From 2ded706f82ea65502c68f5dcb41e63da3210ff8e Mon Sep 17 00:00:00 2001 From: ostrow Date: Mon, 27 Oct 2025 18:30:20 -0400 Subject: [PATCH 01/90] updates from dsa-v2 --- DSA/dsa.py | 444 +++++++++++++++++++++++----------------- DSA/kerneldmd.py | 180 ---------------- DSA/preprocessing.py | 109 +++++++++- DSA/simdist.py | 476 +++++++++++++++++++++++++++---------------- DSA/sweeps.py | 41 ++-- 5 files changed, 673 insertions(+), 577 deletions(-) delete mode 100644 DSA/kerneldmd.py diff --git a/DSA/dsa.py b/DSA/dsa.py index 5036180..9631508 100644 --- a/DSA/dsa.py +++ b/DSA/dsa.py @@ -1,79 +1,83 @@ -from DSA.dmd import DMD -#from DSA.kerneldmd import KernelDMD +from DSA.dmd import DMD as DefaultDMD from DSA.simdist import SimilarityTransformDist from typing import Literal import torch import numpy as np from omegaconf.listconfig import ListConfig +import tqdm +from joblib import Parallel, delayed + + +CAST_TYPES = { + "n_delays": int, + "delay_interval": int, + "rank": int, + "rank_thresh": float, + "rank_explained_variance": float, + "lamb": float, + "steps_ahead": int, + "reduced_rank_reg": bool, + "send_to_cpu": bool, +} + class DSA: """ - Computes the Dynamical Similarity Analysis (DSA) for two data matrices + Computes the Dynamical Similarity Analysis (DSA) for two data tensors """ - def __init__(self, - X, - Y=None, - n_delays=1, - delay_interval=1, - rank=None, - rank_thresh=None, - rank_explained_variance = None, - lamb = 0.0, - steps_ahead=1, - send_to_cpu = True, - iters = 1500, - score_method: Literal["angular", "euclidean","wasserstein"] = "angular", - lr = 5e-3, - group: Literal["GL(n)", "O(n)", "SO(n)"] = "O(n)", - zero_pad = False, - device = 'cpu', - verbose = False, - reduced_rank_reg = False, - kernel=None, - num_centers=0.1, - svd_solver='arnoldi', - wasserstein_compare: Literal['sv','eig',None] = None - ): + + def __init__( + self, + X, + Y=None, + dmd_class=DefaultDMD, + iters=1500, + score_method: Literal["angular", "euclidean", "wasserstein"] = "angular", + lr=5e-3, + zero_pad=False, + device="cpu", + wasserstein_compare: Literal["sv", "eig", None] = "eig", + n_jobs: int = 1, + dsa_verbose=False, + **dmd_kwargs, + ): """ Parameters __________ X : np.array or torch.tensor or list of np.arrays or torch.tensors first data matrix/matrices - + Y : None or np.array or torch.tensor or list of np.arrays or torch.tensors - second data matrix/matrices. - * If Y is None, X is compared to itself pairwise + second data matrix/matrices. + * If Y is None, X is compared to itself pairwise (must be a list) * If Y is a single matrix, all matrices in X are compared to Y * If Y is a list, all matrices in X are compared to all matrices in Y - - DMD parameters: + + DMD parameters : n_delays : int or list or tuple/list: (int,int), (list,list),(list,int),(int,list) number of delays to use in constructing the Hankel matrix - + delay_interval : int or list or tuple/list: (int,int), (list,list),(list,int),(int,list) interval between samples taken in constructing Hankel matrix rank : int or list or tuple/list: (int,int), (list,list),(list,int),(int,list) rank of DMD matrix fit in reduced-rank regression - + rank_thresh : float or list or tuple/list: (float,float), (list,list),(list,float),(float,list) Parameter that controls the rank of V in fitting HAVOK DMD by dictating a threshold of singular values to use. Explicitly, the rank of V will be the number of singular values greater than rank_thresh. Defaults to None. - + rank_explained_variance : float or list or tuple: (float,float), (list,list),(list,float),(float,list) Parameter that controls the rank of V in fitting HAVOK DMD by indicating the percentage of cumulative explained variance that should be explained by the columns of V. Defaults to None. - + lamb : float L-1 regularization parameter in DMD fit - steps_ahead: int - number of steps ahead to predict in DMD - send_to_cpu: bool If True, will send all tensors in the object back to the cpu after everything is computed. This is implemented to prevent gpu memory overload when computing multiple DMDs. @@ -81,7 +85,7 @@ def __init__(self, NOTE: for all of these above, they can be single values or lists or tuples, depending on the corresponding dimensions of the data If at least one of X and Y are lists, then if they are a single value - it will default to the rank of all DMD matrices. + it will default to the rank of all DMD matrices. If they are (int,int), then they will correspond to an individual dmd matrix OR to X and Y respectively across all matrices If it is (list,list), then each element will correspond to an individual @@ -91,133 +95,199 @@ def __init__(self, iters : int number of optimization iterations in Procrustes over vector fields - + score_method : {'angular','euclidean'} type of metric to compute, angular vs euclidean distance - + lr : float learning rate of the Procrustes over vector fields optimization - group : {'SO(n)','O(n)', 'GL(n)'} - specifies the group of matrices to optimize over - zero_pad : bool whether or not to zero-pad if the dimensions are different device : 'cpu' or 'cuda' or int hardware to use in both DMD and PoVF - - verbose : bool + + dsa_verbose : bool whether or not print when sections of the analysis is completed - + wasserstein_compare : {'sv','eig',None} specifies whether to compare the singular values or eigenvalues if score_method is "wasserstein", or the shapes are different """ self.X = X self.Y = Y - if self.X is None and isinstance(self.Y,list): - self.X, self.Y = self.Y, self.X #swap so code is easy + self.iters = iters + self.score_method = score_method + self.lr = lr + self.device = device + self.zero_pad = zero_pad + self.n_jobs = n_jobs + self.dsa_verbose = dsa_verbose + self.dmd_class = dmd_class + + if self.X is None and isinstance(self.Y, list): + self.X, self.Y = self.Y, self.X # swap so code is easy self.check_method() - if self.method == 'self-pairwise': + if self.method == "self-pairwise": self.data = [self.X] else: self.data = [self.X, self.Y] - - self.n_delays = self.broadcast_params(n_delays,cast=int) - self.delay_interval = self.broadcast_params(delay_interval,cast=int) - self.rank = self.broadcast_params(rank,cast=int) - self.rank_thresh = self.broadcast_params(rank_thresh) - self.rank_explained_variance = self.broadcast_params(rank_explained_variance) - self.lamb = self.broadcast_params(lamb) - self.steps_ahead = self.broadcast_params(steps_ahead,cast=int) - self.send_to_cpu = send_to_cpu - self.iters = iters - self.score_method = score_method - self.lr = lr - self.device = device - self.verbose = verbose - self.zero_pad = zero_pad - self.group = group - self.reduced_rank_reg = reduced_rank_reg - self.kernel = kernel - self.wasserstein_compare = wasserstein_compare - self.steps_ahead = self.broadcast_params(steps_ahead,cast=int) - - if kernel is None: - #get a list of all DMDs here - self.dmds = [[DMD(Xi, - self.n_delays[i][j], - delay_interval=self.delay_interval[i][j], - rank=self.rank[i][j], - rank_thresh=self.rank_thresh[i][j], - rank_explained_variance=self.rank_explained_variance[i][j], - reduced_rank_reg=self.reduced_rank_reg, - lamb=self.lamb[i][j], - steps_ahead = self.steps_ahead[i][j], - device=self.device, - verbose=self.verbose, - send_to_cpu=self.send_to_cpu) for j,Xi in enumerate(dat)] for i,dat in enumerate(self.data)] + + # Process DMD keyword arguments from **dmd_kwargs + # These are parameters like n_delays, rank, etc., that are specific to DMDs + # and need to be broadcasted according to X and Y data structure. + self.dmd_kwargs = ( + {} + ) # This will store {'param_name': broadcasted_value_list_of_lists} + + if dmd_kwargs: + for key, value in dmd_kwargs.items(): + cast_type = CAST_TYPES.get(key) + + if cast_type is not None: + broadcasted_value = self.broadcast_params(value, cast=cast_type) + else: + broadcasted_value = self.broadcast_params(value) + + setattr( + self, key, broadcasted_value + ) # e.g., self.n_delays = [[v,v],[v,v]] + self.dmd_kwargs[key] = ( + broadcasted_value # Store in dict for DMD instantiation + ) + + + self._dmd_api_source(dmd_class) + self._initiate_dmds() + + self.simdist = SimilarityTransformDist( + iters, score_method, lr, device, dsa_verbose, wasserstein_compare + ) + + def _initiate_dmds(self): + if self.dmd_api_source == "local_dsa_dmd": + self.dmds = [ + [ + self.dmd_class(Xi, **{k: v[i][j] for k, v in self.dmd_kwargs.items()}) + for j, Xi in enumerate(dat) + ] + for i, dat in enumerate(self.data) + ] else: - raise ValueError('KernelDMD not implemented yet') + self.dmds = [ + [self.dmd_class(**{k: v[i][j] for k, v in self.dmd_kwargs.items()}) for j, Xi in enumerate(dat)] + for i, dat in enumerate(self.data) + ] - self.simdist = SimilarityTransformDist(iters,score_method,lr,device,verbose,group,wasserstein_compare) + def _dmd_api_source(self, dmd_class): + module_name = dmd_class.__module__ + if "pydmd" in module_name: + self.dmd_api_source = "pydmd" + raise ValueError("DSA is not currently directly compatible with pydmd due to \ + data structure incompatibility. Please use pykoopman instead. \ + Note that you can pass in pydmd objects through pykoopman's Koopman class.") + elif "pykoopman" in module_name: + self.dmd_api_source = "pykoopman" + elif "DSA.dmd" in module_name: + self.dmd_api_source = "local_dsa_dmd" + else: + self.dmd_api_source = "unknown" + raise ValueError( + f"dmd_class {dmd_class.__name__} from unknown module {module_name}" + ) + + def fit_dmds(self): + if self.n_jobs != 1: + n_jobs = self.n_jobs if self.n_jobs > 0 else -1 # -1 means use all available cores + + if self.dmd_api_source == "local_dsa_dmd": + for dmd_sets in self.dmds: + if self.dsa_verbose: + print(f"Fitting {len(dmd_sets)} DMDs in parallel with {n_jobs} jobs") + Parallel(n_jobs=n_jobs)( + delayed(lambda dmd: dmd.fit())(dmd) for dmd in dmd_sets + ) + else: + for dmd_list, dat in zip(self.dmds, self.data): + if self.dsa_verbose: + print(f"Fitting {len(dmd_list)} DMDs in parallel with {n_jobs} jobs") + Parallel(n_jobs=n_jobs)( + delayed(lambda dmd, X: dmd.fit(X))(dmd, Xi) for dmd, Xi in zip(dmd_list, dat) + ) + else: + # Sequential processing + if self.dmd_api_source == "local_dsa_dmd": + for dmd_sets in self.dmds: + loop = dmd_sets if not self.dsa_verbose else tqdm.tqdm(dmd_sets, desc="Fitting DMDs") + for dmd in loop: + dmd.fit() + else: + for dmd_list, dat in zip(self.dmds, self.data): + loop = zip(dmd_list, dat) if not self.dsa_verbose else tqdm.tqdm(zip(dmd_list, dat), desc="Fitting DMDs") + for dmd, Xi in loop: + dmd.fit(Xi) def check_method(self): - ''' + """ helper function to identify what type of dsa we're running - ''' - tensor_or_np = lambda x: isinstance(x,(np.ndarray,torch.Tensor)) + """ + tensor_or_np = lambda x: isinstance(x, (np.ndarray, torch.Tensor)) - if isinstance(self.X,list): + if isinstance(self.X, list): if self.Y is None: - self.method = 'self-pairwise' - elif isinstance(self.Y,list): - self.method = 'bipartite-pairwise' + self.method = "self-pairwise" + elif isinstance(self.Y, list): + self.method = "bipartite-pairwise" elif tensor_or_np(self.Y): - self.method = 'list-to-one' - self.Y = [self.Y] #wrap in a list for iteration + self.method = "list-to-one" + self.Y = [self.Y] # wrap in a list for iteration else: - raise ValueError('unknown type of Y') + raise ValueError("unknown type of Y") elif tensor_or_np(self.X): self.X = [self.X] if self.Y is None: - raise ValueError('only one element provided') - elif isinstance(self.Y,list): - self.method = 'one-to-list' + raise ValueError("only one element provided") + elif isinstance(self.Y, list): + self.method = "one-to-list" elif tensor_or_np(self.Y): - self.method = 'default' + self.method = "default" self.Y = [self.Y] else: - raise ValueError('unknown type of Y') + raise ValueError("unknown type of Y") else: - raise ValueError('unknown type of X') + raise ValueError("unknown type of X") - def broadcast_params(self,param,cast=None): - ''' + def broadcast_params(self, param, cast=None): + """ aligns the dimensionality of the parameters with the data so it's one-to-one - ''' + """ out = [] - if isinstance(param,(int,float,np.integer)) or param is None: #self.X has already been mapped to [self.X] - out.append([param] * len(self.X)) - if self.Y is not None: - out.append([param] * len(self.Y)) - elif isinstance(param,(tuple,list,np.ndarray,ListConfig)): - if self.method == 'self-pairwise' and len(param) >= len(self.X): + if isinstance(param, (tuple, list, np.ndarray, ListConfig)): + if self.method == "self-pairwise" and len(param) >= len(self.X): out = [param] else: - assert len(param) <= 2 #only 2 elements max + assert len(param) <= 2 # only 2 elements max - #if the inner terms are singly valued, we broadcast, otherwise needs to be the same dimensions - for i,data in enumerate([self.X,self.Y]): + # if the inner terms are singly valued, we broadcast, otherwise needs to be the same dimensions + for i, data in enumerate([self.X, self.Y]): if data is None: continue - if isinstance(param[i],(int,float)): + if isinstance(param[i], (int, float)): out.append([param[i]] * len(data)) - elif isinstance(param[i],(list,np.ndarray,tuple)): + elif isinstance(param[i], (list, np.ndarray, tuple)): assert len(param[i]) >= len(data) - out.append(param[i][:len(data)]) + out.append(param[i][: len(data)]) + elif ( + isinstance(param, (int, float, np.integer)) or param in {None,'None','none'} or + (hasattr(param, '__module__') and ('pykoopman' in param.__module__ or 'pydmd' in param.__module__)) + ): # self.X has already been mapped to [self.X] + if param in {'None','none'}: + param = None + out.append([param] * len(self.X)) + if self.Y is not None: + out.append([param] * len(self.Y)) else: raise ValueError("unknown type entered for parameter") @@ -225,55 +295,11 @@ def broadcast_params(self,param,cast=None): out = [[cast(x) for x in dat] for dat in out] return out - - def fit_dmds(self, - X=None, - Y=None, - n_delays=None, - delay_interval=None, - rank=None, - rank_thresh = None, - rank_explained_variance=None, - reduced_rank_reg=None, - lamb = None, - device='cpu', - verbose=False, - send_to_cpu=True - ): - """ - Recomputes only the DMDs with a single set of hyperparameters. This will not compare, that will need to be done with the full procedure - """ - X = self.X if X is None else X - Y = self.Y if Y is None else Y - n_delays = self.n_delays if n_delays is None else n_delays - delay_interval = self.delay_interval if delay_interval is None else delay_interval - rank = self.rank if rank is None else rank - lamb = self.lamb if lamb is None else lamb - data = [] - if isinstance(X,list): - data.append(X) - else: - data.append([X]) - if Y is not None: - if isinstance(Y,list): - data.append(Y) - else: - data.append([Y]) - - dmds = [[DMD(Xi,n_delays,delay_interval, - rank,rank_thresh,rank_explained_variance,reduced_rank_reg, - lamb,device,verbose,send_to_cpu) for Xi in dat] for dat in data] - - for dmd_sets in dmds: - for dmd in dmd_sets: - dmd.fit() - - return dmds def fit_score(self): """ - Standard fitting function for both DMDs and PoVF - + Standard fitting function for both DMDs and PAVF + Parameters __________ @@ -281,18 +307,23 @@ def fit_score(self): _______ sims : np.array - data matrix of the similarity scores between the specific sets of data + data matrix of the similarity scores between the specific sets of data """ - for dmd_sets in self.dmds: - for dmd in dmd_sets: - dmd.fit() - + self.fit_dmds() return self.score() - - def score(self,iters=None,lr=None,score_method=None): - """ - Rescore DSA with precomputed dmds if you want to try again + def get_dmd_matrix(self, dmd): + if self.dmd_api_source == "local_dsa_dmd": + return dmd.A_v + elif self.dmd_api_source == "pykoopman": + return dmd.A + elif self.dmd_api_source == "pydmd": + raise ValueError("DSA is not currently compatible with pydmd due to \ + data structure incompatibility. Please use pykoopman instead.") + + def score(self, iters=None, lr=None, score_method=None): + """ + Score DSA with precomputed dmds Parameters __________ iters : int or None @@ -312,25 +343,60 @@ def score(self,iters=None,lr=None,score_method=None): lr = self.lr if lr is None else lr score_method = self.score_method if score_method is None else score_method - ind2 = 1 - int(self.method == 'self-pairwise') + ind2 = 1 - int(self.method == "self-pairwise") # 0 if self.pairwise (want to compare the set to itself) - self.sims = np.zeros((len(self.dmds[0]),len(self.dmds[ind2]))) - for i,dmd1 in enumerate(self.dmds[0]): - for j,dmd2 in enumerate(self.dmds[ind2]): - if self.method == 'self-pairwise': - if j >= i: - continue - if self.verbose: - print(f'computing similarity between DMDs {i} and {j}') - - self.sims[i,j] = self.simdist.fit_score(dmd1.A_v,dmd2.A_v,iters,lr,score_method,zero_pad=self.zero_pad) + self.sims = np.zeros((len(self.dmds[0]), len(self.dmds[ind2]))) - if self.method == 'self-pairwise': - self.sims[j,i] = self.sims[i,j] + if self.dsa_verbose: + print('comparing dmds') + + def compute_similarity(i, j): + if self.method == "self-pairwise" and j >= i: + return None + + if self.dsa_verbose and self.n_jobs != 1: + print(f"computing similarity between DMDs {i} and {j}") + sim = self.simdist.fit_score( + self.get_dmd_matrix(self.dmds[0][i]), + self.get_dmd_matrix(self.dmds[ind2][j]), + iters, + lr, + score_method, + zero_pad=self.zero_pad, + ) + if self.dsa_verbose and self.n_jobs != 1: + print(f"computing similarity between DMDs {i} and {j}") + + return (i, j, sim) + + pairs = [] + for i in range(len(self.dmds[0])): + for j in range(len(self.dmds[ind2])): + if not (self.method == "self-pairwise" and j >= i): + pairs.append((i, j)) + + if self.n_jobs != 1: + n_jobs = self.n_jobs if self.n_jobs > 0 else -1 + if self.dsa_verbose: + print(f"Computing {len(pairs)} DMD similarities in parallel with {n_jobs} jobs") + + results = Parallel(n_jobs=n_jobs)( + delayed(compute_similarity)(i, j) for i, j in pairs + ) + else: + loop = pairs if not self.dsa_verbose else tqdm.tqdm(pairs, desc="Computing DMD similarities") + results = [compute_similarity(i, j) for i, j in loop] + + for result in results: + if result is not None: + i, j, sim = result + self.sims[i, j] = sim + if self.method == "self-pairwise": + self.sims[j, i] = sim - if self.method == 'default': - return self.sims[0,0] + if self.method == "default": + return self.sims[0, 0] return self.sims diff --git a/DSA/kerneldmd.py b/DSA/kerneldmd.py deleted file mode 100644 index 26313cb..0000000 --- a/DSA/kerneldmd.py +++ /dev/null @@ -1,180 +0,0 @@ -from sklearn.gaussian_process.kernels import DotProduct, RBF -from kooplearn.data import traj_to_contexts -from kooplearn.models import NystroemKernel -import numpy as np -import torch - -class KernelDMD(NystroemKernel): - def __init__( - self, - data, - n_delays, - kernel = RBF(), - num_centers=0.1, - delay_interval=1, - rank=10, - reduced_rank_reg=True, - lamb=1e-10, - verbose=False, - svd_solver='arnoldi', - ): - """ - Subclass of kooplearn that uses a kernel to compute the DMD model. - This will also use Reduced Rank Regression as opposed to Principal Component Regression (above) - """ - super().__init__(kernel,reduced_rank_reg,rank,lamb,svd_solver,num_centers) - self.n_delays = n_delays - self.context_window_len = n_delays + 1 - self.delay_interval = delay_interval - self.verbose = verbose - self.rank = rank - self.lamb = 0 if lamb is None else lamb - - self.data = data - - def fit( - self, - data=None, - lamb=None, - ): - """ - Parameters - ---------- - data : np.ndarray or torch.tensor - The data to fit the DMD model to. Must be either: (1) a - 2-dimensional array/tensor of shape T x N where T is the number - of time points and N is the number of observed dimensions - at each time point, or (2) a 3-dimensional array/tensor of shape - K x T x N where K is the number of "trials" and T and N are - as defined above. Defaults to None - provide only if you want to - override the value from the init. - - lamb : float - Regularization parameter for ridge regression. Defaults to None - provide only if you want to - override the value from the init. - """ - data = self.data if data is None else data - lamb = self.lamb if lamb is None else lamb - - self.compute_hankel(data) - self.compute_kernel_dmd(lamb) - - def compute_hankel(self,trajs): - ''' - Given a numpy array or list of trajectories, returns a numpy array of delay embeddings - in the format required by kooplearn. - Parameters - ---------- - trajs : np.ndarray or list, with each array having shape - (num_samples, timesteps, dimension) or shape (timesteps, dimension). - Note that trajectories can have different numbers of timesteps but must have the same dimension - n_delays : int - The number of delays to include in the delay embedding - delay_interval : int - The number of time steps between each delay in the delay embedding - ''' - if isinstance(trajs, torch.Tensor): - #convert trajs to a np array - trajs = trajs.numpy() - if isinstance(trajs,np.ndarray) and trajs.ndim == 2: - trajs = trajs[np.newaxis,:,:] - - data = traj_to_contexts(trajs[0],context_window_len=self.context_window_len, - time_lag=self.delay_interval) - # idx = np.zeros(data.idx_map.shape) - # data.idx_map = np.concatenate((idx,data.idx_map),axis=-1) - for i in range(1,len(trajs)): - new_traj = traj_to_contexts(trajs[i],context_window_len=self.context_window_len, - time_lag=self.delay_interval) - - data.data = np.concatenate((data.data,new_traj.data),axis=0) - - #update index map for consistency - # idx = np.zeros(new_traj.idx_map.shape) + 1 - # new_traj.idx_map = np.concatenate((idx,new_traj.idx_map),axis=-1) - # data.idx_map = np.concatenate((data.idx_map,new_traj.idx_map),axis=0) - - self.data = data - - if self.verbose: - print("Hankel matrix computed") - - def compute_kernel_dmd(self,lamb = None): - ''' - Computes the kernel DMD model. - ''' - self.tikhonov_reg = self.lamb if lamb is None else lamb - #we need to use the inherited .fit method from NystroemKernel - - # data = self.data.reshape(-1,*self.data.shape[2:]) - super().fit(self.data) - - self.A_v = self.V.T @ self.kernel_YX @ self.U / len(self.kernel_YX) - - if self.verbose: - print("kernel regression complete") - - def predict( - self, - test_data=None, - reseed=None, - ): - ''' - Assuming test_data is one trajectory or list of trajectories - - Returns - ------- - pred_data : np.ndarray - The predictions generated by the kernelDMD model. Of the same shape as test_data. Note that the first - (self.n_delays - 1)*self.delay_interval + 1 time steps of the generated predictions are by construction - identical to the test_data. - ''' - if test_data is None: - test_data = self.data - if reseed is None: - reseed = 1 - else: - raise NotImplementedError - - if isinstance(test_data, torch.Tensor): - test_data = test_data.numpy() - if isinstance(test_data,list): - test_data = np.array(test_data) - - isdim2 = test_data.ndim == 2 - if isdim2: #if we have a single trajectory - test_data = test_data[np.newaxis, :, :] - - pred_data = np.zeros(test_data.shape) - pred_data[:, 0:self.n_delays] = test_data[:, 0:self.n_delays] - - #here the hankel matrix should be (ntrials,time,n_delays,dim) - self.compute_hankel(test_data) - - pred = super().predict(self.data) - pred = pred.reshape(test_data.shape[0],test_data.shape[1]-self.n_delays,test_data.shape[2]) - pred_data[:,self.n_delays:] = pred - - return pred_data.squeeze() - #TODO: integrate tailbiting - #split into original trajectories so pred_data matches test_data - # import ipdb; ipdb.set_trace() - - # #reshape into the right 4 dimensions - # test_data = self.data.data.reshape(test_data.shape[0],test_data.shape[1]-self.n_delays,self.context_window_len,test_data.shape[2]) - - # #get the test data into the format of the hankel matrix - # #apply the nystroem predict function - # for t in range(self.n_delays, test_data.shape[1]): - # if t % reseed == 0: - # #need to ignore the current value which is what we're trying to predict - # #hence the -1 at the end - # curr = test_data[:,t-1:t,:-1].reshape(-1,self.n_delays,test_data.shape[-1]) - # pred_data[:,t] = super().predict(curr) - # else: - # past = pred_data[:,t-self.context_window_len:t] - # pred_data[:,t] = super().predict(past) - - # if isdim2: - # pred_data = pred_data[0] - diff --git a/DSA/preprocessing.py b/DSA/preprocessing.py index 3dd5a6a..a44badd 100644 --- a/DSA/preprocessing.py +++ b/DSA/preprocessing.py @@ -46,7 +46,6 @@ def normalize_data(data_list): return normalized_data_list - def coarse_grain(trajectories, bin_size=5, bins_overlapping=0): """ Bin or sum trajectories over time windows, with optional overlap. @@ -76,7 +75,7 @@ def coarse_grain(trajectories, bin_size=5, bins_overlapping=0): for j in range(n_bins): start_idx = j * (bin_size - bins_overlapping) end_idx = start_idx + bin_size - coarse_trajectories[:, j] = np.sum( + coarse_trajectories[:, j] = np.mean( trajectories[:, start_idx:end_idx], axis=1 ) else: @@ -90,7 +89,7 @@ def coarse_grain(trajectories, bin_size=5, bins_overlapping=0): for j in range(n_bins): start_idx = j * (bin_size - bins_overlapping) end_idx = start_idx + bin_size - coarse_trajectories[j] = np.sum(trajectories[start_idx:end_idx], axis=0) + coarse_trajectories[j] = np.mean(trajectories[start_idx:end_idx], axis=0) return coarse_trajectories @@ -213,11 +212,7 @@ def nonlinear_dimensionality_reduction( pca = PCA(n_components=n_components) model = make_pipeline(nystroem, pca) elif method.lower() == "umap": - #assert that umap is installed - try: - from umap import UMAP - except ImportError: - raise ImportError("umap is not installed. Please install it with `pip install umap-learn`") + from umap import UMAP model = UMAP(n_components=n_components, **kwargs) else: @@ -304,4 +299,100 @@ def gaussian_filter(data, sigma, truncate=2.0,causal=True,dim=0,mode='same'): arr=data ) - return filtered_data, kernel \ No newline at end of file + return filtered_data, kernel + + +def coarse_grain_space(data, method='uniform', nbins_per_dim=20, sigma=None, kernel_width=2, scale='standard'): + ''' + Convert continuous data to one-hot encoded spatial bins, optionally with spatial smoothing. + + Parameters + ---------- + data : np.ndarray + Input data of shape (n_conditions, n_timepoints, n_dims) or (n_timepoints, n_dims) + method : str + Method for binning. Currently only 'uniform' is supported. + nbins_per_dim : int + Number of bins per dimension + sigma : float or None + Standard deviation for Gaussian smoothing kernel. If None, no smoothing is applied. + kernel_width : int + Width of smoothing kernel in number of bins + scale : str + Scaling method: 'standard' for zero mean unit variance, or 'minmax' for [0,1] range + + Returns + ------- + encoded : np.ndarray + One-hot encoded data with shape (n_conditions, n_timepoints, n_total_bins) + or (n_timepoints, n_total_bins) + ''' + if method != 'uniform': + raise NotImplementedError(f"Method {method} not implemented. Only 'uniform' is currently supported.") + + # Get input shape and dimensionality + orig_shape = data.shape + if data.ndim == 3: + n_conds, n_time, n_dims = data.shape + data_reshaped = data.reshape(-1, n_dims) + else: + n_time, n_dims = data.shape + data_reshaped = data + + # Scale the data + if scale == 'standard': + mean = np.mean(data_reshaped, axis=0) + std = np.std(data_reshaped, axis=0) + data_scaled = (data_reshaped - mean) / std + elif scale == 'minmax': + min_vals = np.min(data_reshaped, axis=0) + max_vals = np.max(data_reshaped, axis=0) + data_scaled = (data_reshaped - min_vals) / (max_vals - min_vals) + else: + raise ValueError("scale must be 'standard' or 'minmax'") + + # Calculate total number of bins and check if reasonable + n_total_bins = nbins_per_dim ** n_dims + if n_total_bins > 1e6: + raise ValueError(f"Total number of bins ({n_total_bins}) too large. Reduce nbins_per_dim or use different binning method.") + + # Create bin edges + bin_edges = [np.linspace(np.min(data_scaled[:,d]), np.max(data_scaled[:,d]), nbins_per_dim+1) + for d in range(n_dims)] + + # Initialize one-hot encoded array + if data.ndim == 3: + encoded = np.zeros((n_conds, n_time, n_total_bins)) + else: + encoded = np.zeros((n_time, n_total_bins)) + + # Assign data points to bins and handle edge cases + # Vectorized bin assignment and clipping for all dimensions at once + bin_indices = np.stack([ + np.clip(np.digitize(data_scaled[:,d], bin_edges[d]) - 1, 0, nbins_per_dim-1) + for d in range(n_dims) + ]).T + + if n_dims > 1: + flat_indices = np.ravel_multi_index(bin_indices.T, [nbins_per_dim] * n_dims) + else: + # For 1D case, bin_indices is already the flat indices + flat_indices = bin_indices.ravel() + + if data.ndim == 3: + encoded.reshape(-1, n_total_bins)[np.arange(len(flat_indices)), flat_indices] = 1 + else: + encoded[np.arange(len(flat_indices)), flat_indices] = 1 + + # Apply spatial smoothing if requested + if sigma is not None: + from scipy.ndimage import gaussian_filter1d + if data.ndim == 3: + for i in range(n_conds): + for t in range(n_time): + encoded[i,t] = gaussian_filter1d(encoded[i,t], sigma=sigma, truncate=kernel_width) + else: + for t in range(n_time): + encoded[t] = gaussian_filter1d(encoded[t], sigma=sigma, truncate=kernel_width) + + return encoded \ No newline at end of file diff --git a/DSA/simdist.py b/DSA/simdist.py index 8d73078..e15e909 100644 --- a/DSA/simdist.py +++ b/DSA/simdist.py @@ -5,27 +5,41 @@ import numpy as np import torch.nn.utils.parametrize as parametrize from scipy.stats import wasserstein_distance -import ot #optimal transport for multidimensional l2 wasserstein +import ot # optimal transport for multidimensional l2 wasserstein +from DSA import DMD -def pad_zeros(A,B,device): + +def pad_zeros(A, B, device): with torch.no_grad(): - dim = max(A.shape[0],B.shape[0]) - A1 = torch.zeros((dim,dim)).float() - A1[:A.shape[0],:A.shape[1]] += A + dim = max(A.shape[0], B.shape[0]) + A1 = torch.zeros((dim, dim)).float() + A1[: A.shape[0], : A.shape[1]] += A A = A1.float().to(device) - B1 = torch.zeros((dim,dim)).float() - B1[:B.shape[0],:B.shape[1]] += B + B1 = torch.zeros((dim, dim)).float() + B1[: B.shape[0], : B.shape[1]] += B B = B1.float().to(device) - return A,B - + return A, B + +def compute_angle(evec): + ''' + computes the angle between multiple complex eigenvectors + ''' + if isinstance(evec, np.ndarray): + evec = torch.from_numpy(evec).float() + # evec /= torch.linalg.norm(evec, dim=1, keepdim=True) + ang = torch.real(evec.H @ evec) + ang = torch.arccos(ang) + ang[torch.isnan(ang)] = 0 + return ang class LearnableSimilarityTransform(nn.Module): """ - Computes the similarity transform for a learnable orthonormal matrix C + Computes the similarity transform for a learnable orthonormal matrix C """ - def __init__(self, n,orthog=True): + + def __init__(self, n, orthog=True): """ Parameters __________ @@ -33,27 +47,28 @@ def __init__(self, n,orthog=True): dimension of the C matrix """ super(LearnableSimilarityTransform, self).__init__() - #initialize orthogonal matrix as identity + # initialize orthogonal matrix as identity self.C = nn.Parameter(torch.eye(n).float()) self.orthog = orthog - + def forward(self, B): if self.orthog: return self.C @ B @ self.C.transpose(-1, -2) else: return self.C @ B @ torch.linalg.inv(self.C) + class Skew(nn.Module): - def __init__(self,n,device): + def __init__(self, n, device): """ Computes a skew-symmetric matrix X from some parameters (also called X) - + """ super().__init__() - - self.L1 = nn.Linear(n,n,bias = False, device = device) - self.L2 = nn.Linear(n,n,bias = False, device = device) - self.L3 = nn.Linear(n,n,bias = False, device = device) + + self.L1 = nn.Linear(n, n, bias=False, device=device) + self.L2 = nn.Linear(n, n, bias=False, device=device) + self.L3 = nn.Linear(n, n, bias=False, device=device) def forward(self, X): X = torch.tanh(self.L1(X)) @@ -61,17 +76,18 @@ def forward(self, X): X = self.L3(X) return X - X.transpose(-1, -2) + class Matrix(nn.Module): - def __init__(self,n,device): + def __init__(self, n, device): """ Computes a matrix X from some parameters (also called X) - + """ super().__init__() - - self.L1 = nn.Linear(n,n,bias = False, device = device) - self.L2 = nn.Linear(n,n,bias = False, device = device) - self.L3 = nn.Linear(n,n,bias = False, device = device) + + self.L1 = nn.Linear(n, n, bias=False, device=device) + self.L2 = nn.Linear(n, n, bias=False, device=device) + self.L3 = nn.Linear(n, n, bias=False, device=device) def forward(self, X): X = torch.tanh(self.L1(X)) @@ -79,49 +95,55 @@ def forward(self, X): X = self.L3(X) return X + class CayleyMap(nn.Module): """ Maps a skew-symmetric matrix to an orthogonal matrix in O(n) """ + def __init__(self, n, device): """ Parameters __________ - n : int + n : int dimension of the matrix we want to map - + device : {'cpu','cuda'} or int hardware device on which to send the matrix """ super().__init__() - self.register_buffer("Id", torch.eye(n,device = device)) + self.register_buffer("Id", torch.eye(n, device=device)) def forward(self, X): # (I + X)(I - X)^{-1} return torch.linalg.solve(self.Id + X, self.Id - X) - + + class SimilarityTransformDist: """ Computes the Procrustes Analysis over Vector Fields """ - def __init__(self, - iters = 200, - score_method: Literal["angular", "euclidean","wasserstein"] = "angular", - lr = 0.01, - device: Literal["cpu","cuda"] = 'cpu', - verbose = False, - group: Literal["O(n)","SO(n)","GL(n)"] = "O(n)", - wasserstein_compare = 'eig' - ): + + def __init__( + self, + iters=200, + score_method: Literal["angular", "euclidean", "wasserstein"] = "angular", + lr=0.01, + device: Literal["cpu", "cuda"] = "cpu", + verbose=False, + wasserstein_compare: Literal["sv", "eig"] = "eig", + eps=1e-5, + rescale_wasserstein=False, + ): """ Parameters _________ iters : int number of iterations to perform gradient descent - + score_method : {"angular","euclidean","wasserstein"} - specifies the type of metric to use + specifies the type of metric to use "wasserstein" will compare the singular values or eigenvalues of the two matrices as in Redman et al., (2023) @@ -132,13 +154,13 @@ def __init__(self, verbose : bool prints when finished optimizing - - group : {'SO(n)','O(n)', 'GL(n)'} - specifies the group of matrices to optimize over - wasserstein_compare : {,'eig',None} + wasserstein_compare : {'sv','eig',None} specifies whether to compare the singular values or eigenvalues if score_method is "wasserstein", or the shapes are different + + eps : float + early stopping threshold """ self.iters = iters @@ -149,94 +171,154 @@ def __init__(self, self.C_star = None self.A = None self.B = None - self.group = group self.wasserstein_compare = wasserstein_compare - - def fit(self, - A, - B, - iters = None, - lr = None, - group = None, - ): + self.eps = eps + self.rescale_wasserstein = rescale_wasserstein + + def fit( + self, + A, + B, + iters=None, + lr=None, + score_method=None, + wasserstein_compare=None, + wasserstein_weightings = None, + + ): """ Computes the optimal matrix C over specified group Parameters __________ - A : np.array or torch.tensor + A : np.array or torch.tensor or DMD object first data matrix - B : np.array or torch.tensor + B : np.array or torch.tensor or DMD object second data matrix iters : int or None number of optimization steps, if None then resorts to saved self.iters lr : float or None learning rate, if None then resorts to saved self.lr - group : {'SO(n)','O(n)', 'GL(n)'} - specifies the group of matrices to optimize over Returns _______ None """ + if isinstance(A,DMD): + A = A.A_v + if isinstance(B,DMD): + B = B.A_v + assert A.shape[0] == A.shape[1] assert B.shape[0] == B.shape[1] - + A = A.to(self.device) B = B.to(self.device) - self.A,self.B = A,B + self.A, self.B = A, B lr = self.lr if lr is None else lr - iters = self.iters if iters is None else iters - group = self.group if group is None else group - - if group in {"SO(n)", "O(n)"}: - self.losses, self.C_star, self.sim_net = self.optimize_C(A, - B, - lr,iters, - orthog=True, - verbose=self.verbose) - if group == "O(n)": - #permute the first row and column of B then rerun the optimization - P = torch.eye(B.shape[0],device=self.device) + iters = self.iters if iters is None else iters + wasserstein_compare = self.wasserstein_compare if wasserstein_compare is None else wasserstein_compare + score_method = self.score_method if score_method is None else score_method + + if score_method == 'wasserstein': + a,b = self._get_wasserstein_vars(A, B) + device = a.device + # a = a # .cpu() + # b = b # .cpu() + self.M = ot.dist(a, b) # .numpy() + if wasserstein_weightings is not None: + a, b = wasserstein_weightings + assert isinstance(a, (torch.Tensor, np.ndarray)) + assert isinstance(b, (torch.Tensor, np.ndarray)) + assert a.shape[0] == self.M.shape[0] + assert b.shape[0] == self.M.shape[1] + assert a.sum() == b.sum() == 1 + else: + a, b = ( + torch.ones(a.shape[0]) / a.shape[0], + torch.ones(b.shape[0]) / b.shape[0], + ) + a, b = a.to(device), b.to(device) + + self.C_star = ot.emd(a, b, self.M) + self.score_star = ot.emd2(a, b, self.M) *a.shape[0] #add scaling factor due to random matrix theory + # self.score_star = np.sum(self.C_star * self.M) + self.C_star = self.C_star / torch.linalg.norm(self.C_star, dim=1, keepdim=True) + # wasserstein_distance(A.cpu().numpy(),B.cpu().numpy()) + + else: + self.losses, self.C_star, self.sim_net = self.optimize_C( + A, B, lr, iters, orthog=True, verbose=self.verbose + ) + # permute the first row and column of B then rerun the optimization + P = torch.eye(B.shape[0], device=self.device) if P.shape[0] > 1: P[[0, 1], :] = P[[1, 0], :] - losses, C_star, sim_net = self.optimize_C(A, - P @ B @ P.T, - lr,iters, - orthog=True, - verbose=self.verbose) + losses, C_star, sim_net = self.optimize_C( + A, P @ B @ P.T, lr, iters, orthog=True, verbose=self.verbose + ) if losses[-1] < self.losses[-1]: self.losses = losses self.C_star = C_star @ P self.sim_net = sim_net - if group == "GL(n)": - self.losses, self.C_star, self.sim_net = self.optimize_C(A, - B, - lr,iters, - orthog=False, - verbose=self.verbose) - - def optimize_C(self,A,B,lr,iters,orthog,verbose): - #parameterize mapping to be orthogonal + + def _get_wasserstein_vars(self,A, B): + assert self.wasserstein_compare in {"sv", "eig","evec_angle", 'evec'} + if self.wasserstein_compare == "sv": + a = torch.svd(A).S.view(-1, 1) + b = torch.svd(B).S.view(-1, 1) + elif self.wasserstein_compare == "eig": + a = torch.linalg.eig(A).eigenvalues + a = torch.vstack([a.real, a.imag]).T + + b = torch.linalg.eig(B).eigenvalues + b = torch.vstack([b.real, b.imag]).T + elif self.wasserstein_compare in {'evec_angle', 'evec'}: + #this will compute the interior angles between eigenvectors + aevec = torch.linalg.eig(A).eigenvectors + bevec = torch.linalg.eig(B).eigenvectors + + a = compute_angle(aevec) + b = compute_angle(bevec) + else: + raise AssertionError("wasserstein_compare must be 'sv', 'eig', 'evec_angle', or 'evec'") + + #if the number of elements in the sets are different, then we need to pad the smaller set with zeros + if a.shape[0] != b.shape[0]: + if self.wasserstein_compare in {'evec_angle', 'evec'}: + raise AssertionError("Wasserstein comparison of eigenvectors is not supported when \ + the number of elements in the sets are different") + if self.verbose: + print(f"Padding the smaller set with zeros") + if a.shape[0] < b.shape[0]: + a = torch.cat([a, torch.zeros(b.shape[0] - a.shape[0], a.shape[1])], dim=0) + else: + b = torch.cat([b, torch.zeros(a.shape[0] - b.shape[0], b.shape[1])], dim=0) + return a,b + + def optimize_C(self, A, B, lr, iters, orthog, verbose): + # parameterize mapping to be orthogonal n = A.shape[0] - sim_net = LearnableSimilarityTransform(n,orthog=orthog).to(self.device) + sim_net = LearnableSimilarityTransform(n, orthog=orthog).to(self.device) if orthog: - parametrize.register_parametrization(sim_net, "C", Skew(n,self.device)) - parametrize.register_parametrization(sim_net, "C", CayleyMap(n,self.device)) + parametrize.register_parametrization(sim_net, "C", Skew(n, self.device)) + parametrize.register_parametrization( + sim_net, "C", CayleyMap(n, self.device) + ) else: - parametrize.register_parametrization(sim_net, "C", Matrix(n,self.device)) - - simdist_loss = nn.MSELoss(reduction = 'sum') + parametrize.register_parametrization(sim_net, "C", Matrix(n, self.device)) + + simdist_loss = nn.MSELoss(reduction="sum") optimizer = optim.Adam(sim_net.parameters(), lr=lr) # scheduler = optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.999) losses = [] - A = A / torch.linalg.norm(A) - B = B / torch.linalg.norm(B) + A /= torch.linalg.norm(A) + B /= torch.linalg.norm(B) for _ in range(iters): # Zero the gradients of the optimizer. - optimizer.zero_grad() + optimizer.zero_grad() # Compute the Frobenius norm between A and the product. loss = simdist_loss(A, sim_net(B)) @@ -246,14 +328,17 @@ def optimize_C(self,A,B,lr,iters,orthog,verbose): # if _ % 99: # scheduler.step() losses.append(loss.item()) - + #TODO: add a flag for this + # if _ > 2 and abs(losses[-1] - losses[-2]) < self.eps: #early stopping + # break + if verbose: print("Finished optimizing C") C_star = sim_net.C.detach() - return losses, C_star,sim_net - - def score(self,A=None,B=None,score_method=None,group=None): + return losses, C_star, sim_net + + def score(self, A=None, B=None, score_method=None, wasserstein_compare=None): """ Given an optimal C already computed, calculate the metric @@ -273,70 +358,67 @@ def score(self,A=None,B=None,score_method=None,group=None): """ assert self.C_star is not None A = self.A if A is None else A - B = self.B if B is None else B + B = self.B if B is None else B assert A is not None assert B is not None - assert A.shape == self.C_star.shape - assert B.shape == self.C_star.shape + assert A.shape == self.C_star.shape or score_method == 'wasserstein' + assert B.shape == self.C_star.shape or score_method == 'wasserstein' score_method = self.score_method if score_method is None else score_method - group = self.group if group is None else group + wasserstein_compare = self.wasserstein_compare if wasserstein_compare is None else wasserstein_compare with torch.no_grad(): - if not isinstance(A,torch.Tensor): + if not isinstance(A, torch.Tensor): A = torch.from_numpy(A).float().to(self.device) - if not isinstance(B,torch.Tensor): + if not isinstance(B, torch.Tensor): B = torch.from_numpy(B).float().to(self.device) C = self.C_star.to(self.device) - if group in {"SO(n)", "O(n)"}: - Cinv = C.T - elif group in {"GL(n)"}: - Cinv = torch.linalg.inv(C) - else: - raise AssertionError("Need proper group name") - if score_method == 'angular': - num = torch.trace(A.T @ C @ B @ Cinv) - den = torch.norm(A,p = 'fro')*torch.norm(B,p = 'fro') - score_tensor = torch.arccos(num / den) - - if score_tensor.requires_grad: - pi_tensor = torch.tensor(np.pi, device=score_tensor.device, dtype=score_tensor.dtype) - zero_tensor = torch.tensor(0.0, device=score_tensor.device, dtype=score_tensor.dtype) - - score = torch.where( - torch.isnan(score_tensor), - torch.where((num / den) < 0, pi_tensor, zero_tensor), - score_tensor - ) + if score_method == "angular": + num = torch.trace(A.T @ C @ B @ C.T) + den = torch.norm(A, p="fro") * torch.norm(B, p="fro") + score = torch.arccos(num / den).cpu().numpy() + if np.isnan(score): # around -1 and 1, we sometimes get NaNs due to arccos + if num / den < 0: + score = np.pi + else: + score = 0 + elif score_method == 'euclidean': + score = ( + torch.norm(A - C @ B @ C.T, p="fro").cpu().numpy().item() + ) # / A.numpy().size + elif score_method == 'wasserstein': + #use the current C_star to compute the score + assert hasattr(self, 'score_star') + if wasserstein_compare == self.wasserstein_compare: + score = self.score_star.item() else: - score = score_tensor.detach().cpu().numpy() - if np.isnan(score): - score = np.pi if (num / den).item() < 0 else 0.0 - else: - norm_tensor = torch.norm(A - C @ B @ Cinv, p='fro') - if norm_tensor.requires_grad: - score = norm_tensor - else: - score = norm_tensor.detach().cpu().numpy().item() - - + #apply the current transport plan to the new data + a,b = self._get_wasserstein_vars(A, B) + # a_transported = self.C_star @ A #shouldn't this be a? + + M = ot.dist(a, b, metric='sqeuclidean') + score = torch.sum(self.C_star * M).item() + #TODO: validate this + # a_transported = self.C_star @ a + # row_wise_sq_distances = torch.sum(torch.square(a_transported - b), axis=1) + # transported_score = torch.sum(a * row_wise_sq_distances) + # score = transported_score.item() + if self.rescale_wasserstein: + score = score * A.shape[0] #add scaling factor due to random matrix theory + return score - - def fit_score(self, - A, - B, - iters = None, - lr = None, - score_method = None, - group = None): + + def fit_score( + self, A, B, iters=None, lr=None, score_method=None, zero_pad=True, wasserstein_weightings=None + ): """ - for efficiency, computes the optimal matrix and returns the score + for efficiency, computes the optimal matrix and returns the score Parameters __________ A : np.array or torch.tensor first data matrix B : np.array or torch.tensor - second data matrix + second data matrix iters : int or None number of optimization steps, if None then resorts to saved self.iters lr : float or None @@ -350,50 +432,84 @@ def fit_score(self, score : float similarity of the data under the similarity transform w.r.t C - + """ score_method = self.score_method if score_method is None else score_method - group = self.group if group is None else group - if isinstance(A,np.ndarray): + if isinstance(A, np.ndarray): A = torch.from_numpy(A).float() - if isinstance(B,np.ndarray): + if isinstance(B, np.ndarray): B = torch.from_numpy(B).float() assert A.shape[0] == B.shape[1] or self.wasserstein_compare is not None if A.shape[0] != B.shape[0]: if self.wasserstein_compare is None: - raise AssertionError("Matrices must be the same size unless using wasserstein distance") - elif self.verbose: #otherwise resort to L2 Wasserstein over singular or eigenvalues - print(f"resorting to wasserstein distance over {self.wasserstein_compare}") - - if self.score_method == "wasserstein": - # assert self.wasserstein_compare in {"sv","eig"} - # if self.wasserstein_compare == "sv": - # a = torch.svd(A).S.view(-1,1) - # b = torch.svd(B).S.view(-1,1) - # if self.wasserstein_compare == "eig": - a = torch.linalg.eig(A).eigenvalues - a = torch.vstack([a.real,a.imag]).T - - b = torch.linalg.eig(B).eigenvalues - b = torch.vstack([b.real,b.imag]).T - # else: - # raise AssertionError("wasserstein_compare must be or 'eig'") - device = a.device - a = a#.cpu() - b = b#.cpu() - M = ot.dist(a,b)#.numpy() - a,b = torch.ones(a.shape[0])/a.shape[0],torch.ones(b.shape[0])/b.shape[0] - a,b = a.to(device),b.to(device) - - score_star = ot.emd2(a,b,M) - #wasserstein_distance(A.cpu().numpy(),B.cpu().numpy()) + raise AssertionError( + "Matrices must be the same size unless using wasserstein distance" + ) + elif score_method != 'wasserstein': # otherwise resort to L2 Wasserstein over singular or eigenvalues + print( + f"resorting to wasserstein distance over {self.wasserstein_compare}" + ) + score_method = 'wasserstein' + else: + pass - else: - - self.fit(A, B,iters,lr,group) - score_star = self.score(self.A,self.B,score_method=score_method,group=group) - return score_star + self.fit(A, B, iters, lr, wasserstein_weightings=wasserstein_weightings, score_method=score_method) + return self.score( + self.A, self.B, score_method=score_method + ) + +def compute_subspace_angles(A, B): + """ + Computes the subspace angles between two DMD matrices. + Matrices must be square and the same size. + + Parameters + ---------- + A : DMD object or numpy array + First DMD matrix + B : DMD object or numpy array + Second DMD matrix + + Returns + ------- + angles : np.ndarray + Principal angles between the subspaces + """ + + A_mat = val_matrix(A) + B_mat = val_matrix(B) + + # Check matrices are same size + if A_mat.shape != B_mat.shape: + raise ValueError("Matrices must be the same size") + + # Get orthonormal bases via SVD + U_A = np.linalg.svd(A_mat)[0] + U_B = np.linalg.svd(B_mat)[0] + + # Compute principal angles + S = np.linalg.svd(U_A.T @ U_B)[1] + S = np.clip(S, -1.0, 1.0) # Numerical stability + angles = np.arccos(S) + + return angles + +def val_matrix(matrix): + if isinstance(matrix, DMD): + mat = matrix.A_havok_dmd + elif isinstance(matrix, torch.Tensor): + mat = matrix.detach().numpy() + elif isinstance(matrix, np.ndarray): + mat = matrix + else: + raise AssertionError(f" must be tensor, numpy array, or DMD object") + + # Check matrix is square + if mat.shape[0] != mat.shape[1]: + raise ValueError(f"matrix must be square") + + return mat diff --git a/DSA/sweeps.py b/DSA/sweeps.py index 04e89ce..4bb07ba 100644 --- a/DSA/sweeps.py +++ b/DSA/sweeps.py @@ -1,8 +1,8 @@ import numpy as np from tqdm import tqdm -from DSA.dmd import DMD -from DSA.stats import measure_nonnormality_transpose, compute_all_stats, measure_transient_growth -from DSA.resdmd import compute_residuals +from .dmd import DMD +from .stats import measure_nonnormality_transpose, compute_all_stats, measure_transient_growth +from .resdmd import compute_residuals import matplotlib.pyplot as plt from typing import Literal @@ -110,11 +110,11 @@ def sweep_ranks_delays( if isinstance(pred,list): pred = np.concatenate(pred,axis=0) test_data_err = np.concatenate(test_data_err,axis=0) - if featurize and ndim is not None: - pred = pred[:, :, -ndim:] - stats = compute_all_stats(pred, test_data_err[:, :, -ndim:], dmd.rank) - else: - stats = compute_all_stats(test_data_err, pred, dmd.rank) + # if featurize and ndim is not None: + # pred = pred[:, :, -ndim:] + # stats = compute_all_stats(pred, test_data_err[:, :, -ndim:], dmd.rank) + # else: + stats = compute_all_stats(test_data_err, pred, dmd.rank) aic = stats["AIC"] mase = stats["MASE"] if return_mse: @@ -323,10 +323,12 @@ def plot_sweep_results_all_error_types( figsize=(2, 4), xscale='log', aic_scale='symlog', + mase_scale = 'log', plot_herror=False, new_plot_reseeds=False, cmap="gist_gray", - metrics_order=['AIC','MASE','MSE'] + metrics_order=['AIC','MASE','MSE'], + pretty_yticks=False ): """ Plot all error types from sweep_ranks_delays_all_error_types as a 3 x (3*len(reseeds)) grid, @@ -429,8 +431,8 @@ def plot_sweep_results_all_error_types( row_ymaxs[metric_idx] = max(row_ymaxs[metric_idx], np.nanmax(valid_y)) if metric == "MASE": ax.axhline(1, color="black", linestyle="--", linewidth=0.7) - if metric in {"MASE", "MSE"}: - ax.set_yscale("log") + if metric in {"MASE", "MSE"} and mase_scale in {'symlog','log','linear'}: + ax.set_yscale(mase_scale) if aic_scale in {'symlog','log','linear'} and metric == "AIC": ax.set_yscale(aic_scale) if xscale == 'log': @@ -460,14 +462,15 @@ def plot_sweep_results_all_error_types( ax.set_yticklabels([]) ax.yaxis.set_major_locator(plt.NullLocator()) ax.yaxis.set_minor_locator(plt.NullLocator()) - for reseed_idx in range(n_reseeds): - ax = axes[metric_idx][reseed_idx] - # Only set exactly two yticks (min and max), and always set their labels - ax.set_ylim([ymin, ymax]) - ax.set_yticks([ymin, ymax]) - # Set tick labels to formatted numbers (scientific if needed) - ticklabels = [f"{ymin:.2g}", f"{ymax:.2g}"] - ax.set_yticklabels(ticklabels) + if pretty_yticks: + for reseed_idx in range(n_reseeds): + ax = axes[metric_idx][reseed_idx] + # Only set exactly two yticks (min and max), and always set their labels + ax.set_ylim([ymin, ymax]) + ax.set_yticks([ymin, ymax]) + # Set tick labels to formatted numbers (scientific if needed) + ticklabels = [f"{ymin:.2g}", f"{ymax:.2g}"] + ax.set_yticklabels(ticklabels) plt.suptitle(f"{name + '_' if name else ''}{space} tuning", fontsize=14,y=1.05) plt.tight_layout() #rect=[0, 0, 1, 0.97]) From 0884e186bb7143deb5f5fd7bb1b35dc3c0833208 Mon Sep 17 00:00:00 2001 From: ostrow Date: Mon, 27 Oct 2025 18:32:22 -0400 Subject: [PATCH 02/90] add modified version of pykoopman (until they accept my pull request) --- DSA/pykoopman/.readthedocs.yaml | 35 + DSA/pykoopman/LICENSE | 21 + DSA/pykoopman/README.rst | 262 +++ DSA/pykoopman/src/pykoopman/__init__.py | 23 + .../src/pykoopman/analytics/__init__.py | 7 + .../src/pykoopman/analytics/_base_analyzer.py | 89 + .../src/pykoopman/analytics/_ms_pd21.py | 462 ++++++ .../pykoopman/analytics/_pruned_koopman.py | 162 ++ .../src/pykoopman/common/__init__.py | 37 + DSA/pykoopman/src/pykoopman/common/cqgle.py | 234 +++ .../src/pykoopman/common/examples.py | 1045 ++++++++++++ DSA/pykoopman/src/pykoopman/common/ks.py | 189 +++ DSA/pykoopman/src/pykoopman/common/nlse.py | 186 +++ .../src/pykoopman/common/validation.py | 88 + DSA/pykoopman/src/pykoopman/common/vbe.py | 177 ++ .../src/pykoopman/differentiation/__init__.py | 6 + .../pykoopman/differentiation/_derivative.py | 89 + .../differentiation/_finite_difference.py | 12 + DSA/pykoopman/src/pykoopman/koopman.py | 637 ++++++++ .../src/pykoopman/koopman_continuous.py | 154 ++ .../src/pykoopman/observables/__init__.py | 17 + .../src/pykoopman/observables/_base.py | 408 +++++ .../observables/_custom_observables.py | 239 +++ .../src/pykoopman/observables/_identity.py | 89 + .../src/pykoopman/observables/_polynomial.py | 263 +++ .../observables/_radial_basis_functions.py | 293 ++++ .../observables/_random_fourier_features.py | 193 +++ .../src/pykoopman/observables/_time_delay.py | 216 +++ .../src/pykoopman/regression/__init__.py | 22 + .../src/pykoopman/regression/_base.py | 163 ++ .../pykoopman/regression/_base_ensemble.py | 366 +++++ .../src/pykoopman/regression/_dmd.py | 360 ++++ .../src/pykoopman/regression/_dmdc.py | 468 ++++++ .../src/pykoopman/regression/_edmd.py | 248 +++ .../src/pykoopman/regression/_edmdc.py | 239 +++ .../src/pykoopman/regression/_havok.py | 341 ++++ .../src/pykoopman/regression/_kdmd.py | 460 ++++++ .../src/pykoopman/regression/_nndmd.py | 1454 +++++++++++++++++ 38 files changed, 9754 insertions(+) create mode 100644 DSA/pykoopman/.readthedocs.yaml create mode 100644 DSA/pykoopman/LICENSE create mode 100644 DSA/pykoopman/README.rst create mode 100644 DSA/pykoopman/src/pykoopman/__init__.py create mode 100644 DSA/pykoopman/src/pykoopman/analytics/__init__.py create mode 100644 DSA/pykoopman/src/pykoopman/analytics/_base_analyzer.py create mode 100644 DSA/pykoopman/src/pykoopman/analytics/_ms_pd21.py create mode 100644 DSA/pykoopman/src/pykoopman/analytics/_pruned_koopman.py create mode 100644 DSA/pykoopman/src/pykoopman/common/__init__.py create mode 100644 DSA/pykoopman/src/pykoopman/common/cqgle.py create mode 100644 DSA/pykoopman/src/pykoopman/common/examples.py create mode 100644 DSA/pykoopman/src/pykoopman/common/ks.py create mode 100644 DSA/pykoopman/src/pykoopman/common/nlse.py create mode 100644 DSA/pykoopman/src/pykoopman/common/validation.py create mode 100644 DSA/pykoopman/src/pykoopman/common/vbe.py create mode 100644 DSA/pykoopman/src/pykoopman/differentiation/__init__.py create mode 100644 DSA/pykoopman/src/pykoopman/differentiation/_derivative.py create mode 100644 DSA/pykoopman/src/pykoopman/differentiation/_finite_difference.py create mode 100644 DSA/pykoopman/src/pykoopman/koopman.py create mode 100644 DSA/pykoopman/src/pykoopman/koopman_continuous.py create mode 100644 DSA/pykoopman/src/pykoopman/observables/__init__.py create mode 100644 DSA/pykoopman/src/pykoopman/observables/_base.py create mode 100644 DSA/pykoopman/src/pykoopman/observables/_custom_observables.py create mode 100644 DSA/pykoopman/src/pykoopman/observables/_identity.py create mode 100644 DSA/pykoopman/src/pykoopman/observables/_polynomial.py create mode 100644 DSA/pykoopman/src/pykoopman/observables/_radial_basis_functions.py create mode 100644 DSA/pykoopman/src/pykoopman/observables/_random_fourier_features.py create mode 100644 DSA/pykoopman/src/pykoopman/observables/_time_delay.py create mode 100644 DSA/pykoopman/src/pykoopman/regression/__init__.py create mode 100644 DSA/pykoopman/src/pykoopman/regression/_base.py create mode 100644 DSA/pykoopman/src/pykoopman/regression/_base_ensemble.py create mode 100644 DSA/pykoopman/src/pykoopman/regression/_dmd.py create mode 100644 DSA/pykoopman/src/pykoopman/regression/_dmdc.py create mode 100644 DSA/pykoopman/src/pykoopman/regression/_edmd.py create mode 100644 DSA/pykoopman/src/pykoopman/regression/_edmdc.py create mode 100644 DSA/pykoopman/src/pykoopman/regression/_havok.py create mode 100644 DSA/pykoopman/src/pykoopman/regression/_kdmd.py create mode 100644 DSA/pykoopman/src/pykoopman/regression/_nndmd.py diff --git a/DSA/pykoopman/.readthedocs.yaml b/DSA/pykoopman/.readthedocs.yaml new file mode 100644 index 0000000..f915bd9 --- /dev/null +++ b/DSA/pykoopman/.readthedocs.yaml @@ -0,0 +1,35 @@ +# .readthedocs.yaml +# Read the Docs configuration file +# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details + +# Required +version: 2 + +# Set the version of Python and other tools you might need +build: + os: ubuntu-22.04 + tools: + python: "3.10" + # You can also specify other tool versions: + # nodejs: "19" + # rust: "1.64" + # golang: "1.19" + +# Build documentation in the docs/ directory with Sphinx +sphinx: + configuration: docs/conf.py + +# If using Sphinx, optionally build your docs in additional formats such as PDF +# formats: +# - pdf + +# Optionally declare the Python requirements required to build your docs +#python: +# install: +# - requirements: requirements-dev.txt +# - method: pip +# path: . +python: + install: + - method: pip + path: .[dev] diff --git a/DSA/pykoopman/LICENSE b/DSA/pykoopman/LICENSE new file mode 100644 index 0000000..7d426ab --- /dev/null +++ b/DSA/pykoopman/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright 2023 Shaowu Pan, Eurika Kaiser, Brian de Silva, J. Nathan Kutz and Steven L. Brunton + +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 WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/DSA/pykoopman/README.rst b/DSA/pykoopman/README.rst new file mode 100644 index 0000000..a255bb6 --- /dev/null +++ b/DSA/pykoopman/README.rst @@ -0,0 +1,262 @@ +PyKoopman +========= + +|Build| |Docs| |PyPI| |Codecov| |DOI| |JOSS| + +**PyKoopman** is a Python package for computing data-driven approximations to the Koopman operator. + +Data-driven approximation of Koopman operator +--------------------------------------------- + +.. figure:: docs/JOSS/Fig1.png + +Given a nonlinear dynamical system, + +.. math:: + + x'(t) = f(x(t)), + +the Koopman operator governs the temporal evolution of the measurement function. +Unfortunately, it is an infinite-dimensional linear operator. Most of the time, one has to +project the Koopman operator onto a finite-dimensional subspace that is spanned by user-defined/data-adaptive functions. + +.. math:: + z = \Phi(x). + +If the system state is also contained in such subspace, then effectively, the nonlinear dynamical system is (approximately) +linearized in a global sense. + +The goal of data-driven approximation of Koopman operator is to find such a set of +functions that span such lifted space and the transition matrix associated with the +lifted system. + +Structure of PyKoopman +^^^^^^^^^^^^^^^^^^^^^^ + +.. figure:: docs/JOSS/Fig2.png + +PyKoopman package is centered around the ``Koopman`` class and ``KoopmanContinuous`` class. It consists of two key components + +* ``observables``: a set of observables functions, which spans the subspace for projection. + +* ``regressor``: the optimization algorithm to find the best ``fit`` for the + projection of Koopman operator. + +After ``Koopman``/``KoopmanContinuous`` object has been created, it must be fit to data, similar to a ``scikit-learn`` model. +We design ``PyKoopman`` such that it is compatible to ``scikit-learn`` objects and methods as much as possible. + + +Features implemented +^^^^^^^^^^^^^^^^^^^^ + +- Observable library for lifting the state into the observable space + + - Identity (for DMD/DMDc or in case users want to compute observables themselves): + ``Identity`` + - Multivariate polynomials: ``Polynomial`` + - Time delay coordinates: ``TimeDelay`` + - Radial basis functions: ``RadialBasisFunctions`` + - Random Fourier features: ``RandomFourierFeatures`` + - Custom library (defined by user-supplied functions): ``CustomObservables`` + - Concatenation of observables: ``ConcatObservables`` + + +- System identification method for performing regression + + - Dynamic mode decomposition: ``PyDMDRegressor`` + - Dynamic mode decomposition with control: ``DMDc`` + - Extended dynamic mode decomposition: ``EDMD`` + - Extended dynamic mode decomposition with control: ``EDMDc`` + - Kernel dynamic mode decomposition: ``KDMD`` + - Hankel Alternative View of Koopman Analysis: ``HAVOK`` + - Neural Network DMD: ``NNDMD`` + +- Sparse construction of Koopman invariant subspace + + - Multi-task learning based on linearity consistency + + +Examples +^^^^^^^^ + +1. `Learning how to create observables `__ + +2. `Learning how to compute time derivatives `__ + +3. `Dynamic mode decomposition on two mixed spatial signals `__ + +4. `Dynamic mode decomposition with control on a 2D linear system `__ + +5. `Dynamic mode decomposition with control (DMDc) for a 128D system `__ + +6. `Dynamic mode decomposition with control on a high-dimensional linear system `__ + +7. `Successful examples of using Dynamic mode decomposition on PDE system `__ + +8. `Unsuccessful examples of using Dynamic mode decomposition on PDE system `__ + +9. `Extended DMD for Van Der Pol System `__ + +10. `Learning Koopman eigenfunctions on Slow manifold `__ + +11. `Comparing DMD and KDMD for Slow manifold dynamics `__ + +12. `Extended DMD with control for chaotic duffing oscillator `__ + +13. `Extended DMD with control for Van der Pol oscillator `__ + +14. `Hankel Alternative View of Koopman Operator for Lorenz System `__ + +15. `Hankel DMD with control for Van der Pol Oscillator `__ + +16. `Neural Network DMD on Slow Manifold `__ + +17. `EDMD and NNDMD for a simple linear system `__ + +18. `Sparisfying a minimal Koopman invariant subspace from EDMD for a simple linear system `__ + +Installation +------------- + +Language +^^^^^^^^^^^^^^^^^^^^ +- Python == 3.10 + + +Installing with pip +^^^^^^^^^^^^^^^^^^^ + +If you are using Linux or macOS you can install PyKoopman with pip: + +.. code-block:: bash + + pip install pykoopman + +Installing from source +^^^^^^^^^^^^^^^^^^^^^^ +First clone this repository: + +.. code-block:: bash + + git clone https://github.com/dynamicslab/pykoopman + +Second, it is highly recommended to use `venv` to get a local python environment + +.. code-block:: bash + + python -m venv venv + source ./venv/bin/activate + +In windows, you activate virtual environment in a different way + +.. code-block:: bash + + .\venv\Scripts\activate.ps1 + +Then, to install the package, run + +.. code-block:: bash + + python -m pip install -e . + +If you do not have root access, you should add the ``--user`` option to the above lines. + + +Installing with GPU support +^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +After you download the Github package, go to the directory, type + +.. code-block:: bash + + python -m pip install -r requirements-dev.txt + +Documentation +------------- +The documentation for PyKoopman is hosted on `Read the Docs `__. + +Community guidelines +-------------------- + +Contributing code +^^^^^^^^^^^^^^^^^ +We welcome contributions to PyKoopman. To contribute a new feature please submit a +pull request. To get started we recommend installing the packages in "developer mode" +via + +.. code-block:: bash + + python -m pip install -e .[dev] + +This will allow you to run unit tests and automatically format your code. To be accepted your code should conform to PEP8 and pass all unit tests. Code can be tested by invoking + +.. code-block:: bash + + pytest + +We recommed using ``pre-commit`` to format your code. Once you have staged changes to commit + +.. code-block:: bash + + git add path/to/changed/file.py + +you can run the following to automatically reformat your staged code + +.. code-block:: bash + + pre-commit run -a -v + +Note that you will then need to re-stage any changes ``pre-commit`` made to your code. + +Reporting issues or bugs +^^^^^^^^^^^^^^^^^^^^^^^^ +If you find a bug in the code or want to request a new feature, please open an issue. + +Known issues: + +- Python 3.12 might cause unexpected problems. + +Citing PyKoopman +---------------- + +.. code-block:: text + + @article{Pan2024, doi = {10.21105/joss.05881}, + url = {https://doi.org/10.21105/joss.05881}, + year = {2024}, + publisher = {The Open Journal}, + volume = {9}, + number = {94}, + pages = {5881}, + author = {Shaowu Pan and Eurika Kaiser and Brian M. de Silva and J. Nathan Kutz and Steven L. Brunton}, + title = {PyKoopman: A Python Package for Data-Driven Approximation of the Koopman Operator}, + journal = {Journal of Open Source Software}} + +Related packages +---------------- +* `PySINDy `_ - A Python libray for the Sparse Identification of Nonlinear Dynamical + systems (SINDy) method introduced in Brunton et al. (2016a). +* `Deeptime `_ - A Python library for the analysis of time series data with methods for dimension reduction, clustering, and Markov model estimation. +* `PyDMD `_ - A Python package using the Dynamic Mode Decomposition (DMD) for a data-driven model simplification based on spatiotemporal coherent structures. DMD is a great alternative to SINDy. +* `pykoop `_ - a Koopman operator identification library written in Python +* `DLKoopman `_ - a deep learning library for + Koopman operator + +.. |Build| image:: https://github.com/dynamicslab/pykoopman/actions/workflows/run-tests.yml/badge.svg + :target: https://github.com/dynamicslab/pykoopman/actions?query=workflow%3ATests + +.. |Docs| image:: https://readthedocs.org/projects/pykoopman/badge/?version=master + :target: https://pykoopman.readthedocs.io/en/master/?badge=master + :alt: Documentation Status + +.. |PyPI| image:: https://badge.fury.io/py/pykoopman.svg + :target: https://badge.fury.io/py/pykoopman + +.. |Codecov| image:: https://codecov.io/github/dynamicslab/pykoopman/coverage.svg + :target: https://app.codecov.io/gh/dynamicslab/pykoopman + +.. |DOI| image:: https://zenodo.org/badge/DOI/10.5281/zenodo.8060893.svg + :target: https://doi.org/10.5281/zenodo.8060893 + +.. |JOSS| image:: https://joss.theoj.org/papers/10.21105/joss.05881/status.svg + :target: https://doi.org/10.21105/joss.05881 diff --git a/DSA/pykoopman/src/pykoopman/__init__.py b/DSA/pykoopman/src/pykoopman/__init__.py new file mode 100644 index 0000000..b6e344d --- /dev/null +++ b/DSA/pykoopman/src/pykoopman/__init__.py @@ -0,0 +1,23 @@ +from __future__ import annotations + +from pkg_resources import DistributionNotFound +from pkg_resources import get_distribution + +try: + __version__ = get_distribution(__name__).version +except DistributionNotFound: + pass + +from .koopman import Koopman +from .koopman_continuous import KoopmanContinuous + + +__all__ = [ + "Koopman", + "KoopmanContinuous", + "common", + "differentiation", + "observables", + "regression", + "analytics", +] diff --git a/DSA/pykoopman/src/pykoopman/analytics/__init__.py b/DSA/pykoopman/src/pykoopman/analytics/__init__.py new file mode 100644 index 0000000..73a9c1a --- /dev/null +++ b/DSA/pykoopman/src/pykoopman/analytics/__init__.py @@ -0,0 +1,7 @@ +from __future__ import annotations + +from ._base_analyzer import BaseAnalyzer +from ._ms_pd21 import ModesSelectionPAD21 +from ._pruned_koopman import PrunedKoopman + +__all__ = ["BaseAnalyzer", "ModesSelectionPAD21", "PrunedKoopman"] diff --git a/DSA/pykoopman/src/pykoopman/analytics/_base_analyzer.py b/DSA/pykoopman/src/pykoopman/analytics/_base_analyzer.py new file mode 100644 index 0000000..9cdb156 --- /dev/null +++ b/DSA/pykoopman/src/pykoopman/analytics/_base_analyzer.py @@ -0,0 +1,89 @@ +"""module for implement modes analyzer for Koopman approximation""" +from __future__ import annotations + +import abc + +import numpy as np + + +class BaseAnalyzer(object): + """Base class for Koopman model analyzer. + + Attributes: + model (Koopman): An instance of `pykoopman.koopman.Koopman`. + eigenfunction (Koopman.compute_eigenfunction): A function that evaluates Koopman + psi. + eigenvalues_cont (numpy.ndarray): Koopman lamda in continuous-time. + eigenvalues_discrete (numpy.ndarray): Koopman lamda in discrete-time. + """ + + def __init__(self, model): + """Initialize the BaseAnalyzer object. + + Args: + model (Koopman): An instance of `pykoopman.koopman.Koopman`. + """ + self.model = model + self.eigenfunction = self.model.psi + self.eigenvalues_cont = self.model.continuous_lamda_array + self.eigenvalues_discrete = self.model.lamda_array + + def _compute_phi_minus_phi_evolved(self, t, validate_data_one_traj): + """Compute the difference between psi evolved and psi observed. + + Args: + t (numpy.ndarray): Time stamp of this validation trajectory. + validate_data_one_traj (numpy.ndarray): Data matrix of this validation + trajectory. + + Returns: + list: Linear residual for each mode. + """ + + # shape of phi = (num_samples, num_modes) + psi = self.eigenfunction(validate_data_one_traj.T).T + + linear_residual_list = [] + for i in range(len(self.eigenvalues_cont)): + linear_residual_list.append( + psi[:, i] - np.exp(self.eigenvalues_cont[i] * t) * psi[0:1, i] + ) + return linear_residual_list + + def validate(self, t, validate_data_one_traj): + """Validate Koopman psi. + + Given a single trajectory, compute the norm of the difference + between observed psi and evolved psi for each mode. + + Args: + t (numpy.ndarray): Time stamp of this validation trajectory. + validate_data_one_traj (numpy.ndarray): Data matrix of this validation + trajectory. + + Returns: + list: Difference in norm for each mode. + """ + + linear_residual_list = self._compute_phi_minus_phi_evolved( + t, validate_data_one_traj + ) + linear_residual_norm_list = [ + np.linalg.norm(tmp) for tmp in linear_residual_list + ] + return linear_residual_norm_list + + @abc.abstractmethod + def prune_model(self, *params, **kwargs): + """Prune the model. + + This method should be implemented by the derived classes. + + Args: + *params: Variable length argument list. + **kwargs: Arbitrary keyword arguments. + + Raises: + NotImplementedError: If the method is not implemented by the derived class. + """ + raise NotImplementedError diff --git a/DSA/pykoopman/src/pykoopman/analytics/_ms_pd21.py b/DSA/pykoopman/src/pykoopman/analytics/_ms_pd21.py new file mode 100644 index 0000000..bf789bb --- /dev/null +++ b/DSA/pykoopman/src/pykoopman/analytics/_ms_pd21.py @@ -0,0 +1,462 @@ +"""Module for implementing Pan-Duraisamy modes selection algorithm""" +from __future__ import annotations + +import numpy as np +from matplotlib import pyplot as plt +from prettytable import PrettyTable +from pykoopman.koopman import Koopman +from sklearn.linear_model import enet_path + +from ._base_analyzer import BaseAnalyzer +from ._pruned_koopman import PrunedKoopman + + +class ModesSelectionPAD21(BaseAnalyzer): + """Koopman V selection algorithm from Pan, et al.,JFM (2021). + + Aims to extract a low-dimensional Koopman invariant subspace + in a model-agnostic way, i.e., applies to any algorithms for + approximating the Koopman operator. + + See the following reference for more details: + Pan, S., Arnold-Medabalimi, N., & Duraisamy, K. (2021). + Sparsity-promoting algorithms for the discovery of informative + Koopman-invariant subspaces. Journal of Fluid Mechanics, 917. + + + Parameters: + model (Koopman): An instance of `pykoopman.koopman.Koopman`. + validate_data_traj (list): A list of dictionaries that contains validation + trajectories. Each dictionary has two keys: `t` and `x`, which correspond to + time stamps and data matrix. + truncation_threshold (float): When sweeping the `alpha` in the sparse linear + regression solver over the data, we consider any term with an absolute + coefficient smaller than the truncation threshold as zero. Default is 1e-3. + max_terms_allowed (int): The maximum number of terms used to perform sparse + linear regression. It can be inferred by the Q-R plot. Default is 10. + plot (bool): True if the figures should be plotted. Default is False. + + Attributes: + L (int): Total number of terms considered in sparse linear regression. + dir (str): The path where figures are saved. + eigenfunction_on_traj_total_top_k (numpy.ndarray): Evaluations of the best k + eigenfunctions evaluated on all of the validation trajectories. + small_to_large_error_eigen_index (numpy.ndarray): Indices of eigenmodes from + best to worst in terms of linear evolution error. + sweep_index_list (list): A list of a list of bool. It is the final result of + sweeping in the sparse linear regression. It contains which V are + selected at a certain `alpha`. + validate_data_traj (list): A list of dictionaries that contains validation + trajectories. Each dictionary has two keys: `t` and `x`, which correspond + to time stamps and data matrix. + truncation_threshold (float): When sweeping the `alpha` in the sparse linear + regression solver over the data, we consider any term with an absolute + coefficient smaller than the truncation threshold as zero. + """ + + def __init__( + self, + model: Koopman, + validate_data_traj: list, + truncation_threshold=1e-3, + max_terms_allowed=10, + plot=False, + ): + """Initialize the ModesSelectionPAD21 object. + + Args: + model (Koopman): An instance of `pykoopman.koopman.Koopman`. + validate_data_traj (list): A list of dictionaries that contains + validation trajectories. Each dictionary should have the keys 't' + and 'x', corresponding to time stamps and data matrix, respectively. + truncation_threshold (float, optional): When sweeping the alpha in the + sparse linear regression solver over the data, we consider any term + with an absolute coefficient smaller than the truncation threshold as + zero. Defaults to 1e-3. + max_terms_allowed (int, optional): The maximum number of terms used to + perform sparse linear regression. It can be inferred by the Q-R plot. + Defaults to 10. + plot (bool, optional): Set to True to plot the figures. Defaults to False. + """ + + super().__init__(model) + + self.validate_data_traj = validate_data_traj + self.truncation_threshold = truncation_threshold + self.dir = "/" + + if not isinstance(validate_data_traj, list): + raise NotImplementedError("validate_data_traj should be a list.") + + # loop over each validation trajectory + Q_i = [] + for validate_data_one_traj in validate_data_traj: + validate_data = validate_data_one_traj["x"] + validate_time = validate_data_one_traj["t"] + + # 1. residual of linearity equation + linear_residual_list = self._compute_phi_minus_phi_evolved( + validate_time, validate_data + ) + + # 1.1 normalization factor for each psi + eigenfunction_evaluated_on_traj = self.eigenfunction(validate_data.T).T + + tmp = np.abs(eigenfunction_evaluated_on_traj) ** 2 # pointwise square only + dt_arr = np.diff(validate_time, prepend=validate_time[0] - validate_time[1]) + tmp = np.dot(dt_arr, tmp) / (validate_time[-1] - validate_time[0]) + normal_constant = np.sqrt(tmp) + + # 1.2 normalized residual and pick the maximized one over t + tmp = [ + np.max(np.abs(tmp) / normal_constant[i]) + for i, tmp in enumerate(linear_residual_list) + ] + Q_i.append(tmp) + + # compute the mean Q_i for all of the trajectories + Q_i_mean = np.array(Q_i).mean(axis=0) + + # sort Q to get i_1 to i_L + self.small_to_large_error_eigen_index = np.argsort(Q_i_mean)[ + : max_terms_allowed + 1 + ] + + # loop over each validation trajectory - for the second time + R_i = [] + for validate_data_one_traj in validate_data_traj: + validate_data = validate_data_one_traj["x"] + eigenfunction_evaluated_on_traj = self.eigenfunction(validate_data.T).T + + # get reconstruction error with increasing number of V + R_i_each = [] + for k in range(1, max_terms_allowed + 1): + eigenfunction_evaluated_on_traj_top_k = eigenfunction_evaluated_on_traj[ + :, self.small_to_large_error_eigen_index[:k] + ] + sparse_measurement_matrix = np.linalg.lstsq( + eigenfunction_evaluated_on_traj_top_k, validate_data + )[0] + residual = ( + eigenfunction_evaluated_on_traj_top_k @ sparse_measurement_matrix + - validate_data + ) + normalized_err_top_k = np.linalg.norm(residual) / np.linalg.norm( + validate_data + ) + R_i_each.append(normalized_err_top_k) + R_i_each = np.array(R_i_each) + R_i.append(R_i_each) + R_i_mean = np.array(R_i).mean(axis=0) + + # print out the Q-R + QR_table = PrettyTable() + + QR_table.field_names = ["Index", "Eigenvalue", "Q", "R"] + tmp = self.eigenvalues_discrete[self.small_to_large_error_eigen_index] + for i in range(len(R_i_mean)): + QR_table.add_row( + [ + self.small_to_large_error_eigen_index[i], + tmp[i], + Q_i_mean[i], + R_i_mean[i], + ] + ) + print(QR_table) + + # prepare top max k selected eigentraj + eigenfunction_evaluated_on_traj_total = np.vstack( + [self.eigenfunction(tmp1["x"].T).T for tmp1 in validate_data_traj] + ) + self.eigenfunction_on_traj_total_top_k = eigenfunction_evaluated_on_traj_total[ + :, self.small_to_large_error_eigen_index[: max_terms_allowed + 1] + ] + + if plot: + fig = plt.figure(figsize=(6, 6)) + ax1 = fig.add_subplot(111) + ax2 = ax1.twinx() + ax1.plot( + range(1, len(Q_i_mean) + 1), + Q_i_mean[self.small_to_large_error_eigen_index], + "b-^", + label="Max relative psi error", + ) + ax1.set_xlabel("Number of eigenmodes included", fontsize=16) + ax1.set_yscale("log") + ax1.set_ylabel( + "Max linear evolving normalized error", + color="b", + fontsize=16, + ) + ax2.plot( + np.arange(1, len(R_i_mean) + 1), + R_i_mean, + "r-o", + label="Reconstruction normalized error", + ) + ax2.set_ylabel("Reconstruction normalized error", color="r", fontsize=16) + ax2.set_yscale("log") + plt.grid("both") + # annotate the lamda + for i in range(len(Q_i_mean)): + ax1.text( + i, + Q_i_mean[self.small_to_large_error_eigen_index][i], + "{:.2f}".format( + self.eigenvalues_discrete[ + self.small_to_large_error_eigen_index + ][i] + ), + size=10, + rotation=25, + ) + plt.tight_layout() + plt.show() + + def sweep_among_best_L_modes( + self, + L: int, + ALPHA_RANGE=np.logspace(-3, 1, 100), + MAX_ITER=1e5, + save_figure=True, + plot=True, + ): + """Perform sweeping among the best L modes using multi-task elastic net. + + Computes multi-task elastic net over a list of alpha values and saves the + coefficients for each path. + + Parameters: + L (int): The number of eigenmodes considered for sparse linear regression. + ALPHA_RANGE (numpy.ndarray, optional): An array of alpha values on which to + perform sparse linear regression. Defaults to np.logspace(-3, 1, 100). + MAX_ITER (int, optional): Maximum iterations allowed in the coordinate + descent algorithm. Defaults to 1e5. + save_figure (bool, optional): True if the figures should be saved. Defaults + to True. + plot (bool, optional): True if the figures should be plotted. Defaults to + True. + """ + + self.L = L + # options + TOL = 1e-12 + L1_RATIO = 0.99 + + phi_tilde = self.eigenfunction_on_traj_total_top_k[:, :L] + X = np.vstack([tmp["x"] for tmp in self.validate_data_traj]) + num_alpha = len(ALPHA_RANGE) + + # 1. normalize the features by making modal amplitude to 1 for all features + phi_tilde_scaled = phi_tilde / np.abs(phi_tilde[0, :]) + assert phi_tilde_scaled.shape == phi_tilde.shape + + # 2. augment the complex AX=B problem into an AX=B problem with real entries + # since the current package only supports real number arrays + + a = np.hstack([np.real(phi_tilde_scaled), -np.imag(phi_tilde_scaled)]) + b = np.hstack([np.imag(phi_tilde_scaled), np.real(phi_tilde_scaled)]) + phi_tilde_aug = np.vstack([a, b]) + X_aug = np.vstack([X, np.zeros(X.shape)]) + num_data = X.shape[0] + alphas_enet, coefs_enet_aug, _ = enet_path( + phi_tilde_aug, + X_aug, + l1_ratio=L1_RATIO, + tol=TOL, + max_iter=MAX_ITER, + alphas=ALPHA_RANGE, + check_input=True, + verbose=0, + ) + num_total_eigen_func = int(coefs_enet_aug.shape[1] / 2) + + # get the real and imaginary parts from the complex solution + coefs_enet_real = coefs_enet_aug[:, :num_total_eigen_func, :] + coefs_enet_imag = coefs_enet_aug[:, num_total_eigen_func:, :] + assert coefs_enet_imag.shape == coefs_enet_real.shape + + # combine them into a complex array for final results + coefs_enet_comp = coefs_enet_real + 1j * coefs_enet_imag + + # 2.5 remove features that are smaller than 'self.truncation_threshold' + # of the max, because most often + for i_alpha in range(coefs_enet_comp.shape[2]): + for i_target in range(coefs_enet_comp.shape[0]): + coef_cutoff_value = self.truncation_threshold * np.max( + abs(coefs_enet_comp[i_target, :, i_alpha]) + ) + index_remove = ( + abs(coefs_enet_comp[i_target, :, i_alpha]) < coef_cutoff_value + ) + coefs_enet_comp[i_target, index_remove, i_alpha] = 0 + 0j + + # 2.7 given the selected features, do LS-refit to remove the bias of any kind + # of regularization + for i_alpha in range(coefs_enet_comp.shape[2]): + bool_non_zero = np.linalg.norm(coefs_enet_comp[:, :, i_alpha], axis=0) > 0 + phi_tilde_scaled_reduced = phi_tilde_scaled[:, bool_non_zero] + coef_enet_comp_reduced_i_alpha = np.linalg.lstsq( + phi_tilde_scaled_reduced, X + )[0] + coefs_enet_comp[ + :, bool_non_zero, i_alpha + ] = coef_enet_comp_reduced_i_alpha.T + coefs_enet_comp[:, np.invert(bool_non_zero), i_alpha] = 0 + + # 3. compute residual for parameter sweep to draw the trade-off + coefs_enet = np.abs(coefs_enet_comp) + residual_array = [] + for i in range(num_alpha): + residual = np.linalg.norm( + X + - np.matmul(phi_tilde_scaled, coefs_enet_comp[:, :, i].T)[:num_data] + # computed the augmented but only compare the first half rows + ) + residual /= np.linalg.norm(X) + residual_array.append(residual) + residual_array = np.array(residual_array) + + # compute the number of non-zeros + num_non_zero_all_alpha = [] + num_target_components = coefs_enet_comp.shape[0] + for ii in range(coefs_enet_comp.shape[2]): + non_zero_index_per_alpha = [] + for i_component in range(num_target_components): + non_zero_index_per_alpha_per_target = abs( + coefs_enet_comp[i_component, :, ii] + ) > 0 * np.max(abs(coefs_enet_comp[i_component, :, ii])) + non_zero_index_per_alpha.append(non_zero_index_per_alpha_per_target) + non_zero_index_per_alpha_all_targets = np.logical_or.reduce( + non_zero_index_per_alpha + ) + num_non_zero_all_alpha.append(np.sum(non_zero_index_per_alpha_all_targets)) + num_non_zero_all_alpha = np.array(num_non_zero_all_alpha) + + # print a table for non-zero alpha + sparse_error_table = PrettyTable() + sparse_error_table.field_names = [ + "Index", + "Alpha", + "# Non-zero", + "Reconstruction Error", + ] + for i in range(len(ALPHA_RANGE)): + sparse_error_table.add_row( + [ + i, + ALPHA_RANGE[i], + num_non_zero_all_alpha[i], + residual_array[i], + ] + ) + print(sparse_error_table) + + # plot figures + num_target_components = coefs_enet_comp.shape[0] + alphas_enet_log_negative = -np.log10(alphas_enet) + top_k_modes_list = np.arange(L) + + # figure set 1 -- sparsity of Koopman mode in reconstructing each target + if plot: + for i_component in range(num_target_components): + plt.figure(figsize=(6, 6)) + for i in top_k_modes_list: + i_s = self.small_to_large_error_eigen_index[i] + plt.plot( + alphas_enet_log_negative, + abs(coefs_enet[i_component, i, :]), + "-*", + label=r"$\lambda_{}$ = {:.2f}".format( + i_s, self.eigenvalues_discrete[i_s] + ), + ) + max_val = np.max(abs(coefs_enet[i_component, :, -1])) + min_val = np.min(abs(coefs_enet[i_component, :, -1])) + diss = (max_val - min_val) / 2 + mean = (max_val + min_val) / 2 + plt.xlabel(r"-$\log_{10}(\alpha)$", fontsize=16) + plt.ylabel( + "Absolute Value of Coefficients", + fontsize=16, + ) + plt.ylim([mean - diss * 3, mean + diss * 3]) + plt.title(r"$x_{}$".format(i_component + 1)) + plt.legend(loc="best") + if save_figure: + plt.savefig( + self.dir + + "multi-elastic-net-coef-" + + str(i_component + 1) + + ".png", + bbox_inches="tight", + ) + plt.close() + else: + plt.tight_layout() + plt.show() + + # figure set 2 -- reconstruction MSE vs alpha + fig = plt.figure(figsize=(6, 6)) + ax1 = fig.add_subplot(111) + ax2 = ax1.twinx() + ax1.plot(alphas_enet_log_negative, residual_array, "k*-") + ax1.set_xlabel(r"-$\log_{10}(\alpha)$", fontsize=16) + ax1.set_ylabel( + "Normalized Reconstruction MSE", + color="k", + fontsize=16, + ) + + ax2.plot(alphas_enet_log_negative, num_non_zero_all_alpha, "r*-") + ax2.set_ylabel( + "Number of Selected Koopman V", + color="r", + fontsize=16, + ) + + if save_figure: + plt.savefig( + self.dir + "/multi-elastic-net-mse.png", bbox_inches="tight" + ) + plt.close() + else: + plt.tight_layout() + plt.show() + + # 4. find the selected index within the top L best eigenmodes for each alpha + sweep_index_list = [] + for ii, alpha in enumerate(alphas_enet): + non_zero_index_bool_array = ( + np.linalg.norm(coefs_enet_comp[:, :, ii], axis=0) > 0 + ) + sweep_index_list.append(non_zero_index_bool_array) + self.sweep_index_list = sweep_index_list + + def prune_model(self, i_alpha, x_train, dt=1): + """Prune the `pykoopman.koopman.Koopman` model. + + Returns a pruned model that contains most of the functionality of + the original model. + + Parameters: + i_alpha (int): The chosen index from the result of sparse linear regression + to prune the model. + x_train (numpy.ndarray): The training data, but only the `x`. Used to refit + the Koopman V since the Koopman eigenmodes are sparsified. + dt (float, optional): Time step used in the original model. Defaults to 1. + + Returns: + pruned_model (PrunedKoopman): The pruned model with fewer Koopman V, but + similar accuracy. + """ + sweep_bool_index = self.sweep_index_list[i_alpha] + sweep_index = self.small_to_large_error_eigen_index[: self.L][sweep_bool_index] + + pruned_model = PrunedKoopman(self.model, sweep_index, dt) + pruned_model = pruned_model.fit(x_train) + return pruned_model diff --git a/DSA/pykoopman/src/pykoopman/analytics/_pruned_koopman.py b/DSA/pykoopman/src/pykoopman/analytics/_pruned_koopman.py new file mode 100644 index 0000000..d795bf4 --- /dev/null +++ b/DSA/pykoopman/src/pykoopman/analytics/_pruned_koopman.py @@ -0,0 +1,162 @@ +"""Module for pruning Koopman models.""" +from __future__ import annotations + +import numpy as np +from pykoopman.koopman import Koopman +from sklearn.utils.validation import check_is_fitted + + +class PrunedKoopman: + """Prune the given original Koopman `model` at `sweep_index`. + + Parameters: + model (Koopman): An instance of `pykoopman.koopman.Koopman`. + sweep_index (np.ndarray): Selected indices in the original Koopman model. + dt (float): Time step used in the original model. + + Attributes: + sweep_index (np.ndarray): Selected indices in the original Koopman model. + lamda_ (np.ndarray): Diagonal matrix that contains the selected eigenvalues. + original_model (Koopman): An instance of `pykoopman.koopman.Koopman`. + W_ (np.ndarray): Matrix that maps selected Koopman eigenfunctions back to the + system state. + + Methods: + fit(x): Fit the pruned model to the training data `x`. + predict(x): Predict the system state at the next time stamp given `x`. + psi(x_col): Evaluate the selected eigenfunctions at a given state `x`. + phi(x_col): **Not implemented**. + ur: **Not implemented**. + A: **Not implemented**. + B: **Not implemented**. + C: Property. Returns `NotImplementedError`. + W: Property. Returns the matrix that maps the selected Koopman eigenfunctions + back to the system state. + lamda: Property. Returns the diagonal matrix of selected eigenvalues. + lamda_array: Property. Returns the selected eigenvalues as a 1D array. + continuous_lamda_array: Property. Returns the selected eigenvalues in + continuous-time as a 1D array. + """ + + def __init__(self, model: Koopman, sweep_index: np.ndarray, dt): + # construct lambda + self.sweep_index = sweep_index + # self.lamda_ = np.diag(np.diag(model.lamda)[self.sweep_index]) + self.original_model = model + self.time = {"dt": dt} + + # no support for controllable for now + if self.original_model.n_control_features_ > 0: + raise NotImplementedError + + self.A_ = None + + def fit(self, x): + """Fit the pruned model given data matrix `x` + + Parameters + ---------- + x : numpy.ndarray + Training data for refitting the Koopman V + + Returns + ------- + self : PrunedKoopman + """ + + # pruned V + selected_eigenphi = self.psi(x.T).T + result = np.linalg.lstsq(selected_eigenphi, x) + # print('refit residual = {}'.format(result[1])) + self.W_ = result[0].T + + # lamda, W = np.linalg.eig(self.original_model.A) + + self.lamda_ = np.diag(np.diag(self.original_model.lamda)[self.sweep_index]) + 0j + # evecs = self.original_model._regressor_eigenvectors + + return self + + def predict(self, x): + """Predict system state at the next time stamp given `x` + + Parameters + ---------- + x : numpy.ndarray + System state `x` in row-wise + + Returns + ------- + xnext : numpy.ndarray + System state at the next time stamp + """ + + if x.ndim == 1: + x = x.reshape(1, -1) + gnext = self.lamda @ self.psi(x.T) + # xnext = self.compute_state_from_psi(gnext) + xnext = self.W @ gnext + return np.real(xnext.T) + + def psi(self, x_col): + """Evaluate the selected psi at given state `x` + + Parameters + ---------- + x : numpy.ndarray + System state `x` in column-wise + + Returns + ------- + eigenphi : numpy.ndarray + Selected eigenfunctions' value at given state `x` + """ + + # eigenphi_ori = self.original_model.psi(x_col).T + # eigenphi_selected = eigenphi_ori[:, self.sweep_index] + + eigenphi_ori = self.original_model.psi(x_col) + eigenphi_selected = eigenphi_ori[self.sweep_index] + return eigenphi_selected + + def phi(self, x_col): + # return self.original_model._regressor_eigenvectors @ self.psi(x_col) + raise NotImplementedError("Pruned model does not have `phi` but only `psi`") + + @property + def ur(self): + raise NotImplementedError("Pruned model does not have `ur`") + + @property + def A(self): + raise NotImplementedError( + "Pruning only happen in eigen-space. So no self.A " "but only self.lamda" + ) + + @property + def B(self): + raise NotImplementedError( + "Pruning only for autonomous system rather than " "controlled system" + ) + + @property + def C(self): + return NotImplementedError("Pruning model does not have `C`") + + @property + def W(self): + check_is_fitted(self, "W_") + return self.W_ + + @property + def lamda(self): + return self.lamda_ + + @property + def lamda_array(self): + return np.diag(self.lamda) + 0j + + @property + def continuous_lamda_array(self): + check_is_fitted(self, "_pipeline") + return np.log(self.lamda_array) / self.time["dt"] diff --git a/DSA/pykoopman/src/pykoopman/common/__init__.py b/DSA/pykoopman/src/pykoopman/common/__init__.py new file mode 100644 index 0000000..4badea5 --- /dev/null +++ b/DSA/pykoopman/src/pykoopman/common/__init__.py @@ -0,0 +1,37 @@ +from __future__ import annotations + +from .cqgle import cqgle +from .examples import advance_linear_system +from .examples import drss +from .examples import Linear2Ddynamics +from .examples import lorenz +from .examples import rev_dvdp +from .examples import rk4 +from .examples import slow_manifold +from .examples import torus_dynamics +from .examples import vdp_osc +from .ks import ks +from .nlse import nlse +from .validation import check_array +from .validation import drop_nan_rows +from .validation import validate_input +from .vbe import vbe + +__all__ = [ + "check_array", + "drop_nan_rows", + "validate_input", + "drss", + "advance_linear_system", + "torus_dynamics", + "lorenz", + "vdp_osc", + "rk4", + "rev_dvdp", + "Linear2Ddynamics", + "slow_manifold", + "nlse", + "vbe", + "cqgle", + "ks", +] diff --git a/DSA/pykoopman/src/pykoopman/common/cqgle.py b/DSA/pykoopman/src/pykoopman/common/cqgle.py new file mode 100644 index 0000000..f91e230 --- /dev/null +++ b/DSA/pykoopman/src/pykoopman/common/cqgle.py @@ -0,0 +1,234 @@ +"""Module for cubic-quintic Ginzburg-Landau equation.""" +from __future__ import annotations + +import numpy as np +from matplotlib import pyplot as plt +from mpl_toolkits.mplot3d import Axes3D +from pykoopman.common.examples import rk4 +from scipy.fft import fft +from scipy.fft import fftfreq +from scipy.fft import ifft + + +class cqgle: + """ + Cubic-quintic Ginzburg-Landau equation solver. + + Solves the equation: + i*u_t + (0.5 - i * tau) u_{xx} - i * kappa u_{xxxx} + (1-i * beta)|u|^2 u + + (nu - i * sigma)|u|^4 u - i * gamma u = 0 + + Solves the periodic boundary conditions PDE using spectral methods. + + Attributes: + n_states (int): Number of states. + x (numpy.ndarray): x-coordinates. + dt (float): Time step. + tau (float): Parameter tau. + kappa (float): Parameter kappa. + beta (float): Parameter beta. + nu (float): Parameter nu. + sigma (float): Parameter sigma. + gamma (float): Parameter gamma. + k (numpy.ndarray): Wave numbers. + dk (float): Wave number spacing. + + Methods: + sys(t, x, u): System dynamics function. + simulate(x0, n_int, n_sample): Simulate the system for a given initial + condition. + collect_data_continuous(x0): Collect training data pairs in continuous sense. + collect_one_step_data_discrete(x0): Collect training data pairs in discrete + sense. + collect_one_trajectory_data(x0, n_int, n_sample): Collect data for one + trajectory. + visualize_data(x, t, X): Visualize the data in physical space. + visualize_state_space(X): Visualize the data in state space. + """ + + def __init__( + self, + n, + x, + dt, + tau=0.08, + kappa=0, + beta=0.66, + nu=-0.1, + sigma=-0.1, + gamma=-0.1, + L=2 * np.pi, + ): + self.n_states = n + self.x = x + + self.tau = tau + self.kappa = kappa + self.beta = beta + self.nu = nu + self.sigma = sigma + self.gamma = gamma + + dk = 2 * np.pi / L + self.k = fftfreq(self.n_states, 1.0 / self.n_states) * dk + self.dt = dt + + def sys(self, t, x, u): + xk = fft(x) + + # 1/3 truncation rule + xk[self.n_states // 6 : 5 * self.n_states // 6] = 0j + x = ifft(xk) + + tmp_1_k = (0.5 - 1j * self.tau) * (-self.k**2) * xk + tmp_2_k = -1j * self.kappa * self.k**4 * xk + tmp_3_k = fft( + (1 - 1j * self.beta) * abs(x) ** 2 * x + + (self.nu - 1j * self.sigma) * abs(x) ** 4 * x + ) + tmp_4_k = -1j * self.gamma * xk + + # return back to physical space + y = ifft(1j * (tmp_1_k + tmp_2_k + tmp_3_k + tmp_4_k)) + return y + + def simulate(self, x0, n_int, n_sample): + # n_traj = x0.shape[1] + x = x0 + u = np.zeros((n_int, 1), dtype=complex) + X = np.zeros((n_int // n_sample, self.n_states), dtype=complex) + t = 0 + j = 0 + t_list = [] + for step in range(n_int): + t += self.dt + y = rk4(0, x, u[step], self.dt, self.sys) + if (step + 1) % n_sample == 0: + X[j] = y + j += 1 + t_list.append(t) + x = y + return X, np.array(t_list) + + def collect_data_continuous(self, x0): + """ + collect training data pairs - continuous sense. + + given x0, with shape (n_dim, n_traj), the function + returns dx/dt with shape (n_dim, n_traj) + """ + + n_traj = x0.shape[0] + u = np.zeros((n_traj, 1)) + X = x0 + Y = [] + for i in range(n_traj): + y = self.sys(0, x0[i], u[i]) + Y.append(y) + Y = np.vstack(Y) + return X, Y + + def collect_one_step_data_discrete(self, x0): + """ + collect training data pairs - discrete sense. + + given x0, with shape (n_dim, n_traj), the function + returns system state x1 after self.dt with shape + (n_dim, n_traj) + """ + + n_traj = x0.shape[0] + X = x0 + Y = [] + for i in range(n_traj): + y, _ = self.simulate(x0[i], n_int=1, n_sample=1) + Y.append(y) + Y = np.vstack(Y) + return X, Y + + def collect_one_trajectory_data(self, x0, n_int, n_sample): + x = x0 + y, _ = self.simulate(x, n_int, n_sample) + return y + + def visualize_data(self, x, t, X): + plt.figure(figsize=(6, 6)) + ax = plt.axes(projection=Axes3D.name) + for i in range(X.shape[0]): + ax.plot(x, abs(X[i]), zs=t[i], zdir="t", label="time = " + str(i * self.dt)) + # plt.legend(loc='best') + ax.view_init(elev=35.0, azim=-65, vertical_axis="y") + ax.set(ylabel=r"$mag. of. u(x,t)$", xlabel=r"$x$", zlabel=r"time $t$") + plt.title("CQGLE (Kutz et al., Complexity, 2018)") + plt.show() + + def visualize_state_space(self, X): + u, s, vt = np.linalg.svd(X, full_matrices=False) + # this is a pde problem so the number of snapshots are smaller than dof + pca_1_r, pca_1_i = np.real(u[:, 0]), np.imag(u[:, 0]) + pca_2_r, pca_2_i = np.real(u[:, 1]), np.imag(u[:, 1]) + pca_3_r, pca_3_i = np.real(u[:, 2]), np.imag(u[:, 2]) + + plt.figure(figsize=(6, 6)) + plt.semilogy(s) + plt.xlabel("number of SVD terms") + plt.ylabel("singular values") + plt.title("PCA singular value decays") + plt.show() + + plt.figure(figsize=(6, 6)) + ax = plt.axes(projection=Axes3D.name) + ax.plot3D(pca_1_r, pca_2_r, pca_3_r, "k-o") + ax.set(xlabel="pc1", ylabel="pc2", zlabel="pc3") + plt.title("PCA visualization (real)") + plt.show() + + plt.figure(figsize=(6, 6)) + ax = plt.axes(projection=Axes3D.name) + ax.plot3D(pca_1_i, pca_2_i, pca_3_i, "k-o") + ax.set(xlabel="pc1", ylabel="pc2", zlabel="pc3") + plt.title("PCA visualization (imag)") + plt.show() + + +if __name__ == "__main__": + n = 512 + x = np.linspace(-10, 10, n, endpoint=False) + u0 = np.exp(-((x) ** 2)) + # u0 = 2.0 / np.cosh(x) + # u0 = u0.reshape(-1,1) + n_int = 9000 + n_snapshot = 300 + dt = 40.0 / n_int + n_sample = n_int // n_snapshot + + model = cqgle(n, x, dt, L=20) + X, t = model.simulate(u0, n_int, n_sample) + + print(X.shape) + print(X[:, -1].max()) + + # usage: visualize the data in physical space + model.visualize_data(x, t, X) + print(t) + + # usage: visualize the data in state space + model.visualize_state_space(X) + + # usage: collect continuous data pair: x and dx/dt + x0_array = np.vstack([u0, u0, u0]) + X, Y = model.collect_data_continuous(x0_array) + + print(X.shape) + print(Y.shape) + + # usage: collect discrete data pair + x0_array = np.vstack([u0, u0, u0]) + X, Y = model.collect_one_step_data_discrete(x0_array) + + print(X.shape) + print(Y.shape) + + # usage: collect one trajectory data + X = model.collect_one_trajectory_data(u0, n_int, n_sample) + print(X.shape) diff --git a/DSA/pykoopman/src/pykoopman/common/examples.py b/DSA/pykoopman/src/pykoopman/common/examples.py new file mode 100644 index 0000000..ae2ff22 --- /dev/null +++ b/DSA/pykoopman/src/pykoopman/common/examples.py @@ -0,0 +1,1045 @@ +"""module for example dynamics data""" +from __future__ import annotations + +import matplotlib as mpl +import matplotlib.pyplot as plt +import numpy as np +from scipy.linalg import orth + + +def drss( + n=2, p=2, m=2, p_int_first=0.1, p_int_others=0.01, p_repeat=0.05, p_complex=0.5 +): + """ + Create a discrete-time, random, stable, linear state space model. + + Args: + n (int, optional): Number of states. Default is 2. + p (int, optional): Number of control inputs. Default is 2. + m (int, optional): Number of output measurements. + If m=0, C becomes the identity matrix, so that y=x. Default is 2. + p_int_first (float, optional): Probability of an integrator as the first pole. + Default is 0.1. + p_int_others (float, optional): Probability of other integrators beyond the + first. Default is 0.01. + p_repeat (float, optional): Probability of repeated roots. Default is 0.05. + p_complex (float, optional): Probability of complex roots. Default is 0.5. + + Returns: + Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]: A tuple containing the + state transition matrix (A), control matrix (B), and measurement matrix (C). + + A (numpy.ndarray): State transition matrix of shape (n, n). + B (numpy.ndarray): Control matrix of shape (n, p). + C (numpy.ndarray): Measurement matrix of shape (m, n). If m = 0, C is the + identity matrix. + + """ + + # Number of integrators + nint = int( + (np.random.rand(1) < p_int_first) + sum(np.random.rand(n - 1) < p_int_others) + ) + # Number of repeated roots + nrepeated = int(np.floor(sum(np.random.rand(n - nint) < p_repeat) / 2)) + # Number of complex roots + ncomplex = int( + np.floor(sum(np.random.rand(n - nint - 2 * nrepeated, 1) < p_complex) / 2) + ) + nreal = n - nint - 2 * nrepeated - 2 * ncomplex + + # Random poles + rep = 2 * np.random.rand(nrepeated) - 1 + if ncomplex != 0: + mag = np.random.rand(ncomplex) + cplx = np.zeros(ncomplex, dtype=complex) + for i in range(ncomplex): + cplx[i] = mag[i] * np.exp(complex(0, np.pi * np.random.rand(1))) + re = np.real(cplx) + im = np.imag(cplx) + + # Generate random state space model + A = np.zeros((n, n)) + if ncomplex != 0: + for i in range(0, ncomplex): + A[2 * i : 2 * i + 2, 2 * i : 2 * i + 2] = np.array( + [[re[i], im[i]], [-im[i], re[i]]] + ) + + if 2 * ncomplex < n: + list_poles = [] + if nint: + list_poles = np.append(list_poles, np.ones(nint)) + if rep: + list_poles = np.append(list_poles, rep) + list_poles = np.append(list_poles, rep) + if nreal: + list_poles = np.append(list_poles, 2 * np.random.rand(nreal) - 1) + + A[2 * ncomplex :, 2 * ncomplex :] = np.diag(list_poles) + + T = orth(np.random.rand(n, n)) + A = np.transpose(T) @ (A @ T) + + # control matrix + B = np.random.randn(n, p) + # mask for nonzero entries in B + mask = np.random.rand(B.shape[0], B.shape[1]) + B = np.squeeze(np.multiply(B, [(mask < 0.75) != 0])) + + # Measurement matrix + if m == 0: + C = np.identity(n) + else: + C = np.random.randn(m, n) + mask = np.random.rand(C.shape[0], C.shape[1]) + C = np.squeeze(C * [(mask < 0.75) != 0]) + + return A, B, C + + +def advance_linear_system(x0, u, n, A=None, B=None, C=None): + """ + Simulate the linear system dynamics for a given number of steps. + + Args: + x0 (numpy.ndarray): Initial state vector of shape (n,). + u (numpy.ndarray): Control input array of shape (p,) or (p, n-1). + If 1-dimensional, it will be converted to a row vector. + n (int): Number of steps to simulate. + A (numpy.ndarray, optional): State transition matrix of shape (n, n). + If not provided, it defaults to None. + B (numpy.ndarray, optional): Control matrix of shape (n, p). + If not provided, it defaults to None. + C (numpy.ndarray, optional): Measurement matrix of shape (m, n). + If not provided, it defaults to None. + + Returns: + Tuple[numpy.ndarray, numpy.ndarray]: A tuple containing the state trajectory + (x) and the output trajectory (y). + + x (numpy.ndarray): State trajectory of shape (n, len(x0)). + y (numpy.ndarray): Output trajectory of shape (n, C.shape[0]). + + """ + if C is None: + C = np.identity(len(x0)) + if u.ndim == 1: + u = u[np.newaxis, :] + + y = np.zeros([n, C.shape[0]]) + x = np.zeros([n, len(x0)]) + x[0, :] = x0 + y[0, :] = C.dot(x[0, :]) + for i in range(n - 1): + x[i + 1, :] = A.dot(x[i, :]) + B.dot(u[:, i]) + y[i + 1, :] = C.dot(x[i + 1, :]) + return x, y + + +def vdp_osc(t, x, u): + """ + Compute the dynamics of the Van der Pol oscillator. + + Args: + t (float): Time. + x (numpy.ndarray): State vector of shape (2,). + u (float): Control input. + + Returns: + numpy.ndarray: Updated state vector of shape (2,). + + """ + y = np.zeros(x.shape) + y[0, :] = 2 * x[1, :] + y[1, :] = -0.8 * x[0, :] + 2 * x[1, :] - 10 * (x[0, :] ** 2) * x[1, :] + u + return y + + +def rk4(t, x, u, _dt=0.01, func=vdp_osc): + """ + Perform a 4th order Runge-Kutta integration. + + Args: + t (float): Time. + x (numpy.ndarray): State vector of shape (2,). + u (float): Control input. + _dt (float, optional): Time step. Defaults to 0.01. + func (function, optional): Function defining the dynamics. Defaults to vdp_osc. + + Returns: + numpy.ndarray: Updated state vector of shape (2,). + + """ + # 4th order Runge-Kutta + k1 = func(t, x, u) + k2 = func(t, x + k1 * _dt / 2, u) + k3 = func(t, x + k2 * _dt / 2, u) + k4 = func(t, x + k1 * _dt, u) + return x + (_dt / 6) * (k1 + 2 * k2 + 2 * k3 + k4) + + +def square_wave(step): + """ + Generate a square wave with a period of 60 time steps. + + Args: + step (int): Current time step. + + Returns: + float: Square wave value at the given time step. + + """ + return (-1.0) ** (round(step / 30.0)) + + +def sine_wave(step): + """ + Generate a sine wave with a period of 60 time steps. + + Args: + step (int): Current time step. + + Returns: + float: Sine wave value at the given time step. + + """ + return np.sin(round(step / 30.0)) + + +def lorenz(x, t, sigma=10, beta=8 / 3, rho=28): + """ + Compute the derivative of the Lorenz system at a given state. + + Args: + x (list): Current state of the Lorenz system [x, y, z]. + t (float): Current time. + sigma (float, optional): Parameter sigma. Default is 10. + beta (float, optional): Parameter beta. Default is 8/3. + rho (float, optional): Parameter rho. Default is 28. + + Returns: + list: Derivative of the Lorenz system [dx/dt, dy/dt, dz/dt]. + + """ + return [ + sigma * (x[1] - x[0]), + x[0] * (rho - x[2]) - x[1], + x[0] * x[1] - beta * x[2], + ] + + +def rev_dvdp(t, x, u=0, dt=0.1): + """ + Reverse dynamics of the Van der Pol oscillator. + + Args: + t (float): Time. + x (numpy.ndarray): Current state of the system [x1, x2]. + u (float, optional): Input. Default is 0. + dt (float, optional): Time step. Default is 0.1. + + Returns: + numpy.ndarray: Updated state of the system [x1', x2']. + + """ + return np.array( + [ + x[0, :] - x[1, :] * dt, + x[1, :] + (x[0, :] - x[1, :] + x[0, :] ** 2 * x[1, :]) * dt, + ] + ) + + +class Linear2Ddynamics: + def __init__(self): + """ + Initializes a Linear2Ddynamics object. + + """ + self.n_states = 2 # Number of states + + def linear_map(self, x): + """ + Applies the linear mapping to the input state. + + Args: + x (numpy.ndarray): Input state. + + Returns: + numpy.ndarray: Resulting mapped state. + + """ + return np.array([[0.8, -0.05], [0, 0.7]]) @ x + + def collect_data(self, x, n_int, n_traj): + """ + Collects data by integrating the linear dynamics. + + Args: + x (numpy.ndarray): Initial state. + n_int (int): Number of integration steps. + n_traj (int): Number of trajectories. + + Returns: + numpy.ndarray: Input data. + numpy.ndarray: Output data. + + """ + # Init + X = np.zeros((self.n_states, n_int * n_traj)) + Y = np.zeros((self.n_states, n_int * n_traj)) + + # Integrate + for step in range(n_int): + y = self.linear_map(x) + X[:, (step) * n_traj : (step + 1) * n_traj] = x + Y[:, (step) * n_traj : (step + 1) * n_traj] = y + x = y + + return X, Y + + def visualize_modes(self, x, phi, eigvals, order=None): + """ + Visualizes the modes of the linear dynamics. + + Args: + x (numpy.ndarray): State data. + phi (numpy.ndarray): Eigenvectors. + eigvals (numpy.ndarray): Eigenvalues. + order (list, optional): Order of the modes to visualize. Default is None. + + """ + n_modes = min(10, phi.shape[1]) + fig, axs = plt.subplots(2, n_modes, figsize=(3 * n_modes, 6)) + if order is None: + index_list = range(n_modes) + else: + index_list = order + j = 0 + for i in index_list: + axs[0, j].scatter( + x[0, :], + x[1, :], + c=np.real(phi[:, i]), + marker="o", + cmap=plt.get_cmap("jet"), + ) + axs[1, j].scatter( + x[0, :], + x[1, :], + c=np.imag(phi[:, i]), + marker="o", + cmap=plt.get_cmap("jet"), + ) + axs[0, j].set_title(r"$\lambda$=" + "{:2.3f}".format(eigvals[i])) + j += 1 + + +class torus_dynamics: + """ + Sparse dynamics in Fourier space on torus. + + Attributes: + n_states (int): Number of states. + sparsity (int): Degree of sparsity. + freq_max (int): Maximum frequency. + noisemag (float): Magnitude of noise. + + Methods: + __init__(self, n_states=128, sparsity=5, freq_max=15, noisemag=0.0): + Initializes a torus_dynamics object. + + setup(self): + Sets up the dynamics. + + advance(self, n_samples, dt=1): + Advances the continuous-time dynamics without control. + + advance_discrete_time(self, n_samples, dt, u=None): + Advances the discrete-time dynamics with or without control. + + set_control_matrix_physical(self, B): + Sets the control matrix in physical space. + + set_control_matrix_fourier(self, Bhat): + Sets the control matrix in Fourier space. + + set_point_actuator(self, position=None): + Sets a single point actuator. + + viz_setup(self): + Sets up the visualization. + + viz_torus(self, ax, x): + Visualizes the torus dynamics. + + viz_all_modes(self, modes=None): + Visualizes all modes. + + modes(self): + Returns the modes of the dynamics. + + B_effective(self): + Returns the effective control matrix. + + """ + + def __init__(self, n_states=128, sparsity=5, freq_max=15, noisemag=0.0): + """ + Initializes a torus_dynamics object. + + Args: + n_states (int, optional): Number of states. Default is 128. + sparsity (int, optional): Degree of sparsity. Default is 5. + freq_max (int, optional): Maximum frequency. Default is 15. + noisemag (float, optional): Magnitude of noise. Default is 0.0. + + """ + self.n_states = n_states + self.sparsity = sparsity + self.freq_max = freq_max + self.noisemag = noisemag + self.setup() + + def setup(self): + """ + Sets up the dynamics. + + """ + # Initialization in the Fourier space + xhat = np.zeros((self.n_states, self.n_states), complex) + # Index of nonzero frequency components + self.J = np.zeros((self.sparsity, 2), dtype=int) + IC = np.zeros(self.sparsity) # Initial condition, real number + frequencies = np.zeros(self.sparsity) + damping = np.zeros(self.sparsity) + + IC = np.random.randn(self.sparsity) + frequencies = np.sqrt(4 * np.random.rand(self.sparsity)) + damping = -np.random.rand(self.sparsity) * 0.1 + for k in range(self.sparsity): + loopbreak = 0 + while loopbreak != 1: + self.J[k, 0] = np.ceil( + np.random.rand(1) * self.n_states / (self.freq_max + 1) + ) + self.J[k, 1] = np.ceil( + np.random.rand(1) * self.n_states / (self.freq_max + 1) + ) + if xhat[self.J[k, 0], self.J[k, 1]] == 0.0: + loopbreak = 1 + + xhat[self.J[k, 0], self.J[k, 1]] = IC[k] + + mask = np.zeros((self.n_states, self.n_states), int) + for k in range(self.sparsity): + mask[self.J[k, 0], self.J[k, 1]] = 1 + + self.damping = damping + self.frequencies = frequencies + self.IC = IC + self.xhat = xhat + self.mask = mask + + def advance(self, n_samples, dt=1): + """ + Advances the continuous-time dynamics without control. + + Args: + n_samples (int): Number of samples to advance. + dt (float, optional): Time step. Default is 1. + + """ + print("Evolving continuous-time dynamics without control.") + self.n_samples = n_samples + self.dt = dt + + # Initilization + # In physical space + self.X = np.ndarray((self.n_states**2, self.n_samples)) + # In Fourier space + self.Xhat = np.ndarray((self.n_states**2, self.n_samples), complex) + self.time_vector = np.zeros(self.n_samples) + + # if self.noisemag != 0: + # self.XhatClean = np.ndarray((self.n_states**2, self.n_samples), complex) + # self.XClean = np.ndarray((self.n_states**2, self.n_samples)) + + for step in range(self.n_samples): + t = step * self.dt + self.time_vector[step] = t + xhat = np.zeros((self.n_states, self.n_states), complex) + for k in range(self.sparsity): + xhat[self.J[k, 0], self.J[k, 1]] = ( + np.exp((self.damping[k] + 1j * 2 * np.pi * self.frequencies[k]) * t) + * self.IC[k] + ) + + if self.noisemag != 0: + self.XhatClean[:, step] = xhat.reshape(self.n_states**2) + xClean = np.real(np.fft.ifft2(xhat)) + self.XClean[:, step] = xClean.reshape(self.n_states**2) + + # xRMS = np.sqrt(np.mean(xhat.reshape((self.n_states**2,1))**2)) + # xhat = xhat + self.noisemag*xRMS\ + # *np.random.randn(xhat.shape[0],xhat.shape[1]) \ + # + 1j*self.noisemag*xRMS \ + # *np.random.randn(xhat.shape[0],xhat.shape[1]) + self.Xhat[:, step] = xhat.reshape(self.n_states**2) + x = np.real(np.fft.ifft2(xhat)) + self.X[:, step] = x.reshape(self.n_states**2) + + def advance_discrete_time(self, n_samples, dt, u=None): + """ + Advances the discrete-time dynamics with or without control. + + Args: + n_samples (int): Number of samples to advance. + dt (float): Time step. + u (array-like, optional): Control input. Default is None. + + """ + print("Evolving discrete-time dynamics with or without control.") + if u is None: + self.n_control_features_ = 0 + self.U = np.zeros(n_samples) + self.U = self.U[np.newaxis, :] + print("No control input provided. Evolving unforced system.") + else: + if u.ndim == 1: + if len(u) > n_samples: + u = u[:-1] + self.U = u[np.newaxis, :] + elif u.ndim == 2: + if u.shape[0] > n_samples: + u = u[:-1, :] + self.U = u + self.n_control_features_ = self.U.shape[1] + + if not hasattr(self, "B"): + B = np.zeros((self.n_states, self.n_states)) + print(B.shape) + self.set_control_matrix_physical(B) + print("Control matrix is not set. Continue with unforced system.") + + self.n_samples = n_samples + self.dt = dt + + # Initilization + # In physical space + self.X = np.ndarray((self.n_states**2, self.n_samples)) + # In Fourier space + self.Xhat = np.ndarray((self.n_states**2, self.n_samples), complex) + self.time_vector = np.zeros(self.n_samples) + + # Set initial condition + xhat0 = np.zeros((self.n_states, self.n_states), complex) + for k in range(self.sparsity): + xhat0[self.J[k, 0], self.J[k, 1]] = self.IC[k] + self.Xhat[:, 0] = xhat0.reshape(self.n_states**2) + x0 = np.real(np.fft.ifft2(xhat0)) + self.X[:, 0] = x0.reshape(self.n_states**2) + + for step in range(1, self.n_samples, 1): + t = step * self.dt + self.time_vector[step] = t + # self.Xhat[:, step] = np.reshape(self.Bhat * self.U[0,step - 1],\ + # self.n_states ** 2) + # xhat = self.Xhat[:,step].reshape(self.n_states,self.n_states) + # xhat_prev = \ + # self.Xhat[:, step - 1].reshape(self.n_states, self.n_states) + + # forced torus dynamics linearly evolve in the spectral space, sparsely + xhat = np.array((self.n_states, self.n_states), complex) + xhat = self.Xhat[:, step].reshape(self.n_states, self.n_states) + xhat_prev = self.Xhat[:, step - 1].reshape(self.n_states, self.n_states) + for k in range(self.sparsity): + xhat[self.J[k, 0], self.J[k, 1]] = ( + np.exp( + (self.damping[k] + 1j * 2 * np.pi * self.frequencies[k]) + * self.dt + ) + * xhat_prev[self.J[k, 0], self.J[k, 1]] + + self.Bhat[self.J[k, 0], self.J[k, 1]] * self.U[0, step - 1] + ) + + # xhat_prev = self.Xhat[:,step-1].reshape(self.n_states, self.n_states) + # for k in range(self.sparsity): + # xhat[self.J[k,0], self.J[k,1]] += np.exp((self.damping[k] \ + # + 1j * 2 * np.pi * self.frequencies[k]) * self.dt) \ + # * xhat_prev[self.J[k,0], self.J[k,1]] + + self.Xhat[:, step] = xhat.reshape(self.n_states**2) + x = np.real(np.fft.ifft2(xhat)) + self.X[:, step] = x.reshape(self.n_states**2) + + def set_control_matrix_physical(self, B): + """ + Sets the control matrix in physical space. + + Args: + B (array-like): Control matrix in physical space. + + """ + if np.allclose(B.shape, np.array([self.n_states, self.n_states])) is False: + raise TypeError("Control matrix B has wrong shape.") + self.B = B + self.Bhat = np.fft.fft2(B) + + def set_control_matrix_fourier(self, Bhat): + """ + Sets the control matrix in Fourier space. + + Args: + Bhat (array-like): Control matrix in Fourier space. + + """ + if np.allclose(Bhat.shape, np.array([self.n_states, self.n_states])) is False: + raise TypeError("Control matrix Bhat has wrong shape.") + self.Bhat = Bhat + self.B = np.real(np.fft.ifft2(self.Bhat)) + + def set_point_actuator(self, position=None): + """ + Sets a single point actuator. + + Args: + position (array-like, optional): Position of the actuator. Default is None. + + """ + if position is None: + position = np.random.randint(0, self.n_states, 2) + try: + for i in range(len(position)): + position[i] = int(position[i]) + except ValueError: + print("position was not a valid integer.") + + is_position_in_valid_domain = (position >= 0) & (position < self.n_states) + if all(is_position_in_valid_domain) is False: + raise ValueError( + "Actuator position was not a valid integer inside of domain." + ) + + # Control matrix in physical space (single point actuator) + B = np.zeros((self.n_states, self.n_states)) + B[position[0], position[1]] = 1 + self.set_control_matrix_physical(B) + + def viz_setup(self): + """ + Sets up the visualization. + + """ + self.cmap_torus = plt.cm.jet # bwr #plt.cm.RdYlBu + self.n_colors = self.n_states + r1 = 2 + r2 = 1 + [T1, T2] = np.meshgrid( + np.linspace(0, 2 * np.pi, self.n_states), + np.linspace(0, 2 * np.pi, self.n_states), + ) + R = r1 + r2 * np.cos(T2) + self.Zgrid = r2 * np.sin(T2) + self.Xgrid = R * np.cos(T1) + self.Ygrid = R * np.sin(T1) + + def viz_torus(self, ax, x): + """ + Visualizes the torus dynamics. + + Args: + ax: Axes object for plotting. + x (array-like): Dynamics to be visualized. + + Returns: + surface: Surface plot of the torus dynamics. + + """ + if not hasattr(self, "viz"): + self.viz_setup() + + norm = mpl.colors.Normalize(vmin=-abs(x).max(), vmax=abs(x).max()) + surface = ax.plot_surface( + self.Xgrid, + self.Ygrid, + self.Zgrid, + facecolors=self.cmap_torus(norm(x)), + shade=False, + rstride=1, + cstride=1, + ) + # m = cm.ScalarMappable(cmap=cmap_torus, norm=norm) + # m.set_array([]) + # plt.colorbar(m) + # ax.figure.colorbar(surf, ax=ax) + ax.set_zlim(-3.01, 3.01) + return surface + + def viz_all_modes(self, modes=None): + """ + Visualizes all modes. + + Args: + modes (array-like, optional): Modes to be visualized. Default is None. + + Returns: + fig: Figure object containing the visualizations. + + """ + if modes is None: + modes = self.modes + + if not hasattr(self, "viz"): + self.viz_setup() + + fig = plt.figure(figsize=(20, 10)) + for k in range(self.sparsity): + ax = plt.subplot2grid((1, self.sparsity), (0, k), projection="3d") + self.viz_torus(ax, modes[:, k].reshape(self.n_states, self.n_states)) + plt.axis("off") + return fig + + @property + def modes(self): + """ + Returns the modes of the dynamics. + + Returns: + modes (array-like): Modes of the dynamics. + + """ + modes = np.zeros((self.n_states**2, self.sparsity)) + + for k in range(self.sparsity): + mode_in_fourier = np.zeros((self.n_states, self.n_states)) + mode_in_fourier[self.J[k, 0], self.J[k, 1]] = 1 + modes[:, k] = np.real( + np.fft.ifft2(mode_in_fourier).reshape(self.n_states**2) + ) + + return modes + + @property + def B_effective(self): + """ + Returns the effective control matrix. + + Returns: + B_effective (array-like): Effective control matrix. + + """ + Bhat_effective = np.zeros((self.n_states, self.n_states), complex) + for k in range(self.sparsity): + control_mode = np.zeros((self.n_states, self.n_states), complex) + control_mode[self.J[k, 0], self.J[k, 1]] = self.Bhat[ + self.J[k, 0], self.J[k, 1] + ] + Bhat_effective += control_mode + B_effective = np.fft.ifft2(Bhat_effective) + + return B_effective + + +class slow_manifold: + """ + Represents the slow manifold class. + + Args: + mu (float, optional): Parameter mu. Default is -0.05. + la (float, optional): Parameter la. Default is -1.0. + dt (float, optional): Time step size. Default is 0.01. + + Attributes: + mu (float): Parameter mu. + la (float): Parameter la. + b (float): Value computed from mu and la. + dt (float): Time step size. + n_states (int): Number of states. + + Methods: + sys(t, x, u): Computes the system dynamics. + output(x): Computes the output based on the state. + simulate(x0, n_int): Simulates the system dynamics. + collect_data_continuous(x0): Collects data from continuous-time dynamics. + collect_data_discrete(x0, n_int): Collects data from discrete-time dynamics. + visualize_trajectories(t, X, n_traj): Visualizes the trajectories. + visualize_state_space(X, Y, n_traj): Visualizes the state space. + """ + + def __init__(self, mu=-0.05, la=-1.0, dt=0.01): + self.mu = mu + self.la = la + self.b = self.la / (self.la - 2 * self.mu) + self.dt = dt + self.n_states = 2 + + def sys(self, t, x, u): + """ + Computes the system dynamics. + + Args: + t (float): Time. + x (array-like): State. + u (array-like): Control input. + + Returns: + array-like: Computed system dynamics. + + """ + return np.array([self.mu * x[0, :], self.la * (x[1, :] - x[0, :] ** 2)]) + + def output(self, x): + """ + Computes the output based on the state. + + Args: + x (array-like): State. + + Returns: + array-like: Computed output. + + """ + return x[0, :] ** 2 + x[1, :] + + def simulate(self, x0, n_int): + """ + Simulates the system dynamics. + + Args: + x0 (array-like): Initial state. + n_int (int): Number of integration steps. + + Returns: + array-like: Simulated trajectory. + + """ + n_traj = x0.shape[1] + x = x0 + u = np.zeros((n_int, 1)) + X = np.zeros((self.n_states, n_int * n_traj)) + for step in range(n_int): + y = rk4(0, x, u[step, :], self.dt, self.sys) + X[:, (step) * n_traj : (step + 1) * n_traj] = y + x = y + return X + + def collect_data_continuous(self, x0): + """ + Collects data from continuous-time dynamics. + + Args: + x0 (array-like): Initial state. + + Returns: + tuple: Collected data (X, Y). + + """ + n_traj = x0.shape[1] + u = np.zeros((1, n_traj)) + X = x0 + Y = self.sys(0, x0, u) + return X, Y + + def collect_data_discrete(self, x0, n_int): + """ + Collects data from discrete-time dynamics. + + Args: + x0 (array-like): Initial state. + n_int (int): Number of integration steps. + + Returns: + tuple: Collected data (X, Y). + + """ + n_traj = x0.shape[1] + x = x0 + u = np.zeros((n_int, n_traj)) + X = np.zeros((self.n_states, n_int * n_traj)) + Y = np.zeros((self.n_states, n_int * n_traj)) + for step in range(n_int): + y = rk4(0, x, u[step, :], self.dt, self.sys) + X[:, (step) * n_traj : (step + 1) * n_traj] = x + Y[:, (step) * n_traj : (step + 1) * n_traj] = y + x = y + return X, Y + + def visualize_trajectories(self, t, X, n_traj): + """ + Visualizes the trajectories. + + Args: + t (array-like): Time vector. + X (array-like): State trajectories. + n_traj (int): Number of trajectories. + + """ + fig, axs = plt.subplots(1, 1, tight_layout=True, figsize=(12, 4)) + for traj_idx in range(n_traj): + x = X[:, traj_idx::n_traj] + axs.plot(t[0:100], x[1, 0:100], "k") + axs.set(ylabel=r"$x_2$", xlabel=r"$t$") + + def visualize_state_space(self, X, Y, n_traj): + """ + Visualizes the state space. + + Args: + X (array-like): State trajectories. + Y (array-like): Output trajectories. + n_traj (int): Number of trajectories. + + """ + fig, axs = plt.subplots(1, 1, tight_layout=True, figsize=(4, 4)) + for traj_idx in range(n_traj): + axs.plot( + [X[0, traj_idx::n_traj], Y[0, traj_idx::n_traj]], + [X[1, traj_idx::n_traj], Y[1, traj_idx::n_traj]], + "-k", + ) + axs.set(ylabel=r"$x_2$", xlabel=r"$x_1$") + + +class forced_duffing: + """ + Forced Duffing Oscillator. + + dx1/dt = x2 + dx2/dt = -d*x2-alpha*x1-beta*x1^3 + u + + [1] S. Peitz, S. E. Otto, and C. W. Rowley, + “Data-driven model predictive control using interpolated koopman generators,” + SIAM J. Appl. Dyn. Syst., vol. 19, no. 3, pp. 2162–2193, Mar. 2020. + """ + + def __init__(self, dt, d, alpha, beta): + """ + Initializes the Forced Duffing Oscillator. + + Args: + dt (float): Time step. + d (float): Damping coefficient. + alpha (float): Coefficient of x1. + beta (float): Coefficient of x1^3. + """ + self.dt = dt + self.d = d + self.alpha = alpha + self.beta = beta + self.n_states = 2 + + def sys(self, t, x, u): + """ + Defines the system dynamics of the Forced Duffing Oscillator. + + Args: + t (float): Time. + x (array-like): State vector. + u (array-like): Control input. + + Returns: + array-like: Rate of change of the state vector. + """ + y = np.array( + [ + x[1, :], + -self.d * x[1, :] - self.alpha * x[0, :] - self.beta * x[0, :] ** 3 + u, + ] + ) + return y + + def simulate(self, x0, n_int, u): + """ + Simulates the Forced Duffing Oscillator. + + Args: + x0 (array-like): Initial state vector. + n_int (int): Number of time steps. + u (array-like): Control inputs. + + Returns: + array-like: State trajectories. + """ + n_traj = x0.shape[1] + x = x0 + X = np.zeros((self.n_states, n_int * n_traj)) + for step in range(n_int): + y = rk4(0, x, u[step, :], self.dt, self.sys) + X[:, (step) * n_traj : (step + 1) * n_traj] = y + x = y + return X + + def collect_data_continuous(self, x0, u): + """ + Collects continuous data for the Forced Duffing Oscillator. + + Args: + x0 (array-like): Initial state vector. + u (array-like): Control inputs. + + Returns: + tuple: State and output trajectories. + """ + X = x0 + Y = self.sys(0, x0, u) + return X, Y + + def collect_data_discrete(self, x0, n_int, u): + """ + Collects discrete-time data for the Forced Duffing Oscillator. + + Args: + x0 (array-like): Initial state vector. + n_int (int): Number of time steps. + u (array-like): Control inputs. + + Returns: + tuple: State and output trajectories. + """ + n_traj = x0.shape[1] + x = x0 + X = np.zeros((self.n_states, n_int * n_traj)) + Y = np.zeros((self.n_states, n_int * n_traj)) + for step in range(n_int): + y = rk4(0, x, u[step, :], self.dt, self.sys) + X[:, (step) * n_traj : (step + 1) * n_traj] = x + Y[:, (step) * n_traj : (step + 1) * n_traj] = y + x = y + return X, Y + + def visualize_trajectories(self, t, X, n_traj): + """ + Visualizes the state trajectories of the Forced Duffing Oscillator. + + Args: + t (array-like): Time vector. + X (array-like): State trajectories. + n_traj (int): Number of trajectories to visualize. + """ + fig, axs = plt.subplots(1, 2, tight_layout=True, figsize=(12, 4)) + for traj_idx in range(n_traj): + x = X[:, traj_idx::n_traj] + axs[0].plot(t, x[0, :], "k") + axs[1].plot(t, x[1, :], "b") + axs[0].set(ylabel=r"$x_1$", xlabel=r"$t$") + axs[1].set(ylabel=r"$x_2$", xlabel=r"$t$") + + def visualize_state_space(self, X, Y, n_traj): + """ + Visualizes the state space trajectories of the Forced Duffing Oscillator. + + Args: + X (array-like): State trajectories. + Y (array-like): Output trajectories. + n_traj (int): Number of trajectories to visualize. + """ + fig, axs = plt.subplots(1, 1, tight_layout=True, figsize=(4, 4)) + for traj_idx in range(n_traj): + axs.plot( + [X[0, traj_idx::n_traj], Y[0, traj_idx::n_traj]], + [X[1, traj_idx::n_traj], Y[1, traj_idx::n_traj]], + "-k", + ) + axs.set(ylabel=r"$x_2$", xlabel=r"$x_1$") diff --git a/DSA/pykoopman/src/pykoopman/common/ks.py b/DSA/pykoopman/src/pykoopman/common/ks.py new file mode 100644 index 0000000..e356bdf --- /dev/null +++ b/DSA/pykoopman/src/pykoopman/common/ks.py @@ -0,0 +1,189 @@ +"""module for 1D KS equation""" +from __future__ import annotations + +import numpy as np +from matplotlib import pyplot as plt +from mpl_toolkits.mplot3d import Axes3D +from scipy.fft import fft +from scipy.fft import fftfreq +from scipy.fft import ifft + + +class ks: + """ + Solving 1D KS equation + + u_t = -u*u_x + u_{xx} + nu*u_{xxxx} + + Periodic B.C. between 0 and 2*pi. This PDE is solved + using spectral methods. + """ + + def __init__(self, n, x, nu, dt, M=16): + self.n_states = n + self.dt = dt + self.x = x + dk = 1 + k = fftfreq(self.n_states, 1.0 / self.n_states) * dk + k[n // 2] = 0.0 + L = k**2 - nu * k**4 + self.E = np.exp(self.dt * L) + self.E2 = np.exp(self.dt * L / 2.0) + # self.M = M + r = np.exp(1j * np.pi * (np.arange(1, M + 1) - 0.5) / M) + r = r.reshape(1, -1) + r_on_circle = np.repeat(r, n, axis=0) + LR = self.dt * L + LR = LR.reshape(-1, 1) + LR = LR.astype("complex") + LR = np.repeat(LR, M, axis=1) + LR += r_on_circle + self.g = -0.5j * k + + self.Q = self.dt * np.real(np.mean((np.exp(LR / 2.0) - 1) / LR, axis=1)) + self.f1 = self.dt * np.real( + np.mean( + (-4.0 - LR + np.exp(LR) * (4.0 - 3.0 * LR + LR**2)) / LR**3, axis=1 + ) + ) + self.f2 = self.dt * np.real( + np.mean((2.0 + LR + np.exp(LR) * (-2.0 + LR)) / LR**3, axis=1) + ) + self.f3 = self.dt * np.real( + np.mean( + (-4.0 - 3.0 * LR - LR**2 + np.exp(LR) * (4.0 - LR)) / LR**3, axis=1 + ) + ) + + @staticmethod + def compute_u2k_zeropad_dealiased(uk_): + # three over two law + N = uk_.size + # map uk to uk_fine + uk_fine = ( + np.hstack((uk_[0 : int(N / 2)], np.zeros((int(N / 2))), uk_[int(-N / 2) :])) + * 3.0 + / 2.0 + ) + # convert uk_fine to physical space + u_fine = np.real(ifft(uk_fine)) + # compute square + u2_fine = np.square(u_fine) + # compute fft on u2_fine + u2k_fine = fft(u2_fine) + # convert u2k_fine to u2k + u2k = np.hstack((u2k_fine[0 : int(N / 2)], u2k_fine[int(-N / 2) :])) / 3.0 * 2.0 + return u2k + + def sys(self, t, x, u): + raise NotImplementedError + + def simulate(self, x0, n_int, n_sample): + xk = fft(x0) + u = np.zeros((n_int, 1)) + X = np.zeros((n_int // n_sample, self.n_states)) + t = 0 + j = 0 + t_list = [] + for step in range(n_int): + t += self.dt + Nv = self.g * self.compute_u2k_zeropad_dealiased(xk) + a = self.E2 * xk + self.Q * Nv + Na = self.g * self.compute_u2k_zeropad_dealiased(a) + b = self.E2 * xk + self.Q * Na + Nb = self.g * self.compute_u2k_zeropad_dealiased(b) + c = self.E2 * a + self.Q * (2.0 * Nb - Nv) + Nc = self.g * self.compute_u2k_zeropad_dealiased(c) + xk = self.E * xk + Nv * self.f1 + 2.0 * (Na + Nb) * self.f2 + Nc * self.f3 + + if (step + 1) % n_sample == 0: + y = np.real(ifft(xk)) + self.dt * u[j] + X[j, :] = y + j += 1 + t_list.append(t) + xk = fft(y) + + return X, np.array(t_list) + + def collect_data_continuous(self, x0): + raise NotImplementedError + + def collect_one_step_data_discrete(self, x0): + """ + collect training data pairs - discrete sense. + + given x0, with shape (n_dim, n_traj), the function + returns system state x1 after self.dt with shape + (n_dim, n_traj) + """ + n_traj = x0.shape[0] + X = x0 + Y = [] + for i in range(n_traj): + y, _ = self.simulate(x0[i], n_int=1, n_sample=1) + Y.append(y) + Y = np.vstack(Y) + return X, Y + + def collect_one_trajectory_data(self, x0, n_int, n_sample): + x = x0 + y, _ = self.simulate(x, n_int, n_sample) + return y + + def visualize_data(self, x, t, X): + plt.figure(figsize=(6, 6)) + ax = plt.axes(projection=Axes3D.name) + for i in range(X.shape[0]): + ax.plot(x, X[i], zs=t[i], zdir="t", label="time = " + str(i * self.dt)) + ax.view_init(elev=35.0, azim=-65, vertical_axis="y") + ax.set(ylabel=r"$u(x,t)$", xlabel=r"$x$", zlabel=r"time $t$") + plt.title("1D K-S equation") + plt.show() + + def visualize_state_space(self, X): + u, s, vt = np.linalg.svd(X, full_matrices=False) + plt.figure(figsize=(6, 6)) + plt.semilogy(s) + plt.xlabel("number of SVD terms") + plt.ylabel("singular values") + plt.title("PCA singular value decays") + plt.show() + + # this is a pde problem so the number of snapshots are smaller than dof + pca_1, pca_2, pca_3 = u[:, 0], u[:, 1], u[:, 2] + plt.figure(figsize=(6, 6)) + ax = plt.axes(projection=Axes3D.name) + ax.plot3D(pca_1, pca_2, pca_3, "k-o") + ax.set(xlabel="pc1", ylabel="pc2", zlabel="pc3") + plt.title("PCA visualization") + plt.show() + + +if __name__ == "__main__": + n = 256 + x = np.linspace(0, 2.0 * np.pi, n, endpoint=False) + u0 = np.sin(x) + nu = 0.01 + n_int = 1000 + n_snapshot = 500 + dt = 4.0 / n_int + n_sample = n_int // n_snapshot + + model = ks(n, x, nu=nu, dt=dt) + X, t = model.simulate(u0, n_int, n_sample) + print(X.shape) + model.visualize_data(x, t, X) + + # usage: visualize the data in state space + model.visualize_state_space(X) + + # usage: collect discrete data pair + x0_array = np.vstack([u0, u0, u0]) + X, Y = model.collect_one_step_data_discrete(x0_array) + + print(X.shape) + print(Y.shape) + + # usage: collect one trajectory data + X = model.collect_one_trajectory_data(u0, n_int, n_sample) + print(X.shape) diff --git a/DSA/pykoopman/src/pykoopman/common/nlse.py b/DSA/pykoopman/src/pykoopman/common/nlse.py new file mode 100644 index 0000000..b82585f --- /dev/null +++ b/DSA/pykoopman/src/pykoopman/common/nlse.py @@ -0,0 +1,186 @@ +"""module for nonlinear schrodinger equation""" +from __future__ import annotations + +import numpy as np +from matplotlib import pyplot as plt +from mpl_toolkits.mplot3d import Axes3D +from pykoopman.common.examples import rk4 +from scipy.fft import fft +from scipy.fft import fftfreq +from scipy.fft import ifft + + +class nlse: + """ + nonlinear schrodinger equation + + iu_t + 0.5u_xx + u*|u|^2 = 0 + + periodic B.C. PDE is solved with Spectral methods using FFT + """ + + def __init__(self, n, dt, L=2 * np.pi): + self.n_states = n + # assert self.u0.size == self.n_states, 'check the size of initial + # condition and mesh size n' + + dk = 2 * np.pi / L + self.k = fftfreq(self.n_states, 1.0 / self.n_states) * dk + self.dt = dt + + def sys(self, t, x, u): + """the RHS for the governing equation using FFT""" + xk = fft(x) + + # 4/3 truncation rule + # dealiasing due to triple nonlinearity + # note: you could do zero-padding to improve memory + # efficiency + xk[self.n_states // 4 : 3 * self.n_states // 4] = 0j + x = ifft(xk) + + yk = (-self.k**2 * xk.ravel() / 2) * 1j + y = ifft(yk) + 1j * abs(x) ** 2 * x + u + return y + + def simulate(self, x0, n_int, n_sample): + # n_traj = x0.shape[1] + x = x0 + u = np.zeros((n_int, 1), dtype=complex) + X = np.zeros((n_int // n_sample, self.n_states), dtype=complex) + t = 0 + j = 0 + t_list = [] + for step in range(n_int): + t += self.dt + y = rk4(0, x, u[step], self.dt, self.sys) + if (step + 1) % n_sample == 0: + X[j] = y + j += 1 + t_list.append(t) + x = y + return X, np.array(t_list) + + def collect_data_continuous(self, x0): + """ + collect training data pairs - continuous sense. + + given x0, with shape (n_dim, n_traj), the function + returns dx/dt with shape (n_dim, n_traj) + """ + + n_traj = x0.shape[0] + u = np.zeros((n_traj, 1)) + X = x0 + Y = [] + for i in range(n_traj): + y = self.sys(0, x0[i], u[i]) + Y.append(y) + Y = np.vstack(Y) + return X, Y + + def collect_one_step_data_discrete(self, x0): + """ + collect training data pairs - discrete sense. + + given x0, with shape (n_dim, n_traj), the function + returns system state x1 after self.dt with shape + (n_dim, n_traj) + """ + + n_traj = x0.shape[0] + X = x0 + Y = [] + for i in range(n_traj): + y, _ = self.simulate(x0[i], n_int=1, n_sample=1) + # for j in range(int(delta_t // self.dt)): + # y = rk4(0, x, u[:, i], self.dt, self.sys) + # x = y + Y.append(y) + Y = np.vstack(Y) + return X, Y + + def collect_one_trajectory_data(self, x0, n_int, n_sample): + x = x0 + y, _ = self.simulate(x, n_int, n_sample) + return y + + def visualize_data(self, x, t, X): + plt.figure(figsize=(6, 6)) + ax = plt.axes(projection=Axes3D.name) + for i in range(X.shape[0]): + ax.plot(x, abs(X[i]), zs=t[i], zdir="t", label="time = " + str(i * self.dt)) + # plt.legend(loc='best') + ax.view_init(elev=35.0, azim=-65, vertical_axis="y") + ax.set(ylabel=r"$mag. of u(x,t)$", xlabel=r"$x$", zlabel=r"time $t$") + plt.title("Nonlinear schrodinger equation (Kutz et al., Complexity, 2018)") + plt.show() + + def visualize_state_space(self, X): + u, s, vt = np.linalg.svd(X, full_matrices=False) + # this is a pde problem so the number of snapshots are smaller than dof + pca_1_r, pca_1_i = np.real(u[:, 0]), np.imag(u[:, 0]) + pca_2_r, pca_2_i = np.real(u[:, 1]), np.imag(u[:, 1]) + pca_3_r, pca_3_i = np.real(u[:, 2]), np.imag(u[:, 2]) + + plt.figure(figsize=(6, 6)) + plt.semilogy(s) + plt.xlabel("number of SVD terms") + plt.ylabel("singular values") + plt.title("PCA singular value decays") + plt.show() + + plt.figure(figsize=(6, 6)) + ax = plt.axes(projection=Axes3D.name) + ax.plot3D(pca_1_r, pca_2_r, pca_3_r, "k-o") + ax.set(xlabel="pc1", ylabel="pc2", zlabel="pc3") + plt.title("PCA visualization (real)") + plt.show() + + plt.figure(figsize=(6, 6)) + ax = plt.axes(projection=Axes3D.name) + ax.plot3D(pca_1_i, pca_2_i, pca_3_i, "k-o") + ax.set(xlabel="pc1", ylabel="pc2", zlabel="pc3") + plt.title("PCA visualization (imag)") + plt.show() + + +if __name__ == "__main__": + n = 512 + x = np.linspace(-15, 15, n, endpoint=False) + u0 = 2.0 / np.cosh(x) + # u0 = u0.reshape(-1,1) + n_int = 10000 + n_snapshot = 80 # in the original paper, it is 20, but I think too small + dt = np.pi / n_int + n_sample = n_int // n_snapshot + + model = nlse(n, dt=dt, L=30) + X, t = model.simulate(u0, n_int, n_sample) + + # usage: visualize the data in physical space + model.visualize_data(x, t, X) + + # usage: visualize the data in state space + model.visualize_state_space(X) + + print(X.shape) + print(t[1] - t[0]) + + # usage: collect continuous data pair: x and dx/dt + x0_array = np.vstack([u0, u0, u0]) + X, Y = model.collect_data_continuous(x0_array) + + print(X.shape) + print(Y.shape) + + # usage: collect discrete data pair + x0_array = np.vstack([u0, u0, u0]) + X, Y = model.collect_one_step_data_discrete(x0_array) + + print(X.shape) + print(Y.shape) + + # usage: collect one trajectory data + X = model.collect_one_trajectory_data(u0, n_int, n_sample) + print(X.shape) diff --git a/DSA/pykoopman/src/pykoopman/common/validation.py b/DSA/pykoopman/src/pykoopman/common/validation.py new file mode 100644 index 0000000..69ef91f --- /dev/null +++ b/DSA/pykoopman/src/pykoopman/common/validation.py @@ -0,0 +1,88 @@ +from __future__ import annotations + +import numpy as np +from sklearn.utils import check_array as skl_check_array + +T_DEFAULT = object() + + +def validate_input(x, t=T_DEFAULT): + if not isinstance(x, np.ndarray) and not isinstance(x, list): + raise ValueError("x must be array-like OR a list of array-like") + elif isinstance(x, list): + for i in range(len(x)): + x[i] = validate_input(x[i], t) + return x + elif x.ndim == 1: + x = x.reshape(-1, 1) + x = check_array(x) + + # add another case if x is a list of trajectory + + if t is not T_DEFAULT: + if t is None: + raise ValueError("t must be a scalar or array-like.") + # Apply this check if t is a scalar + elif np.ndim(t) == 0 and (isinstance(t, int) or isinstance(t, float)): + if t <= 0: + raise ValueError("t must be positive") + # Only apply these tests if t is array-like + elif isinstance(t, np.ndarray): + if not len(t) == x.shape[0]: + raise ValueError("Length of t should match x.shape[0].") + if not np.all(t[:-1] < t[1:]): + raise ValueError("Values in t should be in strictly increasing order.") + else: + raise ValueError("t must be a scalar or array-like.") + + return x + + +def check_array(x, **kwargs): + if np.iscomplexobj(x): + if x.ndim == 3: + # Handle 3D arrays by processing each 2D slice + result = np.zeros_like(x) + for i in range(x.shape[0]): + result[i] = skl_check_array(x[i].real, **kwargs) + 1j * skl_check_array( + x[i].imag, **kwargs + ) + return result + else: + return skl_check_array(x.real, **kwargs) + 1j * skl_check_array( + x.imag, **kwargs + ) + else: + if x.ndim == 3: + # Handle 3D arrays by processing each 2D slice + result = np.zeros_like(x) + for i in range(x.shape[0]): + result[i] = skl_check_array(x[i], **kwargs) + return result + else: + return skl_check_array(x, **kwargs) + + +def drop_nan_rows(arr, *args): + """ + Remove rows in all inputs for which `arr` has `_np.nan` entries. + + Parameters + ---------- + arr : numpy.ndarray + Array whose rows are checked for nan entries. + Any rows containing nans are removed from ``arr`` and all arguments + passed via ``args``. + *args : variable length argument list of numpy.ndarray + Additional arrays from which to remove rows. + Each argument should have the same number of rows as ``arr``. + + Returns + ------- + arrays : tuple of numpy.ndarray + Arrays with nan rows dropped. + The first entry corresponds to ``arr`` and all following entries + to ``*args``. + """ + nan_inds = np.isnan(arr).any(axis=1) + return (arr[~nan_inds], *[arg[~nan_inds] for arg in args]) diff --git a/DSA/pykoopman/src/pykoopman/common/vbe.py b/DSA/pykoopman/src/pykoopman/common/vbe.py new file mode 100644 index 0000000..2a3cec2 --- /dev/null +++ b/DSA/pykoopman/src/pykoopman/common/vbe.py @@ -0,0 +1,177 @@ +"""module for 1D viscous burgers""" +from __future__ import annotations + +import numpy as np +from matplotlib import pyplot as plt +from mpl_toolkits.mplot3d import Axes3D +from pykoopman.common.examples import rk4 +from scipy.fft import fft +from scipy.fft import fftfreq +from scipy.fft import ifft + + +class vbe: + """ + 1D viscous Burgers equation + + u_t = -u*u_x + \nu u_{xx} + + periodic B.C. PDE is solved using spectral methods + """ + + def __init__(self, n, x, dt, nu=0.1, L=2 * np.pi): + self.n_states = n + self.x = x + self.nu = nu + dk = 2 * np.pi / L + self.k = fftfreq(self.n_states, 1.0 / self.n_states) * dk + self.dt = dt + + def sys(self, t, x, u): + xk = fft(x) + + # 3/2 truncation rule + xk[self.n_states // 3 : 2 * self.n_states // 3] = 0j + x = ifft(xk) + + # nonlinear advection + tmp_nl_k = fft(-0.5 * x * x) + tmp_nl_x_k = 1j * self.k * tmp_nl_k + + # linear viscous term + tmp_vis_k = -self.nu * self.k**2 * xk + + # return back to physical space + y = np.real(ifft(tmp_nl_x_k + tmp_vis_k)) + return y + + def simulate(self, x0, n_int, n_sample): + # n_traj = x0.shape[1] + x = x0 + u = np.zeros((n_int, 1)) + X = np.zeros((n_int // n_sample, self.n_states)) + t = 0 + j = 0 + t_list = [] + for step in range(n_int): + t += self.dt + y = rk4(0, x, u[step, :], self.dt, self.sys) + if (step + 1) % n_sample == 0: + X[j, :] = y + j += 1 + t_list.append(t) + x = y + return X, np.array(t_list) + + def collect_data_continuous(self, x0): + """ + collect training data pairs - continuous sense. + + given x0, with shape (n_dim, n_traj), the function + returns dx/dt with shape (n_dim, n_traj) + """ + + n_traj = x0.shape[0] + u = np.zeros((n_traj, 1)) + X = x0 + Y = [] + for i in range(n_traj): + y = self.sys(0, x0[i], u[i]) + Y.append(y) + Y = np.vstack(Y) + return X, Y + + def collect_one_step_data_discrete(self, x0): + """ + collect training data pairs - discrete sense. + + given x0, with shape (n_dim, n_traj), the function + returns system state x1 after self.dt with shape + (n_dim, n_traj) + """ + + n_traj = x0.shape[0] + X = x0 + Y = [] + for i in range(n_traj): + y, _ = self.simulate(x0[i], n_int=1, n_sample=1) + Y.append(y) + Y = np.vstack(Y) + return X, Y + + def collect_one_trajectory_data(self, x0, n_int, n_sample): + x = x0 + y, _ = self.simulate(x, n_int, n_sample) + return y + + def visualize_data(self, x, t, X): + plt.figure(figsize=(6, 6)) + ax = plt.axes(projection=Axes3D.name) + for i in range(X.shape[0]): + ax.plot(x, X[i], zs=t[i], zdir="t", label="time = " + str(i * self.dt)) + # plt.legend(loc='best') + ax.view_init(elev=35.0, azim=-65, vertical_axis="y") + ax.set(ylabel=r"$u(x,t)$", xlabel=r"$x$", zlabel=r"time $t$") + plt.title("1D Viscous Burgers equation (Kutz et al., Complexity, 2018)") + plt.show() + + def visualize_state_space(self, X): + u, s, vt = np.linalg.svd(X, full_matrices=False) + plt.figure(figsize=(6, 6)) + plt.semilogy(s) + plt.xlabel("number of SVD terms") + plt.ylabel("singular values") + plt.title("PCA singular value decays") + plt.show() + + # this is a pde problem so the number of snapshots are smaller than dof + pca_1, pca_2, pca_3 = u[:, 0], u[:, 1], u[:, 2] + plt.figure(figsize=(6, 6)) + ax = plt.axes(projection=Axes3D.name) + ax.plot3D(pca_1, pca_2, pca_3, "k-o") + ax.set(xlabel="pc1", ylabel="pc2", zlabel="pc3") + plt.title("PCA visualization") + plt.show() + + +if __name__ == "__main__": + n = 256 + x = np.linspace(-15, 15, n, endpoint=False) + u0 = np.exp(-((x + 2) ** 2)) + # u0 = 2.0 / np.cosh(x) + # u0 = u0.reshape(-1,1) + n_int = 3000 + n_snapshot = 30 + dt = 30.0 / n_int + n_sample = n_int // n_snapshot + + model = vbe(n, x, dt=dt, L=30) + X, t = model.simulate(u0, n_int, n_sample) + + print(X.shape) + # print(X[:,-1].max()) + + # usage: visualize the data in physical space + model.visualize_data(x, t, X) + print(t) + + # usage: visualize the data in state space + model.visualize_state_space(X) + + # usage: collect continuous data pair: x and dx/dt + x0_array = np.vstack([u0, u0, u0]) + X, Y = model.collect_data_continuous(x0_array) + + print(X.shape) + print(Y.shape) + + # usage: collect discrete data pair + x0_array = np.vstack([u0, u0, u0]) + X, Y = model.collect_one_step_data_discrete(x0_array) + + print(X.shape) + print(Y.shape) + + # usage: collect one trajectory data + X = model.collect_one_trajectory_data(u0, n_int, n_sample) + print(X.shape) diff --git a/DSA/pykoopman/src/pykoopman/differentiation/__init__.py b/DSA/pykoopman/src/pykoopman/differentiation/__init__.py new file mode 100644 index 0000000..285c279 --- /dev/null +++ b/DSA/pykoopman/src/pykoopman/differentiation/__init__.py @@ -0,0 +1,6 @@ +from __future__ import annotations + +from ._derivative import Derivative +from ._finite_difference import FiniteDifference + +__all__ = ["Derivative", "FiniteDifference"] diff --git a/DSA/pykoopman/src/pykoopman/differentiation/_derivative.py b/DSA/pykoopman/src/pykoopman/differentiation/_derivative.py new file mode 100644 index 0000000..52c94dc --- /dev/null +++ b/DSA/pykoopman/src/pykoopman/differentiation/_derivative.py @@ -0,0 +1,89 @@ +"""Wrapper classes for differentiation methods from the :doc:`derivative:index` package. + +Some default values used here may differ from those used in :doc:`derivative:index`. +""" +from __future__ import annotations + +from derivative import dxdt +from numpy import arange +from sklearn.base import BaseEstimator + +from ..common import validate_input + + +class Derivative(BaseEstimator): + """ + Wrapper class for differentiation classes from the :doc:`derivative:index` package. + This class is meant to provide all the same functionality as the + `dxdt `_ method. + + This class includes a :meth:`__call__` method. + + Parameters + ---------- + derivative_kws: dictionary, optional + Keyword arguments to be passed to the + `dxdt `_ + method. + + Notes + ----- + See the `derivative documentation `_ + for acceptable keywords. + """ + + def __init__(self, **kwargs): + self.kwargs = kwargs + + def set_params(self, **params): + """ + Set the parameters of this estimator. + + Returns + ------- + self + """ + if not params: + # Simple optimization to gain speed (inspect is slow) + return self + else: + self.kwargs.update(params) + + return self + + def get_params(self, deep=True): + """Get parameters.""" + params = super().get_params(deep) + + if isinstance(self.kwargs, dict): + params.update(self.kwargs) + + return params + + def __call__(self, x, t, axis=0): + """ + Perform numerical differentiation by calling the ``dxdt`` method. + + Paramters + --------- + x: np.ndarray, shape (n_samples, n_features) + Data to be differentiated. Rows should correspond to different + points in time and columns to different features. + + t: np.ndarray, shape (n_samples, ) + Time points for each sample (row) in ``x``. + + Returns + ------- + x_dot: np.ndarray, shape (n_samples, n_features) + """ + x = validate_input(x, t=t) + + if isinstance(t, (int, float)): + if t < 0: + raise ValueError("t must be a positive constant or an array") + t = arange(x.shape[0]) * t + + return dxdt(x, t, axis=axis, **self.kwargs) diff --git a/DSA/pykoopman/src/pykoopman/differentiation/_finite_difference.py b/DSA/pykoopman/src/pykoopman/differentiation/_finite_difference.py new file mode 100644 index 0000000..0f50070 --- /dev/null +++ b/DSA/pykoopman/src/pykoopman/differentiation/_finite_difference.py @@ -0,0 +1,12 @@ +from __future__ import annotations + +import numpy as np +from sklearn.base import BaseEstimator + + +class FiniteDifference(BaseEstimator): + def __init__(self, order=1): + self.order = order + + def __call__(self, x, t=1): + return np.gradient(x) diff --git a/DSA/pykoopman/src/pykoopman/koopman.py b/DSA/pykoopman/src/pykoopman/koopman.py new file mode 100644 index 0000000..4330a68 --- /dev/null +++ b/DSA/pykoopman/src/pykoopman/koopman.py @@ -0,0 +1,637 @@ +"""module for discrete time Koopman class""" +from __future__ import annotations + +from warnings import catch_warnings +from warnings import filterwarnings +from warnings import warn + +import numpy as np +from numpy import empty +from pydmd import DMD +from pydmd import DMDBase +from sklearn.base import BaseEstimator +from sklearn.metrics import r2_score +from sklearn.pipeline import Pipeline +from sklearn.utils.validation import check_is_fitted + +from .common import validate_input +from .observables import Identity +from .observables import TimeDelay +from .regression import BaseRegressor +from .regression import DMDc +from .regression import EDMDc +from .regression import EnsembleBaseRegressor +from .regression import HAVOK +from .regression import NNDMD +from .regression import PyDMDRegressor + + +class Koopman(BaseEstimator): + """Discrete-Time Koopman class. + + The input-output data is all row-wise if stated elsewhere. + All of the matrix, are based on column-wise linear system. + This class is inherited from `pykoopman.regression.BaseEstimator`. + + Args: + observables: observables object, optional + (default: `pykoopman.observables.Identity`) + Map(s) to apply to raw measurement data before estimating the + Koopman operator. + Must extend `pykoopman.observables.BaseObservables`. + The default option, `pykoopman.observables.Identity`, leaves + the input untouched. + + regressor: regressor object, optional (default: `DMD`) + The regressor used to learn the Koopman operator from the observables. + `regressor` can either extend the `pykoopman.regression.BaseRegressor`, + or `pydmd.DMDBase`. + In the latter case, the pydmd object must have both a `fit` + and a `predict` method. + + quiet: boolean, optional (default: False) + Whether or not warnings should be silenced during fitting. + + Attributes: + model: sklearn.pipeline.Pipeline + Internal representation of the forward model. + Applies the observables and the regressor. + + n_input_features_: int + Number of input features before computing observables. + + n_output_features_: int + Number of output features after computing observables. + + n_control_features_: int + Number of control features used as input to the system. + + time: dictionary + Time vector properties. + """ + + def __init__(self, observables=None, regressor=None, quiet=False): + """Constructor for the Koopman class. + + Args: + observables: observables object, optional + (default: `pykoopman.observables.Identity`) + Map(s) to apply to raw measurement data before estimating the + Koopman operator. + Must extend `pykoopman.observables.BaseObservables`. + The default option, `pykoopman.observables.Identity`, leaves + the input untouched. + + regressor: regressor object, optional (default: `DMD`) + The regressor used to learn the Koopman operator from the observables. + `regressor` can either extend the `pykoopman.regression.BaseRegressor`, + or `pydmd.DMDBase`. + In the latter case, the pydmd object must have both a `fit` + and a `predict` method. + + quiet: boolean, optional (default: False) + Whether or not warnings should be silenced during fitting. + """ + if observables is None: + observables = Identity() + if regressor is None: + regressor = PyDMDRegressor(DMD(svd_rank=2)) # set default svd rank 2 + if isinstance(regressor, DMDBase): + regressor = PyDMDRegressor(regressor) + elif not isinstance(regressor, (BaseRegressor)): + raise TypeError("Regressor must be from valid class") + self.observables = observables + self.regressor = regressor + self.quiet = quiet + + def fit(self, x, y=None, u=None, dt=1): + """ + Fit the Koopman model by learning an approximate Koopman operator. + + Args: + x: numpy.ndarray, shape (n_samples, n_features) + Measurement data to be fit. Each row should correspond to an example + and each column a feature. If only x is provided, it is assumed that + examples are equi-spaced in time (i.e., a uniform timestep is assumed). + + y: numpy.ndarray, shape (n_samples, n_features), optional (default: None) + Target measurement data to be fit, i.e., it is assumed y = fun(x). Each + row should correspond to an example and each column a feature. The + samples in x and y are generally not required to be consecutive and + equi-spaced. + + u: numpy.ndarray, shape (n_samples, n_control_features), optional (default: + None) Control/actuation/external parameter data. Each row should + correspond to one sample and each column a control variable or feature. + The control variable may be the amplitude of an actuator or an external, + time-varying parameter. It is assumed that samples in u occur at the + time instances of the corresponding samples in x, + e.g., x(t+1) = fun(x(t), u(t)). + + dt: float, optional (default: 1) + Time step between samples + + Returns: + self: returns a fit `Koopman` instance + """ + x = validate_input(x) + + if u is None: + self.n_control_features_ = 0 + elif not isinstance(self.regressor, DMDc) and not isinstance( + self.regressor, EDMDc + ): + raise ValueError( + "Control input u was passed, " "but self.regressor is not DMDc or EDMDc" + ) + + if y is None: # or isinstance(self.regressor, PyDMDRegressor): + # if there is only 1 trajectory OR regressor is PyDMD + y_flag = True + # regressor = self.regressor + x, y = self._detect_reshape(x, offset=True) + if isinstance(self.regressor, HAVOK): + regressor = self.regressor + y_flag = False + else: + regressor = EnsembleBaseRegressor( + regressor=self.regressor, + func=self.observables.transform, + inverse_func=self.observables.inverse, + ) + # regressor = self.regressor + elif isinstance(self.regressor, NNDMD): + regressor = self.regressor + y_flag = False + + else: + # multiple 1-step-trajectories + regressor = EnsembleBaseRegressor( + regressor=self.regressor, + func=self.observables.transform, + inverse_func=self.observables.inverse, + ) + # if x is a list, we need to further change trajectories into 1-step-traj + x, _ = self._detect_reshape(x, offset=False) + y, _ = self._detect_reshape(y, offset=False) + y_flag = False + # if isinstance(x, list): + # x_tmp = [] + # y_tmp = [] + # for traj_dat in x: + # x_tmp.append(traj_dat[:-1]) + # y_tmp.append(traj_dat[1:]) + # x = np.hstack(x_tmp) + # y = np.hstack(y_tmp) + + steps = [ + ("observables", self.observables), + ("regressor", regressor), + ] + self._pipeline = Pipeline(steps) # create `model` object using Pipeline + + action = "ignore" if self.quiet else "default" + with catch_warnings(): + filterwarnings(action, category=UserWarning) + if u is None: + self._pipeline.fit(x, y, regressor__dt=dt) + else: + self._pipeline.fit(x, y, regressor__u=u, regressor__dt=dt) + # update the second step with just the regressor, not the + # EnsembleBaseRegressor + if isinstance(self._pipeline.steps[1][1], EnsembleBaseRegressor): + self._pipeline.steps[1] = ( + self._pipeline.steps[1][0], + self._pipeline.steps[1][1].regressor_, + ) + + # pykoopman's n_input/output_features are simply + # observables's input output features + # observable's input features are just the number + # of states. but the output features can be really high + self.n_input_features_ = self._pipeline.steps[0][1].n_input_features_ + self.n_output_features_ = self._pipeline.steps[0][1].n_output_features_ + if hasattr(self._pipeline.steps[1][1], "n_control_features_"): + self.n_control_features_ = self._pipeline.steps[1][1].n_control_features_ + + # compute amplitudes + if isinstance(x, list): + self._amplitudes = None + elif y_flag: + if hasattr(self.observables, "n_consumed_samples"): + # g0 = self.observables.transform( + # x[0 : 1 + self.observables.n_consumed_samples] + # ) + self._amplitudes = np.abs( + self.psi(x[0 : 1 + self.observables.n_consumed_samples].T) + ) + else: + # g0 = self.observables.transform(x[0:1]) + + self._amplitudes = np.abs(self.psi(x[0:1].T)) + else: + self._amplitudes = None + + self.time = { + "tstart": 0, + "tend": dt * (self._pipeline.steps[1][1].n_samples_ - 1), + "dt": dt, + } + + return self + + def predict(self, x, u=None): + """ + Predict the state one timestep in the future. + + Args: + x: numpy.ndarray, shape (n_samples, n_input_features) + Current state. + + u: numpy.ndarray, shape (n_samples, n_control_features), + optional (default None) + Time series of external actuation/control. + + Returns: + x_next: numpy.ndarray, shape (n_samples, n_input_features) + Predicted state one timestep in the future. + """ + + x = validate_input(x) + + check_is_fitted(self, "n_output_features_") + x_next = self.observables.inverse(self._step(x, u)) + return x_next + + def simulate(self, x0, u=None, n_steps=1): + """Simulate an initial state forward in time with the learned Koopman model. + + Args: + x0: numpy.ndarray, shape (n_input_features,) or + (n_consumed_samples + 1, n_input_features) + Initial state from which to simulate. + If using TimeDelay observables, `x0` should contain + enough examples to compute all required time delays, + i.e., `n_consumed_samples + 1`. + + u: numpy.ndarray, shape (n_samples, n_control_features), + optional (default None) + Time series of external actuation/control. + + n_steps: int, optional (default 1) + Number of forward steps to be simulated. + + Returns: + x: numpy.ndarray, shape (n_steps, n_input_features) + Simulated states. + Note that `x[0, :]` is one timestep ahead of `x0`. + """ + check_is_fitted(self, "n_output_features_") + # Could have an option to only return the end state and not all + # intermediate states to save memory. + + if x0.ndim == 1: # handle non-time delay input but 1D accidently + x0 = x0.reshape(-1, 1) + elif x0.ndim == 2 and x0.shape[0] > 1: # handle time delay input + x0 = x0.T + else: + raise TypeError("Check your initial condition shape!") + # x = empty((n_steps, self.n_input_features_), dtype=self.A.dtype) + y = empty((n_steps, self.A.shape[0]), dtype=self.W.dtype) + + if u is None: + # lifted eigen space and move 1 step forward + y[0] = self.lamda @ self.psi(x0).flatten() + + # iterate in the lifted space + for k in range(n_steps - 1): + # tmp = self.W @ self.lamda**(k+1) @ y[0].reshape(-1,1) + y[k + 1] = self.lamda @ y[k] + x = np.transpose(self.W @ y.T) + # x = x.astype(self.A.dtype) + else: + # lifted space (not eigen) + y[0] = self.A @ self.phi(x0).flatten() + self.B @ u[0] + + # iterate in the lifted space + for k in range(n_steps - 1): + tmp = self.A @ y[k].reshape(-1, 1) + self.B @ u[k + 1].reshape(-1, 1) + y[k + 1] = tmp.flatten() + x = np.transpose(self.C @ y.T) + # x = x.astype(self.A.dtype) + + if np.isrealobj(x0): + x = np.real(x) + return x + + def get_feature_names(self, input_features=None): + """Get the names of the individual features constituting the observables. + + Args: + input_features: list of string, length n_input_features, + optional (default None) + String names for input features, if available. By default, + the names "x0", "x1", ..., "xn_input_features" are used. + + Returns: + output_feature_names: list of string, length n_output_features + Output feature names. + """ + check_is_fitted(self, "n_input_features_") + return self.observables.get_feature_names(input_features=input_features) + + def _step(self, x, u=None): + """Map x one timestep forward in the space of observables. + + Args: + x: numpy.ndarray, shape (n_samples, n_input_features) + State vectors to be stepped forward. + + u: numpy.ndarray, shape (n_samples, n_control_features), + optional (default None) + Time series of external actuation/control. + + Returns: + X': numpy.ndarray, shape (n_samples, self.n_output_features_) + Observables one timestep after x. + """ + check_is_fitted(self, "n_output_features_") + + if u is None or self.n_control_features_ == 0: + if self.n_control_features_ > 0: + raise TypeError( + "Model was fit using control variables, so u is required" + ) + elif u is not None: + warn( + "Control variables u were ignored because control variables were" + " not used when the model was fit" + ) + return self._pipeline.predict(X=x) + else: + if not isinstance(self.regressor, DMDc) and not isinstance( + self.regressor, EDMDc + ): + raise ValueError( + "Control input u was passed, but self.regressor is not DMDc " + "or EDMDc" + ) + return self._pipeline.predict(X=x, u=u) + + def phi(self, x_col): + """Compute the feature matrix phi(x) given `x_col`. + + Args: + x_col: numpy.ndarray, shape (n_features, n_samples) + State vectors to be evaluated for phi. + + Returns: + phi: numpy.ndarray, shape (n_samples, self.n_output_features_) + Value of phi evaluated at input `x_col`. + """ + x = x_col.T + y = self.observables.transform(x) + phi = self._pipeline.steps[-1][1]._compute_phi(y.T) + return phi + + def psi(self, x_col): + """Compute the Koopman psi(x) given `x_col`. + + Args: + x_col: numpy.ndarray, shape (n_features, n_samples) + State vectors to be evaluated for psi. + + Returns: + eigen_phi: numpy.ndarray, shape (n_samples, self.n_output_features_) + Value of psi evaluated at input `x_col`. + """ + x = x_col.T + y = self.observables.transform(x) + ephi = self._pipeline.steps[-1][1]._compute_psi(y.T) + return ephi + + @property + def A(self): + """Returns the state transition matrix `A`. + + The state transition matrix A satisfies y' = Ay or y' = Ay + Bu, + respectively, where y = g(x) and y is a low-rank representation. + """ + check_is_fitted(self, "_pipeline") + if isinstance(self.regressor, DMDBase): + raise ValueError("self.regressor " "has no A!") + if hasattr(self._pipeline.steps[-1][1], "state_matrix_"): + return self._pipeline.steps[-1][1].state_matrix_ + else: + raise ValueError("self.regressor" "has no state_matrix") + + @property + def B(self): + """Returns the control matrix `B`. + + The control matrix (or vector) B satisfies y' = Ay + Bu. + y is the reduced system state. + """ + check_is_fitted(self, "_pipeline") + if isinstance(self.regressor, DMDBase): + raise ValueError("this type of self.regressor has no B") + return self._pipeline.steps[-1][1].control_matrix_ + + @property + def C(self): + """Returns the measurement matrix (or vector) C. + + The measurement matrix C satisfies x = C * phi_r. + """ + check_is_fitted(self, "_pipeline") + # if not isinstance(self.observables, RadialBasisFunction): + # raise ValueError("this type of self.observable has no C") + # return self._pipeline.steps[0][1].measurement_matrix_ + measure_mat = self._pipeline.steps[0][1].measurement_matrix_ + ur = self._pipeline.steps[-1][1].ur + C = measure_mat @ ur + return C + + @property + def W(self): + """Returns the Koopman modes.""" + + check_is_fitted(self, "_pipeline") + # return self.C @ self._pipeline.steps[-1][1].unnormalized_modes + return self.C @ self._pipeline.steps[-1][1].eigenvectors_ + + @property + def _regressor_eigenvectors(self): + """Returns the eigenvectors of the regressor.""" + check_is_fitted(self, "_pipeline") + return self._pipeline.steps[-1][1].eigenvectors_ + + @property + def lamda(self): + """Returns the discrete-time Koopman lambda obtained from spectral + decomposition.""" + check_is_fitted(self, "_pipeline") + return np.diag(self._pipeline.steps[-1][1].eigenvalues_) + + @property + def lamda_array(self): + """Returns the discrete-time Koopman lambda as an array.""" + check_is_fitted(self, "_pipeline") + return np.diag(self.lamda) + 0j + + @property + def continuous_lamda_array(self): + """Returns the continuous-time Koopman lambda as an array.""" + check_is_fitted(self, "_pipeline") + return np.log(self.lamda_array) / self.time["dt"] + + @property + def ur(self): + """Returns the projection matrix Ur.""" + check_is_fitted(self, "_pipeline") + return self._pipeline.steps[-1][1].ur + + def validity_check(self, t, x): + """Perform a validity check of eigenfunctions. + + The validity check tests the linearity of eigenfunctions phi(x(t)) == phi(x(0)) + * exp(lambda*t). + + Args: + t: numpy.ndarray, shape (n_samples,) + Time vector. + x: numpy.ndarray, shape (n_samples, n_input_features) + State vectors to be checked. + + Returns: + efun_index: list + Sorted indices of eigenfunctions based on linearity error. + linearity_error: list + Linearity error for each eigenfunction. + """ + + psi = self.psi(x.T) + omega = np.log(np.diag(self.lamda) + 0j) / self.time["dt"] + + # omega = self.eigenvalues_continuous + linearity_error = [] + for i in range(self.lamda.shape[0]): + linearity_error.append( + np.linalg.norm(psi[i, :] - np.exp(omega[i] * t) * psi[i, 0:1]) + ) + sort_idx = np.argsort(linearity_error) + efun_index = np.arange(len(linearity_error))[sort_idx] + linearity_error = [linearity_error[i] for i in sort_idx] + return efun_index, linearity_error + + def score(self, x, y=None, cast_as_real=True, metric=r2_score, **metric_kws): + """Score the model predictions for the next timestep. + + Parameters: + x: numpy.ndarray, shape (n_samples, n_input_features) + State measurements. + y: numpy.ndarray, shape (n_samples, n_input_features), optional + (default None). State measurements one timestep in the future. + cast_as_real: bool, optional (default True) + Whether to take the real part of predictions when computing the score. + metric: callable, optional (default r2_score) + The metric function used to score the model predictions. + metric_kws: dict, optional + Optional parameters to pass to the metric function. + + Returns: + score: float + Metric function value for the model predictions at the next timestep. + """ + check_is_fitted(self, "n_output_features_") + x = validate_input(x) + + if isinstance(self.observables, TimeDelay): + n_consumed_samples = self.observables.n_consumed_samples + + # User may pass in too-large + if y is not None and len(y) == len(x): + warn( + f"The first {n_consumed_samples} entries of y were ignored because " + "TimeDelay obesrvables were used." + ) + y = y[n_consumed_samples:] + else: + n_consumed_samples = 0 + + if y is None: + if cast_as_real: + return metric( + x[n_consumed_samples + 1 :].real, + self.predict(x[:-1]).real, + **metric_kws, + ) + else: + return metric( + x[n_consumed_samples + 1 :], self.predict(x[:-1]), **metric_kws + ) + else: + if cast_as_real: + return metric(y.real, self.predict(x).real, **metric_kws) + else: + return metric(y, self.predict(x), **metric_kws) + + def _observable(self): + """Returns the observable transformation.""" + return self._pipeline.steps[0][1] + + def _regressor(self): + """Returns the fitted regressor.""" + # this can access the fitted regressor + # todo: future we need to figure out a way to do time delay multiple + # trajectories DMD + # my idea is to manually call xN observables then concate the data to let + # the _regressor.fit to update the model coefficients. + # call this function with _regressor() + return self._pipeline.steps[1][1] + + def _detect_reshape(self, X, offset=True): + """ + Detect the shape of the input data and reshape it accordingly to return + both X and Y in the correct shape. + """ + s1 = -1 if offset else None + s2 = 1 if offset else None + if isinstance(X, np.ndarray): + if X.ndim == 1: + X = X.reshape(-1, 1) + + if X.ndim == 2: + self.n_samples_, self.n_input_features_ = X.shape + self.n_trials_ = 1 + return X[:s1], X[s2:] + elif X.ndim == 3: + self.n_trials_, self.n_samples_, self.n_input_features_ = X.shape + X, Y = X[:, :s1, :], X[:, s2:, :] + return X.reshape(-1, X.shape[2]), Y.reshape( + -1, Y.shape[2] + ) # time*trials, features + + elif isinstance(X, list): + assert all(isinstance(x, np.ndarray) for x in X) + self.n_trials_tot, self.n_samples_tot, self.n_input_features_tot = ( + [], + [], + [], + ) + X_tot, Y_tot = [], [] + for x in X: + x, y = self._detect_reshape(x) + X_tot.append(x) + Y_tot.append(y) + self.n_trials_tot.append(self.n_trials_) + self.n_samples_tot.append(self.n_samples_) + self.n_input_features_tot.append(self.n_input_features_) + X = np.concatenate(X_tot, axis=0) + Y = np.concatenate(Y_tot, axis=0) + + self.n_trials_ = sum(self.n_trials_tot) + self.n_samples_ = sum(self.n_samples_tot) + self.n_input_features_ = sum(self.n_input_features_tot) + + return X, Y diff --git a/DSA/pykoopman/src/pykoopman/koopman_continuous.py b/DSA/pykoopman/src/pykoopman/koopman_continuous.py new file mode 100644 index 0000000..2ba881d --- /dev/null +++ b/DSA/pykoopman/src/pykoopman/koopman_continuous.py @@ -0,0 +1,154 @@ +"""module for continuous time Koopman class""" +from __future__ import annotations + +import numpy as np +from sklearn.utils.validation import check_is_fitted + +from .differentiation import Derivative +from .koopman import Koopman + + +class KoopmanContinuous(Koopman): + """ + Continuous-time Koopman class. + + Args: + observables: Observables object, optional + (default: pykoopman.observables.Identity) + Map(s) to apply to raw measurement data before + estimating the Koopman operator. Must extend + pykoopman.observables.BaseObservables. The default + option, pykoopman.observables.Identity, leaves the + input untouched. + differentiator: Callable, optional + (default: centered difference) + Function used to compute numerical derivatives. + The function must have the call signature + differentiator(x, t), where x is a 2D numpy ndarray + of shape (n_samples, n_features) and t is a 1D numpy + ndarray of shape (n_samples,). + regressor: Regressor object, optional + (default: DMD) + The regressor used to learn the Koopman operator from + the observables. regressor can either extend + pykoopman.regression.BaseRegressor, or the + pydmd.DMDBase class. In the latter case, the pydmd + object must have both a fit and a predict method. + """ + + def __init__( + self, + observables=None, + differentiator=Derivative(kind="finite_difference", k=1), + regressor=None, + ): + """ + Continuous-time Koopman class. + + Args: + observables: Observables object, optional + (default: pykoopman.observables.Identity) + Map(s) to apply to raw measurement data before + estimating the Koopman operator. Must extend + pykoopman.observables.BaseObservables. The default + option, pykoopman.observables.Identity, leaves the + input untouched. + differentiator: Callable, optional + (default: centered difference) + Function used to compute numerical derivatives. + The function must have the call signature + differentiator(x, t), where x is a 2D numpy ndarray + of shape (n_samples, n_features) and t is a 1D numpy + ndarray of shape (n_samples,). + regressor: Regressor object, optional + (default: DMD) + The regressor used to learn the Koopman operator from + the observables. regressor can either extend + pykoopman.regression.BaseRegressor, or the + pydmd.DMDBase class. In the latter case, the pydmd + object must have both a fit and a predict method. + """ + super().__init__(observables, regressor) + self.differentiator = differentiator + + def predict(self, x, dt=0, u=None): + """ + Predict using continuous-time Koopman model. + + Args: + x: numpy.ndarray + State measurements. Each row should correspond to + the system state at some point in time. + dt: float, optional (default: 0) + Time step between measurements. If specified, the + prediction is made for the given time step in the + future. + u: numpy.ndarray, optional (default: None) + Control input/actuation data. Each row should + correspond to one sample and each column a control + variable or feature. + + Returns: + output: numpy.ndarray + Predicted state using the continuous-time Koopman + model. Each row corresponds to the predicted state + for the corresponding row in x. + """ + check_is_fitted(self, "_pipeline") + + if u is None: + ypred = self._pipeline.predict(X=x, t=dt) + else: + ypred = self._pipeline.predict(X=x, u=u, t=dt) + + output = self.observables.inverse(ypred) + + return output + + def simulate(self, x, t=0, u=None): + """ + Simulate continuous-time Koopman model. + + Args: + x: numpy.ndarray + Initial state from which to simulate. Each row + corresponds to the system state at some point in time. + t: float, optional (default: 0) + Time at which to simulate the system. If specified, + the simulation is performed for the given time. + u: numpy.ndarray, optional (default: None) + Control input/actuation data. Each row should + correspond to one sample and each column a control + variable or feature. + + Returns: + output: numpy.ndarray + Simulated states of the system. Each row corresponds + to the simulated state at a specific time point. + """ + check_is_fitted(self, "_pipeline") + + if u is None: + ypred = self._pipeline.predict(X=x, t=t) + else: + ypred = self._pipeline.predict(X=x, u=u, t=t) + + output = [] + for k in range(ypred.shape[0]): + output.append(np.squeeze(self.observables.inverse(ypred[k][np.newaxis, :]))) + + return np.array(output) + + def _step(self, x, u=None): + """ + Placeholder method for step function. + + This method is not implemented in the ContinuousKoopman class + as there is no explicit step function for continuous-time + Koopman models. + + Raises: + NotImplementedError: This method is not implemented + in the ContinuousKoopman class. + """ + raise NotImplementedError("ContinuousKoopman does not have a step function.") diff --git a/DSA/pykoopman/src/pykoopman/observables/__init__.py b/DSA/pykoopman/src/pykoopman/observables/__init__.py new file mode 100644 index 0000000..8a26f3a --- /dev/null +++ b/DSA/pykoopman/src/pykoopman/observables/__init__.py @@ -0,0 +1,17 @@ +from __future__ import annotations + +from ._custom_observables import CustomObservables +from ._identity import Identity +from ._polynomial import Polynomial +from ._radial_basis_functions import RadialBasisFunction +from ._random_fourier_features import RandomFourierFeatures +from ._time_delay import TimeDelay + +__all__ = [ + "CustomObservables", + "Identity", + "Polynomial", + "RadialBasisFunction", + "RandomFourierFeatures", + "TimeDelay", +] diff --git a/DSA/pykoopman/src/pykoopman/observables/_base.py b/DSA/pykoopman/src/pykoopman/observables/_base.py new file mode 100644 index 0000000..2d9af34 --- /dev/null +++ b/DSA/pykoopman/src/pykoopman/observables/_base.py @@ -0,0 +1,408 @@ +"""Module for base classes for specific observable classes.""" +from __future__ import annotations + +import abc + +import numpy as np +from sklearn.base import BaseEstimator +from sklearn.base import TransformerMixin +from sklearn.utils.validation import check_is_fitted + + +class BaseObservables(TransformerMixin, BaseEstimator): + """ + Abstract base class for observable classes. + + This class defines the interface for observable classes. It uses + the transformer interface from scikit-learn. + """ + + def __init__(self): + """ + Initialize a BaseObservables instance. + + Initializes the parent classes with the super function. + """ + super(BaseObservables, self).__init__() + + @abc.abstractmethod + def fit(self, X, y=None): + """ + Abstract method for fitting the observables. + + Args: + X (np.ndarray): The input data. + y (np.ndarray, optional): The target values. + + Raises: + NotImplementedError: This method must be overwritten by any child class. + """ + raise NotImplementedError + + @abc.abstractmethod + def transform(self, X): + """ + Abstract method for transforming the data. + + Args: + X (np.ndarray): The input data. + + Raises: + NotImplementedError: This method must be overwritten by any child class. + """ + raise NotImplementedError + + @abc.abstractmethod + def get_feature_names(self, input_features=None): + """ + Abstract method for getting the names of the features. + + Args: + input_features (list of str, optional): The names of the input features. + + Raises: + NotImplementedError: This method must be overwritten by any child class. + """ + raise NotImplementedError + + def inverse(self, y): + """ + Inverse the transformation. + + Args: + y (np.ndarray): The transformed data. + + Returns: + np.ndarray: The original data. + + Raises: + ValueError: If the shape of the input does not match the expected shape. + """ + check_is_fitted(self, ["n_input_features_", "measurement_matrix_"]) + if isinstance(y, list): + return [self.inverse(y_trial) for y_trial in y] + if y.ndim == 3: + return np.array([self.inverse(y_trial) for y_trial in y]) + + if y.ndim == 1: + y = y.reshape(1, -1) + + if y.shape[-1] != self.n_output_features_: + raise ValueError( + "Wrong number of input features." + f"Expected y.shape[-1] = {self.n_output_features_}; " + f"instead y.shape[-1] = {y.shape[-1]}." + ) + + return y @ self.measurement_matrix_.T + + def __add__(self, other): + if isinstance(self, ConcatObservables): + return ConcatObservables(self.observables_list_ + [other]) + else: + return ConcatObservables([self, other]) + + @property + def size(self): + check_is_fitted(self) + return self.n_output_features_ + + +# learned from https://github.com/dynamicslab/pysindy/blob/ +# d0d96f4466b9c16cdd349fdc515abe9081e5b2cf/pysindy/feature_library/base.py#L235 + + +class ConcatObservables(BaseObservables): + """ + This class concatenates two or more `BaseObservables` instances into a single + `ConcatObservables` instance. + + The concatenated observables are handled in such a way that only the first + observable with the identity mapping is kept, while the identity mapping in + the rest is removed. The same applies to observables that are polynomials with + `include_bias=True`, in which case the bias feature is also removed. + + Args: + observables_list_ (list, optional): A list of `BaseObservables` instances + to concatenate. Defaults to None. + + Attributes: + observables_list_ (list, optional): The list of `BaseObservables` instances + that were concatenated. Defaults to None. + include_state (bool): True if a linear feature (i.e., the system state) is + included. This indicator can help to identify if a redundant linear feature + can be removed. + n_input_features_ (int): The dimensionality of the input features, e.g., + the system state. + n_output_features_ (int): The dimensionality of the transformed/output + features, e.g., the observables. + n_consumed_samples (int): The number of effective samples. This can be less + than the total number of samples due to time-delay stacking. + measurement_matrix_ (numpy.ndarray): This matrix transforms a row feature + vector to return the system state. Its shape is (n_input_features_, + n_output_features_). + + Methods: + fit(X, y=None): Calculates and stores important information such as the + dimensions of the input and output features, the number of effective + samples, and the measurement matrix. + transform(X): Applies the transformation defined by the observables to + input data. + get_feature_names(input_features=None): Returns the names of the features + after transformation. + inverse(y): Applies the inverse transformation to the transformed data to + recover the original system state. + """ + + def __init__(self, observables_list_=None): + """Initializes a ConcatObservables instance. + + Args: + observables_list_ (list, optional): A list of `BaseObservables` instances. + If provided, the first observable must have an `include_state` + attribute. The default value is None. + + Raises: + AssertionError: If the first observable in `observables_list_` does not have + an `include_state` attribute. + """ + super(ConcatObservables, self).__init__() + self.observables_list_ = observables_list_ + assert hasattr( + self.observables_list_[0], "include_state" + ), "first observable must have `include_state' attribute" + self.include_state = self.observables_list_[0].include_state + + def fit(self, X, y=None): + """Sets up observable by fitting the model to the data. + + This method fits each observable in the list to the data, determines the + total number of output features, and sets up the measurement matrix. + + Args: + X (numpy.ndarray): Measurement data to be fit, with shape (n_samples, + n_input_features_). + y (numpy.ndarray, optional): Time-shifted measurement data to be fit. + Default is None. + + Returns: + ConcatObservables: A fitted instance of the class. + + Raises: + AssertionError: If any observable in the list does not have an + `include_state` attribute, or if the shape of the temporary least + squares solution does not match the shape of the measurement matrix. + """ + + # first, one must call fit of every observable in the observer list + # so that n_input_features_ and n_output_features_ are defined + for obs in self.observables_list_: + obs.fit(X, y) + + self.n_input_features_ = X.shape[1] + + # total number of output features takes care of redundant identity features + # for polynomial feature, we will remove the 1 as well if include_bias is true + + first_obs = self.observables_list_[0] + s = 0 + obs_list_contain_state_counter = 1 if first_obs.include_state else 0 + obs_list_contain_bias_counter = ( + 1 if getattr(first_obs, "include_bias", False) else 0 + ) + for obs in self.observables_list_[1:]: + assert hasattr(obs, "include_state"), ( + "observable Must have `include_state' " "attribute" + ) + if obs_list_contain_state_counter > 1 and obs.include_state: + s += obs.n_output_features_ - obs.n_input_features_ + else: + s += obs.n_output_features_ + if obs_list_contain_bias_counter > 1 and getattr( + obs, "include_bias", False + ): + s -= 1 + obs_list_contain_state_counter += 1 if obs.include_state is True else 0 + obs_list_contain_bias_counter += ( + 1 if getattr(obs, "include_bias", False) else 0 + ) + + self.n_output_features_ = first_obs.n_output_features_ + s + + # take care of consuming samples in time delay observables: \ + # we will look for the largest delay + max_n_consumed_samples = 0 + for obs in self.observables_list_: + if hasattr(obs, "n_consumed_samples"): + max_n_consumed_samples = max( + max_n_consumed_samples, obs.n_consumed_samples + ) + self.n_consumed_samples = max_n_consumed_samples + + # choosing measurement_matrix + self.measurement_matrix_ = np.zeros( + [self.n_input_features_, self.n_output_features_] + ) + # 1. if any observable has `include_state` == True + if any([obs.include_state for obs in self.observables_list_]) is True: + jj = 0 + for i in range(len(self.observables_list_)): + jcol = self.observables_list_[i].measurement_matrix_.shape[1] + if self.observables_list_[i].include_state is True: + break + jj += jcol + self.measurement_matrix_[:, jj : jj + jcol] = self.observables_list_[ + i + ].measurement_matrix_ + else: + g = self.transform(X) + tmp = np.linalg.lstsq(g, X)[0].T + assert tmp.shape == self.measurement_matrix_.shape + self.measurement_matrix_ = tmp + + # 1. if first observable does not contain include state but others do + # then we will use the nearest one's measurement matrix + + # otherwise, + + # C comes from the first observable + + # first_obs_measurement_matrix = self.observables_list_[0].measurement_matrix_ + # self.measurement_matrix_[:first_obs_measurement_matrix.shape[0], + # :first_obs_measurement_matrix.shape[1],] = first_obs_measurement_matrix + + return self + + def transform(self, X): + """Evaluate observable at `X`. + + This method checks if the model is fitted and then evaluates the observables + at the provided data, excluding features that are state or bias based on + certain conditions. + + Args: + X (numpy.ndarray): Measurement data to be fit, with shape (n_samples, + n_input_features_) or (n_trials, n_samples, n_input_features_). + + Returns: + y (numpy.ndarray): Evaluation of observables at `X`, with shape (n_samples, + n_output_features_) or (n_trials, n_samples, n_output_features_). + + Raises: + NotFittedError: If the model is not fitted yet. + """ + + # Handle 3D data (multiple trials) by processing each trial separately + if isinstance(X, list): + return [self.transform(X_trial) for X_trial in X] + + if X.ndim == 3: + return np.array([self.transform(X_trial) for X_trial in X]) + + # for obs in self.observables_list_: + # check_is_fitted(obs, "n_consumed_samples_") + check_is_fitted(self, "n_consumed_samples") + num_samples_updated = X.shape[0] - self.n_consumed_samples + first_obs = self.observables_list_[0] + obs_list_contain_state_counter = 1 if first_obs.include_state else 0 + obs_list_contain_bias_counter = ( + 1 if getattr(first_obs, "include_bias", False) else 0 + ) + y_list = [first_obs.transform(X)[-num_samples_updated:, :]] + + # only include those features that are not state + y_rest_list = [] + for obs in self.observables_list_[1:]: + if obs_list_contain_state_counter > 1 and obs.include_state: + y_new = obs.transform(X)[-num_samples_updated:, obs.n_input_features_ :] + else: + y_new = obs.transform(X)[-num_samples_updated:, :] + if obs_list_contain_bias_counter > 1 and getattr( + obs, "include_bias", False + ): + y_new = y_new[:, 1:] + obs_list_contain_state_counter += 1 if obs.include_state else 0 + obs_list_contain_bias_counter += ( + 1 if getattr(obs, "include_bias", False) else 0 + ) + + y_rest_list.append(y_new) + y_list += y_rest_list + + # y_list += [ + # obs.transform(X)[-num_samples_updated:, obs.n_input_features_ :] + # for obs in self.observables_list_[1:] + # ] + y = np.hstack(y_list) + return y + + def get_feature_names(self, input_features=None): + """Return names of observables. + + This method returns a list of feature names, which are created by + concatenating the feature names from all observables in the list. + + Args: + input_features (list of str, optional): Default list is "x0", "x1", ..., + "xn", where n = n_features. Defaults to None. + + Returns: + list of str: List of feature names of length n_output_features. + + Raises: + NotFittedError: If the model is not fitted yet. + """ + check_is_fitted(self, "n_input_features_") + + concat_feature_names = self.observables_list_[0].get_feature_names() + for obs in self.observables_list_[1:]: + if getattr(obs, "include_bias", False): + concat_feature_names += obs.get_feature_names()[ + obs.n_input_features_ + 1 : + ] + else: + concat_feature_names += obs.get_feature_names()[obs.n_input_features_ :] + return concat_feature_names + + def inverse(self, y): + """Invert the transformation to get system state `x`. + + This function approximately (due to some of them use least-square) + satisfies :code:`self.inverse(self.transform(x)) == x`. + + Args: + y (numpy.ndarray): Data to which to apply the inverse. + Shape must be (n_samples, n_output_features) or + (n_trials, n_samples, n_output_features). + Must have the same number of features as the transformed data. + + Returns: + numpy.ndarray: Output of inverse map applied to y. + Shape will be (n_samples, n_input_features) or + (n_trials, n_samples, n_input_features). + + Raises: + NotFittedError: If the model is not fitted yet. + ValueError: If the number of features in `y` does not match + `n_output_features_`. + + """ + + # Handle 3D data (multiple trials) by processing each trial separately + if isinstance(y, list): + return [self.inverse(y_trial) for y_trial in y] + + if y.ndim == 3: + return np.array([self.inverse(y_trial) for y_trial in y]) + + check_is_fitted(self, ["n_input_features_", "measurement_matrix_"]) + if y.shape[-1] != self.n_output_features_: + raise ValueError( + "Wrong number of input features." + f"Expected y.shape[-1] = {self.n_output_features_}; " + f"instead y.shape[-1] = {y.shape[-1]}." + ) + + # dim_output_first_obs = self.observables_list_[0].n_output_features_ + x = y @ self.measurement_matrix_.T + return x diff --git a/DSA/pykoopman/src/pykoopman/observables/_custom_observables.py b/DSA/pykoopman/src/pykoopman/observables/_custom_observables.py new file mode 100644 index 0000000..6d9cefb --- /dev/null +++ b/DSA/pykoopman/src/pykoopman/observables/_custom_observables.py @@ -0,0 +1,239 @@ +"""Module for customized observables""" +from __future__ import annotations + +from itertools import combinations +from itertools import combinations_with_replacement + +import numpy as np +from numpy import empty +from sklearn.utils.validation import check_is_fitted + +from ..common import validate_input +from ._base import BaseObservables + + +class CustomObservables(BaseObservables): + """ + A class to map state variables using custom observables. + + This class allows the user to specify a list of functions that map state variables + to observables. The identity map is automatically included. It can be configured to + include or exclude self-interaction terms. + + Attributes: + observables (list of callable): List of functions mapping state variables to + observables. Univariate functions are applied to each state variable, + and multivariable functions are applied to combinations of state + variables. The identity map is automatically included in this list. + observable_names (list of callable, optional): List of functions mapping from + names of state variables to names of observables. For example, + the observable name lambda x: f"{x}^2" would correspond to the function + x^2. If None, the names "f0(...)", "f1(...)", ... will be used. Default + is None. + interaction_only (bool, optional): If True, omits self-interaction terms. + Function evaluations of the form f(x,x) and f(x,y,x) will be omitted, + but those of the form f(x,y) and f(x,y,z) will be included. If False, + all combinations will be included. Default is True. + n_input_features_ (int): Number of input features. + n_output_features_ (int): Number of output features. + """ + + def __init__(self, observables, observable_names=None, interaction_only=True): + """ + Initialize a CustomObservables instance. + + Args: + observables (list of callable): List of functions mapping state variables + to observables. Univariate functions are applied to each state + variable, and multivariable functions are applied to combinations of + state variables. The identity map is automatically included in this + list. + observable_names (list of callable, optional): List of functions mapping + from names of state variables to names of observables. For example, + the observable name lambda x: f"{x}^2" would correspond to the + function x^2. If None, the names "f0(...)", "f1(...)", ... will + be used. Default is None. + interaction_only (bool, optional): If True, omits self-interaction terms. + Function evaluations of the form f(x,x) and f(x,y,x) will be omitted, + but those of the form f(x,y) and f(x,y,z) will be included. If False, + all combinations will be included. Default is True. + """ + super(CustomObservables, self).__init__() + self.observables = [identity, *observables] + if observable_names and (len(observables) != len(observable_names)): + raise ValueError( + "observables and observable_names must have the same length" + ) + self.observable_names = observable_names + self.interaction_only = interaction_only + self.include_state = True + + def fit(self, x, y=None): + """ + Fit the model to the measurement data. + + This method calculates the number of input and output features and generates + default values for 'observable_names' if necessary. It also prepares the + measurement matrix for data transformation. + + Args: + x (array-like, shape (n_samples, n_input_features)): Measurement data to be + fitted. + y (None): This is a dummy parameter added for compatibility with sklearn's + API. Default is None. + + Returns: + self (CustomObservables): This method returns the fitted instance. + """ + x = validate_input(x) + n_samples, n_features = x.shape + + n_output_features = 0 + for f in self.observables: + n_args = f.__code__.co_argcount + n_output_features += len( + list(self._combinations(n_features, n_args, self.interaction_only)) + ) + + self.n_input_features_ = n_features + self.n_output_features_ = n_output_features + self.n_consumed_samples = 0 + + if self.observable_names is None: + self.observable_names = list( + map( + lambda i: (lambda *x: "f" + str(i) + "(" + ",".join(x) + ")"), + range(len(self.observables)), + ) + ) + + # First map is the identity + self.observable_names.insert(0, identity_name) + + # since the first map is identity + self.measurement_matrix_ = np.zeros( + (self.n_input_features_, self.n_output_features_) + ) + self.measurement_matrix_[ + : self.n_input_features_, : self.n_input_features_ + ] = np.eye(self.n_input_features_) + + return self + + def transform(self, x): + """ + Apply custom transformations to data, computing observables. + + This method applies the user-defined observables functions to the input data, + effectively transforming the state variables into observable ones. + + Args: + x (array-like, shape (n_samples, n_input_features)): The measurement data + to be transformed. + + Returns: + x_transformed (array-like, shape (n_samples, n_output_features)): The + transformed data, i.e., the computed observables. + """ + check_is_fitted(self, "n_input_features_") + check_is_fitted(self, "n_output_features_") + x = validate_input(x) + + if isinstance(x, list): + return [self.transform(x_trial) for x_trial in x] + + if x.ndim == 3: + return np.array([self.transform(x_trial) for x_trial in x]) + + n_samples, n_features = x.shape + + if n_features != self.n_input_features_: + raise ValueError("x.shape[1] does not match n_input_features_") + + x_transformed = empty((n_samples, self.n_output_features_), dtype=x.dtype) + observables_idx = 0 + for f in self.observables: + for c in self._combinations( + self.n_input_features_, f.__code__.co_argcount, self.interaction_only + ): + x_transformed[:, observables_idx] = f(*[x[:, j] for j in c]) + observables_idx += 1 + + return x_transformed + + def get_feature_names(self, input_features=None): + """ + Get the names of the output features. + + This method returns the names of the output features as defined by the + observable functions. If names for the input features are provided, they are + used in the output feature names. Otherwise, default names ("x0", "x1", ..., + "xn_input_features") are used. + + Args: + input_features (list of string, length n_input_features, optional): + String names for input features, if available. By default, the names + "x0", "x1", ... ,"xn_input_features" are used. + + Returns: + output_feature_names (list of string, length n_output_features): + Output feature names. + """ + check_is_fitted(self, "n_input_features_") + if input_features is None: + input_features = [f"x{i}" for i in range(self.n_input_features_)] + else: + if len(input_features) != self.n_input_features_: + raise ValueError( + "input_features must have n_input_features_ " + f"({self.n_input_features_}) elements" + ) + + feature_names = [] + for i, f in enumerate(self.observables): + feature_names.extend( + [ + self.observable_names[i](*[input_features[j] for j in c]) + for c in self._combinations( + self.n_input_features_, + f.__code__.co_argcount, + self.interaction_only, + ) + ] + ) + + return feature_names + + @staticmethod + def _combinations(n_features, n_args, interaction_only): + """ + Get the combinations of features to be passed to observable functions. + + This static method generates all possible combinations or combinations with + replacement (depending on the `interaction_only` flag) of features that are to + be passed to the observable functions. The combinations are represented as + tuples of indices. + + Args: + n_features (int): The total number of features. + n_args (int): The number of arguments that the observable function accepts. + interaction_only (bool): If True, combinations of the same feature + (self-interactions) are omitted. If False, all combinations including + self-interactions are included. + + Returns: + iterable of tuples: An iterable over all combinations of feature indices + to be passed to the observable functions. + """ + comb = combinations if interaction_only else combinations_with_replacement + return comb(range(n_features), n_args) + + +def identity(x): + """Identity map.""" + return x + + +def identity_name(x): + """Name for identity map.""" + return str(x) diff --git a/DSA/pykoopman/src/pykoopman/observables/_identity.py b/DSA/pykoopman/src/pykoopman/observables/_identity.py new file mode 100644 index 0000000..3f7ceb0 --- /dev/null +++ b/DSA/pykoopman/src/pykoopman/observables/_identity.py @@ -0,0 +1,89 @@ +"""module for Linear observables""" +from __future__ import annotations + +import numpy as np +from sklearn.utils.validation import check_is_fitted + +from ..common import validate_input +from ._base import BaseObservables + + +class Identity(BaseObservables): + """ + A dummy observables class that simply returns its input. + """ + + def __init__(self): + """ + Initialize the Identity class. + + This constructor initializes the Identity class which simply returns its input + when transformed. + """ + super().__init__() + self.include_state = True + + def fit(self, x, y=None): + """ + Fit the model to the provided measurement data. + + Args: + x (array-like): The measurement data to be fit. It must have a shape of + (n_samples, n_input_features). + y (None): This parameter is retained for sklearn compatibility. + + Returns: + self: Returns a fit instance of the class `pykoopman.observables.Identity`. + + Note: + only identity mapping is supported for list of arb trajectories + """ + x = validate_input(x) + if not isinstance(x, list): + self.n_input_features_ = self.n_output_features_ = x.shape[1] + self.measurement_matrix_ = np.eye(x.shape[1]).T + else: + self.n_input_features_ = self.n_output_features_ = x[0].shape[1] + self.measurement_matrix_ = np.eye(x[0].shape[1]).T + + self.n_consumed_samples = 0 + + return self + + def transform(self, x): + """ + Apply Identity transformation to the provided data. + + Args: + x (array-like): The measurement data to be transformed. It must have a + shape of (n_samples, n_input_features). + + Returns: + array-like: Returns the transformed data which is the same as the input + data in this case. + """ + check_is_fitted(self, "n_input_features_") + return x + + def get_feature_names(self, input_features=None): + """ + Get the names of the output features. + + Args: + input_features (list of string, optional): The string names for input + features, if available. By default, the names "x0", "x1", ... , + "xn_input_features" are used. + + Returns: + list of string: Returns the output feature names. + """ + check_is_fitted(self, "n_input_features_") + if input_features is None: + input_features = [f"x{i}" for i in range(self.n_input_features_)] + else: + if len(input_features) != self.n_input_features_: + raise ValueError( + "input_features must have n_input_features_ " + f"({self.n_input_features_}) elements" + ) + return input_features diff --git a/DSA/pykoopman/src/pykoopman/observables/_polynomial.py b/DSA/pykoopman/src/pykoopman/observables/_polynomial.py new file mode 100644 index 0000000..5e852de --- /dev/null +++ b/DSA/pykoopman/src/pykoopman/observables/_polynomial.py @@ -0,0 +1,263 @@ +"""moduel for Polynomial observables""" +from __future__ import annotations + +from itertools import chain +from itertools import combinations +from itertools import combinations_with_replacement as combinations_w_r + +import numpy as np +from scipy import sparse +from sklearn.preprocessing import PolynomialFeatures +from sklearn.preprocessing._csr_polynomial_expansion import _csr_polynomial_expansion +from sklearn.utils.validation import check_is_fitted +from sklearn.utils.validation import FLOAT_DTYPES + +from ..common import check_array +from ..common import validate_input +from ._base import BaseObservables + + +class Polynomial(PolynomialFeatures, BaseObservables): + """ + Polynomial observables. + + This is essentially the `sklearn.preprocessing.PolynomialFeatures` with support for + complex numbers. + + Args: + degree (int, optional): The degree of the polynomial features. Default is 2. + interaction_only (bool, optional): If True, only interaction features are + produced: features that are products of at most ``degree`` *distinct* + input features (so not ``x[1] ** 2``, ``x[0] * x[2] ** 3``, + etc.). Default is False. + include_bias (bool, optional): If True, then include a bias column, the feature + in which all polynomial powers are zero (i.e., a column of ones - acts as an + intercept term in a linear model). Default is True. + order (str in {'C', 'F'}, optional): Order of output array in the dense case. + 'F' order is faster to compute, but may slow down subsequent estimators. + Default is 'C'. + + Raises: + ValueError: If degree is less than 1. + """ + + def __init__(self, degree=2, interaction_only=False, include_bias=True, order="C"): + """ + Initialize the Polynomial object. + + Args: + degree (int, optional): The degree of the polynomial features. Default is 2. + interaction_only (bool, optional): If True, only interaction features are + produced: features that are products of at most ``degree`` *distinct* + input features (so not ``x[1] ** 2``, ``x[0] * x[2] ** 3``, + etc.). Default is False. + include_bias (bool, optional): If True, then include a bias column, the + feature in which all polynomial powers are zero (i.e., a column of + ones - acts as an intercept term in a linear model). Default is True. + order (str in {'C', 'F'}, optional): Order of output array in the dense + case. 'F' order is faster to compute, but may slow down subsequent + estimators. Default is 'C'. + + Raises: + ValueError: If degree is less than 1. + """ + if degree == 0: + raise ValueError( + "degree must be at least 1, otherwise inverse cannot be " "computed" + ) + super(Polynomial, self).__init__( + degree=degree, + interaction_only=interaction_only, + include_bias=include_bias, + order=order, + ) + self.include_state = True + + def fit(self, x, y=None): + """ + Compute number of output features. + + This method fits the `Polynomial` instance to the input data `x`. It calls the + `fit` method of the superclass (`PolynomialFeatures` from + `sklearn.preprocessing`), which computes the number of output features based + on the degree of the polynomial and the interaction_only flag. It also sets + `n_input_features_` and `n_output_features_` attributes. Then, it initializes + `measurement_matrix_` as a zero matrix of size `n_input_features_` by + `n_output_features_` and sets the main diagonal to 1, depending on the + `include_bias` attribute. The input `y` is not used in this method; it is + only included to maintain compatibility with the usual interface of `fit` + methods in scikit-learn. + + Args: + x (np.ndarray): The measurement data to be fit, with shape (n_samples, + n_features). + y (array-like, optional): Dummy input. Defaults to None. + + Returns: + self: A fit instance of `Polynomial`. + + Raises: + ValueError: If the input data is not valid. + """ + x = validate_input(x) + self.n_consumed_samples = 0 + + y_poly_out = super(Polynomial, self).fit(x.real, y) + + self.measurement_matrix_ = np.zeros([x.shape[1], y_poly_out.n_output_features_]) + if self.include_bias: + self.measurement_matrix_[:, 1 : 1 + x.shape[1]] = np.eye(x.shape[1]) + else: + self.measurement_matrix_[:, : x.shape[1]] = np.eye(x.shape[1]) + + return y_poly_out + + def transform(self, x): + """ + Transforms the data to polynomial features. + + This method transforms the data `x` into polynomial features. It first checks if + the fit method has been called by checking the `n_input_features_` attribute, + then it validates the input `x`. If `x` is a CSR sparse matrix and the degree is + less than 4, it uses a method based on "Leveraging Sparsity to Speed Up + Polynomial Feature Expansions of CSR Matrices Using K-Simplex Numbers" by + Andrew Nystrom and John Hughes. If `x` is a CSC sparse matrix and the degree + is less than 4, it converts `x` to CSR, generates the polynomial features, + then converts back to CSC. For dense arrays or CSC sparse matrix with a + degree of 4 or more, it generates the polynomial features through a slower + process. + + Args: + x (array-like or CSR/CSC sparse matrix): The data to transform, row by row. + The shape should be (n_samples, n_features). Prefer CSR over CSC for + sparse input (for speed), but CSC is required if the degree is 4 or higher. + + Returns: + xp (np.ndarray or CSR/CSC sparse matrix): The matrix of features, where + n_output_features is the number of polynomial features generated from the + combination of inputs. The shape is (n_samples, n_output_features). + + Raises: + ValueError: If the input data is not valid or the shape of `x` does not + match training shape. + """ + if isinstance(x, list): + return [self.transform(x_trial) for x_trial in x] + + if x.ndim == 3: + return np.array([self.transform(x_trial) for x_trial in x]) + + check_is_fitted(self, "n_input_features_") + + x = check_array(x, order="F", dtype=FLOAT_DTYPES, accept_sparse=("csr", "csc")) + + n_samples, n_features = x.shape + + if n_features != self.n_input_features_: + raise ValueError("x shape does not match training shape") + + if sparse.isspmatrix_csr(x): + if self.degree > 3: + return self.transform(x.tocsc()).tocsr() + to_stack = [] + if self.include_bias: + to_stack.append(np.ones(shape=(n_samples, 1), dtype=x.dtype)) + to_stack.append(x) + for deg in range(2, self.degree + 1): + xp_next = _csr_polynomial_expansion( + x.data, + x.indices, + x.indptr, + x.shape[1], + self.interaction_only, + deg, + ) + if xp_next is None: + break + to_stack.append(xp_next) + xp = sparse.hstack(to_stack, format="csr") + elif sparse.isspmatrix_csc(x) and self.degree < 4: + return self.transform(x.tocsr()).tocsc() + else: + combinations = self._combinations( + n_features, + self.degree, + self.interaction_only, + self.include_bias, + ) + if sparse.isspmatrix(x): + columns = [] + for comb in combinations: + if comb: + out_col = 1 + for col_idx in comb: + out_col = x[:, col_idx].multiply(out_col) + columns.append(out_col) + else: + bias = sparse.csc_matrix(np.ones((x.shape[0], 1))) + columns.append(bias) + xp = sparse.hstack(columns, dtype=x.dtype).tocsc() + else: + xp = np.empty( + (n_samples, self.n_output_features_), + dtype=x.dtype, + order=self.order, + ) + for i, comb in enumerate(combinations): + xp[:, i] = x[:, comb].prod(1) + + return xp + + @staticmethod + def _combinations(n_features, degree, interaction_only, include_bias): + """ + Generate combinations for polynomial features. + + This static method generates combinations of features for the polynomial + transformation. The combinations depend on whether interaction_only is set + and whether a bias term should be included. + + Args: + n_features (int): The number of features. + degree (int): The degree of the polynomial. + interaction_only (bool): If True, only interaction features are produced. + include_bias (bool): If True, a bias column is included. + + Returns: + itertools.chain: An iterable over all combinations. + """ + comb = combinations if interaction_only else combinations_w_r + start = int(not include_bias) + return chain.from_iterable( + comb(range(n_features), i) for i in range(start, degree + 1) + ) + + @property + def powers_(self): + """ + Get the exponent for each of the inputs in the output. + + This property method returns the exponents for each input feature in the + polynomial output. It first checks whether the model has been fitted, then + uses the `_combinations` method to get the combinations of features, and + finally calculates the exponents for each input feature. + + Returns: + np.ndarray: A 2D array where each row represents a feature and each + column represents an output of the polynomial transformation. The + values are the exponents of the input features. + + Raises: + NotFittedError: If the model has not been fitted. + """ + check_is_fitted(self) + + combinations = self._combinations( + n_features=self.n_input_features_, + degree=self.degree, + interaction_only=self.interaction_only, + include_bias=self.include_bias, + ) + return np.vstack( + [np.bincount(c, minlength=self.n_input_features_) for c in combinations] + ) diff --git a/DSA/pykoopman/src/pykoopman/observables/_radial_basis_functions.py b/DSA/pykoopman/src/pykoopman/observables/_radial_basis_functions.py new file mode 100644 index 0000000..217f485 --- /dev/null +++ b/DSA/pykoopman/src/pykoopman/observables/_radial_basis_functions.py @@ -0,0 +1,293 @@ +"""module for Radial basis function observables""" +from __future__ import annotations + +import numpy as np +from numpy import empty +from numpy import random +from sklearn.utils.validation import check_is_fitted + +from ..common import validate_input +from ._base import BaseObservables + + +class RadialBasisFunction(BaseObservables): + """ + This class represents Radial Basis Functions (RBF) used as observables. + Observables are formed as RBFs of the state variables, interpreted as new state + variables. + + For instance, a single state variable :math:`[x(t)]` could be evaluated using + multiple centers, yielding a new set of observables. This implementation supports + various types of RBFs including 'gauss', 'thinplate', 'invquad', 'invmultquad', + and 'polyharmonic'. + + Attributes: + rbf_type (str): The type of radial basis functions to be used. + n_centers (int): The number of centers to compute RBF with. + centers (numpy array): The centers to compute RBF with. + kernel_width (float): The kernel width for Gaussian RBFs. + polyharmonic_coeff (float): The polyharmonic coefficient for polyharmonic RBFs. + include_state (bool): Whether to include the input coordinates as additional + coordinates in the observable. + n_input_features_ (int): Number of input features. + n_output_features_ (int): Number of output features = Number of centers plus + number of input features. + + Note: + The implementation is based on the following references: + - Williams, Matthew O and Kevrekidis, Ioannis G and Rowley, Clarence W + "A data-driven approximation of the {K}oopman operator: extending dynamic + mode decomposition." + Journal of Nonlinear Science 6 (2015): 1307-1346 + - Williams, Matthew O and Rowley, Clarence W and Kevrekidis, Ioannis G + "A Kernel Approach to Data-Driven {K}oopman Spectral Analysis." + Journal of Computational Dynamics 2.2 (2015): 247-265 + - Korda, Milan and Mezic, Igor + "Linear predictors for nonlinear dynamical systems: Koopman operator meets + model predictive control." + Automatica 93 (2018): 149-160 + """ + + def __init__( + self, + rbf_type="gauss", + n_centers=10, + centers=None, + kernel_width=1.0, + polyharmonic_coeff=1.0, + include_state=True, + ): + super().__init__() + if type(rbf_type) != str: + raise TypeError("rbf_type must be a string") + if type(n_centers) != int: + raise TypeError("n_centers must be an int") + if n_centers < 0: + raise ValueError("n_centers must be a nonnegative int") + if kernel_width < 0: + raise ValueError("kernel_width must be a nonnegative float") + if polyharmonic_coeff < 0: + raise ValueError("polyharmonic_coeff must be a nonnegative float") + if rbf_type not in [ + "thinplate", + "gauss", + "invquad", + "invmultquad", + "polyharmonic", + ]: + raise ValueError("rbf_type not of available type") + if type(include_state) != bool: + raise TypeError("include_states must be a boolean") + if centers is not None: + if int(n_centers) not in centers.shape: + raise ValueError( + "n_centers is not equal to centers.shape[1]. " + "centers must be of shape (n_input_features, " + "n_centers). " + ) + self.rbf_type = rbf_type + self.n_centers = int(n_centers) + self.centers = centers + self.kernel_width = kernel_width + self.polyharmonic_coeff = polyharmonic_coeff + self.include_state = include_state + + def fit(self, x, y=None): + """ + Initializes the RadialBasisFunction with specified parameters. + + Args: + rbf_type (str, optional): The type of radial basis functions to be used. + Options are: 'gauss', 'thinplate', 'invquad', 'invmultquad', + 'polyharmonic'. Defaults to 'gauss'. + n_centers (int, optional): The number of centers to compute RBF with. + Must be a non-negative integer. Defaults to 10. + centers (numpy array, optional): The centers to compute RBF with. + If provided, it should have a shape of (n_input_features, n_centers). + Defaults to None, in which case the centers are uniformly distributed + over input data. + kernel_width (float, optional): The kernel width for Gaussian RBFs. + Must be a non-negative float. Defaults to 1.0. + polyharmonic_coeff (float, optional): The polyharmonic coefficient for + polyharmonic RBFs. Must be a non-negative float. Defaults to 1.0. + include_state (bool, optional): Whether to include the input coordinates + as additional coordinates in the observable. Defaults to True. + + Raises: + TypeError: If rbf_type is not a string, n_centers is not an int, or + include_state is not a bool. + ValueError: If n_centers, kernel_width or polyharmonic_coeff is negative, + rbf_type is not of available type, or centers is provided but + n_centers is not equal to centers.shape[1]. + """ + x = validate_input(x) + n_samples, n_features = x.shape + self.n_consumed_samples = 0 + + self.n_samples_ = n_samples + self.n_input_features_ = n_features + if self.include_state is True: + self.n_output_features_ = n_features * 1 + self.n_centers + elif self.include_state is False: + self.n_output_features_ = self.n_centers + + x = validate_input(x) + + if x.shape[1] != self.n_input_features_: + raise ValueError( + "Wrong number of input features. " + f"Expected x.shape[1] = {self.n_input_features_}; " + f"instead x.shape[1] = {x.shape[1]}." + ) + + if self.centers is None: + # Uniformly distributed centers + self.centers = random.rand(self.n_input_features_, self.n_centers) + # Change range to range of input features' range + for feat in range(self.n_input_features_): + xminmax = self._minmax(x[:, feat]) + + # Map to range [0,1] + self.centers[feat, :] = ( + self.centers[feat, :] - min(self.centers[feat, :]) + ) / (max(self.centers[feat, :]) - min(self.centers[feat, :])) + # Scale to input features' range + self.centers[feat, :] = ( + self.centers[feat, :] * (xminmax[1] - xminmax[0]) + xminmax[0] + ) + + xlift = self._rbf_lifting(x) + # self.measurement_matrix_ = x.T @ np.linalg.pinv(xlift.T) + self.measurement_matrix_ = np.linalg.lstsq(xlift, x)[0].T + + return self + + def transform(self, x): + """ + Apply radial basis function transformation to the data. + + Args: + x (array-like): Measurement data to be transformed, with shape (n_samples, + n_input_features). It is assumed that rows correspond to examples, + which are not required to be equi-spaced in time or in sequential order. + + Returns: + array-like: Transformed data, with shape (n_samples, n_output_features). + + Raises: + NotFittedError: If the 'fit' method has not been called before the + 'transform' method. + ValueError: If the number of features in 'x' does not match the number of + input features expected by the transformer. + """ + check_is_fitted(self, ["n_input_features_", "centers"]) + if isinstance(x, list): + return [self.transform(x_trial) for x_trial in x] + + if x.ndim == 3: + return np.array([self.transform(x_trial) for x_trial in x]) + + x = validate_input(x) + + if x.shape[1] != self.n_input_features_: + raise ValueError( + "Wrong number of input features. " + f"Expected x.shape[1] = {self.n_input_features_}; " + f"instead x.shape[1] = {x.shape[1]}." + ) + + y = self._rbf_lifting(x) + return y + + def get_feature_names(self, input_features=None): + """ + Get the names of the output features. + + Args: + input_features (list of str, optional): String names for input features, + if available. By default, the names "x0", "x1", ... , + "xn_input_features" are used. + + Returns: + list of str: Output feature names. + + Raises: + NotFittedError: If the 'fit' method has not been called before the + 'get_feature_names' method. + ValueError: If the length of 'input_features' does not match the number of + input features expected by the transformer. + """ + + check_is_fitted(self, "n_input_features_") + if input_features is None: + input_features = [f"x{i}" for i in range(self.n_input_features_)] + else: + if len(input_features) != self.n_input_features_: + raise ValueError( + "input_features must have n_input_features_ " + f"({self.n_input_features_}) elements" + ) + + output_features = [] + if self.include_state is True: + output_features.extend([f"{xi}(t)" for xi in input_features]) + output_features.extend([f"phi(x(t)-c{i})" for i in range(self.n_centers)]) + return output_features + + def _rbf_lifting(self, x): + """ + Internal method that performs Radial Basis Function (RBF) transformation. + + Args: + x (numpy.ndarray): Input data of shape (n_samples, n_input_features) + + Returns: + y (numpy.ndarray): Transformed data of shape (n_samples, n_output_features) + + Raises: + ValueError: If 'rbf_type' is not one of the available types. + + Notes: + This method should not be called directly. It is used internally by the + 'transform' method. + """ + n_samples = x.shape[0] + y = empty( + (n_samples, self.n_output_features_), + dtype=x.dtype, + ) + + y_index = 0 + if self.include_state is True: + y[:, : self.n_input_features_] = x + y_index = self.n_input_features_ + + for index_of_center in range(self.n_centers): + C = self.centers[:, index_of_center] + r_squared = np.sum((x - C[np.newaxis, :]) ** 2, axis=1) + + if self.rbf_type == "thinplate": + y_ = r_squared * np.log(np.sqrt(r_squared)) + y_[np.isnan(y_)] = 0 + elif self.rbf_type == "gauss": + y_ = np.exp(-self.kernel_width**2 * r_squared) + elif self.rbf_type == "invquad": + y_ = np.reciprocal(1 + self.kernel_width**2 * r_squared) + elif self.rbf_type == "invmultquad": + y_ = np.reciprocal(np.sqrt(1 + self.kernel_width**2 * r_squared)) + elif self.rbf_type == "polyharmonic": + y_ = r_squared ** (self.polyharmonic_coeff / 2) * np.log( + np.sqrt(r_squared) + ) + else: + # if none of the above cases match: + raise ValueError("provided rbf_type not available") + + y[:, y_index + index_of_center] = y_ + + return y + + def _minmax(self, x): + min_val = min(x) + max_val = max(x) + return (min_val, max_val) diff --git a/DSA/pykoopman/src/pykoopman/observables/_random_fourier_features.py b/DSA/pykoopman/src/pykoopman/observables/_random_fourier_features.py new file mode 100644 index 0000000..a9ebec7 --- /dev/null +++ b/DSA/pykoopman/src/pykoopman/observables/_random_fourier_features.py @@ -0,0 +1,193 @@ +"""module for random fourier features observables""" +from __future__ import annotations + +import numpy as np +from sklearn.utils.validation import check_is_fitted + +from ..common import validate_input +from ._base import BaseObservables + + +class RandomFourierFeatures(BaseObservables): + """ + Random Fourier Features for observables. + + This class applies the random Fourier features method for kernel approximation. + It can include the system state in the kernel function. It uses the + Gaussian kernel by default. + + Args: + include_state (bool, optional): If True, includes the system state. Defaults to + True. + gamma (float, optional): The scale of the Gaussian kernel. Defaults to 1.0. + D (int, optional): The number of random samples in Monte Carlo approximation. + Defaults to 100. + random_state (int, None, optional): The seed of the random number for repeatable + experiments. Defaults to None. + + Attributes: + include_state (bool): If True, includes the system state. + gamma (float): The scale of the Gaussian kernel. + D (int): The number of random samples in Monte Carlo approximation. + random_state (int, None): The seed of the random number for repeatable + experiments. + measurement_matrix_ (numpy.ndarray): A row feature vector right multiply with + `measurement_matrix_` will return the system state. + n_input_features_ (int): Dimension of input features, e.g., system state. + n_output_features_ (int): Dimension of transformed/output features, e.g., + observables. + w (numpy.ndarray): The frequencies randomly sampled for random fourier features. + """ + + def __init__(self, include_state=True, gamma=1.0, D=100, random_state=None): + """ + Initialize the RandomFourierFeatures class with given parameters. + + Args: + include_state (bool, optional): If True, includes the system state. + Defaults to True. + gamma (float, optional): The scale of the Gaussian kernel. Defaults to 1.0. + D (int, optional): The number of random samples in Monte Carlo + approximation. Defaults to 100. + random_state (int or None, optional): The seed of the random number + for repeatable experiments. Defaults to None. + """ + super(RandomFourierFeatures, self).__init__() + self.include_state = include_state + self.gamma = gamma + self.D = D + self.random_state = random_state + + def fit(self, x, y=None): + """ + Set up observable. + + Args: + x (numpy.ndarray): Measurement data to be fit. Shape (n_samples, + n_input_features_). + y (numpy.ndarray, optional): Time-shifted measurement data to be fit. + Defaults to None. + + Returns: + self: Returns a fitted RandomFourierFeatures instance. + """ + x = validate_input(x) + np.random.seed(self.random_state) + self.n_consumed_samples = 0 + + self.n_input_features_ = x.shape[1] + # although we have double the output dim, the convergence + # rate is described in only self.n_components + self.n_output_features_ = 2 * self.D + + if self.include_state is True: + self.n_output_features_ += self.n_input_features_ + + # 1. generate (n_feature, n_component) random w + self.w = np.sqrt(2.0 * self.gamma) * np.random.normal( + 0, 1, [self.n_input_features_, self.D] + ) + + # 3. get the C to map back to state + if self.include_state: + self.measurement_matrix_ = np.zeros( + (self.n_input_features_, self.n_output_features_) + ) + self.measurement_matrix_[ + : self.n_input_features_, : self.n_input_features_ + ] = np.eye(self.n_input_features_) + else: + # we have to transform the data x in order to find a matrix by fitting + # z = np.zeros((x.shape[0], self.n_output_features_)) + # z[:,:x.shape[1]] = x + # z[:,x.shape[1]:] = self._rff_lifting(x) + z = self._rff_lifting(x) + self.measurement_matrix_ = np.linalg.lstsq(z, x)[0].T + + return self + + def transform(self, x): + """ + Evaluate observable at `x`. + + Args: + x (numpy.ndarray): Measurement data to be fit. Shape (n_samples, + n_input_features_). + + Returns: + y (numpy.ndarray): Evaluation of observables at `x`. Shape (n_samples, + n_output_features_). + """ + + check_is_fitted(self, "n_input_features_") + if isinstance(x, list): + return [self.transform(x_trial) for x_trial in x] + + if x.ndim == 3: + return np.array([self.transform(x_trial) for x_trial in x]) + + z = np.zeros((x.shape[0], self.n_output_features_)) + z_rff = self._rff_lifting(x) + if self.include_state: + z[:, : x.shape[1]] = x + z[:, x.shape[1] :] = z_rff + else: + z = z_rff + + return z + + def get_feature_names(self, input_features=None): + """ + Return names of observables. + + Args: + input_features (list of string of length n_features, optional): + Default list is "x0", "x1", ..., "xn", where n = n_features. + + Returns: + output_feature_names (list of string of length n_output_features): + Returns a list of observable names. + """ + + check_is_fitted(self, "n_input_features_") + + if input_features is None: + input_features = [f"x{i}" for i in range(self.n_input_features_)] + else: + if len(input_features) != self.n_input_features_: + raise ValueError( + "input_features must have n_input_features_ " + f"({self.n_input_features_}) elements" + ) + + if self.include_state: + # very easy to make mistake... python pass list by reference OMG + output_features = input_features[:] + else: + output_features = [] + output_features += [f"cos(w_{i}'x)/sqrt({self.D})" for i in range(self.D)] + [ + f"sin(w_{i}'x)/sqrt({self.D})" for i in range(self.D) + ] + + return output_features + + def _rff_lifting(self, x): + """ + Core algorithm that computes random Fourier features. + + This method uses the `cos` and `sin` transformations to get random Fourier + features. + + Args: + x (numpy.ndarray): System state. + + Returns: + z_rff (numpy.ndarray): Random Fourier features evaluated on `x`. Shape + (n_samples, n_output_features_). + """ + + # 2. get the feature vector z + xw = np.dot(x, self.w) + z_rff = np.hstack([np.cos(xw), np.sin(xw)]) + z_rff *= 1.0 / np.sqrt(self.D) + return z_rff diff --git a/DSA/pykoopman/src/pykoopman/observables/_time_delay.py b/DSA/pykoopman/src/pykoopman/observables/_time_delay.py new file mode 100644 index 0000000..eb0c9db --- /dev/null +++ b/DSA/pykoopman/src/pykoopman/observables/_time_delay.py @@ -0,0 +1,216 @@ +"""moduel for time-delay observables""" +from __future__ import annotations + +import numpy as np +from numpy import arange +from numpy import empty +from sklearn.utils.validation import check_is_fitted + +from ..common import validate_input +from ._base import BaseObservables + + +class TimeDelay(BaseObservables): + """ + A class for creating time-delay observables. These observables are formed by + taking time-lagged measurements of state variables and interpreting them as new + state variables. + + The two state variables :math:`[x(t), y(t)]` could be supplemented with two + time-delays each, yielding a new set of observables: + + .. math:: + [x(t), y(t), x(t-\\Delta$ t), y(t-\\Delta t), + x(t-2\\Delta t), y(t - 2\\Delta t)] + + This example corresponds to taking :code:`delay =` :math:`\\Delta t` and + :code:`n_delays = 2`. + + Note that when transforming data the first :code:`delay * n_delays` rows/samples + are dropped as there is insufficient time history to form time-delays for them. + + For more information, see the following references: + + Brunton, Steven L., et al. + "Chaos as an intermittently forced linear system." + Nature communications 8.1 (2017): 1-9. + + Susuki, Yoshihiko, and Igor Mezić. + "A prony approximation of Koopman mode decomposition." + 2015 54th IEEE Conference on Decision and Control (CDC). IEEE, 2015. + + Arbabi, Hassan, and Igor Mezic. + "Ergodic theory, dynamic mode decomposition, and computation + of spectral properties of the Koopman operator." + SIAM Journal on Applied Dynamical Systems 16.4 (2017): 2096-2126. + + Args: + delay (int, optional): The length of each delay. Defaults to 1. + n_delays (int, optional): The number of delays to compute for each + variable. Defaults to 2. + + Attributes: + include_state (bool): If True, includes the system state. + delay (int): The length of each delay. + n_delays (int): The number of delays to compute for each variable. + _n_consumed_samples (int): Number of samples consumed when :code:`transform` + is called,i.e. :code:`n_delays * delay`. + """ + + def __init__(self, delay=1, n_delays=2): + """ + Initialize the TimeDelay class with given parameters. + + Args: + delay (int, optional): The length of each delay. Defaults to 1. Or + we say this is the "stride of delay". + n_delays (int, optional): The number of delays to compute for each + variable. Defaults to 2. + + Raises: + ValueError: If delay or n_delays are negative. + """ + super(TimeDelay, self).__init__() + if delay < 0: + raise ValueError("delay must be a nonnegative int") + if n_delays < 0: + raise ValueError("n_delays must be a nonnegative int") + + self.include_state = True + self.delay = int(delay) + self.n_delays = int(n_delays) + self._n_consumed_samples = self.delay * self.n_delays + + def fit(self, x, y=None): + """ + Fit the model to measurement data. + + Args: + x (array-like): The input data, shape (n_samples, n_input_features). + y (None): Dummy parameter for sklearn compatibility. + + Returns: + TimeDelay: The fitted instance. + """ + + x = validate_input(x) + n_samples, n_features = x.shape + + self.n_input_features_ = n_features + self.n_output_features_ = n_features * (1 + self.n_delays) + + self.measurement_matrix_ = np.zeros( + (self.n_input_features_, self.n_output_features_) + ) + self.measurement_matrix_[ + : self.n_input_features_, : self.n_input_features_ + ] = np.eye(self.n_input_features_) + + return self + + def transform(self, x): + """ + Add time-delay features to the data, dropping the first :code:`delay - + n_delays` samples. + + Args: + x (array-like): The input data, shape (n_samples, n_input_features). + It is assumed that rows correspond to examples that are equi-spaced + in time and are in sequential order. + + Returns: + y (array-like): The transformed data, shape (n_samples - delay * n_delays, + n_output_features). + """ + check_is_fitted(self, "n_input_features_") + + if isinstance(x, list): + return [self.transform(x_trial) for x_trial in x] + + if x.ndim == 3: + return np.array([self.transform(x_trial) for x_trial in x]) + + x = validate_input(x) + + if x.shape[-1] != self.n_input_features_: + raise ValueError( + "Wrong number of input features. " + f"Expected x.shape[1] = {self.n_input_features_}; " + f"instead x.shape[1] = {x.shape[1]}." + ) + + self._n_consumed_samples = self.delay * self.n_delays + if len(x) < self._n_consumed_samples + 1: + raise ValueError( + "x has too few rows. To compute time-delay features with " + f"delay = {self.delay} and n_delays = {self.n_delays} " + f"x must have at least {self._n_consumed_samples + 1} rows." + ) + + y = empty( + (x.shape[0] - self._n_consumed_samples, self.n_output_features_), + dtype=x.dtype, + ) + y[:, : self.n_input_features_] = x[self._n_consumed_samples :] + + for i in range(self._n_consumed_samples, x.shape[0]): + y[i - self._n_consumed_samples, self.n_input_features_ :] = x[ + self._delay_inds(i), : + ].flatten() + return y + + def get_feature_names(self, input_features=None): + """ + Get the names of the output features. + + Args: + input_features (list of str, optional): Names for input features. + If None, defaults to "x0", "x1", ... ,"xn_input_features". + + Returns: + list of str: Names of the output features. + """ + check_is_fitted(self, "n_input_features_") + if input_features is None: + input_features = [f"x{i}" for i in range(self.n_input_features_)] + else: + if len(input_features) != self.n_input_features_: + raise ValueError( + "input_features must have n_input_features_ " + f"({self.n_input_features_}) elements" + ) + + output_features = [f"{xi}(t)" for xi in input_features] + output_features.extend( + [ + f"{xi}(t-{i * self.delay}dt)" + for i in range(1, self.n_delays + 1) + for xi in input_features + ] + ) + + return output_features + + def _delay_inds(self, index): + """ + Private method to get the indices for the delayed data. + + Args: + index (int): The index from which to calculate the delay indices. + + Returns: + array-like: The delay indices. + """ + return index - self.delay * arange(1, self.n_delays + 1) + + @property + def n_consumed_samples(self): + """ + The number of samples that are consumed as "initial conditions" for + other samples, i.e., the number of samples for which time delays cannot + be computed. + + Returns: + int: The number of consumed samples. + """ + return self._n_consumed_samples diff --git a/DSA/pykoopman/src/pykoopman/regression/__init__.py b/DSA/pykoopman/src/pykoopman/regression/__init__.py new file mode 100644 index 0000000..4cbc09c --- /dev/null +++ b/DSA/pykoopman/src/pykoopman/regression/__init__.py @@ -0,0 +1,22 @@ +from __future__ import annotations + +from ._base import BaseRegressor +from ._base_ensemble import EnsembleBaseRegressor +from ._dmd import PyDMDRegressor +from ._dmdc import DMDc +from ._edmd import EDMD +from ._edmdc import EDMDc +from ._havok import HAVOK +from ._kdmd import KDMD +from ._nndmd import NNDMD + +__all__ = [ + "PyDMDRegressor", + "EDMD", + "KDMD", + "DMDc", + "EDMDc", + "EnsembleBaseRegressor", + "HAVOK", + "NNDMD", +] diff --git a/DSA/pykoopman/src/pykoopman/regression/_base.py b/DSA/pykoopman/src/pykoopman/regression/_base.py new file mode 100644 index 0000000..6c49394 --- /dev/null +++ b/DSA/pykoopman/src/pykoopman/regression/_base.py @@ -0,0 +1,163 @@ +"""module for base class of regressor""" +from __future__ import annotations + +from abc import ABC +from abc import abstractmethod + +import numpy as np +from sklearn.base import BaseEstimator + + +class BaseRegressor(BaseEstimator, ABC): + """ + Base class for PyKoopman regressors. + + This class provides a wrapper for regressors used in the PyKoopman package. + It's designed to be used with any regressor object that implements `fit` + and `predict` methods following the `sklearn.base.BaseEstimator` interface. + + Note: This is an abstract base class, and should not be instantiated directly. + Instead, a subclass should be created that implements the required abstract methods. + + Args: + regressor (BaseEstimator): A regressor object implementing `fit` and `predict` + methods. + + Attributes: + regressor (BaseEstimator): The regressor object passed during initialization. + + Abstract methods: + coef_ : Should return the coefficients of the regression model. + + state_matrix_ : Should return the state matrix of the dynamic system. + + eigenvectors_ : Should return the eigenvectors of the system. + + eigenvalues_ : Should return the eigenvalues of the system. + + _compute_phi(x_col) : Should compute and return the phi function on given data. + + _compute_psi(x_col) : Should compute and return the psi function on given data. + + ur : Should return the u_r of the system. + + unnormalized_modes : Should return the unnormalized modes of the system. + """ + + def __init__(self, regressor): + # check .fit + if not hasattr(regressor, "fit") or not callable(getattr(regressor, "fit")): + raise AttributeError("regressor does not have a callable fit method") + # check .predict + if not hasattr(regressor, "predict") or not callable( + getattr(regressor, "predict") + ): + raise AttributeError("regressor does not have a callable predict method") + self.regressor = regressor + + def _detect_reshape(self, X, offset=True): + """ + Detect the shape of the input data and reshape it accordingly to return + both X and Y in the correct shape. + """ + s1 = -1 if offset else None + s2 = 1 if offset else None + if isinstance(X, np.ndarray): + if X.ndim == 1: + X = X.reshape(-1, 1) + + if X.ndim == 2: + self.n_samples_, self.n_input_features_ = X.shape + self.n_trials_ = 1 + return X[:s1], X[s2:] + elif X.ndim == 3: + self.n_trials_, self.n_samples_, self.n_input_features_ = X.shape + X, Y = X[:, :s1, :], X[:, s2:, :] + return X.reshape(-1, X.shape[2]), Y.reshape( + -1, Y.shape[2] + ) # time*trials, features + + elif isinstance(X, list): + assert all(isinstance(x, np.ndarray) for x in X) + self.n_trials_tot, self.n_samples_tot, self.n_input_features_tot = ( + [], + [], + [], + ) + X_tot, Y_tot = [], [] + for x in X: + x, y = self._detect_reshape(x) + X_tot.append(x) + Y_tot.append(y) + self.n_trials_tot.append(self.n_trials_) + self.n_samples_tot.append(self.n_samples_) + self.n_input_features_tot.append(self.n_input_features_) + X = np.concatenate(X_tot, axis=0) + Y = np.concatenate(Y_tot, axis=0) + + self.n_trials_ = sum(self.n_trials_tot) + self.n_samples_ = sum(self.n_samples_tot) + self.n_input_features_ = sum(self.n_input_features_tot) + + return X, Y + + def _return_orig_shape(self, X): + """ + X will be a 2d array of shape (n_samples * n_trials, n_features). + This function will return the original shape of X. + """ + if not hasattr(self, "n_trials_tot"): + X = X.reshape(self.n_trials_, -1, self.n_input_features_) + if X.shape[0] == 1: + X = X[0] + return X + + else: + X_tot = [] + prev_t = 0 + for i in range(len(self.n_trials_tot)): + X_i = X[prev_t : prev_t + self.n_trials_tot[i] * self.n_samples_tot[i]] + X_i = X_i.reshape( + self.n_trials_tot[i], -1, self.n_input_features_tot[i] + ) + X_tot.append(X_i) + prev_t += self.n_trials_tot[i] * self.n_samples_tot[i] + return X_tot + + def fit(self, x, y=None): + raise NotImplementedError + + def predict(self, x): + raise NotImplementedError + + @abstractmethod + def coef_(self): + pass + + @abstractmethod + def state_matrix_(self): + pass + + @abstractmethod + def eigenvectors_(self): + pass + + @abstractmethod + def eigenvalues_(self): + pass + + @abstractmethod + def _compute_phi(self, x_col): + pass + + @abstractmethod + def _compute_psi(self, x_col): + pass + + @abstractmethod + def ur(self): + pass + + @abstractmethod + def unnormalized_modes(self): + pass diff --git a/DSA/pykoopman/src/pykoopman/regression/_base_ensemble.py b/DSA/pykoopman/src/pykoopman/regression/_base_ensemble.py new file mode 100644 index 0000000..53f7f16 --- /dev/null +++ b/DSA/pykoopman/src/pykoopman/regression/_base_ensemble.py @@ -0,0 +1,366 @@ +"""module for handling a ensemble of x-x' pair. + +Manual changes are made to add support to complex numeric data +""" +from __future__ import annotations + +from sklearn.base import BaseEstimator +from sklearn.base import clone +from sklearn.base import TransformerMixin +from sklearn.compose import TransformedTargetRegressor + + +class EnsembleBaseRegressor(TransformedTargetRegressor): + """ + This class serves as a wrapper for PyKoopman regressors that utilize ensemble or + non-consecutive training data. + + `EnsembleBaseRegressor` inherits from `TransformedTargetRegressor` and checks + whether the provided regressor object implements the `fit` and `predict` methods. + + Attributes: + regressor (sklearn.base.BaseEstimator): A regressor object that implements + `fit` and `predict` methods. + func (function): A function to apply to the target `y` before passing it to + the `fit` method. The function must return a 2-dimensional array. + If `func` is `None`, the identity function is used. + inverse_func (function): A function to apply to the prediction of the + regressor. This function is used to return predictions to the same space + as the original training labels. It must return a 2-dimensional array. + + Raises: + AttributeError: If the regressor does not have a callable `fit` or + `predict` method. + ValueError: If both `transformer` and functions `func`/`inverse_func` + are set, or if 'func' is provided while 'inverse_func' is not. + + Note: + This class does not implement the `fit` method on its own, instead, it checks + the methods of the provided regressor object and raises an AttributeError if + the required methods are not present or not callable. It also performs some + pre-processing on the target values `y` before fitting the regressor, and + provides additional checks and warnings for the transformer and inverse + functions. + """ + + def __init__(self, regressor, func, inverse_func): + super().__init__(regressor=regressor, func=func, inverse_func=inverse_func) + if not hasattr(regressor, "fit") or not callable(getattr(regressor, "fit")): + raise AttributeError("regressor does not have a callable fit method") + if not hasattr(regressor, "predict") or not callable( + getattr(regressor, "predict") + ): + raise AttributeError("regressor does not have a callable predict method") + + def fit(self, X, y, **fit_params): + """ + Fits the model according to the given training data. + + Args: + X (array-like or sparse matrix of shape (n_samples, n_features)): + Training vector, where `n_samples` is the number of samples and + `n_features` is the number of features. + y (array-like of shape (n_samples,)): Target values. + **fit_params (dict): Additional parameters passed to the `fit` method of + the underlying regressor. + + Returns: + self: The fitted estimator. + + Raises: + ValueError: If 'transformer' and functions 'func'/'inverse_func' are both + set, or if 'func' is provided while 'inverse_func' is not. + + Note: + This method transforms the target `y` before fitting the regressor and + performs additional checks and warnings for the transformer and inverse + functions. + """ + + # transformers are designed to modify X which is 2d dimensional, we + # need to modify y accordingly. + + self._training_dim = y.ndim + if y.ndim == 1: + y_2d = y.reshape(-1, 1) + else: + y_2d = y + self._fit_transformer(y_2d) + + # transform y and convert back to 1d array if needed + y_trans = self.transformer_.transform(y_2d) + # FIXME: a FunctionTransformer can return a 1D array even when validate + # is set to True. Therefore, we need to check the number of dimension + # first. + # if y_trans.ndim == 2 and y_trans.shape[1] == 1: + # y_trans = y_trans.squeeze(axis=1) + + if self.regressor is None: + from sklearn.linear_model import LinearRegression + + self.regressor_ = LinearRegression() + else: + self.regressor_ = clone(self.regressor) + + self.regressor_.fit(X, y_trans, **fit_params) + + if hasattr(self.regressor_, "feature_names_in_"): + self.feature_names_in_ = self.regressor_.feature_names_in_ + + return self + + def _fit_transformer(self, y): + """ + Checks the transformer and fits it. + + This method creates the default transformer if necessary, fits it, and + performs additional inverse checks on a subset (optional). + + Args: + y (array-like): The target values. + + Raises: + ValueError: If both 'transformer' and functions 'func'/'inverse_func' + are set, or if 'func' is provided while 'inverse_func' is not. + + Note: + The method does not currently pass 'sample_weight' to the transformer. + However, if the transformer starts using 'sample_weight', the code should + be modified accordingly. During the consideration of the 'sample_prop' + feature, this is also a good use case to consider. + """ + if self.transformer is not None and ( + self.func is not None or self.inverse_func is not None + ): + raise ValueError( + "'transformer' and functions 'func'/'inverse_func' cannot both be set." + ) + elif self.transformer is not None: + self.transformer_ = clone(self.transformer) + else: + if self.func is not None and self.inverse_func is None: + raise ValueError( + "When 'func' is provided, 'inverse_func' must also be provided" + ) + self.transformer_ = FunctionTransformer( + func=self.func, + inverse_func=self.inverse_func, + validate=True, + check_inverse=self.check_inverse, + ) + # XXX: sample_weight is not currently passed to the + # transformer. However, if transformer starts using sample_weight, the + # code should be modified accordingly. At the time to consider the + # sample_prop feature, it is also a good use case to be considered. + self.transformer_.fit(y) + # if self.check_inverse: + # idx_selected = slice(None, None, max(1, y.shape[0] // 10)) + # y_sel = _safe_indexing(y, idx_selected) + # y_sel_t = self.transformer_.transform(y_sel) + # if not np.allclose(y_sel, self.transformer_.inverse_transform(y_sel_t)): + # warnings.warn( + # "The provided functions or transformer are" + # " not strictly inverse of each other. If" + # " you are sure you want to proceed regardless" + # ", set 'check_inverse=False'", + # UserWarning, + # ) + + +class FunctionTransformer(TransformerMixin, BaseEstimator): + """Constructs a transformer from an arbitrary callable. + + This class forwards its X (and optionally y) arguments to a user-defined function + or function object and returns the result of this function. This is useful for + stateless transformations such as taking the log of frequencies, doing custom + scaling, etc. + + Note: If a lambda is used as the function, then the resulting transformer will + not be pickleable. + + Attributes: + func (callable): The callable to use for the transformation. This will be + passed the same arguments as transform, with args and kwargs forwarded. + If func is None, then func will be the identity function. + inverse_func (callable): The callable to use for the inverse transformation. + This will be passed the same arguments as inverse transform, with args + and kwargs forwarded. If inverse_func is None, then inverse_func will be + the identity function. + validate (bool): Indicate that the input X array should be checked before + calling func. The default is False. + accept_sparse (bool): Indicate that func accepts a sparse matrix as input. + The default is False. + check_inverse (bool): Whether to check that or func followed by inverse_func + leads to the original inputs. The default is True. + kw_args (dict): Dictionary of additional keyword arguments to pass to func. + inv_kw_args (dict): Dictionary of additional keyword arguments to pass to + inverse_func. + n_input_features_ (int): Number of features seen during fit. Defined only + when validate=True. + feature_names_in_ (ndarray): Names of features seen during fit. Defined only + when validate=True and X has feature names that are all strings. + + Examples: + >>> import numpy as np + >>> from sklearn.preprocessing import FunctionTransformer + >>> transformer = FunctionTransformer(np.log1p) + >>> X = np.array([[0, 1], [2, 3]]) + >>> transformer.transform(X) + array([[0. , 0.6931...], + [1.0986..., 1.3862...]]) + """ + + def __init__( + self, + func=None, + inverse_func=None, + *, + validate=False, + accept_sparse=False, + check_inverse=True, + kw_args=None, + inv_kw_args=None, + ): + """Initialize the FunctionTransformer instance. + + Args: + func (callable, optional): The callable to use for the transformation. + This will be passed the same arguments as transform, with args and + kwargs forwarded. If func is None, then + func will be the identity function. Defaults to None. + inverse_func (callable, optional): The callable to use for the inverse + transformation. This will be passed the same arguments as inverse + transform, with args and kwargs forwarded. If inverse_func is None, then + inverse_func will be the identity function. Defaults to None. + validate (bool, optional): Indicate that the input X array should be + checked before calling func. Defaults to False. + accept_sparse (bool, optional): Indicate that func accepts a sparse matrix + as input. Defaults to False. + check_inverse (bool, optional): Whether to check that func followed by + inverse_func leads to the original inputs. Defaults to True. + kw_args (dict, optional): Dictionary of additional keyword arguments to + pass to func. Defaults to None. + inv_kw_args (dict, optional): Dictionary of additional keyword arguments + to pass to inverse_func. Defaults to None. + """ + self.func = func + self.inverse_func = inverse_func + self.validate = validate + self.accept_sparse = accept_sparse + self.check_inverse = check_inverse + self.kw_args = kw_args + self.inv_kw_args = inv_kw_args + + def _check_input(self, X, *, reset): + """Checks the input X. If validation is enabled, it validates the data. + + Args: + X (array-like): Input data to be checked/validated. + reset (bool): Flag indicating whether to reset the validation. + + Returns: + array-like: The original input data, possibly validated if `validate` + attribute is set to True. + """ + # if self.validate: + # return self._validate_data(X, accept_sparse=self.accept_sparse, + # reset=reset) + return X + + def _check_inverse_transform(self, X): + """Checks if the provided functions are the inverse of each other. + + Selects a subset of X and performs a round trip transformation: forward + transform followed by inverse transform. Raises a warning if the round trip + does not return the original inputs. + + Args: + X (array-like): Input data to be checked for inverse transform consistency. + """ + idx_selected = slice(None, None, max(1, X.shape[0] // 100)) + # X_round_trip = self.inverse_transform(self.transform(X[idx_selected])) + self.inverse_transform(self.transform(X[idx_selected])) + # if not _allclose_dense_sparse(X[idx_selected], X_round_trip): + # warnings.warn( + # "The provided functions are not strictly" + # " inverse of each other. If you are sure you" + # " want to proceed regardless, set" + # " 'check_inverse=False'.", + # UserWarning, + # ) + + def fit(self, X, y=None): + """Fits transformer by checking X. + + If ``validate`` is ``True``, ``X`` will be checked. Also checks if the provided + functions are the inverse of each other if `check_inverse` is set to True. + + Args: + X (array-like): The data to fit. Shape should be (n_samples, n_features). + y (None, optional): Ignored. Not used, present here for API consistency by + convention. + + Returns: + FunctionTransformer: The fitted transformer. + """ + X = self._check_input(X, reset=True) + if self.check_inverse and not (self.func is None or self.inverse_func is None): + self._check_inverse_transform(X) + return self + + def transform(self, X): + """Transforms X using the forward function. + + Args: + X (array-like): The data to transform. Shape should be (n_samples, + n_features). + + Returns: + array-like: Transformed data with same shape as input. + """ + X = self._check_input(X, reset=False) + return self._transform(X, func=self.func, kw_args=self.kw_args) + + def inverse_transform(self, X): + """Transforms X using the inverse function. + + Args: + X (array-like): The data to inverse transform. Shape should be + (n_samples, n_features). + + Returns: + array-like: Inverse transformed data with the same shape as input. + """ + # if self.validate: + # X = check_array(X, accept_sparse=self.accept_sparse) + return self._transform(X, func=self.inverse_func, kw_args=self.inv_kw_args) + + def _transform(self, X, func=None, kw_args=None): + """Applies the given function to the data X. + + Args: + X (array-like): The data to transform. Shape should be (n_samples, + n_features). + func (callable, optional): The function to apply. If None, identity + function is used. + kw_args (dict, optional): Additional arguments to pass to the function. + + Returns: + array-like: Transformed data with the same shape as input. + """ + if func is None: + func = _identity + + return func(X, **(kw_args if kw_args else {})) + + def __sklearn_is_fitted__(self): + """Return True since FunctionTransfomer is stateless.""" + return True + + def _more_tags(self): + return {"no_validation": not self.validate, "stateless": True} + + +def _identity(X): + """The identity function.""" + return X diff --git a/DSA/pykoopman/src/pykoopman/regression/_dmd.py b/DSA/pykoopman/src/pykoopman/regression/_dmd.py new file mode 100644 index 0000000..d447571 --- /dev/null +++ b/DSA/pykoopman/src/pykoopman/regression/_dmd.py @@ -0,0 +1,360 @@ +"""module for dmd""" +# from warnings import warn +from __future__ import annotations + +import numpy as np +from pydmd import DMDBase +from pydmd.dmdbase import DMDTimeDict +from pydmd.utils import compute_svd +from pydmd.utils import compute_tlsq +from scipy.linalg import eig +from scipy.linalg import sqrtm +from sklearn.utils.validation import check_is_fitted + +from ._base import BaseRegressor + + +class PyDMDRegressor(BaseRegressor): + """ + PyDMDRegressor is a wrapper for `pydmd` regressors. + + This class provides a wrapper for the `pydmd` regressor. The details about + `pydmd` can be found in the reference: + + Demo, N., Tezzele, M., & Rozza, G. (2018). PyDMD: Python dynamic mode decomposition. + Journal of Open Source Software, 3(22), 530. + `_ + + Args: + regressor (DMDBase): A regressor instance from DMDBase in `pydmd`. + tikhonov_regularization (bool or None, optional): Indicates if Tikhonov + regularization should be applied. Defaults to None. + + Attributes: + tlsq_rank (int): Rank for truncation in TLSQ method. If 0, no noise reduction + is computed. If positive, it is used for SVD truncation. + svd_rank (int): Rank for truncation. If 0, optimal rank is computed and used + for truncation. If positive integer, it is used for truncation. If float + between 0 and 1, the rank is the number of the biggest singular values + that are needed to reach the 'energy' specified by `svd_rank`. If -1, no + truncation is computed. + forward_backward (bool): If True, the low-rank operator is computed like in + fbDMD. + tikhonov_regularization (bool or None, optional): If None, no regularization + is applied. If float, it is used as the Tikhonov regularization parameter. + flag_xy (bool): If True, the regressor is operating on multiple trajectories + instead of just one. + n_samples_ (int): Number of samples. + n_input_features_ (int): Number of features, i.e., the dimension of phi. + _unnormalized_modes (ndarray): Raw DMD V with each column as one DMD mode. + _state_matrix_ (ndarray): DMD state transition matrix. + _reduced_state_matrix_ (ndarray): Reduced DMD state transition matrix. + _eigenvalues_ (ndarray): Identified Koopman lambda. + _eigenvectors_ (ndarray): Identified Koopman eigenvectors. + _coef_ (ndarray): Weight vectors of the regression problem. Corresponds to + either [A] or [A,B]. + C (ndarray): Matrix that maps psi to the input features. + """ + + def __init__(self, regressor, tikhonov_regularization=None): + """ + Initializes a PyDMDRegressor instance. + + Args: + regressor (DMDBase): A regressor instance from DMDBase in `pydmd`. + tikhonov_regularization (bool or None, optional): Indicates if Tikhonov + regularization should be applied. Defaults to None. + + Raises: + ValueError: If regressor is not a subclass of DMDBase from pydmd. + """ + if not isinstance(regressor, DMDBase): + raise ValueError("regressor must be a subclass of DMDBase from pydmd.") + self.regressor = regressor + # super(PyDMDRegressor, self).__init__(regressor) + self.tlsq_rank = regressor._tlsq_rank + self.svd_rank = regressor._Atilde._svd_rank + self.forward_backward = regressor._Atilde._forward_backward + self.tikhonov_regularization = tikhonov_regularization + self.flag_xy = False + self._ur = None + + def fit(self, x, y=None, dt=1): + """ + Fit the PyDMDRegressor model according to the given training data. + + Args: + x (np.ndarray): Measurement data input. Should be of shape (n_samples, + n_features). + Can also be of shape (n_trials, n_samples, n_features), where + n_trials is the number of independent trials. + Can also be of a list of arrays, where each array is a trajectory + or a 3d array of trajectories. + + y (np.ndarray, optional): Measurement data output to be fitted. Should be + of shape (n_samples, n_features). Defaults to None. + dt (float, optional): Time interval between `x` and `y`. Defaults to 1. + + Returns: + self : Returns the instance itself. + """ + + if y is None: + # single trajectory + self.flag_xy = False + X, Y = self._detect_reshape(x) + X = X.T + Y = Y.T + + else: + # multiple segments of trajectories + self.flag_xy = True + X, _ = self._detect_reshape(x, offset=False) + Y, _ = self._detect_reshape(y, offset=False) + X = X.T + Y = Y.T + + X, Y = compute_tlsq(X, Y, self.tlsq_rank) + U, s, V = compute_svd(X, self.svd_rank) + + if self.tikhonov_regularization is not None: + _norm_X = np.linalg.norm(X) + else: + _norm_X = 0 + + atilde = self._least_square_operator( + U, s, V, Y, self.tikhonov_regularization, _norm_X + ) + if self.forward_backward: + # b stands for "backward" + bU, bs, bV = compute_svd(Y, svd_rank=len(s)) + atilde_back = self._least_square_operator( + bU, bs, bV, X, self.tikhonov_regularization, _norm_X + ) + atilde = sqrtm(atilde @ np.linalg.inv(atilde_back)) + + # - V, lamda, eigenvectors + self._coef_ = atilde + self._state_matrix_ = atilde + [self._eigenvalues_, self._eigenvectors_] = eig(self._state_matrix_) + + # self._coef_ = U @ atilde @ U.conj().T + # self._state_matrix_ = self._coef_ + # self._reduced_state_matrix_ = atilde + # [self._eigenvalues_, self._eigenvectors_] = eig(self._reduced_state_matrix_) + self._ur = U + self._unnormalized_modes = self._ur @ self._eigenvectors_ + + self._tmp_compute_psi = np.linalg.pinv(self.unnormalized_modes) + + # self.C = np.linalg.inv(self._eigenvectors_) @ U.conj().T + # self._modes_ = U.dot(self._eigenvectors_) + + return self + + def predict(self, x): + """ + Predict the future values based on the input measurement data. + + Args: + x (np.ndarray): Measurement data upon which to base the prediction. + Should be of shape (n_samples, n_features). + Can also be of shape (n_trials, n_samples, n_features), where + n_trials is the number of independent trials. + Returns: + np.ndarray: Predicted values of `x` one timestep in the future. The shape + is (n_samples, n_features). + """ + # if isinstance(x, list): + # raise ValueError("list of arrays is not supported yet") + x, _ = self._detect_reshape(x, offset=False) + if x.ndim == 1: + x = x.reshape(1, -1) + check_is_fitted(self, "coef_") + y = np.linalg.multi_dot([self.ur, self._coef_, self.ur.conj().T, x.T]).T + # reshape y back to the original shape + y = self._return_orig_shape(y) + + return y + + def _compute_phi(self, x_col): + """ + Compute the `phi(x)` value given `x`. + + Args: + x_col (np.ndarray): Input data, if one-dimensional it will be reshaped + to (-1, 1). + + Returns: + np.ndarray: Computed `phi(x)` value. + """ + if x_col.ndim == 1: + x_col = x_col.reshape(-1, 1) + phi = self.ur.T @ x_col + return phi + + def _compute_psi(self, x_col): + """ + Compute the `psi(x)` value given `x`. + + Args: + x_col (np.ndarray): Input data, if one-dimensional it will be reshaped + to (-1, 1). + + Returns: + np.ndarray: Value of Koopman eigenfunction psi at x. + """ + + # compute psi - one column if x is a row + if x_col.ndim == 1: + x_col = x_col.reshape(-1, 1) + psi = self._tmp_compute_psi @ x_col + return psi + + def _set_initial_time_dictionary(self, time_dict): + """ + Sets the initial values for `time_dict` and `original_time`. + Typically called in `fit()` and not used again afterwards. + + Args: + time_dict (dict): Initial time dictionary for this DMD instance. Must + contain the keys "t0", "tend", and "dt". + + Raises: + ValueError: If the time_dict does not contain the keys "t0", "tend" and + "dt" or if it contains more than these keys. + """ + + if not ("t0" in time_dict and "tend" in time_dict and "dt" in time_dict): + raise ValueError( + 'time_dict must contain the keys "t0", ' '"tend" and "dt".' + ) + if len(time_dict) > 3: + raise ValueError( + 'time_dict must contain only the keys "t0", ' '"tend" and "dt".' + ) + + self._original_time = DMDTimeDict(dict(time_dict)) + self._dmd_time = DMDTimeDict(dict(time_dict)) + + def _least_square_operator(self, U, s, V, Y, tikhonov_regularization, _norm_X): + """ + Calculates the least square estimation 'A' using the provided parameters. + + Args: + U (numpy.ndarray): Left singular vectors, shape (n_features, svd_rank). + s (numpy.ndarray): Singular values, shape (svd_rank, ). + V (numpy.ndarray): Right singular vectors, shape (n_features, svd_rank). + Y (numpy.ndarray): Measurement data for prediction, shape (n_samples, + n_features). + tikhonov_regularization (bool or NoneType): Tikhonov parameter for + regularization. If `None`, no regularization is applied, if `float`, + it is used as the :math:`\\lambda` tikhonov parameter. + _norm_X (numpy.ndarray): Norm of `X` for Tikhonov regularization, shape + (n_samples, n_features). + + Returns: + numpy.ndarray: The least square estimation 'A', shape (svd_rank, svd_rank). + """ + + if tikhonov_regularization is not None: + s = (s**2 + tikhonov_regularization * _norm_X) * np.reciprocal(s) + A = np.linalg.multi_dot([U.T.conj(), Y, V]) * np.reciprocal(s) + return A + + @property + def coef_(self): + """ + The weight vectors of the regression problem. + + This method checks if the regressor is fitted before returning the coefficient. + + Returns: + numpy.ndarray: The coefficient matrix. + + Raises: + NotFittedError: If the regressor is not fitted yet. + """ + check_is_fitted(self, "_coef_") + return self._coef_ + + @property + def state_matrix_(self): + """ + The DMD state transition matrix. + + This method checks if the regressor is fitted before returning the state matrix. + + Returns: + numpy.ndarray: The state transition matrix. + + Raises: + NotFittedError: If the regressor is not fitted yet. + """ + check_is_fitted(self, "_state_matrix_") + return self._state_matrix_ + + @property + def eigenvalues_(self): + """ + The identified Koopman eigenvalues. + + This method checks if the regressor is fitted before returning the eigenvalues. + + Returns: + numpy.ndarray: The Koopman eigenvalues. + + Raises: + NotFittedError: If the regressor is not fitted yet. + """ + check_is_fitted(self, "_eigenvalues_") + return self._eigenvalues_ + + @property + def eigenvectors_(self): + """ + The identified Koopman eigenvectors. + + This method checks if the regressor is fitted before returning the eigenvectors. + + Returns: + numpy.ndarray: The Koopman eigenvectors. + + Raises: + NotFittedError: If the regressor is not fitted yet. + """ + check_is_fitted(self, "_eigenvectors_") + return self._eigenvectors_ + + @property + def unnormalized_modes(self): + """ + The raw DMD V with each column as one DMD mode. + + This method checks if the regressor is fitted before returning the unnormalized + modes. + + Returns: + numpy.ndarray: The unnormalized modes. + + Raises: + NotFittedError: If the regressor is not fitted yet. + """ + check_is_fitted(self, "_unnormalized_modes") + return self._unnormalized_modes + + @property + def ur(self): + """ + The left singular vectors 'U'. + + This method checks if the regressor is fitted before returning 'U'. + + Returns: + numpy.ndarray: The left singular vectors 'U'. + + Raises: + NotFittedError: If the regressor is not fitted yet. + """ + check_is_fitted(self, "_ur") + return self._ur diff --git a/DSA/pykoopman/src/pykoopman/regression/_dmdc.py b/DSA/pykoopman/src/pykoopman/regression/_dmdc.py new file mode 100644 index 0000000..9de8a5d --- /dev/null +++ b/DSA/pykoopman/src/pykoopman/regression/_dmdc.py @@ -0,0 +1,468 @@ +"""module for dmd with control""" +from __future__ import annotations + +import numpy as np +from sklearn.utils.validation import check_is_fitted + +from ._base import BaseRegressor + + +class DMDc(BaseRegressor): + """ + Implements Dynamic Mode Decomposition with Control (DMDc) regressor. + + This class provides an implementation for DMDc, a variant of Dynamic Mode + Decomposition, which is a dimensionality reduction technique used to analyze + dynamical systems. The goal of DMDc is to compute matrices A and B that satisfy + the equation x' = Ax + Bu, where x' is the time-shifted state w.r.t. x and u is + the control input. + + Attributes: + svd_rank (int): Rank of SVD for the input space, i.e., the space of `X` and + input `U`. This determines the dimensionality of the projected state and + control matrices. Defaults to None. + svd_output_rank (int): Rank of SVD for the output space, i.e., the space of `Y`. + Defaults to None. + input_control_matrix (numpy.ndarray): The known input control matrix B. Defaults + to None. + n_samples_ (int): Total number of one step evolution samples. + n_input_features_ (int): Dimension of input features. + n_control_features_ (int): Dimension of input control signal. + coef_ (numpy.ndarray): Weight vectors of the regression problem. Corresponds + to either [A] or [A,B]. + state_matrix_ (numpy.ndarray): Identified state transition matrix A of the + underlying system. + control_matrix_ (numpy.ndarray): Identified control matrix B of the underlying + system. + reduced_state_matrix_ (numpy.ndarray): Reduced state transition matrix. + reduced_control_matrix_ (numpy.ndarray): Reduced control matrix. + eigenvalues_ (numpy.ndarray): DMD lamda. + unnormalized_modes (numpy.ndarray): DMD V. + projection_matrix_ (numpy.ndarray): Projection matrix into low-dimensional + subspace. + projection_matrix_output_ (numpy.ndarray): Projection matrix into + low-dimensional subspace. + eigenvectors_ (numpy.ndarray): DMD eigenvectors. + + Example: + >>> import numpy as np + >>> import pykoopman as pk + >>> A = np.matrix([[1.5, 0],[0, 0.1]]) + >>> B = np.matrix([[1],[0]]) + >>> x0 = np.array([4,7]) + >>> u = np.array([-4, -2, -1, -0.5, 0, 0.5, 1, 3, 5]) + >>> n = len(u)+1 + >>> x = np.zeros([n,len(x0)]) + >>> x[0,:] = x0 + >>> for i in range(n-1): + >>> x[i+1,:] = A.dot(x[i,:]) + B.dot(u[np.newaxis,i]) + >>> X1 = x[:-1,:] + >>> X2 = x[1:,:] + >>> C = u[:,np.newaxis] + >>> DMDc = pk.regression.DMDc(svd_rank=3, input_control_matrix=B) + >>> model = pk.Koopman(regressor=DMDc) + >>> model.fit(x,C) + >>> Aest = model.A + >>> Best = model.B + >>> print(Aest) + >>> np.allclose(A,Aest) + [[ 1.50000000e+00 -1.36609474e-17] + [-1.58023594e-17 1.00000000e-01]] + True + """ + + def __init__(self, svd_rank=None, svd_output_rank=None, input_control_matrix=None): + """ + Initialize a DMDc class object. + + Args: + svd_rank (int, optional): Rank of SVD for the input space. This determines + the dimensionality of the projected state and control matrices. + Defaults to None. + svd_output_rank (int, optional): Rank of SVD for the output space. + Defaults to None. + input_control_matrix (numpy.ndarray, optional): The known input control + matrix B. Defaults to None. + + Raises: + ValueError: If svd_rank is not an integer. + ValueError: If svd_output_rank is not an integer. + ValueError: If input_control_matrix is not a numpy array. + """ + self.svd_rank = svd_rank + self.svd_output_rank = svd_output_rank + self._input_control_matrix = input_control_matrix + + def fit(self, x, y=None, u=None, dt=None): + """ + Fit the DMDc model to the provided data. + + Parameters + ---------- + x : numpy.ndarray, shape (n_samples, n_features) + Measurement data to be fit. + Can be of shape (n_samples, n_features), or (n_trials, n_samples, + n_features), where + n_trials is the number of independent trials. + Can also be of a list of arrays, where each array is a trajectory + or a 2- or 3-d array of trajectories, provided they have the + same last dimension. + + y : numpy.ndarray, shape (n_samples, n_features), default=None + Measurement data output to be fitted. + + u : numpy.ndarray, shape (n_samples, n_control_features), optional, default=None + Time series of external actuation/control. + + dt : float, optional + Time interval between `X` and `Y` + + Returns + ------- + self: returns a fitted ``DMDc`` instance + """ + + if y is None: + X1, X2 = self._detect_reshape(x) + else: + X1, _ = self._detect_reshape(x, offset=False) + X2, _ = self._detect_reshape(y, offset=False) + if u is not None: + offset = u.shape[0] > X1.shape[0] + u, _ = self._detect_reshape(u, offset=offset) + self.n_control_features_ = u.shape[1] + self.n_input_features_ = X1.shape[1] + C = u + + self.n_control_features_ = C.shape[1] + + if self.svd_rank is None: + self.svd_rank = self.n_input_features_ + self.n_control_features_ + if self.svd_output_rank is None: + self.svd_output_rank = self.n_input_features_ + else: + if self.svd_output_rank is None: + self.svd_output_rank = self.svd_rank + + rout = self.svd_output_rank + r = self.svd_rank + + if self._input_control_matrix is None: + self._fit_unknown_B(X1, X2, C, r, rout) + else: + self._fit_known_B(X1, X2, C, r) + + return self + + def _fit_unknown_B(self, X1, X2, C, r, rout): + """ + Fits the DMDc model when the control matrix B is unknown. It computes + the state matrix `A` and control matrix `B` using the Dynamic Mode + Decomposition with control (DMDc) algorithm. + + Args: + X1 (numpy.ndarray): The state matrix at time t. + X2 (numpy.ndarray): The state matrix at time t+1. + C (numpy.ndarray): The control input matrix. + r (int): Rank for truncation of singular value decomposition. + rout (int): Rank for truncation of singular value decomposition on X2 + transpose. + + Returns: + None. Updates the instance variables _state_matrix_, _control_matrix_, + _coef_, _eigenvectors_, _eigenvalues_, _ur, _tmp_compute_psi, + _unnormalized_modes. + + Raises: + ValueError: If the dimensions of X1, X2, and C are not compatible. + """ + + assert rout <= r + Omega = np.vstack([X1.T, C.T]) + # SVD of input space + U, s, Vh = np.linalg.svd(Omega, full_matrices=False) + Ur = U[:, 0:r] + Sr = np.diag(s[0:r]) + Vr = Vh[0:r, :].T + + Uhat, _, _ = np.linalg.svd(X2.T, full_matrices=False) + Uhatr = Uhat[:, 0:rout] + + U1 = Ur[: self.n_input_features_, :] + U2 = Ur[self.n_input_features_ :, :] + + # this is reduced A_r + self._state_matrix_ = Uhatr.T @ X2.T @ Vr @ np.linalg.inv(Sr) @ U1.T @ Uhatr + self._control_matrix_ = Uhatr.T @ X2.T @ Vr @ np.linalg.inv(Sr) @ U2.T + + # self._state_matrix_ = self._reduced_state_matrix_ + # self._control_matrix_ = self._reduced_control_matrix_ + # self._state_matrix_ = Uhatr @ self._reduced_state_matrix_ @ Uhatr.T + # self._control_matrix_ = Uhatr @ self._reduced_control_matrix_ + + # pack [A full, B full] as self.coef_ + self._coef_ = np.concatenate( + (self._state_matrix_, self._control_matrix_), axis=1 + ) + + # self._projection_matrix_ = Ur + # self._projection_matrix_output_ = Uhatr + + # eigenvectors, lamda + [self._eigenvalues_, self._eigenvectors_] = np.linalg.eig(self._state_matrix_) + + # Koopman modes V + self._unnormalized_modes = Uhatr @ self._eigenvectors_ + self._ur = Uhatr + self._tmp_compute_psi = np.linalg.inv(self._eigenvectors_) @ Uhatr.T + + def _fit_known_B(self, X1, X2, C, r): + """ + Fits the DMDc model when the control matrix B is known. It computes + the state matrix `A` using the Dynamic Mode Decomposition with control + (DMDc) algorithm. + + Args: + X1 (numpy.ndarray): The state matrix at time t. + X2 (numpy.ndarray): The state matrix at time t+1. + C (numpy.ndarray): The control input matrix. + r (int): Rank for truncation of singular value decomposition. + + Returns: + None. Updates the instance variables _state_matrix_, _coef_, + _eigenvectors_, _eigenvalues_, _ur, _tmp_compute_psi, _unnormalized_modes. + + Raises: + ValueError: If the dimensions of X1, X2, and C are not compatible. + """ + if self.n_input_features_ in self._input_control_matrix.shape is False: + raise TypeError("Control vector/matrix B has wrong shape.") + if self._input_control_matrix.shape[1] == self.n_input_features_: + self._input_control_matrix = self._input_control_matrix.T + if self._input_control_matrix.shape[1] != self.n_control_features_: + raise TypeError( + "The control matrix B must have the same " + "number of inputs as the control variable u." + ) + + U, s, Vh = np.linalg.svd(X1.T, full_matrices=False) + Ur = U[:, :r] + sr = s[:r] + Vhr = Vh[:r, :] + + self._state_matrix_ = np.linalg.multi_dot( + [ + Ur.T, + X2.T - self._input_control_matrix @ C.T, + Vhr.T, + np.diag(np.reciprocal(sr)), + ] + ) + self._control_matrix_ = Ur.T @ self._input_control_matrix + # self._state_matrix_ = Ur @ self._reduced_state_matrix_ @ Ur.T + + self._coef_ = np.concatenate( + (self._state_matrix_, self.control_matrix_), axis=1 + ) + # self._coef_ = Ur @ self._state_matrix_ @ Ur.T + # self._projection_matrix_ = Ur + # self._projection_matrix_output_ = Ur + + # Compute , eigenvectors, lamda + [self._eigenvalues_, self._eigenvectors_] = np.linalg.eig(self._state_matrix_) + + # Koopman V + self._unnormalized_modes = Ur @ self._eigenvectors_ + self._ur = Ur + self._tmp_compute_psi = np.linalg.inv(self._eigenvectors_) @ Ur.T + + # compute psi + # self.C = np.linalg.inv(self._eigenvectors_) @ Ur.T + + def predict(self, x, u): + """ + Predicts the future state of the system based on the current state and the + current value of control input, using the fitted DMDc model. + + Args: + x (numpy.ndarray): The current state of the system. + u (numpy.ndarray): The current value of the input. + + Returns: + numpy.ndarray: The predicted future state of the system. + + Raises: + NotFittedError: If the model is not fitted, raise this error to prevent + misuse of the model. + """ + check_is_fitted(self, "coef_") + if x.ndim == 1: + x = x.reshape(1, -1) + if u.ndim == 1: + u = u.reshape(1, -1) + u, _ = self._detect_reshape(u, offset=False) + x, _ = self._detect_reshape(x, offset=False) + # y = self.coef_ @ np.vstack([x.reshape(1, -1).T, u.reshape(1, -1).T]) + y = ( + x @ self.ur @ self.state_matrix_.T @ self.ur.T + + u @ self.control_matrix_.T @ self.ur.T + ) + # y = x @ self.state_matrix_.T + u @ self.control_matrix_.T + # y = y.T + y = self._return_orig_shape(y) + return y + + def _compute_phi(self, x_col): + """ + Returns the transformed matrix `phi(x)` given `x`. + + The method takes a column vector or a 1-D numpy array and computes its + transformation using the `_ur` matrix. If the input `x_col` is a 1-D array, + it reshapes it into a column vector before the computation. + + Args: + x_col (numpy.ndarray): A column vector or a 1-D numpy array + representing `x`. + + Returns: + numpy.ndarray: The transformed matrix `phi(x)`. + """ + if x_col.ndim == 1: + x_col = x_col.reshape(-1, 1) + phi = self._ur.T @ x_col + return phi + + def _compute_psi(self, x_col): + """ + Returns `psi(x)` given `x` + + Args: + x: numpy.ndarray, shape (n_samples, n_features) + Measurement data upon which to compute psi values. + + Returns + phi : numpy.ndarray, shape (n_samples, n_input_features_) value of + Koopman psi at x + """ + + # compute psi - one column if x is a row + if x_col.ndim == 1: + x_col = x_col.reshape(-1, 1) + psi = self._tmp_compute_psi @ x_col + return psi + + @property + def coef_(self): + """ + The weight vectors of the regression problem. + + This method checks if the regressor is fitted before returning the coefficient. + + Returns: + numpy.ndarray: The coefficient matrix. + + Raises: + NotFittedError: If the regressor is not fitted yet. + """ + check_is_fitted(self, "_coef_") + return self._coef_ + + @property + def state_matrix_(self): + """ + The DMD state transition matrix. + + This method checks if the regressor is fitted before returning the state matrix. + + Returns: + numpy.ndarray: The state transition matrix. + + Raises: + NotFittedError: If the regressor is not fitted yet. + """ + check_is_fitted(self, "_state_matrix_") + return self._state_matrix_ + + @property + def control_matrix_(self): + check_is_fitted(self, "_control_matrix_") + return self._control_matrix_ + + # @property + # def reduced_state_matrix_(self): + # check_is_fitted(self, "_reduced_state_matrix_") + # return self._reduced_state_matrix_ + # + # @property + # def reduced_control_matrix_(self): + # check_is_fitted(self, "_reduced_control_matrix_") + # return self._reduced_control_matrix_ + + @property + def eigenvectors_(self): + """ + The identified Koopman eigenvectors. + + This method checks if the regressor is fitted before returning the eigenvectors. + + Returns: + numpy.ndarray: The Koopman eigenvectors. + + Raises: + NotFittedError: If the regressor is not fitted yet. + """ + check_is_fitted(self, "_eigenvectors_") + return self._eigenvectors_ + + @property + def eigenvalues_(self): + """ + The identified Koopman eigenvalues. + + This method checks if the regressor is fitted before returning the eigenvalues. + + Returns: + numpy.ndarray: The Koopman eigenvalues. + + Raises: + NotFittedError: If the regressor is not fitted yet. + """ + check_is_fitted(self, "_eigenvalues_") + return self._eigenvalues_ + + @property + def unnormalized_modes(self): + """ + The raw DMD V with each column as one DMD mode. + + This method checks if the regressor is fitted before returning the unnormalized + modes. + + Returns: + numpy.ndarray: The unnormalized modes. + + Raises: + NotFittedError: If the regressor is not fitted yet. + """ + check_is_fitted(self, "_unnormalized_modes") + return self._unnormalized_modes + + @property + def ur(self): + """ + The left singular vectors 'U'. + + This method checks if the regressor is fitted before returning 'U'. + + Returns: + numpy.ndarray: The left singular vectors 'U'. + + Raises: + NotFittedError: If the regressor is not fitted yet. + """ + check_is_fitted(self, "_ur") + return self._ur + + @property + def input_control_matrix(self): + return self._input_control_matrix diff --git a/DSA/pykoopman/src/pykoopman/regression/_edmd.py b/DSA/pykoopman/src/pykoopman/regression/_edmd.py new file mode 100644 index 0000000..409028b --- /dev/null +++ b/DSA/pykoopman/src/pykoopman/regression/_edmd.py @@ -0,0 +1,248 @@ +"""module for extended dmd""" +# from warnings import warn +from __future__ import annotations + +import numpy as np +import scipy +from pydmd.dmdbase import DMDTimeDict +from pydmd.utils import compute_svd +from pydmd.utils import compute_tlsq +from sklearn.utils.validation import check_is_fitted + +from ._base import BaseRegressor + + +class EDMD(BaseRegressor): + """Extended DMD (EDMD) regressor. + + Aims to determine the system matrices A,C that satisfy y' = Ay and x = Cy, + where y' is the time-shifted observable with y0 = phi(x0). C is the measurement + matrix that maps back to the state. + + The objective functions, \\|Y'-AY\\|_F, are minimized using least-squares regression + and singular value decomposition. + + See the following reference for more details: + `M.O. Williams, I.G. Kevrekidis, C.W. Rowley + "A Data–Driven Approximation of the Koopman Operator: + Extending Dynamic Mode Decomposition." + Journal of Nonlinear Science, Vol. 25, 1307-1346, 2015. + `_ + + Attributes: + _coef_ (numpy.ndarray): Weight vectors of the regression problem. Corresponds + to either [A] or [A,B]. + _state_matrix_ (numpy.ndarray): Identified state transition matrix A of the + underlying system. + _eigenvalues_ (numpy.ndarray): Identified Koopman lambda. + _eigenvectors_ (numpy.ndarray): Identified Koopman eigenvectors. + _unnormalized_modes_ (numpy.ndarray): Identified Koopman eigenvectors. + n_samples_ (int): Number of samples. + n_input_features_ (int): Number of input features. + C (numpy.ndarray): Matrix that maps psi to the input features. + """ + + def __init__(self, svd_rank=1.0, tlsq_rank=0): + """Initialize the EDMD regressor. + + Args: + svd_rank (float): Rank parameter for singular value decomposition. + Default is 1.0. + tlsq_rank (int): Rank parameter for total least squares. Default is 0. + """ + self.svd_rank = svd_rank + self.tlsq_rank = tlsq_rank + + def fit(self, x, y=None, dt=None): + """Fit the EDMD regressor to the given data. + + Args: + x (numpy.ndarray): Measurement data to be fit. + Can be of shape (n_samples, n_features), or (n_trials, n_samples, + n_features), where n_trials is the number of independent trials. + Can also be of a list of arrays, where each array is a trajectory + or a 2- or 3-d array of trajectories, provided they have the + same last dimension. + y (numpy.ndarray, optional): Time-shifted measurement data to be fit. + Defaults to None. + dt (scalar, optional): Discrete time-step. Defaults to None. + + Returns: + self: Fitted EDMD instance. + """ + if y is None: + X1, X2 = self._detect_reshape(x) + else: + X1, _ = self._detect_reshape(x, offset=False) + X2, _ = self._detect_reshape(y, offset=False) + + # perform SVD + X1T, X2T = compute_tlsq(X1.T, X2.T, self.tlsq_rank) + U, s, V = compute_svd(X1T, self.svd_rank) + + # X1, X2 are row-wise data, so there is a transpose in the end. + self._coef_ = U.conj().T @ X2T @ V @ np.diag(np.reciprocal(s)) + # self._coef_ = np.linalg.lstsq(X1, X2)[0].T # [0:Nlift, 0:Nlift] + self._state_matrix_ = self._coef_ + [self._eigenvalues_, self._eigenvectors_] = scipy.linalg.eig(self.state_matrix_) + # self._ur = np.eye(self.n_input_features_) + self._ur = U + # self._unnormalized_modes = self._eigenvectors_ + self._unnormalized_modes = self._ur @ self._eigenvectors_ + + # np.linalg.pinv(self._unnormalized_modes) + self._tmp_compute_psi = np.linalg.inv(self._eigenvectors_) @ self._ur.conj().T + + return self + + def predict(self, x): + """Predict the next timestep based on the given data. + + Args: + x (numpy.ndarray): Measurement data upon which to base prediction. + + Returns: + y (numpy.ndarray): Prediction of x one timestep in the future. + """ + check_is_fitted(self, "coef_") + x, _ = self._detect_reshape(x, offset=False) + y = x @ self.ur.conj() @ self.state_matrix_.T @ self.ur.T + y = self._return_orig_shape(y) + return y + + def _compute_phi(self, x_col): + """Compute phi(x) given x. + + Args: + x_col (numpy.ndarray): Input data x. + + Returns: + phi (numpy.ndarray): Value of phi(x). + """ + if x_col.ndim == 1: + x_col = x_col.reshape(-1, 1) + phi = self._ur.conj().T @ x_col + return phi + + def _compute_psi(self, x_col): + """Compute psi(x) given x. + + Args: + x_col (numpy.ndarray): Input data x. + + Returns: + psi (numpy.ndarray): Value of psi(x). + """ + # compute psi - one column if x is a row + if x_col.ndim == 1: + x_col = x_col.reshape(-1, 1) + psi = self._tmp_compute_psi @ x_col + return psi + + def _set_initial_time_dictionary(self, time_dict): + """Set the initial values for the class fields time_dict and original_time. + + Args: + time_dict (dict): Initial time dictionary for this DMD instance. + """ + if not ("t0" in time_dict and "tend" in time_dict and "dt" in time_dict): + raise ValueError('time_dict must contain the keys "t0", "tend" and "dt".') + if len(time_dict) > 3: + raise ValueError( + 'time_dict must contain only the keys "t0", "tend" and "dt".' + ) + + self._original_time = DMDTimeDict(dict(time_dict)) + self._dmd_time = DMDTimeDict(dict(time_dict)) + + @property + def coef_(self): + """ + Weight vectors of the regression problem. Corresponds to either [A] or + [A,B]. + + """ + check_is_fitted(self, "_coef_") + return self._coef_ + + @property + def state_matrix_(self): + """ + The EDMD state transition matrix. + + This method checks if the regressor is fitted before returning the state matrix. + + Returns: + numpy.ndarray: The state transition matrix. + + Raises: + NotFittedError: If the regressor is not fitted yet. + """ + check_is_fitted(self, "_state_matrix_") + return self._state_matrix_ + + @property + def eigenvalues_(self): + """ + The identified Koopman eigenvalues. + + This method checks if the regressor is fitted before returning the eigenvalues. + + Returns: + numpy.ndarray: The Koopman eigenvalues. + + Raises: + NotFittedError: If the regressor is not fitted yet. + """ + check_is_fitted(self, "_eigenvalues_") + return self._eigenvalues_ + + @property + def eigenvectors_(self): + """ + The identified Koopman eigenvectors. + + This method checks if the regressor is fitted before returning the eigenvectors. + + Returns: + numpy.ndarray: The Koopman eigenvectors. + + Raises: + NotFittedError: If the regressor is not fitted yet. + """ + check_is_fitted(self, "_eigenvectors_") + return self._eigenvectors_ + + @property + def unnormalized_modes(self): + """ + The raw EDMD V with each column as one EDMD mode. + + This method checks if the regressor is fitted before returning the unnormalized + modes. Note that this will combined with the measurement matrix from the + observer to give you the true Koopman modes + + Returns: + numpy.ndarray: The unnormalized modes. + + Raises: + NotFittedError: If the regressor is not fitted yet. + """ + check_is_fitted(self, "_unnormalized_modes") + return self._unnormalized_modes + + @property + def ur(self): + """ + The left singular vectors 'U'. + + This method checks if the regressor is fitted before returning 'U'. + + Returns: + numpy.ndarray: The left singular vectors 'U'. + + Raises: + NotFittedError: If the regressor is not fitted yet. + """ + check_is_fitted(self, "_ur") + return self._ur diff --git a/DSA/pykoopman/src/pykoopman/regression/_edmdc.py b/DSA/pykoopman/src/pykoopman/regression/_edmdc.py new file mode 100644 index 0000000..1826a65 --- /dev/null +++ b/DSA/pykoopman/src/pykoopman/regression/_edmdc.py @@ -0,0 +1,239 @@ +"""module for extended dmd with control""" +from __future__ import annotations + +import numpy as np +from sklearn.utils.validation import check_is_fitted + +from ._base import BaseRegressor + +# TODO: add support for time delay observables, so we will +# have n_consumption_. + + +class EDMDc(BaseRegressor): + """Module for Extended DMD with control (EDMDc) regressor. + + Aims to determine the system matrices A, B, C that satisfy y' = Ay + Bu and x = Cy, + where y' is the time-shifted observable with y0 = phi(x0) and u is the control + input. B and C are the unknown control and measurement matrices, respectively. + + The objective functions, \\|Y'-AY-BU\\|_F and \\|X-CY\\|_F, are minimized using + least-squares regression and singular value decomposition. + + See the following reference for more details: + Korda, M. and Mezic, I. "Linear predictors for nonlinear dynamical systems: + Koopman operator meets model predictive control." Automatica, Vol. 93, 149–160. + + + Attributes: + coef_ (numpy.ndarray): + Weight vectors of the regression problem. Corresponds to either [A] or + [A,B]. + state_matrix_ (numpy.ndarray): + Identified state transition matrix A of the underlying system. + control_matrix_ (numpy.ndarray): + Identified control matrix B of the underlying system. + projection_matrix_ (numpy.ndarray): + Projection matrix into low-dimensional subspace of shape (n_input_features + +n_control_features, svd_rank). + projection_matrix_output_ (numpy.ndarray): + Projection matrix into low-dimensional subspace of shape (n_input_features + +n_control_features, svd_output_rank). + """ + + def __init__(self): + """Initialize the EDMDc regressor.""" + pass + + def fit(self, x, y=None, u=None, dt=None): + """Fit the EDMDc regressor to the given data. + + Args: + x (numpy.ndarray): + Measurement data to be fit. + Can be of shape (n_samples, n_features), or (n_trials, n_samples, + n_features), where n_trials is the number of independent trials. + Can also be of a list of arrays, where each array is a trajectory + or a 2- or 3-d array of trajectories, provided they have the + same last dimension. + y (numpy.ndarray, optional): + Time-shifted measurement data to be fit. Defaults to None. + u (numpy.ndarray, optional): + Time series of external actuation/control. Defaults to None. + dt (scalar, optional): + Discrete time-step. Defaults to None. + + Returns: + self: Fitted EDMDc instance. + """ + if y is None: + X1, X2 = self._detect_reshape(x) + else: + X1, _ = self._detect_reshape(x, offset=False) + X2, _ = self._detect_reshape(y, offset=False) + + if u.ndim == 1: + if len(u) > X1.shape[0]: + u, _ = self._detect_reshape(u) + C = u[np.newaxis, :] + else: + if u.shape[0] > X1.shape[0]: + u, _ = self._detect_reshape(u) + C = u + self.n_control_features_ = C.shape[1] + + self._fit_with_unknown_b(X1, X2, C) + return self + + def _fit_with_unknown_b(self, X1, X2, U): + """Fit the EDMDc regressor with unknown control matrix B. + + Args: + X1 (numpy.ndarray): + Measurement data given as input. + X2 (numpy.ndarray): + Measurement data given as target. + U (numpy.ndarray): + Time series of external actuation/control. + """ + Nlift = X1.shape[1] + W = X2.T + V = np.vstack([X1.T, U.T]) + VVt = V @ V.T + WVt = W @ V.T + M = WVt @ np.linalg.pinv(VVt) # Matrix [A B] + self._state_matrix_ = M[0:Nlift, 0:Nlift] + self._control_matrix_ = M[0:Nlift, Nlift:] + self._coef_ = M + + # Compute Koopman V, eigenvectors, lamda + [self._eigenvalues_, self._eigenvectors_] = np.linalg.eig(self.state_matrix_) + self._unnormalized_modes = self._eigenvectors_ + self._ur = np.eye(self.n_input_features_) + self._tmp_compute_psi = np.linalg.inv(self._eigenvectors_) + + def predict(self, x, u): + """Predict the next timestep based on the given data. + + Args: + x (numpy.ndarray): + Measurement data upon which to base prediction. + u (numpy.ndarray): + Time series of external actuation/control. + + Returns: + y (numpy.ndarray): + Prediction of x one timestep in the future. + """ + check_is_fitted(self, "coef_") + u, _ = self._detect_reshape(u, offset=False) + x, _ = self._detect_reshape(x, offset=False) + y = x @ self.state_matrix_.T + u @ self.control_matrix_.T + y = self._return_orig_shape(y) + return y + + def _compute_phi(self, x_col): + """Compute psi(x) given x. + + Args: + x_col (numpy.ndarray): + Input data x. + + Returns: + psi (numpy.ndarray): + Value of psi(x). + """ + if x_col.ndim == 1: + x_col = x_col.reshape(-1, 1) + phi = self._ur.T @ x_col + return phi + + def _compute_psi(self, x_col): + """Compute psi(x) given x. + + Args: + x_col (numpy.ndarray): + Input data x. + + Returns: + psi (numpy.ndarray): + Value of psi(x). + """ + # compute psi - one column if x is a row + if x_col.ndim == 1: + x_col = x_col.reshape(-1, 1) + psi = self._tmp_compute_psi @ x_col + return psi + + @property + def coef_(self): + """Weight vectors of the regression problem. Corresponds to either [A] or + [A,B].""" + check_is_fitted(self, "_coef_") + return self._coef_ + + @property + def state_matrix_(self): + """Identified state transition matrix A of the underlying system. + + Returns: + state_matrix (numpy.ndarray): + State transition matrix A. + """ + check_is_fitted(self, "_state_matrix_") + return self._state_matrix_ + + @property + def control_matrix_(self): + """Identified control matrix B of the underlying system. + + Returns: + control_matrix (numpy.ndarray): + Control matrix B. + """ + check_is_fitted(self, "_control_matrix_") + return self._control_matrix_ + + @property + def eigenvalues_(self): + """Identified Koopman lambda. + + Returns: + eigenvalues (numpy.ndarray): + Koopman eigenvalues. + """ + check_is_fitted(self, "_eigenvalues_") + return self._eigenvalues_ + + @property + def eigenvectors_(self): + """Identified Koopman eigenvectors. + + Returns: + eigenvectors (numpy.ndarray): + Koopman eigenvectors. + """ + check_is_fitted(self, "_eigenvectors_") + return self._eigenvectors_ + + @property + def unnormalized_modes(self): + """Identified Koopman eigenvectors. + + Returns: + unnormalized_modes (numpy.ndarray): + Koopman eigenvectors. + """ + check_is_fitted(self, "_unnormalized_modes") + return self._unnormalized_modes + + @property + def ur(self): + """Matrix U that is part of the SVD. + + Returns: + ur (numpy.ndarray): + Matrix U. + """ + check_is_fitted(self, "_ur") + return self._ur diff --git a/DSA/pykoopman/src/pykoopman/regression/_havok.py b/DSA/pykoopman/src/pykoopman/regression/_havok.py new file mode 100644 index 0000000..0273ebd --- /dev/null +++ b/DSA/pykoopman/src/pykoopman/regression/_havok.py @@ -0,0 +1,341 @@ +"""module for havok""" +from __future__ import annotations + +from warnings import warn + +import numpy as np +from matplotlib import pyplot as plt +from optht import optht +from scipy.signal import lsim +from scipy.signal import lti +from sklearn.utils.validation import check_is_fitted + +from ..common import drop_nan_rows +from ..differentiation._derivative import Derivative +from ._base import BaseRegressor + + +class HAVOK(BaseRegressor): + """ + HAVOK (Hankel Alternative View of Koopman) regressor. + + Aims to determine the system matrices A, B that satisfy d/dt v = Av + Bu, + where v is the vector of the leading delay coordinates and u is a low-energy + delay coordinate acting as forcing. A and B are the unknown system and control + matrices, respectively. The delay coordinates are obtained by computing the + SVD from a Hankel matrix. + + The objective function, \\|dV-AV-BU\\|_F, is minimized using least-squares + regression. + + See the following reference for more details: + Brunton, S.L., Brunton, B.W., Proctor, J.L., Kaiser, E. & Kutz, J.N. + "Chaos as an intermittently forced linear system." + Nature Communications, Vol. 8(19), 2017. + + + Parameters: + svd_rank (int, optional): + Rank of the SVD used for model reduction. Defaults to None. + differentiator (Derivative, optional): + Differentiation method to compute the time derivative. Defaults to + Derivative(kind="finite_difference", k=1). + plot_sv (bool, optional): + Whether to plot the singular values. Defaults to False. + + Attributes: + coef_ (array): + Weight vectors of the regression problem. Corresponds to either [A] or + [A,B]. + state_matrix_ (array): + Identified state transition matrix A of the underlying system. + control_matrix_ (array): + Identified control matrix B of the underlying system. + projection_matrix_ (array): + Projection matrix into low-dimensional subspace of shape (n_input_features + +n_control_features, svd_rank). + projection_matrix_output_ (array): + Projection matrix into low-dimensional subspace of shape (n_input_features + +n_control_features, svd_output_rank). + """ + + def __init__( + self, + svd_rank=None, + differentiator=Derivative(kind="finite_difference", k=1), + plot_sv=False, + ): + """ + Initialize the HAVOK regressor. + + Args: + svd_rank (int, optional): + Rank of the SVD used for model reduction. Defaults to None. + differentiator (Derivative, optional): + Differentiation method to compute the time derivative. Defaults to + Derivative(kind="finite_difference", k=1). + plot_sv (bool, optional): + Whether to plot the singular values. Defaults to False. + """ + self.svd_rank = svd_rank + self.differentiator = differentiator + self.plot_sv = plot_sv + + def fit(self, x, y=None, dt=None): + """ + Fit the HAVOK regressor to the given data. + + Args: + x (numpy.ndarray): + Measurement data to be fit. + Can be of shape (n_samples, n_features), or (n_trials, n_samples, + n_features), where n_trials is the number of independent trials. + Can also be of a list of arrays, where each array is a trajectory + or a 2- or 3-d array of trajectories, provided they have the + same last dimension. + y (not used): + Time-shifted measurement data to be fit. Ignored. + dt (scalar): + Discrete time-step. + + Returns: + self: Fitted HAVOK instance. + """ + + if y is not None: + warn("havok regressor does not require the y argument when fitting.") + + if dt is None: + raise ValueError("havok regressor requires a timestep dt when fitting.") + + self.dt_ = dt + self.n_control_features_ = 1 + + orig_shape = x.shape if isinstance(x, np.ndarray) else None + x, _ = self._detect_reshape(x, offset=False) # time*trials, features + # Create time vector + t = np.arange(0, self.dt_ * self.n_samples_, self.dt_) + + # SVD to calculate intrinsic observables + U, s, Vh = np.linalg.svd(x.T, full_matrices=False) + + if self.plot_sv: + plt.figure() + plt.semilogy(s) + plt.xlabel("number of terms") + plt.ylabel("singular values") + plt.show() + + # calculate rank using optimal hard threshold by Gavish & Donoho + if self.svd_rank is None: + self.svd_rank = optht(x, sv=s, sigma=None) + Vrh = Vh[: self.svd_rank, :] + Vr = Vrh.T + Ur = U[:, : self.svd_rank] + sr = s[: self.svd_rank] + + # calculate time derivative dxdt of only the first rank-1 & normalize + if len(orig_shape) == 2: + dVr = self.differentiator(Vr[:, :-1], t) + + else: + Vrt = Vr.reshape(orig_shape[0], orig_shape[1], -1) + dVr = self.differentiator(Vrt[:, :-1], t, axis=1) + dVr = dVr.reshape(Vr.shape) # TODO: check if this is correct + + dVr, t, V = drop_nan_rows(dVr, t, Vh.T) + + # regression on intrinsic variables v + # xi = np.zeros((self.svd_rank - 1, self.svd_rank)) + # for i in range(self.svd_rank - 1): + # # here, we use rank terms in V to fit the rank-1 terms dV/dt + # # we perform column wise + # xi[i, :] = np.linalg.lstsq(Vr, dVr[:, i], rcond=None)[0] + + xi = np.linalg.lstsq(Vr, dVr, rcond=None)[0].T + assert xi.shape == (self.svd_rank - 1, self.svd_rank) + + self.forcing_signal = Vr[:, -1] + self._state_matrix_ = xi[:, :-1] + self._control_matrix_ = xi[:, -1].reshape(-1, 1) + + self.svals = s + self._ur = Ur[:, :-1] @ np.diag(sr[:-1]) + self._coef_ = np.hstack([self.state_matrix_, self.control_matrix_]) + + eigenvalues_, self._eigenvectors_ = np.linalg.eig(self.state_matrix_) + # because we fit the model in continuous time, + # so we need to convert to discrete time + self._eigenvalues_ = np.exp(eigenvalues_ * dt) + + self._unnormalized_modes = self._ur @ self.eigenvectors_ + self._tmp_compute_psi = np.linalg.inv(self.eigenvectors_) @ self._ur.T + + # self.C = np.linalg.multi_dot( + # [ + # np.linalg.inv(self.eigenvectors_), + # np.diag(np.reciprocal(s[: self.svd_rank - 1])), + # U[:, : self.svd_rank - 1].T, + # ] + # ) + return self + + def predict(self, x, u, t): + """ + Predict the output based on the input data. + + Args: + x (numpy.ndarray): + Measurement data upon which to base prediction. + u (numpy.ndarray): + Time series of external actuation/control, which is sampled at time + instances in `t`. + t (numpy.ndarray): + Time vector. Instances at which the solution vector shall be provided. + Note: The time vector must start at 0. + + Returns: + y (numpy.ndarray): + Prediction of `x` at the time instances provided in `t`. + """ + # if t[0] != 0: + # raise ValueError("the time vector must start at 0.") + x, _ = self._detect_reshape(x, offset=False) + + check_is_fitted(self, "coef_") + y0 = ( + # np.linalg.inv(np.diag(self.svals[: self.svd_rank - 1])) + # @ + np.linalg.pinv(self._ur) + @ x.T + ) + sys = lti( + self.state_matrix_, + self.control_matrix_, + self._ur, + np.zeros((self.n_input_features_, self.n_control_features_)), + ) + tout, ypred, xpred = lsim(sys, U=u, T=t, X0=y0.T) + return self._return_orig_shape(ypred) + + def _compute_phi(self, x_col): + """ + Compute the feature vector `phi(x)` given `x`. + + Args: + x_col (numpy.ndarray): + Input data `x` for computing `phi(x)`. + + Returns: + phi (numpy.ndarray): + Value of `phi(x)`. + + """ + if x_col.ndim == 1: + x_col = x_col.reshape(-1, 1) + phi = self._ur.T @ x_col + return phi + + def _compute_psi(self, x_col): + """ + Compute the feature vector `psi(x)` given `x`. + + Args: + x_col (numpy.ndarray): + Input data `x` for computing `psi(x)`. + + Returns: + psi (numpy.ndarray): + Value of `psi(x)`. + + """ + # compute psi - one column if x is a row + if x_col.ndim == 1: + x_col = x_col.reshape(-1, 1) + psi = self._tmp_compute_psi @ x_col + return psi + + @property + def coef_(self): + """ + Get the weight vectors of the regression problem. + + Returns: + coef (numpy.ndarray): + Weight vectors of the regression problem. Corresponds to either [A] + or [A,B]. + """ + check_is_fitted(self, "_coef_") + return self._coef_ + + @property + def state_matrix_(self): + """ + Get the identified state transition matrix A of the underlying system. + + Returns: + state_matrix (numpy.ndarray): + Identified state transition matrix A. + """ + check_is_fitted(self, "_state_matrix_") + return self._state_matrix_ + + @property + def control_matrix_(self): + """ + Get the identified control matrix B of the underlying system. + + Returns: + control_matrix (numpy.ndarray): + Identified control matrix B. + """ + check_is_fitted(self, "_control_matrix_") + return self._control_matrix_ + + @property + def eigenvectors_(self): + """ + Get the identified eigenvectors of the state matrix A. + + Returns: + eigenvectors (numpy.ndarray): + Identified eigenvectors of the state matrix A. + """ + check_is_fitted(self, "_eigenvectors_") + return self._eigenvectors_ + + @property + def eigenvalues_(self): + """ + Get the identified eigenvalues of the state matrix A. + + Returns: + eigenvalues (numpy.ndarray): + Identified eigenvalues of the state matrix A. + """ + check_is_fitted(self, "_eigenvalues_") + return self._eigenvalues_ + + @property + def unnormalized_modes(self): + """ + Get the identified unnormalized modes. + + Returns: + unnormalized_modes (numpy.ndarray): + Identified unnormalized modes. + """ + check_is_fitted(self, "_unnormalized_modes") + return self._unnormalized_modes + + @property + def ur(self): + """ + Get the matrix UR. + + Returns: + ur (numpy.ndarray): + Matrix UR. + """ + check_is_fitted(self, "_ur") + return self._ur diff --git a/DSA/pykoopman/src/pykoopman/regression/_kdmd.py b/DSA/pykoopman/src/pykoopman/regression/_kdmd.py new file mode 100644 index 0000000..6e33111 --- /dev/null +++ b/DSA/pykoopman/src/pykoopman/regression/_kdmd.py @@ -0,0 +1,460 @@ +"""module for kernel dmd""" +from __future__ import annotations + +from warnings import warn + +import numpy as np +from pydmd.dmdbase import DMDTimeDict +from pydmd.utils import compute_svd +from pydmd.utils import compute_tlsq +from scipy.linalg import sqrtm +from sklearn.gaussian_process.kernels import Kernel +from sklearn.gaussian_process.kernels import RBF +from sklearn.utils.validation import check_is_fitted + +from ._base import BaseRegressor + + +class KDMD(BaseRegressor): + """ + Kernel Dynamic Mode Decomposition. + + See the following reference for more details: + + `Williams, M. O., Rowley, C. W., & Kevrekidis, I. G. (2014). + "A kernel-based approach to data-driven Koopman spectral analysis." + arXiv preprint arXiv:1411.2260. ` + + Args: + svd_rank (int): The rank for the truncation. If 0, the method computes + the optimal rank and uses it for truncation. If positive integer, + the method uses the argument for the truncation. If float between 0 + and 1, the rank is the number of the biggest singular values that + are needed to reach the 'energy' specified by `svd_rank`. If -1, + the method does not compute truncation. Default is 0. + tlsq_rank (int): The rank for the truncation. If 0, the method does not + compute any noise reduction. If positive number, the method uses the + argument for the SVD truncation used in the TLSQ method. + forward_backward (bool): If True, the low-rank operator is computed + like in fbDMD (reference: https://arxiv.org/abs/1507.02264). + Default is False. + tikhonov_regularization (bool or None): Tikhonov parameter for the + regularization. If None, no regularization is applied. If float, + it is used as the λ Tikhonov parameter. + kernel (sklearn.gaussian_process.Kernel): An instance of kernel from sklearn. + + Attributes: + svd_rank (int): The rank for the truncation. + tlsq_rank (int): The rank for the truncation. + forward_backward (bool): If True, the low-rank operator is computed + like in fbDMD (reference: https://arxiv.org/abs/1507.02264). + tikhonov_regularization (bool or None): Tikhonov parameter for the + regularization. + kernel (sklearn.gaussian_process.Kernel): An instance of kernel from sklearn. + n_samples_ (int): Number of samples in KDMD. + n_input_features_ (int): Dimension of input features, i.e., the dimension + of each sample. + _snapshots (numpy.ndarray): Column-wise data matrix of shape + (n_input_features_, n_samples_). + _snapshots_shape (tuple): Shape of column-wise data matrix. + _X (numpy.ndarray): Training features column-wise arranged, needed for + prediction. Shape is (n_input_features_, n_samples). + _Y (numpy.ndarray): Training target, column-wise arranged. Shape is + (n_input_features_, n_samples). + _coef_ (numpy.ndarray): Reduced Koopman state transition matrix of shape + (svd_rank, svd_rank). + _eigenvalues_ (numpy.ndarray): Koopman lambda of shape (svd_rank,). + _eigenvectors_ (numpy.ndarray): Koopman eigenvectors of shape + (svd_rank, svd_rank). + _unnormalized_modes (numpy.ndarray): Koopman V of shape + (svd_rank, n_input_features_). + _state_matrix_ (numpy.ndarray): Reduced Koopman state transition matrix + of shape (svd_rank, svd_rank). + self.C (numpy.ndarray): Linear matrix that maps kernel product features + to eigenfunctions of shape (svd_rank, n_samples_). + """ + + def __init__( + self, + svd_rank=1.0, # 1.0 means keeping all ranks + tlsq_rank=0, + forward_backward=False, + tikhonov_regularization=None, + kernel=RBF(), + ): + """ + Kernel Dynamic Mode Decomposition. + + Args: + svd_rank (int, optional): The rank for the truncation. + If set to 0, the method computes the optimal rank + and uses it for truncation. If set to a positive integer, + the method uses the specified rank for truncation. + If set to a float between 0 and 1, the rank is determined + based on the specified energy level. If set to -1, no + truncation is performed. Default is 1.0. + tlsq_rank (int, optional): The rank for the truncation used + in the total least squares preprocessing. If set to 0, + no noise reduction is performed. If set to a positive integer, + the method uses the specified rank for the SVD truncation + in the TLSQ method. Default is 0. + forward_backward (bool, optional): Whether to compute the + low-rank operator using the forward-backward method similar + to fbDMD. If set to True, the low-rank operator is computed + with forward-backward DMD. If set to False, standard DMD is used. + Default is False. + tikhonov_regularization (float or None, optional): Tikhonov + regularization parameter for regularization. If set to None, + no regularization is applied. If set to a float, it is used + as the regularization parameter. Default is None. + kernel (Kernel, optional): An instance of the kernel class from + sklearn.gaussian_process. Default is RBF(). + """ + self.svd_rank = svd_rank + self.tlsq_rank = tlsq_rank + self.forward_backward = forward_backward + self.tikhonov_regularization = tikhonov_regularization + self.kernel = kernel + + if not isinstance(self.kernel, Kernel): + raise ValueError( + "kernel must be a subclass of sklearn.gaussian_process.kernel" + ) + + def fit(self, x, y=None, dt=1): + """ + Fits the KDMD model to the provided training data. + + Args: + x: numpy.ndarray, shape (n_samples, n_features) + Measurement data input. + Can be of shape (n_samples, n_features), or (n_trials, n_samples, + n_features), where n_trials is the number of independent trials. + Can also be of a list of arrays, where each array is a trajectory + or a 2- or 3-d array of trajectories, provided they have the + same last dimension. + + y: numpy.ndarray, shape (n_samples, n_features), optional + Measurement data output to be fitted. Defaults to None. + + dt: float, optional + Time interval between `x` and `y`. Defaults to 1. + + Returns: + KDMD: + The fitted KDMD instance. + """ + + # if y is not None: + # warn("pydmd regressors do not require the y argument when fitting.") + if y is None: + X, Y = self._detect_reshape(x) + else: + X, _ = self._detect_reshape(x, offset=False) + Y, _ = self._detect_reshape(y, offset=False) + X = X.T + Y = Y.T + + n_samples = self.n_samples_ + if y is None: + self._snapshots, self._snapshots_shape = _col_major_2darray(x.T) + + # total least square preprocessing on X and Y - features, samples + self._X, self._Y = compute_tlsq(X, Y, self.tlsq_rank) + + # compute KDMD operators, lamda, and koopman V + # note that this method is built by considering row-wise collected data + [ + self._coef_, + self._eigenvalues_, + self._eigenvectors_, + self._unnormalized_modes, + ] = self._regressor_compute_kdmdoperator(self._X.T, self._Y.T) + + # Default timesteps + self._set_initial_time_dictionary({"t0": 0, "tend": n_samples - 1, "dt": 1}) + + # _coef_ as the transpose + # self._coef_ = self._regressor_atilde.T + + return self + + def predict(self, x): + """ + Predicts the future states based on the given input data. + + Args: + x: numpy.ndarray, shape (n_samples, n_features) + Measurement data upon which to base the prediction. + + Returns: + numpy.ndarray, shape (n_samples, n_features) + Prediction of the future states. + """ + + check_is_fitted(self, "coef_") + x, _ = self._detect_reshape(x, offset=False) + + phi = self._compute_psi(x_col=x.T) + phi_next = np.diag(self.eigenvalues_) @ phi + x_next_T = self._unnormalized_modes @ phi_next + y = np.real(x_next_T).T + return self._return_orig_shape(y) + + def _compute_phi(self, x_col): + """ + Computes the phi(x) given x. + + Args: + x_col: numpy.ndarray, shape (n_samples, n_features) + Measurement data upon which to compute phi values. + + Returns: + numpy.ndarray, shape (n_samples, n_input_features_) + Value of phi at x. + """ + if x_col.ndim == 1: + x_col = x_col.reshape(-1, 1) + psi = self._compute_psi(x_col) + phi = np.real(self.eigenvectors_ @ psi) + return phi + + def _compute_psi(self, x_col): + """ + Computes the psi(x) given x. + + Args: + x_col: numpy.ndarray, shape (n_samples, n_features) + Measurement data upon which to compute psi values. + + Returns: + numpy.ndarray, shape (n_samples, n_input_features_) + Value of psi at x. + """ + # compute psi - one column if x is a row + if x_col.ndim == 1: + x_col = x_col.reshape(-1, 1) + return self._tmp_compute_psi_kdmd @ self.kernel(self._X.T, x_col.T) + + @property + def coef_(self): + """ + Getter property for the coef_ attribute. + + Returns: + numpy.ndarray, shape (svd_rank, svd_rank) + Reduced Koopman state transition matrix. + """ + check_is_fitted(self, "_coef_") + return self._coef_ + + @property + def state_matrix_(self): + """ + Getter property for the state_matrix_ attribute. + + Returns: + numpy.ndarray, shape (svd_rank, svd_rank) + Reduced Koopman state transition matrix. + """ + check_is_fitted(self, "_state_matrix_") + return self._state_matrix_ + + @property + def eigenvalues_(self): + """ + Getter property for the eigenvalues_ attribute. + + Returns: + numpy.ndarray, shape (svd_rank,) + Koopman eigenvalues. + """ + check_is_fitted(self, "_eigenvalues_") + return self._eigenvalues_ + + @property + def eigenvectors_(self): + """ + Getter property for the eigenvectors_ attribute. + + Returns: + numpy.ndarray, shape (svd_rank, svd_rank) + Koopman eigenvectors. + """ + check_is_fitted(self, "_eigenvectors_") + return self._eigenvectors_ + + @property + def unnormalized_modes(self): + """ + Getter property for the unnormalized_modes attribute. + + Returns: + numpy.ndarray, shape (svd_rank, n_input_features_) + Koopman unnormalized modes. + """ + check_is_fitted(self, "_unnormalized_modes") + return self._unnormalized_modes + + @property + def ur(self): + """ + Getter property for the ur attribute. + + Returns: + numpy.ndarray, shape (n_samples_, n_input_features_) + Linear matrix that maps kernel product features to eigenfunctions. + """ + check_is_fitted(self, "_ur") + return self._ur + + def _regressor_compute_kdmdoperator(self, X, Y): + """ + Computes the KDMD operator given input data X and target data Y. + + Args: + X: numpy.ndarray, shape (n_samples_, n_input_features_) + Training data input. + Y: numpy.ndarray, shape (n_samples_, n_input_features_) + Training data target output. + + Returns: + list + A list containing the following elements: + - koopman_matrix: numpy.ndarray, shape (svd_rank, svd_rank) + Reduced Koopman state transition matrix. + - koopman_eigvals: numpy.ndarray, shape (svd_rank,) + Koopman eigenvalues. + - koopman_eigenvectors: numpy.ndarray, shape (svd_rank, svd_rank) + Koopman eigenvectors. + - unnormalized_modes: numpy.ndarray, shape (svd_rank, n_input_features_) + Koopman unnormalized modes. + """ + # compute kernel K(X,X) + # since sklearn kernel function takes rowwise collected data. + KXX = self.kernel(X, X) + KYX = self.kernel(Y, X) + + # compute eig of PD matrix, so it is SVD + U, s2, _ = compute_svd(KXX, self.svd_rank) + s = np.sqrt(s2) + # remember that we need sigma, but svd or eig only gives you the s^2 + + # optional compute tiknoiv reg + if self.tikhonov_regularization is not None: + s = ( + s**2 + self.tikhonov_regularization * np.linalg.norm(X) + ) * np.reciprocal(s) + + koopman_matrix = ( + np.diag(np.reciprocal(s)) + @ U.T.conj() + @ KYX.T + @ U + @ np.diag(np.reciprocal(s)) + ) + + # optional compute fb + if self.forward_backward: + KYY = self.kernel(Y, Y) + KXY = KYX.T + bU, bs2, _ = compute_svd(KYY, self.svd_rank) + bs = np.sqrt(bs2) + if self.tikhonov_regularization is not None: + bs = ( + bs**2 + self.tikhonov_regularization * np.linalg.norm(Y) + ) * np.reciprocal(bs) + + atilde_back = ( + np.diag(np.reciprocal(bs)) + @ bU.T.conj() + @ KXY.T + @ bU + @ np.diag(np.reciprocal(bs)) + ) + koopman_matrix = sqrtm(koopman_matrix @ np.linalg.inv(atilde_back)) + + # self._regressor_atilde = atilde + self._state_matrix_ = koopman_matrix + + # compute eigenquantities + koopman_eigvals, koopman_eigenvectors = np.linalg.eig(koopman_matrix) + + # compute unnormalized V + BV = np.linalg.lstsq(U @ np.diag(s), X, rcond=None)[0].T + unnormalized_modes = BV @ koopman_eigenvectors + + # compute psi + self._ur = BV # U @ np.diag(s) + self._tmp_compute_psi_kdmd = ( + np.linalg.inv(koopman_eigenvectors) @ np.diag(np.reciprocal(s)) @ U.T + ) + + return [ + koopman_matrix, + koopman_eigvals, + koopman_eigenvectors, + unnormalized_modes, + ] + + def _set_initial_time_dictionary(self, time_dict): + """ + Sets the initial time dictionary. + + Args: + time_dict: dict + Dictionary containing the time information with keys 't0', 'tend', + and 'dt'. + """ + if not ("t0" in time_dict and "tend" in time_dict and "dt" in time_dict): + raise ValueError('time_dict must contain the keys "t0", "tend" and "dt".') + if len(time_dict) > 3: + raise ValueError( + 'time_dict must contain only the keys "t0", "tend" and "dt".' + ) + + self._original_time = DMDTimeDict(dict(time_dict)) + self._dmd_time = DMDTimeDict(dict(time_dict)) + + +def _col_major_2darray(X): + """ + Converts the input snapshots into a 2D matrix by column-major ordering. + + Args: + X: int or numpy.ndarray + The input snapshots. + + Returns: + snapshots: numpy.ndarray + The 2D matrix that contains the flattened snapshots. + + snapshots_shape: tuple + The shape of the original snapshots. + """ + + # If the data is already 2D ndarray + if isinstance(X, np.ndarray) and X.ndim == 2: + snapshots = X + snapshots_shape = None + else: + input_shapes = [np.asarray(x).shape for x in X] + + if len(set(input_shapes)) != 1: + raise ValueError("Snapshots have not the same dimension.") + + snapshots_shape = input_shapes[0] + snapshots = np.transpose([np.asarray(x).flatten() for x in X]) + + # check condition number of the data passed in + cond_number = np.linalg.cond(snapshots) + if cond_number > 10e4: + warn( + "Input data matrix X has condition number {}. " + """Consider preprocessing data, passing in augmented + data matrix, or regularization methods.""".format( + cond_number + ) + ) + + return snapshots, snapshots_shape diff --git a/DSA/pykoopman/src/pykoopman/regression/_nndmd.py b/DSA/pykoopman/src/pykoopman/regression/_nndmd.py new file mode 100644 index 0000000..2518761 --- /dev/null +++ b/DSA/pykoopman/src/pykoopman/regression/_nndmd.py @@ -0,0 +1,1454 @@ +"""module for implementing a neural network DMD""" +from __future__ import annotations + +import pickle +from abc import abstractmethod +from warnings import warn + +import lightning as L +import numpy as np +import torch +from pykoopman.regression._base import BaseRegressor +from sklearn.utils.validation import check_is_fitted +from torch import nn +from torch.nn.utils.rnn import pad_sequence +from torch.utils.data import DataLoader +from torch.utils.data import Dataset + + +# todo: add the control version + + +class MaskedMSELoss(nn.Module): + """ + Calculates the mean squared error (MSE) loss between `output` and `target`, with + masking based on `target_lens`. The `max_look_forward` will determine the + + Args: + max_look_forward + + Returns: + The MSE loss as a scalar tensor. + """ + + def __init__(self, max_look_forward): + super().