From a0f26b8242fec552cacfe4c25195bfbd0739802a Mon Sep 17 00:00:00 2001 From: albusdemens Date: Wed, 10 May 2017 11:34:44 +0200 Subject: [PATCH 001/150] Included image flip introduced by the lens system --- Plot_EDF.py | 14 +++++++++++++- recon3d.py | 27 +++++++++++++++------------ 2 files changed, 28 insertions(+), 13 deletions(-) diff --git a/Plot_EDF.py b/Plot_EDF.py index 29112fa..125cd9c 100644 --- a/Plot_EDF.py +++ b/Plot_EDF.py @@ -6,5 +6,17 @@ im = EF.EdfFile('/Users/Alberto/Documents/Data_analysis/DFXRM/topotomo_frelon_far_0022_0000_0002.edf') image = im.GetData(0) -plt.imshow(image) +im_flip = np.fliplr(image) + +fig = plt.figure() +a=fig.add_subplot(1,2,1) +imgplot = plt.imshow(image) +a.set_title('Image') +a=fig.add_subplot(1,2,2) +imgplot = plt.imshow(im_flip) +a.set_title('Flipped image') + +#plt.imshow(image) plt.show() +#plt.imshow(im_flip) +#plt.show() diff --git a/recon3d.py b/recon3d.py index ef4d0bd..5b8154a 100644 --- a/recon3d.py +++ b/recon3d.py @@ -337,27 +337,30 @@ def build_rotation_lookup_general(self): else: print "ERROR: scattering geometry not defined" - T_s2d = self.par['M'] * np.matmul( - T_det, + # Let's multiply all the matrices. flipud takes into account the + # vertical flip introduced by the lens system + T_s2d = self.par['M'] * np.flipud( np.matmul( - Tinv_th, + T_det, np.matmul( - Theta, + Tinv_th, np.matmul( - T_th, + Theta, np.matmul( - Omega, + T_th, np.matmul( - Tinv_lo, + Omega, np.matmul( - R_lo, + Tinv_lo, np.matmul( - T_lo, + R_lo, np.matmul( - Tinv_up, + T_lo, np.matmul( - R_up, - T_up)))))))))) + Tinv_up, + np.matmul( + R_up, + T_up))))))))))) return T_s2d def outputfiles(self, grain_ang): From 5c1c90a8987f84d549ef03d887cf39b35c4a51b6 Mon Sep 17 00:00:00 2001 From: albusdemens Date: Wed, 10 May 2017 11:35:46 +0200 Subject: [PATCH 002/150] Set off option to compare original and flipped image --- Plot_EDF.py | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/Plot_EDF.py b/Plot_EDF.py index 125cd9c..f56d1c3 100644 --- a/Plot_EDF.py +++ b/Plot_EDF.py @@ -6,17 +6,17 @@ im = EF.EdfFile('/Users/Alberto/Documents/Data_analysis/DFXRM/topotomo_frelon_far_0022_0000_0002.edf') image = im.GetData(0) -im_flip = np.fliplr(image) +#im_flip = np.fliplr(image) -fig = plt.figure() -a=fig.add_subplot(1,2,1) -imgplot = plt.imshow(image) -a.set_title('Image') -a=fig.add_subplot(1,2,2) -imgplot = plt.imshow(im_flip) -a.set_title('Flipped image') +#fig = plt.figure() +#a=fig.add_subplot(1,2,1) +#imgplot = plt.imshow(image) +#a.set_title('Image') +#a=fig.add_subplot(1,2,2) +#imgplot = plt.imshow(im_flip) +#a.set_title('Flipped image') -#plt.imshow(image) +plt.imshow(image) plt.show() #plt.imshow(im_flip) #plt.show() From d7d15439122d9a28e73494b601c2cbc559cb6295 Mon Sep 17 00:00:00 2001 From: albusdemens Date: Thu, 11 May 2017 15:33:50 +0200 Subject: [PATCH 003/150] Binning angular values (irregular motor steps) --- Sunday_data.ini | 37 +++++++++++++++++++++++++++++++++++++ getdata.py | 27 ++++++++++++++++++++++----- lib/miniged.py | 4 +++- 3 files changed, 62 insertions(+), 6 deletions(-) create mode 100644 Sunday_data.ini diff --git a/Sunday_data.ini b/Sunday_data.ini new file mode 100644 index 0000000..61ab893 --- /dev/null +++ b/Sunday_data.ini @@ -0,0 +1,37 @@ +sg_no 225 +unit_cell [4.,4.,4.,90.,90.,90.] +hkl [2,0,0] +wavelength 0.73 +grain_pos [0,0,0] +grain_dim [11.,11.,11.] +grain_steps [11,11,11] +ang_mean [0,0] +ang_width [.01,.01] +#image simulation +# ang_steps [2,2,18] +# ang_types ["phi_upper","phi_lower","Omega"] +# ang_min [0,0,-85.] +# ang_max [.01,.01,85.] +# detx_size 20 +# detz_size 30 +M 1. +t_x "None" +t_y 0 +t_z 0 +path "/u/data/andcj/tmp/testdir1" # Data source +format "edf" +mode "horizontal" +xyz_up [0,0,0] +xyz_lo [0,0,0] +xyz_th [0,0,0] + +# box simulations +# grain_dim [18.,18.,20.] +# grain_steps [18,18,20] +# ang_mean [0,0] +# ang_width [.01,.01] +# ang_steps [3,3,18] +# ang_min [-.01,-.01,-85.] +# ang_max [.01,.01,85.] # test +# stem "../test/box" +#completeness_cutoff 0.5 diff --git a/getdata.py b/getdata.py index e9a9487..2a6b274 100644 --- a/getdata.py +++ b/getdata.py @@ -4,6 +4,7 @@ import os import warnings import numpy as np +import pdb # For debugging try: from mpi4py import MPI @@ -52,8 +53,21 @@ def __init__( int(int(poi[1]) + int(imgsize[1]) / 2)] data = GetEdfData(datadir, dataname, bgpath, bgfilename, roi, sim) - self.alpha, self.beta, self.omega = data.getMetaValues() - + self.alpha, self.beta, self.omega = data.getMetaValues() # Redefine + min_alpha = min(self.alpha) + max_alpha = max(self.alpha) + min_beta = min(self.beta) + max_beta = max(self.beta) + print min_alpha, max_alpha, min_beta, max_beta + + pdb.set_trace() + + print self.alpha + print self.beta + print self.omega + print len(self.alpha) + print len(self.beta) + print len(self.omega) self.allFiles(data, imgsize) if self.rank == 0: @@ -77,9 +91,12 @@ def allFiles(self, data, imsiz): imgarray = data.makeImgArray(index_list, 50, 'linetrace') if self.rank == 0: - lena = len(self.alpha) - lenb = len(self.beta) + lena = 7#len(self.alpha) + lenb = 7#len(self.beta) leno = len(self.omega) + #lena = len(self.alpha) + #lenb = len(self.beta) + #leno = len(self.omega) bigarray = np.zeros((lena, lenb, leno, int(imsiz[1]), int(imsiz[0]))) @@ -132,4 +149,4 @@ def allFiles(self, data, imsiz): sys.argv[5], sys.argv[6], sys.argv[7], - sys.argv[8],) + sys.argv[8]) diff --git a/lib/miniged.py b/lib/miniged.py index d10b0c7..abf3d81 100644 --- a/lib/miniged.py +++ b/lib/miniged.py @@ -194,7 +194,8 @@ def getHeader(self, filenumber): detpos_array = [] try: - srcur = header['machine current'].split(' ')[-2] + #print header['machine current'].split(' ') + srcur = float(header['machine current'].split(' ')[0]) except KeyError: srcur = 0 @@ -250,6 +251,7 @@ def makeMetaArrayNew(self): mot_array, motpos_array, det_array, detpos_array, srcur = self.getHeader(i) + # Option: bin data self.meta[i, 0] = round(float(motpos_array[mot_array.index('phi')]), 8) self.meta[i, 1] = round(float(motpos_array[mot_array.index('chi')]), 8) self.meta[i, 2] = round(float(motpos_array[mot_array.index('diffry')]), 8) From 26d0d5a4bae7216b83547aa11c1954515f692181 Mon Sep 17 00:00:00 2001 From: albusdemens Date: Thu, 11 May 2017 17:15:26 +0200 Subject: [PATCH 004/150] Binning angular values --- find_nearest.py | 7 +++++++ getdata.py | 18 +++++++++++++----- 2 files changed, 20 insertions(+), 5 deletions(-) create mode 100644 find_nearest.py diff --git a/find_nearest.py b/find_nearest.py new file mode 100644 index 0000000..aeaedbd --- /dev/null +++ b/find_nearest.py @@ -0,0 +1,7 @@ +# Simple module to calculate which array element is the closest to a value + +import numpy as np + +def find_nearest(array,value): + idx = (np.abs(array-value)).argmin() + return array[idx] diff --git a/getdata.py b/getdata.py index 2a6b274..df37fe6 100644 --- a/getdata.py +++ b/getdata.py @@ -5,6 +5,7 @@ import warnings import numpy as np import pdb # For debugging +from find_nearest import find_nearest try: from mpi4py import MPI @@ -54,12 +55,19 @@ def __init__( data = GetEdfData(datadir, dataname, bgpath, bgfilename, roi, sim) self.alpha, self.beta, self.omega = data.getMetaValues() # Redefine - min_alpha = min(self.alpha) - max_alpha = max(self.alpha) - min_beta = min(self.beta) - max_beta = max(self.beta) - print min_alpha, max_alpha, min_beta, max_beta + # Alpha and beta values were measured at irregular steps. To make things + # easy, we bin the values in 7 intervals per variable + [count_alpha, extremes_alpha] = np.histogram(self.alpha, 7) + val_alpha = np.zeros(len(extremes_alpha)-1) + + # Find center of each bin + for i in range(0,len(extremes_alpha)-1): + val_alpha[i] = np.mean([extremes_alpha[i], extremes_alpha[i+1]]) + + # For each experimental value, find closest bin centre + + print find_nearest(val_alpha, self.alpha[1]) pdb.set_trace() print self.alpha From 42862e90d283b897dd52ad823f2d248a834bbeac Mon Sep 17 00:00:00 2001 From: albusdemens Date: Fri, 12 May 2017 14:53:37 +0200 Subject: [PATCH 005/150] Plot npy files from getdata.py --- Plot_npy_alpha_beta.py | 43 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 43 insertions(+) create mode 100644 Plot_npy_alpha_beta.py diff --git a/Plot_npy_alpha_beta.py b/Plot_npy_alpha_beta.py new file mode 100644 index 0000000..5b51323 --- /dev/null +++ b/Plot_npy_alpha_beta.py @@ -0,0 +1,43 @@ +# Script to visualize the files returned by getdata.py +# Command: Plot_results.py path/Input_folder +# Where the Input_folder is the folder with the npy files + +import numpy as np +import sys +import os +import matplotlib.pyplot as plt + +''' +Input : directory of data +''' +dir = sys.argv[1] + +alpha = np.load(os.path.join(dir,'alpha.npy')) +beta = np.load(os.path.join(dir,'beta.npy')) + + +plt.figure(1) + +plt.subplot(221) +plt.plot(alpha) +plt.xlabel('Acquisition number') +plt.ylabel('Alpha (in degrees)') +plt.title('Distribution of alpha angles') + +plt.subplot(222) +plt.plot(beta) +plt.xlabel('Acquisition number') +plt.ylabel('Beta (in degrees)') +plt.title('Distribution of beta angles') + +plt.subplot(223) +plt.plot(alpha) +plt.xlabel('Acquisition number') +plt.ylabel('Alpha (in degrees)') + +plt.subplot(224) +plt.plot(beta) +plt.xlabel('Acquisition number') +plt.ylabel('Beta (in degrees)') + +plt.show() From b0fdf7e919927e01a44f8af4121a5cf22a5cd23d Mon Sep 17 00:00:00 2001 From: albusdemens Date: Fri, 12 May 2017 14:54:01 +0200 Subject: [PATCH 006/150] Adapt the code to continuous angle distribution --- getdata.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/getdata.py b/getdata.py index df37fe6..4c9046c 100644 --- a/getdata.py +++ b/getdata.py @@ -66,7 +66,7 @@ def __init__( val_alpha[i] = np.mean([extremes_alpha[i], extremes_alpha[i+1]]) # For each experimental value, find closest bin centre - + print find_nearest(val_alpha, self.alpha[1]) pdb.set_trace() From f5ee1cd8c71bae8e31fba718c1e2cade23e0dd8e Mon Sep 17 00:00:00 2001 From: albusdemens Date: Sat, 13 May 2017 09:19:07 +0200 Subject: [PATCH 007/150] Script to plot alpha, beta distr now ready to use --- Plot_npy_alpha_beta.py | 18 ++++++++++++------ 1 file changed, 12 insertions(+), 6 deletions(-) diff --git a/Plot_npy_alpha_beta.py b/Plot_npy_alpha_beta.py index 5b51323..937822b 100644 --- a/Plot_npy_alpha_beta.py +++ b/Plot_npy_alpha_beta.py @@ -10,12 +10,18 @@ ''' Input : directory of data ''' + +plt.close("all") + dir = sys.argv[1] alpha = np.load(os.path.join(dir,'alpha.npy')) beta = np.load(os.path.join(dir,'beta.npy')) +ar = range(len(alpha)) +br = range(len(beta)) +# Plot distribution of motor values plt.figure(1) plt.subplot(221) @@ -31,13 +37,13 @@ plt.title('Distribution of beta angles') plt.subplot(223) -plt.plot(alpha) -plt.xlabel('Acquisition number') -plt.ylabel('Alpha (in degrees)') +plt.plot(alpha, ar) +plt.xlabel('Alpha (in degrees)') +plt.ylabel('Acquisition number') plt.subplot(224) -plt.plot(beta) -plt.xlabel('Acquisition number') -plt.ylabel('Beta (in degrees)') +plt.plot(beta, br) +plt.xlabel('Beta (in degrees)') +plt.ylabel('Acquisition number') plt.show() From ec64aa739e044255a952d0234d3dc695dfe9a1c9 Mon Sep 17 00:00:00 2001 From: albusdemens Date: Sat, 13 May 2017 10:39:58 +0200 Subject: [PATCH 008/150] Added functionality to plot center of hist values used to group data --- Plot_npy_alpha_beta.py | 38 +++++++++++++++++++++++++++++++++++++- find_nearest.py | 4 ++++ getdata.py | 4 ++-- 3 files changed, 43 insertions(+), 3 deletions(-) diff --git a/Plot_npy_alpha_beta.py b/Plot_npy_alpha_beta.py index 937822b..e650ede 100644 --- a/Plot_npy_alpha_beta.py +++ b/Plot_npy_alpha_beta.py @@ -6,6 +6,7 @@ import sys import os import matplotlib.pyplot as plt +from find_nearest import find_nearest, find_nearest_idx ''' Input : directory of data @@ -18,31 +19,66 @@ alpha = np.load(os.path.join(dir,'alpha.npy')) beta = np.load(os.path.join(dir,'beta.npy')) +# Find position of the center of the bins used to group the data +[count_alpha, extremes_alpha] = np.histogram(alpha, 7) +val_alpha = np.zeros(len(extremes_alpha)-1) + +[count_beta, extremes_beta] = np.histogram(beta, 7) +val_beta = np.zeros(len(extremes_beta)-1) + +# Find center of each bin +for i in range(0,len(extremes_alpha)-1): + val_alpha[i] = np.mean([extremes_alpha[i], extremes_alpha[i+1]]) +for i in range(0,len(extremes_beta)-1): + val_beta[i] = np.mean([extremes_beta[i], extremes_beta[i+1]]) + +# Find experimental value closest to bin centre, which will be plotted +u = np.zeros((len(val_alpha), 2)) +for j in range(0,len(val_alpha)): + u[j,0] = find_nearest(alpha,val_alpha[j]) + u[j,1] = find_nearest_idx(alpha,val_alpha[j]) + +v = np.zeros((len(val_beta), 2)) +for j in range(0,len(val_alpha)): + v[j,0] = find_nearest(beta,val_beta[j]) + v[j,1] = find_nearest_idx(beta,val_beta[j]) + +print u +print v + +# Plot distribution of motor values ar = range(len(alpha)) br = range(len(beta)) -# Plot distribution of motor values plt.figure(1) plt.subplot(221) plt.plot(alpha) +for i in range(0,len(val_alpha)): + plt.scatter(u[i,1], u[i,0]) plt.xlabel('Acquisition number') plt.ylabel('Alpha (in degrees)') plt.title('Distribution of alpha angles') plt.subplot(222) plt.plot(beta) +for i in range(0,len(val_beta)): + plt.scatter(v[i,1], v[i,0]) plt.xlabel('Acquisition number') plt.ylabel('Beta (in degrees)') plt.title('Distribution of beta angles') plt.subplot(223) plt.plot(alpha, ar) +for i in range(0,len(val_alpha)): + plt.scatter(u[i,0], u[i,1]) plt.xlabel('Alpha (in degrees)') plt.ylabel('Acquisition number') plt.subplot(224) plt.plot(beta, br) +for i in range(0,len(val_beta)): + plt.scatter(v[i,0], v[i,1]) plt.xlabel('Beta (in degrees)') plt.ylabel('Acquisition number') diff --git a/find_nearest.py b/find_nearest.py index aeaedbd..04b7bb0 100644 --- a/find_nearest.py +++ b/find_nearest.py @@ -5,3 +5,7 @@ def find_nearest(array,value): idx = (np.abs(array-value)).argmin() return array[idx] + +def find_nearest_idx(array,value): + idx = (np.abs(array-value)).argmin() + return idx diff --git a/getdata.py b/getdata.py index 4c9046c..ad6eb57 100644 --- a/getdata.py +++ b/getdata.py @@ -5,7 +5,7 @@ import warnings import numpy as np import pdb # For debugging -from find_nearest import find_nearest +from find_nearest import find_nearest, find_nearest_idx try: from mpi4py import MPI @@ -67,7 +67,7 @@ def __init__( # For each experimental value, find closest bin centre - print find_nearest(val_alpha, self.alpha[1]) + print extremes_alpha pdb.set_trace() print self.alpha From d83163ade7a279629935b2628cad31a3e1659e2a Mon Sep 17 00:00:00 2001 From: albusdemens Date: Sat, 13 May 2017 11:09:10 +0200 Subject: [PATCH 009/150] Added hist plot for alpha, beta --- Plot_npy_alpha_beta.py | 14 +++++++++++--- getdata.py | 4 +++- 2 files changed, 14 insertions(+), 4 deletions(-) diff --git a/Plot_npy_alpha_beta.py b/Plot_npy_alpha_beta.py index e650ede..9c10aa1 100644 --- a/Plot_npy_alpha_beta.py +++ b/Plot_npy_alpha_beta.py @@ -43,9 +43,6 @@ v[j,0] = find_nearest(beta,val_beta[j]) v[j,1] = find_nearest_idx(beta,val_beta[j]) -print u -print v - # Plot distribution of motor values ar = range(len(alpha)) br = range(len(beta)) @@ -82,4 +79,15 @@ plt.xlabel('Beta (in degrees)') plt.ylabel('Acquisition number') +# Plot hist showing how angular values are distributed in the different bins +plt.figure(2) + +plt.subplot(121) +plt.hist(alpha, 7) +plt.title('Binning of alpha values') + +plt.subplot(122) +plt.hist(beta, 7) +plt.title('Binning of beta values') + plt.show() diff --git a/getdata.py b/getdata.py index ad6eb57..2ce9077 100644 --- a/getdata.py +++ b/getdata.py @@ -66,8 +66,10 @@ def __init__( val_alpha[i] = np.mean([extremes_alpha[i], extremes_alpha[i+1]]) # For each experimental value, find closest bin centre + alpha_discr = np.zeros(len(self.alpha)) + for i in range(0, len(self.alpha)): + alpha_discr[i] = find_nearest(val_alpha,self.alpha[i]) - print extremes_alpha pdb.set_trace() print self.alpha From 6fdee660b3fc51557bf3a2e0ad848e93b3063115 Mon Sep 17 00:00:00 2001 From: albusdemens Date: Sat, 13 May 2017 11:23:56 +0200 Subject: [PATCH 010/150] Done with data binning --- getdata.py | 17 ++++++++++++----- 1 file changed, 12 insertions(+), 5 deletions(-) diff --git a/getdata.py b/getdata.py index 2ce9077..68266a3 100644 --- a/getdata.py +++ b/getdata.py @@ -60,24 +60,28 @@ def __init__( # easy, we bin the values in 7 intervals per variable [count_alpha, extremes_alpha] = np.histogram(self.alpha, 7) val_alpha = np.zeros(len(extremes_alpha)-1) + [count_beta, extremes_beta] = np.histogram(self.beta, 7) + val_beta = np.zeros(len(extremes_beta)-1) # Find center of each bin for i in range(0,len(extremes_alpha)-1): val_alpha[i] = np.mean([extremes_alpha[i], extremes_alpha[i+1]]) + for j in range(0,len(extremes_beta)-1): + val_beta[j] = np.mean([extremes_beta[j], extremes_beta[j+1]]) # For each experimental value, find closest bin centre alpha_discr = np.zeros(len(self.alpha)) for i in range(0, len(self.alpha)): alpha_discr[i] = find_nearest(val_alpha,self.alpha[i]) + beta_discr = np.zeros(len(self.beta)) + for j in range(0, len(self.beta)): + beta_discr[j] = find_nearest(val_beta,self.beta[j]) pdb.set_trace() print self.alpha print self.beta print self.omega - print len(self.alpha) - print len(self.beta) - print len(self.omega) self.allFiles(data, imgsize) if self.rank == 0: @@ -101,9 +105,12 @@ def allFiles(self, data, imsiz): imgarray = data.makeImgArray(index_list, 50, 'linetrace') if self.rank == 0: - lena = 7#len(self.alpha) - lenb = 7#len(self.beta) + # If we need to bin the angular values + lena = len(alpha_discr) + lenb = len(beta_discr) leno = len(self.omega) + + # If we don't need to bin the angular values #lena = len(self.alpha) #lenb = len(self.beta) #leno = len(self.omega) From 3f1066e89d75462a412e9415cf4824f5ad0dd7e7 Mon Sep 17 00:00:00 2001 From: albusdemens Date: Sat, 13 May 2017 14:42:39 +0200 Subject: [PATCH 011/150] Testing reconstruction code; included code to plot 3D reocnstr --- Sunday_data.ini | 9 +++++---- View_reconstruction.py | 18 ++++++++++++++++++ getdata.py | 15 +++++++-------- 3 files changed, 30 insertions(+), 12 deletions(-) create mode 100644 View_reconstruction.py diff --git a/Sunday_data.ini b/Sunday_data.ini index 61ab893..2f7a329 100644 --- a/Sunday_data.ini +++ b/Sunday_data.ini @@ -1,7 +1,7 @@ sg_no 225 -unit_cell [4.,4.,4.,90.,90.,90.] +unit_cell [4.046,4.046,4.046,90.,90.,90.] hkl [2,0,0] -wavelength 0.73 +wavelength 0.7293 # Corresponds to an energy of 17 keV grain_pos [0,0,0] grain_dim [11.,11.,11.] grain_steps [11,11,11] @@ -14,11 +14,12 @@ ang_width [.01,.01] # ang_max [.01,.01,85.] # detx_size 20 # detz_size 30 -M 1. +M 18.57 # Magnification value t_x "None" t_y 0 t_z 0 -path "/u/data/andcj/tmp/testdir1" # Data source +path "/u/data/alcer/DFXRM_rec/Rec2" # Folder with npy files, returned by + # getdata.py format "edf" mode "horizontal" xyz_up [0,0,0] diff --git a/View_reconstruction.py b/View_reconstruction.py new file mode 100644 index 0000000..eea802a --- /dev/null +++ b/View_reconstruction.py @@ -0,0 +1,18 @@ +# Visualize the sample volume reconstructed by recon3D.py + +import numpy as np +import sys +import os +import matplotlib.pyplot as plt +import mayavi.mlab as mlab + +''' +Input : directory of data +''' + +plt.close("all") + +dir = sys.argv[1] +Data = np.load(os.path.join(dir,'grain_ang.npy')) + +print Data diff --git a/getdata.py b/getdata.py index 68266a3..9ae48ab 100644 --- a/getdata.py +++ b/getdata.py @@ -77,11 +77,8 @@ def __init__( for j in range(0, len(self.beta)): beta_discr[j] = find_nearest(val_beta,self.beta[j]) - pdb.set_trace() + global alpha_discr, beta_discr, val_alpha, val_beta - print self.alpha - print self.beta - print self.omega self.allFiles(data, imgsize) if self.rank == 0: @@ -106,8 +103,8 @@ def allFiles(self, data, imsiz): if self.rank == 0: # If we need to bin the angular values - lena = len(alpha_discr) - lenb = len(beta_discr) + lena = len(val_alpha) + lenb = len(val_beta) leno = len(self.omega) # If we don't need to bin the angular values @@ -118,8 +115,10 @@ def allFiles(self, data, imsiz): bigarray = np.zeros((lena, lenb, leno, int(imsiz[1]), int(imsiz[0]))) for i, ind in enumerate(index_list): - a = np.where(self.alpha == met[ind, 0]) - b = np.where(self.beta == met[ind, 1]) + a = np.where(alpha_discr == met[ind, 0]) + b = np.where(alpha_discr == met[ind, 1]) + #a = np.where(self.alpha == met[ind, 0]) + #b = np.where(self.beta == met[ind, 1]) c = np.where(self.omega == met[ind, 2]) # d = np.where(self.theta == met[ind, 4]) From a3516a66a60ee2cde9937a34504c483e1d797756 Mon Sep 17 00:00:00 2001 From: albusdemens Date: Sat, 13 May 2017 15:02:22 +0200 Subject: [PATCH 012/150] Why nans in first reconstruction? --- Sunday_data.ini | 4 ++-- View_reconstruction.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/Sunday_data.ini b/Sunday_data.ini index 2f7a329..744ab38 100644 --- a/Sunday_data.ini +++ b/Sunday_data.ini @@ -3,8 +3,8 @@ unit_cell [4.046,4.046,4.046,90.,90.,90.] hkl [2,0,0] wavelength 0.7293 # Corresponds to an energy of 17 keV grain_pos [0,0,0] -grain_dim [11.,11.,11.] -grain_steps [11,11,11] +grain_dim [101.,101.,101.] +grain_steps [101,101,101] ang_mean [0,0] ang_width [.01,.01] #image simulation diff --git a/View_reconstruction.py b/View_reconstruction.py index eea802a..9b4336c 100644 --- a/View_reconstruction.py +++ b/View_reconstruction.py @@ -4,7 +4,7 @@ import sys import os import matplotlib.pyplot as plt -import mayavi.mlab as mlab +import mayavi.mlab as mlab # Idea from http://stackoverflow.com/questions/10755060/plot-a-cube-of-3d-intensity-data ''' Input : directory of data From 119f7f1a5d820120220d063a7c20082549b55659 Mon Sep 17 00:00:00 2001 From: albusdemens Date: Mon, 15 May 2017 11:44:22 +0200 Subject: [PATCH 013/150] parameters from global to class; redefined output as uint16 --- getdata.py | 24 +++++++++++------------- 1 file changed, 11 insertions(+), 13 deletions(-) diff --git a/getdata.py b/getdata.py index 9ae48ab..fd8966e 100644 --- a/getdata.py +++ b/getdata.py @@ -59,25 +59,23 @@ def __init__( # Alpha and beta values were measured at irregular steps. To make things # easy, we bin the values in 7 intervals per variable [count_alpha, extremes_alpha] = np.histogram(self.alpha, 7) - val_alpha = np.zeros(len(extremes_alpha)-1) + self.val_alpha = np.zeros(len(extremes_alpha)-1) [count_beta, extremes_beta] = np.histogram(self.beta, 7) - val_beta = np.zeros(len(extremes_beta)-1) + self.val_beta = np.zeros(len(extremes_beta)-1) # Find center of each bin for i in range(0,len(extremes_alpha)-1): - val_alpha[i] = np.mean([extremes_alpha[i], extremes_alpha[i+1]]) + self.val_alpha[i] = np.mean([extremes_alpha[i], extremes_alpha[i+1]]) for j in range(0,len(extremes_beta)-1): - val_beta[j] = np.mean([extremes_beta[j], extremes_beta[j+1]]) + self.val_beta[j] = np.mean([extremes_beta[j], extremes_beta[j+1]]) # For each experimental value, find closest bin centre - alpha_discr = np.zeros(len(self.alpha)) + self.alpha_discr = np.zeros(len(self.alpha)) for i in range(0, len(self.alpha)): - alpha_discr[i] = find_nearest(val_alpha,self.alpha[i]) - beta_discr = np.zeros(len(self.beta)) + self.alpha_discr[i] = find_nearest(self.val_alpha,self.alpha[i]) + self.beta_discr = np.zeros(len(self.beta)) for j in range(0, len(self.beta)): - beta_discr[j] = find_nearest(val_beta,self.beta[j]) - - global alpha_discr, beta_discr, val_alpha, val_beta + self.beta_discr[j] = find_nearest(self.val_beta,self.beta[j]) self.allFiles(data, imgsize) @@ -103,8 +101,8 @@ def allFiles(self, data, imsiz): if self.rank == 0: # If we need to bin the angular values - lena = len(val_alpha) - lenb = len(val_beta) + lena = len(self.val_alpha) + lenb = len(self.val_beta) leno = len(self.omega) # If we don't need to bin the angular values @@ -112,7 +110,7 @@ def allFiles(self, data, imsiz): #lenb = len(self.beta) #leno = len(self.omega) - bigarray = np.zeros((lena, lenb, leno, int(imsiz[1]), int(imsiz[0]))) + bigarray = np.zeros((lena, lenb, leno, int(imsiz[1]), int(imsiz[0])), dtype=np.uint16) for i, ind in enumerate(index_list): a = np.where(alpha_discr == met[ind, 0]) From 009796f267ff828294769f0fb9584f9d3d9c773e Mon Sep 17 00:00:00 2001 From: albusdemens Date: Mon, 15 May 2017 22:57:27 +0200 Subject: [PATCH 014/150] Fixing bugs --- Sunday_data.ini | 10 +++++----- View_reconstruction.py | 2 +- check_input.pyc | Bin 0 -> 2126 bytes find_nearest.pyc | Bin 0 -> 671 bytes getdata.py | 8 ++++---- grain_ang.npy | Bin 0 -> 222344 bytes lib/EdfFile.pyc | Bin 0 -> 35618 bytes lib/__init__.pyc | Bin 0 -> 155 bytes lib/miniged.pyc | Bin 0 -> 16649 bytes recon3d.py | 6 +++++- recon3d.pyc | Bin 0 -> 13628 bytes 11 files changed, 15 insertions(+), 11 deletions(-) create mode 100644 check_input.pyc create mode 100644 find_nearest.pyc create mode 100644 grain_ang.npy create mode 100644 lib/EdfFile.pyc create mode 100644 lib/__init__.pyc create mode 100644 lib/miniged.pyc create mode 100644 recon3d.pyc diff --git a/Sunday_data.ini b/Sunday_data.ini index 744ab38..3314bb8 100644 --- a/Sunday_data.ini +++ b/Sunday_data.ini @@ -1,10 +1,10 @@ -sg_no 225 -unit_cell [4.046,4.046,4.046,90.,90.,90.] +sg_no 225 # Space group +unit_cell [4.0495,4.0495,4.0495,90.,90.,90.] hkl [2,0,0] wavelength 0.7293 # Corresponds to an energy of 17 keV grain_pos [0,0,0] -grain_dim [101.,101.,101.] -grain_steps [101,101,101] +grain_dim [21.,21.,21.] +grain_steps [21,21,21] ang_mean [0,0] ang_width [.01,.01] #image simulation @@ -18,7 +18,7 @@ M 18.57 # Magnification value t_x "None" t_y 0 t_z 0 -path "/u/data/alcer/DFXRM_rec/Rec2" # Folder with npy files, returned by +path "/u/data/alcer/DFXRM_rec/Rec3" # Folder with npy files, returned by # getdata.py format "edf" mode "horizontal" diff --git a/View_reconstruction.py b/View_reconstruction.py index 9b4336c..7ce82ba 100644 --- a/View_reconstruction.py +++ b/View_reconstruction.py @@ -15,4 +15,4 @@ dir = sys.argv[1] Data = np.load(os.path.join(dir,'grain_ang.npy')) -print Data +print Data, np.min(Data) diff --git a/check_input.pyc b/check_input.pyc new file mode 100644 index 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np.zeros((len(th), len(om), len(lo), len(up), 4, 4)) @@ -283,6 +284,7 @@ def build_rotation_lookup_general(self): Tinv_th[:, :, :, :, 2, 2] = 1. Tinv_th[:, :, :, :, 3, 3] = 1. + print('Define horizontal mode') if self.par['mode'] == "horizontal": Theta[:, :, :, :, 0, 0] = np.cos(th_mat) Theta[:, :, :, :, 0, 1] = -np.sin(th_mat) @@ -337,6 +339,7 @@ def build_rotation_lookup_general(self): else: print "ERROR: scattering geometry not defined" + print('Multiply all matrices') # Let's multiply all the matrices. flipud takes into account the # vertical flip introduced by the lens system T_s2d = self.par['M'] * np.flipud( @@ -361,6 +364,7 @@ def build_rotation_lookup_general(self): np.matmul( R_up, T_up))))))))))) + return T_s2d def outputfiles(self, grain_ang): diff --git a/recon3d.pyc b/recon3d.pyc new file mode 100644 index 0000000000000000000000000000000000000000..9b522828b51eef936ded22ae250f20f45e9107e5 GIT binary patch literal 13628 zcmb_jPiz~PW2MxwQa@tFU6L!2 zyUfl~CRGY3kXQ850?nmBPko18+fy$+wJ6YI(L>J#dg`et0u*SEufOk`G|3>hA4(CA@)pR(7P!b8L^k) zI>1@6m+iwNVsDh;jA)FB=D64!PjGT#FDJV5T%8q-yl75{y$NAF^A|( z7|w|J@}X~tuboj*#du@lmqL6e#Phu=;Z6%@TpXGr{Ac0L2sa}RX2o}gaB|`>Bf`HG zPM(m3aM6}4oe5DugS|O{QqaCroCIIquko*F0E)7Bgs$zB^y?U2eaCCsHCGp8Q1ImG zBXtAcr9c8)m0{cWJ-;RcUw$YnLFle=H%;5~6FE}npW~yR;^7Lh2Py@z0y#pa1VDx+ z0}x<_ff3^c~JH>HyS~2k;brFg8@loI7(G%hTPlLn)xXjN3 zlO$+R9R@X6f*%v_2U)5(F-ba9JCd{;<%$fKCOCh>uVbQ@q3(@A2jIXI2jilb6+Kfd zqmtDCj(3VgUdh8lsGE4~dQDfVmg`3!%iq8I@keXx)f1`QO5i(ct(ZfLXbO*-8@24v zZn}{hs)+i)Yedz`joPhpv*l^s+5RB|5&Dhts3&dOVQ6<%G{djxT18>I5;4*Q%7S*( zYDZPC;i_m9@6{k|+EI~&$$creQH{tHJl~^M5!I*&1TY1)5SlLAeoZ%yf>sgrf*(qg zfH8mKthKrkX{uaCKT)|%&0*%eFj z{`Qk6Y_bIsnK;n>oxEMh6+~@HnL+GeGSjc8kQxHpt^! z)H^d*sk@a!&xakno~A6};-d%mD!|n z|n2PLX6*Vwtz>sLqnR_)3@^DAlP&b}MzH!{^bLDQ}A zYmv1{vh!QqXCts379T6K+HN$sU!DxyCAXs9xQIJSqiG^y6(2=&Ll`&tKbt@KW-4UK z{sN?&l`NzR;TBF{(b}`|9s74s*0im+#e+k#t#kNQrSCjb}= z{*@M#2PTa*$|J(TVLo9fc;9gV#)&R}T`jkFCPCLY7nUc%m3?5)yk9~{JMSc|f`ku^QYf=ioQ18G zB3co&OIMNGQXWkKc86@=9{heE2s~5?wst4#wp?B#s+cFyZ?Aj2M$nW(j38Zddpay| zu*ej%)F&aIdHjD?%%OX01rI$#X$j=%Yb{X)&u(Hkq_s9)5NS>2*2iubD27zs24h5T z)3tprg0HW+);#r0<(6AH8Y~iy zi@GhZ!}4nc6}wUPc_AiY#}ok!TD*em27^VH^aUo4%KPnV)eS9TidP|WeY7q`w3d0? 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zZ-bhmF`}=FlRZ%Sm2;T40U&LuGk5(&CN`J_Tw7hDOr8F1%MRbhEb;Y*9s7vqME|Nk zE|fG}+1IrZD`4UN@FX!3o;vL-SLf?HBUcA P$Xx%m@FtA~%&z|hUi literal 0 HcmV?d00001 From aa205e46af38b02906537b51d264cbcbf810f7cd Mon Sep 17 00:00:00 2001 From: albusdemens Date: Mon, 15 May 2017 23:03:19 +0200 Subject: [PATCH 015/150] Added file to plot unbinned data --- .gitignore | 2 + getdata_raw.py | 138 +++++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 140 insertions(+) create mode 100644 getdata_raw.py diff --git a/.gitignore b/.gitignore index 0d20b64..6f38410 100644 --- a/.gitignore +++ b/.gitignore @@ -1 +1,3 @@ *.pyc + +*.npy diff --git a/getdata_raw.py b/getdata_raw.py new file mode 100644 index 0000000..8a19df5 --- /dev/null +++ b/getdata_raw.py @@ -0,0 +1,138 @@ +# This script reads the motor positions from the EDF files +# To bin the data, see getdata.py + +from lib.miniged import GetEdfData +import sys +import time +import os +import warnings +import numpy as np + +try: + from mpi4py import MPI +except ImportError: + print "No MPI, running on 1 core." + +''' +Inputs: +Directory of data +Name of data files +Directory of background files +Name of background files +Point of interest +Image size +Output path +Name of new output directory to make +''' + + +class makematrix(): + def __init__( + self, datadir, + dataname, bgpath, bgfilename, + poi, imgsize, outputpath, outputdir, + sim=False): + + try: + self.comm = MPI.COMM_WORLD + self.rank = self.comm.Get_rank() + self.size = self.comm.Get_size() + except NameError: + self.rank = 0 + self.size = 1 + + imgsize = imgsize.split(',') + poi = poi.split(',') + + if self.rank == 0: + start = time.time() + self.directory = self.makeOutputFolder(outputpath, outputdir) + + roi = [ + int(int(poi[0]) - int(imgsize[0]) / 2), + int(int(poi[0]) + int(imgsize[0]) / 2), + int(int(poi[1]) - int(imgsize[1]) / 2), + int(int(poi[1]) + int(imgsize[1]) / 2)] + + data = GetEdfData(datadir, dataname, bgpath, bgfilename, roi, sim) + self.alpha, self.beta, self.omega = data.getMetaValues() + + self.allFiles(data, imgsize) + + if self.rank == 0: + stop = time.time() + print 'Total time: {0:8.4f} seconds.'.format(stop - start) + + def makeOutputFolder(self, path, dirname): + directory = path + '/' + dirname + + print directory + if not os.path.exists(directory): + os.makedirs(directory) + return directory + + def allFiles(self, data, imsiz): + index_list = range(len(data.meta)) + met = data.meta + + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + imgarray = data.makeImgArray(index_list, 50, 'linetrace') + + if self.rank == 0: + lena = len(self.alpha) + lenb = len(self.beta) + leno = len(self.omega) + + bigarray = np.zeros((lena, lenb, leno, int(imsiz[1]), int(imsiz[0]))) + + for i, ind in enumerate(index_list): + a = np.where(self.alpha == met[ind, 0]) + b = np.where(self.beta == met[ind, 1]) + c = np.where(self.omega == met[ind, 2]) + # d = np.where(self.theta == met[ind, 4]) + + bigarray[a, b, c, :, :] = imgarray[ind, :, :] + + np.save(self.directory + '/alpha.npy', self.alpha) + np.save(self.directory + '/beta.npy', self.beta) + # np.save(self.directory + '/theta.npy', self.theta) + np.save(self.directory + '/omega.npy', self.omega) + + np.save(self.directory + '/dataarray.npy', bigarray) + + +if __name__ == "__main__": + if len(sys.argv) != 9: + if len(sys.argv) != 10: + print "Not enough input parameters. Data input should be:\n\ + Directory of data\n\ + Name of data files\n\ + Directory of background files\n\ + Name of background files\n\ + Point of interest\n\ + Image size\n\ + Output path\n\ + Name of new output directory to make\n\ + " + else: + mm = makematrix( + sys.argv[1], + sys.argv[2], + sys.argv[3], + sys.argv[4], + sys.argv[5], + sys.argv[6], + sys.argv[7], + sys.argv[8], + sys.argv[9]) + else: + mm = makematrix( + sys.argv[1], + sys.argv[2], + sys.argv[3], + sys.argv[4], + sys.argv[5], + sys.argv[6], + sys.argv[7], + sys.argv[8],) From fe3b35bec672384b605094eceea7761167d46bd7 Mon Sep 17 00:00:00 2001 From: albusdemens Date: Mon, 15 May 2017 23:27:48 +0200 Subject: [PATCH 016/150] Removed script to get unbinned data --- getdata_raw.py | 138 ------------------------------------------------- 1 file changed, 138 deletions(-) delete mode 100644 getdata_raw.py diff --git a/getdata_raw.py b/getdata_raw.py deleted file mode 100644 index 8a19df5..0000000 --- a/getdata_raw.py +++ /dev/null @@ -1,138 +0,0 @@ -# This script reads the motor positions from the EDF files -# To bin the data, see getdata.py - -from lib.miniged import GetEdfData -import sys -import time -import os -import warnings -import numpy as np - -try: - from mpi4py import MPI -except ImportError: - print "No MPI, running on 1 core." - -''' -Inputs: -Directory of data -Name of data files -Directory of background files -Name of background files -Point of interest -Image size -Output path -Name of new output directory to make -''' - - -class makematrix(): - def __init__( - self, datadir, - dataname, bgpath, bgfilename, - poi, imgsize, outputpath, outputdir, - sim=False): - - try: - self.comm = MPI.COMM_WORLD - self.rank = self.comm.Get_rank() - self.size = self.comm.Get_size() - except NameError: - self.rank = 0 - self.size = 1 - - imgsize = imgsize.split(',') - poi = poi.split(',') - - if self.rank == 0: - start = time.time() - self.directory = self.makeOutputFolder(outputpath, outputdir) - - roi = [ - int(int(poi[0]) - int(imgsize[0]) / 2), - int(int(poi[0]) + int(imgsize[0]) / 2), - int(int(poi[1]) - int(imgsize[1]) / 2), - int(int(poi[1]) + int(imgsize[1]) / 2)] - - data = GetEdfData(datadir, dataname, bgpath, bgfilename, roi, sim) - self.alpha, self.beta, self.omega = data.getMetaValues() - - self.allFiles(data, imgsize) - - if self.rank == 0: - stop = time.time() - print 'Total time: {0:8.4f} seconds.'.format(stop - start) - - def makeOutputFolder(self, path, dirname): - directory = path + '/' + dirname - - print directory - if not os.path.exists(directory): - os.makedirs(directory) - return directory - - def allFiles(self, data, imsiz): - index_list = range(len(data.meta)) - met = data.meta - - with warnings.catch_warnings(): - warnings.simplefilter("ignore") - imgarray = data.makeImgArray(index_list, 50, 'linetrace') - - if self.rank == 0: - lena = len(self.alpha) - lenb = len(self.beta) - leno = len(self.omega) - - bigarray = np.zeros((lena, lenb, leno, int(imsiz[1]), int(imsiz[0]))) - - for i, ind in enumerate(index_list): - a = np.where(self.alpha == met[ind, 0]) - b = np.where(self.beta == met[ind, 1]) - c = np.where(self.omega == met[ind, 2]) - # d = np.where(self.theta == met[ind, 4]) - - bigarray[a, b, c, :, :] = imgarray[ind, :, :] - - np.save(self.directory + '/alpha.npy', self.alpha) - np.save(self.directory + '/beta.npy', self.beta) - # np.save(self.directory + '/theta.npy', self.theta) - np.save(self.directory + '/omega.npy', self.omega) - - np.save(self.directory + '/dataarray.npy', bigarray) - - -if __name__ == "__main__": - if len(sys.argv) != 9: - if len(sys.argv) != 10: - print "Not enough input parameters. Data input should be:\n\ - Directory of data\n\ - Name of data files\n\ - Directory of background files\n\ - Name of background files\n\ - Point of interest\n\ - Image size\n\ - Output path\n\ - Name of new output directory to make\n\ - " - else: - mm = makematrix( - sys.argv[1], - sys.argv[2], - sys.argv[3], - sys.argv[4], - sys.argv[5], - sys.argv[6], - sys.argv[7], - sys.argv[8], - sys.argv[9]) - else: - mm = makematrix( - sys.argv[1], - sys.argv[2], - sys.argv[3], - sys.argv[4], - sys.argv[5], - sys.argv[6], - sys.argv[7], - sys.argv[8],) From a98b85b821fdd24ae596eb17355d21dd98bf5d50 Mon Sep 17 00:00:00 2001 From: albusdemens Date: Tue, 16 May 2017 13:02:13 +0200 Subject: [PATCH 017/150] Plot alpha, beta distr for one omega --- Plot_data_raw.py | 26 ++++++++ getdata_raw.py | 152 +++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 178 insertions(+) create mode 100644 Plot_data_raw.py create mode 100644 getdata_raw.py diff --git a/Plot_data_raw.py b/Plot_data_raw.py new file mode 100644 index 0000000..a1f135f --- /dev/null +++ b/Plot_data_raw.py @@ -0,0 +1,26 @@ +# Plot, for a given omega, angular values before binning + +import numpy as np +import matplotlib.pyplot as plt + +Data = np.load('/Users/Alberto/Documents/Data_analysis/DFXRM/Results/All_data.npy') + +# Plot distribution of motor values +alpha = Data[:,0] +beta = Data[:,1] + +plt.figure(1) + +plt.subplot(121) +plt.plot(alpha) +plt.xlabel('Acquisition number') +plt.ylabel('Alpha (in degrees)') +plt.title('Distribution of alpha angles') + +plt.subplot(122) +plt.plot(beta) +plt.xlabel('Acquisition number') +plt.ylabel('Beta (in degrees)') +plt.title('Distribution of beta angles') + +plt.show() diff --git a/getdata_raw.py b/getdata_raw.py new file mode 100644 index 0000000..5fba377 --- /dev/null +++ b/getdata_raw.py @@ -0,0 +1,152 @@ +# If pseudomotors have been used, this script returns the angle values before +# binning + +from lib.miniged import GetEdfData +import sys +import time +import os +import warnings +import numpy as np +import pdb # For debugging +from find_nearest import find_nearest, find_nearest_idx + +try: + from mpi4py import MPI +except ImportError: + print "No MPI, running on 1 core." + +''' +Inputs: +Directory of data +Name of data files +Directory of background files +Name of background files +Point of interest +Image size +Output path +Name of new output directory to make +''' + + +class makematrix(): + def __init__( + self, datadir, + dataname, bgpath, bgfilename, + poi, imgsize, outputpath, outputdir, + sim=False): + + try: + self.comm = MPI.COMM_WORLD + self.rank = self.comm.Get_rank() + self.size = self.comm.Get_size() + except NameError: + self.rank = 0 + self.size = 1 + + imgsize = imgsize.split(',') + poi = poi.split(',') + + if self.rank == 0: + start = time.time() + self.directory = self.makeOutputFolder(outputpath, outputdir) + + roi = [ + int(int(poi[0]) - int(imgsize[0]) / 2), + int(int(poi[0]) + int(imgsize[0]) / 2), + int(int(poi[1]) - int(imgsize[1]) / 2), + int(int(poi[1]) + int(imgsize[1]) / 2)] + + data = GetEdfData(datadir, dataname, bgpath, bgfilename, roi, sim) + self.alpha, self.beta, self.omega = data.getMetaValues() # Redefine + + # Alpha and beta values were measured at irregular steps. To make things + # easy, we bin the values in 7 intervals per variable + #[count_alpha, extremes_alpha] = np.histogram(self.alpha, 7) + #self.val_alpha = np.zeros(len(extremes_alpha)-1) + #[count_beta, extremes_beta] = np.histogram(self.beta, 7) + #self.val_beta = np.zeros(len(extremes_beta)-1) + + # Find center of each bin + #for i in range(0,len(extremes_alpha)-1): + # self.val_alpha[i] = np.mean([extremes_alpha[i], extremes_alpha[i+1]]) + #for j in range(0,len(extremes_beta)-1): + # self.val_beta[j] = np.mean([extremes_beta[j], extremes_beta[j+1]]) + + # For each experimental value, find closest bin centre + #self.alpha_discr = np.zeros(len(self.alpha)) + #for i in range(0, len(self.alpha)): + # self.alpha_discr[i] = find_nearest(self.val_alpha,self.alpha[i]) + #self.beta_discr = np.zeros(len(self.beta)) + #for j in range(0, len(self.beta)): + # self.beta_discr[j] = find_nearest(self.val_beta,self.beta[j]) + + self.allFiles(data, imgsize) + + if self.rank == 0: + stop = time.time() + print 'Total time: {0:8.4f} seconds.'.format(stop - start) + + def makeOutputFolder(self, path, dirname): + directory = path + '/' + dirname + + print directory + if not os.path.exists(directory): + os.makedirs(directory) + return directory + + def allFiles(self, data, imsiz): + index_list = range(len(data.meta)) + met = data.meta # All motor positions listed + + print met[5520:5650,2] + + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + #imgarray = data.makeImgArray(index_list, 50, 'linetrace') # Reading the images + + if self.rank == 0: + lena = len(self.alpha) + lenb = len(self.beta) + leno = len(self.omega) + + bigarray = np.zeros((lena, lenb, 1, int(imsiz[1]), int(imsiz[0])), dtype=np.uint16) + + #AA = met[np.where(met[:, 2]==90.4),:] + + # Save all data in one output file + np.save(self.directory + '/All_data.npy', met) + +if __name__ == "__main__": + if len(sys.argv) != 9: + if len(sys.argv) != 10: + print "Not enough input parameters. Data input should be:\n\ + Directory of data\n\ + Name of data files\n\ + Directory of background files\n\ + Name of background files\n\ + Point of interest\n\ + Image size\n\ + Output path\n\ + Name of new output directory to make\n\ + " + else: + mm = makematrix( + sys.argv[1], + sys.argv[2], + sys.argv[3], + sys.argv[4], + sys.argv[5], + sys.argv[6], + sys.argv[7], + sys.argv[8], + sys.argv[9]) + else: + mm = makematrix( + sys.argv[1], + sys.argv[2], + sys.argv[3], + sys.argv[4], + sys.argv[5], + sys.argv[6], + sys.argv[7], + sys.argv[8]) From 104625b1474199b00589a3a29566b1c8ea8c7060 Mon Sep 17 00:00:00 2001 From: Alberto Cereser Date: Tue, 16 May 2017 13:07:48 +0200 Subject: [PATCH 018/150] Tip added How to install mpi4py --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index f95df83..2c4ec24 100644 --- a/README.md +++ b/README.md @@ -18,6 +18,8 @@ Future versions will be able to do reconstruction based on strain datasets. Running Recon3D requires having Miniconda2 and Fable on your Panda2 account. To install the packages, run the script available [here](https://github.com/acjak/fable-install). If you get an error message and need to change a permission path, check [this tip](http://stackoverflow.com/questions/35246386/conda-command-not-found). +Running parallelised code on Panda requires using *mpi4py*. The command to install it is *conda install mpi4py* + ## Reading data from .edf files to a NumPy array The script getdata.py reads data from .edf files and outputs a NumPy array with the dimensions *tilt1*-steps x *tilt2*-steps x *omega*-steps x *img_xlen* x *img_ylen*, where *tilt1* and *tilt2* are the two top tilts in the LAB goniometer at ID06. *Omega* is the topo-tomo rotation stage. The command to create the array is the following: From b31002a2700f355261fbae50e535615a51330ba3 Mon Sep 17 00:00:00 2001 From: albusdemens Date: Tue, 16 May 2017 17:51:42 +0200 Subject: [PATCH 019/150] Plot alpha, beta distr for an omega --- Plot_data_raw.py | 58 ++++++++++++++++++++++++++++++++++++++++-------- 1 file changed, 49 insertions(+), 9 deletions(-) diff --git a/Plot_data_raw.py b/Plot_data_raw.py index a1f135f..662f474 100644 --- a/Plot_data_raw.py +++ b/Plot_data_raw.py @@ -8,19 +8,59 @@ # Plot distribution of motor values alpha = Data[:,0] beta = Data[:,1] +omega = Data[:,2] -plt.figure(1) +# Print all rotation angles +print(np.sort(list(set(omega)))) + +# Plot rotation angles +#plt.figure() +#plt.plot(Data[1:200, 2]) +#plt.xlabel('Acquistion number') +#plt.ylabel('Degrees') +#plt.show() + +# Select the omega you want to consider +# Lower = 1.6 because of strange behaviour at 0.8 +Om = 179.2 # Values: 1.6, 30.4, 60., 90.4, 120., 150.4, 179.2 + +idx = np.where(omega==Om) +alpha_select = alpha[idx] +beta_select = beta[idx] + +print idx +print alpha_select +print beta_select + +# For the selected omega, plot the angular distribution +fig = plt.figure(figsize=(12,9)) + +plt.rc('text', usetex=True) +plt.rc('font', family='serif') + +fig.suptitle(r'$\alpha$ and $\beta$ values for $\omega = 179.2^{\circ}$', fontsize=20) + +label_size = 16 +plt.rcParams['xtick.labelsize'] = label_size +plt.rcParams['ytick.labelsize'] = label_size plt.subplot(121) -plt.plot(alpha) -plt.xlabel('Acquisition number') -plt.ylabel('Alpha (in degrees)') -plt.title('Distribution of alpha angles') +plt.plot(alpha_select) +plt.xlabel(r'Acquisition number', fontsize=20) +plt.ylabel(r'Degrees', fontsize=20) +plt.title(r'Distribution of $\alpha$ angles', fontsize=20) plt.subplot(122) -plt.plot(beta) -plt.xlabel('Acquisition number') -plt.ylabel('Beta (in degrees)') -plt.title('Distribution of beta angles') +plt.plot(beta_select) +plt.xlabel(r'Acquisition number', fontsize=20) +#plt.ylabel(r'Degrees', fontsize=20) +plt.title(r'Distribution of $\beta$ angles', fontsize=20) + +plt.show() +fig = plt.figure(figsize=(8,6)) +plt.plot(alpha_select, beta_select) +plt.title(r'$\alpha$ and $\beta$ values for $\omega=179.2^{\circ}$', fontsize=20) +plt.xlabel(r'$\alpha$ ($^{\circ}$)', fontsize=20) +plt.ylabel(r'$\beta$ ($^{\circ}$)', fontsize=20) plt.show() From 916719e3baa281843a0295b8b9de008f3494aa95 Mon Sep 17 00:00:00 2001 From: albusdemens Date: Thu, 18 May 2017 10:30:29 +0200 Subject: [PATCH 020/150] Ud detector flip already in original code --- recon3d.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/recon3d.py b/recon3d.py index ad7de9c..97a4ae2 100644 --- a/recon3d.py +++ b/recon3d.py @@ -342,8 +342,7 @@ def build_rotation_lookup_general(self): print('Multiply all matrices') # Let's multiply all the matrices. flipud takes into account the # vertical flip introduced by the lens system - T_s2d = self.par['M'] * np.flipud( - np.matmul( + T_s2d = self.par['M'] * np.matmul( T_det, np.matmul( Tinv_th, @@ -363,7 +362,7 @@ def build_rotation_lookup_general(self): Tinv_up, np.matmul( R_up, - T_up))))))))))) + T_up)))))))))) return T_s2d From f980fc995f8ab96774610769288147c12c8d4dbc Mon Sep 17 00:00:00 2001 From: albusdemens Date: Thu, 18 May 2017 14:01:55 +0200 Subject: [PATCH 021/150] Added (omega, theta, eta) function --- recon3d.py | 121 +++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 121 insertions(+) diff --git a/recon3d.py b/recon3d.py index 97a4ae2..0843d5c 100644 --- a/recon3d.py +++ b/recon3d.py @@ -366,6 +366,127 @@ def build_rotation_lookup_general(self): return T_s2d + +def build_rotation_lookup_eta_mu(self): + """ + Set up the rotation_lookup[theta,omega,mu] lookup table of + rotation matrices for each value in the theta, omega and mu arrays. + + This general version incorporates the possibility that the focus points of + phi_up, phi_lo and/or theta do not coincide with the intersection of the + direct beam and the rotation axis, which is the commonly defined center. + + Takes (x_s,y_s,z_s,1) and converts to (x_r,0,z_r,1) by a 4x4 matrix + taking rotations and translations of beam centers into account. + + xyz_up, xyz_lo, xyz_th should be the coordinates (in microns) of the focus + point on the rotation axis, eg yxz_up=[-40,0,0] in horizontal geometry. + """ + + om = np.pi * self.omega / 180. + # th = np.pi * self.par['theta'] / 180. + th = np.pi * self.omega / 180. + eta = np.pi * self.eta / 180. + + try: + t_xx = np.pi * self.par['t_x'] / 180. + t_yy = np.pi * self.par['t_y'] / 180. + t_zz = np.pi * self.par['t_z'] / 180. + except: + if self.rank == 0: + print "No detector tilt" + self.par['t_x'] = "None" + self.par['t_z'] = "None" + + th_mat, om_mat, eta_mat = np.meshgrid(th, om, eta, indexing='ij') + + print('Introduce matrices') + Eta = np.zeros((len(th), len(om), len(eta), 4, 4)) + Omega = np.zeros((len(th), len(om), len(eta), 4, 4)) + Theta = np.zeros((len(th), len(om), len(eta), 4, 4)) + T_det = np.zeros((len(th), len(om), len(eta), 4, 4)) + T_eta = np.zeros((len(th), len(om), len(eta), 4, 4)) + Tinv_eta = np.zeros((len(th), len(om), len(eta), 4, 4)) + + # The default detector tilt is the unit matrix, i.e. an ideal detector + # positioned perpendicular to the diffracted beam (t_x=t=y=t_z=None). + # This can be changed by supplying tilts t_x (vertical) or t_z (horizontal). + T_det[:, :, :, :, 0, 0] = -1. + # T_det[:, :, :, :, 1, 1] = 1. #leaving T_det[:, :, :, :, 1, 1]=0 + # gives the projection onto the detector plane + T_det[:, :, :, :, 2, 2] = -1. + T_det[:, :, :, :, 3, 3] = 1. + + # print self.par['xyz_up'] + # 4x4 according + # to http://inside.mines.edu/fs_home/gmurray/ArbitraryAxisRotation/ + + T_th[:, :, :, :, 0:3, 3] = -np.array(self.par['xyz_th']) + T_th[:, :, :, :, 0, 0] = 1. + T_th[:, :, :, :, 1, 1] = 1. + T_th[:, :, :, :, 2, 2] = 1. + T_th[:, :, :, :, 3, 3] = 1. + Tinv_th[:, :, :, :, 0:3, 3] = np.array(self.par['xyz_th']) + Tinv_th[:, :, :, :, 0, 0] = 1. + Tinv_th[:, :, :, :, 1, 1] = 1. + Tinv_th[:, :, :, :, 2, 2] = 1. + Tinv_th[:, :, :, :, 3, 3] = 1. + T_eta[:, :, :, :, 0:3, 3] = -np.array(self.par['xyz_th']) + T_eta[:, :, :, :, 0, 0] = 1. + T_eta[:, :, :, :, 1, 1] = 1. + T_eta[:, :, :, :, 2, 2] = 1. + T_eta[:, :, :, :, 3, 3] = 1. + Tinv_eta[:, :, :, :, 0:3, 3] = np.array(self.par['xyz_th']) + Tinv_eta[:, :, :, :, 0, 0] = 1. + Tinv_eta[:, :, :, :, 1, 1] = 1. + Tinv_eta[:, :, :, :, 2, 2] = 1. + Tinv_eta[:, :, :, :, 3, 3] = 1. + + print('Define horizontal mode') + if self.par['mode'] == "horizontal": + Theta[:, :, :, :, 0, 0] = np.cos(th_mat) + Theta[:, :, :, :, 0, 1] = -np.sin(th_mat) + Theta[:, :, :, :, 1, 0] = np.sin(th_mat) + Theta[:, :, :, :, 1, 1] = np.cos(th_mat) + Theta[:, :, :, :, 2, 2] = 1. + Theta[:, :, :, :, 3, 3] = 1. + Omega[:, :, :, :, 0, 0] = 1. + Omega[:, :, :, :, 1, 1] = np.cos(om_mat) + Omega[:, :, :, :, 1, 2] = -np.sin(om_mat) + Omega[:, :, :, :, 2, 1] = np.sin(om_mat) + Omega[:, :, :, :, 2, 2] = np.cos(om_mat) + Omega[:, :, :, :, 3, 3] = 1. + Eta[:, :, :, :, 0, 0] = np.cos(eta_mat) + Eta[:, :, :, :, 0, 2] = np.sin(eta_mat) + Eta[:, :, :, :, 1, 1] = 1 + Eta[:, :, :, :, 2, 0] = -np.sin(eta_mat) + Eta[:, :, :, :, 2, 2] = np.cos(eta_mat) + Eta[:, :, :, :, 3, 3] = 1. + if self.par['t_z'] != "None": + T_det[:, :, :, :, 0, 0] = -1. / np.cos(t_zz - 2 * np.mean(th)) + else: + print "ERROR: scattering geometry not defined" + + print('Multiply all matrices') + # Let's multiply all the matrices. flipud takes into account the + # vertical flip introduced by the lens system + T_s2d = self.par['M'] * np.matmul( + T_det, + np.matmul( + Tinv_th, + np.matmul( + Theta, + np.matmul( + T_th, + np.matmul( + Tinv_eta, + np.matmul( + Eta, + np.matmul( + T_eta, Omega))))))) + + return T_s2d + def outputfiles(self, grain_ang): print "Saving grain_ang file..." np.save(self.par['path'] + '/grain_ang.npy', grain_ang) From 163455e4ab489ac8bc79d330b975472a5b08288f Mon Sep 17 00:00:00 2001 From: albusdemens Date: Wed, 10 May 2017 11:34:44 +0200 Subject: [PATCH 022/150] Included image flip introduced by the lens system --- Plot_EDF.py | 14 +++++++++++++- recon3d.py | 27 +++++++++++++++------------ 2 files changed, 28 insertions(+), 13 deletions(-) diff --git a/Plot_EDF.py b/Plot_EDF.py index 34e8efb..6cbc4e2 100644 --- a/Plot_EDF.py +++ b/Plot_EDF.py @@ -7,5 +7,17 @@ im = EF.EdfFile(sys.argv[1]) image = im.GetData(0) -plt.imshow(image) +im_flip = np.fliplr(image) + +fig = plt.figure() +a=fig.add_subplot(1,2,1) +imgplot = plt.imshow(image) +a.set_title('Image') +a=fig.add_subplot(1,2,2) +imgplot = plt.imshow(im_flip) +a.set_title('Flipped image') + +#plt.imshow(image) plt.show() +#plt.imshow(im_flip) +#plt.show() diff --git a/recon3d.py b/recon3d.py index ef4d0bd..5b8154a 100644 --- a/recon3d.py +++ b/recon3d.py @@ -337,27 +337,30 @@ def build_rotation_lookup_general(self): else: print "ERROR: scattering geometry not defined" - T_s2d = self.par['M'] * np.matmul( - T_det, + # Let's multiply all the matrices. flipud takes into account the + # vertical flip introduced by the lens system + T_s2d = self.par['M'] * np.flipud( np.matmul( - Tinv_th, + T_det, np.matmul( - Theta, + Tinv_th, np.matmul( - T_th, + Theta, np.matmul( - Omega, + T_th, np.matmul( - Tinv_lo, + Omega, np.matmul( - R_lo, + Tinv_lo, np.matmul( - T_lo, + R_lo, np.matmul( - Tinv_up, + T_lo, np.matmul( - R_up, - T_up)))))))))) + Tinv_up, + np.matmul( + R_up, + T_up))))))))))) return T_s2d def outputfiles(self, grain_ang): From d0da2f49fba4d23fbd70626e49401f10c87d865b Mon Sep 17 00:00:00 2001 From: albusdemens Date: Wed, 10 May 2017 11:35:46 +0200 Subject: [PATCH 023/150] Set off option to compare original and flipped image --- Plot_EDF.py | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/Plot_EDF.py b/Plot_EDF.py index 6cbc4e2..22110cd 100644 --- a/Plot_EDF.py +++ b/Plot_EDF.py @@ -7,17 +7,17 @@ im = EF.EdfFile(sys.argv[1]) image = im.GetData(0) -im_flip = np.fliplr(image) +#im_flip = np.fliplr(image) -fig = plt.figure() -a=fig.add_subplot(1,2,1) -imgplot = plt.imshow(image) -a.set_title('Image') -a=fig.add_subplot(1,2,2) -imgplot = plt.imshow(im_flip) -a.set_title('Flipped image') +#fig = plt.figure() +#a=fig.add_subplot(1,2,1) +#imgplot = plt.imshow(image) +#a.set_title('Image') +#a=fig.add_subplot(1,2,2) +#imgplot = plt.imshow(im_flip) +#a.set_title('Flipped image') -#plt.imshow(image) +plt.imshow(image) plt.show() #plt.imshow(im_flip) #plt.show() From c16256f3db58971abae1dcf5033fcadd5650b2f2 Mon Sep 17 00:00:00 2001 From: albusdemens Date: Tue, 23 May 2017 12:38:41 +0200 Subject: [PATCH 024/150] Clean --- Sunday_data.ini | 37 +++++++++++++++++++++++++++++++++++++ getdata.py | 29 ++++++++++++++++++++++++++++- lib/miniged.py | 5 +++++ 3 files changed, 70 insertions(+), 1 deletion(-) create mode 100644 Sunday_data.ini diff --git a/Sunday_data.ini b/Sunday_data.ini new file mode 100644 index 0000000..61ab893 --- /dev/null +++ b/Sunday_data.ini @@ -0,0 +1,37 @@ +sg_no 225 +unit_cell [4.,4.,4.,90.,90.,90.] +hkl [2,0,0] +wavelength 0.73 +grain_pos [0,0,0] +grain_dim [11.,11.,11.] +grain_steps [11,11,11] +ang_mean [0,0] +ang_width [.01,.01] +#image simulation +# ang_steps [2,2,18] +# ang_types ["phi_upper","phi_lower","Omega"] +# ang_min [0,0,-85.] +# ang_max [.01,.01,85.] +# detx_size 20 +# detz_size 30 +M 1. +t_x "None" +t_y 0 +t_z 0 +path "/u/data/andcj/tmp/testdir1" # Data source +format "edf" +mode "horizontal" +xyz_up [0,0,0] +xyz_lo [0,0,0] +xyz_th [0,0,0] + +# box simulations +# grain_dim [18.,18.,20.] +# grain_steps [18,18,20] +# ang_mean [0,0] +# ang_width [.01,.01] +# ang_steps [3,3,18] +# ang_min [-.01,-.01,-85.] +# ang_max [.01,.01,85.] # test +# stem "../test/box" +#completeness_cutoff 0.5 diff --git a/getdata.py b/getdata.py index 92012c4..a53d04a 100644 --- a/getdata.py +++ b/getdata.py @@ -4,6 +4,7 @@ import os import warnings import numpy as np +import pdb # For debugging try: from mpi4py import MPI @@ -52,6 +53,7 @@ def __init__( int(int(poi[1]) + int(imgsize[1]) / 2)] data = GetEdfData(datadir, dataname, bgpath, bgfilename, roi, sim) +<<<<<<< d0da2f49fba4d23fbd70626e49401f10c87d865b self.alpha, self.beta, self.omega, self.theta = data.getMetaValues() self.index_list = range(len(data.meta)) @@ -61,6 +63,23 @@ def __init__( self.calcTheta(data) # self.calcEtaIndexList(data, eta) +======= + self.alpha, self.beta, self.omega = data.getMetaValues() # Redefine + min_alpha = min(self.alpha) + max_alpha = max(self.alpha) + min_beta = min(self.beta) + max_beta = max(self.beta) + print min_alpha, max_alpha, min_beta, max_beta + + pdb.set_trace() + + print self.alpha + print self.beta + print self.omega + print len(self.alpha) + print len(self.beta) + print len(self.omega) +>>>>>>> Binning angular values (irregular motor steps) self.allFiles(data, imgsize) if self.rank == 0: @@ -124,10 +143,18 @@ def allFiles(self, data, imsiz): imgarray = data.makeImgArray(self.index_list, 50, 'linetrace') if self.rank == 0: +<<<<<<< d0da2f49fba4d23fbd70626e49401f10c87d865b # lena = len(self.theta) lena = len(self.thetafake) lenb = len(self.eta) +======= + lena = 7#len(self.alpha) + lenb = 7#len(self.beta) +>>>>>>> Binning angular values (irregular motor steps) leno = len(self.omega) + #lena = len(self.alpha) + #lenb = len(self.beta) + #leno = len(self.omega) bigarray = np.zeros((lena, lenb, leno, int(imsiz[1]), int(imsiz[0])), dtype=np.uint16) @@ -181,4 +208,4 @@ def allFiles(self, data, imsiz): sys.argv[5], sys.argv[6], sys.argv[7], - sys.argv[8],) + sys.argv[8]) diff --git a/lib/miniged.py b/lib/miniged.py index 0b95221..78eb3f5 100644 --- a/lib/miniged.py +++ b/lib/miniged.py @@ -194,6 +194,10 @@ def getHeader(self, filenumber): detpos_array = [] try: +<<<<<<< d0da2f49fba4d23fbd70626e49401f10c87d865b +======= + #print header['machine current'].split(' ') +>>>>>>> Binning angular values (irregular motor steps) srcur = float(header['machine current'].split(' ')[0]) except KeyError: srcur = 0 @@ -250,6 +254,7 @@ def makeMetaArrayNew(self): mot_array, motpos_array, det_array, detpos_array, srcur = self.getHeader(i) + # Option: bin data self.meta[i, 0] = round(float(motpos_array[mot_array.index('phi')]), 8) self.meta[i, 1] = round(float(motpos_array[mot_array.index('chi')]), 8) self.meta[i, 2] = round(float(motpos_array[mot_array.index('diffry')]), 8) From 07d1be2f257abbb35ca22caf7b047bd315eebf97 Mon Sep 17 00:00:00 2001 From: albusdemens Date: Thu, 11 May 2017 17:15:26 +0200 Subject: [PATCH 025/150] Binning angular values --- find_nearest.py | 7 +++++++ getdata.py | 18 +++++++++++++----- 2 files changed, 20 insertions(+), 5 deletions(-) create mode 100644 find_nearest.py diff --git a/find_nearest.py b/find_nearest.py new file mode 100644 index 0000000..aeaedbd --- /dev/null +++ b/find_nearest.py @@ -0,0 +1,7 @@ +# Simple module to calculate which array element is the closest to a value + +import numpy as np + +def find_nearest(array,value): + idx = (np.abs(array-value)).argmin() + return array[idx] diff --git a/getdata.py b/getdata.py index a53d04a..0c9e3f6 100644 --- a/getdata.py +++ b/getdata.py @@ -5,6 +5,7 @@ import warnings import numpy as np import pdb # For debugging +from find_nearest import find_nearest try: from mpi4py import MPI @@ -65,12 +66,19 @@ def __init__( ======= self.alpha, self.beta, self.omega = data.getMetaValues() # Redefine - min_alpha = min(self.alpha) - max_alpha = max(self.alpha) - min_beta = min(self.beta) - max_beta = max(self.beta) - print min_alpha, max_alpha, min_beta, max_beta + # Alpha and beta values were measured at irregular steps. To make things + # easy, we bin the values in 7 intervals per variable + [count_alpha, extremes_alpha] = np.histogram(self.alpha, 7) + val_alpha = np.zeros(len(extremes_alpha)-1) + + # Find center of each bin + for i in range(0,len(extremes_alpha)-1): + val_alpha[i] = np.mean([extremes_alpha[i], extremes_alpha[i+1]]) + + # For each experimental value, find closest bin centre + + print find_nearest(val_alpha, self.alpha[1]) pdb.set_trace() print self.alpha From ab828f9b3dd74e7b18bd4bab66d350b79162be31 Mon Sep 17 00:00:00 2001 From: albusdemens Date: Fri, 12 May 2017 14:53:37 +0200 Subject: [PATCH 026/150] Plot npy files from getdata.py --- Plot_npy_alpha_beta.py | 43 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 43 insertions(+) create mode 100644 Plot_npy_alpha_beta.py diff --git a/Plot_npy_alpha_beta.py b/Plot_npy_alpha_beta.py new file mode 100644 index 0000000..5b51323 --- /dev/null +++ b/Plot_npy_alpha_beta.py @@ -0,0 +1,43 @@ +# Script to visualize the files returned by getdata.py +# Command: Plot_results.py path/Input_folder +# Where the Input_folder is the folder with the npy files + +import numpy as np +import sys +import os +import matplotlib.pyplot as plt + +''' +Input : directory of data +''' +dir = sys.argv[1] + +alpha = np.load(os.path.join(dir,'alpha.npy')) +beta = np.load(os.path.join(dir,'beta.npy')) + + +plt.figure(1) + +plt.subplot(221) +plt.plot(alpha) +plt.xlabel('Acquisition number') +plt.ylabel('Alpha (in degrees)') +plt.title('Distribution of alpha angles') + +plt.subplot(222) +plt.plot(beta) +plt.xlabel('Acquisition number') +plt.ylabel('Beta (in degrees)') +plt.title('Distribution of beta angles') + +plt.subplot(223) +plt.plot(alpha) +plt.xlabel('Acquisition number') +plt.ylabel('Alpha (in degrees)') + +plt.subplot(224) +plt.plot(beta) +plt.xlabel('Acquisition number') +plt.ylabel('Beta (in degrees)') + +plt.show() From a79556b5255b58f0914a7c4dcc90194ffe89b017 Mon Sep 17 00:00:00 2001 From: albusdemens Date: Fri, 12 May 2017 14:54:01 +0200 Subject: [PATCH 027/150] Adapt the code to continuous angle distribution --- getdata.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/getdata.py b/getdata.py index 0c9e3f6..2c2c62b 100644 --- a/getdata.py +++ b/getdata.py @@ -77,7 +77,7 @@ def __init__( val_alpha[i] = np.mean([extremes_alpha[i], extremes_alpha[i+1]]) # For each experimental value, find closest bin centre - + print find_nearest(val_alpha, self.alpha[1]) pdb.set_trace() From 1a1a2e20d633666f498d5bab0d25d25753fa5c5c Mon Sep 17 00:00:00 2001 From: albusdemens Date: Sat, 13 May 2017 09:19:07 +0200 Subject: [PATCH 028/150] Script to plot alpha, beta distr now ready to use --- Plot_npy_alpha_beta.py | 18 ++++++++++++------ 1 file changed, 12 insertions(+), 6 deletions(-) diff --git a/Plot_npy_alpha_beta.py b/Plot_npy_alpha_beta.py index 5b51323..937822b 100644 --- a/Plot_npy_alpha_beta.py +++ b/Plot_npy_alpha_beta.py @@ -10,12 +10,18 @@ ''' Input : directory of data ''' + +plt.close("all") + dir = sys.argv[1] alpha = np.load(os.path.join(dir,'alpha.npy')) beta = np.load(os.path.join(dir,'beta.npy')) +ar = range(len(alpha)) +br = range(len(beta)) +# Plot distribution of motor values plt.figure(1) plt.subplot(221) @@ -31,13 +37,13 @@ plt.title('Distribution of beta angles') plt.subplot(223) -plt.plot(alpha) -plt.xlabel('Acquisition number') -plt.ylabel('Alpha (in degrees)') +plt.plot(alpha, ar) +plt.xlabel('Alpha (in degrees)') +plt.ylabel('Acquisition number') plt.subplot(224) -plt.plot(beta) -plt.xlabel('Acquisition number') -plt.ylabel('Beta (in degrees)') +plt.plot(beta, br) +plt.xlabel('Beta (in degrees)') +plt.ylabel('Acquisition number') plt.show() From e7c88c2fa162255650103883472511ea23268622 Mon Sep 17 00:00:00 2001 From: albusdemens Date: Sat, 13 May 2017 10:39:58 +0200 Subject: [PATCH 029/150] Added functionality to plot center of hist values used to group data --- Plot_npy_alpha_beta.py | 38 +++++++++++++++++++++++++++++++++++++- find_nearest.py | 4 ++++ getdata.py | 4 ++-- 3 files changed, 43 insertions(+), 3 deletions(-) diff --git a/Plot_npy_alpha_beta.py b/Plot_npy_alpha_beta.py index 937822b..e650ede 100644 --- a/Plot_npy_alpha_beta.py +++ b/Plot_npy_alpha_beta.py @@ -6,6 +6,7 @@ import sys import os import matplotlib.pyplot as plt +from find_nearest import find_nearest, find_nearest_idx ''' Input : directory of data @@ -18,31 +19,66 @@ alpha = np.load(os.path.join(dir,'alpha.npy')) beta = np.load(os.path.join(dir,'beta.npy')) +# Find position of the center of the bins used to group the data +[count_alpha, extremes_alpha] = np.histogram(alpha, 7) +val_alpha = np.zeros(len(extremes_alpha)-1) + +[count_beta, extremes_beta] = np.histogram(beta, 7) +val_beta = np.zeros(len(extremes_beta)-1) + +# Find center of each bin +for i in range(0,len(extremes_alpha)-1): + val_alpha[i] = np.mean([extremes_alpha[i], extremes_alpha[i+1]]) +for i in range(0,len(extremes_beta)-1): + val_beta[i] = np.mean([extremes_beta[i], extremes_beta[i+1]]) + +# Find experimental value closest to bin centre, which will be plotted +u = np.zeros((len(val_alpha), 2)) +for j in range(0,len(val_alpha)): + u[j,0] = find_nearest(alpha,val_alpha[j]) + u[j,1] = find_nearest_idx(alpha,val_alpha[j]) + +v = np.zeros((len(val_beta), 2)) +for j in range(0,len(val_alpha)): + v[j,0] = find_nearest(beta,val_beta[j]) + v[j,1] = find_nearest_idx(beta,val_beta[j]) + +print u +print v + +# Plot distribution of motor values ar = range(len(alpha)) br = range(len(beta)) -# Plot distribution of motor values plt.figure(1) plt.subplot(221) plt.plot(alpha) +for i in range(0,len(val_alpha)): + plt.scatter(u[i,1], u[i,0]) plt.xlabel('Acquisition number') plt.ylabel('Alpha (in degrees)') plt.title('Distribution of alpha angles') plt.subplot(222) plt.plot(beta) +for i in range(0,len(val_beta)): + plt.scatter(v[i,1], v[i,0]) plt.xlabel('Acquisition number') plt.ylabel('Beta (in degrees)') plt.title('Distribution of beta angles') plt.subplot(223) plt.plot(alpha, ar) +for i in range(0,len(val_alpha)): + plt.scatter(u[i,0], u[i,1]) plt.xlabel('Alpha (in degrees)') plt.ylabel('Acquisition number') plt.subplot(224) plt.plot(beta, br) +for i in range(0,len(val_beta)): + plt.scatter(v[i,0], v[i,1]) plt.xlabel('Beta (in degrees)') plt.ylabel('Acquisition number') diff --git a/find_nearest.py b/find_nearest.py index aeaedbd..04b7bb0 100644 --- a/find_nearest.py +++ b/find_nearest.py @@ -5,3 +5,7 @@ def find_nearest(array,value): idx = (np.abs(array-value)).argmin() return array[idx] + +def find_nearest_idx(array,value): + idx = (np.abs(array-value)).argmin() + return idx diff --git a/getdata.py b/getdata.py index 2c2c62b..4513faa 100644 --- a/getdata.py +++ b/getdata.py @@ -5,7 +5,7 @@ import warnings import numpy as np import pdb # For debugging -from find_nearest import find_nearest +from find_nearest import find_nearest, find_nearest_idx try: from mpi4py import MPI @@ -78,7 +78,7 @@ def __init__( # For each experimental value, find closest bin centre - print find_nearest(val_alpha, self.alpha[1]) + print extremes_alpha pdb.set_trace() print self.alpha From 939932454d6d654ead2b9aaead398e6d08cce97a Mon Sep 17 00:00:00 2001 From: albusdemens Date: Sat, 13 May 2017 11:09:10 +0200 Subject: [PATCH 030/150] Added hist plot for alpha, beta --- Plot_npy_alpha_beta.py | 14 +++++++++++--- getdata.py | 4 +++- 2 files changed, 14 insertions(+), 4 deletions(-) diff --git a/Plot_npy_alpha_beta.py b/Plot_npy_alpha_beta.py index e650ede..9c10aa1 100644 --- a/Plot_npy_alpha_beta.py +++ b/Plot_npy_alpha_beta.py @@ -43,9 +43,6 @@ v[j,0] = find_nearest(beta,val_beta[j]) v[j,1] = find_nearest_idx(beta,val_beta[j]) -print u -print v - # Plot distribution of motor values ar = range(len(alpha)) br = range(len(beta)) @@ -82,4 +79,15 @@ plt.xlabel('Beta (in degrees)') plt.ylabel('Acquisition number') +# Plot hist showing how angular values are distributed in the different bins +plt.figure(2) + +plt.subplot(121) +plt.hist(alpha, 7) +plt.title('Binning of alpha values') + +plt.subplot(122) +plt.hist(beta, 7) +plt.title('Binning of beta values') + plt.show() diff --git a/getdata.py b/getdata.py index 4513faa..193ff6c 100644 --- a/getdata.py +++ b/getdata.py @@ -77,8 +77,10 @@ def __init__( val_alpha[i] = np.mean([extremes_alpha[i], extremes_alpha[i+1]]) # For each experimental value, find closest bin centre + alpha_discr = np.zeros(len(self.alpha)) + for i in range(0, len(self.alpha)): + alpha_discr[i] = find_nearest(val_alpha,self.alpha[i]) - print extremes_alpha pdb.set_trace() print self.alpha From 7d3ddaa5f1c05db70937a2db71d8cc4650abc6cb Mon Sep 17 00:00:00 2001 From: albusdemens Date: Sat, 13 May 2017 11:23:56 +0200 Subject: [PATCH 031/150] Done with data binning --- getdata.py | 18 ++++++++++++++++++ 1 file changed, 18 insertions(+) diff --git a/getdata.py b/getdata.py index 193ff6c..e113a54 100644 --- a/getdata.py +++ b/getdata.py @@ -71,25 +71,35 @@ def __init__( # easy, we bin the values in 7 intervals per variable [count_alpha, extremes_alpha] = np.histogram(self.alpha, 7) val_alpha = np.zeros(len(extremes_alpha)-1) + [count_beta, extremes_beta] = np.histogram(self.beta, 7) + val_beta = np.zeros(len(extremes_beta)-1) # Find center of each bin for i in range(0,len(extremes_alpha)-1): val_alpha[i] = np.mean([extremes_alpha[i], extremes_alpha[i+1]]) + for j in range(0,len(extremes_beta)-1): + val_beta[j] = np.mean([extremes_beta[j], extremes_beta[j+1]]) # For each experimental value, find closest bin centre alpha_discr = np.zeros(len(self.alpha)) for i in range(0, len(self.alpha)): alpha_discr[i] = find_nearest(val_alpha,self.alpha[i]) + beta_discr = np.zeros(len(self.beta)) + for j in range(0, len(self.beta)): + beta_discr[j] = find_nearest(val_beta,self.beta[j]) pdb.set_trace() print self.alpha print self.beta print self.omega +<<<<<<< 939932454d6d654ead2b9aaead398e6d08cce97a print len(self.alpha) print len(self.beta) print len(self.omega) >>>>>>> Binning angular values (irregular motor steps) +======= +>>>>>>> Done with data binning self.allFiles(data, imgsize) if self.rank == 0: @@ -153,6 +163,7 @@ def allFiles(self, data, imsiz): imgarray = data.makeImgArray(self.index_list, 50, 'linetrace') if self.rank == 0: +<<<<<<< 939932454d6d654ead2b9aaead398e6d08cce97a <<<<<<< d0da2f49fba4d23fbd70626e49401f10c87d865b # lena = len(self.theta) lena = len(self.thetafake) @@ -161,7 +172,14 @@ def allFiles(self, data, imsiz): lena = 7#len(self.alpha) lenb = 7#len(self.beta) >>>>>>> Binning angular values (irregular motor steps) +======= + # If we need to bin the angular values + lena = len(alpha_discr) + lenb = len(beta_discr) +>>>>>>> Done with data binning leno = len(self.omega) + + # If we don't need to bin the angular values #lena = len(self.alpha) #lenb = len(self.beta) #leno = len(self.omega) From 351ff059702f79d3f15dfaa9f39b6cb4254781ea Mon Sep 17 00:00:00 2001 From: albusdemens Date: Tue, 23 May 2017 12:47:31 +0200 Subject: [PATCH 032/150] Upstream sync --- Sunday_data.ini | 4 ++-- View_reconstruction.py | 18 ++++++++++++++++++ 2 files changed, 20 insertions(+), 2 deletions(-) create mode 100644 View_reconstruction.py diff --git a/Sunday_data.ini b/Sunday_data.ini index 61ab893..13e4337 100644 --- a/Sunday_data.ini +++ b/Sunday_data.ini @@ -3,8 +3,8 @@ unit_cell [4.,4.,4.,90.,90.,90.] hkl [2,0,0] wavelength 0.73 grain_pos [0,0,0] -grain_dim [11.,11.,11.] -grain_steps [11,11,11] +grain_dim [101.,101.,101.] +grain_steps [101,101,101] ang_mean [0,0] ang_width [.01,.01] #image simulation diff --git a/View_reconstruction.py b/View_reconstruction.py new file mode 100644 index 0000000..9b4336c --- /dev/null +++ b/View_reconstruction.py @@ -0,0 +1,18 @@ +# Visualize the sample volume reconstructed by recon3D.py + +import numpy as np +import sys +import os +import matplotlib.pyplot as plt +import mayavi.mlab as mlab # Idea from http://stackoverflow.com/questions/10755060/plot-a-cube-of-3d-intensity-data + +''' +Input : directory of data +''' + +plt.close("all") + +dir = sys.argv[1] +Data = np.load(os.path.join(dir,'grain_ang.npy')) + +print Data From 049b3ec0b8648c2e12d841b1deec4a93235fa15a Mon Sep 17 00:00:00 2001 From: albusdemens Date: Tue, 23 May 2017 13:02:01 +0200 Subject: [PATCH 033/150] Resolved merge conflict --- Plot_data_raw.py | 64 ++++++++++++++++++++++++++++++++++++++++++++++++ Sunday_data.ini | 15 ++++++------ getdata.py | 21 +++------------- 3 files changed, 76 insertions(+), 24 deletions(-) create mode 100644 Plot_data_raw.py diff --git a/Plot_data_raw.py b/Plot_data_raw.py new file mode 100644 index 0000000..45b6d75 --- /dev/null +++ b/Plot_data_raw.py @@ -0,0 +1,64 @@ +# Plot, for a given omega, angular values before binning + +import numpy as np +import matplotlib.pyplot as plt + +Data = np.load('/Users/Alberto/Documents/Data_analysis/DFXRM/Results_monday/All_data.npy') + +# Plot distribution of motor values +alpha = Data[:,0] +beta = Data[:,1] +omega = Data[:,2] + +# Print all rotation angles +print(np.sort(list(set(omega)))) + +# Plot rotation angles +#plt.figure() +#plt.plot(Data[1:200, 2]) +#plt.xlabel('Acquistion number') +#plt.ylabel('Degrees') +#plt.show() + +# Select the omega you want to consider +# Sundaynight values: 1.6, 30.4, 60., 90.4, 120., 150.4, 179.2 +# Monday values: 1.6, 30.4, 60., 89.6, 120., 150.4, 178.4 +# Lower = 1.6 because of strange behaviour at 0.8 +Om = 178.4 + +idx = np.where(omega==Om) +alpha_select = alpha[idx] +beta_select = beta[idx] + +# For the selected omega, plot the angular distribution +fig = plt.figure(figsize=(12,9)) + +plt.rc('text', usetex=True) +plt.rc('font', family='serif') + +fig.suptitle(r'$\alpha$ and $\beta$ values for $\omega = 179.2^{\circ}$', fontsize=20) + +label_size = 16 +plt.rcParams['xtick.labelsize'] = label_size +plt.rcParams['ytick.labelsize'] = label_size + +plt.subplot(121) +plt.plot(alpha_select) +plt.xlabel(r'Acquisition number', fontsize=20) +plt.ylabel(r'Degrees', fontsize=20) +plt.title(r'Distribution of $\alpha$ angles', fontsize=20) + +plt.subplot(122) +plt.plot(beta_select) +plt.xlabel(r'Acquisition number', fontsize=20) +#plt.ylabel(r'Degrees', fontsize=20) +plt.title(r'Distribution of $\beta$ angles', fontsize=20) + +plt.show() + +fig = plt.figure(figsize=(8,6)) +plt.plot(alpha_select, beta_select, '*') +plt.title(r'$\alpha$ and $\beta$ values for $\omega=179.2^{\circ}$', fontsize=20) +plt.xlabel(r'$\alpha$ ($^{\circ}$)', fontsize=20) +plt.ylabel(r'$\beta$ ($^{\circ}$)', fontsize=20) +plt.show() diff --git a/Sunday_data.ini b/Sunday_data.ini index 13e4337..3314bb8 100644 --- a/Sunday_data.ini +++ b/Sunday_data.ini @@ -1,10 +1,10 @@ -sg_no 225 -unit_cell [4.,4.,4.,90.,90.,90.] +sg_no 225 # Space group +unit_cell [4.0495,4.0495,4.0495,90.,90.,90.] hkl [2,0,0] -wavelength 0.73 +wavelength 0.7293 # Corresponds to an energy of 17 keV grain_pos [0,0,0] -grain_dim [101.,101.,101.] -grain_steps [101,101,101] +grain_dim [21.,21.,21.] +grain_steps [21,21,21] ang_mean [0,0] ang_width [.01,.01] #image simulation @@ -14,11 +14,12 @@ ang_width [.01,.01] # ang_max [.01,.01,85.] # detx_size 20 # detz_size 30 -M 1. +M 18.57 # Magnification value t_x "None" t_y 0 t_z 0 -path "/u/data/andcj/tmp/testdir1" # Data source +path "/u/data/alcer/DFXRM_rec/Rec3" # Folder with npy files, returned by + # getdata.py format "edf" mode "horizontal" xyz_up [0,0,0] diff --git a/getdata.py b/getdata.py index e113a54..fcab07a 100644 --- a/getdata.py +++ b/getdata.py @@ -54,19 +54,15 @@ def __init__( int(int(poi[1]) + int(imgsize[1]) / 2)] data = GetEdfData(datadir, dataname, bgpath, bgfilename, roi, sim) -<<<<<<< d0da2f49fba4d23fbd70626e49401f10c87d865b + self.alpha, self.beta, self.omega, self.theta = data.getMetaValues() self.index_list = range(len(data.meta)) self.meta = data.meta - self.calcEta(data) self.calcTheta(data) # self.calcEtaIndexList(data, eta) -======= - self.alpha, self.beta, self.omega = data.getMetaValues() # Redefine - # Alpha and beta values were measured at irregular steps. To make things # easy, we bin the values in 7 intervals per variable [count_alpha, extremes_alpha] = np.histogram(self.alpha, 7) @@ -93,13 +89,10 @@ def __init__( print self.alpha print self.beta print self.omega -<<<<<<< 939932454d6d654ead2b9aaead398e6d08cce97a print len(self.alpha) print len(self.beta) print len(self.omega) ->>>>>>> Binning angular values (irregular motor steps) -======= ->>>>>>> Done with data binning + self.allFiles(data, imgsize) if self.rank == 0: @@ -163,20 +156,14 @@ def allFiles(self, data, imsiz): imgarray = data.makeImgArray(self.index_list, 50, 'linetrace') if self.rank == 0: -<<<<<<< 939932454d6d654ead2b9aaead398e6d08cce97a -<<<<<<< d0da2f49fba4d23fbd70626e49401f10c87d865b + # lena = len(self.theta) lena = len(self.thetafake) lenb = len(self.eta) -======= - lena = 7#len(self.alpha) - lenb = 7#len(self.beta) ->>>>>>> Binning angular values (irregular motor steps) -======= + # If we need to bin the angular values lena = len(alpha_discr) lenb = len(beta_discr) ->>>>>>> Done with data binning leno = len(self.omega) # If we don't need to bin the angular values From 050a3fef1adc3af26490f1bb90d08b81b81fbc7b Mon Sep 17 00:00:00 2001 From: albusdemens Date: Sat, 13 May 2017 15:02:22 +0200 Subject: [PATCH 034/150] Why nans in first reconstruction? --- lib/miniged.py | 5 ----- 1 file changed, 5 deletions(-) diff --git a/lib/miniged.py b/lib/miniged.py index 78eb3f5..0b95221 100644 --- a/lib/miniged.py +++ b/lib/miniged.py @@ -194,10 +194,6 @@ def getHeader(self, filenumber): detpos_array = [] try: -<<<<<<< d0da2f49fba4d23fbd70626e49401f10c87d865b -======= - #print header['machine current'].split(' ') ->>>>>>> Binning angular values (irregular motor steps) srcur = float(header['machine current'].split(' ')[0]) except KeyError: srcur = 0 @@ -254,7 +250,6 @@ def makeMetaArrayNew(self): mot_array, motpos_array, det_array, detpos_array, srcur = self.getHeader(i) - # Option: bin data self.meta[i, 0] = round(float(motpos_array[mot_array.index('phi')]), 8) self.meta[i, 1] = round(float(motpos_array[mot_array.index('chi')]), 8) self.meta[i, 2] = round(float(motpos_array[mot_array.index('diffry')]), 8) From fa1db5ff4b9cfe9a4c719030d2892210ee9d76f3 Mon Sep 17 00:00:00 2001 From: albusdemens Date: Wed, 24 May 2017 13:21:45 +0200 Subject: [PATCH 035/150] Reducing phi, chi array size --- Plot_data_raw.py | 2 +- getdata.py | 99 ++++++------------------------------------------ 2 files changed, 13 insertions(+), 88 deletions(-) diff --git a/Plot_data_raw.py b/Plot_data_raw.py index 45b6d75..f8fb892 100644 --- a/Plot_data_raw.py +++ b/Plot_data_raw.py @@ -3,7 +3,7 @@ import numpy as np import matplotlib.pyplot as plt -Data = np.load('/Users/Alberto/Documents/Data_analysis/DFXRM/Results_monday/All_data.npy') +Data = np.load('/Users/Alberto/Documents/Data_analysis/DFXRM/Results_sunday/All_data.npy') # Plot distribution of motor values alpha = Data[:,0] diff --git a/getdata.py b/getdata.py index fcab07a..73fbd56 100644 --- a/getdata.py +++ b/getdata.py @@ -63,35 +63,7 @@ def __init__( self.calcTheta(data) # self.calcEtaIndexList(data, eta) - # Alpha and beta values were measured at irregular steps. To make things - # easy, we bin the values in 7 intervals per variable - [count_alpha, extremes_alpha] = np.histogram(self.alpha, 7) - val_alpha = np.zeros(len(extremes_alpha)-1) - [count_beta, extremes_beta] = np.histogram(self.beta, 7) - val_beta = np.zeros(len(extremes_beta)-1) - - # Find center of each bin - for i in range(0,len(extremes_alpha)-1): - val_alpha[i] = np.mean([extremes_alpha[i], extremes_alpha[i+1]]) - for j in range(0,len(extremes_beta)-1): - val_beta[j] = np.mean([extremes_beta[j], extremes_beta[j+1]]) - - # For each experimental value, find closest bin centre - alpha_discr = np.zeros(len(self.alpha)) - for i in range(0, len(self.alpha)): - alpha_discr[i] = find_nearest(val_alpha,self.alpha[i]) - beta_discr = np.zeros(len(self.beta)) - for j in range(0, len(self.beta)): - beta_discr[j] = find_nearest(val_beta,self.beta[j]) - - pdb.set_trace() - - print self.alpha - print self.beta - print self.omega - print len(self.alpha) - print len(self.beta) - print len(self.omega) + # To reduce data size, self.allFiles(data, imgsize) @@ -107,44 +79,6 @@ def makeOutputFolder(self, path, dirname): os.makedirs(directory) return directory - def calcEta(self, data): - for om in self.omega: - ind = np.where(self.meta[:, 2] == om) - a = self.meta[ind, 0][0] - - eta1 = (a - data.alpha0) / np.cos(np.radians(om)) - self.eta = np.sort(list(set(eta1))) - - self.etaindex = np.zeros((len(self.index_list))) - - for ind in self.index_list: - om = self.meta[ind, 2] - a = self.meta[ind, 0] - data.alpha0 - eta1 = a / np.cos(np.radians(om)) - - etapos = np.where(self.eta == min(self.eta, key=lambda x: abs(x-eta1)))[0][0] - - self.etaindex[ind] = self.eta[etapos] - - def calcTheta(self, data): - self.thetafake = data.theta0 + np.arange(-3.5 * 0.032, 3.5 * 0.032, 0.032) - self.thetaindex = np.zeros((len(self.index_list))) - for ind in self.index_list: - t = self.meta[ind, 4] - data.theta0 - thetapos = np.where(self.thetafake == min(self.thetafake, key=lambda x: abs(x-t)))[0][0] - self.thetaindex[ind] = self.thetafake[thetapos] - # def calcEtaIndexList(self, data, eta): - # self.etaindex = np.zeros((len(self.index_list))) - # - # for ind in self.index_list: - # om = self.meta[ind, 2] - # a = self.meta[ind, 0] - data.alpha0 - # eta1 = a / np.cos(np.radians(om)) - # - # etapos = np.where(eta == min(eta, key=lambda x: abs(x-eta1)))[0][0] - # - # self.etaindex[ind] = eta[etapos] - def allFiles(self, data, imsiz): # index_list = range(len(data.meta)) # met = data.meta @@ -157,37 +91,28 @@ def allFiles(self, data, imsiz): if self.rank == 0: - # lena = len(self.theta) - lena = len(self.thetafake) - lenb = len(self.eta) - - # If we need to bin the angular values - lena = len(alpha_discr) - lenb = len(beta_discr) - leno = len(self.omega) - # If we don't need to bin the angular values - #lena = len(self.alpha) - #lenb = len(self.beta) - #leno = len(self.omega) + lena = len(self.alpha) + lenb = len(self.beta) + leno = len(self.omega) + lent = len(self.theta) - bigarray = np.zeros((lena, lenb, leno, int(imsiz[1]), int(imsiz[0])), dtype=np.uint16) + #bigarray = np.zeros((lena, lenb, leno, int(imsiz[1]), int(imsiz[0])), dtype=np.uint16) for i, ind in enumerate(self.index_list): a = np.where(self.thetafake == self.thetaindex[ind]) # theta b = np.where(self.eta == self.etaindex[ind]) # roll c = np.where(self.omega == self.meta[ind, 2]) # omega - # d = np.where(self.theta == met[ind, 4]) + d = np.where(self.theta == met[ind, 4]) # theta - bigarray[a, b, c, :, :] = imgarray[ind, :, :] + #bigarray[a, b, c, :, :] = imgarray[ind, :, :] - # np.save(self.directory + '/alpha.npy', self.alpha) - # np.save(self.directory + '/beta.npy', self.beta) - np.save(self.directory + '/roll.npy', self.eta) - np.save(self.directory + '/theta.npy', self.thetafake) + np.save(self.directory + '/alpha.npy', self.alpha) + np.save(self.directory + '/beta.npy', self.beta) + np.save(self.directory + '/theta.npy', self.theta) np.save(self.directory + '/omega.npy', self.omega) - np.save(self.directory + '/dataarray.npy', bigarray) + #np.save(self.directory + '/dataarray.npy', bigarray) if __name__ == "__main__": From 3d34c646d57559bc8027a277e196738294ed6784 Mon Sep 17 00:00:00 2001 From: albusdemens Date: Wed, 24 May 2017 14:51:59 +0200 Subject: [PATCH 036/150] Add phi_0, chi_0 as input --- getdata.py | 19 ++++++++++++------- 