gmslr"] = gmslr
-        return gmslr
-
     def save_components(
-        self, components: dict[str, xr.DataArray], output_dir: str, scenario_name: str
+        self,
+        components: dict[str, xr.DataArray],
+        scenario_name: str,
+        output_prefix: str = "global",
+        output_dir: str = ".",
+        output_format: str = "netcdf",
     ) -> None:
-        """Save SLR components as nc files to a directory.
+        """
+        Stream all global sea level projections to disk in a single file/store.
 
         Parameters
         ----------
-        components: Dict[str, xr.DataArray]
-            Dictionary of component names and their corresponding xarray DataArrays.
-        output_dir: str
-            Directory to save components to.
+        components: dict[str, xr.DataArray]
+            Dictionary of component names and their corresponding Xarray DataArrays.
         scenario_name: str
             Name of the scenario you've run the emulator for.
+        output_prefix: str
+            Prefix for the output file name (default: 'global').
+        output_dir: str
+            Directory to save components to.
+        output_format: str
+            Format to save the output in. Must be either 'netcdf' or 'zarr'.
 
         Returns
         -------
         None
         """
-        Path(output_dir).mkdir(parents=True, exist_ok=True)
         ds = xr.Dataset(components)
-        ds.to_netcdf(os.path.join(output_dir, f"{scenario_name}_global.nc"))
+
+        # Add ProFSea version and scenario metadata
+        ds.attrs["source"] = "ProFSea v3.0"
+        ds.attrs["scenario"] = scenario_name
+        ds.attrs["description"] = "Global sea level rise projections"
+
+        output_format = output_format.lower()
+        if output_format not in ["netcdf", "zarr"]:
+            raise ValueError("output_format must be either 'netcdf' or 'zarr'.")
+
+        Path(output_dir).mkdir(parents=True, exist_ok=True)
+        encoding = {}
+
+        # Sort out Zarr encoding
+        if output_format == "zarr":
+            import numcodecs
+            from numcodecs.zarr3 import Blosc
+
+            compressor = Blosc(
+                cname="zstd", clevel=5, shuffle=numcodecs.Blosc.BITSHUFFLE
+            )
+
+        # Set the encoding/compression dynamically based on the component's actual dtype
+        for name, component in components.items():
+            comp_dtype = (
+                component.dtype.name
+            )  # Captures 'float32' or 'float64' dynamically
+
+            if output_format == "netcdf":
+                encoding[name] = {"zlib": True, "complevel": 1, "dtype": comp_dtype}
+            elif output_format == "zarr":
+                encoding[name] = {"compressor": compressor, "dtype": comp_dtype}
+
+        file_name = f"{scenario_name}_{output_prefix}"
+
+        # Stream the computation and write to disk
+        if output_format == "netcdf":
+            out_path = os.path.join(output_dir, f"{file_name}.nc")
+            with console.status(
+                "[bold cyan]Computing and saving Global NetCDF...[/bold cyan]",
+                spinner="dots",
+            ):
+                # If using Dask, .compute() is required before .to_netcdf()
+                # If arrays are already eager NumPy arrays, .compute() is a harmless no-op
+                if hasattr(ds, "compute"):
+                    ds.compute().to_netcdf(out_path, encoding=encoding)
+                else:
+                    ds.to_netcdf(out_path, encoding=encoding)
+
+            logger.info(
+                f"[bold green]✓ Successfully saved NetCDF:[/bold green] {out_path}"
+            )
+
+        elif output_format == "zarr":
+            out_path = os.path.join(output_dir, f"{file_name}.zarr")
+            with console.status(
+                "[bold cyan]Streaming computation and saving Global Zarr...[/bold cyan]",
+                spinner="dots",
+            ):
+                ds.to_zarr(out_path, encoding=encoding, mode="w", compute=True)
+            logger.info(
+                f"[bold green]✓ Successfully saved Zarr:[/bold green] {out_path}"
+            )
+
+        # Log the shape of the total_gmslr (or the first available component)
+        sample_name = (
+            "total_gmslr" if "total_gmslr" in ds else list(ds.data_vars.keys())[0]
+        )
+        dims_str = ", ".join(ds[sample_name].dims)
+        logger.info(f"Global output shape was {ds[sample_name].shape} ({dims_str})")
 
     def _calculate_drivers(self, T_change: np.ndarray) -> tuple:
         """Calculate the drivers of GMSLR: temperature change and
diff --git a/profsea/components/core/state.py b/profsea/components/core/state.py
index 7fc7296..8f36a2e 100644
--- a/profsea/components/core/state.py
+++ b/profsea/components/core/state.py
@@ -21,6 +21,7 @@ class ClimateState:
     nyr: int
     nt: int
     num_members: int
+    dtype: np.dtype = np.float64
 
 
 @dataclass
@@ -34,6 +35,7 @@ class SpatialState:
     grid_interpolation: str
     output_percentiles: list[int] | np.ndarray
     baseline_yrs: tuple[int, int]
+    dtype: np.dtype = np.float64
 
 
 @dataclass
@@ -47,3 +49,4 @@ class LocalState:
     interpolation_method: str
     output_percentiles: list[int] | np.ndarray
     baseline_yrs: tuple[int, int]
+    dtype: np.dtype = np.float64
diff --git a/profsea/components/core/time_projection.py b/profsea/components/core/time_projection.py
index bb38d1c..941cbcb 100644
--- a/profsea/components/core/time_projection.py
+++ b/profsea/components/core/time_projection.py
@@ -41,7 +41,7 @@ def time_projection(
     time = np.arange(state.end_yr - state.endofhistory) + 1
 
     if fraction is None:
-        fraction = rng.random((state.num_members, state.nt))
+        fraction = rng.random((state.num_members, state.nt)).astype(state.dtype)
     elif fraction.size != state.num_members * state.nt:
         raise ValueError("fraction is the wrong size")
 
diff --git a/profsea/components/global_/antarctica.py b/profsea/components/global_/antarctica.py
index 453e224..7cd7fed 100644
--- a/profsea/components/global_/antarctica.py
+++ b/profsea/components/global_/antarctica.py
@@ -168,58 +168,6 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
 
         return term_slow + term_fast
 
-    # def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
-    #     """
-    #     Projects AIS response using empirical additive bootstrapping aligned
-    #     to the ClimateState ensemble size.
-    #     """
-    #     tas = state.T_ens
-    #     if tas.ndim > 2:
-    #         tas = np.squeeze(tas)
-    #     if tas.ndim == 1:
-    #         tas = np.expand_dims(tas, axis=0)
-
-    #     dt = 1.0
-    #     nm = state.nt * state.num_members
-    #     n_time = tas.shape[1]
-
-    #     preds = np.zeros((nm, n_time))
-    #     tas_int = np.cumsum(tas, axis=1) * dt
-
-    #     # Randomly assign an ISMIP6 model and residual draw to each ensemble member
-    #     model_indices = rng.integers(0, self.n_models, size=nm)
-    #     all_residuals = self.param_ds.param_residuals.values
-    #     n_train_scenarios = all_residuals.shape[1]
-    #     residual_indices = rng.integers(0, n_train_scenarios, size=nm)
-
-    #     for i in range(nm):
-    #         t_idx = i // state.num_members
-    #         m_idx = model_indices[i]
-    #         r_idx = residual_indices[i]
-
-    #         # Extract assigned model parameters
-    #         tau1 = float(self.param_ds.tau1[m_idx].values)
-    #         tau2 = float(self.param_ds.tau2[m_idx].values)
-    #         gamma = float(self.param_ds.gamma[m_idx].values)
-
-    #         general_p = self.param_ds.general_params[m_idx].values
-    #         sampled_residuals = all_residuals[m_idx, r_idx, :]
-    #         total_params = general_p + sampled_residuals
-
-    #         # Slow response
-    #         term_slow = self._impulse_response_term(
-    #             tas[t_idx], tau1, tau2, gamma, total_params, dt
-    #         )
-
-    #         # Fast response
-    #         beta = total_params[2]
-    #         term_fast = beta * tas_int[t_idx]
-
-    #         # Combine
-    #         preds[i, :] = term_fast + term_slow
-
-    #     return preds
-
 
 class AntarcticaDynAR5(Component):
     """
@@ -321,7 +269,7 @@ def project(
         )  # m yr-1 of SLE per K of global warming
 
         if state.fraction is None:
-            fraction = rng.random((state.num_members, state.nt))
+            fraction = rng.random((state.num_members, state.nt)).astype(state.dtype)
         elif state.fraction.size != state.num_members * state.nt:
             raise ValueError("fraction is the wrong size")
         else:
diff --git a/profsea/components/global_/greenland.py b/profsea/components/global_/greenland.py
index 8910dac..e54c3cc 100644
--- a/profsea/components/global_/greenland.py
+++ b/profsea/components/global_/greenland.py
@@ -63,7 +63,7 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
         a_bound = (0.0 - trend_mean) / trend_std
         b_bound = (99999.9 - trend_mean) / trend_std  # Or just np.inf
         trend = truncnorm.ppf(
-            rng.random((nt, state.num_members)),
+            rng.random((nt, state.num_members)).astype(state.dtype),
             a=a_bound,
             b=b_bound,
             loc=trend_mean,
@@ -133,7 +133,11 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
         # random log-normal factor
         fn = np.exp(rng.standard_normal(state.num_members) * fnlogsd)
         # elevation feedback factor
-        fe = rng.random(state.num_members) * (febound[1] - febound[0]) + febound[0]
+        fe = (
+            rng.random(state.num_members).astype(state.dtype)
+            * (febound[1] - febound[0])
+            + febound[0]
+        )
         ff = fn * fe
 
         ztgreen = state.T_ens - dtgreen
diff --git a/profsea/scm_profsea_experiments.py b/profsea/scm_profsea_experiments.py
index e62b592..5f1645e 100644
--- a/profsea/scm_profsea_experiments.py
+++ b/profsea/scm_profsea_experiments.py
@@ -382,7 +382,13 @@ def main(args):
             "glacier": Glacier(),
         }
 
-        global_model = Global(components=slr_components, end_yr=2301, num_members=1)
+        global_model = Global(
+            components=slr_components,
+            end_yr=2301,
+            num_members=1000,
+            dtype="float32",
+            parallel=False,
+        )
         projections = global_model.run(
             scenario=scenario,
             T_change=tas_scen,
diff --git a/profsea/utils/utils.py b/profsea/utils/utils.py
index c4c680e..b5983e5 100644
--- a/profsea/utils/utils.py
+++ b/profsea/utils/utils.py
@@ -1,9 +1,11 @@
 from __future__ import annotations
 
+import logging
 import os
 import zipfile
 from pathlib import Path
 
+import dask
 import dask.array as da
 import numpy as np
 import requests
@@ -20,29 +22,47 @@
 from scipy.spatial.distance import cdist
 
 console = Console()
+logger = logging.getLogger(__name__)
 
 
-def sample_members_2D(array: np.ndarray, percentiles: list | np.ndarray) -> np.ndarray:
+def sample_members_2D(
+    array: np.ndarray | da.Array, percentiles: list | np.ndarray
+) -> np.ndarray | da.Array:
     """
-    Sample real ensemble members from a 2D numpy array.
+    Sample real ensemble members from a 2D numpy or dask array lazily.
 
     Parameters
     ----------
-    array: np.ndarray
+    array: np.ndarray | da.Array
         Input 2D array of shape (realisation, time).
     percentiles: list | np.ndarray
         List of percentiles to sample from the input array.
 
     Returns
     -------
-    np.ndarray
-        Sampled array of shape (len(percentiles), time) corresponding to the closest real ensemble members to the specified percentiles.
+    np.ndarray | da.Array
+        Sampled array of shape (len(percentiles), time). Maintains lazy
+        evaluation if a dask array is provided.
     """
-    # Caculate statistical timeseries, then match with closest real timeseries
-    array_percentiles = np.nanpercentile(array, percentiles, axis=0)
-    distances = cdist(array_percentiles, array)
-    mem_indices = np.argmin(distances, axis=1)
-    return array[mem_indices]
+
+    def _eager_sample(arr, percs):
+        # Calculate statistical timeseries, then match with closest real timeseries
+        array_percentiles = np.nanpercentile(arr, percs, axis=0)
+        distances = cdist(array_percentiles, arr)
+        mem_indices = np.argmin(distances, axis=1)
+        return arr[mem_indices]
+
+    if isinstance(array, da.Array):
+        # Tell Dask to delay this operation until the graph is computed
+        lazy_result = dask.delayed(_eager_sample)(array, percentiles)
+
+        # Reconstruct into a Dask array so downstream Xarray operations continue to work lazily
+        shape = (len(percentiles), array.shape[1])
+        return da.from_delayed(lazy_result, shape=shape, dtype=array.dtype)
+
+    else:
+        # Fallback for standard numpy arrays
+        return _eager_sample(array, percentiles)
 
 
 def interpolate(data: da.array, lats: int, lons: int) -> da.array:
@@ -280,6 +300,11 @@ def save_components(
     """
     ds = xr.Dataset(components)
 
+    # Add ProFSea version and scenario metadata
+    ds.attrs["source"] = "ProFSea v3.0"
+    ds.attrs["scenario"] = scenario_name
+    ds.attrs["description"] = "Spatial sea level rise projections"
+
     output_format = output_format.lower()
     if output_format not in ["netcdf", "zarr"]:
         raise ValueError("output_format must be either 'netcdf' or 'zarr'.")
@@ -310,7 +335,7 @@ def save_components(
             "[bold cyan]Computing and saving NetCDF...[/bold cyan]", spinner="dots"
         ):
             ds.compute().to_netcdf(out_path, encoding=encoding)
-        console.log(f"[bold green]✓ Successfully saved NetCDF:[/bold green] {out_path}")
+        logger.info(f"[bold green]✓ Successfully saved NetCDF:[/bold green] {out_path}")
 
     elif output_format == "zarr":
         out_path = os.path.join(output_dir, f"{file_name}.zarr")
@@ -319,7 +344,7 @@ def save_components(
             spinner="dots",
         ):
             ds.to_zarr(out_path, encoding=encoding, mode="w", compute=True)
-        console.log(f"[bold green]✓ Successfully saved Zarr:[/bold green] {out_path}")
+        logger.info(f"[bold green]✓ Successfully saved Zarr:[/bold green] {out_path}")
 
     dims_str = ", ".join(ds[name].dims)
-    console.log(f"Output shape was {ds[name].shape} ({dims_str})")
+    logger.info(f"Output shape was {ds[name].shape} ({dims_str})")

From 0b65623bf03f5e52f373cfff6b2f7905e6fd2d97 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Wed, 17 Jun 2026 18:00:59 +0100
Subject: [PATCH 260/276] Re-add sum method

---
 profsea/components/core/global_model.py  | 26 ++++++++++++++++++++++++
 profsea/components/core/local_model.py   |  6 +-----
 profsea/components/core/spatial_model.py |  9 +-------
 3 files changed, 28 insertions(+), 13 deletions(-)

diff --git a/profsea/components/core/global_model.py b/profsea/components/core/global_model.py
index 35d71e8..7538975 100644
--- a/profsea/components/core/global_model.py
+++ b/profsea/components/core/global_model.py
@@ -323,6 +323,32 @@ def save_components(
         dims_str = ", ".join(ds[sample_name].dims)
         logger.info(f"Global output shape was {ds[sample_name].shape} ({dims_str})")
 