__init__() + self.max_look_forward = torch.tensor(max_look_forward, dtype=torch.int) + + def forward(self, output, target, target_lens): + """ + Calculates the MSE loss between `output` and `target`, with masking based on + `target_lens`. + + Args: + output (torch.Tensor): The output tensor of shape (batch_size, + sequence_length, features). + target (torch.Tensor): The target tensor of shape (batch_size, + sequence_length, features). + target_lens (torch.Tensor): A tensor of shape (batch_size,) containing the + sequence lengths for each item in the batch. + + Returns: + The MSE loss as a scalar tensor. + """ + + # if target is shorter than output, just cut output off + if target.size(1) < self.max_look_forward: + output = output[:, : target.size(1), :] + + # Create mask using target_lens + mask = torch.zeros_like(output, dtype=torch.bool) + for i, length in enumerate(target_lens): + if length > self.max_look_forward: + length_used = self.max_look_forward + else: + length_used = length + mask[i, :length_used, :] = 1 + + # Compute squared differences and apply mask + squared_diff = torch.pow(output - target, 2) + squared_diff_masked = torch.where( + mask, squared_diff, torch.zeros_like(squared_diff) + ) + + # Compute the MSE loss + mse_loss = squared_diff_masked.sum() / mask.sum() + + return mse_loss + + +class FFNN(nn.Module): + """A feedforward neural network with customizable architecture and activation + functions. + + Args: + input_size (int): The size of the input layer. + hidden_sizes (list): A list of the sizes of the hidden layers. + output_size (int): The size of the output layer. + activations (str): A string for activation functions for every layer. + + Attributes: + layers (nn.ModuleList): A list of the neural network layers. + """ + + def __init__( + self, input_size, hidden_sizes, output_size, activations, include_state=False + ): + super(FFNN, self).__init__() + + activations_dict = { + "relu": nn.ReLU(), + "sigmoid": nn.Sigmoid(), + "tanh": nn.Tanh(), + "swish": nn.SiLU(), + "elu": nn.ELU(), + "mish": nn.Mish(), + "linear": nn.Identity(), + } + # whether to directly encode state in observables + self.include_state = include_state + if self.include_state: + output_size_ = output_size - input_size + else: + output_size_ = output_size + + # Define the activation + act = activations_dict[activations] + + # Define the input layer + self.layers = nn.ModuleList() + + # if linear layer, remove bias + if activations == "linear": + bias = False + else: + bias = True + + if len(hidden_sizes) == 0: + # if no hidden layer, then entire NN is just a linear one + self.layers.append(nn.Linear(input_size, output_size_, bias)) + else: + self.layers.append(nn.Linear(input_size, hidden_sizes[0], bias)) + if activations != "linear": + self.layers.append(act) + + # Define the hidden layers + for i in range(1, len(hidden_sizes)): + self.layers.append( + nn.Linear(hidden_sizes[i - 1], hidden_sizes[i], bias) + ) + if activations != "linear": + self.layers.append(act) + + # Define the last output layer + self.layers.append(nn.Linear(hidden_sizes[-1], output_size_, bias=False)) + + def forward(self, x): + """Performs a forward pass through the neural network. + + Args: + x (torch.Tensor): The input tensor to the neural network. + + Returns: + torch.Tensor: The output tensor of the neural network. + """ + in_x = x + for layer in self.layers: + x = layer(x) + + if self.include_state: + x = torch.cat((in_x, x), 1) + return x + + +class HardCodedLinearLayer(nn.Module): + def __init__(self, input_size, output_size): + + pass + + def forward(self, x): + pass + + +class BaseKoopmanOperator(nn.Module): + """Base class for Koopman operator models. + + Args: + dim (int): The dimension of the state space. + dt (float, optional): The time step size. Defaults to 1.0. + init_std (float, optional): The standard deviation of the initializer. + Defaults to 0.1. + + Attributes: + dim (int): The dimension of the state space. + dt (torch.Tensor): The time step size. + init_std (float): The standard deviation of the initializer. + + Note: + rule for self.init_std: a number between 0.1 and 10 over dt + + """ + + def __init__( + self, + dim: int, + dt: float = 1.0, + init_std: float = 0.1, + ): + """ + Initializes the `BaseKoopmanOperator` instance. + """ + super().__init__() + self.dim = dim + self.register_buffer("dt", torch.tensor(dt)) + self.init_std = init_std + + def forward(self, x): + """ + Computes the forward pass of the `BaseKoopmanOperator`. + + Given `x` as a row vector, return `x @ K.T` + + Args: + x (torch.Tensor): The input tensor. + + Returns: + torch.Tensor: The output tensor. + """ + koopman_operator = self.get_discrete_time_Koopman_Operator() + xnext = torch.matmul(x, koopman_operator.t()) # following pytorch convention + return xnext + + def get_discrete_time_Koopman_Operator(self): + """ + Computes the discrete-time Koopman operator. + + Returns: + torch.Tensor: The discrete-time Koopman operator. + """ + return torch.matrix_exp(self.dt * self.get_K()) + + @abstractmethod + def get_K(self): + """ + Computes the matrix K. + + Returns: + torch.Tensor: The matrix K. + """ + pass + + +class StandardKoopmanOperator(BaseKoopmanOperator): + """ + Standard Koopman operator that only has a diagonal matrix for the Koopman operator. + """ + + def __init__(self, **kwargs): + """ + Initializes the StandardKoopmanOperator. + + Args: + **kwargs: Additional keyword arguments. + """ + super().__init__(**kwargs) + self.register_parameter( + "K", + torch.nn.Parameter( + torch.nn.init.trunc_normal_( + torch.zeros(self.dim, self.dim), std=self.init_std + ) + ), + ) + + def get_K(self): + """ + Computes the Koopman operator. + + Returns: + The Koopman operator. + """ + return self.K + + +class HamiltonianKoopmanOperator(BaseKoopmanOperator): + """ + Hamiltonian Koopman operator that has an off-diagonal matrix for the Koopman + operator. + """ + + def __init__(self, **kwargs): + """ + Initializes the HamiltonianKoopmanOperator. + + Args: + **kwargs: Additional keyword arguments. + """ + super().__init__(**kwargs) + self.register_parameter( + "off_diagonal", + torch.nn.Parameter( + torch.nn.init.trunc_normal_( + torch.zeros(self.dim, self.dim), std=self.init_std + ) + ), + ) + + def get_K(self): + """ + Computes the Koopman operator. + + Returns: + The Koopman operator. + """ + return self.off_diagonal - self.off_diagonal.t() + + +class DissipativeKoopmanOperator(BaseKoopmanOperator): + """ + Dissipative Koopman operator that has an off-diagonal and a diagonal matrix for the + Koopman operator. + """ + + def __init__(self, **kwargs): + """ + Initializes the DissipativeKoopmanOperator. + + Args: + **kwargs: Additional keyword arguments. + """ + super().__init__(**kwargs) + self.register_parameter( + "off_diagonal", + torch.nn.Parameter( + torch.nn.init.trunc_normal_( + torch.zeros(self.dim, self.dim), std=self.init_std + ) + ), + ) + self.register_parameter( + "diagonal", + torch.nn.Parameter( + -torch.pow( + torch.nn.init.trunc_normal_( + torch.zeros(self.dim), std=self.init_std + ), + 2, + ) + ), + ) + + def get_K(self): + """ + Computes the Koopman operator. + + Returns: + The Koopman operator. + """ + return torch.diag(self.diagonal) + self.off_diagonal - self.off_diagonal.t() + + +class DLKoopmanRegressor(L.LightningModule): + """ + Deep Learning Koopman Regressor module using a Feedforward Neural Network + encoder and decoder to learn the Koopman operator for a given dynamical system. + + Args: + mode (str): Type of Koopman operator to use - "Standard", "Hamiltonian" or + "Dissipative". Defaults to None. + dt (float): Time step of the Koopman operator. Defaults to 1.0. + look_forward (int): Number of time steps to predict in the future. + Defaults to 1. + config_encoder (dict): Dictionary containing encoder configurations + - input_size, output_size, hidden_sizes, activations. Defaults to {}. + config_decoder (dict): Dictionary containing decoder configurations + - input_size, output_size, hidden_sizes, activations. Defaults to {}. + lbfgs (bool): Use L-BFGS optimizer. Defaults to False. + + Attributes: + input_size (int): Size of input to the encoder. + output_size (int): Size of output from the encoder. + _encoder (FFNN): Feedforward Neural Network encoder. + _decoder (FFNN): Feedforward Neural Network decoder. + _koopman_propagator (BaseKoopmanOperator): Type of Koopman operator used. + look_forward (int): Number of time steps to predict in the future. + using_lbfgs (bool): Use L-BFGS optimizer. + masked_loss_metric (MaskedMSELoss): Mean Squared Error Loss function. + """ + + def __init__( + self, + mode=None, + dt=1.0, + look_forward=1, + config_encoder=dict(), + config_decoder=dict(), + lbfgs=False, + std_koopman=1e-1, + include_state=False, + ): + super(DLKoopmanRegressor, self).__init__() + + self.input_size = config_encoder["input_size"] + self.output_size = config_encoder["output_size"] + + self._encoder = FFNN( + input_size=config_encoder["input_size"], + hidden_sizes=config_encoder["hidden_sizes"], + output_size=config_encoder["output_size"], + activations=config_encoder["activations"], + include_state=include_state, + ) + + self._decoder = FFNN( + input_size=config_decoder["input_size"], + hidden_sizes=config_decoder["hidden_sizes"], + output_size=config_decoder["output_size"], + activations=config_decoder["activations"], + ) + + if mode == "Dissipative": + self._koopman_propagator = DissipativeKoopmanOperator( + dim=config_encoder["output_size"], dt=dt, init_std=std_koopman + ) + elif mode == "Hamiltonian": + self._koopman_propagator = HamiltonianKoopmanOperator( + dim=config_encoder["output_size"], dt=dt, init_std=std_koopman + ) + else: + self._koopman_propagator = StandardKoopmanOperator( + dim=config_encoder["output_size"], dt=dt, init_std=std_koopman + ) + + self.look_forward = look_forward + self.using_lbfgs = lbfgs + + # self.masked_loss_metric = MaskedMSELoss(1) + self.masked_loss_metric = MaskedMSELoss(self.look_forward) + + if self.using_lbfgs: + self.automatic_optimization = False + + def training_step(batch, batch_idx): + optimizer = self.optimizers() + + def closure(): + + # unpack batch data + x, y, ys = batch + + # get the max look forward in this batch + batch_look_forward = ys.max() + + # encode x + encoded_x = self._encoder(x) + + # future unroll look_forward + phi_seq = self._propagate_encoded_n_steps( + encoded_x, n=batch_look_forward + ) + + # standard RNN loss + decoded_y_seq_rnn = torch.zeros( + (x.size(0), self.look_forward, self.input_size), + device=self.device, + ) + + for i in range(batch_look_forward): + decoded_y_seq_rnn[:, i, :] = self._decoder(phi_seq[:, i, :]) + rnn_loss = self.masked_loss_metric(decoded_y_seq_rnn, y, ys) + + # autoencoder reconstruction loss + # for x + decoded_x = self._decoder(encoded_x) + rec_loss = torch.nn.functional.mse_loss(decoded_x, x) + + # for y_seq + decoded_y_seq_rec = torch.zeros( + (x.size(0), self.look_forward, self.input_size), + device=self.device, + ) + for i in range(batch_look_forward): + decoded_y_seq_rec[:, i, :] = self._decoder( + self._encoder(y[:, i, :]) + ) + rec_loss += self.masked_loss_metric(decoded_y_seq_rec, y, ys) + + loss = rnn_loss + rec_loss + + optimizer.zero_grad() + self.manual_backward(loss) + + self.log("loss", loss, prog_bar=True) + self.log("rec_loss", rec_loss, prog_bar=True) + self.log("rnn_loss", rnn_loss, prog_bar=True) + + return loss + + optimizer.step(closure=closure) + + self.training_step = training_step + + self.save_hyperparameters() + + def forward(self, x, n=1): + """ + Propagates input tensor through the model to obtain predicted output tensor + after n steps. + + Args: + x: Input tensor with shape (batch_size, input_size). + n (int): Number of steps to propagate. + + Returns: + decoded: Output tensor with shape (batch_size, output_size). + + """ + encoded = self._encoder(x) + phi_seq = self._propagate_encoded_n_steps(encoded, n) + decoded = self._decoder(phi_seq[:, -1, :]) + return decoded + + def forward_all(self, x, n): + """ + Forward pass of the Koopman Regressor for a given sequence of input states `x`. + This method returns the decoded sequence for all steps within the horizon `n`. + + Args: + x (torch.Tensor): The input state sequence with shape `(batch_size, seq_len, + input_size)`. + n (int): The maximum horizon for which to generate the output sequence. + + Returns: + decoded (torch.Tensor): The decoded sequence with shape `(batch_size, n, + input_size)`. + """ + encoded = self._encoder(x) + phi_seq = self._propagate_encoded_n_steps(encoded, n) + decoded = torch.zeros(x.size(0), n, self.input_size) + for i in range(n): + decoded[:, i, :] = self._decoder(phi_seq[:, i, :]) + return decoded + + def _propagate_encoded_n_steps(self, encoded, n): + """ + Propagates the encoded tensor linearly in the encoded space for n steps. + + Args: + encoded (torch.Tensor): The encoded tensor of shape (batch_size, + encoded_size). n (int): The number of steps to propagate. + + Returns: + torch.Tensor: The propagated encoded tensor of shape (batch_size, n, + encoded_size). + """ + encoded_future = [] + for i in range(n): + encoded = self._koopman_propagator(encoded) + encoded_future.append(encoded) + return torch.stack(encoded_future, 1) + + def training_step(self, batch, batch_idx): + """ + Defines a training step for the DL Koopman Regressor. + + Args: + batch: tuple of (x, y, ys), representing the input data, + the true output data, and the sequence length for + each sample in the batch. + batch_idx: integer, the index of the batch. + + Returns: + tensor representing the loss value for this training step. + """ + # unpack batch data + x, y, ys = batch + + # get the max look forward in this batch + batch_look_forward = ys.max() + + # encode x + encoded_x = self._encoder(x) + + # future unroll look_forward + phi_seq = self._propagate_encoded_n_steps(encoded_x, n=batch_look_forward) + + # standard RNN loss + decoded_y_seq_rnn = torch.zeros( + (x.size(0), self.look_forward, self.input_size), device=self.device + ) + + for i in range(batch_look_forward): + decoded_y_seq_rnn[:, i, :] = self._decoder(phi_seq[:, i, :]) + rnn_loss = self.masked_loss_metric(decoded_y_seq_rnn, y, ys) + + # autoencoder reconstruction loss + # for x + decoded_x = self._decoder(encoded_x) + rec_loss = torch.nn.functional.mse_loss(decoded_x, x) + + # for y_seq + decoded_y_seq_rec = torch.zeros( + (x.size(0), self.look_forward, self.input_size), device=self.device + ) + for i in range(batch_look_forward): + decoded_y_seq_rec[:, i, :] = self._decoder(self._encoder(y[:, i, :])) + rec_loss += self.masked_loss_metric(decoded_y_seq_rec, y, ys) + + loss = rnn_loss + rec_loss + + self.log("loss", loss, prog_bar=True) + self.log("rec_loss", rec_loss, prog_bar=True) + self.log("rnn_loss", rnn_loss, prog_bar=True) + return loss + + def configure_optimizers(self): + """Configures and returns the optimizer to use for training. + + If using LBFGS optimizer, set `using_lbfgs` attribute to True when + initializing the DLKoopmanRegressor instance. + + Returns: + An instance of torch.optim.Optimizer to use for training. + """ + if self.using_lbfgs: + optimizer = torch.optim.LBFGS( + self.parameters(), + lr=1, + history_size=100, + max_iter=20, + line_search_fn="strong_wolfe", + ) + else: + optimizer = torch.optim.Adam(self.parameters(), lr=1e-3) + return optimizer + + +class SeqDataDataset(Dataset): + """ + A PyTorch Dataset class to handle sequential data in the format of (x, y, ys), + where x is the input sequence, y is the target output sequence and ys is a vector + indicating the maximum look-ahead distance. + + Args: + x (torch.Tensor): The input sequence tensor of shape (batch_size, + sequence_length, input_size). + y (torch.Tensor): The output sequence tensor of shape (batch_size, + sequence_length, output_size). + ys (torch.Tensor): The maximum look-ahead distance tensor of shape + (batch_size,). + transform (callable, optional): Optional normalization function to apply to + x and y. + + Returns: + torch.Tensor: The preprocessed input sequence tensor. + torch.Tensor: The preprocessed target output sequence tensor. + torch.Tensor: The maximum look-ahead distance tensor. + """ + + def __init__(self, x, y, ys, transform=None): + self.x = x.squeeze(1) + self.y = y + self.ys = ys + self.normalization = transform + + def __len__(self): + return len(self.ys) + + def __getitem__(self, idx): + x = self.x[idx].clone() + y = self.y[idx].clone() + ys = self.ys[idx].clone() + + if self.normalization: + x = self.normalization(x) + y = self.normalization(y) + + return x, y, ys + + +class TensorNormalize(nn.Module): + """ + Normalizes the input tensor by subtracting the mean and dividing by the standard + deviation. + + Args: + mean (float or tensor): The mean value to be subtracted from the input tensor. + std (float or tensor): The standard deviation value to divide the input tensor + by. + """ + + def __init__(self, mean, std): + super().__init__() + self.mean = mean + self.std = std + + def forward(self, tensor: torch.Tensor): + """ + Forward pass of the normalization module. + + Args: + tensor (tensor): The input tensor to be normalized. + + Returns: + The normalized tensor. + """ + return torch.divide((tensor - self.mean), self.std) + # return # tensor.copy_(tensor.sub_(self.mean).div_(self.std)) + + def __repr__(self) -> str: + """ + Returns a string representation of the TensorNormalize module. + + Returns: + A string representation of the module. + """ + return f"{self.__class__.__name__}(mean={self.mean}, std={self.std})" + + +class InverseTensorNormalize(nn.Module): + """ + A PyTorch module that performs inverse normalization on input tensors using + a given mean and standard deviation. + + Args: + mean (float or sequence): The mean used for normalization. + std (float or sequence): The standard deviation used for normalization. + + Example: + >>> mean = [0.5, 0.5, 0.5] + >>> std = [0.5, 0.5, 0.5] + >>> inv_norm = InverseTensorNormalize(mean, std) + >>> normalized_tensor = torch.tensor([[-1.0, 0.0, 1.0], [-0.5, 0.0, 0.5]]) + >>> output = inv_norm(normalized_tensor) + + Attributes: + mean (float or sequence): The mean used for normalization. + std (float or sequence): The standard deviation used for normalization. + """ + + def __init__(self, mean, std): + super().__init__() + self.mean = mean + self.std = std + + def forward(self, tensor: torch.Tensor): + return torch.multiply(tensor, self.std) + self.mean + # return tensor.copy_(tensor.mul_(self.std).add_(self.mean)) + + def __repr__(self) -> str: + return f"{self.__class__.__name__}(mean={self.mean}, std={self.std})" + + +class SeqDataModule(L.LightningDataModule): + """ + Class for creating sequence data dataloader for training and validation. + + Args: + data_tr: List of 2D numpy.ndarray representing training data trajectories. + data_val: List of 2D numpy.ndarray representing validation data trajectories. + Can be None. + look_forward: Number of time steps to predict forward. + batch_size: Size of each batch of data. + normalize: Whether to normalize the input data or not. Default is True. + normalize_mode: The type of normalization to use. Either "equal" or "max". + Default is "equal". + normalize_std_factor: Scaling factor for standard deviation during + normalization. Default is 2.0. + + Methods: + prepare_data(): Prepares the data by converting to time-delayed data and + computing mean and std if normalize is True. + setup(stage=None): Sets up training and validation datasets. + train_dataloader(): Returns a DataLoader for training data. + val_dataloader(): Returns a DataLoader for validation data. + convert_seq_list_to_delayed_data(data_list, look_back, look_forward): Converts + list of sequences to time-delayed data. + collate_fn(batch): Custom collate function to be used with DataLoader. + + Returns: + A SeqDataModule object. + """ + + def __init__( + self, + data_tr, + data_val, + look_forward=10, + batch_size=32, + normalize=True, + normalize_mode="equal", + normalize_std_factor=2.0, + ): + """ + Initialize a SeqDataModule. + + Args: + data_tr (Union[str, List[np.ndarray]]): Training data. Can be either a + list of 2D numpy arrays, each 2D numpy array representing a trajectory, + or the path to a pickle file containing such a list. + data_val (Optional[Union[str, List[np.ndarray]]]): Validation data. + Can be either a list of 2D numpy arrays, each 2D numpy array + representing a trajectory, or the path to a pickle file + containing such a list. + look_forward (int): Number of time steps to predict into the future. + batch_size (int): Number of samples per batch. + normalize (bool): Whether to normalize the data. Default is True. + normalize_mode (str): Mode for normalization. Can be either "equal" + or "max". "equal" divides by the standard deviation, while "max" + divides by the maximum absolute value of the data. Default is "equal". + normalize_std_factor (float): Scaling factor for the standard deviation in + normalization. Default is 2.0. + + Returns: + None. + """ + super().__init__() + # input data_tr or data_val is a list of 2D np.ndarray. each 2d + # np.ndarray is a trajectory, and the axis 0 is number of samples, axis 1 is + # the number of system state + self.data_tr = data_tr + self.data_val = data_val + self.look_forward = look_forward + self.batch_size = batch_size + self.look_back = 1 + self.normalize = normalize + self.normalize_mode = normalize_mode + self.normalization = None + self.inverse_transform = None + self.normalize_std_factor = normalize_std_factor + + def prepare_data(self): + """ + Preprocesses the input training and validation data by checking their types, + checking for normalization, finding the mean and standard deviation of + the training data (if normalization is enabled), and creating time-delayed data + from the input data. + + Raises: + ValueError: If the training data is None or has an invalid type. + ValueError: If the validation data has an invalid type. + TypeError: If the data is complex or not float. + + """ + # train data + if self.data_tr is None: + raise ValueError("You must feed training data!") + if isinstance(self.data_tr, list): + data_list = self.data_tr + elif isinstance(self.data_tr, str): + f = open(self.data_tr, "rb") + data_list = pickle.load(f) + else: + raise ValueError("Wrong type of `self.data_tr`") + + # check train data + data_list = self.check_list_of_nparray(data_list) + + # find the mean, std + if self.normalize: + stacked_data_list = np.vstack(data_list) + mean = stacked_data_list.mean(axis=0) + std = stacked_data_list.std(axis=0) + + # zero mean so easier for downstream + self.mean = torch.FloatTensor(mean) * 0 + # default = 2.0, more stable + self.std = torch.FloatTensor(std) * self.normalize_std_factor + + if self.normalize_mode == "max": + self.std = torch.ones_like(self.std) * self.std.max() + + # prevent divide by zero error + for i in range(len(self.std)): + if self.std[i] < 1e-6: + self.std[i] += 1e-3 + + # get transform + self.normalization = TensorNormalize(self.mean, self.std) + + # get inverse transform + self.inverse_transform = InverseTensorNormalize(self.mean, self.std) + + # create time-delayed data + self._tr_x, self._tr_yseq, self._tr_ys = self.convert_seq_list_to_delayed_data( + data_list, self.look_back, self.look_forward + ) + + # validation data + if self.data_val is not None: + # raise ValueError("You need to feed validation data!") + if isinstance(self.data_val, list): + data_list = self.data_val + elif isinstance(self.data_val, str): + f = open(self.data_val, "rb") + data_list = pickle.load(f) + else: + raise ValueError("Wrong type of `self.data_val`") + + # check val data + data_list = self.check_list_of_nparray(data_list) + + # create time-delayed data + ( + self._val_x, + self._val_yseq, + self._val_ys, + ) = self.convert_seq_list_to_delayed_data( + data_list, self.look_back, self.look_forward + ) + else: + warn("Warning: no validation data prepared") + + def setup(self, stage=None): + """ + Prepares the train and validation datasets for the Lightning module. + The train dataset is created from the training data specified in the + constructor by creating time-delayed versions of the input/output sequences. + If `normalize` is True, the data is normalized using the mean and standard + deviation of the training data. The validation dataset is created from the + validation data specified in the constructor in the same way as the training + dataset. If `normalize` is True, it is also normalized using the mean and + standard deviation of the training data. If `stage` is not "fit", + an exception is raised as the `setup()` method has not been implemented + for other stages. + + Args: + stage: The stage of training, validation or testing (default is None). + + Raises: + NotImplementedError: If `stage` is not "fit". + """ + # Load data and split into train and validation sets here + # Assign train/val datasets for use in dataloaders + if stage == "fit": + self.tr_dataset = SeqDataDataset( + self._tr_x, self._tr_yseq, self._tr_ys, self.normalization + ) + if self.data_val is not None: + self.val_dataset = SeqDataDataset( + self._val_x, self._val_yseq, self._val_ys, self.normalization + ) + else: + raise NotImplementedError("We didn't implement for stage not `fit`") + + def train_dataloader(self): + return DataLoader( + self.tr_dataset, self.batch_size, shuffle=True, collate_fn=self.collate_fn + ) + + def val_dataloader(self): + return DataLoader( + self.val_dataset, self.batch_size, shuffle=True, collate_fn=self.collate_fn + ) + + def convert_seq_list_to_delayed_data(self, data_list, look_back, look_forward): + """ + Converts a list of sequences to time-delayed data by extracting subsequences + of length `look_back` and `look_forward` from each sequence in the list. + + Args: + data_list (List[np.ndarray]): A list of 2D numpy arrays. Each array + represents a trajectory, with axis 0 representing the number of samples + and axis 1 representing the number of system states. + look_back (int): The number of previous time steps to include in each + subsequence. + look_forward (int): The number of future time steps to include in each + subsequence. + + Returns: + Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: A tuple containing three + tensors: + 1) The time-delayed input data, with shape (num_samples, look_back, + num_system_states). + 2) The time-delayed output data, with shape (num_samples, look_forward, + num_system_states). + 3) The sequence lengths of the output data, with shape (num_samples,). + """ + time_delayed_x_list = [] + time_delayed_yseq_list = [] + for seq in data_list: + # if self.look_forward + self.look_back > len(seq): + # raise ValueError("look_forward too large") + n_sub_traj = len(seq) - look_back - look_forward + 1 + if n_sub_traj >= 1: + for i in range(len(seq) - look_back - look_forward + 1): + time_delayed_x_list.append(seq[i : i + look_back]) + time_delayed_yseq_list.append( + seq[i + look_back : i + look_back + look_forward] + ) + else: + # only 1 traj, just to predict to its end + time_delayed_x_list.append(seq[0:1]) + time_delayed_yseq_list.append(seq[1:]) + time_delayed_yseq_lens_list = [x.shape[0] for x in time_delayed_yseq_list] + + # convert data to tensor + time_delayed_x = torch.FloatTensor(np.array(time_delayed_x_list)) + time_delayed_yseq = pad_sequence( + [torch.FloatTensor(x) for x in time_delayed_yseq_list], True + ) + time_delayed_yseq_lens = torch.LongTensor(time_delayed_yseq_lens_list) + return time_delayed_x, time_delayed_yseq, time_delayed_yseq_lens + + def collate_fn(self, batch): + """ + Collates a batch of data. + + Args: + batch: A list of tuples where each tuple represents a sample containing + the input sequence `x`, the output sequence `y`, and the maximum + number of steps to predict `ys`. + + Returns: + A tuple containing the input sequences as a stacked tensor, the output + sequences as a stacked tensor, and the maximum number of steps to predict + as a stacked tensor. + + """ + x_batch, y_batch, ys_batch = zip(*batch) + xx = torch.stack(x_batch, 0) + yy = torch.stack(y_batch, 0) + ys = torch.stack(ys_batch, 0) + return xx, yy, ys + + @classmethod + def check_list_of_nparray(cls, data_list): + """ + Check if the input is a list of numpy arrays, and convert data to float32 if + float64. + + Args: + data_list (List[np.ndarray]): A list of numpy arrays representing system + states. + + Returns: + List[np.ndarray]: The input list of numpy arrays converted to float32. + + Raises: + TypeError: If the input data is complex or not float. + """ + # check if data is complex + if any(np.iscomplexobj(x) for x in data_list): + raise TypeError("Complex data is not supported") + + # check if data has float64 + if any(x.dtype is np.float64 for x in data_list): + warn("Found float64 data. Will convert to float32") + + # convert data to float32 if float64 + for i, data_traj in enumerate(data_list): + if "float" not in data_traj.dtype.name: + raise TypeError("Found data is not float") + if data_traj.dtype.name == "float64": + data_list[i] = data_traj.astype("float32") + + return data_list + + +class NNDMD(BaseRegressor): + """Implementation of Nonlinear Dynamic Mode Decomposition (NNDMD). + + Args: + mode (str): NNDMD mode, `Dissipative` or `Hamiltonian` or else (default: None). + dt (float): Time step (default: 1.0). + look_forward (int): Number of steps to look forward (default: 1). + config_encoder (dict): Configuration for the encoder network + (default: dict(input_size=2, hidden_sizes=[32]*2, output_size=6, + activations='tanh')). + config_decoder (dict): Configuration for the decoder network + (default: dict(input_size=6, hidden_sizes=[32]*2, output_size=2, + activations='linear')). + batch_size (int): Batch size (default: 16). + lbfgs (bool): Whether to use L-BFGS optimizer (default: False). + normalize (bool): Whether to normalize data (default: True). + normalize_mode (str): Normalization mode, `max` or `equal` + (default: 'equal'). + normalize_std_factor (float): Standard deviation factor for normalization + (default: 2.0). + trainer_kwargs (dict): Arguments for the `pytorch_lightning.Trainer` + (default: {}). + + Attributes: + coef_ (np.ndarray): Koopman operator coefficients. + state_matrix_ (np.ndarray): State matrix of the Koopman operator. + eigenvalues_ (np.ndarray): Eigenvalues of the Koopman operator. + eigenvectors_ (np.ndarray): Eigenvectors of the Koopman operator. + ur (np.ndarray): Effective linear transformation. + unnormalized_modes (np.ndarray): Unnormalized modes. + + Note: + The `n_samples_` attribute is meaningless for this class. + The `dt` argument is only included to please the regressor class and has no + real use. + + """ + + def __init__( + self, + mode=None, + dt=1.0, + look_forward=1, + config_encoder=dict( + input_size=2, hidden_sizes=[32] * 2, output_size=6, activations="tanh" + ), + config_decoder=dict( + input_size=6, hidden_sizes=[32] * 2, output_size=2, activations="linear" + ), + batch_size=16, + lbfgs=False, + normalize=True, + normalize_mode="equal", + normalize_std_factor=2.0, + std_koopman=1e-1, + include_state=False, + trainer_kwargs={}, + ): + """Initializes the NNDMD model.""" + self.mode = mode + self.look_forward = look_forward + self.config_encoder = config_encoder + self.config_decoder = config_decoder + self.lbfgs = lbfgs + self.normalize = normalize + self.normalize_mode = normalize_mode + self.dt = dt + self.trainer_kwargs = trainer_kwargs + self.normalize_std_factor = normalize_std_factor + self.batch_size = batch_size + self.std_koopman = std_koopman + self.include_state = include_state + + # build DLK regressor + self._regressor = DLKoopmanRegressor( + mode, dt, look_forward, config_encoder, config_decoder, lbfgs, std_koopman + ) + + def fit(self, x, y=None, dt=None): + """fit the NNDMD model with data x,y + + Args: + x (np.ndarray or list): The training input data. If a 2D numpy array, + then it represents a single time-series and each row represents a + state, otherwise it should be a list of 2D numpy arrays. + y (np.ndarray or list, optional): The target output data, + corresponding to `x`. If `None`, `x` is assumed to contain the target + data in its second half. Defaults to `None`. + dt (float, optional): The time step used to generate `x`. + Defaults to `None`. + + Returns: + None. The fitted model is stored in the class attribute `_regressor`. + """ + # build trainer + self.trainer = L.Trainer(**self.trainer_kwargs) + + self.n_input_features_ = self.config_encoder["input_size"] + + # create the data module + # case 1: a single traj, x is 2D np.ndarray, no validation + if y is None and isinstance(x, np.ndarray) and x.ndim == 2: + t0, t1 = x[:-1], x[1:] + list_of_traj = [np.stack((t0[i], t1[i]), 0) for i in range(len(x) - 1)] + self.dm = SeqDataModule( + list_of_traj, + None, + self.look_forward, + self.batch_size, + self.normalize, + self.normalize_mode, + self.normalize_std_factor, + ) + self.n_samples_ = len(list_of_traj) + + # case 2: x, y are 2D np.ndarray, no validation + elif ( + isinstance(x, np.ndarray) + and isinstance(y, np.ndarray) + and x.ndim == 2 + and y.ndim == 2 + ): + t0, t1 = x, y + list_of_traj = [np.stack((t0[i], t1[i]), 0) for i in range(len(x) - 1)] + self.dm = SeqDataModule( + list_of_traj, + None, + self.look_forward, + self.batch_size, + self.normalize, + self.normalize_mode, + self.normalize_std_factor, + ) + self.n_samples_ = len(list_of_traj) + + # case 3: only training data, x is a list of trajectories, y is None + elif isinstance(x, list) and y is None: + self.dm = SeqDataModule( + x, + None, + self.look_forward, + self.batch_size, + self.normalize, + self.normalize_mode, + self.normalize_std_factor, + ) + self.n_samples_ = len(x) + + # case 4: x, y are two lists of trajectories, we have validation data + elif isinstance(x, list) and isinstance(y, list): + self.dm = SeqDataModule( + x, + y, + self.look_forward, + self.batch_size, + self.normalize, + self.normalize_mode, + self.normalize_std_factor, + ) + self.n_samples_ = len(x) + else: + raise ValueError("check `x` and `y` for `self.fit`") + + # trainer starts to train + self.trainer.fit(self._regressor, self.dm) + + # compute Koopman operator information + self._state_matrix_ = ( + self._regressor._koopman_propagator.get_discrete_time_Koopman_Operator() + .detach() + .numpy() + ) + [self._eigenvalues_, self._eigenvectors_] = np.linalg.eig(self._state_matrix_) + + self._coef_ = self._state_matrix_ + + # obtain effective linear transformation + decoder_weight_list = [] + for i in range(len(self._regressor._decoder.layers)): + decoder_weight_list.append( + self._regressor._decoder.layers[i].weight.detach().numpy() + ) + if len(decoder_weight_list) > 1: + self._ur = np.linalg.multi_dot(decoder_weight_list[::-1]) + else: + self._ur = decoder_weight_list[0] + + if self.normalize: + std = self.dm.inverse_transform.std + self._ur = np.diag(std) @ self._ur + + self._unnormalized_modes = self._ur @ self._eigenvectors_ + + def predict(self, x, n=1): + """ + Predict the system state after n steps away from x_0 = x. + + Args: + x (numpy.ndarray or torch.Tensor): Input data of shape + (n_samples, n_features). + n (int): Number of steps to predict the system state into the future. + + Returns: + numpy.ndarray: Predicted system state after n steps, of shape + (n_samples, n_features). + + Note: + By default, the model is stored on the CPU for inference. + """ + self._regressor.eval() + x, _ = self._detect_reshape(x, offset=False) + x = self._convert_input_ndarray_to_tensor(x) + + with torch.no_grad(): + # print("inference device = ", self._regressor.device) + + if self.normalize: + y = self.dm.normalization(x) + y = self._regressor(y, n) + y = self.dm.inverse_transform(y).numpy() + else: + y = self._regressor(x, n).numpy() + y = self._return_orig_shape(y) + return y + + def simulate(self, x, n_steps): + """ + Simulate the system forward in time for `n_steps` steps starting from `x`. + + Args: + x (np.ndarray or torch.Tensor): The initial state of the system. + Should be a 2D array/tensor. + n_steps (int): The number of time steps to simulate the system forward. + + Returns: + np.ndarray: The simulated states of the system. Will be of shape + `(n_steps+1, n_features)`. + """ + self._regressor.eval() + x = self._convert_input_ndarray_to_tensor(x) + x_future = torch.zeros([n_steps + 1, x.size(1)]) + x_future[0] = x + with torch.no_grad(): + for i in range(n_steps): + if self.normalize: + y = self.dm.normalization(x) + y = self._regressor(y, i + 1) + x_future[i + 1] = self.dm.inverse_transform(y) + else: + x_future[i + 1] = self._regressor(x, i + 1) + + return x_future.numpy() + + @property + def A(self): + """Returns the state transition matrix A of the NNDMD model. + + Returns + ------- + A : numpy.ndarray + The state transition matrix of shape (n_states, n_states), where + n_states is the number of states in the model. + """ + return self._state_matrix_ + + @property + def B(self): + # todo: we don't have control considered in nndmd for now + pass + + @property + def C(self): + """ + Returns the matrix C representing the effective linear transformation + from the observables to the Koopman modes. The matrix C is computed during + the fit process as the product of the decoder weights of the trained + autoencoder network. + + Returns: + -------- + numpy.ndarray of shape (n_koopman, n_features) + The matrix C. + """ + return self._ur + + @property + def W(self): + """ + Returns the matrix W representing the Koopman modes. The matrix W is computed + during the fit process as the eigenvectors of the Koopman operator. + + Returns: + -------- + numpy.ndarray of shape (n_koopman, n_koopman) + The matrix V, where each column represents a Koopman mode. + """ + return self._unnormalized_modes + + def phi(self, x_col): + return self._compute_phi(x_col) + + def psi(self, x_col): + return self._compute_psi(x_col) + + def _compute_phi(self, x_col): + """ + Computes the Koopman observable vector `phi(x)` for input `x`. + + Args: + x (np.ndarray or torch.Tensor): The input state vector or tensor. + + Returns: + phi (np.ndarray): The Koopman observable vector `phi(x)` for input `x`. + """ + if x_col.ndim == 1: + x_col = x_col.reshape(-1, 1) + x = x_col.T + + self._regressor.eval() + x = self._convert_input_ndarray_to_tensor(x) + + if self.normalize: + x = self.dm.normalization(x) + phi = self._regressor._encoder(x).detach().numpy().T + return phi + + def _compute_psi(self, x_col): + """ + Computes the Koopman eigenfunction expansion coefficients `psi(x)` given `x`. + + Args: + x (numpy.ndarray): Input data of shape `(n_samples, n_features)`. + + Returns: + numpy.ndarray: Koopman eigenfunction expansion coefficients `psi(x)` + of shape `(n_koopman, n_samples)`. + """ + if x_col.ndim == 1: + x_col = x_col.reshape(-1, 1) + # x = x_col.T + + phi = self._compute_phi(x_col) + psi = np.linalg.inv(self._eigenvectors_) @ phi + return psi + + def _convert_input_ndarray_to_tensor(self, x): + """ + Converts input numpy ndarray to PyTorch tensor with appropriate dtype and + device. + + Args: + x (np.ndarray or torch.Tensor): Input data as numpy ndarray or PyTorch + tensor. + + Returns: + torch.Tensor: Input data as PyTorch tensor. + + Raises: + TypeError: If input data is not a numpy ndarray or PyTorch tensor. + ValueError: If input array has more than 2 dimensions. + """ + if isinstance(x, np.ndarray): + if x.ndim > 2: + raise ValueError("input array should be 1 or 2D") + if x.ndim == 1: + x = x.reshape(1, -1) + # convert to a float32 + # if x.dtype == np.float64: + x = torch.FloatTensor(x) + elif isinstance(x, torch.Tensor): + if x.ndim != 2: + raise ValueError("input tensor `x` must be a 2d tensor") + return x + + @property + def coef_(self): + check_is_fitted(self, "_coef_") + return self._coef_ + + @property + def state_matrix_(self): + return self._state_matrix_ + + @property + def eigenvalues_(self): + check_is_fitted(self, "_eigenvalues_") + return self._eigenvalues_ + + @property + def eigenvectors_(self): + check_is_fitted(self, "_eigenvectors_") + return self._eigenvectors_ + + @property + def unnormalized_modes(self): + check_is_fitted(self, "_unnormalized_modes") + return self._unnormalized_modes + + @property + def ur(self): + check_is_fitted(self, "_ur") + return self._ur + + +if __name__ == "__main__": + pass From 53ef2f1873b07f4604a3044eb04b66a92b8218ac Mon Sep 17 00:00:00 2001 From: ostrow Date: Mon, 27 Oct 2025 18:41:20 -0400 Subject: [PATCH 03/90] add new files for inputDSA --- DSA/base_dmd.py | 186 +++++++++ DSA/controllability_simdist.py | 189 +++++++++ DSA/dmd.py | 234 ++--------- DSA/dmdc.py | 0 DSA/stats.py | 43 +- DSA/subspace_dmdc.py | 737 +++++++++++++++++++++++++++++++++ 6 files changed, 1176 insertions(+), 213 deletions(-) create mode 100644 DSA/base_dmd.py create mode 100644 DSA/controllability_simdist.py create mode 100644 DSA/dmdc.py create mode 100644 DSA/subspace_dmdc.py diff --git a/DSA/base_dmd.py b/DSA/base_dmd.py new file mode 100644 index 0000000..f8e3d28 --- /dev/null +++ b/DSA/base_dmd.py @@ -0,0 +1,186 @@ +"""Base class for DMD implementations.""" + +import numpy as np +import torch +from abc import ABC, abstractmethod + + +class BaseDMD(ABC): + """Base class for DMD implementations with common functionality.""" + + def __init__( + self, + device="cpu", + verbose=False, + send_to_cpu=False, + lamb=0, + ): + """ + Parameters + ---------- + device: string, int, or torch.device + A string, int or torch.device object to indicate the device to torch. + verbose: bool + If True, print statements will be provided about the progress of the fitting procedure. + send_to_cpu: bool + If True, will send all tensors in the object back to the cpu after everything is computed. + This is implemented to prevent gpu memory overload when computing multiple DMDs. + lamb : float + Regularization parameter for ridge regression. Defaults to 0. + """ + self.device = device + self.verbose = verbose + self.send_to_cpu = send_to_cpu + self.lamb = lamb + + # Common attributes + self.data = None + self.n = None + self.ntrials = None + self.is_list_data = False + + # SVD attributes - will be set by subclasses + self.cumulative_explained_variance = None + + def _process_single_dataset(self, data): + """Process a single dataset, handling numpy arrays, tensors, and lists.""" + if isinstance(data, list): + try: + # Attempt to convert to a single tensor if possible (non-ragged) + processed_data = [ + torch.from_numpy(d) if isinstance(d, np.ndarray) else d + for d in data + ] + return torch.stack(processed_data), False + except (RuntimeError, ValueError): + # Handle ragged lists + processed_data = [ + torch.from_numpy(d) if isinstance(d, np.ndarray) else d + for d in data + ] + # Check for consistent last dimension + n_features = processed_data[0].shape[-1] + if not all(d.shape[-1] == n_features for d in processed_data): + raise ValueError( + "All tensors in the list must have the same number of features (last dimension)." + ) + return processed_data, True + + elif isinstance(data, np.ndarray): + return torch.from_numpy(data), False + + return data, False + + def _init_single_data(self, data): + """Initialize data attributes for a single dataset.""" + processed_data, is_ragged = self._process_single_dataset(data) + + if is_ragged: + # Set attributes for ragged data + n_features = processed_data[0].shape[-1] + self.n = n_features + self.ntrials = sum( + d.shape[0] if d.ndim == 3 else 1 for d in processed_data + ) + self.trial_counts = [ + d.shape[0] if d.ndim == 3 else 1 for d in processed_data + ] + self.is_list_data = True + else: + # Set attributes for non-ragged data + if processed_data.ndim == 3: + self.ntrials = processed_data.shape[0] + self.n = processed_data.shape[2] + else: + self.n = processed_data.shape[1] + self.ntrials = 1 + self.is_list_data = False + + return processed_data + + def _compute_explained_variance(self, S): + """Compute cumulative explained variance from singular values.""" + exp_variance = S**2 / torch.sum(S**2) + return torch.cumsum(exp_variance, 0) + + def _compute_rank_from_params(self, S, cumulative_explained_variance, max_rank, + rank=None, rank_thresh=None, rank_explained_variance=None): + """ + Compute rank based on provided parameters. + + Parameters + ---------- + S : torch.Tensor + Singular values + cumulative_explained_variance : torch.Tensor + Cumulative explained variance + max_rank : int + Maximum possible rank + rank : int, optional + Explicit rank specification + rank_thresh : float, optional + Threshold for singular values + rank_explained_variance : float, optional + Explained variance threshold + + Returns + ------- + int + Computed rank + """ + parameters_provided = [rank is not None, rank_thresh is not None, rank_explained_variance is not None] + num_parameters_provided = sum(parameters_provided) + + if num_parameters_provided > 1: + raise ValueError( + "More than one rank parameter was provided. Please provide only one of rank, rank_thresh, or rank_explained_variance." + ) + elif num_parameters_provided == 0: + computed_rank = len(S) + else: + if rank is not None: + computed_rank = rank + elif rank_thresh is not None: + # Find the number of singular values greater than the threshold + computed_rank = int((S > rank_thresh).sum().item()) + if computed_rank == 0: + computed_rank = 1 # Ensure at least rank 1 + elif rank_explained_variance is not None: + cumulative_explained_variance_cpu = cumulative_explained_variance.cpu().numpy() + computed_rank = int(np.searchsorted(cumulative_explained_variance_cpu, rank_explained_variance) + 1) + if computed_rank > len(S): + computed_rank = len(S) + + # Ensure rank doesn't exceed maximum possible + if computed_rank > max_rank: + computed_rank = max_rank + + return computed_rank + + def all_to_device(self, device="cpu"): + """Move all tensor attributes to specified device.""" + for k, v in self.__dict__.items(): + if isinstance(v, torch.Tensor): + self.__dict__[k] = v.to(device) + elif isinstance(v, list) and len(v) > 0 and isinstance(v[0], torch.Tensor): + self.__dict__[k] = [tensor.to(device) for tensor in v] + + @abstractmethod + def fit(self, *args, **kwargs): + """Fit the DMD model. Must be implemented by subclasses.""" + pass + + @abstractmethod + def predict(self, *args, **kwargs): + """Make predictions with the DMD model. Must be implemented by subclasses.""" + pass + + @abstractmethod + def compute_hankel(self, *args, **kwargs): + """Compute Hankel matrix. Must be implemented by subclasses.""" + pass + + @abstractmethod + def compute_svd(self, *args, **kwargs): + """Compute SVD. Must be implemented by subclasses.""" + pass diff --git a/DSA/controllability_simdist.py b/DSA/controllability_simdist.py new file mode 100644 index 0000000..0889847 --- /dev/null +++ b/DSA/controllability_simdist.py @@ -0,0 +1,189 @@ +from typing import Literal +import numpy as np +from scipy.linalg import orthogonal_procrustes + +from DSA.simdist import SimilarityTransformDist + +class ControllabilitySimilarityTransformDist: + """ + Procrustes analysis over vector fields / LTI systems. + Only Euclidean scoring is implemented in this closed-form version. + """ + def __init__( + self, + *, + score_method: Literal["euclidean", "angular"] = "euclidean", + alpha: float = 0.5, + joint_optim: bool = False, + ): + """ + Parameters + ---------- + score_method : {"euclidean", "angular"} + Distance method to use. Euclidean uses Frobenius norm, angular uses principal angles. + alpha : float + Weight (only used if you call fit_score with non-default behavior). + align_inputs : bool + If True, do two-sided Procrustes on controllability matrices (solve for C and C_u). + """ + self.score_method = score_method + self.alpha = alpha + self.joint_optim = joint_optim + + + def fit_score(self, A, B, A_control, B_control, alpha=0.5, return_distance_components=False): + C, C_u, sims_joint_euc, sims_joint_ang = self.compare_systems_procrustes( + A1=A, B1=A_control, A2=B, B2=B_control, align_inputs=self.joint_optim + ) + + + alpha = self.alpha if alpha is None else alpha + score_method = self.score_method + + if alpha == 0.5: + if return_distance_components: + if self.score_method == 'euclidean': + # sims_control_joint = np.linalg.norm(C @ A_control @ C_u - B_control, "fro") ** 2 + # sims_state_joint = np.linalg.norm(C @ A @ C.T - B, "fro") ** 2 + sims_control_joint = np.linalg.norm(C @ A_control @ C_u - B_control, "fro") + sims_state_joint = np.linalg.norm(C @ A @ C.T - B, "fro") + return sims_joint_euc, sims_state_joint, sims_control_joint + elif self.score_method == 'angular': + sims_control_joint = np.trace((C @ A_control @ C_u).T @ B_control) / (np.linalg.norm(C @ A_control @ C_u, 'fro') * np.linalg.norm(B_control, 'fro')) + sims_state_joint = np.trace((C @ A @ C.T).T @ B) / (np.linalg.norm(C @ A @ C.T, 'fro') * np.linalg.norm(B, 'fro')) + + sims_control_joint = np.clip(sims_control_joint, -1, 1) + sims_state_joint = np.clip(sims_state_joint, -1, 1) + sims_control_joint =np.arccos(sims_control_joint) + sims_state_joint =np.arccos(sims_state_joint) + sims_control_joint = np.clip(sims_control_joint, 0, np.pi) + sims_state_joint = np.clip(sims_state_joint, 0, np.pi) + + return sims_joint_ang, sims_state_joint, sims_control_joint + else: + if self.score_method == 'euclidean': + return sims_joint_euc + elif self.score_method == 'angular': + return sims_joint_ang + else: + raise ValueError('Choose between Euclidean or angular distance') + + elif alpha == 0.0: + return self.compare_A(A, B, score_method=score_method) + + else: + return self.compare_B(A_control, B_control, score_method=score_method) + + + def get_controllability_matrix(self, A, B): + """ + Computes the controllability matrix K = [B, AB, A^2B, ..., A^(n-1)B]. + + Args: + A (np.ndarray): The state matrix (n x n). + B (np.ndarray): The input matrix (n x m). + + Returns: + np.ndarray: The controllability matrix (n x n*m). + """ + n = A.shape[0] + K = B.copy() + current1_term = B.copy() # Start with A^0 * B = B + current2_term = B.copy() # Start with A^0 * B = B + + for i in range(1, n): + # current_term = np.linalg.matrix_power(A, i) @ B # Use stable matrix power function + current1_term = A @ current1_term + current2_term = A.T @ current2_term + + # Check for numerical instability + # term_norm = np.linalg.norm(current_term) + # if term_norm < 1e-12 or term_norm > 1e12: + # break + + # Check for linear dependence (rank deficiency) + K_test = np.hstack((K, current1_term, current2_term)) + # if np.linalg.matrix_rank(K_test) <= np.linalg.matrix_rank(K): + # break + + K = K_test + return K + + def compare_systems_procrustes(self, A1, B1, A2, B2, *, align_inputs=False, n=100): + """ + Compares two LTI systems by finding the optimal orthogonal transformation + that aligns their controllability matrices. + + This implements the fast, non-iterative solution to the Orthogonal + Procrustes problem. + + Args: + A1, B1 (np.ndarray): Matrices for the first system. + A2, B2 (np.ndarray): Matrices for the second system. + align_inputs (bool): If True, do two-sided Procrustes (not used in updated version). + n (int): Number of terms in controllability matrix. + + Returns + ------- + C : (n×n) orthogonal state transform + C_u : (p×p) orthogonal input/right transform (identity in updated version) + err : Frobenius residual + cos_sim : cosine similarity between K1 and aligned K2 + """ + # Build controllability matrices: K \in R^{n x p} + K1 = self.get_controllability_matrix(A1, B1) + K2 = self.get_controllability_matrix(A2, B2) + + if not align_inputs: + # One-sided: C = argmin ||K1 - C K2||_F + M = K2 @ K1.T + U, _, Vh = np.linalg.svd(M, full_matrices=False) + C = U @ Vh + K2_aligned = C @ K2 + err = np.linalg.norm(K1 - K2_aligned, "fro") + cos_sim = (np.vdot(K1, K2_aligned).real / + (np.linalg.norm(K1, "fro") * np.linalg.norm(K2, "fro"))) + cos_sim = np.clip(cos_sim, -1, 1) + cos_sim = np.arccos(cos_sim) + cos_sim = np.clip(cos_sim, 0, np.pi) + return C, np.eye(B2.shape[-1]), err, cos_sim + + # Two-sided: C, C_u = argmin ||K1 - C K2 C_u||_F + U1, S1, V1t = np.linalg.svd(K1, full_matrices=False) + U2, S2, V2t = np.linalg.svd(K2, full_matrices=False) + + C = U1 @ U2.T + C_u = V2t.T @ V1t # = V2 @ V1^T + + # import matplotlib.pyplot as plt + # plt.imshow(C_u) + # plt.savefig('C_u.png') + + #TODO: truncate C_u + #TODO: compute error on A and B instead of the observability matrix + K2_aligned = C @ K2 @ C_u + err = np.linalg.norm(K1 - K2_aligned, "fro") + cos_sim = (np.vdot(K1, K2_aligned).real / + (np.linalg.norm(K1, "fro") * np.linalg.norm(K2, "fro"))) + cos_sim = np.clip(cos_sim, -1, 1) + cos_sim = np.arccos(cos_sim) + cos_sim = np.clip(cos_sim, 0, np.pi) + + return C, C_u, err, cos_sim + + @staticmethod + def compare_A(A1, A2, score_method='euclidean'): + simdist = SimilarityTransformDist(iters=1000, score_method=score_method, lr=1e-3, verbose=True) + return simdist.fit_score(A1, A2, score_method=score_method) + + @staticmethod + def compare_B(B1, B2, score_method='euclidean'): + if score_method == 'euclidean': + R, _ = orthogonal_procrustes(B2.T, B1.T) + return np.linalg.norm(B1 - R.T @ B2, "fro") + # return np.linalg.norm(B1 - R.T @ B2, "fro") ** 2 + elif score_method == 'angular': + return np.trace(B1.T @ (R.T @ B2)) / (np.linalg.norm(B1, 'fro') * np.linalg.norm(R.T @ B2, 'fro')) + else: + raise ValueError('Choose between Euclidean or angular distance') + diff --git a/DSA/dmd.py b/DSA/dmd.py index a245a81..6e3e64a 100644 --- a/DSA/dmd.py +++ b/DSA/dmd.py @@ -2,6 +2,10 @@ import numpy as np import torch +try: + from .base_dmd import BaseDMD +except ImportError: + from base_dmd import BaseDMD def embed_signal_torch(data, n_delays, delay_interval=1): @@ -66,33 +70,8 @@ def embed_signal_torch(data, n_delays, delay_interval=1): return embedding -def create_shift_operator(n_features, n_delays, delay_interval, steps_ahead,verbose=False): - """ - Creates the shift operator matrix for a given delay embedding configuration. - - Args: - n_features (int): The number of features (N). - n_delays (int): The number of delays (d). - delay_interval (int): The delay interval (tau). - steps_ahead (int): The number of time steps ahead to predict. - - Returns: - torch.tensor: The shift operator matrix, or None if not constructible. - """ - if steps_ahead != delay_interval: - if verbose: - print("Shift operator is not constructible for the given parameters.") - return None - - embedding_dim = n_delays * n_features - shift_operator = torch.zeros((embedding_dim, embedding_dim)) - - # The bottom (d-1)N rows are the shift part - shift_operator[n_features:, :-n_features] = torch.eye((n_delays - 1) * n_features) - return shift_operator - -class DMD: +class DMD(BaseDMD): """DMD class for computing and predicting with DMD models.""" def __init__( @@ -104,12 +83,11 @@ def __init__( rank_thresh=None, rank_explained_variance=None, reduced_rank_reg=False, - lamb=0, + lamb=1e-8, device="cpu", verbose=False, send_to_cpu=False, steps_ahead=1, - substitute_shift_operator=False ): """ Parameters @@ -162,13 +140,11 @@ def __init__( steps_ahead: int The number of time steps ahead to predict. Defaults to 1. - - substitute_shift_operator: bool - If True, will substitute the bottom (d-1)N rows of the HAVOK operator with a custom shift operator. Defaults to True. """ - self.device = device - self._init_data(data) + super().__init__(device=device, verbose=verbose, send_to_cpu=send_to_cpu, lamb=lamb) + + self.data = self._init_single_data(data) self.n_delays = n_delays self.delay_interval = delay_interval @@ -176,11 +152,7 @@ def __init__( self.rank_thresh = rank_thresh self.rank_explained_variance = rank_explained_variance self.reduced_rank_reg = reduced_rank_reg - self.lamb = lamb - self.verbose = verbose - self.send_to_cpu = send_to_cpu self.steps_ahead = steps_ahead - self.substitute_shift_operator = substitute_shift_operator # Hankel matrix self.H = None @@ -195,50 +167,6 @@ def __init__( # DMD attributes self.A_v = None self.A_havok_dmd = None - self.is_list_data = isinstance(self.data, list) - - - def _init_data(self, data): - # check if the data is an np.ndarry - if so, convert it to Torch - if isinstance(data, list): - try: - # Attempt to convert to a single tensor if possible (non-ragged) - processed_data = [ - torch.from_numpy(d) if isinstance(d, np.ndarray) else d - for d in data - ] - self.data = torch.stack(processed_data) - except (RuntimeError, ValueError): - # Handle ragged lists - self.data = [ - torch.from_numpy(d) if isinstance(d, np.ndarray) else d - for d in data - ] - # check for consistent last dimension - n_features = self.data[0].shape[-1] - if not all(d.shape[-1] == n_features for d in self.data): - raise ValueError( - "All tensors in the list must have the same number of features (last dimension)." - ) - self.n = n_features - self.ntrials = sum( - d.shape[0] if d.ndim == 3 else 1 for d in self.data - ) - self.trial_counts = [ - d.shape[0] if d.ndim == 3 else 1 for d in self.data - ] - return - elif isinstance(data, np.ndarray): - data = torch.from_numpy(data) - self.data = data - # create attributes for the data dimensions - if self.data.ndim == 3: - self.ntrials = self.data.shape[0] - self.n = self.data.shape[2] - else: - self.n = self.data.shape[1] - self.ntrials = 1 - self.is_list_data = isinstance(self.data, list) def compute_hankel( self, @@ -273,9 +201,8 @@ def compute_hankel( print("Computing Hankel matrix ...") # if parameters are provided, overwrite them from the init - # if parameters are provided, overwrite them from the init if data is not None: - self._init_data(data) + self.data = self._init_single_data(data) self.n_delays = self.n_delays if n_delays is None else n_delays self.delay_interval = ( @@ -337,9 +264,7 @@ def compute_svd(self): self.S_mat_inv = torch.diag(1 / S).to(self.device) # compute explained variance - exp_variance_inds = self.S**2 / ((self.S**2).sum()) - cumulative_explained = torch.cumsum(exp_variance_inds, 0) - self.cumulative_explained_variance = cumulative_explained + self.cumulative_explained_variance = self._compute_explained_variance(self.S) # make the X and Y components of the regression by staggering the hankel eigen-time delay coordinates by time if self.reduced_rank_reg: @@ -432,53 +357,26 @@ def recalc_rank(self, rank, rank_thresh, rank_explained_variance): else rank_explained_variance ) - none_vars = ( - (self.rank is None) - + (self.rank_thresh is None) - + (self.rank_explained_variance is None) - ) - if none_vars < 2: - raise ValueError( - "More than one value was provided between rank, rank_thresh, and rank_explained_variance. Please provide only one of these, and ensure the others are None!" - ) - elif none_vars == 3: - self.rank = len(self.S) - + # Determine which singular values to use if self.reduced_rank_reg: S = self.proj_mat_S + cumulative_explained = self._compute_explained_variance(S) else: S = self.S - - if rank_thresh is not None: - if S[-1] > rank_thresh: - self.rank = len(S) - else: - self.rank = torch.argmax( - torch.arange(len(S), 0, -1).to(self.device) * (S < rank_thresh) - ) - - if rank_explained_variance is not None: - self.rank = int( - torch.argmax( - (self.cumulative_explained_variance > rank_explained_variance).type( - torch.int - ) - ) - .cpu() - .numpy() - ) - + cumulative_explained = self.cumulative_explained_variance + + # Get maximum possible rank h_shape_last = self.H_shapes[-1][-1] if self.is_list_data else self.H.shape[-1] - if self.rank > h_shape_last: - self.rank = h_shape_last - - if self.rank is None: - if S[-1] > self.rank_thresh: - self.rank = len(S) - else: - self.rank = torch.argmax( - torch.arange(len(S), 0, -1).to(self.device) * (S < self.rank_thresh) - ) + + # Use base class method to compute rank + self.rank = self._compute_rank_from_params( + S=S, + cumulative_explained_variance=cumulative_explained, + max_rank=h_shape_last, + rank=self.rank, + rank_thresh=self.rank_thresh, + rank_explained_variance=self.rank_explained_variance + ) def compute_havok_dmd(self, lamb=None): """ @@ -495,7 +393,7 @@ def compute_havok_dmd(self, lamb=None): print("Computing least squares fits to HAVOK DMD ...") self.lamb = self.lamb if lamb is None else lamb - + A_v = ( torch.linalg.inv( self.Vt_minus[:, : self.rank].T @ self.Vt_minus[:, : self.rank] @@ -504,28 +402,15 @@ def compute_havok_dmd(self, lamb=None): @ self.Vt_minus[:, : self.rank].T @ self.Vt_plus[:, : self.rank] ).T - self.A_v_learned = A_v - self.A_havok_dmd_learned = ( + self.A_v = A_v + self.A_havok_dmd = ( self.U @ self.S_mat[: self.U.shape[1], : self.rank] - @ self.A_v_learned + @ self.A_v @ self.S_mat_inv[: self.rank, : self.U.shape[1]] @ self.U.T ) - if self.substitute_shift_operator: - self.A_havok_dmd = self.A_havok_dmd_learned.clone() - shift_operator = create_shift_operator(self.n, self.n_delays, self.delay_interval, self.steps_ahead,self.verbose) - if shift_operator is not None: - self.A_havok_dmd[self.n:, :] = shift_operator[self.n:, :].to(self.device) - self.A_v = self.project_A_havok_to_Av(self.A_havok_dmd) - else: - self.A_havok_dmd = self.A_havok_dmd_learned - self.A_v = self.A_v_learned - else: - self.A_havok_dmd = self.A_havok_dmd_learned - self.A_v = self.A_v_learned - if self.verbose: print("Least squares complete! \n") @@ -581,22 +466,6 @@ def compute_reduced_rank_regression(self, lamb=None): if self.verbose: print("Reduced Rank Regression complete! \n") - def project_A_havok_to_Av(self, A_havok_dmd_matrix): - """ - Projects a full A_havok_dmd matrix back to the low-rank A_v space. - """ - if self.U is None or self.S_mat is None or self.S_mat_inv is None: - raise ValueError("SVD must be computed first.") - - A_v_projected = ( - self.S_mat_inv[:self.rank, :self.rank] - @ self.U[:, :self.rank].T - @ A_havok_dmd_matrix - @ self.U[:, :self.rank] - @ self.S_mat[:self.rank, :self.rank] - ) - return A_v_projected - def fit( self, data=None, @@ -683,6 +552,8 @@ def fit( if self.send_to_cpu: self.all_to_device("cpu") # send back to the cpu to save memory + + # print('After DMD fitting in dmd.py', self.A_v.shape) def predict(self, test_data=None, reseed=None, full_return=False): """ @@ -723,34 +594,23 @@ def predict(self, test_data=None, reseed=None, full_return=False): if reseed is None: reseed = 1 - U_r = self.U[:, :self.rank] - S_inv_r = self.S_mat_inv[:self.rank, :self.rank] - S_r = self.S_mat[:self.rank, :self.rank] + H_test_havok_dmd = torch.zeros(H_test.shape).to(self.device) + H_test_havok_dmd[:, :steps_ahead] = H_test[:, :steps_ahead] - # Project to v space - V_test_T = S_inv_r @ U_r.T @ H_test.transpose(1, 2) - V_test = V_test_T.transpose(1, 2) - - V_test_pred = torch.zeros(V_test.shape).to(self.device) - V_test_pred[:, :steps_ahead] = V_test[:, :steps_ahead] - - for t in range(steps_ahead, V_test.shape[1]): + A = self.A_havok_dmd.unsqueeze(0) + for t in range(steps_ahead, H_test.shape[1]): if t % reseed == 0: - v_t = V_test[:, t - steps_ahead] + H_test_havok_dmd[:, t] = ( + A @ H_test[:, t - steps_ahead].transpose(-2, -1) + ).transpose(-2, -1) else: - v_t = V_test_pred[:, t - steps_ahead] - - v_t_plus_1 = (self.A_v @ v_t.unsqueeze(-1)).squeeze(-1) - V_test_pred[:, t] = v_t_plus_1 - - # Project back to full space - H_test_pred = U_r @ S_r @ V_test_pred.transpose(1, 2) - H_test_pred = H_test_pred.transpose(1, 2) - + H_test_havok_dmd[:, t] = ( + A @ H_test_havok_dmd[:, t - steps_ahead].transpose(-2, -1) + ).transpose(-2, -1) pred_data = torch.hstack( [ test_data[:, : (self.n_delays - 1) * self.delay_interval + steps_ahead], - H_test_pred[:, steps_ahead:, : self.n], + H_test_havok_dmd[:, steps_ahead:, : self.n], ] ) @@ -758,14 +618,10 @@ def predict(self, test_data=None, reseed=None, full_return=False): pred_data = pred_data[0] if full_return: - return pred_data, H_test_pred, H_test, V_test_pred, V_test + return pred_data, H_test_havok_dmd, H_test else: return pred_data - def all_to_device(self, device="cpu"): - for k, v in self.__dict__.items(): - if isinstance(v, torch.Tensor): - self.__dict__[k] = v.to(device) def project_onto_modes(self): eigvals, eigvecs = torch.linalg.eigh(self.A_v) @@ -776,4 +632,4 @@ def project_onto_modes(self): ) # get the data that matches the shape of the original data - return projections, self.data[:, : -self.n_delays + 1] + return projections, self.data[:, : -self.n_delays + 1] \ No newline at end of file diff --git a/DSA/dmdc.py b/DSA/dmdc.py new file mode 100644 index 0000000..e69de29 diff --git a/DSA/stats.py b/DSA/stats.py index 1085183..35287af 100644 --- a/DSA/stats.py +++ b/DSA/stats.py @@ -104,6 +104,24 @@ def mse(x, y): return ((x - y) ** 2).mean().item() +def nmse(x,y): + """ + Compute the mean squared error, normalized by the variance of the ground truth. + + x : np.ndarray or torch.tensor + The ground truth time series. + y : np.ndarray or torch.tensor + The predicted time series. + + Returns + ------- + nmse_val : float + The normalized mean squared error between the provided arrays. + """ + x = torch_convert(x) + y = torch_convert(y) + return ((x - y) ** 2).mean().item() / ((x - x.mean()) ** 2).mean().item() + def r2(true_vals, pred_vals): """ @@ -253,6 +271,7 @@ def compute_all_stats(true_vals, pred_vals, rank, norm=True): return { "MAE": mae(true_vals, pred_vals), "MASE": mase(true_vals, pred_vals), + "NMSE": nmse(true_vals, pred_vals), "MSE": mse(true_vals, pred_vals), "R2": r2(true_vals, pred_vals), "Correl": correl(true_vals, pred_vals), @@ -520,27 +539,3 @@ def measure_transient_growth(A): # num_abscissa = np.max(np.real(np.linalg.eigvals((A + np.conj(A).T) / 2))) # return num_abscissa, l2norm return l2norm - -# def get_period(data,dt=None,units='samples'): - -# if data.ndim == 3: -# return np.mean([get_period(i,dt) for i in data]) -# if dt is None: -# fs = data.shape[0] -# dt = 1/fs -# else: -# fs = 1/dt -# chosen_freqs = [] - -# for comp in data.T: -# #channel-wise frequency computation -# freqs, amps = find_significant_frequencies(comp, surrogate_method='rs', fs=fs, return_amplitudes=True) -# chosen_freqs.append(freqs[np.argmax(np.abs(amps))]) - -# period = 1/np.mean(chosen_freqs) -# if units == 'time': -# return period -# elif units == 'samples': -# return period // dt -# else: -# raise ValueError(f"Invalid units: {units}") \ No newline at end of file diff --git a/DSA/subspace_dmdc.py b/DSA/subspace_dmdc.py new file mode 100644 index 0000000..ef8e151 --- /dev/null +++ b/DSA/subspace_dmdc.py @@ -0,0 +1,737 @@ +"""This module computes the subspace DMD with control (DMDc) model for a given dataset.""" +import numpy as np +import torch +#TODO: convert to torch below to match the DMD class + +class subspaceDMDc(): + """Subspace DMDc class for computing and predicting with DMD with control models. + """ + def __init__( + self, + data, + control_data=None, + n_delays=1, + rank=None, + lamb=1e-8, + device='cpu', + verbose=False, + send_to_cpu=False, + time_first=True, + backend='n4sid' + ): + self.data = data + self.control_data = control_data + self.A_v = None + self.B_v = None + self.C_v = None + self.info = None + self.n_delays = n_delays + self.rank = rank + self.time_first = time_first + self.backend = backend + self.lamb = lamb + + + def fit(self): + self.A_v, self.B_v, self.C_v, self.info = self.subspace_dmdc_multitrial_flexible( + y=self.data, + u=self.control_data, + p=self.n_delays, + f=self.n_delays, + n=self.rank, + backend=self.backend, + lamb=self.lamb) + + + + def subspace_dmdc_multitrial_QR_decomposition(self, y_list, u_list, p, f, n=None, lamb=1e-8, energy=0.999): + """ + Subspace-DMDc for multi-trial data with variable trial lengths. + Now use QR decomposition for computing the oblique projection as in N4SID implementations. + + Parameters: + - y_list: list of arrays, each (p_out, N_i) - output data for trial i + - u_list: list of arrays, each (m, N_i) - input data for trial i + - p: past window length + - f: future window length + - n: state dimension (auto-determined if None) + - ridge: regularization parameter (used only for rank selection/SVD; QR is exact) + - energy: energy threshold for rank selection + + Returns: + - A_hat, B_hat, C_hat: system matrices + - info: dictionary with additional information + """ + if len(y_list) != len(u_list): + raise ValueError("y_list and u_list must have same number of trials") + + n_trials = len(y_list) + p_out = y_list[0].shape[0] + m = u_list[0].shape[0] + + # Validate dimensions across trials + for i, (y_trial, u_trial) in enumerate(zip(y_list, u_list)): + if y_trial.shape[0] != p_out: + raise ValueError(f"Trial {i}: y has {y_trial.shape[0]} outputs, expected {p_out}") + if u_trial.shape[0] != m: + raise ValueError(f"Trial {i}: u has {u_trial.shape[0]} inputs, expected {m}") + if y_trial.shape[1] != u_trial.shape[1]: + raise ValueError(f"Trial {i}: y and u have different time lengths") + + def hankel_stack(X, start, L): + return np.concatenate([X[:, start + i:start + i + 1] for i in range(L)], axis=0) + + # Collect data from all trials + U_p_all = [] + Y_p_all = [] + U_f_all = [] + Y_f_all = [] + valid_trials = [] + T_per_trial = [] + + for trial_idx, (Y_trial, U_trial) in enumerate(zip(y_list, u_list)): + N_trial = Y_trial.shape[1] + T_trial = N_trial - (p + f) + 1 + + if T_trial <= 0: + print(f"Warning: Trial {trial_idx} has insufficient data (T={T_trial}), skipping") + continue + + valid_trials.append(trial_idx) + T_per_trial.append(T_trial) + + # Build Hankel matrices for this trial + U_p_trial = np.concatenate([hankel_stack(U_trial, j, p) for j in range(T_trial)], axis=1) + Y_p_trial = np.concatenate([hankel_stack(Y_trial, j, p) for j in range(T_trial)], axis=1) + U_f_trial = np.concatenate([hankel_stack(U_trial, j + p, f) for j in range(T_trial)], axis=1) + Y_f_trial = np.concatenate([hankel_stack(Y_trial, j + p, f) for j in range(T_trial)], axis=1) + + U_p_all.append(U_p_trial) + Y_p_all.append(Y_p_trial) + U_f_all.append(U_f_trial) + Y_f_all.append(Y_f_trial) + + if not valid_trials: + raise ValueError("No trials have sufficient data for given (p,f)") + + # Concatenate across valid trials + U_p = np.concatenate(U_p_all, axis=1) # (p m, T_total) + Y_p = np.concatenate(Y_p_all, axis=1) # (p p_out, T_total) + U_f = np.concatenate(U_f_all, axis=1) # (f m, T_total) + Y_f = np.concatenate(Y_f_all, axis=1) # (f p_out, T_total) + + T_total = sum(T_per_trial) + Z_p = np.vstack([U_p, Y_p]) # (p (m + p_out), T_total) + + H = np.vstack([U_f, Z_p, Y_f]) + + # Perform QR on H.T to get equivalent LQ on H + Q, R_upper = np.linalg.qr(H.T, mode='reduced') # H.T = Q R_upper, R_upper upper triangular + L = R_upper.T # L = R_upper.T, lower triangular + + # Dimensions for slicing + dim_uf = f * m + dim_zp = p * (m + p_out) + dim_yf = f * p_out + + # Extract submatrices from L (lower triangular) + R22 = L[dim_uf:dim_uf + dim_zp, dim_uf:dim_uf + dim_zp] + R32 = L[dim_uf + dim_zp:, dim_uf:dim_uf + dim_zp] + + # Compute oblique projection O = R32 @ pinv(R22) @ Z_p + O = R32 @ np.linalg.pinv(R22) @ Z_p + + # The rest remains the same: SVD on O + Uo, s, Vt = np.linalg.svd(O, full_matrices=False) + if n is None: + cs = np.cumsum(s**2) / (s**2).sum() + n = int(np.searchsorted(cs, energy) + 1) + n = max(1, min(n, min(Uo.shape[1], Vt.shape[0]))) + + U_n = Uo[:, :n] + S_n = np.diag(s[:n]) + V_n = Vt[:n, :] + S_half = np.sqrt(S_n) + Gamma_hat = U_n @ S_half # (f p_out, n) + X_hat = S_half @ V_n # (n, T_total) + + # Time alignment for regression across all trials + # Need to handle variable lengths carefully + X_segments = [] + X_next_segments = [] + U_mid_segments = [] + Y_segments = [] + + start_idx = 0 + for trial_idx, T_trial in enumerate(T_per_trial): + # Extract states for this trial + X_trial = X_hat[:, start_idx:start_idx + T_trial] + + # State transitions within this trial + X_trial_curr = X_trial[:, :-1] + X_trial_next = X_trial[:, 1:] + + # Corresponding control inputs + original_trial_idx = valid_trials[trial_idx] + U_trial = u_list[original_trial_idx] + U_mid_trial = U_trial[:, p:p + (T_trial - 1)] + + X_segments.append(X_trial_curr) + X_next_segments.append(X_trial_next) + U_mid_segments.append(U_mid_trial) + + # TODO: check the time-alignment of Y and X here + # Corresponding output data - align with X_trial time indices + Y_trial = y_list[original_trial_idx] + Y_trial_curr = Y_trial[:, p:p+T_trial-1] + # Y_trial_curr = Y_trial[:, p+1:p+T_trial] + Y_segments.append(Y_trial_curr) + + start_idx += T_trial + + # Concatenate all segments + X = np.concatenate(X_segments, axis=1) + X_next = np.concatenate(X_next_segments, axis=1) + U_mid = np.concatenate(U_mid_segments, axis=1) + + # Regression for A and B + Z = np.vstack([X, U_mid]) + # Ridge regression: (Z^T Z + λI)^(-1) Z^T X_next^T + ZTZ = Z @ Z.T + ridge_term = lamb * np.eye(ZTZ.shape[0]) + AB = np.linalg.solve(ZTZ + ridge_term, Z @ X_next.T).T + A_hat = AB[:, :n] + B_hat = AB[:, n:] + + # Z = np.vstack([X, U_mid]) + # AB = X_next @ np.linalg.pinv(Z) + # A_hat = AB[:, :n] + # B_hat = AB[:, n:] + + C_hat = Gamma_hat[:p_out, :] + + # Estimate noise covariance matrix + # 0) Outputs aligned to X and U_mid (same time indices/columns) + Y_curr = np.concatenate(Y_segments, axis=1) # shape: (p_out, N) + + # 1) Residuals at time t + # Process noise residual (state eq): w_t ≈ x_{t+1} - A x_t - B u_ts + W_hat = X_next - (A_hat @ X + B_hat @ U_mid) # (n, N) + + # Measurement noise residual (output eq): v_t ≈ y_t - C x_t (since D = 0) + V_hat = Y_curr - (C_hat @ X) # (p_out, N) + + # 2) Mean-centering + V_hat = V_hat - V_hat.mean(axis=1, keepdims=True) + W_hat = W_hat - W_hat.mean(axis=1, keepdims=True) + N_res = V_hat.shape[1] + denom = max(N_res - 1, 1) + + # 3) Covariances + R_hat = (V_hat @ V_hat.T) / denom # (p_out, p_out) measurement + Q_hat = (W_hat @ W_hat.T) / denom # (n, n) process + S_hat = (W_hat @ V_hat.T) / denom # (n, p_out) - cross (w,v) + + # 4) Symmetrize + eps = 1e-12 + R_hat = 0.5 * (R_hat + R_hat.T) + eps * np.eye(R_hat.shape[0]) + Q_hat = 0.5 * (Q_hat + Q_hat.T) + eps * np.eye(Q_hat.shape[0]) + + noise_covariance = np.block([[R_hat, S_hat.T], + [S_hat, Q_hat]]) + + info = { + "singular_values_O": s, + "rank_used": n, + "Gamma_hat": Gamma_hat, + "f": f, + "n_trials_total": n_trials, + "n_trials_used": len(valid_trials), + "valid_trials": valid_trials, + "T_per_trial": T_per_trial, + "T_total": T_total, + "trial_lengths": [y.shape[1] for y in y_list], + "noise_covariance": noise_covariance, + 'R_hat': R_hat, + 'Q_hat': Q_hat, + 'S_hat': S_hat + } + + return A_hat, B_hat, C_hat, info + + + + + def subspace_dmdc_multitrial_custom(self, y_list, u_list, p, f, n=None, lamb=1e-8, energy=0.999): + """ + Subspace-DMDc for multi-trial data with variable trial lengths. + + Parameters: + - y_list: list of arrays, each (p_out, N_i) - output data for trial i + - u_list: list of arrays, each (m, N_i) - input data for trial i + - p: past window length + - f: future window length + - n: state dimension (auto-determined if None) + - ridge: regularization parameter + - energy: energy threshold for rank selection∏ + + Returns: + - A_hat, B_hat, C_hat: system matrices + - info: dictionary with additional information + """ + if len(y_list) != len(u_list): + raise ValueError("y_list and u_list must have same number of trials") + + n_trials = len(y_list) + p_out = y_list[0].shape[0] + m = u_list[0].shape[0] + + # Validate dimensions across trials + + for i, (y_trial, u_trial) in enumerate(zip(y_list, u_list)): + if y_trial.shape[0] != p_out: + raise ValueError(f"Trial {i}: y has {y_trial.shape[0]} outputs, expected {p_out}") + if u_trial.shape[0] != m: + raise ValueError(f"Trial {i}: u has {u_trial.shape[0]} inputs, expected {m}") + if y_trial.shape[1] != u_trial.shape[1]: + raise ValueError(f"Trial {i}: y and u have different time lengths") + + def hankel_stack(X, start, L): + return np.concatenate([X[:, start + i:start + i + 1] for i in range(L)], axis=0) + + # Collect data from all trials + U_p_all = [] + Y_p_all = [] + U_f_all = [] + Y_f_all = [] + valid_trials = [] + T_per_trial = [] + + for trial_idx, (Y_trial, U_trial) in enumerate(zip(y_list, u_list)): + N_trial = Y_trial.shape[1] + T_trial = N_trial - (p + f) + 1 + + if T_trial <= 0: + print(f"Warning: Trial {trial_idx} has insufficient data (T={T_trial}), skipping") + continue + + valid_trials.append(trial_idx) + T_per_trial.append(T_trial) + + # Build Hankel matrices for this trial + U_p_trial = np.concatenate([hankel_stack(U_trial, j, p) for j in range(T_trial)], axis=1) + Y_p_trial = np.concatenate([hankel_stack(Y_trial, j, p) for j in range(T_trial)], axis=1) + U_f_trial = np.concatenate([hankel_stack(U_trial, j + p, f) for j in range(T_trial)], axis=1) + Y_f_trial = np.concatenate([hankel_stack(Y_trial, j + p, f) for j in range(T_trial)], axis=1) + + U_p_all.append(U_p_trial) + Y_p_all.append(Y_p_trial) + U_f_all.append(U_f_trial) + Y_f_all.append(Y_f_trial) + + + print("="*40) + print(f"Number of valid trials: {len(U_p_trial)}") + + if not valid_trials: + raise ValueError("No trials have sufficient data for given (p,f)") + + # Concatenate across valid trials + U_p = np.concatenate(U_p_all, axis=1) # (pm, T_total) + Y_p = np.concatenate(Y_p_all, axis=1) # (p*p_out, T_total) + U_f = np.concatenate(U_f_all, axis=1) # (fm, T_total) + Y_f = np.concatenate(Y_f_all, axis=1) # (f*p_out, T_total) + + T_total = sum(T_per_trial) + Z_p = np.vstack([U_p, Y_p]) # (p(m+p_out), T_total) + + # Oblique projection: remove row(U_f), project onto row(Z_p) + UfUfT = U_f @ U_f.T + Xsolve = np.linalg.solve(UfUfT + lamb*np.eye(UfUfT.shape[0]), U_f) + Pi_perp = np.eye(T_total) - U_f.T @ Xsolve + Yf_perp = Y_f @ Pi_perp + Zp_perp = Z_p @ Pi_perp + + ZZT = Zp_perp @ Zp_perp.T + Zp_pinv_left = np.linalg.solve(ZZT + lamb*np.eye(ZZT.shape[0]), Zp_perp) + P = Zp_perp.T @ Zp_pinv_left + O = Yf_perp @ P # ≈ Γ_f X_p + + Uo, s, Vt = np.linalg.svd(O, full_matrices=False) + if n is None: + cs = np.cumsum(s**2) / (s**2).sum() + n = int(np.searchsorted(cs, energy) + 1) + n = max(1, min(n, min(Uo.shape[1], Vt.shape[0]))) + + U_n = Uo[:, :n] + S_n = np.diag(s[:n]) + V_n = Vt[:n, :] + S_half = np.sqrt(S_n) + Gamma_hat = U_n @ S_half # (f*p_out, n) + X_hat = S_half @ V_n # (n, T_total) + + # Time alignment for regression across all trials + # Need to handle variable lengths carefully + X_segments = [] + X_next_segments = [] + U_mid_segments = [] + + start_idx = 0 + for trial_idx, T_trial in enumerate(T_per_trial): + # Extract states for this trial + X_trial = X_hat[:, start_idx:start_idx + T_trial] + + # State transitions within this trial + X_trial_curr = X_trial[:, :-1] + X_trial_next = X_trial[:, 1:] + + # Corresponding control inputs + original_trial_idx = valid_trials[trial_idx] + U_trial = u_list[original_trial_idx] + U_mid_trial = U_trial[:, p:p + (T_trial - 1)] + + X_segments.append(X_trial_curr) + X_next_segments.append(X_trial_next) + U_mid_segments.append(U_mid_trial) + + start_idx += T_trial + + # Concatenate all segments + X = np.concatenate(X_segments, axis=1) + X_next = np.concatenate(X_next_segments, axis=1) + U_mid = np.concatenate(U_mid_segments, axis=1) + + # Regression for A and B + Z = np.vstack([X, U_mid]) + # Ridge regression: (Z^T Z + λI)^(-1) Z^T X_next^T + ZTZ = Z @ Z.T + ridge_term = lamb * np.eye(ZTZ.shape[0]) + AB = np.linalg.solve(ZTZ + ridge_term, Z @ X_next.T).T + A_hat = AB[:, :n] + B_hat = AB[:, n:] + + C_hat = Gamma_hat[:p_out, :] + + info = { + "singular_values_O": s, + "rank_used": n, + "Gamma_hat": Gamma_hat, + "f": f, + "n_trials_total": n_trials, + "n_trials_used": len(valid_trials), + "valid_trials": valid_trials, + "T_per_trial": T_per_trial, + "T_total": T_total, + "trial_lengths": [y.shape[1] for y in y_list], + "X_hat": X_hat + } + + return A_hat, B_hat, C_hat, info + + + + def subspace_dmdc_multitrial_flexible(self, y, u, p, f, n=None, lamb=1e-8, energy=0.999, backend='n4sid'): + """ + Flexible wrapper that handles both fixed-length and variable-length multi-trial data. + + Parameters: + - y: either (n_trials, p_out, N) array, (p_out, N) array, or list of (p_out, N_i) arrays + - u: either (n_trials, m, N) array, (m, N) array, or list of (m, N_i) arrays + """ + if isinstance(y, list) and isinstance(u, list): + # If time_first=True, transpose each trial from (time_points, variables) to (variables, time_points) + if self.time_first: + y_list = [y_trial.T for y_trial in y] + u_list = [u_trial.T for u_trial in u] + else: + y_list = y + u_list = u + if backend == 'n4sid': + return self.subspace_dmdc_multitrial_QR_decomposition(y_list, u_list, p, f, n, lamb, energy) + else: + return self.subspace_dmdc_multitrial_custom(y_list, u_list, p, f, n, lamb, energy) + + else: + # Handle 2D arrays (single trial) by converting to list format + if y.ndim == 2: + y_list = [y] + u_list = [u] + else: + # Convert 3D arrays to list format + y_list = [y[i] for i in range(y.shape[0])] + u_list = [u[i] for i in range(u.shape[0])] + + # If time_first=True, transpose each trial from (time_points, variables) to (variables, time_points) + if self.time_first: + y_list = [y_trial.T for y_trial in y_list] + u_list = [u_trial.T for u_trial in u_list] + + if backend == 'n4sid': + return self.subspace_dmdc_multitrial_QR_decomposition(y_list, u_list, p, f, n, lamb, energy) + else: + return self.subspace_dmdc_multitrial_custom(y_list, u_list, p, f, n, lamb, energy) + + + def predict(self, Y, U, reseed=None): + # Y and U are (n_times, n_channels) or list of 2D arrays + if reseed is None: + reseed = 1 + + # Handle list of 2D arrays + if isinstance(Y, list): + if not self.time_first: + Y = [y.T for y in Y] + U = [u.T for u in U] + + self.kalman = OnlineKalman(self) + Y_pred = [] + for trial in range(len(Y)): + self.kalman.reset() # Reset filter for each trial + trial_predictions = [] + for t in range(Y[trial].shape[0]): + y_filtered, _ = self.kalman.step( + y=Y[trial][t] if t%reseed == 0 else None, + u=U[trial][t] + ) + trial_predictions.append(y_filtered) + Y_pred.append(np.concatenate(trial_predictions, axis=1).T) + return Y_pred # Return as list to match input format + + # print("time_first", self.time_first) + if not self.time_first: + if Y.ndim == 2: + Y = Y.T + U = U.T + else: + Y = Y.transpose(0, 2, 1) + U = U.transpose(0, 2, 1) + + self.kalman = OnlineKalman(self) + if Y.ndim == 2: + Y_pred = [] + for t in range(Y.shape[0]): + y_filtered, _ = self.kalman.step(y=Y[t] if t%reseed == 0 else None, u=U[t]) + Y_pred.append(y_filtered) + return np.concatenate(Y_pred, axis=1).T + else: + # 3D data (n_trials, time, p_out) + # print("Y.shape", Y.shape) + # print("U.shape", U.shape) + Y_pred = [] + for trial in range(Y.shape[0]): + self.kalman.reset() # Reset filter for each trial + trial_predictions = [] + for t in range(Y.shape[1]): + y_filtered, _ = self.kalman.step(y=Y[trial, t] if t%reseed == 0 else None, u=U[trial, t]) + trial_predictions.append(y_filtered) + # print("y_filtered.shape", y_filtered.shape) + Y_pred.append(np.concatenate(trial_predictions, axis=1).T) + return np.array(Y_pred) + + + +class OnlineKalman: + """ + Online Kalman Filter class for real-time state estimation. + + This class maintains the internal state of the Kalman filter and provides + a step method for updating the filter with new observations and inputs. + """ + + def __init__(self, dmdc): + """ + Initialize the Online Kalman Filter with a fitted DMDc model. + + Parameters + ---------- + dmdc : object + Fitted DMDc model containing A_v, B_v, C_v matrices and + noise covariance estimates (R_hat, S_hat, Q_hat) + """ + self.A = dmdc.A_v + self.B = dmdc.B_v + self.C = dmdc.C_v + self.R = dmdc.info['R_hat'] + self.S = dmdc.info['S_hat'] + self.Q = dmdc.info['Q_hat'] + + # Get dimensions + # print("C_shape", self.C.shape) + self.y_dim, self.x_dim = self.C.shape + + # Initialize state storage + self.p_filtereds = [] + self.x_filtereds = [] + self.p_predicteds = [] + self.x_predicteds = [] + self.us = [] + self.ys = [] + self.y_filtereds = [] + self.y_predicteds = [] + self.kalman_gains = [] + + + # def step(self, y=None, u=None, lam=1e-8): + # """ + # Perform one step of the Kalman filter. + + # Parameters + # ---------- + # y : np.ndarray, optional + # Observed output at current time step. If None, the filter + # will predict without observation update. + # u : np.ndarray, optional + # Input at current time step. If None, no input is applied. + + # Returns + # ------- + # y_filtered : np.ndarray + # Filtered output estimate + # x_filtered : np.ndarray + # Filtered state estimate + # """ + # x_pred = self.x_predicteds[-1] if self.x_predicteds else np.zeros((self.x_dim, 1)) + # p_pred = self.p_predicteds[-1] if self.p_predicteds else np.eye(self.x_dim) + + # # Ensure inputs are column vectors + # if u is not None and u.ndim == 1: + # u = u.reshape(-1, 1) + # if y is not None and y.ndim == 1: + # y = y.reshape(-1, 1) + # if u is None: + # u = np.zeros((self.u_dim, 1)) + # if y is None: + # y = np.zeros((self.y_dim, 1)) + + # S_innov = self.R + self.C @ p_pred @ self.C.T + # K_filtered = p_pred @ self.C.T @ np.linalg.pinv(S_innov) + # p_filtered = p_pred - K_filtered @ self.C @ p_pred + # if not np.isnan(y).any(): + # x_filtered = x_pred + K_filtered @ (y - self.C @ x_pred) + # else: + # x_filtered = x_pred.copy() + + # K_pred = (self.S + self.A @ p_pred @ self.C.T) @ np.linalg.pinv(S_innov) + # p_predicted = (self.A @ p_pred @ self.A.T + self.Q - + # K_pred @ (self.S + self.A @ p_pred @ self.C.T).T) + # x_predicted = self.A @ x_pred + self.B @ u + # if not np.isnan(y).any(): + # x_predicted += K_pred @ (y - self.C @ x_pred) + + # # Store results + # self.p_filtereds.append(p_filtered) + # self.x_filtereds.append(x_filtered) + # self.p_predicteds.append(p_predicted) + # self.x_predicteds.append(x_predicted) + # self.us.append(u) + # self.ys.append(y) + # self.y_filtereds.append(self.C @ x_filtered) + # self.y_predicteds.append(self.C @ x_predicted) + # self.kalman_gains.append(K_pred) + + # return self.y_filtereds[-1], self.x_filtereds[-1] + + + def step(self, y=None, u=None, reg_coef=1e-6): + """ + Perform one step of the Kalman filter. + + Parameters + ---------- + y : np.ndarray, optional + Observed output at current time step. If None, the filter + will predict without observation update. + u : np.ndarray, optional + Input at current time step. If None, no input is applied. + reg_coef : float, optional + Regularization coefficient to add to diagonal of P matrices + to maintain numerical stability. Default: 1e-6 + + Returns + ------- + y_filtered : np.ndarray + Filtered output estimate + x_filtered : np.ndarray + Filtered state estimate + """ + x_pred = self.x_predicteds[-1] if self.x_predicteds else np.zeros((self.x_dim, 1)) + p_pred = self.p_predicteds[-1] if self.p_predicteds else np.eye(self.x_dim) + + # Add regularization to p_pred to prevent ill-conditioning + p_pred_reg = p_pred + reg_coef * np.eye(self.x_dim) + + # Ensure inputs are column vectors + if u is not None and u.ndim == 1: + u = u.reshape(-1, 1) + if y is not None and y.ndim == 1: + y = y.reshape(-1, 1) + if u is None: + u = np.zeros((self.u_dim, 1)) + if y is None: + y = np.zeros((self.y_dim, 1)) + + # Use regularized p_pred in computations + S_innov = self.R + self.C @ p_pred_reg @ self.C.T + K_filtered = p_pred_reg @ self.C.T @ np.linalg.pinv(S_innov) + p_filtered = p_pred_reg - K_filtered @ self.C @ p_pred_reg + + # Add regularization to p_filtered to maintain positive definiteness + p_filtered = (p_filtered + p_filtered.T) / 2 # Ensure symmetry + p_filtered = p_filtered + reg_coef * np.eye(self.x_dim) # Add regularization + + if not np.isnan(y).any(): + x_filtered = x_pred + K_filtered @ (y - self.C @ x_pred) + else: + x_filtered = x_pred.copy() + + K_pred = (self.S + self.A @ p_pred_reg @ self.C.T) @ np.linalg.pinv(S_innov) + p_predicted = (self.A @ p_pred_reg @ self.A.T + self.Q - + K_pred @ (self.S + self.A @ p_pred_reg @ self.C.T).T) + + # Add regularization to p_predicted and ensure symmetry + p_predicted = (p_predicted + p_predicted.T) / 2 # Ensure symmetry + p_predicted = p_predicted + reg_coef * np.eye(self.x_dim) # Add regularization + + x_predicted = self.A @ x_pred + self.B @ u + if not np.isnan(y).any(): + x_predicted += K_pred @ (y - self.C @ x_pred) + + # Store results + self.p_filtereds.append(p_filtered) + self.x_filtereds.append(x_filtered) + self.p_predicteds.append(p_predicted) + self.x_predicteds.append(x_predicted) + self.us.append(u) + self.ys.append(y) + self.y_filtereds.append(self.C @ x_filtered) + self.y_predicteds.append(self.C @ x_predicted) + self.kalman_gains.append(K_pred) + + return self.y_filtereds[-1], self.x_filtereds[-1] + + def reset(self): + """Reset the filter to initial state.""" + self.p_filtereds = [] + self.x_filtereds = [] + self.p_predicteds = [] + self.x_predicteds = [] + self.us = [] + self.ys = [] + self.y_filtereds = [] + self.y_predicteds = [] + self.kalman_gains = [] + + + def get_history(self): + """Return the complete history of filter states.""" + return { + 'p_filtereds': self.p_filtereds, + 'x_filtereds': self.x_filtereds, + 'p_predicteds': self.p_predicteds, + 'x_predicteds': self.x_predicteds, + 'us': self.us, + 'ys': self.ys, + 'y_filtereds': self.y_filtereds, + 'y_predicteds': self.y_predicteds, + 'kalman_gains': self.kalman_gains + } \ No newline at end of file From 0c90eacebeafa51c54d137290d2899dddcb4383f Mon Sep 17 00:00:00 2001 From: ostrow Date: Mon, 27 Oct 2025 23:42:06 -0400 Subject: [PATCH 04/90] big alignment of dsa class, fixes on dmdc, simdist_controlalbility, subspace_dmdc. still need to test --- DSA/dmdc.py | 528 ++++++++++++++++++ DSA/dsa.py | 383 +++++++++---- ..._simdist.py => simdist_controllability.py} | 30 +- DSA/subspace_dmdc.py | 5 +- DSA/sweeps.py | 6 - 5 files changed, 805 insertions(+), 147 deletions(-) rename DSA/{controllability_simdist.py => simdist_controllability.py} (91%) diff --git a/DSA/dmdc.py b/DSA/dmdc.py index e69de29..f4052de 100644 --- a/DSA/dmdc.py +++ b/DSA/dmdc.py @@ -0,0 +1,528 @@ +"""This module computes the DMD with control (DMDc) model for a given dataset.""" +import numpy as np +import torch +try: + from .dmd import embed_signal_torch + from .base_dmd import BaseDMD +except ImportError: + from dmd import embed_signal_torch + from base_dmd import BaseDMD + +def embed_data_DMDc(data, n_delays=1, n_control_delays=1, delay_interval=1, control=False): + if control: + if n_control_delays == 1: + if data.ndim == 2: + return data[(n_delays-1)*delay_interval:, :] + else: + return data[:, (n_delays-1)*delay_interval:, :] + else: + embedded_data = embed_signal_torch(data, n_control_delays, delay_interval) + return embedded_data + else: + return embed_signal_torch(data, n_delays, delay_interval) + +class DMDc(BaseDMD): + """DMDc class for computing and predicting with DMD with control models. + """ + def __init__( + self, + data, + control_data=None, + n_delays=1, + n_control_delays=1, + delay_interval=1, + rank_input=None, + rank_thresh_input=None, + rank_explained_variance_input=None, + rank_output=None, + rank_thresh_output=None, + rank_explained_variance_output=None, + lamb=1e-8, + device='cpu', + verbose=False, + send_to_cpu=False, + svd_separate = True, + steps_ahead=1 + ): + """ + Parameters + ---------- + data : np.ndarray or torch.tensor + The state data to fit the DMDc model to. Must be either: (1) a + 2-dimensional array/tensor of shape T x N where T is the number + of time points and N is the number of observed dimensions + at each time point, or (2) a 3-dimensional array/tensor of shape + K x T x N where K is the number of "trials" and T and N are + as defined above. + + control_data : np.ndarray or torch.tensor + The control input data corresponding to the state data. Must have compatible dimensions + with the state data. + + n_delays : int + Parameter that controls the size of the delay embedding. Explicitly, + the number of delays to include. + + delay_interval : int + The number of time steps between each delay in the delay embedding. Defaults + to 1 time step. + + rank : int + The rank of V in fitting DMDc - i.e., the number of columns of V to + use to fit the DMDc model. Defaults to None, in which case all columns of V + will be used. + + rank_thresh : float + Parameter that controls the rank of V in fitting DMDc by dictating a threshold + of singular values to use. Explicitly, the rank of V will be the number of singular + values greater than rank_thresh. Defaults to None. + + rank_explained_variance : float + Parameter that controls the rank of V in fitting DMDc by indicating the percentage of + cumulative explained variance that should be explained by the columns of V. Defaults to None. + + lamb : float + Regularization parameter for ridge regression. Defaults to 0. + + device: string, int, or torch.device + A string, int or torch.device object to indicate the device to torch. + + verbose: bool + If True, print statements will be provided about the progress of the fitting procedure. + + send_to_cpu: bool + If True, will send all tensors in the object back to the cpu after everything is computed. + This is implemented to prevent gpu memory overload when computing multiple DMDs. + + steps_ahead: int + The number of time steps ahead to predict. Defaults to 1. + """ + + super().__init__(device=device, verbose=verbose, send_to_cpu=send_to_cpu, lamb=lamb) + + self._init_data(data, control_data) + + self.n_delays = n_delays + self.n_control_delays = n_control_delays + self.delay_interval = delay_interval + + self.rank_input = rank_input + self.rank_thresh_input = rank_thresh_input + self.rank_explained_variance_input = rank_explained_variance_input + self.rank_output = rank_output + self.rank_thresh_output = rank_thresh_output + self.rank_explained_variance_output = rank_explained_variance_output + self.svd_separate = svd_separate #do svd on H and u separately as well as regression + self.steps_ahead = steps_ahead + + # Hankel matrix + self.H = None + + # Control input Hankel matrix + self.Hu = None + + # SVD attributes + self.U = None + self.S = None + self.V = None + self.S_mat = None + self.S_mat_inv = None + + # Change of basis between the reduced-order subspace and the full space + self.U_out = None + self.S_out = None + self.V_out = None + + # DMDc attributes + self.A_tilde = None + self.B_tilde = None + self.A = None + self.B = None + self.A_havok_dmd = None + self.B_havok_dmd = None + + # Check if the state and control data are list (for different trial lengths) + if not np.all([isinstance(data, list), isinstance(control_data, list)]): + if isinstance(data, list) or isinstance(control_data, list): + raise TypeError("If you pass one of (data, control_data) as list, the other must also be a list.") + + def _init_data(self, data, control_data=None): + # Process main data + self.data, data_is_ragged = self._process_single_dataset(data) + + # Process control data + if control_data is not None: + self.control_data, control_is_ragged = self._process_single_dataset(control_data) + else: + self.control_data = torch.zeros_like(self.data) + control_is_ragged = False + + # Check consistency between data and control_data + if data_is_ragged != control_is_ragged: + raise ValueError("Data and control data have different structure (type or dimensionality).") + + if data_is_ragged: + # Additional validation for ragged data + if not all(d.shape[-1] == control_data[0].shape[-1] for d in control_data): + raise ValueError( + "All control tensors in the list must have the same number of features (last dimension)." + ) + if not all(d.shape[0] == control_d.shape[0] for d, control_d in zip(data, control_data)): + raise ValueError( + "Data and control_data tensors must have the same number of time steps." + ) + + # Set attributes for ragged data + n_features = self.data[0].shape[-1] + self.n = n_features + self.ntrials = sum( + d.shape[0] if d.ndim == 3 else 1 for d in self.data + ) + self.trial_counts = [ + d.shape[0] if d.ndim == 3 else 1 for d in self.data + ] + self.is_list_data = True + else: + # Set attributes for non-ragged data + if self.data.ndim == 3: + self.ntrials = self.data.shape[0] + self.n = self.data.shape[2] + else: + self.n = self.data.shape[1] + self.ntrials = 1 + self.is_list_data = False + + def compute_hankel( + self, + data=None, + control_data=None, + n_delays=None, + delay_interval=None, + ): + """ + Computes the Hankel matrix from the provided data and forms Omega. + """ + if self.verbose: + print("Computing Hankel matrices ...") + + # Overwrite parameters if provided + self.data = self.data if data is None else self._init_data(data, control_data) + self.n_delays = self.n_delays if n_delays is None else n_delays + self.delay_interval = self.delay_interval if delay_interval is None else delay_interval + + if self.is_list_data: + self.data = [d.to(self.device) for d in self.data] + self.control_data = [d.to(self.device) for d in self.control_data] + # Compute Hankel matrices for each trial separately + self.H = [embed_data_DMDc(d, n_delays=self.n_delays, n_control_delays=self.n_control_delays, delay_interval=self.delay_interval).float() for d in self.data] + self.Hu = [embed_data_DMDc(d, n_delays=self.n_delays, n_control_delays=self.n_control_delays, delay_interval=self.delay_interval, control=True).float() for d in self.control_data] + self.H_shapes = [h.shape for h in self.H] + else: + self.data = self.data.to(self.device) + self.control_data = self.control_data.to(self.device) + # Compute Hankel matrices + self.H = embed_data_DMDc(self.data, n_delays=self.n_delays, n_control_delays=self.n_control_delays, delay_interval=self.delay_interval).float() + self.Hu = embed_data_DMDc(self.control_data, n_delays=self.n_delays, n_control_delays=self.n_control_delays, delay_interval=self.delay_interval, control=True).float() + + if self.verbose: + print("Hankel matrices computed!") + + + def compute_svd(self): + """ + Computes the SVD of the Omega and Y matrices. + """ + if self.verbose: + print("Computing SVD on H and U matrices ...") + + + if self.is_list_data: + self.H_shapes = [h.shape for h in self.H] + H_list = [] + Hu_list = [] + for h_elem in self.H: + if h_elem.ndim == 3: + H_list.append( + h_elem.reshape( + h_elem.shape[0] * h_elem.shape[1], h_elem.shape[2] + ) + ) + else: + H_list.append(h_elem) + + for hu_elem in self.Hu: + if hu_elem.ndim == 3: + Hu_list.append( + hu_elem.reshape( + hu_elem.shape[0] * hu_elem.shape[1], hu_elem.shape[2] + ) + ) + else: + Hu_list.append(hu_elem) + self.H = torch.cat(H_list, dim=0) + self.Hu = torch.cat(Hu_list, dim=0) + # H = torch.cat(H_list, dim=0) + self.H_row_counts = [h.shape[0] for h in H_list] + H = self.H + Hu = self.Hu + + elif self.H.ndim == 3: # flatten across trials for 3d + H = self.H.reshape(self.H.shape[0] * self.H.shape[1], self.H.shape[2]) + Hu = self.Hu.reshape(self.Hu.shape[0] * self.Hu.shape[1], self.Hu.shape[2]) + else: + H = self.H + Hu = self.Hu + self.Uh, self.Sh, self.Vh = torch.linalg.svd(H.T, full_matrices=False) + self.Uu, self.Su, self.Vu = torch.linalg.svd(Hu.T, full_matrices=False) + + self.Vh = self.Vh.T + self.Vu = self.Vu.T + + self.Sh_mat = torch.diag(self.Sh).to(self.device) + self.Sh_mat_inv = torch.diag(1 / self.Sh).to(self.device) + + self.Su_mat = torch.diag(self.Su).to(self.device) + self.Su_mat_inv = torch.diag(1 / self.Su).to(self.device) + + self.cumulative_explained_variance_input = self._compute_explained_variance(self.Su) + self.cumulative_explained_variance_output = self._compute_explained_variance(self.Sh) + + self.Vht_minus, self.Vht_plus = self.get_plus_minus(self.Vh, self.H) + self.Vut_minus, _ = self.get_plus_minus(self.Vu, self.Hu) + + if self.verbose: + print("SVDs computed!") + + def get_plus_minus(self, V, H): + if self.ntrials > 1: + if self.is_list_data: + V_split = torch.split(V, self.H_row_counts, dim=0) + Vt_minus_list, Vt_plus_list = [], [] + for v_part, h_shape in zip(V_split, self.H_shapes): + if len(h_shape) == 3: # Has trials + v_part_reshaped = v_part.reshape(h_shape) + newshape = ( + h_shape[0] * (h_shape[1] - self.steps_ahead), + h_shape[2], + ) + Vt_minus_list.append( + v_part_reshaped[:, : -self.steps_ahead].reshape(newshape) + ) + Vt_plus_list.append( + v_part_reshaped[:, self.steps_ahead :].reshape(newshape) + ) + else: # No trials, just time and features + Vt_minus_list.append(v_part[: -self.steps_ahead]) + Vt_plus_list.append(v_part[self.steps_ahead :]) + + Vt_minus = torch.cat(Vt_minus_list, dim=0) + Vt_plus = torch.cat(Vt_plus_list, dim=0) + else: + + if V.numel() < H.numel(): + raise ValueError( + "The dimension of the SVD of the Hankel matrix is smaller than the dimension of the Hankel matrix itself. \n \ + This is likely due to the number of time points being smaller than the number of dimensions. \n \ + Please reduce the number of delays." + ) + + V = V.reshape(H.shape) + + # first reshape back into Hankel shape, separated by trials + newshape = ( + H.shape[0] * (H.shape[1] - self.steps_ahead), + H.shape[2], + ) + Vt_minus = V[:, : -self.steps_ahead].reshape(newshape) + Vt_plus = V[:, self.steps_ahead :].reshape(newshape) + else: + Vt_minus = V[: -self.steps_ahead] + Vt_plus = V[self.steps_ahead :] + + return Vt_minus, Vt_plus + + + def recalc_rank(self, rank_input=None, rank_thresh_input=None, rank_explained_variance_input=None, + rank_output=None, rank_thresh_output=None, rank_explained_variance_output=None): + ''' + Recalculates the rank for input and output based on provided parameters. + ''' + # Recalculate ranks for input + self.rank_input = self._compute_rank_from_params( + S=self.Su, + cumulative_explained_variance=self.cumulative_explained_variance_input, + max_rank=self.Hu.shape[-1], + rank=rank_input, + rank_thresh=rank_thresh_input, + rank_explained_variance=rank_explained_variance_input + ) + # Recalculate ranks for output + self.rank_output = self._compute_rank_from_params( + S=self.Sh, + cumulative_explained_variance=self.cumulative_explained_variance_output, + max_rank=self.H.shape[-1], + rank=rank_output, + rank_thresh=rank_thresh_output, + rank_explained_variance=rank_explained_variance_output + ) + + + def compute_dmdc(self, lamb=None): + if self.verbose: + print("Computing DMDc matrices ...") + + self.lamb = self.lamb if lamb is None else lamb + + V_minus_tot = torch.cat([self.Vht_minus[:, :self.rank_output], self.Vut_minus[:, :self.rank_input]], dim=1) + + A_v_tot = ( + torch.linalg.inv( + V_minus_tot.T @ V_minus_tot + + self.lamb * torch.eye(V_minus_tot.shape[1]).to(self.device) + ) + @ V_minus_tot.T + @ self.Vht_plus[:, :self.rank_output] + ).T + #split A_v_tot into A_v and B_v + self.A_v = A_v_tot[:, :self.rank_output] + self.B_v = A_v_tot[:, self.rank_output:] + self.A_havok_dmd = ( + self.Uh + @ self.Sh_mat[: self.Uh.shape[1], : self.rank_output] + @ self.A_v + @ self.Sh_mat_inv[: self.rank_output, : self.Uh.shape[1]] + @ self.Uh.T + ) + + self.B_havok_dmd = ( + self.Uh + @ self.Sh_mat[: self.Uh.shape[1], : self.rank_output] + @ self.B_v + @ self.Su_mat_inv[: self.rank_input, : self.Uu.shape[1]] + @ self.Uu.T + ) + + # Set the A and B properties for backward compatibility and easier access + self.A = self.A_havok_dmd + self.B = self.B_havok_dmd + + if self.verbose: + print("DMDc matrices computed!") + + def fit( + self, + data=None, + control_data=None, + n_delays=None, + delay_interval=None, + lamb=None, + device=None, + verbose=None, + ): + """ + Fits the DMDc model to the provided data. + """ + # Overwrite parameters if provided + self.device = self.device if device is None else device + self.verbose = self.verbose if verbose is None else verbose + + self.compute_hankel(data, control_data, n_delays, delay_interval) + self.compute_svd() + self.recalc_rank( + self.rank_input, self.rank_thresh_input, self.rank_explained_variance_input, + self.rank_output, self.rank_thresh_output, self.rank_explained_variance_output + ) + self.compute_dmdc(lamb) + if self.send_to_cpu: + self.all_to_device('cpu') # send back to the cpu to save memory + + def predict( + self, + test_data=None, + control_data=None, + reseed=None, + full_return=False + ): + """ + Parameters + ---------- + test_data : np.ndarray or torch.tensor + The state data to make predictions on. + + control_data : np.ndarray or torch.tensor + The control input data corresponding to the test_data. + + reseed : int + Frequency of reseeding the prediction with true data. + + full_return : bool + If True, returns additional matrices used in prediction. + + Returns + ------- + pred_data : torch.tensor + The predictions generated by the DMDc model. Of the same shape as test_data. + + H_test_dmdc : torch.tensor (Optional) + Returned if full_return=True. The predicted Hankel matrix generated by the DMDc model. + + H_test : torch.tensor (Optional) + Returned if full_return=True. The true Hankel matrix. + """ + # Initialize test_data + if test_data is None: + test_data = self.data + if control_data is None: + control_data = self.control_data + + if isinstance(test_data, list): + predictions = [self.predict(test_data=d, control_data=d_control, + reseed=reseed, full_return=full_return) for d, d_control in zip(test_data, control_data)] + if full_return: + pred_data = [pred[0] for pred in predictions] + H_test_dmdc = [pred[1] for pred in predictions] + H_test = [pred[2] for pred in predictions] + return pred_data, H_test_dmdc, H_test + else: + return predictions + + if isinstance(test_data, np.ndarray): + test_data = torch.from_numpy(test_data).to(self.device) + if isinstance(control_data, np.ndarray): + control_data = torch.from_numpy(control_data).to(self.device) + + ndim = test_data.ndim + if ndim == 2: + test_data = test_data.unsqueeze(0) + control_data = control_data.unsqueeze(0) + # H_test = embed_data_DMDc(test_data, n_delays=self.n_delays, delay_interval=self.delay_interval, control=False) + # H_control = embed_data_DMDc(control_data, n_delays=self.n_control_delays, delay_interval=self.delay_interval, control=True) + H_test = embed_signal_torch(test_data, self.n_delays, self.delay_interval).float() + H_control = embed_signal_torch(control_data, self.n_control_delays, self.delay_interval).float() + if reseed is None: + reseed = 1 + + H_test_dmdc = torch.zeros_like(H_test).to(self.device) + H_test_dmdc[:, 0] = H_test[:, 0] + A = self.A_havok_dmd + B = self.B_havok_dmd + + for t in range(1, H_test.shape[1]): + u_t = H_control[:, t - 1] + # print(A.shape) + # print(H_test[:, t - 1].shape) + # print(B.shape) + # print(u_t.shape) + if t % reseed == 0: + H_test_dmdc[:, t] = (A @ H_test[:, t - 1].transpose(-2, -1)).transpose(-2, -1) + (B @ u_t.transpose(-2, -1)).transpose(-2, -1) + else: + H_test_dmdc[:, t] = (A @ H_test_dmdc[:, t - 1].transpose(-2, -1)).transpose(-2, -1) + (B @ u_t.transpose(-2, -1)).transpose(-2, -1) + pred_data = torch.hstack([test_data[:, :(self.n_delays - 1)*self.delay_interval + self.steps_ahead], H_test_dmdc[:, self.steps_ahead:, :self.n]]) + + if ndim == 2: + pred_data = pred_data[0] + + if full_return: + return pred_data, H_test_dmdc, H_test + else: + return pred_data diff --git a/DSA/dsa.py b/DSA/dsa.py index 9631508..92382eb 100644 --- a/DSA/dsa.py +++ b/DSA/dsa.py @@ -1,4 +1,7 @@ from DSA.dmd import DMD as DefaultDMD +from DSA.simdist_controllability import ControllabilitySimilarityTransformDist +from DSA.dmdc import DMDc as DefaultDMDc +from DSA.subspace_dmdc import SubspaceDMDc from DSA.simdist import SimilarityTransformDist from typing import Literal import torch @@ -6,6 +9,11 @@ from omegaconf.listconfig import ListConfig import tqdm from joblib import Parallel, delayed +from dataclasses import dataclass, is_dataclass, asdict +import DSA.pykoopman as pykoopman +from DSA.pykoopman.regression import DMDc, EDMDc +from typing import Union, Mapping, Any +import warnings CAST_TYPES = { @@ -20,26 +28,61 @@ "send_to_cpu": bool, } - -class DSA: +#___Example config dataclasses for DMD # +@dataclass() +class DefaultDMDConfig: + n_delays: int = 1 + delay_interval: int = 1 + rank: int = None + lamb: float = 0 + send_to_cpu: bool = False +@dataclass() +class pyKoopmanDMDConfig: + observables: pykoopman.observables.BaseObservables = pykoopman.observables.TimeDelay(n_delays=1) + regressor = pykoopman.regression.DMD(svd_rank=2) + +@dataclass() +class SubspaceDMDcConfig: + n_delays: int = 1 + delay_interval: int = 1 + rank: int = None + lamb: float = 0 + backend: str = 'n4sid' + +#__Example config dataclasses for similarity transform distance # +@dataclass +class SimilarityTransformDistConfig: + iters: int = 1500 + score_method: Literal["angular", "euclidean"] = "angular" + lr: float = 5e-3 + zero_pad: bool = False + wasserstein_compare: Literal["sv", "eig", None] = "eig" + +@dataclass() +class ControllabilitySimilarityTransformDistConfig: + score_method: Literal["euclidean", "angular"] = "euclidean" + compare = 'state' + joint_optim: bool = False + return_distance_components: bool = False + +class GeneralizedDSA: """ - Computes the Dynamical Similarity Analysis (DSA) for two data tensors + Computes the Generalized Dynamical Similarity Analysis (DSA) for two data tensors """ def __init__( self, X, Y=None, + X_control=None, + Y_control=None, dmd_class=DefaultDMD, - iters=1500, - score_method: Literal["angular", "euclidean", "wasserstein"] = "angular", - lr=5e-3, - zero_pad=False, - device="cpu", - wasserstein_compare: Literal["sv", "eig", None] = "eig", - n_jobs: int = 1, + similarity_class=SimilarityTransformDist, + dmd_config: Union[Mapping[str, Any], dataclass]= DefaultDMDConfig, + simdist_config: Union[Mapping[str, Any], dataclass] = SimilarityTransformDistConfig, + device='cpu', dsa_verbose=False, - **dmd_kwargs, + n_jobs=1, ): """ Parameters @@ -55,32 +98,16 @@ def __init__( * If Y is a single matrix, all matrices in X are compared to Y * If Y is a list, all matrices in X are compared to all matrices in Y - DMD parameters : - - n_delays : int or list or tuple/list: (int,int), (list,list),(list,int),(int,list) - number of delays to use in constructing the Hankel matrix - - delay_interval : int or list or tuple/list: (int,int), (list,list),(list,int),(int,list) - interval between samples taken in constructing Hankel matrix - - rank : int or list or tuple/list: (int,int), (list,list),(list,int),(int,list) - rank of DMD matrix fit in reduced-rank regression - - rank_thresh : float or list or tuple/list: (float,float), (list,list),(list,float),(float,list) - Parameter that controls the rank of V in fitting HAVOK DMD by dictating a threshold - of singular values to use. Explicitly, the rank of V will be the number of singular - values greater than rank_thresh. Defaults to None. - - rank_explained_variance : float or list or tuple: (float,float), (list,list),(list,float),(float,list) - Parameter that controls the rank of V in fitting HAVOK DMD by indicating the percentage of - cumulative explained variance that should be explained by the columns of V. Defaults to None. - - lamb : float - L-1 regularization parameter in DMD fit + X_control : None or np.array or torch.tensor or list of np.arrays or torch.tensors + control data matrix/matrices. + Must be the same shape as X. + If None, then no control data is used. - send_to_cpu: bool - If True, will send all tensors in the object back to the cpu after everything is computed. - This is implemented to prevent gpu memory overload when computing multiple DMDs. + Y_control : None or np.array or torch.tensor or list of np.arrays or torch.tensors + control data matrix/matrices. + Must be the same shape as Y. + If None, then no control data is used. + NOTE: for all of these above, they can be single values or lists or tuples, depending on the corresponding dimensions of the data @@ -90,99 +117,108 @@ def __init__( OR to X and Y respectively across all matrices If it is (list,list), then each element will correspond to an individual dmd matrix indexed at the same position - - SimDist parameters: - - iters : int - number of optimization iterations in Procrustes over vector fields - - score_method : {'angular','euclidean'} - type of metric to compute, angular vs euclidean distance - - lr : float - learning rate of the Procrustes over vector fields optimization - - zero_pad : bool - whether or not to zero-pad if the dimensions are different - - device : 'cpu' or 'cuda' or int - hardware to use in both DMD and PoVF - - dsa_verbose : bool - whether or not print when sections of the analysis is completed - - wasserstein_compare : {'sv','eig',None} - specifies whether to compare the singular values or eigenvalues - if score_method is "wasserstein", or the shapes are different + """ self.X = X self.Y = Y - self.iters = iters - self.score_method = score_method - self.lr = lr + self.X_control = X_control + self.Y_control = Y_control + self.simdist_config = simdist_config + + if is_dataclass(simdist_config): + self.simdist_config = asdict(self.simdist_config) + self.device = device - self.zero_pad = zero_pad self.n_jobs = n_jobs self.dsa_verbose = dsa_verbose self.dmd_class = dmd_class if self.X is None and isinstance(self.Y, list): self.X, self.Y = self.Y, self.X # swap so code is easy + self.X_control, self.Y_control = self.Y_control, self.X_control # swap control too self.check_method() if self.method == "self-pairwise": self.data = [self.X] + self.control_data = [self.X_control] else: self.data = [self.X, self.Y] + self.control_data = [self.X_control, self.Y_control] # Process DMD keyword arguments from **dmd_kwargs # These are parameters like n_delays, rank, etc., that are specific to DMDs # and need to be broadcasted according to X and Y data structure. - self.dmd_kwargs = ( + if is_dataclass(dmd_config): + dmd_config = asdict(dmd_config) + self.dmd_config = ( {} ) # This will store {'param_name': broadcasted_value_list_of_lists} - if dmd_kwargs: - for key, value in dmd_kwargs.items(): - cast_type = CAST_TYPES.get(key) - - if cast_type is not None: - broadcasted_value = self.broadcast_params(value, cast=cast_type) - else: - broadcasted_value = self.broadcast_params(value) + for key, value in dmd_config.items(): + cast_type = CAST_TYPES.get(key) - setattr( - self, key, broadcasted_value - ) # e.g., self.n_delays = [[v,v],[v,v]] - self.dmd_kwargs[key] = ( - broadcasted_value # Store in dict for DMD instantiation - ) + if cast_type is not None: + broadcasted_value = self.broadcast_params(value, cast=cast_type) + else: + broadcasted_value = self.broadcast_params(value) + setattr( + self, key, broadcasted_value + ) # e.g., self.n_delays = [[v,v],[v,v]] + self.dmd_config[key] = ( + broadcasted_value # Store in dict for DMD instantiation + ) + self._check_dmd_simdist_compatibility(dmd_class,similarity_class) self._dmd_api_source(dmd_class) self._initiate_dmds() + self.simdist = similarity_class(**self.simdist_config) - self.simdist = SimilarityTransformDist( - iters, score_method, lr, device, dsa_verbose, wasserstein_compare - ) def _initiate_dmds(self): - if self.dmd_api_source == "local_dsa_dmd": - self.dmds = [ - [ - self.dmd_class(Xi, **{k: v[i][j] for k, v in self.dmd_kwargs.items()}) - for j, Xi in enumerate(dat) - ] - for i, dat in enumerate(self.data) - ] + if self.dmd_has_control and self.X_control is None and self.Y_control is None: + raise ValueError("Error: You are using a DMD model that fits a control operator but no control data is provided for either X or Y") + + if not self.dmd_has_control and (self.X_control is not None or self.Y_control is not None): + warnings.warn("You are using a DMD model with no control but control data is provided, will be ignored") + + + if self.dmd_api_source == "local_dmd": + self.dmds = [] + #TODO: test this for single numpy array + for i, (dat, control_dat) in enumerate(zip(self.data, self.control_data)): + dmd_list = [] + if control_dat is None: + control_dat = [None] * len(dat) + for j, (Xi, Xi_control) in enumerate(zip(dat, control_dat)): + config = {k: v[i][j] for k, v in self.dmd_config.items()} + + # + if self.dmd_has_control: + dmd_obj = self.dmd_class(Xi, control_data=Xi_control, **config) + else: + dmd_obj = self.dmd_class(Xi, **config) + + dmd_list.append(dmd_obj) + self.dmds.append(dmd_list) else: self.dmds = [ - [self.dmd_class(**{k: v[i][j] for k, v in self.dmd_kwargs.items()}) for j, Xi in enumerate(dat)] + [self.dmd_class(**{k: v[i][j] for k, v in self.dmd_config.items()}) for j, Xi in enumerate(dat)] for i, dat in enumerate(self.data) ] + def _check_dmd_simdist_compatibility(self, dmd_class, similarity_class): + self.dmd_has_control = dmd_class in [DefaultDMDc, SubspaceDMDc] or ('pykoopman' in dmd_class.__module__ and self.dmd_config.get('regressor') in [DMDc, EDMDc]) + self.simdist_has_control = similarity_class in [ControllabilitySimilarityTransformDist] + + if self.dmd_has_control and not self.simdist_has_control: + warnings.warn("Warning: You are using a DMD model that fits a control operator but comparing with a DSA metric that does not compare control operators") + if not self.dmd_has_control and self.simdist_has_control: + raise ValueError("Error: Your DMD model does not fit a control operator but comparing with a DSA metric that compares control operators") + def _dmd_api_source(self, dmd_class): module_name = dmd_class.__module__ + if "pydmd" in module_name: self.dmd_api_source = "pydmd" raise ValueError("DSA is not currently directly compatible with pydmd due to \ @@ -190,8 +226,14 @@ def _dmd_api_source(self, dmd_class): Note that you can pass in pydmd objects through pykoopman's Koopman class.") elif "pykoopman" in module_name: self.dmd_api_source = "pykoopman" - elif "DSA.dmd" in module_name: - self.dmd_api_source = "local_dsa_dmd" + if self.dmd_has_control and self.dmd_config.get('regressor') in [DMDc, EDMDc]: + raise ValueError("Pykoopman DMDc and EDMDc are not currently compatible with DSA") + elif ( + "DSA.dmd" in module_name or + "DSA.subspace_dmdc" in module_name or + "DSA.dmdc" in module_name + ): + self.dmd_api_source = "local_dmd" else: self.dmd_api_source = "unknown" raise ValueError( @@ -202,7 +244,7 @@ def fit_dmds(self): if self.n_jobs != 1: n_jobs = self.n_jobs if self.n_jobs > 0 else -1 # -1 means use all available cores - if self.dmd_api_source == "local_dsa_dmd": + if self.dmd_api_source == "local_dmd": for dmd_sets in self.dmds: if self.dsa_verbose: print(f"Fitting {len(dmd_sets)} DMDs in parallel with {n_jobs} jobs") @@ -218,7 +260,7 @@ def fit_dmds(self): ) else: # Sequential processing - if self.dmd_api_source == "local_dsa_dmd": + if self.dmd_api_source == "local_dmd": for dmd_sets in self.dmds: loop = dmd_sets if not self.dsa_verbose else tqdm.tqdm(dmd_sets, desc="Fitting DMDs") for dmd in loop: @@ -236,24 +278,38 @@ def check_method(self): tensor_or_np = lambda x: isinstance(x, (np.ndarray, torch.Tensor)) if isinstance(self.X, list): + # Ensure X_control is also a list + if self.X_control is not None and not isinstance(self.X_control, list): + self.X_control = [self.X_control] + if self.Y is None: self.method = "self-pairwise" elif isinstance(self.Y, list): self.method = "bipartite-pairwise" + if self.Y_control is not None and not isinstance(self.Y_control, list): + self.Y_control = [self.Y_control] elif tensor_or_np(self.Y): self.method = "list-to-one" self.Y = [self.Y] # wrap in a list for iteration + if self.Y_control is not None: + self.Y_control = [self.Y_control] else: raise ValueError("unknown type of Y") elif tensor_or_np(self.X): self.X = [self.X] + if self.X_control is not None: + self.X_control = [self.X_control] if self.Y is None: raise ValueError("only one element provided") elif isinstance(self.Y, list): self.method = "one-to-list" + if self.Y_control is not None and not isinstance(self.Y_control, list): + self.Y_control = [self.Y_control] elif tensor_or_np(self.Y): self.method = "default" self.Y = [self.Y] + if self.Y_control is not None: + self.Y_control = [self.Y_control] else: raise ValueError("unknown type of Y") else: @@ -292,7 +348,7 @@ def broadcast_params(self, param, cast=None): raise ValueError("unknown type entered for parameter") if cast is not None and param is not None: - out = [[cast(x) for x in dat] for dat in out] + out = [[cast(x) if x is not None else None for x in dat] for dat in out] return out @@ -313,7 +369,7 @@ def fit_score(self): return self.score() def get_dmd_matrix(self, dmd): - if self.dmd_api_source == "local_dsa_dmd": + if self.dmd_api_source == "local_dmd": return dmd.A_v elif self.dmd_api_source == "pykoopman": return dmd.A @@ -321,32 +377,39 @@ def get_dmd_matrix(self, dmd): raise ValueError("DSA is not currently compatible with pydmd due to \ data structure incompatibility. Please use pykoopman instead.") - def score(self, iters=None, lr=None, score_method=None): + def get_dmd_control_matrix(self, dmd): + if self.dmd_api_source == "local_dmd": + return dmd.B_v + elif self.dmd_api_source == "pykoopman": + return dmd.B + elif self.dmd_api_source == "pydmd": + raise ValueError("DSA is not currently compatible with pydmd due to \ + data structure incompatibility. Please use pykoopman instead.") + + def score(self): """ Score DSA with precomputed dmds Parameters __________ - iters : int or None - number of optimization steps, if None then resorts to saved self.iters - lr : float or None - learning rate, if None then resorts to saved self.lr - score_method : None or {'angular','euclidean'} - overwrites the score method in the object for this application Returns ________ - score : float + score : float or numpy array similarity score of the two precomputed DMDs + if array is d x d, it is a standard DSA (or whatever you set it to be) + if array is d x d x 3 , you ran simdist_controllability with return_distance_components = True + This means that you returned the following similarity scores: + joint similarity scores (both state and control) + state similarity score (optimized jointly) + control similarity score (optimized jointly) """ - iters = self.iters if iters is None else iters - lr = self.lr if lr is None else lr - score_method = self.score_method if score_method is None else score_method - - ind2 = 1 - int(self.method == "self-pairwise") + ind2 = 0 if self.method == "self-pairwise" else 1 # 0 if self.pairwise (want to compare the set to itself) - - self.sims = np.zeros((len(self.dmds[0]), len(self.dmds[ind2]))) + n_sims = 1 if not (self.simdist_has_control \ + and self.simdist_config.get("return_distance_components")) else 3 + + self.sims = np.zeros((len(self.dmds[0]), len(self.dmds[ind2]),n_sims)) if self.dsa_verbose: print('comparing dmds') @@ -358,14 +421,17 @@ def compute_similarity(i, j): if self.dsa_verbose and self.n_jobs != 1: print(f"computing similarity between DMDs {i} and {j}") - sim = self.simdist.fit_score( - self.get_dmd_matrix(self.dmds[0][i]), - self.get_dmd_matrix(self.dmds[ind2][j]), - iters, - lr, - score_method, - zero_pad=self.zero_pad, - ) + simdist_args = [ + self.get_dmd_matrix(self.dmds[0][i]), + self.get_dmd_matrix(self.dmds[ind2][j]), + ] + if self.dmd_has_control: + simdist_args.extend([ + self.get_dmd_control_matrix(self.dmds[0][i]), + self.get_dmd_control_matrix(self.dmds[ind2][j]), + ]) + sim = self.simdist.fit_score(*simdist_args) + if self.dsa_verbose and self.n_jobs != 1: print(f"computing similarity between DMDs {i} and {j}") @@ -397,6 +463,79 @@ def compute_similarity(i, j): self.sims[j, i] = sim if self.method == "default": - return self.sims[0, 0] + return self.sims[0, 0].squeeze() + + return self.sims.squeeze() - return self.sims + +class DSA(GeneralizedDSA): + def __init__( + self, + X, + Y=None, + dmd_class=DefaultDMD, + device='cpu', + dsa_verbose=False, + n_jobs=1, + # Advanced simdist parameters + score_method: Literal["angular", "euclidean"] = "angular", + iters: int = 1500, + lr: float = 5e-3, + zero_pad: bool = False, + wasserstein_compare: Literal["sv", "eig", None] = "eig", + **dmd_kwargs + ): + #TODO: add readme + # Build simdist_config internally + simdist_config = { + 'score_method': score_method, + 'iters': iters, + 'lr': lr, + 'zero_pad': zero_pad, + 'wasserstein_compare': wasserstein_compare, + } + + dmd_config = dmd_kwargs + + super().__init__( + X=X, + Y=Y, + X_control=None, + Y_control=None, + dmd_class=dmd_class, + similarity_class=SimilarityTransformDist, + dmd_config=dmd_config, + simdist_config=simdist_config, + device=device, + dsa_verbose=dsa_verbose, + n_jobs=n_jobs, + ) + +class InputDSA(GeneralizedDSA): + def __init__( + self, + X, + X_control, + Y=None, + Y_control=None, + dmd_class=SubspaceDMDc, + similarity_class=ControllabilitySimilarityTransformDist, + dmd_config: Union[Mapping[str, Any], dataclass]= SubspaceDMDcConfig, + simdist_config: Union[Mapping[str, Any], dataclass] = ControllabilitySimilarityTransformDistConfig, + device='cpu', + dsa_verbose=False, + n_jobs=1, + ): + super().__init__( + X, + Y, + X_control, + Y_control, + dmd_class, + similarity_class, + dmd_config, + simdist_config, + device, + dsa_verbose, + n_jobs, + ) diff --git a/DSA/controllability_simdist.py b/DSA/simdist_controllability.py similarity index 91% rename from DSA/controllability_simdist.py rename to DSA/simdist_controllability.py index 0889847..6e0b72e 100644 --- a/DSA/controllability_simdist.py +++ b/DSA/simdist_controllability.py @@ -13,35 +13,37 @@ def __init__( self, *, score_method: Literal["euclidean", "angular"] = "euclidean", - alpha: float = 0.5, + compare: Literal['joint','control','state'] = 'joint', joint_optim: bool = False, + return_distance_components=True ): - """ + f""" Parameters ---------- score_method : {"euclidean", "angular"} Distance method to use. Euclidean uses Frobenius norm, angular uses principal angles. - alpha : float - Weight (only used if you call fit_score with non-default behavior). + compare: {'joint','control','state'} + what type of comparison to do on the A and B matrices align_inputs : bool If True, do two-sided Procrustes on controllability matrices (solve for C and C_u). """ self.score_method = score_method - self.alpha = alpha + self.compare = compare self.joint_optim = joint_optim + self.return_distance_components=return_distance_components - def fit_score(self, A, B, A_control, B_control, alpha=0.5, return_distance_components=False): + def fit_score(self, A, B, A_control, B_control): + C, C_u, sims_joint_euc, sims_joint_ang = self.compare_systems_procrustes( A1=A, B1=A_control, A2=B, B2=B_control, align_inputs=self.joint_optim ) - alpha = self.alpha if alpha is None else alpha score_method = self.score_method - if alpha == 0.5: - if return_distance_components: + if self.compare == 'joint': + if self.return_distance_components: if self.score_method == 'euclidean': # sims_control_joint = np.linalg.norm(C @ A_control @ C_u - B_control, "fro") ** 2 # sims_state_joint = np.linalg.norm(C @ A @ C.T - B, "fro") ** 2 @@ -68,7 +70,7 @@ def fit_score(self, A, B, A_control, B_control, alpha=0.5, return_distance_compo else: raise ValueError('Choose between Euclidean or angular distance') - elif alpha == 0.0: + elif self.compare: return self.compare_A(A, B, score_method=score_method) else: @@ -109,7 +111,7 @@ def get_controllability_matrix(self, A, B): K = K_test return K - def compare_systems_procrustes(self, A1, B1, A2, B2, *, align_inputs=False, n=100): + def compare_systems_procrustes(self, A1, B1, A2, B2, *,align_inputs=False): """ Compares two LTI systems by finding the optimal orthogonal transformation that aligns their controllability matrices. @@ -155,12 +157,6 @@ def compare_systems_procrustes(self, A1, B1, A2, B2, *, align_inputs=False, n=10 C = U1 @ U2.T C_u = V2t.T @ V1t # = V2 @ V1^T - # import matplotlib.pyplot as plt - # plt.imshow(C_u) - # plt.savefig('C_u.png') - - #TODO: truncate C_u - #TODO: compute error on A and B instead of the observability matrix K2_aligned = C @ K2 @ C_u err = np.linalg.norm(K1 - K2_aligned, "fro") cos_sim = (np.vdot(K1, K2_aligned).real / diff --git a/DSA/subspace_dmdc.py b/DSA/subspace_dmdc.py index ef8e151..caace53 100644 --- a/DSA/subspace_dmdc.py +++ b/DSA/subspace_dmdc.py @@ -2,14 +2,15 @@ import numpy as np import torch #TODO: convert to torch below to match the DMD class +#TODO: fix time-first argument to match the DMD class -class subspaceDMDc(): +class SubspaceDMDc(): """Subspace DMDc class for computing and predicting with DMD with control models. """ def __init__( self, data, - control_data=None, + control_data, n_delays=1, rank=None, lamb=1e-8, diff --git a/DSA/sweeps.py b/DSA/sweeps.py index 4bb07ba..383e7ca 100644 --- a/DSA/sweeps.py +++ b/DSA/sweeps.py @@ -29,8 +29,6 @@ def sweep_ranks_delays( train_frac=0.8, reseed=5, return_residuals=True, - featurize=False, - ndim=None, return_transient_growth=False, return_mse=False, error_space='X', @@ -53,10 +51,6 @@ def sweep_ranks_delays( Reseed for DMD prediction. return_residuals : bool Whether to return residuals. - featurize : bool - Whether to featurize the data (for special cases). - ndim : int or None - Number of dimensions to use if featurizing. measure_transient_growth : bool Whether to measure transient growth (numerical abscissa and l2 norm). return_mse: bool From bf04fc9e894dd062258306f572b1307918df8508 Mon Sep 17 00:00:00 2001 From: ostrow Date: Mon, 27 Oct 2025 23:59:19 -0400 Subject: [PATCH 05/90] convert subspace_dmdc to working in torch --- DSA/subspace_dmdc.py | 265 ++++++++++++++++++++++++++----------------- 1 file changed, 161 insertions(+), 104 deletions(-) diff --git a/DSA/subspace_dmdc.py b/DSA/subspace_dmdc.py index caace53..d406902 100644 --- a/DSA/subspace_dmdc.py +++ b/DSA/subspace_dmdc.py @@ -1,8 +1,6 @@ """This module computes the subspace DMD with control (DMDc) model for a given dataset.""" import numpy as np import torch -#TODO: convert to torch below to match the DMD class -#TODO: fix time-first argument to match the DMD class class SubspaceDMDc(): """Subspace DMDc class for computing and predicting with DMD with control models. @@ -20,8 +18,10 @@ def __init__( time_first=True, backend='n4sid' ): - self.data = data - self.control_data = control_data + # Convert inputs to torch tensors and store + self.device = device + self.data = self._to_tensor(data) + self.control_data = self._to_tensor(control_data) self.A_v = None self.B_v = None self.C_v = None @@ -31,6 +31,23 @@ def __init__( self.time_first = time_first self.backend = backend self.lamb = lamb + self.verbose = verbose + self.send_to_cpu = send_to_cpu + + def _to_tensor(self, data): + """Convert data to torch tensor, handling lists and numpy arrays.""" + if isinstance(data, list): + return [self._to_tensor_single(d) for d in data] + else: + return self._to_tensor_single(data) + + def _to_tensor_single(self, data): + """Convert single data item to torch tensor.""" + if isinstance(data, np.ndarray): + return torch.from_numpy(data).float().to(self.device) + elif isinstance(data, torch.Tensor): + return data.float().to(self.device) + return data def fit(self): @@ -38,10 +55,26 @@ def fit(self): y=self.data, u=self.control_data, p=self.n_delays, - f=self.n_delays, + f=self.n_delays, n=self.rank, backend=self.backend, lamb=self.lamb) + + if self.send_to_cpu: + self.all_to_device("cpu") + + def all_to_device(self, device): + """Send all tensors to specified device.""" + if self.A_v is not None: + self.A_v = self.A_v.to(device) + if self.B_v is not None: + self.B_v = self.B_v.to(device) + if self.C_v is not None: + self.C_v = self.C_v.to(device) + if self.info is not None: + for key in ['R_hat', 'Q_hat', 'S_hat', 'Gamma_hat', 'singular_values_O', 'noise_covariance']: + if key in self.info and isinstance(self.info[key], torch.Tensor): + self.info[key] = self.info[key].to(device) @@ -51,8 +84,8 @@ def subspace_dmdc_multitrial_QR_decomposition(self, y_list, u_list, p, f, n=None Now use QR decomposition for computing the oblique projection as in N4SID implementations. Parameters: - - y_list: list of arrays, each (p_out, N_i) - output data for trial i - - u_list: list of arrays, each (m, N_i) - input data for trial i + - y_list: list of tensors, each (p_out, N_i) - output data for trial i + - u_list: list of tensors, each (m, N_i) - input data for trial i - p: past window length - f: future window length - n: state dimension (auto-determined if None) @@ -80,7 +113,7 @@ def subspace_dmdc_multitrial_QR_decomposition(self, y_list, u_list, p, f, n=None raise ValueError(f"Trial {i}: y and u have different time lengths") def hankel_stack(X, start, L): - return np.concatenate([X[:, start + i:start + i + 1] for i in range(L)], axis=0) + return torch.cat([X[:, start + i:start + i + 1] for i in range(L)], dim=0) # Collect data from all trials U_p_all = [] @@ -95,17 +128,18 @@ def hankel_stack(X, start, L): T_trial = N_trial - (p + f) + 1 if T_trial <= 0: - print(f"Warning: Trial {trial_idx} has insufficient data (T={T_trial}), skipping") + if self.verbose: + print(f"Warning: Trial {trial_idx} has insufficient data (T={T_trial}), skipping") continue valid_trials.append(trial_idx) T_per_trial.append(T_trial) # Build Hankel matrices for this trial - U_p_trial = np.concatenate([hankel_stack(U_trial, j, p) for j in range(T_trial)], axis=1) - Y_p_trial = np.concatenate([hankel_stack(Y_trial, j, p) for j in range(T_trial)], axis=1) - U_f_trial = np.concatenate([hankel_stack(U_trial, j + p, f) for j in range(T_trial)], axis=1) - Y_f_trial = np.concatenate([hankel_stack(Y_trial, j + p, f) for j in range(T_trial)], axis=1) + U_p_trial = torch.cat([hankel_stack(U_trial, j, p) for j in range(T_trial)], dim=1) + Y_p_trial = torch.cat([hankel_stack(Y_trial, j, p) for j in range(T_trial)], dim=1) + U_f_trial = torch.cat([hankel_stack(U_trial, j + p, f) for j in range(T_trial)], dim=1) + Y_f_trial = torch.cat([hankel_stack(Y_trial, j + p, f) for j in range(T_trial)], dim=1) U_p_all.append(U_p_trial) Y_p_all.append(Y_p_trial) @@ -116,18 +150,18 @@ def hankel_stack(X, start, L): raise ValueError("No trials have sufficient data for given (p,f)") # Concatenate across valid trials - U_p = np.concatenate(U_p_all, axis=1) # (p m, T_total) - Y_p = np.concatenate(Y_p_all, axis=1) # (p p_out, T_total) - U_f = np.concatenate(U_f_all, axis=1) # (f m, T_total) - Y_f = np.concatenate(Y_f_all, axis=1) # (f p_out, T_total) + U_p = torch.cat(U_p_all, dim=1) # (p m, T_total) + Y_p = torch.cat(Y_p_all, dim=1) # (p p_out, T_total) + U_f = torch.cat(U_f_all, dim=1) # (f m, T_total) + Y_f = torch.cat(Y_f_all, dim=1) # (f p_out, T_total) T_total = sum(T_per_trial) - Z_p = np.vstack([U_p, Y_p]) # (p (m + p_out), T_total) + Z_p = torch.vstack([U_p, Y_p]) # (p (m + p_out), T_total) - H = np.vstack([U_f, Z_p, Y_f]) + H = torch.vstack([U_f, Z_p, Y_f]) # Perform QR on H.T to get equivalent LQ on H - Q, R_upper = np.linalg.qr(H.T, mode='reduced') # H.T = Q R_upper, R_upper upper triangular + Q, R_upper = torch.linalg.qr(H.T, mode='reduced') # H.T = Q R_upper, R_upper upper triangular L = R_upper.T # L = R_upper.T, lower triangular # Dimensions for slicing @@ -140,19 +174,19 @@ def hankel_stack(X, start, L): R32 = L[dim_uf + dim_zp:, dim_uf:dim_uf + dim_zp] # Compute oblique projection O = R32 @ pinv(R22) @ Z_p - O = R32 @ np.linalg.pinv(R22) @ Z_p + O = R32 @ torch.linalg.pinv(R22) @ Z_p # The rest remains the same: SVD on O - Uo, s, Vt = np.linalg.svd(O, full_matrices=False) + Uo, s, Vt = torch.linalg.svd(O, full_matrices=False) if n is None: - cs = np.cumsum(s**2) / (s**2).sum() - n = int(np.searchsorted(cs, energy) + 1) + cs = torch.cumsum(s**2, dim=0) / (s**2).sum() + n = int((cs < energy).sum().item() + 1) n = max(1, min(n, min(Uo.shape[1], Vt.shape[0]))) U_n = Uo[:, :n] - S_n = np.diag(s[:n]) + S_n = torch.diag(s[:n]) V_n = Vt[:n, :] - S_half = np.sqrt(S_n) + S_half = torch.sqrt(S_n) Gamma_hat = U_n @ S_half # (f p_out, n) X_hat = S_half @ V_n # (n, T_total) @@ -191,21 +225,21 @@ def hankel_stack(X, start, L): start_idx += T_trial # Concatenate all segments - X = np.concatenate(X_segments, axis=1) - X_next = np.concatenate(X_next_segments, axis=1) - U_mid = np.concatenate(U_mid_segments, axis=1) + X = torch.cat(X_segments, dim=1) + X_next = torch.cat(X_next_segments, dim=1) + U_mid = torch.cat(U_mid_segments, dim=1) # Regression for A and B - Z = np.vstack([X, U_mid]) + Z = torch.vstack([X, U_mid]) # Ridge regression: (Z^T Z + λI)^(-1) Z^T X_next^T ZTZ = Z @ Z.T - ridge_term = lamb * np.eye(ZTZ.shape[0]) - AB = np.linalg.solve(ZTZ + ridge_term, Z @ X_next.T).T + ridge_term = lamb * torch.eye(ZTZ.shape[0], device=self.device) + AB = torch.linalg.solve(ZTZ + ridge_term, Z @ X_next.T).T A_hat = AB[:, :n] B_hat = AB[:, n:] - # Z = np.vstack([X, U_mid]) - # AB = X_next @ np.linalg.pinv(Z) + # Z = torch.vstack([X, U_mid]) + # AB = X_next @ torch.linalg.pinv(Z) # A_hat = AB[:, :n] # B_hat = AB[:, n:] @@ -213,7 +247,7 @@ def hankel_stack(X, start, L): # Estimate noise covariance matrix # 0) Outputs aligned to X and U_mid (same time indices/columns) - Y_curr = np.concatenate(Y_segments, axis=1) # shape: (p_out, N) + Y_curr = torch.cat(Y_segments, dim=1) # shape: (p_out, N) # 1) Residuals at time t # Process noise residual (state eq): w_t ≈ x_{t+1} - A x_t - B u_ts @@ -223,8 +257,8 @@ def hankel_stack(X, start, L): V_hat = Y_curr - (C_hat @ X) # (p_out, N) # 2) Mean-centering - V_hat = V_hat - V_hat.mean(axis=1, keepdims=True) - W_hat = W_hat - W_hat.mean(axis=1, keepdims=True) + V_hat = V_hat - V_hat.mean(dim=1, keepdim=True) + W_hat = W_hat - W_hat.mean(dim=1, keepdim=True) N_res = V_hat.shape[1] denom = max(N_res - 1, 1) @@ -235,11 +269,17 @@ def hankel_stack(X, start, L): # 4) Symmetrize eps = 1e-12 - R_hat = 0.5 * (R_hat + R_hat.T) + eps * np.eye(R_hat.shape[0]) - Q_hat = 0.5 * (Q_hat + Q_hat.T) + eps * np.eye(Q_hat.shape[0]) - - noise_covariance = np.block([[R_hat, S_hat.T], - [S_hat, Q_hat]]) + R_hat = 0.5 * (R_hat + R_hat.T) + eps * torch.eye(R_hat.shape[0], device=self.device) + Q_hat = 0.5 * (Q_hat + Q_hat.T) + eps * torch.eye(Q_hat.shape[0], device=self.device) + + noise_covariance = torch.block_diag(R_hat, Q_hat) + # Add off-diagonal blocks + top_right = S_hat.T + bottom_left = S_hat + noise_covariance = torch.cat([ + torch.cat([R_hat, top_right], dim=1), + torch.cat([bottom_left, Q_hat], dim=1) + ], dim=0) info = { "singular_values_O": s, @@ -268,8 +308,8 @@ def subspace_dmdc_multitrial_custom(self, y_list, u_list, p, f, n=None, lamb=1e- Subspace-DMDc for multi-trial data with variable trial lengths. Parameters: - - y_list: list of arrays, each (p_out, N_i) - output data for trial i - - u_list: list of arrays, each (m, N_i) - input data for trial i + - y_list: list of tensors, each (p_out, N_i) - output data for trial i + - u_list: list of tensors, each (m, N_i) - input data for trial i - p: past window length - f: future window length - n: state dimension (auto-determined if None) @@ -298,7 +338,7 @@ def subspace_dmdc_multitrial_custom(self, y_list, u_list, p, f, n=None, lamb=1e- raise ValueError(f"Trial {i}: y and u have different time lengths") def hankel_stack(X, start, L): - return np.concatenate([X[:, start + i:start + i + 1] for i in range(L)], axis=0) + return torch.cat([X[:, start + i:start + i + 1] for i in range(L)], dim=0) # Collect data from all trials U_p_all = [] @@ -313,61 +353,62 @@ def hankel_stack(X, start, L): T_trial = N_trial - (p + f) + 1 if T_trial <= 0: - print(f"Warning: Trial {trial_idx} has insufficient data (T={T_trial}), skipping") + if self.verbose: + print(f"Warning: Trial {trial_idx} has insufficient data (T={T_trial}), skipping") continue valid_trials.append(trial_idx) T_per_trial.append(T_trial) # Build Hankel matrices for this trial - U_p_trial = np.concatenate([hankel_stack(U_trial, j, p) for j in range(T_trial)], axis=1) - Y_p_trial = np.concatenate([hankel_stack(Y_trial, j, p) for j in range(T_trial)], axis=1) - U_f_trial = np.concatenate([hankel_stack(U_trial, j + p, f) for j in range(T_trial)], axis=1) - Y_f_trial = np.concatenate([hankel_stack(Y_trial, j + p, f) for j in range(T_trial)], axis=1) + U_p_trial = torch.cat([hankel_stack(U_trial, j, p) for j in range(T_trial)], dim=1) + Y_p_trial = torch.cat([hankel_stack(Y_trial, j, p) for j in range(T_trial)], dim=1) + U_f_trial = torch.cat([hankel_stack(U_trial, j + p, f) for j in range(T_trial)], dim=1) + Y_f_trial = torch.cat([hankel_stack(Y_trial, j + p, f) for j in range(T_trial)], dim=1) U_p_all.append(U_p_trial) Y_p_all.append(Y_p_trial) U_f_all.append(U_f_trial) Y_f_all.append(Y_f_trial) - - print("="*40) - print(f"Number of valid trials: {len(U_p_trial)}") + if self.verbose: + print("="*40) + print(f"Number of valid trials: {len(valid_trials)}") if not valid_trials: raise ValueError("No trials have sufficient data for given (p,f)") # Concatenate across valid trials - U_p = np.concatenate(U_p_all, axis=1) # (pm, T_total) - Y_p = np.concatenate(Y_p_all, axis=1) # (p*p_out, T_total) - U_f = np.concatenate(U_f_all, axis=1) # (fm, T_total) - Y_f = np.concatenate(Y_f_all, axis=1) # (f*p_out, T_total) + U_p = torch.cat(U_p_all, dim=1) # (pm, T_total) + Y_p = torch.cat(Y_p_all, dim=1) # (p*p_out, T_total) + U_f = torch.cat(U_f_all, dim=1) # (fm, T_total) + Y_f = torch.cat(Y_f_all, dim=1) # (f*p_out, T_total) T_total = sum(T_per_trial) - Z_p = np.vstack([U_p, Y_p]) # (p(m+p_out), T_total) + Z_p = torch.vstack([U_p, Y_p]) # (p(m+p_out), T_total) # Oblique projection: remove row(U_f), project onto row(Z_p) UfUfT = U_f @ U_f.T - Xsolve = np.linalg.solve(UfUfT + lamb*np.eye(UfUfT.shape[0]), U_f) - Pi_perp = np.eye(T_total) - U_f.T @ Xsolve + Xsolve = torch.linalg.solve(UfUfT + lamb*torch.eye(UfUfT.shape[0], device=self.device), U_f) + Pi_perp = torch.eye(T_total, device=self.device) - U_f.T @ Xsolve Yf_perp = Y_f @ Pi_perp Zp_perp = Z_p @ Pi_perp ZZT = Zp_perp @ Zp_perp.T - Zp_pinv_left = np.linalg.solve(ZZT + lamb*np.eye(ZZT.shape[0]), Zp_perp) + Zp_pinv_left = torch.linalg.solve(ZZT + lamb*torch.eye(ZZT.shape[0], device=self.device), Zp_perp) P = Zp_perp.T @ Zp_pinv_left O = Yf_perp @ P # ≈ Γ_f X_p - Uo, s, Vt = np.linalg.svd(O, full_matrices=False) + Uo, s, Vt = torch.linalg.svd(O, full_matrices=False) if n is None: - cs = np.cumsum(s**2) / (s**2).sum() - n = int(np.searchsorted(cs, energy) + 1) + cs = torch.cumsum(s**2, dim=0) / (s**2).sum() + n = int((cs < energy).sum().item() + 1) n = max(1, min(n, min(Uo.shape[1], Vt.shape[0]))) U_n = Uo[:, :n] - S_n = np.diag(s[:n]) + S_n = torch.diag(s[:n]) V_n = Vt[:n, :] - S_half = np.sqrt(S_n) + S_half = torch.sqrt(S_n) Gamma_hat = U_n @ S_half # (f*p_out, n) X_hat = S_half @ V_n # (n, T_total) @@ -398,16 +439,16 @@ def hankel_stack(X, start, L): start_idx += T_trial # Concatenate all segments - X = np.concatenate(X_segments, axis=1) - X_next = np.concatenate(X_next_segments, axis=1) - U_mid = np.concatenate(U_mid_segments, axis=1) + X = torch.cat(X_segments, dim=1) + X_next = torch.cat(X_next_segments, dim=1) + U_mid = torch.cat(U_mid_segments, dim=1) # Regression for A and B - Z = np.vstack([X, U_mid]) + Z = torch.vstack([X, U_mid]) # Ridge regression: (Z^T Z + λI)^(-1) Z^T X_next^T ZTZ = Z @ Z.T - ridge_term = lamb * np.eye(ZTZ.shape[0]) - AB = np.linalg.solve(ZTZ + ridge_term, Z @ X_next.T).T + ridge_term = lamb * torch.eye(ZTZ.shape[0], device=self.device) + AB = torch.linalg.solve(ZTZ + ridge_term, Z @ X_next.T).T A_hat = AB[:, :n] B_hat = AB[:, n:] @@ -474,12 +515,15 @@ def subspace_dmdc_multitrial_flexible(self, y, u, p, f, n=None, lamb=1e-8, energ def predict(self, Y, U, reseed=None): - # Y and U are (n_times, n_channels) or list of 2D arrays + # Y and U are (n_times, n_channels) or list of 2D arrays/tensors if reseed is None: reseed = 1 - # Handle list of 2D arrays + # Convert inputs to tensors if needed if isinstance(Y, list): + Y = [self._to_tensor_single(y) for y in Y] + U = [self._to_tensor_single(u) for u in U] + if not self.time_first: Y = [y.T for y in Y] U = [u.T for u in U] @@ -495,17 +539,21 @@ def predict(self, Y, U, reseed=None): u=U[trial][t] ) trial_predictions.append(y_filtered) - Y_pred.append(np.concatenate(trial_predictions, axis=1).T) + Y_pred.append(torch.cat(trial_predictions, dim=1).T) return Y_pred # Return as list to match input format + # Convert to tensors + Y = self._to_tensor_single(Y) + U = self._to_tensor_single(U) + # print("time_first", self.time_first) if not self.time_first: if Y.ndim == 2: Y = Y.T U = U.T else: - Y = Y.transpose(0, 2, 1) - U = U.transpose(0, 2, 1) + Y = Y.permute(0, 2, 1) + U = U.permute(0, 2, 1) self.kalman = OnlineKalman(self) if Y.ndim == 2: @@ -513,7 +561,7 @@ def predict(self, Y, U, reseed=None): for t in range(Y.shape[0]): y_filtered, _ = self.kalman.step(y=Y[t] if t%reseed == 0 else None, u=U[t]) Y_pred.append(y_filtered) - return np.concatenate(Y_pred, axis=1).T + return torch.cat(Y_pred, dim=1).T else: # 3D data (n_trials, time, p_out) # print("Y.shape", Y.shape) @@ -526,8 +574,8 @@ def predict(self, Y, U, reseed=None): y_filtered, _ = self.kalman.step(y=Y[trial, t] if t%reseed == 0 else None, u=U[trial, t]) trial_predictions.append(y_filtered) # print("y_filtered.shape", y_filtered.shape) - Y_pred.append(np.concatenate(trial_predictions, axis=1).T) - return np.array(Y_pred) + Y_pred.append(torch.cat(trial_predictions, dim=1).T) + return torch.stack(Y_pred) @@ -549,6 +597,7 @@ def __init__(self, dmdc): Fitted DMDc model containing A_v, B_v, C_v matrices and noise covariance estimates (R_hat, S_hat, Q_hat) """ + self.device = dmdc.device self.A = dmdc.A_v self.B = dmdc.B_v self.C = dmdc.C_v @@ -559,6 +608,7 @@ def __init__(self, dmdc): # Get dimensions # print("C_shape", self.C.shape) self.y_dim, self.x_dim = self.C.shape + self.u_dim = self.B.shape[1] # Initialize state storage self.p_filtereds = [] @@ -639,10 +689,10 @@ def step(self, y=None, u=None, reg_coef=1e-6): Parameters ---------- - y : np.ndarray, optional + y : torch.Tensor or np.ndarray, optional Observed output at current time step. If None, the filter will predict without observation update. - u : np.ndarray, optional + u : torch.Tensor or np.ndarray, optional Input at current time step. If None, no input is applied. reg_coef : float, optional Regularization coefficient to add to diagonal of P matrices @@ -650,51 +700,58 @@ def step(self, y=None, u=None, reg_coef=1e-6): Returns ------- - y_filtered : np.ndarray + y_filtered : torch.Tensor Filtered output estimate - x_filtered : np.ndarray + x_filtered : torch.Tensor Filtered state estimate """ - x_pred = self.x_predicteds[-1] if self.x_predicteds else np.zeros((self.x_dim, 1)) - p_pred = self.p_predicteds[-1] if self.p_predicteds else np.eye(self.x_dim) + x_pred = self.x_predicteds[-1] if self.x_predicteds else torch.zeros((self.x_dim, 1), device=self.device) + p_pred = self.p_predicteds[-1] if self.p_predicteds else torch.eye(self.x_dim, device=self.device) # Add regularization to p_pred to prevent ill-conditioning - p_pred_reg = p_pred + reg_coef * np.eye(self.x_dim) - - # Ensure inputs are column vectors - if u is not None and u.ndim == 1: - u = u.reshape(-1, 1) - if y is not None and y.ndim == 1: - y = y.reshape(-1, 1) - if u is None: - u = np.zeros((self.u_dim, 1)) - if y is None: - y = np.zeros((self.y_dim, 1)) + p_pred_reg = p_pred + reg_coef * torch.eye(self.x_dim, device=self.device) + + # Convert inputs to tensors and ensure column vectors + if u is not None: + if isinstance(u, np.ndarray): + u = torch.from_numpy(u).float().to(self.device) + if u.ndim == 1: + u = u.reshape(-1, 1) + else: + u = torch.zeros((self.u_dim, 1), device=self.device) + + if y is not None: + if isinstance(y, np.ndarray): + y = torch.from_numpy(y).float().to(self.device) + if y.ndim == 1: + y = y.reshape(-1, 1) + else: + y = torch.zeros((self.y_dim, 1), device=self.device) # Use regularized p_pred in computations S_innov = self.R + self.C @ p_pred_reg @ self.C.T - K_filtered = p_pred_reg @ self.C.T @ np.linalg.pinv(S_innov) + K_filtered = p_pred_reg @ self.C.T @ torch.linalg.pinv(S_innov) p_filtered = p_pred_reg - K_filtered @ self.C @ p_pred_reg # Add regularization to p_filtered to maintain positive definiteness p_filtered = (p_filtered + p_filtered.T) / 2 # Ensure symmetry - p_filtered = p_filtered + reg_coef * np.eye(self.x_dim) # Add regularization + p_filtered = p_filtered + reg_coef * torch.eye(self.x_dim, device=self.device) # Add regularization - if not np.isnan(y).any(): + if not torch.isnan(y).any(): x_filtered = x_pred + K_filtered @ (y - self.C @ x_pred) else: - x_filtered = x_pred.copy() + x_filtered = x_pred.clone() - K_pred = (self.S + self.A @ p_pred_reg @ self.C.T) @ np.linalg.pinv(S_innov) + K_pred = (self.S + self.A @ p_pred_reg @ self.C.T) @ torch.linalg.pinv(S_innov) p_predicted = (self.A @ p_pred_reg @ self.A.T + self.Q - K_pred @ (self.S + self.A @ p_pred_reg @ self.C.T).T) # Add regularization to p_predicted and ensure symmetry p_predicted = (p_predicted + p_predicted.T) / 2 # Ensure symmetry - p_predicted = p_predicted + reg_coef * np.eye(self.x_dim) # Add regularization + p_predicted = p_predicted + reg_coef * torch.eye(self.x_dim, device=self.device) # Add regularization x_predicted = self.A @ x_pred + self.B @ u - if not np.isnan(y).any(): + if not torch.isnan(y).any(): x_predicted += K_pred @ (y - self.C @ x_pred) # Store results From 0c3aa9ad0b8645c181d188bb33483c21d8e1ba80 Mon Sep 17 00:00:00 2001 From: ostrow Date: Tue, 28 Oct 2025 00:07:33 -0400 Subject: [PATCH 06/90] fix inits --- DSA/__init__.py | 6 ++++-- DSA/subspace_dmdc.py | 2 +- 2 files changed, 5 insertions(+), 3 deletions(-) diff --git a/DSA/__init__.py b/DSA/__init__.py index 3a12098..7968a98 100644 --- a/DSA/__init__.py +++ b/DSA/__init__.py @@ -1,7 +1,9 @@ -from DSA.dsa import DSA +from DSA.dsa import DSA, GeneralizedDSA, InputDSA from DSA.dmd import DMD -from DSA.kerneldmd import KernelDMD +from DSA.dmdc import DMDc +from DSA.subspace_dmdc import SubspaceDMDc from DSA.simdist import SimilarityTransformDist +from DSA.simdist_controllability import ControllabilitySimilarityTransformDist from DSA.stats import * from DSA.sweeps import * from DSA.preprocessing import * diff --git a/DSA/subspace_dmdc.py b/DSA/subspace_dmdc.py index d406902..eca42d9 100644 --- a/DSA/subspace_dmdc.py +++ b/DSA/subspace_dmdc.py @@ -2,7 +2,7 @@ import numpy as np import torch -class SubspaceDMDc(): +class SubspaceDMDc: """Subspace DMDc class for computing and predicting with DMD with control models. """ def __init__( From f456323c265496a222ff78094b9d915b97771506 Mon Sep 17 00:00:00 2001 From: ostrow Date: Tue, 28 Oct 2025 00:47:21 -0400 Subject: [PATCH 07/90] fix imports --- DSA/dmd.py | 2 +- DSA/dsa.py | 7 +++---- DSA/preprocessing.py | 5 ++++- DSA/resdmd.py | 5 ++++- DSA/simdist.py | 5 ++++- DSA/simdist_controllability.py | 5 ++++- 6 files changed, 20 insertions(+), 9 deletions(-) diff --git a/DSA/dmd.py b/DSA/dmd.py index 6e3e64a..10a6905 100644 --- a/DSA/dmd.py +++ b/DSA/dmd.py @@ -77,7 +77,7 @@ class DMD(BaseDMD): def __init__( self, data, - n_delays, + n_delays=1, delay_interval=1, rank=None, rank_thresh=None, diff --git a/DSA/dsa.py b/DSA/dsa.py index 92382eb..660a76f 100644 --- a/DSA/dsa.py +++ b/DSA/dsa.py @@ -11,6 +11,7 @@ from joblib import Parallel, delayed from dataclasses import dataclass, is_dataclass, asdict import DSA.pykoopman as pykoopman +import pydmd from DSA.pykoopman.regression import DMDc, EDMDc from typing import Union, Mapping, Any import warnings @@ -38,8 +39,8 @@ class DefaultDMDConfig: send_to_cpu: bool = False @dataclass() class pyKoopmanDMDConfig: - observables: pykoopman.observables.BaseObservables = pykoopman.observables.TimeDelay(n_delays=1) - regressor = pykoopman.regression.DMD(svd_rank=2) + observables = pykoopman.observables.TimeDelay(n_delays=1) + regressor = pydmd.DMD(svd_rank=2) @dataclass() class SubspaceDMDcConfig: @@ -481,7 +482,6 @@ def __init__( score_method: Literal["angular", "euclidean"] = "angular", iters: int = 1500, lr: float = 5e-3, - zero_pad: bool = False, wasserstein_compare: Literal["sv", "eig", None] = "eig", **dmd_kwargs ): @@ -491,7 +491,6 @@ def __init__( 'score_method': score_method, 'iters': iters, 'lr': lr, - 'zero_pad': zero_pad, 'wasserstein_compare': wasserstein_compare, } diff --git a/DSA/preprocessing.py b/DSA/preprocessing.py index a44badd..3a04570 100644 --- a/DSA/preprocessing.py +++ b/DSA/preprocessing.py @@ -4,7 +4,10 @@ from sklearn.decomposition import PCA from sklearn.pipeline import make_pipeline from sklearn.kernel_approximation import Nystroem -from DSA.dmd import embed_signal_torch +try: + from .dmd import embed_signal_torch +except ImportError: + from dmd import embed_signal_torch from scipy.signal import convolve diff --git a/DSA/resdmd.py b/DSA/resdmd.py index 82dce85..d82c56f 100644 --- a/DSA/resdmd.py +++ b/DSA/resdmd.py @@ -2,7 +2,10 @@ import matplotlib.pyplot as plt from matplotlib import cm from matplotlib import colors as mcolors -from DSA.dmd import DMD +try: + from .dmd import DMD +except ImportError: + from dmd import DMD import torch import ot from typing import Literal diff --git a/DSA/simdist.py b/DSA/simdist.py index e15e909..99e5d6c 100644 --- a/DSA/simdist.py +++ b/DSA/simdist.py @@ -6,7 +6,10 @@ import torch.nn.utils.parametrize as parametrize from scipy.stats import wasserstein_distance import ot # optimal transport for multidimensional l2 wasserstein -from DSA import DMD +try: + from .dmd import DMD +except ImportError: + from dmd import DMD def pad_zeros(A, B, device): diff --git a/DSA/simdist_controllability.py b/DSA/simdist_controllability.py index 6e0b72e..1528865 100644 --- a/DSA/simdist_controllability.py +++ b/DSA/simdist_controllability.py @@ -2,7 +2,10 @@ import numpy as np from scipy.linalg import orthogonal_procrustes -from DSA.simdist import SimilarityTransformDist +try: + from .simdist import SimilarityTransformDist +except ImportError: + from simdist import SimilarityTransformDist class ControllabilitySimilarityTransformDist: """ From df63ee64871bfda1cd8d518788387da831d2254d Mon Sep 17 00:00:00 2001 From: ostrow Date: Tue, 28 Oct 2025 11:38:24 -0400 Subject: [PATCH 08/90] simplify angular distance --- DSA/simdist_controllability.py | 42 +++++++++++++++++++++------------- 1 file changed, 26 insertions(+), 16 deletions(-) diff --git a/DSA/simdist_controllability.py b/DSA/simdist_controllability.py index 1528865..951391b 100644 --- a/DSA/simdist_controllability.py +++ b/DSA/simdist_controllability.py @@ -35,6 +35,23 @@ def __init__( self.joint_optim = joint_optim self.return_distance_components=return_distance_components + @staticmethod + def compute_angular_dist(A, B): + """ + Computes the angular distance between two matrices A and B. + + Args: + A (np.ndarray): First matrix + B (np.ndarray): Second matrix + + Returns: + float: Angular distance between A and B + """ + cos_sim = np.trace(A.T @ B) / (np.linalg.norm(A, 'fro') * np.linalg.norm(B, 'fro')) + cos_sim = np.clip(cos_sim, -1, 1) + cos_sim = np.arccos(cos_sim) + cos_sim = np.clip(cos_sim, 0, np.pi) + return cos_sim def fit_score(self, A, B, A_control, B_control): @@ -54,16 +71,8 @@ def fit_score(self, A, B, A_control, B_control): sims_state_joint = np.linalg.norm(C @ A @ C.T - B, "fro") return sims_joint_euc, sims_state_joint, sims_control_joint elif self.score_method == 'angular': - sims_control_joint = np.trace((C @ A_control @ C_u).T @ B_control) / (np.linalg.norm(C @ A_control @ C_u, 'fro') * np.linalg.norm(B_control, 'fro')) - sims_state_joint = np.trace((C @ A @ C.T).T @ B) / (np.linalg.norm(C @ A @ C.T, 'fro') * np.linalg.norm(B, 'fro')) - - sims_control_joint = np.clip(sims_control_joint, -1, 1) - sims_state_joint = np.clip(sims_state_joint, -1, 1) - sims_control_joint =np.arccos(sims_control_joint) - sims_state_joint =np.arccos(sims_state_joint) - sims_control_joint = np.clip(sims_control_joint, 0, np.pi) - sims_state_joint = np.clip(sims_state_joint, 0, np.pi) - + sims_control_joint = self.compute_angular_dist(C @ A_control @ C_u, B_control) + sims_state_joint = self.compute_angular_dist(C @ A @ C.T, B) return sims_joint_ang, sims_state_joint, sims_control_joint else: if self.score_method == 'euclidean': @@ -73,7 +82,7 @@ def fit_score(self, A, B, A_control, B_control): else: raise ValueError('Choose between Euclidean or angular distance') - elif self.compare: + elif self.compare == 'state': return self.compare_A(A, B, score_method=score_method) else: @@ -170,10 +179,10 @@ def compare_systems_procrustes(self, A1, B1, A2, B2, *,align_inputs=False): return C, C_u, err, cos_sim - @staticmethod - def compare_A(A1, A2, score_method='euclidean'): - simdist = SimilarityTransformDist(iters=1000, score_method=score_method, lr=1e-3, verbose=True) - return simdist.fit_score(A1, A2, score_method=score_method) + # @staticmethod + # def compare_A(A1, A2, score_method='euclidean'): + # simdist = SimilarityTransformDist(iters=1000, score_method=score_method, lr=1e-3, verbose=True) + # return simdist.fit_score(A1, A2, score_method=score_method) @staticmethod def compare_B(B1, B2, score_method='euclidean'): @@ -182,7 +191,8 @@ def compare_B(B1, B2, score_method='euclidean'): return np.linalg.norm(B1 - R.T @ B2, "fro") # return np.linalg.norm(B1 - R.T @ B2, "fro") ** 2 elif score_method == 'angular': - return np.trace(B1.T @ (R.T @ B2)) / (np.linalg.norm(B1, 'fro') * np.linalg.norm(R.T @ B2, 'fro')) + R, _ = orthogonal_procrustes(B2.T, B1.T) + return ControllabilitySimilarityTransformDist.compute_angular_dist(B1, R.T @ B2) else: raise ValueError('Choose between Euclidean or angular distance') From 1ddac90134e841522f35acb6ad5f1722fc79008d Mon Sep 17 00:00:00 2001 From: ostrow Date: Tue, 28 Oct 2025 11:47:17 -0400 Subject: [PATCH 09/90] fix comparing A on input-dsa: should use SimDist, not Controllabilitysimdist, and switch intelligently under the hood (so that users just specify parameters) --- DSA/dsa.py | 23 +++++++++++++++++++---- DSA/simdist_controllability.py | 4 ++-- 2 files changed, 21 insertions(+), 6 deletions(-) diff --git a/DSA/dsa.py b/DSA/dsa.py index 660a76f..bbea048 100644 --- a/DSA/dsa.py +++ b/DSA/dsa.py @@ -478,7 +478,7 @@ def __init__( device='cpu', dsa_verbose=False, n_jobs=1, - # Advanced simdist parameters + #simdist parameters score_method: Literal["angular", "euclidean"] = "angular", iters: int = 1500, lr: float = 5e-3, @@ -486,7 +486,6 @@ def __init__( **dmd_kwargs ): #TODO: add readme - # Build simdist_config internally simdist_config = { 'score_method': score_method, 'iters': iters, @@ -518,23 +517,39 @@ def __init__( Y=None, Y_control=None, dmd_class=SubspaceDMDc, - similarity_class=ControllabilitySimilarityTransformDist, dmd_config: Union[Mapping[str, Any], dataclass]= SubspaceDMDcConfig, simdist_config: Union[Mapping[str, Any], dataclass] = ControllabilitySimilarityTransformDistConfig, device='cpu', dsa_verbose=False, n_jobs=1, ): + #check if simdist_config has 'compare', and if it's 'state', use the standard SimilarityTransformDist, + #otherwise use ControllabilitySimilarityTransformDistConfig + if isinstance(simdist_config, dataclass): + compare = simdist_config.compare + elif isinstance(simdist_config,dict): + compare = simdist_config.get("compare",None) + else: + raise ValueError("unknown data type for simdist-config, use dataclass or dict") + if compare == 'state': + simdist = SimilarityTransformDist + else: + simdist = ControllabilitySimilarityTransformDist + super().__init__( X, Y, X_control, Y_control, dmd_class, - similarity_class, + simdist, dmd_config, simdist_config, device, dsa_verbose, n_jobs, ) + + assert X_control is not None + assert self.dmd_has_control + diff --git a/DSA/simdist_controllability.py b/DSA/simdist_controllability.py index 951391b..3137d33 100644 --- a/DSA/simdist_controllability.py +++ b/DSA/simdist_controllability.py @@ -58,7 +58,6 @@ def fit_score(self, A, B, A_control, B_control): C, C_u, sims_joint_euc, sims_joint_ang = self.compare_systems_procrustes( A1=A, B1=A_control, A2=B, B2=B_control, align_inputs=self.joint_optim ) - score_method = self.score_method @@ -83,7 +82,8 @@ def fit_score(self, A, B, A_control, B_control): raise ValueError('Choose between Euclidean or angular distance') elif self.compare == 'state': - return self.compare_A(A, B, score_method=score_method) + # return self.compare_A(A, B, score_method=score_method) + raise ValueError('To compute state similarity alone, use the SimilarityTransformDist class') else: return self.compare_B(A_control, B_control, score_method=score_method) From 5ee74891d3aa5694b0ed195062618be5f6bfd387 Mon Sep 17 00:00:00 2001 From: ostrow Date: Tue, 28 Oct 2025 12:01:09 -0400 Subject: [PATCH 10/90] update readme --- README.md | 105 ++++++++++++++++-- ...tutorial.ipynb => dsa_fig3_tutorial.ipynb} | 8 +- 2 files changed, 99 insertions(+), 14 deletions(-) rename examples/{fig3_tutorial.ipynb => dsa_fig3_tutorial.ipynb} (99%) diff --git a/README.md b/README.md index 58538b5..4431f35 100644 --- a/README.md +++ b/README.md @@ -1,14 +1,32 @@ -# DSA -Dynamical Similarity Analysis code accompanying the paper "Beyond Geometry: Comparing the Temporal Structure of Computation in Neural Circuits via Dynamical Similarity Analysis" +# generalized DSA +Computational techniques for Dynamical Similarity Analysis. First introduced in, + +1. "Beyond Geometry: Comparing the Temporal Structure of Computation in Neural Circuits via Dynamical Similarity Analysis" https://arxiv.org/abs/2306.10168 +Abstract: How can we tell whether two neural networks are utilizing the same internal processes for a particular computation? This question is pertinent for multiple subfields of both neuroscience and machine learning, including neuroAI, mechanistic interpretability, and brain-machine interfaces. Standard approaches for comparing neural networks focus on the spatial geometry of latent states. Yet in recurrent networks, computations are implemented at the level of neural dynamics, which do not have a simple one-to-one mapping with geometry. To bridge this gap, we introduce a novel similarity metric that compares two systems at the level of their dynamics. Our method incorporates two components: Using recent advances in data-driven dynamical systems theory, we learn a high-dimensional linear system that accurately captures core features of the original nonlinear dynamics. Next, we compare these linear approximations via a novel extension of Procrustes Analysis that accounts for how vector fields change under orthogonal transformation. Via four case studies, we demonstrate that our method effectively identifies and distinguishes dynamic structure in recurrent neural networks (RNNs), whereas geometric methods fall short. We additionally show that our method can distinguish learning rules in an unsupervised manner. Our method therefore opens the door to novel data-driven analyses of the temporal structure of neural computation, and to more rigorous testing of RNNs as models of the brain. -Code Authors: Mitchell Ostrow, Adam Eisen, Leo Kozachkov +and now including code from the following: + +2. "InputDSA: Demixing then comparing recurrent and externally driven dynamics +Abstract: +In control problems and basic scientific modeling, it is important to compare observations with dynamical simulations. For example, comparing two neural systems can shed light on the nature of emergent computations in the brain and deep neural networks. Recently, (Ostrow et al., 2023) introduced Dynamical Similarity Analysis (DSA), a method to measure the similarity of two systems based on their state dynamics rather than geometry or topology. However, DSA does not consider how inputs affect the dynamics, meaning that two similar systems, if driven differently, may be classified as different. Because real-world dynamical systems are rarely autonomous, it is important to account for the effects of input drive. To this end, we introduce a novel metric for comparing both intrinsic (recurrent) and input-driven dynamics, called InputDSA (iDSA). InputDSA extends the DSA framework by estimating and comparing both input and intrinsic dynamic operators using a novel variant of Dynamic Mode Decomposition with control (DMDc) based on subspace identification. We demonstrate that InputDSA can successfully compare partially observed, input-driven systems from noisy data. We show that when the true inputs are unknown, surrogate inputs can be substituted without a major deterioration in similarity estimates. We apply InputDSA on Recurrent Neural Networks (RNNs) trained with Deep Reinforcement Learning, identifying that high-performing networks are dynamically similar to one another, while low-performing networks are more diverse. Lastly, we apply InputDSA to neural data recorded from rats performing a cognitive task, demonstrating that it identifies a transition from input-driven evidence accumulation to intrinsically- driven decision-making. Our work demonstrates that InputDSA is a robust and efficient method for comparing intrinsic dynamics and the effect of external input +on dynamical systems + +Code Authors: Mitchell Ostrow, Adam Eisen, Leo Kozachkov, Ann Huang If you use this code, please cite: ``` +@misc{huangostrow2025input, + title={InputDSA: Demixing then comparing recurrent and externally driven dynamics}, + author={Ann Huang and Mitchell Ostrow and Satpreet Singh and Leo Kozachkov and Ila Fiete and Kanka Rajan}, + year={2025}, + archivePrefix={arXiv}, + primaryClass={q-bio.NC} +} + @misc{ostrow2023geometry, title={Beyond Geometry: Comparing the Temporal Structure of Computation in Neural Circuits with Dynamical Similarity Analysis}, author={Mitchell Ostrow and Adam Eisen and Leo Kozachkov and Ila Fiete}, @@ -17,6 +35,7 @@ If you use this code, please cite: archivePrefix={arXiv}, primaryClass={q-bio.NC} } + ``` ## Install the repo using `pip`: @@ -32,12 +51,16 @@ pip install -e . ## Brief Tutorial -The central object in the package is `DSA`, which links together the `DMD` and `SimilarityTransformDist` (called Procrustes Analysis over Vector Fields in the paper) objects. We designed an API that should be easy to use them in conjunction (`DSA`) with a variety of datatypes for a range of analysis cases: +The central object in the package is `GeneralizedDSA`, which links together the different types of `DMD` and `SimilarityTransformDist` (called Procrustes Analysis over Vector Fields in the first paper) objects. We designed an API that should be easy to use them in conjunction (`DSA`) with a variety of datatypes for a range of analysis cases: * Standard: Comparing two data matrices X, Y (can be passed in as numpy arrays or torch Tensors) * Pairwise: Pass in a list of data matrices X, which can be compared all-to-all * Disjoint Pairwise: Pass in two lists of data matrices, X, Y, which are compared all-to-all in a bipartite fashion * One-to-All: Pass in a list of data matrices X and a single matrix Y. All of X are compared to Y. +To run the DSA algorithm as it is specified in Ostrow et al. (2023), the class `DSA` in the file `dsa.py` is recommended. This is a restriction / special case of Generalized DSA. To run the InputDSA algorithm as it is specified in Huang and Ostrow et al. (2025), the class `InputDSA` is recommended. + +The `GeneralizedDSA` class generalizes (hence the name) the `DSA` algorithm from Ostrow et al. (2023) to account for the fact that other types of embeddings and DMD models can improve on HAVOK/Hankel DMD (which applies standard ridge least-squares regression on whitened time-delay embeddings). To that end, we have integrated capabilities with PyKoopman (https://github.com/dynamicslab/pykoopman) and PyDMD (https://github.com/PyDMD/PyDMD) to allow for other DMD models. For a brief tutorial, see below. Likewise, other similarity metrics (e.g. Huang and Ostrow et al., 2025) are desirable as well, given the setting. For kernel-like embeddings, functions in the file `preprocessing.py` can be applied. + # DSA has CUDA capability via pytorch, which is highly recommended for large datasets. * Simply pass in `device='cuda'` to the `DSA`,`DMD`,`SimilarityTransformDist` objects to compute on GPU, if you have one. @@ -47,19 +70,81 @@ Depending on the structure of the data, you can also pass in hyperparameters tha * If your parameters are a list of two lists `([a,b],[c,d])`, they will be mapped onto to all data matrices in X and Y with corresponding indices. Will throw an error if there aren't enough hyperparamters to match the data. * If your parameters are a combination of the previous two (e.g. `(a,[b,c])`), the broadcasting behaviors will be combined accordingly. -Our code also uses an API similar to `scikit-learn` in that all the relevant computation is enclosed in the `.fit()`, `.score()`, and `.fit_score()` style functions: + +Our code also uses an API similar to `scikit-learn` in that all the relevant computation is enclosed in the `.fit()`, `.score()`, and `.fit_score()` style functions. The original DSA case can be applied as follows: ``` -dsa = DSA(models,n_delays=n_delays,rank=rank,delay_interval=delay_interval,verbose=True,device=device) +from DSA import DSA +dsa = DSA(models,n_delays=n_delays,rank=rank,delay_interval=delay_interval,verbose=True,device=device,score_method='angular') similarities = dsa.fit_score() ``` +If you wish to use pykoopman/pydmd DMD models, they can applied as follows, using the pk.Koopman wrapper class. We'll use the pydmd SubspaceDMD as an example: +``` +from DSA import DSA +from pydmd import SubspaceDMD #the DMD class you want to use +import DSA.pykoopman as pk +obs = pk.observables.TimeDelay(n_delays=3) #define some nonlinear observables, if you wish -Simple as that! The data matrices can be of shape `(trials,time,channels)` or `(time,channels)`. If you have multiple conditions you wish to test (for example, different control inputs to your system, you can fit them separately or simultaneously. In our tutorial notebook, `fig3_tutorial.ipynb`, we fit two conditions simultaneously and the model works--here, our data matrices are of shape `(condition,trials,time,channels)` which we collapse to `(condition*trials,time,channels)`. +dsa = DSA(compare_dat,dmd_class=pk.Koopman,score_method='wasserstein',wasserstein_compare='eig',observables=obs,regressor=SubspaceDMD(svd_rank=3)) +``` + +Due to the generalization of the method on different DMDs and different similarity metrics, each which have different arguments, we have changed the structure of the DSA class to take in arguments for each of these objects as dictionaries or dataclass config objects. Here are a few examples: +``` +from dataclass import dataclass +@dataclass() +class DefaultDMDConfig: + n_delays: int = 1 + delay_interval: int = 1 + rank: int = None + lamb: float = 0 + send_to_cpu: bool = False +@dataclass() +class pyKoopmanDMDConfig: + observables = pykoopman.observables.TimeDelay(n_delays=1) + regressor = pydmd.DMD(svd_rank=2) + +@dataclass() +class SubspaceDMDcConfig: + n_delays: int = 1 + delay_interval: int = 1 + rank: int = None + lamb: float = 0 + backend: str = 'n4sid' + +#__Example config dataclasses for similarity transform distance # +@dataclass +class SimilarityTransformDistConfig: + iters: int = 1500 + score_method: Literal["angular", "euclidean"] = "angular" + lr: float = 5e-3 + zero_pad: bool = False + wasserstein_compare: Literal["sv", "eig", None] = "eig" + +@dataclass() +class ControllabilitySimilarityTransformDistConfig: + score_method: Literal["euclidean", "angular"] = "euclidean" + compare = 'state' + joint_optim: bool = False + return_distance_components: bool = False -Note that `DSA` performs multiple fits to the data: one `DMD` matrix per data matrix, and then one `SimilarityTransformDist` similarity per pair of data matrices. When you call `score` after `fit_score`, it will only recompute the `SimilarityTransformDist`s. If you wish to recompute the DMDs, call `.fit_dmds()`. The Procrustes Analysis over Vector Fields metric does not have a closed form solution so it may be worth playing around with its optimization parameters. +``` +Then, these are passed directly into the GeneralizedDSA (DSA, InputDSA) classes via the arguments dmd_config, simdist_config for the arguments of each class: +``` +from DSA import GeneralizedDSA, DMD, SimilarityTransformDist +gdsa = GeneralizedDSA(datasets,dmd_class=DMD,similarity_class=SimilarityTransformDist, + dmd_config=DefaultDMDConfig,simdist_config=SimilarityTransformDistConfig) +sim = gdsa.fit_score() +``` + +The logic for InputDSA is equivalent, with a few key things to note. In this setting, there are two types of DMDc models to use-- DMDc (Proctor et al., 2016), and SubspaceDMDc (Huang and Ostrow et al., 2025). If your system is partially observed, we recommend SubspaceDMDc instead. Likewise, there are a few different types of similarity that can be computed. You may wish to apply DMDc-like models but then only compare the A matrix-- in this case, you can set the argument `compare='state'` in the simdist_config object. Otherwise, you have the options `joint,control` which will jointly compare A and B via controllability, or just the control matrix via Procrustes. InputDSA has one other special argument: `return_distance_components`. If this is true, it will return 3 different metrics, encoded in a single numpy array (data x data x 3). They have the ordering: Full Controllability distance, Jointly optimized State Similarity Score, Jointly Optimized Control Score. + + +Simple as that! The data matrices can be of shape `(trials,time,channels)` or `(time,channels)`. If you have multiple conditions you wish to test (for example, if you have different task settings in your system, you can fit them separately or simultaneously). In our tutorial notebook, `dsa_fig3_tutorial.ipynb`, we fit two conditions simultaneously and the model works--here, our data matrices are of shape `(condition,trials,time,channels)` which we collapse to `(condition*trials,time,channels)`. (As of 2025, DSA objects can also take lists of arrays (shape 2D or 3D) to account for different lengths of time in different time series). + +Note that `DSA` performs multiple fits to the data: one `DMD` matrix per data matrix, and then one `SimilarityTransformDist` similarity per pair of data matrices. When you call `score` after `fit_score`, it will only recompute the `SimilarityTransformDist`s. If you wish to recompute the DMDs, call `.fit_dmds()`. The Procrustes Analysis over Vector Fields metric does not have a closed form solution so it may be worth playing around with its optimization parameters, or use the Wasserstein distance. If you only care about identifying topological conjugacy between your systems, you can set `compare_method='wasserstein'`, and `wasserstein_compare='eig'` to compare the eigenvalues of the DMDs of each system with the wasserstein distance (as used in Redman et al., 2024 and upcoming work). Optimizing the PAVF metric over O(n) compares transients as well as eigenvalues. -In the case of a large number of comparisons, it will be more memory effective to use the `DMD` class to fit the models and then the `SimilarityTransformDist` class to compare them, rather than use `DSA`, as `DSA` requires taking in all of the data matrices at once. Using the pieces separately will allow you to stream data, or generate it on-the-fly. This process is simple too (see `examples/fig3_tutorial.ipynb`): +In the case of a large number of comparisons, it will be more memory effective to use the `DMD` class to fit the models and then the `SimilarityTransformDist` class to compare them, rather than use `DSA`, as `DSA` requires taking in all of the data matrices at once. Using the pieces separately will allow you to stream data, or generate it on-the-fly. This process is simple too (see `examples/dsa_fig3_tutorial.ipynb`): * Fit the DMD: with your data: ``` @@ -72,4 +157,4 @@ Ai = dmd.A_v #extract DMD matrix comparison = SimilarityTransformDist(device='cuda',iters=2000,lr=1e-3) score = comparison_dmd.fit_score(Ai,Aj) #fit to two DMD matrices ``` - +This pipeline can also be generalized using different DMDs and comparison methods. diff --git a/examples/fig3_tutorial.ipynb b/examples/dsa_fig3_tutorial.ipynb similarity index 99% rename from examples/fig3_tutorial.ipynb rename to examples/dsa_fig3_tutorial.ipynb index 6071546..dab6216 100644 --- a/examples/fig3_tutorial.ipynb +++ b/examples/dsa_fig3_tutorial.ipynb @@ -199,7 +199,7 @@ }, { "data": { - "image/png": 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\n", 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", 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\n", + "image/png": 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", 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\n", + "image/png": 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", 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\n", 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", 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" ] From a53098c55ea198f9aed3ccaa3bd2f65099a9aa8e Mon Sep 17 00:00:00 2001 From: ostrow Date: Tue, 28 Oct 2025 12:17:54 -0400 Subject: [PATCH 11/90] update simdist to not do wasserstein over anything but eigenvalues --- DSA/simdist.py | 100 ++++++++++++++++++++++++------------------------- 1 file changed, 48 insertions(+), 52 deletions(-) diff --git a/DSA/simdist.py b/DSA/simdist.py index 99e5d6c..3eea468 100644 --- a/DSA/simdist.py +++ b/DSA/simdist.py @@ -135,7 +135,6 @@ def __init__( lr=0.01, device: Literal["cpu", "cuda"] = "cpu", verbose=False, - wasserstein_compare: Literal["sv", "eig"] = "eig", eps=1e-5, rescale_wasserstein=False, ): @@ -158,10 +157,6 @@ def __init__( verbose : bool prints when finished optimizing - wasserstein_compare : {'sv','eig',None} - specifies whether to compare the singular values or eigenvalues - if score_method is "wasserstein", or the shapes are different - eps : float early stopping threshold """ @@ -174,7 +169,6 @@ def __init__( self.C_star = None self.A = None self.B = None - self.wasserstein_compare = wasserstein_compare self.eps = eps self.rescale_wasserstein = rescale_wasserstein @@ -185,7 +179,6 @@ def fit( iters=None, lr=None, score_method=None, - wasserstein_compare=None, wasserstein_weightings = None, ): @@ -266,31 +259,34 @@ def fit( self.sim_net = sim_net def _get_wasserstein_vars(self,A, B): - assert self.wasserstein_compare in {"sv", "eig","evec_angle", 'evec'} - if self.wasserstein_compare == "sv": - a = torch.svd(A).S.view(-1, 1) - b = torch.svd(B).S.view(-1, 1) - elif self.wasserstein_compare == "eig": - a = torch.linalg.eig(A).eigenvalues - a = torch.vstack([a.real, a.imag]).T - - b = torch.linalg.eig(B).eigenvalues - b = torch.vstack([b.real, b.imag]).T - elif self.wasserstein_compare in {'evec_angle', 'evec'}: - #this will compute the interior angles between eigenvectors - aevec = torch.linalg.eig(A).eigenvectors - bevec = torch.linalg.eig(B).eigenvectors + # assert self.wasserstein_compare in {"sv", "eig","evec_angle", 'evec'} + assert self.wasserstein_compare in {"eig"} + + #deprecated: only do wasserstein comparison on eigenvalues (for now, until others are theoretically validated) + # if self.wasserstein_compare == "sv": + # a = torch.svd(A).S.view(-1, 1) + # b = torch.svd(B).S.view(-1, 1) + # if self.wasserstein_compare == "eig": + a = torch.linalg.eig(A).eigenvalues + a = torch.vstack([a.real, a.imag]).T + + b = torch.linalg.eig(B).eigenvalues + b = torch.vstack([b.real, b.imag]).T + # elif self.wasserstein_compare in {'evec_angle', 'evec'}: + # #this will compute the interior angles between eigenvectors + # aevec = torch.linalg.eig(A).eigenvectors + # bevec = torch.linalg.eig(B).eigenvectors - a = compute_angle(aevec) - b = compute_angle(bevec) - else: - raise AssertionError("wasserstein_compare must be 'sv', 'eig', 'evec_angle', or 'evec'") + # a = compute_angle(aevec) + # b = compute_angle(bevec) + # else: + # raise AssertionError("wasserstein_compare must be 'sv', 'eig', 'evec_angle', or 'evec'") #if the number of elements in the sets are different, then we need to pad the smaller set with zeros if a.shape[0] != b.shape[0]: - if self.wasserstein_compare in {'evec_angle', 'evec'}: - raise AssertionError("Wasserstein comparison of eigenvectors is not supported when \ - the number of elements in the sets are different") + # if self.wasserstein_compare in {'evec_angle', 'evec'}: + # raise AssertionError("Wasserstein comparison of eigenvectors is not supported when \ + # the number of elements in the sets are different") if self.verbose: print(f"Padding the smaller set with zeros") if a.shape[0] < b.shape[0]: @@ -341,7 +337,7 @@ def optimize_C(self, A, B, lr, iters, orthog, verbose): C_star = sim_net.C.detach() return losses, C_star, sim_net - def score(self, A=None, B=None, score_method=None, wasserstein_compare=None): + def score(self, A=None, B=None, score_method=None): """ Given an optimal C already computed, calculate the metric @@ -367,7 +363,6 @@ def score(self, A=None, B=None, score_method=None, wasserstein_compare=None): assert A.shape == self.C_star.shape or score_method == 'wasserstein' assert B.shape == self.C_star.shape or score_method == 'wasserstein' score_method = self.score_method if score_method is None else score_method - wasserstein_compare = self.wasserstein_compare if wasserstein_compare is None else wasserstein_compare with torch.no_grad(): if not isinstance(A, torch.Tensor): A = torch.from_numpy(A).float().to(self.device) @@ -391,27 +386,28 @@ def score(self, A=None, B=None, score_method=None, wasserstein_compare=None): elif score_method == 'wasserstein': #use the current C_star to compute the score assert hasattr(self, 'score_star') - if wasserstein_compare == self.wasserstein_compare: - score = self.score_star.item() - else: - #apply the current transport plan to the new data - a,b = self._get_wasserstein_vars(A, B) - # a_transported = self.C_star @ A #shouldn't this be a? + # if wasserstein_compare == self.wasserstein_compare: + score = self.score_star.item() + #non-eig wasserstein comparisons are deprecated until theoretically validated + # else: + # #apply the current transport plan to the new data + # a,b = self._get_wasserstein_vars(A, B) + # # a_transported = self.C_star @ A #shouldn't this be a? - M = ot.dist(a, b, metric='sqeuclidean') - score = torch.sum(self.C_star * M).item() - #TODO: validate this - # a_transported = self.C_star @ a - # row_wise_sq_distances = torch.sum(torch.square(a_transported - b), axis=1) - # transported_score = torch.sum(a * row_wise_sq_distances) - # score = transported_score.item() - if self.rescale_wasserstein: - score = score * A.shape[0] #add scaling factor due to random matrix theory + # M = ot.dist(a, b, metric='sqeuclidean') + # score = torch.sum(self.C_star * M).item() + # #TODO: validate this + # # a_transported = self.C_star @ a + # # row_wise_sq_distances = torch.sum(torch.square(a_transported - b), axis=1) + # # transported_score = torch.sum(a * row_wise_sq_distances) + # # score = transported_score.item() + # if self.rescale_wasserstein: + # score = score * A.shape[0] #add scaling factor due to random matrix theory return score def fit_score( - self, A, B, iters=None, lr=None, score_method=None, zero_pad=True, wasserstein_weightings=None + self, A, B, iters=None, lr=None, score_method=None, wasserstein_weightings=None ): """ for efficiency, computes the optimal matrix and returns the score @@ -426,7 +422,7 @@ def fit_score( number of optimization steps, if None then resorts to saved self.iters lr : float or None learning rate, if None then resorts to saved self.lr - score_method : {'angular','euclidean'} or None + score_method : {'angular','euclidean', 'wasserstein} or None overwrites parameter in the class zero_pad : bool if True, then the smaller matrix will be zero padded so its the same size @@ -446,11 +442,11 @@ def fit_score( assert A.shape[0] == B.shape[1] or self.wasserstein_compare is not None if A.shape[0] != B.shape[0]: - if self.wasserstein_compare is None: - raise AssertionError( - "Matrices must be the same size unless using wasserstein distance" - ) - elif score_method != 'wasserstein': # otherwise resort to L2 Wasserstein over singular or eigenvalues + # if self.wasserstein_compare is None: + # raise AssertionError( + # "Matrices must be the same size unless using wasserstein distance" + # ) + if score_method != 'wasserstein': # otherwise resort to L2 Wasserstein over singular or eigenvalues print( f"resorting to wasserstein distance over {self.wasserstein_compare}" ) From 25c385d3d8b50e103eae5eb4f4efa418aa1d5cdc Mon Sep 17 00:00:00 2001 From: ostrow Date: Tue, 28 Oct 2025 12:18:05 -0400 Subject: [PATCH 12/90] add docstrings for dsa --- DSA/dsa.py | 129 +++++++++++++++++++++++++++++++++++++++++++++++++++-- 1 file changed, 126 insertions(+), 3 deletions(-) diff --git a/DSA/dsa.py b/DSA/dsa.py index bbea048..c21d583 100644 --- a/DSA/dsa.py +++ b/DSA/dsa.py @@ -32,18 +32,65 @@ #___Example config dataclasses for DMD # @dataclass() class DefaultDMDConfig: + """ + Configuration dataclass for DefaultDMD (standard DMD without control). + + This configuration is used to set parameters for the DefaultDMD class when + performing Dynamical Mode Decomposition on time series data. + + Attributes: + n_delays (int): Number of time delays to use in the Hankel matrix construction. + Default is 1 (no delays). + delay_interval (int): Interval between delays in the Hankel matrix. + Default is 1 (consecutive time steps). + rank (int): Rank for SVD truncation. If None, no truncation is performed. + Default is None. + lamb (float): Regularization parameter for ridge regression. + Default is 0 (no regularization). + send_to_cpu (bool): Whether to move computations to CPU. + Default is False (use GPU if available). + """ n_delays: int = 1 delay_interval: int = 1 rank: int = None lamb: float = 0 send_to_cpu: bool = False + @dataclass() class pyKoopmanDMDConfig: + """ + Configuration dataclass for pyKoopman DMD models. + + This configuration is used to set up pyKoopman observables and regressors + for performing DMD analysis with the pyKoopman library. + + Attributes: + observables: Observable function from pykoopman. Default is TimeDelay with n_delays=1. + regressor: Regressor model from pydmd. Default is DMD with svd_rank=2. + """ observables = pykoopman.observables.TimeDelay(n_delays=1) regressor = pydmd.DMD(svd_rank=2) @dataclass() class SubspaceDMDcConfig: + """ + Configuration dataclass for SubspaceDMDc (DMD with control using subspace identification). + + This configuration is used to set parameters for the SubspaceDMDc class when + performing Dynamical Mode Decomposition on controlled systems. + + Attributes: + n_delays (int): Number of time delays to use in the Hankel matrix construction. + Default is 1 (no delays). + delay_interval (int): Interval between delays in the Hankel matrix. + Default is 1 (consecutive time steps). + rank (int): Rank for SVD truncation. If None, no truncation is performed. + Default is None. + lamb (float): Regularization parameter for ridge regression. + Default is 0 (no regularization). + backend (str): Subspace identification backend to use. + Options: 'n4sid', 'custom'. + """ n_delays: int = 1 delay_interval: int = 1 rank: int = None @@ -53,14 +100,48 @@ class SubspaceDMDcConfig: #__Example config dataclasses for similarity transform distance # @dataclass class SimilarityTransformDistConfig: + """ + Configuration dataclass for SimilarityTransformDist (standard similarity transform distance). + + This configuration is used to compute the similarity transform distance between + two DMD matrices, which measures how similar two dynamical systems are. + + Attributes: + iters (int): Number of optimization iterations for finding the similarity transform. + Default is 1500. + score_method (Literal["angular", "euclidean","wasserstein"]): Method for computing the distance score. + 'angular' uses angular distance, 'euclidean' uses Euclidean distance. + Default is "angular". + lr (float): Learning rate for the optimization algorithm. + Default is 5e-3. + zero_pad (bool): Whether to zero-pad matrices to make them the same size. + Default is False. + """ iters: int = 1500 - score_method: Literal["angular", "euclidean"] = "angular" + score_method: Literal["angular", "euclidean","wasserstein"] = "angular" lr: float = 5e-3 zero_pad: bool = False - wasserstein_compare: Literal["sv", "eig", None] = "eig" @dataclass() class ControllabilitySimilarityTransformDistConfig: + """ + Configuration dataclass for ControllabilitySimilarityTransformDist (similarity transform distance with control). + + This configuration is used to compute the similarity transform distance between + two controlled DMD systems, comparing both state and control operators. + + Attributes: + score_method (Literal["euclidean", "angular"]): Method for computing the distance score. + 'angular' uses angular distance, 'euclidean' uses Euclidean distance. + Default is "euclidean". + compare (str): What to compare between systems. + 'state' compares only state operators, 'control' compares only control operators, + 'joint' compares both. Default is 'state'. + joint_optim (bool): Whether to optimize state and control operators jointly. + Default is False. + return_distance_components (bool): Whether to return individual distance components + (state, control, joint) separately. Default is False. + """ score_method: Literal["euclidean", "angular"] = "euclidean" compare = 'state' joint_optim: bool = False @@ -68,7 +149,28 @@ class ControllabilitySimilarityTransformDistConfig: class GeneralizedDSA: """ - Computes the Generalized Dynamical Similarity Analysis (DSA) for two data tensors + Computes the Generalized Dynamical Similarity Analysis (DSA) for two data tensors. + + This class performs Dynamical Mode Decomposition (DMD) on input data and then computes + similarity scores between the resulting DMD models using similarity transform distances. + It supports various comparison modes including pairwise comparisons, bipartite comparisons, + and comparisons with control inputs. + + The class handles: + - Multiple data formats (single arrays, lists of arrays) + - Different DMD implementations (local DMD, pyKoopman, etc.) + - Control inputs for controlled systems + - Parallel processing for efficiency + - Various similarity metrics + + Example usage: + # Compare two datasets + dsa = GeneralizedDSA(X=data1, Y=data2, dmd_class=DefaultDMD) + similarity_score = dsa.fit_score() + + # Pairwise comparison of multiple datasets + dsa = GeneralizedDSA(X=[data1, data2, data3], Y=None) + similarity_matrix = dsa.fit_score() """ def __init__( @@ -109,6 +211,27 @@ def __init__( Must be the same shape as Y. If None, then no control data is used. + dmd_class : class + DMD class to use for decomposition. Default is DefaultDMD. + + similarity_class : class + Similarity transform distance class to use. Default is SimilarityTransformDist. + + dmd_config : Union[Mapping[str, Any], dataclass] + Configuration for DMD parameters. Can be a dict or dataclass. + + simdist_config : Union[Mapping[str, Any], dataclass] + Configuration for similarity transform distance parameters. Can be a dict or dataclass. + + device : str + Device to use for computation ('cpu' or 'cuda'). Default is 'cpu'. + + dsa_verbose : bool + Whether to print verbose output during computation. Default is False. + + n_jobs : int + Number of parallel jobs to use. Default is 1 (sequential). + Set to -1 to use all available cores. NOTE: for all of these above, they can be single values or lists or tuples, depending on the corresponding dimensions of the data From 8d46192cd78065e9669faaaa1496da993e2e4971 Mon Sep 17 00:00:00 2001 From: ostrow Date: Tue, 28 Oct 2025 12:20:17 -0400 Subject: [PATCH 13/90] add docstring for simdist_controllability --- DSA/simdist_controllability.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/DSA/simdist_controllability.py b/DSA/simdist_controllability.py index 3137d33..f962243 100644 --- a/DSA/simdist_controllability.py +++ b/DSA/simdist_controllability.py @@ -18,7 +18,7 @@ def __init__( score_method: Literal["euclidean", "angular"] = "euclidean", compare: Literal['joint','control','state'] = 'joint', joint_optim: bool = False, - return_distance_components=True + return_distance_components: bool =True ): f""" Parameters @@ -29,6 +29,8 @@ def __init__( what type of comparison to do on the A and B matrices align_inputs : bool If True, do two-sided Procrustes on controllability matrices (solve for C and C_u). + return_distance_components: bool + If True, returns the jointly optimized controllability score, the jointly optimize state score, and the jointly optimized control score """ self.score_method = score_method self.compare = compare @@ -186,6 +188,9 @@ def compare_systems_procrustes(self, A1, B1, A2, B2, *,align_inputs=False): @staticmethod def compare_B(B1, B2, score_method='euclidean'): + ''' + compares the B matrices with left procrustes + ''' if score_method == 'euclidean': R, _ = orthogonal_procrustes(B2.T, B1.T) return np.linalg.norm(B1 - R.T @ B2, "fro") From 88d8e312e183c19fd8f02bd504a5abcc8163f208 Mon Sep 17 00:00:00 2001 From: ostrow Date: Tue, 28 Oct 2025 12:24:39 -0400 Subject: [PATCH 14/90] black formatting --- DSA/__init__.py | 2 +- DSA/base_dmd.py | 62 ++-- DSA/dmd.py | 20 +- DSA/dmdc.py | 306 +++++++++++-------- DSA/dsa.py | 304 +++++++++++-------- DSA/preprocessing.py | 105 ++++--- DSA/resdmd.py | 34 ++- DSA/simdist.py | 127 ++++---- DSA/simdist_controllability.py | 102 ++++--- DSA/stats.py | 6 +- DSA/subspace_dmdc.py | 516 +++++++++++++++++++-------------- DSA/sweeps.py | 381 +++++++++++++++--------- setup.py | 20 +- tests/dmd_test.py | 156 +++++----- tests/dsa_test.py | 207 ++++++------- tests/simdist_test.py | 138 ++++----- 16 files changed, 1464 insertions(+), 1022 deletions(-) diff --git a/DSA/__init__.py b/DSA/__init__.py index 7968a98..b830265 100644 --- a/DSA/__init__.py +++ b/DSA/__init__.py @@ -7,4 +7,4 @@ from DSA.stats import * from DSA.sweeps import * from DSA.preprocessing import * -from DSA.resdmd import * \ No newline at end of file +from DSA.resdmd import * diff --git a/DSA/base_dmd.py b/DSA/base_dmd.py index f8e3d28..b3c4716 100644 --- a/DSA/base_dmd.py +++ b/DSA/base_dmd.py @@ -7,7 +7,7 @@ class BaseDMD(ABC): """Base class for DMD implementations with common functionality.""" - + def __init__( self, device="cpu", @@ -32,16 +32,16 @@ def __init__( self.verbose = verbose self.send_to_cpu = send_to_cpu self.lamb = lamb - + # Common attributes self.data = None self.n = None self.ntrials = None self.is_list_data = False - + # SVD attributes - will be set by subclasses self.cumulative_explained_variance = None - + def _process_single_dataset(self, data): """Process a single dataset, handling numpy arrays, tensors, and lists.""" if isinstance(data, list): @@ -51,7 +51,7 @@ def _process_single_dataset(self, data): torch.from_numpy(d) if isinstance(d, np.ndarray) else d for d in data ] - return torch.stack(processed_data), False + return torch.stack(processed_data), False except (RuntimeError, ValueError): # Handle ragged lists processed_data = [ @@ -64,24 +64,22 @@ def _process_single_dataset(self, data): raise ValueError( "All tensors in the list must have the same number of features (last dimension)." ) - return processed_data, True - + return processed_data, True + elif isinstance(data, np.ndarray): return torch.from_numpy(data), False - + return data, False def _init_single_data(self, data): """Initialize data attributes for a single dataset.""" processed_data, is_ragged = self._process_single_dataset(data) - + if is_ragged: # Set attributes for ragged data n_features = processed_data[0].shape[-1] self.n = n_features - self.ntrials = sum( - d.shape[0] if d.ndim == 3 else 1 for d in processed_data - ) + self.ntrials = sum(d.shape[0] if d.ndim == 3 else 1 for d in processed_data) self.trial_counts = [ d.shape[0] if d.ndim == 3 else 1 for d in processed_data ] @@ -95,7 +93,7 @@ def _init_single_data(self, data): self.n = processed_data.shape[1] self.ntrials = 1 self.is_list_data = False - + return processed_data def _compute_explained_variance(self, S): @@ -103,11 +101,18 @@ def _compute_explained_variance(self, S): exp_variance = S**2 / torch.sum(S**2) return torch.cumsum(exp_variance, 0) - def _compute_rank_from_params(self, S, cumulative_explained_variance, max_rank, - rank=None, rank_thresh=None, rank_explained_variance=None): + def _compute_rank_from_params( + self, + S, + cumulative_explained_variance, + max_rank, + rank=None, + rank_thresh=None, + rank_explained_variance=None, + ): """ Compute rank based on provided parameters. - + Parameters ---------- S : torch.Tensor @@ -122,15 +127,19 @@ def _compute_rank_from_params(self, S, cumulative_explained_variance, max_rank, Threshold for singular values rank_explained_variance : float, optional Explained variance threshold - + Returns ------- int Computed rank """ - parameters_provided = [rank is not None, rank_thresh is not None, rank_explained_variance is not None] + parameters_provided = [ + rank is not None, + rank_thresh is not None, + rank_explained_variance is not None, + ] num_parameters_provided = sum(parameters_provided) - + if num_parameters_provided > 1: raise ValueError( "More than one rank parameter was provided. Please provide only one of rank, rank_thresh, or rank_explained_variance." @@ -146,15 +155,22 @@ def _compute_rank_from_params(self, S, cumulative_explained_variance, max_rank, if computed_rank == 0: computed_rank = 1 # Ensure at least rank 1 elif rank_explained_variance is not None: - cumulative_explained_variance_cpu = cumulative_explained_variance.cpu().numpy() - computed_rank = int(np.searchsorted(cumulative_explained_variance_cpu, rank_explained_variance) + 1) + cumulative_explained_variance_cpu = ( + cumulative_explained_variance.cpu().numpy() + ) + computed_rank = int( + np.searchsorted( + cumulative_explained_variance_cpu, rank_explained_variance + ) + + 1 + ) if computed_rank > len(S): computed_rank = len(S) - + # Ensure rank doesn't exceed maximum possible if computed_rank > max_rank: computed_rank = max_rank - + return computed_rank def all_to_device(self, device="cpu"): diff --git a/DSA/dmd.py b/DSA/dmd.py index 10a6905..d2b7b4b 100644 --- a/DSA/dmd.py +++ b/DSA/dmd.py @@ -2,6 +2,7 @@ import numpy as np import torch + try: from .base_dmd import BaseDMD except ImportError: @@ -142,8 +143,10 @@ def __init__( The number of time steps ahead to predict. Defaults to 1. """ - super().__init__(device=device, verbose=verbose, send_to_cpu=send_to_cpu, lamb=lamb) - + super().__init__( + device=device, verbose=verbose, send_to_cpu=send_to_cpu, lamb=lamb + ) + self.data = self._init_single_data(data) self.n_delays = n_delays @@ -364,10 +367,10 @@ def recalc_rank(self, rank, rank_thresh, rank_explained_variance): else: S = self.S cumulative_explained = self.cumulative_explained_variance - + # Get maximum possible rank h_shape_last = self.H_shapes[-1][-1] if self.is_list_data else self.H.shape[-1] - + # Use base class method to compute rank self.rank = self._compute_rank_from_params( S=S, @@ -375,7 +378,7 @@ def recalc_rank(self, rank, rank_thresh, rank_explained_variance): max_rank=h_shape_last, rank=self.rank, rank_thresh=self.rank_thresh, - rank_explained_variance=self.rank_explained_variance + rank_explained_variance=self.rank_explained_variance, ) def compute_havok_dmd(self, lamb=None): @@ -393,7 +396,7 @@ def compute_havok_dmd(self, lamb=None): print("Computing least squares fits to HAVOK DMD ...") self.lamb = self.lamb if lamb is None else lamb - + A_v = ( torch.linalg.inv( self.Vt_minus[:, : self.rank].T @ self.Vt_minus[:, : self.rank] @@ -552,7 +555,7 @@ def fit( if self.send_to_cpu: self.all_to_device("cpu") # send back to the cpu to save memory - + # print('After DMD fitting in dmd.py', self.A_v.shape) def predict(self, test_data=None, reseed=None, full_return=False): @@ -622,7 +625,6 @@ def predict(self, test_data=None, reseed=None, full_return=False): else: return pred_data - def project_onto_modes(self): eigvals, eigvecs = torch.linalg.eigh(self.A_v) # project Vt_minus onto the eigenvectors @@ -632,4 +634,4 @@ def project_onto_modes(self): ) # get the data that matches the shape of the original data - return projections, self.data[:, : -self.n_delays + 1] \ No newline at end of file + return projections, self.data[:, : -self.n_delays + 1] diff --git a/DSA/dmdc.py b/DSA/dmdc.py index f4052de..94c8c5b 100644 --- a/DSA/dmdc.py +++ b/DSA/dmdc.py @@ -1,6 +1,8 @@ """This module computes the DMD with control (DMDc) model for a given dataset.""" + import numpy as np import torch + try: from .dmd import embed_signal_torch from .base_dmd import BaseDMD @@ -8,41 +10,45 @@ from dmd import embed_signal_torch from base_dmd import BaseDMD -def embed_data_DMDc(data, n_delays=1, n_control_delays=1, delay_interval=1, control=False): + +def embed_data_DMDc( + data, n_delays=1, n_control_delays=1, delay_interval=1, control=False +): if control: if n_control_delays == 1: if data.ndim == 2: - return data[(n_delays-1)*delay_interval:, :] + return data[(n_delays - 1) * delay_interval :, :] else: - return data[:, (n_delays-1)*delay_interval:, :] + return data[:, (n_delays - 1) * delay_interval :, :] else: embedded_data = embed_signal_torch(data, n_control_delays, delay_interval) return embedded_data else: return embed_signal_torch(data, n_delays, delay_interval) + class DMDc(BaseDMD): - """DMDc class for computing and predicting with DMD with control models. - """ + """DMDc class for computing and predicting with DMD with control models.""" + def __init__( - self, - data, - control_data=None, - n_delays=1, - n_control_delays=1, - delay_interval=1, - rank_input=None, - rank_thresh_input=None, - rank_explained_variance_input=None, - rank_output=None, - rank_thresh_output=None, - rank_explained_variance_output=None, - lamb=1e-8, - device='cpu', - verbose=False, - send_to_cpu=False, - svd_separate = True, - steps_ahead=1 + self, + data, + control_data=None, + n_delays=1, + n_control_delays=1, + delay_interval=1, + rank_input=None, + rank_thresh_input=None, + rank_explained_variance_input=None, + rank_output=None, + rank_thresh_output=None, + rank_explained_variance_output=None, + lamb=1e-8, + device="cpu", + verbose=False, + send_to_cpu=False, + svd_separate=True, + steps_ahead=1, ): """ Parameters @@ -68,7 +74,7 @@ def __init__( to 1 time step. rank : int - The rank of V in fitting DMDc - i.e., the number of columns of V to + The rank of V in fitting DMDc - i.e., the number of columns of V to use to fit the DMDc model. Defaults to None, in which case all columns of V will be used. @@ -80,7 +86,7 @@ def __init__( rank_explained_variance : float Parameter that controls the rank of V in fitting DMDc by indicating the percentage of cumulative explained variance that should be explained by the columns of V. Defaults to None. - + lamb : float Regularization parameter for ridge regression. Defaults to 0. @@ -93,28 +99,32 @@ def __init__( send_to_cpu: bool If True, will send all tensors in the object back to the cpu after everything is computed. This is implemented to prevent gpu memory overload when computing multiple DMDs. - + steps_ahead: int The number of time steps ahead to predict. Defaults to 1. """ - super().__init__(device=device, verbose=verbose, send_to_cpu=send_to_cpu, lamb=lamb) - + super().__init__( + device=device, verbose=verbose, send_to_cpu=send_to_cpu, lamb=lamb + ) + self._init_data(data, control_data) self.n_delays = n_delays self.n_control_delays = n_control_delays self.delay_interval = delay_interval - + self.rank_input = rank_input self.rank_thresh_input = rank_thresh_input self.rank_explained_variance_input = rank_explained_variance_input self.rank_output = rank_output self.rank_thresh_output = rank_thresh_output self.rank_explained_variance_output = rank_explained_variance_output - self.svd_separate = svd_separate #do svd on H and u separately as well as regression + self.svd_separate = ( + svd_separate # do svd on H and u separately as well as regression + ) self.steps_ahead = steps_ahead - + # Hankel matrix self.H = None @@ -127,12 +137,12 @@ def __init__( self.V = None self.S_mat = None self.S_mat_inv = None - + # Change of basis between the reduced-order subspace and the full space self.U_out = None self.S_out = None self.V_out = None - + # DMDc attributes self.A_tilde = None self.B_tilde = None @@ -140,47 +150,52 @@ def __init__( self.B = None self.A_havok_dmd = None self.B_havok_dmd = None - + # Check if the state and control data are list (for different trial lengths) if not np.all([isinstance(data, list), isinstance(control_data, list)]): if isinstance(data, list) or isinstance(control_data, list): - raise TypeError("If you pass one of (data, control_data) as list, the other must also be a list.") + raise TypeError( + "If you pass one of (data, control_data) as list, the other must also be a list." + ) def _init_data(self, data, control_data=None): # Process main data self.data, data_is_ragged = self._process_single_dataset(data) - + # Process control data if control_data is not None: - self.control_data, control_is_ragged = self._process_single_dataset(control_data) + self.control_data, control_is_ragged = self._process_single_dataset( + control_data + ) else: self.control_data = torch.zeros_like(self.data) control_is_ragged = False - + # Check consistency between data and control_data if data_is_ragged != control_is_ragged: - raise ValueError("Data and control data have different structure (type or dimensionality).") - + raise ValueError( + "Data and control data have different structure (type or dimensionality)." + ) + if data_is_ragged: # Additional validation for ragged data if not all(d.shape[-1] == control_data[0].shape[-1] for d in control_data): raise ValueError( "All control tensors in the list must have the same number of features (last dimension)." ) - if not all(d.shape[0] == control_d.shape[0] for d, control_d in zip(data, control_data)): + if not all( + d.shape[0] == control_d.shape[0] + for d, control_d in zip(data, control_data) + ): raise ValueError( "Data and control_data tensors must have the same number of time steps." ) - + # Set attributes for ragged data n_features = self.data[0].shape[-1] self.n = n_features - self.ntrials = sum( - d.shape[0] if d.ndim == 3 else 1 for d in self.data - ) - self.trial_counts = [ - d.shape[0] if d.ndim == 3 else 1 for d in self.data - ] + self.ntrials = sum(d.shape[0] if d.ndim == 3 else 1 for d in self.data) + self.trial_counts = [d.shape[0] if d.ndim == 3 else 1 for d in self.data] self.is_list_data = True else: # Set attributes for non-ragged data @@ -193,12 +208,12 @@ def _init_data(self, data, control_data=None): self.is_list_data = False def compute_hankel( - self, - data=None, - control_data=None, - n_delays=None, - delay_interval=None, - ): + self, + data=None, + control_data=None, + n_delays=None, + delay_interval=None, + ): """ Computes the Hankel matrix from the provided data and forms Omega. """ @@ -208,26 +223,55 @@ def compute_hankel( # Overwrite parameters if provided self.data = self.data if data is None else self._init_data(data, control_data) self.n_delays = self.n_delays if n_delays is None else n_delays - self.delay_interval = self.delay_interval if delay_interval is None else delay_interval - + self.delay_interval = ( + self.delay_interval if delay_interval is None else delay_interval + ) + if self.is_list_data: self.data = [d.to(self.device) for d in self.data] self.control_data = [d.to(self.device) for d in self.control_data] # Compute Hankel matrices for each trial separately - self.H = [embed_data_DMDc(d, n_delays=self.n_delays, n_control_delays=self.n_control_delays, delay_interval=self.delay_interval).float() for d in self.data] - self.Hu = [embed_data_DMDc(d, n_delays=self.n_delays, n_control_delays=self.n_control_delays, delay_interval=self.delay_interval, control=True).float() for d in self.control_data] + self.H = [ + embed_data_DMDc( + d, + n_delays=self.n_delays, + n_control_delays=self.n_control_delays, + delay_interval=self.delay_interval, + ).float() + for d in self.data + ] + self.Hu = [ + embed_data_DMDc( + d, + n_delays=self.n_delays, + n_control_delays=self.n_control_delays, + delay_interval=self.delay_interval, + control=True, + ).float() + for d in self.control_data + ] self.H_shapes = [h.shape for h in self.H] else: self.data = self.data.to(self.device) self.control_data = self.control_data.to(self.device) # Compute Hankel matrices - self.H = embed_data_DMDc(self.data, n_delays=self.n_delays, n_control_delays=self.n_control_delays, delay_interval=self.delay_interval).float() - self.Hu = embed_data_DMDc(self.control_data, n_delays=self.n_delays, n_control_delays=self.n_control_delays, delay_interval=self.delay_interval, control=True).float() + self.H = embed_data_DMDc( + self.data, + n_delays=self.n_delays, + n_control_delays=self.n_control_delays, + delay_interval=self.delay_interval, + ).float() + self.Hu = embed_data_DMDc( + self.control_data, + n_delays=self.n_delays, + n_control_delays=self.n_control_delays, + delay_interval=self.delay_interval, + control=True, + ).float() if self.verbose: print("Hankel matrices computed!") - def compute_svd(self): """ Computes the SVD of the Omega and Y matrices. @@ -235,7 +279,6 @@ def compute_svd(self): if self.verbose: print("Computing SVD on H and U matrices ...") - if self.is_list_data: self.H_shapes = [h.shape for h in self.H] H_list = [] @@ -249,7 +292,7 @@ def compute_svd(self): ) else: H_list.append(h_elem) - + for hu_elem in self.Hu: if hu_elem.ndim == 3: Hu_list.append( @@ -265,7 +308,7 @@ def compute_svd(self): self.H_row_counts = [h.shape[0] for h in H_list] H = self.H Hu = self.Hu - + elif self.H.ndim == 3: # flatten across trials for 3d H = self.H.reshape(self.H.shape[0] * self.H.shape[1], self.H.shape[2]) Hu = self.Hu.reshape(self.Hu.shape[0] * self.Hu.shape[1], self.Hu.shape[2]) @@ -284,15 +327,19 @@ def compute_svd(self): self.Su_mat = torch.diag(self.Su).to(self.device) self.Su_mat_inv = torch.diag(1 / self.Su).to(self.device) - self.cumulative_explained_variance_input = self._compute_explained_variance(self.Su) - self.cumulative_explained_variance_output = self._compute_explained_variance(self.Sh) + self.cumulative_explained_variance_input = self._compute_explained_variance( + self.Su + ) + self.cumulative_explained_variance_output = self._compute_explained_variance( + self.Sh + ) self.Vht_minus, self.Vht_plus = self.get_plus_minus(self.Vh, self.H) self.Vut_minus, _ = self.get_plus_minus(self.Vu, self.Hu) if self.verbose: print("SVDs computed!") - + def get_plus_minus(self, V, H): if self.ntrials > 1: if self.is_list_data: @@ -341,12 +388,18 @@ def get_plus_minus(self, V, H): return Vt_minus, Vt_plus - - def recalc_rank(self, rank_input=None, rank_thresh_input=None, rank_explained_variance_input=None, - rank_output=None, rank_thresh_output=None, rank_explained_variance_output=None): - ''' + def recalc_rank( + self, + rank_input=None, + rank_thresh_input=None, + rank_explained_variance_input=None, + rank_output=None, + rank_thresh_output=None, + rank_explained_variance_output=None, + ): + """ Recalculates the rank for input and output based on provided parameters. - ''' + """ # Recalculate ranks for input self.rank_input = self._compute_rank_from_params( S=self.Su, @@ -354,7 +407,7 @@ def recalc_rank(self, rank_input=None, rank_thresh_input=None, rank_explained_va max_rank=self.Hu.shape[-1], rank=rank_input, rank_thresh=rank_thresh_input, - rank_explained_variance=rank_explained_variance_input + rank_explained_variance=rank_explained_variance_input, ) # Recalculate ranks for output self.rank_output = self._compute_rank_from_params( @@ -363,17 +416,22 @@ def recalc_rank(self, rank_input=None, rank_thresh_input=None, rank_explained_va max_rank=self.H.shape[-1], rank=rank_output, rank_thresh=rank_thresh_output, - rank_explained_variance=rank_explained_variance_output + rank_explained_variance=rank_explained_variance_output, ) - def compute_dmdc(self, lamb=None): if self.verbose: print("Computing DMDc matrices ...") self.lamb = self.lamb if lamb is None else lamb - V_minus_tot = torch.cat([self.Vht_minus[:, :self.rank_output], self.Vut_minus[:, :self.rank_input]], dim=1) + V_minus_tot = torch.cat( + [ + self.Vht_minus[:, : self.rank_output], + self.Vut_minus[:, : self.rank_input], + ], + dim=1, + ) A_v_tot = ( torch.linalg.inv( @@ -381,11 +439,11 @@ def compute_dmdc(self, lamb=None): + self.lamb * torch.eye(V_minus_tot.shape[1]).to(self.device) ) @ V_minus_tot.T - @ self.Vht_plus[:, :self.rank_output] + @ self.Vht_plus[:, : self.rank_output] ).T - #split A_v_tot into A_v and B_v - self.A_v = A_v_tot[:, :self.rank_output] - self.B_v = A_v_tot[:, self.rank_output:] + # split A_v_tot into A_v and B_v + self.A_v = A_v_tot[:, : self.rank_output] + self.B_v = A_v_tot[:, self.rank_output :] self.A_havok_dmd = ( self.Uh @ self.Sh_mat[: self.Uh.shape[1], : self.rank_output] @@ -401,24 +459,24 @@ def compute_dmdc(self, lamb=None): @ self.Su_mat_inv[: self.rank_input, : self.Uu.shape[1]] @ self.Uu.T ) - + # Set the A and B properties for backward compatibility and easier access self.A = self.A_havok_dmd self.B = self.B_havok_dmd - + if self.verbose: print("DMDc matrices computed!") def fit( - self, - data=None, - control_data=None, - n_delays=None, - delay_interval=None, - lamb=None, - device=None, - verbose=None, - ): + self, + data=None, + control_data=None, + n_delays=None, + delay_interval=None, + lamb=None, + device=None, + verbose=None, + ): """ Fits the DMDc model to the provided data. """ @@ -429,20 +487,20 @@ def fit( self.compute_hankel(data, control_data, n_delays, delay_interval) self.compute_svd() self.recalc_rank( - self.rank_input, self.rank_thresh_input, self.rank_explained_variance_input, - self.rank_output, self.rank_thresh_output, self.rank_explained_variance_output + self.rank_input, + self.rank_thresh_input, + self.rank_explained_variance_input, + self.rank_output, + self.rank_thresh_output, + self.rank_explained_variance_output, ) self.compute_dmdc(lamb) if self.send_to_cpu: - self.all_to_device('cpu') # send back to the cpu to save memory + self.all_to_device("cpu") # send back to the cpu to save memory def predict( - self, - test_data=None, - control_data=None, - reseed=None, - full_return=False - ): + self, test_data=None, control_data=None, reseed=None, full_return=False + ): """ Parameters ---------- @@ -474,10 +532,17 @@ def predict( test_data = self.data if control_data is None: control_data = self.control_data - + if isinstance(test_data, list): - predictions = [self.predict(test_data=d, control_data=d_control, - reseed=reseed, full_return=full_return) for d, d_control in zip(test_data, control_data)] + predictions = [ + self.predict( + test_data=d, + control_data=d_control, + reseed=reseed, + full_return=full_return, + ) + for d, d_control in zip(test_data, control_data) + ] if full_return: pred_data = [pred[0] for pred in predictions] H_test_dmdc = [pred[1] for pred in predictions] @@ -485,20 +550,24 @@ def predict( return pred_data, H_test_dmdc, H_test else: return predictions - + if isinstance(test_data, np.ndarray): test_data = torch.from_numpy(test_data).to(self.device) if isinstance(control_data, np.ndarray): control_data = torch.from_numpy(control_data).to(self.device) - + ndim = test_data.ndim if ndim == 2: test_data = test_data.unsqueeze(0) control_data = control_data.unsqueeze(0) # H_test = embed_data_DMDc(test_data, n_delays=self.n_delays, delay_interval=self.delay_interval, control=False) # H_control = embed_data_DMDc(control_data, n_delays=self.n_control_delays, delay_interval=self.delay_interval, control=True) - H_test = embed_signal_torch(test_data, self.n_delays, self.delay_interval).float() - H_control = embed_signal_torch(control_data, self.n_control_delays, self.delay_interval).float() + H_test = embed_signal_torch( + test_data, self.n_delays, self.delay_interval + ).float() + H_control = embed_signal_torch( + control_data, self.n_control_delays, self.delay_interval + ).float() if reseed is None: reseed = 1 @@ -506,22 +575,33 @@ def predict( H_test_dmdc[:, 0] = H_test[:, 0] A = self.A_havok_dmd B = self.B_havok_dmd - + for t in range(1, H_test.shape[1]): - u_t = H_control[:, t - 1] + u_t = H_control[:, t - 1] # print(A.shape) # print(H_test[:, t - 1].shape) # print(B.shape) # print(u_t.shape) if t % reseed == 0: - H_test_dmdc[:, t] = (A @ H_test[:, t - 1].transpose(-2, -1)).transpose(-2, -1) + (B @ u_t.transpose(-2, -1)).transpose(-2, -1) + H_test_dmdc[:, t] = (A @ H_test[:, t - 1].transpose(-2, -1)).transpose( + -2, -1 + ) + (B @ u_t.transpose(-2, -1)).transpose(-2, -1) else: - H_test_dmdc[:, t] = (A @ H_test_dmdc[:, t - 1].transpose(-2, -1)).transpose(-2, -1) + (B @ u_t.transpose(-2, -1)).transpose(-2, -1) - pred_data = torch.hstack([test_data[:, :(self.n_delays - 1)*self.delay_interval + self.steps_ahead], H_test_dmdc[:, self.steps_ahead:, :self.n]]) + H_test_dmdc[:, t] = ( + A @ H_test_dmdc[:, t - 1].transpose(-2, -1) + ).transpose(-2, -1) + (B @ u_t.transpose(-2, -1)).transpose(-2, -1) + pred_data = torch.hstack( + [ + test_data[ + :, : (self.n_delays - 1) * self.delay_interval + self.steps_ahead + ], + H_test_dmdc[:, self.steps_ahead :, : self.n], + ] + ) if ndim == 2: pred_data = pred_data[0] - + if full_return: return pred_data, H_test_dmdc, H_test else: diff --git a/DSA/dsa.py b/DSA/dsa.py index c21d583..b96eb93 100644 --- a/DSA/dsa.py +++ b/DSA/dsa.py @@ -29,15 +29,16 @@ "send_to_cpu": bool, } -#___Example config dataclasses for DMD # + +# ___Example config dataclasses for DMD # @dataclass() class DefaultDMDConfig: """ Configuration dataclass for DefaultDMD (standard DMD without control). - + This configuration is used to set parameters for the DefaultDMD class when performing Dynamical Mode Decomposition on time series data. - + Attributes: n_delays (int): Number of time delays to use in the Hankel matrix construction. Default is 1 (no delays). @@ -50,35 +51,39 @@ class DefaultDMDConfig: send_to_cpu (bool): Whether to move computations to CPU. Default is False (use GPU if available). """ + n_delays: int = 1 delay_interval: int = 1 rank: int = None lamb: float = 0 send_to_cpu: bool = False + @dataclass() class pyKoopmanDMDConfig: """ Configuration dataclass for pyKoopman DMD models. - + This configuration is used to set up pyKoopman observables and regressors for performing DMD analysis with the pyKoopman library. - + Attributes: observables: Observable function from pykoopman. Default is TimeDelay with n_delays=1. regressor: Regressor model from pydmd. Default is DMD with svd_rank=2. """ + observables = pykoopman.observables.TimeDelay(n_delays=1) regressor = pydmd.DMD(svd_rank=2) - + + @dataclass() class SubspaceDMDcConfig: """ Configuration dataclass for SubspaceDMDc (DMD with control using subspace identification). - + This configuration is used to set parameters for the SubspaceDMDc class when performing Dynamical Mode Decomposition on controlled systems. - + Attributes: n_delays (int): Number of time delays to use in the Hankel matrix construction. Default is 1 (no delays). @@ -91,21 +96,23 @@ class SubspaceDMDcConfig: backend (str): Subspace identification backend to use. Options: 'n4sid', 'custom'. """ + n_delays: int = 1 delay_interval: int = 1 rank: int = None lamb: float = 0 - backend: str = 'n4sid' + backend: str = "n4sid" -#__Example config dataclasses for similarity transform distance # + +# __Example config dataclasses for similarity transform distance # @dataclass class SimilarityTransformDistConfig: """ Configuration dataclass for SimilarityTransformDist (standard similarity transform distance). - + This configuration is used to compute the similarity transform distance between two DMD matrices, which measures how similar two dynamical systems are. - + Attributes: iters (int): Number of optimization iterations for finding the similarity transform. Default is 1500. @@ -117,19 +124,21 @@ class SimilarityTransformDistConfig: zero_pad (bool): Whether to zero-pad matrices to make them the same size. Default is False. """ + iters: int = 1500 - score_method: Literal["angular", "euclidean","wasserstein"] = "angular" + score_method: Literal["angular", "euclidean", "wasserstein"] = "angular" lr: float = 5e-3 zero_pad: bool = False + @dataclass() class ControllabilitySimilarityTransformDistConfig: """ Configuration dataclass for ControllabilitySimilarityTransformDist (similarity transform distance with control). - + This configuration is used to compute the similarity transform distance between two controlled DMD systems, comparing both state and control operators. - + Attributes: score_method (Literal["euclidean", "angular"]): Method for computing the distance score. 'angular' uses angular distance, 'euclidean' uses Euclidean distance. @@ -142,32 +151,34 @@ class ControllabilitySimilarityTransformDistConfig: return_distance_components (bool): Whether to return individual distance components (state, control, joint) separately. Default is False. """ + score_method: Literal["euclidean", "angular"] = "euclidean" - compare = 'state' + compare = "state" joint_optim: bool = False return_distance_components: bool = False + class GeneralizedDSA: """ Computes the Generalized Dynamical Similarity Analysis (DSA) for two data tensors. - + This class performs Dynamical Mode Decomposition (DMD) on input data and then computes similarity scores between the resulting DMD models using similarity transform distances. It supports various comparison modes including pairwise comparisons, bipartite comparisons, and comparisons with control inputs. - + The class handles: - Multiple data formats (single arrays, lists of arrays) - Different DMD implementations (local DMD, pyKoopman, etc.) - Control inputs for controlled systems - Parallel processing for efficiency - Various similarity metrics - + Example usage: # Compare two datasets dsa = GeneralizedDSA(X=data1, Y=data2, dmd_class=DefaultDMD) similarity_score = dsa.fit_score() - + # Pairwise comparison of multiple datasets dsa = GeneralizedDSA(X=[data1, data2, data3], Y=None) similarity_matrix = dsa.fit_score() @@ -181,9 +192,11 @@ def __init__( Y_control=None, dmd_class=DefaultDMD, similarity_class=SimilarityTransformDist, - dmd_config: Union[Mapping[str, Any], dataclass]= DefaultDMDConfig, - simdist_config: Union[Mapping[str, Any], dataclass] = SimilarityTransformDistConfig, - device='cpu', + dmd_config: Union[Mapping[str, Any], dataclass] = DefaultDMDConfig, + simdist_config: Union[ + Mapping[str, Any], dataclass + ] = SimilarityTransformDistConfig, + device="cpu", dsa_verbose=False, n_jobs=1, ): @@ -210,25 +223,25 @@ def __init__( control data matrix/matrices. Must be the same shape as Y. If None, then no control data is used. - + dmd_class : class DMD class to use for decomposition. Default is DefaultDMD. - + similarity_class : class Similarity transform distance class to use. Default is SimilarityTransformDist. - + dmd_config : Union[Mapping[str, Any], dataclass] Configuration for DMD parameters. Can be a dict or dataclass. - + simdist_config : Union[Mapping[str, Any], dataclass] Configuration for similarity transform distance parameters. Can be a dict or dataclass. - + device : str Device to use for computation ('cpu' or 'cuda'). Default is 'cpu'. - + dsa_verbose : bool Whether to print verbose output during computation. Default is False. - + n_jobs : int Number of parallel jobs to use. Default is 1 (sequential). Set to -1 to use all available cores. @@ -241,7 +254,7 @@ def __init__( OR to X and Y respectively across all matrices If it is (list,list), then each element will correspond to an individual dmd matrix indexed at the same position - + """ self.X = X self.Y = Y @@ -259,7 +272,10 @@ def __init__( if self.X is None and isinstance(self.Y, list): self.X, self.Y = self.Y, self.X # swap so code is easy - self.X_control, self.Y_control = self.Y_control, self.X_control # swap control too + self.X_control, self.Y_control = ( + self.Y_control, + self.X_control, + ) # swap control too self.check_method() if self.method == "self-pairwise": @@ -286,76 +302,97 @@ def __init__( else: broadcasted_value = self.broadcast_params(value) - setattr( - self, key, broadcasted_value - ) # e.g., self.n_delays = [[v,v],[v,v]] + setattr(self, key, broadcasted_value) # e.g., self.n_delays = [[v,v],[v,v]] self.dmd_config[key] = ( broadcasted_value # Store in dict for DMD instantiation ) - self._check_dmd_simdist_compatibility(dmd_class,similarity_class) + self._check_dmd_simdist_compatibility(dmd_class, similarity_class) self._dmd_api_source(dmd_class) self._initiate_dmds() self.simdist = similarity_class(**self.simdist_config) - def _initiate_dmds(self): if self.dmd_has_control and self.X_control is None and self.Y_control is None: - raise ValueError("Error: You are using a DMD model that fits a control operator but no control data is provided for either X or Y") - - if not self.dmd_has_control and (self.X_control is not None or self.Y_control is not None): - warnings.warn("You are using a DMD model with no control but control data is provided, will be ignored") + raise ValueError( + "Error: You are using a DMD model that fits a control operator but no control data is provided for either X or Y" + ) + if not self.dmd_has_control and ( + self.X_control is not None or self.Y_control is not None + ): + warnings.warn( + "You are using a DMD model with no control but control data is provided, will be ignored" + ) if self.dmd_api_source == "local_dmd": self.dmds = [] - #TODO: test this for single numpy array + # TODO: test this for single numpy array for i, (dat, control_dat) in enumerate(zip(self.data, self.control_data)): dmd_list = [] if control_dat is None: control_dat = [None] * len(dat) for j, (Xi, Xi_control) in enumerate(zip(dat, control_dat)): config = {k: v[i][j] for k, v in self.dmd_config.items()} - + # if self.dmd_has_control: dmd_obj = self.dmd_class(Xi, control_data=Xi_control, **config) else: dmd_obj = self.dmd_class(Xi, **config) - + dmd_list.append(dmd_obj) self.dmds.append(dmd_list) else: self.dmds = [ - [self.dmd_class(**{k: v[i][j] for k, v in self.dmd_config.items()}) for j, Xi in enumerate(dat)] + [ + self.dmd_class(**{k: v[i][j] for k, v in self.dmd_config.items()}) + for j, Xi in enumerate(dat) + ] for i, dat in enumerate(self.data) ] def _check_dmd_simdist_compatibility(self, dmd_class, similarity_class): - self.dmd_has_control = dmd_class in [DefaultDMDc, SubspaceDMDc] or ('pykoopman' in dmd_class.__module__ and self.dmd_config.get('regressor') in [DMDc, EDMDc]) - self.simdist_has_control = similarity_class in [ControllabilitySimilarityTransformDist] + self.dmd_has_control = dmd_class in [DefaultDMDc, SubspaceDMDc] or ( + "pykoopman" in dmd_class.__module__ + and self.dmd_config.get("regressor") in [DMDc, EDMDc] + ) + self.simdist_has_control = similarity_class in [ + ControllabilitySimilarityTransformDist + ] if self.dmd_has_control and not self.simdist_has_control: - warnings.warn("Warning: You are using a DMD model that fits a control operator but comparing with a DSA metric that does not compare control operators") + warnings.warn( + "Warning: You are using a DMD model that fits a control operator but comparing with a DSA metric that does not compare control operators" + ) if not self.dmd_has_control and self.simdist_has_control: - raise ValueError("Error: Your DMD model does not fit a control operator but comparing with a DSA metric that compares control operators") + raise ValueError( + "Error: Your DMD model does not fit a control operator but comparing with a DSA metric that compares control operators" + ) def _dmd_api_source(self, dmd_class): module_name = dmd_class.__module__ if "pydmd" in module_name: self.dmd_api_source = "pydmd" - raise ValueError("DSA is not currently directly compatible with pydmd due to \ + raise ValueError( + "DSA is not currently directly compatible with pydmd due to \ data structure incompatibility. Please use pykoopman instead. \ - Note that you can pass in pydmd objects through pykoopman's Koopman class.") + Note that you can pass in pydmd objects through pykoopman's Koopman class." + ) elif "pykoopman" in module_name: self.dmd_api_source = "pykoopman" - if self.dmd_has_control and self.dmd_config.get('regressor') in [DMDc, EDMDc]: - raise ValueError("Pykoopman DMDc and EDMDc are not currently compatible with DSA") + if self.dmd_has_control and self.dmd_config.get("regressor") in [ + DMDc, + EDMDc, + ]: + raise ValueError( + "Pykoopman DMDc and EDMDc are not currently compatible with DSA" + ) elif ( - "DSA.dmd" in module_name or - "DSA.subspace_dmdc" in module_name or - "DSA.dmdc" in module_name + "DSA.dmd" in module_name + or "DSA.subspace_dmdc" in module_name + or "DSA.dmdc" in module_name ): self.dmd_api_source = "local_dmd" else: @@ -366,32 +403,47 @@ def _dmd_api_source(self, dmd_class): def fit_dmds(self): if self.n_jobs != 1: - n_jobs = self.n_jobs if self.n_jobs > 0 else -1 # -1 means use all available cores - + n_jobs = ( + self.n_jobs if self.n_jobs > 0 else -1 + ) # -1 means use all available cores + if self.dmd_api_source == "local_dmd": for dmd_sets in self.dmds: if self.dsa_verbose: - print(f"Fitting {len(dmd_sets)} DMDs in parallel with {n_jobs} jobs") + print( + f"Fitting {len(dmd_sets)} DMDs in parallel with {n_jobs} jobs" + ) Parallel(n_jobs=n_jobs)( delayed(lambda dmd: dmd.fit())(dmd) for dmd in dmd_sets ) else: for dmd_list, dat in zip(self.dmds, self.data): if self.dsa_verbose: - print(f"Fitting {len(dmd_list)} DMDs in parallel with {n_jobs} jobs") + print( + f"Fitting {len(dmd_list)} DMDs in parallel with {n_jobs} jobs" + ) Parallel(n_jobs=n_jobs)( - delayed(lambda dmd, X: dmd.fit(X))(dmd, Xi) for dmd, Xi in zip(dmd_list, dat) + delayed(lambda dmd, X: dmd.fit(X))(dmd, Xi) + for dmd, Xi in zip(dmd_list, dat) ) else: # Sequential processing if self.dmd_api_source == "local_dmd": for dmd_sets in self.dmds: - loop = dmd_sets if not self.dsa_verbose else tqdm.tqdm(dmd_sets, desc="Fitting DMDs") + loop = ( + dmd_sets + if not self.dsa_verbose + else tqdm.tqdm(dmd_sets, desc="Fitting DMDs") + ) for dmd in loop: dmd.fit() else: for dmd_list, dat in zip(self.dmds, self.data): - loop = zip(dmd_list, dat) if not self.dsa_verbose else tqdm.tqdm(zip(dmd_list, dat), desc="Fitting DMDs") + loop = ( + zip(dmd_list, dat) + if not self.dsa_verbose + else tqdm.tqdm(zip(dmd_list, dat), desc="Fitting DMDs") + ) for dmd, Xi in loop: dmd.fit(Xi) @@ -460,10 +512,14 @@ def broadcast_params(self, param, cast=None): assert len(param[i]) >= len(data) out.append(param[i][: len(data)]) elif ( - isinstance(param, (int, float, np.integer)) or param in {None,'None','none'} or - (hasattr(param, '__module__') and ('pykoopman' in param.__module__ or 'pydmd' in param.__module__)) + isinstance(param, (int, float, np.integer)) + or param in {None, "None", "none"} + or ( + hasattr(param, "__module__") + and ("pykoopman" in param.__module__ or "pydmd" in param.__module__) + ) ): # self.X has already been mapped to [self.X] - if param in {'None','none'}: + if param in {"None", "none"}: param = None out.append([param] * len(self.X)) if self.Y is not None: @@ -498,8 +554,10 @@ def get_dmd_matrix(self, dmd): elif self.dmd_api_source == "pykoopman": return dmd.A elif self.dmd_api_source == "pydmd": - raise ValueError("DSA is not currently compatible with pydmd due to \ - data structure incompatibility. Please use pykoopman instead.") + raise ValueError( + "DSA is not currently compatible with pydmd due to \ + data structure incompatibility. Please use pykoopman instead." + ) def get_dmd_control_matrix(self, dmd): if self.dmd_api_source == "local_dmd": @@ -507,12 +565,14 @@ def get_dmd_control_matrix(self, dmd): elif self.dmd_api_source == "pykoopman": return dmd.B elif self.dmd_api_source == "pydmd": - raise ValueError("DSA is not currently compatible with pydmd due to \ - data structure incompatibility. Please use pykoopman instead.") - + raise ValueError( + "DSA is not currently compatible with pydmd due to \ + data structure incompatibility. Please use pykoopman instead." + ) + def score(self): """ - Score DSA with precomputed dmds + Score DSA with precomputed dmds Parameters __________ @@ -530,62 +590,76 @@ def score(self): ind2 = 0 if self.method == "self-pairwise" else 1 # 0 if self.pairwise (want to compare the set to itself) - n_sims = 1 if not (self.simdist_has_control \ - and self.simdist_config.get("return_distance_components")) else 3 - - self.sims = np.zeros((len(self.dmds[0]), len(self.dmds[ind2]),n_sims)) + n_sims = ( + 1 + if not ( + self.simdist_has_control + and self.simdist_config.get("return_distance_components") + ) + else 3 + ) + + self.sims = np.zeros((len(self.dmds[0]), len(self.dmds[ind2]), n_sims)) if self.dsa_verbose: - print('comparing dmds') - + print("comparing dmds") + def compute_similarity(i, j): if self.method == "self-pairwise" and j >= i: return None if self.dsa_verbose and self.n_jobs != 1: print(f"computing similarity between DMDs {i} and {j}") - + simdist_args = [ - self.get_dmd_matrix(self.dmds[0][i]), - self.get_dmd_matrix(self.dmds[ind2][j]), + self.get_dmd_matrix(self.dmds[0][i]), + self.get_dmd_matrix(self.dmds[ind2][j]), ] if self.dmd_has_control: - simdist_args.extend([ - self.get_dmd_control_matrix(self.dmds[0][i]), - self.get_dmd_control_matrix(self.dmds[ind2][j]), - ]) + simdist_args.extend( + [ + self.get_dmd_control_matrix(self.dmds[0][i]), + self.get_dmd_control_matrix(self.dmds[ind2][j]), + ] + ) sim = self.simdist.fit_score(*simdist_args) if self.dsa_verbose and self.n_jobs != 1: print(f"computing similarity between DMDs {i} and {j}") - + return (i, j, sim) - + pairs = [] for i in range(len(self.dmds[0])): for j in range(len(self.dmds[ind2])): if not (self.method == "self-pairwise" and j >= i): pairs.append((i, j)) - + if self.n_jobs != 1: n_jobs = self.n_jobs if self.n_jobs > 0 else -1 if self.dsa_verbose: - print(f"Computing {len(pairs)} DMD similarities in parallel with {n_jobs} jobs") - + print( + f"Computing {len(pairs)} DMD similarities in parallel with {n_jobs} jobs" + ) + results = Parallel(n_jobs=n_jobs)( delayed(compute_similarity)(i, j) for i, j in pairs ) else: - loop = pairs if not self.dsa_verbose else tqdm.tqdm(pairs, desc="Computing DMD similarities") + loop = ( + pairs + if not self.dsa_verbose + else tqdm.tqdm(pairs, desc="Computing DMD similarities") + ) results = [compute_similarity(i, j) for i, j in loop] - + for result in results: if result is not None: i, j, sim = result self.sims[i, j] = sim if self.method == "self-pairwise": self.sims[j, i] = sim - + if self.method == "default": return self.sims[0, 0].squeeze() @@ -598,26 +672,26 @@ def __init__( X, Y=None, dmd_class=DefaultDMD, - device='cpu', + device="cpu", dsa_verbose=False, n_jobs=1, - #simdist parameters + # simdist parameters score_method: Literal["angular", "euclidean"] = "angular", iters: int = 1500, lr: float = 5e-3, wasserstein_compare: Literal["sv", "eig", None] = "eig", - **dmd_kwargs + **dmd_kwargs, ): - #TODO: add readme + # TODO: add readme simdist_config = { - 'score_method': score_method, - 'iters': iters, - 'lr': lr, - 'wasserstein_compare': wasserstein_compare, + "score_method": score_method, + "iters": iters, + "lr": lr, + "wasserstein_compare": wasserstein_compare, } dmd_config = dmd_kwargs - + super().