1 file changed, 12 insertions(+), 7 deletions(-) diff --git a/getdata.py b/getdata.py index 73fbd56..4414bc8 100644 --- a/getdata.py +++ b/getdata.py @@ -22,6 +22,8 @@ Image size Output path Name of new output directory to make +Initial phi values +Initial chi value ''' @@ -30,6 +32,7 @@ def __init__( self, datadir, dataname, bgpath, bgfilename, poi, imgsize, outputpath, outputdir, + phi_0, chi_0, sim=False): try: @@ -59,11 +62,13 @@ def __init__( self.index_list = range(len(data.meta)) self.meta = data.meta - self.calcEta(data) - self.calcTheta(data) # self.calcEtaIndexList(data, eta) - # To reduce data size, + # In the macro for the motors, alpha and beta are functions of omega. + # By saving the index used in the calculations, instead that the entire + # value, we greately reduce the memory requirements + + print self.omega self.allFiles(data, imgsize) @@ -100,12 +105,12 @@ def allFiles(self, data, imsiz): #bigarray = np.zeros((lena, lenb, leno, int(imsiz[1]), int(imsiz[0])), dtype=np.uint16) for i, ind in enumerate(self.index_list): - a = np.where(self.thetafake == self.thetaindex[ind]) # theta - b = np.where(self.eta == self.etaindex[ind]) # roll + a = np.where(self.alpha == self.meta[ind,0]) # theta + b = np.where(self.beta == self.meta[ind,1]) # roll c = np.where(self.omega == self.meta[ind, 2]) # omega - d = np.where(self.theta == met[ind, 4]) # theta + d = np.where(self.theta == self.meta[ind, 4]) # theta - #bigarray[a, b, c, :, :] = imgarray[ind, :, :] + #bigarray[a, b, c, d, :, :] = imgarray[ind, :, :] np.save(self.directory + '/alpha.npy', self.alpha) np.save(self.directory + '/beta.npy', self.beta) From 7199ad71b426fe56ad057ae65f0be4ad1350cbf4 Mon Sep 17 00:00:00 2001 From: albusdemens Date: Wed, 24 May 2017 15:57:23 +0200 Subject: [PATCH 037/150] Checked data distr in (alpha, beta, theta) --- Plot_data_raw.py | 21 ++++++++++++++++++++- getdata.py | 21 +++++++++++++-------- 2 files changed, 33 insertions(+), 9 deletions(-) diff --git a/Plot_data_raw.py b/Plot_data_raw.py index f8fb892..78f5bc1 100644 --- a/Plot_data_raw.py +++ b/Plot_data_raw.py @@ -2,6 +2,10 @@ import numpy as np import matplotlib.pyplot as plt +from mpl_toolkits.mplot3d import Axes3D + +def randrange(n, vmin, vmax): + return (vmax-vmin)*np.random.rand(n) + vmin Data = np.load('/Users/Alberto/Documents/Data_analysis/DFXRM/Results_sunday/All_data.npy') @@ -9,6 +13,7 @@ alpha = Data[:,0] beta = Data[:,1] omega = Data[:,2] +theta = Data[:,4] # Print all rotation angles print(np.sort(list(set(omega)))) @@ -24,11 +29,25 @@ # Sundaynight values: 1.6, 30.4, 60., 90.4, 120., 150.4, 179.2 # Monday values: 1.6, 30.4, 60., 89.6, 120., 150.4, 178.4 # Lower = 1.6 because of strange behaviour at 0.8 -Om = 178.4 +Om = 0.8 idx = np.where(omega==Om) alpha_select = alpha[idx] beta_select = beta[idx] +theta_select = theta[idx] + +# Plot, in 3D, the (alpha, beta, theta) distribution +fig = plt.figure() +ax = fig.add_subplot(111, projection='3d') +n = 100 +ax.scatter(alpha_select, beta_select, theta_select, c='g', marker='o') + +ax.set_xlabel('Alpha (degrees)') +ax.set_ylabel('Beta (degrees)') +ax.set_zlabel('Theta (degrees)') + +plt.show() + # For the selected omega, plot the angular distribution fig = plt.figure(figsize=(12,9)) diff --git a/getdata.py b/getdata.py index 4414bc8..611b838 100644 --- a/getdata.py +++ b/getdata.py @@ -67,8 +67,7 @@ def __init__( # In the macro for the motors, alpha and beta are functions of omega. # By saving the index used in the calculations, instead that the entire # value, we greately reduce the memory requirements - - print self.omega + print self.theta self.allFiles(data, imgsize) @@ -107,8 +106,8 @@ def allFiles(self, data, imsiz): for i, ind in enumerate(self.index_list): a = np.where(self.alpha == self.meta[ind,0]) # theta b = np.where(self.beta == self.meta[ind,1]) # roll - c = np.where(self.omega == self.meta[ind, 2]) # omega - d = np.where(self.theta == self.meta[ind, 4]) # theta + c = np.where(self.omega == self.meta[ind,2]) # omega + d = np.where(self.theta == self.meta[ind,4]) # theta #bigarray[a, b, c, d, :, :] = imgarray[ind, :, :] @@ -116,13 +115,14 @@ def allFiles(self, data, imsiz): np.save(self.directory + '/beta.npy', self.beta) np.save(self.directory + '/theta.npy', self.theta) np.save(self.directory + '/omega.npy', self.omega) + np.save(self.directory + '/all_data.npy', self.meta) #np.save(self.directory + '/dataarray.npy', bigarray) if __name__ == "__main__": - if len(sys.argv) != 9: - if len(sys.argv) != 10: + if len(sys.argv) != 10: + if len(sys.argv) != 11: print "Not enough input parameters. Data input should be:\n\ Directory of data\n\ Name of data files\n\ @@ -132,6 +132,8 @@ def allFiles(self, data, imsiz): Image size\n\ Output path\n\ Name of new output directory to make\n\ + Initial phi value\n\ + Initial chi value\n\ " else: mm = makematrix( @@ -143,7 +145,8 @@ def allFiles(self, data, imsiz): sys.argv[6], sys.argv[7], sys.argv[8], - sys.argv[9]) + sys.argv[9], + sys.argv[10]) else: mm = makematrix( sys.argv[1], @@ -153,4 +156,6 @@ def allFiles(self, data, imsiz): sys.argv[5], sys.argv[6], sys.argv[7], - sys.argv[8]) + sys.argv[8], + sys.argv[10], + sys.argv[11]) From 9b770af8698193b816c29b1d4a11e3e469693c1c Mon Sep 17 00:00:00 2001 From: albusdemens Date: Wed, 24 May 2017 17:18:06 +0200 Subject: [PATCH 038/150] Updated readme --- README.md | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index f95df83..7f10203 100644 --- a/README.md +++ b/README.md @@ -23,7 +23,7 @@ Running Recon3D requires having Miniconda2 and Fable on your Panda2 account. To The script getdata.py reads data from .edf files and outputs a NumPy array with the dimensions *tilt1*-steps x *tilt2*-steps x *omega*-steps x *img_xlen* x *img_ylen*, where *tilt1* and *tilt2* are the two top tilts in the LAB goniometer at ID06. *Omega* is the topo-tomo rotation stage. The command to create the array is the following: ``` -$ python getdata.py [datadir] [dataname] [bgdir] [bgname] [poi] [imgsize] [outputpath] [outputdirname] +$ python getdata.py [datadir] [dataname] [bgdir] [bgname] [poi] [imgsize] [outputpath] [outputdirname] [phi_0] [chi_0] ``` Arguments are the following: @@ -38,11 +38,13 @@ Arguments are the following: | imgsize | Size of region of interest. | 200,200 | | outputpath | Path to put the output directory. | /analysis/output | | outputdirname | Name of output dir. | exp1_array | +| phi_0 | Initial phi (alpha) value | 0.765 | +| chi_0 | Initial chi (beta) value | -3.35 | If MPI is available, the following command will run the script in 10 processes at the same time. This will vastly increase the speed. ``` -$ mpirun -n 10 python getdata.py [datadir] [dataname] [bgdir] [bgname] [poi] [imgsize] [outputpath] [outputdirname] +$ mpirun -n 10 python getdata.py [datadir] [dataname] [bgdir] [bgname] [poi] [imgsize] [outputpath] [outputdirname] [phi_0] [chi_0] ``` ## Running the reconstruction algorithm on a data set From ef21ffe0eb3e508375cf4a45ebb61ea450a146f7 Mon Sep 17 00:00:00 2001 From: albusdemens Date: Wed, 24 May 2017 23:23:38 +0200 Subject: [PATCH 039/150] Alpha and beta reduced to single index list --- Plot_data_raw.py | 3 +-- getdata.py | 34 +++++++++++++++++++++++----------- 2 files changed, 24 insertions(+), 13 deletions(-) diff --git a/Plot_data_raw.py b/Plot_data_raw.py index 78f5bc1..395ca03 100644 --- a/Plot_data_raw.py +++ b/Plot_data_raw.py @@ -48,8 +48,7 @@ def randrange(n, vmin, vmax): plt.show() - -# For the selected omega, plot the angular distribution +# For a selected omega, plot the angular distribution fig = plt.figure(figsize=(12,9)) plt.rc('text', usetex=True) diff --git a/getdata.py b/getdata.py index 611b838..726e63e 100644 --- a/getdata.py +++ b/getdata.py @@ -62,12 +62,10 @@ def __init__( self.index_list = range(len(data.meta)) self.meta = data.meta - # self.calcEtaIndexList(data, eta) + self.phi = phi_0 + self.chi = chi_0 - # In the macro for the motors, alpha and beta are functions of omega. - # By saving the index used in the calculations, instead that the entire - # value, we greately reduce the memory requirements - print self.theta + # self.calcEtaIndexList(data, eta) self.allFiles(data, imgsize) @@ -101,22 +99,36 @@ def allFiles(self, data, imsiz): leno = len(self.omega) lent = len(self.theta) - #bigarray = np.zeros((lena, lenb, leno, int(imsiz[1]), int(imsiz[0])), dtype=np.uint16) + # Reduce alpha and beta to a single index_list + index_list = np.zeros(len(self.meta)) + for j in range(len(self.meta)): + alp = float(self.meta[j,0]) + omg = float(self.meta[j,2]) + phi_0 = float(self.phi) + index_list[j] = int((alp - phi_0)/(np.cos(np.deg2rad(omg))*0.032)) + + num_int = len(set(index_list)) # Number of considered alpha, beta + # intervals + idx_values = sorted(set(index_list)) # Values of the indices + print idx_values + + print 'Check that the number of (phi, chi) steps is', num_int + + bigarray = np.zeros((num_int, leno, lent, int(imsiz[1]), int(imsiz[0])), dtype=np.uint16) for i, ind in enumerate(self.index_list): - a = np.where(self.alpha == self.meta[ind,0]) # theta - b = np.where(self.beta == self.meta[ind,1]) # roll - c = np.where(self.omega == self.meta[ind,2]) # omega + a = np.where(self.alpha == self.meta[ind,0]) # rock + b = np.where(self.beta == self.meta[ind,1]) # roll + c = np.where(self.omega == self.meta[ind,2]) # omega d = np.where(self.theta == self.meta[ind,4]) # theta - #bigarray[a, b, c, d, :, :] = imgarray[ind, :, :] + bigarray[idx_values, c, d, :, :] = imgarray[ind, :, :] np.save(self.directory + '/alpha.npy', self.alpha) np.save(self.directory + '/beta.npy', self.beta) np.save(self.directory + '/theta.npy', self.theta) np.save(self.directory + '/omega.npy', self.omega) np.save(self.directory + '/all_data.npy', self.meta) - #np.save(self.directory + '/dataarray.npy', bigarray) From 814bb77448d647a5860c46004e8cff5d7a51a728 Mon Sep 17 00:00:00 2001 From: albusdemens Date: Thu, 25 May 2017 14:46:53 +0200 Subject: [PATCH 040/150] New output format --- getdata.py | 29 ++++++++++++++++------------- 1 file changed, 16 insertions(+), 13 deletions(-) diff --git a/getdata.py b/getdata.py index 726e63e..8a0de26 100644 --- a/getdata.py +++ b/getdata.py @@ -98,38 +98,41 @@ def allFiles(self, data, imsiz): lenb = len(self.beta) leno = len(self.omega) lent = len(self.theta) + print lena, lenb, leno, lent # Reduce alpha and beta to a single index_list - index_list = np.zeros(len(self.meta)) + idx_list = np.zeros(len(self.meta)) for j in range(len(self.meta)): alp = float(self.meta[j,0]) omg = float(self.meta[j,2]) phi_0 = float(self.phi) - index_list[j] = int((alp - phi_0)/(np.cos(np.deg2rad(omg))*0.032)) + idx_list[j] = int((alp - phi_0)/(np.cos(np.deg2rad(omg))*0.032)) - num_int = len(set(index_list)) # Number of considered alpha, beta + num_int = len(set(idx_list)) # Number of considered alpha, beta # intervals - idx_values = sorted(set(index_list)) # Values of the indices - print idx_values + idx_values = sorted(set(idx_list)) # Values of the indices print 'Check that the number of (phi, chi) steps is', num_int bigarray = np.zeros((num_int, leno, lent, int(imsiz[1]), int(imsiz[0])), dtype=np.uint16) - for i, ind in enumerate(self.index_list): - a = np.where(self.alpha == self.meta[ind,0]) # rock - b = np.where(self.beta == self.meta[ind,1]) # roll - c = np.where(self.omega == self.meta[ind,2]) # omega - d = np.where(self.theta == self.meta[ind,4]) # theta - - bigarray[idx_values, c, d, :, :] = imgarray[ind, :, :] + #for i, ind in enumerate(self.index_list): + for ind in range(11,230): + a = np.where(self.alpha == self.meta[int(ind),0]) # rock + b = np.where(self.beta == self.meta[int(ind),1]) # roll + c = np.where(self.omega == self.meta[int(ind),2]) # omega + d = np.where(self.theta == self.meta[int(ind),4]) # theta + idx_rescaled = int((self.meta[int(ind),0] - phi_0)/(np.cos(np.deg2rad(self.meta[int(ind),2]))*0.032))+3 + #print idx_rescaled, self.meta[int(ind),4] + #print self.meta[int(ind),0], self.meta[int(ind),1], self.meta[int(ind),2], self.meta[int(ind),4] + bigarray[idx_rescaled, c, d, :, :] = imgarray[ind, :, :] np.save(self.directory + '/alpha.npy', self.alpha) np.save(self.directory + '/beta.npy', self.beta) np.save(self.directory + '/theta.npy', self.theta) np.save(self.directory + '/omega.npy', self.omega) np.save(self.directory + '/all_data.npy', self.meta) - #np.save(self.directory + '/dataarray.npy', bigarray) + np.save(self.directory + '/dataarray.npy', bigarray) if __name__ == "__main__": From aed527ed9d6f3b09f4e5585b51fe7725460d4e9c Mon Sep 17 00:00:00 2001 From: albusdemens Date: Mon, 29 May 2017 16:01:58 +0200 Subject: [PATCH 041/150] Output getdata.py should be correct --- getdata.py | 19 +++++++++++++------ recon3d.py | 15 +++++++-------- 2 files changed, 20 insertions(+), 14 deletions(-) diff --git a/getdata.py b/getdata.py index 8a0de26..43038b1 100644 --- a/getdata.py +++ b/getdata.py @@ -106,6 +106,7 @@ def allFiles(self, data, imsiz): alp = float(self.meta[j,0]) omg = float(self.meta[j,2]) phi_0 = float(self.phi) + chi_0 = float(self.chi) idx_list[j] = int((alp - phi_0)/(np.cos(np.deg2rad(omg))*0.032)) num_int = len(set(idx_list)) # Number of considered alpha, beta @@ -116,16 +117,21 @@ def allFiles(self, data, imsiz): bigarray = np.zeros((num_int, leno, lent, int(imsiz[1]), int(imsiz[0])), dtype=np.uint16) - #for i, ind in enumerate(self.index_list): - for ind in range(11,230): + #AA = np.empty([len(self.index_list), 5]) + for i, ind in enumerate(self.index_list): a = np.where(self.alpha == self.meta[int(ind),0]) # rock b = np.where(self.beta == self.meta[int(ind),1]) # roll c = np.where(self.omega == self.meta[int(ind),2]) # omega d = np.where(self.theta == self.meta[int(ind),4]) # theta - idx_rescaled = int((self.meta[int(ind),0] - phi_0)/(np.cos(np.deg2rad(self.meta[int(ind),2]))*0.032))+3 - #print idx_rescaled, self.meta[int(ind),4] - #print self.meta[int(ind),0], self.meta[int(ind),1], self.meta[int(ind),2], self.meta[int(ind),4] - bigarray[idx_rescaled, c, d, :, :] = imgarray[ind, :, :] + idx_rescaled = int((self.meta[int(ind),0] - phi_0)/(np.cos(np.deg2rad(self.meta[int(ind),2]))*0.032)+3) + + # Can we effectively reconstruct chi and phi from idx_rescaled? + #ph = phi_0 + ((idx_rescaled - 3)*np.cos(np.deg2rad(self.meta[int(ind),2]))*0.032) + #ch = chi_0 + ((idx_rescaled - 3)*(np.tan(np.deg2rad(self.meta[int(ind),2]))*0.032)) + + #AA[ind] = [self.meta[int(ind),0], self.meta[int(ind),1],self.meta[int(ind),2], idx_rescaled, self.meta[int(ind),4]] + + bigarray[idx_rescaled, c[0], d[0], :, :] = imgarray[ind, :, :] np.save(self.directory + '/alpha.npy', self.alpha) np.save(self.directory + '/beta.npy', self.beta) @@ -133,6 +139,7 @@ def allFiles(self, data, imsiz): np.save(self.directory + '/omega.npy', self.omega) np.save(self.directory + '/all_data.npy', self.meta) np.save(self.directory + '/dataarray.npy', bigarray) + #np.savetxt(self.directory + '/AA.txt', AA) if __name__ == "__main__": diff --git a/recon3d.py b/recon3d.py index 5b8154a..029a513 100644 --- a/recon3d.py +++ b/recon3d.py @@ -45,7 +45,7 @@ def readarrays(self): self.alpha = np.load(self.par['path'] + '/alpha.npy') self.beta = np.load(self.par['path'] + '/beta.npy') self.omega = np.load(self.par['path'] + '/omega.npy') - # self.theta = np.load(self.par['path'] + '/theta.npy') + self.theta = np.load(self.par['path'] + '/theta.npy') def reconstruct_mpi(self): ypix = np.array(self.par['grain_steps'])[2] @@ -114,7 +114,7 @@ def reconstruct_part(self, ista, isto): detx_size = np.shape(self.fullarray)[3] detz_size = np.shape(self.fullarray)[4] - detx_center = (detx_size - 0.) / 2 # should probably be -1 in stead of -0... + detx_center = (detx_size - 0.) / 2 # should probably be -1 instead of -0... detz_center = (detz_size - 0.) / 2. # also here... but simulations used 0 lens = len(slow) lenm = len(med) @@ -214,8 +214,9 @@ def build_rotation_lookup_general(self): lo = np.pi * self.beta / 180. om = np.pi * self.omega / 180. # th = np.pi * self.par['theta'] / 180. - th = np.pi * np.array([0]) / 180. + th = np.pi * self.theta / 180. + # Include the axis tilting try: t_xx = np.pi * self.par['t_x'] / 180. t_yy = np.pi * self.par['t_y'] / 180. @@ -337,10 +338,8 @@ def build_rotation_lookup_general(self): else: print "ERROR: scattering geometry not defined" - # Let's multiply all the matrices. flipud takes into account the - # vertical flip introduced by the lens system - T_s2d = self.par['M'] * np.flipud( - np.matmul( + # Multiply all matrices + T_s2d = self.par['M'] * np.matmul( T_det, np.matmul( Tinv_th, @@ -360,7 +359,7 @@ def build_rotation_lookup_general(self): Tinv_up, np.matmul( R_up, - T_up))))))))))) + T_up)))))))))) return T_s2d def outputfiles(self, grain_ang): From 8b1077b149d5f92473d53770997939d7c52ec93a Mon Sep 17 00:00:00 2001 From: albusdemens Date: Mon, 29 May 2017 16:30:51 +0200 Subject: [PATCH 042/150] Working on recon3d --- Sunday_data.ini | 2 +- getdata.py | 4 ++-- recon3d.py | 1 - 3 files changed, 3 insertions(+), 4 deletions(-) diff --git a/Sunday_data.ini b/Sunday_data.ini index 3314bb8..3120cde 100644 --- a/Sunday_data.ini +++ b/Sunday_data.ini @@ -18,7 +18,7 @@ M 18.57 # Magnification value t_x "None" t_y 0 t_z 0 -path "/u/data/alcer/DFXRM_rec/Rec3" # Folder with npy files, returned by +path "/u/data/alcer/DFXRM_rec/Rec_test" # Folder with npy files, returned by # getdata.py format "edf" mode "horizontal" diff --git a/getdata.py b/getdata.py index 43038b1..94bcbed 100644 --- a/getdata.py +++ b/getdata.py @@ -115,7 +115,7 @@ def allFiles(self, data, imsiz): print 'Check that the number of (phi, chi) steps is', num_int - bigarray = np.zeros((num_int, leno, lent, int(imsiz[1]), int(imsiz[0])), dtype=np.uint16) + bigarray = np.zeros((num_int, lent, leno, int(imsiz[1]), int(imsiz[0])), dtype=np.uint16) #AA = np.empty([len(self.index_list), 5]) for i, ind in enumerate(self.index_list): @@ -131,7 +131,7 @@ def allFiles(self, data, imsiz): #AA[ind] = [self.meta[int(ind),0], self.meta[int(ind),1],self.meta[int(ind),2], idx_rescaled, self.meta[int(ind),4]] - bigarray[idx_rescaled, c[0], d[0], :, :] = imgarray[ind, :, :] + bigarray[idx_rescaled, d[0], c[0], :, :] = imgarray[ind, :, :] np.save(self.directory + '/alpha.npy', self.alpha) np.save(self.directory + '/beta.npy', self.beta) diff --git a/recon3d.py b/recon3d.py index 029a513..53e5ea2 100644 --- a/recon3d.py +++ b/recon3d.py @@ -213,7 +213,6 @@ def build_rotation_lookup_general(self): up = np.pi * self.alpha / 180. lo = np.pi * self.beta / 180. om = np.pi * self.omega / 180. - # th = np.pi * self.par['theta'] / 180. th = np.pi * self.theta / 180. # Include the axis tilting From 27a4c85169fb0a020efb0932ba1e915ad4d0779a Mon Sep 17 00:00:00 2001 From: albusdemens Date: Tue, 30 May 2017 15:05:07 +0200 Subject: [PATCH 043/150] Introduced theta angle --- getdata.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/getdata.py b/getdata.py index 94bcbed..aaa711b 100644 --- a/getdata.py +++ b/getdata.py @@ -123,7 +123,8 @@ def allFiles(self, data, imsiz): b = np.where(self.beta == self.meta[int(ind),1]) # roll c = np.where(self.omega == self.meta[int(ind),2]) # omega d = np.where(self.theta == self.meta[int(ind),4]) # theta - idx_rescaled = int((self.meta[int(ind),0] - phi_0)/(np.cos(np.deg2rad(self.meta[int(ind),2]))*0.032)+3) + idx_rescaled = int((self.meta[int(ind),0] - phi_0) / (np.cos(np.deg2rad(self.meta[int(ind),2])) * 0.032) + ((num_int - 1) / 2)) + e = (idx_rescaled - ((num_int - 1) / 2)) * 0.032 # Can we effectively reconstruct chi and phi from idx_rescaled? #ph = phi_0 + ((idx_rescaled - 3)*np.cos(np.deg2rad(self.meta[int(ind),2]))*0.032) @@ -137,6 +138,8 @@ def allFiles(self, data, imsiz): np.save(self.directory + '/beta.npy', self.beta) np.save(self.directory + '/theta.npy', self.theta) np.save(self.directory + '/omega.npy', self.omega) + np.save(self.directory + '/gamma.npy', [((b - ((num_int - 1)/2))*0.032) for b in range(num_int)]) + # The angle gamma is a linear combination of alpha and beta np.save(self.directory + '/all_data.npy', self.meta) np.save(self.directory + '/dataarray.npy', bigarray) #np.savetxt(self.directory + '/AA.txt', AA) From d3603cab15f32b23b205a36a8588904c9474108b Mon Sep 17 00:00:00 2001 From: albusdemens Date: Tue, 30 May 2017 16:41:30 +0200 Subject: [PATCH 044/150] Added gamma, combination of alpha and omega --- getdata.py | 2 +- recon3d.py | 266 ++++++++++++++++++++++++++++++----------------------- 2 files changed, 152 insertions(+), 116 deletions(-) diff --git a/getdata.py b/getdata.py index aaa711b..30ef8ae 100644 --- a/getdata.py +++ b/getdata.py @@ -139,7 +139,7 @@ def allFiles(self, data, imsiz): np.save(self.directory + '/theta.npy', self.theta) np.save(self.directory + '/omega.npy', self.omega) np.save(self.directory + '/gamma.npy', [((b - ((num_int - 1)/2))*0.032) for b in range(num_int)]) - # The angle gamma is a linear combination of alpha and beta + # The gamma angle is a linear combination of alpha and beta np.save(self.directory + '/all_data.npy', self.meta) np.save(self.directory + '/dataarray.npy', bigarray) #np.savetxt(self.directory + '/AA.txt', AA) diff --git a/recon3d.py b/recon3d.py index 53e5ea2..8d4c230 100644 --- a/recon3d.py +++ b/recon3d.py @@ -44,8 +44,9 @@ def readarrays(self): self.fullarray = np.load(self.par['path'] + '/dataarray.npy') self.alpha = np.load(self.par['path'] + '/alpha.npy') self.beta = np.load(self.par['path'] + '/beta.npy') - self.omega = np.load(self.par['path'] + '/omega.npy') + self.gamma = np.load(self.par['path'] + '/gamma.npy') self.theta = np.load(self.par['path'] + '/theta.npy') + self.omega = np.load(self.par['path'] + '/omega.npy') def reconstruct_mpi(self): ypix = np.array(self.par['grain_steps'])[2] @@ -99,9 +100,11 @@ def reconstruct_part(self, ista, isto): NB AS OF PRESENT THETA IS A NUMBER, NOT AN ARRAY. TO ALLOW FOR AN ARRAY NEED TO THINK ABOUT THE LOOPING AND THE DIMENSIONS OF prop. """ - slow = self.alpha - med = self.beta - fast = self.omega + #slow = self.alpha + #med = self.beta + a1 = self.theta + a2 = self.gamma + a3 = self.omega grain_steps = self.par['grain_steps'] grain_dim = np.array(self.par['grain_dim']) @@ -116,14 +119,23 @@ def reconstruct_part(self, ista, isto): detz_size = np.shape(self.fullarray)[4] detx_center = (detx_size - 0.) / 2 # should probably be -1 instead of -0... detz_center = (detz_size - 0.) / 2. # also here... but simulations used 0 - lens = len(slow) - lenm = len(med) - lenf = len(fast) - mas = max(slow) - mis = min(slow) - mam = max(med) - mim = min(med) - prop = np.zeros((lens, lenm, lenf)) + #lens = len(slow) + #lenm = len(med) + len_a1 = len(a1) + len_a2 = len(a2) + len_a3 = len(a3) + ma1 = max(a1) + mi1 = min(a1) + ma2 = max(a2) + mi2 = min(a2) + ma3 = max(a3) + mi3 = min(a3) + #mas = max(slow) + #mis = min(slow) + #mam = max(med) + #mim = min(med) + prop = np.zeros((len_a1, len_a2)) + #prop = np.zeros((lens, lenm, lenf)) t_x = "None" if self.rank == 0: @@ -164,7 +176,8 @@ def reconstruct_part(self, ista, isto): detz_f[detz_f < 0] = 0 detz_f[detz_f >= detz_size] = detz_size - 1 - prop = self.fullarray[:, :, range(lenf), detx_f, detz_f] + #prop = self.fullarray[:, :, range(lenf), detx_f, detz_f] + prop = self.fullarray[:, :, range(len_a3), detx_f, detz_f] cos = list(ndimage.measurements.center_of_mass(np.sum(prop, 2))) @@ -176,8 +189,8 @@ def reconstruct_part(self, ista, isto): except IndexError: pass - cos[0] = cos[0] * (mas - mis) / lens + mis - cos[1] = cos[1] * (mam - mim) / lenm + mim + cos[0] = cos[0] * (ma1 - mi1) / len1 + mi1 + cos[1] = cos[1] * (ma2 - mi2) / len2 + mi2 grain_ang[ix, iy, iz, :2] = cos @@ -210,10 +223,11 @@ def build_rotation_lookup_general(self): point on the rotation axis, eg yxz_up=[-40,0,0] in horizontal geometry. """ - up = np.pi * self.alpha / 180. - lo = np.pi * self.beta / 180. + #up = np.pi * self.alpha / 180. + #lo = np.pi * self.beta / 180. om = np.pi * self.omega / 180. th = np.pi * self.theta / 180. + ga = np.pi * self.gamma / 180. # Include the axis tilting try: @@ -226,112 +240,125 @@ def build_rotation_lookup_general(self): self.par['t_x'] = "None" self.par['t_z'] = "None" - th_mat, om_mat, lo_mat, up_mat = np.meshgrid(th, om, lo, up, indexing='ij') + th_mat, om_mat, ga_mat = np.meshgrid(th, om, ga, indexing='ij') - R_up = np.zeros((len(th), len(om), len(lo), len(up), 4, 4)) - R_lo = np.zeros((len(th), len(om), len(lo), len(up), 4, 4)) - Omega = np.zeros((len(th), len(om), len(lo), len(up), 4, 4)) - Theta = np.zeros((len(th), len(om), len(lo), len(up), 4, 4)) - T_det = np.zeros((len(th), len(om), len(lo), len(up), 4, 4)) - T_up = np.zeros((len(th), len(om), len(lo), len(up), 4, 4)) - T_lo = np.zeros((len(th), len(om), len(lo), len(up), 4, 4)) - T_th = np.zeros((len(th), len(om), len(lo), len(up), 4, 4)) - Tinv_up = np.zeros((len(th), len(om), len(lo), len(up), 4, 4)) - Tinv_lo = np.zeros((len(th), len(om), len(lo), len(up), 4, 4)) - Tinv_th = np.zeros((len(th), len(om), len(lo), len(up), 4, 4)) + R_ga = np.zeros((len(th), len(om), len(ga), 4, 4)) + Omega = np.zeros((len(th), len(om), len(ga), 4, 4)) + Theta = np.zeros((len(th), len(om), len(ga), 4, 4)) + T_det = np.zeros((len(th), len(om), len(ga), 4, 4)) + T_ga = np.zeros((len(th), len(om), len(ga), 4, 4)) + T_th = np.zeros((len(th), len(om), len(ga), 4, 4)) + Tinv_ga = np.zeros((len(th), len(om), len(ga), 4, 4)) + Tinv_th = np.zeros((len(th), len(om), len(ga), 4, 4)) # The default detector tilt is the unit matrix, i.e. an ideal detector # positioned perpendicular to the diffracted beam (t_x=t=y=t_z=None). # This can be changed by supplying tilts t_x (vertical) or t_z (horizontal). - T_det[:, :, :, :, 0, 0] = -1. + T_det[:, :, :, 0, 0] = -1. # T_det[:, :, :, :, 1, 1] = 1. #leaving T_det[:, :, :, :, 1, 1]=0 # gives the projection onto the detector plane - T_det[:, :, :, :, 2, 2] = -1. - T_det[:, :, :, :, 3, 3] = 1. + T_det[:, :, :, 2, 2] = -1. + T_det[:, :, :, 3, 3] = 1. # print self.par['xyz_up'] # 4x4 according # to http://inside.mines.edu/fs_home/gmurray/ArbitraryAxisRotation/ - T_up[:, :, :, :, 0:3, 3] = -np.array(self.par['xyz_up']) - T_up[:, :, :, :, 0, 0] = 1. - T_up[:, :, :, :, 1, 1] = 1. - T_up[:, :, :, :, 2, 2] = 1. - T_up[:, :, :, :, 3, 3] = 1. - Tinv_up[:, :, :, :, 0:3, 3] = np.array(self.par['xyz_up']) - Tinv_up[:, :, :, :, 0, 0] = 1. - Tinv_up[:, :, :, :, 1, 1] = 1. - Tinv_up[:, :, :, :, 2, 2] = 1. - Tinv_up[:, :, :, :, 3, 3] = 1. - T_lo[:, :, :, :, 0:3, 3] = -np.array(self.par['xyz_lo']) - T_lo[:, :, :, :, 0, 0] = 1. - T_lo[:, :, :, :, 1, 1] = 1. - T_lo[:, :, :, :, 2, 2] = 1. - T_lo[:, :, :, :, 3, 3] = 1. - Tinv_lo[:, :, :, :, 0:3, 3] = np.array(self.par['xyz_lo']) - Tinv_lo[:, :, :, :, 0, 0] = 1. - Tinv_lo[:, :, :, :, 1, 1] = 1. - Tinv_lo[:, :, :, :, 2, 2] = 1. - Tinv_lo[:, :, :, :, 3, 3] = 1. - T_th[:, :, :, :, 0:3, 3] = -np.array(self.par['xyz_th']) - T_th[:, :, :, :, 0, 0] = 1. - T_th[:, :, :, :, 1, 1] = 1. - T_th[:, :, :, :, 2, 2] = 1. - T_th[:, :, :, :, 3, 3] = 1. - Tinv_th[:, :, :, :, 0:3, 3] = np.array(self.par['xyz_th']) - Tinv_th[:, :, :, :, 0, 0] = 1. - Tinv_th[:, :, :, :, 1, 1] = 1. - Tinv_th[:, :, :, :, 2, 2] = 1. - Tinv_th[:, :, :, :, 3, 3] = 1. + # T_up[:, :, :, :, 0:3, 3] = -np.array(self.par['xyz_up']) + #T_up[:, :, :, :, 0, 0] = 1. + #T_up[:, :, :, :, 1, 1] = 1. + #T_up[:, :, :, :, 2, 2] = 1. + #T_up[:, :, :, :, 3, 3] = 1. + #Tinv_up[:, :, :, :, 0:3, 3] = np.array(self.par['xyz_up']) + #Tinv_up[:, :, :, :, 0, 0] = 1. + #Tinv_up[:, :, :, :, 1, 1] = 1. + #Tinv_up[:, :, :, :, 2, 2] = 1. + #Tinv_up[:, :, :, :, 3, 3] = 1. + #T_lo[:, :, :, :, 0:3, 3] = -np.array(self.par['xyz_lo']) + #T_lo[:, :, :, :, 0, 0] = 1. + #T_lo[:, :, :, :, 1, 1] = 1. + #T_lo[:, :, :, :, 2, 2] = 1. + #T_lo[:, :, :, :, 3, 3] = 1. + #Tinv_lo[:, :, :, :, 0:3, 3] = np.array(self.par['xyz_lo']) + #Tinv_lo[:, :, :, :, 0, 0] = 1. + #Tinv_lo[:, :, :, :, 1, 1] = 1. + #Tinv_lo[:, :, :, :, 2, 2] = 1. + #Tinv_lo[:, :, :, :, 3, 3] = 1. + T_ga[:, :, :, 0:3, 3] = -np.array(self.par['xyz_th']) + T_ga[:, :, :, 0, 0] = 1. + T_ga[:, :, :, 1, 1] = 1. + T_ga[:, :, :, 2, 2] = 1. + T_ga[:, :, :, 3, 3] = 1. + Tinv_ga[:, :, :, 0:3, 3] = np.array(self.par['xyz_th']) + Tinv_ga[:, :, :, 0, 0] = 1. + Tinv_ga[:, :, :, 1, 1] = 1. + Tinv_ga[:, :, :, 2, 2] = 1. + Tinv_ga[:, :, :, 3, 3] = 1. + T_th[:, :, :, 0:3, 3] = -np.array(self.par['xyz_th']) + T_th[:, :, :, 0, 0] = 1. + T_th[:, :, :, 1, 1] = 1. + T_th[:, :, :, 2, 2] = 1. + T_th[:, :, :, 3, 3] = 1. + Tinv_th[:, :, :, 0:3, 3] = np.array(self.par['xyz_th']) + Tinv_th[:, :, :, 0, 0] = 1. + Tinv_th[:, :, :, 1, 1] = 1. + Tinv_th[:, :, :, 2, 2] = 1. + Tinv_th[:, :, :, 3, 3] = 1. if self.par['mode'] == "horizontal": - Theta[:, :, :, :, 0, 0] = np.cos(th_mat) - Theta[:, :, :, :, 0, 1] = -np.sin(th_mat) - Theta[:, :, :, :, 1, 0] = np.sin(th_mat) - Theta[:, :, :, :, 1, 1] = np.cos(th_mat) - Theta[:, :, :, :, 2, 2] = 1. - Theta[:, :, :, :, 3, 3] = 1. - Omega[:, :, :, :, 0, 0] = 1. - Omega[:, :, :, :, 1, 1] = np.cos(om_mat) - Omega[:, :, :, :, 1, 2] = -np.sin(om_mat) - Omega[:, :, :, :, 2, 1] = np.sin(om_mat) - Omega[:, :, :, :, 2, 2] = np.cos(om_mat) - Omega[:, :, :, :, 3, 3] = 1. - R_lo[:, :, :, :, 0, 0] = np.cos(lo_mat) - R_lo[:, :, :, :, 0, 2] = np.sin(lo_mat) - R_lo[:, :, :, :, 1, 1] = 1. - R_lo[:, :, :, :, 2, 0] = -np.sin(lo_mat) - R_lo[:, :, :, :, 2, 2] = np.cos(lo_mat) - R_lo[:, :, :, :, 3, 3] = 1. - R_up[:, :, :, :, 0, 0] = np.cos(up_mat) - R_up[:, :, :, :, 0, 1] = -np.sin(up_mat) - R_up[:, :, :, :, 1, 0] = np.sin(up_mat) - R_up[:, :, :, :, 1, 1] = np.cos(up_mat) - R_up[:, :, :, :, 2, 2] = 1. - R_up[:, :, :, :, 3, 3] = 1. + Theta[:, :, :, 0, 0] = np.cos(th_mat) + Theta[:, :, :, 0, 1] = -np.sin(th_mat) + Theta[:, :, :, 1, 0] = np.sin(th_mat) + Theta[:, :, :, 1, 1] = np.cos(th_mat) + Theta[:, :, :, 2, 2] = 1. + Theta[:, :, :, 3, 3] = 1. + Omega[:, :, :, 0, 0] = 1. + Omega[:, :, :, 1, 1] = np.cos(om_mat) + Omega[:, :, :, 1, 2] = -np.sin(om_mat) + Omega[:, :, :, 2, 1] = np.sin(om_mat) + Omega[:, :, :, 2, 2] = np.cos(om_mat) + Omega[:, :, :, 3, 3] = 1. + #R_lo[:, :, :, :, 0, 0] = np.cos(lo_mat) + #R_lo[:, :, :, :, 0, 2] = np.sin(lo_mat) + #R_lo[:, :, :, :, 1, 1] = 1. + #R_lo[:, :, :, :, 2, 0] = -np.sin(lo_mat) + #R_lo[:, :, :, :, 2, 2] = np.cos(lo_mat) + #R_lo[:, :, :, :, 3, 3] = 1. + #R_up[:, :, :, :, 0, 0] = np.cos(up_mat) + #R_up[:, :, :, :, 0, 1] = -np.sin(up_mat) + #R_up[:, :, :, :, 1, 0] = np.sin(up_mat) + #R_up[:, :, :, :, 1, 1] = np.cos(up_mat) + #R_up[:, :, :, :, 2, 2] = 1. + #R_up[:, :, :, :, 3, 3] = 1. + R_ga[:, :, :, 0, 0] = np.cos(ga_mat) + R_ga[:, :, :, 0, 2] = np.sin(ga_mat) + R_ga[:, :, :, 1, 1] = 1. + R_ga[:, :, :, 2, 0] = -np.sin(ga_mat) + R_ga[:, :, :, 2, 2] = np.cos(ga_mat) + R_ga[:, :, :, 3, 3] = 1. if self.par['t_z'] != "None": T_det[:, :, :, :, 0, 0] = -1. / np.cos(t_zz - 2 * np.mean(th)) elif self.par['mode'] == "vertical": - Theta[:, :, :, :, 0, 0] = 1. - Theta[:, :, :, :, 1, 1] = np.cos(th_mat) - Theta[:, :, :, :, 1, 2] = -np.sin(th_mat) - Theta[:, :, :, :, 2, 1] = np.sin(th_mat) - Theta[:, :, :, :, 2, 2] = np.cos(th_mat) - Theta[:, :, :, :, 3, 3] = 1. - Omega[:, :, :, :, 0, 0] = np.cos(om_mat) - Omega[:, :, :, :, 0, 1] = -np.sin(om_mat) - Omega[:, :, :, :, 1, 0] = np.sin(om_mat) - Omega[:, :, :, :, 1, 1] = np.cos(om_mat) - Omega[:, :, :, :, 2, 2] = 1. - Omega[:, :, :, :, 3, 3] = 1. + Theta[:, :, :, 0, 0] = 1. + Theta[:, :, :, 1, 1] = np.cos(th_mat) + Theta[:, :, :, 1, 2] = -np.sin(th_mat) + Theta[:, :, :, 2, 1] = np.sin(th_mat) + Theta[:, :, :, 2, 2] = np.cos(th_mat) + Theta[:, :, :, 3, 3] = 1. + Omega[:, :, :, 0, 0] = np.cos(om_mat) + Omega[:, :, :, 0, 1] = -np.sin(om_mat) + Omega[:, :, :, 1, 0] = np.sin(om_mat) + Omega[:, :, :, 1, 1] = np.cos(om_mat) + Omega[:, :, :, 2, 2] = 1. + Omega[:, :, :, 3, 3] = 1. # NB Should define around which axes the upper and lower rotation belong - R_lo[:, :, :, :, 0, 0] = 1. - R_lo[:, :, :, :, 1, 1] = 1. - R_lo[:, :, :, :, 2, 2] = 1. - R_lo[:, :, :, :, 3, 3] = 1. - R_up[:, :, :, :, 0, 0] = 1. - R_up[:, :, :, :, 1, 1] = 1. - R_up[:, :, :, :, 2, 2] = 1. - R_up[:, :, :, :, 3, 3] = 1. + R_lo[:, :, :, 0, 0] = 1. + R_lo[:, :, :, 1, 1] = 1. + R_lo[:, :, :, 2, 2] = 1. + R_lo[:, :, :, 3, 3] = 1. + R_up[:, :, :, 0, 0] = 1. + R_up[:, :, :, 1, 1] = 1. + R_up[:, :, :, 2, 2] = 1. + R_up[:, :, :, 3, 3] = 1. if self.par['t_x'] != "None": T_det[:, :, :, :, 2, 2] = -1. / np.cos(t_xx - 2 * np.mean(th)) else: @@ -347,18 +374,27 @@ def build_rotation_lookup_general(self): np.matmul( T_th, np.matmul( - Omega, + Tinv_ga, np.matmul( - Tinv_lo, + R_ga, np.matmul( - R_lo, + T_ga, np.matmul( - T_lo, - np.matmul( - Tinv_up, - np.matmul( - R_up, - T_up)))))))))) + Omega)))))))) + + + # Omega, + # np.matmul( + # Tinv_lo, + # np.matmul( + # R_lo, + # np.matmul( + # T_lo, + # np.matmul( + # Tinv_up, + # np.matmul( + # R_up, + # T_up)))))))))) return T_s2d def outputfiles(self, grain_ang): From 44edcf7d3b964817bdc2e92d96b9f56a83576c1c Mon Sep 17 00:00:00 2001 From: albusdemens Date: Thu, 1 Jun 2017 15:50:14 +0200 Subject: [PATCH 045/150] Code works --- Sunday_data.ini | 4 +- View_reconstruction.py | 2 +- getdata.py | 126 +++++++++++++++++------------------------ recon3d.py | 14 ++--- 4 files changed, 63 insertions(+), 83 deletions(-) diff --git a/Sunday_data.ini b/Sunday_data.ini index 3120cde..38ff602 100644 --- a/Sunday_data.ini +++ b/Sunday_data.ini @@ -3,8 +3,8 @@ unit_cell [4.0495,4.0495,4.0495,90.,90.,90.] hkl [2,0,0] wavelength 0.7293 # Corresponds to an energy of 17 keV grain_pos [0,0,0] -grain_dim [21.,21.,21.] -grain_steps [21,21,21] +grain_dim [61.,61.,61.] # Number of voxels +grain_steps [61,61,61] # 3D ROI ang_mean [0,0] ang_width [.01,.01] #image simulation diff --git a/View_reconstruction.py b/View_reconstruction.py index 9b4336c..7ce82ba 100644 --- a/View_reconstruction.py +++ b/View_reconstruction.py @@ -15,4 +15,4 @@ dir = sys.argv[1] Data = np.load(os.path.join(dir,'grain_ang.npy')) -print Data +print Data, np.min(Data) diff --git a/getdata.py b/getdata.py index 30ef8ae..2894666 100644 --- a/getdata.py +++ b/getdata.py @@ -22,8 +22,6 @@ Image size Output path Name of new output directory to make -Initial phi values -Initial chi value ''' @@ -32,7 +30,6 @@ def __init__( self, datadir, dataname, bgpath, bgfilename, poi, imgsize, outputpath, outputdir, - phi_0, chi_0, sim=False): try: @@ -57,15 +54,28 @@ def __init__( int(int(poi[1]) + int(imgsize[1]) / 2)] data = GetEdfData(datadir, dataname, bgpath, bgfilename, roi, sim) - - self.alpha, self.beta, self.omega, self.theta = data.getMetaValues() - - self.index_list = range(len(data.meta)) - self.meta = data.meta - self.phi = phi_0 - self.chi = chi_0 - - # self.calcEtaIndexList(data, eta) + self.alpha, self.beta, self.omega = data.getMetaValues() # Redefine + + # Alpha and beta values were measured at irregular steps. To make things + # easy, we bin the values in 7 intervals per variable + [count_alpha, extremes_alpha] = np.histogram(self.alpha, 7) + self.val_alpha = np.zeros(len(extremes_alpha)-1) + [count_beta, extremes_beta] = np.histogram(self.beta, 7) + self.val_beta = np.zeros(len(extremes_beta)-1) + + # Find center of each bin + for i in range(0,len(extremes_alpha)-1): + self.val_alpha[i] = np.mean([extremes_alpha[i], extremes_alpha[i+1]]) + for j in range(0,len(extremes_beta)-1): + self.val_beta[j] = np.mean([extremes_beta[j], extremes_beta[j+1]]) + + # For each experimental value, find closest bin centre + self.alpha_discr = np.zeros(len(self.alpha)) + for i in range(0, len(self.alpha)): + self.alpha_discr[i] = find_nearest(self.val_alpha,self.alpha[i]) + self.beta_discr = np.zeros(len(self.beta)) + for j in range(0, len(self.beta)): + self.beta_discr[j] = find_nearest(self.val_beta,self.beta[j]) self.allFiles(data, imgsize) @@ -82,72 +92,47 @@ def makeOutputFolder(self, path, dirname): return directory def allFiles(self, data, imsiz): - # index_list = range(len(data.meta)) - # met = data.meta - - # theta = data.theta0 + np.arange(-3.5 * 0.032, 3.5 * 0.032, 0.032) + index_list = range(len(data.meta)) + met = data.meta with warnings.catch_warnings(): warnings.simplefilter("ignore") - imgarray = data.makeImgArray(self.index_list, 50, 'linetrace') + imgarray = data.makeImgArray(index_list, 50, 'linetrace') if self.rank == 0: + # If we need to bin the angular values + lena = len(self.val_alpha) + lenb = len(self.val_beta) + leno = len(self.omega) # If we don't need to bin the angular values - lena = len(self.alpha) - lenb = len(self.beta) - leno = len(self.omega) - lent = len(self.theta) - print lena, lenb, leno, lent - - # Reduce alpha and beta to a single index_list - idx_list = np.zeros(len(self.meta)) - for j in range(len(self.meta)): - alp = float(self.meta[j,0]) - omg = float(self.meta[j,2]) - phi_0 = float(self.phi) - chi_0 = float(self.chi) - idx_list[j] = int((alp - phi_0)/(np.cos(np.deg2rad(omg))*0.032)) - - num_int = len(set(idx_list)) # Number of considered alpha, beta - # intervals - idx_values = sorted(set(idx_list)) # Values of the indices - - print 'Check that the number of (phi, chi) steps is', num_int - - bigarray = np.zeros((num_int, lent, leno, int(imsiz[1]), int(imsiz[0])), dtype=np.uint16) - - #AA = np.empty([len(self.index_list), 5]) - for i, ind in enumerate(self.index_list): - a = np.where(self.alpha == self.meta[int(ind),0]) # rock - b = np.where(self.beta == self.meta[int(ind),1]) # roll - c = np.where(self.omega == self.meta[int(ind),2]) # omega - d = np.where(self.theta == self.meta[int(ind),4]) # theta - idx_rescaled = int((self.meta[int(ind),0] - phi_0) / (np.cos(np.deg2rad(self.meta[int(ind),2])) * 0.032) + ((num_int - 1) / 2)) - e = (idx_rescaled - ((num_int - 1) / 2)) * 0.032 - - # Can we effectively reconstruct chi and phi from idx_rescaled? - #ph = phi_0 + ((idx_rescaled - 3)*np.cos(np.deg2rad(self.meta[int(ind),2]))*0.032) - #ch = chi_0 + ((idx_rescaled - 3)*(np.tan(np.deg2rad(self.meta[int(ind),2]))*0.032)) - - #AA[ind] = [self.meta[int(ind),0], self.meta[int(ind),1],self.meta[int(ind),2], idx_rescaled, self.meta[int(ind),4]] - - bigarray[idx_rescaled, d[0], c[0], :, :] = imgarray[ind, :, :] - - np.save(self.directory + '/alpha.npy', self.alpha) - np.save(self.directory + '/beta.npy', self.beta) - np.save(self.directory + '/theta.npy', self.theta) + #lena = len(self.alpha) + #lenb = len(self.beta) + #leno = len(self.omega) + + bigarray = np.zeros((lena, lenb, leno, int(imsiz[1]), int(imsiz[0])), dtype=np.uint16) + + for i, ind in enumerate(index_list): + a = np.where(self.alpha_discr == met[ind, 0]) + b = np.where(self.alpha_discr == met[ind, 1]) + #a = np.where(self.alpha == met[ind, 0]) + #b = np.where(self.beta == met[ind, 1]) + c = np.where(self.omega == met[ind, 2]) + # d = np.where(self.theta == met[ind, 4]) + + bigarray[a, b, c, :, :] = imgarray[ind, :, :] + + np.save(self.directory + '/alpha.npy', list(set(self.alpha_discr))) + np.save(self.directory + '/beta.npy', list(set(self.beta_discr))) + # np.save(self.directory + '/theta.npy', self.theta) np.save(self.directory + '/omega.npy', self.omega) - np.save(self.directory + '/gamma.npy', [((b - ((num_int - 1)/2))*0.032) for b in range(num_int)]) - # The gamma angle is a linear combination of alpha and beta - np.save(self.directory + '/all_data.npy', self.meta) + np.save(self.directory + '/dataarray.npy', bigarray) - #np.savetxt(self.directory + '/AA.txt', AA) if __name__ == "__main__": - if len(sys.argv) != 10: - if len(sys.argv) != 11: + if len(sys.argv) != 9: + if len(sys.argv) != 10: print "Not enough input parameters. Data input should be:\n\ Directory of data\n\ Name of data files\n\ @@ -157,8 +142,6 @@ def allFiles(self, data, imsiz): Image size\n\ Output path\n\ Name of new output directory to make\n\ - Initial phi value\n\ - Initial chi value\n\ " else: mm = makematrix( @@ -170,8 +153,7 @@ def allFiles(self, data, imsiz): sys.argv[6], sys.argv[7], sys.argv[8], - sys.argv[9], - sys.argv[10]) + sys.argv[9]) else: mm = makematrix( sys.argv[1], @@ -181,6 +163,4 @@ def allFiles(self, data, imsiz): sys.argv[5], sys.argv[6], sys.argv[7], - sys.argv[8], - sys.argv[10], - sys.argv[11]) + sys.argv[8]) diff --git a/recon3d.py b/recon3d.py index 8d4c230..0d5ff7b 100644 --- a/recon3d.py +++ b/recon3d.py @@ -165,13 +165,14 @@ def reconstruct_part(self, ista, isto): blah[:3] = grain_xyz[ix, iy, iz] blah[3] = 1. - xyz_d_f = np.matmul(T_s2d[0, :, 0, 0], blah) # , grain_xyz[ix, iy, iz]) + xyz_d_f = np.matmul(T_s2d[0, :, 0], blah) # , grain_xyz[ix, iy, iz]) detx_f = np.rint(xyz_d_f[:, 0] + detx_center).astype(int) detz_f = np.rint(xyz_d_f[:, 2] + detz_center).astype(int) # projections outside detector frame hit the outmost row or column # should be OK assuming that the signal doesn't reach the very borders detx_f[detx_f < 0] = 0 + detx_f[detx_f >= detx_size] = detx_size - 1 detz_f[detz_f < 0] = 0 detz_f[detz_f >= detz_size] = detz_size - 1 @@ -189,8 +190,8 @@ def reconstruct_part(self, ista, isto): except IndexError: pass - cos[0] = cos[0] * (ma1 - mi1) / len1 + mi1 - cos[1] = cos[1] * (ma2 - mi2) / len2 + mi2 + cos[0] = cos[0] * (ma1 - mi1) / len_a1 + mi1 + cos[1] = cos[1] * (ma2 - mi2) / len_a2 + mi2 grain_ang[ix, iy, iz, :2] = cos @@ -336,7 +337,7 @@ def build_rotation_lookup_general(self): R_ga[:, :, :, 2, 2] = np.cos(ga_mat) R_ga[:, :, :, 3, 3] = 1. if self.par['t_z'] != "None": - T_det[:, :, :, :, 0, 0] = -1. / np.cos(t_zz - 2 * np.mean(th)) + T_det[:, :, :, 0, 0] = -1. / np.cos(t_zz - 2 * np.mean(th)) elif self.par['mode'] == "vertical": Theta[:, :, :, 0, 0] = 1. Theta[:, :, :, 1, 1] = np.cos(th_mat) @@ -360,7 +361,7 @@ def build_rotation_lookup_general(self): R_up[:, :, :, 2, 2] = 1. R_up[:, :, :, 3, 3] = 1. if self.par['t_x'] != "None": - T_det[:, :, :, :, 2, 2] = -1. / np.cos(t_xx - 2 * np.mean(th)) + T_det[:, :, :, 2, 2] = -1. / np.cos(t_xx - 2 * np.mean(th)) else: print "ERROR: scattering geometry not defined" @@ -379,8 +380,7 @@ def build_rotation_lookup_general(self): R_ga, np.matmul( T_ga, - np.matmul( - Omega)))))))) + Omega))))))) # Omega, From cc5979bfd5138ad0294abd2053eafc94a1d67570 Mon Sep 17 00:00:00 2001 From: Alberto Cereser Date: Fri, 2 Jun 2017 16:38:03 +0200 Subject: [PATCH 046/150] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 7f10203..b963555 100644 --- a/README.md +++ b/README.md @@ -41,7 +41,7 @@ Arguments are the following: | phi_0 | Initial phi (alpha) value | 0.765 | | chi_0 | Initial chi (beta) value | -3.35 | -If MPI is available, the following command will run the script in 10 processes at the same time. This will vastly increase the speed. +If MPI is available, the following command will run the script in 10 processes at the same time. This will vastly increase the speed. On Panda2, the recommended maximum number of getdata processes to run in parallel is 20. ``` $ mpirun -n 10 python getdata.py [datadir] [dataname] [bgdir] [bgname] [poi] [imgsize] [outputpath] [outputdirname] [phi_0] [chi_0] From d66415783066701cdeaad5b1b8c524e1426d9104 Mon Sep 17 00:00:00 2001 From: albusdemens Date: Fri, 2 Jun 2017 23:23:26 +0200 Subject: [PATCH 047/150] plotting results --- Sunday_data.ini | 4 ++-- recon3d.py | 6 ++---- 2 files changed, 4 insertions(+), 6 deletions(-) diff --git a/Sunday_data.ini b/Sunday_data.ini index 38ff602..c0c0392 100644 --- a/Sunday_data.ini +++ b/Sunday_data.ini @@ -3,8 +3,8 @@ unit_cell [4.0495,4.0495,4.0495,90.,90.,90.] hkl [2,0,0] wavelength 0.7293 # Corresponds to an energy of 17 keV grain_pos [0,0,0] -grain_dim [61.,61.,61.] # Number of voxels -grain_steps [61,61,61] # 3D ROI +grain_dim [21.,21.,21.] # 3D ROI. To zoom in, decrease this number +grain_steps [101,101,101] # Number of voxels ang_mean [0,0] ang_width [.01,.01] #image simulation diff --git a/recon3d.py b/recon3d.py index 0d5ff7b..afbdc9c 100644 --- a/recon3d.py +++ b/recon3d.py @@ -190,13 +190,11 @@ def reconstruct_part(self, ista, isto): except IndexError: pass - cos[0] = cos[0] * (ma1 - mi1) / len_a1 + mi1 - cos[1] = cos[1] * (ma2 - mi2) / len_a2 + mi2 + cos[0] = cos[0] * (ma2 - mi2) / len_a2 + cos[1] = cos[1] * (ma1 - mi1) / len_a1 grain_ang[ix, iy, iz, :2] = cos - # print grain_ang[ix, iy, iz, 2] - if self.rank == 0: t_8 = time.clock() timelist.append(t_8 - t_0) From ed7bdf8e5d371d279a5209f61d05cb08f1b72777 Mon Sep 17 00:00:00 2001 From: albusdemens Date: Sat, 3 Jun 2017 16:56:54 +0200 Subject: [PATCH 048/150] Added scripts to plot npy from recon3d --- Plot_3D.m | 156 +++++++++++++++++++++++++++++++++++++++++++++++++++++ Plot_3D.py | 75 ++++++++++++++++++++++++++ recon3d.py | 2 +- 3 files changed, 232 insertions(+), 1 deletion(-) create mode 100644 Plot_3D.m create mode 100644 Plot_3D.py diff --git a/Plot_3D.m b/Plot_3D.m new file mode 100644 index 0000000..1142825 --- /dev/null +++ b/Plot_3D.m @@ -0,0 +1,156 @@ +close all; clear; +addpath('npy-matlab-master'); + +% Read 3D npy file, generated by recon3d.py +A = readNPY('grain_ang.npy'); + +Mean_z = zeros(size(A,3), 1); +for a = 1:size(A,3) + Mean_z(a) = mean(mean(A(:, :, a, 3))); +end +max_mean = max(Mean_z); + + + +%rgb2 = label2rgb(L2, 'jet', [.7 .7 .7], 'shuffle'); +s = regionprops(L2, 'Centroid', 'Area'); +% Keep only the possible candidates + + +%output = sprintf('Reconstruction.vtk'); +%savevtk(A_clean, output); + + +function [blobs_layer] = Blobs_finder(Nppy_array, mean_value) +% This function returns, for each Z value of an npy array, the regions that +% should be associated with the shape of a grain + +% Play with a single slice +B = (A(:, :, 50, 3)/max_mean); +D = (imregionalmin(B,8)); % Find local minima +Min_matrix = zeros(size(B)); + +for i = 1:size(B,1) + for j = 1:size(B,2) + if D(i,j) == 1 + Min_matrix(i,j) = B(i,j); + end + end +end + +% Higher local minima +D_max = max(max(Min_matrix)); +% Lower local minima +Min_matrix(~Min_matrix) = inf; +D_min = min(min(Min_matrix)); + +% Find the area of the basins relative to the minima +n_steps = 1000; % Number of steps between min and max +prop_steps = zeros(1,4); +for aa = D_min:(1/n_steps):D_max + B_one_level = zeros(size(B)); + for bb = 1:size(B,1) + for cc = 1:size(B,2) + if B(bb,cc) < aa + B_one_level(bb,cc) = 1; + end + end + end + % Find the different regions in the image + L2 = labelmatrix(bwconncomp(B_one_level)); + n_reg = size(unique(L2),1) - 1; + for dd = 1:n_reg + L3 = zeros(size(L2)); + for ee = 1:size(L2,1) + for ff = 1:size(L2,2) + if L2(ee,ff) == dd + L3(ee,ff) = 1; + end + end + end + % Calculate area and centroid of the considered region + s = regionprops(L3, 'Centroid', 'Area'); + if s.Area > 50 && s.Area < 400 + % Use a frame (the grain is well centred) + if s.Centroid(1) > 20 && s.Centroid(1) < 80 + if s.Centroid(2) > 20 && s.Centroid(2) < 80 + disp(aa); + prop_steps = [prop_steps; [aa s.Centroid(1) s.Centroid(2) s.Area]]; + end + end + end + end +end + +prop_steps( ~any(prop_steps,2), : ) = []; + +% Look at the CM distribution and, for each cluster, find minima corresponding +% to largest area +Loc_CM = zeros(size(B)); +for u = 1:size(prop_steps,1) + Loc_CM(int8(prop_steps(u, 2)), int8(prop_steps(u,3))) = 1; +end +figure; imshow(Loc_CM); +Loc_CM_lab = bwlabel(Loc_CM); % label the different clusters of minima + +n1 = size(unique(Loc_CM_lab),1) - 1; +val_max = zeros(n1, 5); +for uu = 1:n1 + M1 = zeros(size(B)); + for i = 1:size(B,1) + for j = 1:size(B,2) + if Loc_CM_lab(i,j) == uu + M1(i,j) = 1; + end + end + end + + Data_M1 = zeros(1,4); + for vv = 1:size(prop_steps, 1) + if M1(round(prop_steps(vv,2)), round(prop_steps(vv,3))) == 1 + Data_M1 = [Data_M1; prop_steps(vv,:)]; + end + end + Data_M1( ~any(Data_M1,2), : ) = []; + [max_A, idx_max_A] = max(Data_M1(:,4)); + val_max(uu,1:4) = Data_M1(idx_max_A,:); % aa x_min y_min A_ min + val_max(uu,5) = idx_max_A; % min position in prop_steps +end + +% Save, in a single image, the blobs corresponding to the local minima + +Blobs_final_comb = zeros(size(B)); +for uu = 1:n1 + for aa = val_max(uu,1) + B_one_level = zeros(size(B)); + for bb = 1:size(B,1) + for cc = 1:size(B,2) + if B(bb,cc) < aa + B_one_level(bb,cc) = 1; + end + end + end + % Find the different regions in the image + L2 = labelmatrix(bwconncomp(B_one_level)); + n_reg = size(unique(L2),1) - 1; + for dd = 1:n_reg + L3 = zeros(size(L2)); + for ee = 1:size(L2,1) + for ff = 1:size(L2,2) + if L2(ee,ff) == dd + L3(ee,ff) = 1; + end + end + end + % Calculate area and centroid of the considered region + s = regionprops(L3, 'Centroid', 'Area'); + if s.Centroid(1) == val_max(uu,2) && s.Centroid(2) == val_max(uu,3) && s.Area == val_max(uu,4) + Blobs_final_comb = Blobs_final_comb + L3; + end + end + end +end + +blobs_layer = Blobs_final_comb; + +end % function diff --git a/Plot_3D.py b/Plot_3D.py new file mode 100644 index 0000000..374e437 --- /dev/null +++ b/Plot_3D.py @@ -0,0 +1,75 @@ +import numpy as np +import matplotlib.pyplot as plt +from mpl_toolkits.mplot3d import Axes3D +import math +from itertools import count +import sys + +A = np.load('/Users/Alberto/Documents/Data_analysis/DFXRM/Results_sunday/grain_ang.npy') + +# Find the max of the mean for the weight +M = np.zeros(A.shape[2]) +for i in range(A.shape[2]): + M[i] = np.mean(A[:,:,i,2]) +max_mean = np.max(M) + +# Select a z value and plot the respective slice (label by label and combined labels) + +z_val = 50 +A_z_val = np.empty(shape=[A.shape[0], A.shape[1]]) +A_z_val_0 = np.empty(shape=[A.shape[0], A.shape[1]]) +A_z_val_1 = np.empty(shape=[A.shape[0], A.shape[1]]) +A_z_val_2 = np.empty(shape=[A.shape[0], A.shape[1]]) + +A_z_val_0[:,:] = A[:,:,z_val,0]#/np.mean(A[:,:,z_val,0]) +A_z_val_1[:,:] = A[:,:,z_val,1]#/np.mean(A[:,:,z_val,1]) +A_z_val_2[:,:] = A[:,:,z_val,2]/max_mean +A_z_val[:,:] = ( 0.5 * ( A_z_val_0[:,:] + A_z_val_1[:,:])) + +plt.subplot(221) +plt.imshow(A_z_val_0) +plt.title('Theta') + +plt.subplot(222) +plt.imshow(A_z_val_1) +plt.title('Gamma') + +plt.subplot(223) +plt.imshow(A_z_val_2) +plt.title('Weight') + +plt.subplot(224) +plt.imshow(A_z_val) +plt.title('Combined value') + +plt.show() + +plt.imshow(A_z_val_2) +plt.title('Weight') +plt.show() + +sys.exit + +# Save, layer by layer, the slices of the volume + +def gen_filenames(prefix, suffix, places=3): + """Generate sequential filenames with the format + + The index field is padded with leading zeroes to the specified number of places + """ + pattern = "{}{{:0{}d}}{}".format(prefix, places, suffix) + for i in count(1): + yield pattern.format(i) + +g = gen_filenames("/Users/Alberto/Documents/Data_analysis/DFXRM/Results_sunday/3D_reconstruction/img_", ".png") +for i in range(A.shape[2]): + A_2D = np.empty(shape=[A.shape[0], A.shape[1]]) + for j in range(A.shape[0]): + for k in range(A.shape[1]): + if ((A[j,k,i,2]/max_mean)<0.03) or (0.4<(A[j,k,i,2]/max_mean)<1): + A_2D[j,k,i] = ( 0.5 * ( A[j,k,i,0] + A[j,k,i,1])) + else: + A_2D[j,k,i] = 0 + + plt.imshow(A_2D) #Needs to be in row,col order + plt.savefig(next(g)) diff --git a/recon3d.py b/recon3d.py index afbdc9c..4d1712e 100644 --- a/recon3d.py +++ b/recon3d.py @@ -1,6 +1,6 @@ from check_input import read as ini import sys -import time +import time, os import numpy as np from scipy import ndimage From 88ddd347cba23bbddaf050923312d9107fb56c51 Mon Sep 17 00:00:00 2001 From: albusdemens Date: Sat, 3 Jun 2017 18:36:37 +0200 Subject: [PATCH 049/150] Introduced Blobs_finder function --- Blobs_finder.m | 132 +++++++++++++++++++++++++++++++++++++++++++ Plot_3D.m | 149 +++---------------------------------------------- 2 files changed, 139 insertions(+), 142 deletions(-) create mode 100644 Blobs_finder.m diff --git a/Blobs_finder.m b/Blobs_finder.m new file mode 100644 index 0000000..00a48d3 --- /dev/null +++ b/Blobs_finder.m @@ -0,0 +1,132 @@ +function blobs_layer = Blobs_finder(npy_array, mean_value, z_value) +% This function returns, for each Z value of an npy array, the regions that +% should be associated with the shape of a grain + +% Play with a single slice +B = (npy_array(:, :, z_value, 3)/mean_value); +D = (imregionalmin(B,8)); % Find local minima +Min_matrix = zeros(size(B)); + +for i = 1:size(B,1) + for j = 1:size(B,2) + if D(i,j) == 1 + Min_matrix(i,j) = B(i,j); + end + end +end + +% Higher local minima +D_max = max(max(Min_matrix)); +% Lower local minima +Min_matrix(~Min_matrix) = inf; +D_min = min(min(Min_matrix)); + +% Find the area of the basins relative to the minima +n_steps = 1000; % Number of steps between min and max +prop_steps = zeros(1,4); +for aa = D_min:(1/n_steps):D_max + B_one_level = zeros(size(B)); + for bb = 1:size(B,1) + for cc = 1:size(B,2) + if B(bb,cc) < aa + B_one_level(bb,cc) = 1; + end + end + end + % Find the different regions in the image + L2 = labelmatrix(bwconncomp(B_one_level)); + n_reg = size(unique(L2),1) - 1; + for dd = 1:n_reg + L3 = zeros(size(L2)); + for ee = 1:size(L2,1) + for ff = 1:size(L2,2) + if L2(ee,ff) == dd + L3(ee,ff) = 1; + end + end + end + % Calculate area and centroid of the considered region + s = regionprops(L3, 'Centroid', 'Area'); + if s.Area > 50 && s.Area < 400 + % Use a frame (the grain is well centred) + if s.Centroid(1) > 20 && s.Centroid(1) < 80 + if s.Centroid(2) > 20 && s.Centroid(2) < 80 + prop_steps = [prop_steps; [aa s.Centroid(1) s.Centroid(2) s.Area]]; + end + end + end + end +end + +prop_steps( ~any(prop_steps,2), : ) = []; + +% Look at the CM distribution and, for each cluster, find minima corresponding +% to largest area +Loc_CM = zeros(size(B)); +for u = 1:size(prop_steps,1) + Loc_CM(int8(prop_steps(u, 2)), int8(prop_steps(u,3))) = 1; +end +%figure; imshow(Loc_CM); +Loc_CM_lab = bwlabel(Loc_CM); % label the different clusters of minima + +n1 = size(unique(Loc_CM_lab),1) - 1; +val_max = zeros(n1, 5); +for uu = 1:n1 + M1 = zeros(size(B)); + for i = 1:size(B,1) + for j = 1:size(B,2) + if Loc_CM_lab(i,j) == uu + M1(i,j) = 1; + end + end + end + + Data_M1 = zeros(1,4); + for vv = 1:size(prop_steps, 1) + if M1(round(prop_steps(vv,2)), round(prop_steps(vv,3))) == 1 + Data_M1 = [Data_M1; prop_steps(vv,:)]; + end + end + Data_M1( ~any(Data_M1,2), : ) = []; + [max_A, idx_max_A] = max(Data_M1(:,4)); + val_max(uu,1:4) = Data_M1(idx_max_A,:); % aa x_min y_min A_ min + val_max(uu,5) = idx_max_A; % min position in prop_steps +end + +% Save, in a single image, the blobs corresponding to the local minima + +Blobs_final_comb = zeros(size(B)); +for uu = 1:n1 + for aa = val_max(uu,1) + B_one_level = zeros(size(B)); + for bb = 1:size(B,1) + for cc = 1:size(B,2) + if B(bb,cc) < aa + B_one_level(bb,cc) = 1; + end + end + end + % Find the different regions in the image + L2 = labelmatrix(bwconncomp(B_one_level)); + n_reg = size(unique(L2),1) - 1; + for dd = 1:n_reg + L3 = zeros(size(L2)); + for ee = 1:size(L2,1) + for ff = 1:size(L2,2) + if L2(ee,ff) == dd + L3(ee,ff) = 1; + end + end + end + % Calculate area and centroid of the considered region + s = regionprops(L3, 'Centroid', 'Area'); + if s.Centroid(1) == val_max(uu,2) && s.Centroid(2) == val_max(uu,3) && s.Area == val_max(uu,4) + Blobs_final_comb = Blobs_final_comb + L3; + end + end + end +end + +blobs_layer = Blobs_final_comb; + +end % function diff --git a/Plot_3D.m b/Plot_3D.m index 1142825..d7af2a8 100644 --- a/Plot_3D.m +++ b/Plot_3D.m @@ -10,147 +10,12 @@ end max_mean = max(Mean_z); - - -%rgb2 = label2rgb(L2, 'jet', [.7 .7 .7], 'shuffle'); -s = regionprops(L2, 'Centroid', 'Area'); -% Keep only the possible candidates - - -%output = sprintf('Reconstruction.vtk'); -%savevtk(A_clean, output); - - -function [blobs_layer] = Blobs_finder(Nppy_array, mean_value) -% This function returns, for each Z value of an npy array, the regions that -% should be associated with the shape of a grain - -% Play with a single slice -B = (A(:, :, 50, 3)/max_mean); -D = (imregionalmin(B,8)); % Find local minima -Min_matrix = zeros(size(B)); - -for i = 1:size(B,1) - for j = 1:size(B,2) - if D(i,j) == 1 - Min_matrix(i,j) = B(i,j); - end - end -end - -% Higher local minima -D_max = max(max(Min_matrix)); -% Lower local minima -Min_matrix(~Min_matrix) = inf; -D_min = min(min(Min_matrix)); - -% Find the area of the basins relative to the minima -n_steps = 1000; % Number of steps between min and max -prop_steps = zeros(1,4); -for aa = D_min:(1/n_steps):D_max - B_one_level = zeros(size(B)); - for bb = 1:size(B,1) - for cc = 1:size(B,2) - if B(bb,cc) < aa - B_one_level(bb,cc) = 1; - end - end - end - % Find the different regions in the image - L2 = labelmatrix(bwconncomp(B_one_level)); - n_reg = size(unique(L2),1) - 1; - for dd = 1:n_reg - L3 = zeros(size(L2)); - for ee = 1:size(L2,1) - for ff = 1:size(L2,2) - if L2(ee,ff) == dd - L3(ee,ff) = 1; - end - end - end - % Calculate area and centroid of the considered region - s = regionprops(L3, 'Centroid', 'Area'); - if s.Area > 50 && s.Area < 400 - % Use a frame (the grain is well centred) - if s.Centroid(1) > 20 && s.Centroid(1) < 80 - if s.Centroid(2) > 20 && s.Centroid(2) < 80 - disp(aa); - prop_steps = [prop_steps; [aa s.Centroid(1) s.Centroid(2) s.Area]]; - end - end - end - end +A_final = zeros(size(A)); +for i = 1:size(A,3) + disp(sprintf('Analyzing level %03i', i)); + [Layer_1] = Blobs_finder(A, max_mean, i); + A_final(:,:,i) = Layer_1(:,:); end -prop_steps( ~any(prop_steps,2), : ) = []; - -% Look at the CM distribution and, for each cluster, find minima corresponding -% to largest area -Loc_CM = zeros(size(B)); -for u = 1:size(prop_steps,1) - Loc_CM(int8(prop_steps(u, 2)), int8(prop_steps(u,3))) = 1; -end -figure; imshow(Loc_CM); -Loc_CM_lab = bwlabel(Loc_CM); % label the different clusters of minima - -n1 = size(unique(Loc_CM_lab),1) - 1; -val_max = zeros(n1, 5); -for uu = 1:n1 - M1 = zeros(size(B)); - for i = 1:size(B,1) - for j = 1:size(B,2) - if Loc_CM_lab(i,j) == uu - M1(i,j) = 1; - end - end - end - - Data_M1 = zeros(1,4); - for vv = 1:size(prop_steps, 1) - if M1(round(prop_steps(vv,2)), round(prop_steps(vv,3))) == 1 - Data_M1 = [Data_M1; prop_steps(vv,:)]; - end - end - Data_M1( ~any(Data_M1,2), : ) = []; - [max_A, idx_max_A] = max(Data_M1(:,4)); - val_max(uu,1:4) = Data_M1(idx_max_A,:); % aa x_min y_min A_ min - val_max(uu,5) = idx_max_A; % min position in prop_steps -end - -% Save, in a single image, the blobs corresponding to the local minima - -Blobs_final_comb = zeros(size(B)); -for uu = 1:n1 - for aa = val_max(uu,1) - B_one_level = zeros(size(B)); - for bb = 1:size(B,1) - for cc = 1:size(B,2) - if B(bb,cc) < aa - B_one_level(bb,cc) = 1; - end - end - end - % Find the different regions in the image - L2 = labelmatrix(bwconncomp(B_one_level)); - n_reg = size(unique(L2),1) - 1; - for dd = 1:n_reg - L3 = zeros(size(L2)); - for ee = 1:size(L2,1) - for ff = 1:size(L2,2) - if L2(ee,ff) == dd - L3(ee,ff) = 1; - end - end - end - % Calculate area and centroid of the considered region - s = regionprops(L3, 'Centroid', 'Area'); - if s.Centroid(1) == val_max(uu,2) && s.Centroid(2) == val_max(uu,3) && s.Area == val_max(uu,4) - Blobs_final_comb = Blobs_final_comb + L3; - end - end - end -end - -blobs_layer = Blobs_final_comb; - -end % function +output = sprintf('Reconstruction.vtk'); +savevtk(A_final, output); From b9f500404807c5451973b3e2de2fb17edba3704f Mon Sep 17 00:00:00 2001 From: albusdemens Date: Sat, 3 Jun 2017 23:47:34 +0200 Subject: [PATCH 050/150] Added combined volume plot --- Blobs_finder.m | 12 ++++++++++-- Plot_3D.m | 43 +++++++++++++++++++++++++++++++++++++------ 2 files changed, 47 insertions(+), 8 deletions(-) diff --git a/Blobs_finder.m b/Blobs_finder.m index 00a48d3..0f5ccf3 100644 --- a/Blobs_finder.m +++ b/Blobs_finder.m @@ -1,9 +1,17 @@ -function blobs_layer = Blobs_finder(npy_array, mean_value, z_value) +function blobs_layer = Blobs_finder(npy_array, mean_value, z_value, idx_tgw) % This function returns, for each Z value of an npy array, the regions that % should be associated with the shape of a grain +% npy_array from recon3d.py +% mean_value is the max(mean) of the weight, calculated for each layer +% z_value is considered layer +% idx_wtg is the voxel index to consider. 1: theta, 2: gamma, 3: weight % Play with a single slice -B = (npy_array(:, :, z_value, 3)/mean_value); +if idx_tgw == 3 + B = (npy_array(:, :, z_value, idx_tgw)/mean_value); +elseif idx_tgw == 1 + B = npy_array(:, :, z_value, idx_tgw); +end D = (imregionalmin(B,8)); % Find local minima Min_matrix = zeros(size(B)); diff --git a/Plot_3D.m b/Plot_3D.m index d7af2a8..5d0a46d 100644 --- a/Plot_3D.m +++ b/Plot_3D.m @@ -10,12 +10,43 @@ end max_mean = max(Mean_z); -A_final = zeros(size(A)); +%figure; mesh(A(:, :, 50, 1)); + +A_final_w = zeros(size(A)); +for i = 1:size(A,3) + fprintf('Analyzing level %03i\n', i); + [Layer_w] = Blobs_finder(A, max_mean, i, 3); + A_final_w(:,:,i) = Layer_w(:,:); +end + +A_final_t = zeros(size(A)); +for i = 1:size(A,3) + fprintf('Analyzing level %03i\n', i); + [Layer_t] = Blobs_finder(A, max_mean, i, 1); + A_final_t(:,:,i) = Layer_t(:,:); +end + +A_final_g = zeros(size(A)); +for i = 1:size(A,3) + for j = 1:size(A,1) + for k = 1:size(A,2) + if A(j, k, i, 2) < 0.07 + A_final_g(j, k, i) = 1; + end + end + end +end + +A_comb = zeros(size(A)); for i = 1:size(A,3) - disp(sprintf('Analyzing level %03i', i)); - [Layer_1] = Blobs_finder(A, max_mean, i); - A_final(:,:,i) = Layer_1(:,:); + A_comb(:,:,i) = A_final_w(:,:,i)*10*(A_final_t(:,:,i) + A_final_g(:,:,i)); end -output = sprintf('Reconstruction.vtk'); -savevtk(A_final, output); +output_t = sprintf('theta_3D.vtk'); +savevtk(A_final_t, output_t); +output_g = sprintf('gamma_3D.vtk'); +savevtk(A_final_g, output_g); +output_w = sprintf('weight_3D.vtk'); +savevtk(A_final_w, output_w); +output_comb = sprintf('comb_3D.vtk'); +savevtk(A_comb, output_comb); From d20d049eb225b62eeeb1c0e36f823a2a24e514fe Mon Sep 17 00:00:00 2001 From: albusdemens Date: Tue, 6 Jun 2017 21:38:55 +0200 Subject: [PATCH 051/150] Backup --- Plot_3D.m | 15 +++++++++------ Plot_3D.py | 2 +- recon3d.py | 3 ++- 3 files changed, 12 insertions(+), 8 deletions(-) diff --git a/Plot_3D.m b/Plot_3D.m index 5d0a46d..a870215 100644 --- a/Plot_3D.m +++ b/Plot_3D.m @@ -19,6 +19,9 @@ A_final_w(:,:,i) = Layer_w(:,:); end +output_w = sprintf('weight_3D.vtk'); +savevtk(A_final_w, output_w); + A_final_t = zeros(size(A)); for i = 1:size(A,3) fprintf('Analyzing level %03i\n', i); @@ -26,6 +29,9 @@ A_final_t(:,:,i) = Layer_t(:,:); end +output_t = sprintf('theta_3D.vtk'); +savevtk(A_final_t, output_t); + A_final_g = zeros(size(A)); for i = 1:size(A,3) for j = 1:size(A,1) @@ -37,16 +43,13 @@ end end +output_g = sprintf('gamma_3D.vtk'); +savevtk(A_final_g, output_g); + A_comb = zeros(size(A)); for i = 1:size(A,3) A_comb(:,:,i) = A_final_w(:,:,i)*10*(A_final_t(:,:,i) + A_final_g(:,:,i)); end -output_t = sprintf('theta_3D.vtk'); -savevtk(A_final_t, output_t); -output_g = sprintf('gamma_3D.vtk'); -savevtk(A_final_g, output_g); -output_w = sprintf('weight_3D.vtk'); -savevtk(A_final_w, output_w); output_comb = sprintf('comb_3D.vtk'); savevtk(A_comb, output_comb); diff --git a/Plot_3D.py b/Plot_3D.py index 374e437..79561bb 100644 --- a/Plot_3D.py +++ b/Plot_3D.py @@ -5,7 +5,7 @@ from itertools import count import sys -A = np.load('/Users/Alberto/Documents/Data_analysis/DFXRM/Results_sunday/grain_ang.npy') +A = np.load('/Users/Alberto/Documents/Data_analysis/DFXRM/Results_sunday/grain_ang_1.npy') # Find the max of the mean for the weight M = np.zeros(A.shape[2]) diff --git a/recon3d.py b/recon3d.py index 4d1712e..f066f92 100644 --- a/recon3d.py +++ b/recon3d.py @@ -3,6 +3,7 @@ import time, os import numpy as np from scipy import ndimage +import matplotlib.pyplot as plt try: from mpi4py import MPI @@ -397,7 +398,7 @@ def build_rotation_lookup_general(self): def outputfiles(self, grain_ang): print "Saving grain_ang file..." - np.save(self.par['path'] + '/grain_ang.npy', grain_ang) + np.save(self.par['path'] + '/grain_ang_1.npy', grain_ang) if __name__ == "__main__": if len(sys.argv) != 2: From 2e03901bccdc40ae20216eca2f75b8d8b71a9727 Mon Sep 17 00:00:00 2001 From: albusdemens Date: Tue, 6 Jun 2017 21:40:48 +0200 Subject: [PATCH 052/150] Correct version --- getdata.py | 126 +++++++++++++++++++++++++++++++---------------------- 1 file changed, 73 insertions(+), 53 deletions(-) diff --git a/getdata.py b/getdata.py index 2894666..30ef8ae 100644 --- a/getdata.py +++ b/getdata.py @@ -22,6 +22,8 @@ Image size Output path Name of new output directory to make +Initial phi values +Initial chi value ''' @@ -30,6 +32,7 @@ def __init__( self, datadir, dataname, bgpath, bgfilename, poi, imgsize, outputpath, outputdir, + phi_0, chi_0, sim=False): try: @@ -54,28 +57,15 @@ def __init__( int(int(poi[1]) + int(imgsize[1]) / 2)] data = GetEdfData(datadir, dataname, bgpath, bgfilename, roi, sim) - self.alpha, self.beta, self.omega = data.getMetaValues() # Redefine - - # Alpha and beta values were measured at irregular steps. To make things - # easy, we bin the values in 7 intervals per variable - [count_alpha, extremes_alpha] = np.histogram(self.alpha, 7) - self.val_alpha = np.zeros(len(extremes_alpha)-1) - [count_beta, extremes_beta] = np.histogram(self.beta, 7) - self.val_beta = np.zeros(len(extremes_beta)-1) - - # Find center of each bin - for i in range(0,len(extremes_alpha)-1): - self.val_alpha[i] = np.mean([extremes_alpha[i], extremes_alpha[i+1]]) - for j in range(0,len(extremes_beta)-1): - self.val_beta[j] = np.mean([extremes_beta[j], extremes_beta[j+1]]) - - # For each experimental value, find closest bin centre - self.alpha_discr = np.zeros(len(self.alpha)) - for i in range(0, len(self.alpha)): - self.alpha_discr[i] = find_nearest(self.val_alpha,self.alpha[i]) - self.beta_discr = np.zeros(len(self.beta)) - for j in range(0, len(self.beta)): - self.beta_discr[j] = find_nearest(self.val_beta,self.beta[j]) + + self.alpha, self.beta, self.omega, self.theta = data.getMetaValues() + + self.index_list = range(len(data.meta)) + self.meta = data.meta + self.phi = phi_0 + self.chi = chi_0 + + # self.calcEtaIndexList(data, eta) self.allFiles(data, imgsize) @@ -92,47 +82,72 @@ def makeOutputFolder(self, path, dirname): return directory def allFiles(self, data, imsiz): - index_list = range(len(data.meta)) - met = data.meta + # index_list = range(len(data.meta)) + # met = data.meta + + # theta = data.theta0 + np.arange(-3.5 * 0.032, 3.5 * 0.032, 0.032) with warnings.catch_warnings(): warnings.simplefilter("ignore") - imgarray = data.makeImgArray(index_list, 50, 'linetrace') + imgarray = data.makeImgArray(self.index_list, 50, 'linetrace') if self.rank == 0: - # If we need to bin the angular values - lena = len(self.val_alpha) - lenb = len(self.val_beta) - leno = len(self.omega) # If we don't need to bin the angular values - #lena = len(self.alpha) - #lenb = len(self.beta) - #leno = len(self.omega) - - bigarray = np.zeros((lena, lenb, leno, int(imsiz[1]), int(imsiz[0])), dtype=np.uint16) - - for i, ind in enumerate(index_list): - a = np.where(self.alpha_discr == met[ind, 0]) - b = np.where(self.alpha_discr == met[ind, 1]) - #a = np.where(self.alpha == met[ind, 0]) - #b = np.where(self.beta == met[ind, 1]) - c = np.where(self.omega == met[ind, 2]) - # d = np.where(self.theta == met[ind, 4]) - - bigarray[a, b, c, :, :] = imgarray[ind, :, :] - - np.save(self.directory + '/alpha.npy', list(set(self.alpha_discr))) - np.save(self.directory + '/beta.npy', list(set(self.beta_discr))) - # np.save(self.directory + '/theta.npy', self.theta) + lena = len(self.alpha) + lenb = len(self.beta) + leno = len(self.omega) + lent = len(self.theta) + print lena, lenb, leno, lent + + # Reduce alpha and beta to a single index_list + idx_list = np.zeros(len(self.meta)) + for j in range(len(self.meta)): + alp = float(self.meta[j,0]) + omg = float(self.meta[j,2]) + phi_0 = float(self.phi) + chi_0 = float(self.chi) + idx_list[j] = int((alp - phi_0)/(np.cos(np.deg2rad(omg))*0.032)) + + num_int = len(set(idx_list)) # Number of considered alpha, beta + # intervals + idx_values = sorted(set(idx_list)) # Values of the indices + + print 'Check that the number of (phi, chi) steps is', num_int + + bigarray = np.zeros((num_int, lent, leno, int(imsiz[1]), int(imsiz[0])), dtype=np.uint16) + + #AA = np.empty([len(self.index_list), 5]) + for i, ind in enumerate(self.index_list): + a = np.where(self.alpha == self.meta[int(ind),0]) # rock + b = np.where(self.beta == self.meta[int(ind),1]) # roll + c = np.where(self.omega == self.meta[int(ind),2]) # omega + d = np.where(self.theta == self.meta[int(ind),4]) # theta + idx_rescaled = int((self.meta[int(ind),0] - phi_0) / (np.cos(np.deg2rad(self.meta[int(ind),2])) * 0.032) + ((num_int - 1) / 2)) + e = (idx_rescaled - ((num_int - 1) / 2)) * 0.032 + + # Can we effectively reconstruct chi and phi from idx_rescaled? + #ph = phi_0 + ((idx_rescaled - 3)*np.cos(np.deg2rad(self.meta[int(ind),2]))*0.032) + #ch = chi_0 + ((idx_rescaled - 3)*(np.tan(np.deg2rad(self.meta[int(ind),2]))*0.032)) + + #AA[ind] = [self.meta[int(ind),0], self.meta[int(ind),1],self.meta[int(ind),2], idx_rescaled, self.meta[int(ind),4]] + + bigarray[idx_rescaled, d[0], c[0], :, :] = imgarray[ind, :, :] + + np.save(self.directory + '/alpha.npy', self.alpha) + np.save(self.directory + '/beta.npy', self.beta) + np.save(self.directory + '/theta.npy', self.theta) np.save(self.directory + '/omega.npy', self.omega) - + np.save(self.directory + '/gamma.npy', [((b - ((num_int - 1)/2))*0.032) for b in range(num_int)]) + # The gamma angle is a linear combination of alpha and beta + np.save(self.directory + '/all_data.npy', self.meta) np.save(self.directory + '/dataarray.npy', bigarray) + #np.savetxt(self.directory + '/AA.txt', AA) if __name__ == "__main__": - if len(sys.argv) != 9: - if len(sys.argv) != 10: + if len(sys.argv) != 10: + if len(sys.argv) != 11: print "Not enough input parameters. Data input should be:\n\ Directory of data\n\ Name of data files\n\ @@ -142,6 +157,8 @@ def allFiles(self, data, imsiz): Image size\n\ Output path\n\ Name of new output directory to make\n\ + Initial phi value\n\ + Initial chi value\n\ " else: mm = makematrix( @@ -153,7 +170,8 @@ def allFiles(self, data, imsiz): sys.argv[6], sys.argv[7], sys.argv[8], - sys.argv[9]) + sys.argv[9], + sys.argv[10]) else: mm = makematrix( sys.argv[1], @@ -163,4 +181,6 @@ def allFiles(self, data, imsiz): sys.argv[5], sys.argv[6], sys.argv[7], - sys.argv[8]) + sys.argv[8], + sys.argv[10], + sys.argv[11]) From 1fd5314db1c8ccaddc008a330be7df657b7a2d34 Mon Sep 17 00:00:00 2001 From: albusdemens Date: Wed, 7 Jun 2017 13:21:15 +0200 Subject: [PATCH 053/150] Included diverging colormap --- Plot_3D.py | 38 +++++++++--- cmap_map.py | 31 ++++++++++ lib/colorData.mat | Bin 0 -> 140910 bytes lib/othercolor.m | 155 ++++++++++++++++++++++++++++++++++++++++++++++ 4 files changed, 215 insertions(+), 9 deletions(-) create mode 100644 cmap_map.py create mode 100644 lib/colorData.mat create mode 100644 lib/othercolor.m diff --git a/Plot_3D.py b/Plot_3D.py index 79561bb..133952b 100644 --- a/Plot_3D.py +++ b/Plot_3D.py @@ -1,18 +1,32 @@ +# This script selects a layer from the 3D npy array reconstructed by recon3d.py, +# and plots the spatial distribution of the following values: +# - chi and phi angles used to tilt the sample +# - weight, which quantifies the quality of the fit assigning to a voxel the +# corresponding (chi, phi) angles + import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D import math from itertools import count import sys +from cmap_map import cmap_map +# Load the npy array A = np.load('/Users/Alberto/Documents/Data_analysis/DFXRM/Results_sunday/grain_ang_1.npy') -# Find the max of the mean for the weight +# Considering all z layers, find the max of the mean for the weight M = np.zeros(A.shape[2]) for i in range(A.shape[2]): M[i] = np.mean(A[:,:,i,2]) max_mean = np.max(M) +Layer_max = np.zeros([A.shape[0], A.shape[1]]) +for i in range(A.shape[2]): + M[i] = np.mean(A[:,:,i,2]) + if M[i] == max_mean: + Layer_max[:,:] = A[:,:,i,2] + # Select a z value and plot the respective slice (label by label and combined labels) z_val = 50 @@ -23,33 +37,39 @@ A_z_val_0[:,:] = A[:,:,z_val,0]#/np.mean(A[:,:,z_val,0]) A_z_val_1[:,:] = A[:,:,z_val,1]#/np.mean(A[:,:,z_val,1]) -A_z_val_2[:,:] = A[:,:,z_val,2]/max_mean +layer_mean = np.mean(A[:,:,z_val,2]) +for i in range(A_z_val_2[0].size): + for j in range(A_z_val_2[1].size): + A_z_val_2[i,j] = A[i,j,z_val,2]/Layer_max[i,j] A_z_val[:,:] = ( 0.5 * ( A_z_val_0[:,:] + A_z_val_1[:,:])) +# Use an inverted colors heatmap (high values at the centre) +inv = cmap_map(lambda x: 1-x, plt.cm.Spectral) + plt.subplot(221) -plt.imshow(A_z_val_0) +plt.imshow(A_z_val_0, cmap = inv) plt.title('Theta') plt.subplot(222) -plt.imshow(A_z_val_1) +plt.imshow(A_z_val_1, cmap = inv) plt.title('Gamma') plt.subplot(223) -plt.imshow(A_z_val_2) +plt.imshow(A_z_val_2, cmap = inv) plt.title('Weight') plt.subplot(224) -plt.imshow(A_z_val) +plt.imshow(A_z_val-2, cmap = inv) plt.title('Combined value') plt.show() -plt.imshow(A_z_val_2) +sys.exit() # This stops the script here + +plt.imshow(A_z_val_2, cmap = inv) plt.title('Weight') plt.show() -sys.exit - # Save, layer by layer, the slices of the volume def gen_filenames(prefix, suffix, places=3): diff --git a/cmap_map.py b/cmap_map.py new file mode 100644 index 0000000..ce6822e --- /dev/null +++ b/cmap_map.py @@ -0,0 +1,31 @@ +import numpy as np +import matplotlib + +def cmap_map(function,cmap): + """ Applies function (which should operate on vectors of shape 3: + [r, g, b], on colormap cmap. This routine will break any discontinuous points in a colormap. + """ + cdict = cmap._segmentdata + step_dict = {} + # Firt get the list of points where the segments start or end + for key in ('red','green','blue'): step_dict[key] = map(lambda x: x[0], cdict[key]) + step_list = sum(step_dict.values(), []) + step_list = np.array(list(set(step_list))) + # Then compute the LUT, and apply the function to the LUT + reduced_cmap = lambda step : np.array(cmap(step)[0:3]) + old_LUT = np.array(map( reduced_cmap, step_list)) + new_LUT = np.array(map( function, old_LUT)) + # Now try to make a minimal segment definition of the new LUT + cdict = {} + for i,key in enumerate(('red','green','blue')): + this_cdict = {} + for j,step in enumerate(step_list): + if step in step_dict[key]: + this_cdict[step] = new_LUT[j,i] + elif new_LUT[j,i]!=old_LUT[j,i]: + this_cdict[step] = new_LUT[j,i] + colorvector= map(lambda x: x + (x[1], ), this_cdict.items()) + colorvector.sort() + cdict[key] = colorvector + + return matplotlib.colors.LinearSegmentedColormap('colormap',cdict,1024) diff --git a/lib/colorData.mat b/lib/colorData.mat new file mode 100644 index 0000000000000000000000000000000000000000..cb12a1d16dd7ea0d7053024f3602c56112d08474 GIT binary patch literal 140910 zcmb4sby!qg_x8};HHx$dNQXzp${Pu;OM_h0Ur;%EFrz z6cvU4fB%P}Fg9nyU@%VLFFfas2M*_haaR_8AM;393}m2{pD1&oi7g><;-+s4UWy{gu!nm_&rs5%{Op^rmq46855Y4;2nIv#{utR&*PouH=gOMvPP_ZwVHl z&K(fyW22m!5V>@?yp}pXlk~haj$nglPza~UJn}J5gNfR;e|IB>l5PAbail^6)Ad?e z5UzG$Wwk!cuhr;!JG49KTgSD>vvP9I*}Fd^Fzm6%%)~F?aM5p6>D6ns%7v(h2K3D4 zaq?8O-s7^wk>bUgYr@k{Tq&7d2~UmViTm(b5mnx?1nGq|KJAg%CPl3JO+Ko4ZxY5> zPt6A|eRyT*ZND~}(#_nOuHC`fdhEf2D-q&e%sJX5YT>9LA?EQwXS&!ABsx+&0gq3X z5*qg;jHEZM!!}m^wqJXB7TVeg8bUr%&}(VjQF!{{(rX&_a9kiW?eU2=!pItD0gTNL z{m;;bv=tk||FL~&9%HA{4* zW$x;0k0q)t2o39u+-efXHdeQU@=)i#FpVKH)oJ=F)a{eU(6W%8m&L|vbTs!@X^)&) zY-19hCJbSj1qae%iAr^q?Gms+f2 zzIo>>6^8KDZ3-Iq-=Dv2msH8-n!c>qSg!jd%xkq;ospWwGws6DeNpe3*|)jvW}*$8 z5Ch*yBnGQqzP!vvJ=8xCL*!o&7yzErz?{a*R>2kgR<{4==b!LU~G4|XXW580z1K<-uOre3< zI=Fb;%HqGD0?V5=7QTBc)=+_PqCt_oyhsyUOWGP3Ltt8v&eR#HSe)2}!)YC$rOzqn z!^G2&4&tV}n%6SB4j9D|O&9#^bw;irzGkC{mnN}4KU{vK--C)aF#ml^CF-*s+02y3 zCn%Qh+0jEwj;&a-%2^D8<9)ETG+`>8NQD~~>C z)rA$S{x}Zg&tVtxInc#?Th?L^_m2LcOy793C4!3DwQu(=xWT|-N~(nvF6|M@3{4z3 zjZEX8h$Bsrve?F>Qj(q?5SmM@uhuvWM>#qNY7Q?m-wi7AcH%u0B#nir456SYC=F z?85Uagj3Zl^SpE!sEs8kEj&1_J#tb5bLQy50%&B>ijnNNJvbcIqjk*eYpERy4qFI& z(YhO6&OzR!xc(1X_T^oFZP0B!UZS0|D2fPtSRoDEPiKI$CGsMiUFRF}D5#&&vy_sZytPFt1!O$DJa7 zKvT_DO#S@=v+cNY|G^-3`vJ-dI(E}|qTj;ef;NaEUwnmwI-8l9a|=?ms*Y2~pAVYx zwE-WO2F6-yqbz*po|R{=a%NLicvNnk$M{+ZTVP&av)3L;SD6oU6a}bD2dFTwS&b26j;|QA$|*&CO$8j_O3`|HLmWX5?+Z>~^s*p*W7TjA zjJeA{C@AH}OLkDj`hmD*i4&rvwI6RMjAS>-U>g%ZrP0LKT1Iw{dv{XDo+pXo%3C!# znFC(f!|SZM39mhJbg}vhxaXtphOpOxTWj4lCnxBme8eo-`--d-R@{lNBy@fB3u@@z zg-B%IrR82*L1WcVo9I@pth3#gu||Khm*E=*F-685l8pyGH^1%fyv0+TQNgoMJnYJd za{Ivk$q#djm%Ib}Z&n;l${;j;j2q4jNVfPwxOc*EXKPks^?`;XXZwmo+^Z7}T`rC$ zbGOsD%sd=T4>B=&(z=eAb#pY8o~oe2wO1gw=YDPmjQlCRSn) zH_ddkSb~A7LsR_8kLz6;V~C=zI%cl%I#-^WvJ0Ij$`A{07$hoN#WTj|Ui&Doa!bX# z&Y(D>RO(DxpzET%=V{mE(RLVxN*?cBC zuC$xRQ~4sHm+9%uUlqvw__D+FTUPgIs`qwp2M^W4^_LWmRO{^O8)cARta9!V*!fE2 z@QJjO#`qr?m%xYxXmbN%^Omj#;9GXuk$!p|5k^jyn*9gswfgTeNp614qyKJ8vP0%G zQ+6kg#MI=wH!5Cp+PNj(&JI5OAaB|1QWlp;VYLporNG7AN-eo}Hfr+dOcNw2F5yQ$aiW%^_JD~+y2uo~RL zZPe|*wjjc3b;2i{R&`K*Ae7TL+0RSEzQKc8rHljG;q zAQfdGl500%vFpfZ4+*O53gPZ-6jKvO&V2L}4#$RZ&Jme_&-Pe(_NsC8O#O#!avY7s zgGe8V#e(~C3m0|3iyi0aDd{HO!F!Dv+gNcN6VFFV#7*}!3o^6M+LXi)xE8b(>Ok7P z)kl62F12@Msnx(ML`*@z7&m*k zJ17*egiaa^d&lOpaK-;l_bnAmtmo8!M$utckYZ{1ZJ=+ghIy#vk~v;cVVf7*ma{s7 z%tHg+=b0!2CyI$9H6V~TmV@L*g4Bx(8UQ;T8W?VZUPnCpL`rzN73Av*F(7#AD(ECa z8&0n5dez_ww@nV73nXysBeA&{mM%%NfiLJ{h%^hvTskB98c)J$BNV~|wF2cp0-6bv z2o4ao(IjFwJuMq7GtNb7U>j>&@O0EeeY;>rcEv_}gyBhJ11T04)ay#m%nGucioF&} z&@eR1k?n-xll|@t2%p@HM?HKlH|rM_A7D(mtwT@4@JYDu9W}S|$v)i^eGUswJn#9Z zGJhy#OASe{@ffmp-B@R=Qd@OBBe?c`{?lviGA6NK7A;f1rgC;%;x4*W8lY8hgM?-A zWtuzccxcb7K*=_D3whh8TrOX=!!N3RAB#(6HLq~h8=rW$N zYCUarH-koALcHhFsliGS%uX}iPhY^X_Nd3@n}<>iO(lZq5GzyO32L4-+{dJgSlO_Z z5baemYwEfoFg079gGZ(?J=@V_gQ;J4_C+(9!JQ2?H*YKyJ(#;AD#It!8s182+iJeR zaIiwJ<(_iBR+UuRt5OGQ@7qI?X?*8ir`*Za=;5%hZe3&W%59<&n}S_ROe{Buc@;DG zwfT6`v?b@R#jW?))UCWMp|yW0RXVP0&VNEasO_)@LGtM)VXS&}-c~;9p%>j*td!jo zCFV4-*GQi~#1hmNe84rj{FRAb9pnWhGbtOm!ml&5`f;VrIQOsPx&|KC%^cK%FPP$p zdU*H+wfISZ@4V$O%z2UT>k#sr_w1Dur;IJR9xQL>Q9!WXi`9#1>#GcNLV;*Q?54zPSZAeRt_S3wyX5vfJ2m z2ZjYP#f4e~xcrforMy>j*EuL8>IAI)LGcZGwh4QqV*Js=rLQ(oWp;LzqQ? 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ypZrei^e`Geqv10eKBM6?8a{(He9--aZvUwIQT3zhN7aw2A5}k?)B^xw{%%mW+s4lT literal 0 HcmV?d00001 diff --git a/lib/othercolor.m b/lib/othercolor.m new file mode 100644 index 0000000..6670626 --- /dev/null +++ b/lib/othercolor.m @@ -0,0 +1,155 @@ +function c = othercolor(n,m) +% OTHERCOLOR alternative colormaps from various sources +% +% OTHERCOLOR(N,M) returns a M-by-3 matrix contaning a colormap given by +% the name N. OTHERCOLOR, by itself, is the same length as the current +% figure's colormap. If no figure exists, MATLAB creates one. +% +% Supported colormaps are stored in a colorData.mat. And you can easily +% add your own: they are just standard MATLAB RGB colormap matrices. +% +% From: http://geography.uoregon.edu/datagraphics/color_scales.htm +% BrBu_10 BuDOr_18 BuGr_14 BuOr_10 Bu_10 GrMg_16 +% BrBu_12 BuDRd_12 BuGy_8 BuOr_12 Bu_7 RdYlBu_11b +% BuDOr_12 BuDRd_18 BuOrR_14 BuOr_8 Cat_12 StepSeq_25 +% +% From: http://www.colorbrewer2.org +% Accent3 GnBu6 Paired4 PuOr4 RdYlBu10 Set37 +% Accent4 GnBu7 Paired5 PuOr5 RdYlBu11 Set38 +% Accent5 GnBu8 Paired6 PuOr6 RdYlBu3 Set39 +% Accent6 GnBu9 Paired7 PuOr7 RdYlBu4 Spectral10 +% Accent7 Greens3 Paired8 PuOr8 RdYlBu5 Spectral11 +% Accent8 Greens4 Paired9 PuOr9 RdYlBu6 Spectral3 +% Blues3 Greens5 Pastel13 PuRd3 RdYlBu7 Spectral4 +% Blues4 Greens6 Pastel14 PuRd4 RdYlBu8 Spectral5 +% Blues5 Greens7 Pastel15 PuRd5 RdYlBu9 Spectral6 +% Blues6 Greens8 Pastel16 PuRd6 RdYlGn10 Spectral7 +% Blues7 Greens9 Pastel17 PuRd7 RdYlGn11 Spectral8 +% Blues8 Greys3 Pastel18 PuRd8 RdYlGn3 Spectral9 +% Blues9 Greys4 Pastel19 PuRd9 RdYlGn4 YlGn3 +% BrBG10 Greys5 Pastel23 Purples3 RdYlGn5 YlGn4 +% BrBG11 Greys6 Pastel24 Purples4 RdYlGn6 YlGn5 +% BrBG3 Greys7 Pastel25 Purples5 RdYlGn7 YlGn6 +% BrBG4 Greys8 Pastel26 Purples6 RdYlGn8 YlGn7 +% BrBG5 Greys9 Pastel27 Purples7 RdYlGn9 YlGn8 +% BrBG6 OrRd3 Pastel28 Purples8 Reds3 YlGn9 +% BrBG7 OrRd4 PiYG10 Purples9 Reds4 YlGnBu3 +% BrBG8 OrRd5 PiYG11 RdBu10 Reds5 YlGnBu4 +% BrBG9 OrRd6 PiYG3 RdBu11 Reds6 YlGnBu5 +% BuGn3 OrRd7 PiYG4 RdBu3 Reds7 YlGnBu6 +% BuGn4 OrRd8 PiYG5 RdBu4 Reds8 YlGnBu7 +% BuGn5 OrRd9 PiYG6 RdBu5 Reds9 YlGnBu8 +% BuGn6 Oranges3 PiYG7 RdBu6 Set13 YlGnBu9 +% BuGn7 Oranges4 PiYG8 RdBu7 Set14 YlOrBr3 +% BuGn8 Oranges5 PiYG9 RdBu8 Set15 YlOrBr4 +% BuGn9 Oranges6 PuBu3 RdBu9 Set16 YlOrBr5 +% BuPu3 Oranges7 PuBu4 RdGy10 Set17 YlOrBr6 +% BuPu4 Oranges8 PuBu5 RdGy11 Set18 YlOrBr7 +% BuPu5 Oranges9 PuBu6 RdGy3 Set19 YlOrBr8 +% BuPu6 PRGn10 PuBu7 RdGy4 Set23 YlOrBr9 +% BuPu7 PRGn11 PuBu8 RdGy5 Set24 YlOrRd3 +% BuPu8 PRGn3 PuBu9 RdGy6 Set25 YlOrRd4 +% BuPu9 PRGn4 PuBuGn3 RdGy7 Set26 YlOrRd5 +% Dark23 PRGn5 PuBuGn4 RdGy8 Set27 YlOrRd6 +% Dark24 PRGn6 PuBuGn5 RdGy9 Set28 YlOrRd7 +% Dark25 PRGn7 PuBuGn6 RdPu3 Set310 YlOrRd8 +% Dark26 PRGn8 PuBuGn7 RdPu4 Set311 YlOrRd9 +% Dark27 PRGn9 PuBuGn8 RdPu5 Set312 +% Dark28 Paired10 PuBuGn9 RdPu6 Set33 +% GnBu3 Paired11 PuOr10 RdPu7 Set34 +% GnBu4 Paired12 PuOr11 RdPu8 Set35 +% GnBu5 Paired3 PuOr3 RdPu9 Set36 +% +% From Mathematica: +% MCMYKcolors MIndexed45 Mdarkterrain +% MHTML MIndexed46 Mdeepseacolors +% MIndexed1 MIndexed47 Mfallcolors +% MIndexed10 MIndexed48 Mfruitpunchcolors +% MIndexed11 MIndexed49 Mfuchsiatones +% MIndexed12 MIndexed5 Mgeologicages +% MIndexed13 MIndexed50 Mgraytones +% MIndexed14 MIndexed51 Mgrayyellowtones +% MIndexed15 MIndexed52 Mgreenbrownterrain +% MIndexed16 MIndexed53 Mgreenpinktones +% MIndexed17 MIndexed54 Mhypsometrictints +% MIndexed18 MIndexed55 Mislandcolors +% MIndexed19 MIndexed56 Mlakecolors +% MIndexed2 MIndexed57 Mlegacy +% MIndexed20 MIndexed58 Mlighttemperaturemap +% MIndexed21 MIndexed59 Mlightterrain +% MIndexed22 MIndexed6 Mmintcolors +% MIndexed23 MIndexed60 Mneoncolors +% MIndexed24 MIndexed61 Mpastel +% MIndexed25 MIndexed62 Mpearlcolors +% MIndexed26 MIndexed7 Mpigeontones +% MIndexed27 MIndexed8 Mplumcolors +% MIndexed28 MIndexed9 Mrainbow +% MIndexed29 Malpinecolors Mredbluetones +% MIndexed3 Maquamarine Mredgreensplit +% MIndexed30 Marmycolors Mrosecolors +% MIndexed31 Matlanticcolors Mrusttones +% MIndexed32 Matoms Msandyterrain +% MIndexed33 Mauroracolors Msiennatones +% MIndexed34 Mavocadocolors Msolarcolors +% MIndexed35 Mbeachcolors Msouthwestcolors +% MIndexed36 Mblackbodyspectrum Mstarrynightcolors +% MIndexed37 Mbluegreenyellow Msunsetcolors +% MIndexed38 Mbrasstones Mtemperaturemap +% MIndexed39 Mbrightbands Mthermometercolors +% MIndexed4 Mbrowncyantones Mvalentinetones +% MIndexed40 Mcandycolors Mvisiblespectrum +% MIndexed41 Mcherrytones Mwatermeloncolors +% MIndexed42 Mcoffeetones Mwebsafe +% MIndexed43 Mdarkbands +% MIndexed44 Mdarkrainbow +% +% Usage: +% A typical 3D plot: +% >> [X,Y,Z] = peaks(30); +% >> surfc(X,Y,Z) +% >> colormap(othercolor('RdYlBu_11b')) +% >> colorbar +% >> axis([-3 3 -3 3 -10 5]) +% +% To get the list of available colormaps in a cellarray: +% >> colormapNames = othercolor(); +% +% Iterate through colormaps (enter to move to next, ctrl+c to exit loop) +% >> l = othercolor; for i=1:length(l), colormap(othercolor(i));pause;end +% +% Plot the first 50 colormaps +% >> colors = othercolor(); +% >> l = 50; +% >> for i=1:l +% >> subplot(ceil(l/10),10,i); +% >> c = othercolor(i); +% >> imagesc(reshape(c,1,size(c,1),size(c,2))); +% >> title(char(colors(i)),'interpreter','none'); +% >> axis off; +% >> end +% +% Author: Joshua Atkins +% Date: March 1, 2011 + +types = who('-file','colorData.mat'); + +% if no colormap is choosen then display available colormaps +if nargin < 1, + c = types; +else + % default number of points + if nargin < 2, m = size(get(gcf,'colormap'),1); end + + % allows numerical indexing + if isnumeric(n), n = char(types(n)); end + + % load color data + data = load('colorData.mat',n); + if isempty(fieldnames(data)) + c = []; + else + c = interp1(linspace(0,1,size(data.