+    def sum_components(self, components: dict[str, xr.DataArray]) -> xr.DataArray:
+        """
+        Sum the components in-place to get total GMSLR.
+
+        Parameters
+        ----------
+        components: dict[str, xr.DataArray]
+            Dictionary of component names and their corresponding Xarray DataArrays.
+
+        Returns
+        -------
+        xr.DataArray
+            DataArray of the summed global projections.
+        """
+
+        iterator = iter(components.values())
+        gmslr = next(iterator).copy()
+
+        for comp in iterator:
+            gmslr += comp
+
+        gmslr.attrs["units"] = "m"
+        gmslr.attrs["description"] = "Total global mean sea level rise"
+        components["total_gmslr"] = gmslr
+        return gmslr
+
     def _calculate_drivers(self, T_change: np.ndarray) -> tuple:
         """Calculate the drivers of GMSLR: temperature change and
         thermosteric sea level rise.
diff --git a/profsea/components/core/local_model.py b/profsea/components/core/local_model.py
index 1efb777..d3ce230 100644
--- a/profsea/components/core/local_model.py
+++ b/profsea/components/core/local_model.py
@@ -238,10 +238,6 @@ def sum_components(self, components: dict[str, xr.DataArray]) -> xr.DataArray:
         total_rsl = xr.concat(components.values(), dim="component").sum(
             dim="component", skipna=False
         )
-        total_rsl.attrs = {
-            "units": "m",
-            "long_name": "Local total sea-level projections",
-            "source": "ProFSea-Climate v0.1",
-        }
+        total_rsl.attrs["units"] = "m"
         components["total_rsl"] = total_rsl
         return total_rsl
diff --git a/profsea/components/core/spatial_model.py b/profsea/components/core/spatial_model.py
index 6c22662..55906c8 100644
--- a/profsea/components/core/spatial_model.py
+++ b/profsea/components/core/spatial_model.py
@@ -240,13 +240,6 @@ def sum_components(self, components: dict[str, xr.DataArray]) -> xr.DataArray:
         total_rsl = xr.concat(components.values(), dim="component").sum(
             dim="component", skipna=False
         )
-
-        # Optionally, apply attributes so it matches the other DataArrays
-        total_rsl.attrs = {
-            "units": "m",
-            "long_name": "Regional total sea-level projections",
-            "source": "ProFSea-Climate v0.1",
-        }
-
+        total_rsl.attrs["units"] = "m"
         components["total_rsl"] = total_rsl
         return total_rsl

From ebbb004db8be926124c3bb65c55119996f5678f8 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Wed, 17 Jun 2026 19:34:02 +0100
Subject: [PATCH 261/276] Enforce types through global model

---
 profsea/components/core/global_model.py    | 14 ++++-
 profsea/components/core/spatial_model.py   |  4 ++
 profsea/components/core/state.py           |  6 +--
 profsea/components/core/time_projection.py | 12 ++---
 profsea/components/global_/antarctica.py   | 61 ++++++++++++++--------
 profsea/components/global_/expansion.py    |  4 +-
 profsea/components/global_/glacier.py      | 20 ++++---
 profsea/components/global_/greenland.py    | 24 ++++-----
 profsea/components/global_/landwater.py    |  8 ++-
 profsea/utils/utils.py                     |  6 ++-
 tests/core/test_global_model.py            | 52 +++++++++++++++++-
 11 files changed, 151 insertions(+), 60 deletions(-)

diff --git a/profsea/components/core/global_model.py b/profsea/components/core/global_model.py
index 7538975..423d49c 100644
--- a/profsea/components/core/global_model.py
+++ b/profsea/components/core/global_model.py
@@ -212,13 +212,19 @@ def run(
                 if data.ndim > 1:
                     results[comp_name] = data[random_idx][None, :]
 
+        # Print results dtypes
+        for comp_name, data in results.items():
+            logger.info(f"Component '{comp_name}' output dtype: {data.dtype}")
+
         # Output percentiles
         if self.output_percentiles is not None:
             logger.info(
                 f"Sampling {len(self.output_percentiles)} members per component..."
             )
             for comp_name, data in results.items():
-                results[comp_name] = sample_members_2D(data, self.output_percentiles)
+                results[comp_name] = sample_members_2D(
+                    data, self.output_percentiles, dtype=self.dtype
+                )
 
         self.results = self._arr_to_xr(results)
         return self.results
@@ -367,4 +373,8 @@ def _calculate_drivers(self, T_change: np.ndarray) -> tuple:
         # Time-integral of temperature anomaly
         T_int_ens = np.cumsum(T_ens, axis=1)
         T_int_med = np.cumsum(np.median(T_ens, axis=0))
-        return T_ens, T_int_ens, T_int_med
+        return (
+            T_ens.astype(self.dtype),
+            T_int_ens.astype(self.dtype),
+            T_int_med.astype(self.dtype),
+        )
diff --git a/profsea/components/core/spatial_model.py b/profsea/components/core/spatial_model.py
index 55906c8..9ee1a75 100644
--- a/profsea/components/core/spatial_model.py
+++ b/profsea/components/core/spatial_model.py
@@ -37,6 +37,7 @@ def __init__(
         end_year: int = 2301,
         baseline_yrs: tuple = (1995, 2014),
         output_percentiles: list | np.ndarray = [5, 17, 50, 83, 95],
+        dtype: np.dtype = np.float32,
     ) -> None:
         """
         Parameters
@@ -54,6 +55,8 @@ def __init__(
             Tuple defining the start and end years of the baseline period for calculating anomalies. Default is (1995, 2014).
         output_percentiles: list or np.ndarray, optional
             List or array of percentiles to sample from the ensemble for output. If None, outputs all members. Default is [5, 17, 50, 83, 95].
+        dtype: np.dtype, optional
+            Data type for the output arrays. Default is np.float32.
         """
         # Define the path where the data should live
 
@@ -196,6 +199,7 @@ def run(self, member_seed: int = 42) -> None:
             grid_interpolation="linear",
             output_percentiles=self.output_percentiles,
             baseline_yrs=self.baseline_yrs,
+            dtype=self.dtype,
         )
 
         child_seeds = seed_seq.spawn(len(self.components))
diff --git a/profsea/components/core/state.py b/profsea/components/core/state.py
index 8f36a2e..442c731 100644
--- a/profsea/components/core/state.py
+++ b/profsea/components/core/state.py
@@ -21,7 +21,7 @@ class ClimateState:
     nyr: int
     nt: int
     num_members: int
-    dtype: np.dtype = np.float64
+    dtype: np.dtype = np.float32
 
 
 @dataclass
@@ -35,7 +35,7 @@ class SpatialState:
     grid_interpolation: str
     output_percentiles: list[int] | np.ndarray
     baseline_yrs: tuple[int, int]
-    dtype: np.dtype = np.float64
+    dtype: np.dtype = np.float32
 
 
 @dataclass
@@ -49,4 +49,4 @@ class LocalState:
     interpolation_method: str
     output_percentiles: list[int] | np.ndarray
     baseline_yrs: tuple[int, int]
-    dtype: np.dtype = np.float64
+    dtype: np.dtype = np.float32
diff --git a/profsea/components/core/time_projection.py b/profsea/components/core/time_projection.py
index 941cbcb..192a29b 100644
--- a/profsea/components/core/time_projection.py
+++ b/profsea/components/core/time_projection.py
@@ -38,10 +38,10 @@ def time_projection(
     """
     # Create a field of elapsed time since start in years
     timeendofAR5 = state.endofAR5 - state.endofhistory + 1
-    time = np.arange(state.end_yr - state.endofhistory) + 1
+    time = (np.arange(state.end_yr - state.endofhistory, dtype=state.dtype) + 1)
 
     if fraction is None:
-        fraction = rng.random((state.num_members, state.nt)).astype(state.dtype)
+        fraction = rng.random((state.num_members, state.nt), dtype=state.dtype)
     elif fraction.size != state.num_members * state.nt:
         raise ValueError("fraction is the wrong size")
 
@@ -49,9 +49,9 @@ def time_projection(
 
     # Convert inputs to startrate (m yr-1) and afinal (m), where both are
     # arrays with the size of fraction
-    startrate = (
-        startratemean + startratepm * np.array([-1, 1], dtype=float)
-    ) * 1e-3  # convert mm yr-1 to m yr-1
+    startrate = ((
+        startratemean + startratepm * np.array([-1, 1])
+    ) * 1e-3).astype(state.dtype)  # convert mm yr-1 to m yr-1
     finalisrange = isinstance(final, Sequence)
 
     if finalisrange:
@@ -71,7 +71,7 @@ def time_projection(
     #   a = S/t**2-b/t = (S-b*t)/t**2
     # If nfinal=1, the following two lines are equivalent to
     # halfacc=(final-startyr*nyr)/nyr**2
-    finalyr = np.arange(nfinal) - nfinal + 94 + 1  # last element ==nyr
+    finalyr = np.arange(nfinal, dtype=state.dtype) - nfinal + 94 + 1  # last element ==nyr
     halfacc = (afinal - startrate * finalyr.mean()) / (finalyr**2).mean()
     quadratic = halfacc[:, :, np.newaxis] * (time**2)
     linear = startrate[:, :, np.newaxis] * time
diff --git a/profsea/components/global_/antarctica.py b/profsea/components/global_/antarctica.py
index 7cd7fed..4b77270 100644
--- a/profsea/components/global_/antarctica.py
+++ b/profsea/components/global_/antarctica.py
@@ -60,6 +60,7 @@ def _impulse_response_term(
         gamma: float,
         params_1d: np.ndarray,
         dt: float,
+        state: ClimateState,
     ) -> np.ndarray:
         """Computes the slow response term for a single trajectory."""
         n_time = tas_1d.shape[0]
@@ -68,7 +69,7 @@ def _impulse_response_term(
         forcing_base = np.sign(tas_1d) * (np.abs(tas_1d) ** gamma)
 
         # decay_factors1 = np.exp(-np.arange(n_time) * dt / tau1) * (dt / tau1)
-        t_arr = np.arange(n_time)
+        t_arr = np.arange(n_time, dtype=state.dtype)
         decay_factors1 = (t_arr * dt / tau1**2) * np.exp(-t_arr * dt / tau1) * dt
         rate_delayed1 = fftconvolve(forcing_base, decay_factors1, mode="full")[:n_time]
         term_slow1 = alpha1 * (np.cumsum(rate_delayed1) * dt)
@@ -81,21 +82,21 @@ def _impulse_response_term(
         return term_slow1 + term_slow2
 
     def _precompute_delayed_rates(
-        self, tas: np.ndarray, dt: float
+        self, tas: np.ndarray, dt: float, state: ClimateState
     ) -> tuple[np.ndarray, np.ndarray]:
         """
         Precomputes the cumulative delayed rates for all models across all trajectories.
         Returns two arrays of shape (n_models, n_traj, n_time).
         """
         n_traj, n_time = tas.shape
-        cum_rate1 = np.zeros((self.n_models, n_traj, n_time))
-        cum_rate2 = np.zeros((self.n_models, n_traj, n_time))
-        t_arr = np.arange(n_time)
+        cum_rate1 = np.zeros((self.n_models, n_traj, n_time), dtype=state.dtype)
+        cum_rate2 = np.zeros((self.n_models, n_traj, n_time), dtype=state.dtype)
+        t_arr = np.arange(n_time, dtype=state.dtype)
 
         for m_idx in range(self.n_models):
-            tau1 = float(self.param_ds.tau1[m_idx].values)
-            tau2 = float(self.param_ds.tau2[m_idx].values)
-            gamma = float(self.param_ds.gamma[m_idx].values)
+            tau1 = np.array(self.param_ds.tau1[m_idx].values, dtype=state.dtype)
+            tau2 = np.array(self.param_ds.tau2[m_idx].values, dtype=state.dtype)
+            gamma = np.array(self.param_ds.gamma[m_idx].values, dtype=state.dtype)
 
             # Forcing base is model-specific due to gamma. Shape: (n_traj, n_time)
             forcing_base = np.sign(tas) * (np.abs(tas) ** gamma)
@@ -132,25 +133,29 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
         nm = state.nt * state.num_members
 
         # 1. Precompute expensive convolutions for unique (Model x Trajectory) combos
-        cum_rate1, cum_rate2 = self._precompute_delayed_rates(tas, dt)
+        cum_rate1, cum_rate2 = self._precompute_delayed_rates(tas, dt, state)
         tas_int = np.cumsum(tas, axis=1) * dt
 
         # 2. Generate random model/residual assignments for all members
         model_indices = rng.integers(0, self.n_models, size=nm)
-        all_residuals = self.param_ds.param_residuals.values
+        all_residuals = np.asarray(
+            self.param_ds.param_residuals.values, dtype=state.dtype
+        )
         n_train_scenarios = all_residuals.shape[1]
         residual_indices = rng.integers(0, n_train_scenarios, size=nm)
 
         # 3. Create flat mapping array to match the (Trajectory x Member) layout
         # This groups by trajectory: [Traj0_Mem0...Traj0_Mem999, Traj1_Mem0...]
-        t_indices = np.repeat(np.arange(n_traj), state.num_members)
+        t_indices = np.repeat(np.arange(n_traj, dtype=np.int32), state.num_members)
 
         # NOTE: If your required grouping is interleaved [Traj0_Mem0, Traj1_Mem0...]
         # uncomment the line below instead:
-        # t_indices = np.tile(np.arange(n_traj), state.num_members)
+        # t_indices = np.tile(np.arange(n_traj, dtype=state.dtype), state.num_members)
 
         # 4. Vectorized Parameter Extraction
-        general_p = self.param_ds.general_params.values[model_indices]
+        general_p = np.asarray(
+            self.param_ds.general_params.values[model_indices], dtype=state.dtype
+        )
         sampled_residuals = all_residuals[model_indices, residual_indices, :]
         total_params = general_p + sampled_residuals
 
@@ -214,11 +219,18 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
             )
             lcoeff = lcoeff[state.scenario]
 
-            ascale = norm.ppf(state.fraction)
+            ascale = norm.ppf(state.fraction).astype(state.dtype)
             final = np.exp(lcoeff[2] * ascale**2 + lcoeff[1] * ascale + lcoeff[0])
             final = final.reshape(state.num_members, state.nt)
         return (
-            time_projection(state, 0.41, 0.20, final, rng, fraction=state.fraction)
+            time_projection(
+                state,
+                0.41,
+                0.20,
+                final,
+                rng,
+                fraction=state.fraction,
+            )
             + self.d_ant
         )
 
@@ -233,8 +245,7 @@ class AntarcticaSMBAR5(Component):
     """
 
     def __init__(self):
-        # Conversion factor for Gt to m SLE
-        self.mSLEoGt = 1e12 / 3.61e14 * 1e-3
+        pass
 
     def project(
         self,
@@ -255,21 +266,29 @@ def project(
         antsmb: np.ndarray
             Antarctic SMB contribution to GMSLR.
         """
+        # Conversion factor for Gt to m SLE
+        mSLEoGt = 1e12 / 3.61e14 * 1e-3
         # The following are [mean,SD]
         pcoK = [5.1, 1.5]  # % change in Ant SMB per K of warming from G&H06
         KoKg = [1.1, 0.2]  # ratio of Antarctic warming to global warming from G&H06
 
         # Generate a distribution of products of the above two factors
         pcoKg = (
-            pcoK[0] + rng.standard_normal([state.num_members, state.nt]) * pcoK[1]
-        ) * (KoKg[0] + rng.standard_normal([state.num_members, state.nt]) * KoKg[1])
+            pcoK[0]
+            + rng.standard_normal([state.num_members, state.nt], dtype=state.dtype)
+            * pcoK[1]
+        ) * (
+            KoKg[0]
+            + rng.standard_normal([state.num_members, state.nt], dtype=state.dtype)
+            * KoKg[1]
+        )
         meansmb = 1923  # model-mean time-mean 1979-2010 Gt yr-1 from 13.3.3.2
         moaoKg = (
-            -pcoKg * 1e-2 * meansmb * self.mSLEoGt
+            -pcoKg * 1e-2 * meansmb * mSLEoGt
         )  # m yr-1 of SLE per K of global warming
 
         if state.fraction is None:
-            fraction = rng.random((state.num_members, state.nt)).astype(state.dtype)
+            fraction = rng.random((state.num_members, state.nt), dtype=state.dtype)
         elif state.fraction.size != state.num_members * state.nt:
             raise ValueError("fraction is the wrong size")
         else:
diff --git a/profsea/components/global_/expansion.py b/profsea/components/global_/expansion.py
index 1bf5b05..b6945ff 100644
--- a/profsea/components/global_/expansion.py
+++ b/profsea/components/global_/expansion.py
@@ -22,6 +22,8 @@ def __init__(self, OHC_change: np.ndarray, distribution_scaler: float = 1.0):
         self.distribution_scaler = distribution_scaler
 
     def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
+        self.OHC_change = np.asarray(self.OHC_change, dtype=state.dtype)
+
         # check the shape here
         check_shapes(self.OHC_change, state.nyr)
 