__init__( X=X, Y=Y, @@ -632,6 +706,7 @@ def __init__( n_jobs=n_jobs, ) + class InputDSA(GeneralizedDSA): def __init__( self, @@ -640,21 +715,25 @@ def __init__( Y=None, Y_control=None, dmd_class=SubspaceDMDc, - dmd_config: Union[Mapping[str, Any], dataclass]= SubspaceDMDcConfig, - simdist_config: Union[Mapping[str, Any], dataclass] = ControllabilitySimilarityTransformDistConfig, - device='cpu', + dmd_config: Union[Mapping[str, Any], dataclass] = SubspaceDMDcConfig, + simdist_config: Union[ + Mapping[str, Any], dataclass + ] = ControllabilitySimilarityTransformDistConfig, + device="cpu", dsa_verbose=False, n_jobs=1, ): - #check if simdist_config has 'compare', and if it's 'state', use the standard SimilarityTransformDist, - #otherwise use ControllabilitySimilarityTransformDistConfig - if isinstance(simdist_config, dataclass): + # check if simdist_config has 'compare', and if it's 'state', use the standard SimilarityTransformDist, + # otherwise use ControllabilitySimilarityTransformDistConfig + if isinstance(simdist_config, dataclass): compare = simdist_config.compare - elif isinstance(simdist_config,dict): - compare = simdist_config.get("compare",None) + elif isinstance(simdist_config, dict): + compare = simdist_config.get("compare", None) else: - raise ValueError("unknown data type for simdist-config, use dataclass or dict") - if compare == 'state': + raise ValueError( + "unknown data type for simdist-config, use dataclass or dict" + ) + if compare == "state": simdist = SimilarityTransformDist else: simdist = ControllabilitySimilarityTransformDist @@ -675,4 +754,3 @@ def __init__( assert X_control is not None assert self.dmd_has_control - diff --git a/DSA/preprocessing.py b/DSA/preprocessing.py index 3a04570..db5b8b1 100644 --- a/DSA/preprocessing.py +++ b/DSA/preprocessing.py @@ -4,6 +4,7 @@ from sklearn.decomposition import PCA from sklearn.pipeline import make_pipeline from sklearn.kernel_approximation import Nystroem + try: from .dmd import embed_signal_torch except ImportError: @@ -49,6 +50,7 @@ def normalize_data(data_list): return normalized_data_list + def coarse_grain(trajectories, bin_size=5, bins_overlapping=0): """ Bin or sum trajectories over time windows, with optional overlap. @@ -216,7 +218,7 @@ def nonlinear_dimensionality_reduction( model = make_pipeline(nystroem, pca) elif method.lower() == "umap": from umap import UMAP - + model = UMAP(n_components=n_components, **kwargs) else: raise ValueError( @@ -270,9 +272,10 @@ def featurize_data( if len(shape) == 3: return data.reshape(shape[0], shape[1], -1) else: - return data.reshape(shape[0],-1) + return data.reshape(shape[0], -1) + -def gaussian_filter(data, sigma, truncate=2.0,causal=True,dim=0,mode='same'): +def gaussian_filter(data, sigma, truncate=2.0, causal=True, dim=0, mode="same"): """ Applies a causal Gaussian filter to a 1D time series. @@ -287,7 +290,7 @@ def gaussian_filter(data, sigma, truncate=2.0,causal=True,dim=0,mode='same'): kernel_size = int(truncate * sigma + 0.5) t = np.arange(-kernel_size, kernel_size + 1) - kernel = np.exp(-0.5 * (t / sigma)**2) + kernel = np.exp(-0.5 * (t / sigma) ** 2) kernel /= kernel.sum() if causal: @@ -297,18 +300,23 @@ def gaussian_filter(data, sigma, truncate=2.0,causal=True,dim=0,mode='same'): kernel = kernel[::-1] filtered_data = np.apply_along_axis( - lambda x: convolve(x, kernel, mode=mode), - axis=dim, - arr=data + lambda x: convolve(x, kernel, mode=mode), axis=dim, arr=data ) return filtered_data, kernel -def coarse_grain_space(data, method='uniform', nbins_per_dim=20, sigma=None, kernel_width=2, scale='standard'): - ''' +def coarse_grain_space( + data, + method="uniform", + nbins_per_dim=20, + sigma=None, + kernel_width=2, + scale="standard", +): + """ Convert continuous data to one-hot encoded spatial bins, optionally with spatial smoothing. - + Parameters ---------- data : np.ndarray @@ -323,16 +331,18 @@ def coarse_grain_space(data, method='uniform', nbins_per_dim=20, sigma=None, ker Width of smoothing kernel in number of bins scale : str Scaling method: 'standard' for zero mean unit variance, or 'minmax' for [0,1] range - + Returns ------- encoded : np.ndarray - One-hot encoded data with shape (n_conditions, n_timepoints, n_total_bins) + One-hot encoded data with shape (n_conditions, n_timepoints, n_total_bins) or (n_timepoints, n_total_bins) - ''' - if method != 'uniform': - raise NotImplementedError(f"Method {method} not implemented. Only 'uniform' is currently supported.") - + """ + if method != "uniform": + raise NotImplementedError( + f"Method {method} not implemented. Only 'uniform' is currently supported." + ) + # Get input shape and dimensionality orig_shape = data.shape if data.ndim == 3: @@ -341,61 +351,78 @@ def coarse_grain_space(data, method='uniform', nbins_per_dim=20, sigma=None, ker else: n_time, n_dims = data.shape data_reshaped = data - + # Scale the data - if scale == 'standard': + if scale == "standard": mean = np.mean(data_reshaped, axis=0) std = np.std(data_reshaped, axis=0) data_scaled = (data_reshaped - mean) / std - elif scale == 'minmax': + elif scale == "minmax": min_vals = np.min(data_reshaped, axis=0) max_vals = np.max(data_reshaped, axis=0) data_scaled = (data_reshaped - min_vals) / (max_vals - min_vals) else: raise ValueError("scale must be 'standard' or 'minmax'") - + # Calculate total number of bins and check if reasonable - n_total_bins = nbins_per_dim ** n_dims + n_total_bins = nbins_per_dim**n_dims if n_total_bins > 1e6: - raise ValueError(f"Total number of bins ({n_total_bins}) too large. Reduce nbins_per_dim or use different binning method.") - + raise ValueError( + f"Total number of bins ({n_total_bins}) too large. Reduce nbins_per_dim or use different binning method." + ) + # Create bin edges - bin_edges = [np.linspace(np.min(data_scaled[:,d]), np.max(data_scaled[:,d]), nbins_per_dim+1) - for d in range(n_dims)] - + bin_edges = [ + np.linspace( + np.min(data_scaled[:, d]), np.max(data_scaled[:, d]), nbins_per_dim + 1 + ) + for d in range(n_dims) + ] + # Initialize one-hot encoded array if data.ndim == 3: encoded = np.zeros((n_conds, n_time, n_total_bins)) else: encoded = np.zeros((n_time, n_total_bins)) - + # Assign data points to bins and handle edge cases # Vectorized bin assignment and clipping for all dimensions at once - bin_indices = np.stack([ - np.clip(np.digitize(data_scaled[:,d], bin_edges[d]) - 1, 0, nbins_per_dim-1) - for d in range(n_dims) - ]).T - + bin_indices = np.stack( + [ + np.clip( + np.digitize(data_scaled[:, d], bin_edges[d]) - 1, 0, nbins_per_dim - 1 + ) + for d in range(n_dims) + ] + ).T + if n_dims > 1: flat_indices = np.ravel_multi_index(bin_indices.T, [nbins_per_dim] * n_dims) else: # For 1D case, bin_indices is already the flat indices flat_indices = bin_indices.ravel() - + if data.ndim == 3: - encoded.reshape(-1, n_total_bins)[np.arange(len(flat_indices)), flat_indices] = 1 + encoded.reshape(-1, n_total_bins)[ + np.arange(len(flat_indices)), flat_indices + ] = 1 else: encoded[np.arange(len(flat_indices)), flat_indices] = 1 - + # Apply spatial smoothing if requested if sigma is not None: from scipy.ndimage import gaussian_filter1d + if data.ndim == 3: for i in range(n_conds): for t in range(n_time): - encoded[i,t] = gaussian_filter1d(encoded[i,t], sigma=sigma, truncate=kernel_width) + encoded[i, t] = gaussian_filter1d( + encoded[i, t], sigma=sigma, truncate=kernel_width + ) else: for t in range(n_time): - encoded[t] = gaussian_filter1d(encoded[t], sigma=sigma, truncate=kernel_width) - - return encoded \ No newline at end of file + encoded[t] = gaussian_filter1d( + encoded[t], sigma=sigma, truncate=kernel_width + ) + + return encoded diff --git a/DSA/resdmd.py b/DSA/resdmd.py index d82c56f..1b525ca 100644 --- a/DSA/resdmd.py +++ b/DSA/resdmd.py @@ -2,6 +2,7 @@ import matplotlib.pyplot as plt from matplotlib import cm from matplotlib import colors as mcolors + try: from .dmd import DMD except ImportError: @@ -10,14 +11,15 @@ import ot from typing import Literal + def compute_residuals( dmd: "DMD | np.ndarray | torch.Tensor", X: np.ndarray = None, Y: np.ndarray = None, rank: int = None, matrix: Literal["A_v", "A_havok_dmd"] = "A_v", - return_num_denom = False, - tol=1e-6 + return_num_denom=False, + tol=1e-6, ): """ Compute DMD eigenvalues, eigenvectors, and residuals for each mode. @@ -51,12 +53,24 @@ def compute_residuals( # Handle DMD object, numpy array, or torch tensor if hasattr(dmd, matrix): - A = getattr(dmd, matrix).cpu().detach().numpy() if hasattr(getattr(dmd, matrix), "cpu") else getattr(dmd, matrix) + A = ( + getattr(dmd, matrix).cpu().detach().numpy() + if hasattr(getattr(dmd, matrix), "cpu") + else getattr(dmd, matrix) + ) L, G = np.linalg.eig(A) if matrix == "A_havok_dmd": - X = dmd.Vt_minus.cpu().detach().numpy()[:, : dmd.rank] @ dmd.S_mat[:dmd.rank,:dmd.rank].cpu().detach().numpy() @ dmd.U.cpu().detach().numpy().T[:dmd.rank] - Y = dmd.Vt_plus.cpu().detach().numpy()[:, : dmd.rank] @ dmd.S_mat[:dmd.rank,:dmd.rank].cpu().detach().numpy() @ dmd.U.cpu().detach().numpy().T[:dmd.rank] - + X = ( + dmd.Vt_minus.cpu().detach().numpy()[:, : dmd.rank] + @ dmd.S_mat[: dmd.rank, : dmd.rank].cpu().detach().numpy() + @ dmd.U.cpu().detach().numpy().T[: dmd.rank] + ) + Y = ( + dmd.Vt_plus.cpu().detach().numpy()[:, : dmd.rank] + @ dmd.S_mat[: dmd.rank, : dmd.rank].cpu().detach().numpy() + @ dmd.U.cpu().detach().numpy().T[: dmd.rank] + ) + elif matrix == "A_v": X = ( dmd.Vt_minus.cpu().detach().numpy()[:, : dmd.rank] @@ -73,12 +87,16 @@ def compute_residuals( A = dmd L, G = np.linalg.eig(A) if X is None or Y is None or rank is None: - raise ValueError("If passing a raw matrix, must also provide X, Y, and rank.") + raise ValueError( + "If passing a raw matrix, must also provide X, Y, and rank." + ) elif hasattr(dmd, "numpy"): A = dmd.numpy() L, G = np.linalg.eig(A) if X is None or Y is None or rank is None: - raise ValueError("If passing a raw matrix, must also provide X, Y, and rank.") + raise ValueError( + "If passing a raw matrix, must also provide X, Y, and rank." + ) else: raise ValueError("dmd must be a DMD object or a numpy array/torch tensor") diff --git a/DSA/simdist.py b/DSA/simdist.py index 3eea468..37dc9e6 100644 --- a/DSA/simdist.py +++ b/DSA/simdist.py @@ -6,6 +6,7 @@ import torch.nn.utils.parametrize as parametrize from scipy.stats import wasserstein_distance import ot # optimal transport for multidimensional l2 wasserstein + try: from .dmd import DMD except ImportError: @@ -26,10 +27,11 @@ def pad_zeros(A, B, device): return A, B + def compute_angle(evec): - ''' + """ computes the angle between multiple complex eigenvectors - ''' + """ if isinstance(evec, np.ndarray): evec = torch.from_numpy(evec).float() # evec /= torch.linalg.norm(evec, dim=1, keepdim=True) @@ -37,6 +39,8 @@ def compute_angle(evec): ang = torch.arccos(ang) ang[torch.isnan(ang)] = 0 return ang + + class LearnableSimilarityTransform(nn.Module): """ Computes the similarity transform for a learnable orthonormal matrix C @@ -179,8 +183,7 @@ def fit( iters=None, lr=None, score_method=None, - wasserstein_weightings = None, - + wasserstein_weightings=None, ): """ Computes the optimal matrix C over specified group @@ -200,11 +203,11 @@ def fit( _______ None """ - if isinstance(A,DMD): + if isinstance(A, DMD): A = A.A_v - if isinstance(B,DMD): + if isinstance(B, DMD): B = B.A_v - + assert A.shape[0] == A.shape[1] assert B.shape[0] == B.shape[1] @@ -213,11 +216,15 @@ def fit( self.A, self.B = A, B lr = self.lr if lr is None else lr iters = self.iters if iters is None else iters - wasserstein_compare = self.wasserstein_compare if wasserstein_compare is None else wasserstein_compare + wasserstein_compare = ( + self.wasserstein_compare + if wasserstein_compare is None + else wasserstein_compare + ) score_method = self.score_method if score_method is None else score_method - if score_method == 'wasserstein': - a,b = self._get_wasserstein_vars(A, B) + if score_method == "wasserstein": + a, b = self._get_wasserstein_vars(A, B) device = a.device # a = a # .cpu() # b = b # .cpu() @@ -237,11 +244,15 @@ def fit( a, b = a.to(device), b.to(device) self.C_star = ot.emd(a, b, self.M) - self.score_star = ot.emd2(a, b, self.M) *a.shape[0] #add scaling factor due to random matrix theory + self.score_star = ( + ot.emd2(a, b, self.M) * a.shape[0] + ) # add scaling factor due to random matrix theory # self.score_star = np.sum(self.C_star * self.M) - self.C_star = self.C_star / torch.linalg.norm(self.C_star, dim=1, keepdim=True) + self.C_star = self.C_star / torch.linalg.norm( + self.C_star, dim=1, keepdim=True + ) # wasserstein_distance(A.cpu().numpy(),B.cpu().numpy()) - + else: self.losses, self.C_star, self.sim_net = self.optimize_C( A, B, lr, iters, orthog=True, verbose=self.verbose @@ -258,14 +269,14 @@ def fit( self.C_star = C_star @ P self.sim_net = sim_net - def _get_wasserstein_vars(self,A, B): + def _get_wasserstein_vars(self, A, B): # assert self.wasserstein_compare in {"sv", "eig","evec_angle", 'evec'} assert self.wasserstein_compare in {"eig"} - #deprecated: only do wasserstein comparison on eigenvalues (for now, until others are theoretically validated) + # deprecated: only do wasserstein comparison on eigenvalues (for now, until others are theoretically validated) # if self.wasserstein_compare == "sv": - # a = torch.svd(A).S.view(-1, 1) - # b = torch.svd(B).S.view(-1, 1) + # a = torch.svd(A).S.view(-1, 1) + # b = torch.svd(B).S.view(-1, 1) # if self.wasserstein_compare == "eig": a = torch.linalg.eig(A).eigenvalues a = torch.vstack([a.real, a.imag]).T @@ -276,24 +287,28 @@ def _get_wasserstein_vars(self,A, B): # #this will compute the interior angles between eigenvectors # aevec = torch.linalg.eig(A).eigenvectors # bevec = torch.linalg.eig(B).eigenvectors - + # a = compute_angle(aevec) # b = compute_angle(bevec) # else: - # raise AssertionError("wasserstein_compare must be 'sv', 'eig', 'evec_angle', or 'evec'") - - #if the number of elements in the sets are different, then we need to pad the smaller set with zeros + # raise AssertionError("wasserstein_compare must be 'sv', 'eig', 'evec_angle', or 'evec'") + + # if the number of elements in the sets are different, then we need to pad the smaller set with zeros if a.shape[0] != b.shape[0]: # if self.wasserstein_compare in {'evec_angle', 'evec'}: - # raise AssertionError("Wasserstein comparison of eigenvectors is not supported when \ - # the number of elements in the sets are different") + # raise AssertionError("Wasserstein comparison of eigenvectors is not supported when \ + # the number of elements in the sets are different") if self.verbose: print(f"Padding the smaller set with zeros") if a.shape[0] < b.shape[0]: - a = torch.cat([a, torch.zeros(b.shape[0] - a.shape[0], a.shape[1])], dim=0) + a = torch.cat( + [a, torch.zeros(b.shape[0] - a.shape[0], a.shape[1])], dim=0 + ) else: - b = torch.cat([b, torch.zeros(a.shape[0] - b.shape[0], b.shape[1])], dim=0) - return a,b + b = torch.cat( + [b, torch.zeros(a.shape[0] - b.shape[0], b.shape[1])], dim=0 + ) + return a, b def optimize_C(self, A, B, lr, iters, orthog, verbose): # parameterize mapping to be orthogonal @@ -327,10 +342,10 @@ def optimize_C(self, A, B, lr, iters, orthog, verbose): # if _ % 99: # scheduler.step() losses.append(loss.item()) - #TODO: add a flag for this + # TODO: add a flag for this # if _ > 2 and abs(losses[-1] - losses[-2]) < self.eps: #early stopping - # break - + # break + if verbose: print("Finished optimizing C") @@ -360,8 +375,8 @@ def score(self, A=None, B=None, score_method=None): B = self.B if B is None else B assert A is not None assert B is not None - assert A.shape == self.C_star.shape or score_method == 'wasserstein' - assert B.shape == self.C_star.shape or score_method == 'wasserstein' + assert A.shape == self.C_star.shape or score_method == "wasserstein" + assert B.shape == self.C_star.shape or score_method == "wasserstein" score_method = self.score_method if score_method is None else score_method with torch.no_grad(): if not isinstance(A, torch.Tensor): @@ -379,21 +394,21 @@ def score(self, A=None, B=None, score_method=None): score = np.pi else: score = 0 - elif score_method == 'euclidean': + elif score_method == "euclidean": score = ( torch.norm(A - C @ B @ C.T, p="fro").cpu().numpy().item() ) # / A.numpy().size - elif score_method == 'wasserstein': - #use the current C_star to compute the score - assert hasattr(self, 'score_star') + elif score_method == "wasserstein": + # use the current C_star to compute the score + assert hasattr(self, "score_star") # if wasserstein_compare == self.wasserstein_compare: score = self.score_star.item() - #non-eig wasserstein comparisons are deprecated until theoretically validated + # non-eig wasserstein comparisons are deprecated until theoretically validated # else: # #apply the current transport plan to the new data # a,b = self._get_wasserstein_vars(A, B) # # a_transported = self.C_star @ A #shouldn't this be a? - + # M = ot.dist(a, b, metric='sqeuclidean') # score = torch.sum(self.C_star * M).item() # #TODO: validate this @@ -403,7 +418,7 @@ def score(self, A=None, B=None, score_method=None): # # score = transported_score.item() # if self.rescale_wasserstein: # score = score * A.shape[0] #add scaling factor due to random matrix theory - + return score def fit_score( @@ -443,42 +458,49 @@ def fit_score( assert A.shape[0] == B.shape[1] or self.wasserstein_compare is not None if A.shape[0] != B.shape[0]: # if self.wasserstein_compare is None: - # raise AssertionError( - # "Matrices must be the same size unless using wasserstein distance" - # ) - if score_method != 'wasserstein': # otherwise resort to L2 Wasserstein over singular or eigenvalues + # raise AssertionError( + # "Matrices must be the same size unless using wasserstein distance" + # ) + if ( + score_method != "wasserstein" + ): # otherwise resort to L2 Wasserstein over singular or eigenvalues print( f"resorting to wasserstein distance over {self.wasserstein_compare}" ) - score_method = 'wasserstein' + score_method = "wasserstein" else: pass + self.fit( + A, + B, + iters, + lr, + wasserstein_weightings=wasserstein_weightings, + score_method=score_method, + ) + + return self.score(self.A, self.B, score_method=score_method) - self.fit(A, B, iters, lr, wasserstein_weightings=wasserstein_weightings, score_method=score_method) - return self.score( - self.A, self.B, score_method=score_method - ) - def compute_subspace_angles(A, B): """ Computes the subspace angles between two DMD matrices. Matrices must be square and the same size. - + Parameters ---------- A : DMD object or numpy array First DMD matrix B : DMD object or numpy array Second DMD matrix - + Returns ------- angles : np.ndarray Principal angles between the subspaces """ - + A_mat = val_matrix(A) B_mat = val_matrix(B) @@ -497,6 +519,7 @@ def compute_subspace_angles(A, B): return angles + def val_matrix(matrix): if isinstance(matrix, DMD): mat = matrix.A_havok_dmd @@ -510,5 +533,5 @@ def val_matrix(matrix): # Check matrix is square if mat.shape[0] != mat.shape[1]: raise ValueError(f"matrix must be square") - + return mat diff --git a/DSA/simdist_controllability.py b/DSA/simdist_controllability.py index f962243..1e270e7 100644 --- a/DSA/simdist_controllability.py +++ b/DSA/simdist_controllability.py @@ -5,20 +5,22 @@ try: from .simdist import SimilarityTransformDist except ImportError: - from simdist import SimilarityTransformDist + from simdist import SimilarityTransformDist + class ControllabilitySimilarityTransformDist: """ Procrustes analysis over vector fields / LTI systems. Only Euclidean scoring is implemented in this closed-form version. """ + def __init__( self, *, score_method: Literal["euclidean", "angular"] = "euclidean", - compare: Literal['joint','control','state'] = 'joint', + compare: Literal["joint", "control", "state"] = "joint", joint_optim: bool = False, - return_distance_components: bool =True + return_distance_components: bool = True, ): f""" Parameters @@ -35,62 +37,69 @@ def __init__( self.score_method = score_method self.compare = compare self.joint_optim = joint_optim - self.return_distance_components=return_distance_components + self.return_distance_components = return_distance_components @staticmethod def compute_angular_dist(A, B): """ Computes the angular distance between two matrices A and B. - + Args: A (np.ndarray): First matrix B (np.ndarray): Second matrix - + Returns: float: Angular distance between A and B """ - cos_sim = np.trace(A.T @ B) / (np.linalg.norm(A, 'fro') * np.linalg.norm(B, 'fro')) + cos_sim = np.trace(A.T @ B) / ( + np.linalg.norm(A, "fro") * np.linalg.norm(B, "fro") + ) cos_sim = np.clip(cos_sim, -1, 1) cos_sim = np.arccos(cos_sim) cos_sim = np.clip(cos_sim, 0, np.pi) return cos_sim def fit_score(self, A, B, A_control, B_control): - + C, C_u, sims_joint_euc, sims_joint_ang = self.compare_systems_procrustes( A1=A, B1=A_control, A2=B, B2=B_control, align_inputs=self.joint_optim ) score_method = self.score_method - if self.compare == 'joint': + if self.compare == "joint": if self.return_distance_components: - if self.score_method == 'euclidean': + if self.score_method == "euclidean": # sims_control_joint = np.linalg.norm(C @ A_control @ C_u - B_control, "fro") ** 2 # sims_state_joint = np.linalg.norm(C @ A @ C.T - B, "fro") ** 2 - sims_control_joint = np.linalg.norm(C @ A_control @ C_u - B_control, "fro") - sims_state_joint = np.linalg.norm(C @ A @ C.T - B, "fro") + sims_control_joint = np.linalg.norm( + C @ A_control @ C_u - B_control, "fro" + ) + sims_state_joint = np.linalg.norm(C @ A @ C.T - B, "fro") return sims_joint_euc, sims_state_joint, sims_control_joint - elif self.score_method == 'angular': - sims_control_joint = self.compute_angular_dist(C @ A_control @ C_u, B_control) + elif self.score_method == "angular": + sims_control_joint = self.compute_angular_dist( + C @ A_control @ C_u, B_control + ) sims_state_joint = self.compute_angular_dist(C @ A @ C.T, B) return sims_joint_ang, sims_state_joint, sims_control_joint else: - if self.score_method == 'euclidean': + if self.score_method == "euclidean": return sims_joint_euc - elif self.score_method == 'angular': + elif self.score_method == "angular": return sims_joint_ang else: - raise ValueError('Choose between Euclidean or angular distance') + raise ValueError("Choose between Euclidean or angular distance") - elif self.compare == 'state': + elif self.compare == "state": # return self.compare_A(A, B, score_method=score_method) - raise ValueError('To compute state similarity alone, use the SimilarityTransformDist class') + raise ValueError( + "To compute state similarity alone, use the SimilarityTransformDist class" + ) else: return self.compare_B(A_control, B_control, score_method=score_method) - def get_controllability_matrix(self, A, B): """ Computes the controllability matrix K = [B, AB, A^2B, ..., A^(n-1)B]. @@ -106,26 +115,26 @@ def get_controllability_matrix(self, A, B): K = B.copy() current1_term = B.copy() # Start with A^0 * B = B current2_term = B.copy() # Start with A^0 * B = B - + for i in range(1, n): # current_term = np.linalg.matrix_power(A, i) @ B # Use stable matrix power function current1_term = A @ current1_term current2_term = A.T @ current2_term - + # Check for numerical instability # term_norm = np.linalg.norm(current_term) # if term_norm < 1e-12 or term_norm > 1e12: - # break - + # break + # Check for linear dependence (rank deficiency) K_test = np.hstack((K, current1_term, current2_term)) # if np.linalg.matrix_rank(K_test) <= np.linalg.matrix_rank(K): - # break - + # break + K = K_test return K - def compare_systems_procrustes(self, A1, B1, A2, B2, *,align_inputs=False): + def compare_systems_procrustes(self, A1, B1, A2, B2, *, align_inputs=False): """ Compares two LTI systems by finding the optimal orthogonal transformation that aligns their controllability matrices. @@ -157,8 +166,9 @@ def compare_systems_procrustes(self, A1, B1, A2, B2, *,align_inputs=False): C = U @ Vh K2_aligned = C @ K2 err = np.linalg.norm(K1 - K2_aligned, "fro") - cos_sim = (np.vdot(K1, K2_aligned).real / - (np.linalg.norm(K1, "fro") * np.linalg.norm(K2, "fro"))) + cos_sim = np.vdot(K1, K2_aligned).real / ( + np.linalg.norm(K1, "fro") * np.linalg.norm(K2, "fro") + ) cos_sim = np.clip(cos_sim, -1, 1) cos_sim = np.arccos(cos_sim) cos_sim = np.clip(cos_sim, 0, np.pi) @@ -168,36 +178,38 @@ def compare_systems_procrustes(self, A1, B1, A2, B2, *,align_inputs=False): U1, S1, V1t = np.linalg.svd(K1, full_matrices=False) U2, S2, V2t = np.linalg.svd(K2, full_matrices=False) - C = U1 @ U2.T + C = U1 @ U2.T C_u = V2t.T @ V1t # = V2 @ V1^T - + K2_aligned = C @ K2 @ C_u err = np.linalg.norm(K1 - K2_aligned, "fro") - cos_sim = (np.vdot(K1, K2_aligned).real / - (np.linalg.norm(K1, "fro") * np.linalg.norm(K2, "fro"))) + cos_sim = np.vdot(K1, K2_aligned).real / ( + np.linalg.norm(K1, "fro") * np.linalg.norm(K2, "fro") + ) cos_sim = np.clip(cos_sim, -1, 1) cos_sim = np.arccos(cos_sim) cos_sim = np.clip(cos_sim, 0, np.pi) - + return C, C_u, err, cos_sim # @staticmethod # def compare_A(A1, A2, score_method='euclidean'): - # simdist = SimilarityTransformDist(iters=1000, score_method=score_method, lr=1e-3, verbose=True) - # return simdist.fit_score(A1, A2, score_method=score_method) + # simdist = SimilarityTransformDist(iters=1000, score_method=score_method, lr=1e-3, verbose=True) + # return simdist.fit_score(A1, A2, score_method=score_method) @staticmethod - def compare_B(B1, B2, score_method='euclidean'): - ''' + def compare_B(B1, B2, score_method="euclidean"): + """ compares the B matrices with left procrustes - ''' - if score_method == 'euclidean': + """ + if score_method == "euclidean": R, _ = orthogonal_procrustes(B2.T, B1.T) - return np.linalg.norm(B1 - R.T @ B2, "fro") + return np.linalg.norm(B1 - R.T @ B2, "fro") # return np.linalg.norm(B1 - R.T @ B2, "fro") ** 2 - elif score_method == 'angular': + elif score_method == "angular": R, _ = orthogonal_procrustes(B2.T, B1.T) - return ControllabilitySimilarityTransformDist.compute_angular_dist(B1, R.T @ B2) + return ControllabilitySimilarityTransformDist.compute_angular_dist( + B1, R.T @ B2 + ) else: - raise ValueError('Choose between Euclidean or angular distance') - + raise ValueError("Choose between Euclidean or angular distance") diff --git a/DSA/stats.py b/DSA/stats.py index 35287af..8efd601 100644 --- a/DSA/stats.py +++ b/DSA/stats.py @@ -4,6 +4,7 @@ from .simdist import SimilarityTransformDist from .dsa import DSA import warnings + # from dysts.utils import find_significant_frequencies @@ -104,7 +105,8 @@ def mse(x, y): return ((x - y) ** 2).mean().item() -def nmse(x,y): + +def nmse(x, y): """ Compute the mean squared error, normalized by the variance of the ground truth. @@ -518,7 +520,7 @@ def measure_nonnormality_transpose(A): def measure_transient_growth(A): """ - Computes the l2 norm of the matrix (discrete time growth rate). + Computes the l2 norm of the matrix (discrete time growth rate). This is the maximum singular value, which is a measure of the instantaneous growth rate. This can be > 1 even if the matrix is stable. diff --git a/DSA/subspace_dmdc.py b/DSA/subspace_dmdc.py index eca42d9..6408212 100644 --- a/DSA/subspace_dmdc.py +++ b/DSA/subspace_dmdc.py @@ -1,22 +1,24 @@ """This module computes the subspace DMD with control (DMDc) model for a given dataset.""" + import numpy as np import torch + class SubspaceDMDc: - """Subspace DMDc class for computing and predicting with DMD with control models. - """ + """Subspace DMDc class for computing and predicting with DMD with control models.""" + def __init__( - self, - data, - control_data, - n_delays=1, - rank=None, - lamb=1e-8, - device='cpu', - verbose=False, - send_to_cpu=False, - time_first=True, - backend='n4sid' + self, + data, + control_data, + n_delays=1, + rank=None, + lamb=1e-8, + device="cpu", + verbose=False, + send_to_cpu=False, + time_first=True, + backend="n4sid", ): # Convert inputs to torch tensors and store self.device = device @@ -33,14 +35,14 @@ def __init__( self.lamb = lamb self.verbose = verbose self.send_to_cpu = send_to_cpu - + def _to_tensor(self, data): """Convert data to torch tensor, handling lists and numpy arrays.""" if isinstance(data, list): return [self._to_tensor_single(d) for d in data] else: return self._to_tensor_single(data) - + def _to_tensor_single(self, data): """Convert single data item to torch tensor.""" if isinstance(data, np.ndarray): @@ -49,20 +51,22 @@ def _to_tensor_single(self, data): return data.float().to(self.device) return data - def fit(self): - self.A_v, self.B_v, self.C_v, self.info = self.subspace_dmdc_multitrial_flexible( - y=self.data, - u=self.control_data, - p=self.n_delays, - f=self.n_delays, - n=self.rank, - backend=self.backend, - lamb=self.lamb) - + self.A_v, self.B_v, self.C_v, self.info = ( + self.subspace_dmdc_multitrial_flexible( + y=self.data, + u=self.control_data, + p=self.n_delays, + f=self.n_delays, + n=self.rank, + backend=self.backend, + lamb=self.lamb, + ) + ) + if self.send_to_cpu: self.all_to_device("cpu") - + def all_to_device(self, device): """Send all tensors to specified device.""" if self.A_v is not None: @@ -72,17 +76,24 @@ def all_to_device(self, device): if self.C_v is not None: self.C_v = self.C_v.to(device) if self.info is not None: - for key in ['R_hat', 'Q_hat', 'S_hat', 'Gamma_hat', 'singular_values_O', 'noise_covariance']: + for key in [ + "R_hat", + "Q_hat", + "S_hat", + "Gamma_hat", + "singular_values_O", + "noise_covariance", + ]: if key in self.info and isinstance(self.info[key], torch.Tensor): self.info[key] = self.info[key].to(device) - - - def subspace_dmdc_multitrial_QR_decomposition(self, y_list, u_list, p, f, n=None, lamb=1e-8, energy=0.999): + def subspace_dmdc_multitrial_QR_decomposition( + self, y_list, u_list, p, f, n=None, lamb=1e-8, energy=0.999 + ): """ Subspace-DMDc for multi-trial data with variable trial lengths. Now use QR decomposition for computing the oblique projection as in N4SID implementations. - + Parameters: - y_list: list of tensors, each (p_out, N_i) - output data for trial i - u_list: list of tensors, each (m, N_i) - input data for trial i @@ -91,30 +102,34 @@ def subspace_dmdc_multitrial_QR_decomposition(self, y_list, u_list, p, f, n=None - n: state dimension (auto-determined if None) - ridge: regularization parameter (used only for rank selection/SVD; QR is exact) - energy: energy threshold for rank selection - + Returns: - A_hat, B_hat, C_hat: system matrices - info: dictionary with additional information """ if len(y_list) != len(u_list): raise ValueError("y_list and u_list must have same number of trials") - + n_trials = len(y_list) p_out = y_list[0].shape[0] m = u_list[0].shape[0] - + # Validate dimensions across trials for i, (y_trial, u_trial) in enumerate(zip(y_list, u_list)): if y_trial.shape[0] != p_out: - raise ValueError(f"Trial {i}: y has {y_trial.shape[0]} outputs, expected {p_out}") + raise ValueError( + f"Trial {i}: y has {y_trial.shape[0]} outputs, expected {p_out}" + ) if u_trial.shape[0] != m: - raise ValueError(f"Trial {i}: u has {u_trial.shape[0]} inputs, expected {m}") + raise ValueError( + f"Trial {i}: u has {u_trial.shape[0]} inputs, expected {m}" + ) if y_trial.shape[1] != u_trial.shape[1]: raise ValueError(f"Trial {i}: y and u have different time lengths") - + def hankel_stack(X, start, L): - return torch.cat([X[:, start + i:start + i + 1] for i in range(L)], dim=0) - + return torch.cat([X[:, start + i : start + i + 1] for i in range(L)], dim=0) + # Collect data from all trials U_p_all = [] Y_p_all = [] @@ -122,73 +137,85 @@ def hankel_stack(X, start, L): Y_f_all = [] valid_trials = [] T_per_trial = [] - + for trial_idx, (Y_trial, U_trial) in enumerate(zip(y_list, u_list)): N_trial = Y_trial.shape[1] T_trial = N_trial - (p + f) + 1 - + if T_trial <= 0: if self.verbose: - print(f"Warning: Trial {trial_idx} has insufficient data (T={T_trial}), skipping") + print( + f"Warning: Trial {trial_idx} has insufficient data (T={T_trial}), skipping" + ) continue - + valid_trials.append(trial_idx) T_per_trial.append(T_trial) - + # Build Hankel matrices for this trial - U_p_trial = torch.cat([hankel_stack(U_trial, j, p) for j in range(T_trial)], dim=1) - Y_p_trial = torch.cat([hankel_stack(Y_trial, j, p) for j in range(T_trial)], dim=1) - U_f_trial = torch.cat([hankel_stack(U_trial, j + p, f) for j in range(T_trial)], dim=1) - Y_f_trial = torch.cat([hankel_stack(Y_trial, j + p, f) for j in range(T_trial)], dim=1) - + U_p_trial = torch.cat( + [hankel_stack(U_trial, j, p) for j in range(T_trial)], dim=1 + ) + Y_p_trial = torch.cat( + [hankel_stack(Y_trial, j, p) for j in range(T_trial)], dim=1 + ) + U_f_trial = torch.cat( + [hankel_stack(U_trial, j + p, f) for j in range(T_trial)], dim=1 + ) + Y_f_trial = torch.cat( + [hankel_stack(Y_trial, j + p, f) for j in range(T_trial)], dim=1 + ) + U_p_all.append(U_p_trial) Y_p_all.append(Y_p_trial) U_f_all.append(U_f_trial) Y_f_all.append(Y_f_trial) - + if not valid_trials: raise ValueError("No trials have sufficient data for given (p,f)") - + # Concatenate across valid trials U_p = torch.cat(U_p_all, dim=1) # (p m, T_total) Y_p = torch.cat(Y_p_all, dim=1) # (p p_out, T_total) U_f = torch.cat(U_f_all, dim=1) # (f m, T_total) Y_f = torch.cat(Y_f_all, dim=1) # (f p_out, T_total) - + T_total = sum(T_per_trial) Z_p = torch.vstack([U_p, Y_p]) # (p (m + p_out), T_total) - + H = torch.vstack([U_f, Z_p, Y_f]) - + # Perform QR on H.T to get equivalent LQ on H - Q, R_upper = torch.linalg.qr(H.T, mode='reduced') # H.T = Q R_upper, R_upper upper triangular + Q, R_upper = torch.linalg.qr( + H.T, mode="reduced" + ) # H.T = Q R_upper, R_upper upper triangular L = R_upper.T # L = R_upper.T, lower triangular - + # Dimensions for slicing dim_uf = f * m dim_zp = p * (m + p_out) dim_yf = f * p_out - + # Extract submatrices from L (lower triangular) - R22 = L[dim_uf:dim_uf + dim_zp, dim_uf:dim_uf + dim_zp] - R32 = L[dim_uf + dim_zp:, dim_uf:dim_uf + dim_zp] - + R22 = L[dim_uf : dim_uf + dim_zp, dim_uf : dim_uf + dim_zp] + R32 = L[dim_uf + dim_zp :, dim_uf : dim_uf + dim_zp] + # Compute oblique projection O = R32 @ pinv(R22) @ Z_p O = R32 @ torch.linalg.pinv(R22) @ Z_p - + # The rest remains the same: SVD on O Uo, s, Vt = torch.linalg.svd(O, full_matrices=False) if n is None: cs = torch.cumsum(s**2, dim=0) / (s**2).sum() n = int((cs < energy).sum().item() + 1) n = max(1, min(n, min(Uo.shape[1], Vt.shape[0]))) - + U_n = Uo[:, :n] S_n = torch.diag(s[:n]) V_n = Vt[:n, :] S_half = torch.sqrt(S_n) - Gamma_hat = U_n @ S_half # (f p_out, n) - X_hat = S_half @ V_n # (n, T_total) + Gamma_hat = U_n @ S_half # (f p_out, n) + X_hat = S_half @ V_n # (n, T_total) # Time alignment for regression across all trials # Need to handle variable lengths carefully @@ -196,39 +223,39 @@ def hankel_stack(X, start, L): X_next_segments = [] U_mid_segments = [] Y_segments = [] - + start_idx = 0 for trial_idx, T_trial in enumerate(T_per_trial): # Extract states for this trial - X_trial = X_hat[:, start_idx:start_idx + T_trial] - + X_trial = X_hat[:, start_idx : start_idx + T_trial] + # State transitions within this trial X_trial_curr = X_trial[:, :-1] X_trial_next = X_trial[:, 1:] - + # Corresponding control inputs original_trial_idx = valid_trials[trial_idx] U_trial = u_list[original_trial_idx] - U_mid_trial = U_trial[:, p:p + (T_trial - 1)] - + U_mid_trial = U_trial[:, p : p + (T_trial - 1)] + X_segments.append(X_trial_curr) X_next_segments.append(X_trial_next) U_mid_segments.append(U_mid_trial) - + # TODO: check the time-alignment of Y and X here # Corresponding output data - align with X_trial time indices Y_trial = y_list[original_trial_idx] - Y_trial_curr = Y_trial[:, p:p+T_trial-1] + Y_trial_curr = Y_trial[:, p : p + T_trial - 1] # Y_trial_curr = Y_trial[:, p+1:p+T_trial] Y_segments.append(Y_trial_curr) start_idx += T_trial - + # Concatenate all segments X = torch.cat(X_segments, dim=1) X_next = torch.cat(X_next_segments, dim=1) U_mid = torch.cat(U_mid_segments, dim=1) - + # Regression for A and B Z = torch.vstack([X, U_mid]) # Ridge regression: (Z^T Z + λI)^(-1) Z^T X_next^T @@ -242,49 +269,56 @@ def hankel_stack(X, start, L): # AB = X_next @ torch.linalg.pinv(Z) # A_hat = AB[:, :n] # B_hat = AB[:, n:] - + C_hat = Gamma_hat[:p_out, :] # Estimate noise covariance matrix # 0) Outputs aligned to X and U_mid (same time indices/columns) - Y_curr = torch.cat(Y_segments, dim=1) # shape: (p_out, N) + Y_curr = torch.cat(Y_segments, dim=1) # shape: (p_out, N) # 1) Residuals at time t # Process noise residual (state eq): w_t ≈ x_{t+1} - A x_t - B u_ts - W_hat = X_next - (A_hat @ X + B_hat @ U_mid) # (n, N) + W_hat = X_next - (A_hat @ X + B_hat @ U_mid) # (n, N) # Measurement noise residual (output eq): v_t ≈ y_t - C x_t (since D = 0) - V_hat = Y_curr - (C_hat @ X) # (p_out, N) + V_hat = Y_curr - (C_hat @ X) # (p_out, N) # 2) Mean-centering V_hat = V_hat - V_hat.mean(dim=1, keepdim=True) W_hat = W_hat - W_hat.mean(dim=1, keepdim=True) N_res = V_hat.shape[1] - denom = max(N_res - 1, 1) + denom = max(N_res - 1, 1) # 3) Covariances - R_hat = (V_hat @ V_hat.T) / denom # (p_out, p_out) measurement - Q_hat = (W_hat @ W_hat.T) / denom # (n, n) process - S_hat = (W_hat @ V_hat.T) / denom # (n, p_out) - cross (w,v) + R_hat = (V_hat @ V_hat.T) / denom # (p_out, p_out) measurement + Q_hat = (W_hat @ W_hat.T) / denom # (n, n) process + S_hat = (W_hat @ V_hat.T) / denom # (n, p_out) - cross (w,v) # 4) Symmetrize eps = 1e-12 - R_hat = 0.5 * (R_hat + R_hat.T) + eps * torch.eye(R_hat.shape[0], device=self.device) - Q_hat = 0.5 * (Q_hat + Q_hat.T) + eps * torch.eye(Q_hat.shape[0], device=self.device) + R_hat = 0.5 * (R_hat + R_hat.T) + eps * torch.eye( + R_hat.shape[0], device=self.device + ) + Q_hat = 0.5 * (Q_hat + Q_hat.T) + eps * torch.eye( + Q_hat.shape[0], device=self.device + ) noise_covariance = torch.block_diag(R_hat, Q_hat) # Add off-diagonal blocks top_right = S_hat.T bottom_left = S_hat - noise_covariance = torch.cat([ - torch.cat([R_hat, top_right], dim=1), - torch.cat([bottom_left, Q_hat], dim=1) - ], dim=0) + noise_covariance = torch.cat( + [ + torch.cat([R_hat, top_right], dim=1), + torch.cat([bottom_left, Q_hat], dim=1), + ], + dim=0, + ) info = { - "singular_values_O": s, - "rank_used": n, - "Gamma_hat": Gamma_hat, + "singular_values_O": s, + "rank_used": n, + "Gamma_hat": Gamma_hat, "f": f, "n_trials_total": n_trials, "n_trials_used": len(valid_trials), @@ -293,20 +327,19 @@ def hankel_stack(X, start, L): "T_total": T_total, "trial_lengths": [y.shape[1] for y in y_list], "noise_covariance": noise_covariance, - 'R_hat': R_hat, - 'Q_hat': Q_hat, - 'S_hat': S_hat + "R_hat": R_hat, + "Q_hat": Q_hat, + "S_hat": S_hat, } - - return A_hat, B_hat, C_hat, info - + return A_hat, B_hat, C_hat, info - - def subspace_dmdc_multitrial_custom(self, y_list, u_list, p, f, n=None, lamb=1e-8, energy=0.999): + def subspace_dmdc_multitrial_custom( + self, y_list, u_list, p, f, n=None, lamb=1e-8, energy=0.999 + ): """ Subspace-DMDc for multi-trial data with variable trial lengths. - + Parameters: - y_list: list of tensors, each (p_out, N_i) - output data for trial i - u_list: list of tensors, each (m, N_i) - input data for trial i @@ -315,31 +348,35 @@ def subspace_dmdc_multitrial_custom(self, y_list, u_list, p, f, n=None, lamb=1e- - n: state dimension (auto-determined if None) - ridge: regularization parameter - energy: energy threshold for rank selection∏ - + Returns: - A_hat, B_hat, C_hat: system matrices - info: dictionary with additional information """ if len(y_list) != len(u_list): raise ValueError("y_list and u_list must have same number of trials") - + n_trials = len(y_list) p_out = y_list[0].shape[0] m = u_list[0].shape[0] - + # Validate dimensions across trials for i, (y_trial, u_trial) in enumerate(zip(y_list, u_list)): if y_trial.shape[0] != p_out: - raise ValueError(f"Trial {i}: y has {y_trial.shape[0]} outputs, expected {p_out}") + raise ValueError( + f"Trial {i}: y has {y_trial.shape[0]} outputs, expected {p_out}" + ) if u_trial.shape[0] != m: - raise ValueError(f"Trial {i}: u has {u_trial.shape[0]} inputs, expected {m}") + raise ValueError( + f"Trial {i}: u has {u_trial.shape[0]} inputs, expected {m}" + ) if y_trial.shape[1] != u_trial.shape[1]: raise ValueError(f"Trial {i}: y and u have different time lengths") - + def hankel_stack(X, start, L): - return torch.cat([X[:, start + i:start + i + 1] for i in range(L)], dim=0) - + return torch.cat([X[:, start + i : start + i + 1] for i in range(L)], dim=0) + # Collect data from all trials U_p_all = [] Y_p_all = [] @@ -347,102 +384,116 @@ def hankel_stack(X, start, L): Y_f_all = [] valid_trials = [] T_per_trial = [] - + for trial_idx, (Y_trial, U_trial) in enumerate(zip(y_list, u_list)): N_trial = Y_trial.shape[1] T_trial = N_trial - (p + f) + 1 - + if T_trial <= 0: if self.verbose: - print(f"Warning: Trial {trial_idx} has insufficient data (T={T_trial}), skipping") + print( + f"Warning: Trial {trial_idx} has insufficient data (T={T_trial}), skipping" + ) continue - + valid_trials.append(trial_idx) T_per_trial.append(T_trial) - + # Build Hankel matrices for this trial - U_p_trial = torch.cat([hankel_stack(U_trial, j, p) for j in range(T_trial)], dim=1) - Y_p_trial = torch.cat([hankel_stack(Y_trial, j, p) for j in range(T_trial)], dim=1) - U_f_trial = torch.cat([hankel_stack(U_trial, j + p, f) for j in range(T_trial)], dim=1) - Y_f_trial = torch.cat([hankel_stack(Y_trial, j + p, f) for j in range(T_trial)], dim=1) - + U_p_trial = torch.cat( + [hankel_stack(U_trial, j, p) for j in range(T_trial)], dim=1 + ) + Y_p_trial = torch.cat( + [hankel_stack(Y_trial, j, p) for j in range(T_trial)], dim=1 + ) + U_f_trial = torch.cat( + [hankel_stack(U_trial, j + p, f) for j in range(T_trial)], dim=1 + ) + Y_f_trial = torch.cat( + [hankel_stack(Y_trial, j + p, f) for j in range(T_trial)], dim=1 + ) + U_p_all.append(U_p_trial) Y_p_all.append(Y_p_trial) U_f_all.append(U_f_trial) Y_f_all.append(Y_f_trial) if self.verbose: - print("="*40) + print("=" * 40) print(f"Number of valid trials: {len(valid_trials)}") - + if not valid_trials: raise ValueError("No trials have sufficient data for given (p,f)") - + # Concatenate across valid trials U_p = torch.cat(U_p_all, dim=1) # (pm, T_total) Y_p = torch.cat(Y_p_all, dim=1) # (p*p_out, T_total) U_f = torch.cat(U_f_all, dim=1) # (fm, T_total) Y_f = torch.cat(Y_f_all, dim=1) # (f*p_out, T_total) - + T_total = sum(T_per_trial) Z_p = torch.vstack([U_p, Y_p]) # (p(m+p_out), T_total) - + # Oblique projection: remove row(U_f), project onto row(Z_p) UfUfT = U_f @ U_f.T - Xsolve = torch.linalg.solve(UfUfT + lamb*torch.eye(UfUfT.shape[0], device=self.device), U_f) + Xsolve = torch.linalg.solve( + UfUfT + lamb * torch.eye(UfUfT.shape[0], device=self.device), U_f + ) Pi_perp = torch.eye(T_total, device=self.device) - U_f.T @ Xsolve Yf_perp = Y_f @ Pi_perp Zp_perp = Z_p @ Pi_perp - + ZZT = Zp_perp @ Zp_perp.T - Zp_pinv_left = torch.linalg.solve(ZZT + lamb*torch.eye(ZZT.shape[0], device=self.device), Zp_perp) + Zp_pinv_left = torch.linalg.solve( + ZZT + lamb * torch.eye(ZZT.shape[0], device=self.device), Zp_perp + ) P = Zp_perp.T @ Zp_pinv_left O = Yf_perp @ P # ≈ Γ_f X_p - + Uo, s, Vt = torch.linalg.svd(O, full_matrices=False) if n is None: cs = torch.cumsum(s**2, dim=0) / (s**2).sum() n = int((cs < energy).sum().item() + 1) n = max(1, min(n, min(Uo.shape[1], Vt.shape[0]))) - + U_n = Uo[:, :n] S_n = torch.diag(s[:n]) V_n = Vt[:n, :] S_half = torch.sqrt(S_n) - Gamma_hat = U_n @ S_half # (f*p_out, n) - X_hat = S_half @ V_n # (n, T_total) - + Gamma_hat = U_n @ S_half # (f*p_out, n) + X_hat = S_half @ V_n # (n, T_total) + # Time alignment for regression across all trials # Need to handle variable lengths carefully X_segments = [] X_next_segments = [] U_mid_segments = [] - + start_idx = 0 for trial_idx, T_trial in enumerate(T_per_trial): # Extract states for this trial - X_trial = X_hat[:, start_idx:start_idx + T_trial] - + X_trial = X_hat[:, start_idx : start_idx + T_trial] + # State transitions within this trial X_trial_curr = X_trial[:, :-1] X_trial_next = X_trial[:, 1:] - + # Corresponding control inputs original_trial_idx = valid_trials[trial_idx] U_trial = u_list[original_trial_idx] - U_mid_trial = U_trial[:, p:p + (T_trial - 1)] - + U_mid_trial = U_trial[:, p : p + (T_trial - 1)] + X_segments.append(X_trial_curr) X_next_segments.append(X_trial_next) U_mid_segments.append(U_mid_trial) - + start_idx += T_trial - + # Concatenate all segments X = torch.cat(X_segments, dim=1) X_next = torch.cat(X_next_segments, dim=1) U_mid = torch.cat(U_mid_segments, dim=1) - + # Regression for A and B Z = torch.vstack([X, U_mid]) # Ridge regression: (Z^T Z + λI)^(-1) Z^T X_next^T @@ -451,13 +502,13 @@ def hankel_stack(X, start, L): AB = torch.linalg.solve(ZTZ + ridge_term, Z @ X_next.T).T A_hat = AB[:, :n] B_hat = AB[:, n:] - + C_hat = Gamma_hat[:p_out, :] - + info = { - "singular_values_O": s, - "rank_used": n, - "Gamma_hat": Gamma_hat, + "singular_values_O": s, + "rank_used": n, + "Gamma_hat": Gamma_hat, "f": f, "n_trials_total": n_trials, "n_trials_used": len(valid_trials), @@ -465,17 +516,17 @@ def hankel_stack(X, start, L): "T_per_trial": T_per_trial, "T_total": T_total, "trial_lengths": [y.shape[1] for y in y_list], - "X_hat": X_hat + "X_hat": X_hat, } - - return A_hat, B_hat, C_hat, info - + return A_hat, B_hat, C_hat, info - def subspace_dmdc_multitrial_flexible(self, y, u, p, f, n=None, lamb=1e-8, energy=0.999, backend='n4sid'): + def subspace_dmdc_multitrial_flexible( + self, y, u, p, f, n=None, lamb=1e-8, energy=0.999, backend="n4sid" + ): """ Flexible wrapper that handles both fixed-length and variable-length multi-trial data. - + Parameters: - y: either (n_trials, p_out, N) array, (p_out, N) array, or list of (p_out, N_i) arrays - u: either (n_trials, m, N) array, (m, N) array, or list of (m, N_i) arrays @@ -488,11 +539,15 @@ def subspace_dmdc_multitrial_flexible(self, y, u, p, f, n=None, lamb=1e-8, energ else: y_list = y u_list = u - if backend == 'n4sid': - return self.subspace_dmdc_multitrial_QR_decomposition(y_list, u_list, p, f, n, lamb, energy) + if backend == "n4sid": + return self.subspace_dmdc_multitrial_QR_decomposition( + y_list, u_list, p, f, n, lamb, energy + ) else: - return self.subspace_dmdc_multitrial_custom(y_list, u_list, p, f, n, lamb, energy) - + return self.subspace_dmdc_multitrial_custom( + y_list, u_list, p, f, n, lamb, energy + ) + else: # Handle 2D arrays (single trial) by converting to list format if y.ndim == 2: @@ -502,17 +557,20 @@ def subspace_dmdc_multitrial_flexible(self, y, u, p, f, n=None, lamb=1e-8, energ # Convert 3D arrays to list format y_list = [y[i] for i in range(y.shape[0])] u_list = [u[i] for i in range(u.shape[0])] - + # If time_first=True, transpose each trial from (time_points, variables) to (variables, time_points) if self.time_first: y_list = [y_trial.T for y_trial in y_list] u_list = [u_trial.T for u_trial in u_list] - - if backend == 'n4sid': - return self.subspace_dmdc_multitrial_QR_decomposition(y_list, u_list, p, f, n, lamb, energy) - else: - return self.subspace_dmdc_multitrial_custom(y_list, u_list, p, f, n, lamb, energy) + if backend == "n4sid": + return self.subspace_dmdc_multitrial_QR_decomposition( + y_list, u_list, p, f, n, lamb, energy + ) + else: + return self.subspace_dmdc_multitrial_custom( + y_list, u_list, p, f, n, lamb, energy + ) def predict(self, Y, U, reseed=None): # Y and U are (n_times, n_channels) or list of 2D arrays/tensors @@ -523,11 +581,11 @@ def predict(self, Y, U, reseed=None): if isinstance(Y, list): Y = [self._to_tensor_single(y) for y in Y] U = [self._to_tensor_single(u) for u in U] - + if not self.time_first: Y = [y.T for y in Y] U = [u.T for u in U] - + self.kalman = OnlineKalman(self) Y_pred = [] for trial in range(len(Y)): @@ -535,8 +593,7 @@ def predict(self, Y, U, reseed=None): trial_predictions = [] for t in range(Y[trial].shape[0]): y_filtered, _ = self.kalman.step( - y=Y[trial][t] if t%reseed == 0 else None, - u=U[trial][t] + y=Y[trial][t] if t % reseed == 0 else None, u=U[trial][t] ) trial_predictions.append(y_filtered) Y_pred.append(torch.cat(trial_predictions, dim=1).T) @@ -545,7 +602,7 @@ def predict(self, Y, U, reseed=None): # Convert to tensors Y = self._to_tensor_single(Y) U = self._to_tensor_single(U) - + # print("time_first", self.time_first) if not self.time_first: if Y.ndim == 2: @@ -554,12 +611,14 @@ def predict(self, Y, U, reseed=None): else: Y = Y.permute(0, 2, 1) U = U.permute(0, 2, 1) - + self.kalman = OnlineKalman(self) if Y.ndim == 2: Y_pred = [] for t in range(Y.shape[0]): - y_filtered, _ = self.kalman.step(y=Y[t] if t%reseed == 0 else None, u=U[t]) + y_filtered, _ = self.kalman.step( + y=Y[t] if t % reseed == 0 else None, u=U[t] + ) Y_pred.append(y_filtered) return torch.cat(Y_pred, dim=1).T else: @@ -571,45 +630,46 @@ def predict(self, Y, U, reseed=None): self.kalman.reset() # Reset filter for each trial trial_predictions = [] for t in range(Y.shape[1]): - y_filtered, _ = self.kalman.step(y=Y[trial, t] if t%reseed == 0 else None, u=U[trial, t]) + y_filtered, _ = self.kalman.step( + y=Y[trial, t] if t % reseed == 0 else None, u=U[trial, t] + ) trial_predictions.append(y_filtered) # print("y_filtered.shape", y_filtered.shape) Y_pred.append(torch.cat(trial_predictions, dim=1).T) return torch.stack(Y_pred) - class OnlineKalman: """ Online Kalman Filter class for real-time state estimation. - + This class maintains the internal state of the Kalman filter and provides a step method for updating the filter with new observations and inputs. """ - + def __init__(self, dmdc): """ Initialize the Online Kalman Filter with a fitted DMDc model. - + Parameters ---------- dmdc : object - Fitted DMDc model containing A_v, B_v, C_v matrices and + Fitted DMDc model containing A_v, B_v, C_v matrices and noise covariance estimates (R_hat, S_hat, Q_hat) """ self.device = dmdc.device self.A = dmdc.A_v - self.B = dmdc.B_v + self.B = dmdc.B_v self.C = dmdc.C_v - self.R = dmdc.info['R_hat'] - self.S = dmdc.info['S_hat'] - self.Q = dmdc.info['Q_hat'] - + self.R = dmdc.info["R_hat"] + self.S = dmdc.info["S_hat"] + self.Q = dmdc.info["Q_hat"] + # Get dimensions # print("C_shape", self.C.shape) self.y_dim, self.x_dim = self.C.shape self.u_dim = self.B.shape[1] - + # Initialize state storage self.p_filtereds = [] self.x_filtereds = [] @@ -621,24 +681,23 @@ def __init__(self, dmdc): self.y_predicteds = [] self.kalman_gains = [] - # def step(self, y=None, u=None, lam=1e-8): # """ # Perform one step of the Kalman filter. - + # Parameters # ---------- # y : np.ndarray, optional # Observed output at current time step. If None, the filter # will predict without observation update. - # u : np.ndarray, optional + # u : np.ndarray, optional # Input at current time step. If None, no input is applied. - + # Returns # ------- # y_filtered : np.ndarray # Filtered output estimate - # x_filtered : np.ndarray + # x_filtered : np.ndarray # Filtered state estimate # """ # x_pred = self.x_predicteds[-1] if self.x_predicteds else np.zeros((self.x_dim, 1)) @@ -661,14 +720,14 @@ def __init__(self, dmdc): # x_filtered = x_pred + K_filtered @ (y - self.C @ x_pred) # else: # x_filtered = x_pred.copy() - + # K_pred = (self.S + self.A @ p_pred @ self.C.T) @ np.linalg.pinv(S_innov) - # p_predicted = (self.A @ p_pred @ self.A.T + self.Q - + # p_predicted = (self.A @ p_pred @ self.A.T + self.Q - # K_pred @ (self.S + self.A @ p_pred @ self.C.T).T) # x_predicted = self.A @ x_pred + self.B @ u # if not np.isnan(y).any(): # x_predicted += K_pred @ (y - self.C @ x_pred) - + # # Store results # self.p_filtereds.append(p_filtered) # self.x_filtereds.append(x_filtered) @@ -679,35 +738,42 @@ def __init__(self, dmdc): # self.y_filtereds.append(self.C @ x_filtered) # self.y_predicteds.append(self.C @ x_predicted) # self.kalman_gains.append(K_pred) - - # return self.y_filtereds[-1], self.x_filtereds[-1] + # return self.y_filtereds[-1], self.x_filtereds[-1] def step(self, y=None, u=None, reg_coef=1e-6): """ Perform one step of the Kalman filter. - + Parameters ---------- y : torch.Tensor or np.ndarray, optional Observed output at current time step. If None, the filter will predict without observation update. - u : torch.Tensor or np.ndarray, optional + u : torch.Tensor or np.ndarray, optional Input at current time step. If None, no input is applied. reg_coef : float, optional Regularization coefficient to add to diagonal of P matrices to maintain numerical stability. Default: 1e-6 - + Returns ------- y_filtered : torch.Tensor Filtered output estimate - x_filtered : torch.Tensor + x_filtered : torch.Tensor Filtered state estimate """ - x_pred = self.x_predicteds[-1] if self.x_predicteds else torch.zeros((self.x_dim, 1), device=self.device) - p_pred = self.p_predicteds[-1] if self.p_predicteds else torch.eye(self.x_dim, device=self.device) - + x_pred = ( + self.x_predicteds[-1] + if self.x_predicteds + else torch.zeros((self.x_dim, 1), device=self.device) + ) + p_pred = ( + self.p_predicteds[-1] + if self.p_predicteds + else torch.eye(self.x_dim, device=self.device) + ) + # Add regularization to p_pred to prevent ill-conditioning p_pred_reg = p_pred + reg_coef * torch.eye(self.x_dim, device=self.device) @@ -719,7 +785,7 @@ def step(self, y=None, u=None, reg_coef=1e-6): u = u.reshape(-1, 1) else: u = torch.zeros((self.u_dim, 1), device=self.device) - + if y is not None: if isinstance(y, np.ndarray): y = torch.from_numpy(y).float().to(self.device) @@ -732,28 +798,35 @@ def step(self, y=None, u=None, reg_coef=1e-6): S_innov = self.R + self.C @ p_pred_reg @ self.C.T K_filtered = p_pred_reg @ self.C.T @ torch.linalg.pinv(S_innov) p_filtered = p_pred_reg - K_filtered @ self.C @ p_pred_reg - + # Add regularization to p_filtered to maintain positive definiteness p_filtered = (p_filtered + p_filtered.T) / 2 # Ensure symmetry - p_filtered = p_filtered + reg_coef * torch.eye(self.x_dim, device=self.device) # Add regularization - + p_filtered = p_filtered + reg_coef * torch.eye( + self.x_dim, device=self.device + ) # Add regularization + if not torch.isnan(y).any(): x_filtered = x_pred + K_filtered @ (y - self.C @ x_pred) else: x_filtered = x_pred.clone() - + K_pred = (self.S + self.A @ p_pred_reg @ self.C.T) @ torch.linalg.pinv(S_innov) - p_predicted = (self.A @ p_pred_reg @ self.A.T + self.Q - - K_pred @ (self.S + self.A @ p_pred_reg @ self.C.T).T) - + p_predicted = ( + self.A @ p_pred_reg @ self.A.T + + self.Q + - K_pred @ (self.S + self.A @ p_pred_reg @ self.C.T).T + ) + # Add regularization to p_predicted and ensure symmetry p_predicted = (p_predicted + p_predicted.T) / 2 # Ensure symmetry - p_predicted = p_predicted + reg_coef * torch.eye(self.x_dim, device=self.device) # Add regularization - + p_predicted = p_predicted + reg_coef * torch.eye( + self.x_dim, device=self.device + ) # Add regularization + x_predicted = self.A @ x_pred + self.B @ u if not torch.isnan(y).any(): x_predicted += K_pred @ (y - self.C @ x_pred) - + # Store results self.p_filtereds.append(p_filtered) self.x_filtereds.append(x_filtered) @@ -764,9 +837,9 @@ def step(self, y=None, u=None, reg_coef=1e-6): self.y_filtereds.append(self.C @ x_filtered) self.y_predicteds.append(self.C @ x_predicted) self.kalman_gains.append(K_pred) - + return self.y_filtereds[-1], self.x_filtereds[-1] - + def reset(self): """Reset the filter to initial state.""" self.p_filtereds = [] @@ -778,18 +851,17 @@ def reset(self): self.y_filtereds = [] self.y_predicteds = [] self.kalman_gains = [] - - + def get_history(self): """Return the complete history of filter states.""" return { - 'p_filtereds': self.p_filtereds, - 'x_filtereds': self.x_filtereds, - 'p_predicteds': self.p_predicteds, - 'x_predicteds': self.x_predicteds, - 'us': self.us, - 'ys': self.ys, - 'y_filtereds': self.y_filtereds, - 'y_predicteds': self.y_predicteds, - 'kalman_gains': self.kalman_gains - } \ No newline at end of file + "p_filtereds": self.p_filtereds, + "x_filtereds": self.x_filtereds, + "p_predicteds": self.p_predicteds, + "x_predicteds": self.x_predicteds, + "us": self.us, + "ys": self.ys, + "y_filtereds": self.y_filtereds, + "y_predicteds": self.y_predicteds, + "kalman_gains": self.kalman_gains, + } diff --git a/DSA/sweeps.py b/DSA/sweeps.py index 383e7ca..e23c7ea 100644 --- a/DSA/sweeps.py +++ b/DSA/sweeps.py @@ -1,16 +1,20 @@ import numpy as np from tqdm import tqdm from .dmd import DMD -from .stats import measure_nonnormality_transpose, compute_all_stats, measure_transient_growth +from .stats import ( + measure_nonnormality_transpose, + compute_all_stats, + measure_transient_growth, +) from .resdmd import compute_residuals import matplotlib.pyplot as plt from typing import Literal -def split_train_test(data,train_frac=0.8): - if isinstance(data,list): - train_data = [d for i,d in enumerate(data) if i < int(train_frac*len(data))] - test_data = [d for i,d in enumerate(data) if i >= int(train_frac*len(data))] +def split_train_test(data, train_frac=0.8): + if isinstance(data, list): + train_data = [d for i, d in enumerate(data) if i < int(train_frac * len(data))] + test_data = [d for i, d in enumerate(data) if i >= int(train_frac * len(data))] dim = data[0].shape[-1] elif data.ndim == 3 and data.shape[0] == 1: train_data = data[:, int(train_frac * data.shape[1]) :] @@ -18,10 +22,13 @@ def split_train_test(data,train_frac=0.8): dim = data.shape[-1] else: train_data = data[: int(train_frac * data.shape[0])] - test_data = data[int(train_frac * data.shape[0]) :] if train_frac < 1.0 else train_data + test_data = ( + data[int(train_frac * data.shape[0]) :] if train_frac < 1.0 else train_data + ) dim = data.shape[-1] return train_data, test_data, dim + def sweep_ranks_delays( data, n_delays, @@ -31,8 +38,8 @@ def sweep_ranks_delays( return_residuals=True, return_transient_growth=False, return_mse=False, - error_space='X', - **dmd_kwargs + error_space="X", + **dmd_kwargs, ): """ Sweep over combinations of DMD ranks and delays, returning AIC, MASE, non-normality, and residuals. @@ -63,7 +70,7 @@ def sweep_ranks_delays( all_aics, all_mases, all_nnormals, all_residuals, all_num_abscissa, all_l2norm : np.ndarray Arrays of results for each (delay, rank) pair. """ - train_data, test_data, dim = split_train_test(data,train_frac) + train_data, test_data, dim = split_train_test(data, train_frac) all_aics, all_mases, all_nnormals, all_residuals, all_l2norm = [], [], [], [], [] for nd in tqdm(n_delays): rresiduals = [] @@ -76,37 +83,34 @@ def sweep_ranks_delays( rresiduals.append(np.inf) l2norms.append(np.inf) continue - dmd = DMD( - train_data, - n_delays=nd, - rank=r, - **dmd_kwargs - ) + dmd = DMD(train_data, n_delays=nd, rank=r, **dmd_kwargs) dmd.fit() - pred, H_test_pred, H_test_true, V_test_pred, V_test_true = dmd.predict(test_data,reseed=reseed,full_return=True) - if error_space == 'H': + pred, H_test_pred, H_test_true, V_test_pred, V_test_true = dmd.predict( + test_data, reseed=reseed, full_return=True + ) + if error_space == "H": pred = H_test_pred test_data_err = H_test_true - elif error_space == 'V': + elif error_space == "V": pred = V_test_pred test_data_err = V_test_true - elif error_space == 'X': + elif error_space == "X": pred = pred test_data_err = test_data else: raise ValueError(f"Invalid error space: {error_space}") - if hasattr(pred,"cpu"): + if hasattr(pred, "cpu"): pred = pred.cpu() - if hasattr(test_data_err,"cpu"): + if hasattr(test_data_err, "cpu"): test_data_err = test_data_err.cpu() - if isinstance(pred,list): - pred = np.concatenate(pred,axis=0) - test_data_err = np.concatenate(test_data_err,axis=0) + if isinstance(pred, list): + pred = np.concatenate(pred, axis=0) + test_data_err = np.concatenate(test_data_err, axis=0) # if featurize and ndim is not None: - # pred = pred[:, :, -ndim:] - # stats = compute_all_stats(pred, test_data_err[:, :, -ndim:], dmd.rank) + # pred = pred[:, :, -ndim:] + # stats = compute_all_stats(pred, test_data_err[:, :, -ndim:], dmd.rank) # else: stats = compute_all_stats(test_data_err, pred, dmd.rank) aic = stats["AIC"] @@ -117,7 +121,11 @@ def sweep_ranks_delays( dmd.A_v.cpu().detach().numpy() if hasattr(dmd.A_v, "cpu") else dmd.A_v ) if return_transient_growth: - l2norm = measure_transient_growth(dmd.A_v.cpu().detach().numpy() if hasattr(dmd.A_v, "cpu") else dmd.A_v) + l2norm = measure_transient_growth( + dmd.A_v.cpu().detach().numpy() + if hasattr(dmd.A_v, "cpu") + else dmd.A_v + ) else: l2norm = None L, G, residuals, _ = compute_residuals(dmd) @@ -155,10 +163,10 @@ def plot_sweep_results( ranks=None, name=None, save_path=None, - figsize=(10,4), + figsize=(10, 4), return_mse=False, cmap="gist_gray", - error_space='X', + error_space="X", ): to_plot = [aics, mases] if nnormals is not None: @@ -168,33 +176,41 @@ def plot_sweep_results( if l2norm is not None: to_plot.append(l2norm) fig, ax = plt.subplots(1, len(to_plot), figsize=figsize) - cmap = plt.cm.get_cmap(cmap) if isinstance(cmap,str) else cmap + cmap = plt.cm.get_cmap(cmap) if isinstance(cmap, str) else cmap scale_denom = len(aics) + 3 for j in range(len(aics)): - ax[0].plot( - ranks, aics[j], label=f"{n_delays[j]}", color=cmap(j / scale_denom) - ) - ax[1].plot(ranks, mases[j], color=cmap(j / scale_denom),label=f"{n_delays[j]}") + ax[0].plot(ranks, aics[j], label=f"{n_delays[j]}", color=cmap(j / scale_denom)) + ax[1].plot(ranks, mases[j], color=cmap(j / scale_denom), label=f"{n_delays[j]}") ax[1].axhline(1, color="black", linestyle="--") if nnormals is not None: - ax[2].plot(ranks, nnormals[j], color=cmap(j / scale_denom),label=f"{n_delays[j]}") + ax[2].plot( + ranks, nnormals[j], color=cmap(j / scale_denom), label=f"{n_delays[j]}" + ) if residuals is not None: - ax[3].plot(ranks, residuals[j], color=cmap(j / scale_denom),label=f"{n_delays[j]}") + ax[3].plot( + ranks, residuals[j], color=cmap(j / scale_denom), label=f"{n_delays[j]}" + ) if l2norm is not None: - ax[4].plot(ranks, l2norm[j], color=cmap(j / scale_denom),label=f"{n_delays[j]}") + ax[4].plot( + ranks, l2norm[j], color=cmap(j / scale_denom), label=f"{n_delays[j]}" + ) ax[4].axhline(1, color="black", linestyle="--") ax[1].set_yscale("log") ax[0].set_ylabel(f"{error_space} AIC") - ax[1].set_ylabel(f"{error_space} MASE" if not return_mse else f"{error_space} MSE") + ax[1].set_ylabel( + f"{error_space} MASE" if not return_mse else f"{error_space} MSE" + ) if nnormals is not None: ax[2].set_ylabel(f"Non-normal score") if residuals is not None: ax[3].set_ylabel(f"Average residual of eigenvalues") if l2norm is not None: ax[4].set_ylabel(f"L2 norm of matrix") - ax[-1].legend(title='# delays',loc='upper right',bbox_to_anchor=(2,1),borderaxespad=1) + ax[-1].legend( + title="# delays", loc="upper right", bbox_to_anchor=(2, 1), borderaxespad=1 + ) for k in range(len(to_plot)): ax[k].set_xlabel("Rank") ax[k].spines["top"].set_visible(False) @@ -207,28 +223,39 @@ def plot_sweep_results( else: return fig, ax -def predict_and_stats(dmd,test_data,reseed): - pred, H_test_pred, H_test_true, V_test_pred, V_test_true = dmd.predict(test_data,reseed=reseed,full_return=True) - if hasattr(pred,"cpu"): + +def predict_and_stats(dmd, test_data, reseed): + pred, H_test_pred, H_test_true, V_test_pred, V_test_true = dmd.predict( + test_data, reseed=reseed, full_return=True + ) + if hasattr(pred, "cpu"): pred = pred.cpu() - if hasattr(H_test_pred,"cpu"): + if hasattr(H_test_pred, "cpu"): H_test_pred = H_test_pred.cpu() - if hasattr(H_test_true,"cpu"): + if hasattr(H_test_true, "cpu"): H_test_true = H_test_true.cpu() - if hasattr(V_test_pred,"cpu"): + if hasattr(V_test_pred, "cpu"): V_test_pred = V_test_pred.cpu() - if hasattr(V_test_true,"cpu"): + if hasattr(V_test_true, "cpu"): V_test_true = V_test_true.cpu() - if isinstance(pred,list): - pred = np.concatenate(pred,axis=0) - test_data = np.concatenate(test_data,axis=0) + if isinstance(pred, list): + pred = np.concatenate(pred, axis=0) + test_data = np.concatenate(test_data, axis=0) xstats = compute_all_stats(test_data, pred, dmd.rank) hstats = compute_all_stats(H_test_true, H_test_pred, dmd.rank) vstats = compute_all_stats(V_test_true, V_test_pred, dmd.rank) return xstats, hstats, vstats -def sweep_ranks_delays_all_error_types(data,n_delays,ranks,train_frac=0.8,reseeds=5, - return_type:Literal['tuple','dict']='dict',**dmd_kwargs): + +def sweep_ranks_delays_all_error_types( + data, + n_delays, + ranks, + train_frac=0.8, + reseeds=5, + return_type: Literal["tuple", "dict"] = "dict", + **dmd_kwargs, +): """ Sweep over combinations of DMD ranks and delays, returning all error types (AIC, MASE, MSE in X space, H space, V space, with and without reseeds) Will also compute non-normality of the DMD matrix. @@ -250,79 +277,104 @@ def sweep_ranks_delays_all_error_types(data,n_delays,ranks,train_frac=0.8,reseed Returns ------- - + Arrays of results for each (delay, rank) pair. """ - train_data, test_data, dim = split_train_test(data,train_frac) + train_data, test_data, dim = split_train_test(data, train_frac) - if not isinstance(reseeds,list) and reseeds in set([1,'none',None,'',0]): + if not isinstance(reseeds, list) and reseeds in set([1, "none", None, "", 0]): reseeds = [1] - elif isinstance(reseeds,int): + elif isinstance(reseeds, int): reseeds = [1, reseeds] if 1 not in reseeds: reseeds = [1] + reseeds def init_arr(d=3): if d == 3: - arr = np.zeros((len(reseeds),len(n_delays),len(ranks))) + arr = np.zeros((len(reseeds), len(n_delays), len(ranks))) elif d == 2: - arr = np.zeros((len(n_delays),len(ranks))) + arr = np.zeros((len(n_delays), len(ranks))) arr[:] = np.nan return arr - all_aicsx_reseed, all_masesx_reseed,all_msesx_reseed = init_arr(), init_arr(), init_arr() - all_aicsh_reseed, all_masesh_reseed,all_msesh_reseed = init_arr(), init_arr(), init_arr() - all_aicsv_reseed, all_masesv_reseed,all_msesv_reseed = init_arr(), init_arr(), init_arr() + all_aicsx_reseed, all_masesx_reseed, all_msesx_reseed = ( + init_arr(), + init_arr(), + init_arr(), + ) + all_aicsh_reseed, all_masesh_reseed, all_msesh_reseed = ( + init_arr(), + init_arr(), + init_arr(), + ) + all_aicsv_reseed, all_masesv_reseed, all_msesv_reseed = ( + init_arr(), + init_arr(), + init_arr(), + ) - for i,nd in tqdm(enumerate(n_delays)): - for j,r in enumerate(ranks): + for i, nd in tqdm(enumerate(n_delays)): + for j, r in enumerate(ranks): if r is None or r > nd * dim: continue - dmd = DMD(train_data,n_delays=nd,rank=r,**dmd_kwargs) + dmd = DMD(train_data, n_delays=nd, rank=r, **dmd_kwargs) dmd.fit() - for k,reseed in enumerate(reseeds): - xstats, hstats, vstats = predict_and_stats(dmd,test_data,reseed) - all_aicsx_reseed[k,i,j] = xstats["AIC"] - all_masesx_reseed[k,i,j] = xstats["MASE"] - all_msesx_reseed[k,i,j] = xstats["MSE"] - - all_aicsh_reseed[k,i,j] = hstats["AIC"] - all_masesh_reseed[k,i,j] = hstats["MASE"] - all_msesh_reseed[k,i,j] = hstats["MSE"] - - all_aicsv_reseed[k,i,j] = vstats["AIC"] - all_masesv_reseed[k,i,j] = vstats["MASE"] - all_msesv_reseed[k,i,j] = vstats["MSE"] - - if return_type == 'tuple': - return all_aicsx_reseed, all_masesx_reseed,all_msesx_reseed, all_aicsh_reseed, all_masesh_reseed,all_msesh_reseed, all_aicsv_reseed, all_masesv_reseed,all_msesv_reseed - elif return_type == 'dict': - return {'reseeds':reseeds, - 'aicsx_reseed':all_aicsx_reseed, - 'masesx_reseed':all_masesx_reseed, - 'msesx_reseed':all_msesx_reseed, - 'aicsh_reseed':all_aicsh_reseed, - 'masesh_reseed':all_masesh_reseed, - 'msesh_reseed':all_msesh_reseed, - 'aicsv_reseed':all_aicsv_reseed, - 'masesv_reseed':all_masesv_reseed, - 'msesv_reseed':all_msesv_reseed, - 'n_delays':n_delays, - 'ranks':ranks} + for k, reseed in enumerate(reseeds): + xstats, hstats, vstats = predict_and_stats(dmd, test_data, reseed) + all_aicsx_reseed[k, i, j] = xstats["AIC"] + all_masesx_reseed[k, i, j] = xstats["MASE"] + all_msesx_reseed[k, i, j] = xstats["MSE"] + + all_aicsh_reseed[k, i, j] = hstats["AIC"] + all_masesh_reseed[k, i, j] = hstats["MASE"] + all_msesh_reseed[k, i, j] = hstats["MSE"] + + all_aicsv_reseed[k, i, j] = vstats["AIC"] + all_masesv_reseed[k, i, j] = vstats["MASE"] + all_msesv_reseed[k, i, j] = vstats["MSE"] + + if return_type == "tuple": + return ( + all_aicsx_reseed, + all_masesx_reseed, + all_msesx_reseed, + all_aicsh_reseed, + all_masesh_reseed, + all_msesh_reseed, + all_aicsv_reseed, + all_masesv_reseed, + all_msesv_reseed, + ) + elif return_type == "dict": + return { + "reseeds": reseeds, + "aicsx_reseed": all_aicsx_reseed, + "masesx_reseed": all_masesx_reseed, + "msesx_reseed": all_msesx_reseed, + "aicsh_reseed": all_aicsh_reseed, + "masesh_reseed": all_masesh_reseed, + "msesh_reseed": all_msesh_reseed, + "aicsv_reseed": all_aicsv_reseed, + "masesv_reseed": all_masesv_reseed, + "msesv_reseed": all_msesv_reseed, + "n_delays": n_delays, + "ranks": ranks, + } + def plot_sweep_results_all_error_types( return_dict, name=None, save_path=None, figsize=(2, 4), - xscale='log', - aic_scale='symlog', - mase_scale = 'log', + xscale="log", + aic_scale="symlog", + mase_scale="log", plot_herror=False, new_plot_reseeds=False, cmap="gist_gray", - metrics_order=['AIC','MASE','MSE'], - pretty_yticks=False + metrics_order=["AIC", "MASE", "MSE"], + pretty_yticks=False, ): """ Plot all error types from sweep_ranks_delays_all_error_types as a 3 x (3*len(reseeds)) grid, @@ -343,70 +395,82 @@ def plot_sweep_results_all_error_types( If 'by_metric', columns are [aics[0], aics[1], ..., mases[0], mases[1], ..., mses[0], mses[1], ...] (grouped by metric). plot_herror : bool new_plot_reseeds : bool - If True, plot the reseeds in a new plot, with the same number of columns + If True, plot the reseeds in a new plot, with the same number of columns """ fig_axes = [] if new_plot_reseeds: return_dict_new = {} return_dict_plot = {} - for k,v in return_dict.items(): - if (isinstance(v,np.ndarray) and v.size == 0) or (isinstance(v,list) and len(v) == 0): + for k, v in return_dict.items(): + if (isinstance(v, np.ndarray) and v.size == 0) or ( + isinstance(v, list) and len(v) == 0 + ): return_dict_new[k] = [] return [] - elif k in ['n_delays','ranks']: + elif k in ["n_delays", "ranks"]: return_dict_new[k] = v return_dict_plot[k] = v else: return_dict_new[k] = v[1:] return_dict_plot[k] = v[0:1] - fig_axes = plot_sweep_results_all_error_types(return_dict_new,name=name, - save_path=save_path, - figsize=figsize,xscale=xscale, - aic_scale=aic_scale, - plot_herror=plot_herror, - new_plot_reseeds=new_plot_reseeds, - metrics_order=metrics_order) + fig_axes = plot_sweep_results_all_error_types( + return_dict_new, + name=name, + save_path=save_path, + figsize=figsize, + xscale=xscale, + aic_scale=aic_scale, + plot_herror=plot_herror, + new_plot_reseeds=new_plot_reseeds, + metrics_order=metrics_order, + ) return_dict = return_dict_plot - reseeds = return_dict['reseeds'] - n_delays = return_dict['n_delays'] - ranks = return_dict['ranks'] - all_aicsx_reseed = return_dict['aicsx_reseed'] - all_masesx_reseed = return_dict['masesx_reseed'] - all_msesx_reseed = return_dict['msesx_reseed'] - all_aicsh_reseed = return_dict['aicsh_reseed'] - all_masesh_reseed = return_dict['masesh_reseed'] - all_msesh_reseed = return_dict['msesh_reseed'] - all_aicsv_reseed = return_dict['aicsv_reseed'] - all_masesv_reseed = return_dict['masesv_reseed'] - all_msesv_reseed = return_dict['msesv_reseed'] + reseeds = return_dict["reseeds"] + n_delays = return_dict["n_delays"] + ranks = return_dict["ranks"] + all_aicsx_reseed = return_dict["aicsx_reseed"] + all_masesx_reseed = return_dict["masesx_reseed"] + all_msesx_reseed = return_dict["msesx_reseed"] + all_aicsh_reseed = return_dict["aicsh_reseed"] + all_masesh_reseed = return_dict["masesh_reseed"] + all_msesh_reseed = return_dict["msesh_reseed"] + all_aicsv_reseed = return_dict["aicsv_reseed"] + all_masesv_reseed = return_dict["masesv_reseed"] + all_msesv_reseed = return_dict["msesv_reseed"] n_reseeds = len(reseeds) - metrics = ['AIC', 'MASE', 'MSE'] - spaces = ['X', 'V'] if not plot_herror else ['X', 'H', 'V'] + metrics = ["AIC", "MASE", "MSE"] + spaces = ["X", "V"] if not plot_herror else ["X", "H", "V"] data_arrays = [ - [all_aicsx_reseed, all_aicsv_reseed] + ([all_aicsh_reseed] if plot_herror else []), - [all_masesx_reseed, all_masesv_reseed] + ([all_masesh_reseed] if plot_herror else []), - [all_msesx_reseed, all_msesv_reseed] + ([all_msesh_reseed] if plot_herror else []) + [all_aicsx_reseed, all_aicsv_reseed] + + ([all_aicsh_reseed] if plot_herror else []), + [all_masesx_reseed, all_masesv_reseed] + + ([all_masesh_reseed] if plot_herror else []), + [all_msesx_reseed, all_msesv_reseed] + + ([all_msesh_reseed] if plot_herror else []), ] data_arrays = [data_arrays[metrics.index(metric)] for metric in metrics_order] metrics = metrics_order - - cmap = plt.cm.get_cmap(cmap) if isinstance(cmap,str) else cmap + cmap = plt.cm.get_cmap(cmap) if isinstance(cmap, str) else cmap figs_axes = [] for space_idx, space in enumerate(spaces): # Each plot: rows = metrics, cols = reseeds (transpose from original) fig, axes = plt.subplots( - len(metrics), n_reseeds, figsize=(figsize[0]*n_reseeds, figsize[1]), sharex=True, sharey='row' + len(metrics), + n_reseeds, + figsize=(figsize[0] * n_reseeds, figsize[1]), + sharex=True, + sharey="row", ) if len(reseeds) == 1: if len(metrics) == 1: axes = [axes] else: - axes = axes.reshape(-1,1) + axes = axes.reshape(-1, 1) if len(metrics) == 1: axes = [axes] - + # For storing y-limits for each metric row row_ymins = [np.inf] * len(metrics) row_ymaxs = [-np.inf] * len(metrics) @@ -416,20 +480,33 @@ def plot_sweep_results_all_error_types( ax = axes[metric_idx][reseed_idx] for nd_idx, nd in enumerate(n_delays): y = arr[reseed_idx, nd_idx] - ax.plot(ranks, y, label=f"{nd}", color=cmap(nd_idx / (len(n_delays) + 3))) + ax.plot( + ranks, + y, + label=f"{nd}", + color=cmap(nd_idx / (len(n_delays) + 3)), + ) # Update min/max for this row, ignoring nan valid_y = np.asarray(y) valid_y = valid_y[np.isfinite(valid_y)] if valid_y.size > 0: - row_ymins[metric_idx] = min(row_ymins[metric_idx], np.nanmin(valid_y)) - row_ymaxs[metric_idx] = max(row_ymaxs[metric_idx], np.nanmax(valid_y)) + row_ymins[metric_idx] = min( + row_ymins[metric_idx], np.nanmin(valid_y) + ) + row_ymaxs[metric_idx] = max( + row_ymaxs[metric_idx], np.nanmax(valid_y) + ) if metric == "MASE": ax.axhline(1, color="black", linestyle="--", linewidth=0.7) - if metric in {"MASE", "MSE"} and mase_scale in {'symlog','log','linear'}: + if metric in {"MASE", "MSE"} and mase_scale in { + "symlog", + "log", + "linear", + }: ax.set_yscale(mase_scale) - if aic_scale in {'symlog','log','linear'} and metric == "AIC": + if aic_scale in {"symlog", "log", "linear"} and metric == "AIC": ax.set_yscale(aic_scale) - if xscale == 'log': + if xscale == "log": ax.set_xscale("log") if reseed_idx == 0: ax.set_ylabel(f"{space} {metric}", fontsize=10) @@ -442,13 +519,27 @@ def plot_sweep_results_all_error_types( ax.spines["top"].set_visible(False) ax.spines["right"].set_visible(False) axes[-1][reseed_idx].set_xlabel("Rank") - if metric_idx == 0 and reseed_idx == len(reseeds)-1: - ax.legend(title='# delays', fontsize=12, loc='upper right', bbox_to_anchor=(2.3, 1.2), borderaxespad=1) + if metric_idx == 0 and reseed_idx == len(reseeds) - 1: + ax.legend( + title="# delays", + fontsize=12, + loc="upper right", + bbox_to_anchor=(2.3, 1.2), + borderaxespad=1, + ) # Set yticks for each row to be the min and max (rounded) of all the plots on that row for metric_idx in range(len(metrics)): - ymin = 0.75*row_ymins[metric_idx] if row_ymins[metric_idx] > 0 else 1.25*row_ymins[metric_idx] - ymax = 1.25*row_ymaxs[metric_idx] if row_ymaxs[metric_idx] > 0 else 0.75*row_ymaxs[metric_idx] + ymin = ( + 0.75 * row_ymins[metric_idx] + if row_ymins[metric_idx] > 0 + else 1.25 * row_ymins[metric_idx] + ) + ymax = ( + 1.25 * row_ymaxs[metric_idx] + if row_ymaxs[metric_idx] > 0 + else 0.75 * row_ymaxs[metric_idx] + ) for reseed_idx in range(n_reseeds): ax = axes[metric_idx][reseed_idx] # Remove all yticks and labels before setting new ones @@ -466,11 +557,13 @@ def plot_sweep_results_all_error_types( ticklabels = [f"{ymin:.2g}", f"{ymax:.2g}"] ax.set_yticklabels(ticklabels) - plt.suptitle(f"{name + '_' if name else ''}{space} tuning", fontsize=14,y=1.05) - plt.tight_layout() #rect=[0, 0, 1, 0.97]) + plt.suptitle(f"{name + '_' if name else ''}{space} tuning", fontsize=14, y=1.05) + plt.tight_layout() # rect=[0, 0, 1, 0.97]) if save_path is not None: - plt.savefig(f"{save_path}_{space}_metrics_{metrics_order}_reseeds{reseeds}.pdf") + plt.savefig( + f"{save_path}_{space}_metrics_{metrics_order}_reseeds{reseeds}.pdf" + ) # plt.close() figs_axes.append((fig, axes)) - return figs_axes + fig_axes \ No newline at end of file + return figs_axes + fig_axes diff --git a/setup.py b/setup.py index 0e1eff1..9d8f9e0 100644 --- a/setup.py +++ b/setup.py @@ -2,24 +2,18 @@ setuptools.setup( name="dsa-metric", - version="1.0.2", + version="2.0.0", url="https://github.com/mitchellostrow/DSA", - author="Mitchell Ostrow", author_email="ostrow@mit.edu", - description="Dynamical Similarity Analysis", packages=setuptools.find_packages(), install_requires=[ - 'numpy>=1.24.0,<2', - 'torch>=1.3.0', - 'kooplearn>=1.1.0', - 'pot', - 'omegaconf', + "numpy>=1.24.0,<2", + "torch>=1.3.0", + "pot", + "omegaconf", + "pydmd", ], - extras_require={ - 'dev': [ - 'pytest>=3.7' - ] - }, + extras_require={"dev": ["pytest>=3.7"]}, ) diff --git a/tests/dmd_test.py b/tests/dmd_test.py index 0c38a41..d1c28b5 100644 --- a/tests/dmd_test.py +++ b/tests/dmd_test.py @@ -3,99 +3,105 @@ from DSA.dmd import DMD, embed_signal_torch from scipy.stats import ortho_group import torch + TOL = 1e-2 -@pytest.mark.parametrize('delay_interval', [1,2]) -@pytest.mark.parametrize('n_delays', [20]) -@pytest.mark.parametrize('c', [10]) -@pytest.mark.parametrize('t', [100]) -@pytest.mark.parametrize('seed', [21]) -def test_embed_3dvs2d(seed,t,c,n_delays,delay_interval): - #test to make sure 3d and 2d are effectively doing the same thing + +@pytest.mark.parametrize("delay_interval", [1, 2]) +@pytest.mark.parametrize("n_delays", [20]) +@pytest.mark.parametrize("c", [10]) +@pytest.mark.parametrize("t", [100]) +@pytest.mark.parametrize("seed", [21]) +def test_embed_3dvs2d(seed, t, c, n_delays, delay_interval): + # test to make sure 3d and 2d are effectively doing the same thing n = 3 - rng = np.random.default_rng(seed) - data = torch.from_numpy(rng.random((n,t,c))) - embed1 = embed_signal_torch(data,n_delays,delay_interval) - embed2s = [embed_signal_torch(data[i],n_delays,delay_interval) for i in range(n)] - assert np.allclose(embed1[0],embed2s[0],atol=TOL) - assert np.allclose(embed1[1],embed2s[1],atol=TOL) - assert np.allclose(embed1[2],embed2s[2],atol=TOL) + rng = np.random.default_rng(seed) + data = torch.from_numpy(rng.random((n, t, c))) + embed1 = embed_signal_torch(data, n_delays, delay_interval) + embed2s = [embed_signal_torch(data[i], n_delays, delay_interval) for i in range(n)] + assert np.allclose(embed1[0], embed2s[0], atol=TOL) + assert np.allclose(embed1[1], embed2s[1], atol=TOL) + assert np.allclose(embed1[2], embed2s[2], atol=TOL) -@pytest.mark.parametrize('c', [10]) -@pytest.mark.parametrize('t', [100]) -@pytest.mark.parametrize('n', [50]) -@pytest.mark.parametrize('seed', [21]) -def test_embed_1delay(seed,n,t,c): - rng = np.random.default_rng(seed) - data = torch.from_numpy(rng.random((n,t,c))) - embed = embed_signal_torch(data,1) - embed1 = embed_signal_torch(data[0],1) - dmd = DMD(data,1) + +@pytest.mark.parametrize("c", [10]) +@pytest.mark.parametrize("t", [100]) +@pytest.mark.parametrize("n", [50]) +@pytest.mark.parametrize("seed", [21]) +def test_embed_1delay(seed, n, t, c): + rng = np.random.default_rng(seed) + data = torch.from_numpy(rng.random((n, t, c))) + embed = embed_signal_torch(data, 1) + embed1 = embed_signal_torch(data[0], 1) + dmd = DMD(data, 1) dmd.compute_hankel() - assert np.allclose(embed,data,atol=TOL) - assert np.allclose(dmd.H,data,atol=TOL) - assert np.allclose(embed1,data[0],atol=TOL) + assert np.allclose(embed, data, atol=TOL) + assert np.allclose(dmd.H, data, atol=TOL) + assert np.allclose(embed1, data[0], atol=TOL) + -@pytest.mark.parametrize('rank', [10,50,250]) -@pytest.mark.parametrize('n_delays', [1,20]) -@pytest.mark.parametrize('c', [10]) -@pytest.mark.parametrize('t', [500]) -@pytest.mark.parametrize('n', [50]) -@pytest.mark.parametrize('seed', [21]) -def test_dmd_rank(seed,n,t,c,n_delays,rank): - rng = np.random.default_rng(seed) - X = rng.random((n,t,c)) - dmd = DMD(X,n_delays,rank=rank) +@pytest.mark.parametrize("rank", [10, 50, 250]) +@pytest.mark.parametrize("n_delays", [1, 20]) +@pytest.mark.parametrize("c", [10]) +@pytest.mark.parametrize("t", [500]) +@pytest.mark.parametrize("n", [50]) +@pytest.mark.parametrize("seed", [21]) +def test_dmd_rank(seed, n, t, c, n_delays, rank): + rng = np.random.default_rng(seed) + X = rng.random((n, t, c)) + dmd = DMD(X, n_delays, rank=rank) dmd.fit() - rank = min(rank,n_delays*c) - assert dmd.A_v.shape == (rank,rank) + rank = min(rank, n_delays * c) + assert dmd.A_v.shape == (rank, rank) + -@pytest.mark.parametrize('tau', [0.01]) -@pytest.mark.parametrize('t', [1000]) -@pytest.mark.parametrize('c', [5]) -@pytest.mark.parametrize('seed', [21]) -def test_dmd_2d(seed,c,t,tau): +@pytest.mark.parametrize("tau", [0.01]) +@pytest.mark.parametrize("t", [1000]) +@pytest.mark.parametrize("c", [5]) +@pytest.mark.parametrize("seed", [21]) +def test_dmd_2d(seed, c, t, tau): rng = np.random.default_rng(seed) x0 = rng.random((c)) - data = np.zeros((t,c)) + data = np.zeros((t, c)) data[0] = x0 Q = ortho_group.rvs(c) - A = np.eye(c) + tau*Q #\dot{x} = Qx -> x_t+1 ~= x + \tauQx - for i in range(1,t): - data[i] = A @ data[i-1] - dmd = DMD(data,1) + A = np.eye(c) + tau * Q # \dot{x} = Qx -> x_t+1 ~= x + \tauQx + for i in range(1, t): + data[i] = A @ data[i - 1] + dmd = DMD(data, 1) dmd.fit() assert np.linalg.norm(dmd.A_v.flatten() - A.flatten()) < 1e-1 -@pytest.mark.parametrize('n', [500]) -@pytest.mark.parametrize('t', [1000]) -@pytest.mark.parametrize('c', [3]) -@pytest.mark.parametrize('tau', [0.01]) -@pytest.mark.parametrize('seed', [21]) -def test_dmd_3d(seed,n,t,c,tau): + +@pytest.mark.parametrize("n", [500]) +@pytest.mark.parametrize("t", [1000]) +@pytest.mark.parametrize("c", [3]) +@pytest.mark.parametrize("tau", [0.01]) +@pytest.mark.parametrize("seed", [21]) +def test_dmd_3d(seed, n, t, c, tau): rng = np.random.default_rng(seed) - x0 = rng.random((n,c)) - data = np.zeros((n,t,c)) - data[:,0] = x0 + x0 = rng.random((n, c)) + data = np.zeros((n, t, c)) + data[:, 0] = x0 Q = ortho_group.rvs(c) - A = np.eye(c) + tau*Q - #\dot{x} = Qx -> x_t+1 ~= x + \tauQx - for i in range(1,t): - data[:,i] = np.einsum('nn,cn->cn',A,data[:,i-1]) - dmd = DMD(data,1) + A = np.eye(c) + tau * Q + # \dot{x} = Qx -> x_t+1 ~= x + \tauQx + for i in range(1, t): + data[:, i] = np.einsum("nn,cn->cn", A, data[:, i - 1]) + dmd = DMD(data, 1) dmd.fit() - assert np.linalg.norm(dmd.A_v.flatten()-A.flatten()) < 1e-1 + assert np.linalg.norm(dmd.A_v.flatten() - A.flatten()) < 1e-1 -@pytest.mark.parametrize('c', [10]) -@pytest.mark.parametrize('t', [100]) -@pytest.mark.parametrize('n', [50]) -@pytest.mark.parametrize('seed', [21]) -def test_to_cpu(seed,n,t,c): - rng = np.random.default_rng(seed) - X = rng.random((n,t,c)) - device = 'cuda' if torch.cuda.is_available() else 'cpu' - dmd = DMD(X,1,device=device) +@pytest.mark.parametrize("c", [10]) +@pytest.mark.parametrize("t", [100]) +@pytest.mark.parametrize("n", [50]) +@pytest.mark.parametrize("seed", [21]) +def test_to_cpu(seed, n, t, c): + rng = np.random.default_rng(seed) + X = rng.random((n, t, c)) + device = "cuda" if torch.cuda.is_available() else "cpu" + dmd = DMD(X, 1, device=device) dmd.fit(send_to_cpu=True) - assert dmd.A_v.device.type == 'cpu' - assert dmd.H.device.type == 'cpu' + assert dmd.A_v.device.type == "cpu" + assert dmd.H.device.type == "cpu" diff --git a/tests/dsa_test.py b/tests/dsa_test.py index ec05bea..74c1ea2 100644 --- a/tests/dsa_test.py +++ b/tests/dsa_test.py @@ -3,131 +3,142 @@ from DSA import DSA from scipy.stats import ortho_group -@pytest.mark.parametrize('seed', [5]) -@pytest.mark.parametrize('n1', [2]) -@pytest.mark.parametrize('n2', [10]) -@pytest.mark.parametrize('t1', [1000]) -@pytest.mark.parametrize('t2', [1000]) -@pytest.mark.parametrize('c1', [5]) -@pytest.mark.parametrize('c2', [10]) #only these really need to be different -@pytest.mark.parametrize('rank1', [5]) -@pytest.mark.parametrize('rank2', [5,10]) -def test_different_dims(seed,n1,n2,t1,t2,c1,c2,rank1,rank2): - rng = np.random.default_rng(seed) - X = rng.random((n1,t1,c1)) - Y = rng.random((n2,t2,c2)) - dsa = DSA(X,Y,rank=(rank1,rank2),n_delays=10) + +@pytest.mark.parametrize("seed", [5]) +@pytest.mark.parametrize("n1", [2]) +@pytest.mark.parametrize("n2", [10]) +@pytest.mark.parametrize("t1", [1000]) +@pytest.mark.parametrize("t2", [1000]) +@pytest.mark.parametrize("c1", [5]) +@pytest.mark.parametrize("c2", [10]) # only these really need to be different +@pytest.mark.parametrize("rank1", [5]) +@pytest.mark.parametrize("rank2", [5, 10]) +def test_different_dims(seed, n1, n2, t1, t2, c1, c2, rank1, rank2): + rng = np.random.default_rng(seed) + X = rng.random((n1, t1, c1)) + Y = rng.random((n2, t2, c2)) + dsa = DSA(X, Y, rank=(rank1, rank2), n_delays=10) sim = dsa.fit_score() - assert dsa.dmds[0][0].A_v.shape == (rank1,rank1) - assert dsa.dmds[1][0].A_v.shape == (rank2,rank2) - -@pytest.mark.parametrize('seed', [5]) -@pytest.mark.parametrize('n_delays', [10,[[10,20,30],[5,10,15]]]) #only need to test 1 param -def test_param_broadcasting_1(seed,n_delays): - rng = np.random.default_rng(seed) - d1 = rng.random((100,50,10)) - dsa = DSA(d1,d1,n_delays=n_delays) - if isinstance(n_delays,list): + assert dsa.dmds[0][0].A_v.shape == (rank1, rank1) + assert dsa.dmds[1][0].A_v.shape == (rank2, rank2) + + +@pytest.mark.parametrize("seed", [5]) +@pytest.mark.parametrize( + "n_delays", [10, [[10, 20, 30], [5, 10, 15]]] +) # only need to test 1 param +def test_param_broadcasting_1(seed, n_delays): + rng = np.random.default_rng(seed) + d1 = rng.random((100, 50, 10)) + dsa = DSA(d1, d1, n_delays=n_delays) + if isinstance(n_delays, list): delay1 = n_delays[0][0] delay2 = n_delays[1][0] else: - delay1,delay2 = n_delays,n_delays + delay1, delay2 = n_delays, n_delays assert dsa.dmds[0][0].n_delays == delay1 assert dsa.dmds[1][0].n_delays == delay2 assert len(dsa.dmds) == 2 assert len(dsa.dmds[0]) == 1 -@pytest.mark.parametrize('n', [2,5]) -@pytest.mark.parametrize('seed', [5]) -@pytest.mark.parametrize('n_delays', [10,[10,20,30,40,50]]) -def test_param_broadcasting_list(seed,n,n_delays): - rng = np.random.default_rng(seed) - ds = [rng.random((100,50,10)) for i in range(n)] - dsa = DSA(ds,n_delays=n_delays) + +@pytest.mark.parametrize("n", [2, 5]) +@pytest.mark.parametrize("seed", [5]) +@pytest.mark.parametrize("n_delays", [10, [10, 20, 30, 40, 50]]) +def test_param_broadcasting_list(seed, n, n_delays): + rng = np.random.default_rng(seed) + ds = [rng.random((100, 50, 10)) for i in range(n)] + dsa = DSA(ds, n_delays=n_delays) for i in range(n): - if isinstance(n_delays,int): + if isinstance(n_delays, int): assert dsa.dmds[0][i].n_delays == n_delays else: assert dsa.dmds[0][i].n_delays == n_delays[i] assert len(dsa.dmds[0]) == n -@pytest.mark.parametrize('n1', [2,5]) -@pytest.mark.parametrize('n2', [3,4]) -@pytest.mark.parametrize('seed', [5]) -@pytest.mark.parametrize('n_delays1', [10,[10,20,30,40,50]]) -@pytest.mark.parametrize('n_delays2', [11,[11,21,31,41,51]]) -def test_param_broadcasting_2lists(seed,n1,n2,n_delays1,n_delays2): - rng = np.random.default_rng(seed) - ds1 = [rng.random((100,50,10)) for i in range(n1)] - ds2 = [rng.random((100,50,10)) for i in range(n2)] - dsa = DSA(ds1,ds2,n_delays=(n_delays1,n_delays2)) - itrable = zip([n1,n2],[n_delays1,n_delays2]) - for j,(n,delays) in enumerate(itrable): + +@pytest.mark.parametrize("n1", [2, 5]) +@pytest.mark.parametrize("n2", [3, 4]) +@pytest.mark.parametrize("seed", [5]) +@pytest.mark.parametrize("n_delays1", [10, [10, 20, 30, 40, 50]]) +@pytest.mark.parametrize("n_delays2", [11, [11, 21, 31, 41, 51]]) +def test_param_broadcasting_2lists(seed, n1, n2, n_delays1, n_delays2): + rng = np.random.default_rng(seed) + ds1 = [rng.random((100, 50, 10)) for i in range(n1)] + ds2 = [rng.random((100, 50, 10)) for i in range(n2)] + dsa = DSA(ds1, ds2, n_delays=(n_delays1, n_delays2)) + itrable = zip([n1, n2], [n_delays1, n_delays2]) + for j, (n, delays) in enumerate(itrable): for i in range(n): - if isinstance(delays,int): + if isinstance(delays, int): assert dsa.dmds[j][i].n_delays == delays else: assert dsa.dmds[j][i].n_delays == delays[i] assert len(dsa.dmds[0]) == n1 assert len(dsa.dmds[1]) == n2 + # def test_multiple_param_variations(seed,n,n_delays,rank): -# rng = np.random.default_rng(seed) +# rng = np.random.default_rng(seed) # ds = [rng.random((100,50,10)) for i in range(n)] # dsa = DSA(ds,n_delays=n_delays) - -@pytest.mark.parametrize('n', [10]) -@pytest.mark.parametrize('c', [2]) -@pytest.mark.parametrize('t', [100]) -@pytest.mark.parametrize('seed', [5]) -def test_dsa_1to1(n,t,c,seed): - rng = np.random.default_rng(seed) - X = rng.random((n,t,c)) - Y = rng.random((n,t,c)) - dsa = DSA(X,Y) + + +@pytest.mark.parametrize("n", [10]) +@pytest.mark.parametrize("c", [2]) +@pytest.mark.parametrize("t", [100]) +@pytest.mark.parametrize("seed", [5]) +def test_dsa_1to1(n, t, c, seed): + rng = np.random.default_rng(seed) + X = rng.random((n, t, c)) + Y = rng.random((n, t, c)) + dsa = DSA(X, Y) sim = dsa.fit_score() - assert isinstance(sim,float) - -@pytest.mark.parametrize('n', [10]) -@pytest.mark.parametrize('c', [2]) -@pytest.mark.parametrize('t', [100]) -@pytest.mark.parametrize('seed', [5]) -@pytest.mark.parametrize('nmodels', [10]) -def test_dsa_1tomany(n,t,c,seed,nmodels): - rng = np.random.default_rng(seed) - X = [rng.random((n,t,c)) for i in range(nmodels)] - Y = rng.random((n,t,c)) - dsa = DSA(X,Y) + assert isinstance(sim, float) + + +@pytest.mark.parametrize("n", [10]) +@pytest.mark.parametrize("c", [2]) +@pytest.mark.parametrize("t", [100]) +@pytest.mark.parametrize("seed", [5]) +@pytest.mark.parametrize("nmodels", [10]) +def test_dsa_1tomany(n, t, c, seed, nmodels): + rng = np.random.default_rng(seed) + X = [rng.random((n, t, c)) for i in range(nmodels)] + Y = rng.random((n, t, c)) + dsa = DSA(X, Y) sim = dsa.fit_score() - assert isinstance(sim,np.ndarray) - assert sim.shape == (nmodels,1) - -@pytest.mark.parametrize('n', [10]) -@pytest.mark.parametrize('c', [2]) -@pytest.mark.parametrize('t', [100]) -@pytest.mark.parametrize('seed', [5]) -@pytest.mark.parametrize('nmodels', [10]) -def test_dsa_manyto1(n,t,c,seed,nmodels): - rng = np.random.default_rng(seed) - X = [rng.random((n,t,c)) for i in range(nmodels)] - Y = rng.random((n,t,c)) - dsa = DSA(Y,X) + assert isinstance(sim, np.ndarray) + assert sim.shape == (nmodels, 1) + + +@pytest.mark.parametrize("n", [10]) +@pytest.mark.parametrize("c", [2]) +@pytest.mark.parametrize("t", [100]) +@pytest.mark.parametrize("seed", [5]) +@pytest.mark.parametrize("nmodels", [10]) +def test_dsa_manyto1(n, t, c, seed, nmodels): + rng = np.random.default_rng(seed) + X = [rng.random((n, t, c)) for i in range(nmodels)] + Y = rng.random((n, t, c)) + dsa = DSA(Y, X) sim = dsa.fit_score() - assert isinstance(sim,np.ndarray) - assert sim.shape == (1,nmodels) - -@pytest.mark.parametrize('n', [10]) -@pytest.mark.parametrize('c', [2]) -@pytest.mark.parametrize('t', [100]) -@pytest.mark.parametrize('seed', [5]) -@pytest.mark.parametrize('nmodels1', [2]) -@pytest.mark.parametrize('nmodels2', [2]) -def test_dsa_manytomany(n,t,c,seed,nmodels1,nmodels2): - rng = np.random.default_rng(seed) - X = [rng.random((n,t,c)) for i in range(nmodels1)] - Y = [rng.random((n,t,c)) for i in range(nmodels2)] - dsa = DSA(X,Y) + assert isinstance(sim, np.ndarray) + assert sim.shape == (1, nmodels) + + +@pytest.mark.parametrize("n", [10]) +@pytest.mark.parametrize("c", [2]) +@pytest.mark.parametrize("t", [100]) +@pytest.mark.parametrize("seed", [5]) +@pytest.mark.parametrize("nmodels1", [2]) +@pytest.mark.parametrize("nmodels2", [2]) +def test_dsa_manytomany(n, t, c, seed, nmodels1, nmodels2): + rng = np.random.default_rng(seed) + X = [rng.random((n, t, c)) for i in range(nmodels1)] + Y = [rng.random((n, t, c)) for i in range(nmodels2)] + dsa = DSA(X, Y) sim = dsa.fit_score() print(sim.shape) - assert isinstance(sim,np.ndarray) - assert sim.shape == (nmodels1,nmodels2) + assert isinstance(sim, np.ndarray) + assert sim.shape == (nmodels1, nmodels2) diff --git a/tests/simdist_test.py b/tests/simdist_test.py index 9647c01..84d4234 100644 --- a/tests/simdist_test.py +++ b/tests/simdist_test.py @@ -1,6 +1,6 @@ import pytest import numpy as np -from DSA.simdist import SimilarityTransformDist,pad_zeros +from DSA.simdist import SimilarityTransformDist, pad_zeros from scipy.stats import special_ortho_group, ortho_group import torch from netrep.utils import whiten @@ -8,91 +8,99 @@ TOL = 1e-3 SIMTOL = 2e-2 -@pytest.mark.parametrize('device',['cpu']) -@pytest.mark.parametrize('preserve_var',[True,False]) -@pytest.mark.parametrize('dtype',['numpy']) -@pytest.mark.parametrize('score_method',['angular','euclidean']) -@pytest.mark.parametrize('n', [10,50,100]) -@pytest.mark.parametrize('group',['GL(n)','O(n)','SO(n)']) -@pytest.mark.parametrize('seed', [5]) -def test_simdist_convergent(seed,n,score_method,dtype,preserve_var,group,device): - rng = np.random.default_rng(seed) - X = rng.random(size=(n,n)) - if group == 'SO(n)': - Q = special_ortho_group(seed=rng,dim=n).rvs() + +@pytest.mark.parametrize("device", ["cpu"]) +@pytest.mark.parametrize("preserve_var", [True, False]) +@pytest.mark.parametrize("dtype", ["numpy"]) +@pytest.mark.parametrize("score_method", ["angular", "euclidean"]) +@pytest.mark.parametrize("n", [10, 50, 100]) +@pytest.mark.parametrize("group", ["GL(n)", "O(n)", "SO(n)"]) +@pytest.mark.parametrize("seed", [5]) +def test_simdist_convergent(seed, n, score_method, dtype, preserve_var, group, device): + rng = np.random.default_rng(seed) + X = rng.random(size=(n, n)) + if group == "SO(n)": + Q = special_ortho_group(seed=rng, dim=n).rvs() Y = Q @ X @ Q.T iters = 5000 - elif group == 'O(n)': - Q = ortho_group(seed=rng,dim=n).rvs() + elif group == "O(n)": + Q = ortho_group(seed=rng, dim=n).rvs() while np.linalg.det(Q) > 0: - Q = ortho_group(seed=rng,dim=n).rvs() + Q = ortho_group(seed=rng, dim=n).rvs() Y = Q @ X @ Q.T iters = 5000 - elif group == 'GL(n)': - #draw random invertible matrix - Q = rng.random(size=(n,n)) - Q /= np.linalg.norm(Q,axis=0) + elif group == "GL(n)": + # draw random invertible matrix + Q = rng.random(size=(n, n)) + Q /= np.linalg.norm(Q, axis=0) Y = Q @ X @ np.linalg.inv(Q) iters = 80_000 - X,_ = whiten(X,0,preserve_variance=preserve_var) - Y,_ = whiten(Y,0,preserve_variance=preserve_var) - #excessive but we just want to see that it converges - sim = SimilarityTransformDist(lr=1e-2,iters=iters,score_method=score_method,device=device,group=group) - if dtype == 'torch': + X, _ = whiten(X, 0, preserve_variance=preserve_var) + Y, _ = whiten(Y, 0, preserve_variance=preserve_var) + # excessive but we just want to see that it converges + sim = SimilarityTransformDist( + lr=1e-2, iters=iters, score_method=score_method, device=device, group=group + ) + if dtype == "torch": X = torch.tensor(X).float() Y = torch.tensor(Y).float() - score = sim.fit_score(X,Y) + score = sim.fit_score(X, Y) print(score) assert score < SIMTOL -@pytest.mark.parametrize('device',['cpu']) -@pytest.mark.parametrize('dtype',['numpy']) -@pytest.mark.parametrize('score_method',['angular','euclidean']) -@pytest.mark.parametrize('n', [10,50,100]) -@pytest.mark.parametrize('seed', [5]) -def test_transposed_q_same(seed,n,score_method,dtype,device): - rng = np.random.default_rng(seed) - X = rng.random(size=(n,n)) * 2 - 1 - Q = special_ortho_group(seed=rng,dim=n).rvs() + +@pytest.mark.parametrize("device", ["cpu"]) +@pytest.mark.parametrize("dtype", ["numpy"]) +@pytest.mark.parametrize("score_method", ["angular", "euclidean"]) +@pytest.mark.parametrize("n", [10, 50, 100]) +@pytest.mark.parametrize("seed", [5]) +def test_transposed_q_same(seed, n, score_method, dtype, device): + rng = np.random.default_rng(seed) + X = rng.random(size=(n, n)) * 2 - 1 + Q = special_ortho_group(seed=rng, dim=n).rvs() Y1 = Q @ X @ Q.T Y2 = Q.T @ X @ Q - #excessive but we just want to see that it converges - sim = SimilarityTransformDist(lr=5e-3,iters=15000,score_method=score_method,device=device) - if dtype == 'torch': + # excessive but we just want to see that it converges + sim = SimilarityTransformDist( + lr=5e-3, iters=15000, score_method=score_method, device=device + ) + if dtype == "torch": X = torch.tensor(X).float() Y1 = torch.tensor(Y1).float() Y2 = torch.tensor(Y2).float() - score1 = sim.fit_score(X,Y1) - score2 = sim.fit_score(X,Y2) - print(n,score_method,score1) - print(n,score_method,score2) + score1 = sim.fit_score(X, Y1) + score2 = sim.fit_score(X, Y2) + print(n, score_method, score1) + print(n, score_method, score2) assert np.abs(score1 - score2) < SIMTOL -@pytest.mark.parametrize('n2', [10]) -@pytest.mark.parametrize('n1', [50]) -@pytest.mark.parametrize('seed', [5]) -def test_zero_pad(seed,n1,n2): - rng = np.random.default_rng(seed) - X = rng.random(size=(n1,n1)) - Y = rng.random(size=(n2,n2)) - m = max(n1,n2) - sim = SimilarityTransformDist(iters=10) #don't care about fitting - sim.fit_score(X,Y,zero_pad=True) - assert sim.C_star.shape == (m,m) - assert pad_zeros(X,Y,'cpu')[0].shape == (m,m) - assert pad_zeros(X,Y,'cpu')[1].shape == (m,m) -@pytest.mark.parametrize('n', [10]) -@pytest.mark.parametrize('seed', [5]) -def test_ortho_c(seed,n): - rng = np.random.default_rng(seed) - X = rng.random(size=(n,n)) - Q = special_ortho_group(seed=rng,dim=n).rvs() +@pytest.mark.parametrize("n2", [10]) +@pytest.mark.parametrize("n1", [50]) +@pytest.mark.parametrize("seed", [5]) +def test_zero_pad(seed, n1, n2): + rng = np.random.default_rng(seed) + X = rng.random(size=(n1, n1)) + Y = rng.random(size=(n2, n2)) + m = max(n1, n2) + sim = SimilarityTransformDist(iters=10) # don't care about fitting + sim.fit_score(X, Y, zero_pad=True) + assert sim.C_star.shape == (m, m) + assert pad_zeros(X, Y, "cpu")[0].shape == (m, m) + assert pad_zeros(X, Y, "cpu")[1].shape == (m, m) + + +@pytest.mark.parametrize("n", [10]) +@pytest.mark.parametrize("seed", [5]) +def test_ortho_c(seed, n): + rng = np.random.default_rng(seed) + X = rng.random(size=(n, n)) + Q = special_ortho_group(seed=rng, dim=n).rvs() Y = Q @ X @ Q.T - sim = SimilarityTransformDist(lr=1e-2,iters=5000) - score = sim.fit_score(X,Y) + sim = SimilarityTransformDist(lr=1e-2, iters=5000) + score = sim.fit_score(X, Y) C = sim.C_star - assert np.allclose(C.T @ C, np.eye(n),atol=TOL) - assert np.allclose(C @ C.T, np.eye(n),atol=TOL) + assert np.allclose(C.T @ C, np.eye(n), atol=TOL) + assert np.allclose(C @ C.T, np.eye(n), atol=TOL) From 1a9dd034f11af23c338ae7561135aaa1c6fdb48e Mon Sep 17 00:00:00 2001 From: ostrow Date: Tue, 28 Oct 2025 15:35:27 -0400 Subject: [PATCH 15/90] bug fixes --- DSA/dsa.py | 17 ++++++++++------- DSA/simdist.py | 6 +----- DSA/simdist_controllability.py | 11 ++++------- DSA/subspace_dmdc.py | 5 ++++- README.md | 1 + pyproject.toml | 8 ++++++-- setup.py | 5 +++++ 7 files changed, 31 insertions(+), 22 deletions(-) diff --git a/DSA/dsa.py b/DSA/dsa.py index b96eb93..3796065 100644 --- a/DSA/dsa.py +++ b/DSA/dsa.py @@ -676,10 +676,9 @@ def __init__( dsa_verbose=False, n_jobs=1, # simdist parameters - score_method: Literal["angular", "euclidean"] = "angular", + score_method: Literal["angular", "euclidean","wasserstein"] = "angular", iters: int = 1500, lr: float = 5e-3, - wasserstein_compare: Literal["sv", "eig", None] = "eig", **dmd_kwargs, ): # TODO: add readme @@ -687,7 +686,6 @@ def __init__( "score_method": score_method, "iters": iters, "lr": lr, - "wasserstein_compare": wasserstein_compare, } dmd_config = dmd_kwargs @@ -733,10 +731,7 @@ def __init__( raise ValueError( "unknown data type for simdist-config, use dataclass or dict" ) - if compare == "state": - simdist = SimilarityTransformDist - else: - simdist = ControllabilitySimilarityTransformDist + simdist = self.update_compare_method(compare) super().__init__( X, @@ -754,3 +749,11 @@ def __init__( assert X_control is not None assert self.dmd_has_control + + def update_compare_method(self,compare='joint'): + if compare == "state": + simdist = SimilarityTransformDist + else: + simdist = ControllabilitySimilarityTransformDist + return simdist + diff --git a/DSA/simdist.py b/DSA/simdist.py index 37dc9e6..8685b58 100644 --- a/DSA/simdist.py +++ b/DSA/simdist.py @@ -175,6 +175,7 @@ def __init__( self.B = None self.eps = eps self.rescale_wasserstein = rescale_wasserstein + self.wasserstein_compare = 'eig' # for backwards compatibility def fit( self, @@ -216,11 +217,6 @@ def fit( self.A, self.B = A, B lr = self.lr if lr is None else lr iters = self.iters if iters is None else iters - wasserstein_compare = ( - self.wasserstein_compare - if wasserstein_compare is None - else wasserstein_compare - ) score_method = self.score_method if score_method is None else score_method if score_method == "wasserstein": diff --git a/DSA/simdist_controllability.py b/DSA/simdist_controllability.py index 1e270e7..778384f 100644 --- a/DSA/simdist_controllability.py +++ b/DSA/simdist_controllability.py @@ -61,13 +61,10 @@ def compute_angular_dist(A, B): def fit_score(self, A, B, A_control, B_control): - C, C_u, sims_joint_euc, sims_joint_ang = self.compare_systems_procrustes( - A1=A, B1=A_control, A2=B, B2=B_control, align_inputs=self.joint_optim - ) - - score_method = self.score_method - if self.compare == "joint": + C, C_u, sims_joint_euc, sims_joint_ang = self.compare_systems_procrustes( + A1=A, B1=A_control, A2=B, B2=B_control, align_inputs=self.joint_optim + ) if self.return_distance_components: if self.score_method == "euclidean": # sims_control_joint = np.linalg.norm(C @ A_control @ C_u - B_control, "fro") ** 2 @@ -98,7 +95,7 @@ def fit_score(self, A, B, A_control, B_control): ) else: - return self.compare_B(A_control, B_control, score_method=score_method) + return self.compare_B(A_control, B_control, score_method=self.score_method) def get_controllability_matrix(self, A, B): """ diff --git a/DSA/subspace_dmdc.py b/DSA/subspace_dmdc.py index 6408212..33115e9 100644 --- a/DSA/subspace_dmdc.py +++ b/DSA/subspace_dmdc.py @@ -1,4 +1,7 @@ -"""This module computes the subspace DMD with control (DMDc) model for a given dataset.""" +""" +This module computes the subspace DMD with control (DMDc) model for a given dataset. +Code is partially derived from and inspired by https://github.com/spmvg/nfoursid/tree/master +""" import numpy as np import torch diff --git a/README.md b/README.md index 4431f35..27b72fb 100644 --- a/README.md +++ b/README.md @@ -16,6 +16,7 @@ In control problems and basic scientific modeling, it is important to compare ob on dynamical systems Code Authors: Mitchell Ostrow, Adam Eisen, Leo Kozachkov, Ann Huang +Formatted using the Black Style (https://black.readthedocs.io/en/stable/) If you use this code, please cite: ``` diff --git a/pyproject.toml b/pyproject.toml index 6a7186b..80efcc7 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "dsa-metric" -version = "1.0.2" +version = "2.0.0" authors = [ { name="Mitchell Ostrow", email="ostrow@mit.edu" }, ] @@ -17,9 +17,13 @@ license-files = ["LICENSE*"] dependencies = [ "numpy>=1.24.0,<2", "torch>=1.3.0", - "kooplearn>=1.1.0", "pot", "omegaconf", + "pydmd", + "tqdm", + "optht", + "derivative", + "lightning" ] [project.optional-dependencies] diff --git a/setup.py b/setup.py index 9d8f9e0..92f9891 100644 --- a/setup.py +++ b/setup.py @@ -14,6 +14,11 @@ "pot", "omegaconf", "pydmd", + "tqdm", + "optht", #for havok in pykoopman + "derivative", #for pykoopman + "lightning" #for nndmd in pykoopman + "prettytable" ], extras_require={"dev": ["pytest>=3.7"]}, ) From 9732614237d3938aa03dc3ea36a2b88083859dd3 Mon Sep 17 00:00:00 2001 From: ostrow Date: Wed, 29 Oct 2025 17:41:15 -0400 Subject: [PATCH 16/90] fix torch and device things --- DSA/base_dmd.py | 64 ++++ DSA/dmd.py | 8 +- DSA/dmdc.py | 8 +- DSA/dsa.py | 34 +- DSA/simdist.py | 3 +- DSA/subspace_dmdc.py | 851 +++++++++++++++++++++---------------------- 6 files changed, 519 insertions(+), 449 deletions(-) diff --git a/DSA/base_dmd.py b/DSA/base_dmd.py index b3c4716..781fba0 100644 --- a/DSA/base_dmd.py +++ b/DSA/base_dmd.py @@ -2,6 +2,7 @@ import numpy as np import torch +import warnings from abc import ABC, abstractmethod @@ -20,6 +21,7 @@ def __init__( ---------- device: string, int, or torch.device A string, int or torch.device object to indicate the device to torch. + If 'cuda' or 'cuda:X' is specified but not available, will fall back to 'cpu' with a warning. verbose: bool If True, print statements will be provided about the progress of the fitting procedure. send_to_cpu: bool @@ -41,6 +43,68 @@ def __init__( # SVD attributes - will be set by subclasses self.cumulative_explained_variance = None + + def _setup_device(self, device='cpu', use_torch=None): + """ + Smart device setup with graceful fallback and auto-detection. + + Parameters + ---------- + device : str or torch.device + Requested device ('cpu', 'cuda', 'cuda:0', etc.) + use_torch : bool or None + Whether to use PyTorch. If None, auto-detected: + - True if device contains 'cuda' + - False otherwise (numpy is faster on CPU) + + Returns + ------- + tuple + (device, use_torch) - validated device and use_torch flag + """ + # Convert device to string for checking + device_str = str(device).lower() + + # Auto-detect use_torch if not specified + if use_torch is None: + use_torch = 'cuda' in device_str + + # If CUDA requested, check availability + if 'cuda' in device_str: + if not torch.cuda.is_available(): + warnings.warn( + f"CUDA device '{device}' requested but CUDA is not available. " + "Falling back to CPU with NumPy. " + "To use GPU acceleration, ensure PyTorch with CUDA support is installed.", + RuntimeWarning, + stacklevel=3 + ) + device = 'cpu' + use_torch = False # Use numpy on CPU for better performance + else: + # CUDA is available, verify the specific device exists + try: + test_device = torch.device(device) + # Test if we can actually use this device + torch.tensor([1.0], device=test_device) + use_torch = True + except (RuntimeError, AssertionError) as e: + warnings.warn( + f"CUDA device '{device}' requested but not accessible: {e}. " + f"Falling back to CPU with NumPy.", + RuntimeWarning, + stacklevel=3 + ) + device = 'cpu' + use_torch = False + + # Convert to torch.device if using torch + if use_torch: + device = torch.device(device) + else: + device = None # Use numpy (no torch device needed) + + return device, use_torch def _process_single_dataset(self, data): """Process a single dataset, handling numpy arrays, tensors, and lists.""" diff --git a/DSA/dmd.py b/DSA/dmd.py index d2b7b4b..6746d67 100644 --- a/DSA/dmd.py +++ b/DSA/dmd.py @@ -130,7 +130,9 @@ def __init__( Regularization parameter for ridge regression. Defaults to 0. device: string, int, or torch.device - A string, int or torch.device object to indicate the device to torch. + Device for computation. Options: + - 'cpu': Use CPU with PyTorch + - 'cuda' or 'cuda:X': Use GPU (auto-falls back to CPU if unavailable) verbose: bool If True, print statements will be provided about the progress of the fitting procedure. @@ -146,6 +148,10 @@ def __init__( super().__init__( device=device, verbose=verbose, send_to_cpu=send_to_cpu, lamb=lamb ) + + # Smart device setup with graceful CUDA fallback + # DMD always uses PyTorch, so use_torch=True + self.device, self.use_torch = self._setup_device(device, use_torch=True) self.data = self._init_single_data(data) diff --git a/DSA/dmdc.py b/DSA/dmdc.py index 94c8c5b..7076d66 100644 --- a/DSA/dmdc.py +++ b/DSA/dmdc.py @@ -91,7 +91,9 @@ def __init__( Regularization parameter for ridge regression. Defaults to 0. device: string, int, or torch.device - A string, int or torch.device object to indicate the device to torch. + Device for computation. Options: + - 'cpu': Use CPU with PyTorch + - 'cuda' or 'cuda:X': Use GPU (auto-falls back to CPU if unavailable) verbose: bool If True, print statements will be provided about the progress of the fitting procedure. @@ -107,6 +109,10 @@ def __init__( super().__init__( device=device, verbose=verbose, send_to_cpu=send_to_cpu, lamb=lamb ) + + # Smart device setup with graceful CUDA fallback + # DMDc always uses PyTorch, so use_torch=True + self.device, self.use_torch = self._setup_device(device, use_torch=True) self._init_data(data, control_data) diff --git a/DSA/dsa.py b/DSA/dsa.py index 3796065..d11f5c3 100644 --- a/DSA/dsa.py +++ b/DSA/dsa.py @@ -197,7 +197,7 @@ def __init__( Mapping[str, Any], dataclass ] = SimilarityTransformDistConfig, device="cpu", - dsa_verbose=False, + verbose=False, n_jobs=1, ): """ @@ -239,7 +239,7 @@ def __init__( device : str Device to use for computation ('cpu' or 'cuda'). Default is 'cpu'. - dsa_verbose : bool + verbose : bool Whether to print verbose output during computation. Default is False. n_jobs : int @@ -267,7 +267,7 @@ def __init__( self.device = device self.n_jobs = n_jobs - self.dsa_verbose = dsa_verbose + self.verbose = verbose self.dmd_class = dmd_class if self.X is None and isinstance(self.Y, list): @@ -409,7 +409,7 @@ def fit_dmds(self): if self.dmd_api_source == "local_dmd": for dmd_sets in self.dmds: - if self.dsa_verbose: + if self.verbose: print( f"Fitting {len(dmd_sets)} DMDs in parallel with {n_jobs} jobs" ) @@ -418,7 +418,7 @@ def fit_dmds(self): ) else: for dmd_list, dat in zip(self.dmds, self.data): - if self.dsa_verbose: + if self.verbose: print( f"Fitting {len(dmd_list)} DMDs in parallel with {n_jobs} jobs" ) @@ -432,7 +432,7 @@ def fit_dmds(self): for dmd_sets in self.dmds: loop = ( dmd_sets - if not self.dsa_verbose + if not self.verbose else tqdm.tqdm(dmd_sets, desc="Fitting DMDs") ) for dmd in loop: @@ -441,7 +441,7 @@ def fit_dmds(self): for dmd_list, dat in zip(self.dmds, self.data): loop = ( zip(dmd_list, dat) - if not self.dsa_verbose + if not self.verbose else tqdm.tqdm(zip(dmd_list, dat), desc="Fitting DMDs") ) for dmd, Xi in loop: @@ -601,14 +601,14 @@ def score(self): self.sims = np.zeros((len(self.dmds[0]), len(self.dmds[ind2]), n_sims)) - if self.dsa_verbose: + if self.verbose: print("comparing dmds") def compute_similarity(i, j): if self.method == "self-pairwise" and j >= i: return None - if self.dsa_verbose and self.n_jobs != 1: + if self.verbose and self.n_jobs != 1: print(f"computing similarity between DMDs {i} and {j}") simdist_args = [ @@ -624,7 +624,7 @@ def compute_similarity(i, j): ) sim = self.simdist.fit_score(*simdist_args) - if self.dsa_verbose and self.n_jobs != 1: + if self.verbose and self.n_jobs != 1: print(f"computing similarity between DMDs {i} and {j}") return (i, j, sim) @@ -637,7 +637,7 @@ def compute_similarity(i, j): if self.n_jobs != 1: n_jobs = self.n_jobs if self.n_jobs > 0 else -1 - if self.dsa_verbose: + if self.verbose: print( f"Computing {len(pairs)} DMD similarities in parallel with {n_jobs} jobs" ) @@ -648,7 +648,7 @@ def compute_similarity(i, j): else: loop = ( pairs - if not self.dsa_verbose + if not self.verbose else tqdm.tqdm(pairs, desc="Computing DMD similarities") ) results = [compute_similarity(i, j) for i, j in loop] @@ -673,7 +673,7 @@ def __init__( Y=None, dmd_class=DefaultDMD, device="cpu", - dsa_verbose=False, + verbose=False, n_jobs=1, # simdist parameters score_method: Literal["angular", "euclidean","wasserstein"] = "angular", @@ -700,7 +700,7 @@ def __init__( dmd_config=dmd_config, simdist_config=simdist_config, device=device, - dsa_verbose=dsa_verbose, + verbose=verbose, n_jobs=n_jobs, ) @@ -718,9 +718,11 @@ def __init__( Mapping[str, Any], dataclass ] = ControllabilitySimilarityTransformDistConfig, device="cpu", - dsa_verbose=False, + verbose=False, n_jobs=1, + compare = 'joint' ): + #TODO: fix based on making compare argument explicit # check if simdist_config has 'compare', and if it's 'state', use the standard SimilarityTransformDist, # otherwise use ControllabilitySimilarityTransformDistConfig if isinstance(simdist_config, dataclass): @@ -743,7 +745,7 @@ def __init__( dmd_config, simdist_config, device, - dsa_verbose, + verbose, n_jobs, ) diff --git a/DSA/simdist.py b/DSA/simdist.py index 8685b58..9745c34 100644 --- a/DSA/simdist.py +++ b/DSA/simdist.py @@ -6,6 +6,7 @@ import torch.nn.utils.parametrize as parametrize from scipy.stats import wasserstein_distance import ot # optimal transport for multidimensional l2 wasserstein +import warnings try: from .dmd import DMD @@ -460,7 +461,7 @@ def fit_score( if ( score_method != "wasserstein" ): # otherwise resort to L2 Wasserstein over singular or eigenvalues - print( + warnings.warn( f"resorting to wasserstein distance over {self.wasserstein_compare}" ) score_method = "wasserstein" diff --git a/DSA/subspace_dmdc.py b/DSA/subspace_dmdc.py index 33115e9..c37a261 100644 --- a/DSA/subspace_dmdc.py +++ b/DSA/subspace_dmdc.py @@ -1,138 +1,147 @@ -""" -This module computes the subspace DMD with control (DMDc) model for a given dataset. -Code is partially derived from and inspired by https://github.com/spmvg/nfoursid/tree/master -""" - +"""This module computes the subspace DMD with control (DMDc) model for a given dataset.""" import numpy as np import torch +from .base_dmd import BaseDMD - -class SubspaceDMDc: - """Subspace DMDc class for computing and predicting with DMD with control models.""" - +class SubspaceDMDc(BaseDMD): + """Subspace DMDc class for computing and predicting with DMD with control models. + + Inherits from BaseDMD for common functionality like device management and data processing. + """ def __init__( - self, - data, - control_data, - n_delays=1, - rank=None, - lamb=1e-8, - device="cpu", - verbose=False, - send_to_cpu=False, - time_first=True, - backend="n4sid", + self, + data, + control_data=None, + n_delays=1, + f=None, + rank=None, + lamb=1e-8, + device='cpu', + verbose=False, + send_to_cpu=False, + time_first=True, + backend='n4sid', ): - # Convert inputs to torch tensors and store - self.device = device - self.data = self._to_tensor(data) - self.control_data = self._to_tensor(control_data) + """ + Initialize SubspaceDMDc. + + Parameters + ---------- + data : array-like + Output/observation data + control_data : array-like + Control input data + n_delays : int + Number of time delays (past window) + f : int, optional + Future window length (defaults to n_delays) + rank : int, optional + Rank for system identification + lamb : float + Regularization parameter for ridge regression + device : str or torch.device + Device for computation: + - 'cpu': Use NumPy on CPU (fastest for CPU) + - 'cuda' or 'cuda:X': Use PyTorch on GPU (auto-falls back to CPU if unavailable) + verbose : bool + If True, print progress information + send_to_cpu : bool + If True, move results to CPU after fitting (useful for batch GPU processing) + time_first : bool + If True, data shape is (time, features); otherwise (features, time) + backend : str + 'n4sid' or 'custom' for subspace identification algorithm + """ + # Initialize base class + super().__init__(device=device, verbose=verbose, send_to_cpu=send_to_cpu, lamb=lamb) + + # Smart device setup with graceful fallback + self.device, self.use_torch = self._setup_device(device, True) + + # SubspaceDMDc specific attributes + self.data = data + self.control_data = control_data self.A_v = None self.B_v = None self.C_v = None self.info = None self.n_delays = n_delays + self.f = f if f is not None else n_delays # Future window, defaults to n_delays self.rank = rank self.time_first = time_first self.backend = backend - self.lamb = lamb - self.verbose = verbose - self.send_to_cpu = send_to_cpu - - def _to_tensor(self, data): - """Convert data to torch tensor, handling lists and numpy arrays.""" - if isinstance(data, list): - return [self._to_tensor_single(d) for d in data] - else: - return self._to_tensor_single(data) - def _to_tensor_single(self, data): - """Convert single data item to torch tensor.""" - if isinstance(data, np.ndarray): - return torch.from_numpy(data).float().to(self.device) - elif isinstance(data, torch.Tensor): - return data.float().to(self.device) - return data + def _to_torch(self, x): + """Convert numpy array to torch tensor on the appropriate device.""" + if not self.use_torch or x is None: + return x + if isinstance(x, torch.Tensor): + return x.to(self.device) + return torch.from_numpy(x).to(self.device) + + def _to_numpy(self, x): + """Convert torch tensor to numpy array.""" + if not self.use_torch or x is None: + return x + if isinstance(x, torch.Tensor): + return x.cpu().numpy() + return x + def fit(self): - self.A_v, self.B_v, self.C_v, self.info = ( - self.subspace_dmdc_multitrial_flexible( - y=self.data, - u=self.control_data, - p=self.n_delays, - f=self.n_delays, - n=self.rank, - backend=self.backend, - lamb=self.lamb, - ) - ) - + """Fit the SubspaceDMDc model.""" + self.A_v, self.B_v, self.C_v, self.info = self.subspace_dmdc_multitrial_flexible( + y=self.data, + u=self.control_data, + p=self.n_delays, + f=self.f, + n=self.rank, + backend=self.backend, + lamb=self.lamb) + + # Send to CPU if requested (inherited from BaseDMD) if self.send_to_cpu: - self.all_to_device("cpu") - - def all_to_device(self, device): - """Send all tensors to specified device.""" - if self.A_v is not None: - self.A_v = self.A_v.to(device) - if self.B_v is not None: - self.B_v = self.B_v.to(device) - if self.C_v is not None: - self.C_v = self.C_v.to(device) - if self.info is not None: - for key in [ - "R_hat", - "Q_hat", - "S_hat", - "Gamma_hat", - "singular_values_O", - "noise_covariance", - ]: - if key in self.info and isinstance(self.info[key], torch.Tensor): - self.info[key] = self.info[key].to(device) - - def subspace_dmdc_multitrial_QR_decomposition( - self, y_list, u_list, p, f, n=None, lamb=1e-8, energy=0.999 - ): + self.all_to_device(device='cpu') + + + + def subspace_dmdc_multitrial_QR_decomposition(self, y_list, u_list, p, f, n=None, lamb=1e-8, energy=0.999): """ Subspace-DMDc for multi-trial data with variable trial lengths. Now use QR decomposition for computing the oblique projection as in N4SID implementations. - + Parameters: - - y_list: list of tensors, each (p_out, N_i) - output data for trial i - - u_list: list of tensors, each (m, N_i) - input data for trial i + - y_list: list of arrays, each (p_out, N_i) - output data for trial i + - u_list: list of arrays, each (m, N_i) - input data for trial i - p: past window length - f: future window length - n: state dimension (auto-determined if None) - ridge: regularization parameter (used only for rank selection/SVD; QR is exact) - energy: energy threshold for rank selection - + Returns: - A_hat, B_hat, C_hat: system matrices - info: dictionary with additional information """ if len(y_list) != len(u_list): raise ValueError("y_list and u_list must have same number of trials") - + n_trials = len(y_list) p_out = y_list[0].shape[0] m = u_list[0].shape[0] - + # Validate dimensions across trials for i, (y_trial, u_trial) in enumerate(zip(y_list, u_list)): if y_trial.shape[0] != p_out: - raise ValueError( - f"Trial {i}: y has {y_trial.shape[0]} outputs, expected {p_out}" - ) + raise ValueError(f"Trial {i}: y has {y_trial.shape[0]} outputs, expected {p_out}") if u_trial.shape[0] != m: - raise ValueError( - f"Trial {i}: u has {u_trial.shape[0]} inputs, expected {m}" - ) + raise ValueError(f"Trial {i}: u has {u_trial.shape[0]} inputs, expected {m}") if y_trial.shape[1] != u_trial.shape[1]: raise ValueError(f"Trial {i}: y and u have different time lengths") - + def hankel_stack(X, start, L): - return torch.cat([X[:, start + i : start + i + 1] for i in range(L)], dim=0) - + return np.concatenate([X[:, start + i:start + i + 1] for i in range(L)], axis=0) + # Collect data from all trials U_p_all = [] Y_p_all = [] @@ -140,85 +149,98 @@ def hankel_stack(X, start, L): Y_f_all = [] valid_trials = [] T_per_trial = [] - + for trial_idx, (Y_trial, U_trial) in enumerate(zip(y_list, u_list)): N_trial = Y_trial.shape[1] T_trial = N_trial - (p + f) + 1 - + if T_trial <= 0: - if self.verbose: - print( - f"Warning: Trial {trial_idx} has insufficient data (T={T_trial}), skipping" - ) + print(f"Warning: Trial {trial_idx} has insufficient data (T={T_trial}), skipping") continue - + valid_trials.append(trial_idx) T_per_trial.append(T_trial) - + # Build Hankel matrices for this trial - U_p_trial = torch.cat( - [hankel_stack(U_trial, j, p) for j in range(T_trial)], dim=1 - ) - Y_p_trial = torch.cat( - [hankel_stack(Y_trial, j, p) for j in range(T_trial)], dim=1 - ) - U_f_trial = torch.cat( - [hankel_stack(U_trial, j + p, f) for j in range(T_trial)], dim=1 - ) - Y_f_trial = torch.cat( - [hankel_stack(Y_trial, j + p, f) for j in range(T_trial)], dim=1 - ) - + U_p_trial = np.concatenate([hankel_stack(U_trial, j, p) for j in range(T_trial)], axis=1) + Y_p_trial = np.concatenate([hankel_stack(Y_trial, j, p) for j in range(T_trial)], axis=1) + U_f_trial = np.concatenate([hankel_stack(U_trial, j + p, f) for j in range(T_trial)], axis=1) + Y_f_trial = np.concatenate([hankel_stack(Y_trial, j + p, f) for j in range(T_trial)], axis=1) + U_p_all.append(U_p_trial) Y_p_all.append(Y_p_trial) U_f_all.append(U_f_trial) Y_f_all.append(Y_f_trial) - + if not valid_trials: raise ValueError("No trials have sufficient data for given (p,f)") - + # Concatenate across valid trials - U_p = torch.cat(U_p_all, dim=1) # (p m, T_total) - Y_p = torch.cat(Y_p_all, dim=1) # (p p_out, T_total) - U_f = torch.cat(U_f_all, dim=1) # (f m, T_total) - Y_f = torch.cat(Y_f_all, dim=1) # (f p_out, T_total) - + U_p = np.concatenate(U_p_all, axis=1) # (p m, T_total) + Y_p = np.concatenate(Y_p_all, axis=1) # (p p_out, T_total) + U_f = np.concatenate(U_f_all, axis=1) # (f m, T_total) + Y_f = np.concatenate(Y_f_all, axis=1) # (f p_out, T_total) + T_total = sum(T_per_trial) - Z_p = torch.vstack([U_p, Y_p]) # (p (m + p_out), T_total) - - H = torch.vstack([U_f, Z_p, Y_f]) - - # Perform QR on H.T to get equivalent LQ on H - Q, R_upper = torch.linalg.qr( - H.T, mode="reduced" - ) # H.T = Q R_upper, R_upper upper triangular - L = R_upper.T # L = R_upper.T, lower triangular - + Z_p = np.vstack([U_p, Y_p]) # (p (m + p_out), T_total) + + H = np.vstack([U_f, Z_p, Y_f]) + # Dimensions for slicing dim_uf = f * m dim_zp = p * (m + p_out) dim_yf = f * p_out - - # Extract submatrices from L (lower triangular) - R22 = L[dim_uf : dim_uf + dim_zp, dim_uf : dim_uf + dim_zp] - R32 = L[dim_uf + dim_zp :, dim_uf : dim_uf + dim_zp] - - # Compute oblique projection O = R32 @ pinv(R22) @ Z_p - O = R32 @ torch.linalg.pinv(R22) @ Z_p - - # The rest remains the same: SVD on O - Uo, s, Vt = torch.linalg.svd(O, full_matrices=False) + + # Use torch for expensive linear algebra if available + if self.use_torch and H.shape[1] > 100: # Only worth it for larger problems + H_torch = self._to_torch(H) + Z_p_torch = self._to_torch(Z_p) + + # Perform QR on H.T to get equivalent LQ on H + Q, R_upper = torch.linalg.qr(H_torch.T, mode='reduced') + L = R_upper.T + + # Extract submatrices from L + R22 = L[dim_uf:dim_uf + dim_zp, dim_uf:dim_uf + dim_zp] + R32 = L[dim_uf + dim_zp:, dim_uf:dim_uf + dim_zp] + + # Compute oblique projection O = R32 @ pinv(R22) @ Z_p + R22_pinv = torch.linalg.pinv(R22) + O = R32 @ R22_pinv @ Z_p_torch + + # SVD on O + Uo, s, Vt = torch.linalg.svd(O, full_matrices=False) + + # Convert back to numpy + Uo = self._to_numpy(Uo) + s = self._to_numpy(s) + Vt = self._to_numpy(Vt) + else: + # Use numpy for smaller problems or when torch is disabled + # Perform QR on H.T to get equivalent LQ on H + Q, R_upper = np.linalg.qr(H.T, mode='reduced') + L = R_upper.T + + # Extract submatrices from L (lower triangular) + R22 = L[dim_uf:dim_uf + dim_zp, dim_uf:dim_uf + dim_zp] + R32 = L[dim_uf + dim_zp:, dim_uf:dim_uf + dim_zp] + + # Compute oblique projection O = R32 @ pinv(R22) @ Z_p + O = R32 @ np.linalg.pinv(R22) @ Z_p + + # The rest remains the same: SVD on O + Uo, s, Vt = np.linalg.svd(O, full_matrices=False) if n is None: - cs = torch.cumsum(s**2, dim=0) / (s**2).sum() - n = int((cs < energy).sum().item() + 1) + cs = np.cumsum(s**2) / (s**2).sum() + n = int(np.searchsorted(cs, energy) + 1) n = max(1, min(n, min(Uo.shape[1], Vt.shape[0]))) - + U_n = Uo[:, :n] - S_n = torch.diag(s[:n]) + S_n = np.diag(s[:n]) V_n = Vt[:n, :] - S_half = torch.sqrt(S_n) - Gamma_hat = U_n @ S_half # (f p_out, n) - X_hat = S_half @ V_n # (n, T_total) + S_half = np.sqrt(S_n) + Gamma_hat = U_n @ S_half # (f p_out, n) + X_hat = S_half @ V_n # (n, T_total) # Time alignment for regression across all trials # Need to handle variable lengths carefully @@ -226,102 +248,104 @@ def hankel_stack(X, start, L): X_next_segments = [] U_mid_segments = [] Y_segments = [] - + start_idx = 0 for trial_idx, T_trial in enumerate(T_per_trial): # Extract states for this trial - X_trial = X_hat[:, start_idx : start_idx + T_trial] - + X_trial = X_hat[:, start_idx:start_idx + T_trial] + # State transitions within this trial X_trial_curr = X_trial[:, :-1] X_trial_next = X_trial[:, 1:] - + # Corresponding control inputs original_trial_idx = valid_trials[trial_idx] U_trial = u_list[original_trial_idx] - U_mid_trial = U_trial[:, p : p + (T_trial - 1)] - + U_mid_trial = U_trial[:, p:p + (T_trial - 1)] + X_segments.append(X_trial_curr) X_next_segments.append(X_trial_next) U_mid_segments.append(U_mid_trial) - + # TODO: check the time-alignment of Y and X here # Corresponding output data - align with X_trial time indices Y_trial = y_list[original_trial_idx] - Y_trial_curr = Y_trial[:, p : p + T_trial - 1] + Y_trial_curr = Y_trial[:, p:p+T_trial-1] # Y_trial_curr = Y_trial[:, p+1:p+T_trial] Y_segments.append(Y_trial_curr) start_idx += T_trial - + # Concatenate all segments - X = torch.cat(X_segments, dim=1) - X_next = torch.cat(X_next_segments, dim=1) - U_mid = torch.cat(U_mid_segments, dim=1) - + X = np.concatenate(X_segments, axis=1) + X_next = np.concatenate(X_next_segments, axis=1) + U_mid = np.concatenate(U_mid_segments, axis=1) + # Regression for A and B - Z = torch.vstack([X, U_mid]) - # Ridge regression: (Z^T Z + λI)^(-1) Z^T X_next^T - ZTZ = Z @ Z.T - ridge_term = lamb * torch.eye(ZTZ.shape[0], device=self.device) - AB = torch.linalg.solve(ZTZ + ridge_term, Z @ X_next.T).T - A_hat = AB[:, :n] - B_hat = AB[:, n:] - - # Z = torch.vstack([X, U_mid]) - # AB = X_next @ torch.linalg.pinv(Z) + Z = np.vstack([X, U_mid]) + + # Use torch for ridge regression if available + if self.use_torch and Z.shape[1] > 100: + Z_torch = self._to_torch(Z) + X_next_torch = self._to_torch(X_next) + + # Ridge regression: (Z^T Z + λI)^(-1) Z^T X_next^T + ZTZ = Z_torch @ Z_torch.T + ridge_term = lamb * torch.eye(ZTZ.shape[0], device=self.device, dtype=Z_torch.dtype) + AB = torch.linalg.solve(ZTZ + ridge_term, Z_torch @ X_next_torch.T).T + + AB = self._to_numpy(AB) + A_hat = AB[:, :n] + B_hat = AB[:, n:] + else: + # Ridge regression: (Z^T Z + λI)^(-1) Z^T X_next^T + ZTZ = Z @ Z.T + ridge_term = lamb * np.eye(ZTZ.shape[0]) + AB = np.linalg.solve(ZTZ + ridge_term, Z @ X_next.T).T + A_hat = AB[:, :n] + B_hat = AB[:, n:] + + # Z = np.vstack([X, U_mid]) + # AB = X_next @ np.linalg.pinv(Z) # A_hat = AB[:, :n] # B_hat = AB[:, n:] - + C_hat = Gamma_hat[:p_out, :] # Estimate noise covariance matrix # 0) Outputs aligned to X and U_mid (same time indices/columns) - Y_curr = torch.cat(Y_segments, dim=1) # shape: (p_out, N) + Y_curr = np.concatenate(Y_segments, axis=1) # shape: (p_out, N) # 1) Residuals at time t # Process noise residual (state eq): w_t ≈ x_{t+1} - A x_t - B u_ts - W_hat = X_next - (A_hat @ X + B_hat @ U_mid) # (n, N) + W_hat = X_next - (A_hat @ X + B_hat @ U_mid) # (n, N) # Measurement noise residual (output eq): v_t ≈ y_t - C x_t (since D = 0) - V_hat = Y_curr - (C_hat @ X) # (p_out, N) + V_hat = Y_curr - (C_hat @ X) # (p_out, N) # 2) Mean-centering - V_hat = V_hat - V_hat.mean(dim=1, keepdim=True) - W_hat = W_hat - W_hat.mean(dim=1, keepdim=True) + V_hat = V_hat - V_hat.mean(axis=1, keepdims=True) + W_hat = W_hat - W_hat.mean(axis=1, keepdims=True) N_res = V_hat.shape[1] - denom = max(N_res - 1, 1) + denom = max(N_res - 1, 1) # 3) Covariances - R_hat = (V_hat @ V_hat.T) / denom # (p_out, p_out) measurement - Q_hat = (W_hat @ W_hat.T) / denom # (n, n) process - S_hat = (W_hat @ V_hat.T) / denom # (n, p_out) - cross (w,v) + R_hat = (V_hat @ V_hat.T) / denom # (p_out, p_out) measurement + Q_hat = (W_hat @ W_hat.T) / denom # (n, n) process + S_hat = (W_hat @ V_hat.T) / denom # (n, p_out) - cross (w,v) # 4) Symmetrize eps = 1e-12 - R_hat = 0.5 * (R_hat + R_hat.T) + eps * torch.eye( - R_hat.shape[0], device=self.device - ) - Q_hat = 0.5 * (Q_hat + Q_hat.T) + eps * torch.eye( - Q_hat.shape[0], device=self.device - ) + R_hat = 0.5 * (R_hat + R_hat.T) + eps * np.eye(R_hat.shape[0]) + Q_hat = 0.5 * (Q_hat + Q_hat.T) + eps * np.eye(Q_hat.shape[0]) - noise_covariance = torch.block_diag(R_hat, Q_hat) - # Add off-diagonal blocks - top_right = S_hat.T - bottom_left = S_hat - noise_covariance = torch.cat( - [ - torch.cat([R_hat, top_right], dim=1), - torch.cat([bottom_left, Q_hat], dim=1), - ], - dim=0, - ) + noise_covariance = np.block([[R_hat, S_hat.T], + [S_hat, Q_hat]]) info = { - "singular_values_O": s, - "rank_used": n, - "Gamma_hat": Gamma_hat, + "singular_values_O": s, + "rank_used": n, + "Gamma_hat": Gamma_hat, "f": f, "n_trials_total": n_trials, "n_trials_used": len(valid_trials), @@ -330,56 +354,53 @@ def hankel_stack(X, start, L): "T_total": T_total, "trial_lengths": [y.shape[1] for y in y_list], "noise_covariance": noise_covariance, - "R_hat": R_hat, - "Q_hat": Q_hat, - "S_hat": S_hat, + 'R_hat': R_hat, + 'Q_hat': Q_hat, + 'S_hat': S_hat } - + return A_hat, B_hat, C_hat, info + - def subspace_dmdc_multitrial_custom( - self, y_list, u_list, p, f, n=None, lamb=1e-8, energy=0.999 - ): + + + def subspace_dmdc_multitrial_custom(self, y_list, u_list, p, f, n=None, lamb=1e-8, energy=0.999): """ Subspace-DMDc for multi-trial data with variable trial lengths. - + Parameters: - - y_list: list of tensors, each (p_out, N_i) - output data for trial i - - u_list: list of tensors, each (m, N_i) - input data for trial i + - y_list: list of arrays, each (p_out, N_i) - output data for trial i + - u_list: list of arrays, each (m, N_i) - input data for trial i - p: past window length - f: future window length - n: state dimension (auto-determined if None) - ridge: regularization parameter - energy: energy threshold for rank selection∏ - + Returns: - A_hat, B_hat, C_hat: system matrices - info: dictionary with additional information """ if len(y_list) != len(u_list): raise ValueError("y_list and u_list must have same number of trials") - + n_trials = len(y_list) p_out = y_list[0].shape[0] m = u_list[0].shape[0] - + # Validate dimensions across trials for i, (y_trial, u_trial) in enumerate(zip(y_list, u_list)): if y_trial.shape[0] != p_out: - raise ValueError( - f"Trial {i}: y has {y_trial.shape[0]} outputs, expected {p_out}" - ) + raise ValueError(f"Trial {i}: y has {y_trial.shape[0]} outputs, expected {p_out}") if u_trial.shape[0] != m: - raise ValueError( - f"Trial {i}: u has {u_trial.shape[0]} inputs, expected {m}" - ) + raise ValueError(f"Trial {i}: u has {u_trial.shape[0]} inputs, expected {m}") if y_trial.shape[1] != u_trial.shape[1]: raise ValueError(f"Trial {i}: y and u have different time lengths") - + def hankel_stack(X, start, L): - return torch.cat([X[:, start + i : start + i + 1] for i in range(L)], dim=0) - + return np.concatenate([X[:, start + i:start + i + 1] for i in range(L)], axis=0) + # Collect data from all trials U_p_all = [] Y_p_all = [] @@ -387,131 +408,116 @@ def hankel_stack(X, start, L): Y_f_all = [] valid_trials = [] T_per_trial = [] - + for trial_idx, (Y_trial, U_trial) in enumerate(zip(y_list, u_list)): N_trial = Y_trial.shape[1] T_trial = N_trial - (p + f) + 1 - + if T_trial <= 0: - if self.verbose: - print( - f"Warning: Trial {trial_idx} has insufficient data (T={T_trial}), skipping" - ) + print(f"Warning: Trial {trial_idx} has insufficient data (T={T_trial}), skipping") continue - + valid_trials.append(trial_idx) T_per_trial.append(T_trial) - + # Build Hankel matrices for this trial - U_p_trial = torch.cat( - [hankel_stack(U_trial, j, p) for j in range(T_trial)], dim=1 - ) - Y_p_trial = torch.cat( - [hankel_stack(Y_trial, j, p) for j in range(T_trial)], dim=1 - ) - U_f_trial = torch.cat( - [hankel_stack(U_trial, j + p, f) for j in range(T_trial)], dim=1 - ) - Y_f_trial = torch.cat( - [hankel_stack(Y_trial, j + p, f) for j in range(T_trial)], dim=1 - ) - + U_p_trial = np.concatenate([hankel_stack(U_trial, j, p) for j in range(T_trial)], axis=1) + Y_p_trial = np.concatenate([hankel_stack(Y_trial, j, p) for j in range(T_trial)], axis=1) + U_f_trial = np.concatenate([hankel_stack(U_trial, j + p, f) for j in range(T_trial)], axis=1) + Y_f_trial = np.concatenate([hankel_stack(Y_trial, j + p, f) for j in range(T_trial)], axis=1) + U_p_all.append(U_p_trial) Y_p_all.append(Y_p_trial) U_f_all.append(U_f_trial) Y_f_all.append(Y_f_trial) - if self.verbose: - print("=" * 40) - print(f"Number of valid trials: {len(valid_trials)}") + print("="*40) + print(f"Number of valid trials: {len(U_p_trial)}") + if not valid_trials: raise ValueError("No trials have sufficient data for given (p,f)") - + # Concatenate across valid trials - U_p = torch.cat(U_p_all, dim=1) # (pm, T_total) - Y_p = torch.cat(Y_p_all, dim=1) # (p*p_out, T_total) - U_f = torch.cat(U_f_all, dim=1) # (fm, T_total) - Y_f = torch.cat(Y_f_all, dim=1) # (f*p_out, T_total) - + U_p = np.concatenate(U_p_all, axis=1) # (pm, T_total) + Y_p = np.concatenate(Y_p_all, axis=1) # (p*p_out, T_total) + U_f = np.concatenate(U_f_all, axis=1) # (fm, T_total) + Y_f = np.concatenate(Y_f_all, axis=1) # (f*p_out, T_total) + T_total = sum(T_per_trial) - Z_p = torch.vstack([U_p, Y_p]) # (p(m+p_out), T_total) - + Z_p = np.vstack([U_p, Y_p]) # (p(m+p_out), T_total) + # Oblique projection: remove row(U_f), project onto row(Z_p) UfUfT = U_f @ U_f.T - Xsolve = torch.linalg.solve( - UfUfT + lamb * torch.eye(UfUfT.shape[0], device=self.device), U_f - ) - Pi_perp = torch.eye(T_total, device=self.device) - U_f.T @ Xsolve + Xsolve = np.linalg.solve(UfUfT + lamb*np.eye(UfUfT.shape[0]), U_f) + Pi_perp = np.eye(T_total) - U_f.T @ Xsolve Yf_perp = Y_f @ Pi_perp Zp_perp = Z_p @ Pi_perp - + ZZT = Zp_perp @ Zp_perp.T - Zp_pinv_left = torch.linalg.solve( - ZZT + lamb * torch.eye(ZZT.shape[0], device=self.device), Zp_perp - ) + Zp_pinv_left = np.linalg.solve(ZZT + lamb*np.eye(ZZT.shape[0]), Zp_perp) P = Zp_perp.T @ Zp_pinv_left O = Yf_perp @ P # ≈ Γ_f X_p - - Uo, s, Vt = torch.linalg.svd(O, full_matrices=False) + + Uo, s, Vt = np.linalg.svd(O, full_matrices=False) if n is None: - cs = torch.cumsum(s**2, dim=0) / (s**2).sum() - n = int((cs < energy).sum().item() + 1) + cs = np.cumsum(s**2) / (s**2).sum() + n = int(np.searchsorted(cs, energy) + 1) n = max(1, min(n, min(Uo.shape[1], Vt.shape[0]))) - + U_n = Uo[:, :n] - S_n = torch.diag(s[:n]) + S_n = np.diag(s[:n]) V_n = Vt[:n, :] - S_half = torch.sqrt(S_n) - Gamma_hat = U_n @ S_half # (f*p_out, n) - X_hat = S_half @ V_n # (n, T_total) - + S_half = np.sqrt(S_n) + Gamma_hat = U_n @ S_half # (f*p_out, n) + X_hat = S_half @ V_n # (n, T_total) + # Time alignment for regression across all trials # Need to handle variable lengths carefully X_segments = [] X_next_segments = [] U_mid_segments = [] - + start_idx = 0 for trial_idx, T_trial in enumerate(T_per_trial): # Extract states for this trial - X_trial = X_hat[:, start_idx : start_idx + T_trial] - + X_trial = X_hat[:, start_idx:start_idx + T_trial] + # State transitions within this trial X_trial_curr = X_trial[:, :-1] X_trial_next = X_trial[:, 1:] - + # Corresponding control inputs original_trial_idx = valid_trials[trial_idx] U_trial = u_list[original_trial_idx] - U_mid_trial = U_trial[:, p : p + (T_trial - 1)] - + U_mid_trial = U_trial[:, p:p + (T_trial - 1)] + X_segments.append(X_trial_curr) X_next_segments.append(X_trial_next) U_mid_segments.append(U_mid_trial) - + start_idx += T_trial - + # Concatenate all segments - X = torch.cat(X_segments, dim=1) - X_next = torch.cat(X_next_segments, dim=1) - U_mid = torch.cat(U_mid_segments, dim=1) - + X = np.concatenate(X_segments, axis=1) + X_next = np.concatenate(X_next_segments, axis=1) + U_mid = np.concatenate(U_mid_segments, axis=1) + # Regression for A and B - Z = torch.vstack([X, U_mid]) + Z = np.vstack([X, U_mid]) # Ridge regression: (Z^T Z + λI)^(-1) Z^T X_next^T ZTZ = Z @ Z.T - ridge_term = lamb * torch.eye(ZTZ.shape[0], device=self.device) - AB = torch.linalg.solve(ZTZ + ridge_term, Z @ X_next.T).T + ridge_term = lamb * np.eye(ZTZ.shape[0]) + AB = np.linalg.solve(ZTZ + ridge_term, Z @ X_next.T).T A_hat = AB[:, :n] B_hat = AB[:, n:] - + C_hat = Gamma_hat[:p_out, :] - + info = { - "singular_values_O": s, - "rank_used": n, - "Gamma_hat": Gamma_hat, + "singular_values_O": s, + "rank_used": n, + "Gamma_hat": Gamma_hat, "f": f, "n_trials_total": n_trials, "n_trials_used": len(valid_trials), @@ -519,17 +525,17 @@ def hankel_stack(X, start, L): "T_per_trial": T_per_trial, "T_total": T_total, "trial_lengths": [y.shape[1] for y in y_list], - "X_hat": X_hat, + "X_hat": X_hat } - + return A_hat, B_hat, C_hat, info - def subspace_dmdc_multitrial_flexible( - self, y, u, p, f, n=None, lamb=1e-8, energy=0.999, backend="n4sid" - ): + + + def subspace_dmdc_multitrial_flexible(self, y, u, p, f, n=None, lamb=1e-8, energy=0.999, backend='n4sid'): """ Flexible wrapper that handles both fixed-length and variable-length multi-trial data. - + Parameters: - y: either (n_trials, p_out, N) array, (p_out, N) array, or list of (p_out, N_i) arrays - u: either (n_trials, m, N) array, (m, N) array, or list of (m, N_i) arrays @@ -542,15 +548,11 @@ def subspace_dmdc_multitrial_flexible( else: y_list = y u_list = u - if backend == "n4sid": - return self.subspace_dmdc_multitrial_QR_decomposition( - y_list, u_list, p, f, n, lamb, energy - ) + if backend == 'n4sid': + return self.subspace_dmdc_multitrial_QR_decomposition(y_list, u_list, p, f, n, lamb, energy) else: - return self.subspace_dmdc_multitrial_custom( - y_list, u_list, p, f, n, lamb, energy - ) - + return self.subspace_dmdc_multitrial_custom(y_list, u_list, p, f, n, lamb, energy) + else: # Handle 2D arrays (single trial) by converting to list format if y.ndim == 2: @@ -560,35 +562,29 @@ def subspace_dmdc_multitrial_flexible( # Convert 3D arrays to list format y_list = [y[i] for i in range(y.shape[0])] u_list = [u[i] for i in range(u.shape[0])] - + # If time_first=True, transpose each trial from (time_points, variables) to (variables, time_points) if self.time_first: y_list = [y_trial.T for y_trial in y_list] u_list = [u_trial.T for u_trial in u_list] - - if backend == "n4sid": - return self.subspace_dmdc_multitrial_QR_decomposition( - y_list, u_list, p, f, n, lamb, energy - ) + + if backend == 'n4sid': + return self.subspace_dmdc_multitrial_QR_decomposition(y_list, u_list, p, f, n, lamb, energy) else: - return self.subspace_dmdc_multitrial_custom( - y_list, u_list, p, f, n, lamb, energy - ) + return self.subspace_dmdc_multitrial_custom(y_list, u_list, p, f, n, lamb, energy) + def predict(self, Y, U, reseed=None): - # Y and U are (n_times, n_channels) or list of 2D arrays/tensors + # Y and U are (n_times, n_channels) or list of 2D arrays if reseed is None: reseed = 1 - # Convert inputs to tensors if needed + # Handle list of 2D arrays if isinstance(Y, list): - Y = [self._to_tensor_single(y) for y in Y] - U = [self._to_tensor_single(u) for u in U] - if not self.time_first: Y = [y.T for y in Y] U = [u.T for u in U] - + self.kalman = OnlineKalman(self) Y_pred = [] for trial in range(len(Y)): @@ -596,34 +592,29 @@ def predict(self, Y, U, reseed=None): trial_predictions = [] for t in range(Y[trial].shape[0]): y_filtered, _ = self.kalman.step( - y=Y[trial][t] if t % reseed == 0 else None, u=U[trial][t] + y=Y[trial][t] if t%reseed == 0 else None, + u=U[trial][t] ) trial_predictions.append(y_filtered) - Y_pred.append(torch.cat(trial_predictions, dim=1).T) + Y_pred.append(np.concatenate(trial_predictions, axis=1).T) return Y_pred # Return as list to match input format - # Convert to tensors - Y = self._to_tensor_single(Y) - U = self._to_tensor_single(U) - # print("time_first", self.time_first) if not self.time_first: if Y.ndim == 2: Y = Y.T U = U.T else: - Y = Y.permute(0, 2, 1) - U = U.permute(0, 2, 1) - + Y = Y.transpose(0, 2, 1) + U = U.transpose(0, 2, 1) + self.kalman = OnlineKalman(self) if Y.ndim == 2: Y_pred = [] for t in range(Y.shape[0]): - y_filtered, _ = self.kalman.step( - y=Y[t] if t % reseed == 0 else None, u=U[t] - ) + y_filtered, _ = self.kalman.step(y=Y[t] if t%reseed == 0 else None, u=U[t]) Y_pred.append(y_filtered) - return torch.cat(Y_pred, dim=1).T + return np.concatenate(Y_pred, axis=1).T else: # 3D data (n_trials, time, p_out) # print("Y.shape", Y.shape) @@ -633,46 +624,65 @@ def predict(self, Y, U, reseed=None): self.kalman.reset() # Reset filter for each trial trial_predictions = [] for t in range(Y.shape[1]): - y_filtered, _ = self.kalman.step( - y=Y[trial, t] if t % reseed == 0 else None, u=U[trial, t] - ) + y_filtered, _ = self.kalman.step(y=Y[trial, t] if t%reseed == 0 else None, u=U[trial, t]) trial_predictions.append(y_filtered) # print("y_filtered.shape", y_filtered.shape) - Y_pred.append(torch.cat(trial_predictions, dim=1).T) - return torch.stack(Y_pred) + Y_pred.append(np.concatenate(trial_predictions, axis=1).T) + return np.array(Y_pred) + + def compute_hankel(self, *args, **kwargs): + """ + Compute Hankel matrices for SubspaceDMDc. + + This is handled internally within subspace_dmdc_multitrial_QR_decomposition + and subspace_dmdc_multitrial_custom methods. + """ + raise NotImplementedError( + "Hankel matrix computation is integrated into the fit() method for SubspaceDMDc. " + "Use fit() to compute the model." + ) + + def compute_svd(self, *args, **kwargs): + """ + Compute SVD for SubspaceDMDc. + + This is handled internally within the subspace identification process. + """ + raise NotImplementedError( + "SVD computation is integrated into the fit() method for SubspaceDMDc. " + "Use fit() to compute the model." + ) class OnlineKalman: """ Online Kalman Filter class for real-time state estimation. - + This class maintains the internal state of the Kalman filter and provides a step method for updating the filter with new observations and inputs. """ - + def __init__(self, dmdc): """ Initialize the Online Kalman Filter with a fitted DMDc model. - + Parameters ---------- dmdc : object - Fitted DMDc model containing A_v, B_v, C_v matrices and + Fitted DMDc model containing A_v, B_v, C_v matrices and noise covariance estimates (R_hat, S_hat, Q_hat) """ - self.device = dmdc.device self.A = dmdc.A_v - self.B = dmdc.B_v + self.B = dmdc.B_v self.C = dmdc.C_v - self.R = dmdc.info["R_hat"] - self.S = dmdc.info["S_hat"] - self.Q = dmdc.info["Q_hat"] - + self.R = dmdc.info['R_hat'] + self.S = dmdc.info['S_hat'] + self.Q = dmdc.info['Q_hat'] + # Get dimensions # print("C_shape", self.C.shape) self.y_dim, self.x_dim = self.C.shape - self.u_dim = self.B.shape[1] - + # Initialize state storage self.p_filtereds = [] self.x_filtereds = [] @@ -684,23 +694,24 @@ def __init__(self, dmdc): self.y_predicteds = [] self.kalman_gains = [] + # def step(self, y=None, u=None, lam=1e-8): # """ # Perform one step of the Kalman filter. - + # Parameters # ---------- # y : np.ndarray, optional # Observed output at current time step. If None, the filter # will predict without observation update. - # u : np.ndarray, optional + # u : np.ndarray, optional # Input at current time step. If None, no input is applied. - + # Returns # ------- # y_filtered : np.ndarray # Filtered output estimate - # x_filtered : np.ndarray + # x_filtered : np.ndarray # Filtered state estimate # """ # x_pred = self.x_predicteds[-1] if self.x_predicteds else np.zeros((self.x_dim, 1)) @@ -723,14 +734,14 @@ def __init__(self, dmdc): # x_filtered = x_pred + K_filtered @ (y - self.C @ x_pred) # else: # x_filtered = x_pred.copy() - + # K_pred = (self.S + self.A @ p_pred @ self.C.T) @ np.linalg.pinv(S_innov) - # p_predicted = (self.A @ p_pred @ self.A.T + self.Q - + # p_predicted = (self.A @ p_pred @ self.A.T + self.Q - # K_pred @ (self.S + self.A @ p_pred @ self.C.T).T) # x_predicted = self.A @ x_pred + self.B @ u # if not np.isnan(y).any(): # x_predicted += K_pred @ (y - self.C @ x_pred) - + # # Store results # self.p_filtereds.append(p_filtered) # self.x_filtereds.append(x_filtered) @@ -741,95 +752,74 @@ def __init__(self, dmdc): # self.y_filtereds.append(self.C @ x_filtered) # self.y_predicteds.append(self.C @ x_predicted) # self.kalman_gains.append(K_pred) - + # return self.y_filtereds[-1], self.x_filtereds[-1] + def step(self, y=None, u=None, reg_coef=1e-6): """ Perform one step of the Kalman filter. - + Parameters ---------- - y : torch.Tensor or np.ndarray, optional + y : np.ndarray, optional Observed output at current time step. If None, the filter will predict without observation update. - u : torch.Tensor or np.ndarray, optional + u : np.ndarray, optional Input at current time step. If None, no input is applied. reg_coef : float, optional Regularization coefficient to add to diagonal of P matrices to maintain numerical stability. Default: 1e-6 - + Returns ------- - y_filtered : torch.Tensor + y_filtered : np.ndarray Filtered output estimate - x_filtered : torch.Tensor + x_filtered : np.ndarray Filtered state estimate """ - x_pred = ( - self.x_predicteds[-1] - if self.x_predicteds - else torch.zeros((self.x_dim, 1), device=self.device) - ) - p_pred = ( - self.p_predicteds[-1] - if self.p_predicteds - else torch.eye(self.x_dim, device=self.device) - ) - + x_pred = self.x_predicteds[-1] if self.x_predicteds else np.zeros((self.x_dim, 1)) + p_pred = self.p_predicteds[-1] if self.p_predicteds else np.eye(self.x_dim) + # Add regularization to p_pred to prevent ill-conditioning - p_pred_reg = p_pred + reg_coef * torch.eye(self.x_dim, device=self.device) - - # Convert inputs to tensors and ensure column vectors - if u is not None: - if isinstance(u, np.ndarray): - u = torch.from_numpy(u).float().to(self.device) - if u.ndim == 1: - u = u.reshape(-1, 1) - else: - u = torch.zeros((self.u_dim, 1), device=self.device) - - if y is not None: - if isinstance(y, np.ndarray): - y = torch.from_numpy(y).float().to(self.device) - if y.ndim == 1: - y = y.reshape(-1, 1) - else: - y = torch.zeros((self.y_dim, 1), device=self.device) + p_pred_reg = p_pred + reg_coef * np.eye(self.x_dim) + + # Ensure inputs are column vectors + if u is not None and u.ndim == 1: + u = u.reshape(-1, 1) + if y is not None and y.ndim == 1: + y = y.reshape(-1, 1) + if u is None: + u = np.zeros((self.u_dim, 1)) + if y is None: + y = np.zeros((self.y_dim, 1)) # Use regularized p_pred in computations S_innov = self.R + self.C @ p_pred_reg @ self.C.T - K_filtered = p_pred_reg @ self.C.T @ torch.linalg.pinv(S_innov) + K_filtered = p_pred_reg @ self.C.T @ np.linalg.pinv(S_innov) p_filtered = p_pred_reg - K_filtered @ self.C @ p_pred_reg - + # Add regularization to p_filtered to maintain positive definiteness p_filtered = (p_filtered + p_filtered.T) / 2 # Ensure symmetry - p_filtered = p_filtered + reg_coef * torch.eye( - self.x_dim, device=self.device - ) # Add regularization - - if not torch.isnan(y).any(): + p_filtered = p_filtered + reg_coef * np.eye(self.x_dim) # Add regularization + + if not np.isnan(y).any(): x_filtered = x_pred + K_filtered @ (y - self.C @ x_pred) else: - x_filtered = x_pred.clone() - - K_pred = (self.S + self.A @ p_pred_reg @ self.C.T) @ torch.linalg.pinv(S_innov) - p_predicted = ( - self.A @ p_pred_reg @ self.A.T - + self.Q - - K_pred @ (self.S + self.A @ p_pred_reg @ self.C.T).T - ) - + x_filtered = x_pred.copy() + + K_pred = (self.S + self.A @ p_pred_reg @ self.C.T) @ np.linalg.pinv(S_innov) + p_predicted = (self.A @ p_pred_reg @ self.A.T + self.Q - + K_pred @ (self.S + self.A @ p_pred_reg @ self.C.T).T) + # Add regularization to p_predicted and ensure symmetry p_predicted = (p_predicted + p_predicted.T) / 2 # Ensure symmetry - p_predicted = p_predicted + reg_coef * torch.eye( - self.x_dim, device=self.device - ) # Add regularization - + p_predicted = p_predicted + reg_coef * np.eye(self.x_dim) # Add regularization + x_predicted = self.A @ x_pred + self.B @ u - if not torch.isnan(y).any(): + if not np.isnan(y).any(): x_predicted += K_pred @ (y - self.C @ x_pred) - + # Store results self.p_filtereds.append(p_filtered) self.x_filtereds.append(x_filtered) @@ -840,9 +830,9 @@ def step(self, y=None, u=None, reg_coef=1e-6): self.y_filtereds.append(self.C @ x_filtered) self.y_predicteds.append(self.C @ x_predicted) self.kalman_gains.append(K_pred) - + return self.y_filtereds[-1], self.x_filtereds[-1] - + def reset(self): """Reset the filter to initial state.""" self.p_filtereds = [] @@ -854,17 +844,18 @@ def reset(self): self.y_filtereds = [] self.y_predicteds = [] self.kalman_gains = [] - + + def get_history(self): """Return the complete history of filter states.""" return { - "p_filtereds": self.p_filtereds, - "x_filtereds": self.x_filtereds, - "p_predicteds": self.p_predicteds, - "x_predicteds": self.x_predicteds, - "us": self.us, - "ys": self.ys, - "y_filtereds": self.y_filtereds, - "y_predicteds": self.y_predicteds, - "kalman_gains": self.kalman_gains, - } + 'p_filtereds': self.p_filtereds, + 'x_filtereds': self.x_filtereds, + 'p_predicteds': self.p_predicteds, + 'x_predicteds': self.x_predicteds, + 'us': self.us, + 'ys': self.ys, + 'y_filtereds': self.y_filtereds, + 'y_predicteds': self.y_predicteds, + 'kalman_gains': self.kalman_gains + } \ No newline at end of file From 1d3e9d30adf573d11b3007dec5c0ae0553f5eaa3 Mon Sep 17 00:00:00 2001 From: ostrow Date: Wed, 29 Oct 2025 22:58:02 -0400 Subject: [PATCH 17/90] zeros dmd catch, streamlining subspace_dmdc --- DSA/dmdc.py | 5 +++-- DSA/subspace_dmdc.py | 36 +++++------------------------------- 2 files changed, 8 insertions(+), 33 deletions(-) diff --git a/DSA/dmdc.py b/DSA/dmdc.py index 7076d66..32ec2c0 100644 --- a/DSA/dmdc.py +++ b/DSA/dmdc.py @@ -174,8 +174,9 @@ def _init_data(self, data, control_data=None): control_data ) else: - self.control_data = torch.zeros_like(self.data) - control_is_ragged = False + raise ValueError("control data should be present, otherwise use DMD") + # self.control_data = torch.zeros_like(self.data) + # control_is_ragged = False # Check consistency between data and control_data if data_is_ragged != control_is_ragged: diff --git a/DSA/subspace_dmdc.py b/DSA/subspace_dmdc.py index c37a261..4de7801 100644 --- a/DSA/subspace_dmdc.py +++ b/DSA/subspace_dmdc.py @@ -13,13 +13,11 @@ def __init__( data, control_data=None, n_delays=1, - f=None, rank=None, lamb=1e-8, device='cpu', verbose=False, send_to_cpu=False, - time_first=True, backend='n4sid', ): """ @@ -33,8 +31,6 @@ def __init__( Control input data n_delays : int Number of time delays (past window) - f : int, optional - Future window length (defaults to n_delays) rank : int, optional Rank for system identification lamb : float @@ -47,8 +43,6 @@ def __init__( If True, print progress information send_to_cpu : bool If True, move results to CPU after fitting (useful for batch GPU processing) - time_first : bool - If True, data shape is (time, features); otherwise (features, time) backend : str 'n4sid' or 'custom' for subspace identification algorithm """ @@ -66,9 +60,7 @@ def __init__( self.C_v = None self.info = None self.n_delays = n_delays - self.f = f if f is not None else n_delays # Future window, defaults to n_delays self.rank = rank - self.time_first = time_first self.backend = backend @@ -94,7 +86,7 @@ def fit(self): y=self.data, u=self.control_data, p=self.n_delays, - f=self.f, + f=self.n_delays, n=self.rank, backend=self.backend, lamb=self.lamb) @@ -541,13 +533,8 @@ def subspace_dmdc_multitrial_flexible(self, y, u, p, f, n=None, lamb=1e-8, energ - u: either (n_trials, m, N) array, (m, N) array, or list of (m, N_i) arrays """ if isinstance(y, list) and isinstance(u, list): - # If time_first=True, transpose each trial from (time_points, variables) to (variables, time_points) - if self.time_first: - y_list = [y_trial.T for y_trial in y] - u_list = [u_trial.T for u_trial in u] - else: - y_list = y - u_list = u + y_list = [y_trial.T for y_trial in y] + u_list = [u_trial.T for u_trial in u] if backend == 'n4sid': return self.subspace_dmdc_multitrial_QR_decomposition(y_list, u_list, p, f, n, lamb, energy) else: @@ -563,10 +550,8 @@ def subspace_dmdc_multitrial_flexible(self, y, u, p, f, n=None, lamb=1e-8, energ y_list = [y[i] for i in range(y.shape[0])] u_list = [u[i] for i in range(u.shape[0])] - # If time_first=True, transpose each trial from (time_points, variables) to (variables, time_points) - if self.time_first: - y_list = [y_trial.T for y_trial in y_list] - u_list = [u_trial.T for u_trial in u_list] + y_list = [y_trial.T for y_trial in y_list] + u_list = [u_trial.T for u_trial in u_list] if backend == 'n4sid': return self.subspace_dmdc_multitrial_QR_decomposition(y_list, u_list, p, f, n, lamb, energy) @@ -581,9 +566,6 @@ def predict(self, Y, U, reseed=None): # Handle list of 2D arrays if isinstance(Y, list): - if not self.time_first: - Y = [y.T for y in Y] - U = [u.T for u in U] self.kalman = OnlineKalman(self) Y_pred = [] @@ -599,14 +581,6 @@ def predict(self, Y, U, reseed=None): Y_pred.append(np.concatenate(trial_predictions, axis=1).T) return Y_pred # Return as list to match input format - # print("time_first", self.time_first) - if not self.time_first: - if Y.ndim == 2: - Y = Y.T - U = U.T - else: - Y = Y.transpose(0, 2, 1) - U = U.transpose(0, 2, 1) self.kalman = OnlineKalman(self) if Y.ndim == 2: From 5de9f4fd890938be380e7cb3ae85c98fe90ffda1 Mon Sep 17 00:00:00 2001 From: ostrow Date: Wed, 29 Oct 2025 23:54:51 -0400 Subject: [PATCH 18/90] some bug fixes --- DSA/dmdc.py | 11 +++ DSA/dsa.py | 19 ++-- DSA/subspace_dmdc.py | 10 +- examples/all_dsa_types.ipynb | 174 +++++++++++++++++++++++++++++++++++ 4 files changed, 197 insertions(+), 17 deletions(-) create mode 100644 examples/all_dsa_types.ipynb diff --git a/DSA/dmdc.py b/DSA/dmdc.py index 32ec2c0..3561007 100644 --- a/DSA/dmdc.py +++ b/DSA/dmdc.py @@ -115,6 +115,7 @@ def __init__( self.device, self.use_torch = self._setup_device(device, use_torch=True) self._init_data(data, control_data) + self._check_same_shape() self.n_delays = n_delays self.n_control_delays = n_control_delays @@ -214,6 +215,16 @@ def _init_data(self, data, control_data=None): self.ntrials = 1 self.is_list_data = False + def _check_same_shape(self): + if isinstance(self.data,(np.ndarray,torch.Tensor)): + assert self.data.shape[:-1] == self.control_data.shape[:-1] + elif isinstance(self.data,list): + + assert len(self.data) == len(self.control_data) + + for d,c in zip(self.data,self.control_data): + assert d.shape[:-1] == c.shape[:-1] + def compute_hankel( self, data=None, diff --git a/DSA/dsa.py b/DSA/dsa.py index d11f5c3..015fe06 100644 --- a/DSA/dsa.py +++ b/DSA/dsa.py @@ -98,7 +98,6 @@ class SubspaceDMDcConfig: """ n_delays: int = 1 - delay_interval: int = 1 rank: int = None lamb: float = 0 backend: str = "n4sid" @@ -192,10 +191,10 @@ def __init__( Y_control=None, dmd_class=DefaultDMD, similarity_class=SimilarityTransformDist, - dmd_config: Union[Mapping[str, Any], dataclass] = DefaultDMDConfig, + dmd_config: Union[Mapping[str, Any], dataclass] = DefaultDMDConfig(), simdist_config: Union[ Mapping[str, Any], dataclass - ] = SimilarityTransformDistConfig, + ] = SimilarityTransformDistConfig(), device="cpu", verbose=False, n_jobs=1, @@ -512,7 +511,7 @@ def broadcast_params(self, param, cast=None): assert len(param[i]) >= len(data) out.append(param[i][: len(data)]) elif ( - isinstance(param, (int, float, np.integer)) + isinstance(param, (int, float, np.integer,str)) or param in {None, "None", "none"} or ( hasattr(param, "__module__") @@ -713,10 +712,10 @@ def __init__( Y=None, Y_control=None, dmd_class=SubspaceDMDc, - dmd_config: Union[Mapping[str, Any], dataclass] = SubspaceDMDcConfig, + dmd_config: Union[Mapping[str, Any], dataclass] = SubspaceDMDcConfig(), simdist_config: Union[ Mapping[str, Any], dataclass - ] = ControllabilitySimilarityTransformDistConfig, + ] = ControllabilitySimilarityTransformDistConfig(), device="cpu", verbose=False, n_jobs=1, @@ -725,14 +724,10 @@ def __init__( #TODO: fix based on making compare argument explicit # check if simdist_config has 'compare', and if it's 'state', use the standard SimilarityTransformDist, # otherwise use ControllabilitySimilarityTransformDistConfig - if isinstance(simdist_config, dataclass): - compare = simdist_config.compare - elif isinstance(simdist_config, dict): + if isinstance(simdist_config, dict): compare = simdist_config.get("compare", None) else: - raise ValueError( - "unknown data type for simdist-config, use dataclass or dict" - ) + compare = simdist_config.compare simdist = self.update_compare_method(compare) super().__init__( diff --git a/DSA/subspace_dmdc.py b/DSA/subspace_dmdc.py index 4de7801..db0b91f 100644 --- a/DSA/subspace_dmdc.py +++ b/DSA/subspace_dmdc.py @@ -55,6 +55,8 @@ def __init__( # SubspaceDMDc specific attributes self.data = data self.control_data = control_data + if self.control_data is None: + raise ValueError("no control data detected, use DMD or SubspaceDMD instead") self.A_v = None self.B_v = None self.C_v = None @@ -165,7 +167,7 @@ def hankel_stack(X, start, L): Y_f_all.append(Y_f_trial) if not valid_trials: - raise ValueError("No trials have sufficient data for given (p,f)") + raise ValueError("No trials have sufficient data for given number of delays") # Concatenate across valid trials U_p = np.concatenate(U_p_all, axis=1) # (p m, T_total) @@ -533,12 +535,10 @@ def subspace_dmdc_multitrial_flexible(self, y, u, p, f, n=None, lamb=1e-8, energ - u: either (n_trials, m, N) array, (m, N) array, or list of (m, N_i) arrays """ if isinstance(y, list) and isinstance(u, list): - y_list = [y_trial.T for y_trial in y] - u_list = [u_trial.T for u_trial in u] if backend == 'n4sid': - return self.subspace_dmdc_multitrial_QR_decomposition(y_list, u_list, p, f, n, lamb, energy) + return self.subspace_dmdc_multitrial_QR_decomposition(y, u, p, f, n, lamb, energy) else: - return self.subspace_dmdc_multitrial_custom(y_list, u_list, p, f, n, lamb, energy) + return self.subspace_dmdc_multitrial_custom(y, u, p, f, n, lamb, energy) else: # Handle 2D arrays (single trial) by converting to list format diff --git a/examples/all_dsa_types.ipynb b/examples/all_dsa_types.ipynb new file mode 100644 index 0000000..307ee39 --- /dev/null +++ b/examples/all_dsa_types.ipynb @@ -0,0 +1,174 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 37, + "id": "773aa0fd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n", + "Automatic pdb calling has been turned ON\n" + ] + } + ], + "source": [ + "import numpy as np \n", + "import matplotlib.pyplot as plt\n", + "from DSA import DSA, GeneralizedDSA, InputDSA\n", + "from DSA import DMD, DMDc, SubspaceDMDc\n", + "from pydmd import DMD as pDMD\n", + "import DSA.pykoopman as pk\n", + "%load_ext autoreload\n", + "%autoreload 2\n", + "%pdb" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "d452743b", + "metadata": {}, + "outputs": [], + "source": [ + "d1 = np.random.random(size=(20,5))\n", + "u1 = np.random.random(size=(20,2))\n", + "\n", + "d2 = np.random.random(size=(2,20,5))\n", + "u2 = np.random.random(size=(2,20,2))\n", + "\n", + "d3 = [np.random.random(size=(i,20,5)) for i in range(1,10)]\n", + "u3 = [np.random.random(size=(i,20,2)) for i in range(1,10)]\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "88cad354", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(5, 5)" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "#TODO: fix this case\n", + "# dmdc = DMDc(d3,u3,n_delays=2,rank_input=10,rank_output=10)\n", + "# dmdc.fit()\n", + "# print(dmdc.A_v.shape)\n", + "# print(dmdc.B_v.shape)\n", + "\n", + "#TODO: fix this case\n", + "# subdmdc = SubspaceDMDc(d1,u1,n_delays=10,rank=2)\n", + "# subdmdc.fit()\n", + "# print(subdmdc.A_v.shape)\n", + "# print(subdmdc.B_v.shape)\n", + "\n", + "\n", + "#TODO: fix this case\n", + "# subdmdc = SubspaceDMDc(d3,u3,n_delays=2,rank=10,backend='n4sid')\n", + "# subdmdc.fit()\n", + "# print(subdmdc.A_v.shape)\n", + "# print(subdmdc.B_v.shape)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "721bc598", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mitchellostrow/Desktop/Projects/DSAv2/DSAPublic2/DSA/DSA/dsa.py:364: UserWarning: Warning: You are using a DMD model that fits a control operator but comparing with a DSA metric that does not compare control operators\n", + " if self.dmd_has_control and not self.simdist_has_control:\n" + ] + }, + { + "ename": "TypeError", + "evalue": "SimilarityTransformDist.__init__() got an unexpected keyword argument 'joint_optim'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[56], line 9\u001b[0m\n\u001b[1;32m 2\u001b[0m u1s \u001b[38;5;241m=\u001b[39m [np\u001b[38;5;241m.\u001b[39mrandom\u001b[38;5;241m.\u001b[39mrandom(size\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m20\u001b[39m,\u001b[38;5;241m2\u001b[39m)) \u001b[38;5;28;01mfor\u001b[39;00m _ \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m3\u001b[39m)]\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m#works\u001b[39;00m\n\u001b[1;32m 5\u001b[0m \u001b[38;5;66;03m# dsa = DSA(d1s,dmd_class=pk.Koopman,\u001b[39;00m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;66;03m# observables=pk.observables.TimeDelay(),regressor=pDMD(svd_rank=5),\u001b[39;00m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;66;03m# score_method='wasserstein')\u001b[39;00m\n\u001b[0;32m----> 9\u001b[0m dsa \u001b[38;5;241m=\u001b[39m \u001b[43mInputDSA\u001b[49m\u001b[43m(\u001b[49m\u001b[43md1s\u001b[49m\u001b[43m,\u001b[49m\u001b[43mu1s\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 10\u001b[0m sim \u001b[38;5;241m=\u001b[39m dsa\u001b[38;5;241m.\u001b[39mfit_score()\n\u001b[1;32m 11\u001b[0m sim\u001b[38;5;241m.\u001b[39mshape\n", + "File \u001b[0;32m~/Desktop/Projects/DSAv2/DSAPublic2/DSA/DSA/dsa.py:733\u001b[0m, in \u001b[0;36mInputDSA.__init__\u001b[0;34m(self, X, X_control, Y, Y_control, dmd_class, dmd_config, simdist_config, device, verbose, n_jobs, compare)\u001b[0m\n\u001b[1;32m 730\u001b[0m compare \u001b[38;5;241m=\u001b[39m simdist_config\u001b[38;5;241m.\u001b[39mcompare\n\u001b[1;32m 731\u001b[0m simdist \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mupdate_compare_method(compare)\n\u001b[0;32m--> 733\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m 734\u001b[0m \u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 735\u001b[0m \u001b[43m \u001b[49m\u001b[43mY\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 736\u001b[0m \u001b[43m \u001b[49m\u001b[43mX_control\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 737\u001b[0m \u001b[43m \u001b[49m\u001b[43mY_control\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 738\u001b[0m \u001b[43m \u001b[49m\u001b[43mdmd_class\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 739\u001b[0m \u001b[43m 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\u001b[0;32m~/Desktop/Projects/DSAv2/DSAPublic2/DSA/DSA/dsa.py:312\u001b[0m, in \u001b[0;36mGeneralizedDSA.__init__\u001b[0;34m(self, X, Y, X_control, Y_control, dmd_class, similarity_class, dmd_config, simdist_config, device, verbose, n_jobs)\u001b[0m\n\u001b[1;32m 310\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dmd_api_source(dmd_class)\n\u001b[1;32m 311\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_initiate_dmds()\n\u001b[0;32m--> 312\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msimdist \u001b[38;5;241m=\u001b[39m \u001b[43msimilarity_class\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msimdist_config\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[0;31mTypeError\u001b[0m: SimilarityTransformDist.__init__() got an unexpected keyword argument 'joint_optim'" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "> \u001b[0;32m/Users/mitchellostrow/Desktop/Projects/DSAv2/DSAPublic2/DSA/DSA/dsa.py\u001b[0m(312)\u001b[0;36m__init__\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32m 310 \u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dmd_api_source\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdmd_class\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m 311 \u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_initiate_dmds\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m--> 312 \u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msimdist\u001b[0m \u001b[0;34m=\u001b[0m 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+ ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "26c08771", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "dsa_test_env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.18" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From f6f1d1af835824cc98d7dff0b4e097ed8571999c Mon Sep 17 00:00:00 2001 From: ostrow Date: Thu, 30 Oct 2025 09:15:40 -0400 Subject: [PATCH 19/90] bug fixes --- DSA/dsa.py | 14 +++++--------- DSA/simdist.py | 2 +- 2 files changed, 6 insertions(+), 10 deletions(-) diff --git a/DSA/dsa.py b/DSA/dsa.py index 015fe06..62b869b 100644 --- a/DSA/dsa.py +++ b/DSA/dsa.py @@ -120,15 +120,11 @@ class SimilarityTransformDistConfig: Default is "angular". lr (float): Learning rate for the optimization algorithm. Default is 5e-3. - zero_pad (bool): Whether to zero-pad matrices to make them the same size. - Default is False. """ iters: int = 1500 score_method: Literal["angular", "euclidean", "wasserstein"] = "angular" lr: float = 5e-3 - zero_pad: bool = False - @dataclass() class ControllabilitySimilarityTransformDistConfig: @@ -152,7 +148,7 @@ class ControllabilitySimilarityTransformDistConfig: """ score_method: Literal["euclidean", "angular"] = "euclidean" - compare = "state" + compare = "joint" joint_optim: bool = False return_distance_components: bool = False @@ -600,8 +596,6 @@ def score(self): self.sims = np.zeros((len(self.dmds[0]), len(self.dmds[ind2]), n_sims)) - if self.verbose: - print("comparing dmds") def compute_similarity(i, j): if self.method == "self-pairwise" and j >= i: @@ -614,7 +608,8 @@ def compute_similarity(i, j): self.get_dmd_matrix(self.dmds[0][i]), self.get_dmd_matrix(self.dmds[ind2][j]), ] - if self.dmd_has_control: + + if self.simdist_has_control and self.dmd_has_control: simdist_args.extend( [ self.get_dmd_control_matrix(self.dmds[0][i]), @@ -719,7 +714,6 @@ def __init__( device="cpu", verbose=False, n_jobs=1, - compare = 'joint' ): #TODO: fix based on making compare argument explicit # check if simdist_config has 'compare', and if it's 'state', use the standard SimilarityTransformDist, @@ -750,7 +744,9 @@ def __init__( def update_compare_method(self,compare='joint'): if compare == "state": simdist = SimilarityTransformDist + #TODO: check simdist config to make sure it aligns else: simdist = ControllabilitySimilarityTransformDist + #TODO: check simdist config to make sure it aligns return simdist diff --git a/DSA/simdist.py b/DSA/simdist.py index 9745c34..3882093 100644 --- a/DSA/simdist.py +++ b/DSA/simdist.py @@ -462,7 +462,7 @@ def fit_score( score_method != "wasserstein" ): # otherwise resort to L2 Wasserstein over singular or eigenvalues warnings.warn( - f"resorting to wasserstein distance over {self.wasserstein_compare}" + f"shapes are not aligned, resorting to wasserstein distance over {self.wasserstein_compare}" ) score_method = "wasserstein" else: From db4ccff7cb2fdac8ad1012b96a01248edb473db6 Mon Sep 17 00:00:00 2001 From: ostrow Date: Thu, 30 Oct 2025 09:16:51 -0400 Subject: [PATCH 20/90] pykoopman --- DSA/pykoopman/.readthedocs.yaml | 35 - DSA/pykoopman/{src/pykoopman => }/__init__.py | 6 + .../{src/pykoopman => }/analytics/__init__.py | 0 .../analytics/_base_analyzer.py | 1 + .../{src/pykoopman => }/analytics/_ms_pd21.py | 9 +- .../analytics/_pruned_koopman.py | 3 +- .../{src/pykoopman => }/common/__init__.py | 13 - .../{src/pykoopman => }/common/validation.py | 0 .../differentiation/__init__.py | 0 .../differentiation/_derivative.py | 1 + .../differentiation/_finite_difference.py | 0 DSA/pykoopman/{src/pykoopman => }/koopman.py | 1 + .../{src/pykoopman => }/koopman_continuous.py | 1 + .../pykoopman => }/observables/__init__.py | 0 .../{src/pykoopman => }/observables/_base.py | 7 +- .../observables/_custom_observables.py | 7 +- .../pykoopman => }/observables/_identity.py | 1 + .../pykoopman => }/observables/_polynomial.py | 1 + .../observables/_radial_basis_functions.py | 1 + .../observables/_random_fourier_features.py | 1 + .../pykoopman => }/observables/_time_delay.py | 7 +- .../pykoopman => }/regression/__init__.py | 0 .../{src/pykoopman => }/regression/_base.py | 1 + .../regression/_base_ensemble.py | 1 + .../{src/pykoopman => }/regression/_dmd.py | 1 + .../{src/pykoopman => }/regression/_dmdc.py | 1 + .../{src/pykoopman => }/regression/_edmd.py | 1 + .../{src/pykoopman => }/regression/_edmdc.py | 1 + .../{src/pykoopman => }/regression/_havok.py | 1 + .../{src/pykoopman => }/regression/_kdmd.py | 1 + .../{src/pykoopman => }/regression/_nndmd.py | 3 +- DSA/pykoopman/src/pykoopman/common/cqgle.py | 234 ---- .../src/pykoopman/common/examples.py | 1045 ----------------- DSA/pykoopman/src/pykoopman/common/ks.py | 189 --- DSA/pykoopman/src/pykoopman/common/nlse.py | 186 --- DSA/pykoopman/src/pykoopman/common/vbe.py | 177 --- 36 files changed, 43 insertions(+), 1894 deletions(-) delete mode 100644 DSA/pykoopman/.readthedocs.yaml rename DSA/pykoopman/{src/pykoopman => }/__init__.py (71%) rename DSA/pykoopman/{src/pykoopman => }/analytics/__init__.py (100%) rename DSA/pykoopman/{src/pykoopman => }/analytics/_base_analyzer.py (99%) rename DSA/pykoopman/{src/pykoopman => }/analytics/_ms_pd21.py (99%) rename DSA/pykoopman/{src/pykoopman => }/analytics/_pruned_koopman.py (99%) rename DSA/pykoopman/{src/pykoopman => }/common/__init__.py (52%) rename DSA/pykoopman/{src/pykoopman => }/common/validation.py (100%) rename DSA/pykoopman/{src/pykoopman => }/differentiation/__init__.py (100%) rename DSA/pykoopman/{src/pykoopman => }/differentiation/_derivative.py (99%) rename DSA/pykoopman/{src/pykoopman => }/differentiation/_finite_difference.py (100%) rename DSA/pykoopman/{src/pykoopman => }/koopman.py (99%) rename DSA/pykoopman/{src/pykoopman => }/koopman_continuous.py (99%) rename DSA/pykoopman/{src/pykoopman => }/observables/__init__.py (100%) rename DSA/pykoopman/{src/pykoopman => }/observables/_base.py (99%) rename DSA/pykoopman/{src/pykoopman => }/observables/_custom_observables.py (98%) rename DSA/pykoopman/{src/pykoopman => }/observables/_identity.py (99%) rename DSA/pykoopman/{src/pykoopman => }/observables/_polynomial.py (99%) rename DSA/pykoopman/{src/pykoopman => }/observables/_radial_basis_functions.py (99%) rename DSA/pykoopman/{src/pykoopman => }/observables/_random_fourier_features.py (99%) rename DSA/pykoopman/{src/pykoopman => }/observables/_time_delay.py (98%) rename DSA/pykoopman/{src/pykoopman => }/regression/__init__.py (100%) rename DSA/pykoopman/{src/pykoopman => }/regression/_base.py (99%) rename DSA/pykoopman/{src/pykoopman => }/regression/_base_ensemble.py (99%) rename DSA/pykoopman/{src/pykoopman => }/regression/_dmd.py (99%) rename DSA/pykoopman/{src/pykoopman => }/regression/_dmdc.py (99%) rename DSA/pykoopman/{src/pykoopman => }/regression/_edmd.py (99%) rename DSA/pykoopman/{src/pykoopman => }/regression/_edmdc.py (99%) rename DSA/pykoopman/{src/pykoopman => }/regression/_havok.py (99%) rename DSA/pykoopman/{src/pykoopman => }/regression/_kdmd.py (99%) rename DSA/pykoopman/{src/pykoopman => }/regression/_nndmd.py (99%) delete mode 100644 DSA/pykoopman/src/pykoopman/common/cqgle.py delete mode 100644 DSA/pykoopman/src/pykoopman/common/examples.py delete mode 100644 DSA/pykoopman/src/pykoopman/common/ks.py delete mode 100644 DSA/pykoopman/src/pykoopman/common/nlse.py delete mode 100644 DSA/pykoopman/src/pykoopman/common/vbe.py diff --git a/DSA/pykoopman/.readthedocs.yaml b/DSA/pykoopman/.readthedocs.yaml deleted file mode 100644 index f915bd9..0000000 --- a/DSA/pykoopman/.readthedocs.yaml +++ /dev/null @@ -1,35 +0,0 @@ -# .readthedocs.yaml -# Read the Docs configuration file -# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details - -# Required -version: 2 - -# Set the version of Python and other tools you might need -build: - os: ubuntu-22.04 - tools: - python: "3.10" - # You can also specify other tool versions: - # nodejs: "19" - # rust: "1.64" - # golang: "1.19" - -# Build documentation in the docs/ directory with Sphinx -sphinx: - configuration: docs/conf.py - -# If using Sphinx, optionally build your docs in additional formats such as PDF -# formats: -# - pdf - -# Optionally declare the Python requirements required to build your docs -#python: -# install: -# - requirements: requirements-dev.txt -# - method: pip -# path: . -python: - install: - - method: pip - path: .[dev] diff --git a/DSA/pykoopman/src/pykoopman/__init__.py b/DSA/pykoopman/__init__.py similarity index 71% rename from DSA/pykoopman/src/pykoopman/__init__.py rename to DSA/pykoopman/__init__.py index b6e344d..4bbc91d 100644 --- a/DSA/pykoopman/src/pykoopman/__init__.py +++ b/DSA/pykoopman/__init__.py @@ -11,6 +11,12 @@ from .koopman import Koopman from .koopman_continuous import KoopmanContinuous +# Import submodules so they are accessible as attributes +from . import common +from . import differentiation +from . import observables +from . import regression +from . import analytics __all__ = [ "Koopman", diff --git a/DSA/pykoopman/src/pykoopman/analytics/__init__.py b/DSA/pykoopman/analytics/__init__.py similarity index 100% rename from DSA/pykoopman/src/pykoopman/analytics/__init__.py rename to DSA/pykoopman/analytics/__init__.py diff --git a/DSA/pykoopman/src/pykoopman/analytics/_base_analyzer.py b/DSA/pykoopman/analytics/_base_analyzer.py similarity index 99% rename from DSA/pykoopman/src/pykoopman/analytics/_base_analyzer.py rename to DSA/pykoopman/analytics/_base_analyzer.py index 9cdb156..dce5aec 100644 --- a/DSA/pykoopman/src/pykoopman/analytics/_base_analyzer.py +++ b/DSA/pykoopman/analytics/_base_analyzer.py @@ -1,4 +1,5 @@ """module for implement modes analyzer for Koopman approximation""" + from __future__ import annotations import abc diff --git a/DSA/pykoopman/src/pykoopman/analytics/_ms_pd21.py b/DSA/pykoopman/analytics/_ms_pd21.py similarity index 99% rename from DSA/pykoopman/src/pykoopman/analytics/_ms_pd21.py rename to DSA/pykoopman/analytics/_ms_pd21.py index bf789bb..8306b81 100644 --- a/DSA/pykoopman/src/pykoopman/analytics/_ms_pd21.py +++ b/DSA/pykoopman/analytics/_ms_pd21.py @@ -1,10 +1,11 @@ """Module for implementing Pan-Duraisamy modes selection algorithm""" + from __future__ import annotations import numpy as np from matplotlib import pyplot as plt from prettytable import PrettyTable -from pykoopman.koopman import Koopman +from DSA.pykoopman.koopman import Koopman from sklearn.linear_model import enet_path from ._base_analyzer import BaseAnalyzer @@ -303,9 +304,9 @@ def sweep_among_best_L_modes( coef_enet_comp_reduced_i_alpha = np.linalg.lstsq( phi_tilde_scaled_reduced, X )[0] - coefs_enet_comp[ - :, bool_non_zero, i_alpha - ] = coef_enet_comp_reduced_i_alpha.T + coefs_enet_comp[:, bool_non_zero, i_alpha] = ( + coef_enet_comp_reduced_i_alpha.T + ) coefs_enet_comp[:, np.invert(bool_non_zero), i_alpha] = 0 # 3. compute residual for parameter sweep to draw the trade-off diff --git a/DSA/pykoopman/src/pykoopman/analytics/_pruned_koopman.py b/DSA/pykoopman/analytics/_pruned_koopman.py similarity index 99% rename from DSA/pykoopman/src/pykoopman/analytics/_pruned_koopman.py rename to DSA/pykoopman/analytics/_pruned_koopman.py index d795bf4..2333511 100644 --- a/DSA/pykoopman/src/pykoopman/analytics/_pruned_koopman.py +++ b/DSA/pykoopman/analytics/_pruned_koopman.py @@ -1,8 +1,9 @@ """Module for pruning Koopman models.""" + from __future__ import annotations import numpy as np -from pykoopman.koopman import Koopman +from DSA.pykoopman.koopman import Koopman from sklearn.utils.validation import check_is_fitted diff --git a/DSA/pykoopman/src/pykoopman/common/__init__.py b/DSA/pykoopman/common/__init__.py similarity index 52% rename from DSA/pykoopman/src/pykoopman/common/__init__.py rename to DSA/pykoopman/common/__init__.py index 4badea5..bc6769f 100644 --- a/DSA/pykoopman/src/pykoopman/common/__init__.py +++ b/DSA/pykoopman/common/__init__.py @@ -1,21 +1,8 @@ from __future__ import annotations -from .cqgle import cqgle -from .examples import advance_linear_system -from .examples import drss -from .examples import Linear2Ddynamics -from .examples import lorenz -from .examples import rev_dvdp -from .examples import rk4 -from .examples import slow_manifold -from .examples import torus_dynamics -from .examples import vdp_osc -from .ks import ks -from .nlse import nlse from .validation import check_array from .validation import drop_nan_rows from .validation import validate_input -from .vbe import vbe __all__ = [ "check_array", diff --git a/DSA/pykoopman/src/pykoopman/common/validation.py b/DSA/pykoopman/common/validation.py similarity index 100% rename from DSA/pykoopman/src/pykoopman/common/validation.py rename to DSA/pykoopman/common/validation.py diff --git a/DSA/pykoopman/src/pykoopman/differentiation/__init__.py b/DSA/pykoopman/differentiation/__init__.py similarity index 100% rename from DSA/pykoopman/src/pykoopman/differentiation/__init__.py rename to DSA/pykoopman/differentiation/__init__.py diff --git a/DSA/pykoopman/src/pykoopman/differentiation/_derivative.py b/DSA/pykoopman/differentiation/_derivative.py similarity index 99% rename from DSA/pykoopman/src/pykoopman/differentiation/_derivative.py rename to DSA/pykoopman/differentiation/_derivative.py index 52c94dc..26fc6ba 100644 --- a/DSA/pykoopman/src/pykoopman/differentiation/_derivative.py +++ b/DSA/pykoopman/differentiation/_derivative.py @@ -2,6 +2,7 @@ Some default values used here may differ from those used in :doc:`derivative:index`. """ + from __future__ import annotations from derivative import dxdt diff --git a/DSA/pykoopman/src/pykoopman/differentiation/_finite_difference.py b/DSA/pykoopman/differentiation/_finite_difference.py similarity index 100% rename from DSA/pykoopman/src/pykoopman/differentiation/_finite_difference.py rename to DSA/pykoopman/differentiation/_finite_difference.py diff --git a/DSA/pykoopman/src/pykoopman/koopman.py b/DSA/pykoopman/koopman.py similarity index 99% rename from DSA/pykoopman/src/pykoopman/koopman.py rename to DSA/pykoopman/koopman.py index 4330a68..8b008dd 100644 --- a/DSA/pykoopman/src/pykoopman/koopman.py +++ b/DSA/pykoopman/koopman.py @@ -1,4 +1,5 @@ """module for discrete time Koopman class""" + from __future__ import annotations from warnings import catch_warnings diff --git a/DSA/pykoopman/src/pykoopman/koopman_continuous.py b/DSA/pykoopman/koopman_continuous.py similarity index 99% rename from DSA/pykoopman/src/pykoopman/koopman_continuous.py rename to DSA/pykoopman/koopman_continuous.py index 2ba881d..b02fcad 100644 --- a/DSA/pykoopman/src/pykoopman/koopman_continuous.py +++ b/DSA/pykoopman/koopman_continuous.py @@ -1,4 +1,5 @@ """module for continuous time Koopman class""" + from __future__ import annotations import numpy as np diff --git a/DSA/pykoopman/src/pykoopman/observables/__init__.py b/DSA/pykoopman/observables/__init__.py similarity index 100% rename from DSA/pykoopman/src/pykoopman/observables/__init__.py rename to DSA/pykoopman/observables/__init__.py diff --git a/DSA/pykoopman/src/pykoopman/observables/_base.py b/DSA/pykoopman/observables/_base.py similarity index 99% rename from DSA/pykoopman/src/pykoopman/observables/_base.py rename to DSA/pykoopman/observables/_base.py index 2d9af34..62b0fca 100644 --- a/DSA/pykoopman/src/pykoopman/observables/_base.py +++ b/DSA/pykoopman/observables/_base.py @@ -1,4 +1,5 @@ """Module for base classes for specific observable classes.""" + from __future__ import annotations import abc @@ -295,7 +296,7 @@ def transform(self, X): # Handle 3D data (multiple trials) by processing each trial separately if isinstance(X, list): return [self.transform(X_trial) for X_trial in X] - + if X.ndim == 3: return np.array([self.transform(X_trial) for X_trial in X]) @@ -372,7 +373,7 @@ def inverse(self, y): Args: y (numpy.ndarray): Data to which to apply the inverse. - Shape must be (n_samples, n_output_features) or + Shape must be (n_samples, n_output_features) or (n_trials, n_samples, n_output_features). Must have the same number of features as the transformed data. @@ -391,7 +392,7 @@ def inverse(self, y): # Handle 3D data (multiple trials) by processing each trial separately if isinstance(y, list): return [self.inverse(y_trial) for y_trial in y] - + if y.ndim == 3: return np.array([self.inverse(y_trial) for y_trial in y]) diff --git a/DSA/pykoopman/src/pykoopman/observables/_custom_observables.py b/DSA/pykoopman/observables/_custom_observables.py similarity index 98% rename from DSA/pykoopman/src/pykoopman/observables/_custom_observables.py rename to DSA/pykoopman/observables/_custom_observables.py index 6d9cefb..541087f 100644 --- a/DSA/pykoopman/src/pykoopman/observables/_custom_observables.py +++ b/DSA/pykoopman/observables/_custom_observables.py @@ -1,4 +1,5 @@ """Module for customized observables""" + from __future__ import annotations from itertools import combinations @@ -114,9 +115,9 @@ def fit(self, x, y=None): self.measurement_matrix_ = np.zeros( (self.n_input_features_, self.n_output_features_) ) - self.measurement_matrix_[ - : self.n_input_features_, : self.n_input_features_ - ] = np.eye(self.n_input_features_) + self.measurement_matrix_[: self.n_input_features_, : self.n_input_features_] = ( + np.eye(self.n_input_features_) + ) return self diff --git a/DSA/pykoopman/src/pykoopman/observables/_identity.py b/DSA/pykoopman/observables/_identity.py similarity index 99% rename from DSA/pykoopman/src/pykoopman/observables/_identity.py rename to DSA/pykoopman/observables/_identity.py index 3f7ceb0..47d24c0 100644 --- a/DSA/pykoopman/src/pykoopman/observables/_identity.py +++ b/DSA/pykoopman/observables/_identity.py @@ -1,4 +1,5 @@ """module for Linear observables""" + from __future__ import annotations import numpy as np diff --git a/DSA/pykoopman/src/pykoopman/observables/_polynomial.py b/DSA/pykoopman/observables/_polynomial.py similarity index 99% rename from DSA/pykoopman/src/pykoopman/observables/_polynomial.py rename to DSA/pykoopman/observables/_polynomial.py index 5e852de..f749a4f 100644 --- a/DSA/pykoopman/src/pykoopman/observables/_polynomial.py +++ b/DSA/pykoopman/observables/_polynomial.py @@ -1,4 +1,5 @@ """moduel for Polynomial observables""" + from __future__ import annotations from itertools import chain diff --git a/DSA/pykoopman/src/pykoopman/observables/_radial_basis_functions.py b/DSA/pykoopman/observables/_radial_basis_functions.py similarity index 99% rename from DSA/pykoopman/src/pykoopman/observables/_radial_basis_functions.py rename to DSA/pykoopman/observables/_radial_basis_functions.py index 217f485..2363722 100644 --- a/DSA/pykoopman/src/pykoopman/observables/_radial_basis_functions.py +++ b/DSA/pykoopman/observables/_radial_basis_functions.py @@ -1,4 +1,5 @@ """module for Radial basis function observables""" + from __future__ import annotations import numpy as np diff --git a/DSA/pykoopman/src/pykoopman/observables/_random_fourier_features.py b/DSA/pykoopman/observables/_random_fourier_features.py similarity index 99% rename from DSA/pykoopman/src/pykoopman/observables/_random_fourier_features.py rename to DSA/pykoopman/observables/_random_fourier_features.py index a9ebec7..af9f690 100644 --- a/DSA/pykoopman/src/pykoopman/observables/_random_fourier_features.py +++ b/DSA/pykoopman/observables/_random_fourier_features.py @@ -1,4 +1,5 @@ """module for random fourier features observables""" + from __future__ import annotations import numpy as np diff --git a/DSA/pykoopman/src/pykoopman/observables/_time_delay.py b/DSA/pykoopman/observables/_time_delay.py similarity index 98% rename from DSA/pykoopman/src/pykoopman/observables/_time_delay.py rename to DSA/pykoopman/observables/_time_delay.py index eb0c9db..038dd2b 100644 --- a/DSA/pykoopman/src/pykoopman/observables/_time_delay.py +++ b/DSA/pykoopman/observables/_time_delay.py @@ -1,4 +1,5 @@ """moduel for time-delay observables""" + from __future__ import annotations import numpy as np @@ -102,9 +103,9 @@ def fit(self, x, y=None): self.measurement_matrix_ = np.zeros( (self.n_input_features_, self.n_output_features_) ) - self.measurement_matrix_[ - : self.n_input_features_, : self.n_input_features_ - ] = np.eye(self.n_input_features_) + self.measurement_matrix_[: self.n_input_features_, : self.n_input_features_] = ( + np.eye(self.n_input_features_) + ) return self diff --git a/DSA/pykoopman/src/pykoopman/regression/__init__.py b/DSA/pykoopman/regression/__init__.py similarity index 100% rename from DSA/pykoopman/src/pykoopman/regression/__init__.py rename to DSA/pykoopman/regression/__init__.py diff --git a/DSA/pykoopman/src/pykoopman/regression/_base.py b/DSA/pykoopman/regression/_base.py similarity index 99% rename from DSA/pykoopman/src/pykoopman/regression/_base.py rename to DSA/pykoopman/regression/_base.py index 6c49394..cf76532 100644 --- a/DSA/pykoopman/src/pykoopman/regression/_base.py +++ b/DSA/pykoopman/regression/_base.py @@ -1,4 +1,5 @@ """module for base class of regressor""" + from __future__ import annotations from abc import ABC diff --git a/DSA/pykoopman/src/pykoopman/regression/_base_ensemble.py b/DSA/pykoopman/regression/_base_ensemble.py similarity index 99% rename from DSA/pykoopman/src/pykoopman/regression/_base_ensemble.py rename to DSA/pykoopman/regression/_base_ensemble.py index 53f7f16..41c4949 100644 --- a/DSA/pykoopman/src/pykoopman/regression/_base_ensemble.py +++ b/DSA/pykoopman/regression/_base_ensemble.py @@ -2,6 +2,7 @@ Manual changes are made to add support to complex numeric data """ + from __future__ import annotations from sklearn.base import BaseEstimator diff --git a/DSA/pykoopman/src/pykoopman/regression/_dmd.py b/DSA/pykoopman/regression/_dmd.py similarity index 99% rename from DSA/pykoopman/src/pykoopman/regression/_dmd.py rename to DSA/pykoopman/regression/_dmd.py index d447571..130cbb0 100644 --- a/DSA/pykoopman/src/pykoopman/regression/_dmd.py +++ b/DSA/pykoopman/regression/_dmd.py @@ -1,4 +1,5 @@ """module for dmd""" + # from warnings import warn from __future__ import annotations diff --git a/DSA/pykoopman/src/pykoopman/regression/_dmdc.py b/DSA/pykoopman/regression/_dmdc.py similarity index 99% rename from DSA/pykoopman/src/pykoopman/regression/_dmdc.py rename to DSA/pykoopman/regression/_dmdc.py index 9de8a5d..da37439 100644 --- a/DSA/pykoopman/src/pykoopman/regression/_dmdc.py +++ b/DSA/pykoopman/regression/_dmdc.py @@ -1,4 +1,5 @@ """module for dmd with control""" + from __future__ import annotations import numpy as np diff --git a/DSA/pykoopman/src/pykoopman/regression/_edmd.py b/DSA/pykoopman/regression/_edmd.py similarity index 99% rename from DSA/pykoopman/src/pykoopman/regression/_edmd.py rename to DSA/pykoopman/regression/_edmd.py index 409028b..c27d781 100644 --- a/DSA/pykoopman/src/pykoopman/regression/_edmd.py +++ b/DSA/pykoopman/regression/_edmd.py @@ -1,4 +1,5 @@ """module for extended dmd""" + # from warnings import warn from __future__ import annotations diff --git a/DSA/pykoopman/src/pykoopman/regression/_edmdc.py b/DSA/pykoopman/regression/_edmdc.py similarity index 99% rename from DSA/pykoopman/src/pykoopman/regression/_edmdc.py rename to DSA/pykoopman/regression/_edmdc.py index 1826a65..56e8c81 100644 --- a/DSA/pykoopman/src/pykoopman/regression/_edmdc.py +++ b/DSA/pykoopman/regression/_edmdc.py @@ -1,4 +1,5 @@ """module for extended dmd with control""" + from __future__ import annotations import numpy as np diff --git a/DSA/pykoopman/src/pykoopman/regression/_havok.py b/DSA/pykoopman/regression/_havok.py similarity index 99% rename from DSA/pykoopman/src/pykoopman/regression/_havok.py rename to DSA/pykoopman/regression/_havok.py index 0273ebd..ada1a16 100644 --- a/DSA/pykoopman/src/pykoopman/regression/_havok.py +++ b/DSA/pykoopman/regression/_havok.py @@ -1,4 +1,5 @@ """module for havok""" + from __future__ import annotations from warnings import warn diff --git a/DSA/pykoopman/src/pykoopman/regression/_kdmd.py b/DSA/pykoopman/regression/_kdmd.py similarity index 99% rename from DSA/pykoopman/src/pykoopman/regression/_kdmd.py rename to DSA/pykoopman/regression/_kdmd.py index 6e33111..550ff20 100644 --- a/DSA/pykoopman/src/pykoopman/regression/_kdmd.py +++ b/DSA/pykoopman/regression/_kdmd.py @@ -1,4 +1,5 @@ """module for kernel dmd""" + from __future__ import annotations from warnings import warn diff --git a/DSA/pykoopman/src/pykoopman/regression/_nndmd.py b/DSA/pykoopman/regression/_nndmd.py similarity index 99% rename from DSA/pykoopman/src/pykoopman/regression/_nndmd.py rename to DSA/pykoopman/regression/_nndmd.py index 2518761..b9e6018 100644 --- a/DSA/pykoopman/src/pykoopman/regression/_nndmd.py +++ b/DSA/pykoopman/regression/_nndmd.py @@ -1,4 +1,5 @@ """module for implementing a neural network DMD""" + from __future__ import annotations import pickle @@ -8,7 +9,7 @@ import lightning as L import numpy as np import torch -from pykoopman.regression._base import BaseRegressor +from DSA.pykoopman.regression._base import BaseRegressor from sklearn.utils.validation import check_is_fitted from torch import nn from torch.nn.utils.rnn import pad_sequence diff --git a/DSA/pykoopman/src/pykoopman/common/cqgle.py b/DSA/pykoopman/src/pykoopman/common/cqgle.py deleted file mode 100644 index f91e230..0000000 --- a/DSA/pykoopman/src/pykoopman/common/cqgle.py +++ /dev/null @@ -1,234 +0,0 @@ -"""Module for cubic-quintic Ginzburg-Landau equation.""" -from __future__ import annotations - -import numpy as np -from matplotlib import pyplot as plt -from mpl_toolkits.mplot3d import Axes3D -from pykoopman.common.examples import rk4 -from scipy.fft import fft -from scipy.fft import fftfreq -from scipy.fft import ifft - - -class cqgle: - """ - Cubic-quintic Ginzburg-Landau equation solver. - - Solves the equation: - i*u_t + (0.5 - i * tau) u_{xx} - i * kappa u_{xxxx} + (1-i * beta)|u|^2 u + - (nu - i * sigma)|u|^4 u - i * gamma u = 0 - - Solves the periodic boundary conditions PDE using spectral methods. - - Attributes: - n_states (int): Number of states. - x (numpy.ndarray): x-coordinates. - dt (float): Time step. - tau (float): Parameter tau. - kappa (float): Parameter kappa. - beta (float): Parameter beta. - nu (float): Parameter nu. - sigma (float): Parameter sigma. - gamma (float): Parameter gamma. - k (numpy.ndarray): Wave numbers. - dk (float): Wave number spacing. - - Methods: - sys(t, x, u): System dynamics function. - simulate(x0, n_int, n_sample): Simulate the system for a given initial - condition. - collect_data_continuous(x0): Collect training data pairs in continuous sense. - collect_one_step_data_discrete(x0): Collect training data pairs in discrete - sense. - collect_one_trajectory_data(x0, n_int, n_sample): Collect data for one - trajectory. - visualize_data(x, t, X): Visualize the data in physical space. - visualize_state_space(X): Visualize the data in state space. - """ - - def __init__( - self, - n, - x, - dt, - tau=0.08, - kappa=0, - beta=0.66, - nu=-0.1, - sigma=-0.1, - gamma=-0.1, - L=2 * np.pi, - ): - self.n_states = n - self.x = x - - self.tau = tau - self.kappa = kappa - self.beta = beta - self.nu = nu - self.sigma = sigma - self.gamma = gamma - - dk = 2 * np.pi / L - self.k = fftfreq(self.n_states, 1.0 / self.n_states) * dk - self.dt = dt - - def sys(self, t, x, u): - xk = fft(x) - - # 1/3 truncation rule - xk[self.n_states // 6 : 5 * self.n_states // 6] = 0j - x = ifft(xk) - - tmp_1_k = (0.5 - 1j * self.tau) * (-self.k**2) * xk - tmp_2_k = -1j * self.kappa * self.k**4 * xk - tmp_3_k = fft( - (1 - 1j * self.beta) * abs(x) ** 2 * x - + (self.nu - 1j * self.sigma) * abs(x) ** 4 * x - ) - tmp_4_k = -1j * self.gamma * xk - - # return back to physical space - y = ifft(1j * (tmp_1_k + tmp_2_k + tmp_3_k + tmp_4_k)) - return y - - def simulate(self, x0, n_int, n_sample): - # n_traj = x0.shape[1] - x = x0 - u = np.zeros((n_int, 1), dtype=complex) - X = np.zeros((n_int // n_sample, self.n_states), dtype=complex) - t = 0 - j = 0 - t_list = [] - for step in range(n_int): - t += self.dt - y = rk4(0, x, u[step], self.dt, self.sys) - if (step + 1) % n_sample == 0: - X[j] = y - j += 1 - t_list.append(t) - x = y - return X, np.array(t_list) - - def collect_data_continuous(self, x0): - """ - collect training data pairs - continuous sense. - - given x0, with shape (n_dim, n_traj), the function - returns dx/dt with shape (n_dim, n_traj) - """ - - n_traj = x0.shape[0] - u = np.zeros((n_traj, 1)) - X = x0 - Y = [] - for i in range(n_traj): - y = self.sys(0, x0[i], u[i]) - Y.append(y) - Y = np.vstack(Y) - return X, Y - - def collect_one_step_data_discrete(self, x0): - """ - collect training data pairs - discrete sense. - - given x0, with shape (n_dim, n_traj), the function - returns system state x1 after self.dt with shape - (n_dim, n_traj) - """ - - n_traj = x0.shape[0] - X = x0 - Y = [] - for i in range(n_traj): - y, _ = self.simulate(x0[i], n_int=1, n_sample=1) - Y.append(y) - Y = np.vstack(Y) - return X, Y - - def collect_one_trajectory_data(self, x0, n_int, n_sample): - x = x0 - y, _ = self.simulate(x, n_int, n_sample) - return y - - def visualize_data(self, x, t, X): - plt.figure(figsize=(6, 6)) - ax = plt.axes(projection=Axes3D.name) - for i in range(X.shape[0]): - ax.plot(x, abs(X[i]), zs=t[i], zdir="t", label="time = " + str(i * self.dt)) - # plt.legend(loc='best') - ax.view_init(elev=35.0, azim=-65, vertical_axis="y") - ax.set(ylabel=r"$mag. of. u(x,t)$", xlabel=r"$x$", zlabel=r"time $t$") - plt.title("CQGLE (Kutz et al., Complexity, 2018)") - plt.show() - - def visualize_state_space(self, X): - u, s, vt = np.linalg.svd(X, full_matrices=False) - # this is a pde problem so the number of snapshots are smaller than dof - pca_1_r, pca_1_i = np.real(u[:, 0]), np.imag(u[:, 0]) - pca_2_r, pca_2_i = np.real(u[:, 1]), np.imag(u[:, 1]) - pca_3_r, pca_3_i = np.real(u[:, 2]), np.imag(u[:, 2]) - - plt.figure(figsize=(6, 6)) - plt.semilogy(s) - plt.xlabel("number of SVD terms") - plt.ylabel("singular values") - plt.title("PCA singular value decays") - plt.show() - - plt.figure(figsize=(6, 6)) - ax = plt.axes(projection=Axes3D.name) - ax.plot3D(pca_1_r, pca_2_r, pca_3_r, "k-o") - ax.set(xlabel="pc1", ylabel="pc2", zlabel="pc3") - plt.title("PCA visualization (real)") - plt.show() - - plt.figure(figsize=(6, 6)) - ax = plt.axes(projection=Axes3D.name) - ax.plot3D(pca_1_i, pca_2_i, pca_3_i, "k-o") - ax.set(xlabel="pc1", ylabel="pc2", zlabel="pc3") - plt.title("PCA visualization (imag)") - plt.show() - - -if __name__ == "__main__": - n = 512 - x = np.linspace(-10, 10, n, endpoint=False) - u0 = np.exp(-((x) ** 2)) - # u0 = 2.0 / np.cosh(x) - # u0 = u0.reshape(-1,1) - n_int = 9000 - n_snapshot = 300 - dt = 40.0 / n_int - n_sample = n_int // n_snapshot - - model = cqgle(n, x, dt, L=20) - X, t = model.simulate(u0, n_int, n_sample) - - print(X.shape) - print(X[:, -1].max()) - - # usage: visualize the data in physical space - model.visualize_data(x, t, X) - print(t) - - # usage: visualize the data in state space - model.visualize_state_space(X) - - # usage: collect continuous data pair: x and dx/dt - x0_array = np.vstack([u0, u0, u0]) - X, Y = model.collect_data_continuous(x0_array) - - print(X.shape) - print(Y.shape) - - # usage: collect discrete data pair - x0_array = np.vstack([u0, u0, u0]) - X, Y = model.collect_one_step_data_discrete(x0_array) - - print(X.shape) - print(Y.shape) - - # usage: collect one trajectory data - X = model.collect_one_trajectory_data(u0, n_int, n_sample) - print(X.shape) diff --git a/DSA/pykoopman/src/pykoopman/common/examples.py b/DSA/pykoopman/src/pykoopman/common/examples.py deleted file mode 100644 index ae2ff22..0000000 --- a/DSA/pykoopman/src/pykoopman/common/examples.py +++ /dev/null @@ -1,1045 +0,0 @@ -"""module for example dynamics data""" -from __future__ import annotations - -import matplotlib as mpl -import matplotlib.pyplot as plt -import numpy as np -from scipy.linalg import orth - - -def drss( - n=2, p=2, m=2, p_int_first=0.1, p_int_others=0.01, p_repeat=0.05, p_complex=0.5 -): - """ - Create a discrete-time, random, stable, linear state space model. - - Args: - n (int, optional): Number of states. Default is 2. - p (int, optional): Number of control inputs. Default is 2. - m (int, optional): Number of output measurements. - If m=0, C becomes the identity matrix, so that y=x. Default is 2. - p_int_first (float, optional): Probability of an integrator as the first pole. - Default is 0.1. - p_int_others (float, optional): Probability of other integrators beyond the - first. Default is 0.01. - p_repeat (float, optional): Probability of repeated roots. Default is 0.05. - p_complex (float, optional): Probability of complex roots. Default is 0.5. - - Returns: - Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]: A tuple containing the - state transition matrix (A), control matrix (B), and measurement matrix (C). - - A (numpy.ndarray): State transition matrix of shape (n, n). - B (numpy.ndarray): Control matrix of shape (n, p). - C (numpy.ndarray): Measurement matrix of shape (m, n). If m = 0, C is the - identity matrix. - - """ - - # Number of integrators - nint = int( - (np.random.rand(1) < p_int_first) + sum(np.random.rand(n - 1) < p_int_others) - ) - # Number of repeated roots - nrepeated = int(np.floor(sum(np.random.rand(n - nint) < p_repeat) / 2)) - # Number of complex roots - ncomplex = int( - np.floor(sum(np.random.rand(n - nint - 2 * nrepeated, 1) < p_complex) / 2) - ) - nreal = n - nint - 2 * nrepeated - 2 * ncomplex - - # Random poles - rep = 2 * np.random.rand(nrepeated) - 1 - if ncomplex != 0: - mag = np.random.rand(ncomplex) - cplx = np.zeros(ncomplex, dtype=complex) - for i in range(ncomplex): - cplx[i] = mag[i] * np.exp(complex(0, np.pi * np.random.rand(1))) - re = np.real(cplx) - im = np.imag(cplx) - - # Generate random state space model - A = np.zeros((n, n)) - if ncomplex != 0: - for i in range(0, ncomplex): - A[2 * i : 2 * i + 2, 2 * i : 2 * i + 2] = np.array( - [[re[i], im[i]], [-im[i], re[i]]] - ) - - if 2 * ncomplex < n: - list_poles = [] - if nint: - list_poles = np.append(list_poles, np.ones(nint)) - if rep: - list_poles = np.append(list_poles, rep) - list_poles = np.append(list_poles, rep) - if nreal: - list_poles = np.append(list_poles, 2 * np.random.rand(nreal) - 1) - - A[2 * ncomplex :, 2 * ncomplex :] = np.diag(list_poles) - - T = orth(np.random.rand(n, n)) - A = np.transpose(T) @ (A @ T) - - # control matrix - B = np.random.randn(n, p) - # mask for nonzero entries in B - mask = np.random.rand(B.shape[0], B.shape[1]) - B = np.squeeze(np.multiply(B, [(mask < 0.75) != 0])) - - # Measurement matrix - if m == 0: - C = np.identity(n) - else: - C = np.random.randn(m, n) - mask = np.random.rand(C.shape[0], C.shape[1]) - C = np.squeeze(C * [(mask < 0.75) != 0]) - - return A, B, C - - -def advance_linear_system(x0, u, n, A=None, B=None, C=None): - """ - Simulate the linear system dynamics for a given number of steps. - - Args: - x0 (numpy.ndarray): Initial state vector of shape (n,). - u (numpy.ndarray): Control input array of shape (p,) or (p, n-1). - If 1-dimensional, it will be converted to a row vector. - n (int): Number of steps to simulate. - A (numpy.ndarray, optional): State transition matrix of shape (n, n). - If not provided, it defaults to None. - B (numpy.ndarray, optional): Control matrix of shape (n, p). - If not provided, it defaults to None. - C (numpy.ndarray, optional): Measurement matrix of shape (m, n). - If not provided, it defaults to None. - - Returns: - Tuple[numpy.ndarray, numpy.ndarray]: A tuple containing the state trajectory - (x) and the output trajectory (y). - - x (numpy.ndarray): State trajectory of shape (n, len(x0)). - y (numpy.ndarray): Output trajectory of shape (n, C.shape[0]). - - """ - if C is None: - C = np.identity(len(x0)) - if u.ndim == 1: - u = u[np.newaxis, :] - - y = np.zeros([n, C.shape[0]]) - x = np.zeros([n, len(x0)]) - x[0, :] = x0 - y[0, :] = C.dot(x[0, :]) - for i in range(n - 1): - x[i + 1, :] = A.dot(x[i, :]) + B.dot(u[:, i]) - y[i + 1, :] = C.dot(x[i + 1, :]) - return x, y - - -def vdp_osc(t, x, u): - """ - Compute the dynamics of the Van der Pol oscillator. - - Args: - t (float): Time. - x (numpy.ndarray): State vector of shape (2,). - u (float): Control input. - - Returns: - numpy.ndarray: Updated state vector of shape (2,). - - """ - y = np.zeros(x.shape) - y[0, :] = 2 * x[1, :] - y[1, :] = -0.8 * x[0, :] + 2 * x[1, :] - 10 * (x[0, :] ** 2) * x[1, :] + u - return y - - -def rk4(t, x, u, _dt=0.01, func=vdp_osc): - """ - Perform a 4th order Runge-Kutta integration. - - Args: - t (float): Time. - x (numpy.ndarray): State vector of shape (2,). - u (float): Control input. - _dt (float, optional): Time step. Defaults to 0.01. - func (function, optional): Function defining the dynamics. Defaults to vdp_osc. - - Returns: - numpy.ndarray: Updated state vector of shape (2,). - - """ - # 4th order Runge-Kutta - k1 = func(t, x, u) - k2 = func(t, x + k1 * _dt / 2, u) - k3 = func(t, x + k2 * _dt / 2, u) - k4 = func(t, x + k1 * _dt, u) - return x + (_dt / 6) * (k1 + 2 * k2 + 2 * k3 + k4) - - -def square_wave(step): - """ - Generate a square wave with a period of 60 time steps. - - Args: - step (int): Current time step. - - Returns: - float: Square wave value at the given time step. - - """ - return (-1.0) ** (round(step / 30.0)) - - -def sine_wave(step): - """ - Generate a sine wave with a period of 60 time steps. - - Args: - step (int): Current time step. - - Returns: - float: Sine wave value at the given time step. - - """ - return np.sin(round(step / 30.0)) - - -def lorenz(x, t, sigma=10, beta=8 / 3, rho=28): - """ - Compute the derivative of the Lorenz system at a given state. - - Args: - x (list): Current state of the Lorenz system [x, y, z]. - t (float): Current time. - sigma (float, optional): Parameter sigma. Default is 10. - beta (float, optional): Parameter beta. Default is 8/3. - rho (float, optional): Parameter rho. Default is 28. - - Returns: - list: Derivative of the Lorenz system [dx/dt, dy/dt, dz/dt]. - - """ - return [ - sigma * (x[1] - x[0]), - x[0] * (rho - x[2]) - x[1], - x[0] * x[1] - beta * x[2], - ] - - -def rev_dvdp(t, x, u=0, dt=0.1): - """ - Reverse dynamics of the Van der Pol oscillator. - - Args: - t (float): Time. - x (numpy.ndarray): Current state of the system [x1, x2]. - u (float, optional): Input. Default is 0. - dt (float, optional): Time step. Default is 0.1. - - Returns: - numpy.ndarray: Updated state of the system [x1', x2']. - - """ - return np.array( - [ - x[0, :] - x[1, :] * dt, - x[1, :] + (x[0, :] - x[1, :] + x[0, :] ** 2 * x[1, :]) * dt, - ] - ) - - -class Linear2Ddynamics: - def __init__(self): - """ - Initializes a Linear2Ddynamics object. - - """ - self.n_states = 2 # Number of states - - def linear_map(self, x): - """ - Applies the linear mapping to the input state. - - Args: - x (numpy.ndarray): Input state. - - Returns: - numpy.ndarray: Resulting mapped state. - - """ - return np.array([[0.8, -0.05], [0, 0.7]]) @ x - - def collect_data(self, x, n_int, n_traj): - """ - Collects data by integrating the linear dynamics. - - Args: - x (numpy.ndarray): Initial state. - n_int (int): Number of integration steps. - n_traj (int): Number of trajectories. - - Returns: - numpy.ndarray: Input data. - numpy.ndarray: Output data. - - """ - # Init - X = np.zeros((self.n_states, n_int * n_traj)) - Y = np.zeros((self.n_states, n_int * n_traj)) - - # Integrate - for step in range(n_int): - y = self.linear_map(x) - X[:, (step) * n_traj : (step + 1) * n_traj] = x - Y[:, (step) * n_traj : (step + 1) * n_traj] = y - x = y - - return X, Y - - def visualize_modes(self, x, phi, eigvals, order=None): - """ - Visualizes the modes of the linear dynamics. - - Args: - x (numpy.ndarray): State data. - phi (numpy.ndarray): Eigenvectors. - eigvals (numpy.ndarray): Eigenvalues. - order (list, optional): Order of the modes to visualize. Default is None. - - """ - n_modes = min(10, phi.shape[1]) - fig, axs = plt.subplots(2, n_modes, figsize=(3 * n_modes, 6)) - if order is None: - index_list = range(n_modes) - else: - index_list = order - j = 0 - for i in index_list: - axs[0, j].scatter( - x[0, :], - x[1, :], - c=np.real(phi[:, i]), - marker="o", - cmap=plt.get_cmap("jet"), - ) - axs[1, j].scatter( - x[0, :], - x[1, :], - c=np.imag(phi[:, i]), - marker="o", - cmap=plt.get_cmap("jet"), - ) - axs[0, j].set_title(r"$\lambda$=" + "{:2.3f}".format(eigvals[i])) - j += 1 - - -class torus_dynamics: - """ - Sparse dynamics in Fourier space on torus. - - Attributes: - n_states (int): Number of states. - sparsity (int): Degree of sparsity. - freq_max (int): Maximum frequency. - noisemag (float): Magnitude of noise. - - Methods: - __init__(self, n_states=128, sparsity=5, freq_max=15, noisemag=0.0): - Initializes a torus_dynamics object. - - setup(self): - Sets up the dynamics. - - advance(self, n_samples, dt=1): - Advances the continuous-time dynamics without control. - - advance_discrete_time(self, n_samples, dt, u=None): - Advances the discrete-time dynamics with or without control. - - set_control_matrix_physical(self, B): - Sets the control matrix in physical space. - - set_control_matrix_fourier(self, Bhat): - Sets the control matrix in Fourier space. - - set_point_actuator(self, position=None): - Sets a single point actuator. - - viz_setup(self): - Sets up the visualization. - - viz_torus(self, ax, x): - Visualizes the torus dynamics. - - viz_all_modes(self, modes=None): - Visualizes all modes. - - modes(self): - Returns the modes of the dynamics. - - B_effective(self): - Returns the effective control matrix. - - """ - - def __init__(self, n_states=128, sparsity=5, freq_max=15, noisemag=0.0): - """ - Initializes a torus_dynamics object. - - Args: - n_states (int, optional): Number of states. Default is 128. - sparsity (int, optional): Degree of sparsity. Default is 5. - freq_max (int, optional): Maximum frequency. Default is 15. - noisemag (float, optional): Magnitude of noise. Default is 0.0. - - """ - self.n_states = n_states - self.sparsity = sparsity - self.freq_max = freq_max - self.noisemag = noisemag - self.setup() - - def setup(self): - """ - Sets up the dynamics. - - """ - # Initialization in the Fourier space - xhat = np.zeros((self.n_states, self.n_states), complex) - # Index of nonzero frequency components - self.J = np.zeros((self.sparsity, 2), dtype=int) - IC = np.zeros(self.sparsity) # Initial condition, real number - frequencies = np.zeros(self.sparsity) - damping = np.zeros(self.sparsity) - - IC = np.random.randn(self.sparsity) - frequencies = np.sqrt(4 * np.random.rand(self.sparsity)) - damping = -np.random.rand(self.sparsity) * 0.1 - for k in range(self.sparsity): - loopbreak = 0 - while loopbreak != 1: - self.J[k, 0] = np.ceil( - np.random.rand(1) * self.n_states / (self.freq_max + 1) - ) - self.J[k, 1] = np.ceil( - np.random.rand(1) * self.n_states / (self.freq_max + 1) - ) - if xhat[self.J[k, 0], self.J[k, 1]] == 0.0: - loopbreak = 1 - - xhat[self.J[k, 0], self.J[k, 1]] = IC[k] - - mask = np.zeros((self.n_states, self.n_states), int) - for k in range(self.sparsity): - mask[self.J[k, 0], self.J[k, 1]] = 1 - - self.damping = damping - self.frequencies = frequencies - self.IC = IC - self.xhat = xhat - self.mask = mask - - def advance(self, n_samples, dt=1): - """ - Advances the continuous-time dynamics without control. - - Args: - n_samples (int): Number of samples to advance. - dt (float, optional): Time step. Default is 1. - - """ - print("Evolving continuous-time dynamics without control.") - self.n_samples = n_samples - self.dt = dt - - # Initilization - # In physical space - self.X = np.ndarray((self.n_states**2, self.n_samples)) - # In Fourier space - self.Xhat = np.ndarray((self.n_states**2, self.n_samples), complex) - self.time_vector = np.zeros(self.n_samples) - - # if self.noisemag != 0: - # self.XhatClean = np.ndarray((self.n_states**2, self.n_samples), complex) - # self.XClean = np.ndarray((self.n_states**2, self.n_samples)) - - for step in range(self.n_samples): - t = step * self.dt - self.time_vector[step] = t - xhat = np.zeros((self.n_states, self.n_states), complex) - for k in range(self.sparsity): - xhat[self.J[k, 0], self.J[k, 1]] = ( - np.exp((self.damping[k] + 1j * 2 * np.pi * self.frequencies[k]) * t) - * self.IC[k] - ) - - if self.noisemag != 0: - self.XhatClean[:, step] = xhat.reshape(self.n_states**2) - xClean = np.real(np.fft.ifft2(xhat)) - self.XClean[:, step] = xClean.reshape(self.n_states**2) - - # xRMS = np.sqrt(np.mean(xhat.reshape((self.n_states**2,1))**2)) - # xhat = xhat + self.noisemag*xRMS\ - # *np.random.randn(xhat.shape[0],xhat.shape[1]) \ - # + 1j*self.noisemag*xRMS \ - # *np.random.randn(xhat.shape[0],xhat.shape[1]) - self.Xhat[:, step] = xhat.reshape(self.n_states**2) - x = np.real(np.fft.ifft2(xhat)) - self.X[:, step] = x.reshape(self.n_states**2) - - def advance_discrete_time(self, n_samples, dt, u=None): - """ - Advances the discrete-time dynamics with or without control. - - Args: - n_samples (int): Number of samples to advance. - dt (float): Time step. - u (array-like, optional): Control input. Default is None. - - """ - print("Evolving discrete-time dynamics with or without control.") - if u is None: - self.n_control_features_ = 0 - self.U = np.zeros(n_samples) - self.U = self.U[np.newaxis, :] - print("No control input provided. Evolving unforced system.") - else: - if u.ndim == 1: - if len(u) > n_samples: - u = u[:-1] - self.U = u[np.newaxis, :] - elif u.ndim == 2: - if u.shape[0] > n_samples: - u = u[:-1, :] - self.U = u - self.n_control_features_ = self.U.shape[1] - - if not hasattr(self, "B"): - B = np.zeros((self.n_states, self.n_states)) - print(B.shape) - self.set_control_matrix_physical(B) - print("Control matrix is not set. Continue with unforced system.") - - self.n_samples = n_samples - self.dt = dt - - # Initilization - # In physical space - self.X = np.ndarray((self.n_states**2, self.n_samples)) - # In Fourier space - self.Xhat = np.ndarray((self.n_states**2, self.n_samples), complex) - self.time_vector = np.zeros(self.n_samples) - - # Set initial condition - xhat0 = np.zeros((self.n_states, self.n_states), complex) - for k in range(self.sparsity): - xhat0[self.J[k, 0], self.J[k, 1]] = self.IC[k] - self.Xhat[:, 0] = xhat0.reshape(self.n_states**2) - x0 = np.real(np.fft.ifft2(xhat0)) - self.X[:, 0] = x0.reshape(self.n_states**2) - - for step in range(1, self.n_samples, 1): - t = step * self.dt - self.time_vector[step] = t - # self.Xhat[:, step] = np.reshape(self.Bhat * self.U[0,step - 1],\ - # self.n_states ** 2) - # xhat = self.Xhat[:,step].reshape(self.n_states,self.n_states) - # xhat_prev = \ - # self.Xhat[:, step - 1].reshape(self.n_states, self.n_states) - - # forced torus dynamics linearly evolve in the spectral space, sparsely - xhat = np.array((self.n_states, self.n_states), complex) - xhat = self.Xhat[:, step].reshape(self.n_states, self.n_states) - xhat_prev = self.Xhat[:, step - 1].reshape(self.n_states, self.n_states) - for k in range(self.sparsity): - xhat[self.J[k, 0], self.J[k, 1]] = ( - np.exp( - (self.damping[k] + 1j * 2 * np.pi * self.frequencies[k]) - * self.dt - ) - * xhat_prev[self.J[k, 0], self.J[k, 1]] - + self.Bhat[self.J[k, 0], self.J[k, 1]] * self.U[0, step - 1] - ) - - # xhat_prev = self.Xhat[:,step-1].reshape(self.n_states, self.n_states) - # for k in range(self.sparsity): - # xhat[self.J[k,0], self.J[k,1]] += np.exp((self.damping[k] \ - # + 1j * 2 * np.pi * self.frequencies[k]) * self.dt) \ - # * xhat_prev[self.J[k,0], self.J[k,1]] - - self.Xhat[:, step] = xhat.reshape(self.n_states**2) - x = np.real(np.fft.ifft2(xhat)) - self.X[:, step] = x.reshape(self.n_states**2) - - def set_control_matrix_physical(self, B): - """ - Sets the control matrix in physical space. - - Args: - B (array-like): Control matrix in physical space. - - """ - if np.allclose(B.shape, np.array([self.n_states, self.n_states])) is False: - raise TypeError("Control matrix B has wrong shape.") - self.B = B - self.Bhat = np.fft.fft2(B) - - def set_control_matrix_fourier(self, Bhat): - """ - Sets the control matrix in Fourier space. - - Args: - Bhat (array-like): Control matrix in Fourier space. - - """ - if np.allclose(Bhat.shape, np.array([self.n_states, self.n_states])) is False: - raise TypeError("Control matrix Bhat has wrong shape.") - self.Bhat = Bhat - self.B = np.real(np.fft.ifft2(self.Bhat)) - - def set_point_actuator(self, position=None): - """ - Sets a single point actuator. - - Args: - position (array-like, optional): Position of the actuator. Default is None. - - """ - if position is None: - position = np.random.randint(0, self.n_states, 2) - try: - for i in range(len(position)): - position[i] = int(position[i]) - except ValueError: - print("position was not a valid integer.") - - is_position_in_valid_domain = (position >= 0) & (position < self.n_states) - if all(is_position_in_valid_domain) is False: - raise ValueError( - "Actuator position was not a valid integer inside of domain." - ) - - # Control matrix in physical space (single point actuator) - B = np.zeros((self.n_states, self.n_states)) - B[position[0], position[1]] = 1 - self.set_control_matrix_physical(B) - - def viz_setup(self): - """ - Sets up the visualization. - - """ - self.cmap_torus = plt.cm.jet # bwr #plt.cm.RdYlBu - self.n_colors = self.n_states - r1 = 2 - r2 = 1 - [T1, T2] = np.meshgrid( - np.linspace(0, 2 * np.pi, self.n_states), - np.linspace(0, 2 * np.pi, self.n_states), - ) - R = r1 + r2 * np.cos(T2) - self.Zgrid = r2 * np.sin(T2) - self.Xgrid = R * np.cos(T1) - self.Ygrid = R * np.sin(T1) - - def viz_torus(self, ax, x): - """ - Visualizes the torus dynamics. - - Args: - ax: Axes object for plotting. - x (array-like): Dynamics to be visualized. - - Returns: - surface: Surface plot of the torus dynamics. - - """ - if not hasattr(self, "viz"): - self.viz_setup() - - norm = mpl.colors.Normalize(vmin=-abs(x).max(), vmax=abs(x).max()) - surface = ax.plot_surface( - self.Xgrid, - self.Ygrid, - self.Zgrid, - facecolors=self.cmap_torus(norm(x)), - shade=False, - rstride=1, - cstride=1, - ) - # m = cm.ScalarMappable(cmap=cmap_torus, norm=norm) - # m.set_array([]) - # plt.colorbar(m) - # ax.figure.colorbar(surf, ax=ax) - ax.set_zlim(-3.01, 3.01) - return surface - - def viz_all_modes(self, modes=None): - """ - Visualizes all modes. - - Args: - modes (array-like, optional): Modes to be visualized. Default is None. - - Returns: - fig: Figure object containing the visualizations. - - """ - if modes is None: - modes = self.modes - - if not hasattr(self, "viz"): - self.viz_setup() - - fig = plt.figure(figsize=(20, 10)) - for k in range(self.sparsity): - ax = plt.subplot2grid((1, self.sparsity), (0, k), projection="3d") - self.viz_torus(ax, modes[:, k].reshape(self.n_states, self.n_states)) - plt.axis("off") - return fig - - @property - def modes(self): - """ - Returns the modes of the dynamics. - - Returns: - modes (array-like): Modes of the dynamics. - - """ - modes = np.zeros((self.n_states**2, self.sparsity)) - - for k in range(self.sparsity): - mode_in_fourier = np.zeros((self.n_states, self.n_states)) - mode_in_fourier[self.J[k, 0], self.J[k, 1]] = 1 - modes[:, k] = np.real( - np.fft.ifft2(mode_in_fourier).reshape(self.n_states**2) - ) - - return modes - - @property - def B_effective(self): - """ - Returns the effective control matrix. - - Returns: - B_effective (array-like): Effective control matrix. - - """ - Bhat_effective = np.zeros((self.n_states, self.n_states), complex) - for k in range(self.sparsity): - control_mode = np.zeros((self.n_states, self.n_states), complex) - control_mode[self.J[k, 0], self.J[k, 1]] = self.Bhat[ - self.J[k, 0], self.J[k, 1] - ] - Bhat_effective += control_mode - B_effective = np.fft.ifft2(Bhat_effective) - - return B_effective - - -class slow_manifold: - """ - Represents the slow manifold class. - - Args: - mu (float, optional): Parameter mu. Default is -0.05. - la (float, optional): Parameter la. Default is -1.0. - dt (float, optional): Time step size. Default is 0.01. - - Attributes: - mu (float): Parameter mu. - la (float): Parameter la. - b (float): Value computed from mu and la. - dt (float): Time step size. - n_states (int): Number of states. - - Methods: - sys(t, x, u): Computes the system dynamics. - output(x): Computes the output based on the state. - simulate(x0, n_int): Simulates the system dynamics. - collect_data_continuous(x0): Collects data from continuous-time dynamics. - collect_data_discrete(x0, n_int): Collects data from discrete-time dynamics. - visualize_trajectories(t, X, n_traj): Visualizes the trajectories. - visualize_state_space(X, Y, n_traj): Visualizes the state space. - """ - - def __init__(self, mu=-0.05, la=-1.0, dt=0.01): - self.mu = mu - self.la = la - self.b = self.la / (self.la - 2 * self.mu) - self.dt = dt - self.n_states = 2 - - def sys(self, t, x, u): - """ - Computes the system dynamics. - - Args: - t (float): Time. - x (array-like): State. - u (array-like): Control input. - - Returns: - array-like: Computed system dynamics. - - """ - return np.array([self.mu * x[0, :], self.la * (x[1, :] - x[0, :] ** 2)]) - - def output(self, x): - """ - Computes the output based on the state. - - Args: - x (array-like): State. - - Returns: - array-like: Computed output. - - """ - return x[0, :] ** 2 + x[1, :] - - def simulate(self, x0, n_int): - """ - Simulates the system dynamics. - - Args: - x0 (array-like): Initial state. - n_int (int): Number of integration steps. - - Returns: - array-like: Simulated trajectory. - - """ - n_traj = x0.shape[1] - x = x0 - u = np.zeros((n_int, 1)) - X = np.zeros((self.n_states, n_int * n_traj)) - for step in range(n_int): - y = rk4(0, x, u[step, :], self.dt, self.sys) - X[:, (step) * n_traj : (step + 1) * n_traj] = y - x = y - return X - - def collect_data_continuous(self, x0): - """ - Collects data from continuous-time dynamics. - - Args: - x0 (array-like): Initial state. - - Returns: - tuple: Collected data (X, Y). - - """ - n_traj = x0.shape[1] - u = np.zeros((1, n_traj)) - X = x0 - Y = self.sys(0, x0, u) - return X, Y - - def collect_data_discrete(self, x0, n_int): - """ - Collects data from discrete-time dynamics. - - Args: - x0 (array-like): Initial state. - n_int (int): Number of integration steps. - - Returns: - tuple: Collected data (X, Y). - - """ - n_traj = x0.shape[1] - x = x0 - u = np.zeros((n_int, n_traj)) - X = np.zeros((self.n_states, n_int * n_traj)) - Y = np.zeros((self.n_states, n_int * n_traj)) - for step in range(n_int): - y = rk4(0, x, u[step, :], self.dt, self.sys) - X[:, (step) * n_traj : (step + 1) * n_traj] = x - Y[:, (step) * n_traj : (step + 1) * n_traj] = y - x = y - return X, Y - - def visualize_trajectories(self, t, X, n_traj): - """ - Visualizes the trajectories. - - Args: - t (array-like): Time vector. - X (array-like): State trajectories. - n_traj (int): Number of trajectories. - - """ - fig, axs = plt.subplots(1, 1, tight_layout=True, figsize=(12, 4)) - for traj_idx in range(n_traj): - x = X[:, traj_idx::n_traj] - axs.plot(t[0:100], x[1, 0:100], "k") - axs.set(ylabel=r"$x_2$", xlabel=r"$t$") - - def visualize_state_space(self, X, Y, n_traj): - """ - Visualizes the state space. - - Args: - X (array-like): State trajectories. - Y (array-like): Output trajectories. - n_traj (int): Number of trajectories. - - """ - fig, axs = plt.subplots(1, 1, tight_layout=True, figsize=(4, 4)) - for traj_idx in range(n_traj): - axs.plot( - [X[0, traj_idx::n_traj], Y[0, traj_idx::n_traj]], - [X[1, traj_idx::n_traj], Y[1, traj_idx::n_traj]], - "-k", - ) - axs.set(ylabel=r"$x_2$", xlabel=r"$x_1$") - - -class forced_duffing: - """ - Forced Duffing Oscillator. - - dx1/dt = x2 - dx2/dt = -d*x2-alpha*x1-beta*x1^3 + u - - [1] S. Peitz, S. E. Otto, and C. W. Rowley, - “Data-driven model predictive control using interpolated koopman generators,” - SIAM J. Appl. Dyn. Syst., vol. 19, no. 3, pp. 2162–2193, Mar. 2020. - """ - - def __init__(self, dt, d, alpha, beta): - """ - Initializes the Forced Duffing Oscillator. - - Args: - dt (float): Time step. - d (float): Damping coefficient. - alpha (float): Coefficient of x1. - beta (float): Coefficient of x1^3. - """ - self.dt = dt - self.d = d - self.alpha = alpha - self.beta = beta - self.n_states = 2 - - def sys(self, t, x, u): - """ - Defines the system dynamics of the Forced Duffing Oscillator. - - Args: - t (float): Time. - x (array-like): State vector. - u (array-like): Control input. - - Returns: - array-like: Rate of change of the state vector. - """ - y = np.array( - [ - x[1, :], - -self.d * x[1, :] - self.alpha * x[0, :] - self.beta * x[0, :] ** 3 + u, - ] - ) - return y - - def simulate(self, x0, n_int, u): - """ - Simulates the Forced Duffing Oscillator. - - Args: - x0 (array-like): Initial state vector. - n_int (int): Number of time steps. - u (array-like): Control inputs. - - Returns: - array-like: State trajectories. - """ - n_traj = x0.shape[1] - x = x0 - X = np.zeros((self.n_states, n_int * n_traj)) - for step in range(n_int): - y = rk4(0, x, u[step, :], self.dt, self.sys) - X[:, (step) * n_traj : (step + 1) * n_traj] = y - x = y - return X - - def collect_data_continuous(self, x0, u): - """ - Collects continuous data for the Forced Duffing Oscillator. - - Args: - x0 (array-like): Initial state vector. - u (array-like): Control inputs. - - Returns: - tuple: State and output trajectories. - """ - X = x0 - Y = self.sys(0, x0, u) - return X, Y - - def collect_data_discrete(self, x0, n_int, u): - """ - Collects discrete-time data for the Forced Duffing Oscillator. - - Args: - x0 (array-like): Initial state vector. - n_int (int): Number of time steps. - u (array-like): Control inputs. - - Returns: - tuple: State and output trajectories. - """ - n_traj = x0.shape[1] - x = x0 - X = np.zeros((self.n_states, n_int * n_traj)) - Y = np.zeros((self.n_states, n_int * n_traj)) - for step in range(n_int): - y = rk4(0, x, u[step, :], self.dt, self.sys) - X[:, (step) * n_traj : (step + 1) * n_traj] = x - Y[:, (step) * n_traj : (step + 1) * n_traj] = y - x = y - return X, Y - - def visualize_trajectories(self, t, X, n_traj): - """ - Visualizes the state trajectories of the Forced Duffing Oscillator. - - Args: - t (array-like): Time vector. - X (array-like): State trajectories. - n_traj (int): Number of trajectories to visualize. - """ - fig, axs = plt.subplots(1, 2, tight_layout=True, figsize=(12, 4)) - for traj_idx in range(n_traj): - x = X[:, traj_idx::n_traj] - axs[0].plot(t, x[0, :], "k") - axs[1].plot(t, x[1, :], "b") - axs[0].set(ylabel=r"$x_1$", xlabel=r"$t$") - axs[1].set(ylabel=r"$x_2$", xlabel=r"$t$") - - def visualize_state_space(self, X, Y, n_traj): - """ - Visualizes the state space trajectories of the Forced Duffing Oscillator. - - Args: - X (array-like): State trajectories. - Y (array-like): Output trajectories. - n_traj (int): Number of trajectories to visualize. - """ - fig, axs = plt.subplots(1, 1, tight_layout=True, figsize=(4, 4)) - for traj_idx in range(n_traj): - axs.plot( - [X[0, traj_idx::n_traj], Y[0, traj_idx::n_traj]], - [X[1, traj_idx::n_traj], Y[1, traj_idx::n_traj]], - "-k", - ) - axs.set(ylabel=r"$x_2$", xlabel=r"$x_1$") diff --git a/DSA/pykoopman/src/pykoopman/common/ks.py b/DSA/pykoopman/src/pykoopman/common/ks.py deleted file mode 100644 index e356bdf..0000000 --- a/DSA/pykoopman/src/pykoopman/common/ks.py +++ /dev/null @@ -1,189 +0,0 @@ -"""module for 1D KS equation""" -from __future__ import annotations - -import numpy as np -from matplotlib import pyplot as plt -from mpl_toolkits.mplot3d import Axes3D -from scipy.fft import fft -from scipy.fft import fftfreq -from scipy.fft import ifft - - -class ks: - """ - Solving 1D KS equation - - u_t = -u*u_x + u_{xx} + nu*u_{xxxx} - - Periodic B.C. between 0 and 2*pi. This PDE is solved - using spectral methods. - """ - - def __init__(self, n, x, nu, dt, M=16): - self.n_states = n - self.dt = dt - self.x = x - dk = 1 - k = fftfreq(self.n_states, 1.0 / self.n_states) * dk - k[n // 2] = 0.0 - L = k**2 - nu * k**4 - self.E = np.exp(self.dt * L) - self.E2 = np.exp(self.dt * L / 2.0) - # self.M = M - r = np.exp(1j * np.pi * (np.arange(1, M + 1) - 0.5) / M) - r = r.reshape(1, -1) - r_on_circle = np.repeat(r, n, axis=0) - LR = self.dt * L - LR = LR.reshape(-1, 1) - LR = LR.astype("complex") - LR = np.repeat(LR, M, axis=1) - LR += r_on_circle - self.g = -0.5j * k - - self.Q = self.dt * np.real(np.mean((np.exp(LR / 2.0) - 1) / LR, axis=1)) - self.f1 = self.dt * np.real( - np.mean( - (-4.0 - LR + np.exp(LR) * (4.0 - 3.0 * LR + LR**2)) / LR**3, axis=1 - ) - ) - self.f2 = self.dt * np.real( - np.mean((2.0 + LR + np.exp(LR) * (-2.0 + LR)) / LR**3, axis=1) - ) - self.f3 = self.dt * np.real( - np.mean( - (-4.0 - 3.0 * LR - LR**2 + np.exp(LR) * (4.0 - LR)) / LR**3, axis=1 - ) - ) - - @staticmethod - def compute_u2k_zeropad_dealiased(uk_): - # three over two law - N = uk_.size - # map uk to uk_fine - uk_fine = ( - np.hstack((uk_[0 : int(N / 2)], np.zeros((int(N / 2))), uk_[int(-N / 2) :])) - * 3.0 - / 2.0 - ) - # convert uk_fine to physical space - u_fine = np.real(ifft(uk_fine)) - # compute square - u2_fine = np.square(u_fine) - # compute fft on u2_fine - u2k_fine = fft(u2_fine) - # convert u2k_fine to u2k - u2k = np.hstack((u2k_fine[0 : int(N / 2)], u2k_fine[int(-N / 2) :])) / 3.0 * 2.0 - return u2k - - def sys(self, t, x, u): - raise NotImplementedError - - def simulate(self, x0, n_int, n_sample): - xk = fft(x0) - u = np.zeros((n_int, 1)) - X = np.zeros((n_int // n_sample, self.n_states)) - t = 0 - j = 0 - t_list = [] - for step in range(n_int): - t += self.dt - Nv = self.g * self.compute_u2k_zeropad_dealiased(xk) - a = self.E2 * xk + self.Q * Nv - Na = self.g * self.compute_u2k_zeropad_dealiased(a) - b = self.E2 * xk + self.Q * Na - Nb = self.g * self.compute_u2k_zeropad_dealiased(b) - c = self.E2 * a + self.Q * (2.0 * Nb - Nv) - Nc = self.g * self.compute_u2k_zeropad_dealiased(c) - xk = self.E * xk + Nv * self.f1 + 2.0 * (Na + Nb) * self.f2 + Nc * self.f3 - - if (step + 1) % n_sample == 0: - y = np.real(ifft(xk)) + self.dt * u[j] - X[j, :] = y - j += 1 - t_list.append(t) - xk = fft(y) - - return X, np.array(t_list) - - def collect_data_continuous(self, x0): - raise NotImplementedError - - def collect_one_step_data_discrete(self, x0): - """ - collect training data pairs - discrete sense. - - given x0, with shape (n_dim, n_traj), the function - returns system state x1 after self.dt with shape - (n_dim, n_traj) - """ - n_traj = x0.shape[0] - X = x0 - Y = [] - for i in range(n_traj): - y, _ = self.simulate(x0[i], n_int=1, n_sample=1) - Y.append(y) - Y = np.vstack(Y) - return X, Y - - def collect_one_trajectory_data(self, x0, n_int, n_sample): - x = x0 - y, _ = self.simulate(x, n_int, n_sample) - return y - - def visualize_data(self, x, t, X): - plt.figure(figsize=(6, 6)) - ax = plt.axes(projection=Axes3D.name) - for i in range(X.shape[0]): - ax.plot(x, X[i], zs=t[i], zdir="t", label="time = " + str(i * self.dt)) - ax.view_init(elev=35.0, azim=-65, vertical_axis="y") - ax.set(ylabel=r"$u(x,t)$", xlabel=r"$x$", zlabel=r"time $t$") - plt.title("1D K-S equation") - plt.show() - - def visualize_state_space(self, X): - u, s, vt = np.linalg.svd(X, full_matrices=False) - plt.figure(figsize=(6, 6)) - plt.semilogy(s) - plt.xlabel("number of SVD terms") - plt.ylabel("singular values") - plt.title("PCA singular value decays") - plt.show() - - # this is a pde problem so the number of snapshots are smaller than dof - pca_1, pca_2, pca_3 = u[:, 0], u[:, 1], u[:, 2] - plt.figure(figsize=(6, 6)) - ax = plt.axes(projection=Axes3D.name) - ax.plot3D(pca_1, pca_2, pca_3, "k-o") - ax.set(xlabel="pc1", ylabel="pc2", zlabel="pc3") - plt.title("PCA visualization") - plt.show() - - -if __name__ == "__main__": - n = 256 - x = np.linspace(0, 2.0 * np.pi, n, endpoint=False) - u0 = np.sin(x) - nu = 0.01 - n_int = 1000 - n_snapshot = 500 - dt = 4.0 / n_int - n_sample = n_int // n_snapshot - - model = ks(n, x, nu=nu, dt=dt) - X, t = model.simulate(u0, n_int, n_sample) - print(X.shape) - model.visualize_data(x, t, X) - - # usage: visualize the data in state space - model.visualize_state_space(X) - - # usage: collect discrete data pair - x0_array = np.vstack([u0, u0, u0]) - X, Y = model.collect_one_step_data_discrete(x0_array) - - print(X.shape) - print(Y.shape) - - # usage: collect one trajectory data - X = model.collect_one_trajectory_data(u0, n_int, n_sample) - print(X.shape) diff --git a/DSA/pykoopman/src/pykoopman/common/nlse.py b/DSA/pykoopman/src/pykoopman/common/nlse.py deleted file mode 100644 index b82585f..0000000 --- a/DSA/pykoopman/src/pykoopman/common/nlse.py +++ /dev/null @@ -1,186 +0,0 @@ -"""module for nonlinear schrodinger equation""" -from __future__ import annotations - -import numpy as np -from matplotlib import pyplot as plt -from mpl_toolkits.mplot3d import Axes3D -from pykoopman.common.examples import rk4 -from scipy.fft import fft -from scipy.fft import fftfreq -from scipy.fft import ifft - - -class nlse: - """ - nonlinear schrodinger equation - - iu_t + 0.5u_xx + u*|u|^2 = 0 - - periodic B.C. PDE is solved with Spectral methods using FFT - """ - - def __init__(self, n, dt, L=2 * np.pi): - self.n_states = n - # assert self.u0.size == self.n_states, 'check the size of initial - # condition and mesh size n' - - dk = 2 * np.pi / L - self.k = fftfreq(self.n_states, 1.0 / self.n_states) * dk - self.dt = dt - - def sys(self, t, x, u): - """the RHS for the governing equation using FFT""" - xk = fft(x) - - # 4/3 truncation rule - # dealiasing due to triple nonlinearity - # note: you could do zero-padding to improve memory - # efficiency - xk[self.n_states // 4 : 3 * self.n_states // 4] = 0j - x = ifft(xk) - - yk = (-self.k**2 * xk.ravel() / 2) * 1j - y = ifft(yk) + 1j * abs(x) ** 2 * x + u - return y - - def simulate(self, x0, n_int, n_sample): - # n_traj = x0.shape[1] - x = x0 - u = np.zeros((n_int, 1), dtype=complex) - X = np.zeros((n_int // n_sample, self.n_states), dtype=complex) - t = 0 - j = 0 - t_list = [] - for step in range(n_int): - t += self.dt - y = rk4(0, x, u[step], self.dt, self.sys) - if (step + 1) % n_sample == 0: - X[j] = y - j += 1 - t_list.append(t) - x = y - return X, np.array(t_list) - - def collect_data_continuous(self, x0): - """ - collect training data pairs - continuous sense. - - given x0, with shape (n_dim, n_traj), the function - returns dx/dt with shape (n_dim, n_traj) - """ - - n_traj = x0.shape[0] - u = np.zeros((n_traj, 1)) - X = x0 - Y = [] - for i in range(n_traj): - y = self.sys(0, x0[i], u[i]) - Y.append(y) - Y = np.vstack(Y) - return X, Y - - def collect_one_step_data_discrete(self, x0): - """ - collect training data pairs - discrete sense. - - given x0, with shape (n_dim, n_traj), the function - returns system state x1 after self.dt with shape - (n_dim, n_traj) - """ - - n_traj = x0.shape[0] - X = x0 - Y = [] - for i in range(n_traj): - y, _ = self.simulate(x0[i], n_int=1, n_sample=1) - # for j in range(int(delta_t // self.dt)): - # y = rk4(0, x, u[:, i], self.dt, self.sys) - # x = y - Y.append(y) - Y = np.vstack(Y) - return X, Y - - def collect_one_trajectory_data(self, x0, n_int, n_sample): - x = x0 - y, _ = self.simulate(x, n_int, n_sample) - return y - - def visualize_data(self, x, t, X): - plt.figure(figsize=(6, 6)) - ax = plt.axes(projection=Axes3D.name) - for i in range(X.shape[0]): - ax.plot(x, abs(X[i]), zs=t[i], zdir="t", label="time = " + str(i * self.dt)) - # plt.legend(loc='best') - ax.view_init(elev=35.0, azim=-65, vertical_axis="y") - ax.set(ylabel=r"$mag. of u(x,t)$", xlabel=r"$x$", zlabel=r"time $t$") - plt.title("Nonlinear schrodinger equation (Kutz et al., Complexity, 2018)") - plt.show() - - def visualize_state_space(self, X): - u, s, vt = np.linalg.svd(X, full_matrices=False) - # this is a pde problem so the number of snapshots are smaller than dof - pca_1_r, pca_1_i = np.real(u[:, 0]), np.imag(u[:, 0]) - pca_2_r, pca_2_i = np.real(u[:, 1]), np.imag(u[:, 1]) - pca_3_r, pca_3_i = np.real(u[:, 2]), np.imag(u[:, 2]) - - plt.figure(figsize=(6, 6)) - plt.semilogy(s) - plt.xlabel("number of SVD terms") - plt.ylabel("singular values") - plt.title("PCA singular value decays") - plt.show() - - plt.figure(figsize=(6, 6)) - ax = plt.axes(projection=Axes3D.name) - ax.plot3D(pca_1_r, pca_2_r, pca_3_r, "k-o") - ax.set(xlabel="pc1", ylabel="pc2", zlabel="pc3") - plt.title("PCA visualization (real)") - plt.show() - - plt.figure(figsize=(6, 6)) - ax = plt.axes(projection=Axes3D.name) - ax.plot3D(pca_1_i, pca_2_i, pca_3_i, "k-o") - ax.set(xlabel="pc1", ylabel="pc2", zlabel="pc3") - plt.title("PCA visualization (imag)") - plt.show() - - -if __name__ == "__main__": - n = 512 - x = np.linspace(-15, 15, n, endpoint=False) - u0 = 2.0 / np.cosh(x) - # u0 = u0.reshape(-1,1) - n_int = 10000 - n_snapshot = 80 # in the original paper, it is 20, but I think too small - dt = np.pi / n_int - n_sample = n_int // n_snapshot - - model = nlse(n, dt=dt, L=30) - X, t = model.simulate(u0, n_int, n_sample) - - # usage: visualize the data in physical space - model.visualize_data(x, t, X) - - # usage: visualize the data in state space - model.visualize_state_space(X) - - print(X.shape) - print(t[1] - t[0]) - - # usage: collect continuous data pair: x and dx/dt - x0_array = np.vstack([u0, u0, u0]) - X, Y = model.collect_data_continuous(x0_array) - - print(X.shape) - print(Y.shape) - - # usage: collect discrete data pair - x0_array = np.vstack([u0, u0, u0]) - X, Y = model.collect_one_step_data_discrete(x0_array) - - print(X.shape) - print(Y.shape) - - # usage: collect one trajectory data - X = model.collect_one_trajectory_data(u0, n_int, n_sample) - print(X.shape) diff --git a/DSA/pykoopman/src/pykoopman/common/vbe.py b/DSA/pykoopman/src/pykoopman/common/vbe.py deleted file mode 100644 index 2a3cec2..0000000 --- a/DSA/pykoopman/src/pykoopman/common/vbe.py +++ /dev/null @@ -1,177 +0,0 @@ -"""module for 1D viscous burgers""" -from __future__ import annotations - -import numpy as np -from matplotlib import pyplot as plt -from mpl_toolkits.mplot3d import Axes3D -from pykoopman.common.examples import rk4 -from scipy.fft import fft -from scipy.fft import fftfreq -from scipy.fft import ifft - - -class vbe: - """ - 1D viscous Burgers equation - - u_t = -u*u_x + \nu u_{xx} - - periodic B.C. PDE is solved using spectral methods - """ - - def __init__(self, n, x, dt, nu=0.1, L=2 * np.pi): - self.n_states = n - self.x = x - self.nu = nu - dk = 2 * np.pi / L - self.k = fftfreq(self.n_states, 1.0 / self.n_states) * dk - self.dt = dt - - def sys(self, t, x, u): - xk = fft(x) - - # 3/2 truncation rule - xk[self.n_states // 3 : 2 * self.n_states // 3] = 0j - x = ifft(xk) - - # nonlinear advection - tmp_nl_k = fft(-0.5 * x * x) - tmp_nl_x_k = 1j * self.k * tmp_nl_k - - # linear viscous term - tmp_vis_k = -self.nu * self.k**2 * xk - - # return back to physical space - y = np.real(ifft(tmp_nl_x_k + tmp_vis_k)) - return y - - def simulate(self, x0, n_int, n_sample): - # n_traj = x0.shape[1] - x = x0 - u = np.zeros((n_int, 1)) - X = np.zeros((n_int // n_sample, self.n_states)) - t = 0 - j = 0 - t_list = [] - for step in range(n_int): - t += self.dt - y = rk4(0, x, u[step, :], self.dt, self.sys) - if (step + 1) % n_sample == 0: - X[j, :] = y - j += 1 - t_list.append(t) - x = y - return X, np.array(t_list) - - def collect_data_continuous(self, x0): - """ - collect training data pairs - continuous sense. - - given x0, with shape (n_dim, n_traj), the function - returns dx/dt with shape (n_dim, n_traj) - """ - - n_traj = x0.shape[0] - u = np.zeros((n_traj, 1)) - X = x0 - Y = [] - for i in range(n_traj): - y = self.sys(0, x0[i], u[i]) - Y.append(y) - Y = np.vstack(Y) - return X, Y - - def collect_one_step_data_discrete(self, x0): - """ - collect training data pairs - discrete sense. - - given x0, with shape (n_dim, n_traj), the function - returns system state x1 after self.dt with shape - (n_dim, n_traj) - """ - - n_traj = x0.shape[0] - X = x0 - Y = [] - for i in range(n_traj): - y, _ = self.simulate(x0[i], n_int=1, n_sample=1) - Y.append(y) - Y = np.vstack(Y) - return X, Y - - def collect_one_trajectory_data(self, x0, n_int, n_sample): - x = x0 - y, _ = self.simulate(x, n_int, n_sample) - return y - - def visualize_data(self, x, t, X): - plt.figure(figsize=(6, 6)) - ax = plt.axes(projection=Axes3D.name) - for i in range(X.shape[0]): - ax.plot(x, X[i], zs=t[i], zdir="t", label="time = " + str(i * self.dt)) - # plt.legend(loc='best') - ax.view_init(elev=35.0, azim=-65, vertical_axis="y") - ax.set(ylabel=r"$u(x,t)$", xlabel=r"$x$", zlabel=r"time $t$") - plt.title("1D Viscous Burgers equation (Kutz et al., Complexity, 2018)") - plt.show() - - def visualize_state_space(self, X): - u, s, vt = np.linalg.svd(X, full_matrices=False) - plt.figure(figsize=(6, 6)) - plt.semilogy(s) - plt.xlabel("number of SVD terms") - plt.ylabel("singular values") - plt.title("PCA singular value decays") - plt.show() - - # this is a pde problem so the number of snapshots are smaller than dof - pca_1, pca_2, pca_3 = u[:, 0], u[:, 1], u[:, 2] - plt.figure(figsize=(6, 6)) - ax = plt.axes(projection=Axes3D.name) - ax.plot3D(pca_1, pca_2, pca_3, "k-o") - ax.set(xlabel="pc1", ylabel="pc2", zlabel="pc3") - plt.title("PCA visualization") - plt.show() - - -if __name__ == "__main__": - n = 256 - x = np.linspace(-15, 15, n, endpoint=False) - u0 = np.exp(-((x + 2) ** 2)) - # u0 = 2.0 / np.cosh(x) - # u0 = u0.reshape(-1,1) - n_int = 3000 - n_snapshot = 30 - dt = 30.0 / n_int - n_sample = n_int // n_snapshot - - model = vbe(n, x, dt=dt, L=30) - X, t = model.simulate(u0, n_int, n_sample) - - print(X.shape) - # print(X[:,-1].max()) - - # usage: visualize the data in physical space - model.visualize_data(x, t, X) - print(t) - - # usage: visualize the data in state space - model.visualize_state_space(X) - - # usage: collect continuous data pair: x and dx/dt - x0_array = np.vstack([u0, u0, u0]) - X, Y = model.collect_data_continuous(x0_array) - - print(X.shape) - print(Y.shape) - - # usage: collect discrete data pair - x0_array = np.vstack([u0, u0, u0]) - X, Y = model.collect_one_step_data_discrete(x0_array) - - print(X.shape) - print(Y.shape) - - # usage: collect one trajectory data - X = model.collect_one_trajectory_data(u0, n_int, n_sample) - print(X.shape) From 4327124ac77420ce9cec5cf0f9194e2d1276cb72 Mon Sep 17 00:00:00 2001 From: ostrow Date: Thu, 30 Oct 2025 09:38:39 -0400 Subject: [PATCH 21/90] bug fixes for comparisons --- DSA/__init__.py | 5 ++++- DSA/dsa.py | 25 ++++++++++++++----------- DSA/simdist_controllability.py | 6 +++--- 3 files changed, 21 insertions(+), 15 deletions(-) diff --git a/DSA/__init__.py b/DSA/__init__.py index b830265..da9d373 100644 --- a/DSA/__init__.py +++ b/DSA/__init__.py @@ -1,4 +1,7 @@ -from DSA.dsa import DSA, GeneralizedDSA, InputDSA +from DSA.dsa import DSA, ControllabilitySimilarityTransformDistConfig, GeneralizedDSA, InputDSA, SimilarityTransformDistConfig +from DSA.dsa import DefaultDMDConfig as DMDConfig +from DSA.dsa import pyKoopmanDMDConfig,SubspaceDMDcConfig +from DSA.dsa import SimilarityTransformDistConfig, ControllabilitySimilarityTransformDistConfig from DSA.dmd import DMD from DSA.dmdc import DMDc from DSA.subspace_dmdc import SubspaceDMDc diff --git a/DSA/dsa.py b/DSA/dsa.py index 62b869b..02787f5 100644 --- a/DSA/dsa.py +++ b/DSA/dsa.py @@ -13,7 +13,7 @@ import DSA.pykoopman as pykoopman import pydmd from DSA.pykoopman.regression import DMDc, EDMDc -from typing import Union, Mapping, Any +from typing import Union, Mapping, Any, ClassVar, Final import warnings @@ -125,6 +125,8 @@ class SimilarityTransformDistConfig: iters: int = 1500 score_method: Literal["angular", "euclidean", "wasserstein"] = "angular" lr: float = 5e-3 + #class variable, set as final to indicate that it's fixed and immutable + compare: ClassVar[Final] = "state" @dataclass() class ControllabilitySimilarityTransformDistConfig: @@ -139,20 +141,20 @@ class ControllabilitySimilarityTransformDistConfig: 'angular' uses angular distance, 'euclidean' uses Euclidean distance. Default is "euclidean". compare (str): What to compare between systems. - 'state' compares only state operators, 'control' compares only control operators, - 'joint' compares both. Default is 'state'. - joint_optim (bool): Whether to optimize state and control operators jointly. - Default is False. + 'control' compares only control operators, + 'joint' compares both control and state operators simultaneousl. + Default is 'joint'. + If you pass in 'state', it will throw an error -> use SimilarityTransformDistConfig Instead + align_inputs (bool): whether to learn a C_u transformation that aligns the input representations as well return_distance_components (bool): Whether to return individual distance components (state, control, joint) separately. Default is False. """ score_method: Literal["euclidean", "angular"] = "euclidean" - compare = "joint" - joint_optim: bool = False + compare: Literal["joint","control"] = "joint" + align_inputs: bool = False return_distance_components: bool = False - class GeneralizedDSA: """ Computes the Generalized Dynamical Similarity Analysis (DSA) for two data tensors. @@ -586,12 +588,13 @@ def score(self): ind2 = 0 if self.method == "self-pairwise" else 1 # 0 if self.pairwise (want to compare the set to itself) n_sims = ( - 1 - if not ( + 3 + if ( self.simdist_has_control and self.simdist_config.get("return_distance_components") + and self.simdist_config.get("compare") == "joint" ) - else 3 + else 1 ) self.sims = np.zeros((len(self.dmds[0]), len(self.dmds[ind2]), n_sims)) diff --git a/DSA/simdist_controllability.py b/DSA/simdist_controllability.py index 778384f..bc740a9 100644 --- a/DSA/simdist_controllability.py +++ b/DSA/simdist_controllability.py @@ -19,7 +19,7 @@ def __init__( *, score_method: Literal["euclidean", "angular"] = "euclidean", compare: Literal["joint", "control", "state"] = "joint", - joint_optim: bool = False, + align_inputs: bool = False, return_distance_components: bool = True, ): f""" @@ -36,7 +36,7 @@ def __init__( """ self.score_method = score_method self.compare = compare - self.joint_optim = joint_optim + self.align_inputs = align_inputs self.return_distance_components = return_distance_components @staticmethod @@ -63,7 +63,7 @@ def fit_score(self, A, B, A_control, B_control): if self.compare == "joint": C, C_u, sims_joint_euc, sims_joint_ang = self.compare_systems_procrustes( - A1=A, B1=A_control, A2=B, B2=B_control, align_inputs=self.joint_optim + A1=A, B1=A_control, A2=B, B2=B_control, align_inputs=self.align_inputs ) if self.return_distance_components: if self.score_method == "euclidean": From 4374db55c4b202faa55d48d7c55609534c135a83 Mon Sep 17 00:00:00 2001 From: ostrow Date: Thu, 30 Oct 2025 09:48:31 -0400 Subject: [PATCH 22/90] add error for align_inputs = True (need to fix later, quick hack) --- DSA/simdist_controllability.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/DSA/simdist_controllability.py b/DSA/simdist_controllability.py index bc740a9..43763a1 100644 --- a/DSA/simdist_controllability.py +++ b/DSA/simdist_controllability.py @@ -39,6 +39,9 @@ def __init__( self.align_inputs = align_inputs self.return_distance_components = return_distance_components + if align_inputs: + raise ValueError("align inputs is not yet implemented correctly, please switch to align_inputs=False for now") + @staticmethod def compute_angular_dist(A, B): """ @@ -67,8 +70,6 @@ def fit_score(self, A, B, A_control, B_control): ) if self.return_distance_components: if self.score_method == "euclidean": - # sims_control_joint = np.linalg.norm(C @ A_control @ C_u - B_control, "fro") ** 2 - # sims_state_joint = np.linalg.norm(C @ A @ C.T - B, "fro") ** 2 sims_control_joint = np.linalg.norm( C @ A_control @ C_u - B_control, "fro" ) @@ -176,6 +177,7 @@ def compare_systems_procrustes(self, A1, B1, A2, B2, *, align_inputs=False): U2, S2, V2t = np.linalg.svd(K2, full_matrices=False) C = U1 @ U2.T + #TODO: fix this to compute procrustes on individual blocks (B, AB, A^2B, etc) C_u = V2t.T @ V1t # = V2 @ V1^T K2_aligned = C @ K2 @ C_u From 293cd17bce4a6c28b79a146ea8cfc986d8be118c Mon Sep 17 00:00:00 2001 From: ostrow Date: Thu, 30 Oct 2025 10:43:33 -0400 Subject: [PATCH 23/90] add dmdc config --- DSA/dsa.py | 27 ++++++++++++++++++++++++++- 1 file changed, 26 insertions(+), 1 deletion(-) diff --git a/DSA/dsa.py b/DSA/dsa.py index 02787f5..97d77d4 100644 --- a/DSA/dsa.py +++ b/DSA/dsa.py @@ -102,6 +102,32 @@ class SubspaceDMDcConfig: lamb: float = 0 backend: str = "n4sid" +@dataclass() +class DMDcConfig: + """ + Configuration dataclass for DefaultDMDc (standard DMD with control). + + This configuration is used to set parameters for the DefaultDMDc class when + performing Dynamical Mode Decomposition on time series data with control inputs. + + Attributes: + n_delays (int): Number of time delays to use in the Hankel matrix construction + for the state data. Default is 1 (no delays). + input_rank (int): Rank for SVD truncation of the input (control) data. + If None, no truncation is performed. Default is None. + output_rank (int): Rank for SVD truncation of the output (state) data. + If None, no truncation is performed. Default is None. + lamb (float): Regularization parameter for ridge regression. + Default is 0 (no regularization). + delay_interval (int): Interval between delays in the Hankel matrix. + Default is 1 (consecutive time steps). + """ + n_delays: int = 1 + input_rank: int = None + output_rank: int = None + lamb: float = 0 + delay_interval: int = 1 + # __Example config dataclasses for similarity transform distance # @dataclass @@ -752,4 +778,3 @@ def update_compare_method(self,compare='joint'): simdist = ControllabilitySimilarityTransformDist #TODO: check simdist config to make sure it aligns return simdist - From 8b0cae8111b9bf76b9425e46b8c1450ae55bacab Mon Sep 17 00:00:00 2001 From: ostrow Date: Thu, 30 Oct 2025 11:20:44 -0400 Subject: [PATCH 24/90] torch compatibility, dmdc bug, allow passing config in directly without initializing (for defaults) --- DSA/__init__.py | 2 +- DSA/dsa.py | 10 ++++++++-- DSA/simdist_controllability.py | 9 +++++++-- 3 files changed, 16 insertions(+), 5 deletions(-) diff --git a/DSA/__init__.py b/DSA/__init__.py index da9d373..dde8892 100644 --- a/DSA/__init__.py +++ b/DSA/__init__.py @@ -1,6 +1,6 @@ from DSA.dsa import DSA, ControllabilitySimilarityTransformDistConfig, GeneralizedDSA, InputDSA, SimilarityTransformDistConfig from DSA.dsa import DefaultDMDConfig as DMDConfig -from DSA.dsa import pyKoopmanDMDConfig,SubspaceDMDcConfig +from DSA.dsa import pyKoopmanDMDConfig,SubspaceDMDcConfig, DMDcConfig from DSA.dsa import SimilarityTransformDistConfig, ControllabilitySimilarityTransformDistConfig from DSA.dmd import DMD from DSA.dmdc import DMDc diff --git a/DSA/dsa.py b/DSA/dsa.py index 97d77d4..221140f 100644 --- a/DSA/dsa.py +++ b/DSA/dsa.py @@ -123,8 +123,8 @@ class DMDcConfig: Default is 1 (consecutive time steps). """ n_delays: int = 1 - input_rank: int = None - output_rank: int = None + rank_input: int = None + rank_output: int = None lamb: float = 0 delay_interval: int = 1 @@ -283,8 +283,12 @@ def __init__( self.Y = Y self.X_control = X_control self.Y_control = Y_control + + if isinstance(simdist_config, type): #if it's the class itself (not an object) initialize + simdist_config = simdist_config() self.simdist_config = simdist_config + if is_dataclass(simdist_config): self.simdist_config = asdict(self.simdist_config) @@ -311,6 +315,8 @@ def __init__( # Process DMD keyword arguments from **dmd_kwargs # These are parameters like n_delays, rank, etc., that are specific to DMDs # and need to be broadcasted according to X and Y data structure. + if isinstance(dmd_config,type): + dmd_config = dmd_config() if is_dataclass(dmd_config): dmd_config = asdict(dmd_config) self.dmd_config = ( diff --git a/DSA/simdist_controllability.py b/DSA/simdist_controllability.py index 43763a1..edb3468 100644 --- a/DSA/simdist_controllability.py +++ b/DSA/simdist_controllability.py @@ -1,6 +1,7 @@ from typing import Literal import numpy as np from scipy.linalg import orthogonal_procrustes +import torch try: from .simdist import SimilarityTransformDist @@ -63,6 +64,11 @@ def compute_angular_dist(A, B): return cos_sim def fit_score(self, A, B, A_control, B_control): + convert_np = lambda A: A.detach().cpu().numpy() if isinstance(A,torch.Tensor) else A + A = convert_np(A) + B = convert_np(B) + A_control = convert_np(A_control) + B_control = convert_np(B_control) if self.compare == "joint": C, C_u, sims_joint_euc, sims_joint_ang = self.compare_systems_procrustes( @@ -123,7 +129,6 @@ def get_controllability_matrix(self, A, B): # term_norm = np.linalg.norm(current_term) # if term_norm < 1e-12 or term_norm > 1e12: # break - # Check for linear dependence (rank deficiency) K_test = np.hstack((K, current1_term, current2_term)) # if np.linalg.matrix_rank(K_test) <= np.linalg.matrix_rank(K): @@ -156,7 +161,7 @@ def compare_systems_procrustes(self, A1, B1, A2, B2, *, align_inputs=False): # Build controllability matrices: K \in R^{n x p} K1 = self.get_controllability_matrix(A1, B1) K2 = self.get_controllability_matrix(A2, B2) - + import pdb; pdb.set_trace() if not align_inputs: # One-sided: C = argmin ||K1 - C K2||_F M = K2 @ K1.T From bd103d4ed09dfeec2b900c6d7ae5a21ad4319b54 Mon Sep 17 00:00:00 2001 From: ostrow Date: Thu, 30 Oct 2025 12:11:20 -0400 Subject: [PATCH 25/90] fix inputdsa graceful switching of different comparisons based on config that is passed in , generalize to generalizedDSA as a whole (but with the option to still pass in the class) --- DSA/dsa.py | 165 ++++++++++++++++++++++++++------- DSA/simdist_controllability.py | 1 - 2 files changed, 131 insertions(+), 35 deletions(-) diff --git a/DSA/dsa.py b/DSA/dsa.py index 221140f..329bd81 100644 --- a/DSA/dsa.py +++ b/DSA/dsa.py @@ -214,11 +214,11 @@ def __init__( X_control=None, Y_control=None, dmd_class=DefaultDMD, - similarity_class=SimilarityTransformDist, - dmd_config: Union[Mapping[str, Any], dataclass] = DefaultDMDConfig(), + similarity_class=None, + dmd_config: Union[Mapping[str, Any], dataclass] = DefaultDMDConfig, simdist_config: Union[ Mapping[str, Any], dataclass - ] = SimilarityTransformDistConfig(), + ] = SimilarityTransformDistConfig, device="cpu", verbose=False, n_jobs=1, @@ -250,14 +250,18 @@ def __init__( dmd_class : class DMD class to use for decomposition. Default is DefaultDMD. - similarity_class : class - Similarity transform distance class to use. Default is SimilarityTransformDist. + similarity_class : class or None + Similarity transform distance class to use. If None, will be inferred + from the 'compare' field in simdist_config. Default is None. dmd_config : Union[Mapping[str, Any], dataclass] Configuration for DMD parameters. Can be a dict or dataclass. simdist_config : Union[Mapping[str, Any], dataclass] Configuration for similarity transform distance parameters. Can be a dict or dataclass. + If similarity_class is None, the 'compare' field will be used to infer the class: + - 'state' -> SimilarityTransformDist + - 'joint' or 'control' -> ControllabilitySimilarityTransformDist device : str Device to use for computation ('cpu' or 'cuda'). Default is 'cpu'. @@ -291,6 +295,16 @@ def __init__( if is_dataclass(simdist_config): self.simdist_config = asdict(self.simdist_config) + + # Infer similarity_class from simdist_config if not provided + if similarity_class is None: + compare = self.simdist_config.get('compare', 'state') + if compare == 'state': + similarity_class = SimilarityTransformDist + elif compare in ['joint', 'control']: + similarity_class = ControllabilitySimilarityTransformDist + else: + raise ValueError(f"Invalid compare value in simdist_config: {compare}. Must be 'state', 'joint', or 'control'.") self.device = device self.n_jobs = n_jobs @@ -429,6 +443,106 @@ def _dmd_api_source(self, dmd_class): raise ValueError( f"dmd_class {dmd_class.__name__} from unknown module {module_name}" ) + def update_compare_method(self, compare='joint', simdist_config=None): + """ + Update the similarity comparison method and adapt the configuration. + This method can be called to switch between different comparison modes + (state-only, control-only, or joint) after initialization. + + Parameters + ---------- + compare : str + 'state', 'joint', or 'control' + simdist_config : dict, dataclass, or None + Configuration to adapt. If None, uses current self.simdist_config + + Raises + ------ + ValueError + If the comparison method is incompatible with the DMD class + """ + # Validate compare parameter + valid_compare_values = ['state', 'joint', 'control'] + if compare not in valid_compare_values: + raise ValueError( + f"compare must be one of {valid_compare_values}, got {compare}" + ) + + # Use current config if none provided + if simdist_config is None: + simdist_config = self.simdist_config + + # Convert to dict if needed + if isinstance(simdist_config, type): # If it's a class + simdist_config = simdist_config() + if is_dataclass(simdist_config): + simdist_config = asdict(simdist_config) + + # Check if return_distance_components is changing + old_return_components = self.simdist_config.get('return_distance_components', False) + new_return_components = simdist_config.get('return_distance_components', False) + if old_return_components != new_return_components: + warnings.warn( + f"Changing return_distance_components from {old_return_components} to " + f"{new_return_components} will change the output shape of score(). " + f"Previously computed similarities (self.sims) will be cleared. " + f"You will need to recompute similarities by calling score() or fit_score()." + ) + + + if compare == "state": + simdist_class = SimilarityTransformDist + # Extract only parameters relevant to SimilarityTransformDist + adapted_config = { + 'score_method': simdist_config.get('score_method', 'angular'), + 'iters': simdist_config.get('iters', 1500), + 'lr': simdist_config.get('lr', 5e-3), + 'device': simdist_config.get('device', self.device), + 'verbose': simdist_config.get('verbose', self.verbose), + } + # Validate score_method for SimilarityTransformDist + if adapted_config['score_method'] not in ['angular', 'euclidean', 'wasserstein']: + warnings.warn( + f"score_method '{adapted_config['score_method']}' may not be valid " + f"for SimilarityTransformDist, valid options: angular, euclidean, wasserstein" + ) + # State comparison doesn't have return_distance_components, so always treated as False + new_return_components = False + + else: # 'joint' or 'control' + simdist_class = ControllabilitySimilarityTransformDist + # Extract only parameters relevant to ControllabilitySimilarityTransformDist + adapted_config = { + 'score_method': simdist_config.get('score_method', 'euclidean'), + 'compare': compare, # Override with the method parameter + 'align_inputs': simdist_config.get('align_inputs', False), + 'return_distance_components': new_return_components, + } + # Validate score_method for ControllabilitySimilarityTransformDist + if adapted_config['score_method'] not in ['angular', 'euclidean']: + warnings.warn( + f"score_method '{adapted_config['score_method']}' may not be valid " + f"for ControllabilitySimilarityTransformDist, valid options: angular, euclidean" + ) + + # Check compatibility between DMD and new simdist class + # This will update self.dmd_has_control and self.simdist_has_control + self._check_dmd_simdist_compatibility(self.dmd_class, simdist_class) + + if self.simdist_has_control: + if self.X_control is None and self.Y_control is None: + raise ValueError( + f"Cannot use compare='{compare}' which requires control matrices, " + f"but no control data (X_control or Y_control) was provided" + ) + + self.simdist_config = adapted_config + self.simdist = simdist_class(**adapted_config) + + if self.verbose: + print(f"Updated similarity method to compare='{compare}' using {simdist_class.__name__}") + + return simdist_class, adapted_config def fit_dmds(self): if self.n_jobs != 1: @@ -742,45 +856,28 @@ def __init__( Y=None, Y_control=None, dmd_class=SubspaceDMDc, - dmd_config: Union[Mapping[str, Any], dataclass] = SubspaceDMDcConfig(), + dmd_config: Union[Mapping[str, Any], dataclass] = SubspaceDMDcConfig, simdist_config: Union[ Mapping[str, Any], dataclass - ] = ControllabilitySimilarityTransformDistConfig(), + ] = ControllabilitySimilarityTransformDistConfig, device="cpu", verbose=False, n_jobs=1, ): - #TODO: fix based on making compare argument explicit - # check if simdist_config has 'compare', and if it's 'state', use the standard SimilarityTransformDist, - # otherwise use ControllabilitySimilarityTransformDistConfig - if isinstance(simdist_config, dict): - compare = simdist_config.get("compare", None) - else: - compare = simdist_config.compare - simdist = self.update_compare_method(compare) - + # similarity_class will be inferred from simdist_config in parent __init__ super().__init__( X, Y, X_control, Y_control, dmd_class, - simdist, - dmd_config, - simdist_config, - device, - verbose, - n_jobs, + similarity_class=None, # Will be inferred from simdist_config + dmd_config=dmd_config, + simdist_config=simdist_config, + device=device, + verbose=verbose, + n_jobs=n_jobs, ) - - assert X_control is not None - assert self.dmd_has_control - - def update_compare_method(self,compare='joint'): - if compare == "state": - simdist = SimilarityTransformDist - #TODO: check simdist config to make sure it aligns - else: - simdist = ControllabilitySimilarityTransformDist - #TODO: check simdist config to make sure it aligns - return simdist + + assert X_control is not None, "InputDSA requires X_control to be provided" + assert self.dmd_has_control, "InputDSA requires a DMD class with control" diff --git a/DSA/simdist_controllability.py b/DSA/simdist_controllability.py index edb3468..cf1f1da 100644 --- a/DSA/simdist_controllability.py +++ b/DSA/simdist_controllability.py @@ -161,7 +161,6 @@ def compare_systems_procrustes(self, A1, B1, A2, B2, *, align_inputs=False): # Build controllability matrices: K \in R^{n x p} K1 = self.get_controllability_matrix(A1, B1) K2 = self.get_controllability_matrix(A2, B2) - import pdb; pdb.set_trace() if not align_inputs: # One-sided: C = argmin ||K1 - C K2||_F M = K2 @ K1.T From d2a9667b68960f47f037ee6d6d235c7f863b6da8 Mon Sep 17 00:00:00 2001 From: ostrow Date: Thu, 30 Oct 2025 14:39:21 -0400 Subject: [PATCH 26/90] dmdc ragged lists bug fix, starting to fix subspace dmdc but not quite there yet --- DSA/base_dmd.py | 16 + DSA/dmdc.py | 9 +- DSA/subspace_dmdc.py | 642 ++--- examples/all_dsa_types.ipynb | 203 +- examples/fig2_real.ipynb | 5211 ++++++++++++++++++++++++++++++++++ 5 files changed, 5548 insertions(+), 533 deletions(-) create mode 100644 examples/fig2_real.ipynb diff --git a/DSA/base_dmd.py b/DSA/base_dmd.py index 781fba0..32c5d28 100644 --- a/DSA/base_dmd.py +++ b/DSA/base_dmd.py @@ -237,6 +237,22 @@ def _compute_rank_from_params( return computed_rank + def _to_torch(self, x): + """Convert numpy array to torch tensor on the appropriate device.""" + if not self.use_torch or x is None: + return x + if isinstance(x, torch.Tensor): + return x.to(self.device) + return torch.from_numpy(x).to(self.device) + + def _to_numpy(self, x): + """Convert torch tensor to numpy array.""" + if not self.use_torch or x is None: + return x + if isinstance(x, torch.Tensor): + return x.cpu().numpy() + return x + def all_to_device(self, device="cpu"): """Move all tensor attributes to specified device.""" for k, v in self.__dict__.items(): diff --git a/DSA/dmdc.py b/DSA/dmdc.py index 3561007..99650be 100644 --- a/DSA/dmdc.py +++ b/DSA/dmdc.py @@ -311,6 +311,7 @@ def compute_svd(self): else: H_list.append(h_elem) + self.Hu_shapes = [h.shape for h in self.Hu] for hu_elem in self.Hu: if hu_elem.ndim == 3: Hu_list.append( @@ -352,18 +353,18 @@ def compute_svd(self): self.Sh ) - self.Vht_minus, self.Vht_plus = self.get_plus_minus(self.Vh, self.H) - self.Vut_minus, _ = self.get_plus_minus(self.Vu, self.Hu) + self.Vht_minus, self.Vht_plus = self.get_plus_minus(self.Vh, self.H,self.H_shapes) + self.Vut_minus, _ = self.get_plus_minus(self.Vu, self.Hu,self.Hu_shapes) if self.verbose: print("SVDs computed!") - def get_plus_minus(self, V, H): + def get_plus_minus(self, V, H,H_shapes): if self.ntrials > 1: if self.is_list_data: V_split = torch.split(V, self.H_row_counts, dim=0) Vt_minus_list, Vt_plus_list = [], [] - for v_part, h_shape in zip(V_split, self.H_shapes): + for v_part, h_shape in zip(V_split, H_shapes): if len(h_shape) == 3: # Has trials v_part_reshaped = v_part.reshape(h_shape) newshape = ( diff --git a/DSA/subspace_dmdc.py b/DSA/subspace_dmdc.py index db0b91f..0c153c2 100644 --- a/DSA/subspace_dmdc.py +++ b/DSA/subspace_dmdc.py @@ -65,23 +65,6 @@ def __init__( self.rank = rank self.backend = backend - - def _to_torch(self, x): - """Convert numpy array to torch tensor on the appropriate device.""" - if not self.use_torch or x is None: - return x - if isinstance(x, torch.Tensor): - return x.to(self.device) - return torch.from_numpy(x).to(self.device) - - def _to_numpy(self, x): - """Convert torch tensor to numpy array.""" - if not self.use_torch or x is None: - return x - if isinstance(x, torch.Tensor): - return x.cpu().numpy() - return x - def fit(self): """Fit the SubspaceDMDc model.""" self.A_v, self.B_v, self.C_v, self.info = self.subspace_dmdc_multitrial_flexible( @@ -97,34 +80,15 @@ def fit(self): if self.send_to_cpu: self.all_to_device(device='cpu') - - - def subspace_dmdc_multitrial_QR_decomposition(self, y_list, u_list, p, f, n=None, lamb=1e-8, energy=0.999): - """ - Subspace-DMDc for multi-trial data with variable trial lengths. - Now use QR decomposition for computing the oblique projection as in N4SID implementations. - - Parameters: - - y_list: list of arrays, each (p_out, N_i) - output data for trial i - - u_list: list of arrays, each (m, N_i) - input data for trial i - - p: past window length - - f: future window length - - n: state dimension (auto-determined if None) - - ridge: regularization parameter (used only for rank selection/SVD; QR is exact) - - energy: energy threshold for rank selection - - Returns: - - A_hat, B_hat, C_hat: system matrices - - info: dictionary with additional information - """ + def _validate_and_collect_data(self, y_list, u_list, p, f): + """Helper function to validate dimensions and collect data from trials.""" if len(y_list) != len(u_list): raise ValueError("y_list and u_list must have same number of trials") - + # import pdb; pdb.set_trace() n_trials = len(y_list) p_out = y_list[0].shape[0] m = u_list[0].shape[0] - # Validate dimensions across trials for i, (y_trial, u_trial) in enumerate(zip(y_list, u_list)): if y_trial.shape[0] != p_out: raise ValueError(f"Trial {i}: y has {y_trial.shape[0]} outputs, expected {p_out}") @@ -133,10 +97,6 @@ def subspace_dmdc_multitrial_QR_decomposition(self, y_list, u_list, p, f, n=None if y_trial.shape[1] != u_trial.shape[1]: raise ValueError(f"Trial {i}: y and u have different time lengths") - def hankel_stack(X, start, L): - return np.concatenate([X[:, start + i:start + i + 1] for i in range(L)], axis=0) - - # Collect data from all trials U_p_all = [] Y_p_all = [] U_f_all = [] @@ -144,6 +104,9 @@ def hankel_stack(X, start, L): valid_trials = [] T_per_trial = [] + def hankel_stack(X, start, L): + return np.concatenate([X[:, start + i:start + i + 1] for i in range(L)], axis=0) + for trial_idx, (Y_trial, U_trial) in enumerate(zip(y_list, u_list)): N_trial = Y_trial.shape[1] T_trial = N_trial - (p + f) + 1 @@ -151,10 +114,10 @@ def hankel_stack(X, start, L): if T_trial <= 0: print(f"Warning: Trial {trial_idx} has insufficient data (T={T_trial}), skipping") continue - + valid_trials.append(trial_idx) T_per_trial.append(T_trial) - + # Build Hankel matrices for this trial U_p_trial = np.concatenate([hankel_stack(U_trial, j, p) for j in range(T_trial)], axis=1) Y_p_trial = np.concatenate([hankel_stack(Y_trial, j, p) for j in range(T_trial)], axis=1) @@ -169,90 +132,102 @@ def hankel_stack(X, start, L): if not valid_trials: raise ValueError("No trials have sufficient data for given number of delays") - # Concatenate across valid trials - U_p = np.concatenate(U_p_all, axis=1) # (p m, T_total) - Y_p = np.concatenate(Y_p_all, axis=1) # (p p_out, T_total) - U_f = np.concatenate(U_f_all, axis=1) # (f m, T_total) - Y_f = np.concatenate(Y_f_all, axis=1) # (f p_out, T_total) + U_p = np.concatenate(U_p_all, axis=1) + Y_p = np.concatenate(Y_p_all, axis=1) + U_f = np.concatenate(U_f_all, axis=1) + Y_f = np.concatenate(Y_f_all, axis=1) T_total = sum(T_per_trial) - Z_p = np.vstack([U_p, Y_p]) # (p (m + p_out), T_total) + Z_p = np.vstack([U_p, Y_p]) + return U_p, Y_p, U_f, Y_f, Z_p, valid_trials, T_per_trial, T_total, p_out, m + + def subspace_dmdc_multitrial_QR_decomposition(self, y_list, u_list, p, f, n=None, lamb=1e-8, energy=0.999): + """ + Subspace-DMDc for multi-trial data with variable trial lengths using QR decomposition. + """ + U_p, Y_p, U_f, Y_f, Z_p, valid_trials, T_per_trial, T_total, p_out, m = \ + self._validate_and_collect_data(y_list, u_list, p, f) + H = np.vstack([U_f, Z_p, Y_f]) - # Dimensions for slicing dim_uf = f * m dim_zp = p * (m + p_out) - dim_yf = f * p_out - - # Use torch for expensive linear algebra if available - if self.use_torch and H.shape[1] > 100: # Only worth it for larger problems - H_torch = self._to_torch(H) - Z_p_torch = self._to_torch(Z_p) - - # Perform QR on H.T to get equivalent LQ on H - Q, R_upper = torch.linalg.qr(H_torch.T, mode='reduced') + + def calculate_projection_and_svd(H, Z_p): + Q, R_upper = np.linalg.qr(H.T, mode='reduced') L = R_upper.T - # Extract submatrices from L R22 = L[dim_uf:dim_uf + dim_zp, dim_uf:dim_uf + dim_zp] R32 = L[dim_uf + dim_zp:, dim_uf:dim_uf + dim_zp] - # Compute oblique projection O = R32 @ pinv(R22) @ Z_p - R22_pinv = torch.linalg.pinv(R22) - O = R32 @ R22_pinv @ Z_p_torch - - # SVD on O - Uo, s, Vt = torch.linalg.svd(O, full_matrices=False) + O = R32 @ np.linalg.pinv(R22) @ Z_p + Uo, s, Vt = np.linalg.svd(O, full_matrices=False) + return Uo, s, Vt + + if self.use_torch and H.shape[1] > 100: + H_torch = self._to_torch(H) + Z_p_torch = self._to_torch(Z_p) + Uo, s, Vt = calculate_projection_and_svd(H_torch, Z_p_torch) - # Convert back to numpy Uo = self._to_numpy(Uo) s = self._to_numpy(s) Vt = self._to_numpy(Vt) else: - # Use numpy for smaller problems or when torch is disabled - # Perform QR on H.T to get equivalent LQ on H - Q, R_upper = np.linalg.qr(H.T, mode='reduced') - L = R_upper.T - - # Extract submatrices from L (lower triangular) - R22 = L[dim_uf:dim_uf + dim_zp, dim_uf:dim_uf + dim_zp] - R32 = L[dim_uf + dim_zp:, dim_uf:dim_uf + dim_zp] - - # Compute oblique projection O = R32 @ pinv(R22) @ Z_p - O = R32 @ np.linalg.pinv(R22) @ Z_p - - # The rest remains the same: SVD on O - Uo, s, Vt = np.linalg.svd(O, full_matrices=False) + Uo, s, Vt = calculate_projection_and_svd(H, Z_p) + if n is None: cs = np.cumsum(s**2) / (s**2).sum() n = int(np.searchsorted(cs, energy) + 1) n = max(1, min(n, min(Uo.shape[1], Vt.shape[0]))) - + U_n = Uo[:, :n] S_n = np.diag(s[:n]) V_n = Vt[:n, :] S_half = np.sqrt(S_n) - Gamma_hat = U_n @ S_half # (f p_out, n) - X_hat = S_half @ V_n # (n, T_total) + Gamma_hat = U_n @ S_half + X_hat = S_half @ V_n + + X, X_next, U_mid = self._time_align_valid_trials(X_hat, u_list, valid_trials, T_per_trial, p) + + + A_hat, B_hat = self._perform_ridge_regression(X, X_next, U_mid, n, lamb) + + C_hat = Gamma_hat[:p_out, :] + noise_covariance, R_hat, Q_hat, S_hat = self._estimate_noise_covariance(X_next, A_hat, X, B_hat, U_mid) + + info = { + "singular_values_O": s, + "rank_used": n, + "Gamma_hat": Gamma_hat, + "f": f, + "n_trials_total": len(y_list), + "n_trials_used": len(valid_trials), + "valid_trials": valid_trials, + "T_per_trial": T_per_trial, + "T_total": T_total, + "trial_lengths": [y.shape[1] for y in y_list], + "noise_covariance": noise_covariance, + 'R_hat': R_hat, + 'Q_hat': Q_hat, + 'S_hat': S_hat + } + + return A_hat, B_hat, C_hat, info - # Time alignment for regression across all trials - # Need to handle variable lengths carefully + def _time_align_valid_trials(self, X_hat, u_list, valid_trials, T_per_trial, p): + """Helper function to time-align trials for regression.""" + # import pdb; pdb.set_trace() X_segments = [] X_next_segments = [] U_mid_segments = [] - Y_segments = [] start_idx = 0 for trial_idx, T_trial in enumerate(T_per_trial): - # Extract states for this trial X_trial = X_hat[:, start_idx:start_idx + T_trial] - - # State transitions within this trial X_trial_curr = X_trial[:, :-1] X_trial_next = X_trial[:, 1:] - # Corresponding control inputs original_trial_idx = valid_trials[trial_idx] U_trial = u_list[original_trial_idx] U_mid_trial = U_trial[:, p:p + (T_trial - 1)] @@ -261,187 +236,63 @@ def hankel_stack(X, start, L): X_next_segments.append(X_trial_next) U_mid_segments.append(U_mid_trial) - # TODO: check the time-alignment of Y and X here - # Corresponding output data - align with X_trial time indices - Y_trial = y_list[original_trial_idx] - Y_trial_curr = Y_trial[:, p:p+T_trial-1] - # Y_trial_curr = Y_trial[:, p+1:p+T_trial] - Y_segments.append(Y_trial_curr) - start_idx += T_trial - # Concatenate all segments X = np.concatenate(X_segments, axis=1) X_next = np.concatenate(X_next_segments, axis=1) U_mid = np.concatenate(U_mid_segments, axis=1) - # Regression for A and B + return X, X_next, U_mid + + def _perform_ridge_regression(self, X, X_next, U_mid, n, lamb): + """Helper function to perform ridge regression.""" Z = np.vstack([X, U_mid]) - - # Use torch for ridge regression if available if self.use_torch and Z.shape[1] > 100: Z_torch = self._to_torch(Z) X_next_torch = self._to_torch(X_next) - # Ridge regression: (Z^T Z + λI)^(-1) Z^T X_next^T ZTZ = Z_torch @ Z_torch.T ridge_term = lamb * torch.eye(ZTZ.shape[0], device=self.device, dtype=Z_torch.dtype) AB = torch.linalg.solve(ZTZ + ridge_term, Z_torch @ X_next_torch.T).T - AB = self._to_numpy(AB) - A_hat = AB[:, :n] - B_hat = AB[:, n:] else: - # Ridge regression: (Z^T Z + λI)^(-1) Z^T X_next^T ZTZ = Z @ Z.T ridge_term = lamb * np.eye(ZTZ.shape[0]) AB = np.linalg.solve(ZTZ + ridge_term, Z @ X_next.T).T - A_hat = AB[:, :n] - B_hat = AB[:, n:] - - # Z = np.vstack([X, U_mid]) - # AB = X_next @ np.linalg.pinv(Z) - # A_hat = AB[:, :n] - # B_hat = AB[:, n:] - - C_hat = Gamma_hat[:p_out, :] - # Estimate noise covariance matrix - # 0) Outputs aligned to X and U_mid (same time indices/columns) - Y_curr = np.concatenate(Y_segments, axis=1) # shape: (p_out, N) + A_hat = AB[:, :n] + B_hat = AB[:, n:] - # 1) Residuals at time t - # Process noise residual (state eq): w_t ≈ x_{t+1} - A x_t - B u_ts - W_hat = X_next - (A_hat @ X + B_hat @ U_mid) # (n, N) + return A_hat, B_hat - # Measurement noise residual (output eq): v_t ≈ y_t - C x_t (since D = 0) - V_hat = Y_curr - (C_hat @ X) # (p_out, N) + def _estimate_noise_covariance(self, X_next, A_hat, X, B_hat, U_mid): + """Helper function to estimate the noise covariance matrix.""" + W_hat = X_next - (A_hat @ X + B_hat @ U_mid) + V_hat = self.Y_curr - (self.C_hat @ X) - # 2) Mean-centering V_hat = V_hat - V_hat.mean(axis=1, keepdims=True) W_hat = W_hat - W_hat.mean(axis=1, keepdims=True) N_res = V_hat.shape[1] - denom = max(N_res - 1, 1) + denom = max(N_res - 1, 1) - # 3) Covariances - R_hat = (V_hat @ V_hat.T) / denom # (p_out, p_out) measurement - Q_hat = (W_hat @ W_hat.T) / denom # (n, n) process - S_hat = (W_hat @ V_hat.T) / denom # (n, p_out) - cross (w,v) + R_hat = (V_hat @ V_hat.T) / denom + Q_hat = (W_hat @ W_hat.T) / denom + S_hat = (W_hat @ V_hat.T) / denom - # 4) Symmetrize eps = 1e-12 R_hat = 0.5 * (R_hat + R_hat.T) + eps * np.eye(R_hat.shape[0]) Q_hat = 0.5 * (Q_hat + Q_hat.T) + eps * np.eye(Q_hat.shape[0]) noise_covariance = np.block([[R_hat, S_hat.T], [S_hat, Q_hat]]) - - info = { - "singular_values_O": s, - "rank_used": n, - "Gamma_hat": Gamma_hat, - "f": f, - "n_trials_total": n_trials, - "n_trials_used": len(valid_trials), - "valid_trials": valid_trials, - "T_per_trial": T_per_trial, - "T_total": T_total, - "trial_lengths": [y.shape[1] for y in y_list], - "noise_covariance": noise_covariance, - 'R_hat': R_hat, - 'Q_hat': Q_hat, - 'S_hat': S_hat - } - return A_hat, B_hat, C_hat, info - - + return noise_covariance, R_hat, Q_hat, S_hat - def subspace_dmdc_multitrial_custom(self, y_list, u_list, p, f, n=None, lamb=1e-8, energy=0.999): - """ - Subspace-DMDc for multi-trial data with variable trial lengths. - - Parameters: - - y_list: list of arrays, each (p_out, N_i) - output data for trial i - - u_list: list of arrays, each (m, N_i) - input data for trial i - - p: past window length - - f: future window length - - n: state dimension (auto-determined if None) - - ridge: regularization parameter - - energy: energy threshold for rank selection∏ - - Returns: - - A_hat, B_hat, C_hat: system matrices - - info: dictionary with additional information - """ - if len(y_list) != len(u_list): - raise ValueError("y_list and u_list must have same number of trials") - - n_trials = len(y_list) - p_out = y_list[0].shape[0] - m = u_list[0].shape[0] - - # Validate dimensions across trials - - for i, (y_trial, u_trial) in enumerate(zip(y_list, u_list)): - if y_trial.shape[0] != p_out: - raise ValueError(f"Trial {i}: y has {y_trial.shape[0]} outputs, expected {p_out}") - if u_trial.shape[0] != m: - raise ValueError(f"Trial {i}: u has {u_trial.shape[0]} inputs, expected {m}") - if y_trial.shape[1] != u_trial.shape[1]: - raise ValueError(f"Trial {i}: y and u have different time lengths") - - def hankel_stack(X, start, L): - return np.concatenate([X[:, start + i:start + i + 1] for i in range(L)], axis=0) - - # Collect data from all trials - U_p_all = [] - Y_p_all = [] - U_f_all = [] - Y_f_all = [] - valid_trials = [] - T_per_trial = [] + """Subspace-DMDc using custom method.""" + U_p, Y_p, U_f, Y_f, Z_p, valid_trials, T_per_trial, T_total, p_out, m = \ + self._validate_and_collect_data(y_list, u_list, p, f) - for trial_idx, (Y_trial, U_trial) in enumerate(zip(y_list, u_list)): - N_trial = Y_trial.shape[1] - T_trial = N_trial - (p + f) + 1 - - if T_trial <= 0: - print(f"Warning: Trial {trial_idx} has insufficient data (T={T_trial}), skipping") - continue - - valid_trials.append(trial_idx) - T_per_trial.append(T_trial) - - # Build Hankel matrices for this trial - U_p_trial = np.concatenate([hankel_stack(U_trial, j, p) for j in range(T_trial)], axis=1) - Y_p_trial = np.concatenate([hankel_stack(Y_trial, j, p) for j in range(T_trial)], axis=1) - U_f_trial = np.concatenate([hankel_stack(U_trial, j + p, f) for j in range(T_trial)], axis=1) - Y_f_trial = np.concatenate([hankel_stack(Y_trial, j + p, f) for j in range(T_trial)], axis=1) - - U_p_all.append(U_p_trial) - Y_p_all.append(Y_p_trial) - U_f_all.append(U_f_trial) - Y_f_all.append(Y_f_trial) - - - print("="*40) - print(f"Number of valid trials: {len(U_p_trial)}") - - if not valid_trials: - raise ValueError("No trials have sufficient data for given (p,f)") - - # Concatenate across valid trials - U_p = np.concatenate(U_p_all, axis=1) # (pm, T_total) - Y_p = np.concatenate(Y_p_all, axis=1) # (p*p_out, T_total) - U_f = np.concatenate(U_f_all, axis=1) # (fm, T_total) - Y_f = np.concatenate(Y_f_all, axis=1) # (f*p_out, T_total) - - T_total = sum(T_per_trial) - Z_p = np.vstack([U_p, Y_p]) # (p(m+p_out), T_total) - - # Oblique projection: remove row(U_f), project onto row(Z_p) UfUfT = U_f @ U_f.T Xsolve = np.linalg.solve(UfUfT + lamb*np.eye(UfUfT.shape[0]), U_f) Pi_perp = np.eye(T_total) - U_f.T @ Xsolve @@ -451,7 +302,7 @@ def hankel_stack(X, start, L): ZZT = Zp_perp @ Zp_perp.T Zp_pinv_left = np.linalg.solve(ZZT + lamb*np.eye(ZZT.shape[0]), Zp_perp) P = Zp_perp.T @ Zp_pinv_left - O = Yf_perp @ P # ≈ Γ_f X_p + O = Yf_perp @ P Uo, s, Vt = np.linalg.svd(O, full_matrices=False) if n is None: @@ -463,48 +314,13 @@ def hankel_stack(X, start, L): S_n = np.diag(s[:n]) V_n = Vt[:n, :] S_half = np.sqrt(S_n) - Gamma_hat = U_n @ S_half # (f*p_out, n) - X_hat = S_half @ V_n # (n, T_total) - - # Time alignment for regression across all trials - # Need to handle variable lengths carefully - X_segments = [] - X_next_segments = [] - U_mid_segments = [] - - start_idx = 0 - for trial_idx, T_trial in enumerate(T_per_trial): - # Extract states for this trial - X_trial = X_hat[:, start_idx:start_idx + T_trial] - - # State transitions within this trial - X_trial_curr = X_trial[:, :-1] - X_trial_next = X_trial[:, 1:] - - # Corresponding control inputs - original_trial_idx = valid_trials[trial_idx] - U_trial = u_list[original_trial_idx] - U_mid_trial = U_trial[:, p:p + (T_trial - 1)] - - X_segments.append(X_trial_curr) - X_next_segments.append(X_trial_next) - U_mid_segments.append(U_mid_trial) - - start_idx += T_trial - - # Concatenate all segments - X = np.concatenate(X_segments, axis=1) - X_next = np.concatenate(X_next_segments, axis=1) - U_mid = np.concatenate(U_mid_segments, axis=1) - - # Regression for A and B - Z = np.vstack([X, U_mid]) - # Ridge regression: (Z^T Z + λI)^(-1) Z^T X_next^T - ZTZ = Z @ Z.T - ridge_term = lamb * np.eye(ZTZ.shape[0]) - AB = np.linalg.solve(ZTZ + ridge_term, Z @ X_next.T).T - A_hat = AB[:, :n] - B_hat = AB[:, n:] + Gamma_hat = U_n @ S_half + X_hat = S_half @ V_n + + X, X_next, U_mid = self._time_align_valid_trials(X_hat, u_list, valid_trials, T_per_trial, p) + if any([i == 0 for i in X.shape]): + raise ValueError ("too many delays for dataset, reduce number") + A_hat, B_hat = self._perform_ridge_regression(X, X_next, U_mid, n, lamb) C_hat = Gamma_hat[:p_out, :] @@ -513,7 +329,7 @@ def hankel_stack(X, start, L): "rank_used": n, "Gamma_hat": Gamma_hat, "f": f, - "n_trials_total": n_trials, + "n_trials_total": len(y_list), "n_trials_used": len(valid_trials), "valid_trials": valid_trials, "T_per_trial": T_per_trial, @@ -524,8 +340,6 @@ def hankel_stack(X, start, L): return A_hat, B_hat, C_hat, info - - def subspace_dmdc_multitrial_flexible(self, y, u, p, f, n=None, lamb=1e-8, energy=0.999, backend='n4sid'): """ Flexible wrapper that handles both fixed-length and variable-length multi-trial data. @@ -535,18 +349,18 @@ def subspace_dmdc_multitrial_flexible(self, y, u, p, f, n=None, lamb=1e-8, energ - u: either (n_trials, m, N) array, (m, N) array, or list of (m, N_i) arrays """ if isinstance(y, list) and isinstance(u, list): + y_list = [y_trial.T for y_trial in y] + u_list = [u_trial.T for u_trial in u] if backend == 'n4sid': - return self.subspace_dmdc_multitrial_QR_decomposition(y, u, p, f, n, lamb, energy) + return self.subspace_dmdc_multitrial_QR_decomposition(y_list, u_list, p, f, n, lamb, energy) else: - return self.subspace_dmdc_multitrial_custom(y, u, p, f, n, lamb, energy) + return self.subspace_dmdc_multitrial_custom(y_list, u_list, p, f, n, lamb, energy) else: - # Handle 2D arrays (single trial) by converting to list format if y.ndim == 2: y_list = [y] u_list = [u] else: - # Convert 3D arrays to list format y_list = [y[i] for i in range(y.shape[0])] u_list = [u[i] for i in range(u.shape[0])] @@ -560,68 +374,48 @@ def subspace_dmdc_multitrial_flexible(self, y, u, p, f, n=None, lamb=1e-8, energ def predict(self, Y, U, reseed=None): - # Y and U are (n_times, n_channels) or list of 2D arrays + """Predict using the Kalman filter.""" if reseed is None: reseed = 1 - # Handle list of 2D arrays if isinstance(Y, list): - self.kalman = OnlineKalman(self) Y_pred = [] for trial in range(len(Y)): - self.kalman.reset() # Reset filter for each trial - trial_predictions = [] - for t in range(Y[trial].shape[0]): - y_filtered, _ = self.kalman.step( - y=Y[trial][t] if t%reseed == 0 else None, - u=U[trial][t] - ) - trial_predictions.append(y_filtered) + self.kalman.reset() + trial_predictions = [ + self.kalman.step(y=Y[trial][t] if t % reseed == 0 else None, u=U[trial][t])[0] + for t in range(Y[trial].shape[0]) + ] Y_pred.append(np.concatenate(trial_predictions, axis=1).T) - return Y_pred # Return as list to match input format + return Y_pred - self.kalman = OnlineKalman(self) if Y.ndim == 2: - Y_pred = [] - for t in range(Y.shape[0]): - y_filtered, _ = self.kalman.step(y=Y[t] if t%reseed == 0 else None, u=U[t]) - Y_pred.append(y_filtered) - return np.concatenate(Y_pred, axis=1).T + return np.concatenate( + [self.kalman.step(y=Y[t] if t % reseed == 0 else None, u=U[t])[0] for t in range(Y.shape[0])], + axis=1 + ).T else: - # 3D data (n_trials, time, p_out) - # print("Y.shape", Y.shape) - # print("U.shape", U.shape) Y_pred = [] for trial in range(Y.shape[0]): - self.kalman.reset() # Reset filter for each trial - trial_predictions = [] - for t in range(Y.shape[1]): - y_filtered, _ = self.kalman.step(y=Y[trial, t] if t%reseed == 0 else None, u=U[trial, t]) - trial_predictions.append(y_filtered) - # print("y_filtered.shape", y_filtered.shape) + self.kalman.reset() + trial_predictions = [ + self.kalman.step(y=Y[trial, t] if t % reseed == 0 else None, u=U[trial, t])[0] + for t in range(Y.shape[1]) + ] Y_pred.append(np.concatenate(trial_predictions, axis=1).T) return np.array(Y_pred) def compute_hankel(self, *args, **kwargs): - """ - Compute Hankel matrices for SubspaceDMDc. - - This is handled internally within subspace_dmdc_multitrial_QR_decomposition - and subspace_dmdc_multitrial_custom methods. - """ + """Compute Hankel matrices for SubspaceDMDc.""" raise NotImplementedError( "Hankel matrix computation is integrated into the fit() method for SubspaceDMDc. " "Use fit() to compute the model." ) def compute_svd(self, *args, **kwargs): - """ - Compute SVD for SubspaceDMDc. - - This is handled internally within the subspace identification process. - """ + """Compute SVD for SubspaceDMDc.""" raise NotImplementedError( "SVD computation is integrated into the fit() method for SubspaceDMDc. " "Use fit() to compute the model." @@ -629,23 +423,10 @@ def compute_svd(self, *args, **kwargs): class OnlineKalman: - """ - Online Kalman Filter class for real-time state estimation. - - This class maintains the internal state of the Kalman filter and provides - a step method for updating the filter with new observations and inputs. - """ - + """Online Kalman Filter class for real-time state estimation.""" + def __init__(self, dmdc): - """ - Initialize the Online Kalman Filter with a fitted DMDc model. - - Parameters - ---------- - dmdc : object - Fitted DMDc model containing A_v, B_v, C_v matrices and - noise covariance estimates (R_hat, S_hat, Q_hat) - """ + """Initialize the Online Kalman Filter with a fitted DMDc model.""" self.A = dmdc.A_v self.B = dmdc.B_v self.C = dmdc.C_v @@ -653,148 +434,35 @@ def __init__(self, dmdc): self.S = dmdc.info['S_hat'] self.Q = dmdc.info['Q_hat'] - # Get dimensions - # print("C_shape", self.C.shape) self.y_dim, self.x_dim = self.C.shape - # Initialize state storage - self.p_filtereds = [] - self.x_filtereds = [] - self.p_predicteds = [] - self.x_predicteds = [] - self.us = [] - self.ys = [] - self.y_filtereds = [] - self.y_predicteds = [] - self.kalman_gains = [] - - - # def step(self, y=None, u=None, lam=1e-8): - # """ - # Perform one step of the Kalman filter. - - # Parameters - # ---------- - # y : np.ndarray, optional - # Observed output at current time step. If None, the filter - # will predict without observation update. - # u : np.ndarray, optional - # Input at current time step. If None, no input is applied. - - # Returns - # ------- - # y_filtered : np.ndarray - # Filtered output estimate - # x_filtered : np.ndarray - # Filtered state estimate - # """ - # x_pred = self.x_predicteds[-1] if self.x_predicteds else np.zeros((self.x_dim, 1)) - # p_pred = self.p_predicteds[-1] if self.p_predicteds else np.eye(self.x_dim) - - # # Ensure inputs are column vectors - # if u is not None and u.ndim == 1: - # u = u.reshape(-1, 1) - # if y is not None and y.ndim == 1: - # y = y.reshape(-1, 1) - # if u is None: - # u = np.zeros((self.u_dim, 1)) - # if y is None: - # y = np.zeros((self.y_dim, 1)) - - # S_innov = self.R + self.C @ p_pred @ self.C.T - # K_filtered = p_pred @ self.C.T @ np.linalg.pinv(S_innov) - # p_filtered = p_pred - K_filtered @ self.C @ p_pred - # if not np.isnan(y).any(): - # x_filtered = x_pred + K_filtered @ (y - self.C @ x_pred) - # else: - # x_filtered = x_pred.copy() - - # K_pred = (self.S + self.A @ p_pred @ self.C.T) @ np.linalg.pinv(S_innov) - # p_predicted = (self.A @ p_pred @ self.A.T + self.Q - - # K_pred @ (self.S + self.A @ p_pred @ self.C.T).T) - # x_predicted = self.A @ x_pred + self.B @ u - # if not np.isnan(y).any(): - # x_predicted += K_pred @ (y - self.C @ x_pred) - - # # Store results - # self.p_filtereds.append(p_filtered) - # self.x_filtereds.append(x_filtered) - # self.p_predicteds.append(p_predicted) - # self.x_predicteds.append(x_predicted) - # self.us.append(u) - # self.ys.append(y) - # self.y_filtereds.append(self.C @ x_filtered) - # self.y_predicteds.append(self.C @ x_predicted) - # self.kalman_gains.append(K_pred) - - # return self.y_filtereds[-1], self.x_filtereds[-1] - + self.reset() def step(self, y=None, u=None, reg_coef=1e-6): - """ - Perform one step of the Kalman filter. - - Parameters - ---------- - y : np.ndarray, optional - Observed output at current time step. If None, the filter - will predict without observation update. - u : np.ndarray, optional - Input at current time step. If None, no input is applied. - reg_coef : float, optional - Regularization coefficient to add to diagonal of P matrices - to maintain numerical stability. Default: 1e-6 - - Returns - ------- - y_filtered : np.ndarray - Filtered output estimate - x_filtered : np.ndarray - Filtered state estimate - """ - x_pred = self.x_predicteds[-1] if self.x_predicteds else np.zeros((self.x_dim, 1)) - p_pred = self.p_predicteds[-1] if self.p_predicteds else np.eye(self.x_dim) - - # Add regularization to p_pred to prevent ill-conditioning + """Perform one step of the Kalman filter.""" + x_pred, p_pred = self._predict() p_pred_reg = p_pred + reg_coef * np.eye(self.x_dim) - # Ensure inputs are column vectors - if u is not None and u.ndim == 1: - u = u.reshape(-1, 1) - if y is not None and y.ndim == 1: - y = y.reshape(-1, 1) - if u is None: - u = np.zeros((self.u_dim, 1)) - if y is None: - y = np.zeros((self.y_dim, 1)) - - # Use regularized p_pred in computations + u = self._ensure_column_vector(u, self.u_dim) + y = self._ensure_column_vector(y, self.y_dim) + S_innov = self.R + self.C @ p_pred_reg @ self.C.T K_filtered = p_pred_reg @ self.C.T @ np.linalg.pinv(S_innov) - p_filtered = p_pred_reg - K_filtered @ self.C @ p_pred_reg - - # Add regularization to p_filtered to maintain positive definiteness - p_filtered = (p_filtered + p_filtered.T) / 2 # Ensure symmetry - p_filtered = p_filtered + reg_coef * np.eye(self.x_dim) # Add regularization - - if not np.isnan(y).any(): - x_filtered = x_pred + K_filtered @ (y - self.C @ x_pred) - else: - x_filtered = x_pred.copy() - + p_filtered = self._regularize_and_symmetrize(p_pred_reg - K_filtered @ self.C @ p_pred_reg, reg_coef) + + x_filtered = x_pred + K_filtered @ (y - self.C @ x_pred) if not np.isnan(y).any() else x_pred.copy() + K_pred = (self.S + self.A @ p_pred_reg @ self.C.T) @ np.linalg.pinv(S_innov) - p_predicted = (self.A @ p_pred_reg @ self.A.T + self.Q - - K_pred @ (self.S + self.A @ p_pred_reg @ self.C.T).T) - - # Add regularization to p_predicted and ensure symmetry - p_predicted = (p_predicted + p_predicted.T) / 2 # Ensure symmetry - p_predicted = p_predicted + reg_coef * np.eye(self.x_dim) # Add regularization - - x_predicted = self.A @ x_pred + self.B @ u - if not np.isnan(y).any(): - x_predicted += K_pred @ (y - self.C @ x_pred) - - # Store results + p_predicted = self._regularize_and_symmetrize(self.A @ p_pred_reg @ self.A.T + self.Q - K_pred @ (self.S + self.A @ p_pred_reg @ self.C.T).T, reg_coef) + + x_predicted = self.A @ x_pred + self.B @ u + (K_pred @ (y - self.C @ x_pred) if not np.isnan(y).any() else 0) + + self._store_results(x_filtered, x_predicted, p_filtered, p_predicted, u, y, K_pred) + + return self.y_filtereds[-1], self.x_filtereds[-1] + + def _store_results(self, x_filtered, x_predicted, p_filtered, p_predicted, u, y, K_pred): + """Helper function to store filter results.""" self.p_filtereds.append(p_filtered) self.x_filtereds.append(x_filtered) self.p_predicteds.append(p_predicted) @@ -804,9 +472,26 @@ def step(self, y=None, u=None, reg_coef=1e-6): self.y_filtereds.append(self.C @ x_filtered) self.y_predicteds.append(self.C @ x_predicted) self.kalman_gains.append(K_pred) - - return self.y_filtereds[-1], self.x_filtereds[-1] - + + def _predict(self): + """Helper function for prediction step.""" + x_pred = self.x_predicteds[-1] if self.x_predicteds else np.zeros((self.x_dim, 1)) + p_pred = self.p_predicteds[-1] if self.p_predicteds else np.eye(self.x_dim) + return x_pred, p_pred + + def _ensure_column_vector(self, vector, dim): + """Ensure the input is a column vector.""" + if vector is not None and vector.ndim == 1: + vector = vector.reshape(-1, 1) + if vector is None: + vector = np.zeros((dim, 1)) + return vector + + def _regularize_and_symmetrize(self, matrix, reg_coef): + """Regularize and ensure the matrix is symmetric.""" + matrix = (matrix + matrix.T) / 2 + return matrix + reg_coef * np.eye(matrix.shape[0]) + def reset(self): """Reset the filter to initial state.""" self.p_filtereds = [] @@ -818,13 +503,12 @@ def reset(self): self.y_filtereds = [] self.y_predicteds = [] self.kalman_gains = [] - - + def get_history(self): """Return the complete history of filter states.""" return { 'p_filtereds': self.p_filtereds, - 'x_filtereds': self.x_filtereds, + 'x_filtereds': self.x_filtereds, 'p_predicteds': self.p_predicteds, 'x_predicteds': self.x_predicteds, 'us': self.us, diff --git a/examples/all_dsa_types.ipynb b/examples/all_dsa_types.ipynb index 307ee39..704c49c 100644 --- a/examples/all_dsa_types.ipynb +++ b/examples/all_dsa_types.ipynb @@ -2,47 +2,75 @@ "cells": [ { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "id": "773aa0fd", "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np \n", + "import matplotlib.pyplot as plt\n", + "from DSA import DSA, GeneralizedDSA, InputDSA\n", + "from DSA import DMD, DMDc, SubspaceDMDc, ControllabilitySimilarityTransformDist\n", + "from DSA import DMDConfig, DMDcConfig, SubspaceDMDcConfig\n", + "from DSA import SimilarityTransformDistConfig, ControllabilitySimilarityTransformDistConfig\n", + "from pydmd import DMD as pDMD\n", + "import DSA.pykoopman as pk\n", + "%load_ext autoreload\n", + "%autoreload 2\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "52a2ed8c", + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n", "Automatic pdb calling has been turned ON\n" ] } ], "source": [ - "import numpy as np \n", - "import matplotlib.pyplot as plt\n", - "from DSA import DSA, GeneralizedDSA, InputDSA\n", - "from DSA import DMD, DMDc, SubspaceDMDc\n", - "from pydmd import DMD as pDMD\n", - "import DSA.pykoopman as pk\n", - "%load_ext autoreload\n", - "%autoreload 2\n", "%pdb" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "d452743b", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(18, 9)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "d1 = np.random.random(size=(20,5))\n", "u1 = np.random.random(size=(20,2))\n", "\n", - "d2 = np.random.random(size=(2,20,5))\n", - "u2 = np.random.random(size=(2,20,2))\n", + "d2 = np.random.random(size=(3,20,5))\n", + "u2 = np.random.random(size=(3,20,2))\n", "\n", "d3 = [np.random.random(size=(i,20,5)) for i in range(1,10)]\n", - "u3 = [np.random.random(size=(i,20,2)) for i in range(1,10)]\n" + "u3 = [np.random.random(size=(i,20,2)) for i in range(1,10)]\n", + "\n", + "d4 = [np.random.random(size=(i+5,5)) for i in range(1,10)]\n", + "u4 = [np.random.random(size=(i+5,2)) for i in range(1,10)]\n", + "\n", + "d5 = d4 + d3\n", + "u5 = u4 + u3\n", + "\n", + "len(d5), len(d4)" ] }, { @@ -52,41 +80,66 @@ "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "(5, 5)" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" + "ename": "ValueError", + "evalue": "Trial 0: y and u have different time lengths", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[14], line 14\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# dmdc = DMDc(d5,u5,n_delays=2,rank_input=10,rank_output=10)\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;66;03m# dmdc.fit()\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# print(dmdc.A_v.shape)\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 11\u001b[0m \n\u001b[1;32m 12\u001b[0m \u001b[38;5;66;03m#TODO: fix this case\u001b[39;00m\n\u001b[1;32m 13\u001b[0m subdmdc \u001b[38;5;241m=\u001b[39m SubspaceDMDc(d2,u2,n_delays\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m10\u001b[39m,rank\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m,backend\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mn4sid\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m---> 14\u001b[0m \u001b[43msubdmdc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 15\u001b[0m \u001b[38;5;28mprint\u001b[39m(subdmdc\u001b[38;5;241m.\u001b[39mA_v\u001b[38;5;241m.\u001b[39mshape)\n\u001b[1;32m 16\u001b[0m \u001b[38;5;28mprint\u001b[39m(subdmdc\u001b[38;5;241m.\u001b[39mB_v\u001b[38;5;241m.\u001b[39mshape)\n", + "File \u001b[0;32m~/Desktop/Projects/DSAv2/DSAPublic2/DSA/DSA/subspace_dmdc.py:70\u001b[0m, in \u001b[0;36mSubspaceDMDc.fit\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 68\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mfit\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 69\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Fit the SubspaceDMDc model.\"\"\"\u001b[39;00m\n\u001b[0;32m---> 70\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mA_v, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mB_v, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mC_v, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minfo \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msubspace_dmdc_multitrial_flexible\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 71\u001b[0m \u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 72\u001b[0m \u001b[43m \u001b[49m\u001b[43mu\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcontrol_data\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 73\u001b[0m \u001b[43m \u001b[49m\u001b[43mp\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mn_delays\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 74\u001b[0m \u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mn_delays\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 75\u001b[0m \u001b[43m \u001b[49m\u001b[43mn\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrank\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m 76\u001b[0m \u001b[43m \u001b[49m\u001b[43mbackend\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackend\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 77\u001b[0m \u001b[43m \u001b[49m\u001b[43mlamb\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlamb\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 79\u001b[0m \u001b[38;5;66;03m# Send to CPU if requested (inherited from BaseDMD)\u001b[39;00m\n\u001b[1;32m 80\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msend_to_cpu:\n", + "File \u001b[0;32m~/Desktop/Projects/DSAv2/DSAPublic2/DSA/DSA/subspace_dmdc.py:371\u001b[0m, in \u001b[0;36mSubspaceDMDc.subspace_dmdc_multitrial_flexible\u001b[0;34m(self, y, u, p, f, n, lamb, energy, backend)\u001b[0m\n\u001b[1;32m 367\u001b[0m \u001b[38;5;66;03m# y_list = [y_trial.T for y_trial in y_list]\u001b[39;00m\n\u001b[1;32m 368\u001b[0m \u001b[38;5;66;03m# u_list = [u_trial.T for u_trial in u_list]\u001b[39;00m\n\u001b[1;32m 370\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m backend \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mn4sid\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[0;32m--> 371\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msubspace_dmdc_multitrial_QR_decomposition\u001b[49m\u001b[43m(\u001b[49m\u001b[43my_list\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mu_list\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlamb\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43menergy\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 372\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 373\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msubspace_dmdc_multitrial_custom(y_list, u_list, p, f, n, lamb, energy)\n", + "File \u001b[0;32m~/Desktop/Projects/DSAv2/DSAPublic2/DSA/DSA/subspace_dmdc.py:150\u001b[0m, in \u001b[0;36mSubspaceDMDc.subspace_dmdc_multitrial_QR_decomposition\u001b[0;34m(self, y_list, u_list, p, f, n, lamb, energy)\u001b[0m\n\u001b[1;32m 145\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21msubspace_dmdc_multitrial_QR_decomposition\u001b[39m(\u001b[38;5;28mself\u001b[39m, y_list, u_list, p, f, n\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, lamb\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1e-8\u001b[39m, energy\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.999\u001b[39m):\n\u001b[1;32m 146\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 147\u001b[0m \u001b[38;5;124;03m Subspace-DMDc for multi-trial data with variable trial lengths using QR decomposition.\u001b[39;00m\n\u001b[1;32m 148\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m 149\u001b[0m U_p, Y_p, U_f, Y_f, Z_p, valid_trials, T_per_trial, T_total, p_out, m \u001b[38;5;241m=\u001b[39m \\\n\u001b[0;32m--> 150\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_and_collect_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43my_list\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mu_list\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 152\u001b[0m H \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mvstack([U_f, Z_p, Y_f])\n\u001b[1;32m 154\u001b[0m dim_uf \u001b[38;5;241m=\u001b[39m f \u001b[38;5;241m*\u001b[39m m\n", + "File \u001b[0;32m~/Desktop/Projects/DSAv2/DSAPublic2/DSA/DSA/subspace_dmdc.py:98\u001b[0m, in \u001b[0;36mSubspaceDMDc._validate_and_collect_data\u001b[0;34m(self, y_list, u_list, p, f)\u001b[0m\n\u001b[1;32m 96\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTrial \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mi\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m: u has \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mu_trial\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m inputs, expected \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mm\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 97\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m y_trial\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m!=\u001b[39m u_trial\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m]:\n\u001b[0;32m---> 98\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTrial \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mi\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m: y and u have different time lengths\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 100\u001b[0m U_p_all \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m 101\u001b[0m Y_p_all \u001b[38;5;241m=\u001b[39m []\n", + "\u001b[0;31mValueError\u001b[0m: Trial 0: y and u have different time lengths" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "> \u001b[0;32m/Users/mitchellostrow/Desktop/Projects/DSAv2/DSAPublic2/DSA/DSA/subspace_dmdc.py\u001b[0m(98)\u001b[0;36m_validate_and_collect_data\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32m 96 \u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Trial {i}: u has {u_trial.shape[0]} inputs, expected {m}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m 97 \u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0my_trial\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mu_trial\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m---> 98 \u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Trial {i}: y and u have different time lengths\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m 99 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m 100 \u001b[0;31m \u001b[0mU_p_all\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\n" + ] } ], "source": [ "\n", - "#TODO: fix this case\n", - "# dmdc = DMDc(d3,u3,n_delays=2,rank_input=10,rank_output=10)\n", + "# dmdc = DMDc(d5,u5,n_delays=2,rank_input=10,rank_output=10)\n", "# dmdc.fit()\n", "# print(dmdc.A_v.shape)\n", "# print(dmdc.B_v.shape)\n", "\n", - "#TODO: fix this case\n", - "# subdmdc = SubspaceDMDc(d1,u1,n_delays=10,rank=2)\n", + "# subdmdc = SubspaceDMDc(d3,u3,n_delays=3,rank=5,backend='n4sid')\n", "# subdmdc.fit()\n", "# print(subdmdc.A_v.shape)\n", "# print(subdmdc.B_v.shape)\n", "\n", "\n", "#TODO: fix this case\n", + "subdmdc = SubspaceDMDc(d2,u2,n_delays=10,rank=5,backend='n4sid')\n", + "subdmdc.fit()\n", + "print(subdmdc.A_v.shape)\n", + "print(subdmdc.B_v.shape)\n", + "\n", + "\n", + "#TODO: fix this case\n", "# subdmdc = SubspaceDMDc(d3,u3,n_delays=2,rank=10,backend='n4sid')\n", "# subdmdc.fit()\n", "# print(subdmdc.A_v.shape)\n", - "# print(subdmdc.B_v.shape)\n" + "# print(subdmdc.B_v.shape)\n", + "\n", + "#TODO: check predictions for all cases" ] }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 12, "id": "721bc598", "metadata": {}, "outputs": [ @@ -94,57 +147,107 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/mitchellostrow/Desktop/Projects/DSAv2/DSAPublic2/DSA/DSA/dsa.py:364: UserWarning: Warning: You are using a DMD model that fits a control operator but comparing with a DSA metric that does not compare control operators\n", - " if self.dmd_has_control and not self.simdist_has_control:\n" + "Fitting DMDs: 100%|██████████| 3/3 [00:00<00:00, 132.57it/s]\n", + "Computing DMD similarities: 100%|██████████| 3/3 [00:00<00:00, 904.46it/s]\n", + "/Users/mitchellostrow/Desktop/Projects/DSAv2/DSAPublic2/DSA/DSA/dsa.py:408: UserWarning: Warning: You are using a DMD model that fits a control operator but comparing with a DSA metric that does not compare control operators\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(3, 3, 3)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Fitting DMDs: 100%|██████████| 3/3 [00:00<00:00, 147.14it/s]\n", + "Computing DMD similarities: 33%|███▎ | 1/3 [00:03<00:06, 3.07s/it]" ] }, { - "ename": "TypeError", - "evalue": "SimilarityTransformDist.__init__() got an unexpected keyword argument 'joint_optim'", + "ename": "KeyboardInterrupt", + "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[56], line 9\u001b[0m\n\u001b[1;32m 2\u001b[0m u1s \u001b[38;5;241m=\u001b[39m [np\u001b[38;5;241m.\u001b[39mrandom\u001b[38;5;241m.\u001b[39mrandom(size\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m20\u001b[39m,\u001b[38;5;241m2\u001b[39m)) \u001b[38;5;28;01mfor\u001b[39;00m _ \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m3\u001b[39m)]\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m#works\u001b[39;00m\n\u001b[1;32m 5\u001b[0m \u001b[38;5;66;03m# dsa = DSA(d1s,dmd_class=pk.Koopman,\u001b[39;00m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;66;03m# observables=pk.observables.TimeDelay(),regressor=pDMD(svd_rank=5),\u001b[39;00m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;66;03m# score_method='wasserstein')\u001b[39;00m\n\u001b[0;32m----> 9\u001b[0m dsa \u001b[38;5;241m=\u001b[39m \u001b[43mInputDSA\u001b[49m\u001b[43m(\u001b[49m\u001b[43md1s\u001b[49m\u001b[43m,\u001b[49m\u001b[43mu1s\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 10\u001b[0m sim \u001b[38;5;241m=\u001b[39m dsa\u001b[38;5;241m.\u001b[39mfit_score()\n\u001b[1;32m 11\u001b[0m sim\u001b[38;5;241m.\u001b[39mshape\n", - "File \u001b[0;32m~/Desktop/Projects/DSAv2/DSAPublic2/DSA/DSA/dsa.py:733\u001b[0m, in \u001b[0;36mInputDSA.__init__\u001b[0;34m(self, X, X_control, Y, Y_control, dmd_class, dmd_config, simdist_config, device, verbose, n_jobs, compare)\u001b[0m\n\u001b[1;32m 730\u001b[0m compare \u001b[38;5;241m=\u001b[39m simdist_config\u001b[38;5;241m.\u001b[39mcompare\n\u001b[1;32m 731\u001b[0m simdist \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mupdate_compare_method(compare)\n\u001b[0;32m--> 733\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m 734\u001b[0m \u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 735\u001b[0m \u001b[43m \u001b[49m\u001b[43mY\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 736\u001b[0m \u001b[43m \u001b[49m\u001b[43mX_control\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 737\u001b[0m \u001b[43m \u001b[49m\u001b[43mY_control\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 738\u001b[0m \u001b[43m \u001b[49m\u001b[43mdmd_class\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 739\u001b[0m \u001b[43m \u001b[49m\u001b[43msimdist\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 740\u001b[0m \u001b[43m \u001b[49m\u001b[43mdmd_config\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 741\u001b[0m \u001b[43m \u001b[49m\u001b[43msimdist_config\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 742\u001b[0m \u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 743\u001b[0m \u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 744\u001b[0m \u001b[43m \u001b[49m\u001b[43mn_jobs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 745\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 747\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m X_control \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 748\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdmd_has_control\n", - "File \u001b[0;32m~/Desktop/Projects/DSAv2/DSAPublic2/DSA/DSA/dsa.py:312\u001b[0m, in \u001b[0;36mGeneralizedDSA.__init__\u001b[0;34m(self, X, Y, X_control, Y_control, dmd_class, similarity_class, dmd_config, simdist_config, device, verbose, n_jobs)\u001b[0m\n\u001b[1;32m 310\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dmd_api_source(dmd_class)\n\u001b[1;32m 311\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_initiate_dmds()\n\u001b[0;32m--> 312\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msimdist \u001b[38;5;241m=\u001b[39m \u001b[43msimilarity_class\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msimdist_config\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[0;31mTypeError\u001b[0m: SimilarityTransformDist.__init__() got an unexpected keyword argument 'joint_optim'" + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[12], line 27\u001b[0m\n\u001b[1;32m 23\u001b[0m \u001b[38;5;66;03m#fixed\u001b[39;00m\n\u001b[1;32m 24\u001b[0m dsa \u001b[38;5;241m=\u001b[39m GeneralizedDSA(d3s,X_control\u001b[38;5;241m=\u001b[39mu3s,\n\u001b[1;32m 25\u001b[0m dmd_class\u001b[38;5;241m=\u001b[39mDMDc,dmd_config\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mdict\u001b[39m(n_delays\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m,rank_input\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m,rank_output\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m),\n\u001b[1;32m 26\u001b[0m verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m---> 27\u001b[0m sim \u001b[38;5;241m=\u001b[39m \u001b[43mdsa\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit_score\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 28\u001b[0m sim\u001b[38;5;241m.\u001b[39mshape\n\u001b[1;32m 30\u001b[0m \u001b[38;5;66;03m#TODO: check generalized dsa with other data structures for data and inputs\u001b[39;00m\n\u001b[1;32m 31\u001b[0m \u001b[38;5;66;03m#TODO: check generalized dsa with the other comparison metric and changing the config\u001b[39;00m\n", + "File \u001b[0;32m~/Desktop/Projects/DSAv2/DSAPublic2/DSA/DSA/dsa.py:692\u001b[0m, in \u001b[0;36mGeneralizedDSA.fit_score\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 679\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 680\u001b[0m \u001b[38;5;124;03mStandard fitting function for both DMDs and PAVF\u001b[39;00m\n\u001b[1;32m 681\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 689\u001b[0m \u001b[38;5;124;03m data matrix of the similarity scores between the specific sets of data\u001b[39;00m\n\u001b[1;32m 690\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 691\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfit_dmds()\n\u001b[0;32m--> 692\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscore\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Desktop/Projects/DSAv2/DSAPublic2/DSA/DSA/dsa.py:797\u001b[0m, in \u001b[0;36mGeneralizedDSA.score\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 791\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 792\u001b[0m loop \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 793\u001b[0m pairs\n\u001b[1;32m 794\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mverbose\n\u001b[1;32m 795\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m tqdm\u001b[38;5;241m.\u001b[39mtqdm(pairs, desc\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mComputing DMD similarities\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 796\u001b[0m )\n\u001b[0;32m--> 797\u001b[0m results \u001b[38;5;241m=\u001b[39m [compute_similarity(i, j) \u001b[38;5;28;01mfor\u001b[39;00m i, j \u001b[38;5;129;01min\u001b[39;00m loop]\n\u001b[1;32m 799\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m result \u001b[38;5;129;01min\u001b[39;00m results:\n\u001b[1;32m 800\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m result \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "File \u001b[0;32m~/Desktop/Projects/DSAv2/DSAPublic2/DSA/DSA/dsa.py:797\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 791\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 792\u001b[0m loop \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 793\u001b[0m pairs\n\u001b[1;32m 794\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mverbose\n\u001b[1;32m 795\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m tqdm\u001b[38;5;241m.\u001b[39mtqdm(pairs, desc\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mComputing DMD similarities\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 796\u001b[0m )\n\u001b[0;32m--> 797\u001b[0m results \u001b[38;5;241m=\u001b[39m [\u001b[43mcompute_similarity\u001b[49m\u001b[43m(\u001b[49m\u001b[43mi\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mj\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m i, j \u001b[38;5;129;01min\u001b[39;00m loop]\n\u001b[1;32m 799\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m result \u001b[38;5;129;01min\u001b[39;00m results:\n\u001b[1;32m 800\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m result \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "File \u001b[0;32m~/Desktop/Projects/DSAv2/DSAPublic2/DSA/DSA/dsa.py:768\u001b[0m, in \u001b[0;36mGeneralizedDSA.score..compute_similarity\u001b[0;34m(i, j)\u001b[0m\n\u001b[1;32m 761\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msimdist_has_control \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdmd_has_control:\n\u001b[1;32m 762\u001b[0m simdist_args\u001b[38;5;241m.\u001b[39mextend(\n\u001b[1;32m 763\u001b[0m [\n\u001b[1;32m 764\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_dmd_control_matrix(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdmds[\u001b[38;5;241m0\u001b[39m][i]),\n\u001b[1;32m 765\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_dmd_control_matrix(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdmds[ind2][j]),\n\u001b[1;32m 766\u001b[0m ]\n\u001b[1;32m 767\u001b[0m )\n\u001b[0;32m--> 768\u001b[0m sim \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msimdist\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit_score\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43msimdist_args\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 770\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mverbose \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_jobs \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m 771\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcomputing similarity between DMDs \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mi\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m and \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mj\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[0;32m~/Desktop/Projects/DSAv2/DSAPublic2/DSA/DSA/simdist.py:471\u001b[0m, in \u001b[0;36mSimilarityTransformDist.fit_score\u001b[0;34m(self, A, B, iters, lr, score_method, wasserstein_weightings)\u001b[0m\n\u001b[1;32m 468\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 469\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[0;32m--> 471\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 472\u001b[0m \u001b[43m \u001b[49m\u001b[43mA\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 473\u001b[0m \u001b[43m \u001b[49m\u001b[43mB\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 474\u001b[0m \u001b[43m \u001b[49m\u001b[43miters\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 475\u001b[0m \u001b[43m \u001b[49m\u001b[43mlr\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 476\u001b[0m \u001b[43m \u001b[49m\u001b[43mwasserstein_weightings\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwasserstein_weightings\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 477\u001b[0m \u001b[43m \u001b[49m\u001b[43mscore_method\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscore_method\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 478\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 480\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mscore(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mA, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mB, score_method\u001b[38;5;241m=\u001b[39mscore_method)\n", + "File \u001b[0;32m~/Desktop/Projects/DSAv2/DSAPublic2/DSA/DSA/simdist.py:254\u001b[0m, in \u001b[0;36mSimilarityTransformDist.fit\u001b[0;34m(self, A, B, iters, lr, score_method, wasserstein_weightings)\u001b[0m\n\u001b[1;32m 248\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mC_star \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mC_star \u001b[38;5;241m/\u001b[39m torch\u001b[38;5;241m.\u001b[39mlinalg\u001b[38;5;241m.\u001b[39mnorm(\n\u001b[1;32m 249\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mC_star, dim\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m, keepdim\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m 250\u001b[0m )\n\u001b[1;32m 251\u001b[0m \u001b[38;5;66;03m# wasserstein_distance(A.cpu().numpy(),B.cpu().numpy())\u001b[39;00m\n\u001b[1;32m 252\u001b[0m \n\u001b[1;32m 253\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 254\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlosses, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mC_star, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msim_net \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptimize_C\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 255\u001b[0m \u001b[43m \u001b[49m\u001b[43mA\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mB\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43miters\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morthog\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mverbose\u001b[49m\n\u001b[1;32m 256\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 257\u001b[0m \u001b[38;5;66;03m# permute the first row and column of B then rerun the optimization\u001b[39;00m\n\u001b[1;32m 258\u001b[0m P \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39meye(B\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m], device\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdevice)\n", + "File \u001b[0;32m~/Desktop/Projects/DSAv2/DSAPublic2/DSA/DSA/simdist.py:334\u001b[0m, in \u001b[0;36mSimilarityTransformDist.optimize_C\u001b[0;34m(self, A, B, lr, iters, orthog, verbose)\u001b[0m\n\u001b[1;32m 332\u001b[0m optimizer\u001b[38;5;241m.\u001b[39mzero_grad()\n\u001b[1;32m 333\u001b[0m \u001b[38;5;66;03m# Compute the Frobenius norm between A and the product.\u001b[39;00m\n\u001b[0;32m--> 334\u001b[0m loss \u001b[38;5;241m=\u001b[39m simdist_loss(A, \u001b[43msim_net\u001b[49m\u001b[43m(\u001b[49m\u001b[43mB\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 336\u001b[0m loss\u001b[38;5;241m.\u001b[39mbackward()\n\u001b[1;32m 338\u001b[0m optimizer\u001b[38;5;241m.\u001b[39mstep()\n", + "File \u001b[0;32m~/opt/anaconda3/envs/dsa_test_env/lib/python3.10/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1509\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/opt/anaconda3/envs/dsa_test_env/lib/python3.10/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1518\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1519\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1523\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "File \u001b[0;32m~/Desktop/Projects/DSAv2/DSAPublic2/DSA/DSA/simdist.py:64\u001b[0m, in \u001b[0;36mLearnableSimilarityTransform.forward\u001b[0;34m(self, B)\u001b[0m\n\u001b[1;32m 62\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, B):\n\u001b[1;32m 63\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39morthog:\n\u001b[0;32m---> 64\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mC\u001b[49m \u001b[38;5;241m@\u001b[39m B \u001b[38;5;241m@\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mC\u001b[38;5;241m.\u001b[39mtranspose(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m2\u001b[39m)\n\u001b[1;32m 65\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 66\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mC \u001b[38;5;241m@\u001b[39m B \u001b[38;5;241m@\u001b[39m torch\u001b[38;5;241m.\u001b[39mlinalg\u001b[38;5;241m.\u001b[39minv(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mC)\n", + "File \u001b[0;32m~/opt/anaconda3/envs/dsa_test_env/lib/python3.10/site-packages/torch/nn/utils/parametrize.py:368\u001b[0m, in \u001b[0;36m_inject_property..get_parametrized\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 365\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m get_cached_parametrization(parametrization)\n\u001b[1;32m 366\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 367\u001b[0m \u001b[38;5;66;03m# If caching is not active, this function just evaluates the parametrization\u001b[39;00m\n\u001b[0;32m--> 368\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mparametrization\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/opt/anaconda3/envs/dsa_test_env/lib/python3.10/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1509\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/opt/anaconda3/envs/dsa_test_env/lib/python3.10/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m 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DMDConfig(n_delays=20)\n", + "simdistconfig = SimilarityTransformDistConfig(score_method='wasserstein')\n", + "csimdistconfig = ControllabilitySimilarityTransformDistConfig(compare='joint',\n", + " score_method='euclidean', align_inputs=False,return_distance_components=True)\n", "#works\n", "# dsa = DSA(d1s,dmd_class=pk.Koopman,\n", "# observables=pk.observables.TimeDelay(),regressor=pDMD(svd_rank=5),\n", "# score_method='wasserstein')\n", + "dmd_config = SubspaceDMDcConfig(rank=5)\n", + "dmdc_config = DMDcConfig()\n", + "\n", + "dsa = InputDSA(d3s,u3s,simdist_config=csimdistconfig,\n", + " dmd_class=DMDc,dmd_config=dmdc_config,verbose=True)\n", + "sim = dsa.fit_score()\n", + "print(sim.shape)\n", "\n", - "dsa = InputDSA(d1s,u1s)\n", + "#fixed\n", + "dsa = GeneralizedDSA(d3s,X_control=u3s,\n", + " dmd_class=DMDc,dmd_config=dict(n_delays=5,rank_input=5,rank_output=5),\n", + " verbose=True)\n", "sim = dsa.fit_score()\n", "sim.shape\n", "\n", - "\n" + "#TODO: check generalized dsa with other data structures for data and inputs\n", + "#TODO: check generalized dsa with the other comparison metric and changing the config\n" ] }, { "cell_type": "code", "execution_count": null, - "id": "26c08771", + "id": "57132dea", "metadata": {}, "outputs": [], "source": [] diff --git a/examples/fig2_real.ipynb b/examples/fig2_real.ipynb new file mode 100644 index 0000000..ca13ae3 --- /dev/null +++ b/examples/fig2_real.ipynb @@ -0,0 +1,5211 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "52fcf42e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shared parameters:\n", + " System: n=10, m=1, p_out=10, N=10000\n", + " Dynamics: rho1=0.92, rho2=0.82, g1=1, g2=1.5\n", + " Noise: obs_noise=0.0001, process_noise=0.0\n", + " Nonlinearity: nonlinear_eps=0.01\n", + " Model: n_delays=150, rank=10, pf=150\n", + " Evaluation: n_iters=10\n" + ] + } + ], + "source": [ + "\"\"\"\n", + "Figure 2 InputDSA Data Analysis \n", + "\"\"\"\n", + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "# SHARED PARAMETERS - used consistently across all analyses below\n", + "# Only change these values to modify the entire notebook behavior\n", + "n = 10 # latent state dim\n", + "n_large = 50\n", + "m = 1 # input dim \n", + "p_out = 10 # observed dim (partial observation) - gets overridden in some cells\n", + "p_out_small = 2\n", + "N = 10000 # sequence length\n", + "N_small = 1000\n", + "n_Us = 4\n", + "obs_noise = 0.0001\n", + "process_noise = 0.0#1\n", + "nonlinear_eps = 0.01\n", + "input_alpha = 0.001\n", + "g1 = 1\n", + "g2 = 1.5\n", + "rho1 = 0.92\n", + "rho2 = 0.82\n", + "seed1 = 11\n", + "seed2 = 12\n", + "n_delays = 150\n", + "rank = 10\n", + "pf = 150\n", + "n_iters = 10\n", + "backend = 'n4sid'\n", + "\n", + "print(f\"Shared parameters:\")\n", + "print(f\" System: n={n}, m={m}, p_out={p_out}, N={N}\")\n", + "print(f\" Dynamics: rho1={rho1}, rho2={rho2}, g1={g1}, g2={g2}\")\n", + "print(f\" Noise: obs_noise={obs_noise}, process_noise={process_noise}\")\n", + "print(f\" Nonlinearity: nonlinear_eps={nonlinear_eps}\")\n", + "print(f\" Model: n_delays={n_delays}, rank={rank}, pf={pf}\")\n", + "print(f\" Evaluation: n_iters={n_iters}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a08ecb20", + "metadata": {}, + "outputs": [], + "source": [ + "# in this analysis, we are goign to look at how to appropriately compare partially observed systems\n", + "# we will look at the following systems\n", + "\n", + "# 4 systems, made up fo 2 pairings (1,2) (3,4) same intrinsic dynamics, (1,3) (2,4) same read in dynamics\n", + "# we will look at the behavior in the fully observed setting of DSA and AgentDSA, and then in the\n", + "#partially observed setting of DMDc versus subspace DMDc\n", + "#we'll looking at clustering capability across many instantiatons of the data, \n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "4b891d5e", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "# Updated to use DSA package imports\n", + "import sys\n", + "sys.path.insert(0, '..') # Add parent directory to path to import DSA\n", + "\n", + "plt.rcParams['pdf.fonttype'] = 42\n", + "plt.rcParams['ps.fonttype'] = 42\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "f3cdd518", + "metadata": {}, + "outputs": [], + "source": [ + "from DSA import InputDSA\n", + "from DSA import SimilarityTransformDist as SimDist\n", + "from DSA import ControllabilitySimilarityTransformDist as ControlSimDist\n", + "from tqdm import tqdm\n", + "\n", + "def compare_systems_with_InputDSA(Ys, Us, n_delays=150, rank=10, backend='n4sid'):\n", + " \"\"\"\n", + " Compare controlled systems using InputDSA from DSA package.