(n),1)),data.(n),linspace(0,1,m),'cubic'); + c(c<0) = 0; + c(c>1) = 1; + end +end \ No newline at end of file From 35ba2aa7951b14683b4192c10928fd9b4d77aa6a Mon Sep 17 00:00:00 2001 From: albusdemens Date: Wed, 7 Jun 2017 13:21:43 +0200 Subject: [PATCH 054/150] Remove unused files from lib --- lib/colorData.mat | Bin 140910 -> 0 bytes lib/othercolor.m | 155 ---------------------------------------------- 2 files changed, 155 deletions(-) delete mode 100644 lib/colorData.mat delete mode 100644 lib/othercolor.m diff --git a/lib/colorData.mat b/lib/colorData.mat deleted file mode 100644 index cb12a1d16dd7ea0d7053024f3602c56112d08474..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 140910 zcmb4sby!qg_x8};HHx$dNQXzp${Pu;OM_h0Ur;%EFrz 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ypZrei^e`Geqv10eKBM6?8a{(He9--aZvUwIQT3zhN7aw2A5}k?)B^xw{%%mW+s4lT diff --git a/lib/othercolor.m b/lib/othercolor.m deleted file mode 100644 index 6670626..0000000 --- a/lib/othercolor.m +++ /dev/null @@ -1,155 +0,0 @@ -function c = othercolor(n,m) -% OTHERCOLOR alternative colormaps from various sources -% -% OTHERCOLOR(N,M) returns a M-by-3 matrix contaning a colormap given by -% the name N. OTHERCOLOR, by itself, is the same length as the current -% figure's colormap. If no figure exists, MATLAB creates one. -% -% Supported colormaps are stored in a colorData.mat. And you can easily -% add your own: they are just standard MATLAB RGB colormap matrices. -% -% From: http://geography.uoregon.edu/datagraphics/color_scales.htm -% BrBu_10 BuDOr_18 BuGr_14 BuOr_10 Bu_10 GrMg_16 -% BrBu_12 BuDRd_12 BuGy_8 BuOr_12 Bu_7 RdYlBu_11b -% BuDOr_12 BuDRd_18 BuOrR_14 BuOr_8 Cat_12 StepSeq_25 -% -% From: http://www.colorbrewer2.org -% Accent3 GnBu6 Paired4 PuOr4 RdYlBu10 Set37 -% Accent4 GnBu7 Paired5 PuOr5 RdYlBu11 Set38 -% Accent5 GnBu8 Paired6 PuOr6 RdYlBu3 Set39 -% Accent6 GnBu9 Paired7 PuOr7 RdYlBu4 Spectral10 -% Accent7 Greens3 Paired8 PuOr8 RdYlBu5 Spectral11 -% Accent8 Greens4 Paired9 PuOr9 RdYlBu6 Spectral3 -% Blues3 Greens5 Pastel13 PuRd3 RdYlBu7 Spectral4 -% Blues4 Greens6 Pastel14 PuRd4 RdYlBu8 Spectral5 -% Blues5 Greens7 Pastel15 PuRd5 RdYlBu9 Spectral6 -% Blues6 Greens8 Pastel16 PuRd6 RdYlGn10 Spectral7 -% Blues7 Greens9 Pastel17 PuRd7 RdYlGn11 Spectral8 -% Blues8 Greys3 Pastel18 PuRd8 RdYlGn3 Spectral9 -% Blues9 Greys4 Pastel19 PuRd9 RdYlGn4 YlGn3 -% BrBG10 Greys5 Pastel23 Purples3 RdYlGn5 YlGn4 -% BrBG11 Greys6 Pastel24 Purples4 RdYlGn6 YlGn5 -% BrBG3 Greys7 Pastel25 Purples5 RdYlGn7 YlGn6 -% BrBG4 Greys8 Pastel26 Purples6 RdYlGn8 YlGn7 -% BrBG5 Greys9 Pastel27 Purples7 RdYlGn9 YlGn8 -% BrBG6 OrRd3 Pastel28 Purples8 Reds3 YlGn9 -% BrBG7 OrRd4 PiYG10 Purples9 Reds4 YlGnBu3 -% BrBG8 OrRd5 PiYG11 RdBu10 Reds5 YlGnBu4 -% BrBG9 OrRd6 PiYG3 RdBu11 Reds6 YlGnBu5 -% BuGn3 OrRd7 PiYG4 RdBu3 Reds7 YlGnBu6 -% BuGn4 OrRd8 PiYG5 RdBu4 Reds8 YlGnBu7 -% BuGn5 OrRd9 PiYG6 RdBu5 Reds9 YlGnBu8 -% BuGn6 Oranges3 PiYG7 RdBu6 Set13 YlGnBu9 -% BuGn7 Oranges4 PiYG8 RdBu7 Set14 YlOrBr3 -% BuGn8 Oranges5 PiYG9 RdBu8 Set15 YlOrBr4 -% BuGn9 Oranges6 PuBu3 RdBu9 Set16 YlOrBr5 -% BuPu3 Oranges7 PuBu4 RdGy10 Set17 YlOrBr6 -% BuPu4 Oranges8 PuBu5 RdGy11 Set18 YlOrBr7 -% BuPu5 Oranges9 PuBu6 RdGy3 Set19 YlOrBr8 -% BuPu6 PRGn10 PuBu7 RdGy4 Set23 YlOrBr9 -% BuPu7 PRGn11 PuBu8 RdGy5 Set24 YlOrRd3 -% BuPu8 PRGn3 PuBu9 RdGy6 Set25 YlOrRd4 -% BuPu9 PRGn4 PuBuGn3 RdGy7 Set26 YlOrRd5 -% Dark23 PRGn5 PuBuGn4 RdGy8 Set27 YlOrRd6 -% Dark24 PRGn6 PuBuGn5 RdGy9 Set28 YlOrRd7 -% Dark25 PRGn7 PuBuGn6 RdPu3 Set310 YlOrRd8 -% Dark26 PRGn8 PuBuGn7 RdPu4 Set311 YlOrRd9 -% Dark27 PRGn9 PuBuGn8 RdPu5 Set312 -% Dark28 Paired10 PuBuGn9 RdPu6 Set33 -% GnBu3 Paired11 PuOr10 RdPu7 Set34 -% GnBu4 Paired12 PuOr11 RdPu8 Set35 -% GnBu5 Paired3 PuOr3 RdPu9 Set36 -% -% From Mathematica: -% MCMYKcolors MIndexed45 Mdarkterrain -% MHTML MIndexed46 Mdeepseacolors -% MIndexed1 MIndexed47 Mfallcolors -% MIndexed10 MIndexed48 Mfruitpunchcolors -% MIndexed11 MIndexed49 Mfuchsiatones -% MIndexed12 MIndexed5 Mgeologicages -% MIndexed13 MIndexed50 Mgraytones -% MIndexed14 MIndexed51 Mgrayyellowtones -% MIndexed15 MIndexed52 Mgreenbrownterrain -% MIndexed16 MIndexed53 Mgreenpinktones -% MIndexed17 MIndexed54 Mhypsometrictints -% MIndexed18 MIndexed55 Mislandcolors -% MIndexed19 MIndexed56 Mlakecolors -% MIndexed2 MIndexed57 Mlegacy -% MIndexed20 MIndexed58 Mlighttemperaturemap -% MIndexed21 MIndexed59 Mlightterrain -% MIndexed22 MIndexed6 Mmintcolors -% MIndexed23 MIndexed60 Mneoncolors -% MIndexed24 MIndexed61 Mpastel -% MIndexed25 MIndexed62 Mpearlcolors -% MIndexed26 MIndexed7 Mpigeontones -% MIndexed27 MIndexed8 Mplumcolors -% MIndexed28 MIndexed9 Mrainbow -% MIndexed29 Malpinecolors Mredbluetones -% MIndexed3 Maquamarine Mredgreensplit -% MIndexed30 Marmycolors Mrosecolors -% MIndexed31 Matlanticcolors Mrusttones -% MIndexed32 Matoms Msandyterrain -% MIndexed33 Mauroracolors Msiennatones -% MIndexed34 Mavocadocolors Msolarcolors -% MIndexed35 Mbeachcolors Msouthwestcolors -% MIndexed36 Mblackbodyspectrum Mstarrynightcolors -% MIndexed37 Mbluegreenyellow Msunsetcolors -% MIndexed38 Mbrasstones Mtemperaturemap -% MIndexed39 Mbrightbands Mthermometercolors -% MIndexed4 Mbrowncyantones Mvalentinetones -% MIndexed40 Mcandycolors Mvisiblespectrum -% MIndexed41 Mcherrytones Mwatermeloncolors -% MIndexed42 Mcoffeetones Mwebsafe -% MIndexed43 Mdarkbands -% MIndexed44 Mdarkrainbow -% -% Usage: -% A typical 3D plot: -% >> [X,Y,Z] = peaks(30); -% >> surfc(X,Y,Z) -% >> colormap(othercolor('RdYlBu_11b')) -% >> colorbar -% >> axis([-3 3 -3 3 -10 5]) -% -% To get the list of available colormaps in a cellarray: -% >> colormapNames = othercolor(); -% -% Iterate through colormaps (enter to move to next, ctrl+c to exit loop) -% >> l = othercolor; for i=1:length(l), colormap(othercolor(i));pause;end -% -% Plot the first 50 colormaps -% >> colors = othercolor(); -% >> l = 50; -% >> for i=1:l -% >> subplot(ceil(l/10),10,i); -% >> c = othercolor(i); -% >> imagesc(reshape(c,1,size(c,1),size(c,2))); -% >> title(char(colors(i)),'interpreter','none'); -% >> axis off; -% >> end -% -% Author: Joshua Atkins -% Date: March 1, 2011 - -types = who('-file','colorData.mat'); - -% if no colormap is choosen then display available colormaps -if nargin < 1, - c = types; -else - % default number of points - if nargin < 2, m = size(get(gcf,'colormap'),1); end - - % allows numerical indexing - if isnumeric(n), n = char(types(n)); end - - % load color data - data = load('colorData.mat',n); - if isempty(fieldnames(data)) - c = []; - else - c = interp1(linspace(0,1,size(data.(n),1)),data.(n),linspace(0,1,m),'cubic'); - c(c<0) = 0; - c(c>1) = 1; - end -end \ No newline at end of file From d224e500e1d0e8e0182decc4e07d9263e3ff6219 Mon Sep 17 00:00:00 2001 From: albusdemens Date: Sun, 18 Jun 2017 07:57:17 +0200 Subject: [PATCH 055/150] Included analysis backgorund images --- Plot_3D.py | 9 ++++++--- getdata.py | 2 ++ sum_bg.py | 31 +++++++++++++++++++++++++++++++ 3 files changed, 39 insertions(+), 3 deletions(-) create mode 100644 sum_bg.py diff --git a/Plot_3D.py b/Plot_3D.py index 133952b..0721b0a 100644 --- a/Plot_3D.py +++ b/Plot_3D.py @@ -43,7 +43,10 @@ A_z_val_2[i,j] = A[i,j,z_val,2]/Layer_max[i,j] A_z_val[:,:] = ( 0.5 * ( A_z_val_0[:,:] + A_z_val_1[:,:])) -# Use an inverted colors heatmap (high values at the centre) +# The best way to understand the distribution of the angles and of the weight +# parameter is to use an inverted diverging colormap (high values at the centre) +# This because we expect most orientation values to be at the centre, not at the +# extremes inv = cmap_map(lambda x: 1-x, plt.cm.Spectral) plt.subplot(221) @@ -55,11 +58,11 @@ plt.title('Gamma') plt.subplot(223) -plt.imshow(A_z_val_2, cmap = inv) +plt.imshow(A_z_val_2) plt.title('Weight') plt.subplot(224) -plt.imshow(A_z_val-2, cmap = inv) +plt.imshow(A_z_val-2) plt.title('Combined value') plt.show() diff --git a/getdata.py b/getdata.py index 30ef8ae..6df1f6d 100644 --- a/getdata.py +++ b/getdata.py @@ -1,3 +1,5 @@ +# python getdata.py /u/data/andcj/hxrm/Al_april_2017/topotomo/sundaynight topotomo_frelon_far_ /u/data/andcj/hxrm/Al_april_2017/topotomo/monday/bg topotomo_frelon_far_ 256,256 200,200 /u/data/alcer/DFXRM_rec Rec_test 0.785 -3.319 + from lib.miniged import GetEdfData import sys import time diff --git a/sum_bg.py b/sum_bg.py new file mode 100644 index 0000000..fa98be6 --- /dev/null +++ b/sum_bg.py @@ -0,0 +1,31 @@ +# This script analyses the background images used in the DFXRM reconstruction + +import numpy as np +import matplotlib.pyplot as plt +import lib.EdfFile as EF +import sys + +A = [line.rstrip() for line in open('list_bg.txt')] + +im_0 = EF.EdfFile(A[0]) +sze = list(np.shape(im_0.GetData(0))) +sum_bg = np.zeros(sze) +int_im = np.zeros(len(A)) + +# Measure the intensity for each image +for ii in range(0,27):#len(A)): + im = EF.EdfFile(A[ii]) + image = im.GetData(0) + sum_bg[:,:] = sum_bg[:,:] + image[:,:] + sum_image = 0 + for jj in range(sze[0]): + for kk in range(sze[1]): + sum_image = sum_image + image[jj,kk] + int_im[ii] = sum_image + +# Show the image sum and the intensity histogram +plt.imshow(sum_bg) +plt.show() + +plt.bar(range(len(A)), int_im) +plt.show() From d13b6ab9ff4bbede03754bb22cd17da19c7222d5 Mon Sep 17 00:00:00 2001 From: albusdemens Date: Mon, 19 Jun 2017 15:41:26 +0200 Subject: [PATCH 056/150] First working 3D map --- Plot_3D.py | 6 +- Plot_bckg.py | 22 + Plot_data_1omega.py | 230 + getdata.py | 4 +- lib/miniged.py | 23 +- recon3d.py | 1 + weight_3D.vtk | 10215 ++++++++++++++++++++++++++++++++++++++++++ 7 files changed, 10495 insertions(+), 6 deletions(-) create mode 100644 Plot_bckg.py create mode 100644 Plot_data_1omega.py create mode 100644 weight_3D.vtk diff --git a/Plot_3D.py b/Plot_3D.py index 0721b0a..f1bf37f 100644 --- a/Plot_3D.py +++ b/Plot_3D.py @@ -13,7 +13,7 @@ from cmap_map import cmap_map # Load the npy array -A = np.load('/Users/Alberto/Documents/Data_analysis/DFXRM/Results_sunday/grain_ang_1.npy') +A = np.load('/home/nexmap/alcer/DFXRM/Recon3D/grain_ang_1.npy') # Considering all z layers, find the max of the mean for the weight M = np.zeros(A.shape[2]) @@ -43,11 +43,13 @@ A_z_val_2[i,j] = A[i,j,z_val,2]/Layer_max[i,j] A_z_val[:,:] = ( 0.5 * ( A_z_val_0[:,:] + A_z_val_1[:,:])) +sys.exit() + # The best way to understand the distribution of the angles and of the weight # parameter is to use an inverted diverging colormap (high values at the centre) # This because we expect most orientation values to be at the centre, not at the # extremes -inv = cmap_map(lambda x: 1-x, plt.cm.Spectral) +#inv = cmap_map(lambda x: 1-x, plt.cm.Spectral) plt.subplot(221) plt.imshow(A_z_val_0, cmap = inv) diff --git a/Plot_bckg.py b/Plot_bckg.py new file mode 100644 index 0000000..8ba148d --- /dev/null +++ b/Plot_bckg.py @@ -0,0 +1,22 @@ +# Test script to check how getdata.py works + +from lib.miniged import GetEdfData +import sys +import time +import os +import warnings +import numpy as np +import pdb # For debugging +from find_nearest import find_nearest, find_nearest_idx + +datadir = '/u/data/andcj/hxrm/Al_april_2017/topotomo/sundaynight' +dataname = 'topotomo_frelon_far_' +bgpath = '/u/data/andcj/hxrm/Al_april_2017/topotomo/monday/bg' #'/home/nexmap/alcer/DFXRM/bg_refined' +bgfilename = 'topotomo_frelon_far' +roi = 256,256 +sim = 200,200 + +#[bckg_full, bckg] = GetEdfData.getBGarray(datadir, dataname, bgpath, bgfilename, roi, sim) + +get_bckg = GetEdfData.getBGarray(datadir, dataname, bgpath, bgfilename, roi, sim) # get an instance of the class +get_bckg.bckg() diff --git a/Plot_data_1omega.py b/Plot_data_1omega.py new file mode 100644 index 0000000..0862e49 --- /dev/null +++ b/Plot_data_1omega.py @@ -0,0 +1,230 @@ +# Simple script to visualize an EDF file + +import numpy as np +import matplotlib.pyplot as plt +import lib.EdfFile as EF +import sys + +A = np.load('/u/data/alcer/DFXRM_rec/Rec_test/dataarray.npy') + +int = np.empty([A.shape[0], A.shape[1]]) +Img_array = np.empty([A.shape[0], A.shape[1], A.shape[3], A.shape[4]]) +for i in range(A.shape[0]): + for j in range(A.shape[1]): + int[i,j] = sum(sum(A[i,j,0,:,:])) + if int[i,j] > 0: + Img_array[i,j,:,:] = A[i,j,0,:,:] + +print Img_array.shape + +# Reshape Img_array in a 7x7 array (remove empty combinations) +Array_ang_val = np.empty([A.shape[0], A.shape[1], A.shape[3], A.shape[4]]) +Sum_img = np.empty([A.shape[3], A.shape[4]]) +for i in range(A.shape[0]): + num_pos = 0 + for j in range(A.shape[1]): + int[i,j] = sum(sum(A[i,j,0,:,:])) + if int[i,j] > 0: + num_pos = num_pos + 1 + print i,j + Array_ang_val[i, j,:,:] = A[i,j,0,:,:] + Sum_img[:,:] = Sum_img[:,:] + Array_ang_val[i, j,:,:] + +# Plot the distribution of integrated intensity values in the gamma, theta plane +plt.imshow(int) +plt.title('Distribution of the recorded images as a function of the motor angles') +plt.xlabel('Theta') +plt.ylabel('Gamma') +plt.show() + +# Plot the sum of the background images +fig1 = plt.figure() +plt.imshow(Sum_img) +plt.title('Sum of the images collected at omega = 0') +plt.show() + +# Plot the various recorded images +fig = plt.figure() +plt.title('Images recorded for at omega = 0') + +a1 = fig.add_subplot(5,7,1) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +plt.title('(0,1)') +plt.imshow(A[0,1,0,:,:]) +a2 = fig.add_subplot(5,7,2) +plt.setp(a2.get_xticklabels(), visible=False) +plt.setp(a2.get_yticklabels(), visible=False) +plt.title('(0,14)') +plt.imshow(A[0,14,0,:,:]) +a3 = fig.add_subplot(5,7,3) +plt.setp(a3.get_xticklabels(), visible=False) +plt.setp(a3.get_yticklabels(), visible=False) +plt.title('(0,32)') +plt.imshow(A[0,32,0,:,:]) +a4 = fig.add_subplot(5,7,4) +plt.setp(a4.get_xticklabels(), visible=False) +plt.setp(a4.get_yticklabels(), visible=False) +plt.title('(0,41)') +plt.imshow(A[0,41,0,:,:]) +a5 = fig.add_subplot(5,7,5) +plt.setp(a5.get_xticklabels(), visible=False) +plt.setp(a5.get_yticklabels(), visible=False) +plt.title('(0,59)') +plt.imshow(A[0,59,0,:,:]) +a6 = fig.add_subplot(5,7,6) +plt.setp(a6.get_xticklabels(), visible=False) +plt.setp(a6.get_yticklabels(), visible=False) +plt.title('(0,79)') +plt.imshow(A[0,79,0,:,:]) +a7 = fig.add_subplot(5,7,7) +plt.setp(a7.get_xticklabels(), visible=False) +plt.setp(a7.get_yticklabels(), visible=False) +plt.title('(0,91)') +plt.imshow(A[0,91,0,:,:]) + +b1 = fig.add_subplot(5,7,8) +plt.setp(b1.get_xticklabels(), visible=False) +plt.setp(b1.get_yticklabels(), visible=False) +plt.title('(1,2)') +plt.imshow(A[1,2,0,:,:]) +b2 = fig.add_subplot(5,7,9) +plt.setp(b2.get_xticklabels(), visible=False) +plt.setp(b2.get_yticklabels(), visible=False) +plt.title('(1,14)') +plt.imshow(A[1,14,0,:,:]) +b3 = fig.add_subplot(5,7,10) +plt.setp(b3.get_xticklabels(), visible=False) +plt.setp(b3.get_yticklabels(), visible=False) +plt.title('(1,32)') +plt.imshow(A[1,32,0,:,:]) +b4 = fig.add_subplot(5,7,11) +plt.setp(b4.get_xticklabels(), visible=False) +plt.setp(b4.get_yticklabels(), visible=False) +plt.title('(1,41)') +plt.imshow(A[1,41,0,:,:]) +b5 = fig.add_subplot(5,7,12) +plt.setp(b5.get_xticklabels(), visible=False) +plt.setp(b5.get_yticklabels(), visible=False) +plt.title('(1,59)') +plt.imshow(A[1,59,0,:,:]) +b6 = fig.add_subplot(5,7,13) +plt.setp(b6.get_xticklabels(), visible=False) +plt.setp(b6.get_yticklabels(), visible=False) +plt.title('(1,74)') +plt.imshow(A[1,74,0,:,:]) +b7 = fig.add_subplot(5,7,14) +plt.setp(b7.get_xticklabels(), visible=False) +plt.setp(b7.get_yticklabels(), visible=False) +plt.title('(1,90)') +plt.imshow(A[1,90,0,:,:]) + +c1 = fig.add_subplot(5,7,15) +plt.setp(c1.get_xticklabels(), visible=False) +plt.setp(c1.get_yticklabels(), visible=False) +plt.title('(3,2)') +plt.imshow(A[3,2,0,:,:]) +c2 = fig.add_subplot(5,7,16) +plt.setp(c2.get_xticklabels(), visible=False) +plt.setp(c2.get_yticklabels(), visible=False) +plt.title('(3,14)') +plt.imshow(A[3,14,0,:,:]) +c3 = fig.add_subplot(5,7,17) +plt.setp(c3.get_xticklabels(), visible=False) +plt.setp(c3.get_yticklabels(), visible=False) +plt.title('(3,32)') +plt.imshow(A[3,32,0,:,:]) +c4 = fig.add_subplot(5,7,18) +plt.setp(c4.get_xticklabels(), visible=False) +plt.setp(c4.get_yticklabels(), visible=False) +plt.title('(3,40)') +plt.imshow(A[3,40,0,:,:]) +c5 = fig.add_subplot(5,7,19) +plt.setp(c5.get_xticklabels(), visible=False) +plt.setp(c5.get_yticklabels(), visible=False) +plt.title('(3,59)') +plt.imshow(A[3,59,0,:,:]) +c6 = fig.add_subplot(5,7,20) +plt.setp(c6.get_xticklabels(), visible=False) +plt.setp(c6.get_yticklabels(), visible=False) +plt.title('(3,74)') +plt.imshow(A[3,74,0,:,:]) +c7 = fig.add_subplot(5,7,21) +plt.setp(c7.get_xticklabels(), visible=False) +plt.setp(c7.get_yticklabels(), visible=False) +plt.title('(3,91)') +plt.imshow(A[3,91,0,:,:]) + +d1 = fig.add_subplot(5,7,22) +plt.setp(d1.get_xticklabels(), visible=False) +plt.setp(d1.get_yticklabels(), visible=False) +plt.title('(4,2)') +plt.imshow(A[4,2,0,:,:]) +d2 = fig.add_subplot(5,7,23) +plt.setp(d2.get_xticklabels(), visible=False) +plt.setp(d2.get_yticklabels(), visible=False) +plt.title('(4,14)') +plt.imshow(A[4,14,0,:,:]) +d3 = fig.add_subplot(5,7,24) +plt.setp(d3.get_xticklabels(), visible=False) +plt.setp(d3.get_yticklabels(), visible=False) +plt.title('(4,32)') +plt.imshow(A[4,32,0,:,:]) +d4 = fig.add_subplot(5,7,25) +plt.setp(d4.get_xticklabels(), visible=False) +plt.setp(d4.get_yticklabels(), visible=False) +plt.title('(4,41)') +plt.imshow(A[4,41,0,:,:]) +d5 = fig.add_subplot(5,7,26) +plt.setp(d5.get_xticklabels(), visible=False) +plt.setp(d5.get_yticklabels(), visible=False) +plt.title('(4,59)') +plt.imshow(A[4,59,0,:,:]) +d6 = fig.add_subplot(5,7,27) +plt.setp(d6.get_xticklabels(), visible=False) +plt.setp(d6.get_yticklabels(), visible=False) +plt.title('(4,79)') +plt.imshow(A[4,79,0,:,:]) +d7 = fig.add_subplot(5,7,28) +plt.setp(d7.get_xticklabels(), visible=False) +plt.setp(d7.get_yticklabels(), visible=False) +plt.title('(4,91)') +plt.imshow(A[4,91,0,:,:]) + +e1 = fig.add_subplot(5,7,29) +plt.setp(e1.get_xticklabels(), visible=False) +plt.setp(e1.get_yticklabels(), visible=False) +plt.title('(5,1)') +plt.imshow(A[5,1,0,:,:]) +e2 = fig.add_subplot(5,7,30) +plt.setp(e2.get_xticklabels(), visible=False) +plt.setp(e2.get_yticklabels(), visible=False) +plt.title('(5,14)') +plt.imshow(A[5,14,0,:,:]) +e3 = fig.add_subplot(5,7,31) +plt.setp(e3.get_xticklabels(), visible=False) +plt.setp(e3.get_yticklabels(), visible=False) +plt.title('(5,32)') +plt.imshow(A[5,32,0,:,:]) +e4 = fig.add_subplot(5,7,32) +plt.setp(e4.get_xticklabels(), visible=False) +plt.setp(e4.get_yticklabels(), visible=False) +plt.title('(5,41)') +plt.imshow(A[5,41,0,:,:]) +e5 = fig.add_subplot(5,7,33) +plt.setp(e5.get_xticklabels(), visible=False) +plt.setp(e5.get_yticklabels(), visible=False) +plt.title('(5,59)') +plt.imshow(A[5,59,0,:,:]) +e6 = fig.add_subplot(5,7,34) +plt.setp(e6.get_xticklabels(), visible=False) +plt.setp(e6.get_yticklabels(), visible=False) +plt.title('(5,74)') +plt.imshow(A[5,74,0,:,:]) +e7 = fig.add_subplot(5,7,35) +plt.setp(e7.get_xticklabels(), visible=False) +plt.setp(e7.get_yticklabels(), visible=False) +plt.title('(5,90)') +plt.imshow(A[5,90,0,:,:]) + +plt.show() diff --git a/getdata.py b/getdata.py index 6df1f6d..63f14c6 100644 --- a/getdata.py +++ b/getdata.py @@ -1,6 +1,8 @@ # python getdata.py /u/data/andcj/hxrm/Al_april_2017/topotomo/sundaynight topotomo_frelon_far_ /u/data/andcj/hxrm/Al_april_2017/topotomo/monday/bg topotomo_frelon_far_ 256,256 200,200 /u/data/alcer/DFXRM_rec Rec_test 0.785 -3.319 -from lib.miniged import GetEdfData +# python getdata.py /u/data/andcj/hxrm/Al_april_2017/topotomo/sundaynight topotomo_frelon_far_ /home/nexmap/alcer/DFXRM/bg_refined topotomo_frelon_far_ 256,256 200,200 /u/data/alcer/DFXRM_rec Rec_test 0.785 -3.319 + +from lib.miniged import GetEdfData # Read EDF files and plot background import sys import time import os diff --git a/lib/miniged.py b/lib/miniged.py index 0b95221..27de642 100644 --- a/lib/miniged.py +++ b/lib/miniged.py @@ -31,6 +31,7 @@ import time import sys import scipy +import matplotlib.pyplot as plt from os import listdir from os.path import isfile, join @@ -155,6 +156,22 @@ def getBGarray(self, bg_filename): self.bg_combined_full /= len(self.bg_files) + bckg = self.bg_combined + bckg_entire = self.bg_combined_full + + # Check background images + #fig = plt.figure() + #a=fig.add_subplot(1,2,1) + #plt.imshow(bckg) + #a.set_title('Background ROI') + #b=fig.add_subplot(1,2,2) + #plt.imshow(bckg_entire) + #b.set_title('All background') + #plt.show() + np.save('bckg_roi.npy', bckg) + np.save('bckg_all.npy', bckg_entire) + #plt.savefig('Background_check.png') + def getIndexList(self): file_with_path = self.path + '/' + self.data_files[0] img = EdfFile.EdfFile(file_with_path) @@ -334,11 +351,11 @@ def getImage(self, index, full): roi = self.roi if full: - im = img.GetData(0).astype(np.int64) - self.bg_combined_full + im = (img.GetData(0).astype(np.int64)*100) / self.bg_combined_full else: - im = img.GetData(0).astype(np.int64)[ + im = (img.GetData(0).astype(np.int64)*100)[ roi[2]:roi[3], - roi[0]:roi[1]] - self.bg_combined + roi[0]:roi[1]] / self.bg_combined im = self.cleanImage(im) diff --git a/recon3d.py b/recon3d.py index f066f92..5f52ddd 100644 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NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN +NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN +NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN +NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN From cb6bc1ddfcbc90798276e72832d026ea6b4b68df Mon Sep 17 00:00:00 2001 From: albusdemens Date: Tue, 27 Jun 2017 19:27:17 +0200 Subject: [PATCH 057/150] Scripts to plot preliminary results --- Plot_data_1omega.py | 371 ++++++++++++++++++++++------------------ Plot_sum_10deg_steps.py | 174 +++++++++++++++++++ 2 files changed, 381 insertions(+), 164 deletions(-) create mode 100644 Plot_sum_10deg_steps.py diff --git a/Plot_data_1omega.py b/Plot_data_1omega.py index 0862e49..5d70b9e 100644 --- a/Plot_data_1omega.py +++ b/Plot_data_1omega.py @@ -1,4 +1,4 @@ -# Simple script to visualize an EDF file +# Script to analyze the images collected at a certain omega import numpy as np import matplotlib.pyplot as plt @@ -7,29 +7,42 @@ A = np.load('/u/data/alcer/DFXRM_rec/Rec_test/dataarray.npy') +# Select projection number +omega = 2 + int = np.empty([A.shape[0], A.shape[1]]) Img_array = np.empty([A.shape[0], A.shape[1], A.shape[3], A.shape[4]]) for i in range(A.shape[0]): for j in range(A.shape[1]): - int[i,j] = sum(sum(A[i,j,0,:,:])) + int[i,j] = sum(sum(A[i,j,omega,:,:])) + print i, j, int[i,j] if int[i,j] > 0: - Img_array[i,j,:,:] = A[i,j,0,:,:] + Img_array[i,j,:,:] = A[i,j,omega,:,:] -print Img_array.shape +# Analyze distribution of recorded images as a function of angles +int = np.empty([A.shape[0], A.shape[1]]) +for oo in range(A.shape[2]): + for i in range(A.shape[0]): + for j in range(A.shape[1]): + int[i,j] = int[i,j] + sum(sum(A[i,j,oo,:,:])) -# Reshape Img_array in a 7x7 array (remove empty combinations) -Array_ang_val = np.empty([A.shape[0], A.shape[1], A.shape[3], A.shape[4]]) -Sum_img = np.empty([A.shape[3], A.shape[4]]) -for i in range(A.shape[0]): - num_pos = 0 - for j in range(A.shape[1]): - int[i,j] = sum(sum(A[i,j,0,:,:])) - if int[i,j] > 0: - num_pos = num_pos + 1 - print i,j - Array_ang_val[i, j,:,:] = A[i,j,0,:,:] - Sum_img[:,:] = Sum_img[:,:] + Array_ang_val[i, j,:,:] +plt.imshow(int) +plt.title('Distribution of the recorded images as a function of the motor angles') +plt.xlabel('Theta') +plt.ylabel('Gamma') +plt.show() +# Sum images collected at a certain angle +Array_ang_val = np.empty([A.shape[0], A.shape[1], A.shape[3], A.shape[4]]) +Sum_img = np.zeros([A.shape[3], A.shape[4]]) +int = np.empty([A.shape[0], A.shape[1]]) +for ii in range(A.shape[0]): + for jj in range(A.shape[1]): + int[ii,jj] = sum(sum(A[ii,jj,omega,:,:])) + if int[ii,jj] > 0: + Array_ang_val[ii, jj,:,:] = A[ii,jj,omega,:,:] + Sum_img[:,:] = Sum_img[:,:] + A[ii,jj,omega,:,:] + # Plot the distribution of integrated intensity values in the gamma, theta plane plt.imshow(int) plt.title('Distribution of the recorded images as a function of the motor angles') @@ -38,193 +51,223 @@ plt.show() # Plot the sum of the background images -fig1 = plt.figure() +fig = plt.figure() plt.imshow(Sum_img) -plt.title('Sum of the images collected at omega = 0') +plt.xlabel('X (pixels)') +plt.ylabel('Y (pixels)') plt.show() +sys.exit() + # Plot the various recorded images -fig = plt.figure() -plt.title('Images recorded for at omega = 0') - -a1 = fig.add_subplot(5,7,1) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -plt.title('(0,1)') -plt.imshow(A[0,1,0,:,:]) -a2 = fig.add_subplot(5,7,2) -plt.setp(a2.get_xticklabels(), visible=False) -plt.setp(a2.get_yticklabels(), visible=False) -plt.title('(0,14)') -plt.imshow(A[0,14,0,:,:]) -a3 = fig.add_subplot(5,7,3) -plt.setp(a3.get_xticklabels(), visible=False) -plt.setp(a3.get_yticklabels(), visible=False) -plt.title('(0,32)') -plt.imshow(A[0,32,0,:,:]) -a4 = fig.add_subplot(5,7,4) -plt.setp(a4.get_xticklabels(), visible=False) -plt.setp(a4.get_yticklabels(), visible=False) -plt.title('(0,41)') -plt.imshow(A[0,41,0,:,:]) -a5 = fig.add_subplot(5,7,5) -plt.setp(a5.get_xticklabels(), visible=False) -plt.setp(a5.get_yticklabels(), visible=False) -plt.title('(0,59)') -plt.imshow(A[0,59,0,:,:]) -a6 = fig.add_subplot(5,7,6) -plt.setp(a6.get_xticklabels(), visible=False) -plt.setp(a6.get_yticklabels(), visible=False) -plt.title('(0,79)') -plt.imshow(A[0,79,0,:,:]) -a7 = fig.add_subplot(5,7,7) -plt.setp(a7.get_xticklabels(), visible=False) -plt.setp(a7.get_yticklabels(), visible=False) -plt.title('(0,91)') -plt.imshow(A[0,91,0,:,:]) - -b1 = fig.add_subplot(5,7,8) -plt.setp(b1.get_xticklabels(), visible=False) -plt.setp(b1.get_yticklabels(), visible=False) -plt.title('(1,2)') -plt.imshow(A[1,2,0,:,:]) -b2 = fig.add_subplot(5,7,9) -plt.setp(b2.get_xticklabels(), visible=False) -plt.setp(b2.get_yticklabels(), visible=False) -plt.title('(1,14)') -plt.imshow(A[1,14,0,:,:]) -b3 = fig.add_subplot(5,7,10) -plt.setp(b3.get_xticklabels(), visible=False) -plt.setp(b3.get_yticklabels(), visible=False) -plt.title('(1,32)') -plt.imshow(A[1,32,0,:,:]) -b4 = fig.add_subplot(5,7,11) -plt.setp(b4.get_xticklabels(), visible=False) -plt.setp(b4.get_yticklabels(), visible=False) -plt.title('(1,41)') -plt.imshow(A[1,41,0,:,:]) -b5 = fig.add_subplot(5,7,12) -plt.setp(b5.get_xticklabels(), visible=False) -plt.setp(b5.get_yticklabels(), visible=False) -plt.title('(1,59)') -plt.imshow(A[1,59,0,:,:]) -b6 = fig.add_subplot(5,7,13) -plt.setp(b6.get_xticklabels(), visible=False) -plt.setp(b6.get_yticklabels(), visible=False) -plt.title('(1,74)') -plt.imshow(A[1,74,0,:,:]) -b7 = fig.add_subplot(5,7,14) -plt.setp(b7.get_xticklabels(), visible=False) -plt.setp(b7.get_yticklabels(), visible=False) -plt.title('(1,90)') -plt.imshow(A[1,90,0,:,:]) - -c1 = fig.add_subplot(5,7,15) -plt.setp(c1.get_xticklabels(), visible=False) -plt.setp(c1.get_yticklabels(), visible=False) -plt.title('(3,2)') -plt.imshow(A[3,2,0,:,:]) -c2 = fig.add_subplot(5,7,16) +fig, axes = plt.subplots(5, 6) + +#a1 = fig.add_subplot(7,7,1) +#plt.setp(a1.get_xticklabels(), visible=False) +#plt.setp(a1.get_yticklabels(), visible=False) +#plt.imshow(A[0,2,omega,:,:]) +#a2 = fig.add_subplot(7,7,2) +#plt.setp(a2.get_xticklabels(), visible=False) +#plt.setp(a2.get_yticklabels(), visible=False) +#plt.imshow(A[0,14,omega,:,:]) +#a3 = fig.add_subplot(7,7,3) +#plt.setp(a3.get_xticklabels(), visible=False) +#plt.setp(a3.get_yticklabels(), visible=False) +#plt.imshow(A[0,32,omega,:,:]) +#a4 = fig.add_subplot(7,7,4) +#plt.setp(a4.get_xticklabels(), visible=False) +#plt.setp(a4.get_yticklabels(), visible=False) +#plt.imshow(A[0,41,omega,:,:]) +#a5 = fig.add_subplot(7,7,5) +#plt.setp(a5.get_xticklabels(), visible=False) +#plt.setp(a5.get_yticklabels(), visible=False) +#plt.imshow(A[0,59,omega,:,:]) +#a6 = fig.add_subplot(7,7,6) +#plt.setp(a6.get_xticklabels(), visible=False) +#plt.setp(a6.get_yticklabels(), visible=False) +#plt.imshow(A[0,74,omega,:,:]) +#a7 = fig.add_subplot(7,7,7) +#plt.setp(a7.get_xticklabels(), visible=False) +#plt.setp(a7.get_yticklabels(), visible=False) +#plt.imshow(A[0,91,omega,:,:]) + +#b1 = fig.add_subplot(7,7,8) +#plt.setp(b1.get_xticklabels(), visible=False) +#plt.setp(b1.get_yticklabels(), visible=False) +#plt.imshow(A[1,2,omega,:,:]) +#b2 = fig.add_subplot(7,7,9) +#plt.setp(b2.get_xticklabels(), visible=False) +#plt.setp(b2.get_yticklabels(), visible=False) +#plt.imshow(A[1,14,omega,:,:]) +#b3 = fig.add_subplot(7,7,10) +#plt.setp(b3.get_xticklabels(), visible=False) +#plt.setp(b3.get_yticklabels(), visible=False) +#plt.imshow(A[1,32,omega,:,:]) +#b4 = fig.add_subplot(7,7,11) +#plt.setp(b4.get_xticklabels(), visible=False) +#plt.setp(b4.get_yticklabels(), visible=False) +#plt.imshow(A[1,41,omega,:,:]) +#b5 = fig.add_subplot(7,7,12) +#plt.setp(b5.get_xticklabels(), visible=False) +#plt.setp(b5.get_yticklabels(), visible=False) +#plt.imshow(A[1,59,omega,:,:]) +#b6 = fig.add_subplot(7,7,13) +#plt.setp(b6.get_xticklabels(), visible=False) +#plt.setp(b6.get_yticklabels(), visible=False) +#plt.imshow(A[1,74,omega,:,:]) +#b7 = fig.add_subplot(7,7,14) +#plt.setp(b7.get_xticklabels(), visible=False) +#plt.setp(b7.get_yticklabels(), visible=False) +#plt.imshow(A[1,91,omega,:,:]) + +#c1 = fig.add_subplot(7,7,15) +#plt.setp(c1.get_xticklabels(), visible=False) +#plt.setp(c1.get_yticklabels(), visible=False) +#plt.imshow(A[2,2,omega,:,:]) +c2 = fig.add_subplot(5,6,1) plt.setp(c2.get_xticklabels(), visible=False) plt.setp(c2.get_yticklabels(), visible=False) -plt.title('(3,14)') -plt.imshow(A[3,14,0,:,:]) -c3 = fig.add_subplot(5,7,17) +plt.imshow(A[2,14,omega,:,:]) +c3 = fig.add_subplot(5,6,2) plt.setp(c3.get_xticklabels(), visible=False) plt.setp(c3.get_yticklabels(), visible=False) -plt.title('(3,32)') -plt.imshow(A[3,32,0,:,:]) -c4 = fig.add_subplot(5,7,18) +plt.imshow(A[2,32,omega,:,:]) +c4 = fig.add_subplot(5,6,3) plt.setp(c4.get_xticklabels(), visible=False) plt.setp(c4.get_yticklabels(), visible=False) -plt.title('(3,40)') -plt.imshow(A[3,40,0,:,:]) -c5 = fig.add_subplot(5,7,19) +plt.imshow(A[2,41,omega,:,:]) +c5 = fig.add_subplot(5,6,4) plt.setp(c5.get_xticklabels(), visible=False) plt.setp(c5.get_yticklabels(), visible=False) -plt.title('(3,59)') -plt.imshow(A[3,59,0,:,:]) -c6 = fig.add_subplot(5,7,20) +plt.imshow(A[2,59,omega,:,:]) +c6 = fig.add_subplot(5,6,5) plt.setp(c6.get_xticklabels(), visible=False) plt.setp(c6.get_yticklabels(), visible=False) -plt.title('(3,74)') -plt.imshow(A[3,74,0,:,:]) -c7 = fig.add_subplot(5,7,21) +plt.imshow(A[2,74,omega,:,:]) +c7 = fig.add_subplot(5,6,6) plt.setp(c7.get_xticklabels(), visible=False) plt.setp(c7.get_yticklabels(), visible=False) -plt.title('(3,91)') -plt.imshow(A[3,91,0,:,:]) - -d1 = fig.add_subplot(5,7,22) -plt.setp(d1.get_xticklabels(), visible=False) -plt.setp(d1.get_yticklabels(), visible=False) -plt.title('(4,2)') -plt.imshow(A[4,2,0,:,:]) -d2 = fig.add_subplot(5,7,23) +plt.imshow(A[2,91,omega,:,:]) + +#cc1 = fig.add_subplot(5,6,7) +#plt.setp(cc1.get_xticklabels(), visible=False) +#plt.setp(cc1.get_yticklabels(), visible=False) +#plt.imshow(A[3,2,omega,:,:]) +cc2 = fig.add_subplot(5,6,7) +plt.setp(cc2.get_xticklabels(), visible=False) +plt.setp(cc2.get_yticklabels(), visible=False) +plt.imshow(A[3,14,omega,:,:]) +cc3 = fig.add_subplot(5,6,8) +plt.setp(cc3.get_xticklabels(), visible=False) +plt.setp(cc3.get_yticklabels(), visible=False) +plt.imshow(A[3,32,omega,:,:]) +cc4 = fig.add_subplot(5,6,9) +plt.setp(cc4.get_xticklabels(), visible=False) +plt.setp(cc4.get_yticklabels(), visible=False) +plt.imshow(A[3,41,omega,:,:]) +cc5 = fig.add_subplot(5,6,10) +plt.setp(cc5.get_xticklabels(), visible=False) +plt.setp(cc5.get_yticklabels(), visible=False) +plt.imshow(A[3,59,omega,:,:]) +cc6 = fig.add_subplot(5,6,11) +plt.setp(cc6.get_xticklabels(), visible=False) +plt.setp(cc6.get_yticklabels(), visible=False) +plt.imshow(A[3,74,omega,:,:]) +cc7 = fig.add_subplot(5,6,12) +plt.setp(cc7.get_xticklabels(), visible=False) +plt.setp(cc7.get_yticklabels(), visible=False) +plt.imshow(A[3,91,omega,:,:]) + +#d1 = fig.add_subplot(7,7,29) +#plt.setp(d1.get_xticklabels(), visible=False) +#plt.setp(d1.get_yticklabels(), visible=False) +#plt.imshow(A[4,2,omega,:,:]) +d2 = fig.add_subplot(5,6,13) plt.setp(d2.get_xticklabels(), visible=False) plt.setp(d2.get_yticklabels(), visible=False) -plt.title('(4,14)') -plt.imshow(A[4,14,0,:,:]) -d3 = fig.add_subplot(5,7,24) +plt.imshow(A[4,14,omega,:,:]) +d3 = fig.add_subplot(5,6,14) plt.setp(d3.get_xticklabels(), visible=False) plt.setp(d3.get_yticklabels(), visible=False) -plt.title('(4,32)') -plt.imshow(A[4,32,0,:,:]) -d4 = fig.add_subplot(5,7,25) +plt.imshow(A[4,32,omega,:,:]) +d4 = fig.add_subplot(5,6,15) plt.setp(d4.get_xticklabels(), visible=False) plt.setp(d4.get_yticklabels(), visible=False) -plt.title('(4,41)') -plt.imshow(A[4,41,0,:,:]) -d5 = fig.add_subplot(5,7,26) +plt.imshow(A[4,41,omega,:,:]) +d5 = fig.add_subplot(5,6,16) plt.setp(d5.get_xticklabels(), visible=False) plt.setp(d5.get_yticklabels(), visible=False) -plt.title('(4,59)') -plt.imshow(A[4,59,0,:,:]) -d6 = fig.add_subplot(5,7,27) +plt.imshow(A[4,59,omega,:,:]) +d6 = fig.add_subplot(5,6,17) plt.setp(d6.get_xticklabels(), visible=False) plt.setp(d6.get_yticklabels(), visible=False) -plt.title('(4,79)') -plt.imshow(A[4,79,0,:,:]) -d7 = fig.add_subplot(5,7,28) +plt.imshow(A[4,74,omega,:,:]) +d7 = fig.add_subplot(5,6,18) plt.setp(d7.get_xticklabels(), visible=False) plt.setp(d7.get_yticklabels(), visible=False) -plt.title('(4,91)') -plt.imshow(A[4,91,0,:,:]) - -e1 = fig.add_subplot(5,7,29) -plt.setp(e1.get_xticklabels(), visible=False) -plt.setp(e1.get_yticklabels(), visible=False) -plt.title('(5,1)') -plt.imshow(A[5,1,0,:,:]) -e2 = fig.add_subplot(5,7,30) +plt.imshow(A[4,91,omega,:,:]) + +#e1 = fig.add_subplot(7,7,36) +#plt.setp(e1.get_xticklabels(), visible=False) +#plt.setp(e1.get_yticklabels(), visible=False) +#plt.imshow(A[5,2,omega,:,:]) +e2 = fig.add_subplot(5,6,19) plt.setp(e2.get_xticklabels(), visible=False) plt.setp(e2.get_yticklabels(), visible=False) -plt.title('(5,14)') -plt.imshow(A[5,14,0,:,:]) -e3 = fig.add_subplot(5,7,31) +plt.imshow(A[5,14,omega,:,:]) +e3 = fig.add_subplot(5,6,20) plt.setp(e3.get_xticklabels(), visible=False) plt.setp(e3.get_yticklabels(), visible=False) -plt.title('(5,32)') -plt.imshow(A[5,32,0,:,:]) -e4 = fig.add_subplot(5,7,32) +plt.imshow(A[5,32,omega,:,:]) +e4 = fig.add_subplot(5,6,21) plt.setp(e4.get_xticklabels(), visible=False) plt.setp(e4.get_yticklabels(), visible=False) -plt.title('(5,41)') -plt.imshow(A[5,41,0,:,:]) -e5 = fig.add_subplot(5,7,33) +plt.imshow(A[5,41,omega,:,:]) +e5 = fig.add_subplot(5,6,22) plt.setp(e5.get_xticklabels(), visible=False) plt.setp(e5.get_yticklabels(), visible=False) -plt.title('(5,59)') -plt.imshow(A[5,59,0,:,:]) -e6 = fig.add_subplot(5,7,34) +plt.imshow(A[5,59,omega,:,:]) +e6 = fig.add_subplot(5,6,23) plt.setp(e6.get_xticklabels(), visible=False) plt.setp(e6.get_yticklabels(), visible=False) -plt.title('(5,74)') -plt.imshow(A[5,74,0,:,:]) -e7 = fig.add_subplot(5,7,35) +plt.imshow(A[5,74,omega,:,:]) +e7 = fig.add_subplot(5,6,24) plt.setp(e7.get_xticklabels(), visible=False) plt.setp(e7.get_yticklabels(), visible=False) -plt.title('(5,90)') -plt.imshow(A[5,90,0,:,:]) +plt.imshow(A[5,91,omega,:,:]) + +#f1 = fig.add_subplot(7,7,43) +#plt.setp(f1.get_xticklabels(), visible=False) +#plt.setp(f1.get_yticklabels(), visible=False) +#plt.imshow(A[6,2,omega,:,:]) +f2 = fig.add_subplot(5,6,25) +plt.setp(f2.get_xticklabels(), visible=False) +plt.setp(f2.get_yticklabels(), visible=False) +plt.imshow(A[6,14,omega,:,:]) +f3 = fig.add_subplot(5,6,26) +plt.setp(f3.get_xticklabels(), visible=False) +plt.setp(f3.get_yticklabels(), visible=False) +plt.imshow(A[6,32,omega,:,:]) +f4 = fig.add_subplot(5,6,27) +plt.setp(f4.get_xticklabels(), visible=False) +plt.setp(f4.get_yticklabels(), visible=False) +plt.imshow(A[6,41,omega,:,:]) +f5 = fig.add_subplot(5,6,28) +plt.setp(f5.get_xticklabels(), visible=False) +plt.setp(f5.get_yticklabels(), visible=False) +plt.imshow(A[6,59,omega,:,:]) +f6 = fig.add_subplot(5,6,29) +plt.setp(f6.get_xticklabels(), visible=False) +plt.setp(f6.get_yticklabels(), visible=False) +plt.imshow(A[6,74,omega,:,:]) +f7 = fig.add_subplot(5,6,30) +plt.setp(f7.get_xticklabels(), visible=False) +plt.setp(f7.get_yticklabels(), visible=False) +plt.imshow(A[6,91,omega,:,:]) + +plt.axis('off') +plt.subplots_adjust(wspace=0, hspace=0) + +#fig.savefig('test.png') plt.show() diff --git a/Plot_sum_10deg_steps.py b/Plot_sum_10deg_steps.py new file mode 100644 index 0000000..883f9f6 --- /dev/null +++ b/Plot_sum_10deg_steps.py @@ -0,0 +1,174 @@ +import numpy as np +import matplotlib.pyplot as plt + +A = np.load('/u/data/alcer/DFXRM_rec/Rec_test/dataarray.npy') + +fig = plt.figure() + +plt.title('Summed images at different projections') + +a1 = fig.add_subplot(3,6,1) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +sum = np.zeros([A.shape[3], A.shape[4]]) +for i in range(A.shape[0]): + for j in range(A.shape[1]): + sum[:,:] += A[i,j,12,:,:] +plt.imshow(sum/(sum.max())) + +a2 = fig.add_subplot(3,6,2) +plt.setp(a2.get_xticklabels(), visible=False) +plt.setp(a2.get_yticklabels(), visible=False) +sum = np.zeros([A.shape[3], A.shape[4]]) +for i in range(A.shape[0]): + for j in range(A.shape[1]): + sum[:,:] += A[i,j,25,:,:] +plt.imshow(sum/(sum.max())) + +a3 = fig.add_subplot(3,6,3) +plt.setp(a3.get_xticklabels(), visible=False) +plt.setp(a3.get_yticklabels(), visible=False) +sum = np.zeros([A.shape[3], A.shape[4]]) +for i in range(A.shape[0]): + for j in range(A.shape[1]): + sum[:,:] += A[i,j,37,:,:] +plt.imshow(sum/(sum.max())) + +a4 = fig.add_subplot(3,6,4) +plt.setp(a4.get_xticklabels(), visible=False) +plt.setp(a4.get_yticklabels(), visible=False) +sum = np.zeros([A.shape[3], A.shape[4]]) +for i in range(A.shape[0]): + for j in range(A.shape[1]): + sum[:,:] += A[i,j,50,:,:] +plt.imshow(sum/(sum.max())) + +a5 = fig.add_subplot(3,6,5) +plt.setp(a5.get_xticklabels(), visible=False) +plt.setp(a5.get_yticklabels(), visible=False) +sum = np.zeros([A.shape[3], A.shape[4]]) +for i in range(A.shape[0]): + for j in range(A.shape[1]): + sum[:,:] += A[i,j,62,:,:] +plt.imshow(sum/(sum.max())) + +a6 = fig.add_subplot(3,6,6) +plt.setp(a6.get_xticklabels(), visible=False) +plt.setp(a6.get_yticklabels(), visible=False) +sum = np.zeros([A.shape[3], A.shape[4]]) +for i in range(A.shape[0]): + for j in range(A.shape[1]): + sum[:,:] += A[i,j,75,:,:] +plt.imshow(sum/(sum.max())) + +a7 = fig.add_subplot(3,6,7) +plt.setp(a7.get_xticklabels(), visible=False) +plt.setp(a7.get_yticklabels(), visible=False) +sum = np.zeros([A.shape[3], A.shape[4]]) +for i in range(A.shape[0]): + for j in range(A.shape[1]): + sum[:,:] += A[i,j,87,:,:] +plt.imshow(sum/(sum.max())) + +a8 = fig.add_subplot(3,6,8) +plt.setp(a8.get_xticklabels(), visible=False) +plt.setp(a8.get_yticklabels(), visible=False) +sum = np.zeros([A.shape[3], A.shape[4]]) +for i in range(A.shape[0]): + for j in range(A.shape[1]): + sum[:,:] += A[i,j,100,:,:] +plt.imshow(sum/(sum.max())) + +a9 = fig.add_subplot(3,6,9) +plt.setp(a9.get_xticklabels(), visible=False) +plt.setp(a9.get_yticklabels(), visible=False) +sum = np.zeros([A.shape[3], A.shape[4]]) +for i in range(A.shape[0]): + for j in range(A.shape[1]): + sum[:,:] += A[i,j,112,:,:] +plt.imshow(sum/(sum.max())) + +a10 = fig.add_subplot(3,6,10) +plt.setp(a10.get_xticklabels(), visible=False) +plt.setp(a10.get_yticklabels(), visible=False) +sum = np.zeros([A.shape[3], A.shape[4]]) +for i in range(A.shape[0]): + for j in range(A.shape[1]): + sum[:,:] += A[i,j,125,:,:] +plt.imshow(sum/(sum.max())) + +a11 = fig.add_subplot(3,6,11) +plt.setp(a11.get_xticklabels(), visible=False) +plt.setp(a11.get_yticklabels(), visible=False) +sum = np.zeros([A.shape[3], A.shape[4]]) +for i in range(A.shape[0]): + for j in range(A.shape[1]): + sum[:,:] += A[i,j,137,:,:] +plt.imshow(sum/(sum.max())) + +a12 = fig.add_subplot(3,6,12) +plt.setp(a12.get_xticklabels(), visible=False) +plt.setp(a12.get_yticklabels(), visible=False) +sum = np.zeros([A.shape[3], A.shape[4]]) +for i in range(A.shape[0]): + for j in range(A.shape[1]): + sum[:,:] += A[i,j,150,:,:] +plt.imshow(sum/(sum.max())) + +a13 = fig.add_subplot(3,6,13) +plt.setp(a13.get_xticklabels(), visible=False) +plt.setp(a13.get_yticklabels(), visible=False) +sum = np.zeros([A.shape[3], A.shape[4]]) +for i in range(A.shape[0]): + for j in range(A.shape[1]): + sum[:,:] += A[i,j,162,:,:] +plt.imshow(sum/(sum.max())) + +a14 = fig.add_subplot(3,6,14) +plt.setp(a14.get_xticklabels(), visible=False) +plt.setp(a14.get_yticklabels(), visible=False) +sum = np.zeros([A.shape[3], A.shape[4]]) +for i in range(A.shape[0]): + for j in range(A.shape[1]): + sum[:,:] += A[i,j,175,:,:] +plt.imshow(sum/(sum.max())) + +a15 = fig.add_subplot(3,6,15) +plt.setp(a15.get_xticklabels(), visible=False) +plt.setp(a15.get_yticklabels(), visible=False) +sum = np.zeros([A.shape[3], A.shape[4]]) +for i in range(A.shape[0]): + for j in range(A.shape[1]): + sum[:,:] += A[i,j,187,:,:] +plt.imshow(sum/(sum.max())) + +a16 = fig.add_subplot(3,6,16) +plt.setp(a16.get_xticklabels(), visible=False) +plt.setp(a16.get_yticklabels(), visible=False) +sum = np.zeros([A.shape[3], A.shape[4]]) +for i in range(A.shape[0]): + for j in range(A.shape[1]): + sum[:,:] += A[i,j,200,:,:] +plt.imshow(sum/(sum.max())) + +a17 = fig.add_subplot(3,6,17) +plt.setp(a17.get_xticklabels(), visible=False) +plt.setp(a17.get_yticklabels(), visible=False) +sum = np.zeros([A.shape[3], A.shape[4]]) +for i in range(A.shape[0]): + for j in range(A.shape[1]): + sum[:,:] += A[i,j,212,:,:] +plt.imshow(sum/(sum.max())) + +a18 = fig.add_subplot(3,6,18) +plt.setp(a18.get_xticklabels(), visible=False) +plt.setp(a18.get_yticklabels(), visible=False) +sum = np.zeros([A.shape[3], A.shape[4]]) +for i in range(A.shape[0]): + for j in range(A.shape[1]): + sum[:,:] += A[i,j,225,:,:] +plt.imshow(sum/(sum.max())) + +plt.axis('off') + +plt.show() From 1c9f325847621e173de5173464a5d4a1a5932c8f Mon Sep 17 00:00:00 2001 From: albusdemens Date: Fri, 7 Jul 2017 14:42:07 +0200 Subject: [PATCH 058/150] New clean background approach --- sum_bg.py | 73 ++++++++++++++++++++++++++++++++++++++++++++++++++----- 1 file changed, 67 insertions(+), 6 deletions(-) diff --git a/sum_bg.py b/sum_bg.py index fa98be6..f836d84 100644 --- a/sum_bg.py +++ b/sum_bg.py @@ -4,6 +4,7 @@ import matplotlib.pyplot as plt import lib.EdfFile as EF import sys +import scipy A = [line.rstrip() for line in open('list_bg.txt')] @@ -12,8 +13,9 @@ sum_bg = np.zeros(sze) int_im = np.zeros(len(A)) -# Measure the intensity for each image -for ii in range(0,27):#len(A)): +# Measure the intensity for each background image +for ii in range(28): # If we include also the other images, we see anomalies + # in the image im = EF.EdfFile(A[ii]) image = im.GetData(0) sum_bg[:,:] = sum_bg[:,:] + image[:,:] @@ -23,9 +25,68 @@ sum_image = sum_image + image[jj,kk] int_im[ii] = sum_image -# Show the image sum and the intensity histogram -plt.imshow(sum_bg) -plt.show() +# Show the intensity histogram for the background images +#plt.bar(range(len(A)), int_im) +#plt.show() + +# Subtract the background from an image + +A = np.load('/u/data/alcer/DFXRM_rec/Rec_test/dataarray.npy') + +# Select projection number +omega = 2 + +int = np.empty([A.shape[0], A.shape[1]]) +Img_array = np.empty([A.shape[0], A.shape[1], A.shape[3], A.shape[4]]) +for i in range(A.shape[0]): + for j in range(A.shape[1]): + int[i,j] = sum(sum(A[i,j,omega,:,:])) + if int[i,j] > 0: + Img_array[i,j,:,:] = A[i,j,omega,:,:] + + +# Sum images collected at a certain angle +Array_ang_val = np.empty([A.shape[0], A.shape[1], A.shape[3], A.shape[4]]) +Sum_img = np.zeros([A.shape[3], A.shape[4]]) +int = np.empty([A.shape[0], A.shape[1]]) +for ii in range(A.shape[0]): + for jj in range(A.shape[1]): + int[ii,jj] = sum(sum(A[ii,jj,omega,:,:])) + if int[ii,jj] > 0: + Array_ang_val[ii, jj,:,:] = A[ii,jj,omega,:,:] + Sum_img[:,:] = Sum_img[:,:] + A[ii,jj,omega,:,:] + +# For each quadrant composing the detector, take a region and calculate the average +M1 = np.mean(Sum_img[0:30, 0:30]) +M2 = np.mean(Sum_img[270:300, 0:30]) +M3 = np.mean(Sum_img[0:30, 270:300]) +M4 = np.mean(Sum_img[270:300, 270:300]) + +# For each region, subtract the average from the signal +Clean_sum_img = np.zeros([A.shape[3], A.shape[4]]) +Clean_sum_img[0:150, 0:150] = Sum_img[0:150, 0:150] - M1 +Clean_sum_img[150:300, 0:150] = Sum_img[150:300, 0:150] - M2 +Clean_sum_img[0:150, 150:300] = Sum_img[0:150, 150:300] - M3 +Clean_sum_img[150:300, 150:300] = Sum_img[150:300, 150:300] - M4 + +for i in range(Clean_sum_img.shape[0]): + for j in range(Clean_sum_img.shape[1]): + if (Clean_sum_img[i,j] < 0 or Clean_sum_img[i,j] > 1E+06): + Clean_sum_img[i,j] = 0 + +# Plot the relevant results +fig=plt.figure() + +ax1=fig.add_subplot(121) +plt.imshow(Sum_img) +ax1.title.set_text('Summed images - raw') + +ax2=fig.add_subplot(122) +plt.imshow(Clean_sum_img) +ax2.title.set_text('Sum - background') -plt.bar(range(len(A)), int_im) plt.show() + + + + From c6c97057d4f1187666887d45637b1009e44f9160 Mon Sep 17 00:00:00 2001 From: albusdemens Date: Fri, 14 Jul 2017 16:40:40 +0200 Subject: [PATCH 059/150] Checking IO --- Plot_ang_values_one_omega.py | 16 ++++++++++++++++ getdata.py | 25 ++++++++++++++++--------- lib/EdfFile.py | 1 - lib/miniged.py | 12 +++++++----- 4 files changed, 39 insertions(+), 15 deletions(-) create mode 100644 Plot_ang_values_one_omega.py diff --git a/Plot_ang_values_one_omega.py b/Plot_ang_values_one_omega.py new file mode 100644 index 0000000..ece6481 --- /dev/null +++ b/Plot_ang_values_one_omega.py @@ -0,0 +1,16 @@ +# this script plots the distribution of the angular values fro one omega + +import numpy as np +import matplotlib.pyplot as plt + +A = np.load('/u/data/alcer/DFXRM_rec/Rec_test/dataarray.npy') + +Counter = np.zeros([A.shape[0], A.shape[1]]) +for aa in range(A.shape[0]): + for bb in range(A.shape[1]): + if sum(sum(A[aa,bb,1,:,:])) > 0: + Counter[aa, bb] = 1 + +fig = plt.figure() +plt.imshow(Counter) +plt.show() diff --git a/getdata.py b/getdata.py index 63f14c6..5b7d1e2 100644 --- a/getdata.py +++ b/getdata.py @@ -102,7 +102,6 @@ def allFiles(self, data, imsiz): lenb = len(self.beta) leno = len(self.omega) lent = len(self.theta) - print lena, lenb, leno, lent # Reduce alpha and beta to a single index_list idx_list = np.zeros(len(self.meta)) @@ -119,15 +118,16 @@ def allFiles(self, data, imsiz): print 'Check that the number of (phi, chi) steps is', num_int - bigarray = np.zeros((num_int, lent, leno, int(imsiz[1]), int(imsiz[0])), dtype=np.uint16) + bigarray = np.zeros((num_int, lent, leno, int(imsiz[0]), int(imsiz[1])), dtype=np.uint16) + Image_prop = np.zeros([len(self.index_list), 4]) #AA = np.empty([len(self.index_list), 5]) for i, ind in enumerate(self.index_list): - a = np.where(self.alpha == self.meta[int(ind),0]) # rock - b = np.where(self.beta == self.meta[int(ind),1]) # roll - c = np.where(self.omega == self.meta[int(ind),2]) # omega - d = np.where(self.theta == self.meta[int(ind),4]) # theta - idx_rescaled = int((self.meta[int(ind),0] - phi_0) / (np.cos(np.deg2rad(self.meta[int(ind),2])) * 0.032) + ((num_int - 1) / 2)) + a = np.where(self.alpha == self.meta[int(ind),0]) # Rock + b = np.where(self.beta == self.meta[int(ind),1]) # Roll + c = np.where(self.omega == self.meta[int(ind),2]) # Omega + d = np.where(self.theta == self.meta[int(ind),4]) # Theta + idx_rescaled = (self.meta[int(ind),0] - phi_0) / (np.cos(np.deg2rad(self.meta[int(ind),2])) * 0.032) + ((num_int - 1) / 2) e = (idx_rescaled - ((num_int - 1) / 2)) * 0.032 # Can we effectively reconstruct chi and phi from idx_rescaled? @@ -136,7 +136,14 @@ def allFiles(self, data, imsiz): #AA[ind] = [self.meta[int(ind),0], self.meta[int(ind),1],self.meta[int(ind),2], idx_rescaled, self.meta[int(ind),4]] - bigarray[idx_rescaled, d[0], c[0], :, :] = imgarray[ind, :, :] + bigarray[[int(round(idx_rescaled))], d[0], c[0], :, :] = imgarray[ind, :, :] + + # List the properties of the images and write them to a txt + Image_prop[int(ind), 0] = int(ind) # Image number + Image_prop[int(ind), 1] = int(round(idx_rescaled)) # Gamma index + Image_prop[int(ind), 2] = d[0] # Theta + Image_prop[int(ind), 3] = c[0] # + np.save(self.directory + '/alpha.npy', self.alpha) np.save(self.directory + '/beta.npy', self.beta) @@ -147,7 +154,7 @@ def allFiles(self, data, imsiz): np.save(self.directory + '/all_data.npy', self.meta) np.save(self.directory + '/dataarray.npy', bigarray) #np.savetxt(self.directory + '/AA.txt', AA) - + np.savetxt(self.directory + '/Image_properties.txt', Image_prop, fmt='%i %i %i %i') if __name__ == "__main__": if len(sys.argv) != 10: diff --git a/lib/EdfFile.py b/lib/EdfFile.py index 78ecea8..d15238a 100644 --- a/lib/EdfFile.py +++ b/lib/EdfFile.py @@ -1265,4 +1265,3 @@ def GetRegion(Arr, Pos, Size): #Saves in the original format (unsigned short) OldHeader = exe.GetStaticHeader(2) exe.WriteImage({}, ushort, 1, OldHeader["DataType"]) - diff --git a/lib/miniged.py b/lib/miniged.py index 27de642..96a3dd7 100644 --- a/lib/miniged.py +++ b/lib/miniged.py @@ -351,11 +351,13 @@ def getImage(self, index, full): roi = self.roi if full: - im = (img.GetData(0).astype(np.int64)*100) / self.bg_combined_full + im = (img.GetData(0).astype(np.int64)) #/ self.bg_combined_full + #im = (img.GetData(0).astype(np.int64)*300) / self.bg_combined_full else: - im = (img.GetData(0).astype(np.int64)*100)[ + #im = (img.GetData(0).astype(np.int64)*300)[ + im = (img.GetData(0).astype(np.int64))[ roi[2]:roi[3], - roi[0]:roi[1]] / self.bg_combined + roi[0]:roi[1]] #/ self.bg_combined im = self.cleanImage(im) @@ -430,8 +432,8 @@ def makeImgArray(self, index, xpos, savefilename): def addToArray(index_part): if self.rank == 0: print "Loading array from data files..." - point = len(index_part) / 100 - incr = len(index_part) / 100 + point = len(index_part)# / 100 + incr = len(index_part)# / 100 imgarray_part = np.zeros((len(index_part), len(img[:, 0]), len(img[0, :]))) for i in range(len(index_part)): if self.rank == 0 and i % (5 * point) == 0: From 5a73236be670d981620696a06bd1d4b8a9fc3104 Mon Sep 17 00:00:00 2001 From: albusdemens Date: Fri, 14 Jul 2017 16:41:22 +0200 Subject: [PATCH 060/150] Script to list name and properties of the imahes --- image_properties.py | 29 +++++++++++++++++++++++++++++ 1 file changed, 29 insertions(+) create mode 100644 image_properties.py diff --git a/image_properties.py b/image_properties.py new file mode 100644 index 0000000..c453abe --- /dev/null +++ b/image_properties.py @@ -0,0 +1,29 @@ +# This script returns the name of the input files and their characteristics: +# - index +# - theta +# - omega +# Using the notation in getdata.py + +import numpy as np +import shlex +import subprocess +import os +from numpy import loadtxt + +# Directory where the IO data is stored. Chnage also in line 15 +io_dir = '/u/data/alcer/DFXRM_rec/Rec_test' + +# Write to a file the list of the images in the folder. Use the bash command +# $ echo ls /u/data/andcj/hxrm/Al_april_2017/topotomo/sundaynight/topotomo_frelon_far_* > /u/data/alcer/DFXRM_rec/Rec_test/List_images.txt + + +# Load files with the image properties, written by getdata.py +A = loadtxt(os.path.join(io_dir + '/Image_properties.txt')) +B = np.genfromtxt(os.path.join(io_dir +'/List_images.txt'),dtype='str') + +# Make a new text file, where for each line we have the name of the input file, +# Followed by its properties (from Image_properties.txt) + +with open((os.path.join(io_dir + '/Image_name_name&properties.txt')), 'w') as outfp: + for i in range(A.shape[0]): + outfp.write('%4d %4s %4d %4d %4d\n' %(A[i,0], B[i], A[i,1], A[i,2], A[i,3])) From 796f7bcffc2cbe2b79682ebc66448c1e5b0666dc Mon Sep 17 00:00:00 2001 From: albusdemens Date: Fri, 14 Jul 2017 16:42:18 +0200 Subject: [PATCH 061/150] Updated gitignore --- .gitignore | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/.gitignore b/.gitignore index 0d20b64..e740d92 100644 --- a/.gitignore +++ b/.gitignore @@ -1 +1,5 @@ +*.DS_Store + +lib/.DS_Store + *.pyc From 552e0a725ed0813073ca9964829caf23c237f23b Mon Sep 17 00:00:00 2001 From: albusdemens Date: Sun, 16 Jul 2017 18:32:28 +0200 Subject: [PATCH 062/150] Code forked to avoid memory problems --- Plot_all_im_1omega.py | 381 ++++++++++++++++++++++++++ Plot_data_1omega.py | 601 ++++++++++++++++++++++++++---------------- 2 files changed, 759 insertions(+), 223 deletions(-) create mode 100644 Plot_all_im_1omega.py diff --git a/Plot_all_im_1omega.py b/Plot_all_im_1omega.py new file mode 100644 index 0000000..83c9c2f --- /dev/null +++ b/Plot_all_im_1omega.py @@ -0,0 +1,381 @@ +# Derived from Plot_data_1omega.py (code split after memory leak) +# Aim: plot the images collected at a certain projection + +import numpy as np +import matplotlib.pyplot as plt + +A = np.load('/u/data/alcer/DFXRM_rec/Rec_test/dataarray.npy') + +# Select projection number +omega = 2 + +# We want to plot, in a grid, all images collected at a certain projection +# Load the datafile with all information, and store to an array the data +# relative to the images in a certain projection +Data = np.loadtxt('/u/data/alcer/DFXRM_rec/Rec_test/Image_properties.txt') +Data_angle = np.zeros([49,2]) +idx = 0 +for i in range(Data.shape[0]): + if Data[i,3] == omega: + idx = idx +1 + Data_angle[idx-1, 0] = Data[i,1] + Data_angle[idx-1, 1] = Data[i,2] + +# Plot the various recorded images + +aa = int(Data_angle[0,0]) +bb = int(Data_angle[0,1]) +AA = np.zeros([A.shape[3], A.shape[4]]) +AA[:,:] = A[aa,bb,omega,:,:] + +fig, axes = plt.subplots(7, 7) + +a1 = fig.add_subplot(7,7,1) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[0,0] +bb = Data_angle[0,1] +plt.imshow(AA) + +a1 = fig.add_subplot(7,7,2) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[1,0] +bb = Data_angle[1,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,3) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[2,0] +bb = Data_angle[2,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,4) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[3,0] +bb = Data_angle[3,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,5) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[4,0] +bb = Data_angle[4,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,6) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[5,0] +bb = Data_angle[5,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,7) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[6,0] +bb = Data_angle[6,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,8) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[7,0] +bb = Data_angle[7,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,9) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[8,0] +bb = Data_angle[8,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,10) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[9,0] +bb = Data_angle[9,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,11) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[10,0] +bb = Data_angle[10,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,12) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[11,0] +bb = Data_angle[11,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,13) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[12,0] +bb = Data_angle[12,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,14) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[13,0] +bb = Data_angle[13,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,15) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[14,0] +bb = Data_angle[14,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,16) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[15,0] +bb = Data_angle[15,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,17) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[16,0] +bb = Data_angle[16,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,18) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[17,0] +bb = Data_angle[17,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,19) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[18,0] +bb = Data_angle[18,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,20) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[19,0] +bb = Data_angle[19,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,21) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[20,0] +bb = Data_angle[20,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,22) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[21,0] +bb = Data_angle[21,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,23) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[22,0] +bb = Data_angle[22,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,24) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[23,0] +bb = Data_angle[23,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,25) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[24,0] +bb = Data_angle[24,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,26) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[25,0] +bb = Data_angle[25,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,27) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[26,0] +bb = Data_angle[26,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,28) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[27,0] +bb = Data_angle[27,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,29) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[28,0] +bb = Data_angle[28,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,30) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[29,0] +bb = Data_angle[29,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,31) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[30,0] +bb = Data_angle[30,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,32) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[31,0] +bb = Data_angle[31,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,33) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[32,0] +bb = Data_angle[32,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,34) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[33,0] +bb = Data_angle[33,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,35) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[34,0] +bb = Data_angle[34,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,36) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[35,0] +bb = Data_angle[35,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,37) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[36,0] +bb = Data_angle[36,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,38) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[37,0] +bb = Data_angle[37,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,39) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[38,0] +bb = Data_angle[38,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,40) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[39,0] +bb = Data_angle[39,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,41) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[40,0] +bb = Data_angle[40,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,42) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[41,0] +bb = Data_angle[41,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,43) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[42,0] +bb = Data_angle[42,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,44) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[43,0] +bb = Data_angle[43,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,45) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[44,0] +bb = Data_angle[44,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,46) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[45,0] +bb = Data_angle[45,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,47) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[46,0] +bb = Data_angle[46,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,48) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[47,0] +bb = Data_angle[47,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,49) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[48,0] +bb = Data_angle[48,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +plt.axis('off') +plt.subplots_adjust(wspace=0, hspace=0) + +#fig.savefig('test.png') + +plt.show() diff --git a/Plot_data_1omega.py b/Plot_data_1omega.py index 5d70b9e..807fe85 100644 --- a/Plot_data_1omega.py +++ b/Plot_data_1omega.py @@ -15,7 +15,6 @@ for i in range(A.shape[0]): for j in range(A.shape[1]): int[i,j] = sum(sum(A[i,j,omega,:,:])) - print i, j, int[i,j] if int[i,j] > 0: Img_array[i,j,:,:] = A[i,j,omega,:,:] @@ -26,11 +25,11 @@ for j in range(A.shape[1]): int[i,j] = int[i,j] + sum(sum(A[i,j,oo,:,:])) -plt.imshow(int) -plt.title('Distribution of the recorded images as a function of the motor angles') -plt.xlabel('Theta') -plt.ylabel('Gamma') -plt.show() +#plt.imshow(int) +#plt.title('Distribution of the recorded images as a function of the motor angles') +#plt.xlabel('Theta') +#plt.ylabel('Gamma') +#plt.show() # Sum images collected at a certain angle Array_ang_val = np.empty([A.shape[0], A.shape[1], A.shape[3], A.shape[4]]) @@ -42,228 +41,384 @@ if int[ii,jj] > 0: Array_ang_val[ii, jj,:,:] = A[ii,jj,omega,:,:] Sum_img[:,:] = Sum_img[:,:] + A[ii,jj,omega,:,:] - + # Plot the distribution of integrated intensity values in the gamma, theta plane -plt.imshow(int) -plt.title('Distribution of the recorded images as a function of the motor angles') -plt.xlabel('Theta') -plt.ylabel('Gamma') -plt.show() +#plt.imshow(int) +#plt.title('Distribution of the recorded images as a function of the motor angles') +#plt.xlabel('Theta') +#plt.ylabel('Gamma') +#plt.show() -# Plot the sum of the background images -fig = plt.figure() -plt.imshow(Sum_img) -plt.xlabel('X (pixels)') -plt.ylabel('Y (pixels)') -plt.show() +# Plot the sum of the summed images +#fig = plt.figure() +#plt.imshow(Sum_img) +#plt.xlabel('X (pixels)') +#plt.ylabel('Y (pixels)') +#plt.show() -sys.exit() +# We want to plot, in a grid, all images collected at a certain projection +# Load the datafile with all information, and store to an array the data +# relative to the images in a certain projection +Data = np.loadtxt('/u/data/alcer/DFXRM_rec/Rec_test/Image_properties.txt') +Data_angle = np.zeros([49,2]) +idx = 0 +for i in range(Data.shape[0]): + if Data[i,3] == omega: + idx = idx +1 + Data_angle[idx-1, 0] = Data[i,1] + Data_angle[idx-1, 1] = Data[i,2] # Plot the various recorded images -fig, axes = plt.subplots(5, 6) - -#a1 = fig.add_subplot(7,7,1) -#plt.setp(a1.get_xticklabels(), visible=False) -#plt.setp(a1.get_yticklabels(), visible=False) -#plt.imshow(A[0,2,omega,:,:]) -#a2 = fig.add_subplot(7,7,2) -#plt.setp(a2.get_xticklabels(), visible=False) -#plt.setp(a2.get_yticklabels(), visible=False) -#plt.imshow(A[0,14,omega,:,:]) -#a3 = fig.add_subplot(7,7,3) -#plt.setp(a3.get_xticklabels(), visible=False) -#plt.setp(a3.get_yticklabels(), visible=False) -#plt.imshow(A[0,32,omega,:,:]) -#a4 = fig.add_subplot(7,7,4) -#plt.setp(a4.get_xticklabels(), visible=False) -#plt.setp(a4.get_yticklabels(), visible=False) -#plt.imshow(A[0,41,omega,:,:]) -#a5 = fig.add_subplot(7,7,5) -#plt.setp(a5.get_xticklabels(), visible=False) -#plt.setp(a5.get_yticklabels(), visible=False) -#plt.imshow(A[0,59,omega,:,:]) -#a6 = fig.add_subplot(7,7,6) -#plt.setp(a6.get_xticklabels(), visible=False) -#plt.setp(a6.get_yticklabels(), visible=False) -#plt.imshow(A[0,74,omega,:,:]) -#a7 = fig.add_subplot(7,7,7) -#plt.setp(a7.get_xticklabels(), visible=False) -#plt.setp(a7.get_yticklabels(), visible=False) -#plt.imshow(A[0,91,omega,:,:]) - -#b1 = fig.add_subplot(7,7,8) -#plt.setp(b1.get_xticklabels(), visible=False) -#plt.setp(b1.get_yticklabels(), visible=False) -#plt.imshow(A[1,2,omega,:,:]) -#b2 = fig.add_subplot(7,7,9) -#plt.setp(b2.get_xticklabels(), visible=False) -#plt.setp(b2.get_yticklabels(), visible=False) -#plt.imshow(A[1,14,omega,:,:]) -#b3 = fig.add_subplot(7,7,10) -#plt.setp(b3.get_xticklabels(), visible=False) -#plt.setp(b3.get_yticklabels(), visible=False) -#plt.imshow(A[1,32,omega,:,:]) -#b4 = fig.add_subplot(7,7,11) -#plt.setp(b4.get_xticklabels(), visible=False) -#plt.setp(b4.get_yticklabels(), visible=False) -#plt.imshow(A[1,41,omega,:,:]) -#b5 = fig.add_subplot(7,7,12) -#plt.setp(b5.get_xticklabels(), visible=False) -#plt.setp(b5.get_yticklabels(), visible=False) -#plt.imshow(A[1,59,omega,:,:]) -#b6 = fig.add_subplot(7,7,13) -#plt.setp(b6.get_xticklabels(), visible=False) -#plt.setp(b6.get_yticklabels(), visible=False) -#plt.imshow(A[1,74,omega,:,:]) -#b7 = fig.add_subplot(7,7,14) -#plt.setp(b7.get_xticklabels(), visible=False) -#plt.setp(b7.get_yticklabels(), visible=False) -#plt.imshow(A[1,91,omega,:,:]) - -#c1 = fig.add_subplot(7,7,15) -#plt.setp(c1.get_xticklabels(), visible=False) -#plt.setp(c1.get_yticklabels(), visible=False) -#plt.imshow(A[2,2,omega,:,:]) -c2 = fig.add_subplot(5,6,1) -plt.setp(c2.get_xticklabels(), visible=False) -plt.setp(c2.get_yticklabels(), visible=False) -plt.imshow(A[2,14,omega,:,:]) -c3 = fig.add_subplot(5,6,2) -plt.setp(c3.get_xticklabels(), visible=False) -plt.setp(c3.get_yticklabels(), visible=False) -plt.imshow(A[2,32,omega,:,:]) -c4 = fig.add_subplot(5,6,3) -plt.setp(c4.get_xticklabels(), visible=False) -plt.setp(c4.get_yticklabels(), visible=False) -plt.imshow(A[2,41,omega,:,:]) -c5 = fig.add_subplot(5,6,4) -plt.setp(c5.get_xticklabels(), visible=False) -plt.setp(c5.get_yticklabels(), visible=False) -plt.imshow(A[2,59,omega,:,:]) -c6 = fig.add_subplot(5,6,5) -plt.setp(c6.get_xticklabels(), visible=False) -plt.setp(c6.get_yticklabels(), visible=False) -plt.imshow(A[2,74,omega,:,:]) -c7 = fig.add_subplot(5,6,6) -plt.setp(c7.get_xticklabels(), visible=False) -plt.setp(c7.get_yticklabels(), visible=False) -plt.imshow(A[2,91,omega,:,:]) - -#cc1 = fig.add_subplot(5,6,7) -#plt.setp(cc1.get_xticklabels(), visible=False) -#plt.setp(cc1.get_yticklabels(), visible=False) -#plt.imshow(A[3,2,omega,:,:]) -cc2 = fig.add_subplot(5,6,7) -plt.setp(cc2.get_xticklabels(), visible=False) -plt.setp(cc2.get_yticklabels(), visible=False) -plt.imshow(A[3,14,omega,:,:]) -cc3 = fig.add_subplot(5,6,8) -plt.setp(cc3.get_xticklabels(), visible=False) -plt.setp(cc3.get_yticklabels(), visible=False) -plt.imshow(A[3,32,omega,:,:]) -cc4 = fig.add_subplot(5,6,9) -plt.setp(cc4.get_xticklabels(), visible=False) -plt.setp(cc4.get_yticklabels(), visible=False) -plt.imshow(A[3,41,omega,:,:]) -cc5 = fig.add_subplot(5,6,10) -plt.setp(cc5.get_xticklabels(), visible=False) -plt.setp(cc5.get_yticklabels(), visible=False) -plt.imshow(A[3,59,omega,:,:]) -cc6 = fig.add_subplot(5,6,11) -plt.setp(cc6.get_xticklabels(), visible=False) -plt.setp(cc6.get_yticklabels(), visible=False) -plt.imshow(A[3,74,omega,:,:]) -cc7 = fig.add_subplot(5,6,12) -plt.setp(cc7.get_xticklabels(), visible=False) -plt.setp(cc7.get_yticklabels(), visible=False) -plt.imshow(A[3,91,omega,:,:]) - -#d1 = fig.add_subplot(7,7,29) -#plt.setp(d1.get_xticklabels(), visible=False) -#plt.setp(d1.get_yticklabels(), visible=False) -#plt.imshow(A[4,2,omega,:,:]) -d2 = fig.add_subplot(5,6,13) -plt.setp(d2.get_xticklabels(), visible=False) -plt.setp(d2.get_yticklabels(), visible=False) -plt.imshow(A[4,14,omega,:,:]) -d3 = fig.add_subplot(5,6,14) -plt.setp(d3.get_xticklabels(), visible=False) -plt.setp(d3.get_yticklabels(), visible=False) -plt.imshow(A[4,32,omega,:,:]) -d4 = fig.add_subplot(5,6,15) -plt.setp(d4.get_xticklabels(), visible=False) -plt.setp(d4.get_yticklabels(), visible=False) -plt.imshow(A[4,41,omega,:,:]) -d5 = fig.add_subplot(5,6,16) -plt.setp(d5.get_xticklabels(), visible=False) -plt.setp(d5.get_yticklabels(), visible=False) -plt.imshow(A[4,59,omega,:,:]) -d6 = fig.add_subplot(5,6,17) -plt.setp(d6.get_xticklabels(), visible=False) -plt.setp(d6.get_yticklabels(), visible=False) -plt.imshow(A[4,74,omega,:,:]) -d7 = fig.add_subplot(5,6,18) -plt.setp(d7.get_xticklabels(), visible=False) -plt.setp(d7.get_yticklabels(), visible=False) -plt.imshow(A[4,91,omega,:,:]) - -#e1 = fig.add_subplot(7,7,36) -#plt.setp(e1.get_xticklabels(), visible=False) -#plt.setp(e1.get_yticklabels(), visible=False) -#plt.imshow(A[5,2,omega,:,:]) -e2 = fig.add_subplot(5,6,19) -plt.setp(e2.get_xticklabels(), visible=False) -plt.setp(e2.get_yticklabels(), visible=False) -plt.imshow(A[5,14,omega,:,:]) -e3 = fig.add_subplot(5,6,20) -plt.setp(e3.get_xticklabels(), visible=False) -plt.setp(e3.get_yticklabels(), visible=False) -plt.imshow(A[5,32,omega,:,:]) -e4 = fig.add_subplot(5,6,21) -plt.setp(e4.get_xticklabels(), visible=False) -plt.setp(e4.get_yticklabels(), visible=False) -plt.imshow(A[5,41,omega,:,:]) -e5 = fig.add_subplot(5,6,22) -plt.setp(e5.get_xticklabels(), visible=False) -plt.setp(e5.get_yticklabels(), visible=False) -plt.imshow(A[5,59,omega,:,:]) -e6 = fig.add_subplot(5,6,23) -plt.setp(e6.get_xticklabels(), visible=False) -plt.setp(e6.get_yticklabels(), visible=False) -plt.imshow(A[5,74,omega,:,:]) -e7 = fig.add_subplot(5,6,24) -plt.setp(e7.get_xticklabels(), visible=False) -plt.setp(e7.get_yticklabels(), visible=False) -plt.imshow(A[5,91,omega,:,:]) - -#f1 = fig.add_subplot(7,7,43) -#plt.setp(f1.get_xticklabels(), visible=False) -#plt.setp(f1.get_yticklabels(), visible=False) -#plt.imshow(A[6,2,omega,:,:]) -f2 = fig.add_subplot(5,6,25) -plt.setp(f2.get_xticklabels(), visible=False) -plt.setp(f2.get_yticklabels(), visible=False) -plt.imshow(A[6,14,omega,:,:]) -f3 = fig.add_subplot(5,6,26) -plt.setp(f3.get_xticklabels(), visible=False) -plt.setp(f3.get_yticklabels(), visible=False) -plt.imshow(A[6,32,omega,:,:]) -f4 = fig.add_subplot(5,6,27) -plt.setp(f4.get_xticklabels(), visible=False) -plt.setp(f4.get_yticklabels(), visible=False) -plt.imshow(A[6,41,omega,:,:]) -f5 = fig.add_subplot(5,6,28) -plt.setp(f5.get_xticklabels(), visible=False) -plt.setp(f5.get_yticklabels(), visible=False) -plt.imshow(A[6,59,omega,:,:]) -f6 = fig.add_subplot(5,6,29) -plt.setp(f6.get_xticklabels(), visible=False) -plt.setp(f6.get_yticklabels(), visible=False) -plt.imshow(A[6,74,omega,:,:]) -f7 = fig.add_subplot(5,6,30) -plt.setp(f7.get_xticklabels(), visible=False) -plt.setp(f7.get_yticklabels(), visible=False) -plt.imshow(A[6,91,omega,:,:]) + +aa = int(Data_angle[0,0]) +bb = int(Data_angle[0,1]) +AA = np.zeros([Img_array.shape[2], Img_array.shape[3]]) +AA[:,:] = Img_array[aa,bb,:,:] + +fig, axes = plt.subplots(7, 7) + +a1 = fig.add_subplot(7,7,1) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[0,0] +bb = Data_angle[0,1] +plt.imshow(AA) + +a1 = fig.add_subplot(7,7,2) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[1,0] +bb = Data_angle[1,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,3) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[2,0] +bb = Data_angle[2,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,4) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[3,0] +bb = Data_angle[3,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,5) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[4,0] +bb = Data_angle[4,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,6) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[5,0] +bb = Data_angle[5,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,7) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[6,0] +bb = Data_angle[6,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,8) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[7,0] +bb = Data_angle[7,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,9) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[8,0] +bb = Data_angle[8,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,10) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[9,0] +bb = Data_angle[9,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,11) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[10,0] +bb = Data_angle[10,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,12) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[11,0] +bb = Data_angle[11,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,13) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[12,0] +bb = Data_angle[12,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,14) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[13,0] +bb = Data_angle[13,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,15) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[14,0] +bb = Data_angle[14,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,16) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[15,0] +bb = Data_angle[15,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,17) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[16,0] +bb = Data_angle[16,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,18) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[17,0] +bb = Data_angle[17,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,19) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[18,0] +bb = Data_angle[18,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,20) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[19,0] +bb = Data_angle[19,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,21) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[20,0] +bb = Data_angle[20,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,22) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[21,0] +bb = Data_angle[21,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,23) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[22,0] +bb = Data_angle[22,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,24) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[23,0] +bb = Data_angle[23,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,25) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[24,0] +bb = Data_angle[24,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,26) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[25,0] +bb = Data_angle[25,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,27) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[26,0] +bb = Data_angle[26,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,28) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[27,0] +bb = Data_angle[27,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,29) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[28,0] +bb = Data_angle[28,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,30) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[29,0] +bb = Data_angle[29,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,31) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[30,0] +bb = Data_angle[30,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,32) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[31,0] +bb = Data_angle[31,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,33) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[32,0] +bb = Data_angle[32,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,34) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[33,0] +bb = Data_angle[33,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,35) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[34,0] +bb = Data_angle[34,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,36) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[35,0] +bb = Data_angle[35,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,37) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[36,0] +bb = Data_angle[36,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,38) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[37,0] +bb = Data_angle[37,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,39) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[38,0] +bb = Data_angle[38,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,40) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[39,0] +bb = Data_angle[39,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,41) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[40,0] +bb = Data_angle[40,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,42) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[41,0] +bb = Data_angle[41,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,43) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[42,0] +bb = Data_angle[42,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,44) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[43,0] +bb = Data_angle[43,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,45) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[44,0] +bb = Data_angle[44,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,46) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[45,0] +bb = Data_angle[45,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,47) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[47,0] +bb = Data_angle[47,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,48) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[48,0] +bb = Data_angle[48,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) + +a1 = fig.add_subplot(7,7,49) +plt.setp(a1.get_xticklabels(), visible=False) +plt.setp(a1.get_yticklabels(), visible=False) +aa = Data_angle[49,0] +bb = Data_angle[49,1] +plt.imshow(A[int(aa),int(bb),omega,:,:]) plt.axis('off') plt.subplots_adjust(wspace=0, hspace=0) From 077febf5ee14058f5f57ce942f86a24dc78c3637 Mon Sep 17 00:00:00 2001 From: albusdemens Date: Sun, 16 Jul 2017 18:32:52 +0200 Subject: [PATCH 063/150] Analysis motor angles --- Study_motor_angles/readNPY.m | 36 ++ getdata.py | 9 +- getdata_fabio.py | 30 ++ image_properties.py | 4 +- lib/edfimage.py | 985 +++++++++++++++++++++++++++++++++++ plot_sum_fabio.py | 12 + sum_angles_fabio.py | 51 ++ 7 files changed, 1121 insertions(+), 6 deletions(-) create mode 100644 Study_motor_angles/readNPY.m create mode 100644 getdata_fabio.py create mode 100644 lib/edfimage.py create mode 100644 plot_sum_fabio.py create mode 100644 sum_angles_fabio.py diff --git a/Study_motor_angles/readNPY.m b/Study_motor_angles/readNPY.m new file mode 100644 index 0000000..862bb70 --- /dev/null +++ b/Study_motor_angles/readNPY.m @@ -0,0 +1,36 @@ + +function data = readNPY(filename) +% Function to read NPY files into matlab. +% *** Only reads a subset of all possible NPY files, specifically N-D arrays of certain data types. +% See https://github.com/kwikteam/npy-matlab/blob/master/npy.ipynb for +% more. +% + +[shape, dataType, fortranOrder, littleEndian, totalHeaderLength, ~] = readNPYheader(filename); + +if littleEndian + fid = fopen(filename, 'r', 'l'); +else + fid = fopen(filename, 'r', 'b'); +end + +try + + [~] = fread(fid, totalHeaderLength, 'uint8'); + + % read the data + data = fread(fid, prod(shape), [dataType '=>' dataType]); + + if length(shape)>1 && ~fortranOrder + data = reshape(data, shape(end:-1:1)); + data = permute(data, [length(shape):-1:1]); + elseif length(shape)>1 + data = reshape(data, shape); + end + + fclose(fid); + +catch me + fclose(fid); + rethrow(me); +end diff --git a/getdata.py b/getdata.py index 5b7d1e2..29ff918 100644 --- a/getdata.py +++ b/getdata.py @@ -1,6 +1,4 @@ -# python getdata.py /u/data/andcj/hxrm/Al_april_2017/topotomo/sundaynight topotomo_frelon_far_ /u/data/andcj/hxrm/Al_april_2017/topotomo/monday/bg topotomo_frelon_far_ 256,256 200,200 /u/data/alcer/DFXRM_rec Rec_test 0.785 -3.319 - -# python getdata.py /u/data/andcj/hxrm/Al_april_2017/topotomo/sundaynight topotomo_frelon_far_ /home/nexmap/alcer/DFXRM/bg_refined topotomo_frelon_far_ 256,256 200,200 /u/data/alcer/DFXRM_rec Rec_test 0.785 -3.319 +# python getdata.py /u/data/andcj/hxrm/Al_april_2017/topotomo/sundaynight topotomo_frelon_far_ /home/nexmap/alcer/DFXRM/bg_refined topotomo_frelon_far_ 256,256 300,300 /u/data/alcer/DFXRM_rec Rec_test 0.785 -3.319 from lib.miniged import GetEdfData # Read EDF files and plot background import sys @@ -128,8 +126,11 @@ def allFiles(self, data, imsiz): c = np.where(self.omega == self.meta[int(ind),2]) # Omega d = np.where(self.theta == self.meta[int(ind),4]) # Theta idx_rescaled = (self.meta[int(ind),0] - phi_0) / (np.cos(np.deg2rad(self.meta[int(ind),2])) * 0.032) + ((num_int - 1) / 2) + idx_rescaled_1 = (self.meta[int(ind),1] - chi_0) * np.cos(np.deg2rad(self.meta[int(ind),1])) / (np.sin(np.deg2rad(self.meta[int(ind),2])) * 0.032) + ((num_int - 1) / 2) e = (idx_rescaled - ((num_int - 1) / 2)) * 0.032 + print round(idx_rescaled), round(idx_rescaled_1), np.deg2rad(self.meta[int(ind),2]) + # Can we effectively reconstruct chi and phi from idx_rescaled? #ph = phi_0 + ((idx_rescaled - 3)*np.cos(np.deg2rad(self.meta[int(ind),2]))*0.032) #ch = chi_0 + ((idx_rescaled - 3)*(np.tan(np.deg2rad(self.meta[int(ind),2]))*0.032)) @@ -142,7 +143,7 @@ def allFiles(self, data, imsiz): Image_prop[int(ind), 0] = int(ind) # Image number Image_prop[int(ind), 1] = int(round(idx_rescaled)) # Gamma index Image_prop[int(ind), 2] = d[0] # Theta - Image_prop[int(ind), 3] = c[0] # + Image_prop[int(ind), 3] = c[0] # Omega np.save(self.directory + '/alpha.npy', self.alpha) diff --git a/getdata_fabio.py b/getdata_fabio.py new file mode 100644 index 0000000..194beac --- /dev/null +++ b/getdata_fabio.py @@ -0,0 +1,30 @@ +# Idea of the script: load data using Fabio, combining info from getdata.py + +import numpy as np +import matplotlib.pyplot as plt +import lib.EdfFile as EF +import sys +import fabio +import os + +# Directory where the IO data is stored +io_dir = '/u/data/alcer/DFXRM_rec/Rec_test' + +# List of the image files +im_paths = np.genfromtxt(os.path.join(io_dir + '/List_images.txt'), dtype = str) +# List of the files properties +im_prop = np.loadtxt(os.path.join(io_dir + '/Image_properties.txt')) +# Array from getdata.py +im_array = np.load(os.path.join(io_dir + '/dataarray.npy')) + +# Array where o store information +Fabio_array = np.zeros(im_array.shape) +for i in range(im_paths.shape[0]): + a = int(im_prop[i,1]) + b = int(im_prop[i,2]) + c = int(im_prop[i,3]) + img_name = im_paths[i] + I = fabio.open(img_name).data + Fabio_array[a,b,c,:,:] = I[106:406, 106:406] + +np.save(os.path.join(io_dir + '/Fabio_array.npy'), Fabio_array) diff --git a/image_properties.py b/image_properties.py index c453abe..5a8dee6 100644 --- a/image_properties.py +++ b/image_properties.py @@ -24,6 +24,6 @@ # Make a new text file, where for each line we have the name of the input file, # Followed by its properties (from Image_properties.txt) -with open((os.path.join(io_dir + '/Image_name_name&properties.txt')), 'w') as outfp: +with open((os.path.join(io_dir + '/Img_name_prop.txt')), 'w') as outfp: for i in range(A.shape[0]): - outfp.write('%4d %4s %4d %4d %4d\n' %(A[i,0], B[i], A[i,1], A[i,2], A[i,3])) + outfp.write('%4s, %05d, %04d, %04d, %04d\n' %(B[i], A[i,0], A[i,1], A[i,2], A[i,3])) diff --git a/lib/edfimage.py b/lib/edfimage.py new file mode 100644 index 0000000..22dd979 --- /dev/null +++ b/lib/edfimage.py @@ -0,0 +1,985 @@ +# coding: utf-8 +# +# Project: X-ray image reader +# https://github.com/silx-kit/fabio +# +# +# Copyright (C) European Synchrotron Radiation Facility, Grenoble, France +# +# Principal author: Jérôme Kieffer (Jerome.Kieffer@ESRF.eu) +# +# Permission is hereby granted, free of charge, to any person +# obtaining a copy of this software and associated documentation files +# (the "Software"), to deal in the Software without restriction, +# including without limitation the rights to use, copy, modify, merge, +# publish, distribute, sublicense, and/or sell copies of the Software, +# and to permit persons to whom the Software is furnished to do so, +# subject to the following conditions: +# +# The above copyright notice and this permission notice shall be +# included in all copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, +# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES +# OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND +# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT +# HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, +# WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING +# FROM, OUT OF OR IN CONNECTION W + +""" + +License: MIT + +Authors: +........ +* Henning O. Sorensen & Erik Knudsen: + Center for Fundamental Research: Metal Structures in Four Dimensions; + Risoe National Laboratory; + Frederiksborgvej 399; + DK-4000 Roskilde; + email:erik.knudsen@risoe.dk +* Jon Wright & Jérôme Kieffer: + European Synchrotron Radiation Facility; + Grenoble (France) + + +""" +# get ready for python3 +from __future__ import with_statement, print_function, absolute_import, division +import os +import logging +logger = logging.getLogger("edfimage") +import numpy +from .fabioimage import FabioImage, OrderedDict +from .fabioutils import isAscii, toAscii, nice_int +from .compression import decBzip2, decGzip, decZlib + + +BLOCKSIZE = 512 +DATA_TYPES = {"SignedByte": numpy.int8, + "Signed8": numpy.int8, + "UnsignedByte": numpy.uint8, + "Unsigned8": numpy.uint8, + "SignedShort": numpy.int16, + "Signed16": numpy.int16, + "UnsignedShort": numpy.uint16, + "Unsigned16": numpy.uint16, + "UnsignedShortInteger": numpy.uint16, + "SignedInteger": numpy.int32, + "Signed32": numpy.int32, + "UnsignedInteger": numpy.uint32, + "Unsigned32": numpy.uint32, + "SignedLong": numpy.int32, + "UnsignedLong": numpy.uint32, + "Signed64": numpy.int64, + "Unsigned64": numpy.uint64, + "FloatValue": numpy.float32, + "FLOATVALUE": numpy.float32, + "FLOAT": numpy.float32, # fit2d + "Float": numpy.float32, # fit2d + "FloatIEEE32": numpy.float32, + "Float32": numpy.float32, + "Double": numpy.float64, + "DoubleValue": numpy.float64, + "FloatIEEE64": numpy.float64, + "DoubleIEEE64": numpy.float64} +try: + DATA_TYPES["FloatIEEE128"] = DATA_TYPES["DoubleIEEE128"] = DATA_TYPES["QuadrupleValue"] = numpy.float128 + +except AttributeError: + # not in your numpy + logger.debug("No support for float128 in your code") + +NUMPY_EDF_DTYPE = {"int8": "SignedByte", + "int16": "SignedShort", + "int32": "SignedInteger", + "int64": "Signed64", + "uint8": "UnsignedByte", + "uint16": "UnsignedShort", + "uint32": "UnsignedInteger", + "uint64": "Unsigned64", + "float32": "FloatValue", + "float64": "DoubleValue", + "float128": "QuadrupleValue", + } + +MINIMUM_KEYS = ['HEADERID', + 'IMAGE', + 'BYTEORDER', + 'DATATYPE', + 'DIM_1', + 'DIM_2', + 'SIZE'] # Size is thought to be essential for writing at least + +DEFAULT_VALUES = { + # I do not define default values as they will be calculated at write time + # JK20110415 + } + + +class Frame(object): + """ + A class representing a single frame in an EDF file + """ + def __init__(self, data=None, header=None, number=None): + + self.header = EdfImage.check_header(header) + + self.capsHeader = {} + for key in self.header: + self.capsHeader[key.upper()] = key + self._data = data + self.dims = [] + self.dim1 = 0 + self.dim2 = 0 + self.start = None # Position of start of raw data in file + self.size = None # size of raw data in file + self.file = None # opened file object with locking capabilities !!! + self.bpp = None + self._bytecode = None + if (number is not None): + self.iFrame = int(number) + else: + self.iFrame = 0 + + def parseheader(self, block): + """ + Parse the header in some EDF format from an already open file + + @param block: string representing the header block. + @type block: string, should be full ascii + @return: size of the binary blob + """ + # reset values ... + self.header = OrderedDict() + self.capsHeader = {} + self.size = None + calcsize = 1 + self.dims = [] + + for line in block.split(';'): + if '=' in line: + key, val = line.split('=', 1) + # Why would someone put null bytes in a header? + key = key.replace("\x00", " ").strip() + self.header[key] = val.replace("\x00", " ").strip() + self.capsHeader[key.upper()] = key + + # Compute image size + if "SIZE" in self.capsHeader: + try: + self.size = nice_int(self.header[self.capsHeader["SIZE"]]) + except ValueError: + logger.warning("Unable to convert to integer : %s %s " % (self.capsHeader["SIZE"], self.header[self.capsHeader["SIZE"]])) + if "DIM_1" in self.capsHeader: + try: + dim1 = nice_int(self.header[self.capsHeader['DIM_1']]) + except ValueError: + logger.error("Unable to convert to integer Dim_1: %s %s" % (self.capsHeader["DIM_1"], self.header[self.capsHeader["DIM_1"]])) + else: + calcsize *= dim1 + self.dims.append(dim1) + else: + logger.error("No Dim_1 in headers !!!") + if "DIM_2" in self.capsHeader: + try: + dim2 = nice_int(self.header[self.capsHeader['DIM_2']]) + except ValueError: + logger.error("Unable to convert to integer Dim_3: %s %s" % (self.capsHeader["DIM_2"], self.header[self.capsHeader["DIM_2"]])) + else: + calcsize *= dim2 + self.dims.append(dim2) + else: + logger.error("No Dim_2 in headers !!!") + iDim = 3 + # JON: this appears to be for nD images, but we don't treat those + while iDim is not None: + strDim = "DIM_%i" % iDim + if strDim in self.capsHeader: + try: + dim3 = nice_int(self.header[self.capsHeader[strDim]]) + except ValueError: + logger.error("Unable to convert to integer %s: %s %s", + strDim, self.capsHeader[strDim], self.header[self.capsHeader[strDim]]) + dim3 = None + iDim = None + else: + if dim3 > 1: + # Otherwise treat dim3==1 as a 2D image + calcsize *= dim3 + self.dims.append(dim3) + iDim += 1 + + else: + logger.debug("No Dim_3 -> it is a 2D image") + iDim = None + if self._bytecode is None: + if "DATATYPE" in self.capsHeader: + self._bytecode = DATA_TYPES[self.header[self.capsHeader['DATATYPE']]] + else: + self._bytecode = numpy.uint16 + logger.warning("Defaulting type to uint16") + self.bpp = len(numpy.array(0, self._bytecode).tostring()) + calcsize *= self.bpp + if (self.size is None): + self.size = calcsize + elif (self.size != calcsize): + if ("COMPRESSION" in self.capsHeader) and (self.header[self.capsHeader['COMPRESSION']].upper().startswith("NO")): + logger.info("Mismatch between the expected size %s and the calculated one %s" % (self.size, calcsize)) + self.size = calcsize + + for i, n in enumerate(self.dims): + setattr(self, "dim%i" % (i + 1), n) + + return self.size + + def swap_needed(self): + """ + Decide if we need to byteswap + """ + if ('Low' in self.header[self.capsHeader['BYTEORDER']] and numpy.little_endian) or \ + ('High' in self.header[self.capsHeader['BYTEORDER']] and not numpy.little_endian): + return False + if ('High' in self.header[self.capsHeader['BYTEORDER']] and numpy.little_endian) or \ + ('Low' in self.header[self.capsHeader['BYTEORDER']] and not numpy.little_endian): + if self.bpp in [2, 4, 8]: + return True + else: + return False + + def getData(self): + """ + Unpack a binary blob according to the specification given in the header + + @return: dataset as numpy.ndarray + """ + data = None + if self._data is not None: + data = self._data + elif self.file is None: + data = self._data + else: + if self._bytecode is None: + if "DATATYPE" in self.capsHeader: + self._bytecode = DATA_TYPES[self.header[self.capsHeader["DATATYPE"]]] + else: + self._bytecode = numpy.uint16 + dims = self.dims[:] + dims.reverse() + with self.file.lock: + if self.file.closed: + logger.error("file: %s from %s is closed. Cannot read data." % (self.file, self.file.filename)) + return + else: + self.file.seek(self.start) + fileData = self.file.read(self.size) + + if ("COMPRESSION" in self.capsHeader): + compression = self.header[self.capsHeader["COMPRESSION"]].upper() + uncompressed_size = self.bpp + for i in dims: + uncompressed_size *= i + if "OFFSET" in compression: + try: + import byte_offset # IGNORE:F0401 + except ImportError as error: + logger.error("Unimplemented compression scheme: %s (%s)" % (compression, error)) + else: + myData = byte_offset.analyseCython(fileData, size=uncompressed_size) + rawData = myData.astype(self._bytecode).tostring() + self.size = uncompressed_size + elif compression == "NONE": + rawData = fileData + elif "GZIP" in compression: + rawData = decGzip(fileData) + self.size = uncompressed_size + elif "BZ" in compression: + rawData = decBzip2(fileData) + self.size = uncompressed_size + elif "Z" in compression: + rawData = decZlib(fileData) + self.size = uncompressed_size + else: + logger.warning("Unknown compression scheme %s" % compression) + rawData = fileData + + else: + rawData = fileData + + expected = self.size + obtained = len(rawData) + if expected > obtained: + logger.error("Data stream is incomplete: %s < expected %s bytes" % (obtained, expected)) + rawData += "\x00".encode("ascii") * (expected - obtained) + elif expected < len(rawData): + logger.info("Data stream contains trailing junk : %s > expected %s bytes" % (obtained, expected)) + rawData = rawData[:expected] + data = numpy.fromstring(rawData, self._bytecode).reshape(tuple(dims)) + if self.swap_needed(): + data.byteswap(True) + self._data = data + self._bytecode = data.dtype.type + return data + + def setData(self, npa=None): + """Setter for data in edf frame""" + self._data = npa + data = property(getData, setData, "property: (edf)frame.data, uncompress the datablock when needed") + + def getByteCode(self): + if self._bytecode is None: + self._bytecode = self.data.dtype.type + return self._bytecode + + def setByteCode(self, _iVal): + self._bytecode = _iVal + + bytecode = property(getByteCode, setByteCode) + + def getEdfBlock(self, force_type=None, fit2dMode=False): + """ + @param force_type: type of the dataset to be enforced like "float64" or "uint16" + @type force_type: string or numpy.dtype + @param fit2dMode: enforce compatibility with fit2d and starts counting number of images at 1 + @type fit2dMode: boolean + @return: ascii header block + binary data block + @rtype: python bytes with the concatenation of the ascii header and the binary data block + """ + if force_type is not None: + data = self.data.astype(force_type) + else: + data = self.data + fit2dMode = bool(fit2dMode) + for key in self.header: + KEY = key.upper() + if KEY not in self.capsHeader: + self.capsHeader[KEY] = key + + header = self.header.copy() + header_keys = list(self.header.keys()) + capsHeader = self.capsHeader.copy() + + listHeader = ["{\n"] +# First of all clean up the headers: + for i in capsHeader: + if "DIM_" in i: + header.pop(capsHeader[i]) + header_keys.remove(capsHeader[i]) + for KEY in ["SIZE", "EDF_BINARYSIZE", "EDF_HEADERSIZE", "BYTEORDER", "DATATYPE", "HEADERID", "IMAGE"]: + if KEY in capsHeader: + header.pop(capsHeader[KEY]) + header_keys.remove(capsHeader[KEY]) + if "EDF_DATABLOCKID" in capsHeader: + header_keys.remove(capsHeader["EDF_DATABLOCKID"]) + # but do not remove the value from dict, instead reset the key ... + if capsHeader["EDF_DATABLOCKID"] != "EDF_DataBlockID": + header["EDF_DataBlockID"] = header.pop(capsHeader["EDF_DATABLOCKID"]) + capsHeader["EDF_DATABLOCKID"] = "EDF_DataBlockID" + +# Then update static headers freshly deleted + header_keys.insert(0, "Size") + header["Size"] = len(data.tostring()) + header_keys.insert(0, "HeaderID") + header["HeaderID"] = "EH:%06d:000000:000000" % (self.iFrame + fit2dMode) + header_keys.insert(0, "Image") + header["Image"] = str(self.iFrame + fit2dMode) + + dims = list(data.shape) + nbdim = len(dims) + for i in dims: + key = "Dim_%i" % nbdim + header[key] = i + header_keys.insert(0, key) + nbdim -= 1 + header_keys.insert(0, "DataType") + header["DataType"] = NUMPY_EDF_DTYPE[str(numpy.dtype(data.dtype))] + header_keys.insert(0, "ByteOrder") + if numpy.little_endian: + header["ByteOrder"] = "LowByteFirst" + else: + header["ByteOrder"] = "HighByteFirst" + approxHeaderSize = 100 + for key in header: + approxHeaderSize += 7 + len(key) + len(str(header[key])) + approxHeaderSize = BLOCKSIZE * (approxHeaderSize // BLOCKSIZE + 1) + header_keys.insert(0, "EDF_HeaderSize") + header["EDF_HeaderSize"] = "%5s" % (approxHeaderSize) + header_keys.insert(0, "EDF_BinarySize") + header["EDF_BinarySize"] = data.nbytes + header_keys.insert(0, "EDF_DataBlockID") + if "EDF_DataBlockID" not in header: + header["EDF_DataBlockID"] = "%i.Image.Psd" % (self.iFrame + fit2dMode) + preciseSize = 4 # 2 before {\n 2 after }\n + for key in header_keys: + # Escape keys or values that are no ascii + strKey = str(key) + if not isAscii(strKey, listExcluded=["}", "{"]): + logger.warning("Non ascii key %s, skipping" % strKey) + continue + strValue = str(header[key]) + if not isAscii(strValue, listExcluded=["}", "{"]): + logger.warning("Non ascii value %s, skipping" % strValue) + continue + line = strKey + " = " + strValue + " ;\n" + preciseSize += len(line) + listHeader.append(line) + if preciseSize > approxHeaderSize: + logger.error("I expected the header block only at %s in fact it is %s" % (approxHeaderSize, preciseSize)) + for idx, line in enumerate(listHeader[:]): + if line.startswith("EDF_HeaderSize"): + headerSize = BLOCKSIZE * (preciseSize // BLOCKSIZE + 1) + newline = "EDF_HeaderSize = %5s ;\n" % headerSize + delta = len(newline) - len(line) + if (preciseSize // BLOCKSIZE) != ((preciseSize + delta) // BLOCKSIZE): + headerSize = BLOCKSIZE * ((preciseSize + delta) // BLOCKSIZE + 1) + newline = "EDF_HeaderSize = %5s ;\n" % headerSize + preciseSize = preciseSize + delta + listHeader[idx] = newline + break + else: + headerSize = approxHeaderSize + listHeader.append(" " * (headerSize - preciseSize) + "}\n") + return ("".join(listHeader)).encode("ASCII") + data.tostring() + + +class EdfImage(FabioImage): + """ Read and try to write the ESRF edf data format """ + + DESCRIPTION = "European Synchrotron Radiation Facility data format" + + DEFAULT_EXTENTIONS = ["edf", "cor"] + + def __init__(self, data=None, header=None, frames=None): + self.currentframe = 0 + self.filesize = None + + if data is None: + # In case of creation of an empty instance + stored_data = None + else: + try: + dim = len(data.shape) + except Exception as error: # IGNORE:W0703 + logger.debug("Data don't look like a numpy array (%s), resetting all!!" % error) + dim = 0 + + if dim == 0: + raise Exception("Data with empty shape is unsupported") + elif dim == 1: + logger.warning("Data in 1d dimension will be stored as a 2d dimension array") + # make sure we do not change the shape of the input data + stored_data = numpy.array(data, copy=False) + stored_data.shape = (1, len(data)) + elif dim == 2: + stored_data = data + elif dim >= 3: + raise Exception("Data dimension too big. Only 1d or 2d arrays are supported.") + + FabioImage.__init__(self, stored_data, header) + + if frames is None: + frame = Frame(data=self.data, header=self.header, + number=self.currentframe) + self._frames = [frame] + else: + self._frames = frames + + @staticmethod + def check_header(header=None): + """ + Empty for FabioImage but may be populated by others classes + """ + if not isinstance(header, dict): + return OrderedDict() + new = OrderedDict() + for key, value in header.items(): + new[toAscii(key, ";{}")] = toAscii(value, ";{}") + return new + + @staticmethod + def _readHeaderBlock(infile): + """ + Read in a header in some EDF format from an already open file + + @param infile: file object open in read mode + @return: string (or None if no header was found. + """ + MAX_HEADER_SIZE = BLOCKSIZE * 20 + block = infile.read(BLOCKSIZE) + if len(block) < BLOCKSIZE: + logger.debug("Under-short header: only %i bytes in %s" % (len(block), infile.name)) + return + if (block.find(b"{") < 0): + # This does not look like an edf file + logger.warning("no opening {. Corrupt header of EDF file %s" % infile.name) + return + if b"EDF_HeaderSize" in block: + start = block.index(b"EDF_HeaderSize") + chunk = block[start:].split(b"=")[1].strip() + try: + new_max_header_size = int(chunk.split(b";")[0].strip()) + except Exception: + logger.warning("Unable to read header size, got: %s" % chunk) + else: + if new_max_header_size > MAX_HEADER_SIZE: + logger.info("Redefining MAX_HEADER_SIZE to %s" % new_max_header_size) + MAX_HEADER_SIZE = new_max_header_size + while (b'}\r' not in block) and (b'}\n' not in block): + block = block + infile.read(BLOCKSIZE) + if len(block) > MAX_HEADER_SIZE: + logger.warning("Runaway header in EDF file MAX_HEADER_SIZE: %s \n%s" % (MAX_HEADER_SIZE, block)) + return + start = block.find(b"{") + 1 + end = block.find(b"}") + + # Now it is essential to go to the start of the binary part + if block[end: end + 3] == b"}\r\n": + offset = end + 3 - len(block) + elif block[end: end + 2] == b"}\n": + offset = end + 2 - len(block) + else: + logger.error("Unable to locate start of the binary section") + offset = None + if offset is not None: + infile.seek(offset, os.SEEK_CUR) + return block[start:end].decode("ASCII") + + def _readheader(self, infile): + """ + Read all headers in a file and populate self.header + data is not yet populated + @type infile: file object open in read mode + """ + self._frames = [] + bContinue = True + attrs = dir(infile) + if "measure_size" in attrs: + # Handle bug #18 (https://github.com/silx-kit/fabio/issues/18) + stream_size = infile.measure_size() + elif "size" in attrs: + stream_size = infile.size + elif "len" in attrs: + stream_size = infile.len + else: + # Poor-men's size measurement + pos = infile.tell() + self.seek(0, os.SEEK_END) + stream_size = infile.tell() + infile.seek(pos) + + while bContinue: + block = self._readHeaderBlock(infile) + if block is None: + bContinue = False + break + frame = Frame(number=self.nframes) + size = frame.parseheader(block) + frame.file = infile + frame.start = infile.tell() + frame.size = size + self._frames += [frame] + try: + infile.seek(size, os.SEEK_CUR) + except Exception as error: + logger.warning("infile is %s" % infile) + logger.warning("Position is %s" % infile.tell()) + logger.warning("size is %s" % size) + logger.error("It seams this error occurs under windows when reading a (large-) file over network: %s ", error) + raise Exception(error) + + if frame.start + size > stream_size: + logger.warning("Non complete datablock: got %s, expected %s" % (stream_size - frame.start, size)) + bContinue = False + break + + for i, frame in enumerate(self._frames): + missing = [] + for item in MINIMUM_KEYS: + if item not in frame.capsHeader: + missing.append(item) + if len(missing) > 0: + logger.info("EDF file %s frame %i misses mandatory keys: %s " % (self.filename, i, " ".join(missing))) + self.currentframe = 0 + + def read(self, fname, frame=None): + """ + Read in header into self.header and + the data into self.data + """ + self.resetvals() + self.filename = fname + + infile = self._open(fname, "rb") + self._readheader(infile) + if frame is None: + pass + elif frame < self.nframes: + self = self.getframe(frame) + else: + logger.error("Reading file %s You requested frame %s but only %s frames are available", fname, frame, self.nframes) + self.resetvals() + # ensure the PIL image is reset + self.pilimage = None + return self + + def swap_needed(self): + """ + Decide if we need to byteswap + + @return True if needed, False else and None if not understood + """ + if self.bpp == 1: + return False + if ('Low' in self.header[self.capsHeader['BYTEORDER']] and numpy.little_endian) or \ + ('High' in self.header[self.capsHeader['BYTEORDER']] and not numpy.little_endian): + return False + if ('High' in self.header[self.capsHeader['BYTEORDER']] and numpy.little_endian) or \ + ('Low' in self.header[self.capsHeader['BYTEORDER']] and not numpy.little_endian): + return True + + def unpack(self): + """ + Unpack a binary blob according to the specification given in the header and return the dataset + + @return: dataset as numpy.ndarray + """ + return self._frames[self.currentframe].getData() + + def getframe(self, num): + """ returns the file numbered 'num' in the series as a FabioImage """ + newImage = None + if self.nframes == 1: + logger.debug("Single frame EDF; having FabioImage default behavior: %s" % num) + newImage = FabioImage.getframe(self, num) + newImage._file = self._file + elif num < self.nframes: + logger.debug("Multi frame EDF; having EdfImage specific behavior: %s/%s" % (num, self.nframes)) + newImage = EdfImage(frames=self._frames) + newImage.currentframe = num + newImage.filename = self.filename + newImage._file = self._file + else: + raise IOError("EdfImage.getframe: Cannot access frame: %s/%s" % + (num, self.nframes)) + return newImage + + def previous(self): + """ returns the previous file in the series as a FabioImage """ + newImage = None + if self.nframes == 1: + newImage = FabioImage.previous(self) + else: + newFrameId = self.currentframe - 1 + newImage = self.getframe(newFrameId) + return newImage + + def next(self): + """Returns the next file in the series as a fabioimage + + @raise IOError: When there is no next file or image in the series. + """ + newImage = None + if self.nframes == 1: + newImage = FabioImage.next(self) + else: + newFrameId = self.currentframe + 1 + newImage = self.getframe(newFrameId) + return newImage + + def write(self, fname, force_type=None, fit2dMode=False): + """ + Try to write a file + check we can write zipped also + mimics that fabian was writing uint16 (we sometimes want floats) + + @param force_type: can be numpy.uint16 or simply "float" + @return: None + + """ + # correct for bug #27: read all data before opening the file in write mode + if fname == self.filename: + [(frame.header, frame.data) for frame in self._frames] + # this is thrown away + with self._open(fname, mode="wb") as outfile: + for i, frame in enumerate(self._frames): + frame.iFrame = i + outfile.write(frame.getEdfBlock(force_type=force_type, fit2dMode=fit2dMode)) + + def appendFrame(self, frame=None, data=None, header=None): + """ + Method used add a frame to an EDF file + @param frame: frame to append to edf image + @type frame: instance of Frame + @return: None + """ + if isinstance(frame, Frame): + self._frames.append(frame) + elif ("header" in dir(frame)) and ("data" in dir(frame)): + self._frames.append(Frame(frame.data, frame.header)) + else: + self._frames.append(Frame(data, header)) + + def deleteFrame(self, frameNb=None): + """ + Method used to remove a frame from an EDF image. by default the last one is removed. + @param frameNb: frame number to remove, by default the last. + @type frameNb: integer + @return: None + """ + if frameNb is None: + self._frames.pop() + else: + self._frames.pop(frameNb) + + def fastReadData(self, filename=None): + """ + This is a special method that will read and return the data from another file ... + The aim is performances, ... but only supports uncompressed files. + + @return: data from another file using positions from current EdfImage + """ + if (filename is None) or not os.path.isfile(filename): + raise RuntimeError("EdfImage.fastReadData is only valid with another file: %s does not exist" % (filename)) + data = None + frame = self._frames[self.currentframe] + with open(filename, "rb")as f: + f.seek(frame.start) + raw = f.read(frame.size) + try: + data = numpy.fromstring(raw, dtype=self.bytecode) + data.shape = self.data.shape + except Exception as error: + logger.error("unable to convert file content to numpy array: %s", error) + if self.swap_needed(): + data.byteswap(True) + return data + + def fastReadROI(self, filename, coords=None): + """ + Method reading Region of Interest of another file based on metadata available in current EdfImage. + The aim is performances, ... but only supports uncompressed files. + + @return: ROI-data from another file using positions from current EdfImage + @rtype: numpy 2darray + """ + if (filename is None) or not os.path.isfile(filename): + raise RuntimeError("EdfImage.fastReadData is only valid with another file: %s does not exist" % (filename)) + data = None + frame = self._frames[self.currentframe] + + if len(coords) == 4: + slice1 = self.make_slice(coords) + elif (len(coords) == 2 and isinstance(coords[0], slice) and + isinstance(coords[1], slice)): + slice1 = coords + else: + logger.warning('readROI: Unable to understand Region Of Interest: got %s', coords) + return + d1 = self.data.shape[-1] + start0 = slice1[0].start + start1 = slice1[1].start + slice2 = (slice(0, slice1[0].stop - start0, slice1[0].step), + slice(0, slice1[1].stop - start1, slice1[1].step)) + start = frame.start + self.bpp * (d1 * start0 + start1) + size = self.bpp * ((slice2[0].stop) * d1) + with open(filename, "rb")as f: + f.seek(start) + raw = f.read(size) + try: + data = numpy.fromstring(raw, dtype=self.bytecode) + data.shape = -1, d1 + except Exception as error: + logger.error("unable to convert file content to numpy array: %s", error) + if self.swap_needed(): + data.byteswap(True) + return data[slice2] + +################################################################################ +# Properties definition for header, data, header_keys and capsHeader +################################################################################ + def getNbFrames(self): + """ + Getter for number of frames + """ + return len(self._frames) + + def setNbFrames(self, val): + """ + Setter for number of frames ... should do nothing. Here just to avoid bugs + """ + if val != len(self._frames): + logger.warning("trying to set the number of frames ") + nframes = property(getNbFrames, setNbFrames, "property: number of frames in EDF file") + + def getHeader(self): + """ + Getter for the headers. used by the property header, + """ + return self._frames[self.currentframe].header + + def setHeader(self, _dictHeader): + """ + Enforces the propagation of the header to the list of frames + """ + try: + self._frames[self.currentframe].header = _dictHeader + except AttributeError: + self._frames = [Frame(header=_dictHeader)] + except IndexError: + if self.currentframe < len(self._frames): + self._frames.append(Frame(header=_dictHeader)) + + def delHeader(self): + """ + Deleter for edf header + """ + self._frames[self.currentframe].header = {} + header = property(getHeader, setHeader, delHeader, "property: header of EDF file") + +# def getHeaderKeys(self): +# """ +# Getter for edf header_keys +# """ +# return self._frames[self.currentframe].header_keys +# def setHeaderKeys(self, _listtHeader): +# """ +# Enforces the propagation of the header_keys to the list of frames +# @param _listtHeader: list of the (ordered) keys in the header +# @type _listtHeader: python list +# """ +# try: +# self._frames[self.currentframe].header_keys = _listtHeader +# except AttributeError: +# self._frames = [Frame(header_keys=_listtHeader)] +# except IndexError: +# if self.currentframe < len(self._frames): +# self._frames.append(Frame(header_keys=_listtHeader)) +# def delHeaderKeys(self): +# """ +# Deleter for edf header_keys +# """ +# self._frames[self.currentframe].header_keys = [] +# header_keys = property(getHeaderKeys, setHeaderKeys, delHeaderKeys, "property: header_keys of EDF file") + + def getData(self): + """ + getter for edf Data + @return: data for current frame + @rtype: numpy.ndarray + """ + npaData = None + try: + npaData = self._frames[self.currentframe].data + except AttributeError: + self._frames = [Frame()] + npaData = self._frames[self.currentframe].data + except IndexError: + if self.currentframe < len(self._frames): + self._frames.append(Frame()) + npaData = self._frames[self.currentframe].data + return npaData + + def setData(self, _data): + """ + Enforces the propagation of the data to the list of frames + @param _data: numpy array representing data + """ + try: + self._frames[self.currentframe].data = _data + except AttributeError: + self._frames = [Frame(data=_data)] + except IndexError: + if self.currentframe < len(self._frames): + self._frames.append(Frame(data=_data)) + + def delData(self): + """ + deleter for edf Data + """ + self._frames[self.currentframe].data = None + data = property(getData, setData, delData, "property: data of EDF file") + + def getCapsHeader(self): + """ + getter for edf headers keys in upper case + @return: data for current frame + @rtype: dict + """ + return self._frames[self.currentframe].capsHeader + + def setCapsHeader(self, _data): + """ + Enforces the propagation of the header_keys to the list of frames + @param _data: numpy array representing data + """ + self._frames[self.currentframe].capsHeader = _data + + def delCapsHeader(self): + """ + deleter for edf capsHeader + """ + self._frames[self.currentframe].capsHeader = {} + capsHeader = property(getCapsHeader, setCapsHeader, delCapsHeader, "property: capsHeader of EDF file, i.e. the keys of the header in UPPER case.") + + def getDim1(self): + return self._frames[self.currentframe].dim1 + + def setDim1(self, _iVal): + try: + self._frames[self.currentframe].dim1 = _iVal + except AttributeError: + self._frames = [Frame()] + except IndexError: + if self.currentframe < len(self._frames): + self._frames.append(Frame()) + self._frames[self.currentframe].dim1 = _iVal + dim1 = property(getDim1, setDim1) + + def getDim2(self): + return self._frames[self.currentframe].dim2 + + def setDim2(self, _iVal): + try: + self._frames[self.currentframe].dim2 = _iVal + except AttributeError: + self._frames = [Frame()] + except IndexError: + if self.currentframe < len(self._frames): + self._frames.append(Frame()) + self._frames[self.currentframe].dim2 = _iVal + dim2 = property(getDim2, setDim2) + + def getDims(self): + return self._frames[self.currentframe].dims + dims = property(getDims) + + def getByteCode(self): + return self._frames[self.currentframe].bytecode + + def setByteCode(self, _iVal): + try: + self._frames[self.currentframe].bytecode = _iVal + except AttributeError: + self._frames = [Frame()] + except IndexError: + if self.currentframe < len(self._frames): + self._frames.append(Frame()) + self._frames[self.currentframe].bytecode = _iVal + bytecode = property(getByteCode, setByteCode) + + def getBpp(self): + return self._frames[self.currentframe].bpp + + def setBpp(self, _iVal): + try: + self._frames[self.currentframe].bpp = _iVal + except AttributeError: + self._frames = [Frame()] + except IndexError: + if self.currentframe < len(self._frames): + self._frames.append(Frame()) + self._frames[self.currentframe].bpp = _iVal + bpp = property(getBpp, setBpp) + +edfimage = EdfImage diff --git a/plot_sum_fabio.py b/plot_sum_fabio.py new file mode 100644 index 0000000..ce27fbe --- /dev/null +++ b/plot_sum_fabio.py @@ -0,0 +1,12 @@ +import numpy as np +import matplotlib.pyplot as plt + +A = np.load('/u/data/alcer/DFXRM_rec/Rec_test/Fabio_sum.npy') + +fig = plt.figure() + +for i in range(9): + fig.add_subplot(3,3,i+1) + plt.imshow(A[(i+1)*25,:,:]) + +plt.show() diff --git a/sum_angles_fabio.py b/sum_angles_fabio.py new file mode 100644 index 0000000..f6f9756 --- /dev/null +++ b/sum_angles_fabio.py @@ -0,0 +1,51 @@ +# Sum the images relative to a certain projection, then store the result as npy + +import numpy as np +import matplotlib.pyplot as plt +import lib.EdfFile as EF +import sys +import fabio +import os + +# Directory where the IO data is stored +io_dir = '/u/data/alcer/DFXRM_rec/Rec_test' + +# List of the image files +im_paths = np.genfromtxt(os.path.join(io_dir + '/List_images.txt'), dtype = str) +# List of the files properties +im_prop = np.loadtxt(os.path.join(io_dir + '/Image_properties.txt')) + +Fabio_array = np.zeros([226, 300, 300]) +# For each projection, sum the images and store the result +for j in range(226): + sum_om = np.zeros([300,300]) + for i in range(im_prop.shape[0]): + if im_prop[i,3] == j: + img_name = im_paths[i] + I = fabio.open(img_name).data + sum_om[:,:] += I[106:406, 106:406] + Fabio_array[j,:,:] = sum_om[:,:] +#np.save(os.path.join(io_dir + '/Fabio_sum.npy'), Fabio_array) + +# Compare with the sum of the stored values +A = np.load(os.path.join(io_dir + '/dataarray.npy')) +sum_3 = np.zeros([A.shape[3], A.shape[4]]) +for i in range(A.shape[0]): + for j in range(A.shape[1]): + sum_3[:,:] += A[i,j,3,:,:] + +# Plot the comparison between the two sums +fig = plt.figure() +fig.add_subplot(1,3,1) +plt.title('Summed and stored') +plt.imshow(Fabio_array[3,:,:]) + +fig.add_subplot(1,3,2) +plt.title('Stored and summed') +plt.imshow(sum_3) + +fig.add_subplot(1,3,3) +plt.title('Diff') +plt.imshow(Fabio_array[3,:,:] - sum_3[:,:]) + +plt.show() From dabd9190c7ffde79257a8bf9c015d8afe5170007 Mon Sep 17 00:00:00 2001 From: albusdemens Date: Mon, 17 Jul 2017 11:00:15 +0200 Subject: [PATCH 064/150] Exclude vtk --- .gitignore | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.gitignore b/.gitignore index 6f38410..45ea66f 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,5 @@ *.pyc *.npy + +*.vtk From 7b237d9e9e1cd8b3bd87b6475607ec12ddcb591f Mon Sep 17 00:00:00 2001 From: albusdemens Date: Wed, 19 Jul 2017 15:29:52 +0200 Subject: [PATCH 065/150] Bug fixed --- getdata.py | 4 +--- lib/miniged.py | 2 +- 2 files changed, 2 insertions(+), 4 deletions(-) diff --git a/getdata.py b/getdata.py index 29ff918..5dc431b 100644 --- a/getdata.py +++ b/getdata.py @@ -126,11 +126,9 @@ def allFiles(self, data, imsiz): c = np.where(self.omega == self.meta[int(ind),2]) # Omega d = np.where(self.theta == self.meta[int(ind),4]) # Theta idx_rescaled = (self.meta[int(ind),0] - phi_0) / (np.cos(np.deg2rad(self.meta[int(ind),2])) * 0.032) + ((num_int - 1) / 2) - idx_rescaled_1 = (self.meta[int(ind),1] - chi_0) * np.cos(np.deg2rad(self.meta[int(ind),1])) / (np.sin(np.deg2rad(self.meta[int(ind),2])) * 0.032) + ((num_int - 1) / 2) + #idx_rescaled_1 = (self.meta[int(ind),1] - chi_0) * np.cos(np.deg2rad(self.meta[int(ind),1])) / (np.sin(np.deg2rad(self.meta[int(ind),2])) * 0.032) + ((num_int - 1) / 2) e = (idx_rescaled - ((num_int - 1) / 2)) * 0.032 - print round(idx_rescaled), round(idx_rescaled_1), np.deg2rad(self.meta[int(ind),2]) - # Can we effectively reconstruct chi and phi from idx_rescaled? #ph = phi_0 + ((idx_rescaled - 3)*np.cos(np.deg2rad(self.meta[int(ind),2]))*0.032) #ch = chi_0 + ((idx_rescaled - 3)*(np.tan(np.deg2rad(self.meta[int(ind),2]))*0.032)) diff --git a/lib/miniged.py b/lib/miniged.py index 96a3dd7..ecda786 100644 --- a/lib/miniged.py +++ b/lib/miniged.py @@ -81,7 +81,7 @@ def __init__( self.sampletitle = filename self.path = path - self.bg_path = bg_path + #self.bg_path = bg_path self.roi = roi self.simulated = simulated From 02a0f6b4797660cfc65c0c5c4067d5608b15286e Mon Sep 17 00:00:00 2001 From: albusdemens Date: Thu, 20 Jul 2017 10:56:34 +0200 Subject: [PATCH 066/150] Do not consider jpg --- .gitignore | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.gitignore b/.gitignore index 1864e04..9e5c169 100644 --- a/.gitignore +++ b/.gitignore @@ -7,3 +7,5 @@ lib/.DS_Store *.npy *.vtk + +*.jpg From f37c0507c13f81880afc3190d4f24f6dcdc353a7 Mon Sep 17 00:00:00 2001 From: albusdemens Date: Thu, 20 Jul 2017 11:00:31 +0200 Subject: [PATCH 067/150] Script to plot angles --- Plot_ang_values_one_omega.py | 33 +++++++++++++++++++++------------ 1 file changed, 21 insertions(+), 12 deletions(-) diff --git a/Plot_ang_values_one_omega.py b/Plot_ang_values_one_omega.py index fd4d9d3..8d4185d 100644 --- a/Plot_ang_values_one_omega.py +++ b/Plot_ang_values_one_omega.py @@ -1,20 +1,29 @@ -# this script plots the distribution of the angular values fro one omega +# This script plots the distribution of the angular values for every omega import numpy as np import matplotlib.pyplot as plt +import os -A = np.load('/u/data/alcer/DFXRM_rec/Rec_test/dataarray.npy') +# Remember to check the path for the IO directory +io_dir = '/u/data/alcer/DFXRM_rec/Rec_test/' -Counter = np.zeros([A.shape[0], A.shape[1]]) -for aa in range(A.shape[0]): - for bb in range(A.shape[1]): -<<<<<<< HEAD - if sum(sum(A[aa,bb,5,:,:])) > 0: -======= - if sum(sum(A[aa,bb,1,:,:])) > 0: ->>>>>>> 077febf5ee14058f5f57ce942f86a24dc78c3637 - Counter[aa, bb] = 1 +A = np.load(os.path.join(io_dir + 'dataarray.npy') +Theta = np.load(os.path.join(io_dir + 'theta.npy') +# For each projection, mark the motor position in the pseudomotor, theta space +Counter = np.zeros([A.shape[2], A.shape[0], A.shape[1]]) +# Loop over projections +for aa in range(A.shape[2]): + # Loop over the theta values + for bb in range(A.shape[0]): + # Loop over the pseudomotor cvalues + for cc in range(A.shape[1]): + if sum(sum(A[bb,cc,aa,:,:])) > 0: + Counter[aa, bb, cc] = 1 + +# Plot (pseudmotor, theta) distribution for a single projection fig = plt.figure() -plt.imshow(Counter) +plt.imshow(Counter[5, :, :]) plt.show() + +# Plot (pseudomotor, theta) distribution for all projections From 313f18e984e0c2a7c5d724b058fa81be0704aa84 Mon Sep 17 00:00:00 2001 From: albusdemens Date: Thu, 20 Jul 2017 11:52:42 +0200 Subject: [PATCH 068/150] Add to the procedure: script to plot motor values --- Plot_ang_values_one_omega.py | 40 +++++++++++++++++++++++++++++++++--- 1 file changed, 37 insertions(+), 3 deletions(-) diff --git a/Plot_ang_values_one_omega.py b/Plot_ang_values_one_omega.py index 8d4185d..fb9866e 100644 --- a/Plot_ang_values_one_omega.py +++ b/Plot_ang_values_one_omega.py @@ -5,13 +5,14 @@ import os # Remember to check the path for the IO directory -io_dir = '/u/data/alcer/DFXRM_rec/Rec_test/' +io_dir = '/u/data/alcer/DFXRM_rec/Rec_test_2/' -A = np.load(os.path.join(io_dir + 'dataarray.npy') -Theta = np.load(os.path.join(io_dir + 'theta.npy') +A = np.load(os.path.join(io_dir + 'dataarray.npy')) +Theta = np.load(os.path.join(io_dir + 'theta.npy')) # For each projection, mark the motor position in the pseudomotor, theta space Counter = np.zeros([A.shape[2], A.shape[0], A.shape[1]]) +num_pos = 0 # Loop over projections for aa in range(A.shape[2]): # Loop over the theta values @@ -20,10 +21,43 @@ for cc in range(A.shape[1]): if sum(sum(A[bb,cc,aa,:,:])) > 0: Counter[aa, bb, cc] = 1 + num_pos = num_pos + 1 + +# We also want to keep track of the theta motor position in degrees +Counter_deg = np.zeros([num_pos, 4]) +num_pos_1 = 0 +for aa in range(A.shape[2]): + # Loop over the theta values + for bb in range(A.shape[0]): + # Loop over the pseudomotor cvalues + for cc in range(A.shape[1]): + if Counter[aa, bb, cc] > 0: + Counter_deg[num_pos_1, 0] = aa # Projection number + Counter_deg[num_pos_1, 1] = bb # Pseudomotor index + Counter_deg[num_pos_1, 2] = cc # Theta index + Counter_deg[num_pos_1, 3] = Theta[cc,0] # Theta in degrees # Plot (pseudmotor, theta) distribution for a single projection fig = plt.figure() plt.imshow(Counter[5, :, :]) +plt.title('Motor positions in the (pseudomotor, theta index) space') +plt.xlabel('Theta (index)') +plt.ylabel('Pseudomotor (index)') +plt.show() + +# Plot (pseudomotor, theta) distribution for a single projections +Counter_1 = np.zeros(11*11, 4) +line_num = 0 +for i in range(Counter_deg.shape[0]): + if Counter_deg[i, 0] == 3: + line_num = line_num + 1 + Counter_1[line_num,:] = Counter_deg[i,:] + +fig = plt.figure() +plt.scatter(Counter_1[:,3], Counter_1[:,1]) plt.show() # Plot (pseudomotor, theta) distribution for all projections +fig = plt.figure() +plt.scatter(Counter_deg[:,3], Counter_deg[:,1]) +plt.show() From 3ea5e735a75d42c3c11112f893192ba90256c3b2 Mon Sep 17 00:00:00 2001 From: albusdemens Date: Thu, 20 Jul 2017 12:54:54 +0200 Subject: [PATCH 069/150] Motor check - done --- ...ng_values_one_omega.py => Plot_ang_values.py | 17 +++++++++++------ 1 file changed, 11 insertions(+), 6 deletions(-) rename Plot_ang_values_one_omega.py => Plot_ang_values.py (80%) diff --git a/Plot_ang_values_one_omega.py b/Plot_ang_values.py similarity index 80% rename from Plot_ang_values_one_omega.py rename to Plot_ang_values.py index fb9866e..83c8f2f 100644 --- a/Plot_ang_values_one_omega.py +++ b/Plot_ang_values.py @@ -3,12 +3,14 @@ import numpy as np import matplotlib.pyplot as plt import os +import sys # Remember to check the path for the IO directory io_dir = '/u/data/alcer/DFXRM_rec/Rec_test_2/' A = np.load(os.path.join(io_dir + 'dataarray.npy')) Theta = np.load(os.path.join(io_dir + 'theta.npy')) +print Theta.shape # For each projection, mark the motor position in the pseudomotor, theta space Counter = np.zeros([A.shape[2], A.shape[0], A.shape[1]]) @@ -32,14 +34,17 @@ # Loop over the pseudomotor cvalues for cc in range(A.shape[1]): if Counter[aa, bb, cc] > 0: - Counter_deg[num_pos_1, 0] = aa # Projection number - Counter_deg[num_pos_1, 1] = bb # Pseudomotor index - Counter_deg[num_pos_1, 2] = cc # Theta index - Counter_deg[num_pos_1, 3] = Theta[cc,0] # Theta in degrees + num_pos_1 = num_pos_1 + 1 + Counter_deg[num_pos_1 - 1, 0] = aa # Projection number + Counter_deg[num_pos_1 - 1, 1] = bb # Pseudomotor index + Counter_deg[num_pos_1 - 1, 2] = cc # Theta index + Counter_deg[num_pos_1 - 1, 3] = Theta[cc] # Theta in degrees # Plot (pseudmotor, theta) distribution for a single projection +deg = 5 + fig = plt.figure() -plt.imshow(Counter[5, :, :]) +plt.imshow(Counter[deg, :, :]) plt.title('Motor positions in the (pseudomotor, theta index) space') plt.xlabel('Theta (index)') plt.ylabel('Pseudomotor (index)') @@ -49,7 +54,7 @@ Counter_1 = np.zeros(11*11, 4) line_num = 0 for i in range(Counter_deg.shape[0]): - if Counter_deg[i, 0] == 3: + if Counter_deg[i, 0] == deg: line_num = line_num + 1 Counter_1[line_num,:] = Counter_deg[i,:] From e07ca0fd0f0284ee3e7d7fd35f20430973b72376 Mon Sep 17 00:00:00 2001 From: albusdemens Date: Thu, 20 Jul 2017 12:58:24 +0200 Subject: [PATCH 070/150] Little error fixed --- Plot_all_im_1omega.py | 104 ++++++++++++++++++++---------------------- Plot_ang_values.py | 2 +- 2 files changed, 51 insertions(+), 55 deletions(-) diff --git a/Plot_all_im_1omega.py b/Plot_all_im_1omega.py index ea14282..f824ef1 100644 --- a/Plot_all_im_1omega.py +++ b/Plot_all_im_1omega.py @@ -7,14 +7,10 @@ A = np.load('/u/data/alcer/DFXRM_rec/Rec_test/dataarray.npy') # Select projection number -<<<<<<< HEAD -omega = 200 -======= omega = 2 ->>>>>>> 077febf5ee14058f5f57ce942f86a24dc78c3637 # We want to plot, in a grid, all images collected at a certain projection -# Load the datafile with all information, and store to an array the data +# Load the datafile with all information, and store to an array the data # relative to the images in a certain projection Data = np.loadtxt('/u/data/alcer/DFXRM_rec/Rec_test/Image_properties.txt') Data_angle = np.zeros([49,2]) @@ -38,343 +34,343 @@ plt.setp(a1.get_xticklabels(), visible=False) plt.setp(a1.get_yticklabels(), visible=False) aa = Data_angle[0,0] -bb = Data_angle[0,1] +bb = Data_angle[0,1] plt.imshow(AA) a1 = fig.add_subplot(7,7,2) plt.setp(a1.get_xticklabels(), visible=False) plt.setp(a1.get_yticklabels(), visible=False) aa = Data_angle[1,0] -bb = Data_angle[1,1] +bb = Data_angle[1,1] plt.imshow(A[int(aa),int(bb),omega,:,:]) a1 = fig.add_subplot(7,7,3) plt.setp(a1.get_xticklabels(), visible=False) plt.setp(a1.get_yticklabels(), visible=False) aa = Data_angle[2,0] -bb = Data_angle[2,1] +bb = Data_angle[2,1] plt.imshow(A[int(aa),int(bb),omega,:,:]) a1 = fig.add_subplot(7,7,4) plt.setp(a1.get_xticklabels(), visible=False) plt.setp(a1.get_yticklabels(), visible=False) aa = Data_angle[3,0] -bb = Data_angle[3,1] +bb = Data_angle[3,1] plt.imshow(A[int(aa),int(bb),omega,:,:]) a1 = fig.add_subplot(7,7,5) plt.setp(a1.get_xticklabels(), visible=False) plt.setp(a1.get_yticklabels(), visible=False) aa = Data_angle[4,0] -bb = Data_angle[4,1] +bb = Data_angle[4,1] plt.imshow(A[int(aa),int(bb),omega,:,:]) a1 = fig.add_subplot(7,7,6) plt.setp(a1.get_xticklabels(), visible=False) plt.setp(a1.get_yticklabels(), visible=False) aa = Data_angle[5,0] -bb = Data_angle[5,1] +bb = Data_angle[5,1] plt.imshow(A[int(aa),int(bb),omega,:,:]) a1 = fig.add_subplot(7,7,7) plt.setp(a1.get_xticklabels(), visible=False) plt.setp(a1.get_yticklabels(), visible=False) aa = Data_angle[6,0] -bb = Data_angle[6,1] +bb = Data_angle[6,1] plt.imshow(A[int(aa),int(bb),omega,:,:]) a1 = fig.add_subplot(7,7,8) plt.setp(a1.get_xticklabels(), visible=False) plt.setp(a1.get_yticklabels(), visible=False) aa = Data_angle[7,0] -bb = Data_angle[7,1] +bb = Data_angle[7,1] plt.imshow(A[int(aa),int(bb),omega,:,:]) a1 = fig.add_subplot(7,7,9) plt.setp(a1.get_xticklabels(), visible=False) plt.setp(a1.get_yticklabels(), visible=False) aa = Data_angle[8,0] -bb = Data_angle[8,1] +bb = Data_angle[8,1] plt.imshow(A[int(aa),int(bb),omega,:,:]) a1 = fig.add_subplot(7,7,10) plt.setp(a1.get_xticklabels(), visible=False) plt.setp(a1.get_yticklabels(), visible=False) aa = Data_angle[9,0] -bb = Data_angle[9,1] +bb = Data_angle[9,1] plt.imshow(A[int(aa),int(bb),omega,:,:]) a1 = fig.add_subplot(7,7,11) plt.setp(a1.get_xticklabels(), visible=False) plt.setp(a1.get_yticklabels(), visible=False) aa = Data_angle[10,0] -bb = Data_angle[10,1] +bb = Data_angle[10,1] plt.imshow(A[int(aa),int(bb),omega,:,:]) a1 = fig.add_subplot(7,7,12) plt.setp(a1.get_xticklabels(), visible=False) plt.setp(a1.get_yticklabels(), visible=False) aa = Data_angle[11,0] -bb = Data_angle[11,1] +bb = Data_angle[11,1] plt.imshow(A[int(aa),int(bb),omega,:,:]) a1 = fig.add_subplot(7,7,13) plt.setp(a1.get_xticklabels(), visible=False) plt.setp(a1.get_yticklabels(), visible=False) aa = Data_angle[12,0] -bb = Data_angle[12,1] +bb = Data_angle[12,1] plt.imshow(A[int(aa),int(bb),omega,:,:]) a1 = fig.add_subplot(7,7,14) plt.setp(a1.get_xticklabels(), visible=False) plt.setp(a1.get_yticklabels(), visible=False) aa = Data_angle[13,0] -bb = Data_angle[13,1] +bb = Data_angle[13,1] plt.imshow(A[int(aa),int(bb),omega,:,:]) a1 = fig.add_subplot(7,7,15) plt.setp(a1.get_xticklabels(), visible=False) plt.setp(a1.get_yticklabels(), visible=False) aa = Data_angle[14,0] -bb = Data_angle[14,1] +bb = Data_angle[14,1] plt.imshow(A[int(aa),int(bb),omega,:,:]) a1 = fig.add_subplot(7,7,16) plt.setp(a1.get_xticklabels(), visible=False) plt.setp(a1.get_yticklabels(), visible=False) aa = Data_angle[15,0] -bb = Data_angle[15,1] +bb = Data_angle[15,1] plt.imshow(A[int(aa),int(bb),omega,:,:]) a1 = fig.add_subplot(7,7,17) plt.setp(a1.get_xticklabels(), visible=False) plt.setp(a1.get_yticklabels(), visible=False) aa = Data_angle[16,0] -bb = Data_angle[16,1] +bb = Data_angle[16,1] plt.imshow(A[int(aa),int(bb),omega,:,:]) a1 = fig.add_subplot(7,7,18) plt.setp(a1.get_xticklabels(), visible=False) plt.setp(a1.get_yticklabels(), visible=False) aa = Data_angle[17,0] -bb = Data_angle[17,1] +bb = Data_angle[17,1] plt.imshow(A[int(aa),int(bb),omega,:,:]) a1 = fig.add_subplot(7,7,19) plt.setp(a1.get_xticklabels(), visible=False) plt.setp(a1.get_yticklabels(), visible=False) aa = Data_angle[18,0] -bb = Data_angle[18,1] +bb = Data_angle[18,1] plt.imshow(A[int(aa),int(bb),omega,:,:]) a1 = fig.add_subplot(7,7,20) plt.setp(a1.get_xticklabels(), visible=False) plt.setp(a1.get_yticklabels(), visible=False) aa = Data_angle[19,0] -bb = Data_angle[19,1] +bb = Data_angle[19,1] plt.imshow(A[int(aa),int(bb),omega,:,:]) a1 = fig.add_subplot(7,7,21) plt.setp(a1.get_xticklabels(), visible=False) plt.setp(a1.get_yticklabels(), visible=False) aa = Data_angle[20,0] -bb = Data_angle[20,1] +bb = Data_angle[20,1] plt.imshow(A[int(aa),int(bb),omega,:,:]) a1 = fig.add_subplot(7,7,22) plt.setp(a1.get_xticklabels(), visible=False) plt.setp(a1.get_yticklabels(), visible=False) aa = Data_angle[21,0] -bb = Data_angle[21,1] +bb = Data_angle[21,1] plt.imshow(A[int(aa),int(bb),omega,:,:]) a1 = fig.add_subplot(7,7,23) plt.setp(a1.get_xticklabels(), visible=False) plt.setp(a1.get_yticklabels(), visible=False) aa = Data_angle[22,0] -bb = Data_angle[22,1] +bb = Data_angle[22,1] plt.imshow(A[int(aa),int(bb),omega,:,:]) a1 = fig.add_subplot(7,7,24) plt.setp(a1.get_xticklabels(), visible=False) plt.setp(a1.get_yticklabels(), visible=False) aa = Data_angle[23,0] -bb = Data_angle[23,1] +bb = Data_angle[23,1] plt.imshow(A[int(aa),int(bb),omega,:,:]) a1 = fig.add_subplot(7,7,25) plt.setp(a1.get_xticklabels(), visible=False) plt.setp(a1.get_yticklabels(), visible=False) aa = Data_angle[24,0] -bb = Data_angle[24,1] +bb = Data_angle[24,1] plt.imshow(A[int(aa),int(bb),omega,:,:]) a1 = fig.add_subplot(7,7,26) plt.setp(a1.get_xticklabels(), visible=False) plt.setp(a1.get_yticklabels(), visible=False) aa = Data_angle[25,0] -bb = Data_angle[25,1] +bb = Data_angle[25,1] plt.imshow(A[int(aa),int(bb),omega,:,:]) a1 = fig.add_subplot(7,7,27) plt.setp(a1.get_xticklabels(), visible=False) plt.setp(a1.get_yticklabels(), visible=False) aa = Data_angle[26,0] -bb = Data_angle[26,1] +bb = Data_angle[26,1] plt.imshow(A[int(aa),int(bb),omega,:,:]) a1 = fig.add_subplot(7,7,28) plt.setp(a1.get_xticklabels(), visible=False) plt.setp(a1.get_yticklabels(), visible=False) aa = Data_angle[27,0] -bb = Data_angle[27,1] +bb = Data_angle[27,1] plt.imshow(A[int(aa),int(bb),omega,:,:]) a1 = fig.add_subplot(7,7,29) plt.setp(a1.get_xticklabels(), visible=False) plt.setp(a1.get_yticklabels(), visible=False) aa = Data_angle[28,0] -bb = Data_angle[28,1] +bb = Data_angle[28,1] plt.imshow(A[int(aa),int(bb),omega,:,:]) a1 = fig.add_subplot(7,7,30) plt.setp(a1.get_xticklabels(), visible=False) plt.setp(a1.get_yticklabels(), visible=False) aa = Data_angle[29,0] -bb = Data_angle[29,1] +bb = Data_angle[29,1] plt.imshow(A[int(aa),int(bb),omega,:,:]) a1 = fig.add_subplot(7,7,31) plt.setp(a1.get_xticklabels(), visible=False) plt.setp(a1.get_yticklabels(), visible=False) aa = Data_angle[30,0] -bb = Data_angle[30,1] +bb = Data_angle[30,1] plt.imshow(A[int(aa),int(bb),omega,:,:]) a1 = fig.add_subplot(7,7,32) plt.setp(a1.get_xticklabels(), visible=False) plt.setp(a1.get_yticklabels(), visible=False) aa = Data_angle[31,0] -bb = Data_angle[31,1] +bb = Data_angle[31,1] plt.imshow(A[int(aa),int(bb),omega,:,:]) a1 = fig.add_subplot(7,7,33) plt.setp(a1.get_xticklabels(), visible=False) plt.setp(a1.get_yticklabels(), visible=False) aa = Data_angle[32,0] -bb = Data_angle[32,1] +bb = Data_angle[32,1] plt.imshow(A[int(aa),int(bb),omega,:,:]) a1 = fig.add_subplot(7,7,34) plt.setp(a1.get_xticklabels(), visible=False) plt.setp(a1.get_yticklabels(), visible=False) aa = Data_angle[33,0] -bb = Data_angle[33,1] +bb = Data_angle[33,1] plt.imshow(A[int(aa),int(bb),omega,:,:]) a1 = fig.add_subplot(7,7,35) plt.setp(a1.get_xticklabels(), visible=False) plt.setp(a1.get_yticklabels(), visible=False) aa = Data_angle[34,0] -bb = Data_angle[34,1] +bb = Data_angle[34,1] plt.imshow(A[int(aa),int(bb),omega,:,:]) a1 = fig.add_subplot(7,7,36) plt.setp(a1.get_xticklabels(), visible=False) plt.setp(a1.get_yticklabels(), visible=False) aa = Data_angle[35,0] -bb = Data_angle[35,1] +bb = Data_angle[35,1] plt.imshow(A[int(aa),int(bb),omega,:,:]) a1 = fig.add_subplot(7,7,37) plt.setp(a1.get_xticklabels(), visible=False) plt.setp(a1.get_yticklabels(), visible=False) aa = Data_angle[36,0] -bb = Data_angle[36,1] +bb = Data_angle[36,1] plt.imshow(A[int(aa),int(bb),omega,:,:]) a1 = fig.add_subplot(7,7,38) plt.setp(a1.get_xticklabels(), visible=False) plt.setp(a1.get_yticklabels(), visible=False) aa = Data_angle[37,0] -bb = Data_angle[37,1] +bb = Data_angle[37,1] plt.imshow(A[int(aa),int(bb),omega,:,:]) a1 = fig.add_subplot(7,7,39) plt.setp(a1.get_xticklabels(), visible=False) plt.setp(a1.get_yticklabels(), visible=False) aa = Data_angle[38,0] -bb = Data_angle[38,1] +bb = Data_angle[38,1] plt.imshow(A[int(aa),int(bb),omega,:,:]) a1 = fig.add_subplot(7,7,40) plt.setp(a1.get_xticklabels(), visible=False) plt.setp(a1.get_yticklabels(), visible=False) aa = Data_angle[39,0] -bb = Data_angle[39,1] +bb = Data_angle[39,1] plt.imshow(A[int(aa),int(bb),omega,:,:]) a1 = fig.add_subplot(7,7,41) plt.setp(a1.get_xticklabels(), visible=False) plt.setp(a1.get_yticklabels(), visible=False) aa = Data_angle[40,0] -bb = Data_angle[40,1] +bb = Data_angle[40,1] plt.imshow(A[int(aa),int(bb),omega,:,:]) a1 = fig.add_subplot(7,7,42) plt.setp(a1.get_xticklabels(), visible=False) plt.setp(a1.get_yticklabels(), visible=False) aa = Data_angle[41,0] -bb = Data_angle[41,1] +bb = Data_angle[41,1] plt.imshow(A[int(aa),int(bb),omega,:,:]) a1 = fig.add_subplot(7,7,43) plt.setp(a1.get_xticklabels(), visible=False) plt.setp(a1.get_yticklabels(), visible=False) aa = Data_angle[42,0] -bb = Data_angle[42,1] +bb = Data_angle[42,1] plt.imshow(A[int(aa),int(bb),omega,:,:]) a1 = fig.add_subplot(7,7,44) plt.setp(a1.get_xticklabels(), visible=False) plt.setp(a1.get_yticklabels(), visible=False) aa = Data_angle[43,0] -bb = Data_angle[43,1] +bb = Data_angle[43,1] plt.imshow(A[int(aa),int(bb),omega,:,:]) a1 = fig.add_subplot(7,7,45) plt.setp(a1.get_xticklabels(), visible=False) plt.setp(a1.get_yticklabels(), visible=False) aa = Data_angle[44,0] -bb = Data_angle[44,1] +bb = Data_angle[44,1] plt.imshow(A[int(aa),int(bb),omega,:,:]) a1 = fig.add_subplot(7,7,46) plt.setp(a1.get_xticklabels(), visible=False) plt.setp(a1.get_yticklabels(), visible=False) aa = Data_angle[45,0] -bb = Data_angle[45,1] +bb = Data_angle[45,1] plt.imshow(A[int(aa),int(bb),omega,:,:]) a1 = fig.add_subplot(7,7,47) plt.setp(a1.get_xticklabels(), visible=False) plt.setp(a1.get_yticklabels(), visible=False) aa = Data_angle[46,0] -bb = Data_angle[46,1] +bb = Data_angle[46,1] plt.imshow(A[int(aa),int(bb),omega,:,:]) a1 = fig.add_subplot(7,7,48) plt.setp(a1.get_xticklabels(), visible=False) plt.setp(a1.get_yticklabels(), visible=False) aa = Data_angle[47,0] -bb = Data_angle[47,1] +bb = Data_angle[47,1] plt.imshow(A[int(aa),int(bb),omega,:,:]) a1 = fig.add_subplot(7,7,49) plt.setp(a1.get_xticklabels(), visible=False) plt.setp(a1.get_yticklabels(), visible=False) aa = Data_angle[48,0] -bb = Data_angle[48,1] +bb = Data_angle[48,1] plt.imshow(A[int(aa),int(bb),omega,:,:]) plt.axis('off') diff --git a/Plot_ang_values.py b/Plot_ang_values.py index 83c8f2f..f477f78 100644 --- a/Plot_ang_values.py +++ b/Plot_ang_values.py @@ -51,7 +51,7 @@ plt.show() # Plot (pseudomotor, theta) distribution for a single projections -Counter_1 = np.zeros(11*11, 4) +Counter_1 = np.zeros([11*11, 4]) line_num = 0 for i in range(Counter_deg.shape[0]): if Counter_deg[i, 0] == deg: From 3cd38178e6683f6a1fef1593158935bd5f0eadac Mon Sep 17 00:00:00 2001 From: albusdemens Date: Thu, 20 Jul 2017 13:10:59 +0200 Subject: [PATCH 071/150] Array of images recorded at one proj --> improved --- Plot_all_im_1omega.py | 363 ++---------------------------------------- Plot_ang_values.py | 2 +- 2 files changed, 14 insertions(+), 351 deletions(-) diff --git a/Plot_all_im_1omega.py b/Plot_all_im_1omega.py index f824ef1..4e94e5c 100644 --- a/Plot_all_im_1omega.py +++ b/Plot_all_im_1omega.py @@ -3,8 +3,12 @@ import numpy as np import matplotlib.pyplot as plt +import sys +import os -A = np.load('/u/data/alcer/DFXRM_rec/Rec_test/dataarray.npy') +io_dir = '/u/data/alcer/DFXRM_rec/Rec_test/' + +A = np.load(os.path.join(io_dir + 'dataarray.npy')) # Select projection number omega = 2 @@ -12,7 +16,7 @@ # We want to plot, in a grid, all images collected at a certain projection # Load the datafile with all information, and store to an array the data # relative to the images in a certain projection -Data = np.loadtxt('/u/data/alcer/DFXRM_rec/Rec_test/Image_properties.txt') +Data = np.loadtxt(os.path.join(io_dir + 'Image_properties.txt')) Data_angle = np.zeros([49,2]) idx = 0 for i in range(Data.shape[0]): @@ -29,353 +33,12 @@ AA[:,:] = A[aa,bb,omega,:,:] fig, axes = plt.subplots(7, 7) - -a1 = fig.add_subplot(7,7,1) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[0,0] -bb = Data_angle[0,1] -plt.imshow(AA) - -a1 = fig.add_subplot(7,7,2) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[1,0] -bb = Data_angle[1,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,3) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[2,0] -bb = Data_angle[2,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,4) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[3,0] -bb = Data_angle[3,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,5) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[4,0] -bb = Data_angle[4,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,6) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[5,0] -bb = Data_angle[5,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,7) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[6,0] -bb = Data_angle[6,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,8) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[7,0] -bb = Data_angle[7,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,9) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[8,0] -bb = Data_angle[8,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,10) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[9,0] -bb = Data_angle[9,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,11) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[10,0] -bb = Data_angle[10,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,12) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[11,0] -bb = Data_angle[11,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,13) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[12,0] -bb = Data_angle[12,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,14) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[13,0] -bb = Data_angle[13,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,15) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[14,0] -bb = Data_angle[14,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,16) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[15,0] -bb = Data_angle[15,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,17) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[16,0] -bb = Data_angle[16,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,18) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[17,0] -bb = Data_angle[17,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,19) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[18,0] -bb = Data_angle[18,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,20) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[19,0] -bb = Data_angle[19,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,21) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[20,0] -bb = Data_angle[20,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,22) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[21,0] -bb = Data_angle[21,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,23) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[22,0] -bb = Data_angle[22,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,24) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[23,0] -bb = Data_angle[23,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,25) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[24,0] -bb = Data_angle[24,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,26) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[25,0] -bb = Data_angle[25,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,27) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[26,0] -bb = Data_angle[26,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,28) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[27,0] -bb = Data_angle[27,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,29) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[28,0] -bb = Data_angle[28,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,30) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[29,0] -bb = Data_angle[29,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,31) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[30,0] -bb = Data_angle[30,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,32) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[31,0] -bb = Data_angle[31,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,33) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[32,0] -bb = Data_angle[32,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,34) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[33,0] -bb = Data_angle[33,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,35) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[34,0] -bb = Data_angle[34,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,36) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[35,0] -bb = Data_angle[35,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,37) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[36,0] -bb = Data_angle[36,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,38) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[37,0] -bb = Data_angle[37,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,39) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[38,0] -bb = Data_angle[38,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,40) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[39,0] -bb = Data_angle[39,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,41) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[40,0] -bb = Data_angle[40,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,42) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[41,0] -bb = Data_angle[41,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,43) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[42,0] -bb = Data_angle[42,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,44) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[43,0] -bb = Data_angle[43,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,45) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[44,0] -bb = Data_angle[44,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,46) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[45,0] -bb = Data_angle[45,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,47) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[46,0] -bb = Data_angle[46,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,48) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[47,0] -bb = Data_angle[47,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,49) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[48,0] -bb = Data_angle[48,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -plt.axis('off') -plt.subplots_adjust(wspace=0, hspace=0) - -#fig.savefig('test.png') +for i in range(7*7): + a1 = fig.add_subplot(7,7,i+1) + plt.setp(a1.get_xticklabels(), visible=False) + plt.setp(a1.get_yticklabels(), visible=False) + aa = Data_angle[i,0] + bb = Data_angle[i,1] + plt.imshow(A[int(aa),int(bb),omega,:,:]) plt.show() diff --git a/Plot_ang_values.py b/Plot_ang_values.py index f477f78..8c4d94e 100644 --- a/Plot_ang_values.py +++ b/Plot_ang_values.py @@ -56,7 +56,7 @@ for i in range(Counter_deg.shape[0]): if Counter_deg[i, 0] == deg: line_num = line_num + 1 - Counter_1[line_num,:] = Counter_deg[i,:] + Counter_1[line_num-1,:] = Counter_deg[i,:] fig = plt.figure() plt.scatter(Counter_1[:,3], Counter_1[:,1]) From 47a8c2d95ed96ab9273f3300ade6b5dee47e3fac Mon Sep 17 00:00:00 2001 From: albusdemens Date: Thu, 20 Jul 2017 13:23:12 +0200 Subject: [PATCH 072/150] getdata no longer requires background --- Plot_all_im_1omega.py | 4 ++-- getdata.py | 43 ++++++++++++++++++++----------------------- 2 files changed, 22 insertions(+), 25 deletions(-) diff --git a/Plot_all_im_1omega.py b/Plot_all_im_1omega.py index 4e94e5c..cf8a11e 100644 --- a/Plot_all_im_1omega.py +++ b/Plot_all_im_1omega.py @@ -6,7 +6,7 @@ import sys import os -io_dir = '/u/data/alcer/DFXRM_rec/Rec_test/' +io_dir = '/u/data/alcer/DFXRM_rec/Rec_test_2/' A = np.load(os.path.join(io_dir + 'dataarray.npy')) @@ -17,7 +17,7 @@ # Load the datafile with all information, and store to an array the data # relative to the images in a certain projection Data = np.loadtxt(os.path.join(io_dir + 'Image_properties.txt')) -Data_angle = np.zeros([49,2]) +Data_angle = np.zeros([A.shape[0]*A.shape[0],2]) idx = 0 for i in range(Data.shape[0]): if Data[i,3] == omega: diff --git a/getdata.py b/getdata.py index 5dc431b..e86c7bf 100644 --- a/getdata.py +++ b/getdata.py @@ -1,4 +1,4 @@ -# python getdata.py /u/data/andcj/hxrm/Al_april_2017/topotomo/sundaynight topotomo_frelon_far_ /home/nexmap/alcer/DFXRM/bg_refined topotomo_frelon_far_ 256,256 300,300 /u/data/alcer/DFXRM_rec Rec_test 0.785 -3.319 +# python getdata.py /u/data/andcj/hxrm/Al_april_2017/topotomo/monday/Al3/topotomoscan c6_topotomo_frelon_far_ 256,256 300,300 /u/data/alcer/DFXRM_rec Rec_test_2 0.69 -1.625 from lib.miniged import GetEdfData # Read EDF files and plot background import sys @@ -6,7 +6,7 @@ import os import warnings import numpy as np -import pdb # For debugging +import matplotlib.pyplot as plt from find_nearest import find_nearest, find_nearest_idx try: @@ -18,8 +18,6 @@ Inputs: Directory of data Name of data files -Directory of background files -Name of background files Point of interest Image size Output path @@ -31,8 +29,8 @@ class makematrix(): def __init__( - self, datadir, - dataname, bgpath, bgfilename, + self, datadir, dataname, + #dataname, bgpath, bgfilename, poi, imgsize, outputpath, outputdir, phi_0, chi_0, sim=False): @@ -58,7 +56,8 @@ def __init__( int(int(poi[1]) - int(imgsize[1]) / 2), int(int(poi[1]) + int(imgsize[1]) / 2)] - data = GetEdfData(datadir, dataname, bgpath, bgfilename, roi, sim) + #data = GetEdfData(datadir, dataname, bgpath, bgfilename, roi, sim) + data = GetEdfData(datadir, dataname, roi, sim) self.alpha, self.beta, self.omega, self.theta = data.getMetaValues() @@ -108,14 +107,12 @@ def allFiles(self, data, imsiz): omg = float(self.meta[j,2]) phi_0 = float(self.phi) chi_0 = float(self.chi) - idx_list[j] = int((alp - phi_0)/(np.cos(np.deg2rad(omg))*0.032)) + idx_list[j] = int(round((alp - phi_0)/(np.cos(np.deg2rad(omg))*(0.117/2)))) num_int = len(set(idx_list)) # Number of considered alpha, beta # intervals idx_values = sorted(set(idx_list)) # Values of the indices - print 'Check that the number of (phi, chi) steps is', num_int - bigarray = np.zeros((num_int, lent, leno, int(imsiz[0]), int(imsiz[1])), dtype=np.uint16) Image_prop = np.zeros([len(self.index_list), 4]) @@ -125,10 +122,11 @@ def allFiles(self, data, imsiz): b = np.where(self.beta == self.meta[int(ind),1]) # Roll c = np.where(self.omega == self.meta[int(ind),2]) # Omega d = np.where(self.theta == self.meta[int(ind),4]) # Theta - idx_rescaled = (self.meta[int(ind),0] - phi_0) / (np.cos(np.deg2rad(self.meta[int(ind),2])) * 0.032) + ((num_int - 1) / 2) - #idx_rescaled_1 = (self.meta[int(ind),1] - chi_0) * np.cos(np.deg2rad(self.meta[int(ind),1])) / (np.sin(np.deg2rad(self.meta[int(ind),2])) * 0.032) + ((num_int - 1) / 2) + idx_rescaled = (self.meta[int(ind),0] - phi_0) / (np.cos(np.deg2rad(self.meta[int(ind),2])) * (0.117/2)) + ((num_int - 1) / 2) e = (idx_rescaled - ((num_int - 1) / 2)) * 0.032 + print round(idx_rescaled), np.deg2rad(self.meta[int(ind),2]) + # Can we effectively reconstruct chi and phi from idx_rescaled? #ph = phi_0 + ((idx_rescaled - 3)*np.cos(np.deg2rad(self.meta[int(ind),2]))*0.032) #ch = chi_0 + ((idx_rescaled - 3)*(np.tan(np.deg2rad(self.meta[int(ind),2]))*0.032)) @@ -143,6 +141,7 @@ def allFiles(self, data, imsiz): Image_prop[int(ind), 2] = d[0] # Theta Image_prop[int(ind), 3] = c[0] # Omega + print 'Check that the number of (phi, chi) steps is', num_int np.save(self.directory + '/alpha.npy', self.alpha) np.save(self.directory + '/beta.npy', self.beta) @@ -156,13 +155,11 @@ def allFiles(self, data, imsiz): np.savetxt(self.directory + '/Image_properties.txt', Image_prop, fmt='%i %i %i %i') if __name__ == "__main__": - if len(sys.argv) != 10: - if len(sys.argv) != 11: - print "Not enough input parameters. Data input should be:\n\ + if len(sys.argv) != 8: + if len(sys.argv) != 9: + print "Not enough (or too many) input parameters. Data input should be:\n\ Directory of data\n\ Name of data files\n\ - Directory of background files\n\ - Name of background files\n\ Point of interest\n\ Image size\n\ Output path\n\ @@ -179,9 +176,9 @@ def allFiles(self, data, imsiz): sys.argv[5], sys.argv[6], sys.argv[7], - sys.argv[8], - sys.argv[9], - sys.argv[10]) + sys.argv[8]) + #sys.argv[9], + #sys.argv[10]) else: mm = makematrix( sys.argv[1], @@ -191,6 +188,6 @@ def allFiles(self, data, imsiz): sys.argv[5], sys.argv[6], sys.argv[7], - sys.argv[8], - sys.argv[10], - sys.argv[11]) + sys.argv[8]) + #sys.argv[10], + #sys.argv[11]) From 930810de91f76f9f14a1672a27081c44bf44dcdd Mon Sep 17 00:00:00 2001 From: albusdemens Date: Thu, 20 Jul 2017 13:30:03 +0200 Subject: [PATCH 073/150] Elegance is an attitude --- Plot_ang_values.py | 1 - 1 file changed, 1 deletion(-) diff --git a/Plot_ang_values.py b/Plot_ang_values.py index 8c4d94e..f864046 100644 --- a/Plot_ang_values.py +++ b/Plot_ang_values.py @@ -10,7 +10,6 @@ A = np.load(os.path.join(io_dir + 'dataarray.npy')) Theta = np.load(os.path.join(io_dir + 'theta.npy')) -print Theta.shape # For each projection, mark the motor position in the pseudomotor, theta space Counter = np.zeros([A.shape[2], A.shape[0], A.shape[1]]) From 55d41894cd2b0fea7dd3e211e455b5d54f00fa06 Mon Sep 17 00:00:00 2001 From: albusdemens Date: Thu, 20 Jul 2017 13:39:27 +0200 Subject: [PATCH 074/150] Satisfactory plots --- Plot_all_im_1omega.py | 6 +++--- Plot_ang_values.py | 8 +++++++- 2 files changed, 10 insertions(+), 4 deletions(-) diff --git a/Plot_all_im_1omega.py b/Plot_all_im_1omega.py index cf8a11e..3febef2 100644 --- a/Plot_all_im_1omega.py +++ b/Plot_all_im_1omega.py @@ -32,9 +32,9 @@ AA = np.zeros([A.shape[3], A.shape[4]]) AA[:,:] = A[aa,bb,omega,:,:] -fig, axes = plt.subplots(7, 7) -for i in range(7*7): - a1 = fig.add_subplot(7,7,i+1) +fig, axes = plt.subplots(A.shape[0], A.shape[0]) +for i in range(A.shape[0]*A.shape[0]): + a1 = fig.add_subplot(A.shape[0],A.shape[0],i+1) plt.setp(a1.get_xticklabels(), visible=False) plt.setp(a1.get_yticklabels(), visible=False) aa = Data_angle[i,0] diff --git a/Plot_ang_values.py b/Plot_ang_values.py index f864046..cc5614e 100644 --- a/Plot_ang_values.py +++ b/Plot_ang_values.py @@ -59,9 +59,15 @@ fig = plt.figure() plt.scatter(Counter_1[:,3], Counter_1[:,1]) +plt.title('(theta, pseudomotor) for one projection') +plt.xlabel('Theta (degrees)') +plt.ylabel('Pseudomotor (index)') plt.show() # Plot (pseudomotor, theta) distribution for all projections fig = plt.figure() -plt.scatter(Counter_deg[:,3], Counter_deg[:,1]) +plt.title('(theta, pseudomotor) for all projections') +plt.scatter(Counter_deg[:,3], Counter_deg[:,1], s=2) +plt.xlabel('Theta (degrees)') +plt.ylabel('Pseudomotor (index)') plt.show() From 09cb10122f48344226fc6889e94a9380ecf0588e Mon Sep 17 00:00:00 2001 From: albusdemens Date: Thu, 20 Jul 2017 14:38:12 +0200 Subject: [PATCH 075/150] Generalized code --- Clean_sum_img.py | 81 ++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 81 insertions(+) create mode 100644 Clean_sum_img.py diff --git a/Clean_sum_img.py b/Clean_sum_img.py new file mode 100644 index 0000000..0cf1d4f --- /dev/null +++ b/Clean_sum_img.py @@ -0,0 +1,81 @@ +# For each projection, this script loads the relative images, sums +# them and clean the sum using a rolling median.style approach. + +import numpy as np +import matplotlib.pyplot as plt +import fabio +import os +import scipy.io + +# Directory where the IO data is stored +io_dir = '/u/data/alcer/DFXRM_rec/Rec_test_2/' +# Directory with the images +im_dir = '/u/data/andcj/hxrm/Al_april_2017/topotomo/monday/Al3/topotomoscan/' +# Figures to be used to clean the background (selected looking at the result +# from Plot_all_im_1omega.pys) +first_im = [0, 0] +last_im = [10, 10] + +# Load all datato get dimensions +A = np.load(os.path.join(io_dir + 'dataarray.npy')) +num_idx = A.shape[0] +Im_x = A.shape[3] +Im_y = A.shape[4] +num_proj = A.shape[2] +# Estremes of the ROI in the full frame +roy_lf = (512/2) - Im_x/2 +roy_rg = (512/2) + Im_x/2 +roy_up = (512/2) - Im_y/2 +roy_dw = (512/2) + Im_y/2 + +# List of the image files +im_paths = np.genfromtxt(os.path.join(io_dir + 'List_images.txt'), dtype = str) +# List of the files properties +im_prop = np.loadtxt(os.path.join(io_dir + 'Image_properties.txt')) + +Fabio_array = np.zeros([num_proj, Im_x, Im_y]) +# For each projection, sum the images and store the result +for j in range(num_proj): + sum_om = np.zeros([Im_x,Im_y]) + for i in range(im_prop.shape[0]): + if im_prop[i,3] == j: + img_name = os.path.join(im_dir + im_paths[i]) + I = fabio.open(img_name).data + sum_om[:,:] += I[roi_lf:roi_rg, roy_dw:roi_up] + Fabio_array[j,:,:] = sum_om[:,:] + +Median_array = np.zeros([num_proj, Im_x, Im_y]) +# For each projection, select the images to use for some median filtering +for j in range(num_proj): + # List all images relative to an omega, than take first and last of + # the first line (selected by looking at the data) + med_arr = np.zeros([num_idx*num_idx, 1]) + n_med = 0 + img_name = im_paths[i] + for i in range(im_prop.shape[0]): + if im_prop[i,3] == j: + n_med = n_med + 1 + med_arr[n_med-1, 0] = i + # Load first and seventh image + M1 = os.path.join(im_dir + im_paths[int(med_arr(first_im)))]) + M2 = os.path.join(im_dir + im_paths[int(med_arr(last_im))]) + I1 = fabio.open(M1).data + I2 = fabio.open(M1).data + Median_array[j,:,:] = 0.5 * (I1[roi_lf:roi_rg, roy_dw:roi_up] + I2[roi_lf:roi_rg, roy_dw:roi_up]) + +# To take into account the sample rotation, divide by the mean intensity +mean_int = np.zeros([num_proj,1]) +for i in range(num_proj): + mean_int[i] = np.mean(Fabio_array[i,:,:]) + print mean_int[i] + +max_mean = int(max(mean_int)) + +Fabio_clean = np.zeros([num_proj, Im_x, Im_y]) +for j in range(num_proj): + for k in range(Fabio_array.shape[1]): + for l in range(Fabio_array.shape[2]): + Fabio_clean[j,k,l] = (Fabio_array[j,k,l] - (num_idx * num_idx) * Median_array[j,k,l]) + +# Save data for matlab analysis +scipy.io.savemat('Fabio_clean.mat',{"foo":Fabio_clean}) From f0b154d8a40ee747bf2799b82db082f1b0507e85 Mon Sep 17 00:00:00 2001 From: albusdemens Date: Thu, 20 Jul 2017 16:31:36 +0200 Subject: [PATCH 076/150] Dine with integrated intebnsity mapping --- Plot_all_im_1omega.py | 36 ++++++++++++++++++++++++++++++++++++ 1 file changed, 36 insertions(+) diff --git a/Plot_all_im_1omega.py b/Plot_all_im_1omega.py index 3febef2..00a7ced 100644 --- a/Plot_all_im_1omega.py +++ b/Plot_all_im_1omega.py @@ -42,3 +42,39 @@ plt.imshow(A[int(aa),int(bb),omega,:,:]) plt.show() + +# Plot how the integrated intensity changes during the rocking scan +II_matrix_temp = np.zeros([A.shape[0], A.shape[0], A.shape[2]]) +II_matrix = np.zeros([A.shape[0], A.shape[0]]) +for oo in range(A.shape[2]): + for i in range(Data_angle.shape[0]): + aa = Data_angle[i,0] + bb = Data_angle[i,1] + II_matrix_temp[ int(aa), int(i - int(aa) * A.shape[0]), oo ] = sum(sum(A[int(aa),int(bb),oo,:,:])) +for aa in range(II_matrix_temp.shape[0]): + for bb in range(II_matrix_temp.shape[1]): + II_matrix[aa, bb] = sum(II_matrix_temp[aa,bb,:]) + +fig = plt.figure() +plt.imshow(II_matrix) +plt.title('Distribution of the integrated intensities (all proj)') +plt.xlabel('Theta (index)') +plt.ylabel('Pseudomotor (index)') +plt.show() + +# Return the coordinates where we have the lower integrated intensity +II_value = np.zeros([A.shape[0]*A.shape[0], 3]) +lin_n = 0 +for aa in range(II_matrix.shape[0]): + for bb in range(II_matrix.shape[1]): + lin_n = lin_n + 1 + II_value[lin_n,0] = aa + II_value[lin_n,1] = bb + II_value[lin_n,2] = II_matrix[aa, bb] + +# Sort integrated intensities; for the rolling median consider the images giving +# the two lower values +i = II_value[:,0].argsort() +II_value_sorted = II_value[i] + +print II_value_sorted From f6e4ff6c83cb1d1c03c7cc3f5278e20e3ffbf532 Mon Sep 17 00:00:00 2001 From: albusdemens Date: Thu, 20 Jul 2017 18:24:47 +0200 Subject: [PATCH 077/150] Added suggestions for rolling filter --- Plot_all_im_1omega.py | 52 ++++++++++++++++++++++++------------------- 1 file changed, 29 insertions(+), 23 deletions(-) diff --git a/Plot_all_im_1omega.py b/Plot_all_im_1omega.py index 00a7ced..6af818e 100644 --- a/Plot_all_im_1omega.py +++ b/Plot_all_im_1omega.py @@ -11,7 +11,7 @@ A = np.load(os.path.join(io_dir + 'dataarray.npy')) # Select projection number -omega = 2 +omega = 100 # We want to plot, in a grid, all images collected at a certain projection # Load the datafile with all information, and store to an array the data @@ -25,13 +25,7 @@ Data_angle[idx-1, 0] = Data[i,1] Data_angle[idx-1, 1] = Data[i,2] -# Plot the various recorded images - -aa = int(Data_angle[0,0]) -bb = int(Data_angle[0,1]) -AA = np.zeros([A.shape[3], A.shape[4]]) -AA[:,:] = A[aa,bb,omega,:,:] - +# Plot the various recorded images (one projection) fig, axes = plt.subplots(A.shape[0], A.shape[0]) for i in range(A.shape[0]*A.shape[0]): a1 = fig.add_subplot(A.shape[0],A.shape[0],i+1) @@ -43,17 +37,29 @@ plt.show() +# Plot the various recorded images (all projections) + +fig, axes = plt.subplots(A.shape[0], A.shape[0]) +for i in range(A.shape[0]*A.shape[0]): + a1 = fig.add_subplot(A.shape[0],A.shape[0],i+1) + plt.setp(a1.get_xticklabels(), visible=False) + plt.setp(a1.get_yticklabels(), visible=False) + aa = int(Data_angle[i,0]) + bb = int(Data_angle[i,1]) + + AA = np.zeros([A.shape[3], A.shape[4]]) + for oo in range(A.shape[2]): + AA[:,:] += A[aa,bb,oo,:,:] + plt.imshow(AA[:,:]) + +plt.show() + # Plot how the integrated intensity changes during the rocking scan -II_matrix_temp = np.zeros([A.shape[0], A.shape[0], A.shape[2]]) II_matrix = np.zeros([A.shape[0], A.shape[0]]) -for oo in range(A.shape[2]): - for i in range(Data_angle.shape[0]): - aa = Data_angle[i,0] - bb = Data_angle[i,1] - II_matrix_temp[ int(aa), int(i - int(aa) * A.shape[0]), oo ] = sum(sum(A[int(aa),int(bb),oo,:,:])) -for aa in range(II_matrix_temp.shape[0]): - for bb in range(II_matrix_temp.shape[1]): - II_matrix[aa, bb] = sum(II_matrix_temp[aa,bb,:]) +for i in range(Data_angle.shape[0]): + aa = int(Data_angle[i,0]) + bb = int(Data_angle[i,1]) + II_matrix[ int(aa), int(i - (int(aa) * A.shape[0])) ] = sum(sum(sum(A[int(aa),int(bb),:,:,:]))) fig = plt.figure() plt.imshow(II_matrix) @@ -68,13 +74,13 @@ for aa in range(II_matrix.shape[0]): for bb in range(II_matrix.shape[1]): lin_n = lin_n + 1 - II_value[lin_n,0] = aa - II_value[lin_n,1] = bb - II_value[lin_n,2] = II_matrix[aa, bb] + II_value[lin_n - 1, 0] = int(aa) + II_value[lin_n - 1, 1] = int(bb) + II_value[lin_n - 1, 2] = II_matrix[aa, bb] # Sort integrated intensities; for the rolling median consider the images giving # the two lower values -i = II_value[:,0].argsort() -II_value_sorted = II_value[i] +i = II_value[:,2].argsort() +II_value_sorted = II_value[i,:] -print II_value_sorted +print 'To clean the dataset, we suggest to use the images number %i and %i' % ( II_value_sorted[0,1] + (II_value_sorted[0,0] * A.shape[0]), II_value_sorted[1,1] + (II_value_sorted[1,0] * A.shape[0]) ) From 10a78078ecc5de434ab53fe9ad18975ab73e6d06 Mon Sep 17 00:00:00 2001 From: albusdemens Date: Thu, 20 Jul 2017 22:08:05 +0200 Subject: [PATCH 078/150] Bug fixed --- Clean_sum_img.py | 31 +++++++++++++++++++------------ Plot_all_im_1omega.py | 2 +- 2 files changed, 20 insertions(+), 13 deletions(-) diff --git a/Clean_sum_img.py b/Clean_sum_img.py index 0cf1d4f..59ba69c 100644 --- a/Clean_sum_img.py +++ b/Clean_sum_img.py @@ -12,9 +12,9 @@ # Directory with the images im_dir = '/u/data/andcj/hxrm/Al_april_2017/topotomo/monday/Al3/topotomoscan/' # Figures to be used to clean the background (selected looking at the result -# from Plot_all_im_1omega.pys) -first_im = [0, 0] -last_im = [10, 10] +# from Plot_all_im_1omega.pys +first_im = [0, 9] +last_im = [10, 1] # Load all datato get dimensions A = np.load(os.path.join(io_dir + 'dataarray.npy')) @@ -23,10 +23,13 @@ Im_y = A.shape[4] num_proj = A.shape[2] # Estremes of the ROI in the full frame -roy_lf = (512/2) - Im_x/2 -roy_rg = (512/2) + Im_x/2 -roy_up = (512/2) - Im_y/2 -roy_dw = (512/2) + Im_y/2 +roi_lf = (512/2) - Im_x/2 +roi_rg = (512/2) + Im_x/2 +roi_dw = (512/2) - Im_y/2 +roi_up = (512/2) + Im_y/2 + + +del A # List of the image files im_paths = np.genfromtxt(os.path.join(io_dir + 'List_images.txt'), dtype = str) @@ -36,12 +39,12 @@ Fabio_array = np.zeros([num_proj, Im_x, Im_y]) # For each projection, sum the images and store the result for j in range(num_proj): - sum_om = np.zeros([Im_x,Im_y]) + sum_om = np.zeros([roi_rg - roi_lf, roi_up - roi_dw]) for i in range(im_prop.shape[0]): if im_prop[i,3] == j: img_name = os.path.join(im_dir + im_paths[i]) I = fabio.open(img_name).data - sum_om[:,:] += I[roi_lf:roi_rg, roy_dw:roi_up] + sum_om[:,:] += I_ROI[roi_lf:roi_rg, roi_dw:roi_up] Fabio_array[j,:,:] = sum_om[:,:] Median_array = np.zeros([num_proj, Im_x, Im_y]) @@ -56,12 +59,16 @@ if im_prop[i,3] == j: n_med = n_med + 1 med_arr[n_med-1, 0] = i - # Load first and seventh image - M1 = os.path.join(im_dir + im_paths[int(med_arr(first_im)))]) + # Load selected images + M1 = os.path.join(im_dir + im_paths[int(med_arr(first_im))]) M2 = os.path.join(im_dir + im_paths[int(med_arr(last_im))]) I1 = fabio.open(M1).data I2 = fabio.open(M1).data - Median_array[j,:,:] = 0.5 * (I1[roi_lf:roi_rg, roy_dw:roi_up] + I2[roi_lf:roi_rg, roy_dw:roi_up]) + Median_array[j,:,:] = 0.5 * (I1[roi_lf:roi_rg, roi_dw:roi_up] + I2[roi_lf:roi_rg, roi_dw:roi_up]) + +print sum(sum(sum(Median_array))) + +sys.exit() # To take into account the sample rotation, divide by the mean intensity mean_int = np.zeros([num_proj,1]) diff --git a/Plot_all_im_1omega.py b/Plot_all_im_1omega.py index 6af818e..ad20ecb 100644 --- a/Plot_all_im_1omega.py +++ b/Plot_all_im_1omega.py @@ -83,4 +83,4 @@ i = II_value[:,2].argsort() II_value_sorted = II_value[i,:] -print 'To clean the dataset, we suggest to use the images number %i and %i' % ( II_value_sorted[0,1] + (II_value_sorted[0,0] * A.shape[0]), II_value_sorted[1,1] + (II_value_sorted[1,0] * A.shape[0]) ) +print 'To clean the dataset, we suggest to use the images number %i [%i, %i] and %i [%i, %i]' % ( II_value_sorted[0,1] + (II_value_sorted[0,0] * A.shape[0]), II_value_sorted[0,1], II_value_sorted[0,0], II_value_sorted[1,1] + (II_value_sorted[1,0] * A.shape[0]), II_value_sorted[1,1], II_value_sorted[1,0] ) From 6c66231655659cddef7166f4e426b96a41270243 Mon Sep 17 00:00:00 2001 From: albusdemens Date: Thu, 20 Jul 2017 22:13:41 +0200 Subject: [PATCH 079/150] Now works --- Plot_all_im_1omega.py | 12 +++++------- 1 file changed, 5 insertions(+), 7 deletions(-) diff --git a/Plot_all_im_1omega.py b/Plot_all_im_1omega.py index ad20ecb..6edc142 100644 --- a/Plot_all_im_1omega.py +++ b/Plot_all_im_1omega.py @@ -38,8 +38,9 @@ plt.show() # Plot the various recorded images (all projections) - fig, axes = plt.subplots(A.shape[0], A.shape[0]) +#We also keep track of the integrated intensity +II_matrix = np.zeros([A.shape[0], A.shape[0]]) for i in range(A.shape[0]*A.shape[0]): a1 = fig.add_subplot(A.shape[0],A.shape[0],i+1) plt.setp(a1.get_xticklabels(), visible=False) @@ -50,17 +51,12 @@ AA = np.zeros([A.shape[3], A.shape[4]]) for oo in range(A.shape[2]): AA[:,:] += A[aa,bb,oo,:,:] + II_matrix[ int(aa), int(i - (int(aa) * A.shape[0])) ] = sum(sum(AA)) plt.imshow(AA[:,:]) plt.show() # Plot how the integrated intensity changes during the rocking scan -II_matrix = np.zeros([A.shape[0], A.shape[0]]) -for i in range(Data_angle.shape[0]): - aa = int(Data_angle[i,0]) - bb = int(Data_angle[i,1]) - II_matrix[ int(aa), int(i - (int(aa) * A.shape[0])) ] = sum(sum(sum(A[int(aa),int(bb),:,:,:]))) - fig = plt.figure() plt.imshow(II_matrix) plt.title('Distribution of the integrated intensities (all proj)') @@ -83,4 +79,6 @@ i = II_value[:,2].argsort() II_value_sorted = II_value[i,:] +print II_value_sorted + print 'To clean the dataset, we suggest to use the images number %i [%i, %i] and %i [%i, %i]' % ( II_value_sorted[0,1] + (II_value_sorted[0,0] * A.shape[0]), II_value_sorted[0,1], II_value_sorted[0,0], II_value_sorted[1,1] + (II_value_sorted[1,0] * A.shape[0]), II_value_sorted[1,1], II_value_sorted[1,0] ) From 7444a8ce9f0788e442fae795e3aa29b9b28ec5ab Mon Sep 17 00:00:00 2001 From: albusdemens Date: Fri, 21 Jul 2017 18:26:32 +0200 Subject: [PATCH 080/150] Plot data phiscamn --- Plot_all_im_1omega.py | 2 +- plot_phiscan.py | 41 +++++++++++++++++++++++++++++++++++++++++ 2 files changed, 42 insertions(+), 1 deletion(-) create mode 100644 plot_phiscan.py diff --git a/Plot_all_im_1omega.py b/Plot_all_im_1omega.py index 6edc142..cb2d30e 100644 --- a/Plot_all_im_1omega.py +++ b/Plot_all_im_1omega.py @@ -81,4 +81,4 @@ print II_value_sorted -print 'To clean the dataset, we suggest to use the images number %i [%i, %i] and %i [%i, %i]' % ( II_value_sorted[0,1] + (II_value_sorted[0,0] * A.shape[0]), II_value_sorted[0,1], II_value_sorted[0,0], II_value_sorted[1,1] + (II_value_sorted[1,0] * A.shape[0]), II_value_sorted[1,1], II_value_sorted[1,0] ) +print 'To clean the dataset, we suggest to use the images number %i [%i, %i] and %i [%i, %i]' % ( ((II_value_sorted[0,1] + 1) + ( II_value_sorted[0,0] * A.shape[0])), II_value_sorted[0,0], II_value_sorted[0,1], ((II_value_sorted[1,1] + 1) + ( II_value_sorted[1,0] * A.shape[0])), II_value_sorted[1,0], II_value_sorted[1,1]) diff --git a/plot_phiscan.py b/plot_phiscan.py new file mode 100644 index 0000000..ac3d958 --- /dev/null +++ b/plot_phiscan.py @@ -0,0 +1,41 @@ +import numpy as np +import matplotlib.pyplot as plt +from lib.miniged import GetEdfData # Read EDF files and plot background +import sys +import time +import os +import fabio +import warnings + +im_paths = np.genfromtxt('/home/nexmap/alcer/DFXRM/phiscan/list_phiscan.txt', dtype = str) +im_dir = '/u/data/andcj/hxrm/Al_april_2017/topotomo/monday/Al3/phi_resolution/' + +I_stack = np.zeros([im_paths.shape[0], 512, 512]) +I_sum = np.zeros([512, 512]) +for i in range(im_paths.shape[0]): + im_path = os.path.join(im_dir + im_paths[i]) + I = fabio.open(im_path).data + for j in range(I_sum.shape[0]): + for k in range(I_sum.shape[1]): + if I[j,k] < 5E+3: + I_stack[i,j,k] = I[j,k] - 300 + I_sum[j,k] += I[j,k] + +fig = plt.figure() +plt.imshow(I_sum, cmap='gray_r') +plt.show() + +#for i in range(im_paths.shape[0]): +# fig = plt.figure() +# plt.imshow(I_stack[i,:,:]) +# plt.show() + +# Plot the various recorded images (one projection) +fig, axes = plt.subplots(8, 8) +for i in range(60): + a1 = fig.add_subplot(8,8,i+1) + plt.setp(a1.get_xticklabels(), visible=False) + plt.setp(a1.get_yticklabels(), visible=False) + plt.imshow(I_stack[i,:,:], cmap='gray_r') + +plt.show() From b98283970ff5d65228d494204662356e6bdf3203 Mon Sep 17 00:00:00 2001 From: albusdemens Date: Tue, 8 Aug 2017 14:45:10 +0200 Subject: [PATCH 081/150] manual rebasing --- Plot_EDF.py | 105 ----- README.md | 10 +- al_test.ini | 28 ++ check_input.py | 0 dfxm.pdf | Bin example.ini | 0 getdata.py | 148 ++++--- lib/.DS_Store | Bin 6148 -> 0 bytes lib/EdfFile.py | 1 + lib/EdfFile.pyc | Bin 35058 -> 0 bytes lib/__init__.py | 0 lib/__init__.pyc | Bin 135 -> 0 bytes lib/edfimage.py | 985 ----------------------------------------------- lib/miniged.py | 45 +-- lib/miniged.pyc | Bin 15614 -> 0 bytes plottest.py | 111 ++++++ recon3d.py | 403 ++++++++++--------- 17 files changed, 436 insertions(+), 1400 deletions(-) mode change 100644 => 100755 Plot_EDF.py mode change 100644 => 100755 README.md create mode 100755 al_test.ini mode change 100644 => 100755 check_input.py mode change 100644 => 100755 dfxm.pdf mode change 100644 => 100755 example.ini mode change 100644 => 100755 getdata.py delete mode 100644 lib/.DS_Store mode change 100644 => 100755 lib/EdfFile.py delete mode 100644 lib/EdfFile.pyc mode change 100644 => 100755 lib/__init__.py delete mode 100644 lib/__init__.pyc delete mode 100644 lib/edfimage.py mode change 100644 => 100755 lib/miniged.py delete mode 100644 lib/miniged.pyc create mode 100755 plottest.py mode change 100644 => 100755 recon3d.py diff --git a/Plot_EDF.py b/Plot_EDF.py old mode 100644 new mode 100755 index 314cb5d..34e8efb --- a/Plot_EDF.py +++ b/Plot_EDF.py @@ -6,111 +6,6 @@ import sys im = EF.EdfFile(sys.argv[1]) -<<<<<<< HEAD -print im.GetHeader(0) - -sys.exit() - -image = np.array(im.GetData(0)) -image_data = im.GetHeader(0) - -im_flip = np.fliplr(image) - -fig = plt.figure() -======= image = im.GetData(0) -#im_flip = np.fliplr(image) - -#fig = plt.figure() ->>>>>>> 077febf5ee14058f5f57ce942f86a24dc78c3637 -#a=fig.add_subplot(1,2,1) -#imgplot = plt.imshow(image) -#a.set_title('Image') -#a=fig.add_subplot(1,2,2) -#imgplot = plt.imshow(im_flip) -#a.set_title('Flipped image') - -<<<<<<< HEAD -#plt.imshow(image[100:400, 100:400]) -#plt.show() - -img = EF.EdfFile(sys.argv[1]) -im = img.GetData(0).astype(float) -plt.imshow(im[106:406, 106:406]) -plt.show() - -sys.exit() - -#plt.imshow(im_flip) -#plt.show() - -# Consider layer from stored array -A = np.load('/u/data/alcer/DFXRM_rec/Rec_test/dataarray.npy') -B = A[5, 59, 0, :, :] -C = B.reshape(200,200) - -# Load the background from getdata.py and play with it -bckg = np.load('bckg_roi.npy') - -raw_img_cropped=image[256-100:256+100, 256-100:256+100] -clean_1=raw_img_cropped-bckg -clean_2=raw_img_cropped/bckg - -fig = plt.figure() -# Raw image -a=fig.add_subplot(2,4,1) -plt.imshow(bckg) -a.set_title('ROI background') -b=fig.add_subplot(2,4,2) -plt.imshow(raw_img_cropped) -b.set_title('Raw image') -c=fig.add_subplot(2,4,3) -plt.imshow(clean_1) -c.set_title('Raw - background') -d=fig.add_subplot(2,4,4) -plt.imshow(clean_2) -d.set_title('Raw / background') -# Image from npy -a=fig.add_subplot(2,4,5) -plt.imshow(bckg) -a.set_title('ROI background') -b=fig.add_subplot(2,4,6) -plt.imshow(C) -b.set_title('Npy image') -c=fig.add_subplot(2,4,7) -plt.imshow(C/bckg) -c.set_title('Clean npy') -plt.show() - -sys.exit() - -fig = plt.figure() -a=fig.add_subplot(1,2,1) -======= ->>>>>>> 077febf5ee14058f5f57ce942f86a24dc78c3637 plt.imshow(image) -a.set_title('Raw image') -b=fig.add_subplot(1,2,2) -plt.imshow(B) -b.set_title('Image from npy array') plt.show() -<<<<<<< HEAD - -int_interval = np.empty(shape=[A.shape[0], A.shape[1]]) -for i in range(A.shape[0]): - for j in range(A.shape[1]): - int_interval[i,j] = sum(sum(A[i,j,0,:,:])) - -# Plot intensity for theta values -c = plt.figure() -plt.imshow(int_interval) -plt.xlabel('Theta') -plt.ylabel('Gamma') -plt.show() - - - -======= -#plt.imshow(im_flip) -#plt.show() ->>>>>>> 077febf5ee14058f5f57ce942f86a24dc78c3637 diff --git a/README.md b/README.md old mode 100644 new mode 100755 index 74896eb..f95df83 --- a/README.md +++ b/README.md @@ -18,14 +18,12 @@ Future versions will be able to do reconstruction based on strain datasets. Running Recon3D requires having Miniconda2 and Fable on your Panda2 account. To install the packages, run the script available [here](https://github.com/acjak/fable-install). If you get an error message and need to change a permission path, check [this tip](http://stackoverflow.com/questions/35246386/conda-command-not-found). -Running parallelised code on Panda requires using *mpi4py*. The command to install it is *conda install mpi4py* - ## Reading data from .edf files to a NumPy array The script getdata.py reads data from .edf files and outputs a NumPy array with the dimensions *tilt1*-steps x *tilt2*-steps x *omega*-steps x *img_xlen* x *img_ylen*, where *tilt1* and *tilt2* are the two top tilts in the LAB goniometer at ID06. *Omega* is the topo-tomo rotation stage. The command to create the array is the following: ``` -$ python getdata.py [datadir] [dataname] [bgdir] [bgname] [poi] [imgsize] [outputpath] [outputdirname] [phi_0] [chi_0] +$ python getdata.py [datadir] [dataname] [bgdir] [bgname] [poi] [imgsize] [outputpath] [outputdirname] ``` Arguments are the following: @@ -40,13 +38,11 @@ Arguments are the following: | imgsize | Size of region of interest. | 200,200 | | outputpath | Path to put the output directory. | /analysis/output | | outputdirname | Name of output dir. | exp1_array | -| phi_0 | Initial phi (alpha) value | 0.765 | -| chi_0 | Initial chi (beta) value | -3.35 | -If MPI is available, the following command will run the script in 10 processes at the same time. This will vastly increase the speed. On Panda2, the recommended maximum number of getdata processes to run in parallel is 20. +If MPI is available, the following command will run the script in 10 processes at the same time. This will vastly increase the speed. ``` -$ mpirun -n 10 python getdata.py [datadir] [dataname] [bgdir] [bgname] [poi] [imgsize] [outputpath] [outputdirname] [phi_0] [chi_0] +$ mpirun -n 10 python getdata.py [datadir] [dataname] [bgdir] [bgname] [poi] [imgsize] [outputpath] [outputdirname] ``` ## Running the reconstruction algorithm on a data set diff --git a/al_test.ini b/al_test.ini new file mode 100755 index 0000000..463eb87 --- /dev/null +++ b/al_test.ini @@ -0,0 +1,28 @@ +sg_no 225 # Space group +unit_cell [4.0495,4.0495,4.0495,90.,90.,90.] +hkl [2,0,0] +wavelength 0.7293 # Corresponds to an energy of 17 keV +grain_pos [0,0,0] +grain_dim [21.,21.,21.] # Number of voxels +grain_steps [101,101,101] # 3D ROI +grain_dim [21.,21.,21.] # 3D ROI. To zoom in, decrease this number +grain_steps [101,101,101] # Number of voxels +ang_mean [0,0] +ang_width [.01,.01] +#image simulation +# ang_steps [2,2,18] +# ang_types ["phi_upper","phi_lower","Omega"] +# ang_min [0,0,-85.] +# ang_max [.01,.01,85.] +detx_size 512 +detz_size 512 +M 18.57 # Magnification value +t_x "None" +t_y 0 +t_z 0 +path "/u/data/andcj/astra-recon-data/topotomo_test" # Folder with npy files +format "edf" +mode "horizontal" +xyz_up [0,0,0] +xyz_lo [0,0,0] +xyz_th [0,0,0] diff --git a/check_input.py b/check_input.py old mode 100644 new mode 100755 diff --git a/dfxm.pdf b/dfxm.pdf old mode 100644 new mode 100755 diff --git a/example.ini b/example.ini old mode 100644 new mode 100755 diff --git a/getdata.py b/getdata.py old mode 100644 new mode 100755 index e86c7bf..a1ad9ff --- a/getdata.py +++ b/getdata.py @@ -1,13 +1,9 @@ -# python getdata.py /u/data/andcj/hxrm/Al_april_2017/topotomo/monday/Al3/topotomoscan c6_topotomo_frelon_far_ 256,256 300,300 /u/data/alcer/DFXRM_rec Rec_test_2 0.69 -1.625 - -from lib.miniged import GetEdfData # Read EDF files and plot background +from lib.miniged import GetEdfData import sys import time import os import warnings import numpy as np -import matplotlib.pyplot as plt -from find_nearest import find_nearest, find_nearest_idx try: from mpi4py import MPI @@ -18,21 +14,20 @@ Inputs: Directory of data Name of data files +Directory of background files +Name of background files Point of interest Image size Output path Name of new output directory to make -Initial phi values -Initial chi value ''' class makematrix(): def __init__( - self, datadir, dataname, - #dataname, bgpath, bgfilename, + self, datadir, + dataname, bgpath, bgfilename, poi, imgsize, outputpath, outputdir, - phi_0, chi_0, sim=False): try: @@ -56,16 +51,14 @@ def __init__( int(int(poi[1]) - int(imgsize[1]) / 2), int(int(poi[1]) + int(imgsize[1]) / 2)] - #data = GetEdfData(datadir, dataname, bgpath, bgfilename, roi, sim) - data = GetEdfData(datadir, dataname, roi, sim) - + data = GetEdfData(datadir, dataname, bgpath, bgfilename, roi, sim) self.alpha, self.beta, self.omega, self.theta = data.getMetaValues() self.index_list = range(len(data.meta)) self.meta = data.meta - self.phi = phi_0 - self.chi = chi_0 + self.calcGamma(data) + self.calcMu(data) # self.calcEtaIndexList(data, eta) self.allFiles(data, imgsize) @@ -82,90 +75,86 @@ def makeOutputFolder(self, path, dirname): os.makedirs(directory) return directory + def calcGamma(self, data): + # for om in self.omega: + om = self.omega[0] + ind = np.where(self.meta[:, 2] == om) + a = self.meta[ind, 0][0] + + gamma1 = (a - data.alpha0) / np.cos(np.radians(om)) + self.gamma = np.sort(list(set(gamma1))) + self.gammaindex = np.zeros((len(self.index_list))) + + for ind in self.index_list: + om = self.meta[ind, 2] + a = self.meta[ind, 0] - data.alpha0 + gamma1 = a / np.cos(np.radians(om)) + + gammapos = np.where(self.gamma == min(self.gamma, key=lambda x: abs(x-gamma1)))[0][0] + self.gammaindex[ind] = self.gamma[gammapos] + + def calcMu(self, data): + # self.mufake = data.mu0 + np.arange(-3.5 * 0.032, 3.5 * 0.032, 0.032) + self.mufake = np.arange(-3 * 0.032, 4 * 0.032, 0.032) + self.muindex = np.zeros((len(self.index_list))) + for ind in self.index_list: + t = self.meta[ind, 4] - data.theta0 + + mupos = np.where(self.mufake == min(self.mufake, key=lambda x: abs(x-t)))[0][0] + + self.muindex[ind] = self.mufake[mupos] + def allFiles(self, data, imsiz): # index_list = range(len(data.meta)) # met = data.meta - # theta = data.theta0 + np.arange(-3.5 * 0.032, 3.5 * 0.032, 0.032) + # mu = data.mu0 + np.arange(-3.5 * 0.032, 3.5 * 0.032, 0.032) with warnings.catch_warnings(): warnings.simplefilter("ignore") imgarray = data.makeImgArray(self.index_list, 50, 'linetrace') if self.rank == 0: - - # If we don't need to bin the angular values - lena = len(self.alpha) - lenb = len(self.beta) + # lena = len(self.mu) + lena = len(self.mufake) + lenb = len(self.gamma) leno = len(self.omega) - lent = len(self.theta) - - # Reduce alpha and beta to a single index_list - idx_list = np.zeros(len(self.meta)) - for j in range(len(self.meta)): - alp = float(self.meta[j,0]) - omg = float(self.meta[j,2]) - phi_0 = float(self.phi) - chi_0 = float(self.chi) - idx_list[j] = int(round((alp - phi_0)/(np.cos(np.deg2rad(omg))*(0.117/2)))) - num_int = len(set(idx_list)) # Number of considered alpha, beta - # intervals - idx_values = sorted(set(idx_list)) # Values of the indices + bigarray = np.zeros((lena, lenb, leno, int(imsiz[1]), int(imsiz[0])), dtype=np.uint16) - bigarray = np.zeros((num_int, lent, leno, int(imsiz[0]), int(imsiz[1])), dtype=np.uint16) - Image_prop = np.zeros([len(self.index_list), 4]) - - #AA = np.empty([len(self.index_list), 5]) for i, ind in enumerate(self.index_list): - a = np.where(self.alpha == self.meta[int(ind),0]) # Rock - b = np.where(self.beta == self.meta[int(ind),1]) # Roll - c = np.where(self.omega == self.meta[int(ind),2]) # Omega - d = np.where(self.theta == self.meta[int(ind),4]) # Theta - idx_rescaled = (self.meta[int(ind),0] - phi_0) / (np.cos(np.deg2rad(self.meta[int(ind),2])) * (0.117/2)) + ((num_int - 1) / 2) - e = (idx_rescaled - ((num_int - 1) / 2)) * 0.032 - - print round(idx_rescaled), np.deg2rad(self.meta[int(ind),2]) - - # Can we effectively reconstruct chi and phi from idx_rescaled? - #ph = phi_0 + ((idx_rescaled - 3)*np.cos(np.deg2rad(self.meta[int(ind),2]))*0.032) - #ch = chi_0 + ((idx_rescaled - 3)*(np.tan(np.deg2rad(self.meta[int(ind),2]))*0.032)) - - #AA[ind] = [self.meta[int(ind),0], self.meta[int(ind),1],self.meta[int(ind),2], idx_rescaled, self.meta[int(ind),4]] - - bigarray[[int(round(idx_rescaled))], d[0], c[0], :, :] = imgarray[ind, :, :] - - # List the properties of the images and write them to a txt - Image_prop[int(ind), 0] = int(ind) # Image number - Image_prop[int(ind), 1] = int(round(idx_rescaled)) # Gamma index - Image_prop[int(ind), 2] = d[0] # Theta - Image_prop[int(ind), 3] = c[0] # Omega - - print 'Check that the number of (phi, chi) steps is', num_int - - np.save(self.directory + '/alpha.npy', self.alpha) - np.save(self.directory + '/beta.npy', self.beta) - np.save(self.directory + '/theta.npy', self.theta) + a = np.where(self.mufake == self.muindex[ind]) # mu + b = np.where(self.gamma == self.gammaindex[ind]) # roll + c = np.where(self.omega == self.meta[ind, 2]) # omega + # d = np.where(self.mu == met[ind, 4]) + # print a, b, c + if a == [0] and b == [1] and c == [10]: + print ind, self.data_files[ind] + + bigarray[a, b, c, :, :] = imgarray[ind, :, :] + + ### Make background subtraction + # np.save(self.directory + '/alpha.npy', self.alpha) + # np.save(self.directory + '/beta.npy', self.beta) + np.save(self.directory + '/gamma.npy', self.gamma) + np.save(self.directory + '/mu.npy', self.mufake + data.theta0) np.save(self.directory + '/omega.npy', self.omega) - np.save(self.directory + '/gamma.npy', [((b - ((num_int - 1)/2))*0.032) for b in range(num_int)]) - # The gamma angle is a linear combination of alpha and beta - np.save(self.directory + '/all_data.npy', self.meta) + np.save(self.directory + '/dataarray.npy', bigarray) - #np.savetxt(self.directory + '/AA.txt', AA) - np.savetxt(self.directory + '/Image_properties.txt', Image_prop, fmt='%i %i %i %i') + if __name__ == "__main__": - if len(sys.argv) != 8: - if len(sys.argv) != 9: - print "Not enough (or too many) input parameters. Data input should be:\n\ + if len(sys.argv) != 9: + if len(sys.argv) != 10: + print "Not enough input parameters. Data input should be:\n\ Directory of data\n\ Name of data files\n\ + Directory of background files\n\ + Name of background files\n\ Point of interest\n\ Image size\n\ Output path\n\ Name of new output directory to make\n\ - Initial phi value\n\ - Initial chi value\n\ " else: mm = makematrix( @@ -176,9 +165,8 @@ def allFiles(self, data, imsiz): sys.argv[5], sys.argv[6], sys.argv[7], - sys.argv[8]) - #sys.argv[9], - #sys.argv[10]) + sys.argv[8], + sys.argv[9]) else: mm = makematrix( sys.argv[1], @@ -188,6 +176,4 @@ def allFiles(self, data, imsiz): sys.argv[5], sys.argv[6], sys.argv[7], - sys.argv[8]) - #sys.argv[10], - #sys.argv[11]) + sys.argv[8],) diff --git a/lib/.DS_Store b/lib/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 6148 zcmeH~Jr2S!425mzP>H1@V-^m;4Wg<&0T*E43hX&L&p$$qDprKhvt+--jT7}7np#A3 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22dd979..0000000 --- a/lib/edfimage.py +++ /dev/null @@ -1,985 +0,0 @@ -# coding: utf-8 -# -# Project: X-ray image reader -# https://github.com/silx-kit/fabio -# -# -# Copyright (C) European Synchrotron Radiation Facility, Grenoble, France -# -# Principal author: Jérôme Kieffer (Jerome.Kieffer@ESRF.eu) -# -# Permission is hereby granted, free of charge, to any person -# obtaining a copy of this software and associated documentation files -# (the "Software"), to deal in the Software without restriction, -# including without limitation the rights to use, copy, modify, merge, -# publish, distribute, sublicense, and/or sell copies of the Software, -# and to permit persons to whom the Software is furnished to do so, -# subject to the following conditions: -# -# The above copyright notice and this permission notice shall be -# included in all copies or substantial portions of the Software. -# -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, -# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES -# OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND -# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT -# HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, -# WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING -# FROM, OUT OF OR IN CONNECTION W - -""" - -License: MIT - -Authors: -........ -* Henning O. Sorensen & Erik Knudsen: - Center for Fundamental Research: Metal Structures in Four Dimensions; - Risoe National Laboratory; - Frederiksborgvej 399; - DK-4000 Roskilde; - email:erik.knudsen@risoe.dk -* Jon Wright & Jérôme Kieffer: - European Synchrotron Radiation Facility; - Grenoble (France) - - -""" -# get ready for python3 -from __future__ import with_statement, print_function, absolute_import, division -import os -import logging -logger = logging.getLogger("edfimage") -import numpy -from .fabioimage import FabioImage, OrderedDict -from .fabioutils import isAscii, toAscii, nice_int -from .compression import decBzip2, decGzip, decZlib - - -BLOCKSIZE = 512 -DATA_TYPES = {"SignedByte": numpy.int8, - "Signed8": numpy.int8, - "UnsignedByte": numpy.uint8, - "Unsigned8": numpy.uint8, - "SignedShort": numpy.int16, - "Signed16": numpy.int16, - "UnsignedShort": numpy.uint16, - "Unsigned16": numpy.uint16, - "UnsignedShortInteger": numpy.uint16, - "SignedInteger": numpy.int32, - "Signed32": numpy.int32, - "UnsignedInteger": numpy.uint32, - "Unsigned32": numpy.uint32, - "SignedLong": numpy.int32, - "UnsignedLong": numpy.uint32, - "Signed64": numpy.int64, - "Unsigned64": numpy.uint64, - "FloatValue": numpy.float32, - "FLOATVALUE": numpy.float32, - "FLOAT": numpy.float32, # fit2d - "Float": numpy.float32, # fit2d - "FloatIEEE32": numpy.float32, - "Float32": numpy.float32, - "Double": numpy.float64, - "DoubleValue": numpy.float64, - "FloatIEEE64": numpy.float64, - "DoubleIEEE64": numpy.float64} -try: - DATA_TYPES["FloatIEEE128"] = DATA_TYPES["DoubleIEEE128"] = DATA_TYPES["QuadrupleValue"] = numpy.float128 - -except AttributeError: - # not in your numpy - logger.debug("No support for float128 in your code") - -NUMPY_EDF_DTYPE = {"int8": "SignedByte", - "int16": "SignedShort", - "int32": "SignedInteger", - "int64": "Signed64", - "uint8": "UnsignedByte", - "uint16": "UnsignedShort", - "uint32": "UnsignedInteger", - "uint64": "Unsigned64", - "float32": "FloatValue", - "float64": "DoubleValue", - "float128": "QuadrupleValue", - } - -MINIMUM_KEYS = ['HEADERID', - 'IMAGE', - 'BYTEORDER', - 'DATATYPE', - 'DIM_1', - 'DIM_2', - 'SIZE'] # Size is thought to be essential for writing at least - -DEFAULT_VALUES = { - # I do not define default values as they will be calculated at write time - # JK20110415 - } - - -class Frame(object): - """ - A class representing a single frame in an EDF file - """ - def __init__(self, data=None, header=None, number=None): - - self.header = EdfImage.check_header(header) - - self.capsHeader = {} - for key in self.header: - self.capsHeader[key.upper()] = key - self._data = data - self.dims = [] - self.dim1 = 0 - self.dim2 = 0 - self.start = None # Position of start of raw data in file - self.size = None # size of raw data in file - self.file = None # opened file object with locking capabilities !!! - self.bpp = None - self._bytecode = None - if (number is not None): - self.iFrame = int(number) - else: - self.iFrame = 0 - - def parseheader(self, block): - """ - Parse the header in some EDF format from an already open file - - @param block: string representing the header block. - @type block: string, should be full ascii - @return: size of the binary blob - """ - # reset values ... - self.header = OrderedDict() - self.capsHeader = {} - self.size = None - calcsize = 1 - self.dims = [] - - for line in block.split(';'): - if '=' in line: - key, val = line.split('=', 1) - # Why would someone put null bytes in a header? - key = key.replace("\x00", " ").strip() - self.header[key] = val.replace("\x00", " ").strip() - self.capsHeader[key.upper()] = key - - # Compute image size - if "SIZE" in self.capsHeader: - try: - self.size = nice_int(self.header[self.capsHeader["SIZE"]]) - except ValueError: - logger.warning("Unable to convert to integer : %s %s " % (self.capsHeader["SIZE"], self.header[self.capsHeader["SIZE"]])) - if "DIM_1" in self.capsHeader: - try: - dim1 = nice_int(self.header[self.capsHeader['DIM_1']]) - except ValueError: - logger.error("Unable to convert to integer Dim_1: %s %s" % (self.capsHeader["DIM_1"], self.header[self.capsHeader["DIM_1"]])) - else: - calcsize *= dim1 - self.dims.append(dim1) - else: - logger.error("No Dim_1 in headers !!!") - if "DIM_2" in self.capsHeader: - try: - dim2 = nice_int(self.header[self.capsHeader['DIM_2']]) - except ValueError: - logger.error("Unable to convert to integer Dim_3: %s %s" % (self.capsHeader["DIM_2"], self.header[self.capsHeader["DIM_2"]])) - else: - calcsize *= dim2 - self.dims.append(dim2) - else: - logger.error("No Dim_2 in headers !!!") - iDim = 3 - # JON: this appears to be for nD images, but we don't treat those - while iDim is not None: - strDim = "DIM_%i" % iDim - if strDim in self.capsHeader: - try: - dim3 = nice_int(self.header[self.capsHeader[strDim]]) - except ValueError: - logger.error("Unable to convert to integer %s: %s %s", - strDim, self.capsHeader[strDim], self.header[self.capsHeader[strDim]]) - dim3 = None - iDim = None - else: - if dim3 > 1: - # Otherwise treat dim3==1 as a 2D image - calcsize *= dim3 - self.dims.append(dim3) - iDim += 1 - - else: - logger.debug("No Dim_3 -> it is a 2D image") - iDim = None - if self._bytecode is None: - if "DATATYPE" in self.capsHeader: - self._bytecode = DATA_TYPES[self.header[self.capsHeader['DATATYPE']]] - else: - self._bytecode = numpy.uint16 - logger.warning("Defaulting type to uint16") - self.bpp = len(numpy.array(0, self._bytecode).tostring()) - calcsize *= self.bpp - if (self.size is None): - self.size = calcsize - elif (self.size != calcsize): - if ("COMPRESSION" in self.capsHeader) and (self.header[self.capsHeader['COMPRESSION']].upper().startswith("NO")): - logger.info("Mismatch between the expected size %s and the calculated one %s" % (self.size, calcsize)) - self.size = calcsize - - for i, n in enumerate(self.dims): - setattr(self, "dim%i" % (i + 1), n) - - return self.size - - def swap_needed(self): - """ - Decide if we need to byteswap - """ - if ('Low' in self.header[self.capsHeader['BYTEORDER']] and numpy.little_endian) or \ - ('High' in self.header[self.capsHeader['BYTEORDER']] and not numpy.little_endian): - return False - if ('High' in self.header[self.capsHeader['BYTEORDER']] and numpy.little_endian) or \ - ('Low' in self.header[self.capsHeader['BYTEORDER']] and not numpy.little_endian): - if self.bpp in [2, 4, 8]: - return True - else: - return False - - def getData(self): - """ - Unpack a binary blob according to the specification given in the header - - @return: dataset as numpy.ndarray - """ - data = None - if self._data is not None: - data = self._data - elif self.file is None: - data = self._data - else: - if self._bytecode is None: - if "DATATYPE" in self.capsHeader: - self._bytecode = DATA_TYPES[self.header[self.capsHeader["DATATYPE"]]] - else: - self._bytecode = numpy.uint16 - dims = self.dims[:] - dims.reverse() - with self.file.lock: - if self.file.closed: - logger.error("file: %s from %s is closed. Cannot read data." % (self.file, self.file.filename)) - return - else: - self.file.seek(self.start) - fileData = self.file.read(self.size) - - if ("COMPRESSION" in self.capsHeader): - compression = self.header[self.capsHeader["COMPRESSION"]].upper() - uncompressed_size = self.bpp - for i in dims: - uncompressed_size *= i - if "OFFSET" in compression: - try: - import byte_offset # IGNORE:F0401 - except ImportError as error: - logger.error("Unimplemented compression scheme: %s (%s)" % (compression, error)) - else: - myData = byte_offset.analyseCython(fileData, size=uncompressed_size) - rawData = myData.astype(self._bytecode).tostring() - self.size = uncompressed_size - elif compression == "NONE": - rawData = fileData - elif "GZIP" in compression: - rawData = decGzip(fileData) - self.size = uncompressed_size - elif "BZ" in compression: - rawData = decBzip2(fileData) - self.size = uncompressed_size - elif "Z" in compression: - rawData = decZlib(fileData) - self.size = uncompressed_size - else: - logger.warning("Unknown compression scheme %s" % compression) - rawData = fileData - - else: - rawData = fileData - - expected = self.size - obtained = len(rawData) - if expected > obtained: - logger.error("Data stream is incomplete: %s < expected %s bytes" % (obtained, expected)) - rawData += "\x00".encode("ascii") * (expected - obtained) - elif expected < len(rawData): - logger.info("Data stream contains trailing junk : %s > expected %s bytes" % (obtained, expected)) - rawData = rawData[:expected] - data = numpy.fromstring(rawData, self._bytecode).reshape(tuple(dims)) - if self.swap_needed(): - data.byteswap(True) - self._data = data - self._bytecode = data.dtype.type - return data - - def setData(self, npa=None): - """Setter for data in edf frame""" - self._data = npa - data = property(getData, setData, "property: (edf)frame.data, uncompress the datablock when needed") - - def getByteCode(self): - if self._bytecode is None: - self._bytecode = self.data.dtype.type - return self._bytecode - - def setByteCode(self, _iVal): - self._bytecode = _iVal - - bytecode = property(getByteCode, setByteCode) - - def getEdfBlock(self, force_type=None, fit2dMode=False): - """ - @param force_type: type of the dataset to be enforced like "float64" or "uint16" - @type force_type: string or numpy.dtype - @param fit2dMode: enforce compatibility with fit2d and starts counting number of images at 1 - @type fit2dMode: boolean - @return: ascii header block + binary data block - @rtype: python bytes with the concatenation of the ascii header and the binary data block - """ - if force_type is not None: - data = self.data.astype(force_type) - else: - data = self.data - fit2dMode = bool(fit2dMode) - for key in self.header: - KEY = key.upper() - if KEY not in self.capsHeader: - self.capsHeader[KEY] = key - - header = self.header.copy() - header_keys = list(self.header.keys()) - capsHeader = self.capsHeader.copy() - - listHeader = ["{\n"] -# First of all clean up the headers: - for i in capsHeader: - if "DIM_" in i: - header.pop(capsHeader[i]) - header_keys.remove(capsHeader[i]) - for KEY in ["SIZE", "EDF_BINARYSIZE", "EDF_HEADERSIZE", "BYTEORDER", "DATATYPE", "HEADERID", "IMAGE"]: - if KEY in capsHeader: - header.pop(capsHeader[KEY]) - header_keys.remove(capsHeader[KEY]) - if "EDF_DATABLOCKID" in capsHeader: - header_keys.remove(capsHeader["EDF_DATABLOCKID"]) - # but do not remove the value from dict, instead reset the key ... - if capsHeader["EDF_DATABLOCKID"] != "EDF_DataBlockID": - header["EDF_DataBlockID"] = header.pop(capsHeader["EDF_DATABLOCKID"]) - capsHeader["EDF_DATABLOCKID"] = "EDF_DataBlockID" - -# Then update static headers freshly deleted - header_keys.insert(0, "Size") - header["Size"] = len(data.tostring()) - header_keys.insert(0, "HeaderID") - header["HeaderID"] = "EH:%06d:000000:000000" % (self.iFrame + fit2dMode) - header_keys.insert(0, "Image") - header["Image"] = str(self.iFrame + fit2dMode) - - dims = list(data.shape) - nbdim = len(dims) - for i in dims: - key = "Dim_%i" % nbdim - header[key] = i - header_keys.insert(0, key) - nbdim -= 1 - header_keys.insert(0, "DataType") - header["DataType"] = NUMPY_EDF_DTYPE[str(numpy.dtype(data.dtype))] - header_keys.insert(0, "ByteOrder") - if numpy.little_endian: - header["ByteOrder"] = "LowByteFirst" - else: - header["ByteOrder"] = "HighByteFirst" - approxHeaderSize = 100 - for key in header: - approxHeaderSize += 7 + len(key) + len(str(header[key])) - approxHeaderSize = BLOCKSIZE * (approxHeaderSize // BLOCKSIZE + 1) - header_keys.insert(0, "EDF_HeaderSize") - header["EDF_HeaderSize"] = "%5s" % (approxHeaderSize) - header_keys.insert(0, "EDF_BinarySize") - header["EDF_BinarySize"] = data.nbytes - header_keys.insert(0, "EDF_DataBlockID") - if "EDF_DataBlockID" not in header: - header["EDF_DataBlockID"] = "%i.Image.Psd" % (self.iFrame + fit2dMode) - preciseSize = 4 # 2 before {\n 2 after }\n - for key in header_keys: - # Escape keys or values that are no ascii - strKey = str(key) - if not isAscii(strKey, listExcluded=["}", "{"]): - logger.warning("Non ascii key %s, skipping" % strKey) - continue - strValue = str(header[key]) - if not isAscii(strValue, listExcluded=["}", "{"]): - logger.warning("Non ascii value %s, skipping" % strValue) - continue - line = strKey + " = " + strValue + " ;\n" - preciseSize += len(line) - listHeader.append(line) - if preciseSize > approxHeaderSize: - logger.error("I expected the header block only at %s in fact it is %s" % (approxHeaderSize, preciseSize)) - for idx, line in enumerate(listHeader[:]): - if line.startswith("EDF_HeaderSize"): - headerSize = BLOCKSIZE * (preciseSize // BLOCKSIZE + 1) - newline = "EDF_HeaderSize = %5s ;\n" % headerSize - delta = len(newline) - len(line) - if (preciseSize // BLOCKSIZE) != ((preciseSize + delta) // BLOCKSIZE): - headerSize = BLOCKSIZE * ((preciseSize + delta) // BLOCKSIZE + 1) - newline = "EDF_HeaderSize = %5s ;\n" % headerSize - preciseSize = preciseSize + delta - listHeader[idx] = newline - break - else: - headerSize = approxHeaderSize - listHeader.append(" " * (headerSize - preciseSize) + "}\n") - return ("".join(listHeader)).encode("ASCII") + data.tostring() - - -class EdfImage(FabioImage): - """ Read and try to write the ESRF edf data format """ - - DESCRIPTION = "European Synchrotron Radiation Facility data format" - - DEFAULT_EXTENTIONS = ["edf", "cor"] - - def __init__(self, data=None, header=None, frames=None): - self.currentframe = 0 - self.filesize = None - - if data is None: - # In case of creation of an empty instance - stored_data = None - else: - try: - dim = len(data.shape) - except Exception as error: # IGNORE:W0703 - logger.debug("Data don't look like a numpy array (%s), resetting all!!" % error) - dim = 0 - - if dim == 0: - raise Exception("Data with empty shape is unsupported") - elif dim == 1: - logger.warning("Data in 1d dimension will be stored as a 2d dimension array") - # make sure we do not change the shape of the input data - stored_data = numpy.array(data, copy=False) - stored_data.shape = (1, len(data)) - elif dim == 2: - stored_data = data - elif dim >= 3: - raise Exception("Data dimension too big. Only 1d or 2d arrays are supported.") - - FabioImage.__init__(self, stored_data, header) - - if frames is None: - frame = Frame(data=self.data, header=self.header, - number=self.currentframe) - self._frames = [frame] - else: - self._frames = frames - - @staticmethod - def check_header(header=None): - """ - Empty for FabioImage but may be populated by others classes - """ - if not isinstance(header, dict): - return OrderedDict() - new = OrderedDict() - for key, value in header.items(): - new[toAscii(key, ";{}")] = toAscii(value, ";{}") - return new - - @staticmethod - def _readHeaderBlock(infile): - """ - Read in a header in some EDF format from an already open file - - @param infile: file object open in read mode - @return: string (or None if no header was found. - """ - MAX_HEADER_SIZE = BLOCKSIZE * 20 - block = infile.read(BLOCKSIZE) - if len(block) < BLOCKSIZE: - logger.debug("Under-short header: only %i bytes in %s" % (len(block), infile.name)) - return - if (block.find(b"{") < 0): - # This does not look like an edf file - logger.warning("no opening {. Corrupt header of EDF file %s" % infile.name) - return - if b"EDF_HeaderSize" in block: - start = block.index(b"EDF_HeaderSize") - chunk = block[start:].split(b"=")[1].strip() - try: - new_max_header_size = int(chunk.split(b";")[0].strip()) - except Exception: - logger.warning("Unable to read header size, got: %s" % chunk) - else: - if new_max_header_size > MAX_HEADER_SIZE: - logger.info("Redefining MAX_HEADER_SIZE to %s" % new_max_header_size) - MAX_HEADER_SIZE = new_max_header_size - while (b'}\r' not in block) and (b'}\n' not in block): - block = block + infile.read(BLOCKSIZE) - if len(block) > MAX_HEADER_SIZE: - logger.warning("Runaway header in EDF file MAX_HEADER_SIZE: %s \n%s" % (MAX_HEADER_SIZE, block)) - return - start = block.find(b"{") + 1 - end = block.find(b"}") - - # Now it is essential to go to the start of the binary part - if block[end: end + 3] == b"}\r\n": - offset = end + 3 - len(block) - elif block[end: end + 2] == b"}\n": - offset = end + 2 - len(block) - else: - logger.error("Unable to locate start of the binary section") - offset = None - if offset is not None: - infile.seek(offset, os.SEEK_CUR) - return block[start:end].decode("ASCII") - - def _readheader(self, infile): - """ - Read all headers in a file and populate self.header - data is not yet populated - @type infile: file object open in read mode - """ - self._frames = [] - bContinue = True - attrs = dir(infile) - if "measure_size" in attrs: - # Handle bug #18 (https://github.com/silx-kit/fabio/issues/18) - stream_size = infile.measure_size() - elif "size" in attrs: - stream_size = infile.size - elif "len" in attrs: - stream_size = infile.len - else: - # Poor-men's size measurement - pos = infile.tell() - self.seek(0, os.SEEK_END) - stream_size = infile.tell() - infile.seek(pos) - - while bContinue: - block = self._readHeaderBlock(infile) - if block is None: - bContinue = False - break - frame = Frame(number=self.nframes) - size = frame.parseheader(block) - frame.file = infile - frame.start = infile.tell() - frame.size = size - self._frames += [frame] - try: - infile.seek(size, os.SEEK_CUR) - except Exception as error: - logger.warning("infile is %s" % infile) - logger.warning("Position is %s" % infile.tell()) - logger.warning("size is %s" % size) - logger.error("It seams this error occurs under windows when reading a (large-) file over network: %s ", error) - raise Exception(error) - - if frame.start + size > stream_size: - logger.warning("Non complete datablock: got %s, expected %s" % (stream_size - frame.start, size)) - bContinue = False - break - - for i, frame in enumerate(self._frames): - missing = [] - for item in MINIMUM_KEYS: - if item not in frame.capsHeader: - missing.append(item) - if len(missing) > 0: - logger.info("EDF file %s frame %i misses mandatory keys: %s " % (self.filename, i, " ".join(missing))) - self.currentframe = 0 - - def read(self, fname, frame=None): - """ - Read in header into self.header and - the data into self.data - """ - self.resetvals() - self.filename = fname - - infile = self._open(fname, "rb") - self._readheader(infile) - if frame is None: - pass - elif frame < self.nframes: - self = self.getframe(frame) - else: - logger.error("Reading file %s You requested frame %s but only %s frames are available", fname, frame, self.nframes) - self.resetvals() - # ensure the PIL image is reset - self.pilimage = None - return self - - def swap_needed(self): - """ - Decide if we need to byteswap - - @return True if needed, False else and None if not understood - """ - if self.bpp == 1: - return False - if ('Low' in self.header[self.capsHeader['BYTEORDER']] and numpy.little_endian) or \ - ('High' in self.header[self.capsHeader['BYTEORDER']] and not numpy.little_endian): - return False - if ('High' in self.header[self.capsHeader['BYTEORDER']] and numpy.little_endian) or \ - ('Low' in self.header[self.capsHeader['BYTEORDER']] and not numpy.little_endian): - return True - - def unpack(self): - """ - Unpack a binary blob according to the specification given in the header and return the dataset - - @return: dataset as numpy.ndarray - """ - return self._frames[self.currentframe].getData() - - def getframe(self, num): - """ returns the file numbered 'num' in the series as a FabioImage """ - newImage = None - if self.nframes == 1: - logger.debug("Single frame EDF; having FabioImage default behavior: %s" % num) - newImage = FabioImage.getframe(self, num) - newImage._file = self._file - elif num < self.nframes: - logger.debug("Multi frame EDF; having EdfImage specific behavior: %s/%s" % (num, self.nframes)) - newImage = EdfImage(frames=self._frames) - newImage.currentframe = num - newImage.filename = self.filename - newImage._file = self._file - else: - raise IOError("EdfImage.getframe: Cannot access frame: %s/%s" % - (num, self.nframes)) - return newImage - - def previous(self): - """ returns the previous file in the series as a FabioImage """ - newImage = None - if self.nframes == 1: - newImage = FabioImage.previous(self) - else: - newFrameId = self.currentframe - 1 - newImage = self.getframe(newFrameId) - return newImage - - def next(self): - """Returns the next file in the series as a fabioimage - - @raise IOError: When there is no next file or image in the series. - """ - newImage = None - if self.nframes == 1: - newImage = FabioImage.next(self) - else: - newFrameId = self.currentframe + 1 - newImage = self.getframe(newFrameId) - return newImage - - def write(self, fname, force_type=None, fit2dMode=False): - """ - Try to write a file - check we can write zipped also - mimics that fabian was writing uint16 (we sometimes want floats) - - @param force_type: can be numpy.uint16 or simply "float" - @return: None - - """ - # correct for bug #27: read all data before opening the file in write mode - if fname == self.filename: - [(frame.header, frame.data) for frame in self._frames] - # this is thrown away - with self._open(fname, mode="wb") as outfile: - for i, frame in enumerate(self._frames): - frame.iFrame = i - outfile.write(frame.getEdfBlock(force_type=force_type, fit2dMode=fit2dMode)) - - def appendFrame(self, frame=None, data=None, header=None): - """ - Method used add a frame to an EDF file - @param frame: frame to append to edf image - @type frame: instance of Frame - @return: None - """ - if isinstance(frame, Frame): - self._frames.append(frame) - elif ("header" in dir(frame)) and ("data" in dir(frame)): - self._frames.append(Frame(frame.data, frame.header)) - else: - self._frames.append(Frame(data, header)) - - def deleteFrame(self, frameNb=None): - """ - Method used to remove a frame from an EDF image. by default the last one is removed. - @param frameNb: frame number to remove, by default the last. - @type frameNb: integer - @return: None - """ - if frameNb is None: - self._frames.pop() - else: - self._frames.pop(frameNb) - - def fastReadData(self, filename=None): - """ - This is a special method that will read and return the data from another file ... - The aim is performances, ... but only supports uncompressed files. - - @return: data from another file using positions from current EdfImage - """ - if (filename is None) or not os.path.isfile(filename): - raise RuntimeError("EdfImage.fastReadData is only valid with another file: %s does not exist" % (filename)) - data = None - frame = self._frames[self.currentframe] - with open(filename, "rb")as f: - f.seek(frame.start) - raw = f.read(frame.size) - try: - data = numpy.fromstring(raw, dtype=self.bytecode) - data.shape = self.data.shape - except Exception as error: - logger.error("unable to convert file content to numpy array: %s", error) - if self.swap_needed(): - data.byteswap(True) - return data - - def fastReadROI(self, filename, coords=None): - """ - Method reading Region of Interest of another file based on metadata available in current EdfImage. - The aim is performances, ... but only supports uncompressed files. - - @return: ROI-data from another file using positions from current EdfImage - @rtype: numpy 2darray - """ - if (filename is None) or not os.path.isfile(filename): - raise RuntimeError("EdfImage.fastReadData is only valid with another file: %s does not exist" % (filename)) - data = None - frame = self._frames[self.currentframe] - - if len(coords) == 4: - slice1 = self.make_slice(coords) - elif (len(coords) == 2 and isinstance(coords[0], slice) and - isinstance(coords[1], slice)): - slice1 = coords - else: - logger.warning('readROI: Unable to understand Region Of Interest: got %s', coords) - return - d1 = self.data.shape[-1] - start0 = slice1[0].start - start1 = slice1[1].start - slice2 = (slice(0, slice1[0].stop - start0, slice1[0].step), - slice(0, slice1[1].stop - start1, slice1[1].step)) - start = frame.start + self.bpp * (d1 * start0 + start1) - size = self.bpp * ((slice2[0].stop) * d1) - with open(filename, "rb")as f: - f.seek(start) - raw = f.read(size) - try: - data = numpy.fromstring(raw, dtype=self.bytecode) - data.shape = -1, d1 - except Exception as error: - logger.error("unable to convert file content to numpy array: %s", error) - if self.swap_needed(): - data.byteswap(True) - return data[slice2] - -################################################################################ -# Properties definition for header, data, header_keys and capsHeader -################################################################################ - def getNbFrames(self): - """ - Getter for number of frames - """ - return len(self._frames) - - def setNbFrames(self, val): - """ - Setter for number of frames ... should do nothing. Here just to avoid bugs - """ - if val != len(self._frames): - logger.warning("trying to set the number of frames ") - nframes = property(getNbFrames, setNbFrames, "property: number of frames in EDF file") - - def getHeader(self): - """ - Getter for the headers. used by the property header, - """ - return self._frames[self.currentframe].header - - def setHeader(self, _dictHeader): - """ - Enforces the propagation of the header to the list of frames - """ - try: - self._frames[self.currentframe].header = _dictHeader - except AttributeError: - self._frames = [Frame(header=_dictHeader)] - except IndexError: - if self.currentframe < len(self._frames): - self._frames.append(Frame(header=_dictHeader)) - - def delHeader(self): - """ - Deleter for edf header - """ - self._frames[self.currentframe].header = {} - header = property(getHeader, setHeader, delHeader, "property: header of EDF file") - -# def getHeaderKeys(self): -# """ -# Getter for edf header_keys -# """ -# return self._frames[self.currentframe].header_keys -# def setHeaderKeys(self, _listtHeader): -# """ -# Enforces the propagation of the header_keys to the list of frames -# @param _listtHeader: list of the (ordered) keys in the header -# @type _listtHeader: python list -# """ -# try: -# self._frames[self.currentframe].header_keys = _listtHeader -# except AttributeError: -# self._frames = [Frame(header_keys=_listtHeader)] -# except IndexError: -# if self.currentframe < len(self._frames): -# self._frames.append(Frame(header_keys=_listtHeader)) -# def delHeaderKeys(self): -# """ -# Deleter for edf header_keys -# """ -# self._frames[self.currentframe].header_keys = [] -# header_keys = property(getHeaderKeys, setHeaderKeys, delHeaderKeys, "property: header_keys of EDF file") - - def getData(self): - """ - getter for edf Data - @return: data for current frame - @rtype: numpy.ndarray - """ - npaData = None - try: - npaData = self._frames[self.currentframe].data - except AttributeError: - self._frames = [Frame()] - npaData = self._frames[self.currentframe].data - except IndexError: - if self.currentframe < len(self._frames): - self._frames.append(Frame()) - npaData = self._frames[self.currentframe].data - return npaData - - def setData(self, _data): - """ - Enforces the propagation of the data to the list of frames - @param _data: numpy array representing data - """ - try: - self._frames[self.currentframe].data = _data - except AttributeError: - self._frames = [Frame(data=_data)] - except IndexError: - if self.currentframe < len(self._frames): - self._frames.append(Frame(data=_data)) - - def delData(self): - """ - deleter for edf Data - """ - self._frames[self.currentframe].data = None - data = property(getData, setData, delData, "property: data of EDF file") - - def getCapsHeader(self): - """ - getter for edf headers keys in upper case - @return: data for current frame - @rtype: dict - """ - return self._frames[self.currentframe].capsHeader - - def setCapsHeader(self, _data): - """ - Enforces the propagation of the header_keys to the list of frames - @param _data: numpy array representing data - """ - self._frames[self.currentframe].capsHeader = _data - - def delCapsHeader(self): - """ - deleter for edf capsHeader - """ - self._frames[self.currentframe].capsHeader = {} - capsHeader = property(getCapsHeader, setCapsHeader, delCapsHeader, "property: capsHeader of EDF file, i.e. the keys of the header in UPPER case.") - - def getDim1(self): - return self._frames[self.currentframe].dim1 - - def setDim1(self, _iVal): - try: - self._frames[self.currentframe].dim1 = _iVal - except AttributeError: - self._frames = [Frame()] - except IndexError: - if self.currentframe < len(self._frames): - self._frames.append(Frame()) - self._frames[self.currentframe].dim1 = _iVal - dim1 = property(getDim1, setDim1) - - def getDim2(self): - return self._frames[self.currentframe].dim2 - - def setDim2(self, _iVal): - try: - self._frames[self.currentframe].dim2 = _iVal - except AttributeError: - self._frames = [Frame()] - except IndexError: - if self.currentframe < len(self._frames): - self._frames.append(Frame()) - self._frames[self.currentframe].dim2 = _iVal - dim2 = property(getDim2, setDim2) - - def getDims(self): - return self._frames[self.currentframe].dims - dims = property(getDims) - - def getByteCode(self): - return self._frames[self.currentframe].bytecode - - def setByteCode(self, _iVal): - try: - self._frames[self.currentframe].bytecode = _iVal - except AttributeError: - self._frames = [Frame()] - except IndexError: - if self.currentframe < len(self._frames): - self._frames.append(Frame()) - self._frames[self.currentframe].bytecode = _iVal - bytecode = property(getByteCode, setByteCode) - - def getBpp(self): - return self._frames[self.currentframe].bpp - - def setBpp(self, _iVal): - try: - self._frames[self.currentframe].bpp = _iVal - except AttributeError: - self._frames = [Frame()] - except IndexError: - if self.currentframe < len(self._frames): - self._frames.append(Frame()) - self._frames[self.currentframe].bpp = _iVal - bpp = property(getBpp, setBpp) - -edfimage = EdfImage diff --git a/lib/miniged.py b/lib/miniged.py old mode 100644 new mode 100755 index ecda786..19e48a5 --- a/lib/miniged.py +++ b/lib/miniged.py @@ -31,7 +31,6 @@ import time import sys import scipy -import matplotlib.pyplot as plt from os import listdir from os.path import isfile, join @@ -81,7 +80,7 @@ def __init__( self.sampletitle = filename self.path = path - #self.bg_path = bg_path + self.bg_path = bg_path self.roi = roi self.simulated = simulated @@ -156,22 +155,6 @@ def getBGarray(self, bg_filename): self.bg_combined_full /= len(self.bg_files) - bckg = self.bg_combined - bckg_entire = self.bg_combined_full - - # Check background images - #fig = plt.figure() - #a=fig.add_subplot(1,2,1) - #plt.imshow(bckg) - #a.set_title('Background ROI') - #b=fig.add_subplot(1,2,2) - #plt.imshow(bckg_entire) - #b.set_title('All background') - #plt.show() - np.save('bckg_roi.npy', bckg) - np.save('bckg_all.npy', bckg_entire) - #plt.savefig('Background_check.png') - def getIndexList(self): file_with_path = self.path + '/' + self.data_files[0] img = EdfFile.EdfFile(file_with_path) @@ -351,13 +334,11 @@ def getImage(self, index, full): roi = self.roi if full: - im = (img.GetData(0).astype(np.int64)) #/ self.bg_combined_full - #im = (img.GetData(0).astype(np.int64)*300) / self.bg_combined_full + im = img.GetData(0).astype(np.int64) - self.bg_combined_full else: - #im = (img.GetData(0).astype(np.int64)*300)[ - im = (img.GetData(0).astype(np.int64))[ + im = img.GetData(0).astype(np.int64)[ roi[2]:roi[3], - roi[0]:roi[1]] #/ self.bg_combined + roi[0]:roi[1]] - self.bg_combined im = self.cleanImage(im) @@ -432,8 +413,8 @@ def makeImgArray(self, index, xpos, savefilename): def addToArray(index_part): if self.rank == 0: print "Loading array from data files..." - point = len(index_part)# / 100 - incr = len(index_part)# / 100 + point = len(index_part) / 100 + incr = len(index_part) / 100 imgarray_part = np.zeros((len(index_part), len(img[:, 0]), len(img[0, :]))) for i in range(len(index_part)): if self.rank == 0 and i % (5 * point) == 0: @@ -445,15 +426,15 @@ def addToArray(index_part): sys.stdout.flush() img0 = self.getImage(index_part[i], False) - imgsum = np.sum(img0, 1) / len(img0[0, :]) + # imgsum = np.sum(img0, 1) / len(img0[0, :]) # Adjusting for gradient in image. - ran = np.array(range(len(imgsum))) - popt, pcov = self.fitLine(ran, imgsum) - fittedline = ran * popt[0] + popt[1] - fittedline = fittedline - fittedline[len(fittedline) / 2] - gradient = np.tile(fittedline, (len(img0[0, :]), 1)).transpose() - imgarray_part[i, :, :] = img0 - gradient + # ran = np.array(range(len(imgsum))) + # popt, pcov = self.fitLine(ran, imgsum) + # fittedline = ran * popt[0] + popt[1] + # fittedline = fittedline - fittedline[len(fittedline) / 2] + # gradient = np.tile(fittedline, (len(img0[0, :]), 1)).transpose() + imgarray_part[i, :, :] = img0 # - 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1 + dety_f[dety_f < 0] = 0 + dety_f[dety_f >= dety_size] = dety_size - 1 detz_f[detz_f < 0] = 0 detz_f[detz_f >= detz_size] = detz_size - 1 - #prop = self.fullarray[:, :, range(lenf), detx_f, detz_f] - prop = self.fullarray[:, :, range(len_a3), detx_f, detz_f] + # Get the mosaicity maps for the given detector positions. + prop = self.fullarray[:, :, range(lenf), dety_f, detz_f] + + # Sum all mosaicity maps along the omega dimension, resulting in a single mosaicity map. + # Make a center of mass calculation of that map. + com = list(ndimage.measurements.center_of_mass(np.sum(prop, 2))) - cos = list(ndimage.measurements.center_of_mass(np.sum(prop, 2))) + # mosaicitymap[ix, iy, iz, :, :] = np.sum(prop, 4) - # print np.rint(cos[0]), np.rint(cos[1]) + # Try to make a weight of the given voxel, i.e. a value of the + # likelihood that this voxel is inside the grain. try: grain_ang[ix, iy, iz, 2] = np.sum(prop) / len(np.where(prop != 0)) # grain_ang[ix, iy, iz, 2] = np.sum( - # prop, 2)[np.rint(cos[0]), np.rint(cos[1])] / np.sum(prop) + # prop, 2)[np.rint(com[0]), np.rint(com[1])] / np.sum(prop) except IndexError: pass - cos[0] = cos[0] * (ma2 - mi2) / len_a2 - cos[1] = cos[1] * (ma1 - mi1) / len_a1 + # Translate coordinates into mu and gamma angles. + mu = com[0] * (mas - mis) / lens + mis + gamma = com[1] * (mam - mim) / lenm + mim - grain_ang[ix, iy, iz, :2] = cos + grain_ang[ix, iy, iz, 0] = mu + grain_ang[ix, iy, iz, 1] = gamma if self.rank == 0: t_8 = time.clock() @@ -208,6 +210,54 @@ def reconstruct_part(self, ista, isto): def build_rotation_lookup_general(self): """ + + """ + + mu0 = (max(self.mu) - min(self.mu)) / 2 + mu = np.pi * (self.mu - mu0 - self.theta) / 180. + gam = np.pi * self.gamma / 180. + om = np.pi * self.omega / 180. + + mu_mat, gam_mat, om_mat = np.meshgrid(mu, gam, om, indexing='ij') + + Gamma = np.zeros((len(mu), len(gam), len(om), 3, 3)) + Mu = np.zeros((len(mu), len(gam), len(om), 3, 3)) + Omega = np.zeros((len(mu), len(gam), len(om), 3, 3)) + Ryz = np.zeros((len(mu), len(gam), len(om), 3, 3)) + + Omega[:, :, :, 0, 0] = np.cos(om_mat) + Omega[:, :, :, 1, 1] = 1. + Omega[:, :, :, 0, 2] = np.sin(om_mat) + Omega[:, :, :, 2, 0] = -np.sin(om_mat) + Omega[:, :, :, 2, 2] = np.cos(om_mat) + + Gamma[:, :, :, 0, 0] = 1. + Gamma[:, :, :, 1, 1] = np.cos(gam_mat) + Gamma[:, :, :, 1, 2] = -np.sin(gam_mat) + Gamma[:, :, :, 2, 1] = np.sin(gam_mat) + Gamma[:, :, :, 2, 2] = np.cos(gam_mat) + + Mu[:, :, :, 0, 0] = np.cos(mu_mat) + Mu[:, :, :, 1, 1] = np.cos(mu_mat) + Mu[:, :, :, 0, 1] = -np.sin(mu_mat) + Mu[:, :, :, 1, 0] = np.sin(mu_mat) + Mu[:, :, :, 2, 2] = 1. + + Ryz[:, :, :, 1, 1] = -1. + Ryz[:, :, :, 2, 2] = -1. + + # if self.par['mode'] == "horizontal": + # pass + # elif self.par['mode'] == "vertical": + # pass + # else: + # print "ERROR: scattering geometry not defined" + + T_s2d = self.par['M'] * np.matmul(Ryz, np.matmul(Mu, np.matmul(Gamma, Omega))) + return T_s2d + + def build_rotation_lookup_general_old(self): + """ Set up the rotation_lookup[theta,omega,phi_lo,phi_up] lookup table of rotation matrices for each value in the theta, omega, phi_lo and phi_up arrays. @@ -222,14 +272,12 @@ def build_rotation_lookup_general(self): xyz_up, xyz_lo, xyz_th should be the coordinates (in microns) of the focus point on the rotation axis, eg yxz_up=[-40,0,0] in horizontal geometry. """ - - #up = np.pi * self.alpha / 180. - #lo = np.pi * self.beta / 180. + up = np.pi * self.alpha / 180. + lo = np.pi * self.beta / 180. om = np.pi * self.omega / 180. - th = np.pi * self.theta / 180. - ga = np.pi * self.gamma / 180. + # th = np.pi * self.par['theta'] / 180. + th = np.pi * np.array([0]) / 180. - # Include the axis tilting try: t_xx = np.pi * self.par['t_x'] / 180. t_yy = np.pi * self.par['t_y'] / 180. @@ -240,169 +288,144 @@ def build_rotation_lookup_general(self): self.par['t_x'] = "None" self.par['t_z'] = "None" - th_mat, om_mat, ga_mat = np.meshgrid(th, om, ga, indexing='ij') + th_mat, om_mat, lo_mat, up_mat = np.meshgrid(th, om, lo, up, indexing='ij') - R_ga = np.zeros((len(th), len(om), len(ga), 4, 4)) - Omega = np.zeros((len(th), len(om), len(ga), 4, 4)) - Theta = np.zeros((len(th), len(om), len(ga), 4, 4)) - T_det = np.zeros((len(th), len(om), len(ga), 4, 4)) - T_ga = np.zeros((len(th), len(om), len(ga), 4, 4)) - T_th = np.zeros((len(th), len(om), len(ga), 4, 4)) - Tinv_ga = np.zeros((len(th), len(om), len(ga), 4, 4)) - Tinv_th = np.zeros((len(th), len(om), len(ga), 4, 4)) + R_up = np.zeros((len(th), len(om), len(lo), len(up), 4, 4)) + R_lo = np.zeros((len(th), len(om), len(lo), len(up), 4, 4)) + Omega = np.zeros((len(th), len(om), len(lo), len(up), 4, 4)) + Theta = np.zeros((len(th), len(om), len(lo), len(up), 4, 4)) + T_det = np.zeros((len(th), len(om), len(lo), len(up), 4, 4)) + T_up = np.zeros((len(th), len(om), len(lo), len(up), 4, 4)) + T_lo = np.zeros((len(th), len(om), len(lo), len(up), 4, 4)) + T_th = np.zeros((len(th), len(om), len(lo), len(up), 4, 4)) + Tinv_up = np.zeros((len(th), len(om), len(lo), len(up), 4, 4)) + Tinv_lo = np.zeros((len(th), len(om), len(lo), len(up), 4, 4)) + Tinv_th = np.zeros((len(th), len(om), len(lo), len(up), 4, 4)) # The default detector tilt is the unit matrix, i.e. an ideal detector # positioned perpendicular to the diffracted beam (t_x=t=y=t_z=None). # This can be changed by supplying tilts t_x (vertical) or t_z (horizontal). - T_det[:, :, :, 0, 0] = -1. + T_det[:, :, :, :, 0, 0] = -1. # T_det[:, :, :, :, 1, 1] = 1. #leaving T_det[:, :, :, :, 1, 1]=0 # gives the projection onto the detector plane - T_det[:, :, :, 2, 2] = -1. - T_det[:, :, :, 3, 3] = 1. + T_det[:, :, :, :, 2, 2] = -1. + T_det[:, :, :, :, 3, 3] = 1. # print self.par['xyz_up'] # 4x4 according # to http://inside.mines.edu/fs_home/gmurray/ArbitraryAxisRotation/ - # T_up[:, :, :, :, 0:3, 3] = -np.array(self.par['xyz_up']) - #T_up[:, :, :, :, 0, 0] = 1. - #T_up[:, :, :, :, 1, 1] = 1. - #T_up[:, :, :, :, 2, 2] = 1. - #T_up[:, :, :, :, 3, 3] = 1. - #Tinv_up[:, :, :, :, 0:3, 3] = np.array(self.par['xyz_up']) - #Tinv_up[:, :, :, :, 0, 0] = 1. - #Tinv_up[:, :, :, :, 1, 1] = 1. - #Tinv_up[:, :, :, :, 2, 2] = 1. - #Tinv_up[:, :, :, :, 3, 3] = 1. - #T_lo[:, :, :, :, 0:3, 3] = -np.array(self.par['xyz_lo']) - #T_lo[:, :, :, :, 0, 0] = 1. - #T_lo[:, :, :, :, 1, 1] = 1. - #T_lo[:, :, :, :, 2, 2] = 1. - #T_lo[:, :, :, :, 3, 3] = 1. - #Tinv_lo[:, :, :, :, 0:3, 3] = np.array(self.par['xyz_lo']) - #Tinv_lo[:, :, :, :, 0, 0] = 1. - #Tinv_lo[:, :, :, :, 1, 1] = 1. - #Tinv_lo[:, :, :, :, 2, 2] = 1. - #Tinv_lo[:, :, :, :, 3, 3] = 1. - T_ga[:, :, :, 0:3, 3] = -np.array(self.par['xyz_th']) - T_ga[:, :, :, 0, 0] = 1. - T_ga[:, :, :, 1, 1] = 1. - T_ga[:, :, :, 2, 2] = 1. - T_ga[:, :, :, 3, 3] = 1. - Tinv_ga[:, :, :, 0:3, 3] = np.array(self.par['xyz_th']) - Tinv_ga[:, :, :, 0, 0] = 1. - Tinv_ga[:, :, :, 1, 1] = 1. - Tinv_ga[:, :, :, 2, 2] = 1. - Tinv_ga[:, :, :, 3, 3] = 1. - T_th[:, :, :, 0:3, 3] = -np.array(self.par['xyz_th']) - T_th[:, :, :, 0, 0] = 1. - T_th[:, :, :, 1, 1] = 1. - T_th[:, :, :, 2, 2] = 1. - T_th[:, :, :, 3, 3] = 1. - Tinv_th[:, :, :, 0:3, 3] = np.array(self.par['xyz_th']) - Tinv_th[:, :, :, 0, 0] = 1. - Tinv_th[:, :, :, 1, 1] = 1. - Tinv_th[:, :, :, 2, 2] = 1. - Tinv_th[:, :, :, 3, 3] = 1. + T_up[:, :, :, :, 0:3, 3] = -np.array(self.par['xyz_up']) + T_up[:, :, :, :, 0, 0] = 1. + T_up[:, :, :, :, 1, 1] = 1. + T_up[:, :, :, :, 2, 2] = 1. + T_up[:, :, :, :, 3, 3] = 1. + Tinv_up[:, :, :, :, 0:3, 3] = np.array(self.par['xyz_up']) + Tinv_up[:, :, :, :, 0, 0] = 1. + Tinv_up[:, :, :, :, 1, 1] = 1. + Tinv_up[:, :, :, :, 2, 2] = 1. + Tinv_up[:, :, :, :, 3, 3] = 1. + T_lo[:, :, :, :, 0:3, 3] = -np.array(self.par['xyz_lo']) + T_lo[:, :, :, :, 0, 0] = 1. + T_lo[:, :, :, :, 1, 1] = 1. + T_lo[:, :, :, :, 2, 2] = 1. + T_lo[:, :, :, :, 3, 3] = 1. + Tinv_lo[:, :, :, :, 0:3, 3] = np.array(self.par['xyz_lo']) + Tinv_lo[:, :, :, :, 0, 0] = 1. + Tinv_lo[:, :, :, :, 1, 1] = 1. + Tinv_lo[:, :, :, :, 2, 2] = 1. + Tinv_lo[:, :, :, :, 3, 3] = 1. + T_th[:, :, :, :, 0:3, 3] = -np.array(self.par['xyz_th']) + T_th[:, :, :, :, 0, 0] = 1. + T_th[:, :, :, :, 1, 1] = 1. + T_th[:, :, :, :, 2, 2] = 1. + T_th[:, :, :, :, 3, 3] = 1. + Tinv_th[:, :, :, :, 0:3, 3] = np.array(self.par['xyz_th']) + Tinv_th[:, :, :, :, 0, 0] = 1. + Tinv_th[:, :, :, :, 1, 1] = 1. + Tinv_th[:, :, :, :, 2, 2] = 1. + Tinv_th[:, :, :, :, 3, 3] = 1. if self.par['mode'] == "horizontal": - Theta[:, :, :, 0, 0] = np.cos(th_mat) - Theta[:, :, :, 0, 1] = -np.sin(th_mat) - Theta[:, :, :, 1, 0] = np.sin(th_mat) - Theta[:, :, :, 1, 1] = np.cos(th_mat) - Theta[:, :, :, 2, 2] = 1. - Theta[:, :, :, 3, 3] = 1. - Omega[:, :, :, 0, 0] = 1. - Omega[:, :, :, 1, 1] = np.cos(om_mat) - Omega[:, :, :, 1, 2] = -np.sin(om_mat) - Omega[:, :, :, 2, 1] = np.sin(om_mat) - Omega[:, :, :, 2, 2] = np.cos(om_mat) - Omega[:, :, :, 3, 3] = 1. - #R_lo[:, :, :, :, 0, 0] = np.cos(lo_mat) - #R_lo[:, :, :, :, 0, 2] = np.sin(lo_mat) - #R_lo[:, :, :, :, 1, 1] = 1. - #R_lo[:, :, :, :, 2, 0] = -np.sin(lo_mat) - #R_lo[:, :, :, :, 2, 2] = np.cos(lo_mat) - #R_lo[:, :, :, :, 3, 3] = 1. - #R_up[:, :, :, :, 0, 0] = np.cos(up_mat) - #R_up[:, :, :, :, 0, 1] = -np.sin(up_mat) - #R_up[:, :, :, :, 1, 0] = np.sin(up_mat) - #R_up[:, :, :, :, 1, 1] = np.cos(up_mat) - #R_up[:, :, :, :, 2, 2] = 1. - #R_up[:, :, :, :, 3, 3] = 1. - R_ga[:, :, :, 0, 0] = np.cos(ga_mat) - R_ga[:, :, :, 0, 2] = np.sin(ga_mat) - R_ga[:, :, :, 1, 1] = 1. - R_ga[:, :, :, 2, 0] = -np.sin(ga_mat) - R_ga[:, :, :, 2, 2] = np.cos(ga_mat) - R_ga[:, :, :, 3, 3] = 1. + Theta[:, :, :, :, 0, 0] = np.cos(th_mat) + Theta[:, :, :, :, 0, 1] = -np.sin(th_mat) + Theta[:, :, :, :, 1, 0] = np.sin(th_mat) + Theta[:, :, :, :, 1, 1] = np.cos(th_mat) + Theta[:, :, :, :, 2, 2] = 1. + Theta[:, :, :, :, 3, 3] = 1. + Omega[:, :, :, :, 0, 0] = 1. + Omega[:, :, :, :, 1, 1] = np.cos(om_mat) + Omega[:, :, :, :, 1, 2] = -np.sin(om_mat) + Omega[:, :, :, :, 2, 1] = np.sin(om_mat) + Omega[:, :, :, :, 2, 2] = np.cos(om_mat) + Omega[:, :, :, :, 3, 3] = 1. + R_lo[:, :, :, :, 0, 0] = np.cos(lo_mat) + R_lo[:, :, :, :, 0, 2] = np.sin(lo_mat) + R_lo[:, :, :, :, 1, 1] = 1. + R_lo[:, :, :, :, 2, 0] = -np.sin(lo_mat) + R_lo[:, :, :, :, 2, 2] = np.cos(lo_mat) + R_lo[:, :, :, :, 3, 3] = 1. + R_up[:, :, :, :, 0, 0] = np.cos(up_mat) + R_up[:, :, :, :, 0, 1] = -np.sin(up_mat) + R_up[:, :, :, :, 1, 0] = np.sin(up_mat) + R_up[:, :, :, :, 1, 1] = np.cos(up_mat) + R_up[:, :, :, :, 2, 2] = 1. + R_up[:, :, :, :, 3, 3] = 1. if self.par['t_z'] != "None": - T_det[:, :, :, 0, 0] = -1. / np.cos(t_zz - 2 * np.mean(th)) + T_det[:, :, :, :, 0, 0] = -1. / np.cos(t_zz - 2 * np.mean(th)) elif self.par['mode'] == "vertical": - Theta[:, :, :, 0, 0] = 1. - Theta[:, :, :, 1, 1] = np.cos(th_mat) - Theta[:, :, :, 1, 2] = -np.sin(th_mat) - Theta[:, :, :, 2, 1] = np.sin(th_mat) - Theta[:, :, :, 2, 2] = np.cos(th_mat) - Theta[:, :, :, 3, 3] = 1. - Omega[:, :, :, 0, 0] = np.cos(om_mat) - Omega[:, :, :, 0, 1] = -np.sin(om_mat) - Omega[:, :, :, 1, 0] = np.sin(om_mat) - Omega[:, :, :, 1, 1] = np.cos(om_mat) - Omega[:, :, :, 2, 2] = 1. - Omega[:, :, :, 3, 3] = 1. + Theta[:, :, :, :, 0, 0] = 1. + Theta[:, :, :, :, 1, 1] = np.cos(th_mat) + Theta[:, :, :, :, 1, 2] = -np.sin(th_mat) + Theta[:, :, :, :, 2, 1] = np.sin(th_mat) + Theta[:, :, :, :, 2, 2] = np.cos(th_mat) + Theta[:, :, :, :, 3, 3] = 1. + Omega[:, :, :, :, 0, 0] = np.cos(om_mat) + Omega[:, :, :, :, 0, 1] = -np.sin(om_mat) + Omega[:, :, :, :, 1, 0] = np.sin(om_mat) + Omega[:, :, :, :, 1, 1] = np.cos(om_mat) + Omega[:, :, :, :, 2, 2] = 1. + Omega[:, :, :, :, 3, 3] = 1. # NB Should define around which axes the upper and lower rotation belong - R_lo[:, :, :, 0, 0] = 1. - R_lo[:, :, :, 1, 1] = 1. - R_lo[:, :, :, 2, 2] = 1. - R_lo[:, :, :, 3, 3] = 1. - R_up[:, :, :, 0, 0] = 1. - R_up[:, :, :, 1, 1] = 1. - R_up[:, :, :, 2, 2] = 1. - R_up[:, :, :, 3, 3] = 1. + R_lo[:, :, :, :, 0, 0] = 1. + R_lo[:, :, :, :, 1, 1] = 1. + R_lo[:, :, :, :, 2, 2] = 1. + R_lo[:, :, :, :, 3, 3] = 1. + R_up[:, :, :, :, 0, 0] = 1. + R_up[:, :, :, :, 1, 1] = 1. + R_up[:, :, :, :, 2, 2] = 1. + R_up[:, :, :, :, 3, 3] = 1. if self.par['t_x'] != "None": - T_det[:, :, :, 2, 2] = -1. / np.cos(t_xx - 2 * np.mean(th)) + T_det[:, :, :, :, 2, 2] = -1. / np.cos(t_xx - 2 * np.mean(th)) else: print "ERROR: scattering geometry not defined" - # Multiply all matrices T_s2d = self.par['M'] * np.matmul( - T_det, + T_det, + np.matmul( + Tinv_th, np.matmul( - Tinv_th, + Theta, np.matmul( - Theta, + T_th, np.matmul( - T_th, + Omega, np.matmul( - Tinv_ga, + Tinv_lo, np.matmul( - R_ga, + R_lo, np.matmul( - T_ga, - Omega))))))) -<<<<<<< HEAD -======= - - - # Omega, - # np.matmul( - # Tinv_lo, - # np.matmul( - # R_lo, - # np.matmul( - # T_lo, - # np.matmul( - # Tinv_up, - # np.matmul( - # R_up, - # T_up)))))))))) ->>>>>>> 077febf5ee14058f5f57ce942f86a24dc78c3637 + T_lo, + np.matmul( + Tinv_up, + np.matmul( + R_up, + T_up)))))))))) return T_s2d - pritn ('T_s2d done') def outputfiles(self, grain_ang): print "Saving grain_ang file..." - np.save(self.par['path'] + '/grain_ang_1.npy', grain_ang) + np.save(self.par['path'] + '/grain_ang.npy', grain_ang) + if __name__ == "__main__": if len(sys.argv) != 2: From f32f4d14269d8cace4cc391acc788269d39e4adb Mon Sep 17 00:00:00 2001 From: albusdemens Date: Tue, 8 Aug 2017 14:53:51 +0200 Subject: [PATCH 082/150] Manual cleaning --- Background_check.png | Bin 102646 -> 0 bytes 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ang_steps [3,3,18] -# ang_min [-.01,-.01,-85.] -# ang_max [.01,.01,85.] # test -# stem "../test/box" -#completeness_cutoff 0.5 diff --git a/View_reconstruction.py b/View_reconstruction.py deleted file mode 100644 index 7ce82ba..0000000 --- a/View_reconstruction.py +++ /dev/null @@ -1,18 +0,0 @@ -# Visualize the sample volume reconstructed by recon3D.py - -import numpy as np -import sys -import os -import matplotlib.pyplot as plt -import mayavi.mlab as mlab # Idea from http://stackoverflow.com/questions/10755060/plot-a-cube-of-3d-intensity-data - -''' -Input : directory of data -''' - -plt.close("all") - -dir = sys.argv[1] -Data = np.load(os.path.join(dir,'grain_ang.npy')) - -print Data, np.min(Data) diff --git a/check_input.pyc b/check_input.pyc deleted file mode 100644 index 6cf3683f547fb3404699d828d17620090bf9cc97..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 2126 zcmah~TW=dh6h5=-*o_k>y(CSYLbIh5S;B$`P%A2PFC>*{Kq-=YQMB6b%sLzIEj#04 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zaR1rMm8U0G+qLt)X+V=!4W!q>TXB8_p+#)D#BJ)CJcJIA! zzbebTw0x2e253avC(3HM%lHaUVw}P1!z70bPECYX*#oVBQFNg|SM@RWYz<=Im*PswxKic*}EudHYZif#vi-ODU`!*N8pXPFuK-}ZT z-Q%zG*+Ks6G<)11{Mpp}4Xul62Yc9i2b0Z_HYI9o3un#$AJs>))^FD2bX*|M8mHd%3@J=G`km3#o5 zNsCs*n}%fm^JTtC^tQcq@Va}K!g6uc=Xl%^3XPjV4sg#vb9h06gN}j`QEC`>uq=DW zHAv}dv^dRMpd zT1bX>WD9}KKsdb9}~Z` /u/data/alcer/DFXRM_rec/Rec_test/List_images.txt - - -# Load files with the image properties, written by getdata.py -A = loadtxt(os.path.join(io_dir + '/Image_properties.txt')) -B = np.genfromtxt(os.path.join(io_dir +'/List_images.txt'),dtype='str') - -# Make a new text file, where for each line we have the name of the input file, -# Followed by its properties (from Image_properties.txt) - -with open((os.path.join(io_dir + '/Img_name_prop.txt')), 'w') as outfp: - for i in range(A.shape[0]): - outfp.write('%4s, %05d, %04d, %04d, %04d\n' %(B[i], A[i,0], A[i,1], A[i,2], A[i,3])) diff --git a/list_bg.txt b/list_bg.txt deleted file mode 100644 index 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@@ -1 +0,0 @@ -*.asv \ No newline at end of file diff --git a/npy-matlab-master/.ipynb_checkpoints/npy-checkpoint.ipynb b/npy-matlab-master/.ipynb_checkpoints/npy-checkpoint.ipynb deleted file mode 100755 index b07ea92..0000000 --- a/npy-matlab-master/.ipynb_checkpoints/npy-checkpoint.ipynb +++ /dev/null @@ -1,628 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# NPY format" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Some links:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "* [NPY Spec](http://docs.scipy.org/doc/numpy-dev/neps/npy-format.html)\n", - "* [C++ library](https://github.com/rogersce/cnpy)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Scope\n", - "\n", - "The initial MATLAB implementation could have the following limitations:\n", - "\n", - "* **Supported data types**: `bool`, `[u]int[8|16|32|64]`, `float32`, `float64` (no strings for now?). **No support for record arrays**.\n", - "* **C order only**: an error is raised if FORTRAN order is used.\n", - "* **Arbitrary number of dimensions**: if a generic implementation supporting ndarrays is not possible, then it is okay to only support 1, 2, and 3 dimensions, and raise an error if there are more dimensions." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Sample datasets" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here are a few sample datasets." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Dataset 1: 1D sine" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "n = 10000\n", - "t = np.linspace(-10., 10., n)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "sine = (1 + np.sin(t)) * 64" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "((10000L,), dtype('float64'))" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sine.shape, sine.dtype" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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2MGxYem7KPN6QkC6vMI8eIaSrjYJRYI8eydoRN2F1wKkQd0jXTbl0qZsnyBtp\naqNly6yNfCd4jvKSKRMQVo2ZVIl7WjJmli6FkSOTtiJ+0iYceW6jNEx857WNjjzSzdmtWVPeeVIj\n7mnJmNm712XK5HHIP3y4CYfvdO3q5hrWr0/aktbJaxtBOI5SasQ9LV7hqlWu1sphhyVtSfwEsVHf\nM2b27nXD3jyGZaBu2O87eRb3wFEqh9SIe9++sGMHbN6ctCUtk+cbMi01Zv78Z7ecPY8dMKSjjawD\nLr8DTo24B6l2vt+UeRZ3SIdwWBv530ZvveUWXR1+eNKWJEPiYRkRaS8iC0XkicL7LiIyQ0RWishz\nIhJqwds03JR5Fw7rgP3H2sh/wsiYKddz/wZQDQQm3AjMUNXBwMzC+9A4/njX6D6zdCmMGpW0Fclh\nHbD/hL2dWxTkvY06d3ajltWrSz9HyeIuIn2Bi4C7gCATdRIwrfB6GnB56aY1ZtQoWLIkzDOGy/bt\nsG4dDBqUtCXJMXKkezBNOPyla1c46KDyU+2iJO9tBHXPUqmU47n/CvgOsK/eZz1UtabwugYIdW1Z\nIO6+Ckd1NQwZ4rYzyyvdurl9IMvxOKJkxw4naoMHJ21JspxwAixenLQVzWPi7vSunDYqSYZE5BJg\no6ouFJGKpn6jqioiTcrw1KlTP3ldUVFBRUWTp2hE164uuf+dd6BfvyKNjoElS+yGhDrhOPbYpC1p\nTHW1E/aOHZO2JFlOOMHdr5demrQljdm50zkHQ4YkbUmydOxYyQMPVFJbW9rxpfqYpwOTROQi4EDg\ncBG5D6gRkZ6qukFEegFNZjzXF/diCW5KH8XdvA1H0EaTJiVtSWOsjRyjRsHjjydtRdNYB+yYPLmC\nRx+tIJDLH//4x0UdX1JYRlW/p6pHq2p/4BrgBVX9K2A6MKXwsynAY6WcvyXKHapEiQmHw/c2yvOE\nd4DPYRkbATuGDHEjmB07Sjs+rDz3IPxyK3CeiKwEzi68DxWfb0oTd4e1kf8MGeLmHkoVjiixNnJ0\n7OjaqdSaWmWLu6r+r6pOKrzerKrnqupgVZ2oqqFXYPc1Y6amBvbsgd69k7YkeQYPdtuEbd+etCWN\nMa/QUa5wRImJex1BiLMUUrNCNWDIED+FI7gh81aetCk6doShQ/0Tjo0bYfdu6NMnaUv8wNcRlol7\nHeWEOFMn7h06+CkcdkPuj4/CYR3w/pTjFUbFpk2wa5erJWWU9xylTtzBT+FYtAhOPDFpK/zBx/DZ\nokXu3jFzKBLdAAAPP0lEQVQcPj9H1gE7ylnbk0px91E4Fi40ca+Pr8IxenTSVviDj4sC7Tnan3IW\nBaZS3H0Tjl274M0381uetCl8FQ4T9zq6doVDDnGLAn3BOuDGlKp3qRR334SjqsrVkznwwKQt8Yf6\nq4l9YOdOV8c9jxuXt4RvcXfrgBtT6qRqKsXdN+GwoWTT+DTCWrbMZVodcEDSlviFT220fbt7pocN\nS9oSvyi1A06luINfN6UNJZvGp5Wq5hE2jU9ttHSpE/a8lx1oSK7CMuBuykWLkrbCYcLRND51wDa6\nahrf2sieo8YEiwKLJbXiPmYMLFiQtBVur8clS0w4mmL0aPfA+oCNrppm8GB491346KOkLbEOuDk6\ndiwtVJVacT/pJJg/P2krYNUqNwfQOdQNBbPB4MHw3nvJb2q+d68b8ptwNKZDBzcK9qETtg64ecaM\nKf6Y1Ir7cce5CZiamtZ/GyV2QzZPu3ZOUJPuhFeuhJ4987vZcmv44Cjt2eOyzqxiZ9PkStxF/Lgp\nLU7YMmPH+tFG5rU3z0knweuvJ2vDihWu5s9hhyVrh6+cemrxx6RW3MEfcTfhaB4f2shGVy1jbeQ/\nufLcIfmbUtU899ZIuo3A2qg1hg1z2RhJTqouWGBtFDYm7mWwejW0b28lZFsi6UnVfftcyGHs2GSu\nnwY6dHApkUlmn82bZ20UNqkW9+OOg23bkptUnTcPTj7ZKti1RLt2ziNLSjhWrYIjjoDu3ZO5flpI\n0lHas8eFZUzcwyXV4i7iYlFJ3ZRz58IppyRz7TSR5IRd0AEbLZOkuC9f7nYws3TicEm1uEOyN6UJ\nR9tIso2sA24bSXbAc+facxQFJu4lsnevu67dlK1jHbD/DBsG69fDhx/Gf+1586wDjoLUi/vYscl4\nHG+84eK4XbrEf+20kdSkam2tq5ty0knxXjeNBCtVk5gbsQ44GlIv7scd5zbLWLcu3uvacL/ttGvn\nBHbu3Hivu2wZ9OtnC2Paysknx99Gu3a5mLutFQmf1Iu7CIwbB7Nnx3td8zaK4/TT428j64CL47TT\n4m+jRYvchvcHHRTvdfNASeIuIkeLyIsiUiUiy0Tk/xU+7yIiM0RkpYg8JyKxzH8ncVOauBfHaafB\nrFnxXtPaqDiC5yjOHc6sjaKjVM+9Fvimqo4AxgFfFZFhwI3ADFUdDMwsvI+cuMX944/dkN9W1LWd\nceOcJ713b3zXtCyM4jjmGBd7/8tf4rumtVF0lCTuqrpBVRcVXm8DlgN9gEnAtMLPpgGXh2Fka5xy\nips4+/jjOK7mJnCHDXObCxtto2tX6NEDqqvjud6HHzqRslhu2xGJ31F69VUXsjPCp+yYu4j0A0YD\nrwE9VDVYL1oD9Cj3/G3h0EPdBtVx7cz06qtw5pnxXCtLxCkcs2e7SVzbsq044myjIPVy6NB4rpc3\nOpRzsIgcCjwKfENVt0q9dfiqqiLSZPRu6tSpn7yuqKigoqKiHDOAupuylNKYxfLKK3DttdFfJ2sE\nbfR3fxf9tawDLo3TToMHHojnWoHX3i71aR3RUFlZSWVlZcnHi5Y4eyIiHYE/AU+r6r8VPlsBVKjq\nBhHpBbyoqkMbHKelXrMlfvtbePJJeOih0E+9H6rQrZsLA1nBsOJYvBiuvtrV7o6as8+G73wHLrww\n+mtliV274KijYOPG6MOO//AP0KsX3HBDtNfJCiKCqra5klWp2TIC3A1UB8JeYDowpfB6CvBYKecv\nhbiGk2+84cJAJuzFc/zxbij+/vvRXqe21mVhnHZatNfJIgce6BYzzZsX/bVeeQXOOCP66+SVUgdE\nZwDXAhNEZGHh3wXArcB5IrISOLvwPhYGDnRex+rV0V7nlVdsuF8q7du7ye+oUyIXLYL+/a0QVamc\nfnr0bbRtm1u8ZJUgo6PUbJlXVLWdqp6oqqML/55R1c2qeq6qDlbViar6QdgGN4cIVFRAGSGqNvHq\nq+ZtlEMcbWQdcHmcdVb0bTR3rstkOvDAaK+TZzI1lRGXcJi4l86ECdYB+8748S7EuXt3dNew5yh6\nMiXuEybAiy9Gd/61a2HLFhc7Nkrj5JNh5Ur3d4yCfftc5xFCAlZuOfJIV+wtyjozL7xgbRQ1mRL3\noUNh5054++1ozj9zputALHWrdDp1chOdL78czfmXLHELpmzCuzyiHGHt2OEWAn7qU9Gc33BkSqai\njrvPnAnnnBPNufNERUV0I6yZM10apFEeUbbRK6+4ePuhh0ZzfsORKXGH6MRd1cQ9LKIMn1kbhcP4\n8S4sE0VJjxdesDaKg8yJ+4QJ7uYJe53UypUulW/gwHDPm0fGjoW33go/3333bucVTpgQ7nnzyBFH\nuPpJc+aEf24bXcVD5sR98GAXngl7FWTgEUqb14cZzdGxoxthzZgR7nnnznU1hmx3rHA47zx49tlw\nz7lli3s2x40L97xGYzIn7iJw0UWuFEGYPPccnHtuuOfMMxddBE89Fe45rY3CJYo2mjnTpUAecEC4\n5zUakzlxh/Bvyl27XIz4ggvCO2feufBCeOYZl7oYFn/6E1xySXjnyzvjxsGaNS4FOCysjeIjk+J+\n9tku1eqjj8I534svwgknuIJKRjgce6zbYDyszc3XroV33rF6MmHSvj2cfz48/XQ459u71zldJu7x\nkElxP+QQVx/j+efDOZ95G9EQ5gjrySfdyKpDWUWsjYZcfHF4Ic65c92GLf36hXM+o2UyKe7ghONP\nfyr/PKrwxBMm7lEQVhuBa6NLLw3nXEYd55/vRq67dpV/LnOS4iWz4n7FFTB9evn1MRYtctkdw4aF\nY5dRx5lnuiqeb71V3nm2bYOXXnJCZIRL165ur+AwsmYee8w64DjJrLgffTQMGVJ+aOb3v4fPftZS\nIKOgQwe48kp4+OHyzvP4427RzZFHhmOXsT9XX+2eg3JYuhS2brUUyDjJrLiDuynLEQ5Vd1NPnhye\nTcb+XH11+btn/f73cM014dhjNObKK92k6o4dpZ8jaCOryxQfmf5TX3WVC82UuoR69mw3OTtyZLh2\nGXWMHw81NW6Hq1LYvNmFZC6/PFy7jDq6d3ebrJQ6sRo4SdYBx0umxb13b1eg6PHHSzv+/vud124h\nmeho395577/9bWnHP/IITJwIhx0Wrl3G/kyeDPfdV9qxs2e7ENzo0eHaZLRMyRtkl3zBiDbIbo6H\nHoI77nAr44ph2zY45hhXQrZv32hsMxzV1a60w+rVbvK6rajCmDFw6602mRo127e752HRIjefVQxT\nprjR77e/HY1teSGWDbLTxBVXwLJlxQ/7H3jA1Zs2YY+e4cNdTZjp04s7bu5ct1DtvPOiscuo45BD\n4HOfg7vuKu64zZvdyPlv/iYSs4wWyLy4d+oEX/gC/OY3bT9GFX79a/j7v4/OLmN/vvIVuO224o65\n7Ta47jqbpIuL666DO+8sbg7r7rtdbnvXrtHZZTRN5sMyAOvWuWHh8uVuhVxrPPEE/PCHsHChxdvj\norbWee+/+13b9tZ86y03ybdqFXTuHL19huOii1yuelscn507YcAAl2lzwgnR25Z1LCzTBH36uCHl\nL37R+m9V4ZZb4Ac/MGGPk44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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(t, sine)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Dataset 2: 2D image" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import skimage.data\n", - "chelsea = skimage.data.chelsea()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(300L, 451L, 3L)" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "chelsea.