@@ -39,7 +41,7 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
         exp_efficiency = (
             rng.normal(loc=mean_eff, scale=std_eff, size=(state.nt, state.num_members))
             * 1e-24
-        )  # m/YJ
+        ).astype(state.dtype)  # m/YJ
 
         ohc_3d = self.OHC_change[:, None, :]
         # Efficiency shape: (nt, num_members, 1)
diff --git a/profsea/components/global_/glacier.py b/profsea/components/global_/glacier.py
index e525044..4f159f9 100644
--- a/profsea/components/global_/glacier.py
+++ b/profsea/components/global_/glacier.py
@@ -66,24 +66,26 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
         ngl = len(glparm)
         model_indices = rng.integers(0, ngl, size=(state.nt, state.num_members))
 
-        base_factors = np.array([p["factor"] for p in glparm])
-        base_exponents = np.array([p["exponent"] for p in glparm])
-        base_cvgls = np.array([p["cvgl"] for p in glparm])
+        base_factors = np.array([p["factor"] for p in glparm], dtype=state.dtype)
+        base_exponents = np.array([p["exponent"] for p in glparm], dtype=state.dtype)
+        base_cvgls = np.array([p["cvgl"] for p in glparm], dtype=state.dtype)
 
         factors = base_factors[model_indices][:, :, None]
         exponents = base_exponents[model_indices][:, :, None]
         cvgls = base_cvgls[model_indices][:, :, None]
 
-        r = rng.standard_normal((state.nt, state.num_members))[:, :, None]
+        r = rng.standard_normal((state.nt, state.num_members), dtype=state.dtype)[
+            :, :, None
+        ]
 
         T_int_ens_3d = state.T_int_ens[:, None, :]
         # Median shape: (1, 1, nyr)
         T_int_med_3d = state.T_int_med[None, None, :]
 
-        zgl = self._project_glacier1(T_int_ens_3d, factors, exponents)
+        zgl = self._project_glacier1(T_int_ens_3d, factors, exponents, state)
 
         # Passes (1, 1, nyr) + (nt, nm, 1) -> Returns (nt, nm, nyr)
-        mgl = self._project_glacier1(T_int_med_3d, factors, exponents)
+        mgl = self._project_glacier1(T_int_med_3d, factors, exponents, state)
 
         # 6. Apply variance and clip using 3D matrix math
         glacier = zgl + (mgl * r * cvgls)
@@ -94,7 +96,11 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
         return glacier.reshape(state.nt * state.num_members, state.nyr)
 
     def _project_glacier1(
-        self, T_int: np.ndarray, factor: np.ndarray, exponent: np.ndarray
+        self,
+        T_int: np.ndarray,
+        factor: np.ndarray,
+        exponent: np.ndarray,
+        state: ClimateState,
     ) -> np.ndarray:
         """Project glacier contribution by one glacier method."""
         scale = 1e-3  # mm to m
diff --git a/profsea/components/global_/greenland.py b/profsea/components/global_/greenland.py
index e54c3cc..0ecc215 100644
--- a/profsea/components/global_/greenland.py
+++ b/profsea/components/global_/greenland.py
@@ -40,7 +40,7 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
             tas = np.expand_dims(tas, axis=0)
 
         nt = tas.shape[0]
-        time_delta = np.arange(state.nyr)
+        time_delta = np.arange(state.nyr, dtype=state.dtype)
 
         df = self.df
         n_models = len(df)
@@ -48,12 +48,12 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
         model_indices = rng.integers(0, n_models, size=(nt, state.num_members))
 
         # Extract parameters and reshape to 3D: (nt, nm, 1)
-        b0 = df["b0"].values[model_indices][:, :, None]
-        b1 = df["b1"].values[model_indices][:, :, None]
-        b2 = df["b2"].values[model_indices][:, :, None]
-        b3 = df["b3"].values[model_indices][:, :, None]
-        b4 = df["b4"].values[model_indices][:, :, None]
-        b5 = df["b5"].values[model_indices][:, :, None]
+        b0 = df["b0"].values[model_indices][:, :, None].astype(state.dtype)
+        b1 = df["b1"].values[model_indices][:, :, None].astype(state.dtype)
+        b2 = df["b2"].values[model_indices][:, :, None].astype(state.dtype)
+        b3 = df["b3"].values[model_indices][:, :, None].astype(state.dtype)
+        b4 = df["b4"].values[model_indices][:, :, None].astype(state.dtype)
+        b5 = df["b5"].values[model_indices][:, :, None].astype(state.dtype)
 
         # GIS trend values taken from FACTS GitHub repo
         trend_mean = 0.19
@@ -63,12 +63,13 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
         a_bound = (0.0 - trend_mean) / trend_std
         b_bound = (99999.9 - trend_mean) / trend_std  # Or just np.inf
         trend = truncnorm.ppf(
-            rng.random((nt, state.num_members)).astype(state.dtype),
+            rng.random((nt, state.num_members)),
             a=a_bound,
             b=b_bound,
             loc=trend_mean,
             scale=trend_std,
-        )
+        ).astype(state.dtype)
+
         trend_sle = (trend[:, :, None] * time_delta[None, None, :]) * 1e-3
 
         tas_3d = tas[:, None, :]
@@ -131,11 +132,10 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
         febound = [1, 1.15]  # bounds of uniform pdf of SMB elevation feedback factor
 
         # random log-normal factor
-        fn = np.exp(rng.standard_normal(state.num_members) * fnlogsd)
+        fn = np.exp(rng.standard_normal(state.num_members, dtype=state.dtype) * fnlogsd)
         # elevation feedback factor
         fe = (
-            rng.random(state.num_members).astype(state.dtype)
-            * (febound[1] - febound[0])
+            rng.random(state.num_members, dtype=state.dtype) * (febound[1] - febound[0])
             + febound[0]
         )
         ff = fn * fe
diff --git a/profsea/components/global_/landwater.py b/profsea/components/global_/landwater.py
index d1f262b..ab0e74a 100644
--- a/profsea/components/global_/landwater.py
+++ b/profsea/components/global_/landwater.py
@@ -36,14 +36,12 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
         np.ndarray
             Land water storage contribution to GMSLR.
         """
-
-        lw_ds = self.lw_ds
+        self.lw_ds = self.lw_ds.astype(state.dtype)
 
         # Interpolate to annual projections
-        interp_ds = lw_ds.interp(
+        interp_ds = self.lw_ds.interp(
             years=np.arange(2005, 2301, 1), method="linear"
-        ).squeeze()
-        interp_ds["sea_level_change"].values * 1e-3  # mm to m
+        ).squeeze().astype(state.dtype)
 
         # Base array: Shape (n_samples_in_nc, 296)
         lw_base = interp_ds["sea_level_change"].values * 1e-3
diff --git a/profsea/utils/utils.py b/profsea/utils/utils.py
index b5983e5..b248638 100644
--- a/profsea/utils/utils.py
+++ b/profsea/utils/utils.py
@@ -26,7 +26,9 @@
 
 
 def sample_members_2D(
-    array: np.ndarray | da.Array, percentiles: list | np.ndarray
+    array: np.ndarray | da.Array,
+    percentiles: list | np.ndarray,
+    dtype: np.dtype = np.float32,
 ) -> np.ndarray | da.Array:
     """
     Sample real ensemble members from a 2D numpy or dask array lazily.
@@ -50,7 +52,7 @@ def _eager_sample(arr, percs):
         array_percentiles = np.nanpercentile(arr, percs, axis=0)
         distances = cdist(array_percentiles, arr)
         mem_indices = np.argmin(distances, axis=1)
-        return arr[mem_indices]
+        return arr[mem_indices].astype(dtype)
 
     if isinstance(array, da.Array):
         # Tell Dask to delay this operation until the graph is computed
diff --git a/tests/core/test_global_model.py b/tests/core/test_global_model.py
index 6309c09..0035d7a 100644
--- a/tests/core/test_global_model.py
+++ b/tests/core/test_global_model.py
@@ -9,7 +9,7 @@
 class MockGlobalComponent(Component):
     def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
         # Just return an array of 1s with the correct shape
-        return np.ones((state.nt * state.num_members, state.nyr))
+        return np.ones((state.nt * state.num_members, state.nyr), dtype=state.dtype)
 
 
 def test_calculate_drivers_math():
@@ -64,3 +64,53 @@ def test_save_components(tmp_path):
     assert "mock_comp" in ds.data_vars
     assert ds["mock_comp"].shape == (5, 4)
     assert list(ds.dims) == ["member", "time"]
+
+
+def test_return_types():
+    # Import a number of real components to test their return types
+    from profsea.components.global_ import (
+        AntarcticaDynAR5,
+        AntarcticaISMIP6,
+        AntarcticaSMBAR5,
+        Glacier,
+        GreenlandAR6,
+        GreenlandDynAR5,
+        GreenlandSMBAR5,
+        LandwaterAR5,
+        LandwaterAR6,
+        ThermalExpansion,
+    )
+
+    tas = np.zeros((2, 95))  # 2 trajectories, 4 years
+    ohc = np.zeros((2, 95))
+
+    components = {
+        "AntarcticaISMIP6": AntarcticaISMIP6(region="wais"),
+        "AntarcticaSMBAR5": AntarcticaSMBAR5(),
+        "AntarcticaDynAR5": AntarcticaDynAR5(),
+        "GreenlandAR6": GreenlandAR6(),
+        "GreenlandDynAR5": GreenlandDynAR5(),
+        "GreenlandSMBAR5": GreenlandSMBAR5(),
+        "Glacier": Glacier(),
+        "LandwaterAR5": LandwaterAR5(),
+        "LandwaterAR6": LandwaterAR6(),
+        "ThermalExpansion": ThermalExpansion(OHC_change=ohc),
+    }
+
+    global_model_float64 = Global(
+        components=components, end_yr=2101, nt=2, num_members=3, dtype=np.float64
+    )
+    results = global_model_float64.run(scenario="rcp26", T_change=tas)
+
+    for comp_name, data in results.items():
+        assert isinstance(data, xr.DataArray)
+        assert data.dtype == np.float64
+
+    global_model_float32 = Global(
+        components=components, end_yr=2101, nt=2, num_members=3, dtype=np.float32
+    )
+    results = global_model_float32.run(scenario="rcp26", T_change=tas)
+
+    for comp_name, data in results.items():
+        assert isinstance(data, xr.DataArray)
+        assert data.dtype == np.float32

From a2c8998c1bd94a53d218722212fb909b22b89136 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Wed, 17 Jun 2026 19:34:21 +0100
Subject: [PATCH 262/276] Ruff

---
 profsea/components/core/time_projection.py | 12 +++++++-----
 profsea/components/global_/landwater.py    |  8 +++++---
 2 files changed, 12 insertions(+), 8 deletions(-)

diff --git a/profsea/components/core/time_projection.py b/profsea/components/core/time_projection.py
index 192a29b..b1372e8 100644
--- a/profsea/components/core/time_projection.py
+++ b/profsea/components/core/time_projection.py
@@ -38,7 +38,7 @@ def time_projection(
     """
     # Create a field of elapsed time since start in years
     timeendofAR5 = state.endofAR5 - state.endofhistory + 1
-    time = (np.arange(state.end_yr - state.endofhistory, dtype=state.dtype) + 1)
+    time = np.arange(state.end_yr - state.endofhistory, dtype=state.dtype) + 1
 
     if fraction is None:
         fraction = rng.random((state.num_members, state.nt), dtype=state.dtype)
@@ -49,9 +49,9 @@ def time_projection(
 
     # Convert inputs to startrate (m yr-1) and afinal (m), where both are
     # arrays with the size of fraction
-    startrate = ((
-        startratemean + startratepm * np.array([-1, 1])
-    ) * 1e-3).astype(state.dtype)  # convert mm yr-1 to m yr-1
+    startrate = ((startratemean + startratepm * np.array([-1, 1])) * 1e-3).astype(
+        state.dtype
+    )  # convert mm yr-1 to m yr-1
     finalisrange = isinstance(final, Sequence)
 
     if finalisrange:
@@ -71,7 +71,9 @@ def time_projection(
     #   a = S/t**2-b/t = (S-b*t)/t**2
     # If nfinal=1, the following two lines are equivalent to
     # halfacc=(final-startyr*nyr)/nyr**2
-    finalyr = np.arange(nfinal, dtype=state.dtype) - nfinal + 94 + 1  # last element ==nyr
+    finalyr = (
+        np.arange(nfinal, dtype=state.dtype) - nfinal + 94 + 1
+    )  # last element ==nyr
     halfacc = (afinal - startrate * finalyr.mean()) / (finalyr**2).mean()
     quadratic = halfacc[:, :, np.newaxis] * (time**2)
     linear = startrate[:, :, np.newaxis] * time
diff --git a/profsea/components/global_/landwater.py b/profsea/components/global_/landwater.py
index ab0e74a..cd434c2 100644
--- a/profsea/components/global_/landwater.py
+++ b/profsea/components/global_/landwater.py
@@ -39,9 +39,11 @@ def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
         self.lw_ds = self.lw_ds.astype(state.dtype)
 
         # Interpolate to annual projections
-        interp_ds = self.lw_ds.interp(
-            years=np.arange(2005, 2301, 1), method="linear"
-        ).squeeze().astype(state.dtype)
+        interp_ds = (
+            self.lw_ds.interp(years=np.arange(2005, 2301, 1), method="linear")
+            .squeeze()
+            .astype(state.dtype)
+        )
 
         # Base array: Shape (n_samples_in_nc, 296)
         lw_base = interp_ds["sea_level_change"].values * 1e-3

From 02e71ae3474719c9e0ce38dabdadca39ea9c6e53 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Wed, 17 Jun 2026 19:41:25 +0100
Subject: [PATCH 263/276] Update deps

---
 profsea/components/core/global_model.py |  6 +-----
 pyproject.toml                          | 16 +++++++++++++---
 2 files changed, 14 insertions(+), 8 deletions(-)

diff --git a/profsea/components/core/global_model.py b/profsea/components/core/global_model.py
index 423d49c..d09359c 100644
--- a/profsea/components/core/global_model.py
+++ b/profsea/components/core/global_model.py
@@ -373,8 +373,4 @@ def _calculate_drivers(self, T_change: np.ndarray) -> tuple:
         # Time-integral of temperature anomaly
         T_int_ens = np.cumsum(T_ens, axis=1)
         T_int_med = np.cumsum(np.median(T_ens, axis=0))
-        return (
-            T_ens.astype(self.dtype),
-            T_int_ens.astype(self.dtype),
-            T_int_med.astype(self.dtype),
-        )
+        return (T_ens, T_int_ens, T_int_med)
diff --git a/pyproject.toml b/pyproject.toml
index 5bc38a1..894724f 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -5,7 +5,17 @@ build-backend = "setuptools.build_meta"
 [project]
 name = "profsea"
 version = "3.0"
-dependencies = ["numpy", "xarray", "scipy", "dask", "rich", "requests", "regionmask"]
+dependencies = [
+    "numpy",
+    "xarray",
+    "scipy",
+    "dask",
+    "rich",
+    "requests",
+    "regionmask",
+    "netcdf4",
+    "h5netcdf",
+]
 
 [tool.setuptools.packages.find]
 include = ["profsea*"]
@@ -14,8 +24,8 @@ exclude = ["docs*", "tests*"]
 [project.optional-dependencies]
 test = [
     "pytest>=7.0",
-    "pytest-cov",  # Useful for checking test coverage later
-    "pytest-github-actions-annotate-failures"
+    "pytest-cov",                              # Useful for checking test coverage later
+    "pytest-github-actions-annotate-failures",
 ]
 
 # Settings for Ruff formatting

From 3a612b935dad61e7052465a9db11c5751541964a Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Wed, 17 Jun 2026 19:48:10 +0100
Subject: [PATCH 264/276] Add dtype save arg

---
 profsea/utils/utils.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/profsea/utils/utils.py b/profsea/utils/utils.py
index b248638..5d18d8d 100644
--- a/profsea/utils/utils.py
+++ b/profsea/utils/utils.py
@@ -279,6 +279,7 @@ def save_components(
     output_prefix: str = "projection",
     output_dir: str = ".",
     output_format: str = "zarr",
+    output_dtype: str = "float32",
 ) -> None:
     """
     Stream all regional sea level projections to disk in a single file/store.
@@ -295,6 +296,8 @@ def save_components(
         Name of the scenario you've run the emulator for.
     output_prefix: str
         Prefix for the output file name (e.g., 'projection' will result in 'ssp
+    output_dtype: str
+        Data type to save the output in. Default is 'float32'.
 