\n", + " Uses the new update_compare_method() to avoid refitting DMDs multiple times.\n", + " \n", + " Parameters:\n", + " - Ys: list of output data arrays (p_out, N)\n", + " - Us: list of control input arrays (m, N)\n", + " - n_delays: number of delays for DMD\n", + " - rank: rank for DMD\n", + " - backend: 'n4sid' or 'custom' for SubspaceDMDc\n", + " \n", + " Returns:\n", + " - sims_full: joint similarity scores\n", + " - sims_control_joint: control scores from joint optimization\n", + " - sims_state_joint: state scores from joint optimization\n", + " - sims_control_separate: control scores from separate optimization\n", + " - sims_state_separate: state scores from separate optimization\n", + " \"\"\"\n", + " # Transpose data for InputDSA (expects time_first=True by default)\n", + " Ys_T = [Y.T for Y in Ys]\n", + " Us_T = [U.T for U in Us]\n", + " \n", + " # Configure DMD\n", + " # dmd_config = SubspaceDMDcConfig(\n", + " # n_delays=n_delays,\n", + " # rank=rank,\n", + " # backend=backend\n", + " # )\n", + " dmd_config = dict(\n", + " n_delays=n_delays,\n", + " rank=rank,\n", + " backend=backend\n", + " )\n", + " \n", + " # Create InputDSA with joint comparison\n", + " # This will fit the DMDs once and return joint comparison results\n", + " inputDSA = InputDSA(\n", + " X=Ys_T,\n", + " X_control=Us_T,\n", + " dmd_config=dmd_config,\n", + " compare='joint',\n", + " return_distance_components=True\n", + " )\n", + " \n", + " # Fit DMDs and get joint comparison results\n", + " sims_full, sims_state_joint, sims_control_joint = inputDSA.fit_score()\n", + " \n", + " # Update comparison method to 'state' without refitting DMDs\n", + " inputDSA.update_compare_method(compare='state')\n", + " sims_state_separate = inputDSA.score()\n", + " \n", + " # Update comparison method to 'control' without refitting DMDs\n", + " inputDSA.update_compare_method(compare='control')\n", + " sims_control_separate = inputDSA.score()\n", + " \n", + " return sims_full, sims_control_joint, sims_state_joint, sims_control_separate, sims_state_separate\n", + "\n", + "\n", + "#strict comparison metrics, for when we fit and compare separately\n", + "def compare_A(A1,A2):\n", + " simdist = SimDist(iters=1000,score_method='wasserstein',lr=1e-3,verbose=True)\n", + " return simdist.fit_score(A1,A2)\n", + "\n", + "def compare_A_full(As):\n", + " sims = np.zeros((len(As),len(As)))\n", + " for i in range(len(As)):\n", + " for j in range(i+1,len(As)):\n", + " sims[i,j] = compare_A(As[i],As[j])\n", + " sims[j,i] = sims[i,j]\n", + " return sims\n", + "\n", + "def compare_B(B1,B2):\n", + " csimdist = ControlSimDist(score_method='euclidean',compare='control')\n", + " sim = csimdist.fit_score(None, None, B1, B2)\n", + " return sim\n", + "\n", + "def compare_systems_full(As,Bs):\n", + " csimdist = ControlSimDist(score_method='euclidean',compare='joint',return_distance_components=True)\n", + " sims_full = np.zeros((len(As),len(As)))\n", + " sims_control_joint = np.zeros((len(As),len(As)))\n", + " sims_state_joint = np.zeros((len(As),len(As)))\n", + " sims_control_separate = np.zeros((len(As),len(As)))\n", + " sims_state_separate = np.zeros((len(As),len(As)))\n", + " for i in tqdm(range(len(As))):\n", + " for j in range(i+1,len(As)):\n", + " all_sims = csimdist.fit_score(As[i],As[j],Bs[i],Bs[j])\n", + " sims_full[i,j] = sims_full[j,i] = all_sims[0]\n", + " sims_state_joint[i,j] = sims_state_joint[j,i] = all_sims[1]\n", + " sims_control_joint[i,j] = sims_control_joint[j,i] = all_sims[2]\n", + " \n", + " for i in tqdm(range(len(As))):\n", + " for j in range(i+1,len(As)):\n", + " sims_state_separate[i,j] = compare_A(As[i],As[j])\n", + " sims_control_separate[i,j] = compare_B(Bs[i],Bs[j])\n", + " sims_state_separate[j,i] = sims_state_separate[i,j]\n", + " sims_control_separate[j,i] = sims_control_separate[i,j]\n", + "\n", + " return sims_full, sims_control_joint, sims_state_joint, sims_control_separate, sims_state_separate\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "7ba785c8", + "metadata": {}, + "outputs": [], + "source": [ + "def make_stable_A(n, rho=0.9, rng=None):\n", + " rng = np.random.default_rng(rng)\n", + " M = rng.standard_normal((n, n))\n", + " # Make it diagonally dominant-ish and scale spectral radius\n", + " A = M / np.max(np.abs(np.linalg.eigvals(M))) * rho\n", + " return A\n", + "\n", + "def simulate_system(A, B, C, U, x0=None,rng=None,obs_noise=0.0,process_noise=0.0,\n", + " nonlinear_eps=0.0,nonlinear_func= lambda x: np.tanh(x),nonlinear_eps_input=0.0):\n", + " n, m = B.shape\n", + " p_out = C.shape[0]\n", + " N = U.shape[1]\n", + " X = np.zeros((n, N+1))\n", + " C_full = np.eye(A.shape[0])\n", + " C_full[np.where(C == 1)[1],np.where(C == 1)[1]] = 0.0\n", + "\n", + " if x0 is not None:\n", + " X[:, 0] = x0\n", + " else:\n", + " X[:, 0] = np.random.default_rng(rng).standard_normal((n,))\n", + " Y = np.zeros((p_out, N))\n", + " for t in range(N):\n", + " X[:, t+1] = A @ (X[:, t]) + nonlinear_eps * C_full @ nonlinear_func(A @ X[:, t]) + \\\n", + " B @ ((1-nonlinear_eps_input) * U[:, t] + nonlinear_eps_input * nonlinear_func(U[:, t])) + \\\n", + " np.random.normal(0, process_noise, (n,))\n", + " Y[:, t] = C @ X[:, t] + np.random.normal(0, obs_noise, (p_out,))\n", + " return X[:, 1:], Y # states aligned with Y\n", + "\n", + "def smooth_input(m, N, alpha=0.9, rng=None):\n", + " rng = np.random.default_rng(rng)\n", + " w = rng.standard_normal((m, N))\n", + " U = np.zeros_like(w)\n", + " for t in range(N):\n", + " U[:, t] = alpha*(U[:, t-1] if t>0 else 0) + (1-alpha)*w[:, t]\n", + " return U" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "87d14512", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def simulate_As_Bs(latent_dim, input_dim, observed_dim, seq_length,rho1=rho1,\n", + " rho2=rho2, g1=g1,g2=g2, seed1=seed1, seed2=seed2, input_alpha=input_alpha,same_inp=False,n_Us=n_Us,\n", + " obs_noise=obs_noise,process_noise=process_noise,nonlinear_eps=nonlinear_eps,nonlinear_func= lambda x: np.tanh(x)):\n", + "\n", + " A1_true = make_stable_A(latent_dim, rho=rho1, rng=seed1)\n", + " cov_matrix_B1 = np.random.default_rng(seed1).standard_normal((latent_dim, latent_dim))\n", + " cov_matrix_B1 = cov_matrix_B1 @ cov_matrix_B1.T # Make it symmetric positive definite\n", + " B1_true = np.random.default_rng(seed1).multivariate_normal(np.zeros(latent_dim), cov_matrix_B1, input_dim).T * g1\n", + "\n", + " A2_true = make_stable_A(latent_dim, rho=rho2, rng=seed2)\n", + " C = np.linalg.qr(np.random.default_rng(seed2).standard_normal((latent_dim, latent_dim)))[0]\n", + " cov_matrix_B2_rotated = C @ cov_matrix_B1 @ C.T \n", + " B2_true = np.random.default_rng(seed2).multivariate_normal(np.zeros(latent_dim), cov_matrix_B2_rotated, input_dim).T * g2\n", + "\n", + " # Random partial observation: select p_out of n states\n", + " idx_obs = np.sort(np.random.default_rng(seed1).choice(latent_dim, size=observed_dim, replace=False))\n", + " C_true = np.zeros((observed_dim, latent_dim))\n", + " C_true[np.arange(observed_dim), idx_obs] = 1.0\n", + " \n", + " X_trues, Ys,Us = [], [], []\n", + " i = 0\n", + " if same_inp:\n", + " U = smooth_input(input_dim, seq_length, alpha=input_alpha, rng=seed1+i) \n", + " control_labels = []\n", + " state_labels = []\n", + " for a1, As in enumerate([A1_true, A2_true]):\n", + " for b1, Bs in enumerate([B1_true, B2_true]):\n", + " i += 1\n", + " if not same_inp:\n", + " for j in range(n_Us):\n", + " U = smooth_input(input_dim, seq_length, alpha=input_alpha, rng=seed1+ i + j) \n", + " X_true, Y = simulate_system(As, Bs, C_true, U, x0=np.zeros(latent_dim),rng=seed1+i,\n", + " obs_noise=obs_noise,process_noise=process_noise,\n", + " nonlinear_eps=nonlinear_eps,nonlinear_func=nonlinear_func)\n", + " X_trues.append(X_true)\n", + " Ys.append(Y)\n", + " Us.append(U)\n", + " control_labels.append(b1)\n", + " state_labels.append(a1)\n", + " else:\n", + " X_true, Y = simulate_system(As, Bs, C_true, U, x0=np.zeros(latent_dim),rng=seed1+i,\n", + " obs_noise=obs_noise,process_noise=process_noise,\n", + " nonlinear_eps=nonlinear_eps,nonlinear_func=nonlinear_func)\n", + " X_trues.append(X_true)\n", + " Ys.append(Y)\n", + " Us.append(U)\n", + " control_labels.append(b1)\n", + " state_labels.append(a1)\n", + "\n", + " return X_trues, Ys, Us, control_labels, state_labels, (A1_true, A2_true), (B1_true, B2_true)\n", + "\n", + "\n", + "X_trues, Ys, Us, control_labels, state_labels, A_trues, B_trues = simulate_As_Bs(n,m,p_out,N,\n", + " input_alpha=input_alpha,g1=g1,g2=g2,n_Us=1,obs_noise=obs_noise,process_noise=process_noise,\n", + " nonlinear_eps=nonlinear_eps,nonlinear_func= lambda x: np.tanh(x))\n", + "fig, ax = plt.subplots(1, 4, figsize=(8, 2),sharey='row')\n", + "#plot Us and Ys against time\n", + "for i in range(4):\n", + " # ax[0, i].plot(Us[i].T[:100])\n", + " ax[i].plot(Ys[i].T[:100,:],alpha=0.5)\n", + " \n", + " # Remove spines and ticks\n", + " for spine in ax[i].spines.values():\n", + " spine.set_visible(False)\n", + " ax[i].set_xticks([])\n", + " ax[i].set_yticks([])\n", + "# plt.savefig(f'{folder_path}/data_examples.pdf', format='pdf', dpi=300, bbox_inches='tight')\n", + "plt.show()\n", + "\n", + "# X_trues, Ys, Us, control_labels, state_labels, A_trues, B_trues = simulate_As_Bs(n,m,p_out,N_small,\n", + "# input_alpha=input_alpha,g1=g1,g2=g2, same_inp=False,n_Us=4,\n", + "# obs_noise=obs_noise,process_noise=process_noise,nonlinear_eps=nonlinear_eps)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "728cf5a2", + "metadata": {}, + "outputs": [], + "source": [ + "from DSA import DMD,DMDc, SubspaceDMDc\n", + "from tqdm import tqdm\n", + "\n", + "def get_dmds(Ys,n_delays=1,rank=None):\n", + " As = []\n", + " for Y in Ys:\n", + " dmd = DMD(Y.T,n_delays=n_delays,rank=rank)\n", + " dmd.fit()\n", + " As.append(dmd.A_v.numpy())\n", + " return As\n", + "\n", + "def get_dmdcs(Ys,Us,n_delays=1,rank=None):\n", + " As = []\n", + " Bs = []\n", + " for Y, U in zip(Ys, Us):\n", + " dmdc = DMDc(Y.T, U.T,n_delays=n_delays,n_control_delays=n_delays,rank_input=rank,rank_output=rank)\n", + " dmdc.fit()\n", + " As.append(dmdc.A_v.numpy())\n", + " Bs.append(dmdc.B_v.numpy())\n", + " return As, Bs\n", + "\n", + "\n", + "def get_subspace_dmdcs(Ys, Us, p=20, rank=None, backend='n4sid'):\n", + " \"\"\"Fit SubspaceDMDc models using DSA package.\"\"\"\n", + " As, Bs, Cs, infos = [], [], [], []\n", + " for Y, U in zip(Ys, Us):\n", + " model = SubspaceDMDc(Y.T, U.T, n_delays=p, rank=rank, backend=backend)\n", + " model.fit()\n", + " As.append(model.A_v)#.numpy())\n", + " Bs.append(model.B_v)#.numpy())\n", + " Cs.append(model.C_v)#.numpy())\n", + " infos.append(model.info)\n", + " return As, Bs, Cs, infos\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "db4d50cb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[(2, 1000), (2, 1000), (2, 1000), (2, 1000), (2, 1000), (2, 1000), (2, 1000), (2, 1000), (2, 1000), (2, 1000), (2, 1000), (2, 1000), (2, 1000), (2, 1000), (2, 1000), (2, 1000)]\n" + ] + } + ], + "source": [ + "X_trues, Ys, Us, control_labels, state_labels, A_trues, B_trues = simulate_As_Bs(n,m,p_out_small,\n", + " N_small,input_alpha=input_alpha,g1=g1,g2=g2,same_inp=False,n_Us=n_Us,\n", + " obs_noise=obs_noise,process_noise=process_noise,\n", + " nonlinear_eps=nonlinear_eps)\n", + "print([i.shape for i in Ys])" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "3ef1f7f5", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#plot examples of the inputs and the outputs\n", + "fig, ax = plt.subplots(2,2,figsize=(4,2),sharex=True,sharey=True)\n", + "ax = ax.flatten()\n", + "for i in range(4):\n", + " ind = 4*i\n", + " ax[i].plot(Us[ind].T[:100] + 10*np.mean(np.abs(Us[ind])), color=plt.cm.Set2(1), label='Input (u)')\n", + " ax[i].plot(Ys[ind].T[:100, 0], color=plt.cm.Set2(2), label='Output (y)')\n", + " #remove all ticks and lines\n", + " ax[i].set_xticks([])\n", + " ax[i].set_yticks([])\n", + " ax[i].spines['top'].set_visible(False)\n", + " ax[i].spines['right'].set_visible(False)\n", + " ax[i].spines['bottom'].set_visible(False)\n", + " ax[i].spines['left'].set_visible(False)\n", + " \n", + "ax[0].text(1, 0.4, 'Input', transform=ax[0].transAxes, color=plt.cm.Set2(1), va='top')\n", + "ax[0].text(1, 0.2, 'Output', transform=ax[0].transAxes, color=plt.cm.Set2(2), va='top')\n", + "plt.tight_layout()\n", + "# plt.savefig(f'{folder_path}/input_output_examples.pdf', format='pdf', dpi=300, bbox_inches='tight')\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "eef05f5d", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[legend.py:1217 - _parse_legend_args() ] No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Use SubspaceDMDc from DSA package to analyze singular values\n", + "\n", + "\n", + "fig, ax = plt.subplots(2,2,figsize=(9,6),sharey=True,sharex=True)\n", + "ax = ax.flatten()\n", + " \n", + "for j, (Y, U) in enumerate(zip(Ys[::n_Us], Us[::n_Us])):\n", + " # Test different numbers of delays for subspace identification\n", + " nds_all = [10, 25, 50, 75, 100, 125, 150, 175, 200]\n", + " \n", + " for k, nds in enumerate(nds_all):\n", + " # Fit SubspaceDMDc with varying number of delays\n", + " model = SubspaceDMDc(\n", + " Y.T, # SubspaceDMDc expects (T, p_out)\n", + " U.T, # SubspaceDMDc expects (T, m)\n", + " n_delays=nds,\n", + " rank=20, # Use fixed rank for comparison\n", + " backend='n4sid'\n", + " )\n", + " model.fit()\n", + " \n", + " # Extract singular values from model info\n", + " singular_vals = model.info['singular_values_O']\n", + " \n", + " # Convert to numpy if needed\n", + " if hasattr(singular_vals, 'numpy'):\n", + " singular_vals = singular_vals.numpy()\n", + " \n", + " # Plot singular values\n", + " ax[j].plot(singular_vals, '-', label=f'{nds}', \n", + " color=plt.cm.Blues_r(k / (len(nds_all) + 4)))\n", + " ax[j].set_yscale('log')\n", + " ax[j].axvline(x=20, color='k', linestyle=':', alpha=0.5)\n", + " \n", + " ax[j].set_xlabel('Mode Number')\n", + " ax[j].set_title(f'System {j+1}')\n", + " ax[1].legend(title=\"Delays\", loc='upper right', bbox_to_anchor=(1.5, 1), \n", + " fontsize=12, title_fontsize=15)\n", + "\n", + "ax[0].set_ylabel('Singular Value')\n", + "ax[2].set_ylabel('Singular Value')\n", + "plt.tight_layout()\n", + "# plt.savefig(f'{folder_path}/singular_values_subspace_dmdc.pdf', format='pdf', dpi=300, bbox_inches='tight')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "3636fc5c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "========================================\n", + "Number of valid trials: 150\n", + "========================================\n", + "Number of valid trials: 150\n", + "========================================\n", + "Number of valid trials: 150\n", + "========================================\n", + "Number of valid trials: 150\n", + "========================================\n", + "Number of valid trials: 150\n", + "========================================\n", + "Number of valid trials: 150\n", + "========================================\n", + "Number of valid trials: 150\n", + "========================================\n", + "Number of valid trials: 150\n", + "========================================\n", + "Number of valid trials: 150\n", + "========================================\n", + "Number of valid trials: 150\n", + "========================================\n", + "Number of valid trials: 150\n", + "========================================\n", + "Number of valid trials: 150\n", + "========================================\n", + "Number of valid trials: 150\n", + "========================================\n", + "Number of valid trials: 150\n", + "========================================\n", + "Number of valid trials: 150\n", + "========================================\n", + "Number of valid trials: 150\n" + ] + } + ], + "source": [ + "dec = 0 #can change this to look at the efect of using the incorrect ranks\n", + "A_dmd = get_dmds(Ys,n_delays=n_delays,rank=rank- dec)\n", + "A_cs, B_cs = get_dmdcs(Ys,Us,n_delays=n_delays,rank=rank - dec)\n", + "As, Bs, Cs, infos = get_subspace_dmdcs(Ys,Us,p=pf,rank=rank-dec,backend='custom')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "5ae5efa9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "N4SID - A matrix shapes: [(10, 10), (10, 10), (10, 10), (10, 10), (10, 10), (10, 10), (10, 10), (10, 10), (10, 10), (10, 10), (10, 10), (10, 10), (10, 10), (10, 10), (10, 10), (10, 10)]\n", + "N4SID - Ranks used: [10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10]\n", + "N4SID - Backend info: ['unknown', 'unknown', 'unknown', 'unknown', 'unknown', 'unknown', 'unknown', 'unknown', 'unknown', 'unknown', 'unknown', 'unknown', 'unknown', 'unknown', 'unknown', 'unknown']\n", + "\\nEigenvalue comparison (first system):\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\\nComputing similarity matrices...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 16/16 [00:00<00:00, 548.06it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 123.68it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 608.27it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 135.77it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Custom backend silhouette score: 0.685\n", + "N4SID backend silhouette score: 0.669\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "As_n4sid, Bs_n4sid, Cs_n4sid, infos_n4sid = get_subspace_dmdcs(Ys, Us, p=pf, rank=rank-dec, backend='n4sid')\n", + "print(f\"N4SID - A matrix shapes: {[A.shape for A in As_n4sid]}\")\n", + "print(f\"N4SID - Ranks used: {[info['rank_used'] for info in infos_n4sid]}\")\n", + "print(f\"N4SID - Backend info: {[info.get('backend', 'unknown') for info in infos_n4sid]}\")\n", + "\n", + "# Quick comparison of eigenvalues (first system)\n", + "print(\"\\\\nEigenvalue comparison (first system):\")\n", + "eigs_custom = np.linalg.eigvals(As[0])\n", + "eigs_n4sid = np.linalg.eigvals(As_n4sid[0])\n", + "eigs_real = np.linalg.eigvals(A_trues[0])\n", + "\n", + "plt.figure(figsize=(8, 6))\n", + "plt.scatter(eigs_real.real, eigs_real.imag, alpha=0.7, label='True', s=100)\n", + "plt.scatter(eigs_custom.real, eigs_custom.imag, alpha=0.7, label='Custom backend', s=50)\n", + "plt.scatter(eigs_n4sid.real, eigs_n4sid.imag, alpha=0.7, label='N4SID backend', s=50, marker='x',c='k')\n", + "plt.xlabel('Real part')\n", + "plt.ylabel('Imaginary part')\n", + "plt.title('Eigenvalue comparison (first system)')\n", + "plt.legend()\n", + "plt.grid(True, alpha=0.3)\n", + "plt.axhline(y=0, color='k', linestyle='-', alpha=0.3)\n", + "plt.axvline(x=0, color='k', linestyle='-', alpha=0.3)\n", + "plt.show()\n", + "\n", + "# Compute distances using both backends for comparison\n", + "print(\"\\\\nComputing similarity matrices...\")\n", + "_, _, _, _, sims_state_custom = compare_systems_full(As, Bs)\n", + "_, _, _, _, sims_state_n4sid = compare_systems_full(As_n4sid, Bs_n4sid)\n", + "\n", + "from sklearn.metrics import silhouette_score\n", + "silh_custom = silhouette_score(sims_state_custom, state_labels, metric='precomputed')\n", + "silh_n4sid = silhouette_score(sims_state_n4sid, state_labels, metric='precomputed')\n", + "\n", + "\n", + "print(f\"Custom backend silhouette score: {silh_custom:.3f}\")\n", + "print(f\"N4SID backend silhouette score: {silh_n4sid:.3f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "79bb2540", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 16/16 [00:00<00:00, 490.65it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 138.91it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 525.72it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 134.68it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 604.50it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 126.83it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 620.90it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 135.16it/s]\n" + ] + }, + { + "data": { + "image/png": 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dsDIr1lXj119/rRUrVig42L4gTUhI0I4dOwp4FQCcXsQmAE5FfALgRMQmAE5FfEJ+ilUT0+v1yuPx5Fm/fft2RUZGlnhQAFAcxCYATkV8AuBExCYATkV8Qn6KlcTs3r27Jk2a5Gu7XC4dPXpUY8eO1VVXXVVaYwOAU0JsAuBUxCcATkRsAuBUxCfkp1jTyV988UX16NFDzZo1U1pamgYPHqyNGzeqRo0amjNnTpH28ULdxVb70V2dfcuT6i20+jpOvM9q16+Sc+uwOWTXciq01lNkpuyN7ToIEe50q+3NVcTl/BD7duXWcdus9vkhO33L+4/btQsAlI3SiE0BGf7Fm3IWM1fbdUr8StQp6o+cOrrHq9v1J3PXwJSkBk/l1Hk68NnZVt+RI3aNk+YNdlntX1c19C1XPceuyeJfoy53Pc3IOikCUD5KGp/cLr+amLmCk9svGLn8i865C671m7sGpiSZ9FzXQi7/je2mf607K1567WN687w45//R89TWBFBmSuPaKfqPQury+n+8/cJT8LcbfMsx1ZtbfblrYEpSnedzrp063pRm9c1OaW21w0IyrHbm1zn19A42s8frifAbf67moaYCUE5KGp+MfzFvVwHL+W1rve4UrlNOtmkh/V4T4Nfm+ig/xUpi1qtXT2vWrNG7776rNWvW6OjRoxo+fLhuvPFGhYWduQVGAZQvYhMApyI+AXAiYhMApyI+IT/FSmIuW7ZM7dq104033qgbb7zRtz4rK0vLli3TZZddVmoDBICiIjYBcCriEwAnIjYBcCriE/LjMsaYk29mc7vd2rVrl2rWrGmt379/v2rWrJlv8VV/53zwpNWuGpHqWz541J6OfXy/nWXv3/r7Avc7d8Ul9opcU8jPGvKj1eWOjbW3zcqy27nOo9+qDVZXaIA9NT3FE+pbfuGnblbfH4MeLXC8AEpPacSm13/vVGBfsMuOEYc8dqz65/qOvuWxLT6z+t7c3sF+7fGcuFbtaju+HOvfxmqHb0u12knXRfiWM2P8zinInhIVlpTzNL/jDey4tXXEAwJQNkoany4a+ZLV9p8xbvHrO3ROzrKnih0jjF9pndzTnM6+/Vury79kjwIKnuYU8Zn9/+QZHrudkpkzjX3vUbv8xrreTxW4XwClqzSunby7z7Lamabg13hlx6Bu6wb6lt89d5bVN2D9zVa7Z531vuWvW4RafYF161htc+yY1d4xLGeqetXf/UqM+YWyoKM513uZEXbsWvYp105AWSlpfOoROdRekSv1ZTLt73WuULu8jqt+bd9yVoydj8qItp+WnjuGhHz+ndXlrmqXIzPHj1ttV647SmevnWf1HfDa8TIj13TzBceaWX33nrtIZ4piPdjHGCNXPnUB9u/fr/Dw8HxeAQCnH7EJgFMRnwA4EbEJgFMRn5CfU5pOft1110k68VSooUOHKiRXMXiPx6O1a9eqXbt2Bb0cAE4LYhMApyI+AXAiYhMApyI+oTCnlMSMjo6WdCIjHhkZaRVTDQ4O1qWXXqqRI0eW7ggB4CSITQCcivgEwImITQCciviEwpxSEnPatGmSpISEBN1///0luoU3M8M+dGRwum95T4Zdc8mVbs96D3Ll1D5I9dr1CFxZfrcb56r15F8D07N3r9UOiIy09xUclDNe47b6Ylx2jboDJqdGnSe1WM9LAlBMpRmbPtvTwmoH5Cou17nG71bf/OTmKsjeLDuO7fOr+XbkSM4/xiH+NTDfX2W1s65oZbWr7MiJc8dkxyYF2O2siFwxMMVvWwCnXWnFJ7/LHZt/WUtvIf1e+zopzyytXG3/GpieI0esdkAVuy6w3Dkx5nC6X/0orx1/jmXknFBaWpAAlK3SvHZ6Zl9Tq537e5PXr+Ckx9jf67b/Vsu3PK5aF6tv5wb7u9vslNa+5UZ191t9WTt2Wu3A2nFWO2xPTmAM23HU6jMB9pi8VYJybWvX1gRw+pVWfDLp6VbbXStXbU2/epqefXZMCTicEyfyZHfymeLuO4ZfDUzPwYP2fkNDC+zfmmVfK+3xRFttb65qkH+mVStwDJVdsWpiPvDAA1Ztgq1bt2rSpElauHBhqQ0MAE4VsQmAUxGfADgRsQmAUxGfkJ9iJTH79OmjmTNnSpIOHTqkSy65RC+++KL69Omj1157rVQHCABFRWwC4FTEJwBORGwC4FTEJ+SnWEnMH3/8UR07dpQkvf/++4qLi9PWrVs1c+ZMvfzyy6U6QAAoKmITAKciPgFwImITAKciPiE/xSremJqaqsj/1Y9cuHChrrvuOgUEBOjSSy/V1q1bi7SP2tUPW+3ft+bULYmLO2T1hcbadQQSf73It+zZF2L1dWu3xmpHuHPqIPyaZdcU8K+B6U1JsdqB9er6lmPcdg3M/Z4Iqx3tzqmXEriP2k5AeSiN2BQckGW1A1w5xeTOCdll9X3pPsdqp+/OqQ/X7KLtVl+mx65x0rxBzr7St9k1n/xrYAZ++YPVdjdpm7NtrQyrz79ES+DOnLpznmqZAlA+ShqfTIDfhztXnUuXX1FMv5Jzyh3WjB3iZIy9X6vpd0z/GpjeVPvayKo1dRK5YyuA8lMa107VAu0ak5km5yumx68mptcvQJngnFqV1YPt/fi9VGEhOdc85phdq9K/BmbWrt32cdwNfcupDfy+AwbZBwo6mlMrL61GYQWJAZxOJb528vpdH6Wl5TQy/S6I/IXkfPa9YXZ+xxPi95yB3PXEjx+3uvxrYHpzj8GvPzrA/q7mkR3nctcbjgq093MmKdadmE2aNNFHH32kP//8UwsWLFD37t0lSXv27FGUXxF4ACgrxCYATkV8AuBExCYATkV8Qn6KlcR8/PHHdf/99yshIUFt2rRR27Yn7gpauHChWrZsWaoDBICiIjYBcCriEwAnIjYBcCriE/JTrOnk/fv3V4cOHbRr1y5dcMEFvvVdunRR3759S21wAHAqiE0AnIr4BMCJiE0AnIr4hPwUK4k5bdo0DRo0SHFxdu2RSy65pMj72H/UrqtUtXpODZRDfn0ZO8LtF3tzLdew68HFBNk1CLy5izt5PFafK9iubZC7BqYkZW3f4Vt+et1VVl/mBvv2ZW+DnJoE9/T+zB6v7hWA0680YtMP6xvaK3Ldr/7d7359mfbN7IHHctpdwux4k5Vlb/vrqpx9ua6zd1tlh12bKXcNTEmqMXWlb/lolxb2GALt43qa5MTIYL/adwDKTknjkzut6DUkXV67nVUl57XeML/OAL/9FhYm3HYNKP8amJ7kPb7lIHftQseY5sq5BHW7qY8JlJfSuHZ6/sfuBfa5/GKMN8OOIzE/58SC2VH2MQPS7YCU+XV13/KOYdWtvrA9dmzLXQNTkmJm5lw7bZp4qT2mEL+4mOuRCwGHizVxEUApKGl8cvnV9nblrk8ZZH9nkl+dbx3Pye8EhNq1cd2hBT8DxRUWZrU9B+3nuxRWI/Ow197vfo+dB8tUTvw8mGXnzM4kxYrKDz30kGrVqqXhw4drxYoVpT0mACgWYhMApyI+AXAiYhMApyI+IT/FSmLu2LFDM2bM0L59+9S5c2edc845evbZZ7V79+6TvxgAThNiEwCnIj4BcCJiEwCnIj4hPy5jTInm8CQnJ+vtt9/WjBkz9Ntvv6lnz54aPny4rrnmGgUEFJwjPbizntX+8Gi8b7lvxFarb0GqffvwB3tb+ZbTsuxbbgfEfW+1zw/JmRL+3fEEqy/3I+olKcZt30Kcewp5vX6/WH1uv6dhmQZ1chp+p73gp6cFoGwVNzb99YebinyMI5n2dIBvtiT4lvs0XWv1ffDtxVa7at3DvuWDO6OtvsBDdmzKqmWXzQiukulbTrjePo7/FIWUXjn1YyLnrbH6FqTOEoCyV5z4dHRnvNWuEpAztSnVa8eIoybTaj+1+wrfstdvvvhxj30dlenNiT8ZXjsWHU63p0j5C3LnTM3K7LzL6nPH2HHOFZ4zRcpUsePW/N+fLfQ4AE6P4l47tVv4oNX25CpfY/xK2Xj92plfxOY0uh2w+gI/rmq1DzbL+dpa70t7KmjYjqNWO7VBpNXefkXO+Jvc840K447NGZNn716rb5E3sdDXAjg9ihOfftlmlws8NzhnCnamsWPIpsx0q73ieCPfclJ6rNW37bgdm7wm5/hT6n9u9W3Nsq+logPsa7TcU8gfbNjG6guo4jdlPHdZn8b1ra4FPz6pM0WJi3zUqlVLHTp0UNu2bRUQEKCff/5ZQ4YMUePGjbV06dJSGCIAnDpiEwCnIj4BcCJiEwCnIj4hW7GTmMnJyXrhhRfUvHlzde7cWUeOHNFnn32mpKQk7dixQwMHDtSQIUNKc6wAcFLEJgBORXwC4ETEJgBORXyCv2IlMa+55hrVr19f06dP18iRI7Vjxw7NmTNHXbt2lSSFh4frvvvu059//lmqgwWAwhCbADgV8QmAExGbADgV8Qn5CSzOi2rWrKmvvvpKbdu2LXCb2NhYJSUlFdifabxWO8PkDOWw165PUN1t1zi5qVbOk6l2ZFaz+l78vavVbh23zbd8WfQGqy/GZdfA3O+JsMe4IafupX8NTM+RI1Y7cF9OnSjjsc8NQNkojdjk8avVlFtIQJbVDgqwY5Vyvda/5q4/q0ZUkF/MCLBf6/IbUmBgznH9a2B609IKPS6A8lHS+JTqV+fSnSuG+NfAPOa1y50fyQrxLWf51blMzQq22rlrZgbI3o9/jczC+NfA9Bw6bLUDo3Ouq1xZfrEUQJkpjWunmlVSrHbu+nD+dXj9a2JurZJTa65muP3d7LDLrjvnich1veR3bWT8auJ5g/yOG3IK388CCr4WBFB2ShqfDnjt70kHPTkxJs0vH7XXa+eC9mXl1NU9kBlu9R3KsGtV5o5rB7z2fvd4/K6HdMxq7/fk7Nu/BqY31Y6Jub8UulPtGp5nklO6E3PlypX67LPP9Oabb/p+kWbOnKmGDRuqZs2auvXWW5WefuLNdLlcio+PL2x3AFAqiE0AnIr4BMC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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.metrics import silhouette_score\n", + "A_type = [A_dmd, A_cs, As, As_n4sid]\n", + "B_type = [A_dmd, B_cs, Bs, Bs_n4sid]\n", + "names = ['DMD, Partially Observed', 'DMDc, Partially Observed', 'Old Subspace DMDc, Partially Observed', 'Subspace DMDc, Partially Observed']\n", + "for Ai, Bi, name in zip(A_type, B_type, names):\n", + "\n", + " sims_full, sims_control_joint, sims_state_joint, sims_control_separate, sims_state_separate = compare_systems_full(Ai,Bi)\n", + "\n", + " fig, ax = plt.subplots(1, 5, figsize=(15, 3))\n", + " \n", + " # Define data and titles for each subplot\n", + " sims_data = [sims_full, sims_state_joint, sims_control_joint, sims_state_separate, sims_control_separate]\n", + " titles = ['Joint', \n", + " f'State (Joint) \\n {np.round(silhouette_score(sims_state_joint,state_labels,metric=\"precomputed\"),2)}',\n", + " f'Control (Joint) \\n {np.round(silhouette_score(sims_control_joint,control_labels,metric=\"precomputed\"),2)}',\n", + " f'State (Separate) \\n {np.round(silhouette_score(sims_state_separate,state_labels,metric=\"precomputed\"),2)}',\n", + " f'Control (Separate) \\n {np.round(silhouette_score(sims_control_separate,control_labels,metric=\"precomputed\"),2)}']\n", + " \n", + " # Loop through all subplots\n", + " for i, (data, title) in enumerate(zip(sims_data, titles)):\n", + " im = ax[i].imshow(data)\n", + " cbar = plt.colorbar(im, ax=ax[i], shrink=0.2, location='top')#, label='Distance')\n", + " cbar.ax.tick_params(labelsize=10)\n", + " cbar.ax.spines['top'].set_visible(False)\n", + " cbar.ax.spines['right'].set_visible(False)\n", + " cbar.ax.spines['bottom'].set_visible(False)\n", + " cbar.ax.spines['left'].set_visible(False)\n", + " ax[i].set_title(title,y=1.8)\n", + " #loop through all of them and remove x and yticks, then add System as text label for each\n", + " for i in range(5):\n", + " ax[i].set_xticks([])\n", + " ax[i].set_yticks([])\n", + " # ax[i].text(0.5, -0.1, 'System', transform=ax[i].transAxes, ha='center', va='top')\n", + " ax[i].set_ylabel('System')\n", + " ax[i].set_xlabel('System')\n", + " ax[i].spines['top'].set_visible(False)\n", + " ax[i].spines['right'].set_visible(False)\n", + " ax[i].spines['bottom'].set_visible(False)\n", + " ax[i].spines['left'].set_visible(False)\n", + " plt.suptitle(name,y=1.1)\n", + " plt.tight_layout()\n", + " # plt.savefig(f'{folder_path}/{name}.eps', format='eps', dpi=300, bbox_inches='tight')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "2b529073", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 16/16 [00:00<00:00, 523.63it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 110.19it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 474.72it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 133.29it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 594.62it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 95.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Silhouette Scores:\n", + "DMD State: 0.132\n", + "DMDc State: 0.143\n", + "DMDc Control: 0.002\n", + "SubspaceDMDc State: 0.669\n", + "SubspaceDMDc Control: 0.521\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Get the similarity matrices for each method\n", + "sims_full_dmd, sims_control_joint_dmd, sims_state_joint_dmd, sims_control_separate_dmd, sims_state_separate_dmd = compare_systems_full(A_dmd, A_dmd)\n", + "sims_full_dmdc, sims_control_joint_dmdc, sims_state_joint_dmdc, sims_control_separate_dmdc, sims_state_separate_dmdc = compare_systems_full(A_cs, B_cs)\n", + "sims_full_subdmdc, sims_control_joint_subdmdc, sims_state_joint_subdmdc, sims_control_separate_subdmdc, sims_state_separate_subdmdc = compare_systems_full(As_n4sid, Bs_n4sid)\n", + "\n", + "# Print silhouette scores\n", + "print(\"Silhouette Scores:\")\n", + "print(f\"DMD State: {np.round(silhouette_score(sims_state_separate_dmd, state_labels, metric='precomputed'), 3)}\")\n", + "print(f\"DMDc State: {np.round(silhouette_score(sims_state_separate_dmdc, state_labels, metric='precomputed'), 3)}\")\n", + "print(f\"DMDc Control: {np.round(silhouette_score(sims_control_joint_dmdc, control_labels, metric='precomputed'), 3)}\")\n", + "print(f\"SubspaceDMDc State: {np.round(silhouette_score(sims_state_separate_subdmdc, state_labels, metric='precomputed'), 3)}\")\n", + "print(f\"SubspaceDMDc Control: {np.round(silhouette_score(sims_control_joint_subdmdc, control_labels, metric='precomputed'), 3)}\")\n", + "\n", + "# Create 2x3 subplot\n", + "fig, axes = plt.subplots(2, 3, figsize=(6, 5))\n", + "plt.subplots_adjust(wspace=0.1, hspace=0.2)\n", + "\n", + "# Column headers (bold)\n", + "column_headers = ['DMD', 'DMDc', 'SubspaceDMDc']\n", + "for i, header in enumerate(column_headers):\n", + " axes[0, i].text(0.5, 1.75, header, transform=axes[0, i].transAxes, ha='center', va='bottom', fontweight='bold', fontsize=16)\n", + "\n", + "# Row headers\n", + "row_headers = ['State DSA', ['Not Available', 'Input DSA', 'Input DSA']]\n", + "for i in range(3):\n", + " axes[0, i].text(0.5, 1.55, 'State DSA', transform=axes[0, i].transAxes, ha='center', va='bottom', fontsize=12)\n", + "\n", + "axes[1, 0].text(0.5, 1.55, 'Not Available', transform=axes[1, 0].transAxes, ha='center', va='bottom', fontsize=12)\n", + "axes[1, 1].text(0.5, 1.55, 'Input DSA', transform=axes[1, 1].transAxes, ha='center', va='bottom', fontsize=12)\n", + "axes[1, 2].text(0.5, 1.55, 'Input DSA', transform=axes[1, 2].transAxes, ha='center', va='bottom', fontsize=12)\n", + "\n", + "# Data for each subplot\n", + "data_matrices = [\n", + " sims_state_separate_dmd, # top left\n", + " sims_state_separate_dmdc, # top middle \n", + " sims_state_separate_subdmdc, # top right\n", + " None, # bottom left (gray matrix)\n", + " sims_control_joint_dmdc, # bottom middle\n", + " sims_control_joint_subdmdc # bottom right\n", + "]\n", + "\n", + "# Create gray matrix for bottom left - use same size as other matrices\n", + "matrix_size = sims_state_separate_dmd.shape[0]\n", + "gray_matrix = np.ones((matrix_size, matrix_size)) * 0.5\n", + "\n", + "# Plot each subplot\n", + "for idx, (ax, data) in enumerate(zip(axes.flat, data_matrices)):\n", + " row = idx // 3\n", + " col = idx % 3\n", + " \n", + " if idx == 3: # Bottom left - gray matrix with diagonal lines\n", + " im = ax.imshow(gray_matrix, cmap='gray', vmin=0, vmax=1, extent=[-0.5, matrix_size-0.5, matrix_size-0.5, -0.5])\n", + " \n", + " # Add diagonal lines from bottom-left to top-right\n", + " for i in range(matrix_size):\n", + " for j in range(matrix_size):\n", + " ax.plot([j-0.5, j+0.5], [i-0.5, i+0.5], 'k--', linewidth=1)\n", + " \n", + " # Set axis limits to match other plots\n", + " ax.set_xlim(-0.5, matrix_size-0.5)\n", + " ax.set_ylim(matrix_size-0.5, -0.5)\n", + " \n", + " # Remove ticks and labels\n", + " ax.set_xticks([])\n", + " ax.set_yticks([])\n", + " ax.set_xlabel('')\n", + " ax.set_ylabel('')\n", + " \n", + " # Remove spines\n", + " for spine in ax.spines.values():\n", + " spine.set_visible(False)\n", + " \n", + " else:\n", + " im = ax.imshow(data, cmap='viridis')\n", + " \n", + " # Add colorbar on top with only 2 ticks\n", + " cbar = plt.colorbar(im, ax=ax, shrink=0.4, location='top', pad=0.02)\n", + " vmin, vmax = data.min(), data.max()\n", + " cbar.set_ticks([vmin, vmax])\n", + " cbar.set_ticklabels([f'{vmin:.2g}', f'{vmax:.2g}'])\n", + " cbar.ax.tick_params(labelsize=10)\n", + " \n", + " # Remove colorbar spines\n", + " for spine in cbar.ax.spines.values():\n", + " spine.set_visible(False)\n", + " \n", + " # Set custom tick positions and labels (every 4 positions)\n", + " tick_positions = [1.5, 5.5, 9.5, 13.5] # Middle of each group of 4\n", + " tick_labels = ['1', '2', '3', '4']\n", + " \n", + " ax.set_xticks(tick_positions)\n", + " ax.set_xticklabels(tick_labels,fontsize=10)\n", + " ax.set_yticks(tick_positions)\n", + " ax.set_yticklabels(tick_labels,fontsize=10)\n", + " \n", + " # Set axis labels\n", + " ax.set_xlabel('System',fontsize=10)\n", + " ax.set_ylabel('System',fontsize=10)\n", + " \n", + " # Remove spines\n", + " for spine in ax.spines.values():\n", + " spine.set_visible(False)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "2edb4f13", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 16/16 [00:00<00:00, 287.19it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 39.64it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 380.73it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 41.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Silhouette Scores:\n", + "DMDc Full (state): 0.028\n", + "SubspaceDMDc Full (state): 0.377\n", + "DMDc Full (control): -0.001\n", + "SubspaceDMDc Full (control): 0.435\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Get the similarity matrices for each method\n", + "sims_full_dmdc, _, _, _, _ = compare_systems_full(A_cs, B_cs)\n", + "sims_full_subdmdc, _, _, _, _ = compare_systems_full(As_n4sid, Bs_n4sid)\n", + "\n", + "# Print silhouette scores\n", + "print(\"Silhouette Scores:\")\n", + "print(f\"DMDc Full (state): {np.round(silhouette_score(sims_full_dmdc, state_labels, metric='precomputed'), 3)}\")\n", + "print(f\"SubspaceDMDc Full (state): {np.round(silhouette_score(sims_full_subdmdc, state_labels, metric='precomputed'), 3)}\")\n", + "print(f\"DMDc Full (control): {np.round(silhouette_score(sims_full_dmdc, control_labels, metric='precomputed'), 3)}\")\n", + "print(f\"SubspaceDMDc Full (control): {np.round(silhouette_score(sims_full_subdmdc, control_labels, metric='precomputed'), 3)}\")\n", + "\n", + "# Create 1x2 subplot\n", + "fig, axes = plt.subplots(1, 2, figsize=(6, 3))\n", + "plt.subplots_adjust(wspace=0.1, hspace=0.2)\n", + "\n", + "# Column headers (bold)\n", + "column_headers = ['DMDc', 'SubspaceDMDc']\n", + "for i, header in enumerate(column_headers):\n", + " axes[i].text(0.5, 1.55, header, transform=axes[i].transAxes, ha='center', va='bottom', fontweight='bold', fontsize=16)\n", + "\n", + "# Data for each subplot\n", + "data_matrices = [\n", + " sims_full_dmdc, # left\n", + " sims_full_subdmdc # right\n", + "]\n", + "\n", + "# Plot each subplot\n", + "for idx, (ax, data) in enumerate(zip(axes.flat, data_matrices)):\n", + " im = ax.imshow(data, cmap='viridis')\n", + " \n", + " # Add colorbar on top with only 2 ticks\n", + " cbar = plt.colorbar(im, ax=ax, shrink=0.4, location='top', pad=0.02,label='Joint DSA')\n", + " vmin, vmax = data.min(), data.max()\n", + " cbar.set_ticks([vmin, vmax])\n", + " cbar.set_ticklabels([f'{vmin:.2g}', f'{vmax:.2g}'])\n", + " cbar.ax.tick_params(labelsize=10)\n", + " \n", + " # Remove colorbar spines\n", + " for spine in cbar.ax.spines.values():\n", + " spine.set_visible(False)\n", + " \n", + " # Set custom tick positions and labels (every 4 positions)\n", + " tick_positions = [1.5, 5.5, 9.5, 13.5] # Middle of each group of 4\n", + " tick_labels = ['1', '2', '3', '4']\n", + " \n", + " ax.set_xticks(tick_positions)\n", + " ax.set_xticklabels(tick_labels, fontsize=10)\n", + " ax.set_yticks(tick_positions)\n", + " ax.set_yticklabels(tick_labels, fontsize=10)\n", + " \n", + " # Set axis labels\n", + " ax.set_xlabel('System', fontsize=10)\n", + " ax.set_ylabel('System', fontsize=10)\n", + " \n", + " # Remove spines\n", + " for spine in ax.spines.values():\n", + " spine.set_visible(False)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "d85b184f", + "metadata": {}, + "outputs": [], + "source": [ + "#collect statistics now: \n", + "#sample random systems from the set of 4 pairings\n", + "#sample 4 input drives for each system, making 16 diferent systems in total \n", + "#compute silhouette score based on A labels and B labels\n", + "\n", + "def get_silhouette_scores(n,m,p_out,N,n_iters,\n", + " input_alpha=input_alpha,g1=g1,g2=g2,same_inp=False,n_Us=n_Us,\n", + " n_delays=n_delays,pf=pf,rank=rank,process_noise=process_noise,obs_noise=obs_noise,\n", + " nonlinear_eps=nonlinear_eps,nonlinear_func=lambda x: np.tanh(x),\n", + " y_feature_map = lambda x: x, u_feature_map = lambda x: x,backend=backend,\n", + " use_joint_control=True):\n", + "\n", + " silhouette_state_dmdc = []\n", + " silhouette_control_dmdc = []\n", + "\n", + " silhouette_state_subspace_dmdc = []\n", + " silhouette_control_subspace_dmdc = []\n", + "\n", + " silhouette_state_dsa = []\n", + " silhouette_control_dsa = []\n", + "\n", + "\n", + " for i in tqdm(range(n_iters)):\n", + " X_trues, Ys, Us, control_labels, state_labels, *_ = simulate_As_Bs(n,m,p_out,\n", + " N,input_alpha=input_alpha,g1=g1,g2=g2,same_inp=same_inp,n_Us=n_Us, seed1=seed1+i,seed2=seed2+110*i,\n", + " obs_noise=obs_noise,process_noise=process_noise,\n", + " nonlinear_eps=nonlinear_eps,nonlinear_func=nonlinear_func)\n", + " Ys = list(map(y_feature_map, Ys))\n", + " Us = list(map(u_feature_map, Us))\n", + "\n", + " A_cs, B_cs = get_dmdcs(Ys,Us,n_delays=n_delays,rank=rank)\n", + " print('dmdc:', [i.shape for i in A_cs])\n", + " As, Bs, Cs, infos = get_subspace_dmdcs(Ys,Us,p=pf,rank=rank,backend=backend)\n", + " print('subspacedmdc:', [i.shape for i in As])\n", + " A_dmds = get_dmds(Ys,n_delays=n_delays,rank=rank)\n", + " print('dmd:', [i.shape for i in A_dmds])\n", + " sims_full_dmdc, sims_control_joint_dmdc, sims_state_joint_dmdc, sims_control_separate_dmdc, sims_state_separate_dmdc = compare_systems_full(A_cs,B_cs)\n", + " sims_full_subspace_dmdc, sims_control_joint_subspace_dmdc, sims_state_joint_subspace_dmdc, sims_control_separate_subspace_dmdc, sims_state_separate_subspace_dmdc = compare_systems_full(As,Bs)\n", + "\n", + " sims_state_dmd = compare_A_full(A_dmds)\n", + "\n", + " #compute silhouette scores\n", + " silhouette_state_dmdc.append(silhouette_score(sims_state_separate_dmdc,state_labels,metric='precomputed'))\n", + " if use_joint_control:\n", + " silhouette_control_dmdc.append(silhouette_score(sims_control_joint_dmdc,control_labels,metric='precomputed'))\n", + " silhouette_control_subspace_dmdc.append(silhouette_score(sims_control_joint_subspace_dmdc,control_labels,metric='precomputed'))\n", + " else:\n", + " silhouette_control_dmdc.append(silhouette_score(sims_control_separate_dmdc,control_labels,metric='precomputed'))\n", + " silhouette_control_subspace_dmdc.append(silhouette_score(sims_control_separate_subspace_dmdc,control_labels,metric='precomputed'))\n", + " \n", + " silhouette_state_subspace_dmdc.append(silhouette_score(sims_state_separate_subspace_dmdc,state_labels,metric='precomputed'))\n", + "\n", + " silhouette_state_dsa.append(silhouette_score(sims_state_dmd,state_labels,metric='precomputed'))\n", + " silhouette_control_dsa.append(silhouette_score(sims_state_dmd,control_labels,metric='precomputed'))\n", + "\n", + " print(silhouette_state_subspace_dmdc[-1],silhouette_state_dmdc[-1])\n", + " print(silhouette_control_subspace_dmdc[-1],silhouette_control_dmdc[-1])\n", + "\n", + " # print(silhouette_state_subspace_dmdc,silhouette_control_subspace_dmdc)\n", + " return silhouette_state_dmdc, silhouette_control_dmdc, silhouette_state_subspace_dmdc, silhouette_control_subspace_dmdc, silhouette_state_dsa, silhouette_control_dsa\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "e32ce5f0", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 0/10 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "methods = [ 'DMD','DMDC', 'Subspace DMDC']\n", + "state_means = [np.mean(silh_state_dsa),np.mean(silh_state_dmdc), np.mean(silh_state_subdmdc)]\n", + "state_stds = [np.std(silh_state_dsa) / np.sqrt(n_iters), np.std(silh_state_dmdc) / np.sqrt(n_iters), np.std(silh_state_subdmdc) / np.sqrt(n_iters)]\n", + "control_means = [np.mean(silh_ctrl_dsa),np.mean(silh_ctrl_dmdc), np.mean(silh_ctrl_subsdmdc)]\n", + "control_stds = [np.std(silh_ctrl_dsa) / np.sqrt(n_iters), np.std(silh_ctrl_dmdc) / np.sqrt(n_iters), np.std(silh_ctrl_subsdmdc) / np.sqrt(n_iters)]\n", + "\n", + "# Create bar plot\n", + "x = np.arange(len(methods))\n", + "width = 0.35\n", + "\n", + "fig, ax = plt.subplots(figsize=(6,4))\n", + "# Prepare data for violin plots\n", + "state_data = [silh_state_dsa, silh_state_dmdc, silh_state_subdmdc]\n", + "control_data = [silh_ctrl_dsa, silh_ctrl_dmdc, silh_ctrl_subsdmdc]\n", + "\n", + "# Option to create either violin plots or bar plots\n", + "plot_type = 'bar' # Change to 'bar' for bar plots\n", + "\n", + "if plot_type == 'violin':\n", + " # Create violin plots\n", + " violin_parts1 = ax.violinplot(state_data, positions=x - width/2, widths=width, showmeans=True, showmedians=False)\n", + " violin_parts2 = ax.violinplot(control_data, positions=x + width/2, widths=width, showmeans=True, showmedians=False)\n", + "\n", + " # Color the violin plots\n", + " for pc in violin_parts1['bodies']:\n", + " pc.set_facecolor(plt.cm.Paired(0))\n", + " pc.set_alpha(0.8)\n", + " \n", + " for pc in violin_parts2['bodies']:\n", + " pc.set_facecolor(plt.cm.Paired(1))\n", + " pc.set_alpha(0.8)\n", + "\n", + " # Set the color for violin lines (edges) as well\n", + " for key in ['cbars', 'cmins', 'cmaxes', 'cmedians', 'cmeans']:\n", + " if key in violin_parts2:\n", + " violin_parts2[key].set_color(plt.cm.Paired(1))\n", + " # Create legend manually\n", + " # ax.plot([], [], color=plt.cm.Paired(0), alpha=0.8, label='State')\n", + " # ax.plot([], [], color=plt.cm.Paired(1), alpha=0.8, label='Control')\n", + "\n", + "elif plot_type == 'bar':\n", + " # Create bar plots\n", + " ax.bar(x - width/2, state_means, width, yerr=state_stds, alpha=0.8,color=plt.cm.Paired(0))\n", + " ax.bar(x + width/2, control_means, width, yerr=control_stds, alpha=0.8,color=plt.cm.Paired(1))\n", + "\n", + "\n", + "ax.text(0.1, 0.8, 'State', color=plt.cm.Paired(0), fontsize=18, ha='center', va='center', transform=ax.transAxes)\n", + "ax.text(0.1, 0.7, 'Input', color=plt.cm.Paired(1), fontsize=18, ha='center', va='center', transform=ax.transAxes)\n", + "\n", + "\n", + "# Add labels and formatting\n", + "ax.set_xlabel('Method')\n", + "ax.set_ylabel('Silhouette Score')\n", + "ax.set_xticks(x)\n", + "ax.set_xticklabels(methods)\n", + "# ax.legend(loc='upper left')\n", + "\n", + "plt.tight_layout()\n", + "# plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c085ce64", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 5%|▌ | 1/20 [00:50<15:50, 50.01s/it]" + ] + }, + { + "name": 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obs_noise=obs_noise,process_noise=process_noise,nonlinear_eps=nonlinear_eps)\n", + " silh_state_dmdcs.append(ss_dmdc)\n", + " silh_ctrl_dmdcs.append(sc_dmdc)\n", + " silh_state_subdmdcs.append(ss_subdmdc)\n", + " silh_ctrl_subsdmdcs.append(sc_subdmdc)\n", + " silh_state_dsas.append(ss_dsa)\n", + " silh_ctrl_dsas.append(sc_dsa)\n", + "\n", + " print(np.mean(ss_dmdc),np.mean(ss_subdmdc),np.mean(ss_dsa))\n", + " print()\n", + "\n", + "\n", + "silh_state_dmdcs = np.array(silh_state_dmdcs)\n", + "silh_state_subdmdcs = np.array(silh_state_subdmdcs)\n", + "silh_state_dsas = np.array(silh_state_dsas)\n", + "silh_ctrl_dmdcs = np.array(silh_ctrl_dmdcs)\n", + "silh_ctrl_subdmdcs = np.array(silh_ctrl_subsdmdcs)\n", + "silh_ctrl_dsas = np.array(silh_ctrl_dsas)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2662682c", + "metadata": {}, + "outputs": [], + "source": [ + "#for efficiency (if you desire)\n", + "# silh_state_dmdcs = np.array(silh_state_dmdcs)\n", + "# silh_state_subdmdcs = np.array(silh_state_subdmdcs)\n", + "# silh_state_dsas = np.array(silh_state_dsas)\n", + "# silh_ctrl_dmdcs = np.array(silh_ctrl_dmdcs)\n", + "# silh_ctrl_subdmdcs = np.array(silh_ctrl_subsdmdcs)\n", + "# silh_ctrl_dsas = np.array(silh_ctrl_dsas)\n", + "\n", + "# # Save data\n", + "# np.savez(f'silhouette_data_n_use.npz',\n", + "# silh_state_dmdcs=silh_state_dmdcs,\n", + "# silh_state_subdmdcs=silh_state_subdmdcs,\n", + "# silh_state_dsas=silh_state_dsas,\n", + "# silh_ctrl_dmdcs=silh_ctrl_dmdcs,\n", + "# silh_ctrl_subdmdcs=silh_ctrl_subdmdcs,\n", + "# silh_ctrl_dsas=silh_ctrl_dsas,\n", + "# n_uses=n_uses)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d6a705b2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "methods = ['DMD','DMDc','Subspace DMDc']\n", + "#on two plots, plot the mean and std of the silhouette scores for each method across p_out / n\n", + "p_frac = np.array(n_uses[:len(silh_state_dmdcs)])\n", + "\n", + "fig, ax = plt.subplots(2, 1, figsize=(5,4),sharex=True)\n", + "\n", + "# Plot state silhouette scores\n", + "\n", + "for i, state in enumerate([silh_state_dsas,silh_state_dmdcs,silh_state_subdmdcs]):\n", + " ax[0].plot(p_frac, np.mean(state, axis=1), label=methods[i] + ' (State)',color=plt.cm.Set2(i))\n", + " ax[0].fill_between(p_frac, np.mean(state, axis=1) - np.std(state, axis=1) / np.sqrt(n_iters),\n", + " np.mean(state, axis=1) + np.std(state, axis=1) / np.sqrt(n_iters), alpha=0.2,\n", + " color=plt.cm.Set2(i))\n", + "\n", + "for i, state in enumerate([silh_ctrl_dsas,silh_ctrl_dmdcs,silh_ctrl_subsdmdcs]):\n", + " ax[1].plot(p_frac, np.mean(state, axis=1), label=methods[i] + ' (Control)',color=plt.cm.Set2(i),linestyle='--')\n", + " ax[1].fill_between(p_frac, np.mean(state, axis=1) - np.std(state, axis=1) / np.sqrt(n_iters),\n", + " np.mean(state, axis=1) + np.std(state, axis=1) / np.sqrt(n_iters), alpha=0.2,\n", + " color=plt.cm.Set2(i))\n", + "\n", + "ax[0].set_xscale('log')\n", + "ax[1].set_xscale('log')\n", + "ax[0].set_ylim(-0.05,1.05)\n", + "ax[1].set_ylim(-0.05,1.05)\n", + "# Create custom legend with colored text\n", + "from matplotlib.lines import Line2D\n", + "ax[0].text(0.5, 0.5, 'DMD', color=plt.cm.Set2(0), fontsize=12, ha='center', va='center', transform=ax[0].transAxes)\n", + "ax[0].text(0.5, 0.4, 'DMDc', color=plt.cm.Set2(1), fontsize=12, ha='center', va='center', transform=ax[0].transAxes)\n", + "ax[0].text(0.5, 0.3, 'SubspaceDMDc', color=plt.cm.Set2(2), fontsize=12, ha='center', va='center', transform=ax[0].transAxes)\n", + "\n", + "# Add subplot titles\n", + "ax[0].set_title('State', fontsize=16, pad=10)\n", + "ax[1].set_title('Input', fontsize=16, pad=3)\n", + "ax[1].set_xlabel('Number of States')\n", + "fig.text(-0.05, 0.5, 'Silhouette Score', va='center', rotation='vertical',fontsize=16)\n", + "plt.tight_layout()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "5f1c041a", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 0/2 [00:02 11\u001b[0m silh_state_dmdc, silh_ctrl_dmdc, silh_state_subdmdc, silh_ctrl_subsdmdc, silh_state_dsa, silh_ctrl_dsa \u001b[38;5;241m=\u001b[39m \u001b[43mget_silhouette_scores\u001b[49m\u001b[43m(\u001b[49m\u001b[43mn\u001b[49m\u001b[43m,\u001b[49m\u001b[43mm\u001b[49m\u001b[43m,\u001b[49m\u001b[43mp_out_small\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 12\u001b[0m \u001b[43m \u001b[49m\u001b[43mN_small\u001b[49m\u001b[43m,\u001b[49m\u001b[43mn_iters\u001b[49m\u001b[43m,\u001b[49m\u001b[43minput_alpha\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minput_alpha\u001b[49m\u001b[43m,\u001b[49m\u001b[43mg1\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mg1\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 13\u001b[0m \u001b[43m \u001b[49m\u001b[43mg2\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mg2\u001b[49m\u001b[43m,\u001b[49m\u001b[43msame_inp\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43mn_Us\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mn_Us\u001b[49m\u001b[43m,\u001b[49m\u001b[43mn_delays\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mn_delays\u001b[49m\u001b[43m,\u001b[49m\u001b[43mrank\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mr\u001b[49m\u001b[43m,\u001b[49m\u001b[43mpf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 14\u001b[0m \u001b[43m \u001b[49m\u001b[43mobs_noise\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mobs_noise\u001b[49m\u001b[43m,\u001b[49m\u001b[43mprocess_noise\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprocess_noise\u001b[49m\u001b[43m,\u001b[49m\u001b[43mnonlinear_eps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnonlinear_eps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 15\u001b[0m \u001b[43m \u001b[49m\u001b[43mbackend\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbackend\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 16\u001b[0m silh_state_dmdcs\u001b[38;5;241m.\u001b[39mappend(silh_state_dmdc)\n\u001b[1;32m 17\u001b[0m silh_ctrl_dmdcs\u001b[38;5;241m.\u001b[39mappend(silh_ctrl_dmdc)\n", + "Cell \u001b[0;32mIn[32], line 32\u001b[0m, in \u001b[0;36mget_silhouette_scores\u001b[0;34m(n, m, p_out, N, n_iters, input_alpha, g1, g2, same_inp, n_Us, n_delays, pf, rank, process_noise, obs_noise, nonlinear_eps, nonlinear_func, y_feature_map, u_feature_map, backend, use_joint_control)\u001b[0m\n\u001b[1;32m 29\u001b[0m Us \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mmap\u001b[39m(u_feature_map, Us))\n\u001b[1;32m 31\u001b[0m A_cs, B_cs \u001b[38;5;241m=\u001b[39m get_dmdcs(Ys,Us,n_delays\u001b[38;5;241m=\u001b[39mn_delays,rank\u001b[38;5;241m=\u001b[39mrank)\n\u001b[0;32m---> 32\u001b[0m As, Bs, Cs, infos \u001b[38;5;241m=\u001b[39m \u001b[43mget_subspace_dmdcs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mYs\u001b[49m\u001b[43m,\u001b[49m\u001b[43mUs\u001b[49m\u001b[43m,\u001b[49m\u001b[43mp\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpf\u001b[49m\u001b[43m,\u001b[49m\u001b[43mf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpf\u001b[49m\u001b[43m,\u001b[49m\u001b[43mn_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrank\u001b[49m\u001b[43m,\u001b[49m\u001b[43mbackend\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbackend\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 33\u001b[0m A_dmds \u001b[38;5;241m=\u001b[39m get_dmds(Ys,n_delays\u001b[38;5;241m=\u001b[39mn_delays,rank\u001b[38;5;241m=\u001b[39mrank)\n\u001b[1;32m 35\u001b[0m sims_full_dmdc, sims_control_joint_dmdc, sims_state_joint_dmdc, sims_control_separate_dmdc, sims_state_separate_dmdc \u001b[38;5;241m=\u001b[39m compare_systems_full(A_cs,B_cs)\n", + "\u001b[0;31mTypeError\u001b[0m: get_subspace_dmdcs() got an unexpected keyword argument 'f'" + ] + } + ], + "source": [ + "rs = np.arange(2,25,1)\n", + "n_iters = 2\n", + "silh_state_dmdcs = []\n", + "silh_ctrl_dmdcs = []\n", + "silh_state_subdmdcs = []\n", + "silh_ctrl_subsdmdcs = []\n", + "silh_state_dsas = []\n", + "silh_ctrl_dsas = []\n", + "\n", + "for r in rs:\n", + " silh_state_dmdc, silh_ctrl_dmdc, silh_state_subdmdc, silh_ctrl_subsdmdc, silh_state_dsa, silh_ctrl_dsa = get_silhouette_scores(n,m,p_out_small,\n", + " N_small,n_iters,input_alpha=input_alpha,g1=g1,\n", + " g2=g2,same_inp=False,n_Us=n_Us,n_delays=n_delays,rank=r,pf=pf,\n", + " obs_noise=obs_noise,process_noise=process_noise,nonlinear_eps=nonlinear_eps,\n", + " backend=backend)\n", + " silh_state_dmdcs.append(silh_state_dmdc)\n", + " silh_ctrl_dmdcs.append(silh_ctrl_dmdc)\n", + " silh_state_subdmdcs.append(silh_state_subdmdc)\n", + " silh_ctrl_subsdmdcs.append(silh_ctrl_subsdmdc)\n", + " silh_state_dsas.append(silh_state_dsa)\n", + " silh_ctrl_dsas.append(silh_ctrl_dsa)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0a65665b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "methods = ['DMD','DMDc','Subspace DMDc']\n", + "#on two plots, plot the mean and std of the silhouette scores for each method across p_out / n\n", + "\n", + "fig, ax = plt.subplots(1,2, figsize=(8,3),sharex=True)\n", + "\n", + "# Plot state silhouette scores\n", + "\n", + "for i, state in enumerate([silh_state_dsas,silh_state_dmdcs,silh_state_subdmdcs]):\n", + " ax[0].plot(rs, np.mean(state, axis=1), label=methods[i] + ' (State)',color=plt.cm.Set2(i))\n", + " ax[0].fill_between(rs, np.mean(state, axis=1) - np.std(state, axis=1) / np.sqrt(n_iters),\n", + " np.mean(state, axis=1) + np.std(state, axis=1) / np.sqrt(n_iters), alpha=0.2,\n", + " color=plt.cm.Set2(i))\n", + "\n", + "for i, state in enumerate([silh_ctrl_dsas,silh_ctrl_dmdcs,silh_ctrl_subsdmdcs]):\n", + " ax[1].plot(rs, np.mean(state, axis=1), label=methods[i] + ' (Control)',color=plt.cm.Set2(i),linestyle='--')\n", + " ax[1].fill_between(rs, np.mean(state, axis=1) - np.std(state, axis=1) / np.sqrt(n_iters),\n", + " np.mean(state, axis=1) + np.std(state, axis=1) / np.sqrt(n_iters), alpha=0.2,\n", + " color=plt.cm.Set2(i))\n", + "\n", + "# ax[0].set_xscale('log')\n", + "# ax[1].set_xscale('log')\n", + "ax[0].set_ylim(-0.05,1.05)\n", + "ax[1].set_ylim(-0.05,1.05)\n", + "# Create custom legend with colored text\n", + "from matplotlib.lines import Line2D\n", + "ax[0].text(1.4, 0.8, 'SubspaceDMDc', color=plt.cm.Set2(2), fontsize=12, ha='center', va='center', transform=ax[0].transAxes)\n", + "ax[0].text(1.4, 0.65, 'DMDc', color=plt.cm.Set2(1), fontsize=12, ha='center', va='center', transform=ax[0].transAxes)\n", + "ax[0].text(1.4, 0.5, 'DMD', color=plt.cm.Set2(0), fontsize=12, ha='center', va='center', transform=ax[0].transAxes)\n", + "\n", + "# Add subplot titles\n", + "ax[0].set_title('State', fontsize=16, pad=10)\n", + "ax[1].set_title('Input', fontsize=16, pad=3)\n", + "ax[1].set_xlabel('Rank of DMD')\n", + "fig.text(-0.05, 0.5, 'Silhouette Score', va='center', rotation='vertical',fontsize=16)\n", + "plt.tight_layout()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b1ac349b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.1\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 0/2 [00:15 27\u001b[0m ss_dmdc, sc_dmdc, ss_subdmdc, sc_subdmdc, ss_dsa, sc_dsa \u001b[38;5;241m=\u001b[39m \u001b[43mget_silhouette_scores\u001b[49m\u001b[43m(\u001b[49m\u001b[43mn\u001b[49m\u001b[43m,\u001b[49m\u001b[43mm\u001b[49m\u001b[43m,\u001b[49m\u001b[43mp_out\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 28\u001b[0m \u001b[43m \u001b[49m\u001b[43mN\u001b[49m\u001b[43m,\u001b[49m\u001b[43mn_iters\u001b[49m\u001b[43m,\u001b[49m\u001b[43mrng\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43mi\u001b[49m\u001b[43m,\u001b[49m\u001b[43minput_alpha\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.001\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43mg1\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.5\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 29\u001b[0m \u001b[43m 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\u001b[49m\u001b[43mx\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtanh\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 31\u001b[0m \u001b[43m \u001b[49m\u001b[43my_feature_map\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mY_feature_map\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mu_feature_map\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mU_feature_map\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 32\u001b[0m silh_state_dmdcs\u001b[38;5;241m.\u001b[39mappend(ss_dmdc)\n\u001b[1;32m 33\u001b[0m silh_ctrl_dmdcs\u001b[38;5;241m.\u001b[39mappend(sc_dmdc)\n", + "Cell \u001b[0;32mIn[198], line 40\u001b[0m, in \u001b[0;36mget_silhouette_scores\u001b[0;34m(n, m, p_out, N, n_iters, rng, input_alpha, g1, g2, same_inp, n_Us, n_delays, pf, rank, process_noise, obs_noise, nonlinear_eps, nonlinear_func, y_feature_map, u_feature_map)\u001b[0m\n\u001b[1;32m 37\u001b[0m Us \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mmap\u001b[39m(u_feature_map, Us))\n\u001b[1;32m 39\u001b[0m A_cs, B_cs \u001b[38;5;241m=\u001b[39m get_dmdcs(Ys,Us,n_delays\u001b[38;5;241m=\u001b[39mn_delays,rank\u001b[38;5;241m=\u001b[39mrank)\n\u001b[0;32m---> 40\u001b[0m As, Bs, Cs, infos \u001b[38;5;241m=\u001b[39m \u001b[43mget_subspace_dmdcs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mYs\u001b[49m\u001b[43m,\u001b[49m\u001b[43mUs\u001b[49m\u001b[43m,\u001b[49m\u001b[43mp\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpf\u001b[49m\u001b[43m,\u001b[49m\u001b[43mf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpf\u001b[49m\u001b[43m,\u001b[49m\u001b[43mn_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrank\u001b[49m\u001b[43m,\u001b[49m\u001b[43mbackend\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mn4sid\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 41\u001b[0m A_dmds \u001b[38;5;241m=\u001b[39m get_dmds(Ys,n_delays\u001b[38;5;241m=\u001b[39mn_delays,rank\u001b[38;5;241m=\u001b[39mrank)\n\u001b[1;32m 43\u001b[0m _, _, _, sims_control_separate_dmdc, sims_state_separate_dmdc \u001b[38;5;241m=\u001b[39m compare_systems_full(A_cs,B_cs)\n", + "Cell \u001b[0;32mIn[186], line 36\u001b[0m, in \u001b[0;36mget_subspace_dmdcs\u001b[0;34m(Ys, Us, p, f, n_id, backend)\u001b[0m\n\u001b[1;32m 28\u001b[0m \u001b[38;5;66;03m# N4SID identification\u001b[39;00m\n\u001b[1;32m 29\u001b[0m nfoursid \u001b[38;5;241m=\u001b[39m NFourSID(\n\u001b[1;32m 30\u001b[0m df,\n\u001b[1;32m 31\u001b[0m output_columns\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124my\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mi\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(Y\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m])],\n\u001b[1;32m 32\u001b[0m input_columns\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mu\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mi\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(U\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m])],\n\u001b[1;32m 33\u001b[0m num_block_rows\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mmin\u001b[39m(p, f)\n\u001b[1;32m 34\u001b[0m )\n\u001b[0;32m---> 36\u001b[0m \u001b[43mnfoursid\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msubspace_identification\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 38\u001b[0m \u001b[38;5;66;03m# Determine rank - use n_id if provided, otherwise auto-determine\u001b[39;00m\n\u001b[1;32m 39\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m n_id \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "File \u001b[0;32m~/Desktop/Projects/AgentDSA/AgentDSA/n4sid/nfoursid/src/nfoursid/nfoursid.py:90\u001b[0m, in \u001b[0;36mNFourSID.subspace_identification\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 87\u001b[0m y_past, y_future \u001b[38;5;241m=\u001b[39m y_hankel[:, :\u001b[38;5;241m-\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnum_block_rows], y_hankel[:, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnum_block_rows:]\n\u001b[1;32m 88\u001b[0m u_instrumental_y \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mconcatenate([u_future, u_past, y_past, y_future])\n\u001b[0;32m---> 90\u001b[0m q, r \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mmap\u001b[39m(\u001b[38;5;28;01mlambda\u001b[39;00m matrix: matrix\u001b[38;5;241m.\u001b[39mT, \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlinalg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mqr\u001b[49m\u001b[43m(\u001b[49m\u001b[43mu_instrumental_y\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mT\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mreduced\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 92\u001b[0m y_rows, u_rows \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39my_dim \u001b[38;5;241m*\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnum_block_rows, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mu_dim \u001b[38;5;241m*\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnum_block_rows\n\u001b[1;32m 93\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mR32 \u001b[38;5;241m=\u001b[39m r[\u001b[38;5;241m-\u001b[39my_rows:, u_rows:\u001b[38;5;241m-\u001b[39my_rows]\n", + "File \u001b[0;32m~/opt/anaconda3/envs/iblenv/lib/python3.10/site-packages/numpy/linalg/linalg.py:952\u001b[0m, in \u001b[0;36mqr\u001b[0;34m(a, mode)\u001b[0m\n\u001b[1;32m 950\u001b[0m signature \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mD->D\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m isComplexType(t) \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124md->d\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 951\u001b[0m extobj \u001b[38;5;241m=\u001b[39m get_linalg_error_extobj(_raise_linalgerror_qr)\n\u001b[0;32m--> 952\u001b[0m tau \u001b[38;5;241m=\u001b[39m \u001b[43mgufunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msignature\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msignature\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mextobj\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mextobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 954\u001b[0m \u001b[38;5;66;03m# handle modes that don't return q\u001b[39;00m\n\u001b[1;32m 955\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mode \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m'\u001b[39m:\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "> \u001b[0;32m/Users/mitchellostrow/opt/anaconda3/envs/iblenv/lib/python3.10/site-packages/numpy/linalg/linalg.py\u001b[0m(952)\u001b[0;36mqr\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32m 950 \u001b[0;31m \u001b[0msignature\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'D->D'\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misComplexType\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m'd->d'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m 951 \u001b[0;31m \u001b[0mextobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_linalg_error_extobj\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_raise_linalgerror_qr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m--> 952 \u001b[0;31m \u001b[0mtau\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgufunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m 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nonlinear_eps in enumerate(nonlinear_eps_range):\n", + " print(nonlinear_eps)\n", + " ss_dmdc, sc_dmdc, ss_subdmdc, sc_subdmdc, ss_dsa, sc_dsa = get_silhouette_scores(n,m,p_out,\n", + " N,n_iters_local,input_alpha=input_alpha,g1=g1,\n", + " g2=g2,same_inp=False,n_Us=n_Us,n_delays=n_delays,rank=20,pf=200,\n", + " nonlinear_eps=nonlinear_eps,nonlinear_func= lambda x: np.tanh(x),\n", + " y_feature_map = Y_feature_map, u_feature_map = U_feature_map)\n", + " silh_state_dmdcs.append(ss_dmdc)\n", + " silh_ctrl_dmdcs.append(sc_dmdc)\n", + " silh_state_subdmdcs.append(ss_subdmdc)\n", + " silh_ctrl_subsdmdcs.append(sc_subdmdc)\n", + " silh_state_dsas.append(ss_dsa)\n", + " silh_ctrl_dsas.append(sc_dsa)\n", + "\n", + " print(np.mean(silh_state_dmdcs),np.mean(silh_state_subdmdcs),np.mean(silh_state_dsas))\n", + " print()\n", + "\n", + "\n", + "silh_state_dmdcs = np.array(silh_state_dmdcs)\n", + "silh_state_subdmdcs = np.array(silh_state_subdmdcs)\n", + "silh_state_dsas = np.array(silh_state_dsas)\n", + "silh_ctrl_dmdcs = np.array(silh_ctrl_dmdcs)\n", + "silh_ctrl_subdmdcs = np.array(silh_ctrl_subsdmdcs)\n", + "silh_ctrl_dsas = np.array(silh_ctrl_dsas)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a7b0352b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mitchellostrow/opt/anaconda3/envs/iblenv/lib/python3.10/site-packages/IPython/core/pylabtools.py:170: UserWarning: Tight layout not applied. The bottom and top margins cannot be made large enough to accommodate all axes decorations.\n", + " fig.canvas.print_figure(bytes_io, **kw)\n" + ] + }, + { + "data": { + "image/png": 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jPz/f53FXrFjhtn6zZs281pk6dapruW4bptTUVLdtMXXq1MlYtmyZ38/qdDqNcePGeX229PR0IzEx0W1e165djc2bN4d8Pvv376+2//LLLy2XP/jgg+pc6uPExMQYtWrVUufffPzPPvvMa1u9DOc6EvTu3VvtH+c9Ulx22WXqGO+++27EjkFIaXSDFiuxDVhLO3bsUNPOnTslJydH9u/fL19++aVcdNFFylr8+OOPpWPHjrJlyxa/+2ratKns3r1bbeuLt956S73C8gwG3TZMhw4dktzcXPnxxx/lrrvukrS0NFm6dKl0795d5s+f73MfsEYffPBBte2ZZ54p8+bNU2Mz4nNi3qZNm2TChAly1FFHyW+//SZr166VUFi9erXMnTtXff5+/fp5LYclO27cOGUNDx06VFmueXl5snfvXnW+V65cKU8//bSynCsr+A7Ac889F+2mEGKNUcWhxWqfxQprxR+wwLSl1717d78W6+jRo9Xr4MGDLfdVWFhoNGjQQFluet1AFqs//v33X6NVq1ZqvRo1ahjbtm3zWue1115z7evhhx/2u7+CggLjvvvuM7755hsjFO688061/1GjRlkux3nD8rPPPjvgvnJyciqlxVpUVGQ0atRIHSeQh4GQSECLlZQb+vfvL+PHj1fvf/nlF5k9e7bPdS+44AIVO8Q6VvHABQsWyPbt2+Wkk06SFi1alLptiAV/+umnKn538OBBVzs1sEYfeugh9X7gwIGu976Ii4tTcWXEYIOlqKhI3nnnHfUe1qgVsEh1GwKRlJTkFQfX4JyZ485YroEFDM/CFVdcIR06dJA6deqofcEzgKSqJUuW+Ixlf/PNN+rvq666ym3/VvHv/Px8eeWVV9Q5qlWrliQmJqpjXH311cpy9wVi5IMHD1bvp06dGvA8EFLWUFhJmXLddddJ3bp11ftp06b5XA+iCnHFTf7DDz/0Wv7222+r18svv9y2th1//PFyzjnnWLZt+vTpsmvXLvUeruBgMYtZIJYtW6bc30hEQlJWoOSpUEBiVb169Vx/Qyzxt56w3PzQAmGHyEPIkSSEzwE3N85Ljx49XA8AGrQZ+0HSGqhevbrb/jMyMtzWx0NRt27d5JZbbpHvv/9ePcxAWHEMiGWnTp3UOfcFEruAP7c9IdGCwkrKFGTPIhMXfPfdd37XhcVkFlEN4qPIeoUV5cuyC5ezzjpLvSIOa46P6m4vEAkIQiT44Ycf1Cti0LCcrejSpYt6ffnllwOePzMvvvii+kwaxH/NMWcs16SmpqqM7G+//VaysrJk3759Kn67ceNGlXFcWFiour1ABDWIoWM/iLObj6cnHE9TUFAg5513nixfvlxdC4hzwyOQmZkp27ZtU8fA33hoWrdund/z8Pfff6uHEULKE3HRbkBFwMg7LLInNAuhXFOnkTgSq0Xt8Mcdd5xKwoHVhZustnI8Oe2006Rx48bqxosbLBKCANyUuNFDVM2Wll1t06B7EFzEQLsm4RqNFL/++qvLcvYFrGUkNcHCO+WUU6RNmzbSu3dv6dq1q0q8ateuXUhWshWnnnqqmjxBQtXzzz+vBHDKlCnKshwzZkzI+0fSGYQWLmAkp5m/f3RdwjHw/U6cOFG9h7vYE7iykXCGhyyct7PPPjuMT0pIZKCwBsOereL88EmpLMRcdK9Io6OjdvyaNWu63sMaMrsoPWNpiOk99dRTyvWI/pFmC1ZbtJFsmwZZtwCxwEgB96h20/ri9NNPV9Y6XKiwIGGxYYIIAZxLWHro74l+xJEA7nIIq7awQ0Vnc992220+H6rwveMzwS3tC5wnCKs+b4SUF+gKJuUaLZ46pgcxgYsSAmLVHaUis2fPHi9x9yVscFN//vnnSpwQ86xWrdgDgW5OSLyCZQ3BDRc8VKBbD1y7EGgkY+lEpPPPP1+tA7dtqMCNrC3zG264QerXr285Ib4ONm/e7HNf+jzp80ZIeYEWKylz0OdTE8gCPPbYY6Vz584qExUWEmKd6DVyySWXqJt9WbVNW39mK9ZukKil49CBwGcfMGCAmnSGLR44ENtEuT+42YcNG6bOW6iu4VWrVik3PERaA7crEpSwLxwL58mqslMgcP6wvdkL4A+4hANlPftbh5BoQGENhjqNit2nlYU6jaJ6eN1lBPFTX65AT6sVAgEX8Ndff+2aF8m2gZYtW7ret23bVgk7Em4ihRbycMoNQozhJsaEAgpvvPGGyjJGacFAGcaeoKsMRBWZuegyhAxcJDRpFi5cqI4TTslGcxlCtA+JWqV9CIqUy5uQcKGwBoFK9IliTLIyAWsFN2YQbB9PWKeojoRkGSQ7IUEnVLEIFl3pqWHDhq5kKYCavZMnT1aCA1dmJDKDdWzVbDWHA2oIQ1jBP//8E9K5QqYvPh+ykmfNmiWNGnk/hJkt2VCBCGLf6LOLY9khrP5i0oREA8ZYSZkyadIkV39QJKgEA/pAorgERDWS1uqKFStcRSvgRjWDuKLui/noo48Gvc9QrDoU1AeBBisIBPoAazzdytot7KtdutQkPquVqAIMMOALJJz52z88FLqrjL9ylYFAGUndzQaZ0YSUJyispMxAXd1Ro0ap9z179gypi8To0aOV1YopEsKK7jwXXnihsqQw8s3IkSPdliO++PDDD6v3EF8k9gRK0kGbQ+lrqoseoP6vL4IZNcdc3MLTIkThBn/uZt19CVapfgDydJX7K+wRaP9AV3lCtaZArnVf1jvqOuO7Quw3kl2gCAkHCiuJKOhvCUGFOxeJNkg0adKkiXzyySch7QdCjGxXTMgatcstjdKKEHu4S5FpCwsPbbPqAjRixAiXqKOkIaxodAdBMQOzxffaa6+pmOwTTzwR0tBmEFZYlNiHuZiDmYsvvlj1tX3mmWdcVZEARAZJRzfeeKMqwq+zhz3LPcKNDhCvxjaeoN2IfUO8UfRBF8mAtwCVkM444wy3eKsnev9YF9+9L1c1Mplx3pAkBS8G+sZq8Nnfe+891T/XXLjCjC44gaxlX8U0CIkaRhWHRfgjN2xccnKy17BxQ4cODWrYuNWrVwd9/EmTJoU8bFxaWprb0GuYOnfubCxfvjzgsHFjx451GyIO+6lZs6bXUHK9evUytm7daoRCnz591LaTJ0+2XF6/fn23Y8TGxqoh4/Dqeey9e/d6bT9lyhTXOmhv06ZN1Xm76667XOtMnz5dDUWn18O5SkhIUO+x/jvvvOPzfON70+vGxcUZDRs2VOuhPWZ27typ5nkOfZeSkuL2OXCurejbt69ajs9DSHnTDSYvEduAVaMTW2BFwC2ISjoYwgxVgRC3tCrGXlbotsEqhNWFGOLRRx+tqhZhMHZYxYHAtqg2BKsLyUywWGHVwWWJ7h9IeIIVhc9qVb0oENgvuhR98MEH6r0na9askS+++EKtA5cx4rG6zi4seWTyoiIVitRbdbNBxi8sVViJsHDRTxTWqbkvKOLJixYtkscee0x+/vln9b2iOD7KEN53330qFu0LxDtxTmCt67KJVlY76kWjYD/qQMM6RdY3uuLAY4B9IDkMAw2ce+65lt8jssPhBra7pCUhduCAukoVBkkQKDqgwQ1Ed7YnpKyBexSuWMQo4RK2y+1dmUCdZNQyRr1iXXGKkPKkG4yxElKOgNULqxBW5QsvvBDt5pQ79HmBhY7kMELKIxRWQsoZN998syp4P2HChFL3aa1sICP5v//+U0lasBIIKY8wxkpIOQPWGLqiIAYJd1Og2sFVCR3jvummm6LdFEJ8whgrY6yEEEJCgDFWQgghpAyhsBJCCCE2QmElhBBCbITCSgghhNgIhZUQQgixEQorIYQQYiMUVkIIIcRGKKyEEEKIjVBYCSGEEBuhsBJCCCE2QmElhBBCbITCSgghhNgIhZVUWDACDEY7OfXUU6PdFEJKxUknnSRxcXGydu3aaDelXHHmmWdKbGysrFy5UioSFFZiC4WFhUro+vfvLw0aNJCEhAQ13Fnbtm3l7LPPlieffFJ+/fXXaDeT2AAeZPBAY57wfWdkZEibNm1k6NCh8txzz8mOHTv87ufrr7922we28UdBQYHUrVvXtb7VA9XYsWO92gbBwrXYokULGTBggDz00EMh36jXrFmjBqDv1q2b1KtXz3V9d+rUSW655Rb5+eefJVxmzZolP/zwg1x88cXSqlUry3UWLlwow4YNk5YtW0pycrKkpKTIUUcdJb1795Z7771X5s6dK/n5+W7bHDhwQJ0PTJECg85j/xs2bIjI/u+//35xOp3q3FcojCpOdna2sWrVKteEv0lo7Nq1y+jSpQuGH3RNSUlJRo0aNQyHw+Gah7/tZOrUqWq/vXv3tnW/xD843/o7rlevnpoyMjKMxMREt2sgLi7OuPHGG42srCzL/SxevNht/Q4dOvg97syZM93Wt/rex4wZo5bFxMS42oapWrVqbttiOu2004z//vvP7zHz8/ONW265xYiNjXVth33XrFnTiI+Pd9vfGWecYWRmZoZ0LouKiox27dqp3wnuP54UFhYaV199tdd5rVWrllubMC1btsxt2/Xr17uWRYpmzZqp/eO7jBQnnXSSOsZ3331nVBTdoMVKSs1ll10mv//+u6SlpcnTTz8t27dvl5ycHPXEfPDgQVmwYIH83//9n6Snp0e7qcRGLrroImWVYtq1a5fk5ubKzp07Zfr06cpzAS/Ga6+9JieeeKJkZmb63VfTpk1l+fLlfi3Jt99+2zX2ZSCaNGniahum7OxsycrKUlbyddddpyzORYsWSceOHWXFihWW+0D7Bw4cKC+//LIUFRWpz/vdd9+pz7lv3z7Jy8uTf//9V13z9evXV9f53r17JRTmzZsnf/31l3IFw7vjCfY9ZcoU9X7EiBGyevVqdVwcB7+x3377TVmMzZs3l8rKtddeq16ff/55qTAYVRxarKVj9erVrqfijz/+2O+6OTk5th6bFmt0Ldbhw4f7XW/KlCkuj8XQoUP9WqyjR49WryNHjrTc1759+5RFnJKSYtx+++0BLVZYUv747bfflJWt17W6Nu+99161HJ/hzTff9Ls/3DeuvPJKZSWGwgUXXKCO8eqrr3otczqdRoMGDdTym266KaDlm5eXVykt1oMHDyrvCDwE8I6VB2ixkohitjDwdO+PpKQkr3lXXnmlioP5iwPpmB5iuP546623pEePHlK9enWpUaOG9O3bV8WefAEra9SoUdK+fXsVs0L7YOnAwkIcbuPGjT7bCqtlzJgxKqaImBdif5dccon8888/Po/37bffym233Sbdu3eXhg0bKqsJ28G6++STTyQQsFJwzM6dOyvrv1q1anLMMceo2NyMGTMst0Hc7ZVXXpGTTz5ZatWqJYmJicriu/rqq5X1E0muuuoqueuuu9T7jz/+2KdlCC6//HJ1bqdNm6asQ08+/PBDZaldcMEF6rsqLV26dJGpU6eq9/ieJ02a5LYcXhcd873ppptk+PDhfveH7wL7g+UdLPg+Z8+erT73kCFDvJbv2bNHtSOY31ZMTIy6nsy/GcSUNZ5xZ/Pv7dChQ+q3hdg4fgu4tnBNI957/fXXK6vcVyxb/0b69Onjtv9TLeLf8Bg8/vjj0rVrV/X7xO/t6KOPlltvvVU2b97s87Ph99yvXz8VY3/33XelQmBUcWixlo6PPvrI9VS8du3akLeH1YNtYWkEspBgofqyWLUVg/hXenq6W2z3mWee8drnhg0bXNYAJsSrEDczb/e///3Psq2wZHr06KHeJyQkGNWrV3dtg1jeN99843W8Q4cOucXD0tLS3LbDdP311/s8B99++61Ru3Zt17o4rmeczZNt27apuKU5NojjmuPgn376qREpixXs3LlTtVWfN18WKyzGk08+Wb2fO3eu13569uypls2fP9+4//77S22xajp27KjWx/dp5rHHHnPFM7du3WpEAnh4cIxjjjnGcjmsM31+Jk+eHNK+zz//fKNOnTqu7c3xZkzm38TLL7/s9jvAdaW/M0zwEixYsMBt/9ge+8E1hXXw2zHv//zzz3dbH/dWbd3q84r96r+x/ffff+/z8zz99NNqvf79+xsVQTcorBTWUrFu3TrXj6Nfv34hu2rsEFYtUPfcc49x4MABl6hceumlLleeZ+LDVVddpZa1atVKiRZcaSA3N9dYuXKl8cADDxifffaZZVuRhAUBffvtt1VyC0DiSKdOnVw3MbguzeC6Gjx4sNrn3r17XfP3799vvPLKK0ZqaqraFg8qnuCBRX9GCMGiRYtUUgs4fPiwEhu4FM2gXV27dlXb9O3b1/jxxx9dbcW50Q8i+ByhPhCFIqxmUezVq5dfYZ00aZJ6P2zYMLf1/v33XzW/UaNG6nuyU1jvu+8+tT7cjDiXmtNPP13N7969uxEp9HdwySWX+FxHixFeV6xYEdL+g3UFv//+++qc/vrrry53MtzQCPPo3xDc5lZJaMG4gg8cOGA0b95crTdkyBBj+fLlrusX9w983/p3g9+DFbjm9W9d/1ajCYXVBmHNzSswtuzIrDQTPo+dXHHFFW6WFG7k+KHOmDEjoNDaIayYrr32Wq/tcHPo06ePS1zMtG3bVs3/4IMPgv6cuq2Y3n33Xa/lu3fvdlmV48aNM0IBIo3tTj31VK9luBlpyybYrFMtUrACtaB6csMNNwQVvyutsMISx/oNGzb0K6y4AcOKhtjDwtc8+OCDap27775b/W2nsE6bNs3Vhn/++cc1HyIeyItQWk488UR1jCeeeMJvnNrs1cDD26233mq888476oHDH3bEWPEb0g8ZVnHmYIT1/pLvy98DBCxRX94lgIdR/Vn+/PNPo7zrRly0XdEVgT37c+TDuWuksnBR/9bSqF6abftDfKpOnToqloeYHvrcYdIgpoLYIvrhIf4SCUaPHu01D8dC/7fFixerDFBkciLOqOM2QMewQgExSnwWT3AObrjhBhVHQsz0gQceCHqf55xzjnpFf0jEGNEpXselPvvsM/X+kUceUZnXwYB4M8B5j4+Pt1zn0ksvlYkTJ6ps1kiC/p4A598fiLudd955Kp6K84eYNh7+dVztiiuuiFjbPNuns3v19RIJ9LWH68ZfnBrnAH1Vd+/eLUuXLlWTBtnAyJq9/fbbbYk9W/2G0A/9q6++Un1tA8Wa/V2Ld5XE263A7wn5ELgWR44cafk9IY6MPq04b+3atZPyDJOXSKlB0sSzzz6rEhDQvQJJPEhK0CKKLgHokoPuCvhh2A0SRsyJGmbQjQEihZvTH3/84ZqPQgHgnnvuUckpEF90XwgGdMr39YCAZeDPP//06rCP7htvvPGGq4gGEol0soe+wSMpav/+/a5t0I0J22EdbBcMWF8X44DQoyuI1YREIOAvcaSs0eKpu9age8v69etVIYbyfjMNFSQneYq7FUg0Q5IQEsBuvPFGOeGEE1yJSijMgAc4PLyiq1O4bNmyRf0WdGIcfjP62rzjjjvUOtu2bQt5v5s3b1b71r85X9ciHgD1+lagHbq7nj5v5RlarMQ2kOGKGzkmgB86sh5haeEHgxtDr169XD8iu2jUqJHPZchuxI0LP0Y88WtwE1myZImqejNhwgQ1oUIPblDnn3++6uvoq9+tv+PpZbA6IZCo0qMtT2Q2/vjjj25tQ7UiPIkDfWNEn0ttxeh5sOYwBQMsLy3qwfSrDPaBIlz0g0Iw1h/OEc4Z+pvimtECGwlr1dw2z/bVrl1btm7dGtDKLg3IcgbmbF5f4FoZPHiwmsDhw4eVVwgVzXBNIcMboqu9G6HwzTffqKxjXKManbWrrw/0Q8Z1GSrbTR4hZOEHAp/LF+b2lHcorEFQp2aycp9Wps9TFuAGCTfVoEGDVBo/RAKd3e0W1nCAtThz5kzlesXNCF1hILQ//fSTmp555hnllurQoYMtxxs3bpy6AUIwYd3D+sSDiAZCDGEHsK5Lg9krsGzZMlUEoTx0yUI5vkDAUoJbEMUAJk+erFzCOC/wgkSybXCXN27c2DUfxRogrChaESkg5ChegUIqoYLuPQgfQBDxMIJrFdczHqTwUBAs6MICbxJE9fTTT1fdzPBwae4aBy8LfsfhXJdO07WIh5jSFInRD0GhfL5oQWENgsSEOFtjklUNiAliZ6+//rpXP08tJnCB+gLVm/zhz0Vldq3COvQE/V4xATyRw8KGNbtp0yZ1M4EbO5Tj6WUQCLOLD9Y6QBUf9Dv1xJcbT1u8OAeYgrFacePB8SHW+BzRFFZYKXhgAehLGwzo0wphfeKJJ9SNHzE+80OInXz55Zeufq2wCjXol4m4ItqO7xT9jiPxu4Cwmq3mUIGLFHFYCCuED0X8QxEePETCVQuRhzBDsD0pjYu5Xsn1C3AthiussO61peovJl1eYIyVlAk6scLT7aV/aDoO4wnELlAhA8SffBUB//7775XA4AYUSGDQRogeHgAAbqpW7i+4znyhl8FCN39W/fkQH7MCN3ErcMPHwwdumloEAgHrC9uBYLeJFLD84ZbG+bdK+LIC5wjnD6IaSTfw559/7oq7I5HLDBKn8P0hXg13a7CEYtW1bl3sBUMMuTSYk5bM15wOMfhrl74uUWjESlT9XZvmY/jaf4sWLVziWpprUf++cR3p81aeobCSUoGbwrp16wLGTXRlIE9xO+6449Tr/PnzLa1WWC46FuUPWDee4Meub4qowmSOoXkmFpnRlgu2t1oPP/L333/faz7icVqUPSvpaEvTqhYu3HCPPfaYZVtSU1NVzBeg6hKq5AQDhAGgok4gd2ZpLCZ/4NhwewM8sEAsgwXfG7JIkSF67rnn2t42PDTB0tM3/2uuucZtOSxUZNoCZLvrzFZ/1zj251mtyx/IN9AJalbg2vP3EKdBtSp93ZpFR2e+A1/uZn1dorqS1e8Pv0sk9vlCH8OfO/vKkmtx/Pjxyr3uC/zefO1He45Q6awiuILZj5UFIkrF7NmzVbUWVFr58MMPVfEBDTqUz5o1y1U4AZNnpR/0T0tOTnZ1HkelHoA+jY8++qjaNwoyBFMgAp39dYGI7du3G5dffrnPAhFHH320Wt+zU/wvv/xiHH/88Wo7FFjwVSACVWPQl7WgoLhPMDq96xF+6tat61UgAn34sKxJkybG119/rY4FcPxu3bq5VVXyrDeL/oq6YpIuEKE7yaOowZw5c4yzzjrLbRv0XdXVoVBJ5/XXX1c1VzU4P2j/Kaec4rcPcaj9WNGXF0Uw0B79edBmc79UX/1YgyXcfqz4baMqFvqm6pF48F2iIIgV+G7R/1lfQ/gOUR1If+f6u0Hfy/r161t+d/7Ad68rG+mCCVbVuvA9opbwmjVrXNcNvl/UO0bREX0OMQqPJ+g7jGXPPvusZRtQkEGP/IPfn/794rp64403VOESfW1anW9d3AHb+voO9+/fb7Rs2VKt17RpU3WfMBfj2LhxozFx4kR1nXj+xjXou4vt0fe6PMACEQGgsJYOlJ8zd2DHBKHUYmgulYYycVa8+OKLbuuiJKEulfbwww+HVNLQqjShVadzc/t0GTfzMGAoBwex9FXSEBV58B436GBKGqLCjLnEHAoh6JJuOF/z5s3zKawAYorzotfBcXHD81fSEA8pqHakl+Oc4nOaS8lhGjt2rBGJYeNwPkeMGOHzNxVJYfUcNs7zM2NC4QOUtvQHHrrwGTyHjfO8XjANHDjQ8gHCFxBJLThfffWV13KcN8+h4XBMHNt8fWPCg63VOXzooYdc6+Ac4IED0/PPP+/z94ffBkoO6oeil156yef5XrhwoVtxmMaNG6v9X3TRRW7r4QFEF2XRvzlcv/qhWk++BjtAhTQsx++gPEBhDQCFtfTgSXr8+PHGoEGD1A8APxb8cCAEsFYheoGqpeApFk/mECYIFSoG6ZKCwQir/hvWH6w7TKi69OWXX1oeD1YjLFYID6xI3BRw44G1CuHUlrOvKlG4ieGmhWpIEBgIy8UXX2z8/fffPj8jxv687LLLlEWLGySq+6BknD43/oQVoE0o29i+fXvVVkw4PiwpeAasgCX03nvvGQMGDFACg+NiuzZt2qiKWSihiDKOoaC/D6sbPtoDK+q5554zduzY4Xc/kRRW8wQhhFjgho8KP6jk5MtK9QXK+6HyE7wSeECC8GCfJ5xwgrKmfv/9dyMc4JXxVTkMoHIZqi/huzruuOPUbwO/LViSrVu3Vl4Zq9rK5u//qaeeUte1eUxaTy/F9OnT1W8B6+D6wDWGh1pYloFGkcLvFMvM4y/3tlgX19mECRPU71KfQ/xO0TZ4ET7//HNLyx2WOfaJhxBtsZd33XDgH6nCIDZijougqo6vID6p2iBWhFgbYp3+RuMhJFiQcYzqSaiohffoBkbcQawdIw2hohkqqVUE3WDyEiGERAkkSaGgChLf9DB25AjoYoZ+tOgqd/PNN0tFgcJKCCFR5MEHH1TZ30899ZTq3kOO8NJLLylxRS3wYOtklwdYIIIQQqIIil+gdCO6RaFfKVzDpBgUWUFJ1BEjRkhFgsJKCCFRBn2VdX9lcoSK5P41w+QlJi8RQggJASYvEUIIIWUIhZUQQgixEQorIYQQYiMUVkIIIcRGKKyEEEKIjVBYCSGEEBuhsBJCCCE2QmElhBBCbITCSgghhNgIhZUQQgixEQorIYQQYiMUVkJsZtKkSeJwONSA6MReMPILzu3XX3/tNn/atGlq/siRI6PWNkI0FFZSaq688kp1UzNP8fHxUrt2bWnVqpUMGjRIHn/8cVm/fr3f/WzYsMFtH7feemvAY59wwgmu9a2G23rzzTe92hYTEyPp6emqcHbfvn3l7rvvlp9++knsICcnRx5++GE1duRtt93md901a9bIfffdJ926dZN69epJQkKCGiarU6dOcsstt8jPP/8s5QUI2dixY2XGjBlSHrnooovUtfbqq6+qodcIiSpGFSc7O9tYtWqVa8LfJDSGDx+OEZKM+Ph4o169emqqW7eukZSUpObryeFwGEOGDDF2795tuZ/169e7rZ+RkWHk5+f7PO6KFSvc1m/WrJnXOlOnTnUt123DlJqa6rYtpk6dOhnLli0r1bl48skn1b7uuecen+vgM91yyy1GbGys69gxMTFGzZo11Tk0t+mMM84wMjMzjWgzZswY1R5819EE3zHasXjxYq9lkydPVsuuvfbaqLSNVB2yA+gGLVZiGyeeeKLs2LFDTTt37lTW2/79++XLL79UFgWsxY8//lg6duwY0Kpo2rSp7N69W23ri7feeku9wvIMBt02TIcOHZLc3Fz58ccf5a677lIW5tKlS6V79+4yf/58CYeioiJ56aWX1PvrrrvOcp3CwkIZOHCgvPzyy2p9nJfvvvtOtWXfvn2Sl5cn//77rzz99NNSv359WbBggezduzes9lQ1LrnkEklJSVGDhvOckWhCYSURBS7X/v37ywcffCCff/65JCUlydatW2Xw4MF+t7vsssvU6zvvvGO5HKKk42qXXnppWG1LTEyUnj17yvjx45WowpWYn58vQ4cOle3bt4e8vy+++EK2bdsmXbt2laOOOspynQcffFAJN9oNNzXOy0knnaRc5wDz0Y5Ro0bJunXrlJudBAfGwzz33HPVd+jruiGkLKCwkjIDAgsRA7/88ovMnj3b57oXXHCBsj6wzoEDB7yWw5KD+EGUWrRoUeq2Qcw+/fRTiY2NlYMHD7raGQpTp05VrxBmK9De5557Tr2/6aabZPjw4QGFAvuE9e4JRPeGG26Qli1bqocVxGZPOeUUmTx5snrosOLUU091CTq8CYiZtm7dWpKTk6Vu3bpy8cUXK2vZKu6NuLH2EnjGrLGOeV1MADFiPEA1aNBAndfbb7/dbd+LFy9W3zMsc8SX8Xr++efLokWLJFz0udffBSHRgMJKyhS4SHETB7A4fQFRxU0XrtEPP/zQazncfeDyyy+3rW3HH3+8nHPOOQHbZoXT6ZSFCxeq97169bJcBzd7WFNxcXEqaSlYkGxlZs6cOdK+fXt5/fXXVUIYhDU7O1u5lHF+8QCDv32RmZmp2gix3LhxoxJCuN1xnnv06KFEWwNBRGIVvg+AY+Fv84R1PMG+Tj75ZPWwAhH3XOeBBx6Q0047TT777DPZtWuX2j9ekRyFhLJQzo8Zfe5XrFihwhGERAMKKylTYJngxgkgBP644oor3ERUg/gobsC4yfuyDsPlrLPOUq+Iw65duzbo7VauXKkECwKCGLIVsNBA586dpWHDhmG1D6IHyxIx2d69e8vff/+tLHqck4kTJyr39ldffeU3IxndgBD7njt3rhLgrKws+fbbb6Vx48YqzmsWtSZNmqhzobuxICZsjlVjwjqeXHvttXLeeecp4Uf7Dh8+7LJY4f5+7LHH1Pubb75ZCSraA3FHNjR48skn5d133w35/GRkZLgs/EDXFyGRIi5ie66E7MvNln15vi0Bu2lQLV1S4hNC3m5T1j7JLyr0ml8rMUVqJRVbHtHkuOOOk/fff1/FWgsKClzxRU9g0eBmjwQjCIqOWyIBClYQRLVGjRq2t00DUYCLOBh+/fVX9Yr14Vq1YvXq1eq1Q4cOYbcP3ZYghjgXiOnCXQwgqNdff716DxfxlClT5N5777VsP7wAcKWbl8G6fOGFF5TrdtasWcqyxkNQuOAzfvTRRy5rG1Y6ukMZhqHizAAPCEji0qB7FpK/9uzZo64PrDds2DAviz2YY2/atEmFGwLF8gmJBBTWEPhh5zqZs+nPMjvere1PlXY1Q7dspqz5SbYfPug1f2DT9nJOs+Ml2iAeqIGFBHeiFbihIjHpqaeeUskoiAmaLVht0UaybcGik53q1Knjcx2dqVqrVq2w2gZRgmsV3HHHHS5R9bQUH3nkEfXQ8sknnyhx9QRiYyW4SPyBWxjCC2v92GOPlXBBprWVIP7xxx8uTwDcwb4saggrYrZ4YIF7OhT0dxBOAhohdkBXMCnXaPHUWZ6ICcJtCTHu16+flBdgZXkKs938999/KrEK9OnTx3IdiBmSlAAyna1A1rIV8Bzo+Ddcs6UB2dZW6DbBZduuXTvLdZBQ1ahRI7f1Q0F/B/o7IaSsobCSMsd80w5kvcFqQkwSovLDDz8ogYXlhj6LcC9Gs21mYOUBf+5TuDpDtYTNIAap0cJjBdznnuubQZ9dXyBuDeCiLw0QTit0m/y1P5jP4A/9GRAuICQa0BUcAr3qHSVt0+uXaYw1HK5u3dNnjLU8gEQfffP0FV/1tFqXLFmiXMC6Rmwk3MDmtgF0ZQkWLcJWXYM0bdu2VS7a5cuXl7KVopKX7I4v24lVprBn+yOFfjjSDzKElDUU1hBA4k95SP4JRNPU8GJ4ZQGSYnS3FCTMBAOsU8Ts0F0FlhRciKgRHAl0pSdk7foq8uAvrufPhQr3LTJ28ZCAQhKhZgabrUAk5/iKTeuqVr6sxmii27R582a/65XmM+jvwF+8m5BIQlcwKfORX9C9AgRbMQk3V/TN1O7JSFmr6Puoi1YgGzUUEBcEuliCFaiiBFcxyhqiO0mwwPWtLWhUsjJ33bHqT6utehTztwudiKTbEi66Tchs1pnUnvzzzz/KsjevHwr6O2jTpk2p2kpIuFBYSZkxb948VapPJ7ecffbZQW87evRoZbViioSwojvPhRdeqKoWQbxCHX4MdZKRUQtryVxgwQwsVN2X85VXXnHVOvYF+n5eddVVKmELYP8omgFefPFFtdwTVF6CKGHdIUOGiF1Ur149oKs7GNDHV2cko+uQFTr7G91zMPJPKOChBZnHAFW5CIkGFFYSUZDFCkGFO3fAgAEqoQQFBdAVJBR0TV9MKH1nl1safR0h9nAtoxsILEq0zZeb1V+MFTFU8Ntvv/lcD4URUCADlh9EE5YxkrIgCBq0A58TrmiUH/R8wECVIriS8WCCoed08hS8AXqovWuuuSYkV3YgdAbv999/71X2MBQg+I8++qh6P3PmTFUQQndDwivaj642AOuF2ocVMXLEb3GO7LTYCQkFxliJbaCQgxY9CAeqAZkzM3FTRVEHjJkZjfiXWZBh7aHikNm1iexjFFZAacNwQFUi9MHEYAMofmAFMplR2AGWK0oSQkQw6TFicc7MGbkYCcd8riCWWB/nES5fuDuxHVyrejsIN4o92Am68ODYsMbh9kabdD9aiK3O4g32PEEA8ZABy33ChAkqEQsPYXBlA/S/DWdwBZx7AO9DJLLGCQkGXnnENnBj1/VZkRUK9yEKsKOuLYZjg3VmNRh5WaHbBoFPTU1VXT6OPvpo1a8Tg7H76nsZLLBAUX8XlhisJt3twxNYxRATWGdIyELRecQF4WaFpYVYKhK74PKG2HuCesYQJgwthwpKsF4hcqgahW2uvvrqgFm5oYLsbSSdoRoS4rsoZai7wpit7WCBNYrKWqi0hEHm4UJHFi++A5wXXfYyVFAuUVvshEQLBwZllSoMLBcdw9Jje1pVtCEkGGBhwmpC2UWW0ytb8LABbwMsatRQJiRausEYKyE2gsQbWMTPPvtstJtS5dBD/enkJ0KiBYWVEBvp0qWLysbFWKTos0rKBgyYgKH+kISGGC4h0YQxVkJsBn1UkSGM5ChSNqCL0f33368ypfVA64REC8ZYGWMlhBASAoyxEkIIIWUIhZUQQgixEQorIYQQYiMUVkIIIcRGKKyEEEKIjVBYCSGEEBuhsBJCCCE2QmElhBBCbITCSgghhNgIhZUQQgixEQorIYQQYiMUVkIIIcRGKKyElEMuvfRSNUrL4sWLo92USsWGDRvUebUaAef6669X8+fMmROVtpHKA4WVlJorr7zSdbPSU3x8vNSuXVtatWolgwYNkscff1yNmRnsTQ/TrbfeGvDYGH9Tr9+8eXOv5W+++aZX22JiYiQ9PV2NSNG3b1+5++675aeffgp5mLJHHnlETj75ZGnQoIEkJCRIjRo1pH379nLttdeqsVjDHTjqjz/+kPfff1969eolffr08bvuokWL5IYbbpBjjz1WatasqdpRt25d6d27tzz88MMBz3lZgu8Cg5Dj85VH7r33XomNjVXDzzmdzmg3h1RkjCpOdna2sWrVKteEv0loDB8+HApixMfHG/Xq1VNT3bp1jaSkJDVfTw6HwxgyZIixe/duy/2sX7/ebf2MjAwjPz/f53FXrFjhtn6zZs281pk6dapruW4bptTUVLdtMXXq1MlYtmyZ38/qdDqNcePGeX229PR0IzEx0W1e165djc2bN4d8Pvv376+2//LLL32us337dqNv375ux4uLizNq1qxpxMTEuObFxsYaI0eONMoDvXv3Vm3CdxItzNeYFZdddpla9u6775Z520jl0Q1arMQ2TjzxRNmxY4eadu7cKTk5ObJ//3758ssv5aKLLlLW4scffywdO3aULVu2+N1X06ZNZffu3WpbX7z11lvqFZZnMOi2YTp06JDk5ubKjz/+KHfddZekpaXJ0qVLpXv37jJ//nyf+4A1+uCDD6ptzzzzTJk3b54amxGfE/M2bdokEyZMkKOOOkp+++03Wbt2rYTC6tWrZe7cuerz9+vXz3IdnDu0c+HChZKcnCz33Xef/Pnnn5Kfny/79u1Trz///LPcdtttynOAc06CA98veO6556LdFFKBobCSiAKXa//+/eWDDz6Qzz//XJKSkpQbdfDgwX63u+yyy9TrO++8Y7m8qKhIpk2bpsQa8chwSExMlJ49e8r48eOVqMJtDVEaOnSobN++3Wv9iRMnypQpU9R7uFkhqhBXiJumSZMmMmLECPn777+V4MHtHAqTJ09Wr/pBxBO4KLEMAg5X+w8//KDc7O3atXOtD3cmhPeFF15Q7ejWrVvI56aqAtd+o0aN1PVQXl3WpPxDYSVlBgQWIgZ++eUXmT17ts91L7jgAklJSVHrHDhwwGv5ggULlPiddNJJ0qJFi1K3DaL66aefKlE6ePCgq50aWKMPPfSQej9w4EDXe1/ExcUpwcONOljwsKAfJCDuVnz22WfKytZCjxizP2DNf/jhh5bLkBiF81y/fn0Vm8Xr+eefr+K2vtBxasTDIe7XXXedNG7cWD2k4HsYOXKkZGZmWsa5v/nmG/X3VVdd5RbzNsfG9bqnnnqq+vu9995T8WI8RGD+jBkz3B4y3njjDbW8Vq1a6qENbUASUqieAg0ehPRD39SpU8PaByEUVlKm4EaM5BoAi9MXEFXc9PPy8iyF4e2331avl19+uW1tO/744+Wcc86xbNv06dNl165d6j1cwcFiZXX6YtmyZcr9DQvYl2BCTAGSlS688MKw2/DAAw/IaaedpoQanwvnG68QLiR0wdr2x/Lly1UbYWFDSCFyENtnn31WbV9QUOBaF5+nXr16yi0Nqlevrv7WU0ZGhuUxkLwGz8X333+vEsHM1j/c72eddZZy3X777beSlZWlhBVtmDRpkhx33HEyc+ZMCQckjQF/IQFC/EFhJWUKLCPceMF3333nd90rrrjCTUQ1iI9CAHAj9WXZhQtu1gBxWLPVo7u9QAgi5VqFWxcgBg3L2ROIlV5HPwCEA9zyjz32mHp/8803K0FFjBiifsstt6j5Tz75pLz77rt+M8HRzpUrVyphhbDBeoTl+vvvvytx08B1jfOJGDx48cUX3eLdiEV7smTJEnnllVeUy33v3r0qdow26n3ceeedSvhwvNdee01dE/BsrFmzRlm78DAMGzZM/vnnn5DPT5cuXdQr3Og4J4SESlzIW1RhjMy9Iof2ld0BazcUR1JKyJsZOzeKFOZ7L0irJY7qtSXawJpAdxLEWiEW2pLxBBYV3Ixwfa5bt04lBAEk4yAxCqKKLi52t02DripwEeukItChQweJFL/++qvLcrZi48aNylIrTTtg+WmL++KLL5aXX37ZtQzu1pdeekn27Nmjvh+sB3GyihMjDvnFF18oYQN4vfrqq5XVDUH85JNP5P/+7/8kXCDUsJrNLndYupi0VapFGt2NNMccc4yK5eMc4pp59NFHvR7MAgF3MpLZINb4Ts4+++ywPwepmlBYQ8D483sxfp5VZseLueAOkebtQ97OOXeyyN5tXvMdPc4Vx4nnSbRBf0sNLBFYgVbgho7EpKeeekrFHtEHEugbpbZoI9k2DawmgFhepNAJU3Xq1LFcrttQmnYgIUdb4nAHWzFmzBglrBAwCEuPHj281oHFqEXVDPosQ1iRpVwaYLHjGFbAfQ3XM2LCOovXTLVq1VTfZAguXPiIlVp5APyB7wDCapXERkgg6Aom5RotnjqpB1YbYmoQY1/dUSoqsBQ9xd1ukO0KENdEJrEVrVu3VhapeX1Punbtajlfbwe3bWmAp8DXA4ZuExLDfAkmvB0gOztbuYdDRX8H+jshJBQorKTMMd90A1leSNLp3Lmz/Pfffyq+CIGFO/OSSy5Rmbdl1Ta4ST2tWLtBopaOQ1uh21CaduiYoRZAX8AFb17fE7hKrUDcGxQWFkpp8JXQFOxn0O03rx8K+nMg5EBIqNAVHAKO9ieJo9mxZXfA2g3D2iym/7U+Y6zlASS86Jufr/iqp9WKZBa4gL/++mvXvEi2DbRs2dL1vm3btkrYkQ0bKbSQW3Uv0l1n4OZEnBXtwMNFuCC5pzwTjOs2kp9BP2CZH2YICRYKawioxJ9ykPwTCEe94CoRRQMUYEDFIBBsH08ICKojIVaGZCe4MAP13wwXXempYcOGrmQpgJq96FqCilKIO0YiM1i7Pn25UfEQgq4g6MOL/r3I3A3XEty8ebPf9XRlLH+WY7TQbUI/Wl+YK3uF8xn0d+DLHU2IP+gKJmUKsjl1f9BgKybhxojiErpvZKSs1RUrVriKViAb1gwKJ+gbNDJNgyWUQvyIbQJ/hfNR/ACsWrVKJeaE2oZOnTq5Yo86C9kTdFFBxrZ5fTvQ2cXhDk6g0W1CkRGdJe2JLnKB/rn6vAYL9qndx23atClVW0nVhMJKygyUABw1apR6j1KCoXRjGD16tLJaMUVCWNE1AwUXUP0IZRhRQcgMihygTyWA+I4bN87v/hBjRJsD9dW1KkyAfqC+QNEMnaULkUX3Fn8g2Qv9SDXoe6q7EKEylBU6+xoVkey0zNFVxp+rO1hwDiDSyJJ+/fXXLYXxmWeeca0bakYwkqNwHSCOHMnuVaTyQmElEQXlASGocOcOGDBAJYOgni76OYaCrumLCd0s7HJLw+qB2MO1jG4oSBxC26y6AKEGsBZ19K+EFQ23rDnWBxckChYgJvvEE0+ENPwYhBVVkrAPFE6wAoLy0Ucfqfg0hAUlHTHMme5nCyAKsEbvuOMOZXGZLVPsX1vcqEyEghC6Gw9eUe0IXW0A1gu11rE/dBYyLG1cF+GCWLO23DHUG8RVJ37B2sYDG75LxKN9dSnyhy5YgWIUoYoyIQqjisNh4yI3bFxycrLXsHFDhw4Nati41atXB338SZMmhTxsXFpammqPuX2dO3c2li9fHnDYuLFjx7oNEYf9YLg2z6HkevXqZWzdutUIhT59+qhtJ0+e7He9bdu2Gaeeeqrb8XD+a9Wq5TZsHObdf//9Xttjnl4H63sON3fvvfdaHlcvx3cV6rBs+E4TEhJcQ9w1bNhQfWc4T57fF4aY8wd+p2eccYbb58TQffpvfD8zZswIqX0aPRzflClT/LaBVF2yA+gGk5eIbSAGiuQegCd9uP4wCDgG/8ZoK4hbWg1GXlbotsFqS01NVd01jj76aNUnE4UNYBUHAtuigMI111yjkplgscI6QrILumgg4QmWDj6rLiQfCtgvyiei7CDe+wLnFethQHXUUkY9XRQzQHlBZLKim9Lpp58uw4cPVx4CT2CNoq8nKi1hkHe0H9vhHMBq1WUn7QTWM84XLHlYhbDKwx1QHNYoEs1QtB/DByI+DhcwrNkzzjhDFYjAdxvONYLMc7iB7S6XSaoODqirVGHwY0QcyrNLAyHRAG5luHkRh4RL2C63NwkOlHjEgwVczXrAA0JC1Q3GWAkpR8DqRY1cxEkxniopO/Q5R6lGJJ4REi4UVkLKGRhxpmnTpjJhwoRSlwYkwYOhAlHh68Ybb1QWCCHhwhgrIeUMWEyIHWJgcLibIlk7mHjHz2+66aZoN4VUcBhjZYyVEEJICDDGSgghhJQhFFZCCCHERiishBBCiI1QWAkhhBAbobASQgghNkJhJYQQQmyEwkoIIYTYCIWVEEIIsREKKyGEEGIjFFZCCCHERiishBBCiI1QWAkhhBAbobCSCgtGgMGIJKeeemq0m0JIqTjppJMkLi5O1q5dG+2mVCq+/vprdY9o3ry517IzzzxTYmNjZeXKlbYfl8JKbKGwsFAJXf/+/aVBgwaSkJCghjtr27atnH322fLkk0/Kr7/+Gu1mEhvAgwxuVuYJ33dGRoa0adNGhg4dKs8995zs2LEjqJuenrCNPwoKCqRu3bqu9a0eqMaOHevVNggWrsUWLVrIgAED5KGHHgr5ZrpmzRo1AH23bt2kXr16ruu7U6dOcsstt8jPP/8s4TJr1iz54Ycf5OKLL5ZWrVr5XA8Dkc2YMUOGDx8uxxxzjNSoUUMNMYjfG0Ri/PjxsnPnTikvvPDCC+r72LBhg5RH7r//fnE6nep7tR2jipOdnW2sWrXKNeFvEhq7du0yunTpguEHXVNSUpJRo0YNw+FwuObhbzuZOnWq2m/v3r1t3S/xD863/o7r1aunpoyMDCMxMdHtGoiLizNuvPFGIysry3I/ixcvdlu/Q4cOfo87c+ZMt/WtvvcxY8aoZTExMa62YapWrZrbtphOO+0047///vN7zPz8fOOWW24xYmNjXdth3zVr1jTi4+Pd9nfGGWcYmZmZIZ3LoqIio127dup3gvuPL9asWWOccMIJbsdLSEhQ7TD/xpKTk43nn3/eKA80a9ZMtQnfc7TQ1xjaYsVJJ52kln/33Xe26gYtVlJqLrvsMvn9998lLS1Nnn76adm+fbvk5OTIgQMH5ODBg7JgwQL5v//7P0lPT492U4mNXHTRRcoqxbRr1y7Jzc1VFtP06dOV5wJejNdee01OPPFEyczM9Luvpk2byvLly/1akm+//bZr7MtANGnSxNU2TNnZ2ZKVlaWs5Ouuu05ZnIsWLZKOHTvKihUrLPeB9g8cOFBefvllKSoqUp/3u+++U59z3759kpeXJ//++6+65uvXr6+u871790oozJs3T/766y/lCoZ3xwqclx49esiyZcuUlQzvz7p169Tx0Q60Z/HixXLVVVdJfn6+smpJcFx77bXq9fnnnxdbMao4tFhLx+rVq11Pyx9//LHfdXNycmw9Ni3W6Fqsw4cP97velClTXNbU0KFD/Vqso0ePVq8jR4603Ne+ffuURZySkmLcfvvtAS1WXxaK5rffflNWtl7X6tq899571XJ8hjfffNPv/nDfuPLKK43169cboXDBBReoY7z66quWy2Htt2rVSq3TsmXLgPv/448/jGuvvdYoDzSrABbrwYMHlecF3gd43oKFFiuJKGYLA0/3/khKSvKad+WVV6o4GGIxgWJ6iOH646233lJP9tWrV1fxp759+8rcuXN9rg8ra9SoUdK+fXtJSUlR7YOlAwsLcbiNGzf6bCushDFjxqiYYnJysor9XXLJJfLPP//4PN63334rt912m3Tv3l0aNmyorCZsB+vuk08+kUDAGsIxO3furKz/atWqqVgbYnO+rBRYMK+88oqcfPLJUqtWLRWTg8V39dVXy+rVqyWSwIK666671PuPP/7Yp2UILr/8cnVup02bpqxDTz788ENloV1wwQXquyotXbp0kalTp6r3+J4nTZrkthxeFx3zvemmm1Rc0x/4LrA/WN7Bgu9z9uzZ6nMPGTLEch1Y/EhoiomJkQ8++MAyCcdMhw4d5PXXX7dcpj0JiIXjOmjcuLFceumlsnTpUsv1ERvVcWrw559/qmsN1jl+K7j2x40bp64xqzj3xpLfT58+fdxi3ubYuF4Xvy3EO3GtIo6N6xvz//jjD9e6+P7xneD3g983fnetW7eWO++8M2A83xe4V/Tr10/F7999912xDaOKQ4u1dHz00Ucuq2Pt2rUhbw+rB9vC0ghkIcFC9WWxaisG8a/09HS3uNMzzzzjtc8NGzYYDRo0cK2DGJpnvOp///ufZVthyfTo0cMV56pevbprG8TyvvnmG6/jHTp0yC0+lpaW5rYdpuuvv97nOfj222+N2rVru8XXatWq5Rb782Tbtm0qbmmODeK45jj4p59+akTKYgU7d+5UbdXnzZfFCovx5JNPVu/nzp3rtZ+ePXuqZfPnzzfuv//+Ulusmo4dO6r18X2aeeyxx1xx4q1btxqRAB4eHOOYY47xuc7RRx+t1jnrrLPCPg7iuFdccYXbtY7fiPm6mDBhgtd2sI71OvPmzVPxW50rgW30svPOO89tu2eeeUbFtfU6+F2Z493nn3++1/eF9mE/nu1btmyZWg/WpDnGDO+F+VrGMX766aeQLVbw9NNPq3X69+8f9DmlxUoiCqwnDZ7sd+/eXeZtQOwJGYj33HOPijnt379ftm7dqp7Gwd133y3ff/+92zYPP/ywskqQhQlLEk/d2BaxYVjhDzzwgHoyt+J///ufsr4Q80PcDnFktAEZoocPH1ZZsWiDGVgcgwcPls8++0xZKog5Yjush6f01NRUZWnAsvME8TR4A7AdYoKIDeI4+PvQoUMyf/58ZcmZwRP4eeedp+JzsNx//PFHZWXjuNu2bZPbb79d/Q1LEfuPFLDI9TWC+KQ/rrjiCrdYqgYW208//SSNGjVSn8VOzjrrLPW6ZMkS9d1rELMEaDu8C5EAmcD6GFbgGkYMF5x77rlhHwcxYJxTWICwMHHNYdqyZYuylGEp3nzzzep34AvEl8855xxZv369yp3AdfTEE0+ofc6cOVO++OIL17ojR45UFiS8P9pSNse78bcnmAfv0oQJE9S+0T7E61u2bOm6NnSM+aOPPlIxc6z322+/yXHHHafWHzRokOzZsycs7wXAbwTnwhaMKk4oFuvBQ7nGlh2ZZTbl5BaE9Zl27Mmy3B/aHwnMT8OwTvr27ausihkzZgSMW9hhsWKyiis5nU6jT58+ajnaZKZt27Zq/gcffBD059RtxfTuu+96Ld+9e7fLqhw3bpwRCm+//bba7tRTT/VaNmTIEJdlE2zW6aRJk9Q2sAKR2WrFDTfcoNa56aabImaxAljiWL9hw4Z+LdYDBw4oKxpWPyx8zYMPPqjWufvuu9Xfdlqs06ZNc7Xhn3/+cc1v1KhRQC9CaTnxxBPVMZ544gnL5QsWLHC17ccffwzrGDiP2jPi6TEAhYWFrsxYXCu+LFZkPOP35MnAgQPV8quuuiqsGOuYku8L08SJE316a/Q6Vt6MHTt2KIsVy3GthGqx7t2717X/P//807BDN+LskeeqwZ9r98jPy7eX2fEuOP1oad6oRsjbzf1uvew9mOs1v0eHBnJix0ZiN4hP1alTR1lesPwWLlyoJk3Xrl1VbHHYsGGueI3djB492msejoU+arA+YOXBIkWcUcdWAKzWUEGMEp/FE5yDG264QR5//HEVM4XVGyywBgD6QyLGiI7rABYxrFzwyCOPqMzrYEC8GeC8x8fHW64Di37ixIkqmzWSwMoAOP/+QNwMVjbiqTh/iLuh76aOfWmLNhJt82yfzu7V10sk0NcerhsrzBnG4bYD3y0sO8Tz4bnxBNfZgw8+qOKM8CjAorTy1Nx7772Wv11YiXPmzFHx19JQu3ZtFfe3QucfwLJEOz1Bv+Ibb7xRWdCwZvE7CfUagEcJ1iq+k3bt2klpoSuYlBr8aJ999lnZvHmzSrZAEs/RRx/t+iHCXYMuOXAn2eZqMYGEEXT+twLdGHDzwA3anAiBQgEA7mO4sCG+ZlegP3r37u3zAQHLAG40nkkd6L7xxhtvuIpoIIFEJ3ToGzzcs2Y3MroxYTusg+2CAevrYhwQetworSbtPsb3Vl7wdAfjZg/3I9zsdtzwyhPabWkWd7vRiUlIavJ1nFNOOcX1IOcrkQkPx1bAPQ88Qx+hAtFEIQ8rdJuQBOWL0047Tb0ieRBu4lDAb0t3BQzHlWwFhZXYGk/DjRyZnbjA8fQHa1bHWhA/RJ9Au9E/biuQOahvKOb4LwQVcSuIH+I6+GHCikVG8DPPPKPiSOEcTy+D1Wm+2cDyhOii3xz6LsIywM0MGZp44sakMd8YdCUdWHOYggGWlxZ1WD3Yh9WkbyLBPlCEiz4PwVhdsEhwLtDfFIKvBTYS1qq5bZ7tgwUVjJVdGpDlqh9MrdBtKE079DXv75pFhq+2mn3lSPjylOhMf8T0S0NGRkapPgMynAEeoMMRR/057Pot0BUcAu1b1ZFmDYpdiGVB7fTksLbrf3ILKSz0tgzTUqx/wJECN0gICdxF6NKCm/mUKVOUezLawFpE0gVcr3C1InEDCSxIksEEcYUbDU/6doCkESRH4AYG6x7WJx5ENBBi/cSOm0NpMHsFkPCBhKfy0CVLJ6L4Aw8bcLOjw/7kyZOVGxDnBV6QSLYN7nJ9cwYo1oDkISR/RQoIOR6wfD3EmQtGoB09e/YM+1jwhJRnYkss5mh9Bv2AZX6YKQ0U1hConpqopvJOvdql7+dnJxATxM6Q9erZz1OLib8fDbJn/YEsV1+YXatWT8Xo94pJW4roVwhrdtOmTeqhAG7sUI6nl+FGYXa96WxfWOzoC+iJrxqv2pLFOcAUjNWKmwOOD7HG54imsKKvMB5YAPrSBgMylSGsiJnBEkKtafNDiJ18+eWXLlckvBsauB2/+uor1XZ8p5HIDMbvAsLqy40KCw0hFWQGo54w4oihoq95XAf+fiM6nuvPcowWGRkZqlazv8+ADGft1vUVs/bnOdCWaqjb+oKuYFIm6E79nm4vHdvQPwxPIHaBChmgI7qvQt/oZgOBwQ8ukMCgjRA93cEeN1WreM0333zjcx96GSx082fVn++EE06w3A43cX+xJ1ixWgQCAetLdyEIdptIAcsfbmmcf6uELytwjnD+tHsxUm7gzz//3BV3112zNEicwveHeDVKCAZLKN4GFDcAiCH74vrrr1evCB9YPeQFagNi0wDiDAvcCnhr8DnN69tBTEyMLR4Y3Sb8tnztC8mJAAVTQi0gou8duEb1d1JaKKykVOCmEKgfJPpc6spAnuKGPmgAfTGtrFZYLjoW5Q9YN57gR6hviuj/aI6heSYWmdGWC7a3Wg8/xPfff99rPuJgWpQ9K+loS9OqFi7ir4899phlW9C/9fzzz1fvUXUJ/VaDAcIAUK0qkDuztIknvsCx4fYGeGCBWAYLvjdUbUKfyNL04fQFHppQGQog8e2aa65xWw4LFX19AbLddZa1v2sc+/Os1uWPXr16uRLUfAErFS50uPdxDgONFIP+1chz0GDUG+QO4CEFDzme4KETYQrtUfDVdzscqpdk3vvLVwgG9P8GqKmM8I2VtwdJkwB9yENFP7CgkpRdrmD2Y2XlpVIxe/ZsVSkF1VQ+/PBDVe3HXOd01qxZRqdOnVz9xDwr/aAPma7ogv6aqNQD0Kfx0UcfVftGpRd//Vh1P7377rtPbQe2b99uXH755a5ar56jV6CiDdb/9ddfjby8PDUP/fR++eUX4/jjj1fbde3a1bIfK9qDmrXoy1pQUNzXePny5a4RfurWratq25q55JJL1LImTZoYX3/9tatPII7frVs3t6pKnvVg//33X1eVGVQKWrRokaqmAw4fPmzMmTPHqzIP+q7q6lCo0PT666+ruqganB+0/5RTTvHbhzjUfqzoy/vZZ5+p9ujPgzab+6X66scaLOH2Y8VvG1Wx0DdVj8SD73LlypWWx8F3i/7P+hrCd/j999+7vnP93aDSUP369S2/O3/gu8c2uJbQn9QXS5cudVUiwnf55JNPuo3Kg+sX1xT6kuL34nlesL7+DPhN6e9iy5Ytrj7SqJLkWTHM3I/VF/76iQ4bNsz1u/b1/ervK1CfaFRF0hWWULFKn6/ff//d9XtFVac9e/YE3T7NrbfeqtZBv267dIPCSmEtFeiwrX98eoJQajE0l1FDmTgrXnzxRbd1cRPR5dAefvjhkEoaWpUmtCppaG4ftsENyzwMWJ06dZRY+ipp2L17d1dptWBKGq5bt07tU6+HQgi4oerzhZJxvoQVQEzNZehwXIixv5KGeEjp1auXaznOKT6nPq6exo4da0Ri2DiczxEjRvj8TUVSWD2HjfP8zJhOP/10VdrSHxAtfAbPYeM8rxdMKJZg9QDhCzxcobA+tv3qq68CDnZhLk+prwHPaz01NdWroD9EyLOkoXk7fB6rQQBKK6wLFy50KxzTuHFjtd5FF10UsrCi0IwuP6mvPc+ShlZFNIIRVj3IAX5jwUJhDQCFtfRgrMjx48cbgwYNUhcphELX+4S1CtELVNEE1i4sLAgThApVYGD5gGCEVf8N6w8/OEyouvTll19aHg9P+LBYITywIvHDx80XT78QTm05+6oSBSF46KGHVDUk/MghLBdffLHx999/+/yMsDIuu+wyZdHipozqPpdeeqnr3PgTVoA23XPPPUb79u1VWzHh+LCk4BmwAjfV9957zxgwYIASGBwX27Vp00bdbFHrOTc3tIpc+vvwFFGIDdozePBg47nnnlMVcfwRSWE1TxAOPEjh5grLB9V5fFmp/oQNlZ/glcADEmoIY5+oXwuLB5ZTOMCCRBuDGZEGQjx9+nR1DR111FFKRHHdwlo+88wz1TmHx8AXn3zyiVoPD2T4vlAJC9eOr7aXVlgBfsP4nsxjM5u/t2CFFeAawX0G3wF+33iwgOcJ9xezpyyU9mGUIyzHA45VZalwdcOBf6QKg9iIOS6CqjoYqYIQq7glYm2IdfobjYeQYEHGMUasQT9RvEc3MFJ2II6PEXNQLQ1V2uzSDSYvEUJIlECSFJKNkPimh7EjZQO6r6ESGrrzYBACO6GwEkJIFEGtXmR/P/XUU65uLyTyvPTSS0pcUWc82BrcwcICEYQQEkVQ/AKlG9EtCv2dAw1mTuwBBVxQsH/EiBFiNxRWQgiJMuirrPsrk7LBbvevGSYvMXmJEEJICDB5iRBCCClDKKyEEEKIjVBYCSGEEBuhsBJCCCE2UuWFVQ9tpNFDVRFCCCFWeOqE50DtVV5YMeaiWVwxhFYVT5QmhBDiA+iDeahF6AfGQDZT5fux4qSg6klmZqb6GyPJI40anYc9TxYhhJCqbanu379f6YQG+uHp+azywgogolpYAU6a+cQRQgghvvTDkyrvCgbo2NuoUaNoN4MQQkgFArphVVCoylde8qymATM/KytLnE5ntJtDCCGknIYPYan6qtJHYbUAogpfelFRUbSbQgghpJyA7F/k3njGVD2hsBJCCCE2whgrIYQQYiMUVkIIIcRGKKyEEEKIjVBYCSGEEBuhsBJCCCGRFtZDhw7JrFmz5MEHH5SzzjpL6tSpIw6HQ01///13qQ+KKkcPPPCAtG3bVvUDql27tvTt21c++eSTUu+bEEIIiSaW3W1mzJgh559/vuUGq1evljZt2oR9wC1btsgpp5wi69evV3+jo21ubq4UFhaqv0eMGCETJkwQO8BHQ9EHAAHHgwEhhBASFVdw3bp1ZcCAATJmzBh5/fXXbRO6wYMHK1Ft3ry5/PDDD8o6xvT000+rTrf/+9//ZNKkSbYcD6IK4cakBZYQQggpc4sVFYfM48tt2LBBWrRoUWqLVVvCENAlS5ZIx44d3Zbfcccd8sILL0j9+vXVCDMY0q00ZGdnK1EFKFOYkpJSqv0RQgghYVmsnoO22sV7772nXk8//XQvUQUjR45U7todO3bIokWLItIGQgghpNJkBS9evFi99uvXz+dIAe3atVPvKayEEEIqImUmrLt27ZK9e/eq91o8rTj22GPV66pVq8qqaYQQQohtlNlA59u3b3e9b9iwoc/19DLz+lY899xzavIHh34jhBBSaYUViUSa5ORkn+vp8e2QbBSoL+zWrVttbCGxE+TEFRYZkl9QJPn5RVJQ6BT0dkIMHSMu6X7RMa55R96b14lRfxe/J4SQikCZCavdVK9eXcVkA1msgSxf4k1hkVMJYl5+UbEwut4fmZ+XXyh5eJ9XpF7N60NE8WrngISQVS2wxRMGHHaUzC8WZpdwO6zmlWyD1xjf67gEPcZ9m+L1ipe7/221nvu+i5eXHDPGT1vU36a2mD+n6b3b9iUn5shDiekV//GhhJDKK6zmri45OTk+19P9TXU3GV/ceeedagq2u01VoMjplPz8EvErsRTN75UQ6te8QtM6TvW+AH8XOsXpLH9D9KJFxpF/ot2cCodLcE3vi+ebxBh/x7gLsn5wcRNsi2UQefEn9vjPtG+XJ6Lk4cjsmXCbp70bYvZ0eKzv1R7rNh1py5Hjeq6Lv+vXTpHkpAprc5ByQJldPea46rZt2+S4446zXA/LQIMGDaSqACFzWYYmEVQWout9sRjmasvQNB/rFRQWSVERBYdYg+cR9y7rvFZ8MahvK2nZOD3azSAVmDIT1oyMDFVzeM+ePfLXX3/57HKjs4F1dnB5Bjcqs3vUJYxu4lgkuXl6eaHLYtTbFRQ4leu1ohEXFyPxJVNCXKzExx/5Ox5/4zX+yHusj3s5zlnxJOIseTXwn7N4Gc6E4TwyH/lnxcs95mFbX+saJfNLviOn6b0WGJ/rutqo/z7y/sg897/tdHmT6HMoOz/aTSAVnDL1d/Tp00c+/vhjWbBggaUbF8lIEF2AovzRYNW6vZKbpwWw0CSKpphjYbEgIpZY0YiNdRQLoRbBeLMIFgthQsmyuPgj783rQiRjY2KU+wz7i0XcL0a/xqh5Ot5oN9rq0mJa8n+JuHn/XbyeXmRefmQ/3vstfmPapeVx9d9Ow+k6pvnBwZcI+5pfLOLBrGMS9JKHEXHNNz0wmD+D+SFGr2f+TBbLXefPtC/zg4Rb2yz2I1btMZ9f07m1bK/bsiP70d9NpB5oGJMmFUpYhw0bpoR1/vz5snz5cunQoYPbcnSfwQ8JbmCIcDT4dslmOZxTPCBAeQKiZbYAzWIH8Su2IGMlwWw5atE0WZFKELUIWoliyd/llSOxQfVvtJtT4TC7g10CaXrgODLfep75YUOs5lk8tLjNc9u+ZFu37T0feNzb6L4P04OIPobFw4P7Mt8PBLqdzRvWsOdkkyqLT2GFy1azf/9+1/sDBw64LatVq5a6IXve+FC8f+zYsW77PO+886R79+7yyy+/qJrB06ZNkx49ekheXp688sorqk4wePjhh0tdJzhc4mPtrZkBjfIUOIifpRC6uVTd3alHBPCI+JnFUQsmIcFaY0fe8rohpEyEFTFRK3r27On2tx6pJtgfNcZc1cPGYV+ew8bdeOONct1110m0SEzAKclXNx0tbm5C6BE79LQMi4XyyN+xsSYLsUQUPYVRd8cghBBS8SnznPLGjRvLH3/8IU899ZRMnz5djZyTlpamivJjLNYhQ4ZINLnwzGNUt5OcvEJXnFCJYYlAuv4ueU9BJIQQEnDYuMoCh40jhBBSqUe3IYQQQio7FFZCCCHERiishBBCiI1QWAkhhBAbYaVpQsopBuouOotEigpNr4UiRUXFf6MOZFAEmbluZ4Z7UPsKtl1BH1RsITVdHPGJ9uyLVEkorKTqiJQWKCVOWqw8hatknmkdw2p+EeYXSFFRgTgLC8VZWCDOogIxCgvEcBYWvxYVqvd6/SPHLhJHiWjGqHlFEmM41bwYTAZei+hOihKZA0dIzWO6RLsZpAJDYSURRfXmOrhbZN92ESU6JQKjxMZTsEpErKjAtE7BEfErWV8JGESrZH6xeGnhgmgdeYVYqSlCo7nElkyk8rB33zapGe1GkAoNhZXYipGXI7JjvRjb14mxda04d/wnMXnFY+zaBS05EklyCjm6DSkdFFZSOvfqvm1ibP9Pirb+K4Vb/5X4g7vdIl2VXQQLHA4pcsRIYclrUYxDCh0x4nQUvxbp5TElyz3mO2Mc4lSvsWLExIjhKJn032p071jBKN+Yp0b7jokTh1rX4V6YXhfEL0EXutfv3ZfqeUf+chXRP7JDr+1cBfpN2/pb7llk3+p4xdu579Nrv27vPQcHsGiTuXi/x3Hdl3tv27FecCVaCfEFhZUEjXH4kMiO/yRvyz+Sv2WNJO7ZInGFBS4BLe2wCQUu4XFIUYy3CKm/S+a7iVko82Mgelq8IFpasLRoFQsXXh0l82IccRhvT60TExsrMXh14DVG4mJiJQ41oB0xEudALejY4veqLjRe49Tfxe9jJTEmTlLUsuLt9eQo2R8Es3jfOEZJHWkpGYYPE/4yvbcWD4uRbFyvboriLTh+xCoYUQpKKD2O7SX4/sTbot2BRNa8pmedOc9jJ8fGS61EVmgjpYPCSixBDNO5a7Nkb14teRDRXZsk5XCmWhZfMgViZ2KybEitIetTa8jGammSHxevxKlYsIpFy6FEq0SYlCg5igWqRJyK36M2c6x67xIxrKvelwiba3mcJJaImJoXi9c4t/2pGs8eYqUHQvAULpe4ef5dsh4hhHhCYSWKgoN7ZP+GPyV36xpJ2LlR0g/ulninU/DsHszze05srGxIgYhWl82p6ZKbXldqpKRL3ZR0aZVWR3pUqy7xsPa8hKx4wHRf1plrPZPoEUJIeYbCWgXJyjkkuzf+pVy68Ts3SK39O6V6fq7UDnJ79J7cnpyqRHRHWk05nF5P4mtkSL1qNaRpjQw5rloNqRafIClxiZIcF69EkRBCqgoU1koM4lx7crJk5451SkRjd66Xmvt2SIPDh6RpCIMaHYqLV+7cXWm15HB6XXHUaii1U9OlXlodaZFaQ1ITkiUlPlESY3k5EUII74SVhAJnkWw/fFC27d8hhyGiO9ZLzf07pVnWAWlbkmBkxZHkkGKrEok+W6qlye602pKVXleMmvUlvXqG1EqtJRk10iWtWpqkxCWoGGaw5OYVSpFTJ764J5CYk1zUWyw3Na54lmfSTPECnePiSt4pWV6/TujJJwWFRbJlZ5bb/nwf35Rt62qHZxLNkc9p1e70tERp2qB6yO3ctjtLtuw4pPaJPCuMExznmhwSF2f+O0Zi3eY51CvHESYkslBYLTiYlSuffbXW/SZZ/OLCar5bpqJHvwef2Y2GSJMGadKzQ0O3NvgyKLF9TlG+bM85KH+v2yv7NhWqlVEAweHapr6INBADqbrV3YVTvwYsOQct3l0yKTLl5M5pEt/AKbmSK6Ew97v1svdgaNuECz7VsIFtQ94u63C+zFy0TsqKZg2rS2JC6KUl/v5vn/zx965Sn6OYEpGNjXGUiHPJ3x4CffwxGdKobqo4YnS8W4IS5UPZ+eqBxCz0EHRCqgIUVg8Ki5yyZ3+O7CsjIQB5+UXquJ7gxnSgIEd25B6U7bkHZX/mDkncu0nqHdwjLbIPSquC5vJLtR7F6+If3rdKUV+pbE+euStMKNhhaOLIRUWGFKFaVQAa1EmR5KTQbxPzftigfkeebVfdjmKLxVxZ06ZX1xQXI/FxxSIfH+uQ+LjYEoF2t8hd28XFFIs/rXBSTqCwepCTWxiVm2yh4ZQ9eYeUgGLakb1P4vZvlUaH9kmLrEw5Nfug1EVVIxNLkppWDNWqAPc73pOtgWUbDkWWD4rFD66FSs8Di3qwoIWXnN3Gr7C6ssvRdbkkyzw3v1AW/7rZZa23bFxD2h+dQcualBoKqwcpyfESF+eQpg3S1N/mH6t6Z/rNoWtIyf/mmaa/i9+Zf+94X2Q45XBhvhwuypfsonxZLZtlyfIfVTwUInpi9kFpmn1IEgKMXlK/cJd0ObxUuYFR+CAvoZoUJqaKJKWKkVxdYuITJTY+UWLiE458DtWlJbj2en7e9OpJEg7tW9WR3Pwi1/6O7NfUFtf8I/Pc13d4tcnz/JdGHJMS4+Ssk5v7OAemNpka7Pm9mue5td3czpJXFIMIh7Yta6kJ36fTaajYNUTM/dV9XqHTEGeRIYVOZ/Eyz2285h15nxgfXiVk7KOsgOUayFp1qmA4PueReZlZebJtV5brb7jm69VJkWpJ8ZKWUtpyJ6QqQ2H1AE+rzRrUkPq1U13xUO+frLdgei8ttkT35x+WLYcPyNbsA7Ipe59syT4gWbmHpGnBIWmRdVA6ZB9UYlqzIC/oNqJVh1NrSmqtdGlXK1ViM5pIYs16kpiSLlItTRwJxQJYXlxj9WpXjEo2DTNSpaqgk8ggOHiP6pTqVc936vlH1tPLgnFjd2pXT4U4nCXCXizeWsQ9xNxK7E3bFBYVt8MXcBmHg06o08BqBYdzCyispFRQWH0QamIJbgA7cjJlc9Z+2Zy1TzZmFYsoCnpn5OUoEW2bnSkDsg5K45wsiQ0hxlaQkCSFNeuLs3ZDicloLIkZTVV2rlSrrqxTlMEjJBTw0IXnLl0W0W58PUyZxduAJa3+PvLeJfbq75L18d5ptrjdxTjMcLVK3KpXu5prPzqWrAWWkHBxGOFmUVQAsrOzJTW12ArJysqSlBR7LKecwgLZmr1fNmfvl01ZmPbJjsOZKk6aVFgozbIzVXIRxLRFdqak+unu4glq2BbUyBBn7QYSk9FEEuo2FUdqTXFASJOriyOeT9KEhIoWdDfL3Gm22ItFPSE+NqxkLULM8Aryg8rKzc+RLRDRrP3KCsXr3rxstdxhGFI/J1uJaO+sYjHF36E87xYlpxZbonWaSGxGY4mp1UCSklJKrNGUcuPOJaQig99Rcc3paLeEVAUorB5FFpbu2SSbsw7Ixqy9ypWLJCNNakG+NM/OlBOVJXpQmmVlSjIG1Q4SIzZOnDXrS0ydxsqlK3UaSXy1GiouCiF1sHIRIYRUeHgn9+DNf35W7qFYp1Ma5WQVu3OzMqW5RXeXQDhTayoRdZSIqCO9rsSWWKOOxGoR+wyEEEKiB4XVRFx+nly6bYNkHNgdVHcXM0Z8ojhqNxSBkNZppIQ0LhlxUVijacVDpRFCCKn0UFjNxCdI123rJS6AexfZXrA+lRVap8QarV5HHEmpbt1dCCGEVD0orCYQ41TW5q5N7gvgtjWLaO2G4nAlGLG7CyGEkCNQWD2IbXqsSFFhiYDCpdtYJDWd3V0IIYQEBfuxWqBOCfqexsWzuwshhJCQoMVqgRJTWqaEEELCgMFBQgghxEYorIQQQoiNUFgJIYQQG6GwEkIIITZCYSWEEEJshMJKCCGE2AiFlRBCCLERCishhBBiIxRWQgghxEYorIQQQoiNUFgJIYQQG6GwEkIIITZCYSWEEEJshMJKCCGE2AiFlRBCCLERCishhBBiIxRWQgghxEYorIQQQoiNUFgJIYQQG6GwEkIIITZCYSWEEEJshMJKCCGE2AiFlRBCCLERCishhBBiIxRWQgghxEYorIQQQoiNUFgJIYQQG6GwEkIIIWUlrDt27JDbbrtNjjrqKElKSpJ69erJOeecIwsXLgzrYF9//bU4HI6A0549e8L9PIQQQkhUifO1YMWKFXLaaafJ3r171d/Vq1dXgjdnzhz5/PPP5fHHH5d77703rIPGxMRIRkaG3+WEEEJIRcRSwXJycuTcc89VonrCCSfIn3/+KQcPHpT9+/fLXXfdJYZhyOjRo2X+/PlhHbRJkybKGvY11apVq7SfixBCCCk/wjpx4kTZuHGjpKamyuzZs6Vdu3Yuq3X8+PEyaNAgJa733XdfWbeXEEIIqXjC+t5776nXYcOGSaNGjbyWjxo1Sr0uXbpU1qxZE+k2EkIIIRVXWA8dOiRLlixR7/v162e5UY8ePaRGjRrqfbiJTIQQQkiVENbVq1crNy/QLmCvjWJipHXr1ur9qlWrQj7o7t27pVOnTpKSkqKmY445Rq6//npZuXJl6J+AEEIIKc9Zwdu3b3e9b9iwoc8N9TLz+sFy+PBhWbZsmdSsWVOys7Pl33//VdOUKVPkySeflJEjRwbcx3PPPacmfzidzpDbRgghhNgqrBA6TXJyss8Nq1Wrpl6zsrKCPlh6erqKz1500UXKGkbf2KKiIvnhhx9UItSPP/6olkO0Ed/1R2ZmpmzdujXoYxNCCCFR7ccaCTp27KgmM7GxsXLKKafI4sWLVb9ZiOw999wjF198sd/+rMhQtkqs8rRYw7GoCSGEkHDxUi7EPM39Wf25cwG65NhBQkKCjBs3Tr3fsmWLchX7484771Tr+ZvgXiaEEEKiKqzmuOq2bdt8bqiXNWjQwLbGdO/e3fX+v//+s22/hBBCSNSEtU2bNqpeL/jrr798ulh1/9Vjjz020m0khBBCKq6wpqWlSZcuXdT7BQsWWG70yy+/qBKHoG/fvrY1BvvVtGjRwrb9EkIIIWWFZXaQzshFBSar5B+UNQSdO3d29WcNBt0/1oqCggJ56KGHXO5l9HMlhBBCKoWw3nDDDdKsWTNVhWngwIGuIhD4++6775bp06ervzHCjSd66LexY8d6LWvfvr28/PLLKqlIiyy623z//ffK8sUreOKJJzjCDSGEkMrT3Qb9V2fOnKnEDvWA0ecU3VvQZxXxVQgnRPXMM88M6WAQ6FtvvVW9T0xMVG5n9EfNz88vbkxcnDz66KMyfPhwOz4bIYQQUn76sXbo0EENFwfrEWOwohhD7dq1pVu3bnLHHXeEFVvFqDnop4paxLt27VLD0EHE4U7u3bu3jBgxgslQhBBCKjQOw1/gs4KDKlK6ny2sbXMfXUIIISQSMJBJCCGE2AiFlRBCCLERCishhBBiIxRWQgghxEYorIQQQoiNUFgJIYQQG6GwEkIIITZCYSWEEEJshMJKCCGE2AiFlRBCCLERCishhBBiIxRWQgghxEYorIQQQoiNUFgJIYQQG6GwEkIIITZCYSWEEEJshMJKCCGE2AiFlRBCCLERCishhBBiIxRWQgghxEYorIQQQoiNxNm5M0IIqSgYhoF/jsxwiDgctDVI6aGwWmAUFohsWiWCH1lMjIgjViQGv7qYI/PUfNPfjgDzPF4dDke0PyYxYTidInmHRZxFxVNRyauz0P3vIvd5hl4f81zbuG9v4H0Rlhe61nM0aCGONt2Lb+xuk/PIe/H423CKsfJbMVZ+J4L2Ypn3Jwlqlo+ZXjhOHiKOxscEcwbd/nJ+MVlk/46Qj+cmdMESEysxl4wOeTMjc68YsyccmdHuJInpNkAkPYMCS0oFhdUCY8s/Ysx4KfIHUiLsEGl6rMScfKF+ZC6eXMvVm5J5Dre/jf+Wi/HXD8GJvdurflDAq3lZyd+OWHF47c8hjpYdxFGzfsgf09i5USQ/V8QwC1ShSZQ8hMwkUIYSJD3f472VADpiJGbA9R6CFEC08NfB3WLMec3e79ffOTmcKY56zUPfEOcRDwBlheFLwANtVxTedtEmP0dk1yaRcL4bQkqgsFrc8KSooAxvWni8LxGHUNm/U2Tnhki0zNq+gIg1OzbkfTnnThHZu1XKBDwc4DsMZ7uyJJzvOxrtJISEDIXVE1hnZf2kHa5bmO20T7Ai5fpzeRNi3TwCklgtvP3VbigCF7L2JgTfkKBmec1MqxVqC4v3cnRnkdxsm9rliMx3l1hNHB37Hvm7TqPi1xoZ4e2PkBIorB44klPFqNlAHAOuO+I21PEsszvRap52m5ldj06LdUzzkEDhSK8bXmNTa4o0bOV+bKev9pja5avtbm094iYt9Q0sHGFVLnGTe9pNmHzNL35V5zTUhwDcZLsOMO3T3TXudSzz337aqWJ12r2vP5PL3a8/Y8mrORTgtV3xPEethuJod5JpndBPbckJDn7NMB6ownJzlzHqUzVtG+1mkEqIw1CpcZWT7OxsSU1NVe+zsrIkJSVFynd2oikeWLzgyDz1Kv7XsVovmO3Mbmlx/9swiyxu7BCMUD8fklgKCjwEqUSwYi2ESR/HEYzgxPgXLct1i1+ZQEYIiQS0WMsB6gZfTm/ydrSqIlgvhBBiF8wpJ4QQQmyEwkoIIYTYCIWVEEIIsREKKyGEEGIjFFZCCCHERiishBBCiI1QWAkhhBAbobASQgghNkJhJYQQQmyEwkoIIYTYCIWVEEIIsREKKyGEEGIjFFZCCCHERiishBBCiI1QWAkhhBAbobASQgghNkJhJYQQQmyEwkoIIYTYCIWVEEIIsREKKyGEEGIjFFYfGIYR7SYQQgipgMRFuwHlkQN5OfLg77MkJS5R0hOrSa3EalInKVVNeF8rMUW9JsXFR7uphBBCyhkUVg9yCgtka/Z+yXcWSX7+Ydmff1jWH7JeNyk2TmokJCuRrZ2YKnWSU6V2iehCfGskJkusg04BQgipSlBYPch3FsrB/Nyg1s0tKpTcnEOyMwfKu9NruUNE0uKTpGZiNTXVSUwptnqTIL7FU7W4eHE4sCYhhJDKAIXVA1ig1eIS5MR6LSUzP1cy83MksyBXTc4Q465YW2+7MWuf5TrxMbHSLaOZnNPseEmMjZPEmDiJjaGVSwghFRUKqwUd6zRWU4GzSPKKCpRlmldYIAfyc5Q1q8TS4z1eDxfmh3wsHEMJsB8rOcbhUG5nJbyx8ZIQE6us3IP5OS6rmFYvIYSUDyisfoA1iSm1JEepkdR0Ww4LNh+i6yyUvKJCySrIU4lPmQU5xdaumwAXzys0nF7HqR6f5LcdOM7hwgI1ieS45s/ZtFJW7Nuq4rg1EpKkZkI15WYuTrTS7ma4oVOUKBNCCIk8vNuWAmVJxsVLkhQrb93kNK91CpXVWyy8uUUFxSLrIbYNq9UI6/jayi0ynLIv77Ca1h3aY7lucmy8pCci0SpFJVjVTkqV2kmmRKuEJIlhohUhhJQaCmuEiYuJVVNKfKL6u2GKd3/ZfJP45jkLpMgZXCwXwhwsOUUFknO4QLYfzrRcHiMOqZ6Q5Kd7UYoks3sRIYQExGFU4koI2dnZkpqaqt5nZWVJSoqHqlUQYJG6hLeoQPKKitT8/zL3yIH8w8pyPajdzyremyeGitzaByK493Q4U5LjEpRbGTHfhNg4dicihJBQLNYdO3bIE088IXPmzJGtW7dKjRo1pFu3bnL77bdL3759JVwyMzPl6aeflk8//VQ2btwoycnJ0rFjRxkxYoQMHjw47P1WViBeyFTGZKZZWi3XexXrLYn3oi+uZ6wXiU7F74tfYcGGQmp8satYC7y13SsSG+OQxJh4l/jCGl+buVvFfzEvITZWEmLiVAIWLHm40wkhpEpYrCtWrJDTTjtN9u7dq/6uXr26svqcTqfKQH388cfl3nvvDfmAW7ZskVNOOUXWr1+v/oZFmZubK4WFhepviOuECRPEDiqLxWoHTmX1Fmc5QxgPKbHNdYmtjv0WJ1wVzysyXRqNU9LliqN7hHzcjYf2ynvrfvO5PM4R40oS0xNEF9Yw3qP7UXxs8SvmJeJ9SWZ0fIy7UGNCnBvubEICsfNwpnrALHQ6pdAoUkVeMpKL7xeE2C6sOTk50rZtW2VNnnDCCfLOO+9Iu3btlKX5yCOPyLPPPqvEde7cuXLmmWcGfTAcqmfPnvLLL79I8+bN5b333pMTTzxRCevLL7+shBrC/frrr8t1110npYXCGhrF3YsKlfWrrF6X0OYoAWxVo27I+1y5b6vM3rRSyorTG7aRbnWbWy6DdRzrcCjLG++L/y5+/932tbLt8EElzsqyjinu3gQxLxbxYrEvFnB3QY83vy/pClWV0Ql7yIDHNVWkhKv4vRYx9eoskgKj+NVtfsk8rF/g1NsV/31kP8XrYvv+TY6VZqm11P3FiSAIXkve49VXsGvy3z/IrtwjZdVOrt9KTQ2qVVffNSHhYnn1TJw4UYkqRGn27NnSqFEjl9U6fvx4WbduncyYMUPuu+++kIR15syZSlRjYmLks88+U+5fkJSUJKNGjZJt27bJCy+8IA899JAMHz5cEhLcXZ8ksmiLUUoSrRpJutty3LB01yI9BSqaEWwVK7uI81NcQ91sVXu9uzytPrBDuaxtaYOHFW4lzBcd1UVS4xNcAh9MRjbOPx50tLCDYoFyFytbRMwokqYpteSk+kcVi5N4ipW3iOHU4v1PO/+Txdv/kbJiT06W1ElMLfW1glwGgAQ/c5iFEFuEFZYkGDZsmEtUzUAEIaxLly6VNWvWSOvWrYM6mN7v6aef7hJVMyNHjpQXX3xRxXYXLVok/fv3D/XzkAgCSywpNl5NvoB1ogpqOAuU5ds1o5kcXT1DsgrzSm7eJZaHsjZM71039CM3+uLX4mVYV6+nb4BWqAeDMMB+7UKJVZHTbxz7tEat5VBBaFnWOJ/jV34lZUVWfp60Sa8f8nZlndDm73rwh+fDDK5dQiIirIcOHZIlS5ao9/369bPcqEePHiqR6eDBg7Jw4cKghXXx4sV+9wsRh8v5zz//pLBWUFCOMSUmQVKk2NtQv1pw8V/Ec4stSv3e6bIw9d/qVYottIIiaxFGTeZwS1mif6+nuEcqZT7eEfoDgFVxkUgSrmDZUZITVjwsf4g09ud674hRlqb5PUahCoe+DVur2uB6X6hgBhqmhNevnBCfwrp69WrXWKQQOSvgyoWY/vrrr7Jq1SoJhl27drkSoXztFxx77LFKWIPdL6n4FMc8I38c7cKEYJgFG27MYa26uuZrEVexQQuL2Wxdu8X/3KzwYqG3ssIhkOFkQ9tpVQcjYsgED4emKTWlX+NjrYUx4PuSuHeMQ/CfdpWb36PPtcNjvtty1zreVqkZuntJmQnr9u3bXe8bNmzoc0O9zLy+P+ze73PPPacmfyARihCNEjPcuCXyrspQrXB/STYauODPbtLeJe7A05oLSsRwDkx/+xMxq/eBRAyC1UWaRfwcE1JhhBWZtBr0L/VFtWrVXNm2wWD3fpGhjL61hFQlK/yY9NAzswkhZUuFzSlHhrJVYpWnxRqsRU0IIYRERFjNfT3RnzUtzbuwPDh8+LB61f1EA+G5X18Eu98777xTTcH2YyWEEEKiIqzm+Cf6lfrK+MUy0KBBg6AO5Lnf4447zpb9+sNc+8LsiiaEELtA+KqqFwUhAYS1TZs26iKBKP3111+WwgoXK/qv6izeYMjIyJA6derInj171H59dbnR2cDB7tcf2voF9erVK/X+CCHEE1Z1I554pUfC9dulSxf1fsGCBWIFqiehDysIpRh/nz59/O4XyUgQ3VD3SwghhJTrWsEoK3jHHXcokYVl6umWvfDCC2X69OnSuXNn+f3334M+GKo1nX/++aofLKo2dejQwW35XXfdpbrQ4HgbNmwodUlDWNawkAHdNdHj6KOPVklk+F7//fffaDeHEFuvU95biBeGBYcPHzaaNWsGwTU6depk/PXXX2p+ZmamMWrUKDUf07x587y21cvGjBnjtczpdBrdu3dXy1u0aGH89NNPan5ubq4xfvx4IyYmRi17/fXXrZpFKiiNGjVS3yteCSmv8DoldmHZ3Qb9TFEwH+5YWJaolGQ1bFwoBfgBtvvkk09cw8ZhpBvPYeNuvPFGW0a2IYQQQqKBzxI0cNOitOCtt94qLVu2lLy8PKldu7acffbZKkYazlisoHHjxvLHH3/I6NGjVaIUBBUuZ8RfP/roI/nf//5Xms9DCCGElM+BzgmxCzxMITENBT0w0D0h5RFep8QuynZ8J0IIIaSSQ2ElhBBCbITCSgghhNgIhZUQQgixEQorIYQQYiMVdtg4UnHAKEQYPxd9oQkpr/A6JXbB7jaEEEKIjdAVTAghhNgIhZUQQgixEQorIYQQYiMUVkIIIcRGKKyEEEKIjVBYSUjs2LFDbrvtNjnqqKMkKSlJ6tWrJ+ecc44sXLgwrP3t3r1bJk6cKEOGDHHtMyUlRdq2bSs333yzrF271vbPQCo3dl+jVhQVFUmXLl3UUJiYxo4da9u+SSXAtpFdSaVn+fLlRu3atV2D2VevXt01OL3D4TCeeOKJkPcZFxfn2h+m1NRUIyEhwfV3UlKSMW3atIh8HlL5iMQ1asXzzz/vdt2OGTPGlv2SygEtVhIUOTk5cu6558revXvlhBNOUGP1Hjx4UPbv3y933XUXHtDUGLvz588Pab8YjxcD37/11luyfft2OXTokBw+fFi+//576dixo+Tm5soVV1whK1asiNhnI5WDSF2jnmBIuQcffFCaNWumrGFCvIi2spOKgX5Ch0W5ZcsWr+WDBg1Syzt16hTSfr/55hufy3bt2mXUrVtX7ffKK68Mq92k6hCpa9TXfmbOnGk0a9aMFivxghYrCYr33ntPvQ4bNkwNBO3JqFGj1OvSpUtlzZo1Qe8X1qovMjIyZMCAAer9kiVLwmg1qUpE6ho1M2vWLJkxY4YMHDhQWceEWEFhJQGBe1YLW79+/SzX6dGjh9SoUUO9tzNJpHbt2q5kEUKieY1mZ2erhLrk5GR5+eWXS9liUpmhsJKArF69WsWnQLt27SzXiYmJkdatW6v3q1atsu3Y33zzjXpt3769bfsklY+yuEYRV928ebOK0zZv3ryULSaVGQorCQiSijQNGzb0uZ5eZl6/NMycOVN+//139f6qq66yZZ+kchLpa3TZsmXy0ksvyTHHHCN33313KVpKqgIUVhKUC0wDN5gvqlWrpl6zsrJKfcytW7fK9ddfr94jltW/f/9S75NUXiJ5jTqdTrnhhhtUOOKVV16RhISEUraWVHYorKTcgZveoEGDZNeuXapLwxtvvBHtJpEqzKuvviq//fabDB06VM4444xoN4dUACisJCCohGTuK+gL9D8FqampYR8L/VbPO+885QJGVvC8efOkTp06Ye+PVA0idY1u27ZNHnjgAUlLS5Pnn3/ehpaSqgCFlQTEHLPCjcYXelmDBg3COk5+fr4MHjxYFi1aJOnp6aojv042ISQa1+h9990nmZmZKq5avXp15U0xTzphCteunkcIhZUEpE2bNqoeKvjrr798xqF038Bjjz025GOgAtMll1win3/+ubImvvjiC1V5iZBoXqMbN250ZQTDavWcNm3apJY/8cQTrnmEUFhJQHCzQMFxsGDBAst1fvnlF1U+DvTt2zek/eOGN3z4cJk+fbpKPEEn/J49e9rQclJViPQ1SkgoUFhJUKCaja5uY9VVYfz48eq1c+fOIblv4UpD9u+0adNUtiXEtU+fPja2nFQVInGNfv311+oa9TUhuQ6MGTPGNY8QCisJCnQ3wE0EFW5Qzk13sMffiD9BEMHjjz/uta2/obXuuOMOlfUbFxcnH330EbvVkHJ3jRISKnEhb0GqJHDRomADXGiotYrqNjqZA65c3JRwwzrzzDOD3ifiUy+++KJ6j+1xY8Tkb5xNQsryGiUkHCisJGg6dOighuJCosacOXNUEQfU8u3WrZuyPMOJrWoKCgpk586dEWg1qUrYfY0SEg4ODHET1paEEEII8YIxVkIIIcRGKKyEEEKIjVBYCSGEEBuhsBJCCCE2QmElhBBCbITCSgghhIh9/D/9fy9VPfIoVwAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#on two plots, plot the mean and std of the silhouette scores for each method across p_out / n\n", + "methods = [ 'DMD','DMDC', 'Subspace DMDC']\n", + "\n", + "non_eps = nonlinear_eps_range[:len(silh_state_dmdcs)]\n", + "\n", + "fig, ax = plt.subplots(1, 1, figsize=(5, 3))\n", + "\n", + "# Plot state silhouette scores\n", + "\n", + "for i, state in enumerate([silh_state_dsas,silh_state_dmdcs,silh_state_subdmdcs]):\n", + " ax.plot(non_eps, np.mean(state, axis=1), label=methods[i] + ' (State)',color=plt.cm.Set2(i))\n", + " ax.fill_between(non_eps, np.mean(state, axis=1) - np.std(state, axis=1) / np.sqrt(n_iters),\n", + " np.mean(state, axis=1) + np.std(state, axis=1) / np.sqrt(n_iters), alpha=0.2,\n", + " color=plt.cm.Set2(i))\n", + "\n", + "for i, state in enumerate([silh_ctrl_dsas,silh_ctrl_dmdcs,silh_ctrl_subsdmdcs]):\n", + " ax.plot(non_eps, np.mean(state, axis=1), label=methods[i] + ' (Control)',color=plt.cm.Set2(i),linestyle='--')\n", + " ax.fill_between(non_eps, np.mean(state, axis=1) - np.std(state, axis=1) / np.sqrt(n_iters),\n", + " np.mean(state, axis=1) + np.std(state, axis=1) / np.sqrt(n_iters), alpha=0.2,\n", + " color=plt.cm.Set2(i))\n", + "\n", + "\n", + "ax.legend(loc='lower right',bbox_to_anchor=(1.5, 1))\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "jaxenv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.18" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 839143a29538c42c8f4b0c11f2418569f884dd3b Mon Sep 17 00:00:00 2001 From: Ann Huang Date: Mon, 3 Nov 2025 11:11:48 -0500 Subject: [PATCH 27/90] fixed prediction function & delay issue --- DSA/__pycache__/__init__.cpython-39.pyc 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z%=uH3%WImKnADyyz_^px>H$RnX37Zmdx-XiQYTmZR@<9|a#}|oRrrSX&FL{kZaU#Z zF3v5ck~bJtH`J>n8*0xsIf$V2tOS%$hu=Pk9QS=po@dfz(q(cPNw6b|LZRRBdFv;c zNN^GcdJT3(51m0p`a$|l%UCLq%9z!l@)%^sm9|5WQ3u=>a)|5!tj(1Fh{5&0(%8wd n3uD!>r$?>PyGD 100: @@ -188,13 +208,13 @@ def calculate_projection_and_svd(H, Z_p): Gamma_hat = U_n @ S_half X_hat = S_half @ V_n - X, X_next, U_mid = self._time_align_valid_trials(X_hat, u_list, valid_trials, T_per_trial, p) + X, X_next, U_mid, Y_curr = self._time_align_valid_trials(X_hat, u_list, y_list, valid_trials, T_per_trial, p) A_hat, B_hat = self._perform_ridge_regression(X, X_next, U_mid, n, lamb) C_hat = Gamma_hat[:p_out, :] - noise_covariance, R_hat, Q_hat, S_hat = self._estimate_noise_covariance(X_next, A_hat, X, B_hat, U_mid) + noise_covariance, R_hat, Q_hat, S_hat = self._estimate_noise_covariance(X_next, A_hat, X, B_hat, U_mid, Y_curr, C_hat) info = { "singular_values_O": s, @@ -215,13 +235,14 @@ def calculate_projection_and_svd(H, Z_p): return A_hat, B_hat, C_hat, info - def _time_align_valid_trials(self, X_hat, u_list, valid_trials, T_per_trial, p): + def _time_align_valid_trials(self, X_hat, u_list, y_list, valid_trials, T_per_trial, p): """Helper function to time-align trials for regression.""" # import pdb; pdb.set_trace() X_segments = [] X_next_segments = [] U_mid_segments = [] - + Y_segments = [] + start_idx = 0 for trial_idx, T_trial in enumerate(T_per_trial): X_trial = X_hat[:, start_idx:start_idx + T_trial] @@ -235,14 +256,19 @@ def _time_align_valid_trials(self, X_hat, u_list, valid_trials, T_per_trial, p): X_segments.append(X_trial_curr) X_next_segments.append(X_trial_next) U_mid_segments.append(U_mid_trial) - + + Y_trial = y_list[original_trial_idx] + Y_trial_curr = Y_trial[:, p:p+T_trial-1] + Y_segments.append(Y_trial_curr) + start_idx += T_trial X = np.concatenate(X_segments, axis=1) X_next = np.concatenate(X_next_segments, axis=1) U_mid = np.concatenate(U_mid_segments, axis=1) - - return X, X_next, U_mid + Y_curr = np.concatenate(Y_segments, axis=1) + + return X, X_next, U_mid, Y_curr def _perform_ridge_regression(self, X, X_next, U_mid, n, lamb): """Helper function to perform ridge regression.""" @@ -265,10 +291,10 @@ def _perform_ridge_regression(self, X, X_next, U_mid, n, lamb): return A_hat, B_hat - def _estimate_noise_covariance(self, X_next, A_hat, X, B_hat, U_mid): + def _estimate_noise_covariance(self, X_next, A_hat, X, B_hat, U_mid, Y_curr, C_hat): """Helper function to estimate the noise covariance matrix.""" W_hat = X_next - (A_hat @ X + B_hat @ U_mid) - V_hat = self.Y_curr - (self.C_hat @ X) + V_hat = Y_curr - (C_hat @ X) V_hat = V_hat - V_hat.mean(axis=1, keepdims=True) W_hat = W_hat - W_hat.mean(axis=1, keepdims=True) @@ -317,7 +343,7 @@ def subspace_dmdc_multitrial_custom(self, y_list, u_list, p, f, n=None, lamb=1e- Gamma_hat = U_n @ S_half X_hat = S_half @ V_n - X, X_next, U_mid = self._time_align_valid_trials(X_hat, u_list, valid_trials, T_per_trial, p) + X, X_next, U_mid, Y_curr = self._time_align_valid_trials(X_hat, u_list, y_list, valid_trials, T_per_trial, p) if any([i == 0 for i in X.shape]): raise ValueError ("too many delays for dataset, reduce number") A_hat, B_hat = self._perform_ridge_regression(X, X_next, U_mid, n, lamb) @@ -373,40 +399,46 @@ def subspace_dmdc_multitrial_flexible(self, y, u, p, f, n=None, lamb=1e-8, energ return self.subspace_dmdc_multitrial_custom(y_list, u_list, p, f, n, lamb, energy) - def predict(self, Y, U, reseed=None): + def predict(self, test_data=None, control_data=None, reseed=None): """Predict using the Kalman filter.""" + if test_data is None: + test_data = self.data + if control_data is None: + control_data = self.control_data + if reseed is None: reseed = 1 - if isinstance(Y, list): + if isinstance(test_data, list): self.kalman = OnlineKalman(self) Y_pred = [] - for trial in range(len(Y)): + for trial in range(len(test_data)): self.kalman.reset() trial_predictions = [ - self.kalman.step(y=Y[trial][t] if t % reseed == 0 else None, u=U[trial][t])[0] - for t in range(Y[trial].shape[0]) + self.kalman.step(y=test_data[trial][t] if t % reseed == 0 else None, u=control_data[trial][t])[0] + for t in range(test_data[trial].shape[0]) ] Y_pred.append(np.concatenate(trial_predictions, axis=1).T) return Y_pred self.kalman = OnlineKalman(self) - if Y.ndim == 2: + if test_data.ndim == 2: return np.concatenate( - [self.kalman.step(y=Y[t] if t % reseed == 0 else None, u=U[t])[0] for t in range(Y.shape[0])], + [self.kalman.step(y=test_data[t] if t % reseed == 0 else None, u=control_data[t])[0] for t in range(test_data.shape[0])], axis=1 ).T else: Y_pred = [] - for trial in range(Y.shape[0]): + for trial in range(test_data.shape[0]): self.kalman.reset() trial_predictions = [ - self.kalman.step(y=Y[trial, t] if t % reseed == 0 else None, u=U[trial, t])[0] - for t in range(Y.shape[1]) + self.kalman.step(y=test_data[trial, t] if t % reseed == 0 else None, u=control_data[trial, t])[0] + for t in range(test_data.shape[1]) ] Y_pred.append(np.concatenate(trial_predictions, axis=1).T) return np.array(Y_pred) + def compute_hankel(self, *args, **kwargs): """Compute Hankel matrices for SubspaceDMDc.""" raise NotImplementedError( @@ -435,6 +467,7 @@ def __init__(self, dmdc): self.Q = dmdc.info['Q_hat'] self.y_dim, self.x_dim = self.C.shape + self.u_dim = self.B.shape[1] self.reset() diff --git a/examples/all_dsa_types.ipynb b/examples/all_dsa_types.ipynb index 704c49c..d1cc01c 100644 --- a/examples/all_dsa_types.ipynb +++ b/examples/all_dsa_types.ipynb @@ -2,13 +2,36 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "773aa0fd", "metadata": {}, "outputs": [], "source": [ "import numpy as np \n", "import matplotlib.pyplot as plt\n", + "import sys\n", + "import importlib\n", + "\n", + "# Remove any local DSA packages from sys.path to force using the installed package\n", + "paths_to_remove = [p for p in sys.path if 'Degeneracy' in p and 'DSA' in p]\n", + "for p in paths_to_remove:\n", + " sys.path.remove(p)\n", + "\n", + "# Also remove the current directory's parent if it contains a DSA package\n", + "# This prevents importing from /n/home00/ahuang/DSA if it's in the path\n", + "# Uncomment the next lines if needed:\n", + "# if '/n/home00/ahuang/DSA' in sys.path:\n", + "# sys.path.remove('/n/home00/ahuang/DSA')\n", + "\n", + "# Force reload to ensure we get the installed package\n", + "if 'DSA' in sys.modules:\n", + " del sys.modules['DSA']\n", + " # Also remove any submodules\n", + " modules_to_remove = [k for k in sys.modules.keys() if k.startswith('DSA.')]\n", + " for k in modules_to_remove:\n", + " del sys.modules[k]\n", + "\n", + "# Now import from the installed package\n", "from DSA import DSA, GeneralizedDSA, InputDSA\n", "from DSA import DMD, DMDc, SubspaceDMDc, ControllabilitySimilarityTransformDist\n", "from DSA import DMDConfig, DMDcConfig, SubspaceDMDcConfig\n", @@ -16,7 +39,7 @@ "from pydmd import DMD as pDMD\n", "import DSA.pykoopman as pk\n", "%load_ext autoreload\n", - "%autoreload 2\n" + "%autoreload 2" ] }, { @@ -39,7 +62,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "d452743b", "metadata": {}, "outputs": [ @@ -49,7 +72,7 @@ "(18, 9)" ] }, - "execution_count": 7, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -58,6 +81,7 @@ "d1 = np.random.random(size=(20,5))\n", "u1 = np.random.random(size=(20,2))\n", "\n", + "# n_trials, n_timepoints, n_features\n", "d2 = np.random.random(size=(3,20,5))\n", "u2 = np.random.random(size=(3,20,2))\n", "\n", @@ -75,39 +99,10 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "id": "88cad354", "metadata": {}, - "outputs": [ - { - "ename": "ValueError", - "evalue": "Trial 0: y and u have different time lengths", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[14], line 14\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# dmdc = DMDc(d5,u5,n_delays=2,rank_input=10,rank_output=10)\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;66;03m# dmdc.fit()\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# print(dmdc.A_v.shape)\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 11\u001b[0m \n\u001b[1;32m 12\u001b[0m \u001b[38;5;66;03m#TODO: fix this case\u001b[39;00m\n\u001b[1;32m 13\u001b[0m subdmdc \u001b[38;5;241m=\u001b[39m SubspaceDMDc(d2,u2,n_delays\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m10\u001b[39m,rank\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m,backend\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mn4sid\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m---> 14\u001b[0m \u001b[43msubdmdc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 15\u001b[0m \u001b[38;5;28mprint\u001b[39m(subdmdc\u001b[38;5;241m.\u001b[39mA_v\u001b[38;5;241m.\u001b[39mshape)\n\u001b[1;32m 16\u001b[0m \u001b[38;5;28mprint\u001b[39m(subdmdc\u001b[38;5;241m.\u001b[39mB_v\u001b[38;5;241m.\u001b[39mshape)\n", - "File \u001b[0;32m~/Desktop/Projects/DSAv2/DSAPublic2/DSA/DSA/subspace_dmdc.py:70\u001b[0m, in \u001b[0;36mSubspaceDMDc.fit\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 68\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mfit\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 69\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Fit the SubspaceDMDc model.\"\"\"\u001b[39;00m\n\u001b[0;32m---> 70\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mA_v, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mB_v, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mC_v, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minfo \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msubspace_dmdc_multitrial_flexible\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 71\u001b[0m \u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 72\u001b[0m \u001b[43m \u001b[49m\u001b[43mu\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcontrol_data\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 73\u001b[0m \u001b[43m \u001b[49m\u001b[43mp\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mn_delays\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 74\u001b[0m \u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mn_delays\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 75\u001b[0m \u001b[43m \u001b[49m\u001b[43mn\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrank\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m 76\u001b[0m \u001b[43m \u001b[49m\u001b[43mbackend\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackend\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 77\u001b[0m \u001b[43m \u001b[49m\u001b[43mlamb\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlamb\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 79\u001b[0m \u001b[38;5;66;03m# Send to CPU if requested (inherited from BaseDMD)\u001b[39;00m\n\u001b[1;32m 80\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msend_to_cpu:\n", - "File \u001b[0;32m~/Desktop/Projects/DSAv2/DSAPublic2/DSA/DSA/subspace_dmdc.py:371\u001b[0m, in \u001b[0;36mSubspaceDMDc.subspace_dmdc_multitrial_flexible\u001b[0;34m(self, y, u, p, f, n, lamb, energy, backend)\u001b[0m\n\u001b[1;32m 367\u001b[0m \u001b[38;5;66;03m# y_list = [y_trial.T for y_trial in y_list]\u001b[39;00m\n\u001b[1;32m 368\u001b[0m \u001b[38;5;66;03m# u_list = [u_trial.T for u_trial in u_list]\u001b[39;00m\n\u001b[1;32m 370\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m backend \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mn4sid\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[0;32m--> 371\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msubspace_dmdc_multitrial_QR_decomposition\u001b[49m\u001b[43m(\u001b[49m\u001b[43my_list\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mu_list\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlamb\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43menergy\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 372\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 373\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msubspace_dmdc_multitrial_custom(y_list, u_list, p, f, n, lamb, energy)\n", - "File \u001b[0;32m~/Desktop/Projects/DSAv2/DSAPublic2/DSA/DSA/subspace_dmdc.py:150\u001b[0m, in \u001b[0;36mSubspaceDMDc.subspace_dmdc_multitrial_QR_decomposition\u001b[0;34m(self, y_list, u_list, p, f, n, lamb, energy)\u001b[0m\n\u001b[1;32m 145\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21msubspace_dmdc_multitrial_QR_decomposition\u001b[39m(\u001b[38;5;28mself\u001b[39m, y_list, u_list, p, f, n\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, lamb\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1e-8\u001b[39m, energy\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.999\u001b[39m):\n\u001b[1;32m 146\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 147\u001b[0m \u001b[38;5;124;03m Subspace-DMDc for multi-trial data with variable trial lengths using QR decomposition.\u001b[39;00m\n\u001b[1;32m 148\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m 149\u001b[0m U_p, Y_p, U_f, Y_f, Z_p, valid_trials, T_per_trial, T_total, p_out, m \u001b[38;5;241m=\u001b[39m \\\n\u001b[0;32m--> 150\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_and_collect_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43my_list\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mu_list\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 152\u001b[0m H \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mvstack([U_f, Z_p, Y_f])\n\u001b[1;32m 154\u001b[0m dim_uf \u001b[38;5;241m=\u001b[39m f \u001b[38;5;241m*\u001b[39m m\n", - "File \u001b[0;32m~/Desktop/Projects/DSAv2/DSAPublic2/DSA/DSA/subspace_dmdc.py:98\u001b[0m, in \u001b[0;36mSubspaceDMDc._validate_and_collect_data\u001b[0;34m(self, y_list, u_list, p, f)\u001b[0m\n\u001b[1;32m 96\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTrial \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mi\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m: u has \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mu_trial\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m inputs, expected \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mm\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 97\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m y_trial\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m!=\u001b[39m u_trial\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m]:\n\u001b[0;32m---> 98\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTrial \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mi\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m: y and u have different time lengths\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 100\u001b[0m U_p_all \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m 101\u001b[0m Y_p_all \u001b[38;5;241m=\u001b[39m []\n", - "\u001b[0;31mValueError\u001b[0m: Trial 0: y and u have different time lengths" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> \u001b[0;32m/Users/mitchellostrow/Desktop/Projects/DSAv2/DSAPublic2/DSA/DSA/subspace_dmdc.py\u001b[0m(98)\u001b[0;36m_validate_and_collect_data\u001b[0;34m()\u001b[0m\n", - "\u001b[0;32m 96 \u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Trial {i}: u has {u_trial.shape[0]} inputs, expected {m}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\u001b[0;32m 97 \u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0my_trial\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mu_trial\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\u001b[0;32m---> 98 \u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Trial {i}: y and u have different time lengths\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\u001b[0;32m 99 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\u001b[0;32m 100 \u001b[0;31m \u001b[0mU_p_all\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\n" - ] - } - ], + "outputs": [], "source": [ "\n", "# dmdc = DMDc(d5,u5,n_delays=2,rank_input=10,rank_output=10)\n", @@ -120,21 +115,145 @@ "# print(subdmdc.A_v.shape)\n", "# print(subdmdc.B_v.shape)\n", "\n", + "# Testing the case where n_delays >= trial_length / 2 in subspace dmdc\n", + "# prob_15 = 0.8\n", + "# prob_30 = 1 - prob_15\n", + "# n_arrays = 10\n", "\n", - "#TODO: fix this case\n", - "subdmdc = SubspaceDMDc(d2,u2,n_delays=10,rank=5,backend='n4sid')\n", - "subdmdc.fit()\n", - "print(subdmdc.A_v.shape)\n", - "print(subdmdc.B_v.shape)\n", + "# # Generate the list\n", + "# d2 = [np.random.random(size=(np.random.choice([15, 30], p=[prob_15, prob_30]), 5)) \n", + "# for _ in range(n_arrays)]\n", + "# u2 = [np.random.random(size=(d.shape[0], 2)) \n", + "# for d in d2] # match the first dimension from d2\n", + "\n", + "# subdmdc = SubspaceDMDc(d2,u2,n_delays=6,rank=5,backend='n4sid')\n", + "# subdmdc.fit()\n", + "# print(subdmdc.A_v.shape)\n", + "# print(subdmdc.B_v.shape)\n", "\n", "\n", "#TODO: fix this case\n", "# subdmdc = SubspaceDMDc(d3,u3,n_delays=2,rank=10,backend='n4sid')\n", "# subdmdc.fit()\n", "# print(subdmdc.A_v.shape)\n", - "# print(subdmdc.B_v.shape)\n", + "# print(subdmdc.B_v.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "b7b308b7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(500, 10)\n", + "(500, 9)\n" + ] + } + ], + "source": [ + "#TODO: check predictions for all cases\n", + "\n", + "def make_stable_A(n, rho=0.9, rng=None):\n", + " rng = np.random.default_rng(rng)\n", + " M = rng.standard_normal((n, n))\n", + " # Make it diagonally dominant-ish and scale spectral radius\n", + " A = M / np.max(np.abs(np.linalg.eigvals(M))) * rho\n", + " return A\n", + "\n", + "def simulate_system(A, B, C, U, x0=None,rng=None,obs_noise=0.0,process_noise=0.0,\n", + " nonlinear_eps=0.0,nonlinear_func= lambda x: np.tanh(x),nonlinear_eps_input=0.0):\n", + " n, m = B.shape\n", + " p_out = C.shape[0]\n", + " N = U.shape[1]\n", + " X = np.zeros((n, N+1))\n", + " C_full = np.eye(A.shape[0])\n", + " C_full[np.where(C == 1)[1],np.where(C == 1)[1]] = 0.0\n", + "\n", + " if x0 is not None:\n", + " X[:, 0] = x0\n", + " else:\n", + " X[:, 0] = np.random.default_rng(rng).standard_normal((n,))\n", + " Y = np.zeros((p_out, N))\n", + " for t in range(N):\n", + " X[:, t+1] = A @ (X[:, t]) + nonlinear_eps * C_full @ nonlinear_func(A @ X[:, t]) + \\\n", + " B @ ((1-nonlinear_eps_input) * U[:, t] + nonlinear_eps_input * nonlinear_func(U[:, t])) + \\\n", + " np.random.normal(0, process_noise, (n,))\n", + " Y[:, t] = C @ X[:, t] + np.random.normal(0, obs_noise, (p_out,))\n", + " return X[:, 1:], Y # states aligned with Y\n", + "\n", + "def smooth_input(m, N, alpha=0.9, rng=None):\n", + " rng = np.random.default_rng(rng)\n", + " w = rng.standard_normal((m, N))\n", + " U = np.zeros_like(w)\n", + " for t in range(N):\n", + " U[:, t] = alpha*(U[:, t-1] if t>0 else 0) + (1-alpha)*w[:, t]\n", + " return U\n", + "\n", + "\n", + "latent_dim = 10\n", + "input_dim = 2\n", + "g1 = 0.5\n", + "seed1 = 123\n", + "seq_length = 500\n", + "input_alpha = 0.001\n", + "nonlinear_eps = 0.1\n", + "observed_dim = 9\n", + "idx_obs = np.arange(observed_dim)\n", + "A = make_stable_A(latent_dim)\n", + "B = np.random.default_rng(seed1).standard_normal((latent_dim, input_dim)) * g1\n", + "C = np.zeros((observed_dim, latent_dim))\n", + "C[np.arange(observed_dim), idx_obs] = 1.0\n", + "U = smooth_input(input_dim, seq_length, alpha=input_alpha, rng=seed1) \n", + "\n", + "X, Y = simulate_system(A, B, C, U, nonlinear_eps=nonlinear_eps)\n", + "\n", + "X = X.T\n", + "Y = Y.T\n", + "# U = U.T\n", + "\n", + "print(X.shape)\n", + "print(Y.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "e59db62a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DMD prediction MASE: 0.0001704487077985564\n", + "DMDC prediction MASE: 0.6608961494400851\n", + "Using 1 out of 1 trials with sufficient time points.\n", + "DMDC prediction MASE: 0.4358238354387466\n" + ] + } + ], + "source": [ + "from DSA.stats import mase\n", + "X_auto, Y_auto = simulate_system(A, np.zeros((latent_dim, input_dim)), C, U, nonlinear_eps=nonlinear_eps)\n", + "X_auto = X_auto.T\n", + "dmd = DMD(X_auto, n_delays=10, rank=10)\n", + "dmd.fit()\n", + "pred_data = dmd.predict()\n", + "print(f'DMD prediction MASE: {mase(X_auto, pred_data)}')\n", "\n", - "#TODO: check predictions for all cases" + "dmdc = DMDc(X, U.T, n_delays=10, rank_input=10, rank_output=10)\n", + "dmdc.fit()\n", + "pred_data = dmdc.predict()\n", + "print(f'DMDC prediction MASE: {mase(X, pred_data)}')\n", + "\n", + "dmdc = SubspaceDMDc(X, U.T, n_delays=10, rank=10, backend='n4sid')\n", + "dmdc.fit()\n", + "pred_data = dmdc.predict()\n", + "print(f'DMDC prediction MASE: {mase(X, pred_data)}')" ] }, { @@ -244,6 +363,16 @@ "#TODO: check generalized dsa with the other comparison metric and changing the config\n" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "ab0dbe0a", + "metadata": {}, + "outputs": [], + "source": [ + "\n" + ] + }, { "cell_type": "code", "execution_count": null, @@ -255,7 +384,7 @@ ], "metadata": { "kernelspec": { - "display_name": "dsa_test_env", + "display_name": "py39", "language": "python", "name": "python3" }, @@ -269,7 +398,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.18" + "version": "3.9.18" } }, "nbformat": 4, From 7f4f644b92194a363cad32ca9bf6409cb1c7faaf Mon Sep 17 00:00:00 2001 From: Ann Huang Date: Mon, 3 Nov 2025 18:49:19 -0500 Subject: [PATCH 28/90] checked gDSA with various data structures, metrics, and configs --- DSA/__pycache__/base_dmd.cpython-39.pyc | Bin 8329 -> 8329 bytes DSA/__pycache__/dsa.cpython-39.pyc | Bin 26920 -> 27422 bytes DSA/__pycache__/subspace_dmdc.cpython-39.pyc | Bin 17995 -> 19805 bytes DSA/dsa.py | 12 + DSA/subspace_dmdc.py | 150 +++++++--- examples/all_dsa_types.ipynb | 272 ++++++++++++++----- 6 files changed, 326 insertions(+), 108 deletions(-) diff --git a/DSA/__pycache__/base_dmd.cpython-39.pyc b/DSA/__pycache__/base_dmd.cpython-39.pyc index 6124923bcb3dc6800554d7a81ed444d00e842978..a7657d2844e5f6c098e7fa73accd739ca9d4e83b 100644 GIT binary patch delta 726 zcmZ{i%}*0S7{-|{ZKUniR;19D77f96i-P!(MiEHW)X2dDBKUDwH|+>3-EH&k*2Ehg zn-~vFV!ROJpJ4W&@#4vg(Zut3^G3Mn)syoq2?wKl_~n`RdFGurGuyBC>j_KO;{)pZ zF);Kt@jT9C@GAZ;l0T(NG!3K=k%5{Kw!WU8Rq~d>WYpWVx}2uPH~l>;i+-v+a#mSV zv)XN{Aa4Lw@hLUM=7Rmy1IA{>cHy}AnHv;ldU+D@e;#yR89Oj9h%f2wiFwE?{|nEE zt<23eWE#d1x{O%ZIH8o(yq-fzsH9J})i#@*p6h$0BetMmMCm9rE`ly8$RXP^$=cZ9 zEjlOS$M!0Vu$Y3OQ4V?uu~@U`sOGAM{gU05L1Fl##^%NM?6W!|RD+?lxhpl>_2r=L zbBCfRM%|v%eHz}9OnTg+=BC#npSO&}!8q5&?&xAx4tFiG9PV@S%r14F*e%|Y;@9X{ zM(WLu)9tA(YFKwmo)l-I#h^B}#Ky}w8-vQ&DtA?18Sy(5L$xY)a-WNb0lI@CCZc6v zT|vCgjRY_9bF8|iRz`$Q!zlq-Ap9o|eh0OTse4hcv{$&6|Enu@(?#G$&=~g^dni5> ycNOoJ-YEVqy%%NbRjy6kMr(1@4m}>)2()^2}4!m2l#W} z9(D*ir_ALA00f%HByP!(yZ%jrp$i<$_d-n!wMRF*sH2Qe!sWEXj_>o!_Vp47vj>(_7K3PxSIEnZ_4?4%j7R>YVYkG5Z2J*`P!WFrZ zxqcs+ns$gTBi3u2U==D}$EGAJJ?WEWwCas^$MHQ<5xdIJqI3ir7eSX8`eDnfld-mD zHfc)6kM2&)!V(Nkp&axaBaAgVHaF)Q_J`uG9u)dNDPl%`&pxXnLKk5uEz=1V%kjfL z%QtO`q8N2Lw)?bqOJUM84Qi}=ZSu{gme?DoDz^vcvU_K+TNBXwK)LjnrY$$7vuS0~YuOkO>=MLixP`7ErN#M{Hx?1H+>0J{P6nbb!ekI|>HF^if-Gd8A= zz4FM4SHhyDoWA6>h6HQe4n5bY&h<}-e&wy77W>q|hI>o14QF-7k^9is54a6*7%&8YelIj`PI`3{dbVhK^~1X3rrgPb>0OwXdEG$UG*a9< zXEAF7FZzkjg+ctP&+!GTsK~Gqo81v=VD%r7Dq}5M5{B)3xk0sI^A)8#pfogP6{HS5X>MjsyEe1$;)q@-AZuf+ zSl$V_#B98zXhtt)D=<-GD&?Qp>v7ds%R6JHYxg<-^xB z3b9^|bycQOL)~LSs$X?qbx^~IdjL=-PXXqe8o4odDld`C8^x@dvh|#K{*1UneEzW1@>N4G;w7Dy9 zo3=}A+dMuGyOGl0pvd!c-)qthy?_+*f6t8A=%JP4ACWFm zQmW0nHiU<${qX+9>dswy%O}Igi*;x9T-wmLZV1wnKd;X1+OZhvC(i)p2&+T8Urap) z=Hmq9dKTcLY>pKApCH)6+R%EEpkF>kNO4xvjO3cD9?i3AbWgGbWBt&*7Ze|P3eu?D$` z4>s6&iQH)jBY1aUTDXCefid9*PY30P>e(CGOV2PT%$%2ux|4wvea0e2s3_zK?u($$ zIRS1)P=Dkk;aJ#+EDxL(@}ES9AYFK*GHyC}GAPd)(dm#KHe%BuH+m*!)SnD34Ll)4 z!0zO{R=FK>>y5-|aZ1=Pv!FOF9uo37QZ9IUOwJP$w*@qFLqvti zi~w;vNJ9f_n5Zp|__onFcX`C|7r&ZU_>gd(Ateoiw!|ZBg2}mTRsn=$1TgQ4xqF zn0k2kh^9-aPdeIh%N@0BZ9rSA+lDrnB)+LRdagjFqBbJ)In5nq2EimFCHkbMJDNqT{Be8Mb8T^@^_!)*a4>H`z1zR= z%D89}i$qvNLg8Ro1V!*%@LVJu7ZDMs--aN4@QqJ7apPccGvY~JLD0W7MCgUFk6zd& z?3Wrk`A%O?)K;1*Aiodzo_h4=v+MbIM7YjYIr4e*-2~tu=8Jopn&0u`*WTZ^nmbCL zE%(O$t`e5y)wx9qSIN$N7Gs_R!~n+td_=Op??97Xy&4qP-1igZMd=bVGp3Xs+#=8F zF1n;{j-7ir(Q1jDK8FNT-JD>8|9fBE?XUlvzB1Mxa%I4@0k zS5>sSc&{F(EO)=C>|5_>mXN7BbgHECU5nKN-OcK;t`%zQ!EHULVZ2apYQht}Y~M`K zFO1I2qrE9(@)@4W>8`X+ysREPv|PP@&`JK7gsFz`m2HQz?S5hOv-7m5!lBQJ-NbV8rNiqW3O(?q(jJ$ zUpUPR)5;yMj-LG#s|Z~G*~3m||8gPlG(@A$?M2g^GU>kq<7h|h6bA37ijD39r{-K% z;YFqC6th$s%&@TElE20j@BmdQ?2T8zfIh#R{~=LtsgI83OFN0`Pm2EJviTnVUcAp7 z(Mf;bzCO7E!#e>N0Ga_vJ9!}>0k|Dt0WyG5z*hmtB#8q}BE2|JIHoi|dSH*gW4vt) zoQ!)^DBY9jj3;Bwu|zB(_t4XHg&IxoZoPqqT<6Df7e;RZYy)%?P*_|G+ONjdJL#d) zAE}*8NIp;aD7gg!)#s|rqWNhr)MSXxfntFU8eSsm?PR0x(3-*{&`_)ThtRBA(aDbn zFOJvN_;KWe{}RTGGs3G+`E_zCH79a0*B14U;hvjTLYD?u1$YAx1=P+%2z!mQYKF~h zwxWu?=|PxX`X*7G>ZGw+yh+Zyr(upG_o{Yt{Gv~Hg!+Mb^zd8I#9qKDKpy3=VlS{q z*|#;fews%mTU6U=XgsD0Bi*;1x7Iq$*AD0de0J=;4G~s)-m2fBj+BaI)+_;)yxNP> z_1{LDA^F;SL-l4hiZ80;nXYx;CIR}tSTR?al+S`*l+T$N9q*Pl6cMuzm>6}*r`4Y` zz47Xi@2M+BJFU~`scp<{pd=_>&qh$3P`t7|XkvFHOq{cds*~t_<3DJ(;skOI PtJg+vt1AWSnlJw!Q@7#F delta 4774 zcmaJ_Yiv~45#Fz2$=1fHGv};d{H^%*uSJDcULJDMPv%;{EPMKT#XsC4S_pb(d&NBUU_h^$M?Blx zlTIf_U2-W6eckI|fkc!u2GsxpCpvZ-*hqR}gA)dw?L;!{LnvelK)`!Jwr zdM1Wb1!M&=O!`=51Y~qZnNV+3)D=dnG>=%V!ZkaNPGzzc`idJ}i(vrJ4&dEx05t

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z%=uH3%WImKnADyyz_^px>H$RnX37Zmdx-XiQYTmZR@<9|a#}|oRrrSX&FL{kZaU#Z zF3v5ck~bJtH`J>n8*0xsIf$V2tOS%$hu=Pk9QS=po@dfz(q(cPNw6b|LZRRBdFv;c zNN^GcdJT3(51m0p`a$|l%UCLq%9z!l@)%^sm9|5WQ3u=>a)|5!tj(1Fh{5&0(%8wd n3uD!>r$?>PyGD Date: Tue, 4 Nov 2025 09:39:05 -0500 Subject: [PATCH 31/90] changed subspace dmdc code to work with (n_timepoints, n_features) data without transposing --- DSA/subspace_dmdc.py | 28 ++++++++------ examples/all_dsa_types.ipynb | 71 +++++++++++++++++++++++++++++++----- 2 files changed, 77 insertions(+), 22 deletions(-) diff --git a/DSA/subspace_dmdc.py b/DSA/subspace_dmdc.py index a5c245d..918c81b 100644 --- a/DSA/subspace_dmdc.py +++ b/DSA/subspace_dmdc.py @@ -148,8 +148,8 @@ def _check_same_shape(self): def _collect_data(self, y_list, u_list, p, f): """Helper function to validate dimensions and collect data from trials.""" - p_out = y_list[0].shape[0] - m = u_list[0].shape[0] + p_out = y_list[0].shape[-1] + m = u_list[0].shape[-1] U_p_all = [] Y_p_all = [] @@ -159,10 +159,12 @@ def _collect_data(self, y_list, u_list, p, f): T_per_trial = [] def hankel_stack(X, start, L): - return np.concatenate([X[:, start + i:start + i + 1] for i in range(L)], axis=0) + # X is now (n_timepoints, n_features), so we transpose for slicing + # then stack along axis=0 to get (L * n_features, 1) + return np.concatenate([X[start + i:start + i + 1, :].T for i in range(L)], axis=0) for trial_idx, (Y_trial, U_trial) in enumerate(zip(y_list, u_list)): - N_trial = Y_trial.shape[1] + N_trial = Y_trial.shape[0] T_trial = N_trial - (p + f) if T_trial <= 0: @@ -306,14 +308,16 @@ def _time_align_valid_trials(self, X_hat, u_list, y_list, valid_trials, T_per_tr original_trial_idx = valid_trials[trial_idx] U_trial = u_list[original_trial_idx] - U_mid_trial = U_trial[:, p:p + (T_trial - 1)] + # U_trial is now (n_timepoints, n_features), slice rows then transpose + U_mid_trial = U_trial[p:p + (T_trial - 1), :].T X_segments.append(X_trial_curr) X_next_segments.append(X_trial_next) U_mid_segments.append(U_mid_trial) Y_trial = y_list[original_trial_idx] - Y_trial_curr = Y_trial[:, p:p+T_trial-1] + # Y_trial is now (n_timepoints, n_features), slice rows then transpose + Y_trial_curr = Y_trial[p:p+T_trial-1, :].T Y_segments.append(Y_trial_curr) start_idx += T_trial @@ -435,11 +439,11 @@ def _convert_to_subspace_dmdc_data_format(self,y, u): for y_trial, u_trial in zip(y, u): if y_trial.ndim == 3 and u_trial.ndim == 3: for t in range(len(y_trial)): - y_list.append(y_trial[t].T) - u_list.append(u_trial[t].T) + y_list.append(y_trial[t]) + u_list.append(u_trial[t]) elif y_trial.ndim == 2 and u_trial.ndim == 2: - y_list.append(y_trial.T) - u_list.append(u_trial.T) + y_list.append(y_trial) + u_list.append(u_trial) else: raise ValueError("Invalid dimension. Only list of (n_trials, n_timepoints, n_features) or (n_timepoints, n_features) arrays are supported.") else: @@ -449,8 +453,8 @@ def _convert_to_subspace_dmdc_data_format(self,y, u): else: y_list = [y[i] for i in range(y.shape[0])] u_list = [u[i] for i in range(u.shape[0])] - y_list = [y_trial.T for y_trial in y_list] - u_list = [u_trial.T for u_trial in u_list] + y_list = [y_trial for y_trial in y_list] + u_list = [u_trial for u_trial in u_list] return y_list, u_list diff --git a/examples/all_dsa_types.ipynb b/examples/all_dsa_types.ipynb index ad52778..6615607 100644 --- a/examples/all_dsa_types.ipynb +++ b/examples/all_dsa_types.ipynb @@ -10,7 +10,6 @@ "import numpy as np \n", "import matplotlib.pyplot as plt\n", "\n", - "# Now import from the installed package\n", "from DSA import DSA, GeneralizedDSA, InputDSA\n", "from DSA import DMD, DMDc, SubspaceDMDc, ControllabilitySimilarityTransformDist\n", "from DSA import DMDConfig, DMDcConfig, SubspaceDMDcConfig\n", @@ -41,7 +40,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 4, "id": "d452743b", "metadata": {}, "outputs": [ @@ -51,7 +50,7 @@ "(18, 9)" ] }, - "execution_count": 28, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -78,7 +77,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 14, "id": "88cad354", "metadata": {}, "outputs": [ @@ -121,7 +120,7 @@ "\n", "\n", "# passing list of 3D arrayss\n", - "subdmdc = SubspaceDMDc(d5,u5,n_delays=2,rank=10,backend='n4sid')\n", + "subdmdc = SubspaceDMDc(d1,u1,n_delays=2,rank=10,backend='custom')\n", "subdmdc.fit()\n", "print(subdmdc.A_v.shape)\n", "print(subdmdc.B_v.shape)\n" @@ -129,7 +128,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "id": "b7b308b7", "metadata": {}, "outputs": [ @@ -188,7 +187,8 @@ "seed1 = 123\n", "seq_length = 500\n", "input_alpha = 0.001\n", - "nonlinear_eps = 0.1\n", + "nonlinear_eps = 0.0\n", + "nonlinear_eps_input = 0.0\n", "observed_dim = 9\n", "idx_obs = np.arange(observed_dim)\n", "A = make_stable_A(latent_dim)\n", @@ -201,12 +201,63 @@ "\n", "X = X.T\n", "Y = Y.T\n", - "# U = U.T\n", + "U = U.T\n", "\n", "print(X.shape)\n", "print(Y.shape)" ] }, + { + "cell_type": "code", + "execution_count": 22, + "id": "8c4c9ea2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 9.00000000e-01+0.j -4.27762264e-01+0.34055831j\n", + " -4.27762264e-01-0.34055831j 5.72126042e-01+0.47528717j\n", + " 5.72126042e-01-0.47528717j -2.56600620e-04+0.50116322j\n", + " -2.56600620e-04-0.50116322j 3.46246199e-01+0.30316658j\n", + " 3.46246199e-01-0.30316658j -8.50722948e-02+0.j ]\n", + "(500, 9) (500, 2)\n", + "[-4.27762263e-01+0.34055831j -4.27762263e-01-0.34055831j\n", + " 8.99999999e-01+0.j 5.72126042e-01+0.47528717j\n", + " 5.72126042e-01-0.47528717j -2.56600504e-04+0.50116322j\n", + " -2.56600504e-04-0.50116322j 3.46246198e-01+0.30316658j\n", + " 3.46246198e-01-0.30316658j -8.50722947e-02+0.j ]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "

" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Check that subspace dmdc works with (n_timepoints, n_features) data\n", + "print(np.linalg.eigvals(A))\n", + "print(Y.shape, U.shape)\n", + "subdmdc = SubspaceDMDc(Y,U,n_delays=10,rank=10,backend='n4sid')\n", + "subdmdc.fit()\n", + "print(np.linalg.eigvals(subdmdc.A_v))\n", + "\n", + "# plot the two sets of eigenvalues as scatter plots\n", + "plt.figure(figsize=(5,5))\n", + "plt.scatter(np.real(np.linalg.eigvals(A)), np.imag(np.linalg.eigvals(A)), label='True', s=100, alpha=0.5)\n", + "plt.scatter(np.real(np.linalg.eigvals(subdmdc.A_v)), np.imag(np.linalg.eigvals(subdmdc.A_v)), label='Subspace DMDc', s=100, alpha=0.5)\n", + "plt.legend()\n", + "plt.show()\n", + "\n" + ] + }, { "cell_type": "code", "execution_count": 28, @@ -319,7 +370,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": null, "id": "2ea88dc2", "metadata": {}, "outputs": [ @@ -397,7 +448,7 @@ "# sim.shape\n", "\n", "# Should return a 3x3 distance matrix\n", - "#TODO: when doing cross-comparison and using a list of arrays, gDSA treats each array as its own system\n", + "# When doing cross-comparison and using a list of arrays, gDSA treats each array as its own system\n", "dsa = GeneralizedDSA(X=d3, X_control=u3,\n", " Y=d3, Y_control=u3,\n", " dmd_class=DMDc,dmd_config=dict(n_delays=5,rank_input=5,rank_output=5),\n", From e8c93af17fb9c359a5ce5f2228d2d6d26de53efa Mon Sep 17 00:00:00 2001 From: Ann Huang Date: Tue, 4 Nov 2025 09:40:47 -0500 Subject: [PATCH 32/90] changed subspace dmdc code to work with (n_timepoints, n_features) data without transposing --- DSA/subspace_dmdc.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/DSA/subspace_dmdc.py b/DSA/subspace_dmdc.py index 918c81b..8fe7d6f 100644 --- a/DSA/subspace_dmdc.py +++ b/DSA/subspace_dmdc.py @@ -160,7 +160,7 @@ def _collect_data(self, y_list, u_list, p, f): def hankel_stack(X, start, L): # X is now (n_timepoints, n_features), so we transpose for slicing - # then stack along axis=0 to get (L * n_features, 1) + # then stack along axis=0 return np.concatenate([X[start + i:start + i + 1, :].T for i in range(L)], axis=0) for trial_idx, (Y_trial, U_trial) in enumerate(zip(y_list, u_list)): From 7b3b9240f9cfbd8e16285c5f8b4555944fb8b160 Mon Sep 17 00:00:00 2001 From: Mitchell Ostrow <35669245+mitchellostrow@users.noreply.github.com> Date: Tue, 4 Nov 2025 14:40:21 -0500 Subject: [PATCH 33/90] update readme --- README.md | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 27b72fb..6866721 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -# generalized DSA +# Generalized DSA Computational techniques for Dynamical Similarity Analysis. First introduced in, 1. "Beyond Geometry: Comparing the Temporal Structure of Computation in Neural Circuits via Dynamical Similarity Analysis" @@ -8,9 +8,10 @@ https://arxiv.org/abs/2306.10168 Abstract: How can we tell whether two neural networks are utilizing the same internal processes for a particular computation? This question is pertinent for multiple subfields of both neuroscience and machine learning, including neuroAI, mechanistic interpretability, and brain-machine interfaces. Standard approaches for comparing neural networks focus on the spatial geometry of latent states. Yet in recurrent networks, computations are implemented at the level of neural dynamics, which do not have a simple one-to-one mapping with geometry. To bridge this gap, we introduce a novel similarity metric that compares two systems at the level of their dynamics. Our method incorporates two components: Using recent advances in data-driven dynamical systems theory, we learn a high-dimensional linear system that accurately captures core features of the original nonlinear dynamics. Next, we compare these linear approximations via a novel extension of Procrustes Analysis that accounts for how vector fields change under orthogonal transformation. Via four case studies, we demonstrate that our method effectively identifies and distinguishes dynamic structure in recurrent neural networks (RNNs), whereas geometric methods fall short. We additionally show that our method can distinguish learning rules in an unsupervised manner. Our method therefore opens the door to novel data-driven analyses of the temporal structure of neural computation, and to more rigorous testing of RNNs as models of the brain. -and now including code from the following: +and now including code from our new paper: 2. "InputDSA: Demixing then comparing recurrent and externally driven dynamics + Abstract: In control problems and basic scientific modeling, it is important to compare observations with dynamical simulations. For example, comparing two neural systems can shed light on the nature of emergent computations in the brain and deep neural networks. Recently, (Ostrow et al., 2023) introduced Dynamical Similarity Analysis (DSA), a method to measure the similarity of two systems based on their state dynamics rather than geometry or topology. However, DSA does not consider how inputs affect the dynamics, meaning that two similar systems, if driven differently, may be classified as different. Because real-world dynamical systems are rarely autonomous, it is important to account for the effects of input drive. To this end, we introduce a novel metric for comparing both intrinsic (recurrent) and input-driven dynamics, called InputDSA (iDSA). InputDSA extends the DSA framework by estimating and comparing both input and intrinsic dynamic operators using a novel variant of Dynamic Mode Decomposition with control (DMDc) based on subspace identification. We demonstrate that InputDSA can successfully compare partially observed, input-driven systems from noisy data. We show that when the true inputs are unknown, surrogate inputs can be substituted without a major deterioration in similarity estimates. We apply InputDSA on Recurrent Neural Networks (RNNs) trained with Deep Reinforcement Learning, identifying that high-performing networks are dynamically similar to one another, while low-performing networks are more diverse. Lastly, we apply InputDSA to neural data recorded from rats performing a cognitive task, demonstrating that it identifies a transition from input-driven evidence accumulation to intrinsically- driven decision-making. Our work demonstrates that InputDSA is a robust and efficient method for comparing intrinsic dynamics and the effect of external input on dynamical systems From 1412ce8a0a3473dc102cbd88b8d7c127fcfd34e6 Mon Sep 17 00:00:00 2001 From: Mitchell Ostrow <35669245+mitchellostrow@users.noreply.github.com> Date: Tue, 4 Nov 2025 14:41:05 -0500 Subject: [PATCH 34/90] Add abstract for InputDSA paper to README Added abstract for InputDSA paper explaining its significance in comparing dynamical systems. --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 6866721..d240ce2 100644 --- a/README.md +++ b/README.md @@ -12,6 +12,8 @@ and now including code from our new paper: 2. "InputDSA: Demixing then comparing recurrent and externally driven dynamics +https://www.arxiv.org/abs/2510.25943 + Abstract: In control problems and basic scientific modeling, it is important to compare observations with dynamical simulations. For example, comparing two neural systems can shed light on the nature of emergent computations in the brain and deep neural networks. Recently, (Ostrow et al., 2023) introduced Dynamical Similarity Analysis (DSA), a method to measure the similarity of two systems based on their state dynamics rather than geometry or topology. However, DSA does not consider how inputs affect the dynamics, meaning that two similar systems, if driven differently, may be classified as different. Because real-world dynamical systems are rarely autonomous, it is important to account for the effects of input drive. To this end, we introduce a novel metric for comparing both intrinsic (recurrent) and input-driven dynamics, called InputDSA (iDSA). InputDSA extends the DSA framework by estimating and comparing both input and intrinsic dynamic operators using a novel variant of Dynamic Mode Decomposition with control (DMDc) based on subspace identification. We demonstrate that InputDSA can successfully compare partially observed, input-driven systems from noisy data. We show that when the true inputs are unknown, surrogate inputs can be substituted without a major deterioration in similarity estimates. We apply InputDSA on Recurrent Neural Networks (RNNs) trained with Deep Reinforcement Learning, identifying that high-performing networks are dynamically similar to one another, while low-performing networks are more diverse. Lastly, we apply InputDSA to neural data recorded from rats performing a cognitive task, demonstrating that it identifies a transition from input-driven evidence accumulation to intrinsically- driven decision-making. Our work demonstrates that InputDSA is a robust and efficient method for comparing intrinsic dynamics and the effect of external input on dynamical systems From 1c4e9b0b5e2a3a156259bf4d5b5f3be29c2f7e88 Mon Sep 17 00:00:00 2001 From: ostrow Date: Tue, 4 Nov 2025 17:07:35 -0500 Subject: [PATCH 35/90] precompute eigenvalues for wasserstein distance before comparison, resulting in further speedup! --- DSA/dsa.py | 50 ++++++++++++++++++++--- DSA/simdist.py | 108 ++++++++++++++++++++++++++++++++++--------------- 2 files changed, 119 insertions(+), 39 deletions(-) diff --git a/DSA/dsa.py b/DSA/dsa.py index 850b07f..3604559 100644 --- a/DSA/dsa.py +++ b/DSA/dsa.py @@ -703,16 +703,43 @@ def fit_score(self): self.fit_dmds() return self.score() - def get_dmd_matrix(self, dmd): + def get_compare_objects(self, dmd): + """ + Get the comparison objects for similarity computation. + + For Wasserstein distance on states, returns pre-computed eigenvalues to avoid + redundant eigendecomposition. Otherwise returns the full DMD matrix. + + Parameters + ---------- + dmd : DMD object + The fitted DMD model + + Returns + ------- + matrix or eigenvalues : torch.Tensor or np.ndarray + Either the state matrix (for angular/euclidean metrics) or + eigenvalues as 1D complex array (for Wasserstein distance) + """ if self.dmd_api_source == "local_dmd": - return dmd.A_v + matrix = dmd.A_v elif self.dmd_api_source == "pykoopman": - return dmd.A + matrix = dmd.A elif self.dmd_api_source == "pydmd": raise ValueError( "DSA is not currently compatible with pydmd due to \ data structure incompatibility. Please use pykoopman instead." ) + + # Return eigenvalues directly for Wasserstein distance on states + if (not self.simdist_has_control + and self.simdist_config.get("score_method") == "wasserstein"): + if not isinstance(matrix, torch.Tensor): + matrix = torch.from_numpy(matrix).float() + eigenvalues = torch.linalg.eig(matrix).eigenvalues + return eigenvalues + + return matrix def get_dmd_control_matrix(self, dmd): if self.dmd_api_source == "local_dmd": @@ -757,6 +784,16 @@ def score(self): self.sims = np.zeros((len(self.dmds[0]), len(self.dmds[ind2]), n_sims)) + # Pre-compute comparison objects (matrices or eigenvalues) to avoid redundant computation + if (not self.simdist_has_control + and self.simdist_config.get("score_method") == "wasserstein"): + if self.verbose: + print("Pre-computing eigenvalues for Wasserstein distance...") + + self.cached_compare_objects = [ + [self.get_compare_objects(dmd) for dmd in self.dmds[0]], + [self.get_compare_objects(dmd) for dmd in self.dmds[ind2]] + ] def compute_similarity(i, j): if self.method == "self-pairwise" and j >= i: @@ -766,8 +803,8 @@ def compute_similarity(i, j): print(f"computing similarity between DMDs {i} and {j}") simdist_args = [ - self.get_dmd_matrix(self.dmds[0][i]), - self.get_dmd_matrix(self.dmds[ind2][j]), + self.cached_compare_objects[0][i], + self.cached_compare_objects[1][j], ] if self.simdist_has_control and self.dmd_has_control: @@ -777,10 +814,11 @@ def compute_similarity(i, j): self.get_dmd_control_matrix(self.dmds[ind2][j]), ] ) + sim = self.simdist.fit_score(*simdist_args) if self.verbose and self.n_jobs != 1: - print(f"computing similarity between DMDs {i} and {j}") + print(f"finished similarity between DMDs {i} and {j}") return (i, j, sim) diff --git a/DSA/simdist.py b/DSA/simdist.py index 3882093..d027d0b 100644 --- a/DSA/simdist.py +++ b/DSA/simdist.py @@ -193,9 +193,9 @@ def fit( Parameters __________ A : np.array or torch.tensor or DMD object - first data matrix + first data matrix or pre-computed eigenvalues (1D complex numpy/torch array) for Wasserstein B : np.array or torch.tensor or DMD object - second data matrix + second data matrix or pre-computed eigenvalues (1D complex numpy/torch array) for Wasserstein iters : int or None number of optimization steps, if None then resorts to saved self.iters lr : float or None @@ -205,23 +205,55 @@ def fit( _______ None """ - if isinstance(A, DMD): - A = A.A_v - if isinstance(B, DMD): - B = B.A_v - - assert A.shape[0] == A.shape[1] - assert B.shape[0] == B.shape[1] - - A = A.to(self.device) - B = B.to(self.device) - self.A, self.B = A, B + score_method = self.score_method if score_method is None else score_method + + # Check if we received pre-computed eigenvalues (1D complex array) for Wasserstein + precomputed_eigenvalues = False + if score_method == "wasserstein": + # Detect if inputs are 1D complex eigenvalues (torch.Tensor or numpy array) + is_A_complex_1d = ( + (isinstance(A, torch.Tensor) and A.ndim == 1 and torch.is_complex(A)) or + (isinstance(A, np.ndarray) and A.ndim == 1 and np.iscomplexobj(A)) + ) + is_B_complex_1d = ( + (isinstance(B, torch.Tensor) and B.ndim == 1 and torch.is_complex(B)) or + (isinstance(B, np.ndarray) and B.ndim == 1 and np.iscomplexobj(B)) + ) + + if is_A_complex_1d and is_B_complex_1d: + precomputed_eigenvalues = True + # Convert to torch tensors if needed, then to (n, 2) format [real, imag] + if isinstance(A, np.ndarray): + A = torch.from_numpy(A) + if isinstance(B, np.ndarray): + B = torch.from_numpy(B) + + a = torch.vstack([A.real, A.imag]).T.to(self.device) + b = torch.vstack([B.real, B.imag]).T.to(self.device) + # Store for compatibility with score() + self.A = A.to(self.device) + self.B = B.to(self.device) + + if not precomputed_eigenvalues: + # Original logic for matrices + if isinstance(A, DMD): + A = A.A_v + if isinstance(B, DMD): + B = B.A_v + + assert A.shape[0] == A.shape[1] + assert B.shape[0] == B.shape[1] + + A = A.to(self.device) + B = B.to(self.device) + self.A, self.B = A, B + lr = self.lr if lr is None else lr iters = self.iters if iters is None else iters - score_method = self.score_method if score_method is None else score_method if score_method == "wasserstein": - a, b = self._get_wasserstein_vars(A, B) + if not precomputed_eigenvalues: + a, b = self._get_wasserstein_vars(A, B) device = a.device # a = a # .cpu() # b = b # .cpu() @@ -448,25 +480,35 @@ def fit_score( score_method = self.score_method if score_method is None else score_method if isinstance(A, np.ndarray): - A = torch.from_numpy(A).float() + A = torch.from_numpy(A) if isinstance(B, np.ndarray): - B = torch.from_numpy(B).float() - - assert A.shape[0] == B.shape[1] or self.wasserstein_compare is not None - if A.shape[0] != B.shape[0]: - # if self.wasserstein_compare is None: - # raise AssertionError( - # "Matrices must be the same size unless using wasserstein distance" - # ) - if ( - score_method != "wasserstein" - ): # otherwise resort to L2 Wasserstein over singular or eigenvalues - warnings.warn( - f"shapes are not aligned, resorting to wasserstein distance over {self.wasserstein_compare}" - ) - score_method = "wasserstein" - else: - pass + B = torch.from_numpy(B) + + # Check if we have 2D matrices or 1D eigenvalues + is_matrix = A.ndim == 2 and B.ndim == 2 + is_eigenvalues = A.ndim == 1 and B.ndim == 1 + + if is_matrix: + assert A.shape[0] == B.shape[1] or self.wasserstein_compare is not None + if A.shape[0] != B.shape[0]: + # if self.wasserstein_compare is None: + # raise AssertionError( + # "Matrices must be the same size unless using wasserstein distance" + # ) + if ( + score_method != "wasserstein" + ): # otherwise resort to L2 Wasserstein over singular or eigenvalues + warnings.warn( + f"shapes are not aligned, resorting to wasserstein distance over {self.wasserstein_compare}" + ) + score_method = "wasserstein" + else: + pass + elif is_eigenvalues: + # For eigenvalues, different sizes are handled by padding in _get_wasserstein_vars + pass + else: + raise ValueError(f"A and B must both be 2D matrices or both be 1D eigenvalue arrays. Got shapes A: {A.shape}, B: {B.shape}") self.fit( A, From 788918d0d54a334ddb73aef39e63cf42b6f7f6f6 Mon Sep 17 00:00:00 2001 From: ostrow Date: Wed, 5 Nov 2025 13:48:42 -0500 Subject: [PATCH 36/90] add the koopstd tutorial figure --- examples/dsa_koopstd_fix.ipynb | 505 +++++++++++++++++++++++++++++++++ 1 file changed, 505 insertions(+) create mode 100644 examples/dsa_koopstd_fix.ipynb diff --git a/examples/dsa_koopstd_fix.ipynb b/examples/dsa_koopstd_fix.ipynb new file mode 100644 index 0000000..98bfc7b --- /dev/null +++ b/examples/dsa_koopstd_fix.ipynb @@ -0,0 +1,505 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ef5fb65a", + "metadata": {}, + "source": [ + "In this notebook, we'll apply DSA to the setting of the Lorenz attractor, with different parameters governing the behavior. Zhang et al. (2025, ICML) used DSA on this setting with limited success, but we'll show here that it actually does work (and my pull request on their repo for an explanation). We'll also use this setting to illustrate the number of different ways GeneralizedDSA can be applied with different DMD operators. There's no control data here so we'll focus on standard DMD models" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "8b92fb80", + "metadata": {}, + "outputs": [], + "source": [ + "#default imports\n", + "import numpy as np\n", + "import torch\n", + "from scipy.integrate import solve_ivp\n", + "import matplotlib.pyplot as plt\n", + "\n", + "#dsa imports\n", + "from DSA import DSA, GeneralizedDSA\n", + "from DSA import DMD\n", + "from DSA import DMDConfig\n", + "from DSA import SimilarityTransformDistConfig\n", + "from pydmd import DMD as pDMD\n", + "import DSA.pykoopman as pk\n", + "\n", + "rng = np.random.default_rng(2023)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "35270a13", + "metadata": {}, + "outputs": [], + "source": [ + "#lorenz63 class from Zhang et al code (thanks!)\n", + "\n", + "class Lorenz63:\n", + " \"\"\"\n", + " A dataset class for generating Lorenz system trajectories with different rho values.\n", + " \n", + " This class simulates the Lorenz system for multiple rho values and generates\n", + " trajectory clips for each value.\n", + " \n", + " Attributes:\n", + " rho_values (list): List of rho parameter values to simulate.\n", + " initial_state (tuple): Initial state (x, y, z) for the Lorenz system.\n", + " t_start (float): Start time for simulation.\n", + " t_end (float): End time for simulation.\n", + " dt (float): Time step for numerical integration.\n", + " period_length (float): Length of each data clip in time units.\n", + " num_clips (int): Number of clips to extract from each simulation.\n", + " data (list): Generated trajectory data.\n", + " \"\"\"\n", + " def __init__(self, rho_values, initial_state=(-8, 8, 27), t_start=0, t_end=800, \n", + " dt=0.001, period_length=20, num_clips=25, random_seed=None):\n", + " \"\"\"\n", + " Initialize the Lorenz dataset with specified parameters.\n", + " \n", + " Args:\n", + " rho_values (list): List of rho parameter values to simulate.\n", + " initial_state (tuple): Initial state (x, y, z) for the Lorenz system.\n", + " t_start (float): Start time for simulation.\n", + " t_end (float): End time for simulation.\n", + " dt (float): Time step for numerical integration.\n", + " period_length (float): Length of each data clip in time units.\n", + " num_clips (int): Number of clips to extract from each simulation.\n", + " random_seed (int, optional): Seed for random number generators.\n", + " \"\"\"\n", + " if random_seed is not None:\n", + " np.random.seed(random_seed)\n", + " torch.manual_seed(random_seed)\n", + " \n", + " self.rho_values = rho_values\n", + " self.initial_state = initial_state\n", + " self.t_start = t_start\n", + " self.t_end = t_end\n", + " self.dt = dt\n", + " self.period_length = period_length\n", + " self.num_clips = num_clips\n", + " self.data = self.generate_data()\n", + "\n", + " def lorenz(self, state, t, sigma=10, beta=8/3):\n", + " \"\"\"\n", + " Lorenz system differential equations.\n", + " \n", + " Args:\n", + " state (list): Current state [x, y, z].\n", + " t (float): Current time (not used but required by odeint).\n", + " sigma (float): Sigma parameter of the Lorenz system.\n", + " beta (float): Beta parameter of the Lorenz system.\n", + " \n", + " Returns:\n", + " list: Derivatives [dx/dt, dy/dt, dz/dt].\n", + " \"\"\"\n", + " x, y, z = state\n", + " dxdt = sigma * (y - x)\n", + " dydt = x * (self.rho - z) - y\n", + " dzdt = x * y - beta * z\n", + " return [dxdt, dydt, dzdt]\n", + "\n", + " def generate_data(self):\n", + " \"\"\"\n", + " Generate trajectory data for all specified rho values.\n", + " \n", + " Returns:\n", + " list: List of trajectory clips, each with shape (period_length/dt, 3).\n", + " \"\"\"\n", + " data_list = []\n", + " t_span = (self.t_start, self.t_end)\n", + " t_eval = np.arange(self.t_start, self.t_end, self.dt)\n", + " \n", + " # Calculate the index for starting valid data (after transient period)\n", + " valid_start_idx = int(300 / self.dt)\n", + " \n", + " # Store valid data for each rho value for later visualization\n", + " self.valid_data_by_rho = {}\n", + "\n", + " for rho in self.rho_values:\n", + " self.rho = rho\n", + " \n", + " # Define a wrapper function to fix the unpacking issue\n", + " def lorenz_wrapper(t, state):\n", + " return self.lorenz(state, t)\n", + " \n", + " # Solve the Lorenz system using solve_ivp\n", + " solution = solve_ivp(\n", + " lorenz_wrapper, \n", + " t_span, \n", + " self.initial_state, \n", + " method='RK45', \n", + " t_eval=t_eval\n", + " )\n", + " \n", + " # Extract solution data\n", + " solution_data = np.vstack([solution.y[0], solution.y[1], solution.y[2]]).T\n", + " \n", + " # Collect data after transient period\n", + " valid_data = solution_data[valid_start_idx:]\n", + " valid_length = len(valid_data)\n", + " \n", + " # Store the valid data for this rho value\n", + " self.valid_data_by_rho[rho] = valid_data\n", + " \n", + " # Calculate clip size\n", + " clip_size = int(self.period_length / self.dt)\n", + " \n", + " for k in range(self.num_clips):\n", + " # Randomly select clips from the valid data\n", + " max_start_index = valid_length - clip_size\n", + " if max_start_index > 0:\n", + " start_index = np.random.randint(0, max_start_index)\n", + " end_index = start_index + clip_size\n", + " period_data = valid_data[start_index:end_index]\n", + " data_list.append(period_data)\n", + " \n", + " return data_list\n", + " \n", + " def visualize_data(self, time_range=None):\n", + " \"\"\"\n", + " Visualize the generated data with trajectories colored by rho values.\n", + " \n", + " Creates a 3D plot for each rho value showing the Lorenz attractor trajectories.\n", + " \n", + " Parameters:\n", + " -----------\n", + " time_range : tuple, optional\n", + " A tuple of (start_time, end_time) to plot only a specific time range of the trajectories.\n", + " If None, the entire trajectory is plotted.\n", + " \"\"\"\n", + " import matplotlib.pyplot as plt\n", + " from mpl_toolkits.mplot3d import Axes3D\n", + " \n", + " # Determine number of unique rho values\n", + " n_rho = len(self.rho_values)\n", + " \n", + " # Create figure with subplots\n", + " fig = plt.figure(figsize=(4*n_rho, 4))\n", + " \n", + " # Create a subplot for each rho value\n", + " for i, rho in enumerate(self.rho_values):\n", + " ax = fig.add_subplot(1, n_rho, i+1, projection='3d')\n", + " ax.set_title(f'ρ = {rho}')\n", + " ax.set_xlabel('X')\n", + " ax.set_ylabel('Y')\n", + " ax.set_zlabel('Z')\n", + " \n", + " # Get the valid data for this rho value\n", + " valid_data = self.valid_data_by_rho[rho]\n", + " \n", + " # Apply time range selection if specified\n", + " if time_range is not None:\n", + " start_idx = int((time_range[0] - 300) / self.dt) # Adjust for transient period\n", + " end_idx = int((time_range[1] - 300) / self.dt)\n", + " # Ensure indices are within bounds\n", + " start_idx = max(0, start_idx)\n", + " end_idx = min(len(valid_data), end_idx)\n", + " plot_data = valid_data[start_idx:end_idx]\n", + " else:\n", + " plot_data = valid_data\n", + " \n", + " # Plot the trajectory\n", + " ax.plot(plot_data[:, 0], plot_data[:, 1], plot_data[:, 2], linewidth=0.8)\n", + " \n", + " plt.tight_layout()\n", + " plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "e75af356", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "num_clips = 20\n", + "rho_values = [10, 20, 152, 220, 75]# Add labels for different rho values (5 rho values, 40 clips each)\n", + "rho_labels = []\n", + "for rho in rho_values:\n", + " rho_labels.extend([f\"ρ={rho}\"] * num_clips) # 40 clips per rho value\n", + "\n", + "lorenz = Lorenz63(rho_values=rho_values, num_clips=num_clips,period_length=20,random_seed=2023)\n", + "# fig, ax = plt.subplots()\n", + "lorenz.visualize_data(time_range=None)" + ] + }, + { + "cell_type": "markdown", + "id": "7d6521ce", + "metadata": {}, + "source": [ + "A key step in this dataset is to preprocess by whitening the data and subsampling it to take larger timesteps. This ensures that the dynamics can be aptly captured by the DMD. Here we'll subsample by a factor of 10 (i.e. only skipping 10 steps to fit our models to). This allows the system to capture longer timescales (more relevant to comparison) with greater computational efficiency. We could similarly take 10 times as many delays, but that would make fitting slower. " + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "90e66ece", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((2000, 3), 100)" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "subsample = 10\n", + "\n", + "data = lorenz.data \n", + "ss = StandardScaler()\n", + "data = [ss.fit_transform(i) for i in data]\n", + "data = [i[::subsample] for i in data]\n", + "data[0].shape, len(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "0eb18f19", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Fitting DMDs: 100%|██████████| 100/100 [00:03<00:00, 30.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pre-computing eigenvalues for Wasserstein distance...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing DMD similarities: 100%|██████████| 4950/4950 [00:03<00:00, 1617.36it/s]\n" + ] + } + ], + "source": [ + "from DSA import DSA\n", + "# #compute silhouette score\n", + "from sklearn.metrics import silhouette_score\n", + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "dsa = DSA(data,verbose=True, n_delays=70,rank=6,score_method='wasserstein')\n", + "sims = dsa.fit_score()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "e7a0aecd", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mitchellostrow/opt/anaconda3/envs/dsa_test_env/lib/python3.10/site-packages/sklearn/manifold/_mds.py:677: FutureWarning: The default value of `n_init` will change from 4 to 1 in 1.9.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5 [1. 1. 1. 1. 1.]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "from sklearn.manifold import MDS\n", + "from sklearn.metrics import silhouette_score, accuracy_score\n", + "from sklearn.svm import LinearSVC\n", + "from sklearn.model_selection import StratifiedKFold\n", + "import seaborn as sns\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(6,3)) # Add a third axis for decoding accuracy\n", + "\n", + "# Plot heatmap\n", + "sns.heatmap(sims, cmap='gist_gray', ax=axes[0], cbar_kws={'label': 'DSA Wasserstein'})\n", + "rho_values = [10, 20, 152, 220, 75]\n", + "\n", + "lorenz_names = [rf\"$\\rho=$ {rho_values[i]}\" for i in range(len(rho_values))]\n", + "tick_positions = [num_clips/2 + i*num_clips for i in range(5)]\n", + "axes[0].set_xticks(tick_positions)\n", + "axes[0].set_yticks(tick_positions)\n", + "axes[0].set_xticklabels(lorenz_names, rotation=45, ha='right')\n", + "axes[0].set_yticklabels(lorenz_names, rotation=0)\n", + "\n", + "# Perform MDS for visualization\n", + "vis = MDS(n_components=2, dissimilarity='precomputed', random_state=42)\n", + "embedding = vis.fit_transform(sims)\n", + "\n", + "# Create DataFrame for scatter plot\n", + "df = pd.DataFrame()\n", + "df[\"x\"] = embedding[:, 0]\n", + "df[\"y\"] = embedding[:, 1]\n", + "df[\"System\"] = rho_labels\n", + "silhouette_score_ = round(silhouette_score(sims, rho_labels),3)\n", + "\n", + "# Plot scatter with improved styling\n", + "sns.scatterplot(data=df, x=\"x\", y=\"y\", hue=\"System\", ax=axes[1], s=60, alpha=0.8)\n", + "axes[1].set_xlabel('MDS Component 1')\n", + "axes[1].set_ylabel('MDS Component 2')\n", + "axes[1].set_xticks([round(min(df['x']),2), round(max(df['x']),2)])\n", + "axes[1].set_yticks([round(min(df['y']),2), round(max(df['y']),2)])\n", + "handles, labels = axes[1].get_legend_handles_labels()\n", + "# axes[1].legend(handles, labels, bbox_to_anchor=(1.05, 1), loc='upper left', frameon=True, fontsize=8, markerscale=0.7)\n", + "axes[1].legend(handles, labels, frameon=True, fontsize=8, markerscale=0.7)\n", + "\n", + "def compute_decoding_accuracy(sims, rho_labels, n_splits=5, random_state=42):\n", + " \"\"\"\n", + " Computes the decoding accuracy using a linear classifier with stratified k-fold cross-validation.\n", + "\n", + " Parameters:\n", + " - sims: The similarity matrix used as features for classification.\n", + " - rho_labels: The labels corresponding to each sample in the similarity matrix.\n", + " - n_splits: Number of splits for stratified k-fold cross-validation.\n", + " - random_state: Random state for reproducibility.\n", + "\n", + " Returns:\n", + " - avg_test_acc: Average test accuracy over all folds.\n", + " - avg_per_class_acc: Average per-class accuracy over all folds.\n", + " \"\"\"\n", + " # Encode string labels to integers for classification\n", + " from sklearn.preprocessing import LabelEncoder\n", + " le = LabelEncoder()\n", + " y_encoded = le.fit_transform(rho_labels)\n", + "\n", + " # Initialize stratified k-fold cross-validation\n", + " skf = StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=random_state)\n", + "\n", + " # Initialize variables to store results\n", + " test_accs = []\n", + " per_class_accs = []\n", + "\n", + " # Perform stratified k-fold cross-validation\n", + " for train_index, test_index in skf.split(sims, y_encoded):\n", + " X_train, X_test = sims[train_index], sims[test_index]\n", + " y_train, y_test = y_encoded[train_index], y_encoded[test_index]\n", + "\n", + " # Train the classifier\n", + " clf = LinearSVC(max_iter=10000,C=10)\n", + " clf.fit(X_train, y_train)\n", + "\n", + " # Predict on the test set\n", + " y_pred = clf.predict(X_test)\n", + "\n", + " # Compute test accuracy\n", + " test_acc = accuracy_score(y_test, y_pred)\n", + " test_accs.append(test_acc)\n", + "\n", + " # Compute per-class accuracy\n", + " per_class_acc = []\n", + " class_names = np.unique(y_encoded)\n", + " for i, class_label in enumerate(class_names):\n", + " idx = (y_test == i)\n", + " acc = accuracy_score(y_test[idx], y_pred[idx])\n", + " per_class_acc.append(acc)\n", + " per_class_accs.append(per_class_acc)\n", + "\n", + " # Average test accuracy and per-class accuracy over all folds\n", + " avg_test_acc = np.mean(test_accs)\n", + " avg_per_class_acc = np.mean(per_class_accs, axis=0)\n", + "\n", + " return avg_test_acc, avg_per_class_acc\n", + "\n", + "# Example usage:\n", + "avg_test_acc, avg_per_class_acc = compute_decoding_accuracy(sims, rho_labels)\n", + "\n", + "# Plot per-class decoding accuracy\n", + "print(len(avg_per_class_acc),avg_per_class_acc)\n", + "# axes[2].bar(range(len(rho_values)), avg_per_class_acc, color='gray') # Ensure 5 bars are plotted\n", + "# axes[2].set_xticks(range(len(rho_values)))\n", + "# axes[2].set_xticklabels(rho_values, rotation=45, ha='right')\n", + "# axes[2].set_ylim(0, 1.05)\n", + "# axes[2].set_xlim(-1, len(rho_values))\n", + "# axes[2].set_ylabel('Test Accuracy')\n", + "# for i, acc in enumerate(avg_per_class_acc):\n", + " # axes[2].text(i, acc+0.02, f\"{acc:.2f}\", ha='center', va='bottom', fontsize=8)\n", + "# axes[2].text(0.5, 0.95, f\"Avg Test Acc: {avg_test_acc:.2f}\", ha='center', va='top', fontsize=10, transform=axes[2].transAxes)\n", + "\n", + "plt.suptitle(f\"{dsa.n_delays[0][0]} delays, rank {dsa.rank[0][0]}, Silhouette: {silhouette_score_}, Test Acc: {avg_test_acc:.2f}\")\n", + "\n", + "plt.tight_layout()\n", + "# plt.savefig(f\"{save_path}/dsa_silhouette_clustering_{dsa.n_delays[0][0]}nd_r{dsa.rank[0][0]}.pdf\", dpi=300)\n", + "# plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5a5ff5a5", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "dsa_test_env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.18" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From a6640d0576f9906cc4f56be283f1e30e51ef855b Mon Sep 17 00:00:00 2001 From: ostrow Date: Wed, 5 Nov 2025 14:36:00 -0500 Subject: [PATCH 37/90] replicate dsa paper fig 3 --- examples/dsa_fig3_tutorial.ipynb | 431 ++++++++----------------------- 1 file changed, 109 insertions(+), 322 deletions(-) diff --git a/examples/dsa_fig3_tutorial.ipynb b/examples/dsa_fig3_tutorial.ipynb index dab6216..3489faa 100644 --- a/examples/dsa_fig3_tutorial.ipynb +++ b/examples/dsa_fig3_tutorial.ipynb @@ -9,20 +9,20 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "# #install the packages that we haven't added in setup.py\n", - "# ! pip install matplotlib\n", - "# ! pip install scikit-learn\n", - "# ! pip install seaborn\n", - "# ! pip install pandas" + "#install the packages that we haven't added in setup.py\n", + "! pip install matplotlib\n", + "! pip install scikit-learn\n", + "! pip install seaborn\n", + "! pip install pandas" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -33,7 +33,8 @@ "import seaborn as sns\n", "import pandas as pd\n", "\n", - "rng = np.random.default_rng(2023)\n" + "rng = np.random.default_rng(2023)\n", + "np.random.seed(2023)" ] }, { @@ -52,7 +53,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -185,7 +186,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -199,7 +200,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -245,7 +246,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -279,7 +280,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -305,6 +306,38 @@ "models = [x for mtype in models for x in mtype] #list the models by type (bistable,...,line,...,point,...)" ] }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[(200, 1000, 2),\n", + " (200, 1000, 2),\n", + " (200, 1000, 2),\n", + " (200, 1000, 2),\n", + " (200, 1000, 2),\n", + " (200, 1000, 2),\n", + " (200, 1000, 2),\n", + " (200, 1000, 2),\n", + " (200, 1000, 2),\n", + " (200, 1000, 2),\n", + " (200, 1000, 2),\n", + " (200, 1000, 2)]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "models = [i[:,::10] for i in models]\n", + "[i.shape for i in models]" + ] + }, { "cell_type": "code", "execution_count": 7, @@ -312,173 +345,39 @@ "scrolled": true }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Fitting DMDs: 100%|██████████| 12/12 [00:02<00:00, 4.23it/s]\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n" + "Pre-computing eigenvalues for Wasserstein distance...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing DMD similarities: 100%|██████████| 66/66 [00:00<00:00, 646.60it/s]\n" ] } ], "source": [ "#dmd parameters, all others are default for now.\n", - "n_delays = 50\n", - "delay_interval = 20\n", - "rank = 30\n", + "n_delays = 20\n", + "delay_interval = 1\n", + "rank = 15\n", "device = 'cuda' #change this if you have a GPU! Otherwise it will be slow\n", "\n", "#playing around with optimization here, we don't necessarily need the metric to converge to \n", "#get good clustering!\n", - "dsa = DSA(models,n_delays=n_delays,rank=rank,delay_interval=delay_interval,verbose=True,device=device,iters=1000,lr=1e-2)\n", + "# dsa = DSA(models,n_delays=n_delays,rank=rank,delay_interval=delay_interval,verbose=True,device=device,iters=1000,lr=1e-2)\n", + "dsa = DSA(models,n_delays=n_delays,rank=rank,delay_interval=delay_interval,verbose=True,device=device,score_method='wasserstein')\n", "similarities = dsa.fit_score()" ] }, @@ -497,7 +396,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 8, @@ -506,7 +405,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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f3QhFNzDJ2S5cdH97ZQAAvD0VTrUwElBS8dRTT8n48ePNpiTz5s2Te++9V0aMGCErVqwwW6bqualTp+Z5n4SEBElNTfVpRYu4v64+AADeKaVOtTASUFKxc+dOGTJkiPm4T58+cvr0abn77ru95wcMGCDbt2/P8z4xMTFma9WcjaEPAEDIoKciOFNKs9/8dYvU2NhYM3SRrVSpUqbXAQAAhJ+Akoo6derInj17vI/XrVsntWrV8j7Wfd6rVavmbIQAAASZJ8vjWAsnAc3+uP/++yUzM9P7uEmTJj7n//nPf+Zr9gcAACEtzJIBV5KKUaNGXfb8c889d6XxAACAQorFrwAAsGJFTVtIKgAAsGL4wxY2FAMAAI6gpwIAACt6KmwhqQAAwEL3o0LgGP4AAACOoKcCAAArhj9sIakAAMCKpMIWkgoAACzCbXntqy6pSFk02u0QRP53wu0IJKJRnNshiGfnRrdDkFJ/edftEKRcsZJuhyCp51a4HYKUiS3hdgjy09gb3Q5BGpc86XYIIjGl3I5AavVz//cCoStkkgoAAEIGPRW2kFQAAGCVxZfEDqaUAgAAR9BTAQCABYWa9pBUAABgRU2FLQx/AAAAR9BTAQCAFYWatpBUAABgQU2FPQx/AAAQQmbNmiV16tSR2NhYadu2rWzYsCHXa3fu3Cm9e/c210dERMjMmTMvuSYxMVFat24tpUqVksqVK0uvXr0kKSnJ55pbb73V/P8526hRowKOnaQCAAB/wx9OtQAsXrxY4uPjZfLkybJlyxZp3ry5dOvWTY4fP+73+rNnz0q9evVk6tSpUrVqVb/XrFmzRkaPHi3r16+XFStWyIULF6Rr166Snp7uc92IESPk6NGj3vbCCy9IoBj+AAAgRIY/ZsyYYd7chw4dah7PmTNHPv/8c5k7d65MmDDhkuu1B0Kb8ndeLV++3Ofx/PnzTY/F5s2b5ZZbbvEeL168eK6JSVB7KjweNl4BAFxFHOypyMjIkLS0NJ+mx6zOnz9v3ui7dOniPRYZGWker1u3zrFPLTU11fxbvnx5n+PvvvuuVKxYUZo0aSIJCQmmF8SVpCImJkZ2797txK0AALiqJCYmSpkyZXyaHrM6ceKEZGZmSpUqVXyO6+Pk5GRHYsnKypKHH35Ybr75ZpM8ZLv33nvlnXfekVWrVpmE4u2335aBAwcW7PCHjvP4o18EHc+pUKGCt/vmcjRDs2ZpWRcuSkxRRmMAAO7zODilNCEh4ZL3T/1j3A1aW7Fjxw759ttvfY6PHDnS+3HTpk2lWrVq0rlzZ9m3b59ce+21+b5/QO/iWlWqRSNly5a9ZPhDeypKlChhKkbzohnalClTfI491u//ycT+XQMJBwCAguFgUhETE5OvJEKHHqKiouTYsWM+x/XxldY6qAceeECWLl0q33zzjVxzzTWXvVZnnai9e/cGlFQENPzx3HPPmbGYJ554wnSRZDf9Imjhh3789ddf5ytr0/vkbOPvvi2QUAAAuKpER0dLXFycrFy50me4Qh+3a9fO9n31D39NKD766CPzHl23bt08/59t27aZf7XHIhAB9VRoZal2h+g4S8+ePU2PQ9GiRcWJrO1Xhj4AAFfh8EcgdJhk8ODB0qpVK2nTpo0ZIdCpn9mzQQYNGiQ1atTw1mRoceeuXbu8Hx8+fNgkBCVLlpT69et7hzwWLlwon3zyiVmrIrs+Q2s7ihUrZoY49Pwdd9xhyhi2b98uY8eONTNDmjVrFlD8ARcx6NQVrU7VIPWT1mrR/Ax5AABQaLiUVPTt21dSUlJk0qRJ5s2/RYsWZkpodvHmwYMHzYyQbEeOHJGWLVt6H0+fPt20Tp06yerVq82x2bNnexe4ymnevHkyZMgQ00Py1VdfeROYmjVrmgW1Jk6cGHD8tiojNQNasGCBLFq0yEx10UJNAABw5XSoQps/2YlCNl1JM69lHfI6r0mELpDlhCuabtGvXz/p0KGD6bmoXbu2IwEBABCuwx+F3RXP4dQK0ryqSAEAKExIKuxhYQgAACxIKuxhQzEAAOAIeioAALDyMKvRDpIKAAAsGP6wh+EPAADgCHoqAACw8GQx/GEHSQUAABYMf9jD8AcAAHAEPRUAAFh4mP1RyJOKi+fdjkCyDv3idggS1bi12yGI53iK2yFIuWIl3Q5B/vfrGbdDkLplqrodgvyU+tuOhm6KbN7c7RBEiuxwOwKJjGvrdgji2Z8k4YDhD3sY/gAAAFdZTwUAACGC2R/2kFQAAGCRx27hyAVJBQAAFvRU2ENNBQAAcAQ9FQAAWNBTYQ9JBQAAFtRU2MPwBwAAcAQ9FQAAWDD8YQ9JBQAAFizTbQ/DHwAAwBH0VAAAYMHeH/aQVAAAYJHFLqXBTyrS09Pl/fffl71790q1atWkf//+UqFChTz/v4yMDNNyyrpwUWKKkuMAABAWNRWNGzeWkydPmo8PHTokTZo0kbFjx8qKFStk8uTJ5vyBAwfyvE9iYqKUKVPGp037cI39zwIAAIcLNZ1q4SSgpOKHH36Qixcvmo8TEhKkevXq8vPPP8uGDRvMv82aNZPHH388z/vo/5uamurTxt/Vyf5nAQCAw1NKnWrhxPZ4w7p162TOnDmml0GVLFlSpkyZIv369cvz/42JiTEtp18Z+gAAhAhW1AzSlNKIiN+yrnPnzpk6ipxq1KghKSkpNkMBAABh1VPRuXNnKVKkiKSlpUlSUpKpq8imQyD5KdQEACCUhduwhStJhRZj5qRDHjl99tln0rFjR2ciAwDAJUwpdSGpsJo2bZrNMAAAQGHHwhAAAFiE21RQp5BUAABgwewPe9hQDAAAOIKkAgAAP4WaTrVAzZo1S+rUqSOxsbHStm1bs8Bkbnbu3Cm9e/c21+uSDzNnzrR1T10mYvTo0WYGp07C0HseO3Ys4NhJKgAACJFluhcvXizx8fFmYsSWLVukefPm0q1bNzl+/Ljf68+ePSv16tWTqVOnStWqVW3fU7fc0BmcS5YskTVr1siRI0fkrrvuCvjngqQCAIAQMWPGDBkxYoQMHTrU7KelK1cXL15c5s6d6/f61q1bm5mXupq1daXq/N5Tt8p46623zHW33XabxMXFybx582Tt2rWyfv36gOInqQAAwE+hplMtIyPDLBiZs1l36lbnz5+XzZs3S5cuXX5/k46MNI91aww78nNPPX/hwgWfaxo2bCi1atUK+HlJKgAAKMCaikQ/O3PrMasTJ05IZmamVKlSxee4Pk5OTrb1PcrPPfXf6OhoKVu27BU/b8hMKY2ofq3bIUhkidJuhyCRFWq6HYJ4mrV0OwRJPbfC7RCkbhn/45PBdCDV3guJk2qVrux2COJJCbxg7KqUfMjtCPTPXAkHTq5TkZCQYGoacsptqKKwC5mkAgCAq1GMn525/alYsaJERUVdMutCH+dWhOnEPfVfHSY5deqUT2+FnecNj5QTAIAQn1IaHR1tiiRXrlz5exxZWeZxu3btbH3/8nNPPV+0aFGfa3TD0IMHDwb8vPRUAABg4XHpKxIfHy+DBw+WVq1aSZs2bcy6E+np6Wbmhho0aJDUqFHDW5OhPQy7du3yfnz48GHZtm2bWWuifv36+bqn1ngMHz7cXFe+fHkpXbq0jBkzxiQUN910U0Dxk1QAABAi+vbtKykpKTJp0iRTJNmiRQtZvny5t9BSew909kY2XU+iZcvf6+CmT59uWqdOnWT16tX5uqd66aWXzH110SudmaLrWLz++usBxx/h8YTGCufn1i92OwTxpKa4HYJENWjjdgiSmRTYvOSCUKbXC26HILVL+1ZLu4FCzd/seO5Wl78TIp7j7r8+RNYKgULuc+ckFBQf+VKB3n9ttd6O3av90f9PwgU9FQAAWLBLqT0UagIAAEfQUwEAgEUWXxFbSCoAALDwiHOLX4UThj8AAIAj6KkAAMAiKyTmRRY+JBUAAFhkMfxhC0kFAAAW1FTYQ00FAAAIflKxZcsWOXDggPfx22+/LTfffLPUrFlTOnToIIsWLcrXfXQJ0LS0NJ+Wcf5C4NEDAFBAU0qdauEkoKRCNx/Zt2+f+fjNN9+Uv/zlL2aDkscff1xat24tI0aMkLlz5+Z5H90IRTcwydmm/eNj+58FAAAOD3841cJJQDUVe/bskQYNGpiPdaORl19+2SQS2TSxePbZZ2XYsGGXvU9CQoLZDS0nz7ZPA4scAAAU3qSiePHicuLECaldu7bZXlW3UM2pbdu2PsMjuYmJiTEtp3PRRQMJBQCAAhNuwxauDH90795dZs+ebT7WbVU/+OADn/Pvv/++d/92AAAKK2oqgtBT8fzzz5vCTE0otJbixRdfNPu1N2rUSJKSkmT9+vXy0Ucf2QwFAAAUZgH1VFSvXl22bt0q7dq1k+XLl4vH45ENGzbIl19+Kddcc438+9//ljvuuKPgogUAIAgo1AzS4ldly5aVqVOnmgYAwNUoK7wmbTiGxa8AAIAjWKYbAAAL9v6wh6QCAAALNim1h6QCAAAL1qmwh5oKAADgCHoqAACwyIpg+ocdJBUAAFhQU2EPwx8AAOAq66koVsrtCET27XA7AomIq+h2CCKnT7kdgZSJLeF2CPJTarLbIUit0pXdDkEOph13OwSJbPv/3A5BPEmb3A5BIpt2dDsEyUreJ+GAQs3CnlQAABAiWFHTHoY/AACAI+ipAADAghU17SGpAADAgtkf9jD8AQAAHEFPBQAAFhRq2kNSAQCABVNK7SGpAADAgpoKe6ipAAAAjiCpAADAT02FUy1Qs2bNkjp16khsbKy0bdtWNmzYcNnrlyxZIg0bNjTXN23aVJYtW+ZzPiIiwm+bNm2a9xp9Puv5qVOnBhw7SQUAAH5qKpxqgVi8eLHEx8fL5MmTZcuWLdK8eXPp1q2bHD/uf7n8tWvXSv/+/WX48OGydetW6dWrl2k7dvy+7cTRo0d92ty5c03S0Lt3b597PfXUUz7XjRkzRgJFUgEAQIiYMWOGjBgxQoYOHSqNGzeWOXPmSPHixU0i4M/LL78st99+u4wfP14aNWokTz/9tNx4443y2muvea+pWrWqT/vkk0/kD3/4g9SrV8/nXqVKlfK5rkSJwPdgIqkAACAEeirOnz8vmzdvli5duvz+Jh0ZaR6vW7fO7/+jx3Ner7RnI7frjx07Jp9//rnp2bDS4Y4KFSpIy5YtzdDIxYsXC3b2h3aF9OnTRzp2dH+nPAAACorHRi1EbjIyMkzLKSYmxrScTpw4IZmZmVKlShWf4/r4hx9+8Hvv5ORkv9frcX8WLFhgeiTuuusun+MPPvig6eEoX768GVJJSEgwQyDac1JgPRVaPHLrrbfKddddJ88//3yuQedFv7hpaWk+LeP8BVv3AgAglCUmJkqZMmV8mh5zgw6jDBgwwBR15qR1HPr+3qxZMxk1apS8+OKL8uqrr16SDDk+/PHll1/KHXfcIdOnT5datWrJnXfeKUuXLpWsrKwr+gJPe+v9QEMBACDkhz8SEhIkNTXVp+kxq4oVK0pUVJQZoshJH2uNgz96PL/X/+tf/5KkpCS577778vz8ddaJDn/89NNPUqBJhU5XmTlzphw5ckTeeecdk8VopWnNmjXl8ccfl7179+Z5D39f4PHD+wQaCgAAIZ9UxMTESOnSpX2adehDRUdHS1xcnKxcufL3OLKyzON27dr5jVOP57xerVixwu/1b731lrm/zijJy7Zt20w9R+XKlSUoK2oWLVrU1FdoO3jwoOlSmT9/vin00DGhy/E3lnQuuqjdUAAAuCrEx8fL4MGDpVWrVtKmTRvzR3x6erqZDaIGDRokNWrU8A6fPPTQQ9KpUyczXNGjRw9ZtGiRbNq0Sd544w2f+2qZga5noddZaVHnd999Z2aEaL2FPh47dqwMHDhQypUrF/xlunUY5MknnzTzar/66isnbgkAQNgt0923b19JSUmRSZMmmbrFFi1ayPLly73FmPpHvPYgZGvfvr0sXLhQJk6cKI899pg0aNBAPv74Y2nSpInPfTXZ8Hg8Zk0LK/0jX8/r+7iOPtStW9ckFZrgBCrCo8+ST/pEmgHplBOnnfvedwUwN3h2+J+CE0xFuv2Wjbrp4tfvuB2C1BrxrtshyKlfz7gdgtQsHVjXY0E4mOZ/0Z1gOrPx726HIJ6kTW6HIJFN3Z95l5W8T0JBsT/kXRdwJV6uNdCxez100P3X1GAJqKfiwIEDBRcJAAAhgl1K7WHxKwAA4Ai2PgcAwIKeCntIKgAACJFCzcKO4Q8AAOAIeioAALDIcnDvj3BCUgEAgAU1FfYw/AEAABxBTwUAABYUatpDUgEAgEUWaUXhTiou/O1Vt0OQ9B2/uh2ClNi4ze0QJDMl3e0Q5KexN7odgkTmYye/guZJ8d3S2A2Rbf+f2yFIydYj3A5Bika5/3J5MfNvbofgs++EmzLOFewy3bDH/d8SAABCDIWa9pBUAABgQU2FPSQVAABY0FNhT2gMjgEAgEKPngoAACxYUdMekgoAACyYUmoPwx8AAMAR9FQAAGDB7A97SCoAALBg9oc9DH8AAABH0FMBAIAFhZr2kFQAAGBBTYU9DH8AAAB3korXXntNBg0aJIsWLTKP3377bWncuLE0bNhQHnvsMbl48WKe98jIyJC0tDSflpFJWQwAIDRkOdjCSUBJxTPPPGMSh7Nnz8rYsWPl+eefN/8OGDBABg8eLG+++aY8/fTTed4nMTFRypQp49Ne3LLvSj4PAAAcralwqoWTgGoq5s+fb9pdd90l33//vcTFxcmCBQtMUqG0t+KRRx6RKVOmXPY+CQkJEh8f73Ps/Ph77MQPAIDjwisVcCmpOHLkiLRq1cp83Lx5c4mMjJQWLVp4z994443mmrzExMSYltPpKMo7AAAozAJ6J69atars2rXLfLxnzx7JzMz0PlY7d+6UypUrOx8lAABBRE1FEHoqdJhDizTvvPNOWblypRnqGDdunPz3v/+ViIgIefbZZ+Xuu++2GQoAAKHBwwBIwScVWitRrFgxWbdunYwYMUImTJhghkE0udDizZ49e+arUBMAAIR5UqE1FDr7I6d+/fqZBgDA1SLcpoI6hRU1AQCwCLepoE5hygUAAHAEPRUAAFjQT2EPSQUAABYMf9jD8AcAACFk1qxZUqdOHYmNjZW2bdvKhg0bLnv9kiVLzIrWen3Tpk1l2bJlPueHDBliln3I2W6//Xafa06ePGmWjShdurSULVtWhg8fLmfOnAk4dpIKAABCZPGrxYsXm20sJk+eLFu2bDHLNnTr1k2OHz/u9/q1a9dK//79TRKwdetW6dWrl2k7duzwuU6TiKNHj3rbe++953NeEwpdwHLFihWydOlS+eabb2TkyJEB/1yQVAAA4GfxK6f+C8SMGTPMOlBDhw41O4DPmTNHihcvLnPnzvV7/csvv2wShvHjx0ujRo3MWlG6ZYbuKJ6Tbo2hq2Jnt3LlynnP7d69W5YvX242BdWekQ4dOsirr75qdiPPz9YbOZFUAABQgD0VGRkZkpaW5tP0mNX58+dl8+bN0qVLl9/fpCMjzWNddNIfPZ7zeqU9G9brV69ebbbRuP766+X+++83K2HnvIcOeWTv7aX0nvrc3333XUA/GyQVAAAUoMTERClTpoxP02NWJ06cMHtqValSxee4Pk5OTvZ7bz2e1/Xak/GPf/zDbK/x/PPPy5o1a6R79+7mubLvYd23q0iRIlK+fPlcnzfkZ3+kbb00awu2jQequx2CtC962O0QZP/uCm6HII1LnnQ7BJEivmOS4cqTtMntEKRolPsvVRcyL7odgpSIjnU7BEk/f07CgZN7fyQkJJg6iZysO3UXpJyrXmshZ7NmzeTaa681vRedO3d29Lnc/00FAOAqXqY7JiYmX0lExYoVJSoqSo4dO+ZzXB9rHYQ/ejyQ61W9evXMc+3du9ckFXqttRD04sWLZkbI5e7jD8MfAACEgOjoaImLizPDFNmysrLM43bt2vn9f/R4zuuVzuDI7Xr1yy+/mJqKatWqee9x6tQpU8+R7euvvzbPrYWbgaCnAgAAiyyPO2tqxsfHy+DBg03RZJs2bWTmzJmSnp5uZoOoQYMGSY0aNbw1GQ899JB06tRJXnzxRenRo4eZsbFp0yZ54403zHlda0J3GO/du7fpddi3b5/ZWbx+/fqmoFPprBGtu9BZJzrb5MKFC/LAAw+YYZPq1QMrCyCpAAAgRJbp7tu3r6SkpMikSZNMkWSLFi3MdM/sYsyDBw+aWRnZ2rdvLwsXLpSJEyeaXcQbNGggH3/8sTRp0sSc1+GU7du3y4IFC0xvhCYJXbt2NVNPcw7JvPvuuyaR0OEQvb8mIa+88krA8Ud4PC6lYxaH293mdgiy8UBgY0cFof0NFGqqxj3T3f5WSFTNSm6HEBIir7vO7RCk/OC33A6BQs0QK9S8eL5gXysH1r7LsXu98/OHEi7oqQAAwIK9P+whqQAAoACnlIYTZn8AAABH0FMBAEABrlMRTkgqAACwoKbCHpIKAAAsqKmwh5oKAADgTk/F0aNHZfbs2fLtt9+aj3WRDF1HvFevXjJkyBCz0AYAAIUZNRVB6KnQpT91Oc9ly5aZZTz37Nlj1ikvUaKEjBs3Tm655RY5ffp0nvfxu7d8Ft9CAEBo0HUhnWrhJKCk4uGHH5axY8ea5OJf//qXzJ8/X3788Uez1vj+/fvl7NmzZqlQO3vLv3b45yv5PAAAQGFKKrZs2SJ//vOfvY/vvfdec0y3WS1Xrpy88MIL8sEHH+Rrb/nU1FSf9kCN2vY+AwAACmD2h1MtnARUU1G5cmVTR6E1FEqTCd1zvXTp0uaxbmSi+6/b2Vv+dI4NUgAAcBMD8vYE9E6uxZijRo0yO6atWrVKBgwYYLZcLVasmDmflJRktmQFAADhJ6Ceimeeecb0VPTs2VMyMzOlXbt28s4773jPR0REePd4BwCgsGKdiiAkFSVLlpTFixfLuXPnzLCHPs5J92gHAKCwC7daCFdX1IyNjXUsAAAAcHVgmW4AACzCbX0Jp5BUAABgwewPe0gqAACwoFDTHhaHAAAAjqCnAgAAC2Z/2ENSAQCABYWa9jD8AQAAHEFPBQAAFgx/2ENSAQCABbM/CnlSUbZ7NbdDkNsOpbgdgkTVrul2CNK0Y6bbIYjElHI7AomMa+t2CCLJh9yOQCKbdnQ7BLmY+Te3Q5AS0e6vJJx+/pzbIUhMkaJuh4AQFjJJBQAAoSKLFTVtIakAAMCCRbrtYfYHAABwBD0VAABYMPvDHpIKAAAsSCrsIakAAMCCFTXtoaYCAAA4gp4KAAAsGP6wh6QCAAALVtQMYlJx/vx5+fjjj2XdunWSnJxsjlWtWlXat28vd955p0RHR9sMBwAAhE1Nxd69e6VRo0YyePBg2bp1q2RlZZmmHw8aNEhuuOEGcw0AAIW5UNOpFqhZs2ZJnTp1JDY2Vtq2bSsbNmy47PVLliyRhg0bmuubNm0qy5Yt8567cOGCPProo+Z4iRIlpHr16ua9+siRIz730OeLiIjwaVOnTi34pOL+++83wR07dkxWr14tixcvNk0/1mOaVIwePTrgQAAACKWaCqdaIPT9ND4+XiZPnixbtmyR5s2bS7du3eT48eN+r1+7dq30799fhg8fbv6479Wrl2k7duww58+ePWvu88QTT5h/P/zwQ0lKSpI//vGPl9zrqaeekqNHj3rbmDFjJFARngDTqOLFi5usqUmTJn7P/+c//zGZlX4igUh/aoC4LTMkNhSrLCGwe5PbEYjEuD+ExoZi//d1aN3V5e+ESMnmA90OQYqzoVhIbSiWfvanAr3/jdU6OHavLUe/zfe1+v7ZunVree2118xjHQmoWbOmeYOfMGHCJdf37dtX0tPTZenSpd5jN910k7Ro0ULmzJnj9zk2btwobdq0kZ9//llq1arl7al4+OGHTbsSAfdUlC1bVn76Kfdvpp7Tay4nIyND0tLSfFpGKLyRAQDg8PBHhr/3vIwMv/WKmzdvli5duniPRUZGmsdaw+iPHs95vdKejdyuV6mpqWZ4w/percMdFSpUkJYtW8q0adPk4sWLBZ9U3HfffWY85qWXXpLt27ebIQ9t+rEeGzJkiIwcOfKy90hMTJQyZcr4tOn/2hlw8AAAhPrwR6Kf9zw9ZnXixAnJzMyUKlWq+BzXx9mTIqz0eCDXnzt3ztRY6JBJ6dKlvccffPBBWbRokaxatUr+8pe/yHPPPSePPPJIwc/+0DEXLfbQLOavf/2ryXaUZmM6A0SDzSuQhIQEM2aU08Xpl09EAAAojBL8vOfFxMQEPQ4t2uzTp495v549e7bPuZzxNWvWzMzi1ORCk59AYrU1pVQTB20HDhzwmVJat27dfP3/GqA1yPQiUXZCAQAgpNepiPHznudPxYoVJSoqyvT+56SP9T3WHz2en+uzEwqto/j66699eilyq+3Q4Q8tabj++uslKMt0axLRrl0707ITikOHDsmwYcOu5LYAALgqy+NxrOWX9g7ExcXJypUrvce0UFMf6/usP3o85/VqxYoVPtdnJxR79uyRr776ytRN5GXbtm2mnqNy5crurqh58uRJWbBggcydO9fpWwMAEBRuragZHx9v1oFq1aqVmaExc+ZMM7tj6NCh5rzWNNaoUcNbk/HQQw9Jp06d5MUXX5QePXqYuohNmzbJG2+84U0o7r77bjOdVGeIaM1G9ghD+fLlTSKjRZ3fffed/OEPf5BSpUqZx2PHjpWBAwdKuXLlCjap+PTTTy97fv/+/YHeEgAAyG9TRFNSUmTSpEnmzV+nhi5fvtxbjHnw4EHTg5BNV7JeuHChTJw4UR577DFp0KCBWfE6e9mHw4cPe9+39V45aVHmrbfeaoZmNBl58sknzawUHXnQpMJaB1Ig61ToJ6PFmZf73/S8ZkOBYJ2K37BOxf9hnYrfJB8St7FOxW9YpyK81qloVLmNY/faffzyK2JeTQKuqahWrZpZkSt7eW5r0y4WAAAK+/CHU/+Fk4CTCi0i0cU5cpNXLwYAALg6BVxTMX78eFM0kpv69eubcRoAAAqrQGZt4AqSio4dO172vC6MpZWoAAAUVuE2bOGUK1qnAgAAoMDWqQAAoLBj+MMekgoAACwY/rCH4Q8AAHB19VScWfWL2yHIj7sruR2CNI5zf7Gj4z+WcDsEqdWvpNshiGd/ktsh6GpzbkcgWcn73A7BZwVBt6SfP+d2CCGx8FTGxQsSDjyeLLdDKJRCJqkAACBUZDH7wxaSCgAALFjE0R73+xQBAMBVgZ4KAAAsGP6wh6QCAAALhj/sYfgDAAA4gp4KAAAsWFHTHpIKAAAsWFHTHoY/AACAI+ipAADAgkLNEOmpOHbsmDz11FNO3xYAgKBOKXWqhRPHk4rk5GSZMmWK07cFAABX2/DH9u3bL3s+KSkENmECAOAKMPwRpKSiRYsWEhER4fcLnn1c/wUAoLBiSmmQkory5cvLCy+8IJ07d/Z7fufOndKzZ8/L3iMjI8M0n2NZWRITAtsbAwBAT0WQkoq4uDg5cuSI1K5d2+/5U6dO5fnNSExMvKTu4q+1a8n4unUCDQcAAISIgLsGRo0aJXXq5P7mX6tWLZk3b95l75GQkCCpqak+7cHatQINBQCAAsHsjyD1VPzpT3+67Ply5crJ4MGDL3tNTEyMaTn9ytAHACBEMPxhj+NFDIcOHZJhw4Y5fVsAABBuScXJkydlwYIFTt8WAICgzv5wqoWTgIc/Pv3008ue379//5XEAwCA69hQLEhJRa9evXJdpyIb61QAABB+Ah7+qFatmnz44YeSlZXlt23ZsqVgIgUAIEgY/ghSUqHrVGzevDnX83n1YgAAEOr0fcypFk4CHv4YP368pKen53q+fv36smrVqiuNCwAAXO1JRceOHS97vkSJEtKpU6criQkAAFdRqGkPm20AABBCwx+zZs0yK1fHxsZK27ZtZcOGDZe9fsmSJdKwYUNzfdOmTWXZsmWXfC6TJk0yNZHFihWTLl26yJ49ey5ZDmLAgAFSunRpKVu2rAwfPlzOnDkTcOwkFQAAhEhSsXjxYomPj5fJkyebiQ/NmzeXbt26yfHjx/1ev3btWunfv79JArZu3WpmaGrbsWOH9xrdBPSVV16ROXPmyHfffWdGFPSe586d816jCYVuCLpixQpZunSpfPPNNzJy5MiAfy4iPCFSRXLsD+4Pmfy4u5LbIUjjuBS3Q5DjP5ZwOwSp1a+k2yFIRI1qbocgEgLL10c0uMHtEKRsd98NCN2QmZXldggSU6So2yFIxsULEgounj9coPcvGl3DsXtdCCBW7Zlo3bq1vPbaa+axzqqsWbOmjBkzRiZMmHDJ9X379jV1jpoIZLvpppukRYsWJonQt/jq1avLX//6Vxk3bpw5r/ttValSRebPny/9+vWT3bt3S+PGjWXjxo3SqlUrc83y5cvljjvukF9++cX8//nl/isWAAAhxuNgy8jIkLS0NJ+mx6zOnz9vZlfq8ES2yMhI83jdunV+49TjOa9X2guRff2BAwckOTnZ55oyZcqY5CX7Gv1XhzyyEwql1+tza89GYF+4q8C5c+c8kydPNv8SA18Hfib43eA1gtfKUDJ58uRLcg09ZnX48GFzbu3atT7Hx48f72nTpo3fexctWtSzcOFCn2OzZs3yVK5c2Xz873//29zzyJEjPtfcc889nj59+piPn332Wc911113yb0rVarkef311wP6XK+KngrN+KZMmeI38yOG8Ps6hEocxMDXgZ+H0Py9CLaEhAQz5JCz6bGrUcBTSgEAQP7FxMSYlpeKFStKVFSUHDt2zOe4Pq5atarf/0ePX+767H/1mM7+yHmN1l1kX2MtBL148aKZEZLb8+bmquipAACgsIuOjjarVq9cudJ7TAs19XG7du38/j96POf1SmdwZF9ft25dkxjkvEZrOrRWIvsa/ffUqVM+q2V//fXX5rm19iIQ9FQAABAi4uPjZfDgwaZosk2bNjJz5kwzu2Po0KHm/KBBg6RGjRqSmJhoHj/00ENmwckXX3xRevToIYsWLZJNmzbJG2+84d064+GHH5ZnnnlGGjRoYJKMJ554wszo0KmnqlGjRnL77bfLiBEjzIyRCxcuyAMPPGBmhgQy8+OqSSq0W0nn9Oane4kYrv6vQ6jEQQx8Hfh5CM3fi1DWt29fSUlJMYtV6awNHaLQ6Z06BVQdPHjQzMrI1r59e1m4cKFMnDhRHnvsMZM4fPzxx9KkSRPvNY888ohJTHTdCe2R6NChg7mnLpaV7d133zWJROfOnc39e/fubda2KLTrVAAAgMKNmgoAAOAIkgoAAOAIkgoAAOAIkgoAAOCIqyKpCHSbWCfpTm49e/Y002506o5W3QabTi3SDWhKlSollStXNtOEkpKSghrD7NmzpVmzZmbbXG067/mf//ynuGnq1Kne6VTB8uSTT5rnzNl0S+JgO3z4sAwcOFAqVKhgtjrW7ZB1mlmw6O+j9eugbfTo0UGLITMz00yd0yl0+jW49tpr5emnn7a1FfWVOH36tPkZrF27tolDq/V14yY3X5fysxV2Qcfw4YcfSteuXc3PqJ7ftm2bo88PdxT6pCLQbWKdptN09Dk1sXHLmjVrzIv1+vXrzaInOsdYf1k1tmC55pprzJu4Lp6ib1633Xab3HnnnWYrXTfoi/bf/vY3k+gE2w033CBHjx71tm+//Taoz/+///1Pbr75ZilatKhJ7Hbt2mXmsJcrVy6oX/+cXwP9uVT33HNP0GJ4/vnnTbKruz3qLoz6WLeAfvXVVyWY7rvvPvP5v/322/Kf//zH/G7qm7gmfm69LuVnK+yCjkHP69RG/b7gKuIp5HSTldGjR3sfZ2ZmeqpXr+5JTEwMeiz65fzoo488bjt+/LiJZc2aNa7GUa5cOc+bb74Z9Oc9ffq0p0GDBp4VK1Z4OnXq5HnooYeC9ty6SVDz5s09bnr00Uc9HTp08IQS/R5ce+21nqysrKA9Z48ePTzDhg3zOXbXXXd5BgwYELQYzp4964mKivIsXbrU5/iNN97oefzxx115XdLvQdWqVT3Tpk3zHjt16pQnJibG89577wUlhpwOHDhgzm/durVAnhvBVah7KuxsExsOdLMaVb58eVeeX7uddVU3/Uskt6VlC5L22ujKctbtgINFu5G1y7devXoyYMAAs1hNMH366admNT7tFdDhsJYtW8rf//53cfP39J133pFhw4aZbu5g0WEGXZr4xx9/NI+///5702vUvXv3oMWg+yfo70PORYaUDjkEuwcrW362wgbsKtQrap44ccL8wmavNJZNH//www8SjnStdh2/1e7vnCuqBYN27WoSoV2oJUuWlI8++kgaN24c1Bg0mdFhsIIes86NvjDPnz9frr/+etPtr7sxduzYUXbs2GFqXoJh//79pttfhwV1hT39Wjz44INmXwFd/jfYdCxdV/EbMmRIUJ93woQJZo8DrWnRTZr0teLZZ581iV6w6Pdcfye0lkOXQtbXpvfee8+8edevX1/coAmF8ve6mX0OCMukAv7/Stc3MDf+CtI3Ui220p6SDz74wLyBab1HsBKLQ4cOmXXwdfza+pdhsOT8K1jrOTTJ0AK9999/X4YPHx60xFJ7Kp577jnzWHsq9GdCx8/dSCreeust83UJdA+BK6Vfc116WJcw1joX/dnUhFvjCObXQWsptJdG92vQ5ObGG2+U/v37+2zeBFwtCvXwh51tYq9mum770qVLZdWqVaZwMtj0L2H960t32dMZKVqk9fLLLwft+fVFWgt09UW7SJEipmlSowVp+rH+pRpsZcuWleuuu0727t0btOfUin5rIqd/JQd7GEb9/PPP8tVXX5lixWAbP3686a3QTZF09suf//xnGTt2rHcjpmDRWSf6c3jmzBmT+OrsNC2m1uExN+TcCjuncH3dhLMiw22b2KuR1kFpQqHDDbpdrU6hCwX6vcjIyAja8+lGODoEo3+RZjf9i127u/VjTUCDTd9I9u3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" ] @@ -535,13 +434,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "/om2/user/ostrow/anaconda/envs/dmrsa/lib/python3.9/site-packages/sklearn/manifold/_mds.py:299: FutureWarning: The default value of `normalized_stress` will change to `'auto'` in version 1.4. To suppress this warning, manually set the value of `normalized_stress`.\n", + "/Users/mitchellostrow/opt/anaconda3/envs/dsa_test_env/lib/python3.10/site-packages/sklearn/manifold/_mds.py:677: FutureWarning: The default value of `n_init` will change from 4 to 1 in 1.9.\n", " warnings.warn(\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -581,7 +480,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -591,11 +490,11 @@ "nmodels = 5 #3*nmodels total, turn this up as high as you want\n", "sigma = 0.05\n", "\n", - "ntrials = 300\n", + "ntrials = 100\n", "dt = 0.01\n", - "downsample = 20\n", - "n_delays = 50\n", - "rank = 30\n", + "downsample = 10\n", + "n_delays = 20\n", + "rank = 15\n", "\n", "#vary params for model 1\n", "a = np.random.uniform(-5,-3,size=nmodels) \n", @@ -609,17 +508,17 @@ "a1 = np.random.uniform(-2,-5,size=nmodels)\n", "a2 = np.random.uniform(-8,-10,size=nmodels)\n", "\n", - "def fit_dmd(x,n_delays,rank,delay_interval):\n", + "def fit_dmd(x,n_delays,rank,delay_interval=1):\n", " x = flatten_x(x)\n", " #notice how we initialize the dmd separately here, rather than the DSA object itself\n", " dmd = DMD(x,n_delays=n_delays,rank=rank,delay_interval=delay_interval,device='cuda')\n", - " dmd.fit(send_to_cpu=True)\n", + " dmd.fit()\n", " return dmd.A_v.numpy()\n" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 15, "metadata": { "scrolled": true }, @@ -628,7 +527,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "0\n", + "0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/sh/r0d61tjn42s4mc3nxqb0hhq00000gn/T/ipykernel_15994/1281545838.py:28: RuntimeWarning: CUDA device 'cuda' requested but CUDA is not available. Falling back to CPU with NumPy. To use GPU acceleration, ensure PyTorch with CUDA support is installed.\n", + " dmd = DMD(x,n_delays=n_delays,rank=rank,delay_interval=delay_interval,device='cuda')\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "1\n", "2\n", "3\n", @@ -644,165 +557,39 @@ " x1,cond_avg = run_model1([-.1,.1],dict(dt=dt,sigma=sigma,ntrials=ntrials,a=a[i],b=b[i],c=c[i]))\n", "\n", " #x has shape conditions x trials x time x dimension\n", - "# x1 = x1[:,:,::downsample] #here we're downsampling instead of using delay_interval in the dmd class\n", - " dmd1 = fit_dmd(x1,n_delays,rank,downsample)\n", + " x1 = x1[:,:,::downsample] #here we're downsampling instead of using delay_interval in the dmd class\n", + " dmd1 = fit_dmd(x1,n_delays,rank)\n", " models.append(dmd1)\n", " model_types.append('bistable')\n", "\n", " x2,input_optimized = run_model2(cond_avg,dict(dt=dt,sigma=sigma,ntrials=ntrials,eval1=eval1[i]))\n", - "# x2 = x2[:,:,::downsample]\n", - " dmd2 = fit_dmd(x2,n_delays,rank,downsample)\n", + " x2 = x2[:,:,::downsample]\n", + " dmd2 = fit_dmd(x2,n_delays,rank)\n", " models.append(dmd2)\n", " model_types.append('line attractor')\n", "\n", " x3,input_optimized = run_model3(cond_avg,dict(dt=dt,sigma=sigma,ntrials=ntrials,a1=a1[i],a2=a2[i]))\n", - "# x3 = x3[:,:,::downsample]\n", - " dmd3 = fit_dmd(x3,n_delays,rank,downsample)\n", + " x3 = x3[:,:,::downsample]\n", + " dmd3 = fit_dmd(x3,n_delays,rank)\n", " models.append(dmd3)\n", " model_types.append('point attractor')\n" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 16, "metadata": { "scrolled": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0 0 0.0\n", - "0 1 0.019186703\n", - "0 2 0.009880952\n", - "0 3 0.013784869\n", - "0 4 0.018270064\n", - "0 5 0.015367441\n", - "0 6 0.010148779\n", - "0 7 0.016282732\n", - "0 8 0.01389684\n", - "0 9 0.010658683\n", - "0 10 0.016471991\n", - "0 11 0.015901202\n", - "0 12 0.011905524\n", - "0 13 0.016815798\n", - "0 14 0.014998405\n", - "1 1 0\n", - "1 2 0.021556804\n", - "1 3 0.016072713\n", - "1 4 0.0036702405\n", - "1 5 0.018898722\n", - "1 6 0.018109493\n", - "1 7 0.008407826\n", - "1 8 0.021166135\n", - "1 9 0.017249746\n", - "1 10 0.0080381455\n", - "1 11 0.019543765\n", - "1 12 0.016486458\n", - "1 13 0.0062052174\n", - "1 14 0.020368177\n", - "2 2 0\n", - "2 3 0.0107088955\n", - "2 4 0.02125326\n", - "2 5 0.015990915\n", - "2 6 0.01086911\n", - "2 7 0.021016344\n", - "2 8 0.011367685\n", - "2 9 0.009826509\n", - "2 10 0.02084833\n", - "2 11 0.014214892\n", - "2 12 0.0102015\n", - "2 13 0.020632776\n", - "2 14 0.011294038\n", - "3 3 0.0\n", - "3 4 0.016205672\n", - "3 5 0.017034156\n", - "3 6 0.008126642\n", - "3 7 0.017564781\n", - "3 8 0.010036567\n", - "3 9 0.005970233\n", - "3 10 0.016932372\n", - "3 11 0.013024486\n", - "3 12 0.004991472\n", - "3 13 0.016377633\n", - "3 14 0.0110647725\n", - "4 4 0\n", - "4 5 0.018426005\n", - "4 6 0.017557994\n", - "4 7 0.0063851164\n", - "4 8 0.020856904\n", - "4 9 0.016737634\n", - "4 10 0.005545816\n", - "4 11 0.01925493\n", - "4 12 0.01626808\n", - "4 13 0.005015298\n", - "4 14 0.020049619\n", - "5 5 0\n", - "5 6 0.0163266\n", - "5 7 0.018175203\n", - "5 8 0.016562212\n", - "5 9 0.016271744\n", - "5 10 0.017757162\n", - "5 11 0.011503207\n", - "5 12 0.016194634\n", - "5 13 0.017730288\n", - "5 14 0.013685055\n", - "6 6 0\n", - "6 7 0.016080128\n", - "6 8 0.0069996584\n", - "6 9 0.0036702405\n", - "6 10 0.015919933\n", - "6 11 0.012714164\n", - "6 12 0.00534886\n", - "6 13 0.016106054\n", - "6 14 0.011915534\n", - "7 7 0\n", - "7 8 0.020391578\n", - "7 9 0.016231399\n", - "7 10 0.0036539645\n", - "7 11 0.019402957\n", - "7 12 0.01626808\n", - "7 13 0.0045149554\n", - "7 14 0.020455785\n", - "8 8 0.0\n", - "8 9 0.0070758825\n", - "8 10 0.01992735\n", - "8 11 0.010426882\n", - "8 12 0.007453307\n", - "8 13 0.01998112\n", - "8 14 0.008400734\n", - "9 9 0.0\n", - "9 10 0.01592742\n", - "9 11 0.012410495\n", - "9 12 0.004931404\n", - "9 13 0.015799666\n", - "9 14 0.011166656\n", - "10 10 0\n", - "10 11 0.019130701\n", - "10 12 0.015856154\n", - "10 13 0.0041287956\n", - "10 14 0.020103062\n", - "11 11 0\n", - "11 12 0.011820107\n", - "11 13 0.018692581\n", - "11 14 0.0074372957\n", - "12 12 0.0\n", - "12 13 0.015724031\n", - "12 14 0.010517948\n", - "13 13 0\n", - "13 14 0.019738\n", - "14 14 0.0\n" - ] - } - ], + "outputs": [], "source": [ "nmodels_tot = len(models) #should be 3*nmodels\n", "\n", "sims_dmd = np.zeros((nmodels_tot,nmodels_tot))\n", "sims_mtype = np.zeros((nmodels_tot,nmodels_tot))\n", "#notice how we are initializing the similarity transform separately here\n", - "comparison_dmd = SimilarityTransformDist(device='cuda',iters=2000,lr=1e-3)\n", + "# comparison_dmd = SimilarityTransformDist(device='cuda',iters=2000,lr=1e-3)\n", + "comparison_dmd = SimilarityTransformDist(device='cpu',iters=2000,lr=1e-3,score_method='wasserstein')\n", "\n", "for i,mi in enumerate(models):\n", " for j,mj in enumerate(models):\n", @@ -813,27 +600,27 @@ " if j < i:\n", " continue\n", " sdmd = comparison_dmd.fit_score(mi,mj)\n", - " print(i,j,sdmd)\n", + " # print(i,j,sdmd)\n", "\n", " sims_dmd[i,j] = sims_dmd[j,i] = sdmd\n" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/om2/user/ostrow/anaconda/envs/dmrsa/lib/python3.9/site-packages/sklearn/manifold/_mds.py:299: FutureWarning: The default value of `normalized_stress` will change to `'auto'` in version 1.4. To suppress this warning, manually set the value of `normalized_stress`.\n", + "/Users/mitchellostrow/opt/anaconda3/envs/dsa_test_env/lib/python3.10/site-packages/sklearn/manifold/_mds.py:677: FutureWarning: The default value of `n_init` will change from 4 to 1 in 1.9.\n", " warnings.warn(\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -867,7 +654,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "dsa_test_env", "language": "python", "name": "python3" }, @@ -881,7 +668,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.10.18" } }, "nbformat": 4, From ab51bb2fe2579d70bd70869649c0952c0f1d886b Mon Sep 17 00:00:00 2001 From: ostrow Date: Wed, 5 Nov 2025 15:37:42 -0500 Subject: [PATCH 38/90] bug fixes, add tests, add docstrings to DSA and inputDSA --- DSA/dsa.py | 56 +++++- DSA/simdist.py | 6 + tests/dmd_test.py | 4 +- tests/simdist_test.py | 46 +---- tests/test_wasserstein_optimization.py | 257 +++++++++++++++++++++++++ 5 files changed, 328 insertions(+), 41 deletions(-) create mode 100644 tests/test_wasserstein_optimization.py diff --git a/DSA/dsa.py b/DSA/dsa.py index 3604559..df6ba76 100644 --- a/DSA/dsa.py +++ b/DSA/dsa.py @@ -860,6 +860,34 @@ def compute_similarity(i, j): class DSA(GeneralizedDSA): + """ + Dynamical Similarity Analysis (DSA) for Data-driven dynamics comparison + + Attributes: + X : np.array or torch.tensor or list of np.arrays or torch.tensors + First data matrix/matrices. + Y : None or np.array or torch.tensor or list of np.arrays or torch.tensors + Second data matrix/matrices. If None, X is compared to itself pairwise. + dmd_class : class + DMD class to use model fitting. Default is the local Havok, + but pykoopman objects can be passed in, for example + device : str + Device to use for computation ('cpu' or 'cuda'). Default is 'cpu'. + verbose : bool + Whether to print verbose output during computation. Default is False. + n_jobs : int + Number of parallel jobs to use. Default is 1 (sequential). + **dmd_kwargs (dictionary of dmd arguments--for HAVOK, see DMDConfig, repeated here: + DMD Attributes: + n_delays (int): Number of time delays to use in the Hankel matrix construction. + Default is 1 (no delays). + delay_interval (int): Interval between delays in the Hankel matrix. + Default is 1 (consecutive time steps). + rank (int): Rank for SVD truncation. If None, no truncation is performed. + Default is None. + lamb (float): Regularization parameter for ridge regression. + Default is 0 (no regularization). + """ def __init__( self, X, @@ -874,7 +902,6 @@ def __init__( lr: float = 5e-3, **dmd_kwargs, ): - # TODO: add readme simdist_config = { "score_method": score_method, "iters": iters, @@ -899,6 +926,33 @@ def __init__( class InputDSA(GeneralizedDSA): + """ + Dynamical Similarity Analysis (DSA) for controlled systems + + Attributes: + X (required) : np.array or torch.tensor or list of np.arrays or torch.tensors + First data matrix/matrices. + X_control (required) : np.array or torch.tensor or list of np.arrays or torch.tensors + Control data matrix/matrices for X. + Y : None or np.array or torch.tensor or list of np.arrays or torch.tensors + Second data matrix/matrices. If None, X is compared to itself pairwise. + Y_control : None or np.array or torch.tensor or list of np.arrays or torch.tensors + Control data matrix/matrices for Y. Must be the same shape as Y. + dmd_class : class + DMD class to use for decomposition. Default is SubspaceDMDc. + dmd_config: class or dictionary containing parameters of the dmd model + simdist_config: class or dictionary containing parameters used for comparison. + Depending on what is in here (e.g. compare = "state" versus "joint" or "input"), + the type of comparison will be direclty inferred -- "state" yields standard DSA metric, + "input" or "joint" yields the controlalbility metric. + device : str + Device to use for computation ('cpu' or 'cuda'). Default is 'cpu'. + verbose : bool + Whether to print verbose output during computation. Default is False. + n_jobs : int + Number of parallel jobs to use. Default is 1 (sequential). + + """ def __init__( self, X, diff --git a/DSA/simdist.py b/DSA/simdist.py index d027d0b..699160b 100644 --- a/DSA/simdist.py +++ b/DSA/simdist.py @@ -481,8 +481,14 @@ def fit_score( if isinstance(A, np.ndarray): A = torch.from_numpy(A) + # Only convert to float if not complex (preserve complex dtypes for eigenvalues) + if not torch.is_complex(A): + A = A.float() if isinstance(B, np.ndarray): B = torch.from_numpy(B) + # Only convert to float if not complex (preserve complex dtypes for eigenvalues) + if not torch.is_complex(B): + B = B.float() # Check if we have 2D matrices or 1D eigenvalues is_matrix = A.ndim == 2 and B.ndim == 2 diff --git a/tests/dmd_test.py b/tests/dmd_test.py index d1c28b5..a08240a 100644 --- a/tests/dmd_test.py +++ b/tests/dmd_test.py @@ -70,7 +70,7 @@ def test_dmd_2d(seed, c, t, tau): data[i] = A @ data[i - 1] dmd = DMD(data, 1) dmd.fit() - assert np.linalg.norm(dmd.A_v.flatten() - A.flatten()) < 1e-1 + assert np.linalg.norm(dmd.A_havok_dmd.flatten() - A.flatten()) < 1e-1 @pytest.mark.parametrize("n", [500]) @@ -102,6 +102,6 @@ def test_to_cpu(seed, n, t, c): X = rng.random((n, t, c)) device = "cuda" if torch.cuda.is_available() else "cpu" dmd = DMD(X, 1, device=device) - dmd.fit(send_to_cpu=True) + dmd.fit() assert dmd.A_v.device.type == "cpu" assert dmd.H.device.type == "cpu" diff --git a/tests/simdist_test.py b/tests/simdist_test.py index 84d4234..5375a64 100644 --- a/tests/simdist_test.py +++ b/tests/simdist_test.py @@ -3,44 +3,29 @@ from DSA.simdist import SimilarityTransformDist, pad_zeros from scipy.stats import special_ortho_group, ortho_group import torch -from netrep.utils import whiten TOL = 1e-3 SIMTOL = 2e-2 @pytest.mark.parametrize("device", ["cpu"]) -@pytest.mark.parametrize("preserve_var", [True, False]) @pytest.mark.parametrize("dtype", ["numpy"]) @pytest.mark.parametrize("score_method", ["angular", "euclidean"]) @pytest.mark.parametrize("n", [10, 50, 100]) -@pytest.mark.parametrize("group", ["GL(n)", "O(n)", "SO(n)"]) @pytest.mark.parametrize("seed", [5]) -def test_simdist_convergent(seed, n, score_method, dtype, preserve_var, group, device): +def test_simdist_convergent(seed, n, score_method, dtype, device): rng = np.random.default_rng(seed) X = rng.random(size=(n, n)) - if group == "SO(n)": - Q = special_ortho_group(seed=rng, dim=n).rvs() - Y = Q @ X @ Q.T - iters = 5000 - elif group == "O(n)": + + Q = ortho_group(seed=rng, dim=n).rvs() + while np.linalg.det(Q) > 0: Q = ortho_group(seed=rng, dim=n).rvs() - while np.linalg.det(Q) > 0: - Q = ortho_group(seed=rng, dim=n).rvs() - Y = Q @ X @ Q.T - iters = 5000 - elif group == "GL(n)": - # draw random invertible matrix - Q = rng.random(size=(n, n)) - Q /= np.linalg.norm(Q, axis=0) - Y = Q @ X @ np.linalg.inv(Q) - iters = 80_000 - - X, _ = whiten(X, 0, preserve_variance=preserve_var) - Y, _ = whiten(Y, 0, preserve_variance=preserve_var) + Y = Q @ X @ Q.T + iters = 10000 + # excessive but we just want to see that it converges sim = SimilarityTransformDist( - lr=1e-2, iters=iters, score_method=score_method, device=device, group=group + lr=5e-3, iters=iters, score_method=score_method, device=device ) if dtype == "torch": X = torch.tensor(X).float() @@ -77,21 +62,6 @@ def test_transposed_q_same(seed, n, score_method, dtype, device): assert np.abs(score1 - score2) < SIMTOL -@pytest.mark.parametrize("n2", [10]) -@pytest.mark.parametrize("n1", [50]) -@pytest.mark.parametrize("seed", [5]) -def test_zero_pad(seed, n1, n2): - rng = np.random.default_rng(seed) - X = rng.random(size=(n1, n1)) - Y = rng.random(size=(n2, n2)) - m = max(n1, n2) - sim = SimilarityTransformDist(iters=10) # don't care about fitting - sim.fit_score(X, Y, zero_pad=True) - assert sim.C_star.shape == (m, m) - assert pad_zeros(X, Y, "cpu")[0].shape == (m, m) - assert pad_zeros(X, Y, "cpu")[1].shape == (m, m) - - @pytest.mark.parametrize("n", [10]) @pytest.mark.parametrize("seed", [5]) def test_ortho_c(seed, n): diff --git a/tests/test_wasserstein_optimization.py b/tests/test_wasserstein_optimization.py new file mode 100644 index 0000000..2e35ae6 --- /dev/null +++ b/tests/test_wasserstein_optimization.py @@ -0,0 +1,257 @@ +""" +Test to verify Wasserstein distance optimization works correctly. +Tests both SimilarityTransformDist and DSA classes to ensure: +1. Pre-computed eigenvalues produce identical results to matrices +2. Identical systems produce near-zero scores +3. Both torch and numpy complex arrays work correctly +4. DSA correctly caches eigenvalues for efficiency +""" +import pytest +import numpy as np +import torch +from DSA.simdist import SimilarityTransformDist +from DSA import DSA +from DSA.dmd import DMD + + +@pytest.fixture +def random_matrices(): + """Generate random test matrices.""" + np.random.seed(42) + torch.manual_seed(42) + A = torch.randn(5, 5) + B = torch.randn(5, 5) + return A, B + + +@pytest.fixture +def random_data(): + """Generate random time series data for DSA tests.""" + np.random.seed(42) + n_samples = 100 + n_features = 10 + X1 = np.random.randn(n_features, n_samples) + X2 = np.random.randn(n_features, n_samples) + return X1, X2 + + +def test_simdist_wasserstein_with_matrices(random_matrices): + """Test SimilarityTransformDist with full matrices.""" + A, B = random_matrices + + simdist = SimilarityTransformDist( + iters=100, + score_method="wasserstein", + lr=0.01, + device="cpu", + verbose=False + ) + + # Test with matrices + score_from_matrices = simdist.fit_score(A, B) + assert score_from_matrices > 0, "Score should be positive for different matrices" + + # Test with identical matrices (should be close to 0) + score_identical = simdist.fit_score(A, A) + assert score_identical < 1e-3, f"Identical matrices should have near-zero score, got {score_identical}" + + +def test_simdist_wasserstein_with_precomputed_eigenvalues(random_matrices): + """Test SimilarityTransformDist with pre-computed eigenvalues produces same results as matrices.""" + A, B = random_matrices + + # Get score from matrices + simdist1 = SimilarityTransformDist( + iters=100, + score_method="wasserstein", + lr=0.01, + device="cpu", + verbose=False + ) + score_from_matrices = simdist1.fit_score(A, B) + + # Get eigenvalues + eigenvalues_A = torch.linalg.eig(A).eigenvalues + eigenvalues_B = torch.linalg.eig(B).eigenvalues + + assert eigenvalues_A.ndim == 1, "Eigenvalues should be 1D" + assert torch.is_complex(eigenvalues_A), "Eigenvalues should be complex" + + # Get score from pre-computed eigenvalues + simdist2 = SimilarityTransformDist( + iters=100, + score_method="wasserstein", + lr=0.01, + device="cpu", + verbose=False + ) + score_from_eigenvalues = simdist2.fit_score(eigenvalues_A, eigenvalues_B) + + # Scores should be identical (or very close) + diff = abs(score_from_matrices - score_from_eigenvalues) + assert diff < 1e-3, f"Scores should match, got difference of {diff}" + + # Test with identical eigenvalues + score_identical_eig = simdist2.fit_score(eigenvalues_A, eigenvalues_A) + assert score_identical_eig < 1e-3, f"Identical eigenvalues should have near-zero score, got {score_identical_eig}" + + +def test_simdist_wasserstein_with_numpy_arrays(random_matrices): + """Test that numpy complex arrays are handled correctly.""" + A, B = random_matrices + + # Convert to numpy + A_np = A.numpy() + B_np = B.numpy() + eigenvalues_A_np = np.linalg.eig(A_np)[0] # numpy returns (eigenvalues, eigenvectors) + eigenvalues_B_np = np.linalg.eig(B_np)[0] + + assert eigenvalues_A_np.ndim == 1, "Numpy eigenvalues should be 1D" + assert np.iscomplexobj(eigenvalues_A_np), "Numpy eigenvalues should be complex" + + simdist = SimilarityTransformDist( + iters=100, + score_method="wasserstein", + lr=0.01, + device="cpu", + verbose=False + ) + + # Should work without errors + score_from_numpy_eig = simdist.fit_score(eigenvalues_A_np, eigenvalues_B_np) + assert score_from_numpy_eig > 0, "Score should be positive" + + +def test_dsa_wasserstein_caching(random_data): + """Test that DSA correctly caches eigenvalues for efficiency.""" + X1, X2 = random_data + + dsa = DSA( + X=[X1, X2], + Y=None, + dmd_class=DMD, + device="cpu", + verbose=False, + n_jobs=1, + score_method="wasserstein", + iters=100, + lr=0.01, + n_delays=1, + rank=5, + ) + + scores = dsa.fit_score() + + # Check scores shape and properties + assert scores.shape == (2, 2), f"Expected shape (2, 2), got {scores.shape}" + + # Check that diagonal is near-zero (comparing same systems) + diagonal_scores = np.array([scores[i, i] for i in range(len(scores))]) + assert np.all(diagonal_scores < 1e-3), f"Diagonal should be near-zero, got {diagonal_scores}" + + # Check that matrix is symmetric + symmetry_diff = np.abs(scores - scores.T).max() + assert symmetry_diff < 1e-6, f"Score matrix should be symmetric, got max diff {symmetry_diff}" + + # Verify the optimization actually cached the eigenvalues + assert hasattr(dsa, 'cached_compare_objects'), "DSA should have cached_compare_objects" + assert len(dsa.cached_compare_objects) == 2, "Should have 2 groups of cached objects" + assert len(dsa.cached_compare_objects[0]) == 2, "First group should have 2 objects" + assert len(dsa.cached_compare_objects[1]) == 2, "Second group should have 2 objects" + + # Check that cached objects are complex eigenvalues + first_obj = dsa.cached_compare_objects[0][0] + assert first_obj.ndim == 1, "Cached objects should be 1D" + assert torch.is_complex(first_obj), "Cached objects should be complex" + + +def test_dsa_wasserstein_vs_angular_difference(random_data): + """Test that Wasserstein and angular methods produce different results (as expected).""" + X1, X2 = random_data + + # DSA with Wasserstein + dsa_wass = DSA( + X=[X1, X2], + Y=None, + dmd_class=DMD, + device="cpu", + verbose=False, + n_jobs=1, + score_method="wasserstein", + iters=100, + lr=0.01, + n_delays=1, + rank=5, + ) + scores_wass = dsa_wass.fit_score() + + # DSA with angular + dsa_ang = DSA( + X=[X1, X2], + Y=None, + dmd_class=DMD, + device="cpu", + verbose=False, + n_jobs=1, + score_method="angular", + iters=100, + lr=0.01, + n_delays=1, + rank=5, + ) + scores_ang = dsa_ang.fit_score() + + # Both should have same shape + assert scores_wass.shape == scores_ang.shape + + # Both should have near-zero diagonals + assert np.all(np.abs(np.diag(scores_wass)) < 1e-3) + assert np.all(np.abs(np.diag(scores_ang)) < 1e-3) + + # Off-diagonal elements should be different (different metrics) + # But both should be positive and non-zero + assert scores_wass[0, 1] > 0 + assert scores_ang[0, 1] > 0 + + +if __name__ == "__main__": + # Allow running as a script for quick testing + print("=" * 60) + print("Testing Wasserstein Distance Optimization") + print("=" * 60) + + # Create fixtures manually + np.random.seed(42) + torch.manual_seed(42) + A = torch.randn(5, 5) + B = torch.randn(5, 5) + random_matrices = (A, B) + + np.random.seed(42) + X1 = np.random.randn(10, 100) + X2 = np.random.randn(10, 100) + random_data = (X1, X2) + + print("\n1. Testing SimilarityTransformDist with matrices...") + test_simdist_wasserstein_with_matrices(random_matrices) + print("✓ Passed") + + print("\n2. Testing SimilarityTransformDist with pre-computed eigenvalues...") + test_simdist_wasserstein_with_precomputed_eigenvalues(random_matrices) + print("✓ Passed") + + print("\n3. Testing with numpy arrays...") + test_simdist_wasserstein_with_numpy_arrays(random_matrices) + print("✓ Passed") + + print("\n4. Testing DSA with Wasserstein distance caching...") + test_dsa_wasserstein_caching(random_data) + print("✓ Passed") + + print("\n5. Testing DSA Wasserstein vs Angular...") + test_dsa_wasserstein_vs_angular_difference(random_data) + print("✓ Passed") + + print("\n" + "=" * 60) + print("All tests passed! ✓") + print("=" * 60) From a13180ff01c47d93f8bb549b5d79c43da280f9da Mon Sep 17 00:00:00 2001 From: ostrow Date: Wed, 5 Nov 2025 17:01:03 -0500 Subject: [PATCH 39/90] bug fixes and addition of a new tutorial demonstrating all the different ways to use dsa --- DSA/simdist.py | 5 + DSA/simdist_controllability.py | 2 +- DSA/sweeps.py | 11 +- examples/fig2_real.ipynb | 5211 ------------------------ examples/how_to_use_dsa_tutorial.ipynb | 1144 ++++++ 5 files changed, 1158 insertions(+), 5215 deletions(-) delete mode 100644 examples/fig2_real.ipynb create mode 100644 examples/how_to_use_dsa_tutorial.ipynb diff --git a/DSA/simdist.py b/DSA/simdist.py index 699160b..36044a4 100644 --- a/DSA/simdist.py +++ b/DSA/simdist.py @@ -7,6 +7,7 @@ from scipy.stats import wasserstein_distance import ot # optimal transport for multidimensional l2 wasserstein import warnings +from typing import Final try: from .dmd import DMD @@ -142,6 +143,7 @@ def __init__( verbose=False, eps=1e-5, rescale_wasserstein=False, + compare: Final = 'state' ): """ Parameters @@ -164,6 +166,8 @@ def __init__( eps : float early stopping threshold + + compare : str (final). dummy variable for inference of types / config """ self.iters = iters @@ -177,6 +181,7 @@ def __init__( self.eps = eps self.rescale_wasserstein = rescale_wasserstein self.wasserstein_compare = 'eig' # for backwards compatibility + self.compare = compare def fit( self, diff --git a/DSA/simdist_controllability.py b/DSA/simdist_controllability.py index cf1f1da..e700882 100644 --- a/DSA/simdist_controllability.py +++ b/DSA/simdist_controllability.py @@ -21,7 +21,7 @@ def __init__( score_method: Literal["euclidean", "angular"] = "euclidean", compare: Literal["joint", "control", "state"] = "joint", align_inputs: bool = False, - return_distance_components: bool = True, + return_distance_components: bool = False, ): f""" Parameters diff --git a/DSA/sweeps.py b/DSA/sweeps.py index e23c7ea..510d88d 100644 --- a/DSA/sweeps.py +++ b/DSA/sweeps.py @@ -85,15 +85,20 @@ def sweep_ranks_delays( continue dmd = DMD(train_data, n_delays=nd, rank=r, **dmd_kwargs) dmd.fit() - pred, H_test_pred, H_test_true, V_test_pred, V_test_true = dmd.predict( + + # pred, H_test_pred, H_test_true, V_test_pred, V_test_true = dmd.predict( + # test_data, reseed=reseed, full_return=True + # ) + pred, H_test_pred, H_test_true= dmd.predict( test_data, reseed=reseed, full_return=True ) if error_space == "H": pred = H_test_pred test_data_err = H_test_true elif error_space == "V": - pred = V_test_pred - test_data_err = V_test_true + raise ValueError("V space not implemented ") + # pred = V_test_pred + # test_data_err = V_test_true elif error_space == "X": pred = pred test_data_err = test_data diff --git a/examples/fig2_real.ipynb b/examples/fig2_real.ipynb deleted file mode 100644 index ca13ae3..0000000 --- a/examples/fig2_real.ipynb +++ /dev/null @@ -1,5211 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "52fcf42e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Shared parameters:\n", - " System: n=10, m=1, p_out=10, N=10000\n", - " Dynamics: rho1=0.92, rho2=0.82, g1=1, g2=1.5\n", - " Noise: obs_noise=0.0001, process_noise=0.0\n", - " Nonlinearity: nonlinear_eps=0.01\n", - " Model: n_delays=150, rank=10, pf=150\n", - " Evaluation: n_iters=10\n" - ] - } - ], - "source": [ - "\"\"\"\n", - "Figure 2 InputDSA Data Analysis \n", - "\"\"\"\n", - "%load_ext autoreload\n", - "%autoreload 2\n", - "\n", - "# SHARED PARAMETERS - used consistently across all analyses below\n", - "# Only change these values to modify the entire notebook behavior\n", - "n = 10 # latent state dim\n", - "n_large = 50\n", - "m = 1 # input dim \n", - "p_out = 10 # observed dim (partial observation) - gets overridden in some cells\n", - "p_out_small = 2\n", - "N = 10000 # sequence length\n", - "N_small = 1000\n", - "n_Us = 4\n", - "obs_noise = 0.0001\n", - "process_noise = 0.0#1\n", - "nonlinear_eps = 0.01\n", - "input_alpha = 0.001\n", - "g1 = 1\n", - "g2 = 1.5\n", - "rho1 = 0.92\n", - "rho2 = 0.82\n", - "seed1 = 11\n", - "seed2 = 12\n", - "n_delays = 150\n", - "rank = 10\n", - "pf = 150\n", - "n_iters = 10\n", - "backend = 'n4sid'\n", - "\n", - "print(f\"Shared parameters:\")\n", - "print(f\" System: n={n}, m={m}, p_out={p_out}, N={N}\")\n", - "print(f\" Dynamics: rho1={rho1}, rho2={rho2}, g1={g1}, g2={g2}\")\n", - "print(f\" Noise: obs_noise={obs_noise}, process_noise={process_noise}\")\n", - "print(f\" Nonlinearity: nonlinear_eps={nonlinear_eps}\")\n", - "print(f\" Model: n_delays={n_delays}, rank={rank}, pf={pf}\")\n", - "print(f\" Evaluation: n_iters={n_iters}\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "a08ecb20", - "metadata": {}, - "outputs": [], - "source": [ - "# in this analysis, we are goign to look at how to appropriately compare partially observed systems\n", - "# we will look at the following systems\n", - "\n", - "# 4 systems, made up fo 2 pairings (1,2) (3,4) same intrinsic dynamics, (1,3) (2,4) same read in dynamics\n", - "# we will look at the behavior in the fully observed setting of DSA and AgentDSA, and then in the\n", - "#partially observed setting of DMDc versus subspace DMDc\n", - "#we'll looking at clustering capability across many instantiatons of the data, \n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "4b891d5e", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "# Updated to use DSA package imports\n", - "import sys\n", - "sys.path.insert(0, '..') # Add parent directory to path to import DSA\n", - "\n", - "plt.rcParams['pdf.fonttype'] = 42\n", - "plt.rcParams['ps.fonttype'] = 42\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "f3cdd518", - "metadata": {}, - "outputs": [], - "source": [ - "from DSA import InputDSA\n", - "from DSA import SimilarityTransformDist as SimDist\n", - "from DSA import ControllabilitySimilarityTransformDist as ControlSimDist\n", - "from tqdm import tqdm\n", - "\n", - "def compare_systems_with_InputDSA(Ys, Us, n_delays=150, rank=10, backend='n4sid'):\n", - " \"\"\"\n", - " Compare controlled systems using InputDSA from DSA package.\n", - " Uses the new update_compare_method() to avoid refitting DMDs multiple times.\n", - " \n", - " Parameters:\n", - " - Ys: list of output data arrays (p_out, N)\n", - " - Us: list of control input arrays (m, N)\n", - " - n_delays: number of delays for DMD\n", - " - rank: rank for DMD\n", - " - backend: 'n4sid' or 'custom' for SubspaceDMDc\n", - " \n", - " Returns:\n", - " - sims_full: joint similarity scores\n", - " - sims_control_joint: control scores from joint optimization\n", - " - sims_state_joint: state scores from joint optimization\n", - " - sims_control_separate: control scores from separate optimization\n", - " - sims_state_separate: state scores from separate optimization\n", - " \"\"\"\n", - " # Transpose data for InputDSA (expects time_first=True by default)\n", - " Ys_T = [Y.T for Y in Ys]\n", - " Us_T = [U.T for U in Us]\n", - " \n", - " # Configure DMD\n", - " # dmd_config = SubspaceDMDcConfig(\n", - " # n_delays=n_delays,\n", - " # rank=rank,\n", - " # backend=backend\n", - " # )\n", - " dmd_config = dict(\n", - " n_delays=n_delays,\n", - " rank=rank,\n", - " backend=backend\n", - " )\n", - " \n", - " # Create InputDSA with joint comparison\n", - " # This will fit the DMDs once and return joint comparison results\n", - " inputDSA = InputDSA(\n", - " X=Ys_T,\n", - " X_control=Us_T,\n", - " dmd_config=dmd_config,\n", - " compare='joint',\n", - " return_distance_components=True\n", - " )\n", - " \n", - " # Fit DMDs and get joint comparison results\n", - " sims_full, sims_state_joint, sims_control_joint = inputDSA.fit_score()\n", - " \n", - " # Update comparison method to 'state' without refitting DMDs\n", - " inputDSA.update_compare_method(compare='state')\n", - " sims_state_separate = inputDSA.score()\n", - " \n", - " # Update comparison method to 'control' without refitting DMDs\n", - " inputDSA.update_compare_method(compare='control')\n", - " sims_control_separate = inputDSA.score()\n", - " \n", - " return sims_full, sims_control_joint, sims_state_joint, sims_control_separate, sims_state_separate\n", - "\n", - "\n", - "#strict comparison metrics, for when we fit and compare separately\n", - "def compare_A(A1,A2):\n", - " simdist = SimDist(iters=1000,score_method='wasserstein',lr=1e-3,verbose=True)\n", - " return simdist.fit_score(A1,A2)\n", - "\n", - "def compare_A_full(As):\n", - " sims = np.zeros((len(As),len(As)))\n", - " for i in range(len(As)):\n", - " for j in range(i+1,len(As)):\n", - " sims[i,j] = compare_A(As[i],As[j])\n", - " sims[j,i] = sims[i,j]\n", - " return sims\n", - "\n", - "def compare_B(B1,B2):\n", - " csimdist = ControlSimDist(score_method='euclidean',compare='control')\n", - " sim = csimdist.fit_score(None, None, B1, B2)\n", - " return sim\n", - "\n", - "def compare_systems_full(As,Bs):\n", - " csimdist = ControlSimDist(score_method='euclidean',compare='joint',return_distance_components=True)\n", - " sims_full = np.zeros((len(As),len(As)))\n", - " sims_control_joint = np.zeros((len(As),len(As)))\n", - " sims_state_joint = np.zeros((len(As),len(As)))\n", - " sims_control_separate = np.zeros((len(As),len(As)))\n", - " sims_state_separate = np.zeros((len(As),len(As)))\n", - " for i in tqdm(range(len(As))):\n", - " for j in range(i+1,len(As)):\n", - " all_sims = csimdist.fit_score(As[i],As[j],Bs[i],Bs[j])\n", - " sims_full[i,j] = sims_full[j,i] = all_sims[0]\n", - " sims_state_joint[i,j] = sims_state_joint[j,i] = all_sims[1]\n", - " sims_control_joint[i,j] = sims_control_joint[j,i] = all_sims[2]\n", - " \n", - " for i in tqdm(range(len(As))):\n", - " for j in range(i+1,len(As)):\n", - " sims_state_separate[i,j] = compare_A(As[i],As[j])\n", - " sims_control_separate[i,j] = compare_B(Bs[i],Bs[j])\n", - " sims_state_separate[j,i] = sims_state_separate[i,j]\n", - " sims_control_separate[j,i] = sims_control_separate[i,j]\n", - "\n", - " return sims_full, sims_control_joint, sims_state_joint, sims_control_separate, sims_state_separate\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "7ba785c8", - "metadata": {}, - "outputs": [], - "source": [ - "def make_stable_A(n, rho=0.9, rng=None):\n", - " rng = np.random.default_rng(rng)\n", - " M = rng.standard_normal((n, n))\n", - " # Make it diagonally dominant-ish and scale spectral radius\n", - " A = M / np.max(np.abs(np.linalg.eigvals(M))) * rho\n", - " return A\n", - "\n", - "def simulate_system(A, B, C, U, x0=None,rng=None,obs_noise=0.0,process_noise=0.0,\n", - " nonlinear_eps=0.0,nonlinear_func= lambda x: np.tanh(x),nonlinear_eps_input=0.0):\n", - " n, m = B.shape\n", - " p_out = C.shape[0]\n", - " N = U.shape[1]\n", - " X = np.zeros((n, N+1))\n", - " C_full = np.eye(A.shape[0])\n", - " C_full[np.where(C == 1)[1],np.where(C == 1)[1]] = 0.0\n", - "\n", - " if x0 is not None:\n", - " X[:, 0] = x0\n", - " else:\n", - " X[:, 0] = np.random.default_rng(rng).standard_normal((n,))\n", - " Y = np.zeros((p_out, N))\n", - " for t in range(N):\n", - " X[:, t+1] = A @ (X[:, t]) + nonlinear_eps * C_full @ nonlinear_func(A @ X[:, t]) + \\\n", - " B @ ((1-nonlinear_eps_input) * U[:, t] + nonlinear_eps_input * nonlinear_func(U[:, t])) + \\\n", - " np.random.normal(0, process_noise, (n,))\n", - " Y[:, t] = C @ X[:, t] + np.random.normal(0, obs_noise, (p_out,))\n", - " return X[:, 1:], Y # states aligned with Y\n", - "\n", - "def smooth_input(m, N, alpha=0.9, rng=None):\n", - " rng = np.random.default_rng(rng)\n", - " w = rng.standard_normal((m, N))\n", - " U = np.zeros_like(w)\n", - " for t in range(N):\n", - " U[:, t] = alpha*(U[:, t-1] if t>0 else 0) + (1-alpha)*w[:, t]\n", - " return U" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "87d14512", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def simulate_As_Bs(latent_dim, input_dim, observed_dim, seq_length,rho1=rho1,\n", - " rho2=rho2, g1=g1,g2=g2, seed1=seed1, seed2=seed2, input_alpha=input_alpha,same_inp=False,n_Us=n_Us,\n", - " obs_noise=obs_noise,process_noise=process_noise,nonlinear_eps=nonlinear_eps,nonlinear_func= lambda x: np.tanh(x)):\n", - "\n", - " A1_true = make_stable_A(latent_dim, rho=rho1, rng=seed1)\n", - " cov_matrix_B1 = np.random.default_rng(seed1).standard_normal((latent_dim, latent_dim))\n", - " cov_matrix_B1 = cov_matrix_B1 @ cov_matrix_B1.T # Make it symmetric positive definite\n", - " B1_true = np.random.default_rng(seed1).multivariate_normal(np.zeros(latent_dim), cov_matrix_B1, input_dim).T * g1\n", - "\n", - " A2_true = make_stable_A(latent_dim, rho=rho2, rng=seed2)\n", - " C = np.linalg.qr(np.random.default_rng(seed2).standard_normal((latent_dim, latent_dim)))[0]\n", - " cov_matrix_B2_rotated = C @ cov_matrix_B1 @ C.T \n", - " B2_true = np.random.default_rng(seed2).multivariate_normal(np.zeros(latent_dim), cov_matrix_B2_rotated, input_dim).T * g2\n", - "\n", - " # Random partial observation: select p_out of n states\n", - " idx_obs = np.sort(np.random.default_rng(seed1).choice(latent_dim, size=observed_dim, replace=False))\n", - " C_true = np.zeros((observed_dim, latent_dim))\n", - " C_true[np.arange(observed_dim), idx_obs] = 1.0\n", - " \n", - " X_trues, Ys,Us = [], [], []\n", - " i = 0\n", - " if same_inp:\n", - " U = smooth_input(input_dim, seq_length, alpha=input_alpha, rng=seed1+i) \n", - " control_labels = []\n", - " state_labels = []\n", - " for a1, As in enumerate([A1_true, A2_true]):\n", - " for b1, Bs in enumerate([B1_true, B2_true]):\n", - " i += 1\n", - " if not same_inp:\n", - " for j in range(n_Us):\n", - " U = smooth_input(input_dim, seq_length, alpha=input_alpha, rng=seed1+ i + j) \n", - " X_true, Y = simulate_system(As, Bs, C_true, U, x0=np.zeros(latent_dim),rng=seed1+i,\n", - " obs_noise=obs_noise,process_noise=process_noise,\n", - " nonlinear_eps=nonlinear_eps,nonlinear_func=nonlinear_func)\n", - " X_trues.append(X_true)\n", - " Ys.append(Y)\n", - " Us.append(U)\n", - " control_labels.append(b1)\n", - " state_labels.append(a1)\n", - " else:\n", - " X_true, Y = simulate_system(As, Bs, C_true, U, x0=np.zeros(latent_dim),rng=seed1+i,\n", - " obs_noise=obs_noise,process_noise=process_noise,\n", - " nonlinear_eps=nonlinear_eps,nonlinear_func=nonlinear_func)\n", - " X_trues.append(X_true)\n", - " Ys.append(Y)\n", - " Us.append(U)\n", - " control_labels.append(b1)\n", - " state_labels.append(a1)\n", - "\n", - " return X_trues, Ys, Us, control_labels, state_labels, (A1_true, A2_true), (B1_true, B2_true)\n", - "\n", - "\n", - "X_trues, Ys, Us, control_labels, state_labels, A_trues, B_trues = simulate_As_Bs(n,m,p_out,N,\n", - " input_alpha=input_alpha,g1=g1,g2=g2,n_Us=1,obs_noise=obs_noise,process_noise=process_noise,\n", - " nonlinear_eps=nonlinear_eps,nonlinear_func= lambda x: np.tanh(x))\n", - "fig, ax = plt.subplots(1, 4, figsize=(8, 2),sharey='row')\n", - "#plot Us and Ys against time\n", - "for i in range(4):\n", - " # ax[0, i].plot(Us[i].T[:100])\n", - " ax[i].plot(Ys[i].T[:100,:],alpha=0.5)\n", - " \n", - " # Remove spines and ticks\n", - " for spine in ax[i].spines.values():\n", - " spine.set_visible(False)\n", - " ax[i].set_xticks([])\n", - " ax[i].set_yticks([])\n", - "# plt.savefig(f'{folder_path}/data_examples.pdf', format='pdf', dpi=300, bbox_inches='tight')\n", - "plt.show()\n", - "\n", - "# X_trues, Ys, Us, control_labels, state_labels, A_trues, B_trues = simulate_As_Bs(n,m,p_out,N_small,\n", - "# input_alpha=input_alpha,g1=g1,g2=g2, same_inp=False,n_Us=4,\n", - "# obs_noise=obs_noise,process_noise=process_noise,nonlinear_eps=nonlinear_eps)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "728cf5a2", - "metadata": {}, - "outputs": [], - "source": [ - "from DSA import DMD,DMDc, SubspaceDMDc\n", - "from tqdm import tqdm\n", - "\n", - "def get_dmds(Ys,n_delays=1,rank=None):\n", - " As = []\n", - " for Y in Ys:\n", - " dmd = DMD(Y.T,n_delays=n_delays,rank=rank)\n", - " dmd.fit()\n", - " As.append(dmd.A_v.numpy())\n", - " return As\n", - "\n", - "def get_dmdcs(Ys,Us,n_delays=1,rank=None):\n", - " As = []\n", - " Bs = []\n", - " for Y, U in zip(Ys, Us):\n", - " dmdc = DMDc(Y.T, U.T,n_delays=n_delays,n_control_delays=n_delays,rank_input=rank,rank_output=rank)\n", - " dmdc.fit()\n", - " As.append(dmdc.A_v.numpy())\n", - " Bs.append(dmdc.B_v.numpy())\n", - " return As, Bs\n", - "\n", - "\n", - "def get_subspace_dmdcs(Ys, Us, p=20, rank=None, backend='n4sid'):\n", - " \"\"\"Fit SubspaceDMDc models using DSA package.\"\"\"\n", - " As, Bs, Cs, infos = [], [], [], []\n", - " for Y, U in zip(Ys, Us):\n", - " model = SubspaceDMDc(Y.T, U.T, n_delays=p, rank=rank, backend=backend)\n", - " model.fit()\n", - " As.append(model.A_v)#.numpy())\n", - " Bs.append(model.B_v)#.numpy())\n", - " Cs.append(model.C_v)#.numpy())\n", - " infos.append(model.info)\n", - " return As, Bs, Cs, infos\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "db4d50cb", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[(2, 1000), (2, 1000), (2, 1000), (2, 1000), (2, 1000), (2, 1000), (2, 1000), (2, 1000), (2, 1000), (2, 1000), (2, 1000), (2, 1000), (2, 1000), (2, 1000), (2, 1000), (2, 1000)]\n" - ] - } - ], - "source": [ - "X_trues, Ys, Us, control_labels, state_labels, A_trues, B_trues = simulate_As_Bs(n,m,p_out_small,\n", - " N_small,input_alpha=input_alpha,g1=g1,g2=g2,same_inp=False,n_Us=n_Us,\n", - " obs_noise=obs_noise,process_noise=process_noise,\n", - " nonlinear_eps=nonlinear_eps)\n", - "print([i.shape for i in Ys])" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "3ef1f7f5", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#plot examples of the inputs and the outputs\n", - "fig, ax = plt.subplots(2,2,figsize=(4,2),sharex=True,sharey=True)\n", - "ax = ax.flatten()\n", - "for i in range(4):\n", - " ind = 4*i\n", - " ax[i].plot(Us[ind].T[:100] + 10*np.mean(np.abs(Us[ind])), color=plt.cm.Set2(1), label='Input (u)')\n", - " ax[i].plot(Ys[ind].T[:100, 0], color=plt.cm.Set2(2), label='Output (y)')\n", - " #remove all ticks and lines\n", - " ax[i].set_xticks([])\n", - " ax[i].set_yticks([])\n", - " ax[i].spines['top'].set_visible(False)\n", - " ax[i].spines['right'].set_visible(False)\n", - " ax[i].spines['bottom'].set_visible(False)\n", - " ax[i].spines['left'].set_visible(False)\n", - " \n", - "ax[0].text(1, 0.4, 'Input', transform=ax[0].transAxes, color=plt.cm.Set2(1), va='top')\n", - "ax[0].text(1, 0.2, 'Output', transform=ax[0].transAxes, color=plt.cm.Set2(2), va='top')\n", - "plt.tight_layout()\n", - "# plt.savefig(f'{folder_path}/input_output_examples.pdf', format='pdf', dpi=300, bbox_inches='tight')\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "eef05f5d", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[legend.py:1217 - _parse_legend_args() ] No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Use SubspaceDMDc from DSA package to analyze singular values\n", - "\n", - "\n", - "fig, ax = plt.subplots(2,2,figsize=(9,6),sharey=True,sharex=True)\n", - "ax = ax.flatten()\n", - " \n", - "for j, (Y, U) in enumerate(zip(Ys[::n_Us], Us[::n_Us])):\n", - " # Test different numbers of delays for subspace identification\n", - " nds_all = [10, 25, 50, 75, 100, 125, 150, 175, 200]\n", - " \n", - " for k, nds in enumerate(nds_all):\n", - " # Fit SubspaceDMDc with varying number of delays\n", - " model = SubspaceDMDc(\n", - " Y.T, # SubspaceDMDc expects (T, p_out)\n", - " U.T, # SubspaceDMDc expects (T, m)\n", - " n_delays=nds,\n", - " rank=20, # Use fixed rank for comparison\n", - " backend='n4sid'\n", - " )\n", - " model.fit()\n", - " \n", - " # Extract singular values from model info\n", - " singular_vals = model.info['singular_values_O']\n", - " \n", - " # Convert to numpy if needed\n", - " if hasattr(singular_vals, 'numpy'):\n", - " singular_vals = singular_vals.numpy()\n", - " \n", - " # Plot singular values\n", - " ax[j].plot(singular_vals, '-', label=f'{nds}', \n", - " color=plt.cm.Blues_r(k / (len(nds_all) + 4)))\n", - " ax[j].set_yscale('log')\n", - " ax[j].axvline(x=20, color='k', linestyle=':', alpha=0.5)\n", - " \n", - " ax[j].set_xlabel('Mode Number')\n", - " ax[j].set_title(f'System {j+1}')\n", - " ax[1].legend(title=\"Delays\", loc='upper right', bbox_to_anchor=(1.5, 1), \n", - " fontsize=12, title_fontsize=15)\n", - "\n", - "ax[0].set_ylabel('Singular Value')\n", - "ax[2].set_ylabel('Singular Value')\n", - "plt.tight_layout()\n", - "# plt.savefig(f'{folder_path}/singular_values_subspace_dmdc.pdf', format='pdf', dpi=300, bbox_inches='tight')\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "3636fc5c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "========================================\n", - "Number of valid trials: 150\n", - "========================================\n", - "Number of valid trials: 150\n", - "========================================\n", - "Number of valid trials: 150\n", - "========================================\n", - "Number of valid trials: 150\n", - "========================================\n", - "Number of valid trials: 150\n", - "========================================\n", - "Number of valid trials: 150\n", - "========================================\n", - "Number of valid trials: 150\n", - "========================================\n", - "Number of valid trials: 150\n", - "========================================\n", - "Number of valid trials: 150\n", - "========================================\n", - "Number of valid trials: 150\n", - "========================================\n", - "Number of valid trials: 150\n", - "========================================\n", - "Number of valid trials: 150\n", - "========================================\n", - "Number of valid trials: 150\n", - "========================================\n", - "Number of valid trials: 150\n", - "========================================\n", - "Number of valid trials: 150\n", - "========================================\n", - "Number of valid trials: 150\n" - ] - } - ], - "source": [ - "dec = 0 #can change this to look at the efect of using the incorrect ranks\n", - "A_dmd = get_dmds(Ys,n_delays=n_delays,rank=rank- dec)\n", - "A_cs, B_cs = get_dmdcs(Ys,Us,n_delays=n_delays,rank=rank - dec)\n", - "As, Bs, Cs, infos = get_subspace_dmdcs(Ys,Us,p=pf,rank=rank-dec,backend='custom')\n" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "5ae5efa9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "N4SID - A matrix shapes: [(10, 10), (10, 10), (10, 10), (10, 10), (10, 10), (10, 10), (10, 10), (10, 10), (10, 10), (10, 10), (10, 10), (10, 10), (10, 10), (10, 10), (10, 10), (10, 10)]\n", - "N4SID - Ranks used: [10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10]\n", - "N4SID - Backend info: ['unknown', 'unknown', 'unknown', 'unknown', 'unknown', 'unknown', 'unknown', 'unknown', 'unknown', 'unknown', 'unknown', 'unknown', 'unknown', 'unknown', 'unknown', 'unknown']\n", - "\\nEigenvalue comparison (first system):\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\\nComputing similarity matrices...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 16/16 [00:00<00:00, 548.06it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 123.68it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 608.27it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 135.77it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Custom backend silhouette score: 0.685\n", - "N4SID backend silhouette score: 0.669\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "As_n4sid, Bs_n4sid, Cs_n4sid, infos_n4sid = get_subspace_dmdcs(Ys, Us, p=pf, rank=rank-dec, backend='n4sid')\n", - "print(f\"N4SID - A matrix shapes: {[A.shape for A in As_n4sid]}\")\n", - "print(f\"N4SID - Ranks used: {[info['rank_used'] for info in infos_n4sid]}\")\n", - "print(f\"N4SID - Backend info: {[info.get('backend', 'unknown') for info in infos_n4sid]}\")\n", - "\n", - "# Quick comparison of eigenvalues (first system)\n", - "print(\"\\\\nEigenvalue comparison (first system):\")\n", - "eigs_custom = np.linalg.eigvals(As[0])\n", - "eigs_n4sid = np.linalg.eigvals(As_n4sid[0])\n", - "eigs_real = np.linalg.eigvals(A_trues[0])\n", - "\n", - "plt.figure(figsize=(8, 6))\n", - "plt.scatter(eigs_real.real, eigs_real.imag, alpha=0.7, label='True', s=100)\n", - "plt.scatter(eigs_custom.real, eigs_custom.imag, alpha=0.7, label='Custom backend', s=50)\n", - "plt.scatter(eigs_n4sid.real, eigs_n4sid.imag, alpha=0.7, label='N4SID backend', s=50, marker='x',c='k')\n", - "plt.xlabel('Real part')\n", - "plt.ylabel('Imaginary part')\n", - "plt.title('Eigenvalue comparison (first system)')\n", - "plt.legend()\n", - "plt.grid(True, alpha=0.3)\n", - "plt.axhline(y=0, color='k', linestyle='-', alpha=0.3)\n", - "plt.axvline(x=0, color='k', linestyle='-', alpha=0.3)\n", - "plt.show()\n", - "\n", - "# Compute distances using both backends for comparison\n", - "print(\"\\\\nComputing similarity matrices...\")\n", - "_, _, _, _, sims_state_custom = compare_systems_full(As, Bs)\n", - "_, _, _, _, sims_state_n4sid = compare_systems_full(As_n4sid, Bs_n4sid)\n", - "\n", - "from sklearn.metrics import silhouette_score\n", - "silh_custom = silhouette_score(sims_state_custom, state_labels, metric='precomputed')\n", - "silh_n4sid = silhouette_score(sims_state_n4sid, state_labels, metric='precomputed')\n", - "\n", - "\n", - "print(f\"Custom backend silhouette score: {silh_custom:.3f}\")\n", - "print(f\"N4SID backend silhouette score: {silh_n4sid:.3f}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "79bb2540", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 16/16 [00:00<00:00, 490.65it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 138.91it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 525.72it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 134.68it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 604.50it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 126.83it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 620.90it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 135.16it/s]\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from sklearn.metrics import silhouette_score\n", - "A_type = [A_dmd, A_cs, As, As_n4sid]\n", - "B_type = [A_dmd, B_cs, Bs, Bs_n4sid]\n", - "names = ['DMD, Partially Observed', 'DMDc, Partially Observed', 'Old Subspace DMDc, Partially Observed', 'Subspace DMDc, Partially Observed']\n", - "for Ai, Bi, name in zip(A_type, B_type, names):\n", - "\n", - " sims_full, sims_control_joint, sims_state_joint, sims_control_separate, sims_state_separate = compare_systems_full(Ai,Bi)\n", - "\n", - " fig, ax = plt.subplots(1, 5, figsize=(15, 3))\n", - " \n", - " # Define data and titles for each subplot\n", - " sims_data = [sims_full, sims_state_joint, sims_control_joint, sims_state_separate, sims_control_separate]\n", - " titles = ['Joint', \n", - " f'State (Joint) \\n {np.round(silhouette_score(sims_state_joint,state_labels,metric=\"precomputed\"),2)}',\n", - " f'Control (Joint) \\n {np.round(silhouette_score(sims_control_joint,control_labels,metric=\"precomputed\"),2)}',\n", - " f'State (Separate) \\n {np.round(silhouette_score(sims_state_separate,state_labels,metric=\"precomputed\"),2)}',\n", - " f'Control (Separate) \\n {np.round(silhouette_score(sims_control_separate,control_labels,metric=\"precomputed\"),2)}']\n", - " \n", - " # Loop through all subplots\n", - " for i, (data, title) in enumerate(zip(sims_data, titles)):\n", - " im = ax[i].imshow(data)\n", - " cbar = plt.colorbar(im, ax=ax[i], shrink=0.2, location='top')#, label='Distance')\n", - " cbar.ax.tick_params(labelsize=10)\n", - " cbar.ax.spines['top'].set_visible(False)\n", - " cbar.ax.spines['right'].set_visible(False)\n", - " cbar.ax.spines['bottom'].set_visible(False)\n", - " cbar.ax.spines['left'].set_visible(False)\n", - " ax[i].set_title(title,y=1.8)\n", - " #loop through all of them and remove x and yticks, then add System as text label for each\n", - " for i in range(5):\n", - " ax[i].set_xticks([])\n", - " ax[i].set_yticks([])\n", - " # ax[i].text(0.5, -0.1, 'System', transform=ax[i].transAxes, ha='center', va='top')\n", - " ax[i].set_ylabel('System')\n", - " ax[i].set_xlabel('System')\n", - " ax[i].spines['top'].set_visible(False)\n", - " ax[i].spines['right'].set_visible(False)\n", - " ax[i].spines['bottom'].set_visible(False)\n", - " ax[i].spines['left'].set_visible(False)\n", - " plt.suptitle(name,y=1.1)\n", - " plt.tight_layout()\n", - " # plt.savefig(f'{folder_path}/{name}.eps', format='eps', dpi=300, bbox_inches='tight')\n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "2b529073", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 16/16 [00:00<00:00, 523.63it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 110.19it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 474.72it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 133.29it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 594.62it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 95.83it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Silhouette Scores:\n", - "DMD State: 0.132\n", - "DMDc State: 0.143\n", - "DMDc Control: 0.002\n", - "SubspaceDMDc State: 0.669\n", - "SubspaceDMDc Control: 0.521\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Get the similarity matrices for each method\n", - "sims_full_dmd, sims_control_joint_dmd, sims_state_joint_dmd, sims_control_separate_dmd, sims_state_separate_dmd = compare_systems_full(A_dmd, A_dmd)\n", - "sims_full_dmdc, sims_control_joint_dmdc, sims_state_joint_dmdc, sims_control_separate_dmdc, sims_state_separate_dmdc = compare_systems_full(A_cs, B_cs)\n", - "sims_full_subdmdc, sims_control_joint_subdmdc, sims_state_joint_subdmdc, sims_control_separate_subdmdc, sims_state_separate_subdmdc = compare_systems_full(As_n4sid, Bs_n4sid)\n", - "\n", - "# Print silhouette scores\n", - "print(\"Silhouette Scores:\")\n", - "print(f\"DMD State: {np.round(silhouette_score(sims_state_separate_dmd, state_labels, metric='precomputed'), 3)}\")\n", - "print(f\"DMDc State: {np.round(silhouette_score(sims_state_separate_dmdc, state_labels, metric='precomputed'), 3)}\")\n", - "print(f\"DMDc Control: {np.round(silhouette_score(sims_control_joint_dmdc, control_labels, metric='precomputed'), 3)}\")\n", - "print(f\"SubspaceDMDc State: {np.round(silhouette_score(sims_state_separate_subdmdc, state_labels, metric='precomputed'), 3)}\")\n", - "print(f\"SubspaceDMDc Control: {np.round(silhouette_score(sims_control_joint_subdmdc, control_labels, metric='precomputed'), 3)}\")\n", - "\n", - "# Create 2x3 subplot\n", - "fig, axes = plt.subplots(2, 3, figsize=(6, 5))\n", - "plt.subplots_adjust(wspace=0.1, hspace=0.2)\n", - "\n", - "# Column headers (bold)\n", - "column_headers = ['DMD', 'DMDc', 'SubspaceDMDc']\n", - "for i, header in enumerate(column_headers):\n", - " axes[0, i].text(0.5, 1.75, header, transform=axes[0, i].transAxes, ha='center', va='bottom', fontweight='bold', fontsize=16)\n", - "\n", - "# Row headers\n", - "row_headers = ['State DSA', ['Not Available', 'Input DSA', 'Input DSA']]\n", - "for i in range(3):\n", - " axes[0, i].text(0.5, 1.55, 'State DSA', transform=axes[0, i].transAxes, ha='center', va='bottom', fontsize=12)\n", - "\n", - "axes[1, 0].text(0.5, 1.55, 'Not Available', transform=axes[1, 0].transAxes, ha='center', va='bottom', fontsize=12)\n", - "axes[1, 1].text(0.5, 1.55, 'Input DSA', transform=axes[1, 1].transAxes, ha='center', va='bottom', fontsize=12)\n", - "axes[1, 2].text(0.5, 1.55, 'Input DSA', transform=axes[1, 2].transAxes, ha='center', va='bottom', fontsize=12)\n", - "\n", - "# Data for each subplot\n", - "data_matrices = [\n", - " sims_state_separate_dmd, # top left\n", - " sims_state_separate_dmdc, # top middle \n", - " sims_state_separate_subdmdc, # top right\n", - " None, # bottom left (gray matrix)\n", - " sims_control_joint_dmdc, # bottom middle\n", - " sims_control_joint_subdmdc # bottom right\n", - "]\n", - "\n", - "# Create gray matrix for bottom left - use same size as other matrices\n", - "matrix_size = sims_state_separate_dmd.shape[0]\n", - "gray_matrix = np.ones((matrix_size, matrix_size)) * 0.5\n", - "\n", - "# Plot each subplot\n", - "for idx, (ax, data) in enumerate(zip(axes.flat, data_matrices)):\n", - " row = idx // 3\n", - " col = idx % 3\n", - " \n", - " if idx == 3: # Bottom left - gray matrix with diagonal lines\n", - " im = ax.imshow(gray_matrix, cmap='gray', vmin=0, vmax=1, extent=[-0.5, matrix_size-0.5, matrix_size-0.5, -0.5])\n", - " \n", - " # Add diagonal lines from bottom-left to top-right\n", - " for i in range(matrix_size):\n", - " for j in range(matrix_size):\n", - " ax.plot([j-0.5, j+0.5], [i-0.5, i+0.5], 'k--', linewidth=1)\n", - " \n", - " # Set axis limits to match other plots\n", - " ax.set_xlim(-0.5, matrix_size-0.5)\n", - " ax.set_ylim(matrix_size-0.5, -0.5)\n", - " \n", - " # Remove ticks and labels\n", - " ax.set_xticks([])\n", - " ax.set_yticks([])\n", - " ax.set_xlabel('')\n", - " ax.set_ylabel('')\n", - " \n", - " # Remove spines\n", - " for spine in ax.spines.values():\n", - " spine.set_visible(False)\n", - " \n", - " else:\n", - " im = ax.imshow(data, cmap='viridis')\n", - " \n", - " # Add colorbar on top with only 2 ticks\n", - " cbar = plt.colorbar(im, ax=ax, shrink=0.4, location='top', pad=0.02)\n", - " vmin, vmax = data.min(), data.max()\n", - " cbar.set_ticks([vmin, vmax])\n", - " cbar.set_ticklabels([f'{vmin:.2g}', f'{vmax:.2g}'])\n", - " cbar.ax.tick_params(labelsize=10)\n", - " \n", - " # Remove colorbar spines\n", - " for spine in cbar.ax.spines.values():\n", - " spine.set_visible(False)\n", - " \n", - " # Set custom tick positions and labels (every 4 positions)\n", - " tick_positions = [1.5, 5.5, 9.5, 13.5] # Middle of each group of 4\n", - " tick_labels = ['1', '2', '3', '4']\n", - " \n", - " ax.set_xticks(tick_positions)\n", - " ax.set_xticklabels(tick_labels,fontsize=10)\n", - " ax.set_yticks(tick_positions)\n", - " ax.set_yticklabels(tick_labels,fontsize=10)\n", - " \n", - " # Set axis labels\n", - " ax.set_xlabel('System',fontsize=10)\n", - " ax.set_ylabel('System',fontsize=10)\n", - " \n", - " # Remove spines\n", - " for spine in ax.spines.values():\n", - " spine.set_visible(False)\n", - "\n", - "plt.tight_layout()\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "2edb4f13", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 16/16 [00:00<00:00, 287.19it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 39.64it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 380.73it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 41.32it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Silhouette Scores:\n", - "DMDc Full (state): 0.028\n", - "SubspaceDMDc Full (state): 0.377\n", - "DMDc Full (control): -0.001\n", - "SubspaceDMDc Full (control): 0.435\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Get the similarity matrices for each method\n", - "sims_full_dmdc, _, _, _, _ = compare_systems_full(A_cs, B_cs)\n", - "sims_full_subdmdc, _, _, _, _ = compare_systems_full(As_n4sid, Bs_n4sid)\n", - "\n", - "# Print silhouette scores\n", - "print(\"Silhouette Scores:\")\n", - "print(f\"DMDc Full (state): {np.round(silhouette_score(sims_full_dmdc, state_labels, metric='precomputed'), 3)}\")\n", - "print(f\"SubspaceDMDc Full (state): {np.round(silhouette_score(sims_full_subdmdc, state_labels, metric='precomputed'), 3)}\")\n", - "print(f\"DMDc Full (control): {np.round(silhouette_score(sims_full_dmdc, control_labels, metric='precomputed'), 3)}\")\n", - "print(f\"SubspaceDMDc Full (control): {np.round(silhouette_score(sims_full_subdmdc, control_labels, metric='precomputed'), 3)}\")\n", - "\n", - "# Create 1x2 subplot\n", - "fig, axes = plt.subplots(1, 2, figsize=(6, 3))\n", - "plt.subplots_adjust(wspace=0.1, hspace=0.2)\n", - "\n", - "# Column headers (bold)\n", - "column_headers = ['DMDc', 'SubspaceDMDc']\n", - "for i, header in enumerate(column_headers):\n", - " axes[i].text(0.5, 1.55, header, transform=axes[i].transAxes, ha='center', va='bottom', fontweight='bold', fontsize=16)\n", - "\n", - "# Data for each subplot\n", - "data_matrices = [\n", - " sims_full_dmdc, # left\n", - " sims_full_subdmdc # right\n", - "]\n", - "\n", - "# Plot each subplot\n", - "for idx, (ax, data) in enumerate(zip(axes.flat, data_matrices)):\n", - " im = ax.imshow(data, cmap='viridis')\n", - " \n", - " # Add colorbar on top with only 2 ticks\n", - " cbar = plt.colorbar(im, ax=ax, shrink=0.4, location='top', pad=0.02,label='Joint DSA')\n", - " vmin, vmax = data.min(), data.max()\n", - " cbar.set_ticks([vmin, vmax])\n", - " cbar.set_ticklabels([f'{vmin:.2g}', f'{vmax:.2g}'])\n", - " cbar.ax.tick_params(labelsize=10)\n", - " \n", - " # Remove colorbar spines\n", - " for spine in cbar.ax.spines.values():\n", - " spine.set_visible(False)\n", - " \n", - " # Set custom tick positions and labels (every 4 positions)\n", - " tick_positions = [1.5, 5.5, 9.5, 13.5] # Middle of each group of 4\n", - " tick_labels = ['1', '2', '3', '4']\n", - " \n", - " ax.set_xticks(tick_positions)\n", - " ax.set_xticklabels(tick_labels, fontsize=10)\n", - " ax.set_yticks(tick_positions)\n", - " ax.set_yticklabels(tick_labels, fontsize=10)\n", - " \n", - " # Set axis labels\n", - " ax.set_xlabel('System', fontsize=10)\n", - " ax.set_ylabel('System', fontsize=10)\n", - " \n", - " # Remove spines\n", - " for spine in ax.spines.values():\n", - " spine.set_visible(False)\n", - "\n", - "plt.tight_layout()\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "d85b184f", - "metadata": {}, - "outputs": [], - "source": [ - "#collect statistics now: \n", - "#sample random systems from the set of 4 pairings\n", - "#sample 4 input drives for each system, making 16 diferent systems in total \n", - "#compute silhouette score based on A labels and B labels\n", - "\n", - "def get_silhouette_scores(n,m,p_out,N,n_iters,\n", - " input_alpha=input_alpha,g1=g1,g2=g2,same_inp=False,n_Us=n_Us,\n", - " n_delays=n_delays,pf=pf,rank=rank,process_noise=process_noise,obs_noise=obs_noise,\n", - " nonlinear_eps=nonlinear_eps,nonlinear_func=lambda x: np.tanh(x),\n", - " y_feature_map = lambda x: x, u_feature_map = lambda x: x,backend=backend,\n", - " use_joint_control=True):\n", - "\n", - " silhouette_state_dmdc = []\n", - " silhouette_control_dmdc = []\n", - "\n", - " silhouette_state_subspace_dmdc = []\n", - " silhouette_control_subspace_dmdc = []\n", - "\n", - " silhouette_state_dsa = []\n", - " silhouette_control_dsa = []\n", - "\n", - "\n", - " for i in tqdm(range(n_iters)):\n", - " X_trues, Ys, Us, control_labels, state_labels, *_ = simulate_As_Bs(n,m,p_out,\n", - " N,input_alpha=input_alpha,g1=g1,g2=g2,same_inp=same_inp,n_Us=n_Us, seed1=seed1+i,seed2=seed2+110*i,\n", - " obs_noise=obs_noise,process_noise=process_noise,\n", - " nonlinear_eps=nonlinear_eps,nonlinear_func=nonlinear_func)\n", - " Ys = list(map(y_feature_map, Ys))\n", - " Us = list(map(u_feature_map, Us))\n", - "\n", - " A_cs, B_cs = get_dmdcs(Ys,Us,n_delays=n_delays,rank=rank)\n", - " print('dmdc:', [i.shape for i in A_cs])\n", - " As, Bs, Cs, infos = get_subspace_dmdcs(Ys,Us,p=pf,rank=rank,backend=backend)\n", - " print('subspacedmdc:', [i.shape for i in As])\n", - " A_dmds = get_dmds(Ys,n_delays=n_delays,rank=rank)\n", - " print('dmd:', [i.shape for i in A_dmds])\n", - " sims_full_dmdc, sims_control_joint_dmdc, sims_state_joint_dmdc, sims_control_separate_dmdc, sims_state_separate_dmdc = compare_systems_full(A_cs,B_cs)\n", - " sims_full_subspace_dmdc, sims_control_joint_subspace_dmdc, sims_state_joint_subspace_dmdc, sims_control_separate_subspace_dmdc, sims_state_separate_subspace_dmdc = compare_systems_full(As,Bs)\n", - "\n", - " sims_state_dmd = compare_A_full(A_dmds)\n", - "\n", - " #compute silhouette scores\n", - " silhouette_state_dmdc.append(silhouette_score(sims_state_separate_dmdc,state_labels,metric='precomputed'))\n", - " if use_joint_control:\n", - " silhouette_control_dmdc.append(silhouette_score(sims_control_joint_dmdc,control_labels,metric='precomputed'))\n", - " silhouette_control_subspace_dmdc.append(silhouette_score(sims_control_joint_subspace_dmdc,control_labels,metric='precomputed'))\n", - " else:\n", - " silhouette_control_dmdc.append(silhouette_score(sims_control_separate_dmdc,control_labels,metric='precomputed'))\n", - " silhouette_control_subspace_dmdc.append(silhouette_score(sims_control_separate_subspace_dmdc,control_labels,metric='precomputed'))\n", - " \n", - " silhouette_state_subspace_dmdc.append(silhouette_score(sims_state_separate_subspace_dmdc,state_labels,metric='precomputed'))\n", - "\n", - " silhouette_state_dsa.append(silhouette_score(sims_state_dmd,state_labels,metric='precomputed'))\n", - " silhouette_control_dsa.append(silhouette_score(sims_state_dmd,control_labels,metric='precomputed'))\n", - "\n", - " print(silhouette_state_subspace_dmdc[-1],silhouette_state_dmdc[-1])\n", - " print(silhouette_control_subspace_dmdc[-1],silhouette_control_dmdc[-1])\n", - "\n", - " # print(silhouette_state_subspace_dmdc,silhouette_control_subspace_dmdc)\n", - " return silhouette_state_dmdc, silhouette_control_dmdc, silhouette_state_subspace_dmdc, silhouette_control_subspace_dmdc, silhouette_state_dsa, silhouette_control_dsa\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "e32ce5f0", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/10 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "methods = [ 'DMD','DMDC', 'Subspace DMDC']\n", - "state_means = [np.mean(silh_state_dsa),np.mean(silh_state_dmdc), np.mean(silh_state_subdmdc)]\n", - "state_stds = [np.std(silh_state_dsa) / np.sqrt(n_iters), np.std(silh_state_dmdc) / np.sqrt(n_iters), np.std(silh_state_subdmdc) / np.sqrt(n_iters)]\n", - "control_means = [np.mean(silh_ctrl_dsa),np.mean(silh_ctrl_dmdc), np.mean(silh_ctrl_subsdmdc)]\n", - "control_stds = [np.std(silh_ctrl_dsa) / np.sqrt(n_iters), np.std(silh_ctrl_dmdc) / np.sqrt(n_iters), np.std(silh_ctrl_subsdmdc) / np.sqrt(n_iters)]\n", - "\n", - "# Create bar plot\n", - "x = np.arange(len(methods))\n", - "width = 0.35\n", - "\n", - "fig, ax = plt.subplots(figsize=(6,4))\n", - "# Prepare data for violin plots\n", - "state_data = [silh_state_dsa, silh_state_dmdc, silh_state_subdmdc]\n", - "control_data = [silh_ctrl_dsa, silh_ctrl_dmdc, silh_ctrl_subsdmdc]\n", - "\n", - "# Option to create either violin plots or bar plots\n", - "plot_type = 'bar' # Change to 'bar' for bar plots\n", - "\n", - "if plot_type == 'violin':\n", - " # Create violin plots\n", - " violin_parts1 = ax.violinplot(state_data, positions=x - width/2, widths=width, showmeans=True, showmedians=False)\n", - " violin_parts2 = ax.violinplot(control_data, positions=x + width/2, widths=width, showmeans=True, showmedians=False)\n", - "\n", - " # Color the violin plots\n", - " for pc in violin_parts1['bodies']:\n", - " pc.set_facecolor(plt.cm.Paired(0))\n", - " pc.set_alpha(0.8)\n", - " \n", - " for pc in violin_parts2['bodies']:\n", - " pc.set_facecolor(plt.cm.Paired(1))\n", - " pc.set_alpha(0.8)\n", - "\n", - " # Set the color for violin lines (edges) as well\n", - " for key in ['cbars', 'cmins', 'cmaxes', 'cmedians', 'cmeans']:\n", - " if key in violin_parts2:\n", - " violin_parts2[key].set_color(plt.cm.Paired(1))\n", - " # Create legend manually\n", - " # ax.plot([], [], color=plt.cm.Paired(0), alpha=0.8, label='State')\n", - " # ax.plot([], [], color=plt.cm.Paired(1), alpha=0.8, label='Control')\n", - "\n", - "elif plot_type == 'bar':\n", - " # Create bar plots\n", - " ax.bar(x - width/2, state_means, width, yerr=state_stds, alpha=0.8,color=plt.cm.Paired(0))\n", - " ax.bar(x + width/2, control_means, width, yerr=control_stds, alpha=0.8,color=plt.cm.Paired(1))\n", - "\n", - "\n", - "ax.text(0.1, 0.8, 'State', color=plt.cm.Paired(0), fontsize=18, ha='center', va='center', transform=ax.transAxes)\n", - "ax.text(0.1, 0.7, 'Input', color=plt.cm.Paired(1), fontsize=18, ha='center', va='center', transform=ax.transAxes)\n", - "\n", - "\n", - "# Add labels and formatting\n", - "ax.set_xlabel('Method')\n", - "ax.set_ylabel('Silhouette Score')\n", - "ax.set_xticks(x)\n", - "ax.set_xticklabels(methods)\n", - "# ax.legend(loc='upper left')\n", - "\n", - "plt.tight_layout()\n", - "# plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c085ce64", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 5%|▌ | 1/20 [00:50<15:50, 50.01s/it]" - ] - }, - { - "name": 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obs_noise=obs_noise,process_noise=process_noise,nonlinear_eps=nonlinear_eps)\n", - " silh_state_dmdcs.append(ss_dmdc)\n", - " silh_ctrl_dmdcs.append(sc_dmdc)\n", - " silh_state_subdmdcs.append(ss_subdmdc)\n", - " silh_ctrl_subsdmdcs.append(sc_subdmdc)\n", - " silh_state_dsas.append(ss_dsa)\n", - " silh_ctrl_dsas.append(sc_dsa)\n", - "\n", - " print(np.mean(ss_dmdc),np.mean(ss_subdmdc),np.mean(ss_dsa))\n", - " print()\n", - "\n", - "\n", - "silh_state_dmdcs = np.array(silh_state_dmdcs)\n", - "silh_state_subdmdcs = np.array(silh_state_subdmdcs)\n", - "silh_state_dsas = np.array(silh_state_dsas)\n", - "silh_ctrl_dmdcs = np.array(silh_ctrl_dmdcs)\n", - "silh_ctrl_subdmdcs = np.array(silh_ctrl_subsdmdcs)\n", - "silh_ctrl_dsas = np.array(silh_ctrl_dsas)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2662682c", - "metadata": {}, - "outputs": [], - "source": [ - "#for efficiency (if you desire)\n", - "# silh_state_dmdcs = np.array(silh_state_dmdcs)\n", - "# silh_state_subdmdcs = np.array(silh_state_subdmdcs)\n", - "# silh_state_dsas = np.array(silh_state_dsas)\n", - "# silh_ctrl_dmdcs = np.array(silh_ctrl_dmdcs)\n", - "# silh_ctrl_subdmdcs = np.array(silh_ctrl_subsdmdcs)\n", - "# silh_ctrl_dsas = np.array(silh_ctrl_dsas)\n", - "\n", - "# # Save data\n", - "# np.savez(f'silhouette_data_n_use.npz',\n", - "# silh_state_dmdcs=silh_state_dmdcs,\n", - "# silh_state_subdmdcs=silh_state_subdmdcs,\n", - "# silh_state_dsas=silh_state_dsas,\n", - "# silh_ctrl_dmdcs=silh_ctrl_dmdcs,\n", - "# silh_ctrl_subdmdcs=silh_ctrl_subdmdcs,\n", - "# silh_ctrl_dsas=silh_ctrl_dsas,\n", - "# n_uses=n_uses)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d6a705b2", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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rid+BgaGWDjzbOg931zViuzHy8dYZJlZZIawKxrAkWg8zGKow92QCQaQZ6ljIVTh5adphGKeQvh1CLlscQfrUXNlY5Znne8Te77fnk/Rpc3ecIp7a0FNh/iEBHr1cCYVIKCBRQSRArxYP96MdfyCRxl0PbxPyJRbPq8eJR7YhFmVBOOUrqOVA9Ztj+CfH84k2X6D9XZljaH9nRFFyOCtaex/JfSAt33E0kLCbG8NobYyIxs5eA97oYyTxq2xkfjf+1dskftuU47w6RBwN8HvraCONww2a7MeJ3m1rkevvQs4AUqEoMsU8ioUi4ol+NHZuRozEnxqqOMYxTCRaF+C5tvm4J16Pzc5I0grBwEoriKOCEXHwBoPhSnMPid+kuaeAbLEgpp54ICSO3qgdQNwOo5BzRpA+yXTTjgGpueO9YZL24o567O5OignHw5L59WLnZykGknZQavoowvdHtFDTVs1aUrj3ke1IptT3oVP4mOWtiMdUFVBqyIzRr+VA9ezsFAIUjcPpgowYPK2d87H+IOkj4GiFSWicWEHUuzee1zANZLMFSUhj9VGv5HQtjZ+mHmYzU95JeemAKc/AI35l6rGnRdSRhsbBQpP9OOF0bUVmsBfZ4QFkSV6BMDKBgCx7xF832IPGzk2I7d4EK52sPN60kJyzGOvmLMADsTpsyGdA44UfNP4st4JYZYdxZKQOsVAUjk1zjynmnowdwBCJ3ykiaFpC+ozbp4OXpE9ZUk36dOyu3dyLrp6yqcYDbe2M02fETsQ18ZDcaJIZzaTB0QLNKHt7h3H/YztLUTpHLm3CysXNQqRKay+bYsrmGAdDSRK6MiFxIvmPBt4TNXaaljinBq98FJ7gUCMGfqZTltVCiwUahBzYloVgULWSpLa/bdcQuvtH1iCS7mJzlcbPZd47R1I8lkLPazFJ4qe551BGH2loTCQ02Y8TTnIASPTDYQhlNoVsagjZXBoZ00LeDiNr26J5K+IvoL5/b4n4zWyldlm0AkjNXYINbQvwULwO63g+p9IGTX16oRkQO/+RoThawjEUXTt/zrYxZJgYtmyYgSDidlBKNIjWb4WBvCEk7EXvsOhab39aSJ/JUozYWbWsRRyZJK94JOCWT1bRO/sCzzeQyEgRtwef2Fkq3UzBwXIOBAk4kfTMMWn0DWRKgqEWSOIkdWrrnuZO569H7F7UDYUD/2RJwNL0hVq4XS4D7Y0iqN1TMJH4lZ3eQjZfwM5OpfHXKj7nET8L0MXca/M6SuNXxE9HumfqkcgfHcuvMUOgyX4/4BQLQHoYoLkmn4WTywKZYWTTCSF6hlZS48/RlMA6OiT+fAENfZ1ooKmHxJ9XpY09FJmENW85NrctxMPROJ7LJpDkdarQbto4y47guHAdzGBYNP2iYSBpGEiYFpxwBNFQVKJ3ROu3QjCLFtLpPHIu6UtXrcG0qpYpjld7n6GZo0HCNYezYi556MldJYcpnbbc5rVqrAXyI80vQuxNyhzDe+LVlbZeHhFQ0yahqm5e7tzt5EXilcYuMiny9QQcBZDyFzhC+sp/USZ+Rv6wn/C23YPYW4P4+WwouOa2xSSJjAKE1+OxqrewUdL4ORqiz0MTv8Z0hib7A4STywA01aQSSghkU0A6hWx2GFnDQIrOVjpR4ZSI38nl0NC7Gw27NyG6ZwtMjhJ8oI0+Ne8IbJ+zCI9Go1ibTqCvUEmaTaaN1YEoTrCCsOwAnEAYeTuANO3f1KiDYYRidYiE4xK7H7NCsIX0CyXSp7lcyiTb40+6GvH9HUfs6zQbbd89KIlYXrhkNaglK1JXGjudxGKO8WnryhxDc4/bprGktSvnKcF1vFeP4Mdyonpx+pXEX0Q2WxQNn+A5SNaM9tnhavxsMlOL+NlLuMMlfs9sxHsJlGL5lfbP7GWdvasxHaHJ/iDByBbR9kn8rI1DAudnmmYKOWQsC2kriIIVQM4plIk/m0Vjz05F/F3bYFRp84VwDOl5K7B77hI8Hgrj0eE+JIpl4dBg2nhxuAEnmwEEnAIc0xYzT8o0FemTGCP1CEfrYIdiiFpBBIo2MpmCOE0nomga/3TosKV5ZuvOATzx3F7Rnj3nqWdr5+jBs+P7NXcpMFfS2r0+vEa5LaOrrXvTgd4rr0PNXvUSKKBYJOEr4qcjWTKbA0qQeMS/bfdQKYms2o9AjX8eiT8WdAUcs3YV8fM8XvauV7ZBJ3FpTAdosp9ACNGnEkrbL+SAbEbMPMhlkIUjDlZq/EWYFcSPTAaNe7ejoXMTInt3wKiy3xei9RiedwTWNM3Bn8wiBn2Cod6ycVa0BacEwgjmczAKOYkCylg2kqaJvEXCDCIca4AVZthhHeKBMAIWtU/SlCLXAwW1cyH8dB5DwxkhQJLmWE7Usubu2tqZ8er12nU15clKduKfO8tGU+gJ8TtFqUEkNv58QZ4FCZskLc7dPUOK+HuGR0QJ0fxFG/+8triMkMTZ7Gbxkuwrs3eVuUcTv8ZUQZP9JMHJ0KyTEC3fc+qC6wp5ZKnt0/RiWhL25yd+I51CU9c21HduQrh7F4wqismH49jRNh93xOvxZCyOols3P27aOLOuFacyfDOfg5nNwChkRcikYIigsYNhRIJhWOG4mIwcie9Xx5PvqWV7/5k+QSAkLRm+LOPA2v2VRExtmA7YZDpbysIdy4nqOT09W7unzR/Qc+afL4VfsYBiPguD9YWC4XGdT4g/qzT+rGfqyRVFGPA7eURN8ldRPcrGX4v4wyFLNH46vUn89BXw/COyd/mZJRtcISfcz2fsPX8+d20C0pgEaLKfZCinrrLt+526Qv6GgawVRDpgIw1DFfqSeHpF/GYqiaY9W1G/exPCfZ0jzp0NhPBUYyv+2diCtfXNyNNGb1p4YbwVp8WawQIORi4LM5dGIZtGmudldm8wLP4Eg45dZvCGIigGQzAMpe2zugwXFOEr0hfThKXq9zDGv7pWP8M8acOnjZykNpYTdV/aLc1M1LhlzjpCzCou5FDgqCXPaCfOc7KNglRi+flfkXH2plQXDYZjMgXCcZgBfrexr8mfgZh5aOPP0dTjiIknkytWEb8loxn2FSDx7+kehfjbXeKvC5UczeIcltGLO6oag9iVkHXnxsi5VybbLyRK78w71hx5Do3ZC032hxC1nbrDQD7HdE6pkpm2LamXM4L4hxNo2rsD9V1bEeqmqafytTEE9OmGFqxpasMzja0wAiEh/dNjzQixjLJTFOJ3ssMouqGgBTsoGbvFQAAOTOSDIYkmKgRDcNwwRl5Fio/xeoaDoEHCKpdx8HfocvKGZPX67e8kSeYTsPCbInFF5DxfUbRxj8jVXEZBMhWUH4P78QxOEU6hINvl2RULitzdzw79IOIDsCQj2QqGYQYiMGwbthVAIBxFiAIgEocVCI/5nrzuXxRgtPXzs4roKRM/zTwS1VMi/iHs6UmWEtg80HxD5y6jlJjX4BG/H0Lc3kjKdOc+knZlrzvSUnO1rvzZ288j+9GgRlblEQsFkDYtzQ5osp8CjOXUJSlT887abuYsNVeX+FOFDLKFIsKFPJq7dyKyexNCXVtHOHdzhoF19c1i41/X1I7jmjrwgngLwl7tfMeBwQ5buYzMCYZy0qzjeDV7GN4ZoqnHLdlMJyfzCKQ1o1fGwRAtP2SWO3SxZDP/oOgEpeYt9+YSNwkZLqE7eX5mHLybZctt/FwgkZMQ1WcwK9bh8erPVIiOUTj8LpwsW0Yo1Oj5mcfns8Mi2CySv2nCDIWF4M1gRMw8AQoA0fzjCIfjsGjOGpP4maCmwjfpe8hxBCAlIApl4nSb0EgC1+4hdHaPJH6ab2jqYWQSiZaJXwHxYai5Il0a7pTtX31lmoPgE7yu6coVwqP9ekuCwaf1ewKEgzu/+cwbdXkCQI8ADk9osp9iKKfuEJBKjnDqyq8yFBFTT8opIl3ICeGm6PAtFiWJio1UIl3bENq9EaHOzSPj+AE8X9eIp5vaYS1YieNbFiHqa5OoiD8LQ5qpK+IvSI0eFdJZoJM3EEAkFFckIH8uNK/Qz5ATX0OO940ibJfsSe6ipYtW7pqy3MmgNl4swuQy7fpOESbvoWR6oGpry3cniYM2eDEJ2VJCwsqkYaUGgeQAiol+GMkBWMODCKWGYOVzSDW2AW2LkJuzAMPRJhQLWTFhcVRl8locjdBvQc0/GIJpBiSEVZl8YghFmGhWm/yluQyJP6Ns+swQFo3fra/PrGMvqkeZepSNvxbx1wIfbzmqR5m8vPDO0vqq7TJ5gsNLLCsJCJQEhycYHF9pCa8wHPMXKslfkb53DU3+hwc02U8jjOXURYAmlzASrM1TKIimP5zPSOnfkB1AlO0OHQfBvdsR2kXi3wiLx1ZhW6wem5vasbG+GVviDciyw5ZrphE9251XI2yYWByMYkkwgiWBCNrtoLLtC4OwBDHj+AsoOHmYxaJo1Ywq4rLSKFX2qV8bF01cyF3NOT5JZVPIJfrgJBWRB4TIE4ilEqhLD8OuilQaCznLxlBzB0wh/4VIR+tRzGVQzKVRpAObZetI9qEQjEAEdigE02BWso1AKC72/jBHAK7m/4l/3opBCkXaxmGgPVyPExsX4sT6RTIg++3Ox/FUYgf+relEHBmfWyLMv3Q+jX8ObMaq1CKgM4z+2AA62/bAcNz8gYKFSDqKlv5mBHMjS2XvD7waP2Vh4Wntas7RBfMcmhrCKonNV1Ka4rbsXyk71KsFjCb/mQndg3YawQhFRJN36gowPKdupOzUtVJDaICDmKnCKgN2SKJthvNZ9LEvLsM62xch274EQ87LEOjZjdDu52HtfB5hChFWv0wOyrSaDl7TxMZ4I9bVN2FdXRO2x+pK0T3VYD2edZmETASpYrEVwFIriCV2CHPNAIIecdss5KYInOdjlaAEikhQQNFfMNQNMzmIYGoIkdQQYqkk6jPDaM4MYx7NOPuBATuInlAYPaEI8qaJ5UP9mOMKuQDNXXu3A5yehbSMHGqeC2POIjhzFiMdjqNA4qcASPVIyQrTDkg0jxXoF+0/wZFFgDb/mPgYLj/yhTi+ZaGMstYPdOGXGx9DZ3YAFy0+VYiwJRjDxtweHBuYh0y2iEQqi2cTu9BoR3Hk0mYcf/wC/LNnC4yhDF7bcIqYiPpyw3g6tB1b67bhzMLRCOfC4hxmLoB/PlYdIQ9eLgNDYcf8WzOApvqwmJTYjKalKVIKN+Vohb0TvMxlj/ztUmMYRfxBb50m/xkBTfbTEKL1RutlovnBI34xh+SysHNpNGTTiOVzSBrUupm4FZAyCyyPELZVtEyudb5MOPYlSPR3IbVjLSKdW9DKOj8sa1ws4qjBXpmIrBVAV2Mbuprb0d3UjkS8ScW7O8Du3DA2Z4Yx5CZ2MWN3XSErE7IJhKj5M3nLMWCkBxEaTgiRUxtvyaRkWpJJo6HKzLQvsP5PbyiCgVAUyUgMqUgc2Wi95B4YsQZEAmHELVuSzIinssPoHuxGsHs75vbtxcqhPrS4DulQPotQ1zaAEx5AKhDCoJD/YhhtC5ELx1DIZlDIZ5BLJUSQ0sErWj8d2cU8hrp2YHcqiUAohsWRGN6x4nR8/em7cc7Co0RrPjG6AA93bUEgbCAaDuHZ/l7MCdaLcJDmM0HVe4Ba85zmqDhUlxgNONnowE+3P4xuowcXLzx9jLaMnqNYkXJpzlyBfNXcv92de+N4zlUF07R0O/MyhduaoyIAmAynKoKqvASP/AMu+duu+cez+Xv2fn43Tf7TE5rspzkYVQM6T+NNMEhatOdzKhZg57JC+rl0EsPpIUQcA2nLQsLJI53PS0x8mOYWw0ChqR3BpnYUjgO6MsMIdu8Qk09w745STf5gIYcFPbtkIqTEctsCZFsXqOqbuQwy2RQS6QRSmWHksinY+RwihTwivF4hj3o6RscsUlyJnGliSIg8jnSkDvloHYrRBhjxRtixJkRpSzdNtLIRS60T0PbP0YDrDG4yAyi2Lgbal2O4WMDTmSR6B7sQ2LsD7X17sGKoD40cKTEpKpdBZM9WgBOAZDCMgeYO2HMWwW5bjHwohEJO2fzzkihXwPBgN5K5tGj9dPrWWRbqrAAe37kOmVxGKpGe0DIfGzNdOKt9Odbu3oVTWxbjHz2bpE8AyZCmExI3Szv7sdhuxYODG6TInD/mvhRO6WYWM4s36ls/Xnh5BawFxEJwrFzK2v9edBBLX2zdNSgTQYFE0m9rjqClISLlpXk9+i0K6ZwbVqucyzQRlc0+nq9BmX00pgc02c8QyI/aNfMALWWNn8Qdq0cgn0M2nUQgOYhwJi2ll2nqGbCCCIUjivTFSgs4rMc/f6VMhDk8JJowiZ8CwHJNPmY2hfDODTL5MWc/7puW4HQ4iowQeT0Qa4AZb4QRa0Qh1oBiKCo2BVqqR7VWe4TO8EqJ6GGBOXfOkE46lQ0DRdMU52+Qjl+37v/RdhDF1iVA+xFipnk2M4ze/k4E9m5Hm0v+dQx9ZQ0fjpY6NwOcWOwtFEE/bf5zFiHStgTGUAqhYAg2Y/8He5B3gJRNJznQP9gryXGpfA4rYs24c+dzWB6px8ahvbj0yBfg0YEtQtJsDRnNBGCnTbGdC89K5I2DFsSQ7s+JjVz1AeBXd8CxFMNMxeFd5WLzx92XhINZY527HHQjgkrVSZkBLSWrFflTCDDySG1zsKdnWCYPJHwx+zRG0FQvmRxIZVSLS15D5VKUaxrJOlfrl7pGuinMmJCAh2JBRubVyYsHC032M1zjR12z2PSNzLDEkYfijchk0kikBhHIJJHPJJFKDyJhh8T0EA5HxVnqRzFah/Sio2UieZqJPiF+xvNzsqpKNLMcA5OxWItHzUOi+UuCFm3bNLPEaGppQCESV05Z//VqfSEJu1ShmYz0UTH3ORWeKXH5kCqf9AE4liUTAhGYdPay1LM4gE25/yQTr/JpWPkMbApDIX9msoaxguTftgToWCHho89lhtHXtxuBvdvQ0rcHywf7EHML1NVlUqjbvQngxGd+wouQ69qBfP0cRNuXAIEAinzW6QFEssPoY15EPosWw0Yim8Ltm5/AEaEIevdsRS6fRb6YRihmIDxswbJNNNaFVThl0XWMpwuI2qqJO7Vtj/CrYyi4zVuvwlbhSyyTyFU3+kaNIEpROd7fjtjilT2eEUTN9WFpKo/lLXIcy2Dv7RtWAqAvVdHmksucnt/WX8oj8Mw+jQ1sm2kgl3FQJPl79Y3c2kce+XujEf+gRC2riCxXJ3GXa6zzlJ+KY6vWuf+4V0Jp2beu4vq+f9y0h5LgnEyQ2NP5nJj5JMzazSthf4o2/nYmEJrsDwNIP1tO8UbJLA2R+DONyKSHMJhOwcwkxdSTHerBcKIXgWAERdtGnvH0lqVI1O0oJZRAITJvOcz5R4jaGU4OlqJb8nZQyJVZqrZhwjYt2NSoSz/D2pAusx6R58tE7pE7Vdyi4YhT13EjdkxeizZzCb+0pXSzxInTAWwFZR+HZG8HYAXYcCWAfD6LQiYFh47XdAr5fAZJZtvmMzDzGQQywwg4jtx3IBTGYjuERXOWCvnzGWzIDqO/ZyfsvdvR3NeJpYN9iLh5DAzd7OjZhWWbn5HPPZE41rYvwlDrXCyta0dXJoGA6xs4BiYeTPbijcE6JHp3ihM4NdCNfo64+D5yGXQluuS9mRZzFAw8ObAdi+qakA/klYnE1chBP4hEPqlRAJ+040byUHKqvAZ3JCDzkSMA1QrSTZArFaVjKKkKkSWpqfIWhvgeFs9rwLIFjXIsbf3K9KMEAJvAUOsnOApgo3uv2T3P09LA6qZhcQDXx0PIWQaKqXL5jBLJVv3JlEi7TNE+cq76UIO0K09XSebwH1ohZPZN5pWjp9ojqQpTG5crRle+bm8kd5fYOee7KDgqfyXrJlGGraAkKU40NNkfZiDxwW4Qc0mo0Iq2bAqpxAASyX5YjIvPJJBLp2FS45XiZAygMWFYQYmiIakaLqGXCDykNAxpCu6FWTLuv5hDgfVopBesiQC1a+7oEbmUNFD2dJpghKi4s5C3JddAKCYmF7mexcbqSkNnFc8KIre9KSBEzbIN3Jdzr3SAB95jxv0xZWhvZ8IaS0ZkFPnT1FJg3H02jUAqKT8COWcojHl2CB3ty4B5K4UEN2XT6O/ZDqtrG3Kmhaw7KkqZFnYFQ7i9rgGn93TixEfvwsNHHC/JcIORBpzWsgDLUMBix1BhntTakv1I5LNIOQV5bsmurZKpPAQHj6cGsTnZhzctOhbdw/0i4Erk4mbZCpGoJ1z5ziWiVYWDVuznCgb5t+g+IwqJoqFGE+4Qy99RLEcBkGFhbiUAPGcyTTds6iIZ0UVHdSvz2f69PsHcxvX+HgEsjkfy5wiCTek5KPMyeZWQUeU4JJjLTQDzBiIqr8xNIvN957IsGz2xbH9g+LX/6jIVXukJXyZzuaaRn+hHlsAgmZPAJRveKZf2oMkv7/B3pKrQSm8Ey0KuWETKSaCV5s0Jho6znyUg6Q4PDyCR6EMxk1Ix8CxwxlIMhYIkOVnFgiwLufAHaAVVKCKds/xDzOckgSpPuyK1c5Y7KDK5iHNqKYyzZ+EFwyVzauAs+sU5Kz5SW6fG4v6wLBI5k5p4naB03bJJ6KzV79bhqSbyA/ruJfJnbfsU8plhOBnG2g/LSCBHs08uLclldqEAFo+wTBvt//yTCASarfgr3h6O4qGWuXiobZ6wzWUbn8IJ/d1iliIF/2jpUWjMZvDancrs0x8MY8ecRWiwLMzfuhZFPgNqw3YQT9Y14G8dS7A3HEXEMLHADuHUaCOaAsq0w33zpo2CbaPIkFq3LzFRFgIk9bJN3l/TSI21jDEFhuxXNJUgKLojiKIS3CIAGH9fdPsGs1G8TwBI8xZfIxna7En6ngBgo/gDIRa5T7cxDIWA5V5Hlt3Ppn8bl60qwUETFZUG3zrLXV86L8nbv82dPLovJ6aNPi/WMrEJwSsSp1IkOSuGO5py8mI65DqLOQ0c6TgOhovD6MoMojM/iF25hCgD75l/Mk5ZtgoTCa3ZzxLQVBCva5aJmoSUMaBjkqGQjE7hXDJh3axecYDmlFZOR3CxIHH0EktPM0uIpZoBxrXk2aCF7QBpIzZogrFEU5MfFh1NLpkrU4uay+dJKmM84rsbpnTw4oRQFMV4szjCPM0/l6GjO4NCNol8PodMjmafNNocB11HvxD5xjmwikXUJ/vxxvWP4nVFYPCUV6G+qxPZXA7DgRC2NrTipd27MW9IhTESjdk0GnesV88UwO5IDD2N7UC8CYv7u3Dls4/guVPPhRGrR4RlMHJZBHM5Me3IyMiizb2gCr4hjaJpoWDZQvwUAgXLzWVwbfIFz07vM8lR0FWTrjIRqfcjhGO4vXYp1FxTkGinJEDHQLBowJL8aFcIFFQ/APoY/AKgvSUmxd9IziS3nv5UhQDwupmNBd4rr0EFYipg+oSK59OwfCMcmct2t/aT21jHoQnSUMEC/LOWzHFa32Udyd1BwHAk8TFRTKLbSWBvMYkuJ4VcDS/W9qFunDLB302T/SyEaMs0oYgZhdE9vpo9rInvkb831Rr8uaaUiIzJmQ1rigZM+z9HAqLB0wwzDWOupY+sHZCJ5M+wVhabk7o/mZT4N5xsWkYnZrwJ2bpmSb5KBIJIrjwF85+8FwNzFgOMx5+zCHU7n0fHma+XOkK929chsuFR8ZNQENazvAPj9dlTODEgkwfS2ZJH/yo1jJ6NMKYHYhJiUhirk967YAXWty1AmPdrWgibtjSXlybzvH/DkuzpEBvPByOIBCMIsLyzvxyGV0NHsqMV+auMaWqeRQnRzReZUUCCh7xTJQhU5AyFActb5/OGRO6I6YJasWMgFOJowJLRAP90VDKWIi4SIjuUNSxqwqplzXJMIpmTqqhC5kXV46DoLcu8arlqW7Fimzq++riDRVGuw3wG7w1NBvh+GhFDAxbz4RlFWCgg4OQR5lTMoWn5xP9uNNlrlCBROl6UjwsZpno2d1drZ2G0WiQuQ3DMPPC7hEmgNJWwzn+8SWysNDNFmufCaZkv5M+Kobn6VhTWPQoz2Sf1iYqsJNrUjsD6R5BefCyaGG9/xEkIbXka+cXHorNjGcxnHpCOZIOBIOYkB0rPiDFK8UIeL+pWeQ0ehiwb3cEQ5vd1IVnIYVckji1MKBMT2D6+C10sIhgs+T4UDCFfQ3o6/uqDETS6U3MwpvIwaK5x/TGcs/RF2smJ/0PuldotVA0eK2CCLmWx+RcN1QReNHxTtslfRrFcRG447VQIAAkNdW+2XMq5vOzV99/f8syeaaXoCYIRQqHoEw7lbSMEjrveqyHEdp5eSQmvaqkafSjBcKCGcOkT4bAiLf8SAuC4mtOQCRyVLo8QDznZJ5NJ/OAHP8B9992Hnp4efPe738WKFSvwi1/8AieeeCJWrZpY+5LG9ID8yIRkJj46YLpCEoMsW2L3GyP1aGJ2rRsil2GJ6HijZO5aoQhyoSgGF6xA09p/oKd9Idp6dqFryXEI57MoppNSyyfc1A57qBfGSy5CF4VGfxeMwb0I79mKhr3bJcIp4MssrivkUZdgBNQgTu3rKq3vC4aE+HdFYqV5ZyQmjmMP5J0071XMdPvOVqbW3hAIC/E3BCJoDEXQEIygKRhFQygqwoJmCE8IcDSQYaKZax6SPw/Lgh1gtVNq/yZN1HDoBGaFatP14TisnEp7NktV+wuzuYXaqip6jvZeKoTAKAJD7euuN3l/tIyJ+7rmPlVXkX/5XTnSy3l+KUZ5sRYVv7uYPPNikkk6eXQV0ujKp7GHI718Fm2pDFoyWTSns2jMMvExjygT6QwLOfDvykbOsJEHI+JUVJzMYSNrBpG1QmrUORVkv337dnFs7tixQ0j96aefxtDQkGy7++67ceedd+L73//+hN+chsZ0AZu3B4IW6tj4hQXZWheIVm9EYsisPBWBpx/A3K7tSHcsQ6G5XfIBMsUckoN7YSX6hPwLiV6puGm2zAPmLIJhh1Ac7EbvuZej/vHbYeTzyDGDd6gb9mCPEhC+pvRN2YxMxwz0lNaRGlPRegzGGtAfrUNPNI694Rj2BsMYNlRNI8bFpNmcBUXxrfhBk05vNiVTLQQNE012EI3u1GSH0GiH0GAHUef6XSgAaPAQYUDt2mVh+olo7qGRAg4N2UoouYVTRzpw3SGPEH6pjLNXvdM9xlfuoSQYKvapFB5qMwWLBKyO+H4V4ZieACHEV6KizTiX5LdiEUPIoNtJYzg/AGO4Dy2ZBDpSSZyUTqI9NYwmt3LsvpANMWO8EcPulIyEkQw3IpdzEAmFEG5vw5SQ/RVXXIFQKIT169dj/vz5CAbLuY6rV6/GtddeO+E3pqExHeEwuzbRD3vBkXD2bBEbebx1IfLHvAixh/+E5L+9F7HmdnFCh+ONyLUsgDPYqwrCMeKHpZhFozZRt+1ZpOtbkUn0Ih2KIZTtRv+SY1TkEv2jDqScc3CwV5H/YA+sIQqBvlKfYpJTdHhQprn++zRM5ONNKNQ1ocBs5Wg98tE4soEw+lGUBLA+p4g+pyDL/U4RvRwRVBEis4735NIy1UKMwsAKyNToTow+qjcDiNHsJw58lRTnVVP169I+Xi8Ttn+5aq72ZWMd9ZllsiuI3Xf7FeH8TilNoeY5Kz6752VntFymD8V0D+x0P2LpISxOJTE3nUTUJ4RH/VthO9BwA1LRJkXo4SYkwvVIBuuQY5Md9oRg9JqjiNjKO5JbwRyWKYuzv+OOO8Rss3jx4hFecpL/zp07J/zGNDQmCr/663M4alkLjlvZdnDlp3euR/Hun8M46gwYbQsqaNE6+ZXAwiNRv/AoNNAGzkYpTXNhzF0Cg1nIgSDqWuZLqKcx1IP4xn8hPLAXu04+R7THocY5aNj0JEIbHsNQS4dokXYug3R9C5xYA8C5yTBWRRLB5CBCiT6EhnoRHOxDMNELOzlQJjiniMBQj0wV38O0MIddxSSSx1dmWuYWCoaJrGEg404pw5ARAiuXJtlG0zCknlGewsQ0kXPn/NxnmuiSz4Z8Zi8EOpVjLF1hBaRgXYimFAoqITkW01BsT3KV7muyXlGuWuft6+4H33JpYhix/7Na552vcr1TcR35jPL+nBiGHE0n0Jxm9vW+I4gKpoVcrBmZWBMy0WakIw1IheuQDkZlpCPRbSz7LT0fDDQbOVhWQXIpmHMiYcYMQ2ZHNQk9thGvU+UsDjnZZ7NZ1I1y8YGBAdi29vNqTD527hnCfY/tQE9/WobdzNR86emLMLdVRbJMBoq3XKdi6Wj8bemAcco5MI5XuRp+GExtX3R0xTra/cPxJhTrmuD07EL7H76l2izS1t++GP3nXIpgrAEBhrk2daA/HMG9z5rY090k5MXrxsPAwvoMVjQNwzKVieTpvXE819WKY+bHsXRJB4omY/INbNtl4LkdQRwV78LJxnr0Dpn4i/NCBKS6DhAqZtCR78TJqTWYm9s76ncux2dp+MH8iBTrOtW3wKprkdpO2VgdcjTHicnH7YvM+kxGHiEjKaHHKvw4oMhdupIF1SQRT7bks8DNSXEs5rXYCEQmPqlqXCx9/PHH4ze/+Q3OPffcEdv+8pe/4JRTJjoiVEOjEqzWeMvfnsfZZyzCyiXNEi1B8meEx2TBevdXxtxunvuu0Y+9+L/L+x3zIoCTDzSExvxRJGxG37EU2LsVL54fwVELQsik0ujqz+Dv6wPYm4njVcdacJw8Qv0F1IeL2N0bwEmtGYCx+QAe6a5HXagIi+abhadieMhGZBPw6hMcWCzU1j+ATf1R/G/oQrwi9CTmobvUNlI6hzGpzusm5rgJdqXuYnl3/fibx0xHFMD6SoY432nG52dmMXvrONbgiCQRjiEba4TF3JT6FhHKzDBnAT6ad5g4Jb6KbEY0dybh0R9DErdJ7iRtZpUzj8EOIhgISwc0lsqWkGcvNLmqbtRkYlxk/7GPfQxveMMbZPktb3mLzJ999lnceuutEqHz+9//fnLvUmPWg+n5xKplLTJn+N6S+Q2y/Pc1O9E/lMGrX7xMPrNE8A9+8xQ+8vZTSoWsuP2nf3xWzrNwbj3OOWsJIiFbQulu//sWbNk5ILHijfUhXHD2CgkRpPmnoy0urQWrjyP+cM9GETgMPWQ5gbPPWCxFwQiG6v39iZ1Yv7VPBBXX//srV0rJ3117E7j3ke3o7U+jLh7Ey05fKOdmyCsLuwWi9Yi0tomG3bCgiPaFw7j5j+vQV2zFsvYQInt7MM/JoWsgByO+GM1RU0oVw0xhbgwI2wZY1SZJEndMNOUzcNilq64RJy2ykN7q4NHECTj3GGWS7U8Bj22z0JtU2bfL2wtY2ZFXncskWUs1nxead9y2kq45xfbszlzHtH/pUsZlVipV7SdVO0pGtKgqpao7mcpKJtF6Iyf6GegiFV+qu10gncx8HdW9DuylTuxu0K8bXcNzqvIH3rLnfPXq1PgdBaj4TDOODQeNEsbpZhCzpAXJPeeIQDBDEaWxS7iuqhFF4mYmOLuasalQMBQRcme5j+rCg1OFcZH9hRdeiG9+85u48sor8cMf/lDWveMd7xDTzvXXX19T49fQmEiwqBZ/M399YDOOXNKMjrYYwi7pjgfPbuwRsm2IB/GXBzbj7n9uE+Hw7PM9yGYLuPwNx0tkCas9+svwjnYcsXR+A1511hIRKPc/tgN/uX8T3v7aY2TbfY9ulwzSi89bJYKDfWjJM0PJLG752wac+6KlcjwFCYXGpRcci2h4pFOORNHQGJfs1J2DDpavaAUiGUnoOqo9jLW9Obx4QSvWbu3E0UuC6BnIwg4biLREwD5mhjmEeMsClSvhTse2FfCbLqCR2qzj4JZ1Bk6YW8BrlueQLzroTRlo9IiZoZLuvSidnhm6bsE1d1klbBUlRlwiX4SHbQnAUSUaWLpA5WaUnqxLsspm766oFaHj373yyfiOkVAZ13GrYjNFiLjF4krHGu4/rhDwnL0qbtPbXYWQKg2foZtecpkt+Qp2gHWjVIG+gJvMRq09aNsqA3kaY1y/Ftrl3/nOd+Ltb387HnroIXR1daGlpQVnnnnmqLZ8DY2JBCsxvuncVXjk6U7c8dAWqcVCsnzlmUvGdfzRy1tKWvdZJ83Hj//wLM49S6X6pzJ59A9mpEsTSXW8xx1L4nXxwhPn4Zs/XyNJRKzb/vTz3XjLq4+SImDEvDmqmNzaTT1y315FSVaX5DU37xjAMUfUbM8iiEcDkn3qB53Ov/zrOpx16kKs35kUwXL/4zuAWAho6wByfWxWAKOhrVz6opBH3GB4YD9y0Wbs6M0jFh7GGceUnddq7FSGZK6yYJdboZEEL3WQmFQGzlla2YtjUSV6+ZlTQQSCqsUvMfZ0WJbKGbtkXNLW1fWEqH21L73PnuZeWc6yRqXM8soRx8P32SurLNq/G8PvCYOAYSESDEl5D9g2gtTamZhm2uKLob19pmGfZJ/P54XYf/e73+H888/HK17xikNzZxoaVWDDDGrERO9ACn+5fzPu+ed2NDWUM35Hg0e6RH0sKMRFkj9qebNo23+6b5OYW45a1oyzTp5fqtsz2nE05TxIM82WPqQyuRLZsPerl13JuvTV4LV4zKbtT5TWUbv2momMBtaY75hTeT6WD26sC+GBx3fK3H+vtBdLBVSSWFwJFg/JPBO2+hHqWIShwb1oaLKA1gVVsZDlWEbPZDMi2L0U4O6VVVbEz4QrFt5jVUcpzyClERTpcyqp0m6GlGd6UTfuEbFRRdS1yN419fiO9apXyjDQXxzZcOcVT6Kq/r1vgVp6kNq8S+wTUZBv2pM9I23a29slPEhDY7qguSGCo5e34l/r92JOS1QqNHqg1l+LZD0MJrOqvV+IlTgN0co50db/uzs3oKkhjONWtI153HObe7Fxez/ecM5K1MeD0qeVmj1pkB2pWDSLTT44WvCDhHzU8hacM84RiXcP7BZ12rH+SPryyOO2B7eIOWm82LC1D+0tUQSDAemctX5L/4h6OvsLr1TGvs7iJUJNx5pJhzvGZWR629vepjNkNaYU1OQffaazRL6cr9vcI7b7Oc0RaZ7Bnq40ozzyVOeI42k+oQ2dDbv/vmYXVixuEuKmzZx2emrsNL+oui3GPo9j0S9GAtFvQEHz4OPlXBMS2bFHtOKeR7YjMZyVc+/qSogzmKaXTdv7xSHM9Vy3vXOwQqh44DW3dw7h1ruex9zWKJYuUA5pPxiZRJ8C5/siWV7joTW78PSGbjFJEcsWNopwfPzZPaqaZa6A3XtVI5LJgNfgQ+PQY1zifMmSJfjZz36G0047Da973evQ0dEx4oVddtllk3WPGhrS3q5zbxKPPbNHtOhQwMKyhQ14ySkLxZ5/5JIm/Pj3zyIctkUDptbtB0mWzl1G1Sxor8MrzlCa8HAqh7/9YyuGhtn71RTSpLa8r+O4Dxtzf/d/nxTCP+ukeXhyXTl2/SWnLsQDj+/AT/+4ViJzGK1z4StXimb/upcfIfkCNB3RPMA8AYaUerjr4W0iKAhGB61c3IRTjplbkyQZ3bN4Xv2Y5p//99PHZcTBZ0bfwRvPPRLz2pQPgQKOwoKO54ee3CUC7OSj2yUKSePwwrial9CTPuZJ6NWfovrT+4vZ2rxEY2oybzU0ZpRmv3nz5sm/Ew0NDQ2NqSV71sTR0NDQ0Ji52C8XPEsb33vvvejt7UVzc7OYRI45RiWRaGgcbrjoXN2jQWOWkT1j7S+99FL8/Oc/r2guQFs9yyfcdNNNOjRTQ0NDY6aHXrJe/a9+9St85jOfEft9KpWSOT//8pe/lLmGhoaGxgyPxlm6dKmUS/jUpz41YhuJ/sYbb5wxTlwdjaOhoTEbMS7NfteuXVIHpxa4nts1NDQ0NGY42c+bNw8PPvhgzW1///vfZbuGhoaGxgx30L71rW/F5z//eUmu4jIzaDs7O/GLX/xC1n/84x+f/DvV0NDQ0Jhcmz2jcVi/nuTuT9nmoW9+85tx8803T1prQl77i1/8otTR3717t5Ru+P/+v/8PH/jABw6oxoa22WtoaMxGjIuhSeSsjXPVVVfhvvvuK8XZv+QlL5n0OPv3ve99UoTt8ssvx+mnn47bb78dH/zgB+UeajmMNTQ0NDQOULOfKqxZswYnnXQSrrjiCnz1q18trX/Tm94kLREZAUST0v5Aa/YaGhqzEeNy0DK08tOf/nTNbVxPM85kgLH9xIc+9KGK9fycyWRwyy23TMp1NTQ0NGYl2X/jG9+QblW1MGfOHHz961/HZODRRx/F3LlzsWhRufwrwVLLdBY/9thjY9bMrjWx3IOGhobGbMO4yP75558f1TZ/1FFHYePGjZgMMH6/VlhnMBgU4bNzZ7lhhIaGhobGQZI9HbTd3d01t+3dW27YMNFgWYZQqHZ/0XA4LNtrQZoajzKtXr160u5XQ0NDY0ZH4zAK5tvf/jYuuuiiEdu4nmaVyUAkEhHbfC2k02nZvr/gKCWRSJQctRoaGhqHA0488cQxTerj0uwZcnn//ffjBS94Ab73ve/hz3/+s8z5meuvvvpqTAZowqlViiGbzaKnp+eAMnfb2toQjx9YyzVecyJwIOcZ7zHj2W+sfUbbVmt99Tr6Q6bSJzJR72eq39FEvZ/q9VP9fqrv51Cfp2c/jjlU7+iQ/oacceKWW25xlixZ4hiGUZqWLl3q3Hrrrc5k4corr2RYqLN169aK9Q888ICsv+GGG5xDicsvv3zKzjPeY8az31j7jLat1vrqdXwn+/EnNW3fz1S/o4l6P9Xrp/r9VN/PoT7P5ftxzKF6R4fyNzQuzZ5go3HGta9duxYPPPAAnnvuOWzatAmvfe1rJ695hGs2uu666yrW8zOdtBdccAEOJc4///wpO894jxnPfmPtM9q2Wusn6nlMFCbyfqbyHU3U+9mfezpUmAm/ofHsOxN/Q9M6qYp417veJXH+/gxaxt9fc801o8b+a0wNvPIV0/xPatZCv5/Z/Y5G1ewZTvmHP/xhxPq77rpLSJd27xUrVuC73/0uJhN0AJPYb7vtNrz//e+XrFrG/XOdhoaGhsZBavZsQ7hhw4aK0sbr1q2T8gVsQXjOOedg/fr1ePbZZ/HrX/8ar3/968d5SY3DFVpznN7Q72f6Y0o0+4cffhhvfOMbK9Zdf/31Egnzt7/9Db/5zW/w5JNP4uyzz5b1GhoaGhrTF6OSPUMemR3rx1/+8hfR7GnGkYNNE+9+97vFtKKhoaGhMQPJnsMImms8dHV1SfTNWWedVbEfY92ZpKSh4WUpa0xP6Pczu9/RqGS/bNkyMeV4uOOOO8Se9LKXvaxiPwqB1tbWSbk5DQ0NDY1JLpdwySWXSMRLQ0MD2tvbJUuWpE7HrB+sC8+oHA0NDQ2NGUj2bPvHUgheLfn6+nr89Kc/rahHMzw8jJ///Of48Ic/fGjuVkNDQ0NjcpKqmDXLFoCrVq1CLBar2EZbPcMxjzjiCBkBaGhoaGhMT0z7DFoNDQ0NjYPHuGvjaGhoaGjMXGiy19DQ0JgF0GSvoaGhMQugyV5DQ0NjFkCTvYaGhsYswLjJPplMStOQN7zhDZJFy4qYxC9+8QtpZKKhoaGhMcMbjm/fvl0adO/YsUPi7Z9++mkMDQ3Jtrvvvht33nknvv/970/2vWpoaGhoTKZmf8UVVyAUCkn9+scee6yiUM/q1asl01ZDQ0NDY4Zr9iyCxo5UixcvRqFQqNg2f/587Ny5c7LuT0NDQ0PjUGn2bFhSV1dXc9vAwABse1wyQ0NjRuCmm26SCq/PP//8lN5Hf3+/9Fl+/PHHp/Q+NGYR2R9//PHSmaoW2NDklFNOmej70tCY9SDZX3vttZrsNSYE41LJP/axj0kUDvGWt7xF5uw9e+utt+IHP/gBfv/730/M3WhoaGhoTA6cceJb3/qW09DQ4Jim6RiGIVN9fb3zne98Z7yn0NCYEbjxxhsZgeBs2LBBPq9evdo566yznDvuuMM56aSTnEgk4hxzzDHOb3/724rjrrnmGjnuX//6l/PSl75U9ps7d65z9dVXO4VCYcT5N2/eXPN4gtu4XD3xWA2NA8G44+zf+973iiP2tttuw09+8hMx3zAU8z3vec8kiSENjemDjRs3St+G//iP/8Bvf/tbdHR04I1vfGNNu/4FF1yAV7ziFbjllltkJPzZz34Wn/nMZ/brejw/r0P893//Nx566CGZXvOa10zYd9KYXRiXGedHP/qR/JG1tLTIH7EfrHX/xz/+Ee94xzsm6x41NKYc3d3duO+++0pd2U4++WQh5F/96lf4xCc+UbHv5ZdfjiuvvFKW2dltcHAQX/va1/CRj3wEjY2N47oeQ51POumkUovQM844Y8K/k8bswrg0+3e+852i2YzW3ITbNTQOZ5Dk/e0358yZI9O2bdtG7HvRRRdVfL744oul0Q+TETU0pjXZj9XfhGUUdOilxuGO5ubmmtp3Op0esZ49m2t91vkoGlOJUVl6zZo1FSFff/jDH0ZoJqlUSmrj6IbjGhpl7NmzR0wv/s9eAiIRDodL+St+9PT0HNL71JhdGJXsGVbJGF+CCSaf//zna+5HOz7DLzU0NBRox/ds9gQVong8juOOO04+MxOdoPK0cuVKWc7n87j99ttHjBw8pUpDY9LIns6kSy+9VEw41FIYGeA5jPx/jByiUhhoaGgofO9730OxWMRpp50m0WssEshM2IaGBtnO9cuXL5f8Fe7H39E3v/lNZDKZivPwt0VlisKCiY2xWAxLly6VdRoaE2az5x8mNZAlS5aIE5bROPzsn+bOnSu1cmo5qTQ0Zis4KmY9qde+9rUSpvzJT34SV199dWk7fVzcZ+HChaJQfeADH8ArX/lKWfbDNE0RFH19fRIFRyFBc6qGxoHAYLD9vnayLEtifE8//fQR21gFk+urC6RNFBjF8D//8z945JFHZGII3FVXXYXPfe5zk3I9DY0DBbV3mj5zuZwOWtA4/KJx+IdNDWSyQHJnQspTTz01wox0IKB5ipOGhobGbII9VhEmJkx5YNjYpk2bKvah4+jmm28Wc85kgYkrvPa8efOwZcsWsVkeDBhlpKGhoTHbMCrZf+Mb35AhKZ2vnLxCaLW0fi9qZzJA5xWJXkNjJphxOGlozCiyZ30POmdJ5pdddpk4mRhBUE3ERx99tEQKTCfsKzqI3bU0NDQ0ZhNGJfsTTjhBJo88GY3T2tp6KO9NQ0NDQ2OCMK6QgUsuuaTkLP3HP/4hmX7nn3++pJAzXTwYDE6qk3Z/MZZDmY3TNTQ0NGYbxs3QTABZsGCBxA7TrENnKfG6171u1OxaDQ0NDY0ZRPZf+MIXcP311+NTn/oUHn744QrNmRo+SxxraGhoaMxwMw6z+Ej0bKJQnTx1xBFHjFr+WENDQ0NjBmn2jHMfrXkC7fUsc6yhoaGhMcM1e5ZmZYW+l73sZSO2Pfnkkwed6LQv0ITEJC9OxAMPPFAql0AfwnQL/dTQ0NCYkWTPXpssWcBWbJ6Gz3DM9evXS7u1ye5D+9WvfhVbt24tfb733ntlIug01mSvoaGhMQGF0FgWgb00//73v0u1S0bisOzx9u3bceaZZ0oZV5pzZgK80Mt77rlnqm9FQ+OQgj/1fMHhArU1/g9JP5S5+1mXK5/dZE/QMfuzn/1MiL2rq0tqap977rl461vfOqMq/Gmy1zjcUSgUhdQLxSIKBRI8PxdRLI7rpy6QMikjBIFa4ckDbx+/kKi1zTvGWwYcyJ24t1NeLq/336lHUdVMVVrvP8a/zl1IpnPIZvPyTHJ59SzynOcd5AoF93P5OfGZBWwToaDlTjbCnIcshAI2ohEbActU39kVmP7vb1QI0vJy+ZlWrvMfNy3I/nCBJnuNwwEkbpK5kLpH7i5RyX+Og2IRJcLn/vmig2KhiHVb+tDVOwzTNGC5k7fM5MjSOsvbbsKy3H0sA7Zp+pYN1MVDCIfsStJ3/y2Tf+Vc3b9HsI6Qb6GKdBXxFpHzvqNsIzmrzyuXNKO5ITy2oICDh9bsQmf38IQ9+9OOnYtFc+vKwqtKEHok3t2XQq5QRChgIkxBEbRFiIxF6iJYTaAuGpRnOpGYOSq5hsYsAwmbhFhN5qKluzoa1w0msxgYzCAaDSActEuCgCCxeGQesA1YwYAQ5lCysv/tweC4la1YOLdOlOqxdMe1m3qxe29CvgO/18GirTkiJFoeL3iKPYXd/p/PNNi7QwkyT8OvBT57jhY8eORd/d2fXNeFvX2VLSW5p22bCAYsIf5gwEQgYCFoW2rZtnDEokYRsFNC9oy2GVMaGYaOtdcQkuEPYeQPbZRf3hg/yP39rXrXLGmQvn+q17mD6QqTw1RCtG4fkfvNMB6JZHMOBhNp9A9lMJjIYjCZwVAyh2QqVzLPHLO8BSuWNAlpWKYN01JaOiGCQgSHg/p4CB1t6rO8M1fLLvpGAbKvu21fqI8F0VSvmqh796vmZdOKU/oe6jsdCLzRh83vxVGHa0qRUUyNvzvPnLJ0fgPmtcXlOBKsZau599m2SbxqBFM2N7FXRxHpXAGZTB7pbAEZTpk8mupDMA2jJHB5fXVv6n5YOYbzWgKNa2hK4jQaFs+vO4BfwASRPatEVv8gWB+HDls2Un75y18+4TemMZ01zbI92NM8OZ9Ki6B37YMhbhECnq11VMFQ2rO0Y8kOWzoGI+6lWrB45F6o0NIdIZSBRBoDQ1kMJEjoWSSGsxhO5ff588/kCoiEbCUs+F5yudL74vPhHZIkl8xrwOJ5DYqEPbu5j5D9UOYgRaZ+4UAe87bxmgNDqn/uSLt++TtTICjzj89U5Jv7zUqyD81ElgXb4qiEGrcpz5b7cYGEW7aRG6KZl+zlVdcl1D5qO8/hP0au7zsXybYsBCvnZWGmTGXe78J7Tsp85siIJ53OC7Fn8wXXR6D8Bbl8QZF+rohsriBCkH8Tovlbk1NnbFxkf9NNN9Vcz7h3OmnZH1Nj5qNCw3NJoqzllR18ZXtweV9+pnmg4NNYHB8xyg90XMr+xAiM8RB3SfOvOK42cft3Ko8UfPbp0r5+tq9hu3Y38ZkNJTNiS6amPjSsSD2dGV97z2jYRl0sKFMsEkAsYiMSDqBvMC3bRfs1TQRJkiFlZyd5jhdC/iXN3Leuarv7obSv8hd469U6GaA4DhZ11JeegUfYQrwuwY5F2OVn7yNszs3RCdvTsLmfWf33dxDwk7un+PgFgkf6DXEVoSgCgtv5nAruvKjMQd7+/M8b4U3WSPOgjEKNjY1SII09Yd/ylrdM3F1pTF7onUQjVP6BeqTu/ZT9ZK7+SIvyx+mRPjXH4XReyCk5nENiWC2TsGop93SinX3Gon3eX09/Cnc/vL2SWL1//cRbXlW5lzHyui8+ZYH37UeNAOkdTOMfT+52zzTi4pXX9Tb5Vfuq9STgk46a43sWI00MJB/ac59ct3fU58Gzx2MB1MVCiEcDMsXCitQ98lLOU05qmSTvJznP5EE7MZfVdmX6UAStGLmk1fuWSzZw33KFiaZquewgdc9d6/zThLAPBrwfjjZAd0FgHALBby5zR3Lym/P9UajtFADFkvCbaBy0ByAcDmPHjh0TczcaEwqJZHDtg96wUdb7NRBv6On+8XnOPe84JRyKYhumjZhmBU7742DjHy8dUvsCichPyOXFqhCLEai9kT8ohs7tC8Hh3Lg16vGATlK/DduD30Ti/zGTxGj3ppDwSD0aDiActkrCR0jdjZDxtHYSvEeKisjdOe3SEkkzujnA055rizKNCRUIo6BkIqvykXA9Bfq0Ift8Pi8lFNiG7ZhjjpnYu9LYb/APpETsOUXsHnlLaFuhgGy2WHJmEUqLcrUpL6TOMMTOeNuD5YzlfYFxx43xEBrqQhJe5qNo+Ycmh/GdJ4Bjjmip+l4jv2dpufSP97ksFDiLR8b3g2HUw/KFDaOcs6yt+ldWixe/lkvS9js2ywp++blQEDNW++UvWIggBZK7k+fo80IclSPSNXO4Nne/lk5CoWavMfNgMFJK3t+oA4QJxbh+heIYGWVYUV9fjz/96U8TfV8a+wC1bTp3PI2dn2XoWFC2cxI8HT+9Aykxj3T3p4VrXvnCxUIQnoOqFkZTCBnm1lgXRkNdEI11itxJqPz7UBEkyn5fDV6ln7bkihjsqh3c2bIFDSM2jDaiHbneC8BT4PetDjH0QvNKn91/jlrWMoKYa40XKoXP6CF+nu18NFCokqQZGcNYdS9yxjNVlMjc1dK9zxoak0r2LG9cTQw037B0wnnnnYeGBv8PVKMa1RmA1Rpoedvo6zlTXnw1eTZBLxtQwvbytKXn0CMEn1ZJHVUhXnSoMtZ6LJBUTjmmHaGAl0FoiWmBMcClhJe8MvfQdl8yIzDr0C8p/GmMIz+O1I6r1owa3FOt7fsXfAKl0hFbRkWUTLXlv8JOX8NoX+Ur8P8sqlyJtYWUe7zf5q40dZ8JZgxBrKFxoNAZtONEKp1HKpPzhaq586oxfc1h/wQJCpoGlBavzDKevY+x19Teu3pToybLUBPvaIthxWLGYVsVjrOKaIqqpBTP8eZdS0wJfnJyHYCER1YeTY1K7tUp7aPtV3IYVz+T6sN8B1ZFysjyiEga39ZqsvaPPmpx/YjokNrbxhIcXKJWz2eoSV3jUGG/bPb84T/77LPo7e2V/rNHH330rPljZaRJOp2T+OXa5F6bxGo5GA9GUFAjTKVz6O5LS8o7IzpqJb5QG5/bGsO8thjamqNCyIzlpWmHI4CKWiZeHROPsNwQOBUap2zFXnahZz+mRhpgxl8pMWXsNHANDY0ZQvbsVvXJT34Se/eWQ8XmzJkjdeXf9a534XAHBV0mx3DFYskhVjYTuORYqcP5hvyMH8Mo22ok3vj+oSafSuUkwaazZxide5NIZfIj7o/HtjZFJFOQGjxjfHlsxiV4u2iKWSYYDY4ab127+FV5HvBlHk6XMDgNDY0JJPuf/vSnUrP+7LPPxtve9jbMnTsXnZ2dpfXRaBRvfvObMRtAEwijRg4mYiadUXZuRm3sC1t2DuCfT3XW3EY7Oom9oy2OuS1RIWAvG28gkRVSZup8yCV4fmbkCb+DP3mF0Fq5hsbhjXHZ7E844QRpEPLjH/94xLa3v/3teOqpp7BmzRoczjb7PT1JyXSkndVP9jSJpLN5idPmPOPOWUuDpJ5x59yeyeYlzZxYPK8eLzp5/j6vu71zCPc9qvIYeO32VpI7zTNxERaMgvGnW0u9DxJ8wK4geJp1dDSHhsbsxbg0+3Xr1uErX/lKzW3U9C+44AIc7mAo3VPru5XNu0DtXJH6aJXx9gUS/3jQ0hjGWSfPl1j1lsaIRGoogi9KPRLei6TFB03EqzR4TfAaGhr7RfZ1dXWjZslyPbcf7shmC6JljxfUwqXpQciSjEqvnrX3mSaY8YCZlEvmBRTBZwslYeMRfCwShG0rgie5k+Q1wWtoaBwQ2TOW/hOf+ARWrlyJF7/4xaX1Dz30kDhtuf1wRzjsdauxRcv2iJXrPC3aI3XOGZ2yvyiVK/BVlfRX2tMEr6GhMak2ezpjX/KSl0jN+vnz56Ojo0PWUas/4ogjcN9996G9vR2z0Wa/v6hVD8MrEeyFUEoJA8Zhe2Vf3Ton3lwEiiZ4DQ2NidbsGX1DB+wPf/hD3H///RJnv2TJEqlzf+mll0o0jkYlvOJi5UqRI+thSwijmzbP1mVe67fKioblyoXMYGXoo4aGhsb+QmfQHoRm72np/lKmtbR0r1mDX0v3Srd61fEqiV1nV2poaEwsdA/a/QQ7zbAkgVcK2BOVKtHIHLeWXmqtNkYZWg0NDY1DSvbZbBZf/OIX8fOf/xzbtm1DJqNakHmgBsqSx4c7VN9JNbeF0O0SiRMlLd3V5LWWrjGboZqhsCMHe/d5k+O2oKKSU2pHpT5PUtMOjf0ge3ajuuGGGyTq5sILL0QoFMJsA+3l8RhGaOklctdausZhBqdE1KzCV/ARdw0Sr1jv7uvvPTDYA/TsVFxvBwArAHAuy7Y7D8AJBAE7CMO0RwiC8rxaSIxvvSECZvZiXGT/61//Gtdee620H5ytYEd52uJ1+VmNmQqHJJzNAMW8S8jOCMJ2CnkgmwYyKSCbAnIZIJeW4xyu5+R+lnnbQhjLjveRPacCIxRcspdee3C2PAU8+3d1H+O5VypOlisIvLnt+9yxFMaKU8qE7hsl+H+fzvAQkOhTgsTmOYJAIARYQSBIoWL5jnfPNZbSNsLFOVq97QPdTtOBCUTrYfA+DzXZJxIJvPCFL8RsRqnNmIbGTNLMScokbZJ0Poti13Zg4xofYfumPKfc/l2EJN+xrFLzrmgoYEsdQIT2M2JPGrK691ULHAG0L639vUvXN+FsWws8ff+ol3FI9jLKcIUIBYo3qqiF+StgrDx1n7fv7FgPPP84xgWj6uZXvwkmv39T+6En+/PPP19i6V/+8pdP6MU1NDQm2Eae88hdEaUQfj4L5NxpzxZg00HWsSJBioYcBEIRtU40+3xttZ1k1jofWH0xHV4ARw8UKpwKnOfVuqI79yaOPkrL3np3XaxBEfToD8O9r334EoschRSA8cq4eAMwPFD15WpgsBvoPsDe3Hx3fG8TjFGf1qZNm0rLH/zgB/GOd7xDok1e/epXSy37aixbtmzCb05DQ2NsOELuaTiZYRgkdTabIYmKtp5V2jp5j41OCZJzMKKI2vamQOWyf1swBNhhMXkgEJbJCFDzpbpuKvKusI2PXC6Vzt7XdymZgaSNjq/xg2cikg/lbaXJW+/bzztHxxFAXYtPqNQSMDWEymiINlQKNKPiQ3kxHAOaO6rWj8OANYmR8KPG2Vf3nfV2G81eXeADO4zj7DU0pgOEyEnuqQSw+3k4uzcBnZuFoIwX/bsiec/u6wc1b/52SeC0e3uELPu5xKySP9Tn0fpO+52fsr+3XL2+1n5eFI6fqItVhF5F9t4+I4h/jP2qhcVoz9LxBEOxSrh4x1fsXXNxfB6IcR5fiuMOwog1wDhUZpwbb7xxQi+koaGx//Acpk46qUh990Y4uzcD3dtHaKBOalA59UiqQu78hYeUpi5OSRuG5/isJuBqUq4g8vL6mRacIGasilGA4xJ5USnlJYFTa5+KM411kdr7HYiwkHAlE4jVY6IxKtlfcsklE34xDQ2NseGQpMUskwT2bFPaO0l+z1Zle6+FeJMyGdA0Ea6rJHcSB00xNMEEaYYJzTjCPhjI9589X3dM6AxaDY1pEDHjMNSR2vrODXA6tyhHKh2ttRCtB5rnAS0dQPtiGLFGRe52QJEbl0nwJXKf3fHlGvsg+8suuwzjBTWFH/zgB+PeX0NjtkJFx+Qkjt3p6wS2r4PTuUmRO+3wtSDOPpfc5yyGUd9SRe6u85TkHpxacqcdPO8UUeDEkiLesnzm3FFVXd3JMlQxQFOa2puwfNv4mXONSSb7u+66a9zDvdk0LNTQ2BfE8VcKISyHGBZyGWRzWeSKBZiFPIJP/A325n+NPAFJW8h9HtC2CGhsgyFRMUH1WyPRTwG5lwlbTXmXvMvLRan26qEItT/zqwpOAUU4sp2xOSRx/kdyN3wE7/3nQdwHI4SB6RMWcjYtJA6G7Lds2TKe4zU0Zrd93SPzfA4ObeosC5BNw6hvRr5QQDafQS6npgL3K+ZRyOVgwEEoWodGkqIVQL5xDnJN7ci2zkO+YU4p/JEOVTpdDYY8BiMwgmGYllUmwHxOkaSnJZMqXdIbL0jAfiKv1MwLJUIvha4LaStiFzInqZPQhdiLKLjbvf15J36ilmOhSn3zHPzPj9L3ke+ijvUEQZn4K4UEv7kfZSHhFwRmhZAofRah417jMBYS2mavoTEuLb2soTvpFNC/R02DPXD690oSjTPUC6OQR75tIYZOWI1iPi+kxjaSJMsctdsiNVsVg5GMN2DwhJcj1diKophlgiqE0U3lL3px7ULugOnkYWSSlUTnlgmQuQ8SZONqzqX9+Z9BMeP2WBBSV4TrQci6tL6suatltd2/v98Uw3nAtIVEeY1MPo/hQhbJXBaJfAZDubQ8z5AVQMiyETJtmQdNCwHLkmMDcg5L7lcEAe9D7pf3w9GBW1qcwqTqXVUKCb+mr56PEha8V2WNGE1I8HvI3u4zLM1rrnNLlaNyPf+V68j7YUZCuViiemeVx08p2bO6JTtSBQIBWd4XFi1aNNH3pqFxaOvGlDI6XU09OQDQrk4iH+gGODEzkutroPSTHepFIpNCvqjIXTRHy0CQmroVgCWJSyHR6B3DQMyykbeDKHAKBFAkWbrhf5wXeB6X+CR1qIY27JFMidjks9JqRRi4hMN1PFb0cdemLho6P8s1vO9S1n45Dxgk5/LnsTTh32x+AusG9hzU+7ANUwkEy8Yr5x+F5fVtI9+ZK6g881BvOomckxdhETAoMICCASVkHe/ZVQqJkc+tLCBKtK24uWReqkXm3lPwk7l6A/sm8nKFibIpqykUQYTC/1CQ/dKlS6XH7Omnny5dqfYlfWZKUpXGbHeOelmUeZ+mPizx66KlD+xVpD6kzDHjQT4UQS5Sh2wkjmw4jmysAQUrADsUgx0IYmchj3W5YezMprEr1YesU0TQJbOgq+UGXS3XWw5alk/zdeeuNlwXCCFqBxVpl0whbnc0V0CUNGJZ9ohcrfNIhYSttHFFSTTfpPJZDOdzSLqa+FAug4RMabx47hFYVoN0q0GyrQWPMEfq5DWeKc1J+SySrOczSnKUMl1Rk1d4cM9GrB/sqtiHz0uem+UfSajnK3MKBndkIcuGBdvkZCIeCME2rJJg9YStf7la6Na+T+/7l4VjSasvbS+PHEJmQL7XISN7tiBcvnx5aflwtmVpHIaJSP70eK8Oi1snxaHH0FeHxUkNAff+cuxzGiYK0TrkI3XIReLIROJIReuRZq2UQASWHRRitzknWdBcYgfgWDae79+NBxM9Fecj4Wdp4x8tdn4MnDFnKV4+70iXtEffb/NQNzYOdpeIjqRHjVkRuSJwj8xJ7CTYsdCbGcZ4iqKsbJiDhmBEhFJdICykqQRUqCRUMoW8TNliHmnO5XMOmaJaL5O7zHONB+kadXDkHMU8hvazvhtx8bJTxiXc7tm1Ht2ZRIWgoADhs5a5aYrQ4FwJV7WNJdEpaC1wmyL6bKFQMoNNSVIV+8xqHB5IF3JiXqBmyD+qGaWVc/RYmrOAlVs7XSa3PC/L2Q7sVWYXOkuPXw1DtlOTd+c8jmBmKO3D1JIZ2cJkpkAIhVi9kHohUo9stA5pkno0hpxpo0AtWEwxQeThoCEQgRkIwpESuorcc1KGt/zT6nAKMHq2oy1ch3nRBsQCISE5RXCKjPgjV/MyCY72c6dGOh5sT/Thn3sPLtCCQkKRdVjuezxY1ThXJj88X4CKxoEIoLDF0NGJs12/tGMFBrPp0nMsCZERAiSHDJ83fwtjCDgS93iwPdkn00SAQuCdR545Iecace4DOWhgYAAbNmyQRuQLFiyY+LvSGAEhMkJqlhwYSfOH0JcZFnstNQ3RvIJh0TYONcQm7SPqWsul2urpBMByATS3ZIZV6QBOmWF37tZeZ5x6taY8bzkQb3TT/VkQjESsri9hkNlhCRt0TngZ8pEYcoEocpaBrGMgZwJFQ0W+pAwDXcUC9uQz2JXqR2c6gYXxJlw8b4U4DcfC4ngLrjjuFaJZ78/zYYgmSZ/EpOZqamHc/ThBLZLnqUUqntbtkTn/HtRnd9kO7fOePSeuF9Ez8nNtZ2otlJ2cZWem3/Hp2dT96+Bb1xKKozUc99m+q5ypxkhhwnsU4Vo1muAz47nGg8ZgRJQoHuOfDkQ3p/Dhu5kMjPomb7vtNtx999340pe+VLH+C1/4gjQy8doQvulNb8KPfvQj2GOVG9XYPxIUs4M7xPfK0/o0ENGNvHolXvMF079sjahr0p/LYCCfxkAuI3+YESsoBDKYS8sPvT4QHrPTlucwdG/At1ye80edY0EuJs44RVWT210uE7mrWfu/j6+JhvPPPwF9exSxk8BrENW4QaJiYTCGBBYY314ERUHOAPIMATRN5KwosvwcjskPrUhyNG10F/PYm8+iMzOE3ekhsR1XY/fwoNz7vjRSDt8dx5ARFe+BBEN4JFVBYl6sOWuWufbkeODANN6XdKyQie/FGzlQsMXsoGjWY923R9okPj9pF0YhcS9E0vL8ATRRGLYb+qgiYLiO/yn3sqo9U/IxeGVqSp/LNvFyYcuyM1b+45+Mt0/Jjl52MteCMVokjWGUbPtGUG3l96OJqyQ4fOGf/s/nLz5+xHXEse4KbL73rDuns7hSKBSRK+bdeUFMa5OVIzAqQ3/7298e8cdwxx134JOf/CSOO+44vPvd78batWvxne98B6eccgquuOKKSbnBWZFNKaVofRP/aEl+Xuz2UC+QHPRVKrTcKoOcu6QuJWw5N8vrXfIfymWRItnls7CtEFrDEfkRJ52C+uO2AkibFiJ2AHE7CItOKTosGXXi1RWXn5PnaSrFHsj/+aKj/phpWsnlYGZTMDPDMDMptZxNY/Cks+HUt7iCwIEhhajcZddpyP+CfXtg9lc62WqC9yBJRSzZq0IUZc5SAvFGFONNyEViyJoWsoaBghlGwTKR5w+Q5Ee7uWkK6fc7RSH2vbk0OlND6GVdmlFAYp4TUeYYTiSX6gK+JCNF7Hwm6kcsoweo7FFqbvtDUrUJqrxuNC3WrylzH5ru+JnX4vuqJnH/fFQSp83Zqk3ipWfkOU5dG3XlxOtXkro0tfKTuI/0i2N+Hv3PQ30D95yeY9XxruM5VstChesqhUlRfDvOiHON/o48kvYLA0+QU2jTKVx6H753o/IQ1Dvtz6YmzT86Ktk/8cQTuPrqq0dUwgyHw6L104Tj4Wc/+5km+3GF9rlaujen89Az0fidiZx7VQsf+oOK5z4I1LkT0X/8S5FrXwwaAkhIaUZeOAWJcS5aNjKmKbbU2FP3wtg7vuYLFDP7cqHl+rqQkfhpLyrNi3x2h9dCKkC8vlUaudN2zqlYmoeBUBhOKAInGFWZo6KdWpJ4ZPLc1NRhIGvwh6miPjKGiZxhIG3QLGNioFhATy6NnkwSXakEutJDJU17tCG6IvZGzIs1oD1SXxFtQhJXtl9F6CR273yeYy5sM37cc9CNbgf2qKes6ZbnfoIq7kPbVTUcvdBNJXyqMTqJc9kam8Rd52IFiZvl6J5Dlb3qF5KVI4TRhIhTfp5VQqS8Xzm0dTRipxm0YlRSfZ3Stcv3In8TzBWoeM+13w0jgw4p2Xd1dZWicfya/Yte9KIKon/Na16DH//4x5NyczMVQt5+bZ3LdLi5CTpsNEFyl98EyV3KmvJtuI2YQ+Fy382G1oMmez8kI7NQEK2awXsMAaS2m8lnkMykELBMCRsMFos40MAv9g8lSRdsknTQJeygmGlUeKABh1V4JcpF6fVFGd4b6D3iRIl88UYucr8egZSSg3g8HaXcxn3UfhzJUFPPuuSed/dVYXU2YqaFXz73gESe1ELECqDD1djnxRplmSaPSht6AcP5bE1tnUIgxIgcicCwSpmenlbH0D7eh3L8VZKUn3zK2qiPfHz7SP7pfmq7nnbqmZ32TeKK9KeCxMcLb4TD/61DIEyKFe+hTO7VQqIcDls1H+vd+M6r3od16Mi+rq4OyWR5OEuHbE9PD84444yK/err62dtjH0pu1L6d3rkTq28oP5Q2F6MJon+TjVndMhQH8BQvxNfDiw9TvXmZEGrURxhztLjVUs3mic8p6a/UUPFZ7WuWMgjlcsqW6Gk0zOCw5L63cVwDAbt5m7SCHtwOszQRBDsEsoIhgFqxKteICYeywoiGAyL1pcuZOVdC+kVcmLzN4tAEkU8n89iYy6Fo6INeEG8Vc5fJm1lagqWmlcAz6QGsD41BLb1JaGYRmWBLDbPKWU6usk8nPN4UxJm1HZ+B0ZCR1wzFI8loUaFYFWooX9YTCJfN5AW8pobqUNHtBHzYw1C7E3BqOzL7FJlgimIQ9uvrStbtIqRjwfKYXUEl70YbiF4l/RrY3JIqlpLrSQlJRDET+0j9ulG4oezMCm6ZSRqCwwvm9koKRmHhOxXrVqFW2+9VTR3gsv88uecc07Ffps3b0Z7+8R2VJnWLeD8phjXvl6ksGMyDh2LJHUui529f/RuOelhGCTwmsWzyi3SjEhMVT0kSk0klBmkZPvwlg1GkRRdJ2wBiXwaITso8c15sd979vZKR+za1AB+27sDKyN1ODbaiCXheqSLBfS75p1oMAzbMpHJ55DK5ySjM1nIY3MmgeeGB9DtacqWiWUMWaxrhuNq5tTyPT+DIn9T5juzCfwr1T9h74Zhdy+cs2yf9s6z2pfjzPZlmBOuE5Lz7OoUjLTVU1uXxCPWG3PjpuMlbV2ZNcRd4EvIoVDhfDqQZtmJONV3olELEu48SSORAyb7j370o7jwwgvR29srZH7TTTeJY/ass86q2O/Pf/4zTjjhBBzucPr2iPnFoaO0d7ci9gFXW2ds91h9KwkSQV0zUN+qTDOtC1Q6fonYfbHfEh7Ips4Rd5lO1Bqvyt9liOGBhQL6cikM2zaG8jnsyDpoDsWwONrokmxlb1AhX8PAU1v3SvGqtalBmUhiKxrm4MiGdrSF40hy9GAAiaKD7blBSdShrbsaHZF6tDV3ID+ORBQKIdrDvTos/lotB5JQIvVMxiBbqU1TLEhoYc4jdp+2zhEASTtqM5uyWluvJPXRMkQ1NKYzRiX7Cy64AF//+tfxta99TQif5pvqCJ3Ozk7ceeedEo55uMPZ+CSch24FRrH3VoAaOwmdjY7rmwE2l4jWKcekx2MkbxI8i17RlMPPJtvGuc9XSJ6NoN1JWsm5oZUktqowSS+GftC2MJRJYc1QD57s3SGx0pcdeaaQ3Gg4qXWhENj6gS6VkFIs4Jm+3TLRWbu8rlUcmp2pwRHHtkfqcFRjB45qnIsmfo9xgto1p1qoDvOrLK3rr5Hu7lcsosWNifbqqXs2dc8cI8k8DGd0HaXRQFBp7pZfW3eLcfls7DMp8UxD44Aajh+uONCG48Wn7odzx02VKxn2R1JnM4k6l9Rj9TC8DEcpoedmU7pkDuYjuFX91HYfmZfmbju5cYIkP5BNydSdSuC+zudLxEyyfuPSkyUBiPDiflVKf6U2zPXU2p/t340NA12jZhe2hGI4uqkDRzfOLZEsSVclo7j9PGt07az+UxuxvWpNefdRzufb0Vv2wgbpB+D3s91KimJb53OHUUpnV3Z1ZWOvfhYaGocbdCbUeNE4B1i4SvX7JLnHWBMlrAiCJhfTJXQhc9f04mnfQvKVZM75aE7Z8YLkSY2b0SX9mWFsGuzBfZ0bxMnqOSNfv+REqS1CzZb7MZKERMiSsSS+gI/4qNUe2dguE4n7+cEuPNu3G5uHeiTx6qimuTi6sUNMO57gGHLT06Uhh0SeqJBKBS8TsuJjaXtJnJU4tqrcrDev4mBfa4uqzzTnKDOMp61XkLpb8GoqMoY1NKYamuzHCTaNwCmvUjVZPGL37OluZEs1mXsEPxmdhKjB7k0lhLy700k8uncr1vSW4+JPbV2Es+etEns0Y+mZKctbpFZO27NkVZay+Bi9kxOBQKqmBkxiXF7XhlUNc0uZtRJTXsxL4gfTyx3DEVMII1PqGbHjOimrk4z8Vf8q1lctlferWl91QLnU7MhjSeSlSobeCEpDQ0OT/bgRikjuodjNPVIXTb68fKiIhTbqPakhyYrdkezH3bvWY/ewqrFOonv1wmPFzEK7Nk08mWJOInJot49aAdH0aaLxikRJHQ9WaWAqd0E12WDZAzaeEAHgEjjPR+Jncaz6YAQhmyGbdGxaYi5inPq+0vA1NDSmBprsxwmjcY6UN5jKZs4EibkrNSQaPU0s9+zaIKRM0Lxy4ZITxY7OWi6JbFrs09Tm2R2IDtQKR6277CUMqWqLau4Vz6IAYF0VWsVJ5HRw0gzkkXvYZu1tbRbR0Jju0GS/H5hqomcBM5pukrmMOGEf795Wckwe1zQP5y48RowaPemEaOesaBmzQ4gFgkL0o5GyVwTKX+GQGr0qw6sqLvIzyZ4Evz/VGzU0NKYH9K92hoB9PHsyCfSkEvjj9qfFfEOQwF+14Ggc3zQPSen3mREybgvFRZtvDkUPqOMNTTfU3jlpaGjMfEz78TdLKX/2s5+VNokswsbM3uuvv35EGN9MBrVmmk2YnZpwI2u60wnsGR7EzmQ/tiV65TPj4H/y/CMlomd6/6UrzpAQSEbl0LRDezwTqZpCMSkBMNGtzTQ0NGYmpr1m/773vQ/f//73cfnll0s/3Ntvvx0f/OAHJdHrU5/6FGYC6FBVKflMBFLp+f51XuU7rzoeG0FLbXVwP5bLzeOpvt34R9fm0r7Mbj1v4TEiJEj0rAvDiBj2r2RzC5pcNDQ0NGZEUtWaNWtw0kknSfnkr371q6X1bJjCWj2sy9PR0XFIkqpGg9eRp0TkLol7ZM5t3hNW5O3PAlVlAiQL1CV2D16Bquf6O/FU7y70ZYdlPUMj2X+UNvqhfEYiDxsDjIyxRatnExIdDaOhoVGNaa3+/epXv5L5hz70oYr1/Mxtt9xyi2j+hwLMLqWZxCNzRe771sopAEjqdK4OZVVzZ56nKRTBwniz24DYVrXFfQ0ePMJmrLhH9Exseu3i44XQB/KpinDK5nBM12zR0NCYmWT/6KOPSu38RYsWVaw/7bTTpATuY489VvO4fWm2q1ev3u97YVy7atZdHKGVM/IlQSLPKwcpyZxTIq/InRPj2f04sWUBTmhZuM/rsivSivo26Yd5bPN8FN1rjhpOqaGhoTHTyH7Xrl2YN2/eiPXBYBAtLS3YuXPnIbsXlhpY178H/ZmUxLUzOibpI/PxtVSuLFw2GkpFwIpFKcXLaBuv7R/DKdlejgQ/VjilhoaGxowh+1QqJc1RaoGROdxeC2O5ITyb/f6CETB/3v7MuPdXWabhkh2dGacNQdXYO2aHEbFt0f4rqju69nyvrJdq5KHMOsxSbbIjBxVOqaGhMXsxrck+Eokgk8nU3JZOp2X7/uL5559HIpHYb9KnZr97uFzilxq16imqaqF7y14HIM+Q5LWLk+WqtnFec2i17DWIlk9urZdy9RfVlFhdR0NDQ6MaJ554opSlHw3TmjlowqEppxrZbFZaJNYy8ewLbW1tiMdV1cb9gbS6SxcxP9qAhbEmdETrxY7eFIyISYUaN0mfqIjEcdvAkcyVM9ZCsn/AbV2n+pGqol2+srtuz9QQe5patkxD/QPjIno+l4PZZ7RttdZXr7v33ntlmiqM57tP5rnGe8y+9puo91O9fqrfT/X9HOrz9OzHMYfqHR3S35AzjXHllVdKG82tW7dWrH/ggQdk/Q033HBI7+fyyy+fsvOM95jx7DfWPqNtq7W+ep1bYt6ZKkzU+5nqdzRR76d6/VS/n+r7OdTnuXw/jjlU7+hQ/oamtWZ/0UUXyfy6666rWM/PdNKym9ahxPnnnz9l5xnvMePZb6x9RttWa/1EPY+JwkTez1S+o4l6P/tzT4cKM+E3dCjf0aF8P9M6qYp417vehRtvvLEig5Yx9tdccw0+/elPT/XtadQIeZ3mf1KzFvr9zO53NO3JPpfLSY9bEv7u3buxZMkSfOADH5CSCTpTdHpBk8n0hn4/0x+zmuw1Zg40mUxv6Pczu9/RtLbZa2hoaGhMDDTZa2hoaMwCaDOOhoaGxiyA1uw1NDQ0ZgE02WtoaGjMAmiy19DQ0JgF0GSvcUjB/sGnnHKKZEBfeumlU307GlVg4UEmMi5evBh1dXU44YQT8Pvf/36qb0ujCuzet3DhQqkKzHf1+c9/HvuCJnuNQwoWr7v66quFUDSmH/L5vJAIi3ENDAzgS1/6Et7ylrdg/fr1U31rGj6wosBzzz2HwcFB/P3vf8fPfvazUme/GVniWOPww4UXXljqQrZjx46pvh2NKsRisYoyJOeddx5WrlyJRx55ROYa0wOrVq2q+MzOfSzfPha0Zq9RE6z5z/pDr371q6UsNDP7PvnJT46qDX72s5/F0qVLpakM/xBprtFRvTP/He3duxdr167FMcccM0nf4vBGYhLfEUddLNe+YMECuc7b3va2Me9Fk71GTXR3d+Mzn/kMnnrqKZx00klj7sum75/61Kfwyle+Uv44jz/+eKldxD9cjZn7jkg+JJA3velN0hhDY3q9oyuvvBJDQ0N4/PHH8Y53vANNTU1j38ykFE7WmPFIp9POzp07ZXnz5s1SY/uqq64asd8TTzwh26644oqK9RdddJETCoWcXbt21Tw/z3XJJZdM0t3PDkzmOyoUCs7FF1/snHPOOU4mk5nEb3F4Iz3JvyMPX/ziF52PfvSjY+6jNXuNmgiFQuPqBOY5hT70oQ9VrOdnRnbccsstk3aPsx2T9Y5oNqADnV3ifve730nklMb0/h1xFLZx48Yx99Fkr3FQoKN17ty5WLRoUcX60047TZxGjz322Ig/SvYPLhQKMnGZZaw1ps87ojmBdvo//vGPiEajh/huZyce3Y93xN/L9773PfT396NYLOLhhx/GDTfcgLPPPnvMa2iy1zgoUPurpblQG2xpacHOnTsr1n/uc5+TRvF0Lv3kJz+RZYaRaUyPd7R161Z85zvfwZo1a9DR0SEOQE7sKaExPd4Rnby//vWvsWzZMomzf/vb3y4jANr3x4IOvdQ4KKRSKfmDqwVGFHC7Hwzr0x3Gpu87YoKOjqKa3u/Itm3cdttt+30NrdlrHBSomdOmWAs00XC7xtRCv6Ppj0PxjjTZaxwUOPTkELQa2WwWPT0943JOaUwu9Dua/jgU70iTvcZBgXVuOjs7sW3btor1zLik84jbNaYW+h1NfxyKd6TJXuOgcNFFF8n8uuuuq1jPz3QuXXDBBVN0Zxoe9Dua/jgU70g7aDVGBbP4GN7FiXjggQckmoZ47WtfKxl+zAq87LLL8H/+z/+RbL7TTz8dt99+u8QNM01cmwgmF/odTX9cP13e0QGlhWnMCixevFiy+mpNN954Y2m/bDbrfPrTn5b9g8Ggs3LlSucb3/iGUywWp/T+ZwP0O5r+WDxN3pHuQauhoaExC6Bt9hoaGhqzAJrsNTQ0NGYBNNlraGhozAJostfQ0NCYBdBkr6GhoTELoMleQ0NDYxZAk72GhobGLIAmew0NDY1ZAE32GoclbrrpJmnycOedd2ImYc+ePVInpa2tTe5f1/7XmChostfYb9xzzz1CRJz+8pe/HDZEOx3w0Y9+FL///e9l/uMf/xgXXnjhqPsy+Z0di17ykpdISzs2uVi4cCHOPfdcqcfixw9/+EN8/etfP6h7e/zxx0X4bNmy5aDOozE10GSvcVC4+uqrp/oWDivcddddeMUrXoFPfOITeNvb3iZFskbDf/7nf+KNb3yj9Cj9r//6L/y///f/pEUda6D/3//7fyeF7K+99lpN9jMUuuqlxgHj5JNPlkbIv/vd7/D6178esxXJZBKxWGxCztXV1YXGxsZx7UfyZulbPv9a5iANDT+0Zq9xwHjXu96FRYsWSQnWfdXT4/Cfpp1qUEvkepp+qs1Af/3rX/GpT30KCxYsEDJ91ateVWrucMMNN2DFihViujj11FNF6NRCLpfDlVdeKSVi2dqNJg9qqNXI5/P4yle+gqOPPlrO2draire85S0jmklceumlcm/bt2/HxRdfjKamJhx77LH7fFY0yVA48h6am5vx7//+73juuedGPB8+x5/+9KclM9loWvSmTZukqcXq1atrbm9vby8tL1myBA8++KA0E/fO638XLKvL88yZMwehUAhHHHGEjNg4QvDfn9cY/mUve1npHP73tnnzZrzjHe8QkxLPs3LlSnz5y1+W+/TjN7/5Dc444wwRanyvvN573vOefT5DjYOD1uw1DhhsqkBSIAn88pe/FPKbSNCUQeKliYIt2772ta/hda97nZDwj370I7zvfe/D8PCwEApHFhs3bkQgEKg4x3//938LgdLkMTg4KLZsktWjjz4qwoLgdppD/vSnPwmZf/jDH8bOnTtl3/vuuw9PPPGEOEz9OO+887Bq1Sp84QtfkB6hY+GrX/0qPvaxj0mN8i9+8Yvo7e0Vk8sLX/hC6UREsqNtnnOaYc4880z5bkT1dT0sXbpU5r/97W/lnscaDXAE8PGPf1yuW23e8e7v1a9+tYwS+LxZb53fi8KBz5ng/e3YsQM/+MEP5L0cddRRsp73Sjz//PPyfUjeH/jAB0Rw0LdDQUvB9J3vfEf2+9vf/ibPmkKXNd35vrj9D3/4w5jPUGMCMCGFkjVmFe6++26pxf29733PyeVyzhFHHOEceeSRTj6fl+2s0c3td9xxR+mYa665RtZVY/PmzSPqenvHn3rqqXJ+D//5n/8p61nvO5FIlNZff/31sv7WW28dcY5ly5Y5Q0NDpfVr1qxxTNN0LrrootK6X/7ylyOOJ5544gnHsizn4x//eGndJZdcIvtefvnl43pW3d3dTjgcdk455RQnnU6X1j/22GNyH//+7/9esT/P/da3vnVc537Pe94j+8diMefcc891rr32WufBBx90CoXCiH3POusseW61kEwmR6xjXXXe344dO0rr+L55Pb7/apx33nnO0qVLnf7+/or1V1xxhRzz7LPPyuePfOQjTn19fcV71Tg00GYcjYOCbdtixlm3bh1+8pOfTOi5OWLg+T2cddZZMn/rW99aYSP31lOzr8a73/1uxOPx0ucTTjgBZ599Nv785z+XzAs///nPMX/+fNFSu7u7SxPNR9T+qY1W4/3vf/+4vsMdd9whmv9HPvIRMW14oEmHjljeB01IB4JvfetbojEfc8wx0tWI74HPglo/rzteRKNRmRcKBemmxO/O0Q+fz2jmMT/6+vrE5EbTFM1m/mfIEZDneCY4AqGPg/vrVhqHFprsNQ4aNKtwWP+Zz3xGfuwThcWLF1d89kwV9BPUWk8zRTWOPPLImusSiQT27t0rnymoaLahyaR6ol2dztBqLF++fFzfwbO5e2YPP+gfSKVSB+xMZRQObd0PP/ywmKjuv/9+Cdlk42q2u1u/fv24zkPipaCjP4E+CH5vzxfgtdIbCxs2bBDipjmo+vlRoBHeM6SQ5Pc+//zzxbb/5je/GT/72c8m9O9Goza0zV7joEHSYUgek4EY4ufXYD3Ucs562uRosCxrv9YfqKbI46gNf/e73625nXbsapAYpxM40nnRi14kE8nU86PsKzT2oYcewmte8xq84AUvED8CRzN8fxR+9AVUO1fHeu7vfe97Rbsfy8dAAUAHOTV9ChmOQH7xi1+Ic5y+Av8oTGNiocleY0Lwhje8QUwkn//853HVVVeN2E6N0Rvye8sEnXOTCWrttdaRVDznJx2j9957r2iz1Q7eg4VHcmvXrsUpp5xSsY3raELxR85MBEjcBAl7X8KWDa3paKepyi/AbrvtthH7jnaOZcuWlbZ5mvxYoGnunHPOkckzR1Hjp4avo3ImD9qMozEh4I+dZhyGJNbSkL3IF89262mE3/jGNyb1vr7//e+LjdjDk08+KcRGWzJHJARNCUNDQ/jSl7404njeI23PBwqSH0cG1113XUUoI++DWi3vw++XGC9oqvnXv/5Vc9sf//jHEaYjCjeaZKpHPxwl8d35NXiOtv7nf/5nxHk9rZsC2w/PXHPzzTfXFK40MWUyGVnu6ekZsf2kk06qeV6NiYXW7DUmDLQTM7zwn//854htr3zlK0UDpMOUGm1DQ4PEWzN0cjJRV1cn9miaJEg6NFVQm/7sZz9b2odkz8QkxvT/4x//EAcuCZpx47feeqv4JA60Rk1LS4tci6GXDDfktbzQy/r6egnFPBAwDPK0004ThyyJln4M+iFot2c4JoXrZZddVtqf+9JsQkcxNX8KOobKMpSVcfb8zgz7pA+B5p9a5hvmM1Aw8J4pODgS4Lk4eqF2zufM0QvfMU1J3OeZZ56R9/z0009LvD+30X7P6/GeKUi//e1vy7lmc2LeIcEhivrROExDL6tx2223ybbq0EviqaeeclavXu2EQiGnra3N+dCHPuQ888wzo4ZeVh8/2nW98M2rrrpqxDn+/Oc/O//1X//lzJ07V677ohe9yHnkkUdG3DfDFRnCefLJJzuRSMSJx+POqlWrnPe///1yj9Whl/sbOnjzzTc7J554otxDY2Oj8/rXv95Zu3btiP3GG3rJcFLe77/92785S5YskfBO3vfRRx8t37enp6di/4GBAefNb36z09TU5BiGUREG+4tf/MI57rjj5Bzz5s1zPvzhDztPP/30iPdCfPOb35RwVoakVm9nmOZ73/teZ+HChU4gEHDmzJkjIZ9f+cpXnFQqJfv8+te/ljDNjo4OJxgMyvzCCy90Hn/88f16nhr7D4P/HBqxoqGhoaExVdA2ew0NDY1ZAE32GhoaGrMAmuw1NDQ0ZgE02WtoaGjMAmiy19DQ0JgF0GSvoaGhMQugyV5DQ0NjFkCTvYaGhsYsgCZ7DQ0NDRz++P8Bez4jZQ/uOXkAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "methods = ['DMD','DMDc','Subspace DMDc']\n", - "#on two plots, plot the mean and std of the silhouette scores for each method across p_out / n\n", - "p_frac = np.array(n_uses[:len(silh_state_dmdcs)])\n", - "\n", - "fig, ax = plt.subplots(2, 1, figsize=(5,4),sharex=True)\n", - "\n", - "# Plot state silhouette scores\n", - "\n", - "for i, state in enumerate([silh_state_dsas,silh_state_dmdcs,silh_state_subdmdcs]):\n", - " ax[0].plot(p_frac, np.mean(state, axis=1), label=methods[i] + ' (State)',color=plt.cm.Set2(i))\n", - " ax[0].fill_between(p_frac, np.mean(state, axis=1) - np.std(state, axis=1) / np.sqrt(n_iters),\n", - " np.mean(state, axis=1) + np.std(state, axis=1) / np.sqrt(n_iters), alpha=0.2,\n", - " color=plt.cm.Set2(i))\n", - "\n", - "for i, state in enumerate([silh_ctrl_dsas,silh_ctrl_dmdcs,silh_ctrl_subsdmdcs]):\n", - " ax[1].plot(p_frac, np.mean(state, axis=1), label=methods[i] + ' (Control)',color=plt.cm.Set2(i),linestyle='--')\n", - " ax[1].fill_between(p_frac, np.mean(state, axis=1) - np.std(state, axis=1) / np.sqrt(n_iters),\n", - " np.mean(state, axis=1) + np.std(state, axis=1) / np.sqrt(n_iters), alpha=0.2,\n", - " color=plt.cm.Set2(i))\n", - "\n", - "ax[0].set_xscale('log')\n", - "ax[1].set_xscale('log')\n", - "ax[0].set_ylim(-0.05,1.05)\n", - "ax[1].set_ylim(-0.05,1.05)\n", - "# Create custom legend with colored text\n", - "from matplotlib.lines import Line2D\n", - "ax[0].text(0.5, 0.5, 'DMD', color=plt.cm.Set2(0), fontsize=12, ha='center', va='center', transform=ax[0].transAxes)\n", - "ax[0].text(0.5, 0.4, 'DMDc', color=plt.cm.Set2(1), fontsize=12, ha='center', va='center', transform=ax[0].transAxes)\n", - "ax[0].text(0.5, 0.3, 'SubspaceDMDc', color=plt.cm.Set2(2), fontsize=12, ha='center', va='center', transform=ax[0].transAxes)\n", - "\n", - "# Add subplot titles\n", - "ax[0].set_title('State', fontsize=16, pad=10)\n", - "ax[1].set_title('Input', fontsize=16, pad=3)\n", - "ax[1].set_xlabel('Number of States')\n", - "fig.text(-0.05, 0.5, 'Silhouette Score', va='center', rotation='vertical',fontsize=16)\n", - "plt.tight_layout()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "5f1c041a", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/2 [00:02 11\u001b[0m silh_state_dmdc, silh_ctrl_dmdc, silh_state_subdmdc, silh_ctrl_subsdmdc, silh_state_dsa, silh_ctrl_dsa \u001b[38;5;241m=\u001b[39m \u001b[43mget_silhouette_scores\u001b[49m\u001b[43m(\u001b[49m\u001b[43mn\u001b[49m\u001b[43m,\u001b[49m\u001b[43mm\u001b[49m\u001b[43m,\u001b[49m\u001b[43mp_out_small\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 12\u001b[0m \u001b[43m \u001b[49m\u001b[43mN_small\u001b[49m\u001b[43m,\u001b[49m\u001b[43mn_iters\u001b[49m\u001b[43m,\u001b[49m\u001b[43minput_alpha\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minput_alpha\u001b[49m\u001b[43m,\u001b[49m\u001b[43mg1\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mg1\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 13\u001b[0m \u001b[43m \u001b[49m\u001b[43mg2\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mg2\u001b[49m\u001b[43m,\u001b[49m\u001b[43msame_inp\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43mn_Us\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mn_Us\u001b[49m\u001b[43m,\u001b[49m\u001b[43mn_delays\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mn_delays\u001b[49m\u001b[43m,\u001b[49m\u001b[43mrank\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mr\u001b[49m\u001b[43m,\u001b[49m\u001b[43mpf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 14\u001b[0m \u001b[43m \u001b[49m\u001b[43mobs_noise\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mobs_noise\u001b[49m\u001b[43m,\u001b[49m\u001b[43mprocess_noise\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprocess_noise\u001b[49m\u001b[43m,\u001b[49m\u001b[43mnonlinear_eps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnonlinear_eps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 15\u001b[0m \u001b[43m \u001b[49m\u001b[43mbackend\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbackend\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 16\u001b[0m silh_state_dmdcs\u001b[38;5;241m.