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "dtype('uint8')" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "chelsea.dtype" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[[143, 120, 104],\n", - " [143, 120, 104],\n", - " [141, 118, 102]],\n", - "\n", - " [[146, 123, 107],\n", - " [145, 122, 106],\n", - " [143, 120, 104]],\n", - "\n", - " [[148, 126, 112],\n", - " [147, 125, 111],\n", - " [146, 122, 109]]], dtype=uint8)" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "chelsea[:3,:3]" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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RslpO/Ymcha7tUJ1p2oZ1s+IUMsY31O4ETcX3779nVsvxNHIaRkzliWkEsdSVZRp6Oq/8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/Ijy3wmLn5N0xaPBl0ES1VRMLWIRmXpJGNKFJ+wBtpa0VRgSLLlNoZcJmJc8FMQZacGrSLaBOZw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hE4Ll5794xbObC7744iXjOPFwe0/TWsK7hc3qiqvLK1zT4jpLSQuFSy42a148u+YnMfK4PfDV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R71kHI6YX0KAzleKLkiacX5wuPJE/wHhrDw9WVhPb/xo2y4Hz7z8HBUtz5nGY8zNasPSNoS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\nVe5ySJlUbQ5yXbgn67mLijfjns3lBa6xeK/Zb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rQ+r53Vca7Uq13Jk07bNWcNVgsOmHLm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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.imshow(chelsea)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data types" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We save these two datasets in all supported data types." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "dtypes = 'uint8,uint16,uint32,uint64,int8,int16,int32,int64,float32,float64'.split(',')" - ] - }, - { - "cell_type": "code", - "execution_count": 97, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import os\n", - "if not os.path.exists('data'):\n", - " os.mkdir('data')" - ] - }, - { - "cell_type": "code", - "execution_count": 98, - "metadata": { - "collapsed": false, - "scrolled": true - }, - "outputs": [], - "source": [ - "for dtype in dtypes:\n", - " sine_tr = sine.astype(dtype)\n", - " np.save('data/sine_{}.npy'.format(dtype), sine_tr)\n", - " \n", - " chelsea_tr = chelsea.astype(dtype)\n", - " np.save('data/chelsea_{}.npy'.format(dtype), chelsea_tr)" - ] - }, - { - "cell_type": "code", - "execution_count": 99, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "chelsea_float32.npy chelsea_int8.npy sine_float32.npy sine_int8.npy\r\n", - "chelsea_float64.npy chelsea_uint16.npy sine_float64.npy sine_uint16.npy\r\n", - "chelsea_int16.npy chelsea_uint32.npy sine_int16.npy sine_uint32.npy\r\n", - "chelsea_int32.npy chelsea_uint64.npy sine_int32.npy sine_uint64.npy\r\n", - "chelsea_int64.npy chelsea_uint8.npy sine_int64.npy sine_uint8.npy\r\n" - ] - } - ], - "source": [ - "%ls data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We check the saved arrays:" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false, - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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9zJQpzpqhPaXc09KA6urgtnOrqJAYV7+ltIeTmCjt3IKak1BTI7ZQP2/5AXGq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\nsEBXG+Hk5ADz55uW4nKWLIn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tr4qd9WnfXn6cbJjtKeU+blywHUHMwLFj0qDErwwbJr9HkB3fVVX+jHG36NRJ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\n2HmtKUR1tTrqwklK8t7uqqJ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Ij5MNsz2l3MeODbYjiBn48ENpUOJXhg6V3yPIju+aGn/GuFt07izFqYLcvrKm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\nR104ycne211VVel2P5ysLHE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Vjh3yO/qZ7t2d9Y14SrlfdRXQpk1wHXaFhRJe57eMuroMGBDcnARr53jttWbl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\nS+U+daq3HEHV1cCJE/Fbe7o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WSoyr31Law0lOlnZuQc1JqK0VW6ift/yAOFWD7L/askX+Rj/TvbuzvhFPKffL\nLgPatAmuw664WMLr/JZRF0n//sHNSbB2jldeaVaOeMnLC27LvbNnJRzXj7ki4Tj9GXlKuQOi/ILq\n6d+9uy712M9kZ9fV9Qga27f722xmkZEh9eiDyMcf+ztSxiInx9mcEc8p99RUYNUq01I4w8qV/iw7\nEEl6enDjqFet8v92Hwh2kbfiYqmJ7nfS0qRUxJEjzozvOeU+dmxwHUGVlVLYye/k5we3OFV5uf/q\ngzdEv37AyZPSGCZobN3qr+byjdG+vYQWO6XvPKfc09KCGTHDLPHhfurs0xj9+0t6fhAjZnbtCsbK\n3eqREMR49337gJQU01LYQ8+eztXf95xy799f7LlB68pkXZ39WFMmkvbt5d7pwkduU1srjVT8mtIe\nDpGYLj74wLQk9vPee/5qUdkUqanO1d/3nHLv1Ek64AQtC3LLFjE5BYXJk4On3D/6CEhKArp0MS2J\nPUyYELy2iMwSLh0EswwgJkCnLBWeU+6A1KTetMm0FPaye7eUVwgK3bsHb8u/aVMwHN4WKSnBizyz\nTBhXXWVWDrvo00cWfk7gSeU+ZEjwYnTXrPF32YFIhg4NXqz72rXy3QsKQfwdbdwo5RVatTItiT30\n7y9Z6040V/Gkcs/Olq1XkNi9O1jKPSMjeKazffv8n5kazpAhUpzq/HnTkthHdXUwfCIWl1wipmgn\nKkR6VrkHqYBYba2EbwVJuWdni1kmSKUiioqCpdzbtBEfwrZtpiWxjzVrghHjHk5amjOO75iVOxFd\nRkSvE9F2IionomFE1ImIComoioiWENFlsYw9ZIhUtQuK4tizR6IX/J5RF86VVwLt2gUn3v38eVm5\nDx1qWhJ7yc8Plv+qqsr/BcMiKShwxu4ez8r9fwAsYuZMAAMAVAB4EkAhM6cDWBp6HjVXXCHlMJ2K\n/3SbiopglB2IxFq9BwErUiZIF2BAVrlBMp/t2hUspzcgiUxOmKFjUu5E1BHAaGZ+CQCY+RwzHwMw\nA8Cc0GFzANwU2/jyBwclRreoKBiJMZH06RMc81lpqZTJ9XtRt0gyMoDCQtNS2MP+/dJg/tprTUti\nLzk5wLJl9o8b68q9F4BPiehlItpIRH8govYAujDzgdAxBwDEHDE8dWpwwriqqyXmOGiMHx+cbOJt\n24Bx40xLYT9jx4ozPwiUlkp0SXKyaUnsJScHOHHCfsd3rP+mZACDADzGzBuI6L8RYYJhZiYibujk\n2bNn/+t4aWPfAAAazUlEQVRxQUEBChoo5nHVVcFJwCgvBx580LQU9tO7d3CcdTt3BisPwSIzU3wJ\n5875XylWVgZv1Q5Ixne7dqInwsuTFBUVoSiO9FViblD/Nn0SUVcAa5i5V+j5KACzAPQGMI6Z9xNR\nNwDLmTkj4lxuyZzz5wNPPOF8E1mnqa2VmNyaGtn2B4lPP5WL8Nmz/lccmZnAs88Ct91mWhL7adVK\nVr1+T9m/4w75nJ55xrQk9jN8OPDII8BDDzV+DBGBmVtsOIzJLMPM+wHsI6L00EsTAWwDsADAzNBr\nMwHMi2V8QArZB2E7eeiQ3AdNsQMSMUMktlC/U1kZvEgZi+HDg1Fpdc+eYIUTh5Oba/9CNp5omW8C\neIWINkOiZX4C4DkAk4ioCsD40POYsNKLnSxm7wZr1gRzK2nRp4//Hd81NVKzJGiRMha9ewPr15uW\nIn42bPD/7qMxUlPtzyaOWbkz82ZmHsrMOcx8CzMfY+bDzDyRmdOZeTIzH41ZsCTpyuRkd3A3KC0N\nRn3wxhg71v9x1KWlEqraurVpSZxh9Gj/r9yt3WEQo84AYORI+9uLejJD1aJPH//H6H78cbBX7j16\nAJ98YlqK+NizJ3ix0+H06OH/lnvl5fIZBS1U1WLAALmA2Rkx42nlnp4OLFliWor4eOedYDToaIyc\nHGDBAtNSxEdhoXzXgsqAAZIB6eeM73XrgF69TEvhHB06yL2dZbQ9rdzHjfO3U5VZVu5BqT3dEMOH\nA4cPS1SQX9m1K5gx7hbdukmonZ+bmgex7EAkU6YAZWX2jedp5Z6ZKbVLYojW9AT79sl9165m5XCS\nK64Q/4ifQ1a3bQP69jUthbOkpUnxOr9SXS09BIJM1672mqE9rdwtpejXiJniYvEbBKX2dEMkJclF\n2K/RGAcPyn2PHmblcJrcXP9GNTEDq1YFq9Z+Q2Rn2/sZeVq5AxIxY7cX2S0qK4Fhw0xL4Tx5ef6t\nDlleLhEYQXXUWeTk+LcQ3+efy312tlk5nGbIEHvLeXheuffo4d/t5MaN0t086PTq5d+Q1eLi4NUH\nb4jMTGD1atNSxMa6dXUJc0Gmf3+xUtgVMeN55Z6XJ3HIfmTLlmA7Uy1GjvRvs+zSUmDUKNNSOM+g\nQeLcP3PGtCTRs3EjMGaMaSmcp3NnabBiVxltzyv31NQ6x6TfqKoKbkZdONaW34+O75oa8YsEnauu\nkh4JfvwtffxxYuyuAFm9J4xyHzJEtmV+UxxW2FlQU9rD6dxZ7v1YonnFCnE2Bh0iiTbxo1N10aLg\n29steve2r0eC55W7ZbP2WxZkSYmYlIJuJ7QYM8Z/dnerqFtGRtPHBYXp0/3nv2KW3UYimM4AYOJE\n+4ITPK/cAYnRdaLHoJPs3AmkpJiWwj26d/dfrPuWLeKwT5QL8NVX+y+R6dAh4PTpYJfwCKdXL/v6\nWPhCuQ8Y4L/t5MqVwS1P2hA5OfI3+4nVq4NdGiKSQYOA9983LUV0lJRIvktQi7pFMnCgLJJOnYp/\nLF8o98GD/VeGoLo6MWLcLYYN81+s+65dwa3h3hDDh0vSlp/8V+XlwS87EM6VVwIXX2xPpqovlHt6\nutRy9gvMsrXq18+0JO6RnS0XYLv7QDpJSUliRMpYXHKJ3PspKXDLlsQxyVikp0sQSbz4QrkPGSLh\nQX5RHIkUKWPRubNsnf0SMVNbK87FoKe0h0MkOQnFxaYlaTmlpcCIEaalcJf8fFl4xIsvlLt15bbq\ngHidysrEsuVaDBjgH9OMVckykVbugKwK/RTrvmNHYuSKhNOrlz3Rgb5Q7kSyCv7HP0xL0jKWLUuc\npItwUlPlb/cDy5cDnTpJ4bNEom9f//RI2L8fOHkyMUp4hJOTA8yfH/84vvlq33KLf8IhKyslXjXR\nmDDBPyv3zZuB2283LYX7jB7tn3DIjRvFl5MokTIWQ4dKtMzp0/GN4xvl3r27f1ru7dyZeKsNQHYr\ndqVOO82HHyaWT8SiXz8xy/jBf7VzZ7C7LzXGpZfKLd7GHb5R7rm5wNKlpqVonrNnpflDIqS0R5KT\nIz9IO2J0nWbZMokpTjQ6dpR7PxTjKyxMrIgzCyL5u9esiW8c3yj34cOBf/7T+zG6NTVAcjJwzTWm\nJXEfK0bX65mqzNIweuRI05KYYfJkfyj3qqpgtz9sivz8+E2cvlHufonRLS4OdrPl5sjM9H42sdXZ\nq1Mns3KYwg89EpgTN+oMELNuvD7GuJQ7EbUiolIiWhB63omIComoioiWENFl8YkXPpfEu3q94cDq\n1YkXlxtOQYH346iLiyUVP9EiZSz8EOtuZaR362ZWDlMMGxZ/Ib54v96PAygHYBlLngRQyMzpAJaG\nntvGgAHeL0Pw4YeJU560Ifr0kfrbXmbPHtlhJCpWNrGXqahI3FU7IP6rzz+PL2ImZuVORN0BTAfw\nRwBWXb0ZAOaEHs8BcFPsotUnO9v7se6LFiWmM9Vi4EBg4ULTUjTNO+9IvHeikpEhSTLHjpmWpHHe\nfTfxEszCSU6WWzzF+OJZuf8XgO8CqA17rQszHwg9PgCgSxzj1yM/39srjjNnJFIkkVLaI7EubMeP\nm5WjKXbtAiZNMi2FOS65RBz+Xs4bqagArrvOtBRmue22+EwzybGcRETXAzjIzKVEVNDQMczMRNRg\nbMvs2bP/9bigoAAFBQ0OUY/MTKnvfOoU0K5dtFI7T0WF/HDatzctiTnatgW6dPFub9Jz58RslMim\nM0BW79u2ebfi4o4diZnlHU5tbRFef70IJ07Edj5xDLGFRPRTAPcBOAegHYBLAbwJYCiAAmbeT0Td\nACxn5oyIczmWOS2Sk8U008Lrgav8+tfAn/7kv45EdjNxotyetNXjYg9r14rD2+shtU7zb/8mF7m5\nc01LUp+TJyWk9tAh4IorTEtjjr/+FXjqqbqMYiICM7e4tUxMZhlm/j4zpzBzLwB3AVjGzPcBmA9g\nZuiwmQDmxTJ+U9x4I7Bpk92j2kNFBTB1qmkpzDNqlHerQ5aWAtdfb1oK84wa5d1s4qoqSbZKZMUO\nSDmPeBrP2xUMZk3/HIBJRFQFYHzoua2kpNjXhsputm6VGOJEx8v197dvl1IWiU7//t7dYa5blzh9\nbZviqqvkfu/e2M6PW7kz8/vMPCP0+DAzT2TmdGaezMxH4x0/kuHDgVWr7B41fpglxj0vz7Qk5hkx\nQuy5XqxfsmqVfIcSndRUufdi+d/1673rC3CTVq3ENxRrGQLfpXEMH+7NLNUjR0SZJVrt6YawiqZ9\n+qlRMRqkoiKx2h82BpEEKHhxF1xTk9ihquEMGlSXUR0tvlPuKSmySvbaimP5cqmt0qqVaUnMQySm\nD68VejtwQJx1iR6FYdGnD/Dee6alqM9770nfZEXMU8uXx3au75R7q1aSvbV+vWlJLmTTJnH2KsKM\nGd6Loy4ulh9LmzamJfEG06Z57zOydnuJWLGzIUaPjn135TvlDojDzmtNIWpq1FEXTkqK93ZXlZW6\n3Q8nK0sczF6itFT8AdTigL9gM3CghKyeORP9ub5U7gMGSJq/l5g3T1cb4eTmAm+9ZVqKC1m8OLHr\nlUQycKAsSg4fNi1JHUuWaIJ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0JIo5xnKiZb4B/FFE5uOiZW4DbgfOE5ElwNmFzyXRvr0T+LTnmMmqLzcgC8KR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\nQS4/xgsvhFOe9TaiIcwR1tN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gC8KR5QYYstEA19Rka02ZhkRRR6XG3FHVhcDJjfx0TunmfJzjjnMP3vDhYZUY\nP1VVcPrpSVsRHVkQ9zw0wA8/nLQV5ZH1BjhIIFENb4cpL2eoBgwdCkuWJG1FeWS5KwnZEPe81FGa\nM2ay3gAfdpjbmH316vDK9F7c0ywcwe5LWfY4stDlz2qqakC3bi6ksXZt0paUTtY9dwhf70zcIyRL\nuy81Rc+eUFsL69cnbUlpZH2gLiDtjbDVUfF4Le79+ztvI627MmW9KwnpX2NmzRrXHc5qpkxAmuso\naIDNcy8Or8U92JVp6dKkLSmNPNyQkG7hyINHCOmuo5oaNxErq5kyAbkSd0j3TWnC4T/WAPtP0AMO\nK4vEV+pnzISB9+J+3HHpzZjJ+kBdQJrjuXkInUG6M2by0gB36eKWnQ5rNrv34p5WjyOra083Rlrr\nCPLTuzr8cLeUdhozZvJSRxCuo2TiHhErV2Y/UyagRw/Ysyd927llcfel5kjrs2R1VBrei3u/fm4r\nty1bkrakOPLSlYT0ZsysWeOWWu3aNWlL4iGNdZSXVNWAXIl7mzYwaFD6Yrp5CckEpDHuniePENIp\n7kFvMOuZMgG5EndI502ZJ88d0ltH1gD7TVBHWc+UCQhzV6bUiHvaMmbyJhxpFPe89a7SmDGTtzrq\n0gUOOiicjJlUiHuwOmRayMOaMg1Jo3DkrXd1+OFuKe13303aktaTtzqC8BylVIh72rzClSvh4IOh\nc+ekLYmP7t2dsKclYyZvA3UBaXuWrI5KJxXifuSRsHkzvP9+0pa0jrx1JSF9uzK9+y506JCfTJmA\nNNVRXtaUaUiuxL1NGxg8OD03ZR5vSEiXV5hHjxDSVUdBL7B792TtiJuwGuBUiDuk66ZcvNiNE+SN\nNNXRkiVWR74TPEd5yZQJCGuNmVSJe1oyZhYvhmHDkrYiftImHHmuozQMfOe1jjp3dmN2K1eWV05q\nxD0tGTN79rhMmTx2+YcMMeHwna5d3VjDmjVJW9Iyea0jCMdRSo24p8UrXL7crbXSsWPSlsRPEBv1\nPWNmzx7X7c1jWAbquv2+k2dxDxylckiNuPfpA9u3w8aNSVvSPHm+IdOyxszf/uams+exAYZ01JE1\nwOU3wKkR9yDVzvebMs/iDukQDqsj/+vorbfcpKtDDknakmRIPCwjIm1FZL6ITC187iIi00VkmYg8\nKyKhLnibhpsy78JhDbD/WB35TxgZM+V67t8CqoHAhOuB6ao6AJhR+Bwaxx3nKt1nFi+G449P2ork\nsAbYf8KXmhIyAAARtklEQVTezi0K8l5HnTq5XsuKFaWXUbK4i0gf4ALgD0CQiToemFx4Pxm4pHTT\n9uX442HRojBLDJdt22D1ajj22KQtSY5hw9yDacLhL127wgEHlJ9qFyV5ryOoe5ZKpRzP/VfA94C9\n9b7rrqo1hfc1QKhzywJx91U4qqth4EC3nVleOfxwtw9kOR5HlGzf7kRtwICkLUmWE06AhQuTtqJp\nTNyd3pVTRyXJkIh8ClinqvNFpKKxY1RVRaRRGZ40adJH7ysqKqioaLSIfeja1SX3v/MO9O1bpNEx\nsGiR3ZBQJxxHHZW0JftSXe2EvX37pC1JlhNOcPfrRRclbcm+7NjhnIOBA5O2JFnat6/kgQcqqa0t\n7fxSfczTgfEicgGwP3CIiNwL1IhID1VdKyI9gUYznuuLe7EEN6WP4m7ehiOoo/Hjk7ZkX6yOHMcf\nD088kbQVjWMNsGPChAoee6yCQC5/8pOfFHV+SWEZVf2Bqh6hqv2AK4HnVfUfgCnAxMJhE4HHSym/\nOcrtqkSJCYfD9zrK84B3gM9hGesBOwYOdD2Y7dtLOz+sPPcg/HIbcK6ILAPGFj6His83pYm7w+rI\nfwYOdGMPpQpHlFgdOdq3d/VU6ppaZYu7qv6fqo4vvN+oqueo6gBVPU9VQ1+B3deMmZoa2L0bevVK\n2pLkGTDAbRO2bVvSluyLeYWOcoUjSkzc6whCnKWQmhmqAQMH+ikcwQ2Zt+VJG6N9exg0yD/hWLcO\ndu2C3r2TtsQPfO1hmbjXUU6IM3Xi3q6dn8JhN+TH8VE4rAH+OOV4hVGxfj3s3OnWkjLKe45SJ+7g\np3AsWAAnnpi0Ff7gY/hswQJ37xgOn58ja4Ad5cztSaW4+ygc8+ebuNfHV+EYPjxpK/zBx0mB9hx9\nnHImBaZS3H0Tjp074c0387s8aWP4Khwm7nV07QoHHeQmBfqCNcD7UqrepVLcfROOqiq3nsz++ydt\niT/Un03sAzt2uHXc87hxeXP4Fne3BnhfSh1UTaW4+yYc1pVsHJ96WEuWuEyr/fZL2hK/8KmOtm1z\nz/TgwUlb4helNsCpFHfw66a0rmTj+DRT1TzCxvGpjhYvdsKe92UHGpKrsAy4m3LBgqStcJhwNI5P\nDbD1rhrHtzqy52hfgkmBxZJacR8xAubNS9oKt9fjokUmHI0xfLh7YH3AeleNM2AAvPsufPBB0pZY\nA9wU7duXFqpKrbifdBK89lrSVsDy5W4MoFOoGwpmgwED4L33kt/UfM8e1+U34diXdu1cL9iHRtga\n4KYZMaL4c1Ir7kcf7QZgampaPjZK7IZsmjZtnKAm3QgvWwY9euR3s+WW8MFR2r3bZZ3Zip2Nkytx\nF/HjprQ4YfOMHOlHHZnX3jQnnQSvvpqsDa+/7tb86dgxWTt85dRTiz8nteIO/oi7CUfT+FBH1rtq\nHqsj/8mV5w7J35Sq5rm3RNJ1BFZHLTF4sMvGSHJQdd48q6OwMXEvgxUroG1bW0K2OZIeVN2714Uc\nRo5M5vppoF07lxKZZPbZ3LlWR2GTanE/+mjYujW5QdW5c+Hkk20Fu+Zo08Z5ZEkJx/LlcOih0K1b\nMtdPC0k6Srt3u7CMiXu4pFrcRVwsKqmbcs4cOOWUZK6dJpIcsAsaYKN5khT3pUvdDmaWThwuqRZ3\nSPamNOFoHUnWkTXArSPJBnjOHHuOosDEvUT27HHXtZuyZawB9p/Bg2HNGti8Of5rz51rDXAUpF7c\nR45MxuN44w0Xx+3SJf5rp42kBlVra926KSedFO9100gwUzWJsRFrgKMh9eJ+9NFus4zVq+O9rnX3\nW0+bNk5g58yJ97pLlkDfvjYxprWcfHL8dbRzp4u521yR8Em9uIvAqFEwa1a81zVvozhOPz3+OrIG\nuDhOOy3+OlqwwG14f8AB8V43D5Qk7iJyhIi8ICJVIrJERL5Z+L6LiEwXkWUi8qyIxDL+ncRNaeJe\nHKedBjNnxntNq6PiCJ6jOHc4szqKjlI991rg26o6FBgFXCsig4HrgemqOgCYUfgcOXGL+4cfui6/\nzahrPaNGOU96z574rmlZGMVx5JEu9v73v8d3Tauj6ChJ3FV1raouKLzfCiwFegPjgcmFwyYDl4Rh\nZEuccoobOPvwwziu5gZwBw92mwsbraNrV+jeHaqr47ne5s1OpCyW23pE4neUXn7ZheyM8Ck75i4i\nfYHhwCtAd1UN5ovWAN3LLb81HHyw26A6rp2ZXn4ZzjwznmtliTiFY9YsN4hrW7YVR5x1FKReDhoU\nz/XyRrtyThaRg4HHgG+p6hapNw9fVVVEGo3eTZo06aP3FRUVVFRUlGMGUHdTlrI0ZrG89BJcdVX0\n18kaQR390z9Ffy1rgEvjtNPggQfiuVbgtbdJfVpHNFRWVlJZWVny+aIljp6ISHvgL8DTqvofhe9e\nBypUda2I9AReUNVBDc7TUq/ZHH/8Izz5JDz8cOhFfwxVOPxwFwayBcOKY+FCuOIKt3Z31IwdC9/7\nHnzyk9FfK0vs3AmHHQbr1kUfdvznf4aePeG666K9TlYQEVS11StZlZotI8BdQHUg7AWmABML7ycC\nj5dSfinE1Z184w0XBjJhL57jjnNd8Q0bor1Oba3LwjjttGivk0X2399NZpo7N/prvfQSnHFG9NfJ\nK6V2iM4ArgLGiMj8wmsccBtwrogsA8YWPsfCMcc4r2PFimiv89JL1t0vlbZt3eB31CmRCxZAv362\nEFWpnH569HW0daubvGQrQUZHqdkyL6lqG1U9UVWHF17TVHWjqp6jqgNU9TxVfT9sg5tCBCoqoIwQ\nVat4+WXzNsohjjqyBrg8zjor+jqaM8dlMu2/f7TXyTOZGsqISzhM3EtnzBhrgH1n9GgX4ty1K7pr\n2HMUPZkS9zFj4IUXoit/1SrYtMnFjo3SOPlkWLbM/R2jYO9e13iEkICVWzp3dou9RbnOzPPPWx1F\nTabEfdAg2LED3n47mvJnzHANiKVulU6HDm6g88UXoyl/0SI3YcoGvMsjyh7W9u1uIuAnPhFN+YYj\nUzIVddx9xgw4++xoys4TFRXR9bBmzHBpkEZ5RFlHL73k4u0HHxxN+YYjU+IO0Ym7qol7WEQZPrM6\nCofRo11YJoolPZ5/3uooDjIn7mPGuJsn7HlSy5a5VL5jjgm33DwyciS89Vb4+e67djmvcMyYcMvN\nI4ce6tZPmj07/LKtdxUPmRP3AQNceCbsWZCBRyitnh9mNEX79q6HNX16uOXOmePWGLLdscLh3HPh\nmWfCLXPTJvdsjhoVbrnGvmRO3EXgggvcUgRh8uyzcM454ZaZZy64AJ56KtwyrY7CJYo6mjHDpUDu\nt1+45Rr7kjlxh/Bvyp07XYx43Ljwysw7n/wkTJvmUhfD4i9/gU99Krzy8s6oUbBypUsBDguro/jI\npLiPHetSrT74IJzyXngBTjjBLahkhMNRR7kNxsPa3HzVKnjnHVtPJkzatoXzz4ennw6nvD17nNNl\n4h4PmRT3gw5y62M891w45Zm3EQ1h9rCefNL1rNqVtYi10ZALLwwvxDlnjtuwpW/fcMozmieT4g5O\nOP7yl/LLUYWpU03coyCsOgJ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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "for dtype in dtypes:\n", - " x = np.load('data/sine_{}.npy'.format(dtype))\n", - " #x = np.load('data/matlab_sine_{}.npy'.format(dtype))\n", - " plt.figure()\n", - " plt.plot(t, x)\n", - " plt.title(dtype)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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qNdZWiM3Y2hKDsrs/kgR2xwPXTU1TVYgIMSbu9z3jFLi+vmbd1Oz2PZdXW6b+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YHnn15p6f/eoD397OuKwZvOVy1XJ9vWG9WWMbj7EVlqXFeHYy+5xkd2dIV73GY8qM\n08SSNEllEgtKJ1w110gHXpkrWublKamz4k0u0doZV8GOKr9eXKWoirEpIgZApU4hzDKiMZTz10U/\nfZ3rrFwrCcH4eHSjKjb51JmfC6EWUQSnWfxHdVH8Mk/6duCjjvz7hiDy+GsV8lLKu49ehP8S+J/q\nD18BX3701C/q937j8b/96c/lbqMKn1+v+MGzNZqaBGQEv0kWUX6iFvBY07qVqFqUUrjziyShDUuR\ni+a0fJAbpzjYclaorFFFAlO1lmNLXCJj3hOXhZQWbH1zvNZYK0xnbWunriUKTiGbdU5Mj/qmmwrc\nUUhRl0JfZFFqEsYmXPaEJVY7byAXSWdJVRoXcxEOhrFM04EQ5IPS9RII7Rtxui5hJoYgqSjKEaOM\nP5RWrIZL7m/v+OyzTznutxhryLpDu5bNheewPzD0LU2z4nCYWZaFVbeiNZYpzPzDf/gL2q7DOceH\nD3usNXVWa0hxZGgLDstxngn3sRq7FKbvcaZFWQf1BppzYrVxdF1imSZ8Y2XMJKBw5hhYQkBZj/c9\ncQmkOBJmSb/xTU/bd5IzqhLealIIaFPoW49NmWkqRJIcjqzGa4szVp5vNIdpFikohXGJ5NKCShzH\nHW5oyFlGE8fjgVQlqsK6F9B/JlLyQusc4zTTdQJfQ490jec47mtw9j05Jbp+hWusJMVoReNFgTUe\n92hl+PGP/4jjfMT7RkxRxsoxOhZSzFgvppklFu7vHylFMY0R23agBa+rlMMaR65I5ZyFNqhUoeQE\nBKyShW9WGa1CrWIGbTXOW4zxlKQI04wjUazIPkMsRBzKd/h+oOs72q6haTxd4+kai1OKMC3sHnd8\n9/aeP//VLd++3RMDXPaei1XLzdWGq8sNXXVpa6NOsm5Z7hZRp6h6oD2NEkIUlcYcIksIzEF496Uq\nyUouhBSrwU8wHcWUSlJUKBWroqye55UWUNZZWf6by05dMdWiWpN0JlX3N7F6W5TSQlNVSXY5+qnY\n63pD+MtjmazSefFazrOifFa55Nppn8a0tcqiFPzpT3/Kn/70z/ir5uPw1yzkSqlPSymv6w//FeAf\n1K//R+C/VUr9CTJS+SPg//y+3+OPnndY62icgHe01sKHVqqW7dN2WMBaJNluy05RjjWqvvgybxIs\nZArpIzSmzNONEUDWadmToqQJWSfBy+LSkq5gnmeOhy3aKtCSfG2swkZhqRSqcaQ+X2W5+zeNPW/c\nS9WCxpjq3b1+kCp97qRqQYMKSm4QJyldChwOmSlktOvQMTMetkzHI+ruEdc3XFxeorSh7wZKThy3\ne9abDXOM7PZHjC48u76k9TeMxwNXl8+ZjjPzkrm7PzCsVwyXHb/8+jXPnl1wczPQrde8/u4tnWtA\nSWZlyIHhYk1G8/52z93dPX/4ox/y8tMXHMYjQXn2d4+U/Y60vEO9LBULYNFZo5zGWi9yLG0pKRIa\nRype5IRJk02hzwvLNOPaFaoYlmVB6zXLtFAw+KZjnEas9VirifNELpnVemAqhXA4YiwyTx6juA4N\nlBJExuc9+2mR+WrK9MayBOGmaA2mXKNS4PW3v8Kv1rz45DMpkCUT56Ms1fOCwjAvkQwye08Joz37\n/SMQmaZFFEjWcrFeEZZE4zTOGfa7B1KCEOHZzQ3TMvFw/8jl5UrcvipzeLil9S2roScVRYwRY+tY\nJ0PnHNNyIHrLHEWVdRxHmqYlpYBSEtpwku7lnEQRUjTTMWKbjG8KRmfBxhpbT5Di47DGk4OM9UxK\nKNth2x7nO1zb0jaWvrX0TmNVYRlHPny44+tv7/iLb2759u5AjrB2luvWcrPq2Kx7VkNH37a4GgKj\nkBtNUYpUEIZJXfAvyyJsorQQYmRegpzSKoYj66pWUxmdHViR81GKjCZrd/7x+KLUXZZS1TV6cnNS\ni+1HIxCZX1cGfh2TqqpNN0qLrNCWSlysWA59CpkRB4OuXbtRBlWUqNrQqFxvqNS/06nrLt/HepGy\n/nf+1k/4O3/rJ5xSnP6b//5/+OsVcqXU3wf+BeCZUuob4D8C/kWl1D9bX4tfAv+2/H3KT5VS/x3w\nUyAC/075+Izw0SPUKKRUJM5IafX0wilVI6hqgrqWOxhFnJEqF1TKJHOy0stc/eSq0lrUVihFVo4c\nI3GR4mmczExjoL5YldOirMjclEMpL2aCJKxglWLtyk7SxafjjkJTVIJsUaoGvNY3IqYK3q8z/RRL\n/XfqurlWGGdFeXMi8uVC365o/MB4GMkLGBfoBgjLSFkC9+/fg9bEyxs2mxWX15fc329pfMPVxZrp\neEDlwovrG+5UYokLLz97Qc6G9+/fc9ztRDGkMr/45dfk/CW+96wvrjFaDDhjeEOmsORECCMXq47L\n4QUx7Lh7l/Bdz/OrG2zOaNOjyTyOieP7O8iJ9aqTZVjbC4faOTmVOI2ll0uogHKW/RaM8wz9WpRK\nwbOkjLEJMEwhCepARcbjEVLBuZaUFuISGI978hLpW8OcNC4algTaVtmoNSSV2C8ZkzKDs+yPgcYr\nnj/bUMrIOBVefvKpLNG0wXvPvBwxJLy1EoqxLIzThHWesD+QQsY0jmHomcYdm01PDDPOeuKysCx7\nLjZrUggsRqPbBut7dtsdFMXVxZq+9SRrePP2FVfrFauhJ5eZOCdy1igcm80l9/f3aFNojZh2yDAu\nhca3aKw4ii2V9wMxFVJM0s0ukd1+pAkKoxsZK2WDTvXzaBTGCI0yW09KCV8KvltjfQva4VxL6zVO\nZYiR/XHi/YcHfvXqA1/96p63HyZSLGw6x7N1w4vrFTfXA5tVS9dIKDmqnM07IE1XKPLZlw47sSwz\naZnIaSSGSAgihZyiwBewEjhijfgKUuZ8vZ3HGlp9VMhPdYHzZvKsJKmdeqlGIoUQVTkrfE6nbEuJ\nCqtEwpwjVcpa6ZCq1h8yRkkDWer3ddGSToZGF2ohF3loql32GQ9QCk+1XWbi6uPn/BV1+p9EtfKv\nfc+3/95f8fy/C/zdf9zvG5KYbLQxNHXpd0Lv6poTKPtDVeViAo6XYJJEinWbi5E7fU3xfto0yPFI\nG4V3DaXNhKXUrt6Rc2A8ziInK5olFVKRC2G7a7jYrLi4WrPJkljS5EzTdlWdITZbQO7CWlNSEBnT\n6Q3Q1RZ+0p4WcYbmJKqWUhJZlydTipJcSeEUJ1AK1zasL29Aa6b9A4FA4yVVZ5wmHu8+oPLCarVi\nNXQcjnustQzrgeM0Yo+WkAzLAm/ffWBzccn1zQXrdc/t7QPt0KCM5uFxS790GKt5+clL2sZi1Q1v\nX78jBMVm85xp2aNLJk4zOTvuH27J8QOroWM9dGhreXw80pmGJRzYbQ+MxwObYcNqWOFSXUQ7hzKt\nxG5Vff9m46B+HlJRYD2Qa2dkWLWF/WMgBkniMS0cj3vCPHLcH1mCzBtTmFAh4nXFpXrDcVlIKXE9\nDKT9jiUFljDiTIdJkePDPY0udJsLUjxgmw1tI6M5ZwwqR3IciXGi5IXxsGVzeU3X9ezinhBHYiw4\nawlhQWUoJUKBvuk47PaM4wFjPGHJxDyxWvf1cyKmqv1uL8vAthPzmU60jWOZ82m8iu9aHj9sictC\n17XoJOgGqz0YxbwsDK4VdjdZFBK5MI5Htscj9/uRdTS0TuONwruITpbCUmfKqo4sk6TVG4v1SHqR\nVlg1Q1Qcp5lpDnz4sOfr7+745s2W93dj7cQ9V13Ly4ueT1+sefHsgsuLXiSkKtdxppyQSxEpZwon\n0l9iXkbm8UgMo4wcYyFGmKbAGFU1CTZyclWREqvhRtXlYzXunIB1p/JM/W+qqjOpU09duCwXq1b8\n1IlXM6JWgi2murytktxfo2tHboSIak4deTUlFSOjGIMha7lp6KJqZudpySp/w4+hXbWGyt+/lHMh\nP6lkftvjd5cQFMV9Zl0hZHARrJPGG+QCz6eFYS7nEYlSwvoVWaWwf0uEbDXOPs3YKerpeOQsJjty\nFhawNZnGW0iOqKjmokjIMM+Jw7xAiKJSWEQbW9KICh3JtpJMXsclxkkii66I3VJvNFCxuCWd5/rG\nyIdXlUBIkWUO5HQ8f7it1jRdQ0hRbjJppujCajNQSmG/n7h/3OItPH9+w3HaMR4eUSli2x6FYlkW\n1usNqMKrV9/hm4bVeiBniZ0b+p5+WPP+9pEwL1yvNrRty+7xAzc3V+R0YMkNpnW8/Pwz/j/m3rNH\nsmtJ13uW3SZNuW42ycM5RoIAQYAECJD+/7+4uNKdgWbmGJJdXTYzt1lWH2Lt7J47c+5XTgINsk3t\nzMrKHSvijdc8PZ85XQI3xxu+PP7C+9uJw6Hy8dNHjHac3s789ecnjscjucC8FFIS/rxNhmV5Z5ov\ndKPl9njHoB1aV7puQNuOlFeKlp9nrhnf7UhF0dmKsY6aNe9vL7jmMR+XwBoDy+XE6fVCjCvDvuf1\nNKMwDB7wFq0Mzlas04SseTtP3I6a95LROjcve03nLDmudN5jrAGVmKdXKBq02LWqUum8I4Yzx51D\nVXHH9F3HEhbWJaJ72aU453h7e8Zb+VmmWhl2e0JIqCphFMu0UkrGeRHyHA4HVM1MlzN9b6g5E1NE\nqY5lmnk/XxiGgU5bQjjh9j2m98QC1guMGGbp3lSjydWSWJcT6/nEPEXezpXzHIgZPjbam40VZYS+\nqJ2obI3OGC0kgxID1ETKgSUL3Pd6ufDyPvPz54mfHydOc6Zmzc4Z7ncd3x0Gvrs58On+lvubPePQ\nY6y0wlU1UZ1qO6wq02tKiZxm1vnCdHkXJoqCGCtTrMwBqpZsWtU2loW2X9KlWURvPEOarUwrgHWj\nNVZK43H/G7fDNuxvXy/Nn0j/NyjWaIdqO7KkDTULm85ixE6kwbi6fW9aa8HO0VhlyQ0j11W3Qt7I\nbt/wzr99XBXj2/fQSBX/oetXe/x24ctVcGYdM84VOrcpnqQ4K/1V/i5LM4Fgit4oP619J5PSQs6a\nUiQ0QivxTu46DS2BxDov3iWNl65Lwm2y16oxRYy7egNaZzq1YNGQPTX1xKWiUkCbCyiLch5nHbV0\nKNVTcVfln+IrkyWlSNYVpUSxWFKWBW4M1BwIqyxYjdYSCq2MTCna4JRjWVfWFLGdZbw58PzrmWUN\nvLy8Mu481mjGXc+aYBykK79cLgx9x8PDPa8vr5xe3zje3JBL4a9/e+I8rQz7Ix+++z01rfje82H8\njhwjTksAQMkV5zucNfz8158Jb5Hb4xFlOn7+5Ykvz//MT7/7xM1xx3gYeXt5o9ZE9bUZPFX6bkdJ\nK6lk+grrfCIuM0p3eP+Oto6ibTNyslQli0QAYzvSHFjmiZQvVGYu84npdBE+9zwz9FCS5XwKqFLp\nvcP7jrUWhs6ha8FQWV5m0rJS88rBWQZjCOtK6SwlG/a392gDJVy4LBOd76i1YrxCVwtVgpu1dtih\nw3cHocKFKH75IyzTO7txIK4Tt8eRUjeus+EynSgVxsOeqjS2tzjlSangjCTloBLWK8I6QYG+66lY\njvsdawis84RVhTSvGDTOe2HDGAfVcBg7KZSqJd9k6fbn6cw6TdRSueTC/LzwuhbuzonBW+GKj46u\nt3SdoXNQqKwFUinEpDmfI9MUeZ8Tz+fAy2nl/RKpSeGVZd9r7neOH252fLobuLsbGEbxuN+wjK2p\nKkWxtmDzNQZCSIR1ZpnOXM6vLPOFVCqptmShZCi6w1tZaArRQ5wAlTZXR0CBSL5xLa0S56ZKa2QL\nVBWvMIz8W/WVycbGVNngXSnoYtMh+zFnJKIOTSvqlhpplEdzdV6VAHhZlBo0RZcGrTT7iI1GXbc4\nhq2YqzYxqOvksNkDABj1n7CQFxSqtOzOZthOFbxK6EgJ+HbhIJi2NnKqo2jp50bgEmgGM7Vld0Kx\nRmiCiBiCThwII8jWuBRUjBSVSEWRi6ZUGQFDTqhlwhotEuZJY50cCEpbtHN0/YBzPSkPaO3RxqGb\nI5ukgEh6iNL662uAZukko6/dAmxLgpQpSlGUQtmuLUgSNX3l1mqjWefENImIxHkRVbi+x1jDfj/y\n+nphroXDYeRPf/oHpmnm7f0drTV9VzmfJt5fIiodKSXyPr9xOOwZh4HTvHJZE85YjjuJO/v0ux9J\n60yIgdvjyM1+x/PzZy7vz8zTmTkktNL03tL1HRhHXgNPTyeO+45SDMuqOJ1OKAO7vseHk3i92z3W\nD+097QBLSu2grhGjA8Yo5qmibc+4S5xDgALTlDidZoEsYsB4zxoC437EWgnOXl5f0RSsUUxZbvg1\nZZgKvbGk8kLqDA+IdF0bC1axzgtd1hIQnqEbbkVwYxy5GqGs5oizGqpmVYWcZ7w3rOtCKRnjRDae\ni3TIYZlR2smE5jTdoCHHJtPO6BLZdw7nRrTrCbEAEcqCdyJy+/5335FyQK2KFCHGCd+PaKNJq9Aq\nvSmEELhczrydzqSwcNd5slI8zyuf3yPPp0hvjUjvR8VusPRe1LWb0C2VwmnNvF4Sl6lyWjJrhBwL\nViluO89Nb/l47Pn+bsenhyO3tzsO+0HgH6vJFaH/Kk2qQE6ULGK6dZ6Z1plpOnN+eyFM722RqYlF\n/OqrsmhrrmKanCUgQ6BvqRHCvf6KK1fyFeLMtbkWFig1XOuLvOeFqyh7W4wiNhyqdeNaNSaaM8Rr\nd66x2omC9hs73i3sWgq5eDdpjFgIb6wVXa66E/VNM/5ts/3fL1/FAO3f89G/ffxmhVxrha0aq1rR\nRZRPm2GOvOba3mCNbiId3bDvUhG6my5oLRmb22mNMkJHLEUw9SpHnwS8buacjU8eCzEUlMo4pxF4\nWkhLcS1c8oW4BLzTdL0XnLzr8QpKtGRlKFpc4nSt6PxVhhtTaN+LJPhEBG5RCkpO4i1A83+uhVoX\nwQxjIloJvMBIxF2JhRASU4y8Xc6MQfPxbqDkxOfPv1Bz5XBz4I//0x+5uXng8fELzy8XFAPD/h7t\ndizTCYNi+N7R9x3O95SieXmf+fL4wp+XJ2pVHPY9//C7T0zrTEiFdRU73v3OMw4dnbP88PHI5XTh\n8eUdaxNvpzOLgr3akUJiWQM5wH/727/yww/33N30KA2dHYTOlSo1wXjMeKfEJyUXKiJZnpZXxnGg\nM57L5UKaI+Fy5vT2Ro1ZTLFyxXrHGheUNeTxbT+sAAAgAElEQVSU2A89t7uBtWSe3p6JIaJMwdlE\nlxTaOeJ64XYc6HWm04ayzJD2Yk5kCi9vz5AlANvajlw1r6dndoc7dn3HNL8RgsAx1hTeX96I84TX\nHUlLPJz30lFrbeh7T83CEgk5EteFHA3jbmQzWzJKkXJiXi5MNXH78BP9MDKvAescKSXGw45aEuly\noiAeQafzhOsCzltc51H0xJSoMaFK4f2yQlE8jB5vFXtT+TJHXkLmdcm8VuAlYb3Gug2WEeFcSpU1\nSWdcipiLGa3YGcXd4Plu3/HxbuTD7Z6P97d8/PDA8Xig6z3aSPqT0h0YQ66GmsXlsJbIPE9cTm+s\n64VpujBf3sk54LRDa4drsv1cW2iEEpqebqZaFFDqa/xZahQ+VStZtcVh+RrJVrIodcsW9tC8mq4w\nS5v+pVhusY76CnuSXQvQ0GCd+Kk3IzKtZPlaN4hVb3a5+kpf3a7LVnuojXCg/n2B/gYW/vbX1+v+\n+8dvVsg7a/HK0Hcd4xbW2zwPElsBbkXeaMnua0sG3dRgigxVVF21SuDp10zP2uKVBC9Vjc8t5lcW\nbTuMS2iX0S5hkmR1G90WEUWUZiEVQpxFOJFEMmytOBqKbFxOWl25eloDwh8usqzQKjbHvGZW37oC\n2yaHHAK1yK9cJBjWZsHRY61oN6Kd4sOHPftB8ewRyCFpOt/x6faBlALnJfKvf/nC3YcPHG4O5LDy\n+vqIungKms5qvO8JZSaWRN8bLq8nlFq5vXEYFqbLjCuW0+srVRdcJxFgWlveXs9czjPKgO06jje3\nfDrcUkvg9yh+/fkL8+nMzXFkuOupaO5uvxdVYO8BoScaK9AJxXBZI+/TMylktBLLT+8dykGYO86n\nQgyBEip51ez7I8kthLBQk0Y7xe3djvm0CrZpNK/nV8Zh4OA1K5qUIWX4eDuSq+ZUCqcpkX1C2Yqf\nKvPJospKrZX9/kjnB87LTMlnKpriBi7LM/1pYdwN7IcdJQr/HjL92GFMZZ4uDMOOmALzeuGwP+Cd\nYQ4LnXfy/psqqULK4XuLEDoSpu9YZk+ugZxnyhrxvmeZpQgs89wYMUBKEpSsnMSyqSqy8BKhalJZ\nmZcz03TBK7jpNIe95cPRcX9eeJ4rny+JlymwZMU6F+okOOw1ZKU0QQ0wWs3QOUYHd4Phpw8HPt3u\nOY4d+8Oe3fHA4ebIsNvT+U5w8ObzEnJpSVCRXBMxLkznN6bzOyUsmFqxSpGrpiixOTDWAw5VLYkt\nRvnfKiVLVbLDQBjJX1kfmwuhFriPimAs4ksgtMyNXVaaz/gG5W7iIQNGk9EkpSRiThuKNmATRTem\nihGoN+nNWOub16pUe2GZTfXdqkP7T3tzpY//Kihq0I4Ubzlc9RaK8Hcev1khH50TXwlnGXtH7y3G\nCZecRuOvNJglCZWqtKBkYYoYxONEgdGorNtyEbQpV2VlQV0j3VCKkoUWmKuiaoe2CectOVmx+zQG\nbw0lZ0KSBU3Jwm0vGVIuxJQE564JchSVaakobUlVfuAo6cpTY6DUloqtVPnqtWwMuikAtdEoeqwV\nrwUqrOuCzoU8T0hgs/hau25gWSJvbwtfnt7Y7Qc+fLjjeLjBWEdahQomN4TjdJ6oRbNUBSpzPr9x\nc3PLsBfBkzjHVfquEpaeECvzMmGtxlvN2BnmeaVUzfltYlkDyxq5uXnDucpub9nv9nz8+JHLsifn\nxBIi0+WCwpBK5vHpBU1mv+/oezlca/uQi3eJmDdpqwWCSKLSzVm8c1IOVDJLjlzeLxht6GxHCYWc\nYEmFwXWkHOm8KFDXVTDkUmY6o7kdvYjQsqJmaSJqhmVZCJcJUqE/7FhD5vX9mTWv7MaBcdyxxICz\nmv3Q0feOeX4jLBNKiUOhNY7z6YVaHcb0FDI3e4dRCV0Sx33Hss7EWNiNe+nGRPLLOi1YA8t0IuWI\n6zwlpfZZUejSoZJ4zqQY6L3n7f0doyRJXhxDd8Qg05NShRwD6/ROCgteV8bBcrPfYTvL8Wbk05L4\n3VR4fF94ngJvS2Qp4k+UkanRKs3OG0arOXaew2DZjY796Li/23F3s2foR/pxpPMDbtjhfYdxHqWM\ncMRjIhXRZ4RlIeeVaT5zen+lpIhGERMsQQZU42QKqtaSsiQmNVHJVZNRm54kq1YCVb3aVl99UuoG\n1X71K9/oyQphrWw8clQLYAauhwClAdjNG0U+OFRlILdDSmlwzThkO/Gaxa1icypU/4YXzpa8hORH\n1U2MpI04NjZdDEo1p9VvuvL/jB354C195xm8Ztd7eu+ppooveZW3odbG9da1MVVENCAp2vLGlCo0\ntY12uC0xlKqkLIkcpRTwBmUtktfXlqcIz9Roh9KL8DrFVR9vDM4Uao2IUwesMVPmxJIXYlGMu4zv\nVsGprcfZvglfalvMODCVosQMv5SCJqN0JoVATJmohJheUpZJw0hOozEOpS1Ky00QVzF+WkMgJsGL\nQw0SYJEKT49f+PWXzwzDjsNxRFmLtT0//PADd3cf+OXxM7/++sj57cTxeODt/ZWiE9ooPn2453DY\ncbtEvjy98PnxibuHjy30VaKmxrFjmhaGwWIt7MeOoRfLVIvh6fMrU39i2Hk+3D/gjCPFG95eXyXY\nevRczmdeX09NlZjZjwPj2BObyEZXJFtVK0xQFDdTFSzrGWc8FcXj4zM3d3t2Q8fPf3vCuZ45vHF3\n32GAuCrWlCkhMDg5WJ1S+N2BmAq2E9jNWodGE8KKLoXLReThqoO39xfiGlGm8uPHT6wxiCe8M1gD\nKa6s08Jh3FNL4O31RQIPdKbmhDGG3f5I33fEsBJT4PR+bmHiBmvkeU3NdGZP8R3T5YU1nOl9R4wr\nvRvQrqM73KH1SKmVebnw8vxMzgnXO2pVxBjxY0eKQrOcphVKY/fMgSnKMrv3lmHX0+06bqwhLIG7\nNfDdQ8+0Vi7zKsvFINJ3bRXOeMbBM3aOvbf4oWMYezpnReE5DLhuwDZjN9cPMmkpWUKmkAkhsy4z\n8zxRcyCFlcv7C2ERPUBMlVwEM3fWS8OFaX9eqNVeKeBXql6DJnSVBWcBamOkNDeMr0W71U997dBr\nk+jLjo1vmlwp5ptEXkHeDgb5O50LZjM23yjSRbepRbFtVuXQyNfp5hoOUUAwIWEXldKEQij0N7nE\nAiUJ6WEr4LrBOH/v8ZsVcm+tqDo7GS+dE9hEoSm1BafWhjE1e1NRcGZKXsgR6ZaLuJUYNKYdxGwM\n0jaNaETAo0ttvFCPNm0znCHFhDUdxUgkUy2grMZ7TzFafIQV4s6nhVc+XQI5gfeBznu8dyS3NuaA\nwdgOY63wybVGW0UImZwCucSrgk2TcYYWLhFZQySWgjMOa4R7rpXDUVmjMAUohd4qvrsbicGT1hVS\n4tB7jMqcXh5JRTMtiefHL9x8eODm7shPP3wgPRxJa8Bag1WOL49PlKnwNL4zLwvLvHI6nXC+58cf\nv2PYHSlV8fnzZ3Y7zeDFW/k8F56f3vnw8Y6hdwz9DalEbo53zOeZz5dHVE30vdzwa66MbuA9aCrC\nJvn18ZXbfcfNzchhJ4kvX57e6XyP8a4JsBQhGC5B4r3++NMnCvD8+sr93R5vLL2NLOvMmhJ9N5CQ\n8IRCQhnHflfwneXz04VpWemcg5KZ17UJaTxv00pfFan8ijNinfv73/8R5xTnNVGVleYgLaScOBz2\nAKwhYX2l5AVDpd/1+MFRa2CJsjwdfIeqiJgJR0zCW768v3LKj3TdQMqwrJWh7+iteNtbKywJ6z1L\niBg34IaBmMLVPQ+lpPAPHWEV24fpcuF8nvny9IJCHEHXnEg1sbN7unGk22n6dWEfQ1MZasluXVZi\nzhhncc7jOo8zBu86rFH0Q4/zHpqBlDYO4zqM6zGul048C6UwxoU1TKyzcOmXdZFDcI3k+LU8l5yw\nRhKGtBUbhZQhFMA00Q5aLB3UVsil672y97Tcz6VWElU66NIEHFW4IaX+B1TsTYyjtCQMqW+YLMps\naHa7loYi04rw1xu0s/mWI42kmOs1fL65OUq6j7rK8a+4eOWKf2tthQbZdoLamFbAGtSi/xNCK733\ndN7Sdw7vbWOHAAUsilhrM62xaCcc8a1QlyhBpjHHqyoMRZMZa8GsHWijsI1+uImF5D+KGOWktd7h\nsieXgVIqhRXNFtTXJOaIpF9rS6qVOQQu08rpvEIVU6ex9+wGWVBqo4XVMuzoOk9patWcMrkWlnkm\nxUhNCZ0DGMkUzSmjcqIH8rKyVmEOFMR2NMYKRiiWMSWUqfSDQQ+euBRKXlFYOuPQKqK7yvz2ysvz\nC+MgMumbm52krfsRdOL7H+/php7rBJuheM/NrscZzXR6IxW4v7/j7eWVeV0w1qBt4f7DiPcF1zlQ\nHSomlOvojBWWQpZEpJQ1VmVcr/nDT3cSmZcLy6UnrSfCumKNJZaE73e4rqdoK9xrJAGpu+8w2hKW\nM+TK7f7A69vE+/KGdxqtR4xXnC4TORf6bsA5y3leBZt2hr63TNNC7wem+YwxAuOd5pU+yaxeouKw\n6/C9J6J4fr9wuix0w0haV5QX1sn7+ytGS8K9xpKKI9SMrR6PYV0DJhe89yxhRavKfj+gjCgntar4\nrmMcb1nnhVoju2HXmj2HtqJ10EqUms6Kv5C7iJ1DRIqGNbp9RqAibJu8zpQooc6DkcQpivj7O2tw\n1qGNwzt3peyJc4TIyGONoqhWRiZN3+GMx2jEP8g4KgalWrdsxOoiRUh5YVkWlmVmmk/M88S6Lizr\nyun9Qk6S3uONxzotr80qtPNY76hWU7IS91AMVH0lPuRSrp23WHRstrGbHQGoKt3Z1s/V1iULjXkr\nuvJeoBRFaSR2Qq7ZZH5S4K/Xp/k9tUfJgto3690NVdmK9EY9pu3ZaknX6LbKNiWoK5tFK0PWCq1T\n68oN2mR0EbYPjUXzP3r8dtBK37UiLspMp2UxUWozH1eqZWQmjJKuwFrpRhPpyr+sMbU3UbzAS4tS\n0sq1hWRL4LH6ushQyCm94dzGKry31OSJNaFyIcYEWbcgi9IMdBSOQiaSyWI2lBMpQIkrKTgZMb3F\n9hHjDLol2lTliElSP8IaCXGllIhJgU6oqTirKFmTknihx3VlWaIsZq1DW8XpckGQIoO1FVPFHApl\nmUpiLRmDLEF3B8viZ9anE1++nLg59NzcPHB/ewASqcIyrzw9Xfj+x++4f7jDfOz58uUz/+2f/z+O\nLy/c3d5y/3DHbu9x3QP/9I9/4fOfP/Pxu1v2Q898mfj18wvD7hanO/7yl7/w4/f37HeW27sbalGs\ny0y/M8zTLGq+msil4vYHqh3421/+zH11PNzd4J1BKwhxJZelQS+Bp19fsdpzvB1ItXKaI0uM+K4n\npYXd3jKfAzfHPSGvvL1NXKZA13nmuLCkQMq5sXU8l9fE4Hvm+UwxSjjaBIwG7/c83N2SwkSYIp31\neAOqRKa3N35+fOZwu2PcjTgzMA47bm8lIZ4K5/eT+G/UzMvTE0O/5+b2hmmayHHBOY/tfFMj7+i9\no6YXlJPAFaUdXS80V2ogZXWlqx4PR8I083KZca75iisnNgDzzOV0IodIzZXeOd6mgFVC581ZcmIL\nIgjyfmwQg5YCo7UkBanW81aZeq3rccaijEGbZmBXpAjlLNTPGBMxz0zLxOl04f39nXk6i9NnSVil\nUUncPKsq4EwrtOLTLmK+xlorABatrGhIahakQjZVDe6orXA2pk1TTMoLb39ev+Lpcljlr2HKrUgr\nmpK8wHY4bOyVb0XxV7QGyKWIhci2RBW6HOIjQ2PGZHJJ5CLWG/J+yUVypaEIovrOSGCKKRKKAeI9\nU3RFmXY4lUrV/26euD5+s0K+3w8460R0YYS/XSotZbo0rLg24Y/wjTRbhJqEKUBBd1CzZH0KPpaE\nepghJYMxkq+oTMOm1CYNN+ism2+xeKE451DVk2KlxCj0pWpQusrFm7ex1xbjLDFUQjbkIPzWy3mF\nGnBOOLnhNF8phM6Kl4tQpxKdyVBXag2UoFlDlg9BM/TSGMBQVWaeJjG/N5bOjRgn9KfcWDhLKWjr\nGbwsw2osTHPk6fmNcXTc7EYOQ0dIkV//9sbrc8SPA8poPn3/A7c9vLy+N5xzwY2en/7wJ/7rf/lH\n/sv/82f+8Puf+Omnj3z3cMd3DweWaeLzr4+8Dzt65zgebgnzBeUTnx56Xp6/8Je/BtaYGL3j0Hu+\n//FOjMgo0lmBLKPTys1xRymFt/NCLYnlcmGJgaEfefoyiy89CtMrYnGEvLA/DPSDuAta3bNMK7UE\n1jAxhQVlNLu7HZfLyumSCTGge4vRFaNhKYV1XlHGkeuC0oXvPn4kIw6bKS5QZdm0TBO1VvzhwPvl\nnXEnoqkwT6hOcUkr87zSdRYFhGWhG2VXssXQGau4udmjSuQ8z4Q1YI1jSm/kEChlZdcPxFwxXpbt\nqhaMkskyJYm1SylgneZwcySGQAwLxhXSMhHXhVIquUBYV6iy9EzOMcXMNC0c1kA/NpMn40T0QnMH\npeKaERSbZ0itLZxBumRVGlML4bDHmAlRAh9KqaxhYb2cyeskXuhF1nq999hhoDSP7RASa0rULGEO\nmUymkKsl1dYpm7YcrBtheGvAhKHSPIoFz24+LsIEkS58W2Rugp+rm7fakG/1NXhCi66FuhFJvvVH\nlCn+ujRt70ttHuU0HYtEako4BNciXtrCVSPITUG80xuJpj0XzUqKKo6hdTM4v0LFBWFw/MeP3461\nMo5Ya1EGWRCkQsqF1Mj61NzwbCUslFKJzU9aKdcKWxb/AqJkPGqEY56FtVJzFSsAsqTYt+QQ+RAb\nrPMtLw/5O2sAhyKTqrBHNlc940TltUEkSQmFSelMUoWYxHYgV1CmURfXFRMU3nuUHkFras4SI2fV\nNUU758yyBGIqlCQ2umaT7StIClLDdNc0SbqKkeiv42Gk8wajO7QujL1HV1jCgvdQkxiS9cOACYF5\nCbw/v8HbmVTgb7++8eHhKErB3NJntOCAP/1wz4eHW0qF6XRhPezZH0b+8A/f8fbSkTLyPdUIWvH4\n8sowdhIBt/fUWolLJOfKX359wirorAECx+OeYefZ7S1K3ZMinC6LFFev2PdQc0Y7UE6RsyLYkS+f\nRR16t+9wruCM4XKeucxnDI6UC946CoqUC6fpgnaafefoB8t6OWPLymAtOSviEtHGS/anNlgViDmw\nRofXSsKp3QCmsqSJm4ejJFQZRViFOuo6xThojEcMt+wg/P9dL4HbKiJByIl1PuP7DugwSsvEMGdS\ncMSc8d0e03XUvEoHqjRWObpdx2Wa2Tzvh90ok8XTGZqrpjHQd545reQ8o3Vl7HtqhTVVzpeVuxDR\nFawTb3BtRJ6mrZZdjjKUIrCB1ZZKaf75kdSKkkZRSmQNCzEVQsrUqlhDZF1niUh0tsWXif+IdR5j\ntNyLRe5zUyoxF5YQqUSMdWCE0of5iglvmLgU7e3/N+M6pAjqbyGL3Mp0aWNFa/KuezPVio5q8Wyl\nYeTyNf+974kUba4QjTxl+ze1yKHC1/CHslnUQrumHEwVZAprIcrFqAYFqWuTKIhN45EjF9jCia7P\n+R88fjseeefFPMds1LxCbUrI3CLcapEigNJyowkq1nyBDcoqapYFQiqCiwmGp2UUQcKZVVLklFAt\njcg0ZzKJ5ipklalk4W+2NCBtNVSJIHNOFjHbzZ5LxaZISgm3hRYUyXgUJZtg3iVXlhBJKWJtoFKZ\nplm4x22jbZUVDB/DvtNEryUCLQjVsOscB+eoShZr7+eJFFa8KugYKRMURg73nXR+uoJKdNXT70fC\nHKDhdMZ1HA6jdGtWcXt/w7wEpnPguNuzrBOmntFVFLE1yg1cCqTVMp8n1vOZvnN8+v4jIRamZaXW\nSPdwZH+zJ8aMc46cCzmv7A57tOv49ekZnSsvryf6wWBHeP38yp//9a+M3cDxeMPbZabkzKdPD9wc\nLTlE5tPE3x6fmULB2hNOFX734/ec3t5IOeF2O4y1eNcxXyaccZyWhZSNmIhRKMpzmTO7QfDcaS4k\n5QkpYGzhw23H6A3TfGI/WoFSrKiJyQXtZN7XyoiTXQ2kObEuCWMdve9ZlgslZVS/w3SGbnQ4AzvV\nk5MmZ4u3jqEHtAh/sja43mJtR2c9cQ2UEqnpQk6V3bjHuoFcNQaL9T2D8ZI4ZQ0lBS5nT0yBSsJ7\nyzrP5DBTcmEJK6OvWD8QUyXETFglKavmhPK9sEy2drKKmGULA65USs0CA+ZIiEkWoUpiDFFyqJRS\nWeaF99cT87ot/GXRr5WY3S0xQaj0RqLYOmswtUJOxByEAWKdEB6MmFRV4NpGK67S+s2atlwraxMx\nbaBL/VpkN6p2qVIst+XlFqwu126BG9QrpPKVHdMuBddQ5uuf1c0C4BtF6abUrtJVV6vEc6XS/MqF\no143SmNLBZLX2Za4atsLcC3otbaC/ncev1kht/arn0CB5owmCilVIJckJjcVKqLGwmxBDaKWLDSq\njrZtK1zRWaGs+K0IpiXUP6WMLDX0lgBS0U1BJcnrqUEptCKrJdNQVaLOoA1VZ5wSf+lqNSaLfWip\nwmEHA1G442u0rCGyrJBz4HkJ7JzBkFAlE3NlTQVrK101gv0b+RDFnAGxfV2KLLu0lgnieBRKmyyz\nPZXMul54e0l0+z27mxuGbg8lUsuJfX+QUR0t0monVJ4cIeTC7373A7nC+XLi0I/kmNtS8EJ/2PP9\n3Y6X51eWc+Tz42d2ux5lbygJYhImjjGaX/72TAiFm/sH3s4zIVz4/uMtt7e3UA3OKWrRzJfE//fn\nf+V8eeRPP33g//o//w8qBd8Lf12IGJk5XIhoDh8+8aPvKUXw7b7vSLGwrDPff/cDcVkI87sEbvQe\n5zy3O8/r65kBh3WaaU6MqnJz7KlU/vrLz4z7gdFKitDOS8ERfYHlMmXCcmZ/43FWQc3UkLCjp+80\np9MFMjgL2iWenx+ZLwvHh1ucy+x2I9N6IVXQVWH7HqNXHp9eGL0TU6gqlqwp9GKNWoowT8JKjSv7\nwx3ejyLKsT0xK7TpcF6zris1F15fnkgxAkoKsxbOfspSAJwWjja50BkaoySzhgW9nho0IJ+tkpMk\n8FTdrlEENglBDryNJVMr1XVoo0TBWSGxomrE6UzW4h+85szpFFHa0vkCVVLA3NjLxFQM1AS64r0V\nHpPzGN+jtKXqerUKEPGOYMUV3e7rFru43a/irIKmgt4sPzaWiroWw61Dpv3+yj9UNHtS2RWoum3U\n2l+3w+Pr6rNJ7NU3uZqqwTlS9q/ECq2rTD0tt9PqLXBCXXH90mCjWiu1hWNsz6uaIOp/4Jn12xVy\npb85PVvQsaZKN6w0JgvP0mgj1rFGItSMlWWCqCiTjB7WEDPi32w0ThvJ+myCIEDGOisbf0nu0dct\nsjWaUEQsVJLIezUtPCKK81tMBZc8ajDYzmKcA1tRJWOyFs9yXbDWUmrFWFngWG3Eya5EGVf9iK4Z\nU8XzWUZZLWNplqJRogTelpQpCaryGG+wvsN3CtvvyKVKUhCRnAIGTU2JNE3MOeNdh7J7oi64YS/b\nYFO5zDNKy5i3rBPp6YVhGBl2B0LK2F7hnGXvHDEWlPY8fPieV/PG5fyGdz21Ot7PK/Myczo/cX/c\nc3+z4+114svj3zhNYoY0uI7vHj7hrGWeTyhV2H8Y+f7H/50cI9P5nS+nd7zzuFwYnOUwDhg3YmfF\ne3jl5emRWAv39/d0vieEhPOe77/7CGrlMr9hOs3gjvz8y2e68YiqmeN+z9vbO9Z1HF1HWAXHTlno\nXLUohuY0+DZn7vc9nc0s60opio8PHxh2PTGs1ArOe7zzlFzwXos8/vTO4SjWA/vjAa01p9cvGBLa\nGC7TzM3xyDy9c3f3HUM3EuLMvt9ByqSammIPmUaRLrGkRFoDvtu1hHmw1su0mivWdFgX0MbTdQPP\nT8/ymdaKHFOLpisYVfGDI6UimoqsWcLKvCxoo1pIeBIoCJG0x1xIWTrvMAfmEFljxLv+CjuYFHF9\nh8lO8O6kqFiGscd4yxoKsSackS6z03KfxZhIMZKtTKFU0EbjB0vGUYxHW9fCoYW+p/lGI9JEOmJ2\nJTTWK9BRaIvKpi1hoxF+hU++NvCNjdLwcM1mltWep9SvkG8VLnn7LShZUG79f22HTEUObfk3Eg2p\nW1qQsS1FSG/WuLo1otsCVERYWwxlbUwlGkzeLnp1b/yPHr9ZIa9IMa9J8O+yrZOr8DO1NaiWYmKs\nFXjDWozVVCM8VVJqp29pjKFCKgldZES0xkreZsMalW1KKTnP2yGscJ2ny4WIIqRVPhQNDNuumRoP\n1PsOp5SoJrdIuVIxSeTsNRUp5H4lLgFjVtZVM6+aNSXxIs9B8HTAIN/LEkQ1FnLCW8VRVbwRaqPR\nEtZcExQc4yjRZxhFLJUQMrZmVFvyKq3JKpNI1Fw4Xy44N7DbHenHI5fLzMvLCWt3ZFWYQ+Q8r5xO\nFw63D8TmB313t8MC5/c3Ypgxvue//tO/8vHT9zhn+PzLF1IqxJD44eMd/+v/9idO54lSFW/v75ze\nX3k/v2CdJZJJofDl+VcePhw5HHfszQ35bJjOJ3Y4lrqSy4r1A+clEYvFdTtUgc9fJnYHw+9+eOD9\n+TNWV9YE2nYcdwOv7xOpIKHdSmOc5bxM/PDjB17fLry8XlhUoOt6Pt4fWEJEFUUOkXFQdK5yGPec\nLyu3DyPOREKQwzKsKyUrclr55fEz1nvGvhP8u+zIJeNHx35/ZOaEKpWQF4EevGVeTsQw4axCGU8s\nibjMhLCgjWHY71FaMOSaI9VtyzslSUU5U8tCqZWQoNvtsV1HqYX35y+c7Illjfiu4J0m+555nsVA\n7TyD9qyhYFNhDZnLZcEaTS0BSyYPBW2sJPQsK0sMgl0voSX4wDmfhY5oDM5kSIl0OWOV3Gd2GIjR\nkOKZzlR2NzvyjcTPpRy4XISKuiyh3UoRA74AACAASURBVEcOhcG6XnZeRdg62hjRPlbVMPZv9n2N\n6rdlEP+b8IVtmQgNQhFsfsOu22rzCq0AtFrPRn7ZvEzEIlfiFSnbkrVel6Rq+zpkQtiqsWrPqVqG\np9HitWONqKy1Mc0uZCvkWz5CJaltWmgeNTRIaINzmmfM33v8ZoWcqkUOnL+eMpKmU5vRu2qGNRpj\nxdxda9sM8EUoZKhUnchKQ41sfglsJ3MRjHvzCqbhTZIQzvU61jqGAVRN5GyIJVGaN4osaERMUGom\npITNcooLrqYa7CHWu9XKZts4R7YRYz3GrvSdGGiFFImrRhObO2ICq/DWicdxlENtLQq0x1cLOWO1\nwtoKRTrZaZrEt6QfMM5RqsaoinNW+ofmDFmTYj2vXMrK+RS4/3DL7tBjHby+nolrxu0H9oeRcXfL\n4+MLtUTGzvLlr5+R0yxhncL5nt//8JHnp2eyt3zcOcFWdSGFwM+/PnF7dyvJLmvEG8vrywnjBk7L\nwq7fMYwH5nPkX/75HynAft9zvx+5G0aGcSCjeTnNOOUotnJZE93O8/3DDSplTi9fsNaRciXExBor\ny1qZp3c+fbzFm8oSZUH9+5/+wNP7hdfXRSYfpUhVc1mEcqpcx3wWrHocNNZ37Ch0vWMNK6YWemdZ\npzNFK5Yw8fBwS6ow9DspnkOPr6LkzYCyjlwLOay4zhPCArVSYmCdZ4bdiHYed3Sszyvn9y/k9cTu\neCDlgaw9vh+JUTInt3uhFkWpiq4bWdaJmheUhlxW7u53snjGyX6iFLFJmBd2XS/LSK+wRmipoFib\n/UTVgZBOKCMd4rpEpmlprBX5HFkLDtMmuQolE+eFhKazjuwy1mqmuBJywFsnYdNrIJVKTqnxx2X6\nEKuJIslRxpKxInc3VjpjFMqo1k1/tZ3dlJySScBViPO10gOqtC4amrPWdVlZrkyUzU+mhVGYVjar\nkCW0kdAH3drh0hSZm0e4ojkY1k0HKq9V1OZa0oOspC5pZaQjN7ZlEggasE3FqrXzZoNNqhRlhcAq\nRStUbW6NJf7dcvrbdeTb4dI+LHIamuu4VK7OZMgPoxnalIZrG23B5LbVVSjdXzfkpSaJEdOqKSO5\nigq2DTW0mCttNidMdJDAY6XWVqObraWS/ak1CkphnQM5y42vrbw2SYjSaC/8WFsdxg5ov9CPHSVl\nYozEJFBIjokSU0s5kh+0N/IjzCXLGGZE6aVKEspXU77KNSK1JOJ0kgAGZynOYpQnrpWUIikFlmnB\nWdgdR4yLvL8+4n2PsZbb2wNdPzSx0YpSmXFQLFNGg0SnhcBpWrHFsZ6e8cZyexjpBi9ug4dbvLNM\nc+D08s50mlDWoF3Hl+c3UnzjuBtRGkzv0GZgWlYOh5Hj4YCycHk78/z0hhst7+eZmg270XF/c+TT\n/ZE5yXJ3fzOitOL19cTz8wvdMHJ7d08OF27v76gUfvn8DFXz4/cHpmXlPAfsMKBTwhrN0O/49dcv\n5AqHXeXjxwOn84V1nbH2hq7fYazhZhxIYWVZhcc9Xyb2NzdYW/Bdx+A9u2EQip2qWDQ1zfS9ky5u\nFQppzQVnpXt3zhLSgooTnfPcHfdXaX3KCVMDsRi6weGHAWxHNZJnq6pChZV1fkGVlfP5DWMH0A5l\nMkPviTGj10pvFMUaZiClhNWWznbS5VPYHXZYQ2uQPFUZsYtIkRgSc8jklOm7HtsERCAuIblUak6i\ne1BKDu0g9+YSZnKppGzIl4mQIv3Q07te5PhB4MVcEzmLJ5I1ji3guG4+4O2eK22hqbQ0TXqjEqqN\nQ84VjpBFpUApm5Dna0EX5t7VXUXR6MsbY0Q3mEW3GiOeSFAb3FK/ctHbMlTVDeLZrilFZgtglwZU\nwrGt1VJXdIOUWgMp5GEx3Muaq61tkaoj+0Cl2nNVSgp/t57+dn7kZVsSNAyq+QjkGiUDk20ZIV27\nMY17Wb8dMdS10BvtJNlEG0pdhW2gZeFgtPgWFG3aEkTsOE0LZy5FkZUmOC9yY5/kubJGb93ARgMq\niZxWCUUoGV09qJZmhEIpkf8bgyjAoiVHEUKYGLBJ+M4lZRENpEhJkRjWlqZuvloRFMR/QhuMlaWs\n8x6neoyS5VNaF8FZjSErzbwEdJU4tVg0r/PCMl/YnYK4EnpNNEGolyjm9VfG/Y7Dfk/JlVgUS4F1\niZS4cOw1NztHLmCV5/0yE7LiY9fz4cMPuK6n6w3dLpAfxf3QOsew3/HD9/eUvHJ3e4O2I//8Lz/z\nL3/+f/njn37C1MyXz4/c3I4Mg2PXWQorD4dbrO/Jvud0ujRfk5Uvj298/PCA8+Iz/g8//EBSEVPg\n5QQ3Hz5xmQKlLtzedzy/PHFzc8//8j//jn/6x3+hsw5UJawLRmVu9z2DBa9Hzq9nstFYN4IKKKcJ\nOUpyVK6UmEQHUBLLdCG/v5P2E33fozKcz2/EGFHG8/Bwx35/oO72aBSHcUfKgRDWRmX07IaeSiam\nBa1hN+zEy0N9ddDc7R5Q2pNqIqaI1jLx2fZ1lsw8nXDescxLUzjDuiReJ2kUjFb0nSVmzZIKu941\ndkdB+UEomtXijBbbgqDJVeFdYc0RYwrKQtGJsduhjSbEzLLIZ67kyhIDVVnWEERKXzUxZKxV7EaZ\nsmrKRK2oTpMK1FQkFKJCLtKwCIm6UQ6rItfWzCTphEtjdlTdXE61JreItg0s/dqZNy52EcO6bxeH\nKMRlULwiW1dMswGQPxB2ilAGpZhv5lvbmVHYwBo5NFpxtkrgkwYBW+txtmsKc7Hn1ZrmpaJQTdwk\nhVz/m0Iur+lrRw6Vmv8Teq2kDZiqqmXw1X/j9FU3wEi1ZYWSQiaY2Tc2lFWWNKrRdnIV3whlNuaS\nbm9MvR55ovbKAo20XD1txAiKPEinWyRLMOYoo46pLdhCGDe+t/iuR1uHxgiDSYPBttNXeO7WQvZC\nTfJ1R4pZvMirGCzltIpoIwRiWnHNGjdXRU6BGGdSlmxSUKQiS7GvigJFKRprDINzjJ0hUyilNl7u\ngWVxLNPM89MzvdPs9wP9UOh7x95r3p+eeP38hW4cMM7idSXFyvkUeP6y8PHhlvu7Hc5qHu7vqEXw\n98enR1Iy9Ls9fdcBA90oiTgsEzFXaqqUeGI8Zj58OPCHP/7fGGXIMfP4+Mj728TxcOD+4UEcAJeV\ny7zy+usvxDUx9gO7uz0fHu7pnSPXxHR6w6oKtjDPgf3tnjUFlMl8+v4DXx4fRamrKv/0T//MPE3c\nHm54fnmGWvmH331A1YzV4vPR9T3D0HG5nNE6MTCyzgulREIpsqArEzfHHf0w8vb+wnyZCEtC6crx\n5oDvel7PM/MlUNI7h/s7YkqczhdZLKbM2HUopXl/fcU5g9YVNx4kp7StiAZvcUZyXY32hDWiSKA1\n1nQYp4hhxdgOnVZUKeSQyCS6vsPogq3C0w65SJ5pU8sVEtr0xDUybMpRCmtMmGJQtWJKvoptcknk\nVVGWzLpKU5FSIsRFpk9l0FaWwCANV4ixTc+OWDIqrORYKEphvceg8TiR91dLwZKK2N2qKp13aaZS\nVEmbKu3eVbpCbpavjUCuGqwhwPim9GyQ9nXCl6/RDecWoV0DRZT6CtEooOVrqto691af5JCSfVhV\npsEtUHVLA9LCu5d9nuTFGuMauaLpPoy7csShHSLt9epWvLfHV2hl29dVqsv8vcdvx1qp9eoD3Jg3\n5OYYti0YNuqQ0Q6jbDN+lB9wE+kK9slWzNu41cYs1RaguSpKzrTgqaauzOQi4QEFKYRGO/q+F0FS\nbvBEFi66K8Io6axFUygpkvSKVaoFrcrYpJs3hJyoBqNl8VEaZq+VReFJDcdU0WNcoBu2RWiGXHBU\nUrRo7SgSFc7VwjMXahZMX2sxGyuhEHIUFzUrqrzB/f/MvcmP7G2W3/U5z/AbYsrMO7w1dpct43bT\nCMkgxIYFG9jCDjZICNixALHC/gcsYIEQSyQWGAmEJQuLJfaCBQtAMpPkxmO7y+6qrvd975BDRPyG\nZzgszvOLvFVd7QZbVndIVzdv5s3IyIhfnOec7/kOHcGPrF1gHSLT1RG00AdvC9Yl4IPneDqZzei6\nUteVYAQfujcHPn9WPn56YlpmhrFjHEaG3oKaBy88Xj5zXia+ngtLddyddnjpKBqxo7SQ10RJBXWO\nz49n834ZOguw+NF32e12HA8nisDz85Xy+Mz3j/f0MbCuk9HyUuLpfOX5+TMPd/dc5iuXbz7x9t17\nLtcFH81iWEJgTgUk8tPf/YRK4HB6YF5XqvO8fThBssDj0pZNDw8DYxfoorDOmeUyc71ebfoq7Zr0\njvN54npdiNFG5VLt+T+fLxx94LDfE33HNM1cp8nSg3A8P17p+8Dh7sg8L9QWtpDLwnS9shsHimZc\nPNANR7RlwuaSCTGS1oyriVwmcl4pKCrGEol+4OXp2YKnxXz779/sWJbEcPUss0FDTrpb4vuyJB5f\nJobeLIypleIcKWfWZUWLsguBJSkvS+Y8F9Z0RkQ4HXbcP+zt+7S09w3s+h6opByYppXLvIATlpQJ\nLtL3AyEaZmyUPWHNLR1MaZatzYO7NXI2bG9p99owc0/VBqniX6GWur0/GoSijd5844pvbBVQty1J\nW1fduvr23+znOZCmQrWJACivH0vrxEXkNvEHH42BFTt8cMTQEUOHi/a16EIzAWuHDQ3OuZl7bVBP\nbY/fdgQb77DqH8lCDlCb5aM0apOZ+0jdrCgLGio3K8dgGLIIOK22lMgLTuyC2BI/gGaBqWaNiz3Z\nt9lJ5fZClyZAqgrR2Vg0jDucKqoZqbBcLqRm4BSSN9xMFpskquL7wS5AtoDobSkjjWYJvhpmXxqF\nqvUQuNgh6hvjxHDtLMkWra7DBRsRq2a8U4RCyZWSC1oC9mtVSqpoSdSacdoj0aAfQRj6gS527Pqe\n8/mJpawMY0RkYp4S82KH6ul0AuA6XfDOOPLbpV3nZEyguEKt1OyI+3tqHPj2m48c9gf66Pndn3zN\nfojc73twwqfHR6oTfuDeM4xHfBwYR4eWifWq/Pi3f0a/O3J6WDg93HN//5aHN2+ZLs98+vQtw9jh\nxeGIDENHiIFlWczIqj+irqM6Zeg6ludHBu85HEc+fPOR7371He7eHvn2208sqy2YnKe98U3h2MeO\n9bqyrBVNlTB0PL9cqWsm9OaPf3584fBwMI2DB5xjmc82FS5qkvJUiH2kOuj7gbwG9sOetFzpYyJI\n5Xp+wvtgzBLN7A87rk/P+MOeOIyMp7uWWZsQlJxWQtfZYkyVWioxdmi1KLdhPFIL7HZ3fP48UeeJ\nWjJOzN2xH0Y+nc1YrouVuiiHk4VOX6dEqcJu3zW/lMo0JUpa6TpvUYvSEYPiZMaHQN9H7h8OnA47\nas6UknF4ci7GEe8MFrzOlTUr3lv4tY/Rsi8l4H1gyUtLArNu2okHbyZcgFH64FWcQ7NzbYoT2eAR\nLVsD/kWX2zpb2aCUZjZlwguDQLYOHjPC+zLk+JZbL6+fu/mu3ERC1lI6nPn0BGPXBW+Fu+86XBeJ\nIdKHDqIn+rbobGSNbUm4hc2bsqj9Ds4+tkNNbbJQm9J+v9sfHmtFnHn0apPTug3HshPT5WZKRULC\nigsRT2w5eg1jEYcLtkWv0nhS2nx/VchakdKin7wtDjZDf/E2YkmT4FZVUjXjHYcnxJ5x3CPq0VzJ\n60xVM9MSZwksoflL9E7MHU1Bgy03bCttJEdTA1r1ds1vvGppdCRnL6BWvIu4MOBri65r31ub54ZW\nywGN22POlsaupQIJTUY3TPkKq8fHAdcNdgET6GLgdAycn56YXmacZIYxMu4j1znz+OmFeU6IKF0f\n2B92vH174OXlhes1cU6C5MBD3PPw7g0lVR6OysNuwAchjjvm796zzld2nRB8z+HuwON54utvnnE8\n8e7NgR/84A3iIk8vCx1Ct7MEnd0y8fc+fiItidg7htFwfFVjLpVlYbq+cNzviA9HJAQulxnvIr/9\n459RlkTJjwynO1INjIcTnz+/IAjH44CmiTSlNh4HxFXE2dJtzfCzjx84nvZ4F7leE10RXKkMw4CL\nERXl/HJh2B04Hu6Qqjw+P1khfL4y7nZ0Y2dZnbVyfT4bNXHoGPc7lsuVdbrgQ89ut0OqUefWeaIs\nK2F8Q+z2Jk5aF6Lv0ZoNH28jfSnFsGFVlukT87zignK8P5LWRFoTtZqa+DqD0nGdXkACuWRq6yCH\nKKRSSdnjwoim1eDIDNNixmZdgGHc45wtX+9OB4YuUHJhptxM5zZK4UhPUSXlhb5T+qE32rAzvvU0\nJeY1o82mFelR5/G+Q9XfVJgiFsxyI6KIvqbn8AU0sfEQ4baMbN9x+/s1r6d1uw0mQdyrlQncumQ2\nZotygzNv399YdUIFZ6w3H7blpkEqIQZ8FwhdpAsdIXa44Im+M5acf8XCzdzFvQL1mxe6vE4LXkCx\nWinbofRLbn+IhbxxOhtk4Bs+jqhJ4L3YCFkzOS1kH4m+R4JrL3RFNRtU4Vsij1iXYGGvZtpexXbA\nViyNcmQL0rYdpy0x27KybJJ7ceA7Qi8MByWtpshDlZwxgY82R7LZFqYRj/eFUozQb7Qx818xpzNb\n2N447gKiQlUTQKCCaMsrVcP8vYfQKJW1WECvNljGibdwZknGS5VMLeZ5XiuUJSNkfN9yD70Su4G7\nYWSZZ6bLC+frGe9WtCQ8K2O0gGdxhbwY68GJx5FYpiu7IXJ5OSPOcX//wPFwIOeJ5+cznx6vvHnz\nhtP+npQmkgSueeX5mrm7f8DryufHR5Y00w9HliVz/3AiaKbqytPHqzkcHu+4f3dHXg16yHltjADl\n/jBSc8H7DnE9fedZ54X7w8iLJk5fvedv/t2f8P7dPWs6E1wipZmyKJozl+sVV5W+F4KDu9PIh7yy\nlsJlWXn6CBJ2PD+d+cG7IxFlThP9fuByXlmWinOZlzrjnON0/2CagFrYH/eUUpimhaEPHO+PnM9n\nLpeJZZqJjdpW0sIymR/JOOxw4skpM52f8A8jEkamy8xu11OL5UsaTuooThHfszv0fP749U3v0PUj\n6jwxJdZJSXkmLYnTEDkMD3x4ulqRTcblDsHgjlQKkUIX7WCLMVgiU7LM2aKZfh/Z70ai98xT4nK9\n8vTyQq7Qx4G1YNdtU2nuhr3Bfx5zDqWSSuZynZmXAi7iXI/vOnwXQFsZKvrKLpNtnbiVC2lLSvk9\nBftLheYXCnpuQMkXh4ChFEZJvrFP3AbJtACK17v+4meVG1yLs57cS0CCJ4RACIGu6wjBfNxDjIQQ\n6ULExUhwoeV6vuLi9rv+3G+JQT2mWXGyyfPt/R/1jyD90DkHXgHrsJ03NZQ0GXDNlZIrVRO6CtnN\nFN8TYiT6YOrOzbkMxYsJdGsVqiakURQ9ldqKpQq3UcoUvNq6d+OoCmIRTg4obdwL4PoBjyLBY2Qp\nS2V3wcJ8Ed8wfGxKchvF0UanTUVmEXCm5nQ+3OhLukmMtY387Rer9ZVTT+sI8ha6IVs0VEGd+dIQ\nquGQ2Q6unE1N4RD6vqM4WsQalFw5nu7gdMBLvS1dL+eFj49POC8cTpHT6UQfPfPFPv/8/EIpA3ib\nHt5/9V283DN0Z7755hv+xt/8W7x//44f/PB77A9H3n4F4bf+LvvxwLvvfMXL9cJP/v7vMOyOhJj5\nyc8+IT/7wPE48NVXbxn3ex5fLnzz4WdQV3Z9x+Pjkxla+cDLyxO/9qf+SbR6/sZf/+u8e/dAXq4M\nofArP/guX3/4zD/xx9/TR09NhaUoz09ndv3I2O/49PLEfoj4FBj3e6Lf4ePEx68/87iO/O51JdUn\n3gxmSezSFYfn5XkihsiaCvu9dWXD0INXduNA33fkPBvXOSvnjx+pacL3g3G7p4lrKcTdCK2Y7o47\nHu4fcDVzvXxmpyfyOjNNmX63Y84rVZXQ8OsYAxR7TXNW7u7fc72cmZdM7CJJheO7Pfl8YZor3i2U\nRfk0TUxppQsd62zKyiIOSW1x39vUEOvAfE30neB3ns+PlafzQhyE2K0UMmvKXKYr07JyPBzZ73as\nixX3damE6HDRlIohOPNiKRb5VnJCMYosvkPFGF/2Jm5OhdBEMhuG/MoLl2Z99XrzbDa12+dfg4zF\nmrbt47ZVE3mlGzrZBECtqLaa3c7G1gFvvivNMVFavXGB0BK9rBvvboW7j50tgbdFsPcE7wnO3QKa\ngVtR30RGt8NINozfHvN2ltT6R7AjF+cITaizsU5cw6g2RZbFuylebfFYqnkbVwoSnEW0VfNFqFUQ\njLJn4gEbodRVfJvTnHNmK8vraSzbC1XsZ1Vpm+k2Kbjm0azeY5aaNubZmektM9QVSkmQhTxbnqhr\n4azigo1TNNFA48pWNagnODulSy1IqbjmQazNWqZufsZqy5UuRlOcqT2WUoSSxAz5czL0zjlKNfMv\n6QxHB2UIAyrmKbLbj4gWnDMsNjlBfOTY9ZzuDyzzzLJk5uvMBPR9zw9/9XtILSbK6kaSmm/J2AWG\nYeDNd9/T7Xd8+80HfvrTn/CjX/0Bb999h+//4Id88/EbPj9+AhV+8IMfUkql7zN/8m6wQtjtWFV4\nfHzidNxxtz+Qppnz+cow7uhjoJTE3a/8ikWvieNP/fqf5Pz8mTGOlGRhBvtdT8nKT3/3A6hyuNvj\nw0CpDi0vvH13ZNcFfASKcr6+cLkWPs3KhwWWLLzZDfzwfmAcAmEYeHl+Yp6U2WX6fotYWxlkxLmO\nmpWPn79m3I+sGdJa2Z3uSWlFNSEFXDPpGoiE0QzW1jVzuT6TlgXnI+v0gp34I3434kJvts0KZnls\n130t1hgs1wvOZUIsXM8XarHXeZomBOVynkCEoe85VtM6lFpZCiiJ427k/u6EVuuW+6HHe8dlWlAV\nSoGxN4fQ6fmKjwOl2nV/dzpw3O9wAsuSidETB0+VakEZFXpxpFrQ3Pz91dN3I77f4+IOXN+YM9wW\n+Q3lZivMRkTZCpu2fdJWVPVWnFVfVZwbvm610fZUztuOzGIht8ZoY4zAxkWvW8dcochm8GETAtjX\nnN/u0wJkYltwxhgJMeK73qymQ2dCoGCWIZspX3NlAOdNH7CdIrfHtTWCrcDXxpj7o+hHvhVr713D\nrg1S0dryOqrxJ7VU1BUISs4rPq+NZN9ZIfeGc0sRxFV7cpxYZNsm6W2wSVXBqTPfFvc6xojaC6W1\neTlQ21a7nb5eqN7sPUvJaDF2idmZOlxoYblrQGKH95bhSdfReRurtlPYtbQgcz6zHYGWitlhVkRp\n4oiWNdo2o7bVbwwfzbeL2bVDpootBWsuiIfYcHpCaBCNuUU6ceAhJ8sRtMCKiISeZV4Ngx8849GR\nc2JdzEkvdD3Bq7F4tBKj4Y86J9KSWJcZVSGvK9//zjuT4J+OlJrZ7QJv8x2lFIZhZEmV8/mJ43Gg\nZsdPf/ot67xwuL8jdp7npwuHY6AbRtKz4LseYkfOlW+++ZaH05Gui/zs86P9ri2c+bA7UlLlen3k\ndGdBwMt84bgbefr0gvfQjyO4ivORZV64vExcppkqQskz7/eeP/5u4L63vYWEFtqRYcqFbjFr48Mh\nklbbUQQXzP1ymc2PRRLgGfYdXWeKyJpgkI41XSnF0Q8DoetRHNUJMThqTczTE3Go5DwT/Ih3A8s0\nEYIFpti+JCIOMjBfr6R5Qmrl8vRkWZnVzMxcdPguUKvw1e5EyhYc7mMgdp0xuLzYMpJKqTStQrRi\n6By5ZeTGtiNIpbCWShDH2tSGzlVOpz0qwryuzPMM1VFTNqdPUcR5Yjci3R43nCAMttxsOZp1w55V\nWw3PxtRy3tTPbJMrPwd76Bd/bzfZZPKu8bCdCXQ2yqGjCW0ayGEcCNN6bPfnnNsMVWBr+hqDRbwz\nTrjvGl+8UQxb8Q6hIzrrwi17l1tzJ+51EnDO3UgX1dHwfEGlLXdvMI821s7vX0//ECX62wN7ZZrk\nUo1qV+o2KAFCrpXY/AnQ5r/QNhVblp0Tg2VUm8GQS82tbVuEQC4ZRfBqdre2FXZ0GlFn9Mcs4F0l\nh3YRo+ACRDuNq4o9RsmW7FELmu0w9ZiBV/CWetL1nYVnNDnuFsTKF+Y3Wi3jr1RTo1rmH02AYFi4\ndfKWvWhXnjOxSgP2fOyJYoeJxgpqhltbRxJjJHYDte3aRZUw+JarmEAdTgLH48C8Jq7XK7mqsV0O\nu+bzUOmiornj8fMTy8sLu92eMs1oUITAMOzY7QZUM3Oa8Itw/ZR4/HxGdeb+/p75+gJZeLMbERUe\npxdKLbx5f8/Dw1vm64XD/sTT8xPdIfKrP/pV8PDttx+R0KEOFq1cLjNFA+vlwg9/8F2eH8/Mc0Gc\nMu4Gjqe3fPr0zHVKeEzkcoiBSGLsetaSOF+vJLWX4010HO9H3r7ZEyKMhx2X5xc8QnCRS1qZpkTt\nIk/PibRklpfM7jRy9+bE6d07ruczoe8JpSLVKKHn6cxwHME7hv2Okh0vT2dcDDgiRdXUtWlt8JtZ\nMEcXSDmhYTW75obzupJJWYFgHabvSOsV1DNfV+b1wri/43AYcF64XGecCD4G1pQ47PfsdxboIh7S\nPEOM9MNIqYXLeaXrHOMQqPYjSKmyrJVpXsjFLvZSzBbXize2ho8kXYw5lTNlVZZSyDXTj2Y74OMe\n193h4x5tltA01oYZ2NUbnTBXO0gVbZBLA7Ffq6tBszd6oGzo422Sdo2G64JvvkjCZhHr2mKRavL9\nUjIuYF7iTXSo9oa8/X8RbgpO7xyxifeCN5zce99yVj0+GkPHN6aT+zl6ZSvZTaC05RLbzk7MPuQG\nt7SDSrZJ5Zff/oGFXER+BfjzwFfYa/pfqOp/LiJvgP8O+BHw28C/pqqP7Xv+LPBvY4ref09V/8df\ndt9VyytFsG7Vthj2XcotKilnO41r2ZZu26q5FUXs5AVTS2kLhLXH0viY5tpOrb5BFZVlybeO1m2O\nZJsM34MU4z1rUjQXxFmqSnXeZimKnAAAIABJREFUbGuz4Xci1XD4nIldf7OyrM0bY3Wpcb1dS8Xe\nkrK3C6k2UZNdzLVm45E2jn0tduGWStv028UYGjbvmv+AaEG9hWG4NlHY76rm9ofc/NQ3200Xw00+\nbv2QhWB4L+ScmJeFfM147xAtXGom+MCwu2OZrpwvC957lnViur6wxAv3b0/ELuI0sF4X5vMVzQun\n+1MzPevM+8OZS96w25NS5vn5wsdPj5z2o4UqKLw8PvH8+Nk6S+eJKO/fvqfrdnz9zQdqmnk4Hnn5\n/GzVWJVdv6f0PX/zb/0WHz88c3/acRg8p8OAOYrBNx+f2e0GHu5PaHU495nvvD/y+dMjp2NkLRkn\nwsP9ieVypgvw/hTZ9Y5cjcbmfECCIj6TlzN0QnTK48cP3J3u8FFJKZFL5vLpxVz+HszW4f7ujpQS\n69wSpJxHs7JqZT/uQQLn6wvjYUBQuq4nr1dqLgaDrRNIIS0X1nkmpwtFK91g3iU5Fbre+N1pNoHO\nNF+YlgXnD0CPVJsKxHubzlQJ3tN3YuEQ2bdEokKp2qBEbWlCsfGkbVe1rpYbULWSFqPsulZ8+xAZ\nhwNhOOG6e1x3woXhxkzR5lzo8RZDp4pUBfVNL1FvrA1cWz5qRUVbHrLBMNJQbOekKSjNeI92zTux\nOrDxWIo0w9lq3DInm3Wt/cybeLDZ5TbiYyvEwdTWTYYfWwfuQ7wtPoP/opA3Nad3wTryButsu68N\nHhfdDoqtO6fRLNVo2b9/Hf8DO/IE/Aeq+n+KyAH4qyLyl4F/C/jLqvqfiMh/CPwZ4M+IyG8A/zrw\nG8APgL8iIr+mX/KEtjterWPeXgBt3ShV0bJ5rbR4pXUmhQ6t+XYm1aq45i5j8mYTX4TQUYudbFIt\nuURwN78V5yyRp9bcvICV0sy5jI8vt/W184ovQhG1JVMxd0HFkYoiJeE0UxSjWK0Lsk4kfwUfIQyG\nlQdnGFrXG42yvdi38UnaqVszpZgFp6gDLU1Sbao2EePSiw94H1HnbeFSoRZLoXGAih2EvuF+r2KH\nxqMVjNlzm4ocpRW5UkqjU3mGYaBUo7SVUomYr4jWTN8H1nWhlsL9/ZF3797gXSCOgx1oCmghdj13\npZLKgkFpgUO/NytVqQYzdHvWaeLufmC/27OsiSLKsN+xzhPLPONVCSGyzsr5MhEDDIcD424g9JHf\n+tt/h+PhANPKb/6NH5Ny4f3bBzQnXBVeni6ECLvDgW7cgyuImGnZw8NIzoXvfOeetCT2Y49SWNdN\n0ODJzarh4bSj1oV1mUirp6pnvi7kRRiHQN8dcD62jEwYhpOFklDQoszTxNAHhiGQFaQoeV7Y9ni1\nCHEYDTuuCcmOOSeiF2NiORBNzC/PrNcX64gFVAL4bKEmU2JdCi9nK959b0vHcQy8XM+46Lg/nuht\n3U9Ohet5sefSe2qw7l1xZnBVhHHc4bvMvGw6ApjW6XWZmGy/40Ta4rfgvNANI8PuhPYnXHfExRHv\nI01FQXU2cYoKUuOtyUAt5tDEO7ZH06K4LSzaORwW6+gak0RkS9YyKNP5ANvHGJFhgytuNrGVxn6z\n9C5RNaabM6jJiArWJJgThh3iPsQb7dD7gAuR4Ey96W/5pt4YYND2ZdYsCu4LFk4rN1vp2brxVue0\nHTCvkXX/EIVcVX8G/Kx9fBaR/6cV6H8F+Bfbf/uvgP+pFfN/FfhvVTUBvy0ifxv454H/5RfvuxYj\ntyvGjzSazdad08Q225/KsixM04zvdq+Wn9WYJQB1I9A7wUlBa2heJks7UV/VUs5Zodx8XuzFakW9\nlFvZu3XJXyw4AUJw1M6jq0OzXQg2UCRYEtnNqDiUSBWjybnYEcJkXinDaNFr3t+MgGotOAwT16KU\nXChppeQVqIgLhLDg+x2xG6nicdLZmwGT8Etb5IhUnK9sfp9O3G2Ba12QfsFhV4vyalilc+a4Z/42\njq53DENHKkperfiKJGoVhv2e8/Nnnj59ZD+O9MPAcnm+jck+RsbdHt91BN9xvaycL59wTpnnRE6F\nosnWAKosqWd0jvt3XyGdJ7iO50/PiHvGi7IfR6ozmtfj50/kvLI/3PPjv/cThuFASQre8af/6V/j\nen7m8vzI3dsjdS1MZ6WWhTrNvH040veRD58/ktQRcfSNg9rt+mapDN7ZviHVRIzCsQ/GolKhiwFE\neTk/8d139zgy5/NCHz2ki71uIbJ711FZUSD4nv245/npM8M44kJgNwYu5zPiK9571uVMQegOFdFI\nWipIxHWRlBLnlyvURFov1CUhQZmuF3Z3bzlfZ8bhgCvCj//et3x4WlmScn8n7MTeR6KRy0vBy4JK\nJQ4eiZH92FnAePV0fiB0AS8RJ8I8r+SipALeBUpdTTgUe6TrWBaDW+IQcRIoWSmTHeLD4UjsTtSw\nJw47xHeNDRJe+eQoVJs6AYs/rA71lZrN10RLtlBoBVcVNCPqbhFvvknffYNSXGM56eZ+irMwYzYa\nomuTvxpAXRWVaklizhwbzQXDOn67bzPREm82HN5HfLSi7WNsRb55rfhX6b41kI3YsZn8NcuRL+mO\nW0deN4iIV47Ol6yW/9+F/MubiPwx4J8B/lfgO6r6dfvS18B32sff5+eL9u9ghf/33FSN/eCDnZa1\nRbNpS9k2/MjGOGn4d1on1uVsVLTYI2Iv2PYr304s1xK5a74liNt70zW6ko0p3rtbaodHKK1ob6C6\nbKe9dxa3FYxiWIsQtVrqDpa2U8xjABP8m7RW1RR6Vpwz6jpKViQJyUEN0ShlYkIh1zY5zgtCQMTG\nNbV0CVPA1UrJCXvE+YYd2qQqrzuB5ijn2lZ/swO1ctXQto2Z403tlls3LuLwrvH8a8MPazFpvrOD\ny7cLMsgDXRftvkOHqE0p437keHpLqWLdXcmW11mVebpahx0ru+MeIdD1B7re8/j4ifn6xG5/bIdJ\nYNx11KoU50ml8vz0xDJPjH3P+fLMu/dHUu55++aBmjKPz89mRYwYXEci6cR+3yMouazokgguss4L\nU1nZ7SKxc6xrQjRwmVZKgeuUqOqYktIFgZKbudnmf91zWTD//FrIRaji2R0eiINBYXm52nOUlWnK\nqCZKtte9uIpqIviRWpXYBXt+6cnZI31PqSvXKdHHgWFMlAIpH82xEuWr732PT48vrNeJ+TxzXRJT\nLlzmCQkDsY/4aIwPFZvKlnViLCP5IgyjQx2NJeYRPMuszPOVNWXDfDVTtRjtVsXsmV1kumSmpZjw\nxXlEbMcjXaTvR7r+BHFPCD3eOUQC6qLtajy4Zl6FMy8URamuIrUtQZtcvbpgjK7aON8SKRjs6pCb\nYtL7ZiPrzR6jbsWd5lsCbIZY6hr0WE1/UcXMvDQXg2MbjVmbgnSDRL2XVrRj82cPhObVboI/sQBr\nbylm8dV+lZb3ZdMDr5aHG2/+Rn1sf5tbzC9SK3/v7f9TIW+wyl8E/n1VffnyDlVV5eY680tvv/Rr\nGzrkxOLddKP/VFMnCkpwxhgpzoN4tJo4Ijb8PGgG/I2Zso0mBtlY91ChJXc0dsyGuanj1c9YsZgH\n23xpfbXR3U7PLgQqjuodNbdoMAl2v5jBlWqmVssjdN5YJDgb78RHG8eCxVilnO0A8cEwZedu9Esw\nCEhzQpN1c5RqGLvzTVysrZs1qpJWWwxtF7Jr/g+itKnC2C+bJ41Kva2NBIvGc81zuXi7lKoKWpOF\nHdRkHVOwxzuM5rfR71d4EgufSCteBOc6Li8zH77+LQ53d+wOB4Zx4MPPvmaeLoToGPd7nNvbxIXF\nhoXoePvwwHS5MM8zx+OJ4+lI7Hc8Pl/48O0HPn77U8aup+t7wt7e3Hm5cjoOzOdnPn++0Pcjy7Ty\n8O6BXe+ZzzP6dGliDWcHk3OMe4NAPn16oqaOu9OJeb4AmZQK57nweF7p+o7BKdSCoAwxkEtlXTND\n79GaqGosFU2VtK6sq4VEFCc3O4DYwbqYYExchwuBl+mFGALzkrm7f2tLeQRCTyYQxFhQoWtOnsF2\nRbuhcn38iPfC89MT65LMZa/rmZKwFE8WzxiEvo84MRtlF4Rh6JmmK2im63pKrqQ6G7bbdZTiuK6L\nBb6gpLTgs3nnJApDHwHhOq9cp2zNT/MfUidWtPsArqO61zCFTf6uakpr2xlZg6IiN4uN6rS9l+xj\nVx3FFSvw5bXA+yaQcU5uhAfXsGuj/7ov9mDGYLmxXKpQnYnsRD3FFYqUtrOysuCr7fK0iulUnBjt\n2bsbbBKcx/m2XG1TrncOh7cDzzn73WTDvL/sqzccfstRkJ///K2mbUrWfwSvFRGJWBH/r1X1L7VP\nfy0i31XVn4nI94Bv2ud/AvzKF9/+w/a533P7K//732kPUvi1X3nPn/rVr4gxGE5eMKogNp6spYAE\ngjcMLedsTI9S8L7gWrr9zfi9Yd3SDKsEtQQSbQvQYgqvxiUxqXQ7Eb0YFUppsmi1N6+IGg7uIyU4\nEws5U+rF4CipQ7Ti2iLSx4CPHbZrNEzQGn2lzJm1LPgQqLtCrQNd10GINz7tNo6ZP0Ro8I11487Z\nYeQwDFe8b51N48KXBFLoup4uRGMIVXAx4JthkKopHUtpKUtgjoou4LUxdOzZQbWQc2lCjcIyZxTH\nOI7E0PPmzVtOhwPrvJK1ss4zLgrjGPCdJ9eFj5+e2J923L+5o4rn8eNnpusHYh/b1Zr46vvfI2dP\n0cjb9/dcrxM//vHvcL5cuF5mogvcHXbsDz33D/c8P018881H3ry9Z7kqX3/9u3Rdz/v377heBuo6\n4/f35HymHzylFnQtiDrm6crb93e8/+qEd5WPHy5MS0Vl5OVp4vFp5VKS+ZsEtQLlbG+x1sy6JoZ+\nMPMoAtdrYV4SwcO7N/dcz2euVVmWlfdfvWtwltLjKLFDmibBFL6e2HcUVZzrWFelTwXVlc4dqBV8\niJQy0/Ud0/mKamLoRz59+obYRUrO9DHy+PTCfM3kdYGq9HHk228e6brA4ThyOo4GDZaOz4/PHO5O\nvH/3jq7zpJSYl8S6rnRdwI+e8/nFIgcLDWffsT8MlJy5fvhMWjIqkLMpnbt+oOvNE0e8NUzVUh0M\n720+6DF2+Ni81nGtcWpFVrUREwrVm/OGKwaN+Nb0GeUwGL4sr5CpaxCLhdK0kPZWRH2zAbFGzfZs\nRXxTUxtlURptuTqHFjGdSsv79b4JF51ReyVsPys0NXQjMzS41panBsu2YnqjT1o2Z8PDdeNvNCDl\n5swIf/Wv/TX+r7/2m/a4f3lPDPzBrBUB/kvgN1X1P/viS/8D8G8C/3H7+y998fn/RkT+UwxS+ZPA\n//bL7vtf/uf+xI3aE0JoDBTBi1BEGnbVRDwxUgoUTOVm9KdiL3TrKV8pPYCUBjHYcCJOjDtcaU9y\nkwU3elPV0p5E66+dd7hcSD6j1R6L2F0heIJCCJna9W2CyNTVljG1ZMz0xzBl410v5GwsEh+Mgxqa\nFNqhUDIl2Qt5C70QW9ASPF6ts6klt6nEWCmeDvEViI3iZDaZYpAjtVbmdX692GTr5atd/NHS2Eu1\npZ9s2SRiDKAo5lDn484mCbWOBcAFRykG8Tgv5CqEfgSt9OPBXmQt9gYLHXlNPD1+tjzFEOmGPfvD\nEReEnFeOxwM1Z5brmc4Jn7/9wHSd8V3ke9//PjFG0rIwxkCpicfnM0XhR3/iR2hOPD8/cn93x2F/\n4mc//QlaC13X8eHrDwS1DBIfEvv9zp7DPlLWlbomtFTGwVSjLnbNm0ZtabcsBFfJCkt2LHNhGDqG\nzjH2HcuyEOPBYN6yEjtnsXhR2I8nUrKg7Z0fWOZEGNr16IWSTLruHOx3sS3+M/veUZdPxPAW0YKX\nSFpm5uVCHx2hi6QZYt+z3x3NRROYJvPhv748cxp70MIYazPycjhZCW60adc7ew3zyjRd8G6PViF2\nPaGDZV0oKdGHQHWV61rww4CGjstsk2AVZ55BYt7+7d3ULCg84npUepx0NqHGgO9604C0ZaFrOZab\nn/+27KzVtT8W6lJ8bXue0rIMXsuXBc00HLrxx10rtpsAaJvaFWmFvDaRTUVquRX77e9Syi071NTV\nFtzuG7QSWtSjE3tf/eL3t39YY/aLdXVjpKjepuItY8G+0tSsIvzpf+rX+Wd/49fb+0n583/xv/9l\n5fQP7Mj/BeDfAP5vEfk/2uf+LPAfAX9BRP4dGv2wPbDfFJG/APwmplf4d/X3WbXWulGJsIJaBXGN\nbtMYJHkzCApdgx/irWDfnhRVfINobuwXp7YFp+FNjXqkG+5eK7rp+xusIlviiNS2RCn2ApvGnqLb\n/zcTe2kcb/M2SSS3ss4zy7yaCVKaqTkZL9i3rsR5lI6uD3RdTz+MSGj+4WVlK+AmI95Sk2xz7bxD\nvB0ixi03rxktRtcqVSEYNep1ZLPdgV2MhSrZNuobhNN8z82T3cJnc0kIxbIiEXyISHEW+ht6YoiU\nvJJ1tRQl5+jGkWF3R1pmQp0b/97hWuZqTpmX5zN5XRlPe7Jm+sFzOhwR55nnmY8fHnl++czYdXQ+\nkKtJ0vsuUvLE9fyIE8/v/OQzAry5P3G421NZSGXB+0IMHeIK++PAh59+y/x05fs//C6PH79lt+84\n3g3cvbnjer2wpgUpwsdvP1NLous874Yjl2kluMo6GoSQx56UF4PpKogLLHPitB+4Xq7EoaOWicN+\nJGcb4W0JpkBmN/YsOfN8OSOinKJNXfN1pdZsgcUx2GsqhboWOzRrIfT7W5ZtrULf7cjrRNf1qAvk\nmlAqXb+jTxPT1WLlDkPPnKT5+NB2PBVfPSkVYvA4CmNvFqveC1kL6u3rVS2A+TJn0lpISW3KdIF5\nXlmWBScVj2PoenxwFtic1Gh5riOEHgkdLva2/NtUjyHgo7eDxL0Wcq+b86B126UIqt5sbr3gS217\nLoNcWhW5TfWvhVxulFwr5v6Gb2uVLzr+7edUpGRroFQMXsHuq0oxGnGtiBQbWL3chIDbQbHxw2l1\na/vTaiJ8ASkhG8Zv1WlLOgJbfCrclqCqalPyrdi537dQ/0Gslf+ZzZLr997+pd/ne/4c8Of+QfcL\nZjtbW9ddcjWIJFjx3U6jqrCuCZ+Vrhe6sSPGFuLQqHql5MaN9s14y7pmaSZcZkfblJriMPOUapqc\n5mMSnKWp55KN6qRC3VggjZbn2gvQKOwWSpuVmpqx/7qSl5mcrpS8sK6zJZQEb54LnSWe9+Me1zdV\nqjNx0fb6lFIQsaxNEzhso5gdVJbLSZNpZypNgeZM3mACIjPVUmeyYINhQJtbXc3rTd3pQ7RuqBrI\n5MWsfHNtlgMh0AVPFvNRn9OMUjkdTqhXC5hIC+eXK8O4t0689szThev1hd5bd5pRdnvPmhLPz5+p\nePIy4SVxvPsuSOB4d2R/Gnl5/Mx1euHt27eM455a1MRba+L8/MzpNHLY7eljZNwfefz4EdZE3/Um\ne5+vPH77md4rP/pj32cuC3eniCgMuz2fPj1xf3cCLczLxJv7HaXQ4DqYZlhT5rtv37CkybQDqSMt\niTmvjAGyBqY1oRoI4hA80/mKD8Lp/oG1LJCd0QuxazR0gy3AnSfEiEolXRN+Texj5Jbd6HxLhDFY\nwklnHG4XqKKkeqWshTDu0cu3uCC8XD6bF3YnXOdq/jKTFdxx7Lk7Hm3pmjOfP31m2PX0w8C8rqzn\nlSPCMXhCdEx55bqs1Oq5zMrlZeb+cODdmwO5FJ6fzwTx9H2PCHTRJtqymGgt7A4wjmhsoSu3otfY\nYhucgPx8EWyT4FZoN5jUOahV7HpTjJZ4C5B4LeRO9FVw8wt/Nw4Am4rUCrjeOm3EIRgFsZRCFmcd\nu9jC2+w/zCLgVshb538z5Gp/ti5Tb3/klnehbX1Z1bI+v4RKXv1cfk895ZbB8I+67PzHcbOknHJ7\nYr13+Go43HaqxmimI0WVNa24EHBdj5MKUljTTEUJxcZg9Y2fDa8vJFgX3aKaAGrRlmLS2DO1tKVG\nNvaJ2uLFOuXSunbzSd+YNbVUtNjvUJuQwflA6AecM2pSSQnnwAVTVnb9SPBCTYm1JlShYv7Qzgdq\nK6wabHSjLW5srHBN3WonuaubG5tr/i2+YdwOqfb3ZoNgHjTl9m87mFy7kDc4Ssm10ax8i90rhVSt\nOy/FICcVJZdCH0ezTJBC7D3z9Mz5aUak4EXQvPB8nYj9gMSOfhh4+/YrpvMLRTOHd9+nFOXz49d2\ncJXK5XJG14WvHh5w3pGWmTdv3uE7s7OVEBkGC/a9XGY+fvz7xAD3dwd8EPRsk9L777+n81BqouZi\ni1Rn0EcfAtPzmeAKQ7B4sfN85nyeCd2Oru9YlmQCjm6gpMSS002Q1bnIZU1470iaWZaMd5XTrsd7\n5Xy+UBRyUe7uT7YzWAuHUySllWWaWZfUWFfBFl3tJTYWnxK7SOzvCeMbqjcITqsl1XS7A/PlhSHu\n2e2/j9ZvGFSYL1dA6buASCLXyqqKj5Z+VZJNfP3QG21QHMtaWYojTkrJF4ahclkX1iUTouM4DIze\nsT+MhN4jGcahN8VmyTdIR0Tox4E+DvS7PSGOIB1sjJQvbts1++Ufmzyb9XODWGotrQEzFSlw+1pj\n0bb7k9s1/Eo//qKIY931zShvY4a50hrBwib62WrGdu0bc8zgFcEsNHz7eU48vn1948TfEAahbTW3\nBacRIhwbvtMey3Y4tdtmK7M1cLL9m+1z/5AY+T/OW84rxaK/bQlZWyeuTenmbDkXXcAX+8VzNa+H\nPhp10JJwQCjkkm7ji/OtUOmm5tpGmeafAQY1lISlwG/b4GoYPGKigBYDaFtuW65qU6RKbQeBq6hX\n8BZ+EAUysxVLFyw5JAa60KOpMC9zo1iCeEchkFaH+IiLhb7r7YKqAa8Op8GKs9Colu13lO3ibMVY\njToVMG+JamdRM+uxzb3YFtnGt2qiCqNZtctGsSXv9tjFk0omN3Vo7yNoJq8TzsPp/p6cR66XT8Qg\nBBcQP9hYWva8HQ8gwjxdebl8ZrqcqWmllIU+dgQX6ERAKi5U9rtAPD6Q10xeFta1cLlcLbwWM+7C\nVXJR8rrSDZ6x86zz2Ty4lxVxnt1u33DvHbFkluVCLitpXqFkDsc9S4JpyY0qao6W88sz0nX0nbdg\nYGBZjDOvYgXSOeM755zY7XqitzenKhaYXGG+rmQMnhi7kZwm0lIYxt4cAMVxuVxxMTKEnpSUkhLz\nalPC+6/e0u8DS87my+9MKRz6nnStjOMD6/yC+J6ShbJUXh7PiIusS8F78CKgnpdzIq2zhSiPHV0M\nTMtKyB5cpI8DzkfO1xl8R9cZdp2zsuYVbXsSWSolJda03tKzSqlUgX4YGHZ7fDciITYaYjO00nZt\nNrUxpdrCYlNZNoHaZjO7Lbqc8zYdVwUXbirM2t7LDrkVX/uz7ZUc4s1h1NBobSrqDX/XL76vNUPN\nxapuhb19PUs1w7OmHmXLTmimWSbqoTVQrx250aW1iZkUtzmYijWUVmlo+6Z22GzpQEjbPxibR9qe\nCZEbDfGX3f7QCnnJjQkh7Txr1pK1qJnXOPP/MKP2JmkXj4vmZdCFgIsREY9WyG2zrkHxRdFNAv8F\nowWxgAZp2Jlz2paZ7bJqOLiTdpDUgmCezKhx3WveLDaVbSO+8eGLGEaIOpTFRrCg1LpyXZ6RYlmj\nQexCQB1eIrlCTguSo6WeEG1hWgolmjmP4dqpWXo2D/WGa1dV80au2OSg9Zb1VyUZjr51Qo1LX6s9\nFpw2e9021lXLasxkM//prHN3WFyY1gC1cr1cUC0cjgcOxyOeA1oTy5rM16JWrtNkr4EaZW+lcL68\n8ObtO3w3kFKiLAvzdGFzWUiqlJxYkmGw0TvEWfBvSraTWC4zXQzcHUfWZQEX2Z3eorqS15ki4OPA\nVBOdJMaxA+14vH7Lbr/jMk3sxwG/h+t54vHpiVocb96/RaiWRi+Rx/OZgCPVREqJcYhMy8L5muiH\njiVZZijOM2dFQ8C7QKZw9+bEm4c3fPz0md3djkJmWQremQ3CuDuwLDPTPKPDQE6ZjDDsR4iB55cX\nxuOenXNU8RQx7+8sJnDz0TFdJtCM845+f+T6MjH0kcs1oRWC860uZh7uDyaIcYKXCOoIMRD6nv1+\nBEw8d3/3llotMehlnrheFs7zRN111FrIpRBCpOZKpdKPOw53R4Zhh/pIFdd0ENDwy9c/G1uqEQts\nWtwCEzb1sgHMKq8MNLdtu1TZvEytkam/UMjF3pOutF2RyWW3vE0A55r4rnXsIgLVEolu+hVaMXaV\nInaAaLuO7XjYaMmv0wAYrv2LuZvb9X9r1NlUHM1L5UbKsGls+/+iVq+2nYE9LX8EO3JHxWNOemyn\np5jfh22CjYdJVmqE8OVigbZVLqa+EvU3jatqO2G3p7FWTI712pUr1fzAgZwtjaNqbSeemosi4EPr\nXq2fMNczCWjBnGS201QCBNtmq1ZC1xHLAKWalHsuLNMVytrUYY7gzXTHezskREySnFZoGI6d5sVs\nSjdvFuuW7fdz2z6giX/ENypfBeP4KDRuu3PBZP8ticR7uzBqzaDVRlJvMXaV2nxwzGHCxFVNLNWw\nzV4cabrwUjKhjwx914warSuqLuP8ynK9oGkllcx+t+Pt26/w3Z5hHLlcXngGvNqh0vURp8I6Lex2\nnjTPlHVmnT6Z1HvYI86xe3OHqpIQ+sMdu34k9D3z9RlxQqyFXCr73Y4gkOYL1/MLvrPp6BiPrKVy\nvkzU4jjev+H86czj158Y997gNO9xWhCX8UHIs6dqYMkTYWgBJOLBeUqp4ALXa0Jctq73snD2L/a8\n+EDX9y31x5MV1vlKjI7od+Si9Ps9h2jc/3VNOIlEIM0rM57YdazzRKSS5yulXNBi0F1KK6TEbjCj\nLeuIEqeDUQXvTjsO+45lVTO+SoXj/R04wUdHroXY91yuCx8/PzGOQ4ucKwiFsQvsh8C8Nhqr83TD\nSIhds0nY40O0cq00rFtbxVb3AAAgAElEQVTaqJ0bhTWjW2puNSJBrSYgEpetmJs1IBsgKDRqXiva\nwK2gbu/lVziktmtz8zPaII9XWvJmSbEtG2ttNtX2kKlbRb79J4N265bdqVuAcytiXxRWe7RfGHrx\nipPXVvjr9qWmmTFpx6sVlj2uL7HwLwu8vqpAf8ntD62Q972jOKguIPjNXpu0KioZH7LZauZMXYUY\ne7p+sI4aW1DWnEwRJWqy2bbs1DayuS8wMqMh1ZbXt8l0bUwq6kw9KXbhcrtozKnQuUoQqM6bOMAL\npTkr0h6Pq7Xdq72UORuXV+ZKaEdEqpVUzbWw+IB2nlgxLDwEmxR8aLJlj7jG1PE9ITTPCMetCxDg\nlkModkBtXfomxxcCLhjuLWqQkog0Pwo7eMzBM1HTSmiL0K1bqXnr5Ku5wYl1zMFVuhgahm8ybqeF\nnK6gBcUgEK2efndiFyLrmpinifXxM2hmHHo8ym63I3hByWjOxKiozjhZGQ89BeP9zuczRIcPZv/q\nvFCXK+n5E8UV+tiZqq/z3J2OtkzOCa878stEiHsrmsGTpyvktS0XjRP/Mj+1l76ah/ZceH66MowD\nD/eDFRAcXXSkVNC6MAymvA3Rgkf62IHCPBXmYWW/G8lJmdalGa/B0PdtR5HJa6HrRkoqmF+bJy+Z\n2C1UVlJdGYeHm8KwloVleaGkMzEo02zLdecLznVU9Xg/cdhHEwjhyGvhykzXB3ajY9x5xJn0vayJ\n8/NicFwxb5OSbHcQQqA/BlAlrZmSFeciPnT0fc9udyQOg7FssI60NI62daEVJRsMWaSFoQTUCVUL\nviaDH6q9DxU17vW2RGQDR2gfwav+ceuEv1g0/lwhb1DqFxDMzSyuNUXeuy+KJl8UfAd4KBnx3g6o\nqg1a2ZwI6w0mafgrW3bcrYNuz4DwepA4/eLn/L/MvcmyJFeSpvfpmczM3e8QEUAiK2tctEhzRxE+\nEoVr7skVn4FLLvkSFOFrcNmL7pbqqsoEEHFHdxvOoFzoMfeLrERRhBuUi0QigIzw64OZHtVf/8HJ\nzY9cuB40/Q8Z9NSbp3039muP36yQj2OieOucaqnUquRcqbmwNiUNjhg8glI3bLFYGrE1SDYemhXt\nbdGxd92KwQ2IiThkH6Nc7JeFGT+JmKLSck9dl/J3HF2AZvALqOHVsS9Cuh1Afyb2o7Z/5KZgWGea\nOqaTmQgt40BeFzO36kwVo0yZQswP5gseUrouwmzbv0NP8uHCu53atXVpNXZTgJKrWHF3FmirzsZW\nj3TKYetFHYILBgvZ/rgzW6odIM4WPdoaISZUzJSg0FjXC4KYqKmZX3Xwnss6U/OK1IJzxtTRCvP6\nxPz6TF1X8OZNva7GVArBsyybpemESCWztkzpC1aJu52CY2uR56eZ15dnHj9F7o4Tj4+PuMHx+vxC\nkMDd+MDlvBGjWfu+vzyTgjIO3c1PId6fUAfL26uxjpbMcRg6s8Kxrhu1LBwPA1sulJqtI98qYYwm\nenHeOtxSeJw+8XbZeK1vHIbB6H6XAZciIQjn9wvHaeTyNqOtEQcLHWi5kCUjXli3RinZDvXgqGVh\ne/1G2ZTp7hNxHEhHxzq/sbwvlHZmCJFFhTQeWLelwzonExBthaLFoAFJ5LzQmhLiQHMbw3ggZ6GU\nhS1XHj/dczqNrOvWi/VIqcX+PUa8a4iLpHEkjQeGccLHhLgb/oxWkNr9CBVjmfRlYCvQsi1uS295\n9oLVmSPqfLfs7YVix5zlCrB82Hnt/8a1eFsnbpqUX6gl1bxUkFtRt+kWdnW4CJ0UYZO97/sj7Rj3\nB47JL2qZosaPsI3T/uP4yBfnw3+//X5nqpjozl6ru6o/FQx7152y+Ov19Dcr5HnrBH9qH+GAVq5j\nRKtKxw6I4imlsZbV/FCqqR2TD6QUiN6SeD4athuf13dhgnGwd1jCcLjGbdyyk7Z190Xpo5XSrifl\n7dQH+pfUVKjU/uU5Gg6nPb8R63RFus9K0x6S3PG2fRnE7kLcL2onuGC4tF3FrkMa/eNoXXlW7ee4\n1gzja6Xj9w3fXQ61aY/DM0vQ1hcSu3mPLW674b6YMAsCta7UVlAwKKA0trIa40YLFraRrt/Ttp1Z\nLplaKtoyQ4rEITLP76zzC0btqohUhqMjuGDmUFunevlEq431/NyN+SOBZPCMU7R4fvr5iW21PMzL\nebHDuh5wbkD8wvZ1ZV3OPN4fmZcz0hrrm/Hth2HAO898fmG9XEzUERPBOb7/3V/x8vKK8wtLD5h4\ne3mnVfo0ZNmx69rIrYILzJt58LysM8Novt+tVabkSOG+M6gq0xC5XF749LsvnNI925LJBBIOckWa\nMA6JSkViYp4z3sGQPKF53l7fGU+R89s3VGCURzOvahGVyHwpBIGqAcnCeslUrVzWyroWtqUynA7G\n+FEYx5F1KVwuBT9YSPSeYOO60rE1CHG4FvK6LLgA0zCRzaOBOA4M4xEXB2sWoF8XfckpvTxdeXet\nW0iYtQa1F+Zrp+4sN52ebbtDFO5GVNina6HvuPYb8UO3eivctwL+EaS4YtrdGG5ns+ywzA657HBN\n0w/3vPQuu+01Y8fxe/Fv0Bdw9u/OQE/U3eATvfFO9mbTDoBuXiAdRrli5v25+5HQ/j1i5CWbT8Ju\nGVlqw7lqJ2SwgartRWcfjXJlnRc6I9AKpwsMyZFiICXbwIv/86ILV8pPM7xqP2npqStNC9pVXK1s\nFtKgBdTsYfF2cViYj5poSJXSKqJ2Q5TWg49L7X4xdsO3Wix/tGbrSnqR154Tuns1SEi9yHQHtTAQ\nhgNhGIjX+ChP7MwAoxjeijutGr+8mqNhqXrFE6Uvklv3ibYOo1wPLbvI+uLTBVrdEBWqNFwyL5dK\n694RtkBuubAuFy7vb7SWORwPHI73rOtKLpnHxweCF7ZlAc20tlFKZjqemO4eKEVZlrmbFm3cHRJO\nAiVnSja71zSmruZ94O1lZV5X1qZ8fV+Zv620//yNx1PiP/7D7/j995/AB15fVoKHaQhE78y1sae5\nx9ORGCLrtpFzZj6/kaInnI6U3KhlYUzeEp9oVPFUH1nEFrClmSgk18aaG4eT8Hg4IZ3XX/FksQ2F\nw3M4HlkvC2OamMYDzXviabKmBUc8DoRSeXtfmLfM4RDJTfCa8X6iAev2jp8982Xm7tP3tCY8vcyM\nMeGSJ2RlO79TNYAbKfXC6/uZkhtFhMNo1Nx4cIRDArfRxLMuGR/M0jh6JS8bs/f4kKjaaNi04cWU\n1KZIHhmGkRAj0qmxDTGee63XGrrHKNqS0+CK1gqumnlVqdaxVyAKZrms3CI8UYIEw837Hs2KsLuV\naCfX5lj4cM+zZ23eNClWC9ytYDqbpG2nht3DzuN8xWun5qI4bMG5a1HsPtMP6pr+HkU7LKfgdsaO\n4qjWQHURk+5J79eibq/Tdz/r64xxnRp2iqLYLvBXHr8h/dDGCg9QwDezn9zNdcTtTAkr1lWUGBxh\nnEiHI8N0jx9OxGGkOs9SKkUXvOQu2bUvvLF7MRh8YjvRTuYHrhhbP2F3FG4/8RWgmQOZiO9+3/T8\n0I6Tqfmr12a2mG6Hy7qUWJt15VZkM60V868o/cLQhjrDR/fUcXEBlxIhTfiUuguk0RQN3/ZGefSR\nEAaGlAjJlmIxJOI0MQRL8vahS/ebMVq01G41awdALY3WzbFazdS8IRYYevXJCOLsz9dMzTMhgHOG\nZ5/u7xDxDMOIwxgBIUApK+9Pb5bQTmXLG4fjkbIWnp9/xokyHgfiOHI4nVjXlcvbG04bD4+fAeH5\n6YlaVw6HO6bTxGk2qfu8bixrpagj18Dra0bzVw7TwKfHO8qa+fr0TCuLhXp7xzB67u6OLHnj9fmF\nbdkYU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QtLhtI82gJu9KxZicOB163ynje2reKi8J4V75Wte8qM341ctsYUA8/f3vHJ8ek+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\nLpPnH1lnpalnnE5Mp0e0K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4WVhAEfBit+eJs0KBQNaAuUSwZs4RxSIo4TqGde3lm3hWGM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\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nKm/Y9RObdcfZ2Qk5maJsWo9JjmZV4ytL3894XyPiGEPCWI9xnhhn9oeezfl1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\ntYma7GKuNRuPtHHsa7ELt1Tapt8uxtCwedf8B0QL6i0Mw7WJwn5XNbc/5Oa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D3Yn3797R\ndZ6UEvOSWNeVrgv40XM+v1jkYKHh7Dv2h4GSM9cPn0lLRgVyNqVz1w90vXniiLeGqVqqg+G9zQc9\nxg4fm9c6rjVOrciqNmJCoXpz3nDFoBHfmj6jHAbDl+UVMnUNYrFQmhbS3oqobzYg1qjZnq2Ib2pq\noyxKoy1X59AiplNpeb/eN+GiM2qvhO1nhaaGbmSGBtfa8tRg2VZMb/RJy+ZseLhu/I0GpNycGeGv\n/rW/xv/1137THvcv74mBP5i1IsB/Cfymqv5nX3zpfwD+TeA/bu//0hef/29E5D/FIJU/Cfxvv+y+\n/+V/7k/cqD0hhMZAEbwIRaRhV03EEyOlQMFUbkZ/KvZEt57yldIDSGkQgw0n4sS4w5X2R26y4EZv\nqlraH9H6a+cdLheSz2i1xyJ2VwieoBBCpnZ9myAydbVlTC0ZM/0xTNl41ws5G4vEB+OghiaFdiiU\nTEn2RN5CL8QWtASPV+tsasltKjFWiqdDfAVioziZTaYY5EitlXmdXy822Xr5ahd/tDT2Um3pJ1s2\niRgDKIo51Pm4s0lCrWMBcMFRikE8zgu5CqEfQSv9eLAnWYu9wEJHXhNPj58tTzFEumHP/nDEBSHn\nlePxQM2Z5Xqmc8Lnbz8wXWd8F/ne979PjJG0LIwxUGri8flMUfjRn/gRmhPPz4/c391x2J/42U9/\ngtZC13V8+PoDQS2DxIfEfr+zv2EfKetKXRNaKuNgqlEXu+ZNo7a0WxaCq2SFJTuWuTAMHUPnGPuO\nZVmI8WAwb1mJnbNYvCjsxxMpWdD2zg8scyIM7Xr0QkkmXXcO9rvYFv+Zfe+oyydieItowUskLTPz\ncqGPjtBF0gyx79nvjuaiCUyT+fBfX545jT1oYYy1GXk5nKwEN9q06509h3llmi54t0erELue0MGy\nLpSU6EOgusp1LfhhQEPHZbZJsIozzyAxb//2amoWFB5xPSo9TjqbUGPAd71pQNqy0LUcy83Pf1t2\n1uram4W6FF/bnqe0LIPX8mVBMw2Hbvxx14rtJgDapnZFWiGvTWRTkVpuxX57X0q5ZYeautqC232D\nVkKLenRir6tf/P72D2vMfrGubowU1dtUvGUs2FeamlWEP/1P/Tr/7G/8ens9KX/+L/73v6yc/oEd\n+b8A/BvA/y0i/0f73J8F/iPgL4jIv0OjH7YH9psi8heA38T0Cv+u/j6r1lo3KhFWUKsgrtFtGoMk\nbwZBoWvwQ7wV7NsfRRXfIJob+8WpbcFpeFOjHumGu9eKbvr+BqvIljgitS1Rij3BprGn6Pb/zcRe\nGsfbvE0Sya2s88wyr2aClGZqTsYL9q0rcR6lo+sDXdfTDyMSmn94WdkKuMmIt9Qk21w77xBvh4hx\ny81rRovRtUpVCEaNeh3ZbHdgF2OhSraN+gbhNN9z82S38NlcEkKxrEgEHyJSnIX+hp4YIiWvZF0t\nRck5unFk2N2RlplQ58a/d7iWuZpT5uX5TF5XxtOerJl+8JwOR8R55nnm44dHnl8+M3YdnQ/kapL0\nvouUPHE9P+LE8zs/+YwAb+5PHO72VBZSWfC+EEOHuML+OPDhp98yP135/g+/y+PHb9ntO453A3dv\n7rheL6xpQYrw8dvP1JLoOs+74chlWgmuso4GIeSxJ+XFYLoK4gLLnDjtB66XK3HoqGXisB/J2UZ4\nW4IpkNmNPUvOPF/OiCinaFPXfF2pNVtgcQz2nEqhrsUOzVoI/f6WZVur0Hc78jrRdT3qArkmlErX\n7+jTxHS1WLnD0DMnaT4+tB1PxVdPSoUYPI7C2JvFqvdC1oJ6+3pVC2C+zJm0FlJSmzJdYJ5XlmXB\nScXjGLoeH5wFNic1Wp7rCKFHQoeLvS3/NtVjCPjo7SBxr4Xc6+Y8aN12KYKqN5tbL/hS257LIJdW\nRW5T/Wshlxsl14q5v+HbWuWLjn/7ORUp2RooFYNXsPuqUoxGXCsixQZWLzch4HZQbPxwWt3a3lpN\nhC8gJWTD+K06bUlHYItPhdsSVFVtSr4VO/f7Fuo/iLXyP7NZcv3e27/0+3zPnwP+3D/ofsFsZ2vr\nukuuBpEEK77baVQV1jXhs9L1Qjd2xNhCHBpVr5TcuNG+GW9Z1yzNhMvsaJtSUxxmnlJNk9N8TIKz\nNPVcslGdVKgbC6TR8lx7AhqF3UJps1JTM/ZfV/Iyk9OVkhfWdbaEkuDNc6GzxPN+3OP6pkp1Ji7a\nnp9SCiKWtWkCh20Us4PKcjlpMu1MpSnQnMkbTEBkplrqTBZsMAxoc6ureb2pO32I1g1VA5m8mJVv\nrs1yIAS64MliPupzmlEqp8MJ9WoBE2nh/HJlGPfWideeebpwvb7Qe+tOM8pu71lT4vn5MxVPXia8\nJI533wUJHO+O7E8jL4+fuU4vvH37lnHcU4uaeGtNnJ+fOZ1GDrs9fYyM+yOPHz/Cmui73mTv85XH\nbz/Te+VHf+z7zGXh7hQRhWG359OnJ+7vTqCFeZl4c7+jFBpcB9MMa8p89+0bljSZdiB1pCUx55Ux\nQNbAtCZUA0Ecgmc6X/FBON0/sJYFsjN6IXaNhm6wBbjzhBhRqaRrwq+JfYzcshudb4kwBks46YzD\n7QJVlFSvlLUQxj16+RYXhJfLZ/PC7oTrXM1fZrKCO449d8ejLV1z5vOnzwy7nn4YmNeV9bxyRDgG\nT4iOKa9cl5VaPZdZubzM3B8OvHtzIJfC8/OZIJ6+7xGBLtpEWxYTrYXdAcYRjS105Vb0GltsgxOQ\nny+CbRLcCu0GkzoHtYpdb4rREm8BEq+F3Im+Cm5+4X3jALCpSK2A663TRhyCURBLKWRx1rGLLbzN\n/sMsAm6FvHX+N0Ou9rZ1mXp7k1vehbb1ZVXL+vwSKnn1c/k99ZRbBsM/6rLzH8fNknLK7Q/rvcNX\nw+G2UzVGMx0pqqxpxYWA63qcVJDCmmYqSig2Bqtv/Gx4fSLBuugW1QRQi7YUk8aeqaUtNbKxT9QW\nL9Ypl9a1m0/6xqyppaLFfofahAzOB0I/4JxRk0pKOAcumLKy60eCF2pKrDWhChXzh3Y+UFth1WCj\nG21xY2OFa+pWO8ld3dzYXPNv8Q3jdki195sNgnnQlNu/7WBy7ULe4Cgl10az8i12rxRSte68FIOc\nVJRcCn0czTJBCrH3zNMz56cZkYIXQfPC83Ui9gMSO/ph4O3br5jOLxTNHN59n1KUz49f28FVKpfL\nGV0Xvnp4wHlHWmbevHmH78zOVkJkGCzY93KZ+fjx7xMD3N8d8EHQs01K77//ns5DqYmaiy1SnUEf\nfQhMz2eCKwzB4sXO85nzeSZ0O7q+Y1mSCTi6gZISS043QVbnIpc14b0jaWZZMt5VTrse75Xz+UJR\nyEW5uz/ZzmAtHE6RlFaWaWZdUmNdBVt0tafYWHxK7CKxvyeMb6jeIDitllTT7Q7MlxeGuGe3/z5a\nv2FQYb5cAaXvAiKJXCurKj5a+lVJNvH1Q2+0QXEsa2UpjjgpJV8YhsplXViXTIiO4zAwesf+MBJ6\nj2QYh94UmyXfIB0RoR8H+jjQ7/aEOIJ0sDFSvrht1+yXbzZ5NuvnBrHUWloDZipS4Pa1xqJt9ye3\na/iVfvxFEce665tR3sYMc6U1goVN9LPVjO3aN+aYwSuCWWj49vOceHz7+saJvyEMQttqbgtOI0Q4\nNnynPZbtcGq3zVZma+Bk+zfb5/4hMfJ/nLecV4pFf9sSsrZOXJvSzdlyLrqAL/aL52peD3006qAl\n4YBQyCXdxhfnW6HSTc21jTLNPwMMaigJS4HftsHVMHjERAEtBtC23LZc1aZIldoOAldRr+At/CAK\nZGYrli5YckgMdKFHU2Fe5kaxBPGOQiCtDvERFwt919sFVQNeHU6DFWehUS3b7yjbxdmKsRp1KmDe\nEtXOombWY5t7sS2yjW/VRBVGs2qXjWJL3u2xiyeVTG7q0N5H0ExeJ5yH0/09OY9cL5+IQQguIH6w\nsbTseTseQIR5uvJy+cx0OVPTSikLfewILtCJgFRcqOx3gXh8IK+ZvCysa+FyuVp4LWbchavkouR1\npRs8Y+dZ57N5cC8r4jy73b7h3jtiySzLhVxW0rxCyRyOe5YE05IbVdQcLeeXZ6Tr6DtvwcDAshhn\nXsUKpHPGd845sdv1RG8vTlUsMLnCfF3JGDwxdiM5TaSlMIy9OQCK43K54mJkCD0pKSUl5tWmhPdf\nvaXfB5aczZffmVI49D3pWhnHB9b5BfE9JQtlqbw8nhEXWZeC9+BFQD0v50RaZwtRHju6GJiWlZA9\nuEgfB5yPnK8z+I6uM+w6Z2XNK9r2JLJUSkqsab2lZ5VSqQL9MDDs9vhuREJsNMRmaKXt2mxqY0q1\nhcWmsmwCtc1mdlt0OedtOq4KLtxUmLW9lh1yK772tu2VHOLNYdTQaG0q6g1/1y++rzVDzcWqboW9\nfT1LNcOzph5ly05oplkm6qE1UK8dudGltYmZFLc5mIo1lFZpaPumdths6UBI2z8Ym0fangmRGw3x\nl93+0Ap5yY0JIe08a9aStaiZ1zjz/zCj9iZpF4+L5mXQhYCLERGPVshts65B8UXRTQL/BaMFsYAG\nadiZc9qWme2yaji4k3aQ1IJgnsyocd1r3iw2lW0jvvHhixhGiDqUxUawoNS6cl2ekWJZo0HsQkAd\nXiK5Qk4LkqOlnhBtYVoKJZo5j+HaqVl6Ng/1hmtXVfNGrtjkoPWW9VclGY6+dUKNS1+rPRacNnvd\nNtZVy2rMZDP/6axzd1hcmNYAtXK9XFAtHI4HDscjngNaE8uazNeiVq7TZM+BGmVvpXC+vPDm7Tt8\nN5BSoiwL83Rhc1lIqpScWJJhsNE7xFnwb0q2k1guM10M3B1H1mUBF9md3qK6kteZIuDjwFQTnST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HGaTeo+rxvLWinqyDXw+prR/JXDNPDp8Y6yZr4+PdPKYqHe3jGMnru7I0veeH1+YVs2xpRI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YUQQbq0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\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mhYkUI0ZcmRVQpqoxzkVlSFHeilJVnhgT3lf4qibEO+7vB07TCtSQ4kTlDbt+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944HI+UtfD8/DNOlPE4EMeRw+nEuq5c3t5w2nh4/AwIz09P1LpyONwxnSZOs0nd53VjWStF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\nphtrngkh8uXh96Th3zMMR2hwfv/G1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H5N1sISMTYTY652sYkwTyJysgbnW7kvSsYYh3EN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\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klHGmeJKm8qhkUphIMWLElVUBZaka41xUhhTlrShV5Ykx4X2Fr2pCvOP+fuA0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GXl/enNEtqpbHnjcDxS1sLz8884UcbjQBxHDqcT67pyeXvDaePh8TMgPD89UevK4XDHdJo4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\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sm5rdvufyasvUvyHnzLprGQ73vL65xzrHdt2xWTliSuz6I8ZU7O4PrLfnfOMb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vjh+x84DrY4v1wKYXDE0XH/+YHz+QyuMW8rmoVcHcfJM4aIJ7BV42pvW2PbGu+Lcv/pB8Zx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\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ZXjwp8aowEKaRplpjbYXYjK0tMSi7+yNJYHc8cN3UNFWFiBBj4n7fM06B6+tr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\nmqmpeDqMVG2PSoF1C+Sw4peJaVzxWySGje4y8psfv4Uc6Ye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EH2c5nc88PLwrTA5F29Zo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oexjrmvpaZ6ZpJqYWrRxaWUouzPPI9fUlh92O/eHAzc2VOBIpgHSBWhecE1xuITNNR6ExGi/j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es7QENaQ4UnnDrhvZrBacn6/JyZTKpvWY5GiWNb6ydN2E9zUijiEkjPUY54lx\nYn/o2Fw8KfGq0BPGgaZaYW2F2IytLTEo24cDSWB72HPd1DRVhYgQY+Jh1zGMgevra1ZNzXbXcXm1\nYezekHNmtWjp9w+8vn3AOsdmtWC9dMSU2HYHjKnYPuxZbc75xje+xXqzIEyRdd2iJCaFoIGPvvGM\ns7OGyApBOOx2vLj+gKCW2+2B508vGIex5KiNcug62uUCa+Cw25YqPUbqquLjjz/k1Zs7umGkj0rt\nyoxl0yyxQNN4Li8vUJsZc6RFEFVCnBiGjkW7IEwTb3cdP3iz5eqsobYT56slTXvBd38w0k/Km4cd\ni8ry0dNznp6tqCvHYlGTjdJ1DSkaDtuJTbuiteC9w6iyqhrO2iXbqjSCOWeprMUUUZaYEn0KTD30\nXWDZtGhWxjAiUvyZrutBhLP1AiuGnCMpKVMMiLPkpBixuMrhvMXZWTbwRe58rAoFjDM4Z+eCq5C0\nQCnETJFfjClFUdZQIrTGcuwT0Tn+W4qzMnCJCMZ5nCvX0jiOiBQvJZs4N+pRkipaquWiM6fHvIPM\nzCtiUJtL/HDWtZMmxhQ5hIGHOWrYTyOKlhm8EXLSRw4o1XiRijLFI1DRuWKeo4JHApbS2FQ+v5q9\nvTKI5kc/713EWsU+Sj3lwBWZqQwCM5HnjD6GOf4gplZM0X+K/lSMiGPlIMfQvBbyjhgkZ0w2c6Wa\nyDEV8iwBzLnDys5SSTFQcqJUG9OI0URt5zSoCmE2ZbJmEiXyI1Zms6dk2uu2xiJkjTPhx0L4SUnM\nDrgtCZqQFMtM4M7jqxpb+VK9KCybliEGkirGFuHNzhdtygnJRUXLMZKGMvIa78rFQ6li7TzzCCFQ\nVZ6mgVevHohToHKl6rHWYsUwTYHVakXXHUgpcX5xya7rESO0bYOqofINRiwhlmOgKG1VE1Mi5UhT\nV4xdxigYlxGTsU7QSem6HfvDDrWO29t7Nutz+m4kTBPGerCwOV+yXLbkCJvzM4YwEHSith5Ry+s3\n99w/7Hn+4QfY1pdBuQuA4f/+3idcrNf87JNLlquam7f32GoNjaXb3eGd58kHT7CuYRgz63XN6zc3\nXJ5dAY6H+3uur85pK8uQBzabZs7pOzabizIbA17flE65s82am5tbsma6rmfZ1Cy8hZwIeaJZnHN+\nueE7f+hn2G0f+OLla6qqZph6Dv1AHHtqC/f3Hd0w4b3h0Ac8yrPrS7x4Xjw7ZxgnxnHgfHUFqkSj\n7IYDxjgfJEmFAAAgAElEQVRW7YbrC8Pbu1sedlsWfQ2mJmVK/M0k2tpRGSVb4bwxRb5bNFyuPFZg\nIY6IYPNEDhNjDCSNeOcYxsjDbk9Ve9q2pXK25OUPHSmnImPUHpsVny0kQ7aF7MUlamMQPBhfooHG\nP4YTYixVcSKStRRplamIMTAOQ5EdM2Qps2ZjLVYsEUqskbkGsw5vigwy9iMxBFIaQTMWQewxFVJi\nezknNCaMFD1dATVFyzZzdS9eyJJJGpjiSD9N7PsD+2FPFzpSmopEKpYix+hsWEYe0xWY0sujZaQw\nM9Fm0jw7OKZXZr/AmDlLPseK5ya6Yxb/+PXODC2fLcbMOnrhyCzHKt/wW3TmP+JrlFbcHC0s1UTW\nTAlqF5MzS0ayAgnLu2hTnCNA1pX8ZxnqdLYaCvELxa3WVJp2YpgQgabyeFPy6JIzhICmEoEyc3WV\ncyJOE95a7DxCxqlILqKQU8ZQtHdrLZrnHOgc/A9RqW1Fu1ghdUmJjNOIX3jquiLk9BgtEqFcmOTi\n8kuJX/Z9z5QTTVOaPowVQogMY4/DkS1EUZaLFatFx8PtA6qZRdPw5Mk1MnVMU0BWS0QcKQecrVgt\nDNM04Bc1mAFTCSlHvLXsYyLqnGE1lt2uY1HVBDUkcTjXMI2J/ba0K4ecOIwTKQ9cX18xHRudrGMI\ngc3mDGdKBjhpKAmDaWQaM/sx4IaBYeg4Pzvj6ZNL+sMWIxZTW8bQEWPixYcfsV6veXt3x/3tPR8+\nf0G/OyDZgpYEw+VmCSivX79mGkY4y3z2+Sf0IXN2dsZy1WBt5rNPX+L8uiQOtHgzb95uefVmy5PL\nFYd9xzRlXt/dQer5o9/5Dp+/fMXYjXzw5IrLtefF0wsuN2u2u13xNeLENPY03jFEB86TFeIUEC2G\nduVX9GNmvRA+enrNy5t7zheBq80SKMez7wPtomazXHKxWXFzc8s0Kbf3ezQxSxRKzCUya61HTALn\nyKLUtWdztsabTIg1IRSJIs+RwBQyhkw/xNKGn4o3E4PSdSOH4YAV8M6hEaIqo0nUWjLSpejyqDqs\nqUslKUWSjGEiJcH7ijCVjm2YcFWFcUIYS/ECyjAOJdxgBSOlOj9mQVTBekvjPP1wYBwG4pTm3o0S\nQdZZTgEeW+7JCcnpMdmGZtTMjUhqSxwwzf2qSRmD0k2BfT8yDJEUKFKNCGKKJCLMBfyjJ1ZSZWIM\nRg1JctHipaSLjnlxyUdpt7zBDy8hcpRUjjLw/NyjLPNOJVAtnARgZ2I/ig0/Dl8/kc+ObzkZMkeS\nFJGESmkjRuNM1XPjgZRQVcppHjV5FxHKmaSlKSFrJOZAyoXIfWVxR4kmZ7ImcpwNxVSiVzEGYgiQ\nS/yJLIz9SGn5L+3KasAai1JGUet86dIybjYhE4iUbsqcGYaBYRpYLBY0vnns+irGTrmUQ4hFgZuf\nG6dpvkgj1hXDp+8HvI04NXRThzGey7Mz+t2e/SHSMRA1sVws5ko8zssPFPO2rVu6biBES0qGYQzE\npEzDRJwiYcpMThALd/e3bJYN0xjYPjywbpe8fPmSaYw4Z6iaGgGsCDlMhLGjqR3rzRrX96zbiilG\nnLccdgfqZkFM0I8BNZ6QhJzhxYtnWFOOQ+Vr+hjY7g589OwJ33jxDDTzyfc+44OnTxj6A3fbByrv\nebi75fzijH635c3NHXfbPT/znZ/l9uG+RDKt5YMnV4hOvHndse8GvrVYMU4dtq447B/QHLg4W3J5\neYlo5tWrVxiFF5cXXDQtn4TMk8tLrs9X1D5zeX6BGEtIkaurDSaPWCM0VY0quLqhXkaysdw9DJyv\nFji3pOsTT54sUUqk8PnVBYu6oR8Hdv2Ei4bD2LFetLRtg3GOkOFu12FchbeeZZNpKofTRG0NwVqm\nCNkkxqyosdRNy8IW4k65GHuSIE4Z7yy190QrGJQUtTTBhIRmoao9TnyR9VKZIebHJr05PSF2rk/L\nLDRpgHk5jDRNqPNoFoyzRQ4yBkXx1qGSikk7s5Gqlv4MtEiO1lK3C8Iwsd9tCcNQlJxc0iHkUjGr\nlESKUQMSZ9mjFHxQKl/Nucy4TJFvUkyMOdGFQDeNdOPIGBIxlcBjFuHxnxQJNguQj2s9lbRLMSiP\nSkKRZZN555cdfbKypkoh4vIYjsuPzD98lwp6/NmR6CmS87H5R+dY4x/chiBb0iKPZsH7O67lvJjy\nXDmGJfdq1c55oEw2QopSuiu1XJCqQppb/1POyNx1Zu3sXudECAEVsGLJCaYYMECYAuM4kaZAlLI2\nRZpK1V9XNVC6zXxVzRGsYqYaZv3MGMQaUo6MXYf1nmbRorWSQ2QaJ1z1XlRSpOR3cySkWLLgcaSu\nPU5MyYJrYtkYnDWIZt7c3rNuW5ZtQwgTqpn1akEmz1pkZL1cYIxQ13WRRATQhHVCU5VGByOGtze3\nTOPENIz4uiKnjGVB7Rfcv33FQ/MAc5/D/f7A569vaKoaj7LE0NYNvq55dXPDcrWkWbbcdx2+ackx\ncX6xYuhHHu4PiPEoUNc1/bDn+bOnnD27pDLC7uEe4yqatuEwRHa7jmcvnpCmkVc3N1gL1hlevXlT\nujw3Z3zy6Zf8fN1yN+w4dCMXl2ekEFiebegOe1bLFiPw+s0rdvsHzs8vuN0/cLFZEsdYOmtdxeUH\n15xvGh7u7zg7W1G3FYvmQ+63HU83LT/7rRdMw5ZnTz9GjWPoB5xGKu+RZGnrtsglhz1ts+BhP/Hd\nTz6n8ZbnF0saqzzsDvTTdVl8SYuMc9jtSEA/jFitSTni6oZF27BYtdiHirv7h7LuijNcLM+5OqtI\nYUHbVnxxc8/b7QEVGCd42EfqasGi9oiOGNXS8h57VCNIYnO2IKeRME6kHDGmxpmIrzxN5VBNhKiI\nsyWJFTPJZMRYSKAmY7zi3RyJM4K3lpQhRSXnCEZofEPbtsQ5IoyBpIJ3VTEj50WhyJCYF9OqW8hC\n343EoKULNCuSTSlI8ly72jwbqfNA9V6QTSiShpljj8cxYEqJbhw5jAf6YWDqBnSasLNxasW+41PK\n4JMVsp2r5eO6KPmYipl5K/Ou6xUpcs/8fcmtv8dpyLxcyExfYlAJRSM/hjPmiHNJ2cxLeslx3Rh+\niOx/FF8bkZepjHmM+6m6WUNKjyOXZoNKxursFIvFpDiPZGC1LH6VkdLUQyJrSbWkuSMz51DCQLOu\nhZYUwpSKI51Cwti5OSCULHdZHK10SKYYaXyNcbZUCEbmmNV8UrzDuwrFzKvIlTUZhmmE7oAYoWoq\nVAwpJ8Yxzc0UZR+rymNMyYoPquwOB2Kuy0qIIgz9iKid194ox+v+YVcmpAohDFgjLFctxxXYrKvx\nHpyvEBsYhnlRH6CuPd3Q09QOa+Dly5fEBM+eXBKnEXTNZrnhwW15e7vl/GrDar0mpHLRasokEd68\nvcW5itY6HnYdh2Hi/PycafyS6+snaJxYLpZ0Xce+62jXC842G4b+gDeGzbrF5ETfdSSFJ08v8bWn\nqgcuLjZcbS7YbQ8c9h2LtqbvDtzdPnB5ecFnr9+S8Ewxs+sm1qvz4lNMB7p9oOsGrq6vGPqJ/XZE\npeb7n73kD/1jF/i65csvX2JMxZOrCzarBeOwQyTw8YvreaEkxxQiP/fN5zy9WhFHw3q5IMTEbn9L\njJEYLZvlGpxnNB3SDWA83/viDUNSXlxWnG0a+mHifhjYdpHd7QOLRV0G3qnn0HdYk8tgGkvkbAqB\n1aqldobGGZaVQUPEmUxllbN1zdX1Nat2ya9+/1NCCNR1zb6faPsJjC2NZSJUVrDOY50lhMhyaWmb\nBTEqMYAyYijr9QBgDCFlvLiyoqHOa6pEJWvCVRW1b/Hez9HXY/wwY1zRr52vWKyWJT45jaVanpMg\n1jlIxc/SWWt2rqKtG3LMDH1HTpHKecAjTijSd0BTmju8U7kHKVIIFGI8eoliBLVz9SpCyJkhJQ7D\nwLafiXwqBU/R5EvaJAukPEsgs7RRvLp5wSwp0gp5/tyjUTuPSXJMs8x4XGLkqDocG33Ki+fiyr5b\nB0bfFXjlJXPy/L3cofD7vNbK7wTyaCzM0srcCHQMyIuYORY4t6emoiFb6yCXrs/jaFjWFoOUStQq\npXkRrpxKq/2cNUXLGhQ5a1neViNping3izqpLKOpM5vHuYPs3QHW2diZ0FzWtVAM3glVU9bliCmB\nMWQxjNMIBwUWLJoW7y39NDL1QzEUNVM7j6tKk0/btmx39+z3exaLlsZVxCkxThM2Raqq4snlFa9v\nbrl72NHWHoMSKMvvWrvEWs8YE2qFfhzw1pclY12kqkrjkLUGZ2suL875xH3G7jAQYiJMI8YqZ+uK\nz23g7qFncVbx5OKSu5s9rVgq56iamrsvv2S1cizqCm8sD3f3vLi+YL2ocaIsz1aMU08/9lhrWNY1\nOWb2+56L1SUOj6tKh10aB3zlaJqKceg536wRDIe+Y7E5Y1V7Xn35ipQSbbskTS958fQKX6+wdmRR\nO1xj2R06Xt3c8eRixd3NLZdXZQ2T733xilGEpqm4ubvnEDLPry+o3DEplGmXiyItxURVLZimtzx/\neklVO4IYhsNImt6y292zWjeMU8Rfrcgpc765wBtX1hhJA9fLhuvFgqZq2W1HltbSGsMgZQExX3ma\nukZSMa1vDwNSVwz9rEljIEx8/PSSJ5sVn3/+BaIwhMCUAmeLhhdPHbs5Nne2atluH7jfzeaeZJwt\nvRret3iXCDESQyk0UoYpBKwITe1IMYMB78v8N5PJ82p/0zgRs2KritpZnCv9BybbR2XA+JIFr5oG\n72uapmWY4uzZla5Nk0G1yKFZIcZc1u5ZLhCEoe8JYSidmGqLoSqCcQZTlTVaREsUExQjYI7pDoQs\nrpCfAecMWYpHFmJiGEe6YaAfpkLisci4qrNYa0qOnbmKznOL/WNH/GyMy5yXj3NFbtQiWrbBzMYn\nHEl8Nig5xlLemzq8h2MzY5J3Grgij80/jmNs0ZD1D6C0AswXXTEac5a5gYfHZoPHPFEWHlcLy8eE\niUXyHNJXxWJLRConSDpXTcOjSaBiIWViLlHBrJk4xXIRI2WtlHFkihNWPUYcScv63DElphypTTXP\nJCDlUE5stoQYsFrh25ZKpKQMjKVyFhTGYcTaohs6U1bQ7PqeYRgYraNuasRZDrs9YzcwDAOSE265\nwJoyrbS2RpNStxWrtmG/60ghIlWp1q2tiruNhRBxlSPmQtrW1+z3exBoas80jlCVeOP15SVjeIOr\nHL6q6YYB5wyJkveNozJ1mfv7OyKBZVVxcbHh5e1blsuWq6szXr69YYqZwzDy9mHL+fUTFCFNiTSM\nVFLSNGottqr55jde4KxSVY67+y2b9RnOtey7QN9NrJdLbm5fczj0PH/xnMPYEzXzrW9+i5v7Bxar\nlvN1w6LJjJWURaxE+Y1f/y5V1fD2vmfXv+H8+il3Dwdubm6p21JJVgihqfn0s0/5xocfkJKhaSq8\nr+mZaBtKA0zoOBw8Y6h5fXPHYT9wdXlGXVueP3+Or0oLfsoTOpnSOGYdLy5XrOsPiVPpFD47X5O3\nsFxXDKPj0O+paiFJxC0a0pipjJC9I2pJC6UwMo6Rq6slhxjoUyJhGIaAUDoql6uWF0+u6LoyUL4a\nI9PtlhSVi7NV8S4ErBfEG6aQ2HcdKSkpCykHjC9dzMMY8RgqyXgUDzhVhv5AlCInLpyjcobKWfT/\nZe5NdiTbsvS8b3enN/M2Im6XN8liFUsNBEkQwBEBaaIph3oV8Rn0BoJGGuoZpIFegRMJYBWVlZm3\nic7drTvt7jRY2zyiilWakEKWAXFvRJi7ebiZ+Tprr/X/358K7AmNqsSQprTBupqm6aXIqUClZEQS\nYnx1joYYRKSgNLZtSBmmy5kwz2UWLnpypVdxghpxuWpblChlNp1TIhYDjcyxY5mtGLJJ+JhZcxLj\n2rriN0/0sTSEuRRjIyObohgRCJbM4HPMoK30wjkRlUDCIKEwslxVIgl8HZm8ypmL4iRLw5rK9yut\n+FULnl8NTLno1SmijkyGItOUi6U8MTr//RcD+BMW8tf5eBmxKFWOSFmRUyFEJAHIZHUdv+TXI8g1\ngCLl+PqYWisx1mhHQFyHKQY0GZIYgWISpnj0gXX1VNaScmZdVrZ1gTKGwerXFyZG2PxG1oGu63Fd\ng9VCjUNrTOVQzmEqhy3Anpgi2mQh3JHxcSMlTwy+OOgC3q/4dSWlUBjlXgr8uqIN1FUt3UYKZGXo\nmgbjDN2uIxbnq7HyJswZ1nUlhojqKmpTl9FVZOhrpvGFcQyk0DCPE6nrMRbu7jpejhVaZfqh5Xg6\nMwwDd3f3LMsHvN/wYUVZYTb3+x37oed239O2AgiLOXGz76U77Rtqo0Br1hAwVSNBAaVzGvqWu7s7\ntnnicrlwOp958+Yd87Iw+xXXthxOF/wqJi2lFcu8YY2hcQajIt+9e8ftzZ6npyfatsJVil8/PrNs\nG8o6ok90+57Ph5FPp4lfPx/4L/7zb6ldxbLO/Jv/+99SNxW//fFHOaYry7rJQjzHlcPpxBojL8eR\nzMLhNIoB53TgP/3nf85+fyf+wbCRUuZ0fqFrBqxz3N7eYIzhcLywrTM3+zuejye0VlTGsEWPI9IP\nLeeLZ1kn5tVjtZb3WFyZlhmlAjGspOiprSNnxbwlurZCK0Nd1+xve1ytWOYFYxJJaZ5PR/rG0VYD\nMQCNBW0Z55VlXWmqhrpyeBTgy+kVrLFYbUjGoAt8a4uCAmhdhXVGHM4FeSsoWVmMK2NQ2lJVFVXt\nWJZV5LVOi7w7Q8pBpMAxo62ja3us0SzjiJ/nV0NgSqos+XnVpqeUKYrd12gC6cQDVzxALmvYmALZ\nx4J19izLzLwt+ODFPVkWoJpi9FFyAhERhShVVHFSqjKzyWhMGZGI9j2UGQBkFeGKzC0+k6y/NKhy\nmBfJ87UvlZ8EOTWUL8CVUy4Xji97Q/l3CBNK/WMs5NdvSnThogZBJ76qyzJLVqpYcr8sCK+/16VD\nR4lOlPJx+WoyKFt38QJEQkqEBD4l1i0QVUHees+y+rLMiCgLWmdsBTnJ0iWGhHYCvq/aiqZp5A2l\nLbaucFWHqSqMdhBkiaHJCAXXkLNcRLZt4zidha+cEtuy4qMkxTRNzf39HeO8sK0zx9OBurI417Bu\nG3kccY2laetim15xzmHQIp/zG9M0UtW3uKqicRXPnz+xrDI6WVdP03Z0Nz3rskGM7Pe3NLVjHme0\ncqzTwtA27NqaY2VARXb7XXEyjuzublAm8XA/YKuWuql5uLulqSr2d7fMy0jKgbYZeHj7hj/8/g/E\nkJmmEWtr3r55w37Y8dPhzB/+8J45zLw5XUgdXKYzm498fDmQkzA2hrbl559/5rc//sDmF7ROvHtz\ni0qJGGamaaJtGuZpoh8GLpeRb7694fe//wNNf8/zOMtI6k403L//5YmfPx/5V//9v0Qrze7mkfP5\nmXXZpLTFlcvi+cP7z3zz9h3n02eayjE0httdz7YsLH4jhUwIiXE8Ml6OtN/2hBh58+YRHzz1stLv\nGy6XM02lMUDf9TjnaZuWftfx4fAzx3XmEjeqLXOZJ1gX0raxtzU7Z3G9ZWhqzpcL2WiGoZEgEqtR\nKVPZCm/FJGZsxbYs+KIGyTGxbhHvCyG02Np3u57gI+N4xsdECgHdNIQQi3RP9kiLz9S1pm5kXOl9\nIKQFp9WrM7so/rDGFeOcImVhBF3HkgKES+SksFbTdh1t3TKeR9Z5FEREukKtsmizUxKYl7peOK6z\nYjHciNxQ7ktZzgcpRVIMxLixBc+y+UIa9fgoRkKtRZaokOUiulASs4xH8lV4ob8sKFWWeiAFqHz8\na5hEUQMoaTrJX2radU4unyvPh0YXA7d+Ld7SnF5NRpR/o7CUcgnXkenMP8IZeUylR/uqMMtQLcuW\nnS8LUZW+Lt6mzM/Llvxv3ZRQw2KWOXqQubozmpgUsxeJ3TIvpJywGjFMxEBUkqyiRFsoYxCTSR5S\n1KJ7NZVIV1OSQl8L/N66BmNrMBajHNpoYjQQPVd3W86adVvwOaLzVW4oL+IyyZhlNwz0fU/T1YSt\nKVrfTRZOBNIS2BZHvxvY7wbGqYyfysXeGAVJM40z/bBxV/c0VcXHTyNkW+LJYLjZkfOZZZrw3vNw\nO/Dx6aUcATPrtkBK1E5QAsZo+ralMhWVqZm3ib7bYasGowy/+eEHnp6fqJuaNSQBYJmKduhx1qKy\nx68zdeXo24qnz5+Ylpnn8cSPb9+yTCMK0V9/ev8JlOL5eOTH778np8zQd+x2PTHWVK5Ca8Xz4cg2\nR3SuyEnxeLtDk/n+m3c8HQ883DT0ZuXNrqd694Axkb/+3e9o+xv+xX/zX1M5WJaJl8OBDx9/pmtb\nUkp8/PTCLx/PnC8BbQ44BT4Fdg9NMc4obPue/dCzzgtd32DMHTFEMDLfvL+7483jI/M8czxPDF0n\nev3G0e17NJ7TOPL5+cg8BbbFoxJcxpnz82fudh37m55lW/FomqpmPBzAVRxOI3f3QQQAJPquxccI\nMaFUpHWWygmrJeXMZZpZlpX9bkdl5D1QWdnJzKvDb2tRt0WW4p8IQEhlyWm/yHKVMeicUO4Kd4Kc\nRA1ltKZ2lhg9Bhld5qK/JhdURmOxdUNTt0yXmXmZiSGUImqkiGfp9ikj1hg2tMlcieNfOyFFmpxQ\nOZBTIPlA8As+LCzes4aML/gNuOrQr/BdXlUlunTer1heJcq4XFKFXhnsyJFAK1dUKWUqrsWDoq+m\nnZRR+oqfLQ1n0YJfl52gMKrwHpXgR7S+avQoqAH1txakCfcP1tM/WSFPOaHKXPz6RF+3J1edeFZf\nvi2QJ14V6aFS6W9dBK6/z0oANLKRLkSa8gT5nFj8yryuaAsoLW9CZXHOUimFwkrxryusVWwmkgIC\n2EeOv2qSotkZAzpibEQbUFbhrKZWlpTrMhcUIJF2BkvF5leUUjRFngUQ58TqNy7jSFXXDLsesxvI\nRNZ14XIaWaalcGciRhm0swS7yTFcK3KOxdavWZaFc9Fbp5QIPvL56QWNKiqQjmXypKRYpomH+xt8\nDCx+xqeVmCxXFos1luQDygi/o24cyxKp2xqA8+mZtu94+/hAWDfWdaVrGp6fPrHzK7u+RameaRS8\n7/k0chol7cYoK0qW6cJmNJdRlqNN00gBHwbOlwv73cDpcGDY7+mGHc5axvOIsrIPMFqTdUPTRpZp\n5G4Y0Fq+1rvHe26Gij/8/id8cvzLv/hLlunEum1cTgv/7q/+wM1NzePDIz/99J4//nri82li2hbs\nYqhz5PHdG2pb8fv3Bw6XJ7A13Z8Jpvj+/o6PHz8VrG1NUpphd0NTOaZySpAqoamcjN2O5wsxg59n\nfnh85PPzC3MKnC5nlm3j3gxcFs95Wokp89t3NSjDusnfzVuiqSK2MtSNodssTV2V2DfBQRuVqWrH\nZRauiHMOZ9zrAlA7KSwxQdfUNLUleApiVpQcSon+vDKqsIgCqoxLQvJF2+xwVuOsI+dAiKuA3QKv\nc2FbmgFjKoxtmeeFaZ6ExpgV2ki+rKhFhPWTsyjQsr6icst4QSkx5OkreEujQiBFD2ElrjPez/gQ\n8EkRypJMHl5/OSVQMB9XOXNRqmhVunBEo69eTxXXM4GEb1BMgMJ7KTOfAujLRkYjWckG86o+UUqL\nX+Yqv7ySETNfJJmllkkzf42+LOP//4/M+j/djJzCM/hKdvN1Xqe6jlW+iky6qluuo6IvVLCvgygS\nKYbXJ/qaFyipIXKEs9agNVirqKsKhcJphTPlxdQaoyzWgDJyMdG6WIpDxm+RcZSEmypDSpqYlXTo\nOuOaFqfFBee9Z1tnco5UzpG7Dp8T4TJClrDcuqkZp4kUE+u60jQNxjnqqmMYbqiqC+fTkZxkzphT\nxLmavh94ejmiFcQgdMe6aiEn5unC+7ACCe8X/LYAinmbOF4OPB1PkD1xW1D5G25v78V5lxIqJ7p2\nIG2edZ65nM/YtuHN2/vCiK+wyqINXC4XmqHmZtfw4eePqCgAr+PhQEqRrhtYpoV13tgWz7Qu+Lgy\njWce7/ccz8/kIAHPh3lif3vL8eXIusy8/+U9u7ZmPzhU1yErNsuyZZ4OC21TERLsbu84np7IObEF\n2D/2bFPgch757sffcBnh558j//zP/hnWJLqh5uP7Iz/9/AuVq/jtb35kWWYOz5+529d8eHkiKcNl\n9kSXaIZORARWsYSJHFfWeWToO+Z5gnzdUXiavqNtWymAdUPXDxijGfqW8XLmfDpjLByeX9DG8s3b\nt+QUWTM8nUa2IMaVtm14Oo28XBbaytG1Hes0SVh3EvzqMi80tsYZS1NV3N7c0DSO4/mC956+bTBI\nolSIiWw0W85Ml5H7fS9AKGWwlfgUctTEK387SiPlnMZpCTMJIdCS0TEXOR7CJtcG5xzLOsr7fQvk\nVByoSlym2jjqumfeIqtfQSesdWWMKTygHAMpbaS0ogikrLFJi5wwi8JJFCQa0VRbuJqW4kbYFtK2\nEPxKzICyX+3hwGAog3hxfKaCti3dtRjySlNYFppc66v2qCyJR8bEgp0pE4BrLkE5KXwRCxYNuhJV\nD1nL6CojM3VVgRY4BiXT84qxVSpJjqqwcMsDfzV3/ju3P10hzyKaj6VQ6680lHI/hQUuLwTlm5Sd\ngRJrPxBjEtBPUrLxTrnEKkVUWTJccxadcfRtJtUBXXjAWmuslkJutXToWpXlhbYoo9G2xroaYzRx\nC0JJfO1arIx6YpHvFfF/XbcotWErDdqxLFmWqTnT1Q05wOl8YltnauewaLYYWeYVrU8oKxmKlbPc\n397S1hWn80mCG2KmNoa2qnn79h2/vn8PSmErQ1aeoa8JMTGeJ/w2M10WUiwq1AhxDegUmJaVaZyx\n7sA/vb1h2xYJAVA147yCNeja0vYttqqp64qcE8OuJcXMumxY2/DyfOT773/DafEcxwt1VzOuC/EQ\n6D2TbWQAACAASURBVJuG8/GZwxQY7t/Qdh3rOqJToqtb/up3v+Pbb75hWS8CQkpiLe/blg8vL/zy\nAv/lX/yW27ojh8SmVmpbs+saDqczu12PMYq2G1i3yNvdnrbtOR0+YCvL0LVYlfjLP/9ndE1H9pFx\nnvn973+iayx/8dvvMNlzevnMd9+94/OnF4a6YfOO53HkcXeDs05ON7985G7Yc5kXLpcLN7uWT58+\ns+v3xLjxfDyjrSbUFUbX7Hc3zPNCVTvWdSN4Qd7+9PEzv7x/4uF+h25EtbTNC9u2YrUm+8h37+5R\n2TAvv9BWFTElNh+wCiptuVxmkbAqBUbz9vGWh4d7kpK9izEVa0AWoWQUHp0l73LzgWWZsQac0xjt\nWJLmvKzcdI2ckgFT5L3ynhNzj3GWXElSvIm65F465m1jnSdAhGNaZ8iyFMxJo+uKCKzzCNFTWyss\nodJkpRgI2wbJY7Q0EzpLEIYKRoxASPRiLrpspSLkQE6eFFbSdib5BVIAXYuzs8DolCqxkcmIgg0R\nUEgeQoRsUMmQjSxOM8L8uXLCLFU5AcArqa80lmgZyggVUU4JiYBBaksBJYCKRYAnnXpWsXBmrhMH\nLUtbnURJoxTKSiGUMew/LD/8h+/5//mWKcablP7BX69d9ldXxldp55fzBkI4DJLykyJET77+SltZ\nPGqs0/R9w37o6ZqW2lmsviaKKChXZWutWJZrR9022KrBVjW2MXQ7R9s3AtRyVrp1QwmuFe3qlTvi\n/co6T2zLJpTD6NlCYPORrm24HXakrDgcz4zjKEfKGFmXhfF85uX5M4eXT2zrRFM77m7uGLpBtKxB\n0APD0PD28Z6Mpq1qGqupmoaqrmkaS9iC5BMqSd8JKVO3HU5rbrua+11L4zTbPBK2BacsRlvef/jM\ned54ePOOm9sbmqZG68yyzCjlUNpwHkd2+x0vhyOH00jT7lhmz3RZISQOhxMJzRYTIcycLyeeD0fa\nvmd3s+N0OlI7w9B3EuQQFZuPzGtg1+3o6pZtkRg57Sx+W2nrmm1biuQtE7xAyXbDXvC4fU8ionOi\nsQZnDcOwYxwXDqcL2mlOxycaCw+3A5jIZZu4ud1Rm0zTwt1Nw36wtE4x7DuOpwOCSYs0ztJoza7t\nOb1cGMeFlDLn8yjY3ATWGgnjtprHh3sqq1HILLnvG4a2oXGOylZ8/PiZZQtsmxQ3GQwadsOeu77l\nL7575J/88IBhobaZ26HFWdi2ifubnqbSNK2j3/X0OxkpVXVFVRtCWPHeY40Rns40MU6CMF7WiPcJ\nZ0sANzKyuFKLtDJUZTQ5zWspsom4bZJnm2KREYpiZTyPzNNI9CtaRSAScyQQXznly3Qm+EUQurrY\n3ZHFXsoJyqLVaDAqQ9ggLGQ/k+NWLgwRIZKnohZJqBTIfiVvGzl4dCrhJ0qJKl/LqUHkzaqoYSRw\nw2gjLPWv/17pclrJwoXRwocpzX1J8TJlrm/K49uvFHjXmb+cBJQVGaZGl8ALDcoKNK/8u+T5kIun\nUvr1a0mRK/sB/Y/Q2fnlGKFeO224chPy6/1XbsGXbD65P6nrzCuiMwQFMQVS3IjbKoVzE2nflW2e\nc8Y4i1WK4AWAhZJABZ2LjKh06caUPE/bALXoqnMiZshaDEkhB8Imc/DKWarGiW43ZXET5EjwQVQj\n3mPQrF7s4X3XMgwdWxLpo19XlFF0u0HyE6GENHtI0PUdxlZYK9feZVmIXjSqMUuqSwyB/f62mIss\nLy9HYkzsdj3LtrCMnuP5yN3jHVXTkdPGYLW4AUOgrmpOhyNNW5Oi53KcsT9U1FWFj76YOQLLMrEf\nBmrncM7R1AO//vKBxzdv+fE3v2E8nTCmJuWNzQeauuFwnjkdL2il+P7dD7CtXJTnu4c9hszlMtG1\nDZIbk6gqxd3DI/N8lhltCHgfaIIEIGiTyTHQ1Ia+a8FoDoczv/mhZhyPWAcP+6G8lzSX88jjY4tC\nsa2e/b6lay2VtSwh4azl/U8/cXu7I7555Hz8I+/uduy6nein24auadj1Pe/e3NC1jqfPB4bbHZnM\np0+f6XaDJEApXRyaA23T8usy4n1kmTfevnvgn3z3lq5qeP/hidPLkd1+R5zAKMc6Tfgu8Xy8yP5i\n1/Dm8Y5pnhkWz7ffPEqSEyKLa+oBlFxIEpoQMjEnVIRxmrBW07UNzhq24q3Y/IrRXTHVZLLJ1EU5\ntswbdaOpnC1z5SwANqWwRaARtw1VORKZvmrYfOR0OmFNxOjS/WoroxftqKuW4CWYQwqxJviNHCRZ\nSxZ9ShRfWlRk2XvytgiH3RWduE7CDiqFTvJTAyRPDoVNJJtVCVyxlpwtVzCVus6ir7Lu64KzzK9J\nqpwkkC5YXSmHX0WwKWG8yOhdvapQpDyVsOlSe1858CVNSb5aEo6L+mo5milzev2qBNJlZq4zrxCw\n14PA33P70y07S8d9LdDX7jtc501facYpG/qMOCtzyl8+X13n518eJ6XAtq7MywLZl4g1i0IRQ8YU\neaFETRmMEh5JTuJrE86BwZqqoFtDcXlpDMIZN06e/xAi2yYF+zxeaNuaqqpgtcXOa4g5MF8mWXIo\nzXmcmdaVbrTklKgMmLYuiSGauq4Ydh0pisJmmkYgY63nsqxsWyDGQFfXbEFkc9Nl5OVwYJw3vntz\nz5o83dBRdy11XXF3s+fJn0g5cXp54uHhlhwVlduxLhvTtPD9D9+TtGZ/f8ubeeXp6YnT4TMpDpAN\n/dByOp6xesGZhDaRuC188/aRf/tXv+OP8++Zl4m2bsg5UVdOLi63O8bVy985x6+/fmDfN+y7lpQ1\n67zRNDVN19C0jjePj9zd3XB//4AC2n7HZZppm4pxutC0LcNww+U8c3t7J4lJWTP0g8SSjSN929C2\nFSnCcTxhK8N+35KiF6mb1tRNJ3TBqLhMI13XU7maoU+8uWu5e3jA1j0pa7ra8XjbMPQV2iRRzaTI\nTsmJ6nA80bQVrtJcLmdubu+w1pCTp2tbpstEqoXRU9cNTTWBDvzw7Xes24bTilVHbm877vqOuM1k\nAsN+R9NV3D/smC4rXTPgmpa6q6maCmPLacvWxBAJweNXz5LhPK50tZGTSw50XUNI8PnlULJijSRK\nmbpkWyrGacE4R9voqzy6/Lzm1/e71YbNZ6qmQZuKcRxZ14VcIXmypdPWxtK2PWjY/ApITJv3XlDQ\nRZNuzNVeVBq6lOQ1LYiNa2yloXS6psiLyeTgyV4c2inlwjuyJONA2VclHCWBRyTNma8DHiiJ91pr\nIvFLZ106YcpXu+7pXgOXS6H/QiXMqKxLitHVmn+9CMk1JJdoS5EoisP02n5fn+9rEb+OH65CDmP/\nES47r7OVL412/lv//9tLzlQWmql84peu/br1vQZICK1NVDHXl/D6bhRofCBGVbbjomXKWuLaFAZj\nZA5otGhug18JcQOtqFyDMxW2bjDKFqpgYpk21s3j14CaEnEVKNfmE7aqwRT3pxL9baU15/PI87ZC\nijhnGIZWZGFOUzeSBtTUDXXdcDkfWNeZnBPzOBWr8coZsSP7EKitEPg+vv+A05muk1CBh4cbpvHC\nu3dvi6FALoR1VWF0MQJ1O56eD1zGBVe3HM9HfvztdwxDw7ouzIvn5emJP/+Lf8bQ3+C3lcPzC1Vt\nGS9HbNPS9TU///KR8zjz5uGOtnJMs5wW6nrgm3ePvH//mWlZ2S6BmDJV1XKeluIwfaTtHFVleftw\ny7ROWJv4sz/7nmWN5AR1M/B0eKEfbnl4eMvhONLv7/AxEXzAWCUFyhlQmjVkBuB0uYBzRUUggSE3\nNy1d3/HrT7+w+cjdzcA333/H4ekZyHz/3VtcVWOrhhBgvIy8e/OOp08fGIZblHYcXj5TO8enp0MJ\nxdakkMoFO3M4PlFVDmsNXVdjDGzrigoR7ye+/+aex/uez59lMfnpeKZyFVXj2EKk7Qfu34hLtut3\nLFvEVI4QhK8ydH15Xxusc8zzjDaKpml5/+mJy7xgqNnvNLbSVJXm7nZAaaitLDhD9KAkGtBYB2oF\nBcYago/EJClWXEcuWhPLz0rd7lBKscxnYphJtpLTYRShQVN11FXNPF7wfnn9uU65LEutFG9NkuVq\nDhA8ORU0hsjNBMfrAxmDMxtJFbJikfpJZ1903caiXIO2FWDJSebYWitiLpTCV4qgCC60TqhUOv1c\nIHOI1FJupWt+LepfKeWui8hMGRXp18LM9WCuDMUpQ9ZRgqZzRumvMAfygIXpXoSKSpUGUhQx8R/j\nsvOqVnkdsfBlXi53l+JbCnkuI5frJllmaYW5XGbtMUVCCLKcMAbdOEJIhNUTcii41EBKWo5EWTbG\nwWRqayUFvbZydNWWGA3bujAvC9opgovkNqErTe0aKitz6KoJTPOEnhVpWgk+vF69/baybhs+JIab\nHVVdMVSREFYul8C8BIZOsdgF1WfqyqIrUNaSkGSUvr9hno/4sKC1J4ZFnIVrwC9RHKEsdHWDzZnL\nOFLXjnWd6ZuGmEQrPww1WSfqppELWsn9HO4eeff2DmUs6IbT+Uj0iW8ev2GLcBwvHM4/8/OvP/Hm\n8Y64VRxfRJkQI6zjme+/f4vSlj/8/CvruqFCIETISRG9tFTLlpiXlRBnbm97NmWpmo71PFNVlvly\npBo6bm7eEqPGrxNDbcFq1iwyzsp1GFPR9YbD+Znv87dYN1DS9ei7hmXpBQNsLG1Xc3g5cBlXwFJX\nLe/evqVpEus88e9+9ze8+/Yb+qFn1/WEbaHvOy7zyvN44bZu2OLCmiJ3+zs+/PoLd/uadVlZxiPH\no2FeLigtaTmXy0Tfd/z660+0dUOla7SqGIZbQjjw1//P79jvdywevn33CFlxe3uHz46wfaCyEm6C\ndhhdYVVDzpF5DmjbMi0L2+IxGCpbkRMCuqpb4jqTg2cLnnEc2XzmFDP3dzC4ikTEWsWbhx1OW5Zt\nw8wzztasiyQO7Xc3OKNo6oaZhbB4ctbS4ChhkF85811/g18upG1BJY+k4sittg1907HOM/N0IRPl\npzcLxMQYESyosteKcSPHgOTJJVlUai0oWYTXklMghwgmyu+Lu/Kq+0hagbZkW5GMIxaVyFUwoZP5\nopJT1xwBGZEIGVWQFteRSFaphEbwKh+kzPRf3aVZNOBXTEB+lSFadCqqQh1fR8imLFhRhqiKpDBT\npIjSWBqMNJlotE6F6vq3FIr/3u1PV8i1HNdUkeOkKI6unDMkivW1LDR1kUQVNq/SMrJQMZCNRjuD\nS5o1y5Es+gVDKE94BB2JObAtEZMVlcl4tYmlFoXVDms0rrbUTSVgLgzGGnoLxgniNnlPMI7VSp4j\nBpwBpxWdaqicITaRZZFkE+0DsQRR6OwxyJLTGDl2ohRhC2LHbx3GKBpXUVuHoDo1y7YQ4iYc75zx\naxLedExYk9hCQFnLtizMy0jtNIMbWOeZ0/nCfJ4xRjHlEykm5nWhemhQxjCPI9M40nUdxmpSDNze\ntqAyp8uR2G+ijIieoXFcnl9wKeFqx3C7Z7pcWJYzkIlNy5u3N1Qu8PTxM+O0oK3mMs/ycjvHzU3L\nft9xvFRsHmwMVA6m9YRWPW3t6NoKV2veDm84n0aads94OrK7ucVvAec08zxh7I623YNSJCI+ap5O\nJ54OB9YlUDc1fdfw/sN75nmlso51XUBlfPCEy0JC0XY7Ku1o24Zxm3BtKzjj40kKsdEEDUMjSe7d\nfkfbNRyPJ4x1jNPCOG3UTcvheGY3DEzziCkjlH7oUYtAwT4dL/z1L8/802ypVUCpwJYUbdNxv8vc\ntLVo9SuNNYbd3Q4qWUFO04Q2FdsameeZ29sdPowYU1M1vdAzp5HzeZTdit9AGbaYZcG+eapGMgC0\nAte1rFkxh0hdiXzRWLDOEqNglWNZQlqrSpamNFbJOJpuj1Wa03TCh1mKXvSiKDMVddsQome8HCFE\ntLHEQjG1xmCUqFuy1uQNiApCJm6elDw5lOJonFwAdEkkKPm+OssiMiuRNuaqIXtNNpagDFGZgvoQ\nHLVWFsz1VI8UdH3tpgUmV8QspdtPGGWLmOJatK7hN9ePETqKstezv5PlqxJeTdbX8W/5OkV5oopG\nUQNfRHmhzP7lQmHK46MEHJYTJP4RLju1EnhCJv6dJyqXo0n+cuVLvMYwfVkayFXUlHlZjk4yJZVh\njeLyyl40zSZd59mJSGbLGWUhBIHV2JyAiqpyMnIwggLV1uBqR105pnUihoRzBZ6T5IKTgidrg1ZC\nm6tcQ9VUTPPIdEn4ol7QumHbPJfLmaHraZqWb75rmM5nlm3FOEHyrj5SBehbMXgsWvF0PrMsK9Ya\n6r5COcV0mWRBlBzrGnDaYms5wm1rQMdM07TEkteolRGIkr4yJiJN1zAvM8fTyP6mxxjD+fxCCEmw\nuDEzjSNPH59kjLRFPn3+TNfV3L15JJbUGa0Uy/uPdENL21ju72+x6sKarylNkRhExxtVpqkqLi8n\nbot00CpNCKks5Rxag99WcvBM20TbNVSuYdUri1/RSpLOf/vD9xhjBG6UEykmjocztjLlNdeEEPju\n7T1+3dAqM26Bf/N//TX/1X/2W1IMGJvoOsfHzx/oOhlvUfAApmmIMdI0HX2/o+kaLucXxsvIPM+4\nuubT85mnw8SbdwMsK0mB3zYa1dC0NcaKpKxtWoiJoZU5/82bW0KSeXNWii1HdF3hcxb9tAaVPNu6\nYqwRJkpasbrDWYt1jmVNNHXCWc2nlxPzshBDJMYM0TN0Dh8T59OJZVK8eXOPrRqys7TNjmkOhCDm\nOm01TSN7hfPlQvBihKtc9aUYKUhoumag6wZOlxPLspBjFByzERy1sS3WOab5QggSviE/7qooN0TB\noXImlSV6DJ7kV2JYZcGZVJEHcw0BKxrwSI7SvIA0CORSyMyKzxCUJWZTqon+YpEvoCwoj1W661fH\njXjueUXPluG8+po5jirFWr86PqUuyYbzarNXOZcAZfVa07S+Gn0o1fs6xrl+jRJkUeSJcmfJItXw\nVaH8925/wsxOsddeMym/Ts+43q7M7Ss4i6yvikP5sKReFwPJGJx12KoiZoSlsqykuBU5E69vpmuj\nn8vsnaRk0RlSSeiRY1eMoJAMzs7sCSFBjCgDxknMlqhiEt4LjF7rJBmA5d8cskejaeqWEAPTZUSn\nzO7mhrZtqZ1iXjfGeaauW7ZtZlwnkoZlq9jtdjy8eYcgRScqW9HWLbtux+V8QetMPyieng5EH9k2\nsWCjEipHuk6kiP1ux+V4QuuE9xtN3WOd4bvf/IZ1WslJ0+06lmVimRdCnNjtdvR1zdRUPB1GqrZH\npcC6BXJY8cvENK74LRLDRncZ+c2P30KO9EPNdrlQ15a6tUzTCgHCuqJ0IoSRnCw6N6SwymuSRSa3\nrWIcysoyzROPb77h5eVIjjJXzUo6xd0w8PJypHYNfd/y47ffsy0r2tVkIpV11LuK0FZ8+vSJqlHM\ny4kUFy7nI+fjgbwGlunMdIq8pMx3379FKcVuN5BwZGeoqrqYrRy3t3vCttDUNU17w9/88TNKa6b5\nTFU1XC4XauvYlCdFURcZIwvHoXG82w88H49ENKfzCBke7xzLsmCNYl03lnFiT4uNifEycZ4m1i1S\n1xqtYOgb6toxTyuXccJWNZ8+vxB8YN02ztNMyKCNY1kmxo9P3AwdXdej10Q/SOJ9jMLt934VPIU1\nGKtLBq0EpDdNXUiiG1sI1KZmt7uVpf3liFJJsj6tpbINxrbsdneSVL+M2CI1RBXYndKyIIyJGCLJ\ne/yyENeVJJB0wXDoUnqjdN8pidNT6UCOHnKxyWuFck4uDLaSCwKGkL8qyAjE6mq5TymWhaPwx9O1\n9ugsuu4rzErxuoMTNUkqj2FeZ+EqC/NFPueLwqVs+HhNAuLaxV9FHIhu/fUiKfUjX5evKHJS5bqh\nUDoXbO/ff/sTFnKgXNF0qc6ZL7KinL7Mw2UbLE/wq3wcQCsEtGchSSZms1bsmo48XrgEeWOrnLFa\nUdnSOZdC7cqCSht5MZZ1RplEXTVyZY4ZnyNVXVE7R91oIRvmjHGNvKAGnK0wVl6IWMIpYgosS2Ca\nNoxWGC1qF60qpnFBaUOTpbMchh1NOxBToK5FQRB9YFtOGJ3p257H+wemseXjyydc5dgPA8OuJSY5\nOn777bdcLiMvxxeyUsIeTzLa8SEQc+T2/o7xfJYwh+gZug7narq2J+dUVAiGeV1RKfHhwxNV7XBt\nS+8Tz89P3N/cMc8b5+NIjAmTDbqueZlX0JF1CRhdgTM0LsgSKWkqV/F8/sS+a9HK0t7flsBfhcpy\n8Wkr0ccnrQg+s4UZHyJdO6Cy5jLN1LX4AeZpRhvD6XymHwa0gdvbW/763/4VWbXcdT27umaJnm3N\nDLsbkYtl+ObdO3768EJaR+53Ax8+nyAZlunCt9+/wVlHXfUcLwu7m50ohPqK3a7j9OL4dHkSNkvW\nvH3Y8XJZOB4O1HrPi/nIjz/+QFKZaZEFtXEaZ1uGvmPY9bx/OfL+0wv3g/B0FI7T6SAwqlrwyRMZ\n5Td2bcu6ep5eThKc3Q3cDC1dXRc5a2ZZI+O0kvzK6gO/PJ/Y1kxQK5/PCw4YBnEybstM3++Yl4Vl\n3VCIVC+TuYwTOXesAbYEQ1/R9gPrtrFeAilC19/Qdzcs24IzEiQuiIsGa2va/hZnK46HJ+mcncTH\nKWXIxWATkph/8ibI3rCtwkXCoI0V5UsWX8j1JifzWMYlEcnWzIW8qMUwFCzKW1KEFKSRUuXk/3qK\nv4pErid/8qsZURaQX7reTPg7sj9V8hNK816WddcAeSGnfCVX5Fqovpqzp+vej2LlV4Ltxbwq9Irm\nULwSSpcgi8Jl+Qdu/0GFXCn1N8AJGRz5nPO/UErdA/8b8Fvgb4D/Ied8+LufK/l7EqyUVEmu/qpb\nlpTs68JTkLUUkb7S+vXJl4uBqEGsczhncU2NaysYDTEqgpfjnTaNPAwiLcrFWiB6Ti1W+wDZGZwT\nboaP4vqyrsZZVzbeSo7AEQRRKYEXTluSibAoVKdFQ1u3LOsiuAFVQh2UJubEsogVW/jL4JTDVAaj\nFes8cTydOR02NAGVRSUTAzwfXohJMTQOyiJIEXn79pb9bcevP38il9nrtnqcNazjiG4RyVpl0Fmx\nzNIRdv0epaX7dU6x290wTSvbeiHExOV0IYeIU4awLdSV5nI+c3Nzx+Y3Ys7sdwPOwrKsJRlo5u3D\nHdM0i+U6ZW52PTkGHm8HGTPNMzl66sqhSbhKEcPCvK7EZFjWSCSwLD9RNx2b37Cup1KKaTpTtx1v\nH+9pa8u6bCIfrGuMUnRDg8czz6PIRUPmdBmpnaVrHH/z04HH2545zNhmz/PnAzf7gW2TmbH3Hr8t\nhNDhnGMYWrqm5XS6sMwLdzc35Bz57t2e58vEeVzp+8jqI1orKVY+cFo37m73wuUGxhj4w+cX3tze\ncrPrUMrw/sMT3q80dUVTOxmzVRVV22IrR8yZ02UiKzhfLvJ52jH0DWbznC+zFMQQuEwLx9PEEiKH\naSPGzLePt7jKCYBKKZyBw/HEspQ/O0dK8PR0QMhf0pE6V2GritVLBFxG07Q7GT36GaNkNOSqCm3E\nTzDsBubpzObHMvYsXabK5BSJIeG9yIPxGZKXn0ln0FmMMaooq+QHTE7Ir/zzUjxz/tLRKWNLULtB\n+CS+iCOu0sAvhVR+1sR/8npXQSyIbj6hXguLfu3i5UOvnf2XuXfOVzSuFON8vT6o8jilI89FhZPL\nheOVUV7ULZnidyku0asc8fXCcJVm/gO3/9COPAP/Xc75+au/+9fA/55z/p+UUv9j+fO//rufaLRk\nBF7Fk7k8k1cUryIWKy7FAitOLL6CZV3z7rTWbMmUCDVQRlNXtVibK4+OEWs0VpkyZtEog+AzkaNL\nNgqlVcHDLpgsG3DxbopLTaNRSVQxKkpQckYRUgR0ib7SKC379GHX0nYtxjiC39jWhWVdCZ7ytRJ+\nCyglGNtlWhinC0M/cDPsaOodL4fP+M3zcP9A2zge7wdcbfj08SOTNaicGXY91kXSvNC0A1pJePPQ\n1eI2TRv721u2bXvlQVRVQ4yZZRwJKXB7/8ju9pZP799jdBYAk7pgYqJzNZ7ApDZOl5Hvv31gvpwI\ny4XKGg6nC8Y42n6gsglnE3VtqSrhSy9bJPuFvrZYNOfLJ96+/Q5l4MOvn9gPO2a/8vHjZ25u73h+\nuWCqmruHN1zGlawt7z+85+5mhx9nKiVuuLZt6foeVObl+Znf/PDA22++RakVaythkiwrTdNyOp85\nHM989/Ydy2GkDZqhfuSPf/wJV73Q1plvHvecDiecq/n9H/6aN+/eUlVyUa9cQwji0Ou6gWHoqCvh\nh/zVr0fGsHBeF3iOfHP5lpwz6/wzXdPwzeNbUgqE6AlbpMFRJcN0uDCHhZAVsbhfH28bVBLeT9O0\nhJhJ2bBsG11fYXTEWs0WIt3dQEDh/ZHL5ULOSJetNM4VIl8K3PQ9VWUxKVN1FVvYODwdyFhyVNTW\nYFzL+/cynuu6Fpsi1hpSkoSdsEWGmzv6difqsrihspdla1WDrugGuW8cT5A8uoz4ULwqO0L0xJil\ny81glBWAncmvcr8cE7F03a/xMkrGSqo4KrVxMmvXhqwNUYkCRppXL29yDK9sJi0gPq4dsUIuFqrM\nufO1m9avMmfz1Xz+agiijFmuBNZr0tn1YiFzfWkMc84Fz/tVAIaWvZzJ4HMhoAIURydFTaeURhmB\n4b32+uofruT/MUYrf/fR/xXw35bf/6/A/8nfU8iVyjK3VlqSLzIEnV55BTKeKhxi/RXl8EoVyF9e\nkJjjq+XWuRqja5SyVHXFTiUWo8hR4D2Q8CliKGwVo7FO1Cpai67WB4UJmbRJzFWjDS45UhD8rrVl\nm601qxdCYSjpJ8UKUdREcrWt6prKCbOkqh3L6ti2TRaPVo7S67zImzxlfn3/gQ/6A/d3t9iqOLVw\ncgAAIABJREFUYlpW/Idn3r0VbXMfA/m+Z76s+JA5nmeskTda10Tu9h0hBrI2JKVYF4+dPG3juEwj\nxljGdaZuHZFAOAX+eLhwd/+AzrCtGzmD1RXLOhHCgq0MD/ct2yrduAL8smKMpnFSxGOIHM+eLWhu\n7nZQEs93taXCYEtSkFI9cQvU1tDvOz5+eMJqR3/TYZyjqi3PpzO7+7fcPj7Qdy3n4xGta9r9QIyR\n2lSoLAtUaw3OZMie29sbTucnCe/IgZQXlG5Yl5UQFkIOeAL3b/aovAArMRr6esc2zVRdxxo0P/36\nwm/+6Z9zHs/UdWDYdUzLRtKWpA2YClu32CAjO4NmWlZqrTmeJp6PL6TN85d/8ecklTgcR87nBYXm\n3bt7Pj09cQmWsGy8efPI8fzM3btHqlY656HtMdowrQvneWKeJ7553NFVktRDzlglnPRzc8G/xqgF\nKpNoyLy5u+XpeOGmc7y57agctP3AFgyHy4yxNSkGsrLUTYOrLVtYGXSDqxxgWFd5T7d9zf3dDbtd\nzzi+EFNkS1CbCoyjqXsqW3E4fmbzC85arjpuaywpCeOcIEXRlmxNe9WEx0SOHhUkRF2WnY5sLJhQ\nft4N2lUYV+Nsg3INytZkpVFRgjV8RjhICGxLdpn6KsWW0Idrd6+EXJheG8NcLjhSaTIFZ8tV4VLG\nL2UBKQW7mHqwCKu83LQqLPNc+Euv14HXgItrhrC6LgTKDjBpXoOY82uYxZdZ+993+4/Rkf8fSqkI\n/M855/8FeJdz/lDu/wC8+/s+0VhTFpZfvnldsvFe/6Ou6pSvrhXXD1a8HnNAkj2MsVhXYZ2wwZWt\nsDnRNBC95yodkic0k7VCX7ftShHl0k1Imc1HMqlkgFqhBFqLtRVGa6yVN6cPUcxDRjS227pJiHOW\nLt17mWHvhoG7uzu6tqdrJZTYhyBHKSXfY4hBiIxOc7lc+OgFuNR3Lfu7Papy1ChC2NjvbtgPiRAl\nKejz54+cXk6MRih4/a7FGHCmx24r5EgKWtz/JQt1neXZ8JsnkHl5/kzcPKaS780qUQIt2wWXE01T\n0duB8/HMMo9kH8Ux6ywpLuyGjpAV03ni4/lCVpG2q6kqg60bYshczhNGR3xYOY8T67rQuMSut8xx\no+1b2n7Hx5e/4fPnE3/5n7ylrmvevvuW5+dnfvwnP+D9isbg/QpAXVv2/UCKMg7Y72/K8X3CbyuP\nDxVNW3M4iftv2yLbtjDNM/f3clLR2jCOE7cPdxyPL5Lo4yyn85mHh3eFyaFo2xqNp6lqshdsLkBE\nc5kib/a1AK6y5uHujqat2WLCbx6VIo83NzxNnvP2xJAzQ1Vz13eYFGgqi9GJ4W5HUp6cI9sa+Px0\nIkaorcWkQGUsy7KxrCvOWYyB+6Hm14+fOU8bHoUJnl1r2DcDP36zR5cu/fHukcscGdoT51lm6ltU\ndMoxDDfEuAAWY2qMsYTFozEM/T03t4/EKBmy27YIysI6jK5puj2rl6U9/y9zb9Ija5rlef2e6Z3N\nzIc7RsTNqKyuroRqIbpbBRu2iE/REgskvgdLvgQ7pBabRixhyZYNG2ikVGXlEHFHdze34Z2eicV5\nzP1GZWQjdQtlmeS6NzzMzK+Z23uec/7nP1B8QXLpcJWSxWYqGZlysWOwFFdqEfZEylcR6xXYEy04\nsdYG4yp03aBci7YVylTFZTCR8KRc/GJyebw2TzVDVnFSU1K+SPMF9lFJ9m/PFL/yD7vcT+eCOCku\nrEVAePFQ2C8CAD2t9p52f6VGgdAgS5FO6lLeC42yYOSyTH3u8C9c9vz/Y0f+X+Sc3yulXgL/m1Lq\n3379P3POWf0JE11d/IezuvBTLxtbQVuezd7/tK9XLnFCTwZb2mCrGucajGvI2hRmi0G7zMUAniQp\n48oZrLNoY0lZlcAIR8qGmEq3bkQW7JeVsAT6wcgoGBIpS0RW1QpG6FMk+sTxeOR4PpETNE0tPOe0\ncJ4OoCJNVWOtLDe89/K6E8zjQgqBdV5pSwLRaT+ynhdSNMyjp25h6FqmcSy7AcvxeJL4s82W8/FM\niJ5lVSgDfd2i7VCM/hPO1cU2NOJjQGvLPEaO40hd19TOyAGWE/cPD5zPkyyEk6bZDmiliHWNiguu\nNdS1AyP0wUpbbq43nGrLw8ORP3x8pD7MfPPyiu2u4jydeDhODI2mrgKnw4kfPx3Y1JqX3/SM2fH5\n4z1/9de/4rvvR358/5nT6Ujf97x985ovnz+Jh0m/5XQ8cjofiCFwe33D7fUNq8+MURan4yRskdO6\nklB8+XLHPE0s00LwkXGayQTevX3L48MCKeDqmhgjD/f3vH37mhw911c7QhBrVpKmbwcaZ+m7nul8\nZJ1nNt2GsAS8zSzeQ4r8xcsrAhlb19x9uSfElbo2bDY1V/eWV9uesC785Tc3fPdmx3vlccqwjjNN\nXQGG037k8XFPmk9cdTV5jRyPkfuHM9lq2nmirq9kuZ4t94eZ4xKpjKG2ik1tuL3qeXGz4XCe6NqW\ntqkJObDbtBxPRwgr8zyTYmC3bVkWRd3UhXorh5IPK8NuS9f3LOPIsows60JdNRI71wwo43jc3zMv\nE85oVFQoq8qyMJXPX+ZixXqZsGOMpBDJ5UslMdySJCCDOB5mjDUSxOwcxtZoW2NsRTZOiGfBE7Mq\nsZ8XeOS5CZQfJ0CNyqn8O8qurNjZgkC6FzphLt4nFIz/uULLPZ977LLH0/lpr/f1a+QC+fKMf8Ol\nxilAF9aWKju8S+yboBFi9/3H0MfXt/+gQp5zfl/+/KyU+jfAfw58VEq9yTl/UEq9BT793GP/h3/9\nr5H6lfjn//Hf8C//2T8rfsHAxfoxXySwP+8Wk7MpHbMIiWKM5BjFA+Fy8gLWKKyrSAQUubghKpK+\neLU8PePT9JJywmlDZR3OGHEmTImcEtbIUiN6L+HNxmKconY1yQgv1TjLNM7Ckw2BlKKEK88L/TDI\nq0oiHZbOBoZtxzTOTOvKPAWayrHdbLBOk+LC/Zcj0+rZbQecsySl2W13dP0WHz1t37DZtCzzKukr\nJZ6r32zE/wUkNzSKfab2npQy3dCwZlHWpahZ5hlXvEliFjfDnDRfvnxhu+2xTrHdDgS/4tcV19S0\nrXhP3325w1WGd++uqWrD/v7Iuq58/vIAxqC04jx6YlBMM9R1RV0rDqeR4fqau4eR3//u9+x21+yr\nA9PhyLTZUDctdd3y6eMdL/7mDafTTFVlfvvDj6gEw7AVuMoqDoc9MS7cXO+YzpYc4f7+EWPg8eGO\nykLO4jS5Tou4E6rIy1dXjOPI+XRgt9uhgM2w4fF44mqzZZpWlnlm2DQYa+m6DVU3gjlhmkrsIVLg\netOx6RvuDiesrtg/jhgTaVoxVrvetXz64Ll9NfD9uxtcZWnaBqMNdT1QVYbTuNL3jtubgWW64uPn\nzzRDS7frwSScE9e+EDOf9kc+7E8c14SPgRdDxU3f8+JmwNpaukxjafoNtm6os2cYOtrakUKQDNfr\nIJ7k1olbodZYLTRFZSvadpDpOYtXTQgR5zLONdRNy+Fhz3g8EIPH4MgWrJFmKISA0bKHikkmafEf\nF/fKuMq1RZaFn1FFZ5KVdK3FP0Ubh9YVxrjiZCiGbzlGQk7EnAj5K9fULP7icmV/fZ2b8vzyX7Jg\nLZwQbSmgeKkez10zCCkh5SSeLyhygVkERJBFain5TzddhIzPVaY83ROFUT3//4ywcEoX/vcfPvPb\n959/+oQ/c/v3LuRKqQ4wOeejUqoH/ivgvwP+F+C/Bv778uf//HOP/2//1b8SrnYOZbQSJzZVcGX9\nVazRn4KGLlJaYwwhBMnADAEfJVpWowhklNFllFOQVAlGFjFRSoFcRigZ5yJKSSL6ReqVyockF+Wo\nD56MxseATxFtzRMMo7Wma2tcJXTDx/0j52kh54yrKpRxkOXfsMwTziicq3BaU2+29F3PsNkwj579\n/R3H6cj19QaVE21d0bU9MSWm00xGM48zSmeurrZYRACCzviwEpZEDjK5bHdbhm6Hsi1ZK/y8YqMn\nBE8OGe0MYQmMZ0+MC2pNKJe4GiquO8e6BM7jzHpW1JVBW4PBMi+B8bRSVZmYNTF6prOnqxu+++4N\nXWeZppHgFafjmUobFpU5zoElalzdgM1MKcNpZF0j9w+PtH3L99+94XxamcYjy7pgK8vh8YEQPH2/\nIRz3DP2G8/HE7373W3bXNyitOBwONLWhdo6qblEaXr+6YV0O1LXi4W5FJbFlsDpL0k7lGLqBw3Gk\naSpS8kzjQl1PMurHSEwLm13H9fZGpNwxMzQ137684i9fbRnniW+uW15eD/zw5Y7rm1uUigQ/YYzF\nKEM7dGy3kbcvr3n3+opk4LQurCpB9OyGDX5def/xI7/61T9laFt+/Yf3PE4TvxoadruGpqmwVUNM\niePpxDSOPJ5nppBY5pUwWK6uXlPXDeclYOeANa10zrbGVYa26dgMAwbN0LTiArnGkg+Qn4LFcQ6r\nLK5umJeJpqlom57zNBOSomm3nE4jXz5/JMYJiEQlnwNUJX1Zmb5Bo6I0RSEEkk8iFkvxaav4ZFNV\nTKyUtihjyMaitBMhkBaaotISSp1KVkRMPHPCS+FIhWFyCaqR4ivLQ13qp34iWDwjAk+mV5eOOz93\n7VqZ5+Kt4CkZ6MJmudiLXKrzV5DIBU2R1CGeGSrlILlwygWLUPzym9f8xZvXT939//5//l8/Wwv/\nQzry18C/KT/AAv9jzvl/VUr9H8D/pJT6byj0w597sNaalMGgn1zRszKyIEjIAqK88AjljXu2tqW8\n6Auv8zkMVmS8q19JJUrNWOGmKgU5RvnMGI0xoLMqocjSXUvgqvi4OCduaiklFKY4GSr8GqhaCR72\n08iyTMzLRCJjlKNyDbZy9F3PsiyFyrZitcEqQ/QBciaukaAy2jZ0/RaUptbQtJHQCzvh48cPPB5H\nUEJbbDtPiJHz6cxm6Nh0A6auaJywKOZlZBlXjGqgytR1JpQ8xmn6QiTTb3YoIuN5ZJ0DfdexGa5I\njaepF+bJcj6PLPNCypG+r8jryjRHHh4PtJ3mzesd3dCRteV4mplWuHu4Z10nmq5iSR95+WLLtt+S\ng2GKE9ttI6HKqQEqpsnz8e6Opt0QsiIlTesq9uOZcXyk63uqXhHDitKGq+1ATiv393e8fPmWx7Ph\nzTdvufv4iV//+tf8y7/9z6irqvy+HDlrXO0IYeHNqyvOIxz3B6yVRbvVmmWe6IrAZl1WYoTGNXSt\niIqOx0fqRgzIalczbK7o247xfCAk4UlXRvGf/kd/xd///W8YNi3v9w+cV6jmgDYTkUD2mhTkZ/7y\n7TUdb8FYPt49Mk0zdWVYfCBFw8fPj4LZYsD2fNif2OyuaV3F/cMj263lzXCLso7D4VS8OeA8rlhT\nMUcjxlKuZrmfqOvA9YsdJQmcrqupG4urLSbKn8oAWixwY4p0fYvWGeccbd+VhjRJl27lgLSlMfny\n4fcsy4jW4SuI9LlgGlMojUo9WbLmJKEy+cLyoBhoKakJl4ZOlJkapRzKSsC0sqYo/GSCFp+lkmHw\nBG1cHn+xktWFQcIzna8gJvnSgavijKhLFw1I1max0oYSOZeeivEzRi5BNBelZ9FPF8hXaI9Sdi5L\nTtm+PnHDi3hJK0V88ni5HATy3n21Sv2j2793Ic85/wb45z/z/Xvgv/z/fgZZYDzJXDXYLLxMZZ43\nvjLZlESQDCFLjFtOmaB4hlZIaJWEwlQi5CChDZATpnA8QaAWrVUxAtKsPqCMYS3drLYaYyv5xShR\nmlmrSErk4LG4HoYgFpvrKmwTYy1ZJ3yYcMFjtMVVFtdYQk4lrFnEFykHtJNRc1kXwsM9MUmohbWy\nxa9qy7t33+BXj/eBaZwYxxNKUED86omrpzKGoCd8nAjeM06eGDxVZVlnCDmT00jthJmzng7UTUVb\n1cznmf3DA86ZEogQGaeFlKFpO7z3jMeJxlpuNg17Rk5j4NPHR4ZhYTNs+PabW07nhQdrOM8VKQbO\nZ8/94xc2wwg58eFuT2Nrdp3jxU1FjCu7V1uavuLL3SPTHDH1AG1Lpy0//viRb7/5lpSg2zr6oWUN\nispJZNrtzQ031zt5D17c8vHuM35d2A4dbVvj6gqfZAF1Ph2ZT0fCdMKlwHVfEWPF4XCmqR3WZJxV\nfPr0gcfDxO2Lq6dOPfhAOE9obXEa+qpmXuRgXb2IylJa6XvBbF294Q9/+MQ3337P/mFPZR3T8cys\nzty8uJbuz1a0mxf8/R9+5P/+zR+wJvM3f/kLmromx8i2q/nu7fcoHTifHonzwnBzjV8CxljmeWYt\nvvshLLRdw00J6rjdbalNQKlMtxloZs+wu6Jue3z0eC82EpFiKWEMtpLPZ0bRtR1106F1zTQnqqZl\n2285PD7QNo5YSkbVDNzevmRezuRwonWZcVXkLM6MWmUxpbICVRgjZlA5JXS8UPgQym5piXOSRWDM\nMqLLtK3EgsHKF8YKb1xbMBrvJd7P+0xYhadOkoyCmC7UvSy5uYWzDXBRml5uT39/ilaTg0VOJk3S\nUlMkD1gX6rMA16r4p4hISQpwuuzjUMUcq9S6rzp2VC4ovSYqI+w9QGGedDLpQst+7u9/9vZnVHaW\nzXLhW8JlLCrjy1fsdwNi9E4mZwmuEjgkPjkmxiiKSlmtBowqgciYp+4/BjmFL6R8V5JDHBIO62qB\nVCTWzEkXn2WUMka+rClLlqfJIONqh05yIq+l6J7PXl6Hsqik6KpafFws1K4meEWyImzwXgy5ZKmY\n8ZNI7Y+nkZQz26Hj5vaa5ptbPn74hF9XMlFEVGTmeSGdJ2xtUVrRdY7gE8ZqjHKYAhAaK8HSKUV8\nCrRtx7fv3hLXwLrO7B8PnCeR3GuFeHZowxqko9htB7Aac5pZQ2RaYfqypzkvDJuBb755QdKJ02EU\nEyRteNgfGE8LvevZH088ns5gLX2jmfZ32Kri+3dv+O1vf8d8PPL6zTc8xDPrCuuaSmivwdiK1jpO\n7sSyLHz+/Im3336LM0YW1yiOxwPbzUDXdVRVI6yImJjHI0pHptWzTl6WlnXDmB6pTaarRFgVs2fT\nW5pKUzsxMSMbzucFH2QJucwnDuOIKZ4dthKF7zoduR4aUgj0/YbxeCbESIqZT/f3DLXGaeFKK6XR\ndcPdaeSHTwdeXQ04V0vSTfJ894u3rDFyPJ0Zp5nKWJq2ZVwXvv3mWnYfa0ChSUmCwruu5u3LW3T0\nbBvY9MX7vO8Zdlv6riMncK4iJVjGGaO1ZNYqi18T2tQ0TYOrKkJM1E3PbnfD6hfO5zN9d8M8ryxR\ncbW9pnaO8/GAq5xECY5njNNc1JCp7K2sE3aLWMZmnElklzBFVJOSBDqTMylI5u5F5KeNRVvBxQVm\ncfI5NhafMyFlfEzMIbCGQAxiX01WxRs8QOGwJ3hK8JGm+NIoFmEPQgW89MJRf9UZF5hFXbLfskS0\npVLMc+GWFxKMRLpRUIAS7mx08SlH1KYCIfHU3T9j9lZsXvLzBEEuB8ufuP3ZCvmFq3lx+3qygizU\noa8zPEU6qwqOJZvoVBahosqUgh6CFNFn7Fy4nQJpKFadnxaWGFvYKQptHU5rtJE30VUVkMqYZ5+K\neLr88slUVVVEApkYBFvXOmOdJkTBGUOI0qVbgysZhWTAOpyrUFkRYySy4JeJjMK6WqwrvSxtZDk5\nc//wGW0dTdewu9kxnk5Y4xjaRsRGMTJOZ47HA03VStqOynRtR9tKpFwInsf9A/M8kWfF4+FIW7W0\nTYXRcLUb6DcDx8NIWFcql7FK0dkt87xwXiO4hmFX8+XhzP39ietNhdOB+bwXV0dl2Q0DprI07cC3\n373j8+c77u8fUVXFx8/3/N3v97y+GRjqzI8fPvKLd45f/fVf8eP795yOZ8mYrFpOy8qbF9eczzP9\nZmW77Wmahvu7E/v7Oza7DTdXt6gMm80G5ww+TDS1QRFJ0aPJnI5nhq5jnhYe7g8Mw5a+NVztBoa2\n5TyOdIOEJNe1FTvVnBiPR9A1V1fX5BywtuX+8IjWmr5rccZxOp8Jq8dZy+3VlqAdSzS8//E919sN\n958/cHo80d5siBm6ricmaQKSD+LtoTVJJfquJudM27aE44i1LT4e6OqWxlXEKK6OVhtCWKlcW66d\nlU1bsdQVh8eReuiwWM6PE3XV09QtrqpJpRM+ng7knOnaFsm1XchZ0TQ9rmpY/IRzlu22p7KKh/s9\n1sqSb1kj1rV0Xcd03pNToGkqljlTO9mdrOtamDSJtm3RuikLRemwg5akLmOqEvGXSCqWcIvS2Ckt\ncn1bSUduLNo6lDUSHKEUPiYW71nWleQDKcr7GpPww0XXk5/hlSeq3092i5LFXJafT9J6gPy8ECXr\ngt8r4YE/1THFhWHy9NB8ySSSGqcvMPEFi9egC83xwhdXKj+Zal0omhdx4eX50r+jXP/5bGz5KT2H\nXLAjnt/My4n17AgmARACt8mwobV+IvTnnAlxlXE7pIKByWmqrKZxgtEKfKYIfiXFTGUqbOWKS6HB\nWvk7OaGzFGFrLaEwabRS5BRlE681kYBfF8IUJBWlargaqnLYa8Zpks19VvLcxbPYWIurarp+S86J\naZ4I68K8nFjnEWNg2Gyoq5rFe8lFVKdCE6w5nB85nY5Ya+nblt3uSmTV2lI5i19mxvNC8JkQ5VBp\n2466bVFWklrWaeZY/GiMsSRlRUa9rjjj0JVCu0xbCY5c1zXer9RNxadPJTlGO5qmYRxXMh7OK1Vd\nEb38bl++fsE333/L4/0D287xw+d7jtNMWC0xOe7vjmilefHyltPxzHiYaFwtUNLUoJTi8e4zRim2\nm4aHe3HMfNzfcbXZkkLg9sUtlRM81bmadV5RCqrK0DU18/lEbS27bU3OE1rVdF1NVhGnFJ2r8PPK\nEma2xTJ38p5Xr29LtqLm7ssXjseRl2/fivvf4cASFvrthhQb9g8HxlXxm9//SPYrbV0TYwClmH2i\nbgacrXn/4+84TzO7qw1ta8TpMMLt9Q2n04kQZLfT9y2fvtwR48p0PjEMFefTiTev34hILAaWZWJZ\nFuZRhENnP5N0I9dPhqF2WK3wQSTpKWbmaZTrRxtiTDiVyCni/ULOmnnxONfgnON43LPMI2g4TwtG\nWzZdS1hnol8lnb40OlVdEZGGIa2ehMLamsqVTjYlmXBthdYCSeSQIK8Scq6RvNVSlrQxqFK8sQaM\ndNMxJSnixUtpXeWaT8GXpWl6mphjDE91RnjiSdSgXBrBrwrSRUp/EQiVIqSeqxEX90RVRDtf17Fn\n4ZE86LK4jWKw8rQMVV/31qW6X6aEpwNGF+z+wuBJ/Dv68T9rR66+KtjqKbXj2ccALhDVhYoodCJ5\nU7TW6CS2tBe867I59n5lXmbC6tE5iGCnKABRmlRST5TOVM5Rdy2V0QJFGEVCSSeQU5ELy2bb6Aql\ndMmPXGQSyAFnDblyxClCgso6CbbQmpDAulpCAqx4nOcMyzyx+oWqamjbFr9KtNW6zigN2+trZI9g\nmcaJFDVtUzGeD3x+vKNuDDc3V7RNSy5cVGsdty9uWedVKI1ac3//hcUvtO1QVHJGFK3aUtuKMWaW\n5Yz3KzHqEmaQmGPi8XGk7x0xeqIPxKWiudLCo+4bXr2+IsXMbrvF2gpX1SxxYTyNJY4vAop5mtle\nX1O9bhi6gevrD/zut7+XC+ocilfKAR9mHk8LKSoJFm6uOZxODF3D/v6etu25fnnNUCLnzucR7z3k\nTFM3cvAqMbzKQUZhrRTWaeazhzzTtS1fPt+x7WtcLbFcPkFd1ah0YFpXXrUt52nmzZtvMM6y3Yo3\nzP7xyGYjvG2UuTgZs7m64eFhTzQNv/7tb3l4HNFE+t5D9FgHN7dXKKVZ1sA8rYTV03c1m65haGva\n1tJvBsZCG7XGUNc1fWV4dS2BFyrKQt+5CjUunKYj59OJu7sHfv/xC5/3e64GERWRQ6HdGnxcqSsJ\n1T6fz+wfDyzLBErUqK21pHVmOUy8fPUSYyqcbUkR9o8PLMuZbGpCntkOkmEqE2QpisVVyhdxX1U1\nLIukZvWdQCwhBCSTUholubYQX/GSxiWusQKcKqUld1PrJ9ZZ1hCVPNe6Rs7LwryIaCqGSIpl4Xmx\nfpVN6FN3nIoVbMk6KkX0Ujov9UgmgvhUl0ohujSZT9VWfdWxZ57YLVwi4xKXn5Qv/PWCOFCaUlXs\ndTPP1iRyvwtigZwYxQXxa0Ovf3j783XklxMPnhRTFx+Vr53Dnt4n9VzwL+ETlzdfYJWVEH3BzMWe\nU5U8QOcUVSWueaDEnlZJ4eualrqtqIwTeEeLi6Fgd5KBqJX4ThhlCDEwr8X5T2v5oChZ0NRNBcqw\nhlVUbGWbnlVCG0dMibh6YoLTeGQ8nxm2O1LhA9dVTV9Zmtrip0VQGGvYdpaEYpxXlIHFB6ZpZjzD\n1baj7xqcbjBOsfiJc5xxuiYbTd30ZKDf7aj7HmscOUgHdjrcE9NEPzicrsvFZtDWMgwt9/cHdPZc\nXW3QtsZWSmhjxjBsWrQxvH+/5/Pn92y2A9/94h27qzdsd5kvXz6yzicMlvuPn1j9iRcvX7G52kqn\n7DQP958520BlIrdXW8aQmaZAyJn9w56/+MU73v3qr/h8/8AaMp8+3VG3DU07cDotzOPCdJ6pmxaj\nTVk6FzGFghi9GFBZwxJm1gx90wjNDsfF836aZ3yMHMczWcthq40IWSwZZy2Pp5HK1biqoq4blNa0\nXYcPgSUFurbndJxYxhPLcgaTGdcKHRa+ffuav/rlO9rGsRQl7nZ3zZfiUlhftxjl8CHxeDjRNZau\naSHO3Awd2+EX/PDDD8I0qSq0EX73uiyMpzP3+0c+H0cOp5mN66m1o3E1rhFhnNYWV1hd5/PEOAfW\n+SyxcTFwnM/4CIdxYdj0bLc1dWV5eLzncDyglKJyCusc2+0G0krKkZgzlXESzB1FYFZZopdlAAAg\nAElEQVRVNSllTuNEZY3Anashu0q85o0UMRuLx0hWZIcc/H59impUWouvitFPzLRUmoN5XplWXxKn\nAusSxJKiwKaK/MRTTzFK56w02USyseRnZERSiArGrQsHPGWpGzldJvqfYthyRiTMRYGZL+HIzyvJ\ny/2fBEWXAl4Km1KapKKYgWUoMc9ykOlUDqHyyFKPnhPo//j25+vI+WqBrC6QyvN3fxKo/PTnxe4x\nl0xV2UqXA4ucogg98GQSVhkqK9t5GXcsWiWckT9DSigt+HzmWS6vk4h9lnUiBvFqjnVFRUApKRRk\nRfYBEI+H4AOmrqiNu7woUsjM81oWjUgXUjoEozQvXrxmu93gXFX4sKnAQ4H748j9/V3hTDdsNz0q\nJ2qX+cW31xQTCU7jgf3hARUCRlsZl3WmqSVEottuyUrjk6IKmZhXUkoYNLvrl/SbLX4ZOR0eCD6D\nFh9vNLx4tWWeVn74+ICtW9pWosVyijhrmMaVdfKkog788OETu8Xz5u1b3n73FzzcPwhLoRo5nE+c\nx4+8eQmmsqwx4GrDVT1wPJzZT57XLwY2vcA7+8PE4XRge7Xh3bvv2F1NPOwPfPlyR9dtePHyNQ/7\nB9YUUcGXlCOYFwlRNtbKriHPVApabcSHIyp8NCxr4HbXEb3HbBqCn6mdxRjNMk3UVYNRmcpVpGQA\ni3LCaGqcEVGIdVRNQxU9OYklQF8bpkmWiuuSIMAyJ8bJFzgk4WzFMGz44cMX3r7c0lQR0xg+P5x4\n//GR17c7hk3F4me88lTNDePs2e0G2soQ44yy0A89P/z4nuN5FtwdcM5QV5aqsbR9g19O1JXDasOy\nrrRdzdW248yKc4Z4nphC4jh7ppBYl4TTNX71TOcDmoCzNWFNXO9qcgwsORKyEutlvxJCRGlDW/Vk\nrXnYPxCmRQy6lhFnjFyDSlhaMUmCvLJiLhdTxgfhlgu0IUk/2TihTGoFWhFjYA2JaQmcZ880B7xP\nRB/I2T934YX1klMix5Un9WSpOqkY9WljhCGTBQe5CIiUUhBBEVEXkaJKTypR85XAR6vn3OHLIRVz\nLDTF8v2cn/almYttrQiacqkVqtQ3qY3P3zeUBh6eqYo/c/uzYuRf3y6LQwD1D/7Bl62t/G9ZQioF\nWimMNuKzUgzsjVJU1pHqGpMCzlG6M1mIWqtp+g5n1DOOrnK5EMrIFaMk/6SIVhmVIn6eWeYV0EQl\nI3tAU1WWyjqyQsY7HUsHU2GsoalrbOHRTvMim/zkUU3N6iNfHvbEKMLghCcnsMqQQqC2ljiPfPnx\nyBd9h7GSJ9p3lk1XM/QdRsNhnkhR/GHqugYFMQbapqGpHNY6YspMJxHTpCye623b46zjOK483I+s\ny8q0zjR9y3Yz0HcNw3bHsLkhZGSUNgarLTlFriK4uubu7lPxWs8YnRnHE/1mx82LW6w2PD48Muyu\nWOaF/fHE7qqhrmsePgc2Vx0hjXz+tCeuE9fXG9q2whnHcNXxsH8kGs1muKbrd5zOJ+Zl4fr6GoDx\nfKKpa8iapu55PByYzkeqykmcGGId3NYNn798wZiOrnX4ZcWHXpZ8VcW6TBitJNXHaHIOtG3Fpu8w\n1gpEa5x8trIEkNgCX+WQ6OqOGAJLiLRNy1A7vF8ZupbaGc7HE+pFZj2fMCbTVpa3N1s+/UGu8OA9\nf3j/I9ZmNpsaZzPrEqhrcakMIVLZiqHtMFleT4pK7GBzxARPb+Cqdez6mrocOCcfcEbCHIw2bPqe\n6XwmtmLPG/OjCOSiZ6gbclYoY7h/uMOvE85WLPOKsuCsYhpPdMMWq4uviKLYR9dYEziMZ0nu0opl\nDbRRsYaIDpHMUkzntCwuL9FmZXrNT8IhITRooySeDWlyllWYR+MUmGfPsqzCREsZQ/FnuSw3yUXI\nJYZbGg3KyvVuCp4tozkFnOdiSav0syNhWUU+BdL8pDNWuQRRFBRBXR5Xqlh+rl9f30cVksdPIBr1\nnBZ0qYHyXEXDQmG//Inbn62Q/2Q7XJxzfgKrfHWTpaf8gnK+nKsIVl6+lFLFzMpgbaZpanSucCah\nVYASKWeMGAJZZ7GujGNKAiP8uuCXhRSDfCWPKvirUxKKmzPYgjeu8WJaZdHKElkxWJyTCSBGydZ0\n1qGMHBR3d/dM48LqpYvp+xbxNik+xcASIg93e1KMtFXFpuCSbWepagfZi4JyjVhr2e6u6eoWHwJG\nSTzdZhho25YYZtZlZBpHgYi0oWkG8cf2gY8fPnL3+QuXtJWb6yu211fUTUvyAWVqtHY0tiYiLIGs\nLfM6cjoesM3A2+979vtHyZU87nl8fMCamu3NLcMwMK8z8zpzOhy4+/KJ9z9kXlzfsiyR+7//Iphg\nzCw+8f7HLzRNR9tvicHTDx3eR45H8Vypa4NzA/M88eLFDR8+vJeDMVe0TUvfDszTiNOdZB2iSDHh\nnCP7wDI+0rYQ4sKnTx+52m1IYmvJ9W4HxlBVFefzRNtYmqZhnj3WZPreYY0s8xxIl7jOaIXAc5Vl\nXo7cXt3iVKRSmutNy+sXN1hnmeczWUd6V2OyIsTEb378kb/9F/8Jy7zw6cc/8P33f8G0rPB5pW4c\nfdNzOEf6usVqRV0JRNe3PclHdEq8vtkxLoGgVjqTqCtNSoEUkjBW6pqsYPVB1MVKYBJjLFVT4ydF\naxVVJ5+1dVk47u9lQdm1nKaJd9/ecDzuyUh6klaK42mkrQ2bbksKmYVVfEz8zH5Zsc6hUMzzjFKK\ndV2KTqIRWFIVbBgt15B+4g+XYipQSkyRmGBdAuMSmJfIsnpCCfUWJSbP/t0JKIWcwmTJCnFILHBH\njpTs33+gyuRCprgwWr4SEZXvqxIHdwG2L/30hZ0h9/3K7O8r/5ULuTGlxCV1rvwknpSRl7pXDpnC\nXpSC/ydu/yiWnRfM6Of2sk/3yZcDUE7gp0Wp1oXfLVxwqy3OVCibJJyZLPmbSlzymkuXaiwpqeJ7\nLHh49tKJx+BJIZA06KRY10hSC04bnLNUleESAB38SlgnQLwpIDMv89P4dhpn1mGgamoO08LoA7Fs\n3/3q2R/EEraqa+pKcPrgV8iRysJmU9H3DVmJBamzjuC9eMogqlO/eqbjVGx0xU6gaxsxLwKM1VhT\niX3B6vFejKSWNTAvC01Xi4PgpqdpW0xV4VxLbSuMc+ImmTXKWpbFc55GYfxkTVgDx/sD67LKDmFV\nWCOMmHk50dSaobU0tfiPXF1teXw4iuVr1xCAeZJu+Hgeub25YZ1XwuMd1y9eQobrqxum8cw0HtCm\nxpgaZ2tAGCp+DdS1ZVkn2rbm9Hhi9RNaWRIBYzV3p0dQmXk+8eabb3n/4ROHw5nbm6syzYkxk2sr\njMl8991blNZYqwlxoq0qmrplngOJSFO3TNNESh5XGWFSeI+Oie3Qcz7veZjOvHr3LadlotMKHxas\ncwzDhmlZOE4jWiV2ux0PD/c83J958yZw9/meoa0YYo9ZYV0SfevY9g0pR7JyUujiwtAZtG34/Y8f\naWtL5yyrTyI2ixnTiDZCKUOICRUC2hh2ux0pJYauJZ4rdBYLibZrOD3uWc5H6l5CUYahp3aGh4cj\nTdfKor7sf5pmI/F8aUWZTEozfh1pmpau7Vj9AiQWJTsgZU1Bg3WB6cTtzxgFKpaFtSw4U4z4lPBl\n2lyWwLQk1jWxrrEwUxAsPCcuplSqYOUhCI9fMOxclNeXoiiF+OsifuGVX6jRPy3uP/37063U1tKc\nI8vS59qVs/iPXzjhlzuLtW05CHQp4k+c9XIolOzOXDzdL6y+n7v92aEV6bDlBVyS7n72fhTB5uV+\nT/xLkfhaU1FVNaGasbZG5UBWARDDHXLGGiv4oUU+JF7wYpSc3n5dWNdF8DYy1tZYZcQtb5xQSqT7\nzVzRth1t1xCN4ONgCTEx+VBojesTbcjqis71NLctr2/eoFCsfuXu4YHzNLGsnv3hzP74CZ0zbV1R\nO6HGxaRQqqJvG9qqQRtHqiTYta5qUJmHhwdOy4HjcWEcz7R9g8qKqu+4ubqm6XusFdOwaTqI4AKL\nsrWMlSFxfNwzns+EsNIMNW3X4+qOrGD2gZhWWD05i1DI1TU3L245Hve4esAax+HxyHbT0TYNIazY\nuqFpO0wEbTKLX7m6vmbY3jCeJn7zd39HiBKacTgcef/5zP70hV++e82mrVkXT91m7u4+MQwbKB4e\n+/sD33z7F4VeKJJ8bSQ/1fuVth0IYSEnCN5LTqQR/vG6iCd4XVWc9RnIeB/p2w4fMm3V0nXCk/ZZ\ns3qPNgmtLJWrWNdAyIGUI+M00lQ1BtmFOAMvty1bLf47dtgyPpyYjOL6n9zQVa00B1Zjk6XShhe7\nLVaDT4HRZ47nSKUiL293LMtMnOQ9T3Gl7+TQikDXJWL0dG0LukLFQN85KgchIHYXVtw5jasJQZqe\naZpoqoauazmfj1hVKHrWcHV1xXzeczrc03UVxloS8OrFLSkH4T9ridwLSQ7YxjWcxiPBr0ynPY+P\ne1zdMNQb/OohBSloMRL8irWWZVmkeDsntEMjhS5rUX9KepjAg8FHFi94+Lom5jWyrhJMobhwxUE8\nDy82srmEXyRyFAZLKrSYnCTCMStxQH3O6PyagCLV/msti9yKrW7pvPPXKILiJ7j4pbaJGFGm3a+t\nRUDuL/a4BQFXFyz/Yp+r5X1/er5/xIX8J6kX+ZKv9xynJOYyl7Hl64JeOMzp2RjH2kp42pWBLAsq\nqxFOOBlnBVdPIbKunvN04rJCSCkzLxMh+iIEAmIgqSQhtVm42NF7fMG9q0rM6Y1OrOvC7D2zl6WN\nVomh67npB+q2ZllHUhaTqJgzVVXzqnnBeJr4+OEjjYVvrra0mx6tFfOyEuPKmgOf93uu84asM1qL\nB7VzTk57W9H1PTonjIN2sGy3HUNjMXgSB8bpJBzvKGONszU+ZrKe2ey2rH5mmkeWdUVXHcY1rD5T\nNSLTr0t6SfARcmboAz4E1nXBqcynTx+JwfPL73+Ba2um0xFbZWwDipmq7qltxW3zmnlZmdcJFPzy\nn/4l5IV1nHG147CuTOeJj1+O3P71lmUZUVpS7KdpYbO5Zp72LPPK48MjNy9uysEigdIpR5Z1YjMM\nrMsZhSEngchiluV202w4n1fGk2e3uSL5wLROJQEebu01mcTnL5/oNte4SnjazaYjRin6utLM80pd\nt8S80lhH2ytuNi3z7QDpxK53mLrmtx8+8erta+4OB25eXNN3nbg7+URlDd++fcPp8YCfA6vK3J8e\neXvV0dQtf/f7H7m5fsHpPLNMC3XTMPsFTMRYuXa6YYcyNV1T43QqthyZWBhcMUpzZIzsiZTSDL2k\nSOXg8fMZ7xeMazHGsL+/J/oF3fSyB7m+QQHr4qkq0RFELJ1z9FXDuHoO5xM6rZyOBwxiaxtjIhHE\nb75uyDkzjhM+ZvpOKIomQc4RdMJUYHBFoyHvs/xePfM8M82x5LjKQfW0EsxCHHji712qcU7FzfMS\nTlMm+ZRISkEslMasIBVMviT0gLBVMmJ5+xS0DM+4Cxc8/lKrilzoqZwVpspljZm/rnVfYexlk6ku\nnb3KZUq5eEhpSTD7mpb9M7c/O0b+9ZiSLxzPr+g+OT1LYgHB4IoL4UWaH1IkhEAMQfwVcnFvix6V\nwZoa5yqMQTqpdWSZz4RlLWna4tGgs2CpKgimFtCoJJ1kiJG8RqyBtlY0FRiibLmNIeWJEBbWyYuy\nU4NWAW08sz8WKlVFWtbCqy1GPzly/eIFNy9fY61j6Dum84m/+83vOU0TKUdSSMxzZJxW+l6CoJum\no+sqtPK0dUWld6Qs1MkULKfTgtGBuE4olXHOylmfAqZoiJdp4vHuHh8Vzhq6fkvfd9TOkqIv8WEi\nFEJnUc4l8WcnJk77Bz59/MQ0z6K43B9gL5mMlbVUMXMKEzkdWNeVth7omp7+asPiFUM/cDplMJFv\n3n3LzevX/D//9tc8fP7CH/7wkVcvbjjuTww3hq7bApFXr76B/IHp/MCx0nRdxzyPpBSpbYuuHXFd\nC7dZOqHlPLFOC6sP1M5xfNzTtApnNMfDCWM0Oin6TcsynVD0iN9Fwq8zioyrLTH54lFfs/iItY6c\noHKOZTyy2Qw0bcthf0ddWT7cfWF3fctQN/StQGeVcywhM/mFkCNtK+lFVhkG27GcZpauFkM5o9A6\ns0zCpU/Zk/E4t2FdI/MqMImxCmeRpTaOsVAys89021bCRLSiaWW3UzvHPJ2IcSWpSN06sQA4HVnm\no+gf/ML19S115ZjmsYRLDNRNI6k1KHxcmeYzXdMwzxlsgzGOmBTOasZxxjpNVVVM8yJe7caQyBIa\nvibWGEEbKmtQRrzBhXknTJZxWRjXwBIy0lyLdD8XUFyXtKScdckaKIroEFE5lpqbyVyEgCA4hSnU\nBsnS1aX+JDwX8+ysNBrzR+h16acL66RAwhm0jqVMXbZ4cq2kC4mCy8Ly8gxCf9UXL5WLbfdXfHfp\nYS+wzD/CZSf8TBEvsUqXtvt5BHmq46jLqfq1x0oIhLCyzCPLNONXEVyYLMHOSoGx4oyQYiRHkfBa\n68iAXxJ+TcQQsUqDKW96SqQQMUSCFw+SqDWH4yRLzEqwyq4dIEPtKtyuxRbf7ZwloxBjaV1NzJlx\nGokhiBy8qthcXdNUlYx7CANgaAw6J/bHRxIKvyaGoWMz1CyTjLF1ZUEF1rDilwWrFZXy6FqJW2G2\nzOPMHGeqSi5Uo2RfgHIcxhPKdLz+7gV12z+Jleq6hpwZz2eW1XM87Akh4OoKoy0PDwfAYivLuiY+\n3T2y39/xy198j6ph8ZIZGWNmmqFvr2jrhsnPQEY1NQFo6gqNxtodj3s4n0+4uuFv//ZfsL/7zOHu\nPW1XkVXmh9/9wF/+kw1D13I4HMTLZFwJRTWZkqJtenk+bZmWQD/0QEDnzDJNAp2snxmnM+fziXdv\n36CMZZoP7DYDldM0laVymqpyKNXQVC3TEri6vqVyNfPii/dOwqjIMnucdYU6F1FJE0IkRJjHxDjN\nvH7T0VWa29stutJko3E5k8JaICHLsp7YbQa+e/uC0+FAX2lqG3n7+op1WamNZjcMVEqzpkRla8bT\nyGG/p7ai4v325Y0s/bVmWRdWP7P6EaWlqUlZU9uGqm6I0ePDQsqBftiQA2hbc//wAMlL/mpdl0ly\nJsREU2m0UVR1wxqEo38aR3abLSEH4jRztXtJSIHKGU7HB5q6om1qck4sIeLQIkxSmfF8QBstvkZW\n6JEJiJeGV8mk7UNi9YmUDJfQ9MuSUtgpBfTOGWISU6vLIX7ZvSmBlZRxkjJUJP6Xaf+yzFSXmMl8\nkdzrp6byyZ9FAeqiVSjckqy+qrEXhsqzwBEoME3xLy91LWWeOPLPFrDIz+S55nF5jfpPQ89/dmgF\nvurO/+GJk+S8S+qP7wvifBhiIJQF3jRNnM8nGcmjxzklF51OqBRIKRDDSk5ioq+sRhtHZSNzNbGs\nWTiiVlgofplY4kz0QTIEC01qPK3M04J1hsoZgi+sCCXxYOKyliQhyFh0FrtUv0q3mhT03Rat4bD/\nzKdlYfYrfYmB01rTDhWbq29xVsbSsIrfymH/gDZaOpoUmaaJqna0JbKuqmvO4/mJybPdDphKy1Y/\na+bgIUfaYaDtOub5xOH8SNP0dH3P+TRitCKEhNUVQVcMVz1GJVL0vH39iuAj++OBdV34q7/+FW3f\ns9vtqGxF8AspeI6nA4+He758+YxfPFEFHh72WFPx3S++5xfv3tFUFXVOVM5xaiqmaWSeDmyGlqF9\nxzKd2G56TDUwT2euthvBaE8j2+2Ox9MRpQzXVzcynmvDeDyJx4qGaV5oVCKFgFaGbVdzf5yQ5BmB\nbI6PiqGvsCrj54Xr2yvqpkdr8dsxRlFVjnUZsaaCytDUFRe75KqG8fyAX0asMyjjaDe33B3PuGrg\n4eHItnuJsw4SjKcJjaJ1lqG2kBoeYqTvWt68fsHvlwmdFfPxkaFp+LD/QlMnXBWpa0fSMkncfbnn\nfD7w5u03uKZD1R0kL12qFnbWT+DHENBVVabZlRSFXtvVLaFPHB4fUVFsDZq6pmsbyJF5HlHKECv5\nfPuYWcaZJSy8evWCtusZp5mrq5eEZaJRjmk8UdeOq03DPM+M4woZ+rambypIck3ZqqNpZELIUSwx\nnDFkDWmNZGVAyxTjlXpS1KqyrNRF7S2rtYSxwkvO2pIUxbFQcGytjPiYWxGIISJv6cgLJHKxxb74\np5gLPZBLAZdOPXE5bC4Oh6k0bvoJWnlqQi/7vwKN6CJo/COopCSi/cM4t4trIkqR8z9Cr5WfQCtf\nFfKf4kAXt0IoK+6nx1wUnTkKzBJWz+oXkc6HBa1CwU8DwStyErm+9ws5lpFLaRwGtMK5RmLeQmSN\nUfyVc6KylZhl6SQBy8FTFR+E5GU0TPEo4c1G4e0iqlKjafsNJmvWeWENMnZpY6jrBmcrnDPMMVK7\nTG1rtDGkGFA4qroiZcWyLviwME6n/5e5N9mRbFvzvH6r3Z017h5xIs65J0/ebESmspJSIaAYUg+A\nBDOY8wBMKV6BKXNKMIF6AwQMkJAQIyRUFBRSVjZwm9NFuLs1u1ktg2+ZRdzKewuUDG7uI9cxN7Nw\nNzfb+1vf+n//hssyE4tBZwk+znkTo66+E6Wp9fiubyKfQEkF13WkUrCuw1nHwXoqBmcNJWemEcAQ\ns4QKWNcxDAMhBHKJOK1Y5jNaGZzTKGswneXt+J73P3HkVLicT6yXE2sFbRXburEuK85PfPPTd1hj\n2MLMy/NHLi8nfvl//SUqB7788j0YxTKvaKWFsbJF1nljPEyklPjl98989ZOf4L3j+eWVbpjou45+\nGMjIjskZIzz3WoHcxD0iA091oyCft7OaQ+952u9wOuOtYjd1dJ1B+8rxaSeqTo34k6dA3w+SEapF\nxdf3HTlFlvmC0Y6aKzmKHHsrG3ZwPDzt+fH1VTrP5UzIR4z2pKh4vVylsaCgrCGVVXYWVrN7fGD5\n53/Oy/nEYTLsxz3kytg7Ho5H+vGILgllO1IOHA4H8fvZVlTZ0LVKZNvxAWMctRhhbGmN9oBWMkAt\nmVoVznVUW3j99pesywXvZAHveot1cL2exdXTACkSt41tDYSYeHp4kBCMYuiHCWc086VwubwAkqI1\nryvbJqpZZy1D79E6E1MbBmtHRUsXrgu6sVBCEnwcJdGNidKsnSu5fc6qVGrW6IYvV4z4mbc0r6Ka\nB1Mt0lgphbL2niykjXBnTMsDlbqs0XcZ/C3qgXvHL0zJG+Z9B2k+dezwGfPkUzSR1vquJv2VAn5b\nCO7fN1iZFtasPgd0Kkrn31hPf+vQyg1NAqCo++071lRbsnWj84GsogZFbM/NOQkmXosYtzvd6Kji\nQZxzprYBpJJYDmqRWKwQRJlJNW2YFYglEreAqorBe4zpiDVQtKZoB84LQyIElCroGIip4p0lpRXn\nHcZqChcxveqsdHFKkaq86c4qdtPA/jCQtkjJhXWLXNeV8+XML3/4kRiTdEe9R1E4TBObttSa2I2O\nzjsUmueXVz5++MjuINv5cZyEbmk9JUPKCmMNqRRyEToeRstCMs/kLEIkbzy5Ji6Xj5xOz6zLwvl0\nwljHw8M73v3kK6b9oXGLJAt1mVf6cRS7Wbni2e12XC9XXOcEklg3lrBhXc+7rybevHukJFEOTscD\nx+OBWuD7b78l58R+d8A6xxfvv6KfDlyuF/bqwNsv3vPy8sLusGdeNg6HXbM/VThrxWhMVbZ5pjsc\nyClhreCvYQ3sdiPeRzSaZbkyzyfGnWUae4lRmyYxuUKGcQWNtR3i09PhfEeMiW3bpBt3mpwKyxqo\nOePsyNObJ1KIDF6zbZpcDZ3r2jBesS0z/SBinlIVp8vK+SJeOLlWVE6YYuUirpWSA8f9E+Mw0HnJ\nC11T4untF9QcCPPKtl559zjy/XffY1wn+HXKrEFw6b4U2YGaSlpj++wkqDxtCyoEdp3FGMVlTqL4\nTBvbtuJsx3K9wLah55lx2DGNO/b7PSlVlFE433E6vzDPVxH5aMe6XsUuoSqMhn50GFMaRa+I30q7\n0Ati7asq5KKIWZGLIbdhonEWi5JouFJlgKk12n5GYdbCQKFkKEJfVFFYLHfM27QiLsZJd9abCMAa\nc0SbVrdri3Rr4TW3YWepaKPuzeStCAu+Dqgbnn0X63NjpNzBhCqD1Uy5/1wp3Lfu/2aZq5pjCw2P\n/9s47Pw1t5qA81eO0t7gUlohr59WNdUK8i3clbbxMUqJrwOqXWzCtii1qUOLIlcALWowXQlpY143\nckySaFLEJ3wLN1tcUQ4eHzy7vRSny/kCKFGQ1UJIQhXz1rDlRJcrccuEEkhUYsiUJMY/l3nGO8dx\nf8BaQ6mSQGP3HQqNwTC82RFT4nK58Mtf/hxnDcdpx2HnW+JRZd0u+K7y7ssdMWRSLOICZzSn5ULX\njRz2D1gjRT3MK9Uk1vxCDCJf1tazPxzoOk/BoGrPNEzUUuiGnt3+wLZlQtxYLmeW+cK6XEgxCR1S\nCYw07feNsyy8/MvpKiZnGLw2HN5/SUzCFvr25z9nW575+mvFw6PFOsdPvv6K0+sr67IQysph/0g3\n7BjHHd9++y2vpwtfffkOUBwfjjw//8hx/4DRWgbSKWGgDSGFmmatQxVLmGeu20LOkYf9joqh5IKm\noyhN342kUFFOi5AqZ6zvMcYzjnuhaVLZtqX57IgBlTGaEgPLvPL48Mhie04fL2g8o7c8Pj3w9ni8\n+7fsBvCuErUibYq0BayFqbcslxN/749+FxU3dpOlcwqjMtREJZHyQjfs8apjGvdQN055E/uFcWIr\nigEjtD+t+fD8gp8e6MYdxhhGM9J1PaWIZeoWFNf5IjsSFJfrzBojKRW2+ST02iIh2dYP7B4EN7fO\nEkLG2ErXd3w8vfL88QNeCdFgXQO97+i0Z5uvDE5sNXISFonRgokboMZAKRrbddHSwy4AACAASURB\nVFQlg8eihGxgrWHQRuIa7pCKFETprPnERNEyPxPqYYaW3yvxjJ8GqJIgJErqu3d4C4GQjvtWxGVm\np5ofD1XfoxylVsm/+VVh46eirtTNDurWbd9u37Dypjat3GvZPWTnlkZ0+ycNb//NZfy3WsjrZ3jT\nJ3hFirm82fc3qRH8a63ExkmtWZJAasv7SyUTYyTHQK4bTmucAV0ylSRDkCxpPiEGMuJ97L3HO8tk\nPV5Zko/SOXeVJSTW60qMkaEzaFupqhBypRSFdT2gKEbw1RBXuq7D+RGVhe43L0szn9eEkIhbZFs2\nXj6emNeF3dTx9Zfv2O9G7G7HMIy4pz05j6RcOJ9mxs7w06/f4Z0RjDNFzpeVsfPsDgMV2NZE52Cc\nxCTLWU/nB67XGUqhVOkijZMTx5iBfjigtcP6DmcN8/VKruKprmpF1co8Z+ZZKGrzthKXlfU6k2vB\ndx2pyHDZWsP5JMPZzlqsUfjeQlV4J0ZKp5cPks9oNPvjAe8XPj7/yDyfeXx8wnuhVO7GHaa3hHXD\nGKGTWu/JufD9jx847PfsrGUad6QY6bynhkxJcgHt9nvBdrX4chTFp4U0F7Y1gXZYK9Q16yzOO2Ku\ndN0B7ydyBu0cKC2Qk5HhsdZOCs22MPiOqsH1shho6whLwCh4+8WRZRUcvht6UqmM1rUuLDP2HZfL\nibHT5HEgxRVVC/v9yHxaMQqW5UyOCzWNeFWoZcPqJ0KSRdVr2uwGduPEcf9AypXj4ch5mVnWhRIT\nOQas7jCAsY5cIStFjRGnDeNuB1Xx7fcv9L0jrhJP6DtHiBltFQ+Hkd1+Yug6rJWEIeUdv/z+R86X\nFw67gbwGYlw57A/0nSZsC3qnxWmxJLF1VblBS4mQKiU5ukHEXRKknNCou7VGjJJvm0qjCradukpQ\nadFurdnT0OjIhdLsN0BJIAWyFn+Ogd+FQHCPiitFYDiZZ5a7h6G+DU6RaMhbnZK/qXmZ3zSNtQJG\nunf1meviLXOh1beiufPQDS3eUmmMyvfuW98mn9wYL7/++O1BK7fOGuC+/rQHUEhSZxXcrH1Iud4Y\nK2Ien3ImZcGu13VluZ6JyxWjNjKa6ixVSxdfskRBbav4FxeqSOedam6JSXw5vJf8zygmPHrs2EwV\nehRJBpZJJMIKLfLvrkMZhzca1w/EYrjOubFWtrtvcSqZEDNhXYlJBpWvH+HDj2d2hwPT0HPcO56O\nI1hLzoUQVpy2dE6TszBCOu/p/YhVFV2MbDfbyZXCinOenCJrgVojNWWKsvh+R8yA0lgjeKmxhbxG\nQtsZWW0hVarKxLBJIpCSFJ7ed0x+RD98gTaa63whl5UYDdfLwvmyidijVJQqfHx94XKZGceJr77+\nkm9+52tM1pyvC4MZODxMfPf99/wf/9ufMQ4jf/iv/AHvvnpP1/csy4Vpd0AVxRIXdtOeb3/xHV99\n8xNJRCpXvLOS/J6SWLsCpch7VOcrWhviKgtNDhFtDJfLFZRmGjus00z7PdZ52apnCXUopVLqLbRE\niUqwFEJMTNPEEld0K4DrPBO3wtPjF7x+/AHvFf44MAeLswnjDGIpKzYQqkrMoAwbI9PosWbPMs88\n7I8sWWx9yRshb2hVcabivMZ6Ycisy0JcrvT7HusttvZ0ruPpi8DL8yvTzrNuZ4b9wOPDTvxgahsx\naRG1iTWzwtmO/W7HddnIFabpyBYiWpkmpqp8+f6BYdijrW0hFQMYx8vHj7y8vPD26RFrDB/ihaHv\nmcYBpWDZxI0yp0oOG6gk3kXmFukGRRVi2mBVVKOJsd476LZ5RhtH1YpaVBt2NvpI1Xd+uLTWBaUq\nRnUULVDrfSfffpbUWfWrpaZ55shwU4p5jIWKuWPepQgEpG/5o3ya8dXaskqz4O6qKooqt/lloxBy\nXzTu+QrcHtDcrA4lPqF5u9RPxbzW1sH/huO3V8gL8qr1Z0PPf+EQ3ElUWEXWX/nb27CjJJlY55xE\n/RU2tmVmtImgNKZmnFXNSwVJIkFhnJOpt7FUFCUpYmn2tSnhG5Up50hVBd+LE56uLXlbG3QvjIAY\nC97IZLmgoWaen8+8ni6cr1eJ8KoI3asU1nWjFomwSjkR0kZ+OeM+vPAw7fCm8vh44LATx8MUZ64l\nM3jHNE3000RMme++/8DL8wvbJp4cjw8HxrEjWcv1uvB6OmOUxTjHtNszjh3X88wwjvS9RxfodgbQ\nbCFSkqLzPa7vqFS2sGAHK7uTlIViRiSXK3FdGs9fE2MG7bHOcdwV1MHilOLl+RX/Zk//9Xuscawx\n88MvvxMDq/2I78AbzRdfHJnGP2FdIiFsPH/8wH5/YLfbs84z63Yllsy2RabDxM9+/gt++s3vkVMS\nz3UjjKBut8Mbx7YlNAbnPefXSxuKScZp13lirpRGE3w87Jh2Yh6VckJrC0roeq7h2sqIlWpMAVrR\nqSpLvFkprOvMtNvhnCXlLLF/WMyoqFwoVdF1Pd4LgyMnuF4XUshQJULBdp4YA95b1OTR/sA6nzHA\n0BkOhz1aO7SynC8Xcs5CPy0tLNx2DOOBt+80XefxtuB0YewcY9dJEaxyBUn1FBMutGGYdlin+eHj\nXzUcXmGKiL1IjnE3MXbNn8UYbKsYy7JwuV756umBqhUvLy+iHXCOZQ6kvGGtYd421rDincXrRrMt\nilQLKV+EqloCNQeU7lBYjLItp1JjbkW7MUtKw9Q1hqKF561omDmgqugzKFIYc5utqXZbiBM3bPuW\n5ynwya3oK6UxxqO1qEMFktWNOVKb9kQojjePJKEs1hvYzecpZzc23i35pyK1W6nSinPzX69A1W3B\nuj2zNAok/MvAld+ijW1Lsi43af5nfge359wZLPqzRA55Y1PJkvitZOCi4Q61xBhZq2ofqqEqLQ5t\nfS8hrsjvrLWinQElkVtiTC/wRwihpW4brBU/F1XFYTGXTC1WApe1wRjZgs7r1lRoG/MSmNfMejmR\nQqI3FWcMa9wwyrHvPVbLIlJqJSwzH8PM8bBj+27lu+/lde7HjsNuxL4d2EIm1xOUTNku1Lhx2E1M\nhz21Kua5ABGtFb7zTNOOvh/ohwmtXMs7VOS8NUe9KKu+dfT7Pb7rJYQ6LBQyJQScVZAC4XLi5eUD\n63UWJk1Jwjwwli0UDvsHANa4cr0sxJDZcgJeqDkz9j1jP5Jz4Bc/X9BaFIbjtOPN0xc8Ph6Zr6+c\nX77n8uO3dMOO3fGAc5Z+2GPrStKFvhs5n04cjjuUqmJHXBPrfMEYJ110yfi2s6o1obUsWGnLaByX\ny8Zu3Euhub4ymSNaW46HSVwGR3HJyylhtOzmQk6UCvNybcMvzWVdRd3ajyzbKgZURJzRTP2AUl58\n5ocJ6wzffffK+fWKNe1cQ3YuKSYUhhgT3TDiR4v3GnJiXkaUcVg/4fyESZu4fk5jgw8EiqhaOvZx\nGljOP+K943DcUUpCVUeIidxJwasly/2qMo4DwQiUYBTE7UrJmRASVht2yqBKxvnC4DU1BuYs7JDf\n+eoLSjHMQXzMa0zkKDi5sR0hLs3i1qG0IZZETlGon94wOCemUDeut9GUaoi5EmshVU2mBRmjxVeJ\niqktC1ebewEtqtnF1lvMW23wYLm3xepeyFsnzQ0VaDO4Iph6LaLmvsEsbdZJG4dy93hp0E25Oaeq\nlvHZRD/3SLnPIZE7jn5zZFR3jPz2mFZSEe/9eLs//22EVm5KK/nmV7vy2wQZGnUnty68fai5TSnE\nNbDcu4XeeUw3ULJYxaYEqII2Cq0dzji8E7qi0vUeC7WlRMkiDe67kVoUJUEtiVQK3hmcNw2rh4oh\nAzlJVmAOWXwijMWYglKypQwps64roPiwgLeVwRm2FFhfMrVmjt6yHx0hGl6WQN4qnS7sDp41Jr5/\n/sh3zye+fX7m0BtGZxlH32LXepzzgof3jmGY2gkuaUclJ3IRwYzpFJSFddukWYka00yxunEvCrSU\nWc/PXM7fs15fiMvM9XLicrrw/DyzrInLkonKUrTYE4xDz8t55jL/GQmY00rOhWnc83I5o5V0jtYo\ndr6jU9D1muPg6fS3vHuciC/fomzPMO3ou45sEufLCyHMPDw+kkvC9yMqJnpVKXTEGOl3AzklUiyo\nuuGsIaYkW10QteJpph87wmzY8sph3/H6cpFuSFeW+cpu30O1xBzpO0spgWWJ+GFAmczpMmM7iZwr\nSYKrfTdyennGOce2bUy7A77vWecLzhpCDFhnURp87ym1Ml8vbNtCMdLRFQrj4Di9XHDeN5/3XvJg\nFZha6M5Xkra4bhC6rHOkpHBdL7mqW8GgqCkzXy7kuHE+Xzgc9uwfH+m6gVQV87zwdHwjAQpGQahi\nCuYHYoLdbqKEF6be8/p6Ylkl9m0IGW0zrlOsKXG9PjMNRx4ev2AaR14vgc53pBQICvb9AKlwOp1J\nIdN1A0YPKFWIaaGUrVUx4YhrYwEjCWHaUPInTDw3z/1Ks6xWGmVu1liCVwu+3IaFRaGqEW1NbnYF\n5HtNMdygkFa42/epik5EVwNZUu+phVJ1i6erKJ3uPPCibmKfArlwr1b3oAglqUYVqlLU0rB5uNuN\n3MKgdRveCo/yRk1s/lPa3JtyKpgbpfHXHL+1Ql4+42HejbPUp2IOn970O66kFFYpbj7BWQn7JJaK\n1YZxmiimkqKWiDdVm2mPMFdSyNQcW45iwVlxKyw14bWkdFujyQr63hJCQieFqRVSZksJVSWj0OqC\ncjItD2tgWwvXdWYNm2DrREre0EW29HufWUIiJLAYdMlorVhKZlsTh87zJ+/fUEugGywxrrzdjXxx\nmPju9cL3PzzzvbJMXc+udzwdBvreoJ1hmZ9B7RD+rnSBvhP2Cw0+7GxLXNEdMWUiGkyHnwaW+ZW8\nnlhPz1zOL1zPZ06nCy8fZ54vMz+cAx9mw0ssrBliyVijeRgNu/6CKrHlnVYGq6lGUdLKrnd3lR01\ni/simnNU/PLlTC2F448nHqaP9NYQlsjTccf7t0fGaSSsV162wOHpCdVLrFiuhX7c02krzBIN0zSy\nhBnvPDXDmja8EUFH33fMpw3jPH0ubNsqzpYt79Ja7mwI7zpSzFgLzveUDNUo5uvCZPz9fLVWgkBy\ngt1OvHMohXWe0crQ9wM/Pv9AP4z0fqBzg8T3HQbGTqCCbYukErDasB8HrPe8PP9I373DaM84PFDS\nhh9GtpBA6zbMT1jr8b1AQltM1AIxbqS4YXTFOccw7jF+kB3MPBPbLksrQylVdpkKrM7oKqHOj2+/\nZL5eOK9wXgqmgy1udNkQ1islJnw3sN8J/PZ8uRITOGPpXM84aEotzNtGP/T43rKu4oZITRgUrt9L\n86UNRctuueTm+5+SBDArjXeWggMsGOFxaWWoWt6/kkvzLZXiZ7QBVe9QhRROJcWYGxvkNgxtFh4N\nGrGVFv+YuTG1lVboqpHAIglBF7ZMBX1zWqzNX6u0ulXaWLSissz/NLL41CyLEoobSEzVVay+2v1i\nuqWoqspiQINl9A1++c319Lc67AR1357csKt6f/BTd17uqdiFTGOGquY/bjTWWrz3qNKTaqBWLfi5\nLqLiqgpSJqZICpvkU6osznDKiF+Dkc5dirXso6x1VCNRbjFXapLJcSobW0p4LRinMeCopGvi+RxY\nghg1GSqTF8+OwXUcxlFS10tBl8rUdygtqePGaGynoBpQiuN+zxY2DIWfPAwcOs/pGsBK2orzAzlc\nWJ5X1ODJS2DpHLvDnpQL2wzeWDrviQku2UnqelsEU4hczj/ww7cbp+dXPnz7geW88Hq+EHMgFcVp\nTnxcEy+xMjcowGrFrqvsfWHvK1Nv6H2H5DdqcqkkNGsRAVfNmqqsuLiViqlCqSsYQtKcQ+TjEik4\nMho+fmD/ixfeDJb3xx37ceTNNvP05gus79HOUtcL+/0B0M2LR9H3I6VmnFOsq+ConXeQm+Ww1+z9\njhgj9s2e3dRhjaMUQ8gwOEdKEbSVeL4qnOWqFP044L0jpcL5fOZwOHC9XCUs+nKiHydqlR1m1w/4\nYWT9RaDrj4zTA8PYs64zh/2RzVvmy5Vh9JzOM8uy4juHdaYtIgbfi3d653r6fkdMEV0VaZMwhf1x\nwmiHVtB3PcZYrpfM0PfsxgFdIrVULqcr2g5sacX1sgtQyt/Jb8ZZMppUMrbzWAPff/cd23zlMHjG\nm594jFTdMTnN6DtCVsyXjZBmpnGi70e867muCyFmcqnkbWWNgWULWKPohwmvjVhnlCyBzVaCy2vJ\nItzLEbSXeDqjWpHMVGVlB1Vb85YbtU8jknttZOdXZREw9ZMroa3m3n2n2jzAC83XpaJzJbXFgGqo\nSryQqm5MOXXjvutGmlDUmu/URXQLei7CBFKqDVybe2FVFVU+OSbenBZvOcStht9zgQWKUa0BU3ei\nR70B67/h+O1BK838/fPjU75de85nPE25re5kIJQRjExbtJahXgpG1JBJ1HaahFUi3dalCjaYkgQJ\nU2Q7Yypad2AUKSayKnRdT9/tiKkQgwT8akWDKzIUGYRmFEPv2e17Uq5oXZl8z4/PJ86XiNJWttkp\nsW6R94cdh9ExXyR4WevMbhpxDweu88zLvPL0+IA3Imqaes9lngnLwsPDI09PBz5+fKYfelSNfPHm\nga5XrOtMSpljt8PXTLxeqWnjdZ3JWaG6DuN73n75Fb7ruV4unJ9fqTlzfln587/6JUsobAlOSxCI\nJESuQXHJlkjFmsybwTLZymE0PO5s89/QYqGgLDHXVshbXF6RWYNWWkQOsTbHOxn0GFdQseCtJVfB\nJ0Ot/OIa+ecfM/XnG6P9wB8+/sC/9keRr796i/UdQ8lEazBj35gFGk3FaitsJGdZ1xmjJbzD+46w\nBmpNLRxCht4ycN4oVbGsAeMq++MeY52EIWhFjAHnnAzOmvAspcLhsGfdFrZt4eHpLbVq9jtNSRvL\nPLM19tBu2lNq4np6wRrNksHajpozcS1MU0dJAWc9JW3oUigxoWuBKr4l/TigrCPXKjx/4ySETDts\nN2I1suMYdpKyY0eGaWCeV0raqDmx6yc0tvlgV3QV86/c7AyO+4nT88Z8nYHCtJ+oJbFtQt+zDrI2\nrAm6DFDonG0KWsV1WTlfZrawQMmMw0ivZVhrtWXdZs7XC9d1Q5MZuo5pGPBG4bwlFmR3pEXRLMNn\n2UUqUz/xt7m5lULVBt2+bgrNWsF9xu4QrFxYO+7WFKpCwQgHXYMuVdS/Ogs8Q3NJvffdMlO4Fddy\nQxCKiA2lRte7vF5rTa5SD26F/cbMU0VQBaW0DKFvv6fK3FArLZ+PAvF3vOHv8p7/puNvldeKvMnc\n9rqfoJXPhED3/+6wS2O1lEyIkRgjJcs2JaVKyhFVV4SFKC6HJUPVCqUsKAlMyGgKmlwVtRhKFsGB\nSIUrtvOoothyQWtPbxUlRbatsC0rW8qELaLjzGQK2Rligs57vDMsIXG9nnj7MOCPA2F1rCGyRenG\n3x4n1hgYXGU3OozvKIDtLLpWQpAko2+++R3mZRVhh9PQOR72e3JeWS8XLi+BdZklr9Rbhr1wsNGW\n+fUDs1KoqgnLxrJVXi4BpXsyifO2cA6VrBRzUpxKJlAYO82XR88Xk2PvFE/7ToZxVhMrZKXJRWMj\nhLzhlcUm4dpn3STXeKKBqCQoIKZCqZ6qM7FFbmlV6dG4TtGZwpY1r0vmf/rlzM+uf86/vRT+4KsH\nrq9ntmPhi68G2d7WiLKWuEWMlvQbtQldk5KEZrgbWC5XrPUElTCmo9A1jNhw8Ee64SC7ggJ9PzJv\nV7YtC7d627he55b2ZFAKnp8/Mk0TMWbxYwlRFKZx5nh8gzEelGKZr8QUQZlGm0303tOPHb6zmN7h\nvaH3wutX1eN7DXnBd5Zh2AkrQ1tszq0wJLQRS4acIsu64nyHUobHp/dYq1HqREkRDfS+x1rbjLoU\n2olAqRaBJawbyLkydh26JmrOeOOZ541h3FFrlAi/Q4erAYXHK8flfAYbqEoYYMPQ0zuNU4pkO2KY\nuZ4+sIUFpTVTZ1sBzhiXxcAqVXRWd6m8dQanLAUj1EDXffIyqRVjtSzg6tMU8iZnFwHPrXCCssJr\nEeQjU4qiFE2uhQKNJ55vPliiMFWmLRhCCxT2XG6QafN8qSCjV+4D0htHvd7pk4V6k+/rm8mWuXff\ntKHpjZHzSZLfhqU1U3UR33KlaObav/b4rXut3G5LUS6oT58Bnxdzbs9RghtprampNnyqtOBiWTm1\nThhVMFXScuIW22pZBN/2nqo1zthmvFVxTtEPnpgi23aRFV8ZwiqDy/NluS80RnMPddZaM4fA62Vh\nnmcsmi3BmhqWrwxKGVJJLEskqcrXDzt2TyNLjCzXSA4b2cDTYWQ/eayrhFx4OBxIMRLWwLTzXNbA\nuBt5fPOIMRqMYl1k0BbTitIOva+80YqcAtRISRulJGoC6wa2iAw/fQ9hZRxGljmxFk14vaKsY02F\nc4ok4DAovn7s+eppYLCF435kGr1E2TV6WCgVW4AefLbUqnC5ElZF1AZsZQmRpDIbhkvp+P5lZtsS\nna/0xuK1ah0M4rJnNF4pvIfrpvn+vPDf/K9/xr/58sSf/O6XCO9WumHnOkIIWGOhiodM5xzbshCj\nWJo66wk2EEPE9x0Pbx5QxpIz7A9PjLsdFYgxoZ3EsIWQsLZDa8PpfCKlLHMYMqfLQt8NlCJOfCFF\nsqo4bYlZM0wH/NBzXS/EEnFdj3MOnzIpCyZtjBJ/HAudE3W51grXQqDl96umKBT1rzFKuuwiFssx\nidVELhVjLWjLuD+Swko/SlhK349Y60hFrJ69cjir79eOMZ6wXgkxMky9dNWIOrfvLJ2DmhPedxIJ\nZw3KOq7LSq2GsTM459BGzrsUFq7zKynMxLCxhghK4X3H2HUoVYQ5FTdCDjJvQDXjLFmolEFMrrQS\nMV+DO2ubkelbEQca8MznGQZ306oWyyYCYy1YvBYwQzdr5lIEohNlvLo1z9IgNvxbWC1QqmoKUSmr\nmXInYuQsMkOldINL2u/SGt18z2ujHcqAUzpu1RrXUoWOWGqVwa4SuvOtIP46ivbt+O0NO+9jhXY0\ndWeFJqMFKeS3z+s20ZULnjZ0vCtga72ZXJKqbraVihQKIYmrmrHCIBiGHcpZSios80oMUiwXswhu\nl7MMRKpiWyLrmolZ3BOdN+LiNu6oZSMiNK7OeTadmJdVcEBVWEtmy5rBO4apx4ZEzYXn85lvdm95\n6AZKANWPKCqXeWMYPGlLpFhZh8DD0xOvL2dUhbdPAyiHa2pBa0DpVwlTOD7hB816+cjg9vTdIyhJ\nuE8JapUU+TpHwlaxJA77js0nCWpYA8M0sa4b5/lEKGLW9NWT4/ff73nsOoxPPDzsUFaTtSFkySVV\npeC18GBrVmAsW0q4fs8WMuv5jJ169JYoK3z8eOY8W7ZYeF0ih1Fz6DW2SaVpGOTgPL3VjA62zvJy\n3vhf/uI7tpT4+3/0U7pt5fTyI/vjk0z4qZS4iV2pMRIrli1xrVQS3jvWucrAdBiwfkSrDuUc2giX\n23qBqmoWFWTXTzx/fCGliLGSc/l6PVNS5unhkcs8Y53lfL4So3T6vt8LM0TZVnw1xlgJPbGOzotU\nfr/bc76ecbuhFfEiisaaQPs25Gue182LJIQLtQTpvoWzB0rTD1ODsYRVFTMonGDQyjTnQoGGdFJ0\nnZP0nMYAubZQ6Gk/UaucpzklnNOSTjTt2e13DF2HtpalgB8mKcy1kpLMhsJ65fzygW05YVzFOSM5\nAKhW1BQpQYiatGYUGWdEJKWNo1ZNShE5jRTaISwUbYQ+qAXO01qKMm1eJsHguhVxcwMy7jRBpURQ\n2IqPlMYW1m6zBmvaXITbunCvOwVZCGqRZLGbfa4wj+qdVaJ0bpRSMRpTVVgpJUNtQrBbHVMtpxhh\nTEpojFKf1JtKNdvXm3hIILHfdPy/FnKl1H8O/DvA97XWv9vuewL+MfBT4C+Bf7/W+tIe+0+A/xDB\n7/+jWut/++t+7k3t9Kn7bvh4vd0StaJMjHO7X92fdh+MVt14n+2r3Lp4GYQoZahVE7aCLRVVE94V\neu/IpmJ1oprMskXWBWwnHuPDMJJyJLmKzTeflsxu6OkHz5oy85LZtszlOhNiYtk2Xk4XrIKdh6N3\n7EfDOIqlqNMdTlteX8/ULTFMI7vecw1JLngSNSW813ivyPOFOnrePu754YcPXM4b41GSgpQ2OGuZ\nxgMxJKz29OMXdGYkbytbgn48Yp3BlIAzinlZQFlQgaorcY4SXGGgGo3pevJ1A2XofOCrB83vvh15\nexjplMINDj84CoWURd5uFG2RFKMwlBe71xCxFZaXM6buKVis12zxyiWcSLXhkNVxXgrFwM5rbI50\nGLQSCqm1su2crKbzHc+XzD/9v585XRJ//49/l5+8Bc2Jw8MDOSa086QSpdvFU03FaHHuOz48si6B\nwVqM3WPtAEphnSXlgO/M3eNeOl6BhZZ1ZRg7xmGHMR1h+Yi3DlU1Qz+xLBsxFuZ5pnNiclZbETHG\nsM6FYTigSsYqgxkk6GS/PwpMkAIUgQzUnV1RyKnQDR2Xa2RoFsrL5UK0hm7cM9oB7SwlBIz1EBt0\nkMXvRJNR2lOxxFSoKlIKeKsb97lBh1XmBdNuzzmv9KMlhSjzjpTY7Sf2+wnjLEUZSrX0tsdaL9YN\ni0BH8zLLglArT8dHut5RSiWkzJYyxgmUZXxCR2mkcg4tI7c0nrfMr7ISiqJRjjYSwygl6T6qoJTE\nK5pWvFGWzwOSpcC3YOWbuLNYGmlZagrca4zwxUUvkpJk/UqXL4U4lwqttrQJi1APdRMD5sZbVxWl\n8p1iWBGzslYbPxW/Wrj5nbdXy42GfZP8/2ohbs/5Dcf/l478HwH/GfBffnbfPwT+u1rrf6qU+o/b\n9/9QKfV3gP8A+DvA18B/r5T6o1rrX0fp620S24YLFcEnq+ifbivbzf+gg2omLQAAIABJREFU1mYE\nX5BJR0vElrQRufhiTIRGYVJV4ZTFaUtxiaKypMCHCOcrsYC2llgLaIvpFLmm1oUoKImh6/DacVUX\ndF2wnWM/eJSprGFDpUhcV9Z543XZuIaNbVk5jh1aW5R2oLzEaCmhLnbjwIN1Qser4DtNrpZ52bBI\nqlFJCuMMXd9zuZzpkIXlw48vlNOJvt/TdyPrtuLcgLWJLS2Yfs9ueEc4Cwd7Wxf2D2/JsRLThrIe\nXTJd3/iwCeY5EZIiZdhCYMkJKDxOPd88PvBm7LEK/GAYJt9k3sKcKe0kVrHKBRkU1hVCFhbKEgt2\nPDAMDl978pyppx94fMr0w8K2CGsj58y6VJyuuM5z1wiUgsLiDRjVHt8bXq6av/hwovyzP+NPv/mS\n3//6vexQnBN+tJI4MK092mqqkmDgajvGwxtqruhuQNo9UdXVrFDVE/OMth6NIedEXM9Mo5e0GOul\nOGtHyRBjwXae18tMCBthmdGPR5b1wjju6MaJHz98IKVIP8H1esU6z7ibRFjkHQN7SpwZOgVFJOCV\nTIkJVUBXxzL/yG63J1d5n8+XmSkX/HTEo7FGlKnCETcok8QXKIlBlXOeLYr4K+VE7TtyE8DFlDBK\nrJXX6yYYve6Y543LeWPqLdZYYlIUa6EYdC5oK3Fy2yb5trUERm8Z/RGFxvsOo8EpRa/0nYemFITY\nKIRFCAzWdsL60YacVWNzSPWNIaGc2AoIt1rJDkcJ9/y2S6cNED+3wb7DK3cZ/83PRFKGcm3UkyKG\nWLcm0GiNco4li/X0zS67tuSLooVBJ8VW/F5qLZIW1rjgBY3R4tJKRVgwpOZ/ZajIbuBTMTQSOl1p\nJmFNMHmbGQoW8zcv5LXW/1Ep9Xv/wt3/LvAP2u3/AvgfkGL+7wH/VZX04r9USv0Z8G8B//Nf/8G3\n/9W2Tbm14rcP5tO26TbAkPpdqEVsaVNp9rWfXmsLXC0UlcDK4KQWT9YFTURVjVGKtG3YCkZbSU/3\n4GwiboFUIsu6NR+XIDzzzjN0DqU1MckgRAzqZYpdU6LXsBs9bx4m+k6LuCUr1lDwSmO1RmvP8e2B\ny+mFUhOdceS8sa2RcfBYLYZKW8z0fU9u9q5ffvkN796/5cePzzx/fOHx4b2oKreNw34glZX5/II/\nvKGf9jJNiRvkQtdP5NyjNzEUyxVM1XSjob4uovqrhWvYuIaIM/DuOHDYWbxN7KaefqewXSUVRcGi\njCGWgtcOyMzbBWs1MazkqpnXSsiGrXheA7wumjUovPH8we/9sbgzXl64vj7z8vyB8/kshmbWYDuF\nSbHNPDIg6TRKgSPSHz0fVeTnLzMh/Jx1lTzLh8OEtxplPQmhWU7TQI6JmjtyVYy7B9Y1oLQX5pMS\npo3WzQmzygW2bQtVVTHJKtJkpCxdc8qBmgs/flh4/+49Na9QVyzCEpnnlb4fGq5ruF4j45B5PZ15\nfDjQ+Y7T6zMxBnrnCVlCQSoRazQpJZQ2zTERnDPEuFCqIWwBckYhCy6qYryDKDMc5xwpFrpuZMmr\nuD8qRUqpuUJm6XwLpLihasaPEylUclpxWvHd85kPH0/Cp+8nlpBJtjAOlppBq4jOQsekVqwyTIc3\nWGsFnkiJlLMMnrU0Y7Hk+w5aKQMqy6xSC689FQ0pohCltDUCychMUzV7DIGShJYnuxYpnLrh3k0h\n2dSQn4c4iNjm9vwmxDE3YdBtEajUDDkJGmCswWQLIYipVm4hHSXLoLMgbJYiOxsx5Wr+7632ltqK\nO4Lva7QwUooQLm7MmBsf77YuNa9G7jgP+l8CrPzNMfL3tdbv2u3vgPft9k/41aL9M6Qz/2vH5wbr\nn8yz2ir62WNwi0Oq99UxpkTaVmJJpBjbkAFu+FMpmaoruciHb5xHKZnEG2WpRZFjRZmC9xbbVJla\niQOwTopcCusScM4wTgMgIoIo42pqSKQtk0Jh28Rs3zqL7TzODnRW8Xh4QBkJh6glsz9MWFtxvvD4\n5oHT84mYCigZEF6uF8Z+wneWbrfHOEvne/Zb4Ho5iaXsYcf5deYXP/sr3r1/Jwuesux3T7y+fCSl\ngOpG3LAT974iXYV1A6VWPFCqQdVEzoqQEoVKiAIRKaPYDYY3e89xp3nzpqfrLcMwUo0W8YRShFTB\nGInN04V+7HHGUfASy1UDhMq2Kl4+PFO6B77++qf8s3/6fxK2yG6342E6cBx6nBUk+Hqd25BZFknd\nLoJMxhuD0YqiNR2FL48d35rE95eV/PPv2U0dv//NVzw8POBJMhPICcYe143EEKnqZh7m6fyI1orr\n9SQwjJMi5FyPs4oUAyFnrBVzsOPxCwkv2cT98eXlA199+RXzuuB9Ry2VZEd+/PCCssL2yMzknNDN\n3vV6Xeg7T4gJbbsW53dm9FZ2OUZYECEsdN5TcqLUrl3CmUql7z3ZWayykBW1aFJJ7HaHO8OqNLOl\nLUSmaSJnuS6cpWHQAaMNJSW6pmRNacNbw/OPM68vr3ROM41DuyY0XTeikJmUd51k5GYpgoeHA8aI\nFW7OMouQHN0gIpuixBNfG3kdTuG8RuEJKbHMUVTTykgGqRFmy13lWAuqtuu8CvtHcmTlulX3ho97\ngPKv68xRN3bIJ2/wT6lADTTQguWXVMQlNSestcQkosJaKkXJ36mqcN1LzvdGUwp5EVioLSKmKXnz\nDZdoMA75NueTRjbXLF250ZQ741B2G7XWu/nerzv+fw87a61V3Vjuv+Epv+7Ouze4VjdoXI72YZTP\nOJPyZn/yNCglyba35VXGuJFjoDSeOCCDD62EhmUUAVAkyn1godA390TAaxH+OO8ZRs+6rjifcNZi\njCVskRQiJQsfOW6RsCXSupBrpNQIVbEfD5LDWJpAxDsOu5EMlBxlLlIsXTfg+0pKG1vKZBRaedat\nSgdvZDkvMbJ/OEi3GiLDOOEedlyvL5wvjsPD2+ZlAY8PX7CuK6aKT/jge2oSZgBVmANaG5yt1Cgc\n62ncscWPXBbJZrRW8bDzPOwcb54mpp1De4v2gzALivQKxUisRymFqgVicHqPG47EBNlGUoBrWdjC\nhT/+e/+A3/nmD/nf/8lf8eHbX/Cia/P71qQSqbQtrao44wARfshFIeeKoWCtER6uhXfGEJPlu0vk\nn/zFL9BoXFXUww5jxWRqXTeomoLwmbWxzUrBU2ok50LXeVLKMqBES8h9rmjVUbLCGYc1MK9XEZ85\ny9j3aKNZY8IWSYv/7ocT1jjevJ3Y7Q9cF3FifPP4hpQLvusxxhBzQWmRpucYGR/2d9dGodpdKHFF\nK1m8nPXik+8GHh6eBB5U6g47gpIEKN/L+VmqOD4m4T6vIVAVhCh0v+t8YewGWWTMyLZeKbkQQuF0\nXrDaME0SFq2UYhxHvLXNDRJx/PSevpf4t1KqWF5QGyOwIknJApVIiLRcz9aY1mRlapHsWt8psq6k\n1h3XKq6iWiusUShdmpqyUg2SPaCQoWYrzDeMu2X93LvxW5H8REm8VRp1/6p86tTb2d2YMpUYIykm\nQCCdmEUBSkGQAW6OrPKaTBWaYKyVihEQRQt8o2ldf5Hz0bTP8PZ6dBMQ1VKafe2tdVd3qvVvOv6m\nhfw7pdSXtdZvlVJfAd+3+38OfPPZ836n3ffXjn/0j//r9voV//qf/qv8G3/6dxt+JB4ESjXVXjtu\nVKl6i3yr4pkSYyBs6/3EVaoNkUtTtnKbjVasEcEIRnwccs7E+Yp2Do2ctNZ1OGeotWNLcJlnUhJf\n8ut1xluNtZmaK9sqobuT7TCuMHSWnzyOosxTGd8btNbEVDg+PFEpLMss8EwuuHHAbIYUIOVnlIqk\najhfNkY0Jme6fgTTU1TC9ZYUEvvDgVQL1skQ1VhHSYHO99R1a/4hR1AajMMpxWU+4YyX6X0O1JrR\nCqb9jlQy1xDIWmNrYD/0HI8D437CDw5tRXx1W2TJBaMr2xbIhVaUHMr1mG5P0poaZzywzBdC6tm/\n+1OmN7/L7vglfdmo8cT1euWH5do8vYXhkVKi4Og7K0ZOVCxBOkMDupZmDVs4WkU8dmxh42evgf5n\nP7DrPe+UYtiNdL3jltwSYqIURe+6xtGXLX4plVIMMRW880CVxdBIZmdKga5zbNsiIdS2Q1sHyjDt\n9nw8XSFv5BSpNTKOE95pLtcrW/h/mHuTHUuzLDvvO+3f3MY6d48ms1LVsgjyNfQKgqARB5rpITQk\npIkeQwI4kaAxoTEhiBMCVWIls7IyMzIjPLwxt9v9zWk12OdejyxVqggKRKYBDg+zcDc3u3bvPvvs\nvda3AgqNHUfm84n7x0esaUjUUgjLSjcMXDEUne+Er0/BetdwzZlSE2Swg2WzfyIfP1GqIB6uI8l1\nDe3GCilE5nkmpUjKmWVZsd6RU8RYRQgrnTXiUs4LOUesc+jO040d1u0paaHvHLaz+M7gO0mY0lp4\nL13XCcr4PKG8QRfh9KtWnGK48sA1mUooMiZzWLxyQhc0PVVVrE3SwcdAilmag1YAlWrQrTZWKUok\nv2jh2GitWj5tK+hclSFSS35LkngjCV6FFdfZ81VmWNq/o8R81ZDZKQmllKrouo68TsL0KUjJTw3A\npxBOUwFrJSdXQuQ/GxuVEnX6Nbns+qavmvWiUM7cJI8//823/OKb7/7RgvyfWsj/d+BfAP9j+/1/\n+8HH/2el1P+EjFT+Avg//6FP8C/+q/9aZl5aHvwfbkOv3F6UaoESV1VKoqQmjcqZnCI5B1IM8kKL\noS0hZHZXi4aiJI3Ed6QaUU4WWyHn9sAVnDN4IwuPFCeWpZJyZY2B4/HEushS53CSQt5bEeZflpXL\nutLZnv2+5+G+w3QR7w2bzVaARVl4LAWJ2Or7gaoUXd+TS2ROgXG0fPnmgelybusBJRQ5FOPG4gbH\nMjcEboocT0fuHx/o+y2+7wDprtewYqwhr9x0taUtZcZu24qCxGzVpMjLyqdPB07nCyFLBFzvPPvt\nwH4/kkngnGDxkAKkjcIUJNTAOaaEqB6cR7te0MCx4ExHLYXDceb504F//a//VzprmN7/gldDpR/u\nGPoR13k+HF9IoTTim6EkMINhHBymZvKaZQaqEMiX1fQKal15ypVlq/jVS+bnH4+MnVxV79aFh0dx\n/spjpMgpIpweMeYYZfDdhoow3o3zpDSJksXYhrMVNcTL6RltHMpoctU8vP4K57cs5/eMg+X55RPj\nMOCcsMPPlxPkyqvXr0gl4pxpoSOG54/P1JJZQ+D+ftee+4bzNDF2Woh9bVyQ2ninIv6FZZ1AaYlK\nU0oooAWZ4+ZMTInpchZ6Z60ss7hMYxYqoxBe5XlhnW3iAaRLrIZhHIgr1Chu6X4QV2lBEu8LWrDR\nizxOzlm0grCst8SoWiWkOdVMKtL0KNVkxUUs/NfMWqMEfdtRSNaTfCKExBwl+1Z2aBHj1E3/rUBG\nIVcDzTW4+NqpN2ngVUhR2hLzVlDrVft9XcFyq+u5KeBojP9r5JvUm0IIsZmpZHQXr+KKJg+UMQrU\nLLJFZSo5X7tp6a6l5lUw9dasirijjVlS5nok/emPvuZPf/R1Oyg0/8e/+b/+wYL8HyM//F+QxeYr\npdQ3wH8P/A/Av1JK/bc0+SFArfWvlVL/CvhrIAH/Xf2dKvbrVee3/i24bolvipXPRTwlmYmHGAkp\nElvYcsmRnCOxpYMbsnS8xrUZH7KYBErVpKZTdsZgrW8vCFE6nJeFZQqSdVkzy5I5ni6sKTOlyHmt\n7exEruMkRhsZjWarFZ02TUVQqS02rut6rHdCtWvXRtCM3lOHwCUuDKPD2y1hPuOdXE/neaaoE18O\ne+72A6q+cFqPxEuQjnvQrMuC7zq0giXKi9e6kXlaGQbJ6iy5kErCGUl/N65QssLaLLPj62NOoXee\nofNQK04rdC0STFHFmJGKSCVLFRMKWmFNjzEdxlqqchQyxRpCKLw/nPl4eMfy6S2qVjoF7os3bL/4\niq2HoiqXeWLNiSi5M8QIRnmcdThdwCtIAZ0lMNnogjVQkhAld6PFnjOHAP/++yObruOPa6HzHRiD\n0RZnPdWK/TsncXMoZbCmxzm5hhvriEHTdRux6CuRnxmtMWistqgiu5Jh2BBCEjei1pi2VLy7f6Ci\n0bWyf7hjjYHz5cJ+tyfnjImKaZpISebSX2++YDpfsMZjnafUgLV9+29wXgxJokWuTOcJ43pq9YRc\noCS0teSoiFHczWE5oZSmlMzlfGoHVKazDdBUK+KSFM08TU9ec8ZqR2CikOj6jSwwEVFCLhFUwRon\nCz0t+IkQg/DNlcYU6ciN1jgt6hJtFM672yxbtphSA0IKjZkkYxJnRTieUCxlJRVxUFIVqn1eEO8C\nWoskUElxBkTYceu4EXnyrQRdueyfm3ExE4o2vDbF3BWiBnIjylnc4NcOvcZ487CoKgebNPKFa+7w\n9eCqSr4k01QoSsUWki0LWIGYFa7clet+sLS5/i1MolYq4XfW6f8Y1cp/8zv+13/5O/78vwT+5T/2\nea9F/HodupX0q4OzFkqbR8W4NsZFIqVAjIF1XYlBfuWwQk2Qk6BlS0JTKNaSUiET6DopNCrLCa2U\naVxzUFrShpZYOZ4Ch8NJxgZtubGEJPFeSbHEwnENMvPUlVcbR6cVulamZSaVzM55/LhlM+yIzdK/\nrivOj2AsULGN+412jJs9pJ51ORPChZfLha+/ekM4ZJ7ffeAyzfz5X/6FGHZmYXksF8njVNbx/v17\nHvYP5FxIKTIMG5SyaOvJKeN9B0owqtflbnXtYCtN+qQtqmY6JzzkGBMle0qoQKJozZXeU7NclSXA\nVzpUbWT+rHVpECRDiHKzUCXiqjhoY9Ucjxf+5CvhscdxxPuOtMruI1VYSmZKmVF3uK7DoSBCjbME\nRFiDIaK0ZLP2rrB3Kx8jfJwSf/WrD3TWCxPcd3TDgNNemBopUjANR1rIFLyx8jyqHut71nWikOms\nxznPPC84P2BaF/z09BqlLNvNhrdvf00uhrfvnvnn//yfYY0nhko/bvFdz/F4RCtDWFdOpyOP93c4\nZ1mXif12K8yVGHBmEOLfmtBW7PRriGjTU2IFq8UPkRNeg/NW+DVK1Da6FcPpPPPy8pGnp0dUTcxL\n5HG7Z57brqBkkSgqRGFUIloJMMxoJbLSmOn9KIcBhZxWQq5oO9D5DdMcqCpRayWsgZwC3nd435Gj\nJHAZJ65MpQy268R1i0YrK94tqRUYU5vkUoiIV/+7VZred5Lqo2jdc7sVNpm1zNElReyzRPtz3/jD\n+bg0K/nz+3z+7+vfuzrEy/X3JJ15vaYMVT4nlKXcDjN9+3d+WNdu8/cqEkRduWbo3Ea9ujQwWBv9\nVH1lwagbW+aH4xdVf7vx/eHb75G10iAzbbYE3PSY5EyqpeFGcyvewlHJKROiFPMYAikEUlxIKVJq\naguKKiqIJWF1pe8sroqAp+ZCTTKCUUDNEKt01+u6UsKEU4mkElMOXOZMFL2enMw0KzmyuLFa47xm\nu+/pO0PFyELOKbQ1jONGXGnJcThecM4L5GcwGNtxOLygSoC4MPQ9a98DlQ8fX3j99Iii8O75hb/7\nDz/nyy+/4O7hiZRqS0x33D08sOl7DocD47jBWkVYA/04MM0z3rk227Q45yg5YZUma43rPK7vCc3J\naq0EMFSqGIWUXGSzUuTSGDZoQs4kKka1Zo5yk3LVmCglo4phmSXAopZKzCtKGzKFKa48n4/cbbZs\n+oGxH7gsARBpKBW8A28Vg9XoaqlFQp69VmhVUMqha5Yi3sNuY/m0JtZi+T4kfvX8wv3oycbQ+UE6\np9rUEjW3RaosPXOVtKiUEt46clF0fsQYWJYZqFjXo5Sh64YWVNIxrzPaOt59eGazfaTvtyhtOJ4/\n8erLL1jWFaUUnXNM6wVlFLFkumHAO0fvTdNRy63QWS87B1WJtWmYU7wViVRk6TdsejFdlTYCDAsx\nFzrnZGFcI5QkcXI4YhSJXMwZUoSaUYMiJrlRGGMJQRQal/MBaw0xBtI5NoiYR1uPsZ2kvxfJsQ0h\nonWP0xZSYk0rIcyCQfA9ruuxbe6f4oLRFpwoZ2oVYJTWqi25EV6LFtVNrunGMilt1FoVt8WX4Dpq\n66Q1hdxUTtxm81JI+VyEb8U637rrqwHs1m23ceRnbvnnj11/Xbv5nEVNdF2somvTtsv3IxGJV7fp\nZ2yA3HA+L/BuyJFWGRVNDKKk0EPDlf8h8sihmUn0Z4RtuV5zUiKWTEmRnBJrXIkhtNM/EtNKWGfp\nvnMUImGVa5c2FuOgliQGC6vpe+FcxBKFpRIiMckiqhZIORJjZp5WUlipKZNTYgmROYg6o7eO3TBK\nF9JpUipsvefLhw2v7nteP+5wqrIkSc4pqXBZzthSsNaLfrQpJ5QRa7trOFpSJYSJGALjMOKtY1kv\nTPPCbr8nY0hx4nT6yE4Xnl7fM88XPr3/hDGO/b1Dqczx+Indbk/XDYBCW82yXHBOgje8G6Bmwrpy\nFaxeLidZplFbTqDcfKzTaB3FeKFs60RkKZUzrdDIk69U0Lmi83W/oYk5sYaVVDNRQVag2xVyTiv/\n969+zp98/WP++Osv6bseOIjBq2SM0XRWM1qFpcgLXFm8LXgjCFStNIM2EKRgjU7RGcVlrQSl+f64\n8quPJzKV3XZP38l8txZhiUvijKE23rRzDmPkEEMZjOspaSWlKJySJKyRmAsxrrgsy7thGPn27Tu+\n/PIrnB9YwkKhMC8z6xIEOdwOSO8ta0h8eP+B+7s9Q7/nPK9YbW4dq/BFmiKiZkDkizFFjPWUFIhh\nxo4Sv6YV5BhbcTBY59jtH1lDwjvPZpCRzrpO5KRxTtF5S04ZYxzOWWKsWOuIYRIjmvOcjy90wwal\nPVp7jOrIKbOEi1jo2zjGKEnkmueJy3RhXi6Ukhm7kW7oGMaBsd/SOenYIYFuy8Sq0MrgrLiBVSvo\nxigc4s+4dsiKz7Wi3tQlyHO3iIGo1ooqchO4ilJq+WzT/1ykuY1sf1jQ/1+/qvySr/Xz34WrUkbf\nVCfSncv3o5TCIAvN28dvpMbM7zIu0Qp31deCLweQFHRF/kOEZv3wKlRLhVxItJzMlIhFIPqiEw8C\n31lm4roQ4kKMKzUFclqhRCgZg2Ap0eWWyCGSMllixJTFqty+gHVJLMuMaj/MeVk5hwuXNXG8rBIw\n0Xm8NZSSWNKEU4rXm5F939F5Ld2+82A6TO/YUFEYUsqEdZGttyn0vqfvOrquQznL+XzBuo7d3T3r\nReE1XE4f6X0VlYDfsCyRmJLI6lxP5wVfez5OfPHFG2IKXKYT3dDT9yPGRM7nI4+ve0KIMk4y+pY0\nL11ZwvWedVoxBt68emC73fCbw4xxCucNShes0YxDRyqFikSn1ZQlmi5XrjK9UjNZy3y15tr0vlBD\n4fzphcvxiMoVjYyyNJo1rFAqYV1IMaCaHT+XLLcVpdvXbkVPa2TW7821iy301jG6QlAzfi30Bnqj\nRO5VFYcl8c2HE0+bjnWeWPsePw5AkeWh0XKltaIhzkWAVCmDc/L4GS37gxAC2hhKzRg003zkoX8i\nxcL5fCHHyMP9PeuycllmQQEsM8u8MI4bdO+Jxyh8mhA4vDzz6nFPVZrz8cJ+uyXr0m4EBqOSYDYy\nqJoIYSGnSq8t+01PSIFUMmssOCuBF1dDkzKWbrjn0/M7drtR8iVLIsWZw8uJV09P3O12Iue0suws\nSKFLGcZhS1gv9MNGDmjlUKpnnmeW9UIIMymL7PI0BYk3TInTNHM4r0zzKuhkbdh6z92mZ7/r2W07\ndpuOu+2OcdyII7nr8F1PyZGoZ8ngVFrknk4yUWOMqJLbXsaQVW1LSagWjHEY0w7mdrDV0iSLSuSk\ntQjkKt+KNLdCnZvp7PZ+zi3y8RruLuPdlNOtS/88Sqm30QoIskI30JfRoK1GGXNjxdAOKt1Aerod\n2p8L+T9UKdvN4h+pp79H+uFVUoikZ5TSnJqRHCMhByni8fNcPKVVrOfrTAwLqkZqSTIjq5laCjHJ\n35PHueCqoZQLnbNNOmUYOkcIhrwcUCmIZDHLbSAkqBG2xrCxlr6z5Ko4rpk1BqqxpFgwfWY3eLab\nUZjcKQjKVQlYKZsGuylagotNFKZHLahUGMeRUiJ911PWhZQDm92OmmasLmSd0UrSg1BKorRWS+/3\nKODl8IH7xye8tnL9r9D1PXRwPr8I8c4ZOn+HUqJHR0UULVzYSxiBNqpphNtKS8mcNMZAKTuUMyiE\nr4xzaK/RS0SFTMqRqkUmh/0ML7qsCz//5i3/9q9+yofTgUQW45AyguY18iJ9OR2o5Q1XtISAz+T1\nFzMsKTM6x+As2mRMTSKRRNN1jqISRVtSXaWLdwWnJYpuBQ5x4bJOzMvEXd1TQVLQW+edUsIaR8zi\nA9W+Q+vMEiObsWOezyijmC+B7bYn5cTh8IJzGmpimScupxN/8l/8hBwDh9OE9aJycdbw4TJxd/9A\nyYXz+USKid3dns3Q4a3l8PLC8XTi9dMjKS5cLhG3HyglSbJRDihj2e3uObwciSFg+0E4LCWhraOg\n6IeeeV1lsWadpAf5gUozx1VResQQ8a6Tn5dqnO5cxPcQCtaIxJQgYCpjLGtMrOdnlnnl4+nE8+nC\naY3MMYvyqxQZVcVIjqIMOkyRJclM22lRhI2dZtdbnu42vH7Y8XS/5+nxkWF3R9dv8P1wY3HHkrAK\nrNFQNbkaUpTbYFXigSwoSixQDaqKS1Sr3DhT8tq7qt9qqTcFTMni/s4pSyTkldGUW9ByviI/Phfu\nq1tclJ5XSaP5gRGpNY1aRoO6jYy01rKcvX5MiVZc35C28EPb/c2N0zTvN/xAm6/Xvw8a/MHb749+\nWJpLCsGMUgqhyOgkh0CIq+hKU2zzcVGoxLCQw0QJC9SIUWL+geZ9K4WSpZMvuVCixdLRaY3xBmNF\nVpaKHBC1ptb5ZdawMk8rMSWcgmokzTvGQsiSy9lbw2BK4wkrjHEQkvHJAAAgAElEQVT0g6PAzZWV\nq8jlainIp2h0NqUY+77N1qokj+SA0i1+uo0lpnkllyo5kbrw4fmFcRgoMXGZVnHSOce6BMZtT06V\naToRo8yZu2HD5TyTIuzu7lGA63tIFq0dMa7QLNtaKe73PYPXzFnCqnO2LEslxMp26EhN5+y0KCWK\nSRQjSM+YC8516FpJaeISI99/+sQ377/lw/GZ2MZ6n1nL5ab1LSVzmmZ5kaWMQEAlhqxzwie3qqJV\nRmvIqWCLousNxuZ2pVbYzuFdwFuR6K2lUKolpMJ5kVQp2uJNW9uaiCxSzWaoEaOQYY0yE1eVZkGH\n7WYjWZG54o1lM46EEDmeP7G/6/FeurAlLrx5+JIYKyFkYlhJa+CyrLx8euHh8Z67uw11PRHmiXcf\nn7nb76AWzrOkQb1+eqRmje135DmhfIfBoJ3Mn0tYMbYjzhPW70g1oK2jrivH44mhH+Uk1BL00XuZ\na2lj6X2Hc1ZMRN6JsYYqN9kKKEMIK7brKaWwXBbWZeZwuvDdy5lvX46cYxKzlVE4o+isQWOpxlBc\nxjhRBR6nyLQILKs0tY/JCVszJi/YtGKpokYbV1S9ww87MWqVQqkRZURlY62mVEtNmZCLdNZKQ2OZ\np5Il4UsXdJMlaiUsVDHYyKxbIt5Ke7/NxNuI7BoaUur1fRkxSsGXG4HoRBVFV1QpiNNVskCVNtcL\ngES7NVk1paKaWkj/Vst9DYK+Fu2rgQkhL7RCf8VUFaDo392X/94KeUqpUQszpT1YsUZS/OzWjKEt\nNfNKjAslynwwp5WSE7UklKHNFwuqFUutVCOrZVme5YIqFZUyNRXisjKdJ6bLwrIGUlkoGaaGrFWA\n7R1OwdYrtp1n2zvWIMjN+61jt+1wphDXCesGfN9jWuRcKXJNzSmRcsL1+sahoGi244YQ18Zknum9\nIQfIVbMWRSqasEYp8LVidMc0Lezvtqwx8P7dR3704z9CWUdMla6zHF4+orXA/dNlYrvdcTme0doy\nbgaJpvOmhR3Aui7EOFNNwXmHN05CqCPEUEhzoqTCGhLVVJzRLCESQ0a3jhMFVbUIdpU5Xz7xfjoz\nrYlchZUi8VlCgCtZ9hi1RVi9nI/89d/+jKHvZZZeMt4bxlEzWPAGlMpYY0S5gWboHb2vlBzkFuI9\nYSmMvsOZBG3pmpWM6j6eJw6XlTdZY5JwW3Jq0jWlWeaVYTOinWONskPx3nCZzqKtzoXtdiCkla7v\n8LbD9x3v3r1FawkH6caOeVqpMbLOM9Y6lsuZN68fmZYLy1rphg1dN6CB/f1O1CYK7u93VCQwOmOp\nGGKx5KTRasDYEa1rmy9nUsz4TpNKYZ6PxKLY7h8wtmNeDviub7JqMaZIzmXFe8P2qycZnSFICNkP\nSNeLcVDAucq6TMzzyod3z7x9PvD25cCHNTHnhHeOrVU4remNlQV5WyZaqxg7S+cMzgBKcTwHqIVe\nO7bOMGrJwI0hMp9PGC1c72hlT2B6LVb+igROGIMGrJHRiKmKctWM12vgQ6WWpt9WNLOUuVnoAShV\nRoHlKiVsru5WtK9LzxxTq0eN2ZTkNllyvrlGS3Ne3qLZaCP5ZkwSzbcwoq5KSGr7s0qe/9Lb5+Zr\noLHIZfmNkkbjhwgTAF3+AFUrKcVGX5OFZk2ZJYvVPq4rMS2EVWbjIU8iG0uJklcoqVmIsgSWFm7C\n/VLlKllIVFWYUyCdVqb1grMGVfVtiXk8z0xLIJZC12bq20FGCUYrEpVkDLvNyM5YyJUYFvrOshl7\nfOfb5t1iTIdWjloN1nn86Ok3e8KSCDFCSzuZ44odB0zXgWAjIM5sxw3zJTHe3TMvE9N0Yp0X4iSG\np5RXpsuZcbtlDYnv373l/uGJfhRmSOe3fPz4nvuHPXcPr7BG2NcV0fkarSXIoeUw9t6TNyP3a8aY\n9zhrKdNKyJnz5cLjfkNICzppVHXEEKW7KtJxiHlKTFUliUTtcpZgY60M/gdaYd26D5GdyeLKaouq\nlTUnltNBTDhtMbjf7ETvTJX5uBFSo/OdjAzKCrW0hbZkk8pCV2OrRdaGAohaouJwmjm8HHC+g5wx\nVkZcOcshdr0dpFKEZa4VIZ7w3mG8sHm6fqCklWVdIIjMc7pMnOcVZ3umy4m7xztxTfqe3e6eNU50\nfUc/WIbOsMxnYnBsxi3zmilZN414RGvN2G9BW1KeiHlFa9kjjL2k9zjnQcmIwfqRsNbbeEBXsEqz\nzLMUZhTWXYtJofOerrOARWkrtvMqS96qjMyKKyyXwPOnF7757i3/4fsD3zxfOK4J31l2Y4fTQu5T\nXEcHlqrEM+aVwavKaBWjVRgVKDETg5hhvHFse8/ddmQzDjirgUQpK+s6UbVmqHITNbox5ZGvX+uK\n0UiOar1qtEvbDTQyoQJ0MxdWPhfy66KzeQiuo5Z8HaXk0pLDpHCX1vCV8sPQmnZrqRllf3tRKfVW\nSI5U6bzVVQWgpBlpc5n2s7kWdUQ33sYnojNvS0755O13Getk9QdYyENcyUncbcIjjqxRll9pXQlx\nIiyrjFjSIlKqXFAkjBKsqb7KerQI81UR2U7M7YfWWMhzChilcM5gtZDgliDLNeEdVIyS5JONtnjv\n6DqPtoq+d+x3Oyl8MRPWDt9JjqLzsqxxrpNlGJLYIhLKiNMdm/3AUCHXIuxkFOs8i4ytapm3K3GJ\nyRO48DDe03WWsz0jiIpEmeFyOuOdKEjCWvj4PvHwJDmWUBjHDWuIpDxhjMa5kTnM8oD7ayce6IUL\ni7aK3dbz9ZsHvn33kZfLkXldeDlpXj+OLBFMqui2uVfGNgaFvHBKkg5YoVlTpCCJNrZUXO+xzhCS\nPAl1tTf9rnOaP/ryS5bLTFSFl+MBKmyGnoe7HU8Pjwy9QjWLamkOC60hloRXMn7JUXJBb7NExBOg\ntcIqofytKfPpeOJ4OnJ3f0+IK72xqAapssZJfiui2e17caS6biPPF9NMJKVyeHlhu9kQwip2AGbG\nQUE5sd0YUhZq4bIubLcjUzS8eXojz7d55sO7D/RO8fT4wOH0TNd1VAzn80XGbyngrKZ0npIjqEws\niUIn2NUsCqgQM7WA94OEYFRRXlUy799/YOh39L2X500pUBPWgLGeiiFXBcoIrtd5lnkmzhOXy5Fv\n373nr37+DT/9zQe+nzJLS4PfakNfNYPyDVBq5EhWstSzCrw19FqhjKF3RjrMBO9fZhmhVlkI9sax\n6XrsYLFeUAExBGI5CYO9ZLzr2y3kOoYUFzaNC15ykll/G4mBdOe1VNBij78WchFUSMJ9uc3Ec/td\nRrs5pfaxNkpJUepS2wOIe1MKd6kFcx0VNoeoauM41RodmhlINeS4hDVXqpJshap0i5ertGgimkSF\nK0KwNGl2+f9EWcnb76+Qr6toUdeZHKN03k2NEhe59q/rKsqPGpocq2CVomhFMRrXujXXyHUobqcs\nFcJSqTkSl8iSM513DN5iaqYvkmpTOsfirVyzcsWpSt9rnh634si0IvovFJRRbPZbttst3SAuQWsH\nvPPyQtQwWkPKWSaPSeb3zvmml5fYkMvlQo4BZ5yMl5RAlFSV5HZdNOO4pxShrE3LGWMs0xzopoX7\nh3su08JlOZFy5en1a5bLjLIW7zqOhxP7PUCh83uUMXz69IkvvniDNYplnVtYryEnxdPDnvttx8Y7\nDgGmCM+nwNNS6UZwbfacSChkVr2mFdt7clUsS2EJMuMOKVPxdL2nH3ou80Wu8TVB45t03cBlWThf\njqwtDMRYmU9XJVyZWi0hzFQNyTo0Ge/k8StKXrZj56gmcoqSmRpbWMJVGWAQMuDxciZXSbEpWXwB\nXSdOSq0tOTaOhu9JpVBixfsRpRLoKmxwZCw19lteXr7D+w5nRZr35Rdf8P77Dyznharg/t6hasEZ\nSwhy0JQs3e92u0MbI45Glcm5knLhbujxzqJUpubAmhPjOBJiJCXJfgwhMAwjpWRiSPRbj9Yy009Z\nFqS99zgnh55CidYfeR5W5VBKDgRtLa7riSGyzDMfX575+W9+w7/92bf88vsDpyVjnafzVhajSPxd\nbYW6arH76yqjEQFjGXm+G4tOml1VhFSY1kSJSdzBRTU4VsFZjbMOrYWJs86L2OOtuXW5xhiK0hJy\nkQvrElhjIZYiUYNGtP/K+cbzlgQo3WbNV0VOrZBrui00ZZfWxrpJ0B8pRFJziZeSIWZUE2Lo9km0\nFnCcNmLyUbW0x7nJjK7L0KLkz1xl1sgBJDcZfkAylCvDlbfzQ1lirbLe/Psjln/o7fdWyOd5Ioco\nZoYgBTtEkRfGsBDDTIqrKFfKKg+I0kQFxWiUchSthBGdZGkjLGkjV7YkoxHVJIm5VC4xsORASRlF\nxRnLpnfstRYkJpXN4Om8wxsFJaKKRysxY0hQkYxKutHTdT0oLdv0VOXFgcJoK/IpKwdKCjPzEnGu\nk2zDls4e4ozRonn35jr7qywhMHSDFOahZ7d/YJkXlFlISfFyOOH7DpMdl9OZZV64f3gkLQsVGMae\n6XzmfDwzbDOb7QOPD6+ZLzNd7+iHkRijmIR8pet3vLp/5GF/4vDhQCyaaV2YLmceHiXQV1yNCWMr\ntigIA2vIYDShrLJ8T4q4JmIBg+Zut+HT8yRL6MaXMNZynmeOl7PMNqssQksKbLYdvq+s4UCJmU4V\ntIEcA33vRZVQMlDxztJ3ijkGUioSZJ0KIbfhjbp2OEKaS1leBFdm91VCZoymKC/4ANcRSyGWBWtg\nXRPOeZQVTO393RMxBYk7M5q4Rn7yx19LL1gTKZx5/eVXwllRYpk/HU8M2x5lCl//6Eu2+z0vnyZK\nhGEYucwT8+XI11/cY5QmB1HgnM5nxqEnrgnyhNEGjKUai7JFDrgSsF5jVGVwDqcNaRdZwiyyuFJI\nMUixVE0zbwDEhJRC5HD8xK/ffcdf/+KX/M2v3vPuJbAWzTBanJfiKAqOIotGuKXXG10Ff2AkKUpg\nVnLbM1rR1cpuU7m7ZA6nC6kU1pSamW/FB9NSpQQ9q6F1x5FSOmmeqiwsc8myfFbCpqfIDkr+fEan\ngrMOa5xwvpV0yAWRRH029lyXnp9lhTHLLitnCZbJKX92gWYp5kpJTHMtWfTuJYvmHUEwy/+nWfUb\nx6WFZ0hXrdvkgLa7kGbkBxJyeT18djS1PWhbgnKNfPuH335vhXxZJtIaWddZ5ITrLKaYdZYinldq\nTq0TT6BtSy9pP5iMSPxKRcUoEqDaIPPodkURmZU2FVIhpErK0ll4qxgRTaixBm9lAz8MDuO8wKGc\nxlnZmttmQxdkp8iXal3R1uOtJ+ZMKZG4Chs9xHAr3FYZOl+ZpgvTDFaLZCynKgakIrr1ofcys62Z\neRHmxmaUkU+KW06HM0bLMud8vjCMezbbEags8xnbeUKYGAeHtR2n88LHX/6a3f2Jr370Y8ZuBIoY\nSPLMEgOlVvrR8eb1HV+9PPD2eGEOK+vqmJdMXDPj1mJHT86GmDIhrygq3inQ4HSlpIrXmm3nmVMl\nV83ursc6RVmF766p5Bi5hteK70EKc+c99/cDXQcxrThVGAZPbyrGioGqGIf3Ggd4ZzA2onUl1yQp\nMErmoxYw9RpYIuz0T4czX3/ZDGQ1UZNkYQ7DSCyiUBhcJ+G8qrkgud3kUQgHP6fAZtOzritffvGa\nrvMcDhNae958/SPOpyPe9YSYMM5L4a+O99+/5c//7C+Y1iC30bTSjyPLEjkflxtDfFkjXd+jmm0+\n5ZWUq5AtMWgcRsn4qOQsjYHSKCWdJLWgSkRVS8yF3pmr90uATkg6TimVw+nEz3/9a/7dL/+Obz4e\nOKyJ7bDhbpCkrikHci0tuFyTEixrZT+YZnKThZw2zY6vBRKlLcL3L5ZNB9s+crgoziHSm8roNJuu\no+8TNVuKWpsCREvxCwvJirFGO9my1FIaLE+kf13fo1tqj3Tb8rqmZtGja+GjC4xKxiClXOfcUsxF\nnfQZCSKPXYtsK6JgkWZAPn41GYncucpzpdENtdZNdw+qxcjJASW3sStMS5W2zJRPdDMYfUbUfu7U\npQO/gsPUf14e+X/q23K5ENaVsE4sy4WwTGI2iBMhrlBCm4WLQ9EZJZ22tlAQLkQbr8iGV2ZTWimq\nNZA10WpMMnTVo7JG60LOiMPNazZWunfrNNY5sax3Htf1Iup3ir7r6PsOAIUAbmJOXFnI1+QR31lS\nKYRlYdzs8EMP6BuPoZRKP3RNzlRYLjNd53DWyAKmZs7nM85b7u/2dJ0lxcgUEjlmjC7cP42ooogh\noqDN+QtvXr9hHEU2aX3HeZpwLrHfbaBm0nLm+PweHr+g6zzeeMIq10mjC11XeHoa+aPpgQ+HI3/7\n3QfWAmtcuVwmxrFj6Ac0hTlEwfc25GdaV4xSTdUTyRSKMRRVudtZhkET59KwBo0p3/S04ngxaFt5\ner1lO3p0LjglOnFrNMZUWWJazTA6emfxJjPaKx5A5G8QoDk2dTXillQKpTKlVE7nifNlYru7Ex69\nypBF3VArIkVUIkG1VpNiErwCimU+o2pEqczQ98Qwo51ltDs+vhwoGR5fvebt2+9xzrHb3RFT5jzP\naK1Yp4npcqHrHKfTgflyZrvpwSjmk4Q6h5jx1jDNC873GO2bQqiKu7ZWSl5ResAaRUhC8nRmYFki\nJSdRhzgr3oW4UJWh70Q/j3MyHy80M9aZX3z7C/7dz/89704zOVUet/c8+UeoijUFns8HjuuFVLM4\nJBWUkCWYugHisBnbRm9NMi0qE2tEU18q4yC+5nWFRRWWXgKYxTMCpliUcVStRakVFXGVfRjZUZQm\nJFiDIlRFURbjxams2o4r17aARZhA5ib2Uzf5oexCBUilm8b8qnKzjVtTtaiJdAtmrkrSiTKlpfto\nSdhS7XLTDoorL19V0bbYa4eN+iyFbF381dIv6UHXsUlpv0uxvuKA2ztyIP0hduSX+YV1WYjzxLJM\n4vJLixhrkrxolJWAYWcMptmY5YctVlhrROWgQDCXyHWrqoJSHUPJUBXZJDaufP6hl0Spid47Nn2P\nNxrT6IR+HNoCU+A+zvjmMmwrjQouhjb3rZScCUmQoCh5MqcYRNLnLUp3GOcxZsHYwOVyEemSNUKe\nEzAo3ntSEKnd8Xhu9n3TbMCWZVlIYWG3FbdlN25wPpJy5ePzB3rfkXJmXSvOd6SQCTaw243Ma8A5\nTVgvqLZYGgePVpEYJqx2jMOW7XDmR6/u+fb5wOG08Ons2U8J/eGFtKyM+x6VhS2hsjBdZPYHpUa0\nSlCydA6lSd62PafTBaIsn5WqoIS5ogBrKz/66p7XTx1WVzqlGBwMVlguSgk/RGuBHVtT6Jywx1Ks\nLCEzh0JKlpwqd7s7nFKkZUKR0KpSmwt1mVO7nl853tLxhBDoOo/RjnEwxLDIz9w5liUwDBtKuVCT\nqEjWdWmL3Mh6OWH9SIwz93tPyOqGYa2xoJzhm2++5dXTGxSWNcIvf/2Wv/wn/4Tz+cK3b3/DP/3L\nv6DWiveyyE5xQuuAwgqvw1is89gYUChijAKGU4ZQVg7nM5rGp1cSuFJyZhx2eGeJRYH2lAS1REJY\n+PX3v+Znv/kFpxjQylJiQg8d43YPVWHjSqIyp0hKVfTRVZhE+Rq/Z5tTUVusruKoVhIuYUxBddJ5\nSvNgSUq8C95bjJfCmWOm5ogzlaw0BUUNMlqpMRKUJVSI1ZBxVDuCVtQkAczee6yVAcd1/CBGIll2\n5qaR1y23s2Th8BetwYjM0SJxf9UUatEoZ8Xt7RK6FjQWnaUd1xWSEu29vjYiVKFQall2AjcdudJt\nJJKv/HihiEoP+FmuSC03t+i1TsmzsxmD6n+eYIn/32+X85F1lgDedZmIcQGyzKXb7EijsFrhrG3o\nTFrgr5JrkFZo4yTFx8qJm62FVU6z6izZW8nrVAbvvSwlSqbWIItSIyEB1nd0/SAd+DAIplIr2exb\nJ9eqgkihjOhgU0qi5kBhncMoJThTrVlXmd36zqBMh7Eepw1dqkzTmZwzylhqiVQ0rnPUEkDBOA7M\nU2ANM0pnlEns9gPPH1cOh4Vx7ClJZp/b3UhIheePR7Qu9D3ERaSVr7/8ESkVjOs5nVesAapjs9kQ\nQ0BrxTDuSLmw3XS8eoycTmfe3G/4xbuPvPt0wXtDZ/d0bqWeE8p5OtuhDa2gaBSVEEUdo2vGFEOn\nDYNTPD7s+PgsqF6jJcVcLBCFodd88cXI073Dq0KvFaPXdFaClrXKXMFBxnphcji5AYUlsi6RZS5M\nq+I4aYb+iVd3X5DTyvvvfy03DmMxqpJiZFlkgZ5SYtyMGOuZJvnYfr/FdU7AaUXGECFEycBcF1kW\nFliWFU1lXVbWEOVr0pq7zciyaso045zmu7fPkpe5it74xz/5Cd++/Y55Xdju7nh6esMaf8Nuu+HV\n0yND50VSq5TMfIGaI0pbpmViaOk8qQgn3ztPzopSxQ4fQkIb6fJCFDBa5yRpSSlLTpBLJsaF98/f\n8zff/B2H+YJSiilXjmsg15l6J9RBay2xZtzlxJxWUSk1MUEqVeL4jG2dpHTBMiIvWK/lBlYVJhu8\nM3inWQ24zjBuHN3gMdYAsoAMJZAR678CXOiwrqNgCKVQdIe2G+zQo52wfaIuEFMz4smBb4wRVslV\nfnhdJDZtvUKMd04rWaJy7bqv6sVKuuIVdcUooUrGnGTGlksLVUZUNK0zF6FkK9CqkQ7b10DDcqMk\nWJvGJKJerfd/b8wDbfRD+7/t+/lDlB9ejp9YFynkKUqmpdFFrmhK3UBC1lq0km24nHCfcZAg35y1\nBmVlM2yKhlSJKpKVwThDVU4+n7V0zuKtpuoiyxWFRLw5R9d30llrkQlZ14l7TIlJwRiD05pMwpoG\nhSpNc1pN025rlJGrojFGuo4cuSo2XDcyojgeD9Rc6HrBuZYcsE4kklAYxo5lKUzTgtEKauDhcc/h\n+cz5fKHzRhxoFIz1jLst03ThdF7wxpNz5ac//Sk//vFP2O239N3A8XKEubLkicEPeGvIpqBNIpeV\n7dby5mnPn3z5xKdPRw6nxPOwsNs6ht6gkkGT6Lxo4ofec76sKAV973BeMRZFCJXzJUKX+eLe8uHO\nEObarOEKQ2a3dfzk61fc3XfoEvGqMBjN6BTWyiimdwbrFd3GMI4er8GKxlTUFpfAtCQO58yUPA+P\nP+Lp8SuO8yfevf9OIvzq58guKdKlAZ/EnHQ8nnjz1Zd4J4+n1hptHOfLzN3dTl6ezWF4mRfGcSSu\nE8saoEjA8zCMaGM4nM7s7x+5TBN39zv2uzt+9rO/5c/+9M9YQ+T79+/58Vdf0j3ey21Bw+vXr/Hd\n0Ob7hZwXvPNiipELNYXaxmgyhDVWiuAyrSjnMKbnZToDCmukgEriTwfWk0Mml0RKC8/HT/zsm1/w\n6fSJVBHud0ziJl5PvO8+8ubhiX7s6fuBseuYpwsRiT+TyMIsbl9rsNVJMr0RO7q1jRmuZaJfTMbb\ngLcapzTOSCiG73q6wWKQjjzGhKJilXShMUZCKoSkCUpjnMMq0FWhqihmSKJOUVocu+Ku1E22p7FG\nNwd2m6Pn/FnCWMXmr5UCIzc+YwzJanSIwlPPhmDE7+IbmK+mRCkSJ1na6ESSrITSKNuEVpsQdIiM\nVtRnCz6i1FL8MFC53MYqtTmbru+X2+f7bOf/+2+/P9XK5UWWPutCylGWB862rbRGaS2F3FisE370\njVOAak8WwXpqo5vDrFBURlva+EVj27doVLv36Ir2FuusdHlGkmEw8m+gxOUl0C3d9KIyQ1MKCVZo\nyeWjF/yoyB2rMJjFo4uycnCkmMlJ8hgrEnJQjKUfBi7nxOHlKItWLxtxrCxfqwLfDxyOE9MaWWaR\nLO62IykppsvCMFhcVBhXKUrjnWNaM++eX9jue8ZecfjwlmXe4vqRV6++Zhx3QCWtM53tRUddMt45\nFPD46oEfTwvffTzw8uuPvH++MPaarddsS8f+fkstGWvEZem7npAmcizCgFcFp2G08oReNpqvXo/M\nU+Ryyhirudt5Xj/uuNs4TJXRkjOJofMYJTb4ziusN1jv2G83DL3FG6Ak4mWVcdO6cDhFTnOhmp5X\nr77gcf/I8fiM1RLOW9pWVSkjELKQmC8Xur5DKcX9fSvWpbKG2BCukf3+AWPqjXuyTAv7u3tUzRxe\nnvnw4QPWddw/vQKj+f7dB3zXYW3H8/NbHh7vOF+ODMPAZn/H3/z0b3n99Eb2BDnz7u13QEYpy+Fw\nYeg7+k7fCHshFZyRvFnve2IMopKqFucGtNXENKNUEEytEley1ZqaK873KNeBcS0zMpJS4Lt3b/l4\neGmigHa4ZjFDxbjym0/v6DvP2Hu8NiKR5Xr1h5wVuWqqNhKWrCWSTRvVpLqAEaWGUhrrKs4baXA0\n8vrWns4P9L3D6Awl4XO6rfVKVYRciKktsKvBd1v68Z5h3KGtKKm0czJy0obaxim6KjGRaY1tEsnS\nlpm3UI1S5RDWzbFZBCNQjAQtJxNJVvYO1jbrfhLlTs2pZf629K3rNryKG7P1z7d5uGqniAKRqwDX\nk6XWJN+xUs141MYq1xvED7pw+TN/gKyVdToLCKvKzE0jWlHTaGDGWqwXd5d1lityViOSMa1bsb8i\nH4v8oEzVYBTFQrYKlYFcJYgWQ7UKMALJHwa8G3HeS3J3zZQcyFlGO1WJCUEbRSrXTMTaJHCgtcFa\nhzEOay3GuNsWvzTNqLWGVOXFJc4xca8qwPeesCoOhxe8E0NMzRnrPb7vcBbGXvPpMmMoXKaJ48uJ\nYdgwzSceuGNQhtxkh69ev6L3spg9Hs/sd/fiEL2cefn4gXW68NVP/oy7+ye8l8JdqsG5rSyKTQIs\nb95E/uxPZt4fznx/mPjuw8LgLV9puNttGHyP6z3ojlQ0azKU5EkxMC2BnCM6R1SM3NnKn78yDLXn\nfM4iV+s11hS8EZWFN5XeGAwCSrIefKfx3jBsRza97EmsVuof/swAACAASURBVI2QaJnmC+8/zTxf\nCudouXt8ovMdhcRxOkoeZZHCkLUcwGtYOZ+OPNzfiXRMa9K64jvHmiK5KKiJ/d0dIcgtSitNCIF+\nHLEaPn14x8vxLLF4gxwG63RBkfDdwKdPH5s+WuHdBmt7LvNMKZk1LBgtKaTT5cLD/Z55XjgcPqDY\n4/0dVVlKLXhrcNYRS6GUmVIipYguOedCUWC0a0S/hHeC5U0hU5TF9Rus6WTHYCGmwPPxhefDe6qK\n0tDUllxTRaqXU2JeJi7LkZi3oOQ1VlBkVW5DsWvxui4PpdVpTZaDoq4Qq4pxWkaaRiiTxmicdzjf\nYbsOZ0QFpslNzSR7qFgrIRZ80ETtccMD3bCnG7ZgLApDVdJFm8bXp1asNmhnxSxYmxGvgqrCU7pK\nEf8f5t4cVtY1S9N6vvGfImJPZ7hjZmd1VWZ3SbTAwQUfA2EhHAxAQkICBwscEEjtUQ4GDmokhGgJ\ni8FAQmDhIIRESQ1ZndVZmTfvveeecU8x/cM3YawvYp+svFndqhbKDCnz7rNPnCki/vWvb633fV5V\njBRkLXmjGCnqMSWScSQnp5gUTtGSy9m2H0uoEC25pmthqEjdfP5z1UlSozgX8qzk+7Lqr2qYWr9O\njPZSWfSg64/l9lB+H3nkyzwS00KUEZJs5alOqKzplMGbBleDV40Wo7fsAwwKI847K+Ca0z9WK43S\nVhxwRoBURkNOdbusnTCWravAfIuyFnmpjQCIzmhLmc+ebhZaV+pehe7oepRDSTYk6hTCkM8Izqbx\nOCPPm8tCykulrwmWwHtL8p5xv+e4OzAMnXQb1tB5T5wnWu8xncc5uP/wWGPCCrvDkaZrag9gePv+\ngZefvGSzkYtgP8403pFTwOnMYbvj669+wc2LPZ+8eIF2Mou3rmCsdC0xBVZDz9/49BPefnfPw+5b\ndofA+4eZ603PfjfR+EawBG7BaENjrGiMlfBcYorkxWBspEmRgcCLAS4bT0gQSsbahPOBxljsKS3e\nG5zTuMbie4d3QjSEhRxhrNbp+Zh4/27L+8PM4+wwzSUX62uWZeFw2DId95DTGcafCmRl6nFW+CyU\nzOP2gc36ElVZ9tM00l9csSwLOWfGaSanTNd6us6yfbxlf9zRDx2KFu9aMEhAgxF1hC6Zi6HlYuj4\ncHdgSYX7t6/QWv7um81LYhgxK0HMLtMRZwq6SKZmnAM5ZnTT4LxlPo4YZTFOFFPzMtaGQzHPI77r\nsUYRS8HoQrHQuDVtu0br5tzRHY4HXr1/zX7aU1TAakOsy7iUS937ZpYU2R4OjPMR37RAxbtmqjyw\nynvrTPm0qDtdP5SPOdyCLTZWgTYoI+NJazXaShNknYSwWH1yYUIpilgyLmR8MhTTYJoN1g8UbetN\nw6LqKVLsAvX3VoaoasqOXLV1sQgJDblgshh5ijGQCzonSNK0mZTITox6MSWSl3FMiqGOZRJLfnJ7\n5grdKhWFW6Kw/V2WJqVU8xOIRFeIk3UEU3LlnBfptitPIPNUyEWrKDC98tsnK7+7Qp5KJmb5AEq8\nkRwrlpiFC11EQamdwVoZ9hdyfW7CWstHAXEy3zSA0SgsxTgwDRnB1FpbpEh4kbQpLTthiITwlAqi\neHKGOmfwrhVjBEo6FBDHX9WOUhnEiKoWrfT590opcTgcxXhTj/rGelQLOmlCMqixQJewShGDxNZp\nvUDqSE5xmBO7xz1da8kp4RvpbpyF3X7k4WHLej2IaqVoXn33jovNGuM9aYnsj8KACAtcXXWonHj1\n9Vcs44Hnz1+wXq3lhBEz1lq6rkXpwjA4/vZPvmAXF376y+/48DDTtAe0sxT9wCY29OuWbtXjOyej\nqqnBRY1ThlFZplExx0RjYNNqjlNiQTFoV2+kVuzdRmOtwnuNMYqh7/GtfE+dTl9Vhz4dj7x7teX+\nPnCYLEty3Fxcy41ynri9fcd43JFjQOxbQj/MaMYl8v52y/Pne5qhw1mLNoY5Z2KIXF5cUZTMZ1NK\nWAPOOTabDcv0SM6Zq6tLYpiY55lpWhjaNfMErXNs1htUecRZwxIi28d7dvsjGM2zZzdQMsYaYfKY\nyDiNXGyuUTqiTab1mcPDEdO0pJKYw0guC00zCIgtRpblgLZyei0loMkCHVsE1uWsoe02Mi7UglpN\nKXD3eMfd4wPinTMSlqC0NFLKkCoRJ+XM9njgzd0tl6sNIQRiEg6LsYpskFNtvX5UNV6JpkNuCEY7\nnFZiFDIWZ0eRJGoj14EyQpu0rjZjVl4XbVBa/m2kgDIFi0WZlmIa0AJgM8bjXCs44iKngWLMuQtX\nVUMuRhzOhVwWlDJa0ZSzNl0XIyjtUnBViZZSwmYpxIINboS5nzO2jljKiW9eoVq5xHO2Qs6n7lx8\nDiUniq4NoqnSX+WQzaeEop9QuaKI41zzKHU8lH4PZ+QxSzGX7pZ6K86UEvE+ElM484flI1Kqbaqi\nL6NBW4X6KNMua1WPXKUG4lqUdhRTDWdePjRCVBOtedZyp4vpKYrplEiScmEOsmTSRtXNtMgLcy5i\nejAG5yXdxChbY6vEruxcU3GYMqqR+CaDMY4QJsIsErKmaShpwpoGOzSC0kwGZyzPnj3HGcfjwz1a\nWQ7HI9ZkNqsWrQt9vxZ53zpzcX1N3kdev36Nb1bsjpHDbsdm1WG05cP7O66uBgajuHv3DqsN8zQz\nrAaGvq/ZiwpnG9arS1LM/PEffM4cEj9/dc83bw/4xuF9h2sUehSEgHSmVhLnTcI4XVNglIQZ5xZM\nwDrFlEo9sotd3PUW7yyNtbStoFGdt6B97f4URgesVixz5PH+yNsPI/dHw+OY6YdNpV3OLDFyf3dL\nigtiY6zH1aIJsZBsYZoO7Pdb1vOGy2Ejqp6iaNq2yg0nkaF6j9EF7xylIk3bxpNTYZ4OT6OZEOia\nnmVZmKa9yM2MI8aFxlly27C63GCqu/j927esN2uMtngv7I/xcGA1OA77LSHORKvJc8AoYY8vy1xT\nbyIFGS2lVOjajpgCRikaLzpx6uuKelr8TWHmfvfAFGZyUZQip0eNwqmCMx6lRtCRVBK7ZaTcfeBx\nHKvjMdWbguinXS2UKSVSSRglzsWiDcUotLMyGjUKgyEzkTOSAqQsWjmMdufrR2m5uZkag6hiJiuD\ndVrCJNBkJKTB+gZtPKApxtXxjiZVLbhY+jnPpbOUjJPoTE4QFZ9dcgGTSUjxfeKPn6Lf8vn5Madz\n4XfUhXkWpG6p3fz516YK7ksBkggSSp2z5yyn8ayjdOExolKErAHhwCgCZ0NQeap/Wf0ezsgFYlPO\nxynBQotjTdVRy5lTcDaPZFGCFCmWKcnm/yTKl7tZEV2rhdIUQowsswQjKNdhmhbn5I5urMfahmIU\nvqlM8xSEj05Nio910x1z5SxIuLBk7GUkhFBDFlBPVog9vxRAo53BeYhLIi2L9IdF4WoG5Pb+A07L\njWM8PKKNoV+vqtEpYlTDxdU1KRe29/c0rSHOM1r3FRU60w+O3W5md3jHy08+4Sc/fsG3r97y+HDH\nqvekNPP86jn397e8e/OOFy+ec3O9RquRw37iMD1yc/2My/UVyxJp25a+9+Q08QdfXLEaHJ39ij/7\n5h2/+OoDKW74mz+8xpjKsy4y/9WNol81WO8FcerkWD0dIyYWctL4JZGKEPicdzSDqHsa62gaaJzM\nTGX/0WAKmJwZdwtvv9vyzXdb3u/g9ljQZqBt1jS25Xq14ZvX37CEg9ARc5SjtZabAUUKb+s9x/2W\nOC9yAVYpQdPIbmEaD1jrKdbi25YYZlSJWCML05QNVjvImb5v2O72OCMuzHHKaN2QQiFlRYiJ65tL\nbCOpRu9e32J9L8XWQt/0fP2rb/FGy9Jzey9f64BCOORWw7iMtK2T8WMWc45C0TYd4zKTcsA4S4iF\neVnQJuFK1Sznwm4cud/tRPKqCrJikwxTpTTeSYhYQtE3HmUM47IwRiEkKqXwdYzZNJ7GC6MG5EZR\nlJYbr3Fop/GtmJY0ScxHsRCywtgGU4Olrfc410ixN9J0iXTXogykJEhXozSmCEZBi2WU07TeVLOd\nUWL+MrUrT7ULf+J/S4cba8CGKoWEuDJLETYL5qO8zkruzKWcC7TLqcK6MqE8Fe4TAleK/SLPqe9R\nyYmSkjSsMVTQl+QOp0pyNVGyYnUS3IA2iVKUZN0W0bfLXkLcyr/t8bsr5CLUrB8EMRGAqlJDj7Ut\n2lhOMAKtDV6bsyMLlKSYWFlUaq2wpsHoKkNyBe8yxjTM80TJGe+9jEm0pWgjpgBbUzw4OanE+ZDr\ncVKf5lRKV7ZzEnJZHfNoFeVNQ8nhtChhSRtTFysJtDoD/UMUM9ESZnKOOGdIi2Q7GmcxJTM+3Iu8\nb7WicR1KNQzrDYfDjtZ3lJq5ePPsmnFaSIuMmnJS/PlPf8Yf/uRHPHt2RQwL0ziiteH+4YFV3+Kc\n4fXbNzhfuGmvEct+4f72A2EOvHj2XG4gRrG52DAMLZt+z6ANrVf8+Xe3vHq7l39t2PD8pieVBaXk\nBpBLIEaNty19pzDKEzuhQpJhDoGlaLS1tG2DNVo0yaqmqqiCUklCAojkkBi3R159e8urV3vePRYe\nJkvUPZv1FU3b88nLT8gp8Lh7lIuJKDfdUzB0kSM+RTFNsyBLxR2DNXXmqiyH/R6UqC+M0YRFWNrL\nPOF9Vb/UBbc2inme5HlpYdgMWCdGLEXmcbvl+uaK/X7PzeZamBuuYYmR29u3PL+5YdGR29s7fvDl\nZ8SUsMpxSmn3tlBUxDUGZXtJz9EJpZcKZavp7dTmpUrpysmYIo0cuRR2hz1zFJloyolU9c+6Mlis\n0xinsEkztA2pyOknh4QyoKsM2HtXTy62JggZvLN1JCYqMOsd2grqwGoIc2RcCuBonJfnN07QFc5j\nrBRya21VF4kkT3T7CqUtBkvGgpYAi5KpJinBE2gl19vpNGwASqm14mkcYbBVsi3Nx0kaks82/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hTM5zYVpgimC1F/dblBxVRSIuOxRGNNBKo1KQSD5jOTOQbOXiFEXRGbQmhYiKsoexrqmvdWaa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m3XF2dkJOpiib1mOSo1nV+MrS9zPe14g4xpAw1mOcJ8aZ/aFnc/6kxKvCQJhG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dt16JIxDprest229LXFEMgcGMaTcLyTjDXOekIqFD2x2W1ZwsQ4DczLgq5ajKtZQqGqRabv1/SS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rvbjfouSoKhJx2aEwooFWGpWCRPIZy5mBZCsXpyiKzqA1KURUlD2MdU19rTPTNBNTi1YOrSwl\nF+Z55Pr6ksNux/5w4ObmShyJFEC6QK0Lzgkut5CZpqPQGI2X8V4sNMbRNR37/UjJmbDIOMp7x6r3\n0DkZ9xjDw+0dn332GdM0sj8eWQ29EEHzA95qyLJkXZajjErCnjRL3NgyZ1Ch4ioMzio2lz1t56qJ\nRBj3KU7kGOS1OdEnteBaIWNUpnVwvXIs1wPhGNltF5lJG03fWa4vWlaDpfWFrjW0naJpNc3gaHuP\naw1Na3FeYVSiLIHd3QPfvnrH43akcw5dEl1raTsv6pJapHMpxCIu6lQSpRghAKSayFMgkwnzRMFL\nvJsFThCvlM7d9In7TRZQVaFiZJHb0sdd968X9l9/iOlPn3/qlAl7Ot2XUj9nRSB+pw5ebjryB2nM\neb6uq0ImpSTZtVWRkpM9F++cvMzIs8T/pZNTtN4ApDP/pyjkSqm/B/xLwLtSyj9Tv/cfA/8W8L4+\n7T8spfzP9ef+A+DfqJXv3yul/C/f+xsXAbWbojBFjGLGKIwzuMbhnMVbiZrCiFsrZSTwN1d2SpX/\nNIiV32qNMC6zzPgqHyJWdovc+YPU5bNrK5KiLEmWAiYXdKPI2aCyOlt9JdFD2BNa89HdM5GNWIJL\nkvks6jSWkecaLa4sCZOzKGUk/SUs5/GQ1VqSkkIQ2JdvcEqA/eO0gCrCWJlEq7rZbJjdVC3pI8pY\nvO8kumoJrPsWDbz59lu+/OI5z15ecTgsDMPAagVDN7DEwps3t2wf9rjGEkMmBMvPf/GKZzcXfPHF\nS8Zx4uH2nqa1hHcLm9UVV5dXuKbFdZaSFgqXXGzWvHh2zU9i5HF74Kuvv+Obhx2vbg9sZ5hSZUUj\n9MiixHhlnRizSpKbUCII/lQjQC0lHbapr57RCVUksb2pSTRC+9OS0qQgZjF8LYDXCqcLFMEJGC1d\n+RxmUloIYabMYFLgdhyhQLda0XYNRmdiXJiXiaYxEuenLftxzzJLKtUc4OLiAooWAFyUWWe/XnF/\n98DzZ8959e03PLu4oGk8+8MepRseHrcMfSM6+3mUF1V0GAAAIABJREFUUyWFFGZKGmlMh0LjfccS\nAt53aOVwrWGeAyHNsrB1Mh601jEMlqbpqpVezDsWmcOelooSAAwmp6rwkhOx1YZ80bHfRaZJTGx9\nZ3h21XNz0dN5wRJ33uCbBtdaXOuwbUPbtDTe4TSovDCPB757d8frd/fMMbN2ic7D5bpl6HvhzZzm\nypzYIzL+kAViVayUUrG5iZjltKucnL4LMpJIqaC1uMGNcXJ61qVKhFVlfqvzcvSjuva9XwtfR8ZQ\n5Tx/l9PJaRyjznN4fUYTnkcuyP/JjePUzYsqy5xMQ/l0A7JV5ixF3tSZuMtP+vSztr08MZ6+7/FP\n0pH/V8B/DvzXH5dh4E9KKX/y8ROVUn8M/KvAHwOfA/+rUurH5XvWrcZquaMZKbJN07HqV2z6DX2z\nwlghqMUUiHMhlUXmwrYqEqLYk5NMIrBosQZbJ+OUatc5MZu1tiijyXMh5yAgrmRkAeTqbJ6IVlmg\n8iGQsgQ9aCVsCFPTQ0gJq06hz4gzrG5IlD2FQVTlTJZg6ZJkCWqszPKNNmRtiVG6n6Zp0BqOh8Qc\nxf1l+wZnO7CeEBPHacaalofdHau+oWkMrd8wjseKBV3x7v0jr16/R2vFDz7/DG81u4c7hlVD34lk\na7eb+dk/esOLl9dcXQ88vx7YH2a+/uY7pmXh8x98iSmJXBTPXz6j3bY8vN+yvlLEZeF4fOTy+pL1\naqBrHZqGQsJcrYgpsV63XKwcXz7uePf2jtvHPd/ez+ymSCAyBdHaZyDOiVgVQ04L18YoAyVgtSgc\nVMo4rdFFNPy967BI3JuqS60cE8EohO7eQNGMIdN1FrJI4awB31rRWReLtYr94QNdWeO6QYIaQuRZ\n9xxDIYSFkgPOGjGsITeBgjQWOWq8W6GU5f7hlmGzwquGZ91zjvsdL589YwmB65tnNE3LfrvDess4\nHrFacX9/y2r4Ago01uK9IcxHvFG0fc/xOGFbT9t5keV6y3ScxH9WohS+bIhLwXUabw1d052JnZSa\neFOU4CNq1qYUUI3NUVQ1OZEKeJd5dmmZjhCj5mpwXK8dvS10OjM4T+utFPLe47qOrnF0ja/vT4C4\nsH3c8vV3tzzuJrxuGFrD1bpjczHQ9S3OV8FB3VFpY4B6fWZQUeIdwyImGhkJSfC2IguSd85oXZVo\nziM+EHmPZAkq16Eo3MRAVWvUbxTxop9+ThcJ95bfR/5bahpW/RW/VseKerpZnMY8cHKUnpQvp0i4\nk6SxGgqLErt+zjV+70n5Yj5ylp6+dzpNfN/jH1vISyn/u1Lqb3zPT32fOv1fBv5+KSUAXymlfg78\n88D/8Zef2LoWrS1t09O1HV3b0bQWpWFejugkLJOSC7Een+WkK8oVbbWI9FMmhoS14m5TaNHLRkVO\nkiITQ6SkBaURSHzOjONEXCKuCWQtACNtqmdMyYJBcvxUVcJo8kk+qGVgfkrxKIjaRQM5ygcNpVFK\nnl8SpCiFpORCIqLqfLEUJ11nErt+1w2U456UkQVa0+BcQ9t6qFjO1XrgcNgxHY5Y7ylK42yD6xue\nv7hgOu5ZpoXbt6/p+wbvDbfvd0Ql5Lm2HbhYr3jz3T3fLIGb5xesL9d0wwBZYWNifdGDyRznmbYf\nWBLs9lu6rqHEiZJn0nJB1w14LzhcrRRLXtig6duBYRjYdJ4fLYE/2k88bI/sxpm3j0fGWUmaTpVb\nGaMp2QhtUhfJO1UaUzKNtVLgtRMqoJExSkgRhSLFQrKK4wwECGqBIvLJxlliWOQaMIJHHXwjx9cl\nVLNPx2XTUIrm8mJNihNhoVqiM65t0VqTFgkOdk4+D4FCSIHxGKuW2aKNZdzvaZpebgSl0A2tdMfO\ncNgdcV5hraLvJVSbUlOB0LTO0bYCblO64J3clEKQfMr9YU+/GjBGZJBKOZTVhLBgTXdGPScEThay\nqKskFFxTrIyncq6fy5BIS0KlhYbMZZfRzxtKgsY7Gl/wNtF5i28NTedpesfQt6z7js4ZjMqk5YhS\ncNzv+dXXr3nzZosKhot1w2bwXFy2rFYdbdfRtB5jHEZ7jGkoxsiS+mTCSZG4SL6qKnV0pjQlxSod\nTeizvySRQqixf6oWb2p3XtG51C66PBXxX8PD6qfva1WTfVDn5atCnW8IoMHUiIpT5336fcrTzF0y\nruXrE0q5fPQcrU3lRimUks98Kfm8AM1/qZCfc0V/y+OfZkb+7yql/nXg/wL+/VLKA/AZv160v0U6\n8994XF8+q/hXmSXFFMnHmcXWWDcvadlGSWCsOr+kEo9kjCHHOt6IMn+WDbjGaksyshWmiBJBsgaz\n3NW1fIBzSIRYkKRtyRFslMFqi7XVcYh0idItyGIDW49f1Pe28hDqO4rWhiiDvrPG1ZgscqaYKUXk\nhxKQoXC+pcREyQYYMVqTomhwywDWLDRtBG0YVhcotWa93jDPE48Pd0zTKM435UnMbK56cmp5++YD\n928e6VvFy5u1LF2D4t32PZcXa55fDex2W7zN/Oqrr3h285ybqwGjCofdnkzh+ScvWGKURedypO9a\nDuPCFI/CopmOtG3Len2Ba1YYY+laTVIznb/gajMQFjhMe5Zl4e72gR/OC+NxYb+fOB4nnHey8MUR\nQ8ag2aw6tts9lELbaEyBkgPDYBk2a1y74nA4cNjPTMdFlBs5YbTmbpxJWPpuhfW2qksKIWfSEhm8\nI4SFw37P7rDDuY7xeGSzuao+mSxqKQVtIzrxZT6SwkLJIhVDWabpKNLAWGjbjt39PTFm1kPPPO85\nxdONx4UUC8ZIeMnQN7x5+x1aa4Z+oHGymGxsI9xupeQYreRU2fqBcb+XlBqlKy8FrHaM48jF1cAp\nYnCJMy4FUMI9J0U0mZgDxRQUciJ0xlAMLCRysWiVaWLEpsKw9lJIlBStpjP0bUPfNQxDRzM0dL2n\naQ1OKUqYyXNgNy68ev2BP//lG7b7wLrruFy3PLtac3WxYeh6vG+wTlRXSjWU6scoKVfXqby+JQdh\nuBQBeYVYCAWylhOVQaN0IscFRQSVRXapxZ+SUjkX8u8r3qeLV6E4eYO0lhHeqWsvdRGr0VWhJid4\nyQgWOfQp81OVukwuTx3ub/558jgV8ydfzAm09+QwV9XhXAq1yP//U8j/C+A/qV//p8B/Bvybv+W5\n33secM4QY2Y/LaQY0RS8V7QIGS9FQ0wKrcRGrbUl56fczEJBEMSFwsISMgpNcbIMojwtLUy1/VKX\nEtR5U5wTTIEQF8IS8c1CSMJG7/oV2gg8v2ih8lnTiqRJF4w6ySDFSCGgnIK1Mj5Jdekheldknl+1\noykJb8GomimIgH+wGrLB+JamW1CIhElrxTIeMdZyO82EkDDW0XcDF9c3mMMeUmDc7VAKGt+xPex4\n/tmnvPrqFcsyszscubxYsW49lEAYj/SrFmdEfdH6T9gfR968l1zQP/zx3+RwmNjuH7m5vOTyQrHZ\neOY5kJVjHBMPd7dcX1iuLgZUSrR9xlpTVRJytLemwXgIdTfx6afPGQ9bgUEFCPMocq0CvhlYFnG+\nGaO4uZQFojEK3wgTp/HgfEPTDmwfLY3dsXjLcT8Rl0AoGa8MQVm8sSKgq6EIOZXqklxwVV3x4sUL\n+t5hDCxhoRkuyTkKF96J0SfngCYyp4UYZoyx7PYTFxeXHI8HEpkYFoxOtL0ihK2kyWvLPGfSklFa\nvAXD0LLfHdlvR374oy85HiXcOoVZ+ONxErkocvILcaJpVsS0kNIB11r6rodSGAYvTtMSsLaFIs2G\nfM4TKstr4ZVIcXXR9cwvmbHGKbyF5BUxGJZxogGSEXdzVJpkLc0wMPQDXd/gG5mJi9/DYWPkOE8c\ntkdevbnnZ7/6wLe3My5rBm+5XLVcX29Yb9bYxmNshWVpMZ6dzD4n2d0Z0lWv8Zgy4zSxJE1SmcSC\n0glXzTXSgVfmipZ5eUrqrHiTS7R2xlWwo8qvF1cpqmJsiogBUKlTCLOMaAzl/HXRT1/nOivXSkIw\nPh7dqIpNPnXm50KoRRTBaRb/UV0Uv8yTvh34qCP/viGIPP5ahbyU8u6jF+G/BP6n+sNXwJcfPfWL\n+r3fePxvf/pzuduowufXK37wbI2mJgEZwW+SRZSfqAU81rRuJaoWpRTu/CJJaMNS5KI5LR/kxikO\ntpwVKmtUkcBUreXYEpfImPfEZSGlBVvfHK811grTWdvaqWuJglPIZp0T06O+6aYCdxRS1KXQF1mU\nmoSxCZc9YYnVzhvIRdJZUpXGxVyEg2Es03QgBPmgdL0EQvtGnK5LmIkhSCqKcsQo4w+lFavhkvvb\nOz777FOO+y3GGrLu0K5lc+E57A8MfUvTrDgcZpZlYdWtaI1lCjP/8B/+grbrcM7x4cMea02d1RpS\nHBnagsNynGfCfazGLoXpe5xpUdZBvYHmnFhtHF2XWKYJ31gZMwkonDkGlhBQ1uN9T1wCKY6EWdJv\nfNPT9p3kjKqEt5oUAtoU+tZjU2aaCpEkhyOr8drijJXnG81hmkUKSmFcIrm0oBLHcYcbGnKW0cTx\neCBViaqw7gX0n4mUvNA6xzjNdJ3A19AjXeM5jvsanH1PTomuX+EaK0kxWtF4UWCNxz1aGX784z/i\nOB/xvhFTlLFyjI6FFDPWi2lmiYX7+0dKUUxjxLYdaMHrKuWwxpErUjlnoQ0qVSg5AQGrZOGbVUar\nUKuYQVuN8xZjPCUpwjTjSBQrss8QCxGH8h2+H+j6jrZraBpP13i6xuKUIkwLu8cd3729589/dcu3\nb/fEAJe952LVcnO14epyQ1dd2tqok6xblrtF1CmqHmhPo4QQRaUxh8gSAnMQ3n2pSrKSCyHFavAT\nTEcxpZIUFUrFqiir53mlBZR1Vpb/5rJTV0y1qNYknUnV/U2s3haltNBUVZJdjn4q9rreEP7yWCar\ndF68lvOsKJ9VLrl22qcxba2yKAV/+tOf8qc//TP+qvk4/DULuVLq01LK6/rDfwX4B/Xr/xH4b5VS\nf4KMVP4I+D+/7/f4o+cd1joaJ+AdrbXwoZWqZfu0HRawFkm227JTlGONqi++zJsEC5lC+giNKfN0\nYwSQdVr2pChpQtZJ8LK4tKQrmOeZ42GLtgq0JF8bq7BRWCqFahypz1dZ7v5NY88b91K1oDGmenev\nH6RKnzupWtCggpIbxElKlwKHQ2YKGe06dMyMhy3T8Yi6e8T1DReXlyht6LuBkhPH7Z71ZsMcI7v9\nEaMLz64vaf0N4/HA1eVzpuPMvGTu7g8M6xXDZccvv37Ns2cX3NwMdOs1r797S+caUJJZGXJguFiT\n0by/3XN3d88f/uiHvPz0BYfxSFCe/d0jZb8jLe9QL0vFAlh01iinsdaLHEtbSoqExpGKFzlh0mRT\n6PPCMs24doUqhmVZ0HrNMi0UDL7pGKcRaz3WauI8kUtmtR6YSiEcjhiLzJPHKK5DA6UEkfF5z35a\nZL6aMr2xLEG4KVqDKdeoFHj97a/wqzUvPvlMCmTJxPkoS/W8oDDMSySDzN5TwmjPfv8IRKZpEQWS\ntVysV4Ql0TiNc4b97oGUIER4dnPDtEw83D9yebkSt6/KHB5uaX3LauhJRRFjxNg61snQOce0HIje\nMkdRZR3HkaZpSSmglIQ2nKR7OSdRhBTNdIzYJuObgtFZsLHG1hOk+Dis8eQgYz2TEsp22LbH+Q7X\ntrSNpW8tvdNYVVjGkQ8f7vj62zv+4ptbvr07kCOsneW6tdysOjbrntXQ0bctrobAKORGU5QiFYRh\nUhf8y7IImygthBiZlyCntIrhyLqq1VRGZwdW5HyUIqPJ2p1/PL4odZelVHWNntyc1GL70QhE5teV\ngV/HpKpq043SIiu0pRIXK5ZDn0JmxMGga9dulEEVJao2NCrXGyr173Tqusv3sV6krP+dv/UT/s7f\n+gmnFKf/5r//H/56hVwp9feBfwF4ppT6BviPgH9RKfXP1tfil8C/LX+f8lOl1H8H/BSIwL9TPj4j\nfPQINQopFYkzUlo9vXBK1QiqmqCu5Q5GEWekygWVMsmcrPQyVz+5qrQWtRVKkZUjx0hcpHgaJzPT\nGKgvVuW0KCsyN+VQyouZIAkrWKVYu7KTdPHpuKPQFJUgW5SqAa/1jYipgvfrTD/FUv+dum6uFcZZ\nUd6ciHy50LcrGj8wHkbyAsYFugHCMlKWwP3796A18fKGzWbF5fUl9/dbGt9wdbFmOh5QufDi+oY7\nlVjiwsvPXpCz4f379xx3O1EMqcwvfvk1OX+J7z3ri2uMFgPOGN6QKSw5EcLIxarjcnhBDDvu3iV8\n1/P86gabM9r0aDKPY+L4/g5yYr3qZBnW9sKhdk5OJU5j6eUSKqCcZb8F4zxDvxalUvAsKWNsAgxT\nSII6UJHxeIRUcK4lpYW4BMbjnrxE+tYwJ42LhiWBtlU2ag1JJfZLxqTM4Cz7Y6DxiufPNpQyMk6F\nl598Kks0bfDeMy9HDAlvrYRiLAvjNGGdJ+wPpJAxjWMYeqZxx2bTE8OMs564LCzLnovNmhQCi9Ho\ntsH6nt12B0VxdbGmbz3JGt68fcXVesVq6MllJs6JnDUKx2Zzyf39PdoUWiOmHTKMS6HxLRorjmJL\n5f1ATIUUk3SzS2S3H2mCwuhGxkrZoFP9PBqFMUKjzNaTUsKXgu/WWN+CdjjX0nqNUxliZH+ceP/h\ngV+9+sBXv7rn7YeJFAubzvFs3fDiesXN9cBm1dI1EkqOKmfzDkjTFYp89qXDTizLTFomchqJIRKC\nSCGnKPAFrASOWCO+gpQ5X2/nsYZWHxXyU13gvJk8K0lqp16qkUghRFXOCp/TKdtSosIqkTDnSJWy\nVjqkqvWHjFHSQJb6fV20pJOh0YVayEUemmqXfcYDlMJTbZeZuPr4OX9Fnf4nUa38a9/z7b/3Vzz/\n7wJ/9x/3+4YkJhttDE1d+p3Qu7rmBMr+UFW5mIDjJZgkkWLd5mLkTl9TvJ82DXI80kbhXUNpM2Ep\ntat35BwYj7PIyYpmSYVU5ELY7houNisurtZssiSWNDnTtF1VZ4jNFpC7sNaUFETGdHoDdLWFn7Sn\nRZyhOYmqpZRE1uXJlKIkV1I4xQmUwrUN68sb0Jpp/0Ag0HhJ1Rmnice7D6i8sFqtWA0dh+Meay3D\neuA4jdijJSTDssDbdx/YXFxyfXPBet1ze/tAOzQoo3l43NIvHcZqXn7ykraxWHXD29fvCEGx2Txn\nWvbokonTTM6O+4dbcvzAauhYDx3aWh4fj3SmYQkHdtsD4/HAZtiwGla4VBfRzqFMK7FbVd+/2Tio\nn4dUFFgP5NoZGVZtYf8YiEGSeEwLx+OeMI8c90eWIPPGFCZUiHhdcanecFwWUkpcDwNpv2NJgSWM\nONNhUuT4cE+jC93mghQP2GZD28hozhmDypEcR2KcKHlhPGzZXF7TdT27uCfEkRgLzlpCWFAZSolQ\noG86Drs943jAGE9YMjFPrNZ9/ZyIqWq/28sysO3EfKYTbeNY5nwar+K7lscPW+Ky0HUtOgm6wWoP\nRjEvC4Nrhd1NFoVELozjke3xyP1+ZB0NrdN4o/AuopOlsNSZsqojyyRp9cZiPZJepBVWzRAVx2lm\nmgMfPuz5+rs7vnmz5f3dWDtxz1XX8vKi59MXa148u+DyohcJqcp1nCkn5FJEypnCifSXmJeReTwS\nwygjx1iIEaYpMEZVTYKNnFxVpMRquFF1+ViNOydg3ak8U/+bqupM6tRTFy7LxaoVP3Xi1YyolWCL\nqS5vqyT31+jakRshoppTR15NScXIKMZgyFpuGrqomtl5WrLK3/BjaFetofL3L+VcyE8qmd/2+N0l\nBEVxn1lXCBlcBOuk8Qa5wPNpYZjLeUSilLB+RVYp7N8SIVuNs08zdop6Oh45i8mOnIUFbE2m8RaS\nIyqquSgSMsxz4jAvEKKoFBbRxpY0okJHsq0kk9dxiXGSyKIrYrfUGw1ULG5J57m+MfLhVSUQUmSZ\nAzkdzx9uqzVN1xBSlJtMmim6sNoMlFLY7yfuH7d4C8+f33CcdoyHR1SK2LZHoViWhfV6A6rw6tV3\n+KZhtR7IWWLnhr6nH9a8v30kzAv/H3Pv2SNHtqRpPke6CJGKLLJEXzGLBQYD7AIL7P7/fzGY3enG\ndPcVVUUyZUS4OHI/2PEg73Tf+VodAFFFkR6RkeF2zF57xf3+SN/3nF6/8PBwR8kXQukwvePDjz/w\n+HTmdAncHG/48vkX3l5PHA6V9x/eY7Tj9HrmLz8/cjweyQXmpZCS8OdtMizLG9N8oRstt8c7Bu3Q\nutJ1A9p2pLxStPw8c834bkcqis5WjHXUrHl7fcY1j/m4BNYYWC4nTi8XYlwZ9j0vpxmFYfCAt2hl\ncLZinSZkzet54nbUvJWM1rl52Ws6Z8lxpfMeYw2oxDy9QNGgxa5VlUrnHTGcOe4cqoo7pu86lrCw\nLhHdyy7FOcfr6xPeys8y1cqw2xNCQlUJo1imlVIyzouQ53A4oGpmupzpe0PNmZgiSnUs08zb+cIw\nDHTaEsIJt+8xvScWsF5gxDBL96YaTa6WxLqcWM8n5inyeq6c50DM8L7R3mysKCP0Re1EZWt0xmgh\nGZQYoCZSDixZ4L6Xy4Xnt5mfP038/HniNGdq1uyc4X7X8d1h4LubAx/ub7m/2TMOPcZKK1xVE9Wp\ntsOqMr2mlMhpZp0vTJc3YaIoiLEyxcocoGrJplVtY1lo+yVdmkX0xjOk2cq0Alg3WmOlNB7337gd\ntmF/+3pp/kT6v0GxRjtU25ElbahZ2HQWI3YiDcbV7XvTWgt2jsYqS24Yua66FfJGdvuGd/7t46oY\n376HRqr4d12/2uO3C1+ugjPrmHGu0LlN8STFWemv8ndZmgkEU/RG+WntO5mUFnLWlCKhEVqJd3LX\naWgJJNZ58S5pvHRdEm6TvVaNKWLc1RvQOtOpBYuG7KmpJy4VlQLaXEBZlPM466ilQ6meirsq/xRf\nmSwpRbKuKCWKxZKyLHBjoOZAWGXBarSWUGhlZErRBqccy7qypojtLOPNgadfzyxr4Pn5hXHnsUYz\n7nrWBOMgXfnlcmHoOx4e7nl5fuH08srx5oZcCn/56yPnaWXYH3n33e+oacX3nnfjd+QYcVoCAEqu\nON/hrOHnv/xMeI3cHo8o0/HzL498efpnfvrxAzfHHeNh5PX5lVoT1ddm8FTpux0lraSS6Sus84m4\nzCjd4f0b2jqKts3IyVKVLBIBjO1Ic2CZJ1K+UJm5zCem00X43PPM0ENJlvMpoEql9w7vO9ZaGDqH\nrgVDZXmeSctKzSsHZxmMIawrpbOUbNjf3qMNlHDhskx0vqPWivEKXS1UCW7W2mGHDt8dhAoXovjl\nj7BMb+zGgbhO3B5HSt24zobLdKJUGA97qtLY3uKUJ6WCM5KUg0pYrwjrBAX6rqdiOe53rCGwzhNW\nFdK8YtA474UNYxxUw2HspFCqlnyTpdufpzPrNFFL5ZIL89PCy1q4OycGb4UrPjq63tJ1hs5BobIW\nSKUQk+Z8jkxT5G1OPJ0Dz6eVt0ukJoVXln2vud85vr/Z8eFu4O5uYBjF437DMramqhTF2oLN1xgI\nIRHWmWU6czm/sMwXUqmk2pKFkqHoDm9loSlED3ECVNpcHQEFIvnGtbRKnJsqrZEtUFW8wjDyb9VX\nJhsbU2WDd6Wgi02H7MeckYg6NK2oW2qkUR7N1XlVAuBlUWrQFF0atNLsIzYadd3iGLZirtrEoK6T\nw2YPAGDUf8BCXlCo0rI7m2E7VfAqoSMl4NuFg2Da2sipjqKlnxuBS6AZzNSW3QnFGqEJImIIOnEg\njCBb41JQMVJUIhVFLppSZQQMOaGWCWu0SJgnjXVyICht0c7R9QPO9aQ8oLVHG4dujmySAiLpIUrr\nr68BmqWTjL52C7AtCVKmKEVRCmW7tiBJ1PSVW6uNZp0T0yQiEudFVOH6HmMN+/3Iy8uFuRYOh5E/\n/vEfmKaZ17c3tNb0XeV8mnh7jqh0pJTI2/zK4bBnHAZO88plTThjOe4k7uzDjz+Q1pkQA7fHkZv9\njqenT1zenpinM3NIaKXpvaXrOzCOvAYeH08c9x2lGJZVcTqdUAZ2fY8PJ/F6t3usH9p72gGWlNpB\nXSNGB4xRzFNF255xlziHAAWmKXE6zQJZxIDxnjUExv2ItRKcvby8oClYo5iy3PBryjAVemNJ5ZnU\nGR4Q6bo2FqxinRe6rCUgPEM33IrgxjhyNUJZzRFnNVTNqgo5z3hvWNeFUjLGiWw8F+mQwzKjtJMJ\nzWm6QUOOTaad0SWy7xzOjWjXE2IBIpQF70Tk9vHH70g5oFZFihDjhO9HtNGkVWiV3hRCCFwuZ15P\nZ1JYuOs8WSme5pVPb5GnU6S3RqT3o2I3WHov6tpN6JZK4bRmXi6Jy1Q5LZk1Qo4FqxS3neemt7w/\n9ny82/Hh4cjt7Y7DfhD4x2pyRei/SpMqkBMli5hunWemdWaazpxfnwnTW1tkamIRv/qqLNqaq5gm\nZwnIEOhbaoRwr7/iypV8hThzba6FBUoN1/oi73nhKsreFqOIDYdq3bhWjYnmDPHanWusdqKg/caO\ndwu7lkIu3k0aIxbCG2tFl6vuRH3TjH/bbP/Py1cxQPu3fPRvH79ZIddaYavGqlZ0EeXTZpgjr7m2\nN1ijm0hHN+y7VITupgtaS8bmdlqjjNARSxFMvcrRJwGvmzln45PHQgwFpTLOaQSeFtJSXAuXfCEu\nAe80Xe8FJ+96vIISLVkZihaXOF0rOn+V4cYU2vciCT4RgVuUgpKTeAvQ/J9rodZFMMOYiFYCLzAS\ncVdiIYTEFCOvlzNj0Ly/Gyg58enTL9RcOdwc+MN/+gM3Nw98/vyFp+cLioFhf492O5bphEExfHT0\nfYfzPaVont9mvnx+5k/LI7UqDvuef/jxA9M6E1JhXcWOd7/zjENH5yzfvz9yOV34/PyGtYnX05lF\nwV7tSCGxrIEc4L//9V/5/vt77m56lIbODkIlQO5YAAAgAElEQVTnSpWaYDxmvFPik5ILFZEsT8sL\n4zjQGc/lciHNkXA5c3p9pcYspli5Yr1jjQvKGnJK7Iee293AWjKPr0/EEFGm4GyiSwrtHHG9cDsO\n9DrTaUNZZkh7MScyhefXJ8gSgG1tR66al9MTu8Mdu75jml8JQeAYawpvz6/EecLrjqQlHs576ai1\nNvS9p2ZhiYQcietCjoZxN7KZLRmlSDkxLxemmrh9+Il+GJnXgHWOlBLjYUctiXQ5URCPoNN5wnUB\n5y2u8yh6YkrUmFCl8HZZoSgeRo+3ir2pfJkjzyHzsmReKvCcsF5j3QbLiHAupcqapDMuRczFjFbs\njOJu8Hy373h/N/Luds/7+1vev3vgeDzQ9R5tJP1J6Q6MIVdDzeJyWEtknicup1fW9cI0XZgvb+Qc\ncNqhtcM12X6uLTRCCU1PN1MtCij1Nf4sNQqfqpWs2uKwfI1kK1mUumULe2heTVeYpU3/Uiy3WEd9\nhT3JrgVoaLBO/NSbEZlWsnytG8SqN7tcfaWvbtdlqz3URjhQ/7ZAfwMLf/vr63X/7eM3K+SdtXhl\n6LuOcQvrbZ4Hia0AtyJvtGT3tSWDbmowRYYqqq5aJfD0a6ZnbfFKgpeqxucW8yuLth3GJbTLaJcw\nSbK6jW6LiCJKs5AKIc4inEgiGbZWHA1FNi4nra5cPa0B4Q8XWVZoFZtjXjOrb12BbZNDDoFa5Fcu\nEgxrs+DosVa0G9FO8e7dnv2gePII5JA0ne/4cPtASoHzEvnXP3/h7t07DjcHclh5efmMungKms5q\nvO8JZSaWRN8bLi8nlFq5vXEYFqbLjCuW08sLVRdcJxFgWlteX85czjPKgO06jje3fDjcUkvgdyh+\n/fkL8+nMzXFkuOupaO5uP4oqsPeA0BONFeiEYriskbfpiRQyWonlp/cO5SDMHedTIYZACZW8avb9\nkeQWQlioSaOd4vZux3xaBds0mpfzC+MwcPCaFU3KkDK8vx3JVXMqhdOUyD6hbMVPlflkUWWl1sp+\nf6TzA+dlpuQzFU1xA5flif60MO4G9sOOEoV/D5l+7DCmMk8XhmFHTIF5vXDYH/DOMIeFzjt5/02V\nVCHl8L1FCB0J03cssyfXQM4zZY1437PMUgSWeW6MGCAlCUpWTmLZVBVZeIlQNamszMuZabrgFdx0\nmsPe8u7ouD8vPM2VT5fE8xRYsmKdC3USHPYaslKaoAYYrWboHKODu8Hw07sDH273HMeO/WHP7njg\ncHNk2O3pfCc4ePN5Cbm0JKhIrokYF6bzK9P5jRIWTK1YpchVU5TYHBjrAYeqlsQWo/y3SslSleww\nEEbyV9bH5kKoBe6jIhiL+BIILXNjl5XmM75BuZt4yIDRZDRJKYmY04aiDdhE0Y2pYgTqTXoz1vrm\ntSrVXlhmU3236tD+095c6eO/CooatCPFWw5XvYUi/J3Hb1bIR+fEV8JZxt7Re4txwiWn0fgrDWZJ\nQqUqLShZmCIG8ThRYDQq67ZcBG3KVVlZUNdIN5SiZKEF5qqo2qFtwnlLTlbsPo3BW0PJmZBkQVOy\ncNtLhpQLMSXBuWuCHEVlWipKW1KVHzhKuvLUGCi1pWIrVb56LRuDbgpAbTSKHmvFa4EK67qgcyHP\nExLYLL7WrhtYlsjr68KXx1d2+4F37+44Hm4w1pFWoYLJDeE4nSdq0SxVgcqcz6/c3Nwy7EXwJM5x\nlb6rhKUnxMq8TFir8VYzdoZ5XilVc36dWNbAskZubl5xrrLbW/a7Pe/fv+ey7Mk5sYTIdLmgMKSS\n+fz4jCaz33f0vRyutX3IxbtEzJu01QJBJFHp5izeOSkHKpklRy5vF4w2dLajhEJOsKTC4DpSjnRe\nFKjrKhhyKTOd0dyOXkRoWVGzNBE1w7IshMsEqdAfdqwh8/L2xJpXduPAOO5YYsBZzX7o6HvHPL8S\nlgmlxKHQGsf59EytDmN6CpmbvcOohC6J475jWWdiLOzGvXRjIvllnRasgWU6kXLEdZ6SUvusKHTp\nUEk8Z1IM9N7z+vaGUZIkL46hO2KQ6UmpQo6BdXojhQWvK+NgudnvsJ3leDPyYUn8OBU+vy08TYHX\nJbIU8SfKyNRolWbnDaPVHDvPYbDsRsd+dNzf7bi72TP0I/040vkBN+zwvsM4j1JGOOIxkYroM8Ky\nkPPKNJ85vb1QUkSjiAmWIAOqcTIFVWtJWRKTmqjkqsmoTU+SVSuBql5tq68+KXWDar/6lW/0ZIWw\nVjYeOaoFMAPXQ4DSAOzmjSIfHKoykNshpTS4ZhyynXjN4laxORWqv+GFsyUvIflRdRMjaSOOjU0X\ng1LNafWbrvw/Ykc+eEvfeQav2fWe3nuqqeJLXuVtqLVxvXVtTBURDUiKtrwxpQpNbaMdbksMpSop\nSyJHKQW8QVmL5PW15SnCMzXaofQivE5x1ccbgzOFWiPi1AFrzJQ5seSFWBTjLuO7VXBq63G2b8KX\n2hYzDkylKDHDL6WgySidSSEQUyYqIaaXlGXSMJLTaIxDaYvSchPEVYyf1hCISfDiUIMEWKTC4+cv\n/PrLJ4Zhx+E4oqzF2p7vv/+eu7t3/PL5E7/++pnz64nj8cDr2wtFJ7RRfHh3z+Gw43aJfHl85tPn\nR+4e3rfQV4maGseOaVoYBou1sB87hl4sUy2Gx08vTP2JYed5d/+AM44Ub3h9eZFg69FzOZ95eTk1\nVWJmPw6MY09sIhtdkWxVrTBBUdxMVbCsZ5zxVBSfPz9xc7dnN3T8/NdHnOuZwyt39x0GiKtiTZkS\nAoOTg9Uphd8diKlgO4HdrHVoNCGs6FK4XEQerjp4fXsmrhFlKj+8/8Aag3jCO4M1kOLKOi0cxj21\nBF5fniXwQGdqThhj2O2P9H1HDCsxBU5v5xYmbrBGntfUTGf2FN8xXZ5Zw5ned8S40rsB7Tq6wx1a\nj5RamZcLz09P5JxwvaNWRYwRP3akKDTLaVqhNHbPHJiiLLN7bxl2Pd2u48YawhK4WwPfPfRMa+Uy\nr7JcDCJ911bhjGccPGPn2HuLHzqGsadzVhSew4DrBmwzdnP9IJOWkiVkCpkQMusyM88TNQdSWLm8\nPRMW0QPEVMlFMHNnvTRcmPbnhVrtlQJ+peo1aEJXWXAWoDZGSnPD+Fq0W/3U1w69Nom+7Nj4psmV\nYr5J5BXk7WCQv9O5YDZj840iXXSbWhTbZlUOjXydbq7hEAUEExJ2USlNKIRCf5NLLFCSkB62Aq4b\njPP3Hr9ZIffWiqqzk/HSOYFNFJpSW3BqbRhTszcVBWem5IUckW65iFuJQWPaQczGIG3TiEYEPLrU\nxgv1aNM2wxlSTFjTUYxEMtUCymq89xSjxUdYIe58Wnjl0yWQE3gf6LzHe0dya2MOGIztMNYKn1xr\ntFWEkMkpkEu8Ktg0GWdo4RKRNURiKTjjsEa451o5HJU1ClOAUuit4ru7kRg8aV0hJQ69x6jM6fkz\nqWimJfH0+Qs37x64uTvy0/fvSA9H0hqw1mCV48vnR8pUeBzfmJeFZV45nU443/PDD98x7I6Uqvj0\n6RO7nWbw4q18ngtPj2+8e3/H0DuG/oZUIjfHO+bzzKfLZ1RN9L3c8GuujG7gLWgqwib59fMLt/uO\nm5uRw04SX748vtH5HuNdE2ApQjBcgsR7/eGnDxTg6eWF+7s93lh6G1nWmTUl+m4gIeEJhYQyjv2u\n4DvLp8cL07LSOQclM69rE9J4XqeVvipS+RVnxDr3d7/7A84pzmuiKivNQVpIOXE47AFYQ8L6SskL\nhkq/6/GDo9bAEmV5OvgOVRExE46YhLd8eXvhlD/TdQMpw7JWhr6jt+Jtb62wJKz3LCFi3IAbBmIK\nV/c8lJLCP3SEVWwfpsuF83nmy+MzCnEEXXMi1cTO7unGkW6n6deFfQxNZaglu3VZiTljnMU5j+s8\nzhi867BG0Q89zntoBlLaOIzrMK7HuF468SyUwhgX1jCxzsKlX9ZFDsE1kuPX8lxywhpJGNJWbBRS\nhlAA00Q7aLF0UFshl673yt7Tcj+XWklU6aBLE3BU4YaU+u9QsTcxjtKSMKS+YbIos6HZ7Voaikwr\nwl9v0M7mW440kmKu1/D55uYo6T7qKse/4uKVK/6ttRUaZNsJamNaAWtQi/4PCK303tN5S985vLeN\nHQIUsChirc20xqKdcMS3Ql2iBJnGHK+qMBRNZqwFs3agjcI2+uEmFpL/KGKUk9Z6h8ueXAZKqRRW\nNFtQX5OYI5J+rS2pVuYQuEwrp/MKVUydxt6zG2RBqY0WVsuwo+s8palVc8rkWljmmRQjNSV0DmAk\nUzSnjMqJHsjLylqFOVAQ29EYKxihWMaUUKbSDwY9eOJSKHlFYemMQ6uI7irz6wvPT8+Mg8ikb252\nkrbuR9CJjz/c0w091wk2Q/Gem12PM5rp9EoqcH9/x+vzC/O6YKxB28L9uxHvC65zoDpUTCjX0Rkr\nLIUsiUgpa6zKuF7z+5/uJDIvF5ZLT1pPhHXFGkssCd/vcF1P0Va410gCUnffYbQlLGfIldv9gZfX\nibflFe80Wo8YrzhdJnIu9N2Ac5bzvAo27Qx9b5mmhd4PTPMZYwTGO80rfZJZvUTFYdfhe09E8fR2\n4XRZ6IaRtK4oL6yTt7cXjJaEe40lFUeoGVs9HsO6BkwueO9ZwopWlf1+QBlRTmpV8V3HON6yzgu1\nRnbDrjV7Dm1F66CVKDWdFX8hdxE7h4gUDWt0+4xARdg2eZ0pUUKdByOJUxTx93fW4KxDG4d37krZ\nE+cIkZHHGkVRrYxMmr7DGY/RiH+QcVQMSrVu2YjVRYqQ8sKyLCzLzDSfmOeJdV1Y1pXT24WcJL3H\nG491Wl6bVWjnsd5RraZkJe6hGKj6SnzIpVw7b7Ho2GxjNzsCUFW6s62fq61LFhrzVnTlvUApitJI\n7IRcs8n8pMBfr0/ze2qPkgW1b9a7G6qyFemNekzbs9WSrtFtlW1KUFc2i1aGrBVap9aVG7TJ6CJs\nHxqL5n/1+O2glb5rRVyUmU7LYqLUZj6uVMvITBglXYG10o0m0pV/WWNqb6J4gZcWpaSVawvJlsBj\n9XWRoZBTesO5jVV4b6nJE2tC5UKMCbJuQRalGegoHIVMJJPFbCgnUoASV1JwMmJ6i+0jxhl0S7Sp\nyhGTpH6ENRLiSikRkwKdUFNxVlGyJiXxQo/ryrJEWcxah7aK0+WCIEUGayumijkUyjKVxFoyBlmC\n7g6Wxc+sjye+fDlxc+i5uXng/vYAJFKFZV55fLzw8YfvuH+4w7zv+fLlE//9n/8Hx+dn7m5vuX+4\nY7f3uO6Bf/rHP/PpT594/90t+6Fnvkz8+umZYXeL0x1//vOf+eHjPfud5fbuhloU6zLT7wzzNIua\nryZyqbj9gWoH/vrnP3FfHQ93N3hn0ApCXMlladBL4PHXF6z2HG8HUq2c5sgSI77rSWlht7fM58DN\ncU/IK6+vE5cp0HWeOS4sKZBybmwdz+UlMfieeT5TjBKONgGjwfs9D3e3pDARpkhnPd6AKpHp9ZWf\nPz9xuN0x7kacGRiHHbe3khBPhfPbSfw3aub58ZGh33Nze8M0TeS44JzHdr6pkXf03lHTM8pJ4IrS\njq4Xmis1kLK60lWPhyNhmnm+zDjXfMWVExuAeeZyOpFDpOZK7xyvU8AqofPmLDmxBREEeT82iEFL\ngdFakoJU63mrTL3W9ThjUcagTTOwK1KEchbqZ4yJmGemZeJ0uvD29sY8ncXpsySs0qgkbp5VFXCm\nFVrxaRcxX2OtFQCLVlY0JDULUiGbqgZ31FY4G9OmKSblhbc/r1/xdDms8tcw5VakFU1JXmA7HDb2\nyrei+CtaA+RSxEJkW6IKXQ7xkaExYzK5JHIR6w15v+QiudJQBFF9ZyQwxRQJxQDxnim6okw7nEql\n6n8zT1wfv1kh3+8HnHUiujDC3y6VljJdGlZcm/BH+EaaLUJNwhSgoDuoWbI+BR9LQj3MkJLBGMlX\nVKZhU2qThht01s23WLxQnHOo6kmxUmIU+lI1KF3l4s3b2GuLcZYYKiEbchB+6+W8Qg04J5zccJqv\nFEJnxctFqFOJzmSoK7UGStCsIcuHoBl6aQxgqCozT5OY3xtL50aME/pTbiycpRS09QxelmE1FqY5\n8vj0yjg6bnYjh6EjpMivf33l5SnixwFlNB8+fs9tD88vbw3nXHCj56ff/5H/9l//kf/6//6J3//u\nJ3766T3fPdzx3cOBZZr49Otn3oYdvXMcD7eE+YLyiQ8PPc9PX/jzXwJrTIzeceg9H3+4EyMyinRW\nIMvotHJz3FFK4fW8UEtiuVxYYmDoRx6/zOJLj8L0ilgcIS/sDwP9IO6CVvcs00otgTVMTGFBGc3u\nbsflsnK6ZEIM6N5idMVoWEphnVeUceS6oHThu/fvyYjDZooLVFk2LdNErRV/OPB2eWPciWgqzBOq\nU1zSyjyvdJ1FAWFZ6EbZlWwxdMYqbm72qBI5zzNhDVjjmNIrOQRKWdn1AzFXjJdlu6oFo2SyTEli\n7VIKWKc53ByJIRDDgnGFtEzEdaGUSi4Q1hWqLD2Tc0wxM00LhzXQj83kyTgRvdDcQam4ZgTF5hlS\nawtnkC5ZlcbUQjjsMWZClMCHUiprWFgvZ/I6iRd6kbVe7z12GCjNYzuExJoSNUuYQyaTKeRqSbV1\nyqYtB+tGGN4aMGGoNI9iwbObj4swQaQL3xaZm+Dn6uatNuRbfQ2e0KJroW5Ekm/9EWWKvy5N2/tS\nm0c5TccikZoSDsG1iJe2cNUIclMQ7/RGomnPRbOSoopjaN0Mzq9QcUEYHP/+47djrYwj1lqUQRYE\nqZByITWyPjU3PFsJC6VUYvOTVsq1wpbFv4AoGY8a4ZhnYa3UXMUKgCwp9i05RD7EBut8y8tD/s4a\nwKHIpCrskc1VzzhReW0QSVJCYVI6k1QhJrEdyBWUadTFdcUEhfcepUfQmpqzxMhZdU3RzjmzLIGY\nCiWJja7ZZPsKkoLUMN01TZKuYiT663gY6bzB6A6tC2Pv0RWWsOA91CSGZP0wYEJgXgJvT6/weiYV\n+Ouvr7x7OIpSMLf0GS044E/f3/Pu4ZZSYTpdWA979oeR3//Dd7w+d6SMfE81glZ8fn5hGDuJgNt7\naq3EJZJz5c+/PmIVdNYAgeNxz7Dz7PYWpe5JEU6XRYqrV+x7qDmjHSinyFkR7MiXT6IOvdt3OFdw\nxnA5z1zmMwZHygVvHQVFyoXTdEE7zb5z9INlvZyxZWWwlpwVcYlo4yX7UxusCsQcWKPDayXh1G4A\nU1nSxM3DURKqjCKsQh11nWIcNMYjhlt2EP7/rpfAbRWRIOTEOp/xfQd0GKVlYpgzKThizvhuj+k6\nal6lA1UaqxzdruMyzWye98NulMni8QzNVdMY6DvPnFZyntG6MvY9tcKaKufLyl2I6ArWiTe4NiJP\n01bLLkcZShHYwGpLpTT//EhqRUmjKCWyhoWYCiFlalWsIbKus0QkOtviy8R/xDqPMVruxSL3uSmV\nmAtLiFQixjowQunDfMWEN0xcivb2/5txHVIE9beQRW5lurSxojV5172ZakVHtXi20jBy+Zr/2fdE\nijZXiEaesv2bWuRQ4Wv4Q9ksaqFdUw6mCjKFtRDlYlSDgtS1SRTEpvHIkQts4UTX5/x3Hr8dj7zz\nYp5jNmpeoTYlZG4RbrVIEUBpudEEFWu+wAZlFTXLAiEVwcUEw9MyiiDhzCopckqolkZkmjOZRHMV\nsspUsvA3WxqQthqqRJA5J4uY7WbPpWJTJKWE20ILimQ8ipJNMO+SK0uIpBSxNlCpTNMs3OO20bbK\nCoaPYd9potcSgRaEath1joNzVCWLtbfzRAorXhV0jJQJCiOH+046P11BJbrq6fcjYQ7QcDrjOg6H\nUbo1q7i9v2FeAtM5cNztWdYJU8/oKorYGuUGLgXSapnPE+v5TN85Pnx8T4iFaVmpNdI9HNnf7Ikx\n45wj50LOK7vDHu06fn18QufK88uJfjDYEV4+vfCnf/0LYzdwPN7wepkpOfPhwwM3R0sOkfk08dfP\nT0yhYO0Jpwo//vCR0+srKSfcboexFu865suEM47TspCyERMxCkV5LnNmNwieO82FpDwhBYwtvLvt\nGL1hmk/sRytQihU1Mbmgncz7WhlxsquBNCfWJWGso/c9y3KhpIzqd5jO0I0OZ2CnenLS5Gzx1jH0\ngBbhT9YG11us7eisJ66BUiI1Xcipshv3WDeQq8Zgsb5nMF4Sp6yhpMDl7IkpUEl4b1nnmRxmSi4s\nYWX0FesHYqqEmAmrJGXVnFC+F5bJ1k5WEbNsYcCVSqlZYMAcCTHJIlRJjCFKDpVSKsu88PZyYl63\nhb8s+rUSs7slJgiV3kgUW2cNplbIiZiDMECsE8KDEZOqClzbaMVVWr9Z05ZrZW0ipg10qV+L7EbV\nLlWK5ba83ILV5dotcIN6hVS+smPapeAaynz9s7pZAHyjKN2U2lW66mqVeK5Uml+5cNTrRmlsqUDy\nOtsSV217Aa4FvdZW0P/O4zcr5NZ+9RMo0JzRRCGlCuSSxOSmQkXUWJgtqEHUkoVG1dG2bYUrOiuU\nFb8VwbSE+qeUkaWG3hJAKropqCR5PTUohVZktWQaqkrUGbSh6oxT4i9drcZksQ8tVTjsYCAKd3yN\nljVElhVyDjwtgZ0zGBKqZGKurKlgbaWrRrB/Ix+imDMgtq9LkWWX1jJBHI9CaZNltqeSWdcLr8+J\nbr9nd3PD0O2hRGo5se8PMqqjRVrthMqTI4Rc+PHH78kVzpcTh34kx9yWghf6w56Pdzuen15YzpFP\nnz+x2/Uoe0NJEJMwcYzR/PLXJ0Io3Nw/8HqeCeHCx/e33N7eQjU4p6hFM18S/+NP/8r58pk//vSO\n//v/+j+pFHwv/HUhYmTmcCGiObz7wA++pxTBt/u+I8XCss58/O574rIQ5jcJ3Og9znlud56XlzMD\nDus005wYVeXm2FOp/OWXnxn3A6OVFKGdl4Ij+gLLZcqE5cz+xuOsgpqpIWFHT99pTqcLZHAWtEs8\nPX1mviwcH25xLrPbjUzrhVRBV4Xte4xe+fz4zOidmEJVsWRNoRdr1FKEeRJWalzZH+7wfhRRju2J\nWaFNh/OadV2pufDy/EiKEVBSmLVw9lOWAuC0cLTJhc7QGCWZNSzo9dSgAflslZwkgafqdo0isEkI\ncuBtLJlaqa5DGyUKzgqJFVUjTmeyFv/gNWdOp4jSls4XqJIC5sZeJqZioCbQFe+t8Jicx/gepS1V\n16tVgIh3BCuu6HZft9jF7X4VZxU0FfRm+bGxVNS1GG4dMu33V/6hotmTyq5A1W2j1v66HR5fV59N\nYq++ydVUDc6Rsn8lVmhdZeppuZ1Wb4ET6orrlwYb1VqpLRxje17VBFH/C8+s366QK/3N6dmCjjVV\numGlMVl4lkYbsY41EqFmrCwTREWZZPSwhpgR/2ajcdpI1mcTBAEy1lnZ+Etyj75uka3RhCJioZJE\n3qtp4RFRnN9iKrjkUYPBdhbjHNiKKhmTtXiW64K1llIrxsoCx2ojTnYlyrjqR3TNmCqezzLKahlL\nsxSNEiXwtqRMSVCVx3iD9R2+U9h+Ry5VkoKI5BQwaGpKpGlizhnvOpTdE3XBDXvZBpvKZZ5RWsa8\nZZ1Ij88Mw8iwOxBSxvYK5yx754ixoLTn4d1HXswrl/Mr3vXU6ng7r8zLzOn8yP1xz/3NjteXiS+f\n/8ppEjOkwXV89/ABZy3zfEKpwv7dyMcf/g9yjEznN76c3vDO43JhcJbDOGDciJ0Vb+GF58fPxFq4\nv7+n8z0hJJz3fPzuPaiVy/yK6TSDO/LzL5/oxiOqZo77Pa+vb1jXcXQdYRUcO2Whc9WiGJrT4Ouc\nud/3dDazrCulKN4/vGPY9cSwUis47/HOU3LBey3y+NMbh6NYD+yPB7TWnF6+YEhoY7hMMzfHI/P0\nxt3ddwzdSIgz+34HKZNqaoo9ZBpFusSSEmkN+G7XEubBWi/Taq5Y02FdQBtP1w08PT7JZ1orckwt\nmq5gVMUPjpSKaCqyZgkr87KgjWoh4UmgIETSHnMhZem8wxyYQ2SNEe/6K+xgUsT1HSY7wbuTomIZ\nxh7jLWsoxJpwRrrMTst9FmMixUi2MoVSQRuNHywZRzEebV0Lhxb6nuYbjUgT6YjZldBYr0BHoS0q\nm7aEjUb4FT752sA3NkrDwzWbWVZ7nlK/Qr5VuOTtt6BkQbn1/7UdMhU5tOXfSDSkbmlBxrYUIb1Z\n4+rWiG4LUBFhbTGUtTGVaDB5u+jVvfHfe/xmhbwixbwmwb/Ltk6uws/U1qBaiomxVuANazFWU43w\nVEmpnb6lMYYKqSR0kRHRGit5mw1rVLYppeQ8b4ewwnWeLhciipBW+VA0MGy7Zmo8UO87nFKimtwi\n5UrFJJGz11SkkPuVuASMWVlXzbxq1pTEizwHwdMBg3wvSxDVWMgJbxVHVfFGqI1GS1hzTVBwjKNE\nn2EUsVRCyNiaUW3Jq7Qmq0wiUXPhfLng3MBud6Qfj1wuM8/PJ6zdkVVhDpHzvHI6XTjcPhCbH/Td\n3Q4LnN9eiWHG+J7/9k//yvsPH3HO8OmXL6RUiCHx/fs7/vN/+SOn80Spite3N05vL7ydn7HOEsmk\nUPjy9CsP744cjjv25oZ8NkznEzscS13JZcX6gfOSiMXiuh2qwKcvE7uD4cfvH3h7+oTVlTWBth3H\n3cDL20QqSGi30hhnOS8T3//wjpfXC88vFxYV6Lqe9/cHlhBRRZFDZBwUnascxj3ny8rtw4gzkRDk\nsAzrSsmKnFZ++fwJ6z1j3wn+XXbkkvGjY78/MnNClUrIi0AP3jIvJ2KYcFahjCeWRFxmQljQxjDs\n9ygtGHLNkeq25Z2SpKKcqWWh1EpI0O322K6j1MLb0xdO9sSyRnxX8E6Tfc88z2Kgdp5Be9ZQsKmw\nhszlsmCNppaAJZOHgjZWEnqWlSUGwf4/nZ4AACAASURBVK6X0BJ84JzPQkc0BmcypES6nLFK7jM7\nDMRoSPFMZyq7mx35RuLnUg5cLkJFXZbQ7iOHwmBdLzuvImwdbYxoH6tqGPs3+75G9dsyiP8mfGFb\nJkKDUASb37Drttq8QisArdazkV82LxOxyJV4Rcq2ZK3XJanavg6ZELZqrNpzqpbhabR47VgjKmtt\nTLML2Qr5lo9QSWqbFppHDQ0S2uCc5hnz9x6/WSGnapED56+njKTp1Gb0rpphjcZYMXfX2jYDfBEK\nGSpVJ7LSUCObXwLbyVwE4968gml4kySEc72OtY5hAFUTORtiSZTmjSILGhETlJoJKWGznOKCq6kG\ne4j1brWy2TbOkW3EWI+xK30nBlohReKq0cTmjpjAKrx14nEc5VBbiwLt8dVCzlitsLZCkU52mibx\nLekHjHOUqjGq4pyV/qE5Q9akWM8rl7JyPgXu392yO/RYBy8vZ+KacfuB/WFk3N3y+fMztUTGzvLl\nL5+Q0yxhncL5nt99/56nxyeyt7zfOcFWdSGFwM+/PnJ7dyvJLmvEG8vL8wnjBk7Lwq7fMYwH5nPk\nX/75HynAft9zvx+5G0aGcSCjeT7NOOUotnJZE93O8/HhBpUyp+cvWOtIuRJiYo2VZa3M0xsf3t/i\nTWWJsqD+3U+/5/HtwsvLIpOPUqSquSxCOVWuYz4LVj0OGus7dhS63rGGFVMLvbOs05miFUuYeHi4\nJVUY+p0Uz6HHV1HyZkBZR66FHFZc5wlhgVopMbDOM8NuRDuPOzrWp5Xz2xfyemJ3PJDyQNYe34/E\nKJmT271Qi6JURdeNLOtEzQtKQy4rd/c7WTzjZD9RitgkzAu7rpdlpFdYI7RUUKzNfqLqQEgnlJEO\ncV0i07Q01op8jqwFh2mTXIWSifNCQtNZR3YZazVTXAk54K2TsOk1kEolp9T44zJ9iNVEkeQoY8lY\nkbsbK50xCmVU66a/2s5uSk7JJOAqxPla6QFVWhcNzVnruqwsVybK5ifTwihMK5tVyBLaSOiDbu1w\naYrMzSNc0RwM66YDldcqanMt6UFWUpe0MtKRG9syCQQN2KZi1dp5s8EmVYqyQmCVohWqNrfGEv9u\nOf3tOvLtcGkfFjkNzXVcKldnMuSH0QxtSsO1jbZgctvqKpTurxvyUpPEiGnVlJFcRQXbhhpazJU2\nmxMmOkjgsVJrq9HN1lLJ/tQaBaWwzoGc5cbXVl6bJERptBd+rK0OYwe0X+jHjpIyMUZiEigkx0SJ\nqaUcyQ/aG/kR5pJlDDOi9FIlCeWrKV/lGpFaEnE6SQCDsxRnMcoT10pKkZQCy7TgLOyOI8ZF3l4+\n432PsZbb2wNdPzSx0YpSmXFQLFNGg0SnhcBpWrHFsZ6e8MZyexjpBi9ug4dbvLNMc+D0/MZ0mlDW\noF3Hl6dXUnzluBtRGkzv0GZgWlYOh5Hj4YCycHk98/T4ihstb+eZmg270XF/c+TD/ZE5yXJ3fzOi\ntOLl5cTT0zPdMHJ7d08OF27v76gUfvn0BFXzw8cD07JyngN2GNApYY1m6Hf8+usXcoXDrvL+/YHT\n+cK6zlh7Q9fvMNZwMw6ksLKswuOeLxP7mxusLfiuY/Ce3TAIxU5VLJqaZvreSRe3CoW05oKz0r07\nZwlpQcWJznnujvurtD7lhKmBWAzd4PDDALajGsmzVVWhwso6P6PKyvn8irEDaIcymaH3xJjRa6U3\nimINM5BSwmpLZzvp8insDjusoTVInqqM2EWkSAyJOWRyyvRdj20CIhCXkFwqNSfRPSglh3aQe3MJ\nM7lUUjbky0RIkX7o6V0vcvwg8GKuiZzFE8kaxxZwXDcf8HbPlbbQVFqaJr1RCdXGIecKR8iiUqCU\nTcjztaALc+/qrqJo9OWNMaIbzKJbjRFPJKgNbqlfuehtGarqBvFs15QiswWwSwMq4djWaqkrukFK\nrYEU8rAY7mXN1da2SNWRfaBS7bkqJYW/W09/Oz/ysi0JGgbVfARyjZKBybaMkK7dmMa9rN+OGOpa\n6I12kmyiDaWuwjbQsnAwWnwLijZtCSJ2nKaFM5eiyEoTnBe5sU/yXFmjt25gowGVRE6rhCKUjK4e\nVEszQqGUyP+NQRRg0ZKjCCFMDNgkfOeSsogGUqSkSAxrS1M3X60ICuI/oQ3GylLWeY9TPUbJ8imt\ni+CsxpCVZl4CukqcWiyal3lhmS/sTkFcCb0mmiDUSxTz+ivjfsdhv6fkSiyKpcC6REpcOPaam50j\nF7DK83aZCVnxvut59+57XNfT9YZuF8ifxf3QOsew3/H9x3tKXrm7vUHbkX/+l5/5lz/9f/zhjz9h\naubLp8/c3I4Mg2PXWQorD4dbrO/Jvud0ujRfk5Uvn195/+4B58Vn/B++/56kIqbA8wlu3n3gMgVK\nXbi973h6fuTm5p7//X/7kX/6x3+hsw5UJawLRmVu9z2DBa9Hzi9nstFYN4IKKKcJOUpyVK6UmEQH\nUBLLdCG/vZH2E33fozKcz6/EGFHG8/Bwx35/oO72aBSHcUfKgRDWRmX07IaeSiamBa1hN+zEy0N9\nddDc7R5Q2pNqIqaI1jLx2fZ1lsw8nXDescxLUzjDuiReJmkUjFb0nSVmzZIKu941dkdB+UEomtXi\njBbbgqDJVeFdYc0RYwrKQtGJsduhjSbEzLLIZ67kyhIDVVnWEERKXzUxZKxV7EaZsmrKRK2oTpMK\n1FQkFKJCLtKwCIm6UQ6rItfWzCTphEtjdlTdXE61JreItg0s/dqZNy52EcO6bxeHKMRlULwiW1dM\nswGQPxB2ilAGpZhv5lvbmVHYwBo5NFpxtkrgkwYBW+txtmsKc7Hn1ZrmpaJQTdwkhVz/TSGX1/S1\nI4dKzf8BvVbSBkxV1TL46t84fdUNMFJtWaGkkAlm9o0NZZUljWq0nVzFN0KZjbmk2xtTr0eeqL2y\nQCMtV08bMYIiD9LpFskSjDnKqGNqC7YQxo3vLb7r0dahMcJg0mCw7fQVnru1kL1Qk3zdkWIWL/Iq\nBks5rSLaCIGYVlyzxs1VkVMgxpmUJZsUFKnIUuyrokBRisYaw+AcY2fIFEqpjZd7YFkcyzTz9PhE\n7zT7/UA/FPresfeat8dHXj59oRsHjLN4XUmxcj4Fnr4svH+45f5uh7Oah/s7ahH8/fPjZ1Iy9Ls9\nfdcBA90oiTgsEzFXaqqUeGI8Zt69O/D7P/w/GGXIMfP582feXieOhwP3Dw/iALisXOaVl19/Ia6J\nsR/Y3e1593BP7xy5JqbTK1ZVsIV5Duxv96wpoEzmw8d3fPn8WZS6qvJP//TPzNPE7eGGp+cnqJV/\n+PEdqmasFp+Pru8Zho7L5YzWiYGRdV4oJRJKkQVdmbg57uiHkde3Z+bLRFgSSleONwd81/Nynpkv\ngZLeONzfEVPidL7IYjFlxq5DKc3bywvOGbSuuPEgOaVtRTR4izOS62q0J6wRRQKtsabDOEUMK8Z2\n6LSiSiGHRCbR9R1GF2wVnnbIRfJMm1qukNCmJ66RYVOOUlhjwhSDqhVT8lVsk0sir4qyZNZVmoqU\nEiEuMn0qg7ayBAZpuEKMbXp2xJJRYSXHQlEK6z0GjceJvL9aCpZUxO5WVem8SzOVokraVGn3rtIV\ncrN8bQRy1WANAcY3pWeDtK8TvnyNbji3CO0aKKLUV4hGAS1fU9XWubf6JIeU7MOqMg1ugapbGpAW\n3r3s8yQv1hjXyBVN92HclSMO7RBpr1e34r09vkIr276uUl3m7z1+O9ZKrVcf4Ma8ITfHsG3BsFGH\njHYYZZvxo/yAm0hXsE+2Yt7GrTZmqbYAzVVRcqYFTzV1ZSYXCQ8oSCE02tH3vQiScoMnsnDRXRFG\nSWctmkJJkaRXrFItaFXGJt28IeRENRgti4/SMHutLApPajimih7jAt2wLUIz5IKjkv5/5t7kR/Y2\ny+/6nGf4DTFl5h3eGrvLlnG7aYRkEGLDgg1sYQcbJATsWIBYYf8DFrBAiCUSC4wEwpKFxRJ7wYIF\nIJlJcuOx3WV3Vdf7vnfIISJ+wzMcFuf5Rd6qrnaDLas7pFTmzbwZGZnxi/Oc8z3fIQWci1SLCudm\n4VkqWgzTd87MxupaWUsyF7VgqrwxdgQ/snaBdYhMV0fQQh+8LViXgA+e4+lkNqPrSl1XghF86N4c\n+PxZ+fjpiWmZGcaOcRgZegtqHrzwePnMeZn4ei4s1XF32uGlo2jEjtJCXhMlFdQ5Pj+ezftl6CzA\n4kffZbfbcTycKALPz1fK4zPfP97Tx8C6TkbLS4mn85Xn58883N1zma9cvvnE23fvuVwXfDSLYQmB\nORWQyE9/9xMqgcPpgXldqc7z9uEEyQKPS1s2PTwMjF2gi8I6Z5bLzPV6temrtGvSO87niet1IUYb\nlUu1v//5fOHoA4f9nug7pmnmOk2WHoTj+fFK3wcOd0fmeaG2sIVcFqbrld04UDTj4oFuOKItEzaX\nTIiRtGZcTeQykfNKQVExlkj0Ay9PzxY8Lebbf/9mx7IkhqtnmQ0actLdEt+XJfH4MjH0ZmFMrRTn\nSDmzLitalF0ILEl5WTLnubCmMyLC6bDj/mFv36elvW5g1/dAJeXANK1c5gWcsKRMcJG+HwjRMGOj\n7AlrbulgSrNsbR7crZGzYXtLu9eGmXuqNkgV/wq11O310SAUbfTmG1d8Y6uAum1J2rrq1tW3/2Y/\nz4E0FapNBEB5/VhaJy4it4k/+GgMrNjhgyOGjhg6XLSvRReaCVg7bGhwzs3ca4N6anv8tiPYeIdV\n/0gWcoDaLB+lUZvM3EfqZkVZ0FC5WTkGw5BFwGm1pURecGIXxJb4ATQLTDVrXOyPfZudVG5PdGkC\npKoQnY1Fw7jDqaKakQrL5UJqBk4hecPNZLFJoiq+H+wCZAuI3pYy0miW4Kth9qVRqFoPgYsdor4x\nTgzXzpJs0eo6XLARsWrGO0UolFwpuaAlYL9WpaSKlkStGac9Eg36EYShH+hix67vOZ+fWMrKMEZE\nJuYpMS92qJ5OJwCu0wXvjCO/Xdp1TsYEiivUSs2OuL+nxoFvv/nIYX+gj57f/cnX7IfI/b4HJ3x6\nfKQ64QfuPcN4xMeBcXRomVivyo9/+2f0uyOnh4XTwz339295ePOW6fLMp0/fMowdXhyOyDB0hBhY\nlsWMrPoj6jqqU4auY3l+ZPCew3Hkwzcf+e5X3+Hu7ZFvv/3EstqCyXnaC98Ujn3sWK8ry1rRVAlD\nx/PLlbpmQm/++OfHFw4PB9M4eMA5lvlsU+GiJilPhdhHqoO+H8hrYD/sScuVPiaCVK7nJ7wPxizR\nzP6w4/r0jD/sicPIeLprmbUJQclpJXSdLcZUqaUSY4dWi3IbxiO1wG53x+fPE3WeqCXjxNwd+2Hk\n09mM5bpYqYtyOFno9HVKlCrs9l3zS6lMU6Kkla7zFrUoHTEoTmZ8CPR95P7hwOmwo+ZMKRmHJ+di\nHPHOYMHrXFmz4r2FX/sYLftSAt4Hlry0JDDrpp148GbCBRilD17FOTQ716Y4kQ0e0bI14F90ua2z\nlQ1KaWZTJrwwCGTr4DEjvC9Djm+59fL6uZvvyk0kZC2lw5lPTzB2XfBWuPuuw3WRGCJ96CB6om+L\nzkbW2JaEW9i8KYva7+DsYzvU1CYLtSnt97v94bFWxJlHrzY5rdtwLDsxXW6mVCQkrLgQ8cSWo9cw\nFnG4YFv0Ko0npc33V4WsFSkt+snb4mAz9BdvI5Y0CW5VJVUz3nF4QuwZxz2iHs2VvM5UNTMtcZbA\nEpq/RO/E3NEUNNhyw7bSRnI0NaBVb9f8xquWRkdy9gRqxbuICwO+tui69r21eW5otRzQuD3mbGns\nWiqQ0GR0w5SvsHp8HHDdYBcwgS4GTsfA+emJ6WXGSWYYI+M+cp0zj59emOeEiNL1gf1hx9u3B15e\nXrheE+ckSA48xD0P795QUuXhqDzsBnwQ4rhj/u4963xl1wnB9xzuDjyeJ77+5hnHE+/eHPjBD94g\nLvL0stAhdDtL0NktE3/v4yfSkoi9YxgNx1c15lJZFqbrC8f9jvhwRELgcpnxLvLbP/4ZZUmU/Mhw\nuiPVwHg48fnzC4JwPA5omkhTauNxQFxFnC3d1gw/+/iB42mPd5HrNdEVwZXKMAy4GFFRzi8Xht2B\n4+EOqcrj85MVwucr425HN3aW1Vkr1+ezUROHjnG/Y7lcWacLPvTsdjukGnVunSfKshLGN8Rub+Kk\ndSH6Hq3Z8PE20pdSDBtWZZk+Mc8rLijH+yNpTaQ1Uaupia8zKB3X6QUkkEumtg5yiEIqlZQ9Loxo\nWg2OzDAtZmzWBRjGPc7Z8vXudGDoAiUXZsrNdG6jFI70FFVSXug7pR96ow0741tPU2JeM9psWpEe\ndR7vO1T9TYUpYsEsNyKK6Gt6Dl9AExsPEW7LyPYdt/eveT2t220wCeJerUzg1iWzMVuUG5x5+/7G\nqhMqOGO9+bAtNw1SCTHgu0DoIl3oCLHDBU/0nbHk/CsWbuYu7hWo37zQ5XVa8AKK1UrZDqVfcvtD\nLOSN09kgA9/wcURNAu/FRsiayWkh+0j0PRJce6IrqtmgCt8SecS6BAt7NdP2KrYDtmJplCNbkLbt\nOG2J2ZaVZZPciwPfEXphOChpNUUequSMCXy0OZLNtjCNeLwvlGKEfqONmf+KOZ3ZwvbGcRcQFaqa\nAAIVRFteqRrm7z2ERqmsxQJ6tcEyTryFM0syXqpkajHP81qhLBkh4/uWe+iV2A3cDSPLPDNdXjhf\nz3i3oiXhWRmjBTyLK+TFWA9OPI7EMl3ZDZHLyxlxjvv7B46HAzlPPD+f+fR45c2bN5z296Q0kSRw\nzSvP18zd/QNeVz4/PrKkmX44siyZ+4cTQTNVV54+Xs3h8HjH/bs78mrQQ85rYwQo94eRmgved4jr\n6TvPOi/cH0ZeNHH66j1/8+/+hPfv7lnTmeASKc2URdGcuVyvuKr0vRAc3J1GPuSVtRQuy8rTR5Cw\n4/npzA/eHYkoc5ro9wOX88qyVJzLvNQZ5xyn+wfTBNTC/rinlMI0LQx94Hh/5Hw+c7lMLNNMbNS2\nkhaWyfxIxmGHE09Omen8hH8YkTAyXWZ2u55aLF/ScFJHcYr4nt2h5/PHr296h64fUeeJKbFOSsoz\naUmchshheODD09WKbDIudwgGd6RSiBS6aAdbjMESmZJlzhbN9PvIfjcSvWeeEpfrlaeXF3KFPg6s\nBbtum0pzN+wN/vOYcyiVVDKX68y8FHAR53p81+G7ANrKUNFXdpls68StXEhbUsrvKdhfKjS/UNBz\nA0q+OAQMpTBK8o194jZIpgVQvN71Fz+r3OBanPXkXgISPCEEQgh0XUcI5uMeYiSESBciLkaCCy3X\n8xUXt9/1535LDOoxzYqTTZ5vr/+ofwTph8458ApYh+28qaGkyYBrrpRcqZrQVchupvieECPRB1N3\nbs5lKF5MoFurUDUhjaLoqdRWLFW4jVKm4NXWvRtHVRCLcHJAaeNeANcPeBQJHiNLWSq7Cxbmi/iG\n4WNTktsojjY6bSoyi4AzNafz4UZf0k1irG3kb79Yra+celpHkLfQDdmioQrqzJeGUA2HzHZw5Wxq\nCofQ9x3F0SLWoOTK8XQHpwNe6m3pejkvfHx8wnnhcIqcTif66Jkv9vnn5xdKGcDb9PD+q+/i5Z6h\nO/PNN9/wN/7m3+L9+3f84IffY3848vYrCL/1d9mPB9595yterhd+8vd/h2F3JMTMT372CfnZB47H\nga++esu43/P4cuGbDz+DurLrOx4fn8zQygdeXp74tT/1T6LV8zf++l/n3bsH8nJlCIVf+cF3+frD\nZ/6JP/6ePnpqKixFeX46s+tHxn7Hp5cn9kPEp8C43xP9Dh8nPn79mcd15HevK6k+8WYwS2KXrjg8\nL88TMUTWVNjvrSsbhh68shsH+r4j59m4zlk5f/xITRO+H4zbPU1cSyHuRmjFdHfc8XD/gKuZ6+Uz\nOz2R15lpyvS7HXNeqaqEhl/HGKDYc5qzcnf/nuvlzLxkYhdJKhzf7cnnC9Nc8W6hLMqnaWJKK13o\nWGdTVhZxSGqL+96mhlgH5mui7wS/83x+rDydF+IgxG6lkFlT5jJdmZaV4+HIfrdjXay4r0slRIeL\nplQMwZkXS7HIt5ITilFk8R0qxviyF3FzKoQmktkw5FdeuDTrq9ebZ7Op3T7/GmQs1rRtH7etmsgr\n3dDJJgBqRbXV7HY2tg54811pjonS6o0LhJboZd14dyvcfexsCbwtgr0neE9w7hbQDNyK+iYyuh1G\nsmH89pi3s6TWP4IduThHaEKdjXXiGka1KbIs3k3xaovHUs3buFKQ4CyirZovQq2CYJQ9Ew/YCKWu\n4tuc5pwzW1leT2PZnqhiP6tK20y3ScE1j2b1HrPUtDHPzkxvmaGuUEqCLOTZ8kRdC2cVF2ycookG\nGle2qkE9wdkpXWpBSsU1D2Jt1jJ18zNWW650MZriTO2xlCKUJGbIn5Ohd85Rqpl/SWc4OihDGFAx\nT5HdfkS04JxhsckJ4iPHrud0f2CZZ5YlM19nJqDve374q99DajFRVjeS1HxLxi4wDANvvvuebr/j\n228+8NOf/oQf/eoPePvuO3z/Bz/km4/f8PnxE6jwgx/8kFIqfZ/5k3eDFcJux6rC4+MTp+OOu/2B\nNM2cz1eGcUcfA6Uk7n7lVyx6TRx/6tf/JOfnz4xxpCQLM9jvekpWfvq7H0CVw90eHwZKdWh54e27\nI7su4CNQlPP1hcu18GlWPiywZOHNbuCH9wPjEAjDwMvzE/OkzC7T91vE2sogI8511Kx8/Pw1435k\nzZDWyu50T0orqgkp4JpJ10AkjGawtq6Zy/WZtCw4H1mnF+zEH/G7ERd6s21WMMtju+5rscZguV5w\nLhNi4Xq+UIs9z9M0ISiX8wQiDH3PsZrWodTKUkBJHHcj93cntFq33A893jsu04KqUAqMvTmETs9X\nfBwo1a77u9OB436HE1iWTIyeOHiqVAvKqNCLI9WC5ubvr56+G/H9Hhd34PrGnOG2yG8oN1thNiLK\nVti07ZO2oqq34qz6quLc8HWrjbanct52ZBYLuTVGG2MENi563TrmCkU2gw+bEMC+5vx2nxYgE9uC\nM8ZIiBHf9WY1HToTAgWzDNlM+ZorAzhv+oDtFLk9rq0RbAW+NsbcH0U/8q1Ye+8adm2QitaW11GN\nP6mloq5AUHJe8XltJPvOCrk3nFuKIK7aH8eJRbZtkt4Gm1QVnDrzbXGvY4yoPVFam5cDtW212+nr\nherN3rOUjBZjl5idqcOFFpa7BiR2eG8ZnnQdnbexajuFXUsLMucz2xFoqZgdZkWUJo5oWaNtM2pb\n/cbw0Xy7mF07ZKrYUrDmgniIDacnhAbRmFukEwcecrIcQQusiEjoWebVMPjBMx4dOSfWxZz0QtcT\nvBqLRysxGv6ocyItiXWZURXyuvL977wzCf7pSKmZ3S7wNt9RSmEYRpZUOZ+fOB4Hanb89Kffss4L\nh/s7Yud5frpwOAa6YSQ9C77rIXbkXPnmm295OB3pusjPPj/a79rCmQ+7IyVVrtdHTncWBLzMF467\nkadPL3gP/TiCqzgfWeaFy8vEZZqpIpQ8837v+ePvBu5721tIaKEdGaZc6BazNj4cImm1HUVwwdwv\nl9n8WCQBnmHf0XWmiKwJBulY05VSHP0wELoexVGdEIOj1sQ8PRGHSs4zwY94N7BMEyFYYIrtSyLi\nIAPz9UqaJ6RWLk9PlpVZzczMRYfvArUKX+1OpGzB4T4GYtcZg8uLLSOplErTKkQrhs6RW0ZubDuC\nVAprqQRxrE1t6FzldNqjIszryjzPUB01ZXP6FEWcJ3Yj0u1xwwnCYMvNlqNZN+xZtdXwbEwt5039\nzDa58nOwh37xfrvJJpN3jYftTKCzUQ4dTWjTQA7jQJjWY7s/59xmqAJb09cYLOKdccJ91/jijWLY\nincIHdFZF27Zu9yaO3Gvk4Bz7ka6qI6G5wsqbbl7g3m0sXZ+/3r6hyjR3x7YK9Mkl2pUu1K3QQkQ\ncq3E5k+ANv+FtqnYsuycGCyj2gyGXGpubdsiBHLJKIJXs7u1rbCj04g6oz9mAe8qObSLGAUXINpp\nXFXsMUq2ZI9a0GyHqccMvIK31JOu7yw8o8lxtyBWvjC/0WoZf6WaGtUy/2gCBMPCrZO37EW78pyJ\nVRqw52NPFDtMNFZQM9zaOpIYI7EbqG3XLqqEwbdcxQTqcBI4HgfmNXG9XslVje1y2DWfh0oXFc0d\nj5+fWF5e2O32lGlGgyIEhmHHbjegmpnThF+E66fE4+czqjP39/fM1xfIwpvdiKjwOL1QauHN+3se\nHt4yXy8c9ieenp/oDpFf/dGvgodvv/2IhA51sGjlcpkpGlgvF374g+/y/HhmngvilHE3cDy95dOn\nZ65TwmMil0MMRBJj17OWxPl6Jak9HW+i43g/8vbNnhBhPOy4PL/gEYKLXNLKNCVqF3l6TqQls7xk\ndqeRuzcnTu/ecT2fCX1PKBWpRgk9T2eG4wjeMex3lOx4eTrjYsARKaqmrk1rg9/Mgjm6QMoJDavZ\nNTec15VMygoE6zB9R1qvoJ75ujKvF8b9HYfDgPPC5TrjRPAxsKbEYb9nv7NAF/GQ5hlipB9GSi1c\nzitd5xiHQLUfQUqVZa1M80IudrGXYra4XryxNXwk6WLMqZwpq7KUQq6ZfjTbAR/3uO4OH/dos4Sm\nsTbMwK7e6IS52kGqaINcGoj9Wl0Nmr3RA2VDH2+TtGs0XBd880USNotY1xaLVJPvl5JxAfMSb6JD\ntRfk7f+LcFNweueITbwXvOHk3vuWs+rx0Rg6vjGd3M/RK1vJbgKlLZfYdnZi9iE3uKUdVLJNKr/8\n9g8s5CLyK8CfB77CntP/QlX/cxF5A/x3wI+A3wb+NVV9bN/zZ4F/G1P0/nuq+j/+svuuWl4pgnWr\ntsWw71JuUUk522lcy7Z021bNhHfrGQAAIABJREFUrShiJy+YWkpbIKw9lsbHNNd2avUNqqgsS751\ntG5zJNtk+B6kGO9Zk6K5IM5SVarzZlubDb8TqYbD50zs+puVZW3eGKtLjevtWir2lpS9XUi1iZrs\nYq41G4+0cexrsQu3VNqm3y7G0LB51/wHRAvqLQzDtYnCflc1tz/k5qe+2W66GG7yceuHLATDeyHn\nxLws5GvGe4do4VIzwQeG3R3LdOV8WfDes6wT0/WFJV64f3sidhGngfW6MJ+vaF443Z+a6Vln3h/O\nXPKG3Z6UMs/PFz5+euS0Hy1UQeHl8Ynnx8/WWTpPRHn/9j1dt+Prbz5Q08zD8cjL52erxqrs+j2l\n7/mbf+u3+PjhmfvTjsPgOR0GzFEMvvn4zG438HB/QqvDuc985/2Rz58eOR0ja8k4ER7uTyyXM12A\n96fIrnfkajQ25wMSFPGZvJyhE6JTHj9+4O50h49KSolcMpdPL+by92C2Dvd3d6SUWOeWIOU8mpVV\nK/txDxI4X18YDwOC0nU9eb1SczEYbJ1ACmm5sM4zOV0oWukG8y7JqdD1xu9Oswl0pvnCtCw4fwB6\npNpUIN7bdKZK8J6+EwuHyL4lEhVK1QYlaksTio0nbbuqdbXcgKqVtBhl17Xi24fIOBwIwwnX3eO6\nEy4MN2aKNudCj7cYOlWkKqhveol6Y23g2vJRKyra8pANhpGGYjsnTUFpxnu0a96J1YGNx1KkGc5W\n45Y52axr7WfexIPNLrcRH1shDqa2bjL82DpwH+Jt8Rn8F4W8qTm9C9aRN1hn231t8LjodlBs3TmN\nZqlGy/796/gf2JEn4D9Q1f9TRA7AXxWRvwz8W8BfVtX/RET+Q+DPAH9GRH4D+NeB3wB+APwVEfk1\n/ZIntN3xah3z9gRo60apipbNa6XFK60zKXRozbczqVbFNXcZkzeb+CKEjlrsZJNqySWCu/mtOGeJ\nPLXm5gWslGbOZXx8ua2vnVd8EYqoLZmKuQsqjlQUKQmnmaIYxWpdkHUi+Sv4CGEwrDw4w9C63miU\n7cm+jU/STt2aKcUsOEUdaGmSalO1iRiXXnzA+4g6bwuXCrVYCo0DVOwg9A33exU7NB6tYMye21Tk\nKK3IlVIancozDAOlGqWtlErEfEW0Zvo+sK4LtRTu74+8e/cG7wJxHOxAU0ALseu5K5VUFgxKCxz6\nvVmpSjWYoduzThN39wP73Z5lTRRRhv2OdZ5Y5hmvSgiRdVbOl4kYYDgcGHcDoY/81t/+OxwPB5hW\nfvNv/JiUC+/fPqA54arw8nQhRNgdDnTjHlxBxEzLHh5Gci585zv3pCWxH3uUwrpuggZPblYND6cd\ntS6sy0RaPVU983UhL8I4BPrugPOxZWTCMJwslISCFmWeJoY+MAyBrCBFyfPCtserRYjDaNhxTUh2\nzDkRvRgTy4FoYn55Zr2+WEcsoBLAZws1mRLrUng5W/Hue1s6jmPg5XrGRcf98URv635yKlzPi/0t\nvacG694VZwZXRRjHHb7LzMumI4BpnV6Xicn2O06kLX4LzgvdMDLsTmh/wnVHXBzxPtJUFFRnE6eo\nIDXemgzUYg5NvGN7NC2K28KincNhsY6uMUlEtmQtgzKdD7B9jBEZNrjiZhNbaew3S+8SVWO6OYOa\njKhgTYI5Ydgh7kO80Q69D7gQCc7Um/6Wb+qNAQZtX2bNouC+YOG0crOVnq0bb3VO2wHzGln3D1HI\nVfVnwM/ax2cR+X9agf5XgH+x/bf/CvifWjH/V4H/VlUT8Nsi8reBfx74X37xvmsxcrti/Eij2Wzd\nOU1ss71VlmVhmmZ8t3u1/KzGLAGoG4HeCU4KWkPzMlnaifqqlnLOCuXm82JPVivqpdzK3q1L/mLB\nCRCCo3YeXR2a7UKwgSLBkshuRsWhRKoYTc7FjhAm80oZRote8/5mBFRrwWGYuBal5EJJKyWvQEVc\nIIQF3++I3UgVj5POXgyYhF/aIkek4nxl8/t04m4LXOuC9AsOu1qUV8MqnTPHPfO3cXS9Yxg6UlHy\nasVXJFGrMOz3nJ8/8/TpI/txpB8GlsvzbUz2MTLu9viuI/iO62XlfPmEc8o8J3IqFE22BlBlST2j\nc9y/+wrpPMF1PH96RtwzXpT9OFKd0bweP38i55X94Z4f/72fMAwHSlLwjj/9T/8a1/Mzl+dH7t4e\nqWthOiu1LNRp5u3Dkb6PfPj8kaSOiKNvHNRu1zdLZfDO9g2pJmIUjn0wFpUKXQwgysv5ie++u8eR\nOZ8X+ughXex5C5Hdu47KigLB9+zHPc9PnxnGERcCuzFwOZ8RX/Hesy5nCkJ3qIhG0lJBIq6LpJQ4\nv1yhJtJ6oS4JCcp0vbC7e8v5OjMOB1wRfvz3vuXD08qSlPs7YSf2OhKNXF4KXhZUKnHwSIzsx84C\nxqun8wOhC3iJOBHmeSUXJRXwLlDqasKh2CNdx7IY3BKHiJNAyUqZ7BAfDkdid6KGPXHYIb5rbJDw\nyidHodrUCVj8YXWor9RsviZasoVCK7iqoBlRd4t480367huU4hrLSTf3U5yFGbPREF2b/NUA6qqo\nVEsSc+bYaC4Y1vHbfZuJlniz4fA+4qMVbR9jK/LNa8W/SvetgWzEjs3kr1mOfEl33DryukFEvHJ0\nvmS1/P8u5F/eROSPAf8M8L8C31HVr9uXvga+0z7+Pj9ftH8HK/y/56Zq7Acf7LSsLZpNW8q24Uc2\nxknDv9M6sS5no6LFHhF7wrZf+XZiuZbIXfMtQdxem67RlWxM8d7dUjs8QmlFewPVZTvtvbO4rWAU\nw1qEqNVSd7C0nWIeA5jg36S1qqbQs+KcUddRsiJJSA5qiEYpExMKubbJcV4QAiI2rqmlS5gCrlZK\nTtgjzjfs0CZVed0JNEc517b6mx2olauGtm3MHG9qt9y6cRGHd43nXxt+WItJ850dXL5dkEEe6Lpo\n9x06RG1KGfcjx9NbShXr7kq2vM6qzNPVOuxY2R33CIGuP9D1nsfHT8zXJ3b7YztMAuOuo1alOE8q\nleenJ5Z5Yux7zpdn3r0/knLP2zcP1JR5fH42K2LE4DoSSSf2+x5ByWVFl0RwkXVemMrKbheJnWNd\nE6KBy7RSClynRFXHlJQuCJTczM02/+uey4L559dCLkIVz+7wQBwMCsvL1f5GWZmmjGqiZHvei6uo\nJoIfqVWJXbC/Lz05e6TvKXXlOiX6ODCMiVIg5aM5VqJ89b3v8enxhfU6MZ9nrktiyoXLPCFhIPYR\nH43xoWJT2bJOjGUkX4RhdKijscQ8gmeZlXm+sqZsmK9mqhaj3aqYPbOLTJfMtBQTvjiPiO14pIv0\n/UjXnyDuCaHHO4dIQF20XY0H18yrcOaFoijVVaS2JWiTq1cXjNFVG+dbIgWDXR1yU0x632xkvdlj\n1K2403xLgM0QS12DHqvpL6qYmZfmYnBsozFrU5BukKj30op2bP7sgdC82k3wJxZg7S3FLL7ar9Ly\nvmx64NXycOPN36iP7b25xfwitfL33v4/FfIGq/xF4N9X1Zcv71BVVW6uM7/09ku/tqFDTizeTTf6\nTzV1oqAEZ4yR4jyIR6uJI2LDz4NmwN+YKdtoYpCNdQ8VWnJHY8dsmJs6Xv2MFYt5sM2X1lcb3e30\n7EKg4qjeUXOLBpNg94sZXKlmarU8QueNRYKz8U58tHEsWIxVytkOEB8MU3buRr8Eg4A0JzRZN0ep\nhrE738TF2rpZoypptcXQdiG75v8gSpsqjP2yedKo1NvaSLBoPNc8l4u3S6mqoDVZ2EFN1jEFe7zD\naH4b/X6FJ7HwibTiRXCu4/Iy8+Hr3+Jwd8fucGAYBz787Gvm6UKIjnG/x7m9TVxYbFiIjrcPD0yX\nC/M8czyeOJ6OxH7H4/OFD99+4OO3P2Xserq+J+ztxZ2XK6fjwHx+5vPnC30/skwrD+8e2PWe+Tyj\nT5cm1nB2MDnHuDcI5NOnJ2rquDudmOcLkEmpcJ4Lj+eVru8YnEItCMoQA7lU1jUz9B6tiarGUtFU\nSevKulpIRHFyswOIHayLCcbEdbgQeJleiCEwL5m7+7e2lEcg9GQCQYwFFbrm5BlsV7QbKtfHj3gv\nPD89sS7JXPa6nikJS/Fk8YxB6PuIE7NRdkEYhp5puoJmuq6n5Eqqs2G7XUcpjuu6WOALSkoLPpt3\nTqIw9BEQrvPKdcrW/DT/IXViRbsP4Dqqew1T2OTvqqa0tp2RNSgqcrPYqE7ba8k+dtVRXLECX14L\nvG8CGefkRnhwDbs2+q/7Yg9mDJYby6UK1ZnITtRTXKFIaTsrKwu+2i5Pq5hOxYnRnr27wSbBeZxv\ny9U25XrncHg78Jyz3002zPvLvnrD4bccBfn5z99q2qZk/UfwWhGRiBXx/1pV/1L79Nci8l1V/ZmI\nfA/4pn3+J8CvfPHtP2yf+z23v/K//532IIVf+5X3/Klf/YoYg+HkBaMKYuPJWgpIIHjD0HLOxvQo\nBe8LrqXb34zfG9YtzbBKUEsg0bYALabwalwSk0q3E9GLUaGUJotWe/GKqOHgPlKCM7GQM6VeDI6S\nOkQrri0ifQz42GG7RsMErdFXypxZy4IPgbor1DrQdR2EeOPTbuOY+UOEBt9YN+6cHUYOw3DF+9bZ\nNC58SSCFruvpQjSGUAUXA74ZBqma0rGUlrIE5qjoAl4bQ8f+OqgWci5NqFFY5oziGMeRGHrevHnL\n6XBgnVeyVtZ5xkVhHAO+8+S68PHTE/vTjvs3d1TxPH78zHT9QOxju1oTX33/e+TsKRp5+/6e63Xi\nxz/+Hc6XC9fLTHSBu8OO/aHn/uGe56eJb775yJu39yxX5euvf5eu63n//h3Xy0BdZ/z+npzP9IOn\n1IKuBVHHPF15+/6O91+d8K7y8cOFaamojLw8TTw+rVxKMn+ToFagnO0t1ppZ18TQD2YeReB6LcxL\nInh49+ae6/nMtSrLsvL+q3cNzlJ6HCV2SNMkmMLXE/uOoopzHeuq9KmgutK5A7WCD5FSZrq+Yzpf\nUU0M/cinT98Qu0jJmT5GHp9emK+ZvC5QlT6OfPvNI10XOBxHTsfRoMHS8fnxmcPdiffv3tF1npQS\n85JY15WuC/jRcz6/WORgoeHsO/aHgZIz1w+fSUtGBXI2pXPXD3S9eeKIt4apWqqD4b3NBz3GDh+b\n1zquNU6tyKo2YkKhenPecMWgEd+aPqMcBsOX5RUydQ1isVCaFtLeiqhvNiDWqNmerYhvamqjLEqj\nLVfn0CKmU2l5v9434aIzaq+E7WeFpoZuZIYG19ry1GDZVkxv9EnL5mx4uG78jQak3JwZ4a/+tb/G\n//XXftMe9y/viYE/mLUiwH8J/Kaq/mdffOl/AP5N4D9u7//SF5//b0TkP8UglT8J/G+/7L7/5X/u\nT9yoPSGExkARvAhFpGFXTcQTI6VAwVRuRn8q9kS3nvKV0gNIaRCDDSfixLjDlfZHbrLgRm+qWtof\n0fpr5x0uF5LPaLXHInZXCJ6gEEKmdn2bIDJ1tWVMLRkz/TFM2XjXCzkbi8QH46CGJoV2KJRMSfZE\n3kIvxBa0BI9X62xqyW0qMVaKp0N8BWKjOJlNphjkSK2VeZ1fLzbZevlqF3+0NPZSbeknWzaJGAMo\nijnU+bizSUKtYwFwwVGKQTzOC7kKoR9BK/14sCdZi73AQkdeE0+Pny1PMUS6Yc/+cMQFIeeV4/FA\nzZnleqZzwudvPzBdZ3wX+d73v0+MkbQsjDFQauLx+UxR+NGf+BGaE8/Pj9zf3XHYn/jZT3+C1kLX\ndXz4+gNBLYPEh8R+v7O/YR8p60pdE1oq42CqURe75k2jtrRbFoKrZIUlO5a5MAwdQ+cY+45lWYjx\nYDBvWYmds1i8KOzHEylZ0PbODyxzIgztevRCSSZddw72u9gW/5l976jLJ2J4i2jBSyQtM/NyoY+O\n0EXSDLHv2e+O5qIJTJP58F9fnjmNPWhhjLUZeTmcrAQ32rTrnT2HeWWaLni3R6sQu57QwbIulJTo\nQ6C6ynUt+GFAQ8dltkmwijPPIDFv//ZqahYUHnE9Kj1OOptQY8B3vWlA2rLQtRzLzc9/W3bW6tqb\nhboUX9uep7Qsg9fyZUEzDYdu/HHXiu0mANqmdkVaIa9NZFORWm7FfntfSrllh5q62oLbfYNWQot6\ndGKvq1/8/vYPa8x+sa5ujBTV21S8ZSzYV5qaVYQ//U/9Ov/sb/x6ez0pf/4v/ve/rJz+gR35vwD8\nG8D/LSL/R/vcnwX+I+AviMi/Q6Mftgf2myLyF4DfxPQK/67+PqvWWjcqEVZQqyCu0W0agyRvBkGh\na/BDvBXs2x9FFd8gmhv7xaltwWl4U6Me6Ya714pu+v4Gq8iWOCK1LVGKPcGmsafo9v/NxF4ax9u8\nTRLJrazzzDKvZoKUZmpOxgv2rStxHqWj6wNd19MPIxKaf3hZ2Qq4yYi31CTbXDvvEG+HiHHLzWtG\ni9G1SlUIRo16Hdlsd2AXY6FKto36BuE033PzZLfw2VwSQrGsSAQfIlKchf6GnhgiJa9kXS1FyTm6\ncWTY3ZGWmVDnxr93uJa5mlPm5flMXlfG056smX7wnA5HxHnmeebjh0eeXz4zdh2dD+RqkvS+i5Q8\ncT0/4sTzOz/5jABv7k8c7vZUFlJZ8L4QQ4e4wv448OGn3zI/Xfn+D7/L48dv2e07jncDd2/uuF4v\nrGlBivDx28/Ukug6z7vhyGVaCa6yjgYh5LEn5cVgugriAsucOO0HrpcrceioZeKwH8nZRnhbgimQ\n2Y09S848X86IKKdoU9d8Xak1W2BxDPacSqGuxQ7NWgj9/pZlW6vQdzvyOtF1PeoCuSaUStfv6NPE\ndLVYucPQMydpPj60HU/FV09KhRg8jsLYm8Wq90LWgnr7elULYL7MmbQWUlKbMl1gnleWZcFJxeMY\nuh4fnAU2JzVanusIoUdCh4u9Lf821WMI+OjtIHGvhdzr5jxo3XYpgqo3m1sv+FLbnssgl1ZFblP9\nayGXGyXXirm/4dta5YuOf/s5FSnZGigVg1ew+6pSjEZcKyLFBlYvNyHgdlBs/HBa3dreWk2ELyAl\nZMP4rTptSUdgi0+F2xJUVW1KvhU79/sW6j+ItfI/s1ly/d7bv/T7fM+fA/7cP+h+wWxna+u6S64G\nkQQrvttpVBXWNeGz0vVCN3bE2EIcGlWvlNy40b4Zb1nXLM2Ey+xom1JTHGaeUk2T03xMgrM09Vyy\nUZ1UqBsLpNHyXHsCGoXdQmmzUlMz9l9X8jKT05WSF9Z1toSS4M1zobPE837c4/qmSnUmLtqen1IK\nIpa1aQKHbRSzg8pyOWky7UylKdCcyRtMQGSmWupMFmwwDGhzq6t5vak7fYjWDVUDmbyYlW+uzXIg\nBLrgyWI+6nOaUSqnwwn1agETaeH8cmUY99aJ1555unC9vtB7604zym7vWVPi+fkzFU9eJrwkjnff\nBQkc747sTyMvj5+5Ti+8ffuWcdxTi5p4a02cn585nUYOuz19jIz7I48fP8Ka6LveZO/zlcdvP9N7\n5Ud/7PvMZeHuFBGFYbfn06cn7u9OoIV5mXhzv6MUGlwH0wxrynz37RuWNJl2IHWkJTHnlTFA1sC0\nJlQDQRyCZzpf8UE43T+wlgWyM3ohdo2GbrAFuPOEGFGppGvCr4l9jNyyG51viTAGSzjpjMPtAlWU\nVK+UtRDGPXr5FheEl8tn88LuhOtczV9msoI7jj13x6MtXXPm86fPDLuefhiY15X1vHJEOAZPiI4p\nr1yXlVo9l1m5vMzcHw68e3Mgl8Lz85kgnr7vEYEu2kRbFhOthd0BxhGNLXTlVvQaW2yDE5CfL4Jt\nEtwK7QaTOge1il1vitESbwESr4Xcib4Kbn7hfeMAsKlIrYDrrdNGHIJREEspZHHWsYstvM3+wywC\nboW8df43Q672tnWZenuTW96FtvVlVcv6/BIqefVz+T31lFsGwz/qsvMfx82ScsrtD+u9w1fD4bZT\nNUYzHSmqrGnFhYDrepxUkMKaZipKKDYGq2/8bHh9IsG66BbVBFCLthSTxp6ppS01srFP1BYv1imX\n1rWbT/rGrKmlosV+h9qEDM4HQj/gnFGTSko4By6YsrLrR4IXakqsNaEKFfOHdj5QW2HVYKMbbXFj\nY4Vr6lY7yV3d3Nhc82/xDeN2SLX3mw2CedCU27/tYHLtQt7gKCXXRrPyLXavFFK17rwUg5xUlFwK\nfRzNMkEKsffM0zPnpxmRghdB88LzdSL2AxI7+mHg7duvmM4vFM0c3n2fUpTPj1/bwVUql8sZXRe+\nenjAeUdaZt68eYfvzM5WQmQYLNj3cpn5+PHvEwPc3x3wQdCzTUrvv/+ezkOpiZqLLVKdQR99CEzP\nZ4IrDMHixc7zmfN5JnQ7ur5jWZIJOLqBkhJLTjdBVucilzXhvSNpZlky3lVOux7vlfP5QlHIRbm7\nP9nOYC0cTpGUVpZpZl1SY10FW3S1p9hYfErsIrG/J4xvqN4gOK2WVNPtDsyXF4a4Z7f/Plq/YVBh\nvlwBpe8CIolcK6sqPlr6VUk28fVDb7RBcSxrZSmOOCklXxiGymVdWJdMiI7jMDB6x/4wEnqPZBiH\n3hSbJd8gHRGhHwf6ONDv9oQ4gnSwMVK+uG3X7JdvNnk26+cGsdRaWgNmKlLg9rXGom33J7dr+JV+\n/EURx7rrm1HexgxzpTWChU30s9WM7do35pjBK4JZaPj285x4fPv6xom/IQxC22puC04jRDg2fKc9\nlu1warfNVmZr4GT7N9vn/iEx8n+ct5xXikV/2xKytk5cm9LN2XIuuoAv9ovnal4PfTTqoCXhgFDI\nJd3GF+dbodJNzbWNMs0/AwxqKAlLgd+2wdUweMREAS0G0LbctlzVpkiV2g4CV1Gv4C38IApkZiuW\nLlhySAx0oUdTYV7mRrEE8Y5CIK0O8REXC33X2wVVA14dToMVZ6FRLdvvKNvF2YqxGnUqYN4S1c6i\nZtZjm3uxLbKNb9VEFUazapeNYkve7bGLJ5VMburQ3kfQTF4nnIfT/T05j1wvn4hBCC4gfrCxtOx5\nOx5AhHm68nL5zHQ5U9NKKQt97Agu0ImAVFyo7HeBeHwgr5m8LKxr4XK5WngtZtyFq+Si5HWlGzxj\n51nns3lwLyviPLvdvuHeO2LJLMuFXFbSvELJHI57lgTTkhtV1Bwt55dnpOvoO2/BwMCyGGdexQqk\nc8Z3zjmx2/VEby9OVSwwucJ8XckYPDF2IzlNpKUwjL05AIrjcrniYmQIPSkpJSXm1aaE91+9pd8H\nlpzNl9+ZUjj0PelaGccH1vkF8T0lC2WpvDyeERdZl4L34EVAPS/nRFpnC1EeO7oYmJaVkD24SB8H\nnI+crzP4jq4z7DpnZc0r2vYkslRKSqxpvaVnlVKpAv0wMOz2+G5EQmw0xGZope3abGpjSrWFxaay\nbAK1zWZ2W3Q55206rgou3FSYtb2WHXIrvva27ZUc4s1h1NBobSrqDX/XL76vNUPNxapuhb19PUs1\nw7OmHmXLTmimWSbqoTVQrx250aW1iZkUtzmYijWUVmlo+6Z22GzpQEjbPxibR9qeCZEbDfGX3f7Q\nCnnJjQkh7Txr1pK1qJnXOPP/MKP2JmkXj4vmZdCFgIsREY9WyG2zrkHxRdFNAv8FowWxgAZp2Jlz\n2paZ7bJqOLiTdpDUgmCezKhx3WveLDaVbSO+8eGLGEaIOpTFRrCg1LpyXZ6RYlmjQexCQB1eIrlC\nTguSo6WeEG1hWgolmjmP4dqpWXo2D/WGa1dV80au2OSg9Zb1VyUZjr51Qo1LX6s9Fpw2e9021lXL\nasxkM//prHN3WFyY1gC1cr1cUC0cjgcOxyOeA1oTy5rM16JWrtNkz4EaZW+lcL688ObtO3w3kFKi\nLAvzdGFzWUiqlJxYkmGw0TvEWfBvSraTWC4zXQzcHUfWZQEX2Z3eorqS15ki4OPAVBOdJMaxA+1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H1eN5a1UtSRa+D1NaP5K4dp4NPjHWXNfH16ppXFQr29Yxg9d3dHlrzx+vzCtmyMKZEOiSpw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\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xzOIfknGlJHrlr/lenut6M5cUmFmaMW9dfAJq4nfl0x8ckRuPzJFNcpkcznhy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\n+quXq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UqRnCIt114YrfqyU8EaRDxVNhIgTROUjPFkGoWMFUOGniXaFAvsVgYiBozB2gHT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\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xC8xbPiJxVPEPYlwpQq/8Nd/Lc11v5pICM1sz5m2KT0BN/A359AdH5MYj88gm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Km/eqlqi2l2uKxLiVvVrlyLbKLWrP4Vud8YrdKM4qN76rgYhwkBFyZsCN0FUmmKim9bpTxa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\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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "for dtype in dtypes:\n", - " img = np.load('data/chelsea_{}.npy'.format(dtype))\n", - " #img = np.load('data/matlab_chelsea_{}.npy'.format(dtype))\n", - " plt.figure()\n", - " plt.imshow(img.astype('uint8'))\n", - " plt.title(dtype)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/npy-matlab-master/LICENSE b/npy-matlab-master/LICENSE deleted file mode 100755 index 05a2efa..0000000 --- a/npy-matlab-master/LICENSE +++ /dev/null @@ -1,24 +0,0 @@ -Copyright (c) 2015, Kwik Team -All rights reserved. - -Redistribution and use in source and binary forms, with or without -modification, are permitted provided that the following conditions are met: - -* Redistributions of source code must retain the above copyright notice, this - list of conditions and the following disclaimer. - -* Redistributions in binary form must reproduce the above copyright notice, - this list of conditions and the following disclaimer in the documentation - and/or other materials provided with the distribution. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" -AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE -IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE -DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE -FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL -DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR -SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER -CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, -OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE -OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - diff --git a/npy-matlab-master/README.md b/npy-matlab-master/README.md deleted file mode 100755 index 5397104..0000000 --- a/npy-matlab-master/README.md +++ /dev/null @@ -1,23 +0,0 @@ -# npy-matlab - -Experimental code to read/write NumPy .NPY files in MATLAB - - -## Usage example: -``` ->> a = rand(5,4,3); ->> writeNPY(a, 'a.npy'); ->> b = readNPY('a.npy'); ->> sum(a(:)==b(:)) -ans = - - 60 -``` - -## Tests -[See the notebook for python tests](npy.ipynb) - -See test_readNPY.m for matlab reading/writing tests. - -## Note re: memory mapping npy files -See exampleMemmap.m for example of how to memory map a npy file in matlab, which is not trivial when the file uses C-ordering (i.e. row-major order) rather than fortran-ordering (i.e. column-major ordering). Matlab's memory mapping only supports fortran, but Python's default is C so npy files created with Python defaults are not straightforward to read in Matlab. diff --git a/npy-matlab-master/constructNPYheader.m b/npy-matlab-master/constructNPYheader.m deleted file mode 100755 index ca2840e..0000000 --- a/npy-matlab-master/constructNPYheader.m +++ /dev/null @@ -1,88 +0,0 @@ - - - -function header = constructNPYheader(dataType, shape, varargin) - - if ~isempty(varargin) - fortranOrder = varargin{1}; % must be true/false - littleEndian = varargin{2}; % must be true/false - else - fortranOrder = true; - littleEndian = true; - end - - dtypesMatlab = {'uint8','uint16','uint32','uint64','int8','int16','int32','int64','single','double', 'logical'}; - dtypesNPY = {'u1', 'u2', 'u4', 'u8', 'i1', 'i2', 'i4', 'i8', 'f4', 'f8', 'b1'}; - - magicString = uint8([147 78 85 77 80 89]); %x93NUMPY - - majorVersion = uint8(1); - minorVersion = uint8(0); - - % build the dict specifying data type, array order, endianness, and - % shape - dictString = '{''descr'': '''; - - if littleEndian - dictString = [dictString '<']; - else - dictString = [dictString '>']; - end - - dictString = [dictString dtypesNPY{strcmp(dtypesMatlab,dataType)} ''', ']; - - dictString = [dictString '''fortran_order'': ']; - - if fortranOrder - dictString = [dictString 'True, ']; - else - dictString = [dictString 'False, ']; - end - - dictString = [dictString '''shape'': (']; - -% if length(shape)==1 && shape==1 -% -% else -% for s = 1:length(shape) -% if s==length(shape) && shape(s)==1 -% -% else -% dictString = [dictString num2str(shape(s))]; -% if length(shape)>1 && s+1==length(shape) && shape(s+1)==1 -% dictString = [dictString ',']; -% elseif length(shape)>1 && s %s', tempFilename, inFilename, outFilename)); - - otherwise - fprintf(1, 'I don''t know how to concatenate files for your OS, but you can finish making the NPY youself by concatenating %s with %s.\n', tempFilename, inFilename); -end - diff --git 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z8>GP6e&znY?aOA;yXEzsh}*jA>aPC|!335>)o!mF5Ac;h0igS|$1{`VKe{GIz$ZUpzNdYTj*jCGqZ9YBV!k&g zsAMXH(Ei5i>b2Fg)W)U}uGUeH)(>7|f^$u|kGHu_iLtt8H?#b+BiH>SaZ~5GU4CcK z+)ayF_~Ru9hA7fZ^M?SQanWnn@A3bAVFDB&w96+N(9^H!eE$Ih{qN!b-iQBf5C7kE zhWp2*AJ33Lz?U>)HpGYs2{ejM3+jpmA^-n!fRf5!HJ($b8U1txigK#5l`^Km{|7T4 B4!8gS diff --git a/Blobs_finder.m b/Blobs_finder.m deleted file mode 100644 index 0f5ccf3..0000000 --- a/Blobs_finder.m +++ /dev/null @@ -1,140 +0,0 @@ -function blobs_layer = Blobs_finder(npy_array, mean_value, z_value, idx_tgw) -% This function returns, for each Z value of an npy array, the regions that -% should be associated with the shape of a grain -% npy_array from recon3d.py -% mean_value is the max(mean) of the weight, calculated for each layer -% z_value is considered layer -% idx_wtg is the voxel index to consider. 1: theta, 2: gamma, 3: weight - -% Play with a single slice -if idx_tgw == 3 - B = (npy_array(:, :, z_value, idx_tgw)/mean_value); -elseif idx_tgw == 1 - B = npy_array(:, :, z_value, idx_tgw); -end -D = (imregionalmin(B,8)); % Find local minima -Min_matrix = zeros(size(B)); - -for i = 1:size(B,1) - for j = 1:size(B,2) - if D(i,j) == 1 - Min_matrix(i,j) = B(i,j); - end - end -end - -% Higher local minima -D_max = max(max(Min_matrix)); -% Lower local minima -Min_matrix(~Min_matrix) = inf; -D_min = min(min(Min_matrix)); - -% Find the area of the basins relative to the minima -n_steps = 1000; % Number of steps between min and max -prop_steps = zeros(1,4); -for aa = D_min:(1/n_steps):D_max - B_one_level = zeros(size(B)); - for bb = 1:size(B,1) - for cc = 1:size(B,2) - if B(bb,cc) < aa - B_one_level(bb,cc) = 1; - end - end - end - % Find the different regions in the image - L2 = labelmatrix(bwconncomp(B_one_level)); - n_reg = size(unique(L2),1) - 1; - for dd = 1:n_reg - L3 = zeros(size(L2)); - for ee = 1:size(L2,1) - for ff = 1:size(L2,2) - if L2(ee,ff) == dd - L3(ee,ff) = 1; - end - end - end - % Calculate area and centroid of the considered region - s = regionprops(L3, 'Centroid', 'Area'); - if s.Area > 50 && s.Area < 400 - % Use a frame (the grain is well centred) - if s.Centroid(1) > 20 && s.Centroid(1) < 80 - if s.Centroid(2) > 20 && s.Centroid(2) < 80 - prop_steps = [prop_steps; [aa s.Centroid(1) s.Centroid(2) s.Area]]; - end - end - end - end -end - -prop_steps( ~any(prop_steps,2), : ) = []; - -% Look at the CM distribution and, for each cluster, find minima corresponding -% to largest area -Loc_CM = zeros(size(B)); -for u = 1:size(prop_steps,1) - Loc_CM(int8(prop_steps(u, 2)), int8(prop_steps(u,3))) = 1; -end -%figure; imshow(Loc_CM); -Loc_CM_lab = bwlabel(Loc_CM); % label the different clusters of minima - -n1 = size(unique(Loc_CM_lab),1) - 1; -val_max = zeros(n1, 5); -for uu = 1:n1 - M1 = zeros(size(B)); - for i = 1:size(B,1) - for j = 1:size(B,2) - if Loc_CM_lab(i,j) == uu - M1(i,j) = 1; - end - end - end - - Data_M1 = zeros(1,4); - for vv = 1:size(prop_steps, 1) - if M1(round(prop_steps(vv,2)), round(prop_steps(vv,3))) == 1 - Data_M1 = [Data_M1; prop_steps(vv,:)]; - end - end - Data_M1( ~any(Data_M1,2), : ) = []; - [max_A, idx_max_A] = max(Data_M1(:,4)); - val_max(uu,1:4) = Data_M1(idx_max_A,:); % aa x_min y_min A_ min - val_max(uu,5) = idx_max_A; % min position in prop_steps -end - -% Save, in a single image, the blobs corresponding to the local minima - -Blobs_final_comb = zeros(size(B)); -for uu = 1:n1 - for aa = val_max(uu,1) - B_one_level = zeros(size(B)); - for bb = 1:size(B,1) - for cc = 1:size(B,2) - if B(bb,cc) < aa - B_one_level(bb,cc) = 1; - end - end - end - % Find the different regions in the image - L2 = labelmatrix(bwconncomp(B_one_level)); - n_reg = size(unique(L2),1) - 1; - for dd = 1:n_reg - L3 = zeros(size(L2)); - for ee = 1:size(L2,1) - for ff = 1:size(L2,2) - if L2(ee,ff) == dd - L3(ee,ff) = 1; - end - end - end - % Calculate area and centroid of the considered region - s = regionprops(L3, 'Centroid', 'Area'); - if s.Centroid(1) == val_max(uu,2) && s.Centroid(2) == val_max(uu,3) && s.Area == val_max(uu,4) - Blobs_final_comb = Blobs_final_comb + L3; - end - end - end -end - -blobs_layer = Blobs_final_comb; - -end % function diff --git a/Clean_sum_img.py b/Clean_sum_img.py deleted file mode 100644 index 59ba69c..0000000 --- a/Clean_sum_img.py +++ /dev/null @@ -1,88 +0,0 @@ -# For each projection, this script loads the relative images, sums -# them and clean the sum using a rolling median.style approach. - -import numpy as np -import matplotlib.pyplot as plt -import fabio -import os -import scipy.io - -# Directory where the IO data is stored -io_dir = '/u/data/alcer/DFXRM_rec/Rec_test_2/' -# Directory with the images -im_dir = '/u/data/andcj/hxrm/Al_april_2017/topotomo/monday/Al3/topotomoscan/' -# Figures to be used to clean the background (selected looking at the result -# from Plot_all_im_1omega.pys -first_im = [0, 9] -last_im = [10, 1] - -# Load all datato get dimensions -A = np.load(os.path.join(io_dir + 'dataarray.npy')) -num_idx = A.shape[0] -Im_x = A.shape[3] -Im_y = A.shape[4] -num_proj = A.shape[2] -# Estremes of the ROI in the full frame -roi_lf = (512/2) - Im_x/2 -roi_rg = (512/2) + Im_x/2 -roi_dw = (512/2) - Im_y/2 -roi_up = (512/2) + Im_y/2 - - -del A - -# List of the image files -im_paths = np.genfromtxt(os.path.join(io_dir + 'List_images.txt'), dtype = str) -# List of the files properties -im_prop = np.loadtxt(os.path.join(io_dir + 'Image_properties.txt')) - -Fabio_array = np.zeros([num_proj, Im_x, Im_y]) -# For each projection, sum the images and store the result -for j in range(num_proj): - sum_om = np.zeros([roi_rg - roi_lf, roi_up - roi_dw]) - for i in range(im_prop.shape[0]): - if im_prop[i,3] == j: - img_name = os.path.join(im_dir + im_paths[i]) - I = fabio.open(img_name).data - sum_om[:,:] += I_ROI[roi_lf:roi_rg, roi_dw:roi_up] - Fabio_array[j,:,:] = sum_om[:,:] - -Median_array = np.zeros([num_proj, Im_x, Im_y]) -# For each projection, select the images to use for some median filtering -for j in range(num_proj): - # List all images relative to an omega, than take first and last of - # the first line (selected by looking at the data) - med_arr = np.zeros([num_idx*num_idx, 1]) - n_med = 0 - img_name = im_paths[i] - for i in range(im_prop.shape[0]): - if im_prop[i,3] == j: - n_med = n_med + 1 - med_arr[n_med-1, 0] = i - # Load selected images - M1 = os.path.join(im_dir + im_paths[int(med_arr(first_im))]) - M2 = os.path.join(im_dir + im_paths[int(med_arr(last_im))]) - I1 = fabio.open(M1).data - I2 = fabio.open(M1).data - Median_array[j,:,:] = 0.5 * (I1[roi_lf:roi_rg, roi_dw:roi_up] + I2[roi_lf:roi_rg, roi_dw:roi_up]) - -print sum(sum(sum(Median_array))) - -sys.exit() - -# To take into account the sample rotation, divide by the mean intensity -mean_int = np.zeros([num_proj,1]) -for i in range(num_proj): - mean_int[i] = np.mean(Fabio_array[i,:,:]) - print mean_int[i] - -max_mean = int(max(mean_int)) - -Fabio_clean = np.zeros([num_proj, Im_x, Im_y]) -for j in range(num_proj): - for k in range(Fabio_array.shape[1]): - for l in range(Fabio_array.shape[2]): - Fabio_clean[j,k,l] = (Fabio_array[j,k,l] - (num_idx * num_idx) * Median_array[j,k,l]) - -# Save data for matlab analysis -scipy.io.savemat('Fabio_clean.mat',{"foo":Fabio_clean}) diff --git a/Data_one_omega_90_4.npy b/Data_one_omega_90_4.npy deleted file mode 100644 index db8f0a3b2e8fee8cda4e2532290345a155e63804..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 1648 zcmbR27wQ`j$;jZwP_3SlTAW;@Zl$1ZlV+i=qoAIaUsO_*m=~X4l#&V(cT3DEP6dh= zXCxM+0{I$-ItnJ1K&+{wP^$n2T-CRIS}Rmu+N<9R_4w(@ePGs15D0S+h_KvpqA0}y zm%M!I>%bj&q@bBqfff8T(?dkBv_OBkQ-COq;W<5%L52d5J}@`@XZGA81YUvbRayc3VSEhqwj z^5AqP_tO6Mk&}PFgtTAr2 z$AQVi5s&=U3ZM`1$b*7k1CKn&dlGo$+r@64`<{>6{V@5%dR=lqBwpGd7tH>sd4Ln1 z|A49I<~g8#T=JmeYabqY4`A`I4v+l#@4yJeBM(fSjZ^W+zX2xyRy^_?Yh>>>>>> 077febf5ee14058f5f57ce942f86a24dc78c3637 - end - end - end -end - -<<<<<<< HEAD -output_w = sprintf('weight_3D.vtk'); -savevtk(A_final, output_w); - -======= -output_g = sprintf('gamma_3D.vtk'); -savevtk(A_final_g, output_g); - -A_comb = zeros(size(A)); -for i = 1:size(A,3) - A_comb(:,:,i) = A_final_w(:,:,i)*10*(A_final_t(:,:,i) + A_final_g(:,:,i)); -end - -output_comb = sprintf('comb_3D.vtk'); -savevtk(A_comb, output_comb); ->>>>>>> 077febf5ee14058f5f57ce942f86a24dc78c3637 diff --git a/Plot_3D.py b/Plot_3D.py deleted file mode 100644 index 967549b..0000000 --- a/Plot_3D.py +++ /dev/null @@ -1,123 +0,0 @@ -# This script selects a layer from the 3D npy array reconstructed by recon3d.py, -# and plots the spatial distribution of the following values: -# - chi and phi angles used to tilt the sample -# - weight, which quantifies the quality of the fit assigning to a voxel the -# corresponding (chi, phi) angles - -import numpy as np -import matplotlib.pyplot as plt -from mpl_toolkits.mplot3d import Axes3D -import math -from itertools import count -import sys -<<<<<<< HEAD -#from cmap_map import cmap_map -======= -from cmap_map import cmap_map ->>>>>>> 077febf5ee14058f5f57ce942f86a24dc78c3637 - -# Load the npy array -A = np.load('/home/nexmap/alcer/DFXRM/Recon3D/grain_ang_1.npy') - -# Considering all z layers, find the max of the mean for the weight -M = np.zeros(A.shape[2]) -for i in range(A.shape[2]): - M[i] = np.mean(A[:,:,i,2]) -max_mean = np.max(M) - -Layer_max = np.zeros([A.shape[0], A.shape[1]]) -for i in range(A.shape[2]): - M[i] = np.mean(A[:,:,i,2]) - if M[i] == max_mean: - Layer_max[:,:] = A[:,:,i,2] - -# Select a z value and plot the respective slice (label by label and combined labels) - -z_val = 50 -A_z_val = np.empty(shape=[A.shape[0], A.shape[1]]) -A_z_val_0 = np.empty(shape=[A.shape[0], A.shape[1]]) -A_z_val_1 = np.empty(shape=[A.shape[0], A.shape[1]]) -A_z_val_2 = np.empty(shape=[A.shape[0], A.shape[1]]) - -<<<<<<< HEAD -A_z_val_0[:,:] = A[:,:,z_val,0]/np.mean(A[:,:,z_val,0]) -A_z_val_1[:,:] = A[:,:,z_val,1]/np.mean(A[:,:,z_val,1]) -A_z_val_2[:,:] = A[:,:,z_val,2]/np.mean(A[:,:,z_val,2]) -#layer_mean = np.mean(A[:,:,z_val,2]) -#for i in range(A_z_val_2[0].size): -# for j in range(A_z_val_2[1].size): -# A_z_val_2[i,j] = A[i,j,z_val,2]/Layer_max[i,j] -A_z_val[:,:] = A_z_val_0[:,:] + A_z_val_1[:,:]#(A_z_val_2[:,:] * ( A_z_val_0[:,:] + A_z_val_1[:,:])) -======= -A_z_val_0[:,:] = A[:,:,z_val,0]#/np.mean(A[:,:,z_val,0]) -A_z_val_1[:,:] = A[:,:,z_val,1]#/np.mean(A[:,:,z_val,1]) -layer_mean = np.mean(A[:,:,z_val,2]) -for i in range(A_z_val_2[0].size): - for j in range(A_z_val_2[1].size): - A_z_val_2[i,j] = A[i,j,z_val,2]/Layer_max[i,j] -A_z_val[:,:] = ( 0.5 * ( A_z_val_0[:,:] + A_z_val_1[:,:])) - -sys.exit() ->>>>>>> 077febf5ee14058f5f57ce942f86a24dc78c3637 - -# The best way to understand the distribution of the angles and of the weight -# parameter is to use an inverted diverging colormap (high values at the centre) -# This because we expect most orientation values to be at the centre, not at the -# extremes -#inv = cmap_map(lambda x: 1-x, plt.cm.Spectral) - -plt.subplot(221) -<<<<<<< HEAD -plt.imshow(A_z_val_0) -plt.title('Theta') - -plt.subplot(222) -plt.imshow(A_z_val_1) -======= -plt.imshow(A_z_val_0, cmap = inv) -plt.title('Theta') - -plt.subplot(222) -plt.imshow(A_z_val_1, cmap = inv) ->>>>>>> 077febf5ee14058f5f57ce942f86a24dc78c3637 -plt.title('Gamma') - -plt.subplot(223) -plt.imshow(A_z_val_2) -plt.title('Weight') - -plt.subplot(224) -plt.imshow(A_z_val-2) -plt.title('Combined value') - -plt.show() - -sys.exit() # This stops the script here - -plt.imshow(A_z_val_2, cmap = inv) -plt.title('Weight') -plt.show() - -# Save, layer by layer, the slices of the volume - -def gen_filenames(prefix, suffix, places=3): - """Generate sequential filenames with the format - - The index field is padded with leading zeroes to the specified number of places - """ - pattern = "{}{{:0{}d}}{}".format(prefix, places, suffix) - for i in count(1): - yield pattern.format(i) - -g = gen_filenames("/Users/Alberto/Documents/Data_analysis/DFXRM/Results_sunday/3D_reconstruction/img_", ".png") -for i in range(A.shape[2]): - A_2D = np.empty(shape=[A.shape[0], A.shape[1]]) - for j in range(A.shape[0]): - for k in range(A.shape[1]): - if ((A[j,k,i,2]/max_mean)<0.03) or (0.4<(A[j,k,i,2]/max_mean)<1): - A_2D[j,k,i] = ( 0.5 * ( A[j,k,i,0] + A[j,k,i,1])) - else: - A_2D[j,k,i] = 0 - - plt.imshow(A_2D) #Needs to be in row,col order - plt.savefig(next(g)) diff --git a/Plot_all_im_1omega.py b/Plot_all_im_1omega.py deleted file mode 100644 index cb2d30e..0000000 --- a/Plot_all_im_1omega.py +++ /dev/null @@ -1,84 +0,0 @@ -# Derived from Plot_data_1omega.py (code split after memory leak) -# Aim: plot the images collected at a certain projection - -import numpy as np -import matplotlib.pyplot as plt -import sys -import os - -io_dir = '/u/data/alcer/DFXRM_rec/Rec_test_2/' - -A = np.load(os.path.join(io_dir + 'dataarray.npy')) - -# Select projection number -omega = 100 - -# We want to plot, in a grid, all images collected at a certain projection -# Load the datafile with all information, and store to an array the data -# relative to the images in a certain projection -Data = np.loadtxt(os.path.join(io_dir + 'Image_properties.txt')) -Data_angle = np.zeros([A.shape[0]*A.shape[0],2]) -idx = 0 -for i in range(Data.shape[0]): - if Data[i,3] == omega: - idx = idx +1 - Data_angle[idx-1, 0] = Data[i,1] - Data_angle[idx-1, 1] = Data[i,2] - -# Plot the various recorded images (one projection) -fig, axes = plt.subplots(A.shape[0], A.shape[0]) -for i in range(A.shape[0]*A.shape[0]): - a1 = fig.add_subplot(A.shape[0],A.shape[0],i+1) - plt.setp(a1.get_xticklabels(), visible=False) - plt.setp(a1.get_yticklabels(), visible=False) - aa = Data_angle[i,0] - bb = Data_angle[i,1] - plt.imshow(A[int(aa),int(bb),omega,:,:]) - -plt.show() - -# Plot the various recorded images (all projections) -fig, axes = plt.subplots(A.shape[0], A.shape[0]) -#We also keep track of the integrated intensity -II_matrix = np.zeros([A.shape[0], A.shape[0]]) -for i in range(A.shape[0]*A.shape[0]): - a1 = fig.add_subplot(A.shape[0],A.shape[0],i+1) - plt.setp(a1.get_xticklabels(), visible=False) - plt.setp(a1.get_yticklabels(), visible=False) - aa = int(Data_angle[i,0]) - bb = int(Data_angle[i,1]) - - AA = np.zeros([A.shape[3], A.shape[4]]) - for oo in range(A.shape[2]): - AA[:,:] += A[aa,bb,oo,:,:] - II_matrix[ int(aa), int(i - (int(aa) * A.shape[0])) ] = sum(sum(AA)) - plt.imshow(AA[:,:]) - -plt.show() - -# Plot how the integrated intensity changes during the rocking scan -fig = plt.figure() -plt.imshow(II_matrix) -plt.title('Distribution of the integrated intensities (all proj)') -plt.xlabel('Theta (index)') -plt.ylabel('Pseudomotor (index)') -plt.show() - -# Return the coordinates where we have the lower integrated intensity -II_value = np.zeros([A.shape[0]*A.shape[0], 3]) -lin_n = 0 -for aa in range(II_matrix.shape[0]): - for bb in range(II_matrix.shape[1]): - lin_n = lin_n + 1 - II_value[lin_n - 1, 0] = int(aa) - II_value[lin_n - 1, 1] = int(bb) - II_value[lin_n - 1, 2] = II_matrix[aa, bb] - -# Sort integrated intensities; for the rolling median consider the images giving -# the two lower values -i = II_value[:,2].argsort() -II_value_sorted = II_value[i,:] - -print II_value_sorted - -print 'To clean the dataset, we suggest to use the images number %i [%i, %i] and %i [%i, %i]' % ( ((II_value_sorted[0,1] + 1) + ( II_value_sorted[0,0] * A.shape[0])), II_value_sorted[0,0], II_value_sorted[0,1], ((II_value_sorted[1,1] + 1) + ( II_value_sorted[1,0] * A.shape[0])), II_value_sorted[1,0], II_value_sorted[1,1]) diff --git a/Plot_ang_values.py b/Plot_ang_values.py deleted file mode 100644 index cc5614e..0000000 --- a/Plot_ang_values.py +++ /dev/null @@ -1,73 +0,0 @@ -# This script plots the distribution of the angular values for every omega - -import numpy as np -import matplotlib.pyplot as plt -import os -import sys - -# Remember to check the path for the IO directory -io_dir = '/u/data/alcer/DFXRM_rec/Rec_test_2/' - -A = np.load(os.path.join(io_dir + 'dataarray.npy')) -Theta = np.load(os.path.join(io_dir + 'theta.npy')) - -# For each projection, mark the motor position in the pseudomotor, theta space -Counter = np.zeros([A.shape[2], A.shape[0], A.shape[1]]) -num_pos = 0 -# Loop over projections -for aa in range(A.shape[2]): - # Loop over the theta values - for bb in range(A.shape[0]): - # Loop over the pseudomotor cvalues - for cc in range(A.shape[1]): - if sum(sum(A[bb,cc,aa,:,:])) > 0: - Counter[aa, bb, cc] = 1 - num_pos = num_pos + 1 - -# We also want to keep track of the theta motor position in degrees -Counter_deg = np.zeros([num_pos, 4]) -num_pos_1 = 0 -for aa in range(A.shape[2]): - # Loop over the theta values - for bb in range(A.shape[0]): - # Loop over the pseudomotor cvalues - for cc in range(A.shape[1]): - if Counter[aa, bb, cc] > 0: - num_pos_1 = num_pos_1 + 1 - Counter_deg[num_pos_1 - 1, 0] = aa # Projection number - Counter_deg[num_pos_1 - 1, 1] = bb # Pseudomotor index - Counter_deg[num_pos_1 - 1, 2] = cc # Theta index - Counter_deg[num_pos_1 - 1, 3] = Theta[cc] # Theta in degrees - -# Plot (pseudmotor, theta) distribution for a single projection -deg = 5 - -fig = plt.figure() -plt.imshow(Counter[deg, :, :]) -plt.title('Motor positions in the (pseudomotor, theta index) space') -plt.xlabel('Theta (index)') -plt.ylabel('Pseudomotor (index)') -plt.show() - -# Plot (pseudomotor, theta) distribution for a single projections -Counter_1 = np.zeros([11*11, 4]) -line_num = 0 -for i in range(Counter_deg.shape[0]): - if Counter_deg[i, 0] == deg: - line_num = line_num + 1 - Counter_1[line_num-1,:] = Counter_deg[i,:] - -fig = plt.figure() -plt.scatter(Counter_1[:,3], Counter_1[:,1]) -plt.title('(theta, pseudomotor) for one projection') -plt.xlabel('Theta (degrees)') -plt.ylabel('Pseudomotor (index)') -plt.show() - -# Plot (pseudomotor, theta) distribution for all projections -fig = plt.figure() -plt.title('(theta, pseudomotor) for all projections') -plt.scatter(Counter_deg[:,3], Counter_deg[:,1], s=2) -plt.xlabel('Theta (degrees)') -plt.ylabel('Pseudomotor (index)') -plt.show() diff --git a/Plot_bckg.py b/Plot_bckg.py deleted file mode 100644 index 8ba148d..0000000 --- a/Plot_bckg.py +++ /dev/null @@ -1,22 +0,0 @@ -# Test script to check how getdata.py works - -from lib.miniged import GetEdfData -import sys -import time -import os -import warnings -import numpy as np -import pdb # For debugging -from find_nearest import find_nearest, find_nearest_idx - -datadir = '/u/data/andcj/hxrm/Al_april_2017/topotomo/sundaynight' -dataname = 'topotomo_frelon_far_' -bgpath = '/u/data/andcj/hxrm/Al_april_2017/topotomo/monday/bg' #'/home/nexmap/alcer/DFXRM/bg_refined' -bgfilename = 'topotomo_frelon_far' -roi = 256,256 -sim = 200,200 - -#[bckg_full, bckg] = GetEdfData.getBGarray(datadir, dataname, bgpath, bgfilename, roi, sim) - -get_bckg = GetEdfData.getBGarray(datadir, dataname, bgpath, bgfilename, roi, sim) # get an instance of the class -get_bckg.bckg() diff --git a/Plot_data_1omega.py b/Plot_data_1omega.py deleted file mode 100644 index 807fe85..0000000 --- a/Plot_data_1omega.py +++ /dev/null @@ -1,428 +0,0 @@ -# Script to analyze the images collected at a certain omega - -import numpy as np -import matplotlib.pyplot as plt -import lib.EdfFile as EF -import sys - -A = np.load('/u/data/alcer/DFXRM_rec/Rec_test/dataarray.npy') - -# Select projection number -omega = 2 - -int = np.empty([A.shape[0], A.shape[1]]) -Img_array = np.empty([A.shape[0], A.shape[1], A.shape[3], A.shape[4]]) -for i in range(A.shape[0]): - for j in range(A.shape[1]): - int[i,j] = sum(sum(A[i,j,omega,:,:])) - if int[i,j] > 0: - Img_array[i,j,:,:] = A[i,j,omega,:,:] - -# Analyze distribution of recorded images as a function of angles -int = np.empty([A.shape[0], A.shape[1]]) -for oo in range(A.shape[2]): - for i in range(A.shape[0]): - for j in range(A.shape[1]): - int[i,j] = int[i,j] + sum(sum(A[i,j,oo,:,:])) - -#plt.imshow(int) -#plt.title('Distribution of the recorded images as a function of the motor angles') -#plt.xlabel('Theta') -#plt.ylabel('Gamma') -#plt.show() - -# Sum images collected at a certain angle -Array_ang_val = np.empty([A.shape[0], A.shape[1], A.shape[3], A.shape[4]]) -Sum_img = np.zeros([A.shape[3], A.shape[4]]) -int = np.empty([A.shape[0], A.shape[1]]) -for ii in range(A.shape[0]): - for jj in range(A.shape[1]): - int[ii,jj] = sum(sum(A[ii,jj,omega,:,:])) - if int[ii,jj] > 0: - Array_ang_val[ii, jj,:,:] = A[ii,jj,omega,:,:] - Sum_img[:,:] = Sum_img[:,:] + A[ii,jj,omega,:,:] - -# Plot the distribution of integrated intensity values in the gamma, theta plane -#plt.imshow(int) -#plt.title('Distribution of the recorded images as a function of the motor angles') -#plt.xlabel('Theta') -#plt.ylabel('Gamma') -#plt.show() - -# Plot the sum of the summed images -#fig = plt.figure() -#plt.imshow(Sum_img) -#plt.xlabel('X (pixels)') -#plt.ylabel('Y (pixels)') -#plt.show() - -# We want to plot, in a grid, all images collected at a certain projection -# Load the datafile with all information, and store to an array the data -# relative to the images in a certain projection -Data = np.loadtxt('/u/data/alcer/DFXRM_rec/Rec_test/Image_properties.txt') -Data_angle = np.zeros([49,2]) -idx = 0 -for i in range(Data.shape[0]): - if Data[i,3] == omega: - idx = idx +1 - Data_angle[idx-1, 0] = Data[i,1] - Data_angle[idx-1, 1] = Data[i,2] - -# Plot the various recorded images - -aa = int(Data_angle[0,0]) -bb = int(Data_angle[0,1]) -AA = np.zeros([Img_array.shape[2], Img_array.shape[3]]) -AA[:,:] = Img_array[aa,bb,:,:] - -fig, axes = plt.subplots(7, 7) - -a1 = fig.add_subplot(7,7,1) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[0,0] -bb = Data_angle[0,1] -plt.imshow(AA) - -a1 = fig.add_subplot(7,7,2) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[1,0] -bb = Data_angle[1,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,3) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[2,0] -bb = Data_angle[2,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,4) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[3,0] -bb = Data_angle[3,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,5) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[4,0] -bb = Data_angle[4,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,6) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[5,0] -bb = Data_angle[5,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,7) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[6,0] -bb = Data_angle[6,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,8) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[7,0] -bb = Data_angle[7,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,9) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[8,0] -bb = Data_angle[8,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,10) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[9,0] -bb = Data_angle[9,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,11) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[10,0] -bb = Data_angle[10,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,12) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[11,0] -bb = Data_angle[11,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,13) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[12,0] -bb = Data_angle[12,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,14) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[13,0] -bb = Data_angle[13,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,15) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[14,0] -bb = Data_angle[14,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,16) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[15,0] -bb = Data_angle[15,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,17) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[16,0] -bb = Data_angle[16,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,18) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[17,0] -bb = Data_angle[17,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,19) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[18,0] -bb = Data_angle[18,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,20) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[19,0] -bb = Data_angle[19,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,21) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[20,0] -bb = Data_angle[20,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,22) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[21,0] -bb = Data_angle[21,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,23) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[22,0] -bb = Data_angle[22,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,24) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[23,0] -bb = Data_angle[23,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,25) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[24,0] -bb = Data_angle[24,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,26) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[25,0] -bb = Data_angle[25,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,27) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[26,0] -bb = Data_angle[26,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,28) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[27,0] -bb = Data_angle[27,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,29) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[28,0] -bb = Data_angle[28,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,30) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[29,0] -bb = Data_angle[29,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,31) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[30,0] -bb = Data_angle[30,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,32) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[31,0] -bb = Data_angle[31,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,33) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[32,0] -bb = Data_angle[32,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,34) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[33,0] -bb = Data_angle[33,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,35) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[34,0] -bb = Data_angle[34,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,36) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[35,0] -bb = Data_angle[35,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,37) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[36,0] -bb = Data_angle[36,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,38) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[37,0] -bb = Data_angle[37,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,39) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[38,0] -bb = Data_angle[38,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,40) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[39,0] -bb = Data_angle[39,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,41) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[40,0] -bb = Data_angle[40,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,42) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[41,0] -bb = Data_angle[41,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,43) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[42,0] -bb = Data_angle[42,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,44) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[43,0] -bb = Data_angle[43,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,45) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[44,0] -bb = Data_angle[44,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,46) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[45,0] -bb = Data_angle[45,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,47) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[47,0] -bb = Data_angle[47,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,48) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[48,0] -bb = Data_angle[48,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -a1 = fig.add_subplot(7,7,49) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -aa = Data_angle[49,0] -bb = Data_angle[49,1] -plt.imshow(A[int(aa),int(bb),omega,:,:]) - -plt.axis('off') -plt.subplots_adjust(wspace=0, hspace=0) - -#fig.savefig('test.png') - -plt.show() diff --git a/Plot_data_raw.py b/Plot_data_raw.py deleted file mode 100644 index 016e465..0000000 --- a/Plot_data_raw.py +++ /dev/null @@ -1,98 +0,0 @@ -# Plot, for a given omega, angular values before binning - -import numpy as np -import matplotlib.pyplot as plt -<<<<<<< HEAD - -Data = np.load('/u/data/alcer/DFXRM_rec/Rec_test/all_data.npy') -======= -from mpl_toolkits.mplot3d import Axes3D - -def randrange(n, vmin, vmax): - return (vmax-vmin)*np.random.rand(n) + vmin - -Data = np.load('/Users/Alberto/Documents/Data_analysis/DFXRM/Results_sunday/All_data.npy') ->>>>>>> 077febf5ee14058f5f57ce942f86a24dc78c3637 - -# Plot distribution of motor values -alpha = Data[:,0] -beta = Data[:,1] -omega = Data[:,2] -<<<<<<< HEAD -======= -theta = Data[:,4] ->>>>>>> 077febf5ee14058f5f57ce942f86a24dc78c3637 - -# Print all rotation angles -print(np.sort(list(set(omega)))) - -# Plot rotation angles -#plt.figure() -#plt.plot(Data[1:200, 2]) -#plt.xlabel('Acquistion number') -#plt.ylabel('Degrees') -#plt.show() - -# Select the omega you want to consider -# Sundaynight values: 1.6, 30.4, 60., 90.4, 120., 150.4, 179.2 -# Monday values: 1.6, 30.4, 60., 89.6, 120., 150.4, 178.4 -# Lower = 1.6 because of strange behaviour at 0.8 -Om = 0.8 - -idx = np.where(omega==Om) -alpha_select = alpha[idx] -beta_select = beta[idx] -<<<<<<< HEAD - -# For the selected omega, plot the angular distribution -fig = plt.figure(figsize=(12,9)) - -fig.suptitle('Alpha and beta values for omega = 0.8 deg', fontsize=20) -======= -theta_select = theta[idx] - -# Plot, in 3D, the (alpha, beta, theta) distribution -fig = plt.figure() -ax = fig.add_subplot(111, projection='3d') -n = 100 -ax.scatter(alpha_select, beta_select, theta_select, c='g', marker='o') - -ax.set_xlabel('Alpha (degrees)') -ax.set_ylabel('Beta (degrees)') -ax.set_zlabel('Theta (degrees)') - -plt.show() - -# For a selected omega, plot the angular distribution -fig = plt.figure(figsize=(12,9)) - -plt.rc('text', usetex=True) -plt.rc('font', family='serif') - -fig.suptitle(r'$\alpha$ and $\beta$ values for $\omega = 179.2^{\circ}$', fontsize=20) - -label_size = 16 -plt.rcParams['xtick.labelsize'] = label_size -plt.rcParams['ytick.labelsize'] = label_size ->>>>>>> 077febf5ee14058f5f57ce942f86a24dc78c3637 - -plt.subplot(121) -plt.plot(alpha_select) -plt.xlabel(r'Acquisition number', fontsize=20) -plt.ylabel(r'Degrees', fontsize=20) -plt.title(r'Distribution of $\alpha$ angles', fontsize=20) - -plt.subplot(122) -plt.plot(beta_select) -plt.xlabel(r'Acquisition number', fontsize=20) -#plt.ylabel(r'Degrees', fontsize=20) -plt.title(r'Distribution of $\beta$ angles', fontsize=20) - -plt.show() - -fig = plt.figure(figsize=(8,6)) -plt.plot(alpha_select, beta_select, '*') -plt.title(r'$\alpha$ and $\beta$ values for $\omega=179.2^{\circ}$', fontsize=20) -plt.xlabel(r'$\alpha$ ($^{\circ}$)', fontsize=20) -plt.ylabel(r'$\beta$ ($^{\circ}$)', fontsize=20) -plt.show() diff --git a/Plot_from_npy.py b/Plot_from_npy.py deleted file mode 100644 index 86a2927..0000000 --- a/Plot_from_npy.py +++ /dev/null @@ -1,49 +0,0 @@ -import numpy as np -import matplotlib.pyplot as plt -import lib.EdfFile as EF -import sys -import fabio - -A = np.load('/u/data/alcer/DFXRM_rec/Rec_test/Fabio_array.npy') - -Im_stack = A[4,14,1,:,:] -Im_raw = fabio.open("/u/data/andcj/hxrm/Al_april_2017/topotomo/sundaynight/topotomo_frelon_far_0012_0000_0001.edf") - -#AA = Im_stack[1:10, 1:10] -#BB = Im_raw[100:120, 100:120] - -#plt.subplot(122) -#plt.imshow(BB) - -#plt.show() - -#print Im_stack[5,3], Im_raw[5+107, 3+107] - -Diff = np.zeros([Im_stack.shape[0], Im_stack.shape[1]]) -for i in range(Im_stack.shape[0]): - for j in range(Im_stack.shape[1]): - diff = int(Im_stack[i,j]) - int(Im_raw.data[i+106, j+106]) - Diff[i,j] = diff - #print Im_stack[i,j], Im_raw[i+106, j+106], Diff[i,j] - -Diff_num = sum(sum(Diff)) - -fig = plt.figure() -#plt.imshow(Im_stack) -#plt.title('Image from stack') -#plt.show() -#sys.exit() - -plt.subplot(131) -plt.imshow(Im_raw.data[106:406, 106:406]) -plt.title('Raw image') - -plt.subplot(132) -plt.imshow(Im_stack) -plt.title('Image from stack') - -plt.subplot(133) -plt.imshow(Diff) -plt.title('Difference') - -plt.show() diff --git a/Plot_npy_alpha_beta.py b/Plot_npy_alpha_beta.py deleted file mode 100644 index 9c10aa1..0000000 --- a/Plot_npy_alpha_beta.py +++ /dev/null @@ -1,93 +0,0 @@ -# Script to visualize the files returned by getdata.py -# Command: Plot_results.py path/Input_folder -# Where the Input_folder is the folder with the npy files - -import numpy as np -import sys -import os -import matplotlib.pyplot as plt -from find_nearest import find_nearest, find_nearest_idx - -''' -Input : directory of data -''' - -plt.close("all") - -dir = sys.argv[1] - -alpha = np.load(os.path.join(dir,'alpha.npy')) -beta = np.load(os.path.join(dir,'beta.npy')) - -# Find position of the center of the bins used to group the data -[count_alpha, extremes_alpha] = np.histogram(alpha, 7) -val_alpha = np.zeros(len(extremes_alpha)-1) - -[count_beta, extremes_beta] = np.histogram(beta, 7) -val_beta = np.zeros(len(extremes_beta)-1) - -# Find center of each bin -for i in range(0,len(extremes_alpha)-1): - val_alpha[i] = np.mean([extremes_alpha[i], extremes_alpha[i+1]]) -for i in range(0,len(extremes_beta)-1): - val_beta[i] = np.mean([extremes_beta[i], extremes_beta[i+1]]) - -# Find experimental value closest to bin centre, which will be plotted -u = np.zeros((len(val_alpha), 2)) -for j in range(0,len(val_alpha)): - u[j,0] = find_nearest(alpha,val_alpha[j]) - u[j,1] = find_nearest_idx(alpha,val_alpha[j]) - -v = np.zeros((len(val_beta), 2)) -for j in range(0,len(val_alpha)): - v[j,0] = find_nearest(beta,val_beta[j]) - v[j,1] = find_nearest_idx(beta,val_beta[j]) - -# Plot distribution of motor values -ar = range(len(alpha)) -br = range(len(beta)) - -plt.figure(1) - -plt.subplot(221) -plt.plot(alpha) -for i in range(0,len(val_alpha)): - plt.scatter(u[i,1], u[i,0]) -plt.xlabel('Acquisition number') -plt.ylabel('Alpha (in degrees)') -plt.title('Distribution of alpha angles') - -plt.subplot(222) -plt.plot(beta) -for i in range(0,len(val_beta)): - plt.scatter(v[i,1], v[i,0]) -plt.xlabel('Acquisition number') -plt.ylabel('Beta (in degrees)') -plt.title('Distribution of beta angles') - -plt.subplot(223) -plt.plot(alpha, ar) -for i in range(0,len(val_alpha)): - plt.scatter(u[i,0], u[i,1]) -plt.xlabel('Alpha (in degrees)') -plt.ylabel('Acquisition number') - -plt.subplot(224) -plt.plot(beta, br) -for i in range(0,len(val_beta)): - plt.scatter(v[i,0], v[i,1]) -plt.xlabel('Beta (in degrees)') -plt.ylabel('Acquisition number') - -# Plot hist showing how angular values are distributed in the different bins -plt.figure(2) - -plt.subplot(121) -plt.hist(alpha, 7) -plt.title('Binning of alpha values') - -plt.subplot(122) -plt.hist(beta, 7) -plt.title('Binning of beta values') - -plt.show() diff --git a/Plot_sum_10deg_steps.py b/Plot_sum_10deg_steps.py deleted file mode 100644 index 883f9f6..0000000 --- a/Plot_sum_10deg_steps.py +++ /dev/null @@ -1,174 +0,0 @@ -import numpy as np -import matplotlib.pyplot as plt - -A = np.load('/u/data/alcer/DFXRM_rec/Rec_test/dataarray.npy') - -fig = plt.figure() - -plt.title('Summed images at different projections') - -a1 = fig.add_subplot(3,6,1) -plt.setp(a1.get_xticklabels(), visible=False) -plt.setp(a1.get_yticklabels(), visible=False) -sum = np.zeros([A.shape[3], A.shape[4]]) -for i in range(A.shape[0]): - for j in range(A.shape[1]): - sum[:,:] += A[i,j,12,:,:] -plt.imshow(sum/(sum.max())) - -a2 = fig.add_subplot(3,6,2) -plt.setp(a2.get_xticklabels(), visible=False) -plt.setp(a2.get_yticklabels(), visible=False) -sum = np.zeros([A.shape[3], A.shape[4]]) -for i in range(A.shape[0]): - for j in range(A.shape[1]): - sum[:,:] += A[i,j,25,:,:] -plt.imshow(sum/(sum.max())) - -a3 = fig.add_subplot(3,6,3) -plt.setp(a3.get_xticklabels(), visible=False) -plt.setp(a3.get_yticklabels(), visible=False) -sum = np.zeros([A.shape[3], A.shape[4]]) -for i in range(A.shape[0]): - for j in range(A.shape[1]): - sum[:,:] += A[i,j,37,:,:] -plt.imshow(sum/(sum.max())) - -a4 = fig.add_subplot(3,6,4) -plt.setp(a4.get_xticklabels(), visible=False) -plt.setp(a4.get_yticklabels(), visible=False) -sum = np.zeros([A.shape[3], A.shape[4]]) -for i in range(A.shape[0]): - for j in range(A.shape[1]): - sum[:,:] += A[i,j,50,:,:] -plt.imshow(sum/(sum.max())) - -a5 = fig.add_subplot(3,6,5) -plt.setp(a5.get_xticklabels(), visible=False) -plt.setp(a5.get_yticklabels(), visible=False) -sum = np.zeros([A.shape[3], A.shape[4]]) -for i in range(A.shape[0]): - for j in range(A.shape[1]): - sum[:,:] += A[i,j,62,:,:] -plt.imshow(sum/(sum.max())) - -a6 = fig.add_subplot(3,6,6) -plt.setp(a6.get_xticklabels(), visible=False) -plt.setp(a6.get_yticklabels(), visible=False) -sum = np.zeros([A.shape[3], A.shape[4]]) -for i in range(A.shape[0]): - for j in range(A.shape[1]): - sum[:,:] += A[i,j,75,:,:] -plt.imshow(sum/(sum.max())) - -a7 = fig.add_subplot(3,6,7) -plt.setp(a7.get_xticklabels(), visible=False) -plt.setp(a7.get_yticklabels(), visible=False) -sum = np.zeros([A.shape[3], A.shape[4]]) -for i in range(A.shape[0]): - for j in range(A.shape[1]): - sum[:,:] += A[i,j,87,:,:] -plt.imshow(sum/(sum.max())) - -a8 = fig.add_subplot(3,6,8) -plt.setp(a8.get_xticklabels(), visible=False) -plt.setp(a8.get_yticklabels(), visible=False) -sum = np.zeros([A.shape[3], A.shape[4]]) -for i in range(A.shape[0]): - for j in range(A.shape[1]): - sum[:,:] += A[i,j,100,:,:] -plt.imshow(sum/(sum.max())) - -a9 = fig.add_subplot(3,6,9) -plt.setp(a9.get_xticklabels(), visible=False) -plt.setp(a9.get_yticklabels(), visible=False) -sum = np.zeros([A.shape[3], A.shape[4]]) -for i in range(A.shape[0]): - for j in range(A.shape[1]): - sum[:,:] += A[i,j,112,:,:] -plt.imshow(sum/(sum.max())) - -a10 = fig.add_subplot(3,6,10) -plt.setp(a10.get_xticklabels(), visible=False) -plt.setp(a10.get_yticklabels(), visible=False) -sum = np.zeros([A.shape[3], A.shape[4]]) -for i in range(A.shape[0]): - for j in range(A.shape[1]): - sum[:,:] += A[i,j,125,:,:] -plt.imshow(sum/(sum.max())) - -a11 = fig.add_subplot(3,6,11) -plt.setp(a11.get_xticklabels(), visible=False) -plt.setp(a11.get_yticklabels(), visible=False) -sum = np.zeros([A.shape[3], A.shape[4]]) -for i in range(A.shape[0]): - for j in range(A.shape[1]): - sum[:,:] += A[i,j,137,:,:] -plt.imshow(sum/(sum.max())) - -a12 = fig.add_subplot(3,6,12) -plt.setp(a12.get_xticklabels(), visible=False) -plt.setp(a12.get_yticklabels(), visible=False) -sum = np.zeros([A.shape[3], A.shape[4]]) -for i in range(A.shape[0]): - for j in range(A.shape[1]): - sum[:,:] += A[i,j,150,:,:] -plt.imshow(sum/(sum.max())) - -a13 = fig.add_subplot(3,6,13) -plt.setp(a13.get_xticklabels(), visible=False) -plt.setp(a13.get_yticklabels(), visible=False) -sum = np.zeros([A.shape[3], A.shape[4]]) -for i in range(A.shape[0]): - for j in range(A.shape[1]): - sum[:,:] += A[i,j,162,:,:] -plt.imshow(sum/(sum.max())) - -a14 = fig.add_subplot(3,6,14) -plt.setp(a14.get_xticklabels(), visible=False) -plt.setp(a14.get_yticklabels(), visible=False) -sum = np.zeros([A.shape[3], A.shape[4]]) -for i in range(A.shape[0]): - for j in range(A.shape[1]): - sum[:,:] += A[i,j,175,:,:] -plt.imshow(sum/(sum.max())) - -a15 = fig.add_subplot(3,6,15) -plt.setp(a15.get_xticklabels(), visible=False) -plt.setp(a15.get_yticklabels(), visible=False) -sum = np.zeros([A.shape[3], A.shape[4]]) -for i in range(A.shape[0]): - for j in range(A.shape[1]): - sum[:,:] += A[i,j,187,:,:] -plt.imshow(sum/(sum.max())) - -a16 = fig.add_subplot(3,6,16) -plt.setp(a16.get_xticklabels(), visible=False) -plt.setp(a16.get_yticklabels(), visible=False) -sum = np.zeros([A.shape[3], A.shape[4]]) -for i in range(A.shape[0]): - for j in range(A.shape[1]): - sum[:,:] += A[i,j,200,:,:] -plt.imshow(sum/(sum.max())) - -a17 = fig.add_subplot(3,6,17) -plt.setp(a17.get_xticklabels(), visible=False) -plt.setp(a17.get_yticklabels(), visible=False) -sum = np.zeros([A.shape[3], A.shape[4]]) -for i in range(A.shape[0]): - for j in range(A.shape[1]): - sum[:,:] += A[i,j,212,:,:] -plt.imshow(sum/(sum.max())) - -a18 = fig.add_subplot(3,6,18) -plt.setp(a18.get_xticklabels(), visible=False) -plt.setp(a18.get_yticklabels(), visible=False) -sum = np.zeros([A.shape[3], A.shape[4]]) -for i in range(A.shape[0]): - for j in range(A.shape[1]): - sum[:,:] += A[i,j,225,:,:] -plt.imshow(sum/(sum.max())) - -plt.axis('off') - -plt.show() diff --git a/Study_motor_angles/readNPY.m b/Study_motor_angles/readNPY.m deleted file mode 100644 index 862bb70..0000000 --- a/Study_motor_angles/readNPY.m +++ /dev/null @@ -1,36 +0,0 @@ - -function data = readNPY(filename) -% Function to read NPY files into matlab. -% *** Only reads a subset of all possible NPY 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