     Returns
     -------
@@ -324,9 +327,9 @@ def save_components(
     # Set the encoding/compression for each variable based on the output format
     for name, component in components.items():
         if output_format == "netcdf":
-            encoding[name] = {"zlib": True, "complevel": 1, "dtype": "float32"}
+            encoding[name] = {"zlib": True, "complevel": 1, "dtype": output_dtype}
         elif output_format == "zarr":
-            encoding[name] = {"compressor": compressor, "dtype": "float32"}
+            encoding[name] = {"compressor": compressor, "dtype": output_dtype}
 
     file_name = f"{scenario_name}_{output_prefix}"
 

From 616250b49369407bfcc3e0d0c20780be505105ec Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Wed, 17 Jun 2026 20:41:34 +0100
Subject: [PATCH 265/276] Update docs

---
 docs/source/_static/profsea-logo.png | Bin 679505 -> 0 bytes
 docs/source/conf.py                  |   7 ++-
 docs/source/index.rst                |  75 +++++++++++++++++++++++++--
 3 files changed, 78 insertions(+), 4 deletions(-)
 delete mode 100644 docs/source/_static/profsea-logo.png

diff --git a/docs/source/_static/profsea-logo.png b/docs/source/_static/profsea-logo.png
deleted file mode 100644
index 6a55db77d798f1f64c60c3c6f01aa79fc2bc0fd4..0000000000000000000000000000000000000000
GIT binary patch
literal 0
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diff --git a/docs/source/conf.py b/docs/source/conf.py
index 3109f1b..a18e728 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -79,10 +79,15 @@
             "type": "fontawesome",
         }
     ],
+    "logo": {
+        "image_light": "_static/profsea-logo-light.png",
+        "image_dark": "_static/profsea-logo-dark.png",
+    },
 }
 html_sidebars = {
+    "index": [],  # This removes the left sidebar on the landing page
     "**": ["search-field", "sidebar-nav-bs", "page-toc"],
 }
 html_static_path = ["_static"]
-html_logo = "_static/profsea-logo.png"
+# html_logo = "_static/profsea-logo-dark.png"
 html_favicon = "_static/favicon.png"
diff --git a/docs/source/index.rst b/docs/source/index.rst
index 07ca9ff..e3661e7 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -1,8 +1,63 @@
-ProFSea
-=======
+.. title:: ProFSea
 
-**ProFSea is sea-level rise simulator based on statistical emulations of physical modelling experiments and lines of evidence from the IPCC and across the literature. It's modular, easy to setup and self-contained - all you need is global mean surface temperature and ocean heat content forcing anomalies, using any baseline period, and you're good to go.**
+.. grid:: 1 1 2 2
+   :margin: 4 4 0 0
+   :padding: 0
+
+   .. grid-item::
+      :columns: 12 12 8 8
+
+      .. rst-class:: display-2 font-weight-bold
+
+      ProFSea
+
+      .. rst-class:: lead
+
+      **🌊 A modular, easy-to-setup, and self-contained sea-level rise simulator based on statistical emulations of physical modelling experiments and lines of evidence from the IPCC.**
+
+      .. container:: d-flex gap-3 pt-3
+         
+         .. button-ref:: user_guide
+            :ref-type: doc
+            :color: primary
+            :shadow:
+            :class: font-weight-bold
+            
+            Get Started
+            
+         .. button-link:: [https://github.com/MetOffice/ProFSea-tool](https://github.com/MetOffice/ProFSea-tool)
+            :color: secondary
+            :shadow:
+            :outline:
+            :class: font-weight-bold
+            
+            View on GitHub
+
+   .. grid-item::
+      :columns: 12 12 4 4
+
+      .. image:: /_static/logo.png
+         :alt: ProFSea Logo
+         :class: align-center
+         :width: 100%
+
+----
 
+ProFSea makes complex sea-level rise simulations accessible and fast. All you need is global mean surface temperature and ocean heat content forcing anomalies, using any baseline period, and you're good to go. ProFSea development is supported by the MetOffice.
+
+Quick Install
+-------------
+
+ProFSea is available as a Python package. To install it:
+
+.. code-block:: bash
+
+   pip install profsea
+
+.. note::
+   ProFSea relies on standard scientific libraries including ``numpy``, ``xarray``, and ``dask``. Check the :doc:`user_guide` for detailed dependency requirements.
+
+----
 
 .. grid:: 1 1 2 2
    :gutter: 2
@@ -35,6 +90,18 @@ ProFSea
        
        Academic and software references for the project.
 
+Getting in Touch
+----------------
+
+Whether you need help getting started with ProFSea, found a bug, want the emulator to be more awesome, or just want to chat about sea-level modelling, you have a few options:
+
+* **Open an issue** on the `GitHub repository `_ if you find a bug, need a missing feature, or spot a typo in this documentation 👀.
+* **Start a discussion** on GitHub for general questions about emulations, statistical methods, or setting up a new experiment.
+
+Citing ProFSea
+--------------
+
+If you use ProFSea for your research, teaching, or analysis, please credit the project by citing the relevant academic references. See the :doc:`references` page for the full bibliography, methodology papers, and software DOIs.
 
 .. toctree::
    :caption: User Guide
@@ -47,6 +114,8 @@ ProFSea
    :caption: Development
    :maxdepth: 1
    :hidden:
+   
+   documentation
 
 .. toctree::
    :caption: Reference

From 66bc1a42b9d2b57065951c181d8181cd8413b5c5 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Wed, 17 Jun 2026 20:42:00 +0100
Subject: [PATCH 266/276] Update logos

---
 docs/source/_static/profsea-logo-dark.png  | Bin 0 -> 665645 bytes
 docs/source/_static/profsea-logo-light.png | Bin 0 -> 679505 bytes
 2 files changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 docs/source/_static/profsea-logo-dark.png
 create mode 100644 docs/source/_static/profsea-logo-light.png

diff --git a/docs/source/_static/profsea-logo-dark.png b/docs/source/_static/profsea-logo-dark.png
new file mode 100644
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literal 0
HcmV?d00001


From 70226d50f31330d4bbcf958cf6f46c62620e072f Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Wed, 17 Jun 2026 20:48:30 +0100
Subject: [PATCH 267/276] Fix logo

---
 docs/source/conf.py   | 4 +++-
 docs/source/index.rst | 3 ++-
 2 files changed, 5 insertions(+), 2 deletions(-)

diff --git a/docs/source/conf.py b/docs/source/conf.py
index a18e728..b776318 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -89,5 +89,7 @@
     "**": ["search-field", "sidebar-nav-bs", "page-toc"],
 }
 html_static_path = ["_static"]
-# html_logo = "_static/profsea-logo-dark.png"
 html_favicon = "_static/favicon.png"
+html_css_files = [
+    "custom.css",
+]
diff --git a/docs/source/index.rst b/docs/source/index.rst
index e3661e7..6afa9fa 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -38,9 +38,10 @@
 
       .. image:: /_static/logo.png
          :alt: ProFSea Logo
-         :class: align-center
+         :class: align-center transparent-logo
          :width: 100%
 
+
 ----
 
 ProFSea makes complex sea-level rise simulations accessible and fast. All you need is global mean surface temperature and ocean heat content forcing anomalies, using any baseline period, and you're good to go. ProFSea development is supported by the MetOffice.

From 6c102bc5eff89240cbe71beb1bea64f3ba9cda5a Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Thu, 18 Jun 2026 13:24:30 +0100
Subject: [PATCH 268/276] Add custom CSS

---
 docs/source/_static/custom.css | 3 +++
 1 file changed, 3 insertions(+)
 create mode 100644 docs/source/_static/custom.css

diff --git a/docs/source/_static/custom.css b/docs/source/_static/custom.css
new file mode 100644
index 0000000..61ba797
--- /dev/null
+++ b/docs/source/_static/custom.css
@@ -0,0 +1,3 @@
+img.transparent-logo {
+    background-color: transparent !important;
+}
\ No newline at end of file

From 6b0d941ee1f76b1d2961232e2efb4f82e71bc573 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Thu, 18 Jun 2026 13:36:23 +0100
Subject: [PATCH 269/276] Truly unify save method, remove dtype print

---
 profsea/components/core/global_model.py  | 207 +++++++++++------------
 profsea/components/core/spatial_model.py |   1 +
 profsea/utils/utils.py                   |  67 +++++---
 3 files changed, 148 insertions(+), 127 deletions(-)

diff --git a/profsea/components/core/global_model.py b/profsea/components/core/global_model.py
index d09359c..dc6e1a2 100644
--- a/profsea/components/core/global_model.py
+++ b/profsea/components/core/global_model.py
@@ -10,7 +10,7 @@
 from rich.console import Console
 from rich.progress import track
 
-from profsea.utils import check_shapes, sample_members_2D
+from profsea.utils import check_shapes, sample_members_2D, save_components
 from profsea.utils.ui import print_global_preflight
 
 from .base import Component
@@ -88,6 +88,9 @@ def __init__(
         self.endofAR5 = 2100
         self.nyr = self.end_yr - self.endofhistory
 
+    # Inject method!
+    save_components = save_components
+
     def _arr_to_xr(self, arr_dict: dict[str, np.ndarray]) -> dict[str, xr.DataArray]:
         """Convert a dictionary of numpy/dask arrays to xarray DataArrays.
 
@@ -212,10 +215,6 @@ def run(
                 if data.ndim > 1:
                     results[comp_name] = data[random_idx][None, :]
 
-        # Print results dtypes
-        for comp_name, data in results.items():
-            logger.info(f"Component '{comp_name}' output dtype: {data.dtype}")
-
         # Output percentiles
         if self.output_percentiles is not None:
             logger.info(
@@ -229,105 +228,105 @@ def run(
         self.results = self._arr_to_xr(results)
         return self.results
 
-    def save_components(
-        self,
-        components: dict[str, xr.DataArray],
-        scenario_name: str,
-        output_prefix: str = "global",
-        output_dir: str = ".",
-        output_format: str = "netcdf",
-    ) -> None:
-        """
-        Stream all global sea level projections to disk in a single file/store.
-
-        Parameters
-        ----------
-        components: dict[str, xr.DataArray]
-            Dictionary of component names and their corresponding Xarray DataArrays.
-        scenario_name: str
-            Name of the scenario you've run the emulator for.
-        output_prefix: str
-            Prefix for the output file name (default: 'global').
-        output_dir: str
-            Directory to save components to.
-        output_format: str
-            Format to save the output in. Must be either 'netcdf' or 'zarr'.
-
-        Returns
-        -------
-        None
-        """
-        ds = xr.Dataset(components)
-
-        # Add ProFSea version and scenario metadata
-        ds.attrs["source"] = "ProFSea v3.0"
-        ds.attrs["scenario"] = scenario_name
-        ds.attrs["description"] = "Global sea level rise projections"
-
-        output_format = output_format.lower()
-        if output_format not in ["netcdf", "zarr"]:
-            raise ValueError("output_format must be either 'netcdf' or 'zarr'.")
-
-        Path(output_dir).mkdir(parents=True, exist_ok=True)
-        encoding = {}
-
-        # Sort out Zarr encoding
-        if output_format == "zarr":
-            import numcodecs
-            from numcodecs.zarr3 import Blosc
-
-            compressor = Blosc(
-                cname="zstd", clevel=5, shuffle=numcodecs.Blosc.BITSHUFFLE
-            )
-
-        # Set the encoding/compression dynamically based on the component's actual dtype
-        for name, component in components.items():
-            comp_dtype = (
-                component.dtype.name
-            )  # Captures 'float32' or 'float64' dynamically
-
-            if output_format == "netcdf":
-                encoding[name] = {"zlib": True, "complevel": 1, "dtype": comp_dtype}
-            elif output_format == "zarr":
-                encoding[name] = {"compressor": compressor, "dtype": comp_dtype}
-
-        file_name = f"{scenario_name}_{output_prefix}"
-
-        # Stream the computation and write to disk
-        if output_format == "netcdf":
-            out_path = os.path.join(output_dir, f"{file_name}.nc")
-            with console.status(
-                "[bold cyan]Computing and saving Global NetCDF...[/bold cyan]",
-                spinner="dots",
-            ):
-                # If using Dask, .compute() is required before .to_netcdf()
-                # If arrays are already eager NumPy arrays, .compute() is a harmless no-op
-                if hasattr(ds, "compute"):
-                    ds.compute().to_netcdf(out_path, encoding=encoding)
-                else:
-                    ds.to_netcdf(out_path, encoding=encoding)
-
-            logger.info(
-                f"[bold green]✓ Successfully saved NetCDF:[/bold green] {out_path}"
-            )
-
-        elif output_format == "zarr":
-            out_path = os.path.join(output_dir, f"{file_name}.zarr")
-            with console.status(
-                "[bold cyan]Streaming computation and saving Global Zarr...[/bold cyan]",
-                spinner="dots",
-            ):
-                ds.to_zarr(out_path, encoding=encoding, mode="w", compute=True)
-            logger.info(
-                f"[bold green]✓ Successfully saved Zarr:[/bold green] {out_path}"
-            )
-
-        # Log the shape of the total_gmslr (or the first available component)
-        sample_name = (
-            "total_gmslr" if "total_gmslr" in ds else list(ds.data_vars.keys())[0]
-        )
-        dims_str = ", ".join(ds[sample_name].dims)
-        logger.info(f"Global output shape was {ds[sample_name].shape} ({dims_str})")
+    # def save_components(
+    #     self,
+    #     components: dict[str, xr.DataArray],
+    #     scenario_name: str,
+    #     output_prefix: str = "global",
+    #     output_dir: str = ".",
+    #     output_format: str = "netcdf",
+    # ) -> None:
+    #     """
+    #     Stream all global sea level projections to disk in a single file/store.
+
+    #     Parameters
+    #     ----------
+    #     components: dict[str, xr.DataArray]
+    #         Dictionary of component names and their corresponding Xarray DataArrays.
+    #     scenario_name: str
+    #         Name of the scenario you've run the emulator for.
+    #     output_prefix: str
+    #         Prefix for the output file name (default: 'global').
+    #     output_dir: str
+    #         Directory to save components to.
+    #     output_format: str
+    #         Format to save the output in. Must be either 'netcdf' or 'zarr'.
+
+    #     Returns
+    #     -------
+    #     None
+    #     """
+    #     ds = xr.Dataset(components)
+
+    #     # Add ProFSea version and scenario metadata
+    #     ds.attrs["source"] = "ProFSea v3.0"
+    #     ds.attrs["scenario"] = scenario_name
+    #     ds.attrs["description"] = "Global sea level rise projections"
+
+    #     output_format = output_format.lower()
+    #     if output_format not in ["netcdf", "zarr"]:
+    #         raise ValueError("output_format must be either 'netcdf' or 'zarr'.")
+
+    #     Path(output_dir).mkdir(parents=True, exist_ok=True)
+    #     encoding = {}
+
+    #     # Sort out Zarr encoding
+    #     if output_format == "zarr":
+    #         import numcodecs
+    #         from numcodecs.zarr3 import Blosc
+
+    #         compressor = Blosc(
+    #             cname="zstd", clevel=5, shuffle=numcodecs.Blosc.BITSHUFFLE
+    #         )
+
+    #     # Set the encoding/compression dynamically based on the component's actual dtype
+    #     for name, component in components.items():
+    #         comp_dtype = (
+    #             component.dtype.name
+    #         )  # Captures 'float32' or 'float64' dynamically
+
+    #         if output_format == "netcdf":
+    #             encoding[name] = {"zlib": True, "complevel": 1, "dtype": comp_dtype}
+    #         elif output_format == "zarr":
+    #             encoding[name] = {"compressor": compressor, "dtype": comp_dtype}
+
+    #     file_name = f"{scenario_name}_{output_prefix}"
+
+    #     # Stream the computation and write to disk
+    #     if output_format == "netcdf":
+    #         out_path = os.path.join(output_dir, f"{file_name}.nc")
+    #         with console.status(
+    #             "[bold cyan]Computing and saving Global NetCDF...[/bold cyan]",
+    #             spinner="dots",
+    #         ):
+    #             # If using Dask, .compute() is required before .to_netcdf()
+    #             # If arrays are already eager NumPy arrays, .compute() is a harmless no-op
+    #             if hasattr(ds, "compute"):
+    #                 ds.compute().to_netcdf(out_path, encoding=encoding)
+    #             else:
+    #                 ds.to_netcdf(out_path, encoding=encoding)
+
+    #         logger.info(
+    #             f"[bold green]✓ Successfully saved NetCDF:[/bold green] {out_path}"
+    #         )
+
+    #     elif output_format == "zarr":
+    #         out_path = os.path.join(output_dir, f"{file_name}.zarr")
+    #         with console.status(
+    #             "[bold cyan]Streaming computation and saving Global Zarr...[/bold cyan]",
+    #             spinner="dots",
+    #         ):
+    #             ds.to_zarr(out_path, encoding=encoding, mode="w", compute=True)
+    #         logger.info(
+    #             f"[bold green]✓ Successfully saved Zarr:[/bold green] {out_path}"
+    #         )
+
+    #     # Log the shape of the total_gmslr (or the first available component)
+    #     sample_name = (
+    #         "total_gmslr" if "total_gmslr" in ds else list(ds.data_vars.keys())[0]
+    #     )
+    #     dims_str = ", ".join(ds[sample_name].dims)
+    #     logger.info(f"Global output shape was {ds[sample_name].shape} ({dims_str})")
 
     def sum_components(self, components: dict[str, xr.DataArray]) -> xr.DataArray:
         """
diff --git a/profsea/components/core/spatial_model.py b/profsea/components/core/spatial_model.py
index 9ee1a75..1303754 100644
--- a/profsea/components/core/spatial_model.py
+++ b/profsea/components/core/spatial_model.py
@@ -72,6 +72,7 @@ def __init__(
         self.output_percentiles = output_percentiles
         self.start_year = 2006
         self.n_years = self.end_year - self.start_year
+        self.dtype = dtype
 
         if self.output_percentiles is not None and len(self.output_percentiles) > 0:
             self.num_members = len(self.output_percentiles)
diff --git a/profsea/utils/utils.py b/profsea/utils/utils.py
index 5d18d8d..848fd0f 100644
--- a/profsea/utils/utils.py
+++ b/profsea/utils/utils.py
@@ -207,14 +207,14 @@ def fetch_zenodo_fingerprints(
 