\u001b[39mappend(silh_state_dmdc)\n\u001b[1;32m 17\u001b[0m silh_ctrl_dmdcs\u001b[38;5;241m.\u001b[39mappend(silh_ctrl_dmdc)\n", - "Cell \u001b[0;32mIn[32], line 32\u001b[0m, in \u001b[0;36mget_silhouette_scores\u001b[0;34m(n, m, p_out, N, n_iters, input_alpha, g1, g2, same_inp, n_Us, n_delays, pf, rank, process_noise, obs_noise, nonlinear_eps, nonlinear_func, y_feature_map, u_feature_map, backend, use_joint_control)\u001b[0m\n\u001b[1;32m 29\u001b[0m Us \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mmap\u001b[39m(u_feature_map, Us))\n\u001b[1;32m 31\u001b[0m A_cs, B_cs \u001b[38;5;241m=\u001b[39m get_dmdcs(Ys,Us,n_delays\u001b[38;5;241m=\u001b[39mn_delays,rank\u001b[38;5;241m=\u001b[39mrank)\n\u001b[0;32m---> 32\u001b[0m As, Bs, Cs, infos \u001b[38;5;241m=\u001b[39m \u001b[43mget_subspace_dmdcs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mYs\u001b[49m\u001b[43m,\u001b[49m\u001b[43mUs\u001b[49m\u001b[43m,\u001b[49m\u001b[43mp\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpf\u001b[49m\u001b[43m,\u001b[49m\u001b[43mf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpf\u001b[49m\u001b[43m,\u001b[49m\u001b[43mn_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrank\u001b[49m\u001b[43m,\u001b[49m\u001b[43mbackend\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbackend\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 33\u001b[0m A_dmds \u001b[38;5;241m=\u001b[39m get_dmds(Ys,n_delays\u001b[38;5;241m=\u001b[39mn_delays,rank\u001b[38;5;241m=\u001b[39mrank)\n\u001b[1;32m 35\u001b[0m sims_full_dmdc, sims_control_joint_dmdc, sims_state_joint_dmdc, sims_control_separate_dmdc, sims_state_separate_dmdc \u001b[38;5;241m=\u001b[39m compare_systems_full(A_cs,B_cs)\n", - "\u001b[0;31mTypeError\u001b[0m: get_subspace_dmdcs() got an unexpected keyword argument 'f'" - ] - } - ], - "source": [ - "rs = np.arange(2,25,1)\n", - "n_iters = 2\n", - "silh_state_dmdcs = []\n", - "silh_ctrl_dmdcs = []\n", - "silh_state_subdmdcs = []\n", - "silh_ctrl_subsdmdcs = []\n", - "silh_state_dsas = []\n", - "silh_ctrl_dsas = []\n", - "\n", - "for r in rs:\n", - " silh_state_dmdc, silh_ctrl_dmdc, silh_state_subdmdc, silh_ctrl_subsdmdc, silh_state_dsa, silh_ctrl_dsa = get_silhouette_scores(n,m,p_out_small,\n", - " N_small,n_iters,input_alpha=input_alpha,g1=g1,\n", - " g2=g2,same_inp=False,n_Us=n_Us,n_delays=n_delays,rank=r,pf=pf,\n", - " obs_noise=obs_noise,process_noise=process_noise,nonlinear_eps=nonlinear_eps,\n", - " backend=backend)\n", - " silh_state_dmdcs.append(silh_state_dmdc)\n", - " silh_ctrl_dmdcs.append(silh_ctrl_dmdc)\n", - " silh_state_subdmdcs.append(silh_state_subdmdc)\n", - " silh_ctrl_subsdmdcs.append(silh_ctrl_subsdmdc)\n", - " silh_state_dsas.append(silh_state_dsa)\n", - " silh_ctrl_dsas.append(silh_ctrl_dsa)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0a65665b", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "methods = ['DMD','DMDc','Subspace DMDc']\n", - "#on two plots, plot the mean and std of the silhouette scores for each method across p_out / n\n", - "\n", - "fig, ax = plt.subplots(1,2, figsize=(8,3),sharex=True)\n", - "\n", - "# Plot state silhouette scores\n", - "\n", - "for i, state in enumerate([silh_state_dsas,silh_state_dmdcs,silh_state_subdmdcs]):\n", - " ax[0].plot(rs, np.mean(state, axis=1), label=methods[i] + ' (State)',color=plt.cm.Set2(i))\n", - " ax[0].fill_between(rs, np.mean(state, axis=1) - np.std(state, axis=1) / np.sqrt(n_iters),\n", - " np.mean(state, axis=1) + np.std(state, axis=1) / np.sqrt(n_iters), alpha=0.2,\n", - " color=plt.cm.Set2(i))\n", - "\n", - "for i, state in enumerate([silh_ctrl_dsas,silh_ctrl_dmdcs,silh_ctrl_subsdmdcs]):\n", - " ax[1].plot(rs, np.mean(state, axis=1), label=methods[i] + ' (Control)',color=plt.cm.Set2(i),linestyle='--')\n", - " ax[1].fill_between(rs, np.mean(state, axis=1) - np.std(state, axis=1) / np.sqrt(n_iters),\n", - " np.mean(state, axis=1) + np.std(state, axis=1) / np.sqrt(n_iters), alpha=0.2,\n", - " color=plt.cm.Set2(i))\n", - "\n", - "# ax[0].set_xscale('log')\n", - "# ax[1].set_xscale('log')\n", - "ax[0].set_ylim(-0.05,1.05)\n", - "ax[1].set_ylim(-0.05,1.05)\n", - "# Create custom legend with colored text\n", - "from matplotlib.lines import Line2D\n", - "ax[0].text(1.4, 0.8, 'SubspaceDMDc', color=plt.cm.Set2(2), fontsize=12, ha='center', va='center', transform=ax[0].transAxes)\n", - "ax[0].text(1.4, 0.65, 'DMDc', color=plt.cm.Set2(1), fontsize=12, ha='center', va='center', transform=ax[0].transAxes)\n", - "ax[0].text(1.4, 0.5, 'DMD', color=plt.cm.Set2(0), fontsize=12, ha='center', va='center', transform=ax[0].transAxes)\n", - "\n", - "# Add subplot titles\n", - "ax[0].set_title('State', fontsize=16, pad=10)\n", - "ax[1].set_title('Input', fontsize=16, pad=3)\n", - "ax[1].set_xlabel('Rank of DMD')\n", - "fig.text(-0.05, 0.5, 'Silhouette Score', va='center', rotation='vertical',fontsize=16)\n", - "plt.tight_layout()\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b1ac349b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.1\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/2 [00:15 27\u001b[0m ss_dmdc, sc_dmdc, ss_subdmdc, sc_subdmdc, ss_dsa, sc_dsa \u001b[38;5;241m=\u001b[39m \u001b[43mget_silhouette_scores\u001b[49m\u001b[43m(\u001b[49m\u001b[43mn\u001b[49m\u001b[43m,\u001b[49m\u001b[43mm\u001b[49m\u001b[43m,\u001b[49m\u001b[43mp_out\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 28\u001b[0m \u001b[43m \u001b[49m\u001b[43mN\u001b[49m\u001b[43m,\u001b[49m\u001b[43mn_iters\u001b[49m\u001b[43m,\u001b[49m\u001b[43mrng\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43mi\u001b[49m\u001b[43m,\u001b[49m\u001b[43minput_alpha\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.001\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43mg1\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.5\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 29\u001b[0m \u001b[43m \u001b[49m\u001b[43mg2\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43msame_inp\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43mn_Us\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mn_Us\u001b[49m\u001b[43m,\u001b[49m\u001b[43mn_delays\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mn_delays\u001b[49m\u001b[43m,\u001b[49m\u001b[43mrank\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m20\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43mpf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 30\u001b[0m \u001b[43m \u001b[49m\u001b[43mnonlinear_eps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnonlinear_eps\u001b[49m\u001b[43m,\u001b[49m\u001b[43mnonlinear_func\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtanh\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 31\u001b[0m \u001b[43m \u001b[49m\u001b[43my_feature_map\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mY_feature_map\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mu_feature_map\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mU_feature_map\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 32\u001b[0m silh_state_dmdcs\u001b[38;5;241m.\u001b[39mappend(ss_dmdc)\n\u001b[1;32m 33\u001b[0m silh_ctrl_dmdcs\u001b[38;5;241m.\u001b[39mappend(sc_dmdc)\n", - "Cell \u001b[0;32mIn[198], line 40\u001b[0m, in \u001b[0;36mget_silhouette_scores\u001b[0;34m(n, m, p_out, N, n_iters, rng, input_alpha, g1, g2, same_inp, n_Us, n_delays, pf, rank, process_noise, obs_noise, nonlinear_eps, nonlinear_func, y_feature_map, u_feature_map)\u001b[0m\n\u001b[1;32m 37\u001b[0m Us \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mmap\u001b[39m(u_feature_map, Us))\n\u001b[1;32m 39\u001b[0m A_cs, B_cs \u001b[38;5;241m=\u001b[39m get_dmdcs(Ys,Us,n_delays\u001b[38;5;241m=\u001b[39mn_delays,rank\u001b[38;5;241m=\u001b[39mrank)\n\u001b[0;32m---> 40\u001b[0m As, Bs, Cs, infos \u001b[38;5;241m=\u001b[39m \u001b[43mget_subspace_dmdcs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mYs\u001b[49m\u001b[43m,\u001b[49m\u001b[43mUs\u001b[49m\u001b[43m,\u001b[49m\u001b[43mp\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpf\u001b[49m\u001b[43m,\u001b[49m\u001b[43mf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpf\u001b[49m\u001b[43m,\u001b[49m\u001b[43mn_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrank\u001b[49m\u001b[43m,\u001b[49m\u001b[43mbackend\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mn4sid\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 41\u001b[0m A_dmds \u001b[38;5;241m=\u001b[39m get_dmds(Ys,n_delays\u001b[38;5;241m=\u001b[39mn_delays,rank\u001b[38;5;241m=\u001b[39mrank)\n\u001b[1;32m 43\u001b[0m _, _, _, sims_control_separate_dmdc, sims_state_separate_dmdc \u001b[38;5;241m=\u001b[39m compare_systems_full(A_cs,B_cs)\n", - "Cell \u001b[0;32mIn[186], line 36\u001b[0m, in \u001b[0;36mget_subspace_dmdcs\u001b[0;34m(Ys, Us, p, f, n_id, backend)\u001b[0m\n\u001b[1;32m 28\u001b[0m \u001b[38;5;66;03m# N4SID identification\u001b[39;00m\n\u001b[1;32m 29\u001b[0m nfoursid \u001b[38;5;241m=\u001b[39m NFourSID(\n\u001b[1;32m 30\u001b[0m df,\n\u001b[1;32m 31\u001b[0m output_columns\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124my\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mi\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(Y\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m])],\n\u001b[1;32m 32\u001b[0m input_columns\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mu\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mi\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(U\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m])],\n\u001b[1;32m 33\u001b[0m num_block_rows\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mmin\u001b[39m(p, f)\n\u001b[1;32m 34\u001b[0m )\n\u001b[0;32m---> 36\u001b[0m \u001b[43mnfoursid\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msubspace_identification\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 38\u001b[0m \u001b[38;5;66;03m# Determine rank - use n_id if provided, otherwise auto-determine\u001b[39;00m\n\u001b[1;32m 39\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m n_id \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", - "File \u001b[0;32m~/Desktop/Projects/AgentDSA/AgentDSA/n4sid/nfoursid/src/nfoursid/nfoursid.py:90\u001b[0m, in \u001b[0;36mNFourSID.subspace_identification\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 87\u001b[0m y_past, y_future \u001b[38;5;241m=\u001b[39m y_hankel[:, :\u001b[38;5;241m-\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnum_block_rows], y_hankel[:, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnum_block_rows:]\n\u001b[1;32m 88\u001b[0m u_instrumental_y \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mconcatenate([u_future, u_past, y_past, y_future])\n\u001b[0;32m---> 90\u001b[0m q, r \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mmap\u001b[39m(\u001b[38;5;28;01mlambda\u001b[39;00m matrix: matrix\u001b[38;5;241m.\u001b[39mT, \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlinalg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mqr\u001b[49m\u001b[43m(\u001b[49m\u001b[43mu_instrumental_y\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mT\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mreduced\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 92\u001b[0m y_rows, u_rows \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39my_dim \u001b[38;5;241m*\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnum_block_rows, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mu_dim \u001b[38;5;241m*\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnum_block_rows\n\u001b[1;32m 93\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mR32 \u001b[38;5;241m=\u001b[39m r[\u001b[38;5;241m-\u001b[39my_rows:, u_rows:\u001b[38;5;241m-\u001b[39my_rows]\n", - "File \u001b[0;32m~/opt/anaconda3/envs/iblenv/lib/python3.10/site-packages/numpy/linalg/linalg.py:952\u001b[0m, in \u001b[0;36mqr\u001b[0;34m(a, mode)\u001b[0m\n\u001b[1;32m 950\u001b[0m signature \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mD->D\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m isComplexType(t) \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124md->d\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 951\u001b[0m extobj \u001b[38;5;241m=\u001b[39m get_linalg_error_extobj(_raise_linalgerror_qr)\n\u001b[0;32m--> 952\u001b[0m tau \u001b[38;5;241m=\u001b[39m \u001b[43mgufunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msignature\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msignature\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mextobj\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mextobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 954\u001b[0m \u001b[38;5;66;03m# handle modes that don't return q\u001b[39;00m\n\u001b[1;32m 955\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mode \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m'\u001b[39m:\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> \u001b[0;32m/Users/mitchellostrow/opt/anaconda3/envs/iblenv/lib/python3.10/site-packages/numpy/linalg/linalg.py\u001b[0m(952)\u001b[0;36mqr\u001b[0;34m()\u001b[0m\n", - "\u001b[0;32m 950 \u001b[0;31m \u001b[0msignature\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'D->D'\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misComplexType\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m'd->d'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\u001b[0;32m 951 \u001b[0;31m \u001b[0mextobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_linalg_error_extobj\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_raise_linalgerror_qr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\u001b[0;32m--> 952 \u001b[0;31m \u001b[0mtau\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgufunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msignature\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msignature\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mextobj\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mextobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\u001b[0;32m 953 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\u001b[0;32m 954 \u001b[0;31m \u001b[0;31m# handle modes that don't return q\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\n" - ] - } - ], - "source": [ - "#varying eps and observing changes\n", - "\n", - "nonlinear_eps_range = np.arange(0.1,1.1,0.1)\n", - "n_iters_local = 2 # override for this analysis\n", - "\n", - "Y_feature_map = lambda x: x #np.concatenate([x,x**3,x**5],axis=0)\n", - "U_feature_map = lambda x: x\n", - "\n", - "silh_state_dmdcs = []\n", - "silh_ctrl_dmdcs = []\n", - "silh_state_subdmdcs = []\n", - "silh_ctrl_subsdmdcs = []\n", - "silh_state_dsas = []\n", - "silh_ctrl_dsas = []\n", - "\n", - "for i, nonlinear_eps in enumerate(nonlinear_eps_range):\n", - " print(nonlinear_eps)\n", - " ss_dmdc, sc_dmdc, ss_subdmdc, sc_subdmdc, ss_dsa, sc_dsa = get_silhouette_scores(n,m,p_out,\n", - " N,n_iters_local,input_alpha=input_alpha,g1=g1,\n", - " g2=g2,same_inp=False,n_Us=n_Us,n_delays=n_delays,rank=20,pf=200,\n", - " nonlinear_eps=nonlinear_eps,nonlinear_func= lambda x: np.tanh(x),\n", - " y_feature_map = Y_feature_map, u_feature_map = U_feature_map)\n", - " silh_state_dmdcs.append(ss_dmdc)\n", - " silh_ctrl_dmdcs.append(sc_dmdc)\n", - " silh_state_subdmdcs.append(ss_subdmdc)\n", - " silh_ctrl_subsdmdcs.append(sc_subdmdc)\n", - " silh_state_dsas.append(ss_dsa)\n", - " silh_ctrl_dsas.append(sc_dsa)\n", - "\n", - " print(np.mean(silh_state_dmdcs),np.mean(silh_state_subdmdcs),np.mean(silh_state_dsas))\n", - " print()\n", - "\n", - "\n", - "silh_state_dmdcs = np.array(silh_state_dmdcs)\n", - "silh_state_subdmdcs = np.array(silh_state_subdmdcs)\n", - "silh_state_dsas = np.array(silh_state_dsas)\n", - "silh_ctrl_dmdcs = np.array(silh_ctrl_dmdcs)\n", - "silh_ctrl_subdmdcs = np.array(silh_ctrl_subsdmdcs)\n", - "silh_ctrl_dsas = np.array(silh_ctrl_dsas)\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a7b0352b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mitchellostrow/opt/anaconda3/envs/iblenv/lib/python3.10/site-packages/IPython/core/pylabtools.py:170: UserWarning: Tight layout not applied. The bottom and top margins cannot be made large enough to accommodate all axes decorations.\n", - " fig.canvas.print_figure(bytes_io, **kw)\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#on two plots, plot the mean and std of the silhouette scores for each method across p_out / n\n", - "methods = [ 'DMD','DMDC', 'Subspace DMDC']\n", - "\n", - "non_eps = nonlinear_eps_range[:len(silh_state_dmdcs)]\n", - "\n", - "fig, ax = plt.subplots(1, 1, figsize=(5, 3))\n", - "\n", - "# Plot state silhouette scores\n", - "\n", - "for i, state in enumerate([silh_state_dsas,silh_state_dmdcs,silh_state_subdmdcs]):\n", - " ax.plot(non_eps, np.mean(state, axis=1), label=methods[i] + ' (State)',color=plt.cm.Set2(i))\n", - " ax.fill_between(non_eps, np.mean(state, axis=1) - np.std(state, axis=1) / np.sqrt(n_iters),\n", - " np.mean(state, axis=1) + np.std(state, axis=1) / np.sqrt(n_iters), alpha=0.2,\n", - " color=plt.cm.Set2(i))\n", - "\n", - "for i, state in enumerate([silh_ctrl_dsas,silh_ctrl_dmdcs,silh_ctrl_subsdmdcs]):\n", - " ax.plot(non_eps, np.mean(state, axis=1), label=methods[i] + ' (Control)',color=plt.cm.Set2(i),linestyle='--')\n", - " ax.fill_between(non_eps, np.mean(state, axis=1) - np.std(state, axis=1) / np.sqrt(n_iters),\n", - " np.mean(state, axis=1) + np.std(state, axis=1) / np.sqrt(n_iters), alpha=0.2,\n", - " color=plt.cm.Set2(i))\n", - "\n", - "\n", - "ax.legend(loc='lower right',bbox_to_anchor=(1.5, 1))\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "jaxenv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.18" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/how_to_use_dsa_tutorial.ipynb b/examples/how_to_use_dsa_tutorial.ipynb new file mode 100644 index 0000000..10733a8 --- /dev/null +++ b/examples/how_to_use_dsa_tutorial.ipynb @@ -0,0 +1,1144 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Comprehensive Tutorial: How to Use DSA Classes\n", + "\n", + "This notebook provides a comprehensive guide to using the different DSA classes:\n", + "1. **GeneralizedDSA**: The most flexible class supporting different DMD types and similarity metrics\n", + "2. **InputDSA**: Specialized for systems with control inputs (subclass of GeneralizedDSA)\n", + "3. **DSA**: The standard DSA algorithm from Ostrow et al. (2023)\n", + "\n", + "We'll cover:\n", + "- How to pass in different data structures\n", + "- How to configure DMD and similarity metrics (using dataclasses or dictionaries)\n", + "- How to use prediction and stats error to run hyperparameter sweeps\n", + "\n", + "## Table of Contents\n", + "1. [Setup and Data Generation](#setup)\n", + "2. [Data Structure Options](#data-structures)\n", + "3. [GeneralizedDSA Class](#generalized-dsa)\n", + "4. [InputDSA Class](#input-dsa)\n", + "5. [DSA Class (Standard)](#standard-dsa)\n", + "6. [Hyperparameter Sweeps](#hyperparameter-sweeps)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Setup and Data Generation \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import torch\n", + "\n", + "from DSA import DSA, GeneralizedDSA, InputDSA\n", + "\n", + "from DSA import DMD, DMDc, SubspaceDMDc\n", + "\n", + "from DSA import SimilarityTransformDist, ControllabilitySimilarityTransformDist\n", + "\n", + "from DSA import (\n", + " DMDConfig, \n", + " DMDcConfig, \n", + " SubspaceDMDcConfig,\n", + " SimilarityTransformDistConfig,\n", + " ControllabilitySimilarityTransformDistConfig\n", + ")\n", + "\n", + "import DSA.pykoopman as pk\n", + "from pydmd import DMD as pDMD, SubspaceDMD\n", + "\n", + "from DSA.sweeps import sweep_ranks_delays\n", + "from DSA.stats import compute_all_stats\n", + "\n", + "np.random.seed(22)\n", + "torch.manual_seed(22)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Generate synthetic data for demonstrations\n", + "\n", + "We'll create simple dynamical systems for testing\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def generate_linear_system(n_time=100, n_features=5, n_trials=10, eigenvalue_range=(0.8, 0.95)):\n", + " \"\"\"\n", + " Generate data from a stable linear dynamical system.\n", + " \"\"\"\n", + " # Create a random stable system matrix\n", + " A_true = np.random.randn(n_features, n_features)\n", + " # Make it stable by scaling eigenvalues\n", + " eigvals, eigvecs = np.linalg.eig(A_true)\n", + " eigvals = eigvals / np.abs(eigvals) * np.random.uniform(*eigenvalue_range, size=n_features)\n", + " A_true = eigvecs @ np.diag(eigvals) @ np.linalg.inv(eigvecs)\n", + " A_true = np.real(A_true)\n", + " \n", + " # Generate trajectories\n", + " data = np.zeros((n_trials, n_time, n_features))\n", + " for trial in range(n_trials):\n", + " x = np.random.randn(n_features) * 0.1\n", + " for t in range(n_time):\n", + " data[trial, t] = x\n", + " x = A_true @ x + np.random.randn(n_features) * 0.01 # Add small noise\n", + " \n", + " return data, A_true\n", + "\n", + "def generate_controlled_system(n_time=100, n_features=5, n_control=2, n_trials=10):\n", + " \"\"\"\n", + " Generate data from a controlled linear dynamical system.\n", + " \"\"\"\n", + " # Create system matrices\n", + " A_true = np.random.randn(n_features, n_features) * 0.5\n", + " A_true = A_true / np.max(np.abs(np.linalg.eigvals(A_true))) * 0.9 # Make stable\n", + " B_true = np.random.randn(n_features, n_control) * 0.3\n", + " \n", + " # Generate trajectories with control\n", + " data = np.zeros((n_trials, n_time, n_features))\n", + " control = np.zeros((n_trials, n_time, n_control))\n", + " \n", + " for trial in range(n_trials):\n", + " x = np.random.randn(n_features) * 0.1\n", + " for t in range(n_time):\n", + " u = np.random.randn(n_control) * 0.5 # Random control input\n", + " control[trial, t] = u\n", + " data[trial, t] = x\n", + " x = A_true @ x + B_true @ u + np.random.randn(n_features) * 0.01\n", + " \n", + " return data, control, A_true, B_true" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Data Structure Options \n", + "\n", + "DSA classes accept multiple data structure formats:\n", + "\n", + "### Single System Comparison\n", + "- **2D array**: `(time, features)` - Single trajectory\n", + "- **3D array**: `(trials, time, features)` - Multiple trials from same system\n", + "\n", + "### Multiple System Comparisons\n", + "- **Pairwise**: Pass a list of data matrices `[X1, X2, X3, ...]` for all-to-all comparison\n", + "- **Disjoint Pairwise**: Pass two lists `X=[X1,X2,...]` and `Y=[Y1,Y2,...]` for bipartite comparison\n", + "- **One-to-All**: Pass a list `X=[X1,X2,...]` and single matrix `Y` to compare all X to Y\n", + "\n", + "### Lists of Variable-Length Trajectories\n", + "- **List of 2D arrays**: `[array(t1,f), array(t2,f), ...]` - Different length trajectories\n", + "- **List of 3D arrays**: `[array(n1,t,f), array(n2,t,f), ...]` - Different numbers of trials\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2D data shape: (100, 5)\n", + "3D data shape: (10, 100, 5)\n", + "List of 2D arrays: 5 arrays with shapes [(50, 5), (60, 5), (70, 5), (80, 5), (90, 5)]\n", + "List of 3D arrays: 3 arrays with shapes [(5, 100, 5), (6, 100, 5), (7, 100, 5)]\n", + "Mixed list: 8 arrays\n" + ] + } + ], + "source": [ + "# Example 1: Single 2D trajectory\n", + "data_2d = np.random.randn(100, 5) # (time, features)\n", + "print(f\"2D data shape: {data_2d.shape}\")\n", + "\n", + "# Example 2: Multiple trials (3D array)\n", + "data_3d = np.random.randn(10, 100, 5) # (trials, time, features)\n", + "print(f\"3D data shape: {data_3d.shape}\")\n", + "\n", + "# Example 3: List of 2D arrays with variable lengths\n", + "data_list_2d = [np.random.randn(50 + i*10, 5) for i in range(5)] # Variable time lengths\n", + "print(f\"List of 2D arrays: {len(data_list_2d)} arrays with shapes {[d.shape for d in data_list_2d]}\")\n", + "\n", + "# Example 4: List of 3D arrays with variable trial counts\n", + "data_list_3d = [np.random.randn(i+5, 100, 5) for i in range(3)] # Variable trial counts\n", + "print(f\"List of 3D arrays: {len(data_list_3d)} arrays with shapes {[d.shape for d in data_list_3d]}\")\n", + "\n", + "# Example 5: Mixed list (2D and 3D arrays)\n", + "data_mixed = data_list_2d + data_list_3d\n", + "print(f\"Mixed list: {len(data_mixed)} arrays\")\n", + "\n", + "#All data structures are valid inputs for DSA classes!\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. GeneralizedDSA Class \n", + "\n", + "The `GeneralizedDSA` class is the most flexible, allowing you to:\n", + "- Use different DMD algorithms (standard DMD, Kernel DMD, Neural DMD, etc.)\n", + "- Use different similarity metrics (angular, euclidean, Wasserstein)\n", + "- Configure via dataclasses or dictionaries\n", + "\n", + "### 3.1 Basic Usage with Default DMD\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Fitting DMDs: 100%|██████████| 2/2 [00:00<00:00, 218.44it/s]\n", + "Computing DMD similarities: 100%|██████████| 1/1 [00:02<00:00, 2.96s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Similarity matrix shape: (2, 2)\n", + "Similarity between systems: 0.1501\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Generate two similar systems\n", + "data1, A1 = generate_linear_system(n_time=100, n_features=5, n_trials=8)\n", + "data2, A2 = generate_linear_system(n_time=100, n_features=5, n_trials=8)\n", + "\n", + "A1eigs = np.linalg.eigvals(A1)\n", + "A2eigs = np.linalg.eigvals(A2)\n", + "\n", + "plt.scatter(A1eigs.real,A1eigs.imag)\n", + "plt.scatter(A2eigs.real,A2eigs.imag)\n", + "plt.xlabel(\"Real\")\n", + "plt.ylabel(\"Imag\")\n", + "\n", + "# Compare using GeneralizedDSA with default settings\n", + "gdsa = GeneralizedDSA(\n", + " [data1, data2],\n", + " dmd_class=DMD,\n", + " similarity_class=SimilarityTransformDist,\n", + " verbose=True\n", + ")\n", + "\n", + "# Fit and score\n", + "similarity_matrix = gdsa.fit_score()\n", + "print(f\"\\nSimilarity matrix shape: {similarity_matrix.shape}\")\n", + "print(f\"Similarity between systems: {similarity_matrix[0, 1]:.4f}\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.2 Using Configuration Dataclasses\n", + "\n", + "Dataclasses provide type safety and clear documentation of parameters.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Fitting DMDs: 100%|██████████| 2/2 [00:00<00:00, 239.52it/s]\n", + "Computing DMD similarities: 100%|██████████| 1/1 [00:02<00:00, 2.07s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Similarity with custom config: 0.3253\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "# Define configurations using dataclasses\n", + "dmd_config = DMDConfig(\n", + " n_delays=3, # Use 3 time delays\n", + " delay_interval=1, # Consecutive time steps\n", + " rank=10, # Truncate to rank 10\n", + " lamb=0.01, # Small regularization\n", + " send_to_cpu=False # Use GPU if available\n", + ")\n", + "\n", + "simdist_config = SimilarityTransformDistConfig(\n", + " iters=1000, # Optimization iterations\n", + " score_method='angular', # Use angular distance\n", + " lr=5e-3, # Learning rate\n", + ")\n", + "\n", + "# Use configurations in GeneralizedDSA\n", + "gdsa = GeneralizedDSA(\n", + " [data1, data2],\n", + " dmd_class=DMD,\n", + " similarity_class=SimilarityTransformDist,\n", + " dmd_config=dmd_config,\n", + " simdist_config=simdist_config,\n", + " verbose=True,\n", + " device='cpu' # or 'cuda' for GPU\n", + ")\n", + "\n", + "similarity_matrix = gdsa.fit_score()\n", + "print(f\"Similarity with custom config: {similarity_matrix[0, 1]:.4f}\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.3 Using Dictionary Configurations\n", + "\n", + "Dictionaries offer more flexibility and are easier for parameter sweeps.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Similarity with dict config: 0.3126\n" + ] + }, + { + "data": { + "text/plain": [ + "((8, 100, 5), torch.Size([15, 15]))" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Define configurations using dictionaries\n", + "dmd_config_dict = {\n", + " 'n_delays': 5,\n", + " 'delay_interval': 1,\n", + " 'rank': 15,\n", + " 'lamb': 0.001\n", + "}\n", + "\n", + "simdist_config_dict = {\n", + " 'iters': 1500,\n", + " 'score_method': 'euclidean',\n", + " 'lr': 1e-3\n", + "}\n", + "\n", + "gdsa = GeneralizedDSA(\n", + " [data1, data2],\n", + " dmd_class=DMD,\n", + " similarity_class=SimilarityTransformDist,\n", + " dmd_config=dmd_config_dict,\n", + " simdist_config=simdist_config_dict,\n", + " verbose=False\n", + ")\n", + "\n", + "similarity_matrix = gdsa.fit_score()\n", + "print(f\"Similarity with dict config: {similarity_matrix[0, 1]:.4f}\")\n", + "\n", + "data1.shape, gdsa.dmds[0][0].A_v.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.4 Using PyKoopman DMD Models\n", + "\n", + "GeneralizedDSA integrates with PyKoopman for advanced observables and regressors.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Fitting DMDs: 1it [00:00, 321.13it/s]\n", + "Fitting DMDs: 1it [00:00, 418.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pre-computing eigenvalues for Wasserstein distance...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing DMD similarities: 100%|██████████| 1/1 [00:00<00:00, 29.62it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Similarity with PyKoopman: 0.9291\n", + "(8, 100, 5)\n", + "(2, 2)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "# Use TimeDelay observables with SubspaceDMD regressor from pydmd\n", + "observables = pk.observables.TimeDelay(n_delays=10)\n", + "regressor = SubspaceDMD(svd_rank=-1)\n", + "\n", + "# Create configuration\n", + "from dataclasses import dataclass\n", + "\n", + "@dataclass\n", + "class CustomPyKoopmanConfig:\n", + " observables = observables\n", + " regressor = regressor\n", + "\n", + "gdsa = GeneralizedDSA(\n", + " data1, data2,\n", + " dmd_class=pk.Koopman,\n", + " similarity_class=SimilarityTransformDist,\n", + " dmd_config=CustomPyKoopmanConfig(),\n", + " simdist_config={'score_method': 'wasserstein'},\n", + " verbose=True\n", + ")\n", + "\n", + "similarity_matrix = gdsa.fit_score()\n", + "print(f\"Similarity with PyKoopman: {similarity_matrix:.4f}\")\n", + "print(data1.shape)\n", + "print(gdsa.dmds[0][0].A.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. InputDSA Class \n", + "\n", + "The `InputDSA` class extends GeneralizedDSA for controlled systems, comparing both:\n", + "- **Intrinsic (recurrent) dynamics** (A matrix)\n", + "- **Input-driven dynamics** (B matrix)\n", + "\n", + "Two DMD variants are available:\n", + "- **DMDc**: Standard DMD with control (Proctor et al., 2016)\n", + "- **SubspaceDMDc**: Subspace identification approach (Huang & Ostrow et al., 2025) - recommended for partially observed systems\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.1 Basic InputDSA with DMDc\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data shape: (8, 100, 5)\n", + "Control shape: (8, 100, 2)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Fitting DMDs: 100%|██████████| 2/2 [00:00<00:00, 411.19it/s]\n", + "Computing DMD similarities: 100%|██████████| 1/1 [00:00<00:00, 326.15it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "InputDSA similarity: 3.3637\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "# Generate two controlled systems\n", + "data1, control1, A1, B1 = generate_controlled_system(n_time=100, n_features=5, n_control=2, n_trials=8)\n", + "data2, control2, A2, B2 = generate_controlled_system(n_time=100, n_features=5, n_control=2, n_trials=8)\n", + "\n", + "print(f\"Data shape: {data1.shape}\")\n", + "print(f\"Control shape: {control1.shape}\")\n", + "\n", + "idsa = InputDSA(\n", + " [data1, data2],\n", + " [control1, control2],\n", + " dmd_class=DMDc,\n", + " dmd_config=DMDcConfig(\n", + " n_delays=2,\n", + " rank_input=None,\n", + " rank_output=10,\n", + " lamb=0.01\n", + " ),\n", + " simdist_config=ControllabilitySimilarityTransformDistConfig(\n", + " score_method='euclidean',\n", + " compare='joint' # Compare both A and B via controllability\n", + " ),\n", + " verbose=True\n", + ")\n", + "\n", + "similarity = idsa.fit_score()\n", + "print(f\"\\nInputDSA similarity: {similarity[0, 1]:.4f}\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.2 InputDSA with SubspaceDMDc\n", + "\n", + "SubspaceDMDc is better for partially observed systems and handles rank selection more robustly.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Fitting DMDs: 100%|██████████| 2/2 [00:00<00:00, 52.81it/s]\n", + "Computing DMD similarities: 100%|██████████| 1/1 [00:00<00:00, 2021.35it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "SubspaceDMDc similarity: 0.9359\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "# Use SubspaceDMDc backend\n", + "idsa = InputDSA(\n", + " [data1, data2],\n", + " [control1, control2],\n", + " dmd_class=SubspaceDMDc,\n", + " dmd_config=SubspaceDMDcConfig(\n", + " n_delays=3,\n", + " rank=8,\n", + " backend='n4sid' # or 'custom'\n", + " ),\n", + " simdist_config=ControllabilitySimilarityTransformDistConfig(\n", + " score_method='euclidean',\n", + " compare='joint'\n", + " ),\n", + " verbose=True\n", + ")\n", + "\n", + "similarity = idsa.fit_score()\n", + "print(f\"\\nSubspaceDMDc similarity: {similarity[0, 1]:.4f}\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.3 InputDSA Comparison Modes\n", + "\n", + "InputDSA offers three comparison modes:\n", + "1. **'state'**: Compare only A matrices (intrinsic dynamics)\n", + "2. **'control'**: Compare only B matrices (input effects)\n", + "3. **'joint'**: Compare both A and B via controllability Gramian\n", + "\n", + "\n", + "For computational efficiency, you don't have to recompute the dmds each time. Simply run the following:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mitchellostrow/Desktop/Projects/DSAv2/DSAPublic2/DSA/DSA/dsa.py:408: UserWarning: Warning: You are using a DMD model that fits a control operator but comparing with a DSA metric that does not compare control operators\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "control similarity: 0.0043\n", + "state similarity: 0.5079\n", + "joint similarity: 2.2604\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "comparison_modes = ['state', 'control', 'joint']\n", + "results = {}\n", + "\n", + "idsa = InputDSA(\n", + " [data1, data2],\n", + " [control1, control2],\n", + " dmd_class=DMDc,\n", + " dmd_config={'n_delays': 2, 'rank_output': 10},\n", + " simdist_config={\n", + " 'score_method': 'euclidean',\n", + " 'compare': 'control'\n", + " },\n", + " verbose=False\n", + ")\n", + "similarity = idsa.fit_score()\n", + "results['control'] = similarity[0, 1]\n", + "print(f\"{'control':10s} similarity: {similarity[0, 1]:.4f}\")\n", + "\n", + "state_config = SimilarityTransformDistConfig\n", + "joint_config = ControllabilitySimilarityTransformDistConfig\n", + "\n", + "for mode,cfg in zip(['state','joint'],[state_config,joint_config]):\n", + " idsa.update_compare_method(compare=mode,simdist_config=cfg)\n", + " similarity = idsa.score()\n", + " results[mode] = similarity[0, 1]\n", + " print(f\"{mode:10s} similarity: {similarity[0, 1]:.4f}\")\n", + "\n", + "\n", + "# Visualize\n", + "plt.figure(figsize=(8, 4))\n", + "plt.bar(results.keys(), results.values())\n", + "plt.ylabel('Similarity Score')\n", + "plt.title('InputDSA: Comparison of Different Modes')\n", + "plt.ylim([0, 1])\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.4 InputDSA with Distance Components\n", + "\n", + "Set `return_distance_components=True` to get three metrics:\n", + "1. Full controllability distance\n", + "2. Jointly optimized state similarity\n", + "3. Jointly optimized control similarity\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Components shape: (2, 2, 3)\n", + "\n", + "Distance components between systems 0 and 1:\n", + " Controllability distance: 3.4692\n", + " State similarity: 1.5174\n", + " Control similarity: 0.0637\n" + ] + } + ], + "source": [ + "idsa = InputDSA(\n", + " [data1, data2],\n", + " [control1, control2],\n", + " dmd_class=DMDc,\n", + " dmd_config={'n_delays': 2, 'rank_output': 10},\n", + " simdist_config={\n", + " 'score_method': 'euclidean',\n", + " 'compare': 'joint',\n", + " 'return_distance_components': True\n", + " },\n", + " verbose=False\n", + ")\n", + "\n", + "components = idsa.fit_score()\n", + "print(f\"Components shape: {components.shape}\") # (n_systems, n_systems, 3)\n", + "print(f\"\\nDistance components between systems 0 and 1:\")\n", + "print(f\" Controllability distance: {components[0, 1, 0]:.4f}\")\n", + "print(f\" State similarity: {components[0, 1, 1]:.4f}\")\n", + "print(f\" Control similarity: {components[0, 1, 2]:.4f}\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. DSA Class (Standard) \n", + "\n", + "The `DSA` class implements the original algorithm from Ostrow et al. (2023). This was written so that if you have been using DSA, you don't ahve to change your code (backwards compatibility)!\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5.1 Basic DSA Usage\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Fitting DMDs: 100%|██████████| 2/2 [00:00<00:00, 342.95it/s]\n", + "Computing DMD similarities: 100%|██████████| 1/1 [00:02<00:00, 3.00s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "DSA similarity: 0.3565\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "# Generate data\n", + "data1, _ = generate_linear_system(n_time=100, n_features=5, n_trials=10)\n", + "data2, _ = generate_linear_system(n_time=100, n_features=5, n_trials=10)\n", + "\n", + "# Standard DSA with simple interface\n", + "dsa = DSA(\n", + " [data1, data2],\n", + " n_delays=3,\n", + " rank=10,\n", + " delay_interval=1,\n", + " score_method='angular',\n", + " verbose=True,\n", + " device='cpu'\n", + ")\n", + "\n", + "similarity = dsa.fit_score()\n", + "print(f\"\\nDSA similarity: {similarity[0, 1]:.4f}\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. Hyperparameter Sweeps \n", + "\n", + "Finding optimal hyperparameters (n_delays, rank) is crucial for DMD performance.\n", + "We can use prediction error and statistical measures to guide selection.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 6.1 Basic Hyperparameter Sweep\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test data shape: (12, 150, 5)\n", + "\n", + "Running hyperparameter sweep...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:00<00:00, 18.58it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Sweep completed!\n", + "AIC shape: (5, 6)\n", + "MASE shape: (5, 6)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "# Generate a test system\n", + "test_data, _ = generate_linear_system(n_time=150, n_features=5, n_trials=12)\n", + "print(f\"Test data shape: {test_data.shape}\")\n", + "\n", + "# Define parameter ranges\n", + "n_delays_range = [1, 2, 3, 5, 7]\n", + "ranks_range = [3, 5, 8, 10, 12, 15]\n", + "\n", + "# Run sweep\n", + "print(\"\\nRunning hyperparameter sweep...\")\n", + "all_aics, all_mases, all_nnormals, all_residuals, all_l2norms = sweep_ranks_delays(\n", + " test_data,\n", + " n_delays=n_delays_range,\n", + " ranks=ranks_range,\n", + " train_frac=0.7, # Use 70% for training, 30% for testing\n", + " reseed=5,\n", + " return_residuals=True,\n", + " return_transient_growth=True,\n", + " device='cpu'\n", + ")\n", + "\n", + "print(f\"\\nSweep completed!\")\n", + "print(f\"AIC shape: {all_aics.shape}\")\n", + "print(f\"MASE shape: {all_mases.shape}\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 6.2 Visualizing Sweep Results\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Optimal parameters based on MASE:\n", + " n_delays: 5\n", + " rank: 15\n", + " MASE: 0.4495\n" + ] + } + ], + "source": [ + "# Create heatmaps for different metrics\n", + "fig, axes = plt.subplots(2, 2, figsize=(14, 12))\n", + "\n", + "# AIC (Akaike Information Criterion - lower is better)\n", + "im1 = axes[0, 0].imshow(all_aics, cmap='viridis', aspect='auto')\n", + "axes[0, 0].set_title('AIC (lower = better)')\n", + "axes[0, 0].set_xlabel('Rank')\n", + "axes[0, 0].set_ylabel('n_delays')\n", + "axes[0, 0].set_xticks(range(len(ranks_range)))\n", + "axes[0, 0].set_xticklabels(ranks_range)\n", + "axes[0, 0].set_yticks(range(len(n_delays_range)))\n", + "axes[0, 0].set_yticklabels(n_delays_range)\n", + "plt.colorbar(im1, ax=axes[0, 0])\n", + "\n", + "# MASE (Mean Absolute Scaled Error - lower is better)\n", + "im2 = axes[0, 1].imshow(all_mases, cmap='viridis', aspect='auto')\n", + "axes[0, 1].set_title('MASE (lower = better)')\n", + "axes[0, 1].set_xlabel('Rank')\n", + "axes[0, 1].set_ylabel('n_delays')\n", + "axes[0, 1].set_xticks(range(len(ranks_range)))\n", + "axes[0, 1].set_xticklabels(ranks_range)\n", + "axes[0, 1].set_yticks(range(len(n_delays_range)))\n", + "axes[0, 1].set_yticklabels(n_delays_range)\n", + "plt.colorbar(im2, ax=axes[0, 1])\n", + "\n", + "# Non-normality (lower = more normal, better behaved)\n", + "im3 = axes[1, 0].imshow(all_nnormals, cmap='viridis', aspect='auto')\n", + "axes[1, 0].set_title('Non-normality')\n", + "axes[1, 0].set_xlabel('Rank')\n", + "axes[1, 0].set_ylabel('n_delays')\n", + "axes[1, 0].set_xticks(range(len(ranks_range)))\n", + "axes[1, 0].set_xticklabels(ranks_range)\n", + "axes[1, 0].set_yticks(range(len(n_delays_range)))\n", + "axes[1, 0].set_yticklabels(n_delays_range)\n", + "plt.colorbar(im3, ax=axes[1, 0])\n", + "\n", + "# L2 Norm (transient growth measure)\n", + "im4 = axes[1, 1].imshow(all_l2norms, cmap='viridis', aspect='auto')\n", + "axes[1, 1].set_title('L2 Norm of DMD matrix')\n", + "axes[1, 1].set_xlabel('Rank')\n", + "axes[1, 1].set_ylabel('n_delays')\n", + "axes[1, 1].set_xticks(range(len(ranks_range)))\n", + "axes[1, 1].set_xticklabels(ranks_range)\n", + "axes[1, 1].set_yticks(range(len(n_delays_range)))\n", + "axes[1, 1].set_yticklabels(n_delays_range)\n", + "plt.colorbar(im4, ax=axes[1, 1])\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# Find optimal parameters\n", + "best_idx = np.unravel_index(np.argmin(all_mases), all_mases.shape)\n", + "best_n_delays = n_delays_range[best_idx[0]]\n", + "best_rank = ranks_range[best_idx[1]]\n", + "print(f\"\\nOptimal parameters based on MASE:\")\n", + "print(f\" n_delays: {best_n_delays}\")\n", + "print(f\" rank: {best_rank}\")\n", + "print(f\" MASE: {all_mases[best_idx]:.4f}\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 6.4 Using Statistics for DMD Quality Assessment\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DMD Matrix Statistics:\n", + "==================================================\n", + "MAE : 0.0077\n", + "MASE : 0.2627\n", + "NMSE : 0.1039\n", + "MSE : 0.0001\n", + "R2 : 0.8832\n", + "Correl : 0.9397\n", + "AIC : -9.2117\n", + "logMSE : -9.2521\n" + ] + } + ], + "source": [ + "data, _ = generate_linear_system(n_time=100, n_features=5, n_trials=10)\n", + "dmd = DMD(data, n_delays=3, rank=10, device='cpu')\n", + "dmd.fit()\n", + "\n", + "\n", + "pred = dmd.predict()\n", + "\n", + "stats = compute_all_stats(data,pred,dmd.rank)\n", + "\n", + "print(\"DMD Matrix Statistics:\")\n", + "print(\"=\" * 50)\n", + "for key, value in stats.items():\n", + " if isinstance(value, (int, float, np.integer, np.floating)):\n", + " print(f\"{key:25s}: {value:10.4f}\")\n", + " else:\n", + " print(f\"{key:25s}: {value}\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "This notebook covered:\n", + "\n", + "### 1. **GeneralizedDSA**\n", + "- Most flexible class for custom DMD models and similarity metrics\n", + "- Supports configuration via dataclasses or dictionaries\n", + "- Integrates with PyKoopman and PyDMD\n", + "- Offers Wasserstein distance for eigenvalue comparison\n", + "\n", + "### 2. **InputDSA**\n", + "- Specialized for controlled systems\n", + "- Uses DMDc or SubspaceDMDc\n", + "- Compares state, control, or joint dynamics\n", + "- Handles surrogate inputs when true inputs are unknown\n", + "\n", + "### 3. **DSA (Standard)**\n", + "- Simplified interface for the original DSA algorithm\n", + "- Supports all data structure formats\n", + "- Multiple comparison modes (pairwise, one-to-all, disjoint)\n", + "\n", + "### 4. **Data Structures**\n", + "- Single trajectories: 2D arrays\n", + "- Multiple trials: 3D arrays\n", + "- Variable lengths: Lists of arrays\n", + "- All classes handle these formats automatically\n", + "\n", + "### 5. **Hyperparameter Sweeps**\n", + "- Use `sweep_ranks_delays()` for comprehensive parameter search\n", + "- Metrics: AIC, MASE, non-normality, transient growth\n", + "- Combine metrics for robust model selection\n", + "- Workflow: Sweep → Select → Compare\n", + "\n", + "### Best Practices:\n", + "1. Start with hyperparameter sweeps on representative data\n", + "2. Use MASE or combined metrics for model selection\n", + "3. For controlled systems, use SubspaceDMDc for partial observations\n", + "4. Use Wasserstein distance for fast, optimization-free comparisons, especially if dmd models are close to normal\n", + "5. Leverage GPU (`device='cuda'`) for large datasets\n", + "\n", + "For more details, see:\n", + "- Ostrow et al. (2023): https://arxiv.org/abs/2306.10168\n", + "- Huang & Ostrow et al. (2025): https://www.arxiv.org/abs/2510.25943\n", + "\n", + "Feel free to reach out to ostrow@mit.edu with questions or further interest!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "dsa_test_env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.18" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 5eff5b8b83b15525dc0ab2eaa472c9aa9fe7e4e8 Mon Sep 17 00:00:00 2001 From: Mitchell Ostrow <35669245+mitchellostrow@users.noreply.github.com> Date: Wed, 5 Nov 2025 17:02:28 -0500 Subject: [PATCH 40/90] add unmentioned detail to tutorial --- examples/how_to_use_dsa_tutorial.ipynb | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/examples/how_to_use_dsa_tutorial.ipynb b/examples/how_to_use_dsa_tutorial.ipynb index 10733a8..f9809d7 100644 --- a/examples/how_to_use_dsa_tutorial.ipynb +++ b/examples/how_to_use_dsa_tutorial.ipynb @@ -1107,6 +1107,11 @@ "4. Use Wasserstein distance for fast, optimization-free comparisons, especially if dmd models are close to normal\n", "5. Leverage GPU (`device='cuda'`) for large datasets\n", "\n", + + "## Unmentioned topics\n", + "1. Unmentioned is parallelization of comparison (change the n_jobs parameter in the DSA class)\n", + "2. Unmentioned is gpu support (device='cuda') in all classes\n", + "For more details, see:\n", "- Ostrow et al. (2023): https://arxiv.org/abs/2306.10168\n", "- Huang & Ostrow et al. (2025): https://www.arxiv.org/abs/2510.25943\n", From 51602144400e5ba043bfee3bd48c70b81198d04c Mon Sep 17 00:00:00 2001 From: Ann Huang Date: Thu, 6 Nov 2025 16:11:13 -0500 Subject: [PATCH 41/90] updated sweep_ranks_delays to work with DMDc and SubspaceDMDc --- DSA/sweeps.py | 60 ++++++++++---- examples/how_to_use_dsa_tutorial.ipynb | 104 ++++++++++++++++++++++--- 2 files changed, 139 insertions(+), 25 deletions(-) diff --git a/DSA/sweeps.py b/DSA/sweeps.py index 510d88d..e435bbc 100644 --- a/DSA/sweeps.py +++ b/DSA/sweeps.py @@ -1,6 +1,8 @@ import numpy as np from tqdm import tqdm from .dmd import DMD +from .dmdc import DMDc +from .subspace_dmdc import SubspaceDMDc from .stats import ( measure_nonnormality_transpose, compute_all_stats, @@ -9,7 +11,7 @@ from .resdmd import compute_residuals import matplotlib.pyplot as plt from typing import Literal - +import warnings def split_train_test(data, train_frac=0.8): if isinstance(data, list): @@ -33,13 +35,15 @@ def sweep_ranks_delays( data, n_delays, ranks, + control_data=None, train_frac=0.8, reseed=5, return_residuals=True, return_transient_growth=False, return_mse=False, error_space="X", - **dmd_kwargs, + model_class=['DMD', 'DMDc', 'SubspaceDMDc'][0], + **model_kwargs, ): """ Sweep over combinations of DMD ranks and delays, returning AIC, MASE, non-normality, and residuals. @@ -70,7 +74,12 @@ def sweep_ranks_delays( all_aics, all_mases, all_nnormals, all_residuals, all_num_abscissa, all_l2norm : np.ndarray Arrays of results for each (delay, rank) pair. """ + if model_class in ['DMDc', 'SubspaceDMDc']: + assert control_data is not None, "Control data is required for DMDc and SubspaceDMDc" + train_data, test_data, dim = split_train_test(data, train_frac) + train_control_data, test_control_data, dim_control = split_train_test(control_data, train_frac) + all_aics, all_mases, all_nnormals, all_residuals, all_l2norm = [], [], [], [], [] for nd in tqdm(n_delays): rresiduals = [] @@ -83,16 +92,34 @@ def sweep_ranks_delays( rresiduals.append(np.inf) l2norms.append(np.inf) continue - dmd = DMD(train_data, n_delays=nd, rank=r, **dmd_kwargs) - dmd.fit() + + if model_class == 'DMD': + model = DMD(train_data, n_delays=nd, rank=r, **model_kwargs) + elif model_class == 'DMDc': + model = DMDc(train_data, train_control_data, n_delays=nd, rank_output=r, **model_kwargs) + elif model_class == 'SubspaceDMDc': + model = SubspaceDMDc(train_data, train_control_data, n_delays=nd, rank=r, **model_kwargs) + else: + raise ValueError(f"Invalid model class: {model_class}. Valid options are 'DMD', 'DMDc', and 'SubspaceDMDc'.") + model.fit() # pred, H_test_pred, H_test_true, V_test_pred, V_test_true = dmd.predict( # test_data, reseed=reseed, full_return=True # ) - pred, H_test_pred, H_test_true= dmd.predict( - test_data, reseed=reseed, full_return=True - ) + if model_class == "DMD": + pred, H_test_pred, H_test_true= model.predict( + test_data, reseed=reseed, full_return=True + ) + elif model_class == "DMDc": + pred, H_test_pred, H_test_true= model.predict( + test_data, test_control_data, reseed=reseed, full_return=True + ) + else: + pred = model.predict(test_data, test_control_data, reseed=reseed) + if error_space == "H": + if model_class == 'SubspaceDMDc': + raise ValueError("H space not implemented for SubspaceDMDc. Use X space instead.") pred = H_test_pred test_data_err = H_test_true elif error_space == "V": @@ -117,27 +144,32 @@ def sweep_ranks_delays( # pred = pred[:, :, -ndim:] # stats = compute_all_stats(pred, test_data_err[:, :, -ndim:], dmd.rank) # else: - stats = compute_all_stats(test_data_err, pred, dmd.rank) + stats = compute_all_stats(test_data_err, pred, model.rank if model_class in ['DMD', 'SubspaceDMDc'] else model.rank_output) aic = stats["AIC"] mase = stats["MASE"] if return_mse: mase = stats["MSE"] nnormal = measure_nonnormality_transpose( - dmd.A_v.cpu().detach().numpy() if hasattr(dmd.A_v, "cpu") else dmd.A_v + model.A_v.cpu().detach().numpy() if hasattr(model.A_v, "cpu") else model.A_v ) if return_transient_growth: l2norm = measure_transient_growth( - dmd.A_v.cpu().detach().numpy() - if hasattr(dmd.A_v, "cpu") - else dmd.A_v + model.A_v.cpu().detach().numpy() + if hasattr(model.A_v, "cpu") + else model.A_v ) else: l2norm = None - L, G, residuals, _ = compute_residuals(dmd) + if return_residuals and model_class == 'DMD': + L, G, residuals, _ = compute_residuals(model) + residuals = np.mean(residuals) + else: + warnings.warn(f"Residuals not implemented for {model_class}") + residuals = None aics.append(aic) mases.append(mase) nnormals.append(nnormal) - rresiduals.append(np.mean(residuals)) + rresiduals.append(residuals) l2norms.append(l2norm) all_aics.append(aics) all_mases.append(mases) diff --git a/examples/how_to_use_dsa_tutorial.ipynb b/examples/how_to_use_dsa_tutorial.ipynb index f9809d7..ac7660f 100644 --- a/examples/how_to_use_dsa_tutorial.ipynb +++ b/examples/how_to_use_dsa_tutorial.ipynb @@ -34,16 +34,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 1, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -70,7 +70,7 @@ "import DSA.pykoopman as pk\n", "from pydmd import DMD as pDMD, SubspaceDMD\n", "\n", - "from DSA.sweeps import sweep_ranks_delays\n", + "from DSA.sweeps import sweep_ranks_delays, plot_sweep_results\n", "from DSA.stats import compute_all_stats\n", "\n", "np.random.seed(22)\n", @@ -88,7 +88,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -114,7 +114,7 @@ " \n", " return data, A_true\n", "\n", - "def generate_controlled_system(n_time=100, n_features=5, n_control=2, n_trials=10):\n", + "def generate_controlled_system(n_time=100, n_features=5, n_control=2, n_trials=10, nonlinearity=False):\n", " \"\"\"\n", " Generate data from a controlled linear dynamical system.\n", " \"\"\"\n", @@ -134,7 +134,8 @@ " control[trial, t] = u\n", " data[trial, t] = x\n", " x = A_true @ x + B_true @ u + np.random.randn(n_features) * 0.01\n", - " \n", + " if nonlinearity:\n", + " x = np.tanh(x) \n", " return data, control, A_true, B_true" ] }, @@ -1015,6 +1016,89 @@ "print(f\" MASE: {all_mases[best_idx]:.4f}\")\n" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 6.3 Basic Hyperparameter Sweep and Visualization for DMDc / Subspace DMDc" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Running hyperparameter sweep...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 0/5 [00:00,\n", + " array([,\n", + " ,\n", + " ], dtype=object))" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Nonlinear control system with 15 features and 2 control inputs\n", + "data1, control1, A1, B1 = generate_controlled_system(n_time=100, n_features=15, n_control=2, n_trials=20, nonlinearity=True)\n", + "\n", + "# Define parameter ranges\n", + "n_delays_range = [1, 2, 3, 5, 7]\n", + "ranks_range = [3, 5, 8, 10, 12, 15, 20, 30, 40, 50]\n", + "\n", + "# Run sweep\n", + "print(\"\\nRunning hyperparameter sweep...\")\n", + "all_aics, all_mases, all_nnormals = sweep_ranks_delays(\n", + " data1,\n", + " control_data=control1,\n", + " n_delays=n_delays_range,\n", + " ranks=ranks_range,\n", + " model_class='SubspaceDMDc', # can be 'DMDc' or 'SubspaceDMDc' for control systems\n", + " train_frac=0.7, # Use 70% for training, 30% for testing\n", + " reseed=5,\n", + " return_residuals=False,\n", + " device='cpu'\n", + ")\n", + "\n", + "plot_sweep_results(all_aics, all_mases, all_nnormals, n_delays=n_delays_range, ranks=ranks_range, cmap='viridis', name='SubspaceDMDc')" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -1107,11 +1191,9 @@ "4. Use Wasserstein distance for fast, optimization-free comparisons, especially if dmd models are close to normal\n", "5. Leverage GPU (`device='cuda'`) for large datasets\n", "\n", - "## Unmentioned topics\n", "1. Unmentioned is parallelization of comparison (change the n_jobs parameter in the DSA class)\n", "2. Unmentioned is gpu support (device='cuda') in all classes\n", - "For more details, see:\n", "- Ostrow et al. (2023): https://arxiv.org/abs/2306.10168\n", "- Huang & Ostrow et al. (2025): https://www.arxiv.org/abs/2510.25943\n", @@ -1127,7 +1209,7 @@ ], "metadata": { "kernelspec": { - "display_name": "dsa_test_env", + "display_name": "py39", "language": "python", "name": "python3" }, @@ -1141,7 +1223,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.18" + "version": "3.9.18" } }, "nbformat": 4, From 9fd4514d11b78193be69d668e85bfd83db91bdf3 Mon Sep 17 00:00:00 2001 From: ostrow Date: Thu, 6 Nov 2025 21:40:09 -0500 Subject: [PATCH 42/90] bug fix --- DSA/sweeps.py | 3 +- examples/how_to_use_dsa_tutorial.ipynb | 220 ++++++++----------------- 2 files changed, 74 insertions(+), 149 deletions(-) diff --git a/DSA/sweeps.py b/DSA/sweeps.py index e435bbc..60ceeed 100644 --- a/DSA/sweeps.py +++ b/DSA/sweeps.py @@ -78,7 +78,8 @@ def sweep_ranks_delays( assert control_data is not None, "Control data is required for DMDc and SubspaceDMDc" train_data, test_data, dim = split_train_test(data, train_frac) - train_control_data, test_control_data, dim_control = split_train_test(control_data, train_frac) + if control_data is not None: + train_control_data, test_control_data, dim_control = split_train_test(control_data, train_frac) all_aics, all_mases, all_nnormals, all_residuals, all_l2norm = [], [], [], [], [] for nd in tqdm(n_delays): diff --git a/examples/how_to_use_dsa_tutorial.ipynb b/examples/how_to_use_dsa_tutorial.ipynb index ac7660f..6a30413 100644 --- a/examples/how_to_use_dsa_tutorial.ipynb +++ b/examples/how_to_use_dsa_tutorial.ipynb @@ -34,16 +34,16 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 2, + "execution_count": 1, "metadata": {}, "output_type": "execute_result" } @@ -88,7 +88,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -225,8 +225,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "Fitting DMDs: 100%|██████████| 2/2 [00:00<00:00, 218.44it/s]\n", - "Computing DMD similarities: 100%|██████████| 1/1 [00:02<00:00, 2.96s/it]\n" + "Fitting DMDs: 100%|██████████| 2/2 [00:00<00:00, 187.58it/s]\n", + "Computing DMD similarities: 100%|██████████| 1/1 [00:03<00:00, 3.10s/it]\n" ] }, { @@ -235,12 +235,12 @@ "text": [ "\n", "Similarity matrix shape: (2, 2)\n", - "Similarity between systems: 0.1501\n" + "Similarity between systems: 0.8746\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -294,15 +294,15 @@ "name": "stderr", "output_type": "stream", "text": [ - "Fitting DMDs: 100%|██████████| 2/2 [00:00<00:00, 239.52it/s]\n", - "Computing DMD similarities: 100%|██████████| 1/1 [00:02<00:00, 2.07s/it]" + "Fitting DMDs: 100%|██████████| 2/2 [00:00<00:00, 225.10it/s]\n", + "Computing DMD similarities: 100%|██████████| 1/1 [00:02<00:00, 2.16s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Similarity with custom config: 0.3253\n" + "Similarity with custom config: 0.6212\n" ] }, { @@ -362,7 +362,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Similarity with dict config: 0.3126\n" + "Similarity with dict config: 0.4891\n" ] }, { @@ -424,8 +424,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "Fitting DMDs: 1it [00:00, 321.13it/s]\n", - "Fitting DMDs: 1it [00:00, 418.93it/s]\n" + "Fitting DMDs: 1it [00:00, 273.82it/s]\n", + "Fitting DMDs: 1it [00:00, 440.95it/s]\n" ] }, { @@ -439,14 +439,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "Computing DMD similarities: 100%|██████████| 1/1 [00:00<00:00, 29.62it/s]" + "Computing DMD similarities: 100%|██████████| 1/1 [00:00<00:00, 26.33it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Similarity with PyKoopman: 0.9291\n", + "Similarity with PyKoopman: 0.2337\n", "(8, 100, 5)\n", "(2, 2)\n" ] @@ -526,8 +526,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "Fitting DMDs: 100%|██████████| 2/2 [00:00<00:00, 411.19it/s]\n", - "Computing DMD similarities: 100%|██████████| 1/1 [00:00<00:00, 326.15it/s]" + "Fitting DMDs: 100%|██████████| 2/2 [00:00<00:00, 328.84it/s]\n", + "Computing DMD similarities: 100%|██████████| 1/1 [00:00<00:00, 451.49it/s]" ] }, { @@ -535,7 +535,7 @@ "output_type": "stream", "text": [ "\n", - "InputDSA similarity: 3.3637\n" + "InputDSA similarity: 2.9736\n" ] }, { @@ -593,8 +593,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "Fitting DMDs: 100%|██████████| 2/2 [00:00<00:00, 52.81it/s]\n", - "Computing DMD similarities: 100%|██████████| 1/1 [00:00<00:00, 2021.35it/s]" + "Fitting DMDs: 100%|██████████| 2/2 [00:00<00:00, 40.68it/s]\n", + "Computing DMD similarities: 100%|██████████| 1/1 [00:00<00:00, 817.76it/s]" ] }, { @@ -602,7 +602,7 @@ "output_type": "stream", "text": [ "\n", - "SubspaceDMDc similarity: 0.9359\n" + "SubspaceDMDc similarity: 0.5177\n" ] }, { @@ -652,9 +652,16 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "control similarity: 0.0131\n" + ] + }, { "name": "stderr", "output_type": "stream", @@ -667,14 +674,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "control similarity: 0.0043\n", - "state similarity: 0.5079\n", - "joint similarity: 2.2604\n" + "state similarity: 0.3775\n", + "joint similarity: 1.8671\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -735,7 +741,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -745,9 +751,9 @@ "Components shape: (2, 2, 3)\n", "\n", "Distance components between systems 0 and 1:\n", - " Controllability distance: 3.4692\n", - " State similarity: 1.5174\n", - " Control similarity: 0.0637\n" + " Controllability distance: 3.0558\n", + " State similarity: 0.9661\n", + " Control similarity: 0.0629\n" ] } ], @@ -791,14 +797,14 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "Fitting DMDs: 100%|██████████| 2/2 [00:00<00:00, 342.95it/s]\n", + "Fitting DMDs: 100%|██████████| 2/2 [00:00<00:00, 367.31it/s]\n", "Computing DMD similarities: 100%|██████████| 1/1 [00:02<00:00, 3.00s/it]" ] }, @@ -807,7 +813,7 @@ "output_type": "stream", "text": [ "\n", - "DSA similarity: 0.3565\n" + "DSA similarity: 0.5150\n" ] }, { @@ -857,7 +863,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -873,7 +879,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 5/5 [00:00<00:00, 18.58it/s]" + "100%|██████████| 5/5 [00:00<00:00, 15.20it/s]" ] }, { @@ -930,90 +936,36 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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" + "(
,\n", + " array([,\n", + " ,\n", + " ], dtype=object))" ] }, + "execution_count": 14, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Optimal parameters based on MASE:\n", - " n_delays: 5\n", - " rank: 15\n", - " MASE: 0.4495\n" - ] + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "# Create heatmaps for different metrics\n", - "fig, axes = plt.subplots(2, 2, figsize=(14, 12))\n", - "\n", - "# AIC (Akaike Information Criterion - lower is better)\n", - "im1 = axes[0, 0].imshow(all_aics, cmap='viridis', aspect='auto')\n", - "axes[0, 0].set_title('AIC (lower = better)')\n", - "axes[0, 0].set_xlabel('Rank')\n", - "axes[0, 0].set_ylabel('n_delays')\n", - "axes[0, 0].set_xticks(range(len(ranks_range)))\n", - "axes[0, 0].set_xticklabels(ranks_range)\n", - "axes[0, 0].set_yticks(range(len(n_delays_range)))\n", - "axes[0, 0].set_yticklabels(n_delays_range)\n", - "plt.colorbar(im1, ax=axes[0, 0])\n", - "\n", - "# MASE (Mean Absolute Scaled Error - lower is better)\n", - "im2 = axes[0, 1].imshow(all_mases, cmap='viridis', aspect='auto')\n", - "axes[0, 1].set_title('MASE (lower = better)')\n", - "axes[0, 1].set_xlabel('Rank')\n", - "axes[0, 1].set_ylabel('n_delays')\n", - "axes[0, 1].set_xticks(range(len(ranks_range)))\n", - "axes[0, 1].set_xticklabels(ranks_range)\n", - "axes[0, 1].set_yticks(range(len(n_delays_range)))\n", - "axes[0, 1].set_yticklabels(n_delays_range)\n", - "plt.colorbar(im2, ax=axes[0, 1])\n", - "\n", - "# Non-normality (lower = more normal, better behaved)\n", - "im3 = axes[1, 0].imshow(all_nnormals, cmap='viridis', aspect='auto')\n", - "axes[1, 0].set_title('Non-normality')\n", - "axes[1, 0].set_xlabel('Rank')\n", - "axes[1, 0].set_ylabel('n_delays')\n", - "axes[1, 0].set_xticks(range(len(ranks_range)))\n", - "axes[1, 0].set_xticklabels(ranks_range)\n", - "axes[1, 0].set_yticks(range(len(n_delays_range)))\n", - "axes[1, 0].set_yticklabels(n_delays_range)\n", - "plt.colorbar(im3, ax=axes[1, 0])\n", - "\n", - "# L2 Norm (transient growth measure)\n", - "im4 = axes[1, 1].imshow(all_l2norms, cmap='viridis', aspect='auto')\n", - "axes[1, 1].set_title('L2 Norm of DMD matrix')\n", - "axes[1, 1].set_xlabel('Rank')\n", - "axes[1, 1].set_ylabel('n_delays')\n", - "axes[1, 1].set_xticks(range(len(ranks_range)))\n", - "axes[1, 1].set_xticklabels(ranks_range)\n", - "axes[1, 1].set_yticks(range(len(n_delays_range)))\n", - "axes[1, 1].set_yticklabels(n_delays_range)\n", - "plt.colorbar(im4, ax=axes[1, 1])\n", - "\n", - "plt.tight_layout()\n", - "plt.show()\n", - "\n", - "# Find optimal parameters\n", - "best_idx = np.unravel_index(np.argmin(all_mases), all_mases.shape)\n", - "best_n_delays = n_delays_range[best_idx[0]]\n", - "best_rank = ranks_range[best_idx[1]]\n", - "print(f\"\\nOptimal parameters based on MASE:\")\n", - "print(f\" n_delays: {best_n_delays}\")\n", - "print(f\" rank: {best_rank}\")\n", - "print(f\" MASE: {all_mases[best_idx]:.4f}\")\n" + "#our function for visualizing is as follows:\n", + "plot_sweep_results(all_aics, all_mases, all_nnormals, n_delays=n_delays_range, ranks=ranks_range, cmap='viridis', name='DMD')" ] }, { @@ -1025,7 +977,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -1040,38 +992,10 @@ "name": "stderr", "output_type": "stream", "text": [ - " 0%| | 0/5 [00:00,\n", - " array([,\n", - " ,\n", - " ], dtype=object))" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ @@ -1090,7 +1014,7 @@ " n_delays=n_delays_range,\n", " ranks=ranks_range,\n", " model_class='SubspaceDMDc', # can be 'DMDc' or 'SubspaceDMDc' for control systems\n", - " train_frac=0.7, # Use 70% for training, 30% for testing\n", + " train_frac=0.7, \n", " reseed=5,\n", " return_residuals=False,\n", " device='cpu'\n", @@ -1108,7 +1032,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -1117,14 +1041,14 @@ "text": [ "DMD Matrix Statistics:\n", "==================================================\n", - "MAE : 0.0077\n", - "MASE : 0.2627\n", - "NMSE : 0.1039\n", + "MAE : 0.0079\n", + "MASE : 0.1737\n", + "NMSE : 0.0400\n", "MSE : 0.0001\n", - "R2 : 0.8832\n", - "Correl : 0.9397\n", - "AIC : -9.2117\n", - "logMSE : -9.2521\n" + "R2 : 0.9479\n", + "Correl : 0.9736\n", + "AIC : -9.1654\n", + "logMSE : -9.2058\n" ] } ], From 17b025b0dae22970876f92638fcbcd078a4f7832 Mon Sep 17 00:00:00 2001 From: ostrow Date: Thu, 6 Nov 2025 22:31:29 -0500 Subject: [PATCH 43/90] dmdc model tutorial notebook --- examples/dmdc_linear_system_test.ipynb | 765 +++++++++++++++++++++++++ 1 file changed, 765 insertions(+) create mode 100644 examples/dmdc_linear_system_test.ipynb diff --git a/examples/dmdc_linear_system_test.ipynb b/examples/dmdc_linear_system_test.ipynb new file mode 100644 index 0000000..3ae5bc9 --- /dev/null +++ b/examples/dmdc_linear_system_test.ipynb @@ -0,0 +1,765 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# DMDc Test: Controlled Linear Dynamical System\n", + "\n", + "This notebook demonstrates:\n", + "1. Generating data from a controlled linear dynamical system\n", + "2. Fitting a DMDc model to recover the system matrices\n", + "3. Visualizing the eigenvalues of A and singular values of B to verify the fit\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import sys\n", + "sys.path.append('../')\n", + "\n", + "from DSA.dmdc import DMDc\n", + "\n", + "# Set random seed for reproducibility\n", + "np.random.seed(42)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Generate Ground Truth Linear Dynamical System\n", + "\n", + "We'll create a controlled linear dynamical system of the form:\n", + "$$x_{t+1} = A x_t + B u_t$$\n", + "\n", + "where:\n", + "- $x_t \\in \\mathbb{R}^n$ is the state\n", + "- $u_t \\in \\mathbb{R}^m$ is the control input\n", + "- $A \\in \\mathbb{R}^{n \\times n}$ is the state transition matrix\n", + "- $B \\in \\mathbb{R}^{n \\times m}$ is the control input matrix\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ground truth A eigenvalues: [-0.87865666+0.3611959j -0.87865666-0.3611959j -0.63342513+0.09350672j\n", + " -0.63342513-0.09350672j 0.50029664+0.40533415j 0.50029664-0.40533415j\n", + " 0.50450346+0.10018783j 0.50450346-0.10018783j 0.18230014+0.j\n", + " -0.14354022+0.j ]\n", + "Ground truth B singular values: [3.3561271 2.53100558 1.62794133]\n", + "\n", + "A matrix spectral radius: 0.9500\n", + "System is stable: True\n" + ] + } + ], + "source": [ + "# System dimensions\n", + "n_state = 10 # state dimension\n", + "n_control = 3 # control dimension\n", + "n_timesteps = 1000 # number of time steps\n", + "n_trials = 5 # number of trials\n", + "\n", + "# Sample random matrix for A and normalize to have max eigenvalue of 0.95\n", + "A_random = np.random.randn(n_state, n_state)\n", + "eigs_A = np.linalg.eigvals(A_random)\n", + "max_eig = np.max(np.abs(eigs_A))\n", + "A_true = A_random / max_eig * 0.95\n", + "\n", + "# Sample random matrix for B\n", + "B_true = np.random.randn(n_state, n_control)\n", + "\n", + "# Compute actual eigenvalues and singular values\n", + "eigenvalues_A = np.linalg.eigvals(A_true)\n", + "singular_values_B = np.linalg.svd(B_true, compute_uv=False)\n", + "\n", + "print(f\"Ground truth A eigenvalues: {eigenvalues_A}\")\n", + "print(f\"Ground truth B singular values: {singular_values_B}\")\n", + "print(f\"\\nA matrix spectral radius: {np.max(np.abs(eigenvalues_A)):.4f}\")\n", + "print(f\"System is stable: {np.max(np.abs(eigenvalues_A)) < 1}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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hvAAAAKMQXgAAgFEILwAAwCiEFwAAYBTCCwAAMArhBQAAGIXwAgAAjEJ4AQAARiG8AAAAoxBeAACAUQgvAADAKIQXAABgFMILAAAwCuEFAAAYhfACAACMQngBAABGIbwAAACjEF4AAIBRCC8AAMAohBcAAGAUwgsAADAK4QUAABiF8AIAAIwS6O4LAAC71dRaknf8vJRVVElkSLAk9ouQAH8/bryH4XmCR4WX7Oxseemll6S0tFTi4uJk5cqVkpiY2GT9TZs2yfPPPy/fffed3HbbbbJs2TIZN25cZ1wqAC+Tc+i0LPnoazntqKor6xUWLJkTYiV1SC+3Xht+xPMEj+o22rhxoyxYsEAyMzOloKBAh5exY8dKWVlZo/W/+OILmTp1qsyePVu+/PJLeeSRR/R26NAhuy8VgBe+Ic59r8AluCiljipdro7D/Xie0FZ+lmVZYqOkpCQZPny4rFq1Su/X1tZKTEyMzJ8/X9LT06+pP3nyZKmsrJStW7fWld17770ydOhQWbNmTYuPV15eLmFhYeJwOCQ0NLSDfxoAJnVB3LdsxzXBxUl1GkWFBcvuZx6iC8mNeJ7QnvdvW1teLl++LPn5+ZKSkvLjA/r76/3c3NxGz1Hl9esrqqWmqfrV1dX6B66/AYAa49JUcFHUv9rUcVUP7sPzhPawNbycO3dOampqpGfPni7lal+Nf2mMKm9L/aysLJ3UnJtq1QEANTi3I+vBHjxP8Mmp0hkZGbqJybkVFxe7+5IAeAA1q6gj68EePE/wuNlG3bt3l4CAADlz5oxLudqPiopq9BxV3pb6QUFBegOA+tR0aDWrSA3OtZoZ86LqwX14nuBxLS9dunSR+Ph42b59e12ZGrCr9pOTkxs9R5XXr69s27atyfoA0Bi1jouaDq00XNHFua+Os96Le/E8wSO7jdQ06bVr18o777wjhYWFMnfuXD2baNasWfr49OnTddeP029+8xvJycmRl19+WYqKimTx4sWyf/9+mTdvnt2XCsDLqHVcVj8+TLew1Kf2VTnrvHgGnid43CJ1aurz2bNnZdGiRXrQrZryrMKJc1DuiRMn9AwkpxEjRsj7778vzz33nDz77LN6kboPP/xQhgwZYvelAvDSN8bRsVGssOvheJ7gUeu8dDbWeQEAwDwes84LAABARyO8AAAAoxBeAACAUQgvAADAKIQXAABgFMILAAAwCuEFAAAYhfACAACMQngBAABGIbwAAACjEF4AAIBRCC8AAMAohBcAAGAUwgsAADAK4QUAABiF8AIAAIxCeAEAAEYhvAAAAKMQXgAAgFEILwAAwCiEFwAAYBTCCwAAMArhBQAAGIXwAgAAjEJ4AQAARiG8AAAAoxBeAACAUQLdfQHwDTW1luQdPy9lFVUSGRIsif0iJMDfz92XBQAwEOEFtss5dFqWfPS1nHZU1ZX1CguWzAmxkjqkF88AAKBN6DaC7cFl7nsFLsFFKXVU6XJ1HACAtiC8wNauItXiYjVyzFmmjqt6AADPV1NrSe6x7+UfB0r0V3e9ftsWXs6fPy/Tpk2T0NBQCQ8Pl9mzZ8vFixebPeeNN96QBx98UJ/j5+cnFy5csOvy0AnUGJeGLS71qV95dVzVAwB4tpxDp+W+ZTtk6tr/ym82HNBf1b47WtBtCy8quBw+fFi2bdsmW7dulV27dsmcOXOaPefSpUuSmpoqzz77rF2XhU6kBud2ZD0AgHt42hAAWwbsFhYWSk5Ojuzbt08SEhJ02cqVK2XcuHGyfPlyiY6ObvS83/72t/rrzp077bgsdDI1q6gj6wEAOl9LQwDUvFF1fHRsVKfNIrWl5SU3N1d3FTmDi5KSkiL+/v6yd+/eDn2s6upqKS8vd9ngGdR0aDWrqKlfZVWujqt6AADPlOeBQwBsCS+lpaUSGRnpUhYYGCgRERH6WEfKysqSsLCwui0mJqZDvz/aTyVwNR1aaRhgnPvqOOu9AIDnKvPAIQBtCi/p6el6IG1zW1FRkXSmjIwMcTgcdVtxcXGnPj6ap9ZxWf34MIkKc+0aUvuqnHVeAMCzRXrgEIA2jXlZuHChzJw5s9k6/fv3l6ioKCkrK3Mpv3r1qp6BpI51pKCgIL3Bc6mAovpCWWEXAMwdAlDqqGp03Ivf//2DtDOHALQpvPTo0UNvLUlOTtbTnPPz8yU+Pl6X7dixQ2prayUpKan9Vwtjqa6h5AG3uPsyAADtHAKgZhWpoGJ5wBAAW8a8DB48WE95TktLk7y8PNmzZ4/MmzdPpkyZUjfTqKSkRAYNGqSPO6nxMAcOHJCjR4/q/YMHD+p91WIDAADcw9OGANj22Ubr16/XgWXUqFF6ltGkSZPk9ddfrzt+5coVOXLkiF7bxWnNmjWyZMmSuv37779ff33rrbda7K4CAAC+MQTAz7Isr1qbXU2VVrOO1OBdtVIvAADwrvdvPtsIAAAYhfACAACMQngBAABGIbwAAACjEF4AAIBRCC8AAMAohBcAAGAUwgsAADAK4QUAABiF8AIAAIxCeAEAAEYhvAAAAKMQXgAAgFEILwAAwCiEFwAAYBTCCwAAMArhBQAAGIXwAgAAjEJ4AQAARiG8AAAAoxBeAACAUQgvAADAKIQXAABgFMILAAAwCuEFAAAYhfACAACMQngBAABGIbwAAACjEF4AAIBRCC8AAMAotoaX8+fPy7Rp0yQ0NFTCw8Nl9uzZcvHixWbrz58/X+644w7p2rWr9OnTR5566ilxOBx2XiYAADCIreFFBZfDhw/Ltm3bZOvWrbJr1y6ZM2dOk/VPnTqlt+XLl8uhQ4fk7bfflpycHB16AAAAFD/Lsiw7bkVhYaHExsbKvn37JCEhQZepIDJu3Dg5efKkREdHt+r7bNq0SR5//HGprKyUwMDAFuuXl5dLWFiYbq1RLT4AAMDzteX927aWl9zcXN1V5AwuSkpKivj7+8vevXtb/X2cP0RTwaW6ulr/wPU3AADgvWwLL6WlpRIZGelSpgJIRESEPtYa586dk6VLlzbb1ZSVlaWTmnOLiYm57msHAABeFF7S09PFz8+v2a2oqOi6L0y1oIwfP153PS1evLjJehkZGbp1xrkVFxdf92MDAADP1fIgkgYWLlwoM2fObLZO//79JSoqSsrKylzKr169qmcUqWPNqaiokNTUVAkJCZHNmzfLDTfc0GTdoKAgvQEAAN/Q5vDSo0cPvbUkOTlZLly4IPn5+RIfH6/LduzYIbW1tZKUlNRsi8vYsWN1INmyZYsEBwe39RIBAIAXs23My+DBg3XrSVpamuTl5cmePXtk3rx5MmXKlLqZRiUlJTJo0CB93BlcxowZo2cWvfnmm3pfjY9RW01NjV2XCgAAvLnlpS3Wr1+vA8uoUaP0LKNJkybJ66+/Xnf8ypUrcuTIEbl06ZLeLygoqJuJNHDgQJfvdfz4cenbt6+dlwsAAHx5nRd3YZ0XAADM4xHrvAAAANiB8AIAAIxCeAEAAEYhvAAAAKMQXgAAgFEILwAAwCiEFwAAYBTCCwAAMArhBQAAGIXwAgAAjEJ4AQAARiG8AAAAoxBeAACAUQgvAADAKIQXAABgFMILAAAwCuEFAAAYhfACAACMQngBAABGIbwAAACjEF4AAIBRCC8AAMAohBcAAGAUwgsAADAK4QUAABiF8AIAAIxCeAEAAEYhvAAAAKMQXgAAgFEILwAAwCiEFwAAYBRbw8v58+dl2rRpEhoaKuHh4TJ79my5ePFis+c8+eSTMmDAAOnatav06NFDJk6cKEVFRXZeJgAAMIit4UUFl8OHD8u2bdtk69atsmvXLpkzZ06z58THx8tbb70lhYWF8umnn4plWTJmzBipqamx81IBAIAh/CyVDmygwkdsbKzs27dPEhISdFlOTo6MGzdOTp48KdHR0a36Pl999ZXExcXJ0aNHdYtMS8rLyyUsLEwcDodu8QEAAJ6vLe/ftrW85Obm6q4iZ3BRUlJSxN/fX/bu3duq71FZWalbYfr16ycxMTGN1qmurtY/cP0NAAB4L9vCS2lpqURGRrqUBQYGSkREhD7WnD/96U/SrVs3vX3yySe626lLly6N1s3KytJJzbk1FXIAAICPhpf09HTx8/NrdrveAbZqrMyXX34p//nPf+T222+XRx99VKqqqhqtm5GRoZuYnFtxcfF1PTYAAPBsgW09YeHChTJz5sxm6/Tv31+ioqKkrKzMpfzq1at6BpI61hxnK8ptt90m9957r9x8882yefNmmTp16jV1g4KC9AYAAHxDm8OLmr6stpYkJyfLhQsXJD8/X88gUnbs2CG1tbWSlJTU6sdT44nVpsa2AAAA2DbmZfDgwZKamippaWmSl5cne/bskXnz5smUKVPqZhqVlJTIoEGD9HHl22+/1WNYVOA5ceKEfPHFF/KLX/xCr/miZikBAADYus7L+vXrdTgZNWqUDh/33XefvPHGG3XHr1y5IkeOHJFLly7p/eDgYPn888913YEDB8rkyZMlJCREh5iGg38BAIBvsm2dF3dhnRcAAMzjEeu8AAAA2IHwAgAAjEJ4AQAARiG8AAAAoxBeAACAUQgvAADAKIQXAABgFMILAAAwCuEFAAAYhfACAACMQngBAABGIbwAAACjEF4AAIBRAt19AaaoqbUk7/h5KauoksiQYEnsFyEB/n7uviwAAHwO4aUVcg6dliUffS2nHVV1Zb3CgiVzQqykDull5/MDAAAaoNuoFcFl7nsFLsFFKXVU6XJ1HAAAdB7CSwtdRarFxWrkmLNMHVf1AABA5yC8NEONcWnY4lKfiizquKoHAAA6B+GlGWpwbkfWAwAA14/w0gw1q6gj6wEAgOtHeGmGmg6tZhU1NSFalavjqh4AAOgchJdmqHVc1HRopWGAce6r46z3AgBA5yG8tECt47L68WESFebaNaT2VTnrvAAA0LlYpK4VVEAZHRvFCrsAAHgAwksrqa6h5AG32PtsAACAFtFtBAAAjEJ4AQAARiG8AAAAoxBeAACAUQgvAADAKLaGl/Pnz8u0adMkNDRUwsPDZfbs2XLx4sVWnWtZljz88MPi5+cnH374oZ2XCQAADGJreFHB5fDhw7Jt2zbZunWr7Nq1S+bMmdOqc1esWKGDCwAAcK+aWktyj30v/zhQor+qfa9c56WwsFBycnJk3759kpCQoMtWrlwp48aNk+XLl0t0dHST5x44cEBefvll2b9/v/Tq1cuuSwQAAC3IOXRalnz0tZx2VNWVqc/1Ux+P465V5m1recnNzdVdRc7goqSkpIi/v7/s3bu3yfMuXbokjz32mGRnZ0tUVFSLj1NdXS3l5eUuGwAA6JjgMve9ApfgopQ6qnS5Ou5V4aW0tFQiIyNdygIDAyUiIkIfa8rTTz8tI0aMkIkTJ7bqcbKysiQsLKxui4mJue5rBwDA19XUWrrFpbEOImeZOu6OLqQ2h5f09HQ9FqW5raioqF0Xs2XLFtmxY4ce79JaGRkZ4nA46rbi4uJ2PTYAAPhR3vHz17S41Kciizqu6nn8mJeFCxfKzJkzm63Tv39/3eVTVlbmUn716lU9A6mp7iAVXI4dO6a7m+qbNGmSjBw5Unbu3HnNOUFBQXoDAAAdp6yiqkPruTW89OjRQ28tSU5OlgsXLkh+fr7Ex8fXhZPa2lpJSkpqslXnl7/8pUvZXXfdJa+++qpMmDChrZcKAADaKTIkuEPrGTHbaPDgwZKamippaWmyZs0auXLlisybN0+mTJlSN9OopKRERo0aJe+++64kJibqFpnGWmX69Okj/fr1s+tSAQBAA4n9IvSsIjU4t7FRLWoxk6iwYF3Pq9Z5Wb9+vQwaNEgHFDVF+r777pM33nij7rgKNEeOHNEzjAAAgOcI8PfT06GVhquuOffVcVWvs/lZailbL6KmSqtZR2rwrlrZFwAAeP46L215/7at2wgAAJgvdUgvGR0bpWcVqcG5aoyL6ipyR4uLE+EFAAA0SwWV5AG3iKfgU6UBAIBRCC8AAMAohBcAAGAUwgsAADAK4QUAABiF8AIAAIxCeAEAAEYhvAAAAKMQXgAAgFG8boVd50c1qc9IAAAAZnC+b7fmIxe9LrxUVFTorzExMe6+FAAA0I73cfUBjT71qdK1tbVy6tQpCQkJET8/931o1PWmTxW+iouLffaTsbkH3AN+D/hb4DXBt14XLcvSwSU6Olr8/f19q+VF/cC33nqreAP1y+mNv6BtwT3gHvB7wN8Crwm+87oY1kKLixMDdgEAgFEILwAAwCiEFw8UFBQkmZmZ+quv4h5wD/g94G+B1wReF31mwC4AAPButLwAAACjEF4AAIBRCC8AAMAohBcAAGAUwosHOH/+vEybNk0vOhQeHi6zZ8+WixcvNntOaWmpPPHEExIVFSU33XSTDBs2TP72t7+JL90DJTc3Vx566CF9D9S5999/v/zwww/ia/dBUWPvH374Yb2y9Icffii+cg9U/fnz58sdd9whXbt2lT59+shTTz0lDodDTJGdnS19+/aV4OBgSUpKkry8vGbrb9q0SQYNGqTr33XXXfLxxx+L6dpyD9auXSsjR46Um2++WW8pKSkt3jNv/V1w2rBhg/7bf+SRR8QnqNlGcK/U1FQrLi7O+u9//2t9/vnn1sCBA62pU6c2e87o0aOt4cOHW3v37rWOHTtmLV261PL397cKCgosX7kHX3zxhRUaGmplZWVZhw4dsoqKiqyNGzdaVVVVlqnacx+cXnnlFevhhx9WswetzZs3W75yDw4ePGj97Gc/s7Zs2WIdPXrU2r59u3XbbbdZkyZNskywYcMGq0uXLta6deusw4cPW2lpaVZ4eLh15syZRuvv2bPHCggIsF588UXr66+/tp577jnrhhtu0PfBVG29B4899piVnZ1tffnll1ZhYaE1c+ZMKywszDp58qRlsrbeB6fjx49bvXv3tkaOHGlNnDjR8gWEFzdTLz7qzWbfvn11ZZ988onl5+dnlZSUNHneTTfdZL377rsuZREREdbatWstX7kHSUlJ+oXbW7T3PijqRVy9eJ0+fdro8HI996C+Dz74QL8JXLlyxfJ0iYmJ1q9//eu6/ZqaGis6OlqH8sY8+uij1vjx46/5W3jyySctU7X1HjR09epVKyQkxHrnnXcsk7XnPly9etUaMWKE9ec//9maMWOGz4QXuo3cTHV7qKbxhISEujLVBKo+o2nv3r1NnjdixAjZuHGjbjJXH0apmgyrqqrkwQcfFF+4B2VlZfpYZGSkvhc9e/aUBx54QHbv3i2+9rtw6dIleeyxx3Rzs+pGNFl770FDqstIdTsFBnr2x7ddvnxZ8vPz9c/opH5Wta/uRWNUef36ytixY5us7+nacw8a+xu4cuWKREREiKnaex/+8Ic/6NdB1b3qSwgvbqbGrqhfvPrUC676I1THmvLBBx/oP9ZbbrlFr0b75JNPyubNm2XgwIHiC/fg22+/1V8XL14saWlpkpOTo8f9jBo1Sr755hvxpd+Fp59+Wge4iRMniunaew/qO3funCxdulTmzJkjnk5da01NjQ7f9an9pn5eVd6W+t54Dxp65pln9CcRNwx13n4fdu/eLW+++aYeA+RrCC82SU9P14OnmtuKiora/f2ff/55uXDhgvz73/+W/fv3y4IFC+TRRx+VgwcPii/cA9XapKjQNmvWLLnnnnvk1Vdf1YM2161bJ57EzvuwZcsW2bFjh6xYsUJ8+e/Bqby8XMaPHy+xsbE62ML7vfDCC7rlWf3jTQ1y9RUVFRV60oYKLt27dxdf49ltqgZbuHChzJw5s9k6/fv31838qgukvqtXr+ruoKa6AI4dOyarVq2SQ4cOyZ133qnL4uLi5PPPP9ddB2vWrBFvvwe9evXSX9WbVH2DBw+WEydOiCex8z6o4KJ+H1RXS32TJk3SszF27twp3n4P6r+Yp6amSkhIiH4ju+GGG8TTqTedgIAAOXPmjEu52m/q51XlbanvjffAafny5Tq8qH/E3X333WKytt6HY8eOyXfffScTJky45h91qrXyyJEjMmDAAPFa7h504+ucAxT3799fV/bpp582O0Dxq6++0ueoc+sbM2aMHp3uC/egtrZWD2RrOGB36NChVkZGhmWi9twHNUBXzTKpv6nv8dprr1nffvut5Qv3QHE4HNa9995rPfDAA1ZlZaVl2iDNefPmuQzSVIOvmxuw+9Of/tSlLDk52fgBu225B8qyZcv0bMPc3FzLW7TlPvzwww/X/O2rwboPPfSQ/v/q6mrLmxFePGRq6D333KOnPe/evVtP86w/NVRN/7vjjjv0ceXy5ct6+qiaFqfK1PTQ5cuX6xf4f/7zn5Yv3APl1Vdf1S9emzZtsr755hsdZIKDg/X9MFV77kNDJs82as89UMFFzba566679HOvAp1zUzMxTJgeGxQUZL399ts6vM2ZM0dPjy0tLdXHn3jiCSs9Pd1lqnRgYKD+m1fThDMzM71iqnRb7sELL7ygZ5P99a9/dXm+KyoqLJO19T405EuzjQgvHuD777/XL87dunXTb8azZs1y+SNUc/jVG9Jnn31WV/a///1Pr20RGRlp3Xjjjdbdd999zdRpb78HivoXya233qrvgfrXp1oXxGTtvQ/eFF7aeg/UV7Xf2KbqmmDlypVWnz599Buy+te3WuPGSbUmqTelhlPBb7/9dl3/zjvvNPYfLe29Bz/5yU8afb5VkDNdW38XfDW8+Kn/uLvrCgAAoLWYbQQAAIxCeAEAAEYhvAAAAKMQXgAAgFEILwAAwCiEFwAAYBTCCwAAMArhBQAAGIXwAgAAjEJ4AQAARiG8AAAAoxBeAACAmOT/Acq0y7CoWb5RAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "eigs = np.linalg.eigvals(A_true)\n", + "plt.scatter(eigs.real,eigs.imag)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Generate Data from the System\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Generated data shape:\n", + " State data X: (5, 1000, 10)\n", + " Control data U: (5, 1000, 3)\n" + ] + } + ], + "source": [ + "def generate_controlled_trajectory(A, B, n_timesteps, n_trials, noise_std=0.01):\n", + " \"\"\"\n", + " Generate data from a controlled linear dynamical system.\n", + " \n", + " Parameters:\n", + " -----------\n", + " A : ndarray (n_state, n_state)\n", + " State transition matrix\n", + " B : ndarray (n_state, n_control)\n", + " Control input matrix\n", + " n_timesteps : int\n", + " Number of time steps per trial\n", + " n_trials : int\n", + " Number of trials\n", + " noise_std : float\n", + " Standard deviation of process noise\n", + " \n", + " Returns:\n", + " --------\n", + " X : ndarray (n_trials, n_timesteps, n_state)\n", + " State trajectories\n", + " U : ndarray (n_trials, n_timesteps, n_control)\n", + " Control inputs\n", + " \"\"\"\n", + " n_state = A.shape[0]\n", + " n_control = B.shape[1]\n", + " \n", + " X = np.zeros((n_trials, n_timesteps, n_state))\n", + " U = np.zeros((n_trials, n_timesteps, n_control))\n", + " \n", + " for trial in range(n_trials):\n", + " # Initialize with random state\n", + " X[trial, 0] = np.random.randn(n_state) * 0.1\n", + " \n", + " # Generate control inputs (random walk with some structure)\n", + " U[trial] = np.cumsum(np.random.randn(n_timesteps, n_control) * 0.1, axis=0)\n", + " \n", + " # Simulate system\n", + " for t in range(n_timesteps - 1):\n", + " # x_{t+1} = A x_t + B u_t + noise\n", + " X[trial, t + 1] = (A @ X[trial, t] + \n", + " B @ U[trial, t] + \n", + " np.random.randn(n_state) * noise_std)\n", + " \n", + " return X, U\n", + "\n", + "# Generate training data\n", + "X_train, U_train = generate_controlled_trajectory(A_true, B_true, n_timesteps, n_trials)\n", + "\n", + "print(f\"Generated data shape:\")\n", + "print(f\" State data X: {X_train.shape}\")\n", + "print(f\" Control data U: {U_train.shape}\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(2, 2, figsize=(14, 10))\n", + "\n", + "# Plot first few state dimensions\n", + "ax = axes[0, 0]\n", + "for i in range(min(3, n_state)):\n", + " ax.plot(X_train[0, :200, i], label=f'$x_{{{i+1}}}$', alpha=0.7)\n", + "ax.set_xlabel('Time')\n", + "ax.set_ylabel('State Value')\n", + "ax.set_title('Sample State Trajectories (Trial 1, first 200 steps)')\n", + "ax.legend()\n", + "ax.grid(True, alpha=0.3)\n", + "\n", + "# Plot control inputs\n", + "ax = axes[0, 1]\n", + "for i in range(n_control):\n", + " ax.plot(U_train[0, :200, i], label=f'$u_{{{i+1}}}$', alpha=0.7)\n", + "ax.set_xlabel('Time')\n", + "ax.set_ylabel('Control Value')\n", + "ax.set_title('Control Inputs (Trial 1, first 200 steps)')\n", + "ax.legend()\n", + "ax.grid(True, alpha=0.3)\n", + "\n", + "# Plot state norm over time\n", + "ax = axes[1, 0]\n", + "for trial in range(n_trials):\n", + " state_norm = np.linalg.norm(X_train[trial], axis=1)\n", + " ax.plot(state_norm, alpha=0.5, label=f'Trial {trial+1}')\n", + "ax.set_xlabel('Time')\n", + "ax.set_ylabel('$||x_t||_2$')\n", + "ax.set_title('State Norm Over Time (All Trials)')\n", + "ax.legend()\n", + "ax.grid(True, alpha=0.3)\n", + "\n", + "# Plot control norm over time\n", + "ax = axes[1, 1]\n", + "for trial in range(n_trials):\n", + " control_norm = np.linalg.norm(U_train[trial], axis=1)\n", + " ax.plot(control_norm, alpha=0.5, label=f'Trial {trial+1}')\n", + "ax.set_xlabel('Time')\n", + "ax.set_ylabel('$||u_t||_2$')\n", + "ax.set_title('Control Norm Over Time (All Trials)')\n", + "ax.legend()\n", + "ax.grid(True, alpha=0.3)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Fit DMDc Model\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Computing Hankel matrices ...\n", + "Hankel matrices computed!\n", + "Computing SVD on H and U matrices ...\n", + "SVDs computed!\n", + "Computing DMDc matrices ...\n", + "DMDc matrices computed!\n", + "\n", + "DMDc model fitted successfully!\n", + "Recovered A matrix shape: torch.Size([10, 10])\n", + "Recovered B matrix shape: torch.Size([10, 3])\n" + ] + } + ], + "source": [ + "# Create and fit DMDc model\n", + "dmdc = DMDc(\n", + " data=X_train,\n", + " control_data=U_train,\n", + " n_delays=1,\n", + " n_control_delays=1,\n", + " delay_interval=1,\n", + " rank_input=None, \n", + " rank_output=None, \n", + " lamb=0, \n", + " device='cpu',\n", + " verbose=True\n", + ")\n", + "\n", + "# Fit the model\n", + "dmdc.fit()\n", + "\n", + "print(\"\\nDMDc model fitted successfully!\")\n", + "print(f\"Recovered A matrix shape: {dmdc.A.shape}\")\n", + "print(f\"Recovered B matrix shape: {dmdc.B.shape}\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Compare True vs Recovered Matrices\n", + "\n", + "### 5.1 Eigenvalues of A\n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Eigenvalue Comparison (sorted by magnitude):\n", + "======================================================================\n", + "Index True Recovered Error \n", + "======================================================================\n", + "0 -0.8787-0.3612j -0.8780-0.3617j 0.000828 \n", + "1 -0.8787+0.3612j -0.8780+0.3617j 0.000828 \n", + "2 0.5003-0.4053j 0.5015-0.4058j 0.001296 \n", + "3 0.5003+0.4053j 0.5015+0.4058j 0.001296 \n", + "4 -0.6334-0.0935j -0.6324-0.0882j 0.005358 \n", + "5 -0.6334+0.0935j -0.6324+0.0882j 0.005358 \n", + "6 0.5045-0.1002j 0.5048-0.0906j 0.009595 \n", + "7 0.5045+0.1002j 0.5048+0.0906j 0.009595 \n", + "8 0.1823 0.1814 0.000874 \n", + "9 -0.1435 -0.1413 0.002219 \n", + "======================================================================\n" + ] + } + ], + "source": [ + "# Compute eigenvalues\n", + "A_recovered = dmdc.A.cpu().numpy() if hasattr(dmdc.A, 'cpu') else dmdc.A\n", + "eigenvalues_A_recovered = np.linalg.eigvals(A_recovered)\n", + "\n", + "# Sort eigenvalues by magnitude for easier comparison\n", + "idx_true = np.argsort(np.abs(eigenvalues_A))[::-1]\n", + "idx_recovered = np.argsort(np.abs(eigenvalues_A_recovered))[::-1]\n", + "\n", + "eigenvalues_A_sorted = eigenvalues_A[idx_true]\n", + "eigenvalues_A_recovered_sorted = eigenvalues_A_recovered[idx_recovered]\n", + "\n", + "# Plot eigenvalues in complex plane\n", + "fig, axes = plt.subplots(1, 2, figsize=(14, 6))\n", + "\n", + "# Complex plane plot\n", + "ax = axes[0]\n", + "ax.scatter(eigenvalues_A.real, eigenvalues_A.imag, \n", + " s=100, alpha=0.6, label='Ground Truth', marker='o', color='blue')\n", + "ax.scatter(eigenvalues_A_recovered.real, eigenvalues_A_recovered.imag, \n", + " s=100, alpha=0.6, label='DMDc Recovered', marker='x', color='red', linewidths=3)\n", + "\n", + "# Draw unit circle\n", + "theta = np.linspace(0, 2*np.pi, 100)\n", + "ax.plot(np.cos(theta), np.sin(theta), 'k--', alpha=0.3, label='Unit Circle')\n", + "\n", + "ax.set_xlabel('Real Part')\n", + "ax.set_ylabel('Imaginary Part')\n", + "ax.set_title('Eigenvalues of A (Complex Plane)')\n", + "ax.legend()\n", + "ax.grid(True, alpha=0.3)\n", + "ax.axis('equal')\n", + "\n", + "# Magnitude comparison\n", + "ax = axes[1]\n", + "x_pos = np.arange(len(eigenvalues_A))\n", + "width = 0.35\n", + "ax.bar(x_pos - width/2, np.abs(eigenvalues_A_sorted), width, \n", + " alpha=0.6, label='Ground Truth', color='blue')\n", + "ax.bar(x_pos + width/2, np.abs(eigenvalues_A_recovered_sorted), width, \n", + " alpha=0.6, label='DMDc Recovered', color='red')\n", + "ax.axhline(y=1, color='k', linestyle='--', alpha=0.3, label='Stability Boundary')\n", + "ax.set_xlabel('Eigenvalue Index (sorted by magnitude)')\n", + "ax.set_ylabel('Magnitude')\n", + "ax.set_title('Eigenvalue Magnitudes Comparison')\n", + "ax.legend()\n", + "ax.grid(True, alpha=0.3)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# Print numerical comparison\n", + "print(\"\\nEigenvalue Comparison (sorted by magnitude):\")\n", + "print(\"=\"*70)\n", + "print(f\"{'Index':<8} {'True':<25} {'Recovered':<25} {'Error':<10}\")\n", + "print(\"=\"*70)\n", + "for i in range(len(eigenvalues_A)):\n", + " error = np.abs(eigenvalues_A_sorted[i] - eigenvalues_A_recovered_sorted[i])\n", + " true_str = f\"{eigenvalues_A_sorted[i].real:.4f}\"\n", + " if np.abs(eigenvalues_A_sorted[i].imag) > 1e-10:\n", + " true_str += f\"+{eigenvalues_A_sorted[i].imag:.4f}j\" if eigenvalues_A_sorted[i].imag > 0 else f\"{eigenvalues_A_sorted[i].imag:.4f}j\"\n", + " rec_str = f\"{eigenvalues_A_recovered_sorted[i].real:.4f}\"\n", + " if np.abs(eigenvalues_A_recovered_sorted[i].imag) > 1e-10:\n", + " rec_str += f\"+{eigenvalues_A_recovered_sorted[i].imag:.4f}j\" if eigenvalues_A_recovered_sorted[i].imag > 0 else f\"{eigenvalues_A_recovered_sorted[i].imag:.4f}j\"\n", + " print(f\"{i:<8} {true_str:<25} {rec_str:<25} {error:<10.6f}\")\n", + "print(\"=\"*70)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5.2 Singular Values of B\n" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Singular Value Comparison:\n", + "============================================================\n", + "Index True Recovered Relative Error \n", + "============================================================\n", + "0 3.356127 3.355637 0.000146 \n", + "1 2.531006 2.529212 0.000709 \n", + "2 1.627941 1.628247 0.000188 \n", + "============================================================\n" + ] + } + ], + "source": [ + "# Compute singular values\n", + "B_recovered = dmdc.B.cpu().numpy() if hasattr(dmdc.B, 'cpu') else dmdc.B\n", + "singular_values_B_recovered = np.linalg.svd(B_recovered, compute_uv=False)\n", + "\n", + "# Plot singular values\n", + "fig, axes = plt.subplots(1, 2, figsize=(14, 6))\n", + "\n", + "# Bar plot comparison\n", + "ax = axes[0]\n", + "x_pos = np.arange(len(singular_values_B))\n", + "width = 0.35\n", + "ax.bar(x_pos - width/2, singular_values_B, width, \n", + " alpha=0.6, label='Ground Truth', color='blue')\n", + "ax.bar(x_pos + width/2, singular_values_B_recovered[:len(singular_values_B)], width, \n", + " alpha=0.6, label='DMDc Recovered', color='red')\n", + "ax.set_xlabel('Singular Value Index')\n", + "ax.set_ylabel('Singular Value')\n", + "ax.set_title('Singular Values of B')\n", + "ax.set_xticks(x_pos)\n", + "ax.legend()\n", + "ax.grid(True, alpha=0.3)\n", + "\n", + "# Log scale plot (if needed for wide range)\n", + "ax = axes[1]\n", + "ax.semilogy(singular_values_B_recovered, 'o-', label='DMDc Recovered (all)', alpha=0.7)\n", + "ax.semilogy(range(len(singular_values_B)), singular_values_B, 'x-', \n", + " label='Ground Truth', markersize=10, linewidth=2)\n", + "ax.set_xlabel('Singular Value Index')\n", + "ax.set_ylabel('Singular Value (log scale)')\n", + "ax.set_title('Singular Value Spectrum of B')\n", + "ax.legend()\n", + "ax.grid(True, alpha=0.3, which='both')\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# Print numerical comparison\n", + "print(\"\\nSingular Value Comparison:\")\n", + "print(\"=\"*60)\n", + "print(f\"{'Index':<8} {'True':<15} {'Recovered':<15} {'Relative Error':<15}\")\n", + "print(\"=\"*60)\n", + "for i in range(len(singular_values_B)):\n", + " rel_error = np.abs(singular_values_B[i] - singular_values_B_recovered[i]) / singular_values_B[i]\n", + " print(f\"{i:<8} {singular_values_B[i]:<15.6f} {singular_values_B_recovered[i]:<15.6f} {rel_error:<15.6f}\")\n", + "print(\"=\"*60)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. Prediction Performance\n", + "\n", + "Let's verify that the model can accurately predict future states.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "{'MAE': 0.0080215854461753,\n", + " 'MASE': 0.03452452148384243,\n", + " 'NMSE': 2.7266879462026973e-06,\n", + " 'MSE': 0.00010147284380389543,\n", + " 'R2': 0.9999937456506395,\n", + " 'Correl': 0.9999968409538269,\n", + " 'AIC': -9.175519344010073,\n", + " 'logMSE': -9.195719344010072}" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from DSA import compute_all_stats\n", + "# Generate test data\n", + "X_test, U_test = generate_controlled_trajectory(A_true, B_true, n_timesteps=500, n_trials=2)\n", + "\n", + "# Make predictions\n", + "X_pred = dmdc.predict(test_data=X_test, control_data=U_test, reseed=1)\n", + "X_pred_np = X_pred.cpu().numpy() if hasattr(X_pred, 'cpu') else X_pred\n", + "\n", + "\n", + "# Plot predictions vs ground truth\n", + "fig, axes = plt.subplots(2, 2, figsize=(14, 10))\n", + "\n", + "trial_idx = 0 # Plot first trial\n", + "time_window = slice(0, 200)\n", + "\n", + "# Plot first few dimensions\n", + "for dim_idx in range(min(4, n_state)):\n", + " ax = axes[dim_idx // 2, dim_idx % 2]\n", + " ax.plot(X_test[trial_idx, time_window, dim_idx], \n", + " label='Ground Truth', linewidth=2, alpha=0.7)\n", + " ax.plot(X_pred_np[trial_idx, time_window, dim_idx], \n", + " label='DMDc Prediction', linewidth=2, linestyle='--', alpha=0.7)\n", + " ax.set_xlabel('Time')\n", + " ax.set_ylabel(f'$x_{{{dim_idx+1}}}$')\n", + " ax.set_title(f'Dimension {dim_idx+1} Prediction')\n", + " ax.legend()\n", + " ax.grid(True, alpha=0.3)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "\n", + "\n", + "compute_all_stats(X_test, X_pred,rank=dmdc.A.shape[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 7. Matrix Reconstruction Error\n", + "\n", + "Let's compute how well we recovered the A and B matrices.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Matrix Reconstruction Errors:\n", + "============================================================\n", + "A Matrix:\n", + " Frobenius Norm Error: 0.023059\n", + " Relative Error: 0.008991 (0.90%)\n", + "\n", + "B Matrix:\n", + " Frobenius Norm Error: 0.008771\n", + " Relative Error: 0.001946 (0.19%)\n", + "============================================================\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "A_frobenius_error = np.linalg.norm(A_true - A_recovered, 'fro')\n", + "A_relative_error = A_frobenius_error / np.linalg.norm(A_true, 'fro')\n", + "\n", + "B_frobenius_error = np.linalg.norm(B_true - B_recovered, 'fro')\n", + "B_relative_error = B_frobenius_error / np.linalg.norm(B_true, 'fro')\n", + "\n", + "print(\"\\nMatrix Reconstruction Errors:\")\n", + "print(\"=\"*60)\n", + "print(f\"A Matrix:\")\n", + "print(f\" Frobenius Norm Error: {A_frobenius_error:.6f}\")\n", + "print(f\" Relative Error: {A_relative_error:.6f} ({A_relative_error*100:.2f}%)\")\n", + "print(f\"\\nB Matrix:\")\n", + "print(f\" Frobenius Norm Error: {B_frobenius_error:.6f}\")\n", + "print(f\" Relative Error: {B_relative_error:.6f} ({B_relative_error*100:.2f}%)\")\n", + "print(\"=\"*60)\n", + "\n", + "# Visualize matrix differences\n", + "fig, axes = plt.subplots(2, 3, figsize=(16, 10))\n", + "\n", + "# A matrix visualization\n", + "vmax_A = max(np.abs(A_true).max(), np.abs(A_recovered).max())\n", + "im0 = axes[0, 0].imshow(A_true, cmap='RdBu_r', vmin=-vmax_A, vmax=vmax_A, aspect='auto')\n", + "axes[0, 0].set_title('True A Matrix')\n", + "axes[0, 0].set_xlabel('Column')\n", + "axes[0, 0].set_ylabel('Row')\n", + "plt.colorbar(im0, ax=axes[0, 0])\n", + "\n", + "im1 = axes[0, 1].imshow(A_recovered, cmap='RdBu_r', vmin=-vmax_A, vmax=vmax_A, aspect='auto')\n", + "axes[0, 1].set_title('Recovered A Matrix')\n", + "axes[0, 1].set_xlabel('Column')\n", + "axes[0, 1].set_ylabel('Row')\n", + "plt.colorbar(im1, ax=axes[0, 1])\n", + "\n", + "A_diff = A_true - A_recovered\n", + "im2 = axes[0, 2].imshow(A_diff, cmap='RdBu_r', vmin=-vmax_A, vmax=vmax_A, aspect='auto')\n", + "axes[0, 2].set_title(f'Difference (Relative Error: {A_relative_error*100:.2f}%)')\n", + "axes[0, 2].set_xlabel('Column')\n", + "axes[0, 2].set_ylabel('Row')\n", + "plt.colorbar(im2, ax=axes[0, 2])\n", + "\n", + "# B matrix visualization\n", + "vmax_B = max(np.abs(B_true).max(), np.abs(B_recovered).max())\n", + "im3 = axes[1, 0].imshow(B_true, cmap='RdBu_r', vmin=-vmax_B, vmax=vmax_B, aspect='auto')\n", + "axes[1, 0].set_title('True B Matrix')\n", + "axes[1, 0].set_xlabel('Column')\n", + "axes[1, 0].set_ylabel('Row')\n", + "plt.colorbar(im3, ax=axes[1, 0])\n", + "\n", + "im4 = axes[1, 1].imshow(B_recovered, cmap='RdBu_r', vmin=-vmax_B, vmax=vmax_B, aspect='auto')\n", + "axes[1, 1].set_title('Recovered B Matrix')\n", + "axes[1, 1].set_xlabel('Column')\n", + "axes[1, 1].set_ylabel('Row')\n", + "plt.colorbar(im4, ax=axes[1, 1])\n", + "\n", + "B_diff = B_true - B_recovered\n", + "im5 = axes[1, 2].imshow(B_diff, cmap='RdBu_r', vmin=-vmax_B, vmax=vmax_B, aspect='auto')\n", + "axes[1, 2].set_title(f'Difference (Relative Error: {B_relative_error*100:.2f}%)')\n", + "axes[1, 2].set_xlabel('Column')\n", + "axes[1, 2].set_ylabel('Row')\n", + "plt.colorbar(im5, ax=axes[1, 2])\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "This notebook demonstrated:\n", + "\n", + "1. **Data Generation**: Created a controlled linear dynamical system with known eigenvalues of A and singular values of B\n", + "2. **Model Fitting**: Successfully fit a DMDc model to recover the system matrices\n", + "3. **Verification**: Compared the recovered eigenvalues and singular values with ground truth\n", + "4. **Prediction**: Tested the model's ability to predict future states\n", + "\n", + "The results show that DMDc can accurately recover the dynamics of a controlled linear system from data, with the eigenvalues and singular values closely matching the ground truth values.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "dsa_test_env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.18" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 0be3dd88098a34cbc6dfe729b5be59660467c7ee Mon Sep 17 00:00:00 2001 From: ostrow Date: Thu, 6 Nov 2025 22:37:03 -0500 Subject: [PATCH 44/90] input dsa figure 2 working! --- examples/inputdsa_fig2_tutorial.ipynb | 2789 +++++++++++++++++++++++++ 1 file changed, 2789 insertions(+) create mode 100644 examples/inputdsa_fig2_tutorial.ipynb diff --git a/examples/inputdsa_fig2_tutorial.ipynb b/examples/inputdsa_fig2_tutorial.ipynb new file mode 100644 index 0000000..72cdd0c --- /dev/null +++ b/examples/inputdsa_fig2_tutorial.ipynb @@ -0,0 +1,2789 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "900a4676", + "metadata": {}, + "source": [ + "# InputDSA tutorial (Huang and Ostrow 2025 Fig. 2). InputDSA accurately distinguishes partially observed, input-driven dynamical systems\n", + "\n", + "In this analysis, we are going to look at how to appropriately compare partially observed systems\n", + "\n", + "we will look at 4 different dynamical systems, made up of 2 pairings: \n", + "1. (1,2), (3,4) have the same intrinsic dynamics\n", + "2. (1,3) (2,4) have the same read in dynamics -- how the input affects the state\n", + "\n", + "We will compare different types of DMDs (and therefore DSAs): \n", + "1. Dynamic Mode Decomposition (DMD) with delay embeddings (Hankel DMD / Havok). This method must ignore the input\n", + "2. DMD with control (DMDc, Proctor et al., 2016). This method does joint regression on the state and the input to learn an input and a state dynamics operator. However, it assumes full observation of the state, and if not, naive application of delay embeddings to resolve partial observations result in intrinsic dynamics leakage into the input operator (Fig 2a). \n", + "3. Subspace DMDc (N4SID on lifted states, Huang and Ostrow 2025). This method uses subspace identification methods to appropriately separate the effect of input and state on delay embedded data\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "52fcf42e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shared parameters:\n", + " System: n=20, m=1, p_out=10, N=10000\n", + " Dynamics: rho1=0.92, rho2=0.82, g1=0.5, g2=2.0\n", + " Noise: obs_noise=0.01, process_noise=0.0\n", + " Nonlinearity: nonlinear_eps=0.1\n", + " Model: n_delays=150, rank=20, pf=150\n", + " Evaluation: n_iters=10\n" + ] + } + ], + "source": [ + "\"\"\"\n", + "Figure 2 InputDSA Data Analysis \n", + "\"\"\"\n", + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "n = 20 # latent state dim\n", + "n_large = 50\n", + "m = 1 # input dim \n", + "p_out = 10 # observed dim (partial observation) - gets overridden in some cells\n", + "p_out_small = 2\n", + "N = 10000 # sequence length\n", + "N_small = 1000\n", + "n_Us = 4\n", + "obs_noise = 0.01\n", + "process_noise = 0.0\n", + "nonlinear_eps = 0.1\n", + "input_alpha = 0.001\n", + "g1 = 0.5\n", + "g2 = 2.0\n", + "rho1 = 0.92\n", + "rho2 = 0.82\n", + "seed1 = 11\n", + "seed2 = 12\n", + "n_delays = 150\n", + "rank = 20\n", + "pf = 150\n", + "n_iters = 10\n", + "backend = 'n4sid'\n", + "\n", + "print(f\"Shared parameters:\")\n", + "print(f\" System: n={n}, m={m}, p_out={p_out}, N={N}\")\n", + "print(f\" Dynamics: rho1={rho1}, rho2={rho2}, g1={g1}, g2={g2}\")\n", + "print(f\" Noise: obs_noise={obs_noise}, process_noise={process_noise}\")\n", + "print(f\" Nonlinearity: nonlinear_eps={nonlinear_eps}\")\n", + "print(f\" Model: n_delays={n_delays}, rank={rank}, pf={pf}\")\n", + "print(f\" Evaluation: n_iters={n_iters}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "4b891d5e", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "# Updated to use DSA package imports\n", + "import sys\n", + "sys.path.insert(0, '..') # Add parent directory to path to import DSA\n", + "\n", + "plt.rcParams['pdf.fonttype'] = 42\n", + "plt.rcParams['ps.fonttype'] = 42\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "f3cdd518", + "metadata": {}, + "outputs": [], + "source": [ + "from DSA import InputDSA\n", + "from DSA import SimilarityTransformDist as SimDist\n", + "from DSA import ControllabilitySimilarityTransformDist as ControlSimDist\n", + "from tqdm import tqdm\n", + "\n", + "def compare_systems_with_InputDSA(Ys, Us, n_delays=150, rank=10, backend='n4sid'):\n", + " \"\"\"\n", + " Compare controlled systems using InputDSA from DSA package.\n", + " Uses the new update_compare_method() to avoid refitting DMDs multiple times.\n", + " \n", + " Parameters:\n", + " - Ys: list of output data arrays (p_out, N)\n", + " - Us: list of control input arrays (m, N)\n", + " - n_delays: number of delays for DMD\n", + " - rank: rank for DMD\n", + " - backend: 'n4sid' or 'custom' for SubspaceDMDc\n", + " \n", + " Returns:\n", + " - sims_full: joint similarity scores\n", + " - sims_control_joint: control scores from joint optimization\n", + " - sims_state_joint: state scores from joint optimization\n", + " - sims_control_separate: control scores from separate optimization\n", + " - sims_state_separate: state scores from separate optimization\n", + " \"\"\"\n", + " # Transpose data for InputDSA (expects time_first=True by default)\n", + " Ys_T = [Y.T for Y in Ys]\n", + " Us_T = [U.T for U in Us]\n", + " \n", + " # Configure DMD\n", + " # dmd_config = SubspaceDMDcConfig(\n", + " # n_delays=n_delays,\n", + " # rank=rank,\n", + " # backend=backend\n", + " # )\n", + " dmd_config = dict(\n", + " n_delays=n_delays,\n", + " rank=rank,\n", + " backend=backend\n", + " )\n", + " \n", + " # Create InputDSA with joint comparison\n", + " # This will fit the DMDs once and return joint comparison results\n", + " inputDSA = InputDSA(\n", + " X=Ys_T,\n", + " X_control=Us_T,\n", + " dmd_config=dmd_config,\n", + " compare='joint',\n", + " return_distance_components=True\n", + " )\n", + " \n", + " # Fit DMDs and get joint comparison results\n", + " sims_full, sims_state_joint, sims_control_joint = inputDSA.fit_score()\n", + " \n", + " # Update comparison method to 'state' without refitting DMDs\n", + " inputDSA.update_compare_method(compare='state')\n", + " sims_state_separate = inputDSA.score()\n", + " \n", + " # Update comparison method to 'control' without refitting DMDs\n", + " inputDSA.update_compare_method(compare='control')\n", + " sims_control_separate = inputDSA.score()\n", + " \n", + " return sims_full, sims_control_joint, sims_state_joint, sims_control_separate, sims_state_separate\n", + "\n", + "\n", + "#strict comparison metrics, for when we fit and compare separately\n", + "def compare_A(A1,A2):\n", + " simdist = SimDist(iters=1000,score_method='wasserstein',lr=1e-3,verbose=True)\n", + " return simdist.fit_score(A1,A2)\n", + "\n", + "def compare_A_full(As):\n", + " sims = np.zeros((len(As),len(As)))\n", + " for i in range(len(As)):\n", + " for j in range(i+1,len(As)):\n", + " sims[i,j] = compare_A(As[i],As[j])\n", + " sims[j,i] = sims[i,j]\n", + " return sims\n", + "\n", + "def compare_B(B1,B2):\n", + " csimdist = ControlSimDist(score_method='euclidean',compare='control')\n", + " sim = csimdist.fit_score(None, None, B1, B2)\n", + " return sim\n", + "\n", + "def compare_systems_full(As,Bs):\n", + " csimdist = ControlSimDist(score_method='euclidean',compare='joint',return_distance_components=True)\n", + " sims_full = np.zeros((len(As),len(As)))\n", + " sims_control_joint = np.zeros((len(As),len(As)))\n", + " sims_state_joint = np.zeros((len(As),len(As)))\n", + " sims_control_separate = np.zeros((len(As),len(As)))\n", + " sims_state_separate = np.zeros((len(As),len(As)))\n", + " for i in tqdm(range(len(As))):\n", + " for j in range(i+1,len(As)):\n", + " all_sims = csimdist.fit_score(As[i],As[j],Bs[i],Bs[j])\n", + " sims_full[i,j] = sims_full[j,i] = all_sims[0]\n", + " sims_state_joint[i,j] = sims_state_joint[j,i] = all_sims[1]\n", + " sims_control_joint[i,j] = sims_control_joint[j,i] = all_sims[2]\n", + " \n", + " for i in tqdm(range(len(As))):\n", + " for j in range(i+1,len(As)):\n", + " sims_state_separate[i,j] = compare_A(As[i],As[j])\n", + " sims_control_separate[i,j] = compare_B(Bs[i],Bs[j])\n", + " sims_state_separate[j,i] = sims_state_separate[i,j]\n", + " sims_control_separate[j,i] = sims_control_separate[i,j]\n", + "\n", + " return sims_full, sims_control_joint, sims_state_joint, sims_control_separate, sims_state_separate\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "7ba785c8", + "metadata": {}, + "outputs": [], + "source": [ + "def make_stable_A(n, rho=0.9, rng=None):\n", + " rng = np.random.default_rng(rng)\n", + " M = rng.standard_normal((n, n))\n", + " # Make it diagonally dominant-ish and scale spectral radius\n", + " A = M / np.max(np.abs(np.linalg.eigvals(M))) * rho\n", + " return A\n", + "\n", + "def simulate_system(A, B, C, U, x0=None,rng=None,obs_noise=0.0,process_noise=0.0,\n", + " nonlinear_eps=0.0,nonlinear_func= lambda x: np.tanh(x),nonlinear_eps_input=0.0):\n", + " n, m = B.shape\n", + " p_out = C.shape[0]\n", + " N = U.shape[1]\n", + " X = np.zeros((n, N+1))\n", + " C_full = np.eye(A.shape[0])\n", + " C_full[np.where(C == 1)[1],np.where(C == 1)[1]] = 0.0\n", + "\n", + " if x0 is not None:\n", + " X[:, 0] = x0\n", + " else:\n", + " X[:, 0] = np.random.default_rng(rng).standard_normal((n,))\n", + " Y = np.zeros((p_out, N))\n", + " for t in range(N):\n", + " X[:, t+1] = A @ (X[:, t]) + nonlinear_eps * C_full @ nonlinear_func(A @ X[:, t]) + \\\n", + " B @ ((1-nonlinear_eps_input) * U[:, t] + nonlinear_eps_input * nonlinear_func(U[:, t])) + \\\n", + " np.random.normal(0, process_noise, (n,))\n", + " Y[:, t] = C @ X[:, t] + np.random.normal(0, obs_noise, (p_out,))\n", + " return X[:, 1:], Y # states aligned with Y\n", + "\n", + "def smooth_input(m, N, alpha=0.9, rng=None):\n", + " rng = np.random.default_rng(rng)\n", + " w = rng.standard_normal((m, N))\n", + " U = np.zeros_like(w)\n", + " for t in range(N):\n", + " U[:, t] = alpha*(U[:, t-1] if t>0 else 0) + (1-alpha)*w[:, t]\n", + " return U" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "87d14512", + "metadata": {}, + "outputs": [], + "source": [ + "def simulate_As_Bs(latent_dim, input_dim, observed_dim, seq_length,rho1=rho1,\n", + " rho2=rho2, g1=g1,g2=g2, seed1=seed1, seed2=seed2, input_alpha=input_alpha,same_inp=False,n_Us=n_Us,\n", + " obs_noise=obs_noise,process_noise=process_noise,nonlinear_eps=nonlinear_eps,nonlinear_func= lambda x: np.tanh(x)):\n", + "\n", + " A1_true = make_stable_A(latent_dim, rho=rho1, rng=seed1)\n", + " cov_matrix_B1 = np.random.default_rng(seed1).standard_normal((latent_dim, latent_dim))\n", + " cov_matrix_B1 = cov_matrix_B1 @ cov_matrix_B1.T # Make it symmetric positive definite\n", + " B1_true = np.random.default_rng(seed1).multivariate_normal(np.zeros(latent_dim), cov_matrix_B1, input_dim).T * g1\n", + "\n", + " A2_true = make_stable_A(latent_dim, rho=rho2, rng=seed2)\n", + " C = np.linalg.qr(np.random.default_rng(seed2).standard_normal((latent_dim, latent_dim)))[0]\n", + " cov_matrix_B2_rotated = C @ cov_matrix_B1 @ C.T \n", + " B2_true = np.random.default_rng(seed2).multivariate_normal(np.zeros(latent_dim), cov_matrix_B2_rotated, input_dim).T * g2\n", + "\n", + " # Random partial observation: select p_out of n states\n", + " idx_obs = np.sort(np.random.default_rng(seed1).choice(latent_dim, size=observed_dim, replace=False))\n", + " C_true = np.zeros((observed_dim, latent_dim))\n", + " C_true[np.arange(observed_dim), idx_obs] = 1.0\n", + " \n", + " X_trues, Ys,Us = [], [], []\n", + " i = 0\n", + " if same_inp:\n", + " U = smooth_input(input_dim, seq_length, alpha=input_alpha, rng=seed1+i) \n", + " control_labels = []\n", + " state_labels = []\n", + " for a1, As in enumerate([A1_true, A2_true]):\n", + " for b1, Bs in enumerate([B1_true, B2_true]):\n", + " i += 1\n", + " if not same_inp:\n", + " for j in range(n_Us):\n", + " U = smooth_input(input_dim, seq_length, alpha=input_alpha, rng=seed1+ i + j) \n", + " X_true, Y = simulate_system(As, Bs, C_true, U, x0=np.zeros(latent_dim),rng=seed1+i,\n", + " obs_noise=obs_noise,process_noise=process_noise,\n", + " nonlinear_eps=nonlinear_eps,nonlinear_func=nonlinear_func)\n", + " X_trues.append(X_true)\n", + " Ys.append(Y)\n", + " Us.append(U)\n", + " control_labels.append(b1)\n", + " state_labels.append(a1)\n", + " else:\n", + " X_true, Y = simulate_system(As, Bs, C_true, U, x0=np.zeros(latent_dim),rng=seed1+i,\n", + " obs_noise=obs_noise,process_noise=process_noise,\n", + " nonlinear_eps=nonlinear_eps,nonlinear_func=nonlinear_func)\n", + " X_trues.append(X_true)\n", + " Ys.append(Y)\n", + " Us.append(U)\n", + " control_labels.append(b1)\n", + " state_labels.append(a1)\n", + "\n", + " return X_trues, Ys, Us, control_labels, state_labels, (A1_true, A2_true), (B1_true, B2_true)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "728cf5a2", + "metadata": {}, + "outputs": [], + "source": [ + "from DSA import DMD,DMDc, SubspaceDMDc\n", + "from tqdm import tqdm\n", + "\n", + "def get_dmds(Ys,n_delays=1,rank=None):\n", + " As = []\n", + " for Y in Ys:\n", + " dmd = DMD(Y.T,n_delays=n_delays,rank=rank)\n", + " dmd.fit()\n", + " As.append(dmd.A_v.numpy())\n", + " return As\n", + "\n", + "def get_dmdcs(Ys,Us,n_delays=1,rank=None):\n", + " As = []\n", + " Bs = []\n", + " for Y, U in zip(Ys, Us):\n", + " dmdc = DMDc(Y.T, U.T,n_delays=n_delays,n_control_delays=n_delays,rank_input=rank,rank_output=rank)\n", + " dmdc.fit()\n", + " As.append(dmdc.A_v.numpy())\n", + " Bs.append(dmdc.B_v.numpy())\n", + " return As, Bs\n", + "\n", + "\n", + "def get_subspace_dmdcs(Ys, Us, p=20, rank=None, backend='n4sid'):\n", + " \"\"\"Fit SubspaceDMDc models using DSA package.\"\"\"\n", + " As, Bs, Cs, infos = [], [], [], []\n", + " for Y, U in zip(Ys, Us):\n", + " model = SubspaceDMDc(Y.T, U.T, n_delays=p, rank=rank, backend=backend)\n", + " model.fit()\n", + " As.append(model.A_v)#.numpy())\n", + " Bs.append(model.B_v)#.numpy())\n", + " Cs.append(model.C_v)#.numpy())\n", + " infos.append(model.info)\n", + " return As, Bs, Cs, infos\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "db4d50cb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[(2, 1000), (2, 1000), (2, 1000), (2, 1000), (2, 1000), (2, 1000), (2, 1000), (2, 1000), (2, 1000), (2, 1000), (2, 1000), (2, 1000), (2, 1000), (2, 1000), (2, 1000), (2, 1000)]\n" + ] + } + ], + "source": [ + "X_trues, Ys, Us, control_labels, state_labels, A_trues, B_trues = simulate_As_Bs(n,m,p_out_small,\n", + " N_small,input_alpha=input_alpha,g1=g1,g2=g2,same_inp=False,n_Us=n_Us,\n", + " obs_noise=obs_noise,process_noise=process_noise,\n", + " nonlinear_eps=nonlinear_eps)\n", + "print([i.shape for i in Ys])" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "3ef1f7f5", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#plot examples of the inputs and the outputs\n", + "fig, ax = plt.subplots(2,2,figsize=(4,2),sharex=True,sharey=True)\n", + "ax = ax.flatten()\n", + "for i in range(4):\n", + " ind = 4*i\n", + " ax[i].plot(Us[ind].T[:100] + 10*np.mean(np.abs(Us[ind])), color=plt.cm.Set2(1), label='Input (u)')\n", + " ax[i].plot(Ys[ind].T[:100, 0], color=plt.cm.Set2(2), label='Output (y)')\n", + " #remove all ticks and lines\n", + " ax[i].set_xticks([])\n", + " ax[i].set_yticks([])\n", + " ax[i].spines['top'].set_visible(False)\n", + " ax[i].spines['right'].set_visible(False)\n", + " ax[i].spines['bottom'].set_visible(False)\n", + " ax[i].spines['left'].set_visible(False)\n", + " \n", + "ax[0].text(1, 0.4, 'Input', transform=ax[0].transAxes, color=plt.cm.Set2(1), va='top')\n", + "ax[0].text(1, 0.2, 'Output', transform=ax[0].transAxes, color=plt.cm.Set2(2), va='top')\n", + "plt.tight_layout()\n", + "# plt.savefig(f'{folder_path}/input_output_examples.pdf', format='pdf', dpi=300, bbox_inches='tight')\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "eef05f5d", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/sh/r0d61tjn42s4mc3nxqb0hhq00000gn/T/ipykernel_89440/1787994131.py:36: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n", + " ax[1].legend(title=\"Delays\", loc='upper right', bbox_to_anchor=(1.5, 1),\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Use SubspaceDMDc from DSA package to analyze singular values\n", + "\n", + "\n", + "fig, ax = plt.subplots(2,2,figsize=(9,6),sharey=True,sharex=True)\n", + "ax = ax.flatten()\n", + " \n", + "for j, (Y, U) in enumerate(zip(Ys[::n_Us], Us[::n_Us])):\n", + " # Test different numbers of delays for subspace identification\n", + " nds_all = [10, 25, 50, 75, 100, 125, 150, 175, 200]\n", + " for k, nds in enumerate(nds_all):\n", + " # Fit SubspaceDMDc with varying number of delays\n", + " model = SubspaceDMDc(\n", + " Y.T, # SubspaceDMDc expects (T, p_out)\n", + " U.T, # SubspaceDMDc expects (T, m)\n", + " n_delays=nds,\n", + " rank=rank, # Use fixed rank for comparison\n", + " backend='n4sid'\n", + " )\n", + " model.fit()\n", + " \n", + " # Extract singular values from model info\n", + " singular_vals = model.info['singular_values_O']\n", + " \n", + " # Convert to numpy if needed\n", + " if hasattr(singular_vals, 'numpy'):\n", + " singular_vals = singular_vals.numpy()\n", + " \n", + " # Plot singular values\n", + " ax[j].plot(singular_vals, '-', label=f'{nds}', \n", + " color=plt.cm.Blues_r(k / (len(nds_all) + 4)))\n", + " ax[j].set_yscale('log')\n", + " ax[j].axvline(x=rank, color='k', linestyle=':', alpha=0.5)\n", + " \n", + " ax[j].set_xlabel('Mode Number')\n", + " ax[j].set_title(f'System {j+1}')\n", + " ax[1].legend(title=\"Delays\", loc='upper right', bbox_to_anchor=(1.5, 1), \n", + " fontsize=12, title_fontsize=15)\n", + "\n", + "ax[0].set_ylabel('Singular Value')\n", + "ax[2].set_ylabel('Singular Value')\n", + "plt.tight_layout()\n", + "# plt.savefig(f'{folder_path}/singular_values_subspace_dmdc.pdf', format='pdf', dpi=300, bbox_inches='tight')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "3636fc5c", + "metadata": {}, + "outputs": [], + "source": [ + "dec = 0 #can change this to look at the efect of using the incorrect ranks\n", + "A_dmd = get_dmds(Ys,n_delays=n_delays,rank=rank- dec)\n", + "A_cs, B_cs = get_dmdcs(Ys,Us,n_delays=n_delays,rank=rank - dec)\n", + "As, Bs, Cs, infos = get_subspace_dmdcs(Ys,Us,p=pf,rank=rank-dec,backend='custom')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "5ae5efa9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "N4SID - A matrix shapes: [(20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20)]\n", + "N4SID - Ranks used: [20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n", + "N4SID - Backend info: ['unknown', 'unknown', 'unknown', 'unknown', 'unknown', 'unknown', 'unknown', 'unknown', 'unknown', 'unknown', 'unknown', 'unknown', 'unknown', 'unknown', 'unknown', 'unknown']\n", + "\\nEigenvalue comparison (first system):\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\\nComputing similarity matrices...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 16/16 [00:00<00:00, 196.85it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 42.53it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 120.82it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 49.00it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Custom backend silhouette score: 0.975\n", + "N4SID backend silhouette score: 0.956\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "As_n4sid, Bs_n4sid, Cs_n4sid, infos_n4sid = get_subspace_dmdcs(Ys, Us, p=pf, rank=rank-dec, backend='n4sid')\n", + "print(f\"N4SID - A matrix shapes: {[A.shape for A in As_n4sid]}\")\n", + "print(f\"N4SID - Ranks used: {[info['rank_used'] for info in infos_n4sid]}\")\n", + "print(f\"N4SID - Backend info: {[info.get('backend', 'unknown') for info in infos_n4sid]}\")\n", + "\n", + "# Quick comparison of eigenvalues (first system)\n", + "print(\"\\\\nEigenvalue comparison (first system):\")\n", + "eigs_custom = np.linalg.eigvals(As[0])\n", + "eigs_n4sid = np.linalg.eigvals(As_n4sid[0])\n", + "eigs_real = np.linalg.eigvals(A_trues[0])\n", + "\n", + "plt.figure(figsize=(8, 6))\n", + "plt.scatter(eigs_real.real, eigs_real.imag, alpha=0.7, label='True', s=100)\n", + "plt.scatter(eigs_custom.real, eigs_custom.imag, alpha=0.7, label='Custom backend', s=50)\n", + "plt.scatter(eigs_n4sid.real, eigs_n4sid.imag, alpha=0.7, label='N4SID backend', s=50, marker='x',c='k')\n", + "plt.xlabel('Real part')\n", + "plt.ylabel('Imaginary part')\n", + "plt.title('Eigenvalue comparison (first system)')\n", + "plt.legend()\n", + "plt.grid(True, alpha=0.3)\n", + "plt.axhline(y=0, color='k', linestyle='-', alpha=0.3)\n", + "plt.axvline(x=0, color='k', linestyle='-', alpha=0.3)\n", + "plt.show()\n", + "\n", + "# Compute distances using both backends for comparison\n", + "print(\"\\\\nComputing similarity matrices...\")\n", + "_, _, _, _, sims_state_custom = compare_systems_full(As, Bs)\n", + "_, _, _, _, sims_state_n4sid = compare_systems_full(As_n4sid, Bs_n4sid)\n", + "\n", + "from sklearn.metrics import silhouette_score\n", + "silh_custom = silhouette_score(sims_state_custom, state_labels, metric='precomputed')\n", + "silh_n4sid = silhouette_score(sims_state_n4sid, state_labels, metric='precomputed')\n", + "\n", + "\n", + "print(f\"Custom backend silhouette score: {silh_custom:.3f}\")\n", + "print(f\"N4SID backend silhouette score: {silh_n4sid:.3f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "79bb2540", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 16/16 [00:00<00:00, 50.42it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 52.21it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 48.77it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 51.64it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 203.23it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 50.05it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 239.30it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 61.68it/s]\n" + ] + }, + { + "data": { + "image/png": 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OGDFCJSYmxrZJFnzx4sV6mDkAVCRiEwBbEZ8A2IjYBMBWxCeUWRKzTp06sYx4WlqaMYlPQkKCOu6449SoUaOKc0gAKDViEwBbEZ8A2IjYBMBWxCeUWRJz6tSp+nvbtm3VTTfdVKqh462D5izmGU7+teTSIznGeo6rHlz9gFkrZXfErIuQ4qpt4K2BeWf7I431f67+ylhf6qqJmewzz5PpmOfJdOLnqVuiQfoASqosY1N6xKxzuS8S73GeE4jXoPPW5xUbw/F9Uzx1nfY5Zrh1b/fWwPR9schYzx1q1n1K2haPN5c1mWdsu3X5WeZzI21jyzuzzLq/AKpOfBrWIF5DUrQPbY8thzxVdxdktco3jiUH4vWgxHt7uhnrY1vH61E+94U5UeO24+N14vKukRmPn2/v9dQX9sTLxVktYsvzd7cxtgGoWm2nHeFUY/233Pj6joB53BxlvqfKcbWPkn1ZBe6b6bjq8HpLm3nmJ/BleGrYuerOhfaabTRfJP+5DYIZhdcEBWBpfMo1/36dzAJqW3rjQMgVb7LM2KR8ZsInkpubbw3M8LZtxnqgtlmT1zhsjic2HVjBMSbkyU/VJCVKt40dO9aoiblmzRo1adIk9cEHH5TltQFAsRCbANiK+ATARsQmALYiPqHMkpjDhg1TL730kl7etWuXOvbYY9VDDz2kH3/qqadKckgAKDViEwBbEZ8A2IjYBMBWxCeUWRJzwYIFqnfv3nr5jTfeUE2bNtW9MSWx+dhjj5XkkABQasQmALYiPgGwEbEJgK2ITyh1TcyojIwMPbGPkCHkMnuU3+/XE/tIMrMo9kbMugL1AvFabT9m7ze2pXlSrc2D8dpOmY5ZN6CO36wNkOSLv8RdkeQCa2De3PY4Y/1vvy6MLYeVWSMhoMwCBQFXPSq/ZxuAilEWsWldToN8t/3mqfnkFfLF667kOIEi77uhb0qBNTDb/u1LY913VJfY8qJRZi257g02GOu/7o2/nqDfrEkHoOKUNj6FPHV26/vj6w9u62ds61xrk7GeHkmKLXdNWm9sa5sQr60pfnTVqszIDRVYA9NbI7POZ/G4191znqbB3cb6xtx6seUjaq8ztgGoWm2nhfvMtsi27N+PJ9LD8fiTV/so4nrflOiJc959k/zxeRICHdsVWAMzd8PGfK833N5s64VrmefJqu9qO+2jJiZQZeNTwJNIcs+n4i046aqbe8BzPfOwqKCZRvOF43HC5zmntwZmeM8ec3vd3yfPzovPl3+93hA1MYunY8eO6q233lLr1q1Ts2fPVgMHDtSPb926VdUuoFApAJQnYhMAWxGfANiI2ATAVsQnlNlw8ttvv13PEiWzRfXo0UP17Nkzlh0/4ogjSnJIACg1YhMAWxGfANiI2ATAVsQnlNlw8nPOOUedcMIJatOmTerwww+PPd6/f3915plnluSQAFBqxCYAtiI+AbARsQmArYhPKLMk5tSpU9UFF1ygJ/Rxk1nKiyrHU2NydyReBzPJVStOmBWZzDqYIWXWJ9gdyTaf66qRmR6uZWxb6ll318AUd7fvHlt+c/03xraIz6yZ4HfVxMx08q9dAKD8lEVs8vK7/tYDnrjlrdUUdtV1cj8vr+e69/WWO0na5su3BqZw5v8YW97tiWPnNfjaWF+W2jy2/O7WruaJAFSZ+LQj16zJOzsnXp/ymNRVxrZ9kXjtcJHmj9eKW5DR1tj23w2HGev/OPit2PJbOfF2kMjITci3BqbYfcKO2HJklRnH6gYyjPWfs+KxaV1mfWMbgKrddoo4vjxrXua1HnbiAwMjvoL3NSSECq5nV9D1JZqDEXOTPOdxXYcvl/d1QLWJTxGnZGOS3c8TrhqYXk4hschbAzO8K14zPNDIrNd7QPRxxaaw481H1Rwleq3jxo1TTZo0USNHjlRffPFF2V8VAJQAsQmArYhPAGxEbAJgK+ITyiyJuWHDBvXiiy+q7du3q379+qnOnTur+++/X23evLkkhwOAMkFsAmAr4hMAGxGbANiK+IS8+ByndGOft2zZol5++WWd1Fy2bJkaPHiw7qF52mmnKb8//xzp5g3xYUQi2TVFfKaTf/dc4e44m+jzTGFfwLCDDM9x3ecUYU+HXfdQ9TNbml2Wp6z9zFjPdA2b+DXHHBY1pP0P+V4TgPJR0tj08apOxnqjwL7YcnrEHEqZ7Sln4R5e7h0+nu0Zeu4eWvn45v7GtsuazDPWF+1vY6y7h5B/2i3J3PZux3yHdO1e0NDYtuJvNxjrAOyNT7cuPstYbxhKjy1vyo4PLRc9U1ca62ek7I0tX7DqJGPbzc3fN9ZDrlIYbYJmHHt7bytjvXvS+nzjzS3tehjb+i6Olw0SzRN25jv8/ZrOHxvrAOxuO438doSxvnpv/L1Qt7objG37PW2pnEi8fVQ7aMaJvWEzNtQOxktjfPu3o41tob3xcmMi7Bky7h5CnvjOt8a2QL165gtqEI+pvkyzVNl7ayeZ+wKwNj4N7jzOfMCV+vJ5h31702KuoduOP/+SEwfwXIsvx4xNXk4gvn94xa/GtpUvm5Nmd3gyfs0zpz9jbKvdfJ2qKUo9dF6GlcskPzJDufzyLFmyRA0fPlx16NBBzZ07t2yuEgCITQCqCdpOAGxEbAJgK+ITSp3ElEz4gw8+qLp06aKHlO/Zs0fNmjVLrVq1Snf7Pe+883QyEwAqErEJgK2ITwBsRGwCYCviE8okiSlddlu1aqWmTZumRo0apZOWr776qhowYIDenpKSom688Ua1bl3N6dIKoPIRmwDYivgEwEbEJgC2Ij4hL0FVAo0bN1affPKJHkKen0aNGulemfkJeWpX7orEawWYleOkVqUpyVWDIOKpXZBlVMwsOEvrrb3pracZcdWF8tbAHNX6BGN90ur4LO3Ng7sLOCuA8lIWsclrWzgltpzkyzG2JXiiU44reoV8uQXWxNwVTo4tZ4ZDxrZbl5u177o3MOtJndfg69jyrHfPMbbVGWLWwmv+VVpseUHXEoV8ABbEp23Zqcb63M0HxZb7NDH/7pfub2Gs7wnviC13SzPjyf/9dpyx/sOueM3y4xuatZkCrnaRaOpp77hr/XprYH7SLV7LV0xfvzC2vCa3gNpSAKxvO/k9scHcVvLpFwKe57qPFcg2z+mLmPuGa5ntrtykeJxJ9tTADO+M1+gVwTrxtpMKet+ZAqgq8emn6xsY6/5U872cmxMx2yJOputvP1RwHAsmxd/3RTzH8ZbP9HnimnvNiZg1MDte/L2xvvy5eC3gY54z5zb4+XZVYxSrJ+aXX36ph4w///zzsV+kl156SbVr107/gl1++eUqKytLP+7z+VSbNuZkFABQHohNAGxFfAJgI2ITAFsRn1BmScw777xT/fjjj7F1mcRHZoSSYeTjxo1T//3vf9W9995bnEMCQKkRmwDYivgEwEbEJgC2Ij6hzJKYCxcuVP3794+tv/baa6pHjx5qypQp6oYbblCPPfaYmj59enEOCQClRmwCYCviEwAbEZsA2Ir4hIIUq0Dazp079dT2UVKf4JRTTomtH3PMMUWezGdXxKxjkuGqF5fs81bBNG0Pxy+7ScCsa7AtYr6k5oH4sTI99TMzHbNAQeCAeprx9RxPvUx3DUwxpu3xseV7Vn1T4PUDKFtlGZuOTozXdBN+12c9IV/BdZH8Kh6PAj7zM6KMyD5jPdmfEFv+vs5aY1tupK2x/utes57LstR4zbqIJ465a2CKjcelx5ZrvZt/HRgAdsenbqnrjfVDUjbFlldnNjS2JQeyzecmbsizbqVYFzTji9uw2mYtpsVZZq3NjblmXbmfs+KxqXnCznxrYIrzWsbrWyV+0tTYNqt1vpcEwMK2093NPjLW1zSK1/runmC+N9vvmPEpx4m/30r1Jxrbshyz3ZLqT4otD1nXocBryqrviW3uwnQN6uZfA1PaYatd7bJjuxZ4HgD2xqeDXvq91GFMsUpwF5CT8hS6dPyudU/OqVCuY/nCkXxrYIqDL/sutvzkGnPOFqWuVzVFsXpiyi9StGhqdna2WrBggTruuHhB+PT0dBUKmRNUAEB5IzYBsBXxCYCNiE0AbEV8QpklMYcMGaJrX3766adq/PjxKjk5WfXu3Tu2ffHixapDh4I/FQOAskZsAmAr4hMAGxGbANiK+IQyG05+1113qbPOOkv17dtXpaamqhdffFElJMSHRL7wwgtq4MCBRTqW2VHWHEIeKqSbb7I/PoW9d3BkU9fwcREw+gybXXvrelK4fk//Yvfw85+z6xvbmgd3G+vuIeS3tjvW2Pah98UCKFNlGZue2GkOG8pxlbrwe2JIwGf+cYdccSwzYvZKrxM0h3BGnHgA+n5PK2PbzqxkYz3oN8/z7tb4Ne5eYA4jXdDVDOvuIeR1hqwsOBADsDY+tQrtyHebd4j4vog5JHNuRqfY8vIMc+h288Rdxnqn5M2x5Q/3HWpsm7+7jbF+RG1zKNe6zHhb6ZCUjca2NblmG8s9hDyrb/ycGrEJqFJtpxO+vNJYz9oXP07temZ8yg2bb8AiEVfZnlCuuW+uWcYn0bW9zkHmEPBghvkeMLjPXPflxttwvkxzSLsKesoFuYeQf7PE3AagysSnB199xljvGIzHm4insRH2vM9b7wpHjQIFN0waB1JiyxkRM754y5F518OukhrechvHPHeDse4eQv7XNifU2JxTsZKYDRs2VPPmzVO7d+/Wv0yBgPkDmDFjhn4cACoSsQmArYhPAGxEbAJgK+ITyiyJGVWnTp08H69f3+ytCAAVidgEwFbEJwA2IjYBsBXxCaWuiQkAAAAAAAAAFc3nOMWdAx4AAAAAAAAAKg49MQEAAAAAAABYjSQmAAAAAAAAAKuRxAQAAAAAAABgNZKYAAAAAAAAAKxGEhMAAAAAAACA1UhiAgAAAAAAALCaVUnMbdu2qauuukq1bt1aJSYmqqZNm6pBgwapzz//vNTHHjFihDrjjDPK5DoB1CzEJgC2Ij4BsBGxCYC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hYT0xP/30U/Xee++pWbNmqW7duhWZ2OfVV1+tyG4BoFKITQBsRXwCYCNiEwBbEZ+QsCRmw4YN1emnn64qIyUrz7Xs98fGPBbku08rJbXQtewcQp7xyieudVuGHem+b05sv30bfedalxt2J19TfO7jOLUI1PNs6+4G/EVerKtvuyCTGwE1IRGxqciQccfIAs/IySJDIJ1DyAt/XeNe18o99Nwp4B1nmUCeChsAkjQ+tU/d5FrO8sfKUGwNuIdEpfrcQ8SdQ8hDv/3mXteggWs5vCs21GqPYKZrXZ6ODXky+9K6xLZRONUTTL0csamULQHY3naKxzvs0kIBTymesGOworekD4DkiU8dUtxtp2x/LAfV0B9r74gUb9vJMYQ8tGVL/AM5hpN3CLrbZLu1e0h4jmc50xfLSRVmp7nW+dPdebGCBrFtC7Lc+ai6pEJJzClTpiT+TACgkohNAGxFfAJgI2ITAFsRn5CwmpiisLBQvfvuu+qJJ55QO3bsMLetXbtW7dzpndQGAKoPsQmArYhPAGxEbAJgK+ITEtITc9WqVap///5q9erVKi8vT51wwgkqKytLTZgwwSw//vjjFdktAFQKsQmArYhPAGxEbAJgK+ITEpbElFmiDj30UPX111+rJk2aRG8/7bTT1IgR7mnhyyocjtUR8DnqY3rrZQrtKPLmrYHZaOpC1/L2c46IXt8VdtcYKPBMb5+rU+LWRXAKaXftlBRHcbzNIXftl5Ir4QFIpITEJk/pJlcdzHjrRMBfYg3MwnXrXcvB9u2i10OeinAhz4HCngOFHLWbQkXWuffljFShine+B1BJVdF2igiUVlUyHCqxBmZo+3b3vhpml9jWKbJbTx05ORMAyaUqY5MRrpmakt62Uzy0j4DaGZ/8ngkMAo4at951xdy5IqdcdDee719+5am96ainWeo0Cc5t67AKJTHnz5+vFixYoFJTU123d+jQQa1Z457MAgCqC7EJgK2ITwBsRGwCYCviE4pToW454XBYhUJFeyn++uuvZlg5ANQEYhMAWxGfANiI2ATAVsQnJCyJ2a9fPzVp0qToss/nMxP6jB07Vp188skV2SUAVBqxCYCtiE8AbERsAmAr4hMSNpx84sSJ6sQTT1T77ruvys3NVeeee6766aefVNOmTdX06dPLtA/vcH533ctSajA5zjolR5dYA1M0mP5x9HrGbXmudd5acl7p/oLYcXzuOk87w7mu5R3h2ND6dsGcuPsFUDUSEps84cdZOjdOmdxSOWtgisJVv5S5vp3fUb+lyLaedY6SwX9wrA6UElsB2BufAkXqOoVLXOf3fNZ9jvI/4V05JdbAFKGt2xz7df/WHdbueJPibcuVWszJeVKO6zVTMg9AgtpOyls/1xM74m5bRbWA47Wd/J77Fmkf+cq2HwCWt52KfNZ1mdYZrlqVpbRvHO0jb9sp4GkbBTw5KGfNTF0kSeZedt5VJ6hmZ51JYrZt29YUV33ppZfMX+mFOXz4cHXeeeepevXqJf4sAYDYBCCJ0XYCYCNiEwBbEZ+QsCTmBx98oI466iiTtJRLRGFhoVl3zDHHVGS3AFApxCYAtiI+AbARsQmArYhPKI5Pa8/YoDIIBAJq3bp1qnnz5q7bN2/ebG4rbtIfr+7XPVjiukJPZ07HqG7j0MFLotf7NvrOtW5XOM21nOGPDSF/tqt7OGegaxfXsk5z53T9O2NDxrc96u4WXBh2L+/MjR133+brXetePupx9wMAUCUSEZvC6/d0LYccw568wwO8toV3lzh0IBRn2NNZbY90La8dfZRrOWONe5hT4zk/Ra9vP9Ydx4qMhHDcdVdL9/kveuS6Es8JgF3x6YSj7nAt+0KxD3s46P5s+0LumPHUy49Fr+8RzCwxxnnj3ImtD3TvNyXVvZya4lrWhYXR663ed2+bFXSX4Vm7OzaMfXNuhmvd+33udy0DSK62U56OfXlL87njhNf/dgfKfq6ORs74zvu71gVbtnCfk6d0RnjHjuh1X5r7+6IKxxl6nuH+Yjr793+W+XwB1Gx8OmmPa1zLOjfWFvGlp7s39qTFHlowI3q9Q7B+/POM13YKBkss8SPCubF81WFf5rvWZXvKFP6S27jYdpR4tWesrVfbVWhiH8l7ymQ+XvJmyshwN0QBoLoQmwDYivgEwEbEJgC2Ij6h0sPJTz/9dPNXEpjDhg1TaY5fsSQLvnjxYjPMHACqE7EJgK2ITwBsRGwCYCviExKWxMzOzo5mxLOyslyT+KSmpqojjjhCjRgxojy7BIBKIzYBsBXxCYCNiE0AbEV8QsKSmFOmTDF/O3TooEaNGpXYoeOOEgTaM8hdxymVkht211kp8GwccsxD762BGfphmWs5sO9eruVwlrMGSqxWQXF8vtgD8DuuA6h6iYxNX+S5a5GUtTaT2Bou+bghT41M5329NTBb37vAtbzj7CNcy79c3DV6vdBTosUXK0lnOEOkp6wKgGSKT3Fqtnl5myG5jraQs1Zd8YfRJdbA1AWe+Oipp+ms+7Q75G6fBZ0FepVS+eHYtgWhstfEA2Bf22nCZndNzJxwLHbU98dvV330e+cKHTPY0h3oCtdvcC37s7Jcy4EWjpp63jp63ikiHGXTdDnahQDsik+6wNPmyY8t60Agbjtrl3a0abQ7Dvg9VRmdcyF4a2A664UXf5LhEttO6X738u5QLLY621F1TYVqYo4ePdpVE3PVqlVq0qRJ6p133knkuQFAuRCbANiK+ATARsQmALYiPiFhScxBgwapZ5991lzfunWrOvzww9XEiRPN7Y89VndmRQJgF2ITAFsRnwDYiNgEwFbEJyQsifnll1+qXr16mesvv/yyatmypemNKYnNhx56qCK7BIBKIzYBsBXxCYCNiE0AbEV8QnEqNJA+JyfHTOwjZAi5zB7l9/vNxD6SzCwLb91LV/0mb2rVXUrOVVcpxVMALlenlHzMtGDcGpih7350Lfv331uVVWFhrKaC31MnD0D1SERsauyp3eSs4pbiiUVe8erhhh016bzbZqwJx62BmfXSx65lfW5sfTjg3q/PU9cpHHTUdarQz1YAEqHS8cnv/qxrZ+0mT2zyhBtXffCQN0a4opw7zvlSU+LWwPTWefKlxmo1FYbdAScv7K49le+og+ndFkBytZ32SV/j3mc4LXq9vj/+vALrMv+YOLa8bacfdtWLWwMzvGOHe33YHb/KzFs/E0DyxCdPO8XZbvE56mP+sdIdI3Idc63kFKmJ6Y4LAUescraFilOk7eSoP54Xdse8HEcNzD/Wx84pr5CamOXSpUsX9frrr6tffvlFzZkzR/Xr18/cvnHjRtWgQYPy7QwAEoTYBMBWxCcANiI2AbAV8QnFqdBP32PGjDGzRMlsUT169FBHHnlkNDt+0EEHVWSXAFBpxCYAtiI+AbARsQmArYhPKE6F+qAOHjxYHX300WrdunXqgAMOiN7ep08fddppp1VklwBQacQmALYiPgGwEbEJgK2IT0hYEnPKlClqyJAhZkIfJ5mlvMy8teXilCnx1nby1kdxSvG56xOk+2O1Dvw7c937yaoXtwZmePHS6PW04B7uc/LUIPD7Yw8g6Kd2ClATEhGbOqZkupYLdOzznOJz13Tzahx2xxinUJwg13jOT67lXy7uWmINTNHgX7EambvO6OHemSc8+gtjx93Ruu7WTgGSPT5pT01M15LP+8F3L6e7aol745h72e/Yc5G6TUF3DPHWfQrn5MSOGXCvywy660ntdCznBkuuZw7A/rbTwIzYZ1/khLdGr9f3x68Pl+H/SlXE9zu6u5YDLZrHrYEZ3rUret2XFqvZWSxHzWF/hvv7IoDkiU9F6lM6Ptu+NPc67akZnuWL5ZEyfe52SsDT7vI7BjiHc/Pi1tp01sA0qwti7aEMz1e1zEBeiW2p7UFPTc86pELDyW+66SbVokULNXz4cLVgwYLEnxUAVACxCYCtiE8AbERsAmAr4hMSlsRcs2aNmjZtmtq0aZM69thj1d57760mTJig1q9fX5HdAUBCEJsA2Ir4BMBGxCYAtiI+oTg+7e03W04bNmxQzz//vElqLl26VPXv39/00PzLX/6i/P6Sc6Td3xxT4hDxvFx3d91w2L2feb0mR6+3CMTv4u8cNnX04tNVeaQFY8Oogn1Xu9blnXSYa3nj8N3R66Efslzrfrr5unIdF0DlVTQ2bVzT2rVc4AiR3qEDIU/43BGn1EXIsy7gi9334uvdMWLzfu7hnQ2Wu4+TkhMblpDxyifu/Xbp6Fr2FcTi2O493UOt5r1zU4nnC8Cu+HTce9e7lus5hhHlheKXilj5VZvo9XBq2Zt9vQ//zrW8O+RunxV62mfpgVi8+e2o2HBS4U9Pd+88EItzvswM16rZ6x4p8zkCqPm204KfO7mWt4djn/cMv2dopcfcnfuW+fz8Kha/PjisoXtdlrsckN7tLvHjLI+h8+Kfk2u/9eu7lufsnFbm+wKo2fh0yvy/uZYzg7HPfk6he1i331F6x6y/Ova9qTDbU4LC85VPO74jHvnAp3HbTnlh93KG45wWeebI9radnKm7QCN3DHx77cOqrqhQT0wnGVYuk/zIDOXy5lmyZIkaOnSo6ty5s5o3b15izhIAiE0AagnaTgBsRGwCYCviEyqdxJRM+P3336+6detmhpRv375dzZo1S61cudJ0+z3rrLNMMhMAqhOxCYCtiE8AbERsAmAr4hMSksSULrvt2rVTU6dOVSNGjDBJy+nTp6u+ffua9RkZGer6669Xv/zyS0V2DwAVQmwCYCviEwAbEZsA2Ir4hOLEL6JUgubNm6v333/fDCEvSbNmzUyvzJL4HfXgRMAfq0FQ4KiTJLSnllxDf7DYmpci5JnCfmc4t8TaTaXRhY7jeGpgpr39mWs5//jYc+HrGKuPCaD6JCI2pfi8cSJc4rqwozaTKHBs6xXybBtwhjVPiTpfoWfZW7rYV3INzNAy92MLtmoZvV6Y6Y6XAJInPjlrYIr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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.metrics import silhouette_score\n", + "A_type = [A_dmd, A_cs, As, As_n4sid]\n", + "B_type = [A_dmd, B_cs, Bs, Bs_n4sid]\n", + "names = ['DMD, Partially Observed', 'DMDc, Partially Observed', 'Old Subspace DMDc, Partially Observed', 'Subspace DMDc, Partially Observed']\n", + "for Ai, Bi, name in zip(A_type, B_type, names):\n", + "\n", + " sims_full, sims_control_joint, sims_state_joint, sims_control_separate, sims_state_separate = compare_systems_full(Ai,Bi)\n", + "\n", + " fig, ax = plt.subplots(1, 5, figsize=(15, 3))\n", + " \n", + " # Define data and titles for each subplot\n", + " sims_data = [sims_full, sims_state_joint, sims_control_joint, sims_state_separate, sims_control_separate]\n", + " titles = ['Joint', \n", + " f'State (Joint) \\n {np.round(silhouette_score(sims_state_joint,state_labels,metric=\"precomputed\"),2)}',\n", + " f'Control (Joint) \\n {np.round(silhouette_score(sims_control_joint,control_labels,metric=\"precomputed\"),2)}',\n", + " f'State (Separate) \\n {np.round(silhouette_score(sims_state_separate,state_labels,metric=\"precomputed\"),2)}',\n", + " f'Control (Separate) \\n {np.round(silhouette_score(sims_control_separate,control_labels,metric=\"precomputed\"),2)}']\n", + " \n", + " # Loop through all subplots\n", + " for i, (data, title) in enumerate(zip(sims_data, titles)):\n", + " im = ax[i].imshow(data)\n", + " cbar = plt.colorbar(im, ax=ax[i], shrink=0.2, location='top')#, label='Distance')\n", + " cbar.ax.tick_params(labelsize=10)\n", + " cbar.ax.spines['top'].set_visible(False)\n", + " cbar.ax.spines['right'].set_visible(False)\n", + " cbar.ax.spines['bottom'].set_visible(False)\n", + " cbar.ax.spines['left'].set_visible(False)\n", + " ax[i].set_title(title,y=1.8)\n", + " #loop through all of them and remove x and yticks, then add System as text label for each\n", + " for i in range(5):\n", + " ax[i].set_xticks([])\n", + " ax[i].set_yticks([])\n", + " # ax[i].text(0.5, -0.1, 'System', transform=ax[i].transAxes, ha='center', va='top')\n", + " ax[i].set_ylabel('System')\n", + " ax[i].set_xlabel('System')\n", + " ax[i].spines['top'].set_visible(False)\n", + " ax[i].spines['right'].set_visible(False)\n", + " ax[i].spines['bottom'].set_visible(False)\n", + " ax[i].spines['left'].set_visible(False)\n", + " plt.suptitle(name,y=1.1)\n", + " plt.tight_layout()\n", + " # plt.savefig(f'{folder_path}/{name}.eps', format='eps', dpi=300, bbox_inches='tight')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "2b529073", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 16/16 [00:00<00:00, 39.36it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 55.38it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 44.85it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 55.31it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 173.94it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 48.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Silhouette Scores:\n", + "DMD State: 0.901\n", + "DMDc State: 0.887\n", + "DMDc Control: 0.064\n", + "SubspaceDMDc State: 0.956\n", + "SubspaceDMDc Control: 0.747\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Get the similarity matrices for each method\n", + "sims_full_dmd, sims_control_joint_dmd, sims_state_joint_dmd, sims_control_separate_dmd, sims_state_separate_dmd = compare_systems_full(A_dmd, A_dmd)\n", + "sims_full_dmdc, sims_control_joint_dmdc, sims_state_joint_dmdc, sims_control_separate_dmdc, sims_state_separate_dmdc = compare_systems_full(A_cs, B_cs)\n", + "sims_full_subdmdc, sims_control_joint_subdmdc, sims_state_joint_subdmdc, sims_control_separate_subdmdc, sims_state_separate_subdmdc = compare_systems_full(As_n4sid, Bs_n4sid)\n", + "\n", + "# Print silhouette scores\n", + "print(\"Silhouette Scores:\")\n", + "print(f\"DMD State: {np.round(silhouette_score(sims_state_separate_dmd, state_labels, metric='precomputed'), 3)}\")\n", + "print(f\"DMDc State: {np.round(silhouette_score(sims_state_separate_dmdc, state_labels, metric='precomputed'), 3)}\")\n", + "print(f\"DMDc Control: {np.round(silhouette_score(sims_control_joint_dmdc, control_labels, metric='precomputed'), 3)}\")\n", + "print(f\"SubspaceDMDc State: {np.round(silhouette_score(sims_state_separate_subdmdc, state_labels, metric='precomputed'), 3)}\")\n", + "print(f\"SubspaceDMDc Control: {np.round(silhouette_score(sims_control_joint_subdmdc, control_labels, metric='precomputed'), 3)}\")\n", + "\n", + "# Create 2x3 subplot\n", + "fig, axes = plt.subplots(2, 3, figsize=(6, 5))\n", + "plt.subplots_adjust(wspace=0.1, hspace=0.2)\n", + "\n", + "# Column headers (bold)\n", + "column_headers = ['DMD', 'DMDc', 'SubspaceDMDc']\n", + "for i, header in enumerate(column_headers):\n", + " axes[0, i].text(0.5, 1.75, header, transform=axes[0, i].transAxes, ha='center', va='bottom', fontweight='bold', fontsize=16)\n", + "\n", + "# Row headers\n", + "row_headers = ['State DSA', ['Not Available', 'Input DSA', 'Input DSA']]\n", + "for i in range(3):\n", + " axes[0, i].text(0.5, 1.55, 'State DSA', transform=axes[0, i].transAxes, ha='center', va='bottom', fontsize=12)\n", + "\n", + "axes[1, 0].text(0.5, 1.55, 'Not Available', transform=axes[1, 0].transAxes, ha='center', va='bottom', fontsize=12)\n", + "axes[1, 1].text(0.5, 1.55, 'Input DSA', transform=axes[1, 1].transAxes, ha='center', va='bottom', fontsize=12)\n", + "axes[1, 2].text(0.5, 1.55, 'Input DSA', transform=axes[1, 2].transAxes, ha='center', va='bottom', fontsize=12)\n", + "\n", + "# Data for each subplot\n", + "data_matrices = [\n", + " sims_state_separate_dmd, # top left\n", + " sims_state_separate_dmdc, # top middle \n", + " sims_state_separate_subdmdc, # top right\n", + " None, # bottom left (gray matrix)\n", + " sims_control_joint_dmdc, # bottom middle\n", + " sims_control_joint_subdmdc # bottom right\n", + "]\n", + "\n", + "# Create gray matrix for bottom left - use same size as other matrices\n", + "matrix_size = sims_state_separate_dmd.shape[0]\n", + "gray_matrix = np.ones((matrix_size, matrix_size)) * 0.5\n", + "\n", + "# Plot each subplot\n", + "for idx, (ax, data) in enumerate(zip(axes.flat, data_matrices)):\n", + " row = idx // 3\n", + " col = idx % 3\n", + " \n", + " if idx == 3: # Bottom left - gray matrix with diagonal lines\n", + " im = ax.imshow(gray_matrix, cmap='gray', vmin=0, vmax=1, extent=[-0.5, matrix_size-0.5, matrix_size-0.5, -0.5])\n", + " \n", + " # Add diagonal lines from bottom-left to top-right\n", + " for i in range(matrix_size):\n", + " for j in range(matrix_size):\n", + " ax.plot([j-0.5, j+0.5], [i-0.5, i+0.5], 'k--', linewidth=1)\n", + " \n", + " # Set axis limits to match other plots\n", + " ax.set_xlim(-0.5, matrix_size-0.5)\n", + " ax.set_ylim(matrix_size-0.5, -0.5)\n", + " \n", + " # Remove ticks and labels\n", + " ax.set_xticks([])\n", + " ax.set_yticks([])\n", + " ax.set_xlabel('')\n", + " ax.set_ylabel('')\n", + " \n", + " # Remove spines\n", + " for spine in ax.spines.values():\n", + " spine.set_visible(False)\n", + " \n", + " else:\n", + " im = ax.imshow(data, cmap='viridis')\n", + " \n", + " # Add colorbar on top with only 2 ticks\n", + " cbar = plt.colorbar(im, ax=ax, shrink=0.4, location='top', pad=0.02)\n", + " vmin, vmax = data.min(), data.max()\n", + " cbar.set_ticks([vmin, vmax])\n", + " cbar.set_ticklabels([f'{vmin:.2g}', f'{vmax:.2g}'])\n", + " cbar.ax.tick_params(labelsize=10)\n", + " \n", + " # Remove colorbar spines\n", + " for spine in cbar.ax.spines.values():\n", + " spine.set_visible(False)\n", + " \n", + " # Set custom tick positions and labels (every 4 positions)\n", + " tick_positions = [1.5, 5.5, 9.5, 13.5] # Middle of each group of 4\n", + " tick_labels = ['1', '2', '3', '4']\n", + " \n", + " ax.set_xticks(tick_positions)\n", + " ax.set_xticklabels(tick_labels,fontsize=10)\n", + " ax.set_yticks(tick_positions)\n", + " ax.set_yticklabels(tick_labels,fontsize=10)\n", + " \n", + " # Set axis labels\n", + " ax.set_xlabel('System',fontsize=10)\n", + " ax.set_ylabel('System',fontsize=10)\n", + " \n", + " # Remove spines\n", + " for spine in ax.spines.values():\n", + " spine.set_visible(False)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "2edb4f13", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 16/16 [00:00<00:00, 52.96it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 52.17it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 234.84it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 49.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Silhouette Scores:\n", + "DMDc Full (state): 0.036\n", + "SubspaceDMDc Full (state): 0.342\n", + "DMDc Full (control): 0.05\n", + "SubspaceDMDc Full (control): 0.218\n" + ] + }, + { + "data": { + "image/png": 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w1zk5OQ7PxTdbHPjVjlHcII7W6W4wT/XJdUtczMcN0biOhYtr/AEXi3FdINdvcTcKHhSDi6O4ewV3PVHCDYa4yIqL3rmx0JEjR2RrUD4WF/fzwBGu1vUDeAo3DuIBT3ioT26Z/v3339Ply5dla2X+rvOAL1x8zo2XuLuWfSM3nrDo//7v/2TRtnlaSt7+f/7nf2SL9+K0kubGqlyv/M9//lNWpfF1cMtuHoiEu8HZ13t+8sknsrU4F/fzAFBcFM5Vb3yNfF4eUpVbT9tP9GGN0xp+7e+8846cXIPryLlYnls3cz2xuZuqdbEtN6bja+T3ixuDcTUdt+znYmQeHEeNO+kAdzvj18jvDU84kp6eLovU+dyc5vD7wvvxQDLWRcxKuLicqwH4s+UqEfPny90HHQ2562vmKZ+5+x9XS/Lr4/eNqy55GF8l/P5xXTh35VuxYgXt27ePrl+/LlvO84Ra/DrtqyecXgtHcg+8JgAA8ABuAGd9Y8HBmseAB3AEDdsAAAA0CkEcAABAoxDEAQAANAp14gAAABqFnDgAAIBGIYgDAABoFII4AACARiGIAwAAaBSCOAAAgEYhiAMAAGgUgjgAAIBGIYgDAABoFII4AACARiGIAwAAaBSCOAAAgEYhiAMAAGgUgjgAAIBGIYgDAABoFII4AACARiGIe1FqairpdDr65ZdfvHkaAACkOWUUgngxJSYm0tChQ13atnPnznT16lWKjo72+PF5O75B4CU4OJiqVatGvXv3psWLF5PJZLLZ9vDhwzR48GCKiYmhsLAwql+/Po0aNYoyMjKKHHfWrFlkMBjojTfecPmaAcB7kOaAGgRxLwoJCaHq1avLQOsN/fr1kzcJFy5coPXr11OPHj0oKSmJBg4cSIWFhXKbGzduUHx8PFWqVIk2bNhAJ0+epCVLllDNmjXpzp07RY7JNwF/+9vf5F8A0BakOWWQgGJJSEgQQ4YMkf/Pzc0Vzz77rKhataoIDQ0VXbp0EXv37rVsu2XLFsFv8e3bt+XjJUuWiOjoaPHNN9+IZs2aifDwcNG3b19x5coVuT45OVlub73wMZxdh7XNmzfL/RYtWiQfp6SkiKCgIFFQUOD0taWmpopatWqJ/Px8UbNmTbFjxw58OwBKGdIcUIOceAlwjvXLL7+kZcuW0YEDB6hRo0bUt29f+vnnnxX3uXv3Lr355pv0ySefUFpaGl28eJEmT54s1/HfkSNHWnLYvHCRfHH07NmTWrduTatXr5aPuSSAc+UpKSl8w6a670cffUSPPvqoLJ7nv/wYAPwH0hywhyDuJi6KXrBggaw77t+/P8XFxdGiRYuoXLlyqsGvoKCAPvjgA2rXrh21bduWJk6cSJs3b5brIiIi5P6hoaEy+PLCxWPF1axZM1nEzjp16kTTpk2j0aNHU5UqVeS18jVfv37dZp+srCxatWoVPfbYY/Ix//3Pf/5DOTk5xT4/AHge0hxwBEHcTefOnZMBuUuXLpbnOAfboUMHWe+spHz58tSwYUPL4xo1ajhsYFYSnOO2rod/7bXX6Nq1a/LmoXnz5vIvB/qjR49atlmxYoW8Ls7FszZt2lC9evXo888/9+i1AYB7kOaAIwjiPsaB3hoHW2fF3MXFNxGxsbE2z1WuXJlGjBghi/J5PTds4/+bcenB8ePHKSgoyLKcOHECDdwANA5pTmBDEHcT51q5qHvHjh2W5zhnvm/fPlm07i4+ptFodHv/7777Tuawhw8frnoOvn5z63Tefv/+/bJf+6FDhywLP961axd9//33bl8PAHgG0hxwJMjhs+BUeHg4jR8/nl588UXZfatu3bo0d+5c2XDtqaeecvsd5D7c3BXs1KlTMvfMfczt76TN8vLyZDE5B32u4/7mm29kP2/uYvbEE0/IbdatW0crV66kRx55hJo0aSJz/V9//TX997//lV3NzLlwrgZ48MEHi5yjffv2cj36jQOULqQ54AiCeDHxQCpc1Mxmz54tHz/++OOUnZ0tG6txAK5YsSK5a9y4cTIHzMfiRmVbtmyh7t27O9yWgzbXqfP18Dm5Pvu9996jhIQE0ut/K2ThUgGuh//rX/9Kly5dko3mGjduTB9++KG87vz8fPr0009pypQpDs/BOfq33nqLXn/9dcWbCQDwHqQ5oEbH/cxUtwAb3P2Lu5K9//77eGcAwOuQ5oAa1Im76Pbt27JomnPJvXr1cnU3AAC3IM0BV6A43UVPPvmkbLTGxdJDhgxxdTcAALcgzQFXoDgdAABAo1CcDgAAoFEI4gAAABoVkEF8/vz5sr81z53dsWNH2rt3b2lfkuZw9zkeTW7SpEmK2/BY8V27dpXd23jhBn++eK9d/Xy54wWPFc+vY82aNV6/Lii7kOaUrlmzZskxLSIjIykmJoaGDh0qx9pQw113OW2wXwYMGEBaEnBBnMf6fuGFFyg5OVnOLMZ9p3lmMU+PTx7IuAHfwoULqVWrVqrbcUt9nu2M+7LzyG516tShPn360OXLl90+99KlSxX7xRf383333Xe9Npc7gDvfSfCOrVu30oQJE2j37t20adMmOXomp0XmUSkd4ZkezbNF8nLs2DEyGAxyeGpNEQGmQ4cOYsKECZbHRqNRzo09a9asUr0urcjOzhaNGzcWmzZtEt26dRNJSUku71tYWCgiIyPFsmXLLM/xnOt//etf5WdQvnx5+fkozZFunnOdz1vSz/fgwYNybvSrV6/K+dV5XnUAb0Ca438yMjLk737r1q0u7/POO+/I9CsnJ0doSUDlxHn0sfT0dJt+3DxyGT/mnCI4x3ezXJzkTl94HnKW74B5GFoznmqV33se+vXIkSPyLpcHrzhz5ozXPl++Dp56lYs4eTpXAG9BmuOfMjMz5V/rtMgZHl6ah6fm4W21JKD6id+8eVOOI16tWjWb5/kxJvFwjgMtFwdycbo7eOhWnh3NHGQvXrwox2fnv/w8mzx5shwulp/noVy98fk+//zz1LlzZ/TnB69DmuOfw9ROmjRJThPdokULl/bhdjVcnM6BXGsCKoiD+3hc9aSkJFmfxA3G3GkIxzcBXE9u3p9nR+OgyxOv2E/cwpO7MA7w1rO+FRYWytx8RESE5blp06bJxRVfffWVnMnt4MGDxX4NABAYpYnHjh2j7du3u7wPB++WLVvKiaC0JqCCeJUqVWTDBJ7Ryxo/RrGqOi6m5oY4bdu2tTzHATgtLU2OE8+Bl99bR3hecg7i3377rU1jOJ7AhffhY9vvaw7SnEPnaU+tG5t8+eWXtHz5cstz5iIxVz5fDuDnzp2jChUqFJnIhVvS800GgKcgzfEvEydOlMNjc7pVu3Ztl/bhxm+cAZk5cyZpkgjARiYTJ060afjEDZzQsE1dVlaWOHr0qM3Srl078dhjj8n/K5kzZ46IiooSu3btKrLu1KlTsnFJWlqay5+fKw3b1D5fbshm/zr4Gv71r3+JH374weXrAHAV0pzSZzKZZINXbuR6+vTpYu3LaU5oaKi4efOm0KKAC+IrV66UH8jSpUvFiRMnxDPPPCMqVKggrl27VtqXpjn2rdMff/xx8dJLL1kez549W4SEhIhVq1bJ4GleuIW72ZgxY0T9+vXFl19+KYPonj17xOuvvy7WrVvnVhB35/NF63TwJqQ5pW/8+PEiOjpapKam2qRFd+/eVUy/zB544AExatQooVUBVZzORo0aRTdu3KDp06fTtWvXqE2bNrIhlX1jKCg+rr82z1POFixYIFvnPvzwwzbbcX/ZV155Rf6fG7D985//lBPHcP9xLn7s1KkTDRw40K2PAJ8v+Bt8J0vfggUL5F/7MSY4/UlMTHSYfjEeEIbrzjdu3EhahQlQAAAANCqg+okDAACUJQjiAAAAGoUgDgAAoFEI4gAAABqFIA4AAKBRCOIAAAAahSAOAACgUQEZxHmcbx5shP+Cf8JnBIEE32f/lxegcSEgB3vJysqi6OhoOadsVFRUaV8OOIDPCAIJvs/+LytA40JA5sQBAADKAgRxAAAAjUIQBwAA0KiAm8WMhYaGUizdQ8MqjyNDUAjpgoOIDAbSBfFfvfz/b4ueiJ/T60kE8WMdCfm8joR8Tk+k15EpSPf7Xz0JPZGQ2/2+8GO5jv5YpycyGX57Xsi/5nVW/7d+/PtfcrhO/LaO/+p+384giPRCrtOZH+sE6Q0m+VinN5HeIEivE2QwmMigN1EQ/9X9/pcf60wUbDBSkN5EIXrz30L5fKiB/xop2Or/YfoCCtYZKfT3v2G63//Kx4UUIp/Lt1pnojB+nkwUqhMUotNRsE5HoWSgYJ2BgvMNcrYz/qwAAjXN4TTmt3RHOc357f969TSH05IgnVUaUzTN+S3dMW+jkOb8/hz9nkY5THNk2mJeJxynOfrfHuv0nO4I1TTHoBcUxGmNSpoTpOe0pVA1zfkjbSmU6xylOSHE/+dtBYWVkTQnYIN4Q11z0un4mwj+KDRUb5muFEDrkOb4v9AATXNQnA4AAKBRCOIAAAAahSAOAACgUQE52AsAAEBZgJw4AACARiGIAwAAaJTmu5j11o9QXqnTuX1cfUSE4jpTTo7bx9UFBSuuE4UFpCmyw6gCYVLddcPlg4rr9NXPlOSqAEotzdG1b6m6b2GE8u9fn2tUXHcmQb1vs86onNaJMOXjkkoSWf5MiOo5DfnK64xql+ukAjevxa+K6/R69XRFjeCBNhSce+TvpFXIiQMAAGgUgjgAAIBGIYgDAABoFII4AACARiGIAwAAaBSCOAAAgEZpvouZajcybw1G5+y47nZtUzuus2OWysB7Kt09MBAgBCi1bmRi31HVfY192ymuM/xaqLwjT/2pdk13lfNjIlgl7TApr9OrXI6zbmSF4crXG3pLPS0LDVPuu2Y0Kr/OvJzAmmLUVciJAwAAaBSCOAAAgEaVahBPS0ujQYMGUc2aNUmn09GaNWtK83IAIMAhzYFAU6pB/M6dO9S6dWuaP39+aV4GAJQRSHMg0JRqw7b+/fvLBQAAaQ5A8aFOHAAAQKM01cUsLy9PLtZMwkh6naHUrgkAApfDNMdUSHq9ppJOCGCa+ibOmjWLZsyYYfNcLMVRQ11z315ICaY41Zyy9FoBXEhz6tfvSbENehW7HzgL2bBfcV1e//aK63Qh6lNwimDlQlWd2lSkKv3EdUb18BCkMhWpKUjluE6GtMjPU56uVX1Hvfp6J++hVmmqOH3q1KmUmZlps8TqmpX2ZQFAgHKU5tSr3720LwtAmznx0NBQuVhDUToA+DTNQVE6+JFSDeI5OTl09uxZy+Pz58/ToUOHqFKlSlS3bt3SvDQACEBIcyDQlGoQ379/P/Xo0cPy+IUXXpB/ExISaOnSpaV4ZQAQiJDmQKAp1SDevXt3EpgoAwCQ5gAEfsM2AAAA0GjDNkf0ERFeOa4pO1v5nJGRbh+Xx4hXIsLK5lR6AFqizzW6N52ok25koev3Ke846j71i1KbkblQJa+m0t2rwP1kjvQq3c/yo9X3jQjPVVynU+mf9muwSle6AJ4dGTlxAAAAjUIQBwAA0CgEcQAAAI1CEAcAANAoBHEAAACNQhAHAADQKM13MTPl5Ljfp0Clu5daNzK17mfOjqsLUp6hRxTkuz+bmLf6T7g7i1mg9ueAMu9MgkpXUL369151NjKVbmSNE9NVj3tj/P2K66IuKJ/zboxyCOgwUf2cUUG/Kq7LMSq/R3km9bAzt+Z3iutMKunKnryKqsfNF2pTVk8nrUJOHAAAQKMQxAEAADQKQRwAAECjEMQBAAA0CkEcAACgrLVOv3LlCm3fvp0yMjLIZLJt/fjcc8954toAAJDmAHg6iC9dupT+/Oc/U0hICFWuXNlmZi7+P4I4AHgS0hwADwbxl19+maZPn05Tp04lvd6PS+Td7eNc0uO620e6JNfrrdeqek61z16lPyyAhtMcnVFlHIi76tcmglXW69zrB86qLtiluO7nscr7Rl5SHpvCpHZBPFWpSr/rk5nVFdc1iLypetyfVGZzzTYp9z/fdaeR6nEjDMpTnGqZW7+Gu3fv0iOPPFLqPyYAKBuQ5gA45lYUfuqpp+iLL76gkpo1axa1b9+eIiMjKSYmhoYOHUqnTp0q8XEBILAgzQHwYHE6B9+BAwfSN998Qy1btqTgYNuhRN9++22XjrN161aaMGGCDOSFhYU0bdo06tOnD504cYLCw8PduTQACEBIcwA8HMQ3bNhATZs2lY/tG7a5im8C7BuvcI48PT2dHnzwQXcuDQACENIcAA8G8bfeeosWL15MiYmJ5EmZmZnyb6VKlTx6XADQNqQ5AB4M4qGhodSlSxfyJO5rPmnSJHncFi1aONwmLy9PLjb7CSPpdWqz0wCA1vlTmiMKCkkXrPkJICFAuPVNTEpKonnz5tF7773nsQvhuvFjx47JAWTUitRmzJhh81wDfQtqGNSKPE3n5nSizqhNN6oLDqHAgRsr8Bx/SnOiBsdThaG9HW4vgtWrE3VhRsV1olDv1nSizrqRVVqi3P3s16EdFNc1KX9N9ZyXVab+bBx1Q3FdbDnldax5SDlyxzXjj6rrjSIwe1O5FcT37t1L3333Ha1bt46aN29epGHb6tWri3W8iRMnymOlpaVR7dq1FbfjPqIvvPCCzXPDqjxTzKsHAK3xpzSnxcp5xbx6AD8L4hUqVKBhw4aV+ORCCHr22WcpJSWFUlNTKTY21mmRGi/WUJQOEPj8Kc1BUTpoPogvWbLEY8VZn332Ga1du1b2Fb927bfim+joaCpXzr0iFQAIPEhzABxzu5KA+3V/++23tHDhQsrOzrZMipKTk+PyMRYsWCBbpHfv3p1q1KhhWT7//HN3LwsAAhTSHAAP5cR//PFH6tevH128eFG23Ozdu7fMSc+ZM0c+/uCDD1wu2gIAQJoD4MOcOLcUbdeuHd2+fdum2PtPf/oTbd682c1LAQBAmgPg9Zz4tm3baOfOnXIqUmv169eny5cvky+JwgKVle7n9EVYqFvdxCS17mkq3cicHtffqI3Oh1IW8CB/SnNUJ/cyORmxUm29SnJ1N0Y9qVabjUytG1m5NXuVzzlTOQ1kDcMyFNftzGyouC7PpN5F97bxruK6bKHc1e5WofLMaSzS8CsFIr27gyQYjUX7O/7000+yWB0AwJOQ5gB4MIjzJCXvvvuuzcAo3KAtOTmZHnroIXcOCQCANAfAV2On9+3bl+Li4ig3N5dGjx5NZ86coSpVqtCKFSvcOSQAANIcAF8EcR7h6PDhw7IrGP/lXDjP9ztmzBj07wYAj0OaA+DBIM5DFXbu3FkGbV6s+3HyOkwjCgCehDQHwIN14j169KCff/65yPM8cAuvAwDwJKQ5AB7MifMgLY5m+bp16xaFh4eTJrpA+eNx/ZHaa9Wp3QOqz7oEoNU0p/wZ5W6i+kL1fXVG5SS3QKVjT4eJ6arHNan0e1ObjUytG9nWVupDXxviWiuvzFfu+nvTEKN63GXJnRTXlS9vOy2steyLUeTuxIqPNKKyEcTNExDwjykxMdFmYgDucnbkyBFZzA4A4AlIcwA8GMR5YhLzXTH3B7cerY0HYejUqRONGzeuOIcEAECaA+CLIG6eSYhHSZo8ebJ/FZ0DQMBBmgPghYZtf/vb32zqp3hCFB78ZePGje4cDgAAaQ6Ar4L4kCFD6OOPP5b//+WXX6hDhw5yABh+nqcXBQDwJKQ5AB4M4gcOHKCuXbvK/69atYqqV68uc+Mc2N977z13DgkAgDQHwBdB/O7du5aJTrgInVuQ6vV62bCNgzkAgCchzQHwYD/xRo0a0Zo1a+T84Rs2bKDnn39ePp+RkUFRUU766vmSt6bDdHbcQOpHrvpaVfqCYypS8CB/SnMMKjMGG9Vn76QgN2cbjgpSn0azQCh3gr6cV9Gt6URV+4Hzaz1xWnGdvk2c4jpdvnpnepPKdK0moZK2VlCZlpqTpDyVjuJlLSc+ffp02TqdW6l37NiR7r//fkuu/N577/X0NQJAGYc0B8CDOfGHH36YHnjgAbp69Sq1bv3H3Vp8fLy8UwYA8CSkOQAezIlz300e+IVz3VwXbsat1Js1a+bycbgle6tWrWRxGC+co1+/fr07lwQAAQxpDoAHg/hLL71E1apVk9OP7ty5k0oyveDs2bMpPT2d9u/fTz179pRdSY4fP+72MQEg8CDNAfBgEL98+TItW7aMbt68Sd27d5e57zlz5tC1a8oD7TsyaNAgeuihh6hx48bUpEkTeu211ygiIoJ2797tzmUBQIBCmgPgwSAeFBQk677Xrl1Lly5dkuOlL1++nOrWrUuDBw+Wz5tMxZvFiidQWblyJd25c8fSUM5eXl4eZWVl2SwmYXTnJQCAhvhVmlPoZKoyAH9v2GaNi9W5kdvp06flcvToUUpISKCKFSvKeizOqavh7fkHlJubK3PhKSkpFBfnuHvCrFmzaMaMGTbPxVIcNdQ1L+nLAACNKO00p3LXPlT1wX4Oty8MV+9+agpS7iKlV+l+luOk79rJzOqK6xpH3VBctzOzoVvTiTrrRmY6dEJxnaFRrOpxxW3lqV7vqKwT5Z1k6Axe6nKsxZw4u379Or355pvUvHlz+aPhO9R169bR+fPnZdHXyJEj5Q/LmaZNm9KhQ4doz549NH78eLnPiROOvwBTp06lzMxMmyVW53pDOgDQLn9Jcyp37uWFVwfgHp3geUWLieuyecAFrsd++umn6YknnqBKlSrZbMODMPBwrMUt4urVqxc1bNiQFi5c6NL2vQ0jvTLgiP73EekcMWVnuz3Yiy4oWHGdKHBzJIjSojaojZP3fsOVQ4rr9NXPlOSqIAD5U5pzzz/eUVznLCeuz1PJiatkfOOH7/NKTjynUDlne/PxyqrnFBF/TEXtyZz490kxquvdzonrlFf9mDiFylRxekxMDG3dulWxHolVrVpV3iEXF/8AuR4KAABpDoAHg/iuXbvo1q1b9NFHH1me40lPkpOTZeOQoUOH0rx58yg0NFROVVqvXj3V43FRVf/+/WXjlOzsbPrss88oNTVV3nEDACDNAfBgnfjMmTNt+nBzAxHuK87FUdyP8+uvv5YNQVzFxV9cLMZ1VDza2759+2QA7927d3EuCwACFNIcAA/mxLkxyKuvvmp5zN0zeOz0RYsWycd16tSRufJXXnnFpeNZ5+gBAJDmAHgxiN++fVt27zDjenEuDjdr37697MMJAOAJfpnmqLRdC72lPoOhTmXf/GjldXkm9aS6QeRNxXWx5ZQbtuWZlBva3jSoNzBTm41MrfGa8ax6W6mgqsqNisPLKTf+zbxQQfW4Irh4DR4Dsjidf0zmxmr5+fl04MABOYe4GddrBwcrfykAAJDmAJRSEOchUrnue9u2bbJRWvny5alr166W9UeOHJFdNQAAPAFpDoAHi9O5PnzYsGHUrVs3OdIRj58eEvJHP8PFixdTnz59inNIAACkOQC+COJVqlShtLQ0OWoRB3GDwWCz/osvvpDPAwB4AtIcAC8M9sJziTtiP4ISAIAnIM0B8PDY6QAAAFC6EMQBAADK6lSkmqY2gYc39isJZ+cswWQvAOC6vBa/Kq4LDVOfxCg/T7kLbkR4ruK6uTW/Uz3uTypTnDcPUZ6o5LbxruK6Zcl/dB92xGTSuTWdqFo/cBb7yBHFdVmPKl9T3vAc1ePSwSgKRMiJAwAAaBSCOAAAgEYhiAMAAGgUgjgAAIBGIYgDAABoFII4AACARmm/i5lO7T7E5IfX5GY3MqfHLIXXqtatrTS64QH4gF6v/FszGt3PF+lU5ik1OelCmm0Kdeuc2UL5tZQvn6e6r0ko/8bvqHQxU5tO1Fk3sqgVuxXXxSWpD/m9rbABBSLkxAEAADQKQRwAAECj/CaIz549m3Q6HU2aNKm0LwUAygCkORAI/CKI79u3jxYuXEitWrUq7UsBgDIAaQ4EilIP4jk5OTRmzBhatGgRVaxYsbQvBwACHNIcCCSlHsQnTJhAAwYMoF69ejndNi8vj7KysmwWkzD65DoBIDCUNM0RBSqzjQCUpS5mK1eupAMHDsiiLVfMmjWLZsyYYfNcLMVRQ31z387s5fS4at29DG4e1+R/s5ipdSPDrGrghzyR5lQc3oMqjejpcPu8HCddvfKV802/BitnSPbkqZdS7rrTSHHdNeOPiutuFVZXXJd90cmsXxUKFFeJ8sqvJfNCBdXDqs1GptaN7KdO6rOYVR1ZXnnlSNKsUsuJX7p0iZKSkmj58uUUFhbm0j5Tp06lzMxMmyVW18zr1woA2uepNKfi0Ae9fq0Afp8TT09Pp4yMDGrbtq3lOaPRSGlpafT+++/LYiyDwTbXGhoaKhdrep1KzhYAwMNpji5Y+2NkQeAotW9jfHw8HT161Oa5sWPHUrNmzWjKlClFfkwAAEhzAPwkiEdGRlKLFi1sngsPD6fKlSsXeR4AAGkOgB+2TgcAAAD3+FXlTmpqamlfAgCUIUhzQOuQEwcAANAov8qJu2PD5YOlfQnghr412yiu21RKM8gCuEKoTMHpVIjJraEV8oV6Q98IQ67iOqNQzqtFGn5VPqiTtsUiT2UDg/KLEcFOfuAHo9yaTrSqWj9wfo/+ozyNKa0kzUJOHAAAQKMQxAEAADQKQRwAAECjEMQBAAA0CkEcAABAoxDEAQAAtEqUUbm5uSI5OVn+xTm1/94C+DukOYH13voLHf9DZVBWVhZFR0fLqQWjopzMm4tz+v17C+DvkOYE1nvrL1CcDgAAoFEI4gAAABqFIA4AAKBRZTaIh4aGUnJysvyLc2r/vQXwd0hzAuu99RdltmEbAACA1pXZnDgAAIDWIYgDAABoFII4AACARiGIAwAAaFSZC+JpaWk0aNAgqlmzJul0OlqzZo3Xzzlr1ixq3749RUZGUkxMDA0dOpROnTrl1XMuWLCAWrVqJUcv4uX++++n9evXky/Nnj1bvseTJk3y6XkB/AnSHN+ZXQbTnDIXxO/cuUOtW7em+fPn++ycW7dupQkTJtDu3btp06ZNVFBQQH369JHX4i21a9eWX+j09HTav38/9ezZk4YMGULHjx8nX9i3bx8tXLhQ3kgAlGVIc5DmeJUow/jlp6Sk+Py8GRkZ8txbt2716XkrVqwoPvzwQ6+fJzs7WzRu3Fhs2rRJdOvWTSQlJXn9nABagDTHO7LLcJpT5nLi/oAH6WeVKlXyyfmMRiOtXLlS5gi4WN3buNRhwIAB1KtXL6+fCwCcQ5oTuIJK+wLKGpPJJOtrunTpQi1atPDquY4ePSqDdm5uLkVERFBKSgrFxcV59Zx8s3DgwAFZnA4ApQ9pTmBDEPcxzqUeO3aMtm/f7vVzNW3alA4dOiTvwletWkUJCQmyft5bgfzSpUuUlJQk6/3DwsK8cg4AKB6kOYGtTA+7yq0YOXfKrcV9YeLEibR27VrZWjU2NpZ8jYu3GzZsKBuceQO39P/Tn/5EBoPBpiif32e9Xk95eXk26wDKGqQ5nrUGaQ5y4r7A90nPPvusvGFITU0tlQBuLlbjQOot8fHxsgjf2tixY6lZs2Y0ZcoUBHAAH0Ga06zMpDllrjg9JyeHzp49a3l8/vx5WeTMjczq1q3rteKszz77TObCua/4tWvX5PPR0dFUrlw5r5xz6tSp1L9/f/masrOz5fn5BmLDhg3kLfza7Ov5w8PDqXLlyl6v/wfwV0hzkOZ4lShjtmzZIrt52C8JCQleO6ej8/GyZMkSr53zySefFPXq1RMhISGiatWqIj4+XmzcuFH4Wlnr7gFgD2mOb3UrY2lOma4TBwAA0DL0EwcAANAoBHEAAACNQhAHAADQKARxAAAAjUIQBwAA0CgEcQAAAI1CEAcAANAoBHEAAACNQhD3oBs3btD48ePlUKehoaFUvXp16tu3L+3YsaPEx05MTPTZRC0AoA1Ic6DMjZ3uTcOHD6f8/HxatmwZNWjQgK5fv06bN2+mW7dulfalAUAAQpoDZW7sdG+5ffu2HA89NTXV4fqxY8eKAQMG2DyXn58vxzX/8MMP5eMvvvhCtGjRQoSFhYlKlSrJ8c5zcnJEcnJykXHXeTxmdvHiRTFixAgRHR0tKlasKAYPHizOnz9vOQePCT9kyBDx2muviZiYGLndjBkzREFBgZg8ebLcp1atWmLx4sVefX8AwLOQ5gBDEPcQDooRERFi0qRJIjc3t8j6HTt2CIPBIK5cuWJ5bvXq1SI8PFxkZ2fL54OCgsTbb78tg/CRI0fE/Pnz5TpeRo4cKfr16yeuXr0ql7y8PHkTcM8998jJTnj7EydOiNGjR4umTZvK9eYgHhkZKSZMmCC+//578dFHH8mbgL59+8rAfvr0afHqq6+K4OBgcenSJfwqADQCaQ4wBHEPWrVqlczZck66c+fOYurUqeLw4cOW9XFxcWLOnDmWx4MGDRKJiYny/+np6TK4XrhwweGxzTlqa5988okM2CaTyfIcB+9y5cqJDRs2WPbj2cyMRqNlG96na9eulseFhYXyZmLFihUeeR8AwDeQ5gAatnm4furKlSv01VdfUb9+/eT83W3btqWlS5fK9U8//TQtWbJE/p/ry9evX09PPvmkfNy6dWuKj4+nli1b0ogRI2jRokV0+/Zt1fMdPnxYzo3O83hHRETIhedFz83NpXPnzlm2a968Oen1f3zU1apVk+cxMxgMcs7vjIwMVDABaAjSHEAQ97CwsDDq3bs3vfzyy7Rz507Zqjw5OVmue+KJJ+iHH36gXbt20aeffkqxsbHUtWtXSyDdtGmTDOxxcXE0b948atq0KZ0/f17xXDk5OXTffffRoUOHbJbTp0/T6NGjLdsFBwfb7KfT6Rw+ZzKZPPxuAIC3Ic0p2xDEvYwD8p07d+T/ObfL3cQ4N86587FjxxYJpF26dKEZM2bQwYMHKSQkhFJSUuQ6/r/RaLTZnnP5Z86coZiYGGrUqJHNEh0d7e2XBgB+CGlO2YIg7iHcjaxnz54yh33kyBGZg/7iiy9o7ty5NGTIEMt2XKTOXdBOnjxJCQkJluf37NlDr7/+Ou3fv58uXrxIq1evln1A77nnHrm+fv368rinTp2imzdvUkFBAY0ZM4aqVKkij79t2zZ5Ti7Cf+655+inn37y1EsDAD+ENAcY+ol7CNdHd+zYkd555x1ZH81Btk6dOjRu3DiaNm2aZbtevXpRjRo1ZD11zZo1Lc9HRUVRWloavfvuu5SVlUX16tWjt956i/r37y/X83E4QLdr104Wo2/ZsoW6d+8u95kyZQoNGzaMsrOzqVatWrJunY8HAIELaQ4wHbftw1vhOxyAOdBykToHXgAApDngLuTEfYQbjXExOOeuK1SoQIMHD/bVqQGgDEKaUzYgiPsI13Nza/TatWvLRm1BQXjrAQBpDpQMitMBAAA0Cq3TAQAANApBHAAAQKMQxAEAADQKQRwAAECjEMQBAAA0CkEcAABAoxDEAQAANApBHAAAQKMQxAEAAEib/h+uUlO2/yszlAAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Get the similarity matrices for each method\n", + "sims_full_dmdc, _, _, _, _ = compare_systems_full(A_cs, B_cs)\n", + "sims_full_subdmdc, _, _, _, _ = compare_systems_full(As_n4sid, Bs_n4sid)\n", + "\n", + "# Print silhouette scores\n", + "print(\"Silhouette Scores:\")\n", + "print(f\"DMDc Full (state): {np.round(silhouette_score(sims_full_dmdc, state_labels, metric='precomputed'), 3)}\")\n", + "print(f\"SubspaceDMDc Full (state): {np.round(silhouette_score(sims_full_subdmdc, state_labels, metric='precomputed'), 3)}\")\n", + "print(f\"DMDc Full (control): {np.round(silhouette_score(sims_full_dmdc, control_labels, metric='precomputed'), 3)}\")\n", + "print(f\"SubspaceDMDc Full (control): {np.round(silhouette_score(sims_full_subdmdc, control_labels, metric='precomputed'), 3)}\")\n", + "\n", + "# Create 1x2 subplot\n", + "fig, axes = plt.subplots(1, 2, figsize=(6, 3))\n", + "plt.subplots_adjust(wspace=0.1, hspace=0.2)\n", + "\n", + "# Column headers (bold)\n", + "column_headers = ['DMDc', 'SubspaceDMDc']\n", + "for i, header in enumerate(column_headers):\n", + " axes[i].text(0.5, 1.55, header, transform=axes[i].transAxes, ha='center', va='bottom', fontweight='bold', fontsize=16)\n", + "\n", + "# Data for each subplot\n", + "data_matrices = [\n", + " sims_full_dmdc, # left\n", + " sims_full_subdmdc # right\n", + "]\n", + "\n", + "# Plot each subplot\n", + "for idx, (ax, data) in enumerate(zip(axes.flat, data_matrices)):\n", + " im = ax.imshow(data, cmap='viridis')\n", + " \n", + " # Add colorbar on top with only 2 ticks\n", + " cbar = plt.colorbar(im, ax=ax, shrink=0.4, location='top', pad=0.02,label='Joint DSA')\n", + " vmin, vmax = data.min(), data.max()\n", + " cbar.set_ticks([vmin, vmax])\n", + " cbar.set_ticklabels([f'{vmin:.2g}', f'{vmax:.2g}'])\n", + " cbar.ax.tick_params(labelsize=10)\n", + " \n", + " # Remove colorbar spines\n", + " for spine in cbar.ax.spines.values():\n", + " spine.set_visible(False)\n", + " \n", + " # Set custom tick positions and labels (every 4 positions)\n", + " tick_positions = [1.5, 5.5, 9.5, 13.5] # Middle of each group of 4\n", + " tick_labels = ['1', '2', '3', '4']\n", + " \n", + " ax.set_xticks(tick_positions)\n", + " ax.set_xticklabels(tick_labels, fontsize=10)\n", + " ax.set_yticks(tick_positions)\n", + " ax.set_yticklabels(tick_labels, fontsize=10)\n", + " \n", + " # Set axis labels\n", + " ax.set_xlabel('System', fontsize=10)\n", + " ax.set_ylabel('System', fontsize=10)\n", + " \n", + " # Remove spines\n", + " for spine in ax.spines.values():\n", + " spine.set_visible(False)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "d85b184f", + "metadata": {}, + "outputs": [], + "source": [ + "#collect statistics now: \n", + "#sample random systems from the set of 4 pairings\n", + "#sample 4 input drives for each system, making 16 diferent systems in total \n", + "#compute silhouette score based on A labels and B labels\n", + "\n", + "def get_silhouette_scores(n,m,p_out,N,n_iters,\n", + " input_alpha=input_alpha,g1=g1,g2=g2,same_inp=False,n_Us=n_Us,\n", + " n_delays=n_delays,pf=pf,rank=rank,process_noise=process_noise,obs_noise=obs_noise,\n", + " nonlinear_eps=nonlinear_eps,nonlinear_func=lambda x: np.tanh(x),\n", + " y_feature_map = lambda x: x, u_feature_map = lambda x: x,backend=backend,\n", + " use_joint_control=True):\n", + "\n", + " silhouette_state_dmdc = []\n", + " silhouette_control_dmdc = []\n", + "\n", + " silhouette_state_subspace_dmdc = []\n", + " silhouette_control_subspace_dmdc = []\n", + "\n", + " silhouette_state_dsa = []\n", + " silhouette_control_dsa = []\n", + "\n", + "\n", + " for i in tqdm(range(n_iters)):\n", + " X_trues, Ys, Us, control_labels, state_labels, *_ = simulate_As_Bs(n,m,p_out,\n", + " N,input_alpha=input_alpha,g1=g1,g2=g2,same_inp=same_inp,n_Us=n_Us, seed1=seed1+i,seed2=seed2+110*i,\n", + " obs_noise=obs_noise,process_noise=process_noise,\n", + " nonlinear_eps=nonlinear_eps,nonlinear_func=nonlinear_func)\n", + " Ys = list(map(y_feature_map, Ys))\n", + " Us = list(map(u_feature_map, Us))\n", + "\n", + " A_cs, B_cs = get_dmdcs(Ys,Us,n_delays=n_delays,rank=rank)\n", + " print('dmdc:', [i.shape for i in A_cs])\n", + " As, Bs, Cs, infos = get_subspace_dmdcs(Ys,Us,p=pf,rank=rank,backend=backend)\n", + " print('subspacedmdc:', [i.shape for i in As])\n", + " A_dmds = get_dmds(Ys,n_delays=n_delays,rank=rank)\n", + " print('dmd:', [i.shape for i in A_dmds])\n", + " sims_full_dmdc, sims_control_joint_dmdc, sims_state_joint_dmdc, sims_control_separate_dmdc, sims_state_separate_dmdc = compare_systems_full(A_cs,B_cs)\n", + " sims_full_subspace_dmdc, sims_control_joint_subspace_dmdc, sims_state_joint_subspace_dmdc, sims_control_separate_subspace_dmdc, sims_state_separate_subspace_dmdc = compare_systems_full(As,Bs)\n", + "\n", + " sims_state_dmd = compare_A_full(A_dmds)\n", + "\n", + " #compute silhouette scores\n", + " silhouette_state_dmdc.append(silhouette_score(sims_state_separate_dmdc,state_labels,metric='precomputed'))\n", + " if use_joint_control:\n", + " silhouette_control_dmdc.append(silhouette_score(sims_control_joint_dmdc,control_labels,metric='precomputed'))\n", + " silhouette_control_subspace_dmdc.append(silhouette_score(sims_control_joint_subspace_dmdc,control_labels,metric='precomputed'))\n", + " else:\n", + " silhouette_control_dmdc.append(silhouette_score(sims_control_separate_dmdc,control_labels,metric='precomputed'))\n", + " silhouette_control_subspace_dmdc.append(silhouette_score(sims_control_separate_subspace_dmdc,control_labels,metric='precomputed'))\n", + " \n", + " silhouette_state_subspace_dmdc.append(silhouette_score(sims_state_separate_subspace_dmdc,state_labels,metric='precomputed'))\n", + "\n", + " silhouette_state_dsa.append(silhouette_score(sims_state_dmd,state_labels,metric='precomputed'))\n", + " silhouette_control_dsa.append(silhouette_score(sims_state_dmd,control_labels,metric='precomputed'))\n", + "\n", + " print(silhouette_state_subspace_dmdc[-1],silhouette_state_dmdc[-1])\n", + " print(silhouette_control_subspace_dmdc[-1],silhouette_control_dmdc[-1])\n", + "\n", + " # print(silhouette_state_subspace_dmdc,silhouette_control_subspace_dmdc)\n", + " return silhouette_state_dmdc, silhouette_control_dmdc, silhouette_state_subspace_dmdc, silhouette_control_subspace_dmdc, silhouette_state_dsa, silhouette_control_dsa\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "e32ce5f0", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 0/10 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "methods = [ 'DMD','DMDC', 'Subspace DMDC']\n", + "state_means = [np.mean(silh_state_dsa),np.mean(silh_state_dmdc), np.mean(silh_state_subdmdc)]\n", + "state_stds = [np.std(silh_state_dsa) / np.sqrt(n_iters), np.std(silh_state_dmdc) / np.sqrt(n_iters), np.std(silh_state_subdmdc) / np.sqrt(n_iters)]\n", + "control_means = [np.mean(silh_ctrl_dsa),np.mean(silh_ctrl_dmdc), np.mean(silh_ctrl_subsdmdc)]\n", + "control_stds = [np.std(silh_ctrl_dsa) / np.sqrt(n_iters), np.std(silh_ctrl_dmdc) / np.sqrt(n_iters), np.std(silh_ctrl_subsdmdc) / np.sqrt(n_iters)]\n", + "\n", + "# Create bar plot\n", + "x = np.arange(len(methods))\n", + "width = 0.35\n", + "\n", + "fig, ax = plt.subplots(figsize=(6,4))\n", + "# Prepare data for violin plots\n", + "state_data = [silh_state_dsa, silh_state_dmdc, silh_state_subdmdc]\n", + "control_data = [silh_ctrl_dsa, silh_ctrl_dmdc, silh_ctrl_subsdmdc]\n", + "\n", + "# Option to create either violin plots or bar plots\n", + "plot_type = 'bar' # Change to 'bar' for bar plots\n", + "\n", + "if plot_type == 'violin':\n", + " # Create violin plots\n", + " violin_parts1 = ax.violinplot(state_data, positions=x - width/2, widths=width, showmeans=True, showmedians=False)\n", + " violin_parts2 = ax.violinplot(control_data, positions=x + width/2, widths=width, showmeans=True, showmedians=False)\n", + "\n", + " # Color the violin plots\n", + " for pc in violin_parts1['bodies']:\n", + " pc.set_facecolor(plt.cm.Paired(0))\n", + " pc.set_alpha(0.8)\n", + " \n", + " for pc in violin_parts2['bodies']:\n", + " pc.set_facecolor(plt.cm.Paired(1))\n", + " pc.set_alpha(0.8)\n", + "\n", + " # Set the color for violin lines (edges) as well\n", + " for key in ['cbars', 'cmins', 'cmaxes', 'cmedians', 'cmeans']:\n", + " if key in violin_parts2:\n", + " violin_parts2[key].set_color(plt.cm.Paired(1))\n", + " # Create legend manually\n", + " # ax.plot([], [], color=plt.cm.Paired(0), alpha=0.8, label='State')\n", + " # ax.plot([], [], color=plt.cm.Paired(1), alpha=0.8, label='Control')\n", + "\n", + "elif plot_type == 'bar':\n", + " # Create bar plots\n", + " ax.bar(x - width/2, state_means, width, yerr=state_stds, alpha=0.8,color=plt.cm.Paired(0))\n", + " ax.bar(x + width/2, control_means, width, yerr=control_stds, alpha=0.8,color=plt.cm.Paired(1))\n", + "\n", + "\n", + "ax.text(0.1, 0.8, 'State', color=plt.cm.Paired(0), fontsize=18, ha='center', va='center', transform=ax.transAxes)\n", + "ax.text(0.1, 0.7, 'Input', color=plt.cm.Paired(1), fontsize=18, ha='center', va='center', transform=ax.transAxes)\n", + "\n", + "\n", + "# Add labels and formatting\n", + "ax.set_xlabel('Method')\n", + "ax.set_ylabel('Silhouette Score')\n", + "ax.set_xticks(x)\n", + "ax.set_xticklabels(methods)\n", + "# ax.legend(loc='upper left')\n", + "\n", + "plt.tight_layout()\n", + "# plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "5f1c041a", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 0/2 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "methods = ['DMD','DMDc','Subspace DMDc']\n", + "#on two plots, plot the mean and std of the silhouette scores for each method across p_out / n\n", + "\n", + "fig, ax = plt.subplots(1,2, figsize=(8,3),sharex=True)\n", + "\n", + "# Plot state silhouette scores\n", + "\n", + "for i, state in enumerate([silh_state_dsas,silh_state_dmdcs,silh_state_subdmdcs]):\n", + " ax[0].plot(rs, np.mean(state, axis=1), label=methods[i] + ' (State)',color=plt.cm.Set2(i))\n", + " ax[0].fill_between(rs, np.mean(state, axis=1) - np.std(state, axis=1) / np.sqrt(n_iters),\n", + " np.mean(state, axis=1) + np.std(state, axis=1) / np.sqrt(n_iters), alpha=0.2,\n", + " color=plt.cm.Set2(i))\n", + "\n", + "for i, state in enumerate([silh_ctrl_dsas,silh_ctrl_dmdcs,silh_ctrl_subsdmdcs]):\n", + " ax[1].plot(rs, np.mean(state, axis=1), label=methods[i] + ' (Control)',color=plt.cm.Set2(i),linestyle='--')\n", + " ax[1].fill_between(rs, np.mean(state, axis=1) - np.std(state, axis=1) / np.sqrt(n_iters),\n", + " np.mean(state, axis=1) + np.std(state, axis=1) / np.sqrt(n_iters), alpha=0.2,\n", + " color=plt.cm.Set2(i))\n", + "\n", + "# ax[0].set_xscale('log')\n", + "# ax[1].set_xscale('log')\n", + "ax[0].set_ylim(-0.05,1.05)\n", + "ax[1].set_ylim(-0.05,1.05)\n", + "# Create custom legend with colored text\n", + "from matplotlib.lines import Line2D\n", + "ax[0].text(1.4, 0.8, 'SubspaceDMDc', color=plt.cm.Set2(2), fontsize=12, ha='center', va='center', transform=ax[0].transAxes)\n", + "ax[0].text(1.4, 0.65, 'DMDc', color=plt.cm.Set2(1), fontsize=12, ha='center', va='center', transform=ax[0].transAxes)\n", + "ax[0].text(1.4, 0.5, 'DMD', color=plt.cm.Set2(0), fontsize=12, ha='center', va='center', transform=ax[0].transAxes)\n", + "\n", + "# Add subplot titles\n", + "ax[0].set_title('State', fontsize=16, pad=10)\n", + "ax[1].set_title('Input', fontsize=16, pad=3)\n", + "ax[1].set_xlabel('Rank of DMD')\n", + "fig.text(-0.05, 0.5, 'Silhouette Score', va='center', rotation='vertical',fontsize=16)\n", + "plt.tight_layout()\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "dsa_test_env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.18" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 4e1a652f0a7b3a952d7582fdbc4be4ac09d518b4 Mon Sep 17 00:00:00 2001 From: ostrow Date: Sat, 8 Nov 2025 18:10:31 -0500 Subject: [PATCH 45/90] compatibility bw local dmd and pykoopman --- DSA/dmd.py | 2 ++ DSA/dmdc.py | 4 ++-- DSA/subspace_dmdc.py | 3 +++ 3 files changed, 7 insertions(+), 2 deletions(-) diff --git a/DSA/dmd.py b/DSA/dmd.py index 6746d67..2140976 100644 --- a/DSA/dmd.py +++ b/DSA/dmd.py @@ -412,6 +412,7 @@ def compute_havok_dmd(self, lamb=None): @ self.Vt_plus[:, : self.rank] ).T self.A_v = A_v + self.A = A_v #for compatibility with pydmd self.A_havok_dmd = ( self.U @ self.S_mat[: self.U.shape[1], : self.rank] @@ -471,6 +472,7 @@ def compute_reduced_rank_regression(self, lamb=None): @ self.S_mat_inv[: self.A_v.shape[0], : self.U.shape[1]] @ self.U.T ) + self.A = self.A_v if self.verbose: print("Reduced Rank Regression complete! \n") diff --git a/DSA/dmdc.py b/DSA/dmdc.py index 25a005f..0ac43f2 100644 --- a/DSA/dmdc.py +++ b/DSA/dmdc.py @@ -480,8 +480,8 @@ def compute_dmdc(self, lamb=None): ) # Set the A and B properties for backward compatibility and easier access - self.A = self.A_havok_dmd - self.B = self.B_havok_dmd + self.A = self.A_v + self.B = self.A_v if self.verbose: print("DMDc matrices computed!") diff --git a/DSA/subspace_dmdc.py b/DSA/subspace_dmdc.py index 8fe7d6f..cd9f242 100644 --- a/DSA/subspace_dmdc.py +++ b/DSA/subspace_dmdc.py @@ -80,6 +80,9 @@ def fit(self): backend=self.backend, lamb=self.lamb) + self.A = self.A_v + self.B = self.B_v + self.C = self.C_v # Send to CPU if requested (inherited from BaseDMD) if self.send_to_cpu: self.all_to_device(device='cpu') From 6abae78ebe2460f76cf5749b8feaffc12544b456 Mon Sep 17 00:00:00 2001 From: ostrow Date: Sun, 9 Nov 2025 12:49:46 -0500 Subject: [PATCH 46/90] replicate rings with new dsa! --- examples/ring_attractors.ipynb | 1346 ++++++++++++-------------------- 1 file changed, 483 insertions(+), 863 deletions(-) diff --git a/examples/ring_attractors.ipynb b/examples/ring_attractors.ipynb index 45e927a..88e5258 100644 --- a/examples/ring_attractors.ipynb +++ b/examples/ring_attractors.ipynb @@ -21,14 +21,14 @@ "\n", "# #install netrep\n", "# ! git clone https://github.com/ahwillia/netrep\n", - "# ! cd netrep/\n", + "# ! cd netrep #if this does not work, install it via the terminal\n", "# ! pip install -e .\n", "# ! cd .." ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -43,7 +43,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": { "tags": [ "{hidden}" @@ -490,17 +490,17 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "def plot_results(ts,nruns,diffs_dmrsa,diffs_proc,parameter): \n", + "def plot_results(ts,nruns,diffs_dsa,diffs_proc,parameter): \n", " df = pd.DataFrame(dict(ts=np.tile(ts,nruns),\n", - " diffs_dmrsa=diffs_dmrsa.flatten(),diffs_proc=diffs_proc.flatten()))\n", + " diffs_dsa=diffs_dsa.flatten(),diffs_proc=diffs_proc.flatten()))\n", " palette = None \n", " errorbar = 'se'\n", " hue = None\n", - " g = sns.lineplot(data=df, x=\"ts\", y=\"diffs_dmrsa\", hue=hue,\n", + " g = sns.lineplot(data=df, x=\"ts\", y=\"diffs_dsa\", hue=hue,\n", " palette=palette,errorbar=errorbar,c=\"blue\",marker='o',label=\"DSA\")\n", " k = sns.lineplot(data=df, x=\"ts\", y=\"diffs_proc\", hue=hue,\n", " palette=palette,errorbar=errorbar,c=\"orange\",marker='o',label=\"Procrustes\")\n", @@ -511,14 +511,14 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "cuda\n" + "cpu\n" ] } ], @@ -537,8 +537,8 @@ "\n", "#parameters for the run here\n", "nruns = 10 #will do multiple tests and average over\n", - "n_delays = 50\n", - "rank = 1000\n", + "n_delays = 10\n", + "rank = None\n", "diffeo = sigmoid\n", "nneurons = 100 #here we'll keep them fixed across runs, but we varied them in the original code\n", "burn_in = 200\n", @@ -556,7 +556,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { "scrolled": true }, @@ -566,79 +566,35 @@ "output_type": "stream", "text": [ "on run 0\n", - "DSA\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "(1, 8)\n", + "DSA\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Fitting DMDs: 100%|██████████| 1/1 [00:00<00:00, 25.36it/s]\n", + "Fitting DMDs: 100%|██████████| 1/1 [00:00<00:00, 26.63it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pre-computing eigenvalues for Wasserstein distance...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing DMD similarities: 100%|██████████| 1/1 [00:00<00:00, 199.13it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "on run 1\n" ] }, @@ -646,545 +602,274 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_90468/1197694459.py:120: RuntimeWarning: invalid value encountered in divide\n", - " centers = np.sum(\n" + "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "DSA\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "(1, 8)\n", - "on run 2\n", - "DSA\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "(1, 8)\n", - "on run 3\n", - "DSA\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "(1, 8)\n", - "on run 4\n", - "DSA\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "(1, 8)\n", - "on run 5\n", - "DSA\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n" + "DSA\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Fitting DMDs: 100%|██████████| 1/1 [00:00<00:00, 24.53it/s]\n", + "Fitting DMDs: 100%|██████████| 1/1 [00:00<00:00, 25.73it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "(1, 8)\n", - "on run 6\n", - "DSA\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "(1, 8)\n", - "on run 7\n", - "DSA\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", + "Pre-computing eigenvalues for Wasserstein distance...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing DMD similarities: 100%|██████████| 1/1 [00:00<00:00, 238.37it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "on run 2\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", + "/var/folders/sh/r0d61tjn42s4mc3nxqb0hhq00000gn/T/ipykernel_74501/1197694459.py:120: RuntimeWarning: invalid value encountered in divide\n", + " centers = np.sum(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DSA\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Fitting DMDs: 100%|██████████| 1/1 [00:00<00:00, 25.23it/s]\n", + "Fitting DMDs: 100%|██████████| 1/1 [00:00<00:00, 21.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pre-computing eigenvalues for Wasserstein distance...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing DMD similarities: 100%|██████████| 1/1 [00:00<00:00, 258.48it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "on run 3\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", + "/var/folders/sh/r0d61tjn42s4mc3nxqb0hhq00000gn/T/ipykernel_74501/1197694459.py:120: RuntimeWarning: invalid value encountered in divide\n", + " centers = np.sum(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DSA\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Fitting DMDs: 100%|██████████| 1/1 [00:00<00:00, 15.86it/s]\n", + "Fitting DMDs: 100%|██████████| 1/1 [00:00<00:00, 18.51it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pre-computing eigenvalues for Wasserstein distance...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing DMD similarities: 100%|██████████| 1/1 [00:00<00:00, 220.03it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "on run 4\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", + "/var/folders/sh/r0d61tjn42s4mc3nxqb0hhq00000gn/T/ipykernel_74501/1197694459.py:120: RuntimeWarning: invalid value encountered in divide\n", + " centers = np.sum(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DSA\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Fitting DMDs: 100%|██████████| 1/1 [00:00<00:00, 17.86it/s]\n", + "Fitting DMDs: 100%|██████████| 1/1 [00:00<00:00, 21.92it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pre-computing eigenvalues for Wasserstein distance...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing DMD similarities: 100%|██████████| 1/1 [00:00<00:00, 252.79it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "on run 5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", + "/var/folders/sh/r0d61tjn42s4mc3nxqb0hhq00000gn/T/ipykernel_74501/1197694459.py:120: RuntimeWarning: invalid value encountered in divide\n", + " centers = np.sum(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DSA\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Fitting DMDs: 100%|██████████| 1/1 [00:00<00:00, 20.98it/s]\n", + "Fitting DMDs: 100%|██████████| 1/1 [00:00<00:00, 22.40it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pre-computing eigenvalues for Wasserstein distance...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing DMD similarities: 100%|██████████| 1/1 [00:00<00:00, 226.50it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "on run 6\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DSA\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Fitting DMDs: 100%|██████████| 1/1 [00:00<00:00, 13.66it/s]\n", + "Fitting DMDs: 100%|██████████| 1/1 [00:00<00:00, 10.81it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pre-computing eigenvalues for Wasserstein distance...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ "\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "(1, 8)\n", - "on run 8\n" + "Computing DMD similarities: 100%|██████████| 1/1 [00:00<00:00, 286.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "on run 7\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_90468/1197694459.py:120: RuntimeWarning: invalid value encountered in divide\n", + "/var/folders/sh/r0d61tjn42s4mc3nxqb0hhq00000gn/T/ipykernel_74501/1197694459.py:120: RuntimeWarning: invalid value encountered in divide\n", " centers = np.sum(\n" ] }, @@ -1192,79 +877,80 @@ "name": "stdout", "output_type": "stream", "text": [ - "DSA\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", + "DSA\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Fitting DMDs: 100%|██████████| 1/1 [00:00<00:00, 23.87it/s]\n", + "Fitting DMDs: 100%|██████████| 1/1 [00:00<00:00, 26.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pre-computing eigenvalues for Wasserstein distance...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing DMD similarities: 100%|██████████| 1/1 [00:00<00:00, 220.12it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "on run 8\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ "\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "(1, 8)\n", + "/var/folders/sh/r0d61tjn42s4mc3nxqb0hhq00000gn/T/ipykernel_74501/1197694459.py:120: RuntimeWarning: invalid value encountered in divide\n", + " centers = np.sum(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DSA\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Fitting DMDs: 100%|██████████| 1/1 [00:00<00:00, 18.30it/s]\n", + "Fitting DMDs: 100%|██████████| 1/1 [00:00<00:00, 24.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pre-computing eigenvalues for Wasserstein distance...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing DMD similarities: 100%|██████████| 1/1 [00:00<00:00, 254.26it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "on run 9\n" ] }, @@ -1272,7 +958,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_90468/1197694459.py:120: RuntimeWarning: invalid value encountered in divide\n", + "\n", + "/var/folders/sh/r0d61tjn42s4mc3nxqb0hhq00000gn/T/ipykernel_74501/1197694459.py:120: RuntimeWarning: invalid value encountered in divide\n", " centers = np.sum(\n" ] }, @@ -1280,84 +967,36 @@ "name": "stdout", "output_type": "stream", "text": [ - "DSA\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "(1, 8)\n" + "DSA\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Fitting DMDs: 100%|██████████| 1/1 [00:00<00:00, 18.95it/s]\n", + "Fitting DMDs: 100%|██████████| 1/1 [00:00<00:00, 20.08it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pre-computing eigenvalues for Wasserstein distance...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing DMD similarities: 100%|██████████| 1/1 [00:00<00:00, 241.30it/s]\n" ] } ], "source": [ - "diffs_dmrsa = np.zeros((nruns,ts.size))\n", + "from sklearn.decomposition import PCA\n", + "\n", + "diffs_dsa = np.zeros((nruns,ts.size))\n", "diffs_proc = np.zeros((nruns,ts.size))\n", "\n", "for j in range(nruns):\n", @@ -1368,28 +1007,33 @@ " sim.simulate(2,2000,wNoise=0.0) #simulate wth a drive of 2 for 3000 timesteps\n", " run_data = sim.gsR[burn_in:] #remove the burn-in period\n", " d0 = diffeo(run_data,ts[0]) #compare all to d0 (the smallest scale transform)\n", + " d0 = PCA(n_components=15).fit_transform(d0)\n", " data = []\n", " for i,beta in enumerate(ts):\n", " d = diffeo(run_data,beta)\n", + " pca = PCA(n_components=15)\n", + " d = pca.fit_transform(d)\n", + " # print(np.cumsum(pca.explained_variance_ratio_))\n", + " data = []\n", " data.append(d)\n", " proc_score = comparison_shape.fit_score(d, d0)\n", " diffs_proc[j,i] = proc_score\n", " print(\"DSA\")\n", " #we're doing 1-to-many analysis here!\n", - " dsa = DSA(d0,data,n_delays=n_delays,rank=rank,device=device,verbose=True,iters=500,lr=0.01)\n", + " dsa = DSA(d0,data,n_delays=n_delays,rank=rank,device=device,verbose=True,iters=500,lr=0.01,score_method='wasserstein')\n", " dsa_score = dsa.fit_score()\n", - " print(dsa_score.shape)\n", - " diffs_dmrsa[j,:] = dsa_score\n" + " # print(dsa_score.shape)\n", + " diffs_dsa[j,:] = dsa_score\n" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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YuU+h4oGTaRnJYWTKe3um5PiTIocwurN06VKz/s477zSPfeMb38Do0aPN4yeccILf502IDsHOxPS48ILNC2w4iBsKr6L11lS8/z/WC+SBIxWMqfhLQGrf0OwfRz0M+C7Q5wJg2+NW9BS8AxS+D/T7BjD4J9aY3eH3SbTHWFcJlG23oodiJ5UjJVKCcSQizAgLoRMIDQ0Nxp/zxz/+0UQCmPLYu3cv7r333laFDlMuXLqE5Dz7F0NbjbOqCux27RE6fvLKK6+YKAaFH88VRQgjLox2UAB4+3I+/fRTk/LziBzCdCCfx2gKxca+fftwzjnntPmeEydO9Hs/KTwZPfrrX/9qInjf+ta3MGjQoFa3Z9SGopjGc2+YzmLKjVx77bUmXfef//zHvCZFz5gxQShhFaI9sOqo5FPbRI8pqVBfPJky27cU2PdvoHJv0/rEnkDvGVZYpA8NnyZ7FDPDbwT6fdOm0Sh2dv3dRp8G/cj6eiiKOgo/l3iOlCgFijYCFfTvDAJS+mikhMMIC6GTnZ1txMqBAweared977/mvaFHhCkO7zTV8OHDkZ+f32kGW5M+YmSlvdCA3FbjLP6HmvFB+9/bD+h7YTqI54GRD6aSPHgLmvamvdpDy9dl+pERJW8ovLyh+KIIe/XVV/Haa68ZkcqU1MUXX+zzPegb4mfOVGfLFKUnPUXhxFQcX5NihynL3//+9yaiJUTnpqr2AcWf2gIERg0YzQkF9J+wX83+f9u5UB6YOss9y4ob9r0J1f61B0ZZJtwPHFppDculnwNbFgK7n7Ol6vQPBUOc0awcn2Z/T7PqjSMlTIflPI2UcAhhYUbmxZgRGaZYPDCawPv0jfiCEQemq7idh88++8wIoE4ROYT/qZg+au/C6ir+h/QF13uqr9qz+PkfmqKDZeXHH398M5HjCwpEmoLp1fFA/w2FCs3KNArT7O39+bQH+qZKS0ubvS49OC1hdOYXv/iFESVf//rXTQqK8HNk9MYb+nm4jj4iHp/34i2KmZK88sor8fzzz5uUG9NzQnSq54UCh/1mUG/7tnS1iGAqhpGbVdcCyy8ANt9nRQ59gPS/jPmdNRWPmQdknxzeIsebnpOBqU8Do26zUSgKkY9vAlb+FCj2bW0IbKREDzsglCKVIyUOr7JRdxmWI56wEDqeFAYvRn/+859NKoWpB14gPZ6Pyy67rJlZmY+z6oqeEQoc/vVOfwZTM2EDBcrIOcCouTaCQ/iT97m+k0vM2wt9TuxdNGvWLGzYsAFvvvmmiX5ceumljb4pRl4YFXnwwQfx+eefY82aNXjooYfafF1WQ7Fy7tZbb8W2bdvwzDPPNJqbSWVlpTEWs2KKlVIUVzRDU3gRiitGcCiw6OOqqKgwooj7y+8DRcyOHTtMvyVGbfgdINdff73xJPEx7iePx/OaQgQdVlRR4JRstmkX+vO6CvaFKVgBfDIXePM84JPbrKeFhRAZI20KaNprwIQHgD5fCn0aLVAo1o77GnDG88AJPwRik2z0ZcWlwPrfWEESrPdh6XtKrrUesP/OkXVATSsWBBERhI2knzlzpjHKssqG6adx48YZg6nnQkuDKSMM3n+x82LGSAD9Fyw1puhh1VVYwZJRmo5H/qqpERgjOWHUpZNihOeS548GY96nr4Ul/x4ogmhYvv/++435l+nGb37zm22+blZWFp5++mljNqaIpceHgoml64SpJ1aCUbQwTcnXZETHUxnHyitGZfjd4HZMa/H5jPh4zNH0ZfF5J598Mi666CLzPEZ8KHj37NljqstYkcX9FqJTqqpMqqqs61JVfF+KKnpuaCqusdWUhpS+Ni1FUcOy9VBCEUYRWF8JJGYFR2Txj8MTfw70+zrw2cPA/qXA3n8B+f+1TQzZvDAYv1vpAWI5Oivl6HGqco+USOsfNn+givYT42ppoogiWHXFyh6WSvOC6A0v6owIDBw40EQ7hHPRZy0CSlWVbrP9Zjh2oCuiOBxnwAs7TbnlXzSt5x9PHMHAkvDM0aE1FZuGiOX2jzreNhPGu1vvEu/TnB1MMVi0Adi8wHY/Jkk5wIlX2QqyYDYEpJCtOmTL0NlwkCkufu4iLK/fYRvREUKIiIBVOvS+UHhwcnZn/oXPlAknhFPcFK1rWs/UTc4ZNnqTfUro/TbsTcN9pU+Ixl5Gkzi7ipEcpoNYus1Se0bAGNkx64MgRDJHAVP+ZCM6HFfByMv624GdS+zA0KzxwTg69zF1swKOqSxToXWCPcZQn3txTPQJCSFEOKWqzITwd9wTwt9rPiG85yR3M7+z7MU3lHDYJ1NTFH48DzQK0xfE6FZ8iypRCkKafSlE2F+IhmKKHUZ7OgojWIxo5ZwJ7PwbsO1JoGQTsPIKIPdsW/jBCEww3sdEqNJtA0h2V2ZvJDNSIkcjJcIYCR0hhGh3qurzpoZzwUwRHXNC+AXuCeFHz/3rchi1YSk2PTgUKhzjwP2iCGgrShPLyM5xVgjR98IBn4wCBWtwKVNJJ/zADgvd+gdg94vAgTeAg+8A/b8DDPphkIQVR0pkAwl1tgfaoQLrjaLgScrq+OuLoCOhI4QQbcGIBc2/5bv9S1VxVhQjHRQErc16YnSDkRt6b6q8+ogxUsDIDU3F3QeH/vMxxuJioJYd2lOA5N42bcMLfpyf7TwoatigkH1qynfYqeJMb/G1ghEh41yrkbcCx3/b9t/hfLEv/mpNy0N+Chx3cXDexzNSghG4yj12cClTdGakRBAElQgaEjrHIIq92lGDPmPRyhfDXrzox6HYaW+qKjbZ/mXP1AyHSDJlw1RH9WE71ZuCpnFC+OdNz2MqKu9cK256jA99KsQYi8vcoyJirOm5x2Ar9oIxG8pMFh9rRRNHMbCTdEKaPV/BiJZRIE58GCh8D9i80Bq4N90N7Pw7MOx621soGDCSZPrvVLh9SPusf4frIrWc32FI6LQCuy4T9m5pb2dgEZnwM/b+zIUwEYwyd6qKkRletNpz8aXI4V/0m+6x8+yY4mHvLPpEhs0GPrwC2P1Pqgi7vZkQfrqN3vDCGw6VPIxQcL/5M64b0I2m2zwr3Jh+CiY8p+xZwygMx1MwwsUIT3LP4HiQ+Pq9TgN6ngzsft6mtBhFWn2dNXFzYGiwImb0JcUfb31LxeutD6m7e6REMEZWiIBReXkb5Wn79+9HUVGRmanF3jKc+SScFcmhyGGX5czMTNNVWwiTnjGzqnbbC7A/F1xe1D5fDGyYd/Rj7OzbYwLwzsU2YsMS6NxzumZC+LFgg0FGrXiRpgeJx03fiTl+/8bPdAgTFfkCqNhpB6OmsBw9iJ3ueYzb/gTsfNadTowF+l0MDP5pcP01jIYxmseIGFNySb2s2GGKruVC8ehrfagjeg4qL5fQaeNE8ULI5oUUO8K5UORwfISErEClJ1VV4n/pMKMzNOY+n9f6fLuL9wEH37bpn3CAwoKGYAodpqOYnqOxmPsayj/sKBJKmc7aA8QlWLHAi3+w4OtyYOiBN+19+q7YcZmm5WBG1XheTcqyujGI536g6SYFTaOw8bptRBDFUYIVe7xNP5RPUdTKEuwIXBghoRPkE8VOuy2HUQpn0HIwrIhSWqaqzIU1xn+TLUusX2gjMnhxPlBTaDvuhtRYzJ435XZYcFIukMrUVM/wmtrNSjT6mVjtVn3QeoRY2RVMWOnGhoM0mxNGsYZeYyNtXSH0jAe0wR4rRZHL6zZ8rONPX7QqlmKPFkvmZ3xzQXXMJfyyGWoYGGR4IdTFUAgnp6o2AxW7/E9VeVNXBXSjwTaz9YgOL9Q0OIfEWFwK1JQYX7Hx23Qfao83XCuEeBE2TQc5yHOPNSzTv+OrT0+gZJ0EnPIXO07js0XWJ7T2FiBzrG04yIaEnYkREG4xgQ74eI4SQx6BVA/U1bR4jLddvvelUSx5CSWzjhWErYmluLAXS0pdtTPHJ4RwaKqKIofpGxpuAzWN8nXW3Wong3Pq9YbfHb0Nh/kOudJW5XQVjByxaorGYgo4eohYus7KpkhLa1CQlu2wgpQXbPbfCabJl/2BWIa+4y9NETeaxDlSgt8Np+Pyjia5vKJJXlElbwHlsyI5prlA8kSX+Fn1GBPU3VXqqhNOlBDCaamqHXZWlWkA1yvwvzgPLLfmY/p6epwETH8T+PT3R1ddcbgvjbYsMe90YzE7FpdZIcCoDavGGBlxQrlz9SGbzmKXaqYLaSIOpnGXU8s/ewTY90rTuI2B37dDQ7vSmB2JuNwCyTsdV1Nso3C9TgnqW0nodMKJEkI4BHpTit2pKjOGIC3wbskUMxw7QDJGAGPvBNIGe/XRKbLpKu8+Op15XMZYzGGa6UDKcfYvaXpbwtBj0SEa6m0KkOXoFD5JPYLT28cbjvpgw8Eja+x9CsYhPwf6XhRcY7TTqS4E4tMldEKFhI4QUUblAVs6zqoeU1UVYOqDnpG1t9qZSqT/d62J1fv12tMZuaNQbPEv5voKayxmWoppFl6Uo6F3C1Ny7FhNI3l9ZfDGSXigaGRlFiu06N/xjORgT6SeE4P3Pk6mOvRCRw0DhRDOhxEAk6raYqMbqf0Cj3JwmviG39oICqMIo2+3AyVbQnFT3wkChykB9mcxxmIOmswCMobZ1FSg0alIhaXg6YNtvx1jVt5tz4npWxOEyxtfK+9sIOc021F52+M23fnRlXZ6PBsOclK7CGtkRlbqSojoSFVxkGRSB8QAowdMZZjOxmzANMamqrrKqEqDLFNTbKTHSikai9lV2IxMUHM5E32pLrD+HZal009DERjMtB3PvxkY+rz1nzCFxZlag34cHo0fw5Hq0Ed0JHQkdIRwLjSW0mtBj0xHUlU0Ea+b0zSb6oTLbTfdYEQNjtnzpsRGcBi9SMy2Tf0o2IKZonESPGc0KtO/U3ukcyJdjA5uWQgUvGfvM7I36CfA8d9s/p3oivRluFNbDCT0BLLGBvVlJXQ64UQJISI4VZWUE/hf9uyxsnG+9YAwejJ6XtD/Oj0qMlFPY3Gx21icaRvZGWNxuvOMxZ0Fy8UZxeMwT0bjjH8nyLPECj+wUT56hAinl3NgKIezUoy2Ntg1Goj1DLflsbsFp6vWdqEOAhI6nXCihBARAscaFG+xFzhTjdM98Avlp/cAe/9l72dNAMb8zl4wO4OGGitu+L5MuzAtxcnexlgsO2WH0k2lO4DK3U09XYJZNcVozd6X7YwzihmOATn3XWtg3vJQaFoMhJrYNobbjpwTlGikhE4nnCghRCSlqg65U1UBjjSgz4OpKhpc2QRt8BXAoB8Fv6yYxmLPME2+tqfnjenQHJy/fIU7StZsnET34M/zYnpx21NAv68Dh1eGT9PIUNDmcNu5VvB18PstodMJJ0oIEeapKkZwmKriRY1l1oFcxPjcvS8Bm+61gxgpOMbcEfxSYkZt6F1geXh8d/cwzV4yFnc2PN8Ve63g4fnncNVgCkoz2HUo8HzvNga77gFeGmhNumYeh6/X8fe729r2/r6+H+/b2muw4u2iT4EXjmv9HHz9QIfnqqm8XAgRpamqzMAbx7E6i16c/Uvt/Z4n25EO9BkEzVhcDNSWA/HJ7p43fdzG4iB7R4RvaEZnSoWikimkip02tWXGSQRhoClTjHw9Xxd4wvVVBfazZ4VY85HmTbSyOmi4OvG12aCSkdW2zgH/H8R1UgrYB0r8CiEiF140ijdZbwQncAd6sSrZAqydY7slM4U05GfAwMs6XrZthmmW21/uvLjQWNxjkL2wMpIjY3FoYBQnc6RNb9JIXLHPRhjM1PrYjolZmm/bGuzKdgQTH7DRpXYJkNZUia/1rQknV5u7HdjzXa2LSZ7Xts4BxVAXIqEjhIjgVNVn1udCX0sgFyj+AmdfHFbO0AzMv7TH3gH0GBecvjfsxMxhmt0G2gscq3BkLA4fTFWQu6qNgocNB9kPh+sCgSXkFN003fryp3A9K5C6+ELf5fAY2zoHrL5CECJo7URCRwgRWdDfYmZV7bQXpUBTVTQB0zB6YJm93+t02+U40IucNyZ9UQZ0HwR0HyxjcThDgZzKFGI2ULHb3WF5l/WaBDIElSXkNNuS1ga7Op3qNs5BkKqu/EENA2VGFiJyqCq0s6po5EzpQKqqeKOdVcX5RTSQck4V51V1NJXEbrmV+XbiNccymFET6locUVAAswcTRQ+jhck5/kfhGnvIdPFg17Dto1Nkz0WI+ugooiOECH94wSnzVFV1MFW18xnb34RpBqYsmKrKHBWcSBNLmGkwNrOnenT8NUXXw9LzzNH2c2Q6i12WGdkx4yTa+Z2jmGEJOU25FEmctB5tnZEb3OeAf5iwM7IZldJ16SpvJHSEEOENBQQFDv/Kprch0JlC/Kty/W+Agnfs/dyzgVG/DryhoDeMMLH7LpvFMVWlKqrIhpE9lp5TrFa5x0kwwkOx48/3pbMGu0YSDbVAfUVId0FCRwgR/qkqVlfxL8JABcSRtcC6X9mIS0wCMHw20O+bHU9VmblK+dZwnDXKRgFUSeUcYuPcDRyzrW+HYttTjq5ZYxGDhI4QIvxgeooXlpLN9q/ibgF6Xfg6O/4CfP6o9c9wFtG4+bapWzA64dJzwfQXU1WBmqJF+ENRk36ircor32G/m6aTdbaq6CIACR0hRHjBsmwKHHpymCZIDLCxGEXIJ3OBQx/Y+72/ZCs+OmqGpM+Hzd4onDJGAGknBD4VXUQWpspvrJ1Bxuos9t/hZHSmuBTJC1skdIQQ4UP1ITuryqSqcgNPVR1aBXxym/XOsAJqxE1A368GIVVVY1NVvLDRj2MMliKq4HeI3012tGbVHv07jPAkcz5ZWqj3LnxwuewfA4yqhhgJHSFE6OEvQ5o92R+HYqJboFVV9cC2PwFbH6cqsdGWsXdag3BH4eDN6iKgW38gY6h640Q7rKbid4H9dih0mNLi9yOF5eihqS4KGq76FkLF/dMsntuex3jbPKnFi8TY/8Nc+MdGCJHQEUKEQarqM3dVVZqtdgkERoEYxTm82t7v+2Vg+M2BNX3zhr/MjYk5DugxBug2wJpUhSDxqdajxShPKdNZe4C4BPc4iS7+nnjECFoRJa6WosUjTlqIFCNQ4tx/bHhuU7jEWdHCVC3FXOPPePc2Xgufx/8n5jld2yCwJRI6QojQQR8NZ1Wx34hJVQX4C7FgBbB+rm09H5divTh9LgjeGAdW2aQPD1yECefDdGbWeNtlmdPRKXjYDoHLsVKmnugJBYe3KDHrWggVz3ae53njESNGoLh/xniiKm5hwqpDb6HSUqA0ipy4Vh7rYPo3BEjoCCFCnKqqDbyqiuXdny8Gdjxl73cfAoydbydUdxSKJk4aZ9qLFTcqJxbHgt9hDrRM7GmFjjEs77aiolmah7h8p3k80ZDGKEoCEJfqFihuccIBpK0KkdZESkzUfn4SOkKIyExV0RTM3jhF6+x99sUZdn3HBUnjGIdk91/oAfqFRPRCIdL9BBulLNtluwS3N83jS7yIDiGhI4ToOhglKdrkHpXQgVTVwbdtl+PaYmsKZofjvOlBGuPANFpvjXEQHYffzczhOpMhRkJHCNFFqao9tnS8saoqAKMm01ycU8V5VSR9BDDuTht1CfoYhwivnBFCGCR0hBCdC8WDSVVt71iqqmIvsG6ONS+T/pfYqeMdLeVtNsaBwxx7R7WfQQinIaEjhOjcVJVpAJhv2+cHmqrK/x+w4Xd27AJHLYy+Hcg5M0hjHA4Bqf1sJCcYAz6FEGFF2LmcFi1ahAEDBiA5ORlTpkzBypUrW932qaeeQkxMTLOFzxNChAEsy2aHYqaEmFoKROQwGrTpbmDtLVaUZI4Bpj7TcZHDslz6hGrLgIxRQI/xEjlCOJSwiugsWbIEs2fPxuLFi43IWbhwIWbMmIEtW7YgJyfH53PS09PN4x4odoQQIaZyP3DkE1tOm9o3sNco3wmsnQOUfmbvD5wFDPlZx4coNo5xyHKPccjt2OsJIcKasIroLFiwAFdccQUuv/xyjBgxwgie1NRUPPHEE60+h8ImLy+vccnN1S8tIUIKvTRH1prWIEj2/QfKMdm3FHj/UityEjKBCQ+6/TgdFDk1xTbSlDoA6DlRIkeIKCBshE5NTQ1Wr16N6dObSkRjY2PN/RUrVrT6vLKyMvTv3x/9+vXDV7/6VWzcuLHVbaurq1FSUtJsEUIEkfLdbpETb1vgB9JjZ8Nv7SiH+gqgx0nAqX8Dek3teNUXo0wN1Tb9xVEOHZ1iLoSICMJG6BQWFqK+vv6oiAzv5+fn+3zO0KFDTbTnpZdewtNPP42GhgZMnToVe/bs8bn9/PnzkZGR0bhQHAkhggA9L0w1sXkfvThJWf6/BquyVlwG7HnJdooddAUw6RE7fqEjUDxRgDEy1HOybeSmWVVCRA1hI3QC4ZRTTsFll12GcePG4cwzz8Tzzz+PXr164Q9/+IPP7efMmYPi4uLGZffu3V2+z0I4UuSwyzE9OWxVn5jp//P3vGxTVRQ7ST2twBny0yCkqo4AVYW2Lw5TVXxtIURUETZm5OzsbMTFxeHAgQPN1vM+vTftISEhAePHj8fWrVt9Pp6UlGQWIUSQoEjhAMPijUBihv/l2XUVwKb5wL7X7P2eU4Ax8zouSMwYh/1AbIrGOAgR5YRNRCcxMRETJkzAsmXLGtcxFcX7jNy0B6a+1q9fj969e3fingohGn0vNAsbkZPpv8hhE8H3v29FDrskD7kKmPhQx0UOxVP5HiApB8ieDHQ7XvOChIhiwiaiQ1haPmvWLEycOBGTJ0825eXl5eWmCoswTdW3b1/jtSHz5s3DySefjMGDB6OoqAj33nsvdu7ciR//+MchPhIhHE5DvRU5JVusH8cfYy+jQLufAzYvsKXeFCQc49BjXMf2ia/L5n98TY1xEEKEo9CZOXMmCgoKMHfuXGNApvdm6dKljQblXbt2mUosD0eOHDHl6Ny2R48eJiL0/vvvm9J0IUQnipySzTYiw3EO8antfy4b9G38ne10THqdBoz+P/99PT7HOOy3UaVMjXEQQjQR43Lxz6DohOXlrL6iMZmNB4UQ7RAUnDVVts32yGmr2zFLzGkm5nNc7uexAWDlXpuqOvEaYMD3Oj5XiuKpRmMchIgmSvy4fodVREcIEcZwcjj9OKXbbaO91kROrLu8nJ2HWfWU2MOKnE33WJGT0gcYeyeQOSoIqaqD9mfGaCBtYMertIQQjkO/FYQQx6a+BijaCJTvAFJ7tz4xnCInbYAVNVseBGqLbP+aoVcD57wBfHwTcMIPOj5XSmMchBDtREJHCHHswZpFG4CKnUBqHyA2ofVtGcmhyNkwr2kdxQ4nj7MJ4Nj5QNW+jo9xqC22YxwyhvrnERJCRB1hU14uhAhD6iqBok9s1+OUvm2LHHpymK5iJMcXW1g6nmW3C7ScvWKfjeZkjnWPcZDIEUK0jSI6QojW+9FQ5FBccAL5sfwvfJyeHEZwfMH1NUV2u/o6/8c4cBgnDdAZw9XhWAjRbiR0hBBHU1duRzpU5QPdjrNVUseC1VWmcWCmb7HD9Xycr+kP1Yet6Oo+BEgf0nallxBCtECpKyFEc2pLgcMfA5X5NpLTHpFDyr8ADrxhjce+GHotUHPYlpq3BwqnCveA3qyTgMyREjlCCL9RREcI0dzoy3QVoygmktPOv4WqDgIf/RxISAfOfcf+DdWs6upaYMTNQNkX7Xs9RnCqCqz5mV2OO9pQUAgRtUjoCCEs9M8cWWfFiYnktLORH305H10FVO6zwqh4MzDkSmDkrfY1KVIYyaHIaahq5xiHao1xEEIEBQkdIYSN4FDk1JXZ6qr2ihx2JV51je2vk5wLTHwEiE+xoodRHhqP6clpT7pKYxyEEJ2AhI4Q0U5VoRU59RVASu/2ixxWQq253s69YvfjiYtsqskDxU17q6vMGIfDQOpxNpLT0YaCQgjhRkJHiGiGUReKHPam8RYpx4Lbs8vxkbVAfBow8WHbEdlfmKqqOmBvZ4zSGAchRNCR0BEiWmFVFUUOGoCUvPY/jymmdbcBhStsFdSEB4D0oR0b48DeOOyRI4QQQUZCR4hopGIvULTejmXwR2CwO/HGO2wZeUwCMP4+oMdY/99fYxyEEF2EhI4Q0Ub5bqB4vR3FkNTTvzTT5gXA3n/Z3jrj7gSyT/bvvSmU2OGYJuXMcUC344HYdvbpEUKIAJDQESJaoFCp2GUHdMYlWQOxP2z9A7DzWXt71Fwg9yz/nk/zMj1BSRzjMExjHIQQXYKEjhDRInLKdgDFG+0gTH8b8O34K7DtcXt7xC+BvhcGNsYhbbDGOAghuhQJHSGiQeSUbgNKNtmybXYv9ofdzwNbHrC3h1wFHP+t9j/X9MbJt+KKYxxYPt7e8nUhhAgCEjpCOBl6Ykq3AsWfugdupvn3/H1LgY3z7e2Bs4BBl/tXVVWx3z3GYTiQmOHfewshRBCQ0BHCySKn5DPb0C+ph+134w8H3wHW384XAvp9Ezjxaj/eu96KnG4D3cM4E/3efSGECAYSOkI4kYZ6K3BKP7emX6aO/OHQKmDtL61g6XO+HcjZ3pSTMT3vs5GczBESOUKIkCKhI4TToC+GqSqKHPbI4ewpf2BV1prZNvWUcyYw6vb2TzEnlfttRRc7HbO6SwghQoiEjhBOoqEWKN4ElG0DUnJt52J/oDhafa2de9VzMjD2Ttvzxp+5WXzPzNH++4GEEKIT8OPPNCFEWFNfAxzZYCuskvP8Fznlu4CPrgZqS4DMMbbrsT8RGXY75iDPzFFAUpbfuy+EEJ2BhI4QTqC+2o50KN8OpPb2P2XEEvCPfg7UHAK6nwhMWOifr4c9cmpLgfQRdgK6EEKECUpdCRHpsOOwETm7gdS+QGyC/838KHKq8oHU44GJD/nXa4denupCIH1YYBPMhRCiE5HQESKSYSSFIodDOo3I8fO/NKMwq662oyGY7pr0iH+jGWh8Zhl59xOA7kPUDFAIEXZI6AgRqdSVA0c+cUdiAhA5FEmrrwNKPwMSe1qRk5LX/uezjLzSqyGgv+8vhBBdgH4zCRGJ1JYBR9YB1QVW5HCauL+eno9vBIo+AeK7A5MetpPE/fX1qIxcCBHmSOgIEWmwKsqInENukeNnTQHTTet+BRxaCcSlABMftGknfzBl5EkqIxdChD2quhIikqgpAg5/bA3EgYgcjoXYMA84uByITQROWmDFil/7oDJyIUTkIKEjRKRAcXNkLVBbHKDIcQGf3gvs+7dNdY27C+g5yb/XUBm5ECKaUle1tbXIz89HRUUFevXqhawsNQkTolNgmooip64SSOkTWHXT548Au/4BIAYY/Rsg5wz/nt9YRj5cZeRCCOdGdEpLS/Hoo4/izDPPRHp6OgYMGIDhw4cbodO/f39cccUV+Oijjzpnb0VkUXnALrw4i8CpKrDpKvbLYTPAQETO9qeA7U/a2yPnAH2+5N/zTRn5PiBtINB9sMrIhRDOjOgsWLAAd9xxBwYNGoQvf/nLuPXWW9GnTx+kpKTg8OHD2LBhA9555x2cd955mDJlCh566CEMGeKnyVE4ZxxB8Ubr52CH3cQMIDnXVviwGV1cYqj3MDJgZRNLyFHnX+m3N4zifPawvT30WqDf1/17fuM08r4qIxdCRBwxLhd/i7WPSy65BLfddhtGjhzZ5nbV1dV48sknkZiYiB/+8IcIV0pKSpCRkYHi4mITnRJBjkIUvG8vzg3VtucLS5oZjYhLs2XJydlAQncrftSD5WgoLtgMEC4guVdgnwP9OJ/MtbcH/QgY8rPA9oPiNGuCBnUKISLu+u2X0HEaEjqdeXI/txGdbv2ar3fVW0MrhQ8nbVPgxHcDErPtIEgjfNL8N9o6jYo9VuTwPCRlB/YaB5YDa39pz/nxM4HhN/qfcmIZOfeBIkeDOoUQEXj9Vh8dEXxYwlx1AIhPOfoxVvtQzHAhFDsUPeU7gLKttjcLhU5SDpCY6U5zpUSXJ4RTxClyWP4dqLgo/BBYO8eKnL5fBobf4P859JSR9zhJIkcIEbFI6IjgU1cG1JUACRnH3pYDKClouBAabhnxKdliUzYUORQ7yTn2pxE+fk7mjhQYXC3faUWO8TW5z4m/sJngxzcArlog92xg5K/8j5B5ysh7jNU0ciFERCOhI4IPIwF11UBysv/PjUu2CyMZvPDXV9q+MYwQsSzaCAD6e3q5jc3d/Z/WHY7wWMu2A8Wb3Km8dohEX1Agcn4VBWPPk4Gxv/Pf/+RdRt6tf2D7IYQQYYKEjugcI3Iwqqpi3MKGS6O/p9KKnvLdbn9PqvWwcOK2R/hEmr+Hqb7SbVbkUOB40nr+UvaFnUTOiFqPccD4e236y98ycg7qTNM0ciGEM5DQEcGFkYSaQ9ZnE2yMvyetqfKHF2Xj79kFlG634sr4e3oBST2s8GF0JJz9PUbk0Li9GUjKDPy8UZysugqoOQKkDwNOWujbI+VXGbmfg0KFECIM6fCfvlOnTkVubm5w9gbAokWLTBPC5ORk04tn5cqV7Xres88+i5iYGHzta18L2r6IQNNW5U1RmM6EER1GQFL7AGnH25QWfSllnwMFHwAF7wKFK2wFWNXB8Gtc2FBvBQ4jOUaYBShymGb66Oc20tVtADDxocDKwCmWmDLMGOlcH5QQIurocERn5syZKCwsDMrOLFmyBLNnz8bixYuNyFm4cCFmzJiBLVu2ICcnp9XnffHFF7jxxhtx+umnB2U/RAdgRIFemlCkj3hx5mIEj8ttbC61Iof7RGMzDb7099DUzIhPqBoXMhpFkcNojvEb+Rl98RaWH10FVOy2oyEmPWKPP6B0YwrQY4x65QghHEVY9dGhuJk0aRIefth2cW1oaEC/fv1wzTXX4JZbbvH5nPr6epxxxhmmMSG7MhcVFeHFF19stZEhF+86fL6+GgYGMULBKArNrOHWc4UpovoKr8aFcUBcN+vt4dLYuLAL0jUsqS/+1JbTs1s0zdeBwGNhJIf9iuhTmvI4kHpcYGKJpm/2ygm0+7IQQoRpH52wcW3W1NRg9erVmD59euO62NhYc3/FihWtPm/evHkm2vOjH/3omO8xf/58c2I8C0WOCCKMntAIG0japLNhhImpIQqLbsfbn7GxQOUe4PAqoOA9oOBtoGij9amwtLoz/gbwjMYopcjJC1zkUKytucG+Fsv4Jy4KTOR4ysiZrpLIEUI4kLAxIzP9xehMS78P72/evNnnc95991386U9/wtq1a9v1HnPmzDGpsZYRHREkWAbOaI6/lT6hgP6eWHdfHsL95kWfURZGfyhAjDBi/55Md8Sng74jipOiTbY5IodzBnqemPZae4sVaIxK0ZPTfVBg+6MyciGEwwkboRPIFPVLL70Ujz32GLKz29ciPykpySyik+Ck8kg1sVJ0JCa2aFxYbn00nsaFZjBpDhBPgdTdv2Pl6xVtACp2WfN0oL1/WGK//nag4B0gNgmYcD+QMSIwsVSVrzJyIYTjCRuhQ7ESFxeHAwfYGK4J3s/LO9o3sG3bNmNC5hR1D/T0kPj4eGNg5pR10UUwGkIjcmeUlYeCxsaF7rJr+ntqimxlEjO+pqlfFpDcs8nY3FpjPlZ7FX0CVOy1pduBDjDlfmy8C9j/uvUYjb8byDopsNfhcaSojFwI4XzCRuhw0vmECROwbNmyxhJxChfev/rqq4/aftiwYVi/npOdm+BkdUZ6HnjgAaWkupraEmtopbHXaZjGhd3s4j2YtGo/ULHTRmfiUpv69xjh4x5Myu04koHRk46KnC0PAntesEJrzO+AXqcF9loUOazMyhwVuRE4IYSINKFD6J+ZNWsWJk6ciMmTJ5vy8vLyclx++eXm8csuuwx9+/Y1pmL22Rk1alSz52dm2rRDy/WiC6g+bAVBODfnCxbtGUxK7wwNzzWHbX8bihw+L1C2/wn44q/29qhfAb3PDex1vMvIPcJNCCEcTKcIHVZLTZs2Dffee6+J0vjTk6egoABz585Ffn4+xo0bh6VLlzYalHft2mVeW4QZ9HtUH3BO2spf2hpMSnHDaqiO9BX64lng88X29rDZwHFfDex1mHpDg43kBNJrRwghIpBO6aPz1FNPGf8MRcoHH3wAJ9Thi2NEc1ieTaNuoKkZ4Zs9/wI2/MbeHvwTuwQCI07VR4CscRrUKYSIePy5fodVw8CuRkInSJTtsD6Ubl6l+jEs34630R5XXbDeKbrI/x+w9lYbhen/XWDYLwJLDbKMvDLfVmelD42O9KIQwtGU+CF09Oe36BjUyfSgeBrfxbJSKctWJLEKiykS+lQY9Wmo0tluLwXvA+tusyKHqapARY6njLy7ppELIaKToBtedu/ebcYxiCjBlF0XW38ORU7aAOsneT4PeKG3/cn7XM/HxbE5/DHw8U02EpZ3LjDy1sBEDhsfqoxcCBHlBF3oHD58GH/+85+D/bIiXPHMSTI9Z7KATfcAG+YBtUXusvMie5/rw23+VTjCBoVrrgcaqoFepwJj5gVercV0Fcv9VUYuhIhi/E5dvfzyy20+vn379o7sj4g0qg/ZiiJTeZRle734gutHzgEq9oRmsnmkeJ1WXW2Nwz1OAsbdHXgHZU8ZOUWOysiFEFGM30KHzfxiYmLQloeZj4sogP1jqg+601bx1pPjieS0hOsZYfjwpzYlwwtw5mi7cLhltH9n2DWZk8h5nmganrAg8IGfKiMXQojAhU7v3r3xyCOP4Ktf9d3LgwM2/emdIyI8bcVp5RQqNL3SeMwBmL7EDtcn9wIqd9tBksXrgZ1/s48xvZIx2i1+xgDpw4H4FEQNjL5Q5FQX2NlTEx4MvCcRo0G1ZbaMXNPIhRDCf6FDEbN69epWhc6xoj3CQTBywM+a0RxGaVhdNfRa68lpCdfXlABTHgeK1ruXDUDpFpv+OrjcLoSelO6DvcTPaCD1eGdGfXgOV10FVO61s6cmPtLUeDCQMvKqQiBzpD1fQggh/Bc6N910kxnL0BqDBw/Gm2++qVMbLWXl3pEXlpCPuMmWRG952EZ2GMmhyBlxM1D2he0SzKXP+U1dhEs2e4mf9Taywa7CXHb/026XkGFFj0f8ZIwCEiK8EzOjYauuAcq2A0k5wKRHgOTswF7Lu4w8bbAzRaEQQgSAGgaqM3Jg1JYCBe/aAZbeXpJDK4Hk3nYWE1MojE7420eHAsoT8eHPkk+BhpqWX10gbaAVPB6vD+93ZJ5UV0KBt+pa4MgaKwYZ6WIJfiCwjJwen5TeQI/xQFxisPdWCCHCCjUMFJ1PLcvKq+zYB292/t2moIZeDwz+sY0y+NsZmcMw87hMbzI9l37WXPww1cNICJe97kpADtJk2qZR/ITpTCcez8c3W5HDiqiJDwcucgh75TSWkUvkCCGEN+qMLAKDXpCWpc8cZFm4wt7uOdkKoWDA98mggBkJ9HevY4So2C16uBRvBOrLbUSJiwemyUy6y710HxLaeVyueuCTXwOF7wOxScCEB4CMYYG/nikjp8BTGbkQQvgi6L/xp0+fbnrpqJ+Og6HplZVTLSuDONiTje5S+1lB0Zmw+WDOGXbxCAhGd7yNzuU7bN8eLvtfs9tRXGQMb17lxWqwroAppg132BlWnAU2/j6gx7jAX09l5EII0fVC5+KLL0ZhYWGwX1aEW9qKZcypvZuvP7DM/sw9p+vNsKZSa4hd+n3dvZ8lQNFGW8ruSXnVlQJH1trFO1XGaI8n5ZU+DIhLCr55e/P97jRbLDD2TqDXKYG/XrMy8txg7qkQQjiKoAudq666KtgvKcINNgYk3sZfpqloTiZ5ZyMsoFGaYsIjKBhRKd/VPOVVutWan/O5/M9ux2hL+onNU14pffwXb94T3D9f1NQ3aPTcjp0jU0Z+CMgcoTJyIYToDKHDiM0TTzyBFStWID8/36zLy8vD1KlT8YMf/AC9enVRKkB0PRQLLcvKPdO2jTm5t234F45w9ARNv1z6XtTkKyre1Fz8sErMrNsE7Fpit+N4i8bydkZ/RgDxqb7fp+UEd5bG97kAKHgH6PvlpvfuUBn5IJWRCyFEZ5SXf/TRR5gxYwZSU1ONHyc314bNDxw4gGXLlqGiogKvv/46Jk6cCCeVpwmvbsiM3LCayTu9s+42YP9SYMD3gWHXR+7p4n8HVjE1prs+sf18jqoci7VNDb3FT7fjgbhUK6Q4xJTzvRp7CV0NDLvBloG3t8zeZxn5HttYkN4eVVgJIaKUEj+u334LnZNPPhljx47F4sWLj5ppxZe68sor8cknn5hoT7gjoRMATP0cXm0v6t6plDfOs1VPJz9pL/pOgsdHsePt9WFUpSXx3YEzXwYOvAFs+O3Rj4+aCwy5EqjcF9h+UCRRYGadpEGdQoiopsQPoeN36mrdunV46qmnfA7u5Lpf/OIXGD9+vL8vKyKFqoNAbIteLYc+tCKH3X1ZAu40GLnqMcYu3mXdpqzdLX6Y5uJ2PScBb1/cxgT3W+059Le3EJ9jyshHS+QIIYQf+C106MVZuXIlhg3z3fuDj3nSWcJh1FXa/jUtRy/kv2F/0mBLH0w0wJJ0Hq/HVGy8M4XHnuDOknAalOvr/Cwjd7kbIAY4B0sIIaIUv4XOjTfeiJ/85CdmsOc555xzlEfnsccew3333dcZ+yrCohtyBZDUo3mX34NvNZWVRysUL92OA5J6tT3BnULFV9qrrTJyLvTkqIxcCCE6X+iwfDw7Oxv3338/HnnkEdTX15v1cXFxZrI501rf/va3/d8TEf4wmsMZU95Rm0Mf2d40iT2bp3aikXZNcD/c/rRVszLyfkHfXSGEiAYCKi+fOXOmWWpraxubA1L8JCS0GAkgnENDvZ0qntCtlSaBZ0XOQM3OxExwv9neblZ15TXBvT0wFcbqL1Z2aRq5EEKEpmEghU3v3i264wpnUldiIzdJ2c0vxgeW29t5UZy28oal4xQzrK6i8Zj+Gs8Ed65vT2k5y8hZmcXKNvYkipWAFEKIQNFQT9H+/jn1tc0HeXL6Nn07jFj0UKVdIxQzFCqmQi3e/wnuZhp5tq1gU68cIYToEBI6on3wYh2f3HxdvidtNS20E8HDFYobf6qrjiojb6XzshBCiHYTJbXAokOw6ocRHe9p5ZwW7klbRXO1VTAxZeRQGbkQQoSL0NmzZw8aGhqOui0cBkUOZ0LFec23OrIOqDlkB2eySZ7oGJ4ycqarVEYuhBDhIXRGjBiBL7744qjbwmHQSBsb23x6tydt1esMpa06iqeMPH2oysiFECLIdMhY4T0my8+RWSJSYENA+kaapa0a7Dyntqqt+H2o2G0a+pqqIY5H4FRv81NtCI4uIx+iMnIhhOgE5CAVbVPLsvIyINlrrAdnO7GnDk2z2VN8P4+zrzjJO/1EOzqC/WSY/uJrUTxRAVHwxCa5xQ+XKPs6NisjH6YyciGE6ASi7Moi/Ibl47wge4sQT5PAnDOOHvDpoabEXsDTBjatq68B6iubltpStwCqAhr4PqxQirECKC65SQQ5dX6WKSPvpTJyIYToRCR0ROsw/VSZ39yEzHXeQzx9Pq/eLsl5zdezJ4zpC5PR/PUaqq3wYeSHP435ucRd7XXIbkMBxOd6R4C8PUORRmMZ+SiVkQshRCcioSNah2km0xAwvWldyadA1X4rfrJP8f08RmoSM4CkrGOfXYoVRm+4JHoNC2UUqb6qKfrDtBfLr80+ldjoENNfjPZ4xI8RQK1EmMIJlZELIUSXIaEjWscIimogOdlHtdVpVpy09jyWSXfEdEwBw4Z5LZvmceZWs/QXhQ8FEMXQYbf/xz1NnKLHY4IOF/8PhZqmkQshRJcRJr/9RVhSXdh8UCdTSI1DPFtJW1EYUWAk9+qcfWIFV2wakOBVBUYocLzTXxRbjEYxKsQIE6ubzPM9/h+3COrKQaTcl6rDQOZIlZELIUQkCJ1bb70VWVlZR90WDoCpoarC5oKi9HOgYo9NFfU61ffzKC4Ss4AELx9OV2AquBKap9mM/6eFAZr+H1NJxlTYEZsi6wr/jykjP+CeRj4osv1FQggRLUJnzpw5Pm8LB0DBwjRLqtd0ek80p9dU3wZaCgtWUGX0DY9KKeP/cYsXZLbw/1T78P+UtvD/xDT1/jHRn4TABEpjGXk/lZELIUQXo9SVaMMw62pK7Zhqq/8dI21VbgVQUs/wPqvG/5NiF5/+H7cJml4a+n9qy5v7f3hOGs3P7fD/qIxcCCFChoSO8B2BMNPKvaI2ZduB8p02qtHrdN9njWmh1OOB+G6ReVaP5f/xCCB6figEPbfra93Rn4Sm1JfH/2O6SquMXAghQoWEjmilMqgUSPBK93hGPmSffLQQaOyd4wJSWvTOcQK+/D/EO/1FEcQmiZ4O0Mb/U2/L8DNHA4le51IIIUTkCJ1NmzZh165dqKmhr6GJr3zlKwG93qJFi3DvvfciPz8fY8eOxUMPPYTJkyf73Pb555/HnXfeia1bt6K2thZDhgzBDTfcgEsvvTSg9xZuTMM+lpXT29KirLy12VaMbCR0t0bkaMGn/8fVvP9PZ1agCSGE6Dyhs337dlx88cVYv349YmJiGod68japr6/3+zWXLFmC2bNnY/HixZgyZQoWLlyIGTNmYMuWLcjJyTlqe1Z5/epXv8KwYcOQmJiIV155BZdffrnZls8TAVLFOVZejfeYsirbalMxHPvQmtDJGN78edEIv/++/D9CCCFCQsClMddddx0GDhyIgwcPIjU1FRs3bsTbb7+NiRMnYvny5QG95oIFC3DFFVcYsTJixAgjePjaTzzxhM/tp02bZsTW8OHDMWjQILNPY8aMwbvvvhvoYQmTgjnUfFq5Z+RDz8lHp28IS7hpyOXcJiGEEMIJQmfFihWYN28esrOzERsba5bTTjsN8+fPx7XXXuv36zH1tXr1akyfPr1p52JjzX2+17FgRGnZsmUm+nPGGb6jDtXV1SgpKWm2CF9pq4rmRuTGJoHntP4cpqzkQxFCCOEUocPUVPfu3c1tip19+/aZ2/379zdiw18KCwvNa+bm5jZbz/v067RGcXEx0tLSTOrqwgsvNJ6ec8891+e2FGEZGRmNS79+/fzeT8dDEy3x9MFhg8CSzfarknum7+dQGKWGSe8cIYQQIhgenVGjRmHdunUmfUU/zT333GPExh//+EeccMIJ6CoottauXYuysjIT0aHHh+/PtFZL2NSQj3tgREdip0UfmaoDQEK3o6utsiY0H7rpgb1mWD6dGOa9c4QQQkQlAQud2267DeXl5eY2U1gXXXQRTj/9dPTs2dOYiv2FUaG4uDgcOHCg2Xrez8trvWSZ6a3Bgweb2+PGjcOnn35qIje+hE5SUpJZRCuwpJyl5d5Txz3+nLw20lYpx/kuORdCCCFCTMC5BhqAv/a1r5nbFBqbN2826Seakz3Cwx8YDZowYYKJynhoaGgw90855ZR2vw6fQy+OCHDsgzEWuyunKvOB4g12FlTuNN+NBdkrJtWBvXOEEEJEd0SHKav9+/c3K/tmufehQ4fMY4GUlzOtNGvWLFO5xd45LC9n1IhVWOSyyy5D3759TcSG8Ce3ZcUVxc2///1v/PWvf8Wjjz4a6GFFNxw6afrCtEhb9RgHJGUfvT3nQpneOUpbCSGEcJjQ8fTNaQm9MsnJyQG95syZM1FQUIC5c+caAzJTUUuXLm00KLMxIVNVHiiCfv7zn2PPnj1ISUkx/XSefvpp8zrCTzzdfL3Lyj1Cp7VqK/bOSR+m3jlCCCHClhhXa4qlFTxm3gceeMD0vGGfGw+M4nz44YfGa/Pee+8h3KEZmdVXrNxKT/fRHyaa4ODJwg+B1ONs0zs2DVx+gR3sOe1VIDn36PlP3IaTzMN9iKcQQghH4c/12++Izscff2x+Uh+xKzK9NR54m2MbbrzxxkD2W4SS6sO2PNzd2RoH3rQiJ2P00SKnsXdOD9+VWEIIIUSY4LfQefNNXgBhfDOM6kR9JMQJNNQB1e4p2y2bBLZWbVVfDqQPVe8cIYQQzvToPPnkk8HdExE6aCquLQOSc5qiO4dt5A65Z/v288SlKmUlhBAi7OlQK9t33nkH3//+90359969e806Vj1p1lQElpW76uy8KnKQs8oagPQRQGof39tzrhUrroQQQggnCp3nnnvOTAhntRN9O57eNTQG3XnnncHcR9GZ0IvOfjlxXtO28z1pq7N9985hqiultz4XIYQQzhU6v/vd78x08cceewwJCQmN60899VSsWbMmWPsnOhuOcGCExlNWXlMEHF7VetqKJeXx3ZW2EkII4Wyh09qUcJZ7FRUVdXS/RFdBkVNfCcS5ex8dfNt2O+5+ItDteB/bl9pojndjQSGEEMJpQofzp7Zu3XrUevpzunKop+gg1YVATJxXWfmy1qM57J3D7TymZSGEEMKpQofNAq+77jrTIDAmJgb79u3D//t//8/00PnZz34W3L0UnQOFC4WOJ23Fyis2DWytrJzRH/XOEUIIEQ3l5bfccosZoHnOOeegoqLCpLE4GZxC55prrgnuXorOgU3/OK082T2Us4Bpqzog7QQgbaDvsnKmtGLj9IkIIYRwttBhFOdXv/oVbrrpJpPC4oyrESNGIC3Na1aSCG9oPGbVlaes3FNt5Wu2VZ3bx6NxD0IIIaJB6HiPfRg+fHij+BERAgVOVT4Qn9JUfVW4ovWycqatOAqCFVdCCCFENDQM/NOf/oRRo0aZaeVcePvxxx8P3t6JzqOuFKgrafLnFLwHNNQAqccDaYOP7p1TX2OrrSRmhRBCRENEZ+7cuViwYIHx47AzMlmxYgV+8YtfYNeuXZg3b14w91N0ij+nGkhObtEk8JyjxQx9POyCrLSVEEKIaBE6jz76qGkWeMkllzSu+8pXvoIxY8YY8SOhE+ZUHwLi3I0e66uAwvdaLyuvKQG6D27qtSOEEEI4PXVVW1uLiRMnHrV+woQJqKur6+h+ic6kvrp5WXnB+1bspPQB0oc135bjHhjgoT9HCCGEiBahc+mll5qoTkv++Mc/4nvf+15H90t0JjQW03wc361Fk0AfaStum5Bp++cIIYQQTk5dzZ49u/E2K6xoPP7Pf/6Dk08+2axj80D6cy677LLg76kIHjVH7M+YWBvdOfhOG9VWZUCPweqdI4QQwvlCh1PKW6apyLZt28zP7Oxss2zcuDGY+yiCCSuoOK08PtXeP/QBUF9hU1MZI5tvy3RWfDKQnK3PQAghhPOFzptvvtmu7Y4cOWLGQfTo0QMXXHBBoPsmOoPaEltFlZRl7+e/0WRCZoSnZUNBzrVS7xwhhBDR2EfHm+3bt2PhwoU466yz0Lt3bzzyyCOmmaAIQ6HDfjmxiXbW1cG3fM+2YkNBbkeDsnrnCCGEiMbOyPTkvPTSS2bZuXMnzj77bHz/+9/HkiVLkJOjCddhSdVBK3LIoZXu6E5PIHPM0Q0FGclR7xwhhBDRJHT+9a9/4eWXX8arr75qhnpeeOGFuPPOO3HeeechJcU9TkCEJ5xXVX0YSHCXlR9oK23F3jmD1DtHCCFEdAkdDvH86le/in/84x+YOnWq5ltFEiwVp/GY/hz2xzmw3PcQT9M7J0a9c4QQQkSf0Nm8eXPn7InofBjNoYDhcmh1U4+cHuOab2fWZ6h3jhBCiOgyI7NHjj/s3bvX3/0RnUVDPVB9sKkbcmOTwLOA2BZ6t7YcSD3u6PVCCCGEk4XOpEmT8NOf/hQfffRRq9sUFxebGVicZP7cc88FYx9FMOCkchqP2Q3ZVd+UtmpZbWV65yQBSeqdI4QQIvLx60/2TZs24Y477sC5556L5ORk0zCwT58+5jZ75/BxNgs86aSTcM8996iHTrhNK6+vBWITgMOrgRqaktOBrIlH985J6mUfE0IIIaIpotOzZ08sWLAA+/fvx8MPP4whQ4agsLAQn3/+uXmcM65Wr16NFStWSOSEG1XshuyePp7vTlvlnNk8PeXpnZOq3jlCCCGcQUAmDJaRf/Ob3zSLiAA4wJMRHfpzOALiwJu+q61MaisNSOwZkt0UQgghwqIz8rJly8wgT6asunfvbrw7d999N0pLS4O+gyIIUOTUVQBxKUDReqC6wHp1sicfXW2VnAfEqx+SEEKIKBU67IZ8/vnnIykpCbfddht+/etfY8yYMbjvvvuMAfmTTz7pnD0VgVN9yE4fZ1m5J23V64ymDsme3jkuhutydaaFEEJEb+qKJmNPw0BvKioqTEUWOyWvX78emZmZwdxPESicZ2UiOExbuZq6IbestuIMrMRMINE97FMIIYSIxogOjcZXX331UetTU1Px5z//GccddxwWL14crP0TwZpWzlRVySZrSmYKK/vk5ttxm5S+6p0jhBAiuoVOQUEBBg4c6PvFYmNx3XXXmTlYIkxguTj75rC6qjFtdVrzGVbsncM0VrJ65wghhIhyoVNfX29MyK3B3jpbtmzp6H6JYMBUVdUBIC7V3vYInaPSVsV2SjnHPgghhBDRXnX1l7/8xZiSq6qqjnosPT0dRUVFwdg30VGYjjJzq9KA0s+Ayr1AbBKQPbVpGwqg+mqbtqJZWQghhIhmM/Lpp5+O3/72t6aUPD4+HkOHDjVRHHZD5s/c3FwT9RHhMq28CkjO8UpbTQXiU5v32InrZiM6QgghRLQLnbfeesv8ZDdkdkFes2aNWV5++WUTyYlRVCB8qCq0Ix9MtZVniKePtFW3/s3FjxBCCOEQAh5PzfEPXL7zne80rtuxYwdWrVqFjz/+OFj7JwKlvsb2z2G1Vdl2oHwnEJMA5JzWoneOyzYJFEIIIRxIwELHF6zG4vKtb30rmC8rAoGRGnp0UnsDB56161hSzn46HupKgcQMIEm9c4QQQjiTgMzIncmiRYswYMAAU9k1ZcoUrFy5stVtH3vsMeMZ6tGjh1mmT5/e5vZRV1bOVscxcW1UW3l65ySEZBeFEEKIqBI6S5YswezZs3H77bcb38/YsWMxY8YMHDx40Of2y5cvxyWXXII333zTNDLs168fzjvvPOzduxdRDQd3mmnlqUDZF0DZNit4cs5o2oaVVhQ46p0jhBDCwcS4XDRphAeM4HBA6MMPP2zuNzQ0GPFyzTXX4JZbbjnm81ntxcgOn3/ZZZcdc/uSkhJkZGSguLjYlMU7aohnwbtAYg/gi/8HfP6ILSmf+GDTNlUHgYRMm86SgVwIIUQE4c/1O2wiOjU1NaaKi+kn707LvM9oTXvgvK3a2lpkZfn2nFRXV5uT47041p/TUAPEJTVVW+Wd3fQ4tW1dJZCq3jlCCCGcTdgIncLCQhORYR8eb3g/Pz+/Xa/xy1/+En369GkmlryZP3++UYCehdEiR1JVYEc6VOwBSra401bTmh6vL7fVWOqdI4QQwuGEjdDpKHfddReeffZZvPDCC62OqJgzZ44Jc3mW3bt3w3GwQWDNYVtd5ZlUnjXBTib3UFMCpORasSOEEEI4mKCWl3eE7OxsxMXF4cCBA83W835eXtt9Xu677z4jdP73v/9hzJgxrW6XlJRkFkdDf05dBZCa2VRtleudtqq3ZmX1zhFCCBEFhE1EJzEx0YyQWLbMfXF2m5F5/5RTTmn1effcc48ZSbF06VJMnDixi/Y2jKk5Yn9ymGfxRvrNgVyvtFVtKZDQXWkrIYQQUUHYRHQIS8tnzZplBMvkyZOxcOFClJeX4/LLLzePs5Kqb9++xmtD7r77bsydOxfPPPOM6b3j8fKkpaWZJepoqLcCJ6EbsO/fdl2P8UBSdnOhkzFcvXOEEEJEBWEldGbOnImCggIjXihaxo0bZyI1HoPyrl27TCWWh0cffdRUa33zm99s9jrsw/N///d/iDrqSmy3Ywqb/DeOrrZiJVZsvB3yKYQQQkQBYdVHp6txXB8dNgc88jEQlwwsv8Cum/bvJmHDaqyEdHfvnLDJWgohhBDO76MjggCbAJreOW/a+5ljmkdvTO+c4yRyhBBCRA0SOk6BlVY0InuXlXtXW9Wxd06qTMhCCCGiirDy6IgOdkOur7S3D3989BBPlp2n9lPvHCGEEFGFIjpOofqwTUkdfIuuYyB9BJDS26t3Tj2Q0nY/IiGEEMJpSOg4gYY6W1bOTse+ZluZ3jnpQKLvGWBCCCGEU5HQcUraqq7M9tE5vNquyz2nudDhAM+4xJDtohBCCBEKJHScQG2JTU0Vvmt/dj8R6Navee+cpF6h3kshhBCiy5HQiXTYBqkyH4hLaZpt5Z22ogmZKSvvoZ5CCCFElCChE+mwbJypKwqeQyvtutzpXo9zwGdf9c4RQggRlai83Cll5UXrAVcdkHYCkDbAq3dONyCxZ6j3UgghhAgJiuhEOtWFQEx8U7WVtwmZaaukHCAhCgecCiGEEBI6EU59DVBFoRMLFH7QvEmgq8Eak1PVO0cIIUT0otRVpFdb1ZcDRZ/Y6qrU44G0QU2PJXRX2koIIURUo9RVJFNTZCM3niGejObExDT1zklR7xwhhBDRjYROpMIqq6p8K2wK3mvuz2moBWLigGT1zhFCCBHdSOhEKnWlQF0JULQBaKi20Zv0oV69c3rYRQghhIhiJHQiFYqZumrg4NtNTQI9aSv6dlKPU+8cIYQQUY+ETqRSVQBQ1xS82zxtxQaBcalAknrnCCGEEBI6kUh9NVBzGCj+FKivAJJzgYyRTQ0EOdeKFVdCCCFElCOhE7HTysvtEE+S605bsQKroQ5I6R3qPRRCCCHCAgmdSKTmiK2sOvhO8yaBLCmP7660lRBCCOFGQifSYNSG08pLtwB1ZUBSNpA5xqt3Tm8gLinUeymEEEKEBRI6kQY7HlPgHHKPfMg9y1ZXmd45MUByTqj3UAghhAgbJHQi0p9T0ZS28lRbcb165wghhBDNkNCJNKoOAqWfNQmbrPF2PcUPe+fExoV6D4UQQoiwQUInkqirBKqPAIc+tPdzptlRD1wflywTshBCCNECCZ2IS1uVNjUJbKy2cvfOYcWVEEIIIRqR0Ikkqg8DJZ/a8vKEdCBroq3Cqq+x1VaeERBCCCGEMEjoRApsBFh9ADi0yt7POROIjbcVWOyCrJEPQgghxFFI6ERSWXlNCVD4XvO0FdeZ3jnJId09IYQQIhyR0IkU6MMp3gRUFwLxaUDPyTbKw2yVeucIIYQQPpHQiQRcLqDqAHB4pb2fczoQm2jFT0ImkJgV6j0UQgghwhIJnUiAE8qri4CC91s0CSxX7xwhhBCiDSR0IoEapq0+AaoPAnGpQPbJQH0VEJ8EJPcK9d4JIYQQYYuETiRQfQgodM+26nWqNR7XFAGJ2eqdI4QQQrSBhE64w2Gd9Od4hA6rrejZaagBUvuqd44QQgjRBhI6EZG2Wg9U7Qdik4DsU23vHFZeqXeOEEII0SYSOuEOU1QF7zWlreJTbLWVeucIIYQQx0RCJxLKyj1pq9yzbe8cF3vn5IZ674QQQoiwR0InnGGKqugToHKv7ZuTc5rtkJzI3jk9Qr13QgghRNgjoRPOMEV1cLm9zZJy+nJqS929c+JDvXdCCCFE2COhE85UFTZPW7F3TlwSkJQd6j0TQgghIoKwEjqLFi3CgAEDkJycjClTpmDlSvfIAx9s3LgR3/jGN8z2MTExWLhwIRxFfTVweA1QsQuIiQdyzrDGZIqchPRQ750QQggREYSN0FmyZAlmz56N22+/HWvWrMHYsWMxY8YMHDx40Of2FRUVOOGEE3DXXXchLy8PjkxbHXjD3uYAz/juQEO1eucIIYQQkSh0FixYgCuuuAKXX345RowYgcWLFyM1NRVPPPGEz+0nTZqEe++9F9/5zneQlJQER/bPKXy/qUmg6Z3THUjsGeo9E0IIISKGsBA6NTU1WL16NaZPn964LjY21txfsWJF0N6nuroaJSUlzZawxNUAHF4NlO8AYuKAnDNthCc5z/bREUIIIUTkCJ3CwkLU19cjN7d5bxjez8/PD9r7zJ8/HxkZGY1Lv379EJawsmr/6/Z21gRbbcXeOSnqnSOEEEJEnNDpKubMmYPi4uLGZffu3QhLGL0peNfezj3Hq3dOVqj3TAghhIgowqIZS3Z2NuLi4nDgwIFm63k/mEZjenkiws9zZB1QthVADJA7zfpzMkard44QQggRiRGdxMRETJgwAcuWLWtc19DQYO6fcsopiCrqKoG9r9rbPcYD8d1sV+Rk9c4RQgghIjKiQ1haPmvWLEycOBGTJ082fXHKy8tNFRa57LLL0LdvX+Oz8RiYN23a1Hh77969WLt2LdLS0jB48GBELExTFbzVVG3FNBanlCdkhHrPhBBCiIgjbITOzJkzUVBQgLlz5xoD8rhx47B06dJGg/KuXbtMJZaHffv2Yfz48Y3377vvPrOceeaZWL7cPTYhEinZbBeSc5bthpzSF4iJCfWeCSGEEBFHjMvFEdnRCcvLWX1FY3J6ehh0G26oB9bMBj57EMgcA0x40HZI5jDP+NRQ750QQggRcdfvsPDoCDd1JU3dkD1pK5aUS+QIIYQQASGhE06UbAWKN9rbOdMABtvYJFAIIYQQke3REQB2/4NtkYGMETaKw2GeSeqdI4QQQgSKIjrhQl0FkP8/ryaBpdaEHJsQ6j0TQgghIhYJnXChbLttFEh6nabeOUIIIUQQUOoqXNj9PMuugO4nAgndgYRMuwghhBAiYBTRCQca6oB9rzalrdgdOVW9c4QQQoiOIqETDpTvBA6vsbd7nWLHPrAbshBCCCE6hIROOLDnBcBVB6QNsqMeTO+cbqHeKyGEECLikdAJNeyVs/slezv3LPXOEUIIIYKIhE6oqdwPHPrQ3u45xRqRlbYSQgghgoKETlikrWqBbv2BxJ7WhKzeOUIIIURQkNAJNbtfsD9zzgTiEoCkXqHeIyGEEMIxSOiEkuojQMG79nbWJCAxC0hU7xwhhBAiWEjohDpt1VBtRz0k93b3ztFHIoQQQgQLXVVDye7n7M+cM4CENOvREUIIIUTQkNAJFbXlwIE37e0eE4CkHCt2hBBCCBE0JHRCxd6XgfpKK3DSTgBS80K2K0IIIYRTkdAJFbv+YX/mnA4kpittJYQQQnQCEjqhoL4ayP+PvZ010ZqR4xJDsitCCCGEk5HQCQX7XgPqym05efpwIDknJLshhBBCOB0JnVCwc4n92etUO+5BvXOEEEKITkFCp6tpqAX2v2ZvZ00GuvVT7xwhhBCik5DQ6Wr2/w+oLQYSMoCs8TIhCyGEEJ2IhE5Xs2tJ06RydkNW7xwhhBCi05DQ6Uoa6oC9r9jb2acAqb279O2FEEKIaENCpyspeAeoOQTEp1mhw6orIYQQQnQaEjqhqLbiyIdu/YG4pC59eyGEECLakNDpKlwNwO4X7O3caUByry57ayGEECJakdDpKgreB6oPAnGpQM6ZQEJml721EEIIEa1I6HT1bKusCUDaQCA2rsveWgghhIhWJHS6LG31T3s792zbDVkIIYQQnY6ETldw6COgch8QmwzknQckdO+StxVCCCGiHQmdLk9bDeiStxRCCCGEhE6nUF4O1NQABw8CNdUuNOx63j6Qe05UpK2aHX+NvR9tRPs50PFH9+dP9B3Qd6A8TM5BfGje1rlUVQH33AM8+CBQVAScMfpjvHXLDrjiUlGd+w00VDu3d05MjP3pffyZmcC11wK33GIfc7ngaKL9HOj4o/vzJ/oO2POg7wB8noM5c4Dk5K79TkroBBGqVX6w8+Y1rZsx3JqQNxV9Cd0qh2HtB3As48YBTzwB/Pa3Tev4Bfecjx/8AFi3Do4m2s+Bjj+6P3+i74C+A+OOcQ5uvhno1q0Lv5SuKKa4uJh/W5mfwaC62uXKzOTfa56lwbXlviEu1/+D64fn/M1VVuZyZWd7P+6chcfF42t+/E0L1zv5+HUO9B3Q/wF9B/QdcLXrHPBa2ZXXb5mRgwgVKxcPo/ptwIm9P0dVTRL+/t6FKCgA8vLgSHhczMN6H783XO/k4yfRfg50/NH9+RN9B/QdyGvHOSgu7trvpVJXQYQ5SC6eD/ibk23aauknX0J8cnfzBbj7bmvKchqJiUDv3s2P3xuud/Lxk2g/Bzr+6P78ib4D+g4ktuMcZGR07fdSQieI1NZasxXzkNnZwPcuWAckZeO5j76Ba691oaYmBtOmISqOvyVcz1/uTj5+Eu3nQMcf3Z8/0XdA34HaY5wDPk5B1GW4woyHH37Y1b9/f1dSUpJr8uTJrg8//LDN7f/+97+7hg4darYfNWqU69VXXw2ZR4dUVblcWzeXuWp4o3S7q6G2zLVtc5FZHw1UVrpcc+c25Wf5k/e5PlqI9nOg44/uz5/oO6DvQGUnnwN/rt8x/KcLdVWbLFmyBJdddhkWL16MKVOmYOHChfjHP/6BLVu2ICcn56jt33//fZxxxhmYP38+LrroIjzzzDO4++67sWbNGowaNeqY71dSUoKMjAwUFxcjPT09OAdRXwXXxvmI2fIgUFtkhne6hl6LmJFzgLgurqkLYfVZQoLNwzJESfXepQ77MCDaz4GOP7o/f6LvgL4D5Z14Dvy5foeV0KG4mTRpEh5++GFzv6GhAf369cM111yDWzxNKLyYOXMmysvL8corrzSuO/nkkzFu3Dgjlrpc6NSVA5vuATb4iNeNmguMuBmIj7LfdkIIIUSQ8ef6HTZVVzU1NVi9ejWmT5/euC42NtbcX7Fihc/ncL339mTGjBmtbl9dXW1OjvcSVGISAEZyfMH1fFwIIYQQXUbYCJ3CwkLU19cjNze32Xrez8/P9/kcrvdne6a4qAA9C6NFQYWpKi6tPtbFNXVCCCFElBM2QqcrmDNnjglzeZbdu3cH9w0SMu3S6mNdXFMnhBBCRDlhI3Sys7MRFxeHAwcONFvP+3mtdNjien+2T0pKMrk87yWouGqBodf6fozr+bgQQgghok/oJCYmYsKECVi2bFnjOpqRef+UU07x+Ryu996e/Pe//211+06HRmNWV9F47Ins8Cfvc72MyEIIIUT0NgycPXs2Zs2ahYkTJ2Ly5MmmvJxVVZdffrl5nKXnffv2NV4bct111+HMM8/E73//e1x44YV49tlnsWrVKvzxj38M3UGwhJzVVSN/ZT05TFcxkhMlpeVCCCFEOBFWQofl4gUFBZg7d64xFLNMfOnSpY2G4127dplKLA9Tp041vXNuu+023HrrrRgyZAhefPHFdvXQ6VQ8kZu4Xu4VXdkCUgghhBBh2Uenq+mUhoFCCCGE6FQiso+OEEIIIUSwkdARQgghhGOR0BFCCCGEY5HQEUIIIYRjkdARQgghhGOR0BFCCCGEY5HQEUIIIYRjkdARQgghhGOR0BFCCCGEYwmrERBdjacpNDssCiGEECIy8Fy32zPcIaqFTmlpqfnZr1+/UO+KEEIIIQK4jnMURFtE9ayrhoYG7Nu3D927d0dMTEybypFiaPfu3VE5Eyvaj59E+zmI9uMn0X4OdPzR/fmH23eA0oUip0+fPs2GffsiqiM6PDnHHXdcu7fnBxvqDzeURPvxk2g/B9F+/CTaz4GOP7o//3D6DhwrkuNBZmQhhBBCOBYJHSGEEEI4FgmddpCUlITbb7/d/IxGov34SbSfg2g/fhLt50DHH92ffyR/B6LajCyEEEIIZ6OIjhBCCCEci4SOEEIIIRyLhI4QQgghHIuEjhBCCCEci4SOm0WLFmHAgAFITk7GlClTsHLlyjZP3D/+8Q8MGzbMbD969Gj8+9//RrQc/1NPPWU6SXsvfF6k8vbbb+PLX/6y6bDJY3nxxReP+Zzly5fjpJNOMtUHgwcPNuckkvH3HPD4W34HuOTn5yMSmT9/PiZNmmS6pOfk5OBrX/satmzZcsznOeX3QCDH77TfA48++ijGjBnT2AzvlFNOwWuvvRYVn38gxx9Jn7+EDoAlS5Zg9uzZpmxuzZo1GDt2LGbMmIGDBw/6PGnvv/8+LrnkEvzoRz/Cxx9/bH4pcNmwYQOi4fgJ/yPs37+/cdm5cycilfLycnPMFHvtYceOHbjwwgtx1llnYe3atbj++uvx4x//GK+//jqi5Rx44MXQ+3vAi2Qk8tZbb+Gqq67CBx98gP/+97+ora3FeeedZ85Lazjp90Agx++03wPskn/XXXdh9erVWLVqFc4++2x89atfxcaNGx3/+Qdy/BH1+bO8PNqZPHmy66qrrmq8X19f7+rTp49r/vz5Prf/9re/7brwwgubrZsyZYrrpz/9qSsajv/JJ590ZWRkuJwI/0u88MILbW5z8803u0aOHNls3cyZM10zZsxwRcs5ePPNN812R44ccTmRgwcPmuN76623Wt3Gab8H/D1+J/8e8NCjRw/X448/HnWff3uOP5I+/6iP6NTU1BgFO3369GYzsHh/xYoVPsUh13tvTxgBaW17px0/KSsrQ//+/c2At2OpfqfhpM+/o4wbNw69e/fGueeei/feew9Oobi42PzMysqKyu9Be47fyb8H6uvr8eyzz5qIFlM40fb517fj+CPp8496oVNYWGg+1Nzc3GYnhvdb8xtwvT/bO+34hw4diieeeAIvvfQSnn76aTMFfurUqdizZw+igdY+f072raysRDRAcbN48WI899xzZuEvumnTppnUZ6TD7zPTkaeeeipGjRrV6nZO+j0QyPE78ffA+vXrkZaWZrx3V155JV544QWMGDEiaj7/9X4cfyR9/lE9vVwEBhW+t8rnl3v48OH4wx/+gN/+9rc6rVEAf8lx8f4ObNu2Dffffz/++te/IpKhV4U+i3fffRfRSHuP34m/B/idpu+OEa1//vOfmDVrlvEvtXaxdxpD/Tj+SPr8o17oZGdnIy4uDgcOHGh2Yng/Ly/P50njen+2d9rxtyQhIQHjx4/H1q1bEQ209vnTmJeSkoJoZfLkyREvDq6++mq88sorpgqN5sy2cNLvgUCO34m/BxITE00VJZkwYQI++ugjPPDAA+biHQ2ff6Ifxx9Jn3/Up674wfIDXbZsWeNJYQiO91vLTXK99/aElQpt5TKddPwtYeqLIU+mM6IBJ33+wYR/CUbqd4AebF7kGap/4403MHDgwKj6HgRy/NHwe4C/C6urqx3/+Qdy/BH1+YfaDR0OPPvss66kpCTXU0895dq0aZPrJz/5iSszM9OVn59vHr/00ktdt9xyS+P27733nis+Pt513333uT799FPX7bff7kpISHCtX7/eFQ3H/5vf/Mb1+uuvu7Zt2+ZavXq16zvf+Y4rOTnZtXHjRlckUlpa6vr444/Nwv8SCxYsMLd37txpHuex8xx42L59uys1NdV10003mc9/0aJFrri4ONfSpUtdkYq/5+D+++93vfjii67PP//cfO+vu+46V2xsrOt///ufKxL52c9+ZipIli9f7tq/f3/jUlFR0biNk38PBHL8Tvs9wGNjldmOHTtcn3zyibkfExPj+s9//uP4zz+Q44+kz19Cx81DDz3kOv74412JiYmm3PqDDz5oPElnnnmma9asWc1O3N///nfXiSeeaLZnqfGrr77qipbjv/766xu3zc3NdV1wwQWuNWvWuCIVT6l0y8VzzPzJc9DyOePGjTPn4IQTTjCllpGMv+fg7rvvdg0aNMj8YsvKynJNmzbN9cYbb7giFV/HzsX7c3Xy74FAjt9pvwd++MMfuvr372+Op1evXq5zzjmn8SLv9M8/kOOPpM8/hv+EOqokhBBCCNEZRL1HRwghhBDORUJHCCGEEI5FQkcIIYQQjkVCRwghhBCORUJHCCGEEI5FQkcIIYQQjkVCRwghhBCORUJHCCGEEI5FQkcIIYQQjkVCRwghhBCORUJHCOFInnrqKYwYMQKpqakYPnw4Xn311VDvkhAiBEjoCCEcx3PPPYerr74av/71r7FhwwbMmDEDV155Zah3SwgRAjTUUwjhOE499VRMnz4dv/nNb8z9//73v/jWt76FoqKiUO+aEKKLUURHCOEoSktL8cEHH+CCCy5oXPf6669j/PjxId0vIURoiA/R+wohRKewbt06xMbGYuzYsaioqMAzzzyDBx98EC+88ILOuBBRiISOEMJRrF27FsOGDcPq1atx2mmnmXVf//rXcf7554d614QQIUCpKyGE44TOSSedhNGjR+PDDz/EggULsHTpUsybNy/UuyaECAGK6AghHCd0Lr30UqSnp2Py5Mlm2bJlixE9QojoQxEdIYRjqKurw8aNG03fnJa+HU8aSwgRXSiiI4RwDJs3b0ZVVZVJU/Xq1cs0C3z00UfxxRdf4Ec/+lGod08IEQIkdIQQjkpb9e7dGykpKTj99NPRrVs3E8l58803kZeXF+rdE0KEAAkdIYSjhM6UKVNUSi6EaEQeHSGEo4TOmDFjQr0bQogwQkJHCOEYaDqW0BFCeKNZV0IIIYRwLIroCCGEEMKxSOgIIYQQwrFI6AghhBDCsUjoCCGEEMKxSOgIIYQQwrFI6AghhBDCsUjoCCGEEMKxSOgIIYQQwrFI6AghhBDCsUjoCCGEEAJO5f8Dto1gN17VFp8AAAAASUVORK5CYII=", "text/plain": [ "
" ] @@ -1399,7 +1043,7 @@ } ], "source": [ - "plot_results(ts,nruns,diffs_dmrsa,diffs_proc,parameter)" + "plot_results(ts,nruns,diffs_dsa,diffs_proc,parameter)" ] }, { @@ -1411,7 +1055,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -1434,7 +1078,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -1443,15 +1087,45 @@ "text": [ "on run 0\n", "0.0\n", + "[0.25369717 0.45654168 0.61520462 0.73780483 0.82856849 0.88754471\n", + " 0.92879732 0.95389112 0.9700081 0.97930919 0.9854652 0.98951094\n", + " 0.99130669 0.99260625 0.99388587]\n", "0.1\n", + "[0.19048048 0.32985884 0.45220731 0.56204825 0.64656907 0.72225737\n", + " 0.78645379 0.8387057 0.88046408 0.91326671 0.93781133 0.95550473\n", + " 0.96743091 0.9754896 0.98078049]\n", "0.2\n", + "[0.45967021 0.61126547 0.72997665 0.81298848 0.87317053 0.91252379\n", + " 0.94365227 0.96314694 0.9755494 0.98291577 0.98769552 0.99125876\n", + " 0.99318308 0.99419832 0.99518581]\n", "0.30000000000000004\n", + "[0.23221853 0.43225934 0.59113581 0.71408954 0.80805437 0.87151854\n", + " 0.91953221 0.94998535 0.96778991 0.97754717 0.98465794 0.98913341\n", + " 0.99109576 0.99240676 0.99366563]\n", "0.4\n", + "[0.19048048 0.32985886 0.45220733 0.56204827 0.64656909 0.72225738\n", + " 0.7864538 0.83870572 0.8804641 0.91326672 0.93781133 0.95550473\n", + " 0.96743092 0.9754896 0.98078049]\n", "0.5\n", + "[0.19227735 0.33898624 0.46312195 0.57362714 0.66190565 0.7406905\n", + " 0.80478509 0.85580001 0.89642329 0.92684243 0.94889075 0.96370089\n", + " 0.97341818 0.97962759 0.98485685]\n", "0.6000000000000001\n", + "[0.16155528 0.28339831 0.38959138 0.49323849 0.57274959 0.64213237\n", + " 0.70436733 0.76123389 0.80881667 0.84990632 0.8839797 0.91123191\n", + " 0.93278742 0.94931456 0.96168152]\n", "0.7000000000000001\n", + "[0.41582455 0.47106512 0.52099833 0.57023939 0.61476138 0.65558472\n", + " 0.69136598 0.7251092 0.75488265 0.78261983 0.80822032 0.83151158\n", + " 0.85187938 0.87028326 0.88678245]\n", "0.8\n", + "[0.67329322 0.70255427 0.73088931 0.75639627 0.77912073 0.79779598\n", + " 0.8133835 0.82872582 0.84370986 0.85798003 0.87175487 0.88467783\n", + " 0.89732628 0.90904598 0.92008426]\n", "0.9\n", + "[0.68293678 0.71278328 0.74169053 0.76762367 0.79037159 0.80888744\n", + " 0.8247635 0.84028549 0.85518636 0.86945398 0.88327483 0.89639464\n", + " 0.9088834 0.92034872 0.93094254]\n", "1.0\n" ] }, @@ -1459,7 +1133,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_104513/1197694459.py:120: RuntimeWarning: invalid value encountered in divide\n", + "/var/folders/sh/r0d61tjn42s4mc3nxqb0hhq00000gn/T/ipykernel_74501/1197694459.py:120: RuntimeWarning: invalid value encountered in divide\n", " centers = np.sum(\n" ] }, @@ -1467,109 +1141,52 @@ "name": "stdout", "output_type": "stream", "text": [ - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Computing Hankel matrix ...\n", - "Hankel matrix computed!\n", - "Computing SVD on Hankel matrix ...\n", - "SVD complete!\n", - "Computing least squares fits to HAVOK DMD ...\n", - "Least squares complete! \n", - "\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "Finished optimizing C\n", - "(1, 11)\n" + "[0.1106887 0.21309875 0.30240264 0.38665383 0.45767216 0.52288068\n", + " 0.58136988 0.6394324 0.69465593 0.74241949 0.78972251 0.82641405\n", + " 0.86284668 0.88916518 0.91540882]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Fitting DMDs: 100%|██████████| 1/1 [00:01<00:00, 1.17s/it]\n", + "Fitting DMDs: 100%|██████████| 11/11 [00:00<00:00, 23.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pre-computing eigenvalues for Wasserstein distance...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing DMD similarities: 100%|██████████| 11/11 [00:00<00:00, 1261.34it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(11,)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" ] } ], "source": [ "nruns = 1\n", - "ndelays = 50\n", - "rank = 200\n", + "ndelays = 10\n", + "rank = 16\n", "da = 0.1\n", "tSetup = 1000\n", "time = 8000\n", @@ -1585,7 +1202,7 @@ "alphas = np.arange(0,1+da,da)\n", "lneurons = 100 #varying \n", "hneurons = 150\n", - "diffs_dmrsa = np.zeros((nruns,alphas.size))\n", + "diffs_dsa = np.zeros((nruns,alphas.size))\n", "diffs_proc = np.zeros((nruns,alphas.size))\n", "\n", "for k in range(nruns):\n", @@ -1604,24 +1221,27 @@ "\n", " sim.simulate(drive,time,wNoise=wnoise)\n", " d = torch.relu(torch.tensor(sim.gsR))\n", + " pca = PCA(n_components=15)\n", + " d = pca.fit_transform(d)\n", + " print(np.cumsum(pca.explained_variance_ratio_))\n", " data.append(d) \n", " \n", " diffs_proc[k,i] = comparison_shape.fit_score(d0,d)\n", "\n", - " dsa = DSA(d0,data,n_delays=n_delays,rank=rank,device=device,verbose=True)\n", + " dsa = DSA(d0,data,n_delays=n_delays,rank=rank,device=device,verbose=True,score_method='wasserstein')\n", " dsa_score = dsa.fit_score()\n", " print(dsa_score.shape)\n", - " diffs_dmrsa[k,:] = dsa_score\n" + " diffs_dsa[k,:] = dsa_score\n" ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 37, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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" ] @@ -1631,7 +1251,7 @@ } ], "source": [ - "plot_results(alphas,nruns,diffs_dmrsa,diffs_proc,r\"$\\alpha$\")" + "plot_results(alphas,nruns,diffs_dsa,diffs_proc,r\"$\\alpha$\")\n" ] }, { @@ -1644,7 +1264,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "dsa_test_env", "language": "python", "name": "python3" }, @@ -1658,7 +1278,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.10.18" } }, "nbformat": 4, From 3b273a63fd4a1c0c758433b58c686229ae4d8ba9 Mon Sep 17 00:00:00 2001 From: ostrow Date: Sun, 9 Nov 2025 12:50:06 -0500 Subject: [PATCH 47/90] fix scaling of wasserstein --- DSA/simdist.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/DSA/simdist.py b/DSA/simdist.py index 36044a4..2b1f5dd 100644 --- a/DSA/simdist.py +++ b/DSA/simdist.py @@ -279,7 +279,7 @@ def fit( self.C_star = ot.emd(a, b, self.M) self.score_star = ( - ot.emd2(a, b, self.M) * a.shape[0] + ot.emd2(a, b, self.M) #* a.shape[0] ) # add scaling factor due to random matrix theory # self.score_star = np.sum(self.C_star * self.M) self.C_star = self.C_star / torch.linalg.norm( From c7ecae90cc3decc60bbcdd460b41ed90a219a1d3 Mon Sep 17 00:00:00 2001 From: ostrow Date: Wed, 12 Nov 2025 13:39:56 -0500 Subject: [PATCH 48/90] remove subset index bug for time delays (too few timepoints are selected, throwing an error) --- DSA/pykoopman/regression/_base_ensemble.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/DSA/pykoopman/regression/_base_ensemble.py b/DSA/pykoopman/regression/_base_ensemble.py index 41c4949..65b6eda 100644 --- a/DSA/pykoopman/regression/_base_ensemble.py +++ b/DSA/pykoopman/regression/_base_ensemble.py @@ -278,9 +278,9 @@ def _check_inverse_transform(self, X): Args: X (array-like): Input data to be checked for inverse transform consistency. """ - idx_selected = slice(None, None, max(1, X.shape[0] // 100)) + # idx_selected = slice(None, None, max(1, X.shape[0] // 100)) # X_round_trip = self.inverse_transform(self.transform(X[idx_selected])) - self.inverse_transform(self.transform(X[idx_selected])) + self.inverse_transform(self.transform(X)) # if not _allclose_dense_sparse(X[idx_selected], X_round_trip): # warnings.warn( # "The provided functions are not strictly" From ea2b4833965b7290de1eb1ead74c02a17b742b00 Mon Sep 17 00:00:00 2001 From: mitchellostrow Date: Tue, 2 Dec 2025 12:48:58 -0500 Subject: [PATCH 49/90] bug fix --- DSA/base_dmd.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/DSA/base_dmd.py b/DSA/base_dmd.py index 32c5d28..49c2365 100644 --- a/DSA/base_dmd.py +++ b/DSA/base_dmd.py @@ -131,7 +131,7 @@ def _process_single_dataset(self, data): return processed_data, True elif isinstance(data, np.ndarray): - return torch.from_numpy(data), False + return torch.from_numpy(data.copy()), False return data, False From a145afed83042c85a3f5d1c1e36ff9d758c95182 Mon Sep 17 00:00:00 2001 From: ostrow Date: Tue, 9 Dec 2025 15:13:45 -0500 Subject: [PATCH 50/90] bug fix for data handling --- DSA/pykoopman/koopman.py | 227 ++++++--- DSA/pykoopman/observables/_base.py | 13 +- .../observables/_custom_observables.py | 11 +- DSA/pykoopman/observables/_identity.py | 21 +- DSA/pykoopman/observables/_polynomial.py | 21 +- .../observables/_radial_basis_functions.py | 11 + .../observables/_random_fourier_features.py | 11 +- DSA/pykoopman/observables/_time_delay.py | 10 + DSA/pykoopman/regression/_base_ensemble.py | 21 +- examples/how_to_use_dsa_tutorial.ipynb | 146 ++++-- tests/pykoopman_test.py | 439 ++++++++++++++++++ 11 files changed, 796 insertions(+), 135 deletions(-) create mode 100644 tests/pykoopman_test.py diff --git a/DSA/pykoopman/koopman.py b/DSA/pykoopman/koopman.py index 8b008dd..d6de0c7 100644 --- a/DSA/pykoopman/koopman.py +++ b/DSA/pykoopman/koopman.py @@ -11,6 +11,7 @@ from pydmd import DMD from pydmd import DMDBase from sklearn.base import BaseEstimator +from sklearn.base import TransformerMixin from sklearn.metrics import r2_score from sklearn.pipeline import Pipeline from sklearn.utils.validation import check_is_fitted @@ -27,6 +28,73 @@ from .regression import PyDMDRegressor +class FlattenTransformer(BaseEstimator, TransformerMixin): + """ + Flatten various data structures to 2D for regressor consumption. + + Handles: + - 3D arrays (trials, time, features) → 2D (trials*time, features) + - Lists of arrays → concatenated 2D + - 2D arrays → pass through + - 1D arrays → reshape to column vector + + This transformer is inserted after observables to ensure regressors + always receive 2D input, while allowing observables to process + structured data (lists, 3D) that preserves trial boundaries. + """ + + def fit(self, X, y=None): + """Fit method - stateless transformer.""" + return self + + def transform(self, X): + """ + Flatten input to 2D array. + + Args: + X: Input data - can be list, 1D, 2D, or 3D array + + Returns: + 2D numpy array suitable for regressor + """ + if isinstance(X, list): + # Observable returned list of arrays - flatten and stack + flattened = [] + for x in X: + if isinstance(x, np.ndarray): + if x.ndim == 3: + # Flatten 3D: (trials, time, features) → (trials*time, features) + flattened.append(x.reshape(-1, x.shape[2])) + elif x.ndim == 2: + flattened.append(x) + elif x.ndim == 1: + flattened.append(x.reshape(-1, 1)) + else: + flattened.append(x) + return np.vstack(flattened) if flattened else np.array([]) + + elif isinstance(X, np.ndarray): + if X.ndim == 3: + # Flatten 3D: (trials, time, features) → (trials*time, features) + return X.reshape(-1, X.shape[2]) + elif X.ndim == 2: + # Already 2D - pass through + return X + elif X.ndim == 1: + # Reshape to column vector + return X.reshape(-1, 1) + + # Fallback - return as-is + return X + + def inverse_transform(self, X): + """ + Inverse transform - not typically used in this pipeline. + Included for completeness. + """ + return X + + class Koopman(BaseEstimator): """Discrete-Time Koopman class. @@ -146,47 +214,51 @@ def fit(self, x, y=None, u=None, dt=1): "Control input u was passed, " "but self.regressor is not DMDc or EDMDc" ) + # Create FlattenTransformer and composed transform function for Y + # This ensures Y goes through same transformations as X (observable + flatten) + flatten_transformer = FlattenTransformer() + + def transform_y(y_data): + """Apply observable transform then flatten for Y.""" + y_obs = self.observables.transform(y_data) + y_flat = flatten_transformer.transform(y_obs) + return y_flat + if y is None: # or isinstance(self.regressor, PyDMDRegressor): # if there is only 1 trajectory OR regressor is PyDMD y_flag = True - # regressor = self.regressor - x, y = self._detect_reshape(x, offset=True) + x, y = self.split_xy(x, offset=True) + if isinstance(self.regressor, HAVOK): regressor = self.regressor y_flag = False else: regressor = EnsembleBaseRegressor( regressor=self.regressor, - func=self.observables.transform, + func=transform_y, # Composed: observable + flatten inverse_func=self.observables.inverse, ) - # regressor = self.regressor elif isinstance(self.regressor, NNDMD): regressor = self.regressor y_flag = False + # NNDMD handles its own data - skip pipeline setup and return early + # (will add full NNDMD handling in next step) else: - # multiple 1-step-trajectories + # multiple 1-step-trajectories (X and Y provided separately) regressor = EnsembleBaseRegressor( regressor=self.regressor, - func=self.observables.transform, + func=transform_y, # Composed: observable + flatten inverse_func=self.observables.inverse, ) - # if x is a list, we need to further change trajectories into 1-step-traj - x, _ = self._detect_reshape(x, offset=False) - y, _ = self._detect_reshape(y, offset=False) y_flag = False - # if isinstance(x, list): - # x_tmp = [] - # y_tmp = [] - # for traj_dat in x: - # x_tmp.append(traj_dat[:-1]) - # y_tmp.append(traj_dat[1:]) - # x = np.hstack(x_tmp) - # y = np.hstack(y_tmp) + # Create pipeline with observable + flatten + regressor + # X will go through: Observable → Flatten → Regressor + # Y will go through: func (observable + flatten) → Regressor steps = [ ("observables", self.observables), + ("flatten", FlattenTransformer()), ("regressor", regressor), ] self._pipeline = Pipeline(steps) # create `model` object using Pipeline @@ -198,12 +270,12 @@ def fit(self, x, y=None, u=None, dt=1): self._pipeline.fit(x, y, regressor__dt=dt) else: self._pipeline.fit(x, y, regressor__u=u, regressor__dt=dt) - # update the second step with just the regressor, not the + # update the third step with just the regressor, not the # EnsembleBaseRegressor - if isinstance(self._pipeline.steps[1][1], EnsembleBaseRegressor): - self._pipeline.steps[1] = ( - self._pipeline.steps[1][0], - self._pipeline.steps[1][1].regressor_, + if isinstance(self._pipeline.steps[2][1], EnsembleBaseRegressor): + self._pipeline.steps[2] = ( + self._pipeline.steps[2][0], + self._pipeline.steps[2][1].regressor_, ) # pykoopman's n_input/output_features are simply @@ -212,30 +284,49 @@ def fit(self, x, y=None, u=None, dt=1): # of states. but the output features can be really high self.n_input_features_ = self._pipeline.steps[0][1].n_input_features_ self.n_output_features_ = self._pipeline.steps[0][1].n_output_features_ - if hasattr(self._pipeline.steps[1][1], "n_control_features_"): - self.n_control_features_ = self._pipeline.steps[1][1].n_control_features_ + + if hasattr(self._pipeline.steps[2][1], "n_control_features_"): + self.n_control_features_ = self._pipeline.steps[2][1].n_control_features_ # compute amplitudes if isinstance(x, list): self._amplitudes = None elif y_flag: + # Extract data for amplitude computation, handling 3D/list structures if hasattr(self.observables, "n_consumed_samples"): - # g0 = self.observables.transform( - # x[0 : 1 + self.observables.n_consumed_samples] - # ) - self._amplitudes = np.abs( - self.psi(x[0 : 1 + self.observables.n_consumed_samples].T) - ) + n_samples_needed = 1 + self.observables.n_consumed_samples + + # Handle different input structures + if isinstance(x, np.ndarray) and x.ndim == 3: + # 3D: (trials, time, features) - take from first trial + x_for_amp = x[0, 0:n_samples_needed, :] + elif isinstance(x, list): + # List - take from first element + x_for_amp = x[0][0:n_samples_needed] if isinstance(x[0], np.ndarray) else x[0] + else: + # 2D (original behavior) + x_for_amp = x[0:n_samples_needed] + + self._amplitudes = np.abs(self.psi(x_for_amp.T)) else: - # g0 = self.observables.transform(x[0:1]) - - self._amplitudes = np.abs(self.psi(x[0:1].T)) + # Non-temporal observables + if isinstance(x, np.ndarray) and x.ndim == 3: + # 3D: take first sample from first trial + x_for_amp = x[0, 0:1, :] + elif isinstance(x, list): + # List - take first sample from first element + x_for_amp = x[0][0:1] if isinstance(x[0], np.ndarray) else x[0] + else: + # 2D (original behavior) + x_for_amp = x[0:1] + + self._amplitudes = np.abs(self.psi(x_for_amp.T)) else: self._amplitudes = None self.time = { "tstart": 0, - "tend": dt * (self._pipeline.steps[1][1].n_samples_ - 1), + "tend": dt * (self._pipeline.steps[2][1].n_samples_ - 1), "dt": dt, } @@ -589,50 +680,52 @@ def _regressor(self): # my idea is to manually call xN observables then concate the data to let # the _regressor.fit to update the model coefficients. # call this function with _regressor() - return self._pipeline.steps[1][1] + return self._pipeline.steps[2][1] - def _detect_reshape(self, X, offset=True): + def split_xy(self, X, offset=True): """ - Detect the shape of the input data and reshape it accordingly to return - both X and Y in the correct shape. + Split data into X and Y pairs with temporal offset. + + Preserves input structure (list/2D/3D) so observables can handle + trial boundaries appropriately. Does NOT flatten or transform - + just performs the temporal split. + + Args: + X: Input data (1D, 2D array, 3D array, or list of arrays) + offset: If True, split with temporal offset X[:-1], X[1:] + If False, return (X, X) - used when Y provided separately + + Returns: + Tuple (X_split, Y_split) preserving input structure """ - s1 = -1 if offset else None - s2 = 1 if offset else None + if not offset: + # No split needed when Y provided separately + return X, X + + s1, s2 = -1, 1 # X[:-1], X[1:] + if isinstance(X, np.ndarray): if X.ndim == 1: X = X.reshape(-1, 1) - + if X.ndim == 2: self.n_samples_, self.n_input_features_ = X.shape self.n_trials_ = 1 return X[:s1], X[s2:] + elif X.ndim == 3: self.n_trials_, self.n_samples_, self.n_input_features_ = X.shape - X, Y = X[:, :s1, :], X[:, s2:, :] - return X.reshape(-1, X.shape[2]), Y.reshape( - -1, Y.shape[2] - ) # time*trials, features - + # Keep 3D structure: (trials, time-1, features) + return X[:, :s1, :], X[:, s2:, :] + elif isinstance(X, list): - assert all(isinstance(x, np.ndarray) for x in X) - self.n_trials_tot, self.n_samples_tot, self.n_input_features_tot = ( - [], - [], - [], - ) - X_tot, Y_tot = [], [] + # Recursively process each element in list + X_list, Y_list = [], [] for x in X: - x, y = self._detect_reshape(x) - X_tot.append(x) - Y_tot.append(y) - self.n_trials_tot.append(self.n_trials_) - self.n_samples_tot.append(self.n_samples_) - self.n_input_features_tot.append(self.n_input_features_) - X = np.concatenate(X_tot, axis=0) - Y = np.concatenate(Y_tot, axis=0) - - self.n_trials_ = sum(self.n_trials_tot) - self.n_samples_ = sum(self.n_samples_tot) - self.n_input_features_ = sum(self.n_input_features_tot) - - return X, Y + x_split, y_split = self.split_xy(x, offset=offset) + X_list.append(x_split) + Y_list.append(y_split) + return X_list, Y_list + + # Fallback for unknown types + return X, X diff --git a/DSA/pykoopman/observables/_base.py b/DSA/pykoopman/observables/_base.py index 62b0fca..e0b1ad3 100644 --- a/DSA/pykoopman/observables/_base.py +++ b/DSA/pykoopman/observables/_base.py @@ -182,7 +182,8 @@ def fit(self, X, y=None): Args: X (numpy.ndarray): Measurement data to be fit, with shape (n_samples, - n_input_features_). + n_input_features_). Can also be 3D (n_trials, n_samples, n_features) + or list of arrays. y (numpy.ndarray, optional): Time-shifted measurement data to be fit. Default is None. @@ -197,10 +198,18 @@ def fit(self, X, y=None): # first, one must call fit of every observable in the observer list # so that n_input_features_ and n_output_features_ are defined + # Each observable's fit() now handles lists/3D internally for obs in self.observables_list_: obs.fit(X, y) - self.n_input_features_ = X.shape[1] + # Get n_input_features from first element/trial if needed + X_for_shape = X + if isinstance(X, list): + X_for_shape = X[0] + if X_for_shape.ndim == 3: + X_for_shape = X_for_shape[0] + + self.n_input_features_ = X_for_shape.shape[1] # total number of output features takes care of redundant identity features # for polynomial feature, we will remove the 1 as well if include_bias is true diff --git a/DSA/pykoopman/observables/_custom_observables.py b/DSA/pykoopman/observables/_custom_observables.py index 541087f..8c5398b 100644 --- a/DSA/pykoopman/observables/_custom_observables.py +++ b/DSA/pykoopman/observables/_custom_observables.py @@ -79,7 +79,8 @@ def fit(self, x, y=None): Args: x (array-like, shape (n_samples, n_input_features)): Measurement data to be - fitted. + fitted. Can also be 3D (n_trials, n_samples, n_features) or list of + arrays. y (None): This is a dummy parameter added for compatibility with sklearn's API. Default is None. @@ -87,6 +88,14 @@ def fit(self, x, y=None): self (CustomObservables): This method returns the fitted instance. """ x = validate_input(x) + + # Handle lists and 3D by fitting on first element/trial + if isinstance(x, list): + x = x[0] # Fit on first element + if x.ndim == 3: + x = x[0] # Fit on first trial + + # Now x is 2D, proceed as normal n_samples, n_features = x.shape n_output_features = 0 diff --git a/DSA/pykoopman/observables/_identity.py b/DSA/pykoopman/observables/_identity.py index 47d24c0..c252578 100644 --- a/DSA/pykoopman/observables/_identity.py +++ b/DSA/pykoopman/observables/_identity.py @@ -29,8 +29,9 @@ def fit(self, x, y=None): Fit the model to the provided measurement data. Args: - x (array-like): The measurement data to be fit. It must have a shape of - (n_samples, n_input_features). + x (array-like): The measurement data to be fit. Can be 2D (n_samples, + n_input_features), 3D (n_trials, n_samples, n_input_features), or + list of arrays. y (None): This parameter is retained for sklearn compatibility. Returns: @@ -40,12 +41,16 @@ def fit(self, x, y=None): only identity mapping is supported for list of arb trajectories """ x = validate_input(x) - if not isinstance(x, list): - self.n_input_features_ = self.n_output_features_ = x.shape[1] - self.measurement_matrix_ = np.eye(x.shape[1]).T - else: - self.n_input_features_ = self.n_output_features_ = x[0].shape[1] - self.measurement_matrix_ = np.eye(x[0].shape[1]).T + + # Handle lists and 3D by fitting on first element/trial + if isinstance(x, list): + x = x[0] # Fit on first element + if x.ndim == 3: + x = x[0] # Fit on first trial + + # Now x is 2D, proceed as normal + self.n_input_features_ = self.n_output_features_ = x.shape[1] + self.measurement_matrix_ = np.eye(x.shape[1]).T self.n_consumed_samples = 0 diff --git a/DSA/pykoopman/observables/_polynomial.py b/DSA/pykoopman/observables/_polynomial.py index f749a4f..b18b206 100644 --- a/DSA/pykoopman/observables/_polynomial.py +++ b/DSA/pykoopman/observables/_polynomial.py @@ -91,7 +91,8 @@ def fit(self, x, y=None): Args: x (np.ndarray): The measurement data to be fit, with shape (n_samples, - n_features). + n_features). Can also be 3D (n_trials, n_samples, n_features) or + list of arrays. y (array-like, optional): Dummy input. Defaults to None. Returns: @@ -101,17 +102,29 @@ def fit(self, x, y=None): ValueError: If the input data is not valid. """ x = validate_input(x) + + # Handle lists and 3D by fitting on first element/trial + if isinstance(x, list): + x = x[0] # Fit on first element + if x.ndim == 3: + x = x[0] # Fit on first trial + + # Now x is 2D, proceed as normal self.n_consumed_samples = 0 - y_poly_out = super(Polynomial, self).fit(x.real, y) + super(Polynomial, self).fit(x.real, y) + + # Set custom attributes that our code expects + self.n_input_features_ = x.shape[1] + # n_output_features_ is already set by superclass fit() - self.measurement_matrix_ = np.zeros([x.shape[1], y_poly_out.n_output_features_]) + self.measurement_matrix_ = np.zeros([x.shape[1], self.n_output_features_]) if self.include_bias: self.measurement_matrix_[:, 1 : 1 + x.shape[1]] = np.eye(x.shape[1]) else: self.measurement_matrix_[:, : x.shape[1]] = np.eye(x.shape[1]) - return y_poly_out + return self def transform(self, x): """ diff --git a/DSA/pykoopman/observables/_radial_basis_functions.py b/DSA/pykoopman/observables/_radial_basis_functions.py index 2363722..e1c9924 100644 --- a/DSA/pykoopman/observables/_radial_basis_functions.py +++ b/DSA/pykoopman/observables/_radial_basis_functions.py @@ -98,6 +98,9 @@ def fit(self, x, y=None): Initializes the RadialBasisFunction with specified parameters. Args: + x (array-like): The input data, shape (n_samples, n_input_features). + Can also be 3D (n_trials, n_samples, n_features) or list of arrays. + y (None): Dummy parameter for sklearn compatibility. rbf_type (str, optional): The type of radial basis functions to be used. Options are: 'gauss', 'thinplate', 'invquad', 'invmultquad', 'polyharmonic'. Defaults to 'gauss'. @@ -122,6 +125,14 @@ def fit(self, x, y=None): n_centers is not equal to centers.shape[1]. """ x = validate_input(x) + + # Handle lists and 3D by fitting on first element/trial + if isinstance(x, list): + x = x[0] # Fit on first element + if x.ndim == 3: + x = x[0] # Fit on first trial + + # Now x is 2D, proceed as normal n_samples, n_features = x.shape self.n_consumed_samples = 0 diff --git a/DSA/pykoopman/observables/_random_fourier_features.py b/DSA/pykoopman/observables/_random_fourier_features.py index af9f690..08a2085 100644 --- a/DSA/pykoopman/observables/_random_fourier_features.py +++ b/DSA/pykoopman/observables/_random_fourier_features.py @@ -65,7 +65,8 @@ def fit(self, x, y=None): Args: x (numpy.ndarray): Measurement data to be fit. Shape (n_samples, - n_input_features_). + n_input_features_). Can also be 3D (n_trials, n_samples, n_features) + or list of arrays. y (numpy.ndarray, optional): Time-shifted measurement data to be fit. Defaults to None. @@ -73,6 +74,14 @@ def fit(self, x, y=None): self: Returns a fitted RandomFourierFeatures instance. """ x = validate_input(x) + + # Handle lists and 3D by fitting on first element/trial + if isinstance(x, list): + x = x[0] # Fit on first element + if x.ndim == 3: + x = x[0] # Fit on first trial + + # Now x is 2D, proceed as normal np.random.seed(self.random_state) self.n_consumed_samples = 0 diff --git a/DSA/pykoopman/observables/_time_delay.py b/DSA/pykoopman/observables/_time_delay.py index 038dd2b..8e3f244 100644 --- a/DSA/pykoopman/observables/_time_delay.py +++ b/DSA/pykoopman/observables/_time_delay.py @@ -88,6 +88,8 @@ def fit(self, x, y=None): Args: x (array-like): The input data, shape (n_samples, n_input_features). + Can also be 3D (n_trials, n_samples, n_input_features) or + list of arrays. y (None): Dummy parameter for sklearn compatibility. Returns: @@ -95,6 +97,14 @@ def fit(self, x, y=None): """ x = validate_input(x) + + # Handle lists and 3D by fitting on first element/trial + if isinstance(x, list): + x = x[0] # Fit on first element + if x.ndim == 3: + x = x[0] # Fit on first trial + + # Now x is 2D, proceed as normal n_samples, n_features = x.shape self.n_input_features_ = n_features diff --git a/DSA/pykoopman/regression/_base_ensemble.py b/DSA/pykoopman/regression/_base_ensemble.py index 65b6eda..c89e812 100644 --- a/DSA/pykoopman/regression/_base_ensemble.py +++ b/DSA/pykoopman/regression/_base_ensemble.py @@ -80,16 +80,23 @@ def fit(self, X, y, **fit_params): # transformers are designed to modify X which is 2d dimensional, we # need to modify y accordingly. - - self._training_dim = y.ndim - if y.ndim == 1: - y_2d = y.reshape(-1, 1) + + # Handle list inputs (convert to array if list) + if isinstance(y, list): + # Lists should be flattened by observable/flatten transformers + # For now, just note it's a list and it will be transformed by func + y_for_transform = y + self._training_dim = None # Will be determined after transform else: - y_2d = y - self._fit_transformer(y_2d) + y_for_transform = y + self._training_dim = y.ndim + if y.ndim == 1: + y_for_transform = y.reshape(-1, 1) + + self._fit_transformer(y_for_transform) # transform y and convert back to 1d array if needed - y_trans = self.transformer_.transform(y_2d) + y_trans = self.transformer_.transform(y_for_transform) # FIXME: a FunctionTransformer can return a 1D array even when validate # is set to True. Therefore, we need to check the number of dimension # first. diff --git a/examples/how_to_use_dsa_tutorial.ipynb b/examples/how_to_use_dsa_tutorial.ipynb index 6a30413..e3da50b 100644 --- a/examples/how_to_use_dsa_tutorial.ipynb +++ b/examples/how_to_use_dsa_tutorial.ipynb @@ -34,16 +34,16 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 1, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -88,7 +88,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -163,7 +163,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -218,15 +218,15 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "Fitting DMDs: 100%|██████████| 2/2 [00:00<00:00, 187.58it/s]\n", - "Computing DMD similarities: 100%|██████████| 1/1 [00:03<00:00, 3.10s/it]\n" + "Fitting DMDs: 100%|██████████| 2/2 [00:00<00:00, 523.14it/s]\n", + "Computing DMD similarities: 100%|██████████| 1/1 [00:03<00:00, 3.15s/it]" ] }, { @@ -238,6 +238,13 @@ "Similarity between systems: 0.8746\n" ] }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, { "data": { "image/png": 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", @@ -287,14 +294,14 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "Fitting DMDs: 100%|██████████| 2/2 [00:00<00:00, 225.10it/s]\n", + "Fitting DMDs: 100%|██████████| 2/2 [00:00<00:00, 375.01it/s]\n", "Computing DMD similarities: 100%|██████████| 1/1 [00:02<00:00, 2.16s/it]" ] }, @@ -355,7 +362,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -371,7 +378,7 @@ "((8, 100, 5), torch.Size([15, 15]))" ] }, - "execution_count": 6, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -417,15 +424,32 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Automatic pdb calling has been turned ON\n" + ] + } + ], + "source": [ + "%pdb" + ] + }, + { + "cell_type": "code", + "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "Fitting DMDs: 1it [00:00, 273.82it/s]\n", - "Fitting DMDs: 1it [00:00, 440.95it/s]\n" + "Fitting DMDs: 1it [00:00, 186.40it/s]\n", + "Fitting DMDs: 1it [00:00, 386.57it/s]\n" ] }, { @@ -439,14 +463,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "Computing DMD similarities: 100%|██████████| 1/1 [00:00<00:00, 26.33it/s]" + "Computing DMD similarities: 100%|██████████| 1/1 [00:00<00:00, 209.64it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Similarity with PyKoopman: 0.2337\n", + "Similarity with PyKoopman: 0.1168\n", "(8, 100, 5)\n", "(2, 2)\n" ] @@ -511,7 +535,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -522,12 +546,17 @@ "Control shape: (8, 100, 2)\n" ] }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, { "name": "stderr", "output_type": "stream", "text": [ - "Fitting DMDs: 100%|██████████| 2/2 [00:00<00:00, 328.84it/s]\n", - "Computing DMD similarities: 100%|██████████| 1/1 [00:00<00:00, 451.49it/s]" + "Fitting DMDs: 100%|██████████| 2/2 [00:00<00:00, 481.44it/s]\n", + "Computing DMD similarities: 100%|██████████| 1/1 [00:00<00:00, 654.85it/s]" ] }, { @@ -586,15 +615,15 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "Fitting DMDs: 100%|██████████| 2/2 [00:00<00:00, 40.68it/s]\n", - "Computing DMD similarities: 100%|██████████| 1/1 [00:00<00:00, 817.76it/s]" + "Fitting DMDs: 100%|██████████| 2/2 [00:00<00:00, 9.19it/s]\n", + "Computing DMD similarities: 100%|██████████| 1/1 [00:00<00:00, 203.05it/s]" ] }, { @@ -652,16 +681,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 19, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "control similarity: 0.0131\n" - ] - }, { "name": "stderr", "output_type": "stream", @@ -674,6 +696,7 @@ "name": "stdout", "output_type": "stream", "text": [ + "control similarity: 0.0131\n", "state similarity: 0.3775\n", "joint similarity: 1.8671\n" ] @@ -741,7 +764,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -797,15 +820,20 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 21, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, { "name": "stderr", "output_type": "stream", "text": [ - "Fitting DMDs: 100%|██████████| 2/2 [00:00<00:00, 367.31it/s]\n", - "Computing DMD similarities: 100%|██████████| 1/1 [00:02<00:00, 3.00s/it]" + "Fitting DMDs: 100%|██████████| 2/2 [00:00<00:00, 327.50it/s]\n", + "Computing DMD similarities: 100%|██████████| 1/1 [00:03<00:00, 3.46s/it]" ] }, { @@ -863,7 +891,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -875,11 +903,16 @@ "Running hyperparameter sweep...\n" ] }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 5/5 [00:00<00:00, 15.20it/s]" + "100%|██████████| 5/5 [00:00<00:00, 15.70it/s]" ] }, { @@ -936,7 +969,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -948,7 +981,7 @@ " ], dtype=object))" ] }, - "execution_count": 14, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, @@ -977,7 +1010,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -992,10 +1025,33 @@ "name": "stderr", "output_type": "stream", "text": [ - " 0%| | 0/5 [00:00,\n", + " array([,\n", + " ,\n", + " ], dtype=object))" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -1032,7 +1088,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -1133,7 +1189,7 @@ ], "metadata": { "kernelspec": { - "display_name": "py39", + "display_name": "dsa_test_env", "language": "python", "name": "python3" }, @@ -1147,7 +1203,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.10.18" } }, "nbformat": 4, diff --git a/tests/pykoopman_test.py b/tests/pykoopman_test.py new file mode 100644 index 0000000..22e8147 --- /dev/null +++ b/tests/pykoopman_test.py @@ -0,0 +1,439 @@ +""" +Test suite for PyKoopman with 3D data and various observables. +Tests the implementation that prevents trial boundary crossing. +""" +import pytest +import numpy as np +from DSA.pykoopman.observables import ( + TimeDelay, Identity, Polynomial, + RadialBasisFunction, RandomFourierFeatures +) +from DSA.pykoopman import Koopman +from pydmd import DMD as pDMD + +TOL = 1e-10 # Tolerance for Frobenius norm comparisons + + +@pytest.fixture +def basic_3d_data(): + """Create basic 3D test data with distinct values per trial.""" + np.random.seed(42) + n_trials = 3 + n_timesteps = 10 + n_features = 2 + + data = np.zeros((n_trials, n_timesteps, n_features)) + for trial_idx in range(n_trials): + for t in range(n_timesteps): + data[trial_idx, t, 0] = trial_idx * 100 + t + data[trial_idx, t, 1] = trial_idx * 100 + t + 0.5 + + return data + + +@pytest.fixture +def variable_length_data(): + """Create list of 3D arrays with different shapes.""" + np.random.seed(42) + n_features = 2 + + # First array: 2 trials, 12 timesteps each + data_var1 = np.zeros((2, 12, n_features)) + for trial_idx in range(2): + for t in range(12): + data_var1[trial_idx, t, 0] = trial_idx * 100 + t + data_var1[trial_idx, t, 1] = trial_idx * 100 + t + 0.5 + + # Second array: 3 trials, 8 timesteps each + data_var2 = np.zeros((3, 8, n_features)) + for trial_idx in range(3): + for t in range(8): + data_var2[trial_idx, t, 0] = (trial_idx + 10) * 100 + t + data_var2[trial_idx, t, 1] = (trial_idx + 10) * 100 + t + 0.5 + + return [data_var1, data_var2] + + +def compute_manual_embedding(data, n_delays, rank): + """ + Manually embed data with TimeDelay to get ground truth. + This is the reference implementation. + """ + delay_computer = TimeDelay(n_delays=n_delays) + X_list = [] + Y_list = [] + + # Handle 3D array + if isinstance(data, np.ndarray) and data.ndim == 3: + for trial_idx in range(data.shape[0]): + traj = data[trial_idx, :, :] + embedded_traj = delay_computer.fit_transform(traj) + X_list.append(embedded_traj[:-1]) + Y_list.append(embedded_traj[1:]) + + # Handle list of arrays + elif isinstance(data, list): + for data_array in data: + if data_array.ndim == 3: + # List of 3D arrays + for trial_idx in range(data_array.shape[0]): + traj = data_array[trial_idx, :, :] + embedded_traj = delay_computer.fit_transform(traj) + X_list.append(embedded_traj[:-1]) + Y_list.append(embedded_traj[1:]) + elif data_array.ndim == 2: + # List of 2D arrays + embedded_traj = delay_computer.fit_transform(data_array) + X_list.append(embedded_traj[:-1]) + Y_list.append(embedded_traj[1:]) + + X_all = np.vstack(X_list) + Y_all = np.vstack(Y_list) + + # Fit with Identity since embedding already done + k_manual = Koopman( + observables=Identity(), + regressor=pDMD(svd_rank=rank) + ) + k_manual.fit(X_all, Y_all) + + return k_manual.A + + +class TestKoopman3DData: + """Test Koopman with 3D data structures.""" + + def test_3d_timedelay_vs_manual(self, basic_3d_data): + """Test that 3D input with TimeDelay matches manual approach.""" + n_delays = 3 + rank = 5 + + # Manual approach (ground truth) + A_manual = compute_manual_embedding(basic_3d_data, n_delays, rank) + + # New implementation + k_new = Koopman( + observables=TimeDelay(n_delays=n_delays), + regressor=pDMD(svd_rank=rank) + ) + k_new.fit(basic_3d_data) + A_new = k_new.A + + # Compare + diff = np.linalg.norm(A_manual - A_new, 'fro') + assert diff < TOL, f"Matrices differ by {diff:.10e}" + assert A_manual.shape == A_new.shape + + def test_2d_timedelay_backward_compatible(self): + """Test that 2D input still works (backward compatibility).""" + np.random.seed(42) + data_2d = np.random.randn(10, 2) + + k = Koopman( + observables=TimeDelay(n_delays=3), + regressor=pDMD(svd_rank=5) + ) + k.fit(data_2d) + + assert k.A.shape == (5, 5) + + def test_list_of_2d_timedelay(self, basic_3d_data): + """Test list of 2D arrays with TimeDelay.""" + n_delays = 3 + rank = 5 + + # Convert 3D to list of 2D + data_list = [basic_3d_data[i, :, :] for i in range(basic_3d_data.shape[0])] + + # Manual approach + A_manual = compute_manual_embedding(basic_3d_data, n_delays, rank) + + # List approach + k_list = Koopman( + observables=TimeDelay(n_delays=n_delays), + regressor=pDMD(svd_rank=rank) + ) + k_list.fit(data_list) + A_list = k_list.A + + # Compare + diff = np.linalg.norm(A_manual - A_list, 'fro') + assert diff < TOL, f"List approach differs from manual by {diff:.10e}" + + +class TestKoopmanObservables: + """Test various observables with 3D data.""" + + def test_identity_3d(self, basic_3d_data): + """Test Identity observable with 3D data.""" + k = Koopman( + observables=Identity(), + regressor=pDMD(svd_rank=2) + ) + k.fit(basic_3d_data) + assert k.A.shape == (2, 2) + + def test_polynomial_3d(self, basic_3d_data): + """Test Polynomial observable with 3D data.""" + k = Koopman( + observables=Polynomial(degree=2), + regressor=pDMD(svd_rank=5) + ) + k.fit(basic_3d_data) + assert k.A.shape == (5, 5) + + def test_rbf_3d(self, basic_3d_data): + """Test RadialBasisFunction observable with 3D data.""" + k = Koopman( + observables=RadialBasisFunction(n_centers=5, rbf_type='gauss'), + regressor=pDMD(svd_rank=5) + ) + k.fit(basic_3d_data) + assert k.A.shape == (5, 5) + + def test_rff_3d(self, basic_3d_data): + """Test RandomFourierFeatures observable with 3D data.""" + k = Koopman( + observables=RandomFourierFeatures(D=10, random_state=42), + regressor=pDMD(svd_rank=5) + ) + k.fit(basic_3d_data) + assert k.A.shape == (5, 5) + + +class TestKoopmanVariableLength: + """Test Koopman with variable-length trials.""" + + def test_variable_length_timedelay(self, variable_length_data): + """Test TimeDelay with list of different-shaped 3D arrays.""" + n_delays = 3 + rank = 5 + + # Manual approach + A_manual = compute_manual_embedding(variable_length_data, n_delays, rank) + + # New implementation + k = Koopman( + observables=TimeDelay(n_delays=n_delays), + regressor=pDMD(svd_rank=rank) + ) + k.fit(variable_length_data) + A_new = k.A + + # Compare + diff = np.linalg.norm(A_manual - A_new, 'fro') + assert diff < TOL, f"Variable-length differs from manual by {diff:.10e}" + + def test_variable_length_sample_count(self, variable_length_data): + """Verify correct number of samples (no boundary crossing).""" + n_delays = 3 + + k = Koopman( + observables=TimeDelay(n_delays=n_delays), + regressor=pDMD(svd_rank=5) + ) + k.fit(variable_length_data) + + # Array 1: 2 trials * (12 - n_delays - 1) = 2 * 8 = 16 + # Array 2: 3 trials * (8 - n_delays - 1) = 3 * 4 = 12 + # Total: 28 samples + expected_samples = 2 * (12 - n_delays - 1) + 3 * (8 - n_delays - 1) + actual_samples = k._regressor().n_samples_ + + assert actual_samples == expected_samples, \ + f"Expected {expected_samples} samples, got {actual_samples}" + + def test_variable_length_identity(self, variable_length_data): + """Test Identity with variable-length trials.""" + k = Koopman( + observables=Identity(), + regressor=pDMD(svd_rank=2) + ) + k.fit(variable_length_data) + assert k.A.shape == (2, 2) + + def test_variable_length_polynomial(self, variable_length_data): + """Test Polynomial with variable-length trials.""" + k = Koopman( + observables=Polynomial(degree=2), + regressor=pDMD(svd_rank=5) + ) + k.fit(variable_length_data) + assert k.A.shape == (5, 5) + + +class TestKoopmanPrediction: + """Test prediction functionality.""" + + def test_predict_timedelay(self, basic_3d_data): + """Test prediction with TimeDelay observable.""" + n_delays = 3 + + k = Koopman( + observables=TimeDelay(n_delays=n_delays), + regressor=pDMD(svd_rank=5) + ) + k.fit(basic_3d_data) + + # Need n_delays + 1 samples for prediction with TimeDelay + test_point = basic_3d_data[0, 0:n_delays+1, :] + pred = k.predict(test_point) + + assert pred.shape == (1, 2), f"Expected shape (1, 2), got {pred.shape}" + + def test_predict_identity(self, basic_3d_data): + """Test prediction with Identity observable.""" + k = Koopman( + observables=Identity(), + regressor=pDMD(svd_rank=2) + ) + k.fit(basic_3d_data) + + test_point = basic_3d_data[0, 0:1, :] + pred = k.predict(test_point) + + assert pred.shape == (1, 2), f"Expected shape (1, 2), got {pred.shape}" + + def test_predict_polynomial(self, basic_3d_data): + """Test prediction with Polynomial observable.""" + k = Koopman( + observables=Polynomial(degree=2), + regressor=pDMD(svd_rank=5) + ) + k.fit(basic_3d_data) + + test_point = basic_3d_data[0, 0:1, :] + pred = k.predict(test_point) + + assert pred.shape == (1, 2), f"Expected shape (1, 2), got {pred.shape}" + + def test_predict_rbf(self, basic_3d_data): + """Test prediction with RadialBasisFunction observable.""" + k = Koopman( + observables=RadialBasisFunction(n_centers=5, rbf_type='gauss'), + regressor=pDMD(svd_rank=5) + ) + k.fit(basic_3d_data) + + test_point = basic_3d_data[0, 0:1, :] + pred = k.predict(test_point) + + assert pred.shape == (1, 2), f"Expected shape (1, 2), got {pred.shape}" + + def test_predict_rff(self, basic_3d_data): + """Test prediction with RandomFourierFeatures observable.""" + k = Koopman( + observables=RandomFourierFeatures(D=10, random_state=42), + regressor=pDMD(svd_rank=5) + ) + k.fit(basic_3d_data) + + test_point = basic_3d_data[0, 0:1, :] + pred = k.predict(test_point) + + assert pred.shape == (1, 2), f"Expected shape (1, 2), got {pred.shape}" + + def test_predict_multiple_steps(self, basic_3d_data): + """Test prediction with multiple samples.""" + k = Koopman( + observables=Identity(), + regressor=pDMD(svd_rank=2) + ) + k.fit(basic_3d_data) + + # Predict for multiple samples + test_points = basic_3d_data[0, 0:3, :] + pred = k.predict(test_points) + + assert pred.shape == (3, 2), f"Expected shape (3, 2), got {pred.shape}" + + def test_predict_after_variable_length_fit(self, variable_length_data): + """Test prediction after fitting on variable-length trials.""" + k = Koopman( + observables=Identity(), + regressor=pDMD(svd_rank=2) + ) + k.fit(variable_length_data) + + # Predict on new data + test_point = variable_length_data[0][0, 0:1, :] + pred = k.predict(test_point) + + assert pred.shape == (1, 2), f"Expected shape (1, 2), got {pred.shape}" + + +class TestKoopmanNoBoundaryCrossing: + """Test that trial boundaries are never crossed.""" + + def test_no_boundary_crossing_in_embedding(self, basic_3d_data): + """ + Verify that time-delay windows never span across trials. + We do this by checking that manually processing each trial + independently gives the same result as the automatic 3D processing. + """ + n_delays = 3 + rank = 5 + + # Manual approach: explicitly process each trial + A_manual = compute_manual_embedding(basic_3d_data, n_delays, rank) + + # Automatic 3D approach + k = Koopman( + observables=TimeDelay(n_delays=n_delays), + regressor=pDMD(svd_rank=rank) + ) + k.fit(basic_3d_data) + A_auto = k.A + + # If boundaries were crossed, the matrices would differ + diff = np.linalg.norm(A_manual - A_auto, 'fro') + assert diff < TOL, \ + f"Boundary crossing detected! Manual and auto differ by {diff:.10e}" + + def test_sample_count_matches_expectation(self, basic_3d_data): + """Verify that the number of samples matches expected value.""" + n_delays = 3 + n_trials = basic_3d_data.shape[0] + n_timesteps = basic_3d_data.shape[1] + + k = Koopman( + observables=TimeDelay(n_delays=n_delays), + regressor=pDMD(svd_rank=5) + ) + k.fit(basic_3d_data) + + # Each trial contributes (n_timesteps - n_delays - 1) samples + expected_samples = n_trials * (n_timesteps - n_delays - 1) + actual_samples = k._regressor().n_samples_ + + assert actual_samples == expected_samples, \ + f"Expected {expected_samples} samples, got {actual_samples}" + + +@pytest.mark.parametrize("n_delays", [1, 2, 3, 5]) +@pytest.mark.parametrize("rank", [2, 5]) +def test_parametrized_timedelay(basic_3d_data, n_delays, rank): + """Test various combinations of n_delays and rank.""" + k = Koopman( + observables=TimeDelay(n_delays=n_delays), + regressor=pDMD(svd_rank=rank) + ) + k.fit(basic_3d_data) + + # Should successfully fit without errors + # Note: actual rank may be less than requested if not enough features + n_features = basic_3d_data.shape[2] + n_output_features = n_features * (1 + n_delays) + expected_rank = min(rank, n_output_features) + assert k.A.shape == (expected_rank, expected_rank), \ + f"Expected shape ({expected_rank}, {expected_rank}), got {k.A.shape}" + + # Verify we can predict + test_point = basic_3d_data[0, 0:n_delays+1, :] + pred = k.predict(test_point) + assert pred.shape[1] == basic_3d_data.shape[2] + + +if __name__ == "__main__": + pytest.main([__file__, "-v"]) + From fb047cf5e091872f2eb6e80742457126bcee7867 Mon Sep 17 00:00:00 2001 From: ostrow Date: Sat, 10 Jan 2026 16:10:15 -0500 Subject: [PATCH 51/90] dataclass bug fix --- DSA/dsa.py | 8 +++-- examples/how_to_use_dsa_tutorial.ipynb | 49 ++++++-------------------- 2 files changed, 17 insertions(+), 40 deletions(-) diff --git a/DSA/dsa.py b/DSA/dsa.py index df6ba76..b0871df 100644 --- a/DSA/dsa.py +++ b/DSA/dsa.py @@ -332,10 +332,14 @@ def __init__( if isinstance(dmd_config,type): dmd_config = dmd_config() if is_dataclass(dmd_config): - dmd_config = asdict(dmd_config) + # for dataclasses with default entries, __dataclass_fields__ ends up being empty + # dmd_config = asdict(dmd_config) ends up with an empty dictionary + #hardcode fix here + dmd_config = {k: v for k, v in dmd_config.__class__.__dict__.items() if not k.startswith("__") and not callable(v)} + self.dmd_config = ( {} - ) # This will store {'param_name': broadcasted_value_list_of_lists} + ) for key, value in dmd_config.items(): cast_type = CAST_TYPES.get(key) diff --git a/examples/how_to_use_dsa_tutorial.ipynb b/examples/how_to_use_dsa_tutorial.ipynb index e3da50b..6e16a4e 100644 --- a/examples/how_to_use_dsa_tutorial.ipynb +++ b/examples/how_to_use_dsa_tutorial.ipynb @@ -441,45 +441,18 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "Fitting DMDs: 1it [00:00, 186.40it/s]\n", - "Fitting DMDs: 1it [00:00, 386.57it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Pre-computing eigenvalues for Wasserstein distance...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Computing DMD similarities: 100%|██████████| 1/1 [00:00<00:00, 209.64it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Similarity with PyKoopman: 0.1168\n", - "(8, 100, 5)\n", - "(2, 2)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" + "ename": "NameError", + "evalue": "name 'pk' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[1], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Use TimeDelay observables with SubspaceDMD regressor from pydmd\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m observables \u001b[38;5;241m=\u001b[39m \u001b[43mpk\u001b[49m\u001b[38;5;241m.\u001b[39mobservables\u001b[38;5;241m.\u001b[39mTimeDelay(n_delays\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m10\u001b[39m)\n\u001b[1;32m 3\u001b[0m regressor \u001b[38;5;241m=\u001b[39m SubspaceDMD(svd_rank\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 5\u001b[0m \u001b[38;5;66;03m# Create configuration\u001b[39;00m\n", + "\u001b[0;31mNameError\u001b[0m: name 'pk' is not defined" ] } ], @@ -493,8 +466,8 @@ "\n", "@dataclass\n", "class CustomPyKoopmanConfig:\n", - " observables = observables\n", - " regressor = regressor\n", + " observables: object = observables\n", + " regressor: object = regressor\n", "\n", "gdsa = GeneralizedDSA(\n", " data1, data2,\n", From 34151cf53411521b3ea87a15294829c9df561bf9 Mon Sep 17 00:00:00 2001 From: Mitchell Ostrow <35669245+mitchellostrow@users.noreply.github.com> Date: Fri, 6 Feb 2026 11:39:08 -0500 Subject: [PATCH 52/90] Update DSA/pykoopman/__init__.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- DSA/pykoopman/__init__.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/DSA/pykoopman/__init__.py b/DSA/pykoopman/__init__.py index 4bbc91d..9f71276 100644 --- a/DSA/pykoopman/__init__.py +++ b/DSA/pykoopman/__init__.py @@ -6,6 +6,8 @@ try: __version__ = get_distribution(__name__).version except DistributionNotFound: + # Package distribution metadata is not available (e.g., running from source); + # in this case, simply leave __version__ undefined. pass from .koopman import Koopman From bcfd862bf52acbb32632f4518ff5044af0526a92 Mon Sep 17 00:00:00 2001 From: Mitchell Ostrow <35669245+mitchellostrow@users.noreply.github.com> Date: Fri, 6 Feb 2026 11:41:03 -0500 Subject: [PATCH 53/90] Update DSA/pykoopman/common/__init__.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- DSA/pykoopman/common/__init__.py | 29 ++++++++--------------------- 1 file changed, 8 insertions(+), 21 deletions(-) diff --git a/DSA/pykoopman/common/__init__.py b/DSA/pykoopman/common/__init__.py index bc6769f..cb4c649 100644 --- a/DSA/pykoopman/common/__init__.py +++ b/DSA/pykoopman/common/__init__.py @@ -1,24 +1,11 @@ from __future__ import annotations -from .validation import check_array -from .validation import drop_nan_rows -from .validation import validate_input + from .validation import check_array + from .validation import drop_nan_rows + from .validation import validate_input -__all__ = [ - "check_array", - "drop_nan_rows", - "validate_input", - "drss", - "advance_linear_system", - "torus_dynamics", - "lorenz", - "vdp_osc", - "rk4", - "rev_dvdp", - "Linear2Ddynamics", - "slow_manifold", - "nlse", - "vbe", - "cqgle", - "ks", -] + __all__ = [ + "check_array", + "drop_nan_rows", + "validate_input", + ] From a5e931fcb6a3c5080a3d4408371f854af5b3590f Mon Sep 17 00:00:00 2001 From: Mitchell Ostrow <35669245+mitchellostrow@users.noreply.github.com> Date: Fri, 6 Feb 2026 14:26:02 -0500 Subject: [PATCH 54/90] Fix docstring formatting in simdist_controllability --- DSA/simdist_controllability.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/DSA/simdist_controllability.py b/DSA/simdist_controllability.py index e700882..66c5485 100644 --- a/DSA/simdist_controllability.py +++ b/DSA/simdist_controllability.py @@ -23,7 +23,7 @@ def __init__( align_inputs: bool = False, return_distance_components: bool = False, ): - f""" + """ Parameters ---------- score_method : {"euclidean", "angular"} From 67516a915a8db34786367029161201ec97f2e303 Mon Sep 17 00:00:00 2001 From: Mitchell Ostrow <35669245+mitchellostrow@users.noreply.github.com> Date: Fri, 6 Feb 2026 14:26:56 -0500 Subject: [PATCH 55/90] Fix syntax error in setup.py requirements --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 92f9891..905d4ad 100644 --- a/setup.py +++ b/setup.py @@ -17,7 +17,7 @@ "tqdm", "optht", #for havok in pykoopman "derivative", #for pykoopman - "lightning" #for nndmd in pykoopman + "lightning", #for nndmd in pykoopman "prettytable" ], extras_require={"dev": ["pytest>=3.7"]}, From 6d2021bc8d42c2d615123213109fb05d9a8e4b15 Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Fri, 6 Feb 2026 19:28:34 +0000 Subject: [PATCH 56/90] Initial plan From 00c14a88cc1873abe22f3257f792004a2ee7da53 Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Fri, 6 Feb 2026 19:28:44 +0000 Subject: [PATCH 57/90] Initial plan From bcfd5cd0ead9cdeb94dd582b307f42c920300464 Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Fri, 6 Feb 2026 19:30:04 +0000 Subject: [PATCH 58/90] Fix file resource leaks in _nndmd.py using context managers Co-authored-by: mitchellostrow <35669245+mitchellostrow@users.noreply.github.com> --- .gitignore | 18 ++++++++++++++++++ DSA/pykoopman/regression/_nndmd.py | 8 ++++---- 2 files changed, 22 insertions(+), 4 deletions(-) create mode 100644 .gitignore diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..26290cf --- /dev/null +++ b/.gitignore @@ -0,0 +1,18 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# Distribution / packaging +dist/ +build/ +*.egg-info/ + +# PyCharm +.idea/ + +# VS Code +.vscode/ + +# Jupyter Notebook +.ipynb_checkpoints diff --git a/DSA/pykoopman/regression/_nndmd.py b/DSA/pykoopman/regression/_nndmd.py index b9e6018..f9583f7 100644 --- a/DSA/pykoopman/regression/_nndmd.py +++ b/DSA/pykoopman/regression/_nndmd.py @@ -838,8 +838,8 @@ def prepare_data(self): if isinstance(self.data_tr, list): data_list = self.data_tr elif isinstance(self.data_tr, str): - f = open(self.data_tr, "rb") - data_list = pickle.load(f) + with open(self.data_tr, "rb") as f: + data_list = pickle.load(f) else: raise ValueError("Wrong type of `self.data_tr`") @@ -882,8 +882,8 @@ def prepare_data(self): if isinstance(self.data_val, list): data_list = self.data_val elif isinstance(self.data_val, str): - f = open(self.data_val, "rb") - data_list = pickle.load(f) + with open(self.data_val, "rb") as f: + data_list = pickle.load(f) else: raise ValueError("Wrong type of `self.data_val`") From 6b8ad7e62d0115c17a70fd65c925bf6499d1d918 Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Fri, 6 Feb 2026 19:30:05 +0000 Subject: [PATCH 59/90] Fix file resource leaks by using context managers Co-authored-by: mitchellostrow <35669245+mitchellostrow@users.noreply.github.com> --- .../__pycache__/_nndmd.cpython-312.pyc | Bin 0 -> 61819 bytes DSA/pykoopman/regression/_nndmd.py | 8 ++++---- 2 files changed, 4 insertions(+), 4 deletions(-) create mode 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ee17131f614d64965b9176fd04ea6825aa6a99a9 Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Fri, 6 Feb 2026 19:30:25 +0000 Subject: [PATCH 60/90] Remove pycache file and add .gitignore Co-authored-by: mitchellostrow <35669245+mitchellostrow@users.noreply.github.com> --- .gitignore | 14 ++++++++++++++ .../__pycache__/_nndmd.cpython-312.pyc | Bin 61819 -> 0 bytes 2 files changed, 14 insertions(+) create mode 100644 .gitignore delete mode 100644 DSA/pykoopman/regression/__pycache__/_nndmd.cpython-312.pyc diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..4ed2e6a --- /dev/null +++ b/.gitignore @@ -0,0 +1,14 @@ +# Python cache files +__pycache__/ +*.py[cod] +*$py.class + +# Distribution / packaging +dist/ +build/ +*.egg-info/ + +# Virtual environments +venv/ +env/ +ENV/ diff --git a/DSA/pykoopman/regression/__pycache__/_nndmd.cpython-312.pyc b/DSA/pykoopman/regression/__pycache__/_nndmd.cpython-312.pyc deleted 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80efcc7..5b582e5 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -23,7 +23,8 @@ dependencies = [ "tqdm", "optht", "derivative", - "lightning" + "lightning", + "prettytable" ] [project.optional-dependencies] From 0bd1975c7948daf739b80483163df4cac04754ff Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Fri, 6 Feb 2026 19:33:28 +0000 Subject: [PATCH 64/90] Add proper error handling for UMAP import and make it an optional dependency Co-authored-by: mitchellostrow <35669245+mitchellostrow@users.noreply.github.com> --- DSA/__pycache__/__init__.cpython-312.pyc | Bin 0 -> 886 bytes DSA/__pycache__/dmd.cpython-312.pyc | Bin 0 -> 29254 bytes DSA/__pycache__/dsa.cpython-312.pyc | Bin 0 -> 43449 bytes DSA/preprocessing.py | 11 +++++++++-- pyproject.toml | 3 +++ setup.py | 5 ++++- 6 files changed, 16 insertions(+), 3 deletions(-) create mode 100644 DSA/__pycache__/__init__.cpython-312.pyc create mode 100644 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nonlinear_dimensionality_reduction( pca = PCA(n_components=n_components) model = make_pipeline(nystroem, pca) elif method.lower() == "umap": - from umap import UMAP + try: + from umap import UMAP + except ImportError: + raise ImportError( + "umap-learn is not installed. Please install it with " + "`pip install umap-learn` or install DSA with the 'umap' extra: " + "`pip install dsa-metric[umap]`" + ) model = UMAP(n_components=n_components, **kwargs) else: raise ValueError( f"Unknown dimensionality reduction method: {method}. " - f"Supported methods are 'isomap', 'lle', 'pca', and 'kernel_pca'." + f"Supported methods are 'isomap', 'lle', 'pca', 'kernel_pca', and 'umap'." ) reduced_data = model.fit_transform(data) diff --git a/pyproject.toml b/pyproject.toml index 80efcc7..9e08365 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -30,6 +30,9 @@ dependencies = [ dev = [ "pytest>=3.7" ] +umap = [ + "umap-learn" +] [project.urls] Homepage = "https://github.com/mitchellostrow/DSA" diff --git a/setup.py b/setup.py index 905d4ad..67d9cb3 100644 --- a/setup.py +++ b/setup.py @@ -20,5 +20,8 @@ "lightning", #for nndmd in pykoopman "prettytable" ], - extras_require={"dev": ["pytest>=3.7"]}, + extras_require={ + "dev": ["pytest>=3.7"], + "umap": ["umap-learn"], + }, ) From a9c2a1fe0f82163d91effee64ab2435a01f6465e Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Fri, 6 Feb 2026 19:33:46 +0000 Subject: [PATCH 65/90] Remove __pycache__ files and add .gitignore Co-authored-by: mitchellostrow <35669245+mitchellostrow@users.noreply.github.com> --- .gitignore | 63 +++++++++++++++++++++++ DSA/__pycache__/__init__.cpython-312.pyc | Bin 886 -> 0 bytes DSA/__pycache__/dmd.cpython-312.pyc | Bin 29254 -> 0 bytes DSA/__pycache__/dsa.cpython-312.pyc | Bin 43449 -> 0 bytes 4 files changed, 63 insertions(+) create mode 100644 .gitignore delete mode 100644 DSA/__pycache__/__init__.cpython-312.pyc delete mode 100644 DSA/__pycache__/dmd.cpython-312.pyc delete mode 100644 DSA/__pycache__/dsa.cpython-312.pyc diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..07d5edc --- /dev/null +++ b/.gitignore @@ -0,0 +1,63 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +*.manifest +*.spec + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# IDEs +.vscode/ +.idea/ +*.swp +*.swo +*~ + +# OS +.DS_Store +Thumbs.db diff --git a/DSA/__pycache__/__init__.cpython-312.pyc b/DSA/__pycache__/__init__.cpython-312.pyc deleted file mode 100644 index d0723b8e89ac6695ee876550b5b357dc537458e1..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 886 zcma))zi-n(6vyAi`C&UhnubDBA%09qu23VG5mJRKgiyPL=@!e4FXhxb+d4a{!qAa_ zfr&qW0Ve(nCI%KORt6?Eq;8#fcWy%&K%8{G&-=a~_nyvQx?LA=ecF4=-`4=XYvXF6 zZPxi`Jl;VB5fLB>C9+0q(xR5E)4FWX2GPD6`kJ(5*h03cEgkAem%2u)qlU3M*Vqlw zmL1wL+!S5eqdmhdu_61kZ`c-_azF=$9dSc$(JjNSxcRZF;)+{q8m?)iY0>sNKlA`z zqpws~Dtlp~naNy%n*8zBTt|y~C{7YBpdhmaPV7~~nX)Y7@p0u=EW`Ugz&Yl^g*SekNH$jU7$16kny8a8 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z0gmztclSl*Rk@{lUVx*#!rlGHv{yCssz!TN1BBn|`P5owX`UD0&Kt7zNxUZ wQzqJ#Z*ceNFK%Mr`?*M2R~FRRP{7m~I*;s5{u From 4f348a34edfafe92718326f004dbe7697530c8e4 Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Fri, 6 Feb 2026 19:38:20 +0000 Subject: [PATCH 66/90] Initial plan From e73386777ff6efe86e5aaddea84070ce1a687f7c Mon Sep 17 00:00:00 2001 From: Mitchell Ostrow <35669245+mitchellostrow@users.noreply.github.com> Date: Fri, 6 Feb 2026 14:41:32 -0500 Subject: [PATCH 67/90] Update DSA/dsa.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- DSA/dsa.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/DSA/dsa.py b/DSA/dsa.py index b0871df..af1feb3 100644 --- a/DSA/dsa.py +++ b/DSA/dsa.py @@ -511,7 +511,7 @@ def update_compare_method(self, compare='joint', simdist_config=None): f"for SimilarityTransformDist, valid options: angular, euclidean, wasserstein" ) # State comparison doesn't have return_distance_components, so always treated as False - new_return_components = False + else: # 'joint' or 'control' simdist_class = ControllabilitySimilarityTransformDist From 447b4628168cec8cf1821916942555be189d92fd Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Fri, 6 Feb 2026 19:43:04 +0000 Subject: [PATCH 68/90] Fix division by zero in nmse() for constant arrays Co-authored-by: mitchellostrow <35669245+mitchellostrow@users.noreply.github.com> --- DSA/pykoopman/common/__init__.py | 16 ++++++++-------- DSA/stats.py | 11 +++++++++-- 2 files changed, 17 insertions(+), 10 deletions(-) diff --git a/DSA/pykoopman/common/__init__.py b/DSA/pykoopman/common/__init__.py index cb4c649..8240aa0 100644 --- a/DSA/pykoopman/common/__init__.py +++ b/DSA/pykoopman/common/__init__.py @@ -1,11 +1,11 @@ from __future__ import annotations - from .validation import check_array - from .validation import drop_nan_rows - from .validation import validate_input +from .validation import check_array +from .validation import drop_nan_rows +from .validation import validate_input - __all__ = [ - "check_array", - "drop_nan_rows", - "validate_input", - ] +__all__ = [ + "check_array", + "drop_nan_rows", + "validate_input", +] diff --git a/DSA/stats.py b/DSA/stats.py index 8efd601..1ab7e06 100644 --- a/DSA/stats.py +++ b/DSA/stats.py @@ -106,7 +106,7 @@ def mse(x, y): return ((x - y) ** 2).mean().item() -def nmse(x, y): +def nmse(x, y, eps=1e-10): """ Compute the mean squared error, normalized by the variance of the ground truth. @@ -114,6 +114,9 @@ def nmse(x, y): The ground truth time series. y : np.ndarray or torch.tensor The predicted time series. + eps : float, optional + Small constant to prevent division by zero when x has zero variance. + Default is 1e-10. Returns ------- @@ -122,7 +125,11 @@ def nmse(x, y): """ x = torch_convert(x) y = torch_convert(y) - return ((x - y) ** 2).mean().item() / ((x - x.mean()) ** 2).mean().item() + variance = ((x - x.mean()) ** 2).mean().item() + if variance < eps: + # If x is constant (zero variance), return inf to indicate undefined NMSE + return float('inf') if ((x - y) ** 2).mean().item() > eps else 0.0 + return ((x - y) ** 2).mean().item() / variance def r2(true_vals, pred_vals): From 525bda7aa6aa7f01b90c2283ba164e31a701443a Mon Sep 17 00:00:00 2001 From: Mitchell Ostrow <35669245+mitchellostrow@users.noreply.github.com> Date: Fri, 6 Feb 2026 14:43:51 -0500 Subject: [PATCH 69/90] Update DSA/preprocessing.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- DSA/preprocessing.py | 1 - 1 file changed, 1 deletion(-) diff --git a/DSA/preprocessing.py b/DSA/preprocessing.py index db5b8b1..ebf0bed 100644 --- a/DSA/preprocessing.py +++ b/DSA/preprocessing.py @@ -344,7 +344,6 @@ def coarse_grain_space( ) # Get input shape and dimensionality - orig_shape = data.shape if data.ndim == 3: n_conds, n_time, n_dims = data.shape data_reshaped = data.reshape(-1, n_dims) From fe358590a6e3d7e0caa5f339a68fc73cf4e2f17c Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Fri, 6 Feb 2026 19:45:03 +0000 Subject: [PATCH 70/90] Optimize nmse to avoid computing MSE twice Co-authored-by: mitchellostrow <35669245+mitchellostrow@users.noreply.github.com> --- DSA/stats.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/DSA/stats.py b/DSA/stats.py index 1ab7e06..4027979 100644 --- a/DSA/stats.py +++ b/DSA/stats.py @@ -125,11 +125,12 @@ def nmse(x, y, eps=1e-10): """ x = torch_convert(x) y = torch_convert(y) + mse = ((x - y) ** 2).mean().item() variance = ((x - x.mean()) ** 2).mean().item() if variance < eps: # If x is constant (zero variance), return inf to indicate undefined NMSE - return float('inf') if ((x - y) ** 2).mean().item() > eps else 0.0 - return ((x - y) ** 2).mean().item() / variance + return float('inf') if mse > eps else 0.0 + return mse / variance def r2(true_vals, pred_vals): From f8075925a548901b858f679a36bd8eb10dfcc445 Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Fri, 6 Feb 2026 19:45:50 +0000 Subject: [PATCH 71/90] Improve clarity of zero variance handling in nmse Co-authored-by: mitchellostrow <35669245+mitchellostrow@users.noreply.github.com> --- DSA/stats.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/DSA/stats.py b/DSA/stats.py index 4027979..fefb42b 100644 --- a/DSA/stats.py +++ b/DSA/stats.py @@ -128,8 +128,9 @@ def nmse(x, y, eps=1e-10): mse = ((x - y) ** 2).mean().item() variance = ((x - x.mean()) ** 2).mean().item() if variance < eps: - # If x is constant (zero variance), return inf to indicate undefined NMSE - return float('inf') if mse > eps else 0.0 + # If x is constant (zero variance), NMSE is undefined + # Return 0.0 if predictions are perfect, inf otherwise + return 0.0 if mse < eps else float('inf') return mse / variance From ddf84c2f2c4596382cd3062c6db9d875b1735229 Mon Sep 17 00:00:00 2001 From: Mitchell Ostrow <35669245+mitchellostrow@users.noreply.github.com> Date: Fri, 6 Feb 2026 14:47:46 -0500 Subject: [PATCH 72/90] Update DSA/__init__.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- DSA/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/DSA/__init__.py b/DSA/__init__.py index dde8892..b8ba5c7 100644 --- a/DSA/__init__.py +++ b/DSA/__init__.py @@ -10,4 +10,4 @@ from DSA.stats import * from DSA.sweeps import * from DSA.preprocessing import * -from DSA.resdmd import * +from DSA import resdmd From 14177f4cd4b7944316f7c3fa7cf8337bfa2f0d33 Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Fri, 6 Feb 2026 19:59:07 +0000 Subject: [PATCH 73/90] Initial plan From eeef423be5f1168c0f4b46159f3b4936e5598f67 Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Fri, 6 Feb 2026 20:03:05 +0000 Subject: [PATCH 74/90] Remove unused pad_zeros import from tests/simdist_test.py Co-authored-by: mitchellostrow <35669245+mitchellostrow@users.noreply.github.com> --- tests/simdist_test.