     # 1. Check if data already exists
     if target_dir.exists() and any(target_dir.iterdir()):
-        console.log("[bold green]✓ ProFSea assets found locally![/bold green]")
+        logger.info("[bold green]✓ ProFSea assets found locally![/bold green]")
         return
 
     # Create the base directory if it doesn't exist
     data_dir.mkdir(parents=True, exist_ok=True)
     zip_path = data_dir / "temp_fingerprints.zip"
 
-    console.log(f"Initiating download from {zenodo_url}...")
+    logger.info(f"Initiating download from {zenodo_url}...")
 
     # 2. Stream the download with a rich progress bar
     try:
@@ -242,12 +242,12 @@ def fetch_zenodo_fingerprints(
                         progress.update(download_task, advance=len(chunk))
 
     except requests.exceptions.RequestException as e:
-        console.log(f"[bold red]Failed to download data: {e}[/bold red]")
+        logger.error(f"[bold red]Failed to download data: {e}[/bold red]")
         if zip_path.exists():
             zip_path.unlink()  # Clean up partial downloads
         raise
 
-    console.log("Extracting data...")
+    logger.info("Extracting data...")
     try:
         with zipfile.ZipFile(zip_path, "r") as zip_ref:
             # Filter out the __MACOSX directory and its contents
@@ -258,11 +258,11 @@ def fetch_zenodo_fingerprints(
             ]
             zip_ref.extractall(data_dir, members=valid_members)
 
-        console.log(
+        logger.info(
             f"[bold green]✓ Successfully extracted data to {data_dir}[/bold green]"
         )
     except zipfile.BadZipFile:
-        console.log(
+        logger.error(
             "[bold red]Error: Downloaded file is not a valid zip archive.[/bold red]"
         )
         raise
@@ -279,36 +279,44 @@ def save_components(
     output_prefix: str = "projection",
     output_dir: str = ".",
     output_format: str = "zarr",
-    output_dtype: str = "float32",
+    output_dtype: str | None = None,
+    description: str = "Spatial sea level rise projections",
 ) -> None:
     """
-    Stream all regional sea level projections to disk in a single file/store.
+    Stream sea level projections to disk in a single file/store.
 
     Parameters
     ----------
     components: dict[str, xr.DataArray]
         Dictionary of component names and their corresponding Xarray DataArrays.
-    output_format: str
-        Format to save the output in. Must be either 'netcdf' or 'zarr'.
-    output_dir: str
-        Directory to save components to.
     scenario_name: str
         Name of the scenario you've run the emulator for.
     output_prefix: str
-        Prefix for the output file name (e.g., 'projection' will result in 'ssp
-    output_dtype: str
-        Data type to save the output in. Default is 'float32'.
+        Prefix for the output file name (e.g., 'projection' or 'global').
+    output_dir: str
+        Directory to save components to.
+    output_format: str
+        Format to save the output in. Must be either 'netcdf' or 'zarr'.
+    output_dtype: str | None
+        Data type to force the output to (e.g., 'float32'). If None, the dtype
+        is dynamically inferred from each individual component.
+    description: str
+        Metadata description attached to the dataset attributes.
 
     Returns
     -------
     None
     """
+    if not components:
+        logger.warning("No components provided to save.")
+        return
+
     ds = xr.Dataset(components)
 
     # Add ProFSea version and scenario metadata
     ds.attrs["source"] = "ProFSea v3.0"
     ds.attrs["scenario"] = scenario_name
-    ds.attrs["description"] = "Spatial sea level rise projections"
+    ds.attrs["description"] = description
 
     output_format = output_format.lower()
     if output_format not in ["netcdf", "zarr"]:
@@ -324,32 +332,45 @@ def save_components(
 
         compressor = Blosc(cname="zstd", clevel=5, shuffle=numcodecs.Blosc.BITSHUFFLE)
 
-    # Set the encoding/compression for each variable based on the output format
+    # Set the encoding dynamically, overriding with output_dtype if provided
     for name, component in components.items():
+        comp_dtype = output_dtype if output_dtype else component.dtype.name
+
         if output_format == "netcdf":
-            encoding[name] = {"zlib": True, "complevel": 1, "dtype": output_dtype}
+            encoding[name] = {"zlib": True, "complevel": 1, "dtype": comp_dtype}
         elif output_format == "zarr":
-            encoding[name] = {"compressor": compressor, "dtype": output_dtype}
+            encoding[name] = {"compressor": compressor, "dtype": comp_dtype}
 
     file_name = f"{scenario_name}_{output_prefix}"
 
+    # Dynamically adjust console message string based on the prefix
+    log_prefix = "Global " if "global" in output_prefix.lower() else ""
+
     # Stream the computation and write to disk
     if output_format == "netcdf":
         out_path = os.path.join(output_dir, f"{file_name}.nc")
         with console.status(
-            "[bold cyan]Computing and saving NetCDF...[/bold cyan]", spinner="dots"
+            f"[bold cyan]Computing and saving {log_prefix}NetCDF...[/bold cyan]",
+            spinner="dots",
         ):
+            # Let xarray handle streaming directly to avoid RAM overload
             ds.compute().to_netcdf(out_path, encoding=encoding)
+
         logger.info(f"[bold green]✓ Successfully saved NetCDF:[/bold green] {out_path}")
 
     elif output_format == "zarr":
         out_path = os.path.join(output_dir, f"{file_name}.zarr")
         with console.status(
-            "[bold cyan]Streaming computation and saving Zarr...[/bold cyan]",
+            f"[bold cyan]Streaming computation and saving {log_prefix}Zarr...[/bold cyan]",
             spinner="dots",
         ):
             ds.to_zarr(out_path, encoding=encoding, mode="w", compute=True)
+
         logger.info(f"[bold green]✓ Successfully saved Zarr:[/bold green] {out_path}")
 
-    dims_str = ", ".join(ds[name].dims)
-    logger.info(f"Output shape was {ds[name].shape} ({dims_str})")
+    # Safely get a sample name to log the shape (fixes the scope-leak bug)
+    sample_name = "total_gmslr" if "total_gmslr" in ds else list(ds.data_vars.keys())[0]
+    dims_str = ", ".join(ds[sample_name].dims)
+    logger.info(
+        f"{log_prefix}Output shape for '{sample_name}' was {ds[sample_name].shape} ({dims_str})"
+    )

From 27894eae19442d331a6abb8a66621ba3f0d325e7 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Thu, 18 Jun 2026 13:40:54 +0100
Subject: [PATCH 270/276] Update global test

---
 tests/core/test_global_model.py | 6 +++++-
 ukcp18_example_script.py        | 6 ++++++
 2 files changed, 11 insertions(+), 1 deletion(-)

diff --git a/tests/core/test_global_model.py b/tests/core/test_global_model.py
index 0035d7a..cf3744b 100644
--- a/tests/core/test_global_model.py
+++ b/tests/core/test_global_model.py
@@ -53,7 +53,11 @@ def test_save_components(tmp_path):
 
     # Save the output to the temporary directory
     global_model.save_components(
-        components, output_dir=str(tmp_path), scenario_name="test"
+        components,
+        output_dir=str(tmp_path),
+        scenario_name="test",
+        output_prefix="global",
+        output_format="netcdf",
     )
 
     expected_file = tmp_path / "test_global.nc"
diff --git a/ukcp18_example_script.py b/ukcp18_example_script.py
index a0d05f3..de29cda 100644
--- a/ukcp18_example_script.py
+++ b/ukcp18_example_script.py
@@ -37,6 +37,12 @@
 )
 global_model.sum_components(projections)
 gmslr = global_model.results["total_gmslr"]
+global_model.save_components(
+    global_model.results,
+    scenario_name="rcp85",
+    output_format="zarr",
+    output_prefix="global_",
+)
 
 ### Now spatial projections ###
 spatial_components = {

From 14d8a2ea93b19d41b07683f7d7b26537b1eb2d7d Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Thu, 18 Jun 2026 13:44:22 +0100
Subject: [PATCH 271/276] Ruff change

---
 profsea/components/core/global_model.py       |  2 -
 .../tutorial_notebooks/worked_example.ipynb   | 40 ++++++++++++-------
 2 files changed, 26 insertions(+), 16 deletions(-)

diff --git a/profsea/components/core/global_model.py b/profsea/components/core/global_model.py
index dc6e1a2..1c44d29 100644
--- a/profsea/components/core/global_model.py
+++ b/profsea/components/core/global_model.py
@@ -2,8 +2,6 @@
 
 import concurrent.futures
 import logging
-import os
-from pathlib import Path
 
 import numpy as np
 import xarray as xr
diff --git a/profsea/tutorial_notebooks/worked_example.ipynb b/profsea/tutorial_notebooks/worked_example.ipynb
index 1fd20ae..3194d14 100644
--- a/profsea/tutorial_notebooks/worked_example.ipynb
+++ b/profsea/tutorial_notebooks/worked_example.ipynb
@@ -79,7 +79,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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",
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",
       "text/plain": [
        "
        "
       ]
@@ -123,10 +123,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "a1c9b208",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "AntarcticaISMIP6.__init__() got an unexpected keyword argument 'ais_region'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mTypeError\u001b[39m                                 Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[3]\u001b[39m\u001b[32m, line 7\u001b[39m\n\u001b[32m      3\u001b[39m     \u001b[33m\"greenland\"\u001b[39m: GreenlandAR6(),\n\u001b[32m      4\u001b[39m     \"expansion\": ThermalExpansion(\n\u001b[32m      5\u001b[39m         ohc_change\n\u001b[32m      6\u001b[39m     ),  \u001b[38;5;66;03m# only the thermal expansion needs OHC change, so we'll pass it in here\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m7\u001b[39m     \u001b[33m\"wais\"\u001b[39m: AntarcticaISMIP6(ais_region=\u001b[33m\"wais\"\u001b[39m),\n\u001b[32m      8\u001b[39m     \u001b[33m\"eais\"\u001b[39m: AntarcticaISMIP6(ais_region=\u001b[33m\"eais\"\u001b[39m),\n\u001b[32m      9\u001b[39m     \u001b[33m\"peninsula\"\u001b[39m: AntarcticaISMIP6(ais_region=\u001b[33m\"peninsula\"\u001b[39m),\n\u001b[32m     10\u001b[39m     \u001b[33m\"glacier\"\u001b[39m: Glacier(),\n",
+      "\u001b[31mTypeError\u001b[39m: AntarcticaISMIP6.__init__() got an unexpected keyword argument 'ais_region'"
+     ]
+    }
+   ],
    "source": [
     "global_components = {\n",
     "    \"landwater\": LandwaterAR6(),\n",
@@ -134,9 +146,9 @@
     "    \"expansion\": ThermalExpansion(\n",
     "        ohc_change\n",
     "    ),  # only the thermal expansion needs OHC change, so we'll pass it in here\n",
-    "    \"wais\": AntarcticaISMIP6(ais_region=\"wais\"),\n",
-    "    \"eais\": AntarcticaISMIP6(ais_region=\"eais\"),\n",
-    "    \"peninsula\": AntarcticaISMIP6(ais_region=\"peninsula\"),\n",
+    "    \"wais\": AntarcticaISMIP6(region=\"wais\"),\n",
+    "    \"eais\": AntarcticaISMIP6(region=\"eais\"),\n",
+    "    \"peninsula\": AntarcticaISMIP6(region=\"peninsula\"),\n",
     "    \"glacier\": Glacier(),\n",
     "}\n",
     "global_model = Global(components=global_components, end_yr=2301, num_members=1000)"
@@ -152,7 +164,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "84894d79",
    "metadata": {},
    "outputs": [
@@ -215,7 +227,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "5b63dfb5",
    "metadata": {},
    "outputs": [
@@ -256,7 +268,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "47694def",
    "metadata": {},
    "outputs": [],
@@ -279,7 +291,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "09f1fff4",
    "metadata": {},
    "outputs": [
@@ -445,7 +457,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "60d81a5b",
    "metadata": {},
    "outputs": [
@@ -502,7 +514,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "e62d28d9",
    "metadata": {},
    "outputs": [
@@ -559,7 +571,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "5512d606",
    "metadata": {},
    "outputs": [
@@ -720,7 +732,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "061f92d5",
    "metadata": {},
    "outputs": [

From 286db6de6c09d644af97c80cfb5f7c877e2d00ab Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Fri, 19 Jun 2026 13:14:09 +0100
Subject: [PATCH 272/276] Update notebook

---
 profsea/tutorial_notebooks/worked_example.ipynb | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/profsea/tutorial_notebooks/worked_example.ipynb b/profsea/tutorial_notebooks/worked_example.ipynb
index 1fd20ae..aad1a82 100644
--- a/profsea/tutorial_notebooks/worked_example.ipynb
+++ b/profsea/tutorial_notebooks/worked_example.ipynb
@@ -123,7 +123,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "a1c9b208",
    "metadata": {},
    "outputs": [],
@@ -134,9 +134,9 @@
     "    \"expansion\": ThermalExpansion(\n",
     "        ohc_change\n",
     "    ),  # only the thermal expansion needs OHC change, so we'll pass it in here\n",
-    "    \"wais\": AntarcticaISMIP6(ais_region=\"wais\"),\n",
-    "    \"eais\": AntarcticaISMIP6(ais_region=\"eais\"),\n",
-    "    \"peninsula\": AntarcticaISMIP6(ais_region=\"peninsula\"),\n",
+    "    \"wais\": AntarcticaISMIP6(region=\"wais\"),\n",
+    "    \"eais\": AntarcticaISMIP6(region=\"eais\"),\n",
+    "    \"peninsula\": AntarcticaISMIP6(region=\"peninsula\"),\n",
     "    \"glacier\": Glacier(),\n",
     "}\n",
     "global_model = Global(components=global_components, end_yr=2301, num_members=1000)"

From f94b2b8381c2a37306f976be69849428dd1749e4 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Fri, 19 Jun 2026 13:18:55 +0100
Subject: [PATCH 273/276] Update notebook