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/simdist_test.py b/tests/simdist_test.py index 5375a64..1371684 100644 --- a/tests/simdist_test.py +++ b/tests/simdist_test.py @@ -1,6 +1,6 @@ import pytest import numpy as np -from DSA.simdist import SimilarityTransformDist, pad_zeros +from DSA.simdist import SimilarityTransformDist from scipy.stats import special_ortho_group, ortho_group import torch From aa02f37fcffa67b7ca7f1b850616cee311384c3c Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Fri, 6 Feb 2026 20:03:10 +0000 Subject: [PATCH 75/90] Initial plan From b4dde3aacde9c579b0ff7d1d8526c64a5ec218ba Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Fri, 6 Feb 2026 20:07:59 +0000 Subject: [PATCH 76/90] Remove unused import of SimilarityTransformDist Co-authored-by: mitchellostrow <35669245+mitchellostrow@users.noreply.github.com> --- DSA/simdist_controllability.py | 7 +------ 1 file changed, 1 insertion(+), 6 deletions(-) diff --git a/DSA/simdist_controllability.py b/DSA/simdist_controllability.py index 66c5485..03c87b9 100644 --- a/DSA/simdist_controllability.py +++ b/DSA/simdist_controllability.py @@ -1,12 +1,7 @@ from typing import Literal import numpy as np from scipy.linalg import orthogonal_procrustes -import torch - -try: - from .simdist import SimilarityTransformDist -except ImportError: - from simdist import SimilarityTransformDist +import torch class ControllabilitySimilarityTransformDist: From cc2af67cada4a1a23d7579235b84a7dfe3eac1dd Mon Sep 17 00:00:00 2001 From: mitchellostrow Date: Fri, 6 Feb 2026 15:58:35 -0500 Subject: [PATCH 77/90] fixing bugs on inputdsa --- DSA/dmd.py | 37 +++++++++++++++++--------- DSA/dsa.py | 20 +++++++++++--- DSA/preprocessing.py | 9 ++++++- DSA/pykoopman/koopman.py | 2 +- examples/how_to_use_dsa_tutorial.ipynb | 2 +- pyproject.toml | 4 +++ 6 files changed, 55 insertions(+), 19 deletions(-) diff --git a/DSA/dmd.py b/DSA/dmd.py index 2140976..3005b14 100644 --- a/DSA/dmd.py +++ b/DSA/dmd.py @@ -77,7 +77,7 @@ class DMD(BaseDMD): def __init__( self, - data, + data=None, n_delays=1, delay_interval=1, rank=None, @@ -93,13 +93,13 @@ def __init__( """ Parameters ---------- - data : np.ndarray or torch.tensor + data : np.ndarray or torch.tensor, optional The data to fit the DMD model to. Must be either: (1) a 2-dimensional array/tensor of shape T x N where T is the number of time points and N is the number of observed dimensions at each time point, or (2) a 3-dimensional array/tensor of shape K x T x N where K is the number of "trials" and T and N are - as defined above. + as defined above. If None, data must be provided when calling fit(). n_delays : int Parameter that controls the size of the delay embedding. Explicitly, @@ -153,7 +153,8 @@ def __init__( # DMD always uses PyTorch, so use_torch=True self.device, self.use_torch = self._setup_device(device, use_torch=True) - self.data = self._init_single_data(data) + # Allow data=None for deferred fitting + self.data = self._init_single_data(data) if data is not None else None self.n_delays = n_delays self.delay_interval = delay_interval @@ -212,6 +213,10 @@ def compute_hankel( # if parameters are provided, overwrite them from the init if data is not None: self.data = self._init_single_data(data) + + # Ensure data is available + if self.data is None: + raise ValueError("Data must be provided either at initialization or when calling fit()/compute_hankel().") self.n_delays = self.n_delays if n_delays is None else n_delays self.delay_interval = ( @@ -634,12 +639,18 @@ def predict(self, test_data=None, reseed=None, full_return=False): return pred_data def project_onto_modes(self): - eigvals, eigvecs = torch.linalg.eigh(self.A_v) - # project Vt_minus onto the eigenvectors - projections = self.V[:, : self.rank] @ eigvecs - projections = projections.reshape( - self.data.shape[0], self.data.shape[1] - self.n_delays + 1, -1 - ) - - # get the data that matches the shape of the original data - return projections, self.data[:, : -self.n_delays + 1] + # Minimal: eigenfunction values per Hankel row + matching time indices + data slice + if self.A_v is None or self.V is None: + raise ValueError("Call fit() first.") + k = int(self.rank or self.A_v.shape[0]) + eigvals, W = torch.linalg.eig(self.A_v) + Winv = torch.linalg.inv(W) + Vslice = self.V[:, :k].to(dtype=Winv.dtype, device=self.device) + phi = Vslice @ Winv.T.to(dtype=Vslice.dtype, device=self.device) # (T_embed, k) + offset = (self.n_delays - 1) * self.delay_interval + if self.data.ndim == 3: + x = self.data[:, offset : offset + phi.shape[0], :] # keep all features + phi = phi.reshape(x.shape[0], x.shape[1], -1) + else: + x = self.data[offset : offset + phi.shape[0], :] + return eigvals, phi, x diff --git a/DSA/dsa.py b/DSA/dsa.py index af1feb3..ea030fa 100644 --- a/DSA/dsa.py +++ b/DSA/dsa.py @@ -794,10 +794,24 @@ def score(self): if self.verbose: print("Pre-computing eigenvalues for Wasserstein distance...") - self.cached_compare_objects = [ - [self.get_compare_objects(dmd) for dmd in self.dmds[0]], - [self.get_compare_objects(dmd) for dmd in self.dmds[ind2]] + cache_iter = lambda items, desc: ( + tqdm.tqdm(items, desc=desc) if self.verbose else items + ) + # Compute cached compare objects once per DMD. In self-pairwise mode, X and Y + # refer to the same DMD list, so avoid doubling expensive work. + cached_x = [ + self.get_compare_objects(dmd) + for dmd in cache_iter(self.dmds[0], "Caching compare objects X") ] + if ind2 == 0: + cached_y = cached_x + else: + cached_y = [ + self.get_compare_objects(dmd) + for dmd in cache_iter(self.dmds[ind2], "Caching compare objects Y") + ] + + self.cached_compare_objects = [cached_x, cached_y] def compute_similarity(i, j): if self.method == "self-pairwise" and j >= i: diff --git a/DSA/preprocessing.py b/DSA/preprocessing.py index 85200e1..57b9473 100644 --- a/DSA/preprocessing.py +++ b/DSA/preprocessing.py @@ -244,7 +244,7 @@ def nonlinear_dimensionality_reduction( def featurize_data( - data, method="id", pca_downsample=False, pca_n_components=3, **kwargs + data, method="id", pca_downsample=False, pca_n_components=3, include_state=False, **kwargs ): if data.ndim == 3: shape = data.shape @@ -252,6 +252,9 @@ def featurize_data( else: shape = data.shape + # Store original data if we need to concatenate it later + original_data = data.copy() if include_state else None + if method.lower() == "id": pass elif method.lower() in {"rbfsampler", "rbf_sampler"}: @@ -276,6 +279,10 @@ def featurize_data( if pca_downsample: data = pca_reduce(data, n_components=pca_n_components) + # Concatenate original state if requested + if include_state and original_data is not None: + data = np.concatenate([data, original_data], axis=-1) + if len(shape) == 3: return data.reshape(shape[0], shape[1], -1) else: diff --git a/DSA/pykoopman/koopman.py b/DSA/pykoopman/koopman.py index d6de0c7..026bc63 100644 --- a/DSA/pykoopman/koopman.py +++ b/DSA/pykoopman/koopman.py @@ -164,7 +164,7 @@ def __init__(self, observables=None, regressor=None, quiet=False): if observables is None: observables = Identity() if regressor is None: - regressor = PyDMDRegressor(DMD(svd_rank=2)) # set default svd rank 2 + regressor = PyDMDRegressor(DMD(svd_rank=-1)) if isinstance(regressor, DMDBase): regressor = PyDMDRegressor(regressor) elif not isinstance(regressor, (BaseRegressor)): diff --git a/examples/how_to_use_dsa_tutorial.ipynb b/examples/how_to_use_dsa_tutorial.ipynb index 6e16a4e..7a22de4 100644 --- a/examples/how_to_use_dsa_tutorial.ipynb +++ b/examples/how_to_use_dsa_tutorial.ipynb @@ -424,7 +424,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 7, "metadata": {}, "outputs": [ { diff --git a/pyproject.toml b/pyproject.toml index 98fdd7c..f58cef3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,3 +1,7 @@ +[build-system] +requires = ["setuptools>=61.0", "wheel"] +build-backend = "setuptools.build_meta" + [project] name = "dsa-metric" version = "2.0.0" From 69dd4fc8f353ccd8e20ba8b8d708d21218bd824b Mon Sep 17 00:00:00 2001 From: mitchellostrow Date: Fri, 6 Feb 2026 18:08:13 -0500 Subject: [PATCH 78/90] fix some tests and redo whole sweep class --- DSA/dsa.py | 3 +- DSA/pykoopman/__init__.py | 3 +- DSA/sweeps.py | 1353 +++++++++++++++----------- examples/sweep_resdmd_tutorial.ipynb | 521 ++++++++++ tests/dmd_test.py | 1 + tests/dsa_test.py | 4 +- tests/test_sweeps.py | 436 +++++++++ 7 files changed, 1740 insertions(+), 581 deletions(-) create mode 100644 examples/sweep_resdmd_tutorial.ipynb create mode 100644 tests/test_sweeps.py diff --git a/DSA/dsa.py b/DSA/dsa.py index ea030fa..ffae8a6 100644 --- a/DSA/dsa.py +++ b/DSA/dsa.py @@ -872,8 +872,7 @@ def compute_similarity(i, j): self.sims[j, i] = sim if self.method == "default": - return self.sims[0, 0].squeeze() - + return self.sims[0, 0].item() return self.sims.squeeze() diff --git a/DSA/pykoopman/__init__.py b/DSA/pykoopman/__init__.py index 9f71276..cee3333 100644 --- a/DSA/pykoopman/__init__.py +++ b/DSA/pykoopman/__init__.py @@ -1,11 +1,10 @@ from __future__ import annotations -from pkg_resources import DistributionNotFound from pkg_resources import get_distribution try: __version__ = get_distribution(__name__).version -except DistributionNotFound: +except: # Package distribution metadata is not available (e.g., running from source); # in this case, simply leave __version__ undefined. pass diff --git a/DSA/sweeps.py b/DSA/sweeps.py index 60ceeed..7b6bce6 100644 --- a/DSA/sweeps.py +++ b/DSA/sweeps.py @@ -1,607 +1,810 @@ +""" +Sweeps: Parameter sweep utilities for DMD-type models. + +This module provides object-oriented sweeping over hyperparameters +for both local DMD and PyKoopman models. +""" + import numpy as np from tqdm import tqdm from .dmd import DMD from .dmdc import DMDc from .subspace_dmdc import SubspaceDMDc -from .stats import ( - measure_nonnormality_transpose, - compute_all_stats, - measure_transient_growth, -) -from .resdmd import compute_residuals +from .stats import measure_nonnormality_transpose, compute_all_stats +from .resdmd import ResidualComputer import matplotlib.pyplot as plt -from typing import Literal +from typing import List, Dict, Any, Optional, Union, Tuple +from abc import ABC, abstractmethod import warnings +import torch + +# Import pykoopman +from . import pykoopman as pk +from .pykoopman.observables import TimeDelay, Identity + def split_train_test(data, train_frac=0.8): + """Split data into train and test sets.""" if isinstance(data, list): train_data = [d for i, d in enumerate(data) if i < int(train_frac * len(data))] test_data = [d for i, d in enumerate(data) if i >= int(train_frac * len(data))] dim = data[0].shape[-1] elif data.ndim == 3 and data.shape[0] == 1: - train_data = data[:, int(train_frac * data.shape[1]) :] - test_data = data[:, : int(train_frac * data.shape[1])] + train_data = data[:, int(train_frac * data.shape[1]):] + test_data = data[:, :int(train_frac * data.shape[1])] dim = data.shape[-1] else: - train_data = data[: int(train_frac * data.shape[0])] - test_data = ( - data[int(train_frac * data.shape[0]) :] if train_frac < 1.0 else train_data - ) + train_data = data[:int(train_frac * data.shape[0])] + test_data = data[int(train_frac * data.shape[0]):] if train_frac < 1.0 else train_data dim = data.shape[-1] return train_data, test_data, dim -def sweep_ranks_delays( - data, - n_delays, - ranks, - control_data=None, - train_frac=0.8, - reseed=5, - return_residuals=True, - return_transient_growth=False, - return_mse=False, - error_space="X", - model_class=['DMD', 'DMDc', 'SubspaceDMDc'][0], - **model_kwargs, -): - """ - Sweep over combinations of DMD ranks and delays, returning AIC, MASE, non-normality, and residuals. - +class BaseSweeper(ABC): + """Abstract base class for parameter sweeps over DMD-type models. + + This class provides the core infrastructure for sweeping over two hyperparameters + of DMD-type models, computing various metrics (AIC, MASE, MSE, non-normality), + and optionally computing residuals. Subclasses must implement the abstract methods + to define how models are created, how predictions are made, and how to extract + model properties. + Parameters ---------- data : np.ndarray - Input data (trials, time, features). - n_delays : iterable - List or array of delays to sweep. - ranks : iterable - List or array of ranks to sweep. - train_frac : float - Fraction of data to use for training. If greater than or equal to 1, tests on the training set - reseed : int - Reseed for DMD prediction. - return_residuals : bool - Whether to return residuals. - measure_transient_growth : bool - Whether to measure transient growth (numerical abscissa and l2 norm). - return_mse: bool - Whether to return the mean squared error of the prediction in place of MASE - dmd_kwargs : dict - Additional keyword arguments for DMD. - - Returns - ------- - all_aics, all_mases, all_nnormals, all_residuals, all_num_abscissa, all_l2norm : np.ndarray - Arrays of results for each (delay, rank) pair. + Input data for the sweep. Can be 2D (time, features) or 3D (trials, time, features). + Will be automatically split into train/test sets based on `train_frac`. + param1_name : str + Name of the first parameter to sweep over. + param1_values : list or np.ndarray + Values to sweep over for the first parameter. + param2_name : str + Name of the second parameter to sweep over. + param2_values : list or np.ndarray + Values to sweep over for the second parameter. + train_frac : float, optional + Fraction of data to use for training (default: 0.8). + reseed : int, optional + Number of reseeding steps for prediction (default: 1). + compute_residuals : bool, optional + Whether to compute residuals using ResDMD (default: False). + control_data : np.ndarray, optional + Control/actuation data for controlled systems (default: None). + Will be split into train/test sets matching the data split. + **model_kwargs + Additional keyword arguments passed to the model constructor. + + Attributes + ---------- + train_data : np.ndarray + Training portion of the input data. + test_data : np.ndarray + Test portion of the input data. + train_control : np.ndarray or None + Training portion of control data (if provided). + test_control : np.ndarray or None + Test portion of control data (if provided). + dim : int + Dimensionality of the data (number of features). + aics : np.ndarray + AIC values for each parameter combination (after sweep). + mases : np.ndarray + MASE values for each parameter combination (after sweep). + mses : np.ndarray + MSE values for each parameter combination (after sweep). + nnormals : np.ndarray + Non-normality values for each parameter combination (after sweep). + residuals : np.ndarray or None + Residual values for each parameter combination (after sweep, if computed). + fitted_models : list of lists + Fitted model objects for each parameter combination (after sweep). """ - if model_class in ['DMDc', 'SubspaceDMDc']: - assert control_data is not None, "Control data is required for DMDc and SubspaceDMDc" - - train_data, test_data, dim = split_train_test(data, train_frac) - if control_data is not None: - train_control_data, test_control_data, dim_control = split_train_test(control_data, train_frac) - - all_aics, all_mases, all_nnormals, all_residuals, all_l2norm = [], [], [], [], [] - for nd in tqdm(n_delays): - rresiduals = [] - aics, mases, nnormals, l2norms = [], [], [], [] - for r in ranks: - if r is None or r > nd * dim: - aics.append(np.inf) - mases.append(np.inf) - nnormals.append(np.inf) - rresiduals.append(np.inf) - l2norms.append(np.inf) - continue - - if model_class == 'DMD': - model = DMD(train_data, n_delays=nd, rank=r, **model_kwargs) - elif model_class == 'DMDc': - model = DMDc(train_data, train_control_data, n_delays=nd, rank_output=r, **model_kwargs) - elif model_class == 'SubspaceDMDc': - model = SubspaceDMDc(train_data, train_control_data, n_delays=nd, rank=r, **model_kwargs) - else: - raise ValueError(f"Invalid model class: {model_class}. Valid options are 'DMD', 'DMDc', and 'SubspaceDMDc'.") - model.fit() - - # pred, H_test_pred, H_test_true, V_test_pred, V_test_true = dmd.predict( - # test_data, reseed=reseed, full_return=True - # ) - if model_class == "DMD": - pred, H_test_pred, H_test_true= model.predict( - test_data, reseed=reseed, full_return=True - ) - elif model_class == "DMDc": - pred, H_test_pred, H_test_true= model.predict( - test_data, test_control_data, reseed=reseed, full_return=True + + def __init__( + self, + data: np.ndarray, + param1_name: str, + param1_values: Union[List, np.ndarray], + param2_name: str, + param2_values: Union[List, np.ndarray], + train_frac: float = 0.8, + reseed: int = 1, + compute_residuals: bool = False, + control_data: np.ndarray = None, + **model_kwargs + ): + self.data = data + self.param1_name = param1_name + self.param1_values = np.array(param1_values) + self.param2_name = param2_name + self.param2_values = np.array(param2_values) + self.train_frac = train_frac + self.reseed = reseed + self.compute_residuals = compute_residuals + self.model_kwargs = model_kwargs + self.control_data = control_data + + self.train_data, self.test_data, self.dim = split_train_test(data, train_frac) + + # Split control data if provided + if control_data is not None: + self.train_control, self.test_control, _ = split_train_test(control_data, train_frac) + else: + self.train_control = self.test_control = None + + self._aics: np.ndarray = None + self._mases: np.ndarray = None + self._mses: np.ndarray = None + self._nnormals: np.ndarray = None + self._residuals: np.ndarray = None + self._fitted_models: List[List] = None + self._swept = False + + def _validate_control_data(self, model_class) -> None: + """Validate control data against the model class, warning if incompatible. + + Args: + model_class: The model class to validate against. + """ + if self.control_data is not None and model_class is not None: + if not self._is_control_model_class(model_class): + warnings.warn( + f"control_data was provided, but model class ({model_class.__name__}) " + f"may not support control input. Control data may be ignored.", + UserWarning ) - else: - pred = model.predict(test_data, test_control_data, reseed=reseed) - - if error_space == "H": - if model_class == 'SubspaceDMDc': - raise ValueError("H space not implemented for SubspaceDMDc. Use X space instead.") - pred = H_test_pred - test_data_err = H_test_true - elif error_space == "V": - raise ValueError("V space not implemented ") - # pred = V_test_pred - # test_data_err = V_test_true - elif error_space == "X": - pred = pred - test_data_err = test_data - else: - raise ValueError(f"Invalid error space: {error_space}") - - if hasattr(pred, "cpu"): - pred = pred.cpu() - if hasattr(test_data_err, "cpu"): - test_data_err = test_data_err.cpu() - - if isinstance(pred, list): - pred = np.concatenate(pred, axis=0) - test_data_err = np.concatenate(test_data_err, axis=0) - # if featurize and ndim is not None: - # pred = pred[:, :, -ndim:] - # stats = compute_all_stats(pred, test_data_err[:, :, -ndim:], dmd.rank) - # else: - stats = compute_all_stats(test_data_err, pred, model.rank if model_class in ['DMD', 'SubspaceDMDc'] else model.rank_output) - aic = stats["AIC"] - mase = stats["MASE"] - if return_mse: - mase = stats["MSE"] - nnormal = measure_nonnormality_transpose( - model.A_v.cpu().detach().numpy() if hasattr(model.A_v, "cpu") else model.A_v - ) - if return_transient_growth: - l2norm = measure_transient_growth( - model.A_v.cpu().detach().numpy() - if hasattr(model.A_v, "cpu") - else model.A_v - ) - else: - l2norm = None - if return_residuals and model_class == 'DMD': - L, G, residuals, _ = compute_residuals(model) - residuals = np.mean(residuals) - else: - warnings.warn(f"Residuals not implemented for {model_class}") - residuals = None - aics.append(aic) - mases.append(mase) - nnormals.append(nnormal) - rresiduals.append(residuals) - l2norms.append(l2norm) - all_aics.append(aics) - all_mases.append(mases) - all_nnormals.append(nnormals) - all_residuals.append(rresiduals) - all_l2norm.append(l2norms) - all_aics = np.array(all_aics) - all_mases = np.array(all_mases) - all_nnormals = np.array(all_nnormals) - all_residuals = np.array(all_residuals) - all_l2norm = np.array(all_l2norm) - - return_tuples = [all_aics, all_mases, all_nnormals] - if return_residuals: - return_tuples.append(all_residuals) - if return_transient_growth: - return_tuples.append(all_l2norm) - return tuple(return_tuples) - - -def plot_sweep_results( - aics, - mases, - nnormals=None, - residuals=None, - l2norm=None, - n_delays=None, - ranks=None, - name=None, - save_path=None, - figsize=(10, 4), - return_mse=False, - cmap="gist_gray", - error_space="X", -): - to_plot = [aics, mases] - if nnormals is not None: - to_plot.append(nnormals) - if residuals is not None: - to_plot.append(residuals) - if l2norm is not None: - to_plot.append(l2norm) - fig, ax = plt.subplots(1, len(to_plot), figsize=figsize) - cmap = plt.cm.get_cmap(cmap) if isinstance(cmap, str) else cmap - scale_denom = len(aics) + 3 - for j in range(len(aics)): - ax[0].plot(ranks, aics[j], label=f"{n_delays[j]}", color=cmap(j / scale_denom)) - ax[1].plot(ranks, mases[j], color=cmap(j / scale_denom), label=f"{n_delays[j]}") - ax[1].axhline(1, color="black", linestyle="--") - if nnormals is not None: - ax[2].plot( - ranks, nnormals[j], color=cmap(j / scale_denom), label=f"{n_delays[j]}" - ) - if residuals is not None: - ax[3].plot( - ranks, residuals[j], color=cmap(j / scale_denom), label=f"{n_delays[j]}" - ) - if l2norm is not None: - ax[4].plot( - ranks, l2norm[j], color=cmap(j / scale_denom), label=f"{n_delays[j]}" - ) - ax[4].axhline(1, color="black", linestyle="--") - - ax[1].set_yscale("log") - - ax[0].set_ylabel(f"{error_space} AIC") - ax[1].set_ylabel( - f"{error_space} MASE" if not return_mse else f"{error_space} MSE" - ) - if nnormals is not None: - ax[2].set_ylabel(f"Non-normal score") - if residuals is not None: - ax[3].set_ylabel(f"Average residual of eigenvalues") - if l2norm is not None: - ax[4].set_ylabel(f"L2 norm of matrix") - ax[-1].legend( - title="# delays", loc="upper right", bbox_to_anchor=(2, 1), borderaxespad=1 - ) - for k in range(len(to_plot)): - ax[k].set_xlabel("Rank") - ax[k].spines["top"].set_visible(False) - ax[k].spines["right"].set_visible(False) - plt.suptitle(f"{name if name else ''}_tuning_{error_space}") - plt.tight_layout() - if save_path is not None: - plt.savefig(f"{save_path}.pdf") - # plt.close() - else: - return fig, ax - - -def predict_and_stats(dmd, test_data, reseed): - pred, H_test_pred, H_test_true, V_test_pred, V_test_true = dmd.predict( - test_data, reseed=reseed, full_return=True - ) - if hasattr(pred, "cpu"): - pred = pred.cpu() - if hasattr(H_test_pred, "cpu"): - H_test_pred = H_test_pred.cpu() - if hasattr(H_test_true, "cpu"): - H_test_true = H_test_true.cpu() - if hasattr(V_test_pred, "cpu"): - V_test_pred = V_test_pred.cpu() - if hasattr(V_test_true, "cpu"): - V_test_true = V_test_true.cpu() - if isinstance(pred, list): - pred = np.concatenate(pred, axis=0) - test_data = np.concatenate(test_data, axis=0) - xstats = compute_all_stats(test_data, pred, dmd.rank) - hstats = compute_all_stats(H_test_true, H_test_pred, dmd.rank) - vstats = compute_all_stats(V_test_true, V_test_pred, dmd.rank) - return xstats, hstats, vstats - - -def sweep_ranks_delays_all_error_types( - data, - n_delays, - ranks, - train_frac=0.8, - reseeds=5, - return_type: Literal["tuple", "dict"] = "dict", - **dmd_kwargs, -): - """ - Sweep over combinations of DMD ranks and delays, returning all error types (AIC, MASE, MSE in X space, H space, V space, with and without reseeds) - Will also compute non-normality of the DMD matrix. - + + @abstractmethod + def _is_control_model_class(self, model_class) -> bool: + """Check if a model class supports control input. + + Args: + model_class: The model class to check. + + Returns: + True if the model class supports control input, False otherwise. + """ + pass + + @abstractmethod + def make_model(self, p1_val, p2_val): + pass + + @abstractmethod + def get_state_matrix(self, model) -> np.ndarray: + pass + + @abstractmethod + def get_rank(self, model) -> int: + pass + + @abstractmethod + def predict(self, model, test_data: np.ndarray, n_steps: int) -> np.ndarray: + pass + + def _is_valid_param_combo(self, p1_val, p2_val) -> bool: + return True + + def sweep(self) -> "BaseSweeper": + """Run the parameter sweep.""" + n1, n2 = len(self.param1_values), len(self.param2_values) + + self._aics = np.full((n1, n2), np.nan) + self._mases = np.full((n1, n2), np.nan) + self._mses = np.full((n1, n2), np.nan) + self._nnormals = np.full((n1, n2), np.nan) + self._residuals = np.full((n1, n2), np.nan) if self.compute_residuals else None + self._fitted_models = [[None for _ in range(n2)] for _ in range(n1)] + + for i, p1 in tqdm(enumerate(self.param1_values), total=n1, desc="Sweeping"): + for j, p2 in enumerate(self.param2_values): + if not self._is_valid_param_combo(p1, p2): + continue + # try: + model = self.make_model(p1, p2) + self._fitted_models[i][j] = model + + pred = self.predict(model, self.test_data, self.reseed) + pred_np = self._to_numpy(pred) + test_np = self._to_numpy(self.test_data) + pred_np, test_np = self._align_predictions(pred_np, test_np) + + rank = self.get_rank(model) + stats = compute_all_stats(test_np, pred_np, rank) + + self._aics[i, j] = stats["AIC"] + self._mases[i, j] = stats["MASE"] + self._mses[i, j] = stats["MSE"] + + A = self.get_state_matrix(model) + A_np = self._to_numpy(A) + self._nnormals[i, j] = measure_nonnormality_transpose(A_np) + + if self.compute_residuals: + try: + rc = ResidualComputer(model, self.test_data) + self._residuals[i, j] = rc.get_average_residual() + except Exception as e: + warnings.warn(f"Residual computation failed: {e}") + self._residuals[i, j] = np.nan + # except Exception as e: + # warnings.warn(f"Failed for {self.param1_name}={p1}, {self.param2_name}={p2}: {e}") + # continue + + self._swept = True + return self + + def _to_numpy(self, x) -> np.ndarray: + if hasattr(x, 'cpu'): + return x.cpu().detach().numpy() + if isinstance(x, list): + return np.concatenate([self._to_numpy(xi) for xi in x], axis=0) + return np.array(x) + + def _align_predictions(self, pred: np.ndarray, test: np.ndarray) -> Tuple[np.ndarray, np.ndarray]: + if pred.ndim == 3 and test.ndim == 3: + min_t = min(pred.shape[1], test.shape[1]) + return pred[:, :min_t], test[:, :min_t] + elif pred.ndim == 2 and test.ndim == 2: + min_t = min(pred.shape[0], test.shape[0]) + return pred[:min_t], test[:min_t] + return pred, test + + @property + def aics(self) -> np.ndarray: + self._check_swept() + return self._aics + + @property + def mases(self) -> np.ndarray: + self._check_swept() + return self._mases + + @property + def mses(self) -> np.ndarray: + self._check_swept() + return self._mses + + @property + def nnormals(self) -> np.ndarray: + self._check_swept() + return self._nnormals + + @property + def residuals(self) -> Optional[np.ndarray]: + self._check_swept() + return self._residuals + + @property + def fitted_models(self) -> List[List]: + self._check_swept() + return self._fitted_models + + def _check_swept(self): + if not self._swept: + raise RuntimeError("Must call sweep() before accessing results.") + + def get_results(self) -> Dict[str, Any]: + self._check_swept() + return { + "param1_name": self.param1_name, + "param1_values": self.param1_values, + "param2_name": self.param2_name, + "param2_values": self.param2_values, + "aics": self._aics, + "mases": self._mases, + "mses": self._mses, + "nnormals": self._nnormals, + "residuals": self._residuals, + "train_frac": self.train_frac, + "reseed": self.reseed, + } + + def plot( + self, + x_axis_param: str = None, + legend_param: str = None, + metrics: List[str] = None, + figsize: Tuple[float, float] = None, + cmap: str = "viridis", + save_path: str = None, + mase_scale: str = "log", + title: str = None, + ): + """Plot sweep results.""" + self._check_swept() + + if x_axis_param is None: + x_axis_param = self.param2_name + if legend_param is None: + legend_param = self.param1_name + + if x_axis_param == self.param2_name: + x_values = self.param2_values + legend_values = self.param1_values + transpose = False + else: + x_values = self.param1_values + legend_values = self.param2_values + transpose = True + + if metrics is None: + metrics = ["AIC", "MASE", "nnormal"] + if self._residuals is not None: + metrics.append("residual") + + metric_data = { + "AIC": self._aics, "MASE": self._mases, "MSE": self._mses, + "nnormal": self._nnormals, "residual": self._residuals, + } + metric_labels = { + "AIC": "AIC", "MASE": "MASE", "MSE": "MSE", + "nnormal": "Non-normality", "residual": "Avg. Residual", + } + + metrics = [m for m in metrics if metric_data.get(m) is not None] + n_metrics = len(metrics) + + if figsize is None: + figsize = (4 * n_metrics, 4) + + fig, axes = plt.subplots(1, n_metrics, figsize=figsize) + if n_metrics == 1: + axes = [axes] + + cmap_obj = plt.cm.get_cmap(cmap) + n_legend = len(legend_values) + + for ax_idx, metric in enumerate(metrics): + ax = axes[ax_idx] + data = metric_data[metric] + if transpose: + data = data.T + + for leg_idx, leg_val in enumerate(legend_values): + color = cmap_obj(leg_idx / (n_legend + 2)) + y_data = data[leg_idx, :] + ax.plot(x_values, y_data, label=f"{leg_val}", color=color, marker='o', markersize=3) + + if metric == "MASE": + ax.axhline(1, color="black", linestyle="--", linewidth=0.7) + if metric in ["MASE", "MSE"]: + ax.set_yscale(mase_scale) + + ax.set_xlabel(x_axis_param) + ax.set_ylabel(metric_labels[metric]) + ax.spines["top"].set_visible(False) + ax.spines["right"].set_visible(False) + + axes[-1].legend(title=legend_param, loc="upper right", bbox_to_anchor=(1.4, 1), fontsize=8) + + if title: + fig.suptitle(title, y=1.02) + plt.tight_layout() + if save_path: + plt.savefig(save_path, bbox_inches='tight') + return fig, axes + + +class DefaultSweeper(BaseSweeper): + """Sweeper for the local DSA DMD model classes (DMD, DMDc, SubspaceDMDc). + + This sweeper performs parameter sweeps over the local DMD implementations, + typically sweeping over `n_delays` and `rank` parameters. It supports both + autonomous systems (DMD) and controlled systems (DMDc, SubspaceDMDc). + Parameters ---------- data : np.ndarray - Input data (trials, time, features). - n_delays : iterable - List or array of delays to sweep. - ranks : iterable - List or array of ranks to sweep. - train_frac : float - Fraction of data to use for training. If greater than or equal to 1, tests on the training set - reseed : (int, list) - Reseeds for DMD prediction. - dmd_kwargs : dict - Additional keyword arguments for DMD. - - Returns - ------- - - Arrays of results for each (delay, rank) pair. - """ - train_data, test_data, dim = split_train_test(data, train_frac) - - if not isinstance(reseeds, list) and reseeds in set([1, "none", None, "", 0]): - reseeds = [1] - elif isinstance(reseeds, int): - reseeds = [1, reseeds] - if 1 not in reseeds: - reseeds = [1] + reseeds - - def init_arr(d=3): - if d == 3: - arr = np.zeros((len(reseeds), len(n_delays), len(ranks))) - elif d == 2: - arr = np.zeros((len(n_delays), len(ranks))) - arr[:] = np.nan - return arr - - all_aicsx_reseed, all_masesx_reseed, all_msesx_reseed = ( - init_arr(), - init_arr(), - init_arr(), - ) - all_aicsh_reseed, all_masesh_reseed, all_msesh_reseed = ( - init_arr(), - init_arr(), - init_arr(), - ) - all_aicsv_reseed, all_masesv_reseed, all_msesv_reseed = ( - init_arr(), - init_arr(), - init_arr(), - ) - - for i, nd in tqdm(enumerate(n_delays)): - for j, r in enumerate(ranks): - if r is None or r > nd * dim: - continue - dmd = DMD(train_data, n_delays=nd, rank=r, **dmd_kwargs) - dmd.fit() - for k, reseed in enumerate(reseeds): - xstats, hstats, vstats = predict_and_stats(dmd, test_data, reseed) - all_aicsx_reseed[k, i, j] = xstats["AIC"] - all_masesx_reseed[k, i, j] = xstats["MASE"] - all_msesx_reseed[k, i, j] = xstats["MSE"] - - all_aicsh_reseed[k, i, j] = hstats["AIC"] - all_masesh_reseed[k, i, j] = hstats["MASE"] - all_msesh_reseed[k, i, j] = hstats["MSE"] - - all_aicsv_reseed[k, i, j] = vstats["AIC"] - all_masesv_reseed[k, i, j] = vstats["MASE"] - all_msesv_reseed[k, i, j] = vstats["MSE"] - - if return_type == "tuple": - return ( - all_aicsx_reseed, - all_masesx_reseed, - all_msesx_reseed, - all_aicsh_reseed, - all_masesh_reseed, - all_msesh_reseed, - all_aicsv_reseed, - all_masesv_reseed, - all_msesv_reseed, - ) - elif return_type == "dict": - return { - "reseeds": reseeds, - "aicsx_reseed": all_aicsx_reseed, - "masesx_reseed": all_masesx_reseed, - "msesx_reseed": all_msesx_reseed, - "aicsh_reseed": all_aicsh_reseed, - "masesh_reseed": all_masesh_reseed, - "msesh_reseed": all_msesh_reseed, - "aicsv_reseed": all_aicsv_reseed, - "masesv_reseed": all_masesv_reseed, - "msesv_reseed": all_msesv_reseed, - "n_delays": n_delays, - "ranks": ranks, - } - - -def plot_sweep_results_all_error_types( - return_dict, - name=None, - save_path=None, - figsize=(2, 4), - xscale="log", - aic_scale="symlog", - mase_scale="log", - plot_herror=False, - new_plot_reseeds=False, - cmap="gist_gray", - metrics_order=["AIC", "MASE", "MSE"], - pretty_yticks=False, -): + Input data for the sweep. See `BaseSweeper` for details. + param1_name : str, optional + Name of the first parameter to sweep (default: "n_delays"). + param1_values : list or np.ndarray, optional + Values to sweep for the first parameter. + param2_name : str, optional + Name of the second parameter to sweep (default: "rank"). + param2_values : list or np.ndarray, optional + Values to sweep for the second parameter. + model_class : str, optional + Model class to use: "DMD", "DMDc", or "SubspaceDMDc" (default: "DMD"). + control_data : np.ndarray, optional + Control/actuation data. Required for "DMDc" and "SubspaceDMDc". + **kwargs + Additional arguments passed to `BaseSweeper` (train_frac, reseed, + compute_residuals) and to the model constructor. + + Examples + -------- + >>> sweeper = DefaultSweeper( + ... data=X, + ... param1_values=np.arange(1, 5), + ... param2_values=np.arange(1, 15), + ... model_class="DMD" + ... ) + >>> sweeper.sweep() + >>> print(sweeper.mases) + + >>> # With control input + >>> sweeper = DefaultSweeper( + ... data=X, + ... control_data=U, + ... param1_values=np.arange(1, 5), + ... param2_values=np.arange(1, 15), + ... model_class="DMDc" + ... ) + + See Also + -------- + BaseSweeper : Base class with full parameter documentation. + PyKoopmanSweeper : Sweeper for PyKoopman models. """ - Plot all error types from sweep_ranks_delays_all_error_types as a 3 x (3*len(reseeds)) grid, - or, if separate_by_space is True, make 3 separate plots (one for each of X, H, V), with columns as metrics and rows as reseeds. - + + # Model class names that support control input + CONTROL_MODEL_NAMES = ('DMDc', 'SubspaceDMDc') + + def __init__( + self, + data: np.ndarray, + param1_name: str = "n_delays", + param1_values: Union[List, np.ndarray] = None, + param2_name: str = "rank", + param2_values: Union[List, np.ndarray] = None, + model_class: str = "DMD", + control_data: np.ndarray = None, + **kwargs + ): + super().__init__(data=data, param1_name=param1_name, param1_values=param1_values, + param2_name=param2_name, param2_values=param2_values, + control_data=control_data, **kwargs) + self.model_class_name = model_class + + # Validate control data + if model_class in self.CONTROL_MODEL_NAMES and control_data is None: + raise ValueError(f"control_data required for {model_class}") + if control_data is not None and model_class not in self.CONTROL_MODEL_NAMES: + warnings.warn( + f"control_data was provided, but model class '{model_class}' " + f"may not support control input. Control data will be ignored.", + UserWarning + ) + + def _is_control_model_class(self, model_class) -> bool: + """Check if a model class supports control input.""" + if isinstance(model_class, str): + return model_class in self.CONTROL_MODEL_NAMES + return model_class.__name__ in self.CONTROL_MODEL_NAMES + + def _is_valid_param_combo(self, p1_val, p2_val) -> bool: + n_delays = p1_val if self.param1_name == "n_delays" else (p2_val if self.param2_name == "n_delays" else None) + rank = p1_val if self.param1_name == "rank" else (p2_val if self.param2_name == "rank" else None) + if n_delays is None or rank is None: + return True + return rank <= n_delays * self.dim + + def make_model(self, p1_val, p2_val): + kwargs = {self.param1_name: p1_val, self.param2_name: p2_val, **self.model_kwargs} + if self.model_class_name == 'DMD': + model = DMD(data=self.train_data, **kwargs) + elif self.model_class_name == 'DMDc': + model = DMDc(self.train_data, self.train_control, **kwargs) + elif self.model_class_name == 'SubspaceDMDc': + model = SubspaceDMDc(self.train_data, self.train_control, **kwargs) + else: + raise ValueError(f"Unknown model class: {self.model_class_name}") + model.fit() + return model + + def get_state_matrix(self, model) -> np.ndarray: + A = model.A_v + return A.cpu().detach().numpy() if hasattr(A, 'cpu') else A + + def get_rank(self, model) -> int: + return getattr(model, 'rank', getattr(model, 'rank_output', 1)) + + def predict(self, model, test_data: np.ndarray, n_steps: int) -> np.ndarray: + if self.model_class_name == 'DMD': + return model.predict(test_data, reseed=n_steps) + return model.predict(test_data, self.test_control, reseed=n_steps) + + +class PyKoopmanSweeper(BaseSweeper): + """Sweeper for PyKoopman models with dotted parameter names. + + This sweeper performs parameter sweeps over PyKoopman Koopman models, + allowing sweeps over both observable parameters (e.g., time delays) and + regressor parameters (e.g., SVD rank). Parameters are specified using + dotted notation: "component.parameter" where component is "observables" + (or "observable", "obs") or "regressor" (or "reg"). + Parameters ---------- - return_dict : dict - Output from sweep_ranks_delays_all_error_types. - name : str or None - Title for the plot. - save_path : str or None - If provided, save the figure to this path (as .pdf). - figsize : tuple - Figure size. - column_order : {'by_reseed', 'by_metric'} - If 'by_reseed', columns are [aics[0], mases[0], mses[0], aics[1], mases[1], mses[1], ...] (grouped by reseed). - If 'by_metric', columns are [aics[0], aics[1], ..., mases[0], mases[1], ..., mses[0], mses[1], ...] (grouped by metric). - plot_herror : bool - new_plot_reseeds : bool - If True, plot the reseeds in a new plot, with the same number of columns + data : np.ndarray + Input data for the sweep. See `BaseSweeper` for details. + param1_name : str, optional + Name of the first parameter in "component.param" format + (default: "observables.n_delays"). + param1_values : list or np.ndarray, optional + Values to sweep for the first parameter. + param2_name : str, optional + Name of the second parameter in "component.param" format + (default: "regressor.svd_rank"). + param2_values : list or np.ndarray, optional + Values to sweep for the second parameter. + base_observable_class : class, optional + Observable class to use (default: TimeDelay). + base_observable_kwargs : dict, optional + Additional kwargs for the observable constructor. + base_regressor_class : class, optional + Regressor class to use. If None and control_data is provided, + defaults to pk.regression.DMDc. Otherwise uses PyDMDRegressor. + base_regressor_kwargs : dict, optional + Additional kwargs for the regressor constructor. + extra_observables : list, optional + Additional observable objects to combine with the base observable. + control_data : np.ndarray, optional + Control/actuation data for controlled systems. + **kwargs + Additional arguments passed to `BaseSweeper` (train_frac, reseed, + compute_residuals). + + Examples + -------- + >>> # Sweep over time delays and SVD rank + >>> sweeper = PyKoopmanSweeper( + ... data=X, + ... param1_name="observable.n_delays", + ... param1_values=[1, 2, 3, 4], + ... param2_name="regressor.svd_rank", + ... param2_values=[5, 10, 15, 20], + ... ) + >>> sweeper.sweep() + >>> sweeper.plot() + + >>> # With control input + >>> import pykoopman as pk + >>> sweeper = PyKoopmanSweeper( + ... data=X, + ... control_data=U, + ... param1_name="observable.n_delays", + ... param1_values=[1, 2, 3], + ... param2_name="regressor.svd_rank", + ... param2_values=[5, 10], + ... base_regressor_class=pk.regression.DMDc, + ... ) + + See Also + -------- + BaseSweeper : Base class with full parameter documentation. + DefaultSweeper : Sweeper for local DMD models. + + Notes + ----- + The component names in param1_name/param2_name are flexible: + - For observables: "observable", "observables", or "obs" + - For regressor: "regressor" or "reg" """ - fig_axes = [] - if new_plot_reseeds: - return_dict_new = {} - return_dict_plot = {} - for k, v in return_dict.items(): - if (isinstance(v, np.ndarray) and v.size == 0) or ( - isinstance(v, list) and len(v) == 0 - ): - return_dict_new[k] = [] - return [] - elif k in ["n_delays", "ranks"]: - return_dict_new[k] = v - return_dict_plot[k] = v - else: - return_dict_new[k] = v[1:] - return_dict_plot[k] = v[0:1] - fig_axes = plot_sweep_results_all_error_types( - return_dict_new, - name=name, - save_path=save_path, - figsize=figsize, - xscale=xscale, - aic_scale=aic_scale, - plot_herror=plot_herror, - new_plot_reseeds=new_plot_reseeds, - metrics_order=metrics_order, - ) - return_dict = return_dict_plot - reseeds = return_dict["reseeds"] - n_delays = return_dict["n_delays"] - ranks = return_dict["ranks"] - all_aicsx_reseed = return_dict["aicsx_reseed"] - all_masesx_reseed = return_dict["masesx_reseed"] - all_msesx_reseed = return_dict["msesx_reseed"] - all_aicsh_reseed = return_dict["aicsh_reseed"] - all_masesh_reseed = return_dict["masesh_reseed"] - all_msesh_reseed = return_dict["msesh_reseed"] - all_aicsv_reseed = return_dict["aicsv_reseed"] - all_masesv_reseed = return_dict["masesv_reseed"] - all_msesv_reseed = return_dict["msesv_reseed"] - - n_reseeds = len(reseeds) - metrics = ["AIC", "MASE", "MSE"] - spaces = ["X", "V"] if not plot_herror else ["X", "H", "V"] - data_arrays = [ - [all_aicsx_reseed, all_aicsv_reseed] - + ([all_aicsh_reseed] if plot_herror else []), - [all_masesx_reseed, all_masesv_reseed] - + ([all_masesh_reseed] if plot_herror else []), - [all_msesx_reseed, all_msesv_reseed] - + ([all_msesh_reseed] if plot_herror else []), - ] - data_arrays = [data_arrays[metrics.index(metric)] for metric in metrics_order] - metrics = metrics_order - - cmap = plt.cm.get_cmap(cmap) if isinstance(cmap, str) else cmap - figs_axes = [] - for space_idx, space in enumerate(spaces): - # Each plot: rows = metrics, cols = reseeds (transpose from original) - fig, axes = plt.subplots( - len(metrics), - n_reseeds, - figsize=(figsize[0] * n_reseeds, figsize[1]), - sharex=True, - sharey="row", - ) - if len(reseeds) == 1: - if len(metrics) == 1: - axes = [axes] - else: - axes = axes.reshape(-1, 1) - if len(metrics) == 1: - axes = [axes] - - # For storing y-limits for each metric row - row_ymins = [np.inf] * len(metrics) - row_ymaxs = [-np.inf] * len(metrics) - for metric_idx, metric in enumerate(metrics): - for reseed_idx, reseed in enumerate(reseeds): - arr = data_arrays[metric_idx][space_idx] - ax = axes[metric_idx][reseed_idx] - for nd_idx, nd in enumerate(n_delays): - y = arr[reseed_idx, nd_idx] - ax.plot( - ranks, - y, - label=f"{nd}", - color=cmap(nd_idx / (len(n_delays) + 3)), - ) - # Update min/max for this row, ignoring nan - valid_y = np.asarray(y) - valid_y = valid_y[np.isfinite(valid_y)] - if valid_y.size > 0: - row_ymins[metric_idx] = min( - row_ymins[metric_idx], np.nanmin(valid_y) - ) - row_ymaxs[metric_idx] = max( - row_ymaxs[metric_idx], np.nanmax(valid_y) - ) - if metric == "MASE": - ax.axhline(1, color="black", linestyle="--", linewidth=0.7) - if metric in {"MASE", "MSE"} and mase_scale in { - "symlog", - "log", - "linear", - }: - ax.set_yscale(mase_scale) - if aic_scale in {"symlog", "log", "linear"} and metric == "AIC": - ax.set_yscale(aic_scale) - if xscale == "log": - ax.set_xscale("log") - if reseed_idx == 0: - ax.set_ylabel(f"{space} {metric}", fontsize=10) - else: - ax.set_ylabel("") - if metric_idx == 0: - ax.set_title(f"reseed {reseed}") - else: - ax.set_title("") - ax.spines["top"].set_visible(False) - ax.spines["right"].set_visible(False) - axes[-1][reseed_idx].set_xlabel("Rank") - if metric_idx == 0 and reseed_idx == len(reseeds) - 1: - ax.legend( - title="# delays", - fontsize=12, - loc="upper right", - bbox_to_anchor=(2.3, 1.2), - borderaxespad=1, - ) - - # Set yticks for each row to be the min and max (rounded) of all the plots on that row - for metric_idx in range(len(metrics)): - ymin = ( - 0.75 * row_ymins[metric_idx] - if row_ymins[metric_idx] > 0 - else 1.25 * row_ymins[metric_idx] - ) - ymax = ( - 1.25 * row_ymaxs[metric_idx] - if row_ymaxs[metric_idx] > 0 - else 0.75 * row_ymaxs[metric_idx] - ) - for reseed_idx in range(n_reseeds): - ax = axes[metric_idx][reseed_idx] - # Remove all yticks and labels before setting new ones - ax.set_yticks([]) - ax.set_yticklabels([]) - ax.yaxis.set_major_locator(plt.NullLocator()) - ax.yaxis.set_minor_locator(plt.NullLocator()) - if pretty_yticks: - for reseed_idx in range(n_reseeds): - ax = axes[metric_idx][reseed_idx] - # Only set exactly two yticks (min and max), and always set their labels - ax.set_ylim([ymin, ymax]) - ax.set_yticks([ymin, ymax]) - # Set tick labels to formatted numbers (scientific if needed) - ticklabels = [f"{ymin:.2g}", f"{ymax:.2g}"] - ax.set_yticklabels(ticklabels) - - plt.suptitle(f"{name + '_' if name else ''}{space} tuning", fontsize=14, y=1.05) - plt.tight_layout() # rect=[0, 0, 1, 0.97]) - if save_path is not None: - plt.savefig( - f"{save_path}_{space}_metrics_{metrics_order}_reseeds{reseeds}.pdf" - ) - # plt.close() - figs_axes.append((fig, axes)) - - return figs_axes + fig_axes + + def __init__( + self, + data: np.ndarray, + param1_name: str = "observables.n_delays", + param1_values: Union[List, np.ndarray] = None, + param2_name: str = "regressor.svd_rank", + param2_values: Union[List, np.ndarray] = None, + base_observable_class=None, + base_observable_kwargs: dict = None, + base_regressor_class=None, + base_regressor_kwargs: dict = None, + extra_observables: list = None, + control_data: np.ndarray = None, + **kwargs + ): + super().__init__(data=data, param1_name=param1_name, param1_values=param1_values, + param2_name=param2_name, param2_values=param2_values, + control_data=control_data, **kwargs) + + self.base_observable_class = base_observable_class or TimeDelay + self.base_observable_kwargs = base_observable_kwargs or {} + self.base_regressor_class = base_regressor_class + self.base_regressor_kwargs = base_regressor_kwargs or {} + self.extra_observables = extra_observables or [] + + # Warn if the regressor class doesn't support control input + if control_data is not None and base_regressor_class is not None: + if not self._is_control_model_class(base_regressor_class): + warnings.warn( + f"control_data was provided, but base_regressor_class ({base_regressor_class.__name__}) " + f"may not support control input. Consider using a DMDc-type regressor (e.g., pk.regression.DMDc, " + f"pk.regression.EDMDc, or pydmd.DMDc). Control data may be ignored.", + UserWarning + ) + + self._param1_component, self._param1_attr = param1_name.split('.') + self._param2_component, self._param2_attr = param2_name.split('.') + + def _is_control_model_class(self, model_class) -> bool: + """Check if a model/regressor class supports control input. + + Args: + model_class: The model class to check. + + Returns: + True if the model class supports control input, False otherwise. + """ + # Simple check: class name ends with 'DMDc' (covers DMDc, EDMDc, etc.) + return model_class.__name__.endswith('DMDc') + + @staticmethod + def _is_observable_component(component: str) -> bool: + """Check if a component name refers to observables.""" + return component in ("observable", "observables", "obs") + + @staticmethod + def _is_regressor_component(component: str) -> bool: + """Check if a component name refers to regressor.""" + return component in ("regressor", "reg") + + def _build_observable(self, p1_val, p2_val): + kwargs = dict(self.base_observable_kwargs) + if self._is_observable_component(self._param1_component): + kwargs[self._param1_attr] = p1_val + if self._is_observable_component(self._param2_component): + kwargs[self._param2_attr] = p2_val + obs = self.base_observable_class(**kwargs) + for extra in self.extra_observables: + obs = obs + extra + return obs + + def _build_regressor(self, p1_val, p2_val): + kwargs = dict(self.base_regressor_kwargs) + cast_to_int = lambda val: int(val) if isinstance(val, np.integer) else val + if self._is_regressor_component(self._param1_component): + kwargs[self._param1_attr] = cast_to_int(p1_val) + if self._is_regressor_component(self._param2_component): + kwargs[self._param2_attr] = cast_to_int(p2_val) + if self.base_regressor_class: + # Return the regressor instance directly - pk.Koopman handles pydmd regressors + return self.base_regressor_class(**kwargs) + # Default regressor: DMDc if control data is provided, else pydmd.DMD + if self.control_data is not None: + return pk.regression.DMDc(**kwargs) + from pydmd import DMD as PyDMD_DMD + svd_rank = int(kwargs.pop('svd_rank', -1)) + return PyDMD_DMD(svd_rank=svd_rank, **kwargs) + + def make_model(self, p1_val, p2_val): + obs = self._build_observable(p1_val, p2_val) + reg = self._build_regressor(p1_val, p2_val) + model = pk.Koopman(observables=obs, regressor=reg) + train = self.train_data + if isinstance(train, torch.Tensor): + train = train.cpu().numpy() + if train.ndim == 3: + train = train.reshape(-1, train.shape[-1]) + + # Prepare control data for fitting if provided + train_u = None + if self.train_control is not None: + train_u = self.train_control + if isinstance(train_u, torch.Tensor): + train_u = train_u.cpu().numpy() + if train_u.ndim == 3: + train_u = train_u.reshape(-1, train_u.shape[-1]) + + model.fit(train, u=train_u) + return model + + def get_state_matrix(self, model) -> np.ndarray: + """Get the reduced-order state transition matrix. + + Returns the state matrix A from the regressor, which should be + of shape (svd_rank, svd_rank) when SVD truncation is applied. + """ + A = model.A + if hasattr(A, 'cpu'): + A = A.cpu().detach().numpy() + return np.asarray(A) + + def get_rank(self, model) -> int: + reg = model._regressor() + if hasattr(reg, 'svd_rank') and reg.svd_rank > 0: + return reg.svd_rank + return model.A.shape[0] + + def predict(self, model, test_data: np.ndarray, reseed: int) -> np.ndarray: + """Generate one-step predictions using model.predict. + + Uses PyKoopman's predict method which does one-step-ahead prediction. + The predictions are offset by n_consumed samples due to time delays. + + Args: + model: Fitted PyKoopman model. + test_data: Test data to predict on. + reseed: Not used currently (kept for API compatibility). + + Returns: + Predictions aligned with test_data. + """ + test_np = test_data.cpu().numpy() if isinstance(test_data, torch.Tensor) else test_data + + # Prepare test control data if available + test_u = None + if self.test_control is not None: + test_u = self.test_control + if isinstance(test_u, torch.Tensor): + test_u = test_u.cpu().numpy() + + if test_np.ndim == 3: + if test_u is not None and test_u.ndim == 3: + return np.stack([self._predict_single(model, t, u=test_u[i]) + for i, t in enumerate(test_np)]) + return np.stack([self._predict_single(model, t, u=test_u) for t in test_np]) + return self._predict_single(model, test_np, u=test_u) + + def _predict_single(self, model, data: np.ndarray, u: np.ndarray = None) -> np.ndarray: + """One-step predictions for a single trajectory. + + Args: + model: Fitted PyKoopman model. + data: 2D array of shape (time, features). + u: Optional control data. + + Returns: + Predictions array of same shape as data. + """ + n_consumed = getattr(model.observables, 'n_consumed_samples', 0) + n_samples = data.shape[0] + + # Need at least n_consumed + 1 samples to predict + if n_samples <= n_consumed + 1: + return data.copy() + + # Use model.predict for one-step-ahead predictions + # Input: data[:-1], Output: predictions for data[1:] + # But with time delays, we lose n_consumed samples + try: + # Predict uses all but the last sample to predict the next + x_input = data[:-1] # Shape: (n_samples - 1, n_features) + u_input = u[:-1] if u is not None else None + + pred = model.predict(x_input, u=u_input) + + # pred has shape (n_samples - 1 - n_consumed, n_features) + # We need to align this with the original data + predictions = np.zeros_like(data) + predictions[:n_consumed + 1] = data[:n_consumed + 1] + predictions[n_consumed + 1:] = pred + + return predictions + except Exception as e: + warnings.warn(f"Prediction failed: {e}") + return data.copy() + + +def sweep_local_dmd(data, n_delays_values, rank_values, **kwargs) -> DefaultSweeper: + """Convenience function to create and run a DefaultSweeper.""" + sweeper = DefaultSweeper(data=data, param1_values=n_delays_values, param2_values=rank_values, **kwargs) + sweeper.sweep() + return sweeper + + +def sweep_pykoopman(data, param1_name, param1_values, param2_name, param2_values, **kwargs) -> PyKoopmanSweeper: + """Convenience function to create and run a PyKoopmanSweeper.""" + sweeper = PyKoopmanSweeper(data=data, param1_name=param1_name, param1_values=param1_values, + param2_name=param2_name, param2_values=param2_values, **kwargs) + sweeper.sweep() + return sweeper + + +def sweep_ranks_delays(data, n_delays, ranks, control_data=None, train_frac=0.8, reseed=5, + return_residuals=True, model_class='DMD', **model_kwargs): + """Backward-compatible wrapper around DefaultDMDSweeper.""" + sweeper = DefaultSweeper(data=data, param1_values=n_delays, param2_values=ranks, + model_class=model_class, control_data=control_data, + train_frac=train_frac, reseed=reseed, + compute_residuals=return_residuals, **model_kwargs) + sweeper.sweep() + result = (sweeper.aics, sweeper.mases, sweeper.nnormals) + if return_residuals: + result += (sweeper.residuals if sweeper.residuals is not None else np.full_like(sweeper.aics, np.nan),) + return result diff --git a/examples/sweep_resdmd_tutorial.ipynb b/examples/sweep_resdmd_tutorial.ipynb new file mode 100644 index 0000000..77b49b5 --- /dev/null +++ b/examples/sweep_resdmd_tutorial.ipynb @@ -0,0 +1,521 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "fa008edd", + "metadata": {}, + "source": [ + "## Tutorial On Sweeping and ResDMD\n", + "This tutorial will demonstrate the basic interfaces of hyperparameter sweeping and residual dmd, which can be utilized on any dmd model, for a wide range of hyperparameters. These have been fully reimplemented due to the increasing generalization of the DSA package, so like other tutorials in this folder, the goal of this notebook is to demonstrate how the interfaces are designed to be intuitive across many different DSA / DMD instances. \n", + "\n", + "There are 2 high level settings: using a Pykoopman / Pydmd model, or a local model. Then, there can be models that take in control versus not. Finally, the default setting is to sweep ranks and delays (the rank of the principal components regression and the number of delays in the delay embedding, respectively.). But there are other hyperparameters that you can sweep as well. \n", + "\n", + "Let's start with the sweeper class!\n", + "\n", + "### Sweeper class\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "c0c1d638", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import sys\n", + "sys.path.append('../')\n", + "\n", + "np.random.seed(42)\n", + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "e8d66584", + "metadata": {}, + "outputs": [], + "source": [ + "# System dimensions\n", + "n_state = 15 # state dimension\n", + "n_control = 3 # control dimension\n", + "n_timesteps = 100 # number of time steps\n", + "n_trials = 5 # number of trials\n", + "\n", + "# Sample random matrix for A and normalize to have max eigenvalue of 0.95\n", + "A_random = np.random.randn(n_state, n_state)\n", + "eigs_A = np.linalg.eigvals(A_random)\n", + "max_eig = np.max(np.abs(eigs_A))\n", + "A_true = A_random / max_eig * 0.999\n", + "\n", + "# Sample random matrix for B\n", + "B_true = np.random.randn(n_state, n_control)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "e1552288", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Generated data shape:\n", + " State data X: (5, 100, 7)\n", + " Control data U: (5, 100, 3)\n" + ] + } + ], + "source": [ + "def generate_trajectory(A, B, n_timesteps, n_trials, noise_std=0.01,use_control=False,partial_observ_frac=1.0):\n", + " \"\"\"\n", + " Generate data from a controlled linear dynamical system.\n", + " \n", + " Parameters:\n", + " -----------\n", + " A : ndarray (n_state, n_state)\n", + " State transition matrix\n", + " B : ndarray (n_state, n_control)\n", + " Control input matrix\n", + " n_timesteps : int\n", + " Number of time steps per trial\n", + " n_trials : int\n", + " Number of trials\n", + " noise_std : float\n", + " Standard deviation of process noise\n", + " use_control : bool \n", + " Whether or not the control input has any effect\n", + " partial_observ_frac : float\n", + " if < 1, does a partial observation legend on the states with % partial_observ_frac still observed\n", + " will bottom out at 1\n", + " \n", + " Returns:\n", + " --------\n", + " X : ndarray (n_trials, n_timesteps, n_state)\n", + " State trajectories\n", + " U : ndarray (n_trials, n_timesteps, n_control)\n", + " Control inputs\n", + " \"\"\"\n", + " n_state = A.shape[0]\n", + " n_control = B.shape[1]\n", + " \n", + " X = np.zeros((n_trials, n_timesteps, n_state))\n", + " U = np.zeros((n_trials, n_timesteps, n_control))\n", + " \n", + " for trial in range(n_trials):\n", + " # Initialize with random state\n", + " X[trial, 0] = np.random.randn(n_state) * 0.1\n", + " \n", + " # Generate control inputs (random walk with some structure)\n", + " U[trial] = np.cumsum(np.random.randn(n_timesteps, n_control) * 0.1, axis=0)\n", + " \n", + " # Simulate system\n", + " for t in range(n_timesteps - 1):\n", + " # x_{t+1} = A x_t + B u_t + noise\n", + " drive = A @ X[trial, t] + np.random.randn(n_state) * noise_std\n", + " if use_control:\n", + " drive += B @ U[trial, t]\n", + " X[trial, t + 1] = drive\n", + " if partial_observ_frac < 1:\n", + " n_dims = max(1,int(X.shape[-1]*partial_observ_frac))\n", + " X = X[:,:,:n_dims]\n", + " return X, U\n", + "\n", + "# Generate training data\n", + "X_train, U_train = generate_trajectory(A_true, B_true, n_timesteps, n_trials,noise_std=0.0,partial_observ_frac=0.5)\n", + "\n", + "print(f\"Generated data shape:\")\n", + "print(f\" State data X: {X_train.shape}\")\n", + "print(f\" Control data U: {U_train.shape}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "a0b65b61", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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agK+++ooTTzyRAQMGcPfdd1NXV8dTTz3FEUccwfLly1sclJ999tkMHjyYBx98sM0DiAceeIA77riDc845h8suu4yioiKeeuoppkyZwooVK0hLS8PhcDB9+nQaGhq49tprycnJYc+ePXzyySeUl5eTmpra6vZ/+uknMjIyPO+xYLhcLqZPn86RRx7Jo48+SkJCQqvr/v3vf+fUU0/lj3/8Iw6Hg3//+9+cffbZfPLJJ8yYMSPox7bam2++yYsvvsiSJUt4+eWXgdbjH0xXX301WVlZ3HnnndTU1ADGKVjvv/8+c+bMYfjw4ZSUlPDDDz+wbt06xo0bx+23305FRQW7d+/miSeeADoWC5Kfnw/gE1exYsUKEhMTOeigg3zWnThxouf2I488kj179lBYWMghhxzSYrsTJ05sd7KGL7/8kvPOO49jjjmGhx9+GDA+PPz4449cf/31TJkyheuuu47/+7//47bbbvOMx7x88803mT17NtOnT+fhhx+mtraW5557jiOPPJIVK1b4/KwE8vu2vfF4Gz9+vOWZgEIIsT/53//+x4ABA9r9G2i67LLLeOONN5g5cyZ//vOf+eWXX3jooYdYt24dH330kc+6mzdvZubMmVx66aXMnj2bV199lYsuuojx48czYsSIdv9+gBFZdN5553HllVdy+eWXM3ToUAoKCjj88MOpra3luuuuIyMjgzfeeINTTz2V999/31PossI555xD//79eeihh1i+fDkvv/wy2dnZnr8/pm+//ZZ33nmH6667jtjYWJ599llOOOEElixZ0u7nmubefPPNFrFiZkRBe8cdrfnpp58A2lynLf6Ogfx5/fXXSUpKYu7cuSQlJbFw4ULuvPNOKisrmTdvXoce20q33347Q4cO5cUXX+Tee++lf//+rcY/mPx9VnnppZe47rrrmDlzJtdffz319fX89ttv/PLLL/zhD39o9zNaoFo7/gNaHNf16NGDXr16+TQhrVixgnHjxrVofJg4cSIvvvgiGzduZNSoUX4fu7CwkOOPP56srCxuueUW0tLS2L59Ox9++KHnubT1WWnNmjUcccQR9OzZk1tuuYXExETeffddTj/9dD744IMWP6dz5swhLS2Nu+++mw0bNvDcc8+xY8cOT+Z0e+PxNn78eJ544gnWrFkT9M+fEJYIaZ+vEF3kiy++UHa7XdntdjVp0iR10003qQULFiiHw9FiXX+nRk2fPl0NGDDAZ1nfvn0VoH766SfPsgULFiigxanCL7zwQovTs2bPnq0Ade2113qW6bquZsyYoWJiYlRRUZFnOc1ODbn00ktVbm6uT5SBUkrNmjVLpaamtnl6l67raurUqQpQ3bt3V+edd5565plnfMZrauvUm0Bfp9ZON7/vvvtUYmKi2rhxo8/yW265RdntdrVz585Wn0NzzU/zMk9ZS0lJUYWFhT7rulyuFqcelZWVqe7du6tLLrnEZ3lHX/dXX31VAerxxx9vMVZd15VSbZ/yM3bsWJWdna1KSko8y1atWqVsNpu68MILPcvuuusuBfg9Bcm8zbR9+3Zlt9tbxGL8/vvvKioqyrN8xYoVHT6N78gjj1Tjx49vcx1/7ynzZ+GWW25psb6/U9yav/ccDocaOXKkOvroo32WBxqP4M2qeITZs2f7jUdoPqbXXntNAerII4/0iRxQSqnU1FSfeAJ/golH8KehoUENHz5c9e/f3+c0uBkzZrT4WVZKqZqaGp99ZZ7u+I9//KPFujfeeKMCVH19fauPf/3116uUlJQWz91ba6e3VlVVqbS0NHX55Zf7LM/Pz1epqak+ywP9fRvIeEwPPvigAiIiYkcIIfa1iooKBajTTjstoPVXrlypAHXZZZf5LP/LX/6iALVw4ULPMvMY/LvvvvMsKywsVLGxserPf/6zZ1lb8QjmNubPn++z/IYbblCA+v777z3LqqqqVP/+/VW/fv08cUqdiUcwj9GaH3eeccYZKiMjw2cZoAD166+/epbt2LFDxcXFqTPOOMOzrLVIgObHg0q1Ho8QyHGHP3/9618VoKqqqlpdx188QlvHQOZt3seL/j57XHnllSohIcHnWCPQeARvVsUjmONuHo/QfExtfVY57bTT1IgRI9p8HCsi7C699FJlt9t9PouZ2/X3OWzChAnqsMMO8/w7MTGxxXtYKaU+/fRTvz9b3j766KN2YyTa+qx0zDHHqFGjRvnsd13X1eGHH64GDx7sWWbuj/Hjx/t87n/kkUcUoD7++OOAx2P66aefFKDeeeeddtcVoitIPILYLx133HEsXryYU089lVWrVvHII48wffp0evbs2eI0Cu/TZCoqKiguLmbq1Kls3bq1xenhw4cPZ9KkSZ5/H3rooQAcffTRPqcKm8u3bt3aYmxz5szxXDc7OB0OB1999ZXf56KU4oMPPuCUU05BKUVxcbHnv+nTp1NRUdHmaUyaprFgwQLuv/9+unXrxttvv80111xD3759OffccwOehTSY18mf9957j8mTJ9OtWzef53DsscfidrtbnPbVEWeddVaLb53tdrtnYiRd1yktLcXlcnHIIYe0+boF87p/8MEHZGZm+p3grr2ZafPy8li5ciUXXXQR6enpnuWjR4/muOOO89u5+Kc//anNbYIR16HrOuecc47P2HNychg8eLAnHsLspF2wYEGLU/TaU1JSQrdu3YK6j7errroqoPW833tlZWVUVFQwefJky2arDYXLL78cu93usywtLY1ffvmFvXv3dtnjzpkzh7Vr1/L000/7dN3X1dX5nfwjLi7Oc7v3ZSDr+pOWlkZNTQ1ffvll0GP/8ssvKS8v57zzzvN5T9vtdg499FC/kSft/b4NZjzme724uDjosQshxP6usrISIODJGs3jm7lz5/os//Of/wzQIp5g+PDhPhNaZWVlMXToUL/H2q3p378/06dPbzGOiRMneuKmwDiL5YorrmD79u2e09et0Pz4bfLkyZSUlHheO9OkSZMYP3685999+vThtNNOY8GCBR2OBvCno8cdJSUlREVFdXgSYH/HQP54H/9VVVVRXFzM5MmTqa2tZf369R167FDz91klLS2N3bt3s3Tp0i573LfeeotXXnmFP//5zz4ThrV3XOd9TBfosaI/ZmThJ598gtPpDGrspaWlLFy4kHPOOcfzPiguLqakpITp06ezadMm9uzZ43OfK664gujoaM+/r7rqKqKiojy/d4IZjxz/iVCToq3Yb02YMIEPP/yQsrIylixZwq233kpVVRUzZ870OQD78ccfOfbYYz1ZollZWdx2220ALYqR3oVZaCp49e7d2+/y5tk5NpuNAQMG+CwbMmQIQKu5REVFRZSXl/Piiy+SlZXl89/FF18MGKectCU2Npbbb7+ddevWsXfvXt5++20OO+ww3n33XZ+iRluCeZ382bRpE/Pnz2/xHMw8rPaeQyD69+/vd/kbb7zB6NGjiYuLIyMjg6ysLD799NM2xx3M675lyxaGDh3aavREW3bs2AHA0KFDW9x20EEHUVxc3OLUsdaep7dNmzahlGLw4MEtxr9u3TrP2Pv378/cuXN5+eWXyczMZPr06TzzzDMB7VOgw/lOUVFRnhyz9nzyySccdthhxMXFkZ6e7jmFKtAxhiN/+/CRRx5h9erV9O7dm4kTJ3L33XcH9WG0PfPmzeOll17ivvvu46STTvK5LT4+3m8WWX19ved278tA1vXn6quvZsiQIZx44on06tWLSy65hPnz5wc0/k2bNgHGl2TN39NffPFFi98hgfy+DWY85nu9vS9ihBDiQJSSkgIYxbVA7NixA5vNxqBBg3yW5+TkkJaW5jk+MjU/BgejmNL8WLst/v727tixo9VjMPN2qzR/DmYxqPlz8C6qmYYMGUJtbS1FRUWWjaerjztaE8hxLBinxJ9xxhmkpqaSkpJCVlYW559/PhDYZ49w5O+533zzzSQlJTFx4kQGDx7MNddcw48//mjZY37//fdceumlTJ8+nQceeMDntvaO67yP6QI9VvRn6tSpnHXWWdxzzz1kZmZy2mmn8dprr7WbgwtGNIpSijvuuKPF8d9dd90FtPwc2fxnKCkpidzcXM/xXzDjkeM/EWqSaSv2ezExMUyYMIEJEyYwZMgQLr74Yt577z3uuusutmzZwjHHHMOwYcN4/PHH6d27NzExMXz22Wc88cQTLSbIau1b4daWd7Sg5c0cw/nnn+8Ju2/OzPsJRG5uLrNmzeKss85ixIgRvPvuu7z++uttFhyDfZ1aex7HHXccN910k9/bzWJKZ/g7WPjnP//JRRddxOmnn86NN95IdnY2drudhx56yDNJW2vjBetedysFMomCrutomsbnn3/u9/3p3R3x2GOPcdFFF/Hxxx/zxRdfcN111/HQQw/x888/t1lYzcjICOrDkrfY2NiAJgP7/vvvOfXUU5kyZQrPPvssubm5REdH89prr3kmaIhE/vbhOeecw+TJk/noo4/44osvmDdvHg8//DAffvghJ554Yqce7/XXX+fmm2/mT3/6E3/9619b3J6bm8s333yDUsrnoDQvLw8wss3M9byXe8vLyyM9Pd1vF4YpOzublStXsmDBAj7//HM+//xzXnvtNS688ELeeOONNp+D+TP55ptvkpOT0+L2jnxpEsx4zPe6dxacEEIIQ0pKCj169GD16tVB3S/QQogVx9pWTkLVEVZ+XmjtdQumE7ejxx0ZGRm4XC6qqqoC7qz2Fsh+KC8vZ+rUqaSkpHDvvfcycOBA4uLiWL58OTfffHOnJzEOFX/P/aCDDmLDhg188sknzJ8/nw8++IBnn32WO++8k3vuuadTj7dq1SpOPfVURo4cyfvvv9/iWMn7uK55E1JeXp5nbgNz3daO/6DpWNEfTdN4//33+fnnn/nf//7HggULuOSSS3jsscf4+eef2+zaNvf1X/7ylxad8qbmX/60J5jxyPGfCDUp2ooDihmybv5x+d///kdDQwP//e9/fb799nearRV0XWfr1q0+BcqNGzcCtDoLfFZWFsnJybjd7qBnaW1LdHQ0o0ePZtOmTZ7T5ls7AAzmdWptGwMHDqS6utrS5xCI999/nwEDBvDhhx/6jM38ZrY1wbzuAwcO5JdffsHpdPqciuOttdfFnMRrw4YNLW5bv349mZmZJCYmtvn4rY1JKUX//v0DKoiPGjWKUaNG8de//pWffvqJI444gueff57777+/1fsMGzaMDz74IOixBeODDz4gLi6OBQsW+BQDX3vttS593FDJzc3l6quv5uqrr6awsJBx48bxwAMPeD48deRb/o8//pjLLruMM888k2eeecbvOmPHjuXll19m3bp1DB8+3LP8l19+8dwO0LNnT7Kysvj1119bbKO9if1MMTExnHLKKZxyyinous7VV1/NCy+8wB133MGgQYPa/B0CRqE1kN8jgf6+bW88pm3btpGZmRn0xB9CCHGgOPnkk3nxxRdZvHixT5yYP3379kXXdTZt2uQzWVhBQQHl5eUdmuS0I38j+/bt2+oxmHn7vmaeWeJt48aNJCQkeP4GdevWzW/Emb/O4LZel/aOO/wZNmwYYPxd7KoGhkWLFlFSUsKHH37IlClTPMu3bdvWJY8XaomJiZx77rmce+65OBwOzjzzTB544AFuvfVW4uLiOvTe3rJlCyeccALZ2dl89tlnfguj5nHbr7/+6lOg3bt3L7t37/ZMXmeu+/3336Pruk/jxS+//EJCQkJAnzcOO+wwDjvsMB544AHeeust/vjHP/Lvf/+byy67rNXnaJ41FR0dHfDnyE2bNnHUUUd5/l1dXU1eXl6LM83aGo/JfM81n6xXiH1F4hHEfsnsGGvOzLExT4Myv/H2XreioqJLC0JPP/2057pSiqeffpro6GiOOeYYv+vb7XbOOussPvjgA7/dC+2dJrVp0yZ27tzZYnl5eTmLFy+mW7dungNAszjY/CAwmNcpMTHR70HkOeecw+LFi1mwYIHfsbhcrjafR0f5G/svv/zC4sWL271foK/7WWedRXFxsc++NZmPm5CQALR8bXNzcxk7dixvvPGGz22rV6/miy++aHFwEagzzzwTu93OPffc0+JnQSlFSUkJYGTQNX/tR40ahc1ma/eUpUmTJlFWVtalp9LZ7XY0TfPpHNm+fTv/+c9/uuwxQ8Htdrc41S87O5sePXr47IfExMSgTgn87rvvmDVrFlOmTOFf//pXq93Np512GtHR0Tz77LOeZUopnn/+eXr27OkzE/hZZ53FJ598wq5duzzLvv76azZu3MjZZ5/d5njM953JZrN5PvCZz7O130PTp08nJSWFBx980G/+mL/fhe39vg1kPKZly5a1W4QQQogD2U033URiYiKXXXYZBQUFLW7fsmULf//73wE8xzdPPvmkzzqPP/44ADNmzAj68Vv7+9GWk046iSVLlvgcF9bU1PDiiy/Sr18/ny8y95XFixf75Pbv2rWLjz/+mOOPP95zXDtw4EAqKir47bffPOvl5eXx0Ucftdiev2PzQI87/DH/Fvr7Atcq/o7fHQ6Hz3HK/qL5sUhMTAzDhw9HKeU53gn2vZ2fn8/xxx+PzWZjwYIFrX7hPGLECIYNG8aLL77oc6z93HPPoWkaM2fO9CybOXMmBQUFfPjhh55lxcXFvPfee5xyyiltnmlVVlbW4vOIWTA232+tfVbKzs5m2rRpvPDCC347ff0d/7344os+x4rPPfccLpfL82VEIOMxLVu2jNTUVEaMGNHq8xOiK0mnrdgvXXvttdTW1nLGGWcwbNgwHA4HP/30E++88w79+vXzZJIef/zxni6rK6+8kurqal566SWys7P9/lHorLi4OObPn8/s2bM59NBD+fzzz/n000+57bbb2uze+tvf/sY333zDoYceyuWXX87w4cMpLS1l+fLlfPXVV5SWlrZ631WrVvGHP/yBE088kcmTJ5Oens6ePXt444032Lt3L08++aTnwMic9OD2229n1qxZREdHc8oppwT1Oo0fP57nnnuO+++/n0GDBpGdnc3RRx/NjTfeyH//+19OPvlkLrroIsaPH09NTQ2///4777//Ptu3b++S005OPvlkPvzwQ8444wxmzJjBtm3beP755xk+fDjV1dVt3jfQ1/3CCy/kH//4B3PnzmXJkiVMnjyZmpoavvrqK66++mpOO+004uPjGT58OO+88w5DhgwhPT2dkSNHMnLkSObNm8eJJ57IpEmTuPTSS6mrq+Opp54iNTWVu+++u0PPe+DAgdx///3ceuutbN++ndNPP53k5GS2bdvGRx99xBVXXMFf/vIXFi5cyJw5czj77LMZMmQILpeLN99801O0bsuMGTOIioriq6++8vkm3kozZszg8ccf54QTTuAPf/gDhYWFPPPMMwwaNMjng0owvvvuO8/Ed0VFRdTU1Hg6iqdMmeLT0aFpGlOnTmXRokWdfi5tqaqqolevXsycOZMxY8aQlJTEV199xdKlS3nsscc8640fP5533nmHuXPnMmHCBJKSkjjllFP8bnPHjh2ceuqpnoPu9957z+f20aNHewqUvXr14oYbbmDevHk4nU4mTJjAf/7zH77//nv+9a9/+ZzSedttt/Hee+9x1FFHcf3111NdXc28efMYNWqU53dray677DJKS0s5+uij6dWrFzt27OCpp55i7Nixng6GsWPHYrfbefjhh6moqCA2Npajjz6a7OxsnnvuOS644ALGjRvHrFmzyMrKYufOnXz66accccQRPkXaQH7fBjIeMLLSfvvtN6655ppAdqcQQhyQBg4cyFtvvcW5557LQQcdxIUXXsjIkSM9x+HvvfceF110EQBjxoxh9uzZvPjii55T4ZcsWcIbb7zB6aef7tMlF6i2/n605pZbbuHtt9/mxBNP5LrrriM9PZ033niDbdu28cEHHwQU5WS1kSNHMn36dK677jpiY2M9hUrvU+VnzZrFzTffzBlnnMF1111HbW0tzz33HEOGDGkxUev48eP56quvePzxx+nRowf9+/dn6NChAR13+DNgwABGjhzJV199xSWXXGL9CwAcfvjhdOvWjdmzZ3PdddehaRpvvvlmp6LnduzYwZtvvgk0FZzN47++fftywQUXeNadNm0a3377rSVRd+05/vjjycnJ4YgjjqB79+6sW7eOp59+mhkzZnjiJ1r7jNba2XgnnHACW7du5aabbuKHH37ghx9+8NzWvXt3jjvuOM+/582bx6mnnsrxxx/PrFmzWL16NU8//TSXXXaZz7HQzJkzOeyww7j44otZu3YtmZmZPPvss7jd7nZjHN544w2effZZzjjjDAYOHEhVVRUvvfQSKSkpni9w2vqs9Mwzz3DkkUcyatQoLr/8cgYMGEBBQQGLFy9m9+7drFq1yufxHA4HxxxzDOeccw4bNmzg2Wef5cgjj+TUU08NeDymL7/8klNOOUUybUXoKCH2Q59//rm65JJL1LBhw1RSUpKKiYlRgwYNUtdee60qKCjwWfe///2vGj16tIqLi1P9+vVTDz/8sHr11VcVoLZt2+ZZr2/fvmrGjBktHgtQ11xzjc+ybdu2KUDNmzfPs2z27NkqMTFRbdmyRR1//PEqISFBde/eXd11113K7Xa32OZdd93ls6ygoEBdc801qnfv3io6Olrl5OSoY445Rr344ottvhYFBQXqb3/7m5o6darKzc1VUVFRqlu3buroo49W77//fov177vvPtWzZ09ls9l8XoNAX6f8/Hw1Y8YMlZycrAA1depUz21VVVXq1ltvVYMGDVIxMTEqMzNTHX744erRRx9VDoejzefhLTExUc2ePdvzb3+vt0nXdfXggw+qvn37qtjYWHXwwQerTz75RM2ePVv17dvXZ93OvO61tbXq9ttvV/379/esN3PmTLVlyxbPOj/99JMaP368iomJafFYX331lTriiCNUfHy8SklJUaeccopau3atz2PcddddClBFRUUtnqd5W3MffPCBOvLII1ViYqJKTExUw4YNU9dcc43asGGDUkqprVu3qksuuUQNHDhQxcXFqfT0dHXUUUepr776qsW2/Dn11FPVMccc0+rt8+bNa/EeMX8W/PG3X1555RU1ePBgFRsbq4YNG6Zee+01v8+3b9++Pu+L1pj39fef9z6pqqpSgJo1a1a722ztOTUf02uvvaYAtXTpUp/1Ghoa1I033qjGjBmjkpOTVWJiohozZox69tlnfdarrq5Wf/jDH1RaWpoCWrxW3r755ptWn6e/97rb7fb8rMTExKgRI0aof/7zn363vXr1as/vsbS0NPXHP/5R5efnt/0iKaXef/99dfzxx6vs7GwVExOj+vTpo6688kqVl5fns95LL72kBgwYoOx2uwLUN9984/O8pk+frlJTU1VcXJwaOHCguuiii9Svv/7qWSfQ37eBjue5555TCQkJqrKyst3nKIQQB7qNGzeqyy+/XPXr10/FxMSo5ORkdcQRR6innnpK1dfXe9ZzOp3qnnvu8Rw79e7dW916660+6yjV+jH41KlTfY4zlWr970dr21BKqS1btqiZM2eqtLQ0FRcXpyZOnKg++eQTn3XMY83XXnutzedu/u197733PMtaO34zjwm8j5HMzxX//Oc/Pcc+Bx98sM/fQdMXX3yhRo4cqWJiYtTQoUPVP//5T7/HR+vXr1dTpkxR8fHxClCzZ88O+LijNY8//rhKSkpStbW1fm9funRpi9ertWOg1l6LH3/8UR122GEqPj5e9ejRQ910001qwYIFLY4L/B07+tPWcVHz99H48eNVTk5Ou9ts7Tk1H1Nbn1VeeOEFNWXKFJWRkaFiY2PVwIED1Y033qgqKip81mvtM5o/bR3/NX+uSin10UcfqbFjx6rY2FjVq1cv9de//tXvZ7PS0lJ16aWXqoyMDJWQkKCmTp3qd382t3z5cnXeeeepPn36qNjYWJWdna1OPvlkn2M3pdr+rLRlyxZ14YUXqpycHBUdHa169uypTj75ZJ/Ps+b++Pbbb9UVV1yhunXrppKSktQf//hHVVJSEvR41q1bp4CAPxcJ0RU0pfbB10dCCC666CLef//9drs7RWi43W6ioqK47777/E7UJPz7/vvvmTZtGuvXr/c723Ek++yzzzj55JNZtWoVo0aNCvVwRBCs/n178MEHM23aNJ544glLtieEEEL4o2ka11xzjd/IrXBSUVHBgAEDeOSRR7j00ktDPRxLVVVVkZ6ezpNPPiln2ESY119/nYsvvpilS5d65rLpjBtuuIHvvvuOZcuWSaetCBnJtBVCCJomp5OZQYMzefJkjj/+eB555JFQD8Vy33zzDbNmzZKC7QFu/vz5bNq0iVtvvTXUQxFCCCHCQmpqKjfddBPz5s1D1/VQD8dS3333HT179uTyyy8P9VBECJWUlPDyyy9z//33S8FWhJRk2gohDnjvv/8+//jHP9A0rUMZage6zz//PNRD6BLz5s0L9RBEGDjhhBPkDAkhhBCimZtvvpmbb7451MOw3IwZMzo0EZ7Yv2RkZMjxnwgLUrQVQhzwbrrpJjRN45VXXmHo0KGhHo4QQgghhBBCCCEOcJJpK4QQQgghhBBCCCGEEGFEMm2FEEIIIYQQQgghhBAijEjRVgghhBBCCCGEEEIIIcKIZNpaQNd19u7dS3JysswsKIQQQgixDymlqKqqokePHths0o8QKDl+FUIIIYQIjUCPX6Voa4G9e/fSu3fvUA9DCCGEEOKAtWvXLnr16hXqYUQMOX4VQgghhAit9o5fpWhrgeTkZMB4sVNSUrr0sXRdp6ioiKysLOkmiUCy/yKf7MPIJvsvssn+i2xdtf8qKyvp3bu353hMBGZfHr+C/PxGOtl/kU32X2ST/RfZZP9FtlAfv0rR1gLmKWUpKSn7pGhbX19PSkqK/MBHINl/kU/2YWST/RfZZP9Ftq7ef3KKf3D25fEryM9vpJP9F9lk/0U22X+RTfZfZAv18au8Y4QQQgghhBBCCCGEECKMSNFWCCGEEEIIIYQQQgghwogUbYUQQgghhBBCCCGEECKMSKatEEIIIUQEcLvdOJ3OUA+jw3Rdx+l0Ul9fH1QmWHR0NHa7vQtHJtpi1fuuo/s/VOR9J4QQQohQk6KtEEIIIUQYU0qRn59PeXl5qIfSKUopdF2nqqoq6EnD0tLSyMnJkcnG9iGr33ed2f+hIu87IYQQQoSSFG2FEEIIIcKYWTjLzs4mISEhYgtISilcLhdRUVEBPwelFLW1tRQWFgKQm5vblUMUXqx+33Vk/4eKvO+EEEIIEQ6kaCuEEEIIEabcbrencJaRkRHq4XRKR4t28fHxABQWFpKdnS2nrO8DXfG+i6SiLcj7TgghhBChF/6BUkIIIYQQBygzSzQhISHEIwkt8/lHcqZvJJH3nUHed0IIIYQIJSnaCiGEEEKEuUjoTOxKB/rzD5UD/XU/0J+/EEIIIUJLirZCCCGEEEIIIYQQQggRRqRoK4QQQgghhBBCCCGEEGEk4oq2zzzzDP369SMuLo5DDz2UJUuWtLrumjVrOOuss+jXrx+apvHkk0+2WOfuu+9G0zSf/4YNG9aFz0AIIYQQQgghhBBCCCFaF1FF23feeYe5c+dy1113sXz5csaMGcP06dMpLCz0u35tbS0DBgzgb3/7Gzk5Oa1ud8SIEeTl5Xn+++GHH7rqKQghhBBCHBDefvtt4uPjycvL8yy77LLLGDNmDBUVFSEcmdifzZ8/n8TERHRd9yxbvXo1mqZRXFwcwpEJIYQQQgQnooq2jz/+OJdffjkXX3wxw4cP5/nnnychIYFXX33V7/oTJkxg3rx5zJo1i9jY2Fa3GxUVRU5Ojue/zMzMrnoKQgghhBAHhFmzZjFkyBAefPBBAO666y4WLlzIZ599RmpqaohHJ/ZXK1asYOTIkdhsTR9zVq5cSY8ePeQYXwghhBARJSrUAwiUw+Fg2bJl3HrrrZ5lNpuNY489lsWLF3dq25s2baJHjx7ExcUxadIkHnroIfr06dPq+g0NDTQ0NHj+XVlZCYCu6z7f6ncF8zHcbneXPo7oGrquo5Tq8veJ6DqyDyOb7L/IdiDuP/M5m/8BKKVwuPb9axATZUPTtKDuc//993P22WfTvXt3nn76aRYuXEjPnj1RSnHmmWeyaNEijjnmGN577702t2M+f3/HWvvD++G7775j3rx5LFu2jLy8PD766CNOP/30Nu+zaNEi5s6dy5o1a+jduzd//etfueiii7pkfEopGjr7nlMKl8uNW2kQxPsoNsj33cqVKxkzZozPslWrVnmWnXHGGZ733fvvvx/wdoUQQgixH1MKKnZDWu9Qj8RHxBRti4uLcbvddO/e3Wd59+7dWb9+fYe3e+ihh/L6668zdOhQ8vLyuOeee5g8eTKrV68mOTnZ730eeugh7rnnnhbLi4qKqK+v7/BYAvHm0jx+3VHOzIMrmDywW5c+lrCerutUVFSglPLpABGRQ/ZhZJP9F9kOxP3ndDrRdR2Xy4XL5QKgwenm2ndW7fOxPHXuGGKj7UHd54QTTuCggw7ivvvu49NPP2Xo0KE4nU40TeOaa67hwgsv5M033/Q8t9a4XC50XaekpITo6Gif26qqqoJ+LuGmpqaGMWPGcMkll3DmmWe2u/62bduYMWMGf/rTn/jXv/7F119/zWWXXUZubi7Tp0+3fHwNLp1r/rW8U9tQgFI6mmYjmNL/M38cR1wQ77sVK1Zw3XXX+SxbuXIlhxxyCADXX389l1xyCW+88UYQoxBCCCHEfsvVAD8/C3uWwbH3QMbAUI/II2KKtl3lxBNP9FwfPXo0hx56KH379uXdd9/l0ksv9XufW2+9lblz53r+XVlZSe/evcnKyiIlJaVLx5ucXE1UdDVJySlkZ2d36WMJ6+m6jqZpZGVlHTAFh/2N7MPIJvsvsh2I+6++vp6qqiqioqKIijIO29xKQ9P2/fM3xhBc0Xb+/Pls2LABt9tNz549sdvtnqLrMcccw6JFi7DZbJ7n1tZj22w2MjIyiIuL87mt+b8j0YknnuhzTNqe559/nv79+/PYY48BcNBBB/HDDz/wxBNPdEnRNlLU1NSwZcsWn05bXddZsWKF57h+2rRpLFq0KEQjFEIIIURYqS2F7+ZB6VawRUFVvhRtOyIzMxO73U5BQYHP8oKCgjYnGQtWWloaQ4YMYfPmza2uExsb6zcj12azdfmHyGi7DdBwKw6YD6z7G03T9sl7RXQd2YeRTfZfZDvQ9p/NZpwabv4HEBtt59k/jtvnYwn2NPXly5dz7rnn8sorr/D6669z55138tZbbwF4ttP8sjXm8/e37w+U94K3xYsXc+yxx/osmz59OjfccEOXPF5slI1nOvueUwqXy2UU6IOMRwjUtm3b0HWdYcOGeZYtWLCAkpKSFpEJQgghhDjAlWwxCrZ1ZRCbDEfOhe7DQz0qHxFTtI2JiWH8+PF8/fXXnowvXdf5+uuvmTNnjmWPU11dzZYtW7jgggss26aVouzGQa5LVyEeiRBCCCFCQdO0oE4XD4Xt27czY8YMbrvtNs477zwGDBjApEmTWLFiBRMmTAj18CJefn6+38iwyspK6urqiI+Pb3GfYOZk8JelHEzxtDU2dKI78N41x9Ce9PR0NE1jyZIlnHTSSfz888/MmTOHuLg4Bg8e7JMLHch228pSPtAciJni+xPZf5FN9l9kk/0Xpnb8iPbLC+B2QGov1JSbICkbWjkmsnr/Bbq9iCnaAsydO5fZs2dzyCGHMHHiRJ588klqamq4+OKLAbjwwgvp2bMnDz30EGBMXrZ27VrP9T179rBy5UqSkpIYNGgQAH/5y1845ZRT6Nu3L3v37uWuu+7Cbrdz3nnnheZJtiPKZhRt3YEUbWuKYcWbMHQGZA3p4pEJIYQQQkBpaSknnHACp512GrfccgtgzCFw4okncscddzB//vwQj/DAFMycDP6ylDtLKeWZSDfYCe0ClZWVxd13380FF1xAcnIyU6dO5cwzz+Sbb75BNXb6Arjdbs/za0tbWcoHmgMxU3x/Ivsvssn+i2yy/8KMUsRt+h9xWz4DwJk1kpoxl0ItUFvYYvWu2n+BzskQUUXbc889l6KiIu68807y8/MZO3Ys8+fP93Qa7Ny50+dF3Lt3LwcffLDn348++iiPPvooU6dO9WRZ7d69m/POO4+SkhKysrI48sgj+fnnn8nKytqnzy1QUXbj+TndAVTld/0CO38Ge4wUbYUQQgixT6Snp/udJPaTTz6xrAB4oMvJyfEbGZaSkuK3yxaCm5PBX5ayVbq6+HnHHXdwxx13tLmO3W7vdJbygeZAzBTfn8j+i2yy/yKb7L8ws2sJ2q6vICYGddApxIw5j8Q25oroqv0X6HFFRBVtAebMmdNqHELzSQX69evX7mlP//73v60a2j4RVKet22lcuhraXk8IIYQQYh879thjWbVqFTU1NfTq1Yv33nuPSZMmhXpYEWHSpEl89tlnPsu+/PLLNl+/YOZk8Jel3FlKqYAzjLuS9/uud+/ebb7v2spSPhDJaxHZZP9FNtl/kU32XxgpXG1k6w86Fm1cYLGoXbH/At1WxBVtD3Rm0dbpDqBoqxq7cXV3F45ICCGEECJ4X331VaiHEDaqq6t9JsHdtm0bK1euJD09nT59+nDrrbeyZ88e/vGPfwDwpz/9iaeffpqbbrqJSy65hIULF/Luu+/y6aefhuopRAx53wkhhOgSznpw1kJCeqhHItpSvMm47D4ytOMIkJT5I4zdZk5EFkA8gqdoK6ciCiGEEEKEq19//ZWDDz7YE+s1d+5cDj74YO68804A8vLy2Llzp2f9/v378+mnn/Lll18yZswYHnvsMV5++WWmT58ekvELIYQQB7wfn4T/XQeVe0M9EtEaZz2UNx5PZUZGhKh02kaY6MZMW1cgnbZmh63u7MIRCSGEEEKIzpg2bVqbkV6vv/663/usWLGiC0clhBBCiIAoBQWrjYjKXUtgxOmhHpHwp3Sr0dwYnw6JGaEeTUCk0zbCRNnNTttA4hEai7ZuKdoKIYQQQgghhBBCWK62pKnukv9baMciWle80bjMHBzacQRBirYRJspmdtoGEI/g6bSVTFshhBBCCCGEEEIIy3lHIhRtkMngw5WZZytFW9FVzInI3AF12vrPtHXrbmqcNVYPTQghhBBCCCGEEOLAUpXfdF13QeHa0I1F+KcUlJhF28jIswUp2kYcMx7BGUw8QrNM29fWvMZt399GWX2Z1cMTQgghhBBCCCGEOHBUNZt8LE8iEsJOTRHUV4AtCrr1D/VoAiZF2wgTVKetJx7Bt9N2T/Ue3MpNYW2h1cMTQgghhBBCCCGEOHBU5hmXOaOMy/zfQzcW4Z8ZjdCtH0TFhHQowZCibYSJshu7zBlIpq0nHsE301ZvXO5Srub3EEIIIYQQQgghhBCBqmos2g46DtCgYhfUloZ0SKIZcxKyjEGhHUeQpGgbYTrUaev2jUdwNy536VK0FUIIIYQQQgghhOgQtxOqG89izhwM6QOM6/kSkRBWiiMvzxakaBtxzKKty93xicgUxn3dzTpwhRBCCCGEEEIIIUSAqgsABVGxEN8NckcbyyUiIXy4HFC23bguRVvRleyN8QiuTkxE5lbSaSuEEEIIIYQQQgjRKVX5xmVyD9C0plzbvN9ABVC3EV2vbJtRH4tLg8TMUI8mKFK0jTDRZqetHkCmrWcismaZto33laKtEEIIIYQQQgghRAdV7jUuU3KNy8whRtdtQ2VTd6cILTPPNnOQUViPIFK0jTD2oOIRGou1SgevIq+n01YmIhNCCCFEF3n77beJj48nLy/Ps+yyyy5jzJgxVFRUhHBkYn82f/58EhMTPU0KAKtXr0bTNIqLi0M4MiGEEPslcxKy5B7GpT0askcY1yUiITx4JiEbHNpxdIAUbSNMdFDxCF7reHXV6ko6bYUQQoiIpRQ46/f9f0Ge4jdr1iyGDBnCgw8+CMBdd93FwoUL+eyzz0hNTe2KV0Z0FSvecy6v/7rwfbdixQpGjhyJzdb0MWflypX06NGDzMzIOiVSCCFEBDCLtmanLTRFJMhkZKGnVMROQgYQFeoBiOCYnbbuQIq23rEIuhOIMa5K0VYIIYSIXK4GeG/2vn/cs9+A6LiAV9c0jQceeICZM2eSk5PD008/zcKFC+nZsye7du3iggsuoLCwkKioKO644w7OPvvsLhy86BRL3nMKu67ApgFBnJoY5Ptu5cqVjBkzxmfZqlWrGDNmjLzvhBBCWK/S7LT1Ktqak5EVrgO3Y9+PSTSpLYW6MtBskD4g1KMJmnTaRpgoe1OmrWqv80B5F22l01YIIYQQ+9bJJ5/M8OHDuffee/nwww8ZMcI4XTAqKoonn3yStWvX8sUXX3DDDTdQU1MT4tGK/cGKFSsYPXq0zzKzkCvvOyGEEJZy1EJ9uXE9OadpeUpPSMgw6jCF60MyNNHIjEZI6xvUl8DhQjptI0yUrakzwaUrou1tdCoor8nK3EaBVimFwij2upXb372EEEIIEc6iYo3uw1A8bpDmz5/P+vXrcbvddO/e3bM8NzeX3FyjIyUnJ4fMzExKS0tJTEy0bLjCQpa85xRul4uoqCiC6rQN4n1XU1PDli1bfDptdV1nxYoVXHrppfK+E0IIYS0zGiEuFWK8/pZomhGRsHWREZHQM8fv3cU+4D0JWQSSom2EifLK53K5FdH2NlbWW3baehdqpdNWCCGEiECaFhGdAsuXL+ecc87hlVde4fXXX+fOO+/krbfearHesmXLcLvd9O7dOwSjFAGx4j2nFOCCqKgum7l527Zt6LrOsGHDPMsWLFhASUlJi8gEed8JIYTotCo/0QimnNGwdRFa/m/Q8/h9Oy7RxFO0jbw8W5B4hIjj3Vnr8poV1y8/8Qi6V/etU3daOjYhhBBCCIDt27czY8YMbrvtNs477zzuvfdePvjgA1asWOGzXmlpKRdeeCEvvvhiiEYq9icZGRlomsbSpUsB+Pnnn5kzZw5xcXEMGdL0YU3ed0IIISxh5tmm9Gh5W84oQIPynWhmhILYt9xOKNtuXJeirdgXNE3DrNu63O1k2noXdRsLtN6dthKPIIQQQgirlZaWcsIJJ3Daaadxyy23AHDooYdy4okncscdd3jWa2ho4PTTT+eWW27h8MMPD9VwxX4kNzeX++67j/PPP5++ffvy/PPPc/bZZzNy5EjsduP0NHnfCSGEsEzVXuPSX6dtXAp06wdAdInk2oZE2XajgTE2GZK6t7t6OJJ4hEiTt4pxDUvYqw3EpY9pe12fTlvjunenrcQjCCGEEMJq6enprF/f8sPJJ598gsvVlLF/0UUXcfTRR3PBBRfs6yGK/djtt9/O7bff7vc2ed8JIYSwVFW+cemv0xYgdzSUbiVm71IYdTzEJOy7se0D9U43j32xgf6ZSfzh0D6hHk5LZjRCxuAui2bqatJpG2m2LuKoms/p6dweQDyCd6dty3gEKdoKIYQQIhR+/PFH3nnnHf7zn/8wduxYxo4dy++//x7qYYn9nLzvhBBCWEYpqNxjXE9uZaKxnuMBiCpeg/bfOfDbe1BfuY8G2PU25FextaiGr9cVsC4vDJ+XJ892cGjH0QnSaRtpbHY0TcOm9ADiEbw6bd1GPIJ30VbiEYQQQggRCkceeSR6e18+C2Exed8JIYSwTF0ZuBoArfVT77OGoibNQV/6JjRUwOr3Yd1/YdAxMOxkSMzcp0O2Wl5Fnef6O0t3cefJw7HZwqSjVSko3mRcj9A8W5BO28ij2dE0sKHj0tsp2vrEI7TMtJWJyIQQQgghhBBCCCGCZEYjJGWBPbr19fodSeWUe1BHXA/d+oPbARs+h/9dDyVb9s1Yu8je8nrP9V2ltfy4pTiEo2mmcC3UloA9BjIGhXo0HSZF20hjs6NhFG3d7XUK6JJpK4QQQgghhBBCCGEpzyRkreTZetNs0GcSnPAQHHW7MUGZ7oJt33XpELua2Wk7KDsJgI+W76HeGSZndG/43LjsPwWi40I7lk6Qom2k0WxomoamdJztxSO0k2nr1sPkh0kIIYQQQgghhBAiUlTmGZcpuYHfR9OMyckOOsX4d8lm68e1jyilyKswOm3Pm9iH7JRYKuqcfL46L8QjA6qLYPevxvWhJ4Z2LJ0kRdtI4+m0dbefaetdtPWTaSudtkIIIYQQQgghhBBBMuMRAum0bc48Xb9sO7gjsy5TUeekzuFG06Bnt3hmju8FwILVBZTWODq9fZdbp7K+g5GemxYACnJGQWqvTo8llKRoG2l8Mm2DiUcwfhF4Z9q6VGT+chBCCCGEEEIIIYQImco9xmVyTvD3TeoOMUlGnaZ8h7Xj2kfMPNus5Fii7TbG9enGkJxknG6dD5fv7tS2lVL8/etN/PndVSzbURbcnV0NsGWhcX1IZHfZghRtI4/Njk0Dmwp2IjLJtBVCCCGEEEIIIYToFN0N1YXG9ZQOdNpqWlO3bQgnI3PrCqXaqSu1wsyz7ZEaD4CmaZx7SG8AFm8pYVtxTYfH9cPmYtburUTXFS99t5WtRdWB33n79+CogaRs6HFwh8cQLqRoG2k0u3GBjtMdTKetxCMIIYQQQgghhBBCdEpNkdEkZ4+GhIyObSNjoHFZssm6cQXB4dK5939ruOPj1bjaqy35sbcxzzY3Ld6zrF9mIpMGGq/Hv5fu7FBBuLLeybu/Gp26aQkxON06//f1JoqrG9q/s1KwYb5xfcgJYIv8kmfkP4MDjc2GTdOwKzfudjttW05E5hOPIEVbIYQQQgghhBBCiMBV7jUuk3ONrtmO8BRtrZ2MbHdZLTUN7dd6ftxczO6yOvLK6ymsCqAg2kxeudFpm5sa57P8zHG9iLbb2FxQzfKd5UFv992lu6htcNE7PYF7TxtB7/QEqupdPPnVRmod7TyvgjVQsQuiYmHAtKAfOxxJ0TbSaOZEZHrHJiLzysGVTFshhBBCCCGEEEKIIFTlGZcdybM1mfEIlXuN0/ktMH91Pnd9vIb7P12Hw9V696zLrfP56jzPv82og2DkmZ22zYq26YkxHDu8OwBfrysIaptr9laweEsJmgazD+9HYmwU1x0zmNSEaPLK63lu0Za2u4I3fm5c9p8CMYlBPXa4kqJtpLF5T0TWTtFWb5lp691p6/a+XQghhBBCCCGEEEK0zSzapvTs+DbiUiExy7heurXTQ/pybQHv/boLgMLK+jYLpr9sK6Wk2uH5t1mADVRNg4vKOqMxsIdXPILpqKFZaBpsyK8iP8BtO1w6//zZmJTt6GHd6a/lw7pPSI+zccMxQ4iNtrF2byVv/rzDf+xCdSHsXmZcH3JCUM8nnEnRNtJodjRNQ1Oq7W8YlAK83siNmbbKa5nEIwghhBBCCCGEEEIEwROP0IlOW2jqti3uXK7twvUF/HvJTgCG5SYD8MlveVQ0Fla96brik9+MonO3xBiAgAurJrMzt1tiDHHR9ha3ZyTFMqpnGgDfbiwMaJuf/LaXwsoG0hJiOOPgnrD0ZVjxJqz7L30yErhyykA0DX7YVMyCNX4K0hsXAApyRkNqr6CeTziTom2ksZnxCO62O22bd9G2kmnb0ZkCW+PUW/5SEEIIIcSB5+233yY+Pp68vKbT7y677DLGjBlDRUVFCEcm9mfz588nMTHRJxJs9erVaJpGcXFxCEcmhBBiv1GVb1wm9+jcdjIHG5clWzq8iW82FPKvn42C7YmjcvnL8UPpl5lIvdPNxyv3tFj/1x1lFFbWkxAbxZkHG53CwXba7i33H43gbepQo4v4x80lbUY1AOwpr2P+auM1/cOhfYiPthnZtABr/wO1pYzpnca5E/oA8MWafN8NOOthy0Lj+tD9p8sWpGgbgbTGeAQ3Lr2tTttmtzUWbb0PYBUKvfl6nbClfAs3fnsjC3cutGybQgghhIhMs2bNYsiQITz44IMA3HXXXSxcuJDPPvuM1NTUEI9O7K9WrFjByJEjsXnNGL1y5Up69OhBZmZmCEcmhBBiv+Csh9oS43pKbue2ZXbalmxuPFs6ON9tLOKfi41IgekjcjhrXE80TWPWhN6e23eV1nrWV0rx6W9Gl/Bxw7vTL9PIfc2rqAuqoc/stM1NbRmNYBrVM5VuiTHUNLhYtqOs1fWUUvzjp+24dcXY3mmM65NmvL6N8zLhaoBVbwMwaWAGABV1Tt9C8NZvwFkLSd2hx7iAn0ckiAr1AESQbFFomPEIbfxQqWadtu6WnbZgTEZmp2U7e0dsr9yOS3extWIrR3O0JdsUQgghhC+lFA7d0f6KFouxxaAFMUOypmk88MADzJw5k5ycHJ5++mkWLlxIz549KS8v59hjj8XlcuFyubj++uu5/PLLu3D0ojOseM8ppXC5Xbg1d1Dvo2DfdytXrmTMmDE+y1atWsWYMWPkfSeEEKLzqhu7PGOSIDa5c9vq1g80G9SXQ20pJGYEfNfFW0r4x+LtgFGAPfuQXp6/l4O7J3NIv3R+3V7Kv5fu5C/HD0XTNFbuKmd3WR1x0XaOGZZNbJQNm02jwalTVuskvTEuoT1mp22PtNY7be02jcmDM/nvyr18u7HIU3BtbtGGIjYXVhMbbeOPh/U1noMZPxGdYBRjt30Hg48nMWMQsdG2xvE66J4SZ3Qpr/yXsf6wGRDEMUMkkKJtpDEnIlM6DR2IR2jeWevSXcTaYy0ZmhmNIFm5QgghRNdx6A7+vOjP+/xxH5v2WNDHDCeffDLDhw/n3nvvZcGCBYwYMQKA5ORkvvvuOxISEqipqWHkyJGceeaZZGQE/mFF7DtWvOcUCqUrNJuGRuAfqIJ9361YsYLrrrvOZ9nKlSs55JBD5H0nhBCi8yobY586m2cLEBULaX2gbLvRbRtg0VbXFe/+ugul4OiDsjl3Qu8WX3CefUgvVu4qY31eFSt3lTO2d5ony/aoYdkkxhrlwKzkWAoq6smrqAu4aBtIpy3AlMFZ/G9VHpsKqthbXtdi0rKCynrebZw87cyDezU9vjnRW/ZwiEmEbd/CstfRjr+f9MQY8srrKa5uoHtMA3z/uNGV22McDD4+oPFHEolHiDRaY9EWve1M2+adto0FVX9FW6u4/OTmCiGEEOLANX/+fNavX4/b7aZ79+6e5Xa7nYSEBAAaGhpQSlmesy8OPDU1NWzZssWn01bXdVasWMGYMWPkfSeEEKLzShvzZ62a7Mo7IiFAW4qqqaxzEh9j59xDWhZsATKTYjl+uFFYfvfXXfy2u4LtxTVE220cP6LpmCw3xeiWzSsPLNe23ummtMY4+ya3jU5bMCYqG9PLiMRatKHI5za3rnj5+604XDrDcpM55qDsphvNTtuUXBgzyyhul2yG7d+TkWh8kVtaVQs/PAm1xZCcC4fP2e+6bEE6bSOPZkMD7MqNy91GHm0QnbZWMbclnbZCCCFE14mxxfDYtMdC8rjBWL58Oeeccw6vvPIKr7/+OnfeeSdvvfWW5/by8nKmTp3Kpk2bmDdvnuSNhjEr3nNKKVwuF1FRUUHHIwRq27Zt6LrOsGHDPMsWLFhASUmJp5Ar7zshhBAdphTsWmJczx1rzTYzBsLmr6BkU8B3MTNix/ZOI8reei/mjNG5/LC5mMLKBl74zig2TxmSRUpctGed3LR4Vu4qJ68ysKJtQWU9SkFibJTPdlozbWg2K3eV89OWYmaO70VMlDHez1fnsbWohrgYO5cc0d/32MDstE3OhYR0GHEGrPo3rHyLrKz/B0DSunehdq1R0J3yF6Mjdz8kRdtIY7OhaaCh426z07bZba1l2nZFp23zgrEQQgghLKNpmmXRRl1l+/btzJgxg9tuu43zzjuPAQMGMGnSJFasWMGECRMASEtLY9WqVRQUFHDmmWcyc+ZMn25cET6seM8ppbArO1H24Iq2wcjIyEDTNJYuXcpJJ53Ezz//zJw5c4iLi2PIkCGAvO+EEEJ0QsVuqC4AWxT0GGvNNs1O29KtoOtga/uEeKUUy3caRduD+6S1uW5ctJ0zx/Xk9R+30+DUsds0ThjpG+uQm2p0y+Y3Rh60pynPtu1oBNOIHilkJMVQUu1g6fZSjhiUyc6SWj5eaXTT/vHQPmQkNTvG8HTa9jAuh86AzV9DTREjyheyty6KjOqvID0BJs2xrus5DEk8QqRpnIjMpnScwUxE1kqnrZVRBp5OWyWdtkIIIcSBqrS0lBNOOIHTTjuNW265BYBDDz2UE088kTvuuKPF+t27d2fMmDF8//33+3qoYj+Tm5vLfffdx/nnn0/fvn15/vnnOfvssxk5ciR2u+/Eu/K+E0IIEbTdS43LnNEQHVjRsl0pvYxuUVcDVO5ud/VdpXWUVDuIttsY0SO13fWPGJhJ73QjGuiIQZktcmtzGou2eRWBddqaebZtTULmzWbTmDIkC4BvNxbhcOm8/MNWdF0xrm83Jg1oluPrckBNsXE9Ode4jIqBcRcC0KdgIcdU/QenW4cRZ0LviQGNI1JJp22kacy01dA7FI/QlZ22MhGZEEIIIdLT01m/fn2L5Z988gkul3GMUFBQQEJCAsnJyVRUVPDdd99x1VVX7euhiv3Q7bffzu233+73NnnfCSGE6BSzaNvrEOu2abNB+kAoXGvktqb1aXN1s8t2ZM8U4qLtba5rbF7j6mkD+WFzMdNHtJw8zey0rah1UutwkRDTdpnQLO7mpARWtAU4clAmH6/cy5bCal74dgt7yupIjovigkl9W559U10AKIhOgDivonSvCdB9BLE7V2FXLrbFDGfgqLMDHkOkkk7bSKPZwn4iMinaCiGEEKItO3bsYPLkyYwZM4bJkydz7bXXMmrUqFAPS+zn5H0nhBCiw2pKjAgDNGuLtuA1GdmWdlc1i7bj+nQLePPZKXGcOa4XibEtC7IJMVGkJhjZtPkBdNvuLTc7bQPvNE5LiGFs7zQAVu4qB+CiI/r7z8Q1oxGSc30nFtM0GDeb6JhYyu2ZfJxwJmo/nHisOem0jTQ2OzY0bMqNSw+m09b4d/OirdkdawUp2gohhBAiEBMnTmTlypWhHoY4wMj7TgghRIftbpyALGuIbweoFTxF281trlZQWc+esjpsNo3RjUVQK+SmxlFR6yS/op4BWUmtrudy6xRWNQDBFW0Bpg7JYnnjBGpHDs70FHFbMCchM/NsvXXri/30Z/j3u2txqhgq612kxrc/GVokk07bSGOzN3Xatplp26yguw8zba3cphBCCCGEEEIIIURIeaIRuiBD1Szalu80sm1bYRY9h+Ukk+Sna7ajclKNAuzedjptC6sa0HVFbLSNbgnBFUtH9EhhZM9UBmQlMmtCGxEQnk7bllEOAFGJ3UhMNArLpTWOoMYQiaTTNtJoNjQCiUdoVrR1Gx21+yLT1sruXSGEEEIIIYQQQoiQaaiCwnXGdaujEQAS0iG+G9SVQek2yB7md7WORCMEIrcxnza/os4oHK98G3ocDIOP84koMCchy02Nb5lF2w5N0/h/xw1pf8W2Om0bZSTFUF7roLSmgf6ZiUGNI9JIp22k0exomoZNtTMRWYtO232XaetuHs0ghBBCCCGEEEIIEYn2LDdqLGl9Wu0A7RRNg4yBxvVWIhLKax1sLaoB4OA+aZY+fG6aUbStLM6Dbx6Evcvh11dg0UNQW+pZb2+50YlrTl7WJcyibXJuq6ukJ8YAUFK9/3faStE20njHI7TVadtKpm3zTlsrowzMDluJRxBCCCGEEEIIIcR+wcyz7TWh6x7Dk2u7ye/NK3aWAzAgK5G0hBhLHzo3NZ44vZbDd72IXlsKiZlgj4a8VfDZjbDzF6Cp0zbYPNuANVQZ/0FgRVuJRxBhR7OjAZpyt5Np27xoa3TBKuV7n66aiEwpFXS7vBBCCCGEEEIIIUTYcDUYxUvYR0Vb/522ZjTC+L7WRiMAdIvROb3yn6S5iqiP7kvCsfeCqw5+egrKtsMPj0P/qRSVHgp0YadtVb5xmZAB0a0/RmaSUbQ9EDJtpdM20gTbaWtv/AZmH2TaulTTtqTbVgghhLCOrrcRiXQAONCff6gc6K/7gf78hRBCAPm/G/WUxEzo1q/rHid9IGh2qCn2dLaaqhtcrM83OlCtzrNFd6P99BR91R4cWhxbRl4HiRmQ2guOfwCGnw5oqG2LmLLlMWL0OnJTu6jTtnKPcdlOBEV6YixwYMQjSKdtpNFsaGiNRdu2Mm0bi6ZRseB2eDptm2faWpk/63Q3de26lZsoeXsJIYQQnRITE4PNZmPv3r1kZWURExMTsWeyKKVwuVxERUUF/ByUUjgcDoqKirDZbMTEWHs6oPCvK953Hdn/oSLvOyGEEB67vKIRuvLvV0wCHHQyrP0YlrwIWUOMycmA33aVo+uKnt3iyU6xsMtVKVj6Cuz5leiYWP6b+EcmuTIYZd5uj4Kx50GPg2lY9CjJrl0Mcm4gK3mydWPwZnbathGNAJCRaHbaNnTNOMKIVNUiTWOnraZU2522ZnE2KtbIBNHdoFTLTlvVNZ22Lt1FrD3Wsm0LIYQQByKbzUb//v3Jy8tj7969oR5Opyil0HUdm80WdNEuISGBPn36YLPJSWL7Qle87zqz/0NF3ndCCHGA092wZ5lxvdfErn+8UefA3pVQvgN+eRGm3gSa5olGsLzLdvUHsOVrQGPP8MvI25Htya31kT2MoowJsG0XQ+x52G1d9Hfc7LRN6dHmamambVW9C4dLJyZq//07LUXbSKMZRVs7blzuNjptzS5cMx4BBbq7xWlelsYj6C6/14UQQgjRcTExMfTp0weXy4XbHbnxQ7quU1JSQkZGRlBFMLvdHhHdmfsbq993Hd3/oSLvOyGEEBSuA0c1xCRB1tCufzx7FBw+B+bfCnuXw5avqe97FKv3VAIWF22rCuD3943rEy4lPmY87NhCfkW939X32HsRB/RVXdhEEGCnbUKMndhoGw1OndIaBzldlbEbBiKuaPvMM88wb9488vPzGTNmDE899RQTJ/r/xmPNmjXceeedLFu2jB07dvDEE09www03dGqbIWczJiKzoePWVesTfpkdtd7drroLHSnaCiGEEJFG0zSio6OJjo4O9VA6TNd1oqOjiYuLi4iinbD2fSf7XwghRJdQCip2GxmsVn/RtnupcdnrELDZrd12a9L6wJhZsOKfsPwfbHD2wunWyUiKoXe6hVmyG+cDCnLHwODjyC03OmzzKur91pm2qlyGA1nufHDWtzlRWIcoBVV5xvV2Om01TSM9MYa88npKahr266JtRB0xvfPOO8ydO5e77rqL5cuXM2bMGKZPn05hYaHf9WtraxkwYAB/+9vfyMnxH2Qc7DZDTrOjaRqacqMUuFuLSDCzaqO8Mrh0V4tMW6uKq0opn23JRGRCCCGEEEIIIYToUlsXwWd/gR+fNAp/VlEKdv9qXO81wbrtBmLoDMg+CFwNOH94Gk3pHNIv3bqzP5x1sPWbxsc6EYCs5Fg0TaPe6aaizumzulKKDZUxVNtTiYvSoHSrNePwVlNsTPhmi4LErHZXz2icjKy0Zv+ejCyiiraPP/44l19+ORdffDHDhw/n+eefJyEhgVdffdXv+hMmTGDevHnMmjWL2Fj/+arBbjPkbDY0QANoK9fW02kbY67tU7S1a8a3RFZl2jbfjnTaCiGEEEIIIYQQokttXWRc7vwZ1nxk3XYrdkFtMdijIWe0ddsNhM0Gh11DgxZDdOkmxtX+wJTB7RcyA7Z1kVG4Tc6F3LEARNttZCUbdbO8ZhEJS7aVsrusjqLY3iTFRkHxRuvGYjK7bJO6B9TVnJFkNCiWVO/fRduIiUdwOBwsW7aMW2+91bPMZrNx7LHHsnjx4n26zYaGBhoammapq6w08kV0XW+RGWs1XWmejn8NNw6Xmxi7n29b3G40pQCb8YZ3O1EuBy7dhVKKaHs0LpcLp9tpyZgbnA0or2+1rNru/kbXdc9EHCIyyT6MbLL/Ipvsv8jWVftP3g9CCCHEAaq2FIo2NP37t3ehWz/oOa7z2877zbjMHuF7BvO+kpTFsszTSdj5Ise6viXHfSbQv/PbVaoxGgEYepJPpESP1DgKK+vJq6jjoNwUAOqdbt79dTcAvQaNJrpsNxRv6vw4mqtszMptJ8/WZE5GVrKfd9pGTNG2uLgYt9tN9+7dfZZ3796d9evX79NtPvTQQ9xzzz0tlhcVFVFf7z+02Sq6o45kpxNdd+NsqCe/oJCUuJa7MaashASHA2ddHVEOF5rbQWVhPpVVlTgcDmJVLA6ng/LKckuiIGqcNTgcTT8shcWFxNb5724+kOm6TkVFBUopyXOLULIPI5vsv8gm+y+yddX+q6qqsmxbQgghhIggu38FFGQMMoq1m7+Cn56C6Q9CSmDFv1bl/25c5ozq7Cg7RCnFf8oHcljsQUxL2GF0EU+e2/kN711uTPgVnQD9p/jclJMaB7t8O20/+z2P8loHmUmxjDl4Iiz8DEo2GcVfKzOEPXm2wRVtS2sa2lkzskVM0Tac3Hrrrcyd2/TDUllZSe/evcnKyiIlJaVLH1t3NuCKjibK7iY+xk5aegaZSX6KoxXJaDExxCSloNUlQYMiMz2NhOoEYmpiSElIoaG2gfiEeLKzszs9rrL6MmJimr59SklLIbtb57e7v9F1HU3TyMrKkoJDhJJ9GNlk/0U22X+Rrav2X1zc/jv5hBBCCCHasOtn47LPYTDkRCjfaZy6/908mP4ARHdw4i6XAwrXGNdz93E0QqM1eyspqXGyJnUqp8e8DYVrrSmUbvjcuBx4dIvJxHJTjdcrv7FoW1hVz4I1+QCcM6E3MZmJRuZsfYWRQZtkYWSD2WnbziRkpgMl0zZiiraZmZnY7XYKCgp8lhcUFLQ6yVhXbTM2NtZvRq7NZuv6D5FR0aAZEQl2FAqtlcc0fpg1e5SRwdI4eZmO8YEpLioOTdNw47ZkzG7cPqHYSpMuqNZomrZv3iuiy8g+jGyy/yKb7L/I1hX7T94LQgghxAGovgIK1hrXex8K9iijE3X+rVC5BxY/A5P/3LEiZ/FGY1Ks+G6Q2tvacQfo241FAAwaNgbbzg+hocooSnfr2/GNVuxu7CDWYMgJLW7OTTOKuHvLjaLte7/uxuVWHJSbwrg+acZrmdYXSrcYr5GVRduq4OIRvDNtlVLWTdIWZiLmKDcmJobx48fz9ddfe5bpus7XX3/NpEmTwmabXU9r/L+Gho7T3UqOmzkRmWZrCnHWXegY68fYjDe4VROGNd+OTEQmhBBCCCGEEEKILrF7KaCgW39IajzLN76bUai1RRm3r/mwY9vOb8yzzRllbQRAgCrqnKzcVQ7AkUNzIGuocUPh2s5t2Oyy7XWI34JrTopRtC2vdbB8ZxnLd5ShaRrnHdqnqSiaOdi4tHIyMpcDakqM6wF22qbFR6Np4NYVlXX7b/0pYoq2AHPnzuWll17ijTfeYN26dVx11VXU1NRw8cUXA3DhhRf6TCrmcDhYuXIlK1euxOFwsGfPHlauXMnmzZsD3mbY0TTQbGga2JQbt678r6cai7maHWzRxnXdhd64PNpuLLOquOrUnT7/lqKtEEIIIYQQQgghusSuJcZln0N9l2cOhgmXGtd/e8/oLg1W3irjMkR5tj9tLkbXFQOyEumdngDdRxg3dKZo21AF2741rg89ye8qibFRpMQbtaLXftwOwNHDsumZ5hUzYRZtSzZjmep8QBk5u7GBRY5G2W2kJZiTke2/ubYRE48AcO6551JUVMSdd95Jfn4+Y8eOZf78+Z6JxHbu3OlzitzevXs5+OCDPf9+9NFHefTRR5k6dSqLFi0KaJthSbMbpxei43S3UrTVGzttbXYjHgFAd+FuXB5rN+IdrCqumts1SdFWCCGEEEIIIYQQlmuogvzVxvXeh7a8feDRRlF37wrY+TOMmhn4tusroGy7cT0ERVulFN9tKgZgypDGbtjsxqJtQSdybTd/bUQ+dOsH2Qe1ulpuahyVdU5qG1wkxkZx2thmna+ZQ4zLsu1Gh2xUTIttBK2ycRKy5NygnltGYgxlNQ5KaxwMsDCpIZxEVNEWYM6cOcyZM8fvbWYh1tSvXz+UaqWoGeA2w5HSbGiATekBdto27ma309Npa8YjuJXb372D1qLTVknRVgghhBBCCCGEEBbbs8yIhEzr0/rp9H0OM4q2u34JrmhrFoPT+hpxC/vYhoIqCivriY22MaFfurEwfQDYY8BR3bFcW90Nm74wrg89sc3CaG5qHBvyqwA4Y1xPEmOblQ0Ts4xu2IZKKNvWFN3QGVXBTUJmSk80O23338nIIioeQTSy2bFpGhqqjUzbxuU270xbd5fFIzTfTvPOWyGEEEIIIYQQQohO2/mLcemvy9bUc7wxx0/5TqgqaH295rzzbEPgu8YJyA4bkEFcdGMtxx4FWcOM6x2JSNi1BGpLjGJrn8PbXLVXegIAvdMTmDrYT/uqpjV12xZvCn4s/ng6bXOCuptZtC2Voq0IK42ZtnbayLQ1i6atZNqa8QjNO2Q7SjJthRBCCCGEEEII0aUcNU2F1baKtrHJTTEAu5cEtm2lIK9x27ljOj7GDqpucLFsRxkAk5sXTM1c24I1wW9450/G5aBj2o0zOGJgJrMm9uG6YwZjs7XSkZs5yLgssaho6+m07RnU3TKTjLpWSfX+m2krRdtIpNmN+ciUG5feWqetWbS1NcUj6E5PHIJZtLUqHqF5kVbiEYQQQgghhBBCCGGpPctBdxmn0qf2anvdXhONy10BFm0r90BdqVFDMTtb96HFW0pwuRW90xPol5Hge6NnMrJ1RnE5UEpB0Qbjeo+D214XiImycdzw7p4uVr/CrNNW4hFEWDEybY2JyFxtTESmK8W20noaVONu9uq0tTweoVmRVjpthRBCCCGEEEIIYaldZjTCYe1PWtVrgnFZvAnqytrfttllm32QNRNsBUEp5YlGmDokC635c+vWH6JiG3NtdwS+4eoCY3I1W5SxDSukDwQ0I3KhtrRz22qoMp4TGBORBTMMiUcQYcnstEXhamMisvJaJws3FLOuoM5Y5nZ5OmvNicisKq463b7xCJJpK4QQQgghhBBCCMs46yFvpXG998T210/MaCwwKti9rP3180MXjVBQ2cDe8jrsNo1DB6S3XME717YgiFxbs8s2vb91hejoOGMSOOh8t63ZZZuQYWw3CBlJxvOprnfR4No/a1BStI1ENhs2wKb0NiYic+Nw6+jYqDffu7qzRaatVcXVFhORWRS7IIQQQgghhBBCCMHeFeB2QlJ36NYvsPuYxd32cm3dzqa82BBMQrYurxKAgdlJJMRE+V8pe7hxWRhErm3xRuMyc2gnRudH5mDf7QfD5YDdv8LiZ+HbvxnLgoxGAIiPtnsmayursWa+pnDTyjtBhDcbmqZhx91mPIJSCh0bTnM3e8UjxNgbO20typ6ViciEEEIIIYQQQgjRZXb9bFz2CSAawdR7Iqx62yjIOmogJtH/esUbwe2AuFRI62vNeIOwtrFoOzw3pfWVmufaBvIaFK03Lq3O6M0cDJu/Cm4yssL1sPFzo/ju8po8LDYZhpwY9BA0TSM9MYa95XWU1DSQkxpcp24kkKJtBFK2qMaJyPQ24xF0BbpmQ6dlpq1ZtG1ebO2o5p21MhGZEEIIIYQQQgghOs3lgE0LjO5MgN6HBn7flB6Q0tOYZGzvCuh3pP/1zDzbnFGBF4QtopRiQ34VAAe1VbT15NrWQNl2I/KgLQ3VULHbuJ41xJrBmszJyEq3gttlxDe0pSofFt5nTCIHRhxC74nGZHFZw8DWsSCAjKTGom31/plrK0XbSKQZnbY2dFx6K/EIuhtdVyjNu9PW7SmumvEISil0pWPTOpeUIZ22QgghhBBCCCGEsIyuw/bv4bd3obbYWJYzCtIHBLed3hNhzUewa0nrRdv83xu3P7rj4+2gnaW11DS4iIu20z+zlU5gaMy1PcjI9S1c237R1uyCTepudBBbKTnX6Fp21BgTo2UMbHv9FW8aBdusoTButrEPLSiOZ+znk5FJpm0kstnRABs67vY6bbHhNnezuynT1pyIDKzJtTWLtBqaz7+FEEIIIYQQQgghAqaU0RU7/2b4+VmjYJuQAYf+CabdFlCx74s1+Ty3aAsutw69JhgL964wunaba6gyOkYhJHm2a/ca0QhDuidjt7Xz3Lo35toGMhmZOQmZ1dEIYOyDDDPXtp2IhPzVRpe0ZoOJVxgFXou6mdMTjYbEEinainCh0LBpYFNunK1l2io3ulLomg0XRjAzusvTaRttj/asakVEgtNtbMOMXbBqgjMhhBBCCCGEEEIcQDZ8Bov+BuU7IToBxpwHJz8JA48K6DT6kuoG3v11N79uL2VDQZXR1ZmQaWTW5v/W8g75qwEFqb0hId3yp9MecxKy4T3aiEYwZTfm2hY15tq2xVO0tTgawWRORtZWrq2uw/J/GNcHHQupvSwdQrqn07ahnTUjkxRtI5HNjqZpaOi424pHUAqFDZcyi7ZOVOMPtXenrRX5s+Y24qLiLNumEEIIIYQQQgghDjDbfzAuB0yDU/8PRpwOUTFt3cPH1+sKPbWPoqoGo6uz1yHGjbuW+K5csgV+f9e4nrvvoxGcbp2NBdUAHJSb3P4d0pvl2rbG7WoqpnZFpy00FW13/9rUqdzctkVGfEJ0Aow62/IhZCRJPIIIN5odTTPiEdrqtFWN8QguTzxCU6et3WbHrhnFXCvjEeKj4n3+LYQQQgghhBBCCBEQt8vosAUYeRbEBlDI9FLvdPPtpiLPv4uqGjswe080LvcsA90NznpY9gYsuB0q90JMktEJuo9tLarB6dZJiY+mZ1p8+3ew2Y1cWzBybVtTvgPcTiN3NqWnNYNtrvtIoyDsqoeFDzTtN5OzDlb927g+8iyIC6CTOEhmp21JtcNTqN+fSNE2Eml2NDRsqo1MW11vjEewe01E5vJk2to1O3abUbS1osBqRizE2eMs26YQQgghxIHimWeeoV+/fsTFxXHooYeyZMmSVtd9/fXXjbOuvP6Li4vbh6MVQgghukjFLmPCqugESMwK+u7fbyqm3tHUmFZoFm2zhhmFWUc1rP4QPvuzEcOAgr5HwMmPQ0qPDg97fX4l81fnB104XJtXAcCwnGS0QHNeuzdGJLSVa1u03rjMHGJZfmwLNjtMvRkyBhmv68L7jQK4ae3HUF9hTIQ25IQuGUK3hBg0Ddy6oqKu89Gf4UaKthFIaZpXp20r8QhKb4xH0HCpxt3slWlr02xE2YxirhUFVnMbsVFGCLT5OCK0lFIU1xXvl984CSGEEPuLd955h7lz53LXXXexfPlyxowZw/Tp0yksLGz1PikpKeTl5Xn+27Fjxz4csRBCCNFFyrYZl+n9gy426rriy7X5AIzulQZ4ddra7E0Tkq1+H2oaJzebejMccR3EpXZ4yC63znOLtvDer7tYur0sqPuuy6sCAsyzNZlF28K1RmasP8UbjcvMLsqzNcUkwLRboVs/o0D79X1QXQjVRbDuf8Y6B18A9qgueXi7TSMtobHbdj+MSJCibSTS7J6JyFrttFVu9MZ4BGfjRGTK3ZRpa9NsRNuMycisyJ81O23NeASZiCw8fLv7W+7+6W4W5y0O9VCEEEII0YrHH3+cyy+/nIsvvpjhw4fz/PPPk5CQwKuvvtrqfTRNIycnx/Nf9+7d9+GIhRBCiC5i5rR26x/0XVfsKqOk2kFibBSnjTW6ZouqG5qamPoc1rimZnR+zngMeo7r9JDX51dRXW/UVb7bWNTO2k3qHG62FtUAcFBuEEXbbv0gKg6ctVC8oeXtSnlNQtZFebbeYpPgqNuMGIa6Uvj6Hlj6stExnT28KU+4i2Qk7r+5tl1T6hZdq3EiMhs6rlaLtjq6bsQjuBpX0fWmVnG71pRpa0WnrVmkjbUbnbZOff9rS49EeTV5ABTUFIR4JEIIIYTwx+FwsGzZMm699VbPMpvNxrHHHsvixa1/6VpdXU3fvn3RdZ1x48bx4IMPMmLEiFbXb2hooKGhaWblykpjpmpd19Fb69KxkK7rKKX2yWMJ68n+i2yy/yLbgbb/tJKtoBQqrV/rXaStWLA6H4Vi2pBMclJiUSjqHC4q6xwkx0VDzmg48s+QmGkUPiHox/BnybYSFEbhZW1eBfnltWSnxDVuvvX9tz6/Al3pZCfHkZ4QHcQ+1qD3YWhbv4EV/0Ide49vV3JNEVptKdjsqG79LXmO7YpJhqNuR/v6HqjKNzptNQ118AVGEbkLz/7tlhCNQlFUWW/5z0lX/fwFuj0p2kYgpdnQAA0dV2sTkeluFKpxIrLGRV6FVKvjETyZtlHGLyaJRwgPDS7jw1mDu6GdNcW+opSi0lFJamzHT78RQgix/yguLsbtdrfolO3evTvr16/3e5+hQ4fy6quvMnr0aCoqKnj00Uc5/PDDWbNmDb169fJ7n4ceeoh77rmnxfKioiLq6+s7/0Taoes6FRUVKKWw2eRkv0gj+y+yyf6LbAfU/lM6aQUbwe2gUk9GbyMmqLkdpfWs2V1KlE1jZIZGeWkxcZqbino3G3bk0adbY/Z7TB9wAkFsuy0uXfHTxnwanDqpcVFU1Lv4dPk2ZgzPANref0s2FtPQ4KBXblybkUj+aLnTSN24EPaupua3z3HmNnWzxuz5hQSHA3dqP6pKKzr/JIMZ16g/kfzLo9jqSnH0OpxaZ4Jlr3VrEnDQ0OBg/e4ixmVb+zPSVT9/VVVVAa0nRdtIpNmNTFul09BKdV7pbnQddM2GUxnfuOi6yxOIYdfsnqKtFQVWs/ArE5GFF7NY63Dvf6cJRKoPN33IN7u+4fpx1zO42+BQD0cIIUQEmjRpEpMmTfL8+/DDD+eggw7ihRde4L777vN7n1tvvZW5c+d6/l1ZWUnv3r3JysoiJcX62Zyb03UdTdPIysra/4sO+yHZf5FN9l9kO6D2X8VuNDsQm0Jm/5GgBf58P1y3hdjYGA4fmMGgPkY0Qu+sMuoLq9BjksjOTu+SIa/aXY5uiyI7LZpZE3rzwndb+b3QwQWTM4my29rcf7uri4iNjWHC4B4dGF82jJ2JtvoDYnfOR408FuxGTAA7C9FiYlB9xhKfnW3NEw1mXNnzYO9KYvodQZI5pi40yhHDou3VlDTYyLb4+XbVz1+gE8hK0TYSabZ24xF03Y0CFBrOxonI3C4HNP68aJrWJZ22ZqatFG3Dg6doq0vRNlzsqd4DGNEVUrQND1WOKkrrS+mb0jfUQxFCHIAyMzOx2+0UFPhGGRUUFJCTkxPQNqKjozn44IPZvHlzq+vExsYSGxvbYrnNZttnRQBN0/bp4wlryf6LbBG3/5TquhnvI1DE7b+Oqthp7PdufdGCmLiquLqB5TvL0dCYPiLX8zplp8SxubCaompHl712y3YYjzuhXwbj+6aTGr+byjonv++tYnzfboD//VdR52RveT0aGsN7pHZsfMNPg63fGHEImxYY/wZjEjJNQ8seBqF4zyRmwOBj9tnDDchMRkOjoLIeh1sRF223dPtd8fMX6Lb285/4/ZStcSIy3Ljc/jttdbdRNNWx42zczc0zbaM064q2nk5biUcIK9JpG37MfSH7JHy8tvo15i2dR35NfqiHIoQ4AMXExDB+/Hi+/vprzzJd1/n66699umnb4na7+f3338nNze2qYQohxL6z/Qd453zY8VOoRyL2tdJtxmV6cJOQLVxXiFIwvEcKvdMTPMuzko0vK4uquiYu0OHSWbGzHICJ/bsRZbdxxKBMoP0JydbnGdnyvdMTjLzdjoiOgzGzjOtrPoL6CnDUQvkuY1nmkI5tN8KkJkSTlhCDUrCrtDbUw7GUFG0jkNJsgIZN6ThbybR1u42iqULDqRvfMuiNhVUNzafT1opJw5rHI8hEZOEhnIu2De4G3ln/DpvKNoV6KPtUvdvIDZSc4fBRVGccUJXUl4R4JMJUVl8mRXRxQJk7dy4vvfQSb7zxBuvWreOqq66ipqaGiy++GIALL7zQZ6Kye++9ly+++IKtW7eyfPlyzj//fHbs2MFll10WqqcgROiU7zIKFWL/oJRRfNJdsOKf4Aq/zzGiC5U1Fm27BV60rXO4+XaTcTx//HDfM1SyzaJtddd89lq9t4J6p5tuiTEMzEoCYMpgo2i7Zm8FJW087trGou3w3E5GFPWfarxezjr4/T0o2QQoSMyChK6JhAhH/TKMYv32EinailDTzE5bHVcrmba6bhRtdc2GA9+ira0xF6ZLMm3NTltdOm3DQTjHI6wpXsP3e77n822fh3oo+5R02oafcP5y40D1+LLHeXjJw9S56kI9FCH2iXPPPZdHH32UO++8k7Fjx7Jy5Urmz5/vmZxs586d5OXledYvKyvj8ssv56CDDuKkk06isrKSn376ieHDh4fqKQgRGuW74POb4fvHQj0SYZXSrVCx27heWwJbvm57fbH/UKpDnba/bCuh3uEmNy2OkT19C6Bmp21hZceKtrquWj27GWDptlIAJvTrhtYY55GdEsew3GSUgh82F/u9n1KKdY1F24M6W7TVNBh3oXF901ewufFnJmtY57YbYfpmJgKwo6QmxCOxlmTaRqLGTFsNhbu1TFuveAS3sqFQuN1OIBa7zSji2jXj0pJ4BGVsI9Yea9k2ReeFczHK7DitdlaHeCT7lnTahh/z50P2SXhw627K6ssAqGyo9GSli9BqcDf4TGIqrDdnzhzmzJnj97ZFixb5/PuJJ57giSee2AejEiLM5a0E5YaijcYpwTEJ7d5FhLmti4zL2GRoqII1/4GBx0BU109mJEKspgictWCLgpReAd9tW7FRpDukb7qncGoyi7bltQ4cLp2YqMD7Fl1unbv+uwZdwS0nDiM13jfCoMHlZtXucgAm9PPtaJ0yOIv1eVV8v6mYGSNb5tMXVTVQUu3AbtMY3D0p4DG1qvtw6DUBdi+FXb8Yyw6QaARTU6ft/lW0lU7bSKTZ0QCbcrcbj6BrNnTsKNV6p60lE5G5jTgE70xbpfyPTew7Da7wLdqaYzrQOunCuUBYXFfMK7+/wo7KHaEeyj7j1t2e34Hmz4sILe+fjXD8OTkQOd1O7vnpHh77VTrZhBBhpmBt4xVldGiK8LVnOSx5CRxtFFTcTtjxo3H9sKsgIRPqy2Hzl/tkiCLEzC7btD4QxCRke8qMz5O9urX8oj85NsozKVVxkBEJ+ZX15FfUU1hZz3OLtrTouP19dwUNTp2MpBj6N3Z5mg7u043E2CjKahys3lvZYttrGpcNyEqybtKsg883Ct6mrKHWbDdC9M0w9kF+RT31zv3nzG8p2kYgZbN5JiJrrdNWNXbaKjTcWhSKNuIROhll4NbdKIxxeHdEyWRkoeXSXZ59EI6FDzP3uCuKtpWOyrDMw/QuEIZj7vOvBb+yonAF3+/+3vJtbyjdwJbyLZZvt7MO5AJhWX1ZWD5n758NszNdhFZ5QzmVjkp2Ve1CV62fIiiEEPuUrkPR+qZ/lxxY8yREFF2HJS/C5q/g9/dbX2/PMqOoG58OuQfDyDON5Wv+A045JtjvdSDPVinF3gqzaNuy017TtA5PRmYWgwE2FVTxzq+7fG5fst2MRmjZ4RsTZePwgRkAfL+paUKyijonb/68g3/9shMwJk6zTHIODJluXI+Oh9Te1m07AqTGR9Mt0ZiMbOd+NBmZFG0jkWZH08yJyFrLtDWW69hwYwcFeuMH4eZFW6fqXPHIjEaAponIwNqibVFtEQU1BZZt70Dg3V0bjgVCc3z1rnrLiwBPLX+Kh355iGpHeEUvhHuB0Ow0rXFae0pJg7uB51Y9xzMrnwm7vGvvn5Nw3CddpaKhgrt/uptnVz4b6qG0UO9q+lAWjmcJdJUaZw0vrHqBlYUrQz2UFryL5977RwghQqpip3Eqtal4c+jGItqWvwrqjOgjNn0BNa1M/rr1W+Oy/xSw2YwJlpKyoaHSuN+BytVgTMy2v+tAnm1RVQMNTp0oe1NxtrmOFm13NxZtezZ28C5cV8iPjRm19U43v+0yJkCc2N//ZF9ThmQBsGp3BYXVDj5euZdbP/yNResLUUoxulcaxw/vHtSY2jXyLOg9EUbPMn6GDjB90xsjEor3n4iEA28v7g8a4xHazLRt/KWuNDu6Zkcp5Smimlm2UZo18Qje94+NivW7vDPcupvHlj3GI0sf8cQwiPZ5F6AcbkfYxVV4F2OsLAIopcivzcet3JQ3lFu2XSv4FAjD8FR8s7hf67L2m8kqRxUu3YXD7Qi7SfHCvpDubmBdyTrLc8ILagtwKzcFteH3ZZj3e+RAKhCuLVnL78W/s2jXolAPpQXvv73h+HMihDhAFa4zLmMbO9VKNhsTGYnws+27xiuaUXz8/b2W69SWGhnFAAOmGpf2KKMIBbD2Y3AeWLFqAFTsRvvgUlK//gv88ISR+WsWwPcnyivipFu/gO+2u9x4T+SmxmO3aX7X8UxG1sGi7bShWZw6tgcA/1i8nW3FNazaVY7TrZOdEkufdP9Z2j3S4hmUnYSuFI8u3Mn/fttLg1Onf2YiN50wjOuPHWxdNIIpJhEm/xmGnmDtdiNEP89kZNJpK0JIaTY0Dey0nmmrzExbbLg1u1c8gkLD+GVmVTyC+WHOptmI0qI827eqyFDpqKTaUU2Du8HyYtL+rPkH63DrtvUej5X7tc5V5ylQh3OBMNzGBk1FZas7bb23F27vw3DvtJ2/bT7PrHyGJXlLLN1ubWNnUjh2snp/oRGO+6SrmFEx4ficw/3LDSHEAcos2g46FjS7kX1a20oHpwgdR40xORLA+IuMy62LoGKP73o7fgSlQ+ZgSOnRtLzfZOO0b0c1bJy/L0YcXnYtAd2F5qpH2/UL/PwcfPQnmH8brP7QyAHeH9SVGR3Vmg3S+gZ8t91t5NmaOhyPUG4cL/dMS+DUMT0Y0zsNl1vxzDebWbTRiDzwF43gzey2dSvonhzHVdMGcvuMgxiakxzUWERg+jXm2u5Pk5FJ0TYS2ZriEZRS6H66bXXdq2jbOBGZGwVKYbc1dtpaNBGZGY8QZYtC0zRLJzgDfLolO7pNp9vJ/y3/Pz7f9rklY4oEzT9Yh9sHbe/inZW5tj4FwjA7iAn3wkdX5QzXep26GG5FQu/TvsNxn5TWG1lZhXWFlm7X/KLE6XaGXRd+g960Hw6kTFvz5yTcvtgA35+NA23ySCFEmFKqqWibO8aYuAigWHJtw86OxUZhMbWXkbfZ8xBAwW/vNK2jlFHIBeg/zff+NjuMnGlcX/c/cDQeV7pdsPtXo/v0vYvg85uNvNyK3ftXx3Xje7qh71GokWdC+gBjeekW4zXcuTiEg7OQmWeb0hOiYgK+W9MkZP67XQGyzaJtdeDHlXUONyXVjsZtx6NpGpdN7k/31DjKahxszK8CjKJtWw4bkMFpY3pw9pgs7jl1OIe0U+QVndM303gfFFTuP5ORSdE2EmnGRGQaxpvQ5adoq8yirWZrnIiscaowpTyZtmbx1qp4BLNYa9V2TVYUbXdV72Jj2Ua+3f2tJWOKBM0LUOFWLPMej3dRr7OqnU05tuFW/GgeWRFuPPEIFu4P8C2kW32af2eFe6dtV3U/1zWeXqhQYTdppPc+Ccefk65iFkPD8TmH+8+JEOIAVJVndOXZoiBjIGQOMpaXSK5tpxWsgcq91m1vm5lTOxU0DcacC2iw6xcoaZyktnSrUWy1R0PfSS230fcIo/vWUQPL/wFLXoKProDv5sHOn43YhLLtRuzCp3+GT/4frHy7KSM1UikFxRsBcPQ4FEadAyc8BKc/Z+SWQuQ/R1MH8myhqRs20E7bQJsVzO2mJcSQGGvUORJiorj26EGeSIPctLg2HxfAbtM4ZUwPDuuXSpRdym9dLSWuaTKy/SUiQd41kUizAxo2owyLS285iZNZtFXY0L07bVFNmbZmR6zqXBHFLPRE26J9tmtVIaCsvimzp6NjNT9w1jprLe8q21K+hU+2ftKpYpTD7bC8UNb8g3+4FTB9Mm0t7KYL51Pxw73T1twnTt1paZeyd/xFuD3vSCmkW1209d4n4fa8vfdJOGbaLslbwhPLnqDKUWXpds19Em4/IxD++0QIcQAqXGtcZg42Cn0ZZtHW6EqsrHfy/aYiHC5rJ7vd7+Wtgq/vNf5zW/BFe2VeY9FRg35HGsvS+jRdX/Vv49Lssu01wcjkbM5mg1FnN677DWz+yijgxneDYTPguHvh0D9Bj3FGIb8qD9b+B+bfYhShI1XlXiMWwh6DO6VX0/KEdMgda1yv2BWSoVnO7LQNIs/W6dbJrzCOUXqmtV48TU+IQdM0XG5FWW1gn3GaT0Jmyk2N58qpA+iWGMNJI3OlazYM9c/cvyISokI9ABE8ZXbaKuMgpM1OW2wozfhPN27wdNpGa9GN97em09Ys2ppFYatmia9oqGjxWMEyP3DqSqfeXU98VNvfiAXjg00fsLNyJ4PSBjEsfViHtjFv6TwqHBXcd8R9xNr9z3oZrOYfrMOtEOCd6Wrl6bY+nbZhHo+glAqrP/Q+3c+uWlLtqZZsVyIrOs78OfF+X1vB+0uicPtyo6u+0LHKD3t/YGv5VtaWrOXQ3EMt2675ezDc9geE/z4RQuz/ahpcfL+pmMo6J6eO7UFc4XrjhuzhxmXGYOOydCvVdfU8PH8T+RX17CmrY9bEPqEZdKRxu2DZG8b1ujLIXwU9x3dum2aXbe4Yo9BoGnW2cVp//m+wd6WRZwswYFrr2+ozySjWlm2HXhONwm/2cKOgC5A1FAYeZcQn7F0BGz4zOq+3fQ/dR3TueYRKY5ctGQONYrQ3MxKkfD8p2pZuNy67Bd5pm1dej1KKhNgo0hKiW10vym4jMymGoqoGiqoaSE9sP35hT+MEZ738FINH90rj0bPTAh6n2Lf6ZiSwfEcZO6RoK0LG7JTVGuMRmk9GphR6Y/et3ligVZrdk2lrdTyC+QHT7LA1L53Kmg+eZQ1NnbYdLQQ3PxXfyqKt2Qnc0U5ZpRR5NXkAFNYW0ju5tyXjat49F27ddD4TkVnYZexdIAy3yb68i4JKKVzK5fnyJBw0L6SnxlpTtPXJtA3jfeI9AVa4MIvcVnfiR0qnbbiNDZrGVO3omkK6w+0Iuy90pNNWCBEqe8rr+HpdAYu3lHi6ZrcW1/CXujXGB9msxoaJlB4QnYDuqOGfC34kvyIFgIXrCzn6oGyyk+NC8wQiyaYvoNJrcrBt33euaKsUbPvOuD5gqu9tyd1h4NHGY/74d3DWQnw6dB/V+vY0DY65s/3HjUmAfkdAfJrRMbx7CbgvA3sElj4ai7Yqc0jL21IbO2/ry6G+EuJS9t24rFZfCbXFxvUgOm13e0UjtHfclJUc6ynaBjIJWCATnInw1DQZmcQjiFBp/DYxSmuMR3A3O+1Hd2M23+qNu1jXohqXNRVtLZuIrFmmbZTWGI9gUadteX2553pHO5C8P3BaeZqxW3d7Prh3tBjl/fp7P9fOat4NFW7FMu+Oy66aiCzcCj7Ni4LhNj7vfWLlz0k4R1aEe1an+XpZ3mnrioxCejgWCM2fE6v3iffvwXDLfvbZJ9JpK4Roh79JkoOhlGLVrnIe+2IDd/5nNd9uMGIOenWLJy7GTv7enWzfuRM3NjCLWZqGSh/AztJa6vauJy7GTr/MRNy64sPle9p+QAH1FUYeLMCgY43LPb82TfrVEQWrobYEohMaJx9rZuSZYI8xCrYA/ac0dc1aIesgiEs1YhQKfrduu/tS0Qbj0l/RNjoeErOM65EekVC23bhMzjGK7gHyRBi0EY1gMnNtC6vaP45RSgU0wZkIT30zGicjq6inzhFec3d0hBRtI5Bq7LS1m0Xb5gdGSvfkturYPffRGzttm2fadjZ71lO01bpmIjLvTtvOZtqCb7Gis6qd1eYUbx0+7du7iOX9XDurRaZtmJ2W7v28LY1HcITxRGR6mBdtu6r72RXGhfRwj0foojxun+7nMNsn4V5I76rICu/fg+FWSPfZJ2HYkS6ECB+7y2qZ++5K3vhpe4e38fPWUv7v602s3VuJpsG4vt246YRh3H3qCP583BD6qV1UN7j4tTKNeprOWFpSlUF5rZNc926umTaI2ZP6oWmwdFspW4us/Z293/ntXaN42q0fHHIpJOeC22lMFtZRWxujEfoeDlF+Tkc382hNzbtxO8tma5qsa2cnnkeoNFQ3dT5nDva/Tlpf47J8574ZU1cp3WpcBtFlC3gVVtsv2mZ7TUbWnoo6JzUNLjRNIydVuvQjTXJcNBlJxu+cHaWRH5EgRdtIZHbK0php2yIeoanTVjWeJuC2RbXItDWLrJZl2tqtn4hMV7olmbbeHzitPKXVe2wd/ZDtU7Stt65o27zYEW7FD+99YmXR1rsoH25F2+bFsXDbJz5FWwu/3KhzNu3fcNsnYV+0bfy9oiv9wOlID/OuTvP1qnRUWrpd70J6uL0Xw737WQgRHlxunZe/30ZVvYtlOzp+TLtmr3F8PbZ3Gn87azTXHDWIoTnJaJrGgKwkzu9fg92mscbZgye+2ki9083C9QV8UWCc8jwto4LhPVLok5HAYQMyAHhv2W7LJyPeb5Rth81fG9fHzTaKnf2nGP/e/n3Htumsayr4tpVTe9ApRkFy4NFGxIXV+kwyLncvsWZitX2pcVI9knMgtpXog7TGWL2K3ftmTF3F7LQNIs8WvHJnAyjaZgVRtDU7eLunxBITJSWzSNTXjEgojvyIBHkHRiIzk1YzJyLzF49gHJQommXa0rLT1vJMW4uKwQBVjip01fT8OjsRGVhbjPL+0N7RYtS+KtqGW2HGu8htZVenT6dtmHUXR1Ih3cp4BO+OxHB7H3rvA13pYXdauvd72MrfXZHy5UY4dnWar1eNw7qfEaWUb6dtmP2ceP++DsdCuhAiPHz6ex67So2/LzUNLqobOvY3dUtjV+y0odlkJrWcoDezdisDshIpThzI5oJqHvxsHW/9spOC6F7kpMaRQ6lxSjxw5rheRNk1NuZXsWp3RYttHfCUgmWvAwr6HAbdGyd26zfZuCxYCzUlwW9358/gdhgduxmDWl8vJhGOvx8OvTL4xwhEJEckFDcWbTOHtr5OamPRtnxH14+nK5VtMy7TBwR8l5oGF2U1xvFJj0DiEZKMjtnCIIq2PSXPNmKZEQn7w2RkUrSNRJ6JyFqLR2gq2noybc2+XK/JTTxF2w5GDpiaxyNYVQwGKG8o9/tYweqqTFvvTtuOFgi9n1NpQ2mnx2QK+6JtF3XahnWBMMwzbbsqRiRSCoQQXoV0pZRPscyq313hXiAM5+5npVSXZNrWu+s9UTsQfl84eb9HpNNWCOHPjpIaPvnNmFjXZjM+a+RXBH98V1HnpLCyAU2DgdmJLVeoK4OqPBJjojnnhGOJj7Gzp6wOpWD80H50z+0NKCjZAkB6YgzHDc8B4P1lu3B3Mm93v7PrFyhcB/ZoGHt+0/KkLMg+CFAd67bd1hiN0H+KMYFYqERyREJbebYms9O2fJdRgI9EjlqoyjeuBxGPYHbZpifGkBDT/iRz2SnGF0A1DS5qHW3XFJo6eCXPNlLtT5ORSdE2AqnGzFh7a/EIuhuj+Vbz/JF026KMTls/mbaWd9paVAyGlp2nVsQjWNnV6d1p29HCh/f9rJyIrEXRNowyEr0LH9B1E5F1tEColOLl31/mg40fWDUsILwL6brSfSJNrNonSqmwPhW/eddgOBUJ3crtcyqnVUXCene9z3bD6XcDhHfR1qVcnuJqlaPKsu02/7sk+0QIEUmcbp1XftiGrivG9+vG0O5GTEFBZfC/L8wu2x5p8f4LMYXrjcu0PvTrkc2fjx9KZlIsE/unc8FhfdHM7M+SzZ67nDQqh8TYKPLK6/l+U1HQYwpLNSVGDm1nTot3OWDFm8b1g04zCrXezG7bbd8FVxCs3GsUgtGgv8U5tR3R+zDjcvfSyIlI0N1N8QhZbRRtk3uALQpc9VBTvG/GZjUzGiEhA+JaiYHwI9iJwuKi7STHGb9TiqvaPs7aXWYclwUywZkIT/0yjaJtYWV9u0X6cCdF20hkTkSG2WnbLB5BKXQUumbzfLGpe0q8TZm2ZvG2s0Vbt24UesxirVXbha7ptLWyO8qKTFvv51TWUOYTB9EZZnEsITrB59/hwK3cPl1lXVUg7GjRtrS+lJWFK/lm1zeWFijCOR6h+Wtl1ZcbzQuE4dZp27yjMZx+TpqPxap90qJAGEbPGZp1dTZ7/4Sa9/ulwd1gWUds89+B4fS7AcI/Z1gIEVr/XbmXPWV1JMdFcf5hfeneOHFPfkXwvy82FxrH6YOyk/yvULTOuMw+CID+mYn87axRXDl1IFF2W9Op+F5F24SYKE4Z08Mz1npn5M8mzi/PweoP4POb4ff3O1aMXP+JUehLyIDhp7a8vc9hRkGwck9TYa09pdvg63uN67mjITEj+HFZLXu4kQnrqIaC1aEeTWDKd4KrAaLjmyIQ/LFHNWUBV0ToZGSFa43L1iZba4WnsBpEhIEn17a69d9Nuq7IKzdu7y3xCBErKTaqaTKyCO+2laJtJDLjDTyZti3jEZRuRCPERhsFVF2ze+IRLO+0VcaH1mhbs4nI9M4fEFlVtPXJT7XwtG8r4hG8x6aUorLBmsltzA/WKTEpLR4n1JoXiawq2ta56nyK3h0tRnkXFgtrCzs9LlM4F20P1AJh8wJUOJ363bzAbVU8QvPfgeF2Kr73z4VSytKcYafbyfrS9ZactQHWfQnY/HdguP2cSDyCEKI1W4qq+Xy1EYtwwaR+pMRFk5PSWLStDP73xZbGou3ArFaKtoW+RVvAE/0GQEZj4ad4k0936FFDs8hKjqWizskXawuCHldYKdsO+Y35rLoLfn8P5t/clIEaqK3fGJdjZkFUy+xgYhKh1yHG9W3ftb+9Pcvhq7uMCIvUXjCxi3Jqg2WzQZ9Djes7fw7tWAJl7suMwe3HS6T2Mi7Ld3XtmLqK+V7uPiqou+0OYhIyk1m0LWzjLIDCqgacbp2YKJtnfRGZzMnIpGgr9j0tgHgEpdCxEe9TtDXiEcxOW09xVXWuuGp+4O+KeITmcQFmgThY3lmiVk4e4xOPYEGnLViXa2t+yE6KTvL5dzhoXoyqd1nTTdeiGNXBrk7v1yq/Jr9TY/JmFqNi7bEtHifUWhRtLfpyo3nRNtw6bcM507b5a2VVgTDcT8Vvvk+s7Oz8audXPL3iab7bHcCHTz9a7BOHRfvEot9dXUXiEYQQ/jhcOq/+sA2l4NAB6Yzv2w3AU7QtCLJo63TrbG+cNMZvp21DVVNhKmuY/41062d0hzZU+pwuHmW3cfaoVM4qfYmYRfdTH8mny677xLjscxgcfp3RRVqxG764A359DZwBvO61pVBdCGjQ85DW1zMjEnb8aJyy35qNX8C3jxjdoTmj4Lh7w6PL1hRpEQnFG43LrDYmITOl9TEuyyOw09ZZ39QVnzMy4LsppTzxCMFEGGQnG7+biqpbP5YxO3h7pMX7fiEkIk7/TDPXNrInI5OibQRSZryBWbRtEY/QWLTVbCTEGEVbtxaF8We2qWhrdsZ29sOhWXRs3mlrZTyCeYp/WHfadvB1bH4/q3JtzW6o5BgjVywcC4Rm17dCWdJt27yA0tFilPc+Kai1rhvDLHaY+yScih9d1dVZ4/LdTmfeh7uqdllWJDOZ+8A8KLNyn+yq3MXtP9zOz3kd6+roqu7n5j9r4VYg7Mqc4e2V24GWeemBav47pcppTa5tJHXaWplBLoSIbP9ZuYf8inpSE6L5w6F9Pcu7pxpfThdUBvel/M7SWlxuRVJcFNn+OtyKNgIKknMhPs3/RqJiIK1xLCVenacNVYzb/Az91S5y6rewfvNm//cPpdJtsGtJ2/mxNSWw4yfj+kGnQr8j4OTHG7NjFWycD1/8FZqdRdNiAjazY7lbX4hpIxM0dyzEJEF9BeT/1vJ2pWD5m/DrK8bjDzgKpt5idOmGk0iLSCgOYBIyU2pj0bYiAjtti9Yb3eKJmZDUPeC7ldU6qXO4sdk0chvjWALhiUeoaqtoG3wHrwhPfTOM3207pGgr9rnGQpdN899pq3QXugLl3WmL2Wlrfaat2VHbPNPWyniErPisTm3T+wOnVd1qSimfiWg6eopx8/t1tJjQnPmcw7FAaBY+4qPjPcV+KwoBzQuEHd4n3kXbGuuKtuY+6arIinUl63y+SAhG89fKqi83mhd/O1ogzK/J5+ElD/Py7y9bMSwP8+fC3CdW/pwsL1xORUMFq4s79uFgn3XahnGBEKw9Hd+MO7GiCx8s7LQN833i/XPhcDvCKmdYCBEahVX1fNkYMzB7Uj+SYpsmDctMjMVu03C5FSU1gf8+21TQmGebleS/w83Mvswe3vaGMgYal+Yp5o4a+OZBtPIdpMYbx51bt2wMeFxdrmI3fP8YzL/FuNzwWevrbpwPym3EQ5jPMzYZJl0NR90O0QlG8a5ovecu24trmPP2Cj5YVdT0+9vMBm6tY9lkj4K+hzdu6Iem5Y4a2PotfH2PkY0LMPpcOPRK4z7hxmaD3hON6+EekVBX1tQFbWY0t8XstK3cGxldxN7MAnr3ke3HQHgxu2FzUuKMLOsABRKPsKc8+A5eEZ7MeITCygbW5VkTQRkKUrSNRDbfeITm35w6XEZhU8dGvKfT1u7ptG2eadvZ4mrzTlt74/g6G4+glPIUMDPjM4GOf9D2/sBZ56yz5ANnjbPGJ1rCivxUsC4ewexW66pOW6fu7PB7xywQxthiiI8y/iBaUSQ0oy/M93iHu5+9CphWxiM07372ju3orE1lm3hm5TO8ufbNDt3fLCB7iugW/pz4PE4H34cl9SUAbK3YamkGq7kPuuLnZE/1nk5ts/n9uirTNpwKhEopz3isjhFx6S6K64xTZa0q2nZVp22DHj5fsrl0V4sJMmUyMiHExyv2ouuKET1TGdM7zec2m00jO8X4HR7MZGRbitqZhMyTZ9tOodGc0KhkEzhq4ZsHoXQrxCYTn2ucbl68dxsutzWT/3ZYdREsfhY+/YvRYWta9TZU7Gm5vqMWNn9pXB92Ssvbc0dDz/HGdTMnFFi1uxynW+en7RVNeb5FjZ2cXtnAreo/xbjctQS2LIRFD8OHV8DPzxr7xBYFh18LI88MqvC2z/WZZFzu44iEpdtLWbajNPDjavPLhrTebXdBmxIzjUxi3QXV1n1u2ScK1hiX3QOPRoCOd8OaHfwlNY5Wf/7NbQczwZkIT0mxUUzonw7AUws3eSa6jDRStI1AzeMRnG6dxXsXe07BdTqdnvXMTls3UW1m2namONMi01azphhc7azGrdxoaJ6ibUe7gn0mtrHoVPwKh29HoxWn4oM1nbZKKc9+6YpiVI2zhjt+vINnVj7Toft7FwjNoq0l8QiNnYhpcWmANZEVhbWFLQoWHeFdjOqKTtv1pUZHRfPJ+wJlji01NhUwfi9YMT6zQGju584W0nWls7t6d6fHBcbvE/OLl67otDWLth0uEOpdW7TVMD5YhVM8gvdzTok19kmd25rT8Yvrij1/66z6kq3LMm3DaHI4758J8z1j5RdOQojIs6u0ll+2GV+mnjWup991gs21VUp5JiHzW7StrzAKr9B+gcfsTizdBoseMjIzY5Lg6L+SNnAC0XaNpIYC1udb88Vb0HQ3LHsdPrkBtn0LKOg1AU6aBzmjjWiDn59tmSG79Rtw1hnxED3H+d927mjjMq8pysAsQgG8v2wPv2/d0342sLeMQZCcA24H/PIC7F1uFAhTesLImTDjMeh3ZMBPP2RCEJGwpaia5xdt4dlvtvDIgg3sLQ/gmKYoiGgEMArlqb2N65GUa9tQZfyMAnQfEdRd93SwsJoaH0203YZSilI/ZwE0uNwUVRm/s3p1C6BgLsLeJUf0Z3iPFBqcOk9+tTEioxKkaBuJzHgEjA+f9a4G3lr/Fm+tewun7sTZ2GmrNLvndAG3WeJVTZ225iV0riu2eTyCVRORmdmuyTHJxNhjjG12sGjb/AOwFcWPyob/z95/x0ly1ef++LtCp5meHDfn1QatsgQCCSEQSGCCbIzB93uvrwFj/7CxucYRLhfsaxtzMQ44XAdsbF+wDSaaKBAooiztrqTV7mrz7uzu5Jme6Vxd4ffHqVNd3VPd02k2yP3sa1/T01Ndfao+dU7Vec7zeT6lEvtmCULp29sK0tawDRz3+vBI2xYShM9OP0vKSHn+kPXCU9pq4ZaStjKuvZFeoAmFo+9cWY7FbHa26baZtrk0Ji0k0o8njnvf0wjkdRgPxb2xoRUeqnIfzcbE37/Gkq3x7PK3pdU2Iikj5VlVNHzMbj+R8WgZaevG5FL2uwboCrW2fdIaAVq3yNYqy4psQYx/clH1UoyJpmjeeN1W2rbRxn9ufHXvORwHbtzU76WflmPEI21ru6/OpAwWsgU0VQne5/n9gCMKjXX0V99Z1yrhqWqboqBTqANe8z+hbyNK9xq6YyH6zGn2nmmNJVndOHE/vPhd0b7RPfD6P4BX/bpIc3/Z/0+0d/YYHPyP4mcsEw67tgk734TtCNuDJcKb0T3i5/wpyIm5ikwlX9cbwcHhO/c/QLZgVvcG9kNRispeSdS+8VPCS/eqtwtC93LARbBIuP9w8dnjyESS3/nGC3xt31kMs4ogRBYhq5W0haKP8+VE2k4eBBxxTS3Xp8sgLQzqJVYVRWGwS/AKUwG+tucTORwHuqK6Z6XSxuWNsK7yS7dvZetInKxh8cffP+JdP5cL2qTt5YgypW3O9ZezHZuCVcA0C+5mGroqVDGWomJJpa1aWogMmvO1XaK0bVEhsvm8eJDqjfY2tU/bsb2JtjzmVpAfUmkrydZGJ9nymIZjw0DxuJuBV1wJhXgo3lT7gvD8tEi5KliFhlTaXjy0kHf+Wkna9kX6Sr6n7vaVkfytKEbmJzniYRGTVhGEBbvA6cXT3utGIK+PsBYmFmqhZYUk0luofj6z2JoHUnn+VUWlQ+8oea9ZSJUtNB8Tee5arbSVqupGCcz53Dx/ue8veXb62Za0C4rnP6yFiepiwt8qT1t/P25UybpSnrZy/Fspv+tmIGMS0SJEdJFW2Eqf4TbaaOPywpHJJM+dTaAoCj9+bbDKFmDULQ40sVDb851MW90w0EFYD5iint8nfq6+dvmdKT4v0FBMeL32bxa/d6+mJxaiz5pm/5nExfHonnL9Zne8CV7zERj0+ZZ2DsAN7xKvD3ylqEQcewIyM0IpuvFVfOGpMX7vWwd5+OhM6b5jfa7HqQOTB8gVLM+/810vW8UVI10MZU5yciZNrm9b7W3e+lr4qf9XJGp71zV27Bcb0iLh3DPVC761AMlcgSdPCtu79716C1et7cWyHb717Dgf+8YBXjgfUIfCKhQV5UNX1P5lMh6Jy6gYmVQ7j9ZnjWBatqdYbsR3drhLjE1BxcgaVfC2cWkjGtL4H6/dzsbBTtJ5kz/+3os1Z4FcCmiTtpcjVKm0FaStYRYnn5ZjUSgIElBRVVSPtNXdrW1USu0RoDmCVaYXlxcia5a0lWnevZFej2xtRL3rJ2H6ooLMKy9Y1Qikik4WSWuWmBnuEKRtykg1nRorU1dDWshTKbcqBdqwDC8V38Ep8fWtZx9QZo9QaJ09goxzKwqRQWuKkZWQUVq05L1mMZYc89rcMBklLSu0kEdgtoIkbJX62X9cZ5KtIW393qmSjGpV2vfZZNHCoVl1sVyEMCyjJf1YxkSSto1eMwdnD3J47jDfOVGlYEqd8PcT6Wnbqn7iV9o2q0iXNgH+YpTNQBLpzfaTlYB/QafVMWmjjTYuLziOw1f3ikXJV20f9NS0QZD2CBM1ToyPuX62W4YCrBFsC8bdBcJaSFuAnW8WqtPb/2cpKdq9mnhEp5Mc+XTC89G9oJg9Jn5WIqs23irsEmxT2CRYBTj0TfG37Xcyk3O4/0VxT3vhfEBxHam2HX/OU5T1xsL0RHXe9+otbFHGMEybr5/rrt3XV1GEb+rljsHtIms1vwiZ1tQRqYQfHZ3Bsh02DnZyw8Z+fuW1W/nF27fQ0xFiajHPn3z/CB/9jwN85ZmzHJtKYtuOUEjbpiDn4yO1f5m0R1i4NJS2k4s5vvT0WHVPa68I2Z669j2VzGPZDpGQymA8XHfbZDGyINJWqtLb1ggvPcTCGr/6uu2s7YuxkC3wR997kZnU5fE82yZtL0NIT1uPtPVNuG3HpmBK0lZDc83ghT1Cqaetoije66aUtmUq1lYpbf2krfTJbYSwkBNOBcWbELci7XvREA9JXpG0BokPeZ56Ij0ewdqoL6mEJOCiWpSwKvbZqkn24bnDJXFoKCZu+/yFyFqhtJVxlaSt5VgN+dGWH9NEpnlTf79aTca5VcSMtEaA5tXFITVEZ0ikJa6EPUIrlLbjqfGWnLuVJNL9vrvNLuh0hbs8krAVMZF9zSMIG1R1ynN1LnXOW8RqFv5+4iltW5SK7198afSYy72fW2aP4MbEI9IvIZ9hfz9p5XjdRhuXPKZfhEf/UpBl86dWXJV3OeDQZIZj0ylCmspbrl5dddsRV2k7lzaqp4K7qOpnO3MEChnhSztQozp0dI+rYi3bXo+gxofojur0m9PsPZOobX+tQj4FyXHxemBr8DaKAje9VxB3iTPw4P+B+ZOghWDb6/nms6IIHAiLhCWQpO3E84y5/o1SORjXbW7qWUBTFZ5IDvO5x09fHLXxxYIehm732k2c8t5OZAy+8sxZ5gN8ThuBbTs88OI0ALdfIYQ5iqJw/YZ+/uDuPbx25wiKonBuPst3nh/nD79zmP/xxf3c+9BDLOYKLrlcR1E3qbRNTUHh4ioIXzi/wO996yD3HJjgT+89QsYI4AQyc7B4HlBqK4bng1corDeG0kDhu6G4S9oGEHZF24W20valiHhE54Ovv4KRnijzaYMvPd2aOikrjTZpezlCetJigeNg+KpfmrbpFSLz2yOYfk9btehl6y9G1igk6Vhuj9DMPqHo7dobac4ewa8Skqn4LbFHcEkKj7S1m7QKUEOeom4u19zKr1QL+pVRrSIIn595vuT3Zoj0VpMAXiEyl4xqtH3yM5K8bKXSNqJFWq5WKydtm7kOw2rYU9q2wh5B7sOvFm0E/jg6OCVK1kYRSKS3KC29FfYI8nMRLeKNXa0gCVfCZ/jQ3KGm2+VvS0k/aZH62W+P0KzfdX9MeK+1irSVMfEsKy5Bpe1KjF1ttHFJ47kvwqmHYd/n4bu/BV/9eXjk03D8PlEU6yUIy3b41Pde5CNff56Hj06XqDBt2+E7h4TH/x27RujtqK5w64roxMIajgNTyeokUtawPIVbIGkrrRFWXS18SZtF9xrPImHfmfkLS1pKlW18BCJdlbeL9sCNPydeT7jP3ptfzZQR5pFjxVoLM6k8qXzZ/Gh4F6g6ZGaYnRD2WR4JNXOUmOawdvVqknofPzo6E6zWfSmjb6P4OX/Ke+tbz43znefH+buHT7TkejhwfoGZVJ6OiM6Nm/pK/hYLa/yXl63nz955De991WZu2tRPLKyRzpssnjnAiek08x0b6/vCaI/4D7CwMkTUI8dm+NN7j/DUqbmK5+j+w1P86b1HyRoWiiKuz3985NTS7aXKtn8zRAL6fBU0q4aV1i3PnU3w8NHpsn2L+ejqBmwX2rg80BML8bOv2AgIu5/LYdGqTdpejvApZRWcEqWt5ViYluVuphftETwHXNtT1wKegrUZVewS0rYF+4QiKdoX7SsSweVVVGuAn5iRJFwrC5ENxAa89xo5Zr+/q/SubNbXVh5zVI96ZJTt2E3HxHbspaRtAwpjT2nrI21b6p/qI20bIT/kMa3tWgvARHqi6QE9iPhoBTHjOA4nFk6UvNfM4kaJz3ALLCu8VPxocwrC8nPVCosEf0xaqbQ1bbOE6G/WsiKshVs6di3xtG0FaTvbGtLWU3WqrU3FTxfSJeeuWUX6QFSM+zkz1xJVbKvsESzb4vRia1VTQernNmnbxn8KSG/Iwe0iLTy/CKcfhSf+Fr71qzDVmnHvUsLYXIZD44uMJ3L80yOn+MjXD/CjozOYls0TJ+cYXzToCOm84crlC08piuJZJCznHXhiJoXjwEA8HEwGn9srfq65ru5jCkT3GrqiIQbtGaYW8xe2KI0kbcsVwEFY/zJhlQCAAjvexDefG8dxHHav6WHYPb9L1LZ6xPNDtc8/B8BaSUJNC4uzvg1XcdsOkX7/6PEyX9yXOvrcol0+0lZ6Kh+ZSPL06ebri9x/WJCBt2wdIKJrgdvEIzov3zzAL9y2hU+/81o+ek2anY4QYhyxGiju1rte/GyxRYJtO/z7U2N89kcnOXBugb954Dgf/Y8XSshb07L53OOn+byr3L55ywC/edcONFVh7+l5z87Dw0Rjfrbg851tkFjdtaqbq9f1YloO//TIKf7xkZMYps1irsBittDUvtu4PLBxoBNFUVjMFpjPXDrZbZXQJm0vQziu0lZBWCSUkLa2henaI6iqWqq0dVx7BF/YJRnazKRTflaStS0vRNZCpa2X9t0CgrDcHgEaU+n5lbb9UaHekirjRuFPZw1pxYJzzZKEpxZOkTJSxPSYR6g0pGT1peK3qhCZ4zheUaB4OO5dMw2Rtu4xrY2vRUEhY2aaVtTJwj0RLeLFpBWk7WRmkkwhU1JYsJnrMKwWFenN9hPHcZaoOhtdPJDtk4tOsvBaMwhS2rZC1TmRnsByLM/SoGH1s6+fyIKCzdojOI7jkfHN2iP4yejDc4dbQhR6RLreWlVneTHBZhcPusPdXkpe2miOSLdsa4ntQqMxeWz8Mf7oqT/iWye+1VSb/AiyEWnbI7TxkkduQZC0KCLF/m2fhTt+B658m6h0bqThvt+H049d7Ja2FJK4GoxH6IrqTCfz/OMjJ/nI1w/wFdfL9q4rR+iM6NV240Eq2iYXq4/jx6fFOBqosk3PwMIYoAilbSvQvQZNVdgeE77kF9QiYdbNjqpkjVCO638WVl8He36SSaeXx44Lle1br1nNxgHxvHZqNtgiwcEhOvsCUFTaKi5py/AuXrlFLEDuO5MgV2guQ/KyQq8kbcWzZK5QVHoDfPGpsabOx3Qyz/PnEkDRGqEqHAft+X9nw8G/ZiCmcCa8laczdfjZSvS0vhhZrmDxV/cf43svCKu4GzYKVfD5RNYjbx87Psuf/uAIDxyeQlHgbdev5T23bGL7SBdvv0G06QtPjhUXFxzH52e7u+42eRYG/Y0Rq6qq8Muv2cqPX7cGRRHewx//ziGeHUsAwvM2Ggom2tt4aSCsq96YeHLmIvia14k2aXs5QhYiUxQUxy6ZfJqOWSRttVKlrbj1OKi+tCJpldCIgtX7TpeAkUSUt88m7BEcxyGRSwCCWJD7bKYQWVgL06m7arUmJ9mO4xSVwJE+r/haM6pOXdW9FPJWkbYRLYKu6B650Kwi7LkZsVq/a2CXp7hqxn6gpBBZkySAYRveNdcZ6vS8fJtpXywU8/xxJ9LN+dr6Fw8k8dEKr85jCaHY2NSzqRjnVqmfmyQI81bei0mrLCvWdwkVwViy+QdSef5bnfYtrRHWdBWrardK/dzs4kHWzOIgyNVmVZ3+z6UL6Zaon/1K21aqOmURMrkw1qy6OKJFPCI9WWiuGJl/HGhW/SwV3g+efdBbKGoWgQX72krbNl7qkOnF8SGhWtR04bt41U/BXX8Ia28QxYIe+TM49K2XjN/tUZe0vXX7IJ9421W8/YZ1HnmbyBr0RDVeu7MGEsqFVIKOVytGBBybFONoVWuEwW3V7QTqgetpuiGUAGDfmeaVlTXBcWD2qHhdK2kbicOrfwv2/CTffPY8juOwZ20PW4bibBoU85pgX9urKFgOo9lj6IotCHTbghn3+4euYNNgJ8PdEQzTZu+FOgeXAqTSNjUJhSwnptM4DvR2hBmIh5lPG9xzoPHn/gePTOM4lKihK8JIC8/iF74KgLLzx/hG73/j8GTa8y2uGdLXNtEape182uAT3z3M/rEEuqbw86/azPtevYVP/uRVvOWa1R55+/cPn+DweJJISOWXbt/KG/es8uYkd+wc5tr1vVi2w988eFz42yYnIDMrLDyGdtTVplzB8gqINaOGVRSFN121ml993XbiUZ2xuQz/9MippvfbxuWDzUNi/Dwx3XwW40rjsiNt/+qv/oqNGzcSjUZ52ctexpNPPll1+y996Uvs2LGDaDTKnj17+M53Sqtc/+zP/qywGfD9v+uuu1byEJqHZ48AKhYFH+HqV9qWeNo6vkJkAUrbugmFhXPeKp5HwCmtK0SWMTPefv2FyMr3WYuyy09gtkpBmDWzXvu6I91NFZbyKxwlQdgqe4SIFkFRlJYVI3tuWpC2Vw1d5Sk7GyEI5Wda6WkrVbYhNURYLSqMm1ECh9Uwo50iPalcqVcvJDHjt6woWI0pMP2QfrabezcXY9KEz3BIC3metmmzuZuYTEfXVZ0OvcNTnjbST+RntvRuAQQ51ez17CfSV4K03di90XuvGaI6rLbOHkH2M7/KvVkfZIlWWCT4rV28xY0WkI+SzFzXJSY0jfY9/zXTFRbkgRx7GoVcHPEv6DTruZszczw+/nhT7ZIIUtq2ihBuo41LFpK0lco1P/QI3PJrsN2dL+z7HDzzT2DXX/j0UoLjOBydEuTptuEuoiGNu64c5f/85FX81I3r2DwY5yevHq6Y6h2EVa7SdqqKPYJtOxx3ScetQwGkrCRtV19b8/cuC5e0HVQW0ClwZjZzYaqIp6YgnxRklfRVrRETCzkePyFVtmJReKMkbWcD5jX9m0k7UUKOwe7oNCFNRVscAzMH4U7oXY+iKLx8s1DbPnGiuXoarYZlO5yZzdRPXNaCaA+43vQkznBsWtzHt4/EeceNos9/98C4Rw7WA8O0PZ/U268Yqr7xwln43ofFNa6F4OZfYuDW9xIJh8nkTcbm65yv9kh7hOaFDWdmM/zetw8yNpehK6rzG3fu4GXutdIR1nnrNWtKyNvBeIQPvWEn164v9e9VFIV3vXITA/Ew08k8//zoaRypsh3cJsbTOlCaDRBaZuvlsXt1Dx978262+BaMGlXwtnF5QS56nQxa9LrEcFmRtl/84hf54Ac/yMc+9jH27t3L1VdfzZ133snU1FTg9o8++ig//dM/zXve8x727dvH3Xffzd13382BAwdKtrvrrrsYHx/3/v/bv/3bhTicxlFmj1Ao97Q13TRiTUN1V7lMxVXaOo6nCgWf/2w9ClbLhHv/l/hvFZYobVvhaZvIJwChmAxpIW/f/n2enk3zgS/s5/7DwfGXCLJHaFatJq0RpELPI+EaIGb8nsAeadtCpa3/ZzPp+JPpSaYyU2iKxq6BXU1Za0hiwa+0bVbVKcmsjlAHiqI0Ryr7lMAjHSI9qdliZN51qIa968XBadpGRJK2W3u3NlVMy5+K79mINBkTuTjSGRK+QU0R6e5n+qP99EZ6cXAYW2zuobSkOFwLFYSySNr67vUlFgn1wq9+lqrOZklbGdOOUIfXRxzHaSgzQh6TXNg4OHewqbZB6XjtWVa0UGkrfaodGjtm/9glY9Ls/UT2kw69ozhWt8Cy4oGxB1piWRHkadsmbdt4yUOSHj1rg/+uqiJt/dr/Kn4/cg/86E/AvHSKCNaL2bTBQqaAqipsHCwW+InoGnfuHuXDb9zBrtHOuvY50iXGjIkqpO25RJacYREJqawpr9huGsUiXK0kbaM9EOogpCpc0yvatu9CWCRIP9veDYKkqwPfePYcjgNXr+v1yIb1/R0oilBELpT7MioKEzHhm7tbOQWAPi/9dK8Q6h/wSNsXzi+ykL10vB2/98IEv/vNF/izHxwha6yAdUNf0SLhuEsEbh2Oc936Pnau6sa0HP796fqfM58+PUcqZ9LfGebqtb2VN5w4IAjb5AR0DMLr/jdsepWw7RgRixeHxuvM5HHHKyuTIJ9qfC55LpHlE/ccYiFTYFVvlP/5Y7sCVfCSvP2zd1zDH/z4lazrDy4M1hnR+YXbtqCqCk+fmuPEAXdReaR+P9sXJ8Q52T7aItU90N8Z5jfvvILX7x5hIB7m+vX9Ldt3G5cuvEyF2QZU7RcYlxVp+yd/8ie8973v5V3vehe7du3ib/7mb+jo6OCzn/1s4Paf/vSnueuuu/iN3/gNdu7cye/93u9x3XXX8Zd/+Zcl20UiEUZHR73/fX19gfu7ZKAo4KqCVcfG8JE+pm16hchUn9LWclRXaVtWiKyRAl+5BZHKUchCoaiIlWSwZ2XgtuvAuQW+/MxZrDo6g7RGkCSmVPH6iY/DE0nSeZNnzyaq7ktOfv2FyJolo6Q1gkxlleRHIxNtPwkg7RHmcpWrctaCctK2GSWwhLRG2Na3jZgea4qo9hMzLVPausSJJFKaiYm/OFyrlLYlBKEWWfJ+I5jPzTOXm0NBYWP3xpYQ1WE17Cltm1WkewShu7+WEOlaiPXdQknQbDr+SvinOo7D2ZQgbdfG1zblX+wn+lultJUx9ffhZtt31dBVAJxcONkSSw1ofdEr2X/XxIuWFc1kRoS0EPGwa49gNGePIMe+mB5r2u/aPx7PZGc4MHOgyta1IcjapW2P0MZLHp7StgJpC+J5fOeb4ZUfEMrJs0/BgS9fmPatAI5OiueoDf0ddalpq2G4W9xbUzmTVD54kfq4q3LcNNiJ5s5bPEwdBMuAWF/dytSqUBRPbXt9n7hvXRB7AK8IWY3WCC7OJ7I8eVIoYe++pngfi4Y0zzc4yNf2hL4ZgI2mKFirz7nWCMPFlPSR7iibBjtxHIenTl46alupqHzh/CKf+O4h5tItXhBxrydn/qR3DW4djqMoCj/9MqFC3nt6nhfOL9S12wdeFCrb264Y8mwKl2DhLDz8x2DmhafrXR+H/s3en3eukqTtYn3HFIpidgxxeGKRz377oYbnkl98aox8wWb7aBcffuNOhrqqq2F1TUXXqtNKW4bivP36teA4zBzbi2HZMLqn7rYdca1UrhhpHWkL4hjeceN6PvmTV7N+IJh8buOlhdU9MSIhlXzBZnyZYpkXG5cNaWsYBs888wx33HGH956qqtxxxx089lhwEYDHHnusZHuAO++8c8n2DzzwAMPDw1xxxRW8733vY3Z2tmpb8vk8i4uLJf8BbNu+IP8dx8FRVBRAwaJgijRPx3EwLROz4CptVQ1FcXBwMFx7BAcHBcXbl6ZoOI6DYRm1tyE7732fXchh2iaOa7tg2zYqqmiLbWLbNl96eozvPH+ew+MLNX/HXFaQlj3hHrFPRfWOT26TzhdwcMjkzar7yhVyOI4jVJ1azCtY1UwMFvILOI5DV6gL27bRVV2cR3P58+g4TsnvMk1XUzR6wj04jkPezJM20g23Tx5zWA1j2zYhNYTjOOTMXMP7fG7qOeGjNbhHHLOie22td195Ky9iooSIqlFRHMnMYllWw+1L5pM4jkOH3lESk4baZ4r26egMxYZwHIfx1HjFGNYUE9ONiRYGB6992UK24WM+Nn8Mx3FYG19LWA23JCa6ohPVREwyRqbhttm2TdIIjkkj16FhGl771sXX4TgOpxdON9QuGT8vJkqYkBLyzl0z1+F8bp60kUZBYSQ2UrLfuo/ZMryxIaaLsStpJJuKScpI4TgOMS2G6v5rtn3DsWGGYkPYts3h2cNNtc8br5UQYSUc2Efq7X+mZTKVmcJxHFZ1rAKoebyudB2GlBCdupjkJvPNxSRtpEVM9JjXhw3LaOg6lP24O9yN4zjcd+a+ptrm32dIEdYzzY5bjYyftfxvo42WwXGKhXyC7BHKseEV8LJfEK/PPH7Z+tsek9YIIwG+sg0iGtLo6xQLhJMVJsWSnNs2vIw1glKBAGsU3YL83NEpvv/oZJJkboWVpvX62br4xrPncRy4dn3vEkJp40BRLVaO50yxyD2UOwNGuqi0HdpZsp1U20r7hUsB4wtiQVPXFM7OZ/mDbx9ibK75QtIe3GJk6YnjZA3LLUwkzu2a3pjn3fxvT57BtGq7x5yZzXB8KoWmKty6rYI1Qm4BHvgEFDIwdAW8+kNC+e3DzlXdABydStb83RKz+ggFy6Ewe5pkhYUS0Y5FePG7cM+H4bu/BRlB2B84t8AL5xbQVIV3vWIjHeHaig7WgtftGuGa7kUidoYFQ4X+LXV9Pm9aXir79tHWjVNt/OeEqipscMfPk5e4r23reuEKY2ZmBsuyGBkpraQ4MjLC4cOHAz8zMTERuP3ERNFY/K677uInfuIn2LRpE8ePH+fDH/4wb3jDG3jsscfQtOBV5j/8wz/kd3/3d5e8Pz09TS63siy9bdssLCzQa5iYZoGCkyeXTeOExOrj9Ow0yWSSbtvGMk1Si4vk8wbJrIFhWliqQ3IhyVRYpIrms3kMw2B6dpopp7rNgIQ+fYq4Ib5vYeIc2Zy4qSbmEhT0AgvpBQzDIEWKqakpZhdS5PMmp8enGdBqOz9nps9gGAa6oTM1NUUinxD7NFOeHcbUbIJ83mBmIVXRIgNgen4awzAwMgaZREa8NgwmJidKVMf14MyUaJ9W0JiamsLMmRiGweTMJL2F3oqfk/FznGJBuMX0IoZhkEwkSZgJdFsnY2Y4dv4Yo7HRhto3k5jBMAxyqZxoX77Yvn6z/pSPVCHFYbfa7KgzytTUFEbWKF471HbtSCSSbjwXUyTVJIZ7PZ0ZP+Mpb+vF+ZnzGIaBk3dKYjI9O82UXV/7vJgsJOnr7BPXizHB2Ymz6OhLYlgLymNiG4L0Gp8ax4o1lva1/+x+DMNgSB0qifPU7BQ9hZ7ld+CDjEl6MU2n0YlhGMxZc1X71nI4Py1iYudtpqamsPKWOJfTE8Ry9cV5IeWOKwsp4mocwzB4cepFpobqa5+/D3oxSedYmFvwrsNzE+dKVKj14MWFF0VMokPMz85jGiZGQRyznqnvlruQXPBi4jgOhmEwszDTXExmymJSsDBsg/NT58lH6lNP+mOyNrSWs8ZZnjrzFKtZ3XD75hbmvH6S0lIYhsGivegdc9AYuhxm87Nkc1l0RcdcNLELooDn+NQ4uUh99+xEKuEds521MQyD8blxprpaE5OF2eJ1eH7yvKdOr7l9bj++ZvAafpD8AQcmD/Dc6ecavpcAzC7MejHJFMQ9dC7Z2NjQSPxqQTLZnNq5jTZKkEuAkQIUj9hbFmtvFGrb1CQkxz0V5+WEY74U8VZipDvCfNpgciHHlqGl+5YqxyV/cxw4v1e8Xn1dS9sEeDHqzk+xrn8PY3MZnh1b4JZtg63/LhD2cnMnxeuBbTV/7Ox8hqdPCULtrdcsvR43DXby2PFZTs2UEpqGaXM800FCGyAaKsCxH6AU0hCLl6g6AW7c1M8Xnhrj5EyaycUcI8sVz1phGKbt+cn+xp07+KdHTzKeyPGJ7x7mfa/ewpVr6nvGDYRrj5CdOYWiWmwa7CpRer/1mtU8fmKW8USO+1+c5nW7RirtCYBkrsAXnxYZYNdv6KMnFnD/Ng146FOQnob4MNz664E2GWv7YsSjOqmcyanZNFuDFjQqYBxBNg+YE0ws5Oj2+75aJozvhxP3w/n9opiixKN/gX37R/iSawnxmh3DyxdRqxOKonBTTNR9OKWuZ0ir77n4xHQay3bo6wwzFK/PC7eNNoKwabCTIxNJTs6kVm7sbwEuG9J2pfDOd77Te71nzx6uuuoqtmzZwgMPPMBrX/vawM986EMf4oMf/KD3++LiIuvWrWNoaIju7u4Vba9t2yiKQiTWgWFaxByVbEhDDQuCobu3m3wkhKqqRDo66e/rJRJJoEfjKLaKpqr09/UzPCwG9J7xHsJGmK6eLu+9ZZHSUNzv6+2OEnZfrxpeRVSPYiQNwifDhCIhhoeHUfTzRCIqoVi85u+wZ23C4TBrB9cyPDxMKBciHA6jqZq3DzWSJhLJghauut9oUrRxoGeA9avWE35RtDfeF/fSW+uFsqgQDodZ3b+a4eFhes72MG1O09nTWbUtMn5DQ0PehFU/phN2wowMjjDcPcxozyhnk2dRO1WGB2uv0OtHeDpMOBlmqH+I4eFhes/1MlWYIt5Tewz8OH7+OOFwmHVd69i2Vjxo9k70Es6Hlz3mwPaNhQnnw4wMjLBmaA0d0Q5M2yTeF2cgNlB3+wD0lE44HGa0b1Qc83gvYSNMZ3f97dNP6YQLIiab+jfRd6JPpKV3wnDn8JIY1oLwTJhwOMxw/zDDw8N0d3Zj5SzifXGGuxuL8/SpacLhMFevu1pch2M9zFlzoj8P1bfP0JkQYSPM8MAw67rWET4exlZshoaGvAqw9SKUEf12uFccc8/pHpJOku7eboYH6mzf6RDhgmjf+q71hM+ESTpJ4n1xr6BWLfD3wfCs20/6hlgzsoZIJCJUigPddIcbG8v3Z/cTDofZOrTVi3Muk6Ort4vhvjqvw9O6d8whNUT4XBgn5DTUhyUi2UhJTLo6ukgaSbr7uhmON37NDA8Ms3dhL2eNs81dM1Mhwikxdq0aWEX4eBhUvGMOGkOXw/Ss6Cer46sZGRkhHouTLqTp7u9muLPxsUHLaoRnwxChuZjkREwGewZZM7qG8CH3/jrQ69li1Ao5tm5ftZ1FdZF9U/t4PvM8V224quH2hSZCxbGrY5jw2TBqRG3omBuJXy2IRi8uwdDGSwzSGiE+DHqNC3ihGAzvFP6r5565eKTt5EGYPwlbXguh2vtFxjA5lxAijHoIolow2h3l8Hgy0Nf2XCLL1GIeRSlW8vaQHBeFu1S9oTTqZSGtLxbPcd2GPsbmMnz7+fNcu76XzsgKTJETpwVJFu6ErtoX0qTK9roNfYGeoRt9voyO43j334kFkbky2bGdkHYQDn9TfGBgK5SRZT2xELtXd3Pg3AKPn5gNJIcvJKaSORwHomGNLUOdfOgNO/mr+4/x4kSST//wKP/95o3NEyzxUdAjZLPz9EZn2TpcaoXSEdZ523Vr+edHT/G1fWdxHIdXXzFMWF967zpwboHP/ugkC9kCmqpw15UB8XUceOJvYOYIhDrgtt+GaPCzpqIo7Bjt5ulTcxwcT9bVJ08U+tkMrDVOYj37Zeg1IZsQi1HJcWFxKNG/GdbdBC98DaYOcuS+f+bs/B5iYY03Xb0yY9g2+yRngGcL67jedipbSATA87MdiTf8nNlGG35sdsfPE5d4MbLLhrQdHBxE0zQmJ0t9JScnJxkdDb7xjY6O1rU9wObNmxkcHOTYsWMVSdtIJEIksnR1R1XVlk5CKkFRFBRVFBlTHQfLMdHcgcvGxnbTKHRNJ6Rrwg5B0bABBQdN04qEoaqjKAo2du1tNxa9FCXLzHmDZlgPo6oqYT2MoiiYjomqqhQsYcmQNmr/joSRQFEU+mP9Jfu0HTHhUxSFrGGjoJAxrKr7LdgFQXTrEcJ6mKgeJW/lyVpZutXGiJmkkURRFHqjvYIg1yMlx1wNiqKUXCumbXrtU1WV/mg/51LnSBiJhq8nwzZQFIWYHis5fwW70NA+D8weQFEUrh6+2vu83KflVD//QZAxkddMR6iDpJEkb+cbPuaMmUFRFOKRuDhmLVxzTMphOqUxGe0c5cTCCaayU6ztWrskhrXAsERMonoUVVWJ6tGmYpIpZBhPj6MoClv7tjZ9zP5+Eg8XH4bydr4uUtSPnCXGh3hYxCSiiX5ScOo/Zn/7uqPdDMQGmMvNcS59jiv6r6hrXzJ+sp9EQ1E0TSOshTEso+GYAJxPn0dRFNZ2rS2JiUXj/SSiR7xibmkz3dR9JmtlURSFznCnN3alCqmmr5mNPRvRVZ35/DzTuWnPC7peyH1GQ1E6wh3eGGNjex7s9fa/6ew0iqIw0jnixSRjZpo+5u5IN4qikDEzTcUkb+W9mOiaTkgLCX/6Jtv3mvWvYf/0fp6Zeoa7t91NV7gxIqbgFGMSC8VQFIW81fhY3cj4uRwuxLNXG/+J4PnZ1mCN4Mea6wVpe36f8Lq90EhOwgMfB6sAL34HbvoFWFXbgs3xqTSOIzxoAxWCTUCqNoNI2x8eEvO0a9f3LSVKz7kq2+GddRHQNUMS64vnec3tQ/zo6DRTi3n+7qETfOC12+oik2rCrCgcy8DWmq0exuYyPHNqHkURys8grOsT98rFbIH5TIF+145ibF4ob42h3SiFQzh5oWh2hncQ9O0v29zvkbZvuXr1RSXFxhfEtbK6Rzwrd0Z0fvV12/nnR0/x2PFZ/unRU+xa3e0da0NQVehZR/rsNEP6BFuGXrlkk1u2DvLEyVkOjyf54lNj3HtwkruvXcPNmwdQVQXDtPnq3rPce1Bcx6t6o/z8rVuCPVEPfAVOPyIKit/6QeipTozvXNXF06fmODS+yFvqIFBfyPawGeix5ug59jXoLctsi/bCxltg823QK+wz6BjAeuQvMZ/9Emu7O3nFjbcRX4mFi9OP0rtwkHOqwhF1M2fmMt6iQy2QfrbbW+xn28Z/XshiZGfnsximHbgocyng0mxVAMLhMNdffz0//OEPvfds2+aHP/whN998c+Bnbr755pLtAe69996K2wOcPXuW2dlZVq1a1ZqGrxQUDUUB1bEp+FIbLMfCMkWqtaZrXpqHgYoF4PojSshJcF0V7HNFQ/aCW9RGQSkWIlOKhchs26HgkshVfXXKIAuR9UZ6S/YJglADyBTEz4JlY5iV/X7Ki3J5BX3MxldUFg3hY1xeiKyZAksyFrL4WiKfaLh9/sIxQFMVyQ3L4PCcsEbYM1hUOniFvpoo5hNWRftaUYxMFiLr1DtL29dIITI3jnIfI50iJWoiM1HxM8uh1cXhTi6exMFhMDbYkoJ4Xky0MCEt5O2rmWJksmiWJH3lNd5I8TqvAJTbLlmM7GzybMPt8wqRuTFpRZGlc0mR9rWmSzyMe2NDE8cc1oqFyLKFbFNFCmU8y4vDNdWPtTARLcKWXuFNdmj2UMPtq1Swr5kiirII2UiH6Mey2FezBftkTOT9oFHI4m1yHJTjYlOF0tQQm3o2sb57PaZt8si5RxpuX0khshYWh2ujjUsWkrTtrZO0XX2t+Dl1uFTNdiHgOPDUZwRhiwLpGbj/D+CJv62pLUddP9tWq2wBr1DW5EIpaZvKmzx6THio3rEzIPXc72e7EugcFipeyyBuLfD+27cR0lQOnFvgK3sbf7aoiAb8bL++TzxT3Lix3/NbLYfwYhX3D7+v7VmXtA2v3gN+O7gyP1uJ69b3EdZVphbznm/oxYIkbUd7ioRjSFN5zy2bWD/QgeM4HJ1s3hYn17WefMFm0JxgS4AtiKoqfPB1V/Czr9xIb0eYubTBZ390kt/55gs8fHSaP/j2QY+wvX3HMP/rTbuWEramAcfvg+e/JH6/8d01Kcelr+3xqRR5szYbtYVsgVP5XvZ23MKpyBUcjd8IV74NbniPsGK48+Nw9/+F6/5bkbAF2PQqDsauw7Rs3pL5Kq/ZuALWA7PH4fH/i6ooTK6+g1l9hIN1FForWDYnXN/RHaMrm9ncxn8e9HeG6Y6FsG2HM630zG4xLhvSFuCDH/wgn/nMZ/jnf/5nDh06xPve9z7S6TTvete7APiZn/kZPvShD3nbf+ADH+Cee+7hj//4jzl8+DC/8zu/w9NPP8373/9+AFKpFL/xG7/B448/zqlTp/jhD3/IW9/6VrZu3cqdd955UY6xZqiCtFWwSwhXy7Gw3Embqumo7iqp6WiIab5T4uMqJ+z1kbbFAdYsuCbxrmJXvgawbEtUhnSRytX2HY7jMJ8XFVwlaev39ZNtzRrFG5j/dTnKiRk50W6myvlCXhDXMoXaI+CaJMugSNrO5xqvYisn1HKCLUmARibah+cOU7AL9Ef7SyqvN3TtuCgnlSWB1AxpKwlCaXnhETMNkGUyjvIYRzuEanAyPVnxM8vus+w6lD8bJT9OJEQlYEmUQfGYm4mJPGZJtGYLzcdE9jkZ72bIMnmM67vEw+bp5OmG29dqIt2wDKYywudzbXxtyT4bOWavn/gIQgenKSJdjnsyvl77GiGVyxY3dg3sAuDQXPOkbVgLo6u6t2CXsxr3i5cxkaStHA+bOmYt5ClX5XXeKJYQ6VrjRLrXj7UQiqJw+7rbAXjo7EMNjQsQTKTLYo1ttPGSREL4Unrp87WiaxS6VoFjwfhzrWuPZYr/1XDqYaHyVXW46w9huzuPOX4ffPvXhWVDFayUny0IewSAycXScePhI9MULJu1fTG2lxc/K2TBraWwYqStpkPcJYsXz7F+oIN337IJgHsOTPBEq4tyzbpFwAZqK750aibN/rEEigJvqaCyldjgEoWnZvykrXh+WzU0UCSKFQUGg/10oyGNa9f3AvD4ibma2rhSGHetOlb3lCqsFUXhCldledS9ZpvBeUXEf6M2XVFZKouK/eFP7OEnr19LLKxxbj7LPz1yirPzWbqiOr/y2m3815dvIGLn4MSDsP9f4cFPwjd+Bf79Z8TiCcCON8HWOwK/pxzDXRH6OsNYtuP1z+VwaiYNisKj3Xfxzd7/xve67oarfgq2vx7W3SiuPXVpzZ6FTIHPZG5jXh9ic9wg9MT/bW1Bxcyc8PK1CrDqGrjm/wPg4PnaSduTM2kKlk13LMRId9vPto3WQFEUT217sRerquGyIm3f8Y538KlPfYqPfvSjXHPNNezfv5977rnHKzZ25swZxsfHve1f8YpX8K//+q/83d/9HVdffTVf/vKX+frXv86VV14JgKZpPPfcc7zlLW9h+/btvOc97+H666/n4YcfDrQ/uKSgaKgoqGWkrWmbWJartNV0dFdpW3BULJwlSltPFevUQ9omit9nFklbb5/uzcByLLJGcb+pfG0T5KyZ9SaevdHeJfuXx5vxEbVSdRsEPwkARbKimYl2udK2UQWhZVveA6yntI20jrSVx9wMGfX8zPMA7BnaU5Iq1YyCUJKisl2xkFhJb6WqsxmyTF5jkjyRSlup2GsEknRaEpMGiH6AYwnx8F9C2rZA8e3FxFX9NaNIl/GUhGMzSmA/gQlFpe2ZxTMNt6988UAucjRKEJ5PncfBIR6Oews6rVA/h7QQuqp77Wxm7ConCFul6gTYOSDUO0fnjzZ0DfrbEVHdxQ090nD7JCRpO9wxXNLepmKiFknbrJltmBCVn4elY1dD/cQuXRy6dvhaeiI9LBqL7J3c21D7gpS2Dk7DY1cbbVzScJzG7RFAWCRAUSXaLE4/Bl/7BfjWB2DxfPA2uUXY+//E6z0/Cf2b4IZ3w2s/JkjJ7JwgkJ77UuDHTZ+CbdsKkLYD8QiaqlCwbObSYtywbIcfHhZj8x27Rpam4k88L/xf4yMr6w/ss0gAuGlTP2/YI7It//GRU5yebdEk3kgX41ej0vY/9ovtX755gFU+xWkQir62xedoSdqu7YvBqLDJsLrXg17ZauLlm0VdiSdPzmLZF29hTiptV5Wn9oOniK2VyKyGYwVxvBuU6WW3Desqb9iziv/ztqu468pRIiGVq9b28rtvvZKr1/WKjR79C3j8/8LB/xALJalJwBEettte75GVtUBRFE9te2i8NlWxVFpfMSqeT6aTeUyrcjaqxH88e460pXNg07vp7eqEiefEMbQCpgEP/7EYh7pXwyt/hd1rewGh8K+WLeuH3xqh7WfbRitRJG2bH1NWCpeNp63E+9//fk8pW44HHnhgyXtvf/vbefvb3x64fSwW43vf+14rm3fh4CptNcfCtIvkpWX77RF0zx7BdISnLY5dorRt1h7BLIibql8JqyvFyypXKE7ca1XaShVrh97hTTwVRdgvWI7lI22L+0vn61faViU+TAMcO9BDq2AVvEl2udK2XqLCT3hK8qQ/2g/gqY0bQSsVhBNpYQmwtbf0IdNTg7XAfsCzR2iBqjMeipfsu15S2XGcpfYIrkJvKjOF7dT2cFGO8uuwWbLs9KJQmG7pWUraNkOKlittm1Gke0S6XkZGtYAglErbmewMmUKmId/dnCnGr1Ypbc+lXGuE+BrvgbJR9XPQdRgPxZmz5pojbSsobZsi0t19rO5cTU+kh4X8AscXjrOjf0fd+ywfu6JalEwh07AKP2tmvXuKJG0bHa8dxylZ3OjQhY+g4zikC2lvEa+RNkJxHPTsbBq4DuV1Ju/tuqpz69pb+dbxb/HA2Qe4cfTGuic7/piE1TAKCg4OOTNXYmHRRhsvCWTnoZAR6eSNkIWrr4XD3xKkrePU7F26BEYanv5HoaAFMFLwg9+B13ykNK0ZYN/nIJ8U7+/weemO7II3/hE8+wXhcXvoP4TXbtmz7Zm5DAXLpjOis6qn9d6xmqow1BVhYiHHxGKOgXiEfWfmmU8bxKM6L9sUUIB28gXxs0ZP3obRvQZ4ChbPeW/9xLVrGJvLcODcAn9x3zE++uZddEeb9PmVfrbxYYguf684Pp3iubOivseba/Az3TTgkrYzohjZYs5kMVtAUWB1bwzir8OZP0m2/3qq0b+7VnUTj+okcyYHzy+yZ21j97VmYNsOEz5P23LIhYWz8xlyBYtoaKlytFY8n+rmtSj0qWnR92N9y36mM6Lz9hvW8ZPXry29ny6edxdrFNh2B3SvFWr9njXCR7aBsWDnaBePHpvh8PgiXLv8dXBqRjzjXbuuj5MzafIFm5mU4VmUBOFcIstDRwRpfdcrb0RJIZTBz31R+EkP1Vc3ogSOA0/+nVCZhzvhVb8J4U5GQw69HWESGYOjU0l2r17+OvMXIWujjVairbRtY+WgqMLTFhvTKU48bcfGlpM2zedp62ie0rZ50raYyiA9bf1KWP/rjI+0rdXT1rNGcFW2ElLBa9ompmWTLxTJs2r2COWqU+l5WpH4cBy457fhmx8Q5G0ZFgxBAITUUP0ehGNP0PPD34DJA0ApobjE0zaXaDj9tJz4aMbTtlyBKdGoPYKf+JCEljyPzaRAl6fiN+rVaTkWjmsmIvcxEBtAV3VM22Qu11jKmDw2qVRrxh5hbHEM0zaJh+MeEQWNW0KYtumR0eX9pKWp+A2mpZu26fUFeYwdoQ4GY6Jy8JlkY2rbSp62jV6HkrSV1gjQuPrZtE3vOvRiUsuC0zLwVJ1NEum2Y2M5YtyVx6goikfUNuprW9GPu0EiXapsu8Jd3nXY6OKGf3tZYE7GJGk07q1X0dO2zvYFEf0At6y+BU3ROLN4htlc/em+3j1UDXvFFKG46NFGGy8pSJVtfAS0Boi6oR0QikF+sZgOXy8mDsB3fsMlbBXY9Vbo3SBEEz/43SIBCMKG4eRDYrubfl6k/PuhR+C6nxHHYxUCbRKkYnHL0DIV2Zvw6ZUWCVOLYjz5wSExNt+2fSi48MvMEfGzgv9qy1CmtAXhY/oLt21mpCfKfNrg/95/vHnVqWeNUJ/K9uYtA14ht2pY0xdDUxXSeZOZlOH52Q51RQWpGeuFW38dc2h31f3omspNm4R45PFW20PUiJl0noJlo6kKg/GlC4O9HWEG4mEcB08h3ggs2+HYnElCGxBF8Obrs9ta0leO3it+rr4Wbvw5uOIuGL1SEMENLt7scJW2p2bTJWKlIDiO4ykFNw11MtxVuQCgH1995iyOA9dt6GPbSBdsvl0UKnNs2Pf5htrt4dA3xDimqPDK/wHdQsWuKAq7Votje6EGiwTTsjk+LY6tXYSsjVZDZipMLeZJ1VGD6UKiTdperlA1FEXoXSyf0rbgFErsESRpW8BV2pZ52kp7BDn5XhaOIx5EXZjupK3EHsFnv5ApFAmpVM6siYSUBbikn62E55XrWGQLpe1NV7mRLfFPXU5BaBXEinsuAamlhacW3eOXlcP9+16WjDr7NIqR9LzO5Paaonn76g53oyAqpjda4CZvliltayGVCznY9y8wUzrJKE9Ll2hUKeoneeU+JIHUqKrTsAzvXDartPUfjyQIVUX1yNFGLRKWkFFNpH0fXxATti09W0oeGj1StAWK72aVtlJ9CEs9bes95qD2AazrEqmrjVoktNrT9mxKTPb93s+NKln9xyyv5VaQtuVEeqOkckn7fMTGzn4xwT44e7DutjmO4xHmHpHepGVFuZ+tv731Ljj5z5E8b10hMXmQhRAbQbnStlFP2yCiH4TPt/T6rjebwU8ElxPpzSyytdHGJYuFMfGz3iJkEpoOq64Wr+u1SDANYXNw3+9BZlYQra/733DNf4HXflQQfkZK/H36RTDzovgYCA/bCl6lKApseKV4fXppUULpDbqtmoLt+S+jfPXn6Hj2s2DXOGfwYcRV+o0v5Dgzm+HoZBJVVXjNjuGlGxdyMH9KvG5G5VcLut37dZn1REdY5/23byUa0jg6mWT/WKLhr/jGs+d58uknSWQMzL7l/WyPTSV54dyCq7KtrTB2SFNZ1+/62s6mS60R6oRUPu8fS1wUi4QJrwhZFFUNJju3DLkWCdON33vPzmcwTJtEdDWRkAqJxmskUMjBiQfE6+2vb3w/ZejvDDPSE8Vx4Mhk9WOdSxskcyaqqrCur8NT104sVL5XG6bN8+eEGOnua92+oCiw56fcnZ5wixvWCduGw9+G/f8mfr/uZ5ao5ne5hHQtvran5zLkCyIboJFruo02qiEe0Rl2F8dOXaJq2zZpe7lC0VAA1bGwnOJEzbZtbLdYge6zRyjYKnaAp23dStt8Uqy8uQgkbVUNBfG9fqWtZTvkCsunlidcz9xy0tZPLpQra2tR2i6xR6jk1eknRNJLPY6k0rYnXEzlqFXVqWRdywND3HjLJ8Mgzp9Ms21E1WnapkfCy2OuSYF57mmxIvrcF0veLk9Ll2jUx9dPXi2xR2gwBVoSJpqi1U/AJc7Aga96qmp5PApKidWHJH2kXUQ9sOyirUc5kV5VaZsYgx+6kzMfgvxsoXGiOkjx7RHpDSptDdvwrkPZ5+q6ZnwLPEHtA9jQvQFoTGkb1E+aUT87jsP5lJj0relaWrCv0cUDVVG9Y26WtHUcZwlB2GghMj+B6SfSdw7sREFhPD1ety+36RQX9sq9n+VCVL2Q/bVEkd4gUS3HLl3VvcVP6WvbjNJ2ifq5wQWxIKJfopnFA/l8IQn0ttK2jZc0mvGzlVh9nfi5TPGvElgmPPRJQXSAKFb0hk/C0HbxeyQurBGGd4oiXff9Pjzy55Cago4BuPqd1fe/4RXi5/iz4lneheMUixxV9LM98FV4/kvgOITPPwlP/I0gZepAsRhZjnsPicXvGzb00dsRXrrx7DEx1+gYgM7Bur6nbrjKP7LzS5TEq3tj3LpNfH+jpG0yV+A/9p7Fmj7CqdkMn3jS5HOPneL4dKqikOXr+8SzxC1bBzy1ZC3Y6CtGJklbSeTWgy1DnXRGdHIF66KkC59PuH62VXx85QJDM7628rP6wCYxb5ULBY3g9CPCViU+IgpttRA7XX/awxPVnzOkn+2a3hhhXfX63MRC5bnVuUQWy3bojOilVhTxYYh0C1/pes9LYgzu/V+uz7YjxrLtdy3ZTCptx+YyLOaqP48dcY992/Ay2QBttNEgNrtq2xNt0raNlkLRUBUFUV7Mwp1TYTkWtl9pq0ilrVpU2vqeEeomFHx+thBM2vp/zxZK95usoRiZtEeQNgHl+yw4BdJG/Urbmj1t/Uqk9NLUIOmP2B3p9t6rmfiQJIb7YCgLwJWfP+lrK1XH9cA/0V9CfFQjoyShnCk95or2CA2m4sv2aYrmWV40S9p6frbh4s285lT8/f8qiOqzT5Vsr6t6yYNBM8XI/Oe9LoLw9CPCSuPYD0vePpcUafgbuzeWvN8wke5TActjblZpKz+nKZoXi5r7yVP/AP/xfm9i6U/59sekGaVtUD+pKSb5JJx5fInyYDY3S87MoSlaqaqzQZuOoMWSZknbrJn1CLhyy4plybz5UzB9xPvVT2D6Y9IZ6mRVXEyCzybP1tU+/zmSsWjWOiVIadswgRlgPSBjkjIamzgWrIIX63IivdFrRlGUJfeURhcP/H1BXivSRqSRxY022rjkIZW2PWurb1cNq68BSQJlalh8dxxRvGjieWFn8KrfhJveu7SuQigGr/6QKCplGWKxHeDG94i/VUPvOuF5a5ve8w6IQkWL2QKaqrDB9UUtwcHiYr6z8RZQFJRTDwufyjosvGSK/+nZNE+4afd37BoJ3njGXaiupBxuJcKdRR/TxfElf5YFpp47m8BuQHU6Npely04QJ4Ou65xxhnngxWk+/u1DfPhrB/jbB4/zhSfP8N3nx3n0+Az3vzjFofFFNFXhTTV42fpRLEaWZmxOPIOtCSjktRwURWHHKkEUHhxvLOOvGYy7JGM1f+WtQ6J91cjv5SBJ265VrmVFnfYIHhwHjn5fvN72usZ9rCtgh1eMrHosTrp+ttKf01PaLla+V0tV4cbBzlIyVFGKVh4zR2trqGXC818WFoOzx0TBuxt/TvwPOCc9sZCnmj28TKG1F90iZLLAWhtttBqer20TlisriTZpe7nC9bQV5UAs5HOEYRU8JayuayWFyCx3su63a5ekmVVrqlM5aVtWvEhCThgzRumks5ZiZJIUXWKPoBRVweW+PpkqhciWeNq6k+yKZJThez8zs+TP0rJAFiGD4vEuSwK4xKjikrblxZ8kJGHdiNJWHq+maF67avKFlMqLshhXamPDarUymwBoHWkrlWpQB6mcEqSOjI1UxIbKvOxGO0aBxkhbP1EtY1ITMSNjki1VLMoYy2tZolGyp7x4ETSvtPVbI3hFuWolMM88Lvre3EmgstpbkrZzubm6SbNq/aSqqvP5L8OP/hROPljytiQoV8VXlZzHRpWscizx9xOPIGwwFV/GMqSGvHNZ0zXjOELx/cPf9RacggjM8nbWS4rKmOiq7t2balKkV4Hsr3LRBRon0oNiIpW2zcZEQVlK2lY7f44Dz/07HL+v2L4KVjb+9+o9Znne/Qsm0trlpay0NQyDF198EdO8NL3N2lghOI5PadsEaRvtgQE3E6YWi4R9nxeLtIoGt/4arL2+8rZ6BG77TVhzg/h9wytgTZXt/ZBq21NFiwRpjbBpsHOpt+zhb8P+fxGvr/opuPn9pK9+jyBgTtwPT/19zcStJJCSORPLdtg81OmluC+BXCAcqr+YZUPwfG2XLjRuG44TC2ukciYnGqgsPjafYaRwlnhEZ/euPXzgzt3cvGWAsK4ytZjjyZNz3Htwki8/c5Z/ePgkn39MEIe3bhsM9HOtho2yGNlsxiM+1zWYSr6rRqJwJSDT+auRtmv6YkRCKjnD4lyi8txBWPwEq8KlR+rIeteCY/F8YC2TZTFzVCzQaCHY/Or6P78MdrhE5blElmSVefRpV2m7wVVc16K0lepcqdIugVw0ma2BtJ09Lsja578kFoZWXwc/9ifLkthFX9uFitvYtuONU23Sto2VwqYhWYys8YWglUSbtL1coUp7BBvHo2PBsExUN+VX93na5h3VI3YVn3+tR4Q6NU5Mygi9QgWlrZxw58xSoqIWc2eZUltJaStI29qUtpZtLUmBlgqzykpbH0kVZI8QpLStxUu0kCsSwi7B5BGEZcSHJKylVUQ98Owg9OLDXk0Tdllgzkh5KsIgP0OJRguRSSLCf8wxVyHSKEEoCTvp2+jf/7KkctYlxsssK8pj4ilt040rbf2V1mtTdboxKbsOygu5STSrpvOTPc0qbT0iPVR8EKyJILTM4nFLpW2F4+0IdXhp7/VaJJQr8P2vq8Yk5ca/zP9OFiHz+9lCEwRhwGJJs0rbcj9bqNE/Nbcg+odtegsIQQSmt88mj9m/T5mKXzUmL35XFO0pU7Q5jsN0RozhgfYIDVpC+GPSrD2C366i3CO96vlLTcKBr4jq8u7DpTe2BhRPanRBx+snvvtJKwpHXqrIZDK85z3voaOjg927d3PmjBhXfvmXf5lPfOITF7l1baw4MnMi20rRoKs+leMSrJEWCXurb3f423D4W+L1y/9/RT/catBCgty98+Nw8y/X3ibpazv5gjeWe0XIyq0RjnzPTW8Grnyb+A8UVt2A8/JfAhQ49gN45p9qIm67ozrRcFE2csfOCipbxykWIRvcXtNhNY2AYmQSuqayZ42wLNs/VplYqoSxuQyjhbPEwhrK4FZ2r+7h527dzJ++4xp+8fYtvOPGddx55Sg3bxlg1+puVvfG2DjYyZvrVNmCsHMIaYLINC2HSEhlqKs+4ldCkrbHp1LkCjUKe1oAx3E475K2q6uohDVVYfPg8hYJ//bkGL/0L3v51nPnS4iYRMZgNmWgKLBx7RphBYBTVNrXg6PfEz83vBIirScVu6Ihz+bi+GwwASuKkIlnQ3le/AsllYqY+ZW2S1Cr0jafFD7bC2PiPL7iV8TCUudA9c8Bu1aJvnXw/GJFomxsPkPOsIiGNdb11W/30UYbtWBdXweqqpDMmcymG6ttspJok7aXK9xCZJrikrbuQJc3TRTXCEHTdXTpaeuoRaWtb1Cs39O2dMW14FNG+SF9c3OF+pW2yxUiCyJty3/3mhuQ2tmpL2eP4CdtlyptJWnr97StaULsJ96k0rbCJFsS1tIqoh4EEYR1qTrBI3Atx/LSqZcobRu0RwiyW5CqzqpKW9uuODEoL3jlb19VtVohW7TDKCcIy0lbN706XUhX9kOugHK1t/91vUpb27Erkv3N+qeWxESStg0S6fJzJaRtLZYV/n4SYI9QjtVxMbGp12tYEk5BMVnWHgGWEITLkbatiMmyWQLLQMZEkm41t8+v9HbHhkr9xN/mRhcPAon0aurnEw8Ib+qJ50vensvNUbALaIrGQLQ4ebgU1c8xX2pzTYts0sbGMsBdPPUW2AKUtjWNhwEIWtDxCpFVU9qe+pFQ8+UbI7MvFj70oQ/x7LPP8sADDxCNFlVed9xxB1/84herfLKNlwQkYdM1KgqKNQPpazv5fGX13ulHi8To1T8Nm15V+/5VVah51TqmcvFhl4hxREYLcHSq6BXp4dgP4OnPite73gp73l66n423wMt+Qbw+ck9RjVsFiqJ4yr+ejhDXb+gL3nDhrHgO18LQu6HmQ2sKXjGyc4F/vsa1SNg/Vv8zuSBtx4iFNBgo2j1EQxrXb+jn9btH+akb1vFzt27m115/Bb9395X8rzftCvb6XQaaqrDep5hc0xtr2P9zqCvCQDyMZTscXaYAViuxmDPJ5E0UpWipUQlbh6uTtou5Ag+8OIVlO3xt7zn+4r5jHnkpVbZr+zqIhnXoc6+1+ZP1NTi34PUltrWuAFk5pNr26HTwPGk6mSdrWOiawupe138+pNHTIe794wHFyPKmxTnXP3hjkDXKwBZAEQKmXJUFi4kDYi4VH4Ef+2PY+MqaLSK2jcTRVIW5tMFUMvg570Wfn22lwnRttNEswrrqLQpcDC/v5dAmbS9XuEVQQorjkrbi7bxZQHVsVAUURfUGN8sGx/2MP+iSCK3bHsFV3Zi+1Ek/5O/ZMqVtchmlbd7Ke8RdNdI26950pZK40gpikMefnGTnrXwwWb0MaevZI9TraesnPlySsRL5Jj1tG7FHkBPpINK2JlUneMSZnzRoFUEYRMB59giVKpubBnzrA/DAHwb+OYi0rUn97CfeXKVtJQVhWAt7cZnJLb0uqkGed6kahDotK/JJoUCl9HwHtRHqt6yoRqQ3q7SViyT+/Vcljvz9RKqfqxCE8tqpl4wKUtrW5NXpEek1krYNLm4EjQ1NE4QBStvaVPiJ4usyIr18wc7f5nqvw0DSVqbiV1N15t3zUebHLf1sB2ODXvYHNG4VEGQ/4NkjNOhpW14YDmrtJ4nia/e+XE39XNN4GICG1c/7Pg+P/nlgtsqljK9//ev85V/+JbfccksJ2bF7926OHz9+EVvWxgVBK/xsJfo2QqwfzDxMHVz694kD8Nhfidfb7xTk6IWAVNuefoRU3mTcJW08pe3kQXjy78XrHW8SZHIQAbPldrjxveL1oW8uKZgaBJmC/dodI+hahSmo3M/gtuaJ81rhkbZLlbYAe9b2oKoK44kcU4u1ZxiYls1UIsmwed4lbbe2orVV4SffGilCJqEoCjtXwCLBtivbFUDRGmEwHllq11EGaa8hCdhyPHpsFst26O0Io2sKz44l+N/fPMiZ2cxShblcIKjX1/b4fSILaWBr0RJlBXClq/Z+9nwqsPi2JJnW93eU9C1pMTEZQNqOzWVxHIfuWIi+jqXP14Q7iyr0mWOVGzfxnPi55nqIdlfeLgDRkObF4OD54OvsiPSzHWlbI7SxsvAsEi5BX9s2aXu5wlWy6mqp0tawTVQcVEUBVfMKkVmOg+U+c2k+grbuwkWStI0L1WElT1uptM2X+cEtp7SV1ghRPVpCcPnbatomadfDdiAuJpLLKW0jWmRJgSWooCL0E4fZeY8skwhS2nrpwDWTURmw7YoKwr6Iq7Sts/o6FGMZpFar2j6/IsolBDzSm8qFbfzEx/NnF/j1Lz3LgXOVV2S9tG8tgLQ1s8HpMakJ4T07/myp57D8s0tixUMB9ghViXQf8ZYvtUcIIqNGO4Wv7VRuqvI+A1BNaVsTQQgeceYnXcoVdc2qOv3XYbOetoGp+LWQeX4iPV+m6gxI+26aINQbVKT72lmwC8xmBWEolb/Ntq+aqrNhewQ3lkFEes0LTmWK9ECCsMFCX4EEYS1EurQmKCNtpZ+t3xoBms8S8F+HcsxplEiXC1V+P+6GifQa1M+NKm1lHPyvKxLpjlPsJ5H6JnAXG9PT0wwPDy95P51OtytW/2eA52e7rvl9KQqsvla8lr62Rkao8h77K3jok4LsWfcyuO5nW168qCLWvxxQYOYop0+fAmCkJ0p3NCT67WN/CTiw8Va49r9Wb9e2O8R2UFQbVsGPX7eWX7x9K2/cM1p5I68I2QWyRoAiaZucXPLMD9AR1tk+Isb6/WOJmnd7PpFjde4YYcUi1DVQJMBWEH5v0rUN+tlKSIuEZouRydT9Lzx5hl//0rP88r/u8wqlleO86786WsXPVmLLcCeKAlOLeRaypfdzx3F4+KhYNHzz1av40Bt2MhAPM53M8/HvHOKJE+IZbotL0hSVtqdqPzDbhqM/EK+3va72zzWA3au7GemOki3YPHhk6WLoKc/PtlQx6/naBiw2eNYIA52V72+yH1bytXWcImm76qrlDiMQu1dXvs4cx+GIq/Te3vazbWOFsdm1CTnRVtq20So4spiRgihE5r5vmCYKliBtFQ1NE4OwbTtYiNdqkD1CvZ62nWJSU7CKFcT9kL9n3ZQweS9YztO2kjVCeVszrr+SNOmvVIgsSLmlKqpHEgaqCEtIQaeE2DNt0yNMeiJLSduqxExZMSkK6YqTbGmPkDSSdRMLQUrbmtpXQhCKtsrvLq8QX7JPX/v2jc0znzbYV+Wh1iN7/P6pLmHh4AQTAYZv8AxQQjRsjxCgtA1qn4Qkf2Zzs0v+Vg3V0r4rts9xigpCWOIlqinakpgELcLMpw0eOTaDWa5sSM94+wy6DiXZmjNz2E6AKqKQgwf+j0ijDEC1mNRLEFYtsNRoqnvAPpdVENpWUYmfnS96ifrINb9iEuog0h0Hnv0CnH2mtH2ttEcIUtrWQlSXxETcA6oShE0WvQrMEqhkj2AaQskGSzIjpNLWX4QMfP1kOSI9PQvf/S2v2Fc1pW2jnrZBNiJ19xP3vlzNRqSVhSOXtUcwc4KMAvD5jF8OuOGGG/j2t7/t/S7H2L//+7/n5ptvvljNauNCoRVFyPyQvrZnHoP7fh+++l63kOVDYtwa2Q03v78+i4MG4DgOjx6f4at7z/K555Lsza/ixHSKfQ+La33bcFzcg574W7H41TVaseL7Eqx/ufg59uSy3rbxiM71G/qqL4BIpe3QFbUcWmvQ0S8yCB0L0r5F+anD8MAn4J4Pc/2oGEPrIW3H5jNsyR8iFtJQ1t54QYh5vzfp2ib9P3e6ZNrYXIbFXH33DoCpxRz/sf8c//PrB/j9bx3k3oOTLGQLFCybh48GZ6xJ5ffqnuUJ546w7vnelqttj02lmFjIEdZVXrZpgI2DnXz0zbvZs7aHgmV7JK+0WKBvo/iZOF1zcT3O7xVFc8NxWP+K2j7TIBRF4Q1XisWOew9NYpilz+UnZ8SzxKYyb1ppMRFkj+AVIRuscp0s52ubHBfPXqoOQzuXPY4g+BXdtl167s/OZ0nnTSIhlQ1NKMfbaKMWyP5zejaNZdc4DlwgXKC8kzZaDvcBTy/ztBWFyGzhnuBT2gLY7kvVR8B4hchq9bT1SNtB8TkrD8S9/UjIVNR8Qey3tyPMfNpoirT1F77KuPuRBvuZCib5QQpCEORH1swGK9YKGeHj6rgTtvS08AGjODFXFbVE1VmTiqnMAxMjXVFB2BnqJKSGKNgFFvILDMYGK++3DEFp335iy3GcpQ/Mtl1KjLpKWy9Fu4rC0X/tLGTE8STKDbwnDoiJwObbAhWEuqqjKRqWY5E1s0uIr1LS9iwMlqaYSZVbkD1CdTLKr+qsXvQKiqq6cmJ5NpVnMWcueVjydt2I0raQERMIr60J8XaFwnD+9/xkz5efOcvjJ2ZRFHjFFvc6KmRF4SY9Anf/dZHsCVA/g1BA+88tINI9z+8VqoStdyxpSyAZVUuBqiqqzsACSxXiLMfESpPDakrbijHxp8DbpmhftNv7blVRlyrSAwi4ZK7ATMoovV6mDsILXxNZDGuvDyRFZQwKdgHDMgKvgWrwYhKk6qw1S6CcSA8qRNasf2o9Slt/TGpU2tbkrQxw9klxfZ94ELa8pqrSNmtmMW0zUKFfDUE+w7UR6Ynia1eRHpTFsGSfrYjJcosbcrFJ1T0rpcsFH//4x3nDG97AwYMHMU2TT3/60xw8eJBHH32UBx988GI3r42VhOMrQtTbAqUtwMiVoh/kFoqe212rRBrxmusEybHChC3A4Ykk//Bw0atzOreN1+QOMmruhYFXcNXaHrEAe/Yp0d5XfgBCyysdARi9SvTzzAzMnWguRTybKBb7vJBKW0URhefmT8LCOUFCvfBVmDrkbXLd6sP8C8McmUyRzpt0RpYf68dmU2zKHybWqcG6m1byCDyMdkcZ7o6QNSzWN0lydUdDrO2LcXY+y+HxJDdt6q/5s/cenOSLT53x+M+QpnLN+l6GuyJ8+7lxnjk9z0/ftG7JM9q4q7Rd1Vvb9bd1OM65+SzHplJct77ok/yQSwrfuLGfmFsALx7R+cBrt/HN58b5xv5zDHVFGHLFP3StFte+mRfXYFcVNbjEEbcA2ZbbQa/fg7hevHxTP198XGchW+DR4zO8+grxbGPbDmfmgguKrXLJ78kgpa0kbYP8bCUGXR/m2WNirlg+XslxbeiK2seMMmwa6CQW1sgaFidn057thWnZnghoy1C8sqVKG220CKPdUaIhjVzB4nwi25TFTKvREGl7/Phx/vEf/5Hjx4/z6U9/muHhYb773e+yfv16du/e3eo2thEE6Wnr2SOItw3LRMVGxVXa+gy7i0rbImkrydXaPW0T4qdLYpp2daVtzvW0HYgL0ja5jD1Cwt2/VJr6IS0X/IXIpNI2a5iBZGSQcgtcIikbnGbsGGmOTaWwbIftI12oPk8+aY3QFe4q+a6GlLY+0rac9FYUhb5oH1OZKeZyc3WRtpJQDCJtQUzay8+HSC/2rSiVedpWUzj6iQW5El+SpmSZ8PCnBFE4cmUg2aMoCrFQjJSRCi5G5idmpBLGB6kgrNseoYrSNlCt5pIh5cr0P/3BESYWcvzRT15NX+fScyVVgv4U42U9bfOlqoFypW0gWRZwHU4lxfUwtegjWOZOCFK4kIFCtrhPX5x1VSeshTEsg3QhvZS0lTHJzoniVGU+Vo172gZYVtQQE3+cbdvhf3/rIIoCH33TrkDitmrRq4pkVJmaMjML0e66Vad/99AJDp5f5Hfesrv4QCBT8qQ/aZASWIuiKiq2Y5MupJdeA7lFeO6LsPn2JQsbUMGyQquTSK+lEFmAf6rjOHzzuXHW9sVKJlZ+BC04Letp649JOWmbFpP/0Y7SyVfQ2GXZDpbtlHroybHGXTQKUrJ2hjpRUHBwSBfSJRkYtaAl9ghSaVslS6DSeJgrWER0dfnFjQDv54qFI6U/eqTrwqV8twi33HIL+/fv5xOf+AR79uzh+9//Ptdddx2PPfYYe/bsudjNa2MlkZ4RhI2qQ7wGwqYWhKJw7X8T1k4juwVRewFS5MtxeEL0yXX9HVyzrpcu5fWse/qHbFKS7Hh1D2u6k/D9fxYbX/NfoH9z7TvXw8IG4szjQm3bDGkr1Xw9a4Wf5oVEzxpB2j7x10WhgKoLkn1hjN7FI6zu3cD5RJbnzy3w8s0D1fcHZM4fJGanCccGG1Yh1gtVVfjwG3di28IvtFnsWt3N2fksh8YXayJtHcfhy8+c5Z4DojjszlXdvGLrANet7yMa0jBMmx8emiKRMTg+nWLrcGnKu1SErqrBHgFg61CcB1+c5rivGFnGMHnqpHiWfNX2oZLtFUXhLVev5qaN/UIBLe9Rmi5sUeZPCl/b5UjbhbOuLYACW1fWGkFC11Ru29LLPUcXuefABLduG0JTFcYXc+QLNpGQyqqy4m0jPeLePbmYw7Ydr9ZNrmB5/sFVSduedWJRxsyJQn3lC1rjrjXCaOP3R1UV/sl7T89zz4EJumMhTs+kGZvPYFpibrq97WfbxgWAqipsHOzg8HiSEzPpS4q0rXvJ4sEHH2TPnj088cQTfPWrXyWVEoPks88+y8c+9rGWN7CNCnAJTM0tRCYV3AWrIEhbFVDVEtLWdklbLcgeoWalrTsZc+0RTNf7aQlp65KQhjvJHewUN41UvrqySSptgya+/rbKwmMDLkHmOJANUNsGkQBQJPeCSNtsJkU6b5ErOOJm4Uu5lUXIyttXrmQNxBLSNlVVQSjVxgn/5BzYd2aezzx0gtwy6uISBaFvEh9IBJSTUWXEUVD7vCJ2juWlzy9mRVwSftI2cbroE5xLVEzhrVr4ym9ZsbC0uq9U2galfTuOU/n69pO2Zh6sQkV1tv89P/FhmDbjiRyOQ8XKp3m7stLWsIzga6Y8JtLKoJYUaF/7ZEzm/OrnuRPF10a6omqyajq+X/2cWFq4QX6m7uJwVVSdtZKicxmDsbkMZ2YzFYsfNkbalhPpcyXfXZVI98XkXEL0h7F533lNnBE/zRzYlkds+/ueoijVPVTHnhBqqee+ENj8IFVnbernRPF1DTEJOuaxuSz/se8cn3+8cpGPoAWnZZW2/n5SyHhjRd7Ke/eTJZ62AR7kn7znML/91edKx1WpuqtSpFBRFDrDjReIa7wQWRUiPSgzIoCcP5/I8iv/to/PP3Gm4tcEHbOntK1kWSEXdCKX5yRry5YtfOYzn+HJJ5/k4MGDfP7zn28Ttv8ZIPt716rWFsC64i549W/BzjddFMIW8Iou3b5jmLuvXcNrr9nKwNab6ImFWDv3BMqjfw5WAVZdA1e8sf4vWOuqSMeeqD21PAjTh8XPoR2N76NRyNgYadBCsP0uePOfC5sIgMmDXLPWLQZVg0WC4zhEJ4Tdkb7u+gtXVA3oioboCSos1QBk6nqlIlF+mJbNP/zopEfYvu36tfza67fzii2DHoEc1lWuWdcLwNOnSudFuYLFvPusuqoGewQo2hucnEl7Bc6eODlHwbJZ1RstetaWYbQnuvQcSYuEM49Wv44LOXjk0+L1muuha6Tyti3GyzZ00xnWmU7mefqUeAaV3rQbBjo9UlZisDOCrimYlsOsbx5wejaD40BfZ7j6taKq0O8uxJT72lomTB4Qr0cb87OVkP7Je0/P88DhKU7OpDEth1hY46q1vdy6rXbxUhttNIMfv3YtH3nTLl65ZfmFuQuJuknb3/7t3+b3f//3uffeewmHiw/xr3nNa3j88eVN6NtoEVyFbAhB2kqVpGGZKI7tetqq6CVKW/ejAYXIavK0LeRATvjiYuVSKm3LJ+5yv3mf0haWL0QmJ7B+dd6StvqUtj0dIUJuukRQMbJKZEo1gjC5KAjLhDYg/Ex8pK1U2naHS1WF/uOvSBDKSbarksZIB1aIl+iPihXtuXyprcLX953j8ROzFYt9eemsaimxUJWcWUIQJoDaCtuAOGbHcTylbSLjI6/lQzgIgrDCNeMvRrYEfoJQTqx8kOR7kNJWHkfGMPnrB47zzGnfQ2I5kZ5P1nTMfjJqNl0kLpIVPL/kOQ9S2kIFciZf9oBcxX9WopyYcRzHUz0nMpVI21RFhV7VYmT+mAQUbpAxCfRPrdlnePmiV0Gp+LMpn/o7WyEmAWSUR9pWIqOWKG1rUD+XKYEdxwkm0v0Vi410RRsMeT4DiXRpXzNzVKSxlcFT2up1xiQgFb+WmPj7yXRKELKL2UJFn6igY64/JkJtK4s4xvQY8TJf1XJS2bRsjk2lWMgUir5vjuNT2rqkbYXMA49INwJI23wSXvzu0nbK5q6APUJgZkTA4saRySSW7XB0srIfb6D6WVtO/Xz5krZnzpyp+r+NFYBpiOfLi41W+9leIjAtm+NT4n68bdg3Fm5wPTgPf0sce7QHbv7FxtTxa64TqtTkeGA2VM3wipBta3wfjWLz7YJ42vlmeMtfwg3vgs4B4emphSG/yPW9Ymx7/tzC0joBZZhPG6xLHxDOC9tW1u90JbF9pAtVVZhJ5b3MrSDkChZ/cd8xHjs+i6IovPuWTbxxz6rALI4bNopsm6dPz5eIFuT9tzsWqsl+AoRNXldUx7IdTs+K++nDR8S87VXbhuorILn51WKOduZxYVcVBMeBp/5eLLRHe+DG99S+/xYgoqvcsVMsRH/n+XGvyBuUFqGTUFWF4S63GJnP17ZojVCDklD2x5ljpe/PHRdCg3Ac+jbVeygluHFTP9tHu9g+2sWdu0f5+Vdt5g9/Yg9/8dPX8oE7ttHbsfL2E220AWIhaNNg5yVnx1F3a55//nl+/Md/fMn7w8PDzMwEm4q3sQJwlba6auNg+5S2rj2CW4hMURQURRR4chSptG3Q01YSAlrYqwhdsEzAWaK0lVYGeXcS3u8qYlN5s7ISFZ/SKsAHz08wZ12CtjOs0+F6FQUVIwtSbonPuVXYzaVK23RaTGBn9BEsxxE+XS4WDHEOypW2fgKtIvnhqvIsWRTHR8wEEXC90V6gSD6AIHwm3TT3SkUBgrw6YRkiQBIKklB21b3V0m39lg4Fu0DetD1jfEHgutdUGWlbieypTtr6yJD0TMkEr+BTx/pVnbqqo7jq8oJd4MC5RZ4+Ncc39vuUutlSQrxE/Vwt7dtPRiX9pG11VWeJ0tZ3TgNVhOUEUFlMqvkMS/Vz3rQ95cGcn7SdPe77nspK26oEYQlpG6C0dcko/wKMbJ9f/SxV8x78RLqZB9OoOxV/JlVDTKpYVliOFTwmlhebcq+fav24nCxL+sZAj7S1rdLFiCrXYbUsAS8mMo2tDLJvlRDpy6mfHac0Fb/cZ7jK4oZ/rJlJyYWEyosbQeP1sgRheT9xx2vZp6Qq1A+PVHaP2X+NzMprJ5conk+rAKZRkZyvWozsxe/CM/8EB74a2PxApe1y9giWWXrcrtK20oKYv83+sUtef+VVt/0ItEdwz2nFQmRywekyK0IGsHHjRjZt2lTxfxsthuPAff8bvvH+igsbFwwvUdJ2bD5LwbLpjOilKedrbxSKUomb3y9IqEYQihWVdmNPNLYP04A513f3YihtO/rhNf8Trv2vEOstvq/pMCysDdYXTtAV1ckaFkenAhbpfJg48yJdVoJQKIK+5toVbPjKIhrSPI/RQ+PBfTSZK/Cp773IgXMLhDSVX37NVl65tbIycvfqHiIhlfm0UVKlXfrZjtZojQBClCLVtsemUpyZzXB6No2mKtxcr1JueAdc/y7x+rkvwunHlm5z9F449bCYL73yf4jr5gLjNTuGiYRUzs5nee7sgqe0rWRzIM/nhM/X1vtMhVocJRiQvrZlSlvPGuHKpr254xGd37prB7911w5+6sZ1vGzzAMPd0fpI9zbaeAmj7h7W29vL+Pj4kvf37dvHmjVrWtKoNmqAOziGXHsESQIUbBMFB1XBU+MKiwQbxyWwFD9pW489giRto93gTtpMbHCWkrbyd2mPMODaIzgOpAMUsRKV7AygrBCZu49YWKMj4pK2haXHUFGt5irNAu0RXNJ2Vh91lbZFT9tFdzJarrTVVM0jquUxzKTyRUKqkPUqnNvxVe7BFomZoOI1fRGxEi1TfAFm04ZHwi1HEJafw6qKOjnJ7nLblk2A41S1R9BUDdUleQ3LWKJoXMgURMBlJWAoIQgbVtriQPK895uMoaIoJcSHoii+67DYvvGFnFBI2FZRrSZJrHyq7kJfftK2IpFuLo2Joii1Eekh95ikp20NPsOyjf6YzLtF4jDSxUIf7u+VrkPPHqEBpW01/1R5HM+OJfiVf9vHd5937ylmXqS4l3xPqrolRICq00/aVlLayn7ib1OJ93NQTHJl6mdXFVxVdVqm6vS3x1MEJ8dFYTPvy9MV4yxjEpiK749J+cM1wcXh/ONCRZsOf9uk0rYOohrK1c/BY1fQ4oEkCCvbiJTHZLZ0XwH9xPOTdsdCf7+V5PISG5Yq12FVywqpHJ94bunfqECkL6d+zpVlWZTFJHDsCrIRcUnbVM6sqBoLjMmylhWXr9J237597N271/v/xBNP8Dd/8zds376dL33pSxe7eS89zB4TmQH5JEwdXn77lYRH2raoCNklAqmk3zIULyVAQlFY93LxeudbYFVz6c1eoa2zT1XexrbEonHQWD5/UtxrIt2iIOelhOFdAKhTB7naTe3fv4xFQu6EyD7NDOy+7AoylmPnKjGWB1kkZAyT/3PPYU7OpOmM6PzGXVd456gSwrrK1WvFNjLFH2A8IUjF1XWQtlC0SDg+neLBo2LOdt2GPrqiDVhEbH89XPEG8frxvyr6LIN4vdfn/Tyyq/79twCdEZ1Xbxdq2289d96z2qpUDHm0O4C0dVXJVf1sJaRPdWKsaHcHMPGs+wVNjh1ttNHGsqibtH3nO9/Jb/3WbzExMYGiKNi2zSOPPMKv//qv8zM/8zMr0cY2guAShCqlhcgKtoXqWOLBTCmStg6OR9pqdoOkrZwcR3uF2hYwARy7Mmnr2iN0RDSiriK2mkVCJcLRv89coUhcdoQ1OsLi/XSA0jZI4QhF4iOItDVy4r0ZfaRoj+Ce4EpKW/93GJZBImPw4a8+zx9//4j4o1QPhjqwZZG1fHVPWz9ZIeFPbVmsl7R1J+2BE21JEEqDecsAM1dVTed/37TNJYRlImsIctBPMPhT8SuoOpcnbSkhVCRR0hnqXLIi6xUOs01SrrepZTtMJvMuYeuIlD7pZeazRwgi0oO8OmtSdVZQ/clzUDUmcgJZRyEyEASOX0GXMyyhUJdKFm/D5dXPwaStj6BaPCdUMvJPVlEdW6J+VkrVzydmUjgO7D+bKDlG9Iin5ie/WDUmQWTUjJ8grBATeR79Sltd1X1ZAlXUz5Jgq4FIl9egVBf7CUtPaVuuVK7idy37SVWlLZRONFwE2SP41c+WY5ErWDx/1pf6KWMi22EVoJCrOnYFkY6zqRoWN+TYpS5V2vr/XvqhcqXtXMl3B/UTeR1JktMfk2nZznIbFr91ihZM2gYpbe18kqxh4STGSi0NZHOrWVZUUtqW27rU4GkbGJP08v1kOaVtIJEuz8NlSNpeffXVJf9vuOEG3vve9/KpT32KP//zP7/YzXvp4eSDxddzxytvt9JwnGKfLy+0c5lDKkK3jQQo3294N7zmfwkCqlmsvUGoD+dPQXJy6d8dBx76I/jeh0X2QTnkAv/Q9kuvgKEssjR1kKvXimeT/WcSVTMH9fGnAbDX3LTizVtp7F4tjvnQ+GLJMZuWzV8/cJzxRI6ejhC//YYdnip3OdywUShUnz5VtEiQStta/WwlJGl7ZDLJEyfEwm1THqjX/gysvk487zz0R5CaFnOZh/9ELCysexnseFPj+28BXr97BE1VODFd9H4d6gpeHPCUtu75zRgmUy6Bu6EWe4SOfugYBJxipp6RLr5uk7ZttLHiqJu0/fjHP86OHTtYt24dqVSKXbt28apXvYpXvOIVfOQjH1mJNrYRBFWStsLPVt5CpT2Cpig+pa0K2N5DkOoUyU1JUFhOZfWrBznhjPaIdCFVp4ADjl3R09Zw/XMjukqX609UrRhZkCKxfJ/Zgvi8okAspHn2CNkABW8lArNSgaVcwcLOC+JjTh/GtB1xg3aJx0pKWygl9MYXcli2w6mZtFDbykl2rAdHkljLeNoGkVF+0rYS+b2c0jYwDVoShJ1DRUVANlFVQQilfqLlKbaJTKHUGgGgkKlIcHkEYWAqfgrLdrDdNGR/6neQn62E/xz6C1Kdm88WrRGivUWCcJnicIFenQ3aI5S3b+mHJJG+XvzMLYJtVVU4qorq9emCXVhCjs1njFI/W6iqfq7m/ezkU0wnXTW5Y5eQXDImqqIuURf7z6G8hsfmMuKhXaoSY31FwmeZxY0gIn22hEivThCWx0TailQlCPs2iJ/ZUoIwkCzzXeflMZlN58VxyyJkElWUttXsERYXExwcXxQ+y2Wkre3YVVWdIK7Dbz83zp/94Aj3v+hmGMixq2uVWOCAEgIzkKgO8M8uIQiXUT/7rV1Casgj+qsubshjcj3IvX6yzDXj9+IG37VT7stYpZ9Iz9wgpe2p81O8OJkUCwmTLyz5e7XicAWrQmFLaVchU5nzi+A41f2uffuUmPfFpJJFQlA/kV6MI9sAAQAASURBVKStg1Mhc+PyJW0r4YorruCpp6ooCNuoH5ZZmn48e6zytiuN9LRYrFb1S0/l2QQcx/GKkJX42UqEO0RqcytI0kiXp0jl7JNL/37ifji/T7w+cg8cv6/07x5pexGsEZZD30Zxjylk2B2dRdeEx+v5hQoWMclJQotjOCh0bbn8SduNA51EQirpvMnYnHiOcByHf3tqjIPnF4mEVP7Ha7ezurd2snXPGmGRMOezSJDnc1VvfUrb9f2daKpCKifs8wbiYa+wVUNQVXjlr4jn79wCPPRJUXgsOydEHi9/30VfWOjtCJdYUGwaXCpckSiStuJ+Lr1/B+Lh2tXIg1vFTzlOTx4Uz/5dq7w6N2200cbKoW7SNhwO85nPfIbjx4/zrW99i89//vMcPnyYz33uc2iathJtbCMIUmmrCqLSdid2pm2hYgtrUulhq4Djs0cILERmV/eaBSAv7RHciaIW9kjbSp62coIYDWnEXdK2ErEF1ZW2IUXcWDIusRsL6yiK4pG26XJvTKoUIqugVjs3l0Rzi7Jl1U5yunvTd4mARUOQtlWVtrZRcoxn5jI+0rYfR5ILPoVjrR6E44s5onaG1cYpFrPBKqxKxHdVVadM+450CRITIJeoShD63y9YhSUpz4lsAaZdpbG8Pnyp+OVkiiQtgrwrC9kkhycWeXjeVSkHEIR+RafXPh9BmPaRtmfnM0WCsKMPIu5kJp+snoovlcW+wn1+0rbSgoSntNVKH0S9NO1qpG33GkABHMgtVPWthNJjLo+JIG3dlXFfQbxK/SQWqkykz8zPcS6R5XTC/Q6fRYI/DX+J+tl3zUgiPV+whVez10/8pG2yJkuIUqVtDfYIlfpJTYp0l7SVqs4q7fOPjeWWFfmCTbZgBZO2FYjgalkCkzOzGKYtrEkWzpaksfkV7H5Vp67qXowKdoFJt9CIlwbpEYS9JernSgSmv83+DI5SVWdwTILOo6Io1T1Upfq53/UbdT1tq6qfywpH+glL79op9wSukiUgPW2DCpEtLIhrOlewlpC2juMEEunymnQQ6uzziSz3HJjwqZ8T4qdc0LHNkgWxWmxEHMcpKYRXsWBfgGVRWA17RHpgTGQ/uQw9bRcXF0v+LywscPjwYT7ykY+wbdtFKI70Usb4ftF/5Rg5d6J6xfaVxNHvi5/dazzBw0sB08k8i9kCmqqwoZY06Gax7mXiZ7mvbXoGnnHTygdc8uepfygStY4DM+7z4uD2lW9nvVA1j5COzB5i1yoxB9h/JhG4uXH6CQzT5lx4E2tGhy9UK1cMuqZyxYi4/x8cF88GPzw0xQOHp1AUeO+tm1lfi2LTh7CucpVrkfDMqXlMy2bKrdlRr9I2rKsl3qy31luALAihGNz22+LZJ3FG3L/1CNz6a0XbsouMN1w56nHH1WwOpD1CImOQK1j1+dlKSF9bKQjwrBH21NXmNtpoozE07Bq9fv163vjGN/JTP/VT7QfZiwH3oVJBELDyOde0TVSnWIgMhNLWwQaPtC3aI/gnd8uqbXNlpK0ewayitPUXGwrrKvGoVNo2Rtpq7jHnXKVtR0j8HnPtEczUHJx+FJ78jPhvmR7xsURpqwcTH+enZr3XhhIhpbskYWYG27GLpG14KWnrT7n1T4BPzxZJWyfWhy0n575CZNVS8UuVtlnuWPwqb5v/e64c+1ehVClDpXNY7uNY+iGfMkrGtxaC0KdYW6q0NYpKW7eIA0aqMkFYxdN2fiFBwXJ4obBKLC4E2CMEKW397fMrk0uUtrH+IrlgpDxCtpZCZI7jCAWd44DjVPTqDPJPhWJMqhKE0Z5iUYzsfFXVafkxl8dkPl0o2iPIiZORKqaSl/un6pU9bRcXEgCc1d2iLT7S1iPS9aUPhP7FDX9MTs+mK5K2NRW9co/Bsh3m0sXjrrRIVGlskARhcExcIlOSZUZKFEqr4iWqKErp4kZZe2ZTBiRce4TuNe73FBcPymNSzR4hmxZjtKgH6JQUnJPEe1gLLxlv/IpvGRNhXeFUiImPtK2iLpbb5AoWGd+4v1w/achGxCPSXU/bKvYI1byfZ1MGjm0L7zYQxw2lY1cF9XOyUGqP4DgO+Yx4z7QdmHy+5O9+H+Egpa3c5otPjfGlp8d4/IQ7ZsmYxEeKmRH+xY0q1ilym4VsQdj/uCgZKxJnROE0qxB4P1EUpTZFeuTyI217e3vp6+vz/vf397Nr1y4ee+wx/vqv//piN++lBWmNsPUOQdyW+61fKBz9ARz6pni9660X/vtXENIaYdNgJ2H9AlTCXnuD+DlztLgw7jjwxN+IApmD2+B1/1uQu7YJD31KELrJCXF/VXXo37zy7WwEI7vFz8kXuHqdeE7ee2Y+cNP08UdxgInuK+luxFf1EoTnazu+yPNnF/jCU2Kx+SevX8u16/sa2ueNG8Xnnj49x2RSZB5FQxp9HfWfs62uLYOiwC1ViqDVhc4BuO03i/ZQL3vfJVWocLg7yqu2D6EocM363orbdUZ0utw5+ORirj4/W4lBWYzsmOjTsgjZqqsbaXobbbRRJ5YyRcvg3e9+d9W/f/azn224MW3UAVcl5yguaYtU2pooSNJWbKOpADaOIrQxik8lqPkUBZZjoVe7JMpJWy3skbZBSlvbwSWLhT2CVNpW87StNuks2iOIbWIhFc4+zZXnHmDt7H62Pb0Afb6V3rU3LG+PUEZGTc7O0wVYahhHUUlqvcAEpKdJFQSJoaB4yio//MrYpE9xeWY2A53FtG/HT9raveKzVbww/UrbiYU8N1iClNiw8BQ8+Am45VchXLzxLluIzDJ49PgMc2mDH9uzSqxGe6StnyBMUNCrE4T+Y5bquZ5YiIVsgfRioqhWW30tTDzvqjrF4kE9qfjJRXHtndfXY9mPoScnhNeUFvLIK79SzWufn4zKF6/1c4ks9Eulbb9PaZvCUOySY6t0vCAWIHIFizsX/511xkkOmreBuR300s9WUtrWREZFugRxlJ0vUT9XtKzwkTOLuVKlweLifHFSPLpHKFuMNAVXsV9JkR4Uk2xaEJgn1Y04TKP4lbbu9oHqZ1/7/As4p+cyvEz1EenyO32etlVT3d3zMp8pLVi1rH9qlX6y9ENuTOIj4iHeKkB2rqrnrmxjwS4EL27Mz7HOJRoZ3in6jE9pWx6TSvYIhmljZpKowIS+mu0siviOXgkEp+H7jzlv5QWR7sYklTOZSuYZ8ZO2sp012iPIbfzKZyiLSWpapNJuelVFpWhUi7LAQvV+4iltZ0uKKFZSPyuKguNu52+PYdokF+fpNlKAIlRfY0+IsaFSTKQ9QpnSdnIxj+6ed9O2ITUljtdNJZT9RFO0kmOWhS0tx/I80gEOji9wy7bBpepnU/jtVYtJ+eKGX/kMPtI2n4T7/kB8R3ykYkwiWoScmaugfpZjVxPpqRcJ999/f8nvqqoyNDTE1q1b0fW6H5nbqAQjBef2itdbbhdEwOwxsdDUNXrh2nF+Hzz9D+L1nrfDxldeuO++AJBFyLYGWSOsBDr6BbkzcxTOPi2KOh37oXj+00Lw8l8UopOX/6IgahOnhWfolteKz/dvLhJklxokaTt1iGtv7OJfVYVTM2lOTKfY7PdxzSawJoWC2Fx1w0Vo6Mpgl+tre2QiyfEpUY/glm2D3Lm78f565ZoewrrKbMrgsePi+WK0J9qQSvbqdb1874UJbtzYT19n8PNxQxjYAm/4pMhIHL70rDv+28s38BPXrfXm2JUw0hMlmUsxsZArKm3rIW37NgkxWC4BU4fEXEJRi5YobbTRxoqi7mXX+fn5kv9TU1Pcd999fPWrXyWRSKxAE9sIRImnrV9pK+wRVAWfp63ikroKGkpJJXBdKQ7yyxYjk6StnIhJpa0dXIjMdhwcLBQFwprqrfIl865H7FfeC4/+Rcn3S7Vv1UJkbnGz7cYL8NAfsWbmR/SZ01gOwndKKqNyixVJYEkmGZZR4vE3MydIo3BMkLKLqktQp2dYcO0hOsOdJWS3RIl/ql9BOFeqICyStinvnNdSYClXsEhkDMK2mCSbDjjjz8EPfgfSPoWwTGctq1brJ8s+99hpvrb3XFElIBWEFewRgkgA/z4LdkGkZIOXIhWad1NoutcUJ2FGpiKxIFPxy5W2Rj5POiPIjVl9mIIWAxxYPA8so7TVisfsj8l0Mk8hJdKo6RgAScIbtak6LcfCdmzPGmGzcYSYneKGuW/jfOsDwqvNZ0NS6TqsShAaPrWajElmvmoKtP99f0xk37NmXD/bziGIuyl7RqaiJYRHpJctbswupDANcexn9E3YNmLy5Q5EkoyKBaSQVVLanvEp0gWRLmOSqnod+ol0oXwuJfa8uBdyIv32/H4w8xU9bSWxXjUVP9IliGWAzFxVVSf4FmDKVPgA2elT4kXnoIgLlNqIlMWkkj3CyckEqiM+c0z3KSLk98g0fD1gccN3Hfpjcnwq5Ru7eiEq7REqF+Uq3x+4amIfPIJw8gW457dh7/+DYz+oqLStrur0ez8rgkhfRgkMpf2kXPm7OOGq0eND4lqEEqVteUy6Qq49Qpmn7YnJBLojPjOnubGdPOD93SPSQ7Elk1T/OZQ2IofHk676OSE2ivX5fG2XiUnZ/WSuEmn79GeLpHAuUXFxo5qdzeVsj3DbbbeV/L/11lvZsWNHm7BtNc48Lp5Fe9eL5zaprryQxcjmTsKP/lT4Mm66Da5824X77guEY9NiTLpgpC34LBIeF4tU+/6f+P3qny4WfQ1F4VW/IeYT86dgr2udMHTFhWtnvehdL+79lkFP9gw3bRL3hu8fLFOHn3uGbMFkKrSaoZE1F6GhK4M1vTG6YyEKlk2uYLF9tIv/9vINTdkQRHTNs0i477A4j6t66vOzlbhitIs/+PE9vOuVmxpuT0V0r74kCVsQmS/LEbZQtEg4Pp32npNrKkImoYeL9RwOfEX8HNgqfLHbaKONFUfdT6Ff+9rXlrxn2zbve9/72LJlS0sa1UYNcK0PUMRkzk/aKkvsERRwPW1VEJNaF5qqoaB43nlVEaC0LeAAwfYIkrQN66p7UxHbpHImTBwQROGZx72Vd/+EPFBp6xLMeZe07UW0p9C9nm8719O38Wp+8a5rhFn86UchnywSMwFFr+RxZ8wMPVoPjuMwm3CJ2biYhM8rPeAA6Zmq1gj+NvvJMhDFw8zYnOhssT4c013Z9Cm3aiE+Jt1Kn52KOKbvdL+TXeFHCCfOwPf/J9z2W9C/yZtEV1IQpo0chsif5hv7z3Pd+j6UElVnr3idTWC43r01edq6arX1/R08f3aBzsVjEEc8hHvF11IYVmdJeyQq2SMcPTuByOJVMJQouY5RYpnTQpHYt8EjCKsVIitYBSSXJxV2qfkp+kCQb/Kh00hTqKAE9u8PRJxnUgaKY9Gjm6QNyKhxrNQs+hN/Cwe/ISYp627yCMBKnrZLyCjH8Sltu30LEQkKIXNJW/wo8bR1lbYbBjo5cG4Bdd4lbQe2lFhCFGKxks9KVFLaHhmbRHxCYTK0FlPR0My8WHnvGq1KpPtjkixT2jrqvDBxifWJiTSItO9QFa/OMn9SSRBerZ+ib+45hhZm4WuOZ4ehOA6xNa/CUILtESoS6Y7jS/vuEmR/ahKyc1VVnf42+hXpg/EIM6k8hVmXIOzd6ItJGiNcfcFpCWl7fhKxNKJwQtuM7TyNOnNUtFtRalY/+2NyfDrFKzyC0Eek12hZYTkWlm15BGEkpJIv2IK4PvJ9UUFc2vJk5ysShBWJdNsqKrIlgZlLQGZ2WSI9rIUxLAPDLiptFUWcrowk0rvXlsSkEikqlbaZQgbLtrxFvbHJaQQFoXAktJOXsU8Q1VtuF4dcqE6kZ81sCZG+kBVFLlf7iXS5iJpL1OT9LBc3Aon004+Je6dEPlWZSHdjtCQmVgFcv+jLpRDZN77xjZq3fctb3rKCLfnPA+XUw+LFxlvFz4EtcJQSS5cVRXoWHvw/4lod2Q03/fxFLyzUaqTyJuMJ0T8vKGm79ibY93mhxnv00+IcD10BV7yxdLv4kPAHve/3imKSS7EImYSiiGvlzOMw8Tx37n4jjx2f5elT88xcn2cw7t63zj5FzrA4HtnF9f0vHUJLURR2rerm8ROzDHdH+KXbt6JrzVtu3LCxj6dPzZEviGe+ev1s/RhtkPD9zwBJhj95Uoh8hrsjdNZA9pZgYKsQP8jF57Y1QhttXDC0RDqgqiof/OAHefWrX81v/uZvtmKXbSwHT+npkrbSHsExlyptFUUUIlOk0rbUu1ZXdQp2oQbS1lVj+j1tHQKVtkV7BIuwe1Mv8bSVHo62Cclx6FnrTQ51VQ9UnpYrbTtd0sXs38aJ/C622u4Dk0+hF1REBcTDRywUI1PIkC6k6Yn0MJ3Kg5FBUaCrqweSMO+4E+LMDIuuGrW7QspnJeLDcSC9MEMPCNI25074zRxmFdVkOfExsZBDcwp06hZ5R2EsvJm5V76G0b1/JgjMH3wM55Zf81SJlTxtk/niJPvsfJa9p6a4Xk68S5S2CxQsVzVbh1pNrtz2pU/gdDooQ1cU7RuqKAgrqToPnx5nC5BXo6AopKOr6Muc9nxtJUHYGQ4go9x2Zwp5TEtcP5sGOzgxnSa3OC1yDWJ9xQlDPolhhwPbB6WKaMMymE7miTg5IiGNnGnzTwMf5Iqdk/Sf/La4rn/0JzjbXl+T0vbQ+CJhXWXLUFx4v8k2heOlnrYugVXRssJHii5kFfeYBWkbXjwtiPS+TaISMriWFVrgMVeKyfFzE1yJiImtaOQ61hLJnhGKma7RYiGyADJKtjuZz2HbsqCiQiZvkk/OEAURE0ma5lMUVLviMZf7f86k8mhOgTcn/oWFrCDFrEwPmqKItEvTQE1NkO+o7v28REFopMAdZ4l0FxWYmTkKanXLCr+6WPaTjYOdzKTyKInTwm68b0PRpsNIUtDsJccHRdI1a2ZLCMIzk9OMImIyFVqD6WiE84siJb9rxCN5K9kjgIxJ0Vri+HQaHGlZ0esjCBcxnCpjV9nihkzF3zDQybHxBFeOfxnSh9z9utYf+cp+1xUXN/x2BOG4INJzCaF+XoZI9/cTqX5e3Rvj3HyWwpxbGK53XZG0zScpEKz47gx1eouAqULKK1Q5MT3DFYiYHFM34Th7USYPFIn0ZSwrABbz2RLv2UPji6z22yNEizGpZtMh4+Q4DpZTJNKHuiJMJ/MYyVl46u/FxpFusahqVC7MWDEmcrEJpcS251LG3XffXdN2iqJgWct4/7exLNTMjChApaiwwbUjkB7r8yfBtkX19pWCkRHWUtl54U9566+B9tJTUh9z/WxHe6K1V4hvBbpGhHp6/pSwSZC2CEGk+PAOuOFdog4FStE381LFyJWCtJ18gXV7fpKdq7o5NL7IDw9N8o4b14ORwRl/jmzB4kTXTu7uvzQKVrUKb71mNZ0RnTt2Ddek7qwFe9b0ENJUCm6hzVW9beJ1JTDiKm1l9lld1ggSg9uKRRsBRq9qRdPaaKONGtCyp6Ljx49jmsuQfm20Dq6KVnrGOo54GLJcewSlxNNWxcHBkfWey8hZOcGrStpaZnGC7CltIxU9bXVVx7aF0jaii7Z6nrZ5E+ZPFzd2C75U87OF4qRRKm07XNI2FHWVZ4bb/kgxhbdSCjQs9YYcm8sScXJEdQ09KsimGcclgNPTFX1Jy9tXsAskXeVWR0QHxyG/6KbiR3txfCnjhqu0qoX4GF/IEbWzRHQNTVMxlCgJtRde/3ti9d/MYzz1dx6BX05Gye9IGaVk1L17j4vPKKqYZEuCMJeoqqaDsrRv95jX9HagU2C4cE4U3xnaUZy8FzIUKqQte0rbQtbzJHUch2PnRMqUsEWAZGREfGDxLFC96JVs96JLVOuaIkhRx8FMupYS/lT8fNGyIuiYFUUpsVyYSeWJ2lnCmoqjRzHVMNNrXgdv+QvY9noAzKmDXkwqqdUW8xn+9N4j/OF3DvPM6fki8aGFRKEhmYqfTSxrjyD7omEZS4j0eNrtdwNbSoj0SqpEqbQ1LMM7L47jMDYxLU6XImKS7iwtRubFpIqqM5kTMQnrKmv7YuA4ZBfEfon1FS0rlvFPlf6fsp0zKYOYnaFDF9f0D7p/gsVbPwZv+3u4+f0AWEYKyw62YqlYsE+qbPWImOB7MaldaZs3897YsMmt2qsnxXVM7/raYuIjwiXpZ9sOE9Pies4rUSxFJ9flFkubPVqybTXvZ0lyy7n12bk0ViYhfon1+cbWxWI/CSDSSxY3bINZV+a+rQfemvhnti88JnrE1T8NV/0UAFY+6e2zUkwqEoShDrFI2Tkgfk/P1HE/yXs+vjIm9oIbk+41PiK9sqetqqjeeZWLSIZpM+va7eSVKOOhdZiKLogi19rFs6yoISYSh8cXi1kvFewRliu+ZlgGc+l88ZgdhyvPfkHc4/s2wo4fE+chn/L2WdEeoVxp62UIxC8b5aJt2zX9bxO2rUH4/BPixcjuYp/tWi3GVjPv3dtXDI//X1FsL9orqsNfJosL9UL62W67kCpbibU3Fl9f8/9V9yneeocgdV/xy8Xx7FKF9LWdOQKm4fm5PnRkhoxhwviz5A2DOXWAZHiUka6XFgE53B3lv7xsPcMtPK5oSOOqdcW4r25CadtGZZSrkDc0QtoO+BZVQh1iLtFGG21cENRN2n7wgx8s+f+rv/qrvPOd7+Qd73gH73jHO1aijW0EwSVkcQuRaYpUZdrgWMGFyDxP21JPRanUMp0qpK30PFXUIsGl+wqRKQGkrePgYBMJiXZ4nrY5n9IWvNeVUsj9+wTIu5PxKC55FXHVgIY7ofIpoyql20KR/JAE09n5DGEnTyysEYqIm9m07ZIURhrDJa2X8630q+l2r+4m7OTI5dyJd6xPVMd1yTvTPeZKqk6XZsewDSYWc4SdHNGQJnxxFUWcy3AnvPyXxCFnZsGl5yspo5KGaEtfZ5hoSGN+bkakxobdSbZU2mYTy3t1ut+RLuQ9y4XejhCbtSlUxyKvd4miTXJS5NgY7jEHWVaAUI3LuJ2Zy2BkFtFUV/0MzOuuF6tLrMjiP4FK2zLSNh4JsaYvRsTJYUjyOtZfYhWwnBemp9CzC0wn84SdnKjKLEmbfEF4PG1+NQA5WZQn4JhlTBJZoaZzHIe/ffA4R8fOuxt0i5j4lLa1+qdmCnlPubBhoJOonSFmzGE7jlDa+lPxK5CO0kYEiqTf2fksZi6JpkI0LvrHYtT1bXMXY6qSUW67F/Jim3hEZ0N/R1k/8RPpi8suHvgVy4JIzxDWVaxQJ4di15Ho2Cj25x6z6bMWqKjqNPPMpPKkpWrebyECpUrbKl6i/nYn83lPNblxsAPFsYll3GJ9vetLU/ErKBw1VfP6SnHsyqIYKTRVQXWVl6m46+s245K2hcrq5/KY9HeG6e8ME7ZzZLJuP4n2ltgjVCOqFaU4/hQsobTVHJNXnvwz1hknKChh0i//IOy+2yPn8/IeQ0BMKnna+u0qADrcitGZmapEPxRjlcjlpPCVjR6R7sakZ21plkCFmABecUo5Ho3NZwhZGXRVwQzFsZQQuR5XTTj5AlDsJ9WUtolcKZF+6vwkjrQ4ivYUjz23UJWo1hTN68t+9fOmwU525vYykjqIo2pw8y95440/JvUp0sEJxysWAWzjPzEch/A5l7TddGvxfVUt+tqupEVCZg7OPiVe3/abXlHAemBaNn/+w6P84yMnW9y41kIqbbeNXASbkk2vEs+5q6+D7Xctv/3m2y6PInBdq8TziW3CzItcuaabVb1RcgWLRw+egue+IFS2kV2s7e9AVS+PhauLjRs3iucpTVUY6lo6X2ujeQzFIyX+w3KRui50jRafU0d2+7J+22ijjZVG3bkN+/btK/ldVtb94z/+Y9797ne3rGFtLAN3oLRdewSNMGC6CkUbVVV9hchUV5HretqWK22VGpS2/iJkctD3KW3LyQpB2uIqbV17BFdpa2YWQJ0vbpwQqajVVLFynwCGVSBMkbQNx1w/wbxL2vqJhVDlSawk+SSZMTaXIWLniIU0dHefSSuEHe1ALWQouJ6YFdNt3e/IGnlyBdGWPWt6OHb0MFnDEqSeLA4WiUMmT8HMgaZVVXUaloFhGUx4SlsVxxFt94oGuceStw2wbUKhSMXCNhm3gNRQV4TtI3H2P3WEyYU8PSNdYkpfYo9Qm6rTr5qMhjS2OIJQXezaQlxRQAsLsto2McwsqNqSmITUkFcxPWtmiepR9o8liNg54pEQ6WgcLJjVXNI2OQGWWV1p616XKZe07YrqrO3rIG4vipiE48JcX14ztiliwvJkj7RH6LCzhMMqji2+f7E8JkYSGCCshZfExGufT/1s2Q5ffuQQv6KZdPa6D0fS0zY7T8Ea9c5XEGS7E9ksECUSUunrCLHamRDtiQwSi8RFPADHNimYeVCUQAVhVI+SNbNkChm6w928cH6RiJ2jM6yTd1WIc+FV4gNlSttAewS1NCbxqC48d+2kG5NONya+tPQabDqyZIXPcDJPzMkS1lUcV+nrFf9yY1IwM0AYTdGWZAnIczCTTvPbX3kOXVV55dYB3tA/yyAU1aZ+pa2rOlmOVF7IZYEOYmGN4a4oPdYcdsHA0XpR4qOQEWpZJ5+kYOsln/WjM9TpxQTgyGTSjYlG0l3EWujYwGrwipFVVdpKe4RcDogTj4QY7o5wfGGMjGnS1d1T2k/yixTCetVjDmkhCnbB9X7OM2iO06VMc06P8e89P8cv9+4hDsWYGCkIKyiKsmQRsKKn7RIi3VXtZWYxtOrXTGk/0eiM6Ax3RYjZKZT8ItAtlLYLrvVQPokVVkvOlx8yc0MqbU9Op4nYOTrCuregk+zZQdfCizD5PGx/fbFgXwBp6y045bJAnLV9HUyn8iiZBFndoiPeKxTfntJ2kYJWmVQuv5/Mu6Ttts4sfclvYztQ2P12wr3rhaUGYBhJCIOCsqSfyKyBpTFJYdoOB6Ys/uWbB/ndt+4W5+AyQjqd5sEHH+TMmTMYRqni/ld+5VcuUqteIpg9hpqZgli8WLBKon+L8EGdO+75PrccYy5hPLi9YZXYi5NJnh1LAPDj166htyP4WeFiwjBtTroV4i+K0jY+DG/7BzEPuUwU9zVB+tqeehgmX0AZ3cPrd43yL48cIfTwJ3H6EywoPezteCU31VPk6T85rl7by7Xre1nb1+HWYWmj1dA1laGuCFOLORSlziJkEooCwzvFwtea61rfyDbaaKMi6n6Svv/++1eiHW3UC1mIDEEOqoogbW3HASxUNF8hMsC1R6jkaQt46cKB8IqQFf1cHS0kCpEF2SMoAfYIrtK2I3NWeJ0qKuB4Stvl7AeKpK0gxaK4JG/MnfRbNgXLJuRPZ1WDPW3Bp7Q1i/YIO1wlq7RcADCj/YQLGQyXtF2OBJCqTk1VuGK0i047Sc60sKK9eI8i4U7IzFKwcqB1ViWjDMvAMA0mF3OsdgRpq6jiGD0lkx4BRSXvOOBYgefQK0RWcAuahTVet2uUk8/kyBYsJvJhVkGRBHAsCkbaa0fgMft8FwG6Xd+0taYg4mdjmwRxpAh/QyubwLLyoHYsIT6kz3DKSJExM/TRx/6xBL1Ojp5YiGwkDhmYp8tLozQXz3vXTTygUrmMSdolquMRnVU9UeL2IqbtYIR7Ccvz55LKBTMLPhuEoJiAUHzPpg36XKWtorhEfzlpa+bAcQKvQRmntKt+3jjYSWdYwzye4sR8mvVDUeGFXAeRXrQfEMfcEwuhKAobFWEzkepcL4qIucdsWQUcuwBaOHCfHaGOEoLw0PgiUSdLVzREIhIHA2b0UUARxb5yC1XtEWRMpLdyV0Rn/UAHcWuRjGHhRPtEP5FEnJmj4MZ4ueswW8gznzHos131c1gS6ZK0FTHKmymgNzAm8r2ZdArHEePKAy9OM5l9jp8w0nTFwww6DoqntJ3HsAZL2lEO2W5BEHbQHQvRGwsxZE3gALnONcRU1avCW7AMUYhNUSuStjPZGY8gPDqVIupkRFGJcBwcmIm5VX7nT4FpFNXP1Yh02U+iOluG4kwcTpLOW8VFA/+CWEdwQUGJsBomQ4ZMIcditkCvnSWkqeQjfczqI76YiP3kjBTQRURbuuDkt0fIFyzOLeQ5nZnDOX6K1XMZTqdzPHbPYX55a6+4ttMzGJ2jVdsn7ycLLinaHQ0xFI/Qb05jmDZOxyBKKOotHhTySXDV/kELOnL8SbrK+hMzIiYdEQ1cQne2a5sYDycPCjuQKjEpWqcIIr0nFqK/M8zcsSQpy6JjqCwmuUUK0eUV6YZlkDHyYpxyHNYc+WdSisFZfT2r1t/JCBTHrvwihEOBC04eaVumtB2bmCQ5keSkvop5vcCLE0muXd8X2J5LEfv27eONb3wjmUyGdDpNf38/MzMzdHR0MDw83CZtm4VbgMxZeyNKqGyxQpKosydW7vslaVtOGNeBZ8cWvNcnZ9Jcu/7SI23PzKWxbIeuqH7xlIsvQZ9gwEfaimJML9/UR/L7X6Enc5q5rkF+MPpustMx1vW1SdtaEdZV3v+aS9zP+CWA0e4oU4s5RnuiREMNqmRveDesu6lYRLKNNtq4IFhBp/82VhSuitZxK6yrjpik2Q7YWKKGQ5nS1pFKW5la6UJOXgtltgkl8Ejbou+QJSfDVe0RikrbzrCOosBQYUJ4nY7sEhunZ8BI16W0BYg4Lskbi3sL+RnD8iaxZm4Ry61MvpynbdawmEmJVPdYWEOLdHq2DkZEqLcK7jlYjiyTCsLuWIiBzjADWgbHgaTqK2AW6sRxHEyzNjJqJp3BMG1iCDJKddV0nqrTJUUNHLCtYE9DtUhsAXRGdOIRnZevEd/9wqxIz0fTPXKr4JJvlQhMGffFvEsQdoTAcRgxBBE/GdngO+YOQfK7iwNBx+z3tZ1LG5yZzbgEoY4WdUmRvCWqugPpOZH6raBUTfuWStZ4VCca0lgTEWTJgtLlO39xHMfxlLbLKVmnUxkcx6GDLCFNKbZPklEuYZnHAccMJAjL29cbC/GLt29lU7eNZTs8dDrLxELO7XeKINLdmCxnjyAXDzwinXEA5iJri8cc6nCvmcr+pP5iZAXL5sUJoeqMR3VU10ZkwdRF8RGA+dPVSVutjEiP6qzr6yDuCIVePuT2k3AcUNyYZCu2D4pjw2wmi+NAp5JD1xTUqIhvkUh3PXrtAjh24DmUZJSMyereGNes6yXmZFjMFbj/ZIbf/eZBZiz32LLzy6fil/n49sRCqKrCRlV4+Kaiq90NO0BR3X5S2VtZntdUIYXjOBydTBK1s3RGiv1kzukWqmDbhPlTRXuEakpbH5G+ZaiTTjtJxjBx5KKBu2hn55JYVdrn3+dMKu3GJI+uKRCR6mdfoT3AKKSB4MUN+d5kMskHv/Qcf/LAGH/70An2HjnDXNrgfEbnxYkkR5LuZ2spRCYXnHJyvNbp6wwzYE1hO5CPuzHx1NlZUUCM4EJfXSHXHkEqbWfSRO0sHWEN1T3GmfBakTJspERMaihElnIXxLqiOjvcRcBUrlC0THHvyVZuoer9zn8uptOif252ThGaOQh6hHu738ZCrtRiKO9aHZR7cUOpjQiIlPGvPHOWbz/1IoZlo3d08+E37risCFuAX/3VX+XNb34z8/PzxGIxHn/8cU6fPs3111/Ppz71qYvdvMsbloly5lHxOmjC3++StonTS55TW4LcAkwdFq8bJG0dx/FUtoCnZr3UcGSyaI1QvuDSRpMYuVL8nD0OhSzhZ/8f12vHsRWNL3X+NAfT4j657iVWhKyNyx+r3SJvDRUhk+joF/Yn7XGljTYuKGpaBr322mtrvunv3bu3qQa1USNkITLX01ZBx8Fxlba262nrkraKUiRtlaWetl4hslo8bX2kbUGuojt2oO+isEewheINoTztCOsMmuNYtkNoeJdIcc/MwsLZqv6zsNTGIeyStkooRjRkkDUssoZFj+staOQXgX5gado3FImPdCHN2Xkxee7VTXRVkFkdYZ18wSAX7icOFHIJUKsobcuJj6guFI4x8fus1YGkbZ1wp9BIu5PsIBIAipPs84tCvTUcMVEVBS3SBYbPHgEg1EEuZ4NtBU6yZftyPtIW4LoRnaOqwkQuxN4zCa7f0CcIASNV9PFdxiog5ZK23VEdFs8RI8esEuI8I74GdNZO2ppZnp1KALCu0yakqeguAZfOm9CzBuaOk0oIT7mOUEfgGCW/I+NT2gKsdUnbWbsTz9EuEsfKzgu/yFBs2eJrM+k0EGYwYqGgoEU6IecjCFUVQjHhnbsMkZ7xxSQa0njLzi5OTWokrCif+v6LfPiNO+mPdEF+0YvJskpb3+IBwGjhHFlgKryWK7wGdFLIzYMtjqF88QWKJF+mkOH4dIqCZdOr54mGVPRYFyTdmPRtFP15GYJQnr90QcYkRFhXWRfNwgLM2nHWgHggjMQxc4vimtGWJwinU2lAZSBSQEFBjcQh67NHcL2gDVxFepV+knZJ23X9MX7+VVtY6Bwg+UyYF4gzNpfhqYlu3uAS6V5MKqmz3feThuwn4vfViiBt58OrxHUoifR8AmwLTddQlaVrq3LsyhQyTCfzLGQLxMjREdbQol2QhZRhiRTgc0/D7NGaCpHJY+6K6qzv76AHQaQnlS4xdrljawHhnY6iL6tIn06L7x2MGG5MusD2qZ9dJWveKYAdTKRLT9vzi4vkTYuIrrB1KM6VHSqrtChjEUEMJhR3hM3OYZi1+XF7pG00REhTWa2KjIpkeJQouIsv8poxCeudgWONtNtJGSlSeZOpxTw7nCwdYV0ccxqShgPDu+D8Xpg8UJP3c9Lz49bZuaqbY3aStGFiRXrQACLinlzw3++WIapFP4HVoRQKCon4Vhb0AeFtDkWiupABequO1Tkrx8RCjs88fIJTM2lusbP0d4bZfe1WQkMXIS27Sezfv5+//du/RVVVNE0jn8+zefNmPvnJT/Lf//t/5yd+4icudhMvX4zv53Rujp5QJ+HRPUv/Hh8WCwZGSthmtbrIzdhTgCO8cxvwsgUYX8gxkyp6a5+6REnboy5pu/ViWCO81BEfEtdqagoe+XM4v5eBeJj/p93Ni+kRQIyja9tK2zYuMbxu1wgZw+INV1YpDNhGG21ckqiJtL377rtXuBlt1A1VTOSlpy2OW0HdJUr9hch0TQG/0raCp21Ve4RsQvz0kbamtGioZI9QprQFoaobNF2lbd9GUXwnMwvzp8lHxf6CiBTwK4JdhZftPjiHYnSEBWGbNkzodUlb199V1ULLklFj85JYcM9BqIOOsMZ8GjKhXrG//CLEYsuq6dKuB55HzETEpHuiEGOT3DgcX5bAhOIke2IxBUQZjljgQMgly5L+Qi/hOIbjEgtVqodnTYMIRQIzaqcZjIfJWR1889nzXLe+FyXaCwtnxaQ9FFlWdSpVk92xEEwdJqQqTITWMZ9zStvnkmUhNRRIfPhJ2/1nEgBs7AZy7jHjEoTdovBVemEMCFZ0QvG8Zl01mCyGNxIS8Z4sdLBDbhzpcu0+llEQusc8mxa+qAMhNyU51g0L5THpJJefFUR6gGWFXKDIForEDEDEyrB5qJNnC73Mpw3uPTjBO2J9grQtpEENTpv3t9sj0mMhyCfpthNkgXNKMJEe0oJj4lfanp0Wizfr426xO1fVmcqZsHYjnHmcwtxxT3laLRU/41PaAqxx+8mk2cEar31xjFyiquoUymMSZyDkbh8TBGayTJGezy+AHdxPZJwyBVkoTXxnj5Kjp6+DzZ2j3DcPSQMxHuYSFNyU+HqUtgAj1qQg0rVhtsuNI3GM/HzF9kHpgtNRt9jMqpgoQCkXN1I5Ewa3CtJ25ggZpXIhsnJv5XhUR9dU1sbysCjGrm4QKvxQhzheqwCqvixBOJvOAjEG3X6iRbsgQ5EgdP2u85YYG6rbiIj2Xbe2i1983Q7UJx8CM0pPrA8WIOF0iMVKx/Jisry1S2lMVjMj2q0PCyLdta0w8wmwLPRI8GNTd9gtAFdIcXJaEDmDEbEIqMcEaZvKmSK19vxemHyBjOuFXM3T1h+TtX0x+rQMlg0zVodYEnMzSwp2QdxPVL3iIqDc51wmizd22YjidRYsZEptRHJYYAfHxK9+/t1vvoBh2nREdF471MH6hQ7ouMSrwFdAKBQSNQGA4eFhzpw5w86dO+np6WFsbOwit+7yxhee/Tsetie5c+Bm3hRUwEZRBFE7/qxQMbactG3eGmG/q7Lt6wwznzY4MZPGcZxLSs3qOA7Hp12lbZu0XRmMXAmp+8RYDoRu/FlG5nbz4hGxEDvcHWk8/byNNlYIvR1h/vsrNl7sZrTRRhsNoCbS9mMf+9hKt6ONeuESstIeATQUXPUttshacCceqqLg1OBpW7UQmVTaRoop/qartNVY+sBaYo/ge3DpDkO/NY1pR6B3gyBtz++DxGmMIZG2Xc0ewcHBtE0cHEKOn7TNMYshipHpnS4JUADbJBKOByujfMTHWEoQNH1h9xyERLEggIwuVFxGPilI22XS0qVnrFQ4DusZ5oBzOd/E10eWKSiCcA/apzvJnkymgSiDkUIJgVlSnTvcQQ67IkHoKW2tPBGKSlvySYa6olhpoSDceybB9W7qbcHMCdJ2mbR0Sdr2xEIw8yIhTeV8aAPzGV8Rl3CHl/ZdaX+SUErkUhyeENfv2k4bchDpENdeMm9CzzrxvcnzEI94VhdLzp/7PdmCgUKRFB3SMkwD5w0fWVIWk4rEh6e0FSq0vlABLAh3+Mgyb59xz36gOpGeJ0qRwCSfJKSpXLFqNd87C3PpgvAVTZwWRHokvixZlir41M9zJwhpKgtaPzN533GF4xjYVQlC/+LGwfNiHFjdYUEOwi4xk5JKWyAzdwI6XY/ioLTvMnVxlxuTkXCWNHAuF8UrbyCJdNsUBaoqKdI9gjANxOnTDbAq9ZO4ZyNSzbIia+bR8cVEkoCxbph399nR75K2GdCXV52mvLFBByNNlyTSGV7aPidYnQ2l9ghzc6JdqzrcBR15HeZNGHA94maOkekTsQi0ESmLiadIj+axgbO5iI9U7qJgLIJtoilaRbKiSBCmgRi9mhuTDkHaeupnae2SWax4Hco4SSK90x2bZSEy3V0wWczb0NEnPG0lkb7c4oZRNl7bM6SBKWWwuKATjpPPz4kFsQr9To5BSSPJiRlBmIxG3KyQDmnTUYBtu8UHpg6SjQrNezVrl7QvJoqisLGjAAtwOh0WpK0eBj2KUUiJsVWPVY6JJmMiFpz6dQMMBNGf9nukh0ELYRQyFfuJXFw9PbeAZdpsHOzkl27fSv9T34cFil67lxmuvfZannrqKbZt28Ztt93GRz/6UWZmZvjc5z7HlVdeebGbd1lj45U/zcP7E9yvpLnTKhBRA7Kq+l3Sdu54a788n4TJF8TrpvxsEwDcuXuUrzxzlqxhMZXMM9IdLDa4GBhfyJHOm4Q0lfX9bbXnimDkSjh+n3h9xRthx4/x+oUsD7mkbVtl20YbbbTRRivR9rS9XOGSF7ZbiMxxVI+0tXEEaesSgbpaprQt8wrTXMVDVdJWetpKHz3A1Nz9O0s31xQN2xYEsl9pO8ocqmNhKDHoHBTELUDizPL2CKou9ukW29JkAZRQjM6IS7AapptW3VUz8ZEupBmbK9ojyH12hMQ5TmrimI1CdauAoBRjgH5V7Pt0OoptuyerBoUjFCfZMp21z1WrSQKzlCCszdM276YNxyNF4kNXFXZuEvrGHx2d8QpfGS5JshxBmPanfU8fJqSpjIfWF4mZsvZVUr9Jku/o9Bym5TDUFaFLFpPrLCptnW7hN5lOT4LjVFTaej6+ZikZ1YMgdE5no+J6AggXCUJd1SvGRO5TEB/Qq7vtk6RyWUw8Ir2KL2SurH2SjAp5pGjBKwZVKFT3d/UU334FoUvaToXWMJ8JiIlTOSaSUJrPJjk1K67DkagYd2RMUnnT68vp5DlwbDr0CpYVkkg3S5W2A6rY96mM71qLdBeJ/grtg+J1OJcVMenR3NT4oJiEqvcTueDhqbPLY9LpI6pjohiZIX2Gl7ERSed9KvzEGcKaSkrrYSpXSqQXluknfnsEqbQdchecov5jHtgCKNjpaXLuwls1ywrpdy3HrmFdnM+TKV875Nha4fyV71OoOqFXl4UjRfsWyxY38lUWnKQ9goxJkbQVxxSK+cbDDuFBbhjVvZ8Dx658kjjifJ61+0vaV23xBXxEupHihFTahl2v45ivn/RtFEpWM082PQUEK209otq7n5RmbhxL+q6ZaPeyC2KwdOzq0Vz/bveaSWSCFjeqK9LT7nh0/YY++jvDvgXey4u0tSwxpn384x9n1apVAPzBH/wBfX19vO9972N6epq/+7u/u5hNvOxxw7pb6R+4ghQ2j44/GryRV4ysxaTtuWeEpUvveuhe1dAukrmCp2C9bn0v693q67K/XyqQ94TNQ53oWnuatyJYfS0MbIWtd8B1PwPAqp4YV63tBWDzYBOeoW200UYbbbRRhrrv5pZl8alPfYqbbrqJ0dFR+vv7S/63cYGgSILWVc06qkvJOqCKwkyyEJmqFj1tNVhqj+AqWA+Oz3M+kQ3+Pkna+pW2ruXA/5+9Pw+X5KjvfOFP5F7L2XuV1JJaC0ggISGwhQDjTZhlPDb2NcPFY+uFAXuuH+PHHtl4hrGNWXzHK2D84jt6DN7mneGaAXO5nsEWZmSzGRBGSEKAdiT13n36rLXmGu8fEZGZVZVZpwUtiYb6PU+rW+dkZUXmLyKq8hPf+P6qHhEdyyGVEkmSe9oC7M1UBfvtYL+Cq4tKMcnmIUJdAGqa0jbVgM0VEZbQsE37z4IuRAbgzykIkE55yHbKnra62IsGhHjNHARvW9ozMO6DlDtCgEE8ao/QyraxLdiixYltDZq9FkkJENaFecg+rQvHLGoY1WhpSBEmJRDcVkWvdtjOGqWjnrYM1UP2/j1K7bc5iKCxqApApdNzktsPaNizZHWhewrHsTjhHmB7GJOW2hdpMFN3PgMvHjq9BsC1BxYRGoiZa05SSRjsAtulm0WQRnnl9on2aYAxTBSMMICwmWwhBGxkbU53dc5Lqs4zAYSbA91nRKjbZ1SdSQkEF1C0Cujl0HY8JxoQ+u0SjDI52aFQWm4/YJS2ObQVnHTOZ6NXVj8Xiwd1wNFAvkc31pES9i8GBFJde1BePGgsgT9PjxTiwY4gfTim6pyXqh8ejxolv9O5HWFZ+Zo3BxpwWervIidjlhX6nFXjpOgzo1A5z0n5mptaha/9YncqANUvq/A3HsO1LU47+1jr1SjSa3JsVJ3rww4nt4YIgVIXA35pbsBtwOIBBmQQTSl6lS9umAUd9b5LesHp0b7PMNZza7CgcxJPzYk556YG6XNWONK+6gWd6UrbCWirvYT9dmlxo7lL/WonkJ7b2Zhx4sDW0QKkD0oLDuVxUnPNc9rvtxN18uJESxpUB61F9buhXlTcq9S2/Z76PDyTnBiQvttVY+/BbZswMZ93Gtqm0+euYpyoc8zrucv06a3BKLQ1IH2a0naQjC5SmpxQMyd/q8b555/Pf/gP/4H5+Xm+//u/H1D2CLfeeivb29vccccdXHPNNU94O/74j/+Yiy++mCAIuP766/nCF74w9fgPfvCDXHHFFQRBwNVXX83f/u3fPuFt/EbDsRxuvOhGAP7Xof9VLRRYvkT9vXUE9Hx5VuLQN2+NcM+RLaSEA8tNVto+BzWY+1YrRvbgSfVZNfOzfQLDa8JL/k/47p8ZKcj0b154Ma/+7gv5gSv3THnxLGYxi1nMYhaPLx43tH3rW9/KO9/5Tl71qlextbXFzTffzI//+I9jWRZvectbnoAmzqIytD1CirZHkDZgIZBI8/0hL0SGhrbqiHFo61ouwzjjf3z5CH/yqa9Xv5+BtuVCZNp+wTGAqhSqEJnUStti6/+uWFWw3/C00mHuPKUaToaEA1UApkppBcon1wDAeWeowDQCbI+mfojvRUVF8nAKmIECRq0POkRpimtbBBjLhQIEb0llt6AeiqMdYZmBPfOBA1IiBhsErk3Xnuextb5uXyv3T90JEGZS5sVy2jn4UIBASujm19xS1yyrr9nAhiiLkEha3iiMCtoKQHWHCQQLpIDUFdh3umYDFnYNHgXAWb6I2G4gZQnOlK65DqQ03AZSSg6tbwJwzYFFMIq5xhy2pTp3L8pg/nwFo5JB5fbicvvCXDXpQhpjRR0Cx6ZrL+RF6MbVz3UxDj5MThoasEop6ZnFA7c1tR/mOUlHwYzJSaOlctIJFRRNAJntkBPbQNvS4oFW2q6657E9jElSPW+U1HR112wWNw5vqjngGfsX8pw0NSyL04wwzWDpYnoyg7hfm5PCpkODdF+NEyfcxncsetY8h8w48dtnBNInciIMLFPt65RAuiyB9Gn+qWE66jNMqGBU0F4ECqVtKiWp7l87LW4UOXFype1pZx/r4yBdTu+HBogf29oA4PzFBm6qi1qVQL+UEnY9jT4ZxD0826tcJBovUtjWc5cfb+I5Fj3RLtRkj3NxY2uoc4IB6UZpOw4I6xec8pxoQNjKdwloaKuhaHeYQGuXXtyYDtKLcVIUImPrCJ5jsWbvGSk2hK/9wrP6uctAzO1wQC9MsC2R90MzN+SK771XkUnJUH/mTVc/j/bDRtrBsy22meMhrag7Y5A+tuDUEnrxRY/jEWjrT7fpMHkyORkfJ+ea0vbnf/7n+dCHPsSVV17J93zP9/AXf/EX9LUi+cmKD3zgA9x888385m/+Jl/60pe45ppreMlLXsKpU6cqj//sZz/Lq1/9al73utdx55138opXvIJXvOIVfOUrX3lS2/144nn7nkfbbbM53OQLJyqAdHNZfdbJjM8++P9yUi9sfFMR9eHEl9W/vxlrhCPqM/CaA2q8FNC2+8217yyGlJL7TqjvD5fvObfG4LdDzAUuNz5j78hzzyxmMYtZzGIW32w8bmj73/7bf+O9730vv/zLv4zjOLz61a/mfe97H29+85v5/Oc//0S0cRZVYRn/2gp7BJNVDXZt22JaITJb2CRphiTlxNawUAmakDJXY5pt8wCJqIe2ruWSSdW+wC262VKkoO1pR0Nb28mLSkV6q2jdQ6dtKcsFgJZWleE2QIgc2hZK27YGH/UP2fl21jBEknBg0UOkZWirQXCcQXNFPcCmUS1IycGHBpjzDVepjrKEpuvQt+Y4tG4A4c5boM05wzhDktDyHXwNZpxgjqZ+SM4tEtzmVGWUa7naE1jl2sAys521Mbeo70cCwaK+3jh/bVWYc+bX3FeFWsTup7PYNCCtgLaRnH7NTadJP0rpJwManq2KaGjllvDbORjohSnMn0coJcTDqVYBEqn8jdEwaqBAl+f7DEWDo0ZdfoYwyrVcUilz1V9DwygnmCPQfSbPidfK1cVmi3c5TJ7iNEaSlXKioa3JyTBBBgt64UBvuZ4CCCUyB3CL1hB6p3FswZp3HlIyUiXeAMKdlLbHtzcBuHL/XKFwbBYgvTtUW78VIBzQ8mqKw9kuUspc8T0XOKoPypSG59C32sU4yXMyfZyYcxp/UpOTloZlWVYC6SVV504gXSIrclIC6c3lkZxMA+kSmSvS5xsubD6G5yho2wuTQsnqldTFO+RktafG7mWlcTIC0pMMVi5VOYm6U0G6lJIwKy0exH1IY1qeTc+ey7cFly0rdtolICkWnExOTPu2B6OK9HDKNRsgGqahXnCyVE70Nbc0SFc5WVGLG8nOC04qJ6X5euswnmOx7uxhrRuNtM/0w7prNn2pE4ZIMi5camDFuvibAelhrM657yql9o66ILNqpa3JSXlBJwkR8YB2oMbJfcc7+s3P3EZEIulo65TmeE7KIH2HBScD0ofpEIlU9g1Zli/onGvQ9jd+4zd46KGHuO2227jkkkt4wxvewP79+/mZn/kZbr/99ielDe985zv5mZ/5GV772tfyjGc8g1tuuYVms8mf/dmfVR7/7ne/m5e+9KW88Y1v5Morr+Ttb3871113He95z3uelPZ+I+HaLi/Y8wIAPv7Yx8nyugylWL6Uv842eP+DH+K997x38jvp441jX1Lfe+f2w8IF39ApkjTjK0c1tB3bAn9ovV8shD7Fcf/JDhu9iMCzefq+c2sMzmIWs5jFLGYxi+p43ND2xIkTXH311QC02222ttSXmB/+4R/mox/96Nlt3SzqQ0Nb84VXSltbJJSUtpZR2haFyKqUto7lIDVgjdNs1GsQ9IOl2Rpb2CNMVdoKmyxThcg846klJfPDYwCcsEoV7BcvBGDYV1W765S2QA6mW3YBV4HCHiHUbffncy/RKlgG6iHbEhaDOCVlyEXzpe2wpUJkgyiB1m4Ft9Jox+225iF7PnALQNiaJxUOj61XqDp3grZJSkbCvoWgeCD2WrkqM1dveW0FMWuu2bM9Bb0lOQQmCfP+0J43W70zQreAADsV5cqywg+5kWmw09zFoi7ss2mKkbmt/JzT7BG2hzEZIVefv4BDptqor9nYB3TCGBYuUHArGdaqqc01Z7Kk6uwrdZvdWgEhOLrxOKGt7RInGRkpLd/BSYqczuc5KaDo0OSkxrJCSplbiYznpDm/CECaSUKngLZCTC9el2WQSHWO+cEhAMTc/hwe5QXiSgCzDnw3nAZRktGN+gghuGJ3I4eUwm/n9gG9MC1B23qlrYLekOk5pZwTt7VIJuxCkV7KyU5b8aM0I5Oq+IqXqtc7jbl8HBc52UHV6QSlxY1EA8xBPge2NEgfRimJv5Qv5phrq2tflkGq++G8b8PmIWxL0AnUotVoTqbbiOT2CP0OEsnTd3l5n/Gb8zi2BulhArueRk/bIzQr4KBpXyolme4zLc+BwaY+3xyJ8ErQdq5YENvB0zZJJalU/dXXlhrm/kkpVfvK1yzr5y6VkxRJquwR4h7ozz8zTrrDBNlYVufSOZlmWZFm5Nc8FziwfRTXtthw9xAlmYLAMOJ3vZM9Tj9KyYi5bMUr9RkFRZNUKpA+t5++Gyi7nSyrVj9bHkmm5gUhTE7U50mz0SASPvedMIup80RS2wHVfD6BHnuZJM5ihIAgG83J9qBkt+NP361iQLrJyXzgaIhe2OGci/F93/d9/OVf/iUnTpzgHe94B/feey833HADz3zmM3nnO9/5hL1vFEXccccd3HjjjfnPLMvixhtv5HOf+1zlaz73uc+NHA/wkpe8pPb4b5X47l3fTcttsdpf5UsnvzTx+8+58GnZhajHid4J7t+4/5t7w8Maul/4vJGt7OOxFW4Rj9V8MHH/yQ7DOGW+4eYK291zPk3fIUllbrH1VMc/PaSspb7roqURa7JZzGIWs5jFLGZx7ka9TKYmLrjgAo4fP86FF17IpZdeyt///d9z3XXX8c///M/4fjU4mcUTEBrYpCWlLdrTtrBH0FBVFyLLDPIc+1LqWI62MlDnOt0NVfEiE0Zl6zZBg53D632Od7W6rEIpYc4JGb6r4dJgAz/tAoJj7C4O1tA2GqxDe3kqCFDXCU2rKEIGFErbuPC0naamA1XdvuW2GMQdGgbaHkVdo+3k9gH9KIX2rjNS2ipVZ0kZta0esoP53bCt7puUi4XCUU4vHONaLsMkQxKzbz6Ao8YvsMVcEHFyq6SO8lpKaSuTyqrMCs5r9bWVqi/0Xa3Usl18v4FtCdJM0hNtZaiRJTjCmlqUS8GtGM+xcJNCSZwrbfslpe0O9gNNp0kvTEkJuer8BQVm8pvRGgWE8xraxlOgbd4+db2eY4Gx4ZjfBdsUD1tee8dt6eacUSqRxOxue9ApoO1c0OHUdljKSXtqP1TjBJCQESkYpWEZloPvN/EcSwEkuw1kkMW41vTidQY4+q6Ft/Wo+sXyJSxte6x1o6IYmVF813juglJ1dsKYlIRLd7doYB5OBbhN2r7DVj9WIH3pYgUI4wGtOlWn5ZGmGRkZgWerQil5TlagC4fWjVpvPgeEOy1uRInKyUrLRYRFP5xvbDGIUrYHCfsX0Nu+66GogXkqJ7EC6SYntkur2UQIJfTs2fPKmzo9g5zo3QyBZ+MNVhVUtF3shX2wFbHWjdi/0Ci2pU+Bti23pcZpHCGJuXxJP5wLC+E2aPsum/2I7jBh18r59G0X0oxmDZBwLAUeJDFN31HqaQMIF3ZBVxXbkVIigjP3T40Sdc1LDQdLLzg5jTma/ib9MGF7mCiFZr7gVD1OfNvXRSjVOGl7drEN3/FpNdXnQJpJQm85n6ttYU9dcErSjIyEhmfj2hZsHcESgqh1PqSw1o3U4ptfshGpuWZHOFjCoh8lzBFzyYL+XLQcfL+Ba1vEaUZnmBDM+Qy8BgyhIarb59kK2mYkND0HyxK5TVF7aTdsCx453aMfJTT9+TO2R4j058l8w8WKC3W2EF2tVk/UNXstBYJr5kPf9smkVItiRGpuHq7rm9tQu2jO4Wi327z+9a/n9a9/PR/96Ee56aabeOMb38jNN9/8hLzf6dOnSdOUvXv3jvx879693HfffZWvOXHiROXxJ06cqH2fMAwJw8L6Y3tbfb/Lsowsm/wud7YjyzJcy+VF57+Iv3v077j10Vt59u5n53PnY9uP8VebX0MCS0nMhpR84tAneNri076xN0yGiKN3gpTI878LKq5xkAz48IMf5vPHP8+z9zybf3PVv5k45q5DG0gkV58/j5QyV/9evNLkq8e2eHi1w4XL1YtiT1aEccoXH11HIrnhkuUnJJ9ZliGlfFL6yizOfszyd27HLH/ndszyd27HE5W/Mz3f4/5W/WM/9mPcdtttXH/99fzCL/wCP/VTP8Wf/umfcujQIf7dv/t3j7uhs/gGw3jaGqVtZoG0ERINbUWuKDCFyDgDpS3Aei/i0hJTHfezjdOM37n1PtL4CLYAt6KzFSA4w9OqLzYfw7EEG84K21EJbhil7XAD2su5gqcqpFRwNhBGaauOLZS2ZotxO3+InQYWWm6LYaSUthe0tUJIq3cLpW0Krd0K9CTTlbYTyq2TCny0Fnbh9ASDOGWtn7B3USu3ZIZbo5gEBZDCOEWSsH/OydVjStWpHriLrfjNqQpCAEsP+cDX15pX+p5HWBbtQAM4GeDrFLnUK1MK2JMq0G+K0PhtFpra03IwBm2nAMKG0yDJMjJCVYncnM9tgGXl9gjdMIb9F5RyUr8t3YCP3HNRqzpbiwqkn9gekqQZTgnMTFOrebZHnKpz7mtK6Jh+02LOKIFL9ghDoyCsyIkQAqFL+XmuVACzXH1dCNq+w3oS0ZFtfKRS503JiWu5xKlS6C3obfgALB1kOdHV43vjStt6QNh0mnSHCRmptkYwau9m3j7Q/XDffvpCQJbRqgGEJieSNL9fBhC2l/ZCF05thwpGeWfmaatAulakN4Fh4dk7Fzic3CopbfNt3/U5sXVOXEcpd401Av48Ql9zZ5jQEXM4uk9PG8eu5RLrfjgfuLCp1M8sHGDJbnB0KyopbQuQPm1r/zCWIGGhVRQhw2uBEMwFjoK2oSp81W8uQ3SMZs0XgwIQxsU4MTlZ2IU7sOiFCSe3Q/Y9Dk/bKM3IiNnTErBV7AhYaDgK2g5izl/cGVRbwkLouStfcAr155I3h+/YpcWNhXzBydvBvsGMk/mGqyCwvmZ78QJYSzjdDZWqLh8nWe04EULgWh6DKEUSc9BsSvFa+dy60YvoDGN2z/lKaQs0RLUazrXcHKTnXte6fUF7mT0EnNoe8sDJLtcGZ2aP4FouUSrJSNndsKCjPkedxjxt/ySdYcJWP9bQtlCkV+1+UWp/T/87puHasKXHyTmqsi1Hv9/nv//3/86f//mf85nPfIZLL72UN77xjU91s77p+O3f/m3e+ta3Tvx8dXWV4fAsFv6qiSzL2Nra4srWlfxd8nc8tv4Yn3rwU1y5eCW9uMf/dd//RT+Da1OPf5H6/J/DPnccu4P7l+5nyV963O/nnvgSrUGXrLHMdtyEMX/gr3e+zocf+zBbkZpPbj9yO9+39H35bgZQuwI+/+AJwjDhwlY24jG84iWEYcQ9j5zkmY+/eWc17jjcYbs3YFfLZZ4+p06dffWvyZ+UEsuaKXnPtZjl79yOWf7O7Zjl79yOJyp/nU7njI47Y2j7nve8h5/6qZ/id37nd/KfvepVr+LCCy/kc5/7HJdffjn/8l/+y8ff0ll8Y2HsEXQhskxaIEtKW6sACI6l7BGo8bR1hENGobRdKxdggRK0VU+hp7shwyglTgVtR9bbI+gfO7b+x8ZjOLbgtLO/2BYLhdI2VP5+Z6K0DSbsESY9bUP9kF0HMAFc0SBOM1KG7GuMQttycTPZXMkLkU0rbKPUdAmBaykApwGh1VzigqUmj5zucmQz5BkX7VPno9pewoSyR8hwSdjfKgEXt7BHmFR1TgELesg3XH2uMiAE5nLVZIrw25BUK6nL7YszpZqcL0Nbr53bI5S3fe9UgV1BW4kkVPCoZAcBlKBtCu29hBoQejVVpl3LJU2N9YBRfOviP4u7CU7YDKOU41tDDgRtpZo8A6/OSOd5X0P3N9sFx8uVwGVou1NBPAMIG56+zzn4VjlpBw7rvYhuIrDcBqTgTekzCvZkBSA047e1i6Weuu8bvfGc1MOeptukF6VIMi7dG0C0lr8WyC0rumEClkUvmIfeFo3Bdu39mwDpZit+e4WVtlIDH1rvc0VDK+bTHRTptkucqDzvC/R4sBxwPHUPKI+Tpl48qN/qbkB64I0tbmgY1Q40tM08mrajc1I/TkxOFCB0CpC+eCErUrVhrWtsRJo7WqcIIQhjNRdetOIVqtOxcWL6Yd9R52nW9BsFbVWxyhwQDjcBsFrLXLSryUMnuzy82mXf4nyxuDHNHsFSNiIqJwlsoXaIOD7zgctxhiNFCqf5cQPYqPfKx0kO0lVO5gKHtW7EdubjWfaZjZNMjZOFhgvbR9UvmissLizA2lrxWWh8hqcsOAEkiY2U4HsZu9wCwoPKiYK2KicDR52nWo+ucpJmCnq3g9FxQmORK+fmOLU95L7j21x7QQnaTtslYHs6JzF7ghQ6AAK8FgsNV0HbQcwB3e6d8mzpnAR+ppSSeRGy+crjz4X47Gc/y5/92Z/xwQ9+kCRJ+Imf+Ane/va386IXvegJfd9du3Zh2zYnT44W3jp58iT79u2rfM2+ffse1/EAb3rTm0bUwtvb2xw4cIDdu3czP//E5y3LVF/ZvXs3Lw5fzMcf+zhf2P4CL7jsBfzfd/3fDMWQC5Yv5jVpk2Z3lavnz+f+aIOvhV/jxw782ON/w4ceQHge8vLvIyipkqM04m8e/hs+eeSTAJy3cB5CCFb7qxzlKN+z53vyY49uDuilFq1GwAuecVGxewy4JvL41KM9VkOLPXv2fOM35izEV+/axPc9fuCZ500osM9WlPM3gw7nXszyd27HLH/ndszyd27HE5W/IKgXK5bjjKHtr/3ar/Grv/qr/NiP/Rive93r+IEf+AEAbrjhBm644YZvrJWz+MbD2CNozzyZ2boYGWSCXIkLSmlrCpHZZ6C0XTXwwMSY0nZVq3MSIZASHL2FchQUG3sEsG39kL35GLalCu8MY+Wf69oWNJbAn2OYTPcnVdeprsvFQFu1Hc0AuUFsPG3nigJQU84XJ+oBtxUkNCigCTBij5A0lhRiTaOpvpWJJtVzDX3/NfigucxFKGh7dCsEyyZ2XIjAq+cKuJZLmKTYxOwL0qJ9llWCtkUhMuXjm9SrlaV6jZ/DKAM+NLQNXGBAN0xoeG3oTQcfhdI2UZ6GWwVkrbVHkBlujZ22bwekmQRCpcLcMkrbMUA4TMB2iNwGhAN8DcfHQ4EZ5T/QNtLhvoKOornCBYsNHjrV5djmgAMXaRgFuNNgj62UrJCwJyjUg8X9G4W2qh/We2siNSB0x2FUAdLNNTf8Ngyr1e0mjGoyVxD2C/XbUktD2/4Y6JdprVrZt3xS3fWafgrDAsyray5BWyDymtCDIK5W+RSAsAzSN/QbLHPRSou1bsRja32uuFSDdJni1mwjB6MgVOfc5RdqeyD3Gd4elEC6qFcQAlgGpPvV46Ttu8CQbpjg+vNqHE/Jiekz0oD0DQNtL2I50tDWgHR/Z1gG0NfrFBesuKpoWOma22M5Gei5uVnTRGVZodo3rkgnWORSr51D2xfsndvRKgDKStuE3X5x7xFC9UtGVfg7LW7kIN2vWdzwXda6Ed0wZa6xCPER3CytbZ/x3M1zsvWI+sXCBay01X3PPwv99o6etkAO0vcvOoiosE2Boh+anPQ1XG3UTDUj41jPK/nnSWOJK3fN88n7V7n3+DZctnBGlhVKXaxysscv7j1CsNBwObIxGNsZkU1f3DBzl6fvczQ6Ts6l+L3f+z3+/M//nAceeIDnPve5/P7v/z6vfvWrmZt7cq7F8zye85zncNttt/GKV7wCUA8It912G294wxsqX3PDDTdw22238Uu/9Ev5zz7+8Y9P/U7u+36llZllWU/aQ6QQAsuy+MGLfpBPHvkkhzuHueXLt/Dw1sP4js+/vebf0rrzr6B3mu/39/BAvMntx2/nX176Lzm+GXPHYxu8/Or9BG797gYAkkgVIRMCcdENoK/vse3H+Iuv/gWr/VWEEHzP+d/DKy5/BZ858hn+n4f+H+48dSffe+B789Pcc3QbgeDK/fM0/NHxdcnuOQSCk9tDwkTmO7Se7FjrhjxwsoNA8ILLn1ggYPI3gw7nZszyd27HLH/ndszyd27HE5G/Mz3XGb/jiRMnuOWWWzh27BgvfvGLOXjwIG9/+9s5fPjwN9zIWXwTYRlPW6O0FVqFKpXgdkJpqzS5lUrbcXuEHaDtqW0FTGOhSo44iKJYVOmcpqBJXgth4zFsC067+wHoGbWtELB4oXpAjAc7QFt1Xa6xR3CMp23J6xTAnyc6g2I5w1C9brElC/9UTyttNVDqhylxQ127KkRWv2XZVBBumUswMKqxxIFldd5jW/r+6YI7jqwHC1FsKS9HkbLLLZSCAHP64aE7AggVFPXqAJc0W/GrAWEOe4YJka9gw1TwkW/hTVVxpdL5Fhtj9ghGQUi9KjHNXF3HRuK6KYyBDwOUTN8JPZV/r79ReT4DPgAaJicGRjWWOX9Jvf7IxgBsl1j7MO6k0DMwasUtrg2oLkS2A4wyIN2bUBCO5qQTJkRaVbhTTuJc1emW1G9zLOUgvay0VWahdTYYUSoRUrVdWOEU9bPOiZ57/Dp7BK1+BknL15NDnpNFLtTj5NBaX1sF6NftoPg2Bft2eSUYRRmkn5nPMIA0ixv5OClsP9Q5S+MkUHlyx+bV8Ws2qs75hgud4+oXCxcoGxBgvWdUne2S0rZ6HEsp6YUqX+ctOSMKd2DURgTo59C2ut+U/VNbBjqUFpwu3a3O+/CpblEcTmZ4U75CFIr0uMiJb0C6UT8Xix754sFOgNA1gHBSka6uOSHSu0K8tD4nhd+1Vj9vaaXt/Pnsaqt+UShtW8RT/F1N9DRI37cwmZPxBZ2BnmvqQLpv+xWKdD3PBYt5To5uDpElD/ephcjs0uKGOzpOJkC639ZzV/3Cp9Aq8aBG/Xwuxe///u/z0pe+lLvvvpvbb7+dn/3Zn33SgK2Jm2++mfe+97385V/+Jffeey8/93M/R6/X47WvfS0AN910E29605vy43/xF3+RW2+9lXe84x3cd999vOUtb+GLX/xiLeT9Vos5b47nn/98AB7YeACAm55xE/ta+2DlUgCeORiy0lihn/T55xP/zH//4mE++uXj/O09x3d+gxP3qO+mjWVYuQyANEv5ky//Cav9VRb8BX7+2p/nVVe8Ct/2uW7vdQA8vPkwW8Z+Bbj78CYA1x5YnHiLhYbLSttDSnh0rTfx+ycrPvf1NaSEp++by+evWcxiFrOYxSxm8e0RZwxtG40GN910E//4j//Igw8+yE//9E/zp3/6pxw8eJCXvvSlfPCDHySOqx/SZ/EEhPG01dAWaZNJgTD2CCV/RUsIQCKNPcIYTLGFrQorGHuE3pg9gtkaHCwCyh5BvbcqDOMiIB17jRRIXRHNtpXvKNvHEAgGLVUtPVcjAnLhgC4q1Z8KbTNtj+CMKW0Le4REFYjw2jvDMqAfqvMttFAV4ivOGacZfUc92AqZYUfVX8xty87blyv0chi1xEUaRh3ZCpFSEms4MU2htz3QwNGTOOmomm5uHBC6TYYabPk1gCvT0Ntzxh+yFeTIt1WHMbEGkd5O0DZTVZvm/bRYEBgpRKYBoRBEGnjXKUWjSBXUsy1BlA13BISRBt+e2TY8FrawtXIXAsMytD0CzWUuKENbCpC+MxRVCrjlMfBRZY8QSQW4/BrYPwkIR30h2yU4H+v3mQajjLo4I2UhcEbg1lIOCHVOHJ9Iw9o6KLo9jLHwVPEtEdfnZGigrTpfrWWF7ZZAuh4ng9I4WVH97rH1Hlg2kd7aP1XJahX2CEtuAaQBBeQoA8LC07ZuAUZm6ud+DSCcqwDpOwLCsvezyXGwwIpe4Sl8htuqzyBrVfjdMEFmjm4Loz7D5fYZQKhz0kyr+/WIIj3QXwtKgPCyHBAOGOITa7/0aeOkKA6XjnruUsrJhD3CFJCemQUn/Z5jlhVlP+lI58mbBtK10ja3Edk6on5RUtqe7hYgPVcXi3po2x2q+7Jn3pocJxNKWzUXN2ru4Yi1iwHpJXsEs0AkpaSLWRCT0xc3LGWPkBGz5Opr0/fKFB8tcrJzQTzTBz1X3+exuetcimPHjvGud72Lq6666ilrw6te9Sr+4A/+gDe/+c1ce+213HXXXdx66635VvdDhw5x/HgBK5///Ofz/ve/nz/5kz/hmmuu4UMf+hAf+chHntJreLxx44U3Yuvvqy+5+CVcu+da9YvdVwJgnfoKL9qvlMOfPPJJDq+r70H/eP8qw7h+/gHg0U+rvw98d17j4YGNB9gKt2i5LX7t+l/jypUr88OXgiUuWbgEieRLJ78EqO9XD6+q+f9ZFyxUvs3BXaq/P3L6qYG2Ukr+6SG1g+gFl+16Stowi1nMYhazmMUsnrj4hrS9l1xyCW9729t45JFH+Lu/+ztWVlZ4zWtew/nnn3+22zeLujBKW63SFNikqfpSOm6PYJS2Mi9Eliramv/eQWE342kb5ZVxgeJBUYM9Y48gRUomhFbajqpzozRDoNpo2xK2DgMKplrNZYARX9to4Tz1jx2UtmmmQfAEtDUPsDCMs2I7q8zwphQICnVBNN/NiodsvRW/4drmez5bcQK2i4tA6O31VWGUwE2z6zp/yFaetpYQdMOUjX5Moj0NnSngowxtxyHAxFZ82yHScMavgTPmIdudgLajMKo7TNQ2d8CtUUxC2SoA5izdPssBx2ehWbTPKJBjR79/DVjohik2Po4tGCSDCbWa2U6fQ1udIH9M6W1CCFGyhNDl5/OcLHP+orrGo5vqQTDSOZkGo8JYkEmQImHBHvWtnB9Xdbq6EBkQ1Jwzy3MyBqMMSNc56UUJscnJFBjl2QoQQsa8E42A9CVdHG5zECslvBDEtoGi1e3rDBMsXBzbIkzLSttxVaeGtjvkxBEOujsQuAbaGkX6Mhctq/59YmuobFRykF5/zTaO9lZOWHIK6ATFOJnwfkbi1xSBMuPYqctJCVQbkO5OA+l58bV4QpG+XALpUkpwvLxf14HqbZ0T2xKkxDt62vb0PNasuYfKP9XMNfo9S7sEFpouDU/5tW4OkjPKiSMc4jQlI2Yxh7ZjgLCcE2lAejUgzMfJxC4BlZOyTYfZJeCNfS6VwxSHyxXpxtN24QC7c6VtlC8C7rRLoB8l9LX6eWXeGinKWG6fmRsGOsd10NbYiICkaT4SS/YIjm3RNHmOBZHZJbADqFZK25RFuwDSUORkonBkluBbdYp0U7DPjJPRxY1zKVy3HsY/mfGGN7yBxx57jDAMuf3227n++uvz333iE5/gL/7iL0aOf+UrX8n9999PGIZ85Stf4eUvf/mT3OJvLpaCJV571Wv50ct+lH9xyb8ofrFyKTRXIAm5QbTxbI9D20dYDZW1TD9M+MyDp+tPHHbgyD+rf19SWB3ccfIOAK7bcx1Nd9JR2qhtv3RKQdt7jmwhJRxYbrJSo2A9uEud56mCtg+v9ji1PcRzLJ5z0VNcDW0Ws5jFLGYxi1mc9fimDBmEEDiOgxBCKQdnStsnL3JPW/UgKbBJM6W0zWDEHsHWnraZNBhVwUwTyh5BmlcyjFN6UelB0mwTM/YIBtqSIrEUtB1T2oaxhrYChEhLFewvYk5vmy9D22FbFc4Q8XCqJ1+Sqi5rS+M/q6Ctawt9nerhOa98zXQF3FB7EDp2OqG0FULknmmd4RBsT20H7tU/KBgla8OVmiBvql80lvEci/0LiuYeWu/nsGwaINzqa+DnVkHbMaUtEOp+4dc8uJv21QJCv1AQxrq6uZvUj2tHOLlqsmmVlMBCMOc7eU6MytEobb0aqNzRqk7bMtC2+ppzewT9Oq/GP7V8zQ1XX6+5N42l3B5hrRsxjFMSrSibBuA6ppvYGXYyat9QVmACCqQbAFdzziw1ORnfij/qadsZJkSOycl0GGUg+Zyt22dAesNF1W6TOTCLcmhbAwgHMQIX1xJEaVQC6dXq4hyk1+RECJFD0cAD0gSGuh9qQLjQcJESjm0OckX6tJz0Q+XVIkRCk8FI++bHFzecIJ8b/DqQno6D9HH1czH2cpBeA6nBKG0zJBkLzhDtAQL+PEtNlZMklXm/iXdYPDA5cWyhQbrJifF3HfO01e/XqOk3SpGum2Sm3xK0hdHt80X76nOSZLaydiFl3tKq67GcFD7DemcE9QtOBto6tlF1jkLRdmk+jAxIn5KTkeJwflE0kvZullpKWR4lmcqJ7RDpz9S6fni6E+nFDYFjZ/WLG0b9LIz6ud5GJMl3CZjFjU31t971YtS2W4OYWC92ukl9TiyMB3nMvGWUtnXQtk0oM7VLoGZxI0vVa/LFjWgUpM9iFmcS1+65lhdf9GKscj8TAi74LgCax+/mu/Z9F8M4Y5O780P+/msn8sWmiXjss+qzfvEiWDoIQJzF3L2qXm/g7Hg8e8+zEQge2XqE9eE6dx3ZBOCaA9UqW3jqlbaffVh9J33ORUs7+/zOYhazmMUsZjGLcy6+IWh7+PBh3va2t3HJJZfw4he/mGPHjvHe9753ZNvWLJ7g0A+QCakCo9gkKVBlj2AJZG6PoCVXpYdtVTSsUNrCmK+t8bRtLCKlLJS2pGQIHDGptA2TFIGFJbQa2BTeWbp44uEVIGqrLV1eliLM+1WEUROLMaWtECIvUtWPUhCCUCuP6sAMQBhpCOykRTGfkvrCWCRshwMNbQX0VmvPlwNCj1FAqIG38bU9PAJt6x+y17u6MJabFWDGHQWE3VBZQiRZQqrNrP0a0JpqQGiPwyh/DPYME2INCL20HhAKIUg1SG+KUZgndHEbgA1tkRDpLcFejY+vUnUGOJZVCW1bJRiVZimpAaLG+7YiMl28zndlAWb8ebAd2r6TK4KPbg5UcTjODNp6ZXV2hfpZSkmapSS5x2sdSFfXZNvjOZksKhXr/u7tAKNio5qkBICFWthYaIwWIzM5qeuH3TBRIH1CaVvjM2wWS6JqewSATOp+4GZ6fpFqztLjxKhPNwdxrn6etvjS1W/lutlEAai8YJ+BUUIQajhQ57ubGkBYN07Kqs4zyYld5KRlxonbANvBsa0ciK7pedcAwjr18/ZALW44lqVBes1WfD3HmkzUFYcTQuRzl+8pb/Hcp9xA25JiOQfpUxZ0BkNjj5PiJmMgfVxpazvoTQ/4WU1OklFoKyYKkZXVz9rapcaiAwpFuiRlwRoF6a5t5eMkz8kOivStQTymSB+dD8cXdPr6eutAuvKFL81dWTbyWQyl+zgyTurn64G5ySKlIU1O2iPnmlDaUj/2EvN5YuvXnMOetrP4FowLn6f+PvJFXrT/BQzjlC4Pc3CvmuPWuhF3PFbtZ8/D/0iUZHw6uzovKHjv2r0MkgEL/gKXLV5W+bIFf4FLF5Wf7heO38FXjqoxd+2BegXrRStNhICNXlTYQT1JESUZX3hEfa95/qUza4RZzGIWs5jFLL4d44yhbRRF/NVf/RU/9EM/xMGDB3nve9/LT/7kT/LAAw/wD//wD/zrf/2vCYKaivWzOPuhoUOCRKCgbZwKBNoewZq0R8gLkcGIr60jjNK2eDA7Xfa1NQ+K/jzbg4RYS7IkKZkUlZ62YaKUtpYQpFlJabt40aQaEdQWcicgQMDmodrLNkpbkRloWwDWRu5rqx6qc/BRC8skw1iDBasEbb0ytNUgIAoVtBUC+vVKWwMwAy8rlGoaEALsahXFuWJdZGga+FjvqmtxnUlAaCCFlBoepVEO6+u2BedKZUuDB6NwrIBR+RboKTBKSlnAHqNwLD2wF7626hrNNdflpBMm2Hg4tqAf90vF4Yw9gnr9IEoZxEOlIAX8qdC2pGQt+dmaOH9RQbcTW8OS5259ToxlhWtntWAmyyT9KFXwxkDbmpzEydhW/BoY1RnGuYfvtJxAsbgxAm11LLfUNRoP1Z1ysj3Q9giWmAptzeJBrmKNp+REwx7flSU/28Xcd7Dsr2kWN5wpMKoz1DlxyjkZhVGDKCVJM5IsITNzQ1wzTnRObKta1WmKAJbVz9MAoSsK9XMgJ3OyMlaMLJ8baqCyWtxwcGzBMJn0fs6LFBobEZOTaSA9NX7Xspi7HB+04r7sQ2sA5jTF9/ZQXe9ITvQ1z5dAupQSKWVpvq6+5qRuwWnM47sbnmFOcj9uCGTJH1e3Y9eYr22ct6/6mreHhSK9epyMWqcM9M6WZlw/lkdA+nATBZYF+Gpxo1zQrVCk189dplCa52YF9K6xR5BAaBYBaxZ0ksQsOJ37nraz+BaMXU9XC3lxn/N7a8xZBwBJ6H6VH7hiDwC3fuXEqJ0XwPojpGtf56G1IX916sK8aJmxRnjO3uco66SaMCrc2x75PGGcsdj0uHhl0krBRODanKe/RzzZatu7j2wyiFKWWh5X7j/3bElmMYtZzGIWs5jFznHG0Hbfvn285jWvYX5+nv/xP/4Hjz32GL/1W7/FJZdc8kS2bxZ1kSttJUIYT1sLJGRCVhQi05625ovqmNJWSnJ7BIDTWk1LEhaKq2CB1W7xECxJlNIWURyjowxtkywpKW0vqlbaphG4DQVFDeAdC6Vc1F3WQFunWCgwxVp6kVb8GaXttIds6WoLh2TCHgEKpW1nOADHU4B6ij2CAQuem01sL4ZRtVWsFY5OzUN2lGQ5IHTsdAICjHgaDhMNCB0cBE5SDSsKZVS1gtBAAKW0NWq6eqgQJlmuBnOlaV/x4LDYNJB6VEHo1uRE2SMoQBhlkwrCloboAJvDAVgOAnDMdUy5Zs/JoK9z0lypaGNMYhuQXg+jNnrq3tn2JEh3batkqaFBumVjT82JAelj4KPCPzVX08XDEV/qcgziNPfxzXPiT+bEKIJiDWbqAWE8Bm1HYY8B/VGS0Y8ipF5Q8qJBbRsT3Wc8p3qcFErMpFCkTwOEAzV3ORUgveXZWNqmw4wTKaYDQgPS83GSQ8dRn2EFCHVOkqFSQ1ZEmEjQRQp9ObmFfLlVeKhCSZE+Ze5SStvpOTGKb+Or7MeD2jampkihm474PpsoK21z/9QpIL2rp1M1d40vbrj6PdXiRpRFpcWNupyo97SscVWnWdzQUDRMiFyzSyCGmgWdJLXy7hkwungF5BXYzWdhrkivWdxQSlulnI7SqOQzPO5pq5W2erdBI4kqcyKlzBcBPaeksg0W8kXZkcUNs0tgCqg20HZknIzZIwyilCjJSGRpcWOHnIg8J+emp+329vYZ/5nFkxiWlVskcPgLLMhrADgW3sULL1/CtS0eW+tx34nRz3/59U9wdHPA16ynMbSaHFrvE6Yh95y+B1DQdloYi4QH1h4hYpNnX7g4FfICHNyl5rYnG9r+00Pq++jzL13ZsY2zmMUsZjGLWczi3Iwzhra//uu/zuHDh/nQhz7Ey172MizrjF86iyciRAFtLRS0jRIQSKQQo4XIbG2PIMmr9FLa4lm2RzAepHklc6PEtBxwG7mfLUBGSoaFAzD28K7sETS07a0qFavlwPwFI8pBE8N0CE4DH6tWaTuIU4TeVp1VKG2NKnZglLZmC3QN7Mm3s44DQm0/oM6pC1/FEViqEBlT7BuS/CFb1kDbQqFXKByrH4hPbg9BOtreIpkAM+p849DWVhYO5tixiBPVPsvSxehq7BE6YUJY9tytAQHbGlRYFohkFMxAAQJypa2xM6iBAJ1hgtDbvsOkrFZTebYtkSuqNwYK2roIRJZMLBzAKPhw3ZLStgwIS6q/nRSO6lrU364NSTh5zYXiL1Y5EQ4+oriWsTCAUBj1czgKkIrzpUXRMFnyYB6L7UGixp4lkHFJQajDFCPL7RFykD4tJ1WFyIqCfQaKbgz6hfpZykK9XookzXJVp+OkNdC28OqM9GfNtKJSmz29uGFlyLGcCCEqx4mLwEmq76HJCcLAqDFFenkrvu0AAg8KD+uxMCpMywLiSUBolLYbOUifnpPcZ9iusUfIVfiSfpQQaQ9zH1Go10sRJVnuM+yOgPTF/JhiK7655umWEEb9bNvpRE48x8rH8fYwzhc3pp0z0nOXsBI1d42B9JEiitpuQ+2MqC4c2R1mysLHAmF828vQdk4rbY0iXewA0rVlhTvhMzwKbY3ieyAVvG3W5CRMsmLxpTYneu4alnduTMmJ7u62nU5A5aZn49jGg1znROg816iBw9jMXfFYTs4taLu4uMjS0tLUP+aYWTzJkVsk/DNJdz8ObaQ15JHO13jB5coO4GNfPVEcn8acuud/sd6L+FqgFLNHNwbcferLRGnErsYuLpy7cOpbznlzXL54OdvDmC4P8uwLF3ds5lMBbbf6MV85qj5PZtYIs5jFLGYxi1l8+4az8yEqbr755ieyHbN4vGGZQmRgjXnaZkKOFCIrlLYSK4e2BQywLVvbI6TsnvM5sTVkLYe2m+rvYBGEyP1s5xsuxwcpqbFHGHtQjJKs8LTdPqJ+OH+e8hEtqdTy49MIvIaCCjXQth8pEKwKKUWAW6mKNd6aBeypfuDc7Mc5jIqyqEZpa3xyhzkgrIO2UZLlnrGek8F2vdJ2exCz1NRF0GogwMntIRYOgWORypQ07KpCcmOA8OSWAuBBU8GoAKsSlkkpc2grLAOBCx9HKOwHskzSTzMQloLAw01w902cU8EoB8eyiMeUW1Blj/DNqTrNNQ+ilK1BHywL3/TpsKu2c5eiF6WgQb9jZ4WnbckeoezlmJn2TQEfG9qywnMs4mhbTaJj7VvthGwPE7xgOkiXUhLFpZwkYWFdMmYJIaWkl2YgbKVuH2yMWHmYUCpMB2EJ4jEoD4U9woYe43mhtJpr3i7nJJmEtkII2r7D9iBmY9ADYeEIG1sItShQ6q+gciJwQIBt1Shtg0JBmJ4BjNrsKSjpORZR1MWHkX44H7hs9WO2hzGOp2xEvBqQLqXMAaFVkxMzjuM0o5/EYDu40lLXUupb+T0cxAhsbMsiMvNHqc8YD9+1PCfTQfX2MMmLXlWBdM+x8F2LMM7Y6PeV7YSw1fwadiagWi9UYB6hF3Qqc1LMXUmek3pVpwHprm2Rhp2JcTLfcBlEKduDBNdTixseAlExd6WZzLfiI2K1SJjG6rrGvJ/7UUKYRmpnRGopO5vFAxX3UM//VlzkJCjUzxNKWzM31ADMrUExF46AdN2+lucghGKbvTBlkIYgLBpYlTkxiyVCACIZ/SzWMVcq6JbbN0wpytgZGJCeIcOOcrjX7TMe5GvdiK1BjO3EpV0Ck+dMM0kclxY3kmGxg+ccg7b/+I//+FQ3YRZ1secZ4LVJ+lssDB5h3rsC33mQL5/+Mj/8jJ/kk/ef4p4jWxzdHHD+YoPjX/00p1ZP07Pmee4N38fxL58gSjI+deh2YGdrBBP7/CtJ0i8wsB/i6Xt37s+XlIqRSSmfFNXrPz+6jpSSS3a32Lcws6ebxSxmMYtZzOLbNc4Y2s7iWyxKSluBxMImSS2EMTko2SMYT9tcaSsZtUcQDhlKabtvPuDE1jCHs7nSVhcIMj+/eKXFfUdSUmOPMAYeh3GWq/2SLQ1tFy8CRv0gTYRpqJW2AraOKCVwCTwD9MNUgw9Bkg5R0LaktC0XIgNC/Z25ztfQbGd1baEesnNP20mlbS8KwXYV6DGqu7FQBZvUgzlWUoCPCkC4PUxygFkHZtZ7kVLTOeq4OOpMQNv5knJ3Lhkq8CGqYZRSKuvS8CIpVLa6IBKMwp5ONATbxc00qJ6bhLYGVNh2CRBWqDo3tU9ipAvh1W3h7U7AqMmcKIuEkM1hHxD4umo64Ta0VirPZ1mCjKSktC1yslAqiuQHWm1Yq/bL2BwU0DYadGnASD+cKwHHuTllWREIqzYnRk2HiIucaGU7FJYLwzilW87JYAMWzp84Zw6PbEEcTao6TU7WjapzB0DYKeVEKdInQXrLtzW0VXDH0/6ahJ2JfmPGiS10TqaA9O1BjB8YkF4PCE3BPgVtOwraVijSt4cxrVaItOxa9XMvKsaJrMmJ71jYliDNJN1wCLaHmwh1LSuXTpyz2DoviMPJnCxr/1RTANIo0ussIbbzXQIWUc3iRtt3COOItYG6RmE5ev6aXDzIPXItQZzFO1hWxLgmJ1O24m/1yznRixula54PXE5uDdkaxLTbZnHDqlzc6EVJkRNirFgfYzm5RU4Ziqpx4uGl9UpbA9IdOyEON9UP/aJC/IqxrDA+w2L6IuCWKQ5nC8JkOPF5YlmqWGZ3mLA5CLW6WKvwK3KixonKscqJbmMVSB/G2L5u35ScbGpo69kWcdRR6vBSnylD25ZeBPRrFpxMgUIEZDIqxontTiyefavH937v9z7VTZhFXVg2XPBdDO/9OJcNv8Lq4vOxrIf42trXuOkZDs++cIkvPbbBrV85wf/+XQf4yqf/X3ZL6F3wAl529XnceXiLB1fX+fLqV5lv2Dx373PP6G3D7oWAhRdssjZcZW9r79Tjz1sMcG2LQZRycjt8UiDq/SfVmHv2hTMF+CxmMYtZzGIW384x8zg4V6NUiMwobYVOpypEVgBPZXmQqRIm2pevXIjMtdxcabtXf9Fcn1DajkHbXU0kZaXt6INi2R4h7RxTP1xS0LZKaRsmITgevuUpoNw5PnHJ/Vht+3YslE8u5EVyoPC07UepKjakwbVf8xC7qeGWa+lt3+Yhu6y01SC4p4teeQilyK1QiuYA07JU+/r1arV+lBDpfDm1Ckd1va6Gu9EUe4TyFuM6GNUNE4XYBWQymbBGMGG2VveisFAXG2Aw3sZBgoWt4JEBKaXz5cVt+kZBqC+h1tO2ABVV4AMK5el2qPLq5dB20te2Gxr1myBO4wLglAFhUChtc4VjTZ9RIF3gCKU8jce8OmG0INKoZUV1TiytpktlPJqTklKnsK0Y6sUDq34rfqlfV4L01qinbZ6TM1Haxv2S6rR8zfoeDlW+fFuPy4oFjm5ut6AXSyosK8ogvVA4VuckTjOtmrS1+nkyJ/MlRaKyrKhXPyvQ72FZgjgrwSivnedECFHMY9EQLL2gY65lLIwiXeWk3h5hvTeekxpbEqMStQVh3ANtf1BWFxuP142+yolne0r9VTFOOmExd0VZVPStCvVzeZy4Ub2qc70bA0IDwlElMIwWNjN+3EEdwNR9xtY5EbmKtRgntiWKwpH5OBHFosBYGEW6a1lExh5hRGmr7RE6kVJfn9E4UUrbsNyvSnY7Zhyv93X7LVftjKjKiT6fnY+TaZYVcWGpMUVpu6X9uF27em4o59jkpG7u6mhfZVsIwiwsjZNzS2VbFZubm7zjHe/g9a9/Pa9//et517vexdZWvS3SLJ7guPB6hnHKJeG9PG3xEtpem2Ey5KHNh3jpVWpR8Pavr/HnH/8iu7v34zsWL3zpTyCE4MByky4P0Ysi9rf2s7+9f8e3k1LytWMhTQ4w33D40qkv7fgax7a4SBcr+/rpanuqsxlSSh4+pd7nsj2zwn+zmMUsZjGLWXw7xwzanquhfWuVp63Moa1AKmhb8rS1x5W2MOJpq+wRlNJ2v4a2vTBhGKel4ifqYXa1WyhtJQmpFEr9WWGPgLZHSHqn1A8X1BbVqkJkYRoCAt/ANFO4rBTGHsERkvyVzqQ9Qj9KRj0SawrRbPUjrLIvpIFCZWjrmnNq+Gbu33ASRnWGRrmlFYkVarW27+Qsrp/p7cM1D9kKAgsCV8GDKDIPxZMQoOzV6VOt6uyFaQFE09JDdqkgEhQArhdFStVp7BFq26jUyonxZRxTbkHJP1XbMVRBUSklHb1N27YFYVQCGe4kFO0MtarTFKOrBB9a1WkUhP0KpW2ztMU4VzhWwzKzaBHooj9xYqByGaQX6uciJ9W+leWcqOJF1dXX863f2lvZM/YIFZHDI1tUWlbkStueglFm+aGqH0opRz1t85yIEXWx6Yc5tDVKu0oFYTyak3ycVBS9KuekZpxs9CI9t7nYQhDl3tRl9XPV4kb1ODHtcywxmhN/NCdz5cUN25sOCAdJSf1cVYisKIYXp1mRkwooKqXUiyUapJtrsBwwCmeKPrOpcxKYcRJVLG4Mi/PFaZ3S1kDWnXMSJindMM3nhiiugLblwmY6J3W7BDoapNuWYJgOERVzTfmau+Wc1BSOHMnJsEL93PIQolgUmJYTgK1Bon2GBbFZbHL8fBcDFHPDxlC137IdpUCuyYko98MKe4SRxQ39M6/GO1tKyUY/QWDh2RAZy4NSvy7mwtI4EdXq57ICf5gMa8fJuRZf/OIXufTSS3nXu97F+vo66+vrvPOd7+TSSy/lS1/aGd7N4gmIvVfTlT6trMPT3eNcvetqAO45fQ+X7m5z2d42aSaxH/s0lpCc97TraC5fAMCBpSYdHmAQpzxn3/QCZCaObAw4tR2yaD2dOd/ljpN3nNHrjK/tPz+ygawpwnm2YrUbKhsTS3DxSmvnF8xiFrOYxSxmMYtzNr5haBtFEffffz9JUl1JeRZPQgiLGKn4LTYan9ZCW5BY2ouw7GnrWE6utG37Tl4gZq0XjVSsDpOULQ3fLlpRSluJhS0n7RHCJCsKkRloqtW6ZT/IMNFWBqmCYV5Db2+vUtpqewQHSSKl2oZZeiA2KqtemGpY5io/vgpYBspnNd/2nfSRRq1WUYhsGEeAwDUgKJxU3ZiHWHcK+BBC0DZwOdXQNkuhoiL5trYUaDg+SEliwHiNPcIoIJwE1b0SEI2yaKK4kol2bjNRVhBuTpwPSp62tkVUoYo1qs5emBAlKXEObSfBRz9KyTKpthhbJWg7Bj5aZdUp06GtUhfbCnzEveK+jChtTb9JMEsPbhJW5mS1q94zcDyQGVE2qTotF9ozhci8OkA4LEHqEZA+JScGpNdA262+WTywiOOSIlGH8RlOUkmnDKMqQPpkToyKtVmpBN4yIN2eDtJHvD+nAMJhnGJa5cZDyLKJ85mFpIbrIWRKjD5mzD/VvHeYhsjc37UKKqcjgLA2JzlID0s5ma7qdPS29PHztX0H11Zz9lpvSGJ8hityEiYZcZrlqs5c4e61RnJioPLmwChtpynSS4CwvOBUAoRmronTjFDzCDfqKz+CsTCKYcdysUWaL9ZU5aSs6vSxKhc3FEgvbFNExWJE+Zr72h7BRShP24ooz12FjUgB0h3bYlEvcJzc7iOnKFnjNKOv7QIc22KY52SsfQaka/VzYAdT1M8FSFc52VS/qFA/J6lkmOekV5mTXpQSxpkCy0LqnIiRz7uFUk6MfUOdpYbaJeDjWJYaJ+doEbLx+Hf/7t/xIz/yIzz66KN8+MMf5sMf/jCPPPIIP/zDP8wv/dIvPdXN+84M2+ER7+kAXD78agFtV+9BSslLn7kPpOQZgy9x/mKDhWe+OH/p8lxKn8MMoozn7DkzaHvn4U0Avnv/s/FshxO9E3z26GfV97op8YLLdmFbgi8f2eSzD1fbspyteEirbC9aaeI5M/3NLGYxi1nMYhbfzvG4P+n7/T6ve93raDabPPOZz+TQIVU06hd+4Rf4nd/5nbPewPH44z/+Yy6++GKCIOD666/nC1/4wtTjP/jBD3LFFVcQBAFXX301f/u3fzvyeyklb37zm9m/fz+NRoMbb7yRBx988Im8hLMWmbCQqCSOKG2RFfYIUqvRDLSt8rTNcG1rtACLgRDBIqe132LDs1lsegiRIYVAZEwobQtoC0mu6FEPc8YPEgq1rYG2vnnIrVD29CMF4GyRkSBHVLZQUtrGY9vSKx44wahEldJWZqlS71qOgsHmnBoCDPT15dC2ohjZ9qAEAZKwcosxQNs30FZvVa3Zpr1loK3rg0wJcxhVpTqN823f0z0Iy8qtSVBRPqdRdU5V2vYLVWJs4HwJpLQ8O8/1Rn9YAh+TMMp4HAe2jyX0tm+YAB8FtNWqzhyk16nVlEo0NtDD8SdUopZuY6+ckwp4ZMZA01E2HqZKfdnD0QDHzjBRth+WrbZ9VykIw7HCa3WqTlN5Po6Kbd+1Slut+BuxbyhAimtb+fnWekNkbj8wCaPynDgqJ1VgHiZBemDub00/zAFh3C/uSwmkN9yikn1fL+x4NedbM/OS60OmFwZsF5xCdVouAJgvboh6ewRhYFka1cIoYz9goK1S2lY/qOcg3RKFVUDpfEKI3Nf2VKen5iHAq1h8MYs5vh0osFxhPQAFVN4OVV59s4OgBtrm6uyaBafAtfFd1Vd6Gp7vmBPHR5icOMHI51JR2EzvjBD187VR2irblLDwtK1b3IjL6ud6T9ti7tLnC0bnwhWdk5Pdbikn9ePEES62RWmcVI9jo7T18wWn6muuXtxYzI/xHOV3DdDXOXEllTkxhQc928WSeu7ymmAVXwNzkN4f/zyps0coChTKCrXyuRhf/OIX+ff//t/jOMVCoeM4/Oqv/ipf/OIXn8KWfWfH3UJB273bd3PF0tNxLZf14TrHese49sAir7q4x5XtPsuL83DgefnrTsf3AxI73U3DPjPv1zsPqbH23Rfv45o91wDw/vvez6/906/x4Qc/zIneicrXHVhu8qPXKp/5999+qKgN8QTEzBphFrOYxSxmMYvvnHjc0PZNb3oTd999N5/4xCcIgsJP9MYbb+QDH/jAWW3ceHzgAx/g5ptv5jd/8zf50pe+xDXXXMNLXvISTp06VXn8Zz/7WV796lfzute9jjvvvJNXvOIVvOIVr+ArX/lKfszv/d7v8Ud/9Efccsst3H777bRaLV7ykpcwHNYX8/hWiUSraYWQWmVrQZU9gijsESzz0FyGtiWlrWtbo/6K21rxOrePU9vqnuyZU3m37YwMC0vKCaVtpKGtLUr+s15RpXrc17aAtvqBrwIs5PYIllTQ1h2HtvqBPUzHVEKT5wLlaWvUW2QpEZk6Z0mtlitt9fV5RpVUaY9QgqLRtvaZFLnC2EQObZMYhK2h7eRDsYEzTdcvAOEY+GgHJUA4Yo9Qp7RV29JHt31PAx87eNoOS6rO3CpgFEYZZedqt5+3vUo12Q1j/f4qr2ENjMq3pesq7nlOKvxTO2FhjxCZYkON5ZEcCyFymDKMSzmp6DfGB7bpeiDTAnyMKBxLqs7M+ELWFFjK7RHGczIKj/KcJGEB0mt9hstK20mQDuQKwlOdEoyKBhMKvc5Q5WRe52RYty3dWJ5okO7uANJzewSzGGCPFhU0lezVNZfHyeQ9PK2Vtm0vKOVktH1lRXoBCKfZIyjF5DQbkXYO0hUgdMV0/1SzeJDbN4wDQj3vrnZ6xTip2Iq/rQHhnK/m4Xybe01OtnN7BJOTekBo54p0fc6xBSdzHwdJGbJO5tjkpOl5em6VE32wvLV/ZJfAFO9nx9gj5OC7Oif9RENboaFthfJ0e1jYI0Tmnoydb7dewDy1rXIiALuifWaBbT5oIBCE5v6Ng3SjSB8YG5EpIF1D0dzTtsIeAdQikUQy1OPEQ1R+Pq31zOKGGidxxThZqFM/19ojqMJrqUxJzEKmd25DpPn5+VyMUI7Dhw8zN3duA+lzNbphwtfkQRLh0Yw38baO8PRlBXHvWb0HIQQ/FNzLnvkAceHzR2od3L32JTzHYp6nc3i92iqrHGvdkENrfYSAaw4s8pNX/CT/4pJ/waK/SD/u8w+H/oHf+vxv8Yd3/CEPbkwKPF521T4u29tmGKe87zNfJ8ueGJuEh2bQdhazmMUsZjGL75h43ND2Ix/5CO95z3t44QtfqLb16XjmM5/Jww8/fFYbNx7vfOc7+Zmf+Rle+9rX8oxnPINbbrmFZrPJn/3Zn1Ue/+53v5uXvvSlvPGNb+TKK6/k7W9/O9dddx3vec97AKWy/cM//EN+/dd/nR/90R/lWc96Fv/lv/wXjh07xkc+8pEn9FrORiRaISMo7BEEkIJS/+nIlbZle4R0FNpm2tPWsQUrudJ2UNgUzJ+XqwZ2z6nf23aGRCAyUaG01YXISEmRusp3oUacK3mxAuqhFPDNQ/M0aEtWA22LQmT5A6fQYGYMECpfyBiBpfxJZUqIHAFHoBR/AEMDCM0DaQUgLLbblirEB/MjkBXI7RGGifIMrIK2WSZzONP2/AJ81MKoYjurb3whx0BFGXxMhbYGYCba01aISmUxGF9Itd02zuH8KKgwHqpr/R4IBwHKx3FMRdjJr1c9cIV5YbjRnLTKxeEoQ9vJPtMrqTrzwjtjIAoKWDECqivglvHmbTpa1SnlJFSu8E8Narw6zbbvHKRXbJ2HUk7MVnwxxR4hVxAW42r8fMZD9XS/D0LNG45MJxZftjW0ndPQNqqBUbniOzJK2/pxkittbUFsbEbGQDoUfVv1Q6cWpBtVZ9sL1OKGzKbmJExD5BRAWPZBnuYzPFdW4ds7FCIbJLroFcRjOw9MmJyc0osbLgKRDCbmLpOThUCNi7AO2pqc5Ir0+sWNQoVvEZmxbnsTc+x8w0WSKUA4JSfGHqGlc1IFCMtFtHYChN2Sf2qSJaTGzmBCyar7TK5+tlThvAp17EjBvrgaAhul7aleP198EfGkJYRZYFtsjOXErwHpWv0cmJxUedqW566oWxQALCltQY0TSUqcZUVOKubr9Z76DGuVFwGnQNvcZ7hmscTYAZmdFMOhno/OcaXtq171Kl73utfxgQ98gMOHD3P48GH+6q/+ite//vW8+tWvfqqb9x0ZxzYHpMLl1NyVqr8d+jzP2vUsAL589J/gi38Oj/2TOvjS789ftzHc4OubX6fhObS5/Iyg7Z2HNgG4fO8c84FL4AS87ODLeNsL3sb/cc3/wdW7rkYgeGjzIf7z3f+ZQTI6t1iW4PUvvITAtXnoZJdbv1qtyv1moh8lHN1U73vZnnN7vM1iFrOYxSxmMYud43FD29XVVfbs2TPx816vNwJxz3ZEUcQdd9zBjTfemP/MsixuvPFGPve5z1W+5nOf+9zI8QAveclL8uMfeeQRTpw4MXLMwsIC119/fe05v5XCKG3Nf4tCZJP2CGeitAWJJWT+oNrbOKmOsxxo7cm3hufQ1sqQWFiZhLTKHsHCRtsOuGMemGNK22GqYM90aKvtEdCFyMahrVGwRonyADRKW+TEVvdumJBqBUTLDQooWgMIwzRCInGNirRCyZTbI1gWkQEjFYDQKG0VtDWFaEYfirtRgpTKrzgHH1MAYTdMVDEYoyCU6QRI74VpXoFdedpOV9oOtarTq7FHkFIWSluRKTAj7ErQA3C62wchcIWt5ooxYGag7bw/HUa18jwbkG7AR91Wd3sM2i5OHDcCCKcoY43StuV6xVb8mi3Q3ZI9godQ4GhCXZzqrfjjqs66nJTsEabmxME1ORnzrYTCa3it1wfLwjPK/JqcLBilbTJdQTiRk9ricO6O48QAwihJpqrmjYKw7ZfsEcbaV3jaxvk4yb2fpyxuxFlMagBYDYBTfUbnJAkrVe4T4wQm+s1yOSdCwzeYmLtyQBhokG6K5o1fc16USy9uTLGe6RglsCWIcpC+VAHSHSQpSaoW4tw6VecISK9e3CgXIlPjxKkFhN3QFCJT/TSH/dPGibDwjJK1YqzkPsMiI5K6MOeY+tlYBa1qywoXoT4Tx+bWrTwnau6K01B9prrj87XuhxMgvb6IomMJQtN+tzGy+AlmnMSjOZmyuNH0SgtONernrUGsPpPznFQvbggsGtriIazJybkWf/AHf8CP//iPc9NNN3HxxRdz8cUX85rXvIaf+Imf4Hd/93ef6uZ9R8YxDSg7e56rfnD48zyzuwWr93Ho6/+Lrfs/qsbl/mth19Py133umPoef9HcQVzaHN6oLiJYjjsPq8WHZx9YHPm5JSyu2nUV//aaf8vbXvA2djV2EaURXz391Ylz7J7z+cnrLwTgI3ce5dDazrD48cTXV3tICXvm/XzMzmIWs5jFLGYxi2/feNzQ9rnPfS4f/ehH8/83oPZ973sfN9xww9lr2VicPn2aNE3Zu3fvyM/37t3LiRPVK9knTpyYerz5+/GcEyAMQ7a3t0f+AGRZ9qT8kVKSZRkxAgk4uRWCpZS2UpIh8uMFUhcNk1hCAdosifLfW1haaQtSxiw3XSSSZOOIsk1o7yUDTm4PkEh2tVz1OpGRCQEZyDgcaePQAFaZEssM6bVGft/ybCSSzkC1I0zUQ67nzav3HG5PXHcvTAALm4xYZmROY+T3gWMhkapy+bCPBHzLVdc72Bo5dqMXIpE0PRvf8ZBZwlBmSHf0nA1H3b1ExmSZxPXn9Pk2J9pXQFtBGHbUccHixHFt30ZKCJMIadm4UpANOyPHbJba59kuMksIZYZ0myPHNV11zZmUbA/7SGHhC0u9dzh6zm6o2meLwoNQSknmtStzM0wipOXgSJCDTbI0HTluECW5otrB5LlJpvun+bOot++u93sqx7ZX2b7tgQLjc0GAlJIwGajjxq7ZtK8fD9X53HZtn+logGlbgiiqz8l8oPTbUZoihY0jIRs7X5qmrPcipDTbvpWqczwnpn1JlrEd9pDCxpOi8po7g0iNE0vZiMSDTX3N1TkJ01JOoj5Z1B8bIzFxqq1JyjkZm58WA5WT090uUoJr5oWxa97qq5zM+zvlRPXDXtwfzcnY9ZqcoK85mjJO5nybDHU9eU7GxnGWZax11ViZ8wJklhBJSeaOzTd6nMRpRjcaIoWNi4VMY7J4MJaTGHSfkVIyrMlJ29c5SSKksHDtpjquvz46TsJYjxMHh7ToM6U5OssylvS8e7rXQwKuZXIyeg9NThYa6v2iJCTNsomcNF3Vvm400Dlp1Y+TgZ6vLUGk5wVZOXc5pZw4uBKy4WROVrtDJJK25+dz13hO2n6Rk06o5i4PgYyHI59PWZZpKGrh6UXHOOoiYTLPuh8O9eeJE6jPk6y3NpGTYVzkJJYZ0gnIhD2ak4bKyVqvh0Tgmbm1ZpwsBg39+RwTyYxs4nNPta8TqpwEXv04UYpq1Q/DcEvnZKEiJzapzolle1hANtioHSdtz0emCSEVOSnPXcNBXrBPht2J+b8zjNVnlKPmhsGwepxU/THfYc72n2820jTl85//PG95y1vY2Njgrrvu4q677mJ9fZ13vetd+L6/80lmcdbj2KZaeLLPv05Z6XRPsXDHX3BxHAOCe3YdgO//j/B9/yFfaFobrPHxxz4OwAvO+x6AHZW23TDh/hNq0ejaCxdrj1sKlnjOXlXU7K7VuyqPef6lK1x30RJpJvmTTz9MlHzz/dOEsUa4dPfMGmEWs5jFLGYxi++EcHY+ZDT+03/6T7zsZS/ja1/7GkmS8O53v5uvfe1rfPazn+WTn/zkE9HGb7n47d/+bd761rdO/Hx1dfUJ98LNsoytLfUAF0cKlJJJwjAiERnYGWGW0usP6Gmv341+TJIkpHZGFMaEUUR//TRRS/0+zVLSNCWTsLFxGjKPMIyI+48SNiJia4HeqVMcOrVJGMbYcY9TpyRpFpJmkMUxvc4G3ZK38MZ2lySRyCQiTBJ6iTXyexkNCcOIo6vrnFoRbHY2iaKI/hDCKIJ0lc0xr+LTm9skkUR6EWGW0g1T+qVjMqnuA8DRVXU+S9qEcUTn+GOkw6K7P3KqTxhGLPuQhilxFNKNQ7phmt83QN/niMQOGQxDQtcijCLi9eMjxwGc2uyQRJClMd3OGmEUESXOaBuzDBEPCMMBQy8itSCNY7ZWjxK2TlW2Lx7ExGFIP4npxky8r50l9OOMExtrRFGEyGzCJKJz7DHS+TQ/7uTaFnGYkWUJ/WGf3nYPJ4rodGPS0jmj3oAwjBhYA6JEIuOEMO6zdfQRZEkdeLoXE4YRwhWk0ZBBkjBIbbbH2qeuN+Lo6S6RE2FlFmEc0T1xhCQpznd0dYMwjHBiQZRE9IZdwkgQhhmD0jkHffW+3bTHQhgSRYIwisi2Vifee3WzQxJLsjShG3YIo4hBKAjHfbDjAcNwwDCMSFF9emv1KGG7OK4fpXT7Q5ASJ82IoyG9JKYbZhM5IY0JE8mJ9TWiKMbOBGESsX3sMbJWsVPh5Po2cQgyTYmiiK3eCdpRRLcXkdTlJHaQqSDMIrYPPzRyvlPdiDCMsFyLNBzQTxIGmVORkx5hGHFsfZOoGWFhE0YR3ROHSaLCD/CYyUlqEUURSTigHznEw4xhOSf6fbezHq0oIopVTuTWKlsVOUljyJKEzvZpwigiTJyRHI/kJIpHczI3Oj5PbXZJMomVpiThkH4S0Q3lyLgr5+T42ipxkmInGSERW0cfRTaKImgnN7aJwhSyTOWkfxI7iuj247Gc9IucRC6p8AmjPt2jD5GU5hozTnCEzknMIHMncxKq8x3b2EAuRQhpFXPXoFhfNePEjn01x8VDOrELw3QkJ6HOSV928OYiktiqzcnprQ5JrOauzvaqmuNSZ7Jfx/08JyCI4pjBqSMjOQE4enqbMExwMklixklEbU6Onl4lilOsJNU5eQxZUmyubmyrfigtwjCk2+sRheFETuK+6td9e0AUeSRZkzCK6B1/lFjszo9bMzlxherTSczAncxJNtT3MN2k3QjzcdI5/hjpfAFhjpxaV+1LEqIowo5COnGGPUhG5pqoFxKGER2xxWIrInHrc7K21SVNIEsTtjZVTpLMH/kcVSfVnydRjLBU+4arRxkujB53eHWTMIxYymAYDenFEZ1ITow7K0sYxJnKSaJzkgzZPHZoZBfFKZ0TPxNEUcRm9xS7K+au8Sh/h7Gsx60bqI1Op9q7/vGEbdv80A/9EPfeey8HDx7k6quvPgstm8U3G0Zpu29lEeTz4ZFPQmsXVy09k0eHR7hn79N44f5rRl7zoQc+RJzFXL50OT948Hpuu/MrHNsckKQZjl3d7758WC08XLDUyGs31MW1u6/lY49+jK+e/iphGuLbo0BfCMFNN1zEw6e6HN8c8uEvHeF//+4Lv/GbUIoHT6m+PvOzncUsZjGLWcziOyMeN7R94QtfyF133cXv/M7vcPXVV/P3f//3XHfddXzuc597Qr/g7tq1C9u2OXny5MjPT548yb59+ypfs2/fvqnHm79PnjzJ/v37R4659tpra9vypje9iZtvvjn//+3tbQ4cOMDu3buZn5+vfd3ZiCzLEEKwe/duTgYNrNjCdzx83yPGJ5MCLIvW3DwtbWPh9iNsRyCERbM1hz/YxJtvgf69lBJhWVgSdu1aZDFYwPdP0d5ex3U9vP2X09y9m156BN8XPO3C/eye83E9gbBsXOHS8h2aJdsM21vDczw8R4Bj01zcM/L7/btj/OMDLK/Jnj17sB6x8BKPvfsO4ntqm/CelUXlq6hDOKfxPB/PsZCJRXtpN+0xq47F9jEGcYoIPDzPoxnM4wuB1/by6wV4cPs0vu+xb3meuL1ApyPAdWgt7cnvW3HO45yIJLbrsrzrfPyjHr6djhwnpSTmML4X0Ax8rKiH73l4ew6MtDHLMvae7OP6Q6SwsF2fudTHaboj7Xu4s5a3b2l+CfeUQDp2Zft2LZ7i5PaQzLHwPI+55iL+MMSbb4ycU7jrBH6L1PfwPBfb6qg2nncRzBfHJd4A318lJcPzG7Rbi/hxzO45DxaK47ZPdfF9Dydo4ToW0rFoLOwmGGvfhR0b/+EOsSXwPI/Ab+ALbyInwuvh+30O7F7m/jUP2c9w3ABvZR9zpeMW4hTfPw5IXM9jeeV81WdEPPHeiTiG7zZoBB6ip69370Uj7wtwwWmJe2gVKWxsx6eVeBDYI8cd3Rjg+x5N12apvcCxjs7J8t7JnCyc5HQ3zHPSai7iRym75huwUjrWOY3vJ7iBj+fZ2MNItXH/RSPHjeTE82gv7MXvrbOrZY20cSPrnFFODqYB/lc3GUrwPI+G19Q58UfvjdfF9wdcuGcvXz7pgQ24LvO79jNfOq61kOD7J8hIcV2PlZXzdE4S/N27R7bZJ+IonhvQCHxE1M1zMjeek3VwHzuu1H5ukyB18YPR690axNjuYRwE+5aWObqmctJe2TcxN+xeOMlqNyRzbFzXpdWYw08ddi80YbE0Ru1TBH6G25jDcWKsYajauO8i2FUcN3T6eP4qqUjxvBbzS3vwkx5eY7SNZpws+ConODaNxcmcyGCI759mkGXMex5Nr4lveXhzwWhO3I7OyW7uOukje6IyJ8FcjO+fYFv3meXl/Son1mhOpJQk4ogeJz5Wlqjr3X1gol8f2AD30SMgbBp+Gz8VeIEYaV+aSYbZYXzfYv/SMhtb6jOgKid7Fk9xqjNEug6e79MK2viZx+6FxsiclFon8X3JfGuBmG2E6OP5Psv7LhzJSVd08fxVMiSe57HQPh+/fxwvYKSNHZ2Teb+F56g+01jcO5GTxeUM3z9BD4HjejS8hs7J6DiROicH9q7w5VMt4i7guizsPn/kOLsZ4vsn6ZucLO6rHCdpJknFYXy3QTPwcSydk5XzRj5HAS7YAPfRR0FYNIIWfpzh+Yz0BYChPIHve+xZXGTYB+E4zI3NrSYnJ7aHZI6N5wc0vQY+HnsWm9AqwHcijuP7sDy/yGY0AGuox8loTsaj/B3mbELbcmHcbyauuuoqvv71r3Pw4MGzcr5ZfPNxbEtB2/2LARx8PVz5w7BwgGf1jvM/b/9P3L9+/wg4/crpr3DP6XuwhMWrnv4q9jQDAtdmGKec2B5ywVKz8n3uPLwJwHUXTdr1jMcFcxewHCyzPlzn3rV7uXbPtRPHzAUur33BQf7wfz3Ax792kqfvm+PZF+587mmRZpJHTiu7khm0ncUsZjGLWcziOyO+oW/Ml156Ke9973v5whe+wNe+9jX+63/9r0+4IsHzPJ7znOdw22235T/Lsozbbrut1pbhhhtuGDke4OMf/3h+/MGDB9m3b9/IMdvb29x+++1TrR5832d+fn7kDyiP3SfjjxACy7JILWWH4AobgVCFvwRkAizLyY93HRtQ/ge25arXyyz/PcJCSFWMSNgw3/AIXJuldE1tuVw4j61hSppJbMti11yg26E8bUUmEWk00sYozRA4WDIjBaxgbuT384GHQNCLMn18hBCCRmMJYSnPUyvuj7ymH6dYOFhkJEJiuc2Je9P0HASCbhQihMB3G/pcvZHjOmGKQLDY9PAdHyEzIsDyWhPnbAcOkoQsA7+xhBACEW6PHDNIJJlEF8uxiOOBet/G8sT55nwHKZQHoYLek9faCZOx9qXEAiy/PXG++cDV91K9p++11Xsng5HjepHxSFS5juO+buPi6PmaKjdxpnwavUBdszV2zaaNbb+ByFJiQPhzE+1baqnzbYc6J3ag2zd6zV2dk5WWar+QKVHFNQeeg2tbSGLSDILGojo+i7GyOD9OIhjoPuPaFkmic9JcmmjjYtMDUpJM4jmqf4/3ma2hut6llodrucoXUoBVcc15TuIhQggCr1WTkxQLi5bup1G0rY4L5kf7TMMDJIn23vSbK+q44dboPYzUPWwFASJLSADhz0+0b6UdIBBshUMQ4Nl+Zfu6eT9sqDGfpcRCTuSkHbhYQiBJSKUkCBbzHFppmB+XZBAlWT5OEtMHmytTcpLhObrPRN2RYzYHqn0LTZeGF0wfJ42anIyNvZ7uhy1P5SSOOpU5mW94QEaSSXUPm7v0OBnNiZlr2r7KSYxEBAsT7ds1p3ISpRGplMU1T8wNeu5qBfi2X8rJ3EROBIIMdb6GmbtkNpKTOFMgwIyTOO7pnEzOXeWc+DU52dI+yI5lsdBoTM3Jgs5JN9I5MfN1xdxl5hqAKO4iAGvsPhY5UWAwMDkZbo7eQzNOPL+Uk8lxEniOLvSVEKf1Odkempz4BE6g567JnCw0fQSCRIZkEhqN0jjJis/Qgd5KnXsrRzonjeq5S5KQZBLfaVbO1ZlUCxwCwWKjWeozk9e8qOf/rvk8cZsTn6FCiHy+XvDVOApNv2lM9u3xP+Y7zNn+czbit37rt/iVX/kV/uf//J8cP3680gprFk9e9MKELV0A9PzFBjgeLF4IQrC/tZ+VxgpJlnD/+v0AxGnMBx/4IADff+D72dfahxCCC5bU3HF4vdrXNkxS7jmifJmffWBnsCqE4Nl7ng3AnafurD3u6gsW+MErlQXb+z7zCCe2vrndeEc2+oRxRsOz1f2YxSxmMYtZzGIW3/bxuL/l2rbNqYqtb2tra9i2XfGKsxc333wz733ve/nLv/xL7r33Xn7u536OXq/Ha1/7WgBuuukm3vSmN+XH/+Iv/iK33nor73jHO7jvvvt4y1vewhe/+EXe8IY3AOpL1y/90i/xW7/1W/zN3/wN99xzDzfddBPnnXcer3jFK57QazkbkWhVjqP/FthYQMJYITKhCpEBCDFZiEx5YFr6HClCCJZbHkvJKnGawfz5rHZU4ZVdbQ/bEqRZim2hPG0lE4VZIl2IzCIhnVasKVRfxkNdyMx3guLYsSrnRSGyrLIQGRSFw3q6+E5doZdN/RCw2PSUOiNLicgmCpEBND3ld5pmEtcUTAq3RgoYdXRF94brYQlRVIivKUQmNfiwba+yKJcpbDMfuDkgjMgmivlAUWSpF6t7WFQkHz2ngVEN1yuqh1cUqWp5DogUkAoema3jg/WR47YHqg/N+X5RId6fVH4o0ANb2jrENYV0xgo2mXs4H/hYwqq9ZiEELe2tmWQZnjeniuXBSJ57UYKUqOJrQhBNyYkq5pOQZBmu2eY41v82dBGyxWYJ2tbkxBQcMv3Qq8lJVwOuhuuDzFTFdpgo5qM8bROQyrPabe5SvxjLiXm4zQtAISsLAy21VPvCJCLNwNPFhGqLwzVcAludM6wYz0II2r6jCiJlUhUis3WBlFJhqZ4uPGgLF9tSCwfqBlTkJHDJSEg0LFMNHh3H6z3V51daHp7lFQUFq8aJzklemMuZzEmWSfqRamPLDUBmeZHEqkJkkhikqi/nNlfUL/rj48TkpDxOJnPi2hYLTXXNUZLV5sScb77hEDgqJ8OKsefYFg1P+QInqVRzoclJ6T6aHLuWmrsi836mj5WinBM3L/I1Ok5MwauVtodnm7kmm+gz6hpMTtTY9Crm6yjJCGP1+dXyApCSMDPjZPxzxSUjRkpll5OPk7FCZEVOgqk5UdfhI0mI0gzX1tccjhZLK8/XnuVBasbJaD/0HAvftfK5y/fapZwU5yzmhSAHouoH1eNEmpyYz8SxcbKmvbg9x2LeLxXerJivTWGjTmg+Q/VnYqkf9qJUFy+FOb8BMss/w8/1QmQvf/nLufvuu/mRH/kRLrjgApaWllhaWmJxcZGlpW9OJTmLxx/Htcp2qaXEBOUQQnD1LiUYuef0PQB87LGPsTZYY9Ff5OWXvDw/9oJl1Y8Pb1T72t5zZIs4zVhpexxYPjMYes0eZcnwldNfyRe5q+JfPfcCLt87xzBK+f/+w4MMorT22J3iwZOFn+0TWfx5FrOYxSxmMYtZfOvE47ZHkCVQVY4wDPE8r/J3Zyte9apXsbq6ypvf/GZOnDjBtddey6233poXEjt06NCI2uL5z38+73//+/n1X/91/uN//I9cfvnlfOQjH+Gqq67Kj/nVX/1Ver0eP/uzP8vm5iYvfOELufXWW8/aVrsnMuIc2lpKUYeFEJCCkszqsK0C2mLpB8TSF8wkkwgcIAahjtsTSFpZhyhpwNx+Th9WD3CmmnYiEw2DLciYgLZhooohWTJTEHm82ryGtp1hogocGWhr++qhL9weeYhNM0kY63OSkUhZCW0bnrruAgI0gfVJaDvQAK7hspn6IA3sqYK2CpilmcQL9ENbGqtrdlU/KQBmgwxT0b01BdoqoOhYPhBVgJkClg1tD6QGCxXgwwDwfjyk5ZarxFfDqIbjE4ZdBVL8NowplGxLELiqWUkqceeqYZQBFXO+TzdMa9u3qCHAIB7SlhLXDoB4KiD0bZ9BllSCD/WeDrKvcuI7us8MNlS/aSlQY8BH0/MRSAVEHaCxOHG+hYaCUXEqNVSezMmGBqJLzQKkx7KmfaWcNBwI3MmchEmqFkVQMCrsrROSqbE7tnjg2Ba+KyHWOTEwahwQDouc9If1IN13bOYCh+1hQpSkuE4AdOsBYeDi2d7UnLR8h2yoAGFgB+DPQ39Njb222i7dDQ0Q9RHocWLNV46ThRykG0DYh2gc2uqctMqAkKk56cUDEBB4bWBjBCr34zRfi2l5ARsDVdgMISb6tudYuE4GCWRS4BhoO9gYvYd5TgLCqB6kg5pfD/U1IMxzMgoIt8040TlB6gWYmrkhi+LSONE5ibqA+tw0OWn7ai6Lkh6wCM3lifOVFzccpwFsVQBCNZcv54r0hASrGtqanEQhlg++22a8H5r22Zag7TZA6j5YkZPAtbBEChIEDsL0q4mcmGv22ezqcRJUWxsttzyy0wlxakB6VU5Ujhca4zmZ7Idt3yGLI5JMjxNvTi2+hNvQVvYDZi5sezoncR/wK+eueT13JVmG6+h5Y7g1ckyx6OqXxolTmZOFRrG4YQca2g6GlVA58GyabgOyhCEZCKty4fNcin/8x398qpswi1Ic2VDf5c6rUZVevetqPnH4E9xz+h5O9k7y8UdV8bH/7Wn/24jP7AGttD1SU4zs0w+eBuD6gytnDEMPzh9kwV9gK9zi/vX7uWrXVZXHObbFz33fpbztf3yNE1tD/vQzX+fnv/+ybwi6PrSqxuHMGmEWs5jFLGYxi++cOGNo+0d/9EeAWtl+3/veR7tdfGFI05RPfepTXHHFFWe/hWPxhje8IVfKjscnPvGJiZ+98pWv5JWvfGXt+YQQvO1tb+Ntb3vb2Wrikxap/sLnlqCtJZQ6sgzibEug5LBg5UrbYqU/0dXmESAV8uVCVz309USLFb/Nqc4mAHvmNLTNEmxbQ9tUQjoGbTVgFTLV0Hb0C+ZKS53n1HbIMInIpIJXnu2ph+ftoyMwYBCrdqlzZkq9W6W01dC2F2uVUK7aHVNGaQC30HRxe0bJKmpBcEZCKgWeP6d8dtNIPRhraFuoRAM2kUTpUPl/VsAo37FwbanoutB0dAxGbZXUdJnQCsIaQNjW6uJBMqQF+J4GQnHxcFKGUQ3XJxxo8FEDjxqBhAjSDJwaVed26ZqPbxioPNm+pmfj2hZZqsGH2wA6I+BDSpmDgLlAQ1tZreoESkpbqfpMDm1LYMHAKM8nM2o6qxpUGBiVZRLbDlT7xmDUVq60dUksV4MZpgLCQRLSAKUGhjHVqerTtiVoOEqFGRrIWvEw1/QVtBU4iGad+tnkpMHJjXrQD7B7zufwUAFClZPuZE5CkxNHPQDLVIHlqn4YOGRbMWmWFTnpr40oljslWJZkGXGWqP0elepnJ8+JlaufR3Oy0VM5WS6rn6U1VZE+iEMCD5wK9XOucPRsgjPIScMDEpUTckX6GCAcFND29NYOqs6W2uoeJRK3MZmTJM3oh6OLG2QpoajJSUn9rEB6VU4K1WmIJE6GKicGQpfvoVbaZhka2qJ2HZTCKG2XWwYQpkRS7LBLYMCcj1JoQ2VO2oFDQwPC0Ch3x3IihKDhSxiCkE7RrwabI8eVc7K5rQGrXw1ti5xkuMFkTqIkY6iVc/MNp1Cki6wyz3OBS9bV6mfHV31rMLqwaHagzPkBXSBONLStUT8b+x47h7aj6ufT3Spoa1e2z+SkHw+ZC8Bz54D1MZBu5hlzvYlSe1fk5FyLgwcPcuDAgQmgJqXk8OHDT1GrvnPjuLYTOH+xWkhx6eKlNJwG3ajLf777P5PKlCuWr+Da3deOHHcgV9pO2iOs9yK+ekzNY99z+eQYqwshBNfsvoZPHfkUd566sxbagloM+fnvv5Tf+bv7uPPQJh+95zg//Kzzzvi9TDx0agZtZzGLWcxiFrP4Toszhrbvete7APXF9ZZbbhmxQvA8j4svvphbbrnl7LdwFrVhDA4cYawNlFdpghxR2jolpa0w28hH7BFk8Vqpfr5PKBi04eziQgqlzu4ytLUEUliKB6exsgsQQhXlSo3SNlEYeEztt3fep+U79MKEh09v5j/3bK9kj1A8xBpQETiu8khEglMFWDUsi0MQJYBZA0UXGy7+0MAoe8IqAKDpGqWto8BQMA+90wp8zCm1moFRi0GDzSwh1hCaYGHifABNH+iDFB7Qq7VHWGi49GKzFb8afJit+MMkBFx8fxIQmvvnu1YOo6Ip8Eg7GiClhWhVqzoN+J4PGoUSuOJ8QghW2h6ntvS274YGCyWoHCZZrjrNAeE0GBVoGJWWoC1Ubvtuez7baazVdAuVUKHl2QiRKetnYQDhKOgvlLYeG5Gjc1INCE1OBskQcAjynFTAKN9R8CZLlHK3JieBp6i7lHYtINwyCm2/ZPlRA6N2tX3kahkQMqFwNKC/7Rc5iUSNqrMECHPFPIzBKAOBG2ykseqDbiNf/ChHw7URVqqU/Fb1tvR1DdKXWh6O7eqt7q5SL463L8+JgrZBxTjJYVlgcpIqBWHd4oYH9IHMLgHCalXnfCPYcdyttH0FRZMMz5nclm76tBCClmdrS4iEyJKVCse27xY2IjVza75Y4geEWUqUpbUgPXAtbCuDDDJRsxU/B4ReydrFrlR8l0H6HOCbvlrKSaFUdrT1QAmkV0TDy2AISKdQptaon+f9BqQ6J9OUtsQkaaZ3bjDSD825XNui4dp4wlZWJ2KK0paItAzSx64533XgB3SRRPEA7KV8F0E5AtfCqsqJUYgDpzVI3z3nl6xdqtu3mENbnZN850b5msfnLjNOzn2QdPDgQY4fP86esQJt6+vrHDx4kDT9xre2z+Lxx7HN6Upbx3J4xsozuOPkHZwenMYWNv/q6f9qArqfv9hACLVgszWIc0U5wGceOo2UcMX+OfbMP75ddtfuuZZPHfkU96zeQ5IlOFb9Y9Ulu9v89A0X8Rf/9CgfufMoFy43edYFi2f8Xuu9iI2eqv9wcNfk2J3FLGYxi1nMYhbfnnHGnraPPPIIjzzyCN/7vd/L3Xffnf//I488wv3338/HPvYxrr/++ieyrbMYC+Np6+XQ1lKFyEBtU9QhhMhtDxD6C2VatkfQ/rNCkGiYuytTW8VOodRW49A2zmIcIZC4pJkmO9qPM9RFVAS2LlA1+XAohODS3eoB74FT6oHasRz1hdc8uJfUYD2tZGp6PmTacmGK0rav/V3dCnAkpcw9bRdKajXlaVsBj8r2CLYHvgaxpS2oOWQNAtCAUHptsKu/wDcMFMX4u9Z72uZgpkY1OR84SEoWE54BHwUU7YTFQ7ZRWk0DhPnzjJyiIByWrjmrt0cA2DMXIEkIE6PqpBJGubaF71g6JwmhrINRSmmbynFAWPQZAwjn/QZkBtouVrZPCKGUrAAGfIyB/o2S0raAUfX2CBJJqG1DvBxGla5ZA8J2Dql3UD97+iFUOsXW9Rp7hMVGoK+ZWpCyq+2TERMl6dScNH1H2TNYrvKunGaPQFyMkwqVe6Eg9CGL1ZirgIOgc6KvOaPkaVuy6MmVtuXicLWetmosDidyMgnLCkhdP+4AAtNnxgFhqY1G1bngN0rQtkbV2fYK/9QKQFhYiDiqSJSe5+sU6e1AA8J0DBCWzxkWgJA0KtpnuxPny5WsQCp0TpIQkig/Zk3nZKXl4+WK9Lq5q7y4UV5km7RHaAfawzdX4FePE39kcUP3rbg/0sbcfiY4c0/bOMlK3tTF/SvvihBCoIw/IITKuWsucHIIHDjBDosbgYLUMgVEMReXQhXVU5+5qVncQI6cr+xJ7wobZP18bUD6UOfE8yehbTFOXO2rnDCU1b7F51pIKSu3rXe73XPCNuvbLY7uAG2B3NcW4MaLbmRPc8/EMYFrs3tO5e9Iydc2yySffmAVgO+5fPfjbt9li5fR9tr0kz4PbT604/Hfc/luvu+KPUgJf/Kpr3Nq+8wLkz14Uo3pC5ebE/6+s5jFLGYxi1nM4ts3Hren7czv61snjFbWzgFtSWlrjX6hExiIYJS2JWhrlLaCHNouJqfpAkcz9dCbQ9u2+tJr7BGEhrZSSkQSguOXoK0FMtV+hpMPxJfuafHlI5s8tLqpK9hrklnxEFsUB/LoxqkuRFbhP+sbMDPE80oAswQpBnHhJbrQdHFtDeBktdLWd/UDcSa10nYS2nZyT8MGbKl/J41FJrGHCgNtM/Q/xrZAm4JNC00Xd1hSq9WqTpUfK4BvwGQJfPRDA72dUQhcA498DQGktEuAcG3kGAM+lrSCUEHg+q34psCSW7EtPd+iHYzDqGoQoKBtQpIKdT1V4GNYAh9bGqQHi9Rt3m36wBBSA9LTWIEeR+Vos6S0PdlxtQ+yXQujJClxpu5jkOdk0h6hVVKxhlNUnbnSNrNGQXpJUZcDwkZjR5C+a85XbUwzXHcSzOQKRw07jTvgkGqQbopAJRmjIH1EyaqueSFowCDSOVmqzYnZ6p7k7y5Vv9H9bK0EbVcjnZMadfZc4JYWN1x8s/hSAQhbJWg7nKJWDlyVk6wMCNNIQULdhsLvdGdAuKtVjJNc1RkX7Ssv5gAYZhxaVt5PR655wkZkMs9mnCw0mjCMiMiQjeXanASuhAGk0leLgzJT485RC3w5tG17bMQCpKxVZy80VN8KkwjwSzYiVYp0d2RxQ/pzlW30XTNOHNVPbVeN5eFm7q08uriR6MWN6l0RKy1P5SQtz12T0NYo90wWQserVPWruSssFOkV6mcDRRf8BvSjYu6qWQQMPDVO0sxV/S7qqQUsrR429gi753y6WilaV9hyIYe2ZpxULDiV5oam04QsYTDl8+RciJtvvhlQEPw3fuM3aDaLOS5NU26//Xauvfbap6h135nRj5J8R8/+hXpg/sxdz2TBX6DpNHnJxS+pPe7AcoNT20MOrw945nlqvH/t+DbrvYim73DdhY+/0JwlLJ6161l89thnufPUnVyxvLNN3Ku/6wBH1vs8dKrL/+/zj/HLP/T0M3qvmZ/tLGYxi1nMYhbfmfG4oS3AkSNH+Ju/+RsOHTpEFEUjv3vnO995Vho2i52jUmnLpD2C+qXa+l0UIiu2+EXaykCUlLZz0SkAjqSL9MIkf4jcNaceSeMsxhYCcEiFQyrBMUpb7T/r2i4iS0hxK2GeUdo+sraJvQulBINKAGeq7bY8n24vJUMinWDiob2p1QeDJMQDfANYS+cyD9mBZ+M7dsmrU1YWIvP1LSugrX4wLUFbs110udnMVcxRMF8LbQ2AS8qAMI3Bdke2QM/5Dp5QMCqu2W47r2FZmmUIBK55cC7BnlzV6ZdVnfUwKofKmTXqC5mlYNlIKXPwsdRonoHS1kcSa2hbv+3bAELPqNUsuxJGFcXhtHLYm+wzBnwvBgGsG5A+LSfq70R6EzAqSbMcVCw2XVwEyEzlpEoJrBWOiQbpXgUg7JXUz67lFtu06xSEevFAZiWvzixRAMmfQ0qZ9+3lhgIp0RQ19a62l+ekyku0Uyp4BaC7LFEtjLKRxKSZU69+NjAqaEAaK2eVxmLtB5EBcEnmguMrVWfYAb9NlhWK+eWmx1ZoYFSNfUPgaEidAm4pJ4XqqluntK25h145J46v+kLcVzBd39PCiqUJaUIsxRRVp85JWp2TwkdajxOt6I0cn6poG1VnJrSqc3IXgwHVIzlpLtfmJJ+7Mu2JOtzSBQBXkFKy3jVKW4/uuspJLIS6P2MxPwLSfQIzX5dVnWVFuuOX7BHOQGkrhFLX91ZVTgy0zdXPBtra9fYIWv2cpBK7YsFpewyke5nJSfVMUyxujKufJxecFhsNOB0hgaS1XDt35Z9Rqa1yHPWUr62+neVCZOG6OndsOROLu6AWCgGiLEJKp7S4MQn65wKHhqMU5P0pC07nQtx5552AUtrec889I4V1Pc/jmmuu4Vd+5VeequZ9R4axRlhsejS9+seVhtPgLc9/C4D6LK2JA0tN7nh0Y0RpawqQPe+SZTznjDcfjsS1e67ls8c+y92rd/Oqp78KS0w/j2Nb/H+efzG/8ZGv8MDJDkma4dg7v/fMz3YWs5jFLGYxi+/MeNzQ9rbbbuNHfuRHuOSSS7jvvvu46qqrePTRR5FSct111z0RbZxFTRho6+q/BRZCSDLJiD0CFIxF5NC28LQ1SltLQCpTkBJvcBIhYMPaxQN6S1bLd/IvzkmWIITAsRwS4ZJmGY7edmyUtoElIM1IBZWw8eCuFkLA5qDPXJrtoLQ10NYFrV5MHHfiIbbp27oNegt0WeGoFYllawQA3/Ig0x6EFQDOcdR7Z1JgW3ahtC21r1Bu+VhZSgZEfps617Egh1E+oAvFRV1oLE1st/VkSRlVoQSeM4BQq+lErqabVNq2fEcpi9Ppij+vrFYLFguIOdyC5jJhkhHpPC81G4VabUrRq4yUMM3wvMn2FduBVU4CoyCsALZgoLIsWVZUeNrmBZsaubI88tpTwIe6njjV6tlwW+WktcLWIEZKVTRsznfwtGdx3Vb8Oe25a4pyVeWkM1LkK9jRHsHLFd+2st3w51Ub++vgzzGI09yqRKmftQ9yzfl2t1VOoiQrinKVfIY7Y0pbo3Oqz4mFJCNJs1pP21yRrgEhqMWNnQBhagBhEubwSOVEbWVeaLh4p/U4EValLYkqNhfnOwP8CvVzZ7zwWjbdP9VzVE6yVM+3jaUC2i5cQJikhLE6Zjnw9Lb06gJQYPxTVfE1aVfZI5i5wSht1f0ZVlgZQAHSk8zd0Wd4sdHILW6ixkJtTkw/TAwgHG7l5+uEqtigEOpajq8aQGhXgn6Vk5RMZmpRrCInheeuLnolk1oFPhQ5SVMNJBtLGtpuqlMnWb4IuBQ4yn8Wq3YBa853cm/lWOpRUBrHudK2aeYulZPQrh4nam6ISDJbFyKr8LQ1OWk285zEzWnQVs9HiR7vneM5mB9Eab5AtHvOZ1XnpA4qtz0HBGQyUZ8pFTs3OqWikYXS9tz2tDW7yF772tfy7ne/m/n5c1c1/O0SxzanFyErxzRYayIvRrauPuc6w5g7Dynbpxd9A9YIJp629DSaTpNu1OXhzYe5fOnyHV+zfyHI6zocWu9zye7pY2cYpxxeVxB7Bm1nMYtZzGIWs/jOise9rPymN72JX/mVX+Gee+4hCAL++q//msOHD/O93/u9vPKVr3wi2jiLmki05YGTQ1sbC0iRqkDYSGhFGPpBdsLT1kaglbb9NUQa4zguW/YS951QD+R75vzSa9RDm2e7xMIjySSko9C2bRk1qayEjYFrc8FSk4yIfpiUlLYVhci0PcK8a4G+7rgCVLQ0VA71g65fViRqj75yETJAPwhLDUUnoa1rG6sAjTHMw31ZaTsoHmJzoDdFdeTr5/k4swp1r34oLtR0WrmV6ofsGhhlCtsgwRZeARHLqs7IKAjtM/JPzS0hUhusktpWWyQY8O27Fm1bAd14CtzaM2+UtilOGdpqwNGpURCGNTAqB0fTil7lajUfS28JjqdABaMuTlIx0QdNEbLFpqtAuslJDYwyvpVSgiNK/q4VMKrlOaWK7vVqNaM6TcuAEFTleYo+GHg2TVJATgXzyy0PKVQbU4ynbckeIff91DkxCsK6nDjq+GQKSM99hgMPoUF6HEwZJ/qaFYwaVYmulzyGLUvgZiYn1bix7TvqegGwsStA+uRW/JRI1ufELYN0KKxEtP+zuYeubdFGX+80j1zXxnN035fTcqKh7Q45KcZxNrYVv8InN/AQul/HNapTAN8p52Q0x2taZTvfcHFsC1d/zsR12/pdG8dWYzPJsgKkV3juzgXFOJm6uKEX2Qpoq8+pc2LmGtsSzJuc1CiBQfv46sWDocnJyILdmCLd9MMaKNrwQJKRplmt0rZY3PCxzVxT48etrtlAW6tk36PGibFGaPmO6l9msaRmnFiWQFl/qoKiuT1CxeJBobRNGXybeNr++Z//+QzYfovETkXIHm8cWFLnObY1JE4zPvfwGmkmuXhXKwe630g4lsPVu5Wv7l2rd53Ra8p1HR5e7e1wNHx9tYeUkuWWx3KrekFoFrOYxSxmMYtZfHvG44a29957LzfddBMAjuMwGAxot9u87W1v43d/93fPegNnUR9GK1u2RxBCIoFsHCQJ4zNrPG0LpW1c8rSNsxi2j6mfN3Yjhc19x9XD3+4aaJuYYmSJKUSmHprbloY4lgZ/FXHZnjYZCb0oLSltzUPipNJ23i5sHeJxCwjUtnmASENbz2sXBXX0Q+eE0tYAQqh8cHec0hZoKLbRlrYYFwo4p4BHUx5gfX3OJLEmIOuER2I6HUY5toXrahCBXzpf2Uu08Ooc9bStgVG2gaJjgFAXvjq+pR6m9swFuPpep0gyp1oRs6vtI0VClkEq9MORTJVyktFq5FBWq1WDD9eonzNLbUWsBISFJYQrd4a2roZRUWJNQNZNDQiXmqqPeka5W7G9GMB3bBwNMS3capBezomBtlN8gR2TEwNtxwDhSPE6o84TVmVBKXU+K4eig0znzfj4UoZlZpyo9x/WADjjg5xlli4oWFH0qgQIXW3RElUolU145ZyM5Xi9V2zDB3JAmNjVObEtkSt3hXRLOa4uelV42p7BOEl0TgxYM9C2NC94uq/HllMJ+k2YNg4Sk5MoX2Qrnw/A1/06rMmJ52qAmWklWsWCWAHg3AI4TllwcvXcFSViYi5c6xbb8IG8H9aNE4CWvkwpbezc2qXasqLpNCFNGYj6nJj5Ohlf3BhuAsVcMxe4+Lqvx3b14ouJYpxoeJRG+TjZHp+vdb8Oa6458Aykrve0za85cPKchAbGVkSek7gEbXVOVkt+tlCMk6jmMxmKnCSpxAv0PFOjSG+6zcIeYQrsP1ei1+vxG7/xGzz/+c/nsssu45JLLhn5M4snL842tF1ueTQ8myyTHN8c8qkHTQGyXd/0ua/dfS0Ad526C1kqRDktLt2jPvuM7cG0mPnZzmIWs5jFLGbxnRuP2x6h1WrlPrb79+/n4Ycf5pnPfCYAp0+fPrutm8XUSHN7BANti4fEDCg/MppCZNKAv5KnbZJmyg9XCNIshd5RdUh7P6RwZEN9cS5D21hDK992tT1CP1famm3zbZGwDSQ1sBGUr212n1La+rY+f75dtORPWoK2DoJEWCQyGT9dXogsykKgoQClN6fUiOE2tHfnD9kFgNPwqEY1aZRgmQGYY0rbOC22284Fbg5ZY/9MYBTQagOnRrZ9QwEBXHO+KQ/ZDS+DGCz8Qi2chJAmYDsjgLCfqzqd2i3BRkGYJPp+NJdhjVzVaSo6X7DUwDVV2S2HWCb4TMIK17aU5UIE/dhVnssyVcDMDUa22wK5WrkWRplt6QakVxa9KsCCm8aEQOTWP/zlW4xjCxZGgaNR2pot0LmCcIp3XeBKSLRC24DJeJDbdJTBxyAHhPV2BsW2bwOjTIE4lZOTugr1rraHE6t/S8smyRIFUSui4emcJGM2Hc5yPk5yn+HyOKkIN8+J/n0VjCpBUS9NiNgBpLsGRomJ8xlou6ShrZdoGDUlJw0vgxDECEgvrFMKn2GbyC4tbtT4DJtrjtM6pW0B0t1YtTu2rNoK9aDhdx+6ice4dUqRk/EFnek5STPlWZ6P94px0vZVTkKUtUtduBpgRrE9AdJPd4vCcEC+oDMtJ00/gx4I6VQq0jslgOmU/VNrFsXMfB0n+p6Mg/SS/Ywbq59VLQCWw8wN3bhUfG1snOQgfQdo65TGiW3ZlYsb+dzlO3hpzACIgvqcmAWdMBEFpO6p72OnO2Mg3YDqKZ8nut4oWWYjgsm5tVMC6cbTdkCGdNu1BezOlXj961/PJz/5SX76p3+a/fv3147TWTzxcWxLfY6ddwb2CGcSQggOLDd54ESHTz5wiuObQzzH4vqDK9/0ua9YvgLP9tgKt3h0+1EOLhzc8TWF0vYMoO3Mz3YWs5jFLGYxi+/YeNzQ9nnPex6f+cxnuPLKK3n5y1/OL//yL3PPPffw4Q9/mOc973lPRBtnURPjSluw8gemWIhR/zujtBXG07awR1BKW+Vjl2RJrrQVCxfAenGKKqWt77jEYlxpq96rJdR7pFOg7WV72khiBnGKY4152iahOqfjMdDb+1t2pKCthlHj0XRtJFIVUSHQW+fbGtoqsLA5KLbwQmnbd81DrHkgzjJVgEuMbT81D7CWJWh5dqFkcusfNIxPbhRPKm3zLdC50tao1eofsj1XvUbIkj0CKMWaPU9Xe9q2fYdYuDtuxXdtbW2Rwyj9UKMBYVkB4ya6k1gOcRoX8H0sGl4GEXRDVBtLnrHdsUJkO4EPVysIZWarIh7lrfMGihqw4DkF3PLqoa1R2oZlQBgZe4RRpa2f7KxW870UBkBWUnUiVZ799kghslR42he4HqTbtlE4lrw6IYdRh3VxlQNLTbxYW3dYDnEW10JboyDsRUJXne9qleNyoYrV/TBIp6s6jbJYZs5oTqJOnpNcQeiVtmlXWJKYMP0wjC3wRz1eNzS0XdY5cbX9yTSQ3vQldIDMLsaJzJR1itvIQXrbd+kJYyPCjqrOOBlTdeqcbOZAz8WN1ZjBckiyRHlLV4SxH+iFpZxEPQVtx7bin/k4cXVORovDZZnM++G8BoQwHdrmgDBm0rJiTP3sx2cAbfPCYW5hFWMU345XstRw1YKUTBhO8U817ZvMySYwaj/j6cWN1LJJs1RB1Iow8LsXotpgctJcrtgZMV2FXx4ncZrhji1ShkmaL3zOBW6+KDbNbsfMDcNIwNx+9cNttfhqQHqutI13Hif7F204rsedGSd6EVBadmm+dmk4au6SQOT6VM/+50783d/9HR/96Ed5wQte8FQ35Ts6+lGSz/FnS2kL6vNRQVulsn3uxcs0vOmLNmcSru1y9a6ruePkHXz6yKfPCNqaug4bvYj1XlRre5BlMge7l+85d4v9zWIWs5jFLGYxi28sHrc9wjvf+U6uv/56AN761rfygz/4g3zgAx/g4osv5k//9E/PegNnUR/G09YtedoKoX6WjeldzM9ToygqFyIznrZCKPVq5zgA3vL5I+eotUfIoa16GMyhrcbK6ZStsbvaHr6XISX0BrrNblMpMSEHrT1TSEvEOAioVdraSFQxtSxj1Fsz3+pe+JMC+BrohTUPsZalVcnSYRhno9tPpRzx9xMUD+3xFFWnq7lXnAqysYrkExAgMeCjXvFj7BHIPFUR3BktmFNW2vpJpIrvCFHA2LGwbeMLaSmAYFSdRmm7UUBbKx5gI8CycwV2VZgK590BBZyJi4IgUCp6lU6HUZZIQCjrgV6YFjlOY0hCkpL6uW0N8wWMuMa+AQowE454dY7bIxjQX3hh1m2FNCDdwldexMb+Q+ck97T1y0Wv6kF6OSdJmkFz1LLCFCm5YLmBUwKE03OirrkzyCYWD0xOjGWFuebacSLSqTkJk5Q41TCqnJMpIN2oJhUgHLNH6I+qOo39wLRxYiwcpHTB9sHA7LGczAWOKhKVTi9EZuaGJBVkmZyAtmaXwr75YATaRllU20bTD7tDJhd0xu0R9OJBaFVfsxCxzomGn2bxQOekH6fGVpqWFeJqhfs0kG764TAqjRO962DcHsHJLSHqc9LQVQez1NZzv+5fURcp5YiqU9kjJPSRUxY39IJYohTN45625UWxPCf29HHi5Dkp9YWxnREGpAcmJzX90LaSPCe9MJlYpDR90LYEgYhKlhX1OzeKcSJg/jz1w+2jICWrudLWjBP1OT0NpO9ZUPPuIBKjfvRxjzDJinEcOLiWi60XD/o1CzrnUiwtLbG8vPxUN+M7Pg7pYmELTTcvgHs24sCy+rwx896LzoI1gokXXfAiBIIvnPgCd566c8fjTV0HmK62fWStxzBKCTyb85fOHsCexSxmMYtZzGIW50Y8bmh7ySWX8KxnPQtQVgm33HILX/7yl/nrv/5rLrroorPewFnUh3nE9HJoq8qJQWGdUIT+hio0OCoXIkslYKkiZlmaK22bKwdGzrC7PQltA0dB25FCZLGGZabIyxQ1ohCCXXPq95v9zPyw5L2oK2DrczasSMnDa5S2nm0poAeksrog0uYYFPXzbd81YEGkCAEWjiqIZmCB3iI7Uhwo6uLqp4FpgNCyFOAS0iG0TMGhUTBTQFt1X1NhkWmoMh6uVm9lUuc3346vHnzK2749DSIjr1lZ2AxAaAAncBTsMdu++6pwx/EtU9W5AWEHF7EjIDTFw7YHpUJMOSAcVdp6O/guxjLGFgKBSzdKlBexAXBhJ4f8QkAz7eAZqCzqveackj9p4pjicKP2CIvGUkODdCnsysUDAMdRr5GZycko7CnbN/ioLdfhlCJVViknvSgdAelSyhGlrYi6Oic2cVqfEwMIO4PJ9o0rbf3ELG5Uj5NYRnlOOmE8mpOoOwKj/HhT58RRiu+asPOc2MTumPp5wh5BjxNL27xUhFEWZ6n2lS1ZJKSZzAsetgMHXzggU63qrAbpljALOq4q9jcGbR89rfr3wd0tnKinZmetSK8LAwhHx8kowCyKFE5XssZZhG0JLBz1WrdRGidFThqejTNcz3MybZzYVqFkjQMNPDonAFjrVYP0mPrFjZW2ansvFConBhhHPfpRmr+uHTg0LB9kSowkrtnJIHROhHTU58aYPcJ6T7VpoeHihtrPeIe5y8wNo+OkxzAuVLHzZ7jIFqYhjiWwcHVORkF12Q5C9NdUToQ91RdYWGbnhiBp7weEmlvDbVa7aq42i67F4kbt6dgzr95rGFkMU1nkJCza59oWvmMhZEZD71YZTGnjuRJvf/vbefOb30y/39/54Fk8IXG6G/K+Tz8CnH07AANJAfYuBGf1/JcuXsqLL34xAO+/9/2sD9d3eAVcqt//4Sm+tncf3gTgqvMWsKcsgM1iFrOYxSxmMYtvz/iGoO3a2trEzzc3N2dFGp7kMEpbp/QdztEPipMYSW9BFZOetnGaYZlCZHEf+iq/83sLCG9bIt8aDoWnbcP1a+0RmlI9LCZTFD0AS/qhfa1Tgjdj6lgDU1oi0kpbuxLMCCFyNR1SF0Qa88I0yqjF3J9UP2TXuPFFGnwIHFUQzXaKh9jhdmm7rQODzRwQRlPARyoTHHNOgsr2GTWdF6uH7GlKVsvWVhSJ1i+OFSMbKUSm32daAahEt8/CUWCn5J+62glJM4nnWEq9FXVVTnZQ2hof362+nGhfJxz16gy0+nlYAz6i1MAoV7VPlFR/USc/X8t3sMItDTDdqbDMwHmBTU8UkAIKdXbug6zVatOgqO1osJuNgfSoR5JmDPVCRMt38i3QkSgdNxaJTDQU1SC9VBzudDdiGKXYlmD/QgBh94xAuqPtB7b7slA/6/b1wlGQ7utxEtY8M0ZphGOLQmlbzkm4PeJnKwabqn32TjlJipygFzd0/60DhNOu2XNMYS7ju1vkpBclherUc2jqfw9kPUhPibHMOAlHoW2cpBzWarGLV1rF4sYOqk6jFFWAULcv7NKLUqXmpZSTZHpOCkColbai7A28nRfrmwscGGhoa7t5IcfK0DmxcOgE+9TPto9BmuQ5WTGqzlxdXL3IBgUg7AzUNuDy3GD6TODauLZFI0vzWXpQ87micqIWNzrDUk7CDmRp7g150UoTERULTtOu2dJFNbf7EpnnpJP72fquReCq6zD9sG5xI0xDbMvCwlPtG8lJZ8TPlt5pPCxwvB1zohZ0XLZjC1oKpsutI6wZewS96OpGKieZsGpz4rkZji0Q0uXIRn8kJ7kCP3CU32vUpaGvdfBt4P/6jne8g4997GPs3buXq6++muuuu27kzyye2NjoRfzBx+5noxexdyHgX19/dsUg5y828tIFL7p891n3LH75wZdz0fxFDJIBf/nVv6xdaDdx6W41tqYpbe/S0PaaA/XFCGcxi1nMYhazmMW3bzzuPUePPvooaToJy8Iw5OjRo2elUbM4s8g9bUuw0UUVIUvHv4dqT1tpNiVPeNqqIlyJBrZ4bRYXlxHiEaSU7JrzsUor/LnS1nVJhKegrVHaTnjaToe2C5qOnO7IokDPmDq2r7e6B8QKTFtWLfjwPQmh2qatflCcK0xShvpchdJWXUtiCTKZYY3BgBwQpm7eDoIFpWIdbtEZKigw33BhsFEAwilgJs5ibMtCpDZ9GbACtfYI+VZ3ocBClWessNSD+SS07ZNlMrcKaPkOnvZ2nObvGqURjmUhUq2anCtUnUc3VDvPW2yoXIVdrQabDj7MFt7NXgbLBhD2iZKMMC6220JZQVh9LgOjMLAMVJ4HGwp8CAUt2r5T5GQHGKW8Xy2s1KUrAxYAwm2klBP2CE48wEIV+qoH6XU56Y0ogVueja/HXeg2aqvYR5mGopkG6Ys6J+E2h9eUIv28xQaObUEJRk2DogYQbg8kmdtSq3hj7Wt7o4CwTkE4TId6ccPNYWCRky6dTFlxzPkODE7l42RaTlQRNQuROnRkg0WAwQZJmuXAzHjaOpFWxumcBEwqMdsNNdd0hnqMj+SkUJ3alqCpF4V6lkAKq3JJR40TgTA5WVlUv0hjjp5SqvSW76jFjbCDh0W0gz2CUbIOI4idpprFol5+vU3fUTmmBAhrzlXkxCuNkzYMNyHs0IlVe9u+A/1TGtp6O85dJidb1hLLjg9JyHDzKH39HrtyQFjkJMqiSh/f+abAspT6+ejmgAP+HHRPQtSjQ2GNACCiLg0sugL6WcRiTftsy8LKlJJ179xCXjyst7WWF1F82p45eHQbF0G8w+KG2XmQZjah1VQ9K+pO7IoA8POcVMOaYTrEsQQSNweg+HNqV0nYoTNUALcdONA7oecub2qfUeNEYKUO24OY5YULoLdK//RhomQFIUqLGyWQXud3HaYhTc8hHjg8crrPZf4c9FYVtM1GF3MIVU4QNgM5ZQHmHIlXvOIVT3UTvmNjexjzB39/P6udkN1zPm/8oaePjK2zEZ5j8V0XL/PoWo8XXPbNFyAbD8dyeM0zX8Nvf+G3eXjzYT726Md42cGX1R5/mS5G9tia+i7kOaPfP093Q45uDBACnnXB4llv7yxmMYtZzGIWs/jWjzOGtn/zN3+T//tjH/sYCwvFim+aptx2221cfPHFZ7Vxs5geRmnrlSCKIwQRkFKoPNX20nGl7ainLdhYAtKBhrbz52FbguWWy1o3GrFGALU9HaDpeMrTNi2Utma7aDsrtvVPq5be8hUcGkYWq92QPXPBJLQNDbQNc6Vt3bb0+QbQKVUPL6l2t7Ri0rUtGloZ5cf6QVPYhGmoqmGXrzWNc4WjUfzizynv33Cb7YE6/3zgwnDzjNRqqhCRwIodelLf26hHlGQTUFnEPQ0W7NpzGmibpDq/JZ/cXlTcp5ZXgrbOFGibb6vWRWf2aECYxpw4rbb85cVBoq7yVdYFlurC1lt4e6EgtgsYZWCSbYk8J4HuS8O69qURtm0hcXPYVu4zXQN7AgeGW4WqcxqMSmNcWyBSm+3M53zdvkFpC7SxR7DSIS5CAbjanGh7kGTcHqE3ogQWQuDrSu/RFB9ks3iglLYx+Etqq3uWcPKk2p5+wVKhRj0Tpa3QqkkpHfr4tHX7tkt+tmaxpoBR1QpyA/ozs+0bRnOSlXIy2DgjQJird1OHDWcPBwA6J9nsdJBS9RmjSBdxX42TKYsHS21d5Gto0w0T5kv2A+PF8Fq6iJa0bIbpcGJeAAMw9Vb3MAFnTuU56nL0uLKZUcVmxGhOpoD0jFjlOXPoyUCByRIgnA+Kj21fF5XaKSfSKNJhZD4sF16jv65UnWeyuGELROqowmgLB2DtIbaPfx1o0/SdXHVqa7/rVI+TVtkfVUciI5qegxi6PLza5UBZ1ZkVqk7zsyYWXctikAymXHNJkS6EWmQbbHDo6FGkhD3zAQtNF8LtM1Lhm50RVurQlQ0NbXsTfrZQ5KTuDg6TIY4tSI3SFkp2QB06Q7Pg5EL/NL6BtjvmxNI5iZWv7bE76a0+Bqyw2PRy0O+EXQQg9dzQYLJfR2lEw7NJBx6PrfVGFje6aVGETP1MQ1tb1ObkXIrf/M3ffKqb8B0Z3TDhHR+7nxNbw/8/e38eJslVn/ninxNbLpVZ+9b7vqm1tBCSkBAIhACBBRYGBozneowx2AOGGdvXHvx4fDH2b8ww9449Y8zMMHjsMfZ4MIwxRoDB7JtYhKTWvrV636q6uvbcIjMifn+cOJGRmZGZVZnZ6qom3ufRo66qzMgT+Y2IzPic97xfhvos/u9X7wuib3qtX7591yXZrtJYeoy37nsrn3jiE3zx2BfZN7SPnYPRKxHHsgmySbkq4ORsjt11jcYOn5wHYPd4Npi8ihUrVqxYsWL9ZGnF3wCU+0AIwb/4F/+i5m+mabJ9+3b+43/8jz0dXKzWUm5aK+QMVdC2EvKFeXiBec+lsRGZctoKIaj4uX/0yyZkI5mEhLbZWmir4FzKtKhg1jlt/fxZt7pc2fEcDBF9uFU8m5Spo9kmR6aXfWirupIv8eS5RRnhoAkf2iKhbRNAuG3UgmlYKvggIwSOwtEICiIblaJ/EyuhaD2csUO5kDVOW/DjEcYBH/bkZ4Ml0K2cUWWnHICFxQDaVsGMoVcBJnYV2jYFXD60tcv+exwCH8o1mfQdhJbfuKpkNHewlB0fzKglxoYVwKi5C+eBpMyz9V9D5ae22meXinwfXYNF15Lu4nKucbkt1UgIuxmMciWYcTBrnbYgwQehJcYKEK7A/WzoGgKDBScRbEvl2aYTBpah4bouopzHQmCL5vus+TVZyMk8TxFRkz7V5GtpSv7JaN57PXACq5oIIZss5WaYnTkPDLJFOZhLCqS3jqwou2VMXUOrGCy5SR/aVo/DbAhGWQEgjHYQymXfAsI1CS/7dkKAMIC2KwGEGgKdOa9PnnfFBXJTMu9wKG1VJ4NWcJ54okTS1NDKSZ6ZWuKFIRhVBZiyJubCKbk9M0W+nI+EtmryQKgYEZA1sZeZnj4HjLJjtLqcfkU1ccqYhoawDRadhA9tczUNtJQUSG+WC6xcnRWMYP/CTfbCkRXVa1drV2c1BsMHhD60LVw4DlzNSBi0+I5vp8XkRtEpkrZ0ikWD5y7keFkEIAxgRWmJFNL5nK9EZ44qaKvV1GQICnOcOXcOGGPvRDUiol1khed5lN0yloKirsWoP5ZqlI2pHhw4WUtedK5y2GlbPU+qID2cdS3jEdpDW9uxMdXnSaESfH7bs6eAF9R8fqvJjVYTTtJpq5PH4NhMDsaqMR1LldC11X8fpNP2yoC2Sg888ABPPvkkAAcPHuT666+/zCO6cpW3K/zxV57h9FyBgZTJb756X+DWX6+6acNNPDn7JPefv5//+fj/5P03vZ90RINHIQS7xjIcPjXPkelGaPvw6XkADm0ZfB5GHStWrFixYsVai1oxtHVdeVO4Y8cO7r//fkZHe9dxNVZnqvgBjGbYaRs0Iqs+zvGqOYBu0Igs5LT1M201AZWC3zihfwMg3ZTPnF+qOvjUc3xgmrbCmba18QhVaKv7yzejDzfbsUknDIRt8tyFHLfuGg3Agltc5H//6CQAL9s3huU+HOSnNoO2m4bk6yz6GYlaCBzVNyEDEBXfNSminaxyua1cit8IbRek2wz/xn1hXkL0Ns6tsucvMUZnUTkx65xbAYzyAWFek07gKLn+AulcUfP3udqILMiztXRwKlg+mC8bzV0sykEowvED6WEJ9ObOA9ur0Dacn9pknwPw4cOoBccKIiGCJmSJRgdhsQn4UIDQbQJtl71qhi+h/NT28QgS9sy6VdA2vyCjB1Q0AhBAW1q4n1MJmZFbKOmcnitUHYTlfDVL1N/npN/IqaibTV3pYdAf7HNqGHIzLF6cAgbZGsROtM+0ralJRdZkA0hYVuc6BUgEMMqNHKOCZU3dz04YEM6tzP3sln0YZcp4gKEdcO4whakjwDaGM2FAuOyfJ81hVK6cI5MwqOSTPH2+Ftou6yGoDDB7lDQaC2aafCXPCI1LaWsAYSkECBdOMzczRT20VZMHzaBoxa3geI5sqIjBgspDtpeDeITA1em6IVdndE1KlRKGLnCxqgBTAcLSYrXOCQOWLgbQttW1K4iswJDXq0HZsNKZPUkjtM1hISi2ANVlp0xfwsBGOm3ZHKqJqDsO8xdJo4FmkC83gbau7YP+UPyA34xs5sJ5JLT134Piolyp0iKyQo3bMjQ026xO6ISbUKrPk0qRhP+57AjZEE+va85VqpQwNA2HRARIX6qFtjMhaNsKpPv7HID0yc0AuAtnQFTjKnAdCKBt8zxu27VJWzoCk6nFIvZkHxbI67UdykH2x5xCA605SF9Pmp6e5q1vfSvf/OY3GRwcBGTPhpe//OV88pOfZGxs7PIOcB3rh0cv8uDJOWbmFhHmLIWyQ8F2WC5VgiiZ33j1Pib6mzdxXU968943c3ThKBcLF/nbp/+WXzj4C5Gf7bvGJbStz7Ut2A5Pn5erzWJoGytWrFixYv3katWNyI4dOxYD2zWichCPUP2dakQWRl2u5wY5ma7i9GGnrRty2hbn5S99p84brt/EL9++ixfvrq25upHtMy3KwpIRC6pRkZ9PmnZVFquO0wS+gXQe9Vk6Gla1g65/E/vc6XOcmSvQlzD46UOboFL04xGaN1EZyepomsB1DE7M5mucZSoeYSAE4Cjn5LLgJlC02vQqFI/gQ1uvuMD5Bbmfg2mzJj+1nZtOOVnnHR9ylPMBmKnJcbOXfRDcHEZpmmyIVC6bHJ1ZjszqzCRMyE1jeR4IrWkOJignq+/qDKDtCK7nUVqUERqbhhqdts1qon6fMOQ2Z221tDbX6Or0vABGlfEiu84rgKlhhAChcmcvhppohSIr2kQFBMu+MbjopPzmax6l6eeAajQCVCMrWm2z5BTIJA00Ejx2ZqGmJkGH+IQBToX00rTcdSNB0YkOhQiPLwBw6WEc16O8LCdbqk7b9g2WVE0sVZNyuCZ1S6ArNgk/y9wVIjKaROUMi6h4BHspOI5q3M96c4AJYVenLgHh0HY5hlnltA2fJzn/PGkO0vOVPH0JA50ET59fDkVW1LlOAS4+R0poYPY1BYQ1kxvFKkh3XI/SkjxPtgfQdrHt5EYYEArMuprUwbJyTi6dB5knHfE+Vpte1eUMg3Sk1zhtL0qA2aYm0p0t5IRTwY9HALyFU4BcnQH4gLDQ1oUvnbYGGiZTC0WKohrxsVQ/eXDhKdJCw/NBepTCIH0pBNJdz2NpXsaQ7JnIgFMGf8Ku1Xmifm8qx3fEJFtwvS4ty88SIT+joj5P1IRT0EQRamsS3ufcBUwhG5G1nAQMr9wo+PEIALkZDDe0UsbPTTdVBnmTmiiwnE2k8Dy4aFejEIJrVwjapoV0KxfK699p+973vpelpSUef/xxZmdnmZ2d5bHHHmNxcZH3ve99l3t461qn5wrcf3yWp6bzHJ1Z5vxCkYVCGcf1GEiZ/N+v2ledDL4ClDbTAah9YOoBzubORj5u97j8HHruwnLN953Hzy7guB7j/UkmB64MkB0rVqxYsWLFWr1W7LT9/ve/z8WLF7n77ruD333iE5/gAx/4ALlcjnvuuYePfOQjJBLre0nTelLFXxKbECKIPzCCv1XlhDpue6KxEVnFcQHZaKdSXAAxBFnptM0kDG7aMdz42q5yb1pcECauB065iA7YPtxJOHnZ/6VN1qnt2PQlDJaQnaqLZYeklaHiejx3+hxkJTzOJAwo59s6bcuuLd10BYMnzy2yY3sVHCmn7WAq5AYrF0ggWG7jtHXCTlsfEC4uzHJxWYKlXWMZeHy+usS4XYMlXeCgM1epLsVvgAAQuNUQzd1qJbdIf9JAyyc4fGqB3ZnwUnzlOtVh6ZzclpFsG98gnay1MKpUcUlXFkhaehWYhVydzbapfi9hlMFMCEY1uDorJRJ+x2VPaNhuY/M1BT5EE/ChnLYZ5bQVK1xi7IOZxUIZRnbB6VmcmWeBqyKcthIQNoMphUqB/qSJXkjw6JkFXrOjSTzC0llMz8XUDMqGRa6ca74U33c4hp22xbJDxllkMG2FlpEvtoXKQU385f21YKYOEPouViCAoqZWG69RckrouoRRav9qalIO12TOjwpo40hXTa+Ug3DDdjmE+RMADPcpQOjWAMJm+6yctnkvyen5PAUSMtHTzgU5yNmEIeHW8hR9aGCtHhAWyg5pZ5HhPkuey5USOHbb8QXQ1j8OL9pVp+1S2NHvj9lEIISGJwQlpxR5nhiaoBx22iZCoDp87hVmV7wU3/BB8GKxDINbAdBzUxgZO2h4FQBCIQFhszrny3kMTTCc7oc8nCsY7PCfvxScxyZ4Hlx4mhQantnXMtNWbwDpg+Rth1RlkYG0KTPa/ZgYE31FNUmaJgKNmbK/f6VlFr26nGF7GQPQNBPXH0v9kmiVaathhRqRhWJEFEi3dMjPrawm9U7bRBYS/djOMsPOBUYz+4LxAVi6CUI0B9X+tWEim2G5BOeKRuDCX6qf0Fk842faWleE0/ZLX/oSX/3qVzlw4EDwu6uuuoqPfvSjvOpVr7qMI1v/um7LANmkTim3xKaJUfoSJmlLJ2XpDKbMIHf5StKOgR3sHdrL07NPc2zhGJsymxoes32kD00TLOTLXMzZgTP+8Kl5AA5tGWh4TqxYsWLFihXrJ0cr/ob0+7//+zz++OPBz48++ijveMc7uPPOO3n/+9/Pvffey4c+9KFLMshY0VKNyJJC8NYbt/K2m7cGYMUJZRzWZtoqaFt1vpYdF4GB7pUlCBUaZCZav3YQj2BR8eFNueRnXpZd8DxMp9AWsIJ0Wpm6xlAqjechM/QSWc4vFDDsZTYPpbh9r78ksVyoNiJrsk3bkdBWw+Spc4tVcFQpsZSTN5WDNQ69vL9EVouEjrZjhxqR1cYjzMxIh+T+yX7ZfCfIT12Bq9N3ss6Vq+7nxZwEHYET2PPk+Fq4wTzPo1Au0J8y0bE4fGoOFCgINfrKJAxYPCdho5Gg7JQjXaxQhcpB0yuAtA8I3QU2Daaqy/xCrs5WNQFI+eDjQrGa19sAAXxILfyaNHOrqQZQy8r9HMRgLNe61YrzK2rKFW4qJaHtbgD02SNA6JhxHYRTagvg8pU8Wd9p++z0MiXlIKxxPxswLx2KaSsLiNbLvrVQzjAEgLDPXWTLsL/9ig1Oue346msyXVLgKdcICMt5DCEwNBMQTR3pRgtXZzUSQvNrsrKmV9LVaUpXp++0NZdPIzyH4T41PnneWG0chAoQjvXJY+VUTqvuc9h16jt501YWNIOcv/2o8en17ufUIHm7Qp+zFHLZSlhm+k7gdjVJmwkEggu2ETy/Go9QrZMQAlOTELFUaX2eRC/F92uil6FcWEX2c8jVmRyARJZyxWG4coHRTBU0A1hCAsJWIB1g66CcHDydr9akBirnZmQ8gjDw/JzhyPfQVe5ssyayYrlUoc9dYu9E1m8MJ2NPLCMBNB9fuCYAF0rqOhUxyebXJOF/Jra9dtXXJDRhMsAyeA4JobeN1PA8L+S09bc5sAm74jJUucC4ctr6x6HlHzNNJ9n8fZ7MynGdDWqyXD1mkob8fDr3sIyssLJXRKat67qYZmPeu2maQUxYrM60ezzLnQcmuHFrP4e2DLJvMsuW4TSjmcQVCWyVtmblxNappVORf7cMLYg2UqvNXNfjkdMLAFwXRyPEihUrVqxYP9Fa8bekw4cP84pXvCL4+ZOf/CQ333wzH//4x/n1X/91/uRP/oRPfepTl2SQsaKlMm114M6rJrhj/4RqM1YTj1DNtBU4gdO2Im+4kI3INHQM15YgODMBemsTtoJzpmaimXLZVtn2oW3FxfKKaMLP2F2B0xZg19gQIJeInSuaXFy2SXp53nbztqCDPWW1nLV5PELJKfnL0k2emVqmoiUliAYKSzLPtcbJWs7LZcZNus4rp62GSb7uJnthTi63PbRlUL6fQTzCCrp9+zfZF0sGCFk5e1FuL8itLOcBryWAKzklPDyySRNdpDg3X2S2oiBCPgDN6YRR47T18JpDVjeimU96mFLZJeMsslEt1XMqUAkBzGYZif570WfJ503VQNvGDvFCCEzhA8IIGCVrovmuzha5kIYbOBJXkp+qml4tFsswugeA5KIEeEE8gg+LrFbHjCMnQBKGzmR2ANf1OLroVfc5DAjnZWZzX1Ie/60AYUN+anqYQtkh4y6yZagajQBgCb1lVEBjTaowUAHCbAgQAlg+jIrapmqwFAmjSkvBcdSv5cFzVwYInbLvIPRrkp0EI4lTthl2ZhhKV12PAKZmSEDY5DhU7+3u4UEAji2qiYeq6zSTMGBWRmKk+2STwdbxCJoP0v3XTA2Ttx363KVQnq0EhKaeBJqPr7qCQUK2mpoU65pe+VA0ofvQttlSfL2ZI70aCTHoLVXH1+KYcT03yLQNXJ1CwMAWbMdlpDJVjUdQx4zfXK/ZNpfLcj92jcrM4BNLBPunAGEmacAF2RQqnZkEobdx2tbXZIhcAG2rTcgALDPVcnwKbGb882Q6uHblGxuRqX32a9L8PJHXrqWGnOHQeeLOyz8lsi1dser8MRVI9/fZzW6kXHF9p62qiX9tMPzP7CbHYbEiP8s3DfqTG8vVCbpgQidpwNxxKMyR0hOQyFwR0PaOO+7gX/2rf8XZs9Wl7GfOnOHXfu3Xar4Dx4q1Um3JygiZk4snmz5m15iKSMj5/5erpFKWzm7/b1eqKm6Fe5+7l/9y+L/wV0/8FZ898lm+dvJr3H/+fp6dexbHddpvJFasWLFixbqCteJ4hLm5OSYmqu7Lb33rW7zmNa8Jfr7xxhs5dSp6FjnWpZFy2hoht2QUtHVdFyEEAkHFC3F61wHdoOJ4gIbulnDwqnl4LVS9UTSx/JtZx/YbFVUckm4BzQBd6DJ/NiIDEyQEUDeje0YHefzkLEemlzmeX+Q2YEOywq7JUDfdcl4etEKn7DXLEpUd4lNmgnLZ5bmZPPusDJQWKeYWgcEGaGv5jVRaZdoKDPLlqtO24nqUc/OQhms2D0C5AI5ddcW2yYU0/FzIJbsCY7tg5hkSF58EDtQ4twAszQChRd64qxvlhG6wY3yAZ6aWefqiyy0gnVvhLNFZBW19kOLamHqjqyi8z/XLvjPuIqnBap4tqIzEFg7CEPiYRy6B9hIeopxrzK309zmpW9i0cKvVOxxDgHCx4sMU4QNWYbSEyiABhqnLGIyFQhmG9wICvThLn7VYBYRq2bdaYhxRZ7VMWCA4tHmMrz05zVMXXQ7471kNIDwrb+Qy6TGg0hTa2o6N7o8vDAiLtkOfs8RY0IRMAbjWDkL1+4yVYBaYsQ3chIcWyncNL/sGSOgJ8lShTv37p9dn2lpVQLjkO6IHXN/tZ2Wbvn9QbZQml/f7rk4hYGgb5ZNTjJbPMdJXCwhNvXpc18v13OBc2Ts6xFNTJZ6dc3m1v3/LTggQnjwKQDqzAZz5tvEIlTpXZ952yHiLjIaakAGYPiBsnoMsj/VMIkkBmSXqJj00O8eiW9eIrFStyRLRgFBmk8ql+MH4gppUmyhmXB9g+isI2h0z6jxRrk6nfzNlx/OhbZ3TtkVNAJb9x+0dG+VbLHNsUchrg50LIjWyCQNOPA1AanA75M9EAkLP8+R5rKJd/H12EgPk7Qppscw21Z296IN0o3VN1DUjk0iyBMyULRzNQyvnWKoUAS10vZZ1TujyMzHy2uU3h5PxCNIlK/xrlxeOR3Dm5fj8yZz2mbs6rop2AZascTxgxJ1p+DwxFbRtc73ePCiPh3MFg0raw6jJ4zbh9GEAUiN7QOSvCGj7p3/6p7z+9a9n+/btbNkiYdupU6e4+uqr+eu//uvLPLpY61Fb+6XT9uzyWb+5ZuN3rl1jfXztSYJmZCoa4ZpNA1e0C7nslPmzx/6Mx2ceb/qYV2x9BW/Y84bncVSxeqVipchCaYFcJUe+nCdXzpEr50gaSW7ZcEtkY75YsWLFitWoFUPbiYkJjh07xpYtW7BtmwcffJAPfvCDwd+XlpYil5TFunRyfFgbLmI109YLPU45bTUqAdZFum11g4rrItDRnJJMdfTzbFtJOcIMzcCw5E1vJeS0TXsFNCEwfMdRs5ny8I3ovskRYJbHzixgOi4vFbClX5d5kD5kDBqRtYhcsB0bgWDzQD9LM/DU+UX2JSS0tXMLIOqhbaFl/EBNIzIFPpL9LBXLJNwCWwdN6WRalM4c00i2B4SheIRS2cUevwZr5hmyc4+DcYD+VC0sC8BHC2ibMlNcNz7EM1PLPHqhIqFtOR+MOW3psHgOXQh0M42DhAp9Zl/T8Sko6nkeIj1CsSKX4meH6qCtkWi5BDoMCJd1QdFNYDseCVEiV5THTQAIlZO1xT6Hm/nkbMcHHzJneHZuhllRQmiCEV1uy/SjB9qCdH+fS2WXIibJwS1Uzj7KeOUMQ+kX+ftcBwjb1OTazYN87clpHptxuEdIGJUzVM6wAQsS2qb7xiF/tq3TthzKjHWTgxKks8imId/9rFynbQChei/SZoJlQ6NgJyk7LglRZrkgxx+OrIDWrs6iUwyWpRdsB8f10BWMsqsOwj7l6vT/1q5RmqFrVDAo2A52xUUf2EbZ+SFjlXMM9dW6Tls5CMNA6cDoEJ/jPCeXNSoJF8POsazJ91RObkho2zewFWbnW4J0w8+7VpMbS1oGu+LSJ5bZpiIrlPt5hYAwbSZImjpFN4VdcUmaDoViHjBq4hHkNuVx2HwpvnR12hWXUsUh4b/vbnGZou/C7wsA4SDgNHd1+uMzdAlFc6UKFcdlOSE/M8bcC/L9g+px6NekndN25/AISavAUi5BseySCjnSs0kTpqXTNjW8C/JnIkF6xavg4aFrAi80eXC6mMBxoV9fZvOgOk9kTQw/SqbpKgH/PEkZFklTp2QnKVdcTF1DrxQoa31VkF5fEzf6PNF9kO55nsyw9DNtK4VFtQCGlC1XhVipYWCh6bVLHUspwyKPYLlUwXU9LuoyUmiDuBiKsvFrord22ub8/RhJZxnLCoqzKQq2Q19pmaI/cZlJGnD2MADp8Wvg4g+bOtLXk7Zs2cKDDz7IV7/6VZ566ikADhw4wJ133nmZRxZrvWokOULakNno55bPBRA3rF1+M7KTs3lKFYeHT88D/iquK1Qlp8THHv4Yz8w9g6mZ/NTOn8LxHJbsJZbsJabz05xaOsWTs0/yBmJouxZUdso8t/AcuwZ2RRo+wvreme/x6Wc+3fRerd/q5+rRqy/FMGPFihXritOKoe1rX/ta3v/+9/PhD3+Yz372s6TTaV7ykpcEf3/kkUfYtWvXJRlkrGipRmThIur+HV84ec31XNnMGkG5BtqWgSRlx0Ogo7slCXtX4LQNgIowsJK+07Ysb1BLZZchV0Fbs+bx9VKgQSDYMZzF1DXKjostEoz1p7EMTd5oKmjrZ2si9OYg2L+53TI8wBMz8OS5JX46kcX1PLzSEiRD+aQVG9yKH48Q7bRV8Qg1mbZWlgXfZXb9hL8tv7GN5cPDdsu+NQ2ZR+nC8vA1DPN3DC8+jTbkNDptFfiIuHEPAKGR4tCWQT7941M8edHBMT10O8eyaiplOFCQY+xLDbNYKZAr5xhONjaaU05gDQPH9SiWXXRrELvskqTApmw13gCqbrV2sMfUJeCemnewKw4JQ6OYXwYM6dyCAMAlfShadBpdndUGUCau68nGT4ksdsXlzMUljJEyr7l2O1nPB+k+qGpWE8d1cD0XTZMRAJ4Li8Uy5tBOKs4jTJZPNcYjtHCrKXiRNtLsnZDH9XTRoKi7pMizrPk10coyqxPoy26E/Nmmrk7ZIV6C/mLZoey4XKz04XpgCZuJpH/WK0CooG0TMBOuyUjG4tycQ6nikTCglF8C0g0wKtECwKnsZ12Y4EHOrtCfyOB6Hudn5iFlIzSTlCNz+qzEAGC3Bf26Bqbf7HCxWEZPbQZg0jlfbbwWnCf+cRhxnijwmjSSDKQsNg6kWJxNsWxXGDTyLPvO/YwowrLMqk4PbofZRyIdhI7r4HgOut8Aqmg7VByXYzn5nqUNlzRFwAzVpK/p+xcet6VbjGQsztgVbFfDdCXsRx9oiEdI6nKfm0FbTQNDWLImJYeEAoR2Hs1z8DSdZNmvSXII3Jm2S/ETuklFaHiex1KxwqwpV+Fs4EIVENbVJKrOZaccvFbGyrBrtI8ThRQ5u0KitEShVAEhyIgCLJ6R7+voXjj97UhAWA+VC35NnprTmAAypoawl2QObxCP0AdU2oJ0S7cY7rM4W3YoiQQ4JZJuAZL98nMqYp8j4xEqRTQhyFgpXBt+5+8f5bYtFm+yHYRYRngOiYSFUbwo3+v0KOQX2k5uJE2LgpApPUulClOMkAJGvXkZY6Mb1ckNs/l5ArBUlsdr1syybcTjqfkUebtCorgMnofQNPoowswzAKQ2HoKLP7winLYAQghe+cpX8spXvvJyDyXWFSAhBFv6t/D07NOcXDoZCW1H+iwG0iYL+TL3H5vj3HwRIQRXb7oym5Dly3n+28P/jaMLR7F0i3953b9kz9CemscslBb4ne/+DueWz0U22uy1Km6FB6ceDL5zan6smobGUHKIAyMHWj39J0JfOv4lvnz8y2zJbuEd17yD0dRow2M8z+NLx7/EF45+AZCrgTJWhrSRps/sY6Yww0xhhtNLp2NoGytWrFgr1IrX3PzBH/wBhmFw++238/GPf5yPf/zjWJYV/P3P//zP4866z7PKnoQ0ZqiXlOY7bMNOW9dzEQhAw/EEqC7w/s1oxQk7bVcJbTUDy3fauuWivzzVJekVZKat78xrFo+gQIOlW5iGzo4xCTWGMgnGx/zmY/7NNa4LlZLvtG2eaatubncMS3h69MIyZaOPiuORdPPomqjCnpp8Ur15IzLltPVdRg6Cab+z+7Vj/vtZnAfATAzUjCNKFa+CQNCfkl9C55Kb8RL9UCmwoXyyAZa1WsIbhraTA0kmBpLkvaRcQu+5FIvy74MVCQFIZMm0yU+1HRtNiMBZuVQqcz6vUxYmhiYY8N2SVVendKu1q4mlW4xnk3hCo4APPfOyvg35qW0AoRZqwrRcquBqFsfmbBzXY/+wx+uu2wgF362mQHozgOm/rwLBYEq+14uFCvnsDjxgY+V0Q1RAq/Ep8JoyUliGxv4NWUpa0s+b9LALchuD5fPyCakh+lISnivnYVgqKkDTQPNzqXOlCqeWHGyRJGnqaKX5mvEZRl/NvjXbZ0u3GMskQQgKJHA9L6hrpq4mCaM1IBRCkPIzrpeLFWZtgyMXCkwvlUi6ee6+diNWSQJC6epsXxMNLThPFgtl5hKy+/ZG7xzBwrr6Zd8R21THetqQx+q+ySxFLSWd5K5DpSj/ni34MT+ZCdJ+TSIBYQgqq5oslyocm7UpainSlhEcf9V4BPna7VyTpmbK6AchyIskFdcl6eYxdY1EPSA0mx+HJaeEQJD1r9F/e/8p/uibZ3ji3CJPnl0k5ebIJAyEmnBKDbUcX/g8VqsBFotlpoS8Vg+yFBw7gQu/hZNV1UQIQcpIsWs8Q1FLkbcdHMfB9GyEgPS8bAZI/yZSaflaUZMbwfg0A83PCc+VHJ65UKCopckkdSjM+2+OXxO/geFKQTpAgSQV1yPhFWpXbahoF/8Ya3aeAPzzm3ezfVR+Ln3rWJ6np5Y4fjFH0itIt7I/mWOmR1qOT20voVvB59piocy5ch8VYZLUXchN1+yzpY7DJttc8mMeslaWHaNpisKvSaWC6dlkkwZi6jHwXMhuINUvYwQKlULT5pZrXV//+te56qqrWFxcbPjbwsICBw8e5Dvf+c5lGFmsK0GqGVmzXFshRJBr+7mH5QTVvsmMXI1zhWnZXuYjD32EowtHSRkp3nv9exuALcBAYoDBxCAeHicWT1zycf3g3A/4xBOf4FNPf4pPPf0pPvnUJ/nkU5/kb576Gz56+KM8O/fsJR/DWtdz8zLv/9TSKT78ow/z2MxjNX93PZe/ffpvA2D76u2v5v+7/f/jg7d+kH9z07/hV6//VV686cUAnMude34HHytWrFjrWCuGtqOjo3z7299mbm6Oubk53vCG2qUqn/70p/nABz7Q8wHGai7ltNW9qq/W9CTGqIR+V3XaalRcr9pkzHeqlh0X09PQ3DLznoOTqWYXN1M40zaRktDALZcoVeTrJt2877SVsKWd0zbpw5bbdo+SThj8/C3b0JP9/oN8QOjDSd0HrO22OZrpYyRj4bgeF2yLsuuS9PL0p8yqGyy8FL9Js5ew07ZoO7iux5HpZZbow9AEW9M+jFCAUMGoJrBM5S4C9CckTFkqOVQmrsH1YJv9bIObLoC2LZZ9p/zHHNo8SEWYzJd8gF/wb8ArEgKQ3UDGlDcH6uY8ap8BMglZv+VihTMLRZa1fpKmjlAwyl4djLI0izG/k/myZ0n3sw866gFh0t+fZk5bOT553ORKDl9+YooZ20LX4OevH0HXRADSVU3ajQ9g0D+eFwplZlPbANjknkMoGBEAwnTDc5VUTdL++3Lt5gEcYTJvy/q7PtjqK/jNbga30udD1ihAqI51CeDk+7dUrHBqNk9Oz5Iy9cDpXQ8Im8YjBIDLYjQrYVTOh1FJt4CmCfos5aiudei1glFZS77u95+7yO/d+wQXyxa6JnjnzWPcc/2m4DwxU4Ny020Aoambgct5sVhhSozhCp0+zQ7AVhWkNz8OFeRTcSD7J7M4wmTRJgBwQkBqyb85HN4Z1C9qckO9Rn1Njs/kyWn9siYBtPXzU31A2K5hn6mZASDMeVVA2J8yGpysiRUAwoGUfMyPj8/y+LklFt0kHjCgl3j5/nHIywkdMz1cM456haGymlhaLFS4UNJZ1gewdA0WTteMr1VMh5qgyJiZAFpUMFmyPSqO3Oe+hIE2I5epM7Y/uM5FuToDwGpYAexYLJZ5dnqZnJaVv1M1KfruYlWTNueJBOnqPElRcVwyzlIttG0DRT3PC65n120a49/+1AF++7UHuHHnGLaWpFiWcD4TgrZW31jNvtUr7ARWcSaLxTIXlm3m9DEsQ4eFM/7OKJDe13SbJacUjDtrZdk2IuFvroycPPDycoLNj0Zg4/Wk/Bq7ntsygmYt6z/9p//EO9/5Tvr7+xv+NjAwwC//8i/zR3/0R5dhZLGuBCl37aml5r0/FLS9uCzPoes2D17ycT3fWigt8J8e/E+cWjpFn9nH+17wPnYM7Gj6+O0D2wE4vnD8ko9N5epuyW7h0Pghrhu7jmtGr2E8LRuSPjj14CUfw1qW53mcWZafJWPpMQqVAv/t4f/Gvc/di+u5lJ0yf/7Yn/PdM99FIPhn+/4Zr9v1uobc2sn0JADnc+ef932IFStWrPWqVafbDwwMoOt6w++Hh4drnLexLr2CTNuQs0U5bd2axznIz0wN1/VANUFwldO2wovyz5BGo6AJjpUutn1tdYNrCAPLB49epUTJd6ImvAKaBvoKM21Vt+0X7x7lT956iGs3D9Y0lpIvWvRfs3VzswAE60n2T8obsNN5nYrjkXLzDNbl2UJ12XfTeAQhoS1AvizzxgpaH/0pE02BT9/BZSq3WrPlrKFxD/rREkvFMktDBwHYWX6WpFkbP9DKGVUPba/bMghCcKGo43keFeWaLPluq+yGAFxFwaiKWwncUll/fMulCmfmC+T0fpKmFsQs1Dtt2+WnSqetD7jcBI4PCIWAjFXnZPW3WapEN4cDyPrQ9vGzC/z9Q2coaGk2DqYYMf3nqJqs0NVpaAYDKQUIy8zoY1SERVqzYckHrPXu54hthuMRgGCJ40XbxHZcLEfWLJnzYcrAFvqs5jUJg5D+UE1OzRYkSLf0UE2Ug7Cv5T6Ha6K6yy+7CSqOS8J3+60GEFZrIrf1xUfPkStVMNL97J3IcM2of0yryY1UJ4CwzFzRZVYfk4Bw7njN+MwW54nK6VQgds+EvEGer1iUKi4Jt0DaMtDnZJ4tI7uC8yTK1RnES2gm2VTIaTuzTE7rJ53QG5y27QBhbYyI7yL3Ev4qgULVgQ+h8yQ6ciHc5PENh7Zz045h7rp6kl948Xb2b9vIwY39/P5rtvHThzYFx46VHm09vtBknZpYWiyWmVm2uWhMYBoaLJyqG1+LmvjHuppE2jnWh9AEi06CYsUh6fqA8IIPbcf3B+dUsVLE9dya7YUnIpTr9Mi07MJeNHyQ7k/k1IP0dsehdNrKY3tan6DseLxk+YuM6iF4rM4Tvyb150n42powEggh2D2e4Zdv38V1u7Yw0Z9gU7rCi3YMQ+6CfF0fGKzoPPHdzwuFMjPLJWaNMSxDBNES9Z8nUdcGNZFnaiYJPcG2kTQIwaIrs4YTboFsQodzD8snbDyEpVnBtaJQXp8RCQ8//DB33XVX07+/6lWv4oEHHngeRxTrStKWrHSjq2ZkUdo9Xttf4ErMs/3kU5/kfO48A4kBfu2GXwvel2ba0S+B7vHF45d0XI7r8MycjHv52f0/yy9d80u889p38svX/TJv2vsmAB6+8PC6XUnQC80WZylUCuhC5/03vZ/bt9wOwJePf5k/fehP+ejhj3J4+jC60PnFa36Rl25+aeR2JvsktJ3KTzV8hseKFStWrGhduS1Jr3B5nocTmWkr/+9ExCMIhHTaasppW4ELT/Pa0/+ZFy9/nf0kIDXMYxebd3FVUs4/UzNJ+8vJXdelVPYdkKKIQKD7MNTxoqGtch2Fs6oCUORnL1ahrd9Uymjt3g2D4AMbJLQ9vqTL2AY3H7mctd1SfCFEMMa8XeERH9pmk0bg2FLgI6FgVBPHUXjcA76rc7FQYTa7HxBMulNV12Rwk908gzC8FB9g93iGdMIg5yXJ2Q7CB6vJ4pR8Qv8Gsn4H+SinbfiGYiBRXep+dr7AsjYggbLvzKt3brVzEFp61Wm7ULF8N11Ruum0OkBoNgeE6nfKqfzZh87iuB6DQ8MM91nBuILICj8Ooi0s00wG0iFAmHeYMjdi6hrM+EvjlDvbau/qVDUZz8rYipJIMp8vk/AKpCwdfdEHXINbAxgVFY+g3j9NVKMClksVTs3lWa53daqarBQQamYAbRccS7o63WI1rgKqNbGiAaHjOsFx3Z+o3ni+4sAEL9izlYShV8/jANqOtB5fExh1MWdzwdiAqQuYO1YzvlaTB6om6n3uT5psGkpR0lIsFMokvaJ0e/tNyBjaETw2CqSHAabKYz5xMc9SsULeyNY5bWuX4red3NCqgHDBUSC9WHXgh/a52XkSrtELtozzy7fv4s0v3MJL9owxODiMqWvBtSFwdbYBhGHXqYoLWSiUmc2VuKhPSJA+7zuVSwraZmr2LazAaeu/L2nLYMNAiqKWYrFQIekVGTA9mPXrHHLaQqPbNjwRoZz7D5yQNUgNjKAJEXLa+tC2TQZ5+DwZ9p22Pxx4DUvmKFlnnhef+bNgQjEA1VY0tA2vGkj6zcCUEn0DbBhI8Wsv3cSde7JQkY+1svIGdyXxDTXu56USc8aorImCtqXaa0PUNsPRCEII0pYhr11aiqWiPE82eDPy8063YOxAEG9Rv4/rSVNTUy2b6RqGwYULF57HEcW6kjSSHCFtpnE8h7PLZyMfs3W4T64QAiYHkoz3JyMft151aukUj848ikDwnkPvCeBdK23rl6udTiyeuKTA9MTSCUpOibSZZnN2c83f9g7tJWWkWLQXObZw7JKNYa1LuWwn+yZJ6AnevPfN/MLBX8DSLZ6Ze4Yj80dIGknefejdXD9+fdPtjKRGMDWTilvhYqG9SShWrFixYsXQdt1KAhL55U53qzOVhg9ryyFo63gO+PEIrheCtg9+Ar7y/zBon6Oopdi5640wuKUho6j560tnYiLp55k6HnZR3rBlkDfTRotmTVC9aYxsMJCoi0fwHTxmm20WK1UQfGCDhJOn8zrFskPSKzDYF3KE+9ts1ThG/a7Pkq978mKec/NFClpG3iT7N//K1Wn4GYTNoHI4P3UgpZZVl5l3U0ybGzF0UXUxBdC2+U22cjYpwKRrgms29VPSkszmbCzPv/nPK6ftxsBB2AoQCkTg6lxU0Fbv96GtgsoKArTJTw2Bj/F+P8e3YvqAsFAFhJVSsLy6mVtNNYACyCbkPnuex0Da5Jqdm2V+c6nO/ZxuDW2jwUeZubzNlLFZQtuLR2r22ViB+1m5OgGu2TRAUaSYy9sk3QIZS4f5KrRVbsNW+alWKLfy/EKRuZxNTu+vi0fwwVGb5mvhfR7zAeF8xZSuTq9QBwjlNhP+GOtrEv75Jbsn2DKc5t0v38Xbbt6Knqo7j/1xtsvqDIP+gZCrcy5nc0GB9LkTNeOzWsAoBV7VsQ9+rq1ISZDuFhg2StXIheEdQf1sx244n4M8bs0Kjt9Hz8zL9ylbBwj9fTdUTVYwuaGW4s9WEpRdjxctf40DS/fJ5olQhbb+NpvVRCAwNKPmbzXX1nKxOhGhluKvwHUaOG0LZS4u28wYE7IhVxCP0L4my/5jwjXZNdZHUaRYLMqabHHPgOdAahj6xjA0A9NfLVJ/rtS4n/3z5Knz8r0fHJZAuj7T1lIZ5M1iOtzGmpwvWfxo+7soammGiqfhvj+Rmett9ll9Nlm61bBktLqyZLF6DCay1W25diS0qJlw8msys1ySDeL0cTlhEsQjKMd382tDGNoq7RiRNVkuVUi4BbaWfOfzxNVgyPdEff5EXb/WgzZt2sRjjzX/7vPII4+wYcOG53FEsa4kCSECV2mziATL0KSzHX/F1BWmLx//MgA3TNzAxszGFT1na/9WhBAslBaYV7n9l0BPzz4NSECrGpApGZoRNMx6aPqhSzaGta7TS/KzPQy1Xzj5Qn7rxt9iY2Yjw8lh/vUL/jX7hve13I4mNCb6ZAxfnGsbK1asWCtTDG3XqcpuGT/zADMEaHVPxSNUf+d5HqoHasXxQPOXKZ9/FIAn0y/kr0f+FTuv+lmE0DifO89MYabl66sl/oZm0JdK4KHheC52yV+qjx9loLd22labqERB29BNLARgoV1OrnLUZcwMg2mLyYEkBaSbLlXvtC237vbtem4w9j4/t/IHR+XM8OCQyk1VTttaB6HtRN9kq+3VLzFeKJQ5bu3F0DQ4d1g+uA6KRoGF+ngEkFloJZFioSABYcrU0NTy/uxkcEPeykFoaAbZpA+OcjYXlkosa/2yEVJ9PMIKHYSqwZIQMquzWHbkUvykCbk2nDOzAAEAAElEQVSL8NXfk0vehU5iQGbAtQKEyqkM8Isv3kGiz++0rAChctqmWgPCsKtTHR8LhTLz+TLnzS3S1RlAW+VwbO4gVOAiXJNrNg1Q0lJyibFXYMTIy/oKDfo3tcxPDY9PAcInz8nzQuvzj8OgJmrZd/+K9tnQjCDTdtFJYDtyCfQ4c3DyB/DwJwMQpwBSM1enEIKbd4zze68/yA3bpOMcK3QeO5VgfCteil+XnzobOG21Bqdtq/iBwGkbAun7J7OUtFTQPHGT699AZDeA1dfS1RnOElUg/dkpeT70B4CwLh7BB4Rtm8NpFqO+I/0B43qZx+oucvW5v4N73wdPfaH6PjapSTB55S/Dr5FaxWAvVY8bI4mVrALMSEAYURPptLW5aIz7TttT4HlV93OLCZ1wpq3SrvEMJS0lo1O8AhvLx+UfxvcHn3kpXdalPrYiDL1Vvqvaj9Ex39VVmJN57ura6u9zW5Aecj/P5mzOOYPcO/DP0c0EnHkAHviLah63D8WbOW3rXbbyBUIrSxS07RsLALXneZGfo8G1NfR5cmxGjiOfmpTXhsUzsialOqgcce1S0Fa5nwG2jaQpaSlcD5JensmcD203Hgoe0ypreD3ota99Lb/7u79LsdjoFC4UCnzgAx/g7rvvvgwji3WlqF0zMoDXXbeRg5sGuPNA+94S60lnls9wePowAsFdO5rHkNTL0i02ZWTz0XYRCcVKsePrj4K2+4aigeOh8UPAT3ZEwull+T1Q1UNpsm+S377pt/ngrR9scCk3U5xrGytWrFir05XXlvQnROGbN8NthLaOV++0FVWnbXJA5uUNbIEbf4mv/VMOu+KStTLsGtjFkfkjPDbzGC/b8rKmrx9296QtgyVh4rhlXFt+YepD3qwaZhLKxeZNwyorgLZqqXtZuXebN7bxPK/q3vJhwYEN/Tx9IU3Z8Rh1z9F37vOw502QHq5m2pppINdwkx1+jYyV4AIlHjktIe3GyQmYphEQ+m41kGDZ1GuXXIZhWRhGLRTKnLD2YJa/B+ce8cGCWorf3KGnvqSqZm4A12we4JSexLFlvvCIWfKht5CNyGwJntV7FVYQfaFXAeEzU3IfvdSQhGV1Tlur3RLjkEPPMjQG0xalxSR5u0KCIpud0/DlP5bvoZWBl/wGicJJmG4EH2Gn8s6Rfr7DHK++elLmxl5UDsJFCSlUs6F2gNAJ1SSlGhhVMDSHKXMzpqfB/EmolBABjMpArnVNlPsMYO9Elqd0+XPSLTDg+Utts5NgWPS5fcFzHddBV5Mr1C5LD2d1AmSHxiFPg4PQbOO0DUPHtGWQsnRKIkW+5HBz8euMHfkezKRCzxAk+sZh7vGmID2pJ5s7CO3lap6oZmD6MSJlp4zneQ3Pq41HCDlt8za2MYmlaTKmo7TUAG2jQLUC6arhG8iaHBFyHxNugcmK70gc3imHKTRSRopCpUCunKtxH9YsxfebOzr+tXhkdAKWkYDQ86ogXbk6V+AuziYMTF3jLNv430O/SabyHd6cekBu88FPBM9JWtGAsOUqhjAgVFEn6ZEGQGiI2q8I4eu+Ok9OzeVxXI95c1xm2trLcowKiqprQ8R5EuV+3j2e4WyoJuNF/6ZubH/wmJSRoug03qDX1CTkLtY0wcTEJBxDjk1dsxFBTZrG7YSg6IDfxNJxPU7N5lmwtjJ39bsYfebP4Nl/Cp7T7DgMPu+MVpOUy5D3oW16JMh7V9urd00H57FWXSVwctb/3BjcAHlNRi3kL1Yjhlo4vpfK8r3pt6oNuXaM9vGAX5N+Z56h/DHoM2HDoeAxCtpG5T+vB/3bf/tv+cxnPsPevXv51V/9Vfbtk/Dmqaee4qMf/SiO4/A7v/M7l3mUsdazVtKM7NrNg7KnwxUm5bI9NH5oRbEIYW3v387ppdMcXzjedNn9TGGGP/zhH2I7NgOJASbSE4ynx5lIT7Axs5G9Q3sbv5v4KjmlIPZg//D+yMccGD6ApVvMFmc5tXQqqOVPklQ8QhSYbfbeNtNkZhKmYmgbK1asWCtVDG3XqeQNpoaOQBCOR/D/3pBpC6BJqHDLe+Sy4s03gm5Qdu4HwNQF14xesyJoG45H6LMM5oSJ49pQki6VFPLGTTdSUJ5vDm1X5LT1b7ArYWhbimxuVnJKVWesqaBtlu8+sYE5Y4yhygU2nPkSfO5bsPUW8N8ny+qDSq7hJjv8c8ZK+q8rn7N980YJbYvz/hJjP77BB4Qgb/jroa1yKYfBx5LvtJ0yN4OWkTfXF4+EYFS2YTxKUYAwbRkMDAxCARJukUkxL//QNyIB4QriEUzNDHIhT8/57uUAEKr8VDk+Qzkc27jVFBQayyYoTSfJFx128DS7TjwIgxYMboWX/iZkxkmclBm8zQChpVu8ZM8YBzcOBA64mmOmtCRzmyEAhKoRUP0XzHBGc+C0zZfRNMGyPoBmDYO3LLM162rSytUZdmpahsbQ0BDkIeEVGXf9uIpB+eU/XL98JV8LCEMwT0FbdRwOjY7DSSJAemtXZ9hBCDCaSTC7MEZ+Wb4XmmHC8C4Y2i7/m7iK5JLMe20GCMOAKVA4m1odN8lBTP+xHh4Vr4Ip6iY3QlBZnSczSyWWixXQkhiDGyA/7ddEOb6b1yQKEGaTJqlMPxTl5Mao7b+HPrRVjy9UCg3LvqPOE6WJiQ1wHLm/lVL1OEy2rkl4QkcIwUjG4vxCkRMLZdz0zdxy25vZxGPw+N/LiTehYflN9hqW4kfkhQeqAYRVaNsOEIYd8+o8Ob8gX6e/L41IboClc7IZWXCe9Nc8N6wop+1kfxLX6oMipN1lBvPHIS1qoG1ST4LTuBS/phGZWR379pE0iYz/GsX56uoNqw/TX97fLB4hDEV1TTCUNpnN2SwU/FptvwX6ivDQXwXPUedeq8mNBtVkuPuf332jGJqBJrSgsVzYKQ7RLnxXTR7094E2IWsy80x1fFbzSIioeIQtw2nu8yecdhcfw0x60o2erboB1bjWq9N2YmKC++67j3/5L/8lv/3bvx246YQQvPrVr+ajH/0oExNXlvsx1vMr5bRVzcjU96ErXedz53loSsYKrMZlq7R9YDvfPfNdji02z5O9//z9wfV/obTAQmkhaCwG8JZ9b+Elm18S+dwj80dwPIfh5DCjqdHIx1i6xVUjV3F4+jAPX3j4Jw7a5sv5IH92c2ZlbtpWCpy2+RjaxooVK9ZKFEPbdaqKWwEh0EE6Mn1VnbZVkOt4DkJQbUTWv1H+hwQ/ypRr6BoHRw/y90f+nmfnnqVYKda4N5XCyzQNzSCd0KkImU/q2UVAkPRzVA0zDQUiASuEoG1L51Ftpq1hSngaBYIVBDA1M4BR+yb7qegJ/tfwe9lZeor3jD0Fy0fh+HeqL2X2QaXxJrYGzFjV02W8P8HIcGjZt3IQ6hZGIhvcZJedMtR9L49c9l2ssFio4AkNe/RqyD8CZw+HAJwPbVcYjwCwYXSEynkJo/rwwUxWZvKppa9R0LYGAviAUB0jgyMbqtDWdUPLvgdrnttsmwoKjWcTLIoUpYrLEBewRBI2vRBu/VXwm66pY6IZ+EjoCR9shY6d8DGjamJlgqZhIN/DepAVbipVrUl1X/Sx3XDhMFx8tupWa7EUPyrTFmBydITyGekgHKn4X1YHZM6drulNXZ1hqFwPCEfHN0poW5yX8QO2ctO1jkcIuxJBgvSHktexoA9T0lL81G0vZMP+WkeMlT8duc2VTb4sV8FyarAGEJadxhvYqPNkNue7R00NY2SHhLbzJxoc360ybVNGitCcFiPDwzAj3c9DxdOQoAbaNnMQRp0nIOH8+LiffVmYrwJC3WzZKC38e/XejGQSnF8oBhCuvy8FG14BO26HUz8E3SJhyL+tymkbPk9UTdIj6EJHCIHneZFjrAHpQfwAwVjJbPEB4bNVUN3Caauc/uGl+EIIBgaGYBE2lY9jJctgDgaTG9A8HiF8Hodrsmc8CyoWpzBXzSFP9gefEyuJTlH7qY5DQMLr/T8FuWl45stgZUj4x0yzeISWGe72UtCEDH/VhqVbFCvFyOt/zbUrVXsOjWUSoG2SNbkgl/9iJDFNPxc+ymmroK1Zvf4kTZ1U3wDkYNC5iK5laqIRYP07bQG2bdvGF7/4Rebm5jhy5Aie57Fnzx452RYrVpcaTg6TNtPky3nOLp8Nmmxd6fry8S/j4XHt2LUNS+tXou392wE4tXiKiltpzGgHHpx6EIA3730z2/q3MZWfYjo/zXPzz3Fk/gjfOv0tbtt0W6Qj9JlZCXf3De9r6Rg9NHaIw9OHOTx9mNftet2q92OlOrF4gufmn+Olm18aua+XQ8plO5Qcavhe24mU2/p87nykkSJWrFixYtUqzrRdpwqgKUI2aQHwPNSCaqfeaSsAtODmX6nshFy6mmAiPcFoahTHc3hq9qnI175QqHZQVs6/srDkavRCHjyPpKsAqw8pvGhI0Rr2RDci031HonKshqXATNpMB18CMgmDLcNpPKHxXPIqvDs/CK/+Q9h2q8wTBawmTZHCN8Qpq7pc/drNgwjfNUdxoeogTA2BEMENftRNdhjAKWfUUrHMfEE+1t1wnXzgucNVt1oLGNUM2m6ZkDf8CbfIiFsHbX1nW87ONeRz1TS9qgeEY2PyPfNcCQmDJdBtml7VuTrHsgnyWtXxOLPttfDS/zsAtlA9JtSS4mB8Ti3YqlEYRqm4gNRQDRCMAhVhOK/Ah11xsSvy/EhM+jlnF480LPtutRS/viabJqSLI+kVGLR9aBuCUcoF2uAgDC3RziZqwczGyYlqTRbPoIik4Y+v6bLv+ppkEnhC56y1nYvGBP3pxnNSOQTrO8SX3Kr7uUE1NfHPk/RwAAgh+rgJn3sDdTBqMG0hhrbLHy4ekW5WQo2+Vui0BZgYlS7sIWeGVGUeEDC8I/i7ukFp1vSq/jzZOpJG9xvf4Vb8mgCJfix/IqJdPII6Xkczte9nAOV0A7a/GLbcGLznnYH0xZDTdhghREuIGRVZoTSascDPoeaC/9mhGXIVA9HXwmY1GR2WNRkvn8HQBIztC/JsIQRtWzhtVaYtwJ6JjLw2AzhlWJ7y34f+4P1rm63sr5gY6aurSdKQY3vBL8Chn4Obf6V5TVrFI9Rk2vqfsf6qDVXDVo0yLc1qOE/GsgkY8CGJctomMsG+rNRpCzA4WAWXhi5qohGgulJANcZczxoaGuLGG2/kpptuioFtrJ5pJc3IrjRN56f58fkfA3DX9tW7bAEm0hOkjBRltxzZuOp87jzncufQhc6NkzeyfWA7N2+4mdfteh3vuvZdWLrF+dz5IAKhXupep1merdLB0YPoQmcqP3XJlvWX3TL//ZH/zmee/QxfPfnVS/IanSiIRuiByxZgNDWKLnRsx2a2ONuTbcaKFSvWlawY2q5TKaetiag6bV0ngLaVUGSCjEcQVadtzXaqP5u6hhAyIgHg8YuPR772F45+AYCrRq4ioSdIGBqOv7R5OZfHoIyJH5/gOxzbOW1bwx7ViEzFIySr70GdopbbAhyYlABLCKRDbGQXvPhfwev/FF78r7G2314zHqXwDXG6BtoOQNKHypVSFQL4YKAVCAjiEfRqPqnnVZcZGxt9aDt7tNrMp8VS9wDamrWAcGBgkKSpkfCKDDk+mPEd1gqSeHgNja+i8lOVNg31gb8cm9yFaiTECpd9K1gwnk1ywtrLo6mb+OLA28jtf1MNlIEQtG0Rj9CgAHyE8lNTg2hCQxd60zGGG0AlTZ2EWb00piwdc2yv/GHm2SpIT7aAtqrplVHrSBgcGCJhaiTdPNlSI7Rt1owsvEQ7DAiTps5YNlWtybzf4MRMYfnbarrsux5G1QHCbLLR4RHAniYxItH5qaHzOJjckIAwmNyIAoQ1udm6bKjka6TPkpENAFNPVF+qRWasqkk9INw4JsHYhvJJ2QSwf0PN5EEA0uubXrnR58mOkT4JVdWk07x/c57IBvvbdnJDOW37at/P/oiaNDtPAldnq1UMdsj9nJagtBXQC7uzswmj5pQd7rNgUAKJABCaaawWwLHZ9Xp8dCT4t6HXRiNANb+7IdM2HCMSeq/2TGTBSFTrqs6TZP/qaxI6T/oSBobuXys0Da56PWy5MRjfqhqRRUVW9Mljs9V5UlOT+km2TCK45jN3XP7fyrQE882g7chwCNqaCRi/qubv670RWaxYz4eUu/bE4onLPJLnR/90/J/w8Dg4erDjSAEhRPDcKPD60LSMXtg3vK/BBZo209wwcQMA3z3z3YbnLtlLAZDcN9wa2qaMFPtH5GfR4enDq9uJFer+8/ezUJK9GL587MtBJMHllnqPNmVX75SOkqEZjKWlsSSOSIgVK1as9oqh7TqVBJYCvcZp60rnLbWNyFSmbdCILLwd32krhAigyNWjVwPw2MxjDS7MU0uneGDqAYBgeZAQAmHKG/NcPkfCLUgHndCDpmFtM221FmDBKUsw6i8ZVUuMo7aZs6OdWwc2SIAymLbQQvCHvhHYdguJJh3Ya5y2fkZiwtTYN5EFMw1q6dKc/wU8NSgf3wpGhdxqhq7R5wMf5erMDo1XgZTK3G2SWwnNnbZYfQylLRJugTHPBzNZuSTJ0Izg8fURCbXNkOpcnYOpAO4EMAowFFRu14E9tBS/rCX4Zv/reS55VY0rTqkdtG3tzl6syU8Nv3bLmvjAqj80nqG0JSE/QsIU/5wIAGEd3HI9l6J/rDYsI7MybOhPslOfZjAhQLcgU81JDBzQlTqQ3gQQbh5KyXNNuQgXTgWvo/al2bkXhqLgA56Q+iNqEjhtK3VO25VmU4cd6YRq0mrZtyabP4WdnUPpELRVkzpmCkst+64DcJ7nBQ7Ahpu67CB9CR3Nc0gYmszxDf/diAbpzQDhjtG+mn0MAKGVaQ8IFYALcoargFAI0TCJAi3Ok5U0eazLtA2/djuQrmm145HQ1r8pV+74kKszqibqPQ3HIwBsGBtBXaUNXWuEtv5x2AykW7rFxsEk4/1JbtwxXB1n/eRGor/ltTr8e/W+DIectipruV7N3sMg0zYidijItC3MVVcJ+NC25XkSqompazUrQkazFvT7zigVmWRlWl4LF215PtVD2/HRatajPnkQjNpJnhjaxorVXirX9ifBaTtTmOFH538EdO6yVVIRCVGwW0HbZk3Kbt14a/C4+tUZKvd2Y2ZjwzUvSteNSVPF4QuHVzTu1cjzPL524muAvOaX3TL/55n/0/PX6USnl2Q0Vq+ctgAb+uTKv/PLMbSNFStWrHaKoe06VdktgxAYQshsUQDPkRCX6Exb0BqctmVH/mzqVZC5a3AXCT3Bkr3EyaWTNY///HOfB+AFEy8IlnkBCN/NlS/kSboFdCHA6kP3oaaKc6hXy0xbI1mFoqWlIEvU8JfbrmYJ9NWb+nnddRv52ZuiZ/oDZ6zfyV4pfMO+eUjelN6wbViCBCFARSTMK2hbB6MiborDTdyg8ca/P1XblRvNwLSiszDLTjnYXhS0He9P8JJtSTbq8/J32Y3Bn4NcW7sW2oZhWV+iCgAG0qYEzAraLihXZxrTr1/ZLTeAfmhc9j2era13lKuzGYyqz/2sfZL/pdutwJJyPw/WvHbUcVM/vlpAaEqH3kDIYaAZmP4xVg+qw9AiqiaDaYurRg1MXZN5tiG7YgAI7eaAMPxebR72AaRajq8mD0Iwqv6YVqrP6hxrqEkjtG3mIF9RI7JKUeZ+QvU8WclS/AiQPtxnyeNQnX8AVl/T7RUqBTx/AqTe/YyVYftIH3snMiRNvSbPFto7bS2tdnKjEdqqmmSbXmfq9znK1dmfNCJz39RjG6Ct2wLaKkhq56pL8f1mfauZ3Agvxx/NJOQERDiDr4Wr03bt4NpV77RNpPvZNJRiciCJZapJk6raxSOYmknC0PnDN1zNr9weem49SF9BTeqzn8Pu5/o4gmC3m9RETXZEO239CadyHvBAN4Pfreo8CY1ppC8hneM1r1OtSf157LhO8J7WA4wNoyNkEgbDfRbapkY4ciVk2saKdamlvjefWz7XdPLuStFXTnwF13PZP7yfHQM72j+hhdTzjy8cr/n9VG6Ks8tn0YTGtWPXRj53e/92NmY2UnbLAURWUtEI+4f3Rz21QdeOXosQgtNLp5kpzKxyL1rr0ZlHmcpPkTJSvPf696IJjUdnHuXRC4/29HVWq4pb4ezyWQA2Z3sHbYNc29hpGytWrFhtFUPbdapqpi1Vp20oHiEMbV3PBSHjEZpl2gZLPJEw8cDIAUB+iVA6MneExy8+jhCCu3feXbMdzXfaFgoFkl4BTUNCW39JelOnbSs3mBC1EQlBI7K+pttsttxWCME912/ihm3R+XQKXHl4NdsNOxwPbuznd37qAP/8RSHwq260FSzznVytAKGKR1A3zmE4lk74ME/l2kIN+Ki4FVlPX+oGWSAaQYDVh0CQtWcQbkXCFL+xDVQbzdQ7bcPgyAg5tzYN+gAypZy2ykHYV5MZG5U1HI4fALmsOB1y6bWCts3campbNTISEnZAFSqrmrRY9h3OGYZaGDOY9l9nZE/wO89M1zgIw7BHQVtLtxqbSFi1kwnhaASAPisaEIZrkjC0wBW/ZahJTRKZ2mM6oiatln3rmiBpNn48NHPaqp+bA0JRO74VxIjUOxzDNQncjoEjnVoHYV2N1WROs5qYukZaNRqsg7YK8rbKtE1ZOq86OMEdB8ar8FsBwkV5sxOOR/DwIieyAtek3uh+jjpHoL3TNhIQBs5WL5gMC5y2LWpSD/prAGHGAk2HgdBNndXcaauW4Zua2Qj7rT5GMwkm+5Mwsrt6Tvtq14hMba8BcvsTOCqXOjy50awm9RM6tSA9Gtq2jaxoBdKV0qPBhI7an/rzDiJq4o9pIG1iGZq85qRCn3staqI+CwSiYeLTSGXZPZ5h63AaNjZCW+Vgj522sWI1l2pG5nhOAMKuNJXdMt889U1+cPYHALxmx2u63qaKlZjKT9V8Fj90Qbps9w/vb7hmKQkhuG3TbYCMSFDf1zzP4+lZ2aCxXTSCUsbKsGdQfhd8ePrhDvakub56QmbY3rbpNnYM7OCOrXcA8OlnPt10JUgzzRRmepa7O5WfwvEckkaSkeRI+yesUIHT9hLlA8eKFSvWlaQY2q5TqXgEI5xp6zlBPEI409bxHL/QjU7bSoTTFqoRCY/PyFxbz/P43HOfA+RSo/H0eM3jNX+5Z7FYIOkW0ISoAUdNmyG5LbIwoXZpdZ3TNmqb6stcXz0ca6Pw64dvtMNgRgjBzrEMCaPqPg2cfgoCpGvdaq3AR+C0Dd34B5mVo3ur+YtWXw3UCG8zcG4ZyUZAod4DBSIyEzJ30Zf6gtsAbeuWzStYFEDbdD0g7A8gQPj5YQXgI/S4sNu21bLvolOMdj9HuTqFqGaoqvgGH1iEnaf1agQf1fEM9fljHtkd/M4z0jXQOHwsNmtCJgddD2231PzYZzSpSV1UwJAPkreP1Lk6C34MRiLbviZ1DsKEoQcQrj9ltnR12o5dW5NW57F/LZAP9B3EKj91NTUJOdKjoW118sDzaidfmjnw5QuEnbeidptUs6IboG2dw/EtN27l527eVn3fVE3UOEKAENpHp4AE1QrQ1zf+UmrmFG2Z/awbtfutW8Gx2cyFGR5zPSCEUE0GQsd0yP1cP+HUsiZhgDnW6IBSILpppm3UhA7UwkuQmbZ665oEEzr+48LxCO2ctg01adWIzLBkHZT6qnEEQ0k57tlSY8OW+uuhOk/GwnEn/aFVAuFs5brxKZCesTJoou7rYWYSJq+F7bcFMTthxfEIsWK1lxAiiEg4uXiyzaPXlxzX4b4z9/HB+z7I/3nm/+B4DteMXsOuwV3tn9xGWSvLaEpeE8MrAB+aktD20Pihls9/4cQLMTWzpiHZTGGG2eIsutDZPbi75fPDUq/Vy4iEo/NHObpwFF3ovGzLywAJuwcTg8wWZ/ny8S+veFu5co7/cP9/4EM//BBHF452PbYzS36ebWZT5PfCThU4bXPnI1e5xIoVK1asqmJou06lGpEZ4Uxb1wkK6tY5bYWQmbZOvdPWj1YwtNoP4oMjBxEITi2dYr44z+MXH+fowlFMzeQ12xtnzXVL3kTrjk3C86GtlUXXJOBsGo/QymkLtdmLymnbAto2c9q2k67pgSs4DG0Dp60efXMejE+pzmkbCQGU01ZXTtsqjBoId4eflA3h6p2s4W0qp9mKAGG2dplsEI/QBBAqqKycwJvqXZ0KwFl9GMJA+BMG7Rp9KSkXYcrSa5zewfD996cewLWEtlCtiXKl+WC9pavTrQcfEU7b0RC0NVO1NQk5OxW0aFiGD41uunqnrQ+wmjUiU6/5i7ft4J/fso3taim+Aumh12lXkyjApTJU27k6PbyabarzuG1NlBRIb+L4C/8uMmc4gLahJZdWX1MApzKC2wLCgU1g1jpTm9Vk1YAwkcXQVlcTIUTg7Gzm6lT5qA01aZUzDFWQDtJlW+fqbAWV6wFhNmlUJ7PCExGhTNvw8yF0ra4/J6C2JuON0DZYit8kHqHpcdhQk/4Vnyfq3EuaerBKoBlIb3aetGxEBrXnSboKbcdScoXEhfyFpuNTx4w6TmriTsLRLqFJwPqVJc2akAFywu+O34Fb3xs59Bjaxoq1MqmmWvXxY+tVnudx//n7+YMf/AF/89TfMF+aZyAxwFv3v5V3XPOOnr2Octsq6Dqdn+bM8hk0oQVZs80U1ZDs6Tnpst0xsKP552SE1GsdWzgWNA3rVl858RUAbtpwEwN+v4SEnuBNe98ESBfuVG5qRdv66omvki/ncTyHP3/0zxsi0FYr1YSsl9EIAGPpMQSCQqUQZKnHihUrVqxoxdB2nUqCv3qnrSd/FqLmRkwCXBmPUA9tldO2HpplrWzwBenRmUcDl+3tW25nUDVzCcnwoa3plUm6eTRBAPNgBY3Imn1hUjfv4XgE/4bS8ZyG2dmW7q02UmMI32QHTttmYCacqQkrWvbdmGlbvfGvcW5tvlH+PzuJECISBDdtQgZ1DkIasg0V2G6Waav24a6rJ3nBtqFqtES6bnlUIiPHtwoAB1WnbVR2KjR3P7eHUU0AYYvIinoo2tD0CqSD0H9PXLOvBvRHgnQzoiaGVZv5WQdt1RLjplmd/vu3bzLLy/eF3O6pOmibyK68JiH4rEB6s5qEYdiqahKGcLoZHJutJjda5QwPq5oMbat5jWYATr2fkSDdsKrL74cbHUFBPEKTpfjh969GEdBWCBGc960mD8LnicpQbdf0CqJrEtn0CqrRLlBzTq8o07bOaTsSdnXWOG0zNeOrAelNmkYCctJqcJscY4TTtlk8Qv3kS4PqP7tWcJ7UN4cDGOlrjOwIq+PzJHztCjltlcPsQqER2tbX5ODGfixD49rNg9UHhZ22oYZ4UDvh1BLatlEAbcuF2DUVK1YLXUnNyEpOiY889BH+8vG/ZKYwQ8bK8DN7fobfu+X3uG3TbY1xRF1INSM7vngcgAenHwRktMFKvvO/eNOLgWpDstVGIygNJAaCjN2HL6wsIqFYKTaNODifO8+jM48iENy59c6av103dh1XjVyF4zl86plPtb22LpQW+OapbwLy++R8aZ5PPPGJrq7J6jjdlNnU5pGrk6mZjKXlhOS53LmebjtWrFixrjTF0HadSjltTUS1K3QQjyBqMiyV0xY0nLoPbpVpa0U4Ha8ekxEJ9x69l7PLZ0kZKV657ZWR41HQ1qAsnbaaXBLd1mm70pvY0lII2lYBUH1WZ6dOW4huHtMeAvTX/lzX9Krlctu6+AGoc25tfwnc/ltw6Ofk4yPAgopHiIS2mi6buSnVO23996iZg1CN7wVbh3jPy3dXMz8bXJ3yy7L6ch4F6KNyaMf7fRjVxNWpa3owhqiarBzaDtbsz0qXpSsNpf1/a9UmVZ6CjhE1KZRbOG2hCjAT/Q3Qv2lkRYRTuUb1NfHfgwAQRsUPOI0gXbnzmtVEE1r0eeK0q0noPEkNV12dqsFSRM5w/T6rMSUtPchZJrtB5hiDzHBuAuCC2JRmN3aqJsONzVICkF4PCFft6pQ1UeOLOk+i3LvK4T6ejYavQojImqwKpIegbXCeRNXEqZ3Q2Twk35ttI6FjPTwR4ddEHYfhc6/ttfpV/z94/UeqMTEhheMRwjej9ZMvDWqIR5Dn30rOk3Cdb901wnh/kv2T0XBTE1rktSscZxOpJtBW3djO5Bsb39RPiF2/dYiPvu0F3LQjdE2oi0cwNCOIPwjvcwBtzc6hreM5TSdpY8WKVW1Gdnb5bOQ1Z72o5JT4r4f/K8/MPYOlW9y9624+eOsHuWPrHc1Xp3Wh7QPbAdmMzPM8Dk8fBuD6scaM7cjnhxqS/eDcDwKn7b6h1UFbgENjhwAJgNup7JT58P0f5v3feT/fOvWtBoD6tZNfA+CasWuY6Juo+ZsQgjfvfTOGZvD07NNtX++fTvwTZbfM9v7tvO/692FqJk9cfIJ/OvFPq9i7qjzP4/TyaQA2Z3rrtIXaiIRYsWLFitVcMbRdp1KZtjpUnbZBPILAdaPjEeobkamM2/p4BIBrRuXyfAU8XrHtFU2hh5Xwoa1XrmbaWpmWrjLP81YAbX3YU1oMlrubYWhbd3PYjdM2ymFWDzAbFIZumhGAkGYNkQDKXrRbDeqcW0LAphsC6BjVQbxlPALUgpkm8QjqRl0pyllWo3pXp+/KagZF8+V8ACvUsi+QcOHQlkFedbAxHzHYdAtA2DyyorWrc7VL8YN4BIAx+eXea9HVXdWkObT1j826PFuoHrcNTlu3zXFYD6P82q5kn2th1CjXbh7kZfvGol+H6Jq0zE+FumXf1eNnRa5Ovybj/fIaM9kfAl5CSEcmBO9rVE5uUJN697mSOjfGr2r4k8oZzpVzkTm+q4a2Tc4Tz/Mia/L66zbyKy/bxYt3j9JMkTVZafQMRNYkapl7/XF49aZ+PvC6g7z1xhCoTY+E8rj962FETq66VkfGI4B0QJvRcDOIhAh9hoTH17wmg7U/++9Bs8kDx3WCCcfwNl91cJIP/cw1tQ7jOqnc2nCdVwfSG+MRFu3FhuZmUaBfq/8879/Y8BpRn1FL5c6dtgk9Ebjc6yc4YsWKVZVqRuZ6Lmdz67MZmQK2R+aPkDSSvPf693LX9rtWFTOwWm3ObEYXOrlyjqdmn+L00mmEEFw7du2Knh9uSPalY18iX86T0BNBXMVqdGj8EALBs3PPRsbWhHX4wmEu5C9gOzaffubTfOShj3CxcBGQztgfnfsRAHduuzPy+WPpscAw86lnPsVssTHbHGC2OMt3T8voh9fteh2bs5t58743A/D55z7Ps3PPrno/50vz5Mt5hBBB47BeSm0zdtrGihUrVmvF0HadqibTFg88DzwHXfjxCCEHquM5/q1UYyMy5bSNyhTd2LeRwcQgIG/iXr7l5U3HYybkTbrh2SSDTNvW8QgVr9qYpi3syc8FDX2MJtDW87xgqX838Qjhm+woiFL7pLCDcDBwELZqsFTvtB0ILXtuttw2PIYVxyMAWCFIVR+P0CzTNsKBWSPDqoULiVpAWF9r9eU0Y2VqHGaZhMF7X7GnGrsQoagu7AH40Fbg6kwOrCqrU00yqGZDpq7Vuk7334133Vspbr+zdptuBEiPikeAELRtvFFol2nb9Dg001XHKTQAwnpoGwaEYRA8OZDkX925h93jzaFNy5qsBBCGYGbLhn11rsntI2l+5WW7+KWX1Llht98mIaEPXKNqoo7xpteFl/w63PWh2rgFXwr01gPCtq7O5AAQgmf+cdmsJuG4l/A2+xIGN24fxjKaf1xH1UTlp64M2ladtsFS/Bb5qWp8Qgi2jqRrxyaEbKQIQcOqwP0cuh52syrCFGawiiMMCNvmDIfjEcxUEIsRbhwWVrhGTbfZROrxq4usCL0XfdWJk7SZDo7D+rpERc80KDVUvS6qCJ+ICadu4hGEEME1L861jRWrudZ7MzLbsflvD/83jswfIaEneM+h9wRxAZdSpm4GuaqfPfJZAPYO7W0+8Rch1ZBMfW7sGdrTUYTDSGqEAyMHAPje2e+1fOz3znwvGKulWzwz9wz/7of/ju+e+S5fP/l1HM9h5+BOdg7sbLqNV217FZuzm1m2l/nYwx9rmLwD+Mdj/4jjOewd2htEPtyy4RZumrwJD4+/eOwvVp0dq/JsJ9OTl8Q93anT9unZp/n6ya8znZ/u+ZhixYoVay0qhrbrVArKGgoKuA64TvBzfaatEDLTtt5pq6CtqTc6bYUQ3LzhZkDO2raaQVdO2yDTVqPGaeu4jfEI4ZvFts1yctUPZmGmg6WdNQ2qXDtwRa3mS1ywD61cnStx2q4QRtVn2obzQ5s1GwpvMwyjgniEdoDQSDbkObbLtG26z1C7HL/O4VjvVlM5jOOpcVar5xsQKpAx3Gfxlhu38PYXb6/tlpvsh6vuwfPfy6hl1W3jEdSy5xb5qWW3XLPNtk5bIWqdnYlaN139Podr1BQEN1HU5Eb7eITQ+Rga54oiK/QqILxx+zAbBuqO9b2vhjf9ReCCjtpmEI9gNIG2iWwQfVEvUzODOodhetsmhbpReywqV2eTyYPwz6u9Oeq6JqHzuZXzJZhwaje+W94Dr/gAjMjmfVGguptVEUKIyGZkbSMrrL5qpnRocqfZtSv882pv7FtNsjVvRKbGJBqyw9X1c6ZQG5HQdvIA5PXhtl+DF/1LyMrlt1ExIt1AW2ie/xwrVqxaqZ4R660Zme3Y/NVzfxUA21+9/lefF2CrpCISFEx8wfgLVvX8cEMygP3DjZnpK5Vy7X7/7Pcjv1eChJFH5o8gEPxfV/1f/PZNv82uwV3Yjs0nn/pkEI3wyq3R0XNKpm7yrmvfRcbKcGb5DH/1xF/VrPyZzk/zg3M/AODuXXcHvxdC8Jb9b2Gyb5JFe5G/fPwvaxpVt1MQjdDjJmRKnUDbilvhY498jM88+xl+//u/z7//0b/ny8e/3NbxHCtWrFjrWTG0XadS8QgBtPUc8FwZlyBEzYdyK6etE8QjRB8Kr93xWj546we5deOtLceTSMobaNOzZTwCMtO2Vc6pAo6GZgSuqcYN+zePy37XVCMBmha5XQUfDc1YtSsKQuAjdKPe1sVU47RthFGRTlsfuKub+nCmbUunbdRS/HKbeAQFRPo3Bo5TpXCmbeSy71bvYTgiQcUjRLjpoAptlYNvNYqCtm1dpzVO28Hgn6uJRwC5BPrmnSMNjw0rCoq2jaw49M8lPNnWeE6ljFQwIRF2QLfdZ4iuSRMoqs49gWgNeyLUS5DezJ0NK5w8UAod25HuZ3WeNJvcaLlpEQmj2ro6oQpDjYR0qIceX38cqp8FIlihsFJFuToDp63RpCZhMBc6dtRN1FRuqmGybcU1SQ7AxFWNLveIpledOG2hCgjDrs628QhCVCMSQsdks2tDeNJOiMaJzVZSYFbVxPO8KkhvVhM1ptSghP4hqVzbemdR2wkdpYmrYOfLgh+jrg3dQtugGVnstI0Vq6VUru2zc8+um8Z9tmPzsUc+xvHl4ySN5PMObKHajAzkZ/N1Y9etehuqIRl0B20PjhxkIDFArpzj4enohmTKZXtw9CBDySHG0mP86xf8a35mz88E1+DJvkmuHr267esNJ4d51zXvQhc6h6cP84/H/jH42xeOfgHP8zg4erDBsZvQE/zSNb8UuHz/+PE/5j8/+J/5n4/9Tz575LN889Q3eW7+ucjXPL106fJsASbSEwgEuXKuIaqtmc4tn8N2bDShIYTg9NJp7n3uXj74/Q/y73/074MGc7FixYp1JSmGtutUtfEISKet56KrRmR1sQEq07ZZIzIjwmkLshnUSKo1uAJIBPEIqhEZYLVuRNbWFQVVAOfnM6p80sDBG9pueAn0am+ww+OIcqs1X24bDW1bZtrWLcVPmTrZpIGuCYYzzd+LyKZXlXZNr3xom23MjVVu5LJbrgXV7eIRoNZpq1ydTWCUcoYp6LAaKbih8jlhBcdNjatzMPhnK5C+KkAYUhRID2rSLD+1b0TCk4iJCiFEZOOrFS2BrqlJ63gE5WoYSY0EkHilChzplUZou7IYkeo41f5EOW3butybjU8dh05vXJ0QakZWbqxJa5DuXxPCgLDJPoezmld7/YrKT23vtI2ORxhJjmDpFo7nNLg6VwSqIxR1nvSsJuHzZCWuU1WT0CqJZvEIK3YWR6j+8yQM1Ns6bfsar5Uq11ZNgoH8bFdjXK1jPmrCqWfQthxD21ixWunAyAGSRpKZwgzPzD1zuYezIn3lxFdk0zHN4t3Xvft5B7ZQC21XG40Q3sYrtr6CO7bewUR6ov0TmkjXdF68UQLg7575bsPfy06ZH57/IVB15YL8nnfH1jt4/03v5+VbXs7br377ij/zdw7u5C373wLAF499kcPThzmzfIYHpx4E4O6dd0c+b7Jvkp878HPoms68Pc+R+SP8eOrHfPXEV/k/z/wf/viBP+abp77Z8DwFbTdlNzX8rReydIth/zvhSt22J5ZOADLa4kO3fYi37X8b+4b3BQD3C0e/cEnGGitWrFiXU6sP8om1JqSctqb6nPedtgYy0zYMM6XTVkho69RDW/mzFZFpuxolkyFo6xbQRELGI1QWQ+OtlXKCNb2BhVrYA3KZP62X23bq3Ipyq7UFM0ZC/lcpRbo6o2BU4LT1X08IwW+8ah+likMm0fyUjIKOwXLbZhmJCtb6y5TDsjQLUzMpu2WW7WUSKQl36jN3IxVeuut/aW7WdE4tWeqV03ZVrs5wTVoAwhXBngi1cnU2BeltlDEzLNvLNbEVK4LKCoZqRpBv2yweQS0t7GTJW72DEKr7v1qnrdpWlDOvU5AeNbmhwF7TeIQ2igKEKwKYAbRtXIrfzGm7WvgWHoOqieu5K4e2Wm2MgxCCifQEp5ZOcS53rqab9YomD6LGFzEhFmTadnDTDaGl+P751qyRW4OiaqKuDXWTbMFkRAcrN+rjdtT/W7rbN70Adt0BW29p+JOa9AqD9JrM3dVC2zqQ7nle9/EIEedJrFixGpXQE9w0eRPfPv1tvnPmO0EGaTPly3k0oTX/rneJVXbLfOfMdwB4/dbXXxZgC/J7ZNpMky/nuX78+o62IYTgDXve0JPx3LrxVv7x+D9yZP4I53Png5UqIBuQ5ct5BhODXDXS2OR0om+CN+59Y0eveW75HN849Q0+8cQn2Ni3EQ+PQ+OHAgd3lG6YuIFdA7t46vRT6H06i/Yic6U5pvJTPHnxSf7u2b9jU2YTe4b2AHJFlvq82ZS5NNAWJFC+WLjIudy54LVb6cSihLbb+7eTsTLcuulWbt10K6cWT/Hh+z/M6eXTvllp9eadWLFixVqrip2261TSaduYaSu9e7XQVmbaAhFO24oPbZs5bVeqZErerCW8IpZXqjYiUzEGXiO0XZnTtu7m0b8p1IXc0zAM7ta51SrTtuVNu3JshZ22TbqRh8cczkjcMpxu2fwpPL7wNts6bQ+8TmZL7nl1w5+EEMF7FV6K3zarEyLzU5vBKOUMU06x1Sgyq7PdEuj65nC+VhSPsFpAGAHS28YjtFErQLgiB2Ei29AQrx5Un12WHas76QYcBeCU67Z5lmh0pq26wVG5aWH10v3ctauzDhDWjG8l50noOtYMpKvIik4AobqRrweE0GJSrH+jBLbDOxuiU6JybcOuzk7PE3Xuep5Hzu5ukq1+Kf6Kc5rT/uTRCuJsVhRL0kT1E06qvkkj2fxm0kzBzb8MGxq7oUc1iKvJQe5ywqlQKQTfG7JmHI8QK9alllqm/8iFR1goLTR93JK9xO//4Pf5wx/+4WU7tx6cepBle5nBxCBXD7Zfyn+pJITgnl33cMPEDdw4eeNlG4fSYHKQa0avAQigtpKKRnjxphevekVTO92z+x4OjBzAdmyOLx5HIJq6bMPqt/rZntnODRM38Iptr+BNe9/Eu697NzdO3ojnefyPx/4Hs8VZoPo9cSAx0PFE3ko0mV5dru3xheNANRdaaWNmI4ZmYDt2wyqhWLFixVrviqHtOpWEoCKAl3gueI4fj1Db+MtxHT8eQTYiC+dnVeMRujsUrEQSTUCfK506Mh6hLxhfVCOyoJN2K6etkag2jgF5UwuRmbbdghl1kx3VAKrlTXu/71YcqM5wN8t3hd7CqLaAUDdllqEe7eBVX8RqXJ11TbkiFV6K77/fzeIl1M1IR/EIfk2UKxtWshS/SSOyJvENsEJQHaFIp61fk6bxCG2k3KBRTa9awyi/JivI6jybk1/GO3FP1NfE9dxg+01dnclQg6VQTZQrZCo3FUAtpU6BWb3T1vO8qvu505qYtTXxPG9l7uxB/6ZioOpobgYIVUOasFNnpao/99T/hRDNG2ilh+F1/xle/jsNf9qQkdA2fBPVTfM6VRN1vc5X8njIz6GuQbp/voWvOy2vXfteC1f9NOypNn5pmmm70rzYCNXXJMgYbtHQs5XG07IR2XxpPjh21Hh1oa8aCtQfh8plmzSSHXcJj6FtrFgr16bMJnYO7MT1XL5/7vtNH/fl419m2V5mtjjLV0989XkcoZTnecHS+ZdsfknzHhTPk27ddCtvv/rtHV9Ley0VffCjcz8KrvfhBmQv2vCinr+mrum8/eDbg8+FGydv7Oi7A8jvCT+7/2fZnN3Msr3Mxx/5OGWnXG1CdonybJWivm80U8kpBY+rh7a6pgcTzlFGgFixYsVaz4qh7TpV4NZUN2qB07YxHkE2JROocjuhZmSqMZmpdbmMxEiiawLNc9AECLMPNL1lI7K28A2kAywM4VpA22C5bYfOrajl8ysCM7e8G175BzBajSBo5epUruNeAEJ1c9ypq7Ol07ZlPILvVjPTARCOaiqlZrtTRqqjuIBIp20797ORkLAaVh2PsFqXY30khOd5QZ5jp/EIUTVZ0fgmr4GJq2HvXcGvoqICXM/l3LJ0UG7MbFz1+OprEn4/m2d1ZuHQ2+CGfwFm9TEDiQEGE4N4eJxaOlUzRnUN63Zyo1ApBICw05rUA8KKWwm22fL6teUmuOvfw3U/G/yqWYyIagSyc7C2ichK1NTVqbdwdQL0jdbUQynKaRuGzN1m2qpJoqSRbA6V20g1lVNAfsVNwzJj8lgMTTw1cz+vaDVIE9XHiISdtp0obaSD67y6rnYzvvrPk26jESCGtrFirVYK+H3vzPdqGggrXSxc5Dunqw7Or538GhcLF5+38QEcWzjGqaVTmJrJrRtaNyX+SdSB4QOMpEYoVAo8MPUAUHXZXj16NUPJoVZP71hpM817r38vd++6mzftfVNX27J0i3de807SZppTS6f430/97+A7WScxWqvRapy2JxdP4uExkBhgIDHQ8Hc1VpXFGytWrFhXimJou05VhbbKaeuA5/ghxbWNyFxcvz2ZD21DTttKj5y26Al0H/yqaASgZTxC22xSpRVCW7Xcts/qzLkVlZ+6oiXQiSyM7a35VTMIEB5zL5bidwttI522K9nngc1w8A0SwtWNLwyVw03IOsmXqnd1hh2OLUHF9pfC8K4a93OryIqO3c91DYzKbjmAjd26OsPNfFYE0q0+eMXvwu5XBL+KOmYu5C9QdstYutWTnOFwVmdLAHfVT8O+1zT8WrklTi6eDH4XPm9WO7lRfxwq0GrpVscOwvpGZOFjqGVNhIDhHdVJBKKvDZ7ncXThKAC7Bnatenz1zeG6gXlQdftO5aaCVRJhV+dqz+X6yYNu88ehCtLVNbDTvF2IXsUA1YmTXkDRFX/eNZEQIlitoCJnunECNzhtyz607TAaAaqfQ+EYkVixYjXX9ePXkzbTzBXneOLiEw1//8LRL+B4DnuH9rJ3aC8Vt8Lnnvvc8zrGb5z6BgAvnHxhxxnkV7KEEDUNycINyFQExqXSUHKIu7bf1fH3zbBGUiO84+p3IBD86PyP+PH5HwOXNs8Wqt83Fu3Ftp8d6ntiuCFdWGqsqm9DrFixYl0piqHtOpW6kQ6grSsbkSmnreu5QQyC67pBPIJ8bmM8gtllpi2GVYW2WtUduxKnbVvnUSto6/XOaRs4CN1GV2fHGY5Rrs4uAaHapuM6wb87/cKm3qsap+1KcnyFgOveCjtfFvwqKhKimyZk0OjqLLvlwOHYEn7c/C646w/BqO6Dev/CsQMg30flcFm1+7kO9qgvnEKIjrJJodFpW3Erwbm86mXpEe5nFY0w2TfZUc5aPbQNA8JOwPzW/q0AHF88HvxuxUvdI1QP0lW9O3XZQrUmCgCrbWtCW7VTNOracLF4kYXSArrQm96MtFKD07bLpfgjyREs3cLxnN64Ouudtl1eq6F5PEIn512zRmSPXHgE6A1I7xbaQjUXXF1Xu2leVw/SldO2v7755ypUD9JjxYrVWqZuBsvn6zNRzyyf4f7z9wPw07t/mjfseQMCwQNTD3Bs4djzMr654hyHLxwG4GVbXva8vOZ61C0bb0EXOicWT3Dv0XtbNiBby9o3vI979twDVD8bLjW0TRpJBhODQHu3rfqeWB+NoBRD21ixYl2piqHtOlXZkx+mNU5b1w0ybYHA8ed4TtCIDOqhrR+P0AunrQ+Awk7bVpm2K4YANdBW3hRGOQh71YhsVU2v2mwr7NpVCuIRumzmE8557RQEKFdyzVL8LptyhUF6N03IABKG77T1lxaH38/V1kTBsDNLZ2rAbXi8vWjmAxJedNq5VgF4NcYa1+lqaxIRCaG+zHb6Rbz+2O4WEKov3+F4hF66OhVI7/S6AI0Owk6vCxAdnaKiEbb2b+3IKVo/4dQLV+dEegKoRiR0el2AxvNEXW86XRUBoaX4viO9G6jc7PPkqdmnAHjBxAtWvc1Wjcg6VdCMzL+uqvF2EjFRD9IX7UWgO5Cu9i2GtrFirVzKpfnEzBM10Qf3PncvHh7Xj1/Ptv5tbMlu4eYNNwPwmWc/U9Of4lLpO2e+g+d57Bnac8nh3XpW1spyaPwQAF8/+XXg0jQgez50x5Y7giZvlm511I9itVJu23bQ9sTiCaA9tJ0rzsUrPmLFinVFaf19msQCQvEIWthpW41HgCq0dT0XgUCIqExbPx6h20xb3cTQVTwCQbd4dTPpeE7DF8xw7mJLhaGtf1PYymnbMbTVGkFrp+6t0dQoAsFCaaGhK7CCH6u90a6HyurG2NKtjnMhA6et3ejq7HhZeggQBtC2wy99zVydpmau+svwYHKQyb5JPDyemXsm+H1XHdjrnbbtGsOtQKom9VmdbeMHIhQFCFVH4I19q8+zhcaadNowTGlrVjptZwozVVDdxTbra9LtZA6E4hF64epsAW13Dqw+zxYaQfqKVzG0UH2ubTfxA/WTB+p605XTtr4mXYD0qCaFD08/jOu5bMxs7ElzuJ44beviEbpx2jaAdL8mcaZtrFjPryb6Jtg7tBcPj/vO3gfIz4THZh5DCMHdO+8OHvu6Xa/D0i2OLRzjwekHL+m4yk6Z7575LgC3b779kr7WlSCVTwzy+9otG265jKPpXKox2Us2vYQ37XnT8wKeo3L067VkLzFbnAWqTWzrlTbTDCdlXn3sto0VK9aVpBjarlNV4xF8iOO54DpofjxC+DEK3mp+uUPMtndOWyFkAyj8eIQ6py005tqu3GkbWq5Z57StybTtMiexfim+53kd3xT3mX3Bsu/6nDJVj07du4Grs9xdni00xiOEocVqgVSrRmQdO22bNL3qNJv0wPABAJ68+GTwuzBE79bVGThtu8gXU8+tr4mpt2mwFDW+VtC2gyZkUHU/q1p067RNm+kARikXRTdZnfWZsQrqdVOTPkNez+rdzx3lp0Y4+oM828HVL8OHxqZXK2ry2Eb1HZ07jYqBEBSti6zoZaZtL+IRwufJA9OyoUwnLltoHlnRdpKyhcZTslO4ikfoZp/rJzd60YhMnWMxtI0Va3VSwO8H535Axa3wD0f+AYBbN97KRN9E8LiBxACv3PZKAP7hyD/UrA7ote6fup98Oc9wcphrx669ZK9zpWj34O5ggu/q0asZDDXCXW+ydIu37H8Lt256fhrPBU7bfHOnrfp+OJGeaPl9Lo5IiBUr1pWodQNtZ2dn+bmf+zn6+/sZHBzkHe94B8vLyy2fUywWec973sPIyAiZTIY3vvGNTE1N1TxGCNHw3yc/+clLuSs9kQKggdPWc8FzkD+JmseovE71WOWuBaj40NboNtMWEH5+qC6qmbbhG/z6iIQVw56aeISk/xpqX+Q+ep7Xs3gEdZMddgd3AioOjEhAGIa24fzUVTtt68BHL12dUdB21a7OOvBRcSvMFuSseLeZtupY6dattn9kPwBPzT4V1Fa9n71o5qPcsb3MT6266jtYAh3h9lMgvdOljs3cz904CLdla5uRdeXqrHN89yLTtj6yohuAWQ/Sl+3lAIx26rStd7KqHNVuAGEzp20vIiG6XRUBjZEVvThmgqZc9hLPzEo3/g3jN3Q0vobIikr37ufRtLyOzhXnKLvlnrifGzJtrc4zbWOnbefq5Dvuy172sobvr7/yK7/yPI04Vi917di1ZK0sC6UFPvnUJzm6cBRTM3nN9sbmna/Y+goGEgPMFmeDJmG9lud5fPPUNwHpsl2Py/yfbwkheMOeN7C1fys/tfOnLvdw1pXU940zS2eaxn60i0ZQ2pSNoW2sWLGuPK2bT+Gf+7mf4/HHH+crX/kKn//85/n2t7/Nu971rpbP+bVf+zXuvfdePv3pT/Otb32Ls2fP8jM/8zMNj/uLv/gLzp07F/x3zz33XKK96J0UyAmcrG4FPBchRJAtqyCpgoTqS1eI2YbiEbo/FKyE3yRMDzlttZDT1u3QaRvuVuvDk/oGZ7ZrB//utLttPeCqWTbfwU2xakDw9OzTQS3CbuNOGyzVxyN0A6PUe6WWxoZh1GpdnaomCnzMFmfx8LB0q2MQ0FATtztAuHtwN7rQmS3OMp2fluPtIquzHkb1AqSHXZ2e51Vdkz1Yiq9ctgOJga7Pk166OpUrXX0p7wak1zvSe5Fpq55rO/I600uAqVy2k32THdfkUjhtlfNlKjeF4zqXJNO2G6etOsfKbpmyU+5JZIUa30PTD+HhsbV/a8fRLvWNyIJJSqPzyY2smcXSLTw8ZguzvZk88M+1XmTaqppU3MoldQBeierkOy7AO9/5zprvr//hP/yH52G0sXotQzO4daN0Nf7g3A8AuH3L7ZFuTUu3+OldPw3Al49/OZhw6aWenX+Ws8tnsXSLWzauz2X+l0MHRw7yWzf+Fpuzmy/3UNaVtmS3kDSSLNqLNfFlYa0U2m7OyPf+9NLp3g4yVqxYsS6j1gW0ffLJJ/nSl77En/3Zn3HzzTdz22238ZGPfIRPfvKTnD17NvI5CwsL/I//8T/4oz/6I+644w5uuOEG/uIv/oL77ruPH/zgBzWPHRwcZHJyMvgvmezcCfN8qcF95zryP6hCW68W2kY5bW1H/tvsgdN2dDDLtpE0k/3JALRqQgvgnxqP0oqXi9Y4bX0wXAdtFXQ0NKOjm3ZoXIqvIIVAVGMoVqFt2W2kjTT5Sp4TSydqth3eh5WqHiwoaNsNIFRLYQuVAhW3EryfHS37rsuFVLmLo6nRjptyqWNDHSvdNBsCWWO1BF01GerlUvdexCMoQOh5HoVKoat812bQVrkaOlG907YX+anqS/jJpTqnbQ9gVC8ybcPnWKFS6G5Zuopv8MfXbZ5teJv1+andOG1HkiNYuoXjOcwUZnriSA/cz7Zfky4bkQl/VUm+ku/q2lBfkwenZFZkpy5baNGIrIuaCCEYT/sRCYUL1cmDLo7DepDeTTxCUk/W1CTWytTJd1yldDpd8/21v79zp3Ssy6tbN94anD8pI8Wrtr2q6WNvnLyRLdktlJwSf/zAH/P4xce7eu2yW+Zi4SLHFo7x8IWH+eLRLwJw8+TNXX2fiRVrJTJ1kxsnZPMzlesclud5HF88DqzAaeuvIjuXOxfZBDtWrFix1qM66170POv73/8+g4ODvPCFLwx+d+edd6JpGj/84Q95wxve0PCcBx54gHK5zJ133hn8bv/+/WzdupXvf//7vOhFLwp+/573vIdf+qVfYufOnfzKr/wKb3/72zuGTM+X1AeRGc60VXBWaNhUgWaQaRvltO1Vpi2gG0mG0v7NYwi0mpoZONTCWnmmbQto6ztXw2Cm09opsNDQ9KqDLFGQLuN9w/t4aPohnrj4BDsHdlYd0pq+6m3WZ3X2AtqmjTQCgYeMl+jKuVW3RFvlLnaaZwvN3c+dgnmA/cP7eWbuGZ6afYrbt9zeXVZnE1dnN+5nUzexdAvbscmVcz11AqvlYt10gW6aM9zB+JQ2ZzcHjfvmi/O9aXrl1yRX8eMRurjx1IRGykhRqBTkedJF06tmTtvdg7s7Hl8QI1KpixHpwtUphGAiPcGppVOcy53rac5wL5y2QghSRop8JS9BejeNyELn8UJpIQDp109c3/H4GuIRetCIDOQk2Oml01zIX6jWpItJNtuxKTvl4NjpBtrW12QgMdDxtn6S1Ml3XKX/9b/+F3/913/N5OQkr3vd6/jd3/1d0unm17pSqUSpVG22urgoHdau6+KGvxxeIrmui+d5z8trrTcNJYY4OHKQR2ce5c6td5LUky3fp7fueyv/5eH/wlRuiv/y0H/h4MhB3rDnDUykJ5o+B+Tk1PHF4xyZP8JzC89xeul08B26Xi/Z9JKaMcT1W99ay/V70YYX8e3T3+ah6Yd4U+lNNRPtFwsXydk5dE1nQ9+GluMfTgxjaRYlp8T53PmuTAprTWu5frHaK67f+talqt9Kt7cuoO358+cZHx+v+Z1hGAwPD3P+fHRo+fnz57Esi8HBwZrfT0xM1Dzn93//97njjjtIp9P80z/9E+9+97tZXl7mfe97X9PxXM4vveqAKbtlPM9DFzqe5+E5FXDKCM9D839Xdsq4rovjOv5jNTw8bMcJxlmuuHh46GLlB00zCd0EP4vIM1IBHdbQ8DwP27FrXqNYKeJ5HpZmtX5tK4NQ29UT4LroyH20K3KbS/YSnueRNtId74cpTDzPo+JVKFfKlColPM/D1MyOt3lg+AAPTj3IEzNP8Nrtr+1qm4YwavY5Z8vl8+2+2LdT2kyzbC+zWFqsjk+sfnyqJmVXHnfTuWk8z2MkOdLx+CzNCo5lu2Kv/JhpoX1D+/A8j6dnn8au2NiOvap9Dl+0G2pS7k1NUkaKUqXEsr0c1MTQjNXXxL8WqHNP5YW1+9LbSuo8KVVKVJxKUJOEluhqm5N9k5xdPsvxheOUHH+fxer32cCo2ee8ncfzPFJ6KrhGd/KhmzJS5Mt5cnauu/MkVJNSucSJxRN4nseO/h3d18Qp4ThO9TwRnZ8nAJPpSU4unuTM0hlSRqrra1epUsJ1XZbtZXm91ld/vQ7XL2WkyJVzNedJN9cu27F54PwDuJ7LzoGdDFqDHb9/ap+LlSKu61KoFLq+dgGMJkfxPI/p/DRJI9nxeRI+DhdKC/I7gqZ3dR6DdNznyjnydh431bidy/2ldy2qk++4AG9729vYtm0bGzdu5JFHHuHf/Jt/w9NPP81nPvOZps/50Ic+xAc/+MGG31+4cIFisdj5TqxQruuysCCPN60HsVxXml41+ioOpA6wN7mX6enplo9NkOBXdv4K3zr/LX5w4Qc8dO4hHj7/MC8aexG3TdyG7dos2ossV5ZZKi8xb89zJneGM/kzDSvfQF4TMmaGjJEha2bZ078HLacxnauOI67f+tZarl+CBMP6MOcL5/nK01/h1vFqE7RH5x7Ftm02pTcxNzPXdltD2hAnCyd57NRj6MN628evF63l+sVqr7h+61uXqn5LSyuLOLqs0Pb9738/H/7wh1s+5sknn2z59271u7/7u8G/r7/+enK5HP/v//v/toS2l/NLrzpglvPL0iFj65Rsm9zFC2j5WVK2jWc42LbN9IVpzLzJ0vIStm1TKVdwSjYXZmbIuHJWfSmXo1QqszA/x7TR3dgzJQfDls6fxaUiLvKLXsWuYFdspi5MIVJVd+n88jy2bbM8v8x0pcWX00qJQX+7C/M5vMI0+eU8tm0ztzjH9PQ0p2dPY9s2oizaftFtprJbxvZf58zUGWaKM9i2jYvb8TZHnVFs2+bZmWc5fvY486V5ypUyju2sepuLxUVs22bRWWR6epoLcxewbZtyvtzx+AD0io5t25yaOoXrufJY0Sur3ubSkjzOFpYXmJ6e5sTMCWzbxiyZHY/PcZ1qTc6fYXp2Gtu2sQt2x9u0PAvDNViyl3jw+IPSOWnb2MWVbTN80Q5q4vk1mZc1sZc7Hx+AVtaCmhSdoqxzcfV1Vuf+Um6Jqakpjl48iu3YJIqJ3pwn588wMyfPk2K+2NU+D4thjtvHefTMowxag3KfCx3sc07u8yJ+TRZlTYqLRaa96c4/dG2wbZvT06eDa0OxsPp9Do/vweMPUigWyJpZKosVppc6e//UMQJwduosFxcuYts2heVCVzVJV9LYts1z088xmZrsuM7hfT53/hwL+QUACguFGiCwEoXr59ketm1zZvoMF5fkPhdzqx/fYq56Hn/3+HexbZudiZ1dvXf5Sj6oybnz55hfkp93+cU803S+XbNkYts2J2ZOMJYc63if1bVhMbfI8XPHsW2bfrOfCxcudDw2IKjJ6enTpEuNjs/L/aX3+dSl/o4bzry95ppr2LBhA694xSt47rnn2LVrV+Rzfvu3f5tf//VfD35eXFxky5YtjI2NPS/RCq4rey+MjY3FN61NtJ3tq3r8z2/8eV6dfzV/f+TveXzmcX48/2N+PP/jpo/XTZ3hxDC7Bnexe3A3O/p3MJQckiuv2qwAi+u3vrXW63dn+U4+9fSneCL/BD899tPB8biwsIBlWeyf2N8wwRWlPXN7OH/mPHkzv6LHrxet9frFaq24futbl6p+K41lvazQ9jd+4zf4hV/4hZaP2blzJ5OTkw03JJVKhdnZWSYnJyOfNzkpbzDn5+dr3LZTU1NNnwNw88038wd/8AeUSiUSiehljJfzS686YIzzBpZjkRUpEkULa7AfzBLCskhannQZDw8yPjBO6kwKq2ShJ1PYjsXA4DDj43IJpGFNkUgIJsZHGR/tPF8QgP4hxKJccjm6cTukhgDoS/dRLpYZGBpgvL/64alZGhYWG8Y3MJ5p86F69euhUmRs8y4QguH8MNasRaovxfj4OGbJxLIsxgfGO/6A9jyPRCKB53kMDA9QypewLItsX7bjbY4zzvaz2zm7fJaL+kWGh4YxDZNMOrPqbRpFA+uIhaZpjI+PY1w0sCyLieGJrr6UjJ0eY8FdwMpaGEJus7+vf9XbzCfzWCcsjITB+Pg4+aN5LMti94bdjA93Pr5UMoXjOvQP95Mqp7Asi+GB4a72+bqZ63hg6gGmmWZiYALLshjIDqxom+GLtigIrKMWuqkzPj6OdlrDsiw2jG3oanyjZ0aZdWZJZBPori7P5+zgqrdZSpWwjlvolk5iMIGruySNJFdtvarjOIPwedI/3E9iMYFlWYwNjnW1zwfLB3ls6THmmWdTdpPc5/7V73NluYJ1TO7z+Pg43jMelmexeWIz45nxjj90R8+OctG5SCKbIGX6x2H/6o9De8kOxjevz2NZFleNX8XEROslra3kei7Wk/LaOzA8ID8fLIvx4c6vhwD7tH18c+abLIkldmR2dLzP5eVysM99w31YloUQgq0btq66K3m4fqPnZE2S2SRJN4llWYwMjqx6fG7OxTpmUdEqTJWnSCQSvGzPy7pa3l92y1hP+TUZGZCfd45/bRjovCa7zd1Y5y3yIk+qL9XxPtspO7g2WFlLHi/Z7o4XqF67Uv2pyG1d7i+9z6cu5XfcKN18880AHDlypCm0TSQSkd9tNU173m4ihRDP6+v9JGhDZgPvPvRuHr/4OJ955jNM5aeCJrCDiUEGEgMMJAbY0LeB3YO7u+o1ENdvfWst1+/GyRv57JHPcj53nlPLp9g+sB2AU8unEEKwfXD7isa9uX8z4qzgbO7smtzPbrSW6xerveL6rW9divqtdFuXFdqOjY0xNtY+7/KWW25hfn6eBx54gBtukI1Bvv71r+O6bvAltV433HADpmnyta99jTe+8Y0APP3005w8eZJbbmneCfXw4cMMDQ01BbZw+b/0CiGoeBWEEJiavPkVIDNthUAXAiEELi6apuHig15Np4zAo3qAOK6HQJAw9O7HbibB/xIoElnwt2dqMhPWo9ZZU3bLMgPPTLV/7Rt/seZHS5f77XgOmqaRd/IIIcha2a72Q2WJVrwKFeR7nNATXW3zqpGrOJc7x1NzT3HTxE0IIbB0a9XbTBrJYJ8RMtNWCEHaSnc1vmwiixCCfCUfZAJ3Mr6EkQiOTQTMFmdl45y+8a7Gl9ST5L08Za9M2Sv3piajV/Hg9IM8Pfd0cPOymn1WF+2EKfe57JbRNI2CI2uSsTJdjS9jZRBCyO0huq+JW+Fc/pzMKe2b6CrrFGQuZ8kpUfbK2K6NEIKkkexqn7cPbEcIwanlU+wb3tf5eWImg5qo47r+2tDJh646N4pOMbj+JozVH4fh8+TY4jGEEOwe2t3Ve6ehYekWZVeeI6u6trbQxuxGhBBM56eDOne7z6oefWYfht7ZVxBVv7SVDmqi9rmT49Ay5OeJ68nPyt2DuxnyJx07lSXksasid0puqSc1meibQAjBbGk22GY314ayW2a5sizPkUR3n58g43ZUTZpt63J+6X0+dSm/40bp8OHDAGzYcOVkOMZanQ6OHOSqF12F7dpY/v1BrFjrRWkzzaHxQ9x//n7uO3sf2we247gOp5ZOAbC9f/uKtqP6NpxZOnOphhorVqxYz6vW3rfcCB04cIC77rqLd77znfzoRz/ie9/7Hr/6q7/KW9/6VjZu3AjAmTNn2L9/Pz/60Y8AGBgY4B3veAe//uu/zje+8Q0eeOAB3v72t3PLLbcETcjuvfde/uzP/ozHHnuMI0eO8F//63/lD//wD3nve9972fZ1JfI8r9rQSt30ek6oEZn8ncqscv3f65rM9XFcL9iW7fjP0XvwxU41WNFNMKrNYFSTlx+e/2EwbtdzgwZGnTRmCRqR+dvrRTfy8FhKTqnaLb2DJi9hXTVyFQBPXHyiu6ZSoXGU3TJFR8ZZdNOIDKrNgJbspa46sAcNlpwyc8U5HM9BFzpDyS7hhz+WklPqanxh7R/aD8DJxZMs2HKpdjeNvipuBc/zgkZk3dZENWBYLi931SE+3PTq7LLsQt5NEzKlpCFdbb2sycbMRnShky/nOZc7J7fZzT475SBHFKhpatGJVCOzXDlXvTZ007zOsYMmZLsGo11xnW5XXRtUnTrVSHIES7dwPIezOXn8dNv0qhdNyJTUeZav5LtrylV37N4wcUPXYxNCkNSr50mp0ptGZAOJAZkr7Lmcz8m8066arzk2y7asSTdNyJRUTVSjzFjt1cl33Oeee44/+IM/4IEHHuD48eN87nOf4+d//ud56UtfyrXXXns5dyfWZZaa2I6Bbaz1qFs3yizbH0/9WDYTy5+XsV56om2TPaWNmY0IBIv2Ikv22ovOiRUrVqzVal1AW5Adcvfv388rXvEKXvva13Lbbbfx3//7fw/+Xi6Xefrpp8nn88Hv/viP/5i7776bN77xjbz0pS9lcnKypkGDaZp89KMf5ZZbbuHQoUN87GMf44/+6I/4wAc+8Lzu22qlICyA6QNa3Cq01QMXrYS2Ct7qouquVao48t+m3oNDQYHauhu/2zffDsD3znyPP33oT2vgIBDc2K7qpeqgrQIBfUZvoK3t2MEYO11GrrRzcCeWbrFkL3Fi8UTH2wwDLNuxg5vingFCOwQIu4G2bpmZwgwgO52vdvlzvcKAsFcd2AeTg0z2TeLh8ejMo0B34ANkl3hVk7TRvHv3SqRqki/nq6C/AxilnlN2y4HjYEOmexdWFCDstiaGZrA5uxmA5+afAzrcZ/849PCCL+umZnY9+RLUJAQIuzlPXM+lWCmS0BM9Aenq/S86xZ6BdCFEcJN0alE6XbqZcHI9l8WSbNzZ7QQbVM+zQqXQkwknkPt8aPxQ12MLj0U5gaGzz7uwhBCMpkYBqtC2i8kNx3OCiasY2l4+rfY7rmVZfPWrX+VVr3oV+/fv5zd+4zd44xvfyL333nu5diFWrFixupaK77AdmwemHgjum7b1b1vxRERCTzCalp+TZ5Zjt22sWLHWvy5rPMJqNDw8zN/8zd80/fv27dsDR5VSMpnkox/9KB/96Ecjn3PXXXdx11139XScz4fCXV8VvMRzJLhFdoANP66Z01Y6dn2nrdZDp22i1kH14k0vJmtl+cvH/5Ij80f48I8+zD/b988AEIjqPqxC6jlqH3Nl6bTNWN25twIY5dpVMNPBDXFYpmayd2gvj808xsMXHg5+t1rJOAxTNoJy7J65OtWN+nJ5uScOQtdzmcpPAQRfmrpRGBB2486u14HhA5zPnefk4kmge5BerBQDqNxtTcKuTrWv3YAZgBNL8ovv5szmrsYGlwYQAmzNbuXE4glmi7NAd8chwHxpHqi+n91ITQip8w46q0n9+7RzcGfXExvh7fYSpANs6NvAqaVT5Ctyv7s9T+ZKsvNzL5y2qq75cr56HHZ5nuwd2tsTeAnV9z/s9Ok2mgRgLD3Gudy5riZ0wsfhbEGeb1mzd9BWHS+xVqbVfsfdsmUL3/rWt56PocWKFSvW8yYhBLduvJXPPfc5fnD2B4HRYFv/tlVtZ3NmMxfyFzizdIb9w/svxVBjxYoV63nTunHaxqqq4lWCfxvqZtN1JbgFDB/ahqMI5GNrnbaO66HuAYyeOm0bHVTXjl3Lb974m4ynx5kvzfPxRz4uH6p3lrmlIiCCeAQf2nYLZ9QNfzgeoRcwSkUkqBnfTt27Yedkr5y2Cp7kyrkAAnQD0gHOLcvl7WOp9nl+7aScacVKFRB265oE2D9S+yWuU5Cu9nvRXgx+3yv3c66c64mrE+BCXnaF39DXW6etAtXdLsUH2DZQ+6W8kzobmhFcUxQg7DYaASBl+jCqW/dz3XG2a6D7aAQgcil+t65OaHRmd3IcGpqBn7zOXLGHNYmIR+h4fP4x04toBCU1loWSdLLqQu965QY0Xle7iXYBuFi8CPTWaVusFLveVqxYsWLF+snTzRtuRgjB0YWjgdlltdB2U1auYDq9fLrn44sVK1as51sxtF2HUqBSCIGmNzptNa2J0zZw4Hr+dqquDbOXmbZNbsYn+yb5zRt/k2tGr8FDvnanoKch01Y5bbt0b9W4OruAAPVS0Fap0xv3MFRWN8VdQ1urmmnbTTxC2OF2JifhdC+gbVRNeuEg3D24OzgnoHMQrGqpwIylW4GrvVPVgPQu3M+60ANYBvJ8G04OdzU2qAWE3Tgc67UtWwdtuzxPAqdtl3EV4W3kK/mu3MVh0A/SadsLBUvxK9Wl+L1y2obV8SoB//xS0LYnTluj0Wnb6TGzZ3APw8lhDo0d6npcSqomakKnFy5bkE7bsDq5dqmVGwAXCz2EtmYcjxArVqxYsTrXQGKAq0euBggy1ztx2gKcXoqhbaxYsda/Ymi7DqVgrKmZoJbVuuFGZLXQVv0/iBRwI6BtLzovbzwEA1tg+21NH5IyUrzr2nfxUzt/CoDx9HhHL6X2peyW8Tyv59C25JS6ahpWr9HUaM2NdidO1vD4luylAHz3Kj81V851BT6EEAEEVU7bXsQjhJfi9yrTVm0j3ACqY0BY56brCSA0q4Cwm+MwDMsANvZt7ElzkiinbS+A1ETfRA0I7daRvlCUNemFqzMcWdEtqFb7pQltxd2Q2ylyKX4PzpPJvsmanzuuif+8SxGPUKgUuppwAnjv9e/l/7nl/+lJlIaSmtxQOb69cD5D42RYx8ehf56oc7gX0DYM0mPFihUrVqxO9OJNLw7+3W/1M5gYXNXzVa+AqfxU8P0gVqxYsdar1k2mbayqVDyCIQxQTkHPCeIRdM0AvLbxCOWK/L0QAq0XmbYDm+Gn/r+2DxNC8Jodr+HGyRs7vkkMO21t1w4+kLuFM+FGZN04HKN01chVTOemgc6hrRqLggC60DvelpLKMVwuL1c7sHcBy5yKE7iseuG0jWwO14N4BID9w/t5Zu4ZoHsAF0DbHuanhiMrunECq/etFw2vIATSQzm+vXDaakJja3YrR+aPyG12CODUWBQg7ElNQo3IlLu9G9BfqBTYmt3aEye/2iZUXZ31jt5ONZIcwdKtrrOL1Xul8oq7zR+HWvez+rzrdHxCiCB2p1dqcNr2AKIDQSOy+tdZrSzNIk8VrsaNyGLFihUr1lrQgeEDDCQGWCgtrKoJmdJgYpC0kSZfyXM+d54t2S2XaKSxYsWKdekVO23XodTNqaEZoG7K3cZGZArWOur3dY3Iyn4TMsvoAbDtQKOp0Y5vYoNMW68SuGx1oXd9U1zTiKyHDZZAfgFR6hTABa5Ov9t3ykx17ZxUXdzDnd27ddOBhCC9WIqvahpeip/QegM/elmTXi7FV4CwWCkG8KNbWAaN+aSdKlwTNbnRq6Xf4SVwvXI/98RpG3IQBqC6y5qEnd7dStVEAcKknuyJq1oIwUR6Ivi525ooB2YvnLZBpm2XjcguldRYgpr0IPcZYCg5VBvt0qUjXamXNYmhbaxYsWLF6lS6pvOyLS8D4OrRq1f9fCFEkGur+onEihUr1npVDG3XoRwvBGG1sNO2Nh5BwV21jL7eaVtxan+/nhR22uZsPxrBynQNKaLyU3vltN0ztCcA591C0V4uxTc1MwA+ypnYC0A4nBzuidNPwcCSU+oaltVrc3ZzAPQ6jqzQ6kB6lxnDIJ2hKotW1bpbWAbVjK9upY6X5fJycH3plYtwa//W4N+9Worfy8gK13NZLsuMtU5BvzpGLiW07VU9oDbXttvJDaVeRlbUZCv36NrQC6kaqHO4VzXRhFbjtu02+xnke9ltFjfE0DZWrFixYvVGd269kw/c8gFu3XhrR89Xq8vOLK0vaPvt09/m7575OzzPa//gWLFi/URo/dG6WNV4BM0ABSldt+q01WqhbdNMWwVte9GE7HlWGNoqgNILCBDl6uwVBEjoCXYP7gZ65yDslXNLLYtVS5e7jQqAxiW8nepS1kQIwU2TN6EJrePogCA/tYfxCJrQAvjRy5rU55N2qgAQ+s5s6J3DMey07XjywK+JamDRi2uDpVmBu1Ftt9Pz+J7d93DXjrs6co80HV8o7xp6DG1DDu1eAELoTTxCeIJETR70apKtF6qvSa+u11CbF96LSbZ+q7/rMUG1JmW3HLjwY8WKFStWrNVKCMFYeqxjQ04AbdeR0/bo/FE+9fSn+Mapb3B88fjlHk6sWLHWiGJouw6l4g4aM22l01b3gaaKR2jItPVq4xF60oTseVaN07ZHTcig1mnbbWObKN2x5Q7GkmNBV9ROx9dLVyeE8jr9pcsd56eGnjee6qzJXL0i4xF6CKR+Zs/P8OGXfnjVnWmVAqdtD93PUIW/3eb4KjAznBzuWZOlehhl6VZPluKDzFBV4+wUBNc/rxfQVgjRsJ1Ox7dveB9377wbTfTu2tvgtO1RXAXUOm27bXql1IuaGJrRcH1eS05bBWnVZ0kvr1vh62svXPi9yLMF+ZmkVgkUnNhtGytWrFixLo/U6rLTy6cD12q+nOeRC4/wD0f+gUcuPHI5h9cgx3X426f/NvhZNXWOFStWrLgR2TpUjdNWLWd0q43IwnmvEIK2ugFUYqdtCykgUdP0qofOratGruJ9V72P8YHOgOalAoT1rrdeuOnCTrBupEBHvpwPXOO9BDNCiK7gd72rs5cgfaYwE/zcMcD036uNmY09GRc0AsJe1+Oe3ffw7NyzNVEJq1H9eHpVk7SZDvY56nUup1RNVFxMLwFh2KHd7eQByPzxpN4b12naSAfXaiFETdbr5Vb9OdtLp+1YutrksReTbL2CtkIIkkaSQqVAoVzomYM3VqxYsWLFWo0m+ybRhEa+nOd/P/W/ObF4grPLZ4OVObrQ+Xe3/bu2K3+evPgk86V5XrThRT0zKETpO2e+U+MKPps7e8leK1asWOtL689iGSsAV6ZmVhuReaFGZEEMgvzZrcu6DRqROb7TVl9/h4ECAJcyHuFSOG27ldrvXje2qXcpdwoBwrmwY6mxFo9cueoBIazNmqgvgb1ys9Yfz93WpNP4hyipmgTLvnsE4JRu3Xgr/+Lgv+g4Z7h+0qEXS/GhcZJkLR2Hylnb64xhkO7noeQQSSNJ1uwM7oXfq17kjyuFzzdL653juxeqPz56eZ6o+BmBCCZqV6swVO4VtIVQg7hKvmfbjBUrVqxYsVYjUzeDRqr3nb2PM8tn8PAYT48zkBjA8Rx+eO6HLbexZC/xsUc+xv968n/x+MXHL9lYF0oLfP7o5wHYObATgLPLMbSNFSuWVOy0XYdSWbUy09YHriGnbQBt/Z8VvFUxCJUGaLt2bnJXKgVzPLwAHPXEaRsRj7AWMxLVMp+eOW3roW0PupH3GtqqOgvROaS4FLpUrs4GaNthTbb3b+fJi09y1chVvRgWUK3JWpzYgMbx9Oo8SZm1te1Vjm8vVD+WXkJbIQS/deNv4bhOT5y2vbhWK4XPt7V2HNbXoNfN4QSiKwB+KeIRQIL02eJs3IwsVqxYsWJdVr16+6v59ulvsymziT1De9g1uIuBxAD3nbmPv3nqb/jOme9wx9Y7mn6OfvfMd4P77s8f/TwHRw5eksnhzx75LMVKka39W3nDnjfwH3/8H2On7U+AHNdhtjhbs3oqVqworR3yEWvFqm1EFsq0dVWmbW0jMuW01XX5e1fFI7gqHmH9OW3DDrz50jzQm0xbdVMdjkdYS2CmHpjUQ6RO1at4hEvZiEzl7Sb0xNpy02mXBhD2Ctq+dudruWPrHT1dml0Px3oJo3qh+veqV5Cwz6jdTqdO4Euh+gzbXtekW6gXPk96ca1WCp9vaw3aNjhte3gODiWHeMc17+jqvaxx2nbooI6SAukxtI0VK1asWJdTL5x8IS+cfGHD72+YvIG/e/bvmCnM8MzcM+wb3tfwmIpb4TunvxP8fHrpNIcvHOb68et7OsZn557l/vP3IxC8Zd9bmOybRCBYtpdZspd6Oqkaa23pH577B75+8uu8bf/buHXTrZd7OLHWsNYfrYsVOGd1oYNqIua6QSMy5UJ0PAfP80JdtRXMrXXaGtraAWArVdhpqfJde+m0DTe9WksgoB4QpvQeQdu6G/9uu5EPJAY6duTV61Iu++6F6vfzUsUjdHMc9hIWRW1vLZ0jUDseQzN65pYP18TUzDU9ebDWzpNwTXrptA2fb2tpVQRcWqctwKHxQ+we2t3x8y9Fpi1UYyBiaBsrVqxYsdaiEnqCmyZvAmSWbJQenHqQRXuRgcQAr9z2SgC+cPQLgRmqF6q4FT719KcAuHXTrWzr30ZCTzCSGgHiiIQrWcVKke+e+S4Anzv6OYqV4mUeUay1rBjarkM1ddrWxyO4ThCREDwecL3aRmTrMdNW1/SgQ7Vy2vZZPWxE5trY7hqEtpdoKX79DXvHHeJ9aNKraARoBB1rDcw0gPRLFI+wlo7DSw2julX9UvxLkZ/aq0mJXqk+L7XeeXu5FX6/eum0/UmNR+iFwudJr+MRoLo6IlasWLFixVprum3zbQA8cuGRwACk5Hke3zj1DQBesvklvHLbK0kbac7nzvPj8z9e9WuVnBILpYUg3k7p26e/zbncOdJmmtfven3we9U8OIa2V65+PPXjwCC2bC/ztZNfu8wjirWWtXbWdsZasRSINTQDfPeszLRV8Qhm8Ljwh4Oh1TYiq/hxCsY6zLQFuf9lt8yyLRuR9QIEhJ22Kl5iLUHChqX4l6rpVZeZtr3M5llP4AN6F49Qv501dRyu8XiE8Ph6BdGhbin+GopNgUvb9KoXCr9fvZhgU1pXNemx471bXSqnrTrnik7sGokVK1asWGtTmzKb2DGwg2MLx/j+ue9z1/a7gr8dXTjKqaVTmJrJbRtvI22muXPbnXzuuc/xxWNf5AUTL1hxRFa+nOeD3/8guXIOUzMZSg4xkhphODkcAOB7dt1Tcy+2MbORRy48EufaXsH63pnvAbB3aC/PzD3D105+jds23cZAYuAyj+zS68TiCSpuhV2Duy73UNaN1p/FMlYttK3JtFVO22qmba3TVpZbQVu7omIT1udhUP9h2Yslt1GZtmvJUVc/ll6BmYZGZB3u81XDV5GxMhwaO9SDUUmteWj7POQM60JHE2vnPK2vwVpzOIYBd0/zU821m58a12Tt7fNav3bVZNpeAmhbKMfxCLFixYoVa+3qxZteDMB9Z+6riT1QLtsXTr4w+D5++5bbyVgZZgoz/PDcD1f8Go/OPEqunANkA9/p/DRPXnyS7535HiWnxPb+7dyy8Zaa52zsk07bc8vnOt+5WGtWJxdPcmrpFLrQ+cWrf5Ft/duwHZsvHfvS5R7aJZft2PznB/8zf/LgnzBXnLvcw1k3WjsUINaKpRyghgg5bT03iEcw/BtXx3NqPoBM/cpz2obVS6etArawttxbDfEIl6gRWaf7fHD0IB+67UMcHD3Yi2EBfnYo1WN0LUF0qK2JLvSeHS/hpldrHUatOVdn6P3qlRu9fltrrSZr3mkbGt9PaiOyXrq+eyE1Pku3egqU1X7mK3E8QqxYsWLFWru6YfwGUkaK2eIsT84+CcDFwkUenn4YgJdveXnw2ISe4NXbXg3APx77R8pOeUWv8ejMowC8evur+b1bf4/3Xv9e3nbgbdy14y5esukl/MLVv9AQ4xXEI+TONkQq9FqHpw/zpeNfuuSvc6Wp7JT5xslvsGgvrvq5Ksv2+vHryVgZ7tl9j/z92e8ylZvq5TDXnI4vHsd2bBzP4fD04cs9nHWjGNquQ0U6bd2KbEYGgSOvPtO2vhGZyrQ11mGmLdRCW13oPbnpjLrpX1PL0uvjEXq0FD+pJ2VjO18rXfITpV43ZxJC1NRlLUF0qB1PykxdkvzUtQajDM2oOV7WHEgPL8XvZdOrNbwUf105ba1Lk2m7lq7VsH6ctr3uTK2uXXEjslixYsWKtZZl6iY3b7gZgO+eliDt26e/jYfHvuF9ATxVum3TbQwmBpkvzfO9s99ru/2yU+aJi08AcGjsEKOpUfYN7+PWjbdy9867ecv+tzCaGm143mhqFF3o2I7NxeLFbnezqWzH5i8f/0s+/9zneW7+uUv2OleivnPmO/zds3/HJ5/65KqeV6wU+fGUjMVQTu89Q3s4OHoQz/O49+i9PR/rWtLRhaPBvx+68NBlHMn60vqkdT/hqmlEpqIN3JDT1r9xrXiVwGkrhED34azrQ9uyI/9mauvfaZuxMj2BZfU31brQg7iJtaBL5dwSQgQgxdTMnoPXbhXOglxz4CPs6uwRRIda2LjWYBTU7vdac3WGIXIvaxLeVjcTG5dCuqbXjGmt5aeGj5eegvQ1PLmx1rOf1edHr/PTgniEGNrGihUrVqw1rts2yYZkj808xvncee47ex9Q67JVMnWTu3bI7NsvH/8yJafUcttPzz2N7dgMJgbZnN284jEZmsFk3yRwaZuRPTX7FGVXOoafW2gPbY/OH+VvnvybhsZtP4k6vXQagMdnHl9V49UHph7AdmzG0+PsHtwd/P71u16PQHB4+jDHFo71fLxrRUfnq9D22Pyx+FhaoWJouw7lhLNrRTgeQcUdmMHjFLTVhY7hw9nAaev/31yvTltRBRS9ggCa0GrAx5qDACF3n0D0FAKoJctrzTUJtXVYazUJA9VeAsKEngjcrGsNEEItgFprNQmfJ72MRwhfZ9baPkPtmNYaIAxfVy5ZPMIacz9rQqu5Pqy1muwf3s+rtr+Ke3bd09PtxtA2VqxYsWKtF032TbJ7cDceHh975GMUKgXG0mMcHImOenvRhhcxmhplyV7iW6e+1XLbD1+QMQvXjl27akNMOCLhUumRC48E/w47IJvps0c+y31n7+Ovn/zrn/g4hfP58wCrXuavHNov3vTimmNiU2YTN224CYB/OPIPV+T763pucJxlrAweXnCOxGqt9UnrfsJV67RtbESm+TDT9dwgHkETGpp/YXA9FY9w5WTa9tK5FQYfa83hGAYfKaN3S/Gh+h6utX2GWtix1sBHTU16lDEM0v2sarIWAeGarknoGO7ltSHsbI9rsjqFgWovoe1ar4kak6mZa2rVBshr1+t3vZ6dgzt7ut0Y2saKFStWrPUk5ba9kL8ASJdts3ssQzN4zY7XALJhmXKq1sv13P8/e/cdHlWZ/g38e6amTnqFUAKh9yIiIoggilhXVl1UcFfcVSxY1hXXVdSfoq66Kq6FXVdd18r6Yl0rUgTpRamhkwDpdTKT6c/7x3BOZpJJMklmMjPJ93NducicOXPOPfMkw5N77nM/Sj/bEWkj2hxTVmwWgOAtRuYZHwAcqznWYqLQ6rTieO1xAMD+iv3YeHpjUOKKBEIIFJuKldtyu4PWnDKdUhYgm5A5ocn9s3NnQ6PS4HD1Yeyt2BuweMNFkakIFocFerUeF/S6AACws5QtEvzBpG0EkhOxWknr3dO2hfYIKkmlJGflClubM7IrbYOVmPFMLoRbEsDzOQcyQQg09DUM96RtOI9JICttgYYq0XCrIAQAvSaME4RBuhRfrVIrbQfC/fckXMdEp9YFtJrfs5I6HK8SkMfB8/elq5PfB9tyuSAREVGojEwfqcwXozXRSp/b5ozNGItEfSKMNiO2l2z3uc/xmuOos9UhWhPtdRm8v5RK2yC1RzhafRQmuwkxmhhoVVqY7WaUmJtfBOtYzTG4hEtZHPrjQx+joj54/XbDWaWlEjanTUnsH6o6hGpLdauP21bhTu7KC5A1lhSVhCk9pwBwVzV3tWpbuW9yn4Q+GJM+BgBwuOpwuxZz624iM1vXzTlcPiptXQ2Vtpozfxw7XN5JW6XS1tWo0rYr9LQNYOVWOFfaesYW6JXI5f88wi0pCoR30tZrIbIAj4lS/RyGyahw/nAjWH2GPY8Xjon0cG6PkBqdCp1ah17xvQJ6XK1Kq7QRCecxCbe+z8Ekvw/aXfZmK5CIiIjChValxeSekwEA5/U8r9U5lEalwZQcd3Lth4IffCbXfil3tx4YkjKkXW3O5KRtiblE+ds/kOQq26GpQ9Hb0BuAd7/Rxg5VHQIAjMsch9yEXFidVry7/90ul1j0h9waITMmE7kJuRAQ2F7qO3kvszgs+LnS3QpAXoDMl5l9ZiJKE4ViUzHyq/IDF3QYkH+++if2R0p0CnoZekFAeLXpIN+YtI1ATqWiVuOzp63qTCK3cXuELtfTNkjtETz/ow63ZFlQk7bahoXIwk0kVBACge2fCjT8XIdjMiqcK229qp+DNSZhlqgGvBOD4VbZGauNxeOTHsfC0QsDelxJkpSrDsJ5TMJtYbhgitZEK5U4FoclxNEQERG1blbfWVg0ZhEuyb3Er/0nZU+CTq3D6brTOFh1sMn9ciKqPa0RACBJn4QoTRRcwoVSc2m7jtEcIRp6iY5MG6m0SWppMTI5aTsweSCuH3I9tCotDlYdxI+nfgxobJFAbo2QGZuJcZnjAKDZimvZ9pLtsLvsTRYgayxGG4OzMt29bdefWh+giMOD/POVm+D+eRudPhoAWyT4IzKzdd2cd0/bM0PouRCZ3B7Bo9JWLamhPpO0dZ5J1tojvKetXF0FBK+nbbglyzqlqjPMk7bhlpgJZiKdY9I+nh+2BPK9AWgY43D7QAdoiEklqbwWagwXsdrYoPwsR0L1c7h9sBFMkiQpSWq2SCAiokigklTon9QfKsm/9EiMNgZnZ50NAPih8Aev+4pNxSg1l0ItqTEkZUi74pEkCdmxwWmRUGwqRnl9OTQqDQanDFaSaMdqjvnc37OfbV5iHtJj0nF5/8sBuC/jL68vD2h8wVZjrcFjGx/DJ4c/adfjPZO2o9NHQ5IkFNQWoMTUfHsJZQGy7EmtrkkjV+L+XPYzaqw17Yox3FRZqlBlqYIkSeiT0AcAMCptFADgYNVB1NnqQhdcBGDSNgJ5tUeQmrZHUKubJm1VkqpJ0tYR4T1tg9UeIZyTUZIkKUmPQCcI+xr6QpIk5RKZcOJZNRhuiRnPeIJ1KX44JgjDufo5Sh0FCZLXYm6BEs6JdLmqU6/WB3SRwnBn0BkABL6qOhC6Y3sEgIuRERFR1zc1ZyokSNhbvtcrYSdX2Q5IHtChv9ey4tyLkZ02BTZp+3O5u8p2YNJA6NV69E3oCwAoNZfCaDM22V/uZ5sclYyU6BQAwJSeU9A/sT9sThv+s+8/EdUmYWPRRpSaS7Hu5Do4z+RP2kJO2mbFZiFeF4/ByYMBNF9tu69in7IAmVxF25IecT3cbReE6DILvsn9bHvG9VT+bkyLSUPP+J4QQijtRMi3yMzWdXPe7RHODKHHQmTqM0kkz/YIakmt9LR1nnlTtbvcCV1thFbaerVH0AWp0jbMkrYAgpa0zTHk4JnznsEV/a8I6HEDIZwThJ4/h4FOGg1JGYIYTQwGJQ8K6HEDIZzHRKfW4dpB1+K6QdcFPLYxGWOQHpPe7sqJYOqOVZ0AcEX/KzCr76yw/j0Jt3YVwfbH8X/E81OfD8sPAYmIiAIhPSYdQ1OHAgDWFK5Rtsv9YoenDu/Q8YNVabu7zB2f3LohVhuLzNhMAL6rbeXWCHlJeco2SZJw/ZDroVPrcLj6MD498ikKawsj4sPaXaW7AAA2pw0n60626bFCCK9KWwAYl+FukbCtZFuT5HWtrRbv7HsHADA+dbzPBch8ObfHuQDcLRLkIry2yK/Mx7GaY2GTTD9a4+5n2y+xn9f2UemjADSMCfkWftdPUquUSlvJYyEy4fJYiOxMpa1wwOVqWmnralRpq1FFZu4+aAuReVROhmM1nVatBRyBT9oCwTlmIIRzIl2ufra77AF//QYmD8TT5z0dllWT4Zy0BVpu8t8Ro9NHKz2Ywo08Dt2pfyrgXoVWvtQq3HTXRHq8Lj7UIRAREQXd+TnnY0/5Hmwu3ozZ/WbDKZw4XnMcADAitX39bGXyYmSBTNrWWGtwovYEJEgYljpM2d43oS+KTcU4Un2kSR9eX0lbwL3I7JX9r8SH+R/i+xPf4/sT3wNwL26dGpWKjNgMzOwzE+kx6QGLv6PKzGU4aWxI1B6pPtKmD5hrbbWod9RDkiSkxaQBcCe/tSotSs2lKDQWopfBveiuEAL/3vtvGG1GZMVl4cIeF/p9ntHpo/HfQ/9FtbUa+yr2eY1Vaw5WHcSyncsAuBPLE7Mn4qzMs0I6N5OTtnIrDtnotNH44sgXOFB5AGa7OSyvmgsHkZmt6+a8e9rKSVtnk0pbp8vptRCZuvFCZBHe09YzoRq0hcjCMGkrJwHCNcEaDJ6XFodb0hZoaF8QjDEJx4QtEN6J9O5Kfu/ieIQP+b2rO71fExERdRcDkgagR1wP2Jw2bDi1AXvK9kBAoJehFxKjEjt0bDlpW2mpDNjCnnLrht6G3kjQJyjb5QrIxpW2jfvZNnZuj3Nxab9LkZuQq1SR1tnqcLz2ODYXbcbKQysDEnegyIteyQumHq4+3KbHF5mKAABp0WlKniBKE6UkVbeVbFP2/b7gexyoPACtSovfDv1tm/IKWrUWE7MmAkCbF3tbd3Kd8n2xqRgrD63EQ+sfwj93/xMHKg+06ViBUO+oxynjKQBNK20zYjOQHZcNl3CxRUILmLSNQC5lwTHP9ggu4Ez5u9zT1imcEHBvU0kqaBpV2tpdXaenbdAWIgvD5IdcCdydkgDhPiYTsyaiX2I/9IjrEepQOo2cINSqtH4v2kDB1V37p4azszLPwuCUwRifOT7UoRAREVGASZKE83udDwBYe3ItdpTuAIAm1artEauNVRKr8iX5HSUnxhrHJ1dAFhgLYHfZle2++tl6kiQJM/vMxD3j7sFTk5/Cs1OexZ/O+hOuG3QdAHc/13BalFRO2p6V5e4te7j6cJtaCDRujSCT53nbS7bDJVw4VnMMnx/5HAAwZ8CcJvv745zscwAA+8r3oaK+wq/HVFmq8HOZu2fxPePuwbWDrkUvQy84hRO7Snfh5Z0vY/2p9W2OpSOO1xyHgEBqdKrXBwUy+QpGeWyoKf6lHYG8Km3lZIlouhCZZ6WtWlI3qbS1O+Tkb3hW8rVGTtqqJXVAkxSeScFwrLSVE9QGvSHEkXQez/ENx8uMr8y7EnePvdvrg4SujlWd4YdjEn5yDDlYOGohcuJzQh0KERERBcHYjLGI18WjxlqjVDJ2tDWCTE72+VqMLL8yH+8feB8mu8mvY9U76nGw8qA7vkZJ27ToNMTp4uBwOVBYW6hsb641QnOiNFHIic/BpB6TkBWb5U4Wlu1q9XFmuzno/VfL68tRaCyEBAmX5l4KrUoLs92MEnNJ6w8+w3MRMk+DUwYjWhONGmsN9pTvwZt73oRLuDAmYwwmZk9sV7wZsRkYkDQAAgI/nf7Jr8dsOL0BQgj0T+yP3IRcnNvjXNw//n4snrBYSSx/efRLWJ3WdsXUHkdq3IuQyQveNSYnbQ9UHAirBH84YdI2Aik9bT3bIzjtAOQete5Eo4BQ9vVsjyAvROaI9EpbyZ0gi9PFBfQScs+kYDgmP67MuxJX9L8Cg5LCb9GdYAn3MemOlAWWwjCJ3l0NSRmCnvE9WdVJRERE1Em0Ki3O63mecjs1OrVJUq+95Kv4Gve1LTYV4/VfXseGUxvwYf6Hfh1rf8V+OIUT6THpTSo/JUlCX4M7qSb3HwUakrb9E/u3OfaxmWMBuKtPW1JoLMRDGx7C0i1LUV5f3ubz+Ete7CovKQ+JUYlKErEtLRKaq7TVqrTKolpv7nkTlZZKpESn4NqB13YoTyEvSLbx9EYlr9Mch8uBjac3AgAm95zsdV+PuB6YO3guUqNTYbQZvVooBNvRat+LkMkyYzORGZsJp3Bib8XeTosrkkRmtq6bk6tn3QuRnans87iMQe2R1LI5be5tHpW2zjMLkNnP9LSN2KTtmeceowlsw2qvS/FV4ZcgzInPwfTe06GWE/bdgOeYMEkYHvQaJm3DTXpMOh446wGMzRgb6lCIiIiIuo1ze5yrXKE5PHV4wAqK5OSv3EsVcFfMLv9lufJ3/o6SHdhTvqfVY8mXzTfXukFOqslJW69+tn5W2noalzEOAHCw8iBqrDXN7vft8W9hc9pwuu40/rr1r0qiONDk1hVyclV+voer/E/ayuPgq92B/HztLjtUkgrzh87v8MJaI9JGIF4Xj1pbbatjvLt8N2qsNYjXxWNk2sgm92tUGlzc92IAwHcnvkO9o75DsfnD4XIoP0P9EnwnbYGGMfnm+DedElekicxsXTfnVWkrt0dwNnzyovZINNqd7mSuSlJBLTWqtD2TvI3UhcjkpK3c9DxQvNojqMOvPUJ35LUQWRgm0ruj/on9MSRlCM7POT/UoRARERERhUy8Lh4X9rkQ8bp4TOoxKWDHlRcjk9sjCCHwn33/Qam5FIn6ROXS+w8OfNDiYmUOlwP7KvYBaL51g9zX9mjNUQghlH62SVFJSIlq2s+2NanRqehj6AMB0Wy/0or6CqUCNjM2Eya7Cct2LsNPp/xrB+CvivoKFNQWQIKkJAjl6mF/+9oabUaY7CZIkJARk9Hk/rykPCTqEwEAl/a7tNl2AG2hUWmU3ratLUj240n3/edkn9Nsy77xmeOREZMBs92MNYVrOhwf4P4QYdnOZfjvwf8qay/JTtWdgs1pQ4wmpsW+vpN7TEaCPgHFpmK8uedNOM+0/SQ3Jm0jkHdP2zPVlqLhB1vtkWi0udyfwKkkFdTqhoXIhBBwuM5U2qoi88fAoHP3dG3PfyIt8UwK8lL88BClcSdtdWpdQFthUPvp1XrcNuo2nNPjnFCHQkREREQUUhf3vRhLJy9t16JTzcmKzYIECXW2OtTZ6/B9wff4uexnqCU1bh5+M+YMmIPU6FRUW6vx+dHPmz3OwaqDqHfUI04Xhz4JfXzuk2PIgUalQZ2tDmX1ZQ39bBPz2v3317hMd/XptuJtPu9fU7gGAgIDkwfi/vH3Y0zGGLiEC+8deA//PfjfgCXv5MRw/6T+Sg6hT0IfqCQVqq3VqLRUtnoMuTVCcnSyzxyBSlLh9yN/jxuH3IjpvaYHJG7AnYSVICG/Mh9l5rJmYztYdRASJKWlgi8qSYVLci8BAKwqWOV3P+SWbDi1AfmV+VhTuAZv733ba8yOVJ/pZ5vYt8WfoQR9Am4ZcQu0Ki32VezDx4c+7nBcXUlkZuu6Mc9fAq+eth5Uaq3ySyGv/uhZaQsANqcL8gdKkVppOyp9FOYNnYdL+10a0ON6Xu4djguRdUep0ak4t8e5uKTvJaEOhYiIiIiIKOh0ah1Sot0FSj+V/qQkZucMnIM+CX2gU+tw7aBrAQDrCtfhWM2xJsc4VHUIb+19CwAwMm0kVJLvFJBWpUWv+F4A3NW2bV2EzJfR6aMhQcLx2uNN+tXWO+qVBbam9ZoGnVqHm4bepCQV1xSuwas/v6q0gegIudJXXvQKcP/NLz9fObnYkub62XrKic/BWVlnBbTIKCU6BYNTBgMAPjvymc9EtlyFOyx1GJKiklo83uj00ciOy4bFYcGqglUdis3pcnpV7G4v2Y639r6lXBkut9poqTWCrLehN+YNnQcAWHdyXcAqgbsCJm0jjFO0nrSFpFIW6ZLbI3j2tAUAq6OhdD1Sk7YalQbjM8cjQZ8Q0OOGe0/b7kiSJFw76Fpc0PuCUIdCRERERETUKeTFyH4s+RFCCEzMnohJ2Q0tGAYlD8JZmWdBQOC9/e95LVi18fRGvLzzZZjtZvQy9MLs3Nktnku+pH9/xf4O9bOVJegTMCB5AABgW4l3te3G0xthdVqRGZuJIclDALj/5ru478W4efjN0Kl1OFB5QLnsv70qLZU4XnscEqQmvV7lvraHqlvvo1tsPpO0jQlcJbW/pveeDkmSsLN0J97c+6ZSmAe4ew9vLtoMAF4L4jVHkiTl52BN4RoYbcZ2x7WrbBeqrdWI08Xht8N+C7Wkdse45004XA5lETK59UZrRqWPwuX9LwcAfHzwY+wt58JkAJO2EcfzTVgtqRt62iokQJKURaq82iN4JG0t9obkb6S2RwgWr6Qt2yMQERERERFRCGTFZSnf94rvhV8P+HWTSs6r8q5CrDYWRaYifF/wPVzChZWHVuLd/e/CKZwYnT4ai8YsQrwuvsVzyUnMnaU7O9TP1pO8QJdniwTPCs3zc85v8nxGpY/C5f3cybtdZbs6dH65NUJuYm6TYi+5r60/lbZFde5FyDzHo7MMSBqAm4ffDLWkxq7SXXh116uwOq0A3K+rxWFBanQqBiUP8ut4w1OHo5ehF2xOG749/m2741pdsBoAcF6P8zAmYwxuGXELNCoNfi77GS/teAm1tlqoJTV6G3r7fczpvaZjYvZECAi8secNnKo71e74ugpm6yKMnLSVJOlM0rZRpe2ZZK36zHb5cgK1Su3VHsFqd1faqlQSVKrIrLQNFrZHICIiIiIiolCTL+GPVkfjt8N+63Oh7DhdHK4ecDUA4KujX+GVXa8ol75f1Pci/HbYb/0qRpIrbeUFpTrSz1Y2Mm0k1JIaxaZiJQH3c9nPqLRUIlYbi7Myz2r2cQBwvOY4aqw17T6/nLT1bI0g65fYDxIklJpLUWurbfE4JeYSAPC5CFlnGJk2EreNug06tQ4Hqw5i2Y5lMNlNSmuEyT0n+z1WkiTh0lx3i8kfT/2Iaks1APe4n6o7hfWn1mPFwRUoNBY2e4yjNUdxvPY4NCoNJvecDAAYmjoUvx/xe2hVWqU1Qi9DrzYt7i5JEq4ZeA0GJA2AzWnDq7te7VA1cFfApG2EkUvhNSqN+5eycXuEM5W38oqB8v4S3MlZ+ffY6nBX2mojtDVCMHn+h9aWNxgiIiIiIiKiQBmeOhy/Hvhr3DzgZqW/rS/jMsZhSMoQOIUTByoPQKPSYN7QeZidO9vvZF68Lh7pMenK7Y60RpDFaGMwLHUYgIZq2x8KfgDgTjQ29/d2YlQieht6Q0Bgd/nudp27ylKlJA99JW1jtDFK5ax8Kb8vZrtZSRwHcqG5thqYPBB3jr4TMdoYHK89jqWbl+Kk8SS0Ki3Ozjq7TccalDwI/RL7weFy4F97/4VlO5fhj2v/iKWbl+KDAx9gbeFavLrr1WaT2XKV7fjM8V4V3INTBuMPI/+g5FT8bY3gSaPS4ObhNyM9Jh3V1mqsOLiizcfoSpi0jTAO4a60lXvW+l1pe+a26swbtuVMpa2GrRGa8Ky0ZU9bIiIiIiIiCgVJkjC5x2SkR6e3ut81A69BrDYW8bp43DnmTozPHN/m83km2QKRtAWAsRljAbgXqjpa7a7QVEvqVnuwytW2P5f93K7zKq0REpq2RpDJi2S11NdW7mebqE9EtCa6XbEESp+EPrh7zN1I0Ceg2loNwP36xmpj23Qcz962R6uPIr8yH1anFTq1DgOSBiA1OhW1tlq8vfdtpfJaVmmpVNpWTM2Z2uTYA5MH4o7Rd2Bi9kSf9/sjRhuDm4beBEmSsKNkhzKW3ZEm1AFQ28jtEeSetU0rbeXkrDsZKydt5dsatQSnSyiVtpG6CFkweSZq2dOWiIiIiIiIwl1KdAqWnLMEWpVWufK2rXITcrGpaFNA+tnKhqcOh06tQ6WlEu/ufxcAcFbWWTDoDC0+bmTaSHx25DMcrDwIs92MGG1Mm867s3QnAN9VtrL+Sf3x46kfW+xrW2w6swhZCKtsPWXFZeHusXfj5Z0vo9JSiSk5U9p1nLykPFze/3IUm4rRx9AHfRP6IjsuGypJhWJTMZ7Z+gzyK/PxzfFvcHHfi5XHrS1cCyEEBiQNUBbKa6xvQl+l3UZ75RhycGHvC/HN8W/wQf4HyEvKa3NyuitgmWWEkZO2ypuwJAHwSLw20x5BTvI2rrTlImRNaVQaJEUlIVoTjThtXKjDISIiIiIiImpVtCa63QlbABibORbjMsbhyv5XdrifrUyr1ipVs3Jv2PNzzm/1cRmxGciIyYBTOLG3Ym+bzlliKsHRmqOQILWctD2zGNkp4ymY7Waf+8hJ26zYzl+ErDmp0al4cMKDeGTiI8iJz2n3cWb0noEbhtyAyT0no2d8T6XYLzM2E9cMvAYA8L+j/0N+ZT4AwOq04qfTPwEApvWa1sFn0bqL+l6EzNhM1NnqsCK/e7ZJYMYuwjjFmV60kkfvF89q2zPfy2/UNlejStszi44pPW01rLRtTJIk3D/+fvx5wp/Z05aIiIiIiIi6Bb1aj/nD5mNMxpiAHndc5jjl+4HJA5Edl+3X40amt69FwsaijQCAIalDkBiV2Ox+CfoEpEanQkDgWO0xn/vISduM2NAsQtYcnVrXYp/jjpqQNQETsydCQOCtvW+hxlqDjac3ot5Rj/SYdAxNGRq0c8u0Ki1uGHIDJEjYVrINv5T9EvRzhhsmbSNMk/YIgFJd6/7euz2C3emutFWdGWqVij1t/RGvi2/xzZ2IiIiIiIiIWjcwaaCyYNUFvS7w+3Fyhe6+in1KbqM1DpcDm4o2AQDOyT6n1f37Jbr72h6uOuzz/iJTEYDwqrTtLHMGzEFWbBaMNiPe3vs21hSuAeDuZRuoSuzW9Db0xgW93T8zHxz4ACa7qVPOGy6YsYswTdojAIDX92cqaiXv9ggqlXelrcV+ptKWPW2JiIiIiIiIKEg0Kg1uG3Ubbh5+M4akDPH7cb3ieyFRnwib04b8qny/HrOnfA/qbHWI18X7VQ0qt0jw1dfW6rSiylIFIHx62nYmnVqH3w3/HXRqHQ5WHUR5fTliNDGYkDWhU+O4pO8lSI9JR62tFh8f/LhTzx1qTNpGGJ9JWx+VtnIlrrwQmVrZLrdHOFNpq+aPABEREREREREFT058Dkalj2rTYyRJwoi0EQD8b5Gw8bS7NcLZWWf71d9XTtqeqD3RpJpXbo0Qr4vvlotgAe5k9bUDr1VuT+oxCXq1vlNj0Kq1uH7I9ZAgYUvxFuwp39Op5w8lZuwijEOcSdpKntW1TVslyEnaxj1t1Y0rbVWstCUiIiIiIiKi8CO3SPil7Bc4Xc4W962yVGFfxT4AwMTsiX4dPzU6FQn6BDiFE/sq93ndJydtu2OVraezss7CzD4z0dvQG+f3an0RuWDITchVzv3fg/+FECIkcXQ2Jm0jjO9K26YLkTWutFWStlKjnrastCUiIiIiIiKiMNQvsR9iNDEw2U04WnO0xX03FW2CgED/xP5Ij0n36/iSJGFQ8iAAwD9/+Sf+e/C/sDgsADyStjHdO2kLAJf2uxR/HP9HGHSGkMVwSe4liNZEo7y+HAerDoYsjs7EjF2E8bkQmappewSlp628EJlSaev+1+qQe9ryR4CIiIiIiIiIwo9GpcGw1GEA3NW2zRFCKK0R/FmAzNNVeVdhbMZYCAisKVyDxzc9jl2luxoWIYvrfouQhSO9Wo9xGeMAQFlsrqtjxi7CyElbraRt2NhCpa2Au2S8oaeteze5py0XIiMiIiIiIiKicDUy3d0i4eeyn5u9LD6/Kh+VlkpEa6Lb3Ds3VhuLm4bdhNtG3YbU6FTUWGvwz93/xN7yvQCAjJiMDsVPgXN29tkAgF2lu2C2m0McTfAxaRthnMJdIevVHqGFnrbK5jNtEVRnetjW29zHUbOnLRERERERERGFqcHJg6FVaVFpqcTJupM+9/np9E8AgHEZ46BT69p1niEpQ/DnCX/GzD4zoZbUShFcd+9pG056xfdCVmwW7C47tpdsD3U4QcekbYTxt6dt41US5SSu5kyStqHSlj8CRERERERERBSedGodBqcMBuCutm2szlaHn0vd28/p0bbWCI1p1Vpc2u9SLJ6wGMNTh2Ni9sSQ9nElb5IkKYvMbSzaGOJogo8ZuwjjM2nrR6Vt44XIGnrastKWiIiIiIiIiMLXyDR3i4QtRVuwt3wvnC6nct+W4i1wCidy4nOQE58TkPNlxmbi9yN/j7mD5ypXLlN4GJ85HipJhYLaApyqOxXqcIKKSdsI02qlrbLgmHfStqGn7ZmFyOyuM8fhjwARERERERERha/hqcMRo41BpaUSr/78Kv6y4S9YeWgliuqKlNYIcgUmdW3xuniMSBsBANh0umsvSBYxGbvKykrMnTsXBoMBiYmJ+N3vfoe6uroWH7N8+XJMnToVBoMBkiShuro6IMcNJbvLDqBxpa3HMMptECTv9ghKpa2yENmZSltNxPwIEBEREREREVE3FKONwX3j7sPUnKmI1cai1laLVQWr8MTmJ1BsKoZWpcW4jHGhDpM6ydlZ7gXJthRvUYobu6KIydjNnTsXe/fuxXfffYcvvvgC69atwy233NLiY8xmMy666CI8+OCDAT1uKDmE+4fRq/2Bj562jSttG5K27n/lBRc1XIiMiIiIiIiIiMJcekw6rh5wNZ449wncMuIWjEgboeQ6xmWOQ4w2JsQRUmcZnDwYCfoEmOwm7CnfE+pwgkbT+i6ht3//fnz99dfYunUrxo1zf3KybNkyzJo1C88++yyys7N9Pm7RokUAgDVr1gT0uKHkb09b+Y1L2aVRpa2MSVsiIiIiIiIiihQalQYj0kZgRNoIGG1GHKs5hoHJA0MdFnUitUqNszLPwncnvsPGoo0YlT4q1CEFRUQkbTdu3IjExEQlsQoA06dPh0qlwubNm3HllVd26nGtViusVqtyu7a2FgDgcrngcrnaFYu/HE4HhBBQQ62cS4KklM4KSQW4XFBDDSGX0wKQhASXywWVBAg0bNeopKDHTA1cLheEEHzNIxjHMLJx/CIbxy+yBWv8+PNARETUfXn2N6Xu5eyss/Hdie+wr3wfaqw1SNAnhDqkgIuIpG1xcTHS09O9tmk0GiQnJ6O4uLjTj7t06VI8+uijTbaXlZXBYrG0Ox5/VBurYXfYYTKaUFpaCgCIrbdCa7MBAGwmM8ylpagz1sF2ZhsA1NbUolRdCrPJBKu1YXudsQalpQLUOVwuF2pqaiCEgIqLwEUkjmFk4/hFNo5fZAvW+BmNxoAdi4iIiIgiQ0ZsBnITc3G0+ig2F23GhX0uDHVIARfSpO0DDzyAp59+usV99u/f30nR+G/x4sW45557lNu1tbXIyclBWloaDAZDUM99rjgX6VHpGJkzEumJZxLOcQZINToAgC4+AXHp6Ui2JkNXplMel5KcgvT0dCQa6qHXNySW05KTkJ6eEtSYqYHL5YIkSUhLS2PCIUJxDCMbxy+ycfwiW7DGLyoqKmDHIiIiIqLIMTFrIo5WH8XGoo2Y0XsGJKlrtQANadL23nvvxfz581vcJzc3F5mZmUpVqczhcKCyshKZmZntPn97j6vX66HX65tsV6lUQf8jckzGGPSUeiI9Mb3hXGo1cOYHU1JrAJUKWrXW64dVo9JApVJBo1a72ymcodOq+YdvJ5MkqVN+Vih4OIaRjeMX2Th+kS0Y48efBSIiIqLuaUzGGKw4uAJl5jIcrTmKfon9Qh1SQIU0aZuWloa0tLRW95s4cSKqq6uxfft2jB07FgDwww8/wOVyYcKECe0+f7CO2+k8Fx2T3IuSqSW11y7yQmSNFx7T8A8dIiIiIiIiIiKKMHq1HmPSx2BT0SZsL9ne5ZK2EZGxGzx4MC666CIsWLAAW7ZswYYNG3D77bfj2muvRXZ2NgDg1KlTGDRoELZs2aI8rri4GLt27cLhw4cBALt378auXbtQWVnp93Ejgsoj934mCatWeSdt5SSuqnHSVt21SseJiIiIiIiIiKh7GJU+CgCwu3w3hOhaazZFRNIWAN59910MGjQIF1xwAWbNmoVzzz0Xy5cvV+632+3Iz8+H2WxWtr322msYPXo0FixYAAA477zzMHr0aHz22Wd+HzcieFbVnvleI3kXUcuVtupGI65rvIGIiIiIiIiIiCgCDEgaAK1KiypLFU6bToc6nIAKaXuEtkhOTsZ7773X7P19+vRpklFfsmQJlixZ0qHjRgSv9gi+K20bkrbeSVoNk7ZERERERERERBSBdGodBiQPwN7yvdhdvhs94nqEOqSAYcauK/BM0Kpa7mmrlhr3tGV7BCIiIiIiIiIiikzDU4cDAPaW7w1xJIHFpG1X4GshsmZ62qobJWm1rLQlIiIiIiIiIqIINSxlGADgeM1xGG3GEEcTOMzYdQVeC5G11tOWC5EREREREREREVHXkBiViJ7xPSEgsLei61TbMmnbFfjqaSv5rrRt3A5Bq+KPABERERERERERRa5hqe5q2z3le0IcSeAwY9cVeLZCaG4hsjPJWVXjpK2GlbZERERERERERBS55L62+yr2we6yhziawGDStiuQmiZtG7dHaK7SVsNKWyIiIiIiIiIiimC94nvBoDPA5rThSPWRUIcTEMzYdQWeVbUq3wuRSXAna1VS44XIWGlLRERERERERESRS5IkDE0dCgDYXb47xNEEBpO2XYFXT9szSdvGPW3lBco8krRqlQRJYtKWiIiIiIiIiIgim9wiYU/5HgghQhxNxzFp2xX4UWmrOpPY9ay01bDKloiIiIiIiIiIuoCByQOhUWlQUV+BYlNxqMPpMCZtuwKvnrZnKmqb6Wmr9uhpy362RERERKH1xBNP4JxzzkFMTAwSExP9eowQAg8//DCysrIQHR2N6dOn49ChQ8ENlIiIiCjM6dV6DEgaAKBrtEhg1q4r8Kq0dQ9pk562Zyps1R6Vtlo1h5+IiIgolGw2G+bMmYNbb73V78c888wzeOmll/Daa69h8+bNiI2NxcyZM2GxWIIYKREREVH4G5Y6DIC7RUKkY9auK/CqtD2TtG3c09ZHpS0XISMiIiIKrUcffRR33303hg8f7tf+Qgi88MILeOihh3D55ZdjxIgR+Pe//43Tp0/jk08+CW6wRERERGFOTtoeqzmGOltdiKPpGCZtuwKVj4XIGlfa4kylrYo9bYmIiIgi1bFjx1BcXIzp06cr2xISEjBhwgRs3LgxhJERERERhV5yVDJ6xPWAgMC+in2hDqdDNK3vQmFP8rEQWeNKWxV72hIRERFFuuJi96IaGRkZXtszMjKU+3yxWq2wWq3K7draWgCAy+WCy+UKQqTeXC4XhBCdci4KPI5fZOP4RTaOX2Tj+IXGkJQhOGk8iV/KfsG4jHHtPk6wxs/f4zFp2xWomi5E1jhpq5LbJrA9AhEREVFQPfDAA3j66adb3Gf//v0YNGhQJ0UELF26FI8++miT7WVlZZ3SC9flcqGmpgZCCKhYOBBxOH6RjeMX2Th+kY3jFxrZUjZsNht2nt6JotSiJlej+ytY42c0Gv3aj0nbrsBHpa0kSVBLajiFE0BDElfj1R6BbxhEREREgXbvvfdi/vz5Le6Tm5vbrmNnZmYCAEpKSpCVlaVsLykpwahRo5p93OLFi3HPPfcot2tra5GTk4O0tDQYDIZ2xdIWLpcLkiQhLS2Nf7RGII5fZOP4RTaOX2Tj+IVGmkhD0skkmOwmWKOt6JPQp13HCdb4RUVF+bUfk7ZdgVelbUNSVq1Sw+l0J2199bTVMmlLREREFHBpaWlIS0sLyrH79u2LzMxMrFq1SknS1tbWYvPmzbj11lubfZxer4der2+yXaVSddofkZIkder5KLA4fpGN4xfZOH6RjeMXGrmJudhTvgfHjMeQm9S+D8uB4Iyfv8fiT0xXIDVtjwA0VNeqJBUkyUfSVsX2CEREREShVFBQgF27dqGgoABOpxO7du3Crl27UFfXsNrxoEGDsHLlSgDuPxwWLVqE//u//8Nnn32G3bt348Ybb0R2djauuOKKED0LIiIiovCSm+BO1B6rORbiSNqPlbZdgWeG3qPqVu1jUTI12yMQERERhY2HH34Yb7/9tnJ79OjRAIDVq1dj6tSpAID8/HzU1NQo+9x///0wmUy45ZZbUF1djXPPPRdff/2135faEREREXV1ctL2SPURCCGUYkZfthZvhdluxoi0EUiKSuqsEFvFpG1XIKl8fq+R3MPr+YOplrgQGREREVG4eOutt/DWW2+1uI8Qwuu2JEl47LHH8NhjjwUxMiIiIqLI1dvQG2pJDaPNiApLBVKjU5vdd3XhahTUFiBKE4UJWRM6McqWsdSyK1B55N6lViptPRK1GrZHICIiIiIiIiKiLkar1iInPgcAcLT6aLP7GW1GFNQWAAAGJQ/qlNj8xaRtV+DZ01blu6etcrfE9ghERERERERERNS19UvsBwA4UnOk2X0OVB4AAPSM74kEfUKnxOUvZu26ApXnQmQe7RHOVOB6Vtp6VtfqmLQlIiIiIiIiIqIuqG9CXwAtV9ruq9gHABiSMqRTYmoLZu26gmZ62rZeacv2CERERERERERE1PXkJroXIys2FcNsNze5XwiB/ZX7AQCDkwd3amz+YNK2K1D53x7Bs9KW7RGIiIiIiIiIiKgrMugMSI1OhYDAsdpjTe4vMBagzlYHvVqvVOWGE2btugLPnrY+FiLzqrRVSZCLbbVciIyIiIiIiIiIiLqo3AR3ta2vFgn7K9xVtoOSByktRsMJk7ZdQSuVtp49bYGGFgmstCUiIiIiIiIioq5KbpFwrKZppa3cz3ZwSvi1RgCYtO0avHraei465v6UwLPSFmjoZcuetkRERERERERE1FX1S+gHADheexwOl0PZbrablURuOPazBZi07Rq82iM0XYhMrfJdaatjpS0REREREREREXVRmbGZiNZEw+a04VTdKWV7flU+BAQyYzOREp0Swgibx6xdV+DZd0PlkbT10dMWaFiMTMOetkRERERERERE1EVJkqQsMubZIkFpjRCmVbYAk7Zdg+SRfPVsjyCdaY/QaJhVKrZHICIiIiIiIiKirk/ua3uk+ggAQAihJG2HpAwJWVytYdK2K2huITK50lblu9JWy/YIRERERERERETUheUmuJO2R2uOQgiBIlMRaqw10Kq06J/YP8TRNY9Zu67Aq6etR9JW7mkrefe0VSvtETj8RERERERERETUdfUx9IEkSaix1qDSUon9FfsBAAOSBkCr1oY4uuYxa9cVaPSAWguodV79beVkbeOetpmGaEiShHSDvlPDJCIiIiIiIiIi6kw6tQ458TkA3H1t91bsBQAMTgnffrYAoGl9Fwp7ai0w5QF3b1t1w5BqziRwG1fa3nZ+P5isDiTG6Do1TCIiIiIiIiIios6Wm5CLgtoC7K/cr/S2Ded+tgArbbuOzGFAxlCvTXJPW0nyXnBMq1YxYUtERERERERERN2C3Nd2a/FWOIUTqdGpSItOC3FULWPStgtrrqctERERERERERFRd9EvsR8AwCVcAIDByYObFDmGGyZtuzC5PULjnrZERERERERERETdRYI+AclRycrtcG+NADBp26U1txAZERERERERERFRdyJX26olNQYkDwhxNK1jNq8Li9HGAACiNdEhjoSIiIiIiIiIiCh0+if2d/+b1B96tT7E0bROE+oAKHjGZYyDzWnDqPRRoQ6FiIiIiIiIiIgoZM7OOhtO4cTQlKGhDsUvTNp2YVGaKEzrNS3UYRAREREREREREYWUWqXGeT3PC3UYfmN7BCIiIiIiIiIiIqIwwqQtERERERERERERURhh0paIiIiIiIiIiIgojDBpS0RERERERERERBRGmLQlIiIiIiIiIiIiCiNM2hIRERERERERERGFESZtiYiIiIiIiIiIiMIIk7ZEREREREREREREYYRJWyIiIiIiIiIiIqIwwqQtERERERERERERURhh0paIiIiIiIiIiIgojDBpS0RERERERERERBRGmLQlIiIiIiIiIiIiCiNM2hIRERERERERERGFESZtiYiIiIiIiIiIiMKIJtQBdAVCCABAbW1t0M/lcrlgNBoRFRUFlYo590jD8Yt8HMPIxvGLbBy/yBas8ZPnX/J8jPzTmfNXgL+/kY7jF9k4fpGN4xfZOH6RLdTzVyZtA8BoNAIAcnJyQhwJERERUfdkNBqRkJAQ6jAiBuevRERERKHV2vxVEixL6DCXy4XTp08jPj4ekiQF9Vy1tbXIyclBYWEhDAZDUM9Fgcfxi3wcw8jG8YtsHL/IFqzxE0LAaDQiOzubFSxt0JnzV4C/v5GO4xfZOH6RjeMX2Th+kS3U81dW2gaASqVCz549O/WcBoOBv/ARjOMX+TiGkY3jF9k4fpEtGOPHCtu2C8X8FeDvb6Tj+EU2jl9k4/hFNo5fZAvV/JXlCERERERERERERERhhElbIiIiIiIiIiIiojDCpG2E0ev1eOSRR6DX60MdCrUDxy/ycQwjG8cvsnH8IhvHr3vj+Ec2jl9k4/hFNo5fZOP4RbZQjx8XIiMiIiIiIiIiIiIKI6y0JSIiIiIiIiIiIgojTNoSERERERERERERhREmbYmIiIiIiIiIiIjCCJO2RERERERERERERGGESdsI8/e//x19+vRBVFQUJkyYgC1btoQ6JPJh6dKlGD9+POLj45Geno4rrrgC+fn5XvtYLBYsXLgQKSkpiIuLw69+9SuUlJSEKGJqzlNPPQVJkrBo0SJlG8cu/J06dQrXX389UlJSEB0djeHDh2Pbtm3K/UIIPPzww8jKykJ0dDSmT5+OQ4cOhTBikjmdTvzlL39B3759ER0djX79+uHxxx+H57qpHL/wsW7dOlx66aXIzs6GJEn45JNPvO73Z6wqKysxd+5cGAwGJCYm4ne/+x3q6uo68VlQsHH+Ghk4f+1aOIeNPJy/Ri7OXyNLJM1fmbSNIB9++CHuuecePPLII9ixYwdGjhyJmTNnorS0NNShUSNr167FwoULsWnTJnz33Xew2+248MILYTKZlH3uvvtufP7551ixYgXWrl2L06dP46qrrgph1NTY1q1b8frrr2PEiBFe2zl24a2qqgqTJk2CVqvFV199hX379uG5555DUlKSss8zzzyDl156Ca+99ho2b96M2NhYzJw5ExaLJYSREwA8/fTTePXVV/Hyyy9j//79ePrpp/HMM89g2bJlyj4cv/BhMpkwcuRI/P3vf/d5vz9jNXfuXOzduxffffcdvvjiC6xbtw633HJLZz0FCjLOXyMH569dB+ewkYfz18jG+Wtkiaj5q6CIcdZZZ4mFCxcqt51Op8jOzhZLly4NYVTkj9LSUgFArF27VgghRHV1tdBqtWLFihXKPvv37xcAxMaNG0MVJnkwGo0iLy9PfPfdd2LKlCnirrvuEkJw7CLBn/70J3Huuec2e7/L5RKZmZnir3/9q7Kturpa6PV68f7773dGiNSCSy65RPz2t7/12nbVVVeJuXPnCiE4fuEMgFi5cqVy25+x2rdvnwAgtm7dquzz1VdfCUmSxKlTpzotdgoezl8jF+evkYlz2MjE+Wtk4/w1coX7/JWVthHCZrNh+/btmD59urJNpVJh+vTp2LhxYwgjI3/U1NQAAJKTkwEA27dvh91u9xrPQYMGoVevXhzPMLFw4UJccsklXmMEcOwiwWeffYZx48Zhzpw5SE9Px+jRo/GPf/xDuf/YsWMoLi72GsOEhARMmDCBYxgGzjnnHKxatQoHDx4EAPz8889Yv349Lr74YgAcv0jiz1ht3LgRiYmJGDdunLLP9OnToVKpsHnz5k6PmQKL89fIxvlrZOIcNjJx/hrZOH/tOsJt/qoJ6NEoaMrLy+F0OpGRkeG1PSMjAwcOHAhRVOQPl8uFRYsWYdKkSRg2bBgAoLi4GDqdDomJiV77ZmRkoLi4OARRkqcPPvgAO3bswNatW5vcx7ELf0ePHsWrr76Ke+65Bw8++CC2bt2KO++8EzqdDvPmzVPGydf7Kccw9B544AHU1tZi0KBBUKvVcDqdeOKJJzB37lwA4PhFEH/Gqri4GOnp6V73azQaJCcnczy7AM5fIxfnr5GJc9jIxflrZOP8tesIt/krk7ZEQbZw4ULs2bMH69evD3Uo5IfCwkLcdddd+O677xAVFRXqcKgdXC4Xxo0bhyeffBIAMHr0aOzZswevvfYa5s2bF+LoqDUfffQR3n33Xbz33nsYOnQodu3ahUWLFiE7O5vjR0TUSTh/jTycw0Y2zl8jG+evFCxsjxAhUlNToVarm6zuWVJSgszMzBBFRa25/fbb8cUXX2D16tXo2bOnsj0zMxM2mw3V1dVe+3M8Q2/79u0oLS3FmDFjoNFooNFosHbtWrz00kvQaDTIyMjg2IW5rKwsDBkyxGvb4MGDUVBQAADKOPH9NDz98Y9/xAMPPIBrr70Ww4cPxw033IC7774bS5cuBcDxiyT+jFVmZmaTBakcDgcqKys5nl0A56+RifPXyMQ5bGTj/DWycf7adYTb/JVJ2wih0+kwduxYrFq1StnmcrmwatUqTJw4MYSRkS9CCNx+++1YuXIlfvjhB/Tt29fr/rFjx0Kr1XqNZ35+PgoKCjieIXbBBRdg9+7d2LVrl/I1btw4zJ07V/meYxfeJk2ahPz8fK9tBw8eRO/evQEAffv2RWZmptcY1tbWYvPmzRzDMGA2m6FSeU9P1Go1XC4XAI5fJPFnrCZOnIjq6mps375d2eeHH36Ay+XChAkTOj1mCizOXyML56+RjXPYyMb5a2Tj/LXrCLv5a0CXNaOg+uCDD4RerxdvvfWW2Ldvn7jllltEYmKiKC4uDnVo1Mitt94qEhISxJo1a0RRUZHyZTablX3+8Ic/iF69eokffvhBbNu2TUycOFFMnDgxhFFTczxX3hWCYxfutmzZIjQajXjiiSfEoUOHxLvvvitiYmLEf/7zH2Wfp556SiQmJopPP/1U/PLLL+Lyyy8Xffv2FfX19SGMnIQQYt68eaJHjx7iiy++EMeOHRP/7//9P5Gamiruv/9+ZR+OX/gwGo1i586dYufOnQKAeP7558XOnTvFiRMnhBD+jdVFF10kRo8eLTZv3izWr18v8vLyxHXXXReqp0QBxvlr5OD8tevhHDZycP4a2Th/jSyRNH9l0jbCLFu2TPTq1UvodDpx1llniU2bNoU6JPIBgM+vN998U9mnvr5e3HbbbSIpKUnExMSIK6+8UhQVFYUuaGpW4wkvxy78ff7552LYsGFCr9eLQYMGieXLl3vd73K5xF/+8heRkZEh9Hq9uOCCC0R+fn6IoiVPtbW14q677hK9evUSUVFRIjc3V/z5z38WVqtV2YfjFz5Wr17t8/+7efPmCSH8G6uKigpx3XXXibi4OGEwGMRNN90kjEZjCJ4NBQvnr5GB89euh3PYyML5a+Ti/DWyRNL8VRJCiMDW7hIRERERERERERFRe7GnLREREREREREREVEYYdKWiIiIiIiIiIiIKIwwaUtEREREREREREQURpi0JSIiIiIiIiIiIgojTNoSERERERERERERhREmbYmIiIiIiIiIiIjCCJO2RERERERERERERGGESVsiom5g/vz5uOKKK0IdBhERERGRXzh/JaLuThPqAIiIqGMkSWrx/kceeQQvvvgihBCdFBERERERUfM4fyUiap0k+C5IRBTRiouLle8//PBDPPzww8jPz1e2xcXFIS4uLhShERERERE1wfkrEVHr2B6BiCjCZWZmKl8JCQmQJMlrW1xcXJPLy6ZOnYo77rgDixYtQlJSEjIyMvCPf/wDJpMJN910E+Lj49G/f3989dVXXufas2cPLr74YsTFxSEjIwM33HADysvLO/kZExEREVEk4/yViKh1TNoSEXVTb7/9NlJTU7FlyxbccccduPXWWzFnzhycc8452LFjBy688ELccMMNMJvNAIDq6mpMmzYNo0ePxrZt2/D111+jpKQEv/71r0P8TIiIiIioO+D8lYi6EyZtiYi6qZEjR+Khhx5CXl4eFi9ejKioKKSmpmLBggXIy8vDww8/jIqKCvzyyy8AgJdffhmjR4/Gk08+iUGDBmH06NH417/+hdWrV+PgwYMhfjZERERE1NVx/kpE3QkXIiMi6qZGjBihfK9Wq5GSkoLhw4cr2zIyMgAApaWlAICff/4Zq1ev9tlf7MiRIxgwYECQIyYiIiKi7ozzVyLqTpi0JSLqprRarddtSZK8tsmr+rpcLgBAXV0dLr30Ujz99NNNjpWVlRXESImIiIiIOH8lou6FSVsiIvLLmDFj8PHHH6NPnz7QaPjfBxERERGFN85fiSiSsactERH5ZeHChaisrMR1112HrVu34siRI/jmm29w0003wel0hjo8IiIiIiIvnL8SUSRj0paIiPySnZ2NDRs2wOl04sILL8Tw4cOxaNEiJCYmQqXifydEREREFF44fyWiSCYJIUSogyAiIiIiIiIiIiIiN360RERERERERERERBRGmLQlIiIiIiIiIiIiCiNM2hIRERERERERERGFESZtiYiIiIiIiIiIiMIIk7ZEREREREREREREYYRJWyIiIiIiIiIiIqIwwqQtERERERERERERURhh0paIiIiIiIiIiIgojDBpS0RERERERERERBRGmLQlIiIiIiIiIiIiCiNM2hIRERERERERERGFESZtiYiIiIiIiIiIiMIIk7ZEREREREREREREYYRJWyIiIiIiIiIiIqIwwqQtERERERERERERURhh0paIiIiIiIiIiIgojDBpS0RERERERERERBRGmLQlIqKAWrJkCSRJCnUYTWzZsgU6nQ4nTpxo1+PfeustSJKE48ePK9umTp2KqVOnBiZAH9asWQNJkrBmzZo2P7Y947Bv3z5oNBrs2bOnzecjIiIib8ePH4ckSXjrrbdCHQoFAOe4gcM5LpF/mLQl6kZ2796Nq6++Gr1790ZUVBR69OiBGTNmYNmyZV77Pfnkk/jkk0/afZ59+/ZhyZIlXv/xB4L8n7skSdi+fXuT++fPn4+4uLiAnjPYNmzYgCuvvBIZGRnQ6/Xo06cPfv/736OgoCDUoXnp06eP8tq39BXOf5T8+c9/xnXXXYfevXv7vP+ss86CJEl49dVXA3ZOeVLZ2lcwJ8VtMWTIEFxyySV4+OGHQx0KERF1M0eOHMHvf/975ObmIioqCgaDAZMmTcKLL76I+vr6oJ03WPPWtuAcN3Q4x20fznGJOocm1AEQUef46aefcP7556NXr15YsGABMjMzUVhYiE2bNuHFF1/EHXfcoez75JNP4uqrr8YVV1zRrnPt27cPjz76KKZOnYo+ffoE5gk0smTJEnz++edBOXZnWbZsGe666y7k5ubijjvuQFZWFvbv349//vOf+PDDD/G///0P55xzTqjDBAC88MILqKurU27/73//w/vvv4+//e1vSE1NVbafc845uP766/HAAw+EIsxm7dq1C99//z1++uknn/cfOnQIW7duRZ8+ffDuu+/i1ltvDch5r7rqKvTv31+5XVdXh1tvvRVXXnklrrrqKmV7RkaGz8efd955qK+vh06nC0g8/vjDH/6AWbNm4ciRI+jXr1+nnZeIiLqvL7/8EnPmzIFer8eNN96IYcOGwWazYf369fjjH/+IvXv3Yvny5UE5d2fMW9uCc9zOxTlu+3COS9Q5mLQl6iaeeOIJJCQkYOvWrUhMTPS6r7S0NDRBtdOoUaPwxRdfYMeOHRgzZkzQzmM2mxETExOUY2/YsAGLFi3Cueeei6+//trrPLfeeismTZqEq6++Gnv37kVSUlJQYvDFZDIhNja2yfbGCfzi4mK8//77uOKKK3z+gaPRhNd/L2+++SZ69eqFs88+2+f9//nPf5Ceno7nnnsOV199NY4fPx6QP9xGjBiBESNGKLfLy8tx6623YsSIEbj++uubfZzFYoFOp4NKpUJUVFSH42iL6dOnIykpCW+//TYee+yxTj03ERF1P8eOHcO1116L3r1744cffkBWVpZy38KFC3H48GF8+eWXIYywgRACFosF0dHRQTk+57jBwzku57ic41IkYnsEom7iyJEjGDp0aJOELQCkp6cr30uSBJPJhLffflu5rGX+/PkAgBMnTuC2227DwIEDER0djZSUFMyZM8frcrK33noLc+bMAQCcf/75yjE8+xV99dVXmDx5MmJjYxEfH49LLrkEe/fu9fu53HHHHUhKSsKSJUv82v+VV17B0KFDodfrkZ2djYULF6K6utprn6lTp2LYsGHYvn07zjvvPMTExODBBx9UepE9++yz+Pvf/47c3FzExMTgwgsvRGFhIYQQePzxx9GzZ09ER0fj8ssvR2VlZasxPf7445AkCW+//XaTSXO/fv3wzDPPoKioCK+//joA4Nlnn4UkST57VS1evBg6nQ5VVVXKts2bN+Oiiy5CQkICYmJiMGXKFGzYsMHrcfJlTfv27cNvfvMbJCUl4dxzz/XrNW2Jrz5TkiTh9ttvx4oVKzBkyBBER0dj4sSJ2L17NwDg9ddfR//+/REVFYWpU6f6vETRn+fUnE8++QTTpk1rtv/Ve++9h6uvvhqzZ89GQkIC3nvvvbY96Q6QL4n84IMP8NBDD6FHjx6IiYlBbW2tz35fP/74I+bMmYNevXpBr9cjJycHd999t1+Xjn733Xc499xzkZiYiLi4OAwcOBAPPvig1z5arRZTp07Fp59+GuinSkRE1MQzzzyDuro6vPHGG14JW1n//v1x1113KbcdDgcef/xx9OvXT7ns/sEHH4TVavV6XJ8+fTB79mysX78eZ511FqKiopCbm4t///vfyj6tzVvlY3zzzTcYN24coqOjlbnZ0aNHMWfOHCQnJyMmJgZnn312h5PLnON64xy3dZzjunGOS10Vk7ZE3UTv3r2xffv2Vpuvv/POO9Dr9Zg8eTLeeecdvPPOO/j9738PANi6dSt++uknXHvttXjppZfwhz/8AatWrcLUqVNhNpsBuC91ufPOOwEADz74oHKMwYMHK8e/5JJLEBcXh6effhp/+ctfsG/fPpx77rl+9xIzGAy4++678fnnn2PHjh0t7rtkyRIsXLgQ2dnZeO655/CrX/0Kr7/+Oi688ELY7XavfSsqKnDxxRdj1KhReOGFF3D++ecr97377rt45ZVXcMcdd+Dee+/F2rVr8etf/xoPPfQQvv76a/zpT3/CLbfcgs8//xz33XdfizGZzWasWrUKkydPRt++fX3uc80110Cv1+OLL74AAPz617+GJEn46KOPmuz70Ucf4cILL1SqFX744Qecd955qK2txSOPPIInn3wS1dXVmDZtGrZs2dLk8XPmzIHZbMaTTz6JBQsWtBh7R/z444+49957MW/ePCxZsgT79+/H7Nmz8fe//x0vvfQSbrvtNvzxj3/Exo0b8dvf/tbrsW19Tp5OnTqFgoKCZitWNm/ejMOHD+O6666DTqfDVVddhXfffTdgz9tfjz/+OL788kvcd999ePLJJ5u9XGzFihUwm8249dZbsWzZMsycORPLli3DjTfe2OLx9+7di9mzZ8NqteKxxx7Dc889h8suu8znHwVjx47Fnj17UFtbG5DnRkRE1JzPP/8cubm5fl8uf/PNN+Phhx/GmDFj8Le//Q1TpkzB0qVLce211zbZ9/Dhw7j66qsxY8YMPPfcc0hKSsL8+fOVYoHW5q0AkJ+fj+uuuw4zZszAiy++iFGjRqGkpATnnHMOvvnmG9x222144oknYLFYcNlll2HlypXtfi04x/XGOS7nuJzjUrcniKhb+Pbbb4VarRZqtVpMnDhR3H///eKbb74RNputyb6xsbFi3rx5TbabzeYm2zZu3CgAiH//+9/KthUrVggAYvXq1V77Go1GkZiYKBYsWOC1vbi4WCQkJDTZ3tjq1asFALFixQpRXV0tkpKSxGWXXabcP2/ePBEbG6vcLi0tFTqdTlx44YXC6XQq219++WUBQPzrX/9Stk2ZMkUAEK+99prXOY8dOyYAiLS0NFFdXa1sX7x4sQAgRo4cKex2u7L9uuuuEzqdTlgslmafx65duwQAcdddd7X4fEeMGCGSk5OV2xMnThRjx4712mfLli1er7/L5RJ5eXli5syZwuVyKfuZzWbRt29fMWPGDGXbI488IgCI6667rsU4fPnrX/8qAIhjx441uU8+ricAQq/Xe+3/+uuvCwAiMzNT1NbWKtvl11bety3PyZfvv/9eABCff/65z/tvv/12kZOToxz722+/FQDEzp07vfZ78803mzznKVOmiClTprR4fk9lZWUCgHjkkUeUbfLPdW5ubpPfMfk+z98lX7+HS5cuFZIkiRMnTijbGo/D3/72NwFAlJWVtRrne++9JwCIzZs3+/3ciIiI2qqmpkYAEJdffrlf+8tzqJtvvtlr+3333ScAiB9++EHZ1rt3bwFArFu3TtlWWloq9Hq9uPfee5Vtzc1bPY/x9ddfe21ftGiRACB+/PFHZZvRaBR9+/YVffr0Uead8jzyzTffbPF5cY7LOS7nuJzjEvnCSluibmLGjBnYuHEjLrvsMvz888945plnMHPmTPTo0QOfffaZX8fw7N9lt9tRUVGB/v37IzExsdVqAMB92Up1dTWuu+46lJeXK19qtRoTJkzA6tWr/X4+CQkJWLRoET777DPs3LnT5z7ff/89bDYbFi1aBJWq4e1uwYIFMBgMTS5h0+v1uOmmm3wea86cOUhISFBuT5gwAQBw/fXXe/W2mjBhAmw2G06dOtVs7EajEQAQHx/f4nOMj4/3+hT4mmuuwfbt23HkyBFl24cffgi9Xo/LL78cgHsxgkOHDuE3v/kNKioqlNfYZDLhggsuwLp16+ByubzO84c//KHFOALlggsu8OqhJb+Gv/rVr7xeC3n70aNHAbTvOXmqqKgAAJ990xwOBz788ENcc801ymVl06ZNQ3p6eqdXIsybN8+vHnme+5hMJpSXl+Occ86BEKLZ3wUASmuUTz/9tMXXC2h4rcrLy/2InIiIqH3keU5rcyLZ//73PwDAPffc47X93nvvBYAmc7shQ4Zg8uTJyu20tDQMHDhQmWP4o2/fvpg5c2aTOM466yyvS+7j4uJwyy234Pjx49i3b5/fx2+Mc1w3znE5x+Ucl4jtEYi6lfHjx+P//b//h6qqKmzZsgWLFy+G0WjE1Vdf7dfksr6+Hg8//DBycnKg1+uRmpqKtLQ0VFdXo6amptXHHzp0CIB7wpCWlub19e2337Z5QbS77roLiYmJzfb9kntjDRw40Gu7TqdDbm5uk95ZPXr0aPZynV69enndlie3OTk5Prd79t5qTJ68yRPb5hiNRq+J3pw5c6BSqfDhhx8CcC+GsWLFClx88cUwGAwAGl7jefPmNXmN//nPf8JqtTYZq+YuXwu09r6G7XlOvgghmmz79ttvUVZWhrPOOguHDx/G4cOHcezYMZx//vl4//33W534BZK/41BQUID58+cjOTkZcXFxSEtLw5QpUwCgxdfhmmuuwaRJk3DzzTcjIyMD1157LT766COfz1F+rZrrj0ZERBQI8vyltTmR7MSJE1CpVF6r1gNAZmYmEhMTm8ztGs89AHfSpqV5WmO+/n8+ceJEk/klAKWtgq/+rG3BOS7nuJzjco5LBADhtfQhEXUKnU6H8ePHY/z48RgwYABuuukmrFixAo888kiLj7vjjjvw5ptvYtGiRZg4cSISEhIgSRKuvfZav/7jl/d55513kJmZ2eT+tq7GKlciLFmypMVPX/3V0ifAarW6Tdt9TZ5k/fv3h0ajwS+//NLsPlarFfn5+Rg3bpyyLTs7G5MnT8ZHH32EBx98EJs2bUJBQQGefvppZR/5Nf7rX/+KUaNG+Tx2XFyc1+1grYDcWHtfw/Y8J08pKSkAfP+RIVca/PrXv/b52LVr13r1fQsmf8bB6XRixowZqKysxJ/+9CcMGjQIsbGxOHXqFObPn9/i72F0dDTWrVuH1atX48svv8TXX3+NDz/8ENOmTcO3337rNQ7ya5WamtrxJ0ZERNQMg8GA7OzsVtdcaMzfhEt75mmNddY8yRPnuJzjyjjH5RyXujcmbYm6OXnCVFRUpGxrbiL83//+F/PmzcNzzz2nbLNYLE1WqW3u8f369QMApKenY/r06R0JW7Fo0SK88MILePTRR5VLY2S9e/cG4F5AIjc3V9lus9lw7NixgMXQVrGxsTj//PPxww8/4MSJE0qcnj766CNYrVbMnj3ba/s111yD2267Dfn5+fjwww8RExODSy+9VLlffo0NBkPInl+gdfQ5DRo0CABw7Ngxr+0mkwmffvoprrnmGlx99dVNHnfnnXfi3Xff7bQJrT92796NgwcP4u233/ZalOG7777z6/EqlQoXXHABLrjgAjz//PN48skn8ec//xmrV6/2em2PHTsGlUqFAQMGBPw5EBEReZo9ezaWL1+OjRs3YuLEiS3u27t3b7hcLhw6dMhrsbCSkhJUV1f7nFO1pj0Vd71790Z+fn6T7QcOHFDu7yjOcTnHbQ3nuA04x6Wuiu0RiLqJ1atX+/xkXO4N5nl5VWxsbJNELOD+tLjxMZYtWwan0+m1LTY2FgCaHGPmzJkwGAx48sknm6xqCwBlZWV+PRdPciXCp59+il27dnndN336dOh0Orz00ktecb/xxhuoqanBJZdc0ubzBcpDDz0EIQTmz5+P+vp6r/uOHTuG+++/H1lZWfj973/vdd+vfvUrqNVqvP/++1ixYgVmz56tvN6Ae0XUfv364dlnn0VdXV2T87bnNQ61jj6nHj16ICcnB9u2bfPavnLlSphMJixcuBBXX311k6/Zs2fj448/htVqDejz6Qi5UsDz51kIgRdffLHVx1ZWVjbZJld1NH6O27dvx9ChQ7163BEREQXD/fffj9jYWNx8880oKSlpcv+RI0eU/+dmzZoFAHjhhRe89nn++ecBoF1zu+bmrS2ZNWsWtmzZgo0bNyrbTCYTli9fjj59+mDIkCFtjqMxznE5x+Ucl3NcIlbaEnUTd9xxB8xmM6688koMGjQINpsNP/30Ez788EP06dPHa3GCsWPH4vvvv8fzzz+P7Oxs9O3bFxMmTMDs2bPxzjvvICEhAUOGDMHGjRvx/fffK5fmyEaNGgW1Wo2nn34aNTU10Ov1SuP7V199FTfccAPGjBmDa6+9FmlpaSgoKMCXX36JSZMm4eWXX27zc7vrrrvwt7/9DT///LPX5C4tLQ2LFy/Go48+iosuugiXXXYZ8vPz8corr2D8+PG4/vrr2/+CdtB5552HZ599Fvfccw9GjBiB+fPnIysrCwcOHMA//vEPuFwu/O9//2uysEB6ejrOP/98PP/88zAajbjmmmu87lepVPjnP/+Jiy++GEOHDsVNN92EHj164NSpU1i9ejUMBgM+//zzznyqHRaI53T55Zdj5cqVEEIoFTXvvvsuUlJScM455/h8zGWXXYZ//OMf+PLLL3HVVVcF/Hm1x6BBg9CvXz/cd999OHXqFAwGAz7++GO/evM99thjWLduHS655BL07t0bpaWleOWVV9CzZ0+vhVTsdjvWrl2L2267LZhPhYiICIC72vC9997DNddcg8GDB+PGG2/EsGHDlLnqihUrMH/+fADAyJEjMW/ePCxfvhzV1dWYMmUKtmzZgrfffhtXXHFFuyoHW5q3NueBBx7A+++/j4svvhh33nknkpOT8fbbb+PYsWP4+OOPvRYH6wjOcRtwjusb57ic41LXxqQtUTfx7LPPYsWKFfjf//6H5cuXw2azoVevXrjtttvw0EMPeV129fzzz+OWW27BQw89hPr6esybNw8TJkzAiy++CLVajXfffRcWiwWTJk3C999/32RF3czMTLz22mtYunQpfve738HpdGL16tVIT0/Hb37zG2RnZ+Opp57CX//6V1itVvTo0QOTJ09udlXb1iQmJmLRokV49NFHm9y3ZMkSpKWl4eWXX8bdd9+N5ORk3HLLLXjyySeh1Wrbdb5AufvuuzFu3Dg899xzeOGFF1BTU4OsrCzMmTMHf/7zn5u9tO6aa67B999/j/j4eKXixNPUqVOxceNGPP7443j55ZdRV1eHzMxMTJgwoUlVQ6To6HP67W9/i5dffhkbNmzAueeei9LSUnz//fe47rrrmu03dsEFFyAmJgb/+c9/wmZCq9Vq8fnnn+POO+/E0qVLERUVhSuvvBK33347Ro4c2eJjL7vsMhw/fhz/+te/UF5ejtTUVEyZMgWPPvqoV7XBqlWrUFlZiXnz5gX76RAREQFw/x/1yy+/4K9//Ss+/fRTvPrqq9Dr9RgxYgSee+45LFiwQNn3n//8J3Jzc/HWW29h5cqVyMzMxOLFi1tdm6E5Lc1bm5ORkYGffvoJf/rTn7Bs2TJYLBaMGDECn3/+eUCrXDnH9cY5blOc43KOS12bJNrShZ2IiChCXXDBBcjOzsY777wT6lDC2hVXXAFJkrBy5cpQh0JEREREreAc1z+c41IkYtKWiIi6hc2bN2Py5Mk4dOhQQBYI6Yr279+P4cOHY9euXRg2bFiowyEiIiKiVnCO2zrOcSlSMWlLREREREREREREFEYC0yGdiIiIiIiIiIiIiAKCSVsiIiIiIiIiIiKiMMKkLREREREREREREVEYYdKWiIiIiIiIiIiIKIwwaUtEREREREREREQURjShDqArcLlcOH36NOLj4yFJUqjDISIiIuo2hBAwGo3Izs6GSsV6BH9x/kpEREQUGv7OX5m0DYDTp08jJycn1GEQERERdVuFhYXo2bNnqMOIGJy/EhEREYVWa/NXJm0DID4+HoD7xTYYDEE9l8vlQllZGdLS0lhNEoE4fpGPYxjZOH6RjeMX2YI1frW1tcjJyVHmY+Sfzpy/Avz9jXQcv8jG8YtsHL/IxvGLbKGevzJpGwDyJWUGg6FTkrYWiwUGg4G/8BGI4xf5OIaRjeMX2Th+kS3Y48dL/NumM+evAH9/Ix3HL7Jx/CIbxy+ycfwiW6jnr/yJISIiIiIiIiIiIgojTNoSERERERERERERhREmbYmIiIiIiIiIiIjCCHvaEhERERGRT06nE3a7vcPHcblcsNvtsFgs7OnnQavVQq1WhzoMIiIiCkNM2hIRERERkRchBIqLi1FdXR2w47lcLhiNRi4a10hiYiIyMzP5uhAREZEXJm2JiIiIiMiLnLBNT09HTExMhxOKQgg4HA5oNBomJ88QQsBsNqO0tBQAkJWVFeKIiIiIKJwwaUtERERERAqn06kkbFNSUgJyTCZtfYuOjgYAlJaWIj09na0SiIiISMGGUkREREREpJB72MbExIQ4ku5Bfp0D0TuYiIiIug4mbYmIiIiIqAlWxHYOvs5ERETkC5O2RERERETU7SxZsgSjRo1q02MkScInn3wSlHiIiIiIPDFpS0REREREEU2SpBa/lixZ0uQx9913H1atWhXQONatW4dLL70U2dnZTPASERFRh3AhMiIiIiIiimhFRUXK9x9++CEefvhh5OfnK9vi4uKU74UQcDqdiIuL89oeCCaTCSNHjsRvf/tbXHXVVQE9NhEREXUvrLQlIiIiIqKIlpmZqXwlJCRAkiTl9oEDBxAfH4+vvvoKY8eOhV6vx/r165u0R9i6dStmzJiB1NRUJCQkYMqUKdixY0eb4rj44ovxf//3f7jyyisD/AyJiIiou2HSloiIiIiImiWEgM3hCsmXECJgz+OBBx7AU089hf3792PEiBFN7jcajZg3bx7Wr1+PTZs2IS8vD7NmzYLRaAxYDERERET+YnsEapHZbkahsRD9E/tDrVKHOhwiIiIi6mR2p8DfVx/u4FEEXC4XVCoVAMnvRy08vz90Gv/3b8ljjz2GGTNmNHv/tGnTvG4vX74ciYmJWLt2LWbPnh2QGIiIKLI5XU7kV+Wjb0JfRGuiQx0OdXGstKUWbSvZhlUFq7CjtG2XhhERERERhZNx48a1eH9JSQkWLFiAvLw8JCQkwGAwoK6uDgUFBZ0UIRERhbsdpTuwpnAN1p5cG+pQqBtgpS21qMZaAwDYV7EPY9LHsNqWiIiIqJvRqiUsPL9/h44hhIDD4YBGo4Ek+V85q1UHpsoWAGJjY1u8f968eaioqMCLL76I3r17Q6/XY+LEibDZbAGLgYiIIpcQAgerDgIAjtccR72jntW2FFRM2lKLLE4LAMBkN+FE7QnkJuaGOCIiIiIi6kySJHW4RYEQAiqooNGo2pS07UwbNmzAK6+8glmzZgEACgsLUV5eHuKoiIgoXJSaS5XCNpdw4Uj1EQxLHRbiqDrH2sK1OFpzFL8a8CsYdIZQh9NtsD0CtcjisCjf763YG8JIiIiIiIiCJy8vD++88w7279+PzZs3Y+7cuYiOblsFVV1dHXbt2oVdu3YBAI4dO4Zdu3axxQIRURdwqPoQAECr0gIA8ivzQxlOp7E4LNhXuQ/1jnocrupoj3tqCyZtqUWeSdtCY6HyqRIRERERUVfyxhtvoKqqCmPGjMENN9yAO++8E+np6W06xrZt2zB69GiMHj0aAHDPPfdg9OjRePjhh4MRMhERdRKXcCkJy0k9JkGSJJSYS1BlqQpxZMF3rOYYhBAAgAIjP4TsTGyPQM2yu+ywu+wAgMzYTBSbirG3Yi/OyT4nxJEREREREfk2f/58zJ8/X7k9depU5Y9NT0uWLMGSJUuU26NHj8bWrVu99rn66qu9bvs6jqfmzkVERJHtlPEUzA4z9Go9BiYNxLGaYzhRewL5Vfk4O+vsUIcXVIerG6pri0xFsDqt0Kv1IYyo+2ClLTXL6rACAFSSCqPSRwEADlQegMPlCGFURERERERERESd52C1ewGy/on9oVapMTBpoHt75cEu/WGdxWHBybqTAIAoTRSEEDhpPBniqLoPJm2pWfWOegBAtCYafQx9EKuNhcVhwdGaoyGOjIiIiIiIiIgo+OwuO47VHAMA5CXlAQD6JPSBTq1Dnb0Op+pOhTK8oJJbI6REpWBA0gAAwInaEyGOqvtg0paaJfezjVJHQSWpMCRlCABgbzkXJCMiIiIiIiKirq+gtgA2pw1x2jhkxWYBADQqDfon9gcA5Fd13QXJ5NYI/RL7oXd8bwDu9Y66cnVxOGHSlppV73RX2kZpogAAg5MHQ5IkFJmKUFFfEcrQiIiIiIiIiIiC7mCVuzVCXlIeJElStsstEo5WH4XdaQ9JbMHk2RqhX2I/ZMVlQaPSwGQ3ocLCnFBnYNKWmqVU2p5J2sbp4tDX0BcAsK9iX8jiIiIiIiIiIiIKNovDorQDkFsjyDJjM5GgT4DdZe+SbSQ9WyMkRSVBo9KgR1wPAGyR0FmYtKVmyUnbaE20sm1oylAA7vL/rvhJEhERERERERERABytOQqXcCE5Khmp0ale90mSpPR5latxuxLP1giy3gZ3i4SC2oKQxNTdMGlLzbI4G3raynrG94RBZ4DNaVN+gYmIiIiIiIiIuppDVYcAQEnONiZvP2k8iTpbXafFFWyNWyPIcuJzAADF5mJYndaQxNadMGlLzTI7zAC8K20lScLQVHe17d4KLkhGRERERERERF1Pna0Op+tOAwD6J/X3uU+CPgFZsVkQEF2q2rZxawRZgj4BifpECCFQWFsYwgi7ByZtqVmNe9rKBiYNhAQJpeZSmO3mUIRGRERERERERBQ0h6sPQ0AgKzYLBp2h2f0GJrsXJMuvyocQorPCCypfrRFkcouEE0b2tQ02Jm2pWc0lbWO0MTDo3W9YVdaqTo+LiIiIiKijlixZglGjRrXpMZIk4ZNPPglKPEREFF7kytnmWiPI+iX2g1pSo8pShQpLRWeEFlTNtUaQ9TL0AgAU1hZ2mSR1uGLSlprlayEyWZLeXR5fZWHSloiIiIhCS5KkFr+WLFnS5DH33XcfVq1aFdA4li5divHjxyM+Ph7p6em44oorkJ+fH9BzEBFR8JntZpTXl0OChNzE3Bb31av16BHfAwBwqu5UZ4QXVM21RpBlxWZBq9LC7HC/RhQ8TNqST0II1DvrAXgvRCaTf3ErLZWdGhcRERERUWNFRUXK1wsvvACDweC17b777lP2FULA4XAgLi4OKSkpAY1j7dq1WLhwITZt2oTvvvsOdrsdF154IUwmU0DPQ0REwVVtrQYAxOvifRayNZYdmw0AKKorCmZYASOEwDfHv8GHBz7ET6d/wum603AJFwDgSM0RAL6rbAFAo9KgR5w7SX2ili0SgolJW/LJ5rIpZe6N2yMAQHJUMgBW2hIRERFR6GVmZipfCQkJkCRJuX3gwAHEx8fjq6++wtixY6HX67F+/fom7RG2bt2KGTNmIDU1FQkJCZgyZQp27NjRpji+/vprzJ8/H0OHDsXIkSPx1ltvoaCgANu3bw/wMyYiomCSW0EmRiX6tX923JmkrakoIloGFJuKcaT6CCosFdhVugufHP4Eb+55E9+f+B6FRvcCY80lbYGGvrYFxoJOibe7YtKWfKq3u6tsdWodNCpNk/sT9YkA/Evamu1mlJhKAhofEREREXUSIQCHrWNfTo+vtjwugH/4PvDAA3jqqaewf/9+jBgxosn9RqMR8+bNw/r167Fp0ybk5eVh1qxZMBqN7T5nTU0NACA5ObndxyAios5XbakG0JD7aE1adBrUkhr1jnqlSjecHag8AADoEdcDA5IGQK/Ww+q04mDVwRZbI8hyDDkAgBJTidJakwKvaTaOCIDFeWYRMh+tEYCG9ghmhxkWh8VnNa7sm+PfoMhUhF8P/DVSo1MDHywRERERBY/TDvz4XAcPIqByuQCVCoDk/8Mm3wtodB08t9tjjz2GGTNmNHv/tGnTvG4vX74ciYmJWLt2LWbPnt3m87lcLixatAiTJk3CsGHD2vx4IiIKHbnSVl7PpzVqlRoZsRk4XXcap02nW0x4hprdZVdaIIzLHIcecT3gEi4Um4pxvOY4SswlGJ0+usVjGHQGJEUlocpShUJjIfKS8joj9G6HlbbkU73jTD/bZpKxOrUOcdo4AC1X2zpcDhSbiwEAFfWRv4oiEREREUWmcePGtXh/SUkJFixYgLy8PCQkJMBgMKCurg4FBe279HPhwoXYs2cPPvjgg3Y9noiIQkeptPWzPQIQOX1tj9cch81pQ5w2TolZJamQHZeNc3qcgyvzrkSfhD6tHqd3/JkWCbVskRAsrLQln+Ty9pYqaJOiklBnr0OltRJZcVk+96mor1D6uRht7b+0jIiIiIhCRK11V7x2iIDL4YBKo0GbKm3V2g6et0FsbGyL98+bNw8VFRV48cUX0bt3b+j1ekycOBE2m63N57r99tvxxRdfYN26dejZs2d7QyYiohBwuBxK/sLfSlsAyIp150WKTOGdtM2vygcADEweCElqw//JjfRO6I1dZbtwqPoQ8pLy0MvQK1Ah0hmstCWf5PYI0ermV0n0ZzGysvoy5fs6e12AoiMiIiKiTiNJ7hYFHflSe3y15XEd+GOyrTZs2IA777wTs2bNwtChQ6HX61FeXt6mYwghcPvtt2PlypX44Ycf0Ldv3yBFS0REwVJjrYGAgE6tQ7Sm+ZxIY5mxmZAkCUabMWyL1sx2s7LQ2MCkgR06VnZsNvon9odLuPD18a9RbCoORIjkgUlb8qm19ghAQ1/bFpO25oakbbi+aRERERER5eXl4Z133sH+/fuxefNmzJ07F9HR/v+xDrhbIvznP//Be++9h/j4eBQXF6O4uBj19fVBipqIiAJNXkgsSZ/UpkpUrVqLtOg0AMDputPBCK1FQggcrjqM8vrmP3CUFxrLiMloU+sHXyRJwgW9LkBOfA4cLge+PPol22IGGJO25JPcHqGlT5VYaUtEREREXcUbb7yBqqoqjBkzBjfccAPuvPNOpKent+kYr776KmpqajB16lRkZWUpXx9++GGQoiYiokCTcxztSWqGskXC/sr9+PbEt/jk8CfNFs0drDoIwN0aIRDUKjUu6nMRMmMzYXVa8cXRL1BjrQnIsYlJW2qGP5W2ifpEAO5krM3ZtNeXw+VAhaXhU5Y6W53S35aIiIiIKBjmz5+P6upq5fbUqVMhhEBiYqLXfkuWLMGuXbuU26NHj8bWrVtRX1+PgwcP4uqrr8bx48exaNEiZR8hBK644opmzy2E8Pk1f/78gDw3IiIKPs9K27aS1/vp7KSt0WbET6d/AgDYnDZ8f+J7uITLa5/y+nKU15dDJanQP7F/wM6tVWsxq+8sJEclw2Q34YujX8BsNwfs+N0Zk7bkkz8LkUVpohCjiQHgu9pWXoRMp9YBAOwuO6xOaxCiJSIiIiIiIiLquCrrmUrbM4VqbSFX2lZZqjotcSmEwJrCNbA5bUiNToVWpUWRqQjbS7Z77Xew0l1l28fQp8VcT3tEaaJwab9LYdAZUGOtwedHPmf+JwCYtCWf/FmIDPDoa2ttmrSVWyNkxGQobRbY15aIiIiIiIiIwpEQAtWWagDta48QrYlW8iSBWJgrvzIf7+5/F1uLtzZ75fKBygMoNBZCLakxo/cMnNfzPADAtpJtSgwu4VJaIwxIHtDhuHyJ1cbi0n6XIkYTgwpLBVYXrObV1h3EpC355E97BKAhaVtpqWxyn7wIWVpMGuJ0cQAAo51JWyIiIiIiIiIKP2aHGXaXHZIkIUGX0K5jZMdmA+hYiwSXcOGnUz9hVcEq1FhrsLV4K34o+AFOl9NrvzpbHTac3gAAOCvrLCRFJWFA0gDkJeVBCIHvTnwHq9OKk8aTMDvMiNJEoXd873bH1ZoEfQJm5c6CSlLhaM1R7KvcF7RzdQdM2lITTpdT6VHbWtK2pcXI5Erb9Oh0xGvjAbjfUIiIiIiIiIiIwo2c2zDoDFCr1O06htzX9rTpdLseb3Va8eXRL7GrbBcAIDcxF5IkIb8qH18e+1LJ1wghsPbkWticNqTHpGNk2kgAgCRJOK/neTDoDDDajFhbuBb5VfkAgP6J/dv9vPyVHpOOs7POBgBsOLXBZ5Ef+SfikrZ///vf0adPH0RFRWHChAnYsmWLX4/74IMPIElSk4UDhBB4+OGHkZWVhejoaEyfPh2HDh0KQuSRQ+47IkFClNq/StvGSVvPRcjSYtIQr2PSloiIiIiIiIjCV0cWIZPJfW3LzeU+F21vSZWlCv89+F8UGguhUWkwo/cMXNTnIszqOwtalRYnjSex8tBK1NnqcLDqIE7UnoBKUmFar2lQSQ0pPr1aj+m9p0OSJByuPozDVYcBAAOTBrb7ebXFyLSRyInPgcPlwPcnvofD5eiU83Y1EZW0/fDDD3HPPffgkUcewY4dOzBy5EjMnDkTpaWlLT7u+PHjuO+++zB58uQm9z3zzDN46aWX8Nprr2Hz5s2IjY3FzJkzYbFYgvU0wp7Z4W6WHaWJgiRJLe4rv5EZbUbYXXZlu7wIWZQmCnHaOL/bIxhtRuwq3cWG1URERERERETUqeSCtPb0s5XF6+IRr4uHgECJucTvxxXUFuDjQx+jxlqDOG0crux/JfKS8gAAvQ29cXn/y5V+sR8f+hjrT60HAIzPHK9cBe0pMzYT4zPGAwAEBBL1iUiPSW/382oLSZIwrdc0RGmiUF5fjk1FmzrlvF1NRCVtn3/+eSxYsAA33XQThgwZgtdeew0xMTH417/+1exjnE4n5s6di0cffRS5uble9wkh8MILL+Chhx7C5ZdfjhEjRuDf//43Tp8+jU8++STIzyZ8WRzuhLU/qwlGa6IRpYmCgECNtUbZLrdGSItOgyRJfrdH2Fa8DT+d/gn7K/a3N3wiIiIiIiIiojYLRKUt0NDX9nSdfy0SLA4Lvj7+NWxOG7Jis3D1gKuRFpPmtU96TDquGnAVEvWJMNlNsDqtSItJw+j00c0ed0zGGGTHuWMZkjKk1cK8QIrVxmJazjQAwC9lv+BE7YlOO3dXoQl1AP6y2WzYvn07Fi9erGxTqVSYPn06Nm7c2OzjHnvsMaSnp+N3v/sdfvzxR6/7jh07huLiYkyfPl3ZlpCQgAkTJmDjxo249tprfR7TarXCam2oBK2trQUAuFwuuFyudj0/f7lcLgghgnoes90MIQT0Kr1f50nUJaLIXoQKcwWS9e5Pd0pNpRBCIDUqFS6XCzGaGAghUGurbfGYZeYyCOFOAAf7tQyFzhg/Ci6OYWTj+EU2jl9kC9b48eeBiIiIAkWutJVbQbZXZlwm8qvyUWQqQl9D31b3P1ZzDA6XA0lRSbis32XN9p016Ay4Ku8qfHfiO1TUV2BajndbhMZUkgqX9L0EhXWF6GPo096n0259EvpgeOpw7C7fjR8KfsCvB/4asdrYTo8jUkVM0ra8vBxOpxMZGRle2zMyMnDgwAGfj1m/fj3eeOMN7Nq1y+f9xcXFyjEaH1O+z5elS5fi0UcfbbK9rKws6G0VXC4XampqIISAShWcQumimiKYTCbYYGu19QQASBYJJpMJR4uPIsHuXl3xSOkRmKwmqOpVKC0tRb2jHiaTCSaYUFRc5PMNSAiBk5Un4XA5UIQilGpbP3ek6Yzxo+DiGEY2jl9k4/hFtmCNn9HYcuslIiIiIn/YnXbU2d1XByfoEzp0LLnSttRcCmecs9X9j9QcAeDfQmFRmihc2u9SuISrxYStTKvWIjcht9X9gmVi9kQUmYpQXl+OHwp+wOzc2Z1a8RvJIiZp21ZGoxE33HAD/vGPfyA1NTWgx168eDHuuece5XZtbS1ycnKQlpYGg8EQ0HM15nK5IEkS0tLSgvYHa4EoQKw5FunJ6UhPb73fSV+pL047T0NEC6Snp8PhcsBebEesJhaDeg5y93IRAgkVCXC4HIhJivH5Bmi0GaEv1kMPPTQxGr/OHWk6Y/wouDiGkY3jF9k4fpEtWOMXFdV6OyciX5YsWYJPPvmk2QIPXyRJwsqVK5ssbkxERJFPbo0QpYlCtCa6Q8dK1CciWhMNs92MCmsFspDV7L4WhwWFxkIA7qStv/xJ2IYDjUqD6b2n46P8j1BoLESVtcpnD15qKmKStqmpqVCr1Sgp8W7iXFJSgszMzCb7HzlyBMePH8ell16qbJMvn9NoNMjPz1ceV1JSgqyshl+gkpISjBo1qtlY9Ho99Hp9k+0qlapT/oiUJCmo57I6rZAkCTG6GL/OkRKdAkmSUG2thkqlQlW9+3KCaG00DHqD8glKvC4e1dZqmBwmJEU3vdSgxlaj7Gt2mLvsH+TBHj8KPo5hZOP4RTaOX2QLxvjxZ4EAtFqx88gjj2DJkiVe2+677z7ccccdAY3j1Vdfxauvvorjx48DAIYOHYqHH34YF198cUDPQ0REgReofraA+/+lrNgsHKk+ghJLCYZhWLP7Hqs5BiEEUqJSOtyWIVwlRyUjKzYLp+pO4XTdaSZt/RQxs1ydToexY8di1apVyjaXy4VVq1Zh4sSJTfYfNGgQdu/ejV27dilfl112Gc4//3zs2rULOTk56Nu3LzIzM72OWVtbi82bN/s8ZndR76gHAL8/WZLfVGpsNXC6nE0WIZPF684sRmb3vRhZpaVS+d7kMEEI0fbgiYiIiKjbKSoqUr5eeOEFGAwGr2333Xefsq8QAg6HA3FxcUhJSQloHD179sRTTz2F7du3Y9u2bZg2bRouv/xy7N27N6DnISKiwJOTton6xIAcLyvOXRxYUl/S4n5ya4R+if0Cct5w1SOuBwDgZN3JEEcSOSImaQsA99xzD/7xj3/g7bffxv79+3HrrbfCZDLhpptuAgDceOONykJlUVFRGDZsmNdXYmIi4uPjMWzYMOh0OkiShEWLFuH//u//8Nlnn2H37t248cYbkZ2d3a0vebI43X15o9T+XW4Yq42FTq2DEALV1mqUmd1J2/QY7/YGcdo4AO42CL7IDb8B92RaTh4TEREREbUkMzNT+UpISIAkScrtAwcOID4+Hl999RXGjh0LvV6P9evXY8mSJV5X123duhUzZsxAamoqEhISMGXKFOzYsaNNcVx66aWYNWsW8vLyMGDAADzxxBOIi4vDpk2bAvyMiYgo0AK1CJksJz4HAFBkLlISwo15tkbo6knbnvE9AQCn606zSM9PEdMeAQCuueYalJWV4eGHH0ZxcTFGjRqFr7/+WllIrKCgoM2XyN1///0wmUy45ZZbUF1djXPPPRdff/11t+6PZnG4k7b+VtpKkoQkfRJKzCWoslR5Vdp6itO5k7Z1Nt+VtlXWKq/bJrsJMdqYNsVORERERIElhIDD5ej4MZwOCEm0afERjUoTsMVKHnjgATz77LPIzc1FUlIS1qxZ43W/0WjEvHnzsGzZMggh8Nxzz2HWrFk4dOgQ4uPj23w+p9OJFStWwGQydeur+IiIIkWgK22To5LRx9AHe017sb1kO2b0mdFkn+7QGkGWFp0GrUoLi8OCCksFUqMDu/5UVxRRSVsAuP3223H77bf7vK/xxKuxt956q8k2SZLw2GOP4bHHHgtAdF2DXOEapfE/cZ0U5U7altWXocJSAQBIi/FO2srtEYz2ppW2QgjlUy2NSgOHywGT3YQ0pDXZl4iIiIg6j8PlwD92/6NDxxBCwOVyQaVStSkJu2D4AmjV2g6dW/bYY49hxoymfzDLpk2b5nV7+fLlSExMxNq1azF79my/z7N7925MnDgRFosFcXFxWLlyJYYMGdLuuImIKPjkK4eBwFXaAsDYjLHYW7QXh6oP4SzrWU0WZT9cfRhA16+yBQC1So3M2EwUGgtxqu4Uk7Z+iKj2CBR8Qgil0rYtSVu5ifSR6iMQQiBKE6W0Q5C11B6h3lHvXgAN7mbdgLvSloiIiIgoEMaNG9fi/SUlJViwYAHy8vKQkJAAg8GAuro6FBQUtOk8AwcOxK5du7B582bceuutmDdvHvbt29eR0ImIKMiMdiMcLgdUkkopOAuE9Jh09IjpASEEtpVs87rP4rAo/V27Q9IWaGiRcMp4KsSRRIaIq7Sl4HK4HHAKJwAgWu1fewSg4fKBWlstgKaLkAEeC5HZ6iCE96Vx8iJkBr0BBp0BAJO2REREROFAo9JgwfAFHTqGvPiXRtO2dgcaVeD+XImNjW3x/nnz5qGiogIvvvgievfuDb1ej4kTJ8Jms7XpPDqdDv379wcAjB07Flu3bsWLL76I119/vd2xExFRcFVbqgEACfoEqKTA1jeOTB6JtVVrcbDqIMZljFOqbbtTawSZvBjZadNpuIQr4K91V8OkLXkxO8wA3BPktlyK1vgNpvEiZIB7wTIJEpzCiXpHvVe/WqXhtz5J2c6kLREREVHoSZLU4RYFQghIQoJGHbgetYG2YcMGvPLKK5g1axYAoLCwEOXl5R0+rsvlgtVq7fBxiIgoeJTWCPrAJ09To1KRE5+Dk3Unsb1kO6b1crfj6U6tEWSp0anQqXWwOW0oM5chIzYj1CGFNaa0yYvSGkHdtoXYDDqDVyVE40XIAHciWE7I1tm9FyOTFyFLikpCrNZdBWFyMGlLRERERJ0jLy8P77zzDvbv34/Nmzdj7ty5iI72/8ozAFi8eDHWrVuH48ePY/fu3Vi8eDHWrFmDuXPnBilqIiIKBGURsqjEoBx/XIa7RU9+VT5qrDXdsjUCAKgkFbJjswG4q22pZUzakheLs+39bAF3BYbnCouNFyGTyX1t62yNkraWhqStvI/Zbm5TDOFGCBHqEIiIiIjIT2+88QaqqqowZswY3HDDDbjzzjuRnt706rGWlJaW4sYbb8TAgQNxwQUXYOvWrfjmm29aXACNiIhCz/Pq32DIjM1ETnwOhBDYWbqzW7ZGkPWId7dIOGk8GeJIwh/bI5AXudI2WtO2qgLAvRhZeX25z0XIZHG6OJSYS2C0ey9G5vkGqVapATStxo0kVqcVHxz4AFmxWbiwz4WhDoeIiIio25g/fz7mz5+v3J46darPD9OXLFmCJUuWKLdHjx6NrVu3eu1z9dVXe91u7UP5N954o+0BExFRyCmVth7FaIE2LmMcCo2F2F+5H6XmUgDdq8pWJve1LTYVw+lyKjkgaoqVtuSl3lEPoO2VtkBDX1tfi5DJ4rXuxciMtoakrcVhUXrperZHsDgscLgcbY4jHJTXl8NkN+F47XFW3BIRERERERGFKZvTpqypE6z2CACQFZeFnvE9IYRAeb27Z3r/xP5BO1+4SolKQZQmCnaXXUlek29M2pIXpT1CG3vaAsDQlKEYlDwIZ2ed3ew+8Tp30tazPYL8iVacNg46tQ5R6ihlBUE5mRtp5OfncDmU15SIiIiIiIiIwot85W+sNhZ6tT6o55J72wLuRbmCmSQOV5IkITvO3df2VN2pEEcT3pi0JS/1dnelbXvaI0RpojCt17Rm+9kC7vYIALzaI1RaKgE0VOpKkqRU20ZqX1v5U7rG3xMRERERERFR+JALyRL0CUE/V3ZcttIeoDtW2cp6xvUEwKRta5i0JS9yVWh7krb+8FVp67kImay5BcsihWc/Xn+eQ0V9BUpMJcEMqd2EELA77aEOg4iIiIiIiCjgqqzBXYSssRm9Z2BKzhSMTBvZKecLR559bSO1LWZnYNKWvMgLkbWnp60/5GRsvaMedpc7Edi40hYAYrQxAACTIzKrVD2ra1tbUE0Igc+OfIaVh1cqPYXDyfpT6/HGnjdQUV8R6lCIiIiIiIiIAsbhcuBYzTEA3jmJYIrRxmBoytBuvQBXoj4RMZoYOIUTxabiUIcTtpi0JS8dWYjMH3q1HlqVFgBgsrkTm/KlCMn6ZGU/uT1CpLYW8Kq0bSVpW++oR72jHi7hUhLY4eRk3Um4hItvpERERERERNSl7CzdiSpLFaI10RiQNCDU4XQbkiShR7y72vZ03ekQR+MupgvHnAeTtuSlIwuR+UOSpIa+tjYj7E47jDZ3f1vPBtz+9LQ12834oeCHsPzFkhPSjb/3pdZWq3wvt4oIF0IIpb1Da8lnIiIiIiIiokhRaanE9pLtAIDJPSYHrXiNfJNbJISyr60QAgW1Bfjvof/i/x36fygzl4UsFl+YtCWFS7hgdVgBBK+nLdDQIsFoNyq9Y6I10V7n9KfS9mDVQRyoPIBtJduCFqsnIQQKawuVFhLNcbqcMDsaks2ei675IietgfBL2tpcNqWNRaQnbeU340hd3I6IiLqmpUuXYvz48YiPj0d6ejquuOIK5Ofnt/q4FStWYNCgQYiKisLw4cPxv//9rxOiJSIi6hqEEFhTuAYu4UIfQx/0S+wX6pA6lxDAgS/dX0KEJASlr625OCTr6JyuO41PDn+CL45+gTJzGbQqbdhd/cykLSksDgsE3L+swfyEyaAzAHAv0OVrETKgIWnbUqJQ7rFabakOQpRNHak+gs+Pfo4Npze0uF/jPryttXjwTNrKrSLChWds/rSqcLgcOFV3Ck6XM5hhtcupulP44ugXWFO4JtShEBERKdauXYuFCxdi06ZN+O6772C323HhhRfCZGr+/92ffvoJ1113HX73u99h586duOKKK3DFFVdgz549nRg5ERFR5NpTvgfFpmLo1Dqc1/M8SJIU6pA6V/EvQNGZL1N5SEIw6AyI08ZBCIEiU1GnnbfUXIrPj3yOTw5/giJTEdSSGiPTRmLu4LkYmDyw0+LwB5O2pJBbI+jVeqik4P1oKO0R7EblU4zkqGSvfWI1rbdHqLC4k7ZGm7FTVhssMZe4z9vKglxyOwS15G4qXmerg2jhkyvPxHRnVdo6XA5YndZW95NbIzT+vjm/lP2CTw9/il/Kf+lQfMEg/6zJ1d1ERETh4Ouvv8b8+fMxdOhQjBw5Em+99RYKCgqwffv2Zh/z4osv4qKLLsIf//hHDB48GI8//jjGjBmDl19+uRMjj3xLlizBqFGj2vQYSZLwySefBCUeIiLqHEabEZuKNgEAzs46W8lRdBt2C3B0TcPtmoKQhOHZ1/Zk3clOOWehsRAfH/wYhcZCSJKEoSlDMXfwXEzqMQkx2phOiaEtNKEOgMKHfNl/sPu4yO0R6mx1sDltANwrB3qSK23tLjtsTht0ap3X/S7hUhKcAgK1ttomid9Ak5N+tbZaCCGa/SROTsKmRqeixFwCp3Ci3lHf7BuAZ0/bOnsd7E47tGptgKNvIITAxwc/Rp29DjcMuaHJa+vJM6FssptafN4AUF7v/oROTnCHE7lqWE6id7tPUomIKCLU1NQAAJKTm5/XbNy4Effcc4/XtpkzZ7aYTLRarbBaGz6wra11zz9cLhdcLpfXvi6XC0II5StQ5GMF8pgylarlgoOHH34YS5Ys8dp277334vbbb29zPP6+Lk899RQefPBB3HnnnXjhhRdaPZ6vsQgX8s9EuMZHLeP4RTaOX2AJIbCmYA1sThsyYjIwOGlwUF/bsBy/4+shWRuu6BFVJ4Cs0SEJJTM6EwfEAZSZyzrlNdpevB0u4UJOfA4m95iMBH0CADR77mCNn7/HY9KWFHLSNpj9bAEgXhcP4ExC8EzbksbtEbRqLXRqHWxOG0x2U5PEYq21Fk7RcAl+taW605K2NqcNVqe12eS23EbAoDfAaDPC7DDDZDc1m7T1bEEAuFskpMWkBTByb3X2OqVKudJSiczYzGb39YzN7rLD5rJBr9a3un9ntaxoC7m3cGtJdGV/mxEalSbovw/tYXFYYHValf9giIioa3C5XFi0aBEmTZqEYcOGNbtfcXExMjIyvLZlZGSguLj5xVmXLl2KRx99tMn2srIyWCze/frtdjtcLhccDgccjsBczSSEgNPpnrsF44PTgoKGKqEVK1bg0Ucf9WoXERcXpzwXOZaoqChERUW1+Tk6nc5WH7Nt2zYsX74cw4cPhxCixf0dDgdcLhcqKiqg1Qbvg/uOcLlcqKmpgRCi1QQ5hR+OX2Tj+AXWMeMx7C/ZD5WkwrCUYSgrC+7CU+E2flJ9JWLy10ASArassdAVbYfr5H6YUyYCIShsspltMJlMKLYXozSuNKjnqrJW4WDJQUiQMDR1KKw1VpSi5XMGa/yMxpbXPpIxaUuKemc9ACBKHeRKW7k9gs2oVCn4SrjGamNhc9pQZ69rktRt3Bw62L1grU6rV0/XWltts0lbuTo1VhuLWF0szA4z6ux1SEPTRKwQQkl0xmpjYbKbUGWtCmrStqy+4T+lWltti0nbxj2FjTYj9NHNJ23l/WusNXAJV1DbbLSVZ3uHlpLogHu8PzjwAWK0MfjNoN+EXVXuF0e/QFl9GW4YfEP3u5SHiKgLW7hwIfbs2YP169cH/NiLFy/2qs6tra1FTk4O0tLSYDAYvPa1WCwwGo3QaDTQaAL750KwkpI9e/ZUvk9KSoIkScq2NWvWYNq0afjyyy/xl7/8Bbt378Y333yDNWvW4NNPP8XOnTsBAFu3bsWf//xn7Ny5E3a7HaNGjcLzzz+PMWPGeJ1LrVa3+LrU1dVh3rx5WL58OZ544glIktTi/hqNBiqVCikpKYiKCs+Vy10uFyRJQlpaWlgkHahtOH6RjeMXOBaHBfvL9iM2NhbjM8djQMaAoJ8zrMZPCGD3akgxMRAp/RAzdDZQdxiSy4G4eC0QE9xCOF90Vh1ia2IhqdyvUTD/9t5XuA+xsbHITchF3x59/XpMsMbP3//vmbQlRWe1R4jVxEKCBJdwl4Pr1DrEaJom0GK1saiyVPnsaytXisqCnbRt3Gu21lqL9Jh0n/vKics4bRzitfEoQ1mz/WDrHfVKP96ecT2RX5Uf9CrVMrNH0tZa28KeTfvYmuwmpEan+tzX4XIoiW2ncMJoM4ZVJahn1XBzSXRZtaUadpcdNdYav6pyO5NLuFBWXwYhBCotlUzaEhF1Ebfffju++OILrFu3zisB6UtmZiZKSrxbEZWUlCAzs/kPYvV6PfT6ph+8qlSqJn+EqFQqSJKkfAkhAHvHVnUWQgBOZ9tbFGm1bf4DTt6/8b+LFy/Gs88+i9zcXCQlJWHt2rVe98vJ1mXLlkEIgeeeew6XXHIJDh06hPj4eK/jtxTT7bffjksuuQQzZsxQkrYt7S/f72sswkkkxEjN4/hFNo5fYOyt3AuL04Lk6GSMzRjbaa9n2Ixf+WGg6jig1kDKmwFodEBCNlBdCKn2JBDn+2/9YIrXx0OSJDiEA3ZhD1oRYb2jHoeqD0GSJIxKH9WmsQjG+Pl7LCZtSVHvcFfaBvtycLVKjVhtrJLcTI5K9jmRlRcj86xwlcmVtmkxaSgzlwU9ads4SezZh7YxeSGyOG2c0pu3ccWqzLMqNyU6BagCKq2VPvcNlFJzQ/l/S88DaEh0RmmiYHFYmn0eQNMEb7W1OuhJW7PdjF/Kf8Hw1OHKa+2L3WVXfr6B1hdV80zw1lhrwippK/cWBhpaPhARUeQSQuCOO+7AypUrsWbNGvTt23rlx8SJE7Fq1SosWrRI2fbdd99h4sSJwQnSbkf568s7dAgBdz84lUoFCf4nYVN/fwuga77/fls89thjmDFjRrP3T5s2zev28uXLkZiYiLVr12L27Nl+neODDz7Ajh07sHXr1g7FSkREgWVz2vBLmXvB7PEZ46FWqUMcUSdzOoDD37u/7zmuoao2IQeoLgRqCoHsUZ0ellalRbQmGvWOetTZ64JWRLivYh+cwom0mLQWrzYON/yYhhSd1dMWaOhrCzRdhEwmJ8p8JW0r6t1J1H4J/QB0XqWt/EdGS8lOz0SsXAXp6zkADcnBeF280gIimJW2QghlsTCg5efhEi6YHO64M2Pcb2pyQtqXxgnEYI8JAOwo3YEdJTuws3Rni/s1jrul5HPj+zvjebSFV5uHFsZDll+Zj/cPvN+kWjxcyAvDERF1VwsXLsR//vMfvPfee4iPj0dxcTGKi4tRX9/wYeONN96IxYsXK7fvuusufP3113juuedw4MABLFmyBNu2bcPtt98eiqcQMcaNG9fi/SUlJViwYAHy8vKQkJAAg8GAuro6r365LSksLMRdd92Fd999N2zbHBARdVd7K/bC6rQiUZ+I3MTcUIfT+U5tA+qrAF0s0HtSw/bEXu5/qwtDExe8W2gGg9PlxJ5yd5/7Eakjwq79YUtYaUsKuRIx2O0RgDO/lGfyTc0tIBanPZPwdHgnphwuB2ps7pWVcxNysaloEywOCywOS9Bilyt7M2MzUWQqavbNxCVcMDvc7RzidHGIs7X85uOZtJWT19XW6qD1g62z13lVnLbUHkGu6FRJKqTFpOF47fEWk51NFlTrhMXIik3uBVdaS0g2Tig3l0SXeSazwy1p6xlba8lnADhQeQBVliocqznWpDd0qB2pPoJvjn+D8ZnjMT5zfKjDISIKiVdffRUAMHXqVK/tb775JubPnw/AvciW52V055xzDt577z089NBDePDBB5GXl4dPPvmkxcXLOkSrdVe8doAQAg6nExq1us3tEQIlNrb5q3IAYN68eaioqMCLL76I3r17Q6/XY+LEibDZbH4df/v27SgtLfXqget0OrFu3Tq8/PLLsFqtUKu7WWUXUXvY64HS/UByXyA6vOavFJnsLjt+Lv0ZADAmY0xYrb3SKaxG4PiZfvm5UwGNR7skQw9AUgGWGqC+GohO7PTwWmsr2VFHa46617XRxKBfYr+gnCNYmLQlhcV5pqdtkBciAxoSsgCaTSTJl7s3riastlZDCAG9Wo8EfYKygFe1tRqZmuCUuctJwT4JfVBkKmq2QtVsNyu92qI10Q2J52aShPJx4rRxiNfFQy2pg9oPVu5nK79mJrsJTpfT56Uhcsyx2lilMrqlZKectNWpdbA5baiyBrey0+FyKFXD/rZ5kLVaaevxn0WNtaadEQZH4968/u4fbslnoCHpXmIuaWVPIqKuy5+rDdasWdNk25w5czBnzpwgRNSUJEkdb1EgBCSHA5JGE7YVLhs2bMArr7yCWbNmAXBXzpaXl7fyqAYXXHABdu/e7bXtpptuwqBBg/CnP/2JCVsif7hcwO7/AjUn3SvZpw0Ecs4GDFmhjowiWH5lPswOM+K0cchLzAt1OJ3LWgfs+X+A0+7+Pcoc7n2/RgfEZwK1p90tEkKQtFUqbYPU/k9uizE0dSg0qshKg3azjxeoJZ21EBng3R6huaRtc+0R5NYIci9czwrVYLA4LEoMvQ29AbiThPJCap6U1giaWKgkFWJ1DT1tff1RJifUDDoDVJJKeS6tVY6293Lysnp30jYnPgcalQYCotUq4DhtnJJ8bulyBTnR2TPOvXhKsJOd5fXlyhg0Nx6NY5OrutvS0zbckp2eidrWnocQQtk/3JLPQMPr7E+bByEEdpTswOm608EOi4iIuqm8vDy888472L9/PzZv3oy5c+ciOtr/tmHx8fEYNmyY11dsbCxSUlKCVwVN1NUcX+dO2KrU7pXuSw8A298Cdr0HVBxxbyNqA6fLqbTTG50+uuv0sjVXAiX73L1qm1NXBuz4tzshq40CBlzs/jCkscQc978hapEg5xuCUWlbYipBibkEKkmFoSlDA378YGPSlhSd2dNW/qXUqDSI18b73EeptHWYvJKUcquClOgUAAh60lZOoMZp45CkT4JKUnklwzzJyV35k6JYTSwkSHAJl1dbApn8piQnsROjEgG0/Fw2FW3CW3vfwuGqw21+LvIiZOkx6TDoDACar1KVn1+8Lt6rYri5hLF8nJz4HGVfm9O/ywnbw3NBNSGaTz4DDZ/YyQ3Hm0uiN94fcCc7W0oIB4JLuPD18a+x7uS6Vvf1fJ4tjQcAmB1mJfZwTNrKPzP+fKJ62nQam4o2Ye3JtcEOq12cLiecLmeowyAiog544403UFVVhTFjxuCGG27AnXfeifT09FCHRdR9VBwBTmx0fz/4UmD874DMYe5Lt6tOAL98BPz8PsA5F7XBoepDMNqMiNHEYFDKoFCHEzj7PnV/bXkdKPrZXaXuqeIIsPPf7rYHMcnAmHlAfIbvYyWc6WtbE5qkrZwP8edK0rb6pdxdZZuXmBdWC4z7K7Lqgilo7C477C47gM5J2qbHpCNKE4Wc+JxmL5GL0cRAggQhBOod9covmJy0lasm/Ul0doR8vqSoJEiShHhdPGqsNai11iqJT5nnImQAoFapEaONgclucq+E6NF6QgihJKvkN6nkqGQcwZFmK21dwoU95Xtgc9rw7YlvUVZfhglZE/zqySOEUCpt06LTYNAZUGmpbDZpq1Ta6uKUimG7yw6bywa9Wt/s/inRKYjRxMDsMKPKUoWM2Gb+Y+ggz6Qt4O7P21xLCTk5nhmbif0V+5Ukuq83bavTqiSbJUmCUzhRZ69rMtaBVGouxdHqowCAs7POhk7d/CWonuNld9lhdVqbrY73TPCaHWbYnLYWj93Z5PhsThvsTju06ub7FspJZzmJHk59qIQQ+OjgR3AJ1/9n77+j5Mju+274U1Wdu6d7csAMBoMMLHax2ITNiVxySYoUuWJWIEX55eMjWZb10rKPaD+WVzLPoWSfR6af80rmY1t6KK9kiTTFIJLLJCx3l5uxAQtgkYEBJueZzrGq3j/u3JoO1d0zwIQeoD7nzJme7urqeytN9fd+7/fHp/d9uqHa5uDg4HAj8uu//utWHjCIvGC7Qc4nn3ySJ5980vr7tttu4+jRoyXLfOxjHyv5e6WzneyiLRwcHGzIxOD098Tj3tuhc794vP9DsP0hGDkKY28J8Xb+MrRtrlxKh43BMA3enHwTgFs7b8Wtrl5O+oZimpBajO/JxODM0zD0Cgw8KM6d0Tfgwj+K5Vq2wYEnwF1D54n0CQduak7k33rtjXVrxXJm9l4NyXySCwvC7Haw4+Cqrnu9cL5ZOgBLLltVUdflQhZwB/jsTZ/lsf7Hqi6jqZolIBdHJFSIttJpu0aFryxnr084e2s5VOU07+LMXinglo8aFYuD0plbzzU8lZoip+csofutqbf4waUfWPuvFol8gkwhg6IotPnbCHsX+1GlGJnlAnY34VbdllBrN2XBMA2rYFyTp2nNhXRYykGVIqQsTmeHvPhHPBHbY8puWZ/LZ+2PaGZtXaoy2xVqbzPTNK3tryCOgZUUh2skt21Wz5LVs9bf9dy2st+GaaxZVdGrJZFPMJ+ZJ5qNLqtteSPfUPvCwcHBwcHBwWFDMQzhGMynhRNw57tLX/dFYNdj0LWYxTlzfv3b6LApGYwOspBdwKN5NuXU+KrkkiIWQVFg56NCkE3NifPo5T+D8z8Vgm3PrXDwk7UFWxDRCaHFmSUbEJEgTWypfGpVZy+enDmJaZr0BHvoCHSs2nrXE0e0dQBKoxHWqziEptavHlyea5vTc5YoUi7aRrPRq856rYUlEvvF50k3p504U+60hSVBtlzslO/3u/yWUC7zfecyc7Z9GY6LC+j2yHYe2/YYLtXFcHyYvz//91bWbzVkEbI2Xxsu1bXseATZ/lpF1WTcgKqoBFyBZUdWRLPRq7ooZwoZS/TaHtlurcuO4iiLkCdUN+Rc7pcmT9OaR29IikXbWmJeupBGN3UUFOtYqZX7Uy7oNpJQWH7+1Mu1Ld5f1QYaNoric2g5U3p+cvkn/K/T/6tudrWDg4ODg4ODw3VFtft+mWPr8sBNHwGtyoTg9sUCUrMXnGxbh7qYpskbk28AcLD9YEPNOLxmpKnI2wT998A9vwnbHxTnUDa+KOa+C/a+X+RDL4cNjEjwu/wihhLTMoNdK6Zpcmr2FLB5XbbgiLYOi8xnl3JbG4niXFtYwqHsCwABAABJREFUElCD7qA1JbzJ04SqqOimvibVBq14BK8QyWo6bfOVTlsrVDtvL9oWF2WTgnBWz9pm4ErRdmvTVva07OGXdv+SFdfwrfPfYjA6WLUfU2kRJ9Dh76jbj+L2yfYXF1WrtmyTp6mkOJw8ruwYT4zzN6f/hu9c+A55PV91Odu+LEYjRLwRugJdNfshc10VRSHoDi6Jz1VEQqsv7iZrf6ylaGuaJuPJcevvWp8l+xhwB6z9V80xDDZO2xpu5NWiYBSYSdevtF3etnrnbvHy69GPlVDctuWE50+lpjAxLbd4o7EWg18ODg4ODg4ONzgXjsBz/xFe/nM4+fdw+QWYuQCT7yzl2O79gMjerEbzNiHoZuOQaMz7KIfGYSg+xEx6Brfq5paOWza6OauLnGXsW4wHdHlh4AG457eEWHvwk9B/t33RsWpYxciG7F+//IIoCLgGs1BlDCWsXjGyucwc6UIal+piIDywKuvcCBzR1gFYcvqtVf7o1SJF21Q+BcBsRrhJpcsWRKSDJa7ViUgwTZOcniOWizGVmmI4NsxQbKhqoal0IW2Jp/Iz5cXEzu1X7k6F6g7V8jxbALfqtv4uF+8yhQyTSXFz0t8kRsHa/e18bM/H6GvqI2/kOTJ0pKoAKp22clqAFY+Qi1WINHk9b01dl+2RBeNsnbZlBdWkC7RWrIAUmCdTk/zkyk9WVOxLil1dgS5LvKzmIpWCWtAVRFXUqnEVdn1ZD6dtNBstEehruWFl28Ke8JKDexnxCPIYXGvHMMDLYy/zjbPfqDmAADZO2xriM5T+824kxzCUXgvqOW3zRt7a343mGAY4N3+Ovzj5F9YAkYODg4ODg4PDqiAjDTJRmD4Hgz+HE/8bTv2DeL44x7Yamgtatpeuz8GhCm9NvQXAgbYD61K3Z11JL4jfi7GEFm6/EGtbt698nZE+8Ts5A7lU6Wtjb4lzdv6KyJ4uL3q2Csjv6asVhSc1g85AJ9py3cYNiCPaOgClIlgjYTltFwWduXRpvqyknriW03N889w3+X+O/z/8jxP/g78+9dd889w3+d6l7/H9S9/n+PRx2/dJl22Tp8kqklTNoWqaptXOkngEd+14hGLRtrgv5S7V0cQoJibN3uaS9/hdfj6444NEvBFyes4K2i5vW3ERsuLPzek5MnppJq4UlD2ax5pGUusiKreF7Gvx/qgmxo4mR63HV2JXeHb42WU7/IovwFKwrxaPUb6dpdhZTSSM5WPWcsXRG7UYig3x9vTbV+VQlC5bmVFb67OKhX5LfK4xEin73tck/gGvtUhomiaXoqKg2nhivOay8piRESm1/jkbplEihjaaaLsSp22J+NxgjmGAS9FL5PQcQ7EqI+xFxHIxvnX+W1yYr7zmODg4ODg4ODhYGPqSO+/AE7Dr3dB9MwTbhRMw0luZY1sNKyLBEW0dqpPKp6zvI9edyxaWzifptF0NPEFxToKIK5FER0RGLojzdWEYhl5evc9dRJrElhM3txykMbE72L0q69soHNHWgbyRt6YzN9oBXe6KLM+XldSbxj4YHWQqNWUJiJqiEXQHLSHv7NxZ2/fJzMliZ690qKYL6RJXa7qQFtPwUQi4Atbz1RyRUuiRIrBEulTLXcPS+dYf7q9op6qo7G8VI9On505XvB7Px0uKkIFw9crtWy7mFRchK++HndhZLoyWRFbYiHFZPcvMYrXLh/oeQkHhzNwZjk4crVi2HNM0rXiErkCXFcmgm3rVvN3itlWLq7CWt3HaxnNxCkbBdnnDNPjJlZ/w4uiLnJg5Ubf95ch/JlvDYjpKLTes5Zz1hOr2ozjLtzfUW3fdq0E0G7X2Qa1oDFjqixxEqOW0TeVTJeJ/o4mdK8m0LRZtG9FpK9u0nJulSwuXmEhOcHL25Fo3y8HBwcHBwWEzk4mCaQinbMde2HoY9n8IDn8eHvpXcNuvVc+xLadtlxCO4pOQabx7KYfG4HLsMiYmnYHOCpPUdYHUCvzNq7ve5kWtQUYkZOPwzrfFwEvHXhFhAiIqITpqv46rpFotoKvFmk3eYMbEleKItg5Mp6YxTbMk77NRKI9HsERbX6loK/NmqznwBmNiqvahjkN8/pbP838c/D/47IHP8vE9H0dVVGYzs9a6i7HybBeFVACv5sWreQF7sSbgDpTY74vdwsVOzPKp6+V9KRa9TNO0nG9bm7ba9nFf6z4URWEiOVHRFymQyiJkkmquYaugmmfJMVwrVqBcGFUVtaZLdTwxjolJxBvh5vabeajvIQBen3ydd2bese2fJJaLkSlkUBWVNn8bqqIuRSTYiHnFQidUdz6XL9/kbsLv8uPRPJiYVTNz5zJz5PQcAK+Mv7JiF6h02u5t2QtUzzMuaZunqW48Qs7IWe2Sx0y6kLaeWwtGE0v/uOsJxLIvPcGekr/tkH1UFfEvK5atjPTYSEquA3VuMoqzextNfDbNpeN8OTdLcr80mvPZwcHBwcHBocFILX438bdWZmyq2spyNz1BCG8Rj2ed2T4O9siots2cZVqTtXDaAkQWtYbokBBq3/kOZBPCgbvvF6D7FhFjYhpw+h+gkF21j15O/N9yyRQy1vfRRjMmrhRHtHUoybNVVvIPcx0oFjxT+RTpQhoFpUREhaJIAZtq7AWjwHBMuFR3tezCrbmtfvpcPkvQspviK8XP8jiG4jxYSbFoW94HBQXDNEgVlrJh5Hsr4hEWc2mKnbYL2QUS+QSqorIluKWinfJz5T+l07OlbtvyImRWP6qJtnZO2yrZvGAf9VCrGNlYYgxYcoAeaD/AnV13AvD8yPPWFHs7ZDRCu7/dEqCtftg4F4tFWCgtqFaR5VuUNyodvPVc3MUxAAWjsKKYh3Qhba23r6nPOt7r5fOG3eES8blWLITP5SPgDlg5TrXE1OnUNH975m+5tFB9+9diJLE0jSaWi6FXqxDM0jHXExKibfmgRjGyL52BTsvBvVrTZqphmAZHJ47WjQkoGIWSc2IlBdUyhYyVHd0IZPSMJeovZ/vK60Qqn6rqRN8oCkaBb5//Ni+MvrCs5SeSE3VzmB0cHBwcHByukrQUbVtqL7dc2naJ345o62BDXs8zEhffSwYiAxvbmLXAMJZc5uWZtteKLEaWmIKzT4toBJcXbv6o+K0osOd94AuLXF0Zm3Ct6HmaXKvntJWaQcQb2fR5xo5o62Ad0N2BxhuBkDED6ULainAIe8O4VXfJclJYS+QT5I3SQlxjiTHyRp6gO1ghWgLsbhG5SOcXzpeIRqZpVnX2WsXIisRO6QYud86qimoJuVIIyepZSxypiEdYdNrGc3GrL0NxIRxtCW2xsnXtkBEJZ+fPlogosghZZ6CzZHlLfC4TO6XwZFdQLafnStyapmnai7aL/0DsBEiZZ7sltCRA39V9F/tb92Ni8tPLP2U2PWvbRxmNUNwXK9fWxrlYXiQt6FoS0csdrXJZt+q23NT1cm0tp2zrXlyqi9HEKKdmT9kuW44cMGnxteB3+Uvyecsp3s4hT8gSeHVTr8gkhkohfTn5vKdmTzGfmef5keerFrSrhmmalhgv/67mTi4+/qXTNm/kqwqYxVEisj9r7e68HLvM0YmjPDfyXM3lZNukCzin52puu/KbkEaKSChuSzKfrCvEyuuEiblqBQNWi+nUNOPJcd6ZeafuIIppmjw9+DQ/GvzRqk3HcnBwcHBwcCgivWjiCLTWXm65tC3m2s5fgcLazSJz2JwMx4fRTZ2wJ1xhvrouyMaE01XVwLvK0Q/eJjG4YpowcVKItPt/sfTcdftEvImiwMQJmKqMZ1wRc5fg539KaELMuK1nglkOsoB7I2pcK8URbW9wTNNcOqAb0Dbud/ktMUSOlpULqHI5KbKVizmXY5cBMTXCzkm8Pbwdl+oimo1axbpACMWZQgYFxRIgJXbOTitSoKgImaTcpSqFAZ/LVyHCyr6YmFZfZJ5ttWgESX+4n6A7SKaQ4XJU9Lu4CFm7v92+H1WctsUCtFtbEjKLXXipQsrK8g26lvpuxTyUuZ+L82yl0xZEQaqHtz7M1qat6KbO0Un7fNviPNvyftgJeeUCtKZq1mhbuWu4WOCVx4pVVK0sY1gihde9LXu5u+duAF4ae6mqYFmMFHzlP5NawmpWz1oifsgTwqW6qvYDKh3GUqCv5bSVAzipQoq3p9+u2/5i5jJzpAtpXKrLOker5drKtvldfvwuPz6XD6iRM1wUv1FLoC8mq2fJFCrF7OUiHbbxXLxmpITsS7O32SraV+tGo/y1RooWKD9ma+UMQ6lreDnH+3oi26ObetW4EYm81puYa5777ODg0Fg8+eSTHDp0aEXvURSF73znO2vSHgeH65bUKjttg+0iy9MowPzl1Vmnw3WDjEYciNh//9/0yO+lvsjKokWWS3OR5jDwALTvslmmH/rvFY/P/hBi4+Jn9qIQe4ePwtCry4tPuPwCmAZNi8XPcnrummcjTqSujyJk4Ii2NzyxXIxUIYWqqBWCXiOgKIolgkq3qZ1oqyiKrbhmmuZSnk2VqRFuzc228DagNCJBumxtnb0eIRwVCxXJnBA47HKBy0O17ZypxX2R8Q/zmXkKRsFyMNYTbe0KktkVIZPUy7Qtb59dHqzsS9AdLMnyrRYrUJxnWy5wq4rKvVvExX9wYbAim1c3dMs1XCzaVnOoFjs67YqqlQtosXys5PVa/ZB9T+QTKIpCV6CLg+0H6Qn2kDfyPDf8XF2HnxR8ZUSAdQxX+SwQQqc8HuX2s3M5WsL7Yl/kuqs5O/NGntnMkrv52PSxFYme8hjtDnZbx1k1QbI8GmS5OcMhT8g692qJnQWjwNfPfJ2vn/36VWX4FmdIg33sikT2JewJL1U8reHWlK/VymHeKKpdB+zIG/mS46ORxGcobU89F3BxPxtNfL4aasWSODhczyiKUvPnySefrHjP7/3e73HkyJFVbceTTz5Z8dn79u1b1c9wcNh0pIsybVcDRVly286eX511OlwXGKbBldgVALZHtm9wa9aItcqzlbSLWit07IVt91dfbuABkS9dyMIbXxM/x78Bp78HF/4RLj4Dl56t/VnRUaugmTu9gFcVJphrmf1mmIZlTOwKbu4iZOCItjc8dvmgjYYUpqpFFUjsBK+Z9AzJfBK36i5xdZazu7kyIsEqQuatHBGWgkuxGLAcp61cxhKt3PbTGYr7Mp4cp2AUCLgCy5resa9tHwoKw/Fh4R5eFDnLi5DBkvsykUtYX/RN06wQ/CTFebCSagK0FJ6T+WSJcFaeZ1tOu7+d7ZHtmJi8NflWyWuzmVl0U8ejeSwxFZaE1VguZlvszat5SxzNlvM5Z++0LY6sqFlQbdEp2+HvsLKSH9n6CJqiMRwf5szcGds+ghAWpWtYRgTUcsPabWf52NZpmy9dXoqd1ZyExQUJ23xt5PQcb069WbX95cgiZL2h3poZ03Z9qZWXXLK8e8lpWytWYDY9SyKfIJlPWoMXK2E+O19yjNdyXxYL0HbnRzGGaVj7RR7/jRqPAMsTn633NpjYWSyG18vnLe5Lo/UDxGDi9y5+z4rgqcXFhYv89xP/nbNzZ9ehZQ4OjcX4+Lj185WvfIVwOFzy3O/93u9Zy5qmSaFQIBQK0da2+lNnDxw4UPLZL7ywvHxtB4frEr0gKtDD6sUjALTtFL9nL4ip3JsNZ5B1TZhITpApZPBqXus71nVHekH8Xu08W0n7LrjnN+HAE7WdvKomYhK8ocWohhCEOqBlANoXB1XG316KR7Fj+NWlx4ZOE+LzrqV+yVxmjryRx626q2pHmwlHtL3BkU6/RraNlxf2KneLSuwyVGU0wtamrTVF6f5wPx7NQzKftIQ4KTi12owIFxcikyKhvLCUC51QWlANKgW1coqdtsXRCMuZ3hH2hOlr6gPgzNwZKxrBLs834AqgKRomptX+dCGNbuoVcQdgL65VE229mtfKJC4WvWSxquI823Lu6LoDgHML50r2Z3E0QvG2CHvCKCjk9FzJVOjyPFuJ3B/l/wwsR2eRW1oKkKlCqmKaht350+Jr4XDPYQBeHHuxqvA1nZrGMA0CroAlEltu2DLxGeyPmWr9KO6LHBiwzo8qzk45gNMV6LJiHk5Mn1jWKKdpmraibTWxs0K0reJ8lhSfW7VyfyXFMSfHp49jmEbdPhRTXnys3PFdjOW09dZ32qbyKUzTRFEUy11dz2mbyqc4OXPymgp9PT/yPP/vyf+37r6UfZHXyloO1UbO5oWVOW2Lj7tG6wcI1/twfNia6leL4fgwhmlY/zccHG4kuru7rZ9IJIKiKNbfZ86coampiR/+8IfccccdeL1eXnjhhYp4hKNHj/Ke97yH9vZ2IpEIDz/8MG++ufwBTInL5SppT3t7481mc3BYN9LzQlR1eaDse9010dwvCiPlUhAbq798o5CYhre/Ds/9R5EH6rCqyIjAbeFtVszidYd02vqb1+4z/M3Li14ItMK9vw0P/Su475/DXf8fOPRpuOVj0LpdDE5cecn+vel5mDknHi9m84Z0MZhxLU7bYpft9XAMbP4eOFwTxUJNo1IsHKqKajkGy7HcfUU5mvWiESQu1WVNn7iwICISajltQ+4QCgoFo0C6kMY0TUvItItHkOKUFA+qiYnlfVnILlhZvlvDtaMRipERCWfmztgW7pIoilJRjEy2MeAOlMQdQJFIaBOPYNcXKbBJATyrZ60CY7Wcz52BTrY2bcU0Td6aWnLbVjteXarLaluxU65qzIPH3tlp1xeP5rHE53Kh0Io3KBvFvbXjVjoDneT0HM+PPG/bRyvPNthtCdDVxOeSthXHPNSIFSg/xuR5kylkbGMPrG0b7GJbeBs9wR50U+eNyTds21/MbGaWrJ7Frbrp8HcsW7SVYnU15zNURlxY8Qi5aNX4CXnMy8+S14HlIqNYakVjSOR5E/aEly8+u0N14yokr028xvMjz3N0wj7juR45Pcep2VOkC+m6Qp48vuXxXGuEW0aJSIG30RyqdteBajRyNq9pLuXsLufmVfal0frhsPkxTRO9YGzIT72ooZXw+7//+/zxH/8xp0+f5uDBgxWvx+NxPvvZz/LCCy/wyiuvsHv3bj7wgQ8Qj6+sKMr58+fZsmULO3bs4Fd+5VcYGhqq/yYHh+sV6bLzt65u/qaqQesO8XgzRCTkknD2R/D6X4jCSwCjKx8UcqiOaZrWIPd1G40ApZm2jYCi2J/bAw+K3xMnlnKtixl5XQzotO6ANpGbG1osLHgtxchknm0ja1wroTHnwzusC3kjz0xaFIRqZKdtsXO1xdtSISRKigUW0xTO0Zn0DAoK/U39dT9nd/Nuzs6d5eLCRR7ofcASbe2cvZqqEXQHSeQTxHIxVEW1nHB28QgVTtsaQqfsJwjhWBb56gv11e2DZCAygM/lI5lPWp9ZLbM47Akzn5m3vuRXEzqhMuYBaruGW3wtjCfHLTFoLDFWNc+2nDu67rAiBu7supOQJ2SNmtkJ0GFvmEQ+QTQbtY5nO+ds8d/lDrxq+yXijZAqpFjILtDuE9uxWIAuF21VReXRrY/yjXPf4HLsMpejlysGDuxcui7VRcgTIp6Ls5BdKHGZ2wn9VlZymShVMAqkCqmSZdyam6A7SDKfJJqNWsW/JNaI5KKL+e6eu/nOhe9wau4UhzoPlcRRlCMHFnpCPWiqZgmSmUKGdCFtFUyTSKFyOU5buU9k0b4mRRSJk320O45kJEi7v52Z9AxvT7/NzuadVdtfTF7PWxEeB9sP8vPRn9d02hYfM1JcrhbzIM+xkDtkCdbJfJKCUag6E0AeJ2fnznK4+3DV6181rsSuWE7jWv3QDd1q95bQFobjw8ty2nYHuxmJj1ju8EYo9lBehK5upm0DxyNk9Ix1XK1EtK3XZweHlWLoJm/88PI1rcMEDMNAVVVWcqW44/0DaK7Vubb80R/9Ee95z3uqvv6ud72r5O//9t/+G83NzTz33HN88IMfXNZn3H333Xzta19j7969jI+P84d/+Ic8+OCDnDx5kqamVa7y7eCwGZB5tqsZjSBp2yUq18+chx2PLD1vmpCYglxCCEIbeX+iF2DkKAy9BIuCFG27RKxDfFyIuZ7a34sclsd8dp5oNoqqqHVrwawJU6chNgrbH4ayYuOrylrHI6wWkV4RkzBzHi7/HG768NJr+YyITgDYepfVp6ZcBtyBa3LabobZ5CvBcdrewBRnWNq5QxsF6XIE+6gCScQbKXEpyqkR3cHuiogFO3pDvfhcPtKFNOfmz5HVsygsFTgrRzpUo9moJZr5XD5b8cVyEhaSJbmW1UTbsDeMqqiW2NIeaF9WHyQu1cXelr3W36qiVo2VKC+IVE3oLOmHXTyCTT5vufu5Xp5tMVtCW+gJ9mCYBsemj5HVs5bjzE60tSsOV02EteuHYRokC0nb5e1ybSeTk5YAbbdv2vxt3NpxKwAvjL5A3shbr5mmaTltywXfahm65cW7qvUDlsQdl+rCpy2Js3JflztHEzmR/6ooihWjsSW0hf5wP6Zp8trEaxX9K6Y4GgGEQCzbVt4P0zQrjv9ambblx6OmataxZheRkDfyljj5yNZHUBWVieSE9c+7HqOJUQzToMnTZI3Qx7Ix23iCrJ61IjMinsiS+FxFMCsW3v0uPx7Ng4lZVSjMG3nr3EkVUpYDeCVcil6yHtcTn01MXKqLTr84v6qJz8V96Qn2WLMO5ECBHYZp8OzwsxyfPr7CHqyc8uNiJfEImULmmqvVribFfaknKMvBShD7rvia4+DgILjzzjtrvj45OcnnP/95du/eTSQSIRwOk0gkVuSUff/738/HP/5xDh48yOOPP87TTz/NwsIC3/jGN661+Q4OmxPpsPNXzl68Ztp2gqJCckY4ejNRuPIyHP0f8PpfiqJIU6dW/3OXi56HN78mijEVctDUDbf9Chz8OIQ6hbg8d6neWhyWifz+39fUh0fzrO+H5zNw5gcwfBQG7Wdargp6Xgj90DhO21pIt+3UaTGQIhk/JvoS6oCW7eJ8AEK5UpPbSkkX0tb980qdtqs5s2c1cZy2NzBSxOgKdjWEO6oaxU66WkHSbtVtuRSj2aiVZ1svGkGiqRq7mndxcuYkr0+8DgjxtJoDLuwJM8YY8Vzcci1WE78D7gCKomAYBol8gkwhg6IoVUVbVVGJeCNWrMDVjBTub9vP29Ni9KrV11q9H0X5vLAk1Nhl85Y7O03TrOkatqbJL07hkOJerTzbYu7supPvXfoep2ZPWUJt2BO2FUntsk6ruYaLC0ZJd6B8rCpqyUABLOXBFoudxfEGtdp/fv48sVyMY1PHuKv7LkCI2Fk9i0t1VTigI94Iw/HhCmHVEi+L9ou1P3KJEpdjsShafG43e5sZT45XiD8yGqHN11ZSsO2ennsYig1xfv48t3XeZuvWNkzD2hbFYnyzr5lEPsF8Zr5kG5XEHZQ5bcv7IZ+D0uJwEW+EWC5GNButOJZmUjOYiMGozkAnu1uEg/749PFljbZKYbS/qZ+gO4hH85DTc0Sz0YqBj3IXcHFchZ3rtPh4VBSFsCfMTHqGWDZme22bS8+V3DycmTuzoqleBaNQks+7rGze4piHXLyqe1YeYxFvxLruxrKxqg768eQ4p2ZPoSgKe1r2VDi9l4NpmpybP4c7X9u5IK8Bct+tJB5B/u31e1fcvrXA7npWjXQhXTK4EM/Fr4viCw6Ngaop3PH+gWtahyz+5XK5VnTfqWqrd48aDNZ2s332s59ldnaW//Jf/gvbtm3D6/Vy7733ksvlar6vFs3NzezZs4cLFy5c9TocHDY1xfEIq43bD5E+WBiCt/9OiLblwsvIUeg6sPqfvRymz4gMW7cPdj0GXTcvuX7bdgoRa/YidN+yMe27zpCRaNvDGxCNMHFciJAgjrmOveLYXG1knq3LI47/RqepCzr3wdQZ4ba9+aMi53ZE6C30HRbnRFAYh5oKedDzV12ITM4ebfY2r/j7RuwHT6PPzhB86CG82xsnXsNx2t7ASKGmO9DYtvHlirawJBJOpiYtgXAlF+1dzSJLRQoYbb7qFYWliBTLxayLSjXBQlVUK5t3KiNGmDyaB69WXRiQxcjg6kTbVl+rJVLZFSGTWP2Qmbb56k5b2b+cniOn58joGUsksBN5ZR8WsgtkCpll5dkW09fUR2egk4JR4MXRFwF7ly3Yi7bVXMNBVxAFBcM0rOzYYhdk+ZdJu2xTmZVTSwj0aB7u33I/AG9Ovmm1TYqcnYHOiunuVj+KClTl9Jzl/isWL2U/dFMvycCt1u9qGa3F0QjFtPvb2d0iKn++Mv6KbR+nU9Pk9BwezVMi6lbLtZVt87v8uFV3zX5A0fFYdHzZbSPJVFqcX/KYl27ni9GLyxqxlbmv/eF+FEWxokrkAEoxxXm2sHR+6KZORq/MDS4X3i23fpViZDK+Rvb3cuwyqXx1N2s5I/ER8kbeiqdI5pO2ecZgL9rmjTw5w16oKHbYy/bVcoLKvpimWeL+XQnvzL7DkaEjvDZT2/ktzzN5nckUMlVdp3kjb20Tqx8NVIys+PxJ5BM1i+qV39w2Uj8cNj+KoqC51A35WU9jwYsvvsjv/M7v8IEPfIADBw7g9XqZmZm5pnUmEgkuXrxIT891WsXcwaEeaxmPAEtV6tMLQrBt7oe974e7/6nIvY2Nb1yhsvHFGUZ9h4UwW3w9W8zwZO4SGCsrmutQSSqfsupaLNe0tWoYxpII6YuI4/DM00si7moiRVtfZGNjP1bCwIOirdPnxPk4dRqycREL0nmTWMblBX8zIdUDuWTd+95qLOc7ejX06AJ6LI6irSyObq1xRNsbFNM0N03WR7HwtFzR9p3ZdzBMg2Zvs+WSXA49wZ4S4bVYOC1HugRjuZhVQKlWzIQUQqRoWyy+2SHFIrfqvmph/b4t99ET7OGWjuqjt8XiMyyJl3YirEfzWFNNEvnEUtEyV8DWydvkaUJVVHRT59z8uWXn2UoUReGOrjsALDGvnmgr+6EbuiVwlTttNVWz3LpS6KgVC1EcWWCaJrqpWzcF5fEG5exs3klfUx+6qVvCc7UCZiWflakUn72at2Sqj6ZqJYKcxM6dWt6PYoqLkJVzuPswiqIwFBviwnylS2gsKW6CtwS3lFTnrCfaFrfNbn+UL1+8X6xID5t4BJln2xEQom27v53eUC+maXJiunaF3mg2auVgScFPXj+KCxxKyiMrXKrLcmnb5TCVR4mUD5hU9CUt+rIjsoOuQJflNF0uUhzd2bzT2n524nNxG8LeMG7VbY1M2wndhmksFVXzhGruD4kcsAG4uHBx2X0o5szcGQBmsrUFFLlf2v3tS9erKrlY8nmP5rGE/noxBK+Ov8pfnPiLms7l1aJ4mxYXvLSjvI9Orq2Dw8rZvXs3Tz31FKdPn+bVV1/lV37lV/D7V+Zk+r3f+z2ee+45Ll++zEsvvcQTTzyBpml8+tOfXqNWOzg0MIUcZBf/P61FPAJAzyHovQN2PAz3/paIH9hySIjEnaI4M6P1C+vaMvI6vP11OP09uHAEhl4VRZXmLtUX5FJzwgGsKNB9c+XrTVuEU7KQhdjI1bXPwWIwNoiJSWegc9nfNVeN2QtCTHX74PZfA28IUrPCWbrabJY822KC7Utu98HnYWTRgNF7B2hFGkKwg4DiQsmLQu8rMatILDOSzffaWpimiREX1yq1wfLnN51o+2d/9mcMDAzg8/m4++67ee216o6bb33rW9x55500NzcTDAY5dOgQTz31VMkyv/7rv46iKCU/73vf+9a6GxtOLBcjXUijKmrVAlWNgltzc3vX7RzsOFhX6CwXpVY6yqYoiuW2hdoicbErq57TFpZEp8m0uJBUi0aQSEFve2T7iosPSbqD3Tyx+4ma+1i6/XJ6jkwhs+Q4tcmohaL80VyybkE1GfMAcGpW5Ekt12UrGQgPlDieq2XTyGMjXUiT1bMi7gATTdEqCmHB0r4qF23t+hL2hEvykuez8xSMAl7Na4nr1VAUhQd6H0BRFC7HLjMYHawp2sps3mguak2Nr9U2uyJedlEKsLSvZbE+EOK2FAfttm3EG+G2ztsAeGb4GcsxKZFFyHqbSver3C7loq1dNi9U7g9JtXgEsBcJpZheLO5Lt+2puVNWNIMdV2JXALFfpNgnrwG2TttcqdMWahdVKxY6S/pRx2nb4e9gX9s+AE7PnV5W3pJhGlZEzI7IDisPvJrQKPsij7/iqIdyUvmUFZsQdAcrBn5q9QVgJDGy4puwhcyCtW/ThXTN98vjIuKN1OxH8fMhd2hZjmGA8/PnyepZzsyeWVEfroZqgx52lLe70YqqOThsBv7iL/6C+fl5br/9dn7t136N3/md36Gz036wuBojIyN8+tOfZu/evXziE5+gra2NV155hY6O6rOeHByuW2Q0gtu/dlO5XR7Y817Ydl9lxmevMH8wdXopB3S55NNCqJ27BBMnYfg1uPgMnP6+EHJPfqv2+ycWzQIt2+2zR1VVFEkDEZHgcE3IPNuB8MD6f/jIUfG75xB4m2DPop40/BpER1f3sxZjBzeVaAuw7X6RPz13CeKTQqzdclvpMqEuVEUhtDggstKIBMM0rO8LKzW9mZkMZl58ruaItlfP17/+db7whS/w7//9v+fNN9/k1ltv5fHHH2dqasp2+dbWVv7tv/23vPzyyxw/fpzPfe5zfO5zn+PHP/5xyXLve9/7GB8ft37+9m//dj26s6FI0ajD31E167SRuKfnHkv4qkV50bCrybOR08GhttO2uPq7VRXexp0qkeJBvSJkkv5wPx/d/VEe6ntoeQ2/Styq23IHLmQXrGJC1fpSLK5VEweLkeKdFIuWm2crURSF27tutx63B+wFaI/mscTZYiE95AnZHjfF4jPUFkZdqst6fj47b7mle4I9y5q22eprtYTD50eeJ5qNoqDYjgDKeIaCUbBcddWyee36AdUjLqQoJaMtQOyXglHAo3mqFt073H2YrU1bKRgFfjT4I2s6uW7o1rWkXIyP+JaE1eKpLVWLw3nsxbVyobO4H7FcrETAlNmzUBoJsi28jWZvMzk9Z7k17bDybMP91nM14xFyS+5Uqx82+wPKsnwXB0Qsgd5GfNYN3XKndgQ62N28G5fqYj4zbzmjazGeHCdTyODVvGwJbbHE59nMrO3ysg2yL3L/2N0sWfvEHUJV1LqirW7o1vkfdAcxTdPKHFsu5Q7j5eTzlhSHsxHRAWL5pWv3chzDeT1vrX8wtrI+rBTTXCpSJ6/RtURbuV/kgMNyRNvp1HRDFV5zcFgtfv3Xf52FhQXr70ceeQTTNGlubi5Z7sknn+TYsWPW37fddhtHjx4lnU5z7tw5Pvaxj3H58mV+93d/11rGNE0+8pGPVP3sv/u7v2NsbIxsNsvIyAh/93d/x86dO1enYw4Om421jkaoR3iL+DF0GDu2svfODYJpCMF156Oiwn3XAWgZWBKfFqoUKTSMJdG2Vl5t2+K1YfbGzbyWs+F+PvJzjgwd4YeDP+S7F77LN85+g78/9/c178skeT1vGUlWUv9hVUhMLTqqVegV31lp3y2OFdOEs0+DTUHjq0aKtv7m1VvnehBohZ6DS393HwRPWZ0aWYysIL4zrXTW2FxmjryRx6N5VlzXQY+Lz1IDARRXY+ljm0q0/dM//VM+//nP87nPfY6bbrqJr371qwQCAf7yL//SdvlHHnmEJ554gv3797Nz507+xb/4Fxw8eJAXXnihZDmv10t3d7f109KyRlM3Goha06E3M5GiUUyfy3dV/evwd7CreRfbwttqnux+lx+X6sLEtEZ0ajltZfErSa0oBUlXsGtdKl9KoWYsIaa6u1QXPs0+uNtyrhWJtrUE6PJ4ipU6bUFM776963Ye6nvIykG1o9iBWZxRa0e5s7OWMFq+bpmbupJokbu67iLoDlpCbKuv1TbTWFM1SzySLjtrUKBGznCxKFUtHsGtuq3l5Q2QdS0IVC9IqCoqj217jLAnTCwX46dXfophGkynp8kbeXwuX0X+c5O7CZfqwjCNkun/dvEIxX0rFgmLhevyeIRi57NkOj2NiUnIHSopVqcoiiWan5g5YZuPVDAK1vFfnCFdnMtc/j6rL+76TtvyomWwdN7Fc/GKdc9n59FNHY/mIewJ49E87IyIG/tawrNEiqIDkQFURbX2j53YWSwOyv1iDTLZ3CyVH4/1smDltnOrbm5pF19czi+cr9uH4vZJ0dajiuthNfE5b+StYybijVgCebWbvuKZBeVFGe0o/txoNlo1bqIezw0/x/cufg/d0Ksuky6kyek5FBRrsKuW40D2RV6X6mXajifG+d/n/jdHrhxZafMdHBwcHByWh1WEbAO/X0u37dhbK8uOnVt0v3bshf57RCGxm34RDn0aesR9JYM/ryx8BrBwWWR2un3Qvqf6Z7TuEPEJyZmlrNIbjMHoID8f/TknZk5wdu4sg9FBRhOjzKRnmExNWgXCazEcH0Y3dcKe8PoXYZVZth17Sh3Vux4Tma3JGbj84up9XnGm7WZj232guoTA3XdX5euLxchC+RyY5oqdttJMVOt7bTUMKdqGG8tlC9BYEnINcrkcb7zxBl/84het51RV5bHHHuPll1+u+37TNHnmmWc4e/Ysf/Inf1Ly2rPPPktnZyctLS28613v4ktf+hJtbdULUGWzWbLZJWdKLCa+GBmGgbHGIeKGYYi8jWv8nPHEOKZp0uHrWPM2rydBLYimaBSMAv2hfjC5qgDrx/ofEw/qvL/J3SRGdBYt/AEtUHV7Bl3CYSZ/Qu5Qw2z7kDuEaZqMxEcwTbOkreVIp1w8GyddEHkzIVf1voTdYWs9EW8Ev+a/qn4f7joMUPO9Te4mxs1xFjILGBhWX+zeI/sYz8YxDMPKq622DyOeCEPmkHA6pifRfBpdga5l90VTNO7tuZefXvkpQM33ht1hFjILzGfm2RLcQiwbq3rMyH4ksglxDTINy4EacFX2JewJk8glmE/P0+nvZCIxIa4F/trXAq/q5b3b3su3L3ybodgQr469KgYtTJOeQI/t8RJ2h5nNzDKXmSvJb7XbL+X7AyCejWOaJi7VhVf1Ws+rqARcARL5BAuZBWuAYTIxiWmatPvbK/qyq3kXL4+9TDQb5UriCp0dpdNdR+Oj5PU8QXeQFk+L9f6gK4iKSsEoEM1ELYHSNE3b/WLXD4BYprLfAS0gCrAZOrFsrETInk5OY5omrd5Wa9vuadnDmbkznJ87z73d91ribzmmaXJx/iKmabK9aTuGYdDsacY0TWbTs+i6XnIjk8qnLHFQtq9aP0r2oVssK68fyXySbD5b0a7p1GJffK3siOzg5bGXGYuPkcgmSsT1aownx4lmo7hVN/tb9/Ni7EVmUjO2x+tCWkR/eDQPHtVjXa9i2Zjt8sV9CblC1rIFvVCS0SyZTc2WHOeXFi5Z8SHLZSo1xcmZk6JvifGqsw/mM/NW2yKeCKZpEs1Eq56n8hrWE+jhSvQKsWysYl8XMxofxTRNrsSukMln1mWAcLXuYezW6+Dg4ODQgKQWB4v9G+S0BejYBxePCBF15pyoZF8Pw1iKLJAFw4rZdi9MHBcOy/nL0Frm7pQFyDoPlGZ2luP2Q7gXoiPCbSsF5huIi1Gxnfua+ugL9eHVvLg1N3kjz3PDz3F+4Tz3brm35j3jiRnhat4R2bGuxSvJpWDyHfG4987S1zwB2PO4iNEYfgV1ayusMG7Hls2YaSvxReC2XxXOYzv3vb8FNDchRYN8esVO22LRdqXoi5peo0UjwCYSbWdmZtB1na6u0h3Q1dXFmTPVXUfRaJTe3l6y2SyapvHnf/7nvOc977Fef9/73scv/dIvsX37di5evMi/+Tf/hve///28/PLLaFWqxn35y1/mD//wDyuen56eJpOxr8y9WhiGQTQqvpip6tUZpQtGgaGZIUxMXCkXU3n7eInNijvnJpqNEm4KV43OWC3MtEkytTQNOrWQIq/ah9JnMhmSyaR1jGRjWaayjbHtjaRBMpnkQvoCBaNA2Ky+7XLxHMlkkjFzjLSeJplNkovnmDLsl9czOsmk2Ea9Wu+a7hMjJfoxNC2mKiWTSQregu1nZuNZ0Q9jjEnfJJPzk+imLvZLunJ5uY3eyb3DQnKBgBGAOEwll9+fJrOJiBlhLDVGKByqvi3Sou1XJq/QoXcwNjtGMrO4nc3S9xT3Y8o/RbKQJJFIoKCQnE+SVtJV191SaOHC5AWS+SSutGtZ++Zg4CA/n/w5Px/8OX7NT1pPEwgEbN+rZBWSySSD44MEMgFM02R8fpy8kRfbObP0nmxC9GNcH2fKL54fT42TTCYJu8NMT09Xrjst1q2GxbXwwsQFkskkHp/Htj29Wi/Ho8d5Zv4ZFFMpybw6PnOcZDJJT7in4rO0vEYsG+PC2AW2BoULN11IE42LmIvUQoqskq3aD4ChhSGSySRttJW0TckqJPNJBscG6QksZRxfmBZ9cbvc1vIu04WaU5nPz3N08Ci7wjZfIoCZzAyTC5O4VBeetIep7BQFo0AqmSJJkqHxoZKc5+nMNMlkkoArwNyM+HKVSyye5/pYST8ARqdHSSaT6B7dals+nSdn5Lg0dqki5/nizEWSySQul4vMQga/7mcmM8Prg6+zr7n+F6ejU0dJJpPsbNqJK+Mik8lwefpyRbsAhhJiO/u8PqampsjH80vnh69y+dHZUZLppFjOTJJOpTFMgytjV2xnTlycFn3xqB5yRo4TIyfoZWWzB16aesm6Jp4fO4+r2f5WbDA2KI5/f5h8Im9dd6e89ufpxPwEWT2LJ+MhmUySJMnwxHDVWRODU4NWO04MnbCO7bVkNe5h7IjHnaJrDg4ODg2JjEfYSKet5hJZo1deEgXJliPaxsdFpq3LC5G+ytd9EZHHOfK6KDbVMiAcsyDeN7MY61Q8HbwabTsXRdtLN5xoWzAKVk2Jw92HK2Yxnp07y0RygpMzJzncc9h2HZPJSUYToyiKUrP49powfkwIkE3d9sdJx17o3I8yeQrfpR9B77ZrizXIZ0ThOticTluAcI0i3ooCwQ6aUhOQS1atSVENOYN0JbNhJUaiMYuQwSYSba+WpqYmjh07RiKR4MiRI3zhC19gx44dPPLIIwB86lOfspa95ZZbOHjwIDt37uTZZ5/l3e9+t+06v/jFL/KFL3zB+jsWi7F161Y6OjoIh2sXyrpWDMNAURQ6Ojqu+gvPWGKMQDBAwB1gYMvA+o5GrQMfavoQM+kZ9rTsWfO+9eX7mJ8R0348mofe7upf3oP5IME5IQIEg0G292y3KrRvNP2ufi7mxCinFy+9rb1VC29k/VmCySCaT4O8cBZu695Gm9/enR4uhAnOi37v791PZ8sqjDBWYcA9wIXsBRS/gqqoBPUgfZ19dLZWfqYe1AkmgmgejabWJnwTPhQUBnoGbAu/Zf1Z3km/Q8Es4PP52N6xnZ7uGv90qvCJjk8wl5krKZRVTr/az1BhCMWv0NnZiTKtENTEdi5/n+yH6lHp7OxkPDlOMBikydNEd1flP6x++hnTx1ACCuHWMPq4TtATZP/W/bYF28rp7OxE9+u8Pf02AEGCHNh6wHYq0jZjGzPmDEpA9CNdSOOZ8ODBw8CWgZKoCzNoEowHUdyKdezNzs0SjAbpaeqpOB77sn3E5+JoIc16LTubJRgMsmfLHjqbKrfvu9reRe5yjjMTZ3g98TqusIvbOm5DURRiczGCwSC39N1CZ3Ppe/vT/eQWcqhB1fqsieQEwWCQkDtET9fScWDXD4CLhYsE00F620vPrd5EL3pcx93kprNt6fl8LE8wGGRXz66S4/dO7uS1ideYYor7Ou+z3UeD44MEg0F2RHawpXvJxdk9300sF0Nr0kq2T3Q+SjAYpCe4tJ2r9QNAS2oEC0G2dm612rxlYQvT6Wk8YQ+dkdLlC4kCwWCQnd076Wzr5DblNl4ae4lZdbZugZ+CUWBmaoZgMMhdA3cRcoV4buI58u487R3tFW7YMcYIBoP0NfeJY7Xs/ChHnVUJqkH6u/vpDnbTPd9NNBvFE/HQGbK5bsR1goUgd3XfxdGJo6RIEWoJLcsxDELsn5qYIhgU10Tdp1fdBoO62I99bX30R/p5O/k2irdyf4DIcnONu3DhYlfvLjoSHaTyKbwRb9Vrjb6gEzREO5Lu5IqLLV0Nq3EPY4fP1xj/Sx0cHBwcypDxCBuVaSvZchsMvSKcsYkpKzuzKjJjtnU7VCsI3X+PyMmNjop8W5lPO3lKZOiGOiG0DMdf2y649JyIVNDzUGUm1fXISHyEnJ4j6A7auiMPth9kIjnBO7PvcHvX7ba1eN6aeguAPS176taMWVUMHUbfFI/77lwS7cvZ/V7M2BjqzCi8/Xdw+6+J2ISrQebZegKiAN/1SKiL0OwZyCVWFI+Qyqes+L+rich0nLarQHt7O5qmMTlZWoBlcnKS7u7qSrqqquzaJdxIhw4d4vTp03z5y1+2RNtyduzYQXt7OxcuXKgq2nq9XrzeyixKVVVX9UtINRRFuabPmkpPoSgKPaGeqm7izUxHsIOO4PpU6I34IpYw3ORpqrlPQp4QqqqiKApezYvf7W8YwbzZ11zSliZv9b40eUWhrFguhm6KqbfNvuaqywc8ATqDncSyMbaGt67pOSL7EcvFcKtuFEUh4o3YfqbsR7KQJFFIoCgKQXcQt8v+RqnF32JtI0VR6An2XFVfPKqH7lDt0b9WX6voRz6GgUFaT4u++Cr7EvaGURSFVCFl9UdRlKrHo+xHLB9jJjNjbaPyzOVa3Nd7H7OZWUYTowRcAdr8bbbHcotPfFY0F0VVVattAVcAr6v0Gir3R0pPgSJydGWf7Pah7Ec8H0dVVTKFDPF8HEVR6Ax22vbdq3r5hR2/ABkYKYzw2sRrxHIxbu+83Wpjf6S/4r2t/laUqMJCbsF6TR4z5fsk7AtX9KN4+fJzq9nXzHBimFg+Zj1vmiZz2TkhbgVLxa39bft5ffJ1JlITxHKxisxogMvxyyiKwo7mHSXvbQ+0E8/HWcgt0K8uFVuT2634PK7WDxC5qoqiEPaGreWbfc3MZGas/VHMfGbeKiKoqiq7W3bz8vjLTKYmSRVSNQsZjsRGyBk5Qp4QfeE+TENEH5iYRHPRisGi8r7I80Mee8XHqWEa1vPyGIt4I8RyopBheT9M02Q+K/oyEBngcuwyM+kZRpIj7GtdhmsHOLdwDgMDl+pCN3VmM7NVryOxfAxFUWjxtVj/a+z6AaLwnaIooiCj20+zt5l0IU0in6BHrRxcMkwRCSPXM5ocvarrmWEaXIldoS/UVzWuo5xrvYexYz3uvRwcHBwcVkg+I6aPw8Y6bQF8YZE5OnVGuG33vr/28lK0tYtGkHiboPc2GD4Kg88v5dNOCGMD3QerC3nFBDvEurJxISpL8fcG4FL0EiCKh9l9l9ge2W7VBLmwcKHifmsuM2etY6VxVdfM9FmxzzxB6NhffTlPAG79NMbP/x+U1By8/bdw6y9XFuFaDps5GmG5hDoIqW7Ix1Yk2kqXbYuvxbZuTD2MeOM6bTfNXa7H4+GOO+7gyJGlghmGYXDkyBHuvffeZa/HMIySPNpyRkZGmJ2dpadn5Q66zYJlGw+s3DbuUEpxBmWtImQgvqjKwj0hT6hhBFuoLApVa5RS9kE3RQEdmTtUiw/v/DCf3vfpZbvRrhZZSCiZT9YtkhZ0BVFQREGt1HTNZUHk5WrK0iBHT3DtrhGyoF40G7WKIrlVt+0054ArYPUjXUhXLUJmrduztO7iImQrQVVU3jvwXva07OG+3vuqHsuyiJcc9ay1TwLuAIqiYJomqXyqZHm7AmzF/QCsfRjxRmo62FVF5e6Ou3mg9wEUFM7MneHbF74NiGui3T95Od1/QY5us1Tkqbwvfpe/oh9A1cJ4dsWvYrkYOT2HpmgVDuaQJ8TWsJjGfma+MhpoPjPPfGYeVVEZiAyUvCbXVV6MrLwIWa1+wFIxrGKxtVoRr1Q+JcR3FKsYWsgTss6fCwu1qyXLAmS7W3ajKmLQq9nTDNgXI4vmxPEgs4eD7iCKIs6PVKG0H8l8EtM0xUDC4rXJKqpmU4wsVUiRKWRQEEKqjNe4HL1csw8SwzSsLNvbu0R14bnsHIUqFYXlsR3xRqz/LwWjUFJ8TyKPL3muyH1ZLQsslhWDbpqioSoq0Wx0WdWZy3l57GV+OPhDXp+sXyTEYXNil23vsPo429nhukRGI3iCImZgo5HRA5MnRYRBNTIx4cZVFCHE1qL/XuGMjU8IoTc+KX5UDboOLK9dirIkDs/Wvi+6njBMwyqcuyNiv501VbOK2B6fPl5xrZQu2+2R7etfgGx08d5ny221c4sBfBHSez+M6Q1BYhqOf10MaqyUzVyEbLkEO2lSPZBLkilkrNpBtZhITnB04ihw9RqXHl902q7xzPmrYdOItgBf+MIX+O///b/zV3/1V5w+fZrf/M3fJJlM8rnPfQ6Az3zmMyWFyr785S/z05/+lEuXLnH69Gn+r//r/+Kpp57iV3/1VwFIJBL8q3/1r3jllVe4fPkyR44c4cMf/jC7du3i8ccf35A+1mN6KM7YmTix2Rr/aOowmbw6ocahkmKRw05YKkd+8V7XqRvLIOgOlgiStQRoj+YpKViznL54NM+aC7YAPs1ntU2KytX6oqma1abx5DhQuy/SjSe5mmkXyyXkDqEqKoZpWG2rJvQX9yOeiy8JnVXcixFvBAWFnJ7jcuwycHXXAr/Lz2PbHmNPS/WKuM3eZkCIXVk9W1O0VRWVoEvsKykK1lpeioRSaJI5xB3+5bnsb2m/hV/Y8Qt4NI8lgm1tss/0lOLzfHbeulm0EzplP+S1oHh0WD4u74sUn6UIDEsCdJu/zbYY1v5WMZp/avYUZ+fOohu69Zq8+e0N9VYI0NVEW7kN5Tat1Y+sniWn50Rf3Et9kduhXPibTc9a6y4e3NnZLFwkFxcuVvRPkilkrGO0+Dhr9baWrLsYuR1le4qPq3IBs1jolNvZKphnI9rOLX75DHvDuFU32yOi6MhQfKiq8FrMldgV4rk4Xs3Loc5D+Fw+qzhcOaZploi2LtVlXcvsXAfxfOm5In/b9QOWjoEWX4uV+zUUG6rbh2KS+aQlQo8lxlb0XofGx+0W52sqlaqzpMNqILez3O4ODtcFjRKNIIlshVAH6AWYOFF9ubnFe5OmnvrT2D3BJTF48HlRnAyECLsSJ6V0185ehBtkEGc0MUpWz+Jz+aoWZQUxy8yluphJz1jfi0Dc18nB/VV12eoFEXkxfBROfRde+Sq88J/hpf+fePzaf4fX/1+xjKoJ0XYZmL4WOPgpUXwuPgEnvrGUT7tcpIHkWnJxG51gB15Fw6MXQM/XdNvOZ+b50eCP+Nb5bzGTnsGlurip7aYVf6SRy2FmxL5oRKftpolHAPjkJz/J9PQ0f/AHf8DExASHDh3iRz/6kVWcbGhoqGSKXDKZ5Ld+67cYGRnB7/ezb98+/vqv/5pPfvKTAGiaxvHjx/mrv/orFhYW2LJlC+9973v5D//hP9jGHzQCsZk0C+NpUltzNHesPAslmU9arqf2QPsatPDGokS0rTHFVyJFjkYTbeV0+oXsAlAqxtgRcoeY0xcFjCqOzo1ACqtS9Aq4ArbZRxI53cYSRusI783eZmbTs0Q8kWXlv14tqiKmac9n5hmJjwD13c/JfFI4jPO1RVsp/iTyCWs71crXvRY8msfaxvOZ+SWh02t/zDR5mkjkE0JMC9o7OiVS7MzqWTKFzFX1pT/czxO7nuDpwadJ5BPsaLYf5Y94xdT0nJ4jmU8S8oQsAdDu+A+5Q5aA3h3spmAUSOaT1mvFFIvP0vU5nRZ9qSZAbwtvI+KNEM1GOTJ0hFfGX+GW9lu4qe0ma4qYnWOhWLSVnwXVBejyfsCS8Olz+UpE2Gpi50xmBoB2f+n/m53NO3lx9EUmU5PEcjHb7Xhx4SKGadDmayt5f7OnmZHCSIXTVjd0q33FAyzlx5WkXOgs6Ue2UuyUnycjGdr97dbxPZoYZVt4W8V7ipEC5/7W/bhVNx3+Dobjw8xkZioGgVKFFHkjj4JitUme54lcouI4Lx/gqOZ8ruiLr40WXwtjiTGG48MrKuDx5uSb1uDYTHoG3dBt88AdNieaptHc3GwVGwwEAtc8Q8g0TQqFAi6Xq6FmG20kpmmSSqWYmpqiubn5uowtc7iBSckiZA0i2iqKEFjP/kgUENtyu71DcnZRtK0VjVDM1rth7E3hzk2K+x56bl1Z25q3geoSTsrkjBCXr3Ok0WB7eLutSUHid/nZ07KHU7OnOD593BJ4j00dwzRNekO9V1V4qoL5y3DpWbEfiwwRS9g4YztvAm99DcAi2A63fhre/l9C9D3xTTj4ieXnGN8ITlu3D3wRQnE3c4vFyKSBRpLKp3h98nXemX1HfKdBYV/rPg73HK47+9kOY7GgreLzonoaLyt4U4m2AL/927/Nb//2b9u+9uyzz5b8/aUvfYkvfelLVdfl9/v58Y9/vJrNW3NcHnEzV8gZV/X+qZS4+W7xtZQUAXK4OtyaG7/LT7qQXtYFYmvTVk4oJ6o6+jaSsDdsibb18k2D7qDl1FqOWL2eRDyRZcUdgBBBppiyBLV6AnSbv42LCxfp9q99tEizt7lEtK3VtqAnCCkhclpiorv68s2+ZksQ1RStQlBbTSLeCMl8kmg2Wj+yoshJaJpmzeXdmtsSzKLZqHVt6wis7Ca3zd/Gp/Z9inQhXXUbu1QXYU+YaDbKfHaekCdUVeiExXMiiXVcyd+aolWI/WFPGAWFvJEnXUgTcAeYSdsLncXt+ejuj/LO7DucmD5BMp/klfFXeH3ydQpGAQXFcoEW0+xtrhCfC0bBij+wi0kZT46XjHBXi6yQImk8F8cwDevmW/ZFRiNIgu4gPaEexhJjXFy4aOuQkO6Jva17S55v8bRAqtJpG8/FMTFxqS4CriV3i+yHFGklVmRFsWO4hthZLHSCGCTaHtnOyZmTXI5erinaLmQWGI4Po6BwoF1Ml2z3twvRNjUDZXUcpcs25AlZA09NniZL5C6nWjyCnfgMwpkAIq+5L9THK+OvMJoYXbbwmsglODV7ytoOuqkzl5lb8fnn0NjImhFSuL1WTNPEMAwr399hiebm5po1OhwcNiXSabvRebbFdN0Ml18Q4tfIa7CtrKirXhDiHSw/W9YTgL674PKLYBpCxGupvA+ricsDzf2ioNncxetetDVNcykaoYppopiDHQc5NXuKweigVbvk9NxpAG7vvH01GgTnf7okunsCEO4VbutwD3jDYAjnJ0ZhUdQ1hdi+Upq64OAnRbbtwhAc+19wy8eX58y+ETJtAUKdhGaFaFt+/z6XmeNb579lzfwbCA9wz5Z7rikeo5GLkMEmFG1vdFxu8UW4kLMb/amP/AK93CnEDvWRX7yXc6HY27qXyI4I3eHGuzGXX/L9Ln9dQb9YsGk013Cxw66eoFz+er3lb+24FZfiorWw9o6BYicp1HYBS9EpkUssiTc1+hLxRBhBiMEdgY41dce1eIWLbz47X1dQlm1O5BOkCikM00BBsaa3lyMFYSksKihXdW1zq27cntrHfKuvVYi2mXl6Q72WkGnnGpb7Sva3OLKiXKgodj7HcjH8Ln9d0RaE0/WOrju4teNWLi5c5O3pt633dQW7bONINFWzBgPmMnOWY9jExK26KwRluT+KYwWqZfMG3UEr0iORT1jXEyms2vVld/NuxhJjXFi4UCHaRrNRxpPjKCjsai51usjYjWQ+SbqQttpt5dl6IiXb2a4fxX8XnyuyX+lCmryeL3ETy74UC9AD4QEh2sYu85D5UFUh6uSscNn2h/uta5TcJtJZXYwcQJN9LW6nbTxCudNWZtrmS0V0iRWP4G2h3d9uDT5OpCboDfXa9qGYt6beQjd1eoI9aKrGSHyEqdSUI9peZyiKQk9PD52dneTz9TPl6mEYBrOzs7S1tTnF44pwu92Ow9bh+kRm2jZKPAIIR+OOR+H09+DKi0LE9RXdyy1cEcKcNwShFcSH9R0W7t1CVqzzaq5xbbuEaDt7EfrvWfn7NxGTqUmS+SQezbOs+45WXytbm7YyHB/m5PRJXKqLglGg3d9OX1PftTdoYUgItpob7vwNMdCwloOL4S0iKuHENyA2Bm89JYTbWueKad4YTluA0GKu7eIMM0lez/Pjyz8mp+do97fzQO8DNaM1louRaNwiZOCItpsO6bTV89fmtHW+WK0e7+5/N/PZ+WVPy6g1/WMjsabgLsM5aydyNArFbsHlxDwUU68vHs3DrR23rprrqBaRsn/GtdomHaqzmVnyhvhiXVO0Lc7mXeNsa/lZC9mFuk7b4gxVuWzAHagqKkc8EcYYs4pZNfuaS/KWVxMpns1nhPhsmiaaopU4OiWyf1JcswTeKk7esDdMIp8gmo0ScodIF9IoimJNw6+FS3Wxt3Uve1r2MJoYZTA6yP626hVsW32tlmjbH+4vcQyXC47FgwESu0gBENe1Jk+TKJ6XFXEHBaPAfFa4bOz6sj2ynedHn2c6Nc2l6CXCHpEV61bdnJ07C0BvU2/FsexRPYQ9YeL5OLPpWetm3cqzLRPS7fpR3Jfi9Xs1Lz6Xj0whQzQXtYRVwzQsd2pxX7aEtuBW3STzSabT07bxHHk9z5k5UTROFtOApcHT2fRshbBanGdb3g+74mLl51Y1ER1EjIQUhVv9rSiKQn9TP2fnzzIUG6r75anYZXtn952MJkYt0fYAyyy64rCp0DRtVURFwzBwu934fD5HtHVwuN4xzcaLR5B0HYCxtyA6AhefgQMfWXptTsRM0bpzZaKd2wd7PyCKnG09fHXtatsB5xHtyqVWlom7yZA1DQbCAzWj7Iq5pf0WhuPDnJo7hYLYN3d03bE6MzfG3hS/u25ev0GGSC/c9hlRlCw1tyjcfkI4e+3IJYTLV1Guf9E22ElIcUMuYd2vm6bJz4Z/xnxmnqA7yAd3fHDVauY0utPWuWPaZEinbf4q4xEcp+3qE3AHljVC2Oj0NfXhUl1sa6o/zaM4CqKeMLrerMRpWx5p0Uh9kU5bSb1MWxCj1lDfLb2eoq3MIJpMTtYVlC0nYZFjuFYshOyHHIzq9K9NNi8s5cHOZ5eyeZs8TbY3iuUFo6pFCkisYmS5mOW6bPW2LvsmFoQjrq+pjwf7Hqzp0JX9kFP9pdBZfExI7Jydtfoi1yG3z3xGFG7zal7b5QPuAH0hIbj+aPBHfOPsN/ib03/D1975Gq9Pioq8e1v2VrzPrh9Q6rSt1w9YEnHLnd92+bzRbBTd1K2oDIlLdbE1LOJuLkcv27b13Pw5cnqOiDdSEo0T8UbwaB50U7cE4eLPk8tI5DWgXLQ1TINkoTQzWeaUQ2VEQjQXxTAN3KrbuuZJ4Xs4Pmzbh2LenHrTctn2hfosoVqehw4ODg4ODuTTS0WWGq1okqLA7veK31OnYf6KeN40YVYYAZadZ1tM5z645WP1i5dVw98ips6bhohuuE4xTbNmDYZqyJoOOT1HVs8S8UZs48BWTDYO0yKSi95ViFpYCcE2uP3XINQphPpjf7OUqVyOdNl6m0QBtOuZUCch1Q25FImsuO89OXOSCwsXUBSFxwceX9Ui50a8sZ22jmi7yXB5xC7TryIeQRYqUlDWNMPSYXPS7m/nn9z8TzjcU390eLPEIywn01ZSXlhpoymeFg11RNtFUUpm+9QTq4vX3RlcO6Gz+LNkrmvQHawqRsr9sZyCalApNK7lDAIpPkunLVTfJ5bTNlcm2lbpS3ExsuVEI1wLcmq/nB5fM5u3LOYBqscjFK9DCo7Ffanmgrir+y66Al1EvBECrkDJYEOTp6nqzbjcPsW5tnZCZ7V+FGcml+8XuzxYub1afa0VfdkeFm28HLtc0U7TNDkxIypUH2g7UPJeRVGs/SG3VUVfPJWDUOWircyAVhW1ZCCqWnG4uXRlX6SYPJOesTKO7Sh22d7VfReKoliDJXPZOfL6tU+hd3BwcHC4DpDRCL7w8ossrSdNXbBlMZrpwk/BMITbMb0gBLGWgY1p17YHxO+R1yGX3Jg2rDEz6RniuXjJwPdyUBSFg+0Hrb9v67xtdWawjr8thPJInxBP1xtvE9z2q9C6XURznPimaFM5N0qeLYCvmSaXH0yDRHKKieQEL469CMB9W+5bncJzRejxRadtuHEKrBfjxCNsMjRZiOwq4hFkcaZmX3NDiVMOjcNys02lGODVvHg171o2acVI4Sdv5OuLtkViTa3M2I1AipsFo4CqqLbT8CUrjXmIeCMMhAdwa+41dxc3eZrQFM2qMr8cx3Aqn1oqxFRjv5SLc3ZT01eLFq8QbdOFtHUttXOnwlKb04U0BaNQNx6h2Gkr3cjtgbURbYvFZ9M0re1sl80r91VWz1r5rrIvdvulXCS0CnfViHnoDnbz0T0fLXnONE3yRh6X6qp6My7XWeK0rdKX4n7k9BwezSP6VMX5bVeMTIrDdtnl/eF+FBRm0jPEcjFrO+SNPGfnzjKXmcOlutjXuq/ivR2BDsaT40ynp9nLXqv/0jVcPMAi11u8P6C0CFmxKGzl2paJvFaebVEl3oA7QLu/nZn0DMPx4Yrib5I3p97EMA16gj3WDJOQJ2QVBZxJz9ATqjKtz8HBwcHhxqFRoxGKGXgQpk5BYlpMjzcWTVHN/aIw2EbQvhuauiE+AUOvwK53b0w71hDpsu0P96+4MPq+1n28Pf02LtXFnpY9194YQxdRGbD+LttiXF6RaXv2aZg4CWd/KIqftRaZF26UPFsAVSXUtAVi54gnJ/nJ5Z9gmAY7m3eWCPerhRET98pqqLHMaBJHtN1kuD2yENlViLaL027Xcgqxw41Bs6+ZB3sfrDo9fCNRFIUH+x5kPjNfUbG+nIArgIKCiVlzGv5GoCgKEU+E2cxs3e0ccAdQFAXTNIH6MQ+qovKBHR9Y1fbW+qyIN2IJRbVEW7/Lbwm8k0kR9VBrvxS/ttwM2KvFrbkJuUMk8gmuxMQ0ump98WpeS3BP5pN1nbZS/I1mo5YAt1YRNhFvBE3RKBgFYrlYTaetR/Pg0Tzk9ByJfIImpclyTNs6bcvEzqt1DSuKUjeb2HIMp+cwTKPkc8v7Ut6PVq3VEp/tokTsHKq1BGi/y093sJvx5DgXFy4S8US4sHCBy7HLFIwCIGIefC5fxXutYmSppWJkyXySglEoiTgo70c8H6dVE1+Eqzm/7cRnEI5YqBSg+8P9NUVbO5etpMPfQTKfZDI16Yi2Dg4ODg6QXoz98bfUXm4j8QRg+8Nw7scw+PxSjMPVRCOsFooC2x+C49+A0TdFPq63MYWkWgzHhvn+4PfpDfVyd/fddAWX4thknu1KohEkbs3Np/d9Gli+2agmM+chmxDHQrv9gPW6oWqw74OgqDB+HE7/A9zxuaVCeZkF8bvR4kbWiGC4D2VMwcjFSeQTRLwRHt366KprD2ahgJESs8y0cGOea048wiZDW8y0NXQDXV+ZcCu/FK6Vg8vhxuKWjlsYiAxsdDNs2de6j3u33Fv3oq6pmpWHs5wCbOuNLEZWzzmrKipBV1HOcINFVhS7BWu1TVEUaz9IkbfWfvFoHsuB3OptXfFo/UqRzsR6zllFUSwnaiwXK3FC2iHFtXQhba17reIRVEW1+jGbma0p2kJRcbhcwhJsNUXD7/JXLGs5hrMxTNO03Kn1Bk+uBlm0TDdFUa1kPmkV87I7xsqLeNUS0uvFI9ghYxxeHnuZH13+ERcWLlAwCoQ9YW7vup37eu+zfZ8V85CZtQZdpMs27AlXfCGxK0a2kpgHWIpHKN8v/U39AAzFh6y2FGPnspVIl7scHHZwcHBwuMGR8QjrVdTpauk5JKISClmIC8MArSsXE1eV1h2iSJVREG7bTcjZ+bOYpslIfIS/P//3/Ojyj6xCuAvZBVRFZSA8cFXr1lRtdQRbWCpA1nMItAbwM8q8ZZlxe+q7IroDbqx4BEALdRNQXJBL4lJdvG/gfWtScFqPi/toxe1C8VUaLBoBR7TdZGgu1RKi9BW6bR2nrYNDJTIDstGETliakr8cF3BJ1EODCdDNRTcX9foi94eJEI3qxVZIl+pa5tlKiqeTQ+2+yONpJj2DbuooKFX74tW8JS5MWaBqrZDC42h8VDg6Uaoe/1aOaj5eIg7aDYgUT9+fzcyS1bMoilKx3VYDRVGWipGlZ61ohCZPk22kQrWcYTtXuhTR47m4iGrQ85bwWU2A3h7Zbn1uyB3iUMchPrr7o/zK/l/hnp57qg4otHhb0BSNnJ6zBPSF7IJoh83xVd4PWBpEKO+LnWO4YBQsUbh8v3QFuvBoHjKFTIn4apomx6aO8c7sO0Cly1a+F5xiZA4ODg4Oi2wGpy2AqgqRTBJo23ihWVFEdAOIqftyWvwmYiI5AUBPsAcFhUsLl/i7M3/HT974rzBxgj5/x5re6y6L5IwoQqcosOXQxralGM0NB54QER3RERh8Vjx/I8UjAIQ66NT8kEvycN/Dazaj0kgsFSFrtBnEkgYYTnBYCYqioLrFwZTP6Xj8y9uFqXzKKULm4GDDQHiAhexChXOsEbi5/WYKRoGb22+uu2xxAaK1zqldKct12kJl2+stL6elbwtvu+r2LZdykatW2+T+GE+MAyLCopYrIOwJkylkgLVz2Uqk2CkLZwXdwaptk/sjkUtYomS1frs1NwFXgFQhxWB0EBCiZLXCc9dKu7+dydSkFSECNXKGi8RnKMqBtRngkNmwuqlbBTxNTPwuf9VKtRFvhI/u/ii6qdMV6Fr2TZ+marT525hKTTGdmibijVgCdHkxwuL2Fgux1eIR5N/pQtrKwF3ILmCaJh7NU3LNkG3pDfUyGB1kOD5MZ6CTVD7FkaEjDMeHARHzYHetlIMm0WyUTCFjGwXh4ODg4HCDYJqbI9NWEumD7ptFlmjHKuSkrgYtAyJbd2EIrrxcKiw3OKl8ilguhoLCB3Z8gEQuwWsTrzG4cIm5mdNg6OycGxHHyUaKZDLLtm1X4wmhgVbY+wvwzrdh6FUI94KcOXWDxCMQ7OTdga0kjTwti7PB1gI91thFyMARbTclrsWIBH0FxcikaybijThFyBwciriz+05u77p9daqPrjJBd5D7e+9f1rLFYmejuYbtiilVI+hZEpK8mrfuKPxd3Xexu2X3ugxGSecziGiGWsKU3AfjyfGSv6sR8UYsl+Ja5dlKpGgrxb5qQieUip1SiKw1KBD2hkkVUlxaEEUm1nK/WMXI0rNWrq2MaCinwmmbtxc6QURIhD1hotkosVzMcr7WG+G/Wrd3u79diLbpaXa17LJEW7v9Is8f6a6F6vEIPpfPysCN5WK0+dusmIc2X5utsLy1aSuD0UGGYkO0+dr42fDPSBfSuFQX92+5n5vabrJ9n8/lswTn6dT0iqpBOzg4ODhcZ+QSoOeFILdZBKY974f2PRsfjSBRFNj+ILz1NzD+NvQd3ugWLZuJlHDZtvhaRNFqv5f3b38/E/MXOTr6NgXVYEcqDsOvQf/da9cQ0xSi58IQ9N4BfXeBe/HevZCDiePi8UYWIKtF5z6I3gUjR+HUP4j+qC5osBmVa4bbh8ffiicThcQUtKyNQceILzptQ427XRtPpXCoi+ZactouFykGrGV1dQeHzUojCrYrRYqdbtWNV/NucGtKafGJKeBu1V3h7itnpeKzS3Wt2+yBYqdtPfFZRiFk9WzJ39UoFhvXuj8riXkozrStV1ANlvpRq3DXaiGjCmbTRdm83trZvOWZttUE6OJogXp5tteKFOll4bZaom15P0zTrBqPAJURCbIv1SIrtjYJsXUiOcHTg0+TLqRp87XxsT0f40D7gZoOYtmPydRk1WUcHBwcrleuxK7wjbPfsK7lNzTSZeuLiOJKmwHNBR17xdT0RqG5H1q3g2nAlRc3ujXLRhYTltFJkm40PhTczhNNu/EqGlx6FmJja9eQsTdh+izk03D5BXjlz0TBuXwaJk8K4TbQCi3b164N18rORyG8ReQbgzinGnQK/5rQtHgMXcNxYuRyGJlM1df1eOM7bTe/UnEDol2F0/Zqq3g7ODhsDqRg0+RpvDwer+blQzs/xId2fqjuVPliUbee0Lne+F1+qwDXckVb6+86o+LFYuNaX6dlES+7zy7HcqjmE0viYA0xvXxd7b6160vr4pTLRD7BVFIMTNZ12i72oVY8ApQW8VrLgmqwtL9n0jOYplkzHkH2Q4q2GT1DYfFG3raomrdUtJ3PiIzBagJ0xBsh4o1YmdIHOw7y0T0fXZZg7RQjc3BwuJE5NnWMmfQMx6aObXRTNpb4JJz5gXgcXPt6A9c92x8SvydPoiz+D290ZJ5td7C79AUp5rduFwK5aYhCW/nqgtpVk56Hiz8Tj3tuhWC7EGkvvwiv/LkQcQG23NbYIqiqwYGPLDmEGy3GYa0J94nfsdGrenthZob5p55i/q//GiOXs13GiIl7ajXUWDNVi3HiETYhmlvFSEPBcdo6ODgssjW8lT0te6wq9o3GltCWZS1XLDw1WswDCCEtXUjXF23LBLR6y8vohSZPU9Xc1NVCFgeT/xdqOm09S05bSU3Rtmxda+m09WpemjxNxHNxS4ytmmkrHcP5BHkjT6qQAqr3RT5f7LRdq760+ltRFIV0Ic1kalIUrlPsi8PJ51L5FLqhW+JtwBWwHRAJu5eKqgHLcg3f0XUHJ2ZOcLj78IqyouX9hVOMzMHB4UajYBQsoepy7DK6oa9edfvNxOQ7cPZp0AuiANnOd210izY/4S3Qtgtl5jyBU9+A2QEIdQpBPNgOwU7wrO1940rQDd0avK0q2gZaYdsDEJ+A9AKc+yHc9JHVE09NE87+UER0NG+Fve8Xz0+fhSsvQGIayAl3dfctq/OZa4kvAjd9GM7+CLpu2ujWrC+RxToKsdEVZyAXpqeJfve7GGkxKJAfGcW7o/J7spEQ98hauPG+d0oc0XYTorkV8kAhtzynrVOEzMHh+setunls22Mb3Yxrptih2oiibV9TH+PJ8cob0TLKRdt6ruHOQCcP9j64btfoVl/rskTboCuIgijKJR2gtfpSvK6AK7DmAnSbr80SJBXshU4QheAURcE0TavfLtWFT7PPJZb9mExNki6kUVCqRgpcK27VTYu3hbnMHBcWLlifbxfb4nf50RQN3dSF+7meY9i75BjOG3lii0Usaom2+1r3sa9134r70eHvQEEhmU+SyCXqussdHBwcrhemUlPopjDT5PQcY8kxK27mhsAw4NLPREYpQNtO2P8hcPs3tl3XCzsfxYyPoyQnUaKjpVPFFRUOfVpEKTQAs5lZCkYBr+atnDGULipO5/YJB+mbT8HUGWh+a/WyZcfehPkrQpTd+4Eloa9zn3D4zpwTOcFtuzbPMdq6A+79rY1uxfoT6hZu41xKuKcDy4sqKxFsFQVMk/zIcIVoaxoGemIx07ap8b53Spx4hE2IjEdYrmhbXISsXlEfBwcHh43Eq3mtqfuNFo8AcGfXnXzmps+wo7l2oYrybOF6ApaiKNzScQs9oZ5VaWc9ikW7WvEImqqVCK8KSs39Uux0XUuXraRY5A66g1XjN1RFtSJExhLiy07IHaoaJSK3iRSqw97SSInVRubBXly4CFR3DCuKsuR+zidqFlSD0kzbhcwCJiY+l8+K+VhN3JrbOq6m0o7b1sHB4cZhNFE6dXcwOrhBLdkAcik4/vUlwXbbvXDzxzaPGLYZCLbD3b9J6uZfxrzpF2HbfdC+G7xNImJATvVvAKTjvCvYVXmPZTltF+8Pw1tgxyPi8YUjotjUcpi9CNPnxGBBOcWxCDserRT5FEUItwc/0bgFyByW0FwQWlmubX5qioXvfAcjncHV1UnTux4FIDc8XLGskUyCYYKmogZr113ZSBzRdhOy0kJk0ykh2l5tZWsHBweH9UJRFEvsa8SZAcWiWT2KhbR68QjrjRTXPJqnqttUUizSBtyBmlM+/S6/JZyuh2hb/BnVhE6J3G/jyXFghTEPa5RnK2kPiGM9mU8C9nm2kuJc26stqLZWudfyPsOJSHBwcLiRkIOBMlJmMDqIaZob2aT1wdDh2N/A/GVRwOvAE0KEUx2JYdVRNQx/G3TeBDsehls+Brf9qnDazl+B2PjVrffyC3DyWyLvdRWQxUjLi5ChFyCzIB4XC6lbDwtntlGAd75Tvx2zF+H4N+Dk38PrfwFTp8W0eRC/zzy9GIvQD713rEqfbkRM0yQ3MoKZz290U0ojEuqQn5wi+t3vYmayuLq7iHz4w3h27ABFQZ+bR08kS5Y3YotFyEKNVxOmGOeKuglZaSEy6bSVTh4HBweHRuZ9A+/j43s+TrOveaObck1IsdOjeRpulkNPqIfeUC+3dtxa9yZlJTnDiqJYxcDWQ3QvFlPribZS2JRVjWv1xefyleyztRagy/8/VyuoBkv9iOfideMRQp4QCgoFo8BIYgSoHY1wrcgvaXKw2MHBweF6pzjP9u6eu3GrbpL55I0xeDVxHJIzwlV7+2fF9HOH9cPfDJ37xePhV1b+/oVhGPy5yHodfWNVmlS1CFlmQYiqLg8U37MoCuz7BfCGIDUL539cfeXZOJz+3uL7VHHsvfMdId5On4XRN2FhSLgz932gsQuMNTjZc+eJfvs7xJ/52UY3BcKLom10pOZihZkZS7B193QT+fCHUb1eVJ8PV7v4TpIfLV3HZohGAEe03ZRobsdp6+DgcP0ScAeui+uVFNKquSA3Erfq5sO7Psxd3XfVXba4/cuJrDjcc5i9rXvXpShe2Bu2nL3LddrmDeEaqNeXYrftWgqdUClw13Layn7Ec/G68Qgu1WXFWwzFhoC17Ys8bydTkzeGy8zBweGGR+bZBlwB2nxt9IdFtuhg7DqPSNALcOUl8Xjb/RDa/Pdtm5L+e8Tv6bNL8QPLwTTh4pGlv4dfgXzmmpqSzCeJ5+IoKJVO29Ss+O1vrRRTPUFRaEtRYOIkjB+vXLlhCME2nxaF2O79ZzDwgBCBE9PCLXz+J2LZHe8ShfAcrhopbmYvnMdIpTa2MVK0TU7XdGInX34FM5vFvaWH8C/+IqpnyXzh3toHQH6kVLS1nLYNXIQMHNF2U+JagdM2lU9ZVbUdp62Dg4PD+iFFwc1ekKm4/cvpy/bIdt7d/+41zYCVqIpq/W9r8da+QS9vez3XcHHW71rHI3g0T4noXCtnWIrJiXxiKR5hGVEP6UIaWFvRts3XhqZo5PSclQfs4ODgcD0j82y3hLagKIo1YHnd59qOvw2ZmHBIbrlto1tz4xLqFPECprmUK7wcpk6JSAXNLQTOfAZGVvB+G+RMplZfa+UMMyvPtso9SHM/DDwoHp//sRBiixl+Zam42E0fEcfd9gfhnt+CgfuFeCvX42TVXjOFSbEvMUwyZ85ubGN8YfFjmhC3z7U1kklyQ1cACL3rXSWCLYBnqygMmRseLjEV6HFxH62GHNHWYZXRXIuFyPIGplHbyTKTngGcImQODg4O682ull30h/s52HFwo5tyTZTEIzSga/ihvod4sPdBy91UjfK21xVtF8VOl+qqKaKuFtJtqypqzbbJ/TGfmSdTEK6YWq7h8ra3+NbOfaKpmtUPpxiZg4PDjYAUbXtDwg22LbwNVVGZz8wzn5nfyKatHXoehqTL9j4hpDlsHNJtO3ECson6y+t5uPSseLztvqViYCNHRWG5q6RqNAJAuqwImR3b7oPW7cLFfeo7S67K6KiIcQDY/V4IFq3D7YftD8Hdvwk3/SLc/FEnFuEaMfN5CnNLru3MqVOrNnvKSKWIfu97LHzzm+THV5DDHN4ifkftc22z58+DYeLq7sLVUnmf6+7pAU3FiCfQFxaW2rMo2jpOW4dVR8YjYJoUCrXdtjJPqTPQudbNcnBwcHAoIuwJ88EdH2Rr09aNbso1USx21hM6N4I2fxu3dNyCqtS+pSl32tZzDctc2RZfS911rwbSMRz2hGt+nhRoZdEyj+bBq3mrLl8c8xBwBfC71rait1OMzMHB4UahYBQsd+GWkBAVvJrXEnCvW7ft2FtCHPSFofvWjW6NQ2SrELWMAoy+Xn/54deES9oXhr67oGMvNHUJkfRqsnEXsYqQBbsqXyyOR6iGosD+DwkXbXIGLvxUOIBPfRdMQ+T3dlcxQngC0HUA3LWL6zrUpzAzA4aJ6vehuN3o8/MUxuwdrita7/w8C9/8JrnLV8iPT7Dwzb8n/szPMDLLiOUIi3gDYvbtyJw5A4Bvn32utuJ24+7uASA/siT86rFFp21TYxWMLscRbTchiqosuW3r5NpKp60TjeDg4ODgcDWsNB6hUSkWnxUUgq5gzeW3R7aztWkrt3Wsz7TP/nA/qqJa1cerEXKL4mIlf9dwlZRk89b6srRKOMXIHBwcbhQmU5NWnm1xFvmOyA7gOhVtCzkYelk83na/47JtBBQF+u8Vj0ffhEK2+rLZ+NL+2/GIiEdQFNj+8OL73xDLrBDd0K3B2u6AjdM2tQynLYh82/2/KNo0fhzeegoyUVF0bc/7HBftOiCjEVzdPXj37AYgferUNa0zNzLKwje/iR6NoUXCePftBSDzzjvM/83fkDl7trabN7KYaxsbETEJxe2dmaEwPQOainf37qqrcPeJdeRHhgEwTRMjsei0bWrs7zeOaLtJcXk0AAq55Tltr4eiPg4ODg4O649P8xHxRvC5fHWLfTUybs2NzyUcGEF3EE3Vai4fcAf40M4Psatl13o0j3Z/O//kln/C/b3311xOUzWruBjUF9KLRdt6ub+rgbzfmE5PY5j1s/cdHBwcGpXJ5CQnpk9UvZaNJYTrS+bZSgYiA+L9qUlrVsR1w+gbYgq9vxm6b9no1jhI2ncLQbSQhbFj1Zcb/LmIRwhvgc6blp5v3SGEMb0AV15e8cdPp6fRTd3+XjGXEgXEYHkFwlq2iSJjIBy3iioKlTku2nUhPyX0I1dnB76bxDGSu3gRI1tjMKAGmTNniP7DdzEzWdw93TR/7GOE3/Memn/pCbTWFoxUmvhPfkr0O99FXywMVkGoC1SXcF6XFdyTmbve7dtRfdWPESvXdmQE0zQxUynMgg6KghpyRFuHNcDlqe+0TRfSVhGy8srUDg4ODg4Oy0FRFD6x5xP88r5fXpfiYmuJjBZoxJgHYNnbt1iILX5su6x3fZ22Ld4WPJqHglFgLrOCStYODg4ODcY/Dv0jPx/9OSdmTti+buXZNvWWPB90B61ZB5ejl9e0jetKIbs0fX7gAagz+OmwjigK9N8tHo+8JsTXcuKTMHFcPN717lLXqqKIbFiA8WOQXljRx1vRCIGuytk/Ms/W27RUMKwe/Yv5tgA7Hl7KNHVYcwpTYqaUu7MTV1cXWlsrZr5A9ty5Fa3HNE2Sr7xK/Kf/CLqBd/cuIh/+MGpAGA/cvb20fOpTBO+7F8XtIj8yQuwHP8DUbfQtVYOmRQd3bCnewDQMsmcXRdu99tEIEldnJ4rbjZnJUjj3Ovoz/xkWhlCDQRStsa9ljmi7SXG5l4qRVUNOTYx4IzXz7hwcHBwcHGpR7FLdzEixdjPHPEBZZEWNImQgcmw1RdyMtvnqTEtcBRRFYU/LHm5uvxmX4kybdXBw2JxEs1Gi2SgAr0+8bhV+lBTn2fYGeyvevz0iBKdL0Utr3NJ1ZOSocLoF2qDzwEa3xqGczgMiDzabgMmTpdPITRMuHhG/O/dDpK/y/S0DwuVq6HDlpRV9dM0iZMuNRihGVeHmj8Gdn1sqtOaw5hi5HPq8KKDo6uxEURT8B8S5nnnnnRUVJEsdPUrq6FEAAnfeQdPjj6O4S80JiqYRuOMOWj71KVS/j8LMLOljx+xXaEUkLIm2+aEhjFQK1e/Ds612QWJF03D39kI+Rf6l/40Ri0N0GC2wzIGEDcQRbTcpy4lHkA4Xx2Xr4ODg4OCANWWvOHtwM1LsFK4nQCuKwp3dd7Kzeee6FSV9qO8hHup7iGZf87p8noODg8NqM5IYsR5n9SxvTL5R8rrMsw26g7bRQVK0HUmMkNWvblpxQ5FPiwJWsOiydWSEhkNzQd9h8fjsD+HZP4Zn/wSe/0/wwn+G+StiivmOR6qvQ2bbTpyomIZeCzmAIR3mJcgiZIEVzvbRXEvuSod1obAYjaCFmyxHrHfvXhSXRmF6xnLh1kOPxUi/Ia6ZoYcfInjvvTXrL2jNzQQfEJEYqddeo7AoHJcQXhRto0vXZisaYc+eZbllPT2dMHWK/OQ8ejoHpoGanVhWnzYS52q7SdFkPEK+ejyCvEFY60rRDg4ODg4Om4HbOm/jwd4HuaV9c+fwFRdVqxePAHBH1x08PvA4quLc9jk4ODgsh9G4cHP1BEXF8RMzJyznLVTPs5W0+Fpo8bVgmiZXYlfWocVriGHAuR+LeIRgu3BqOjQmWw6V5saahohKkMXJ+u8RecTViPRC2y7xvnM/Fvm3dUjkEiTyCRRFsRdtZTzCOkQ0XQ8U5uftRcv1+Gwrz3ZpkF/1+fDs2AlA5tQ7y1pP8uVXMAs67t5efLcs757bu3cvnv6tmAWdxLPPVbp6pWibmoV8BiObJTcoZjJ499WORgDAMHAnT0A+TT5WQG8WDmItMyJmEDQwzt37JsXtru+0laKtE43g4ODg4OAgBjFv6bhl00c9rCQewcHBwcFhZZimaTlt7+65m61NWzFMg5fHlgo0jcTF61tC1bM2pdt2MDq4hq1dY/QCnPo2TJ0WBaHKs1AdGguXF+7+p/DA/xfu/x2477fhnt8Uz939T5cKfNVix8PCkTt/GY5/va6gJaMR2nxtuDWbbH4rHsERbeuRu3KF+b/9Wxa+/g2MdHrdP98SbbtKxXffAVGQLHvuPGYuV3Md+YkJkX+rKAQfuL+mw7YYRVEIPfKIlW+bPX26dAFvSAw4mCbEx8iev4BZ0NHaWnF1dNT/gEs/Q9MnUX1ezLa95OYBdwDVo8LYm8tq40bhiLabFG0ZhchyujihHNHWwcHBwcHh+kG6a1VFJeAObHBrHBwcHK4vZjOzZAoZ3KqbrkAX9225DwWFS9FLjCXGyBt5q/CSXZ6tZHtYiLZDsSEKhk1hqEZHz8PJv4fpc0LEu/mXoHXHRrfKoR6KAm4feIKi+Je/WQimgdblCe6hTrj1k0IAXhiGY38N2XjVxeW5YJtnaxhLRc0c0bYm+dFRYj/8IegGZj6/4sJfq0FhUuzLYqctiKJhWiSCmcuRvXix6vtN0yT5wgsA+Pbtxd25slguLRIhcFgU1Eu8+CJGMlm6gCxIFx0le/bM4ufsqy8MT5yA4ddQFAX3rQ+DJ4SRyUBkK5rfLaJfCrXF6I3EEW03KS5LtK3utJWirUdr/HBlBwcHBwcHh+XR7G3mUMch7t9yvxN54ODg4LDKSBdtT6gHTdVo87exv01EArw09hITyQkM06iaZyvpDHQScofIG3mGYkPr0vZVo5CF49+AuUsiW/SWj0H77o1ulcN60dwPt/2qcDcmpuHNp6pm3EqnrW00QjYKRkGI/jXOlRud/OQk0e//ADNfQA0GAcicOrWiwl/XipFKoceEOF/uXFUUBd9N+612VSN34QL58QkUt4vAPVdXQM5/6FZcHR2YmSyJn79Q+mJYFNDTxy6QHxsHRcG7Z2/tFUZH4eyPxONt9+E+UNSuYAdqa4fI7B5/+6raux44d/qbjOfOTfP1tya5Mi/s8rWctk48goODg4ODw/WHoijc13sft3Rs7mxeBwcHh0ZERiP0hfqs5w53H8atuplKTfHS6EtA9TxbiaIo7GrZBcC5hfV3zV01+TS8/bewMAQuDxz8FLRu3+hWOaw3oU4h3AZaIROFN/8nxMZKFikYBabTojiVrdNWCr3+Zqd4XRUKMzNE/+EfMHM53H19NH/iE6Lw18zssgt/rUo7psVnaS0tqN5K/ci7bz+oCvmxcdInTlQIymahQPJlESHjv+12tNDVxXcpqkroXY+CqpA9f57sYFG8TETMbMicfgcw8fRvRQsFq69Mz8M73xYDB+27YftDePqWrusoCtreB8Xj4VfBqK6tbSTOmbPJSOd0YhmdnCJOkkLecdo6ODg4ODg4ODg4ODhcK7qhW0XG+pqWvtwH3AFu77odEPEJUDvPVrKnZQ8AV6JXLENNQ5NPw7H/BbFxcPvh1l+G5q0b3SqHjcLfIoTbpu6lYyMxZb08nhi3XOe2hVGdPNuaFObniX73u5iZLO6ebiK/8AG0UHDFhb9Wg7wVjWCfD6uFgvhuEtm2iWefI/b9H5TEF6SPn0CPxlCDQQK3Hbqmtrg7O/EfEutIPPMzYj/5CfFnfkbijbMkz06SGZyEXArv3joFyEbfFNEevgjs/5AQaSMRtMhizFjAj9J7m3CUZ+MiRqEBcUTbTYZbE6O5cgygkNOr2ubljYFHdURbBwcHBwcHBwcHBweHWkymJikYBfwuP22+tpLXDnYcLCn+WOzErUabr40WXwu6qTd+QbJCVhSeSkyJPNRDvwLhno1ulcNG4wnCoUXxXs/D0CvWS0NxEfuxtWmrves8LUXbtsrXbnDyU1NEv/NdjFQaV0c74Q99CMUjdBsZRZA9dx4zn1+X9khXb60c2tAjjxB68AEUl0bu8mXm//ZvyV66hJFKkTp6FIDgvfdY/bgWgocPo0XCGKkU2bPnyLzzDukTJ0gNpzAyORQzjXdHjRkAhRwMLRaPHLhfZDQvkmnv5vUrcwznNBH/slXk6DL0ishhbjBcG90Ah5Xh0oTOrmvib9MwMXQTzVV5kcwZTiEyBwcHBweHtUbXdfLrdFO9mTEMg3w+TyaTQV3BNEm3242maWvYMgcHBwfBaGIUgN5Qb4UI5Vbd3N1zN0eGjtDkabJ3FpahKAq7m3fz2sRrnJ8/z77WOs6wjUIviKJj0mF76Jch2L7RrVoTshcvogYCuHscQXrZuLyw893wxtdg+gzk3g2eIFdiVwDYFt5m/76UcKXjd5y2AEYuR/b8BTKnTlGYEo5lrbWFyC/+YkkkgbuvDy0SRo/GyF66hG9vndzWVUC2x9Vlk028iKIo+A8dwr11K/Gf/pTC9AyxHzxtFSlzdXTg3bc61zjF7SbykY+QuzKEWchDoYBZKGAOK5gTp/DePIDidldfwejrwh0eaIWu0jix4eYe8jqcNgMc0A3cPYfgykuQnofp09B1YFX6sFo4ou0mw6UuOm1NUFQF0zAp5HQ0V+mXH8M0nHgEBwcHBweHNcQ0TSYmJlhYWNjopmwKTNPEMAzi8Xj9Sr9lNDc3093dveL3OTg4OKwEWYSsOBqhmD0te1AUhRZvy7KvR7tbhGg7Eh8hlU8RcAdWrb2rgmHAqe/A/BXQ3HDwE9etYJsfHyf29A9RvF7afuNzKC5HDlk24R7xExuH8beJ9dzMQnYBRVGqni9L8Qg3ttM2PzlJ7sUXmZ+aBn1xzrSm4t2xg+ADD6IGSq8JiqLg3beP1KuvkXnn1JqLtnoiIaIOVAVXe/1z39XWRvPHPkbqtddIvfkWejQKQPCBB1b1Pk0Lh/HfcnPpk7vb4UQMvHHIZ8Dtq3xjPrPkCB94oCJPeSrYxuVHfxHd6+X8ZIKbtoSh704Y/LkQbztvgga633SuUpsMt3TamiYuj0Y+U6CQNyj30ub0HK54mvY3LqM0T8KAEx7v4ODg4OCwmkjBtrOzk0Ag4AiKdTBNk0KhgMvlWva2Mk2TVCrF1KIDpMdxRjk4OKwROT3HRGoCEE5bOxRFsXJql0vEG6Er0MVkapILCxc42HHwmtu6apgmnP0BzJwH1QW3fAzC9bN6Nyvpt0WFeDObJT82hqe/f4NbtMnovQNi34extxjyi6iQ7kC3/czeQk7khMINnWmbOXOG2E9+ip5MYgaDuNpa8d10E769eyvE2mJ8+/eTeu0o+dFR9IUFtObmNWuj5bJtba3tXi1CcbkI3ncfnm3bSLz4Iu6eLXj67K+bq0rzNvCFIROD0/8AN3+sssjdyGsi7iXYDh37K1YxHc+i+8W2PzkaFaJt7x2iGFlyBmYviMJlDYIj2m4yZKZtXjdxuVXyGZFrW05WzxIcmSMwkyB/5hx+R7R1cHBwcHBYNXRdtwTbtrYb20GyXK5GtAXw+/0ATE1N0dnZeV1GJTz//PP8p//0n3jjjTcYHx/n29/+Nh/5yEeqLv/ss8/y6KOPVjw/Pj5Od7dNBW8HB4e6jCXGME2TsCdMxBtZ1XXvbtnNZGqS8/PnG0e0NU248I8wcRIUFQ58BFoGNrpVa4Yej5O9eNH6O3f5cl3RNnflCkYyaRVguuHp2A8XjkAmxtC4yDDtD1fZhjLP1u0XPzcg2cFB4keOAKBt6yfy0EN4eiujV+zQmprw9G8ld2WIzOnTBO+9d83aaYm2NfJsq+Hu7aXlE59Y7SZVx+WBmz8Kbz0Fsxdh8DnYWXQ/lEvBiDg2GXiwQtDNFnSiaRFppigwupBmNpGlLeSHLbfDwlBJ/m0j4BQi22TITNu8buDyiC8thVxlWHJOz+FK5XCp2rqFVzs4ODg4ONwoyAzbQA2XhMPqIbfz9ZodnEwmufXWW/mzP/uzFb3v7NmzjI+PWz+dV/GFy8HBQSDzbKtO9b4GdjXvQkFhMjVJNBtd9fVfFUMvw8jr4vG+X2goZ9lakDlxAgwTZTE7NHf5ctWC3gBGNkvs6aeJH3mG/KKodcOjuaDnVgqmwejEWwD0N1URbW/waIT8+DjxH/8YDBPvvr24H30U95YtKxq09u0XLtHM6TOYa1ggqzA5CYCrs3qebUPR1A17PyAeD70Ck6eWXht+Rbi8Q53QURkrMZMQEaJNPhfb24MAnByLiRe3PwS3fwaaG8uB74i2mwyZaVvQTVwesfvsRNusnkXL5NEUR7R1cHBwcHBYK5xIhPXhet/O73//+/nSl77EE088saL3dXZ20t3dbf2spMCbg4NDKVaebWj1RduAO2CJwefnz6/6+ldMdBQGnxePd78Hum+uvfwmx8znSb/zDgChhx9GcWno0Rj6/HzV9+QuXcIsiBmt+ZGRdWnnpmDLbYzrKfKpWQKGSbu/SgaqLEJ2A0YjFGZniX7/+5j5Ap6BbYQeffSq7mM827ej+n0YyST5oaE1aKmYBZW/BqfthtF1APrvEY/P/gDiE5BNwOgb4rntD9vm0k7HswB0NHm5pVfMqDg9HqOgG6BqDZVlK3HiETYZMtM2bxi4fItO23xlPEJOz+FK59BUF2Yut65tdHBwcHBwcHBwWHsOHTpENpvl5ptv5sknn+T++++vumw2myWbzVp/x2LCWWIYBsYaOngkhmFYxegcNh/X+/5L5VPMpGcAkdG5Fv3cGdnJUGyIc/PnuK3jtnUdjCrZf3oeTn8PxTAwu24SU4Kv0/0qyZw5g5HOoEXCuHfuQDt9mvzQEJlLlwhUyQpNnz1rOXGzQ0P4Dh1avwaX0VDnnzfMFX8IMwF92Qymado7llOzKKaJ6Wu57o+vYvR4nOh3v4uRzuDq6Sb0nvdgKsrV7T9VxbN7N+m3j5N65x1c15DBnBseJv3mm/huvhnvzp1L7Y1GMdIZ0FTU1pbGOMaWy8CDkJhEmb2EeeKb0NyPUshjhrdAy3bb4246lsY0TdqCHvpb/AS9GolMgfOTcfZ2N9l+zFqdf8tdnyPabjJkpm09p23OyKGlc2hKwHHaOjg4ODg4OFw1Tz75JN/5znc4duzYst+jKErdXFaHq6enp4evfvWr3HnnnWSzWf7H//gfPPLII7z66qvcfvvttu/58pe/zB/+4R9WPD89PU0mk1nrJmMYBtFoFNM0HUfwJqQR9p9pmqQKKTJGhpyeI2tkyepZ8kaebn837b76Vc+rMRgfJJlM0uJtITGfIEFiFVsuCOkhMqkMI8kRzo6cpdW7fg7E4v3nG3kBz/QwhidIKnIIrvOp/6Zpkn3xRcxkEveBm8jPzFCIhMknk6RPnCDRV+msNlNpMmfOitxfgHPnyYyNo7g2JlO9Ec6/Yk7rCvlcjo7pUabGR0DzVCzjn7yMlkySTpvo1/kxJjEzGbJP/xAzGkVpjuA9fJjphYVr2n9GRwfZZJLkyZOkb7oJxb/yfGAzlyP77e9gptNw5iza9u2477kbxedDvzRILplE7WhnenZ2xevecNruJTA1jDo7DrPjAKR7b0KfnrZd/OLYDMlkHjXnYWZGZ4tf563ZJC+fGaFFtY/yWKvzLx6PL2s5R7TdZMhM24Ju4HJXd9pmc2m0bB6Xz4lHcHBwcHBwcBDUc3b9+3//73nyySdLnvu93/s9/vk//+er2o6VFt5yKGXv3r3s3buU1Xbfffdx8eJF/vN//s889dRTtu/54he/yBe+8AXr71gsxtatW+no6CAcDq95mw3DQFEUOjo6GkJ0cFgZjbD/fnrlp1xYuGD72uX8ZT7V8ykC7qvLGT+VPUUwGGR/x/41zYa+KXsTl6KXmNfm2de5b80+pxxr/7nTaIkLEAxi3vJxQm2Nld24FuSGh4nl8ijNzbTcdx+q14vu8zF/4iQkk7SGw6g+X8l70m8fRwsEcHV1YiSSGMkkYb2AZ0vPhvRhPc+/eC7O9y99nx2RHdzdc7ft67mmEJ54hD2+ZrzmNHTeVrqQaYKWRwkGCfTthuDVD6hsJqLf/S5aoYDa3UXkox9FC4WAa9x/nZ0snDhBYXKK4Nwc/ttuq/+eMpIvvYSmqijtbZjZLExNof70Hwk+/BAFvUA6GMS3cyehzRSPUEzks/DW/0Qp5DCbtxLYdYdtzIFhmOSUGMGgh73bemgJeHigqYXzC5dZKIC3qYWI323zvrU5/3xl151qOKLtJsO9mGmbr+e0TcTABE1xOaKtg4ODg4ODAwDj4+PW469//ev8wR/8AWfPnrWeCy1+wQDhTtJ1nVAoVPL8aiALb/3Gb/wGv/RLv7Sq675ROXz4MC+88ELV171eL15vZUVkVVXXTYRTFGVdP89hddnI/ZcpZLgUu4SiKARcAXwuH17Ni8/lYy4zRzQb5YXxF3jfwPtWvG7TNBlNjqIoCv3h/jXt397WvQzGBrkYvch9vfeta0SCYuRRz/1QfOaWQygd13fhMUn2+AkURcF/035ciy5FtbkZV3sb+uwcheERfHv3lLwnd+G8eM/eveSnp8meOYs+Ooq6bdtGdAFYv/Pv1Nwporkob02/RV+4j61NW0teH0mOoKgq3R0H8KdyMPYm9N5eKpJl42AUQNVQgm1wA1zz8xMTFEZGUVwazR/+MK6ywdBr2X/+AzeTmPoZmeMnCNx6K4pr+TJeYX6ezPHjKIpC+LHHUANB4kf+EX12jsSPf4Li0lAUBU9X1+b939zUCTd/FIZfRdn5LtDsHfHzqSy6CV63RmvQi6IoNAe9bGsLcmU2xanxOPfvsh9gWIvzb7nr2qR75cbFVZxp65aibaXTNh8XVUk1VThta1XGdHBwcHBwcLgxKC5aFYlEUBTF+vvMmTM0NTXxwx/+kDvuuAOv18sLL7zAk08+yaGiLL+jR4/ynve8h/b2diKRCA8//DBvvvnmitpxtYW3HKpz7Ngxeno2xgXm4LDWDMeHMU2TFl8Lv37zr/OpfZ/iid1P8P7t7+fxgcdRFIVLC5e4tHBpxeuO5WLEc3FURaUnuLbnUH+4H4/mIZFPMJ4cr/+GVcQ7/AJKJgq+COx817p+9kahLyyQu3IFAN/BgyWveQcGAMhdvlz6nmiUwsQkKAqeXbvxLOaI5oZXXozMLBRIPP88yVdexSwUVt6BdcYwDc7Nn7P+fm74OfJ6qQFsKCYKYvX33QuaC5IzEB0uXZEsQuaLiOJONwDpt94CwLdnD67W1Y0+8e3bi9oUwkgkyCwW1FsuyRdfAt3As60fz8AA7q5OWj7xCQJ33QmqYhXbc3V1rWqb153W7XDrpyBU3S08kxC1ntqCnpIBM1mQ7NRYDMNoPN3MEW03Ga6STNvFeAQbp20+IYpLaIompic4blsHBwcHB4c1xTRNcgVj3X9We2D293//9/njP/5jTp8+zcGyL7kgMrg++9nP8sILL/DKK6+we/duPvCBDyw7m8uhkkQiwbFjx6zc4MHBQY4dO8bQYrXoL37xi3zmM5+xlv/KV77Cd7/7XS5cuMDJkyf53d/9XZ555hn+2T/7ZxvRfAeHNedKTAhv28KVTsd2fzu3LU7Pfn7keTKFlWU0jySEGNcV6MKtVU6NXU1cqosdkR0AnJ8/v6afVcLsRdzTi2LPvl8AV6Xr/nokffw4mCaegW24WlpKXvNI0XboCmZRQaDsebFf3H29aKEg7l6ReVuYnsZIp5f92aZhEPvxj0URqaNHWfj7b6FHo9fYo7VlNDFKMp/Eo3kIuUPEcjFem3jNel03dEbi4nzpb9kFXTeLF87/BE59F45/A958Cs78QDwfsM8Ivd7QFxbIXhQDRlcTX1APxeUicOddAKRef2PZM6lzQ0PkBgdBVQg+8IAlVCouF8F77qH5Yx/H1d2Fu7cXrez8uB6ZjotirB1Npde/HR0hAh6NRLbApZnkRjStJk48wibDs+i01Q0T1bUo4Npk2hYSIjzfpQhh18znUTyVAeEODg4ODg4Oq0NeN/mzn9nnLa4l/+zRXXhcqzfF9o/+6I94z3veU/X1d72r1KH13/7bf6O5uZnnnnuOD37wg6vWjhuJ119/nUcffdT6W2bPfvazn+VrX/sa4+PjloALkMvl+Jf/8l8yOjpKIBDg4MGD/OM//mPJOhwcrhcM07BE24HwgO0yd3bdyaWFSyxkF3h57GUe7V/+uTCWGAOgr6myINVasLt5N2fmznApeokH+x5EVdbYR5VPw7kfAmD23YnSsnFT/NcTI5cjc/oMAH6bAUhXdzeKz4uZyVIYH8fd24tpmmQWI4N8e0RkghYKorW1os/OkR8dxbtrV93PNk2TxM9+Ru7SoChe5nJRmJpi/uvfoOmxd+PdsWMVe7p6nJ0Tfd/dvJuByAA/uPQDjk8fZ2fzTrqD3Ywnx8kbefwuPx3+DthyO4wdg8S0+CmneWvlc9ch6bffFoMD2/pxta2NUO3bv4/0G6+jx+KkT54kUEccNnWdxM9/Dojj38796+7qpOXjH1+T9jYi0wkxoFcu2mqqwk1bwrx+eZ53xqLs6lzdSLBrZdM5bf/sz/6MgYEBfD4fd999N6+99lrVZb/1rW9x55130tzcTDAY5NChQxXFGUzT5A/+4A/o6enB7/fz2GOPcf78Oo56rhCXWvSlbPELmp43KmzcekK4XVR1SbR1cHBwcHBwcKjHnXfeWfP1yclJPv/5z7N7924ikQjhcJhEIlEiKjqsjEceeQTTNCt+vva1rwHwta99jWeffdZa/l//63/NhQsXSKfTzM7O8rOf/cwRbB2uWyaTk2T1LF7NS3ew23YZl+ri0a3iHDg9d5rh+LDtcuWYpslofBSALaEtq9PgOmwJbcGreUkX0kwkJ9b+Ay/+DCWbwPC3wPaH1/7zGoTs6dOYuRxaSwvu/sqCa4qq4lnMqM0uRiTos7Poc/MoLg3Pzp3Wsp6tQnzMDS3vuEq+9BKZU6dBUWh6/HFaPvUp3D3dmNkssR88TeLnL2DqlcarjSSn57gUFW7Rva172Rbext6WvZiYPDv8LLqhMxRfjEZo6heuzaYuOPARGLgfdr0b9n0ADjwhpqnf9U9ga2Uhs+sNI50mc/o0sDYuW4miaQTuEm7b9JtvYuZyNZfPnDyJPjeP6vcROHx4zdq1mZiJi21WLtoC3LxFRCQMziSJZxpLO9tUTtuvf/3rfOELX+CrX/0qd999N1/5yld4/PHHOXv2rG2Vz9bWVv7tv/237Nu3D4/Hw/e//30+97nP0dnZyeOPPw7Af/yP/5H/+//+v/mrv/ortm/fzr/7d/+Oxx9/nFOnTi27mtt6oqmKlfFtKIoI/DZNCjkdj29pdxpJYet2KeI5I5fjxkiTcXBwcHBw2BjcmsI/e7S+A2ctPnc1CQaDNV//7Gc/y+zsLP/lv/wXtm3bhtfr5d577yVX5wuEg4ODw9VwOXYZEHmwtVypPaEebm6/mZMzJ3lu+Dk+ufeTdeMOFrILpAopNEWjK7A+mY6aqjEQGeDs3FkuLlxcW7F4YRjG3wYgs+1dhNY4/qFRMDIZUq+/AYD/1oNVC755BgbInj0ncm3vv5/sOZHn6tm2DbWocKO7r4/0sbfJj9QXbVNvvkX6TZFv2vSuRy1XbeSJJ0i+/Arpt94ifewY+Ylxwu//AFqo9v/c9eJS9BIFo0DEG7HOhft672MoPsRcZo43p95cyrMNF4ngnfuB/RvQ4sYgc/IkZr6Aq6Mdd9/auvW9e/eSev0N9GhUuG1vv912OSOVIvmqMDcG7rm35Fi+UUnlCiSyBRQF2oKV26Ml6KGvxc/IfJrzUwlu72+cuIhN5bT90z/9Uz7/+c/zuc99jptuuomvfvWrBAIB/vIv/9J2+UceeYQnnniC/fv3s3PnTv7Fv/gXHDx40Kqsa5omX/nKV/g//8//kw9/+MMcPHiQ//k//ydjY2N85zvfWceeLR9FUSy3rW6aVjEyPV+aa2skhGiryeBvx2nr4ODg4OCwpiiKgselrvvPelYfB3jxxRf5nd/5HT7wgQ9w4MABvF4vMzMz69oGBweHG4daebbl3NNzj20WZzVkNEJXsAuXun5+Jplreyl6ae0KRhs6nPsRAGbPQYym9XESNwLJl17GSKXQWlrw7a8uKHq2bQNVQZ+bF7mkizNuvYvRCBJ3b69YLhqrmUubOXWK5IsvAhC8/z58N91kvaZoGqEH7if8C7+A4vVSmJgk/uMfleTpbiQyGmFvy17rvsLv8vNg74MAvD75OnOZORSUdYsSaXTMQoH08ROAcNmu9f2YomkEDi+5bY0qg+XJV1/FzGZxdbTju+nGFdSLkXm2zX43Hpe9DHrfrnY+dkcft21tXseW1WfTOG1zuRxvvPEGX/ziF63nVFXlscce4+WXX677ftM0eeaZZzh79ix/8id/AogiDxMTEzz22GPWcpFIhLvvvpuXX36ZT33qU7brymazZLNZ6+9YTBT9MgwDY40vuoZhoKkKpmmSzeuoLgUza5LLFPAGS522KiYqKqZpomdzaA3yD+FGxjBEwZi1Pk4c1g5nH25unP23uWmk/SfbIn82I7Lddr+L+1T++u7du3nqqae44447iMVi/Ot//a/x+/227yvfNvLveDzOhQtL+b+XLl3irbfeorW1lX6baaxyXXb3Wo1wPDg4OKwNsVzMEor6myqvDeV4NA8Pb33YyuLc3bKbzkD1auKjCRGN0BvqXbU2L4etTVtxq26S+SSTqcmqsQ/XxMhRSM6A2w87HoH5xOp/RgOSHx0l844outb06CMoruqSh+r14u7ZQn50lOQrr6LH4igejxWbYC3n8eDu7iY/Nk5ueAR/JFKxruzgIPFnfgYIAa+aC9K7Yzvaxz/Gwjf+N/mxcdLHjlVddr2I5WLWubCntVSw3tm8k+0L2xmMDgLQGejE7/KvexsbkczZsxipFGootKys49XAu2cPqaOvoy8skDl+nEBRpJWRy5F67SiZd04BEHzgQRR1U/k014yZhCxCVn02fW9zYx7Xm0a0nZmZQdd1urpKp610dXVx5syZqu+LRqP09vaSzWbRNI0///M/t4prTExMWOsoX6d8zY4vf/nL/OEf/mHF89PT02QyK6tWulIMwyCfzVDI6UxOTZPOJEkn80xOTJPWvUvLzEdRdIOcqmEkk+QnJ9ACjXkQ3kgYhkE0GsU0TVTnAropcfbh5sbZf5ubRtp/+XwewzAoFAoUCoUNbcvVIsVO2X59MV+vvE9SoJbPffWrX+W3fuu3uOOOO+jr6+M//If/wO///u9b20Oi63rJ36ZpWp/x6quvlhQ7+5f/8l8C8Gu/9mv8xV/8RUVbC4UChmEwOzuL2106vTcej1/9RnBwcGhorkSFy7Y72I3Ptbzoum3hbexq3sWFhQucnDnJu/rfZbucaZqW03a9RVuX6mJbeBsXFi5waeHSykXb6bNw8RmRGbrlNih3+KUX4LIoQsTOd4E7AFz/oq1ZKBD/2bMA+A4cEA7ZOngGBsiPji65bHfuQHFXxki4t24lPzZOfmQY/80HSl7TEwniP/1HME18+/cRvP++mp/pamkh9OADxI88Q/KVV/D09+Nqb19mL1efc3MiFqI31EvYEy55TVEUHup7iNHEKDk9VxqNcANjmibpY8cA8N96K4q2PmGUiqoSOHyY+E9+Quqtt/DdcguKx0P23HmSL75oxWT6D96Cp299r2uNjHTatoc8G9ySlbNpRNurpampiWPHjpFIJDhy5Ahf+MIX2LFjB4888shVr/OLX/yiVdUXhNN269atdHR0EA6Ha7zz2jEMg3BwhqziIdLSiqfNjaqniDQ109HZBEAmFceraaBptG4dQB8dI9TUhM8m99dhfTEMA0VR6Ojo2HDBweHqcPbh5sbZf5ubRtp/mUyGeDyOy+XCVcPF08j8xm/8Br/xG79h/f3ud7/b1rX6R3/0R/zRH/2R9fddd93F0aNHS5b55Cc/WfJ3Lfer2+2u+lnVcLlcqKpKW1tbRc2BRqxB4ODgsDpciQvRdiAysKL33dR2ExcWLjAYHUQ39KXIuCKK82xruXHXip3NO4VoG73EvVvuXdnU6pHXhTB77scQH4fd7wWZV2uacP6noBeguR+6bxHP3QCk3nwTfX4eNRAgeN+9y3qPZ/uAFWkAldEI1nJbt5J69TVyw8OYpmntL9M0SRw5Iqajd3USevTRZe1L7/79ZC8NkhscJP7Tn9L88Y/buoJN0yQ3MkLh8mXSo6OQy2Fks5iZLGgqofvvRw0EltVXO0zT5Oz8YjRC617bZYLuIO/d9l5OzZ3iQNsB22VuNHKXL4uidR4PvpvXd5t4d+8i9fpR9Ll5ki++iL4QJT8qnNJaJELooQfxDAysa5saHSna2hUha3Q2zbeM9vZ2NE1jcnKy5PnJyUm6u6uPTKqqyq5Fq/qhQ4c4ffo0X/7yl3nkkUes901OTtLT01OyzkOHDlVdp9frxWsT5qyq6rp8iXRpCjlToWCCx+tCURSMwpLrKBdfABRMrxt3MIShKCiFwoZ/wXUQKIqybseKw9rg7MPNjbP/NjeNsv9UVWTJyh+H+hR/yV3pNpPb2W7fb/Sx4ODgsDbk9TyjcSFELCfPtpgtoS34XX7ShTSjiVFbd6B02XYHu9c1z1bS39SPS3URy8WYSc/QEehY3hsLOYiJ7YKiwPhxSEzCgV8CfzPMnIfZC6BqsOdxq3D19U5hfp7U668DEHzwAdRlDuhpzc1okQh6NIoa8FctJuXq7ETxeDAzWQrT07gXDVGZ48fJDQ2juF00vec9y3ZcKopC07seZf5vJyjMzJJ67TWC95U6dI1kkvgzPyM7OEg+mSQZDFb8/zTTacIf+tBV34tMpiaJZqO4VJeVtWxHf7jfcdkWkX7rGCAc3apnfd2biqoSPHyY2I9+bEUhKG4XgTvuENm6m9RMsFYUdIO5pKjxtBlF201zl+vxeLjjjjs4cuSI9ZxhGBw5coR7713eKJp8j8yj3b59O93d3SXrjMVivPrqqyta53ojC5EVdBOXR+zCQm7JrZKNLwCgBAIoixcQ0ylE5uDg4ODg4ODg4OCwSRhJjKCbOmFPmBbvyip5q4pqCVAXFy7aLiMzPLeENqZAl1tzWzm9l6KXlv/GhSFRZMzfDAc/KTJr45Pwxtdg+hyc/4lYbuthCG7clPv1xDRNEs/8DHQDz8A2vLt3L/u9iqLg2SmOFe+evVUzQBVNs+IW8sPDABTm5ki+9BIAwfvvx9WywuM0ECD06KMApN58i/zYmPVa9tIg83/3d+QuX0Zxaah9vXj37sF/60ECd91F8L57UVwauStDZE6cWNHnFnNuXkQj7IzsxKNtvqnjG0F+cko4W1UF/60HN6QNnl27cHWJgQPvzh20/PIvE7jrLkewtWEumcMwTfwejZB3822fTdXiL3zhC3z2s5/lzjvv5PDhw3zlK18hmUzyuc99DoDPfOYz9Pb28uUvfxkQ2bN33nknO3fuJJvN8vTTT/PUU0/xX//rfwXEBfp3f/d3+dKXvsTu3bvZvn07/+7f/Tu2bNnCRz7ykY3qZl1cmgI65HWDJina5nXr9VxsAQAlGLDyeBzR1sHBwcHBwcHBwcFhs3AlJqIR+sP9V+Ui3NG8g3dm3+FS9BIPGQ/9/9n77zBJrvu8F/9UVVfnNDnv7uzuYHexi43IgQABgggEryiRIkXDFMB7TV5Z5I8Xl+aVSduXBmnKomwr8NqmrECLMmmJQTQpMYEEAS5A5AWwC2yOs5PzTOdU4fz+qO6emZ08O3HnfJ5nnunuqjp1uqu7uvo973m/kyISVjPPdiJbo1u5FL/ExdhFbq6/eX7Pc8wpCEVFK1S2wqHH4eT3IdkPJ77nLPNFYfMdy9XtNUfu1CmM3l4U3UXw7rsX/H4J3HwzrupqPFtndpoCuFuaKbS3U+jqwrd/P8mnf4EwLdybN+Hds2dRffds3Yp3105yp8+Q/MUviL7//aRffa1cTM1VXUXgXe/CsixCtbWTZpcoLhep539F+sUX0ZubcVVWLmjfpm1yfszJ8r2yANmVGAODFNov4T94sGwM26hkXn0FAE9bG1ootCp9UBSFyPveh51OL3iwYKMxWM6z9azL2XHrSrT90Ic+xNDQEJ///Ofp7+9n//79PPXUU+VCYp2dnZNOYul0mt/93d+lu7sbn8/Hzp07+eY3vzkpd+33fu/3SKfTfPzjHycWi3HnnXfy1FNPrel8tLLT1ha43M7Fx0SnbSEVB0ANBMqirV0orHAvJRKJRCKRSCQSiWThCCHKou2W8JZFtdEUbMLr8pIzc/SmemkJt5SXrXaebYkt4S2oikosH2M0N0qVr2rujUaLrtzKosDoi8KBj8D5nzlRCTA54/Yax06ny25X/823oC2ixoyi63h3TJ/nOhG9xXkPmX19pF9+BXNwEMXrIXjvfVclBgXe8Q6Mnh6seILR//E/EKYFioJv/34Ct96CUFUYHJyynXfvXgodHRQ6Okn+/OdOLu4CCmJ1JDrIW3kCemDWwYtCZyeJn/wEYZgobjf+gwcX9TyvBQqdnRQ6OkFzIgpWE9XtXvFohvXIUGr95tnCOhNtAT75yU/yyU9+ctplhw8fnnT/S1/6El/60pdmbU9RlCkFNtY6elGYNi0bze0cwolOWyOZAEALBlHc0mkrkUgkEolEIpFI1g/D2WHSRhpd1RcdX1CKSDg1cooLsQuTRNvVzrMt4dbctIRa6Eh0cCl+aW7RNhuDzCgoqlNkrITmgh0PQ1UbCAuqti1rv9cKQgiSzz6LyOVx1dTg279vWfenVVSgBoPYqRTZo0cBCN1zD1owcFXtqm43wfveRfwHP0CYFmooSOhd9+NudoRUMUPhTkVRCN57H7Fv/R3m0DCZV1+dkos7G2dHnQJk11Vch6pMHwuRv9RO4qmfguX0IX/x4oYVbYUQ5QEC3w03oEWjq9shybwYLhUhC65P0XbdZNpKxnFpziieYQn0aZy2RioJFEVbvTjyIkVbiUQikUgkEolEsg64nLgMQHOo+apE1W1RR7xsT7Rj2eMml9XOs51IKXv3UmweubalaIRwI+hXzAxVFKi5Dmp3LXEP1y6548cpXO5AcWmE7n/XjHm0S4WiKLhbxguVeXZct6D83NlwNzcRetd9+G88RMWHP1wWbOdCCwYm5eIWunvmtZ0tbLpT3QC0VUz/HHLnzpH46U+crODNziCB2T+AlUrPax/XGvlz5zCHhh238Y03rnZ3JPNACFF22laH1qcrWYq265BSPIJh2Wi6cwiNwvhFiFl02rqCIem0lUgkEolEIpFIJOuKUjTC5vDmq2rnyogEWDt5tiW2RLagKAojuRHi+fjsK48WRdvK1uXv2BrHHBkh/eKLAARuvx1X1TyiJZYAfZMjXqqhIMG7717Str07dxK47TZUz8IcgZ5t2/Duvh6EIPmLp7GLhddnYzQ3immbuDU3Vd6pr13u1CmSP38abIFnx3WEH3kEV70TS1loX0DhvGsEYZqkX34ZAP+Nh1B9vlXukWQ+JHImecNGUxWqAtJpK1khxjNt7bLT1jJshBCAk+sD4AqFZaatRCKRSCQSiUQiWTdkjAyDGSe/82pF21JEAsDF+EUAxvJjayLPtoTP5SuLxxdjF2de0bZh7LJzu2Jji7bCNEn+/OdOEbAtm/Hu3bti+/a0tRG6716iv/7rCxZXl5PgnXeiRSLYyRSp556bc/2hzBAA1b7qKXm82bffJvnMsyAE3j27Cd1/P4qq4tnmONfzF2d5n16jZN8+jp1MoQaD+Fbw/Sa5OoaLLtvKgBtNXX9FyECKtusSTR2PR9DcziEUtsAybYRlYWUc0dYdjJRFW+m0lUgkEolEIpFIJGudzmQnADX+GgL61WWFAmyLOELTpfglbGGvmTzbiUzs44wke8HMO7EIoYYV6tnaJP3yy5jDI6h+H6F7713RivCKouC9/nq0SGTF9jkfFLeb0LvvB0Uhf/YcxjSFyyYylHVE2xpfzaTHC11dpJ57HgDf/v0E77mn/Pp6tjoDIEZPD3Yut9RPYc1i53JkXn8dgMCtt5Q1FsnaZyi5vouQgRRt1x1HB4/yVuI54mYfpiXQNBVVKxYmK9jYmQyWbSFUBU8wjOKWmbYSiUQikUgWz5NPPsn+/fsXtI2iKPzgBz9Ylv5IJJJrm56kk8nZEmqZY8350RQaj0joSfWU82zXQjRCidZIKwoKg5lBEoXE9CuVohEqtsAyZ7euZQodHWSPvQVA6L77UANXL+xfK+j19eWM3ewbb8y6bslpW+MfF22FEGRefRUA7+7dBO68Y5IgrkWjaFWVYAsKly8vce/XLpnX30Dk87iqq/Ds2LHa3dkwvHRhmL/61SXO9icX3UZJtK1ep0XIQIq2646R7AgjhX5ydhyzWEXS5Z4g2qbTWMLC8uq4XR7ptJVIJBKJRFJGUZRZ/5588skp23zmM5/hmWeeWdJ+/MEf/AE33XQToVCI2tpa3ve+93H27Nkl3YdEIlmf9KaLebOBpRFVVUWlNezECVyMXVxTebYl/Lqf+kA9AB3xjulXKhUh28DRCHYmQ/IXzveRb+8NuLdsWd0OrUH8Nx4CIH/xEubo6LTr2MJmODsMQK1vPCLE6OrC6OtHcWkEbrl5WgezZ+vGikiwEgmybzuDBIHbb1/2YncSByEEx3viJHMmPznex1Mn+smb1twbXkFJtK2VTlvJShHQA6gKmCJHwSyJtk6urWlY2KkUlm1i+dx4NI/MtJVIJBKJRFKmr6+v/Penf/qnhMPhSY995jOfKa8rhMA0TYLBIFVLXODlueee4xOf+ASvvPIKTz/9NIZh8O53v5t0emNWpJZIJA6JQoJkIYmiKGURcynYHt0OwNnRs2TNLC7VNclhuBYo5fd2JKcRbY0sJByxeSMXIUv+8pfYmQxaVSWB229f7e6sSVxVVbi3toIQZGZw247mRrGEhVtzE/E4MQ9CCDKvvQaAd8+eGR3Mnm3FiISurg1hDEu/8gpYNnpLc7kInWT5SeRMMgULRQFFgdN9Cb75Sic9sey82+iLZ4lnDVRFkfEIkpXDEW0VCnYG03YKj7n0qU5b8wrRFsvJu5VIJBKJRLJxqa+vL/9FIhFHGCneP3PmDKFQiJ/+9KccOnQIj8fDCy+8MCUe4ciRI9x///1UV1cTiUS4++67efPNNxfUj6eeeorHH3+c3bt3s2/fPr7+9a/T2dnJG3NM55RIJNc2fak+wHH/6drS5UY2BhvxaB4s4fweWkt5tiVKom1PsgfDvkIMG+sAISBQDd61laW6UlixGIVL7aAohN/9bpkrOgv+G28CIH/uHFY8PmX5dEXIjO7ussvWd+DgjG1r1dVo4RDCMCl0di5D79cOdiZD/tx5oOiyXcHs5I3OQMLJTK4NefnNG1sI+3QSWYPvvt7FSxeGsYpa2Gy81u44zXc1hPDq2rL2dzmRou06w6/7URUwRBbTmuq0tVIpTNvC8rlxa+5JX2YbYSRMIpFIJJJVQwgwCyv/J+a+cF0In/3sZ/nyl7/M6dOn2TtNheRkMsljjz3GCy+8wCuvvEJbWxsPP/wwyeTiM8fixR+VlZWVi25DIpGsf0p5sw3BpS20pakaWyNby/cbA41L2v5SUOmtJKgHsYRVzvUtI6MRyJ07B4C7pRlXdfUq92Zto9fV4t68CWxB5o2pg6qlImSlaIRJLtvdu9GCM+cEK4qCe4NEJBj9AyAEWmUFem3t3BtIloz+uCPa1kc8NEV9PHrLJnY1hBECXm0f5R/f6kHMcv07mMxxaSiNosBNW9b3teXaGl6UzElQD6IpCobIYVhFp+3ETNtUGkuYWD4dt+pG0TQUl4YwLUe09XpXs/sSiUQikVy7WAb86o9Wfr93/QtwuZesuS9+8Yvcf//9My6/9957J93/i7/4C6LRKM899xyPPPLIgvdn2zZPPPEEd9xxB3v27Fnw9hKJ5NqhL+04bZcjb3ZbdBunR08vW/tXi6IobA5v5uTISTqTnWyJbHEWCDFehGyDRiMIIcifdUTbjVYIyhY2RweP4s17qWX+wqH/xhspdHSSO3Ma/803oQWD5WVlp63fEb+Nnh6M3j7QVHwHZ3bZlvBs20r22DEKlzsQloWirV8X42yY/c75SG9Y2kEkydz0F522dWFHv/LqGg/uqae1OsDTp/q5PJzhZG+CPU3Tzzw40j4GwHV1ISoCS3eNvBpIp+06w6/7URQw7CxGMe5gPB7BwkgmHKNPMR4BGC9GJnNtJRKJRCKRzMGNN9446/KBgQE+9rGP0dbWRiQSIRwOk0ql6FzkNMlPfOITnDhxgm9961uL2l4ikVwbpAop4vk4CkubZ1uiKdhElbeKCm8Ftf616Zor59omOsZdZJlRyMVB1SDSsoq9Wz3MwUGsWAxFd+HZunXuDa4hLsUu8UrfK7ww8MKCttMbG9EbG8GyyR49Wn7csq0pRcgyrzouW9/u3ZPE3Zlw1dej+n2IfB6jp2fO9dcrRl8/AHr9/M9Hs7k/JfPDtkW5gFh9eLLpcEd9iNu2OYMNz58fIp03p2w/kspzftCZ/bXeXbYgnbbrjoArgKoqCCyypvNGLscjFGwKKWd6oe3zlHOaFF2HbE7GI0gkEolEspxouuN6XY39LiGBGYqPlHjssccYGRnhK1/5Cps3b8bj8XDbbbdRWMTg8Cc/+Ul+9KMf8fzzz9Pc3LzYLkskkmuA3rRTaKvKV1U2nywlmqrxmzt+EwVlzWZTNgWb0BSNZCHJaG6UKl/VeDRCpGVJZ1WsJ/JnzwLgbt2K4t5Yr0F/xhEO44U4sXyMSt/8RSj/TTcS/4d/JHfyJP5Dh1D9fsbyY5OKkBW6ezB6ex2X7aFD82pXUVXcrVvJnTxJ/uJF3NdggS5hWZiDAwC45um0fepEP5dH0jx6yyZCXpm5vFhGMwUKpo3bpVLhn/p5P9AS5Ux/gsFEnufODfHwDZOPz5HLYwgB22qD67oAWQnptF1naKqG3+WMNmTNNEKI8XgEw8JIOqKtFgyWL0ZKX2xStJVIJBKJZBlRFOcH9Ur/rbD48OKLL/KpT32Khx9+mN27d+PxeBgeHl5QG0IIPvnJT/L973+fZ599ltbWjTnlVyKRjFMqQrac0QWqoq5ZwRZA13Qag07ebmeyOHtho0cjWBb5804xKO+O61a5NytPKcoAoD3evqBt9ZYWXHW1CMMke+zYpPZKRcjKWbbXXz8vl20JzzbH8Vxov7xm3aXCMLDz+UVtaw4PI0wLxetBi0bn3pcQXBxKkS1YtA+nF7VPiUMpz7Y25EFVp56vVVXh/l11KAqc7U9Oer3jGYOz/Y7L9pbW9e+yBSnarksCLj8AeTuDZQtcxUp4RqaAWXBOSlowVF6/HI8gRVuJRCKRSCRXSVtbG9/4xjc4ffo0r776Ko8++ig+n29BbXziE5/gm9/8Jn/7t39LKBSiv7+f/v5+stnsMvVaIpGsdZarCNl6Y2JEApYJscvOgg1UhMywDbqSXZi2idHVhZ3Jovp96C0bKx7CFna5aBgsXLRVFAV/MfIoe/wEVizG0FgPimlR66l2XLY9PaCp+Ofpsi2hNzejuN3Y6TRmf/+Ctl0pEj/9KaN//XWsVGrB25p9xTzb+oZ5DfRkDYuC6RSK7xmT1zJXw0CiVIRs5npMtWEvBzdVAPDsmcHya3/k8ii2EGyp9pfzcNc7KyLaZrNZeqbJOjl58uRK7P6aI6g7oq1h5zDtcaetkcpiCQvbreH2jP94kpm2EolEIpFIloqvfe1rjI2NcfDgQT7ykY/wqU99itoFVlX+sz/7M+LxOPfccw8NDQ3lv29/+9vL1OvlQV7jSiRLQ8bIEMvHUFBoDDSudndWlZJo25fuIx/vdIRbtx+CazOHd6kZy43x9+f+nh9e/CHHh4+TKxUga2u7ZgtezcRobhTTNtEU53kPZgbJGJkFteFubcVVXYUoFBj9xjcR3/xfbP7+61R+6xni3/8+AN5du9BCoTlamoyiabi3bAEgf+nSgrZdCaxUikJHJ8IwFiUqG8Vt9Ib55dmOZcYNct1j2TXrPr6Sy8NpLg0tXNReTkpFyK7Ms72SW7dWEfbpJLIGL18aIZkzONWXAK6NLNsSyy7a/v3f/z1tbW285z3vYe/evbz66qvlZR/5yEeWe/fXJEE9gKIoGCKDYdnlTFsjncO0zUlFyEA6bSUSiUQikUzl8ccfJxaLle/fc889CCGIXjEN8Mknn+RYcVolwIEDBzhy5AjZbJZz587xgQ98gMuXL/PEE0+U1xFC8L73vW/GfQshpv17/PHHl+S5rQTyGlciWTp6U06ebaW3Eq/r2nBHLZaIJ0LUE0UIQddwcQAoWLfiUTirwcXYRb53/nuM5ZzK7/1j3RTaHUHQs2PHanZtVShFGdQF6qj2VCMQtCcW7rYN3H47iteDQJA1HdE34HLy6xWPp+zGXSjliIQLF9ac1lDo6CjftiZc68yXUhEyV/38nP+xzLhBLpU3iWfX1usxHZmCyT8c6+Uf3+olnlkb/TUtm+Gk81rWzeK0BXC7VO7d6QxmHe0c4+lTA1i2oKnCR3OFf9n7ulIsu2j7pS99iTfeeINjx47x13/91/wf/8f/wd/+7d8CsrLeYvG7/KgKGCKLYQlcunMYrVwB07axvG7c2nhgs8y0lUgkEolEIlla5DWuRLJ0lIqQlfJcNzrliIRRJ8uVQPUq9mb5sYXNSz0v8bPLP6NgFQi7wwBkL55HGCZaNIprgTM6rgVK0Qg1vhpagk40xEIjEgDcmzdT/bGPof6zD3PpN26i7/230/g7n6Dyox+l6qOPL9hlW2530yZUvw8rkST5zDNr6rvPuArR1komsVMpUBX02pp5bXOl6Nm9DiIS2ofT2EIgBJwdSK52dwAYSuWxhcDv1gh5XHOu31odYGd9CCGgY8QZkLhWsmxLLLtoaxgGdXV1ABw6dIjnn3+eP//zP+eLX/zimg6BX8v4NB+aojjxCJaN5nIOoygURVu/G486jdO2IEVbiUQikUgkkqVAXuNKJEtHyWkrRVuHkmjbmSgWeQrMTzhaj2SMDP9w4R84NnQMgP01+/n1tl93Fl64jCUsPDuu25Dn1YHMAAC1/lpaAo5o253spmAtLvZwMDMIqkJ1uB7N50MLBspawWJQ3G7CDz4Imkr+/AUyr7626LaWEmFZFLq6y/cXKtqW4hRcVdVlA9xcxIrOWk/RULdeRNsSZ/oTa0J0LxUhq4945/2Zf8d1NXiLdZ7qI142VV47LltYAdG2traWt99+u3y/srKSp59+mtOnT096XDJ//C4/qlqMR7AFiqqg6Sp2oUDBAtOrzxCPIDNtJRKJRCKRSJYCeY0rkSwNWTPLaG4UgIbAxi5CVqIh0IBbc5PNxRi0smtWtDUsg7yVX/T2OTPHd899l750H27NzQNbHuD2ptsJ6AGChoZ3ME7WzOK97rol7PX6wLRNRrIjANT6aonoESKeCLaw6Ux0LqrN4eww4Dh3lwq9qYnQPfcAkDlypJxBvJoYff1OPZ+i6LdQ0XahebYAY8V4hF31jku8e2xh2cMrjWWLsjMVYCRVYDi1+npRqQjZQoqIBTwu7r++juqgm7uvq7nmBniWXbT9xje+MaU4hdvt5u/+7u947rnnlnv31yROPIKCIbKYllMlT3OpiIKBKQSW74p4BJlpK5FIJBKJRLKkyGtciWRp6Es5VdorvBX49WvLIbVYNFWj2VsNlkGnmQT/2otHMCyD75z7Dt889U0ShcSi2jgxfIK0kSbsDvP+tvezLbqtvKx+oAACshV+tCuy1jcCo7lRbGHjdXkJuUMoikJruBVgwbm2JQYzgwDU+Jd2EMB7/fX4Dh4AIPXsM2XRc7UodFwGxjN37WwOO5eb9/ZGn3NOmm+erRCCWDEeYVdDGFVRSObWdq5t91iGgmkT8Ghsqw0CcLZ/9SMSyk7bBYi2ANtrg3zkti00Rn3L0a1VZdlF2+bmZurrpx+huOOOO5Z799ckPs2HqoJh58mZJlAUbQ0D0xKYV4q2pUxbGY8gkUgkEolEsiTIa1yJZGko59kGZDTCRDa5nJzRDsUC1/ymaK8krw+8TjwfJ2/lea7ruQVPrTZsg+PDxwG4ueFmKrwVk5ZX9jgCUmJTxZRtNwJlgdU37hxsjTiibUeiA8u2pt3OFva0x8KyrXHnrn/p84EDt92Gu7UVYVokfvwTrOTqCYClImTubdtQA07Btfm6bYVhYA45WcLzddpmDYuCaaMoUB10Uxd2Zj33rOGIhEvFaITW6iC76p1zzWpHJOQMi7Gi+L0Qp+21ztzJvpI1h1fz4lI0wCKZTwERNF1FFApYNlg+9wzxCFK0lUgkEolEIlkora2ti5pu98QTT/CpT31qGXokkVw7yDzb6dmsOL/nBhVBxsisKRfyWG6Mt4beAkBRFLqSXZwbO8eOyh3zbuPc6DmyZpaQO8T26PZJy8zRUQLxPKgKg3UbU7wpibYTBdY6fx1+l5+MmaE31UtLuGXSNvF8nB9d+hFezct7tr4Hr2v8tRvLj2EJC7fmLhd6W0oUVSX87vuJ/a//hTk0TOJHPyLy/vejzjMTdqmwkkmskVFQFNybNpGLnsROp7FiMfQZBlonYg4Ogi1QAwHUeRZoK7lsgx4XLk2lqcJHXzxH91iG6xuX/rW+WoQQXBpyRNutNQE2Vfpxu1SSOZOeWJbmitU515SiEaJ+HZ9bW5U+rEWWVLSVF7Qrg6Io+Fx+YhRIFZwPm6Y6oqxhg+XTp49HKKx+RolEIpFIJBLJeuPrX//6orbbsmXLkvZDIrnWyFv5svtPiraTCeRTVGs+ht1+OpOd7KzcudpdAhzB54WeF7CFzebwZuoD9bza9yov9LxAS6hlXuKyLexy4bF9NftQlckTgPPnzuFz+cnURUiSWo6nseQMZYZoj7ezp3rPkgjsQxnH7TlRtFUUhS2RLZwaOUV7on2SaJs1s/z40o+J5+PEifOT9p/w3m3vRVcdLWA65+5So7jdhN/zHmLf+S7m8AjZ118ncPvty7KvmShcdly2ekM9qteLFo1i9PTM22k7Mc92vq9TSbSN+h0NprnCz+uXx+iJrU2n7Ui6QCJr4FIVNlX60TWV7bVBTvUmONufnLdoK4Qga1ik8iaVfjcu7eom8g8knHxs6bKdzJKKtvKCduXwufxAjKThiLaK7ZwoTMDyzFSITDptJRKJRCKRSBbK3XffvdpdkEiuSfpSfQgEEU+EgB5Y7e6sLdJDbNZDDOt+OhIda0a0vRS/RFeyC03RuLPpToJ6kIuxiwxnh3mx90Xu33z/nG20x9uJ5+N4NA+7KndNWZ6/eBGvy0umpZqsmV1zTuPpeK77OQYzgxwfPs4dTXewo2LHosVRwzLKxfmujDJojbQ6om28nbua7kJRFEzb5Kn2p4jlYwT0AKZt0p/u5+nLT/Ng64OoiloWgZc6z/ZKtFCIwB23k3z6FxS6u1npT3U5GmHzZqc/xTzkeYu2fY5o65qHK7dErFiELOpzdJfGqBdFccTcZM4gsMZco+3FaISWomALsLM+xKneBOcGUtyzoxZNnfre7Y/nONETJ5EzSOZMkjkDw3LiFMI+nfcfbCoL14uhfxFFyDYCSyraygvalSOoO2HRqYIz8qiazomi4NKdqQDTZdpK0VYikUgkEolEIpGsEcrRCDLPdjJCOKKtK8wb7gBdyS5M28Slrm66oWEZvNDzAgAHag8Q8UQAuKflHr537nucHztPW7SNLZEtM7YhhODo4FEAbqi+AV3TJy03x8awRsfQNBeuzS1AhpHcyJoWbTNGpiyK5q08z3Y+y/mx89zTcg8h9/ym2E9kODuMQBDQAwT0ALZtl5c1BZvQVZ20kWYwM0itv5ZnOp+hL92HW3PzyNZHKFgF/vHiP3I5cZnnup7jnpZ7GMoWRVvf8oq2AHqj83k2h4YQhlE2kS03wjQxuruAxYm2QgjMfqcImd4wvyJkALFiwbGKgPM8PS6N2pCXgUSOnliW64qFvtYKl4YcDWlrzbik3lLhJ+DRSOctOkbSbK2Z3OehZJ7vvdlNwbS5EpeqkMgafOf1Ln7jYDPVQc+UdebDQKkIWUSKthNZ9kJkkuXB73K+tNIlp62RAwQF3RnFkU5biUQikUgkS8GTTz7J/v37F7SNoij84Ac/WJb+SCSSa4dyETIZjTCZfBLMPHWuAEF/DQWrQGeic7V7xesDr5M20oTcIQ7UHSg/XuuvZV/NPsBxnBasmWP5etO9DGYG0RSNPdV7piwvtLcD4G5upjLiuB1LERprle5UNwJBpbeSWxtuRVM0upJdfOvMtzg5fHLBxZ0Gs1PzbEu4VBebwpsAaE+083Lvy1yMXURVVB7c8iBVvioagg3cv+V+FBROj57mlb5XGM4Oz9jmUqOGQk4BMFs4GbErhNHbizBM1EAArboaAK0iCjii7VzHwYrFsLM5FJeGq7j9fCjFI0R848a5pgofAN2jaysiIVMw6SuKo63V46Ktqiq01TkDDGf7JxeRyxYsfvhWLwXTpinq4/7r63j/wWY+escW/n/3bud/v7OVmpCHdN7iu693019sfyEkcwapvImqKNQsUvS9VpGi7TrFX5w+lCqKtqqRd0aGipVFpxVtZaatRCKRSCQbGkVRZv178sknp2zzmc98hmeeeWZJ+/Fnf/Zn7N27l3A4TDgc5rbbbuOnP/3pku5jKWltbWXr1q0L/vv//r//b7W7LpGsWQzLKLv/pGh7BWnndVH8lWyvuA6A87Hzq9mjScXH7mi6o5yVWuKmhpuIeCKkjTSv9L0yYzsll+2uql3TumfzFy8C4N66lSpfFUA5KmCt0pVw3J2bw5s5WHeQD+74IPWBegzb4Lnu53i64+kFtTcxf3Y6WiOtABwfOl7OBn5nyztpDjWX19ka2co7Wt4BOK+5LexlK0J2JYqioDc4gnspI3YlKEcjbNlcjqbQwmFQFYRhYqfTs25vFvvqqq1Fcc3P1S6EIJYtxiP4xz8TzUXRdq3l2rYPpxECasMeQt7Jn+Gd9Y5oe3EoVXbU2rbgJ8f7iGcNIj6d9+5rZE9ThE1VfqLFHNuAx8UHDjXTEPGSMyy+92Y3XaOZBfWrVISsKujG7ZIy5URkIbJ1SrAo2maKoi2FLDYCy+VGQZn0JVoWbU0TIcSyBY9LJBKJRCJZ2/T19ZVvf/vb3+bzn/88Z8+eLT8WDI5PhxNCYFkWwWBw0uNLQXNzM1/+8pdpa2tDCMHf/M3f8Gu/9mscPXqU3bt3L+m+lgJZt0EiWXr6M/0IIQjqwUVNIb+mSTuuSALVbK/YzrGhY3QkOjAsY0qcwEowsfjYpvAmWsOtU9bRVZ27m+/mHy/+IyeGT7AlvKXsCC0xnB2mM9GJglJ25k7ESqUx+wcAcLe2Umk6t0dyS+O0tYXN2dGzhD1hmoJNS9KmEILOpOOCLj3fCm8F79v+Pk4Mn+DFnhe5ELvALflbynESc1ESbWdyxW4Ob0ZVVIxiXZtbGm5hR+WOKevtrtpNxshwpP8IsLxFyK7EVV9P/sLFckbsSlDocI6De9P4+07RNLRwBCsWw4rF0Ga5ninn2dbNP882a1jkDUfgjPjGP5tNUR+KAqPpAum8uaDnsZyU8mwnumxL1Ie9RHw68azBpeEUO+vD/OrCMJ2jGdwulffua8Q3Qz6vV9f4jYPN/ONbvXSNZvjB0R4e2dc47X6moz8ui5DNhCxEtk4JuouirVmMR8hnEcLG1t24Nfekk3Ep0xYhwDDAvfhwaIlEIpFIJOuX+gmFNSKRCIqilB87fPgw73znO/nJT37Cv/k3/4bjx4/z85//nMOHD/ODH/yAY8eOAXDkyBH+1b/6Vxw9ehTDMNi/fz9/8id/wsGDB+fdj/e+972T7v/+7/8+f/Znf8Yrr7yyJkXb2eo2ZLNZfD7fCvZGIrk26E87AklDcP7ZkRuGotOWQA01vhoingjxfJz2RDvXFZ23K0l7op2uZBeqopaLX01Hc6iZnZU7OTN6hh9d+hHNoWYO1B6gOdiMoii8Neg4dbdGt04rYJaiEVz1dWjBIJU5R5Qcy41hCxtVWbwDL5aL8UznMwxkBnBrbv73Pf/7VbVXYjg7TNbMoqs69f7x71hVUdlbs5dL8Uv0pnrpSHSwt2bvnO3lrTzxfByYuWiYR/PQEmqhI9HB9VXXc7B25u/fG+tuJGtmOTF8YoqIvpyUMmHN/r4VMY5ZsRjW2BioCnpLy6RlWjRaXB6D5ubpGwCMcp7tQoqQOe/RkNdVLuoFjohZFfQwnMzTE8sSXQO+OcsWdIw4Dtit1VPFa0VR2Fkf4tX2Uc72J7FteLNjDIB3X19HTWj22AK3S+V9+xv58fE+Lg2l+eFbvbz/UDNN0bmvkUpO23op2k5hxQqRyQvapSXodj5kWTODEAK1kMMWAkvT8WpXfJhcLlAUEMIJApeirUQikUgkS44QAtNeeTeFS3Ut6Y+hz372s/yn//Sf2Lp1KxUVFRw+fHjS8mQyyWOPPcZ//s//GSEEf/RHf8TDDz/M+fPnCYUW7pazLIvvfve7pNNpbrvttiV6FivHnXfeyRtvvDHpsTNnzrBz59qo9C6RrFVKou1EoUtSJFNy2jrOyO3R7bwx8AYXxi6simh7KXYJgL3Ve+d0i97ZdCcCwbmxc3Qnu+lOdlPjr+H6yus5FzsHOEXMpqNwyYlG8GzdCkDEE0FTNEzbJJFPEPVGF9x3IQQnR07yUu9L5e/oglUglo9R6a1ccHtXUnLZNoea0dSpLsQt4S30pnppj7fPS7QtuWzD7jA+18z6yTtb3slAZoDN4c2zXgMoisJdTXexr2bfijraXdXVoKnY2Rx2PF4uCLZcFDqd46A3NKJ6Jush8ylGZufzWKOOQKnXL1y0jfqnaizNFb5x0bZi3k0uG91jGQqmTcCjUReeXoDdURRtLw9n6CwKvLe0VpbzbufCpak8stcRbi8Opni7KzanaCuEoL8o2tZFZJ7tlaxY+Ul5Qbu0hIuibcEyKNgFyKYdp62m49YmnzAURUHRdUShgF0oOKHgEolEIpFIlhTTNvnL43+54vv92A0fW9Lpsl/84he5//77Z1x+7733Trr/F3/xF0SjUZ577jkeeeSRee/n+PHj3HbbbeRyOYLBIN///ve5/vrrF93vleaHP/whp06dIpVK0dXVRcsEZ8+HPvQh3nrrrVXsnUSytrGFzUDGmfounbZXIMQkpy1QFm07k53kzBxe18q60UqZsvM5Vm7NzX2b7uOm+pt4a/AtTo+eZigzxHOZ5wBoCjZNO+3fzucpdHc7bRRFW1VRqfBWMJwdZjQ3umDRNm2kebbzWbqSXeV958wcI7kRhjJDSyPaFgvEbQpN72JtjbTyUu9L9KZ753XshjLOsZ/JZVvCr/vL2bZzoSjKvKMZlgrF5UKvrcXo68fo719+0XZCnu2VzEe0NQcGQAi0SHhBekk5z9Y39TqsOerjWGeM7rEsuyuW1sBoWDbHe+Kk8ya3bq2a5PKdiUvlaITgjEJ/VdBDbdjDYCKPLWBrTYDbtlUtqG+aqnDTlgouDqa4NJzGtGxcs/RvLGNQMG10TaE6IEXbK1n2hN8f/vCH/OEf/mH5gnYiH/rQh5Z799csXpeOpujYtiBVSKHk0tjYWOpU0RYm5Noaxkp3VSKRSCQSyTrixhtvnHX5wMAAH/vYx2hrayMSiRAOh0mlUnR2Lqyy+Y4dOzh27Bivvvoq//yf/3Mee+wxTp06dTVdX1H27NlDKBRieHiYxx57jG3btvGOd7yDD33oQ+j6ymdOSiTridHcKAWrgFtzL4lwdk2Ri4FlgqqBz7HnVfmqqPRWYgub9nj7inbHsq2yaLuQYxV2h7mr+S7+6a5/yo11N+LRPCiKwo3103/HFC53gC3QKitwVYzbEkvFyBaaa9uV7OJbZ75FV7ILTdG4o+kO/rdt/xtNISfLtuRovRryVp7+jOMYn1gEbCIRT4RKb+Wk7NvZKPWrzl931f1bbVz1jshvTMjTXw6EYWCUBP/N04i2FVFgdtG2nGe7AJctQLzstJ36vd9UFGpHUgVyxdzbq8W0bI52jvHXL7bz3NkhXr88xgsXhufcTgjBpSFHtN1aM7sovbPeKVZXGXDz4J76Rc3mqg97CXpcFEybrrHZi7H1Fou11Ya8qOoayJFYYyy703bPnj10dXWVL2g7OjpoamqioaFBXtBeBS5NRVd9WCJNOjWGZhUQwotQXXiujEegmGubTjuZthKJRCKRSJYcl+riYzd8bFX2u5QE5nCYPPbYY4yMjPCVr3yFzZs34/F4uO222ygUCgvaj9vtZvv27QAcOnSII0eO8JWvfIU///M/X3TfV5LW1lZ+93d/lz179vCOdzgVunt6eujo6GDPnj2r3DuJZG3Tl3JEnDp/3ZLkil5TlIqQ+atAHX9ttke381r/a1yIXWBX1a4V6068EMcWNrqqE3aHF7y9X/dzc8PNHKg9QN7Kl2P+ruTKaIQSVd6Fi7ambfJs57PkrTzVvmretfldZcG5xuc4WIeyQwt+LlfSk+xBCEHUE53VybolsoXR3Cjt8bkziUui7VxO2/WA3lBP9iiY/ctbjMzo6UGYFmooiFY5dWCh7LRNxBGWhaJNjbEoib6lLN75MjZLPILf7aIq6GY4macvmedqEoVNy+Zkb4Ijl0dJ5pyYj5DXRTJncqwzxrbqIJuq/DNuP5IukMgauFSFloqZ1wPY3xLFp2tsrvLjcU1feGwuFEVhW22At7riXBhMzVqQ7Ex/EmDW/m9klk20TSaThEIheUG7TOiaglvxYYg06eF+oorAdmkINHRFOm0lEolEIllpFEVZlareK82LL77IV7/6VR5++GGA8uD81WLbNvl8/qrbWW5GR0epnPCjsHR9C9DU1ERT09JUJJdIrmVK7sT6gMyzncIV0Qgl2iraeK3/NbpT3WSMDH59ZQSO0ey4y/Zq8tN1TZ/xO1IYBoUOx4Xq3rpt0rKSaFvqx3w4N3aOtJEmoAf4jbbfmDS4WYpmGM4OX3Vxs5Jzdq4CX63hVt4ceJOuZBembc442JoxMqSMFApKWVxez7jqnM+3OTLqxDQuU22d5MXLdIykadx23bTvUTUQQNFdCMPESiQmObkB7FyuXITMvWn+0qoQYjweYRqnLYzn2vYnFjawPZHRdIHvH+0hkR0venZLaxXXN4Z57twgb3XF+fmpfv7prZvx6tOLrGeLwmhLpR+3a/b3vKYqXN+48AGaK9leE+KtrjiXhlLYdu20Ltp41qBr1MnO3dVw9fu8Flm2Yc277rqL/gkjKlde0N5+++2Ew/KgLBZdU9EVH7YtyA4PoCkCy+Oc/N3MLNraC3TBSCQSiUQikUykra2Nb3zjG5w+fZpXX32VRx99dMHFZj/3uc/x/PPPc/nyZY4fP87nPvc5Dh8+zKOPPrpMvV46qquraWlp4ZFHHuFf/+t/zXe+8x3Onj2LEGK1uyaRrBtKTlsp2k7DDKJtxBOh1l/rTHOOX1qx7iwmGmGhFLq7EYaBGgziqp38vCt9zn7j+TiGPbcByRY2RwePArCvZt8UgTTqiaKrOqZtlp/bYhBCzJlnW6LWX0tAD1CwCuX3/nSUXLZRb3TayMP1hhYMoEXCIMSyum0vvXWGvniOw2kvedOaslxRlFlzbY2urnI0hxaZf/ZvzrDJF2MPItNk2gI0RZ3BlY7RHAVzcREJh88OksgaBD0u3rmzlsdv38INzRE0VeHO7TVU+HWSOZPDZ6eP/HijY4zX2p33+nXzLCi2FDRV+PDqGpmCRW98+oiE030JwBGTZ3oNNzrLJtoeOHCAW265hTNnzkx6/NixY2VnhmTxuFQFXfVhC0F+dAhFAeF1RlV0e5p4BOm0lUgkEolEsgR87WtfY2xsjIMHD/KRj3yET33qU9TWTi0qMxuDg4P89m//Njt27OC+++7jyJEj/OxnP5u1ANpa4fjx43z5y1/m+uuv58iRI3ziE5/g+uuvJxgMcsstt6x29ySSNU+qkHLchIpCvV+KtlMoi7bVUxZtjzqRMufHzs+7uYyR4bnu5xjIDiyqO2XR1reMou0lR4T2bG2d4pT0u/x4XV4EglguNmdbF2MXiefjeDQPu6t2T1muKEo5eqBU9GsxjOXHSBkpNEWbs0CboihsDjtZq+2JmTOJS5ENtb6FfaeuZUpu21Jm7JUI2yZz9CjGIkVdKx4nMTCMUBQGA1UcPjv9MZ1NtM1fvgyAe/OWBe275LINeV0zFgLbUu0n6HWRzFv86vzC329doxk6RjJoqsIHb2xhf0t0UlEvt0vlgT31KAqc7ktyfiBZXiaE4OWLIzx/ztnvTVsq2dWwcqKtpirlWIQLg6kpy4UQnOp1RNvrpct2RpZNtP3rv/5rHn/8ce68805eeOEFzp07xwc/+EEOHTqENk2GiGRhOE5bP5YtMEadL1LL5xxOF45Aa9mCi0MpcoblZNqCzLSVSCQSiUQCwOOPP05swo+Xe+65x8nmu6LC85NPPsmxY8fK9w8cOMCRI0fIZrOcO3eOD3zgA1y+fJknnniivI4Qgve9730z7vtrX/saly9fJp/PMzg4yC9+8Yt1IdgC7N69m0cffZT/8B/+Az//+c8ZHBzkRz/6EQ0NDdx3332r3T2JZM3Tl3achlXeqg0RKbMgbAsyRffnLKJtX7qPVGGqCHIlhm3w4/Yfc2rkFM/3P0/BWvisy1KW7HI5bYVtU2h3hEz3tm1TliuKUt73XLm2Qoiyy3Zvzd4Z318lUfRqcm1LLtvGYCO6Ovf7uDXSCkB7vH3GmRnXUp5tCb2hGJEwML0omztxgvQLL5L40Y+wFxGRFDt/iUzBIldRDW6dU72JchTARGYSbYUQGMVCqu4tU4uYzcZY2tFWZnOIelwaD1xfh6LA8Z4EF4fm/txO7NuLxSJjNzRFiMwQwdAQ8XHzFucz8syZQVJ5EyEEvzo/zCuXnM/MHdurubOt+qoiThbD9lonw/rCYGrK+74nliWeNXC71PJ6kqksa+r7F77wBT796U9z//33s2fPHpLJJC+//DI//OEPl3O3GwKXpqArXmwBxpjzxW6WRFvb+TCf7kvwj8d6efHCsHTaSiQSiUQikSwDiqLw0EMP8c1vfnNSNJhEIpme/rTzOWkILKzgz4YgO+YIt5oO3uiUxUF3sPy6XYhdmLUpIQTPdj5bdpNmrSxHBo4sqDuGbZDIO064UrbsUmP29WFncyhez4xFoOaba9uZ7GQ4O4yu6txQfcOM65VE0ZJIuhi6kl3A3Hm2JZqCTeiqTtpITysWJwvJ8oBGKXf3WsBVP+60vVK0E6ZJ5vU3ALCzOTKvv77g9vtPO58D35bN3NxaEi4HSOQm6x7jom180uPm4CB2Jovidk95/9m24B+O9fCPb/Vi21OF9pLTtmKaImQTaan0c0ODI0o+fWqAdN6c13O7OJSmL57D7VLLz20mbtlaRW3YQ7Zg8YtTAzx7ZpA3OsYAuGdHzZzbLxebq/zomkIyZzKUnCzKl1y219WF5szZ3cgs2yszMDDA//V//V986Utf4vrrr0fXdR5//HFuvvnm5drlhsKlquiqD8W2MYujRabfOZyacHJ7SkHVvfGczLSVSCQSiUQiWUZuvfVWfvnLX652NySSNU9JmJJ5ttOQLhZ1DFTDDI64too2YG7R9vWB17kYu4iqqByqOwTA8eHjjGRnd6tOZCw3hkDgdXnxuRaWXT5f8sVoBPeWLSgzzMgtRTPM5bR9c+BNAHZX7cbr8s64XkkUHcmOYNlTM1DnwrAMelI9wNx5tiVcqouWUAsAl+OXJy2zhc0vOn5BwSpQ66+9tkTb6moUXUcUClijk0X33MmT2Ol0eVZw9q23sOLx6ZqZFmFZxC52AFCzczu3tFbREPGSN2x+dqJ/ktA6k9O2cNnZ3r2pZcr7bziV59JQmouDKS6PpKfsP55x9JaZipBN5FBLiOqgm2zB4ulTA3Pm4Nu24KWLzvngQEuUgGf64nUlNFXhgd31uFSF9uE0b3fHURS4//o6DmyqmHXb5UTXVLZME5FQMG3OF+8vRdGza5llE21bW1t5/vnn+e53v8sbb7zB9773PT7+8Y/zH//jf1yuXW4odE3BrfjxZgsYZgF0F2bxe0krOm2zhvMFNJoqYBdPQNJpK5FIJBKJRLJ4gsEgt912G7/zO7/DV7/6VV588UVGRkZ46qmnSCanTsmUSCTjGJZRFt6k03YaZihCNpGtka0oKAxmBonnpxe4zo+d50i/46q9u/lubq6/mZZAC0IIXuh5Yd6FEycWIVuOadVCiPE822miEUqUnbazFA7rTfXSl+5DVVT21e6bdb9hdxi35sYS1qKKkfWme7GFTcgdIuqJznu7UkTC5cTlSY+/3v86fek+3Jqb+zffj6pcO65DRVVx1dUBk3NthWGQecMR2QO33457UwtYNumXXpp32/meXuLJDKbHy6a2FjRV4cE99bhdKt1jWd7oHCuvWxJt7VQKMcHIVijn2U6NRugaGy+edawrNmV5LDt/0dZV7NtEUXU2TvcnGEkV8OoaBzfPT3StDnq4fbvzWVEVhYf2NLCnaf6F1ZaLbTXFiIQJ0RAXBlMUTJuoX6cxMvMAi2QZRdv//t//O0ePHuU973kPAA8++CC//OUv+ZM/+RM+8YlPLNduNwyKouBzefFnCthCYIcDWIpjs3cJ56SRK1YytIUgaTtfslK0lUgkEolEIlk8f//3f8+v/dqvEYvF+MpXvsLdd99NbW0tjzzyCP/iX/yL1e6eRLKm6c84U6SDepCge4NlGBo5GLsMna/CmZ9ArGvqOvMQbf26n+ZQMwDfPfddXuh5YVKBroH0AM92PgvA/pr97KraBcCN1TeiKRo9qR4uxi7Oq8ulOILlyrPNvfUWViKJortwt7TMuF5p/2kjTdacvgr9GwPONPudlTsJ6IFZ96soStnNOphdeERCKc92U2jTgsTsTeFNKCgMZ4dJFJyp4d3J7nLf726+m4hn9UW2paaca9vfV36s5LJVQ0G81+8icOedoCjkL1zE6OmZV7sDpy9g2QKzrpH6sOMEj/rd3H2d8/l56cIIA4kcAKrXi+pzxMGSm9dOpzEHneM/nWjbExt/r3WMZBhNj4u9QgjGMs79iG/2eIQS1UEPd7Q5WdXPnxtiJDV9hq9p2bx80Rncurm1Aq8+/5pQBzdV8O7ddXzwpmZ21K9c0bHZaK0OoKkKI6lC+TU81TdegGylc3bXG7N7rK+C3/qt35ry2MGDB3nppZd46KGHlmu3Gwrd5SKUFQgBhZAXQ+QBN6rlfKhLTluAsQLUgSxEJpFIJBKJRHIVPPjggzz44IPl+5lMhvb2dqqqqqivl9O9JZLZKOfZBjeIy3boHAyegtTAeIGxEoMnYe+HIDphev3EeIRZuK3xNhKXE8Tzcd4eepu3h96mJdTCdRXX8XLvy1jCYnN4M7c23lreJqSHOFB7gDcG3+DF3hfZHN48ZyG4kiu6yrf0ebb5CxdIvfAiAP6bbynH+U2HW3MTdodJFBKM5kZpCjZNWj6UGaIr2YWCwoHaA/Paf42vhu5kt5P5u8Cn15l0RNuW8MxC83T4XD7qA/X0pfu4HL9MW0Ubz3Q+g0Cws3JnOfriWuNKp+1El63/xptQNA1XVRXe3deTO3GS1AsvEv3gb84p5g2ddSJCom1bUdXxdXc3hrk8kub8QIqfHu/jn966GZemokWj2Nl+rFgMV00NhWIBMldtLWpgstAvhKCn6LSN+HTiWYO3umO8c4cj9ucMm3zRJDcfp22JAy1RLg+n6RjJ8NTJfn7zUMuUPNfjPXGSOZOQ18Xe5ui82wZnQGJ349oS/r26Rkulj8vDGS4MpthRH6JrNIOiwC4ZjTAnK+6737JlCy8twPIumRmXqhDKOKJtNqCXnbaKPZ1o60yBkZm2EolEIpFIJEuH3+9n9+7dUrCVSOZBSbSt92+Az4uRhZPfh8HT44KtNwI110GkGSwTjn8XEr3OMst0CpHBrE5bgGpfNf9k5z/hka2PsDm8GQWFrmQXz3Q+Q8bMUOmtnHaa/f7a/YTdYdJGmtcH5i76VIoOWOoiZEZfH8mnnwYh8N6wB9+B/XNuU+GpRJwPceK5bizLnrTszUFHANxesX3eTtWy03aBxcj60/3E83EURaE52LygbWE8IqE93s6znc+SNtJEPVHuarprwW2tF/Ti96MVi2Fns47LNpNBC4fw7tpZXi9wyy0objfm4CD5s2dnbdNKpUn2DiJQaNw1OVpDURTetasOn1tjLGPQF3fctlfm2s4WjTCcKpAzLNwulXt2OJ/HU70J8qajsZSKkIW8LnRt/rKaoii8e3c9Xl1jMJHnL391iZ+d7KdrNIMQgrxp8Vq787m7pbVqQW2vZbbXOK7fi0MpThddti0VfsLe+QveG5Vlc9rORkXF6gUhX0vomkowYyEQJH0Cco4wq1jOBzs/QbQdzTtfbDIeQSKRSCQSiUQikaw0trAZyAwAG8RpO3IBhA2+Cmi7H0IN4PY7yyzDEWzHOuCtb8H+fwIozvq6F+YRHaEoCpvCm9gU3kQ8H+fkyEnOjJ5BUzQe3vowbm3qlG1d1bmj6Q5+2v5T3hp6i12Vu4h6o9O2nzNzpA2n+FKFd+l+v5tjYyR+/GOEaeFubSX4jnfMa3p0RFRCLEW8kCY9lidc7SNv5TkzeoZLMScXd74uW4AavyPEjeZGMW0Tlzq7NJIxMhzpP8KpkVMANAebp32N56I10spLvS+VC5lpisa7t7x7Ttfzekb1+dAqKrDGxjC6u8suW9+hGycV/1L9fvw3HiL90sukX3oZz9at5SJlV5K81E4qb5KPVLG5eaoz3atrNES8XBpKM5ou0FLpL4u2ZiyGsCwKnU5EiXvLzNEIDREvrdUBKgNuRtMFTvcl2d8SJVYsQhbxLfy4BT0uHtnbwC9ODxDLGJzqTXCqN0HYpxP16WQKFhV+nd3XkAt1a00A5Qz0x3Mkc85rJwuQzY8lFW1bW1sXlUfxxBNP8KlPfWopu7Ih0FWFQMZA6DDqtUATaKqGsBw7f7YwLtoOFwQCIUVbiUQikUgkkgUir3ElkqtnNDdKwSrg1tzLlpG6phg+7/yv3QVVVxTZ0nTY8wF4+9sQ73aE28b9zrJADSzwfBPxRLi98XZubbgVIZzfhDOxJbyFTeFNdCY6+VXPr3hk6yPTnt/Gco7rN6gH8WieBfVnJuxMhsQPf4idzeGqqyX87vtR1Pk5Cd1JR8jOGFn6Boc4lrvM2dGzGLbz+3ZbdBvVvtljJSYS0kN4XV5yZo6R7Ah1gbpp1zNtk+PDx3lj4A0KluOu3BrZyp1Nd857XxOJeCJUeCvKr+/tjbcvqN/rFb2hHmtsjNSvXpjWZVvCt28fuRMnsBJJMkePEbjl5mnb6zvlfL70zZsIeqaXtSoDbke0LWbPTnTaGn39iEIB1e8rxzdMpHssA0BT1IeiKOxrifLLM4O81RVjX3OkLNpG/QsX7gFaKv08fvsWeuM5TvcmODuQJJE1SBSLm92+vXpS5MN6J+Bx0Rjx0RPLks47DubttRss13yRLKlo+/Wvf31R223ZsmUpu7FhcJt5FFPB1mHEXXBEW0XDMm0MS2DajvNWVRTyaOQMm6AUbSUSiUQikUgWhLzGlUiunlI0Qp2/bsq0/XVDPgmv/zVEW2D3r8+8nmXCqOP+pPq66ddxuWHvB+Gtv4NEH3S87DzuX7yApyoqzKHzKIrCXU138XfJv6Mr2UVPqqdc2GwipTzbSt/SCOzCMIj/6EdY8QRaJEzkPe+Z0UU5bb/jjnCcNtL84vRzKNuSgOMCvqH6BnZWThUAZ21PUajx1dCV7GIoOzStaNuV6OK57ufKRcOqfdXc2XQnjcHGBe3rSrZHt3Ok/witkVb2VO+5qrbWC676ejh1GjvtuLd9N0522ZZQXC4Ct99O4qmfkT36Jt7rd6GFJhfUErbN2PnLANTu2DaljRIVRUF1LD1VtC10ONvrm6YWk5uYZ9tc6bjjdzWEePHCMKPpAp2jGWJFIbhiAXm2U56rotAU9dEU9XH3jhouDaU5058g6HHRdg0Kmttqg2UH83V1oWsm+mG5WVLR9u67717K5qblv/7X/8p//I//kf7+fvbt28d//s//mZtvnn705S//8i/5H//jf3DixAkADh06xL//9/9+0vqPP/44f/M3fzNpuwceeICnnnpq+Z7EEuHNJLAUF3mfm6SZAM3liLaGTa6YtaKpCtVBD6MJF5mCiV9m2kokEolEIlkATz75JD/4wQ84duzYvLdRFIXvf//7vO9971u2fq0kK3GNK5Fc6/Slncrx9YF1nGfb9zYU0jB4BlpHwT+DoBnrcCIQPEEIzfJ8XR6nGNmxv4VUMVt1jjzbpSDiibCrchcnR05yduzstKJtKc92KVzRwrZJ/PznmAODKF4P4fe+d0rhp9kw8hZ2UkNVVGxho6RdbAlv4YaaG2gONi+6+nytv5auZNe0ubaJQoKftP8ES1gE9AC3NNzCjoodS1Lp/kDtAWr9tTQFm5akvfWA3jAeiaJFwnh3ziyyu7dvR298G6O3j8RTTxH99V9HcY1LV8bAAIl4Esul03zd1GiDEpUBR7QdLYm2ESfvWOTy5M85Tt3p8mxH0wUyBQuXqlAXcgYLPC6N6xvCHOuKcawrRqY4q3khRchmQ9dUdtSH2FEfmnvldcr2miDPnxsCZDTCQlhX0va3v/1tPv3pT/Nv/+2/5c0332Tfvn088MADDA5OHx5++PBhPvzhD/PLX/6Sl19+mZaWFt797nfT09Mzab0HH3yQvr6+8t/f/d3frcTTuWrcqSSaopMLeLGFXY5HsEybXPEk4tM1akMehMtFJm/JeASJRCKRSDYwiqLM+vfkk09O2eYzn/kMzzzzzLL16ctf/jKKovDEE08s2z4kEsnqUy5Ctl5FWyFg4MT4/f63Z163FI1Q1TZ31IHug32/BYFqZ91oy9X3dR5cV+E4gNvj7eWIgYmMZB2n7dUWIRNCkHruOQqX2lFcGpH3vAfXAmvcxAYyKMDOxu00BhvZE97P/c0P0BJquSrRs5RrO5QZmrLspd6XsIRFQ6CBf7Lzn7CzcueSCawu1cXm8OY5c3SvJbSKChSvI4D6Z3DZllAUhdB996F4PJj9A6QOH0YIUV4+dOYihiUw6hpoqpxZ/C85bZM5k4Jpo+g6ashxsNqpFKgK7k2bpmxXzrON+nBNcIPua4kC0D6cZiSVByDiW1w8wkYk4te5q62aW1oraYx4V7s764Z1lWn7x3/8x3zsYx/jox/9KAD/7b/9N3784x/z3//7f+ezn/3slPX/5//8n5Pu/9Vf/RXf+973eOaZZ/jt3/7t8uMej2ddVvx1pxOouMgFPM4sGM3GpbgwDZuc4RQe8+oqNSEPJzUX6YIJlo2wrFlPkhKJRCKRSK5N+vr6yre//e1v8/nPf56zEyo0B4Pj0/GEEFiWRTAYnPT4UnLkyBH+/M//nL179y5L+0uFzLSVSK6OVCFFspBEURTq/evvdxcAiV7IjI7f7z8BW94BV2ayCgEjRdG2um1+bbsDcOijUEiBL7ok3Z2L+kA9IXeIZCFJZ6KTbdHxaeZCiCVz2mZff53ciZOgKITe/W70xoVHC8QGnHzR67ZvZrg7RT5tkInnidT4r6pvtb5aAEbzoxi2ga46rsmuZBeXYpecKInmu67pImErhaIohN71LqyRETyzuGxLaNEo4QcfIP6PPyR3+gyumhp8+/YBMHjmAgDhba1os+S++twaPrdGtmARyxSoDXvRolHsZAoAvb4B1TtVPOwuRiM0RX2THq8MuNlc5adjJINhOSLyYgqRbWRu3LIB8syXmHWTaVsoFHjjjTf43Oc+V35MVVXe9a538fLLL89rP5lMBsMwqKyc/EY5fPgwtbW1VFRUcO+99/KlL32JqqqZRxTz+Tz5fL58P5FwMm5s28a27Xn1ZbHYto0QAtu2caUSqIqLTMCNXwhQbVRFxTQs0nkDIQQel0pVQMfSNNJ50/kBls9Pe3KSLD8Tj59kfSKP4fpGHr/1zVo6fqW+lP7WC3UTim2Ew2EURSk/dvjwYe69915+/OMf8//+v/8vx48f52c/+xmHDx/mH/7hHzh69CjgCK3/+l//a44ePYphGOzfv58//uM/5uDBg5P2Nd1rU7ovhCCVSvHoo4/yF3/xF/z+7//+rK9ladl011or8X6QmbYSydVRctlWeavWrwDWf9z5X7sTxi47+bZj7VOLjCX7IZ9yio1FZ566PQXNtWKCLTgi2vbodo4OHuXc2LlJom3GzJC38igoVHgX5oqdSO7UKdKvvApA8B134dk2c/7oTFiWTXzIEdGidX5yKYN82iAdK1y1aBvQA/hdfjJmhpHsCPWBemxh82LPiwDsqdqzIYqErRSe1lZobZ33+u5NmwjccTvpF14k9cILaJWVuKqriXf2AtBw/dyDIpV+Nz2FLKMTRFujq9tpf8vUz+ekPNsK35Tl+1uidIw4gwhBjwu3a11NXpesQ9ZNpu3w8DCWZU36sQHOj48zZ87Mq41/+S//JY2NjbzrXe8qP/bggw/yG7/xG7S2tnLx4kX+1b/6Vzz00EO8/PLLaDO4Uf/gD/6AL3zhC1MeHxoaIpfLLeBZLRzbtonH4wghMIcGMAsWCZeGSKfBVAkXTJLxJPEBi3Q6Td5jIbIuMrkcybxBLJHE7OktTwuQrCwTj586z0qpkrWFPIbrG3n81jdr6fgZhoFt25imiWmaQFGQLN5eUVyuRblAS2Jnqf+W5UQrffazn+UP//APaW1tpaKigmeffda57iiuF4vFePTRR/njP/5jhBD86Z/+Ke95z3s4deoUoQnFQizLKm8D485dcMSC3/3d3+Whhx7innvu4Utf+tKkfVyJaZrYts3IyAi6PlnwSSaTC37uC0Vm2kokV0dvuiiyBBrmWHONYpkweMq53bAf9AD0vOEIuVeKtiWXbeVWR4hdw7RVtHF08CgdiQ5yZg6vyzH2jGYdl23EE1n0FP7C5cskf/lLAPyHDuJb5IyK5HAO27Jx+1z4w24CUQ8jPSnSsfzcG8+BoijU+GvoSHQwmBmkPlDPieETjOZG8bq83FR/01XvQ3J1+PbvxxweJn/mLImnnsJ1wz5SOYN8MMLmlto5t68IuOmJZcu5tq5iMTKYPs82ljFI5U00VaF+min8W6oCRHw68ayxZHm2EslsrO1vkSXky1/+Mt/61rc4fPgw3gku09/6rd8q377hhhvYu3cv27Zt4/Dhw9x3333TtvW5z32OT3/60+X7iUSClpYWampqCIeXN1DZtm0URaE6GqUPm4TbjV5dRSCggg0Bn59AIIDpDRIIWNRXh2lqqKO5Jo/L5wfdTXU0gmsWJ7Fk+Sgdv5qamlUXHCSLQx7D9Y08fuubtXT8crkcyWQSl8uFq1gcQxQKDH/tayvel+qPfxxFX/gPh9JrWOp/abD6i1/8Ig8++OCk9RRFKa93//33T2rnL//yL6moqODFF1/kkUceKT+uaVp5m4nous63vvUtjh07xmuvvYarKDpP3MeVuFwuVFWlqqpq0nUcMOW+RCJZW1i2xcXYRYBpC16tC0YugJkHT8hxz+o+R7QdPg9G1rlfYvic83++0QirSLWvmipvFSO5ES7FL3F91fUAjOScPNtK38KnMhsFi8svX8J97Dn8qsCzcwf+225bdB/H+tOA47JVFAV/xMkQTcevXrQFpxhZR6KDocwQGSPDa/2vAXBLwy1lEVuyeiiKQuid78QaG8McGKT/8K8QgNbSQmQeomllwFlnLO3kNmvFWddqKIg2jSZSyrOtj3jRtanXmqqqcGhzBc+eGaQxOtWJK5EsNetGtK2urkbTNAYGBiY9PjAwMGce7X/6T/+JL3/5y/ziF7+YMzNt69atVFdXc+HChRlFW4/Hg8fjmfK4qqor8iNSURREMommKNhuD6o3hKJkQHN+CCmKQrbg/LD1e3RUVaU27CXn0p3HLWvVf+xuZBRFWbH3imR5kMdwfSOP3/pmrRy/kpBZ+it2DoWVrwI9qQ8L3G66/zfddNOk9q5cPjAwwL/5N/+Gw4cPMzg4iGVZZDIZurq6pmw38b4QAkVR6Orq4oknnuDpp5/G5/PNuP50z3G6Y7/a7wWJRDI7HYkOsmYWv8vP5vAC4gLWEqUCZHW7nQzbYB0EayA15Dhwmw45y7Mx5zFFhcqFRwGsBm0VbYz0jXB+7HxZtC3l2S6mCFnHG930PPc2Przs3FdD6N57F13ASwhBbMAR0SrqnSiEQMQDikIha1LImbi9Vydp1PicYmSD2UFe63+NglWg2lfNrspdV9WuZOlQXC7CD7+H2He+Q3zIyaOt3jG/z1epGNloxnHa6i0tBO64A72hftr3ZfeYE33QPIsgu7c5QkPES2VAFiGTLD/rRrR1u90cOnSIZ555hve9732A47h55pln+OQnPznjdv/hP/wHfv/3f5+f/exn3HjjjXPup7u7m5GRERoa1vbUHWsshqpAIRhGV/2Ac3LRdeeQZrPO9EKv7vyQqQl56HDppAsZhDG1OqhEIpFIJJKrRNep/j8/vir7XUoCgZkrMQM89thjjIyM8JWvfIXNmzfj8Xi47bbbKBQK82r/jTfeYHBwcFIGrmVZPP/88/yX//JfyOfzM0ZUSSSS9cepUSdWYEflDlRlHQ6yFNIw4jiFqb/B+a8oUL8XLjzjRCSURNsRp0ASkWZwX13e6kqxvWI7r/S9Qm+ql1QhRdAdHHfaLrAIWTqep/fFkwjDpBCIEHjg3VdVADsdy2PkTTRdJVTliGiaS8UX1MkmC2TihasXbf2OaBvLxYjlYgDc1XTX+nyvXsNowQChhx5k7E//GtPtoXnH/LJxS8JqLF0oDx77Dx6Ycf3ucp7tzJ9fRVGoDUsXtmRlWDeiLcCnP/1pHnvsMW688UZuvvlm/vRP/5R0Os1HP/pRAH77t3+bpqYm/uAP/gCAP/zDP+Tzn/88f/u3f8uWLVvo73cC8EtVkFOpFF/4whd4//vfT319PRcvXuT3fu/32L59Ow888MCqPc/5YMXG0FQFwxfGpYyPArndLrAgVyiJts6XZE3QQ7vLRTplIub5o0oikUgkEsn8URQF3Ne+6+LFF1/kq1/9Kg8//DAAXV1dDA8Pz3v7++67j+PHj0967KMf/Sg7d+7kX/7LfykFW4nkGiJVSNGV6AJYv87FgVMgbAg3QGBCUaq63XDxl5Doc9y1wRonLgHWRTRCibA7TEOggb50HxdiF9hXs4+x3BiwcNG2/dnjWPEkiqqib9tGPgdXkzAwNuAYkyI1flR13BUZiHjIJgukY3midVdfjCygB0gbTgzDdRXX0RBcuIHLMmyMvIU3KHNOl4tEqJLztz+Ipqk8UhOaewMg7NXRVAXTFiRyJhHfzMcnnjVI5kxUZfo8W4lkNVhXou2HPvQhhoaG+PznP09/fz/79+/nqaeeKhcn6+zsnDRF7s/+7M8oFAp84AMfmNTOv/23/5Ynn3wSTdN4++23+Zu/+RtisRiNjY28+93v5t/9u383bfzBWsIaG0NVFArBMBrjJxS3W4cs5HIWKOAribYhD0JzkTNsCtk8a/vZSSQSiUQiWau0tbXxjW98gxtvvJFEIsH/8//8P5NiDuYiFAqxZ8+eSY8FAgGqqqqmPC6RSNY3Z0bPIBA0BhuJeqOr3Z3FMVAcZKq7YfLj7oBThGz4PPS/DZvvgFins6xq+8r28Sppq2ijL93HubFztEZaMW0TTdGIeCLzbiPWOcLwW5dREIS2N2N5vaRieQLRxf/yjPU7om0pGqFEIOphuDu5pLm27fF2dFXn1oZbF7StbQsGOxL0nothFix23tZAuFpmnS4Hl4fTmP4gLdWBafNmp0NVFSr8OsOpAmPpAmGvi3SsgDfgwuWePEhcikaoC3twu6TTWrI2WFeiLcAnP/nJGeMQDh8+POn+5cuXZ23L5/Pxs5/9bIl6trI48QgKRiCExvhokcetI7KQy5vgVctO24DHhdvrfGGOJTLMb1xKIpFIJBKJZDJf+9rX+PjHP87BgwdpaWnh3//7f89nPvOZ1e6WRCJZYwghOD16GljHLtvUECQHQNWgdprn0LDPEW0HTkCw1nHkBqrBv/ACXqvJtug2ftXzK4azw+WicRXeikkRAWbBmiJylbBtmws/fA1hWdQ0eoneeB29F+KkY4sXVXNpg2yygKIqRGomi6CBaLEYWWx8yvvVsDWylfZ4O7c23ErQHZzXNkIIxvozdJ8ZJZcajx+MDWSkaLtMtA87bugt1bPHOF1JRcDNcKpAX1+KzJk4qbEc3qDO7jub0PTx93jPPKIRJJKVZt2JthLnC8IaG0NVoRAIM3EMyON2k0NQyFvgVctOW4BQyE8WiMXTbFrxXkskEolEIllLPP744zz++OPl+/fccw9CiCnrPfnkkzz55JPl+wcOHODIkSOT1rlyVtN07czGlQPvEolk/dOd6iZZSOLW3GyNbl3t7iyOksu2cuv0GbWlxwsZuPSc89g6c9kC+Fw+NoU20ZHo4M3BN4HJRcj62+N0nhihdnOYTXuqJkUVAAy+cpJkfwJNg63vu4OC6gOuTrSNFaMRQpXeKWKxL+xGURWMvEkhZ+HxXZ2ssaNyB62RVtza/CKO0rE8nadGSI7kAHB5NCLVPkZ6UiRHc1fVF8n05AyL3pjz2rZWLUy0DQkFtTNDR3eerUXBN5cy6Dg5wtb9NeX1Snm2TRVSdJesHaTnez2STiNME03TMPwBVDE+5cTjdmMLMAo2AL4JX3DhkHPyiSUyK9tfiUQikUgkEolEsqE4PeK4bK+ruA5dXYc5n7YNAyed2/V7p19H1ZxsW4B80vlffd3y920ZaKtwcngLllP/pNI37hYe7XUcjoMdCc6/1o9pWOVlZiJF+7NOsbnGg1vwN9TgjzjiZzZlYBn2ovpTEm2ny6zVNBVfqOS2XZqIhPkKtrGBDCdf6CU5kkPVFBrboux7ZwvNu5zXK5MoYJmLe86SmekczWALQWXATcQ/v/NJIWty6egQ6VNxlJRBzrCo3Rxm26FaUBSGu5KM9KQASOYM4lkDRYHGqMyzlawdpGi7DrHjcQC0aARUDU34CLlDRDwRPB4Ppm2DLVAU8EzIYomEnS+8uBRtJRKJRCKRSNYMzz//PO9973tpbGxEURR+8IMfzLnN4cOHOXjwIB6Ph+3bt/P1r3992fspkcyXrJnlUvwSsI6jEcbaIZ8C3etk185E/b7x2+4AhBuXv2/LQGu4FZc67lgtFSGzDLssjKqaSnwoy+kX+8hnDIQQdP7weXJ5cIf8bHngEABurwuPXwchFpU7axassmN1pkJjgaIwvFS5tvPtV/vbwyAEFfUB9r6zheadlWi6isfnwu1zIWxBamzl+rRRWGg0ghCCs6/2M9ydxKur2GGdfKufLXurqWoM0rjdyWu+fHyYfMYou2xrQ148LlkQVbJ2kKLtOkQURVu9ogIAU8Bv7fwtPrjjg+i6hmkJsAReXZuU7xMtirapVBbbXti0RYlEIpFIJBLJ8pBOp9m3bx//9b/+13mt397eznve8x7e+c53cuzYMZ544gn+2T/7Z+u2VoPk2uP82HlsYVPtq6bGXzP3BmuRgRPO/9rdjqN2JoI1EKp3bldth6vMV10tdE2nNdJavl8SbRMjWYQt8AR0dt3RgO51kUnkOfHMZYZeeoveczFQFLbcvw+XZ1z0LRUgSy3QCZvPmvSciyFsgS/kxhuY3lVZaj+zRE7b+dB5chQjZ+INutl6sAb3FbEMoUrHoZmSEQlLihCCy0XRdus8RdvkSI5ssoDqUtl/TzOi2U8GJ2YBoPG6CoIVXizD5uLRIbpGHWNbs4xGkKwxZKbtOqQs2lZWggGGKcpTjjSXimE5TlvvFRUPQyEfmqogDIPRTIHq4OIreUokEolEIpFIloaHHnqIhx56aN7r/7f/9t9obW3lj/7ojwDYtWsXL7zwAn/yJ3/CAw88sFzdlEjmhRCiHI2wbl22QsDYZef2dAXIrmT7fXD5Rdh067J2a7lpi7Zxfuw8bs1NUHcKciWGc9i5HPrIBQojr9AQy9De5yJhuHDCIzSCrY007G2e1FYg6mG0NzWv+IJCzmS0L81ob3qS4FnZMLNAVxJt0/GlKUY2F2P9aYa7k6AobN1fjaZN9b+FqrxOru2YFG2XksFknkzBwu1SaYzOT1Qd6nLiSqqaglRW+wl6XKTyJmOZAg0RH6qqsPVADSd/1UNyJMel3ixEdTZVyiJkkrWFFG3XIaV4BHdVJfSDLQSWLdBUBU1XsWyBYk/OswVQ3W78bo2EaTCUzEvRViKRSCQSiWQd8vLLL/Oud71r0mMPPPAATzzxxIzb5PN58vlx8SSRSABO1XfbXv78Rdu2EUKsyL4kS89Cjt9AZoDh7DCaorEtsm19HvNcHCWfRigqBOucfNvZCDfD3g85t9fg853v8WsJtnBz/c1UeCoQQiCEID6UodDTg9u4jJHIowKtEYXuZJiUCKKFI2y+bw8oTGrfH9YRQpAay824X9sWtB8bYrQv4wjlAIpCqNJDZUOA6pbgjNt6Ai5QwMib5NIFJ45hmTALFu1vDSGEoH5rGH/EPW2//FE3QgiSIzlM05pSsG2xbPTz56XBJEIIWip8KIg5Zw2bBYvR3hRCCKqaA9i2TdTvIpkzGEnmqQs5Oojbp7Hp+kqOvdJHvi+D2xekKepd8td5ox+/9c5yHb/5tidF23WISCQABXdlBfQ7wdmGZaOpWtFpOx6PMBFF1wm4XaiWyVAyz66GVei8RCKRSCQSieSq6O/vp66ubtJjdXV1JBIJstksPt9UJ9If/MEf8IUvfGHK40NDQ+Ryy+8Ks22beDyOEAJVlQlt642FHL9XB18lnU7TGmolMZogQWKFerl0aKMX8KXTWIEassOjq92dq2Yhx6+ZZsjD4OAgRt5iqH8Ma2QYvGMUDhxCratDCQRo9HgY68ljWwLLnWVwcPJ5xDYF6UyGdFrQ0+VC90yNmEgM5ug+77w/fBGdcI2HcI0H3asBOYZHZj83WUqebNqg61If4drlKx7VcypBfDSH2+9CjxoMDg5Ou54Qglwhi2XYdF3qxRdeGiF5o58/3748TDpdIKzqM772ExntzpJMpPAEXaTzMTKDCqqRJZ1Oc6l3kGrXhPeVG4ZFFqNQINyXZrBvAE1f2td4ox+/9c5yHb9kMjmv9aRou86wCwVEOgOBAO7KClQljS0EZnG0yaWr5UJk04m2fo+GmjcZTMpwdIlEIpFIJJKNwuc+9zk+/elPl+8nEglaWlqoqakhHA4v+/5t20ZRFGpqauSP1nXIfI+fYRkMDQwRCAS4tfVWaoO1K9jLJSR5EiUQQDReR6h2nT6HCSz28zfcncKvxVBcFuFokKq77kLRx4XI+jlMQMP1JtlEAb8epqJ2atRBvGOAQCBAw/YIzTsr592vEtlmjcGOBB41SG3twrefD2P9acxUmkAwyK47GghGZ5+tGm+B+ECm2KfIkvRhI58/MwWTjIgTCOgcbGsm6JldwhJCMHSml0AgwKbrq6irc77fWvNuOpIgdD+1Ez7TQghSdSn0EYv6oJ90n8q2QzVLGrexkY/ftcByHT+vd34DTVK0XWfYsRgAqt+P5vPh0hQKpsC0HGu15lLLhch8V4q2bjcBtwsl44i2K5H9I5FIJBKJRCJZWurr6xkYGJj02MDAAOFweFqXLYDH48HjmSo2qKq6Yj8iFUVZ0f1Jlpa5jp8Qghd6X8AQBlFvlOZQ8/r9rZEeAEVBCTfCNfJ+XcznLzWax06liLgN3PX1aNOcQ2YjVOEllzTIJAyqGifv1yhYJIZzKIpCdXN4UeeFYIWXoc4kmYRxVecVo2Ax1JlEc6l4/C68fh2334Vl2nSeGENRFBq2RwlXzp2nGqnykRjMko4VlvRct1HPn52jOUChNuwh7HPPuX46liebNNBcKjUtofLrVRX0oCgKsaw56TUcSORIGTb6Jj+VeIgNZBjqSFG/dWkE9xIb9fhdKyzH8ZtvW1K0XWeYY2MAaBVRAHRNoWBCoSTa6ipG0Wl7Zaatouv43BqaWSBnWCTzJmHv8mX/SCQSiUQikUiWnttuu42f/OQnkx57+umnue2221apRxIJHB8+ztmxsyiKwt3Nd69fwVYISPY5t8ONq9uXVUQIQWI4i51IEnAX0JuaFtxGIOphqDM5bTGy0d40whb4Ix784bnFuJnaB8gkrs6Q1Hc+Rv+l+OQHFQXNpWAZNr6Qm6brovNqK1jpuOeSozlpkloCLo+kAWitnrko3USGOp0p5xX1AVwT9JCKgPMei2eNcj0ggAuDTtzklk0RtoSDdBwfpuv0KIGoh1Dl8kVuSCTzRcr86wyrLNpWAOAqqvOm5cQjlJy2ii3wXFHRUtF1VEUhqDnr9sdlVUuJRCKRSCSz8+STT7J///4FbaMoCj/4wQ+WpT/XIqlUimPHjnHs2DEA2tvbOXbsGJ2dnYATbfDbv/3b5fV/53d+h0uXLvF7v/d7nDlzhq9+9at85zvf4f/+v//v1ei+REJvqpcXe18E4LaG22gONa9yj66CzCiYBdBc4K9e7d6sGrm0QT5rIlIJ/LqxaNEWHPejEJOLR430OGJZVVNw0X30BXVUTcUybHJpY9HtJEed38WBqAdfyI2qqSAElmGjqAqt+2qcx+ZBIOJG1RTMvHVVfZI4heo6RjLA/ERby7QZ6XXeVzWbQpOWhTwudE3BsgWJrHNchBBl0XZ7bZDazSEqG4MIW3DxTSfTWSJZbaRou86wxmLAuGira84I0bhoqziZtoDnimqVpfyhiA4IQfdYZgV6LJFIJBKJZK2gKMqsf08++eSUbT7zmc/wzDPPLGk/nnzyySn73rlz55LuYz3x+uuvc+DAAQ4cOADApz/9aQ4cOMDnP/95APr6+soCLkBrays//vGPefrpp9m3bx9/9Ed/xF/91V/xwAMPrEr/JRubVCHFzy7/DCEEbRVt7KvZt9pdujqSvc7/YP01E42wGBJDOUQ+j0+kUV0qen39gtvwFwXQK0XVXNogNZoDRaGqaX4OyulQVAV/xHFQpmOFRbVhmTaZhLPt9hvruOGeZg49tJn9929i1x2N7Lm7iWDF/GMhVE0ti9WpUVlH5mroS+TIGRZeXaM+PLfrdbQvjWXYePw6oarJ6yuKUnbbjmac4z2aLjCaLqCpCq3VARRFoXVvNd6gm0LW5OLRQYQtpuxHIllJZDzCOsPTth2XZaI3OKnvenHEzygKtaqmlouS6Vwh2rqdk1TY60KxTHrGsnPvcPQSdLwEOx4G//KEu0skEolEIlkZ+vr6yre//e1v8/nPf56zZ8+WHwsGxx1PQggsyyIYDE56fKnYvXs3v/jFL8r3Xa6Ne1l6zz33THGhTeTrX//6tNscPXp0GXslkcyNaZs8dfkpsmaWKm8V97Tcs/6ngyf7nf+hOapsXeM40QgJwrqBXlc3qQDZfCmJqqnRHOlYHl+wKJoV3ZDhai9u79Wd+wNRD6nRHKmxHNXNC/+uSsfyCFvg9rlwe53p9Iqi4Pa6Ft23YKWX5EiO5GhuiuNTMn8uDzvRCFuq/Kjq3OeVUjRCzabQtOehSr+bwUSesXQBasajETZX+fG4nGOv6SrbD9Vy6oVeEkNZes7HaN5RsVRP6ZrDNCzOvTpAIOph856q1e7ONcnGHTpcp3i2b0e/+WZcxYqHLm1yPAKAWTw/ua88sblcoCiEydA8+gbDyTzZwhyW/85XIdYFna8s2XOQSCQSiUSyOtTX15f/IpEIiqKU7585c4ZQKMRPf/pTDh06hMfj4YUXXpgSj3DkyBHuv/9+qquriUQi3H333bz55psL7ovL5ZrUn+rqjTsNWSJZr/yq+1cMZgbxaB4ebH0QXb0G6mUkik7b8MYVbYUtSIxksZKLz7MtESy5Tscc16kQguFuR4y7mmiEcvtFF+xgR5K+i/FZB8Cmo9SvYIV3yQYcQhNybSWLp70k2s4jGiGbKpTd29Ut07+vyk7btOO0vTDkiLbbaiav7w+72bLXuSbpPR8jNihnKM/EaG+a1FiOgfY4ieF5mAIlC0aKtuucUjyCUSxEZtsCs7TsSqetoqDoOq6xi7SljhLNddETm+UEZNuQ6HFuD58FW2a6SCQSiUQyE0IILNNe8b+F/kCdi89+9rN8+ctf5vTp0+zdu3fK8mQyyWOPPcYLL7zAK6+8QltbGw8//DDJZHJB+zl//jyNjY1s3bqVRx99dNL0f4lEsraxhc2bA29yevQ0Cgr3b76fiGdpq62vCrYFqUHn9gZ22qbjeUzDhlQCn8u8KtE2UDGeawuQiRfIpQqomkpl/eKjEUpU1geobgmBEHSdGuHSsSGs4m/j+ZAac4TVhUQgzEWwwgOKQj5tUMiZc28gmUIyZzCUzKMosKVq7vfJcKcjwEbrfDM6pCuLou1YpkA8YzCYyKMqyhTRFqC6OUjt5jAIQfuxYRmTMAOjfeny7c5To0t+TSqR8QjrnlIhspJomzdthAIK4GLqSKHiUhFmjpCuEM730TWWZXvtDFM2MsNgFbOHjBzEOqGydTmehkQikUgk6x7bErzx08srvt9DD21Bcy3ddOQvfvGL3H///TMuv/feeyfd/4u/+Aui0SjPPfccjzzyyLz2ccstt/D1r3+dHTt20NfXxxe+8AXuuusuTpw4QSgkp5JKJGsVIQQXYxd5te9VYvkYADc33Mym8KbV7dhSkR4C2wSXB3wbd0p0YjiLyOfxixSKS0Wvq1t0WyWnbSZRwLZshntK4pofTb96D5lTKKyaQMRD56kRRrpT5FIGbTfW4fbNLncIISY4bZdOtHXpGv6wm0w8T3I0R1Xj4h3F6Xie3gsxhDtPcbLthuDysGMuqw978bm1Wde1bcFQdzEaoWXma4gKf8lpa3BhyFm/qcI3Y/ubdlcy0pvCyJtkUwb+sHvBz+NaxshbJIadQQ/VpZKJ5xnpSVHdLK/jlhLptF3nuEqFyIojPznDAk1BUxWENXWUQ8EEBEGXRig/QPdsubYll22JobPTryeRSCQSieSa4cYbb5x1+cDAAB/72Mdoa2sjEokQDodJpVILcso+9NBD/OZv/iZ79+7lgQce4Cc/+QmxWIzvfOc7V9t9iUSyDAgh6M308r3z3+Nnl39GLB/Do3m4o+kODtYeXO3uLR3JYu53qAHWezbvVZAYzmEnEgTdBfTa2nJtlMXg9rlweTSELUjHC4wURdvF5M/OhKIo1LWG2XFLPS63RjqW5+SveueMJ8ilDcyChaop+CNLJ9rCuAi82GJkpmHRcWKEk7/qZbQnxeCl9NwbXUNcGnbeJ/OJRoj1ZzDzFrrXRbTWP+N6Ub+OojiayYmeBADba2d+H6qaWhZqMwlZVO5KxvrTIASBqIfGtigA3WfGFuR0l8yNdNquc9zaZKdt1rBAVXCpKpY59cOiCOdk49dUAvkhRpPpckXGKcSLom24ARJ9TkRC27s3dBVViUQikUhmQtUUDj20ZVX2u5QEArP/QHrssccYGRnhK1/5Cps3b8bj8XDbbbdRKCyucjdANBrluuuu48KFC4tuQyKRLA9ZM8vP2n/GuYFzBAIB3JqbfTX72F+7H7d2jTnPEkXRdgPn2VqWTXI05+TZ6sZVRSOAI6gGox5iAxl6z8cw8xYut0a4xrdEPR4nXO1j912NnD8yQCZR4MzLfVx/ZyOBGQTZkqDqj3jmVehqIYQqvQxeTiw411YIwUhPmq5Toxj58WiFQsbEyJl4/NfYZ24aCqZN54jjtJ0uuuBKBi47Amx1SxBlluOoayohr04ia5RzbbfVzH7N4w97SI7kSMcLVDfP9xlsDErRCBUNAepbwwx1JMlnDAYuxWls27gzFZYaqb6tN8w8SnYUDOckVnbaWuNOW6EpuDQFy5hGtLWdLw2XEARc4M8Pz+y2LYXwb7oddC8UMhDvWuInJJFIJBLJtYGiKGgudcX/VrpS+4svvsinPvUpHn74YXbv3o3H42F4ePiq2kylUly8eJGGho0rlEgka5XX+1+nJ9WDpmjsrdnLo7se5eaGm689wRYmO203KKnRHMIWaJk4bs26atEWIFCMSIgXCzpVNgaWXCQt4fHr7LqjkXC1D2ELhjpnzlsv5dmGKrxL3o9SMbJMojDt7/LpyKYKnHm5n0tHBzHyJt6gzo5bG/AV3Z7JsY3h9rw8ksa0BVG/TnVw9vNMOp4nOZJFURXqNofnbLsyMF4ssTHqJeSdvXiiP1J02sY3xms/XyZGI1Q2BFA1leadjlDbeyEus5yXECnarjdO/yOBE/8Ths4BUzNty05bTZneaVsUbYVlE/a5COcH6B6bphiZkYXMiHM70gzV1zm3ZUSCRCKRSCQbmra2Nr7xjW9w+vRpXn31VR599FF8voU5pj7zmc/w3HPPcfnyZV566SV+/dd/HU3T+PCHP7xMvZZIJIvBsi0uxBwH/Dvq38EdjXfg12eefryusQxIFwegNrBomxjKYefz+O0kiqag19dfdZsl0bbEUkYjTIfmUqnf5hTGG+1Lz1hEqpxnW7m00QjgxEJ4/DoIQSo2t9s2OZrj1Au9JEeyqJpC885K9ryjiUiNrywAL9S1u165MOhEI2yvDc45MD3Q7rhsK+oDc2YYw3iuban9uSi5tDOJgiyyNYFSNII/4sFbFMIrGwMEoh5s06bnXGx1O3gNIUXb9Ya7eGIpOCcyvei0NayJmbagq+oMTtuiq9ZbTdirE8r30xObxmlbctn6K8Hth5qdzv2hM77zWkIAAQAASURBVCBPVhKJRCKRbFi+9rWvMTY2xsGDB/nIRz7Cpz71KWoXWB2lu7ubD3/4w+zYsYMPfvCDVFVV8corr1BTU7NMvZZIJIuhK9lF1szic/lo8l+943JNkxoAYYM7AJ6NW0gnPpzFTiYJuA30urqryrMtMVG09QT0KSLuchCu9uFya5h5i8TIVLHTNCyySWeKfHAZnLYw7rZNTrP/iSSGs5x9tR/LsAlWetlzdzONbVHUYhRiqZ3UHO1cC5iWTfuwM+1+LlHVyFvljOS61rldtgCVgfH383yiF7xBHbU4izmfke7REqVohMrG8XgJRVHYtLsKgKHOJJnE4mOzJOPITNv1RukCIu9M83AVT+Sm7Qi0OcMGVUHTmOq0FQLFcly1dngLYf0ioeEBziVyU3NtS0XIwo3O/4otThXVQhri3RBtWZanJ5FIJBKJZGV4/PHHefzxx8v377nnnmldJE8++SRPPvlk+f6BAwc4cuTIpHU+8IEPTLo/lxvlW9/61sI7LJFIVpxzY87svrZoG6pyjft9ynm2jRu2CFliJOtM508kCeiFJYlGANDdGp6ATj5tUN00t3tyKVBVhcqGAIMdCUZ7U0SuyNAtuWw9AR3dM019lyUgWOlhuDvpFHazxbSRELHBDBdeH8C2BJEaH9tvqkPTJn/WUi6BaQsySQPTsHBNV4/mGqFzNEPBtAl5XdSHZxfTBzsSCNsphFUq/DYX9REvigINES/ReeQDq6qCL+QmHcuTjufLrtKNjFGwygMRlQ2TM4FDlV4qGgKM9aXpOj3Kjluu3qm/0bnGv3mvQUpOW8MZ2dCvyLTNFizQFHRVxbzSaVtIoag2oCACDbh1NyGtgMeI03ul27ZchKz4Ra1qUN3m3JYRCRKJRCKRSCQSyTVNwSrQHm8HoK2ibZV7swIkizMNQxtPZBBC0HshxtlX+p0pz8YouiaWTLQFaGyLEqn1U7tlfo7IpaDkAhzrz2BfEZFQEm2XI8+2RLjKV9xXjref7aL/UnzSbNix/jTnjziCbbTeT9s0gm33WIb/9XYf5+N5bNsuF0+7VilFI2yrmV3ct23B4GXHyFa/NTLvgYDakJcP37yJ9+5rnHef/BMiEiQwVowcmRiNMJGWXZUoqkJ8MMNIb2oVenhtIUXb9YanKNrmS/EIUzNtxUyZtpkRFE0D3evk+oQbCXl1wvn+ycXIbHv8oiU84YtaRiRIJBKJRCKRSCQbgouxi1jCIuqJUuPbANElyX7n/wbLszUKFudeG6D79CjCFlRWaTTpA6AuTZ5tiZqWEDtuqV82V+t0hCq96F4XZsEiMTTZpFQqQrYcebYlvEGd1n016F4XhaxJ58kR3nq2i+4zowx1JrnwxiDCFlQ0BNh+qK4chzCR0u/0pCboGsvOGbWwnrFtwcWh+UUjjPamMfImutdFxRVuz7moC3vxu+c/6TxQLkY2u2jbfWaUk7/queaLcI2VohFmeN29AZ36VidT+tLRIeJD09RQkswbKdquN9zFD0Yx09alTpNpqyq4VBV7OtHWpYLLhzAMiDQR9jm5tpNE28wImAXQdAhMuECraAWX24lmKGXeSiQSiUQikUgkkmuOUjTCjsodKzKdfVmxTOh7C458Dd78BhSuEBGMHGRGndsbSLRNjuY4+XwP8cEMqqbQuq+GpmgaTWXJ8mxXE0VVym7biY4/YYvxImTL6LQFqNkUYt+9zbTuq8Eb1DELFr3nY7S/NYSwBVXNQbYfrJ02OgFgMOn00/Yq9MdzdHYnlrW/q0n3WJacYeFzazRFZy5wKoRgoD0OQN2W0Iyv3VLhDzufg3Q8P2P8k2XZ9F+Kk47l6Tw5uqz9WU2MwnhG9MQ82ytp3llBRUMAYQvOHxkkMTJNHSXJvJCi7XqjXIgsDbZddtqOZ9oW4xE0ZWo8QmbMEW11H6JQgEgLYa+LUL6fwWSOvGk56yW6nf/hRlAnvEU0F1Rtd24PnVmuZzgtr18e5aWLw7Jio0QikUgkEolEssykCil6U45JY11HIxhZ6HgJXvkqnPkJpAad+hzHv+uYVEoki3m2vqhThHkDMNKT4szLfRSyJt6gzvV3NlGzKYTZ6xx3vXH+08fXMlUTIhKs4uzUTLKAbdpouoovuPwZpaqmUrMpxA13N7P9xrpyIbaazWG27qtBmUV0HCqKtpEqD0LAiQujZPPGsvd5OXijY5Qfv903rjtcwYUhJ+5gW01wViE2NZYnHcujago1m5Y/bsMXdqOoCmbewshN3/fkcA67aKQb7U0RG7g23aWx/sys0QglFFVh28FaIrV+bMvm3GsDZXe7ZGFI0Xa94Q4gFAVFCDDSuLQrnbY2qI4Dd+Z4BL/jtA034dFdREnhMjP0xoofopKLNjzNF3U5IuHsikUk5AyLX50f5tVLo4ymZY6MRCKRSNYOcjBxZZCvs0SyspwbO4dA0BBoIOxeuQzSJcPIwvmn4eX/ApeecwwvnhBsuRN0r/N75+T3wS4KMCXRdgPl2XY88xaZo8fwdJ2gJX8GcfoYuTNnKHQ7Bp6lzLNdTQJRDx6/jm3axAcct1/ZZRv1zCqYLjVKsTja9Xc2cuDdm2ndWz3r/nOGRSLrCLTv3lOFx+8ib1j88s3+lerykhHPGvzq/DDnBpL88szQlOVCCC4Ozi8aYaDdcRtXNgZXJG5D09SyQDlTrm1s0BFpNd2R2DpOjJQHCa4lRouO9ZmiESaiqgptN9YSrvZhmzZnX+0nHb+2M5mXAynarjcUFaEXR3/zqXGnrWUjhCBbdNq6NBXLFJN/5JTiEfRiPILuhUB1MSJhgO6x4mhQuQhZ89T9V251YhNy8fHcpyJCCHpiWcwlPjlNFGq7xqStXiKRSCSrj64XL94z16aTYq1Rep1Lr7tEIlk+hBCTohHWHUI4gmz3604sQrAWdr0Xbv3n0HoX3PCbzgzC0Utw5sfO+mXR9tpwl85FoX+A5IUu7HyBqnw35vmzZF57jeTTv8BOppw824ZrIyZCUaZGJJTzbJc5GmG2Ps1HbCy7bH06Ia+L/TuqADjXPsbFofVV4OlYV6zs+Trdl+BM/+SYh754jlTexO1SaamYORohnzUZ63fE3fqtkWXr75UEisXIZhId48XM5M17qnH7XOQzBr3nYyvVvRVhvtEIE1E1lbab6ghWerEMm7Ov9MuCbgtk/unLkjWD0ANAGgppdH81AKYtKFg2li2cTFtNASGwTYGmK2AZkE9MFm0BIi2EvF2Ecv30jGWdUenMiLMsPM0XtaZD1TYYPONEJExY59xAip8c72NrTYD/bV/jkmVfTRRtu8cy7G+JLkm7EolEIpEsFk3TiEajDA4OAuD3+9d/5uMyI4TANE1cLte8XyshBJlMhsHBQaLRKJq2cgVsJJKNykhuhNHcKJqisS26bbW7s3AGTsBYhyPM7v4Nx3Qy8ZwTaXYeP/73MHASdD8kNo7TVghB7NnnsYWCXhml8qH9kEpiJRJY8QR2Mol729Z1n2c7kcrGAH0XYsQHM1iGTWq06LRdxiJkS8Fg0hHIakJOP7dsCtPVnqA3Y/CLUwM03Da1oFbetNBVddE5r0IIzg4kqQ97ifqX5j2QNy1O9DgZtJur/HSMZHj2zCANER8RnzMYe2HQEaG3VgdwTVOQrcTg5QTCFoSrfeWs2ZXAH3FD9/TFyHIpg3zaQFEVKur8aC6F80cG6L8Yp6oxiDd4bchu841GuBLNpXLdzXWcfaWfdCzP2Vf72X1nI27f2npdLMsmPpidl4t4JVlbr5JkXgg9AEYaCklcoVI8gk2u4DhcXS4Vl6kibIFZzOohOwZCoHj9oOlOpi1AuFiMLN7PqUSewlgPbgBfxXjRsyup2Tku2m69p3wRVHLqXhpKc2EwRVtdaEme72TRNosQQv4wlkgkEsmqU1+sql0SbiWzI4TAtm1UVV3w93g0Gi2/3hKJZHk5O3oWgM2RzXi0tS1qTaGQgQvPOLc33+mYTaajahvsfA+c/iF0H3EeU5QNIdrmTp4i0zeKolUR2Lkd/851KMwvEH/YjTfoJpcqMNiRIJ8xQFEIRlfHaTtfBhOOuFwb8gAmoUovLZU+4n0GybzJ06cG2FIVYDRdYCRdYCxdIJU3qQ55+Cc3b0JbhHB7aTjNT4/3Ux/x8uGbNy3J8zjRk6Bg2lQF3fza/ib+/o0uemM5fnainw8cakZRxkXb2aIRcimDwQ7HoVvXurKxLROLkV1JbMjRQUKVXjRdpaI+QEV9gLH+NB0nhrnulroV7etSYhoW8aEs8cFsOad3MaKmS9fYcUs9p1/qI5sscP71AXbe3oA2i0C/kmQSBS6+OUg2WWDHrfVEatZOtrkUbdchQveDAeRT5UxbISBZDCT3uTU0U8UsWFiGDT7K7lklWAVYE5y2zXhdGpViDCyD0b5u6gEis2QYVW5zRq6zMScPqrhuafoGwOGzQ2yq8uNxXb0jZiwzLtpmCxbDqUJ5tHE6uscydI9luXlL5bJXkpRIJBLJxkVRFBoaGqitrcUw1mdRkJXEtm1GRkaoqqpCVed/ka7runTYSiQrhC1szo+dB2BHxTqMRrj0S2fmYKAaWm6efd36Pc66F37h3PdXgWudidQLxM5mSb/8EgVLQ29qxF+xdoSJ5URRFKoaA/ScK9B7IQaAP6SX80fXKkMp5/d1TcgDtlMwzu1xsa0qwNs5i0tDaS4NpadsN5zMc7wnvqgZqpeHnfb64zmyBQuf++q+f21bcKwrBsCBlgo0VeHB3Q1889UOemJZXrs8ytaaAPGsgUtV2Fw1vSCYTRU483I/lmHjj3iI1q7se9cfcUTbQtbEKFjoE16X+KATjRCpGY912LSnivhwluRIjuHuFKyjU4tt2Qx2JBnrT5MayyPs8chNl0ejqnn2zOGZcLk12m6q49QLvaRjeS6/PczW/TWrasgTQjDQnqDr9CjCFuietSeRrr0eSebEiUcACin0CT96UnkTAI+uoelF0bZUjCwzCoASqgYGxkVbbwQ8ISKeFMHCIImBDup1pi9CVsLlhppd0H8c+t+GSBNCCEaKjlivrpHKm7x8cYR7dtRe9fMtOW09ukresOkey8wo2goheOpEP8mcSXXQM2eIuUQikUgkV4umaVJUnAe2baPrOl6vd0GirUQiWTl6kj1kzAxel5dNoaVx2a0YYx3Q97Zze8dDoM7jvNxykzODseNlJ0bhGifzyiuIXB4rWI+rrg6Pf+PkhFc2Bug5N+aYmli9PNv5Ylh2+XdwTchDJp5GURRCVV7MgsXBKj+XFZOwT6cy4C7/9cZyPH9uiFcvjbCrIbRgE1Xn6HhWf/dY5qpnz14cSpHIGvjcGjsbnLYifp17d9by1Il+Xr00ykDCiYHYXB3A7Zp6fZBNFTjzUj9G3sQfdrPjlvoVLSAHjlPUE9DJpw0y8UJZoLUsm+RIUbSdICR7fC6arqug69QI3afHqLt+/cSNjPSk6Tw5Ur7vC7mJ1PqI1voJVnqvyhjnDehsP1TL2Vf7GelO4Q97aNi2ctnEEynkTC4dGyJRzCOO1vtp3VuzIsXtFoK8Yl6H2KXYgnwKVVVwFT80yZwj2vp0Da14sit9KZWdtpGiNd+yEZblTAOKNBP2uQjl+8iNdDnLw3NUC23Y6/wfPAVmgVjGoGDauFSFB/c404qOdcUYLJ6AryRvWrx4YZhLcwSom5ZNvFgx8/oGZwrEbMXI+uK58usw074lEolEIpFIJBLJVM6OOdEI26Pb0eYjeq4VLBPOPeXcbjzg5NbOl633wC3/p/P/GsYeHCJ36jQAatseFEXBs4BcyvWOL+TGHxk3/qz1PNvhVB4hIODRCE5w/4UqHbG5VnPxkdu28Gv7m7irrYbdjREaIj72t0SJ+nUyBYs3O2IL2mc8axDLjM8cmijgLpY3O8cA2NscKRdRB9jVEGZnfQhbiLJbuG0aw1U2eYVge2vDqolqpYiEzISIhORIDtsSuH0ufKHJn6e61jD+sBuzYDF4aaojeq2SSTqDBRUNAfbe28IN9zSz6foqwtW+JZnJHK72sel6p6he1+lRYoMrX9R3tC/Nied6SAxlUTWVLXurabuxbs0JtiBF23XJuNM2CVAO6k7mivEIuoarONVj3GlbFG3DNePtlHJtIy0EPC5q02cp5LNOsbHAHA7ZSIuTe2sWYPgsw8WpG1VBD63VAXbWhxACfnF6EHuCnR6cL6C/e7WT19pHeeb0IEKI6fYAwFjGQAjHZbuj3hmZ6ynm2k7HuYFk+fZgcvrKjhKJRCKRSCQSiWQy/el+LsUvAXBdxXWr3Jv5IQoFYt//AZkf/40zs9AdWJz46q+cnzN3nSJsG+OVl0EIPDt3YHqd31XewMaaeDux4v1ad9qW8myvnGFaEm1TY7lJ09ZLaKrCHdudYuVvdo6RKZjz3mdXUaQtmcK6rlK07Y1l6Y3l0FSFfc3RKcvfubOWcLEQmaootFZPjkbIJp1IhLJge9vqCbYAgaLon55QjCw+NB6NcOU0f1VV2HyDcyziAznMgrVCPb06cilHV4rU+BZUcGwh1G4JUbMpBEJw8c3B8j5XgthghguvD2AWLAJRD7vf0Ujt5vCarZskRdt1yLho64zW6Npkp61XV8edtqbtBN5mi/EIwRqU4hSJ8VzbJjwuFY+ZwrAEZqAe5pq2qCjjbtu+t8t5O9VBZ/TpHdfV4NFVBhI53i5WigQ425/k20e6GCuO4KXyJulZTl6lPNtKv5u6kBe3SyVnWJPyc8uvixDlAHOAgURuVkFYIpFIJBKJRCKRQG+qlx9e/CGmbdIUbKLOvz4K5xi9vRjt50j/6hnsvAHb3wX62hbjVoPcyZPYI6MoHjfB228nnynG6m2geASAqsYAqqbiDbrx+Ne2YF0yINWGJr+f/WE3mq5iGXbZEXklbbVB6sJeCqbNq+2j895nyVl7Q3MERXEMVInc4sW0o50xAHbWhwhMkxXq1TUe3FOP2+UYtLz6uCCbTRU4/XKfI9hGPI5ge5X5uldLKdc2k5gg2hZdopEZMnZDlV58YTfCFoz2rg+3bb6o1SyXYAtOzvTmPVUEK71Yhs25IwMYKyRqD3U6Rr+qpiC77mjEF1zb0RVStF2HCPcE0da2yyNhiYnxCEWnrWnYUEg5jlhFBW8URXc+fGXRNlCLy+0tV5dMe+d5kVa3xxFvY53ERgaA8ZHAgMfFHducUaUXLwyTyBkcPjvIT473UTBtWir9RIsXCQOzxBiUcnwqAm5UVaEp6mTHTBeR0J9wohHcLhVVUcgUrHLO71qhZyzLD44PTSs6SyQSiUQikUgkK01XsosfXfoRhm3QFGzi4daH16zj6EqsZBJGLoBtkU/6oXbXandpzWFnMmReeQUA/y23YOvesuNvrQuXS43Hr7Pn7iZ23la/5t/jQ2XRdrLTVlGVsks4OTr972hFUbiz6LY93h0nnplbeBVClEXbtroQ9WFnH4t128azBucHHXHs4OaKGddrivr4+Du28sDuyRpEz9kYZt5yBNtb61ddsIXxeIRc2sAybXJpg1zKQFEVwtUzDxZVFwt3DffMHg25FhC2KA/qLKdoC6BqKtsP1eL2ucilChx7upOTv+qh8+QIo31pjPzSi7imYZWF9vptkXVRuF6KtusQ4fIhFMVx0BrpcjxCquS0dWuTnbbFaAS8EdBcKG7nZFOOR1BVCDtuW4Cke57Fw7zh8cD+/hMAVAfHv1RuaIpQH3FG+L7xckd5pO2mLZX8xoEmGiKOAFua+jEdY0XRtirg9Lml0tmme2zql8f5AeckuLU6QGXxBLPWIhKOdcUYShm83R2b1/p98Sx5c31Mo5BIJBKJRCKRrC86Eh385NJPMG2TllALD299GF1bP+5Le6AdcnFQVHLZSsdQIplE5s2jiHwBtbIS75495NLOb0bd6yr/ZtxIeAM6bu/Si9WdIxmePjVAdgncgpYtyvGD0xXgLkUkJEdmNj9tqvKzqdKPZQtevjQ85z6HUnmyBQu3S6U+7KWl0nGOdo3OXE9mNo51xRACNlf5J2kE06Fr6hQRPV3MjW3ZVbkmBFsAt9eF7nWBEGQShXI0QrDCg0ufuY9VjQEURSE9liebmt4dvVbIZ02ELVA1Fd27/K+72+ui7aY6vEEdYQvSsTz9l+JceH2Aoz/v4OSvepbUgTvWn8G2hJNxHV7bDtsSG+8sfS2gqE5eE0A+hbso2uYM583sdY07bS3DdvKdAPxO2LMadDKMrLGx8TYjzWXRdsw1nns7J/V7MW2bwOgpEPakLxVVVbhvZy2KAgXTxu1See++Bu5sq0ZVFerCzrqDyZm/bEYmOG0BmiucL4/useykrFwhBOeL0QhtdUFqitNIZhOEV4OSiNw/j35dGkrxrde6+OWZweXu1oIpmDb/89UOnjrRt9pdkUgkEolEIpHMwkxxYZfil/hp+0+xhPX/Z+8/gyTJ7vNe+DlpKsub7q72bqbHz47ZmVmPBRbAEkuABgsCBEiKggSGqHupSykU+CAJERIpkoqXFANB6ep9GYREChckLyhSIEESJEB47GIdZs3seD/T3pTp8ib9eT+czCzTVdXVPd0z3bv5i+iYnuqsrDyZWZknn/Oc5489kT348J4PQ+S2SbAtrwJ3XwDkwpau1lhhGbzw90LPV6CnUlu6/mZUWcelFxYwc2l9EWwnYFarkC8zc41w+hQIxzlTn99tLtvthFKK715L4PJiHi/duvdzMFNWYZgUHoFDxLf2OxnqrTltO8UBvmc/c9teXyl2fOYGao7a0ZgPPEcwFvM7r280clDRDVy2IhJPjbd32bbDMEzH7dlc3OtBE7AiEsp5Zd1oBBvRKyDQw96XXtjZbls7W9YbEO6bGz0QkXD8/WM48ew49j7cj/6JMHwhaz/nFBRSmxs4aIUdUdFjCem7AfdKvVvxBFk8glqCwPsa/uTz8OAFdmFlTltbtO0BAAgD/dAWF6GtJOA9coT9LToBSeBQFaMwjQ2MOPTuQ8n0wGNkMUJX4BUPNfy5P+zFBw8NYGa1jPfs63PEV/tvQHthlVKKXF2mLQDEgxIkkYOimUiVFAxY60gUFBSqGjwCh4neAIqyjmvLnQXh+42sGchX2UVwtaRAM8yGCprNzKyyC8rddBmmSXeUdX8pV0WyoCBZUPDMwf6G/KFWqLoJk9J1l3NxcXFxcXFxcdk6yloZf3nzL1HVqxA4ASInOv+uyquglGIqOoVnx58Fv52FuO7+AEjfYrPzjv0sENqazFwztQAAIKE+UADytWsIxjdgQNkgy7fzqBZVVEsahvZFIfl29uN09cJFUE2DEI/DGBkBcH/yKt9tLGSrznPelaUCjo9GMRjZfLay/QwbD0kghKwRTQMRDzieQFcMyGWtbSbnQNiLg4Mh3Fgp4tXbq3j+4ZG2nzm7ygRI22E7FPVC4AhKio5sRUNPoDuNYCFbwUu30lB1E71BDyZ6OwuarZBLGkApBA//QAuPtcIfkZBLVFDOKiik2XGKxH3rvAuIDkrIzhhYXShh9GBsxwqGsj2o8wCuD5JPgDQadOIk7r6dQnqh6Aj494qmGCikmQDcOxLcknXeD1yn7W5Fsk4ypejEI9j4RB5Cg9PWikewRFtxgHWS9GSi9qboGMr7fxo3e59FobqBsHFeQDqwDwCwR7/TcpFjoxH81InhBsEWYAIsIVYxshbZswVZh2ZQ8BxxRhgbcm3r8nXsvJw9fQGIPOcIwjspO7Z+W0y6fnTDcp7dBBTNdKbH7BTsbQPW38eUUvzVuQV86dWZLZkutNUs5qr4/vUEVN180Jvi4uLi4uLi4rKl3M3dRVkrw6QmVENFWSsjr+SRrqZBKcXB2EH82MSPba9ga5pAbpb9rhSBt/8USN++9/VqMswMczX6Hn0PW/3Nm6DG9vQ31aqO1JzlFKYUqzvcMWcqCqoXLwIAfKdPOSKRHY/gDexcwXl2tYz/+fJ0y0i8ncjVZXZe2LVmXriRvKeC2Mk2ebY2HM8hELXMS9Od3etPTvWCIwTT6XLbfFrdMLGUY2LWuCXaijyHoRbP3e3IlFV87cISvvLmAlbyMjwCh/fuj29KnHTcnkFxx4mbttM2s1yGaZgQvUJX0+yDvRJ4kYNa1R2xdydSc9o++EEdezaA0kUmczdklsqgJkUgKu2I9nWLK9ruVjyWaKuWITY5ML0iB05grxm6CVQb4xGEwUEAgJ5ereXaAhAHj6Dq6UFR3thIxqK0HwAwoMwBavc3Vo/AOSN2rYqROUXI/GKDy7Q+IgGwohGsPNv9/Wy/2IJwUW4tCG8lRVnD3dT6nbZm1+9Kvv3FWtVNpOuqgbYqvPYgWSnUtqdTITkAKCo6VvIyqqrhhNvvJH54M4UL83mns9UJ06SYz1RgmJvvhLm4uLi4uLi43C8Wy4sAgIf7H8bPH/p5/OyBn8Xz+57HT+z9CXxs38fwgfEPgCPb/EhYWmFFkQUJiE0ChgZc/ktg4c17Wi3NzcGQVUD0wXviDLhAAGZVhjozsyWb3czS7RxMg4KzIuXSC8V7Eua2G/nSJVBFAd8Tg2dqynm9Fo+wc0WLiwt5FKoaLi3kH/SmrIuqm7htxfT9+EOD8AgclvMyrq8UN73OWhGy9m7d4f0RAEBypuC4B1sR9XtwbDQMAHj5drrlObucl6EZFAGJd2rJADUBt9MzXFnR8f3rCfzpa7O4kyyBIwTHRyP4p09OYrIv0KGV7alaz8H2FPmdhC3QUut5MBL3dSUsczxBzzDbHzt5wOd+FSHrBtvtaw803SurS2y/7yaXLeCKtrsXD8ulZfEIjYfRW++0VTQWzg84oi0fDIILBgFKG3Kfwtb0noK8sZGMJSOMsqcPAQ8Bklc39F579LCV6zTTlGdrYxcjW8xVYZgUqaKCfFWDyBPnxuAROMSsSIXtLEZmmhRfPbeIvz2/hLnVzoKkHQNhZwcnO4idiYIMs+6Gej9GmWXNwLevrKzbDkopVvK1fZpYJ583USdOb7b66HahGaZzXNYTnwHg/EIOf3VuEed3aOXPudUKihv8/rq4uLi4uLi8M6GUYqm0BADYE9mDmDeGuD+O4eAwJsITGAoO3R8XW26O/RsdB45/Ehg6wQoq3/oO+zE3N9uJLt9k7/XFwIVC8B46CACQr13fqi13UKo6UnNMhJt6OA6O5yCXNJSy2z8bTl9dhVkub+g9VFVRPX8eAOA/c6bhONsCyE4WbW1zy1IHk8tO4VayCFU3EfOL2NcfxKN72OzWl62IgI1CKXVE21ZFyGwicT/6J5gYO30hzWbYtuGxPb3wCBxW8jJuJtY+x9ii7HiPv+FcqRUBb6wnY1NWdHz57CwuzOdhUoq98QB+8fFxfPDwAALS5p3cVcvt6QvuvHPU4xMg1BVGi/SvH41g02eJhZnlMjPX7UBsp620A5z4W+m0Vao6ShkZIDXxfLfgira7lbpCZCJfu7ByhEASOKcSqFEts06RIAFiLU9GHOgHAGiJWkRCyMsuiiVF79pJaJoUqyUFycBB+D0CsHyefV6XOLm2LYTVbLkxz9YmHpTgFXmouolksXbjmbSiEZx124JwF2LcZrmRKDri8mymc2fKbuMBK/NmpcN22fEDMasz1e5GuZWcnc7gylIBP1wnPD9b0Zyid8D6Ymd9lMJOm+JUL453c57YgvZsdud1IOczFfzVuQV88/JKV8sv5qod3d4uLi4uLi4uu5tVeRWyLkPgBMR925fzui5ZKxohOgFwPHDww8DeZ9hrC28CV/96Q88PNsbSTQAAFxsE4XlIh1htDXV2ZsMi53os386BmhShXi+iA370DN0fx5y+uorsn/85cn/1V6Ba98KFfPUqzKoMPhKGtH+/87phmNDknR2PUJQ1lKyZkoVq7fedytUlNlvvyHAEhBA8PBZF1C+ipOh4Yyaz4fXlqxpU3YTAkXVzZMcO90Dyi1AqGuautf+sgCTg9AQrCPbK7TR0o1EwtEVbO8/WZiDkhUfgIGsGUi3i+l66lUJZMRDzi/jE6VF89OQIeoPtheZuqRYt0XYHOm0JIfBbEQkgBJG+7kXbQEyCNyjCNExklrf2GrUVmCaFUt1BTltLtFVlA6ZxbyJ3xjJdhXq88Hh35rWvHa5ou1uxM23VYoNQ6RU5EELAi3WiLcBctnWjZk5EwkpN4Al4ePAcAaXo+uaYq2rQDIp8aB8kjwcopYBSYv03WnQSVjOV1k5bQghGYna+TtXJsz0wEGpcdwdBeCswTYrXp2s3x8UOEQaKbiBrtefQAOvk5Spa24zX5Txb17HRCDwCB1U3W94ot4r6Cp+pooKK2v7420JfnxV4n682irjtlgeY4Hs/nKCaYULR188zqxeUV8tqx9FwSqkjtGcqndv8ILA7W8t5eU1HrJmKquOv3lrAX51bgHaPN8DtwDSpG0Hh4uLi4uJyj9gu26HA0PZm1nbCNID8PPs9NsH+JQSYeAI4+jGAE4DUTSB5bWPrVYowMwkABFz/GABA6OmBMDgAmBTyzZtb1oR6l+3IAVZAqNcqlLO6VLpnMaET8vXrgElh5AuoXrjQ1XuorqNy7m0AgO/UaRCu9qxoT33mRa7BLbiTaDYVLOd2VkxcPfmKhoVsFYQAh4bYs6jAc3jvATZI8tZsFvkNugTtZ9feoAR+nULUvMhhz4k+AEBqtoBcsr1B5tR4DAGJR76q4eJiLXZC1gzHhDPeJNpyHMForHWu7XymgmvLRRAC/PhDQ2sE381iGqbjrPSFHrxw2IpAmGkYwZi0oe8RIcQpsrUTIxKUCisAxwncjigAJ0o8i6OhNTF5s6wuMV2sd2R3uWwBV7TdvdiZtkrJCTwHAJ910ag5bSts4NqKRrCxi5FpiaTzGiEEIWvUoVthzZ66EYtEwPWzKUlYudx1M1hFTJY92ywU2g7W3hYjjGPWzePSYh65igaBI5jsbfwC2oJwN9PeASYUyZqBgqwhXVLWLZp1M8lcth5rXycKSlvRL11SQSkQ9AqI+gTHQdtq2yiljpg4HPU5N8r1XKqporLhaAuby4uFhm1f6CBA23m2E70BRDu0AwAMkzp/81vnZqd1bwWmSfGVNxfwxZdnOorPQKNoSyk6CuP5ak1kp7SzSP8gWLQ6tIZJsVpWOy67nJdhmBSqbu5It+0/XF7B//jh3a4Gj/JVDW/MZHak+Ozi4uLi4vIgWSyxPNuRYPuK8dtOcZll2Io+INDk9u0/BEw8yX6/+wO2XLdkZ2BWNcATAB/tcV72HjoMAJCvXes6b5aqKso/Ogs9m2359+VbtsvWh7Dlqgv3eeHxCTA0E9mV7ZlJRimFcvOW8//KW+e6chDL167DLJfBBYNOZISNUt45RYba0TwbcXkH9lVt7JoY4z1+hL21fbq3L4CJXj8Mk+LFFrMYq6qB5XzrmZR2dFu7ImTNhPt8GNjD8m1nLqahtzGWeAQOT+xlAu/ZuxnHgLKQZXpBb9DjzLytxxZj5+ueRXXDxPevMx3h+GgEg5H22bsbRS7roCYFL+4M4bAV8fEQgj1ejByIbvi9vaNM3C+kq1tWYGurUMo1l+1OKABHCIHXjki4h1zbalFFJa+AcASxIVe0dblf2E5brQKh7ih6BUu0tZy2VK3CNAng72l4uxCPAxyBWSrBKNVGeewLdaHLkYy0JXL1BSWgxwq434DTVhL4WvZsXTZqVTUcgSzqXyva2sXIClV2oZvsCzjiqY2dAVSU9bYC7O1kCX/00l38/75/C//3927hD164g//50jT+9LVZ/D+vTjvCcTOmSXH2LnPZnpmIIeQVYFLaVry0ncT2zXfAcgG3ikiwHbgCR9Af8jptnc+0FwnTJQX/6/U5/PErM7gwn9tQUQTTpHh7jnVSbdG+U/as3XEainiddrTLtV0tKdBNCq/I4/BQeN11bwW3UyUkCjJkzcB0un3HllLqjNzb7e4k8Dd3GBd20Ki/bpgN2cHrCbENOcM7LLLCLuYgawZmV9d/MHnpVgov30rjwnxu+zfOxcXFxcVll1CfZzscHH5wG+JEI4w3zPpzGHsUkEKAXNhYYbLsLCtC5ouCC9Zm20kH9oMIPIzVTEPtjk5UL19G5Y03UHrxxTV/UyoaUvOWy/Zg1Hm93jGX3ibHnL60BLNUApEkCPE4E5dff73je6hhoHruLQCA/+GTIELjNGBlF+TZ2n1u27hiz0DcKBfmc/izs3PIVTqbGQCWzfrandUNFbCmlDqi7ZHhcMPfCCF434E4OEJwJ1nCxYUczs1l8Q+XlvHFl6fxhRfv4M9fn8c3Li+vEW5TJeu5Mdx9zMDooRikgAi1qmPuSvuYhKPDYfQGPZA1w4luaBeNYGO7bxezVWcm3FuzWWTKKgISjyen+rrezm6oL0K2E4TDVniDIo48NYxIfOPuYsknOIM/qzusToq8Awd17GuVPUtgM9gu20jcB3GHzjDoxK4TbX//938fk5OT8Hq9eOyxx/B6hxvXH/7hH+Lpp59GLBZDLBbDs88+u2Z5Sil+7dd+DUNDQ/D5fHj22Wdx69atNmvcQYh+gDCruGTWbmRe6yTkOALCEUCvwjC5NU5b4vFA6GWv1UckhDfptI2HpJowXG09St2OVo5YOxoh5BXWiLEAm5rvq/vC7R9YWwHQK/KOEzRZbO1offVOGkVZh2bUbpY8RyDyBIpm4u8uLLWcZn8rWUKmrMIr8jgxFsVItBbS3opksXHE1B6NbCUSLlkdk4GwFzxHHFfxYq59ru2lxTwMk0I3Kb5/PYlvXFrpevr+rWQJRVmH38PjfdZUnnYVQjXDRNq6kQ5GvBgI24XkWouEdqdrMCLVjdJ27nhdmM/hy2dnN5VFTCltyI7qVFQtX9VQscTxI5ag3OkzbYE9aH1HttsxDLAYg5dupZCvdv4+JotMHLfZSM7wTnMML+erTs7weu1gwjtbZqeJzwAr6vjVcwtdic+UUrx2ZxV3Ujur8+bi4uLisjtZlVehGApETkS/v//BbYhThGyi9d95sZZvO/cqoHRxH6S05rT1RsEHa84pTpLg2bMXAKBc6y5yQVtcsv5dhCk39j2WbudBTYpwnw/h3sbsyj7LMZdPVaHKW5+7Kt9gEQ/S1F4En34Pe+3KVeirq23fo9y6BaNQBOf3wXv06Nq/W8LHTigy1ArTpE5//JSVwZooKOtGf7XijZkMEgUZL9zoLN5TSvHNyyv40d1VvHQr3fX6F7JVFKoaPAKHqfjaZ9HeoITjY8wB+71rSbx4I4XrK0WnX08IcCtRwg9uJB3DDaXUMTJ1KkLWDC9w2HsyDhCC9HwR2ZXWfU+OI3jPPiaynp/LoSBrzvNSczSC046AB34PD82gWM5Xka9oTkTgew/E4RW3VgTbyUXItoreugGfjZitthtbtN1J1wd7W+RNupIppY443jO89nu6G9hVou1f/MVf4LOf/Sx+/dd/HefOncOJEyfw3HPPIZlMtlz+hRdewM///M/jBz/4AV577TWMjY3hQx/6EBYXF51lfvd3fxf/7b/9N3zhC1/A2bNnEQgE8Nxzz0GWd+40DABMsLWKkUlmTazwWRdNQgh4gQBaBYZB1oi2ACA4EQlri5EVuux4OE7bkAT42I0VSnFD05v6HeGv5ta0i5D1BluHjxNSy9cROII9fa1t7gMdcm0Xc1WsllSIPME/eXIS/+f7pvCvPrgf/+qD+/GZp/Yg5BWQKav41pVEw8WUUoqz06yz9PB4FF6RdzJ2F9u4L5tFW1vsXMnLay7UK47Qyba9LyhBEjmr8NradmiGiWvWKO/hoTA4QnAzUcSfnZ3rSvQ6Z7lsj49GMd7rB0cIchWtZdSCXbgrKAkIeUX0hzo7bR3RNuzDcNQLjhAUqlpbEVI3TLx6ZxXJgoKvvr3Y1uncjtnVCpIFxTFyzGYqbW+ES7naKPawJbp3cqjafzs1HgXAzv1OwjilFJcX81i9hyzi719P4s2ZLF653bkDuWSdd05UR4ccZ0opEsVGV+5mOsLbRb2IvJLvvO9Kiu5EKCzl5G0v1rdRLs7nMbtaaci+bsd8poof3V3Fd64mdlTnzcXFxcVld7JQXAAADAWHwJEH9Mhn6ECebYeTZ9uKgaNAaBDQVWDm5fXXW8kAShGGbADeMLhQY10L72FWkEy+eRNU7/xMQymFtrLM/mNSqLOzzt/ksoa047KNrXmvNygi2OMFKMXq4tYWFaKGAeXObQCAdPAgxJERSFN7AUpRfvXVlu8xikVULIOS7+RJEHGt6GULH94d6rRNlxVoBoVH4LCnNwC/h4dh0g3XKMlXNRSt59npdLnjAPqdVMkxrNxJlTrWuKjnilWA7OBAqKHGTD1P7O1Fb5CJnnvjATwx1YufOTWCX3lmCj9xbAiEABcX8njtDnu2LKsGKqoBQqyZrBsg1OPF4F5mRJm9vNq2X7ynL4DRmA+6SfGdKwlkKxo4QhwTUjOEEMd8M5ep4Ps3EtBNivEePw421ZTZCuqdtu9UeoYC4AQOcklDKbt9dWs2ys522m5OtK3kVShlDRzPITa4NbnL95tdJdr+3u/9Hn75l38Zn/nMZ3DkyBF84QtfgN/vxxe/+MWWy3/5y1/Gv/gX/wInT57EoUOH8Ed/9EcwTRPf+973ALCb9H/9r/8V//7f/3t89KMfxfHjx/Enf/InWFpawt/8zd/cx5ZtEku09Rg10dYr1g4pDw0wDegmD3ija95u59rqK/WibfdO26pqODfDvqCHZVWJVp7NBty2tvBXfzO2MzljLaIRbOwM273xICSh9QhfrdDZ2ovhpQUWwH5oMIyeAHPu2mHvAUnATx4fhsCxKS31osutZAmrJRWSyOHkWBQAnJvcSr66poCSbpjIlFh7+qztiQclcISgohprBPIlJ8+W7ReOI3VO3rVuwtvJEhTNRNgn4rmjA/jkI6MI+0Tkqxr+4o15vD2XbSsCLeaqWMnLEDiCE2MRSALvCMqtXKrNgrI9Alyoai3zY1cs1/BgxNuw7nYRCXfTZUcIraoGvnpuYUM5vbbL9sRoFB6BQ1U12nby7KlWQxGfM3CQbVNgTDdMx1W+ty+AqE8ApZ3dtjcSRXznagJfeWthU8XXEgUZtxJsVHBmtdxRkLTPmYdG2Gj+aql9vnK2okHRTIg8gd/DQzdpy5iOraZbIbI+diJd6uysqBfZVd10BpF2Cvb3NVlU1hWUbYd9VTXWzSR+EMytVnA72Z0L+HayiG9eXnFzhl1cXFweIHY0woPNs10CTJ09s7QwkDgQAuz7IPt9+TwrbNyJ7AwAwIQPIHxDPAIAiGNj4IJBUFmBOjPTcVVGLgcq1/oP6vS08/vyHctlG/ch1NM6s9OJSJgvbumgqzo7Byor4AIBiMMs3sL/xBMAR6DOzEKdm2tYXltZQe5//28Y+QLLsj12rOV6nXiEHeSkqydhDdgPhL3gOIKh6OYiEpqfmX54M9WyL6YZJl68WTNH2DFd66HoBm5bxbCboxHq8Yo8Pv3EJP6P903hoydH8PjeXkz0BuAVeewfCOGDh9jz+NnpDM7NZR2XcU/A01YI7sTowRhESYBa1ZFLtH7eIoQ4hdJssXowInV0zNou3PPzOcykK+A5gvcf6t+W+ALZctp638FOW16oCYjtjtODoD7Tdqcg3WOmre2yjQ74nbpPu41ds9WqquKtt97Cs88+67zGcRyeffZZvPbaa12to1KpQNM09PSwafzT09NYWVlpWGckEsFjjz3WcZ2KoqBQKDT8AIBpmvflh1LKfvcEQCkFrzNbPaUUksA5y3FmFZQCuhCGSbg16+H6+9nociIBQ9ehqTqCHg6UUhSq2rrbkSxUQSlF2CtA5Ah73Rtl21deXff98t1paJkM+gIiKKXIV1SUZfa5qyXmQI36xLbvPzwYxE8dH8T7D/a1XaYv6AGlFCv5asPrJVnFzQTrXD00HGr53v6QB88c7HNiFG4nizAMA6/dSYNSipOjEXh41u6oT4DXcsMu5ypN+0mGYZrwihwCItu/HAH6gqI1vbu2fFXVkC6ytveHJOf10agXlFLMZcprtvPSAsuwPTIYBKUUAyEJP//IKPb2+aEbJn5wPYlvXl6Gphtr3vvWTAaUUhwaDMJrnTujMR/7rNW1n7WUY8d8IMy2zcMTS8Bcu48rioZMWWVtCXpgmiZGrHbMt2iHaZq4vJhzjknML6JQ1fBXb86jJKvrnk+L2TLmMxVwBHh4POLss+lUqc3yzIU7GJbgFTiEvDxzoTa1wz6GusGOYUjiMRjydGyHaZq4upgHpRQVRcffX1xquf87/bxyK+V8r2XVwGK20nI5wzCctuyL+xHw8DBNikRhbTtM08SStWxf0OMcj4VM63XbP9+5uoIvvTKNXEXZUBvqz+s/eW0Gf3Z2FnqH/aBqOlasc4wjTCxPFuS2y9vno/3T6XjU/zjX0G38qaoaVvJs+xTNQLrUvh2maWK5ri3rHY/7/VNVNfzN2wv4uwuLyHdxDrx4I4WrS3lcX85vy/bcj+Pn/mzfz3YdPxcXlxomNbFU3gGi7Xp5tvVEx4H4QRZ9cOf766x3GtQ0YRImevChximvhOMg7d8PAA3O2VZoS2w/cX4mDqozs6A6K4SUWWIP+8P7om3f3zMcAMcTq9DN1g26KjetaIT9+0E49sguxGLwHT8OACi/8gqode2Tb9xE/q//GmalCqGvF9FPfBycZ63xhZrUiXHYqU7bmqnC2/DvRouR2TO3jo1E4BV5pEuq44yt59xsFoWqhpBXwJlJ5qa2ZzB24laiBM2giPlFZxs3w7HRCJ6cYgMaL95IOUahbouQNcPxHPrG2PchNVdsu9xA2ItDg7XBjnZ5ts7frRorisbOuTOTMfS0KBZ+r5gmddye/new0xaAk2tbzOyMGd6mYUKp7jzR1t4WpaJveGBMlXWkLdG2d2T3FSCz2ZlDbC1Ip9MwDAMDljvUZmBgANevX+9qHf/23/5bDA8POyLtipXl2mqdK3U5r8389m//Nn7jN35jzeupVGrbYxVM00Q+z8QgX9WAWC6jmllGucwupJWiiGSSXejkYgKapiKvilBaREhQSiFrKlAuo3LpOqZvUXAhAWW5CkWuIJHwdhw9u7VcQrlcRlzyOhEVkspBLJehLN2FRtdOI3LakclA+duvgcSi8D7/PARTQb6q49rMEkajEuYTGZRlA2a1gGSyvXsuCKCQraDtrVU3US6XUS4Dc0sr8FqjK+cXSygUS4gHRUAuICm3XkNcACZCwNWVMr569g6ODwcwlyhCEjiMeLWGaI4gUZEuy7g8vQxhpNZ5vJEoo1wuIyZKSKVSzvHzUgXlchk35hKIEtaxWMwrKJXKCEk8KvkM7HE3r6GhXC7jplzB8qDgOILzVR03FtIgBIiLwYbteXSQRwACzs7m8ebtMpZTWTx7IOZMoc9XdVyaTYJSYMwfcN7rN9l2XZuX8XCcNJwDt5fSKCsGRM3rnGcSVVAuV3FjdgV+o3bzn8/JKJfLiPgEFHOrKALw09q6T/Y1rrusGLg6lwKlwLg/gD1BD/7uSgELqTL+9IdlfORIL6QOo2Pfv5FBuSzjYL8fciGLEGGfdXlGw2SgcWRO0U3MJbOgFBDUIpLJCrxUwXJZxvW5FUh6Y+f/6jI7hj0edgyDREG5rOPanIbD0bU3j4pq4Oo8a4vIE9xeLOPvTRlPTEbabn89KwUVV+bS4AgwEPJguaDi7TuLEMfXjuTnqjrS2QI7J6oF+KAgUZZxfWYZYovcnhvzeZTLZfgiBD6qo1wu4+qsvmYf2ZRVA2dvJkAp8NdnK/jxQz0bHlU/O1vAnOUavhwjGAy37oQtFxTkiyX4PRwiARHzWQXXZpfBDba+yd5aSKNcVhH1CchVdVydNTDibe9qNkyKm8kyorzChGFu+8Yt57IyiqXaVLxrM8s42N+6Q0wpxZ2lNKpWR/ja3AoGPTvHNTyXlZEvsuN3ZXoJe3tbT58DgKpmYCmdAwBcm00gLmxtO+rvgZs5fpRS5Ko6oj5h3fO4qhn4wa0cDvb7MdXXvs0u3XOvx68dxWL7B1MXl3cj6WoaqqHCw3vQ59vaIkEbImcJpp2iEerZ+wywehvI3AVW7wC9U2uXMU0gN8fybKUoiMCD+NZeoz3jY6i+/TbUuXlQStte8+3aHt7DhyHfuAmzVIK2sAAlPAhDMyF4+LYuWwAQRB7RgQAySyWkF0oIRDcntjU0UVWhzjDHr3TgQMPf/I88AvnadejpVcjXrsEsllB54w3W5j17EPrQj7UUbAFAk01Qk4IXeIjenVmMx452syPubEHUHqjvtg9qz4ab6g+iN+jBCzdSePVOGgcGazM0C7LmzNJ7en8cg2Ev3pzJYj5bQUHWEPa2F65qBcgi9+w2fXRPD6qagbfnco44HQ9tXgiOj4ewfDuHfKoKpaK1LTr35FQfbiVLMEyKid7OglbELyJizeSM+kU8MtnTcfnNIpc0do6K3I49R7cK+7pSzikwDBP8JpzVW4lS0QHK9r3g2TneTo9PAAiBaZjQFAMeb3cSpq4ZuHl2BbpiwBsUEYnv3r78rhFt75Xf+Z3fwZ//+Z/jhRdegNe7+YsgAHzuc5/DZz/7Wef/hUIBY2NjiMfjCIfbT4/YCkzTBCEE8XgcnDwKUp5Gb0BEoMoutCP9fejvZ0JNXtSQEz3wxUYR729dgCC/dy+0+QXoeRU+bwQ84RAMcqAUCMV64fe0P0X0NEUgYGBqpAf9/daUp/IkiLwIvxdAm88EADmTQSkQAFQNPeEw9g4ZuJkoQRcDiPVGYQp5BALAgYmhjtvQDUN9VRSqOog3jP4ePyilWLxVQSAQwHsOD6C/v/Mx++m+OLRzC1jKybi6aiIQCODxvT0YG26c5nVYFpFS0qgSL/rr2n5pNYFAQMfUSAz9/T3O8Tto+jFbTEDmastPl1YRCMjYNxBsWEecUvTOyZA1E/CF0R9hF50bt9IIBAKY7PNj79jQmm0fGAD2jJTxjcsryOkmXphT8NGTwwh5RVy+noTfH8CevgAOTNSqCvf0mnh5nhW2EgJR9Fp5SkVZA4Q8QiJweHLEEX8PyCJWqmmovK9hm++WVhEIKNg/FHJej1nrNkwKKRRDtC7+4vXpDPz+AEZiPuy3tufTvb34ypsLqKgGfrSs4/mTwy2nCq2WFKSUPILBAD5wfAI9AQ+kkIbzSR0lE4j09DZEaMyuluH3FxHxiZgcZfttf0VAUl6F1tQOAFCTKwgEdBwc7UF/fwwHNBNXiyUohCAc610znejtuSz8/gCGol6cGY/h7y4uY7oAHKY+HFgn+4lSihfnFxEIBHBsNIKRqA/fvLyCrC6u2S4ASC7mEQiwbKqhwQHsr4pIKatQBX/L5eUZGYEAcHB8AD0BDy4kdZQpQW9f3BkMqOf8fA5+P7u+ZDUgpXudGIZuyFZUzBQLCATYOvJUwvH+eMtlZ8oZBAIyDgwE0RPwIKNm1pxXNqZJUSVFBAIi3nu4H9+7lkSZ8ojH4207z2/MZHAuoSPImfilQ3Hw/MY7g2VFx+1kCUeGwx2nrd3IpxEI1E23bNMOgOWucZ4CbMNCmQptl30QXM+lnOOn8n70tzl+AIs4CQSYwFumrc/Ze6HhHrgJ0e+t2Sxeul3ABw714/ho5/P4rdkscrqAW3ngiSM753jsZu71+LXjXvt1Li7vNOxohKHAg8yz1YAC2462Rcia8fcAI6eB+deZ2za2B2i+VhSXAV2BoRHAEwQXCLa874tDQyACD7NUgpHLQYi1NpNoy0y0FYaG4FFVyJcuQ7k7jbzV1wn3+Vhx5w70jQWRWSphdbGEsSM94NZZfj3Uu3dBNR18NAqh6Z7Leb3wP3IG5ZdfQemFFwBryr/v1MMIPPGE48ptud4qiwCT/OsPXD4IFL0WEWWLtQNhVhOjrLBIuYhvfQdgUWb1MwhhcXPjPX5cmM8hW9HwxnQW79nPBjJevpWGZlCMxHw4MMDOo5GYD4vZKm6sFNsKk7mKisVsFYQAh4fuPdOVEIL3HYhD1gxcW2aDkJt12gLMmRju86GQriI1X8JoizxmgAmxP3F8CLmKhuEu3MIPjUTwxkwGzx4e2FR0QzdUrUhBX9CzI8/RrUTyC/D4WJRFKaM8cFGxPs92J+17jiOQfAKUigaloncl2hqGiZuvJ1ApqBAlAQceHQT3gEXxe2HXbHlfXx94nkeirmgWACQSCQwODnZ87+c//3n8zu/8Dr797W/juDWlBIDzvo2uU5IkhMPhhh+AxTXcjx9CCPvdGwIhBB6jCkKYa9HvFZ3lRC0LQgDTN9B2XZ6hIRBCUE1kQQiBqQMBD7uRlxSz43asllUQQtAf9tZeD/Sy7VPyHd9LC0Vnm81MBoMRHwghSJdV5GUdAIHPIyAgiR3X081P/bo5jsNcVkZB1uH18Dg4FF73/aLA46dOjCDkZRcwSeRxaqJnzXJjPQEQQrBckAEQ5/VUSQMhBIMRX8PxG4xa21VSneUTRdXqMPgb1s3zvLP+pbzC9iEIrq+w/Xh8NNZ2+6f6Q/jkmTEEvQJWyxq+8tYilvKy894zk41t8YgCRmJ+EEKwaH0Wx3FIWtsWD3vh9Qhr9m+qpDasJ1FUQAjBULTWFkkUMGS1ezFXWzchBNes7Tk6HHFe7w168TOnR+H18FjKyfjahWWUVGNNG9+ay4MQgn39IfSF2PkYC0iIBTygaPwsjuOwUrD3s895bSjC2pwsqmvW39yWgCRYYnbteNT/XE+UQAjBkaEI9g+G8cgeJtZ/73oKuare8XxbyMlYyskQeQ6P7+3F3ngQHEeQKWst275cUKzCfP7G41Fcu10GBdLW+TgU9SMe8sIvCdBNON+P5p9byZLzPSeE4KXbqy23o93Py7dXYVKWE00IwXS6UruGrWmLzNrSE2h7Xtk/2aoO3aCQRB5HhiMQeQ5VzURebr9tt1NldoxLGm4myxu+llAQfO3iMl64mcbb852vcUt51pZ9A+w6nWhxXjV/t/pCEniOQ1E2Ou5jQgj+4XICf3thCSbd/nvPQk52rtetvh+NbVGcZXNVHbLe+T6ymZ925083P7eS7ByYWa2su6x9XLIVDapBt30/b+fPD2+l8ddvL8G4D+fLdh6/Tj8uLi41Fkqs+NcDjUbILwCmAUihWrHibph4itXJKKeBuVdZXEI9lnvXFPsAQtYUIbMhoghhiA3Ma035rzZmtQojy+pwiENDkPbuBcBybfNJNt+tGyEl0ueD6BWgq4bzvnvBiUY4eKCleOI7dgx8JMwEW55D6IMfQPCppzoKtgCgypZou0PzbJMFhRmHvAICEttGkeec2hPd5traLtv+EKupwXMET1sZrm/PZZGvaljIVnBjpQhCgGcO1gb8jwyx5/pry4W2U7Ffuc2Khk32Bpwi3vcKIQQ/dmQQR4fDGOvx31PkAsDctoCVtdyhrsJUPIjTE7GuRLpH9/TgXzwztW6Uwr0gF9/5ebY2hBCEetlx3gkRCbKdd70Do1Psa5YtLHeCmhR3ziVRysjgRQ4HHx/cUXEPm2HX9HI9Hg9Onz7tFBEDmGPje9/7Hp544om27/vd3/1d/NZv/Ra++c1v4syZMw1/27NnDwYHBxvWWSgUcPbs2Y7r3DFI7GLM67WwdHv6P9QKOI0V2jKk9tMXhH4WDVFJWIXDKEXIciR2Kp5kmBSr1khYPFh3U7E7ZesUIjPyeed3PZl0psAkCwqy1pexZ4tGeexCZwmrGNnFhRwAdlPudpQwIAn4yRND6At68N798ZZB7fGgBI/AQdFMpMvsswyTOsWRmkdMe/weeASWg5upsOzXZacI2doO4missRjZdLqEimogIPHY09d5SstA2ItPnRlHT8CDoqzjK28uQDMo4iHJWW899RVCbexta+5E9Ie8IAQoyjrKCrvYs4xbpeXyze0AWCGtXEWDR+DWOFH7Q1589OQIRJ5gIVvFn742i/PzOacjla9quLHCRqWbR8Qneu12NFaMrS9C5nyO1SHMVzVU1VoxsqpqIGdVqxysa8tIi3YAzPWbLCjgCHHa8tRUH0ZjPqi6ia9fXGpbJIxS6nQEj49FEfKK8Io8hq3tnEmvrXy7ZBXuss8Z+7vUqqhaqqjApBQBiUfYy0TUWpG7tR3hfFXDUk4GIcBHTw5jOOqFqpv43rVEV5lCd1Ml3E2VwXMEzz88Ap5jAlimRaEt06RYyrFzbCTqc/Z1pqxC0dcWh6ufPifynLP8YpvicPmK1lCQ8OXb6Zbr7cQbMxlnHbcS7adjK7rhLHdmgl0TV0tq28JcdiG4sZjfKe63lGv/YJIqKbiZKGImXcGlxXzb5baCiqo7RfgAtt+biy3Ws9KUOWcf03aouolbiWLHdW4VrDAh257lvLzuOVz/cNjpeDwIdMPEX761gO9cTay7rKIbOD+fw1ym0vb74eLi8s7BpCZWysw9+kBF25wllHaTZ1uP6AX2PM1+n34JuPEPTPy1sYuQ8VEAa/Ns6/GMjQEA1PmFln+3XbZ8Twyc1wtxZATE44FaqqK4yKbNdyPaEo44eYmri2v7ahvBLJehzs8DALxN0QjO5wkCQh/6EKR9U4h+9KPwHjnS1bptp+1OzbO1+0P1/XOg1v9eXqdPYWPf60bqnnH29gUw1uOHblK8dCuFF26wYnfHRiLOsyIA7OsPQuAIVktqy2LGc6sV3EwwsffJfR2K620CniP40NFBfOL0KIR7dAXGBv0QPDzUqo58auvu/dvtwKxaRch87/A8Wxs7IqG4+uD7Z47TdgcK5raQrFQ6FyOjlGL6Yhq5lQo4nmD/IwPwt4nl203sGtEWAD772c/iD//wD/HHf/zHuHbtGn7lV34F5XIZn/nMZwAAn/70p/G5z33OWf4//+f/jP/wH/4DvvjFL2JychIrKytYWVlBqcRETkII/vW//tf4T//pP+FrX/saLl26hE9/+tMYHh7G888//yCauDE8rHMgaLXOgc9jiYmFJQg8BUQ/dNr+iycOMtFWzsugBruRB6ybREFu/6XIVlQYJoVH4BD21Y3W2qKtXGDTotpg5HPO73oy5QgV+armVFGP+bfmC2aLpcmCjIKsYdoSvo6PRje0nqGID//4iUkcazOlluMIhqONwtFq2YoCELk103k4jjjbtpKXHZFN5An6gmunxIxaAfBLOSaaXF60spSGIi2ntTcT8Yv45JkxZxsBtB1ZtSuELmQrsCut2mKMLQraeATOCaK3hbSc1RaBW9sWO8h+3iqIBQBXLOFpf3/QiV2oZyTqwy88NuGIhj+4nsRX3lxApqzi3FwWJqUY6/E3iKoAnHym2dWasGqaNXF8qG5feEUeUeuGYAs7QK0DGfOLDWL9aBux87olIE/2+Z3vI8cRfOTYEAISK4Tw/eutRc87qTISBRkegcMjkzVXyqQlyk83ibZlRUeuwqZ/2W33eXjnXKsXKevbMhCu5VXbHdpWYo4tTI5EfQh5RfzYkUEIHMFMutKymEM9umHihzdZh/jh8SgGwl6M9bDPuttCfE6VFKi6CUnk0Bf0wO8REPaJoHRtO4DaIMKgdT467ci1drjcsir8Dke9iPgElBUDZ+9mOrahnmRRblg+XVJbis8A+46alCJqFacISgJMSlt2/gFgxbrmDUa8zvezk0h4J1nbf69PZzYsPgMsx+3//dEsvn+9s+hnn999QQ+8Ig+9biCqGUqpc44NRtZvBwC8cieNv7+4jJdvpzsutxXMZSqOYUvWDGQr7e9RJUVHse4euJ74fL+Zy1Qwn6ng8mIeFbVzB9Z2LgE7T3x2cXHZelKVlJNn2+vbWlFpQ2w0z7ae4VPA1AeY2Lt8ATj/Z4BaZs8WeSbAGmAD41ywvWgrWqKttrjoPOfUo68ss+UsRy7heXgmJ1FWPdAzGfhCHpan2AU9Q2w7cskKjDaDtN2g3L4NmBTCQD/4aLTtcuLgIMIf/jDEke6Fec2OR9ihrjOnbxdpfHawzQvdFiOzDRX1xhRCCN57oA+EsCJiqaICSeTw5FRj5rNX5LHPihq82tTXNUyKF26yGiAnRqMNYu9Og+M59I2ydiTn1i+stlOoFu14hJ15jm41YatORDmnwLyH68ZWoDjxCDvPiS/52TYpHfruADB/LYP0fBGEI5g61e/s393OrhJtP/WpT+Hzn/88fu3Xfg0nT57E+fPn8c1vftMpJDY3N4fl5WVn+T/4gz+Aqqr4xCc+gaGhIefn85//vLPMv/k3/wb/8l/+S/zzf/7P8cgjj6BUKuGb3/zm7shH87ALMW9UEfYywclr53YWFsDzJiCFYGjtLwCc3w8+EoZqcDDLTAjwE1u0bf+lsJ1X8aDUKPqJPkCwbrTVXNv3NzttvWJNaLppiV69wS0SbS0HZbai4dwsKz411uPfloqXI1EmSC5aD8a22LRmP1nYwkaiIDsP0/1hb0sRti/ogc/DQ9VN3E6WMLPKjtdDI93nKPs8PH7m1CiOjURwYCDUNl+1PyRBEplrOFViwnOizeg3W77RzbxcJ/A2t2Uo4oXAsWyqbEVjTrskG0g52iErtSfgwSfPjOH9h/rhETgs5qr48o9mcXmBnUuPtsidGo35wBGCXEVDrsI6AatlFapuwiNw6As0dgptQTpRJxKu5BtFqPp1A0C6pDiOVkqpU3H28FDjcQlIAj5ybAgcIbi2XMT/e3YOlxbyjuvWNCleu8OEq4fHog1ZzraTej5TaXBr2udMb1BqEJSd86rY2LldaRI6gZr4vJirOgK9zU2reNhBq7psT8DjuApevJnqeI04b2WHBSQej+5hx2Yqzq5Zd5KlNcvb4uCIFZ9Rv522ENjQliZxcDRqDzS0FqVuJ2tteWKSHZu353JYbSNA1mOYFN++koBJKfb1BzHZxz6rndu29rDAIjcGrG1caTGtzzCpc50YDHud86qTI/JOirWFIwQV1cC52dy6bahHN0x8/eIyUkUFFxfyHWdVzFtuezYoYk9PbP3QlK1oUDQTAkecvNhOIiGl1DkXLs7nHKf+dlE/cwDoPM2y+VgtrTMl0zAp/u7CkjNQsd3UD0St9xBb//elDVbfdnFx2X0slhYBMJftA8uz1VWgYD2TdZtnWw8hwPhjwLGfBQQPE2rf+hKw+JYTuaDIJoqKhukqwcu30s79qh4hHgfn84KqKvTE2kFKzXpuFOti8Tx7JlFSRRjZ3IYyJgNRJvCauonCPTgbZSsaoZ3L9l6oz7TdaVBKkXD63I373TZZpIpK29lqNiVFR9YyNIw0zVzsD3lxdLj2rPHkVF/N8FSH3Ye/0TQT6Px8DqslFT4PjyemHuCASJfYEQn5ZBVqBzPWTsE0qeP29IXeHaKtFBAgSgJMg6KUe7BFiOszbXca9uwApdz+PE7NF7Fyh+kCk8f7EGtTyHo3sqtEWwD41V/9VczOzkJRFJw9exaPPfaY87cXXngBX/rSl5z/z8zMgFK65uc//sf/6CxDCMFv/uZvYmVlBbIs47vf/S4ObMNNclvwBADCgQPw6dO9+MXHJ+AE3xeWIIlWp2YdGzkXH4BmcDAtB7LP6uAVO1zcbadVX/PUBULWjUgwq1VQuXZRMvJ5mIriCGYVa2r6Vjlt/R4BISuw+sI8+yKvV4Bms9Q7bSmlSNnRCOHWgwD1olS7+AEbllvKOh8v3kw64nN0g/tJ5Dk8e2QAP3F8qK1Dl+Nq0+bnMhWkS6wwmVfkEWsxpWrAEsZth+pKoeYcbEbgOQxZ657PsClGqm4i5hfXDcEnhODkWBS/+PgEJvvYFCfdpA0uznokgXeOiS1y2ELNYNiL5kIRdjsSdSJhotC6AxmQBPQEPKC0JhQu5qooyjo8AtcysmI05scHDvVD5AnSRQXfvZbAH718Fy/eTOHcXBbpkgpJ5HBqojH7rS/oQcgrQDNogyhpDw6MRBv3m92O5qnqK845VmtLX5AJ9KpuOucrwIosJAoyOEIcxwEAPDwWWzcmoaToODvNXKnv2Rd3isDttUTb5byMUpNAV2tLbdvatUPVTUdstc+xwQgrVFGUdeSrjSJkQdawnGcxD1PxIMaiXkzFAzApxQs3UutOk399OoNUUYHPw+MDh/qxv591gm+1EJ+B2vlgf1+d73l+bWes/rvFnLn2YIC6Jt4CYDEPqSKL33j/IZbPdm4uu67bsp4Xb6acfUppTZxvhS10jvf4MRj2We1oLfzZr/eHJWdmQKKgtI2FSBUV5z6jmxRvzXaO1WlHvqrhZqLY8ThSSjFrDXT1hVqfV/XYx8o+hol851iI2dUybidLeGs2i2SLQYathFLa4Lpfb7povTidKMhrBmeakTWj69xAFxeXnYct2g4Hh9dZchvJzwPUBLwRwBfd/Hp6p4BT/5QVKJMLyF76Fq6vFPCdFR9eeHsWVxYLeGVZxhszGXztwlJDvBXA+o3i6CiAtREJVNehJ5lr0s6+BQBxYgIlXYIpywhI6+cn1n9WbIj1/TLLm4tIMHI56CsJgBBI+/dvah3toJRCszJtd6IoU1R0lBQdHCFrIuXCXhEhL5u1lFjnHrvozBCSWsbZPTnVi5BXwHDUi+NtzCLjPX4EJQFV1XDutyVFx4/usgiz9+zra7nunYYv5EGwxwtqUqTn28d67RSUsgZqUnAC17XDfbfTkGu7+uAG1g3DhFq1Mm134PXBybTt4LRdXWTPM0P7o4iP3XuBwJ3ErhNtXeogxIlIEPVqLZ/VNIHCIrySBkhhVEtqZ1GiJw4KAqPEbkq2BNjJfVVz2rYQ2dYRbW2XLRcIgA+zL5SeSjmOWGezttAJa4umdp6n7fjbagbDzEVasXJQU4XWebY2tgMvXVQdd14rJ6uNLYSUFdbp2ojLdqPYEQnzmUqd27S1Y7jmUJUbsnlbibZATQiZz1Zw1XKmHhmOdJ2TFPGJeP7kCJ47OojJPj8+eLi/7XudiISMLdqujUZo1Q6gabp3C+G9OZ/3ulXxdX9/sG1e8rHRCP7Z03vx3gNxRP0iFM3EudksXrrFXLZnJnrWdAQJIZi02lGfa2tP2W7OQK45n2s3/4paEzPrv2v1An29IGznBI/1+BpcvxxHGmISXriZwuxqueFB6eVbaai6iaGIt6GqblASnHNiOlVrB6W0Zf5Y8/GwYecZK1QRtApVeIRaoYpmd6ftsh2O+Jzl33sgDoEjmMtUnL+3IlmQ8bolQL//YD8CkoCpeBCcVewt2xSRoOiGs712W+yBmJaO4abvVkASnIGRVi7VO+mSs+5jIxEMhJmAbovk63F5MY+LC3kQAsdpbx/rZgqyhlxFA2cV7Rvq4Bhm7bMHa3wIewXnAaudOHrHOgfsQbVLi/k1D9zrkSmr+PPX5/D1i8sdxedsRUNR1sFzxMkZ7uQ6tUXLw0Nh+DwsFiJZbL+8PfMBwKbF527JVbSGgYlOLmDatP+bB2da8f3rSfz56/O4vrJ7plO6uLyTuZC6gHOJc1CN1pE89RimsUPybO8hGqGZQC9w6tOgPXswnS4jW9Gwwg1CqJYh8gQDQ70I+0Souok3ZtbeC52IhPnGYmR6Og2qG+B83oYYAkUhMAMxcIRCyrbOwm1HjyXa5hKVTU11Vm7dAgB4xkbBBbbWJabJBkyDgnBkRwpitsu2L+Rp2Yce6jIiwY7JGmlRswNgpotfemoPPnlmbI15w4bjiDPLzJ5B9/KtFFTdxGDEi6PD2/f8tdX0T7B2pOZKXdWjeJBUrSJkvuDW1LXZLeyEYmS2g1Xw8BBbuM8fNPbsAF0xWs4iNw0TpYxlKhzZHp3nQeKKtrsdyTop1bqH1XISMHRIfh7w+GFoJnS1fcfBCLLpHWa5BArAQ9lFslDdhNMW6Fq05aNRCHHmFNOTqQZhU+AIwltUjRNoFE0fGu4uA3YzCDznCLHz2UrNadtGtA1JAgISD5NSp9hVp2qh9dlMXpHHvm0Sn4FaMbKlXNUR82ynXTPxkARCmJicq2hIW3lE7URbe92zq6wwDiFoEPe6gRCCI8NhfOzh0TU5u/XYxcjmMxUYJsWyXbirhThut8MuqmYXJRM44uQu12OL6AvZKnTDxE0rN7U5GqEZr8jj9EQM//TJSTz/8Ijjyg15BZwci7Z8z554LdeWUsrEF2vwZI1oG25sB1ATB3sCnjWicE20rU0tvGlN/W8VoVEfk3B+LoevnlvEF168gz966S7+9vyi08F95uBaMd2JSEjVrlmrZdXJc67PB2vVDqDe/dx43O12NEcL2KLsvoHa9yXiE3Hayg1+8WaqpRvUMCm+dZXFIuwfCOKA9X6fh3ec3c1u26UcE5SjftG5htntKFS1NREA9TnDNvbxbJWjascJTMUDIITgPftYFtulhTzy6+Q8JQsyfnCduYqe2NuLDxzqB0cIEgW5ZT7vnOVOHwhLkATe2d/ZitZSXK0vPkgIqWtHa1HRdq88vrcX/WEJqm7i3Fz3gmdB1vDVcwvO7IzLHYqy2S7bkajPuf6slpSWecCmWcsfHox4nWtyu3Yw52v9d6fUMTrkXpm22hK2IoU6uYALVR0V1QDPkYZrejt0w8Rd67t5bja34x/wXFze6VS0Cl5ZfAU/Wv4Rvnzty7iUugTDbD+4laqmoJkavIIXvd4HmWdbV4RsKxB9KOz7GO74TyLnn8D7HnkEj40GcXqiBz/91AF84FA/AODCfG6N6cQuRqYlEjCV2qCVtsSiEYTBoYa+SiFdBR+LIiBq0GamN7SZwZgE0SvA0EwU0hsTYKimoXrlCgBA2oZZn/bMS49PaCtWPkiaaxU04xQjW2cmiP3MMtZGtAWYKLueKGj35afTbCbNtWVWfOz9Lfq2O5nYUAC8yEGpaCikd/YsmqpV5PzdUoTMxi5GVsoo686G2i5kqwDcToxOAQBB5CFYYnIrt205r8I0TAgSvyMLqd0rrmi727FybaHUOaXybFoUHx1xKu3ZF8FW6N4wQAiopoMqCsS6Qi2tcoPyFQ1lxQBHCHoDLcTI9UTbnC3aRiD0s06Wnko1iDXRgGdLOxS2aEoI8NA2RSPY2BmhV5YKUHUTIk/aRj0QQhrEmohPREBqf7HsDXjgty5Yh4ZC91xZtBO9AQ8CEg/NoI7o1U5QFnkOvVbBsUuLeZiUIigJCLVpy2DYC5Enzvk10etHaAtF+nr6QxL8Vhbw3VTJKT7USlCWBN5xeCeLitOBjIeklkJ/fa7tteUiFM1EyCs0iOudIIRgT18Azz88gl9+71784uMTLQuxAayAG88R5KsaMmUWXWBSipBXWDPAUd8OW+BszoBtbEetyB2lrNBUuqSC5xqjEeo5NR7Djx0ZwMHBkOMMLco67lruyaPD4ZaftTdey+e1j78tsg5FfA37WRJ49FrtqHeptnM/14qR1TqlZUV3RKrmtjwy2YOQV0BR1vHGdAaqbkLWDJQVHQVZw6t30kjXxSLUd9JrEQmNLtX6PNv6dvS0aAfQOme4XVG1qmo4bbOjJsZ7/Zjo9cMwKV67276YV1U18HcXl6GbFHvjATy6pwc+D+8MarRyVdptsV339fEoze3QjNoggn1Nc0TbFg9YJUVHosAiK/b0BfDYHmsQYD7XMhaimbKi46tvLaAo604e+ny20lYstWMeJnrZlMeQV2hb5K4+97rH73EGA9oVI8uUVRSqGgSrGKVJKc7P5dZtQzPzmQr+9LWZdZ26tgB9YjTiFIdLtSlyt2y5n+MhyXmA7eRSWsrJ0AzWCUgU5JbucBcXl/tHTsk5v1f1Kl5afAn/6/r/wq3srZaDKk40QmD4wQlLmgwUmdt3U3m2bVjMK1iInkF+6qMY9gkQOA5EksB5PJjs9WMk5oNuUvyoqcgoHw4zJ61JoS0uOq87RciGhxqWzycr4KMxBDwq9ETSmYnYDYQQxAbZPXOjEQmVt9+GWSyBCwUh7du3ofd2gy10dCPKyJoB/T4XRerUTwVqEXTLebntgGJF1bFqPfPadUY2SzwkoT8swTApvnGJnSsPDUfabt9Ohec59FrOw+Tszo5IqHfavpvwhUQIHh6mYaL8gHJt7evDThY8a8XI1hoLi6usvxvu9e2qQZVucUXb3Y6nhdO2YHVIwsNO9b9Ooc2qQsH5/fDwBsxSCVRjFdyB1sXIZjOsEzIU9bYWmPxWQai2om0OAMBHInVO2yR8Ht5xDvVsUZ6tzViPH3vjTBjYSgdvK2zBZaVO8OskQA82OOzWz3R9eDyGnoAHp8ZiHZe9VwghGLOEJ9PqHHXqqAxYwvgVq9LqoOW2awXPkQZ3aH1RgK2GEOKITj+yppD3Bte6TW3swYOVvLxuB7I+1/YVq4jYocHwpm4WQUnomI/lEThHDJ5ZLbfMgG3VDruoWitxsLasBI/AQdYMpEuq47Kd6PW33SZCCB4aieAjx4bwT5/ag195ZgqfOD2K9x6I49E9PXjvgXjL9/UGPIj6RehmLWPUaUsLsduONknUCU12W5od1va+yJRVJ+P1drIEStkxbP7uizyHZw6y7Tw7ncHv/+A2/uCFO/gfP7yL//nSNN6cYdewDxzqb4iIAICp/gAIYaJfvcO1Oc/WZqBFO2TNcByu9eeY3Y7mPNi7adaW/rDkCJUAHLft9ZViS/HONCm+eWUZhaqGqF/Ec0cHnXPUnv53Y6UxE5ZSivmM5VbpqT342NnOzZEHqaLixM+ErbgD+3q2lFubo2rHYwyGvVbkRAB9Iea2fXsdwVPWTfzN+SVkKxrCPhE/e2YUozEfKAWuLa0Vnw2zlgVtXws6uYDrzy+OI04G91Ku2vJB0Y5GGO3x4VFLfL60mO9KfLa5vJjHV88tIl1S8aO7q20LrWiGiQXruEz2BWr7uF1kRV1UzXrO5/q22FyYz3XdBhcXl62noLJr2nBwGO8bfR/8gh8FtYDvzH4HX7n5FXxr5lv4+t2v429v/y2+euurOJ88D+ABRyOs3mKB6f4ewLt108jtvsJw1AezyPopfIg9B9XPPLmylF8ze0QcY7m22vw8ACvfdZkJy/VFyAzDRDEjg/OIiI6EAUqhzsx0vY3UMBojErp0zRmlEqrnzgEAAk8+CSKu/6xSlDW8dme163uN/SzobVGbwoZSinNzWfyPH97FV99ebLvcVmOa1MmDb+e07Q+xCLqqFUHXilqeradlgbGNYrttDav2wFPWObbbsAuS5RIVaMrGYqjuJ/K71GnbkGv7gCISbKdtp+vDg0ZyipGt/f4XrDxgez++03BF292OHY+gtBBtIyPwBtlFz/4itkKuaOCDAfhFDWa5DE0xHNdjq2JkdkGniZ42I5i201YpAMba9xv5HIDGeAQjl4Opqk7hoa3MswWYOPPRkyP3pdKnXRDJpt5B3G752u/rOzQf3dODf/LkJCL34aJaL9bE/GJHUdEWpezOY6eYh/p1e0Uee1sU7dpKxi03YbpoT99uv5/ri6qtrJPNC9TEOXu6+KENxjxsBDtGYTpdcYSX5mgEG3ub7ZzhTgI0ZzkEAeauvLnSPhqhHV6Rx1iPH6cnYniqQ4EGQojjEr2TKjfm2bZoi915T1h5oiVFR1HWQcha0dYr8uizrnn2Ou34gv1tHMNT8WBLNzHPEXgEDifGIi33g98jOG5a220ra7U822bRtr7ooI29bMQnNojCzHHPwzAbC27YGbB7+xq3tz/sxcHBEBs8uM0GDyhlGazn53P42wuLmElXIPIEP3l8uOHYTMWDEHmCXEVzBH6ACd8lRYfAkYbvci2ft1H4W64TOm1BuC/ABgNU3US63Cgm37Wyee1zmhCCx/awAb+357MtYwsAlsn67eusMFxA4vHxUyMIeUVn4OfqcmGNsLqcr0LVTfg9vBNzMtghZ9ieemm3dcBy2ldUY02ROwCOu3yyN4DJXj96gx6ouokrS+3jGmwopXj5VhrfsWI4OMJmILTLGV7IVqGbzGHfG/DUMv7auIBrxQe9TIS2ivW1cyTbAymnrdzfm4nShorc2Szlqnj5Vnvnt4uLS3fkFXYdiUkxHO07in90+B/hkcFHIHIi0tU07uTuYLYwi8XSIlbKK1AMhQ1Wh7colmCjUAosvMF+Hzy+pateqhvgNYrsHsIFa/fn4agPe+MBUAq8eqfx+uMZZ/ujMjOLb19ZwQtv3oZRKQM858z6A4BSRoZpUHh8AsIHJwEA6vTdrrav/PrrSP/3/w5y9U0IIgddNRz317rvffVVUE2HODzUdQGyF26k8KO7q07u/noottM20NppW1F1fO3CEl68kYJhUizlqm0LiW416bICzaCQRK7tMyDP1WYnthuoXOhgAtgMhwZDzjPdk1O9WyIEPwgCEQmBqARqUsxfy4A+oCn4naAmRbW0892e28WDLkam7AKnrWNGbHLamiZ18mztqIl3GjsztMKle5qdtkoJqOZYDkB4BF4rY7DaIq/QRqno4IJB+CsaiiVLtO0VkS6uLUZmmLRummkboU30A4IH0FVAzrMCAvXrsDNtIxFwfj+4UBBmsQQ9mcKTU3H4PXzbXM/dgCQwYcAWW1plodbTkGW5w6bctHLYtaO5kFynnFmAjV7fTZVwZCiyrTEPwNpztZOgbG/3Sl6GYrnd2o36A2wa/MUFdk7HQxL6gp2P972wpy+AF26ksJitQuBZJ7KdaGuLz4mCjFxFg6KZEDjSdvtGon7MpCu4uJBHtsKmettRBlvNVDyAc7NZp6BISWEFoloJyo64llcaiir1BqWWTv+RmA/pkorFXBWjMb8j3tpxBs0QQvCTx4eg6CZ4joAjBBxBV27p/f1BzGcquJUs4cxkj+XEZHm2zXEf9cXIKKUghDQIas3bNBL142aiiMUsa4dmmJizBLWp/rXH5cmpXtxKlDCdLuMv31pAsihDaQrq/+DhgTXXI4/AYSoexPWVIq6vFJz9PZ+tDQrUfz+bj4e9n+zrXf2ACGcJvrOrFSzlZGcASzNMzFv3kT1159i+eBA9AQ8yZRUX5vN41BJxbSqqjm9cXEaiqKI3KuFjD48ias3K2NcfxA9ucMhVNOfY29jZvOM9fmd77X1uT7OsP97NmckCz2EgLGEpJ2MxV3U+E2BCvR2bsKeP5QyfGo/hO1cTeHsuh5NjsbYZ6pph4ltXVnDLKqD22N4eSAKHH95M48JCDg+NrHXt207YyV72WfV5u83t0A3TyeYdCvvgETj0hTxIFhQs52SEBxvP0YKsIV1SQQgbHFzMVbGSl3F5sbDmWLRD0Q28ensVFxZyoJTNyNmuwp8uLu8GbKdtWGKOP5EX8cjgIzjaexR383dBKQXP8RA4AQInQCQiwlIYEWl7o8Dakp8HigmAF4ChE1u22oqqO+7Z4YgP5h02sMUFG++HT+3rw3S6jFuJElbysnMdF0dGoBgmblydw43YMnyrSezXTQSHhkCE2uNwPmlNse3zQRqfQuVHZ6HOz8NUVXCe9oYSdWYGlbOvAwDkCxfgETJQ+w8is1xBJN55mr62sgLlxk0AQOA9T3fV/6iqhpMLfzdVaju7qR5b6GgVjzC3WsE3ryyjrLA6DgCgmxTZirqu+WQrcGa4hNrP0gPYPWUxV8VyTm45S2/Rme10b9EINn6PgA8e7ke+quHYyAP6Tm0RQ1NR3H4rgfR8EaqsY+pU/44qOKVUdFCTguO5HZurup3YYmMxI4OarGDg/UQu2deHnSvaOk7bJqd9OaewPFsPD19o527/veA6bXc7kiVE2KKt7bIN9AGC5IyW2F/EZiilUCoaiNcLn6DBVBRQkyJkdWCai5GtFGSougmfh29bXAuE1OXaNo7+mrIMKrOHSD7MOqBOREIqiZ6ABx84NLBrRzJt6kd4m8XMZrwijyemenFyPLquwHu/ifhEZxr2es7ZvqDkjEa3ckE2E5QEfOqRcRzb5oxh+7P66vZtO6ETsOIsCHPVGSaFz8M3TEVvpt5RudFiahsl6vegJ+CBaRUik0TOyXxtJh6stcOOO+gPt87mBWrnrP1QtCcegCRsz/dwOOKDz8ND1gzHIcJyjtfekvqCbJtljbkcO8U8ALUMs8VcFXdSJZiUsjiBDp0QQgi8Ig+R58B3UZzCZl9/EISwh418VauLRlj7sNAblCBwBIpmOtP6nCJkLb5bzdPeZ1cr0AyKsE9EvIXwHvV7cGyUXVPnMxUoGstknej144mpXvzCY+NtC+TZEQk3E0VnKqctqo41zajos9oha4aTDw20LyDSakr+fIa1JeQVGtrCccQRB8/NZZ2IgExZxXevJvA/X5rGXIY5hj96crjheukROMcRfaUpImHWaovtuAfaT7NUdAOrdmRFi+JwzY7W+UwFJqWI+UVHzD00GEJA4lGU9TWZxzZlRcdfvrWAW4kSeI7guaODeHKqD0eHIxA4glRRaekCnrUe0if7WFvsmR0lRUexqchdqqTAMCn8Hh5hnx1Z0T5neNYqpjYU8cIr8jgxGgUAXFzIdTXF926qhD99bRbn55lge3Q43Da+xcXFpTtsp23E09hX8ot+PNT3EI7Fj+FI7xEciB3A3shejIXHHpxgC9RctgPHAM/WCGdA7R7Sa017N0vsuYcPNfa7+oISDg2ye5098wQAUgrFW2UBFdWAP70Cby4NWTMhDDbl2abY50T6feBjMfCxGGCYKH33u6Bma9epUSqj+N3vAgA8E+MgkoRAZQXy5StIX1vo6GqklKL00ksAAO/hQxAH+tsuW8/NRNEpQJmtaMh2MOfYyGXbaVvrDxkmm+3x1bcXUFYM9AY9+LlHx50+/GqHmihbSbtB7GZqA65r72Es4os9Y27lveehkQie2te3I4u3bYSe4QD2nR4AJ3AopKq4+tISKoX7c3y7oVK0oxHEd2Qm6Xr4Qx7wIgdTN1HO39/jYmgmNKsP6Q3uXMHcFvPlpthPe0ZDqLfzoM9uxhVtdzuepniE/AL7N8yyrGzRVqloLR+6dM2EoZkgoghJMMCZKiiAgOXia3ba2g+M4z3+zjevNsXI7DxbLhAAsUasRbsYWTLVua27CLuzwHNtirU18fje3h1bjfTp/X3Y1x90hJ12sGJk7Jj2tXFBPkgme9cWU2qFyHPoCdaE0MFw5xtAQBIw0etHQOKdB4XtZLIuSmI44mv7PRR45qoDWL4m0FlIt4vD2RzcQDTCRuE44kyLtwtgtZvKxnPEEedW6gojtRVtrfWkioozPX3fNjn9ApLgfNdvJ0tt82wB1g57AMd2d3YSoOuLX5kmxZ0Uu8ZPxQNtz8cnp/pwZjKG9x6I4xceG8evvG8KP3NqFI/v7e147Cd6A/CKPMqKgYVsFaZJMZ+1RdvGttRPT7S3v6LqKFQ1NlgTabzejbQQbe04gb0t2nJwIISoX0RVNfDDmyn87flF/PGrM7i0mIduUgxGvPjw4d6WESdHhtn373ay5Ai+9ZEV43UCdPPxsEnkFVAKhJuKQrYTO22n0566c0zgOUfwfGs2uyau4U6qhD87O4eVvAyvyONnTo042+4VeRywrrUX5hvjFXIVFdmKBo4QR0wXec75fjQLyst18S72fh6OtM+1tV289syEAwNB+DxMfLbjLFpRUXV849Iy/vb8klMY7uOnRvGho4MdI3VcXFzWp9lpu6OpZoH0Lfb76JktXfWidX2z7ylOPEJobV/liale8BzBXKaCudUKbieL+Mqb88hF++H38BiqZODNpCBrRkMRMrWqo1pUAUIQ6WPFbILPPAPwHJQ7d1F64YU113Nqmih++9swqzKEeB/CH/kIYp/6JMLDUfCmitLlG0h877W2gq9y8yb0lQSIKML/+BNd7w+772SbJaab8sib0VUDhjX7xhY+KKX4+4tLeGMmA0qBYyMR/Pyj44iHJKc/35wNvF10GsSux773r5ZVp0iyzaI126kn4OlY1PndTM9wAEeeGobkF6FUNFx9ZWnDBfO2CyfPNvjuyrO1IdyDy7W1i5AJHh7CDu63ea0BJ1XWGwbD7EiJcO871yiws1QVl41jZ9pqFcA0gcIS+78l2nq8PDieAzUp1BaV9uxQeikogSOASAzAMOAlrQuRzdZV4O5IO9HWjkaIRp3Xak7bd45oO9Hrx2Qfy/ds52zcLewfCOGnTgx39fBti0/rjZQ/CA4OhMBzBAcGguuK4wN1Dr5uqsQ+f3IEv/TUnvvSSdxTF/XQyTEMsGlmQC2bulNbWDwBW59H4BrE4e3AnjJtP/90ckXY59VyXl4zdb2ZoCQg6hdBKZxp6/u3UYC21311KY9ksXWerY1TjKwgoyDrqKgGEw9bOOztgQ9VN5EqKY7Q2WmquVfk8fT+OE5PxJwiWt1gfy8A9iCYKimOU3egxbTIwaZcW1sc7Al41rizm3NUKaWO0NmczQswQf+RSea2vbSYx91UGYQAU/1B/OyZUXzqzCgG2hSoGI54EfOLUHXTcZfPZyqglLmzmiMrnKJqdfm8K07MQ5Nj2H5QLKlObjel1BE69zRFsBwfjULkCZIFxRHzK6qOf7i0jK+dX0JJ0dET8ODnHhlb48w+bs0+uJUoOlnZADBjxTwMR70N+3moTTGyVoMC9rLpotpQ7Kw++mjSaovAc8500PNNArLN3VQJf/zqLG6sFEEIcGYyhn/8xESDq9nFxaU70iUFr95OO99NxVAg69bDqGcXiLaLb7Gbes8eNuNvC2nO8XcKkQXX3kciPtGZxfXNK8v4+4vL0AyKnv17cGQ4jHghCU+pwETbgQHnfbbLNhiVIFgz/jyjIwg/9xxACOQrV1F57bWGz6q88Sa0xUUQUUTouedABAF8JIKen/04eg+wAmeJN24i95d/CfnGDVCt9lxFVRXlV9n6/GdOgw921+/KVVQs5WQQAjw8HgVQK+7ZDtuZJkg8eGtW01uzWdxNlSFwBD9xfAjPHhlwZjzZubKr90G0rS/Kut7zQ0ASsKeP5Rb/3YUlfPvKipOBv9ChPoJLDX/YgyNPDyMc98HUTdx+M4HFG2sHmO831aKVqfoOnd7eDSHLKFHMdJeFvVXYLvydnGcLAKKXB8cTUJNCsWaDmyZF0c6zfYcWIQNc0Xb3I/oBwrFOkpIHiqwSKiKsSiohpBaR0KLSnhM6HZJARBECZ4JqGiTYTtua0FtVa46ltnm2Nm2dtrZoW5u61VyM7J2AyHP42MOju7bK6GZ5ZE8Pjo1E8EiX+Yf3k/6wF//8vXvx/oPrTz2rdyV2yrO14Tiy7bm8NiMxn+NitqfQt6NZ2BwKd+7I2oMxrDjV9rZnvMfv5KYRUhOTWmEfj1uJIlSdiYntYiGARjG7L+jZ8sKG9dhFzNIlFZSygn3N4qDNkCMS1sTnvqDU8typLw73+nQGsmbA5+G37WHEdtLfTpUcUXU01trJPViXBwsAiQ6OYY/AOY7WpVwVyaKCkqLDI3Btxe3DQ2HEQxJEnuDEWAT/5IlJ/PSJYYzG/B0HXAghOFJXkAyoFc4cb1E4c9jJg605Guwpl83fHZ+Hd84jWzxIFhWUFQMegVvzXfR5eCdv763ZLG6sFPEnr83iuiVuPjLZg3/02DhiLc7NwbAX/WEJuklxdbkmls440QiN99/hNsXIlp3pprX9HPaKCHkFmLSxyJ1drK05+ujYaASEMPF7tdRYTO7tuSy+dmEJsmYgHpLw84+O4+n98W2/dri4vFN58UYKZ6czeHOGxQYVFHYd8wk+ePgd7j7TFWD5Avt99JEtXbWqm0hahTKHoz5QSmGW7UJkrQcyH9vTA4/AoawYoBQ4MRbBR95/AqIkwUcNEFCUpQC4QO16aou24XjjvUmamkLoA+8HAFTeOofKuXNsuxYWUXmDxUEE3/8MhFjMeQ8RBAz/2GOQpvaiqPugrSRR/PZ3sPr/fAnFH/wAWiKByrm3YZZK4CNh+E6e7Hp/2Pe3iV6/M7C2mKu2LeAJgDmIAXh8TIxeylXxyu1VAMAzB/vXFFy1Zwlmmq7728HbczlncLW+KGs7fvL4EM5MxkAIi0P68o/msJir1vJse1zRdj1ED4+Djw5icK91/tzMOuf/g8IuQubb4cLhdhKuK0bWbbG4SkFFeqF4T8XlFFu03cF5tgDr5zfn2r4b8mwBV7Td/RACeKwOx+pdwNQB0VcTTVGzktsXw3pqofQiOJ+Xiba6DomyB+OSojuZSXOWY6kvJCG4nqOwrdM2B4AVIbPhAgHW6aIUxjvIbftuJOIT8eyRAYTbiFYPGq/Id+U+bBBtd5hrmOcIPvzQIJ7e37eugFefp+yry7Vsx8NjUfzYkQE8c3D9ghb3ikfgHDdePCR1zM+1j0FZMZzlOx3H+v2yr00Bsq0iWBeRAHQufmGLmqmi4jxcDEbWumxtbDHOngK4py+wbZlqI1EfQl4BimbirVl23W7Os7Wxj0e6qEIzzIZp+K2oz7W1HcPjPf62Ax08R/Dzj47j/3zfFD5waKClsNmOw0MhEMKKkeQqat3skLUDjU47SgpU3Vw3smKoSeS1xe2xNm15eDwKQthy37i0jKpqoM8SN9+zv69t+wkhOD4SBQBcXMiDUgrdMLGQbXTCOtsVrZ1XtkOvrLSPrGiVM2yL2xNN0Udhr4i9lrv7wkIOAHM1/OBGEi/cSIFSlvf383UZiC4uLhtHN0xn0OjKUgGmSWvRCLvBZbtyiRUg9vcCPXu3dtV5GSZlOegRnwharYLqBkBIW9HW7xHw9P4++Dw83ncwjvcf7AcvChBHRpyZY7lg7VmJmhSFtJVnG1/bt/IeOYLAU08CAMqvvIrKubdR/Pa3AUrhPXIY3oMH17wn3OeDNBiHcPQE6ENnwIdDoIoC+fIV5P73VxzBN/Dkkw3F0DpBKcX1ZeYyPjwURizgQdQvwjCpk0Xf6j2JaTYAGIiKkDUD37i0DJNSHBwM4aGRteeXHROWq2rQjdbRDltBWdFxbo71OR7f27vO0gyB5/D0/jg+fmoUIa+AfFXDV96cd2Y7uU7b7iAcwfjRXgzsYc/ky7dzD2xbqElr8QhtZlO9G/CHWa6toZlOxm8n1KqOa68u4e7bKcxcSm/aLW078Xdynq2NHe9ia1h2NMI7Oc8WcEXbdwZ2RELqOvs3PMLEXItOTlsnlN4vgPh8ltNWB29SCBwBpUDJctvO2nl3bR7kG/BZTks5D5i1kV8707ZetAU6RyRQw0D+a19D7q//BtRoP4rs4rJV9IckHB4K45HJnh2Zybg3HsSZyZ51b059Aclxs66XzQuwjvBDI5H71ubjo0zUWi8LOOYXGzKS15s+V+/gtJ2w28m+gdpndHJ4hH0CfB4ehklxzcqjG+zgfm6Ov+gUjXCvEEIct60t/LVypwJASBIQlGpuzZV1IitGLFFxMSc72ah71onf4DfpXg95Rccx/uqdVRSqGniOtHyIC1muU0q7i6xozrW1na/N0Qg2Ub/HOWYcIXh8by9+oUtx8+BgCB6BQ66iYS5TwWKu6hRv62vKe2vlnrWF9N4WkRWO+FwXp9CcZ1vPSSuf99pyESVFx99dXML5uRwA4D37+/Ds4f5dHwPk4vKgSRQVaAZ74C4pOmZWy7UiZPexsJhpUnz94jL+4dJyVwUIAbDZfgtvst9HTzc8g2wFi7nGae9Onq3fD8K3768cH43i/3jvXpwajzn9H8/4GLwiu7esBnocY0opp0BXDfAih2C09WCq/9Qp+E49DAAov/IKzHIZfE8Mwaefbrk8xxHEBgLgPB7I/fsR+/SnEfnY85AOHgCxrsvi6Cg8U1Nd74slq/CpR+Cc+4t9P73bJiIhl6igUlDBCxxiI15852oCRVlH1C/ig4db19MIeHh4RR6UApnK9s2CPDu9ClU3MRjxYv8G+2tjPX784uMTODwUBqXsNIx2mO3k0pqhfREQjqC4KqOw+mDctkpVh2lQcDyBtI7B5J0M4QiCsZrbthOUUty9kHKyqlNzRcxfzWxKuK3pQTv/u+M4bcu2aGvNkHgH59kCrmj7zsAuRpafZ/9GRhr+bDtt5RYVQBudtj6InAmqa9AUEyEvu2jaOYSOE6ebrDpPAOBFdgeVa9M7W2XaAoDQz0RbLZlcsyr58mWos3PQFhagLS+v/9kuLvcIxxH8+EODeM/+3R1vwdUVW9ppjmGAPWj8X+/fh1NWJls7CCEbiqyI+EQ8tqcHj+7pWSNwbQf7+oMghD2ndnJ4EEKcbVesTlan4zIY8TpimMiT7q6990B9scGAxLeNoCCEONt9bZlFVog8QV+bootO4ZCSgmRBASGsCNl2cWSICRw3VorW53vbFka0t205Lzsu23iodWSFLdom8jKKsuaI1ZN97Y/LBw7147G9PfiFx8ad4jjd4BE4HBligxkXF/KOq3eit3Uhuvp2AHV5ti0KttntsAvilRXdmXrc6hwb6/GhJ+CBqpv4k9dmGjIQH+li8MjFxWV9FppckpeXCg/EaXt1uYCbiSKurxRxcbF1lvUaVu+wmXWCBAwc2/JtWpNnW2LXdi60vsjXfH0Sx8bgEThwBKhGe1GoapDLGu6+zUwjkbgfpMN1OvDkk/AePcLWLfAIP/ecU1i5FbEhdk3NLJcBCnhGRxH+0IfQ80u/hPBPfAThj3x4Q9fQ61Y0wr7+WoyVnQ8/nS6vLZRGKRZv5gAA/XvCuL4q406qDJ4j+MixobaznAghTh9gu4qRZcoqLi2w9jy9v29T9xKvyOPHHxrETx4fQm/Qg1PjsfXf5NKAxysgPsb6f8u3cvf9802TYvYyi+rwhTwdv3/vBrotRpaYKaCQqoLjOYwcYOf9yt38phzTuyXTFgCkANOn5Ir2rsmzBVzR9p2BZD1o2zfqcDvRtkUhMjvTNiCAs5y20HVoiu6MVBZlHatlFSVFh8i3diytgZA1EQmmLIPK7IvFhxs7oO2ctma1ivLrrzv/V+/eXf+zXVxcHB6Z7MF4jx9Hh3fm9EqR57rqqNcLtetVFyaE4Ml9fXhq3+YeAjZK2CviJ44N4cMPDa3r8KgXnyWRQ6zDqLbIcxiwRPfx3sC2Z4XGg7WK0WPr5Mfaoq39ANnfofBZQBIQs4rDAexYdpNbt1mm4gFIYm1fdcpgr+XzVmt5tm0GBWJ+ET4PD92keHMmC0qZwNvpmAckAU9O9SHewrm7HnYhnbupMm4lmLNsso1wb0ck2G1oV1ANYMfZI3BQNBOrZdUZkO0PSy2LKRJCcGIsCoANNvg9PD5xZnRNBqKLi8vmsd2kJ8bY9346VUaizLJt75fTVjNMvHZn1fn/K7fTKClrnx3WsMCm+WP4JCBs7UCpYVLnujYSW78I2XrwsRh8R4/CnDoANRjBSqqMa68uQ6lokAIixo50rslACEHwmWcQfOYZRJ5/HkJf58H9SNwHwcNDk3Ws3K2J4JwkQdq7F5zU/b1BN0zcsIpsHq6boWTXOqioBhKFxgzaXKKCSl4BJ3AgvR68PlcTSdeb9WHnuGdamH62gldup2FSir3xQMdoqW7YPxDCp5+YdO5VLhvDdtvmU1WUsp3Fwq2EmhR3304hn6yA4zmMH+0uIuOdTKin5rRVq62vv9WSioVr7P4wdrgHIwdjzr5buJ7FynSXA25gLmddYXEzOz3TFqjl7ioV/V2TZwu4ou07A09dp4VwQGio4c92oLem6NC1WryAaZhQZfZ/yS+CeH1OITJVNhqctnY0wkjM1/2UVVu0rbCLiu2y5QKBNaPSYj8rDmVkc6B1xcgqr78OKivO8srduw+8uqWLy25ibzyIj58e3fXTxezs16AkILRepvYDYP9AqMGp2o56Ea2byIqjwxGIPHGmqG8nhBA8MsmKtzw00lkksIVN3Zpaup77eahusG/vNsY8ACzm41DdsejkULaPx0qd07ad+5kQ4ix/yXKgrRfzcC/0BSWMxHwwKUVJ0cER0jZn2M4/XsrJMM1aTEKrtnBczbm+lKs69/fmrNx6Dg+FEPWLiIck/Nwj4w3FzVxcXO4NJkyy7+yxkSiGo16YlOJ2mhkZ7pfT9u25HEqKjrBPxGDEC1U38cOb69SaKCWB7Ax7/hg5veXblLJiI7xibfaHUWLXLC648YEjQghCH/gAuKefARQTt88moMk6fCEPDj851NXUbMJx8B17COLQ0LrLcjyHscNMCF68mW0ZVdct0+kyFI3NhKyPgeK52kwcO4IIYC7bJcs52TMWxHdupmCYFFPxAE52IW7aubbpbXDaLuWquJ0sgRDgPe+yos07EckvoneE9c2W7lO2LaUU0xfTyCyVQDiCfWf6HcHy3UwgKsHjE6CrBq68tLTGcWtaQrdpUETiPvRPsuvg4N6I47idu7yK9EKxq8/LrbCB+1CPBF7c+dJgLdNWe9fk2QKuaPvOwFP3oBWMrxnl5kUOoiVy1Ltt1aoBUApO4CB4OHB+n1OITFMMhH01p20tGmEDD6hNTtt2ebaAVYwsEAAohZ5OAwD01VVUL18GAISf+xCIKMAsllrm3rq4uLyz2dMXxMnxKJ45GN/VN+aNxDwArMjTr35gv1O0bbs5PBTG//X+fW3FQZuBsLchtnC9nOH6GRrbKXTaPDQcASFM5I8H2zuZ+kMSeI6gohod3ak2TqaiJVZPbnNbTtSJ9UNRb9u86XhIgsgTyJqB26kSVN2ER+DQ42/tehuO1Im2mfWjjySBxz99chL/6LFxRHaBE8PFZTeRLMpQdRNekUdf0IOjwxGY1MBsdhWUUoSlexNtTZMiWZSh6O3rQlRVA2/MMJPFk1O9+OChfhDCYmbsgZ2W2Fm28QOAd+sdwYtONELtodyOR+C7iEdoR0Cn4GbLKFc0BKISDj05BI93ewaE+8aCCMd9MA2KmYubLxZ0zYr8OTgYWjOzxb6vzqRrMRv5ZBXlnAKO53BdU5GraAhKPH7syEBX/Sg78ihTUtZZcmNQSvHSLfYsd3Q4gt4O92iX+8fwvihACHIrFZTzW3vMm6GUYu5KBun5IghHMHWqH9H++9PP3elwHMGhJ4bgD3ugKTquv7aM5GzB+fvyrRzKOQW8yGHPicZnouEDUQzstWZrXEgju9Lh2m2RS7BlogPb3zffCmzR1tBMFjuDd340AuCKtu8MpLqR5vBoy0VaFSNz8kv8Aggh4LxepxCZqZsIWKMt2bLqVDrvqgiZzRrR1sqzjUVbLl4fkUApRfnllwGTQpraC8/kJMSxMQCAOj3T/Ta4uLi8I+A5gvcf7Mf+XT4l2+fhnSmHQ7u4wrFH4NBX96C1XmTFWI8fAkfQF5LuS85wf9iLj58axc+cGmkb2wAwV65ddIxSdnwivvaiZH1xOK/IY6gL4f1e2NcfhN/DhNpOTlieI+i3tuXcLLvnDnaIrLDbcStRQlU1IImc49ZtByFkVw+YuLjsVBayten/hBAWPcJVUVUNVBTAL2xczMhXNVxayOPvLy7hCz+8gy//aA5/dnaubdzB6zMZqLqJeEjCocEQ+sNex435/etJaIa59k2UAukb7PfhUxvexm5oLkIGAEbRzrTdXH+gkK6icqsIYlBoEoeDjw9C9GxfAVZCCCaP9YHjORTSVaTnS+u/qYmqajjFLw8PrRXx7ftDoiCjpOhWli27F2gRATfSzNX6/n2xrovN2k7bXFWD3ur4b5I7qRKWcjJEnuCJKXc6/E7BGxTRM8TOo+Xb3U+vB5iAplR1GLrZ1aDE4o0sEtYU/j0n+pzPdWF4AyIOPzWMnuEgqMkGe6YvplHMyI4TevJYHzxNMwMIIRg/0oO+sZD1vlXQDgUldc1AwXKrxgZ2h2jO8ZzT7oo1uPBOL0IGADtvjqnLxqmPR2gqQmbjDYoorlYbipHVFyEDAOLzgecoOIOJuX7CRFu7wxTyCo7Y0BXNom0+B6C10xYAhP5+qDMz0JJJcNMzUOfmAZ5D4Kmn2Hbu3Qv17jTU6bsIPPZo99vh4uLisoN47uggVgpy23zS3cJg2ItUUekqsiLiE/GPn5iAJPD3Tfhbzy1sMxjxOlOT14us6A9JEDgC3aSY7PV3FIS3Ap4j+MChflxdLuChkc5uu+GID4vZaq0t6xS5I6QWbzHes/1tcXFxaY1tjLCFSY/AYbgHOJ8HChWh62smpRQXFvI4P5dFtrJ2Gn6uouGv3lrAJ06PNuRX56saLsznADQWhHpiqhe3kyXkKhremMng8T1Nea+VVUCTAV4AIq1NI/cCpXRNETIAMItM9OQ2kWlrGiZuv5WExBPQgAB52AuhSxHzXvAGRIweimHuyirmrq4i0u/bkLP3ZqIIw6ToD0sNA6Y2AUnAYMSLlbyMmXQZo6KIck6BRinOV5j79pHJHgyG2rut16zTw0MSWf55pqKiP3Tvg5SGSfHyLTaj8tR4DMEdGHf1bmZ4fxSZpRIyy2VUiyp8ofWf+5WKhisvLUFX2bnF8QS8yEMQ2UxewhEQACAEhDDnfyFlmcGO9aFvdHebMbYLXuAwdSoOf8SDhetZpGYLSM0VAUrRMxx04iyaIYRg8ngfcskKNEVHPlVFtI0gm09WQU0Kb9CzK4qQ2Uh+wcn7fTfk2QKu0/adgVT3pQ0Pt1zEKUZWro2w20XIbJs552MdIt5koxbeptOjXdXqttiirZwHTMPJtG0r2tpO25UEyq+8AgDwnzzpLO+ZnAQIgZ5KO+tycXFx2W0MRpiDabe7Fu3p9GM9nYuW2UT9Hvi20c20WerzWTsJnQBz5triwXZn89rsHwjhoydH1i3eZhcjs+nUlvp8SKCzi9fFxWX7ME3qmCPG6nJKh3rYgEqh7IGsrS+0UUrxyu1V/OB6EtmKBo6wwsFPTPXi548F8M8mEugnWWTKKr56bgEVtfY88NqdNAyTYrzHj/G6wS5J4PG+A6xv/uYMe28D+QVkC17M5ydBt+GRMlvRUFUNCHU53NQwYFoi5GYKkZXzKnTVQCjggTnmR0kzoOpb5yLtxMBkGIGoBEMzcfH1FfyPH95pKPzWiWtW0c9Dg+0H7+yIhDupEpZu5UABzJg6VLD7wWPNovs6EEKc+8SaY79Jrizlka1o8Hl4nJ6Mbck6XbYOf9iD2CCLK+zGbWuaFHfeTjmCLQCYBoUm66gWVRRXZRRSVeRTVeSTFeQSFUewHTvcg4HJnVkkeadACMHwvigOPDLA8mYphegVMHmss0Od44gj6qYX2jv7cwl2Ld0tLlsbqS6m692QZwu4Ttt3Bp4AMHKKFQHwRlsu4msRj+A4bS1BtybashuzBwAhcCp+d8q7a4kUYqPvhg7I+Y6ZtgBz2gK17FvO74fvzBnn75zPB3F4GNriItTpafhOntzY9ri4uLi4bBn7+oP4xOlRxEO7O4+uXuzsJmf42SMDWMnLODBwf0TbbmmON1gvZ3g46kPamn1zvzKTXVxcGkmVFKi6CUlsjJwRhCoCEg+B+nFtuYCHx9sLXJRS/OBGEhfmmcjy5FQvTgz74c3eApZfAGYXAAAfk8L4Mv0w0iUVXz23iE+cHkVB1nDdykp9T53L1mZffxB7+gKYTpfxgxtJvGek9rBM84uYXoxBD/YisFJBz/DWDv7YDuTBiBe8NRPALJfZgwnPgfg3ft0qWUV9YnEffGUDVdVArqI68TLbCeEI9pyI48pLi7h2cxXlmIAfKavwilzH43s7WcJyXgYhaCiy2czevgBeu7OKhfkiBqiIRFFBMsbBI/D48aODzj7cCL0BCUs5GZnSvYu2umHi7F2Wm/zYnh5Iws4bxHUBhvZFkV0pY3WphOEDUcf41YrFm1mUMjJ4kcND7x2BIPLQNQO6ZkJX2b/UBADK9ATKrlfegIhw3zt/SvtWER3w4+h7RpCYKaBvLAihCwNE32gQibt55BJl6JqxZkaBaVLkkky0jQ7urj6gN1CTMN8NebaAK9q+czjwXMc/O07bkgZKKQgha5y2xBJtBapBMwwYqomgJKAo6yAEDaPvrTByORS++S34z5yGtG8fU3x9MaCUgplbAZWZg7edaMsHA+D8fmcEPfDE4+A8jdMypL17oC0uQrnrirYuLi4uDxJCSNcRBDuZkCRgJOpDQdbWuFVbEfGJHXNvHxQ+D4+YX0S2oiHiE9d15g5Hfbi4kEdf0IOwd+e1x8Xl3cBClvV5R6K+hoiSglpAf8gLvRTE5aVC29kZpknx7asJXFsugBDgQxMCjmivA29cBXRLaLPe59cL+MTxIL5ypYhUUcFXzy3CI3CglBW3GmghXBLC8uTnMzOYz1Tx1dU8emIqBI7D2LWbyOSDKFIeC28tobcUhSRw1g+PiV5/QwzDRmmVZ2taebZ8MLQpd1Upy55FgjEvYlRBVTWQrWj3RbQFmJMxMhZE7vYqOEWHGRDw4s0UQl4B+/rXCrK3k0V8/eIKAODYSKTj/oyHJAQlAZWZPJa9BuaJAYhevO9AHLGAB6a5cUexnWu7ugVO20uLeZQUHSGvgGMjW1+0zmVrCMYkRPr9yCcrmLu6in2n+sHxa530+VTVceNOHu9z3I+8yGF3D+XvTLxBERMPdZ8B7Q974At5UC2qyCyV0T/R6GouZWQYmglB4hGM7q4jVu+0fTfk2QJuPMK7BskvgHAEpmFClQ1QSiFbTluvnWkriiACz4qR6To02UDIylsaDLevWm0j37wJPZVC6cUXQTXL0WtFJBhJNsrPBQIgnvb5OEI/m4YlxOOQDh9e83fPnj0AAG1pCWa12m3zXVxcXFxcWkIIwSdOj+KXntqz650/dnG79Vy2AHBwIISn9vXhQ0cHt3uzXFxc2mAXIRuNNT54FtQC+oIe+IUQ0kUFicLaau66YeLrl5ZxbbkADsDzvYs4svSXwNJ5Jtj6YsDe9wGP/wsgzGpexJRFfPzUKPweHomCjPlMBTxH8NRUH1sppcD1bwA3v+1MtYv4RTxuFYzKVDQs52SspLPIp2RUNRMp1YP52QLOzWTw2p1VvHAjhW9dWcHfX1y6p31j59mO1O0bo7T5PFtKKYpZ5rQNxiRE/ex5JFvZmqn/3ZLyAaaHQ1jgsdfkQSnwD5dWnPba2IKtSSkODYbw/oP9HddLCMFExAdS0TGfqcDo8WCqP4ijw5ufgm7HI6yW1p5/G0E3TLw5w2qcPLqnB0ILEdBl5zByIAoQgtxKBddeXYZSbSxgqMo67r6dAihF/0QYvcM7a+aRC7se9I2xgaBWEQlZKxoh2u8H2WU1Dez8XUF6d+TZAq7T9l0D4Qgkvwi5pEIuqSDEA1M3AULgsZ22hIB4fRA4g4m2ioGo34OlnIzJvvWnPJkFlrdkVqqQr12D7/jxmmibWgbQ3mVr4zt5ElTVEHzv0y1H0PlIBEK8D3oqDXVmBt4Wwq6Li4uLi8tGeKcU4To9EUNJ1nF6Yv2sQI4jeHSDGYcuLi5bR32e7Ui0NmuBUoqCWoDAczg8MIC5FPD6TAaHBkMwKYVhsqnGNxNFzK5W4DUr+Bn/eQzkWF8bvVPA2GNAdNxx2SI2CeQXgOwMeocfxs+cGsVfnVtAVTVwbDSCiO1cKiwByxfY78MPA0FmpjgzEcNgWMJyIoVYrAdCfhort3sghMMQ4xEYJkWkLwTTy6OqGbidLGElr0AzTIibEOiKsoZ8VQMhjfncpi3ahjYuEillHbpigOMJAhEPYnkmSObuo2hrmBRXl4sw+yUMqDyGJQno5XA3VcbXLizhU2fGEAt4cCtRxDcuMcH28FAIHzoy2NV9akAUcB0A9fAIBj34scMD95T3aBegzlU16Ia5abG13mV7ZMjNMd3pBGNeHHh0AHffTqGcU3Dlh4uYOtWPSNwHSimmL6ShKTp8IQ/Gjrr9iJ1K70gA89cyKGVkyGXNmXlNKUVuxcqz3WXRCAAQiEjYcyIOKdB9oc7djivavovwBgVLtNWdaQ4eL9/QCeB8Poh8AdA0qIqOxw/1Ieb34OHx6LrrN/IF5/fq22/De/QoiC3aplcABMBHO4u2nrExeMbGOi8zuYeJttPTrmjr4uLi4uJi0ReU8PHTW1/F3cXFZetJlxUomgmPwKG/Lhu8olegmzoICE6PDWMutYw7yRLuJNe6peLKLH7C8zZiusHqSEx9kImtzQ+ysUlg5mUgOwtQinhIwifPjOFWotiYp7p6q+73245oS6zCZqLqRX9/EFo2gxT1IRTtwcjeGArpKoYDPowejIFSij986S7KioF0SWko9tgtSznmiI2HpIYZEIYdjxDaeMV522Xrj0jgeA4xS6jOVrROb9tS7qZKKCk6/D0Seksc1IqGDz42irJiIFGQ8TfnF3Fmogffv57csGALABFw4DkCzcfjQ0cH7rn4Z1ASIIkcFM1EtqJtKsPeddnuTqL9fhx9ehi33kyikldw4+wKxg6xa0U+WQHHc5g61Q/ePZ47Fo9XQCTuQz5ZQXqhhNGD7PhVixqUigaOJ7s2Wzg+vvF7wG7G/Za9i/BZo6XVkuoUIWsOF+d83oZ4hIhPxKN7eroaJTfyVpVJjsAoFKHculVz2mbSAAA+Gr3ndnj2sogEdW6uFsPg4uLi4uLi4rKL+f3f/31MTk7C6/Xisccew+uvv9522S996UtshlTdj9f77ijIcU+YBqDJD3orANSiEYaj3sY8W4WZIIKeICZ6gjg5HsVw1IuRmA/jPX5M9vkx1Svhafomnhd+hJhoAMF+4PRnrMLELQS+8DAgeACtCpQSAJiL8rG9vfAIdX38dJNo24b8fJJtYzyKvjHmerUrkRNCnHzclfzm9rUdFTAcbRQUzKIdj7DxB3Y7zzYUY9tWH49A7arL28yFBfasdGwsikCECaBKXsVHTw4j4hORq2j47rWEJdiGNyTYAoBSUHF4KIwPnhrCRO+9F4YjhDgRCZlN5tq6Ltvdi+QXcfipITbNnlLMX8tg/horJjfxUC/84faRhy47g75Rdn1eXSg517lcogwACPf5wAuuHLgbcI/Suwg7/0Mpa2uKkNkQn4+JthqLR+gWqmmsoisA34mTAIDKm2+BeqMAACObAagJXtCA+deBS38JvPJ/Aze/teF2CPE4uFAQVNOhzi9s+P0uLi4uLi4uLjuJv/iLv8BnP/tZ/Pqv/zrOnTuHEydO4LnnnkMymWz7nnA4jOXlZedndnb2Pm7xLuX8l4HX/r9AuiZIUkoxk59BUS3e101ZdPJsrempyxeAs/8D+dwMACDsCTuFwD71yDg+eWYMHz89io89PIqf7lnAGXEWQUkAxh8DTv0TINDX/sM4HohOsN8z062XqWSAcrom+hYWAbWydjnTQG6Z7avo2AAicT9ACCp5BaqVfdkfYsJoqyzebrBjI0Yt0dZUFJRfew3awjwAVrx4o5QyVp5tDxNLo5bTVtFMVLXun3k2y2pJwXymAkKAh0YjCFtVz4urMgKSgOcfHnHqhzDBdmBDgi2lFKWcgqAkYO/E1hX66glIzvZvFNdlu/vheQ57TvRh8nifk33aMxx0BmtcdjbRQT94kYNS0VBcZdfArBWNEB3YfdEI71bceIR3EbZoK5d1iBLrVNVX3wNYPAJz2mrQVQOmYbasGNmMPV2JeDzwP/oI5KtXYWSzUBfTkDgBZlkG8j8CP50GMnWj5ktvs9wtX7TrdhBCIO3di+qFi1Cn70KynLcuLi4uLi4uLruR3/u938Mv//Iv4zOf+QwA4Atf+AK+/vWv44tf/CL+3b/7dy3fQwjB4KBbSK5r5AKQX2S/X/kqcPingf5DmC/O4xvT34DES/jwng9jODgMADDLZSh37sCUZVBFBdVUUEUBVVWIY+Pwn3p405tCaX2erY8V/Zp5BZDzKCz8CPBKiEhthDfTBJbPs98PPMfctd0Qm2RO2uwMMPHE2r/bztrouOXITQKZO8DgscaPz6+gUBIBTkBkfBCixCMYk1DKyMglK+ifCGMgzIS+ZHHjTltZY7EKADAYEFA5dw6Vt94Cldlr4vAQhOHhDa1T1wxUi8wpGrSctiLPIeQVUJR1ZCsa/J7tfSy+uMhctnvjQYS9IoxeL1bu5lGwhJSegAe/8Og4EkUZ++LBDWetyyUNhsae2/yhrXNA2rm2q5tw2rou23cGhBD0T4QRiEoopGX0T4TeNVmiux2e59AzFEBqroj0QgneoIhyjl1LXdF29+AOd72LsKMQlKqOaondeJudtpzPB55QEIOJuppqdrVuOxqBj4TBeTzwHWcdvMpb52CKEZiqDpgG+EgQ6N0HTH0AiIywTurKpQ23xbN3LwBAnZ4GNbvbRhcXFxcXFxeXnYaqqnjrrbfw7LPPOq9xHIdnn30Wr732Wtv3lUolTExMYGxsDB/96Edx5cqV+7G5u5fcHPuXEBaTcPVvgJVLmCnMAAAUQ8HX7nwNN7M3WTGwb34LpRd/iMrZ11E9fx7ylatQbt+BOjeP8quvwlQ3X8BqfrGIakWDyFtRAsVlQGZ96XzmNmBoCHvaiFy5GSZAi15g8Hj3HxqbZP/mFwCjRbyYHY3Qu5/11YGWEQnF+QWYJoEYCsFvTfGP9rOH/1ySObjseIRMWYWqb6yfvpKXQU2KkeQM1P/95yi/8iqorIDviSH8Ex9B5Gd+BpxnY6JkKcNECikgQpRqOa8xOyJhk1P/u0XVTVxbZrEXJ0aZGB/q8QKEQC6pUGX23BXxizgwENpUccySJcQEop4trQbfF9xcPILrsn3nEYhIGJqKuFPqdxl9YyxOJrNcRmaJzYwOxrzweF3/5m7BPVLvIgQPB17kYGgmynlbtG102hKvD4QAvMk6c5qsQ/Ktf5qYjmjLOiK+EydQPX8eejIJefgQ0DsDLtYP8r5/BXDWhV4KMsfDykVg4qna610gDg2BeCWYVRn68jLEkZGu3+vi4uLi4uLislNIp9MwDAMDAwMNrw8MDOD69est33Pw4EF88YtfxPHjx5HP5/H5z38eTz75JK5cuYLR0dbF6BRFgaLUpjgXCkxEMk0T5n0YADdNE5TS+/JZLcnOgFAKOnIGMBSQ5Yug1/4ec14O1BtEzBtDVs7iOzPfQYHextjiAgjPw3voEIgkgXg8IB4Pqm++BbNSgZZIQhzZmOMTYNPh335hHqSqYvDhPhBQmImrIFbeYF6XQYsrCImh1vtq6TxrR/wwQDjmvO0GbwzwBEGUImhuvibiAoBWAXJzbL09U4BWBpl5BXT1DqBrAMc7xy83nwSlQHgwAkopKKUIx72g1yjyySo0TYdP5BCQeJRkHYl8FSOx7ovdLGQrCCzPYez2WzDiQXChIPyPPgrp4EEQjnM+cyMUMlVQShGMehr2acQngFKKTFnZ1vPy+nIesmog6hcxGvXCNE1wAoE3KKBaUFFIV9EzfG8ZtEWrjf6wp2VbNvv9i1r7KFtWoGp61+LrxYUcirKGkFfAoYHgg/vev0N44NdPl3viQR4/f0SExydAqWhYuJ5h1+x+r3subYDtOn7drs8Vbd9FEELgC3pQysrM4QrAG2hy2vpZp4qnKijQda6tYXX8uTBzBXA+H7xHj6J6/gIql28BoWHw/cONwmzfQeYSkAtAdhroneq+LTwPz+QklOs3UP7RWQSfeR+E3t6u3+/i4uLi4uLislt54okn8MQTtSnuTz75JA4fPoz//t//O37rt36r5Xt++7d/G7/xG7+x5vVUKgVZ3v7iXKZpIp/Pg1IKbgMD9TbUMABVBfFtrtq1f/4yOKWMqhmCEX0InkIZyspbSCfvwIxO4qmH/hUuGZdwNXcVN1/5a1SKHoyf/iDMo0cb1qP6vDBSKai3bkIQN/4oNXtjFRcSb8MwAjhgBpBMJOC/+yY4tQw9MoG0fAGqJkMtKEiqjZnGRKvAP/s2iGmiIo7A7JB53AqJi0Esr0C9+zbUsdrUWCF9Hd5SCYa/l8UIUAF+1QRXzqJ69zyM8BhM00Qum0VxLg1No0BYcjKXKaVQDRla2cDMzUWEeiX4qIJEWcb12WWIWvf5l9fn0uAWZyFQHfLgAMQnn0JB4IF0ekNtrWd5NodyWUUYXGNOtFJCuVzG7LKBA+HtETAopXjlWhrlsoaH+sJIpVLO30xeRrlcxfzdFejCvVVDX5nLQC7rUEwByaS+5u+b/f5RSqHJFagGxa25ZfQ2FbFuRVkx8IPLaVRUAw8PRJBZ3fyxc2Hc6/XT5cHyoI8fH9BQTpWd/+uchGRye2cYvJPYruNXLHaXpe+Ktu8yvAGRibYAeJE5b+vhrMrDgqlAA6DKXYq2eSba8uFa/pbv4YdRvXQJVGfr4KNN2Vy8AAw8BCy8ydy2GxBtAcB39CiUmzehLS0h+7/+HNKB/Qg8+ij4aHRD63FxcXFxcXFxeVD09fWB53kkEomG1xOJRNeZtaIo4uGHH8bt22uns9t87nOfw2c/+1nn/4VCAWNjY4jH4wiHtz9v0jRNEEIQj8c39dBTfuklVC9eQuRjz0PcYKYp5DyIYMIUgvBNPAQi+YH+T+DaxSrEmXkMKjmMydMYO/zjGL/iw0LhCoqchktTGp7r64HA1R6ZKlNTqKxmIOkGQv39G27HhTfnoJgGTOQx0uNHv08HEU1QbxTq6Y/D+M6L8FATe/0ipL6m9c+/DuLzgYYG4Z882voDOkGPg1Tn4EcOqN/21CsggQDoxKlamyZOgixfhB9ZoP80TNOElsujaAjwSATjJ46DlyRnFfJeAcnZAgTdj/7+XuwvC0gpq1AFP/q73E+GSVGhBQyqVfTHQoifOAlpeGjj7axvskkxb1QRCIgYmxpqyHvdwwVwMWXAFD1db2MnVN2EyJOGvM/lfBVVFBAJSXjqyAR8nlo8g2AEoGST4I17+3zDMDFHKggEKManhuBpMUvyXr5/4wMqlvMyOF8Y/f1rxWVKKVbLKu6myribLmMlL4OIXgyGBDx1ZMKNRtgC7vX66fJgedDHLxLUUEmxAu6SX8TYniE3l3gDbNfx81ra23q4ou27DLsYGcC+sM1fVtu9wOtMtO3eaVvLtLXhg0F4Dx2CfOWq9bcWBRUGjzPRNn2LVaj1dB+ILQ4PI/ZzP4fK2bNQ7tyFcuMmlFu34D18BP5HzoAP3duItYuLi4uLi4vLduPxeHD69Gl873vfw/PPPw+APSB873vfw6/+6q92tQ7DMHDp0iV85CMfabuMJEmQ6kQ2G47j7ttDJCFk05+nzc6BANDu3oXUJgKiLfk5gBCUruSgT/9vRD72PISeHixEh0BiExivyuDmXgOKyxi/zcEX248LvSUkzVVMF6ZxsOegsyqxfwCEEJir6Q23Q63qSK+WYFIKAmAldxcnoAGEgMQPoMRRkOAAvOUUfKlrQH/tc0EpkLjElh0+uaFYMYeevez95RSgy6zfbehsxhshIPGDtfX27QdWLoFk7rIcYI5DdTkJgCDc64XY5HiODbJiN/lUFYQQDEV9IIQgVVTAcRyyK2UkZ4uYPNa7Jp7NJlGswjBMBKtF+HpDEPvv/QG5XFRATQrBwyMQlhqefXqD7P+Fqg5CyD2JGG/OZPDSrTQEjiDoFRD2igh5BWQrKgghODgURsDb2O5Inx+EEFZETKcQ6wTdjVDKKgAFPD4R3kD7vN/Nfv/6Ql6sFBRkq1rDeymlODeXxYX5PPLVWk4yxxEMhr1438E4PJtwo7u05l6uny4Pngd5/HxBCeE+H4qrMmJDAfD85q4172a24/h1uy73G/8uwxuoF23X3kQ5W7Q1VVDThCavnV7TDKUUphWP0CzM+h5+mHX0gNYO2NAAEBpkBSESl7tthoPQ24vwRz6C6Cc/Cc/kBGBSyFeuIPvlL0Nrcqy4uLi4uLi4uOxEPvvZz+IP//AP8cd//Me4du0afuVXfgXlchmf+cxnAACf/vSn8bnPfc5Z/jd/8zfx7W9/G3fv3sW5c+fwi7/4i5idncU/+2f/7EE1YVuhqupEcWmLixtfQW4OVDegZg2YlQoKX/8GdLmKhdICEB3H2KHnAV6AducytNf/HlGOR/8Tz7C3KrmGVQnxPgCAnsmA6uv3k+vJp6vIVksAAI/AYXZlDord/40fQl7JA6FBhDkPkL4JqLXprCgsAeU0m6nWf2Tj+wBg9SSCcSYA52bZa7lZVphMCrI+uU1sEuB4oJoFKhkAQHmFFZaKDsfWrDrU5wXHc1CrOqpFDf0h5iDKVjQkFou4/VYS+WQFybn200GXclWIlRLCIgEniq0NHxukmGEzDIMx7xpRNuwVwXMEuklR6OKZpx2yZuDsNNtHukmRq2iYy1RwZamApRz7/BOj0TXvEyUePsv5W1zdfERJOcuyqoOxtYMyW0FPYG0xMtOk+N61JH54M418VYPAEeyNB/BjRwbwy0/vxc89Oo6hyOaiTFxcXLaeiYf6EB8PYWjq3q+rLvcXd+jrXUa909bbYpSbSBLAEQicCarrXTltzXKZRSBwBFywMbNKiMXgP3MG6uwsxHauiKETQHEFWL4AjD7iiLwbQRzoR+Snfgra0hJKr7wCfSWB4re+hejP/dyGK8y6uLi4uLi4uNxPPvWpTyGVSuHXfu3XsLKygpMnT+Kb3/ymU5xsbm6uwZGRzWbxy7/8y1hZWUEsFsPp06fx6quv4siRTYp5Oxw9k3HqMejpVZiVCjh/l7OzKAWys9ALMiCxh1Ujl8PyN/4ayh4ZHkFC/+R7gfhRVP7w/wMYKrzCIgLqCEApSmqpYXVcMAjO52XFcFczEAe6n9aeT1ZQUMrQRR1RIQgtW8I1YRYn/cNAz14UVi8DUhCRQD8zNKxcAsYfZ29evsD+jR9iNSE2S2wSKKWAzDTQf5jNdgOA3v2NfXBBAqLjbLnVWzAGzqCSqcADIDq+NrKA5zmE+7zIJSrIJSoY3h9FxCcin67i0mvLCEvssbOUUda812YxJ0MqZBH2CuB7e0G2wNFkf16op5XLnCDiE5Epq8hVVER86+e1tuLtuRxU3URf0IOfPjGCgqyhKOsoyhoKso6egAcD4daCaqjXi2pRRTEjo2doc8XISjlLtI1uj2jba4m2qyUm2homxbeurODGShGEAO89EMdDwxF4BNcP5uKyU/GHPdhzIv6gN8NlE7hX1ncZUkBwOmRSYK1mTwgB5/VB5ExQTesq09bMW9EIoRBIC6t94PHHEPvUJ5283DX0H2GugXKauQjuAXF4GJGf/mlwoSCMfAGlF1+8p/W5uLi4uLi4uNwPfvVXfxWzs7NQFAVnz57FY4895vzthRdewJe+9CXn///lv/wXZ9mVlRV8/etfx8MPP/wAtvr+oKdXG/6vLW2gv1jNAkoRelEBvGHwPTEQgUf25mVEry5iJDgCjnDQSgZUbhIIDcC/tw+h5StA4gqKKxeAxBUgOwOU0yC6Ar7XctvWFZVaD0op/v/s/XeQJNl93Y9+0pf31d6Nn52Z3Z31C2IXhgAWAEESBCkRhEiRoijz9ELx4omhF+/xH0n8S4qQ4vf0IqSQRMqAFCWSIAFSBAEuzGKxMOvNeN/TPe1deZM+8/1xq6q7p83MOgAL1ImYmO6uW1n3m5mVefPcc8/ZWK1jug7NTJOJ1BjUXc5aJYL8IVA0obQFUgOnxJuWzgjS2bNhTdiNMXz/3de+G7IHxP+VWbHtUoe0LRzZ2Tbf+VvpBvW1OjgmEd0jMjy+66Yzg4JIr661xdtlBXm+TcN0SWTFc0CrahME4Y73hmHIUtVEb1RJRFTUzj5+u+hmeXQ/f0efOyKWStvd9fU7wXJ93pgXCuTHDuZJxzTGczFOjKR47GCej50Y5KHJ7J7WC8mc6NfbUtp2SNv4u6W0TQjSttp2sVyfvz63xNWVBoos8TP3DvPgRLZP2PbRRx99vEvoX11/wqAocs8WIbJH+qcci6LKAdyl0ra7XE1+qyEWWkSoBmBTRfA2IBsGqaeeAknCvnIV6+rVt73NnwSEYYgzP09gmj/srvTRRx999NFHH3304HfT5zvEl7OwcPdv7tgAeG4EJAXj0GESH/4wNbtO5tIi4xXxONR+5RWQFSJP/DzKg58mqUbArAjS9tJfwZk/hpd/H773/0UtvwLNVbz1u7fiatccys06Dj5+WmYiO4hqOzRtiemoWKlWd8SYOj1wH6i6IJyrt2DtsrAwiOUgvTthuhdaVZuZs+tUVjpWC+lxYXtg1QQRbDdB0SAzufPN3ZDg2iLVm7NIhKTzEkR2X16bGRCkbbNiU98wkeZaSEGIbcgce98QiiYT+AFmfWdqeaXtYjo+sWaVuK72bCjeDmzTwzE9JFkivocKNRvTO5//1pLUz85Xsd2AfELnyEDizm+4Dcm8IG3bdQfP2fnc5Tk+V19aYeFKedf3O1trTL87pG3SUNFVmSAM+cKr89xcb6EpEj93/whHB/sZIn300Ucf7yb6pO1PICZP5Rk5kunN7N4OKRLdZo8Q7jIbvhV+V2n7dnynuqqBtUvgvbVB01ZoIyPEHnkEgOa3n+v18ScNoe/jzM8TBsEd2zo3b1L7y/9D41vf+gH0rI8++uijjz766OPu0FXaGgeFSvRN+dpWOqStLQgttVhAOXKI5UlBsGVeFBP8zuwtkCViDz8MIw+QePA3ID1KM5YhSI9DLC8sAwDVcGHjGt5LX4TF10SY1x1QWzcptZrYUZuBRJ5MxGFA0sFNcNYpE4YhdVuQtqlYHgZOijcuvQEr58TPw/fftY2Y3XaZfn2Ni99dZH2uwey5EmEYCjI4NSoaTT8r/s8dFKvebkc0A/ECYRBQu3EDgPRIbs/P1KMqsbQBYcjVl1aIyjJhVKU5aKAock/t2qzstEhYqgrRQN5pIksSauHtk7ZdlW00qaPsoQTtkrbVt0Da2p7P63NVAB49kHtLQWZ6RBX2dWHY89/dirlLZWprbZauV2nVdu63rjXCfjW+XUiStM0iQVdlfuGBUQ4U3pqdQx999NFHH3ePPmn7E4jMQIyx4zkkefeBhRzdVNoShri7zPpuhV/bPYTsTSE9LtQDvgvrl9/6drYg9sjDaCPDhI5D/etfJ/TvrBr+cUPrhRep/eX/wXzjjTu2taenAXDn5990sMaPGvxmE69UunPDPvroo48++ujjRxphGPbu6ZH77gNJwi9XCFqtO7yTTuDWHKEf4NnisUctFllqLbFx3xgMFdF9icbXvwGAcfRoLzg3nplCyh8iLB6jfeoX4LF/BE/+Njzxz1Dv/xgoGn65THjla/DSf4L5l/clb2vrJlWriRW1mEgPEguXGVCiyNIQa1aJpdYSDVeEdKX0FIx07C7Wr0FtESQZBk/dsWTP8Zm7VOLcswuUFjt+vJKEa3tYzY4FQHZK/G93QsEKR/feYP4wbUvDbVnIckBydGjvtkBmQIRPhUFIPh8lmIjRcDzajtfzle2SqVuxWDWRHZtMKPqo5PN3rPVO6PrZ7hfQ1bNHaL15e4RzCzUs1ycX1zk68NYVp8m82Ge3k7bV1TYb85vBbYvXKjve2wshe5f8bLvIJ8T2o7rC33pojLHsXXpK99FHH3308bbQJ2372AE5GkGSQOkMmu5kkeDXhYo1iCS4/urqrrPEd4QkwdB94uflc2/+/bttUpZJfuxjSIaBt7Iqlr39BCF0XaxLwv/Mvn5j/7ZhiDs/33mfh7uy8q73791CGIbUvvQXVL/whZ51Rx999NFHH3308d5E0GwS2jYoMtrwMGpBkHnO4qJYnTX7vb3ttdolcFp4LQ+0BFLEQE4mmW/MgywT//hHURIdsk3qqGw7kCWZhCbUuE13SxiZFkE59VGkAz9FmD6A72rCYuDGM3D5//QC07bC9wKaFYu63cKKWRzMDRJ3bqFJMsMRER73/cXvE4YhiqQQ1+KQHITUMISd1VL5Q2Dsv/y+tNTk3LMLrEzXCIOQVCHKySdHSRWEwrW+0Rmjd0lbEGRw1wZhNxSOUGsKwi4RtZCzewQLd5AbSYAkYcQ1Tr1/hFwngGutbt9RaWvUKyQjKko6/Y4ECXfJ4b1WFwJkOwrSuuXi+XdemdaF4wW8dkuQqI8eyCHvIYa5G+zma+u7AbPnhS1IdjgOkkR1pb1j3zWr4j172T+8U3h4MsvpiQy//PA4g6m3EYTXRx999NHHm0KftO1jB6SomO1VQqEWcO8QRhZ0iLFqW6ey3GLu4ltUOA6dEgPH2oIIJXsHoKRSJD/8IfxA4toz11h4aX/ysotG2aK2fnfervaNGzS++U1C587LqsIg6O2vdxv29LR4yEEEZfjNvRUp3vo6QXuz3i6BuxdC16X25S/T/M533pnO3gX8anXfGnrtNjbwazVCz8e5desH0LN3D0GrRePb38ar7FRW9NFHH3300cdPArx1MSZUs1kkRUEbFUv73esX4LX/ATPfhStfhfLMzjd3rRH8BEgyarGIJEmCtAXGBo6Q+plPIsfjRO+/HzWb3fb2Lmnb9ZrtQpIk1IFBSI3gTX4Kjn1C+MSuX4OFnSKBRsnC9TzqNPBVj3uiCjG5CpJC0ThGGMKGKepMGanNZfbDpzc3svXn3faT4zNzZgPP8YkmdY4+OsSxx4eIZwxSBTG2r290xnrJ4Z7VA+kx0KJ7bzg5QssR+yEedyE+sG8/Yimd+z40xqkPjKJHVAY7pO1q3RLEoiRht10cc1OV3LQ9qm0XoxtCVnj7KlvfC2h3vHMT+5C2cV1BV2XCEGrm3attzy1UMR2fTEzj2Nv0dU11fG1bNQffFcTx3OUyjulhxDUOPlCkMCaOwVa1bRiEtKqdGt+lELIusnGdDx8bIBd/+2R6H3300Ucfd48+advHDsgd0lYNxSBgP6Vt4Dg9ss/vDPhaVRvbfAvL643k5kx/NyH3HYBx5Ail7HGqlsHNZy7geXdQDrsBl755g8vfvLZtQLkbwjCk+d3vYV2+gnXt+h37Yr7xBvYXv4R55sybKeEtwbp4cdvvzq3ZPdu6c3MASJrwM3PuQNo6t27hzN7CPHvuTSUnv1X4tRqVP/kTal/64h39ebf2/U7kM4gwk8YzzxBYbz21991C+9VXsc5foP3iiz/srvTRRx999NHHDwXdELLucnltdBTqi7gv/xW0y2LCH+Dq34B3m4KzF0ImlnKrxSJNp0nFqiAhMZoYRRscJP/3f5PEk0/s+OykLsi4ptPc8VrXc9UrVYSVwaGPiBemn4Xq9vFHbd2k3G7RjphE9QjD7UWiERcpnkMLo4xHJnpt0/oWu7GBE8I+LDkofGf3wfpcg8APiKV0Tn1glMxgrEf+pjrL7+slU2RVyFvUtd0w4L0gy7QkYYkQyaYEOX0HRBJaz191oKPKXG3YKKpMLCVIv62K0eWOn23RbaLKMso74GfbqtqEQYgeVTGiu/j1diBJ0qZFQvvuSFvXf+dUtiC8gI3Ypq9tbd1k/ZaYKDhwXwFFkRk5kkGSJWpr7d6qxnbDIfADFE0Wvrh99NFHH3382KFP2vaxA13SVgm6pO3exGXQCfiSoxH8cPN06iXUvlnkD3c28M4pJMvLLZqpCSRFwTdtqpfn9m1fXaxgXryMefkqlbndk1q7CGo1gqYYyLvz+28XwL4uiN32K6+8qyShVy7jLi0TSBLO5AnCEJzZ2T3bO3Pi4SJ6vwiE89bW9+2fPX2z97N57p2xs9gP5oULhK6HX6vj3cG6wZnbPA7OwuIdSd7mc89hXbqM+frr70hf7wS/2cK6fPnuwuFuiVqchYW7au+urBC022+7j+80Qt+n8oUvUP3il0QISh999NFHH33cJbp+tmqhAHYTrfQ8lGfwmyZ+fAIe+8ciMMuqwc1vb74xDDdJW1sQjWqxyEJzAYBirEhE3X+Zd4+0dXchbYtFse3u5PXogzBwj7AzuPSX4GyOhWvrJiWzgRW1GEkUkEvXUOSQSHEQgCPGPb22KSO15UN0ePQfwUO/KYjWPRAGIauzguQbPJDekVsRT+somozvbqpPOfwxOPkLot/7wLE8nMgo6BGUyXv2bbsbukvpV2tiXNlVhG71tV3skLaFDjmuFopv+nNuR5cU7loy7Ic3G0Z2bqFG2/FJRzWOD6Xu/Ia7QLKjtq2utZk9JyYqBqZSPZV0JK5tqm2vCsK41Qkhi2eMtxSC1kcfffTRx48++qRtHzsgRTqkrS8GAs4+9ghdz1A5ldqmyK0s708c+V7A9VdXWZ6ubX8hOyn+ry8Jn7J9EIYhnru/ataxPGbPbSApCnpRpN1unJne9z3rr98QBFkYUr66sP/2t6QXO/P7E2t+s4nfST8ObeeuwsG2InRd3MVF2q+9RuvFlwjdvdUA1kWhVK4kDjLXLrLRjuHOL+waMBY4Du7yEgCRe+5ByWVFAN0eycyh728jgO1r1wjMu7OSeCvY6s0LYM/ssvxxS1tveVn8osiEto23trZne79axS+Lga91+fIPJICt8fTf0PjmM1iX9g/c86tV/M6kSGjZd1Q0OwuLVP/sz6l95SvvWF/fKbjLy3ira7hLSz8QZfa7idBxcGZn++RzH3300ccPCF5n7KRqDrz635Cb86iZOOQP46YeEoTtsU+KxouvQ2VW/NxcA9cilFT8pri/q8VizxphPDl+x89O6IIkaziNHa9tJW3DMBT5DMd+BuKCXObSX0EQYLddrKZD1WrhGQ3usRfBqoOiER8eBiDpZslHhJI4a2y3aECSxL99UFlp45geqq6QH43veF2SpZ7atta1SNBjgmS+w7ZbVRv0BNFj74Pi4X3b7oZiwkCShAVC0/Z29bVdqloQ+GRc8fzwTtgjdEnhu7EN6CptLy7VmV5v7nmPD8OQW6UWr90Soo5HD+RQ3qbKtosuabs2W8duuxgxjfHjuW1tumrb+oZJfcPcJKbfZT/bPvroo48+fnjok7Z97IAc65C2nhjU7WeP4NcEaauk0njOZrtG2dr3fevzDSrLLeYvlzeTbAGiWTH4DgOobV9aFoYh7brD6kydG6+tceYb87z+9C2uvri862eFYcjMWeHtFUsbHHr/AQDKsxt7+s8GQUDp2lLv9+pcaV9yxl1YxPIUKmaEwLLxVlf3bNvzV9XFwNA8e3bf5OMwDLFnZmh+93tUvvAFNn7v96h+6S9oPf8C7VdeofXCC7u/z/OwrghCsJ0YQY7HaZIQpO/S0o727sIiBCFKOkU7iKCMiICJvSwS3IUFQsdBjsVQi0VCz99Gqr7TsKenCS2bum3QcrR9FcPu0hKh5yMnExgHxPHeqrzdse2bmwRw0Da3KYjfDbjLy7jLQinsTO/vr3x7v+9k9WDfECpub2UVdx+i+p1CGASYZ8/i7nPOd7HVW/huLCsa33qW8h/+4V15GP+g0fze96l9+a+xLlz4YXeljz766OPHHqHr4lerQIhafhWcNiSKaO/7JUgOb45rslPCogA6NgkOVMV91JdzhH6ApGnI6TQLDTEhfzekbVITStvdSFsll+tNEAeNzuuqDic/A4oqyONb36O+YREGPqY7zaB5nkNuJ/th8qeIZYVtg1l3+djUx3ho8CGO5o6+6f20MiMmeQcmk8jK7o93yU4YWWPjzU20t2pizBxPvzUvU12VyXd8UFfrVo9EbdVsfD/A9nzWGhZ6s05Ck5AMg6aj8urfzLJys7bfpvdEGG56vd5NQNeBQhxNkSi3HP7qzBJ/9OItLi/XCQLxDGA6Pq/dKvP552f50uuLtGyhsr1n+J1R2cKmr20XU/flUbTtx9KIaRQnxDm5cLXSU9rejZq4jz766KOP9yb6pG0fOyBHxI1fcS3CMNyftK2LwZSSSffayYoMYUh1dXe1bRiGrM02ur+wdL26vUGmo7atzPTaz18q8/rXbnHhuQVuXdigvNTs2TbU1k0ufndxR5rq+lyD2lobWZE4eLpI/sQESsTAdiTqF3f3n61fm8dpOchSiCyF2OXG5jKyXepwFhaZr6VYsvPUbKNnM7Abur6x6omTqIMDhK5H+7XX9mzffukl6n/9FcwzZ/BW1yAIkeNx3MEDrLVitM6cw1nYqYa1p28KZWYkiasnkAAnlscLpF2DuZw58bd2ZpzL319isSVm9d353VXGXWJTP3iA6P33AWCeP39Xy/ffCszz57E8heXYUebqadxSZc9gru7+1ycm0MbEw9hedQA4HdWukhID4HebiNvqZewsLOxrQdGtRUmntv2+G8IwxJmZ7f3+bpLovc+4eInmd75L4+mn76g63Xre7VcHQGBZWJcv4dfq2Jff/TreDMIgwLkplPrOzTsT/IFl0fj2t38gJPqbRRgEwo/76tUfdlf66KOPPvaEV65AGCJjI/lCncrpX0M7JJbpuwtb7vGHPgyRFJhVmHlu0xrBF/d4tVigbJcxPRNN1hiMDd7x87faI9x+r5MUBTUnFKHbVpHEC0JxCzD7fWoXXsObf40gvIJMwEjxIDz464K07fi7tusOuUiOx4YfQ5PfnDdpq2rTLFtIssTA1N4kYldp2yjbBP7dj9m2LsF/q+j52tYtjJiKZqiEQUi75rBSswhDyNkNDFVBLRRYulYj8AIWrpSx79Jndiscy8e1PSRZInYXZPNwOspvvv8Aj0zl0FWZjabD0xdW+Pzzs3z1/DL/9bs3+c61DaptF12VOT2e4W89PPaOqWxBELJGR/FbnEiSLsZ2bTdyOIOsSDTLFmbj7onpPvroo48+3pt4z5G2//E//kempqaIRCI89thjvPzyy3u2vXjxIr/0S7/E1NQUkiTx7//9v9/R5l/9q3+FJEnb/h0/fgdD/h9zSJEISBKaEoDn4e5jj9DztE0me0rb7rKs8vLuKrlGycJqOsiKGOhsLDa3q22zU+L/jq9teanF8nQV3w2QVZl0McrY8SzHf2qYkx8YJZLQcEyPy88vsXarThiGWE2XuYti6dLY8RyxlI6qK6QPiMTb9Td2Vzmuv3a104UC8YhP6LpUpnf3UPWrVcy6hR1oaMNDVMzInr62YRDgdMhDZXSU2OOPA8Kr1W/sVG9Y167RfuVVACIn7iH51FPkfuPXyfz6r7OaPUU5NkXVitD81jM7VMPdADJn5GjP30rOZGg5+jZir4sumdzUxDK/hhfBCRWxPP+2voVBgDMjyCrj0CGMI0eQoxGCRrNHgO6GwLaxZ2awr1/HunQJ8+xZYfPw0ss9r7rd4K6u4a2s0vAiaENDkEjTdvdW2zqd/V9VBlhupQhCcFeWCXZRVgemiduxUkg+9RTIkli+v09/3g78Wg17+iYV0+BmcxDH3Z1EB2FB0X0Qjb/vfYCoYy+FuLe+3vNWBrCvXtuz7TuBMAgw3xAewH69gbe2t+WB32jglza9od3lpX2tPZxbt6CjbLEuX/mB2BDYMzO0XnzxjhMP3toagSmIdndp/zpAkPTW+Qs0n3vuHevrOwVndhbzzBmazz57V7Ygfr2+52RJH3300ce7hV4ImVQVY5qhe0GLiDAyWcKv1TfHKqqxaZOw8CqUxbjEcwShtdUaYSQxgnIXgVoJTdgjOL6DE+y8r6rFThjZ7dY/gydh9EHCEOqzs7StFo1oEytzmOIj/xjSowA9QtFuu9tWrL0ZdFW2uZE4emTvwK1oUkMzVAI/2CF02AtCsfr2SdvBqEJq7jqtZ79NaJokch1f27LV87MdDoTYw43naJTE3wI/ZP7ym7/3tDr1RZM6yh7K49sRN1SeOFLgt544wPsPF4jqCjXT5epKAy8IKSYNPnrPIP/wyYN8+PgAqcg7H/w1eSrPwFSKiRN720PoUZXi5CY5b8Q0NOPO53IfffTRRx/vTbynSNs//dM/5bd/+7f5l//yX/L6669z//338/GPf5y1PVRM7XabgwcP8m/+zb9haGhoz+2ePHmS5eXl3r/vfe9771YJ7wlIsowcMVDlgNDzcG1vT+Kka49APEXgizYDnYFEfcPc1XO2G5RQGE+SGYztVNt2fW2ba3itRo98HTmS4aGPT3Ls8WFGjmRJ5aPE0wYnnhghOxQnDEJmz20wc3aDm2fWCfyAVCHK4IHNgU3+XrFkvrpY6yy320RgmpRnxKC7cPoImVGR3lu5vrxr7e7CInXbQEkkULJZWq5Oc3GDwN45EPZWVghtGyliIBXyaGNjaGNj4Ae0X3ll+3ZX12h+61sARB98gORHPkLk2FGUVIryUht36QY6izT8CH6tvs0mwatUhBetJGGlhE+aoskoqRRNz8Cv1bYRL8I3tU4gSTQDocCQFIVmVLz39qXs3soKQdtEMgy00VEkVSVy8iQA5tndA8n8Wo3KH/0R9b/+CvWnv0bjmW/R/M53qXz3JS587Roz//vpPT1xrYtC+Wqlx5A0DSWbobkH+ew3W/ilMl4os1zR2Vj3aOs5CHb353VmZyEMUYsFtOFh9Kkp8Vn7qG1Dx8G6dAl/drZD4Jl3TSqa584RBiEVYww3M0jFjO6p1nSXlwUhGI1SNwYhkQQ/2NXeAujtD/3gAZR0itBxsKf3925+O7CvXxdKWE/GD6R9rR66YWrq0CByMrFvHcC2Y+vXanj7tH0nELTbNL72ddqvvIp9/Q6WFVsmC0LP75H+d2rvraz+yBGe3b6Froe7sr/FReh5VP/sz6n+6Rf2tXT5YaH5ve+z8Xu//yO3j/voo4+3D69UAs9GlTrjzRERmiXres9TdpvaNncQhkWoKoEHqoHXEBNTciHPTE0QuXdjjQCgKVovrGx/X9uNnW8+9BFa2hReoLEeybGYmyKdO4Kmbio/VU3pqSu7NgRvBo7lUV4S1+WhA+l920qSRKpjkVAv3V0Yrt328BwfSZaIpt68PYJfq9H83vfJ/PWfMXDhVfwrl2i++OI2X9ulquhLvrN/y65QN8czBkgS5aUm9dKbtXR460RzRFN49ECO33riAB8+PsBDk1l+5dFxfvWxCe4dS6Or797jc2YwxtS9hR22CLdj+FC6Z4NxN569ffTRRx99vHfxniJt/6//6//iH/7Df8hv/uZvcuLECf7zf/7PxGIx/vt//++7tn/kkUf4t//23/Irv/IrGMbeNzRVVRkaGur9KxQK71YJ7xlI0aggbV2XwA/x3Z0KtDAI8BtiEB1GhRJBVmTiGYNIQicMQmpr2wdZjuX1bBMGJlOMHhVhC9vUtnocEmIQvPDGDVzbI5LQe+b7t0PVFA4/PMDYPTmQJDbmGzQrFoomc+B0cVuaam4yj5JO03J12peubNtO8/xlTFtBjsfJHx8he0QQl7XF6q7LyNzFRWqWgZxKosUjyJEIlbax/eGhW3dHzaqPjyPJMpIkEX/8MUAEYHXJhqDVov7VrxK6HvrUZE9lKfZ3yPK1EjSWkaQQL5PFCyTMc+d7NgndZfHqxASNjvBy+FAGSVEwowXCcDvx1O2XkxklCDb3U13ptL3NWqBnjTA1iaSIWf3IvfcKleriIt7G9oeWwLKoffmvCdomcjyONjKCPjWJceQwlcxRLDnByoZM/Zvf2kF+BpaFfe2asHVIiiWMSkaQtu7y0g5rga7K2UoMCi85oBEZ6ry2c0l+VxmsTwkiP3rvveL9V67uqlINw5D6175O81vP4jz7bapf+DNK//W/Ufovv0f5f/9vGt/+9p7Ky8C2sS5ewvYVgvwQSjZL09Fwbs3tqnLsqp8rsQlmzm6wpoqHy718hru1GAcPEjlxQtRxB4sEv1bD29h400rWMAxpv/oqpqty05lgoZ7CvjG953bcjv2GtsWyYi+LhND3e+dkV71kXbmya9t3Cu3XXu8dt64v8F5wZkUtUkTcT/azevAbjW0P8fYPwIbAbzSoP/30nudJF7fbaXSP0V5wl5YI2m1C18W+vv8++kEj9DysCxcIbRv76rU7tndmZyl9/vN33Ec/DIRhiHPr1o8kMd5HHz8seBslaK6gJg3IjPfGhwD6mPDg3zExe+inwRDEX5gexyuVcAKHbzZfY6W1giRJTKYme80dy2Pm3Maetl5dte2dwsh2QFGpFT8OE4+zlIgTyjKTmZ2WDF217V52XPth7VaDMAhJ5CJ3RVCmCmKCvn6XvrZdlW0sqeFcuUy4j63TVrjLy9S+8hXK//OPMN94gyg+fiSG64esv3GBpUaDmxstnju7zEK5BWFI2mzgBRLVttgfEyfzDHT8W+culgmDux+v9Lxe34Y6WFOEDcIHjhYZTke3PU/8sKFHVEaOCJI+O7QzeK6PPvroo48fH7xnSFvHcXjttdf46Ec/2vubLMt89KMf5YU9ApnuFtevX2dkZISDBw/yq7/6q8ztE1z0kwI5EkWWQAmFUnY3X9ug2RTLmBUZXxMz5t3lOdlh4cN0u0XCemdwmcxHiKV04hljd7VtZoqWqbF2QwyCJ0/l9wxWAKEeGDmc4dhjQ6i66MPUvQWM6PZlYtGkRnS0SBBKlM7d6BFNYRiy8fo1QiSSk4MYMY3UkQlUOcCrNWisb68jDEMas8vYvoqaTjF5Ko+STlG1Ilizu/jGdtSG2uTmQ4I2PIx+4AAEAe0v/wHh1a9R+8pXCZpNlGyW5FNPIcmbNVdW21gbayiyR8TwkBUHe0x4ujW/9YwgOTsElz92lMALUN0Sgwv/DdmqEMQzOL6yq7+oGRcPEbmRBIom40WStF0Nd2F+2z7q+nkahw71tqEkEr3fzXPnN/eR51H/ylfxKxXkZILML/8ymV/6RdI/93OoP/XTtPMHMUbzeD5Uri1gnd98L4B95Qqh62HFi0iJBHpURTYMHC2J67LDWqBbS3uLR11LTgvS9zaCJnTdTSL9oCBttfFxlHRaqGl3IabMN94QhLciIxeLyDFxjgeOy8xNj3PfXqLyjWd3JS+tixcJXZd2pICcTiPH41hyHNf2diWPun1rqcJfuCmlCcLdSVu/2RQPi5KEPjmJcfyejtXDMl65vKM9gFcuU/njP6byx39C+b//Dxrf/Cb29ev7euz2+nbzJn65Qj1IoB8+TNMzsCt1/F1sJULfx5lfIAjh+kqS6UpWWFYs7BFyt7xMaNvI0QiJJ58EwL5+Y1+rhzAM8ev1t2Sj4DebWBfO03I0NtpR7NlbuyrlRdtW76E8/sgjor972KHAFoJXFdcj++rVd93qofncd7Cv36D53e/u285bWydob5ITd/IZ3jrRY90FMfqDhDM/3yPdnX2ORxftN84QNJr7+on/sODcvEntr75M45ln7qq9eeHitjDFHyX8IGxN+vjxRxiGeOtr0FhBSUVg9KFtr2ujwmJgB2mrReDEz0NqmCB1D7XmBhcrV1hQ6+iKzlOTT5E2BOFlNV0uf3+Z9Vt15i7tbo+U0sWKrabT3PGams+DJBG0WrtOuNRKFl4I66q4Hx/K7Vz5F08LYrFduzvLgi4CP2DtlhBPbF1Vth+6SttW1d5VkHE7uuSnVl2m+a1ncW9bHbYbvI0Nql/6Es7NGQhD9IlxMj/3s7Q+/bex0jkuLVQ49/wrrDZs2i2X0AmZjEIkcKnYUTAixNIGiazB6LEsiibTrtlsLOzc/7shDMO3pbR9r2DkSJYHPj5JbqRP2vbRRx99/Dhjb+OjHzFsbGzg+z6Dg9tnqAcHB7nyNpRYjz32GJ///Oc5duwYy8vL/O7v/i5PPvkkFy5cIJlM7voe27axtzzY1+tiwBQEAcG7FMbURRAEhGH4rn8OkQhhGCLj4ochtulixLefLm6lQhiGKMkkju2LnzWJIAjIDERZulahutrGdT0URSYIQlY7nrOFiUSvhuHDaSorLdYXGgwdShFJaISpcWYXZwm9KrmROMm8cVc1J/MGpz44gmP5xFL6ru/JHR2hefUm9bKLPTeHPj4uVLMbFshxcifGCYIAOZcjmYBKzad0bYnkwJHeNrxymWrFB0kiM5knOxTDKGRorqxSurZE4kN+b0Y+aLdxV8XyY2V0FGu9jpNy0SMa0UcfwT77fayZaYLFC7jKJFJ6gOTPfBI0rdf/MAxZul4hbG0wkG0iSSGL6ypm8T4S9Vt41RqVL30Jv9VGjsdpalnCsEEqmEEObeLBLdz0MRolDX1xEc+ykBQFZ36eIAhpSGnCMCQ7HEVWYM32qDgxYq0q7vo6aqGAt76OV6sjqSrq2Bj1UhskiXhaxzh5CuvadawrV4g+/phI/v3mN3EWF5F0neTP/AxSLNqrZ+FKmbC5hty8iZZQqFk6ie99D2VoCLVQEGrOc+eFP3HhAIQBWe88dSeGnUnTbK4Qn76JfuRIb/84c3MEQUhLShGGIaquECQSVJcj5Etl3HodJdHxppufJ3Bc5GQCOZ/v9Us/cQ/t51/APH8e4557Ns/1pSWazz8PYUjs/U/iDw6QKRaRfJ/16XWsFxdxZmZZOz+HXnyV2EObD5ah79M+c4YwDDHzB6BDZiiZNA1znfj09DYyP2i1cNfWcQMFW4kjhSFhLEF92SC9UcJrNJDjmwN0a1qoXNWhQYhEkBCqVmdmFvPiReLvf/+28z/0fepf/zqBI4guv9XCvHQZ89JlkCSk4hDJJx5HHxnZ8d0Jw5DWK68QBCHt7IRI4k6ladpVktevE8vltrV3l5YIbJu2lMSWDFAUWo6GtL6xow4A++ZNUcvEJPLQEHI6Lbyjr18nsuV4bEXz2WexLl5CLRSInL4f48iRngp8N2y9hrZffhnf8VjiAI7nobdXSUxPE9nF19yenRHnWXqIcitHwpMI96gDwJ4RtURPn8Y8ew6vVsdZWEQb3blfe/vX96GjxH+zcBYWsDt2G95GCWdtDXWPVSP2TXHOaMPDuMvLuGtreM1mbyJiW5/CEHtmpkfCuauruOUySibzpvt4twjDkNZzzxFaNomPfXTb8bz9Htg9ZwDclVW8drsXpnk7AsfBWVyAMMSZX9jz2P2wYF29Jvb33ByeZSHrey9F9tbXaXzrW6DI5P7+30feZzXRDwONp79GGAbEHnlk23n4bo1h3vUxUR8/FAStFmF5EQJXBH4Vjm57XRseFr629QZ+rYaS3mIPkJkgfPA3OP/yX7NcuYqVjZOLFfjE1CfIRDKAWJp/7eWVnpes1XRxbX+HP2hC7yht3Z1KW0nXUTIZ/EoFb2MDfcs1pV13aJYtmrZH3SgT0WRGkwM7ttFV2r5Ze4TSYgvP9tGjKrm7VFt2w67stkujbAnhxD7okp+6XQUgWFi446SMPX0TAjEuSX7kI6idscGBYIOLU8cYOfcCk+uzNE4ex/AkTtwzxKBUofqGRJUcqiwzdDCFJElohsLo0SxzF0vMXymTHY6havv7t1otV+RgKDLRxDvvO/ujBE3ve9n20Ucfffy44z1D2r5b+OQnP9n7+b777uOxxx5jcnKSL3zhC/zWb/3Wru/51//6X/O7v/u7O/6+vr6OdZfLht4qgiCgVqsJQlV+94TSjm3jt1rYRh1bibG6tI4VbH8Q9mZncVst5GyG9vI6rVYLKeKxtrYmyA3fwm35zFxZJFU0qK/ZVEs1FF3Gk1usrW0qvaSIR7Nkc/m1W4zek6I8L1GuS6hylViqwtram1ftNPc4FL7m4MXjlMoN1l56Cd0wsL/3PKUmhLk4vu6wPncNwgAlp+NstFm6MEPs6ObDgHflCusNCTeqQdRjfWMdfSSGc8ZjacEkcuMGcufhwZueFvspn2PhzCrLN6tMG2VUXSGimuiWimLniS3WMRLzyB/5GUqOA1u8mlsVh7WFMkZjhURuFY8orhNl7eY88ZP34T/zDegoPNRDB1mc3cBumAxL07RaLXR7GlOZJPAjROobuGfPgq7jVKs4Wpyq6SHLPnbQhKhHu92mGSZINpdwzp9HPXkS94038FotlMkJZm+ucutMFQAjoZIdiRDVDKiWsZ9/ntBx8M6eA1lCf+L9lIOgV49Zd1mYrqBWFhlPr7Dg5CkFWVLVVVpf+hLGz/4swdoazuIioaax5ugE7WWM6OsY7SiO8iilRoh2+RLt++9DUhSCUgl7Y4M2MRp+iOqYFIZj1Co2TpAg0lrHOXsO9chhcX6/8QZ+q4U6MS7CzpwALaIQFgriOzwzi3XxInKxSGia2F/+MmGrjXLwAG6xQL1aFQ8tocT01Rp+2CSM+azVwHjmW+hhiDIxIc63mzdxVtcIjBhlT4NGnUzGpmEYlNZC9AsXaJ040VNVe9dv4LZa1KMjtB0HOipT30+gtko4Z8+iHj7cOzfss+cIWi20dAans4/9wUGcCxdpv/oqzQMHe2pPAPeNM3gzs0iGgf5zP0fYqBMsLOAvLlJdc1ldMClc/zLDn34/8sD2h0t/QZDTthShkUsgtVoE0Qil5QDtzBkiBw5sa++ePYfXarGRHqPdOT/XgiRSa25HHWEYYp8/T9hq4aaSmOvruEODeIuLmC+/gpHfGcrhLy/jvNxR/bRa1G7dQorFUO45jnr0qAhVvA3da2hQr+O88gqmo9EeyhHKLdbLIeprr2HcRj4DOOfO4bdaLBlp7LkqCS9N0VrAOXcOdYvyHDpL9q9cBd/Hy2TwCnn86zewXn4J/TYSvdevWg3nq18Fw0B79DGUsdFd2+2GMAhw/uZpzLrNcjtP1mhgv/wK2sMP7drePn9eTA4kB5HWqqj1Ddxz51AOHtzZr2oVe2m5pzAPVlaxX3oJ7YEH7rp/bxbejRu4L4mQ0WYh3/suwfZ7oCRJ2OcvEJomKLLwSz57FuW287ALf3YWp7Gp1HJeeQW1Yyfyw0bo+ViXLoIr7FLcs+dQJif2bO+eOYPX+U45r77Wu7b9KCB0HKwL54Vn+8GDyFsI1XdrDNPYJdCzj/c+/I0NaCyjxA2k8QfhtuAwSdfRBgdxl1dwFxe3kbau7/LNuW9SvfZ90iEUx47y2JFfQlMEiVdda3Pj1TUCPyCeMfCcALvt0qxYO5ab72ePAMIiwa9U8NbX0bdMwq5Mi4AwJybTdqrkIxr56M57WazjFWu1XHwvQLkLz9QwDHsZEYNTqV3tw/ZCqhhl/ZZLfcPcl7QNg5BWVYxBDFPYeIWWjb++jrJPVojTsdyJnDjRI2wBHj+Q58TgB3D9BWi3qaUcynaSoOnhOyUatoGvJ4gYCrnhzWMwMJVi7VYDq+mwdL26b0gXbLF0SOtvar/00UcfffTRx48i3jOkbaFQQFEUVle3B6asrq7uGzL2ZpHJZDh69Cg3buwdSPM7v/M7/PZv/3bv93q9zvj4OMVikVTq7pYnvVUEQYAkSRSLxXeVtG0PD9FeWCAdU2nF4yTiaQYGtgcctG5MY8bjRMbGURJp6vGAfDHJwIBQ1diHVVZn6shuhIGBIuWbK8TjcYaPZBgcym7bVvzhNJe+t4TXlIiqKdobJnoyx2R2joGYBQM7lQlvFbmsT+lSDatWR1kpk9d1llarKGqa+IEJJg8NIr/8n8B3MO79IBszM/h1l1wm37NeWH/hDUI5RnygwMHUKurCZVIj91LNZXEbKtFqi3RHBdo4cwY7HscdP4FTlYlEIsRjMSQCWLqMnZ7CbOQo+Q6Hxy2G7z0hEpC34NrMCnHZZaBgkR4YgOIxUmvzmKFNdOhBjMdqWBcuin350GMsvVxDlaoMFSQ0Nc6QDBtVEy87StStEK03kKNRzHgcL3+MRDxOejDG0MggYRjSXIJ6YwC/skGi2SI1MEClXMaPx4ndez83liDeVZOE0FgMqUdPEy1fJHd5FiM0MeJxEj/900RObFdIXptdJR6LkvdLTA77VJoK2sAQcq1JzKkTuXaNwDTR4nH8Q6eI+AkUZ41iXiEalSjXwNNzxFSHtOehDw/Tnp+nHY9jJQ6RSCTIjyUYP5GjtTqPnR5CsUokW02SAwOEYUilUiWIx0mdPs38kkR5ySI9GGP8niFi99+HffUaxsoqiRMnqH/5r1GQUMZGSX/606CqyLJMsVhkbaaBLqmozgJ+AmxtAE2x0V97nfTUFEo+T+3Zb6PF41hHHibuJoi1rzDhX8FJ/BSuniUqOWT8AK1zHeueL/XcAeLxOIXxJBvzDdzUCIZTIt5qkex8HwLHodyoQzxO5sEHkNMZZEUmLBSonL9A0GqRbLUwOoSOt7ZGdfoGRjxO8uNPYRzqkHSnT+NaHutfv0nk2k3aLRn1+RfIfOYXen59ALXvfV8cl5H7SOhpVF3BjURw15aIOhtkVXXbA1qlXsOLxZEy473zxU+NErVLRNvtXh0gQvSqfgCpJLnTp5F1Hf/xx6lcvQaNBlnD2PZAHnoe1a9/Ay0exzh+HCWTweqQkVy5ind1Bv3kPWSefN8OpaYkSUTPnUOLxmjnDpAcHCRImXjVNSLVCvlUaptaM/R9yvUGXjSBnB4iHovjp8aJWRUiW45HF87sLPVIBDmVJHv0KF4iQW1pGWl9g1w2i6TtVP7UX3oZVVHB8+H559EPHSL+xPtR9ljxsRXWpcs0bZuKPoQ+fJj2/DRj62tki8Udql2/2aRi2VhGmtX2ALIRMBkziTR31gFgLi7SisfRJiYwjh2l+Y1voqytC6X5Horg1vMvYF+/RvynfgrjyJFd2+yFwDSpXL6MFEkSAvH1dVIPP7z5+pZ7oL+2Rk2WkbJZjGPHsM6fx9ijDoDG+QvY8ThyNEpgmqilEpl38N6yG+zr12l957skfvrDwg5nDzizs9R1A3Rx7Y+0miT26Vu1XsfrfKf0cpnUu1zHm4F97RqNSBQlmyFz9Oi28+TdGsNE9lBX9/EewNplaG3AxOOgbL82egvTYNVQR3IwfHrXt2tjY7jLKzgLCz1Pd4A31t5gpjbDUNVkKjXJgZMf7RG2GwsNZs5uEAYh6WKUww8PMnepzPotl2bZ3kHaJnVxHd7NHgGEB7t97Rrulsl22/QoLYn26/E2ge2TjSV7VgtboUdUtIiKa3m06w7J3J3P50bJol2zkRWZ4sSd7xNbkcpHWL9Vv2MYmdl0CfwAWZVRW2W68glnbh59j2evwLLwVsV+0Ce2TzzJskQmEaF93720XnwJdeEGYfEBmhWLnLlByYwip6MMTKa2WaLJssTEiRzXXl5hdabOwIRYlbcXmpW372fbRx999NFHHz8qeM942uq6zkMPPcQzW7zegiDgmWee4X1bwpreLprNJtPT0wwPD+/ZxjAMUqnUtn8gPHZ/EP8kSXrXP0OJxZAkCTV0kSQJzwl2tAmbDbF0KZvBd4XqSY+ovddzIwkkSaK2ZmI1PZolC0mWGZxK79hWMhclOxRHAq6/vErghcQLGQbzbeTarXe0Nt3QSI8XkGNRmpZK8+tfp2lpyIkE2QNF1PYasmsiBz7JeAND9Qlabeorjd7+L98qgySRHc+gL34HubFMbP7rZORZcFqsXV4WbQFvYQE/lFlupZEkifxYjIc+McWpsescHlpk/GBI8YNPYIwOshxMUrl8aVt/zYZLfcNCMksMF5vIA8eR84fIpS0ks0p1pU3iiScwDh0k/ugjtB0DSZJIyCV0LUSKpolFPXR3AzmdxfJ0vLk5vIV5JEmibRSQJIncUFwce0WhOJFCTaep2lG85SWCSoWgXEFSZEpBHqftYcQ0Tn90gslTBaJJHSmbo2JHuDHrsFbTiD3yMLFTJ7fV0qo61NdNZLvKWKGKLEvk0hay18I9/qhQzl28hDsziyRJOAMHkSSJjLqKokgk4w6qvUGYzuMEKt4tcW54C4uiFj2PJElkh+IYEY3sUBw1naFmR/EWRZtgbY3QNJENA8soUFluI0kS9TWTS99dZsM4gB/KuNM3MF94EW9+HlnXSH/yk6iRSO8c8L2Q5ekakllmYrBKKu6g56O00xPgeTT/5m/wZmbwNzaQdQ07M45ESFae79VBKoftq7izs9vOlyCUsNR0z6s5kY2gpNPU7WivVlmW8RcXkYIQNZPBUROcfWaBy99fxrECoidPiH145bLYdhDQfOYZpBAiR48QOXp027FZuFoltJtEjHVCRaLVgsaXv0xQqYjPWl7GW15GUhWs7DhS4DLqPofaWiBMZsXxmJnZvD602wSlMk6o4UdTKKqMEdOQkmmaTgRvfmHbtcy7dQtJkjDGxlAMA1mW0VIpjMkJUcfVa9v6a505Q1CrocTjJD/4ARKPPkL+7/0GqY8/RZgd5MZ6ikvPLVL78lfBdbdfu2o1nGvXAQm7KM4xJRbDjyRxPQV3Sx2yLOOvrIDrYqop5Li4rpFI0/b0HXXIsozbqSVy4ACKoqCPjaGkkuC6uLd2Xs+8+XncuTkkRSZ66hSSIuPevEntj/8Y6/XXkYKd19/e/cDzMF96SdRSOIRaKOBgYNdMgpWVnZ91a058VxJDyJqGH01jehrews46RC2ifeTgAaKHDiHrGkG9TrC+vmt/glIJ6403CJstml//Bq3vfGff/t/+z3zpJQLTZtYc4mYlhzkzB7a96z2we86U41Nc28ji+MqedUiShDcnakl84EkkWcZfWSVsNvfsS9hoUP+Lv8B89dW3dN8NGw1az36b0LIwX31t37buzIy45+Zyoq/z83t+JpaFv7aOJElIkiQ8om/bR+/GPykIkO5irOPevCnOmSNHUBRl12PxbvSvj/corj0Ns9+DN/4nWLVtL3k3hPe0OnEEIruLInq+tvML2/zPZ+uzEIYcDwcpxgZ6E5CrM3VuvrFOGITkxxIceXQIRZVJZAXB16jsJDJ7pK27O2mrFAosVNo8+8IVvnZxBcv1Wb1ZEwFh+QjztvBDn0gP7DnZFe+Gkd2lRULX3zU/lugJCu4W3TCyds3eNbOii65iNar5sCU01d0n+8Odn4cwRMll95x0jJw6haQq6PUVgkaDdsOlulCl7Woo8RgDkzvflxmMkR6IEQYhc5d39x6+vd8/zn62ffTRRx99/OTgPTXK/e3f/m1+//d/nz/4gz/g8uXL/JN/8k9otVr85m/+JgC//uu/zu/8zu/02juOw5kzZzhz5gyO47C4uMiZM2e2qWj/+T//5zz33HPMzs7y/PPP85nPfAZFUfjc5z73A6/vRwlSRAzoVE8MfFxr56DOr4rBtZxK43UGfVt9wJLZCKqh4LsBN8+IAWt2MLYjHKyL0aNCfRv4IUgSUw+OI0lA5VbPB/SdQmYwhloo0rB1vPUNGraOOlAkMxCD6uZgVGlcJ5UTg77KVRF04ZfLVGsgyTKFQhN8D4wE6HGGBhvQLrFx7iLB/Bt4q6v4bYtlM4uvx4gmdQYOxlHKl4jXz5HPWox++KOc/IWHGHroECBx85V5KiubYRYr0zUIQ3L6EhHdh+IxyEyQyzjg29SWSoSSSvpTnyL++OPU1k0IfNLygtjAkY8jSZBWV5GjOq0gStBu461v4PoSliwUJZmCDvMvQ2OF/GgcOR7DJIpphrSeF2F/XmGctQWRODx1bwEjpjF0MM29Hxrj+GMDFFLr4DSpBBFWI4cIbkv6XbwqltcV4utEDB8SRXLpNpgVGl4U4/Rp0TAM0UZHaZgquCZpvQSSjKxIJJUNlGSUpqPjzMwQOg7u8hK2p+AaSSRZIpOPgO9SGEsgJxPU3Bh+y8Tf2MCZEcE96sQkc1c6/RlLkhkSDwMbDZVpa4z1qkrr9dcBSHzwgzv8QZevV/HdgFi4TiHTJp9pI1lVrPFTyKk0fr1B/emvAaAdOUq95oFVIxNvIcuQkldR0klaro7T8T/11tcJTIs2cYjFMWIakYRGfiyBnEhQ82IE7XYv9KtbizY1xfylCr4b0KraXPzOEo3kBGEIzvwCfr1O66WX8MsV5FiMxAc/uO3Bsb5hUlpoQmOJVNLBKGjUI0MEpkXtL/8Sr1LpBTcph47TNiVorpMzVkh6M6iZNE1Hx56e7m2zGxTXjg0gqSrJfLRXR/22OkQtswDoU1PMni9x9pl56hsmRsdf1rpyueej51UqtF99FYD4k0/0/DwlRcE4epTSwSdQJ0bx/YD1G+tUv/hF/C3Lp70zZyAMCUYP4CkRZKdOgmXUbE6cV7ettOiGipnJESRAkqU96wjDsNden5oS/ZKknk+uffXqtm2Hvk/zu98DIHrf/SR/+sNkP/tZtJERQtej/v2XmP1vf469cFvQTgft118naLexIjnIF5FkGSWXpenou4aGdUPFzKhQZsqJBA0/RtBq7QiTCywLd3lJ1DI5iaTr6AeEOtu+tnsgWasTDNpVRVvnL1D98y/iV6u7tt8Kd2kJ6+IlWo6OPHWYMJqg0VaxdwkGBLBnZghCqJLH1+NU3ThBs4m/SwCft7ZG0G4j6TrG4cM9omevbQO0nn8ed2mZ9ksv0/jmN4Xn8F0iDEMa33wGx/JZbsQxlzd2T5enY2/RrWX0Adq+Ljw699hnztwchCFqsYhaLEAQ9vyM90L7lVeoP/21PYP27gS/VqP8h/+T6hf+jHAf/9jQcXrffeM225A++tgBzwa3Q5I2VuHV/yHGfJ3XvDmRWaEee2zPTWjDw8iJBEG7TfN73weEInbD3EA1XTJhFGQJNZ/H9wPmL4vrw9ChNAdPF5E7y+e76tZW1Sbwt5/jXXuEltvCC7xtr1muz9eWXRYqJkqzwZVbG/zB92a4fGkDAGMoSsOroioSE+m9FfFdi4TWXYSRBX7QGycWxhJ3bH87NEPpfV69ZO7Zrkt+RmTxvxwVzwbuyjLBHgGhvZDXicldX+9uxzh2HE0JkcsrhJ7H/KLY5/mDBfTI7s8JEydzSLJEdaVNcxdyHRCe+3XRtz5p20cfffTRx48D3lOk7Wc/+1n+3b/7d/yLf/EvOH36NGfOnOHpp5/uhZPNzc2xvLzca7+0tMQDDzzAAw88wPLyMv/u3/07HnjgAf7BP/gHvTYLCwt87nOf49ixY/zyL/8y+XyeF198keKWJcE/iZBjYmCm+GIw51jejjZ+J4BNSadwOyEOW2f7JVnqLTHrJuIOTO29hCueMcgMCW+tgckkifEpkFVwWmLp3DuIdDGKWsjT8nQcX8YKI6i5HOmB6DbSFrNKdkgM6Cuz4oG7MT2P5akoiTg5/7JoN/44PP5PKDz5ITRdxnGh/OxXcP7m/0d1dZ2WFEOWJQ6eLqC4VbgmyDwm3w/ZSSRJ4sD77yWfMQnNOjdevEV1tY3VcikttcCqMpwtgx6H1BgoGtGhYQzdI2iVqXb8gcMgpL5hglUlHW9DJA35Q5AeJRU3kcwKZnwzzM+MDyFpGvGMgb72Ctx4Bl7/n+jNGbKDcZRUmqoVwZmdJQxhJRgmDEJyI4ltPmiSJJGufZ+TDzQ5/nCIkZcp3Spz/ZVVfE8MxOsbJvUNE0mCkUiH8Dn00ySTAZrUxms18A/fjzooHmqUe+4V541ZIZO0IDMB6XHSCRtFcWh6Efx6A/PCBeGdqGWQIhGSuQjKhf8FL/wH0tE6mqESxAWp6Mwv9NLWa9ERrKaLZqhMnMpx9JEhjr9vmHjaQMpkWFmuMz1rEkwe2xGCZbc91m41IPAYT99EkiCbtpD9Nlajjfahp5B0vTfZ4E+dwHcDVLdMPCoeJlLxNorqiTpqdfxSqadeMZPDQmE8KBTvueE4sqpgG1lsTwTIhUGwScClx2iUTEFq56MEfsDcTYslbQrPh+Zzz2G+cQaAxIc/3HvwAvGAc+tCCXyHgdgi44M1JN/EGTqElCsQtE1qX/oSzq05kCWc8XsgDInL6+haQDreRjFCmq6Bt77RI5p6tUTF+ZYZjJEfSSDJMi09hxdIOHPzog+midu5dtuZEdZv1bHbLldfWqGhDyAZBkGjidsJQGl++znwA/TJiR3L71dn6zTX66jWHEbGoeon8Etlqn/25yJwa30df2YWJAln/KS4HlhnyVmvo8QQ58nCAkF703PbuXWLMIS2JiaWRg5nkGSZpl4kCOnVASIgKmg2kTQNJ1Hg3LPzrNysYRw7JrY1N7ctYdy6cAG/UkGORog9+ggAaqFA+hc/Q/yjH2XeLDK7IHHjf32d1gsvbCMO/UYD8403xH6buhdJlpFDBzWXo2Hr2NM3trUPXRd3YR7Xl7F1QapKskzTKAqCf0sd3b4SdBRTHRI2ckwEAdnXr+8g75yFhd55kv75nyP9cz+LHI3gra9T+dMvbCP1b0fo+zS+/W0AzIHDKMkkarEgyOfLO8NG/WoVv1Sm7euQSInzyhgQddzaqQLrEenjY4Lc75w3e5G23sYG9o1pkCSQJewrV6l/5SvblHz7wTxzBndpiVUrTVUdZK0Vw7p0ade27tKymCAJ0qw3DFakjnKwQ37urGVW1DI1idHxhb59omFbLaUSrRdfwr5+nfqXv3zXNXQReh71v3maoNXCW1/vkbK79u3WLULPp6XnMZXEHQOL+vgJh91RrioaJAfBNeHsn8DCq4SLZ/HrbdBiKAfu23MTkqqS/NhHQZKwLl7Enp5mriGuAUOWgSZrQsGuqtTXTQI/wIhpjN+T2zZ5acRUVEMRPq63qV2jahRVFkRiy928fpeaNn/y8hzTdZ8gFmc8G2XQb2GumFxdqnOjbrLse5h+jaShUojuHg4JEEsLgrFLOO6H2rqJ7wboUbWnEH6zSHbUto2NvS0Sml2lrS8mPbXxMaRUEoIQd5eJRBEKK+4j+sT4vp8fPX2/aFdbxa9WcTwFSdMYPr7381c0oZMfEST1xvzuqmez7hAGIozWiL1nXAD76KOPPvroY0+8p0hbgH/6T/8pt27dwrZtXnrpJR57bHP2/dvf/jaf//zne79PTU0RhuGOf9/uPBgC/Mmf/AlLS0vYts3CwgJ/8id/wqG+OqTn56i4grS9XWkbWBZhR7GjpFJblLbbT6ns0CaxZ8S13pKsvXDw/iIHHygycSIHigrpMfFCde+HxLuC78GFL8Hci4BQNOjJKFIqy1orjpLPEc/F0DWg3lGoZsSAM5uuIhFirtexWi4bVwW5lCmqqNaqIJaHToGioUw9Tv7eUxDJsrIcpblQZbkeRXZWGfO+RWztO0Smn0byXUFCTm6GEkmxNAfvzZBLm4SNVW68tsrM2Q0IQ9LaBvGoK5KTO8tApfxBcikTzCrlZfEQ0azaeI6P6pRIRB3RXpKgeA+puA3tDexIBi8QDyrtmCBIswUVFjqBToHYV4XIIkoqSdWKEIRQMmPYegpVV5g8dVsIxPo1WDqDpMgMH01ydKKE3F6lttbm6osruI7PQkdlW8y3MWQT9BhkppCyk2RTJpgVKqsWmV/4BTK//MtYHXI5Lm2gqQHkD8PAcdJJC8kqY0fzBCG0O0FUreggEpAxNqC+BK6FfOEL5AshSipF1TKwLl7Er1RwQ4W1hjgXx+7J9pKIU4UoJ99X5MjoTXTNxVV05swMKzdr28iHtZst4YUXrZGOmRAvoObHSSctMCvUWhrJpz6GpCoYx47SsIT3WkZdFurx5BCZhI1kVbCiBfwA7Js3cebmCENoqcIXNhOtwov/Gb16hVQhipJOUbMN3Pl5vJUVAtMCw2ClJPo/eCDN8fcNMX4ijyRLtKODTJezlC/dhDAkcs9xjIPbfTVXbtYwGw6qW2asWCMedYkYHqFdx3/kIyi5LEFbXAeMI0eoNyUIPLKq+B6kkxaSU8WK5kUd09OEvo8zN48XSFiaWNKaGYgSS+nEUjpyKk3DNsSybjaVg3Iux/KimCDqPjzPnC9TSR8mDMG6fFn4Bi4sIKnKDsWw1XRZuFyB1gajxSpqRCYcHcVJFghaLWpf+guaz3yrU8th6qYKdpNMtEo6YSF7TUwtQ+CHIv2aDjlYqWAGGkEshaLJDB9Oo0VUSKRoOjru/CZJ2CXUtPExFq7XsZoucxdLrJUkMSERhD2iMDBNWp3Ardhjj/cUwyAmQkrhAMGBI6iaRaWp0HzlNap/9ud4HSVp64UXCD0fdXiEBikwK4xY30D2SrSlOL5pbyPYnIUFQs+nraWRYjFiaQNFkwliadqutq2OrbXoU1PMXSwx/cYa8sgocjRC0DbFMtgOwjCk9fzzAERPnULJZNCnpsh89rNow0MEtsPsF59l+cvfwm+2uB3m2bP4pTJEIphZ4YOo5vM0PQNndQ3vNhVwb8IiMYykigdzL5bG8tQdddxeCyD8nGUJb30Dr1LZ0b79sjguxuFDpD/1KSRNxbk1R/Uv/3Ibob8bvHKZ9osv4gdgDR1GGxmm4RiYV64Ruu7Ovt0UZHZ3ssaLZrE8padY24rud6tbS4+0vW2iYVstHZU8gLu8Qu0rX921H3uh+d3v4qytc7OSYb6WxDx/Yc+29vQ0YQhryiiXv79MbW1vFV8ffWCLyX8iaXjg78LgSQgDuP4N/LN/A2GIVJhETuyvJtXHxog9KMIRG9/6FvMrYmJ4zBbCga41QmVZfEeyQ7EdNgWSJPXUto2yteO128PIrq82+JNX5qm0XZIRlQceOMxoNsYnB2QmUZGQWNNDXpmtYAZVEhF11xCyLrr2CGbD2bFK6XaUl8Q1NDcc39Nu4U5I5UWttY3dv6OBH2A2BIGs22JlnZLLo4yMAOx6nfXLZTFpqSponXZ7Qc3l0CfGiWlO75qWyEVIZPf38813lMXl5dYORTRsEs3xjPGW900fffTRRx99/CjhPUfa9vGDgRQVZKvimYQIpe1W0sqviYG2HI8jaRquLQZOt/tqpQpRFE2cZoNTqTsOoFRdoTCW3AwgyE6J/yuzb6+g8k1Yvwoz3wHPFsrQYhR9apJW7gD62BiZgSg0lgXBq8fg4IcBiCjrRFWHwLapzqxTmhe1F7KdB/3iMdA2yeihU2MQSVH2DjLjPEigp8gMKgyl1pEWX0NpbxDqMTjx8z0Ctgtp+F4OjpbJKgsEfkijZEIYMhyd3vysLrIHyKVNsGrUVpv4fiCsEcKQlNwhBwuHe+/TtYCYtIGSENYCQQhtRajn0t5VsUwxXoCheyEMSK99DUNv4QUyZTPKhjSArGuMn8hts8HAbsLVr4qfxx+FAx8gk7Q4NnAFVZNpViwuPLdIs2whKxIjyY46I39E1J87JOowK1RWWoSKijY4INTDgU+mQw6SPwSFY0QMHz2oIiVitByN0HU75KCoJWMJ5aFQabcp1L6OEo/ScAycsnjwKBnjhMgkspHtSwvDEOnaVxmIr/DIUyHDJ6OEjVXmLpa4/soqru3TKFs01m2QJMZTHUKseBxyh8hnhNVDaamJPjVF7rd+i8RHPkJ1tQ1um0ykJPp19OMYuofhb6CkkrRdHfvqNdyVFSxPJYglkVWZZOX7YFbg6lcppJsd8jmCs7iE3VHWNdOTWC0PVVcYPix8cIcPpTnxxAiJ0QJeq8Lsgs1Sw0B7ZLv/t216LF2rAjCRuYWqCg/kQqYl6lh3SX/6F1CyWUFAn35QKLnNKplkG1SdiO5heOvI6bSoY3oab2WF0HFoSwlBDiZ1jPlvwtWnyQ3HUNJpQT4vLRF6Xs/mwS1O9hTDJ58cZfhwBoCNsMBiI4l1Y5rW94SVQOyRR7YHkwUhN8+uE/gBKXmZkWKDbEoQsc59H0QbHxPnysYGyBLyqQdp1x0kq0I2aRIxPHRvHTmbpeVoPWK1S3qa8WEkRSFdjCErMrmReMdnuFNHhwTrkoNOfpz6uikmTYCFy2WqaTEhaF0RFgmtl14itG3UQp7Iyc0AHYDqapvl6SpS7RbRPMiFJC0pibe+TvVP/5Tmd7+H3bE/CO99FM/2UcxVhvJNdGcFOZOj7WrbbAy6FhTtjs1DdjAmFPW71BEGQU8t7WZHWblZo7TQZOZcGa0zsWlt3fb0NN7qGpKmEdsSHKYkk6Q/8xlqI/ez1Ehw7ZVVNv7wj2i98EJvqb5fr/dIUv/EYwTI6OY8WvsWJLOYroZ1ZbvatnvOmBFBxqi6smsdgFCIdgKCvNwI06+vYblKb/mufZuNhLe+Lkh7SSL26KPoU1Okf+EXkCIG3uoa1S9+Cb+23Xuzi9D3aXz9G4Sej1WYQs4XkdNpAi1Cu+XvUBuHobA28AMwDTFZo2Q6dSwu7iBX3eUVQttGjkZQBwZQMhnUgc5kwPROiwS/Vuudy4kPfUjcrxcWqD/9tbuye7CuXMG6cJGGE8EbPUzNjtC8ubirdUPoujizs9i+gp/ICuV/oR8Q1sc+sDu2NUZSqG3v+Tk4/FGQZLxaEyQFdfLEXZFvscceQx0YwDdNrGefgzCk0BZjFbVYJAjC3qqkzBZBwVZ0CcPu0vua6fLSzRLfv7HBUjlkttTi2etzfPX8Mn99bhnHCxjPxfg7j01QOCgmm1a/e46BRoUHDuUojiXxQgcnaJOMaPuStnpURdXFZGWXLN0Nvh9QWRV15EbevDVCF6l8FEmWsFsutrlzNV27q1g1FOSGGO8quSzyiFgJsOuKhs49Qxsd3TVw83ZET58mqrq969x+q/E2+x1Bi6h4jk91l0mhnp9thwTvo48++uijj/c6+qRtH7tCjoqBq4oIHwiDEM/ZnNH2a1VAWCOEYYjndpS2t5G2siwxeSpPYSz5ptNtAch2PLGqt2AfH707otZRhAU+lMRDc3ogiqzr6GNjSKpKequfbWYCUiMQLyArIZmUGMAvvXQV0xRcYz7eaTtyettHpY4fIKq6+JaDKaVRi6Mc/tyvId33twmHTuFHc3DPp8VDyu0oHEHWDQ4NLpCOCyVF3GiT1GugRUS/uogXiKUjGKpL0KpSWzOprbXBrpOO1kX7dKd9JAXpMdJxC9mt406exJ04AbE4ug6xivAHZepJOP4pGH8UWYaicgU5aLHSjCNncqSL0R0kJ1e+IpY0JgZg6gOCwNQiJJUy95xw0KMiERlgYDKFXu8QJF0COneAZMxB9yv4tktt3STYYvOQSbQglhP/jARSdoJ0wkLRPFquGJQ3XQM5mSSqNInYi4IYffDXIZolRom4e51QUanZBm1Xpa7kQZKYOJnf/jA496JIspZk9Hs/yZGJMpOZm8iBRXW1zYXvLHLrvFD8FUcMYnaHJCkeh9xBMgkLxa3gtB0aJQtZ17FNH7vlIlkV0nFbHMPkMFI8TypuohgBTVcXJEgQ0tLzyIZBOm4iNxZ7521m/SuoURVXjtA2wbxwAT+ADV88BI4cyfQUwwDxpMbJgTcYHm4gKTLtaJwL319j8WqlZ1kxd7FE4AckUyF5ZVYQjEeeopAxwarQLLVxJZ3sr3yW3K//Ou0gSuCHGEGZqOHB8P1I0TSpWBs1Ak1Xx1tdw7xwEYB2YkRMkMSqsHQGlt4gH1xCikZpE8exAtzFRZxbQmG87gr7geJECiOqMn5Pjqn7CiiJOHUyzK6oOLUGSj5H9IEHtn11VmbqNMsWSmhxIDcjBOZZQT6XVy0SP/MpIieEzYV6/DgNUzxQJpU1QVZLkI41UWIKTUcQf36ztenNGxXkYCbehMt/TT7nI0WjNMIkvuvjLi8LcrCTml1yBKFcGEswdlzUtdZOsmHF8dbXsa5dw+rsp/iTH0DaMoFjm57wAXdNBuJLDOWbqJqDdeoD6JMThJ6PeeYMAMbxYzTcKIQhWW0ZWYZMrIGS0GnYwvc5cJyO1+4sQQhmx+YhMxgjNxIXdZAk6NQB4K2uEpgWkmGw3thUAFeWW2yoYgWEM32T0HUJg4DWC2IVQ/T0aeT49uT1jcU2FbVIJO0g+U3KDZX2q69R/oM/pP3aazSfe47Q9dBGR2lGBsCzyXkXyQTTqCmDhiMmNbp2DKFl4S4tY3sybjSNJEuMHc9ur2Npqff53WOoDgywMGNSWmxy/ZVVlAOCfLavX982KdnqqWwPo+YEkaoNDZH5W38LOZmgstrm4u9/lfoLL/dsgrpov/oa3vo6UsTAGr8XKQyQzRJqIS+sHi5ut0jw1tYJGk3aYQwpKVTpUjRKkxSht70OUcusqGViktVbDUqLTYwjYnLO3sUiof366xCE6JMTRO89Repnf1aohmdnaXz96/v603obGzQ7K5PaI/egDQygpNM0bK33Hd/Wt7k5QtejrWaQ43GS+SiK0h9m9rEPnM4Sd70zrpAkGH8E7v8snheF3BTq4NAdN+O7AUEokXzqY7RCC225TGG6TKwuCDy1WKRZtsRKJEMhuYeaM9nJMGiWbcIw5Nkrazw/XeLlmTKLZViuWpxfWuXqiiCbH5rM8osPjBLTVaL33ot++DAbzQjO/DxD5ct89r4iD0ypjGQijKayGMreVgaSJG36zO5jWVBdbRN4wuIhnnnrxKSiycQ7lgylhcaO15tbyM+gKlZ3qLkc8vAQyBJ+rbZj8qo70aeN72+N0IU2MUG8mCKmuURVl8KhvT1/u5BkicKoOF9KizstEvohZH300Ucfffy4oT+a7mNXSIqCZBjIEiiyeKjb6msbdB5U5VQK3w0IO0u5VEPZsa3CWJKDDxRR1LdwuiWGQDXAc4QKttcBH5bPwrk/g/LMnbdTW9j8eUOQhumiUBmAUGklMsZ20laSYFh4bmWTgqir3RK+tqmEha54Qpma3j44VRJxisObg8XJkzmMZESoXo//LOapX90ko2+HosHACWQZjgzOcuD+IoeHu6rZoyBv2b+ShJQ/QDYtlI/rt+rCh61dJp2wIHdou5J34B5SCRtaG9jZMdwD9wrfVGUeye+obIvHRN2HPwKHPkwx00JL+Ki6i15MMXlvYTvJufiaUDHLKpz4tLC0UFSh1gWitXPc8/4R4hkDI64xPNASylxVh0xnH8RySPEc2VQbLGH10Cxb+G6A5pWJRVxRSxfF46QSNrJTpW0IwrIdLSKpKpmwQ1oM3Sv88e7/LJIRpxBdRQlrVE2D5UYCJZulOJ7Y7gW3cQNmnhM/H/mYICQLhxjMt7hncolIQse1PMyGg6xIjObXxXkYL0CiKAj+WIpssg1WTXgRA9UVoYhJKesoSigUw5K0afXg1DAjmz53ZseyIhN0yO2B45AYQPHa5JwzKMkEVSsCfkDJjhPEUhgxjYGpLcnaYQjXnkYpX+XYwzIPf1wlnWoTtCosXqtw7tkF5i6WqCy3kGSJyYF1cY6lxyF/CD0RFbYPVo2NhSaSqiLH41Q6tWTURdE+fxiKRzt11DE7xKZ97do2D9iMu6mSNFa+TzLSRk6lqNuGUJs6Di0lSds3kBVhP9A7bSdTHHt0CCPm0KpUuD7nUx5+kHZz83pkNh0Wr4qHyoniOobuQ+4gqYyModj4zSrVNZvkRz5C9jd+HfXRR0UtvkNW61xXOnXIfoN2JA9hiH31Cu7iIq4v4+gpkCTS1e/CynniS08TiSpIqbQgR+fne+Sgmx6kXvNBkhg5nGHkSJbRY1kkVWVdGWGjHaXxjW9AGGIcOog+NtqrJQhCpl9fw3N84qwxMVQT5LNv0yw30D78cRIf+iCSpiJFDGKPPy4Caew62bi4LqcTNjJt2mqG0POFCnZ9naDVwgyjEEuIIJrGOVLyEqomEyQytF2tt0y1qxgOBieEmkmSGD0mjud6VaEiFwhdF3tmBuvSZfxqFTkaIfrgdjK9vmEye34DmqukMg7GoEF7/CRyLkdo27Sef0H4zSoy0Sc/ID7LLJNLm2JyRvVohXGCVqtHCPgLCxCGmLEBZMMgmYuQH0mgKBJePLfDWqB7XLziuJgMAuy2y0ItCYqCX632QsLctTWcmzMdle0j22pRs1n893+KRbtAacNi7tsXKP/BH1L94pcwL17EmZ+n/aqwa4n81JM0GgFU5xjyX0bRLBqdyYCtdgzOjJj4MdOjSLJMfjSBLEs4sRy2J++wSOgpubOjzF0oMf3GOnZO3IPcxcVtfsl+s4V1Wfiuxx56CAB9bJTUJz8Jiox9Y5rGM8/s6jsbOA71v3ma0PWQRsaxksNiHwwM0LB1rMuXdqiA7RtiQrSdHBVWNYO7qxn76KOHrUrbrchO4WUfhOQISn5vdSqA5/qcfXaeS99bQk5n2LhffB8mr1YJm22QJJRCoWcjlR2M9cZ+tyOWNpAVCc/xaTUcFirinndyJMXJoUFGMhEmixKPHczxiw+O8oGjm0Fmkqbhn36ScPwQqgKJyiy1L3yBQXuJyXx8X5VtF7kRMeG1Ml3D32XpP2yxRhh569YIXXTFFIvXqj2ys4vRljh1AABbe0lEQVR2Vah9o6pH6PlIqoKcSiHpOtqQuB5s9UEPXbc3yaRP7h1CthWSJBE7fT8Hs1UO5aroA3t7/m5F1yKhutruZWoA+F6A2RTXpT5p20cfffTRx48L+qRtH3ui62urSWJAtNXXthdClkr3BkyKJvcGr+9cJ+RNdWn1FvguLLwKL/4nuPJVKN0Qlgf7wXOgsbL5e3kafA9VU3qDunQxihT6W/xsOwPOwVMgK6QzLZTA6gRLheSTnWC04dO95c9bUTg2TFK3KcTaDN5/d4PXHjqEp1y6QnFYw2he7W50Z9tc19e20rNGiIVr6FogSN5tnTpKMu4gOzXcZksoFAKPrHNevD715PZaJh4nevopMoMSRiZk3HuWyMXPw/SzUJ2H5pr4GeDQTwvysovhDnFTuoEhtTj55Cj3fWgMrd4hVfOHBbm7tY6eRUK793CVVpY65OBW0vYYqYQDThMvnhZBcvFBcFpkwpmOUudR0Taahfs+S77goege7VoNW0+jxSOMHc9tbrNVgsv/RxzfkQdg9EHx91FBdMQbZzn5vgKF8SSSLDFwMIF2u2JYkiB3iEK6De0K5aUmgR90bB48MmpHMdetpXicVFz42noxUYfry9haEjyLtNs57pPvh1O/BFqUvLGEEtaoWQaOL1NRBpEUhbHj2e3fvZvfFpMakgQnPk3i0AnuObDOoeIMRkzDtTxWbgqFzNCBNLFWJ1Bv4J5eHfmuRcJCU/iBB6GwebAbZGNVQbynx7fUUcKLZ3F8cVtpexrEk6jYJOzrne0egDAgb76EkohTtcRy8zCEsjaKJEkMTqW2J0cHAenys5w6OE0sGaJkYpRXa1z8ziIXv7vI2q06M2c2CPxQKMHpKBmHTiEVDlHoqG3X5wQ5oCST+E4oVERmhWzKguQQjDwo6jBL+Ikcji/TfuXVjgdsBikaJWGYaKY4jlJ9kZy2iJJKUbcjuPPzPUKtrAkvv/xInEhCKHpHj2YZPZpFLRRYqWqsr1kEkkz8/Zu+1gALV8pCMaxKHMpeRpaFH3g2ZUK7zPpck+i995L7e3+P3N/5O1iehmN6yHZnsiaSIpWwkM0Sfqoovh9Xr/asEczUCJIsk47WkG58E/nSX5DNBp06DJw5QXB2a6lowls6OxRj9GiWsXtySMC6Mkrd1rEuXepZG8QefhhZ31R+mU2H66+uivBCdY7jB4Q/tR968KGfI/mxj6KkBGkQe/AhWm5EKMh8EdiXTljivOrW0bGVCDpeuu24UOBlMj5K+TLpju+zqEOQnVs9YKuyuEYlchFkRaZedqgkDwKbgWRdj2zjyJGeyraL0mKT2atNjDwYCYdaGMMPZdylJZrfepbaX/4fCEKMI0doJ0YIg5BYuMpgvonsVnHieVxf3qa27XrAtg3Rt/xYgmQ+gpJOCUuXLUuQ/Xodv1wBWaLidiZpwpBbN0woDoqJhi32C+aZM+AHaCPDaKObEwP65CSpj38c09e4+uIK07//ZzS+9S3M8+dxV1cJXZfmM88IIj6ZwDnxPggDjMoZFGcZU07gtp1tyt7Q83BmZ/ECCTuyqeTuo4990SNtdy7z9zs+1mphfyKvtmbi2T5mw6G02GRmUKI9miWti++IkskgaZq4f0EvIHc3yLLUGxfemq/j+iFRXeFjJwZ58tA4k/k44wWZnzpUYDK/czsrN2toAwOMf+JR9HyGoNnE+vJXyZ6fp+DodwzmK4wnxT3a9lib3al+9d1ArKhik+B9OyiMJ8gOxwmDkBuvr+G7m0RxV2kbCcXnKdlcb0WI1gkZ2+prK+yOfOSEmBi/W0SOH0PJZFCy2bt+XyylE0sbhEHYI7EBWjUbwhA9qm4fR/TRRx999NHHexh90raPPSHHhE+r2iFttyptu562SiaN2wsh26myfUeQ7QQnLb0BL/xHuP4NMdDXOwPWxrJYnr8XGksi2MJIiH+e0ws2GzmcIZLQGDyQFu26fraxjiJCj0H+MHo2Rhyh5JN8h0KuJtSlgyd3/cjogQkmM3WGszb6HcIYdiA1Ij7f92D6W5vK1K6/77Z9M0U85qLTEJ60bpu0URaK3Nz2wCkiKeTsGMm4De0SgR+itJZIGo1Nle3tGL6fw5/4IEdPaQwW2tDaEBYCb/wRvPrfRWhZ/tAmydlFPC/I9jCE5XNiv0mIwDLYSSjnDpKIOuheicALBMFmN8lEakJ9vNUWQo+jFcaJRx3UGJiPfgryA6jtBRIxR1gVxLaQLclB9Ad+kdyIgpHTMGJ1RqVX0W59E+ZfFn268OfivEiPCZXtln4Ry4HnoJQucvB0kQc/PkFuSN5UeBfv2WyfP0QybqN5G/huQGmxRbNiCw/YRFsc12jnoSReRE1miUcslAg0PYNmGEdOpYgHS+iqL45hYgCiGTj5GVIJF0Or45tN5mpppHSWeMbY/vA292IvcI+jnxDHdexhJFkiH17j3ocNxu7JoWgykYTOyFggJjUkWew7gPxhsikLxS5ht10aJWsz5M6tkIw54nspK5AaRY3FiRsmSlSm6QjSrh0bQNI00kpHlZs7BCc/A9EsuegGqr+G6anYniKWv0eyKJrM0BaVLb4HF78Ey+dIpCUe/dvj3PuQSU6aRpIlWlWb2XMbNCsWiiZz4ICDZFXFOZM/AvnDFDJtaJdolEysjgKnsSEe7OLyBrrmi0mEzARqxCChN1ESGk1H7ykJ24lhoRyUOh7GEUEG5MyXUKKa6P9qCWduDstTaEji9a4nbxejRzNMTDSRzDVWyz43nBGunm8xd6lEZUVMpKxMCzL9wAGHCDWx0uDAB4Xatl1mY76B7wXIkQhyPL6pHtNWhLD+wAdQdZWE3kBJGiIobWER++pVEXLXIQczfodwCzxy1msoqRQ1x8DbKOOurOBtlHADhZorzq3hQ+ne/8XJFEoux0I9Re3mCkGrhZJKEjl1qler6/hce3kV3w1IGE0OFhdQ5JBirgXtEmu3GkSOHyf7a79G9nO/QuyxRyktNSEMyGninFE1SdSRjtF0dJyZmwStFv7iIn4gYWli/2Y2vgGX/oqsPCvIZyeCV6rgNxqCSHAcfCNGtSUe4idOCNsNEH7JDVvHvn4dd3VNeOXuorItL7WEZYXVoJhYIzGgoQ2m8D/8C8R/6n0oeXHNkRMJEh/8gJgUc03ysQ10LSCh1lEzSZqOjn31CqHv41Uq+OUKVihC7mRFJqWWyBU14fvsRPArld4kaZdID/PDVMvi3NSjKo7psaZOEIZgXxfHNTBNrAsiMKyrst2KdnyIpcz9NKsNFm82aZy7TPPbz1H9wp+x8V9+T6hmFZnUJz5Bed0Fs8pQbIF4uIKaTdN0dMzz53vbc+bmhY+1mkGKx4mldIxonzTp4w7okbapbX8OWi0RgClJOyZPbkdtfXP8d+vaGhtWmdJDB8lkO+rwQoFW1RGTW6pM6g4+y4lOGNl8xzJgLBsVQWT69iCy29EoWzTLFpIsMXJ6nOwv/22M48cwnTbpK0ukv/w8lT/8QxrPPos9Pd3z9N4KWZYYOZIBYPlGdRuJClBZbRP4IZGE1rNSeDuQJIkD9xXQoyp2y2X2wkbP8sxqiWuM4XbG+7lNQrVrf+DML/S8sbsTZfrkxJtSAEuaRvZzv0L273xum03QndC16tpqkdDqqIP7Kts++uijjz5+nNAnbfvYE1KkQ9qGnYCdrUrbrqdtKoW3RwjZO4aulYBVF+RsNAPHPgGP/98F2RiGULm19/u71gjpcUHkAGwIVVVmMMZ9Hx4Xy+Rvt0boYvh+JFkiG98QRE+4QTQfEWSYvruSSBsfJ/bIIyQ/8pFesvldQ5J6aluWz4r/b1em9j4oipQaFio8qwrtEumkJZTC6i6D1uI9pDsWCQQe6WBaED1TT+yqGAbQR46Q+fDnkJ74f4jwtMETYtthKOo/9jO7v3fkgc0aggBa6yJUS1a32x0AZCaQFJVcvAJOmzAIkawyqYQliEv5tnNr4DjphI3ULlEu+UieRUaaE92Y2B62BUB2koFHHkfNxIlHLQbU67D4Otx4Bi58EdplQcSd/MwOCwpGOoT04usQhsiKjFqbQQp8QcJuVRhnJpEUhXysDK7J/OWyUNtJJbFkf6tiWJKgeJx0wkL26oT3/RT+ve9DIiATdAi1sS3EUXYS6chHKeZtFKWN7QYo6RTjR2JIThPMKiy8tkX9/OFNv+VYrkeUy8uvMHI4wwNPTXLqAyMo5Y51QXZq83zOTqGoMtlYBVyTjYWmWIIPpNWVTWuEbh2FY2QSFrJb69k7tI2i8OL1O4rhsYfEeXPi02i6RFpbRZY9arbBWjuBkkoxeCC96Yvt2XD+C+K7Kqtw8jNIp36RVCbk8MAtTp+2GT+RF0pWSWLq3gJ6rVNL8VhnouMAhhGSNkQd6/PiYbtL2maVzrUhf1gc9/wRYZHgNnp1CA/YjPi+eJ3t3/PzkDtITLeIWtMQjdNwdELXo+TnkONxssPx7Q/Vrgnn/5zR4HkOHnOJ5XRUw6JZbrMyXeP6K6tMvy78cAcPpskFnf02eBKKx0gnHQyq+KbZUxaFYSjS0F2TbGRNEO/5I5A/tGn1EBuAMMSv1XB8BT+SQgoc0r64BiIrpLwbaEGNIJKk7Wo0v/tdAGqRYZAVkvnNRG9Jkpg6lSc7mQddY27ZpVr38I4/TLvpY7ddXMfn+iur2C0XI6ZxZHBGXGfyhxjMtZDsGo21Bq2ajaQoqIUCgR9SWxNWL7lUU6TJ5w+RSVrIYZu2nif0fOGx6nqYahpiMQylTcQTKykyzReQFfAiaWxfWCQ4s+Le0EhMEAZhL528MJZgYCqFkk6z2MpgVto0vvY1AIyjR1C3KL4qKy2m31gjDEIKkUUOjFQYLjTALLO2ZBM5/QDZz32O7K/9KtnP/jIuGo2SBWaZfFoQSpmkhaK5NKUkQdvEmZnBuSmsEazUqAi5M6rIZ/+IzMbTSIqCY2RwfLmntu3aPNRjoyJwshDlyMODSLJEQ0pTtQzc5WX8ZhPz3HlC10UtFtBuW6q8crPG9VfXkBWT2HgULSvTHL0PfXJC+Nl31ICJJ57AjeVo14SSPp9uk0laqHFouBG81TXc1VUA7GlxzTLTwhohHWuI62offeyHPewR3E5wYFcluxfCsHPd6GC1tAF1jUJ2lPynfg5tdJToffdSWRXXzMxAbDPodg90/W7XOve88ay4LyY0QRI2neauitml61VAkIl6REXSdYyf/gCzD41gDqaIG0n8egPrwkXqX/0bSv/1v/b8s7eiMJYgktDwHJ/V2e2e2eVlQVDmRhJvihgNPa9HFt8OVVc49OAAkixRWmiysdCkXXOEfU9MQ6oLOxd1i02FWiwiRQxCx8HrXAN6pO3ExI7PuBMkVX1ThC10lMaSRLNs9Qjmvp9tH3300UcfP47ok7Z97Imu0lZDzFx3w6SCdpugKQazSirVs0d415S2sbwgClMjIln40X8sCEFF3VSfVmb3fv9W0rbQJW2v9R5Me9hK2m5F9gBEUgyN2Iyos0xlZoQf2m0BZFshSRLxxx/DOHLkbqvcjsGT24nQrgJyN+QO9CwSFLtEIups1nk7ikdJJW2w61CeIRPvqmz32X4XWlT068Sn4f3/TxH09dBv7rqsERAkoRYVD2Xl6Z6XMLkDglDbio6aNpdqC2IXSCrrqEq4k+AFKBwjlRQWCbgm1BZJJ0yhjE0O7tqd7ImTHP3Mz3Ds0x9BPvEzMPk+Qe4lBsQ5duqXdq9l6F7Rv9ZGT6Gtljuk6sDx7cepYxmQz4g6vM53I610AsXyt9VSFOQzZoWWp9NydGisirCreEHUsxWjD5I/NokxkiFaDMi1vk/q8n+G5/+DsAy5/nXRbuIxmHh8+3vHHxP/r14Eu4EsS8JSYW2LNcJtdRQyLTDLlJdbghz0HbLaSsfqYEvfOn6wtEs4gwfh6En8zABSa410tCH2b1cxnxqGgx8inzZRaLDRUHFjWdSIxtDBjtrKbsCZ/y0mY1Qd7vvbUDwqwvU6lhXa8gsMH0xx74fGeOjjk+SHo5u1dBXwWgQy40LdaVbYmG/iOj6tqgt2jWyiKRT7yU7QTfGYOB7tDaxYkSCEdhCBeBLdXSOmW+J8SY/BsU8gaTo5YwVFdahZBran0DSKSNBTSwFCyfza56F0A0lRmfj5p3j0KYXTh+c4OFqiOJkimhTfiUQuwviROGx0SdtToMeR0iMM5MTxWJ2tE4Yh7ZqD3XaR7Yrod2Zc1Fw4RiZpQbuMFRugYzlOOzGIpGmk5FXhVZ6dhIn3CRca7yJKMk7NNvBWVvEDiZoiPIqHD22pBZACh8OZCyTVBTzLZKmmMLOic/G7i5x9Zp43vnZLWDxoMkdPp9CqnVoOfAA9m+98z8us3twkJKqrbQI/wAg7PtaFI1A4SiZpIpllrOQgQcimzUOHHMx0v1uAGlqkw1nkjkWCOzfXC1+rINR6gwc2FX0TJ/MkCzHI5Jmvp3GrNaGyfWRzsqSy0uLGa4KwzQ/pHEheQJIgn3PQVRe3WqK02EKSJNRsFjkWo9xRfiXlNaHkTg0L32ezjJ0aJgjBunSpR6C0o+KalUH8rrfnSWrVTuiXsKwIXRd3YYEghKovahiYShHPGIwezSLrOmvyCLYrYV++jHlOTPjFHnqoR+6EQcitCyXmLpYg8ClqMxw7WEWNydSVNNGnfobcb/0Wub/3G2Q/9ytE77uP0kITwpCMuoyqhmS6PtzJIVHHhQvCgmKmE3Kni/2cbTwPL/2X3gRpH33sQODj2y3my22em23jbfFwtTtezNoWv+/d0K47uLZQ0A5MpajZdViJMpGaQBseJvOLn0EbGel5y2eH7mzZkcgaBGFIvWaDFzCWFWPhhJZAQsIPfUxv++quRtmittZGkqVtKyxma7M0x3O4T72fsf/bPyX1s58iev99KJkMBCHm62/s8IaWZImRI2LSaOVmrRf06zl+j6DOv0lrBLsTfNl87ju7Es7JXITRo6Lfty6U2FgQ17B4xsCviMkXJbupeJZkGX1cjJWduXn8RkNYt0gS2tjYm+rbW4UeUUkXxbEpdfrbJW0TfdK2jz766KOPHyP0Sds+9kTX01YJBGnbVdqaZ89CGKIODCDH43hde4R3S2nb8eXkod+AoVPbw7W6RFBljzCyINgkbTPjguRVdXBaUN982Mf3oNb5PbNdlYQsw9C9RCdyTB6tkbuvKEio2wLI3lFEUpu1KepO8m4rcgdJxh0O5Gc5MjArds9epK2RJDowTDzqoFlLZBLWvirbPSHLkB7tLRPfFYoKw/eJn5fegPWuN+8efcsdJB51MYIS+A5pteNDfDvRCaDHSIwMI8shNFaQWquiltuJyi2QJInMaA59cEoEzB38EJz6RXjkt+Cxf7RJ3N0OLbKpfF54FTwbpd5Rdu9GducPEYu4RELhx4fdIBOtbXrAbkVigHgujip7+PUNAs9HtxYEaTX2yM7jIknE7n+K5GAWVYXxwU5ysySL/a1FBGF78MM7+5UeFWRj4IsAORDq59aGULLeblmRP0wy5mD4wrLCbrtIVkX4piaHthPc6QliKQ1VciB0qBVOIqkqiWBOEO+jD22vZewRsgeGMXIRZM1Gz8cYjt5EvfxFQUA//x8E0alF4f6/s90aZOwRUWtjBco3kSQJRevYVbimIGEzW9rnD5NJWKhuCdf2mLtQIgxCopSJGt5mMBxAdopYHDTaSPEIbmEcZ/Kk8IANZ0WzkQdE+0gaDn+UXLqNEtZoWhKrrThyOkNmMCZSucNQnPuv/0+hhI5m4MG/C6MPIU29H0P3KVgvcuBEh3z+xBT3vG8YeeOKuCbF8mKyCiB/hEKmjWyVaddsWlV70/tZW0ORw031c/4Q0WiITgM5HqHtCULYjAnf03RXyT18WijTYzlysQoKTeq2ITyGzQgk00STOukB8WBMGIrv8cu/j7L0CqceaDNyUKJwUCXuL6JFVGRF7EtZkTj80CDR9jVxziUHxXlTOMpgvgntMqWlZs92pxeuoy1s+ljnDxONBOhhHTkZp9WpQ4Tc5SAMyPidyaCpJ8T7g8soMU342s7O4ler1JwoYUx4Rea2+FnKssThB4pEMhpWrcLsXJvl6BGuXzY59+wCr3/tFtdf6XjyjiQ4OLiEFIgASnn0AYbyTWiXWJ6u9cI4ARFC6Lvkjc495cjHiUU9tKCGlE7RckTgm7e6hhvI2HoKPJt0sKmCy3rnkFMparaBu7CIMzdH6Pk0lCy+aqBHVbIdz9jhQ2mS+QhSJsdiI0XzpZcJLRslk0E/JK6fvhdw/dVVVmfENWN8qMbUwCrphEUi6hA01lmZronvUzKJWigQBiEbi01wGhQSFVA0ce/wq8jZLG1Xw752DfvGDULbxlJTBJE4atgizpr4nt4+CdpHHx2UKmUuLFaZr9m8vuxyflGcm36ziT0jxnTRe+/ddxtdEjNViDBwIEndrUFdpxgO99qYTQez4SDJ0ua1bB+ouoKjSgRhSMyDXFxcdxRZIaaJ79ztFglL18Rkc340QSS+qQyerorv9KHMIaG8PXCAxAc+QPbXfhU5mSB03R1hgyBI2WhSF2rbzuRWZUWsQoom9d4k392iuz+DVqsXJHw7hg9lSBWiBF7ARmdVSiyl4XeCE9Ut9ggA+kTXImGuV4M2NNh7dvhBID8qxiEbC01cW6z0AIil3751RB999NFHH338qKBP2vaxJ6SoGNwqvpi5di2PwLIwzwkfu9gjD4u/d9SE6rultN0PmQmxrNms7r4Us7kqwstUA2KFzhLoDrHRVX6C8LMNbvOz3Yqh+5BkmchwCiWiCZXt20ztvSM6ikIGTgil515IjoBqUEzVSCVsoWS8PYl5C6SB49xzYJ37jq6ipvN3p7J9qxg+Lf4v3xTBZZK0aVFxO/KHkCSYzN2ikKwKD8/k4J61yIMiAIv6IsmoiZIdfvcIgu6xKN2AxVeRgoAwloN4cWfbnKgjbyxC4KN6FaF+7nrAboUkIQ0cE8etVYL2BploFcmICYXlLpBUjeN/62e5/zd/mehT/ww+9P+BD/2/4QP/L3jin4lQuL3Oza7advF14eG71glEyh8UhO9WdI5HwVgSpBuQUtdRlC3kYBeyjFQ8KgjddonyUhPMChljQ5DVQ7fVIkkoJz9Ftihh5A10c4ZB92UoTW8ul00MwAN/V5zPW6HHN603bn1/UzG/Kvw7GTixfWInfxhZhmJE1NElBzeD4bbUomhIhUPCesOuEJx+Ais1JjyJ9XVRy9bjMnQfsdFJEukAnBqNIIGSSjIy2Ibr3xQe3FefFteWwhF46O9tTg4M3y9IXKfdI9EVTRYq/m4tQ/duHsvCETQ1IGcsQ+CzNtsQpG3gk1O32DwAqAZSbkoEeTk17KHDBHoEK5LrHJeSIMQLRwWxduQpUnEbnRK+H9B0dCpBBikWY/hwWig1zQqc/3O48CVxjKIZ9Id/mWO/+rOcPFblVOEVHngyw0OfnOKhT07xwFOTpAsRWD6zWW+njkTMJSGvEvo+a7caIlxnvQ12k3ysJK536QnQY0jZcVGHW8dKCwWXLUfxI3Fka4Ok0RT7ceoJGDhOJmGiOqvYoYHZyY6sakNIisLAVGozNT4IYO0K+uX/xdHsyyhYmI5EO4RGycJqOj2lfH4swaHTBaRuLaMPQvEoxWwLxd7AathUOiFHZsMRdgJ2hWzSFOdxahgpMyasHvwWZnq0d95aqWEkTSMmbQgf6+QQ6DFy+hpy0MAiimO6tF56iTCEmjGCBNtqkWSJg6eLGAMF2q7GeiNCEEJ49H5Wbta59soKZ5+Zp7ra7pDpAwzLZ5EkkDJjjAzUob3B2mx9m3d9vWTiWh6q0wm5yx9GSo+IOiSbdqQoLCueE2GgVnZcTI6py+K0LRzb3aanj59ohGHIuYUaf/XSFdqOj6eKZe4vz5SxPV94MQch2ujotiX5u6G2Lr536WKMcriOnzFRZRVvaXPMVOmobFOFKKp2d+PUVqdZQVG22RAkdTEeabqbPqqNskVt3ewoZDO9vzu+w3xDhCAeTG+feJckCeOwGAt1QxC3vS5LjHSUryszNTzH703S5UffnMo2dF3cTngjgLu8vGs7SZY4+EBxm9VZVLEJPR9JU5HT6W3ttY4Ngre6hn3t+ra//aCQHYohqzJ22+1NSEUS+l0f5z766KOPPvp4L6BP2vaxJ+QuadtZBuZYPua5c4SOg1rIox8QSlD33Vba7gdV31Si7aa27VkjjG0SOV1F4cb1TcJnLz/bLqKZTQWurO5JqL2jKByGx/4xHPn4/u1kebsScS9StIviMWRFEsq8t6KyfTOI5UTfuvs5M7GnDzDRLEQzZOJtDkZeEgrN28nBrSgcZajQQtd8hgoNoRh8t2qJFzbrmP2++Fvx+O6fF8tBNMNApkEq1mIs01UO7lHLwD2k48L/ktqCWNY++tDuHsYdyIqMGouKNm+m5vzhTrCaLbyGe9YIJ3avI5YjnxEELGGw3QP2dhSOdqwFSmI/1ZeF+nno/t2JGz3O8BMfwEjoTB41UCYegKMfF0rUJ39bKKDjezywjz8mvoe1RfHd9ezNZdi3hwN26ih06wDwLHLGmiDRbw/4Kxzrkc8b8w1hP9BaFhMEg/dut/aQJDj6CfJ5n+hoguiATKbxIombfwoLrwhyU9U7qu5fEkRpF7ICU0+Kn+deANcSP7fLUJ0X295aSyfEbiDTEFYPi03slotsd0PuctsD+ApHesfDGT6C9nOfQ9INIt4yEcPv2H50zrHcAeThk+TSJrLUZKmRIEhmMaIquYwDV74CL/2emLSQFZj8KXjkHwg1bPG4uL76Hsx8Ryg1VRlFlaG+tKnkHujUkhiESIrBbB3MKmuzdcrLLRGu01U/5w5u9q1wVHwnWiWs9CghEmbhgFA/Swvisj50n9hfBz+MqiuklFWUiEzdNmi5Gm40i6zKFCcSHaX56/Dy78HFv4D6Mul0yKlPjnHwQZUDuescOmFw/H3DnPrgGA88NcmhBwaQ6nPi3FY0cf1PjaFEowxmqmDVWL5RJQzDXihOWltHU4PNlQX5Ix3LihJmcrh3STQT4v6VYVb8YfRBmHwCXfNJujeRk0kajoFfKovgPiONrEgUJ7ZMZoUhhl/mwNAyil9mfanE1bU4N1ZjzF8uU11p4zk+WkTl+PuGycXK0FgV+/jEp0mnAxJag8Css9wJwgN6S6RzxvLmCo7O8ZDMMmZ6jDCEsBOo1I4UhPq5q+Qe2l8l2cdPHizX55vXKnzryhqy+/9v786D5Lrqu+F/7+3b+75Nz6LZNDPSSJasXbJsgwELL/FLYkwM9usQ4/DAE5CJheslAQJ2pSgwe7G5cKDKSaoCsUMFHCCYRI9szGMsS5a8IcuWZO3SaPaZ7p6l13veP04v0zPdMy1bo+4rfT9VKs1093SfO2eWM7/7u98zCZ/DjM3L2+BzmDGVyuKl48NIvPYaAMB+5fxfP5l0Vm70CcAbtuNU/BQQmYLX6sVY/1Sh6/J8ohHyxiCjGtzZ0t+v+VzbmZ22Z3NdtqFWd0mX7YnYCWRFFj6rDwHb3M3U8vFZqRMnIFKpOfcHcrno2bSO02+MITYs1+KB5gqRVBWkzpyBSBdPxqT7yhdtARk5sHStPBmtmhRYM7koNJ9/ToauyeWSm5MJgfQZuTZ4K3m2b4dJUwtXT/Qfkz+7GI1ARESXGhZtqaJ80VbLFW3T0ylMvixz8uwzcvIyhU7bGn05FSISTsy9L5rrLvDOyNgKLJWFh6lR+Uc4UDnPdqb8plCNqysXHi80R2De4l1BoLP49uzL3GezuoCeG2ThZTG7bPPyXZHA/GNTlGJ+bUr+kVU2zzbP4oCntRlrl/fD1+iuHLtwoeS6bRWRy92r9LnLHYdZ09Hb+CYarLmvwUoRF64IvGEbFGRhysbhdmdKP2cXkqoWv45P/F52qJvMlT/PgS7YLFmE3COwqRPwu+Ly68dVJjfY3wGPTwDZFDA5CGt2EDZbVhahKnC2LcWae+5AcNudsmDbsl5+ry7UnWd1F6M3Tj4nL9nXc3EC5WIugl2wWzNwa/L73aaPywgKX9vc1wp2wePJAOlpZCYngEwSbqVPdhiXmxebB4H1m6GYVCjpGJr8g7mO3CuA1X8OXH2fzE8uV1xvWClPCGSSwOk98rZ8l20uS7tAUYBQN5z2FBzqSOFEiNcynOt+njWHwR543Eko6QkkxicwcCIKZJLwqbnCe74LPq/rPQgEsrB4zRA2M8whHxqVV6Du+zFw7lVA6PLnzMaPAkuvK3b/KwrQfb18u/+PQGxGQSC/kWJDb7GTW1GA0DIEPNOwZIeRSWVx6qCcl4Clb+4JjlAPPM4klFQMGc0Gx5/fiUSkC0hPyzzbmRs32n1A2xZZfBZRxBIWjEw5YPL7EG51Q5s8C+x7FDj837KAb7YBHdcAWz+JwLYPoW39EkT8MQRjv4MnaIPDYylmtfe9JP+PrJJfM6paiHpQEyOYHE8iPpLAyNlJQOgIaicL8zDzONTUOLJWJ9JWN3RVxZTZKyNUzLkiarhXXslh98PvGIfJlEQsKb9GR5NOmDweBJpd8iRprE92dD//Q2DfowhO/gENTQlAVaE5MjAjCV+jA60rg1h5bTPWXN8qN5Q7s694LDYPlFAXWiIxYGoEQydlt202rRc2uQvZ+mUES6ALCC2Tx5EcR9bpQcokfxemLU6kTA4o02Pw2mLye3R21BBd1s6MTeFf95zCidEETKqCLc1m9Da6YXP5cHWX3NDzjT1/RDI+CdXpLDQGVBIbSkDoAjaXBTanGadip6A4s2hqCkLoAv3HY0hNZzAxlgAUBb5Ideu2dFbHsC6LnLYsoM/I2s132uaLtrGRacTyXbbdpZ2ox8blRoNdvq6ym4ZpDWGYvB6IdKawyeBMiqKgZbmMJBg6GZObmnqtJYXhauRzwE1e+fsk3dc37+N9EQeWbW5Ez6ZGIJrLsw3OLToDgKWt+D2u2KzQGhrOa2wXQnCJLGLrWfk70eFjNAIREV1aWLSlihSbLNoqiWmoJgWZwUGkp9Iw+Xywdhf/qE4n5YK2Jp22QLFgOXZCXvKaJ0Rpp22eZi121w0fnj/PdqZQN7D1k0DPey/UyC+cYLc8Lk+TLAItpGW9LLwsdsQDIIupdp8s8ixUUJ5Z2LQ4il3UlbRvlcfbff3iH0uwW+aYAtBtvvLRCHn548h3f3qaKm/YpiiwtPSgt3MYvR3DMDXLjacWTeNq2fGZyXX2hHrmbgyXF5THsdR3BFcuz3UOBrrKf65VEyxNMs8XI0dlN16wq7T780Jq3SILSWMngBPPyttmb+BXOA5ZOGtyyG78kCNXHCxXrNasMIfbZaTF5Agw0Q+fa1oWeF3l59zWuRbtG9rRtioEz1V/Kgu1K/9Ufm7nO+miqkDnO+XbZ14AkhNA/4xohDLHoShAxHpK/nwTAn4td8Jpdvez1QXN3wyXQ3bbxkcSwMRA8VhmdzFbXXCv3gqLQ4XVnYR57FWExWvydYLdcuPBNXeU7372NBe7go/ukh+TSRbjN/LRCHmh3HHY5HFk0zqQSSJozhVhZxagbV6YfA2y03lqBKNjAomJLDAxKDuiA0tLi9utV8Ef0uQeiIkkpiwBqBYNkexe4KWfyM5fi0P+HL9qu/z8W+Tl2ejZJudr/FSxeA7IjumhXJzOzJMQoWUwazpCNvl75vgrw7IzOxWDzzEhv+fzJxEcAZg8QbgdCSiJcYit22B+759BmCwwJwfgtKdlwVazypOKne+E3zsNkxjDZNKERH6TO1VFpN0FHPsdsP9f5NdNIirHHerBstvejTW3dWLtunGsa9qDZRvCaOrywuW3yY0Hp8eL0UAtMuIIYRk140I/9KzAuaNRjPZPQs/qsIkxOO0p+TVjtgHOIEzuANyOaShJGb0BAIlgBxRFgVs9J08iRK4ojSmhy96Bs1FMJDLw2jV8cOMS9PoBBQpgcWNZxIUGjxWOY4fQNz4N26oroJjmX1MWohEa7Iin4hhNjEKBghUr5Tpu6FQcw2dkcdXlt8Jiq+IEOID+aAJZkwKLVYPVpGIyWuyCnR2P0Hd4HAAQbpWZ2XnpbBonc9n3Xb7yJ0VlRIL8/km++WbZx/giDjhndI6e7wZkQgikTpwAADi2yMz/7NgY9KmpeT/OF3HAG7YjM5rPs61UtC3m9FtaW6HU4HveE7TBYi/OLTttiYjoUsMVNVWkOmTRViSnoZkVpPsHkM6qcGzcULIwK3Ta1qpo62qUf0xmUjKbNm96TG44pppk7utM+a7M4cMz8myd5fNsZ7J55+aS1gOrG7jqE3LTpotRiD0fqkkWfDb9r/k3LgOKGcWALMYsdCy+NmDzx+bfqO1CUdXCRmfp8Kr5x+Zvl5eE583XMQwA4RVwO1KycLNk8wUY7DxM5mJGL1A+GiHP2yYfn5osdk2W2xguL9yL5oYYnLYEIoFJYMmGyo99u+y+YlZuIndJd6TCseS6d332KNZv1tFoy/2BXOlYwsvgdSeAqWEgPiAva5+v+1lRELn6OjS++08WLtTOFlomC3vZNPDaz+WxaNbynePeVsBsQ8A1BosyCQ1T8NnGym9yBwDhYtQDhIBpul8WcZvXlh2KumQ9Ak12QOiIBCdhaloObPwr4Mrb5UZ281l6Xa7geVr+XB18XR6TIzB3bF5ZAAy7R6FmZFHFro7DbsvIAvDskxaFyIpRDByPARBw6mdhMesyGmEmzQJt2XXwejOwN1tgbfHAH98N2+jL8v7mtcDmjwNLNs49WWH3A+1yQzMcfUpubAcUO429LTKjNi+3sWWjdwhKKl64HNtvGy5mP8/8OZGPepgaRXxCQTxtA4QOr3JKPmxmob5hBWzBMJyONBQlidNRD1SvDy6PAufJJ4CTu3OP65XRG9fsAFb/OdSWtfBfdQMcbg3KRL+M3pip70VZVPd3FE9CBLqgaBpa/P1AagJDJ2O5zzMQsuVOcMz8egx1w++Rx5EId8L93m2yeJtNw6fkTiIwGoFmedfyBmxo9+P9q0OIeGxAKpcLa3VDURRc4wPsY8M4N5FCunv5vM8lhEB0SH5/FqIRAEScEYQbfXB4LNAzOs7miqr+KrtsAeD02BSgKAg2OKBAZtbmzYxHiA1PIzYsu2ybZmTZAsDJ2ElkRRZeqxdBW+V15cyIBL1MRIKiKGhZVtwALHCeRdvM4BD0yUkoZjOsXUtlnAGAdH9/VR+fHZVXQZgqFG3Nzc1QNLlmu9jRCHmKqiCYi4xQVAUODzttiYjo0sKiLVVU2AE2q0MZ7INIp6E7PLAuK3ZLCl0UirbmWmxEBshiWr5DdmZEQj4awd00t4gS7JF/TMfOAQMyP61inq1RmO2VOyZrzeKURbaFaJZiIS28YlGH9Ja0rIfY+kmkI2vnf5zJXBq1MV82LyCLdl3vlhEBlXJcL6SW9fLrxeYpxouUY9KKXenZdPkM2Jn8nQj4s7iiawg2v3f+574QZuYYe5fIols5qqlQ2DedfAoqhNxIrlIXcLAHfm8CSnoSLssErC4bEJ6/iPCWKYoseALFjv+GFeU3H1TlJeomVeCKnmGsXjYis5/zkS+z5YuEiRgwOQSPNQrVapMbRFUYS+uNN2HZtV1o+ZMPAle8X24GWA2bt7jR3dGninECTWvm/lxVVSDYDU0TaHAPy6HackWECnnJ8jjGITIZKIlx+OxR2TFbrrgduQKBFhcURUAZO46Ib1h25K+7C1h+c2m28Gytm+VjU1Oym1XXi8fSPCvqI7expc2SRcA+XLh5TjTC7OOYHsPEyBTGzk0CUyPwOePy8zfzSg9FAbrejYBnGlYvIMJN0NxWRCb+j/w9Z9JkN/cV7wfCy0q/XqxuGYEDyAzueO5zm0kBfS/Lt5dsLD5eswCBpbLb1jQCPSswFU0C2TSC5vyxzIysyB/HqOxCbO3CRDQDTA7JfGV3Y3VXfNBlxWY24R09IZhNuT898ptO5jYbDZx5Ex67GfGGJdh7bnre55qOp5GazkA1qfAEbTgVk0XbNncbFEVBY5e8Kkbo8pJ5f2Ox2HlsaAL7T45Cz90325lR+drNLbIQODFWLNrmO21jyXghyzbc5obVXrrGPBo9CkBuQFYuGiHPFArB5PNBZLKFGIPZvA12tCx1oq3XW9LNW43UcXl1iaW9DYqmwdwkGxjmy7XNE7qO7Pg4gMqdtorZDPu69TA3N8HStcDJ6UUUbnND1VR4G+xQTfzTloiILi38zUYVKWYzFLNcIOqn5cLP1N1bcslaOlewhaJAM9fwyykfkTA6YzOyctEIeVZX8dL7fAfhfHm2dPEs/xO5GVVogUJnrVg91RX388Vni7N8zupMiiK7eOfJf72gLE7ZobzhnoW7QmcWa8plwM5k0mTBEQBaNy3+SRBHoNjRV6F7tCB3HMrkSMn7ZVkccDQ2Y1XXAHraR2ThcTE77P2dgG9GN+p8XYq5IqU5+ibMcVkYqHgsdh/swQAs5gwwKiMrSjYgK8Pk8MK37hoo7nniPyppvUr+bJ0el4VCRa28aWMuKqXVdQQrrgqj0Zrvfi5zLM4QbD43rOYUMD0G09QwfO5p+dzl5kVR4N/wDlitWfg8Kbiv2Cy/1qv5Ga+a5MkTQBY4jz8ji0tme/kc61wBvMl+FIoCmJVpeMzDuRMes+J23I2wuh2wWxIQU+NITWegTA7A60zKeZn9/eLvgL8jAtVqgtk0Csvoq/DbhuUVIes/MnfTvZkaVsoTDUIHXv+VjAEaOCBjK+z+uZ/n8AooCtDiOFYcrjUGqzkju4tnnnTztMDitMFhSQDT4zh5YERmi2b6i5vcES2kULR1QU8kkDpyBK0BO6Lty3DwXAwjE8mKHxodlJf3u4M2CEXgTFyu99o88ns80OwqXDJvd1tgc8m1bDqr48kD/fj94WEc6IvOed50Vkd/TBZpO1vllUETo0mIXIZ4vmibHNcxPjwF1aSgudtX+hx6ulBErhSNkKcoCqw9+YiEI2Ufo0ejsD73S1h2/xfEzAiwKqRO5Iq2uXxgc0uuaHtu/lxbAMhGoxCZLBSzBtVT+Sop51Vb4PvAB6BaaxdLYHOZsfb6VnRvqPIkIxERkYGwaEvzUu2y21YTKVnAjZQWQDMpuYDULCoUtYZdqvnuv1if/KMUmFG0LXPZMFDMV81v481NU+qD2V6+0G40kVWywNb1nvrs4LY4q9tQb2aEwEIxDwDQ/V5ZdJ+90dViWXYTsPGeysXBvNlxGwsdS2g57LYMzGaxcEH47VIUYOm7ZJHT1QB45okiKGykOAJMDJZu4FfuqRuWo6N5DI3+KILeqcWdF81SzOgF5ImXSlnO/k5A1aAkxuGeOgBFZGS3abmsaEUBQj2y6DwxAEtmWGYnz87KnTmU4BKsuet9WPahD0LpfMf5RVb42oqFx1PPy/+briz/HIGlgKrBgRGsXGfBiu5RGeXq75zbLZ3bhC0fkYBMEm51QEYpNJb/+rVfcZ2MjUhPocEfh9rYC2y4u2K+cslrLbtRfo9PDgMn/i9wdr+8r2XD3J9JwS5A1eDRBuFyypiHsD3X+T27mzk3H37PNDA1KgtoqSn4LIPya7ihDq+SoPoihMzwBgCrG4mDr0OkM/AvacKS5R0QAvjD0ZGKH16IRmiw49zkOaT1NOyaHWG7/L5QVQXNuciCcJu78HGnRqeQysh16x/eHMFUKlPyvH3j08jqAm6bhkjEKfdzSGVx5vUxnHljFP1vxGE67QVOupDKJhFu85TkqQLA6dhppPU03BZ3YTzzyefapk6ehJ4sLVSLbBaxnTshEklkozFkx8YWfL68bCyGzNCwzM1vl+tbc1MTACAzOAhRJo6h5ONHc5uQ+QPzdgvXC81ikrndRERElxgWbWleil1eRqqpOsxNjUinS+9PJ3PRCLXKs82z++U/oQNjJ+UfA1Oj8o/LSlmMMzfFsjgXb8MkujyZbcDqP69YjDEMq1t25ZmrjAjQLLLofrH+yFNNspN5odezOArFUGGyLHxiILJSbiDXuqWwAd2i8i4BNn1UbvY137Fo1tKOUU/z/MX30DL43Em0NUWhBloX/7L1xivl5w2YGycwk2Ypnmw7mdtIbnYG7Ezh5Qj7J6GlRhH2xQBfy8LH4gwtnKNdSde75dc8IMdUKdNYsxSu9HCmjsE2MU/HMFAsPk+P5DaGS8j5rBTt4WpAx4Z2RMIJNG7eDKy8df5u95ksTmDZzfLtU8/L4q1mkQXoOcdhBQKdcj+2Jf1YtiGEoJI/ljIRFDOLzwAwOSi7n4Ndi7uRIl0aMtNyLwEAwuxC4oDc+M+2ajWu7g5BUYCjgxM4F50bk5BN64WcWV+DAyeip/BaXxRDY6UniBraPVj73jZEOos/A94cnCi8nUhn8eyR4ZKPOTMmX2+J3wGTSYXTJ38GnDs6jr4j4+g/FoV52AMkTEgjjabuub8b8tEIXd6uqoqdpmBQZs1m9UKcQd7UC/uQ6R8ovJ8ZHFzw+fLyG5CZm5qg5tbyqtsN1eUCdIH0As9VKNoGKvxsIiIioouCRVuaV36hZ3Fo0MJhpBKlXQk1z7OdKR+RMHYCiOU6hJyhyvmFjkDxj36j59kSLaZVtwFb733rBbB6kSs6Z3ydC8cdmO3Aho/I4t3F4gxVV/CaWURbKC/ZGS6ekLoY3c+KAlx5h9zALP8zuZJ8B2c293tlvk3uPC1weKxYv6IPkcD43A3ILjSLU3bJA/JzXKmoChRPZvT/MRcLoVSeF18bXB4FFjUBJXYGPs/0gnEC7vXXo/2D/wumzs3n/3sqvKz0xFHjlZWLvrn4B/PYIfisQ1D0tDxpUy7exdcOhxMwK1NAMg7T9ABc9hSjEag6+WgEsx3pM2eRjUahWK2wLetB0GXFyib5u+a5N+d228ZGpiF0AavTDJvTjBfOHkFsOoN43IvBeGmnqsWmFQqnWV3g2NAkAODaHrn2e60vhr7xYmH4zJiMXVjil+vGtpUBhFrdCLe5Een0oqnLB2+nGVgyCf9qFRZbaZdtRs/gZEzmQC/1VbdBqqIosHbLn4XJI28Wbk/39WFq3z4AgBaSWffpgYG5T1BBIc+2s/hzWFGUQrdtum/+iITMiCzaasGLkLNPREREFbFoS/PK7xjrXnMFFJMJ6US25P58p61W605boLjp0dhxuYM5UDkaIS+/GQv/0CSqTDWV3xjLaFo2Qqx4H5Kt76j1SN6emcXNhYq2iiI3q1p+8/wZqBeS2VbdBmahnmIR0mSeP6Imd0k+AAiTuXy+7IXWtEYWn1f86fyPC3bLWIBETL7vbqocC6GaoIS60ds5hJWd/bDZ1eqORX0by7XubfKEi0mT0QiVBLuL0RundsvbZs7RTCYNSnAp/J4EMHocXntMbnJXTYQK0YxohOk/5rpsV/RCscjNVLcsDUJVFJwancJgLFHyodHBXDRC2I7J1CTeGJIn6d2mRhzsi1V8ybNj00iks3BYTNjQ5scVzbIw/NQbg9B1gVRGR39UFn1b/fLqBafPiqVrw+hcE0b7qiBaVwYQ7nZAaZpG2jk15zVOx08jlU3BaXYi4qg+XzWfa5s6fQp6IgE9mUR8505ACNhW9MK+QX7fZgaq67TVUymkzsrPi6Wzo+S+fK5t5tz8m5Flx/LxCOy0JSIiqqXzCHmjy5Fz0yZYOzqQ8YaBp88gncxACFHoXEjXU6etr03+4Tw1Kne6Bxa+BLp5HdC45u39QUxExqCqsnB5HpeY1iW7Txbi9HT5DNjZXA3yX72xOGVkRfSM7MpdKHe2aS3EuVeRjqypPiLg7aqm+Gy2y03H8hthzs6AnS20DLaB1wBkgfBqGVmwmMx2uRFbNlW6odicx9nkyc+RN4snPuc7KRBahubwESiKQGNwAmhYc37ZwXT5ynXa6rAhdUp2ptpWFTvCvXYzlkVceKM/jhdPjeGmVbI7VAiB8dwmZL4GB/adPYypZBZOzQ+zasMb/XG8oycEzTR3TffmkHzNpWEXVFXBtT0hHB2axFA8iVfPRuGzm6ELAY/dDK+j8knK/GZkE6mJOfcdG5cb+XX5qotGyNMCAZiCAWRHRpE6dgypM2eQjcVh8nrgfOc7IaZloTozMgyRyUDR5v8+S588CWR1mPx+aLOKroVO23P9ELoOpcz6V+g6Mrn8XHbaEhER1RYrVTQvxWKBuaUF1txGC3pWIJMu7l6byWfaWuvgS8lsK2Yp5i+9W6jTFmDBloiMp3UT0H618WNd2rbKzOAlmxZ+rKcJeMf/h1TzVYs/rvM1MyN9oe7n3OZlAMrnyy4Gi2P+gm3ezNzqhbqfg12wWID2piisluzCmwES5eUKnqmRaUAX0MKhOcXFDe3y/UP9E4gl5In4xEQaqekMVJMCd9CG504dAgBcGemC26Yhkc7i+PDknJcTQuDooLy9u0F2wTssGq7plgXJ544O49CAXDfmoxEqcZtl0TaeipfcntWzOB6TJ266vOffcW7rkSd7Jnc/j+Shw4CqwP3e90K1WKB6PHJj4KyOzEjlDdrykoVohI4595kCAShWK0Q6LTcqKyMbjQJZHYrZDNXtLvsYIiIiujhYraKqqCa1EIEwMyIh32lbF/EIQDEiAZCFAKNncBIRXcpC3cDWT5ZurjYfRanPQnV4uexo9TQt3P2sWWRkxbIbqzuxeDGFeuQVK4AsLs/XOWu2F+fNEZCb4hFVIyljDFIDcWR1BcqSufnXDR4bWgMO6ELgpVPjAIDokOw4dQdsiKcyODIqu3Tf2dmLFbkc3IPn5kYk9McSmEhmYNFUtM4oyq5q9iLisSGZ1gvRCvlohEpcFln0HUmMYNfJXfifE/+DJ48/iV8e/WUhGqHRWSYHegGWbnmyR5+SncSOTZsKXbGKokBrkFdLZBbItRW6jtRJ+Xmxds79vCqqCnOTHF/mXPlc28ImZH7/eXUMExER0YXHoi1VzWKThdnUdHEzskxSdt3WT9G2o/j2QtEIREREF4LFCVz1CWDtXdUVlUPdQMv6+itAm+1ybADQsHLhx7esl/+3bqm/Y6H6lYxDZHUk+sdxbMyHw+dcmIwm5zxsY67b9sDZKKaSGQydkt2t3gYH/u/xo0jrSQSdDqwItxWKtseHJzGRLN00981B2dm7NOQsiU5QVQXv6W0o+dJdEpi/09Zr9UJRFKSyKRwaO4Q3x9/E8ehxnJuUGbHdvu63VOjU/H5oYblBmrmpEY6NG0vvb5BRLZkF4n3SfecgEkmodhu0xvLF42JEQvlc23w3rxYMVH8AREREtCgYPkZVM9s0IJYqbD4GFDciq4tMW0B2+mgWIJNi0ZaIiC6ei5Wzu9iW3yLjKqrpfg4vB677O8YM0flJTSA1FMdIMoK05oDJZseJV4ex8ppmKGqx4NkedCDksmB4IoXn9/bBFc9As5jgaXRgz++PAADWNi2FSTUh4DSh2WdD33gCb5yLYWOHLDgKIQpF266GuRsENnptWN3ixatnovDazfDY5t90067ZcXPHzRieHoZJNcGk5P6pJphVM9o8VV41UIbz2ncgcfA1OLdunZM1q0Vkp216gU7bVC4awdzeXjavFgDMzbIrPt13rmSfirzsqMyzzW9GTERERLXDoi1VrdBpm5jRaZvOFW3rpdNWNcmMxKFDpdl8REREtDCzrfq4CoAFWzp/yTimz8UxlFwBU7O8BH9yPIlzx6Jo7vYhq2fx6vCrWOJagvXtfvzPi304fGQYa5f40HZFEEdHpzCS7IPNrGJ9UzFDemWTF33jCRw8F8OGdvm8I5MpjE+loakKOoLOssO5plt2uHaGyt8/W4e3Ax3ejrf9aZjNsqQFliUtZe8z5+IRsmPj0FMpqJa5GxgKIQpF23LRCHlaQwNgUqFPTSE7Pj4nTzgzmuu0ZdGWiIio5rjSpqpZbLLGn8pl2mazOrK5Tcm0etiILK/9amDjPfJyVSIiIiKqD9k0kJzG2T47sooZzpYQOq+URdO+w2NITKTx8tDL2N23G785/hssDdlgH04hlc5iyqIg0OzAvpODmMwOI+Kxod1bPMHQE3FBUxWMTKQwEJNxC/ku27agAxat/FrVZjbh+hURLA3P7cStF6rTCdXtAoRAZnCo7GOyw8PIRqNQNBMsbZVPvCiaBnMkF7cwKyIh3deH7Pg4AHbaEhER1YM6qrRRvTPb8huRyU7bTEoWbBVVganCQpiIiIiICACU9CSmhhIYToWhaBa0b25HqNUFb4MDelbg8Et9eHHgJQDAZHoSuw/sR8SkQagKztgFzo4ncCJ6GqoCdIca4LV6C89tM5vQnYtAOHguCqBYtO0uE41gNPlu28xg+YiE5NGjAABLezuUMp24Jc81K9dW6Dom9+zF+M9/AWR1aA0NUN3uCzV0IiIieotYaaOqze60zczIs+XuskREREQ0HzU1gVPHrdAVCzwtPvibXFAUBR2rg1A1FUdOn0TqnAqryQqRVnDolTPwOxVojXaMpDL4n4MDiGX7EXZbsNTXPuf5VzbLDckO9U9gZCKJoXgSqqJgacj4RVstUnkzMiEEkkfeBABYurrn3D/bzFzbbDSK6M9/jqm9ewEhYO1dDu/7b+XanoiIqA6waEtVM8/KtM1vQqbVS54tEREREdWtZDSGoWEboJrQvqmtUBi0OswIddsxODUInHXiHeHrEBhshZ4BhtQ+rFwtIxSiUynEM/2IeGxoc8+NAGj1O+C2aUiks/g/r8uO1Ba/HfZLYK1aKNqW2YwsOzqK7Pi4jEbo7Fj4uRobAUVBdnwcY489jvS5figWC9w33ADPe99bNjOXiIiILj4Wbalq+U7bTDILXRdIp4qdtkRERERE8xk4OA49pcNrSyBwRelmWSdtb0B3JeEyuTF90IrGZAcUBRgMH0VLOAVVUZAUE7DbUnBZLWhxzd20S1UVrGiS3bZ94wkAl0Y0AgBo4TAAIBuLQ5+aKrkv+aaMRjC3tlVVcFVtNmhBmVkrUimYmxrhv/MO2JYvu8CjJiIioreDRVuqmmZRoaiyIyKdyBTjES6B7gUiIiIiWjwTowlET08CCtDSYYZiNhfuiyajODh6EGifQKt3CZKTaTjNDizpCUFxZvHHkb24otmNiewAmn12NDmbYDaZy77OylzRNq8rfGlsTKtarTD5/QDmRiQk3zwCALB2d1X9fNblvVA0ExybN8N7220weTwLfxARERFdVCzaUtUURSnJtc132mpWfhkRERERUXlCCJw5NAY9GodfGYZ3eWm0wb7+fRBCoK2hGb2r5H1Whxnv3rIJZtWMgakBLIlEsaknC6/djFZ3a8XX8jstaPbZAABNXhvctvLFXSPSInIzsvRAsWibGR1FdnQMMKmwdHZW+tA5HOvXIfi//zecWzZDUbmWJyIiqkf8DU3nZWaubSapy9vYaUtEREREFUQHpxHti0JJTKFB64Olq6dw38j0CA6PHQYAbGnagqYuL7rWN6B3ayPcNjfWR9YDAPb0P4+h6X4AQJtnbp7tTJs7g7BoKta3+xfpiGrD3CCLtjM7bZNv5jYga22FarWe1/OxWEtERFTftFoPgIwl32mbntlpy6ItEREREVUwfGYC2fFx+NEHe9AK1RMs3Le3fy8EBJb6lqLBIYuSwZZiDu2a8BocHDmIeCoOAHBoDgRtQcynM+TE9nd3L8KR1FZhM7LBAQghoCgKUkdlnq21q/poBCIiIjIGnl6l85LvtE0nskgnuREZEREREc2va30YzdoAguI0rI0ewOoGAPRP9uN49DgUKNjcuLnsx2qqhq1NWwvvt7pboSjKRRl3vdFCIUBVoE9NQ4/HkRkbQ2Z4BFAVWJYurfXwiIiI6AJj0ZbOSzHTNoMMO22JiIiIaAEikYAregyakoE54gUsLgghsOfcHgDA8sByBGyBih/f5etCk7MJANDpqz639VKjaBq0YAiAjEjId9laWluh2my1HBoREREtAsYj0HmxFDJts4WirZkbkRERERFRBakTJ4B0Eia3DSZ/AFBVHB8/hrMTZ2FSTNjYuHHej1cUBbcsvQXD08OF4u3lSos0IDM0hPTAANKnzwAALIxGICIiuiSx2kbnJR+PMD2Rgp4VAACN8QhEREREVEHq9Gkgm4LW4AasbmT0DJ7rew4AsLZhLTwWz4LPYTFZ0OxqvmyjEfK03GZkqaPHkBkaAlQFVkYjEBERXZLYaUvnJR+PkMnl2aqaCpOJtX8iIiIiKs+9bRus/gySx58GrG68MvQKYqkYnGYn1jesr/XwDMWc24wsG43K91taoNrttRwSERERLRJW2+i85DttC+8zz5aIiIiI5qGoKsx+G1S7BROqhhcHXgQAXNV0Fcwmc41HZyymQACKudh3Y+3qruFoiIiIaDGxaEvnxWRSSzYeMzMagYiIiIgWkpoAADw/dQZpPY1GZyOW+ZfVeFDGo6gqtHA4944C69LLd2M2IiKiSx2LtnTeZhZqNW5CRkREREQLScYxkJ3GkcQgFCi4tuXayz6f9q3SGmREgrm5GarTWePREBER0WJhpi2dN4tdw3Q8BYDxCERERES0MJGIYXd6ADAF0RvoRYOjodZDMiz7miuRjUbh2Lih1kMhIiKiRcSiLZ23kk5bFm2JiIiIaD5C4I34SQzrCTgtLmxp2lLrERmayeOB9/+5pdbDICIiokXGa9vpvFlszLQlIiIiouokkzHsmToLANjYvBUOs6PGIyIiIiKqfyza0nmz2IsN2uy0JSIiIqL57Dv7B0yLDLyaE6sa1tV6OERERESGYLii7cMPP4yOjg7YbDZs2bIFe/furfjY1157DR/4wAfQ0dEBRVHwne98520/J5V215q5ERkRERERzSNsssGpaNjs7IRJ5Ql/IiIiomoYquL2+OOP4/7778eDDz6IF198EWvWrMGNN96IwcHBso+fmprC0qVL8dWvfhWNjY0X5DmJnbZEREREVL1l1iD+X/dytDiaaj0UIiIiIsMwVNH229/+Nj72sY/hnnvuwcqVK/HII4/A4XDg0UcfLfv4TZs24Rvf+AbuuOMOWK3WC/KcNLvTlkVbIiIiorfrfK/8+tnPfobe3l7YbDasXr0av/nNby7SSN+C5ATMigphdtV6JERERESGYZiibSqVwv79+7Ft27bCbaqqYtu2bdi9e3fdPOflwGw1wRWwwemzsmhLRERE9Dad75Vfzz33HO6880589KMfxUsvvYRbb70Vt956Kw4cOHCRR16l9DQAQFhYtCUiIiKqlrbwQ+rD8PAwstksIpFIye2RSARvvPHGRX3OZDKJZDJZeD8WiwEAdF2HrutvaSzV0nUdQohFf52FLL9Kfs6EEBBC1HQsRlIv80dvHefQ2Dh/xsb5M7bFmr9L4eth5pVfAPDII4/gv/7rv/Doo4/is5/97JzHf/e738VNN92Ez3zmMwCAL33pS9i5cyd+8IMf4JFHHrmoY69KzzaIjmuRYvwYERERUdUMU7StJw899BD+4R/+Yc7tQ0NDSCQSi/rauq4jGo1CCAFVNUyjNOVw/oyPc2hsnD9j4/wZ22LNXzwev2DPVQv5K78+97nPFW5b6Mqv3bt34/777y+57cYbb8QTTzyxmEN9e0wW+Y+IiIiIqmKYom0oFILJZMLAwEDJ7QMDAxU3GVus5/zc5z5XslCOxWJobW1FOByGx+N5S2Oplq7rUBQF4XCYf7AaEOfP+DiHxsb5MzbOn7Et1vzZbLYL9ly18Fau/Orv7y/7+P7+/oqvU8srxfKvw0554+L8GRvnz9g4f8bG+TO2Wl8pZpiircViwYYNG7Br1y7ceuutAORB7tq1C/fee+9FfU6r1Vp2YzNVVS/KH5GKoly016ILj/NnfJxDY+P8GRvnz9gWY/74tVCdWl4pBrBT3ug4f8bG+TM2zp+xcf6MrdZXihmmaAsA999/P+6++25s3LgRmzdvxne+8x1MTk4W8r/+8i//Ei0tLXjooYcAyMvNDh48WHj77NmzePnll+FyudDd3V3VcxIRERERLZa3cuVXY2Ojoa4UA9gpb3ScP2Pj/Bkb58/YOH/GVusrxQxVtP3Qhz6EoaEhPPDAA+jv78fatWvx29/+tnB52KlTp0o+iX19fVi3bl3h/W9+85v45je/ieuuuw6/+93vqnpOIiIiIqLF8lau/Nq6dSt27dqFHTt2FG7buXMntm7dWvF1an2lGMBOeaPj/Bkb58/YOH/GxvkztlpeKWaooi0A3HvvvRUXsPlCbF5HRweEEG/rOYmIiIiIFtP5Xk1233334brrrsO3vvUt3HLLLXjsscewb98+/OhHP6rlYRARERHRBWS4oi0RERER0aXkfK8mu/rqq/HTn/4UX/jCF/D5z38ePT09eOKJJ7Bq1apaHQIRERERXWAs2hIRERER1dj5XE0GALfffjtuv/32RR4VEREREdUKAzWIiIiIiIiIiIiI6giLtkRERERERERERER1hEVbIiIiIiIiIiIiojrCoi0RERERERERERFRHWHRloiIiIiIiIiIiKiOsGhLREREREREREREVEdYtCUiIiIiIiIiIiKqIyzaEhEREREREREREdURrdYDuBQIIQAAsVhs0V9L13XE43HYbDaoKmvuRsP5Mz7OobFx/oyN82dsizV/+fVXfj1G1bmY61eA379Gx/kzNs6fsXH+jI3zZ2y1Xr+yaHsBxONxAEBra2uNR0JERER0eYrH4/B6vbUehmFw/UpERERUWwutXxXBtoS3Tdd19PX1we12Q1GURX2tWCyG1tZWnD59Gh6PZ1Ffiy48zp/xcQ6NjfNnbJw/Y1us+RNCIB6Po7m5mR0s5+Firl8Bfv8aHefP2Dh/xsb5MzbOn7HVev3KTtsLQFVVLFmy5KK+psfj4Te8gXH+jI9zaGycP2Pj/BnbYswfO2zPXy3WrwC/f42O82dsnD9j4/wZG+fP2Gq1fmU7AhEREREREREREVEdYdGWiIiIiIiIiIiIqI6waGswVqsVDz74IKxWa62HQm8B58/4OIfGxvkzNs6fsXH+Lm+cf2Pj/Bkb58/YOH/GxvkztlrPHzciIyIiIiIiIiIiIqoj7LQlIiIiIiIiIiIiqiMs2hIRERERERERERHVERZtiYiIiIiIiIiIiOoIi7YG8/DDD6OjowM2mw1btmzB3r17az0kKuOhhx7Cpk2b4Ha70dDQgFtvvRWHDh0qeUwikcD27dsRDAbhcrnwgQ98AAMDAzUaMVXy1a9+FYqiYMeOHYXbOHf17+zZs/iLv/gLBINB2O12rF69Gvv27SvcL4TAAw88gKamJtjtdmzbtg1Hjhyp4YgpL5vN4otf/CI6Oztht9vR1dWFL33pS5gZwc/5qx+///3v8b73vQ/Nzc1QFAVPPPFEyf3VzNXo6CjuuusueDwe+Hw+fPSjH8XExMRFPApabFy/GgPXr5cWrmGNh+tX4+L61ViMtH5l0dZAHn/8cdx///148MEH8eKLL2LNmjW48cYbMTg4WOuh0SzPPPMMtm/fjueffx47d+5EOp3GDTfcgMnJycJjPv3pT+NXv/oVfvazn+GZZ55BX18fbrvtthqOmmZ74YUX8I//+I+48sorS27n3NW3sbExXHPNNTCbzXjyySdx8OBBfOtb34Lf7y885utf/zq+973v4ZFHHsGePXvgdDpx4403IpFI1HDkBABf+9rX8MMf/hA/+MEP8Prrr+NrX/savv71r+P73/9+4TGcv/oxOTmJNWvW4OGHHy57fzVzddddd+G1117Dzp078etf/xq///3v8fGPf/xiHQItMq5fjYPr10sH17DGw/WrsXH9aiyGWr8KMozNmzeL7du3F97PZrOiublZPPTQQzUcFVVjcHBQABDPPPOMEEKI8fFxYTabxc9+9rPCY15//XUBQOzevbtWw6QZ4vG46OnpETt37hTXXXeduO+++4QQnDsj+Lu/+ztx7bXXVrxf13XR2NgovvGNbxRuGx8fF1arVfzbv/3bxRgizeOWW24Rf/VXf1Vy22233SbuuusuIQTnr54BEL/4xS8K71czVwcPHhQAxAsvvFB4zJNPPikURRFnz569aGOnxcP1q3Fx/WpMXMMaE9evxsb1q3HV+/qVnbYGkUqlsH//fmzbtq1wm6qq2LZtG3bv3l3DkVE1otEoACAQCAAA9u/fj3Q6XTKfvb29aGtr43zWie3bt+OWW24pmSOAc2cEv/zlL7Fx40bcfvvtaGhowLp16/DjH/+4cP/x48fR399fModerxdbtmzhHNaBq6++Grt27cLhw4cBAK+88gqeffZZ3HzzzQA4f0ZSzVzt3r0bPp8PGzduLDxm27ZtUFUVe/bsuehjpguL61dj4/rVmLiGNSauX42N69dLR72tX7UL+my0aIaHh5HNZhGJREpuj0QieOONN2o0KqqGruvYsWMHrrnmGqxatQoA0N/fD4vFAp/PV/LYSCSC/v7+GoySZnrsscfw4osv4oUXXphzH+eu/h07dgw//OEPcf/99+Pzn/88XnjhBfzN3/wNLBYL7r777sI8lft5yjmsvc9+9rOIxWLo7e2FyWRCNpvFl7/8Zdx1110AwPkzkGrmqr+/Hw0NDSX3a5qGQCDA+bwEcP1qXFy/GhPXsMbF9auxcf166ai39SuLtkSLbPv27Thw4ACeffbZWg+FqnD69Gncd9992LlzJ2w2W62HQ2+BruvYuHEjvvKVrwAA1q1bhwMHDuCRRx7B3XffXePR0UL+/d//HT/5yU/w05/+FFdccQVefvll7NixA83NzZw/IqKLhOtX4+Ea1ti4fjU2rl9psTAewSBCoRBMJtOc3T0HBgbQ2NhYo1HRQu699178+te/xtNPP40lS5YUbm9sbEQqlcL4+HjJ4zmftbd//34MDg5i/fr10DQNmqbhmWeewfe+9z1omoZIJMK5q3NNTU1YuXJlyW0rVqzAqVOnAKAwT/x5Wp8+85nP4LOf/SzuuOMOrF69Gh/+8Ifx6U9/Gg899BAAzp+RVDNXjY2NczakymQyGB0d5XxeArh+NSauX42Ja1hj4/rV2Lh+vXTU2/qVRVuDsFgs2LBhA3bt2lW4Tdd17Nq1C1u3bq3hyKgcIQTuvfde/OIXv8BTTz2Fzs7Okvs3bNgAs9lcMp+HDh3CqVOnOJ81dv311+OPf/wjXn755cK/jRs34q677iq8zbmrb9dccw0OHTpUctvhw4fR3t4OAOjs7ERjY2PJHMZiMezZs4dzWAempqagqqXLE5PJBF3XAXD+jKSaudq6dSvGx8exf//+wmOeeuop6LqOLVu2XPQx04XF9auxcP1qbFzDGhvXr8bG9eulo+7Wrxd0WzNaVI899piwWq3in//5n8XBgwfFxz/+ceHz+UR/f3+th0azfOITnxBer1f87ne/E+fOnSv8m5qaKjzmr//6r0VbW5t46qmnxL59+8TWrVvF1q1bazhqqmTmzrtCcO7q3d69e4WmaeLLX/6yOHLkiPjJT34iHA6H+Nd//dfCY7761a8Kn88n/vM//1O8+uqr4s/+7M9EZ2enmJ6eruHISQgh7r77btHS0iJ+/etfi+PHj4uf//znIhQKib/9278tPIbzVz/i8bh46aWXxEsvvSQAiG9/+9vipZdeEidPnhRCVDdXN910k1i3bp3Ys2ePePbZZ0VPT4+48847a3VIdIFx/WocXL9eeriGNQ6uX42N61djMdL6lUVbg/n+978v2trahMViEZs3bxbPP/98rYdEZQAo+++f/umfCo+Znp4Wn/zkJ4Xf7xcOh0O8//3vF+fOnavdoKmi2Qtezl39+9WvfiVWrVolrFar6O3tFT/60Y9K7td1XXzxi18UkUhEWK1Wcf3114tDhw7VaLQ0UywWE/fdd59oa2sTNptNLF26VPz93/+9SCaThcdw/urH008/Xfb33d133y2EqG6uRkZGxJ133ilcLpfweDzinnvuEfF4vAZHQ4uF61dj4Pr10sM1rLFw/WpcXL8ai5HWr4oQQlzY3l0iIiIiIiIiIiIiequYaUtERERERERERERUR1i0JSIiIiIiIiIiIqojLNoSERERERERERER1REWbYmIiIiIiIiIiIjqCIu2RERERERERERERHWERVsiIiIiIiIiIiKiOsKiLREREREREREREVEdYdGWiIiIiIiIiIiIqI6waEtEdBn4yEc+gltvvbXWwyAiIiIiqgrXr0R0udNqPQAiInp7FEWZ9/4HH3wQ3/3udyGEuEgjIiIiIiKqjOtXIqKFKYI/BYmIDK2/v7/w9uOPP44HHngAhw4dKtzmcrngcrlqMTQiIiIiojm4fiUiWhjjEYiIDK6xsbHwz+v1QlGUkttcLtecy8ve9a534VOf+hR27NgBv9+PSCSCH//4x5icnMQ999wDt9uN7u5uPPnkkyWvdeDAAdx8881wuVyIRCL48Ic/jOHh4Yt8xERERERkZFy/EhEtjEVbIqLL1L/8y78gFAph7969+NSnPoVPfOITuP3223H11VfjxRdfxA033IAPf/jDmJqaAgCMj4/jPe95D9atW4d9+/bht7/9LQYGBvDBD36wxkdCRERERJcDrl+J6HLCoi0R0WVqzZo1+MIXvoCenh587nOfg81mQygUwsc+9jH09PTggQcewMjICF599VUAwA9+8AOsW7cOX/nKV9Db24t169bh0UcfxdNPP43Dhw/X+GiIiIiI6FLH9SsRXU64ERkR0WXqyiuvLLxtMpkQDAaxevXqwm2RSAQAMDg4CAB45ZVX8PTTT5fNFzt69CiWLVu2yCMmIiIiossZ169EdDlh0ZaI6DJlNptL3lcUpeS2/K6+uq4DACYmJvC+970PX/va1+Y8V1NT0yKOlIiIiIiI61ciurywaEtERFVZv349/uM//gMdHR3QNP76ICIiIqL6xvUrERkZM22JiKgq27dvx+joKO6880688MILOHr0KP77v/8b99xzD7LZbK2HR0RERERUgutXIjIyFm2JiKgqzc3N+MMf/oBsNosbbrgBq1evxo4dO+Dz+aCq/HVCRERERPWF61ciMjJFCCFqPQgiIiIiIiIiIiIiknhqiYiIiIiIiIiIiKiOsGhLREREREREREREVEdYtCUiIiIiIiIiIiKqIyzaEhEREREREREREdURFm2JiIiIiIiIiIiI6giLtkRERERERERERER1hEVbIiIiIiIiIiIiojrCoi0RERERERERERFRHWHRloiIiIiIiIiIiKiOsGhLREREREREREREVEdYtCUiIiIiIiIiIiKqIyzaEhEREREREREREdWR/x/b9A8eGbTwtwAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(2, 2, figsize=(14, 10))\n", + "\n", + "# Plot first few state dimensions\n", + "ax = axes[0, 0]\n", + "for i in range(min(3, n_state)):\n", + " ax.plot(X_train[0, :200, i], label=f'$x_{{{i+1}}}$', alpha=0.7)\n", + "ax.set_xlabel('Time')\n", + "ax.set_ylabel('State Value')\n", + "ax.set_title('Sample State Trajectories (Trial 1, first 200 steps)')\n", + "ax.legend()\n", + "ax.grid(True, alpha=0.3)\n", + "\n", + "# Plot control inputs\n", + "ax = axes[0, 1]\n", + "for i in range(n_control):\n", + " ax.plot(U_train[0, :200, i], label=f'$u_{{{i+1}}}$', alpha=0.7)\n", + "ax.set_xlabel('Time')\n", + "ax.set_ylabel('Control Value')\n", + "ax.set_title('Control Inputs (Trial 1, first 200 steps)')\n", + "ax.legend()\n", + "ax.grid(True, alpha=0.3)\n", + "\n", + "# Plot state norm over time\n", + "ax = axes[1, 0]\n", + "for trial in range(n_trials):\n", + " state_norm = np.linalg.norm(X_train[trial], axis=1)\n", + " ax.plot(state_norm, alpha=0.5, label=f'Trial {trial+1}')\n", + "ax.set_xlabel('Time')\n", + "ax.set_ylabel('$||x_t||_2$')\n", + "ax.set_title('State Norm Over Time (All Trials)')\n", + "ax.legend()\n", + "ax.grid(True, alpha=0.3)\n", + "\n", + "# Plot control norm over time\n", + "ax = axes[1, 1]\n", + "for trial in range(n_trials):\n", + " control_norm = np.linalg.norm(U_train[trial], axis=1)\n", + " ax.plot(control_norm, alpha=0.5, label=f'Trial {trial+1}')\n", + "ax.set_xlabel('Time')\n", + "ax.set_ylabel('$||u_t||_2$')\n", + "ax.set_title('Control Norm Over Time (All Trials)')\n", + "ax.legend()\n", + "ax.grid(True, alpha=0.3)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "3a4db4a7", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sweeping: 0%| | 0/7 [00:00,\n", + " array([,\n", + " ,\n", + " ],\n", + " dtype=object))" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from DSA.sweeps import PyKoopmanSweeper\n", + "import pykoopman as pk\n", + "from pydmd import SubspaceDMD,DMD\n", + "sweeper = PyKoopmanSweeper(\n", + " data=X_train[0],\n", + " control_data=None,\n", + " param1_name=\"observables.n_delays\",\n", + " param1_values=np.arange(3,10),\n", + " param2_name=\"reg.svd_rank\",\n", + " param2_values=np.arange(1,25),\n", + " reseed=1,\n", + " base_regressor_class=SubspaceDMD\n", + " # base_regressor_class=pk.regression.DMDc, # Must use DMDc or EDMDc\n", + ")\n", + "sweeper.sweep()\n", + "sweeper.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "57fb8535", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sweeping: 100%|██████████| 9/9 [00:00<00:00, 10.77it/s]\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from DSA.sweeps import DefaultSweeper\n", + "# With control data\n", + "sweeper = DefaultSweeper(\n", + " data=X_train,\n", + " control_data=None, #NOte that we can still pass this in, even if it's none\n", + " param1_name=\"n_delays\",\n", + " param1_values=np.arange(1,10),\n", + " param2_name=\"rank\",\n", + " param2_values=np.arange(1,25),\n", + " compute_residuals=True #\n", + ")\n", + "sweeper.sweep()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "9a225798", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(
,\n", + " array([,\n", + " ,\n", + " ,\n", + " ], dtype=object))" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sweeper.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e0a5c88f", + "metadata": {}, + "outputs": [], + "source": [ + "sweeper" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "afc7a96a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Generated data shape:\n", + " State data X: (5, 100, 5)\n", + " Control data U: (5, 100, 3)\n" + ] + } + ], + "source": [ + "#now, let's generate data with input\n", + "# Generate training data\n", + "X_train_control, U_train_control = generate_trajectory(A_true, B_true, n_timesteps, n_trials,use_control=True,noise_std=0,partial_observ_frac=0.5)\n", + "\n", + "print(f\"Generated data shape:\")\n", + "print(f\" State data X: {X_train_control.shape}\")\n", + "print(f\" Control data U: {U_train_control.shape}\")\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fb5f1e37", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sweeping: 0%| | 0/4 [00:00,\n", + " array([,\n", + " ,\n", + " ], dtype=object))" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from DSA.sweeps import DefaultSweeper\n", + "# Without control data\n", + "sweeper = DefaultSweeper(\n", + " data=X_train_control,\n", + " control_data=None,\n", + " param1_name=\"n_delays\",\n", + " param1_values=np.arange(1,5),\n", + " param2_name=\"rank\",\n", + " param2_values=np.arange(1,15),\n", + " model_class=\"DMD\"\n", + ")\n", + "sweeper.sweep()\n", + "sweeper.plot()#not so clear, if we just use regular dmd now" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "094fe74c", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sweeping: 100%|██████████| 4/4 [00:01<00:00, 3.62it/s]\n" + ] + }, + { + "data": { + "text/plain": [ + "(
,\n", + " array([,\n", + " ,\n", + " ], dtype=object))" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from DSA.sweeps import DefaultSweeper\n", + "# Without control data\n", + "sweeper = DefaultSweeper(\n", + " data=X_train_control,\n", + " control_data=U_train_control,\n", + " param1_name=\"n_delays\",\n", + " param1_values=np.arange(1,5),\n", + " param2_name=\"rank\",\n", + " param2_values=np.arange(1,15),\n", + " model_class=\"SubspaceDMDc\"\n", + ")\n", + "sweeper.sweep()\n", + "sweeper.plot()#not so clear, if we just use regular dmd now" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0cfe5a52", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "dsapublic-env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.19" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tests/dmd_test.py b/tests/dmd_test.py index a08240a..faae52a 100644 --- a/tests/dmd_test.py +++ b/tests/dmd_test.py @@ -103,5 +103,6 @@ def test_to_cpu(seed, n, t, c): device = "cuda" if torch.cuda.is_available() else "cpu" dmd = DMD(X, 1, device=device) dmd.fit() + dmd.all_to_device() assert dmd.A_v.device.type == "cpu" assert dmd.H.device.type == "cpu" diff --git a/tests/dsa_test.py b/tests/dsa_test.py index 74c1ea2..c7f4122 100644 --- a/tests/dsa_test.py +++ b/tests/dsa_test.py @@ -109,7 +109,7 @@ def test_dsa_1tomany(n, t, c, seed, nmodels): dsa = DSA(X, Y) sim = dsa.fit_score() assert isinstance(sim, np.ndarray) - assert sim.shape == (nmodels, 1) + assert sim.shape == (nmodels, ) @pytest.mark.parametrize("n", [10]) @@ -124,7 +124,7 @@ def test_dsa_manyto1(n, t, c, seed, nmodels): dsa = DSA(Y, X) sim = dsa.fit_score() assert isinstance(sim, np.ndarray) - assert sim.shape == (1, nmodels) + assert sim.shape == (nmodels,) @pytest.mark.parametrize("n", [10]) diff --git a/tests/test_sweeps.py b/tests/test_sweeps.py new file mode 100644 index 0000000..d6136a8 --- /dev/null +++ b/tests/test_sweeps.py @@ -0,0 +1,436 @@ +""" +Tests for sweeps module: LocalDMDSweeper, PyKoopmanSweeper, and convenience functions. +""" + +import pytest +import numpy as np +from DSA.sweeps import ( + LocalDMDSweeper, + PyKoopmanSweeper, + sweep_local_dmd, + sweep_pykoopman, + sweep_ranks_delays, + split_train_test, +) +from DSA.dmd import DMD + + +# ============================================================================= +# Test Data Fixtures +# ============================================================================= + +@pytest.fixture +def data_2d(): + """2D data: (time, dim)""" + np.random.seed(42) + return np.random.randn(200, 5) + + +@pytest.fixture +def data_3d(): + """3D data: (trials, time, dim)""" + np.random.seed(42) + return np.random.randn(10, 100, 5) + + +@pytest.fixture +def data_list_2d(): + """List of 2D arrays""" + np.random.seed(42) + return [np.random.randn(100, 5) for _ in range(5)] + + +@pytest.fixture +def data_list_3d(): + """List of 3D arrays""" + np.random.seed(42) + return [np.random.randn(3, 80, 5) for _ in range(4)] + + +# ============================================================================= +# Test split_train_test +# ============================================================================= + +class TestSplitTrainTest: + + def test_split_2d(self, data_2d): + train, test, dim = split_train_test(data_2d, train_frac=0.8) + assert train.shape[0] == 160 + assert test.shape[0] == 40 + assert dim == 5 + + def test_split_3d(self, data_3d): + train, test, dim = split_train_test(data_3d, train_frac=0.8) + # For 3D with shape[0] > 1, splits along first dimension + assert dim == 5 + + def test_split_list(self, data_list_2d): + train, test, dim = split_train_test(data_list_2d, train_frac=0.8) + assert isinstance(train, list) + assert isinstance(test, list) + assert len(train) == 4 # 80% of 5 + assert len(test) == 1 + assert dim == 5 + + +# ============================================================================= +# Test LocalDMDSweeper +# ============================================================================= + +class TestLocalDMDSweeper: + + def test_sweeper_creation_2d(self, data_2d): + """Test sweeper can be created with 2D data.""" + sweeper = LocalDMDSweeper( + data=data_2d, + param1_values=[2, 3], + param2_values=[5, 10], + ) + assert sweeper.param1_name == "n_delays" + assert sweeper.param2_name == "rank" + assert len(sweeper.param1_values) == 2 + assert len(sweeper.param2_values) == 2 + + def test_sweeper_creation_3d(self, data_3d): + """Test sweeper can be created with 3D data.""" + sweeper = LocalDMDSweeper( + data=data_3d, + param1_values=[2, 3], + param2_values=[5, 10], + ) + assert sweeper.dim == 5 + + def test_sweep_runs_2d(self, data_2d): + """Test sweep completes on 2D data.""" + sweeper = LocalDMDSweeper( + data=data_2d, + param1_values=[2, 3], + param2_values=[5, 8], + train_frac=0.8, + ) + sweeper.sweep() + + assert sweeper._swept + assert sweeper.aics.shape == (2, 2) + assert sweeper.mases.shape == (2, 2) + assert sweeper.mses.shape == (2, 2) + assert sweeper.nnormals.shape == (2, 2) + + def test_sweep_runs_3d(self, data_3d): + """Test sweep completes on 3D data.""" + sweeper = LocalDMDSweeper( + data=data_3d, + param1_values=[2, 3], + param2_values=[5, 8], + ) + sweeper.sweep() + + assert sweeper._swept + assert sweeper.aics.shape == (2, 2) + + def test_sweep_with_residuals(self, data_2d): + """Test sweep with residual computation.""" + sweeper = LocalDMDSweeper( + data=data_2d, + param1_values=[2], + param2_values=[5], + compute_residuals_flag=True, + ) + sweeper.sweep() + + assert sweeper.residuals is not None + assert sweeper.residuals.shape == (1, 1) + assert not np.isnan(sweeper.residuals[0, 0]) + + def test_invalid_rank_skipped(self, data_2d): + """Test that invalid rank combinations are skipped.""" + sweeper = LocalDMDSweeper( + data=data_2d, + param1_values=[2], # n_delays=2, dim=5 -> max_rank=10 + param2_values=[5, 15], # 15 > 10, should be skipped + ) + sweeper.sweep() + + assert not np.isnan(sweeper.aics[0, 0]) # rank=5 valid + assert np.isnan(sweeper.aics[0, 1]) # rank=15 invalid + + def test_get_results(self, data_2d): + """Test get_results returns correct structure.""" + sweeper = LocalDMDSweeper( + data=data_2d, + param1_values=[2, 3], + param2_values=[5, 8], + ) + sweeper.sweep() + + results = sweeper.get_results() + assert "param1_name" in results + assert "param1_values" in results + assert "aics" in results + assert "mases" in results + assert "mses" in results + assert "nnormals" in results + assert results["param1_name"] == "n_delays" + assert results["param2_name"] == "rank" + + def test_fitted_models_stored(self, data_2d): + """Test that fitted models are stored.""" + sweeper = LocalDMDSweeper( + data=data_2d, + param1_values=[2], + param2_values=[5], + ) + sweeper.sweep() + + model = sweeper.fitted_models[0][0] + assert model is not None + assert hasattr(model, 'A_v') + + def test_plot_runs(self, data_2d): + """Test that plot method runs without error.""" + sweeper = LocalDMDSweeper( + data=data_2d, + param1_values=[2, 3], + param2_values=[5, 8], + ) + sweeper.sweep() + + fig, axes = sweeper.plot() + assert fig is not None + assert len(axes) >= 1 + import matplotlib.pyplot as plt + plt.close(fig) + + def test_error_before_sweep(self, data_2d): + """Test that accessing results before sweep raises error.""" + sweeper = LocalDMDSweeper( + data=data_2d, + param1_values=[2], + param2_values=[5], + ) + + with pytest.raises(RuntimeError): + _ = sweeper.aics + + +# ============================================================================= +# Test PyKoopmanSweeper +# ============================================================================= + +class TestPyKoopmanSweeper: + + def test_sweeper_creation(self, data_2d): + """Test PyKoopman sweeper can be created.""" + sweeper = PyKoopmanSweeper( + data=data_2d, + param1_name="observables.n_delays", + param1_values=[2, 3], + param2_name="regressor.svd_rank", + param2_values=[5, 10], + ) + assert sweeper._param1_component == "observables" + assert sweeper._param1_attr == "n_delays" + assert sweeper._param2_component == "regressor" + assert sweeper._param2_attr == "svd_rank" + + def test_sweep_runs_2d(self, data_2d): + """Test PyKoopman sweep completes on 2D data.""" + sweeper = PyKoopmanSweeper( + data=data_2d, + param1_name="observables.n_delays", + param1_values=[2, 3], + param2_name="regressor.svd_rank", + param2_values=[5, 8], + ) + sweeper.sweep() + + assert sweeper._swept + assert sweeper.aics.shape == (2, 2) + assert sweeper.mases.shape == (2, 2) + + def test_sweep_runs_3d(self, data_3d): + """Test PyKoopman sweep on 3D data (flattened internally).""" + sweeper = PyKoopmanSweeper( + data=data_3d, + param1_name="observables.n_delays", + param1_values=[2], + param2_name="regressor.svd_rank", + param2_values=[5], + ) + sweeper.sweep() + + assert sweeper._swept + assert sweeper.aics.shape == (1, 1) + + def test_with_extra_observables(self, data_2d): + """Test PyKoopman sweeper with extra observables.""" + from DSA.pykoopman.observables import RandomFourierFeatures + + sweeper = PyKoopmanSweeper( + data=data_2d, + param1_name="observables.n_delays", + param1_values=[2], + param2_name="regressor.svd_rank", + param2_values=[10], + extra_observables=[RandomFourierFeatures(D=20, gamma=1.0)], + ) + sweeper.sweep() + + assert sweeper._swept + assert not np.isnan(sweeper.aics[0, 0]) + + def test_get_results(self, data_2d): + """Test get_results for PyKoopman sweeper.""" + sweeper = PyKoopmanSweeper( + data=data_2d, + param1_name="observables.n_delays", + param1_values=[2], + param2_name="regressor.svd_rank", + param2_values=[5], + ) + sweeper.sweep() + + results = sweeper.get_results() + assert results["param1_name"] == "observables.n_delays" + assert results["param2_name"] == "regressor.svd_rank" + + def test_plot_runs(self, data_2d): + """Test that plot method runs without error.""" + sweeper = PyKoopmanSweeper( + data=data_2d, + param1_name="observables.n_delays", + param1_values=[2, 3], + param2_name="regressor.svd_rank", + param2_values=[5, 8], + ) + sweeper.sweep() + + fig, axes = sweeper.plot() + assert fig is not None + import matplotlib.pyplot as plt + plt.close(fig) + + +# ============================================================================= +# Test Convenience Functions +# ============================================================================= + +class TestConvenienceFunctions: + + def test_sweep_local_dmd(self, data_2d): + """Test sweep_local_dmd convenience function.""" + sweeper = sweep_local_dmd( + data_2d, + n_delays_values=[2, 3], + rank_values=[5, 8], + ) + + assert sweeper._swept + assert sweeper.aics.shape == (2, 2) + + def test_sweep_pykoopman(self, data_2d): + """Test sweep_pykoopman convenience function.""" + sweeper = sweep_pykoopman( + data_2d, + param1_name="observables.n_delays", + param1_values=[2, 3], + param2_name="regressor.svd_rank", + param2_values=[5, 8], + ) + + assert sweeper._swept + assert sweeper.aics.shape == (2, 2) + + def test_sweep_ranks_delays_backward_compat(self, data_2d): + """Test backward-compatible sweep_ranks_delays function.""" + result = sweep_ranks_delays( + data_2d, + n_delays=[2, 3], + ranks=[5, 8], + return_residuals=False, + ) + + aics, mases, nnormals = result + assert aics.shape == (2, 2) + assert mases.shape == (2, 2) + assert nnormals.shape == (2, 2) + + def test_sweep_ranks_delays_with_residuals(self, data_2d): + """Test sweep_ranks_delays with residuals.""" + result = sweep_ranks_delays( + data_2d, + n_delays=[2], + ranks=[5], + return_residuals=True, + ) + + aics, mases, nnormals, residuals = result + assert aics.shape == (1, 1) + assert residuals.shape == (1, 1) + + +# ============================================================================= +# Test with List Data +# ============================================================================= + +class TestListData: + + def test_local_dmd_sweeper_list_2d(self, data_list_2d): + """Test LocalDMDSweeper with list of 2D arrays.""" + sweeper = LocalDMDSweeper( + data=data_list_2d, + param1_values=[2], + param2_values=[5], + ) + sweeper.sweep() + + assert sweeper._swept + assert sweeper.aics.shape == (1, 1) + + +# ============================================================================= +# Test Edge Cases +# ============================================================================= + +class TestEdgeCases: + + def test_single_param_value(self, data_2d): + """Test with single parameter values.""" + sweeper = LocalDMDSweeper( + data=data_2d, + param1_values=[3], + param2_values=[10], + ) + sweeper.sweep() + + assert sweeper.aics.shape == (1, 1) + assert not np.isnan(sweeper.aics[0, 0]) + + def test_large_n_delays(self, data_2d): + """Test with larger n_delays (more delay embedding).""" + sweeper = LocalDMDSweeper( + data=data_2d, + param1_values=[5, 10], + param2_values=[20, 30], + ) + sweeper.sweep() + + # Should complete without error + assert sweeper._swept + + def test_train_frac_1(self, data_2d): + """Test with train_frac=1.0 (test on train data).""" + sweeper = LocalDMDSweeper( + data=data_2d, + param1_values=[2], + param2_values=[5], + train_frac=1.0, + ) + sweeper.sweep() + + assert sweeper._swept + assert not np.isnan(sweeper.aics[0, 0]) + + +if __name__ == "__main__": + pytest.main([__file__, "-v"]) From f8e782c7b0129256c8edaa68c8409f3140bcd881 Mon Sep 17 00:00:00 2001 From: mitchellostrow Date: Sat, 7 Feb 2026 11:54:04 -0500 Subject: [PATCH 79/90] add option for sweeping over multiple observable params --- DSA/sweeps.py | 163 +++++++++++++++++++-- examples/sweep_resdmd_tutorial.ipynb | 208 +++++++++++++++++++++++---- 2 files changed, 325 insertions(+), 46 deletions(-) diff --git a/DSA/sweeps.py b/DSA/sweeps.py index 7b6bce6..1570a63 100644 --- a/DSA/sweeps.py +++ b/DSA/sweeps.py @@ -514,7 +514,8 @@ class PyKoopmanSweeper(BaseSweeper): allowing sweeps over both observable parameters (e.g., time delays) and regressor parameters (e.g., SVD rank). Parameters are specified using dotted notation: "component.parameter" where component is "observables" - (or "observable", "obs") or "regressor" (or "reg"). + (or "observable", "obs"), "regressor" (or "reg"), or "extra_obs.{index}" + for extra observables. Parameters ---------- @@ -522,7 +523,10 @@ class PyKoopmanSweeper(BaseSweeper): Input data for the sweep. See `BaseSweeper` for details. param1_name : str, optional Name of the first parameter in "component.param" format - (default: "observables.n_delays"). + (default: "observables.n_delays"). Supported components: + - "observables", "observable", "obs": base observable parameters + - "regressor", "reg": regressor parameters + - "extra_obs.{index}": extra observable parameters (e.g., "extra_obs.0.D") param1_values : list or np.ndarray, optional Values to sweep for the first parameter. param2_name : str, optional @@ -540,7 +544,14 @@ class PyKoopmanSweeper(BaseSweeper): base_regressor_kwargs : dict, optional Additional kwargs for the regressor constructor. extra_observables : list, optional - Additional observable objects to combine with the base observable. + Additional observable objects or classes to combine with the base observable. + Can be a mix of: + - Observable instances (fixed, not swept) + - Observable classes (can be swept via "extra_obs.{index}.param" syntax) + extra_observable_kwargs : list of dict, optional + Default kwargs for each extra observable class. Only used when the + corresponding entry in extra_observables is a class (not an instance). + Length must match extra_observables. control_data : np.ndarray, optional Control/actuation data for controlled systems. **kwargs @@ -560,6 +571,18 @@ class PyKoopmanSweeper(BaseSweeper): >>> sweeper.sweep() >>> sweeper.plot() + >>> # Sweep TimeDelay n_delays and RandomFourierFeatures D + >>> sweeper = PyKoopmanSweeper( + ... data=X, + ... param1_name="obs.n_delays", + ... param1_values=[3, 5, 7], + ... param2_name="extra_obs.0.D", + ... param2_values=[50, 100, 200], + ... base_observable_class=pk.observables.TimeDelay, + ... extra_observables=[pk.observables.RandomFourierFeatures], # class, not instance + ... extra_observable_kwargs=[{"include_state": True}], # default kwargs + ... ) + >>> # With control input >>> import pykoopman as pk >>> sweeper = PyKoopmanSweeper( @@ -582,6 +605,7 @@ class PyKoopmanSweeper(BaseSweeper): The component names in param1_name/param2_name are flexible: - For observables: "observable", "observables", or "obs" - For regressor: "regressor" or "reg" + - For extra observables: "extra_obs.{index}" (e.g., "extra_obs.0", "extra_obs.1") """ def __init__( @@ -595,7 +619,8 @@ def __init__( base_observable_kwargs: dict = None, base_regressor_class=None, base_regressor_kwargs: dict = None, - extra_observables: list = None, + extra_observables: Union[List,Any] = None, + extra_observable_kwargs: List[dict] = None, control_data: np.ndarray = None, **kwargs ): @@ -608,6 +633,16 @@ def __init__( self.base_regressor_class = base_regressor_class self.base_regressor_kwargs = base_regressor_kwargs or {} self.extra_observables = extra_observables or [] + if not isinstance(self.extra_observables, list): + self.extra_observables = [self.extra_observables] + self.extra_observable_kwargs = extra_observable_kwargs or [{} for _ in self.extra_observables] + + # Validate extra_observable_kwargs length + if len(self.extra_observable_kwargs) != len(self.extra_observables): + raise ValueError( + f"extra_observable_kwargs length ({len(self.extra_observable_kwargs)}) must match " + f"extra_observables length ({len(self.extra_observables)})" + ) # Warn if the regressor class doesn't support control input if control_data is not None and base_regressor_class is not None: @@ -619,8 +654,17 @@ def __init__( UserWarning ) - self._param1_component, self._param1_attr = param1_name.split('.') - self._param2_component, self._param2_attr = param2_name.split('.') + # Parse parameter names into components + self._param1_parsed = self._parse_param_name(param1_name) + self._param2_parsed = self._parse_param_name(param2_name) + # Keep legacy attributes for backward compatibility + self._param1_component, self._param1_attr = param1_name.split('.', 1) + self._param2_component, self._param2_attr = param2_name.split('.', 1) + # Handle extra_obs.N.param format for legacy attrs + if self._param1_parsed['type'] == 'extra_obs': + self._param1_attr = self._param1_parsed['attr'] + if self._param2_parsed['type'] == 'extra_obs': + self._param2_attr = self._param2_parsed['attr'] def _is_control_model_class(self, model_class) -> bool: """Check if a model/regressor class supports control input. @@ -644,24 +688,113 @@ def _is_regressor_component(component: str) -> bool: """Check if a component name refers to regressor.""" return component in ("regressor", "reg") + @staticmethod + def _is_extra_obs_component(component: str) -> bool: + """Check if a component name refers to extra observables.""" + return component.startswith("extra_obs") + + def _parse_param_name(self, param_name: str) -> dict: + """Parse a parameter name into its components. + + Handles formats: + - "obs.n_delays" -> {'type': 'observable', 'attr': 'n_delays', 'index': None} + - "reg.svd_rank" -> {'type': 'regressor', 'attr': 'svd_rank', 'index': None} + - "extra_obs.0.D" -> {'type': 'extra_obs', 'attr': 'D', 'index': 0} + + Args: + param_name: Parameter name in dotted notation. + + Returns: + Dict with 'type', 'attr', and 'index' keys. + """ + parts = param_name.split('.') + + if len(parts) < 2: + raise ValueError(f"Invalid parameter name: {param_name}. Expected 'component.param' format.") + + component = parts[0] + + if self._is_observable_component(component): + return {'type': 'observable', 'attr': parts[1], 'index': None} + elif self._is_regressor_component(component): + return {'type': 'regressor', 'attr': parts[1], 'index': None} + elif self._is_extra_obs_component(component): + # Format: "extra_obs.{index}.{attr}" or "extra_obs{index}.{attr}" + if len(parts) == 3: + # "extra_obs.0.D" + try: + index = int(parts[1]) + except ValueError: + raise ValueError(f"Invalid extra_obs index in: {param_name}. Expected integer.") + return {'type': 'extra_obs', 'attr': parts[2], 'index': index} + elif len(parts) == 2: + # Try "extra_obs0.D" format + import re + match = re.match(r'extra_obs(\d+)', component) + if match: + return {'type': 'extra_obs', 'attr': parts[1], 'index': int(match.group(1))} + raise ValueError(f"Invalid extra_obs format: {param_name}. Use 'extra_obs.N.param' or 'extra_obsN.param'.") + else: + raise ValueError(f"Invalid extra_obs format: {param_name}. Use 'extra_obs.N.param'.") + else: + raise ValueError(f"Unknown component: {component}. Expected 'obs', 'reg', or 'extra_obs'.") + + @staticmethod + def _cast_to_int(val): + return int(val) if isinstance(val, np.integer) else val + def _build_observable(self, p1_val, p2_val): + """Build the composite observable with swept parameters. + + Args: + p1_val: Value for param1. + p2_val: Value for param2. + + Returns: + Combined observable (base + extras). + """ + # Build base observable kwargs = dict(self.base_observable_kwargs) - if self._is_observable_component(self._param1_component): - kwargs[self._param1_attr] = p1_val - if self._is_observable_component(self._param2_component): - kwargs[self._param2_attr] = p2_val + if self._param1_parsed['type'] == 'observable': + kwargs[self._param1_parsed['attr']] = self._cast_to_int(p1_val) + if self._param2_parsed['type'] == 'observable': + kwargs[self._param2_parsed['attr']] = self._cast_to_int(p2_val) obs = self.base_observable_class(**kwargs) - for extra in self.extra_observables: - obs = obs + extra + + # Build and add extra observables + for i, extra in enumerate(self.extra_observables): + extra_kwargs = dict(self.extra_observable_kwargs[i]) + + # Check if param1 targets this extra observable + if self._param1_parsed['type'] == 'extra_obs' and self._param1_parsed['index'] == i: + extra_kwargs[self._param1_parsed['attr']] = p1_val + # Check if param2 targets this extra observable + if self._param2_parsed['type'] == 'extra_obs' and self._param2_parsed['index'] == i: + extra_kwargs[self._param2_parsed['attr']] = p2_val + + # Build extra observable: if it's a class, instantiate it; if instance, use as-is + if isinstance(extra, type): + # It's a class, instantiate with kwargs + extra_obs = extra(**extra_kwargs) + else: + # It's already an instance + if extra_kwargs: + warnings.warn( + f"extra_observables[{i}] is an instance, not a class. " + f"Cannot apply swept parameters. Pass a class instead to sweep its parameters.", + UserWarning + ) + extra_obs = extra + + obs = obs + extra_obs return obs def _build_regressor(self, p1_val, p2_val): kwargs = dict(self.base_regressor_kwargs) - cast_to_int = lambda val: int(val) if isinstance(val, np.integer) else val if self._is_regressor_component(self._param1_component): - kwargs[self._param1_attr] = cast_to_int(p1_val) + kwargs[self._param1_attr] = self._cast_to_int(p1_val) if self._is_regressor_component(self._param2_component): - kwargs[self._param2_attr] = cast_to_int(p2_val) + kwargs[self._param2_attr] = self._cast_to_int(p2_val) if self.base_regressor_class: # Return the regressor instance directly - pk.Koopman handles pydmd regressors return self.base_regressor_class(**kwargs) diff --git a/examples/sweep_resdmd_tutorial.ipynb b/examples/sweep_resdmd_tutorial.ipynb index 77b49b5..8377075 100644 --- a/examples/sweep_resdmd_tutorial.ipynb +++ b/examples/sweep_resdmd_tutorial.ipynb @@ -57,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 3, "id": "e1552288", "metadata": {}, "outputs": [ @@ -66,7 +66,7 @@ "output_type": "stream", "text": [ "Generated data shape:\n", - " State data X: (5, 100, 7)\n", + " State data X: (5, 100, 15)\n", " Control data U: (5, 100, 3)\n" ] } @@ -127,7 +127,7 @@ " return X, U\n", "\n", "# Generate training data\n", - "X_train, U_train = generate_trajectory(A_true, B_true, n_timesteps, n_trials,noise_std=0.0,partial_observ_frac=0.5)\n", + "X_train, U_train = generate_trajectory(A_true, B_true, n_timesteps, n_trials,noise_std=0.0,partial_observ_frac=1)\n", "\n", "print(f\"Generated data shape:\")\n", "print(f\" State data X: {X_train.shape}\")\n", @@ -136,13 +136,13 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 4, "id": "a0b65b61", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -202,7 +202,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 5, "id": "3a4db4a7", "metadata": {}, "outputs": [ @@ -210,14 +210,66 @@ "name": "stderr", "output_type": "stream", "text": [ - "Sweeping: 0%| | 0/7 [00:00,\n", + " array([,\n", + " ,\n", + " ],\n", + " dtype=object))" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from DSA.sweeps import PyKoopmanSweeper\n", + "import pykoopman as pk\n", + "from pydmd import SubspaceDMD,DMD\n", + "sweeper = PyKoopmanSweeper(\n", + " data=X_train[0],\n", + " control_data=None,\n", + " param1_name=\"observables.n_delays\",\n", + " param1_values=np.arange(1,10),\n", + " param2_name=\"reg.svd_rank\",\n", + " param2_values=np.arange(1,25),\n", + " reseed=1,\n", + " base_regressor_class=SubspaceDMD\n", + " # base_regressor_class=pk.regression.DMDc, # Must use DMDc or EDMDc\n", + ")\n", + "sweeper.sweep()\n", + "sweeper.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "d83efaf6", + "metadata": {}, + "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "Sweeping: 100%|██████████| 7/7 [00:00<00:00, 10.47it/s]\n" + "Sweeping: 100%|██████████| 7/7 [00:00<00:00, 11.19it/s]\n" ] }, { @@ -230,13 +282,13 @@ " dtype=object))" ] }, - "execution_count": 10, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", "text/plain": [ "
" ] @@ -249,15 +301,19 @@ "from DSA.sweeps import PyKoopmanSweeper\n", "import pykoopman as pk\n", "from pydmd import SubspaceDMD,DMD\n", + "#we can also sweep other parameters here! here we sweep the dimension of random fourier features\n", "sweeper = PyKoopmanSweeper(\n", " data=X_train[0],\n", " control_data=None,\n", - " param1_name=\"observables.n_delays\",\n", + " param1_name=\"observables.D\",\n", " param1_values=np.arange(3,10),\n", " param2_name=\"reg.svd_rank\",\n", - " param2_values=np.arange(1,25),\n", + " param2_values=np.arange(1,15),\n", " reseed=1,\n", - " base_regressor_class=SubspaceDMD\n", + " base_regressor_class=SubspaceDMD,\n", + " base_observable_class = pk.observables.RandomFourierFeatures,\n", + " base_observable_kwargs = {'gamma':0.1,'include_state':False},\n", + " extra_observables = pk.observables.TimeDelay(n_delays=5)\n", " # base_regressor_class=pk.regression.DMDc, # Must use DMDc or EDMDc\n", ")\n", "sweeper.sweep()\n", @@ -267,25 +323,97 @@ { "cell_type": "code", "execution_count": 15, - "id": "57fb8535", + "id": "2920979a", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "Sweeping: 100%|██████████| 9/9 [00:00<00:00, 10.77it/s]\n" + "Sweeping: 0%| | 0/7 [00:00" + "(
,\n", + " array([,\n", + " ,\n", + " ],\n", + " dtype=object))" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Sweep TimeDelay n_delays AND RandomFourierFeatures D\n", + "sweeper = PyKoopmanSweeper(\n", + " data=X_train[0],\n", + " control_data=None,\n", + " param1_name=\"obs.n_delays\", # Sweep TimeDelay.n_delays\n", + " param1_values=np.arange(3, 10),\n", + " param2_name=\"extra_obs.0.gamma\", # Sweep RFF.D (first extra observable)\n", + " param2_values=[0,0.1],\n", + " base_observable_class=pk.observables.TimeDelay,\n", + " extra_observables=[pk.observables.RandomFourierFeatures], # Pass CLASS, not instance\n", + " extra_observable_kwargs=[{\"include_state\": False,'D':10}], # Optional default kwargs for RFF\n", + " base_regressor_class=SubspaceDMD,\n", + " base_regressor_kwargs={'svd_rank':10}\n", + "\n", + ")\n", + "sweeper.sweep()\n", + "sweeper.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "57fb8535", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sweeping: 33%|███▎ | 3/9 [00:00<00:00, 19.35it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sweeping: 100%|██████████| 9/9 [00:00<00:00, 10.80it/s]\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -305,7 +433,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 7, "id": "9a225798", "metadata": {}, "outputs": [ @@ -319,13 +447,13 @@ " ], dtype=object))" ] }, - "execution_count": 15, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -340,17 +468,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "e0a5c88f", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "sweeper" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "afc7a96a", "metadata": {}, "outputs": [ @@ -359,7 +498,7 @@ "output_type": "stream", "text": [ "Generated data shape:\n", - " State data X: (5, 100, 5)\n", + " State data X: (5, 100, 7)\n", " Control data U: (5, 100, 3)\n" ] } @@ -377,7 +516,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "fb5f1e37", "metadata": {}, "outputs": [ @@ -392,7 +531,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Sweeping: 100%|██████████| 4/4 [00:00<00:00, 29.34it/s]\n" + "Sweeping: 100%|██████████| 4/4 [00:00<00:00, 23.39it/s]\n" ] }, { @@ -404,13 +543,13 @@ " ], dtype=object))" ] }, - "execution_count": 12, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -437,7 +576,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "094fe74c", "metadata": {}, "outputs": [ @@ -445,7 +584,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "Sweeping: 100%|██████████| 4/4 [00:01<00:00, 3.62it/s]\n" + "Sweeping: 0%| | 0/4 [00:00], dtype=object))" ] }, - "execution_count": 13, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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" ] From 972fd98e0779a73fc2c96eeed6b276d52f7544d6 Mon Sep 17 00:00:00 2001 From: mitchellostrow Date: Sat, 7 Feb 2026 15:05:04 -0500 Subject: [PATCH 80/90] resdmd updates --- DSA/dmdc.py | 2 + DSA/resdmd.py | 1129 +++++++++++++++++++++++--- examples/sweep_resdmd_tutorial.ipynb | 349 +++++--- 3 files changed, 1259 insertions(+), 221 deletions(-) diff --git a/DSA/dmdc.py b/DSA/dmdc.py index 0ac43f2..86be392 100644 --- a/DSA/dmdc.py +++ b/DSA/dmdc.py @@ -122,6 +122,7 @@ def __init__( self.delay_interval = delay_interval self.rank_input = rank_input + self.rank = rank_input self.rank_thresh_input = rank_thresh_input self.rank_explained_variance_input = rank_explained_variance_input self.rank_output = rank_output @@ -437,6 +438,7 @@ def recalc_rank( rank_thresh=rank_thresh_output, rank_explained_variance=rank_explained_variance_output, ) + self.rank = self.rank_input def compute_dmdc(self, lamb=None): if self.verbose: diff --git a/DSA/resdmd.py b/DSA/resdmd.py index 1b525ca..8259c2f 100644 --- a/DSA/resdmd.py +++ b/DSA/resdmd.py @@ -1,150 +1,961 @@ +""" +ResDMD: Residual-based analysis of DMD eigenvalues. + +This module provides tools for computing and analyzing eigenvalue residuals +for DMD-type models, including both local DMD and PyKoopman models. + +Supports DMD with control (DMDc) models by subtracting out control effects +before computing residuals on the autonomous dynamics. +""" + +import warnings import numpy as np import matplotlib.pyplot as plt from matplotlib import cm from matplotlib import colors as mcolors +from typing import Literal, Optional, Tuple, Union try: - from .dmd import DMD + from .dmd import DMD, embed_signal_torch + from .dmdc import DMDc, embed_data_DMDc + from .subspace_dmdc import SubspaceDMDc except ImportError: - from dmd import DMD + from dmd import DMD, embed_signal_torch + try: + from dmdc import DMDc, embed_data_DMDc + from subspace_dmdc import SubspaceDMDc + except ImportError: + DMDc = None + SubspaceDMDc = None + embed_data_DMDc = None + import torch import ot -from typing import Literal -def compute_residuals( - dmd: "DMD | np.ndarray | torch.Tensor", - X: np.ndarray = None, - Y: np.ndarray = None, - rank: int = None, - matrix: Literal["A_v", "A_havok_dmd"] = "A_v", - return_num_denom=False, - tol=1e-6, -): - """ - Compute DMD eigenvalues, eigenvectors, and residuals for each mode. +# ============================================================================= +# Control Input Handling Helpers +# ============================================================================= +def _requires_control(model) -> bool: + """ + Check if a model requires control input for residual computation. + Parameters ---------- - dmd : DMD object, np.ndarray, or torch.Tensor - DMD object (with A_v, Vt_minus, Vt_plus, rank) or matrix. - X : np.ndarray, optional - Left-hand side data matrix. - Y : np.ndarray, optional - Right-hand side data matrix. - rank : int, optional - Rank of the DMD model. - matrix : Literal["A_v", "A_havok_dmd"], optional - Matrix to compute residuals on. Must be either "A_v" or "A_havok_dmd". Default is "A_v". - return_num_denom : bool, optional - Whether to return the numerator and denominator of the residual. Default is False. - + model : DMD, DMDc, SubspaceDMDc, or pk.Koopman + Fitted model. + Returns ------- - L : np.ndarray - Eigenvalues. - G : np.ndarray - Eigenvectors. - residuals : np.ndarray - Residuals for each eigenpair. - normalized_residuals : np.ndarray or None - Normalized residuals (if available, else None). + bool + True if the model is a control model requiring control_data. """ + # Local DMDc or SubspaceDMDc + if hasattr(model, 'B_v') and model.B_v is not None: + return True + + # PyKoopman with control (check via B property) + if hasattr(model, '_pipeline'): + try: + B = model.B + if B is not None: + return True + except (AttributeError, ValueError): + pass + + return False - # Handle DMD object, numpy array, or torch tensor - if hasattr(dmd, matrix): - A = ( - getattr(dmd, matrix).cpu().detach().numpy() - if hasattr(getattr(dmd, matrix), "cpu") - else getattr(dmd, matrix) - ) - L, G = np.linalg.eig(A) - if matrix == "A_havok_dmd": - X = ( - dmd.Vt_minus.cpu().detach().numpy()[:, : dmd.rank] - @ dmd.S_mat[: dmd.rank, : dmd.rank].cpu().detach().numpy() - @ dmd.U.cpu().detach().numpy().T[: dmd.rank] - ) - Y = ( - dmd.Vt_plus.cpu().detach().numpy()[:, : dmd.rank] - @ dmd.S_mat[: dmd.rank, : dmd.rank].cpu().detach().numpy() - @ dmd.U.cpu().detach().numpy().T[: dmd.rank] - ) - elif matrix == "A_v": - X = ( - dmd.Vt_minus.cpu().detach().numpy()[:, : dmd.rank] - if hasattr(dmd.Vt_minus, "cpu") - else dmd.Vt_minus[:, : dmd.rank] - ) - Y = ( - dmd.Vt_plus.cpu().detach().numpy()[:, : dmd.rank] - if hasattr(dmd.Vt_plus, "cpu") - else dmd.Vt_plus[:, : dmd.rank] - ) - rank = dmd.rank - elif isinstance(dmd, np.ndarray): - A = dmd - L, G = np.linalg.eig(A) - if X is None or Y is None or rank is None: - raise ValueError( - "If passing a raw matrix, must also provide X, Y, and rank." - ) - elif hasattr(dmd, "numpy"): - A = dmd.numpy() - L, G = np.linalg.eig(A) - if X is None or Y is None or rank is None: +def _validate_control_input(model, control_data, data) -> None: + """ + Validate control data matches model requirements. + + Parameters + ---------- + model : fitted model + The DMD-type model. + control_data : array-like or None + Control input data. + data : array-like + State data (for shape validation). + + Raises + ------ + ValueError + If model requires control but none provided. + + Warns + ----- + UserWarning + If control_data provided but model doesn't use control. + """ + requires = _requires_control(model) + + if requires and control_data is None: + raise ValueError( + f"Model of type {type(model).__name__} requires control input, " + "but control_data was not provided." + ) + + if not requires and control_data is not None: + warnings.warn( + f"control_data was provided, but model of type {type(model).__name__} " + "does not use control input. Control data will be ignored.", + UserWarning + ) + return + + if control_data is not None: + # Validate temporal structure matches + data_arr = data.cpu().numpy() if hasattr(data, 'cpu') else np.asarray(data) + ctrl_arr = control_data.cpu().numpy() if hasattr(control_data, 'cpu') else np.asarray(control_data) + + # Check dimensionality matches + if data_arr.ndim != ctrl_arr.ndim: raise ValueError( - "If passing a raw matrix, must also provide X, Y, and rank." + f"data and control_data must have the same number of dimensions. " + f"Got data.ndim={data_arr.ndim}, control_data.ndim={ctrl_arr.ndim}" ) - else: - raise ValueError("dmd must be a DMD object or a numpy array/torch tensor") + + # Check temporal dimension matches (axis 0 for 2D, axis 1 for 3D) + if data_arr.ndim == 2: + if data_arr.shape[0] != ctrl_arr.shape[0]: + raise ValueError( + f"data and control_data must have the same number of time points. " + f"Got data.shape[0]={data_arr.shape[0]}, control_data.shape[0]={ctrl_arr.shape[0]}" + ) + elif data_arr.ndim == 3: + if data_arr.shape[0] != ctrl_arr.shape[0] or data_arr.shape[1] != ctrl_arr.shape[1]: + raise ValueError( + f"data and control_data must have the same number of trials and time points. " + f"Got data.shape={data_arr.shape[:2]}, control_data.shape={ctrl_arr.shape[:2]}" + ) - # L = L[np.abs(L) > tol] - # G = G[:, np.abs(L) > tol] - # rank = len(L) - if hasattr(dmd, "rank"): - L = L[:rank] - G = G[:, :rank] +def _subtract_control_effects( + Y: np.ndarray, + B: np.ndarray, + U: np.ndarray +) -> np.ndarray: + """ + Subtract control effects to isolate autonomous dynamics. + + Computes Y_corrected = Y - B @ U.T (or Y - U @ B.T depending on shape). + + Parameters + ---------- + Y : np.ndarray + Output data matrix. Shape (T, rank). + B : np.ndarray + Control matrix. Shape (rank, control_dim) or compatible. + U : np.ndarray + Control input matrix. Shape (T, control_dim) or compatible. + + Returns + ------- + Y_corrected : np.ndarray + Y with control effects removed. Shape (T, rank). + """ + # Y is (T, rank), U is (T, control_dim), B is (rank, control_dim) + # We want Y_corrected = Y - (B @ U.T).T = Y - U @ B.T + if B.ndim == 1: + B = B.reshape(-1, 1) + if U.ndim == 1: + U = U.reshape(-1, 1) + + # Compute control contribution: (T, control_dim) @ (control_dim, rank) = (T, rank) + control_contribution = U @ B.T + Y_corrected = Y - control_contribution + + return Y_corrected + + +# ============================================================================= +# Core Residual Computation +# ============================================================================= + +def _compute_residuals_from_matrices( + X: np.ndarray, + Y: np.ndarray, + eigenvalues: np.ndarray, + eigenvectors: np.ndarray, + return_num_denom: bool = False, +) -> Tuple: + """ + Core residual computation from projected data matrices and eigendecomposition. + + shared implementation used by both local DMD and PyKoopman. + + Parameters + ---------- + X : np.ndarray + Projected input data matrix. Shape (T, rank). + Y : np.ndarray + Projected output data matrix. Shape (T, rank). + eigenvalues : np.ndarray + Eigenvalues of the state matrix. Shape (rank,). + eigenvectors : np.ndarray + Eigenvectors of the state matrix. Shape (rank, rank). + return_num_denom : bool + Whether to return numerators and denominators. + + Returns + ------- + residuals : np.ndarray + Residuals for each eigenpair. + normalized_residuals : np.ndarray + Normalized residuals (relative to persistence baseline). + numerators : list (optional) + If return_num_denom=True. + denominators : list (optional) + If return_num_denom=True. + """ + rank = len(eigenvalues) + + # Compute Gram matrices XtX = np.dot(X.T, X) XtY = np.dot(X.T, Y) YtY = np.dot(Y.T, Y) YtX = np.dot(Y.T, X) - residuals = np.zeros(rank, dtype=np.complex64) - persistence_residuals = np.zeros(rank, dtype=np.complex64) + + residuals = np.zeros(rank, dtype=np.complex128) + persistence_residuals = np.zeros(rank, dtype=np.complex128) numerators = [] denominators = [] + for i in range(rank): - denominator = np.dot(G[:, i].conj().T, np.dot(XtX, G[:, i])) + g = eigenvectors[:, i] + lam = eigenvalues[i] + + denominator = np.dot(g.conj().T, np.dot(XtX, g)) numerator = np.dot( - G[:, i].conj().T, + g.conj().T, np.dot( - YtY - np.conj(L[i]) * XtY - L[i] * YtX + np.abs(L[i]) ** 2 * XtX, - G[:, i], + YtY - np.conj(lam) * XtY - lam * YtX + np.abs(lam) ** 2 * XtX, + g, ), ) residuals[i] = numerator / denominator numerators.append(np.real(numerator)) denominators.append(np.real(denominator)) + + # Persistence baseline (lambda = 1) persistence_numerator = np.dot( - G[:, i].conj().T, np.dot(YtY - XtY - YtX + XtX, G[:, i]) + g.conj().T, np.dot(YtY - XtY - YtX + XtX, g) ) persistence_residuals[i] = persistence_numerator / denominator + normalized_residuals = np.abs(residuals) / (np.abs(persistence_residuals) + 1e-10) + if return_num_denom: - return L, G, residuals, normalized_residuals, numerators, denominators + return residuals, normalized_residuals, numerators, denominators else: - return L, G, residuals, normalized_residuals + return residuals, normalized_residuals -def plot_residuals(L, residuals, cmin=None, cmax=None): +def compute_residuals( + dmd: "DMD", + data: "np.ndarray | torch.Tensor", + Y: "np.ndarray | torch.Tensor" = None, + control_data: "np.ndarray | torch.Tensor" = None, + matrix: Literal["A_v", "A_havok_dmd"] = "A_v", + return_num_denom: bool = False, +): + """ + Compute DMD eigenvalues, eigenvectors, and residuals for each mode. + + Parameters + ---------- + dmd : DMD or DMDc object + Fitted DMD object (with A_v or A_havok_dmd, U, S_mat_inv, rank, n_delays, delay_interval). + For DMDc models, also requires B_v and control-related SVD matrices. + data : np.ndarray or torch.Tensor + Input data matrix. Can be 2D (T x N) or 3D (K x T x N). + If Y is not provided, X and Y will be constructed by splitting along the time axis. + Y : np.ndarray or torch.Tensor, optional + Right-hand side data matrix. If not provided, will be constructed from data. + control_data : np.ndarray or torch.Tensor, optional + Control input data. Required for DMDc models. Must have the same temporal + structure as data (same number of time points and trials). + matrix : Literal["A_v", "A_havok_dmd"], optional + Matrix to compute residuals on. Must be either "A_v" or "A_havok_dmd". Default is "A_v". + return_num_denom : bool, optional + Whether to return the numerator and denominator of the residual. Default is False. + + Returns + ------- + eigenvalues : np.ndarray + Eigenvalues of the state matrix. + eigenvectors : np.ndarray + Eigenvectors of the state matrix. + residuals : np.ndarray + Residuals for each eigenpair. + normalized_residuals : np.ndarray + Normalized residuals. + numerators, denominators : list (optional) + If return_num_denom=True. + + Notes + ----- + For DMDc models (when control_data is provided), residuals are computed on the + autonomous dynamics by subtracting out the control effects: + + Y_corrected = Y - B @ U + + This allows assessment of eigenvalue quality for the A matrix alone. + """ + # Validate control input + _validate_control_input(dmd, control_data, data) + + # Check if this is a DMDc model + is_dmdc = hasattr(dmd, 'B_v') and dmd.B_v is not None and control_data is not None + + # Get parameters from the DMD model + n_delays = dmd.n_delays + delay_interval = dmd.delay_interval if hasattr(dmd, 'delay_interval') else 1 + steps_ahead = dmd.steps_ahead if dmd.steps_ahead is not None and hasattr(dmd,'steps_ahead') else 1 + device = dmd.device + + # For DMDc, we need rank_output for state and rank_input for control + if is_dmdc: + rank_output = dmd.rank_output if hasattr(dmd, 'rank_output') else dmd.rank + rank_input = dmd.rank_input if hasattr(dmd, 'rank_input') else dmd.rank + rank = rank_output + else: + rank = dmd.rank + + # Convert to torch tensor if needed + if isinstance(data, np.ndarray): + data = torch.from_numpy(data).to(device) + else: + data = data.to(device) + + if control_data is not None: + if isinstance(control_data, np.ndarray): + control_data = torch.from_numpy(control_data).to(device) + else: + control_data = control_data.to(device) + + # Construct X and Y if Y is not provided + if Y is None: + if data.ndim == 3: + X_data = data[:, :-steps_ahead] + Y_data = data[:, steps_ahead:] + if control_data is not None: + # Control at time t affects transition from x_t to x_{t+1} + U_data = control_data[:, :-steps_ahead] + else: + X_data = data[:-steps_ahead] + Y_data = data[steps_ahead:] + if control_data is not None: + U_data = control_data[:-steps_ahead] + else: + if isinstance(Y, np.ndarray): + Y = torch.from_numpy(Y).to(device) + else: + Y = Y.to(device) + X_data = data + Y_data = Y + if control_data is not None: + U_data = control_data + + # Compute delay embeddings for state data + H_X = embed_signal_torch(X_data, n_delays, delay_interval) + H_Y = embed_signal_torch(Y_data, n_delays, delay_interval) + + # Compute delay embeddings for control data if present + if is_dmdc: + n_control_delays = dmd.n_control_delays if hasattr(dmd, 'n_control_delays') else 1 + H_U = embed_data_DMDc( + U_data, + n_delays=n_delays, + n_control_delays=n_control_delays, + delay_interval=delay_interval, + control=True + ) + if isinstance(H_U, np.ndarray): + H_U = torch.from_numpy(H_U).to(device) + + # Flatten if 3D (combine trials and time) + if H_X.ndim == 3: + H_X = H_X.reshape(-1, H_X.shape[-1]) + H_Y = H_Y.reshape(-1, H_Y.shape[-1]) + if is_dmdc: + H_U = H_U.reshape(-1, H_U.shape[-1]) + + # Get the appropriate matrix and compute eigendecomposition + A = getattr(dmd, matrix) + if hasattr(A, "cpu"): + A = A.cpu().detach().numpy() + # Note: The DMD convention uses Y ≈ X @ A^T, so for the residual + # ||Yg - λXg||^2 to be small, we need g to be an eigenvector of A^T + eigenvalues, eigenvectors = np.linalg.eig(A.T) + + # Project into appropriate coordinates based on matrix type + if matrix == "A_havok_dmd": + # For A_havok_dmd, data is already in the right space (observation space) + X = H_X.cpu().detach().numpy() if hasattr(H_X, "cpu") else H_X + Y = H_Y.cpu().detach().numpy() if hasattr(H_Y, "cpu") else H_Y + + if is_dmdc: + # Get B_havok_dmd and project control + B = dmd.B_havok_dmd + if hasattr(B, "cpu"): + B = B.cpu().detach().numpy() + U = H_U.cpu().detach().numpy() if hasattr(H_U, "cpu") else H_U + # Subtract control effects + Y = _subtract_control_effects(Y, B, U) + + elif matrix == "A_v": + # For A_v, project into V coordinates: X_proj = X @ U @ S_mat_inv[:rank, :rank] + # DMDc uses Uh/Sh_mat_inv, regular DMD uses U/S_mat_inv + if is_dmdc: + U_proj_mat = dmd.Uh[:, :rank] + S_inv_mat = dmd.Sh_mat_inv[:rank, :rank] + else: + U_proj_mat = dmd.U[:, :rank] + S_inv_mat = dmd.S_mat_inv[:rank, :rank] + + X_proj = H_X @ U_proj_mat @ S_inv_mat + Y_proj = H_Y @ U_proj_mat @ S_inv_mat + + X = X_proj.cpu().detach().numpy() if hasattr(X_proj, "cpu") else X_proj + Y = Y_proj.cpu().detach().numpy() if hasattr(Y_proj, "cpu") else Y_proj + + if is_dmdc: + # Project control into its reduced space + Uu = dmd.Uu[:, :rank_input] + Su_mat_inv = dmd.Su_mat_inv[:rank_input, :rank_input] + U_proj = H_U @ Uu @ Su_mat_inv + U = U_proj.cpu().detach().numpy() if hasattr(U_proj, "cpu") else U_proj + + # Get B_v and subtract control effects + B_v = dmd.B_v + if hasattr(B_v, "cpu"): + B_v = B_v.cpu().detach().numpy() + + # Subtract control effects: Y_corrected = Y - B_v @ U.T + Y = _subtract_control_effects(Y, B_v, U) + else: + raise ValueError(f"matrix must be 'A_v' or 'A_havok_dmd', got {matrix}") + + # Truncate eigenvalues/vectors to rank + eigenvalues = eigenvalues[:rank] + eigenvectors = eigenvectors[:, :rank] + + # Use shared core computation + result = _compute_residuals_from_matrices( + X, Y, eigenvalues, eigenvectors, return_num_denom + ) + + if return_num_denom: + residuals, normalized_residuals, numerators, denominators = result + return eigenvalues, eigenvectors, residuals, normalized_residuals, numerators, denominators + else: + residuals, normalized_residuals = result + return eigenvalues, eigenvectors, residuals, normalized_residuals + + +def compute_residuals_pykoopman( + model, + test_data: np.ndarray, + control_data: np.ndarray = None, + return_num_denom: bool = False, +): + """ + Compute residuals for a fitted PyKoopman model. + + Parameters + ---------- + model : pk.Koopman + Fitted PyKoopman model. Can use DMD, EDMD, DMDc, EDMDc, or PyDMD regressors. + test_data : np.ndarray + Test data. Shape (T, N) or (K, T, N). + control_data : np.ndarray, optional + Control input data. Required for models with control (DMDc, EDMDc, PyDMD DMDc). + Must have the same temporal structure as test_data. + return_num_denom : bool + Whether to return numerators and denominators. + + Returns + ------- + eigenvalues : np.ndarray + Eigenvalues of the state matrix. + eigenvectors : np.ndarray + Eigenvectors of the state matrix. + residuals : np.ndarray + Residuals for each eigenpair. + normalized_residuals : np.ndarray + Normalized residuals. + + Notes + ----- + For control models (DMDc, EDMDc, PyDMD DMDc), residuals are computed on the + autonomous dynamics by subtracting out the control effects: + + Y_corrected = Y - B @ U + + This allows assessment of eigenvalue quality for the A matrix alone. + """ + # Ensure test_data is numpy + if isinstance(test_data, torch.Tensor): + test_data = test_data.cpu().numpy() + if control_data is not None and isinstance(control_data, torch.Tensor): + control_data = control_data.cpu().numpy() + + # Validate control input + _validate_control_input(model, control_data, test_data) + + # Check if this is a control model + is_control = _requires_control(model) and control_data is not None + + # Split data into X and Y with temporal offset BEFORE transforming + # This correctly handles trial boundaries - each (X[k,t], Y[k,t]) pair is aligned + if test_data.ndim == 3: + X_data = test_data[:, :-1] # (K, T-1, N) + Y_data = test_data[:, 1:] # (K, T-1, N) + if is_control: + U_data = control_data[:, :-1] # (K, T-1, control_dim) + else: + X_data = test_data[:-1] # (T-1, N) + Y_data = test_data[1:] # (T-1, N) + if is_control: + U_data = control_data[:-1] # (T-1, control_dim) + + # Transform to observable space - handles 3D structure automatically + H_X = model.observables.transform(X_data) + H_Y = model.observables.transform(Y_data) + + # Flatten if 3D (combine trials and time) + if H_X.ndim == 3: + H_X = H_X.reshape(-1, H_X.shape[-1]) + H_Y = H_Y.reshape(-1, H_Y.shape[-1]) + if is_control: + # Account for samples consumed by observable (e.g., TimeDelay) + n_consumed = getattr(model.observables, 'n_consumed_samples', 0) + U_data = U_data[:, n_consumed:] # (K, T-1-n_consumed, control_dim) + U_data = U_data.reshape(-1, U_data.shape[-1]) + elif is_control: + n_consumed = getattr(model.observables, 'n_consumed_samples', 0) + U_data = U_data[n_consumed:] + + # Get state matrix and eigendecomposition + # Note: PyKoopman uses Y ≈ X @ A^T convention, so for the residual + # ||Yg - λXg||^2 to be small, we need g to be an eigenvector of A^T + A = model.A + eigenvalues, eigenvectors = np.linalg.eig(A.T) + + # Get projection matrix and project into reduced space + ur = model.ur + rank = ur.shape[1] + + X = H_X @ ur # (total_samples, rank) + Y = H_Y @ ur # (total_samples, rank) + + # Handle control if present - subtract control effects + if is_control: + B = model.B + # Subtract control effects: Y_corrected = Y - U @ B.T + Y = _subtract_control_effects(Y, B, U_data) + + # Truncate eigenvalues/vectors to rank + eigenvalues = eigenvalues[:rank] + eigenvectors = eigenvectors[:, :rank] + + # Use shared core computation + result = _compute_residuals_from_matrices( + X, Y, eigenvalues, eigenvectors, return_num_denom + ) + + if return_num_denom: + residuals, normalized_residuals, numerators, denominators = result + return eigenvalues, eigenvectors, residuals, normalized_residuals, numerators, denominators + else: + residuals, normalized_residuals = result + return eigenvalues, eigenvectors, residuals, normalized_residuals + + +def compute_residuals_subspace_dmdc( + model: "SubspaceDMDc", + test_data: np.ndarray, + control_data: np.ndarray, + return_num_denom: bool = False, +): + """ + Compute residuals for a fitted SubspaceDMDc model. + + Parameters + ---------- + model : SubspaceDMDc + Fitted SubspaceDMDc model. + test_data : np.ndarray + Test data (observations). Shape (T, N) or (K, T, N) or list of arrays. + control_data : np.ndarray + Control input data. Must have the same temporal structure as test_data. + return_num_denom : bool + Whether to return numerators and denominators. + + Returns + ------- + eigenvalues : np.ndarray + Eigenvalues of the state matrix A. + eigenvectors : np.ndarray + Eigenvectors of the state matrix A. + residuals : np.ndarray + Residuals for each eigenpair. + normalized_residuals : np.ndarray + Normalized residuals. + + Notes + ----- + For SubspaceDMDc, the system operates in latent state space: + + x_{t+1} = A @ x_t + B @ u_t + y_t = C @ x_t + + Residuals are computed in the latent space by: + 1. Projecting new observations to latent states via the oblique projection + 2. Subtracting control effects: X_next_corrected = X_next - B @ U + 3. Computing residuals on (X, X_next_corrected) using the A matrix eigenstructure + """ + if control_data is None: + raise ValueError("control_data is required for SubspaceDMDc residual computation.") + + # Convert to numpy if needed + if isinstance(test_data, torch.Tensor): + test_data = test_data.cpu().numpy() + if isinstance(control_data, torch.Tensor): + control_data = control_data.cpu().numpy() + + # Get model parameters + p = model.n_delays # past window + f = model.info.get('f', p) # future window (usually same as p) + rank = model.info['rank_used'] + + # Get projection matrices from the model + Gamma_hat = model.info['Gamma_hat'] # (f*p_out, rank) + + # Get A and B matrices + A = model.A_v + B = model.B_v + + # Convert data to list format for processing + y_list, u_list = model._convert_to_subspace_dmdc_data_format(test_data, control_data) + + # Collect Hankel data using the same method as fitting + # This gives us the properly aligned data for projection + try: + U_p, Y_p, U_f, Y_f, Z_p, valid_trials, T_per_trial, T_total, p_out, m = \ + model._collect_data(y_list, u_list, p, f) + except ValueError as e: + raise ValueError(f"Failed to process test data for residuals: {e}") + + # Project to latent space using oblique projection + # Following the subspace identification approach: + # 1. Compute the oblique projection O = Y_f @ Pi_perp @ pinv(Z_p @ Pi_perp) + # 2. SVD of O gives us U_n, S_n, V_n + # 3. X_hat = S_half @ V_n projects the data to latent space + # + # For new data, we use the Gamma_hat to project: + # X_hat_new = pinv(Gamma_hat) @ Y_f_new (approximately) + + # Compute pseudo-inverse of Gamma_hat for projection + Gamma_pinv = np.linalg.pinv(Gamma_hat) # (rank, f*p_out) + + # Project Y_f to latent space + X_hat = Gamma_pinv @ Y_f # (rank, T_total) + + # Time-align the latent states for regression + # X_hat[:, t] corresponds to time t, we need X[t] and X[t+1] pairs + # Also need U at time t + X_segments = [] + X_next_segments = [] + U_segments = [] + + start_idx = 0 + for trial_idx, T_trial in enumerate(T_per_trial): + X_trial = X_hat[:, start_idx:start_idx + T_trial] + + # Current and next states + X_trial_curr = X_trial[:, :-1] + X_trial_next = X_trial[:, 1:] + + # Get control input aligned with current state + original_trial_idx = valid_trials[trial_idx] + U_trial = u_list[original_trial_idx] + # U_trial is (n_timepoints, n_features), extract the aligned portion + U_mid_trial = U_trial[p:p + (T_trial - 1), :].T # (m, T_trial-1) + + X_segments.append(X_trial_curr) + X_next_segments.append(X_trial_next) + U_segments.append(U_mid_trial) + + start_idx += T_trial + + # Concatenate across trials + X = np.concatenate(X_segments, axis=1).T # (T_total-n_trials, rank) + X_next = np.concatenate(X_next_segments, axis=1).T # (T_total-n_trials, rank) + U = np.concatenate(U_segments, axis=1).T # (T_total-n_trials, m) + + # Subtract control effects: X_next_corrected = X_next - B @ U.T + Y_corrected = _subtract_control_effects(X_next, B, U) + + # Compute eigendecomposition of A + # Note: SubspaceDMDc uses X_next = A @ X + B @ U in column format, + # but after transposing to row format, we have Y ≈ X @ A^T. + # So for ||Yg - λXg||^2 to be small, we need g to be an eigenvector of A^T + eigenvalues, eigenvectors = np.linalg.eig(A.T) + + # Truncate to rank + eigenvalues = eigenvalues[:rank] + eigenvectors = eigenvectors[:, :rank] + + # Use shared core computation + result = _compute_residuals_from_matrices( + X, Y_corrected, eigenvalues, eigenvectors, return_num_denom + ) + + if return_num_denom: + residuals, normalized_residuals, numerators, denominators = result + return eigenvalues, eigenvectors, residuals, normalized_residuals, numerators, denominators + else: + residuals, normalized_residuals = result + return eigenvalues, eigenvectors, residuals, normalized_residuals + + +# ============================================================================= +# ResidualComputer Class +# ============================================================================= + +class ResidualComputer: + """ + Computes and plots eigenvalue residuals for DMD-type models. + + Supports local DMD, local DMDc, SubspaceDMDc, and PyKoopman models + (including those with control: DMDc, EDMDc, PyDMD DMDc). + + Parameters + ---------- + model : DMD, DMDc, SubspaceDMDc, or pk.Koopman + A fitted DMD-type model. + test_data : np.ndarray + Test data for residual computation. Shape (T, N) or (K, T, N). + control_data : np.ndarray, optional + Control input data. Required for models with control. + Must have the same temporal structure as test_data. + model_type : str, optional + Model type: "auto", "local_dmd", "subspace_dmdc", or "pykoopman". + Default "auto". + + Attributes + ---------- + eigenvalues : np.ndarray + Eigenvalues of the state matrix. + eigenvectors : np.ndarray + Eigenvectors of the state matrix. + residuals : np.ndarray + Residuals for each eigenpair. + normalized_residuals : np.ndarray + Normalized residuals. + + Example + ------- + >>> # For autonomous DMD + >>> rc = ResidualComputer(model, test_data) + >>> eigenvalues, eigenvectors, residuals, norm_resid = rc.compute() + >>> rc.plot(title="Eigenvalue Residuals") + + >>> # For DMDc with control + >>> rc = ResidualComputer(dmdc_model, test_data, control_data=control_test) + >>> eigenvalues, eigenvectors, residuals, norm_resid = rc.compute() + """ + + def __init__( + self, + model, + test_data: np.ndarray, + control_data: np.ndarray = None, + model_type: str = "auto" + ): + self.model = model + self.test_data = test_data + self.control_data = control_data + self.model_type = self._detect_model_type(model) if model_type == "auto" else model_type + + # Validate control input + _validate_control_input(model, control_data, test_data) + + # Results (computed lazily) + self.eigenvalues = None + self.eigenvectors = None + self.residuals = None + self.normalized_residuals = None + self._computed = False + + def _detect_model_type(self, model) -> str: + """Detect the model type for dispatch.""" + # Check for SubspaceDMDc first (it has A_v but also has 'info' with Gamma_hat) + if hasattr(model, 'A_v') and hasattr(model, 'info') and model.info is not None: + if 'Gamma_hat' in model.info: + return "subspace_dmdc" + + # Local DMD or DMDc + if hasattr(model, 'A_v') and hasattr(model, 'n_delays'): + return "local_dmd" + + # PyKoopman + if hasattr(model, '_pipeline') and hasattr(model, 'A'): + return "pykoopman" + + raise ValueError(f"Cannot detect model type for {type(model)}. " + "Please specify model_type explicitly.") + + def compute(self, return_num_denom: bool = False): + """ + Compute residuals for the model. + + Parameters + ---------- + return_num_denom : bool + If True, also return numerators and denominators. + + Returns + ------- + eigenvalues : np.ndarray + Eigenvalues. + eigenvectors : np.ndarray + Eigenvectors. + residuals : np.ndarray + Residuals for each eigenpair. + normalized_residuals : np.ndarray + Normalized residuals. + """ + if self.model_type == "local_dmd": + result = compute_residuals( + self.model, + self.test_data, + control_data=self.control_data, + return_num_denom=return_num_denom + ) + elif self.model_type == "subspace_dmdc": + result = compute_residuals_subspace_dmdc( + self.model, + self.test_data, + self.control_data, + return_num_denom=return_num_denom + ) + elif self.model_type == "pykoopman": + result = compute_residuals_pykoopman( + self.model, + self.test_data, + control_data=self.control_data, + return_num_denom=return_num_denom + ) + else: + raise ValueError(f"Unknown model_type: {self.model_type}") + + if return_num_denom: + self.eigenvalues, self.eigenvectors, self.residuals, self.normalized_residuals, nums, denoms = result + self._computed = True + return self.eigenvalues, self.eigenvectors, self.residuals, self.normalized_residuals, nums, denoms + else: + self.eigenvalues, self.eigenvectors, self.residuals, self.normalized_residuals = result + self._computed = True + return self.eigenvalues, self.eigenvectors, self.residuals, self.normalized_residuals + + def plot( + self, + cmin: float = None, + cmax: float = None, + ax: plt.Axes = None, + figsize: tuple = (6, 6), + title: str = None, + ): + """ + Plot eigenvalues on the complex plane, colored by residuals. + + Parameters + ---------- + cmin : float, optional + Minimum value for color scale. + cmax : float, optional + Maximum value for color scale. + ax : matplotlib.axes.Axes, optional + Axes to plot on. If None, creates new figure. + figsize : tuple + Figure size if creating new figure. + title : str, optional + Plot title. + + Returns + ------- + fig, ax : matplotlib figure and axes + """ + if not self._computed: + self.compute() + + residuals_real = np.real(self.residuals) + + if cmin is None: + cmin = np.min(residuals_real) + if cmax is None: + cmax = np.max(residuals_real) + + if ax is None: + fig, ax = plt.subplots(1, 1, figsize=figsize) + else: + fig = ax.get_figure() + + cmap = cm.viridis + norm = mcolors.Normalize(vmin=cmin, vmax=cmax) + + # Plot unit circle + theta = np.linspace(0, 2*np.pi, 100) + ax.plot(np.cos(theta), np.sin(theta), 'k--', alpha=0.3, linewidth=1) + + sc = ax.scatter( + np.real(self.eigenvalues), + np.imag(self.eigenvalues), + c=residuals_real, + cmap=cmap, + norm=norm + ) + cbar = plt.colorbar(sc, ax=ax, orientation="vertical") + cbar.set_label("Residual") + + ax.set_xlim(-1.1, 1.1) + ax.set_ylim(-1.1, 1.1) + ax.set_xlabel("Real") + ax.set_ylabel("Imaginary") + ax.set_aspect('equal') + + if title: + ax.set_title(title) + + ax.spines["top"].set_visible(False) + ax.spines["right"].set_visible(False) + + return fig, ax + + def get_average_residual(self) -> float: + """Return mean absolute residual.""" + if not self._computed: + self.compute() + return float(np.mean(np.abs(self.residuals))) + + +# ============================================================================= +# Utility Functions +# ============================================================================= + +def plot_residuals(eigenvalues, residuals, cmin=None, cmax=None): """ Plot eigenvalues on the complex plane, colored by residuals. Parameters ---------- - L : np.ndarray + eigenvalues : np.ndarray Eigenvalues. residuals : np.ndarray Residuals for each eigenpair. @@ -153,71 +964,133 @@ def plot_residuals(L, residuals, cmin=None, cmax=None): cmax : float, optional Maximum value for color scale. """ - - plt.scatter(np.real(L), np.imag(L), c=residuals) + residuals_real = np.abs(residuals) if cmin is None: - cmin = np.min(residuals) + cmin = np.min(residuals_real) if cmax is None: - cmax = np.max(residuals) + cmax = np.max(residuals_real) cmap = cm.viridis norm = mcolors.Normalize(vmin=cmin, vmax=cmax) - sm = cm.ScalarMappable(cmap=cmap, norm=norm) - sm.set_array([]) - cbar = plt.colorbar(sm, orientation="vertical") + sc = plt.scatter(np.real(eigenvalues), np.imag(eigenvalues), c=residuals_real, cmap=cmap, norm=norm) + cbar = plt.colorbar(sc, orientation="vertical") cbar.set_label("Residual") plt.xlim(-1, 1) plt.ylim(-1, 1) plt.xlabel("Real") plt.ylabel("Imaginary") - # plt.show() def compute_inverse_participation_ratio(residuals): + """Compute inverse participation ratio from residuals.""" if isinstance(residuals, list): residuals = np.array(residuals) - - inv_resid = 1 / residuals + residuals = np.abs(residuals) + inv_resid = 1 / (residuals + 1e-10) num = np.sum(inv_resid) ** 2 denom = np.sum(inv_resid**2) return num / denom -def clean_spectrum(L, G, residuals, epsilon): +def clean_spectrum(eigenvalues, eigenvectors, residuals, epsilon): """ - remove the eigenvalues with value greater than epsilon + Remove eigenvalues with residual greater than epsilon. + + Parameters + ---------- + eigenvalues : np.ndarray + Eigenvalues. + eigenvectors : np.ndarray + Eigenvectors. + residuals : np.ndarray + Residuals. + epsilon : float + Threshold. + + Returns + ------- + eigenvalues, eigenvectors, residuals : filtered arrays """ - mask = residuals < epsilon - return L[mask], G[:, mask], residuals[mask] + residuals_real = np.abs(residuals) + mask = residuals_real < epsilon + return eigenvalues[mask], eigenvectors[:, mask], residuals[mask] -def thresh_topn(L, G, residuals, n): - # pick the top n eigenvalues with the smallest residuals - sorted_resid = np.sort(residuals) +def thresh_topn(eigenvalues, eigenvectors, residuals, n): + """ + Keep the top n eigenvalues with smallest residuals. + + Parameters + ---------- + eigenvalues : np.ndarray + Eigenvalues. + eigenvectors : np.ndarray + Eigenvectors. + residuals : np.ndarray + Residuals. + n : int + Number to keep. + + Returns + ------- + eigenvalues, eigenvectors, residuals : filtered arrays + """ + residuals_real = np.abs(residuals) + sorted_resid = np.sort(residuals_real) if n > len(sorted_resid): n = -1 topn = sorted_resid[n] - mask = residuals <= topn - return L[mask], G[:, mask], residuals[mask] + mask = residuals_real <= topn + return eigenvalues[mask], eigenvectors[:, mask], residuals[mask] -def format_eigs(eig1): - if isinstance(eig1, list): - eig1 = np.array(eig1) - # sort eigenvalues by real magnitude - eig1 = eig1[np.argsort(np.abs(eig1.real))] - - eig1 = np.vstack([eig1.real, eig1.imag]).T - return eig1 +def format_eigs(eigenvalues): + """Format eigenvalues as 2D array sorted by real magnitude.""" + if isinstance(eigenvalues, list): + eigenvalues = np.array(eigenvalues) + # Sort by real magnitude + eigenvalues = eigenvalues[np.argsort(np.abs(eigenvalues.real))] + return np.vstack([eigenvalues.real, eigenvalues.imag]).T def compute_ot_distance(a, b): - # check if a has imaginary compnents, if so convet to 2d array + """ + Compute optimal transport distance between two sets of eigenvalues. + + Parameters + ---------- + a, b : np.ndarray + Eigenvalue arrays (can be complex). + + Returns + ------- + score : float + OT distance. + C : np.ndarray + Transport plan. + """ + # Convert complex to 2D if needed if np.iscomplexobj(a): a = np.vstack([a.real, a.imag]).T if np.iscomplexobj(b): b = np.vstack([b.real, b.imag]).T M = ot.dist(a, b) - a, b = np.ones(a.shape[0]) / a.shape[0], np.ones(b.shape[0]) / b.shape[0] - score = ot.emd2(a, b, M) - C = ot.emd(a, b, M) + a_weights = np.ones(a.shape[0]) / a.shape[0] + b_weights = np.ones(b.shape[0]) / b.shape[0] + score = ot.emd2(a_weights, b_weights, M) + C = ot.emd(a_weights, b_weights, M) return score, C + + +# Module exports +__all__ = [ + "ResidualComputer", + "compute_residuals", + "compute_residuals_pykoopman", + "compute_residuals_subspace_dmdc", + "plot_residuals", + "compute_inverse_participation_ratio", + "clean_spectrum", + "thresh_topn", + "format_eigs", + "compute_ot_distance", +] diff --git a/examples/sweep_resdmd_tutorial.ipynb b/examples/sweep_resdmd_tutorial.ipynb index 8377075..357eb20 100644 --- a/examples/sweep_resdmd_tutorial.ipynb +++ b/examples/sweep_resdmd_tutorial.ipynb @@ -57,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 17, "id": "e1552288", "metadata": {}, "outputs": [ @@ -66,7 +66,7 @@ "output_type": "stream", "text": [ "Generated data shape:\n", - " State data X: (5, 100, 15)\n", + " State data X: (5, 100, 1)\n", " Control data U: (5, 100, 3)\n" ] } @@ -127,7 +127,7 @@ " return X, U\n", "\n", "# Generate training data\n", - "X_train, U_train = generate_trajectory(A_true, B_true, n_timesteps, n_trials,noise_std=0.0,partial_observ_frac=1)\n", + "X_train, U_train = generate_trajectory(A_true, B_true, n_timesteps, n_trials,noise_std=0.0,partial_observ_frac=0.1)\n", "\n", "print(f\"Generated data shape:\")\n", "print(f\" State data X: {X_train.shape}\")\n", @@ -136,13 +136,24 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 18, "id": "a0b65b61", "metadata": {}, "outputs": [ + { + "ename": "IndexError", + "evalue": "index 1 is out of bounds for axis 2 with size 1", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[18], line 6\u001b[0m\n\u001b[1;32m 4\u001b[0m ax \u001b[38;5;241m=\u001b[39m axes[\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mmin\u001b[39m(\u001b[38;5;241m3\u001b[39m, n_state)):\n\u001b[0;32m----> 6\u001b[0m ax\u001b[38;5;241m.\u001b[39mplot(\u001b[43mX_train\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m:\u001b[49m\u001b[38;5;241;43m200\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m, label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m$x_\u001b[39m\u001b[38;5;130;01m{{\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mi\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m1\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;130;01m}}\u001b[39;00m\u001b[38;5;124m$\u001b[39m\u001b[38;5;124m'\u001b[39m, alpha\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.7\u001b[39m)\n\u001b[1;32m 7\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_xlabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTime\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 8\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_ylabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mState Value\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", + "\u001b[0;31mIndexError\u001b[0m: index 1 is out of bounds for axis 2 with size 1" + ] + }, { "data": { - "image/png": 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HhroXS5YsieOPPz4OPvjgqK2tjRtvvDG++OKLIq12ZPrDH/4QM2bMiCOPPDJGjRoVzz333H+9Zs2aNfGd73wnCoVCfPOb34zHHnts2Nd5oDA/lQazU+kwP5UO81NpMDuVhtzmp2wEWrFiRVZRUZE9+uij2V/+8pfsqquuyg4//PCso6NjwPNff/31rLy8PLvnnnuyt956K7vllluygw46KHvzzTeLvPKRZ6h7cdlll2VLly7NNmzYkG3atCn74Q9/mFVXV2d//etfi7zykWWo+/CVDz74IJs4cWJ2zjnnZN///veLs9gRbqh7sXPnzmzq1KnZBRdckL322mvZBx98kK1ZsybbuHFjkVc+8gx1L5544omsUChkTzzxRPbBBx9kL730UjZhwoTsxhtvLPLKR5ZVq1ZlCxYsyJ555pksIrJnn302ef7mzZuzQw45JGtqasreeuut7Je//GVWXl6erV69ujgLHsHMT6XB7FQ6zE+lw/xUGsxOpSOv+WlEBqRp06Zlc+fO7fvvnp6e7Mgjj8xaWloGPP+SSy7JLrzwwn7H6urqsh/96EfDus4DwVD34j/t2rUrO+yww7LHH398uJZ4QNibfdi1a1d25plnZr/61a+y2bNnG4D2kaHuxUMPPZQdc8wxWXd3d7GWeMAY6l7MnTs3+973vtfvWFNTU3bWWWcN6zoPJHsyAP3sZz/LTj755H7HGhsbs+nTpw/jyg4M5qfSYHYqHean0mF+Kg1mp9JUzPlpxH0LW3d3d6xbty4aGhr6jpWVlUVDQ0O0tbUNeE1bW1u/8yMipk+fPuj57Jm92Yv/9Pnnn8eXX34ZRxxxxHAtc8Tb23244447Yty4cXHFFVcUY5kHhL3Zi+effz7q6+tj7ty5UVNTE6ecckosWrQoenp6irXsEWlv9uLMM8+MdevW9b1Ve/PmzbFq1aq44IILirJm/s09e3iYn0qD2al0mJ9Kh/mpNJid9m/76p49el8uqhRs3749enp6oqampt/xmpqaePvttwe8pr29fcDz29vbh22dB4K92Yv/dNNNN8WRRx652x929tze7MNrr70WjzzySGzcuLEIKzxw7M1ebN68OX7/+9/H5ZdfHqtWrYr33nsvrrvuuvjyyy+jubm5GMsekfZmLy677LLYvn17nH322ZFlWezatSuuueaauPnmm4uxZP6fwe7ZXV1d8a9//SsOPvjgnFa2fzM/lQazU+kwP5UO81NpMDvt3/bV/DTi3oHEyLF48eJYsWJFPPvss1FZWZn3cg4Yn332WcycOTOWL18eY8eOzXs5B7ze3t4YN25cPPzwwzFlypRobGyMBQsWxLJly/Je2gFnzZo1sWjRonjwwQdj/fr18cwzz8SLL74Yd955Z95LA4gIs1OezE+lxfxUGsxOI8+IewfS2LFjo7y8PDo6Ovod7+joiPHjxw94zfjx44d0Pntmb/biK/fee28sXrw4XnnllTjttNOGc5kj3lD34f33348PP/wwZsyY0Xest7c3IiJGjx4d77zzThx77LHDu+gRam/+TkyYMCEOOuigKC8v7zt24oknRnt7e3R3d0dFRcWwrnmk2pu9uPXWW2PmzJlx5ZVXRkTEqaeeGjt27Iirr746FixYEGVlviZTDIPds6uqqrz76GswP5UGs1PpMD+VDvNTaTA77d/21fw04nasoqIipkyZEq2trX3Hent7o7W1Nerr6we8pr6+vt/5EREvv/zyoOezZ/ZmLyIi7rnnnrjzzjtj9erVMXXq1GIsdUQb6j6ccMIJ8eabb8bGjRv7HhdddFGcd955sXHjxqitrS3m8keUvfk7cdZZZ8V7773XN4RGRLz77rsxYcIEw8/XsDd78fnnn+826Hw1mP778wspBvfs4WF+Kg1mp9Jhfiod5qfSYHbav+2ze/aQPnJ7P7FixYqsUChkjz32WPbWW29lV199dXb44Ydn7e3tWZZl2cyZM7N58+b1nf/6669no0ePzu69995s06ZNWXNzsx9Du48MdS8WL16cVVRUZE8//XT2t7/9re/x2Wef5fUSRoSh7sN/8lNE9p2h7sWWLVuyww47LPvxj3+cvfPOO9kLL7yQjRs3LrvrrrvyegkjxlD3orm5OTvssMOy3/zmN9nmzZuz3/3ud9mxxx6bXXLJJXm9hBHhs88+yzZs2JBt2LAhi4js/vvvzzZs2JB99NFHWZZl2bx587KZM2f2nf/Vj6H96U9/mm3atClbunTpXv0YWnZnfioNZqfSYX4qHean0mB2Kh15zU8jMiBlWZb98pe/zI466qisoqIimzZtWvbHP/6x7/+de+652ezZs/ud/9vf/jY77rjjsoqKiuzkk0/OXnzxxSKveOQayl4cffTRWUTs9mhubi7+wkeYof6d+P8ZgPatoe7FG2+8kdXV1WWFQiE75phjsrvvvjvbtWtXkVc9Mg1lL7788svstttuy4499tissrIyq62tza677rrsn//8Z/EXPoK8+uqrA/67/9Xv/ezZs7Nzzz13t2smT56cVVRUZMccc0z261//uujrHqnMT6XB7FQ6zE+lw/xUGsxOpSGv+WlUlnnvGAAAAACDG3GfgQQAAADAviUgAQAAAJAkIAEAAACQJCABAAAAkCQgAQAAAJAkIAEAAACQJCABAAAAkCQgAQAAAJAkIAEAAACQJCABAAAAkCQgAQAAAJAkIAEAAACQ9L80BjiR+m9uIAAAAABJRU5ErkJggg==", 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" ] @@ -202,7 +213,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 19, "id": "3a4db4a7", "metadata": {}, "outputs": [ @@ -210,30 +221,36 @@ "name": "stderr", "output_type": "stream", "text": [ - "/orcd/data/fiete/001/om2/ostrow/dmrsa/DSApublic/DSA/examples/../DSA/pykoopman/__init__.py:3: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", - " from pkg_resources import get_distribution\n", - "Sweeping: 100%|██████████| 9/9 [00:01<00:00, 8.53it/s]\n" + "Sweeping: 0%| | 0/9 [00:00,\n", + "(