---
 .../tutorial_notebooks/worked_example.ipynb   | 68 ++++++++-----------
 1 file changed, 28 insertions(+), 40 deletions(-)

diff --git a/profsea/tutorial_notebooks/worked_example.ipynb b/profsea/tutorial_notebooks/worked_example.ipynb
index 3194d14..f233611 100644
--- a/profsea/tutorial_notebooks/worked_example.ipynb
+++ b/profsea/tutorial_notebooks/worked_example.ipynb
@@ -79,7 +79,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
        "
       ]
@@ -123,22 +123,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "a1c9b208",
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "AntarcticaISMIP6.__init__() got an unexpected keyword argument 'ais_region'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
-      "\u001b[31mTypeError\u001b[39m                                 Traceback (most recent call last)",
-      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[3]\u001b[39m\u001b[32m, line 7\u001b[39m\n\u001b[32m      3\u001b[39m     \u001b[33m\"greenland\"\u001b[39m: GreenlandAR6(),\n\u001b[32m      4\u001b[39m     \"expansion\": ThermalExpansion(\n\u001b[32m      5\u001b[39m         ohc_change\n\u001b[32m      6\u001b[39m     ),  \u001b[38;5;66;03m# only the thermal expansion needs OHC change, so we'll pass it in here\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m7\u001b[39m     \u001b[33m\"wais\"\u001b[39m: AntarcticaISMIP6(ais_region=\u001b[33m\"wais\"\u001b[39m),\n\u001b[32m      8\u001b[39m     \u001b[33m\"eais\"\u001b[39m: AntarcticaISMIP6(ais_region=\u001b[33m\"eais\"\u001b[39m),\n\u001b[32m      9\u001b[39m     \u001b[33m\"peninsula\"\u001b[39m: AntarcticaISMIP6(ais_region=\u001b[33m\"peninsula\"\u001b[39m),\n\u001b[32m     10\u001b[39m     \u001b[33m\"glacier\"\u001b[39m: Glacier(),\n",
-      "\u001b[31mTypeError\u001b[39m: AntarcticaISMIP6.__init__() got an unexpected keyword argument 'ais_region'"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "global_components = {\n",
     "    \"landwater\": LandwaterAR6(),\n",
@@ -164,7 +152,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "84894d79",
    "metadata": {},
    "outputs": [
@@ -227,13 +215,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "5b63dfb5",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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BdhQs2ueOHxePuKXxFVeXjx8vIh43aZLHvMmhegAAF8FQUUE6bo7FrWoDAz06N27QaqVnCkfiFS2bml4aDQ2ZNUs2Ne3tTHEbMef+KZwL72bFMNdjx45JT3Nb2uYCAK7/cV2Xm2vqqdLU5NG+8cbqakr9+mtK3r6d6isq5Jhvt24Uv2iR9EwJiY4mT8UqMU9PT5fpQjw9iCs8eZoQz/5k9Hq9PM4bops2bZLNzE8++cTR6wbA7eHtrOZLl0xOldJSGRyh6dHDI6PxqvPn6fSXX1I6l9s3Nsqx4B49aOzdd9PI2293201Nu4s5i3NycjK9/fbbMueTE/Pe3t7k7+8vQ50ZHur82GOPSak/HwcAdB7uoyIdDvPypNuhpnt38nKh0nJ7TvPhVErewYOWTc3ugwZJPnzwzJnkjT4znXez8Oks7NyDvKGhQaYK3XDDDYpMF4KbBbjl9J/iYpPd8PJl8goOJo0NnfrcgWaDgfIOHKCkrVupJD3dcjzuxhslH86bm67w6cSodjcLX8SxY8fKDQBg5+Kf9HTScym+l5cppeJB/cYba2oofedOSt6xg2pLS+WYxteXhs6eLS1ouw8cqPQSVQ3cLACowG5oOHeOdJmZpuIf7qfiQdN/eIIPF/lkfv+9JR8eGB5Oo5YsoVGLF7tdpaajgJgDoCCG8nKJxmVohK+vx9gNOZ1UmJAgm5qtOxdGDRwoUThbDH082D/fGSDmACiAUav9ZYOT7YZc/OMBdkMe/MD+cBbxipbZv5xSGjBlijhT+txwg0e8mTkCiDkAzrYbXrhAWt7gLC+X8W2eYDesLSuj1K++Eo84e8UZ34AA6Vw45s47KbxvX6WX6PLYVcy//PJLuvvuu+35lAC4DZwP57y4bHAajR5hNyzNypJ8OPdLadbr5RhPtGcBhz9cQTHnAqGsrCzplMjdE8189dVX9NJLL1FmZibEHID2KjjPnCFDTo6pn4qbb3By//DcAwcoZccOyxAIpteoUZJKGXjLLS41js3txJyrPHnC0LmWPghLliyhDRs20L333isDnn/961/Tzp07HblWAFwvpXLxImkzMqi5rMw0h9ONNzg5lcL9w9P+7/8spfYaHx+Z5MMiHjN8uNJLdGusFvMXX3xR2t++9dZbtHnzZvrss89kqPMDDzwgIh4SEuLYlQLgap7xrCzSc/DT3Gzqp+KG1Yr8hnUxNVW84VzowwU/5v7ho+64Q/qHw1roHKx+dR0/fpy++eYbGj9+PN1yyy0i5v/2b/9Gjz76qGNXCIALwaX3+vx8aYol49s4pRIYSO7oSsneu5dStm+nSzk5bVIpY5Yto4HTpsloNqBCMS8tLaU+LbPywsPDpYPi9OnTHbk2AFwrpVJaKhuchpIS6TGuiYlxu5RK9cWL4khJ37XL4krhoQ9cpcki7omtZ11OzPlFqWlVWsxfXzkyDgCPTamYXSoGg9ulVPiNigc/cNtZnm5vbnjFrhSepzni9tspMCxM6WV6PD62/ELZwWKONGpra6VTYmuBZypaNj4A8JiUSlaWCLomNNStUira+nrK2rNHUinmARAMN7riKLz/lClwpbiimH/00UeOXQkArpZSycgwpVTczKVSlpcnqRQWcl1Dg6XAZ/j8+TR62TKK7NdP6SWCrog59ym/HjqdTgZTAODunnFtairpeRYup1SiotwipaLXail3/34R8dbe8PDYWEmlsJBjAIS6sdurkH3o7HQxtFiTAHA3uIeK9tQpyY27S0qlsrhYfOEZ335r2dDkgh4u7OGuheiV4jq4fkgBgBNorq8n7cmTZDh/3hSNu/DmP3vBeSOTo/DCkyctx3lDk2dpcr+UoKgoRdcIbAdiDoAVPVVEyEtLTf1UXDStUnvpEqXt2iW2wrqyMtNBLy/qd+ON0je83+TJ2NB0YVzzVQmAk2iurKSmEyeouaLCJRtjsY2Q+4ZzFN7aVhgYEUEjFyyQSDy0Vy+llwmcKeY89/N6cAMuANwJQ1kZaRMSqLmqyuVGuDVUVUkenPPhPNneTO+xYyUKH4QKTc8Vcx7azNar9uY/m4+7izULALYcipBzl0MX6TcuUfipU5JGOXPokKXlrF9QEA2fN09EPLJ/f6WXCZQW87NsxQLAA9AXFpI2MVGmAUlqReVCzrnwjN27Kf2bb6jm4kXL8ehhw6TZFY9g83UD5w2wk5j3Q6EAcHP40yWPcdOlpMh9ca2oVMjZkZJ/5IgIOM/QNOfCOQofNncujVy4kHoMHqz0MoEaxZzL9Ovr66lvq/FOaWlp9Oc//5nq6upo6dKldP/99ztqnQA4FBZD6a+SkUHEg5VDQ1V5xauKi0XAM777jurLyy3He48ZIwLOvcN9/P0VXSNQuZg/8cQT1KtXL/rLX/5i6aI4bdo06t27Nw0aNIgeeeQRKRh68MEHHbleAOyO0WAgbUqKtK2VmZxBQaqrzjxz8KDkwosSEy3HA8PDpTKTx69FxMUpukbgQmJ+9OjRNv1ZPvnkE4qMjKSkpCTy8fGRCP3tt9+GmAPXK89PSiL9mTOqq+osP3tWBDxzzx5qaqnOZF943KRJIuADpk5Fz3AVYWRziFYrlcJ846EkXt26cYtZdYn5xYsXZdKQmb1799KyZctEyJnFixfT+vXrHbNKAByAsbHRVJ5fWEia8HDyUkF6QtvQQLl791LaN99QSXq65XhwdLRUZo647TYK7dlT0TWCX7pmimizeLc4h7iPPTde8+7VS15TMpzESSk7q8U8NDSUKisrLRuhPHlo9erVlsd5o6iJfzAbYPHftm2bDIIODAykqVOn0muvvUbDhg2z6XkAsBVuWcvWQ8OFC4qX53NEV5qZKdWZOXv3WjoVco+U/lOnUvzChRQ7cSKqMxVOxZE56tZq+ZdG5O1t6pjZowd5s+spNJQ0ISHkFRysSE2C1WJ+4403yvzP999/XwS4pqaGZs2aZXk8OzubYmNjbfrmBw4ckFz8pEmTSK/X07//+7/TvHnzpGlXkMrylsB9aL58mZpOnjRVdbKHXKGqzsaaGmkzy6mU8jNnLMfD+val+Ntvp2Hz52N+ptLpEq1W0iWc3uJPbizU3lFR5M0Rd0iIKfL28yM14GVsrwqoHTg3PmfOHBFxFt4//OEP9Mc//tHyOG98sgC/++67nV7MpUuXKDo6WkT+1ltv7fD86upqCgsLo6qqKvnkAEBHcH8VKQaqqTF5yJ0cQfGf2/nTpyUK5wHIBp1OjvO8zEHTp0sUzlWaarVEuhtGlj9Ol7Bot06X+Pqaou7ISNON91M46g4MdPjvprO6ZlMFaEZGBh0+fJh69uxJkydPbvP4fffdRyNHjqSuwItneGO1PTiN0zqVwz80ANaiLyoyFQM1NTm9qrO+okLshGwrrCoqshyPGjhQLIXsDQ8ICXHaejwVI0fZHHU3Nv6SLvHxkaib39w55cbCLeLN6RIX6sVjdWTuaHgZS5YsocuXL9PBgwfbPeeVV16hdevWXXUckTno6LXFbhUpBmKHQUSEU4ScC3u4xSxH4fmHD8t9hqsxuSqTo/Do4cMRhTs6XaI1pUx4mAg7S2STsls3EW9Jl4SFmXLdKkmXdDYyt1rM2YpoDQ899BB1Bs6d79q1iw4dOtSmMKmjyJzz9BBzcN1ioIwM0mdmOq0YqKa0VJpc8a2mpMRyPGbkSMmFD541i/xUZIF0S3eJTmfKc3O6hKPuiIi26ZKgINW+iTpczHlwc3BwsFgRr/Vf+OJ0ZqDzU089RTt27KCffvqpjf2xI5AzBx39cWuTk0mfm+vwYiCDXk/njh6ltJ07qeDECUt5vX9IiKW8vvvAgfiF2TtdYvZ0G1vcJSzcnCbhdAlH3C6YLnF4znzEiBFUUlJCDzzwAP3qV7+iMWPGUFfhNwUW8u3bt9P+/fttEnIAOhzxlphI+nPnTI6DgACHldezJzxz927Ji5vhcWss4INuvZV8VPLx3eU3Kc3CzZuUZndJYCB5x8ZahFtN7hJnY7WYcx+WY8eO0YcffihOk8GDB4vPfOXKlZ12knBq5dNPP6WvvvqKQkJCpDCJ4Xcl9p0D0Bm4ba3FQ875cTv/cRu0Wso7dIjSd+5sW14fEUEjuLx+4UIKv0aqEFiZLjG7S1rcPrxJqfH3NxXjmNMlfFNxusQlNkAbGhroiy++kPJ+Lh7iJlss8v42VtBd65fAz8u9XjoCaRbQ7mQg9pCXl5uKgew44o1F/OTmzZSyY4dl+DHK6+20SWl2l3D6xJwu4dRYS7pECnJYvF10ZJ+qcubtwTnul19+Wf4tKyujiIgIciYQc3CVh/zUKWqurra7h7w0O5t+WL+eKvLz5X5wjx6m8voFC1BebyUiNTrdL+kSdpdwusTsLuGIu6UEnt0lxGkUD4y6qx2dMzdTXFxMH3/8sUTP3PqWc+gbNmxwupAD0FokDOwhT0qyu4ecNzYTNm+mk//6l1gLOZVy61NPSS6cy+3B9Uvg27hLzOkS7l3Su7eIt1RRKlgC705YLeaff/65CDhXZ86fP5/eeOMNWrhwIXnjBQ0UFnJdTg7p09LsPlCCuxZyNH4pJ0fuc4XmjOeeo8CwMLs8v1t3DOT7LZuUEmn36PFLMQ6LtwekS5yNTdbEuLg42fCMiYm55nlPP/00OQukWTwbjvx0aWnSh5w/kstHczvAEXjSF1/Q0Q8/pGadTuyF0599lobMnOmRH/uvRCSDo27Oc7d2l7R0DJR0Cfu6zZuUAQG4bmrKmffv37/DXwg/fqZVwyBHAzH3XFr3IZeP6tw32g5UFhXRD6++ShdbIv1+N91EM3/3Owru3p082tPdEnHLJiVj3qQMC1NFx0B3wuE58/yWjR8AlMbY0GDqQ15UZLc+5CxYKV99RYc3biR9UxP5dutG0554QjY4PSkat2xStnaXtO4YaC6BN4u3h3q61QgSV8ClYKeKeMhLS00baHboQ1598SLt/dOfLJ7xvuPG0azf/97tXSqWdEl7JfC8SRkT0zZd4oSOgcAJYs7e8h9//JEWLVok99euXdumTwpvhHJL3AAHVdoBYCgrM1kPKytN1sMubr6zmHH/lINvv026+noZhDz1N7+h0UuWuGWqwJIuMTeeMncM9PMz+bm5ayCnrMyblDA3uKeYc6OtnTt3WsT873//O8XHx1sqNXlaEA93fu655xy3WuCx6IuLSZeYSM0NDXbxkNeWldG+P/+Zzh07Jvd7xsfTnBdfdJvKzTYl8I2NpnQJ72txuqRbNymB9+YUldlhgnSJ54j55s2brxJqLsUf2NI8aNOmTTLQGWIO7O4hP3tWGmaxe0WEvAsf9fn5eDTbgb/+lZp4QIWvL920ahXdcO+9Lu0bv8rTzWLOXSK5BJ493S19uiVd0q0b0iWeLOY8Fm7o0KGW+5xOYbti67Fy3GsFALu2r83MlBa27EvmcV1doaGykva/+aZM+GF6DBlCc9aupSgXa/DW3hR46dNt9nRzusQ81sxDSuCBDWLONhluf9t6xFtrmpubbR7oDICz2teeOXSI9v3lL9Rw+bJE4BMffJAmrFxJ3i4gdJZct9lhYp4Czx0DW02BF589PN0ei9WvZB4YkZqaSsOGDWv38eTk5GsOlQDAFli0pH1tQUGX29fy0OSDf/ubDE5mIvv3l9x49DVex6ps92qejhMaSj7R0Sbhbom83XGjFjhYzG+//XZ66aWXpIT/SscKO114nBs/BkBX4EHLYj0sKemy9fDc8eO09/XXqa6sTERv3PLlNPmRR8hbRZt97aZMWmZS+vTs+UvXQL7BGgjsUQHKgyl4qLOfnx89+eSTkj/njSh2sbCzRa/XU2Ji4nVL/e0NKkDdC0NFBWm5fS2nQrpgPdTW19PPGzbI1B8mrG9ficZ7xceT0sif25UpE/Z1m2dScv9184QcO3jogevh8ApQFunDhw/Tb3/7W3rxxRcto+NY0OfOnUvvvPOOU4UcuBc8SIJTK821taauh51MHxQlJdGPr71GNS2DTsbceSdNefRR8lWo/qFd8eaUSVAQ+QwYYCrKQcoE2AGbdn94rNvu3btlzmdubq4c44lDkZGR9lgL8FTrYX6+yXqo13e6fa2usZGO/uMfdHrrVrkfEhNDs194Qao5FdusbGn7Kt7ukBBTvps3K9nfzT1MUE0J7EintvJZvNmKCIA9rYeSI++EwF1IS5PmWFVFRXKfx7bd8v/+H/nZqfmWzZE3i7d5s5IjbxZvjDcDDkb9vizgvl0PU1K6ZD3kMW7HPv6YErdskTeGoO7dadbzz1O/yZPJkUg5fEODacPSnDYJDjaJd8u0HIg3cDYQc+CS1kMZ4/bqq1Rx9qzcHzZ3Lk176ikKsFNP8/Zg8eZGX2wV1HAHwbg4KWSSodFImwCFgZgDl7IeXjXGLTycZqxZQ4OmTXOKiPv07k0+gwd3aZMWAEcAMQfOtR4mJFBzRUWnrIcyxu3VV+kSTxbiMW633moa4xYe7jgRr6qSQQwi4oMGkSYmBhuXQJVAzIFTMFy8aGpf2wnrYbtj3J55hobMmuUQYeWcOLfZFRHv0wciDlwCiDlwKGI9PHeOtKdPy6anrdbDq8a4TZ5MM59/3iFj3ETEORL38pJOg76cTkEkDlwEiDlwrPUwO1uGLnNKhUvTrRVyZ45x4zcZFnF+Vp6u4ztkiEnEkRMHLgTEHDgELgBiEWcx554i7P6wZYzbj3/6ExU7eIybRcSNRvKOjiafIUPIu2dPiDhwSSDmwCEbh9qkJNKfO2ca+tsyjcqqMW7ffEMH33nHNMYtIICmPvaY3ce48RuN5MRZxHv0EHcKp1UQiQNXBmIO7ApvcIr18OJFk//ayg6FV45x6zVqFM3mMW59+thXxDkn3jKxiNMpIuIuPGEIADMQc2A3DOXlba2HVgx+4Gg8+8cf6ae33pIxbt6+vjR59Wq64e677TbGTUaqVVWZer9ERppEvE8fTOABbgXEHNjHscIDl5OSqLm+njTR0ValLGSM2//8D+X99JPc7zF0KM1du1YGSNgD3kQVEddq5VOCH4t4bCxEHLglEHNgF8eKPiNDctDWWg9ljNsbb4igcwQ+6aGHaPz999tljJuIeHW15O65T4rvmDHkExeH/uDArYGYg07DEa/M6TxzxjRcwQrHSlNtraRULGPcBgyQaJyHK9vjE4KxpkaaYPFwB9/4ePLp18/qvD0ArgzEHHS+x0piogyVsLZZVsGJE2I5tIxxu+8+mvzww10e4yYiXltLRk7xcPfCMWPId8CALs0OBcDVgJgDmzGUlor1UMa7cSFQB6kRbUMDHX73XUr9+mu7jnETEa+rEyHnlrO+o0aZpvc4oY85AGpD0bZvP/30E91xxx3Um+1hXl60Y8cOJZcDrBBPXW4uNR05Il0EJT/egZAXnz5NW1avtgj5mGXL6L733uuSkPM6eKO1ubRUBiD7jhhBATNnkl98PIQceCyKRuZ1dXU0duxYWrVqFd11111KLgVYkx9PTSXDmTNEfn4dluZzCf7RDz6gpC+/lI1RGeP2+99T3/Hju3StOR/OKR7Og3Oxj/RPCQvD7w94PIqK+YIFC+QG1A1XS3KjLMmP8xSdDnLRF9PTZajy5YICuT/y9ttNY9w6MU3oyna03P/cp39/U09xLkrCHE0AXC9n3tTUJDcz1TwwADjWP15YSLrUVImGOyoE0jU0SJtaGapsNFK3qCia9bvfUf8pU7reyZAHQ8TGmkSc1wERB8B1xXz9+vW0bt06pZfhEXATKh60rMvJEeeJFAJdR0ALExLEN1594cIvY9yefJICQkM7/f35EwF/b+9evUydDDtYAwCejEuJ+dq1a2nNmjVtIvPY2FhF1+SOcCRsTqt4hYRcd1ORfeM/b9hA6d98I/eDo6Np5po1nR6qbG6CJe1oo6NNIo5OhgC4l5j7+/vLDTgprdKB7ZCrOPe/+SbVl5fL/dFLl9KURx8lv05YA7l/inQybN0Ei/unoKc4AO4n5sDBbpW0NNLn5ZkGSVwnpcH9xg/+7W909vBhuR8eG0uznn+eeo8ZY58mWH37opMhAK4k5rW1tZSbm2u5f/bsWUpKSqLIyEiKi4tTcmke1+1Ql5wsxUBcBn+t/uMGnU5mcZ745BOxHnJPlRuWL6cbH36YfGys4rQ0weJRcuHhpiZYLOK+vnb6qQDwLBQV85MnT9LMmTMt98358Icffpj++c9/Krgyz4CjYl1eHukzM8nY2GhyiVyj7WxRUhIdePNNunzunNznKHz6s89S1IABtos490/h78dNsAYPFpcK+qcA4MJiPmPGDMnTAufDOXFdSgrpi4rEN36tbof1FRX087vvWhpjBYaH082PP07D5s2zyVnSRsS5CdaIEaYmWNgDAcAuIGfuiZucBQWkS083leRfYxpQs8EgJfhcxamtq5OJ9aPuuINu+vWvKSAkpHOdDENCyGfYMPLt3x9NsACwMxBzD4L7mbCIG/Lzia6zycme8YNvv00VZ8/KfW5PO+O55yhmxIgudTLkyk2NlfNAAQC2ATH3lGj8/HnSpaWZRrpdoyS/qrhYUipsOWT8Q0Np8iOP0KjFi60e4WYR8bo6k4ijkyEATgFi7uZwjlqbnk76lii7vZFu2vp6OrlpkzTFatbp5PHRS5bQjY88YnUF51UiPnq0KRLvQj8WAID1QMw9IRrnvuPtWA55UzLju+/o6PvvU/3ly3IsduJEaYplrUtFNjZbp1Mg4gAoAsTcDWluaJC+KpZonJ0qV0TjBSdP0uGNG6msxecf1qePiDg3xbLGpdLGncIbm5wT79cPkTgACgExd7dovKhIhFxy4zzO7Ypo/FJuroh44cmTcp/b0k588EEau2yZVePbpGKThyXzxPvQUJM7hS2G2NgEQFEg5m5Cc22tiDjbDo3t5Ma5BP/Yhx9S1g8/SHtajY+P5MVZyAOtGO4gXQy55TD3TjFPvOeKTfjEAVAFEHMXh9MdhnPnTNE4N8cKCyNNK6dKY00NJWzeTKe3bZPNTWbIrFl00+rVFNa7t3VDIaqrJfXiHRVFPgMHkjeP+UPZPQCqAmLuwnCXQd7g1J8/L90NW/vGuXdK8o4dlLBpk7SpZfqMG0dTf/Mbihk2rGNnSkOD5MTJ11f6ifOgZG9uRWulRREA4Fwg5q46OCI3l/Q5OabKylZVnAatVnqLn9i0ydKaNnLAALr5N7+huBtvvO7mpsWZ0tAgPnSfQYNMm5qY7AOA6oGYuxAylf7iRdJySuXSJfLq1s0SjXP5feZ330lHw5qSEjmfhyizV5yn/lyv6Ifbz4ozhTc12V4YHy/NrzAoGQDXAWLuIjTX1ZEuM5P03LWwudnS4ZBFPHvfPjr+8cdUVVQk5/LszUkPPCCDlK/lUJEGZ5wP50n3ROTFm5oDBpg2NTsY2AwAUB8Qc5XDUTMLuD4ry9QYq8VuyGKc99NPdOyjj6iCe60QUUBYGE24/35xqfhcw2Ui+fC6Orlxasand2/y7tfPlA+/zlQhAIC6wV+vmlMqpaUSjRtKSkR4NTEx8tjZI0fo+Ecf0aWcHLnvHxxM45YvpzF33nnNkW2tUymcnmF/uKRSIiMxJBkANwBirtaUSlYW6TniZl93ZCSRRiMNsE7+618WEfcNDKSxd99N4+69VwS93VRKY6MpleLlZUql9O8vszWvN6QZAOB6QMzVllLJzyd9drbJMx4aSkY/P8o9cEDcKeaWtL4BATRqyRIaf999Miyi3SpNdqU0NkpRD0fg3nFx5B0Tg1QKAG4KxFxFLhVJqbBLhfPdUVGyscndDC8XFMh5vt260Zhly+iGe+65qmrTsqHJnnLeIOV+KTySjSfcc57dhqlAAADXA2KuMM1VVSaXCjtReHMyNJSy9u2Tqk3uL85wCoXTKZwTv3LKTxtvuJ+fRN8+HIX36oW5mgB4EBBzheAUiAxTzssTIW4OCKDM/fvp1JYtVHPxopzDvcQ5CudonBtiXTMKZ2/4iBEy3V4KiBCFA+BxQMwVyIvLDE7Oi1dVkba5mVJ//JGSt22jhpae4t0iIuiG5ctlwo9fq26EkgtnW+GVUTjbCtHwCgCPBmLuzIZYFy7I5ibnxetraij5++8pdedO0tXXWyo22WI4YsEC2eRs40jhKJy7HXIufNAgSaMgCgcAmIGYO2Nzs7xcin5YzMsLCyllzx7K/OEHSxdD7p0yYcUKGjxzJnm3FO5w/xXJhTc1SZEQV2ZyGkUcKVb0HQcAeBYQc0dvbubkkL6gQCb7cCTOk+/N9Bo9WkS83003SZ6bo3eOwDmVQt7eUu3pO3KkaTMzJAS5cADANYGYO6johzc2G7KyKOv77ynlu+/ocmGhPMYDIwbcfDONu+ceEXNzGsXAAs4FQkFB5D10qJTZm/uvAABAR0DM7e1Qyc+nqmPHKHn7dkrfu5eauCd4i0ecG1+NvfNOCu3VS8rqedCylNdzYU+vXr+kUdDoCgBgIxBzO8CCzKmU4l276PSXX1LekSPSzZBh4WZ/+Eje1PT3lxQK91ohHibBaRRzZWZoKNIoAIBOAzHvArxJ2ZSTQ+kffEApO3bQpTNnLI9xCuWGu++m/pwPb2r6pbCHPeHsRomJIU1UVJs5nQAA0Fkg5p30ipcfOkRJb79NGd9+S02c7+Yhyr6+NGTGDCny6REbS0a2HFZVkZc5D852Qs6Do9UsAMDOQMxtwNDURHmbNlHSxo1UcOKE5Tj7w0fdcQcNnzGDAloGHcsAZI7Ae/Yk7x49YCcEADgUiLkVVOfnU8rf/kapmzdbRrIxcZMm0ajbbqPYkSNJo9FIn3ARb7OAoyoTAOAkIObXwKDTUd6OHZS8YQOdO3BAPODmplfD58yh+JkzKYz934GBvwg4p1DgRAEAKADE/AoqsrMp5b33KPWf/6SGlun2TO+RIyWNMnjqVPINDycNWwmjoxGBAwBUgeJi/s4779Drr79OFy5coPj4eHrzzTdp2rRpTl1DfVkZZX/5JWX8619UfPiw5XhgaCgNnz5dIvHIoUOlEpMFXFwoKKkHAKgIRcX8s88+o2effVYE/eabb6aNGzfSggULKD09neLi4hz6vbV1dZT39deU8emnlL97NzXr9ZaNy9ixY2nk/PnUf8YM8mMfePfuplmZcKEAAFSKl1HqyZVh8uTJNH78eNqwYYPl2IgRI2jp0qW0fv36Dv9/dXU1hYWFUVVVFYWGhlr1PVm0d69aRTnbt5OuxVLIdB8wgIbOmEFDFy2isDFjTPlvLuSBDxwA4EQ6o2uKRuZarZYSEhLoxRdfbHN83rx5dLhVqqM1TU1Ncmv9Q9uKxseHqvLzRci5OlME/LbbKHr2bIm+Na36hwMAgKugmJiXlZWRwWCgmJiYNsf5/sWWSTtXwtH6unXruvy9p3HU39BAPTkCZwshom8AgIujeC35lSPOOOtzrbFna9eulY8e5lthSydCW+l7yy3Ud+5c8uGeKBByAIAboFhk3r17d/L29r4qCi8tLb0qWjfj7+8vNwAAACqJzP38/GjChAm0Z8+eNsf5/tSpU5VaFgAAuCSKWhPXrFlDDz74IE2cOJGmTJlC7733HhUUFNDjjz+u5LIAAMDlUFTMly9fTuXl5fSf//mfUjQ0atQo+uabb6hfv35KLgsAAFwORX3mSvkxAQDA3XRNcTcLAACArgMxBwAAN0DxRltdwZwh6kwlKAAAqBGzntmaAXdpMa9pmXwfGxur9FIAAMDu+sa5c4/YAG1ubqbz589TSEhIu1Wj/A7HQs+Voq64QYr14/rj9eN5f79Go1GEvHfv3jLBzCMic/5B+/bt2+F5fCFd8cVgBuvH9cfrx7P+fsNsiMjNYAMUAADcAIg5AAC4AW4t5tyU6+WXX3bZ5lxYP64/Xj/4+/WIDVAAAAAeEJkDAICnADEHAAA3AGIOAABuAMQcAADcANWLOQ9xnjRpklR5RkdH09KlSykrK6vNObyH+8orr0jFVGBgIM2YMYPS0tLanNPU1ERPPfWUjKsLCgqixYsXU1FRUZtzLl++LMMy2LDPN/66srJSFevnY1zl2vp23333qWL927Zto/nz58u15XUlJSVd9Txqvv7WrF+J69/R2nU6Hb3wwgs0evRouab8+nnooYekKtoVrr2161fza/+VV16h4cOHy/ojIiJozpw5dOzYMWWuv1HlzJ8/3/jRRx8ZU1NTjUlJScaFCxca4+LijLW1tZZzXn31VWNISIhx69atxpSUFOPy5cuNvXr1MlZXV1vOefzxx419+vQx7tmzx3jq1CnjzJkzjWPHjjXq9XrLObfddptx1KhRxsOHD8uNv160aJEq1j99+nTjo48+arxw4YLlVllZ2eZ7KbX+Tz75xLhu3Trj+++/z84oY2Ji4lXPo+brb836lbj+Ha2dv/+cOXOMn332mTEzM9N45MgR4+TJk40TJkxwiWtv7frV/NrfvHmzXNe8vDw5b/Xq1cbQ0FBjaWmp06+/6sX8Svgi8R/cgQMH5H5zc7OxZ8+eIohmGhsbjWFhYcZ3331X7vMv3tfX17hlyxbLOcXFxUaNRmPcvXu33E9PT5fnPXr0qOUcfnHxMX6hKbl+8wv6mWeeuebzKrX+1pw9e7ZdMVTz9bdm/Wq5/tdbu5njx4/LOefOnXOpa3+t9avl2lu7/qqqKjnnhx9+cPr1V32a5Up4+gYTGRkp/549e5YuXrxI8+bNa1NsM336dDp8+LDcT0hIkI90rc/hj3Q8ps58zpEjR+TjzeTJky3n3HTTTXLMfI5S6zezefNm+agWHx9Pzz//vKVrpJLrtwY1X39bUPr6W7N2PofTEOHh4S557a9cv1quvTXr12q1MseYv+/YsWOdfv1dqtEWf5LgIdC33HKLXAyGhZCJiYlpcy7fP3funOUcPz8/yWldeY75//O/nBe7Ej5mPkep9TMrV66kAQMGUM+ePSk1NZXWrl1Lp0+fpj179ii6fmtQ8/W3FqWvvzVrb2xspBdffJHuv/9+S2MnV7r27a1fDde+o/Xv3LlTcvj19fXUq1cvWRe/8Tj7+ruUmD/55JOUnJxMhw4duuqxK1vg8sVvry3u9c5p73xrnscZ63/00UctX/OLaciQITRx4kQ6deoUjR8/XvH1dwY1Xf+OUPr6d7R2jv5YULgt9DvvvONy1/5661f62ne0/pkzZ8qmeVlZGb3//vt07733yiZoewLtyOvvMmkW3g3++uuvad++fW3a3vK7NXPlO1hpaakl2uVz+CMQ7xhf75ySkpKrvu+lS5euipqdvf724Bexr68v5eTkKLp+a1Dz9e8szrz+Ha2dhZAFhFN2HBW2jmpd4dpfb/2u8NoPCgqiwYMHS2rkgw8+IB8fH/nX6dffqHJ4g/CJJ54w9u7d25idnd3u47yB+Nprr1mONTU1tbsByrvmZs6fP9/uJsSxY8cs5/CGRFc3Ueyx/vZg10vrzRil1m/LBqgar78161fq+luzdq1Wa1y6dKkxPj6+jYPCVa59R+t3ldd+awYNGmR8+eWXnX79VS/mv/3tb0XY9u/f38aaVF9fbzmHnSB8zrZt2+QXvWLFinatiX379pVdZrYHzZo1q1170JgxY2QnmW+jR4/usr3JHuvPzc0V69yJEydEcHbt2mUcPny4cdy4capYf3l5uQggr4tfgLxzz/f5PFe4/h2tX6nr39HadTqdcfHixXJd2TrX+hwOCNR+7a1Zv5pf+7W1tca1a9fK98vPzzcmJCSINdHf319sis6+/qoXc/7jau/G/s/W76D8TsgRLl/IW2+9VUSxNQ0NDcYnn3zSGBkZaQwMDJQLVVBQ0OYc/qNeuXKleL75xl9fvnxZ8fXzOvkYr93Pz0/e+Z9++mlZrxrWz1+3d445OlH79e9o/Upd/47Wbv4k0d5t3759qr/21qxfza/9hoYG47JlyyRy57VxAMZvTmyvbI2zrj9a4AIAgBvgMhugAAAArg3EHAAA3ACIOQAAuAEQcwAAcAMg5gAA4AZAzAEAwA2AmAMAgBsAMQcAADcAYg4AAG4AxBx4FFylzXMaeebnlXDrVR4IUFBQoMjaAOgKEHPgUXB/6I8++kj6TW/cuNFynNuv8nDhv/71rxQXF2fX78ktXgFwNBBz4HHExsaKaPP4MRZxjtZXr15Ns2fPphtvvJFuv/12Cg4Oll7SPCWdhw6Y2b17t0yb4bFmUVFRtGjRIsrLy7M8np+fL28Yn3/+uUyVDwgIoE2bNin0kwJPAo22gMeydOlSqqyspLvuuov++Mc/0okTJ2SCDU+2eeihh6ihoUGidb1eT3v37pX/s3XrVhHr0aNHU11dHb300ksi4DxpRqPRyNc84qx///70xhtv0Lhx42SmK899BMCRQMyBx8LTXngMWXl5OX355ZeUmJgo6ZfvvvvOck5RUZFE8llZWTR06NB2p8HweLCUlBR5LrOYv/nmm/TMM884+ScCngzSLMBjYRF+7LHHaMSIEbRs2TKZpM6jwTjFYr4NHz5czjWnUvhfHjg8cOBAGW/Gws1cuWnKET4AzsSlBjoDYG94XiPfGB4mfMcdd9Brr7121Xk8dZ3hxzlS58G9nDrh/8MROc95vHIuJADOBGIOQKtBwZwT53y3WeBbw+mYjIwMccFMmzZNjl1r2jwAzgZpFgBaeOKJJ6iiooJWrFhBx48fpzNnztD3339Pv/rVr8hgMFBERIQ4WN577z3Kzc2VTdE1a9bg+gFVADEHoAVOm/z8888i3FxUxOkT3sTkQiJ2qvBty5Ytklvnx5577jl6/fXXcf2AKoCbBQAA3ABE5gAA4AZAzAEAwA2AmAMAgBsAMQcAADcAYg4AAG4AxBwAANwAiDkAALgBEHMAAHADIOYAAOAGQMwBAMANgJgDAAC5Pv8fULk5AL0xgAcAAAAASUVORK5CYII=",
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",
       "text/plain": [
        "
        "
       ]
@@ -268,7 +256,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "47694def",
    "metadata": {},
    "outputs": [],
@@ -291,18 +279,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "09f1fff4",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "
        [14:59:35] ✓ ProFSea assets found locally!                                                             utils.py:188\n",
+       "
        [13:17:18] INFO     ✓ ProFSea assets found locally!                                                                \n",
        "
        \n"
       ],
       "text/plain": [
-       "\u001b[2;36m[14:59:35]\u001b[0m\u001b[2;36m \u001b[0m\u001b[1;32m✓ ProFSea assets found locally!\u001b[0m                                                             \u001b]8;id=4042414;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/utils/utils.py\u001b\\\u001b[2mutils.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=4042415;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/utils/utils.py#188\u001b\\\u001b[2m188\u001b[0m\u001b]8;;\u001b\\\n"
+       "\u001b[2;36m[13:17:18]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m \u001b[1;32m✓ ProFSea assets found locally!\u001b[0m                                                                \n"
       ]
      },
      "metadata": {},
@@ -311,11 +299,11 @@
     {
      "data": {
       "text/html": [
-       "
        [14:59:35] INFO     Baseline period = 1995 to 2014                                                                 \n",
+       "
                   INFO     Baseline period = 1995 to 2014                                                                 \n",
        "
        \n"
       ],
       "text/plain": [
-       "\u001b[2;36m[14:59:35]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Baseline period = \u001b[1;36m1995\u001b[0m to \u001b[1;36m2014\u001b[0m                                                                 \n"
+       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Baseline period = \u001b[1;36m1995\u001b[0m to \u001b[1;36m2014\u001b[0m                                                                 \n"
       ]
      },
      "metadata": {},
@@ -457,7 +445,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "60d81a5b",
    "metadata": {},
    "outputs": [
@@ -474,11 +462,11 @@
     {
      "data": {
       "text/html": [
-       "
        [14:59:50] ✓ Successfully saved Zarr: ./stabilisation_projection.zarr                                  utils.py:322\n",
+       "
        [13:17:33] INFO     ✓ Successfully saved Zarr: ./stabilisation_projection.zarr                                     \n",
        "
        \n"
       ],
       "text/plain": [
-       "\u001b[2;36m[14:59:50]\u001b[0m\u001b[2;36m \u001b[0m\u001b[1;32m✓ Successfully saved Zarr:\u001b[0m .\u001b[35m/\u001b[0m\u001b[95mstabilisation_projection.zarr\u001b[0m                                  \u001b]8;id=4042421;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/utils/utils.py\u001b\\\u001b[2mutils.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=4042422;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/utils/utils.py#322\u001b\\\u001b[2m322\u001b[0m\u001b]8;;\u001b\\\n"
+       "\u001b[2;36m[13:17:33]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m \u001b[1;32m✓ Successfully saved Zarr:\u001b[0m .\u001b[35m/\u001b[0m\u001b[95mstabilisation_projection.zarr\u001b[0m                                     \n"
       ]
      },
      "metadata": {},
@@ -487,11 +475,11 @@
     {
      "data": {
       "text/html": [
-       "
                   Output shape was (5, 295, 180, 360) (percentile, time, lat, lon)                            utils.py:325\n",
+       "
                   INFO     Output shape for 'sterodynamic' was (5, 295, 180, 360) (percentile, time, lat, lon)            \n",
        "
        \n"
       ],
       "text/plain": [
-       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mOutput shape was \u001b[1m(\u001b[0m\u001b[1;36m5\u001b[0m, \u001b[1;36m295\u001b[0m, \u001b[1;36m180\u001b[0m, \u001b[1;36m360\u001b[0m\u001b[1m)\u001b[0m \u001b[1m(\u001b[0mpercentile, time, lat, lon\u001b[1m)\u001b[0m                            \u001b]8;id=4042428;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/utils/utils.py\u001b\\\u001b[2mutils.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=4042429;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/utils/utils.py#325\u001b\\\u001b[2m325\u001b[0m\u001b]8;;\u001b\\\n"
+       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Output shape for \u001b[32m'sterodynamic'\u001b[0m was \u001b[1m(\u001b[0m\u001b[1;36m5\u001b[0m, \u001b[1;36m295\u001b[0m, \u001b[1;36m180\u001b[0m, \u001b[1;36m360\u001b[0m\u001b[1m)\u001b[0m \u001b[1m(\u001b[0mpercentile, time, lat, lon\u001b[1m)\u001b[0m            \n"
       ]
      },
      "metadata": {},
@@ -514,13 +502,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "e62d28d9",
    "metadata": {},
    "outputs": [
     {
      "data": {
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",
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",
       "text/plain": [
        "
        "
       ]
@@ -571,18 +559,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "5512d606",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "
        [15:00:02] ✓ ProFSea assets found locally!                                                             utils.py:188\n",
+       "
        [13:17:52] INFO     ✓ ProFSea assets found locally!                                                                \n",
        "
        \n"
       ],
       "text/plain": [
-       "\u001b[2;36m[15:00:02]\u001b[0m\u001b[2;36m \u001b[0m\u001b[1;32m✓ ProFSea assets found locally!\u001b[0m                                                             \u001b]8;id=4042434;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/utils/utils.py\u001b\\\u001b[2mutils.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=4042435;file:///Users/gregorymunday/Documents/Papers/ProFSea/ProFSea-tool/profsea/utils/utils.py#188\u001b\\\u001b[2m188\u001b[0m\u001b]8;;\u001b\\\n"
+       "\u001b[2;36m[13:17:52]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m \u001b[1;32m✓ ProFSea assets found locally!\u001b[0m                                                                \n"
       ]
      },
      "metadata": {},
@@ -591,11 +579,11 @@
     {
      "data": {
       "text/html": [
-       "
        [15:00:02] INFO     Baseline period = 1995 to 2014                                                                 \n",
+       "
                   INFO     Baseline period = 1995 to 2014                                                                 \n",
        "
        \n"
       ],
       "text/plain": [
-       "\u001b[2;36m[15:00:02]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Baseline period = \u001b[1;36m1995\u001b[0m to \u001b[1;36m2014\u001b[0m                                                                 \n"
+       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Baseline period = \u001b[1;36m1995\u001b[0m to \u001b[1;36m2014\u001b[0m                                                                 \n"
       ]
      },
      "metadata": {},
@@ -666,12 +654,12 @@
     {
      "data": {
       "text/html": [
-       "
        [15:00:05] WARNING  The following site(s) may be over land: Land point. Expect NaN values for all components.      \n",
+       "
        [13:17:54] WARNING  The following site(s) may be over land: Land point. Expect NaN values for all components.      \n",
        "                    Either try increasing your grid resolution or check your site coordinates.                     \n",
        "
        \n"
       ],
       "text/plain": [
-       "\u001b[2;36m[15:00:05]\u001b[0m\u001b[2;36m \u001b[0m\u001b[33mWARNING \u001b[0m The following \u001b[1;35msite\u001b[0m\u001b[1m(\u001b[0ms\u001b[1m)\u001b[0m may be over land: Land point. Expect NaN values for all components.      \n",
+       "\u001b[2;36m[13:17:54]\u001b[0m\u001b[2;36m \u001b[0m\u001b[33mWARNING \u001b[0m The following \u001b[1;35msite\u001b[0m\u001b[1m(\u001b[0ms\u001b[1m)\u001b[0m may be over land: Land point. Expect NaN values for all components.      \n",
        "\u001b[2;36m           \u001b[0m         Either try increasing your grid resolution or check your site coordinates.                     \n"
       ]
      },
@@ -732,13 +720,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "061f92d5",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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From 8f79b39b53ec9ae50e9fa74a678274c9c4a39c59 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Fri, 19 Jun 2026 13:38:59 +0100
Subject: [PATCH 274/276] Update landing page text

---
 docs/source/index.rst | 16 ++++++++++++++--
 1 file changed, 14 insertions(+), 2 deletions(-)

diff --git a/docs/source/index.rst b/docs/source/index.rst
index 6afa9fa..c89964e 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -11,9 +11,13 @@
 
       ProFSea
 
+      .. rst-class:: display-8 font-weight-light
+      
+      (pronouced "pro-fe-see")
+
       .. rst-class:: lead
 
-      **🌊 A modular, easy-to-setup, and self-contained sea-level rise simulator based on statistical emulations of physical modelling experiments and lines of evidence from the IPCC.**
+      **🌊 The Met Office's modular, accessible, fast sea-level rise simulator based on statistical emulations of physical modelling experiments and lines of evidence from the IPCC and across the literature.**
 
       .. container:: d-flex gap-3 pt-3
          
@@ -44,7 +48,15 @@
 
 ----
 
-ProFSea makes complex sea-level rise simulations accessible and fast. All you need is global mean surface temperature and ocean heat content forcing anomalies, using any baseline period, and you're good to go. ProFSea development is supported by the MetOffice.
+ProFSea makes complex sea-level rise simulations easy and efficient. All you need is global mean surface temperature and ocean heat content forcing anomalies, using any baseline period, and you're good to go.
+
+We simulate sea-level change contributions from:
+
+- Antartic Ice Sheet 🇦🇶
+- Greenland Ice Sheet 🧊
+- Glacier melt 🏔️
+- Thermal expansion 🌡️
+- Landwater ⛰️
 
 Quick Install
 -------------

From 3ffced8d835ec4b9b209776dcac2dfbcbfc75be2 Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Fri, 19 Jun 2026 14:00:13 +0100
Subject: [PATCH 275/276] Add MO logo

---
 docs/source/_static/mo-logo-light.png | Bin 0 -> 94911 bytes
 docs/source/_templates/mo_logo.html   |   7 +++++++
 docs/source/conf.py                   |   1 +
 docs/source/index.rst                 |   2 +-
 4 files changed, 9 insertions(+), 1 deletion(-)
 create mode 100644 docs/source/_static/mo-logo-light.png
 create mode 100644 docs/source/_templates/mo_logo.html

diff --git a/docs/source/_static/mo-logo-light.png b/docs/source/_static/mo-logo-light.png
new file mode 100644
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diff --git a/docs/source/_templates/mo_logo.html b/docs/source/_templates/mo_logo.html
new file mode 100644
index 0000000..c0ff11a
--- /dev/null
+++ b/docs/source/_templates/mo_logo.html
@@ -0,0 +1,7 @@
+
\ No newline at end of file
diff --git a/docs/source/conf.py b/docs/source/conf.py
index b776318..1536fd7 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -71,6 +71,7 @@
     "show_nav_level": 2,
     "header_links_before_dropdown": 4,
     "secondary_sidebar_items": [],
+    "navbar_start": ["mo_logo", "navbar-logo"],
     "icon_links": [
         {
             "name": "GitHub",
diff --git a/docs/source/index.rst b/docs/source/index.rst
index c89964e..6a84909 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -17,7 +17,7 @@
 
       .. rst-class:: lead
 
-      **🌊 The Met Office's modular, accessible, fast sea-level rise simulator based on statistical emulations of physical modelling experiments and lines of evidence from the IPCC and across the literature.**
+      **🌊 The Met Office's modular, accessible, and *fast* sea-level rise simulator based on statistical emulations of physical modelling experiments and lines of evidence from the IPCC and across the literature.**
 
       .. container:: d-flex gap-3 pt-3
          

From 58cc60ce96087c44b0ba3b744af0fc508a73ebbd Mon Sep 17 00:00:00 2001
From: Gregory Munday 
Date: Fri, 19 Jun 2026 17:30:30 +0100
Subject: [PATCH 276/276] Update logo

---
 docs/source/_static/mo-logo-dark.png  | Bin 0 -> 17485 bytes
 docs/source/_static/mo-logo-light.png | Bin 94911 -> 18367 bytes
 docs/source/_templates/mo_logo.html   |   6 +++---
 docs/source/index.rst                 |   2 +-
 4 files changed, 4 insertions(+), 4 deletions(-)
 create mode 100644 docs/source/_static/mo-logo-dark.png

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diff --git a/docs/source/_templates/mo_logo.html b/docs/source/_templates/mo_logo.html
index c0ff11a..01fd91a 100644
--- a/docs/source/_templates/mo_logo.html
+++ b/docs/source/_templates/mo_logo.html
@@ -1,7 +1,7 @@
 
\ No newline at end of file
diff --git a/docs/source/index.rst b/docs/source/index.rst
index 6a84909..8c7f871 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -17,7 +17,7 @@
 
       .. rst-class:: lead
 
-      **🌊 The Met Office's modular, accessible, and *fast* sea-level rise simulator based on statistical emulations of physical modelling experiments and lines of evidence from the IPCC and across the literature.**
+      **The Met Office's modular, accessible, and *fast* sea-level rise simulator based on statistical emulations of physical modelling experiments and lines of evidence from the IPCC and across the literature.**
 
       .. container:: d-flex gap-3 pt-3
          

        
     
-    
        
+    
        
 
        
+    
+    
        
+
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