,\n", " array([,\n", " ,\n", - " ],\n", + " ,\n", + " ],\n", " dtype=object))" ] }, - "execution_count": 5, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -252,7 +269,8 @@ " param2_name=\"reg.svd_rank\",\n", " param2_values=np.arange(1,25),\n", " reseed=1,\n", - " base_regressor_class=SubspaceDMD\n", + " base_regressor_class=SubspaceDMD,\n", + " compute_residuals=True\n", " # base_regressor_class=pk.regression.DMDc, # Must use DMDc or EDMDc\n", ")\n", "sweeper.sweep()\n", @@ -261,7 +279,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 22, "id": "d83efaf6", "metadata": {}, "outputs": [ @@ -269,28 +287,36 @@ "name": "stderr", "output_type": "stream", "text": [ - "Sweeping: 100%|██████████| 7/7 [00:00<00:00, 11.19it/s]\n" + "Sweeping: 0%| | 0/7 [00:00,\n", + "(
,\n", " array([,\n", " ,\n", - " ],\n", + " ,\n", + " ],\n", " dtype=object))" ] }, - "execution_count": 6, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -313,7 +339,8 @@ " base_regressor_class=SubspaceDMD,\n", " base_observable_class = pk.observables.RandomFourierFeatures,\n", " base_observable_kwargs = {'gamma':0.1,'include_state':False},\n", - " extra_observables = pk.observables.TimeDelay(n_delays=5)\n", + " extra_observables = pk.observables.TimeDelay(n_delays=5),\n", + " compute_residuals=True\n", " # base_regressor_class=pk.regression.DMDc, # Must use DMDc or EDMDc\n", ")\n", "sweeper.sweep()\n", @@ -322,7 +349,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 26, "id": "2920979a", "metadata": {}, "outputs": [ @@ -330,35 +357,29 @@ "name": "stderr", "output_type": "stream", "text": [ - "Sweeping: 0%| | 0/7 [00:00,\n", + "(
,\n", " array([,\n", " ,\n", - " ],\n", + " ,\n", + " ],\n", " dtype=object))" ] }, - "execution_count": 15, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -373,12 +394,13 @@ " param1_name=\"obs.n_delays\", # Sweep TimeDelay.n_delays\n", " param1_values=np.arange(3, 10),\n", " param2_name=\"extra_obs.0.gamma\", # Sweep RFF.D (first extra observable)\n", - " param2_values=[0,0.1],\n", + " param2_values=[1,0.1],\n", " base_observable_class=pk.observables.TimeDelay,\n", " extra_observables=[pk.observables.RandomFourierFeatures], # Pass CLASS, not instance\n", " extra_observable_kwargs=[{\"include_state\": False,'D':10}], # Optional default kwargs for RFF\n", " base_regressor_class=SubspaceDMD,\n", - " base_regressor_kwargs={'svd_rank':10}\n", + " base_regressor_kwargs={'svd_rank':10},\n", + " compute_residuals=True\n", "\n", ")\n", "sweeper.sweep()\n", @@ -387,7 +409,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 27, "id": "57fb8535", "metadata": {}, "outputs": [ @@ -395,23 +417,23 @@ "name": "stderr", "output_type": "stream", "text": [ - "Sweeping: 33%|███▎ | 3/9 [00:00<00:00, 19.35it/s]" + "Sweeping: 0%| | 0/9 [00:00" + "" ] }, - "execution_count": 6, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -433,7 +455,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 28, "id": "9a225798", "metadata": {}, "outputs": [ @@ -447,13 +469,13 @@ " ], dtype=object))" ] }, - "execution_count": 7, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -468,28 +490,7 @@ }, { "cell_type": "code", - "execution_count": 8, - "id": "e0a5c88f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sweeper" - ] - }, - { - "cell_type": "code", - "execution_count": 9, + "execution_count": 29, "id": "afc7a96a", "metadata": {}, "outputs": [ @@ -516,7 +517,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 34, "id": "fb5f1e37", "metadata": {}, "outputs": [ @@ -524,34 +525,28 @@ "name": "stderr", "output_type": "stream", "text": [ - "Sweeping: 0%| | 0/4 [00:00,\n", + "(
,\n", " array([,\n", " ,\n", - " ], dtype=object))" + " ,\n", + " ], dtype=object))" ] }, - "execution_count": 10, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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"text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -568,7 +563,8 @@ " param1_values=np.arange(1,5),\n", " param2_name=\"rank\",\n", " param2_values=np.arange(1,15),\n", - " model_class=\"DMD\"\n", + " model_class=\"DMD\",\n", + " compute_residuals=True\n", ")\n", "sweeper.sweep()\n", "sweeper.plot()#not so clear, if we just use regular dmd now" @@ -576,7 +572,25 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 41, + "id": "55f61190", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Automatic pdb calling has been turned ON\n" + ] + } + ], + "source": [ + "%pdb" + ] + }, + { + "cell_type": "code", + "execution_count": 46, "id": "094fe74c", "metadata": {}, "outputs": [ @@ -584,14 +598,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "Sweeping: 0%| | 0/4 [00:00], dtype=object))" ] }, - "execution_count": 11, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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" ] @@ -625,13 +639,25 @@ " data=X_train_control,\n", " control_data=U_train_control,\n", " param1_name=\"n_delays\",\n", - " param1_values=np.arange(1,5),\n", + " param1_values=np.arange(1,10),\n", " param2_name=\"rank\",\n", - " param2_values=np.arange(1,15),\n", - " model_class=\"SubspaceDMDc\"\n", + " param2_values=np.arange(1,25),\n", + " model_class=\"SubspaceDMDc\",\n", + " compute_residuals=False\n", ")\n", "sweeper.sweep()\n", - "sweeper.plot()#not so clear, if we just use regular dmd now" + "sweeper.plot()" + ] + }, + { + "cell_type": "markdown", + "id": "2e72767d", + "metadata": {}, + "source": [ + "We've seen the flexibility of the sweeper algorithms to different settings of DSA. In the previous code, you might have seen a parameter called ```compute_residuals``` that added a 4th plot. This is our impementation of ResidualDMD (ResDMD, Colbrook et al., 2021,2022, and more) that develops a method for precise error control on \\textit{individual} dmd eigenvalues. This is an alternatve method of error that has been helpful for understanding which modes are worth keeping, and is more in line with Koopman Operator theory than the prediction error metrics. We've found that studying these residuals can afford more accurate comparisons\n", + "\n", + "\n", + "In this next section, we'll go over the interface of ResDMD for different methods, and demonstrate its efficacy on this above problem" ] }, { @@ -639,6 +665,143 @@ "execution_count": null, "id": "0cfe5a52", "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(100, 100, 15) (15, 15)\n" + ] + }, + { + "data": { + "text/plain": [ + "2.3121872292023155e-16" + ] + }, + "execution_count": 98, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from DSA import DMD, DMDc, SubspaceDMDc\n", + "import DSA.pykoopman as pk\n", + "from pydmd import DMD as pyDMD\n", + "from pydmd import SubspaceDMD\n", + "\n", + "from DSA.pykoopman import Koopman\n", + "\n", + "from DSA.resdmd import ResidualComputer\n", + "\n", + "X_train, U_train = generate_trajectory(A_true, B_true, n_timesteps, 100,\n", + " noise_std=0.0,partial_observ_frac=1,use_control=False)\n", + "X_test, U_test = generate_trajectory(A_true, B_true, n_timesteps, 10,\n", + " noise_std=0.0,partial_observ_frac=1,use_control=False)\n", + "print(X_train.shape, A_true.shape)\n", + "\n", + "true_evals = np.linalg.eigvals(A_true)\n", + "\n", + "model = DMD(n_delays=1,rank=15)\n", + "model = Koopman(regressor=pyDMD(svd_rank=15),observables=pk.observables.TimeDelay(n_delays=2))\n", + "model.fit(X_train)\n", + "\n", + "rescomput = ResidualComputer(model,X_test)\n", + "residuals, normalized_res, num, denom = rescomput.compute();\n", + "\n", + "\n", + "\n", + "fig, ax = rescomput.plot()\n", + "ax.scatter(true_evals.real,true_evals.imag, c = 'r', marker='x')\n", + "rescomput.get_average_residual()" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "id": "b7e7784b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(10, 100, 3) (10, 100, 3) (15, 15)\n" + ] + }, + { + "ename": "AttributeError", + "evalue": "'NoneType' object has no attribute 'shape'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[113], line 14\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[38;5;66;03m# model = SubspaceDMDc(X_train,U_train, n_delays=10,rank=15)\u001b[39;00m\n\u001b[1;32m 12\u001b[0m \u001b[38;5;66;03m# model = DMDc(X_train,U_train, n_delays=10,rank_input=15,rank_output=15)\u001b[39;00m\n\u001b[1;32m 13\u001b[0m model \u001b[38;5;241m=\u001b[39m Koopman(regressor\u001b[38;5;241m=\u001b[39mpkDMDc(svd_rank\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m15\u001b[39m),observables\u001b[38;5;241m=\u001b[39mpk\u001b[38;5;241m.\u001b[39mobservables\u001b[38;5;241m.\u001b[39mTimeDelay(n_delays\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m))\n\u001b[0;32m---> 14\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43mU_train\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 16\u001b[0m rescomput \u001b[38;5;241m=\u001b[39m ResidualComputer(model,X_test, U_test)\n\u001b[1;32m 17\u001b[0m residuals, normalized_res, num, denom \u001b[38;5;241m=\u001b[39m rescomput\u001b[38;5;241m.\u001b[39mcompute();\n", + "File \u001b[0;32m/orcd/data/fiete/001/om2/ostrow/dmrsa/DSApublic/DSA/examples/../DSA/pykoopman/koopman.py:270\u001b[0m, in \u001b[0;36mKoopman.fit\u001b[0;34m(self, x, y, u, dt)\u001b[0m\n\u001b[1;32m 268\u001b[0m filterwarnings(action, category\u001b[38;5;241m=\u001b[39m\u001b[38;5;167;01mUserWarning\u001b[39;00m)\n\u001b[1;32m 269\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m u \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 270\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_pipeline\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mregressor__dt\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdt\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 271\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 272\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pipeline\u001b[38;5;241m.\u001b[39mfit(x, y, regressor__u\u001b[38;5;241m=\u001b[39mu, regressor__dt\u001b[38;5;241m=\u001b[39mdt)\n", + "File \u001b[0;32m~/.conda/envs/dsapublic-env/lib/python3.10/site-packages/sklearn/base.py:1365\u001b[0m, in \u001b[0;36m_fit_context..decorator..wrapper\u001b[0;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1358\u001b[0m estimator\u001b[38;5;241m.\u001b[39m_validate_params()\n\u001b[1;32m 1360\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[1;32m 1361\u001b[0m skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[1;32m 1362\u001b[0m prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[1;32m 1363\u001b[0m )\n\u001b[1;32m 1364\u001b[0m ):\n\u001b[0;32m-> 1365\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfit_method\u001b[49m\u001b[43m(\u001b[49m\u001b[43mestimator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m 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\u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n", + "File \u001b[0;32m/orcd/data/fiete/001/om2/ostrow/dmrsa/DSApublic/DSA/examples/../DSA/pykoopman/regression/_base_ensemble.py:113\u001b[0m, in \u001b[0;36mEnsembleBaseRegressor.fit\u001b[0;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[1;32m 110\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 111\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mregressor_ \u001b[38;5;241m=\u001b[39m clone(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mregressor)\n\u001b[0;32m--> 113\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mregressor_\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_trans\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mfit_params\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 115\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mregressor_, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfeature_names_in_\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m 116\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfeature_names_in_ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mregressor_\u001b[38;5;241m.\u001b[39mfeature_names_in_\n", + "File \u001b[0;32m/orcd/data/fiete/001/om2/ostrow/dmrsa/DSApublic/DSA/examples/../DSA/pykoopman/regression/_dmdc.py:134\u001b[0m, in \u001b[0;36mDMDc.fit\u001b[0;34m(self, x, y, u, dt)\u001b[0m\n\u001b[1;32m 132\u001b[0m offset \u001b[38;5;241m=\u001b[39m u\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m>\u001b[39m X1\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m 133\u001b[0m u, _ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_detect_reshape(u, offset\u001b[38;5;241m=\u001b[39moffset)\n\u001b[0;32m--> 134\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_control_features_ \u001b[38;5;241m=\u001b[39m \u001b[43mu\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshape\u001b[49m[\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m 135\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_input_features_ \u001b[38;5;241m=\u001b[39m X1\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m 136\u001b[0m C \u001b[38;5;241m=\u001b[39m u\n", + "\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'shape'" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "> \u001b[0;32m/orcd/data/fiete/001/om2/ostrow/dmrsa/DSApublic/DSA/DSA/pykoopman/regression/_dmdc.py\u001b[0m(134)\u001b[0;36mfit\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32m 132 \u001b[0;31m \u001b[0moffset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mX1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m 133 \u001b[0;31m \u001b[0mu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_detect_reshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moffset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moffset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m--> 134 \u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_control_features_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m 135 \u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_input_features_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m 136 \u001b[0;31m \u001b[0mC\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\n", + "*** Invalid frame count (.shape)\n", + "None\n" + ] + } + ], + "source": [ + "from DSA.pykoopman.regression import DMDc as pkDMDc\n", + "\n", + "X_train, U_train = generate_trajectory(A_true, B_true, n_timesteps, 10,\n", + " noise_std=0.0,partial_observ_frac=.2,use_control=True)\n", + "X_test, U_test = generate_trajectory(A_true, B_true, n_timesteps, 10,\n", + " noise_std=0.0,partial_observ_frac=.2,use_control=True)\n", + "print(X_train.shape,X_test.shape, A_true.shape)\n", + "\n", + "true_evals = np.linalg.eigvals(A_true)\n", + "\n", + "# model = SubspaceDMDc(X_train,U_train, n_delays=10,rank=15)\n", + "# model = DMDc(X_train,U_train, n_delays=10,rank_input=15,rank_output=15)\n", + "model = Koopman(regressor=pkDMDc(svd_rank=15),observables=pk.observables.TimeDelay(n_delays=2))\n", + "model.fit(X_train,U_train)\n", + "\n", + "rescomput = ResidualComputer(model,X_test, U_test)\n", + "residuals, normalized_res, num, denom = rescomput.compute();\n", + "\n", + "\n", + "\n", + "fig, ax = rescomput.plot()\n", + "ax.scatter(true_evals.real,true_evals.imag, c = 'r', marker='x')\n", + "rescomput.get_average_residual()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "72f8c75c", + "metadata": {}, "outputs": [], "source": [] } From 8d6a36a98217fd41fef59c70f59db035dd310254 Mon Sep 17 00:00:00 2001 From: mitchellostrow Date: Sat, 7 Feb 2026 15:17:18 -0500 Subject: [PATCH 81/90] resdmd with control bug fixes --- DSA/dmdc.py | 4 +++- DSA/resdmd.py | 4 ++-- DSA/subspace_dmdc.py | 8 +++++--- DSA/sweeps.py | 12 ++++++------ 4 files changed, 16 insertions(+), 12 deletions(-) diff --git a/DSA/dmdc.py b/DSA/dmdc.py index 86be392..55fa6ad 100644 --- a/DSA/dmdc.py +++ b/DSA/dmdc.py @@ -215,6 +215,7 @@ def _init_data(self, data, control_data=None): self.n = self.data.shape[1] self.ntrials = 1 self.is_list_data = False + def _check_same_shape(self): if isinstance(self.data,(np.ndarray,torch.Tensor)): @@ -240,7 +241,8 @@ def compute_hankel( print("Computing Hankel matrices ...") # Overwrite parameters if provided - self.data = self.data if data is None else self._init_data(data, control_data) + if data is not None and control_data is not None: + self._init_data(data, control_data) self.n_delays = self.n_delays if n_delays is None else n_delays self.delay_interval = ( self.delay_interval if delay_interval is None else delay_interval diff --git a/DSA/resdmd.py b/DSA/resdmd.py index 8259c2f..15a5608 100644 --- a/DSA/resdmd.py +++ b/DSA/resdmd.py @@ -425,8 +425,8 @@ def compute_residuals( if is_dmdc: # Project control into its reduced space - Uu = dmd.Uu[:, :rank_input] - Su_mat_inv = dmd.Su_mat_inv[:rank_input, :rank_input] + Uu = dmd.Uu[:, :rank_input].cpu().numpy() + Su_mat_inv = dmd.Su_mat_inv[:rank_input, :rank_input].cpu().numpy() U_proj = H_U @ Uu @ Su_mat_inv U = U_proj.cpu().detach().numpy() if hasattr(U_proj, "cpu") else U_proj diff --git a/DSA/subspace_dmdc.py b/DSA/subspace_dmdc.py index cd9f242..9f0caac 100644 --- a/DSA/subspace_dmdc.py +++ b/DSA/subspace_dmdc.py @@ -69,11 +69,13 @@ def __init__( self.rank = rank self.backend = backend - def fit(self): + def fit(self,data=None,control_data=None): """Fit the SubspaceDMDc model.""" + data = self.data if data is None else data + control_data = self.control_data if data is None else control_data self.A_v, self.B_v, self.C_v, self.info = self.subspace_dmdc_multitrial_flexible( - y=self.data, - u=self.control_data, + y=data, + u=control_data, p=self.n_delays, f=self.n_delays, n=self.rank, diff --git a/DSA/sweeps.py b/DSA/sweeps.py index 1570a63..3fd96b6 100644 --- a/DSA/sweeps.py +++ b/DSA/sweeps.py @@ -221,12 +221,12 @@ def sweep(self) -> "BaseSweeper": self._nnormals[i, j] = measure_nonnormality_transpose(A_np) if self.compute_residuals: - try: - rc = ResidualComputer(model, self.test_data) - self._residuals[i, j] = rc.get_average_residual() - except Exception as e: - warnings.warn(f"Residual computation failed: {e}") - self._residuals[i, j] = np.nan + # try: + rc = ResidualComputer(model, self.test_data) + self._residuals[i, j] = rc.get_average_residual() + # except Exception as e: + # warnings.warn(f"Residual computation failed: {e}") + # self._residuals[i, j] = np.nan # except Exception as e: # warnings.warn(f"Failed for {self.param1_name}={p1}, {self.param2_name}={p2}: {e}") # continue From 05b7a25da9b6c5fee6413142ed8ea67caeebac05 Mon Sep 17 00:00:00 2001 From: mitchellostrow Date: Sat, 7 Feb 2026 16:06:19 -0500 Subject: [PATCH 82/90] kalman smoothing for postprocessing latent state inference --- DSA/subspace_dmdc.py | 182 ++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 181 insertions(+), 1 deletion(-) diff --git a/DSA/subspace_dmdc.py b/DSA/subspace_dmdc.py index 9f0caac..ec39865 100644 --- a/DSA/subspace_dmdc.py +++ b/DSA/subspace_dmdc.py @@ -478,6 +478,125 @@ def subspace_dmdc_multitrial_flexible(self, y, u, p, f, n=None, lamb=1e-8, energ return self.subspace_dmdc_multitrial_custom(y_list, u_list, p, f, n, lamb, energy) + def get_training_latent_states(self, return_aligned=True): + """ + Get the latent states from training data. + + Parameters + ---------- + return_aligned : bool, default=True + If True, returns time-aligned (X, X_next, U) triplets. + If False, returns raw X_hat matrix. + + Returns + ------- + If return_aligned=True: + X : np.ndarray, shape (T_total, rank) + Current latent states (row-major format). + X_next : np.ndarray, shape (T_total, rank) + Next latent states (row-major format). + U : np.ndarray, shape (T_total, m) + Control inputs aligned with X (row-major format). + If return_aligned=False: + X_hat : np.ndarray, shape (rank, T_total) + Raw latent states from training. + """ + X_hat = self.info['X_hat'] + + if not return_aligned: + return X_hat + + # Need to re-align using stored info + p = self.n_delays + valid_trials = self.info['valid_trials'] + T_per_trial = self.info['T_per_trial'] + + # Get training data in list format + y_list, u_list = self._convert_to_subspace_dmdc_data_format(self.data, self.control_data) + + X, X_next, U, _ = self._time_align_valid_trials( + X_hat, u_list, y_list, valid_trials, T_per_trial, p + ) + + return X.T, X_next.T, U.T + + def project_to_latent(self, test_data, control_data, return_aligned=True, method='smooth'): + """ + Project new observations to latent states using Kalman filtering/smoothing. + + This method uses the learned state-space model (A, B, C) to infer latent + states from observations, which projects into the same latent space as training. + + Parameters + ---------- + test_data : np.ndarray or list + Test observations. Shape (T, N), (K, T, N), or list of (T_k, N) arrays. + control_data : np.ndarray or list + Control inputs. Must have the same structure as test_data. + return_aligned : bool, default=True + If True, returns time-aligned (X, X_next, U) triplets ready for dynamics. + If False, returns raw latent states per trial. + method : str, default='smooth' + 'filter' - use Kalman filtering (causal, uses only past observations) + 'smooth' - use Kalman smoothing (non-causal, uses all observations) + + Returns + ------- + If return_aligned=True: + X : np.ndarray, shape (T_total, rank) + Current latent states (row-major format). + X_next : np.ndarray, shape (T_total, rank) + Next latent states (row-major format). + U : np.ndarray, shape (T_total, m) + Control inputs aligned with X (row-major format). + If return_aligned=False: + x_latents : list of np.ndarray + Latent states for each trial, each shape (T_trial, rank). + """ + # Convert to list format for consistent processing + y_list, u_list = self._convert_to_subspace_dmdc_data_format(test_data, control_data) + + X_all = [] + X_next_all = [] + U_all = [] + x_latents_all = [] + + for trial_idx, (y_trial, u_trial) in enumerate(zip(y_list, u_list)): + T_trial = y_trial.shape[0] + + # Run Kalman filter forward pass + kalman = OnlineKalman(self) + for t in range(T_trial): + kalman.step(y=y_trial[t], u=u_trial[t]) + + # Get latent states + if method == 'smooth': + # Run backward smoothing pass + x_smoothed, _ = kalman.smooth() + # Stack into (T, rank) array + x_latent = np.hstack(x_smoothed).T # (T, rank) + else: + # Use filtered states + x_latent = np.hstack(kalman.x_filtereds).T # (T, rank) + + x_latents_all.append(x_latent) + + if return_aligned: + # Create (X[t], X[t+1], U[t]) triplets + X_all.append(x_latent[:-1]) # (T-1, rank) + X_next_all.append(x_latent[1:]) # (T-1, rank) + U_all.append(u_trial[:-1]) # (T-1, m) + + if not return_aligned: + return x_latents_all + + # Concatenate across trials + X = np.concatenate(X_all, axis=0) + X_next = np.concatenate(X_next_all, axis=0) + U = np.concatenate(U_all, axis=0) + + return X, X_next, U + def predict(self, test_data=None, control_data=None, reseed=None): """Predict using the Kalman filter.""" if test_data is None: @@ -628,4 +747,65 @@ def get_history(self): 'y_filtereds': self.y_filtereds, 'y_predicteds': self.y_predicteds, 'kalman_gains': self.kalman_gains - } \ No newline at end of file + } + + def smooth(self, reg_coef=1e-6): + """ + Perform RTS (Rauch-Tung-Striebel) smoothing on the filtered states. + + This is the backward pass that uses future observations to improve + state estimates. Must be called after running the forward filter pass. + + Parameters + ---------- + reg_coef : float + Regularization coefficient for numerical stability. + + Returns + ------- + x_smoothed : list of np.ndarray + Smoothed state estimates, shape (x_dim, 1) each. + p_smoothed : list of np.ndarray + Smoothed covariance estimates. + """ + if len(self.x_filtereds) == 0: + raise ValueError("Must run forward filter pass before smoothing.") + + T = len(self.x_filtereds) + + # Initialize with final filtered state + x_smoothed = [None] * T + p_smoothed = [None] * T + x_smoothed[-1] = self.x_filtereds[-1].copy() + p_smoothed[-1] = self.p_filtereds[-1].copy() + + # Backward pass + for t in range(T - 2, -1, -1): + # Get filtered and predicted values at time t + x_filt_t = self.x_filtereds[t] + p_filt_t = self.p_filtereds[t] + + # Predicted values for t+1 (computed during forward pass at time t) + # p_predicteds[t] is P_{t+1|t} computed after processing observation t + p_pred_tp1 = self.p_predicteds[t] + + # Regularize for numerical stability + p_pred_tp1_reg = p_pred_tp1 + reg_coef * np.eye(self.x_dim) + + # Smoother gain: J_t = P_{t|t} @ A^T @ P_{t+1|t}^{-1} + J_t = p_filt_t @ self.A.T @ np.linalg.pinv(p_pred_tp1_reg) + + # x_{t+1|t} = A @ x_{t|t} + B @ u_t + x_pred_tp1 = self.A @ x_filt_t + self.B @ self.us[t] + + # Smoothed state: x_{t|T} = x_{t|t} + J_t @ (x_{t+1|T} - x_{t+1|t}) + x_smoothed[t] = x_filt_t + J_t @ (x_smoothed[t + 1] - x_pred_tp1) + + # Smoothed covariance: P_{t|T} = P_{t|t} + J_t @ (P_{t+1|T} - P_{t+1|t}) @ J_t^T + p_smoothed[t] = p_filt_t + J_t @ (p_smoothed[t + 1] - p_pred_tp1) @ J_t.T + p_smoothed[t] = self._regularize_and_symmetrize(p_smoothed[t], reg_coef) + + self.x_smoothed = x_smoothed + self.p_smoothed = p_smoothed + + return x_smoothed, p_smoothed \ No newline at end of file From 88488e97ddc2458eeddffa9ed2d6e7166c33c9fd Mon Sep 17 00:00:00 2001 From: mitchellostrow Date: Sat, 7 Feb 2026 16:07:00 -0500 Subject: [PATCH 83/90] resdmd for subspacedmdc fix with kalman smoothing for test error --- DSA/resdmd.py | 109 ++++--------- examples/sweep_resdmd_tutorial.ipynb | 223 +++++++++++---------------- 2 files changed, 120 insertions(+), 212 deletions(-) diff --git a/DSA/resdmd.py b/DSA/resdmd.py index 15a5608..89b187d 100644 --- a/DSA/resdmd.py +++ b/DSA/resdmd.py @@ -578,9 +578,11 @@ def compute_residuals_pykoopman( def compute_residuals_subspace_dmdc( model: "SubspaceDMDc", - test_data: np.ndarray, - control_data: np.ndarray, + test_data: np.ndarray = None, + control_data: np.ndarray = None, return_num_denom: bool = False, + use_training_latents: bool = False, + projection_method: str = 'smooth', ): """ Compute residuals for a fitted SubspaceDMDc model. @@ -589,12 +591,22 @@ def compute_residuals_subspace_dmdc( ---------- model : SubspaceDMDc Fitted SubspaceDMDc model. - test_data : np.ndarray + test_data : np.ndarray, optional Test data (observations). Shape (T, N) or (K, T, N) or list of arrays. - control_data : np.ndarray + If None and use_training_latents=True, uses training data. + control_data : np.ndarray, optional Control input data. Must have the same temporal structure as test_data. + If None and use_training_latents=True, uses training control data. return_num_denom : bool Whether to return numerators and denominators. + use_training_latents : bool, default=False + If True, uses the exact latent states from training (stored in model.info['X_hat']). + This should give near-zero residuals for training data since A was fit to these states. + If False, projects test_data to latent space using Kalman filtering/smoothing. + projection_method : str, default='smooth' + Method for projecting observations to latent states (only used if use_training_latents=False): + - 'smooth': Kalman smoothing (uses all observations, better estimates) + - 'filter': Kalman filtering (causal, uses only past observations) Returns ------- @@ -615,88 +627,33 @@ def compute_residuals_subspace_dmdc( y_t = C @ x_t Residuals are computed in the latent space by: - 1. Projecting new observations to latent states via the oblique projection + 1. Projecting observations to latent states via Kalman smoothing/filtering 2. Subtracting control effects: X_next_corrected = X_next - B @ U 3. Computing residuals on (X, X_next_corrected) using the A matrix eigenstructure """ - if control_data is None: - raise ValueError("control_data is required for SubspaceDMDc residual computation.") - - # Convert to numpy if needed - if isinstance(test_data, torch.Tensor): - test_data = test_data.cpu().numpy() - if isinstance(control_data, torch.Tensor): - control_data = control_data.cpu().numpy() - # Get model parameters - p = model.n_delays # past window - f = model.info.get('f', p) # future window (usually same as p) rank = model.info['rank_used'] - - # Get projection matrices from the model - Gamma_hat = model.info['Gamma_hat'] # (f*p_out, rank) - - # Get A and B matrices A = model.A_v B = model.B_v - # Convert data to list format for processing - y_list, u_list = model._convert_to_subspace_dmdc_data_format(test_data, control_data) - - # Collect Hankel data using the same method as fitting - # This gives us the properly aligned data for projection - try: - U_p, Y_p, U_f, Y_f, Z_p, valid_trials, T_per_trial, T_total, p_out, m = \ - model._collect_data(y_list, u_list, p, f) - except ValueError as e: - raise ValueError(f"Failed to process test data for residuals: {e}") - - # Project to latent space using oblique projection - # Following the subspace identification approach: - # 1. Compute the oblique projection O = Y_f @ Pi_perp @ pinv(Z_p @ Pi_perp) - # 2. SVD of O gives us U_n, S_n, V_n - # 3. X_hat = S_half @ V_n projects the data to latent space - # - # For new data, we use the Gamma_hat to project: - # X_hat_new = pinv(Gamma_hat) @ Y_f_new (approximately) - - # Compute pseudo-inverse of Gamma_hat for projection - Gamma_pinv = np.linalg.pinv(Gamma_hat) # (rank, f*p_out) - - # Project Y_f to latent space - X_hat = Gamma_pinv @ Y_f # (rank, T_total) - - # Time-align the latent states for regression - # X_hat[:, t] corresponds to time t, we need X[t] and X[t+1] pairs - # Also need U at time t - X_segments = [] - X_next_segments = [] - U_segments = [] - - start_idx = 0 - for trial_idx, T_trial in enumerate(T_per_trial): - X_trial = X_hat[:, start_idx:start_idx + T_trial] - - # Current and next states - X_trial_curr = X_trial[:, :-1] - X_trial_next = X_trial[:, 1:] - - # Get control input aligned with current state - original_trial_idx = valid_trials[trial_idx] - U_trial = u_list[original_trial_idx] - # U_trial is (n_timepoints, n_features), extract the aligned portion - U_mid_trial = U_trial[p:p + (T_trial - 1), :].T # (m, T_trial-1) + if use_training_latents: + # Use the exact latent states from training - should give near-zero residuals + X, X_next, U = model.get_training_latent_states(return_aligned=True) + else: + if control_data is None: + raise ValueError("control_data is required for SubspaceDMDc residual computation.") - X_segments.append(X_trial_curr) - X_next_segments.append(X_trial_next) - U_segments.append(U_mid_trial) + # Convert to numpy if needed + if isinstance(test_data, torch.Tensor): + test_data = test_data.cpu().numpy() + if isinstance(control_data, torch.Tensor): + control_data = control_data.cpu().numpy() - start_idx += T_trial - - # Concatenate across trials - X = np.concatenate(X_segments, axis=1).T # (T_total-n_trials, rank) - X_next = np.concatenate(X_next_segments, axis=1).T # (T_total-n_trials, rank) - U = np.concatenate(U_segments, axis=1).T # (T_total-n_trials, m) + # Project test data to latent space using Kalman filtering/smoothing + # Returns time-aligned (X, X_next, U) in row-major format + X, X_next, U = model.project_to_latent( + test_data, control_data, return_aligned=True, method=projection_method + ) # Subtract control effects: X_next_corrected = X_next - B @ U.T Y_corrected = _subtract_control_effects(X_next, B, U) diff --git a/examples/sweep_resdmd_tutorial.ipynb b/examples/sweep_resdmd_tutorial.ipynb index 357eb20..e0e6143 100644 --- a/examples/sweep_resdmd_tutorial.ipynb +++ b/examples/sweep_resdmd_tutorial.ipynb @@ -57,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 3, "id": "e1552288", "metadata": {}, "outputs": [ @@ -136,24 +136,13 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 6, "id": "a0b65b61", "metadata": {}, "outputs": [ - { - "ename": "IndexError", - "evalue": "index 1 is out of bounds for axis 2 with size 1", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[18], line 6\u001b[0m\n\u001b[1;32m 4\u001b[0m ax \u001b[38;5;241m=\u001b[39m axes[\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mmin\u001b[39m(\u001b[38;5;241m3\u001b[39m, n_state)):\n\u001b[0;32m----> 6\u001b[0m ax\u001b[38;5;241m.\u001b[39mplot(\u001b[43mX_train\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m:\u001b[49m\u001b[38;5;241;43m200\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m, label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m$x_\u001b[39m\u001b[38;5;130;01m{{\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mi\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m1\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;130;01m}}\u001b[39;00m\u001b[38;5;124m$\u001b[39m\u001b[38;5;124m'\u001b[39m, alpha\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.7\u001b[39m)\n\u001b[1;32m 7\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_xlabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTime\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 8\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_ylabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mState Value\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", - "\u001b[0;31mIndexError\u001b[0m: index 1 is out of bounds for axis 2 with size 1" - ] - }, { "data": { - "image/png": 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", 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"text/plain": [ "
" ] @@ -167,7 +156,7 @@ "\n", "# Plot first few state dimensions\n", "ax = axes[0, 0]\n", - "for i in range(min(3, n_state)):\n", + "for i in range(min(3, X_train.shape[-1])):\n", " ax.plot(X_train[0, :200, i], label=f'$x_{{{i+1}}}$', alpha=0.7)\n", "ax.set_xlabel('Time')\n", "ax.set_ylabel('State Value')\n", @@ -213,7 +202,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 8, "id": "3a4db4a7", "metadata": {}, "outputs": [ @@ -221,36 +210,30 @@ "name": "stderr", "output_type": "stream", "text": [ - "Sweeping: 0%| | 0/9 [00:00,\n", + "(
,\n", " array([,\n", " ,\n", - " ,\n", - " ],\n", + " ],\n", " dtype=object))" ] }, - "execution_count": 19, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -270,7 +253,7 @@ " param2_values=np.arange(1,25),\n", " reseed=1,\n", " base_regressor_class=SubspaceDMD,\n", - " compute_residuals=True\n", + " compute_residuals=False\n", " # base_regressor_class=pk.regression.DMDc, # Must use DMDc or EDMDc\n", ")\n", "sweeper.sweep()\n", @@ -279,7 +262,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 9, "id": "d83efaf6", "metadata": {}, "outputs": [ @@ -287,14 +270,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Sweeping: 0%| | 0/7 [00:00" ] @@ -349,7 +325,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 10, "id": "2920979a", "metadata": {}, "outputs": [ @@ -357,7 +333,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Sweeping: 100%|██████████| 7/7 [00:00<00:00, 82.95it/s]\n" + "Sweeping: 100%|██████████| 7/7 [00:00<00:00, 35.82it/s]\n" ] }, { @@ -371,13 +347,13 @@ " dtype=object))" ] }, - "execution_count": 26, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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" ] @@ -409,7 +385,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 11, "id": "57fb8535", "metadata": {}, "outputs": [ @@ -417,23 +393,16 @@ "name": "stderr", "output_type": "stream", "text": [ - "Sweeping: 0%| | 0/9 [00:00" + "" ] }, - "execution_count": 27, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -455,7 +424,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 12, "id": "9a225798", "metadata": {}, "outputs": [ @@ -469,13 +438,13 @@ " ], dtype=object))" ] }, - "execution_count": 28, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -490,7 +459,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 13, "id": "afc7a96a", "metadata": {}, "outputs": [ @@ -517,7 +486,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 14, "id": "fb5f1e37", "metadata": {}, "outputs": [ @@ -525,7 +494,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Sweeping: 100%|██████████| 4/4 [00:00<00:00, 23.18it/s]\n" + "Sweeping: 100%|██████████| 4/4 [00:00<00:00, 9.02it/s]\n" ] }, { @@ -538,13 +507,13 @@ " ], dtype=object))" ] }, - "execution_count": 34, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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" ] @@ -572,25 +541,7 @@ }, { "cell_type": "code", - "execution_count": 41, - "id": "55f61190", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Automatic pdb calling has been turned ON\n" - ] - } - ], - "source": [ - "%pdb" - ] - }, - { - "cell_type": "code", - "execution_count": 46, + "execution_count": 15, "id": "094fe74c", "metadata": {}, "outputs": [ @@ -598,14 +549,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Sweeping: 0%| | 0/9 [00:00], dtype=object))" ] }, - "execution_count": 46, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -662,7 +606,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 47, "id": "0cfe5a52", "metadata": {}, "outputs": [ @@ -676,16 +620,16 @@ { "data": { "text/plain": [ - "2.3121872292023155e-16" + "1.601763411203178e-16" ] }, - "execution_count": 98, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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" ] @@ -728,68 +672,75 @@ }, { "cell_type": "code", - "execution_count": 113, - "id": "b7e7784b", + "execution_count": 22, + "id": "99bdd082", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "(10, 100, 3) (10, 100, 3) (15, 15)\n" - ] - }, - { - "ename": "AttributeError", - "evalue": "'NoneType' object has no attribute 'shape'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[113], line 14\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[38;5;66;03m# model = SubspaceDMDc(X_train,U_train, n_delays=10,rank=15)\u001b[39;00m\n\u001b[1;32m 12\u001b[0m \u001b[38;5;66;03m# model = DMDc(X_train,U_train, n_delays=10,rank_input=15,rank_output=15)\u001b[39;00m\n\u001b[1;32m 13\u001b[0m model \u001b[38;5;241m=\u001b[39m Koopman(regressor\u001b[38;5;241m=\u001b[39mpkDMDc(svd_rank\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m15\u001b[39m),observables\u001b[38;5;241m=\u001b[39mpk\u001b[38;5;241m.\u001b[39mobservables\u001b[38;5;241m.\u001b[39mTimeDelay(n_delays\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m))\n\u001b[0;32m---> 14\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43mU_train\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 16\u001b[0m rescomput \u001b[38;5;241m=\u001b[39m ResidualComputer(model,X_test, U_test)\n\u001b[1;32m 17\u001b[0m residuals, normalized_res, num, denom \u001b[38;5;241m=\u001b[39m rescomput\u001b[38;5;241m.\u001b[39mcompute();\n", - "File \u001b[0;32m/orcd/data/fiete/001/om2/ostrow/dmrsa/DSApublic/DSA/examples/../DSA/pykoopman/koopman.py:270\u001b[0m, in \u001b[0;36mKoopman.fit\u001b[0;34m(self, x, y, u, dt)\u001b[0m\n\u001b[1;32m 268\u001b[0m filterwarnings(action, category\u001b[38;5;241m=\u001b[39m\u001b[38;5;167;01mUserWarning\u001b[39;00m)\n\u001b[1;32m 269\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m u \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 270\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_pipeline\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mregressor__dt\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdt\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 271\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 272\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pipeline\u001b[38;5;241m.\u001b[39mfit(x, y, regressor__u\u001b[38;5;241m=\u001b[39mu, regressor__dt\u001b[38;5;241m=\u001b[39mdt)\n", - "File \u001b[0;32m~/.conda/envs/dsapublic-env/lib/python3.10/site-packages/sklearn/base.py:1365\u001b[0m, in \u001b[0;36m_fit_context..decorator..wrapper\u001b[0;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1358\u001b[0m estimator\u001b[38;5;241m.\u001b[39m_validate_params()\n\u001b[1;32m 1360\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[1;32m 1361\u001b[0m skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[1;32m 1362\u001b[0m prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[1;32m 1363\u001b[0m )\n\u001b[1;32m 1364\u001b[0m ):\n\u001b[0;32m-> 1365\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfit_method\u001b[49m\u001b[43m(\u001b[49m\u001b[43mestimator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/.conda/envs/dsapublic-env/lib/python3.10/site-packages/sklearn/pipeline.py:663\u001b[0m, in \u001b[0;36mPipeline.fit\u001b[0;34m(self, X, y, **params)\u001b[0m\n\u001b[1;32m 657\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_final_estimator \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpassthrough\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 658\u001b[0m last_step_params \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_metadata_for_step(\n\u001b[1;32m 659\u001b[0m step_idx\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m,\n\u001b[1;32m 660\u001b[0m step_params\u001b[38;5;241m=\u001b[39mrouted_params[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msteps[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m][\u001b[38;5;241m0\u001b[39m]],\n\u001b[1;32m 661\u001b[0m all_params\u001b[38;5;241m=\u001b[39mparams,\n\u001b[1;32m 662\u001b[0m )\n\u001b[0;32m--> 663\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_final_estimator\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mXt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mlast_step_params\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfit\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 665\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n", - "File \u001b[0;32m/orcd/data/fiete/001/om2/ostrow/dmrsa/DSApublic/DSA/examples/../DSA/pykoopman/regression/_base_ensemble.py:113\u001b[0m, in \u001b[0;36mEnsembleBaseRegressor.fit\u001b[0;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[1;32m 110\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 111\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mregressor_ \u001b[38;5;241m=\u001b[39m clone(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mregressor)\n\u001b[0;32m--> 113\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mregressor_\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_trans\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mfit_params\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 115\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mregressor_, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfeature_names_in_\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m 116\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfeature_names_in_ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mregressor_\u001b[38;5;241m.\u001b[39mfeature_names_in_\n", - "File \u001b[0;32m/orcd/data/fiete/001/om2/ostrow/dmrsa/DSApublic/DSA/examples/../DSA/pykoopman/regression/_dmdc.py:134\u001b[0m, in \u001b[0;36mDMDc.fit\u001b[0;34m(self, x, y, u, dt)\u001b[0m\n\u001b[1;32m 132\u001b[0m offset \u001b[38;5;241m=\u001b[39m u\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m>\u001b[39m X1\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m 133\u001b[0m u, _ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_detect_reshape(u, offset\u001b[38;5;241m=\u001b[39moffset)\n\u001b[0;32m--> 134\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_control_features_ \u001b[38;5;241m=\u001b[39m \u001b[43mu\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshape\u001b[49m[\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m 135\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_input_features_ \u001b[38;5;241m=\u001b[39m X1\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m 136\u001b[0m C \u001b[38;5;241m=\u001b[39m u\n", - "\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'shape'" + "Automatic pdb calling has been turned ON\n" ] - }, + } + ], + "source": [ + "%pdb" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "id": "b7e7784b", + "metadata": {}, + "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "> \u001b[0;32m/orcd/data/fiete/001/om2/ostrow/dmrsa/DSApublic/DSA/DSA/pykoopman/regression/_dmdc.py\u001b[0m(134)\u001b[0;36mfit\u001b[0;34m()\u001b[0m\n", - "\u001b[0;32m 132 \u001b[0;31m \u001b[0moffset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mX1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\u001b[0;32m 133 \u001b[0;31m \u001b[0mu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_detect_reshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moffset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moffset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\u001b[0;32m--> 134 \u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_control_features_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\u001b[0;32m 135 \u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_input_features_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\u001b[0;32m 136 \u001b[0;31m \u001b[0mC\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\n", - "*** Invalid frame count (.shape)\n", - "None\n" + "(10, 200, 1) (10, 200, 1) (15, 15)\n" ] + }, + { + "data": { + "text/plain": [ + "3.176992673021038e-13" + ] + }, + "execution_count": 99, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ "from DSA.pykoopman.regression import DMDc as pkDMDc\n", "\n", - "X_train, U_train = generate_trajectory(A_true, B_true, n_timesteps, 10,\n", - " noise_std=0.0,partial_observ_frac=.2,use_control=True)\n", - "X_test, U_test = generate_trajectory(A_true, B_true, n_timesteps, 10,\n", - " noise_std=0.0,partial_observ_frac=.2,use_control=True)\n", + "X_train, U_train = generate_trajectory(A_true, B_true, 200, 10,\n", + " noise_std=0.0,partial_observ_frac=.1,use_control=True)\n", + "X_test, U_test = generate_trajectory(A_true, B_true, 200, 10,\n", + " noise_std=0.0,partial_observ_frac=.1,use_control=True)\n", "print(X_train.shape,X_test.shape, A_true.shape)\n", "\n", "true_evals = np.linalg.eigvals(A_true)\n", "\n", - "# model = SubspaceDMDc(X_train,U_train, n_delays=10,rank=15)\n", - "# model = DMDc(X_train,U_train, n_delays=10,rank_input=15,rank_output=15)\n", - "model = Koopman(regressor=pkDMDc(svd_rank=15),observables=pk.observables.TimeDelay(n_delays=2))\n", + "U_zeros = np.zeros_like(U_train)\n", + "model = SubspaceDMDc(X_train,U_train, n_delays=20,rank=15)\n", + "# model = DMDc(X_train,U_train, n_delays=1,rank_input=15,rank_output=15)\n", + "# model = Koopman(regressor=pkDMDc(svd_rank=15),observables=pk.observables.TimeDelay(n_delays=2))\n", "model.fit(X_train,U_train)\n", "\n", "rescomput = ResidualComputer(model,X_test, U_test)\n", - "residuals, normalized_res, num, denom = rescomput.compute();\n", - "\n", + "residuals, normalized_res, num, denom = rescomput.compute()\n", "\n", "\n", "fig, ax = rescomput.plot()\n", From 68383f1bebb6309ca6171033838c77a6eb4f938d Mon Sep 17 00:00:00 2001 From: mitchellostrow Date: Sat, 7 Feb 2026 16:39:24 -0500 Subject: [PATCH 84/90] pykoopman handle dmdc correctly --- DSA/pykoopman/koopman.py | 31 ++++++++++++++++------------ DSA/pykoopman/regression/_dmd.py | 20 +++++++++++++++--- examples/sweep_resdmd_tutorial.ipynb | 24 ++++++++++++++------- 3 files changed, 51 insertions(+), 24 deletions(-) diff --git a/DSA/pykoopman/koopman.py b/DSA/pykoopman/koopman.py index 026bc63..ef3fe33 100644 --- a/DSA/pykoopman/koopman.py +++ b/DSA/pykoopman/koopman.py @@ -227,7 +227,7 @@ def transform_y(y_data): if y is None: # or isinstance(self.regressor, PyDMDRegressor): # if there is only 1 trajectory OR regressor is PyDMD y_flag = True - x, y = self.split_xy(x, offset=True) + x, y, u = self.split_xy(x, u=u, offset=True) if isinstance(self.regressor, HAVOK): regressor = self.regressor @@ -681,8 +681,7 @@ def _regressor(self): # the _regressor.fit to update the model coefficients. # call this function with _regressor() return self._pipeline.steps[2][1] - - def split_xy(self, X, offset=True): + def split_xy(self, X, u=None, offset=True): """ Split data into X and Y pairs with temporal offset. @@ -692,15 +691,16 @@ def split_xy(self, X, offset=True): Args: X: Input data (1D, 2D array, 3D array, or list of arrays) + u: Control input (same shape as X), optional offset: If True, split with temporal offset X[:-1], X[1:] - If False, return (X, X) - used when Y provided separately + If False, return (X, X, u) - used when Y provided separately Returns: - Tuple (X_split, Y_split) preserving input structure + Tuple (X_split, Y_split, u_split) preserving input structure """ if not offset: # No split needed when Y provided separately - return X, X + return X, X, u s1, s2 = -1, 1 # X[:-1], X[1:] @@ -711,21 +711,26 @@ def split_xy(self, X, offset=True): if X.ndim == 2: self.n_samples_, self.n_input_features_ = X.shape self.n_trials_ = 1 - return X[:s1], X[s2:] + u_split = u[:s1] if u is not None else None + return X[:s1], X[s2:], u_split elif X.ndim == 3: self.n_trials_, self.n_samples_, self.n_input_features_ = X.shape # Keep 3D structure: (trials, time-1, features) - return X[:, :s1, :], X[:, s2:, :] + u_split = u[:, :s1, :] if u is not None else None + return X[:, :s1, :], X[:, s2:, :], u_split elif isinstance(X, list): # Recursively process each element in list - X_list, Y_list = [], [] - for x in X: - x_split, y_split = self.split_xy(x, offset=offset) + X_list, Y_list, u_list = [], [], [] + for i, x in enumerate(X): + u_elem = u[i] if u is not None else None + x_split, y_split, u_split = self.split_xy(x, u=u_elem, offset=offset) X_list.append(x_split) Y_list.append(y_split) - return X_list, Y_list + u_list.append(u_split) + u_result = u_list if u is not None else None + return X_list, Y_list, u_result # Fallback for unknown types - return X, X + return X, X, u diff --git a/DSA/pykoopman/regression/_dmd.py b/DSA/pykoopman/regression/_dmd.py index 130cbb0..a2acd78 100644 --- a/DSA/pykoopman/regression/_dmd.py +++ b/DSA/pykoopman/regression/_dmd.py @@ -73,9 +73,23 @@ def __init__(self, regressor, tikhonov_regularization=None): raise ValueError("regressor must be a subclass of DMDBase from pydmd.") self.regressor = regressor # super(PyDMDRegressor, self).__init__(regressor) - self.tlsq_rank = regressor._tlsq_rank - self.svd_rank = regressor._Atilde._svd_rank - self.forward_backward = regressor._Atilde._forward_backward + if hasattr(regressor, '_tslsq_rank'): + self.tlsq_rank = regressor._tlsq_rank + elif hasattr(regressor, '_dmd_operator_kwargs'): + self.tlsq_rank = regressor._dmd_operator_kwargs['tlsq_rank'] + else: + raise ValueError("can't find tlsq_rank") + + if hasattr(regressor, '_svd_rank'): + self.svd_rank = regressor._Atilde._svd_rank + elif hasattr(regressor, '_dmd_operator_kwargs'): + self.svd_rank = regressor._dmd_operator_kwargs['svd_rank'] + else: + raise ValueError("can't find svd_rank") + if regressor._Atilde is not None: + self.forward_backward = regressor._Atilde._forward_backward + else: + self.forward_backward = False self.tikhonov_regularization = tikhonov_regularization self.flag_xy = False self._ur = None diff --git a/examples/sweep_resdmd_tutorial.ipynb b/examples/sweep_resdmd_tutorial.ipynb index e0e6143..ace547a 100644 --- a/examples/sweep_resdmd_tutorial.ipynb +++ b/examples/sweep_resdmd_tutorial.ipynb @@ -690,7 +690,7 @@ }, { "cell_type": "code", - "execution_count": 99, + "execution_count": 141, "id": "b7e7784b", "metadata": {}, "outputs": [ @@ -704,16 +704,16 @@ { "data": { "text/plain": [ - "3.176992673021038e-13" + "2.2266363006886167e-13" ] }, - "execution_count": 99, + "execution_count": 141, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -723,7 +723,6 @@ } ], "source": [ - "from DSA.pykoopman.regression import DMDc as pkDMDc\n", "\n", "X_train, U_train = generate_trajectory(A_true, B_true, 200, 10,\n", " noise_std=0.0,partial_observ_frac=.1,use_control=True)\n", @@ -736,11 +735,12 @@ "U_zeros = np.zeros_like(U_train)\n", "model = SubspaceDMDc(X_train,U_train, n_delays=20,rank=15)\n", "# model = DMDc(X_train,U_train, n_delays=1,rank_input=15,rank_output=15)\n", - "# model = Koopman(regressor=pkDMDc(svd_rank=15),observables=pk.observables.TimeDelay(n_delays=2))\n", - "model.fit(X_train,U_train)\n", + "# model = Koopman(regressor=pk.regression.DMDc(svd_rank=15),observables=pk.observables.Identity())\n", + "# model.fit(X_train, u = U_train) # use this for Koopman\n", + "model.fit(X_train,U_train) # use this for DMDc or SubspaceDMDc\n", "\n", "rescomput = ResidualComputer(model,X_test, U_test)\n", - "residuals, normalized_res, num, denom = rescomput.compute()\n", + "residuals, normalized_res, num, denqom = rescomput.compute()\n", "\n", "\n", "fig, ax = rescomput.plot()\n", @@ -755,6 +755,14 @@ "metadata": {}, "outputs": [], "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "13f71085", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { From a42cc4ad7a3dc2517dc83861fb318c57c3d6a2ca Mon Sep 17 00:00:00 2001 From: mitchellostrow Date: Sat, 7 Feb 2026 17:07:52 -0500 Subject: [PATCH 85/90] small bugs and tutorial fixes --- DSA/pykoopman/regression/_dmd.py | 4 +- DSA/subspace_dmdc.py | 5 +- DSA/sweeps.py | 2 +- examples/all_dsa_types.ipynb | 224 +++++++++------- examples/how_to_use_dsa_tutorial.ipynb | 358 +++++++++++++------------ examples/sweep_resdmd_tutorial.ipynb | 8 +- 6 files changed, 332 insertions(+), 269 deletions(-) diff --git a/DSA/pykoopman/regression/_dmd.py b/DSA/pykoopman/regression/_dmd.py index a2acd78..ee68057 100644 --- a/DSA/pykoopman/regression/_dmd.py +++ b/DSA/pykoopman/regression/_dmd.py @@ -73,14 +73,14 @@ def __init__(self, regressor, tikhonov_regularization=None): raise ValueError("regressor must be a subclass of DMDBase from pydmd.") self.regressor = regressor # super(PyDMDRegressor, self).__init__(regressor) - if hasattr(regressor, '_tslsq_rank'): + if hasattr(regressor, '_tlsq_rank'): self.tlsq_rank = regressor._tlsq_rank elif hasattr(regressor, '_dmd_operator_kwargs'): self.tlsq_rank = regressor._dmd_operator_kwargs['tlsq_rank'] else: raise ValueError("can't find tlsq_rank") - if hasattr(regressor, '_svd_rank'): + if hasattr(regressor._Atilde, '_svd_rank'): self.svd_rank = regressor._Atilde._svd_rank elif hasattr(regressor, '_dmd_operator_kwargs'): self.svd_rank = regressor._dmd_operator_kwargs['svd_rank'] diff --git a/DSA/subspace_dmdc.py b/DSA/subspace_dmdc.py index ec39865..99ea075 100644 --- a/DSA/subspace_dmdc.py +++ b/DSA/subspace_dmdc.py @@ -71,8 +71,9 @@ def __init__( def fit(self,data=None,control_data=None): """Fit the SubspaceDMDc model.""" - data = self.data if data is None else data - control_data = self.control_data if data is None else control_data + if data is None and control_data is None: + data = self.data + control_data = self.control_data self.A_v, self.B_v, self.C_v, self.info = self.subspace_dmdc_multitrial_flexible( y=data, u=control_data, diff --git a/DSA/sweeps.py b/DSA/sweeps.py index 3fd96b6..64629b8 100644 --- a/DSA/sweeps.py +++ b/DSA/sweeps.py @@ -222,7 +222,7 @@ def sweep(self) -> "BaseSweeper": if self.compute_residuals: # try: - rc = ResidualComputer(model, self.test_data) + rc = ResidualComputer(model, self.test_data, self.test_control) self._residuals[i, j] = rc.get_average_residual() # except Exception as e: # warnings.warn(f"Residual computation failed: {e}") diff --git a/examples/all_dsa_types.ipynb b/examples/all_dsa_types.ipynb index 6615607..5700a44 100644 --- a/examples/all_dsa_types.ipynb +++ b/examples/all_dsa_types.ipynb @@ -2,10 +2,19 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "773aa0fd", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/orcd/data/fiete/001/om2/ostrow/dmrsa/DSApublic/DSA/DSA/pykoopman/__init__.py:3: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " from pkg_resources import get_distribution\n" + ] + } + ], "source": [ "import numpy as np \n", "import matplotlib.pyplot as plt\n", @@ -22,7 +31,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "52a2ed8c", "metadata": {}, "outputs": [ @@ -40,7 +49,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "id": "d452743b", "metadata": {}, "outputs": [ @@ -50,7 +59,7 @@ "(18, 9)" ] }, - "execution_count": 4, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -77,7 +86,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 4, "id": "88cad354", "metadata": {}, "outputs": [ @@ -128,7 +137,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 23, "id": "b7b308b7", "metadata": {}, "outputs": [ @@ -152,7 +161,7 @@ " return A\n", "\n", "def simulate_system(A, B, C, U, x0=None,rng=None,obs_noise=0.0,process_noise=0.0,\n", - " nonlinear_eps=0.0,nonlinear_func= lambda x: np.tanh(x),nonlinear_eps_input=0.0):\n", + " nonlinear_eps=0.0,nonlinear_func= lambda x: np.tanh(x),nonlinear_eps_input=0.0,autonomous=False):\n", " n, m = B.shape\n", " p_out = C.shape[0]\n", " N = U.shape[1]\n", @@ -203,13 +212,16 @@ "Y = Y.T\n", "U = U.T\n", "\n", + "\n", + "\n", + "\n", "print(X.shape)\n", "print(Y.shape)" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 6, "id": "8c4c9ea2", "metadata": {}, "outputs": [ @@ -217,22 +229,20 @@ "name": "stdout", "output_type": "stream", "text": [ - "[ 9.00000000e-01+0.j -4.27762264e-01+0.34055831j\n", - " -4.27762264e-01-0.34055831j 5.72126042e-01+0.47528717j\n", - " 5.72126042e-01-0.47528717j -2.56600620e-04+0.50116322j\n", - " -2.56600620e-04-0.50116322j 3.46246199e-01+0.30316658j\n", - " 3.46246199e-01-0.30316658j -8.50722948e-02+0.j ]\n", + "[ 0.8973288 +0.06928948j 0.8973288 -0.06928948j 0.51192657+0.j\n", + " 0.31817322+0.46791128j 0.31817322-0.46791128j -0.61083921+0.53853946j\n", + " -0.61083921-0.53853946j -0.80198335+0.j -0.14361156+0.j\n", + " -0.33773263+0.j ]\n", "(500, 9) (500, 2)\n", - "[-4.27762263e-01+0.34055831j -4.27762263e-01-0.34055831j\n", - " 8.99999999e-01+0.j 5.72126042e-01+0.47528717j\n", - " 5.72126042e-01-0.47528717j -2.56600504e-04+0.50116322j\n", - " -2.56600504e-04-0.50116322j 3.46246198e-01+0.30316658j\n", - " 3.46246198e-01-0.30316658j -8.50722947e-02+0.j ]\n" + "[ 0.8973288 +0.06928948j 0.8973288 -0.06928948j 0.31817322+0.46791128j\n", + " 0.31817322-0.46791128j 0.51192656+0.j -0.61083921+0.53853946j\n", + " -0.61083921-0.53853946j -0.14361156+0.j -0.33773262+0.j\n", + " -0.80198335+0.j ]\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -260,7 +270,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 25, "id": "e59db62a", "metadata": {}, "outputs": [ @@ -268,28 +278,27 @@ "name": "stdout", "output_type": "stream", "text": [ - "DMD prediction MASE: 0.0001704487077985564\n", - "DMDC prediction MASE: 0.6608961494400851\n", - "Using 1 out of 1 trials with sufficient time points.\n", - "DMDC prediction MASE: 0.4358238354387466\n" + "DMD prediction MASE: 2.9924139443833934e-07\n", + "DMDC prediction MASE: 0.4920231093673477\n", + "DMDC prediction MASE: 0.3430521570675024\n" ] } ], "source": [ "from DSA.stats import mase\n", - "X_auto, Y_auto = simulate_system(A, np.zeros((latent_dim, input_dim)), C, U, nonlinear_eps=nonlinear_eps)\n", + "X_auto, Y_auto = simulate_system(A, np.zeros_like(B), C, U.T, nonlinear_eps=nonlinear_eps)\n", "X_auto = X_auto.T\n", "dmd = DMD(X_auto, n_delays=10, rank=10)\n", "dmd.fit()\n", "pred_data = dmd.predict()\n", "print(f'DMD prediction MASE: {mase(X_auto, pred_data)}')\n", "\n", - "dmdc = DMDc(X, U.T, n_delays=10, rank_input=10, rank_output=10)\n", + "dmdc = DMDc(X, U, n_delays=10, rank_input=10, rank_output=10)\n", "dmdc.fit()\n", "pred_data = dmdc.predict()\n", "print(f'DMDC prediction MASE: {mase(X, pred_data)}')\n", "\n", - "dmdc = SubspaceDMDc(X, U.T, n_delays=10, rank=10, backend='n4sid')\n", + "dmdc = SubspaceDMDc(X, U, n_delays=10, rank=10, backend='n4sid')\n", "dmdc.fit()\n", "pred_data = dmdc.predict()\n", "print(f'DMDC prediction MASE: {mase(X, pred_data)}')" @@ -297,7 +306,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "721bc598", "metadata": {}, "outputs": [ @@ -305,9 +314,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "Fitting DMDs: 100%|██████████| 3/3 [00:00<00:00, 168.46it/s]\n", - "Computing DMD similarities: 100%|██████████| 3/3 [00:00<00:00, 134.51it/s]\n", - "/n/home00/ahuang/DSA/DSA/dsa.py:408: UserWarning: Warning: You are using a DMD model that fits a control operator but comparing with a DSA metric that does not compare control operators\n", + "Fitting DMDs: 100%|██████████| 3/3 [00:00<00:00, 455.34it/s]\n", + "Caching compare objects X: 100%|██████████| 3/3 [00:00<00:00, 39199.10it/s]\n", + "Computing DMD similarities: 100%|██████████| 3/3 [00:00<00:00, 3006.67it/s]\n", + "/orcd/data/fiete/001/om2/ostrow/dmrsa/DSApublic/DSA/DSA/dsa.py:412: UserWarning: Warning: You are using a DMD model that fits a control operator but comparing with a DSA metric that does not compare control operators\n", " warnings.warn(\n" ] }, @@ -322,8 +332,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "Fitting DMDs: 100%|██████████| 3/3 [00:00<00:00, 643.33it/s]\n", - "Computing DMD similarities: 100%|██████████| 3/3 [00:06<00:00, 2.22s/it]\n" + "Fitting DMDs: 100%|██████████| 3/3 [00:00<00:00, 407.15it/s]\n", + "Caching compare objects X: 100%|██████████| 3/3 [00:00<00:00, 38716.65it/s]\n", + "Computing DMD similarities: 100%|██████████| 3/3 [00:06<00:00, 2.23s/it]\n" ] }, { @@ -332,7 +343,7 @@ "(3, 3)" ] }, - "execution_count": 18, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -370,7 +381,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "2ea88dc2", "metadata": {}, "outputs": [ @@ -378,44 +389,46 @@ "name": "stderr", "output_type": "stream", "text": [ - "/n/home00/ahuang/DSA/DSA/dsa.py:618: UserWarning: When using cross-comparison with a list of arrays, gDSA treats each array as its own system.\n", - "If arrays within X (and Y) are samples from the same system, switch to using X=[X,Y], X_control=[X_control,Y_control], Y=None, and Y_control=None.\n", + "/orcd/data/fiete/001/om2/ostrow/dmrsa/DSApublic/DSA/DSA/dsa.py:622: UserWarning: When using cross-comparison with a list of arrays, gDSA treats each array as its own system.\n", + "If arrays within X (and Y) are samples from the same system, switch to using GeneralizedDSA(X=[X,Y], X_control=[X_control,Y_control], Y=None, Y_control=None.)\n", " warnings.warn(\n", - "/n/home00/ahuang/DSA/DSA/dsa.py:408: UserWarning: Warning: You are using a DMD model that fits a control operator but comparing with a DSA metric that does not compare control operators\n", - " warnings.warn(\n", - "\n", - "Fitting DMDs: 100%|██████████| 9/9 [00:00<00:00, 2031.58it/s]\n", - "\n", - "Fitting DMDs: 100%|██████████| 9/9 [00:00<00:00, 2208.82it/s]\n", - "\n", - "\u001b[A\n", - "\u001b[A\n", - "\u001b[A\n", - "\u001b[A" + "/orcd/data/fiete/001/om2/ostrow/dmrsa/DSApublic/DSA/DSA/dsa.py:412: UserWarning: Warning: You are using a DMD model that fits a control operator but comparing with a DSA metric that does not compare control operators\n", + " warnings.warn(\n" ] }, { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[46], line 35\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Check generalized dsa with other data structures for data and inputs\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# Self-comparison (using X and X_control)\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 29\u001b[0m \u001b[38;5;66;03m# Should return a 3x3 distance matrix\u001b[39;00m\n\u001b[1;32m 30\u001b[0m \u001b[38;5;66;03m#TODO: when doing cross-comparison and using a list of arrays, gDSA treats each array as its own system\u001b[39;00m\n\u001b[1;32m 31\u001b[0m dsa \u001b[38;5;241m=\u001b[39m GeneralizedDSA(X\u001b[38;5;241m=\u001b[39md3, X_control\u001b[38;5;241m=\u001b[39mu3,\n\u001b[1;32m 32\u001b[0m Y\u001b[38;5;241m=\u001b[39md3, Y_control\u001b[38;5;241m=\u001b[39mu3,\n\u001b[1;32m 33\u001b[0m dmd_class\u001b[38;5;241m=\u001b[39mDMDc,dmd_config\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mdict\u001b[39m(n_delays\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m,rank_input\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m,rank_output\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m),\n\u001b[1;32m 34\u001b[0m verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m---> 35\u001b[0m sim \u001b[38;5;241m=\u001b[39m \u001b[43mdsa\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit_score\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 36\u001b[0m sim\u001b[38;5;241m.\u001b[39mshape\n", - "File \u001b[0;32m~/DSA/DSA/dsa.py:704\u001b[0m, in \u001b[0;36mGeneralizedDSA.fit_score\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 691\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 692\u001b[0m \u001b[38;5;124;03mStandard fitting function for both DMDs and PAVF\u001b[39;00m\n\u001b[1;32m 693\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 701\u001b[0m \u001b[38;5;124;03m data matrix of the similarity scores between the specific sets of data\u001b[39;00m\n\u001b[1;32m 702\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 703\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfit_dmds()\n\u001b[0;32m--> 704\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscore\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/DSA/DSA/dsa.py:809\u001b[0m, in \u001b[0;36mGeneralizedDSA.score\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 803\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 804\u001b[0m loop \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 805\u001b[0m pairs\n\u001b[1;32m 806\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mverbose\n\u001b[1;32m 807\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m tqdm\u001b[38;5;241m.\u001b[39mtqdm(pairs, desc\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mComputing DMD similarities\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 808\u001b[0m )\n\u001b[0;32m--> 809\u001b[0m results \u001b[38;5;241m=\u001b[39m [compute_similarity(i, j) \u001b[38;5;28;01mfor\u001b[39;00m i, j \u001b[38;5;129;01min\u001b[39;00m loop]\n\u001b[1;32m 811\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m result \u001b[38;5;129;01min\u001b[39;00m results:\n\u001b[1;32m 812\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m result \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", - 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"File \u001b[0;32m~/DSA/DSA/dsa.py:780\u001b[0m, in \u001b[0;36mGeneralizedDSA.score..compute_similarity\u001b[0;34m(i, j)\u001b[0m\n\u001b[1;32m 773\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msimdist_has_control \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdmd_has_control:\n\u001b[1;32m 774\u001b[0m simdist_args\u001b[38;5;241m.\u001b[39mextend(\n\u001b[1;32m 775\u001b[0m [\n\u001b[1;32m 776\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_dmd_control_matrix(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdmds[\u001b[38;5;241m0\u001b[39m][i]),\n\u001b[1;32m 777\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_dmd_control_matrix(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdmds[ind2][j]),\n\u001b[1;32m 778\u001b[0m ]\n\u001b[1;32m 779\u001b[0m )\n\u001b[0;32m--> 780\u001b[0m sim \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msimdist\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit_score\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43msimdist_args\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 782\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mverbose \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_jobs \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m 783\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcomputing similarity between DMDs \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mi\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m and \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mj\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", - "File \u001b[0;32m~/DSA/DSA/simdist.py:471\u001b[0m, in \u001b[0;36mSimilarityTransformDist.fit_score\u001b[0;34m(self, A, B, iters, lr, score_method, wasserstein_weightings)\u001b[0m\n\u001b[1;32m 468\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 469\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[0;32m--> 471\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 472\u001b[0m \u001b[43m \u001b[49m\u001b[43mA\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 473\u001b[0m \u001b[43m \u001b[49m\u001b[43mB\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 474\u001b[0m \u001b[43m \u001b[49m\u001b[43miters\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 475\u001b[0m \u001b[43m \u001b[49m\u001b[43mlr\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 476\u001b[0m \u001b[43m \u001b[49m\u001b[43mwasserstein_weightings\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwasserstein_weightings\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 477\u001b[0m \u001b[43m \u001b[49m\u001b[43mscore_method\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscore_method\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 478\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 480\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mscore(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mA, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mB, score_method\u001b[38;5;241m=\u001b[39mscore_method)\n", - "File \u001b[0;32m~/DSA/DSA/simdist.py:254\u001b[0m, in \u001b[0;36mSimilarityTransformDist.fit\u001b[0;34m(self, A, B, iters, lr, score_method, wasserstein_weightings)\u001b[0m\n\u001b[1;32m 248\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mC_star \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mC_star \u001b[38;5;241m/\u001b[39m torch\u001b[38;5;241m.\u001b[39mlinalg\u001b[38;5;241m.\u001b[39mnorm(\n\u001b[1;32m 249\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mC_star, dim\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m, keepdim\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m 250\u001b[0m )\n\u001b[1;32m 251\u001b[0m \u001b[38;5;66;03m# wasserstein_distance(A.cpu().numpy(),B.cpu().numpy())\u001b[39;00m\n\u001b[1;32m 252\u001b[0m \n\u001b[1;32m 253\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 254\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlosses, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mC_star, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msim_net \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptimize_C\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 255\u001b[0m \u001b[43m \u001b[49m\u001b[43mA\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mB\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43miters\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morthog\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mverbose\u001b[49m\n\u001b[1;32m 256\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 257\u001b[0m \u001b[38;5;66;03m# permute the first row and column of B then rerun the optimization\u001b[39;00m\n\u001b[1;32m 258\u001b[0m P \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39meye(B\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m], device\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdevice)\n", - "File \u001b[0;32m~/DSA/DSA/simdist.py:338\u001b[0m, in \u001b[0;36mSimilarityTransformDist.optimize_C\u001b[0;34m(self, A, B, lr, iters, orthog, verbose)\u001b[0m\n\u001b[1;32m 334\u001b[0m loss \u001b[38;5;241m=\u001b[39m simdist_loss(A, sim_net(B))\n\u001b[1;32m 336\u001b[0m loss\u001b[38;5;241m.\u001b[39mbackward()\n\u001b[0;32m--> 338\u001b[0m \u001b[43moptimizer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 339\u001b[0m \u001b[38;5;66;03m# if _ % 99:\u001b[39;00m\n\u001b[1;32m 340\u001b[0m \u001b[38;5;66;03m# scheduler.step()\u001b[39;00m\n\u001b[1;32m 341\u001b[0m losses\u001b[38;5;241m.\u001b[39mappend(loss\u001b[38;5;241m.\u001b[39mitem())\n", - "File \u001b[0;32m~/.conda/envs/py39/lib/python3.9/site-packages/torch/optim/optimizer.py:493\u001b[0m, in \u001b[0;36mOptimizer.profile_hook_step..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 488\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 489\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[1;32m 490\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must return None or a tuple of (new_args, new_kwargs), but got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mresult\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 491\u001b[0m )\n\u001b[0;32m--> 493\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 494\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_optimizer_step_code()\n\u001b[1;32m 496\u001b[0m \u001b[38;5;66;03m# call optimizer step post hooks\u001b[39;00m\n", - "File \u001b[0;32m~/.conda/envs/py39/lib/python3.9/site-packages/torch/optim/optimizer.py:91\u001b[0m, in \u001b[0;36m_use_grad_for_differentiable.._use_grad\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 89\u001b[0m torch\u001b[38;5;241m.\u001b[39mset_grad_enabled(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdefaults[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdifferentiable\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[1;32m 90\u001b[0m torch\u001b[38;5;241m.\u001b[39m_dynamo\u001b[38;5;241m.\u001b[39mgraph_break()\n\u001b[0;32m---> 91\u001b[0m ret \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 92\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 93\u001b[0m torch\u001b[38;5;241m.\u001b[39m_dynamo\u001b[38;5;241m.\u001b[39mgraph_break()\n", - "File \u001b[0;32m~/.conda/envs/py39/lib/python3.9/site-packages/torch/optim/adam.py:218\u001b[0m, in \u001b[0;36mAdam.step\u001b[0;34m(self, closure)\u001b[0m\n\u001b[1;32m 210\u001b[0m \u001b[38;5;129m@_use_grad_for_differentiable\u001b[39m\n\u001b[1;32m 211\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mstep\u001b[39m(\u001b[38;5;28mself\u001b[39m, closure\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 212\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Perform a single optimization step.\u001b[39;00m\n\u001b[1;32m 213\u001b[0m \n\u001b[1;32m 214\u001b[0m \u001b[38;5;124;03m Args:\u001b[39;00m\n\u001b[1;32m 215\u001b[0m \u001b[38;5;124;03m closure (Callable, optional): A closure that reevaluates the model\u001b[39;00m\n\u001b[1;32m 216\u001b[0m \u001b[38;5;124;03m and returns the loss.\u001b[39;00m\n\u001b[1;32m 217\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 218\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_cuda_graph_capture_health_check\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 220\u001b[0m loss \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 221\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m closure \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", - "File \u001b[0;32m~/.conda/envs/py39/lib/python3.9/site-packages/torch/optim/optimizer.py:436\u001b[0m, in \u001b[0;36mOptimizer._cuda_graph_capture_health_check\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 420\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_cuda_graph_capture_health_check\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 421\u001b[0m \u001b[38;5;66;03m# Note [torch.compile x capturable]\u001b[39;00m\n\u001b[1;32m 422\u001b[0m \u001b[38;5;66;03m# If we are compiling, we try to take the capturable path automatically by\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 429\u001b[0m \u001b[38;5;66;03m# Thus, when compiling, inductor will determine if cudagraphs\u001b[39;00m\n\u001b[1;32m 430\u001b[0m \u001b[38;5;66;03m# can be enabled based on whether there is input mutation or CPU tensors.\u001b[39;00m\n\u001b[1;32m 431\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m 432\u001b[0m \u001b[38;5;129;01mnot\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mcompiler\u001b[38;5;241m.\u001b[39mis_compiling()\n\u001b[1;32m 433\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mbackends\u001b[38;5;241m.\u001b[39mcuda\u001b[38;5;241m.\u001b[39mis_built()\n\u001b[1;32m 434\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mcuda\u001b[38;5;241m.\u001b[39mis_available()\n\u001b[1;32m 435\u001b[0m ):\n\u001b[0;32m--> 436\u001b[0m capturing \u001b[38;5;241m=\u001b[39m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcuda\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mis_current_stream_capturing\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 438\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m capturing \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mall\u001b[39m(\n\u001b[1;32m 439\u001b[0m group[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcapturable\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m group \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mparam_groups\n\u001b[1;32m 440\u001b[0m ):\n\u001b[1;32m 441\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[1;32m 442\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAttempting CUDA graph capture of step() for an instance of \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 443\u001b[0m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\n\u001b[1;32m 444\u001b[0m \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m but param_groups\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m capturable is False.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 445\u001b[0m )\n", - "File \u001b[0;32m~/.conda/envs/py39/lib/python3.9/site-packages/torch/cuda/graphs.py:30\u001b[0m, in \u001b[0;36mis_current_stream_capturing\u001b[0;34m()\u001b[0m\n\u001b[1;32m 25\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mis_current_stream_capturing\u001b[39m():\n\u001b[1;32m 26\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"Return True if CUDA graph capture is underway on the current CUDA stream, False otherwise.\u001b[39;00m\n\u001b[1;32m 27\u001b[0m \n\u001b[1;32m 28\u001b[0m \u001b[38;5;124;03m If a CUDA context does not exist on the current device, returns False without initializing the context.\u001b[39;00m\n\u001b[1;32m 29\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m---> 30\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_cuda_isCurrentStreamCapturing\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + "name": "stderr", + "output_type": "stream", + "text": [ + "Fitting DMDs: 100%|██████████| 9/9 [00:00<00:00, 434.61it/s]\n", + "Fitting DMDs: 100%|██████████| 9/9 [00:00<00:00, 406.73it/s]\n" ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pre-computing eigenvalues for Wasserstein distance...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Caching compare objects X: 100%|██████████| 9/9 [00:00<00:00, 8832.18it/s]\n", + "Caching compare objects Y: 100%|██████████| 9/9 [00:00<00:00, 8515.39it/s]\n", + "Computing DMD similarities: 100%|██████████| 81/81 [00:00<00:00, 1457.22it/s]\n" + ] + }, + { + "data": { + "text/plain": [ + "(9, 9)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -452,14 +465,14 @@ "dsa = GeneralizedDSA(X=d3, X_control=u3,\n", " Y=d3, Y_control=u3,\n", " dmd_class=DMDc,dmd_config=dict(n_delays=5,rank_input=5,rank_output=5),\n", - " verbose=True)\n", + " verbose=True,simdist_config={'score_method':'wasserstein'})\n", "sim = dsa.fit_score()\n", "sim.shape" ] }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 12, "id": "997a05d3", "metadata": {}, "outputs": [ @@ -467,24 +480,26 @@ "name": "stderr", "output_type": "stream", "text": [ - "/n/home00/ahuang/DSA/DSA/dsa.py:408: UserWarning: Warning: You are using a DMD model that fits a control operator but comparing with a DSA metric that does not compare control operators\n", - " warnings.warn(\n", - "\n", - "Fitting DMDs: 100%|██████████| 1/1 [00:00<00:00, 1006.55it/s]\n", - "\n", - "Fitting DMDs: 100%|██████████| 1/1 [00:00<00:00, 1508.20it/s]\n", - "\n", - "\u001b[A\n", - "Computing DMD similarities: 100%|██████████| 1/1 [00:01<00:00, 1.72s/it]\n" + "Fitting DMDs: 100%|██████████| 1/1 [00:00<00:00, 577.73it/s]\n", + "Fitting DMDs: 100%|██████████| 1/1 [00:00<00:00, 696.73it/s]\n", + "Caching compare objects X: 100%|██████████| 1/1 [00:00<00:00, 8305.55it/s]\n", + "Caching compare objects Y: 100%|██████████| 1/1 [00:00<00:00, 10058.28it/s]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing DMD similarities: 100%|██████████| 1/1 [00:02<00:00, 2.92s/it]\n" ] }, { "data": { "text/plain": [ - "()" + "0.0" ] }, - "execution_count": 39, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -495,25 +510,40 @@ " dmd_class=DMDc,dmd_config=dict(n_delays=5,rank_input=5,rank_output=5),\n", " verbose=True)\n", "sim = dsa.fit_score()\n", - "sim.shape" + "sim" ] }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 13, "id": "6a5de212", "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, { "name": "stderr", "output_type": "stream", "text": [ - "/n/home00/ahuang/DSA/DSA/dsa.py:408: UserWarning: Warning: You are using a DMD model that fits a control operator but comparing with a DSA metric that does not compare control operators\n", - " warnings.warn(\n", - "\n", - "Fitting DMDs: 100%|██████████| 3/3 [00:00<00:00, 190.57it/s]\n", - "\n", - "Computing DMD similarities: 100%|██████████| 3/3 [00:00<00:00, 1423.73it/s]\n" + "Fitting DMDs: 100%|██████████| 3/3 [00:00<00:00, 105.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pre-computing eigenvalues for Wasserstein distance...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Caching compare objects X: 100%|██████████| 3/3 [00:00<00:00, 3265.74it/s]\n", + "Computing DMD similarities: 100%|██████████| 3/3 [00:00<00:00, 714.53it/s]\n" ] }, { @@ -522,7 +552,7 @@ "(3, 3)" ] }, - "execution_count": 43, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -546,11 +576,19 @@ "sim = dsa.fit_score()\n", "sim.shape" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8f0e06c0", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "py39", + "display_name": "dsapublic-env", "language": "python", "name": "python3" }, @@ -564,7 +602,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.10.19" } }, "nbformat": 4, diff --git a/examples/how_to_use_dsa_tutorial.ipynb b/examples/how_to_use_dsa_tutorial.ipynb index 7a22de4..c5b004d 100644 --- a/examples/how_to_use_dsa_tutorial.ipynb +++ b/examples/how_to_use_dsa_tutorial.ipynb @@ -34,18 +34,16 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 1, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" + "name": "stderr", + "output_type": "stream", + "text": [ + "/orcd/data/fiete/001/om2/ostrow/dmrsa/DSApublic/DSA/DSA/pykoopman/__init__.py:3: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " from pkg_resources import get_distribution\n" + ] } ], "source": [ @@ -70,11 +68,14 @@ "import DSA.pykoopman as pk\n", "from pydmd import DMD as pDMD, SubspaceDMD\n", "\n", - "from DSA.sweeps import sweep_ranks_delays, plot_sweep_results\n", + "from DSA.sweeps import DefaultSweeper\n", "from DSA.stats import compute_all_stats\n", "\n", "np.random.seed(22)\n", - "torch.manual_seed(22)\n" + "torch.manual_seed(22)\n", + "\n", + "%load_ext autoreload\n", + "%autoreload 2\n" ] }, { @@ -88,7 +89,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -163,7 +164,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -218,15 +219,16 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "Fitting DMDs: 100%|██████████| 2/2 [00:00<00:00, 523.14it/s]\n", - "Computing DMD similarities: 100%|██████████| 1/1 [00:03<00:00, 3.15s/it]" + "Fitting DMDs: 100%|██████████| 2/2 [00:00<00:00, 114.21it/s]\n", + "Caching compare objects X: 100%|██████████| 2/2 [00:00<00:00, 26715.31it/s]\n", + "Computing DMD similarities: 100%|██████████| 1/1 [00:02<00:00, 2.31s/it]" ] }, { @@ -247,7 +249,7 @@ }, { "data": { - "image/png": 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/bNiwRh+Tltcvnzz88MNrLd+iUme4dHtnYe1jOqWDK7lkxbcadCBP0lAGxoMCoNWs81xW9dGPYWf8z+8N15f+Z/7/rDyiwfmuVpqvSv+NvKwsxoPa4HGgli1bFs8//3wxhEBta1St1BLVEtIQBieccEIMHjy4GDrh2muvLe6yS5cRk/Q9fNtvv33Rjyk5++yz46CDDoqf/vSnccQRR8SECRPimWeeKVrQWkUa2+Ib/3ed40At3/SjcaAeXP6/40CllifjQAHQJqztXNY9jQN12Ufrd9h/jfVV3fvEc586P26csl08u6z/R+NA1RvKIJ3/ymkcqA0axmDixIlFWEkdu9d4wqqqorN3S0lDGNQOpJmGI0gtX2l4g+Tggw+OnXfeOW6//fYGA2n+6Ec/qhtIM31/X85Ami1yG6SRyAEodzVteyTylh7GYIMCVAoi6cuC07hPq9/lVml8Fx4AlJ9FbXEcqNQRO11Oq/TwBADQbAHqa1/7WjzxxBMb8lAAgLK3QZfwli5dGl//+tejV69esddee0WnTp0arD/rrLOiUriEBwDlZ1ELX8LboLvw0te1PPTQQ9GlS5eiJSp1HK+Vfq+kAAUA0CwB6oc//GFccsklxRf4pjGhAADak+oN/WLf9LUtwhMA0B5tUIBKg1neeeedzb81AACVegkvDZSZBqR88MEHY++9916jE/nVV1/dXNsHAFAZAeqFF16IQYMGFb/PmDGjubcJAKDyAtTjjz/e/FsCAFCJAerII49cb5k0jMG///u/f5xtAgConACVBqQCAGjvsgLUbbfd1nJbAgBQJoyCCQCQSYACAMgkQAEAZBKgAAAyCVAAAJkEKACATAIUAEAmAQoAIJMABQCQSYACAMgkQAEAZBKgAAAyCVAAAJkEKACATAIUAEAmAQoAIJMABQCQSYACAMgkQAEAZBKgAAAyCVAAAJkEKACATAIUAEAmAQoAIJMABQCQSYACAMgkQAEAZBKgAAAyCVAAAJkEKACATAIUAEAmAQoAIJMABQCQSYACAMgkQAEAZBKgAAAyCVAAAJkEKACATAIUAEAmAQoAIJMABQBQqQFqwYIFcdxxx0X37t1jiy22iJNPPjnef//9dT7m5z//eRx88MHFY6qqquK9997baNsLAFSusglQKTzNnDkzHn744bj//vvjySefjFNPPXWdj1m6dGmMHDkyLrjggo22nQBA5asqlUqlaONeeumlGDBgQDz99NMxePDgYtnEiRPj8MMPjzfffDP69Omzzsc/8cQT8bnPfS7+/ve/F61X67J8+fJiqrVo0aLo27dvLFy4sGjJAgDavnT+7tGjR4udv8uiBWry5MlF8KkNT8nw4cOjuro6pkyZ0qz/1rhx44oXvHZK4QkAoOwC1Ny5c2ObbbZpsKxjx47Rs2fPYl1zGjNmTJFWa6c5c+Y06/MDAOWvVQPU6NGji87d65pefvnljbpNnTt3Lpr66k8AAPV1jFZ03nnnxYknnrjOMv369YvevXvHvHnzGixfuXJlcWdeWgcA0G4CVK9evYppfYYNG1YMQTBt2rTYb7/9imWPPfZY1NTUxNChQzfClgIAlFkfqD322KMYjuCUU06JqVOnxqRJk+KMM86IUaNG1d2B99Zbb8Xuu+9erK+V+kc999xz8eqrrxbzL7zwQjGfWq4AACo6QCV33HFHEZAOOeSQYviCz3zmM8VAmbVWrFgRr7zySjH2U60bb7wxBg0aVASv5LOf/Wwxf99997VKHQCAylAW40BV8jgSAEDzMw4UAEAbUzaX8AAA2goBCgAgkwAFAJBJgAIAyCRAAQBkEqAAADIJUAAAmQQoAIBMAhQAQCYBCgAgkwAFAJBJgAIAyCRAAQBkEqAAADIJUAAAmQQoAIBMAhQAQCYBCgAgkwAFAJBJgAIAyCRAAQBkEqAAADIJUAAAmQQoAIBMAhQAQCYBCgAgkwAFAJBJgAIAyCRAAQBkEqAAADIJUAAAmQQoAIBMAhQAQCYBCgAgkwAFAJBJgAIAyCRAAQBkEqAAADIJUAAAmQQoAIBMAhQAQCYBCgAgkwAFAJBJgAIAyCRAAQBkEqAAADIJUAAAmQQoAIBMAhQAQCYBCgAgkwAFAJBJgAIAqNQAtWDBgjjuuOOie/fuscUWW8TJJ58c77///jrLn3nmmbHbbrtF165dY8cdd4yzzjorFi5cuFG3GwCoPGUToFJ4mjlzZjz88MNx//33x5NPPhmnnnrqWsu//fbbxXTVVVfFjBkz4vbbb4+JEycWwQsA4OOoKpVKpWjjXnrppRgwYEA8/fTTMXjw4GJZCkOHH354vPnmm9GnT58mPc/dd98d3/zmN2PJkiXRsWPHRsssX768mGotWrQo+vbtW7RcpdYvAKDtS+fvHj16tNj5uyxaoCZPnlxctqsNT8nw4cOjuro6pkyZ0uTnqX0R1xaeknHjxhUveO2UwhMAQNkFqLlz58Y222zTYFkKQT179izWNcX8+fPj0ksvXedlv2TMmDFF0Kqd5syZ87G2HQCoPK0aoEaPHh1VVVXrnF5++eVmacY74ogjisuAF1988TrLdu7cuWilqj8BANS39mtZG8F5550XJ5544jrL9OvXL3r37h3z5s1rsHzlypXFnXZp3bosXrw4Ro4cGd26dYt77703OnXq1CzbDgC0X60aoHr16lVM6zNs2LB47733Ytq0abHffvsVyx577LGoqamJoUOHrrPlacSIEUWr0n333RddunRp1u0HANqnsugDtcceexStSKecckpMnTo1Jk2aFGeccUaMGjWq7g68t956K3bfffdifW14OvTQQ4s77m655ZZiPvWXStOqVatauUYAQDlr1RaoHHfccUcRmg455JDi7rujjjoqfvazn9WtX7FiRbzyyiuxdOnSYn769Ol1d+j179+/wXPNmjUrdt55541cAwCgUpTFOFCVPI4EAND8jAMFANDGlEUfKACAtkSAAgDIJEABAGQSoAAAMglQAACZBCgAgEwCFABAJgEKACCTAAUAkEmAAgDIJEABAGQSoAAAMglQAACZBCgAgEwCFABAJgEKACCTAAUAkEmAAgDIJEABAGQSoAAAMglQAACZBCgAgEwCFABAJgEKACCTAAUAkEmAAgDI1DH3AQBA+VpVU4qpsxbEvMXLYptuXWLILj2jQ3VVa29W2RGgAKCdmDjjnbjk/70Y7yxcVrdsux5dYuwXB8TIPbdr1W0rNy7hAUA7CU/f+eX0BuEpmbtwWbE8rafpBCgAaAeX7VLLU6mRdbXL0vpUjqYRoACgwqU+T6u3PNWXYlNan8rRNAIUAFS41GG8OcshQAFAxUt32zVnOQQoAKh4aaiCdLfd2gYrSMvT+lSOpnEJDwAqXBrnKQ1VkKweomrn03rjQTWdAAUA7UAa5+mGb+4bvXs0vEyX5tNy40DlMZAmALQTKSR9YUBvI5E3AwEKANqRdJlu2Ce2au3NKHsu4QEAZBKgAAAyCVAAAJkEKACATAIUAEAmAQoAIJMABQCQSYACAMgkQAEAZDIS+XqUSqXi56JFi3JfWwCgldSet2vP481NgFqPxYsXFz/79u3bIjsAAGjZ83iPHj2a/XmrSi0VzSpETU1NvP3229GtW7diJ6QgNWfOnOjevXu0lwTf3urcXuvdHuvcXuvdHuvcXuvdnus8e/bsqKqqij59+kR1dfP3WNICtR7pRd9hhx2K39OOSNJB2F4OxFrtsc7ttd7tsc7ttd7tsc7ttd7tsc49evRo0TrrRA4AkEmAAgDIJEBl6Ny5c4wdO7b42V60xzq313q3xzq313q3xzq313qrc8vRiRwAIJMWKACATAIUAEAmAQoAIJMABQCQSYCq51/+5V/igAMOiE033TS22GKLJr2AaSD3iy66KLbbbrvo2rVrDB8+PP7yl780KLNgwYI47rjjigG90vOefPLJ8f7770dbkbt9b7zxRjGoaGPT3XffXVeusfUTJkyItmBD9snBBx+8Rn2+/e1vNyiTRr494ogjimNom222ie9///uxcuXKaCty653Kn3nmmbHbbrsVx/eOO+4YZ511VixcuLBBuba0r8ePHx8777xzdOnSJYYOHRpTp05dZ/l0zO6+++5F+b322iseeOCB7Pd4W5BT75tvvjkOPPDA2HLLLYsp1Wn18ieeeOIa+3TkyJFRrnW+/fbb16hPelyl7+vGPrfSlD6nymVfP/nkk/HFL36xGFE8bdtvf/vb9T7miSeeiH333be4C7F///7F/v+4nxVrSF/lwkcuuuii0tVXX10699xzSz169GjSy3LZZZcVZX/729+W/vSnP5W+9KUvlXbZZZfSBx98UFdm5MiRpX322af0xz/+sfRf//Vfpf79+5eOOeaYNvOy527fypUrS++8806D6ZJLLiltvvnmpcWLF9eVS4fXbbfd1qBc/delNW3IPjnooINKp5xySoP6LFy4sMHrsueee5aGDx9eevbZZ0sPPPBAaeutty6NGTOm1Fbk1vuFF14oHXnkkaX77ruv9Oqrr5YeffTR0q677lo66qijGpRrK/t6woQJpU022aR06623lmbOnFnsry222KL07rvvNlp+0qRJpQ4dOpSuuOKK0osvvlj60Y9+VOrUqVNR75z3eGvLrfexxx5bGj9+fHGcvvTSS6UTTzyxqOObb75ZV+aEE04ojpf6+3TBggWlcq1zOj67d+/eoD5z585tUKYS9/Xf/va3BnWeMWNGccyn16Nc9vUDDzxQ+uEPf1j6zW9+U3zW3Hvvvess//rrr5c23XTT4lye3tfXXXddUeeJEydu8OvYGAGqEenAakqAqqmpKfXu3bt05ZVX1i177733Sp07dy79+te/LubTzks7/Omnn64r8/vf/75UVVVVeuutt0qtrbm2b+DAgaV//Md/bLCsKQd6OdU5Baizzz57nW/y6urqBh/KN9xwQ/GhvXz58lKl7Ou77rqr+OBZsWJFm9vXQ4YMKZ1++ul186tWrSr16dOnNG7cuEbLf+Mb3ygdccQRDZYNHTq0dNpppzX5Pd4W5NZ7dSn8d+vWrfSLX/yiwUn1y1/+cqmtyq3z+j7X28u+vuaaa4p9/f7775fNvq6vKZ81P/jBD0qf+tSnGiw7+uijSyNGjGi21zFxCe9jmDVrVsydO7do5q3/3TupKXDy5MnFfPqZLpUMHjy4rkwqn75jb8qUKdHammP7pk2bFs8991xxOWh1p59+emy99dYxZMiQuPXWW4sm8nKu8x133FHUZ88994wxY8bE0qVLGzxvugS07bbb1i0bMWJE8cWWM2fOjNbWXMdiunyXLgF27NixTe3rDz/8sDgW678fU93SfO37cXVpef3ytfustnxT3uOtbUPqvbp0HK9YsSJ69uy5xmWQdCk6XcL9zne+E3/729+inOucLlfvtNNOxRfNfvnLX27wvmwv+/qWW26JUaNGxWabbVYW+3pDrO993RyvY+LLhD+G9GZL6p8wa+dr16Wf6aBs8KJ37Fh8UNWWaU3NsX3pDbnHHnsU/cfq+/GPfxyf//zni/5ADz30UHz3u98tPsBSH5pyrPOxxx5bfPim6/DPP/98nH/++fHKK6/Eb37zm7rnbexYqF1XCft6/vz5cemll8app57a5vZ12rZVq1Y1ug9efvnlRh+ztn1W//1bu2xtZVrbhtR7delYTsd1/RNK6gNz5JFHxi677BKvvfZaXHDBBXHYYYcVJ5gOHTpEudU5BYMU7Pfee+/ij4Crrrqq+MxKISp9YXx72Nepj8+MGTOKz+z62vK+3hBre1+nP2Y/+OCD+Pvf//6x3zPtIkCNHj06Lr/88nWWeemll4pOpO2x3h9XOhh/9atfxYUXXrjGuvrLBg0aFEuWLIkrr7yyxU6qLV3n+qEhtTSljqaHHHJI8YHziU98Iip9X6cPn9TxdMCAAXHxxRe36r6m+Vx22WVFh//UAlG/U3Vqpah/vKfgkY7zVC4d9+Vm2LBhxVQrhaf0h99NN91U/FHQHqTglPZlaiWur9L29cZS8QHqvPPOK+4wWJd+/fpt0HP37t27+Pnuu+8WJ9NaaX7gwIF1ZebNm9fgcemurHR3U+3jW7PeH3f77rnnnqL5//jjj19v2dQUnj6oli9f3iLfRbWx6ly/Psmrr75afNikx65+F0c6FpJy39eLFy8u/krt1q1b3HvvvdGpU6dW3deNSZcP01/Lta95rTS/tvql5esq35T3eGvbkHrXSq0wKUA98sgjxUlzfcdQ+rfS8d7aJ9WPU+da6RhOYT/Vpz3s6/RHTQrKqbV4fdrSvt4Qa3tfp64H6e7K9Bp+3OOn0OTeUu1Ibifyq666qm5ZuiursU7kzzzzTF2ZBx98sM11It/Q7Usdq1e/I2tt/vmf/7m05ZZbllpbc+2Tp556qniedLdO/U7k9e/iuOmmm4pO5MuWLSuVa73TMf3pT3+62NdLlixp0/s6dQw944wzGnQM3X777dfZifwf/uEfGiwbNmzYGp3I1/Uebwty651cfvnlxbE5efLkJv0bc+bMKY6V//iP/yiVa51X7zi/2267lc4555yK39e157VUl/nz55fdvt6QTuTpjuj60t3Gq3ci/zjHT7EtTS7ZDvz3f/93cVtv7S356fc01b81P73h0q2U9W97Tbc+pgPt+eefL+5kaGwYg0GDBpWmTJlSnHTTbeBtbRiDdW1furU51Tutr+8vf/lL8SZLd3KtLt32fvPNNxe3g6dy//Zv/1bcVpqGiijHOqdb+H/84x8X4WPWrFnF/u7Xr1/ps5/97BrDGBx66KGl5557rrhltlevXm1uGIOceqcTSLorba+99ipeg/q3Oaf6trV9nW5NTieJ22+/vQiMp556avH+rL0z8lvf+lZp9OjRDYYx6NixY3HSTLfzjx07ttFhDNb3Hm9tufVOdUp3Ut5zzz0N9mntZ136+b3vfa8IV+l4f+SRR0r77rtvcby0hT8GNqTO6XM9/cHw2muvlaZNm1YaNWpUqUuXLsUt7JW8r2t95jOfKe5EW1057OvFixfXnY9TgErDDaXf0zk7SfVN9V59GIPvf//7xfs6DdnR2DAG63odm0KAqifdypl2zurT448/vsZ4N7XSXy0XXnhhadttty12xiGHHFJ65ZVX1hiHI52kUihLf/GddNJJDUJZa1vf9qU31eqvQ5KCQd++fYvkvroUqtLQBuk5N9tss2LsoRtvvLHRsuVQ59mzZxdhqWfPnsV+TuMnpTdn/XGgkjfeeKN02GGHlbp27VqMAXXeeec1uN2/3Oqdfjb2nkhTKtsW93Ua82XHHXcsAkL6KzONeVUrtaKl9/nqwzJ88pOfLMqnW59/97vfNVjflPd4W5BT75122qnRfZoCZLJ06dLiD4H0B0AKlKl8Gicn5+TS1ur8T//0T3Vl0748/PDDS9OnT6/4fZ28/PLLxf596KGH1niuctjXj6/lc6i2nulnqvfqj0mfS+k1Sn/s1j9vN+V1bIqq9L8NusgIANBOGQcKACCTAAUAkEmAAgDIJEABAGQSoAAAMglQAACZBCgAgEwCFABAJgEKoInSlzZ/5Stf8XoBAhRQOeGmqqqqmDp16hS77LJL/OAHP4hly5a19qYBFahja28AQHMZOXJk3HbbbbFixYqYNm1anHDCCUWguvzyy73IQLNyCQ+oGJ07d47evXtH3759i0ttw4cPj4cffrhYV1NTE+PGjStaprp27Rr77LNP3HPPPXWPXbVqVZx88sl163fbbbf413/911asDdCWaYECKtKMGTPiD3/4Q+y0007FfApPv/zlL+PGG2+MXXfdNZ588sn45je/Gb169YqDDjqoCFg77LBD3H333bHVVlsVjz311FNju+22i2984xutXR2gjRGggIpx//33x+abbx4rV66M5cuXR3V1dVx//fXF7z/5yU/ikUceiWHDhhVl+/XrF0899VTcdNNNRYBK/aYuueSSuudKLVGTJ0+Ou+66S4AC1iBAARXjc5/7XNxwww2xZMmSuOaaa6Jjx45x1FFHxcyZM2Pp0qXxhS98oUH5Dz/8MAYNGlQ3P378+Lj11ltj9uzZ8cEHHxTrBw4c2Ao1Ado6AQqoGJtttln079+/+D0FodTP6ZZbbok999yzWPa73/0utt9++zX6TSUTJkyI733ve/HTn/60aKXq1q1bXHnllTFlypRWqAnQ1glQQEVKl+8uuOCCOPfcc+PPf/5zEZRSy1K6XNeYSZMmxQEHHBDf/e5365a99tprG3GLgXLiLjygYn3961+PDh06FP2cUuvSOeecE7/4xS+KYDR9+vS47rrrivkkdSx/5pln4sEHHywC14UXXhhPP/10a1cBaKO0QAEVK/WBOuOMM+KKK66IWbNmFXfcpbvxXn/99dhiiy1i3333LVqpktNOOy2effbZOProo4uxo4455piiNer3v/99a1cDaIOqSqVSqbU3AgCgnLiEBwCQSYACAMgkQAEAZBKgAAAyCVAAAJkEKACATAIUAEAmAQoAIJMABQCQSYACAMgkQAEARJ7/H1jKpNzSXQZXAAAAAElFTkSuQmCC", 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SpNzcXF3gQ33VOn78uMrKyhQbGxvQHhsbq7y8vCrXycvLs+ovSRkZGfJ4PP5XQkLChRcvnb2JzlJHnajUdqzwVBCKAQCgDupwLGvUn3MB6hSgfvazn2n9+rPXKCdNmqT7779f1113nUaPHq2f//znQS2woc2ePVter9f/OnToUHA23Cb2/H1+4JhiKrV1jI4KQjEAANRBHY5ljfpzLkCdnsJ79tlnVV5+9hTdPffco4svvlibN2/WjTfeqDvvvDOoBVZo3769IiIi/BN3VsjPz1dcXFyV68TFxVn1lySXyyWXy3XhBf9Ql6Fnn1DwHdXZC3LVq7gHKru8t7+t4h6oxG7tgl8bAAC1cd5jWZDugeoyNJhV14s6nYFyOp2KjPxX9hozZoyefPJJTZ8+XS1btgxacedq2bKlBg8erA0bNvjbysvLtWHDBiUnJ1e5TnJyckB/SXr33Xer7V+vnBFnH++UdDYOVa38n7+P887cEXADuSSl39CHG8gBAKFT47Hsn++Tp/3zvwOXm3++/7+loyT963hXodxIDjnkSFsQFvNB1XkeqFOnTumzzz7TsWPH/GejKtx4Y/3M4TBjxgxNmDBBQ4YMUWJiop544gkVFRVp0qRJkqTx48erc+fOysjIkCTdd999SklJ0R//+EeNGjVKa9as0SeffKJnn322Xuo7rz43Sr/6fzXOA1XS+uw8UG+XDPS3xTEPFACgsajuWOaOl9IWnF1+yVWVljvc8dpxxUwt39JJOad6nJ0H6pypDEpah9c8UHWaxiAzM1Pjx4/X8ePHK2/Q4VBZWVlQiqvK0qVL/RNpDhw4UE8++aSSkpIkSddee626du2q1atX+/uvW7dOv//97/0TaS5atMhqIs16eQySmcgBAOGukc9EXt/TGNQpQPXs2VPDhw/X3LlzKz3l1tTwb+EBABB+GuU8UPn5+ZoxY0aTD08AAABVqVOA+sUvfqGNGzcGuRQAAIDwUKdLeMXFxfrlL3+pDh06qF+/fmrRokXA8nvvvTdoBYYal/AAAAg/9X38rtNTeC+99JLeeecdRUVFaePGjXI4/nWDs8PhaFIBCgAA4IfqFKB+97vfad68eZo1a5aczjpdBQQAAAhbdUo/p0+f1ujRowlPAACgWapTApowYYLWrl0b7FoAAADCQp0u4ZWVlWnRokV6++231b9//0o3kT/22GNBKQ4AAKAxqlOA2rlzpwYNGiRJ2rVrV1ALAgAAaOzqFKA++OCDYNcBAAAQNqwC1C233HLePg6HQ//93/9d54IAAAAaO6sA5fF46qsOAACAsGEVoFatWlVfdQAAAIQNJnICAACwRIACAACwRIACAACwRIACAACwRIACAACwRIACAACwRIACAACwRIACAACwRIACAACwRIACAACwRIACAACwRIACAACwRIACAACwRIACAACwRIACAACwRIACAACwRIACAACwRIACAACwRIACAACwRIACAACwRIACAACwRIACAACwRIACAACwRIACAACwRIACAACwRIACAACwRIACAACwRIACAACwRIACAACwRIACAACwRIACAACwRIACAACwRIACAACwRIACAACwRIACAACwRIACAACwRIACAACwRIACAACwRIACAACwRIACAACwRIACAACwFDYBqqCgQOPGjZPb7VZMTIwmT56skydP1rjOs88+q2uvvVZut1sOh0MnTpxomGIBAECTFjYBaty4cdq9e7feffddvfHGG/rwww81derUGtcpLi5WWlqa5syZ00BVAgCA5sBhjDGhLuJ89u7dqz59+mjr1q0aMmSIJCkzM1MjR47Ut99+q/j4+BrX37hxo37605/qH//4h2JiYmrsW1JSopKSEv97n8+nhIQEeb1eud3uCx4LAACofz6fTx6Pp96O32FxBiorK0sxMTH+8CRJqampcjqd2rJlS1A/KyMjQx6Px/9KSEgI6vYBAED4C4sAlZeXp44dOwa0RUZGql27dsrLywvqZ82ePVter9f/OnToUFC3DwAAwl9IA9SsWbPkcDhqfO3bt69Ba3K5XHK73QEvAACAc0WG8sMfeOABTZw4scY+3bt3V1xcnI4dOxbQXlpaqoKCAsXFxdVjhQAAAJWFNEB16NBBHTp0OG+/5ORknThxQtu2bdPgwYMlSe+//77Ky8uVlJRU32UCAAAECIt7oC6//HKlpaVpypQpys7O1qZNmzRt2jSNGTPG/wTe4cOH1bt3b2VnZ/vXy8vL044dO7R//35J0s6dO7Vjxw4VFBSEZBwAAKBpCIsAJUkvvviievfurWHDhmnkyJH6yU9+omeffda//MyZM/r8889VXFzsb1u+fLkGDRqkKVOmSJKuueYaDRo0SOvXr2/w+gEAQNMRFvNAhVJ9zyMBAACCj3mgAAAAGhkCFAAAgCUCFAAAgCUCFAAAgCUCFAAAgCUCFAAAgCUCFAAAgCUCFAAAgCUCFAAAgCUCFAAAgCUCFAAAgCUCFAAAgCUCFAAAgCUCFAAAgCUCFAAAgCUCFAAAgCUCFAAAgCUCFAAAgCUCFAAAgCUCFAAAgCUCFAAAgCUCFAAAgCUCFAAAgCUCFAAAgCUCFAAAgCUCFAAAgCUCFAAAgCUCFAAAgCUCFAAAgCUCFAAAgCUCFAAAgCUCFAAAgCUCFAAAgCUCFAAAgCUCFAAAgCUCFAAAgCUCFAAAgCUCFAAAgCUCFAAAgCUCFAAAgCUCFAAAgCUCFAAAgCUCFAAAgCUCFAAAgCUCFAAAgCUCFAAAgCUCFAAAgCUCFAAAgCUCFAAAgCUCFAAAgCUCFAAAgCUCFAAAgCUCFAAAgKWwCVAFBQUaN26c3G63YmJiNHnyZJ08ebLG/tOnT1evXr3UqlUrXXrppbr33nvl9XobsGoAANAUhU2AGjdunHbv3q13331Xb7zxhj788ENNnTq12v5HjhzRkSNHtGTJEu3atUurV69WZmamJk+e3IBVAwCApshhjDGhLuJ89u7dqz59+mjr1q0aMmSIJCkzM1MjR47Ut99+q/j4+FptZ926dbr99ttVVFSkyMjIKvuUlJSopKTE/97n8ykhIUFer1dut/vCBwMAAOqdz+eTx+Opt+N3WJyBysrKUkxMjD88SVJqaqqcTqe2bNlS6+1U/BCrC0+SlJGRIY/H438lJCRcUO0AAKDpCYsAlZeXp44dOwa0RUZGql27dsrLy6vVNo4fP6758+fXeNlPkmbPni2v1+t/HTp0qM51AwCApimkAWrWrFlyOBw1vvbt23fBn+Pz+TRq1Cj16dNHjzzySI19XS6X3G53wAsAAOBc1V/LagAPPPCAJk6cWGOf7t27Ky4uTseOHQtoLy0tVUFBgeLi4mpcv7CwUGlpaYqOjtZrr72mFi1aXGjZAACgmQtpgOrQoYM6dOhw3n7Jyck6ceKEtm3bpsGDB0uS3n//fZWXlyspKana9Xw+n0aMGCGXy6X169crKioqaLUDAIDmKyzugbr88suVlpamKVOmKDs7W5s2bdK0adM0ZswY/xN4hw8fVu/evZWdnS3pbHgaPny4ioqKtGLFCvl8PuXl5SkvL09lZWWhHA4AAAhzIT0DZePFF1/UtGnTNGzYMDmdTt1666168skn/cvPnDmjzz//XMXFxZKk7du3+5/Q69GjR8C2cnNz1bVr1warHQAANC1hMQ9UKNX3PBIAACD4mAcKAACgkSFAAQAAWCJAAQAAWCJAAQAAWCJAAQAAWCJAAQAAWCJAAQAAWCJAAQAAWCJAAQAAWCJAAQAAWCJAAQAAWCJAAQAAWCJAAQAAWCJAAQAAWCJAAQAAWCJAAQAAWCJAAQAAWCJAAQAAWCJAAQAAWCJAAQAAWCJAAQAAWCJAAQAAWCJAAQAAWCJAAQAAWCJAAQAAWCJAAQAAWCJAAQAAWIoMdQEAAKDhlJUbZecW6FjhKXWMjlJit3aKcDpCXVbYIUABANBMZO46qnl/3aOj3lP+tk6eKKXf0EdpfTuFsLLwwyU8AACagcxdR3XXC9sDwpMk5XlP6a4Xtitz19EQVRaeCFAAADRxZeVG8/66R6aKZRVt8/66R2XlVfVAVQhQAAA0cdm5BZXOPJ3LSDrqPaXs3IKGKyrMEaAAAGjijhVWH57q0g8EKAAAmryO0VFB7QcCFAAATV5it3bq5IlSdZMVOHT2abzEbu0asqywRoACAKCJi3A6lH5DH0mqFKIq3qff0If5oCwQoAAAaAbS+nbS07dfqThP4GW6OE+Unr79SuaBssREmgAANBNpfTvpuj5xzEQeBAQoAACakQinQ8mXXRzqMsIel/AAAAAsEaAAAAAsEaAAAAAsEaAAAAAsEaAAAAAsEaAAAAAsEaAAAAAsEaAAAAAsEaAAAAAsMRP5eRhjJEk+ny/ElQAAgNqqOG5XHMeDjQB1HoWFhZKkhISEEFcCAABsFRYWyuPxBH27DlNf0ayJKC8v15EjRxQdHa3CwkIlJCTo0KFDcrvdoS6tQfh8vmY3Zql5jrs5jllqnuNujmOWmue4m/OYDx48KIfDofj4eDmdwb9jiTNQ5+F0OnXJJZdIkhyOs/9atdvtbja/iBWa45il5jnu5jhmqXmOuzmOWWqe426OY/Z4PPU6Zm4iBwAAsESAAgAAsESAsuByuZSeni6XyxXqUhpMcxyz1DzH3RzHLDXPcTfHMUvNc9yMuf5wEzkAAIAlzkABAABYIkABAABYIkABAABYIkABAABYIkCd4z//8z81dOhQtW7dWjExMbVaxxijuXPnqlOnTmrVqpVSU1P15ZdfBvQpKCjQuHHj5Ha7FRMTo8mTJ+vkyZP1MIK6sa3v66+/lsPhqPK1bt06f7+qlq9Zs6YhhnReddkn1157baXx/PrXvw7oc/DgQY0aNUqtW7dWx44d9dBDD6m0tLQ+h2LFdtwFBQWaPn26evXqpVatWunSSy/VvffeK6/XG9CvMe3rZcuWqWvXroqKilJSUpKys7Nr7L9u3Tr17t1bUVFR6tevn958882A5bX5jjcGNuN+7rnndPXVV6tt27Zq27atUlNTK/WfOHFipX2alpZW38OwYjPm1atXVxpPVFRUQJ+muK+r+rvlcDg0atQof5/Gvq8//PBD3XDDDYqPj5fD4dBf/vKX866zceNGXXnllXK5XOrRo4dWr15dqY/t34pKDPzmzp1rHnvsMTNjxgzj8Xhqtc6CBQuMx+Mxf/nLX8ynn35qbrzxRtOtWzfz/fff+/ukpaWZAQMGmI8//tj87//+r+nRo4cZO3ZsPY3Cnm19paWl5ujRowGvefPmmTZt2pjCwkJ/P0lm1apVAf3O/bmEUl32SUpKipkyZUrAeLxer395aWmp6du3r0lNTTU5OTnmzTffNO3btzezZ8+u7+HUmu24d+7caW655Razfv16s3//frNhwwbTs2dPc+uttwb0ayz7es2aNaZly5Zm5cqVZvfu3WbKlCkmJibG5OfnV9l/06ZNJiIiwixatMjs2bPH/P73vzctWrQwO3fu9PepzXc81GzHfdttt5lly5aZnJwcs3fvXjNx4kTj8XjMt99+6+8zYcIEk5aWFrBPCwoKGmpI52U75lWrVhm32x0wnry8vIA+TXFff/fddwFj3rVrl4mIiDCrVq3y92ns+/rNN980v/vd78yrr75qJJnXXnutxv5fffWVad26tZkxY4bZs2ePeeqpp0xERITJzMz097H9OVaFAFWFVatW1SpAlZeXm7i4OLN48WJ/24kTJ4zL5TIvvfSSMcaYPXv2GElm69at/j5vvfWWcTgc5vDhw0Gv3Vaw6hs4cKD593//94C22vyih0Jdx5ySkmLuu+++ape/+eabxul0BvxRfvrpp43b7TYlJSVBqf1CBGtfv/zyy6Zly5bmzJkz/rbGsq8TExPNPffc439fVlZm4uPjTUZGRpX9f/WrX5lRo0YFtCUlJZk777zTGFO773hjYDvuHyotLTXR0dHm+eef97dNmDDB3HTTTcEuNWhsx3y+v+vNZV8//vjjJjo62pw8edLf1tj39blq87fmt7/9rbniiisC2kaPHm1GjBjhf3+hP0djjOES3gXIzc1VXl6eUlNT/W0ej0dJSUnKysqSJGVlZSkmJkZDhgzx90lNTZXT6dSWLVsavOYfCkZ927Zt044dOzR58uRKy+655x61b99eiYmJWrlypUwjmHbsQsb84osvqn379urbt69mz56t4uLigO3269dPsbGx/rYRI0bI5/Np9+7dwR+IpWD9Lnq9XrndbkVGBv5TmqHe16dPn9a2bdsCvo9Op1Opqan+7+MPZWVlBfSXzu6ziv61+Y6HWl3G/UPFxcU6c+aM2rVrF9C+ceNGdezYUb169dJdd92l7777Lqi111Vdx3zy5El16dJFCQkJuummmwK+l81lX69YsUJjxozRRRddFNDeWPd1XZzvex2Mn6PEPyZ8QfLy8iQp4IBZ8b5iWV5enjp27BiwPDIyUu3atfP3CaVg1LdixQpdfvnlGjp0aED7o48+qp/97Gdq3bq13nnnHd199906efKk7r333qDVXxd1HfNtt92mLl26KD4+Xp999plmzpypzz//XK+++qp/u1X9LlQsC7Vg7Ovjx49r/vz5mjp1akB7Y9jXx48fV1lZWZX7YN++fVWuU90+O/f7W9FWXZ9Qq8u4f2jmzJmKj48POKCkpaXplltuUbdu3XTgwAHNmTNH119/vbKyshQRERHUMdiqy5h79eqllStXqn///vJ6vVqyZImGDh2q3bt365JLLmkW+zo7O1u7du3SihUrAtob876ui+q+1z6fT99//73+8Y9/XPB3RmoGAWrWrFlauHBhjX327t2r3r17N1BFDaO2475Q33//vf785z/r4YcfrrTs3LZBgwapqKhIixcvrreDan2P+dzQ0K9fP3Xq1EnDhg3TgQMHdNlll9V5uxeqofa1z+fTqFGj1KdPHz3yyCMByxp6XyN4FixYoDVr1mjjxo0BN1WPGTPG/9/9+vVT//79ddlll2njxo0aNmxYKEq9IMnJyUpOTva/Hzp0qC6//HI988wzmj9/fggrazgrVqxQv379lJiYGNDe1PZ1Q2nyAeqBBx7QxIkTa+zTvXv3Om07Li5OkpSfn69OnTr52/Pz8zVw4EB/n2PHjgWsV1paqoKCAv/69aG2477Q+l555RUVFxdr/Pjx5+2blJSk+fPnq6SkpF7+jaKGGnOFpKQkSdL+/ft12WWXKS4urtJTHPn5+ZIU9vu6sLBQaWlpio6O1muvvaYWLVrU2L++93VV2rdvr4iICP/PvEJ+fn6144uLi6uxf22+46FWl3FXWLJkiRYsWKD33ntP/fv3r7Fv9+7d1b59e+3fvz/kB9ULGXOFFi1aaNCgQdq/f7+kpr+vi4qKtGbNGj366KPn/ZzGtK/rorrvtdvtVqtWrRQREXHBvz+SeAqvKrY3kS9ZssTf5vV6q7yJ/JNPPvH3efvttxvdTeR1rS8lJaXSE1nV+Y//+A/Ttm3bOtcaLMHaJx999JGRZD799FNjzL9uIj/3KY5nnnnGuN1uc+rUqeANoI7qOm6v12t+/OMfm5SUFFNUVFSrzwrVvk5MTDTTpk3zvy8rKzOdO3eu8Sbyf/u3fwtoS05OrnQTeU3f8cbAdtzGGLNw4ULjdrtNVlZWrT7j0KFDxuFwmNdff/2C6w2Guoz5XKWlpaZXr17m/vvvN8Y07X1tzNnjmsvlMsePHz/vZzS2fX0u1fIm8r59+wa0jR07ttJN5Bfy+2MMT+EF+Oabb0xOTo7/kfycnByTk5MT8Gh+r169zKuvvup/v2DBAhMTE2Nef/1189lnn5mbbrqpymkMBg0aZLZs2WI++ugj07Nnz0Y3jUFN9X377bemV69eZsuWLQHrffnll8bhcJi33nqr0jbXr19vnnvuObNz507z5Zdfmv/6r/8yrVu3NnPnzq338dSG7Zj3799vHn30UfPJJ5+Y3Nxc8/rrr5vu3buba665xr9OxTQGw4cPNzt27DCZmZmmQ4cOjW4aA5txe71ek5SUZPr162f2798f8JhzaWmpMaZx7es1a9YYl8tlVq9ebfbs2WOmTp1qYmJi/E9G3nHHHWbWrFn+/ps2bTKRkZFmyZIlZu/evSY9Pb3KaQzO9x0PNdtxL1iwwLRs2dK88sorAfu04m9dYWGhefDBB01WVpbJzc017733nrnyyitNz549G8X/DBhjP+Z58+aZt99+2xw4cMBs27bNjBkzxkRFRZndu3f7+zTFfV3hJz/5iRk9enSl9nDY14WFhf7jsSTz2GOPmZycHPPNN98YY4yZNWuWueOOO/z9K6YxeOihh8zevXvNsmXLqpzGoKafY20QoM4xYcIEI6nS64MPPvD30T/nu6lQXl5uHn74YRMbG2tcLpcZNmyY+fzzzwO2+91335mxY8eaNm3aGLfbbSZNmhQQykLtfPXl5uZW+jkYY8zs2bNNQkKCKSsrq7TNt956ywwcONC0adPGXHTRRWbAgAFm+fLlVfYNBdsxHzx40FxzzTWmXbt2xuVymR49epiHHnooYB4oY4z5+uuvzfXXX29atWpl2rdvbx544IGAx/1DzXbcH3zwQZXfCUkmNzfXGNP49vVTTz1lLr30UtOyZUuTmJhoPv74Y/+ylJQUM2HChID+L7/8svnRj35kWrZsaa644grzt7/9LWB5bb7jjYHNuLt06VLlPk1PTzfGGFNcXGyGDx9uOnToYFq0aGG6dOlipkyZYnVwaQg2Y/7Nb37j7xsbG2tGjhxptm/fHrC9privjTFm3759RpJ55513Km0rHPZ1dX+HKsY5YcIEk5KSUmmdgQMHmpYtW5ru3bsHHLcr1PRzrA2HMY3guXIAAIAwwjxQAAAAlghQAAAAlghQAAAAlghQAAAAlghQAAAAlghQAAAAlghQAAAAlghQAAAAlghQAFBLEydO1M033xzqMgA0AgQoAE3CxIkT5XA45HA41KJFC3Xr1k2//e1vderUqVCXBqAJigx1AQAQLGlpaVq1apXOnDmjbdu2acKECXI4HFq4cGGoSwPQxHAGCkCT4XK5FBcXp4SEBN18881KTU3Vu+++K0kqLy9XRkaGunXrplatWmnAgAF65ZVX/OuWlZVp8uTJ/uW9evXSn/70p1ANBUAjxxkoAE3Srl27tHnzZnXp0kWSlJGRoRdeeEHLly9Xz5499eGHH+r2229Xhw4dlJKSovLycl1yySVat26dLr74Ym3evFlTp05Vp06d9Ktf/SrEowHQ2BCgADQZb7zxhtq0aaPS0lKVlJTI6XRq6dKlKikp0R/+8Ae99957Sk5OliR1795dH330kZ555hmlpKSoRYsWmjdvnn9b3bp1U1ZWll5++WUCFIBKCFAAmoyf/vSnevrpp1VUVKTHH39ckZGRuvXWW7V7924VFxfruuuuC+h/+vRpDRo0yP9+2bJlWrlypQ4ePKjvv/9ep0+f1sCBAxt4FADCAQEKQJNx0UUXqUePHpKklStXasCAAVqxYoX69u0rSfrb3/6mzp07B6zjcrkkSWvWrNGDDz6oP/7xj0pOTlZ0dLQWL16sLVu2NOwgAIQFAhSAJsnpdGrOnDmaMWOGvvjiC7lcLh08eFApKSlV9t+0aZOGDh2qu+++29924MCBhioXQJjhKTwATdYvf/lLRURE6JlnntGDDz6o+++/X88//7wOHDig7du366mnntLzzz8vSerZs6c++eQTvf322/riiy/08MMPa+vWrSEeAYDGijNQAJqsyMhITZs2TYsWLVJubq46dOigjIwMffXVV4qJidGVV16pOXPmSJLuvPNO5eTkaPTo0XI4HBo7dqzuvvtuvfXWWyEeBYDGyGGMMaEuAgAAIJxwCQ8AAMASAQoAAMASAQoAAMASAQoAAMASAQoAAMASAQoAAMASAQoAAMASAQoAAMASAQoAAMASAQoAAMASAQoAAMDS/wdYyqTcM2jpmAAAAABJRU5ErkJggg==", "text/plain": [ "
" ] @@ -294,22 +296,30 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "Fitting DMDs: 100%|██████████| 2/2 [00:00<00:00, 375.01it/s]\n", - "Computing DMD similarities: 100%|██████████| 1/1 [00:02<00:00, 2.16s/it]" + "Fitting DMDs: 100%|██████████| 2/2 [00:00<00:00, 934.14it/s]\n", + "Caching compare objects X: 100%|██████████| 2/2 [00:00<00:00, 32513.98it/s]\n", + "Computing DMD similarities: 0%| | 0/1 [00:00 2\u001b[0m observables \u001b[38;5;241m=\u001b[39m \u001b[43mpk\u001b[49m\u001b[38;5;241m.\u001b[39mobservables\u001b[38;5;241m.\u001b[39mTimeDelay(n_delays\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m10\u001b[39m)\n\u001b[1;32m 3\u001b[0m regressor \u001b[38;5;241m=\u001b[39m SubspaceDMD(svd_rank\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 5\u001b[0m \u001b[38;5;66;03m# Create configuration\u001b[39;00m\n", - "\u001b[0;31mNameError\u001b[0m: name 'pk' is not defined" + "name": "stderr", + "output_type": "stream", + "text": [ + "Caching compare objects X: 100%|██████████| 1/1 [00:00<00:00, 1871.62it/s]\n", + "Caching compare objects Y: 100%|██████████| 1/1 [00:00<00:00, 3398.95it/s]\n", + "Computing DMD similarities: 100%|██████████| 1/1 [00:00<00:00, 610.26it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Similarity with PyKoopman: 0.4217\n", + "(8, 100, 5)\n", + "(10, 10)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" ] } ], "source": [ "# Use TimeDelay observables with SubspaceDMD regressor from pydmd\n", "observables = pk.observables.TimeDelay(n_delays=10)\n", - "regressor = SubspaceDMD(svd_rank=-1)\n", + "regressor = SubspaceDMD(svd_rank=10)\n", "\n", "# Create configuration\n", "from dataclasses import dataclass\n", @@ -473,7 +495,7 @@ " data1, data2,\n", " dmd_class=pk.Koopman,\n", " similarity_class=SimilarityTransformDist,\n", - " dmd_config=CustomPyKoopmanConfig(),\n", + " dmd_config=CustomPyKoopmanConfig,\n", " simdist_config={'score_method': 'wasserstein'},\n", " verbose=True\n", ")\n", @@ -508,7 +530,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -519,17 +541,13 @@ "Control shape: (8, 100, 2)\n" ] }, - { - "name": "stderr", - "output_type": "stream", - "text": [] - }, { "name": "stderr", "output_type": "stream", "text": [ - "Fitting DMDs: 100%|██████████| 2/2 [00:00<00:00, 481.44it/s]\n", - "Computing DMD similarities: 100%|██████████| 1/1 [00:00<00:00, 654.85it/s]" + "Fitting DMDs: 100%|██████████| 2/2 [00:00<00:00, 577.01it/s]\n", + "Caching compare objects X: 100%|██████████| 2/2 [00:00<00:00, 36792.14it/s]\n", + "Computing DMD similarities: 100%|██████████| 1/1 [00:00<00:00, 2384.48it/s]" ] }, { @@ -537,7 +555,7 @@ "output_type": "stream", "text": [ "\n", - "InputDSA similarity: 2.9736\n" + "InputDSA similarity: 2.6703\n" ] }, { @@ -588,15 +606,16 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "Fitting DMDs: 100%|██████████| 2/2 [00:00<00:00, 9.19it/s]\n", - "Computing DMD similarities: 100%|██████████| 1/1 [00:00<00:00, 203.05it/s]" + "Fitting DMDs: 100%|██████████| 2/2 [00:00<00:00, 138.12it/s]\n", + "Caching compare objects X: 100%|██████████| 2/2 [00:00<00:00, 24818.37it/s]\n", + "Computing DMD similarities: 100%|██████████| 1/1 [00:00<00:00, 2744.96it/s]" ] }, { @@ -604,7 +623,7 @@ "output_type": "stream", "text": [ "\n", - "SubspaceDMDc similarity: 0.5177\n" + "SubspaceDMDc similarity: 0.7325\n" ] }, { @@ -624,7 +643,6 @@ " dmd_config=SubspaceDMDcConfig(\n", " n_delays=3,\n", " rank=8,\n", - " backend='n4sid' # or 'custom'\n", " ),\n", " simdist_config=ControllabilitySimilarityTransformDistConfig(\n", " score_method='euclidean',\n", @@ -654,14 +672,21 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 14, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "control similarity: 0.0079\n" + ] + }, { "name": "stderr", "output_type": "stream", "text": [ - "/Users/mitchellostrow/Desktop/Projects/DSAv2/DSAPublic2/DSA/DSA/dsa.py:408: UserWarning: Warning: You are using a DMD model that fits a control operator but comparing with a DSA metric that does not compare control operators\n", + "/orcd/data/fiete/001/om2/ostrow/dmrsa/DSApublic/DSA/DSA/dsa.py:412: UserWarning: Warning: You are using a DMD model that fits a control operator but comparing with a DSA metric that does not compare control operators\n", " warnings.warn(\n" ] }, @@ -669,14 +694,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "control similarity: 0.0131\n", - "state similarity: 0.3775\n", - "joint similarity: 1.8671\n" + "state similarity: 0.5910\n", + "joint similarity: 2.2566\n" ] }, { 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", 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", 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" ] @@ -737,7 +761,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -747,9 +771,9 @@ "Components shape: (2, 2, 3)\n", "\n", "Distance components between systems 0 and 1:\n", - " Controllability distance: 3.0558\n", - " State similarity: 0.9661\n", - " Control similarity: 0.0629\n" + " Controllability distance: 3.8842\n", + " State similarity: 2.2045\n", + " Control similarity: 0.0599\n" ] } ], @@ -793,20 +817,23 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", - "text": [] + "text": [ + "Fitting DMDs: 100%|██████████| 2/2 [00:00<00:00, 489.16it/s]\n", + "Caching compare objects X: 100%|██████████| 2/2 [00:00<00:00, 31536.12it/s]\n", + "Computing DMD similarities: 0%| | 0/1 [00:00" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -915,22 +935,17 @@ "n_delays_range = [1, 2, 3, 5, 7]\n", "ranks_range = [3, 5, 8, 10, 12, 15]\n", "\n", - "# Run sweep\n", - "print(\"\\nRunning hyperparameter sweep...\")\n", - "all_aics, all_mases, all_nnormals, all_residuals, all_l2norms = sweep_ranks_delays(\n", + "\n", + "\n", + "sweeper = DefaultSweeper(\n", " test_data,\n", - " n_delays=n_delays_range,\n", - " ranks=ranks_range,\n", - " train_frac=0.7, # Use 70% for training, 30% for testing\n", - " reseed=5,\n", - " return_residuals=True,\n", - " return_transient_growth=True,\n", - " device='cpu'\n", + " param1_name = \"n_delays\",\n", + " param1_values=n_delays_range,\n", + " param2_name='rank',\n", + " param2_values=ranks_range,\n", + " compute_residuals=True\n", ")\n", - "\n", - "print(f\"\\nSweep completed!\")\n", - "print(f\"AIC shape: {all_aics.shape}\")\n", - "print(f\"MASE shape: {all_mases.shape}\")\n" + "sweeper.sweep()\n" ] }, { @@ -942,27 +957,28 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(
,\n", - " array([,\n", - " ,\n", - " ], dtype=object))" + "(
,\n", + " array([,\n", + " ,\n", + " ,\n", + " ], dtype=object))" ] }, - "execution_count": 23, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", + "image/png": 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" ] }, "metadata": {}, @@ -970,8 +986,7 @@ } ], "source": [ - "#our function for visualizing is as follows:\n", - "plot_sweep_results(all_aics, all_mases, all_nnormals, n_delays=n_delays_range, ranks=ranks_range, cmap='viridis', name='DMD')" + "sweeper.plot()" ] }, { @@ -983,48 +998,25 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 22, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Running hyperparameter sweep...\n" - ] - }, { "name": "stderr", "output_type": "stream", "text": [ - "/Users/mitchellostrow/Desktop/Projects/DSAv2/DSAPublic2/DSA/DSA/sweeps.py:168: UserWarning: Residuals not implemented for SubspaceDMDc\n", - " warnings.warn(f\"Residuals not implemented for {model_class}\")\n", - "100%|██████████| 5/5 [00:10<00:00, 2.14s/it]\n" + "Sweeping: 100%|██████████| 5/5 [00:10<00:00, 2.20s/it]\n" ] }, { "data": { "text/plain": [ - "(
,\n", - " array([,\n", - " ,\n", - " ], dtype=object))" + "" ] }, - "execution_count": 24, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ @@ -1036,20 +1028,52 @@ "ranks_range = [3, 5, 8, 10, 12, 15, 20, 30, 40, 50]\n", "\n", "# Run sweep\n", - "print(\"\\nRunning hyperparameter sweep...\")\n", - "all_aics, all_mases, all_nnormals = sweep_ranks_delays(\n", + "\n", + "sweeper = DefaultSweeper(\n", " data1,\n", + " param1_name = \"n_delays\",\n", + " param1_values=n_delays_range,\n", + " param2_name='rank',\n", + " param2_values=ranks_range,\n", " control_data=control1,\n", - " n_delays=n_delays_range,\n", - " ranks=ranks_range,\n", - " model_class='SubspaceDMDc', # can be 'DMDc' or 'SubspaceDMDc' for control systems\n", - " train_frac=0.7, \n", - " reseed=5,\n", - " return_residuals=False,\n", - " device='cpu'\n", + " compute_residuals=True,\n", + " model_class='SubspaceDMDc'\n", ")\n", - "\n", - "plot_sweep_results(all_aics, all_mases, all_nnormals, n_delays=n_delays_range, ranks=ranks_range, cmap='viridis', name='SubspaceDMDc')" + "sweeper.sweep()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(
,\n", + " array([,\n", + " ,\n", + " ,\n", + " ], dtype=object))" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sweeper.plot()" ] }, { @@ -1061,7 +1085,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -1070,14 +1094,14 @@ "text": [ "DMD Matrix Statistics:\n", "==================================================\n", - "MAE : 0.0079\n", - "MASE : 0.1737\n", - "NMSE : 0.0400\n", + "MAE : 0.0078\n", + "MASE : 0.2682\n", + "NMSE : 0.0728\n", "MSE : 0.0001\n", - "R2 : 0.9479\n", - "Correl : 0.9736\n", - "AIC : -9.1654\n", - "logMSE : -9.2058\n" + "R2 : 0.8992\n", + "Correl : 0.9481\n", + "AIC : -9.1745\n", + "logMSE : -9.2149\n" ] } ], @@ -1162,7 +1186,7 @@ ], "metadata": { "kernelspec": { - "display_name": "dsa_test_env", + "display_name": "dsapublic-env", "language": "python", "name": "python3" }, @@ -1176,7 +1200,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.18" + "version": "3.10.19" } }, "nbformat": 4, diff --git a/examples/sweep_resdmd_tutorial.ipynb b/examples/sweep_resdmd_tutorial.ipynb index ace547a..e9751fb 100644 --- a/examples/sweep_resdmd_tutorial.ipynb +++ b/examples/sweep_resdmd_tutorial.ipynb @@ -606,7 +606,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 145, "id": "0cfe5a52", "metadata": {}, "outputs": [ @@ -620,16 +620,16 @@ { "data": { "text/plain": [ - "1.601763411203178e-16" + "1.7968946616506403e-16" ] }, - "execution_count": 47, + "execution_count": 145, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] From ea0d14c99f1788b63d15a9c149c6caef0150d01c Mon Sep 17 00:00:00 2001 From: ostrow Date: Fri, 13 Feb 2026 16:23:00 -0500 Subject: [PATCH 86/90] add flag for differentiability --- DSA/simdist.py | 51 +++++++++++++++++++++++++++++++++++--------------- 1 file changed, 36 insertions(+), 15 deletions(-) diff --git a/DSA/simdist.py b/DSA/simdist.py index 2b1f5dd..8513b43 100644 --- a/DSA/simdist.py +++ b/DSA/simdist.py @@ -143,7 +143,9 @@ def __init__( verbose=False, eps=1e-5, rescale_wasserstein=False, - compare: Final = 'state' + compare: Final = 'state', + differentiable=False, + sinkhorn_reg=0.1, ): """ Parameters @@ -166,8 +168,15 @@ def __init__( eps : float early stopping threshold - + compare : str (final). dummy variable for inference of types / config + + differentiable : bool + If True, use ot.sinkhorn2 instead of ot.emd/ot.emd2 for Wasserstein + distance, enabling gradient flow through the score. + + sinkhorn_reg : float + Entropy regularization for Sinkhorn (only used when differentiable=True). """ self.iters = iters @@ -182,6 +191,8 @@ def __init__( self.rescale_wasserstein = rescale_wasserstein self.wasserstein_compare = 'eig' # for backwards compatibility self.compare = compare + self.differentiable = differentiable + self.sinkhorn_reg = sinkhorn_reg def fit( self, @@ -277,14 +288,21 @@ def fit( ) a, b = a.to(device), b.to(device) - self.C_star = ot.emd(a, b, self.M) - self.score_star = ( - ot.emd2(a, b, self.M) #* a.shape[0] - ) # add scaling factor due to random matrix theory - # self.score_star = np.sum(self.C_star * self.M) - self.C_star = self.C_star / torch.linalg.norm( - self.C_star, dim=1, keepdim=True - ) + if self.differentiable: + self.score_star = ot.sinkhorn2( + a, b, self.M, reg=self.sinkhorn_reg + ) + # No transport plan needed for differentiable mode + self.C_star = None + else: + self.C_star = ot.emd(a, b, self.M) + self.score_star = ( + ot.emd2(a, b, self.M) #* a.shape[0] + ) # add scaling factor due to random matrix theory + # self.score_star = np.sum(self.C_star * self.M) + self.C_star = self.C_star / torch.linalg.norm( + self.C_star, dim=1, keepdim=True + ) # wasserstein_distance(A.cpu().numpy(),B.cpu().numpy()) else: @@ -404,14 +422,15 @@ def score(self, A=None, B=None, score_method=None): score : float similarity of the data under the similarity transform w.r.t C """ - assert self.C_star is not None A = self.A if A is None else A B = self.B if B is None else B assert A is not None assert B is not None - assert A.shape == self.C_star.shape or score_method == "wasserstein" - assert B.shape == self.C_star.shape or score_method == "wasserstein" score_method = self.score_method if score_method is None else score_method + if score_method != "wasserstein": + assert self.C_star is not None + assert A.shape == self.C_star.shape + assert B.shape == self.C_star.shape with torch.no_grad(): if not isinstance(A, torch.Tensor): A = torch.from_numpy(A).float().to(self.device) @@ -435,8 +454,10 @@ def score(self, A=None, B=None, score_method=None): elif score_method == "wasserstein": # use the current C_star to compute the score assert hasattr(self, "score_star") - # if wasserstein_compare == self.wasserstein_compare: - score = self.score_star.item() + if self.differentiable: + score = self.score_star # keep as tensor for gradient flow + else: + score = self.score_star.item() # non-eig wasserstein comparisons are deprecated until theoretically validated # else: # #apply the current transport plan to the new data From 147c39de574dba77c1366ca590e9e982d2a951bd Mon Sep 17 00:00:00 2001 From: Mitchell Ostrow <35669245+mitchellostrow@users.noreply.github.com> Date: Sat, 14 Feb 2026 16:53:34 -0500 Subject: [PATCH 87/90] Update DSA/pykoopman/__init__.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- DSA/pykoopman/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/DSA/pykoopman/__init__.py b/DSA/pykoopman/__init__.py index cee3333..375d42e 100644 --- a/DSA/pykoopman/__init__.py +++ b/DSA/pykoopman/__init__.py @@ -4,7 +4,7 @@ try: __version__ = get_distribution(__name__).version -except: +except Exception: # Package distribution metadata is not available (e.g., running from source); # in this case, simply leave __version__ undefined. pass From 6bf0b7afd6f063d2ecdc92c81d4ca6d62aeec372 Mon Sep 17 00:00:00 2001 From: ostrow Date: Sat, 14 Feb 2026 17:10:13 -0500 Subject: [PATCH 88/90] update pyproject, clean and make more pythonic --- DSA/__init__.py | 2 +- DSA/resdmd.py | 101 ++++++++++--------------------------------- pyproject.toml | 6 ++- tests/test_sweeps.py | 1 - 4 files changed, 28 insertions(+), 82 deletions(-) diff --git a/DSA/__init__.py b/DSA/__init__.py index b8ba5c7..dde8892 100644 --- a/DSA/__init__.py +++ b/DSA/__init__.py @@ -10,4 +10,4 @@ from DSA.stats import * from DSA.sweeps import * from DSA.preprocessing import * -from DSA import resdmd +from DSA.resdmd import * diff --git a/DSA/resdmd.py b/DSA/resdmd.py index 89b187d..3d5f20c 100644 --- a/DSA/resdmd.py +++ b/DSA/resdmd.py @@ -13,16 +13,16 @@ import matplotlib.pyplot as plt from matplotlib import cm from matplotlib import colors as mcolors -from typing import Literal, Optional, Tuple, Union +from typing import Literal, Tuple try: from .dmd import DMD, embed_signal_torch - from .dmdc import DMDc, embed_data_DMDc + from .dmdc import embed_data_DMDc from .subspace_dmdc import SubspaceDMDc except ImportError: from dmd import DMD, embed_signal_torch try: - from dmdc import DMDc, embed_data_DMDc + from dmdc import embed_data_DMDc from subspace_dmdc import SubspaceDMDc except ImportError: DMDc = None @@ -57,12 +57,8 @@ def _requires_control(model) -> bool: # PyKoopman with control (check via B property) if hasattr(model, '_pipeline'): - try: - B = model.B - if B is not None: - return True - except (AttributeError, ValueError): - pass + if getattr(model, 'B', None) is not None: + return True return False @@ -180,7 +176,6 @@ def _compute_residuals_from_matrices( Y: np.ndarray, eigenvalues: np.ndarray, eigenvectors: np.ndarray, - return_num_denom: bool = False, ) -> Tuple: """ Core residual computation from projected data matrices and eigendecomposition. @@ -197,8 +192,6 @@ def _compute_residuals_from_matrices( Eigenvalues of the state matrix. Shape (rank,). eigenvectors : np.ndarray Eigenvectors of the state matrix. Shape (rank, rank). - return_num_denom : bool - Whether to return numerators and denominators. Returns ------- @@ -206,10 +199,6 @@ def _compute_residuals_from_matrices( Residuals for each eigenpair. normalized_residuals : np.ndarray Normalized residuals (relative to persistence baseline). - numerators : list (optional) - If return_num_denom=True. - denominators : list (optional) - If return_num_denom=True. """ rank = len(eigenvalues) @@ -221,8 +210,6 @@ def _compute_residuals_from_matrices( residuals = np.zeros(rank, dtype=np.complex128) persistence_residuals = np.zeros(rank, dtype=np.complex128) - numerators = [] - denominators = [] for i in range(rank): g = eigenvectors[:, i] @@ -237,8 +224,6 @@ def _compute_residuals_from_matrices( ), ) residuals[i] = numerator / denominator - numerators.append(np.real(numerator)) - denominators.append(np.real(denominator)) # Persistence baseline (lambda = 1) persistence_numerator = np.dot( @@ -248,10 +233,7 @@ def _compute_residuals_from_matrices( normalized_residuals = np.abs(residuals) / (np.abs(persistence_residuals) + 1e-10) - if return_num_denom: - return residuals, normalized_residuals, numerators, denominators - else: - return residuals, normalized_residuals + return residuals, normalized_residuals def compute_residuals( @@ -260,7 +242,6 @@ def compute_residuals( Y: "np.ndarray | torch.Tensor" = None, control_data: "np.ndarray | torch.Tensor" = None, matrix: Literal["A_v", "A_havok_dmd"] = "A_v", - return_num_denom: bool = False, ): """ Compute DMD eigenvalues, eigenvectors, and residuals for each mode. @@ -280,8 +261,6 @@ def compute_residuals( structure as data (same number of time points and trials). matrix : Literal["A_v", "A_havok_dmd"], optional Matrix to compute residuals on. Must be either "A_v" or "A_havok_dmd". Default is "A_v". - return_num_denom : bool, optional - Whether to return the numerator and denominator of the residual. Default is False. Returns ------- @@ -293,8 +272,6 @@ def compute_residuals( Residuals for each eigenpair. normalized_residuals : np.ndarray Normalized residuals. - numerators, denominators : list (optional) - If return_num_denom=True. Notes ----- @@ -445,23 +422,17 @@ def compute_residuals( eigenvectors = eigenvectors[:, :rank] # Use shared core computation - result = _compute_residuals_from_matrices( - X, Y, eigenvalues, eigenvectors, return_num_denom + residuals, normalized_residuals = _compute_residuals_from_matrices( + X, Y, eigenvalues, eigenvectors ) - if return_num_denom: - residuals, normalized_residuals, numerators, denominators = result - return eigenvalues, eigenvectors, residuals, normalized_residuals, numerators, denominators - else: - residuals, normalized_residuals = result - return eigenvalues, eigenvectors, residuals, normalized_residuals + return eigenvalues, eigenvectors, residuals, normalized_residuals def compute_residuals_pykoopman( model, test_data: np.ndarray, control_data: np.ndarray = None, - return_num_denom: bool = False, ): """ Compute residuals for a fitted PyKoopman model. @@ -475,8 +446,6 @@ def compute_residuals_pykoopman( control_data : np.ndarray, optional Control input data. Required for models with control (DMDc, EDMDc, PyDMD DMDc). Must have the same temporal structure as test_data. - return_num_denom : bool - Whether to return numerators and denominators. Returns ------- @@ -564,23 +533,17 @@ def compute_residuals_pykoopman( eigenvectors = eigenvectors[:, :rank] # Use shared core computation - result = _compute_residuals_from_matrices( - X, Y, eigenvalues, eigenvectors, return_num_denom + residuals, normalized_residuals = _compute_residuals_from_matrices( + X, Y, eigenvalues, eigenvectors ) - if return_num_denom: - residuals, normalized_residuals, numerators, denominators = result - return eigenvalues, eigenvectors, residuals, normalized_residuals, numerators, denominators - else: - residuals, normalized_residuals = result - return eigenvalues, eigenvectors, residuals, normalized_residuals + return eigenvalues, eigenvectors, residuals, normalized_residuals def compute_residuals_subspace_dmdc( model: "SubspaceDMDc", test_data: np.ndarray = None, control_data: np.ndarray = None, - return_num_denom: bool = False, use_training_latents: bool = False, projection_method: str = 'smooth', ): @@ -597,8 +560,6 @@ def compute_residuals_subspace_dmdc( control_data : np.ndarray, optional Control input data. Must have the same temporal structure as test_data. If None and use_training_latents=True, uses training control data. - return_num_denom : bool - Whether to return numerators and denominators. use_training_latents : bool, default=False If True, uses the exact latent states from training (stored in model.info['X_hat']). This should give near-zero residuals for training data since A was fit to these states. @@ -669,16 +630,11 @@ def compute_residuals_subspace_dmdc( eigenvectors = eigenvectors[:, :rank] # Use shared core computation - result = _compute_residuals_from_matrices( - X, Y_corrected, eigenvalues, eigenvectors, return_num_denom + residuals, normalized_residuals = _compute_residuals_from_matrices( + X, Y_corrected, eigenvalues, eigenvectors ) - if return_num_denom: - residuals, normalized_residuals, numerators, denominators = result - return eigenvalues, eigenvectors, residuals, normalized_residuals, numerators, denominators - else: - residuals, normalized_residuals = result - return eigenvalues, eigenvectors, residuals, normalized_residuals + return eigenvalues, eigenvectors, residuals, normalized_residuals # ============================================================================= @@ -768,15 +724,10 @@ def _detect_model_type(self, model) -> str: raise ValueError(f"Cannot detect model type for {type(model)}. " "Please specify model_type explicitly.") - def compute(self, return_num_denom: bool = False): + def compute(self): """ Compute residuals for the model. - Parameters - ---------- - return_num_denom : bool - If True, also return numerators and denominators. - Returns ------- eigenvalues : np.ndarray @@ -792,34 +743,26 @@ def compute(self, return_num_denom: bool = False): result = compute_residuals( self.model, self.test_data, - control_data=self.control_data, - return_num_denom=return_num_denom + control_data=self.control_data ) elif self.model_type == "subspace_dmdc": result = compute_residuals_subspace_dmdc( self.model, self.test_data, - self.control_data, - return_num_denom=return_num_denom + self.control_data ) elif self.model_type == "pykoopman": result = compute_residuals_pykoopman( self.model, self.test_data, - control_data=self.control_data, - return_num_denom=return_num_denom + control_data=self.control_data ) else: raise ValueError(f"Unknown model_type: {self.model_type}") - if return_num_denom: - self.eigenvalues, self.eigenvectors, self.residuals, self.normalized_residuals, nums, denoms = result - self._computed = True - return self.eigenvalues, self.eigenvectors, self.residuals, self.normalized_residuals, nums, denoms - else: - self.eigenvalues, self.eigenvectors, self.residuals, self.normalized_residuals = result - self._computed = True - return self.eigenvalues, self.eigenvectors, self.residuals, self.normalized_residuals + self.eigenvalues, self.eigenvectors, self.residuals, self.normalized_residuals = result + self._computed = True + return self.eigenvalues, self.eigenvectors, self.residuals, self.normalized_residuals def plot( self, diff --git a/pyproject.toml b/pyproject.toml index f58cef3..003d136 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -28,7 +28,11 @@ dependencies = [ "optht", "derivative", "lightning", - "prettytable" + "prettytable", + "scipy", + "scikit-learn", + "matplotlib", + "joblib", ] [project.optional-dependencies] diff --git a/tests/test_sweeps.py b/tests/test_sweeps.py index d6136a8..cbe33c2 100644 --- a/tests/test_sweeps.py +++ b/tests/test_sweeps.py @@ -12,7 +12,6 @@ sweep_ranks_delays, split_train_test, ) -from DSA.dmd import DMD # ============================================================================= From 2eac6e3b654789c8caa17a9d2f29e1db3251d553 Mon Sep 17 00:00:00 2001 From: ostrow Date: Sat, 14 Feb 2026 18:24:27 -0500 Subject: [PATCH 89/90] updates for uv, simpler imports, resolving comments --- DSA/__init__.py | 6 +- DSA/base_dmd.py | 4 +- DSA/dmd.py | 5 +- DSA/dmdc.py | 5 +- DSA/pykoopman/koopman.py | 7 - DSA/pykoopman/regression/__init__.py | 2 - DSA/pykoopman/regression/_nndmd.py | 1455 -------------------------- DSA/resdmd.py | 16 +- DSA/simdist.py | 17 +- DSA/sweeps.py | 2 - README.md | 15 + pyproject.toml | 5 +- setup.py | 1 - 13 files changed, 51 insertions(+), 1489 deletions(-) delete mode 100644 DSA/pykoopman/regression/_nndmd.py diff --git a/DSA/__init__.py b/DSA/__init__.py index dde8892..ce42c5d 100644 --- a/DSA/__init__.py +++ b/DSA/__init__.py @@ -1,6 +1,6 @@ from DSA.dsa import DSA, ControllabilitySimilarityTransformDistConfig, GeneralizedDSA, InputDSA, SimilarityTransformDistConfig from DSA.dsa import DefaultDMDConfig as DMDConfig -from DSA.dsa import pyKoopmanDMDConfig,SubspaceDMDcConfig, DMDcConfig +from DSA.dsa import pyKoopmanDMDConfig, SubspaceDMDcConfig, DMDcConfig from DSA.dsa import SimilarityTransformDistConfig, ControllabilitySimilarityTransformDistConfig from DSA.dmd import DMD from DSA.dmdc import DMDc @@ -8,6 +8,6 @@ from DSA.simdist import SimilarityTransformDist from DSA.simdist_controllability import ControllabilitySimilarityTransformDist from DSA.stats import * -from DSA.sweeps import * +from DSA.sweeps import PyKoopmanSweeper, DefaultSweeper from DSA.preprocessing import * -from DSA.resdmd import * +from DSA.resdmd import ResidualComputer diff --git a/DSA/base_dmd.py b/DSA/base_dmd.py index 49c2365..37fd166 100644 --- a/DSA/base_dmd.py +++ b/DSA/base_dmd.py @@ -74,7 +74,7 @@ def _setup_device(self, device='cpu', use_torch=None): if not torch.cuda.is_available(): warnings.warn( f"CUDA device '{device}' requested but CUDA is not available. " - "Falling back to CPU with NumPy. " + "Falling back to CPU. " "To use GPU acceleration, ensure PyTorch with CUDA support is installed.", RuntimeWarning, stacklevel=3 @@ -91,7 +91,7 @@ def _setup_device(self, device='cpu', use_torch=None): except (RuntimeError, AssertionError) as e: warnings.warn( f"CUDA device '{device}' requested but not accessible: {e}. " - f"Falling back to CPU with NumPy.", + f"Falling back to CPU.", RuntimeWarning, stacklevel=3 ) diff --git a/DSA/dmd.py b/DSA/dmd.py index 3005b14..57b3847 100644 --- a/DSA/dmd.py +++ b/DSA/dmd.py @@ -552,8 +552,11 @@ def fit( """ # if parameters are provided, overwrite them from the init self.steps_ahead = self.steps_ahead if steps_ahead is None else steps_ahead - self.device = self.device if device is None else device self.verbose = self.verbose if verbose is None else verbose + + # Validate and set device with graceful fallback + if device is not None: + self.device, self.use_torch = self._setup_device(device, use_torch=None) self.compute_hankel(data, n_delays, delay_interval) self.compute_svd() diff --git a/DSA/dmdc.py b/DSA/dmdc.py index 55fa6ad..95cb2b6 100644 --- a/DSA/dmdc.py +++ b/DSA/dmdc.py @@ -504,8 +504,11 @@ def fit( Fits the DMDc model to the provided data. """ # Overwrite parameters if provided - self.device = self.device if device is None else device self.verbose = self.verbose if verbose is None else verbose + + # Validate and set device with graceful fallback + if device is not None: + self.device, self.use_torch = self._setup_device(device, use_torch=True) self.compute_hankel(data, control_data, n_delays, delay_interval) self.compute_svd() diff --git a/DSA/pykoopman/koopman.py b/DSA/pykoopman/koopman.py index ef3fe33..dca95c3 100644 --- a/DSA/pykoopman/koopman.py +++ b/DSA/pykoopman/koopman.py @@ -24,7 +24,6 @@ from .regression import EDMDc from .regression import EnsembleBaseRegressor from .regression import HAVOK -from .regression import NNDMD from .regression import PyDMDRegressor @@ -238,12 +237,6 @@ def transform_y(y_data): func=transform_y, # Composed: observable + flatten inverse_func=self.observables.inverse, ) - elif isinstance(self.regressor, NNDMD): - regressor = self.regressor - y_flag = False - # NNDMD handles its own data - skip pipeline setup and return early - # (will add full NNDMD handling in next step) - else: # multiple 1-step-trajectories (X and Y provided separately) regressor = EnsembleBaseRegressor( diff --git a/DSA/pykoopman/regression/__init__.py b/DSA/pykoopman/regression/__init__.py index 4cbc09c..ae84260 100644 --- a/DSA/pykoopman/regression/__init__.py +++ b/DSA/pykoopman/regression/__init__.py @@ -8,7 +8,6 @@ from ._edmdc import EDMDc from ._havok import HAVOK from ._kdmd import KDMD -from ._nndmd import NNDMD __all__ = [ "PyDMDRegressor", @@ -18,5 +17,4 @@ "EDMDc", "EnsembleBaseRegressor", "HAVOK", - "NNDMD", ] diff --git a/DSA/pykoopman/regression/_nndmd.py b/DSA/pykoopman/regression/_nndmd.py deleted file mode 100644 index f9583f7..0000000 --- a/DSA/pykoopman/regression/_nndmd.py +++ /dev/null @@ -1,1455 +0,0 @@ -"""module for implementing a neural network DMD""" - -from __future__ import annotations - -import pickle -from abc import abstractmethod -from warnings import warn - -import lightning as L -import numpy as np -import torch -from DSA.pykoopman.regression._base import BaseRegressor -from sklearn.utils.validation import check_is_fitted -from torch import nn -from torch.nn.utils.rnn import pad_sequence -from torch.utils.data import DataLoader -from torch.utils.data import Dataset - - -# todo: add the control version - - -class MaskedMSELoss(nn.Module): - """ - Calculates the mean squared error (MSE) loss between `output` and `target`, with - masking based on `target_lens`. The `max_look_forward` will determine the - - Args: - max_look_forward - - Returns: - The MSE loss as a scalar tensor. - """ - - def __init__(self, max_look_forward): - super().__init__() - self.max_look_forward = torch.tensor(max_look_forward, dtype=torch.int) - - def forward(self, output, target, target_lens): - """ - Calculates the MSE loss between `output` and `target`, with masking based on - `target_lens`. - - Args: - output (torch.Tensor): The output tensor of shape (batch_size, - sequence_length, features). - target (torch.Tensor): The target tensor of shape (batch_size, - sequence_length, features). - target_lens (torch.Tensor): A tensor of shape (batch_size,) containing the - sequence lengths for each item in the batch. - - Returns: - The MSE loss as a scalar tensor. - """ - - # if target is shorter than output, just cut output off - if target.size(1) < self.max_look_forward: - output = output[:, : target.size(1), :] - - # Create mask using target_lens - mask = torch.zeros_like(output, dtype=torch.bool) - for i, length in enumerate(target_lens): - if length > self.max_look_forward: - length_used = self.max_look_forward - else: - length_used = length - mask[i, :length_used, :] = 1 - - # Compute squared differences and apply mask - squared_diff = torch.pow(output - target, 2) - squared_diff_masked = torch.where( - mask, squared_diff, torch.zeros_like(squared_diff) - ) - - # Compute the MSE loss - mse_loss = squared_diff_masked.sum() / mask.sum() - - return mse_loss - - -class FFNN(nn.Module): - """A feedforward neural network with customizable architecture and activation - functions. - - Args: - input_size (int): The size of the input layer. - hidden_sizes (list): A list of the sizes of the hidden layers. - output_size (int): The size of the output layer. - activations (str): A string for activation functions for every layer. - - Attributes: - layers (nn.ModuleList): A list of the neural network layers. - """ - - def __init__( - self, input_size, hidden_sizes, output_size, activations, include_state=False - ): - super(FFNN, self).__init__() - - activations_dict = { - "relu": nn.ReLU(), - "sigmoid": nn.Sigmoid(), - "tanh": nn.Tanh(), - "swish": nn.SiLU(), - "elu": nn.ELU(), - "mish": nn.Mish(), - "linear": nn.Identity(), - } - # whether to directly encode state in observables - self.include_state = include_state - if self.include_state: - output_size_ = output_size - input_size - else: - output_size_ = output_size - - # Define the activation - act = activations_dict[activations] - - # Define the input layer - self.layers = nn.ModuleList() - - # if linear layer, remove bias - if activations == "linear": - bias = False - else: - bias = True - - if len(hidden_sizes) == 0: - # if no hidden layer, then entire NN is just a linear one - self.layers.append(nn.Linear(input_size, output_size_, bias)) - else: - self.layers.append(nn.Linear(input_size, hidden_sizes[0], bias)) - if activations != "linear": - self.layers.append(act) - - # Define the hidden layers - for i in range(1, len(hidden_sizes)): - self.layers.append( - nn.Linear(hidden_sizes[i - 1], hidden_sizes[i], bias) - ) - if activations != "linear": - self.layers.append(act) - - # Define the last output layer - self.layers.append(nn.Linear(hidden_sizes[-1], output_size_, bias=False)) - - def forward(self, x): - """Performs a forward pass through the neural network. - - Args: - x (torch.Tensor): The input tensor to the neural network. - - Returns: - torch.Tensor: The output tensor of the neural network. - """ - in_x = x - for layer in self.layers: - x = layer(x) - - if self.include_state: - x = torch.cat((in_x, x), 1) - return x - - -class HardCodedLinearLayer(nn.Module): - def __init__(self, input_size, output_size): - - pass - - def forward(self, x): - pass - - -class BaseKoopmanOperator(nn.Module): - """Base class for Koopman operator models. - - Args: - dim (int): The dimension of the state space. - dt (float, optional): The time step size. Defaults to 1.0. - init_std (float, optional): The standard deviation of the initializer. - Defaults to 0.1. - - Attributes: - dim (int): The dimension of the state space. - dt (torch.Tensor): The time step size. - init_std (float): The standard deviation of the initializer. - - Note: - rule for self.init_std: a number between 0.1 and 10 over dt - - """ - - def __init__( - self, - dim: int, - dt: float = 1.0, - init_std: float = 0.1, - ): - """ - Initializes the `BaseKoopmanOperator` instance. - """ - super().__init__() - self.dim = dim - self.register_buffer("dt", torch.tensor(dt)) - self.init_std = init_std - - def forward(self, x): - """ - Computes the forward pass of the `BaseKoopmanOperator`. - - Given `x` as a row vector, return `x @ K.T` - - Args: - x (torch.Tensor): The input tensor. - - Returns: - torch.Tensor: The output tensor. - """ - koopman_operator = self.get_discrete_time_Koopman_Operator() - xnext = torch.matmul(x, koopman_operator.t()) # following pytorch convention - return xnext - - def get_discrete_time_Koopman_Operator(self): - """ - Computes the discrete-time Koopman operator. - - Returns: - torch.Tensor: The discrete-time Koopman operator. - """ - return torch.matrix_exp(self.dt * self.get_K()) - - @abstractmethod - def get_K(self): - """ - Computes the matrix K. - - Returns: - torch.Tensor: The matrix K. - """ - pass - - -class StandardKoopmanOperator(BaseKoopmanOperator): - """ - Standard Koopman operator that only has a diagonal matrix for the Koopman operator. - """ - - def __init__(self, **kwargs): - """ - Initializes the StandardKoopmanOperator. - - Args: - **kwargs: Additional keyword arguments. - """ - super().__init__(**kwargs) - self.register_parameter( - "K", - torch.nn.Parameter( - torch.nn.init.trunc_normal_( - torch.zeros(self.dim, self.dim), std=self.init_std - ) - ), - ) - - def get_K(self): - """ - Computes the Koopman operator. - - Returns: - The Koopman operator. - """ - return self.K - - -class HamiltonianKoopmanOperator(BaseKoopmanOperator): - """ - Hamiltonian Koopman operator that has an off-diagonal matrix for the Koopman - operator. - """ - - def __init__(self, **kwargs): - """ - Initializes the HamiltonianKoopmanOperator. - - Args: - **kwargs: Additional keyword arguments. - """ - super().__init__(**kwargs) - self.register_parameter( - "off_diagonal", - torch.nn.Parameter( - torch.nn.init.trunc_normal_( - torch.zeros(self.dim, self.dim), std=self.init_std - ) - ), - ) - - def get_K(self): - """ - Computes the Koopman operator. - - Returns: - The Koopman operator. - """ - return self.off_diagonal - self.off_diagonal.t() - - -class DissipativeKoopmanOperator(BaseKoopmanOperator): - """ - Dissipative Koopman operator that has an off-diagonal and a diagonal matrix for the - Koopman operator. - """ - - def __init__(self, **kwargs): - """ - Initializes the DissipativeKoopmanOperator. - - Args: - **kwargs: Additional keyword arguments. - """ - super().__init__(**kwargs) - self.register_parameter( - "off_diagonal", - torch.nn.Parameter( - torch.nn.init.trunc_normal_( - torch.zeros(self.dim, self.dim), std=self.init_std - ) - ), - ) - self.register_parameter( - "diagonal", - torch.nn.Parameter( - -torch.pow( - torch.nn.init.trunc_normal_( - torch.zeros(self.dim), std=self.init_std - ), - 2, - ) - ), - ) - - def get_K(self): - """ - Computes the Koopman operator. - - Returns: - The Koopman operator. - """ - return torch.diag(self.diagonal) + self.off_diagonal - self.off_diagonal.t() - - -class DLKoopmanRegressor(L.LightningModule): - """ - Deep Learning Koopman Regressor module using a Feedforward Neural Network - encoder and decoder to learn the Koopman operator for a given dynamical system. - - Args: - mode (str): Type of Koopman operator to use - "Standard", "Hamiltonian" or - "Dissipative". Defaults to None. - dt (float): Time step of the Koopman operator. Defaults to 1.0. - look_forward (int): Number of time steps to predict in the future. - Defaults to 1. - config_encoder (dict): Dictionary containing encoder configurations - - input_size, output_size, hidden_sizes, activations. Defaults to {}. - config_decoder (dict): Dictionary containing decoder configurations - - input_size, output_size, hidden_sizes, activations. Defaults to {}. - lbfgs (bool): Use L-BFGS optimizer. Defaults to False. - - Attributes: - input_size (int): Size of input to the encoder. - output_size (int): Size of output from the encoder. - _encoder (FFNN): Feedforward Neural Network encoder. - _decoder (FFNN): Feedforward Neural Network decoder. - _koopman_propagator (BaseKoopmanOperator): Type of Koopman operator used. - look_forward (int): Number of time steps to predict in the future. - using_lbfgs (bool): Use L-BFGS optimizer. - masked_loss_metric (MaskedMSELoss): Mean Squared Error Loss function. - """ - - def __init__( - self, - mode=None, - dt=1.0, - look_forward=1, - config_encoder=dict(), - config_decoder=dict(), - lbfgs=False, - std_koopman=1e-1, - include_state=False, - ): - super(DLKoopmanRegressor, self).__init__() - - self.input_size = config_encoder["input_size"] - self.output_size = config_encoder["output_size"] - - self._encoder = FFNN( - input_size=config_encoder["input_size"], - hidden_sizes=config_encoder["hidden_sizes"], - output_size=config_encoder["output_size"], - activations=config_encoder["activations"], - include_state=include_state, - ) - - self._decoder = FFNN( - input_size=config_decoder["input_size"], - hidden_sizes=config_decoder["hidden_sizes"], - output_size=config_decoder["output_size"], - activations=config_decoder["activations"], - ) - - if mode == "Dissipative": - self._koopman_propagator = DissipativeKoopmanOperator( - dim=config_encoder["output_size"], dt=dt, init_std=std_koopman - ) - elif mode == "Hamiltonian": - self._koopman_propagator = HamiltonianKoopmanOperator( - dim=config_encoder["output_size"], dt=dt, init_std=std_koopman - ) - else: - self._koopman_propagator = StandardKoopmanOperator( - dim=config_encoder["output_size"], dt=dt, init_std=std_koopman - ) - - self.look_forward = look_forward - self.using_lbfgs = lbfgs - - # self.masked_loss_metric = MaskedMSELoss(1) - self.masked_loss_metric = MaskedMSELoss(self.look_forward) - - if self.using_lbfgs: - self.automatic_optimization = False - - def training_step(batch, batch_idx): - optimizer = self.optimizers() - - def closure(): - - # unpack batch data - x, y, ys = batch - - # get the max look forward in this batch - batch_look_forward = ys.max() - - # encode x - encoded_x = self._encoder(x) - - # future unroll look_forward - phi_seq = self._propagate_encoded_n_steps( - encoded_x, n=batch_look_forward - ) - - # standard RNN loss - decoded_y_seq_rnn = torch.zeros( - (x.size(0), self.look_forward, self.input_size), - device=self.device, - ) - - for i in range(batch_look_forward): - decoded_y_seq_rnn[:, i, :] = self._decoder(phi_seq[:, i, :]) - rnn_loss = self.masked_loss_metric(decoded_y_seq_rnn, y, ys) - - # autoencoder reconstruction loss - # for x - decoded_x = self._decoder(encoded_x) - rec_loss = torch.nn.functional.mse_loss(decoded_x, x) - - # for y_seq - decoded_y_seq_rec = torch.zeros( - (x.size(0), self.look_forward, self.input_size), - device=self.device, - ) - for i in range(batch_look_forward): - decoded_y_seq_rec[:, i, :] = self._decoder( - self._encoder(y[:, i, :]) - ) - rec_loss += self.masked_loss_metric(decoded_y_seq_rec, y, ys) - - loss = rnn_loss + rec_loss - - optimizer.zero_grad() - self.manual_backward(loss) - - self.log("loss", loss, prog_bar=True) - self.log("rec_loss", rec_loss, prog_bar=True) - self.log("rnn_loss", rnn_loss, prog_bar=True) - - return loss - - optimizer.step(closure=closure) - - self.training_step = training_step - - self.save_hyperparameters() - - def forward(self, x, n=1): - """ - Propagates input tensor through the model to obtain predicted output tensor - after n steps. - - Args: - x: Input tensor with shape (batch_size, input_size). - n (int): Number of steps to propagate. - - Returns: - decoded: Output tensor with shape (batch_size, output_size). - - """ - encoded = self._encoder(x) - phi_seq = self._propagate_encoded_n_steps(encoded, n) - decoded = self._decoder(phi_seq[:, -1, :]) - return decoded - - def forward_all(self, x, n): - """ - Forward pass of the Koopman Regressor for a given sequence of input states `x`. - This method returns the decoded sequence for all steps within the horizon `n`. - - Args: - x (torch.Tensor): The input state sequence with shape `(batch_size, seq_len, - input_size)`. - n (int): The maximum horizon for which to generate the output sequence. - - Returns: - decoded (torch.Tensor): The decoded sequence with shape `(batch_size, n, - input_size)`. - """ - encoded = self._encoder(x) - phi_seq = self._propagate_encoded_n_steps(encoded, n) - decoded = torch.zeros(x.size(0), n, self.input_size) - for i in range(n): - decoded[:, i, :] = self._decoder(phi_seq[:, i, :]) - return decoded - - def _propagate_encoded_n_steps(self, encoded, n): - """ - Propagates the encoded tensor linearly in the encoded space for n steps. - - Args: - encoded (torch.Tensor): The encoded tensor of shape (batch_size, - encoded_size). n (int): The number of steps to propagate. - - Returns: - torch.Tensor: The propagated encoded tensor of shape (batch_size, n, - encoded_size). - """ - encoded_future = [] - for i in range(n): - encoded = self._koopman_propagator(encoded) - encoded_future.append(encoded) - return torch.stack(encoded_future, 1) - - def training_step(self, batch, batch_idx): - """ - Defines a training step for the DL Koopman Regressor. - - Args: - batch: tuple of (x, y, ys), representing the input data, - the true output data, and the sequence length for - each sample in the batch. - batch_idx: integer, the index of the batch. - - Returns: - tensor representing the loss value for this training step. - """ - # unpack batch data - x, y, ys = batch - - # get the max look forward in this batch - batch_look_forward = ys.max() - - # encode x - encoded_x = self._encoder(x) - - # future unroll look_forward - phi_seq = self._propagate_encoded_n_steps(encoded_x, n=batch_look_forward) - - # standard RNN loss - decoded_y_seq_rnn = torch.zeros( - (x.size(0), self.look_forward, self.input_size), device=self.device - ) - - for i in range(batch_look_forward): - decoded_y_seq_rnn[:, i, :] = self._decoder(phi_seq[:, i, :]) - rnn_loss = self.masked_loss_metric(decoded_y_seq_rnn, y, ys) - - # autoencoder reconstruction loss - # for x - decoded_x = self._decoder(encoded_x) - rec_loss = torch.nn.functional.mse_loss(decoded_x, x) - - # for y_seq - decoded_y_seq_rec = torch.zeros( - (x.size(0), self.look_forward, self.input_size), device=self.device - ) - for i in range(batch_look_forward): - decoded_y_seq_rec[:, i, :] = self._decoder(self._encoder(y[:, i, :])) - rec_loss += self.masked_loss_metric(decoded_y_seq_rec, y, ys) - - loss = rnn_loss + rec_loss - - self.log("loss", loss, prog_bar=True) - self.log("rec_loss", rec_loss, prog_bar=True) - self.log("rnn_loss", rnn_loss, prog_bar=True) - return loss - - def configure_optimizers(self): - """Configures and returns the optimizer to use for training. - - If using LBFGS optimizer, set `using_lbfgs` attribute to True when - initializing the DLKoopmanRegressor instance. - - Returns: - An instance of torch.optim.Optimizer to use for training. - """ - if self.using_lbfgs: - optimizer = torch.optim.LBFGS( - self.parameters(), - lr=1, - history_size=100, - max_iter=20, - line_search_fn="strong_wolfe", - ) - else: - optimizer = torch.optim.Adam(self.parameters(), lr=1e-3) - return optimizer - - -class SeqDataDataset(Dataset): - """ - A PyTorch Dataset class to handle sequential data in the format of (x, y, ys), - where x is the input sequence, y is the target output sequence and ys is a vector - indicating the maximum look-ahead distance. - - Args: - x (torch.Tensor): The input sequence tensor of shape (batch_size, - sequence_length, input_size). - y (torch.Tensor): The output sequence tensor of shape (batch_size, - sequence_length, output_size). - ys (torch.Tensor): The maximum look-ahead distance tensor of shape - (batch_size,). - transform (callable, optional): Optional normalization function to apply to - x and y. - - Returns: - torch.Tensor: The preprocessed input sequence tensor. - torch.Tensor: The preprocessed target output sequence tensor. - torch.Tensor: The maximum look-ahead distance tensor. - """ - - def __init__(self, x, y, ys, transform=None): - self.x = x.squeeze(1) - self.y = y - self.ys = ys - self.normalization = transform - - def __len__(self): - return len(self.ys) - - def __getitem__(self, idx): - x = self.x[idx].clone() - y = self.y[idx].clone() - ys = self.ys[idx].clone() - - if self.normalization: - x = self.normalization(x) - y = self.normalization(y) - - return x, y, ys - - -class TensorNormalize(nn.Module): - """ - Normalizes the input tensor by subtracting the mean and dividing by the standard - deviation. - - Args: - mean (float or tensor): The mean value to be subtracted from the input tensor. - std (float or tensor): The standard deviation value to divide the input tensor - by. - """ - - def __init__(self, mean, std): - super().__init__() - self.mean = mean - self.std = std - - def forward(self, tensor: torch.Tensor): - """ - Forward pass of the normalization module. - - Args: - tensor (tensor): The input tensor to be normalized. - - Returns: - The normalized tensor. - """ - return torch.divide((tensor - self.mean), self.std) - # return # tensor.copy_(tensor.sub_(self.mean).div_(self.std)) - - def __repr__(self) -> str: - """ - Returns a string representation of the TensorNormalize module. - - Returns: - A string representation of the module. - """ - return f"{self.__class__.__name__}(mean={self.mean}, std={self.std})" - - -class InverseTensorNormalize(nn.Module): - """ - A PyTorch module that performs inverse normalization on input tensors using - a given mean and standard deviation. - - Args: - mean (float or sequence): The mean used for normalization. - std (float or sequence): The standard deviation used for normalization. - - Example: - >>> mean = [0.5, 0.5, 0.5] - >>> std = [0.5, 0.5, 0.5] - >>> inv_norm = InverseTensorNormalize(mean, std) - >>> normalized_tensor = torch.tensor([[-1.0, 0.0, 1.0], [-0.5, 0.0, 0.5]]) - >>> output = inv_norm(normalized_tensor) - - Attributes: - mean (float or sequence): The mean used for normalization. - std (float or sequence): The standard deviation used for normalization. - """ - - def __init__(self, mean, std): - super().__init__() - self.mean = mean - self.std = std - - def forward(self, tensor: torch.Tensor): - return torch.multiply(tensor, self.std) + self.mean - # return tensor.copy_(tensor.mul_(self.std).add_(self.mean)) - - def __repr__(self) -> str: - return f"{self.__class__.__name__}(mean={self.mean}, std={self.std})" - - -class SeqDataModule(L.LightningDataModule): - """ - Class for creating sequence data dataloader for training and validation. - - Args: - data_tr: List of 2D numpy.ndarray representing training data trajectories. - data_val: List of 2D numpy.ndarray representing validation data trajectories. - Can be None. - look_forward: Number of time steps to predict forward. - batch_size: Size of each batch of data. - normalize: Whether to normalize the input data or not. Default is True. - normalize_mode: The type of normalization to use. Either "equal" or "max". - Default is "equal". - normalize_std_factor: Scaling factor for standard deviation during - normalization. Default is 2.0. - - Methods: - prepare_data(): Prepares the data by converting to time-delayed data and - computing mean and std if normalize is True. - setup(stage=None): Sets up training and validation datasets. - train_dataloader(): Returns a DataLoader for training data. - val_dataloader(): Returns a DataLoader for validation data. - convert_seq_list_to_delayed_data(data_list, look_back, look_forward): Converts - list of sequences to time-delayed data. - collate_fn(batch): Custom collate function to be used with DataLoader. - - Returns: - A SeqDataModule object. - """ - - def __init__( - self, - data_tr, - data_val, - look_forward=10, - batch_size=32, - normalize=True, - normalize_mode="equal", - normalize_std_factor=2.0, - ): - """ - Initialize a SeqDataModule. - - Args: - data_tr (Union[str, List[np.ndarray]]): Training data. Can be either a - list of 2D numpy arrays, each 2D numpy array representing a trajectory, - or the path to a pickle file containing such a list. - data_val (Optional[Union[str, List[np.ndarray]]]): Validation data. - Can be either a list of 2D numpy arrays, each 2D numpy array - representing a trajectory, or the path to a pickle file - containing such a list. - look_forward (int): Number of time steps to predict into the future. - batch_size (int): Number of samples per batch. - normalize (bool): Whether to normalize the data. Default is True. - normalize_mode (str): Mode for normalization. Can be either "equal" - or "max". "equal" divides by the standard deviation, while "max" - divides by the maximum absolute value of the data. Default is "equal". - normalize_std_factor (float): Scaling factor for the standard deviation in - normalization. Default is 2.0. - - Returns: - None. - """ - super().__init__() - # input data_tr or data_val is a list of 2D np.ndarray. each 2d - # np.ndarray is a trajectory, and the axis 0 is number of samples, axis 1 is - # the number of system state - self.data_tr = data_tr - self.data_val = data_val - self.look_forward = look_forward - self.batch_size = batch_size - self.look_back = 1 - self.normalize = normalize - self.normalize_mode = normalize_mode - self.normalization = None - self.inverse_transform = None - self.normalize_std_factor = normalize_std_factor - - def prepare_data(self): - """ - Preprocesses the input training and validation data by checking their types, - checking for normalization, finding the mean and standard deviation of - the training data (if normalization is enabled), and creating time-delayed data - from the input data. - - Raises: - ValueError: If the training data is None or has an invalid type. - ValueError: If the validation data has an invalid type. - TypeError: If the data is complex or not float. - - """ - # train data - if self.data_tr is None: - raise ValueError("You must feed training data!") - if isinstance(self.data_tr, list): - data_list = self.data_tr - elif isinstance(self.data_tr, str): - with open(self.data_tr, "rb") as f: - data_list = pickle.load(f) - else: - raise ValueError("Wrong type of `self.data_tr`") - - # check train data - data_list = self.check_list_of_nparray(data_list) - - # find the mean, std - if self.normalize: - stacked_data_list = np.vstack(data_list) - mean = stacked_data_list.mean(axis=0) - std = stacked_data_list.std(axis=0) - - # zero mean so easier for downstream - self.mean = torch.FloatTensor(mean) * 0 - # default = 2.0, more stable - self.std = torch.FloatTensor(std) * self.normalize_std_factor - - if self.normalize_mode == "max": - self.std = torch.ones_like(self.std) * self.std.max() - - # prevent divide by zero error - for i in range(len(self.std)): - if self.std[i] < 1e-6: - self.std[i] += 1e-3 - - # get transform - self.normalization = TensorNormalize(self.mean, self.std) - - # get inverse transform - self.inverse_transform = InverseTensorNormalize(self.mean, self.std) - - # create time-delayed data - self._tr_x, self._tr_yseq, self._tr_ys = self.convert_seq_list_to_delayed_data( - data_list, self.look_back, self.look_forward - ) - - # validation data - if self.data_val is not None: - # raise ValueError("You need to feed validation data!") - if isinstance(self.data_val, list): - data_list = self.data_val - elif isinstance(self.data_val, str): - with open(self.data_val, "rb") as f: - data_list = pickle.load(f) - else: - raise ValueError("Wrong type of `self.data_val`") - - # check val data - data_list = self.check_list_of_nparray(data_list) - - # create time-delayed data - ( - self._val_x, - self._val_yseq, - self._val_ys, - ) = self.convert_seq_list_to_delayed_data( - data_list, self.look_back, self.look_forward - ) - else: - warn("Warning: no validation data prepared") - - def setup(self, stage=None): - """ - Prepares the train and validation datasets for the Lightning module. - The train dataset is created from the training data specified in the - constructor by creating time-delayed versions of the input/output sequences. - If `normalize` is True, the data is normalized using the mean and standard - deviation of the training data. The validation dataset is created from the - validation data specified in the constructor in the same way as the training - dataset. If `normalize` is True, it is also normalized using the mean and - standard deviation of the training data. If `stage` is not "fit", - an exception is raised as the `setup()` method has not been implemented - for other stages. - - Args: - stage: The stage of training, validation or testing (default is None). - - Raises: - NotImplementedError: If `stage` is not "fit". - """ - # Load data and split into train and validation sets here - # Assign train/val datasets for use in dataloaders - if stage == "fit": - self.tr_dataset = SeqDataDataset( - self._tr_x, self._tr_yseq, self._tr_ys, self.normalization - ) - if self.data_val is not None: - self.val_dataset = SeqDataDataset( - self._val_x, self._val_yseq, self._val_ys, self.normalization - ) - else: - raise NotImplementedError("We didn't implement for stage not `fit`") - - def train_dataloader(self): - return DataLoader( - self.tr_dataset, self.batch_size, shuffle=True, collate_fn=self.collate_fn - ) - - def val_dataloader(self): - return DataLoader( - self.val_dataset, self.batch_size, shuffle=True, collate_fn=self.collate_fn - ) - - def convert_seq_list_to_delayed_data(self, data_list, look_back, look_forward): - """ - Converts a list of sequences to time-delayed data by extracting subsequences - of length `look_back` and `look_forward` from each sequence in the list. - - Args: - data_list (List[np.ndarray]): A list of 2D numpy arrays. Each array - represents a trajectory, with axis 0 representing the number of samples - and axis 1 representing the number of system states. - look_back (int): The number of previous time steps to include in each - subsequence. - look_forward (int): The number of future time steps to include in each - subsequence. - - Returns: - Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: A tuple containing three - tensors: - 1) The time-delayed input data, with shape (num_samples, look_back, - num_system_states). - 2) The time-delayed output data, with shape (num_samples, look_forward, - num_system_states). - 3) The sequence lengths of the output data, with shape (num_samples,). - """ - time_delayed_x_list = [] - time_delayed_yseq_list = [] - for seq in data_list: - # if self.look_forward + self.look_back > len(seq): - # raise ValueError("look_forward too large") - n_sub_traj = len(seq) - look_back - look_forward + 1 - if n_sub_traj >= 1: - for i in range(len(seq) - look_back - look_forward + 1): - time_delayed_x_list.append(seq[i : i + look_back]) - time_delayed_yseq_list.append( - seq[i + look_back : i + look_back + look_forward] - ) - else: - # only 1 traj, just to predict to its end - time_delayed_x_list.append(seq[0:1]) - time_delayed_yseq_list.append(seq[1:]) - time_delayed_yseq_lens_list = [x.shape[0] for x in time_delayed_yseq_list] - - # convert data to tensor - time_delayed_x = torch.FloatTensor(np.array(time_delayed_x_list)) - time_delayed_yseq = pad_sequence( - [torch.FloatTensor(x) for x in time_delayed_yseq_list], True - ) - time_delayed_yseq_lens = torch.LongTensor(time_delayed_yseq_lens_list) - return time_delayed_x, time_delayed_yseq, time_delayed_yseq_lens - - def collate_fn(self, batch): - """ - Collates a batch of data. - - Args: - batch: A list of tuples where each tuple represents a sample containing - the input sequence `x`, the output sequence `y`, and the maximum - number of steps to predict `ys`. - - Returns: - A tuple containing the input sequences as a stacked tensor, the output - sequences as a stacked tensor, and the maximum number of steps to predict - as a stacked tensor. - - """ - x_batch, y_batch, ys_batch = zip(*batch) - xx = torch.stack(x_batch, 0) - yy = torch.stack(y_batch, 0) - ys = torch.stack(ys_batch, 0) - return xx, yy, ys - - @classmethod - def check_list_of_nparray(cls, data_list): - """ - Check if the input is a list of numpy arrays, and convert data to float32 if - float64. - - Args: - data_list (List[np.ndarray]): A list of numpy arrays representing system - states. - - Returns: - List[np.ndarray]: The input list of numpy arrays converted to float32. - - Raises: - TypeError: If the input data is complex or not float. - """ - # check if data is complex - if any(np.iscomplexobj(x) for x in data_list): - raise TypeError("Complex data is not supported") - - # check if data has float64 - if any(x.dtype is np.float64 for x in data_list): - warn("Found float64 data. Will convert to float32") - - # convert data to float32 if float64 - for i, data_traj in enumerate(data_list): - if "float" not in data_traj.dtype.name: - raise TypeError("Found data is not float") - if data_traj.dtype.name == "float64": - data_list[i] = data_traj.astype("float32") - - return data_list - - -class NNDMD(BaseRegressor): - """Implementation of Nonlinear Dynamic Mode Decomposition (NNDMD). - - Args: - mode (str): NNDMD mode, `Dissipative` or `Hamiltonian` or else (default: None). - dt (float): Time step (default: 1.0). - look_forward (int): Number of steps to look forward (default: 1). - config_encoder (dict): Configuration for the encoder network - (default: dict(input_size=2, hidden_sizes=[32]*2, output_size=6, - activations='tanh')). - config_decoder (dict): Configuration for the decoder network - (default: dict(input_size=6, hidden_sizes=[32]*2, output_size=2, - activations='linear')). - batch_size (int): Batch size (default: 16). - lbfgs (bool): Whether to use L-BFGS optimizer (default: False). - normalize (bool): Whether to normalize data (default: True). - normalize_mode (str): Normalization mode, `max` or `equal` - (default: 'equal'). - normalize_std_factor (float): Standard deviation factor for normalization - (default: 2.0). - trainer_kwargs (dict): Arguments for the `pytorch_lightning.Trainer` - (default: {}). - - Attributes: - coef_ (np.ndarray): Koopman operator coefficients. - state_matrix_ (np.ndarray): State matrix of the Koopman operator. - eigenvalues_ (np.ndarray): Eigenvalues of the Koopman operator. - eigenvectors_ (np.ndarray): Eigenvectors of the Koopman operator. - ur (np.ndarray): Effective linear transformation. - unnormalized_modes (np.ndarray): Unnormalized modes. - - Note: - The `n_samples_` attribute is meaningless for this class. - The `dt` argument is only included to please the regressor class and has no - real use. - - """ - - def __init__( - self, - mode=None, - dt=1.0, - look_forward=1, - config_encoder=dict( - input_size=2, hidden_sizes=[32] * 2, output_size=6, activations="tanh" - ), - config_decoder=dict( - input_size=6, hidden_sizes=[32] * 2, output_size=2, activations="linear" - ), - batch_size=16, - lbfgs=False, - normalize=True, - normalize_mode="equal", - normalize_std_factor=2.0, - std_koopman=1e-1, - include_state=False, - trainer_kwargs={}, - ): - """Initializes the NNDMD model.""" - self.mode = mode - self.look_forward = look_forward - self.config_encoder = config_encoder - self.config_decoder = config_decoder - self.lbfgs = lbfgs - self.normalize = normalize - self.normalize_mode = normalize_mode - self.dt = dt - self.trainer_kwargs = trainer_kwargs - self.normalize_std_factor = normalize_std_factor - self.batch_size = batch_size - self.std_koopman = std_koopman - self.include_state = include_state - - # build DLK regressor - self._regressor = DLKoopmanRegressor( - mode, dt, look_forward, config_encoder, config_decoder, lbfgs, std_koopman - ) - - def fit(self, x, y=None, dt=None): - """fit the NNDMD model with data x,y - - Args: - x (np.ndarray or list): The training input data. If a 2D numpy array, - then it represents a single time-series and each row represents a - state, otherwise it should be a list of 2D numpy arrays. - y (np.ndarray or list, optional): The target output data, - corresponding to `x`. If `None`, `x` is assumed to contain the target - data in its second half. Defaults to `None`. - dt (float, optional): The time step used to generate `x`. - Defaults to `None`. - - Returns: - None. The fitted model is stored in the class attribute `_regressor`. - """ - # build trainer - self.trainer = L.Trainer(**self.trainer_kwargs) - - self.n_input_features_ = self.config_encoder["input_size"] - - # create the data module - # case 1: a single traj, x is 2D np.ndarray, no validation - if y is None and isinstance(x, np.ndarray) and x.ndim == 2: - t0, t1 = x[:-1], x[1:] - list_of_traj = [np.stack((t0[i], t1[i]), 0) for i in range(len(x) - 1)] - self.dm = SeqDataModule( - list_of_traj, - None, - self.look_forward, - self.batch_size, - self.normalize, - self.normalize_mode, - self.normalize_std_factor, - ) - self.n_samples_ = len(list_of_traj) - - # case 2: x, y are 2D np.ndarray, no validation - elif ( - isinstance(x, np.ndarray) - and isinstance(y, np.ndarray) - and x.ndim == 2 - and y.ndim == 2 - ): - t0, t1 = x, y - list_of_traj = [np.stack((t0[i], t1[i]), 0) for i in range(len(x) - 1)] - self.dm = SeqDataModule( - list_of_traj, - None, - self.look_forward, - self.batch_size, - self.normalize, - self.normalize_mode, - self.normalize_std_factor, - ) - self.n_samples_ = len(list_of_traj) - - # case 3: only training data, x is a list of trajectories, y is None - elif isinstance(x, list) and y is None: - self.dm = SeqDataModule( - x, - None, - self.look_forward, - self.batch_size, - self.normalize, - self.normalize_mode, - self.normalize_std_factor, - ) - self.n_samples_ = len(x) - - # case 4: x, y are two lists of trajectories, we have validation data - elif isinstance(x, list) and isinstance(y, list): - self.dm = SeqDataModule( - x, - y, - self.look_forward, - self.batch_size, - self.normalize, - self.normalize_mode, - self.normalize_std_factor, - ) - self.n_samples_ = len(x) - else: - raise ValueError("check `x` and `y` for `self.fit`") - - # trainer starts to train - self.trainer.fit(self._regressor, self.dm) - - # compute Koopman operator information - self._state_matrix_ = ( - self._regressor._koopman_propagator.get_discrete_time_Koopman_Operator() - .detach() - .numpy() - ) - [self._eigenvalues_, self._eigenvectors_] = np.linalg.eig(self._state_matrix_) - - self._coef_ = self._state_matrix_ - - # obtain effective linear transformation - decoder_weight_list = [] - for i in range(len(self._regressor._decoder.layers)): - decoder_weight_list.append( - self._regressor._decoder.layers[i].weight.detach().numpy() - ) - if len(decoder_weight_list) > 1: - self._ur = np.linalg.multi_dot(decoder_weight_list[::-1]) - else: - self._ur = decoder_weight_list[0] - - if self.normalize: - std = self.dm.inverse_transform.std - self._ur = np.diag(std) @ self._ur - - self._unnormalized_modes = self._ur @ self._eigenvectors_ - - def predict(self, x, n=1): - """ - Predict the system state after n steps away from x_0 = x. - - Args: - x (numpy.ndarray or torch.Tensor): Input data of shape - (n_samples, n_features). - n (int): Number of steps to predict the system state into the future. - - Returns: - numpy.ndarray: Predicted system state after n steps, of shape - (n_samples, n_features). - - Note: - By default, the model is stored on the CPU for inference. - """ - self._regressor.eval() - x, _ = self._detect_reshape(x, offset=False) - x = self._convert_input_ndarray_to_tensor(x) - - with torch.no_grad(): - # print("inference device = ", self._regressor.device) - - if self.normalize: - y = self.dm.normalization(x) - y = self._regressor(y, n) - y = self.dm.inverse_transform(y).numpy() - else: - y = self._regressor(x, n).numpy() - y = self._return_orig_shape(y) - return y - - def simulate(self, x, n_steps): - """ - Simulate the system forward in time for `n_steps` steps starting from `x`. - - Args: - x (np.ndarray or torch.Tensor): The initial state of the system. - Should be a 2D array/tensor. - n_steps (int): The number of time steps to simulate the system forward. - - Returns: - np.ndarray: The simulated states of the system. Will be of shape - `(n_steps+1, n_features)`. - """ - self._regressor.eval() - x = self._convert_input_ndarray_to_tensor(x) - x_future = torch.zeros([n_steps + 1, x.size(1)]) - x_future[0] = x - with torch.no_grad(): - for i in range(n_steps): - if self.normalize: - y = self.dm.normalization(x) - y = self._regressor(y, i + 1) - x_future[i + 1] = self.dm.inverse_transform(y) - else: - x_future[i + 1] = self._regressor(x, i + 1) - - return x_future.numpy() - - @property - def A(self): - """Returns the state transition matrix A of the NNDMD model. - - Returns - ------- - A : numpy.ndarray - The state transition matrix of shape (n_states, n_states), where - n_states is the number of states in the model. - """ - return self._state_matrix_ - - @property - def B(self): - # todo: we don't have control considered in nndmd for now - pass - - @property - def C(self): - """ - Returns the matrix C representing the effective linear transformation - from the observables to the Koopman modes. The matrix C is computed during - the fit process as the product of the decoder weights of the trained - autoencoder network. - - Returns: - -------- - numpy.ndarray of shape (n_koopman, n_features) - The matrix C. - """ - return self._ur - - @property - def W(self): - """ - Returns the matrix W representing the Koopman modes. The matrix W is computed - during the fit process as the eigenvectors of the Koopman operator. - - Returns: - -------- - numpy.ndarray of shape (n_koopman, n_koopman) - The matrix V, where each column represents a Koopman mode. - """ - return self._unnormalized_modes - - def phi(self, x_col): - return self._compute_phi(x_col) - - def psi(self, x_col): - return self._compute_psi(x_col) - - def _compute_phi(self, x_col): - """ - Computes the Koopman observable vector `phi(x)` for input `x`. - - Args: - x (np.ndarray or torch.Tensor): The input state vector or tensor. - - Returns: - phi (np.ndarray): The Koopman observable vector `phi(x)` for input `x`. - """ - if x_col.ndim == 1: - x_col = x_col.reshape(-1, 1) - x = x_col.T - - self._regressor.eval() - x = self._convert_input_ndarray_to_tensor(x) - - if self.normalize: - x = self.dm.normalization(x) - phi = self._regressor._encoder(x).detach().numpy().T - return phi - - def _compute_psi(self, x_col): - """ - Computes the Koopman eigenfunction expansion coefficients `psi(x)` given `x`. - - Args: - x (numpy.ndarray): Input data of shape `(n_samples, n_features)`. - - Returns: - numpy.ndarray: Koopman eigenfunction expansion coefficients `psi(x)` - of shape `(n_koopman, n_samples)`. - """ - if x_col.ndim == 1: - x_col = x_col.reshape(-1, 1) - # x = x_col.T - - phi = self._compute_phi(x_col) - psi = np.linalg.inv(self._eigenvectors_) @ phi - return psi - - def _convert_input_ndarray_to_tensor(self, x): - """ - Converts input numpy ndarray to PyTorch tensor with appropriate dtype and - device. - - Args: - x (np.ndarray or torch.Tensor): Input data as numpy ndarray or PyTorch - tensor. - - Returns: - torch.Tensor: Input data as PyTorch tensor. - - Raises: - TypeError: If input data is not a numpy ndarray or PyTorch tensor. - ValueError: If input array has more than 2 dimensions. - """ - if isinstance(x, np.ndarray): - if x.ndim > 2: - raise ValueError("input array should be 1 or 2D") - if x.ndim == 1: - x = x.reshape(1, -1) - # convert to a float32 - # if x.dtype == np.float64: - x = torch.FloatTensor(x) - elif isinstance(x, torch.Tensor): - if x.ndim != 2: - raise ValueError("input tensor `x` must be a 2d tensor") - return x - - @property - def coef_(self): - check_is_fitted(self, "_coef_") - return self._coef_ - - @property - def state_matrix_(self): - return self._state_matrix_ - - @property - def eigenvalues_(self): - check_is_fitted(self, "_eigenvalues_") - return self._eigenvalues_ - - @property - def eigenvectors_(self): - check_is_fitted(self, "_eigenvectors_") - return self._eigenvectors_ - - @property - def unnormalized_modes(self): - check_is_fitted(self, "_unnormalized_modes") - return self._unnormalized_modes - - @property - def ur(self): - check_is_fitted(self, "_ur") - return self._ur - - -if __name__ == "__main__": - pass diff --git a/DSA/resdmd.py b/DSA/resdmd.py index 3d5f20c..2ec6619 100644 --- a/DSA/resdmd.py +++ b/DSA/resdmd.py @@ -10,9 +10,6 @@ import warnings import numpy as np -import matplotlib.pyplot as plt -from matplotlib import cm -from matplotlib import colors as mcolors from typing import Literal, Tuple try: @@ -30,7 +27,6 @@ embed_data_DMDc = None import torch -import ot # ============================================================================= @@ -768,7 +764,7 @@ def plot( self, cmin: float = None, cmax: float = None, - ax: plt.Axes = None, + ax=None, figsize: tuple = (6, 6), title: str = None, ): @@ -792,6 +788,10 @@ def plot( ------- fig, ax : matplotlib figure and axes """ + import matplotlib.pyplot as plt + from matplotlib import cm + from matplotlib import colors as mcolors + if not self._computed: self.compute() @@ -864,6 +864,10 @@ def plot_residuals(eigenvalues, residuals, cmin=None, cmax=None): cmax : float, optional Maximum value for color scale. """ + import matplotlib.pyplot as plt + from matplotlib import cm + from matplotlib import colors as mcolors + residuals_real = np.abs(residuals) if cmin is None: cmin = np.min(residuals_real) @@ -968,6 +972,8 @@ def compute_ot_distance(a, b): C : np.ndarray Transport plan. """ + import ot + # Convert complex to 2D if needed if np.iscomplexobj(a): a = np.vstack([a.real, a.imag]).T diff --git a/DSA/simdist.py b/DSA/simdist.py index 8513b43..17babba 100644 --- a/DSA/simdist.py +++ b/DSA/simdist.py @@ -1,13 +1,12 @@ import torch import torch.nn as nn import torch.optim as optim -from typing import Literal +from typing import Literal, Final import numpy as np import torch.nn.utils.parametrize as parametrize -from scipy.stats import wasserstein_distance -import ot # optimal transport for multidimensional l2 wasserstein import warnings -from typing import Final + +from ot import dist, emd, emd2, sinkhorn2 try: from .dmd import DMD @@ -273,7 +272,7 @@ def fit( device = a.device # a = a # .cpu() # b = b # .cpu() - self.M = ot.dist(a, b) # .numpy() + self.M = dist(a, b) # .numpy() if wasserstein_weightings is not None: a, b = wasserstein_weightings assert isinstance(a, (torch.Tensor, np.ndarray)) @@ -289,15 +288,15 @@ def fit( a, b = a.to(device), b.to(device) if self.differentiable: - self.score_star = ot.sinkhorn2( + self.score_star = sinkhorn2( a, b, self.M, reg=self.sinkhorn_reg ) # No transport plan needed for differentiable mode self.C_star = None else: - self.C_star = ot.emd(a, b, self.M) + self.C_star = emd(a, b, self.M) self.score_star = ( - ot.emd2(a, b, self.M) #* a.shape[0] + emd2(a, b, self.M) #* a.shape[0] ) # add scaling factor due to random matrix theory # self.score_star = np.sum(self.C_star * self.M) self.C_star = self.C_star / torch.linalg.norm( @@ -605,4 +604,4 @@ def val_matrix(matrix): if mat.shape[0] != mat.shape[1]: raise ValueError(f"matrix must be square") - return mat + return mat \ No newline at end of file diff --git a/DSA/sweeps.py b/DSA/sweeps.py index 64629b8..3362c82 100644 --- a/DSA/sweeps.py +++ b/DSA/sweeps.py @@ -12,13 +12,11 @@ from .subspace_dmdc import SubspaceDMDc from .stats import measure_nonnormality_transpose, compute_all_stats from .resdmd import ResidualComputer -import matplotlib.pyplot as plt from typing import List, Dict, Any, Optional, Union, Tuple from abc import ABC, abstractmethod import warnings import torch -# Import pykoopman from . import pykoopman as pk from .pykoopman.observables import TimeDelay, Identity diff --git a/README.md b/README.md index d240ce2..2d14c7b 100644 --- a/README.md +++ b/README.md @@ -53,6 +53,21 @@ cd DSA/ pip install -e . ``` +You can also create an envirnoment with `uv` for fast, reliable dependency managemen +``` +# 1. Create virtual environment +uv venv + +# 2. Install DSA with all dependencies +uv pip install -e ".[dev]" --python .venv/bin/python + +# 3. Install Jupyter support +uv pip install ipykernel jupyter --python .venv/bin/python + +# 4. Register kernel for VS Code/Jupyter +.venv/bin/python -m ipykernel install --user --name=dsa-uv --display-name="DSA (uv)" +``` + ## Brief Tutorial The central object in the package is `GeneralizedDSA`, which links together the different types of `DMD` and `SimilarityTransformDist` (called Procrustes Analysis over Vector Fields in the first paper) objects. We designed an API that should be easy to use them in conjunction (`DSA`) with a variety of datatypes for a range of analysis cases: diff --git a/pyproject.toml b/pyproject.toml index 003d136..7624d17 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -27,7 +27,6 @@ dependencies = [ "tqdm", "optht", "derivative", - "lightning", "prettytable", "scipy", "scikit-learn", @@ -43,6 +42,10 @@ umap = [ "umap-learn" ] +[tool.uv] +# PyTorch ships CPU-only wheels on PyPI for macOS, which is what we want. +# No special index needed — uv resolves from PyPI by default. + [project.urls] Homepage = "https://github.com/mitchellostrow/DSA" Issues = "https://github.com/mitchellostrow/DSA/issues" \ No newline at end of file diff --git a/setup.py b/setup.py index 67d9cb3..b4e0697 100644 --- a/setup.py +++ b/setup.py @@ -17,7 +17,6 @@ "tqdm", "optht", #for havok in pykoopman "derivative", #for pykoopman - "lightning", #for nndmd in pykoopman "prettytable" ], extras_require={ From f1caab0f2720a9365254ffdad11f25865c1abdc0 Mon Sep 17 00:00:00 2001 From: ostrow Date: Sun, 15 Feb 2026 13:28:18 -0500 Subject: [PATCH 90/90] update notebooks and packages to run seamlessly with provided uv installable environment --- DSA/__init__.py | 2 + DSA/pykoopman/__init__.py | 11 - DSA/sweeps.py | 9 +- examples/all_dsa_types.ipynb | 156 ++--- examples/dmd_lorenz.ipynb | 124 ++-- examples/dmdc_linear_system_test.ipynb | 121 ++-- examples/dsa_fig3_tutorial.ipynb | 100 ++- examples/dsa_koopstd_fix.ipynb | 67 +- examples/how_to_use_dsa_tutorial.ipynb | 159 ++--- examples/inputdsa_fig2_tutorial.ipynb | 922 +++++++++++++------------ examples/ring_attractors.ipynb | 288 ++++---- examples/sweep_resdmd_tutorial.ipynb | 467 +++++++++++-- setup.py | 5 +- 13 files changed, 1349 insertions(+), 1082 deletions(-) diff --git a/DSA/__init__.py b/DSA/__init__.py index ce42c5d..8614237 100644 --- a/DSA/__init__.py +++ b/DSA/__init__.py @@ -1,3 +1,5 @@ +__version__ = "2.0.0" + from DSA.dsa import DSA, ControllabilitySimilarityTransformDistConfig, GeneralizedDSA, InputDSA, SimilarityTransformDistConfig from DSA.dsa import DefaultDMDConfig as DMDConfig from DSA.dsa import pyKoopmanDMDConfig, SubspaceDMDcConfig, DMDcConfig diff --git a/DSA/pykoopman/__init__.py b/DSA/pykoopman/__init__.py index 375d42e..49cb69f 100644 --- a/DSA/pykoopman/__init__.py +++ b/DSA/pykoopman/__init__.py @@ -1,14 +1,3 @@ -from __future__ import annotations - -from pkg_resources import get_distribution - -try: - __version__ = get_distribution(__name__).version -except Exception: - # Package distribution metadata is not available (e.g., running from source); - # in this case, simply leave __version__ undefined. - pass - from .koopman import Koopman from .koopman_continuous import KoopmanContinuous diff --git a/DSA/sweeps.py b/DSA/sweeps.py index 3362c82..38d5876 100644 --- a/DSA/sweeps.py +++ b/DSA/sweeps.py @@ -16,6 +16,7 @@ from abc import ABC, abstractmethod import warnings import torch +from matplotlib.pyplot import plot, savefig, subplots, cm, tight_layout from . import pykoopman as pk from .pykoopman.observables import TimeDelay, Identity @@ -346,11 +347,11 @@ def plot( if figsize is None: figsize = (4 * n_metrics, 4) - fig, axes = plt.subplots(1, n_metrics, figsize=figsize) + fig, axes = subplots(1, n_metrics, figsize=figsize) if n_metrics == 1: axes = [axes] - cmap_obj = plt.cm.get_cmap(cmap) + cmap_obj = cm.get_cmap(cmap) n_legend = len(legend_values) for ax_idx, metric in enumerate(metrics): @@ -378,9 +379,9 @@ def plot( if title: fig.suptitle(title, y=1.02) - plt.tight_layout() + tight_layout() if save_path: - plt.savefig(save_path, bbox_inches='tight') + savefig(save_path, bbox_inches='tight') return fig, axes diff --git a/examples/all_dsa_types.ipynb b/examples/all_dsa_types.ipynb index 5700a44..692b603 100644 --- a/examples/all_dsa_types.ipynb +++ b/examples/all_dsa_types.ipynb @@ -5,16 +5,7 @@ "execution_count": 1, "id": "773aa0fd", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/orcd/data/fiete/001/om2/ostrow/dmrsa/DSApublic/DSA/DSA/pykoopman/__init__.py:3: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", - " from pkg_resources import get_distribution\n" - ] - } - ], + "outputs": [], "source": [ "import numpy as np \n", "import matplotlib.pyplot as plt\n", @@ -32,24 +23,6 @@ { "cell_type": "code", "execution_count": 2, - "id": "52a2ed8c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Automatic pdb calling has been turned ON\n" - ] - } - ], - "source": [ - "%pdb" - ] - }, - { - "cell_type": "code", - "execution_count": 3, "id": "d452743b", "metadata": {}, "outputs": [ @@ -59,7 +32,7 @@ "(18, 9)" ] }, - "execution_count": 3, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -86,7 +59,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "id": "88cad354", "metadata": {}, "outputs": [ @@ -137,7 +110,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 4, "id": "b7b308b7", "metadata": {}, "outputs": [ @@ -221,7 +194,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "id": "8c4c9ea2", "metadata": {}, "outputs": [ @@ -229,20 +202,20 @@ "name": "stdout", "output_type": "stream", "text": [ - "[ 0.8973288 +0.06928948j 0.8973288 -0.06928948j 0.51192657+0.j\n", - " 0.31817322+0.46791128j 0.31817322-0.46791128j -0.61083921+0.53853946j\n", - " -0.61083921-0.53853946j -0.80198335+0.j -0.14361156+0.j\n", - " -0.33773263+0.j ]\n", + "[-0.9 +0.j 0.80763046+0.32343476j 0.80763046-0.32343476j\n", + " 0.06137112+0.71249655j 0.06137112-0.71249655j -0.49680702+0.j\n", + " -0.25875772+0.32447627j -0.25875772-0.32447627j 0.39053652+0.j\n", + " 0.180503 +0.j ]\n", "(500, 9) (500, 2)\n", - "[ 0.8973288 +0.06928948j 0.8973288 -0.06928948j 0.31817322+0.46791128j\n", - " 0.31817322-0.46791128j 0.51192656+0.j -0.61083921+0.53853946j\n", - " -0.61083921-0.53853946j -0.14361156+0.j -0.33773262+0.j\n", - " -0.80198335+0.j ]\n" + "[-0.9 +0.j 0.80763046+0.32343476j 0.80763046-0.32343476j\n", + " 0.06137112+0.71249654j 0.06137112-0.71249654j -0.49680702+0.j\n", + " -0.25875772+0.32447626j -0.25875772-0.32447626j 0.180503 +0.j\n", + " 0.39053651+0.j ]\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -270,7 +243,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 6, "id": "e59db62a", "metadata": {}, "outputs": [ @@ -278,9 +251,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "DMD prediction MASE: 2.9924139443833934e-07\n", - "DMDC prediction MASE: 0.4920231093673477\n", - "DMDC prediction MASE: 0.3430521570675024\n" + "DMD prediction MASE: 2.4295793244821465e-07\n", + "DMDC prediction MASE: 0.5514180132934315\n", + "DMDC prediction MASE: 0.39012201230797877\n" ] } ], @@ -306,7 +279,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "id": "721bc598", "metadata": {}, "outputs": [ @@ -314,10 +287,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "Fitting DMDs: 100%|██████████| 3/3 [00:00<00:00, 455.34it/s]\n", - "Caching compare objects X: 100%|██████████| 3/3 [00:00<00:00, 39199.10it/s]\n", - "Computing DMD similarities: 100%|██████████| 3/3 [00:00<00:00, 3006.67it/s]\n", - "/orcd/data/fiete/001/om2/ostrow/dmrsa/DSApublic/DSA/DSA/dsa.py:412: UserWarning: Warning: You are using a DMD model that fits a control operator but comparing with a DSA metric that does not compare control operators\n", + "Fitting DMDs: 100%|██████████| 3/3 [00:00<00:00, 586.86it/s]\n", + "Caching compare objects X: 100%|██████████| 3/3 [00:00<00:00, 40590.04it/s]\n", + "Computing DMD similarities: 100%|██████████| 3/3 [00:00<00:00, 1557.68it/s]\n", + "/Users/mitchellostrow/Desktop/Projects/dsa_test/DSA/DSA/dsa.py:412: UserWarning: Warning: You are using a DMD model that fits a control operator but comparing with a DSA metric that does not compare control operators\n", " warnings.warn(\n" ] }, @@ -332,9 +305,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "Fitting DMDs: 100%|██████████| 3/3 [00:00<00:00, 407.15it/s]\n", - "Caching compare objects X: 100%|██████████| 3/3 [00:00<00:00, 38716.65it/s]\n", - "Computing DMD similarities: 100%|██████████| 3/3 [00:06<00:00, 2.23s/it]\n" + "Fitting DMDs: 100%|██████████| 3/3 [00:00<00:00, 662.82it/s]\n", + "Caching compare objects X: 100%|██████████| 3/3 [00:00<00:00, 88612.06it/s]\n", + "Computing DMD similarities: 100%|██████████| 3/3 [00:07<00:00, 2.36s/it]\n" ] }, { @@ -343,7 +316,7 @@ "(3, 3)" ] }, - "execution_count": 8, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -381,7 +354,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 8, "id": "2ea88dc2", "metadata": {}, "outputs": [ @@ -389,19 +362,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "/orcd/data/fiete/001/om2/ostrow/dmrsa/DSApublic/DSA/DSA/dsa.py:622: UserWarning: When using cross-comparison with a list of arrays, gDSA treats each array as its own system.\n", + "/Users/mitchellostrow/Desktop/Projects/dsa_test/DSA/DSA/dsa.py:622: UserWarning: When using cross-comparison with a list of arrays, gDSA treats each array as its own system.\n", "If arrays within X (and Y) are samples from the same system, switch to using GeneralizedDSA(X=[X,Y], X_control=[X_control,Y_control], Y=None, Y_control=None.)\n", " warnings.warn(\n", - "/orcd/data/fiete/001/om2/ostrow/dmrsa/DSApublic/DSA/DSA/dsa.py:412: UserWarning: Warning: You are using a DMD model that fits a control operator but comparing with a DSA metric that does not compare control operators\n", - " warnings.warn(\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Fitting DMDs: 100%|██████████| 9/9 [00:00<00:00, 434.61it/s]\n", - "Fitting DMDs: 100%|██████████| 9/9 [00:00<00:00, 406.73it/s]\n" + "/Users/mitchellostrow/Desktop/Projects/dsa_test/DSA/DSA/dsa.py:412: UserWarning: Warning: You are using a DMD model that fits a control operator but comparing with a DSA metric that does not compare control operators\n", + " warnings.warn(\n", + "Fitting DMDs: 100%|██████████| 9/9 [00:00<00:00, 1970.70it/s]\n", + "Fitting DMDs: 100%|██████████| 9/9 [00:00<00:00, 1698.48it/s]\n" ] }, { @@ -415,9 +382,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "Caching compare objects X: 100%|██████████| 9/9 [00:00<00:00, 8832.18it/s]\n", - "Caching compare objects Y: 100%|██████████| 9/9 [00:00<00:00, 8515.39it/s]\n", - "Computing DMD similarities: 100%|██████████| 81/81 [00:00<00:00, 1457.22it/s]\n" + "Caching compare objects X: 100%|██████████| 9/9 [00:00<00:00, 9346.06it/s]\n", + "Caching compare objects Y: 100%|██████████| 9/9 [00:00<00:00, 11335.96it/s]\n", + "Computing DMD similarities: 100%|██████████| 81/81 [00:00<00:00, 1378.46it/s]\n" ] }, { @@ -426,7 +393,7 @@ "(9, 9)" ] }, - "execution_count": 14, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -472,7 +439,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 9, "id": "997a05d3", "metadata": {}, "outputs": [ @@ -480,17 +447,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "Fitting DMDs: 100%|██████████| 1/1 [00:00<00:00, 577.73it/s]\n", - "Fitting DMDs: 100%|██████████| 1/1 [00:00<00:00, 696.73it/s]\n", - "Caching compare objects X: 100%|██████████| 1/1 [00:00<00:00, 8305.55it/s]\n", - "Caching compare objects Y: 100%|██████████| 1/1 [00:00<00:00, 10058.28it/s]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Computing DMD similarities: 100%|██████████| 1/1 [00:02<00:00, 2.92s/it]\n" + "/Users/mitchellostrow/Desktop/Projects/dsa_test/DSA/DSA/dsa.py:412: UserWarning: Warning: You are using a DMD model that fits a control operator but comparing with a DSA metric that does not compare control operators\n", + " warnings.warn(\n", + "Fitting DMDs: 100%|██████████| 1/1 [00:00<00:00, 812.38it/s]\n", + "Fitting DMDs: 100%|██████████| 1/1 [00:00<00:00, 646.57it/s]\n", + "Caching compare objects X: 100%|██████████| 1/1 [00:00<00:00, 26886.56it/s]\n", + "Caching compare objects Y: 100%|██████████| 1/1 [00:00<00:00, 5309.25it/s]\n", + "Computing DMD similarities: 100%|██████████| 1/1 [00:01<00:00, 1.57s/it]\n" ] }, { @@ -499,7 +462,7 @@ "0.0" ] }, - "execution_count": 12, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -515,20 +478,15 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 10, "id": "6a5de212", "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [] - }, { "name": "stderr", "output_type": "stream", "text": [ - "Fitting DMDs: 100%|██████████| 3/3 [00:00<00:00, 105.11it/s]\n" + "Fitting DMDs: 100%|██████████| 3/3 [00:00<00:00, 171.00it/s]\n" ] }, { @@ -542,8 +500,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "Caching compare objects X: 100%|██████████| 3/3 [00:00<00:00, 3265.74it/s]\n", - "Computing DMD similarities: 100%|██████████| 3/3 [00:00<00:00, 714.53it/s]\n" + "Caching compare objects X: 100%|██████████| 3/3 [00:00<00:00, 2990.95it/s]\n", + "Computing DMD similarities: 100%|██████████| 3/3 [00:00<00:00, 928.49it/s]\n" ] }, { @@ -552,7 +510,7 @@ "(3, 3)" ] }, - "execution_count": 13, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -576,21 +534,13 @@ "sim = dsa.fit_score()\n", "sim.shape" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8f0e06c0", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "dsapublic-env", + "display_name": "DSA (uv)", "language": "python", - "name": "python3" + "name": "dsa-uv" }, "language_info": { "codemirror_mode": { diff --git a/examples/dmd_lorenz.ipynb b/examples/dmd_lorenz.ipynb index 4a616f4..6e3c630 100644 --- a/examples/dmd_lorenz.ipynb +++ b/examples/dmd_lorenz.ipynb @@ -1,53 +1,12 @@ { "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "a61e6f81-6e9e-49c0-babf-a16219290dbc", - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext autoreload" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "167784bf", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: tqdm in /Users/mitchellostrow/opt/anaconda3/envs/dsapublic/lib/python3.11/site-packages (4.65.0)\n", - "Requirement already satisfied: scipy in /Users/mitchellostrow/opt/anaconda3/envs/dsapublic/lib/python3.11/site-packages (1.11.1)\n", - "Requirement already satisfied: numpy<1.28.0,>=1.21.6 in /Users/mitchellostrow/opt/anaconda3/envs/dsapublic/lib/python3.11/site-packages (from scipy) (1.25.1)\n" - ] - } - ], - "source": [ - "! pip install tqdm\n", - "! pip install scipy" - ] - }, { "cell_type": "code", "execution_count": 3, "id": "d29cfc8e-40bf-4a51-8420-345b8e54bb06", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mitchellostrow/opt/anaconda3/envs/dsapublic/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - } - ], + "outputs": [], "source": [ - "%autoreload 2\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from scipy.integrate import solve_ivp\n", @@ -128,11 +87,18 @@ "metadata": {}, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 1/1 [00:10<00:00, 10.17s/it]\n" - ] + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "911b78bf93f44442b6bd8662cec429b6", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/1 [00:00" ] @@ -271,7 +237,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 11, "id": "44c64307-90c4-426b-bba0-4929375f36db", "metadata": {}, "outputs": [ @@ -279,22 +245,24 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 3.12 ms, sys: 3.67 ms, total: 6.79 ms\n", - "Wall time: 16.1 ms\n" + "CPU times: user 9.73 ms, sys: 8.38 ms, total: 18.1 ms\n", + "Wall time: 3.1 ms\n" ] }, { "data": { "text/plain": [ - "{'MAE': 2.5213026358451924,\n", - " 'MASE': 0.5486326863713192,\n", - " 'MSE': 12.68645078434762,\n", - " 'R2': 0.8358856973464476,\n", - " 'Correl': 0.9128922820091248,\n", - " 'AIC': 8.923584201972321}" + "{'MAE': 2.5167064420307153,\n", + " 'MASE': 0.5478299945180521,\n", + " 'NMSE': 0.17307321464288092,\n", + " 'MSE': 12.60745008437796,\n", + " 'R2': 0.8366451689614438,\n", + " 'Correl': 0.9133078455924988,\n", + " 'AIC': 8.917337561150338,\n", + " 'logMSE': 2.534287915760268}" ] }, - "execution_count": 13, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -306,7 +274,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 12, "id": "35d37dc0-efdd-49fb-a296-feb6c17c4aeb", "metadata": {}, "outputs": [], @@ -317,7 +285,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 13, "id": "f7628177-2a3b-4c7a-a859-3cc203d3e367", "metadata": {}, "outputs": [], @@ -327,13 +295,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "29a2c838-9367-40e3-927c-b805c394a6db", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -355,7 +323,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "id": "a5a1bd0a-472b-429a-80e9-0998855b957e", "metadata": {}, "outputs": [ @@ -363,22 +331,24 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 1.77 ms, sys: 729 µs, total: 2.5 ms\n", + "CPU times: user 4.97 ms, sys: 1.15 ms, total: 6.12 ms\n", "Wall time: 1.68 ms\n" ] }, { "data": { "text/plain": [ - "{'MAE': 2.5992292470271003,\n", - " 'MASE': 0.5639169094725991,\n", - " 'MSE': 13.477207460761758,\n", - " 'R2': 0.8256031498871144,\n", - " 'Correl': 0.9070751070976257,\n", - " 'AIC': 10.70919812055686}" + "{'MAE': 2.5789991592384194,\n", + " 'MASE': 0.571164058723301,\n", + " 'NMSE': 0.1865428760401078,\n", + " 'MSE': 13.510877507404555,\n", + " 'R2': 0.8233529400789129,\n", + " 'Correl': 0.9058391451835632,\n", + " 'AIC': 10.711693300496558,\n", + " 'logMSE': 2.6034951022983597}" ] }, - "execution_count": 16, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -399,9 +369,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "DSA (uv)", "language": "python", - "name": "python3" + "name": "dsa-uv" }, "language_info": { "codemirror_mode": { @@ -413,7 +383,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.10.19" } }, "nbformat": 4, diff --git a/examples/dmdc_linear_system_test.ipynb b/examples/dmdc_linear_system_test.ipynb index 3ae5bc9..418ddc5 100644 --- a/examples/dmdc_linear_system_test.ipynb +++ b/examples/dmdc_linear_system_test.ipynb @@ -47,18 +47,18 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Ground truth A eigenvalues: [-0.87865666+0.3611959j -0.87865666-0.3611959j -0.63342513+0.09350672j\n", - " -0.63342513-0.09350672j 0.50029664+0.40533415j 0.50029664-0.40533415j\n", - " 0.50450346+0.10018783j 0.50450346-0.10018783j 0.18230014+0.j\n", - " -0.14354022+0.j ]\n", - "Ground truth B singular values: [3.3561271 2.53100558 1.62794133]\n", + "Ground truth A eigenvalues: [-0.47397087+0.82331745j -0.47397087-0.82331745j -0.45512345+0.j\n", + " -0.00880071+0.70984946j -0.00880071-0.70984946j 0.21876129+0.j\n", + " 0.20142906+0.39072123j 0.20142906-0.39072123j 0.01263768+0.35684693j\n", + " 0.01263768-0.35684693j]\n", + "Ground truth B singular values: [4.32455287 3.18211775 1.78226893]\n", "\n", "A matrix spectral radius: 0.9500\n", "System is stable: True\n" @@ -93,22 +93,22 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 22, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -131,7 +131,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -206,12 +206,12 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -278,7 +278,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -294,7 +294,7 @@ "\n", "DMDc model fitted successfully!\n", "Recovered A matrix shape: torch.Size([10, 10])\n", - "Recovered B matrix shape: torch.Size([10, 3])\n" + "Recovered B matrix shape: torch.Size([10, 10])\n" ] } ], @@ -332,12 +332,12 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { - "image/png": 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AAAiHSlsAAFAitm0zGz/e7OabzYYONTvnnNyfuq7bdT/yuvvuu61Hjx5xsVkOPfRQu+aaa2K9GgAAACmvVatW9tRTTxVa2fnhhx+63//88093/aeffnLXJ0yY4K5rvopUc+6559pJJ51kiYikLQAAiLpffzW7/HKzESPMpk41K1tWfZ1yf+q6btf9Wq6kZh2++uqrrV27dla5cmVr1KiRDRgwwJ577jk3m2uiJnQVbBd0KY5UDuIBAACiJTMz0y699FJr0aKFVapUyRo3bmyDBw+277//PmRiNdpWrFhhRx99dMj7DjzwQHd/rVq13PVXX33Vateuvdf/p5cc9i4VK1Z08fd9993nJkXD3qE9AgAAiColYu++22zVKrO2bc0qVsx7f4MGZllZZgsW5C6ny377Re//X7hwoUvQKhB94IEHrGvXri5w/vnnn+3FF1+0ffbZx0444YSQj9VEARUqVLB4dMMNN9gll1ziv77//vvbRRddZBdeeGHI5bOyslzgDAAAgJJ36qmnuvjrtddeszZt2tiqVats/Pjxtnbt2lLZ/EoSK1GanZ2d7z7FhLq/pIwbN872228/27lzp3333Xd2wQUXWJMmTez888+3ZLArRscIVNoCAICoURHrY4/lJmw7dsyfsPXodt2v5bR8NItfL7vsMitfvrxNnz7d/vnPf1qnTp1c4HziiSfaZ599Zscff7x/WVUEqPpWSdxq1arZ/fff727XbW3btnUB7r777mv//e9//Y8JHm4mqlLVbapaDaxeVaDep08fN3usKhx+//33POv60EMPuSrgGjVquKB2x44dYV9X9erVXbDtXcqVK+ce510//fTT7YorrnAtDerXr+8qOwpbV91/2GGHudvr1KnjbtcQMk9OTo7ddNNNVrduXfd/qNoXAAAAeSm+mjhxoj388MMutmrZsqX17dvXbr31Vn+xgFocyMknn+xiLu/6ggULXJyqmFDxnk7MKwkabPPmzXbGGWe4mFVFCCNHjsxzf0FVvIEjq/T7sGHDbOPGjf4KWcV499xzj3Xp0iXfY9W668477yxwl9erV8/Finrd//d//+cKKGbOnJknptTzN2vWzBVT6DnHjBkTcv08P/30k7tN8WpgdfDYsWNdfK9tddRRR7kKYs/u3bvtuuuuc8tpnRTHBlf86v896KCD/Mscd9xxbh94vPj5nXfesUMOOcSN2lPhR82aNS09PT3Pc2l7a39o35QEkrYAACBqJk/OraBVhW1ho/V1v5bT8lOmROf/VyXDl19+aZdffrkLoEL/v3lXTEGqgmdV4p533nn2wQcfuNYK119/vf3yyy928cUXu8D266+/LvL63H777fb444+7BLISyXp+z//+9z/3f6saWPerGuHf//637Q1VdijRrGF4zz//fKHLN2/e3N577z33uxLKCnqffvrpPM+n7Th16lR75JFHXLCdkZGxV+sIAABQHErIhbsoKRjpsrpEsmxRKIGoi5J4qjYNZdq0ae7nK6+84mIu7/qWLVvsmGOOcSf7f/zxR5eIVJHBkiVL8jz+0Ucfte7du7tlbrnlFhevFicuUyGB+uMqCan10EUjuhSnzpkzx79eov9r9uzZLhaOlOLaGTNmWL9+/fy3Kb5UTPzYY4+551NxgZLZf/zxR5HWfdu2be45VFDx7bffum2kdffo/1Byd9SoUa7id926dS62D7R161aX2NV6apuXLVvWHQsEv4e8baxtcsopp7gCCe27QLo+ZMgQV0hREmiPAAAAokInsceOzU3GRjoqX8tpeZ1oV8FnMduy+s2fP9+dTVd1bCBVnnpVrEroqgrCc+aZZ+YJRFXBoGpTVeyKgropU6a4ANGrSo2UKnd1ht4L/I499li3Hjpjr2BZ1bXesDH1/lJVRUHVtoVp3769S656vMqEcFStqypaadiwYb7eZt26dbPhw4f7n/vZZ591wW1aWlqx1xEAAKA4Pv/887D3KY4JTBKqGjNc4lXVlUpcehR/qa1BsMDRWYXRyXklC9W2SifOe/Xq5WJAJfoUT0kD9Qgzc/FWYKsCJWJ18dx7770u0fjxxx+7UVQeVa8qnpQOHTq4k/RPPvlkkeMyneBXb1sVMgSuh5LOSqYqEalqX9Hveh0atVYQbU8lP7Ud1UpALbzOPvts//2Ko2+++Wa3PUSxuAoiFA8HVwwXZNeuXW77akScaPuoqMCj51N1s5KsomX1XghuYxFICV7tm99++y1PpbFGr3nPI2r54PUGVrHF6tWr3XsyVFV0SlbaKouuP5qmTZtG3LxZJdb6Y1H5tZoh648omN4gKkvXAZT+yH/44YcSegUAACSvrVvN5s1TIFy0x2l5Pa4k5wfTd7uGWHm9tgKpfUEgnU1XUBxI13V7UXlBuii4EwV43v8TeHAh/fv3t73Ru3dvi6bA9RcvQAUAAIDlSwYuX77cJVtVLevlo0LloQKp0lbVohryr4SukqeKE4MrbYPjRF0vTnxaECWd33rrLVdEoATsm2++mWekWDhqJaBYe9asWW402UcffeRPMG/atMltl2jE11WrVvUnbINjU7V7UEI1ML5WMj041ld1r4o0lIhWtbHXpiJ4ewc/Tu0udCyhkWgyevRo1w7i4IMPtpKSUJW2KmHW2Qe9YQKz3eEsWrTIVbRo0o433njDVYZ4zZB19sB7Y6mCRtl37Vhl5XWfhgjqTA0AAIiMcqGa96Bq1aJtsfLlzbZvN1OBaZiOBhHTCVqd2A3uHetVB1SpUiXfY8K1UQhHVQQS2B9LZ/1DCZywwGvLEDz0KpqCX0tR1jWU4AkX9BpKcv2TnQoQNLRRQwZ1UKEqmpNOOqnAx+iAT7Hqr7/+6tpZ3HHHHXn6DgMAkCrUQiCc4PZXXs4nEoMGDbJoUTGgKl91UR9Y5aA0aqmg724lbNXmQNWoimUVr2rIfajq35KmQkkVPSpGUUWu4katS2EUo2jdRcln9YjV6490PoRIY9YKIWLT4J61kbxGJVtfeuklVxSq2FYVtsHbO9QxgvanCj+VkFYVskbrBb/3UrbS9uijj3ZDB9VrIhJKxLZu3dr1tNCbRmXTerOpfNzzxBNPuDMJ2tCdO3d2j1HmXuXRAAAgcpUq5SZgQ0xYWyAtX66cgty939oa7qYgWcP4dbK3OBQzaLhZIF1XnBA4tC1w0oPAib6K8v+oV2wgtWGIpkjWVQG5FLV3G4pfgBDpMECvAEFtObTfNExPBwvBw/wAAEgFausU7uIl/SJZVpdIlo0GxY+BMamSjsExl+JMJXWV6+ratatrWRCqxVVwnKjriieLQ/FfqNhPlannnHOOS0jqonYGoYoeCqPtl52d7RKhqmZVcrSk4+tatWq5Is3A+FrroJPlgfNfqLhDJ8GPOOIIt/3Wr18f8f8xdOhQW7x4sT3zzDOunYK2VUlKqErbopo8eXK+MyY626KAV/Tm0c5TvwuP/tD1GD02HA2rDBxaqVJvUXae6pPIaVvpjAjbLPGxL2NLf0f6PNMXki468eSdgdRQm8zMTHe7vpS9ZbzrOhuqJJeoSfvcuXPdsBLNIu8FP96Mojp7qr6gosBDgYS+7AMvOiurn/r/S/KMI0rnb9J7Hu9SGFXYtm+vVgTqIRv5/7N2rYYbqQo2ty/u3lJCTDPCakiTKhs0xF/vZU2qoPe4hqkFvp7g16dqh9NOO83NaquY4JNPPrH333/fVUBoOVVQHHDAAfbQQw+54VQakqXAL/C5vOcL/j3wtquuusqdNFZLAw0P06ggVVOqKjh4/QJ/Bgpe9+DrkaxrixYt3N+rXqcqWBSYa1heqOcLXqdY89YvVAwWj/GFChB0iVRgAYLowEKTaqgAoSgVRBJuUhXt+8AD3cKS94EHsaWxrPajt+7e+6401sH7/IvnZb3v6ERatjivrbDvs8D3cDIvW9h7uLSXDfW3GYvPiFgtm2yfEYHi4fMkkvgzcNnClMaySgb+85//dLGdYk9NTKWJrjTXgCbc8igeUw9U9UbVsZOOuzRvgGLN4447zj3nXXfdlScOD0xyqhesRukoLn333Xfts88+y7NO4WLR4NejSlMdK2pddEJZx5C6iOZb8JKp+j8L2hbefXr9K1eudNWxmtxXE4/ppLO2g5ZRfK2qW7U2UIythLCSsmoxoPt1u443tYyKNefNm+ePfwJfR/C+CV43TRymuFfHuR07dnSFmhs2bPAvq/YTOv598cUXXYJXCVgvJxhq2wW/Vj1eI/9vvPFGO/LII22fffbxLxd4/Bv4XMWZ2C4lkrZ6wzRq1CjPbbquJOv27dtdNl0bLtQyOqgL58EHH7QRI0bku12Jkb2ZPCTV6ENIySG9iYPPiiGxsC9Ljv4+dJJISVAvSNMX4tKlS93t+szRz8AvFH0Jeu1ddKZSX5rhKFDwvkCUzNEkTpqRU59nwX+XgQcWa9ascTOJhqPG+F5vICV41TdIryEwuesFBjqbi/j8m1TQpefyEv2RGDSojE2ZUtZ27vRFNBmZRiHl5JSxtDQd/EUnEaggVD1sFdTedttt7u9F7zklvK699lrXNinw9XgnNDwKmBXgKVDUiV69lzV8Solgb7kXXnjBLr74YpcY1vtdsYESnt5zeX9Xgdsu8Kcu6numvw1NyqC/ZVVXaNIGBeHesl6gJ6FOhHj7x1tWl+B9Vdi6Ku7RwYECVrWgUgXByy+/HPL5vORopO+Hkqb10ProczF4uNzmzZst2QsQilJcMGbMGP/BWEETt3zxxRcRT9zy5Zdfhh26qYOagQMH+q+rTZni71B0QHfooYf6r3/zzTf+/af3oD7PvAlTdFIhcJsoie0djAXT901gclvbU++VUPQdGzjsVlU6BfVvDpycRgflgZVBwfS83ne4DlD/+uuvsMtqfb3qd31/FzSZoCqEvH2qah8NRQ1H29eb2VoVRjoYDkf7zZuUUM+p5w5H7wfv5K/WtaCYQ70AVUmlfaptoNnDw9HJLFVlifogBlZJBVPcowN9WbVqVYHzk6iCzYtP9F6YNGlS2GWVsPD6Juo9NnHixLDL6rPVmwBT7121NQlHz+klQxRz6W8jHK2r1ln0t1ZQlb22gbaF6G+4oAmblKgI7NX46aefhl023GdE8N9mrD4jgvEZUbzPCPXk9GJ9xSax+oxQTOKdENA+Dp6HwBNYpKJ4VX9L4eg94X2uFmVZxRgFjdrSiXHFl+Il5ZT8VPyokTJ6vBJ6Z511louxvOM1JXGvv/56+89//uP+FvVZqESlRobrJL62l2JD7/tbf2ei7aLJdFVdq4m39Heh5KQSh4GJRr0+Pdb7G1FiVs/h5aq8/azerEowq1BBxTv6P70etFpv7Tvdrv3irUMo3vb0vpv1faf9qJFvKhTwHquKVB1j6rXr+1Wfg+qdq88YbxklUnW/tqOKLO69916XCNc6e3k877Mn+P/3Xr9aSikRq/9Px0GKazVqSY/X4/R5pf9XyV21RFDCXNtR8b+eS8sEbrvA42xd137WNlOvX/0MXBd9Hnr03tHzaZ31nRAcO3sTFReGI+Vi0B+c3gge7Xx9SSoIUdk3IqMPCv3BaLuRtE1s7Mu9pw90fXl5H+z6UvV+6otCX5ZeIlbBhvelq0DBCxaU/NRF1bDesgpm9AXh3acv0cDfNWu816tHX/z6otGXqW73eld6QUDgZ5wXAGpdFIwroNJPb7ZQBSDeOuh1BScRwh0U6fFKCGuddCGhG9u/Sb3PFLR475lIHHSQ+sqazZ9fxjp2VKIx/LKKgRYtyl1+wAC9Ny1q9L2sFgmFCVfhpKBYl3B0EB18sB/4XDpQCn5uHSAH36Zg1qt8LUhwQlJ0QBAoXJKgsHUVVSTrUtjzaVKJeKL3pd7jOsDRwVOg4OvJWIAQarhiuOICPSZcb7jA5KQOPsIlbbWtA5dVIitcj2R9bwQvG664Qf9f8LI6MPLeq/pu9E5C6bskcFkVYXgHtoW9Ni0b7sBT34mRLiuBy+qgtrBlvaRtJMt6CYNIlvW+j5WALGhZfbd7CbFIlvUSbZEs671fCltW3+/ewbaXRChoWe97R78XtqwXCxW2rNbR22aFbV8t6yW6tVxhy3rvicJeW+CyXoIgHK2jt6z2SUHLKm7yltU+KWhZba/gv/twwn1GBP9txuozIhifEcX/jPCS8bH8jFD86hUNBJ9UD6TbvZMFhRUX6D7vPRrtZb3Pdq2rfr/99tvdJZh3TCaaoEyFAIHPo5NUqrT1/j4Vw+iEu7aDl0CcOXNmvufV94X3vFpW29v7XYlX77qWUULY22/ea1MCWZfAdfHohJnWobDt0KxZszwnZ/SdHJjoDHzsTTfdlCdG0bKB9ytW1kkZ0fasXr26/7m1HZTA1SXwMTrZqc/KwNvuv/9+l9gOt391EkgTpgUmYgO3lfaHrus9FqrQQwUhOl5Wwty7P9Sy3mel1j34BJb3/xWmjC9exrcVkTZIYZM36GyRsvOaXMyjEmxVJ+jDQhtNH2Lp6el5nkcZeX0RRHpgojeaEh16TpK2kdObV18kSuyQtE1s7MvIt5O+FPShrc8NfZF6nxn64A9XuarPO51t9KpIdNZOlSQ6WNcXun7qyz0a7QiisS+9ZJD3eB0k6jkDk7u6Ta9D1/U5rW0RqkJGr8tL4Oqi/k7eARRKdj+KDp6UGNTw7KIkwH791UxzDqxapYoiBZT5l1HcooIP5aMUu+0pOkIQr9JVyQtajhTtPRrv8Vkksayq91QFE9jKS9VzqhhRsiRU0jZUpa2+P3QwHGo7JEJ7BB3YBJ6EYuhzfLU8KMqyul/7UydaCvpMoz1CYrRHCP7bjMc2BrRHiLw9gr4nvL6isfw80Xe7qnJVaR4u/gz+/IikjUIqLevFjsV5Xv1dv/POO2602pIlS1z7hmitb/Dyibbs1q1b3cgaxW4nnniia+MQblnRMa/ey4rDgt/Lei5t28Li1KSutO3fv3++YSEacqjbvbMSGnqjoRhewKwPEl1XaToA7A19SCsZ6yVpg4dX6IPb+4BWYkEJNp1NDEzGhkrIKnmpnpfxKjhJqNeg4eqFnSX3HquzltpWXpJXF509FZ1o85K22q46A6zhWbp4VTaIvf32y03aPvZYbmJWu1gj47xJyjQ6WX8KSujeeCMJWyAcnajSyaxAuq7vjnCTggSOwAiu8glVsR2sKCd6SmNZxeY68NS6h3qOeFtflo1sJIhXJR+pokwIlMzLxtN7rbC/zdJYB5aN3nbw/ja1XKy3r5e8Lcq6FOXEdrIvq+NNb06Sgh4b7j7FHhq5qVYFOi4r6fVNpGUfe+wxV8WrAlEltQt7f3rv5VAxWKTv7YRK2uogXv0WPaqsUF8ovZE0iYaqEJYtW2avv/66u1896zQ0UiXY6tH21Vdf2f/+9788jZrV5kCVtSrD1vBjVeUq462qBgAoSv9QDfnR55H35aakbXB/bAW3OthW4jHwjJquB/YLSxXBZ4B1dt87w+/1nNJnvz6XdQncZjoLHNhvSwkMna30kri6RGvWVxQvcTtypGa1VS9NM+0qjczVLtFb/aijdHI1d/IxAMUrQAAAAIimBB2MXyrUe1iX0pRQSVs1+dfscx6vr6ySrq+++qorU1b5tkdD5ZSg1aQjmrlOvTbU7DlwQgI1DtaBvybgUN8wNW/XRA3B/cMAIHCYlVqoKEmrizepoWgYj5e0VYLRa4HgJWnDVUYhP52NVAVyYEP3QNqm+lz3+popSa6Lqm9FfZu8faGErwIQVTKj9KiN2uGHm+mrW3MEqFWdRgbp9ih08wASTkkUIAAAACA5JVTSVs2CC8r6K3Eb6jEFzXAuaoVAOwQA4XhDTERD9lXlFDyJj9qtBFbZelWk6teKkhFclatqZyVwdfFmMw5MjOiipLkeo1YUGvYTyVBh7D39+Wi+uz1z3gEpqyQKEAAAAJCcEippCwClwZs1VRM56af6y+6///7+5KyuK2mrSTSUpNVPVXAySVDsKPmqJKwu4ahvkCpxlRDRRftLLRSUxG3fvj0TIgJI2AIEAAAAJB+StgCwZ0IstUpRmxRN8qLKzcCEYGC17cCBA13yFomjS5cu1qlTJ5eE135WQl7DlNXaQjPU7rvvvv5ldb83IRwAAAAAALFA0hYAzOyHH35wCT2PkrLqba0qTFXTBlbRkrBNTJqUTG0RdNlvv/1c1a0StIFVb/p9xowZLmmvxK32v2ZQVTU1ldQAAAAAgNJC0hZAStGEVKqm1aVv377+nqZK5KniUgk6XerUqUOSLsmpklYT/wTauXOnm+BMFbiqxNVFvXDVEqNp06aun2S4idEQYOLE3FnH0tIK3ywZGbmzkw0cyCYEAAAAgD1I2gJIepqYSklaTfCiJJxHQ+T32Wcf93ubNm2sXbt2MVxLxAMlZwcMGOAqbdesWePeI3rfKKG/cOFCV61L0jaChO3o0X9fLyhxq4Rtevrf10ncIkirVq3smmuucRcAAACUjLvvvts+/PBD++mnn2K+idXPv0ePHvbUU09ZqiNpCyBpqVpSk7eoutajIe6arErVtIGTVmmSKsCjCuwmTZq4S9euXV0bhWXLlvmT/KLex3PmzHG3qQq3WrVqbMDghK2XkA2VuA1O2HqPi0Li9txzz7XXXnvN/V6+fHnX4qRbt252xhlnuPsC/96VFFy8eLG99dZbdvrpp+d5HrXR+O233+yVV15xjwtc3kvyq42KqvYvueQSO/zww4u9zn/++ae1bt3af13V/nrv3Xvvvda/f/9iPy8AAECyuPji0v3/Xnih6I9RsdCDDz5on332mS1dutQVfKg4aOjQoXbOOedY1apVLRETuiNGjChwmYImWg1nwoQJdthhh7njdk0QjfzIUgBIGjk5Oa4iMnD4+7Zt21x1pJJqvXr1ssGDB9sBBxzgEi+VKlWK6foiMSjBp8Sc3j9qneBZvny5bd682ebOnWtfffWVTZw40RYsWJDnPZhyQr12JWaVoC0oYVvQ44vpqKOOclXSSoZ+8cUXLiC8+uqr7bjjjnMTDwZq3ry5S8wGmjJligu6QyXj77nnHvfcv//+u73++usuyBw0aJDdf//9e73e48aNc8/97bffus+t448/3p0gSGRZWVmxXgUAAIASp5F5PXv2tC+//NIeeOABV0A0efJku+mmm+zTTz91cV44gRNhx5sbbrjBxafeRS3jvHjYuwQi9osekrYAEt6mTZvs119/tYyMjDzDOVQFpwq4I4880nr37u0qIr0etsDe6tKlixu2o8nKVMGtNhyqytT7cNKkSakZrKiidsiQghO34RK2elwkPXAjpJMyqqjX370S7rfddpt99NFHLoH76quv5ln2//7v/+ybb76xv/76y3/bqFGj3O2q1A2m5L2eWz2RDz74YHvxxRftzjvvtLvuusslcj36XFKSuGbNmu4xAwcOdIn9gmjSOz233l9aZ32+aaJEzy+//GJHH320myhPJxPOOuss18oj8OTVI4884io6tA20joHJ5J9//tlVBOuklv6viy66yN82RgcY+tzUezmQkt2BVcTfffedey16DiW8r7rqqjwjGnRSTBXCZ599tnvt+j8ieZzakShJrftVdfzGG28UuK0AAADiyWWXXeZix+nTp9s///lP69Spk2vDd+KJJ7rKW8U5Hh0/PPfcc3bCCSe4IgEvXtNtbdu2dZNf77vvvvbf//7X/xgVI+hxgce8itt0m6pWRT/12PHjx1ufPn1cZe+BBx6YJ0aVhx56yMWSilHPP//8AgtPFHd6c7/ooqIoLx7WRaPVrrjiCtfOSqNZVShV2LrqfhVViDefjDeyzYtpb7rpJjdiTv+Hqn1TEUlbAAlJZyL1Qa/qRiVbdFZTSTJVPu7evdu/nCYYC5V0AfaWTgAo8aTK7bS0NDeUXUGF9/5UsOQJruxM2cTtzTeXSsI2HCUeu3fvbu+//36e2xWwKrj0WiqoQv+dd96x8847L+LnVmJTw8KUGBa101BCV4lTVWLPmDHDPV+k74Xt27e7Kl7x3ksKdPUaVMGhg4ExY8a4KlwdFHhuvfVWF4QriayTCG+++aZ7faIEqV6nAuNp06bZu+++6yo+FGTLEUcc4aqG33vvPf/z6fNU20IJbFHSWVXMp556qs2ePdvdp2Ss9xyexx57zG1rVZhoXSJ5nAJ1Jc6//vprS09Pt3//+98ukQsAABDv1q5d606AX3755WHbpikxGUiJyJNPPtmdVFec+MEHH7iY8vrrr3cn6i+++GIbNmyYi42K6o477rDHH3/cxYw6Hg6Ma//3v/+5/1vVwLpfLeEUd+0NxdGKWb///nt7/vnnC11ex1FezKmEsqp1n3766TzPV61aNZs6daorSFBlr4pjUg2ZDAAJ548//rB58+a5s2+Bw9dVUeZVPQKlSYk5VRfqomRbYJWtknRKjKmqUffrPZr0vARscII2qIKzNBO2no4dO7qkYTAFsgqQb7/9dpcwVIWDKqkjpYS9ThLpZJKMHDnS9TB7++23/RX+HTp0KPR5VAmhzzQljpUE1igBr8r12WefdQlbBdiBFcEKevWZqIBbwa6WU8800es46KCD3O9K4KqKQslg72BCy6rq4+GHH3afo6qU0HKquBBVaShZrGSrqEebErjexGDt27e3Z555xg455BBXGaJKXdE6a3t6LrjgggIft2TJElcFrari/fff3y3z8ssvuwoVAACAeDd//nwXu6k6NpAqT70qViV0FXN5zjzzTJeU9XjzL6hiV6677jrXsksnw72q1Ejdd999Ls6SW265xY499li3HorVNMGXYj0v3tOyOl7ZmzZviu2UXPV4MXE4qtb1Cl4UQwf3tNV8FMOHD/c/t2JWxaUqlkklVNoCiHuq9PIStF6CTNc17FYTBemDW0M/9GFPwhaxpqHdStZ5NHRdlbfqj6qgS2fKFy1aFNd9q0q04jaGCVtRMB3qc0KBrNoEqJesEqFFqbIN9dwaCqZWAEVtyaIKVFWnqvJALQ7Ua9d7jlmzZrn3j4aoeRcloUWVrJoYb+fOna5iNhTdr+rXwOqPAQMGuM9Tb8icEqsasqaezaIWBdo2XiCtdVB7icB1UPWunkPva48+kwMV9jitm6pAlKT26LUxKQUAAEhkOiGtuFDHrYrTAgXHS4qHFJsF0nXdXlRKenp0Yl+8EUx6vn79+uVZfm8nvg2M4aIhcP2915CKI7CotAUQt1StqIN5naVThZo3s7om51HCloN5JAL1YDr00EPd+1gzyCoxqOFOCpbUxF9njpXoTUpKyGrChVAVtkoCxuBMuba791kSSAlD9YfVGX0Nw9LwtKIOicvMzPQ/d3H3qapm9Z7QRVXap5xyikviav303vGqYoMpkFWbmL2lKldV56pC+NJLL3XbIbAHsNZBQ/XUjzaYRjt4gocFFvY4VQoDAAAkKp1s18n74N6x6mkbLjYM10YhHI3G8goFPOEKQQILB7yigsBCqGgLfi1FWddQggsfypQpU6LrH6+otAUQd9R3UcOX1bNGB/JK3gbOnq7kBQlbJBI16lfPW6/3raoMVUG+ePHiPIFM0lHfqVAJW9HtpdyXSr1l1TPMG+ofTNW16pGtySLU97Uo1JZAwelJJ53krw5Qz+29qageMmSI+7zz+oJpQjVNbqY2GzowCLwoUPZOAGjoWChqNaCK18DJv9R3TOsdOJRP1baqsP3kk0/cfaq09Wgd1Cs3+P/XJbCPc7DCHqeqWiWp1fvXo4Oe4EnRAAAA4pFaoSnW1zD+wFirKBSrKTYLpOudO3d2v3tt1tT/1RM40VdR/h8VKQTSiMBoimRdvdgxcE4a5EXSFkDcWL9+vWuErsSKklk6k6bkrIZaBA/fABKREnBKuKknlYYgKWDSjK4eJbW8937CU0I21KRjgXR/CSVuNfxMLSk0IdjMmTNdH1glY4877jg7++yzQz5G+0PtLNSSoCCa8FDPrUmz1E7hoosucr3ANOuvkpCiCbY2bdrkesTqc029uDX7b3D1RUFUUXDllVfao48+6nrcqg/aunXrXL8zTSSmlghjx451vdAU7KpH2c033+xm2lXfWt2vAFy9Yb1krJZRv1tVe6vVgp5fFcbeZGXectpmej1KHKsljUfPP2nSJPf6FHjrdWnyteCJyIIV9jgljTVRmapxdRCh5K364CZtFToAAEg6msxLJ6HV9kAtrzTCS7Hf6NGjbe7cua6Pa0FuvPFGN8JJ/f4VKz3xxBNuAt0bbrjB3a+4SJMga9JZPbeKDTThWFFpsjO1A1PMqyIpjTRTYUA0RbKuLVu2dPHup59+6kasaWQW8iJpCyCumrd7Z+KUQNCEPOoJqXYI9KpFstGkBF6CTxSkKMmmKnOduFA7hYRN3oZL2AZNMFCSidsxY8a4lgFKkisZqASlJr5SorCggFlVEoUlCu+66y733Np/Snhu3LjRVbcqMRn4PNqP2q+aBEInn1566aUi97hVglXVuqra0Gehqi2UoD3yyCNd1bYm9tLJLW8I2p133ukmANM6Kgl92mmn+ft/6QSBkrxK/KoNghKy6n+r5w6k19W3b1/3XlQCN5AqiBV0K8DX57MmRtP/pXUrSCSP04GDrmt7qS2EkuHqVQ4AAJAI1GJKba0GDRpkt956q5tLQAncf/3rXy7xeu+99xb4eI3Y0ugtTTymHrgvvPCCi4/Uas2jZKsSw4otFQeqcKCoFB8qZtSJfj2PikbUFivaClvXffbZx0aMGOEmStPxf2FFAKmojC+px2WWDlXSaNIZHbSpzyYio2SEDiR1QOYdbCJ19qUeo/6eSlx5lYZKJGgGcX3ZaTg5Sh9/l7GjRJze/zp54c3cqqpIJdB0Froon5PR2o9aD/WVVp9WrcteJWy9SccKux95KExTsKsqbU5eFe09SnyWHNuB76Xkwv5MHuzL5BJP+7NY8Sf8iB2TK05lIjIApR4QKDGlaisNX9YHWJcuXdx9devWdRcgFan6U38PStDqbLeXvNUwdv2uysi47+UcSULW+xm8nHedxC0AAAAAkLQFUHpn/JYvX+56+niN2TUEmYpaIC9VN3jJW6/yVpWWmrws7oWqhghVQRsucUs1BQAAAAA4VNoCKHGa2EcTLKn0XzSpTYcOHaxFixYxH34DxCv9bagfq/5ONPGVhsd7NFGTZmRVYrewCQ1K1cCBuT9Hjy685UFw4nbo0L8fDyChXXxx9J6rTBmzu++2pH+d8sIL0X0+AACQ2EjaAihxq1atcglbJZ3Ur7ZNmzZ5ElAACk7eqt+RR/3GvIuqcNXzVsnduDkB4iVe1Ze3sFYH3v2qsCVhCyCckSPNMjM1bCe5s6NkgQEAQACyJgCiTu0P1A7BG87dvn17N4GOkksVK1ZkiwN7QZP3aSbaP/74w7Zt22a//vqr/fnnn643tCaPiAtFScDSwxYAAAAA8iFpCyBqNGmSKv/Uh7NevXrWv39/d7sStZ07d2ZLA1Ggilq1TGjWrJktXbrU5s6d606UqGWCkrY9e/akkh0AAAAAEhxJWwB7bdeuXa7qTy0QVGEr6rO5e/fu+Oq3CSRh8rZp06Y2b948W7RokTtxUqFCBf/fIQAAAAAgMZG0BbBX/vrrL/vll18sMzPT9d1Uha2qauvWrcuWBUqB+kPrb06TkulEiVqRKGmr3/X3qcSubgMAAAAAJA6StgCKbdmyZfbTTz9ZTk6OVatWzfbff39X9Qeg9OlvMJD63K5Zs8bf71YnVAAAAAAAiSFOppoGkIiaNGliderUsU6dOrn+tY0bN471KgHYQ72k1Sph06ZNNmnSJJs+fbqbuAxF06pVK3vqqadKZLO9+uqrVrt27Zg/BwAAAIDSOQYoCiptAURMLRDUN7NPnz6un6YuAwYMcEOxV69ezZYE4kjz5s2ta9eurt/04sWLbcWKFbZq1Spr27attW/fPqn7TR966KHWo0ePfIGWEpzXXHONbdiwIeLnmjZtWp4qZrWa+OCDD+ykk04q9LFff/21Pfroo26SuO3bt7vg7+ijj7brrrvO9tlnHzvttNPsmGOOKeKrAwAASFEXX1y6/98LLxRp8XPPPddee+01fwsztQzs1q2bnXHGGe4+HT97FBcqRn/rrbfs9NNPz/M8++23n/3222/2yiuvuMcFLi+VK1e2Ro0aWd++fe2SSy6xww8/vNgvUaPyWrdu7b+uoiwdQ9x33302cODAYj8vooNKWwCF2rlzp82cOdOmTJnikj5K3HrolQnEd7Wtgq6DDz7Y6tev71qZKIn7888/x3rVEkaDBg2satWqRX7cCy+8YIMGDXIjEN577z0XeD///PNuwsbHH3/cLVOlShVr2LBh2OfIysraq3UHAABA6TrqqKNcsYSSoV988YUddthhdvXVV9txxx1n2dnZ+YoslJgNpGPulStX5mt9Jvfcc4977t9//91ef/11N9pK8eb999+/1+s9btw499zffvuta3mo9dWxfyLLSoJYmqQtgLBUQauzeaoWU/9aJWh1Fk4THgFIHDVr1nQtTNR3unr16tahQ4dYr1JcUOWCKmYfe+wx1+5FfX8vv/xy27VrV8ihUfpdTj75ZPd56F0PtnTpUrvqqqvcZdSoUa7yV8sqef6f//zH7rrrrpCtDe6++25XIaxl9FmrKgpRZfDFF1/sKip0m3oUf/rpp2Ff10cffWS9evVyy7Zp08ZGjBiR7yABAAAA0VepUiV30l6jqhSP3XbbbS42UwJXsV+g//u//7NvvvnGTR7sUeyo21WpG6xGjRruuTXRsOLKF1980e68804XWyqR6/n111/t+OOPd8cAeowqZhcsWFDgeisO1nMrztQ6q8WaRot5NPm4RozpWEIx6VlnneXmz/CoOOSRRx6xdu3auW2gdQxMJqtoRBXBKlrQ/3XRRRfZli1b3H1ffvmli1uDR8NdffXVeaqIv/vuO/da9BxKeCvW3rp1q/9+xdv33nuvnX322e616/+I5HEaNaztpfsVg7/xxhsWL0jaAghJH9Lff/+9zZ492yUwatWq5T7o9CEe6gsEQPxTIKYEYmDlqCpv165dW+Tn2r17d9iLgrZIl9WlsGVLkk5KKYjVTw1nUzAdHFAHtkoQVUSoEsG7Huzdd991Z/ZvuummkPcX1IN2/vz5rjL3/fff90/0qCBSfYlHjx7tKnYfeuihsO0tJk6c6AJVBblaVhW/ej3RqMAAAABA0Snx2L17dxffBVLyc/Dgwf6WCpp/4p133rHzzjsv4udWzKdiKyWGRcVWRxxxhEucfvXVVzZjxgz3fJGewFdLL1XxeqP2RMlUvYaePXu6eTLGjBnjqnD/+c9/+h936623uhhVSWTFoG+++aZ7faIEqV6nWi8oflasrMreK664wt2v9VV8rBjYo2OAd955xyWwRfG6qphPPfVUl6PQfUrGes/hUTGGtvWPP/7o1iWSx6mQQ4lzHQ+kp6fbv//977hp/0jmBUBIc+bMsfXr17sEbceOHd1ZK1ohAIkv8O9Yydq5c+e633VWWZMKRtrr9vPPPw97n4b89+vXz3997NixYZOvOtN+4IEH+q8rgAseyqSkZUlR8Pjss8+6163PumOPPdbGjx9vF154YchWCaKgsqCJF5UI19l9Ve8WlV67AmXv/9K2U3Cr4Hffffd1t6l6NhxV1d5yyy12zjnn+JdVxYESyMOHDy/y+gAAAGDvKc5U0jCYEqrXX3+93X777S5hqPknNPIqUuqbq9hb7Rhk5MiRruBKvXK9pGsko+wUj6vnrhLHSgL37t3bJVNFsbIStg888ECeimBVrc6bN8/FvE8//bRbzotB9ToOOugg97sSuDt27HAxrtf2Qcsqxn/44Yddcld9fbXc+eef7+5XPL5hwwaXbJUHH3zQJXA1P4Vojo5nnnnGDjnkEHvuuef8I9SUXNb29FxwwQUFPm7JkiWuCvqHH35woxLl5ZdfdsdF8YCkLYCQVFGrIRadO3f2fwACSC5KLGrokoIV9arWGXMFiUqkpgpN9BCYqFbQubc9fxXoFvckl9rPeAlbUbVts2bNIm5pMWvWLDdKIrCyVglzBcoKwovTnxcAAABWIvGhCgbUBku9ZJUILUqVbajnViyoycIrVKhQpOdQBaoSy2qDoJP9GqnlPYeeU1Woao0QTJWsSq5qHhwvyRuqIEzVr4F9erWOGlGmnIOStkqsHnDAAbZ8+XLXU1ctCo499lj/CDWtg5Lega0L9Lr1HDqO8ZKsmjQ9UGGPU9JZhWpKUnu0HQoaGVeaSNoCcB9aCxcudB+0StKKPlDVgwdA8lIgpgBKgZECGiX1NAxflfWFnV0+5phjwt4XHJBqOFSkNJlCNJLRmvArmAJKVR4ECg5ote7B7R2KSglW/f9qoVDUatvgSSfUW6so1BtM1bannHJKvvs4AQcAABAbSlxqZFswJQzVH1YjotRD9oMPPijS82rkXGZmpv+5ixo7elQ1qypUXdRKQXM4KIGrNguKL72q2GCKdZVL2FuqclV17ttvv22XXnqp2w6vBrQs0zooua1+tMFUhBIuli7scUraxjN62gIpTtVXmqFSQ2+9s2QAUosqO9Xr1ptkUMOrJk+eXOBjVJ0a7qKhVZEuG9yOobD7I6E2AjNnzsx3u27b20nYlOQtrM/ukCFD3HA0TcYQSlE+Z7t16+YmNos0oNTJNlUsaBKI4EvwfgEAAEDJU29ZjeTyhvoHU3WtJiQ78cQTXeuuolBbAsV4mlxXunbt6kZdBU6sW1SKZZVMVm9XL77U5GYq7AiOL5UkVaJXyWK1NAhFxSAqEAmc/EvrqPX22n+Jqm1VEfvJJ5+4+4499lj/fVoH5SxCxbheG4hQCnucqmqVpFbvX49i6XjJixC9Ayls5cqVNmHCBDfroxIjqriLl2EAAEqXAjMlCDUsSUFXQX1T453OzivJqTPqGg6lwOuJJ55wvb0Ce1wVh4JVBaT6/FTf73CVCk8++aQLotWXS0H44sWLXXCqM/3qMRsp9dvSJJAKnjMyMtwwLvXd0gQQoWj2YPULU7WtgmtVdahi4Y477ij2awYAAEBkNHpVcaImBFPBgPrAKhl73HHHucliwyU1dUyuyW4LsnnzZvfcmjRL7RQuuugiu++++1xbLCUhRRNsaVLxM844w00aprkW/vvf/7p4OFIaeaY4WhOLaSTe5ZdfbuvWrXPPqbkWVOyleReGDRvmihk0muvmm292bRUUh+p+FYapN6yXjNUy6ner6l21WrjyyitdhbE3WZm3nLaZXo9i30qVKvnv0/NrRKBen9qH6XVp8rXgiciCFfY4JY01UZlidFU6K3mrPrjFrViONpK2QArSB6vOdOkDV2fgNFz44IMPzjOsAEDqVt0edthhts8++/hv09nnwqpL44kSzgpkNcma2i1oUrT//e9/bqZaBWV74/HHH3fJUyVmNSFDOJdddpl9+eWXLmDX8DKdxVcAqNYNN9xwQ5F7jKk/lwJltbBRQBxuf6gVxaeffur+bw0zUxJeCWSvihoAAAAlRyfW1TJAJ/oVdypBqYmvlCgsaASZ5pQoLFGok/N6biVolfBUOy4VEygxGfg8SqiqLYBO/qtX60svvVTkHrdKsCpXoAnD1EpNxQeKP4888khXzauJvVTw5Y3kuvPOO11xhNZRSejTTjvNVq9e7e7TnApaJyV+FZ8qIav+t3ruQHpdffv2dUUXSuAGUnGJCiFUmKGCBsXh+r+0bgWJ5HFKluu6tpdajCkZrsnd4kEZn5pZYq/oLIaSXvqD0cEYIqOegfoj1h8DQzZL13fffeevENMHo84u7c0+YF8mD/ZlcojWflT7FPWoUiJXw4cUSBY0/AjRpzBNSXNVQhd3crNkpveoqo/Vxy24Zy/xWey2w8UXR++5ypTJsbsbDLeGmZlWNlqHLS+8EHevU16waD9hdF5nNBFnJA/2ZXKJp/1Z0Hc7CkfsmFxxKpW2QApSFZo+NPr37+/OgsX6ixlAfNNnhAJADY/ShfO9AAAAAFCyypfw8wOIA9u3b3eXunXruusq/VfvmOJM8AMgtai6U0Oa9FNni7OystzQKN3GZwgAAAAAlAzK64Akp2Eu6uGiJuRqiu4h2QIgUkrYqjpfs8PqdyVt1SdLCVwAAAAAQPSRtAWSmGZt/OGHH1wDcfWiTKSJhADEH01gUKNGDddf1euXBQAAAACIPtojAElIydlZs2a5WculRYsWboZHetcCKI7AHrb6HFHFrSr3K1WqxAZFTNFfGQCA5MR3PBJdNN7DJG2BJKPetdOmTXOzEGoYc5cuXaxVq1axXi0ACVpZK5p8TNX6we0SAgMSffZoee8xQGnQezPwvQoAAJIz/gRSMU4laQskmT/++MMlbCtWrGh9+vSxevXqxXqVACQo9b6uXbu2640t3oRkwdTbVpOUiT57VIEbajkUndeGQi0p2KZ5t4sCYb039R6lTzsAAKkVfyI0YsfkilNJ2gJJZr/99nMH+B07dnRfcACwNxo3bux+eoFzuMBEvbO9HrcKTJS8JcDee9q2OTk5ri0F2zM/BcLeexQAAKRO/InQiB2TK04laQskOB3ML1261PWt9ZIlvXr1ivVqAUgSShQ2adLEGjZs6BKzBVm1apXNmTPHBYuasKxbt24ueYu9+4xfu3atGzVBX/K8NNSMClsAAFI7/kRexI7JFacmXNJ25MiR9uijj9rKlSute/fu9q9//cv69u0bctlDDz3Uvvnmm3y3H3PMMfbZZ5+5388991x77bXX8tw/ePBgGzNmTAm9AiB6NCR5+vTp7oBekwK1b9+ezQugRCjoKCzwaNmypUvW/vDDD7Z+/XrXX7tfv35WvXp19speBN4K+tRDmKQtAABIJZHEn8iL2DG5lLUE8s4779h1111nw4cPt5kzZ7qkrRKs4Urm33//fVuxYoX/8ssvv7g/+H/84x95ljvqqKPyLPfWW2+V0isCim/Tpk327bffuoSteh3WrFmTzQkg5urWrWsDBw60atWquT63OrkEAAAAAEjiStsnnnjCLrzwQhs2bJi7/vzzz7uK2VGjRtktt9wS8sAx0Ntvv+16fAYnbTVhCv3QkEhUaa4TF7t373aJEVWbU8kGIF7oc+mggw5ykyIGfxcDAAAAAJKo0laVOjNmzLBBgwb5b9MwQV2fPHlyRM/x8ssv2+mnn+4OJgNNmDDB9UrZd9997dJLL3WVi0C8WrJkiWuJoIRtgwYNXEUbCVsA8Ua9bPUZFTg64I8//ojpOgEAAABAokiYSts1a9a4JFWjRo3y3K7rc+fOLfTx6q+n9ghK3Aa3RjjllFOsdevWtmDBArvtttvs6KOPdongcL1T1DtUl8ADUa93iC6IjLaVN7MhIrNt2zabNWuW22aaeEyT/KhJe6y3IfsyebAvk0O87cfs7Gz3vap2CZs3b3afXfRnTcx9mUjYZgAAAEhkCZO03VtK1nbt2jXfpGWqvPXofh1Itm3b1lXfHnHEESGf68EHH7QRI0bkuz0zM9MdkCLygykNndXBKAfvkWvWrJlLemg2Tb3n4gH7MnmwL5NDPO5HtUnQSVadQF2+fLnrS68JtpB4+zJR6LsSAAAASFQJk7StX7++q3xdtWpVntt1vbB+tFu3bnX9bO+5555C/582bdq4/2v+/Plhk7a33nqrmxAtsNK2efPmbhgok0EV7UBUVaLabhyIhqcDdVV2a+ZwUSuPeMO+TB7sy+QQj/tRn136rlSrI1XeqlVCv379rEqVKrFetbgWj/syUXjfmwAAAEAiKp9IvfF69+5t48ePt5NOOsl/IKPrV1xxRYGPfffdd13Sa+jQoYX+P0uXLnU9bVXFGI4mLtMlmA6mOKAqGh2Ist3C03v8xx9/dFVWAwYMiOsDUPZl8mBfJod43I86yao+3FOnTnUnVL///nvr37+/1ahRI9arFtficV8mArYXAAAAEllCRf+qbn3ppZfstddeszlz5rhJw3TQN2zYMHf/2Wef7apgQ7VGUKK3Xr16eW7fsmWL3XjjjTZlyhT7888/XQL4xBNPtHbt2tngwYNL7XUBoagSTYmNFStWuLYbXu9kAEhkGpGixK1+6oRqJH3pAQAAACDVJEylrZx22mmuh+ddd91lK1eutB49etiYMWP8k5MtWbIkX1XF77//bt999519+eWX+Z5P7RZmz57tksAbNmywpk2b2pFHHmn33ntvyEpaoLRkZWW5hK3el+XLl7f999/fte0AgGSgUQMHHnigOwHbuXPnWK8OAAAAAMSdhErailohhGuHoMnDgu27776uJ2go6qM3duzYqK8jsDe2b9/uqr9VCa62IOr5WLt2bTYqgKSiScg0+WcgVd5y0hQAAAAAEqw9ApDsvB6PStiqEk19bEnYAkgFmgD066+/diMMAAAAACDVkbQF4qzyTG07qlevbgcddJD7CQCpMOniqlWrbNeuXTZ58mRbv359rFcJAAAAAGKKpC0QR9QOQTOpq8JW7TsAIBWoH71awWjCUE3CqBYx69ati/VqAQAAAEDMkLQF4qCH7YoVK/zX1RZByVsASCWadFGJW0266CVu165dG+vVAgAAAICYIGkLxNCOHTvcUODp06fbsmXL2BcAUpraw/Tt29caNGhgu3fvtqlTp5K4BQAAAJCSSNoCMZKVleUqyTT5WNWqVd2wYABIdUrc7r///v7E7aZNm2K9SgAAAABQ6sqX/n8JQJPtKGG7efNm1w5BfWz1EwDwd8WtJidr0qQJmwQAAABAyqHSFihl6tWoIb8bN260SpUquYStKm0BAHknJwtM2Oqzc8uWLWwiAAAAACmBpC1QinJycmzatGm2fv16q1Chgh1wwAFWvXp19gEAFGDnzp02adIkd9m2bRvbCgAAAEDSI2kLlOYfXNmyVqtWLTdLuhK2NWvWZPsDQATtEnw+n0veqrWMeoIDAAAAQDIjaQuUss6dO9uhhx5qtWvXZtsDQAR0oqtfv36ulYwmb1SLGU1SBgAAAADJiqQtUMJUHbZo0SLXGsFTpUoVtjsAFIEma1TitmLFirZhwwabMWOG+3wFAAAAgGRE0hYoYb///rv98ssv9sMPP7CtAWAvqAd43759XbuEVatW2ezZs9meAAAAAJISSVugBC1dutT++OMP9/s+++zDtgaAvVSnTh3r3bu3lSlTxiVud+zYwTYFAAAAkHTKx3oFgGS1bt06mzVrlvu9Xbt21rx581ivEgAkhUaNGlmvXr1cb3C1TQAAAACAZEPSFigB27Zts2nTprk+to0bN7aOHTuynQEgipo2bZrnenZ2tpuwDAAAAACSAe0RgChT4kD9a7OysqxWrVquGkzDeAEAJWPlypU2btw4N8IBAAAAAJIBSVsgyrZs2eJ6LGrIrjdhDgCg5Pz111+2a9cud8Js69atbGoAAAAACY+kLRBl6rF40EEHuYQtvRYBoORpRIMmKFPiVq1pNOIBAAAAABIZSVsgSnbv3u3/vXr16q41AgCg5GlEQ58+faxSpUq2efNm/ySQAAAAAJCoSNoCUbBmzRrXTzEzM5PtCQAxoJENStyqh/jy5ctt4cKF7AcAAAAACYukLbCX1D9x+vTpbuKxpUuXsj0BIEbq1q1r++23n/v9t99+sw0bNrAvAAAAACSk8rFeASCRqX/i1KlT3U/1U+zWrVusVwkAUlrr1q1dsrZixYq0qQEAAACQsEjaAsXk8/ls5syZrtK2SpUqtv/++7u+igCA2OrRo4drkwAAAAAAiYr2CEAxqV/i6tWrrWzZsta3b183AQ4AIPYCE7Y5OTmuxy0AAAAAJBIqbYFi2Lhxo82ZM8f93qVLF6tZsybbEQDicETElClTbO3atbZ7925r3rx5rFcJAAAAACJCpS1QDNWrV7cWLVpYkyZNrGXLlmxDAIjTitv69eu732fPnu1OuAEAAABAIiBpCxSDetdq0rHevXuz/QAgjrVv394aNWrk2iRMmzbNsrKyYr1KAAAAAFAokrZAEWzatMkNt/Uw0Q0AxDd9Tvfs2dOqVatm27dvdxNIBn6OAwAAAEA8ImkLRGjz5s323Xff2dSpUy07O5vtBgAJokKFCtanTx83SiIzM9MWLFgQ61VCChs5cqS1atXKKleubP369bMffvihwOWfeuop23fffa1KlSquL/O1115rO3bsKLX1BQAAQGyQtAUioAlsZsyY4X6KDvwBAIlDE0Z27drV/T5//nzbtWtXrFcJKeidd96x6667zoYPH+6qvrt3726DBw+21atXh1z+zTfftFtuucUtrwlQX375Zfcct912W6mvOwAAAEoXSVsgAr/88ourtK1UqZIbZktbBABIPKpSVI/bgQMHuupboLQ98cQTduGFF9qwYcOsc+fO9vzzz1vVqlVt1KhRIZefNGmSDRgwwM4880xXnXvkkUfaGWecUWh1LgAAABJf+VivABDvli9fbkuWLHG/9+rVyyVuAQCJqWPHjrFeBaQoTYKnUTu33nqr/7ayZcvaoEGDbPLkySEfc+CBB9ro0aNdkrZv3762cOFC+/zzz+2ss84K+//s3LnTXQL78Ysm49OlNJQpE83nyjF1oc6J5pNGaTtEc5Ukx6L9hKWzv4tC70H1FS+t9yJKDvsyubA/kwf7MjFE+j1I0hYowLZt22zWrFnud1Vn1a9fn+0FAEli7dq1LmlWp06dWK8KUsCaNWtcm6VGjRrluV3X586dG/IxqrDV4w466CCX6FJP/UsuuaTA9ggPPvigjRgxIt/t6udcWr1wGzSIbtJ2Q81a5itTxspGaxLBMO0oYvk6ZbVF+wmj8zqjfZC6ceNG937W5y8SF/syubA/kwf7MjFoJHckSNoCBfjxxx/dAVLdunXdJCAAgOQZRaGqRw1NP+SQQ6x8eUIixJ8JEybYAw88YP/+97/dpGXqx3z11Vfbvffea3feeWfIx6iSV31zAytt1RqkQYMGrrdzacjMjG7StrZvozVYsyZ6SduGDePudUpDi/YTRud1RjuZoDZjej+StE1s7Mvkwv5MHuzLxKAJaSPBEQpQAE1aM3v2bNcWgT62AJA8GjZsaFWqVHEjKn777Tfr1q1brFcJSU6jdTSR6apVq/LcruuNGzcO+RglZtUK4YILLvDHJVu3brWLLrrIbr/99pBJL7VxCtXKScuWVpIsWrlVj5oGKGEbtaRtlLZDtF9nWYv2E8ZnJati6tJ8P6LksC+TC/szebAv41+k34F8UwIFUEWKhiTqwB4AkDxUWauJJWXx4sW2Og6HESO5VKxY0Xr37m3jx4/PUw2j6/379w/5GJ1UCA7qlfgVDS8HAABA8iJpCwTRQZCqWAAAya1evXrWpk0b9/tPP/3kJooCSpLaFrz00kv22muv2Zw5c+zSSy91McewYcPc/WeffXaeicqOP/54e+655+ztt9+2RYsWWUZGhqu+1e1e8hYAAADJifYIQBBVXP36669uhvG2bduyfQAgiXXq1MlN0KTJANQOp0+fPrFeJSSx0047zb3f7rrrLlu5cqX16NHDxowZ45+cbMmSJXkqa++44w43xFE/ly1b5vqAKmF7//33x/BVAAAAoDSQtAUCbN++3VW+aLgiFSwAkPyUIFObhIkTJ9qKFStszZo1rvcoUFKuuOIKdwk38VhwG4/hw4e7CwAAAFILSVsggKqssrOzrW7dutayZUu2DQCkgFq1armKW/UcJWELAAAAIB6QtAX20LBDTUSjqqvu3bu74YgAgNRAOxwAAAAA8YSJyAAzN/nML7/84rZF+/btrXr16mwXAEhRGnGhHrcAAAAAECskbQEzN/GYErc1atSwdu3asU0AIEVt2LDBvvrqK5s2bZrrbw4AAAAAsUDSFjCzmjVrusk+1BYhcNZmAEBq0UgLtcfZunWrLViwINarAwAAACBFkZ0C9vQyHDRokNWpU4ftAQApTCfwOnfu7H7/448/bPv27bFeJQAAAAApKOGStiNHjrRWrVpZ5cqVrV+/fvbDDz+EXfbVV1911TKBFz0ukM/ns7vuusuaNGliVapUcYk7HaQhNWj/eypUqBDTdQEAxId99tnH6tWrZ7t37/b3OwcAAACA0pRQSdt33nnHrrvuOhs+fLjNnDnTDWUfPHiwrV69usBh7ytWrPBfFi9enOf+Rx55xJ555hl7/vnnberUqVatWjX3nDt27CiFV4RY2rhxo33zzTe2bt06dgQAII+uXbu6k70rV64sMM4AAAAAAEv1pO0TTzxhF154oQ0bNswNXVSitWrVqjZq1Kiwj9EBV+PGjf2XRo0a5amyfOqpp+yOO+6wE0880bp162avv/66LV++3D788MNSelWIld9++83NDv7nn3+yEwAAeWhiytatW7vfVW3LpGQAAAAASlPCJG2zsrJsxowZrn2BRxNG6frkyZPDPm7Lli3WsmVLa968uUvM/vrrr/77Fi1a5CpoAp+zVq1aru1CQc+JxLdq1Spbs2aNew917Ngx1qsDAIhD++67r2urpH7napUAAAAAAKWlvCUIJdh0wBRYKSu6Pnfu3LAHW6rCVQWthsI/9thjduCBB7rEbbNmzVzC1nuO4Of07gtl586d7uLZtGmT+6kqHCpxIqdtpWrn0t5m+j+9qqk2bdq4A3L2W2LuS0Qf+zI5sB+jQyf2Dj74YH/P81h8xrEv927bAQAAAIkqYZK2xdG/f3938Shh26lTJ3vhhRfs3nvvLfbzPvjggzZixIh8t2dmZtILt4gHU0qmK9mnA+PS8tdff9myZcvcQbh6HtOrMHH3JaKPfZkc2I/Jg31ZfGqBBAAAACSqhEna1q9f38qVK+eGtQfSdfWqjYSSdD179rT58+e7697j9BxNmjTJ85w9evQI+zy33nqrmxAtsNJW7RcaNGjgkoCI/EBUPYe13Uor0ZednW0//fSTa4PRpUsXN0M4EnNfomSwL5MD+zH6tm/f7kZptGrVyn3WlRb2ZfFpJA0AAACQqBImaVuxYkXr3bu3jR8/3k466ST/gYyuX3HFFRE9h9or/Pzzz3bMMce465pgRIlbPYeXpFUCdurUqXbppZeGfZ5KlSq5SzAlq0hYFY0SfaW53TTJ3K5du/wTzLC/EndfouSwL5MD+zG6Fi9e7EZmbN261Q499NBS/axjXxYP30cAAABIZAmTtBVVt55zzjnWp08f69u3rz311FPu4GnYsGHu/rPPPttVTqp9gdxzzz12wAEHWLt27WzDhg326KOPuoOuCy64wH8QdM0119h9991n7du3d0m8O++805o2bepPDCO5qEJKCffy5ctzMAcAiJjiBLXXUdyxdOlSa9GiBVsPAAAAQIlJqKTtaaed5vrG3nXXXW6iMFXHjhkzxj+R2JIlS/Ik4tavX28XXnihW1YzP6tSd9KkSda5c2f/MjfddJM7ALvoootcYveggw5yz8mQuuQV2AoDAIBIWywpcavJTOfNm+cmNKWSEwAAAEBJSaikragVQrh2CBMmTMhz/cknn3SXgqjaVhW5uiC5exHqgFsVtgAAFHe0xoIFC9x3ik4U6zoAAAAAlASaTyIl/Pjjj653sSq1AQAoDlXWdujQwf2ualv1ygcAAACAkkDSFklv1apVtnbtWsvOzrbq1avHenUAAAmsefPmVrVqVdu5c6frkw8AAAAAJYGx4khqOTk59ttvv7nf27RpY1WqVIn1KgEAErzatlOnTq4fPpORAQAAACgpJG2R1JYtW2ZbtmyxihUrWrt27WK9OgCAJNC0adNYrwIAAACAJEd7BCQtn89n8+fPd7+3bdvWTUQGAEBJjOoAAAAAgGgiaYuktXLlSldlq2QtM3wDAKJN/dK//fZbNykZAAAAAEQTSVskrY0bN7qfrVu3tvLl6QQCAIiuXbt2ue+aRYsWWVZWFpsXAAAAQNSQyULS6tixozVr1sz1swWApDZxotmOHWZpaYUvm5FhVrmy2cCBpbFmSa1x48ZWq1Ytl7hVO57OnTvHepUAAAAAJAmStkhq1atXj/UqAEDJJ2xHj/Zf9Q1Ksy1bzDZtMqta1axGDbMyZQIStunpfz+WxG1UThBOnTrVVdu2adPGKishDgAAAAB7iaQtko762JYtW9aqKlsBACmSsN2Vbbb66XT7/CWzz3cdYU2amK1YYda+vdngwWYDtmVY5U8DErZeopfE7V5p2LCh1a1b19atW+eqbbt06bJ3TwgAAAAAJG2RjH777TdbvXq1devWzVq0aBHr1QGAkqOWCGa2br3Z7Fm51bWNLN16tPPZhlbdrGxZs6lTzbK/yLByWenWrbtZ3Tr5H4+906FDB5syZYotWbLE9t13XzcBJgAAAADsDSYiQ1LZtGmTrVq1yv1er169WK8OAJSstDT7s88QmzE9N2Fbs6ZZ7dpmh6573zru+NEaNDA7uXqGnZCV7u7XckrwOkOGRNYDF4Vq0KCB1ahRw3bv3m3Lli1jiwEAAADYa7RHQFLR0FRp2rSpVatWLdarAwAlats2sxGT0qxONbOTKgW0PjCztqsmWcs/J1jV7RvcKVolczdsyK3IPeCxIVaZhG1UaRKynJwca9SoUXSfGAAAAEBKotIWSWPr1q22fPly93u7du1ivToAUOImTzZbsMBsfZ80m9lmSL77q+zcmOe6KnE/rjjEJlWjwrYkets2btzYyvhnfQMAAACA4iNpi6SqsvX5fK7KqaYyEwCQxHw+s7FjzZQjrFjRbE6z0InbQD+1G2Iz6qbZmDG5j0fJUMUtAAAAAOwNkrZICjt27LClS5e636myBZAKtm41mzdP/bv/vk2J2+2VaodcXrfrfi2vx6m1AkrmBOK4ceNsg3pRAAAAAEAxkbRFUtDBcdmyZd3kY3Xr1o316gBAidu50yw726x8QHf6TkszrMrO0MlC3a77tfzu3TrZxU4qCZs3b7adO3fawoUL2cAAAAAAio2kLZKC+gimpaVZjx49Yr0qAFAqKlXKTdgqcStKyPZamHcysmC6f7/lGVaunFnlyqWznqmmTZs27qd6rGsUCAAAAAAUB0lbJI3y5ctb1apVc69MnGiWkeG/T70bt2wxW7s292eeXo5aTssDQAKpVs2sQ4fcz7VwCdvtlWrlu63ngnQ7tmKGeR+XiK5atWq5ER/qsf7nn3+yeQEAAAAUS8CgSiBxh6LWqFHj7xuUgB092v2qIqfvq6a5yXrUw9EbSqxEx+DBZgO2ZVjlTwMSHQMHxuAVAEDRaQIyfY5lf5FhPVam5zsNu6DRgfZjw8HW8a/x/oSuNz/WMdvSrcw4M0tLY9OXULXtunXrbPHixda+fXsrp9JmAAAAACgCkrZIaFu3brUJEyZYzZo1beDAgVb2++/9Cdt1681m35Bun1U0N1u6Jt9RZZkSt1On5iY6ymWlW7fuZnXrmP9xJG4BJAqdeNLn2KZNZrUD5h/7sfUptrZ+N7PM3MnJRIlbLVezplnDRmaWvueEFYnbEmnZo5Ef27Zts2XLllmLFi2i/58AAAAASGq0R0BCW7JkiftZuXJlNxGZN7OOErYzpptLUJyQlW4nV8+wBg3M6tQx91PXdbvu13Ja3qH/IIAEUrl2ZXfiqUoVTciYW0k7s80Qm9tsUJ7lZjVMsw/LD3HLde9uVsE7ZUtj2xJRpkwZa926tfudCckAAAAAFAeVtkhYOTk5/qRty5Ytc29MS3N5V1XYbt/+d+WZNzRYFWf+3o9lc+9XomP2LLMDHhtilak4A5BIBg60umbW+1+j3efYxxWH2IwtaVY/M8claDMzzdasye3j3bZPmnUYYFZn2p4K26FDGVlQgpo3b25btmyxVq1aleR/AwAAACBJkbRFwlq5cqVlZWW5KttGjTTWN5d62Kolwgk1807Ko0Rtp2XjrMrODXlu11BhJTpyqqXZ4aW29gAQ3cTtARt2uM+x8mPM/vjDLCsrt/K2Xz+zo44y699fFblpZi33VNjSw7tEVahQwbp161ay/wkAAACApEXSFglLE7yIegVqKKqomkyTjqmHbYvqf1fYeoITtvJTu9zKNCU6Djssd3IfAEgoAwdaZTN34kmfY1u2mK1YYdakiVn16kGfa4woAAAAAIC4R09bJOwEZGs05ndP0vbv283mzTM36ZhaIai3Y0F0v5bT8nrctm0lvuoAUKKUoK1WLXcUgX5yIiq2NmzYYD/99JObkAwAAAAAIkXSFgnJO/ht2LChVVHjxj127jTLzjYrv6eGXAnZ7ZUCplQPoNu9WdW1/O7dzEMGAIgunWD866+//KNDAAAAACASJG2RkNq3b2/9+vWzDh065Lm9UqXcBKwSt6JJx0K1RBDdrvtFy5crx0TqAIDoatasmfu5du1a28ZwDgAAAAARImmLhKQetqqyrVOnTp7bNRRYedy1a3MTtsE9bYO5ycmWZrjl9biqVUt4xQEAKUWTZdavX9/9TosEAAAAAJEiaYukot6Ngweb9V6XYT3m50/YhmqVoOW0vGZXp/cjAKCkqm3VJgEAAAAAIkHSFgllx44dNm7cOPv111/N5/OFXGbAtgw7ISvdNm3KP+nY+/0ezjc5mZbT8gduzW2VAABANDVp0sTKlSvnJtHUxGQAAAAAUBiStkgoK1eutO3bt9v69etdi4R8MjKs8qfp1q27meYn07FxTk5uwtabdEw/dV23634t1727ucfp8QAARFP58uWtcePG7velS5eycQEAAAAUqnzhiwDxY8WKFf6qpZAqV3Y/6tYx693HbPYss48rDrEZW9KsXubfk5TN3ZJmSyqanVAz3SVs/a1x9zweAIBot0hQlW1VmqcDAAAAiABJWySMnTt3utm3C0zaDhyY+3P0aJe4PeCxIZZTLc3KjzGbN89s+3azcuXM+vUzO+qoNOu/zazSJ3t63w4d+vfjAQCIogYNGtjhhx/ONgUAAAAQEZK2SKjWCOpjW6tWrYIrlbzE644dVjktzXSIfNhhZtu2uZtcMa0enttdIc2s0p4KWxK2AIASErKlDwAAAACEQdIWCdcaoWnTpoUvHJSA1bFytWq5l3zScnvdAgBQ0nJycmzVqlVWv359q1ChAhscAAAAQEhMRIaEkJWVZWvWrCm4NQIAAHFu8uTJNn36dP+JSAAAAAAIhaQtEqYyqVWrVtawYUOrFrJcFgCA+KfvMa/lDwAAAACEQ3sEJITKlStbly5dYr0aAADslUaNGtncuXPd6JHdu3dbOc2OCQAAAABBqLQFAAAoJTVr1nQnIpWwXbt2LdsdAAAAQEgkbRH31q9f7yqSfD5frFcFAICoVNuKJiQDAAAAgFBI2iLu/fHHH27ilvnz58d6VQAA2GskbQEAAAAUhqQt4lp2drZlZma63xs3bhzr1QEAYK/Vr1/fypYta9u3b7ctW7awRQEAAADkw0RkiGsaOpqTk2PVq1e3GjVqxHp1AADYa5p8rFevXu57Td9vAAAAABCMpC3iGlW2AIBk1KRJk1ivAgAAAIA4RnsExDVNQOYNJQUAAAAAAABSAUlbxK2tW7e6fn/q+1e3bt1Yrw4AAFFvATR9+nRbuXIlWxYAAABAYidtR44caa1atbLKlStbv3797Icffgi77EsvvWQDBw60OnXquMugQYPyLX/uuedamTJl8lyOOuqoUnglKMzatWvdT+079f8DACDZvudWrFjhLgAAAACQsEnbd955x6677jobPny4zZw507p3726DBw+21atXh1x+woQJdsYZZ9jXX39tkydPtubNm9uRRx5py5Yty7OckrTeQZMub731Vim9IhRE++vggw+2Tp06saEAAEmnUaNG7qfiGJ/PF+vVAQAAABBHEipp+8QTT9iFF15ow4YNs86dO9vzzz9vVatWtVGjRoVc/o033rDLLrvMevToYR07drT//Oc/lpOTY+PHj8+zXKVKlaxx48b+iyo7EXuqeq5Vqxb7AwCQlBRvVKhQwbKysmzDhg2xXh0AAAAAcaS8JQgd0MyYMcNuvfVW/23qdaqWB6qijcS2bdts165d+fqjqiK3YcOG7uDp8MMPt/vuu8/q1asX9nl27tzpLp5Nmza5n0oI64LIaFupsohtlvjYl8mDfZkc2I+JQ/HG8uXL3UgfnagMxr4sPuILAAAAJLKESdquWbPGdu/e7R9K6NH1uXPnRvQcN998szVt2tQlegNbI5xyyinWunVrW7Bggd1222129NFHu0RwuD6qDz74oI0YMSLf7ZmZmbZjx44iv7ZUPpjauHGjS9wqAR9ILSzWr19vTZo0KTCBjvjfl0gs7MvkwH5MrFEl+vxUDBJq0k32ZfFt3rx5r/YNALOLL47uVihTxuzuBiN14GQWrbYwL7wQd6/zBYvyE0bpdQIAEkvCJG331kMPPWRvv/22q6rVJGae008/3f97165drVu3bta2bVu33BFHHBHyuVTtq966gZW26r/aoEEDq1mzZgm/kuShA1EdrGq7BSf6/vzzT9u6datVrFjRVUEjcfclEgv7MjmwHxOH2jzpRKU+Q+vXr5/vM5R9WXyB8R4AAACQaBImaasDGVW+rlq1Ks/tuq4+tAV57LHHXNJ23LhxLilbkDZt2rj/a/78+WGTtuqBq0swHWiRsCoaHaQGbzdVa6rKVrcpYcs2Tdx9icTEvkwO7MfEUKNGDatSpYq7ZGdnh0w0si+Lh+8jACi5imJXNX13fG7hqFdOU+QMIEYSJmmrisvevXu7ScROOukkd5s3qdgVV1wR9nGPPPKI3X///TZ27Fjr06dPof/P0qVLbe3atW5YPmJDk7HowFWTs1C5DABIZkrIqm0TCUYAQMIZGX+tLkoEWWAAMZJQJXFqSfDSSy/Za6+9ZnPmzLFLL73UDaEfNmyYu//ss8/OM1HZww8/bHfeeaeNGjXKWrVqZStXrnSXLVu2uPv188Ybb7QpU6a44fhKAJ944onWrl07Gzx4cMxeZ6pT/2JRxbMOZgEASGYkbAEAAAAkbKWtnHbaaW6yr7vuusslX3v06GFjxozxT062ZMmSPAc+zz33nGVlZdmQIUPyPM/w4cPt7rvvdu0WZs+e7ZLAqu7UJGVHHnmk3XvvvSHbH6D0k7YAAKQKTbgabhJUAAAAAKkloZK2olYI4dohaPKwQKqeLYj6x6ltAuKHWl6sW7fO/U7SFgCQCtTL/fvvv3cnkNUqgQm0AAAAACRUewQkvx07driZtFXpXL169VivDgAAJU6tgHTSUslb78QlAAAAgNRW5KRtmzZt3ERdwVQdovuAvaGE7WGHHeYqjQAASBV16tRxP9evXx/rVQEAAACQiElbtRxQz7VgO3futGXLlkVrvZDimJQFAJBK6tat635SaQsAAACgSD1tP/74Y//v6gNbq1Yt/3UlccePH2+tWrViqwIAABSz0nbjxo1MSAYAAAAg8qTtSSed5P/9nHPOyXNfhQoVXML28ccfZ5Oi2NTL78svv3S9bPfff3+rWLEiWxMAkDLtgTQBmXq7K3HrVd4CAAAASE0RJ201QYa0bt3apk+fbvXq1SvJ9UIK2rJli2VlZdmmTZvciQAAAFKt2nbFihWuRQJJWwAAACC1RZy0lV27drnJxnQwQdIW0abKIqlZs6abSRsAgFTSsGFD9/1Xo0aNWK8KAAAAgERK2qr6cfbs2SW3NkhpXtI2sF8yAACpokWLFu4CAAAAAGWLugmGDh1qL7/8MlsOUUfSFgAAAAAAAChipa1kZ2fbqFGjbNy4cda7d2+rVq1anvufeOIJtiuKNQkZSVsAQKrT9+G2bdvcZJz0dwcAAABSV5ErbX/55Rfr1auX67c2b948+/HHH/2Xn376qWTWEklPB6g6IVC2bFmrXr16rFcHAICYmDJlin311Ve2atUq9kCSGjlypLVq1coqV65s/fr1sx9++KHA5Tds2GCXX365NWnSxCpVqmQdOnSwzz//vNTWFwAAAAlSafv111+XzJogpeXk5FijRo3c70rcAgCQijSCac2aNbZly5ZYrwpKwDvvvGPXXXedPf/88y5h+9RTT9ngwYPt999/dxPRBcvKyrK0tDR3X3p6uu2zzz62ePFiq127NvsHAAAgyRU5aQuUBFVu9+3bl40LAEhp3miTzZs3x3pVUALURuzCCy+0YcOGuetK3n722Weu9dgtt9ySb3ndvm7dOps0aZK/XYaqdAEAAJD8ipW0nT59uv3vf/+zJUuWuAqAQO+//3601g0AACDlTmIKlbbJRzHzjBkz7NZbb/XfptFFgwYNssmTJ4d8zMcff2z9+/d37RE++ugja9CggZ155pl28803W7ly5UI+ZufOne7i2bRpk39Uky6loUyZaD5Xjvm0/tF80ihth2iukuRYtJ8wJ+5eY7zuz7jfl3H4OuN1XybE/iylz+Ki0PeD+uqX1vcESg77MjFE+rdW5KTt22+/bWeffbYbyvXll1/akUce6XrbqvfaySefXJx1BVw/2/LlKfwGAKQ2r9J269atHDglGbW92L17t78dlEfX586dG/IxCxcudD2O/+///s/1sZ0/f75ddtlltmvXLhs+fHjIxzz44IM2YsSIfLdnZmbajh07rDQ0aBDdxNCGmrXMV6aMlfUpRRQFq1fH3euU1RbtJ1wdd68xXvdn3O/LOHyd8bovE2J/Rul1RjuBpInBlbilXWFiY18mhkhH1RU5S/bAAw/Yk08+6c74qxrk6aefttatW9vFF1/sJkgAivOh8sUXX7hhf4cffribMRsAgFRUpUoVV0Gp5J4m6axatWqsVwkxjpHUz/bFF19074vevXvbsmXL7NFHHw2btFUlr/rmBlbaNm/e3FXp1qxZs1TWOzMzuomh2r6N1mDNmuglhkL0D47165SGFu0nbBh3rzFe92fc78s4fJ3xui8TYn9G6XVG+/umTJky7ruCpG1iY18mBk1IWyJJ2wULFtixxx7rfldyTZUg+uO+9tprXcIt1Jl9oCDbt2/3f7iQsAUApDpV26raRWfgSdrGH1WrRhpoB6pfv75LvGp0WiBdb9y4ccjHqCBCJ7UDWyF06tTJVq5c6dothIqbKlWq5C7BdBBeWgfi0crfeDQwWUmhqCWGorQdov06y1q0n7Bs3L3GeN2fcb8v4/R1xuO+TIj9GacTbyuvU5rfFSg57Mv4F+nfWZH/GuvUqeMv49UMtr/88ov7fcOGDa4iBCgq732j6iIAAFKdqiLbt2/vb5WA2NOJ5XvvvdfFvtovalsgd955p7388ssRPYcSrKqUHT9+fJ7n1XX1rQ1lwIABriVCYN8ztSVTMpcT3QAAAMmtyEnbgw8+2DIyMtzv//jHP+zqq692s+CeccYZdsQRR5TEOiJFKm2pJgIAwFzbqY4dO/onJUPs3Xffffbqq6/aI488kidZ2qVLF/vPf/4T8fOobcFLL71kr732ms2ZM8cuvfRSN2pt2LBh7n7NGxE4UZnuX7dunYu3laz97LPPXKsytSkDAABAcitye4Rnn33WP4nB7bff7oZsTZo0yU499VS74447SmIdkSKVtiRtAQBAPHr99dddX1kVKFxyySX+27t37x52ErFQTjvtNDch2F133eVaHPTo0cPGjBnjn5xsyZIleYbLqep67Nixrg1Zt27dXKWvErg333xzlF8hAAAAEjpp++eff7oqW/XQOuSQQ1x1wS233FJya4eUQNIWAKJLvdw0iGHdOjW5N6tWTb2t2MqJNgply5YtVrdu3VivCszc5F/t2rXLty3UtmDXrl1F2kZXXHGFu4QyYcKEfLepdcKUKVPYDwAAACkm4qTt119/bccdd5x/KHv58uVt1KhRNnTo0JJcP6QAkrbRSdBs3Wq2c6cmICFBA6QqDVyYPNls7NjcpO2ff5pp/qIOHcwGD1byR6MaYr2WKIzX59Tn89F6Kk507tzZJk6caC1btsxze3p6uvXs2TNm6wUAAIDkFXHSVhMtpKWl2XPPPedmzFUrhJtuuomkLfaaZlPWLMdMuFJEEyfajg077PuqaS5BM2+eWXa2TqiESNCoD7XK7QYO5B0LJKOJE+3PuTtsxKQ0W7Agd1Libt1y//5VBDh1qpkK9dq2NRt+YIa16sjnQTzT8Hh9L6odldeSCrGldgbnnHOOq7hVUv3999+333//3bVN+PTTT9k9AAAAiF3S9pdffnG9azVbrTz66KP2wgsv2Nq1a61evXrRXzOkDE22Eti/DRGYONHW/Wu0zZ5l9llFsxl100x/hkrQKHGbL0EzPf3vx5K4BZLy8+CP6WZ1qpm17ZPmKu5r1TLLysqtxG/QIPf3OtMz7I9v0q1mHzM36J7Pg7ilE+RK2O7cudPK0Nsi5k488UT75JNP7J577rFq1aq5JG6vXr3cbSpqAAAAAGKWtN20aZOriPRo0qgqVarYxo0bSdoCMUjQzJieO/z5hJrp1qK62ZwGfx80hkzQ1DGz0aNzFyBRAySHiRNt16u5J3D0eXBSpXSbudpsbvMj8i3afXWG9cpOtw3bzS0/4NXRVkF38HkQl1RpK0rcKt5C7A0cONDN7QAAAADE3URkmr22lkp3gnquqQrXc8IJJ0R3DZHUsrOz3QQe3sEpCqeWCF6Cpnbt3Nt6LcytpJ3TLC18guYgswr6i2eoLZA8duyw1at0YtWsZs2Az4MyPlvboJt/sU5LM/yfE1pOy+tx+/B5ELe870VV2pK0BQAAAFJPkZK26uUV7OKLL/b/ruF7u3fvjs6aISWsWLHCZsyYYc2bN3fDDFE49bBVSwRV2AYKTNyGTdBcPcSMYZxA0vANSrPPXzJrZOmuj62n56L3bW7z3ZZpg/N8Hoi33OdVh9gFg9KsTAzWG5G1RxB62sZOnTp1Im5NsW7duhJfHwAAAKSWiJO2qqoFom2bpjoPqChCwdSbUpOOqYetWiIEJmJE1zstG2dVdm7w30aCBkheW7eafZaVZj3bmh22Nu/nQdtVk6zlnxOs6va/Pw88P7YdYj9mpdmZ28yqVSvFFUaxKm0RG0899ZT/d83hcN9999ngwYOtv2b5NLPJkye7UWiarBcAAACIaaUtEG1eBRFDPyNP0MybZ27SMa+HbXDiNjBh6yFBAyQn5fM0+eCvTdPcxGP5Pw825nvMzDZD7NdqabZ7e263FJK28Vvl2aFDB6tevXqsVyVlBY4wO/XUU90kZFdccYX/tquuusqeffZZGzdunF177bUxWksAAAAkq4DBlEDpUz9bodK2aAma8ntOt6gVghIwBXEJmqZpps4ltK8EkouKMfV5oM+FSD8PtJyWL1dOQ/BLbVVRRJpDYN9997UmTZqw7eKAKmqPOuqofLfrNiVtAQAAgGgjaYu4SNpWqODmMEcREjQeJWC2V9ozI1kQ3U6CBkheqpLt0EFDtyP/PBAtr8dVrVqaawskrnr16tlHH32U73bdpvsAAACAaKM9AmIqOzvbKlasSNK2iAmaqVPNGjTIvU2TDIVqiSC6XffP3ZJm/fqRoAGSjeZIGjzYbMoUs6wss+6rcz8PfGXLhP08mNUwzfXHVtFghHMsIUa2bt1q27dvZ16BODBixAi74IILbMKECdZPX6im7+KpNmbMGHvppZdivXoAAABIQiRtEfNKW5K2e5egCe5hGazH/HRbUlEJmjQSNEAS0pxIbdua1ZmeYb2yC/480OfFkiVmbfuk2QEHlNoqopgmTpzoJiLr0qUL2zDGzj33XOvUqZM988wz9v7777vbdP27777zJ3EBAACAmCZtNSnD+eefbwcffHBUVwSpqXHjxm6SFSVuEZ0EjYZAB1bebtpkdkLNdDtgq67lDo0GkDzU4mD4gRn2xzfptmG7Wc2aZmX2ND/aXqmWVd2e+3mQk5P7eXB0lXTrMECP4/Mg3qnfu5K23qSdiC0lZ9944w12AwAAAOIzabtx40YbNGiQtWzZ0oYNG+aSuPvss0/JrB2SXufOna1hw4ZWtiztlfcmQeNtPm+SIQ2BVoWtEjRVqph1725W+dN0M006lEaiBkgqGRnWanq61exjNntWbmLWV9ZsYY0DbUr7wdZ52XjruSD3BI8+L/R5UGdaullLPg/iXeXKlW3Tpk2WpaEViKklKlEvQIsWLUptXQAAAJAaipy0/fDDDy0zM9P++9//2muvvWbDhw93SVxV35544on0JgVikKCRH9sOsV+rpVl2prketmqJoApbl6Cps+ex6Xsqc0ncAskhI8P/d123jtmAg8xWrzL7vNoptrBmN9u+3uzH+mnWtKnZMdvSrVGj3MkMHT4PEqLSVlRti9hq1aqVlSmgCfTu3btLdX0AAACQ/IrV07ZBgwZ23XXXucvMmTPtlVdesbPOOssNcx86dKhddtll1r59++ivLZJKTk6Om4gMRVS5cv4ETdUh9mNWmu3eblaunIZw5vaw7b/NrNIn6SEfDyAJBP09Vyhvts/VQ+y8w4+wv/5abdWr51bbqxVCmXEBidowj0d8qVChgvvJd2Xs/fjjj/l68uu2J554wu6///6YrRcAAACS115NRLZixQrLyMhwl3LlytkxxxxjP//8sxvy/sgjj9i1114bvTVF0lm/fr199dVX1qxZMzv88MNjvTqJY+DA3J+jR/sTNBcMSrMzt5mp7aFyMGqhkFsQlGZWKSBRM3To348HkFSfB86QIa6SvkxOjvssqFv37/Yp/gp7Pg8Shtc6iCrO2OuuYStB+vTpY02bNrVHH33UTjnllJisFwAAAJJXkZO2qiz4+OOPXXXtl19+ad26dbNrrrnGzjzzTKupZnlm9sEHH9h5551H0haFvpfcm9A/VhdFTtQoS6sEjZlVq5Z7ycdL1CiDQ8IWSPrPgwLxeZBQdEJcfD5frFcFYey77742bdo0tg8AAACirsjZsiZNmrhh7WeccYb98MMP1qNHj3zLHHbYYVa7du1orSOSPGnrDf9EERUlAUsPWyC58XmQlOrXr+9+0h4h9jQhXCAl0jXi7O6776YlGAAAAOIjafvkk0/aP/7xDzejcThK2C5atGhv1w1JjqQtAAAFJ23r1q1rq1evZjPFmGLb4InIlLht3ry5vf322zFbLwAAACSv8kVNsg0bNsx69uxpXbp0Kbm1QkogaQsAABLB119/na/fsCbmbdeuHW2eAAAAEPukrYaxt2jRggkxEBUkbQEAKPh7cuvWrbZt2zY2U4ypyvbAAw/Ml6BV64pvv/3WDj744JitGwAAAJKTN6d0xG6//Xa77bbbbN26dSWzRkgZ3mzY3kQrQEqZONEsI8N/VfMMbdlitnZt7s888w5pOS2fithOSGFqi/DNN9/YnDlzYr0qKU/zNYSKfTdu3OjuAwAAAGLe0/bZZ5+1+fPnW9OmTa1ly5ZWLWi6+pkzZ0Zz/ZDEatWqZQ0bNrQaNWrEelWA0k9Ejh7tft2xw+z7qmk2dqzZvHmq2jJTIVeHDmaDB5sN2JZhlT9NL96EU4mO7YQUpyH4gSc5ETvqXxvc01bWrl2bLxYGAAAAYpK0Pemkk6LyHwOtWrWyqlWrusQtkIqJyHXrzWbfkG6fVTSbUTfN6tUzq1o1N3E7dapZ9hcZVi4r3bp1N6tbx/yPS4nELdsJ8Cdtc3Jy2Boxcsopp7ifStiee+65VqlSJf99SqbPnj3btU0AAAAAYp60HT58uMXSyJEj7dFHH7WVK1da9+7d7V//+pf17ds37PLvvvuu3Xnnnfbnn39a+/bt7eGHH7ZjjjkmT+WEXtNLL71kGzZssAEDBthzzz3nlgWAqFNp7Z6E7YzpZtu3m51QM91aVDeb0yDNv9jBOzOsx8p027Qpd7neffYkbvc8PumxnQB/+yCStrEdFeTFixoZVKVKFf99FStWtAMOOMAuvPDCGK4hAAAAklWRk7ax9M4779h1111nzz//vPXr18+eeuopGzx4sP3+++8hqzUnTZpkZ5xxhj344IN23HHH2ZtvvukqhdXCoUuXLm6ZRx55xJ555hl77bXXrHXr1i7Bq+f87bffrHLlyjF4lQCSWlqay0eqwlYJ29q1c2/utTC3BcKcZmnWaWlG7vWyufdv2GA2e5bZAY8Nscppfyd2kxrbCaDSNg688sor/tFBN9xwA60QAAAAEL8TkWko2GOPPeaqWxs3bmx169bNcylJTzzxhKtmGDZsmHXu3NklbzW8ftSoUSGXf/rpp+2oo46yG2+80Tp16mT33nuv9erVy/Xl9aomlPi944477MQTT7Ru3brZ66+/bsuXL7cPP/ywRF8LzGbNmmVffvml/fHHH2wOpBT1sP244hCrWTPv7UrUnjL1Zn8C16PltPykaimSsN2D7YRUR6Vt/NCoLHrXAgAAoDQVOWk7YsQIlzw97bTT3Iy5qnxVvy/1Xbv77rtLZi3NLCsry2bMmGGDBg3y36b/U9cnT54c8jG6PXB5URWtt/yiRYtcm4XAZTQMTlW84Z4TAPaGz2du0jH1sP2p3ZB891fZuSHfbVpOy48Zk/v4VMB2ApiILNZ0on/9+vXu9549e7rr4S4AAABAzNsjvPHGG67/67HHHuuStGo/0LZtW1elOmXKFLvqqqusJKxZs8ZV+TZq1CjP7bo+d+7ckI9RQjbU8rrdu9+7LdwyoezcudNdPJvUdHJPzzn6zkVO20rVzmy35NqXKNiWLWYqLq9f32xugyPMyvis56L3wy7/Y+tTbG6zI6x+Zo57nB5fkhOVx8u+jPftFO/iZT9i72gf6iLsy6Lb222mUVjexGNMxAsAAIC4T9oqmdm1a1f3e/Xq1V21rahnrPrBpgL1yFXFcbDMzEzbkSqTBEXBunXrbOvWrbZ27VpbvXp1rFcHe3lgrM8CJRe82c4Rms7xNGlipjxA9epmaxt0s61VZ1rF7K35ls0qX83W7tvNGthq09w3WVlmK1bktktI9n0Z79sp3sXLfsTe2bJli5skddeuXe57kn1ZNJs3b96r7R84+W6sJ+IFAABA6ily0rZZs2a2YsUKa9GihauwVU9SDQubNm2avxqhJNSvX9/1dlu1alWe23VdvXVD0e0FLe/91G1NlB0IWKZHjx5h1+XWW291bSECK22bN29uDRo0sJqpnCUoIvVAVn+4evXqhZxIDomVICpTpoz7GyCpULCqVXMTisqjNWhg1nHpOKu26M+Qy1awTKu3bbbNbTbIMjO1nXMTmSVdaRsP+zLet1O8i5f9iL2juKp27druhLC+J9mXRVMSE8qqXZcS6MFVvIqLAQAAgJgmbU8++WQbP3686/t65ZVX2tChQ+3ll1+2JUuW2LXXXmslpWLFita7d2/3f3tD1BQw6/oVV1wR8jH9+/d3919zzTX+2zIyMtzt0rp1a5e41TJeklYJ2KlTp9qll15a4EFUqAS1DqY4oIqctpWSCmy35MC+jEyNGmbt25tNnWo2cEeG9Vr4XoHL91rwnpmvjM3Zkmb9+uVWnZYpY0m/LxNhO8W7eNiP2DuqlNZ+LF++PPuyGKL53p83b56df/75NmnSpJD7SC28AAAAgJgmbR966CH/75qMTJUFmrSrffv2dvzxx1tJUnXrOeecY3369LG+ffvaU0895YbXDxs2zN1/9tln2z777OPaF8jVV19thxxyiD3++OOuB+/bb79t06dPtxdffNHdryBbCd377rvPrb+SuGrx0LRpU3qXASgRSiQOHmyW/UWG9ViZnm86yO2VauebjKzH/HRbUtHsqKPSUiYRyXYC/u7JSuI99hRrKnn+6aefutFZiiEBAACAuEraBlPVqle5WtKUJFbf2Lvuusv11lV17JgxY/wTianaN/DA5sADD7Q333zT7rjjDrvttttcYvbDDz+0Ll26+Je56aabXOL3oosucn3jDjroIPecJTGkDnnVqFHDtb1QiwQglQzYlmHlstJd39batf++fWabITanWZp1WqrK0nT/7VruhJrpdoBr55pmqYLthFSnWEQnlNXbFrH1008/2YwZM6xjx47sCgAAAMRv0vaPP/6wr7/+OmRPLyVUS5JaIYRrhzBhwoR8t/3jH/9wl3BUKXHPPfe4C0pXmzZt3GR29LNFSsnIsMqfplu37mYzpptt2JA7YdZP7XITtuL9VIWtEraaXKt7d3OPM51PSkuBxC3bCXDfkTrRzGSdsde5c2dbs2ZNrFcDAADn4oujuyE0gOTuBiM1u7p6/0TnSV94Ie5e5wsW5SeM0usEopa0femll1y/V1VIqh9s4PAw/V7SSVsASGh7qvjr1jHr3cds9iyzjysOsRlb0qxepln58mbZ2WZzt6S5lgiqsFXCtk6dvI9PemwnAHHk4YcfdqOzHnjgAevatatVqFAhz/1MRAsAAICYJ23V//X++++3m2++Oeorg9SkSTyAlDFwYO7P0aNd4vaAx4ZYTrU0Kz9GE92Ybd9uVq6cucm01MO2/zazSp/saZUwdOjfj092bCfAsrOzbdeuXe4nYmvQoEHu5xFHHJHndiYiAwAAQNwkbdevX19guwEgUvPnz7cffvjBVax0VykhkCq8hOSOHVY5Lc0ON7PDDjPbts3d5IpMq1bNHabkethW2lN5mioJWw/bCSnur7/+stmzZ7vetpokFbGjtmAAAABAXCdtlbD98ssv7ZJLLimZNULK0KRx6omsKiIg5QQlYJWg1Zx8IeflS4UetuGwnZDCvHkDAidZRWwccsghbHoAAADEd9K2Xbt2duedd9qUKVNC9vS66qqrorl+SGLee4ekLQAA+ZG0jR+qeA5F8zmoErpFixZWqZKGRQAAAAAxStq++OKLbjbjb775xl2CA1eStogUSVsAAMLbvXu3+1lOja4RUz169Mgz+W6omOa0006zF154wSVxAQAAgFJP2i5atGiv/1PAvfnK5779qLQtpokTcxugRjJ0PiMjNXuiAqmCz4OkrrQtKFmI0vHBBx+4SXhvvPFG69u3r7tNffkff/xxGz58uJss7pZbbrE77rjDHnvsMXYLAAAASj9pC0RLxYoV3U9mxS5mgmb0aP9V36A027rVbOdOM43OVF9U/zG+Erbp6X8/lsQtkNSfBwWeyOHzIKFQaRs/7r//fnv66adt8ODB/tvUJqxZs2aubZgSuNWqVbPrr7+epC0AAABKL2l73XXX2b333uuCUf1ekCeeeCI6a4aUqbTNysqK9aokbIJmV7bZ6qfT7fOXzD7LSrPsbG1Xsw4dzHRcOWBbhlX+NCBh6yV2SNwCyZmw9U7QHHFE4QlbPg/injcSxfu+ROz8/PPP1rJly3y36zbd57VQWLFiRQzWDgAAAMkooqOAH3/80X/goN/DYfgeilppW7t2bWvQoIH5fD7eP5FSSwQzW7febPYss02bzBpZuvVsa/Zr09zE7dSpZtlfZFi5rHTr1t2sbp38jweQBEL9Paenmy/HZ9s7dLN163I7o1SblGFl3kuP7PGIG/Xr13c/meAq9jp27GgPPfSQm9vBGymk2Fi36T5ZtmyZNWrUKMZrCgAAgJRK2n799dchfwf26s1XvrzrC9ewYUMStkWRlmZ//mn2x4Pptn27Wc2aZmXLmh22Nt1q1TKb0yzNDt6ZYT1WpruE7ozpZr377EncDhkSWQ9cAInB+3veU0Hrqu9XmS255X2b3GW3fb1hsPVam2FHb0235s3NGjYyq+B98/N5EPdatGjhht+vXr061quS8kaOHGknnHCC2x/dunVz20MVtmph8emnn7rrCxcutMsuuyzltxUAAACig/F2QILZts1sxKQ0q1PN7KRKeSvnei1Mt07LxlmVnRvMyprVrm22YUNuRe4Bjw2xyiRsgeSz5+963Uvp/up7X1mzTusnWduFE6zK9g22eoeZ8n46yeOq7y/kBA5QFAceeKCbjPeNN96wefPmudv+8Y9/2Jlnnmk1atRw18866yw2KgAAAGKXtD355JNDVkXqtsqVK1u7du1cALvvvvtGax0BBJg82WzBArO2fdJs5urcRG0gl7ANoCTNxxWHWE61NDucLQkkJbVGeX+lWfdN6e5vvkx5s10VzaqW2WhlKue2SMjJyU3ojlw5xE5pmmb7xXqlUSC1DdqxY4dVqFCBLRUnlJy95JJLYr0aAAAASBFFTtrWqlXLPvzwQ9eLtHfv3u62mTNn2oYNG+zII4+0d955xx5++GEbP368DRgwoCTWGUlkxowZ7sDUa5OAgvl8ZmPH6iSJegLntkKQ4MRtoJ/aDbEZW9Ks/Bizww7LfSyA5Kq+f+wxswWWZtV7mvVelG6+EMupjcqCnkMsY0uaLXzMbORIs6pVY7DCiIgStuPGjXO/77///my1OPHbb7/ZkiVL8k2iqtYJAAAAQEyTto0bN3aVtM8++6yV1RGgqXonx66++mpXgfD222+7KoSbb77Zvvvuu6iuLJKPErbqB+dNdIeCbd1qplGZ9er9fZsSt/6WCEG2V6rt7q+Xmfs4JXeqVWMrA0lZfd/WbG7FNOu8fJxV3rUx5OfB3OZp1jYrd/kpU8wOp/w+rpO2wiRk8UH9ajXaTH1sNbpM8Yt4o88UywAAAADRlJt1LYKXX37ZrrnmGn/C1j1J2bJ25ZVXuhl1FbxeccUV9ssvv0R1RZG8k5FJcMUKQtu50yw7W9vt79s6Lc0ImbAV3a77tbyOJ5koHkju6vtIPg+0nJYfMyb38YhPO/WBb2ptUTnWqwIzV5zQunVrNylc1apV7ddff7Vvv/3W+vTpYxMmTGAbAQAAIPZJ2+zsbJs7d26+23WbV2WgA4xQfW+BYFWqVHE/t6kEFIWqVCk3YavErSgBU1BrBNH9+y3PsHLlcvtaAkjO6vtIPw+0nJb3qu8R30lbKm3jw+TJk+2ee+6x+vXru2IFXQ466CB78MEH7aqrror16gEAACAJFTlpq5lxzz//fHvyySdd+wNd9LtuO/vss90y33zzje23H1OcoHAkbYtGrQ06dDBbuzZ8gkZDoIP1XJBux1bMoH8lkKTV9z3Xhvs8qJXvNi2n5am+j2+0R4gvKkxQGzBR4nb58uXu95YtW9rvv/8e47UDAABAMipyT1slaBs1amSPPPKIrVq1yt2m69dee63rYyuakOyoo46K/toi6ZC0LRoVsA8ebJb9RYb1WJme77TLzDZDcnvcBiR0NWO8HLMt3cpoTpu03MnLACRH9X3vdRnWb0W6WVAl/YJGB9qPDQdbx7/G50vo9luSbtlNVH3P50G8oj1CfOnSpYvNmjXLtUjo16+fi4MrVqzoWoO1adMm1qsHAACAJFTkpG25cuXs9ttvd5dNmza522rWrJlnmRYtWkRvDZESSdvt27fHelUSxoBtGVYuK93051e7dv6ErXg/lajRcvoTbdjIzNL3JG5I3AJJodqkDDt6a7qt3pG3/cmPrU+xtfW7mWXm/TzwqL+1Hlf1e51pJXEbj2iPEF/uuOMO26p+JGY2YsQIO/74423gwIFWr149NwkvAAAAEPOkbaDgZC1QnKRt7dq1rXr16paTk5NngjuEVrl2ZevW3WzGdLMNG3ITsj+1+zth65nVMM2WLDE7ukq6de9uVsH7a6exLZA0ylSpbM2bm61enVtVr49QncCZ2+wIa2Cr/csFJm696ns9To9HfGrQoIFVqFDBxVq7du2K9eqkvMEa5rJH+/bt3VwO69atszp16jCPAwAAAOInaZuenm7/+9//bMmSJZaVlZXnvpkzZ0Zr3ZACVLmtShWStUUwcKDVNbPe/xpts2eZfVxxiM3Ykmb1Mv+epEw9bzUrfNs+adZhgFmdaXsq7IYOdY8HkCRU6bfDrOYVo11V/YKeStimWRnbk5kNoMSt+1z4Md2d7Kl3NZ8H8axVq1bup05orlZWHjFx3nnnRbTcqFGjSnxdAAAAkFqKnLR95plnXGuEc8891z766CMbNmyYLViwwKZNm2aXX355yawlgJCJ2wM27LCcamlWfkzuTPDqMlGunFm/fmZqK92/v6qZ08xa7qmwJWELJJ3KaQOt5e1mo/+zwzK2pFnbrNxet8F0jvWDLWmW1t7srAsru8cBKNirr77qJhvr2bOn+XTWAwAAAIjXpO2///1vN+nCGWec4QLZm266yU3AcNddd7lhYkBx6EBI1USqvEWEBg508w4dbmaHHWa2bVtun0rlZqtWzZ20zI8etkBSa332QDult9nCx8wWLMhtk1Cxotn69WYaWe+vvm9rduqNadaqc6zXGAXJzs62HTt2WLVq1dhQMXbppZfaW2+9ZYsWLXKFCkOHDrW6dXXaFAAAAChZRW4gqpYIBx54oL8f6ebNm93vZ511lgtqgaKaP3++ff755zZnzhw2XjEpQatj+3r1cn/mSdgCSAn77Wc2cqTZ3Xeb9e2bm6RV9b162Kr6Xrf/+99mnUnYxr01a9bY119/bd99912sVyXljRw50lasWOGKFD755BNr3ry5/fOf/7SxY8dSeQsAAID4qrRt3Lixq6jVULEWLVrYlClTrHv37q4CgWFjKA5NtKIq220qFQUAFJuq7A8/3OyQQ8z++susenWdYA1RfY+4tmXLFveTStv4UKlSJTfCTJfFixe7kWaXXXaZq4j+9ddf3WSqAAAAQMwrbQ8//HD7+OOP3e8aJnbttddaWlqanXbaaXbyySdHfQWR/Koqm2Aa3k/SFgCiQQlatUrRKG6q7xM3aUsyMP5o4tQyZcq4QoXdu3fHenUAAACQxIpcaat+tqqKFE08Vq9ePZs0aZKdcMIJdvHFF5fEOiLJkbQFAOBvXuupGjVqsFniwM6dO+3999+3UaNGuZYVxx13nD377LN21FFHuSQuAAAAEBdJWwWngQHq6aef7i5Acak3sqhiRQdGGoYIAECqotI2fqgNwttvv+162Z533nlu/ob69evHerUAAACQAoqctBXNaDx79mxbvXq1v+rWo4pboCh0EqBy5crufaUWCSRtAQCpSt+F6pWqIfj0tI29559/3s3h0KZNG/vmm2/cJRRV4gIAAAAxTdqOGTPGzj77bDezcTAdYNDfC8WhIaA6UN20aZPVqVOHjQgASOnWCGodpJOawSfHUboU8yq+BQAAAOI+aXvllVfaP/7xD7vrrrusUaNGJbNWSDkNGza0ChUq+FslAACQipSs7dChg5UvX6zBUIiyV199lW0KAACAmCjyEcGqVavsuuuuI2GLqNKwQwAAUp1aIuy7776xXg0AAAAAMVbkKW+HDBliEyZMKJm1AQAAAAAAAIAUV+RK22effda1R5g4caJ17drVDWkPdNVVV0Vz/ZBitm7dahUrVsz3vgIAIBVkZma6alu1C6KXKgAAAJC6ipy0feutt+zLL7+0ypUru4rbwAMK/U7SFsU1ZcoUd7Das2dPa9asGRsSAJBSdu3a5b4L5eijj6avLQAAAJDCipy0vf32223EiBF2yy23uFmNgWipXr26S9pu3LiRpC0AIOVs3rzZ/dSJcSYiAwAAAFJbkbOuWVlZdtppp5GwRdTVqlXL/VTSFgCAVLNhw4Y834cAAAAAUleRk7bnnHOOvfPOOyWzNkhpgUlbn88X69UBAKBUrV+/3v2sU6cOWx4AAABIcUVuj7B792575JFHbOzYsdatW7d8E0Y98cQT0Vw/pFh7BLXcyM7Otm3btrmJWAAASBXr1q1zP+vWrRvrVQEAAACQaEnbn3/+2U0UJb/88kue+5jlGHtDCdsaNWq4SttNmzaRtAUApIzt27fbjh07XCxVu3btWK8OAAAAgERL2n799dclsybAnhYJStrq0qRJE7YJACClWiPUrFnTypUrF+vVAQAAAJBoSVugJDVu3NgqVapkDRs2ZEMDAFJGvXr1rFevXkz0CgAAAKBoSdtTTjklouXef//9SJ8SyKdRo0buAgBAKtEJy3322SfWqwEAAAAg0ZK2GrYOAAAAAAAAAIiTpO0rr7xSsmsC7JGdne1m0K5QoYLVqVOH7QIASGqbN2+2VatWWf369ZmEDAAAAIBT1hKEknj/93//5ybo0KzK559/vm3ZsqXA5a+88krbd999rUqVKtaiRQu76qqr3ARXgTRLc/Dl7bffLoVXhHAWLlxoU6dOdT8BAEh2StjOmTPH5s+fH+tVAQAAABAnEmYiMiVsV6xYYRkZGbZr1y4bNmyYXXTRRfbmm2+GXH758uXu8thjj1nnzp1t8eLFdskll7jb0tPT81URH3XUUf7rSgojdlRp9Pvvv9uaNWvM5/O5RDoAAMlq/fr17mfdunVjvSoAAAAA4kRCJG1VfTJmzBibNm2a9enTx932r3/9y4455hiXlG3atGm+x3Tp0sXee+89//W2bdva/fffb0OHDnXD78uXL58nSdu4ceNSejUojPZHuXLlLCsryw0ZVXU1AADJSqODhJZAAAAAABKqPcLkyZNdIs9L2MqgQYOsbNmybhh9pNQaQQnAwIStXH755a66s2/fvjZq1ChX3YnY0X6tV6+e+13VtgAAJKutW7e6k5T67mPS19QwcuRIa9WqlVWuXNn69etnP/zwQ0SPU/sujT466aSTSnwdAQAAEHsJUWm7cuVKa9iwYZ7blHjVMELdFwkl/+69917XUiHQPffcY4cffrhVrVrVvvzyS7vssstcr1z1vw1n586d7uLZtGmT+5mTk+MuiIy2lRLkobaZt29Xr17tDmyQuPsSiYV9mRzYj4lD8Yn2l9eaKfhzlH1ZfPH4nfTOO+/YddddZ88//7xL2D711FM2ePBg1xYqONYN9Oeff9oNN9xgAwcOLNX1BQAAQIombW+55RZ7+OGHC22NsLeUVD322GNdb9u77747z3133nmn//eePXu6ipdHH320wKTtgw8+aCNGjMh3e2Zmpu3YsWOv1zeVDqZU/axknyqMAuk23acEesuWLelrm8D7EomFfZkc2I+JQ8k6fX6qNYJOVAZjXxafWizFmyeeeMIuvPBCNzeDKHn72WefuZFeiotD2b17t5vbQbHnxIkTbcOGDaW81gAAAEi5pO31119v5557boHLtGnTxvWbDT6QUV9a9YArrBetAnZNMlajRg374IMPrEKFCgUur6oHVeSqkrZSpUohl7n11ltdlURgUrh58+bWoEED+q8WgQ5ENcxP2y040afbNIu2Jp2rWLEiff4SeF8isbAvkwP7MXF4bRF0YjnURGTsy+JT+4F4ojYYM2bMcHFk4P5Xyy+1AgtHo8JUhXv++ee7pC0AAABSQ0yTtkrw6FKY/v37u6oCBbq9e/d2t3311VfuQEZJ1nCUTNWQMyVfP/7444iC959++sklCMMlbEX3hbpfgTcJq6JRoi/cdlMPY7WtqF69ehGfFfG2L5FY2JfJgf2YGNSiaf369S5hq30WCvuyeOLt+0itMFQ126hRozy36/rcuXNDPua7776zl19+2cWnkYqHNl5h3srFfK4c02wTOdF80ihth2iukuRYtJ8wJ+5eY7zuz7jfl3H4OuN1XybE/oyzfRnP+zPu96V70vhqx0RrrcQQaUyWED1tO3Xq5KplNZxMw8hUfXnFFVfY6aefbk2bNnXLLFu2zI444gh7/fXX3YRiClCPPPJI27Ztm40ePdpd94JWJYrLlStnn3zyia1atcoOOOAAl9DNyMiwBx54wPUMQ+wV1NsNAIBkm3wTCB4tdtZZZ9lLL73kJsyNVDy08YqgJqNIiYQNNWuZTyeHozVZcIhWJLF+nbLaov2Eq+PuNcbr/oz7fRmHrzNe92VC7M8425fxvD/jfl9G8X0bLbTWSq42XgmRtJU33njDJWqVmNUBzqmnnmrPPPOM/34lctUXTklamTlzpk2dOtX93q5duzzPtWjRIje5lVolaAbfa6+91vXi1HJerzEAAAAgWpR4VdGACgYC6Xqodl8LFixwE5Adf/zx+aoyNCGv4t62bdvGZRuvzMzoJhJq+zZagzVropdIiFJhQDRfpzS0aD9hw7h7jfG6P+N+X8bh64zXfZkQ+zPO9mU878+435fuSeOr2IzWWsnVxithkrYaNvjmm2+GvV9JWCVePYceemie66GoelcXxC9VUK9cudJat24dstcfAACJaPv27TZp0iSXrNtvv/1ivTooBerRrzZf48ePt5NOOsl/YKXrKkwI1rFjR/v555/z3HbHHXe4yoynn37aJWLjtY1XtI73PRrMqiRC1BIJUdoO0X6dZS3aT1g27l5jvO7PuN+Xcfo643FfJsT+jMN9Ga/7M+73pXvS+GrHJLTWin+RxmQJk7RFatIEdMuXL7dq1aqRtAUAJNX3m0YHqWc/UocqYM855xzXt1/tvJ566inbunWrDRs2zN1/9tln2z777ONaHKgCo0uXLnkeX7t2bfcz+HYAAAAkH5K2iGsayrd06VI3dFAVJwAAJANviDz921PLaaed5nrL3nXXXW4kUY8ePWzMmDH+ycmWLFkSdxOoAQAAIDZI2iKu6SBGpf3qx7ZlyxarXr16rFcJAIC9snv3bluzZo373UvWIXWoFUKodggyYcKEAh/76quvltBaAQAAIN5wKh9xTZPFqdpWVqxYEevVAQBgr61du9YlbjX8vbQmhgIAAACQWEjaIu41adLE/VRvWwAAkqU1AlW2AAAAAMIhaYu4p5m1vRYJmqwDAIBERtIWAAAAQGHoaYu4V7FiRatfv74bSrpr165Yrw4AAMWm7zJV2Kqnrb7bAAAAACAUkrZICH379mU2ZQBAwitXrpx17do11qsBAAAAIM7RHgEJoWxZ3qoAAAAAAABIDWTCkFDUHmHz5s2xXg0AAIps27ZtlpmZaT6fj60HAAAAoEAkbZEwVq5caWPHjrVZs2bFelUAACiyxYsX25QpU/geAwAAAFAokrZIGLVr13bVSevXr7ft27cXvPDEiWYZGf6rKmrassVs7drcn3mKnLSclgcAoITo+2vp0qXud01EBgAAAAAFYSIyJIzKlStb3bp1bd26dbZixQpr06ZN6AWVgB092v26Y4fZ91XTbOxYs3nzzLKzzcqXN+vQwWzwYLMB2zKs8qfpfz924MBSejUAgFSydu1a27Fjh1WoUIGkLQAAAIBCkbRFQmnSpEnBSduAhO269Wazb0i3zyqazaibZvXqmVWtmpu4nTrVLPuLDCuXlW7dupvVrWP+x5G4BQBEm1dl27RpUybXBAAAAFAo2iMgoehgV5S4VcVSPntuU8J2xnSzTZvMTshKt5OrZ1iDBmZ16pj7qeu6XfdrOS0f+HgAAKJl9+7dtnz5cvd7s2bN2LAAAAAACkXSFgnZIkH++uuv/AukpdmO44bY7Flmantbu7ZZ2bJmvRamW6eluT1u9VPXdbvu13JaXo/T4wEAiPZEmkrcVq1a1f8dBgAAAAAFoT0CEk7Lli1dpa2Stu3bt893v3rYqiXCCTUDetXansTtsnFWZeeGPLfXrGn2ccUhllMtzQ4v8bUHAKSaNWvWuJ9U2QIAAACIFElbJGRf26ysrJAHvz6fuUnH1MO2RfXcRG2g4ISt/NRuiM3Ykmblx5gddphZmTIluvoAgBTTvXt3d8JRo0UAAAAAIBIkbZFwypUrF3oSMjPbutVs3jxzk47NaZDb6iA4cRtoZpshNqdZmtXLzH3ctm1m1aqV2KoDAFJUbfXjAQAAAIAI0dMWCc+n8to9du40y842K7/ndIQSstsrhT5Q1u26X7T87t3MQwYAiK6cnBw2KQAAAIAiI2mLhJWZmWnff/+9zZ8/339bpUq5CVglbr1Jx0K1RBDd7k1OpuXLldNEZ6Wz7gCA5Ldp0yYbO3as/fzzz7FeFQAAAAAJhqQtEtbOnTvdhGSLFy/2V9uqtUGHDmZr1+YmbAtqjeCfnGxphltej6tatZRWHgBKiD4Ot2xRwjD3Z8BgBJQyTZiZnZ1tO3bsYNsDAAAAKBJ62iJhNW3a1H755Rfbvn27q7pt2LChm0Rs8GCz7C8yrMfK9HynJdQSIbjytsf8dFtS0eyoo9KYhAxAYpo40XZs2GHfV01zkzH+8YcmbTRbscKsffvcz8X+/fecmMrIyB1WMHBgrNc6qSlZq6SttGjRItarAwAAACDBkLRFwipbtqw1b97cFi5c6KptlbSVAdsyrFxWuqsyC5z3xZt0LLgCV8udUDPdDtiqa7k9bgEgYUycaOv+NdpmzzL7rKLZjLppVr9+bruYsmXNpk41mzLFrG1bs+EHZlir6QEjEEjclhglbHft2mXVqlXzfz8BAAAAQKRoj4CE1rJlS/dz1apVucNPMzKs8qfp1q27WZUqZhs2aBKYvxO2op+6rtt1v5br3t3c41wFGgAkWMJ2xvQ9J6Cy0u3k6hnWoIFZ9ermfnbsmJuwrTM9w/54MN3Wrd/z2NGj3eMRfWrZs2jRIvd7mzZtrIyGgQAAAABAEZC0RUKrXr261a1b1x0gL1myxD+TWN06Zr37mNWsafZxxSH2wZY0y8w0W79eE5iZu67bdX+fPmZ16ux5QmYiA5BA1BJBFbbbt+eOLFBlrUYSdFw6Ls9y3Vdn2EnZ6W45Lb9rz2SNRq/VErF69WrbunWrVahQwZo1a1Yy/wkAAACApEbSFgmvVatW7qeStr6DDjIbOtSfuD3gsSF23NNp1q9fbsWtEhb6qeu6vf/jQ/5O2OpxDBUGkEDUw9Y7ARWo56L3rdmaH93vgS1htJwqclevMrMhQ8zSaAlTErwqW/WyLV+eTlQAAAAAio4jif9v707go6rO/48/IZCELeyEnbDv+ybgDoJirfhDKy5F+StUFCvFutCKCBRRq9al1K2itkKxWrWiFosKLsi+7/sOYd8hIcD8X99j7zgJSQhhklnyeb9el8nM3Jm5c8/McO5zn/McRLyqVatalSpVrHr16j/e4AVeU1Mt4aqr7Eozu+IKs+PHf0wqUzKtJuP5cbTqVWbx/8uwJWALIIL4fOYmHVMN21qlfsywDVRv1w9We9N0K3Hip8kXlYkrn5e40e7ufpUxaD9/tG7d2jZt2uQv4QMAAAAA54ugLaJiQrIOHTpkvDFTAFYB2pIlf1zOQqYZgAh07JjZmjVmFSqYraz0Y8Zs5sBt8bRDZz1uYb0bbeHJq+zW49n8JuKCJSQkWGMVEwYAAACAPKI8AgAAESgtzezUKTNv9L03yWJOdP/yalfZ6dOUswUAAACAcEbQFlEjPT3d1q5da0uWLAn1pgBAvouP/zFgq8CtR4HbE/Fls1xft+t+rR8by7yL+UH/B82ePdsOaNZLAAAAALgABG0RNVJTU23VqlW2efNmO3LkSKg3BwDylUobNGxotm/fT7dp0rHiaT/VsA2k23W/1tfjVNsbwXPmzBk3Adnu3bvtuIqoAwAAAMAFIGiLqFG6dGk3IZmsW7cu1JsDAPlKtbp79vxxQrKTJ38M2GauaZtZ63UfWLv9U+3qq73JGBEsO3bssLS0NFfPVhNkAgAAAMCFIGiLqNKgQQN3uX37djKdAES9zp3N6tUzKzcv64DtifgyGa4fPmz285MfWJdjUwtwKwuHDRs2uMvk5GQ3QSYAAAAAXAiOKhBVypYta5UqVTKfz2fr168P9eYAQL5SiYMRXabaNcc+sIMHNUT/p/vWJ3WxjzuOdZOP6XbdX7y4WatWZgmffmA2lcBtsOzbt88OHTrkgrW1a9cO2vMCAAAAKLwI2iJqs223bNnihqoCQNSaOtWS531g7dqbJSb+mEmr4Oz08v9nqxLa2J49Zh8dvco+ibvR3d++vVm5cv977AcEboNlzZo17rJmzZoWFxcXtOcFAAAAUHgRtEXUqVChgpUrV85NCuMNVwWAqJSQ4C7KlzPrerFZu3Zmuy650RZV7O7q3CrDtlMns5+9eJV1fu7GnwK2mR6PC8uy3bt3r8uy9U4aAgAAAMCFKnrBzwCEoYYNG7q6tjVq1Aj1pgBA/rnkkh8v333XihU1q/7AjXZ396us79EztnOnmebDKlXKm3TsKrP4/2XYyu23//R45Fn58uWtVatWbmRHcdWfAAAAAIAgIGiLqFS5cmW3AEDU8wKvqalmV11lis+WLPljuQRd/hiw/Z+rrvopw5aAbVDExMRYrVq1gvNkAAAAAPA/BG0BAIh05xOA9QK3uGAqw6OyCAAAAAAQbBxpIKodPXrUFi5caBs3bgz1pgAAosjOnTvt66+/dqV4AAAAACDYCNoi6ieI2bZtm61du9ZOnToV6s0BAEQBn89nq1evthMnTriTgwAAAAAQbARtEdVq1qxpJUqUcBPEbNiwIdSbAwCIAjt27LAjR45YsWLFrG7duqHeHAAAAABRiKAtoppqDTZp0sT9vW7dOkvVRD0AAFxglq3Uq1fPBW4BAAAAINgI2iLqVatWzcqVK2enT5/2H2gDAJAXKrlz7Ngxi4uLszp16rATAQAAAOQLgrYoFJo1a+Yut2zZYocPHw715gAAItCZM2f8J//q169vRYsWDfUmAQAAAIhSBG1RKCjTVhm3XpkEAADO1+7du93kYwkJCZacnMwOBAAAAJBvIiZou3//frvtttssMTHRypYta3fdddc5Z2y+/PLLLSYmJsNyzz33ZFhHmZfXXnutm6yqcuXK9tBDD9mpU6fy+d0gFFTbtmHDhtayZUsaAABw3qpUqWJdu3a1Fi1aWGxsLHsQAAAAQL6JmHF9Ctju3LnTpk6daunp6da/f38bOHCgTZw4McfHDRgwwEaNGuW/ruCsRzVOFbDVQdgPP/zgnr9fv35uUpEnn3wyX98PCp7avlGjRux6AECelS9fnr0HAAAAIN9FRKbtypUrbcqUKfbXv/7VOnXqZBdffLG9/PLLNmnSJNuxY8c5A3UKynqLMnU9//3vf23FihX27rvvWuvWre2aa66x0aNH27hx4+zkyZMF8M4Qytm/jx8/TgMAAM5J/1+oLAIAAAAAFJSIyLSdOXOmK4nQvn17/23du3e3IkWK2OzZs+2GG27I9rETJkxwQVkFbK+77jobPny4P9tWz6shjklJSf71e/bsaYMGDbLly5dbmzZtsnzOtLQ0t3i8ia00QYkW5I72lYKnBb3PdOA9b948d3nllVcykUwEtyWCj7aMDrRjcC1atMiVadIJXq8+ekGhLS9s3wEAAACRKiKCtikpKa7ebCDN2KwhirovO7feeqvVrl3bHWAtWbLEHnnkETfr84cffuh/3sCArXjXc3resWPH2siRI8+6fc+ePZaamnre768wH0wdOnTIBfsUgC/I11VbKXNqzpw5bgZwRGZbIvhoy+hAOwbPrl273ASW+m3TKBxNRlaQaMu8O3LkSBBbAgAAAChEQdtHH33Unn766XOWRsgr1bz1KKO2atWq1q1bN1u/fr3Vq1cvz887bNgwGzp0aIZM25o1a1qlSpUylF/AuQ9ENTmc9ltBB/o6d+7ssm0PHjzo2kwzgSMy2xLBRVtGB9oxOFT7Xid9y5QpYw0aNLDk5GQraLRl3vF/OwAAACJZSIO2Dz74oN155505rlO3bl1X2iBzZsupU6fcUEXdl1uqhyvKmFHQVo9VpmXmjBrJ6Xnj4+PdkpmCVQSszo8CfaHYb9WrV7dNmza5z9CaNWvckFdEZlsi+GjL6EA7Xjj9/6BySCVLlnQTWYbq9422zBv+PwIAAEAkC2nQVll5WnKTFamMyPnz51u7du3cbV9//bXLPvECsbmtSSfKuPWed8yYMS4g7JVfmDp1qsu8bNq0aR7fFSKF2vj777+3rVu3ukzpChUqhHqTAABh4tixY25kjjRv3txiY2NDvUkAAAAACpGISIlr0qSJXX311TZgwACXGTtjxgwbPHiw9e3b1z8hyPbt261x48b+zFkdaI0ePdoFepVR+cknn1i/fv3s0ksvtZYtW7p1evTo4QJ3v/zlL23x4sX2xRdf2GOPPWb33Xdflpm0iC7lypVzNY9F7a9hsAAAyNKlS93JYZ3UPZ9RPQAAAABQaIK2MmHCBBeUVU3aXr162cUXX2yvv/66//709HQ3yZgml5K4uDj78ssvXWBWj1Mphj59+tjkyZP9j1HWzKeffuoulXV7++23u8DuqFGjQvIeEZoTAqp5p8+LPkMAAGhSxbJly7pJT5VlCwAAAACFqjzC+ShfvrxNnDgx2/s1OYgOsjwa7v7NN9+c83mVafn5558HbTsRWYoVK2ZdunSxEiVKuJqBAADo/wOd8FX9e/0/AQAAAAAFLWKCtkB+0QQzAABkRsAWAAAAQKhETHkEIL+ppu3KlSttw4YN7GwAKKSTj6luviY/BQAAAIBQImgL/E9KSoqtW7fOVq1a5Q7cAQCFh0osLVq0yPbv3+9q5AMAAABAKBG0Bf6nevXqVrFiRZdxu2TJEvYLABQiGmWhgK0mH2vRokWoNwcAAABAIUfQFgjQsmVLK1KkiO3du9e2bt3KvgGAQuDIkSNulIU0a9bMTU4JAAAAAKFE0BbINCmZZgyX5cuXW1paGvsHAKLYmTNnbMGCBe4yKSnJatWqFepNAgAAAACCtkBmdevWtTJlylh6erotW7aMHQQAUWzNmjV2+PBhi4uLs1atWoV6cwAAAADAIdMWyCQmJsZat27tLnfv3m0nTpxgHwFAlE4+duDAAX95nPj4+FBvEgAAAAA4RX+8ABAoMTHRBW4rVKhgxYsXZ+cAQBTSybmLLrrI9uzZY5UrVw715gAAAACAH0FbIBs1atRg3wBAIQjcErAFAAAAEG4ojwDkgsokbNy4kX0FAFFAmbVLly6106dPh3pTAAAAACBLZNoC53Dw4EGbPXu2y8bSBGXly5dnnwFAhNIkk4sWLbLU1FQ3+VijRo1CvUkAAAAAcBYybYFzKFu2rFWvXt1NWLNgwQJ3wA8AiEzKsFXAtmTJkla/fv1Qbw4AAAAAZImgLZALmlVcB/gnTpxwGVoAgMizY8cO2759uxs50bZtW4uNjQ31JgEAAABAlgjaArlQtGhRa9eunRUpUsRSUlJs06ZN7DcAiCDHjh2zxYsXu78bNGjgRlEAAAAAQLgiaAvkkurZNmnSxP29fPlyO3z4MPsOACLAqVOnbO7cue5SdckVtAUAAACAcEbQFjgPdevWtaSkJDtz5ozLuAUAhL/jx49bWlqaJSQk+EdNAAAAAEA4KxrqDQAiTevWrW3v3r1WrVq1UG8KACAXEhMT7dJLL/UHbgEAAAAg3BG0Bc5TXFwcAVsAiAA+n89NOibFixd3CwAAAABEAsYHAhdAWVvz5s2zo0ePsh8BIIykpqbatGnTKGUDAAAAICIRtAUuwLJly2znzp02Z84cO3nyJPsSAMKA6o7rhNqxY8ds9erVLuMWAAAAACIJQVvgAjRv3txKlCjhAgMKEChQAAAI/Qm1AwcOWLFixaxDhw7+EgkAAAAAECkI2gIXID4+3jp27GhFixa1ffv22dKlS9mfABBCW7dutc2bN7u/27Zt606sAQAAAECkIWgLXKDSpUu7wIAyubZs2WIbNmxgnwJACBw6dMiWLFni/m7UqJFVrlyZdgAAAAAQkQjaAkGQlJRkTZs2dX8vX77cdu3axX4FgAKkuuJz5851ZWr0m9ygQQP2PwAAAICIRdAWCJK6detarVq1rGTJkm4BABQclampUqWK+/1t06YNdWwBAAAARLSiod4AIJq0aNHCTp8+7Sa/AQAUnCJFirjJIdPT0/kNBgAAABDxyLQFgvmFKlIkQ7BAs5drqC4AIH9s3749w+8sJ80AAAAARAOCtkA+zmA+Y8YMW7x4MfsYAPLB+vXrbcGCBTZnzhzz+XzsYwAAAABRg6AtkE+KFy/uLrdt22Zr165lPwNAkDNsV6xY4f6uVKkSNWwBAAAARBWCtkA+qVixoqtxK6tWrbIdO3awrwEgCPbu3WuLFi3yTwJZr1499isixrhx4yw5OdkSEhKsU6dOLlM8O2+88YZdcsklVq5cObd07949x/UBAAAQPQjaAvmodu3aVqdOHff3woULbc+ePexvALgAhw8ftrlz57o6ttWqVbOmTZuyPxEx3nvvPRs6dKiNGDHClfZo1aqV9ezZ03bv3p3l+tOnT7dbbrnFpk2bZjNnzrSaNWtajx49XKY5AAAAohtBWyCfNWvWzKpWreoCDAo07Nu3j30OAHlw/PhxmzVrlp06dcqNZmjTpg1lERBRnn/+eRswYID179/fnXB49dVXrUSJEjZ+/Pgs158wYYLde++91rp1a2vcuLH99a9/df2Jr776qsC3HQAAAAWLoC2Qz2JiYqxt27ZWuXJlO336dLbZNACAnKWmprrf0cTERGvfvr0VKUI3BpHj5MmTNn/+fFfiwKPPsK4riza3Jy7S09OtfPny+bilAAAACAdFQ70BQGGggzIFGDScsVatWqHeHACISApUde3a1eLi4qxYsWKh3hzgvGsx66RDUlJShtt1XbXvc+ORRx5xZUECA7+ZpaWluSWwpIgoQ1dLQYiJCeZznTGftj+YTxqk/RDMTZIzFuwnPBN27zFc2zPs2zIM32e4tmVEtGeYtWU4t2fYt6V70oL5vzW39H+9z+crsP/zkTe5bR+CtkABiY2NzRCw1ZdUB1XFixenDQAgG+p0njhxwg0hF2XZAoXRU089ZZMmTXJ1bjWJWXbGjh1rI0eOPOt21dVXtnpBqFQpuIGEg4llzBcTY0V8CikEQZBGPQXzfcpuC/YT7g679xiu7Rn2bRmG7zNc2zIi2jPM2jKc2zPs29I9aXiNpFWc4dChQ64Pzai08HXkyJFcrUfQFggBZdrMmzfPZb8oa8wLRgAAfqLOpiZxVFmZjh07MiQcEU11mHUCd9euXRlu1/UqVark+Nhnn33WBW2//PJLa9myZY7rDhs2zE125lFfQxOYVapUqcBOegRz3lUFEsr6DlmlvXuDF0ioXDkoTxPs+WUrW7CfsHLYvcdwbc+wb8swfJ/h2pYR0Z5h1pbh3J5h35ZB/NwGM2irEo36f5+gbfjK6QR8IIK2QIiCtqpLp4wX1bFT4Da3X1oAKAzU4VTAdseOHa7jqXqgQCRTWY927dq5ScR69+7tbvMmFRs8eHC2j3vmmWdszJgx9sUXX7hSS+cSHx/vlsx04FZQB2/BOt73aDCrgghBCyQEaT8E+30WsWA/YZGwe4/h2p5h35Zh+j7DsS0joj3DsC3DtT3Dvi3dk4bfHAvqOxfk//s4f7ltG1oQCNGBW+fOnV2GrYK3CtwSkAAA8weyFixY4AK2Xk3wc2UiApFAGbBvvPGGvfPOO7Zy5UobNGiQHTt2zPr37+/u79evn8uU9Tz99NM2fPhwGz9+vCUnJ1tKSopbjh49GsJ3AQAAgIJA0BYIEWXWKnCrSx18KXCrGaEBoLAHbOfPn287d+4kYIuoc/PNN7tSB48//ri1bt3aFi1aZFOmTPFPTrZlyxb32fe88sor7qTujTfeaFWrVvUveg4AAABEN8ojACGkTNsuXbrYjBkzXM252bNn20UXXWRFi/LVBFA4A7aq960an8qw7dChg1UOszphwIVSKYTsyiFokrFAmzZtYocDAAAUUmTaAiFWsmRJF6gtVqyYm0GQIY8ACvPEYwrcKmCriccI2AIAAAAorEjnA8KAZnNW4FYBi7Jly4Z6cwAgJGJjY112rU5g8VsIAAAAoDAjaAuEicwBioMHD7oJy1RCAQCi1enTp2379u1Wq1Ytf+CWgC0AAAAKi1/9KnjPFRNj9sQTwXs+hBZBWyAMKcts1qxZLnihDNzSpUuHepMAIF8CtqrlvW/fPktNTbWGDRuylwEAAIALMW6c2Z49qj0WnP342mu0R4hQ0xYIQ6pvm5CQ4IIYmqTswIEDod4kAAiqU6dOuZNTCthq8sWKFSuyhwEAAAAg0oK2+/fvt9tuu83V/tSwybvuuivHCZs0225MTEyWy/vvv+9fL6v7J02aVEDvCsiaArZdu3a1cuXKWXp6us2cOdP26EwZAESBtLQ0F7DV/+0K2GpEQfny5UO9WQAAAAAQNiImaKuA7fLly23q1Kn26aef2rfffmsDBw7Mdv2aNWvazp07MywjR460UqVK2TXXXJNh3bfeeivDer179y6AdwScO9u2c+fObvZ0DSGeM2eO7dixg90GIKLphOv333/vRhB4v3M6QQUAAAAA+ElE1LRduXKlTZkyxebOnWvt27d3t7388svWq1cve/bZZ61atWpnPUa1QKtUqZLhto8++sh+8YtfuMBtIGXuZl4XCKeZ1BctWuQm6pk/f77LSlMgFwAisSSCSr6cPHnSTbLYqVOns/5PBgAAAABESKathoYrsOoFbKV79+5WpEgRN4FJbijYpcCXyipkdt9997laeh07drTx48ebL1jFmoEg0Oe8TZs2lpycbBUqVKDuI4CIpZNOTZo0cZm1l1xyCQFbAAAAAIjkTNuUlJSzMgt14Kf6d7ovN9588013oNilS5cMt48aNcquvPJKl/Hz3//+1+699143dPPXv/51jrX4tHgOHz7sLs+cOeMW5I72lQLk7LPcadasmSuT4O070f5THeZQoy2jB20ZHcKtHZVZGxcX5/6uUaOGVa9e3f12hcv2hbNwa8tIwj4DAABAJAtp0PbRRx+1p59++pylES7UiRMnbOLEiTZ8+PCz7gu8TdmMx44dsz/+8Y85Bm3Hjh3r6uNmpomiUlNTL3h7C9PB1KFDh9zBqLJJcX7WrFljx48ft+bNm7uTGKFEW0YP2jI6hEs7ajtWrFhhBw8edKNZvMAtIq8tI9GRI0dCvQkAAABAnoU00vPggw/anXfemeM6devWdfVmd+/efVZdPM06nZtatB988IELbvXr1++c66q+3ujRo10mbXx8fJbrDBs2zIYOHZoh01YTn1WqVMkSExPP+Rr46UBUmVbabxyInh9lg+sgXvtw7dq1ru6tssVDhbaMHrRldAiHdkxPT3eliXQyVMFa1eimHndktmWkSkhICPUmAAAAAJEZtNUBiJZz0czSytLRwV+7du3cbV9//bU7kFGQNTelEX7+85/n6rVU91a19rIL2Iruy+p+HUxxQHV+dCDKfjt/OjnQtWtXmzdvngvgamIffTdUmzlUaMvoQVtGh1C2o0a4qOa8Mh2LFSvmfp+SkpIKfDuiBd/JvKFPBgAAgEgWESkbqkV79dVX24ABA2zOnDkuQDV48GDr27evVatWza2zfft2a9y4sbs/0Lp16+zbb7+1u++++6znnTx5sv31r3+1ZcuWufVeeeUVe/LJJ+3+++8vsPcG5JVqOl966aVukj7Vi5w1a5Zt3LiRHQogpDQK4LvvvnMBW2U66gQTAVsAAAAAiMKJyGTChAkuUNutWzeXOdGnTx976aWXMgzDXL16tSuDEGj8+PFu0pMePXqc9ZzK/hk3bpz95je/cbXi6tevb88//7wLDgORQAERTa63ZMkS27ZtmzsBoaHIqnMLAAVNZYt0AkmTJmpEgOrYFi9enIYAAAAAgGgN2iqrUJOJZSc5OdkFXjNT5qyWrCh7VwsQyVQnUpPoKUCiifsqVKgQ6k0CUEiVLl3alQ8qWbKktW/fPuSTJAIAAABApOJoCogS9erVs6pVq2aYkEx1n6npByA/aaSLRq6ILlWHXqMA+O0BAAAAgCivaQsgdwIDtqmpqW7CPpVNAID8sHfvXps2bZpt3rw5w+8QAVsAAAAAuDAEbYEopUnJNIP7woULbfny5VmWDwGAvNDvydq1a1392rS0NNuyZQu/MQAAAAAQRJRHAKJU48aNXbbbmjVrbMOGDW5G97Zt27phywCQVydPnnQng3bv3u2u16xZ01q0aGExMTHsVAAAAAAIEjJtgSilAEqjRo3cZECarGzfvn02ffp027lzZ6g3DUCEOnDggH3zzTcuYKuTQq1bt3aLfmMAAAAAAMFDpi0Q5TQ5WWJioi1YsMAOHjxo8+bNcxm31atXD/WmAYggqpP9ww8/uAkOS5Ys6U4I6bcFAAAAAM7lV78K7j567bXo3+cEbYFCQAGWrl27ulIJKSkpVqVKlVBvEoAIo9IqDRs2tMOHD1urVq2saFG6EAAAAABC5FfRHwXmiAsoJDSUWXVuFXTxZnbXZELbtm2zGjVqUI8SQJblEOLi4tyJH6lfvz6/FQAAAABQAKhpCxQyXsBW1q9fb4sWLXJDnk+cOBHS7QIQPk6fPm0rVqywGTNm2Ny5c911YbIxAAAAACgYBG2BQj7cWUOc9+/f7yYX2r59e6g3CUAYZNd+++237qSOsvHLlCnj6tgCAAAAAAo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" ] @@ -354,16 +354,16 @@ "======================================================================\n", "Index True Recovered Error \n", "======================================================================\n", - "0 -0.8787-0.3612j -0.8780-0.3617j 0.000828 \n", - "1 -0.8787+0.3612j -0.8780+0.3617j 0.000828 \n", - "2 0.5003-0.4053j 0.5015-0.4058j 0.001296 \n", - "3 0.5003+0.4053j 0.5015+0.4058j 0.001296 \n", - "4 -0.6334-0.0935j -0.6324-0.0882j 0.005358 \n", - "5 -0.6334+0.0935j -0.6324+0.0882j 0.005358 \n", - "6 0.5045-0.1002j 0.5048-0.0906j 0.009595 \n", - "7 0.5045+0.1002j 0.5048+0.0906j 0.009595 \n", - "8 0.1823 0.1814 0.000874 \n", - "9 -0.1435 -0.1413 0.002219 \n", + "0 -0.4740-0.8233j -0.4736-0.8233j 0.000363 \n", + "1 -0.4740+0.8233j -0.4736+0.8233j 0.000363 \n", + "2 -0.0088-0.7098j -0.0116-0.7094j 0.002827 \n", + "3 -0.0088+0.7098j -0.0116+0.7094j 0.002827 \n", + "4 -0.4551 -0.4571 0.002004 \n", + "5 0.2014-0.3907j 0.2046-0.3867j 0.005143 \n", + "6 0.2014+0.3907j 0.2046+0.3867j 0.005143 \n", + "7 0.0126-0.3568j 0.0138-0.3626j 0.005851 \n", + "8 0.0126+0.3568j 0.0138+0.3626j 0.005851 \n", + "9 0.2188 0.2231 0.004293 \n", "======================================================================\n" ] } @@ -445,12 +445,12 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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"text/plain": [ "
" ] @@ -467,16 +467,16 @@ "============================================================\n", "Index True Recovered Relative Error \n", "============================================================\n", - "0 3.356127 3.355637 0.000146 \n", - "1 2.531006 2.529212 0.000709 \n", - "2 1.627941 1.628247 0.000188 \n", + "0 4.324553 4.321076 0.000804 \n", + "1 3.182118 3.182414 0.000093 \n", + "2 1.782269 1.781281 0.000554 \n", "============================================================\n" ] } ], "source": [ "# Compute singular values\n", - "B_recovered = dmdc.B.cpu().numpy() if hasattr(dmdc.B, 'cpu') else dmdc.B\n", + "B_recovered = dmdc.B_havok_dmd.cpu().numpy() if hasattr(dmdc.B_havok_dmd, 'cpu') else dmdc.B_havok_dmd\n", "singular_values_B_recovered = np.linalg.svd(B_recovered, compute_uv=False)\n", "\n", "# Plot singular values\n", @@ -533,12 +533,12 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { - "image/png": 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vX//6F771rW9hx44dWLhwIb/PGWecUfLzGoaBvXv3IhgMQhCEUfhLCCGEEELIaDJNE7FYDNOmTYMoUp5BqWicSwghhBAyNca5gsluOUFOO+00XHjhhTj00EOhaRquuuoqvPXWW3jnnXfg9/uL3ue5557DscceywO+Z511Fm6//XYetH311Vdx4IEHlvS8e/bswcyZM0f5ryGEEEIIIaNt9+7dmDFjBu3YEtE4lxBCCCFkaoxzJzRo219XVxcaGxvx1FNP8cBsMRdccAESiQTuv//+3LojjjgCK1euxA033FDS80QiEVRXV/OdU1VVhfHIeGB/W0NDA2WK0L6h9wz9P9FnzQSgz2HaN/S+mXz/T9FolJ9kD4fDCIVCY/Y8Uw2NcysHfffQvqH3Df0/0WfNxKLPYdo3lfq+KXWcO6HlEYoNMpna2tpBb/P888/ji1/8YsG6U089FXfffXfJz5MticACtuMVtE2n0/y5aHof7Rt6z9D/E33WjD/6HKZ9Q++byfv/RKWsRra/aJw78ei7h/YNvW/o/4k+ayYWfQ7Tvqn0981w41ypknbK5z//eRx11FFDljlob29HU1NTwTp2ma0fjCzL/Cc/op19TvYz1thzsITm8XiuyYb2De0Xes/Q/xN91kws+hymfVOp7xkaNxFCCCGEkP1ZxQRtP/3pT/N6ts8888yoPzarf3vNNdcMWM/SnVn0fKyxgw6WRcwOcCjTlvYNvWfo/4k+a8YffQ7TvqH3zeT7f2LNGQghhBBCCNlfVUTQ9oorruA1ap9++ulhG000Nzejo6OjYB27zNYP5sorrywoqZCtHcHqU4xXeQSW8kw1bWnf0HuG/p/os2Zi0Ocw7Rt630y+/yePxzNmj00IIYQQQkilm9CgLcvQ+MxnPoO77roLTz75JObOnTvsfY488kg89thjvJRC1iOPPMLXD8btdvOf/tiBxnhlvrKDm/F8vsmE9g3tF3rP0P8TfdaMP13XoaoqD8BpmgZFUeg7qh/aN2O7X5xOJxwOx6DX05iJEEIIIfs6zmW/2QxrGlcUon0ztvtmuHHupAjaspIIt99+O+655x4Eg8FcXVrWOc3r9fLliy++GNOnT+clDpjPfe5zOO644/Czn/0MZ555Jv7+979j7dq1uOmmmybyTyGEEEImBXbClH3fsk6l2ctsYMKmolPDp4H7ivbN2O6X6upqPluK3nuEEEII2Vc0zi1vX9E4d2z3zWiMcyc0aPu73/2O/z7++OML1t9yyy348Ic/zJd37dpVENles2YND/R+85vfxFVXXYWFCxfi7rvvHrJ5GSGEEEIs2YBtY2MjfD4fX8eyJiVJosBZkQEb7Zux2S/sMZLJJDo7O/nllpYW+hclhBBCyD6hcW55YzEa547NvhnNce6El0cYDiub0N/73/9+/kMIIYSQ8qaKZQO2dXV1ue9iGrANPk6hfTN2+yU7q4oNaNl7cjSmkBFCCCFk/0Tj3PLQOHds981ojXOpwCohhBCyn2C1mZhshi0hEy37Xsy+NwkhhBBCRoLGuWQqjnMpaEsIIYTsZ6h+KKkU9F4khBBCCI0tyFQk7GPvB4aCtoQQQgghhBBCCCGEEFJBKGhLCCGEEDKGvvOd72DlypUVsY9Z89fPf/7zE70ZhBBCCCFkCqBx7tiioC0hhBBCJk1H4M997nNYsGABPB4PmpqacNRRR+F3v/sd79A6WQe6bOrUUD8jwRq5svuyxnOEEEIIIaSy0Ti3dE/uR+NcaaI3gBBCCCFkONu2beMB2urqanz/+9/H8uXL4Xa7sW7dOtx0002YPn06zjnnnKL3ZcX/nU5nRe7kL3/5y/jEJz6Ru3zooYfiYx/7GC6//PKit1cUBS6Xaxy3kBBCCCGEjCUa51ponDsQZdoSQgghpOJ96lOfgiRJWLt2Lc4//3wsWbIE8+bNw7vf/W488MADOPvss3O3ZWfeWfYtC+L6/X5873vf4+vZuvnz5/Og56JFi/DnP/85d58dO3bw+73++uu5dezsPbstO5uff1b/sccew+rVq3lH2DVr1mDjxo0F2/rDH/6QZwEHg0F85CMfQTqdHvTvCgQCaG5uzv04HA5+v+zlCy+8EFdccQUvaVBfX49TTz110G1l69g2sutPOOEEvr6mpoav//CHP5y7rWEY+OpXv4ra2lr+HCzblxBCCCGETAwa59I4dzAUtCWEEFIR4qk0duzeA9M0J3pTSIXp6enBww8/jE9/+tM8CFtM/zICLBB53nnn8Uzcyy67DHfddRcvrfClL30Jb731Fj7+8Y/j0ksvxRNPPFH29nzjG9/Az372Mx5AZoFk9vhZ//znP/lzs2xgdn1LSwt++9vfYl/cdtttPHj87LPP4oYbbhj29jNnzsS///1vvswCym1tbfjlL39Z8HhsP7744ov48Y9/jGuvvRaPPPLIPm0jIWRqSCoatnbF6buYEELGCY1zaZw7FCqPQAghZMJpmobXbvoEgul29Bx5BVYde9ZEb9J+5Xv/2YCYrLPQ57g9Z8jrxLfPXlrSbbds2cIDCCw7Nh/LPM1msbKA7o9+9KPcdR/84Ad5UDbrAx/4AM82ZZkMzBe/+EW88MIL+OlPf5rLSi0Vy9w97rjj+PLXv/51nHnmmXw7WJ3d6667jmfXsh/m//2//4dHH310yGzb4SxcuJAHV7NYJu1QWLYuy6JlGhsbeUmJfCtWrMDVV1+de+zrr7+eZw+ffPLJI95GQsjkZxgmvnv/O+iMynjvqhk4Y3nLRG8SIYRMynFuOWNdGufSOHcoFLQlhBAy4Xp3vYNguo0v175+A0BB23EVSWuIpLRxHsruu5deeolP9f/Qhz4EWZYLrmPlC/KtX7+e14rNx2rk5meglooFPbNYJi3T2dmJWbNm8efJr1HLHHnkkSPK6M1atWoVRlP+9mf/Brb9hJD9WzilItHXiTlaGza1+SloSwiZEmicWx4a51YWCtoSQgiZcGmVnf22qLoxoduyPwp5JIi8vMD4ZtqWasGCBbz8Qf/asaymLeP1egfcZ7AyCoMRRatiVH55DtbArJj8pmbZsgwseDxW+v8t5WxrMf2bsrG/YSy3nxAyOaRlGef33YSAHsGO9h4Apc2GIISQSjYR49xyxro0zqVx7lAoaEsIIWTCqYqSW9Z0qmk73r5xxmJem7V/XdhKUVdXx6fus2n8n/nMZ8oOyDKscRmrCXvJJZfk1rHLS5daQYmGhgb+m9V/Pfjgg/lyfqOvcp6H1Yq9+OKLc+tYGYbRVMq2shq4jK7bJ0QIIWQoat8eHrBlFnc/DODztMMIIZMejXNpnDuZUdCWEELIhFOVZG75Oe9xWGKaFRtAJBODNfNi5QxY2QPW6ItN3WIZpy+//DI2bNgwbAmBr3zlKzj//PN5kPOkk07CfffdhzvvvJPXm81m6x5xxBH44Q9/iLlz5/JyAd/61rfK3k7W7IzVzmXbybb3r3/9K95+++1cVvBoKLat3/zmNwtuM3v2bP4/dP/99+OMM87g9wkEAqO2DYSQqUfW9IL6toQQQsYHjXNtNM4tZM2vI4QQQiaQJqdyy0nBi5is0etBCsyfPx+vvfYaD7heeeWVOOigg3hg9Ne//jW+/OUv47vf/e6Qe+zcc8/l9WtZ47Fly5bhxhtvxC233ILjjz8+d5ubb76ZN8VjAeDPf/7zwz5mMRdccAEP9n71q1/lj7Nz50588pOfHPVXs/+2soZn+aZPn45rrrmGN0pramrCFVdcMerbQAiZWtS872I9r/wKIYSQsUXj3EI0zrUJZn5BtP1ENBpFKBRCJBJBVVXVmD8fqxPHsmBYB+dsHTpC+4beM/T/RJ81tjee+DfMl27iy49VnYfzL7gYM2t99Dk8ytLpNLZv386zMz0eD1/HhgEs+FfJ5REmCu2bsd8vxd6TEzVemyponFs5JtsxwBsvPwXz8e/zZfafveKrD43Z98Jk2zfjifYN7Rd6z4wMjXPLQ+Pcsd83ozHOpW9IQgiZYsx0FOH7r0bs0Z8A+uTIWNVVO7tHFVwIJ0tvqkQIIYSQfadodkNCltWTVqlBISGEEDKRqKYtIYRMMZ3/uwUdb7/As2RmNyxB9UFnodL1euagw7sKC9NvY2F6HdS2RcCMoyZ6swghhFSwO1/dg7U7+3DREbOxpIWysfdVV2Ax1lZfBIepQxE9+Jyqw+tyjMprRQghhJDyUaYtIYRMMdqul3JZMuEdb2Iy6PbMwnb3ErjMNObL7wCd70z0JhFCCKlgcVnDf9a1oSOSxsNvd0z05kyZRmQ73YuwzbMUe1zzkFbtxmSEEEIIGX8UtCWEkClG1Y2inaArGTswTIjB3GU90Tuh20MIIaSy9SUUZDtzxGUqqTMa+pdDSCqTYwxBCCGETFUUtCWEkCkmbTrtZbXyatoahoktnfGC2nmyZhQEbY0kBW0JIYQMrjeh8N8OU6Xg4ijpn1lLmbaEEELIxKKatoQQMsX8d/oVOCF6NV9WZBmV5m8v78Lj6zuxuCWIr5y6mK8zUlEYgn0eUUj1TeAWEkIIqXR9SQVnhG/HXGUjnsf5AJZP9CZNeo3tT+N9vc/AayTwum8NlHg9gNBEbxYhhBCy36KgLSGETDHdqouFPa2qtukoKk18y/O4qPs+rEseCsNYBFEUcNDu23Bk7/rcbRxyeEK3kRBCSGVL9LVZNdABHNv9NwAfnuhNmvQCsW0Iqbv48vGx+2D2HABg7kRvFiGEELLfovIIhBAyxURkIC16+bIoR1Bpjmm7DdV6N46JPYiUkinfoBVmBEtKFKZeeaUdCCGEVIZUpCe3zEq560amwC0ZOS1VeDGdoL1JCCGETCDKtCWEkClEM0wkFQ33hz4EVXBB8FVjFSqLnu0cww660yn4PU4IWrrgNqZpIhnthb+mcQK2kBBCSKXTYl0Fl1OqjoCbDm32Rf/vYl2moC0hhBAykSjTlhBCppB0tBsnRu/BLGULr0nXpXqhsRSkCmLkBW3TiRj/LeqFB4pMLFx4QE4IGdyOHTsgCAJef/11fvnJJ5/kl8PhkZcaGY3HIGSs6P2DtkphEy2y70Fbg4K2hBBCKsCO/XicS0FbQgiZQpRIBw5MrcVhiScwW9nM18XlyikzoOoG8mK2SKeyQduBDdOSEQraEtuHP/xhPrBiP06nE01NTTj55JNx8803wzAKT0zMmTOH3+7vf//7gF24bNkyft2tt9464Pbsx+v18svnn38+Hn/88VEZYGZ/6urqcMopp+C1114b85d2zZo1aGtrQyhUWhOh448/Hp///Of36TEIGU/bzZaCy6kK+q6brMR+5REMJcl/K5qB21/chTte2cNnwhBCCBldNM4tz5r9aJxLQVtCCJlClKTdeCwl+vnvSEodk+diB27d8YHB1qGkFQ2G4MhdlpMx/jj5QdseqQm7XAsR1WiaKyl02mmn8cEVC4Y++OCDOOGEE/C5z30OZ511FjStMGAzc+ZM3HLLLQXrXnjhBbS3t8Pvt/438l177bX8sTdu3Ig//elPqK6u5kHhH/zgB/v8Mjz66KP8sf/73/8iHo/j9NNPH/SsvqqOzv+ry+VCc3MzDxZP5GMQMhZYVu12cRb2uObl1qWp/uo+638C1VCs8ghrd/bisfUdeHBdG17dVdkZSYQQMlnROLd0rv1onEtBW0IImULUpN14bKayFQcnnkWqc9uYPNdvn9yKr93xJu58dU/J95F1E2t9x+Yuq6k4ZEWFw7QCbm3OWbi97jO4p+YStLnnj8l2k8nL7XbzwdX06dNxyCGH4KqrrsI999zDA7j5mbPMhz70ITz11FPYvXt3bh3LymXrJWngCYFgMMgfe9asWTj22GNx00034Zvf/CauueYaHsjNevvtt3mQuKqqit/nmGOOwdatW4fcbpZhyx579erV+OlPf4qOjg68+OKLuUzcf/zjHzjuuOPg8Xjw17/+ld/nD3/4A5YsWcLXLV68GL/97W8LHvOll17CwQcfzK9nj9s/e7fYlK9nn32WZxr4fD7U1NTg1FNPRV9fH8/uYPvql7/8ZS4rmG1bscf497//jQMPPBCBQABz587Fz372s4LnZVnK3//+93HZZZfx/cP2J9uXhIymvqTCf/8vcBr+Wftx/KXuc0gadKJvX7BGbo7+pYoymbadUTuYu7OH6twSQshYoHFuZYxzly1bxp934cKFFTHOpaAtIYRMIXoqWhC0PTr+IIz2d0b9eVh2bGTTszgr/Bfs3GjVFio1O6pPasBO10Js9ixHAj7IKeugkBGcntzyWGUIk6nlxBNPxEEHHYQ777yzYD0rn8AGa7fddhu/nEwmeXCUDbJKxbJ42XudBYaZ1tZWHtBlg2pWOuGVV17hj9c/y3corPwCoyhW0In5+te/zp9r/fr1fJtZ4Pbb3/42vve97/F1bHD4rW99K/e3sGxdFjheunQp34bvfOc7+PKXvzzk87IaYO9617v4fZ5//nk888wzOPvss6HrOh/EHnnkkbj88st5RjD7YZnK/bHnYmUjLrjgArz66qu4+uqr+Xb1D5izAW52gP2pT30Kn/zkJwsC34Tsq96E9f/T7ZyGDudM/r2SVCurfvtkI6saXKb9ucQIqvX97Ox4A0fEH8WK5Avo6WyboC0khJD9D41zXxn3ce6FF16IN998k49x2Xh8ose5dEqaEEKmEEO2g7ZZSqJv1J9H1gycEb6dLy/Yw76kzirxfjo2eVbwH+Yc9zQoeY1O3B5fbrkvSUHbcbXhfmDDf4a/Xe1c4LivFq576sdA7/bh77v4TGBJae+VcrBMVDa46o8FVL/0pS/hG9/4Bu644w7Mnz8fK1euLPlxa2tr0djYyM/GM7/5zW943StWK5fV1WUOOOCAkh+Pncn/7ne/y7NUDzvsMKRSVv1IVmPrPe95T+52LBjKBoTZdSyj9Z133sGNN96ISy65BLfffjuv4/vHP/6RZwKwjIA9e/bwQeNgfvzjH/MBZn7GLrtf/hQxlpnAMoIH8/Of/5wPiNkglgWq2cCYBZV/8pOf8CyGrDPOOIMPYpmvfe1r+MUvfoEnnngCixYtKnlfETKUvngSfj2ChFgFZKY1plVqRLYv0mn2edSvXm2mxq2v5y0cmniSLz/WM4fenISQyYfGuQPQOHfwcS5L2pg3bx42bNgw4eNcCtoSQshUkh4YtNWTox+0zQab+OMbJm8w5nQMP3mDZdWGtB4oohuy4OWZt0razuzx+vxAJlZLmbbjTE0Bqd7hb5euLbIuUtp92XOMATawKlaP6swzz8THP/5xPP3007w0QjlZtsUem53FZ+UQsgHbUrFGB6IoIpFI8AEgy/hlmcDZYDALpmax27ByCx/5yEd4RkAWC5JmGyWwQOmKFSt4wDaLZRAMhW37+9//fuwL9rzvfve7C9YdddRRuO6663gmg8Nh1atm25bF9h0LBHd2du7TcxOSL93bisu6f8JrpL/pPRz/C56BpEJB233Bgt5sX7pMGYvT1gwa9n9tXWmPI/akXPy2Hqddn54QQioejXOLonHu8ONclqk7keNcCtoSQshUIscGrDKSA5uGvLy9B89tasfpK2fhgKZg2U+TThY+TyylojbgHvZ+Yvd6XNzzC778ov9EJJXzkXDW8jq2kqngyJkzcdq6u1Cb3oMAiz+f/qeyt42MkNMLeIsEZPvzhIqvK+W+7DnGABtksWzU/ljt2osuuohnrrIasnfddVdZj9vT04Ourq7cY2dLG5SLBWlZViqrbcsanPWX3xiNlT5gfv/73+Pwww8vuF12sDgSI932kegf1GYDWpYZTMhoSUesgyPR1DFb2YTeVCMQYVmi02gnj3Sfmk48VXU2X34qeBZUwQWXS8Lx7H8474Rwk9aKtt4I5jaV8JlPCCGVgsa5A9A4d3KMcyloSwghU4ikWsFUTXBC4s29TCsLsp8dD/0Kx0VfwAu7TsGcS78AlyTuU9A2Gg2jNtA07P20tF2/lmXbaqqOtCGiR7Lua4amowl9qNI64GDffboKOMrLaiQjtPgsYIl1wF62/uUSxhGrLbtu3Tp84QtfKHo9y65lzb9YHVbWlKAc7Mw6y5A999xzc2fWWV1ZVVXLyrZldbNYaYZSsAzcadOmYdu2bbxpWjGsQdmf//xnpNPpXLbtCy+8MOTjsm1/7LHHeGO1Ylh5hFxW3SDY87ImD/nYZVYiYl8CyoSUS4t25JZrtG6cGL0bfb3sxMRhtDNHKJ1XE1gRrc8VWTVgsAZlih20PS3yD/TtWYa5TYUnlQghpKLROHcAGudOjnEuNSIjhJApxKFaWXqyMwRFsrL3RLkwaKuoOhZGrQDPit6H8epra8t+HjVVGLRNRXtKu59sB22PiT2IxTv/yuvjZrklEabXykTUDSAdK+1xyf5BlmW0t7fzhmCsERZr0MWmMbGmXBdffPGgA7Du7m7ccsstQz52LBbjj717925eTuFjH/sYbwR27bXXYsGCBfw2V1xxBaLRKG9QsHbtWmzevJkHT0e7+QALrP7gBz/Ar371K2zatIkHpdn2s1pbzAc/+EF+Vp+VT2C1bv/zn//wwPRQrrzySrz88su8Bher/8tqdP3ud7/j+ybbDZdlI7OSDWxdsYwBVh+YBX5ZXV62XSyAff311w/bHIKQ0WbGrfdtPj3v+4WUL60VP2kTS2twq4Wll+LtW2gXE0LIKKNxbmWNc//0pz/xfhYTPc6loC0hhEwVponN0gHY6VqAPv88aK4qvlpiB1um3VwkmbIbfzHq8zdAU8tr+hVzNeayY5lUtHfQOknsJ8vod1DNAsr5zWNYjTzBa2dDxnqpDiaxPfTQQ2hpaeEDr9NOO40X/WeBzXvuuWfIM+CsLMFw5QFYd1j22CxAy0oqRCIRPProo/jKV75S8Dgss5eVMDjuuOOwatUqXsag3Bq3w/noRz+KP/zhDzxQu3z5cv5crHNttkwDa2R233338WDuwQcfzBut/ehHPxryMVmWwMMPP4w33niDN0FjNXDZfmMlJBg2IGX7kJVxaGhowK5duwY8xiGHHIJ//vOfvNwDe15WdoIFtfObMxAyHsSUdRDmEO1a1qZS+N1GypNW2OycgboiMbjMdME6taeExpOEEELKQuPcyhjn/v3vf+fjbzbGZYkUEz3OFcz8o+n9BMuSYc082AFZVZUV1BhLLIrPChOzDtRsmiWhfUPvGfp/GguyquGym1+A2+3CwqYgjt19I/x96/l1yz7zTzh9Vi3S9rY9aP/TRwruW33AGkw79Yt4bneKZ76euLhxyMZiT23qwp3/ewNOU0ZKDODM1QtxxorCWoJdMRk/fmgD/G4JXz99MQ/IvnTPDXBtsOuKRvxzIR7+cby49kVeP++UY4+BvOVp+Nb/k19fc8pXMfvgd+3zvqHPYQubTr99+3Ye/MtOq2fDANbkig1qijXz2p/Rvhn7/VLsPTlR47WpYqqPc2VNx+O/+iRa1J3wuxxIZBqQdTUciZMv+w4qyWT67nnt6ftgvvA7KIILHa5Z6BXrebB28dHnwfHINwtuG/bOwvGf/X1Jj8salbJxRcAtTdp9M95o39B+offMyNA4tzw0zh37fTMa41yqaUsIIVMEm8KYVeVxwvTYDY8SkV5UZ4K2MQRwa/2XMFvejBNi9/J1ya0v4LXffxK3V30KuuCEz+XAMQsbBn2ulKIj7rAbUkXznjvriQ2d6I3L/Gd9WxQHz6qBofbLtNWScHe/hZOiViA3EJ+OvmBd7vp0dOAUWEIIIfuvcFJF0LAabLJ67ClVh2ECgkqZtvtCk1Nwmjo8ZgrztW2YrVllX+KdKxDod9tgei8SKRl+79ANSNlMmqvuWod4WsNXT1uMBY39H4kQQgghQ6HTmoQQMkXE0naJg6BHAvz1iDmq0eGcgVhKzl2X0ICYowZv+Q7DW95D+TpFN+BOd2FF8kW4jBS62vcM+Vz5JQ2YSGpgeYW9u7fjsu6f4IO91yORtA6mTSVVcBtRS8FQ7ECu5PHCkxe0VeJ9ZewBQgghU11vLAW/btVY1fzNuRIJglr4/ULKo+d9F2seu0yRGm7NLWeTjRymhs69O4Z9zC2t3dBi3RA0Ga/vou9zQgghpFyUaUsIIVM007Z3wXtwT8zq7vxZZzNmZq5LyPbt0gdeiMirWxHSe3kAd3XyKRwdfwiSNgM45o+DPpcQ3oWZ8g7Ioge9UiOieQFj/riqjmU7b4XfiPIfbc9zwOLzYfY7qHYbaSTSKWQnizhdPng9Xljt1AAtSQd5hBBC8r7rwp1wwaruZlbNgBBuB3QVokaZtvvCzAvaGt46iHGrpnwspSDmWgCfEUetzwExagVxw3s3AfMXDfmY4t61uKz7V3w5sveDAC6ht/IIxWUN7ZE05jf4qZQRIYTsRyhoSwghU4Rzy0P4ZPgfSEtVCKQ+gnTdkqKZsPlB2+VzWhBv+BFea23F8sWLkLjj0/AgBTFtTT0dTEP7kzg3/CRf3uRZjpTEnmtx7vqtXXH0iA1ogHVwp6ViRTOhJFOBmrauY1xePwR3dS5oa6QKO1YTQgjZv6X6WNDW4qxqQEryAqoKSUvxGnRUm3tk9LyZMIKvNre8Q6vHGzUn8eWL54QRetHq4J3u3DbsY6qyHUhPY+hSCmRwmm7gO/e+jb6EggsOnYlTljXT7iKEkP0EBW0JIWQy2/Qw0PEWcPBFUBN98BkJBLQUHG4Jbq/d0T4/E1bs2YyDkq8hLXhRZTbi8OWzgeWz+HWPuKqBVC/AMm7UNOAsLJiek9el+4D0OoR7WZDXbm62uSOOre6lWJx+3bq5lgkUawOnr5p52bQujw+eYA2s/B7ATEdGvGsIIYRMPbsd03F3/ZcQ1MO4bMFBMLa+DKSivGlWWjXgdTkmehMnJZN952c4Ag0wshfYydbMcGL63EWIv5i5fe/24R8z0Ztb9seHD/KS4rr7evGu3dfDZcqQXlsMLLuKdhUhhOwnqKYtIYRMVsleYO3NMHe/ADzzC+hJOzvWE6xFVV7QNj/T1tf9Bo6NPYBTonegSmkreEjTa2XXqIYBM9kz+HMr2VxYi6RGobNOMBmbO2NIi77cZT1t3f6Jxovx57rPI+Kws3gcafugzuX2IeB14/ngyXgyeDZeq7Kye8jod6YmpBLsD+/F3/zmN5gzZw7vGnz44YfjpZdeGvS2t956K88Uzf/p3214f9eX1BF11KDVNRehprnQXVVIiX4kRT9SysCmmKQ4lpXcGk7xLE4ur1Gou6quoIxRVnNTC3RnELogIZw2+WMMRcgbRzT2vUovxQglNAEt6i7UaR1wpzpoPxIyjP1hbEH2n/ciZdoSQsgkZUZbsasnzmvZzk5vgIQWZEOzvqoauJ0GTo/8HV4jjuCW2cDhVxUEUBmPr6rwQdmUyF52MAfEw10IhqYXfW7WQCyf10ggnlIR8rv4AeDWzgQCeUFbyFaZg7DhRVhyYLdrPkIso5c9pWZn07o9Xh6k2FR3EqIpFTWO7CRYMhpcLhdEUcTevXvR0NDALzOapkGSJJpW3A8LSNC+GZv9wh5DURR0dXXx92T2vTjV/OMf/8AXv/hF3HDDDTxge9111+HUU0/Fxo0b0djYWPQ+VVVV/Posmu5fqC+p5vZLyOvE64u+gGe3dPN1C1Ud9ilBMpR739iLe1/fiwWNAXz99MUFM2G8oUZkL7EM5uz+9rslvLjoy3i1EzAFB45KaQj57BPE/Wmaimzes25SBvRIpTS279hnrUkN9wgZAo1zy0Pj3LHbN6M5zqWgLSGETFK9HXtyB697w2n4HTvAcm1Z3kswVAOPJGF++m0IMKHG7GwYM6/GnCdQGLR1+O3smmRfB4Kziz+3qBY2fGGdpGPxKEL+euzoSWJG8h0kxEDuekG2AsVs6iqzybMCXVILFNGDw+OPw4MkWANw0eXl11d5JB60ZT9Uo3D0sEHD3Llz0dbWxgO3DNu/7Cwwu46CQ4Vo34z9fvH5fJg1axZ/rKno5z//OS6//HJceuml/DIL3j7wwAO4+eab8fWvf73ofdg+bW6mmpWD6UnI/He1zwlRFODLK4fAmmCS0ry5uw8tyk7sbG9GTNYgaNngLOAJNeRud3DyWcxStkD2NEAQVqO6fhrMLivbszOWLjloq9IEzxGTkxE4Ms33JNXuA0AIKUTj3PLQOHfs981ojHMpaEsIIZNUuHNPbjml6lB06wslLfoRcLOMSgGKMwi3GoWYyXTl8gKu3n6Zts5gfW45GbUyl4pxaPY0yqxEtAdoqsfW1k6cGf4rDxZniaoVtJU164CaTWtlP8zqxFP8t8myakXr8I6XduhL8ZIL7G/zuejrarSwM71s8MDOHuu6zgckPT09qKurm7KBs5GifTO2+8XhcEzpDG+WYfHKK6/gyiuvzK1j++ukk07C888/P+j94vE4Zs+ezffzIYccgu9///tYtmzZoLeXZZn/ZEWj1uc9u/94TBFlz5E9uBlrSUXD/N6noMGJ6sAMGMZyuCURZub7hjXarKRpseO5b8q1sO0+LO57El1SM7oiK3JBWwgSnN7CsQGblq8Z1t9R53fm9ndnNI35Df5Bn8NQ7NIKmuko2A+VvG8mWv99I7WuzY2oPGrffrvP6D1D+6YUbFwxY8aMgnFub28vamtraZxb5H+K9s3Y7Zv8cW6xz+1SP8vpKJgQQiapVG9rbvnRqvNwQuxeCNChOoM8YMuwWn9Qo5CUqFXzgAVHMk3EHKIA0W1nwzLevOwaJZptB9aPaVpduvutTrKgLcsA3vkWmjLXss1gpW4dahyaqmB59H9QRDcijjrscc3jt2G1b1k9Qqdkd5audgFB3WqsFovMg6/BzgAm+44NHpxOJ/9hAwb2m9XNpKBtIdo3xdF+KU13dzc/YGxqaipYzy5v2LCh6H0WLVrEs3BXrFiBSCSCn/70p1izZg3efvttfhBazA9+8ANcc801A9azKXnptB00G8v3A9tWFmQa68+Q3X1JHBH9LyRT4yWBOjsPh5qKQ5YVfv3ejm40Oe0A9kQbz31TDrY9i3of59/Pdcpe7Ny+GU+5T2Uhf9S4gfekFOwSZ0ITJMxRt/L7yKIXnZ2dEJVEbn9v29uF+cHBs5vVVBTuzEFpWjP4/St931SC/vsm2tcJf2Y/qiYK9uP+hN4ztG9G+r5JJBI8eEafNbRvKul9E4uVNnOCgraEEDJJqZF2XuGM1Tnb7l6Ck6J38W7PhiuYu43hrgYSe2AaOpRkFC5/CA7NCtoaDi9L+yp4TH91I6xDMUCPF29EpikpmObAM4NyrM+q/9P2Fr8siQJckoikokPSEkglYjg6/hC/brd3cS5oe2fNR/jvmbU+rM481pLwk1jVfaf1uO31QMMx+7SvCCFkMjjyyCP5TxYL2C5ZsgQ33ngjvvvd7xa9D8vkZXVz8zNtZ86cyetWs/q443Fgw04Esecb6wPi1q4NcAgGIIhw1c/hdYHnb1+Ps9MPwm2mUKtfgMbG+agU47lvys1Y7hDYlE/rsqQn0e6dD9VlwKj2YvrMObim4eMI6H2Y1/0zfhtnsIHv74WGgJOUvyCk96Gucz4aj7li0OfZwYoiZP5uh4CCOs6Vum8qQf99s8thZelnNdTXQcjMTNqf0HuG9g29b+h/aip93pTaaJaCtoQQMkml0ymwCrAxRwhuw24gYrhDuWXBYy/Hwl2o9VXBkekQbTjzGoVlBKvr0ZNpdmEk+6zn6d6Bzkd+Bc+0pWg87qNIJexSC9lMWkZJhLGnL4XG1DZ+mTUsea3pfdjQJyAl+jAjZt/P5XKjWuvmDU7Sgg9RqRZup/2F6PRX239nzGpYRgghk0l9fT2fGtfRUdjtnV0utWYty4I/+OCDsWXLlkFv43a7+U9/7CBjvIJh7MBmPJ4v0bMb2TC0u86qEVel92Jx+g2+Tol3VlwAcLz2TTnisoH/BU/HMbEH+eV0Xxs0fS4ECPC6JP6+9boc8CXsckqit5r/DfXVQRyctMp7qH3akH9XftNS0VAG3LYS902lyN83RmbclqWw8V+/ngT7C3rP0L6h9w39T02Vz5tSH5e+IQkhZBJizVb+UHUFftfwbTzU9DF4jbwDK489kBf9dh/tRKQbaUXPBXhN18A6dDVBL+6quRS3130Gj7Vczte13Xstene9jY6X/oVU907IqQQMwcrwMDz24+vJPmzZ241G1SrbINXMQFfzMdjqWYq9rjmIRO0pIPUI46Ke63BB7w1YmbIO/jySnTXiygvasmAwIYRMxvrRq1atwmOPPVaQucEu52fTDoWVV1i3bh1aWlrGcEsnD6VnZ27Z3zCH/5a89uwSPW3VTydDi6RURBz293eqry23zGoEM6zBm8+w96fDX2Ptd68XsmTtc0eya8jnuaPWGkdkg7ZkZEzZDn4z6VRhM1hCCCFTF2XaEkLIJNQatgbwmujCwnnT0NbZhQeMC+GUw1hcf2Dudq6AXQs2Ee6GL5WAwIsoAKZzYNDW73LwKZKabsIlW0FULdLOf+sG0N25F0bTCvym8Ro4TBUnNCYwbf3NSIoBpMwA9O3rUJN5fN+MA+Fz218zsVgEuVwwXx0QsQ6+XYZVc9HjtIO2nmANsocoKgVtCSGTFCtbcMkll2D16tU47LDDcN111/EaaZdeeim//uKLL8b06dN5XVrm2muvxRFHHIEFCxYgHA7jJz/5CXbu3ImPfvSjE/yXVIiIXcs91Gw1s3R67NrshkJB21LE0iqiohWEZczIHsx2boQquFFrWLWTvU4WtE0MmAHDMo80bz3csRgcSgSmJkPIq0mfpekGeswQOp3T0KjuhWiovITSVG08OJbM/pm2KXqfE0LI/oKCtoQQMgmxMgRZM2p8mF03B3+OALKgYEmTdSDLeKrqoGaW09FupJIpdEstvPaf12tn2WSxg6lavwudURnhpAo90YeUajcZCRt+SIp1WReccDQtxp+7v8QPxGZ6fFjYdn+uyVnNnJXw99mB2Gg8hmybM9FflynCACxJvwYRBmoiKwBYtQi9wdpc0FZPRUZxzxFCyPi54IILeEOwb3/722hvb8fKlSvx0EMP5ZqT7dq1q2B6XF9fHy6//HJ+25qaGp6p+9xzz2Hp0qX0srHvlthevh8khwh3rRVcdPvy6rjLhcEtUlw0XZhpW5/eiXMSb/Jl1Xc4q66Mo3vuQEPUmgnD93Mgb2aNrwmIbef9TSNde1HdYo87sljjMSYhViHuSECDE6qqweVy0stSrryg7fOBk3GaOPCk+2A6Y2m83RrFqjk1qPLQvieEkMmGgraEEDIJteYFbafXeDG/IYC3WsNo743iyPl2dq2/mhUisGjxHsTgw9/qPs0vn3FAS67xV75qnxW0TSk62rety9WsfdV3NOZKzajOC+Ky6ZNVXgmRpIq94RQOj2/JZeyKTUsRDPeiRdkJj5mEmbfNpq8OoihAzzz4ovQb0FN2WQd/VS2ylWzNFJVHIIRMXldccQX/KebJJ58suPyLX/yC/5CBkrKKgJyZ+eGtBzLZne688gimQtPGSyHteQlnR/6TuxzQ7ZOjgtNqjBI0C7M5PUE7aOuoagQypZpjPa1Fg7ZJWeO/76/+v9y61aYAF725y6dkg7YC1vqOxbElBm3ZCfUbHnoFzp6N2LjwcHziXcto7xNCyCRDQVtCCJmEfFv/g5OiOxF21GK6fwFckohPn7AAnZ2dPFM2K1A3HU/7jkBCDKDWsxTzFesgKtsorJhmRwxC6nUEjChib3Xn1rc7Z6ImpeTq3eWCth4nD9oKuowmzZq66ghNA3y1aIk8jvf1/Z6vk037oE7whiCIEmBk84ABMXOgyASDIV43VzR1CLLdwIwQQsj+qbuzDU7TqotqBKfl1rv9dnkEUNC2JEJkN2Yo24pf52QtTlntosJmpb6QfULYFbIb6SV6rezn/lgT0RXJFyALHnRLzehxNkNh2bcDKymQYWQbuimCm02J4n0NSrGjO46jdt2IOq0DbVs2A++yyrAQQgiZPChoSwghkwzLnPD1rMOc1BY4HQKqPJ8a9Lah2gY8FTyLL88XA2jKZL4wfrdduiDfLG0XDorewZcFxb5Nh3MGWpIqZsTewnHR5yCLXgS181Dlsb5KPEYSrc45aFF3wTvDqqvr9FUh+4xSyg4ASy4fDMkLaHlBW5fXPiB0OqBIAXjUCETFbmBGCCFk/xTtsJuQOWpm5pZd3iCLY/Gp+kK/2p+kOCOZncsCbPQcxIOBy1MvFXwXC3lB2x3uRVhSXZ+77K9tRratmBy2sp/708J7cFzMKpn0mu8oPOM8HSorjk/KJmSDtqIV8U6rpe3HXW89xwO2zKzY67TnCSFkEqKgLSGETDLRlAafbHVsdniCgGvwaXJOh4igR0IsraE3oSCZqUfL+FzFvwLcVXY2DSuRwCTEIOKsLl0iDldqI1akXuTrXcZJODjyIpb2vs67TP+19jMQJQm/PHKx9Vj+UK42raTEeA1ba7v9UFg2T9rOonW47ExbRncGATUCBwva8qNxal5CCCH7q64UEHYvRo3WjWn1s3PrBYcLYDM3dA2iRkHbkqT6covPBk5Fs7o7F7R1ZIK1Qt7Y4u2qo3Gu2z6xGqibkSthpEc7iz6FnNcsSxat73eeaUvKPlH/p9rPwq0n4TUSCOp9UBKsVIUdRB+MsflRe5mNowghhEw6FLQlhJBJZk9vFAE9E+wMWM1shlLjd/GgLWss5m19Duf1PcqnK1anLmHXDri9r7pxwDq/EcMnu74LT9QJtWU1byLGuH1VqNO7Yai7rfsacTQ1zIbLH+KXXZlu0ww7XAg76uE0Zfg9AchOf+5x8g8Uc7d3h4DkHpiGDjUVhdNnPSYhhJD9zzZhBp7L1Ef9zqK82pyCwGduCHosN42cDE2UszVsBSTFAFymnLvOkcm0dbjt7+RqZ+F0/Jr6JvRAgMC+2RPFg7Zayq4vfET8MbQou6CFPwvUHkAvTxlYVm1a8KJF34mzwn/h6/Q97wcOshq3DibS24WavnX248DNA8Cs4SwhhJDJg4K2hBAyyXS170F1JmfVXd0y7O1rvE60GykeeFV7dubq2PmRLnr7YE0D8gsSvOI/FqsST0MyFQiqCke6F9lcGbc3gJSvJneZBW0PaLLrC3oDdqB1m3sJHqj+EF++qmEhq51Q8LySuzDTdtPsC/G6I4qUGMCPTC/sFiiEEEL2N51R+zurIVhYGHV31SGIxpNISUEcQYGpYTkVK2grO4MwBbEgaCtlgrViXqZttZQthmAJeN3YGDgUKcMJwzMDhxR5DjUdh10BH5itbIaRpMai5UpmehHwerYZujL8yYk3uoEHaj+FD/T+hl92mCo0w+RltQghhEweFLQlhJBJJtzJgraWQP30YW9/ZPcdOLbrKb7c7V+YW+/JZMP2F6qqQo/ggctMI+qowXOBUxDUwzgg/SY03YQnsTdX8sDlD8Lpr87VrbWCtsGiQVtW8za37HQg1q+sg6PfZWdVIxKZg4toSoWaiuGl/96O4IwlOP64k4b9uwkhhEwdHZmgbcjn5N8h+dZPew82tVunGy/WDbil4jXbCZBWVHg0a185fDXwIVXQlEzyWN/FLpcT2arzNWJhkJBla749/QLsDacgiQIuKxIo15VEQdCW0VQ7OExKky1rlR+0NZXhy4C8sTuMbmcL2pyzeK8B1thVVhQ4vYUnyAkhhFS2/t+lhBBCKlwq06mZHR6FGmcMe3sWVM3yp9pyyx6/HVzNV+11Iu6osm5vWPVkE5L1GDy/N2VlyuiCBJ/XB3fAzoE9K3I7FrjsTBq32wNddPFlr5kXtJVEtM6/EG96D7e3s1+mbdDjzC1H0yq2/ec6zNxzP4Iv/gLhsF2PjxBCyNSWklXEU1a2Z1PVwKCTLy+Im63FToqLRXqy3+YwPdX4eO9PMU9en7vemald68uWYQKwvOuBAY9TH7CCiOxkbiSRwjubt+I3T2zB5g4rIKyn7fIIWbpSfIYPGZwS68ahiSewJP1qbp2hDp1py2oHv9NmvX5qXrBXTlHNZ0IImWwoaEsIIZMIq0emRa1OzS5JhKuqedj75AdVvYbVGIQlxLh9xYO2kkOE4rJq3TpMDR4zhep6+3lkLZv14eHBV0/e43tcDni8dtkDlnmjSf4BmbZupwPOQC3UTCdkxplXP48JefOCtikNwY6Xc9skR4rX0COEEDL19O18E5/suhYf6LkeBymvD7je68oL2qoUtB1KIsyCthbBVwvNW9jQypnJtBXmnZBbt/cAq5ZwvtqAy3593nkC2t1XoP7Nm3Dv/17h64wi2aAUtC2fFm7jNYEPSr5grxymPMLG9liu6dtW9xK87luDl/3HQzaoNAIhhEw2VB6BEEImkZisIaB082W3JALB4YO23lA9+k9IZNMZBZdde7Y/v2R3Ga4XY6hraAF2WJeNzFWqw8sDvLV1tegWBV4rjdXPRaCh4LF0px9Q+njX43P6buNNTzzSKn6Q3SFNx9ve1bw52cxA4YFjtRDHyuSz/H6pt6YhkH1intlDB+WEELK/iHXs5Cfs6rV2qEVmd2eDtoKp86xcwMoWJQOlo125ZVYewUw3Qoi28st/qfscvlY3ly/PnjUbG1d/FalIJ048+pwBj9PAgramyWfkJJ+/GYZhYFH6DayPHMFPMJtKkUxbjcojlEsukrGMYRrudb96Nw5O9CHhCGBP/dF4K2llqZ8EO9BOCCFkcqCgLSGETCLdMRlVulUaQHI6Ae/w7bn81Q3o3/rDkDyAOHjNPykzPZJZI6yDOzSwhqwuWZmxrurpOKAlBFlR4Z+/ZsDtDFcQSNiNSFKOIA/2+lwObPUs4z/M6bXTBwRtj4k9aF3YamfdMppa2BSFEELI1CX37sot+xpmDbh+QdejWNR5N2+YaXRcAzQeMc5bOHn0iTV4038iD7YurT8ADi0FM1M5ic2I8bhduZkyp77rXYM+zjSzA5/quoYH07O1b9ucs7HbMRNxWSuaDapTTduysTITA/JjtaHLTNTu+A+OToWRcISwbeXJeHJDZ8FMKUIIIZMHBW0JIWQS6UkoWO85BHVaOw5u9APi8FVugjWFma+MkSlZMJi22edgdvfbUAUXEgvPxrRQEP2H+oYzU87AUwXXMZ+Hq3sTsOzcAY/1+pyP4nVnAh/r+j5vbmY4rDSpKrUbByee4eUXdrkWwC2tKrifr6oOEftIr+A6XaOgLSGE7C+0cGuupluo2coEzedyiBBM63uBNa0kg+sSG/BS4ES+fOD0hXCl23OzcUJ674Amb4MJ1Taiz8y2IbVs8ixHi7ITfb3TEYcXpiOEgJ77Jqeg7QiocnxAfqw4TKatqCX4mI2dXM8vNSWrVskEQgghkwcFbQkhZBLpisl4zX8UX56/an5J9/EE6+EQBeh55QVMVrJgCK6GefhD/dd4s7FLm1oQrHKjV3Dw7sMMC7Iavrxsp9lHWj/Fnt/rg4kkL4GQy/IFEJA7cHT8Ib7MmpU5HYW5JMFQDey2aRa2PTc0fAufDS0q6W8nhBAy+ZnJ3tx3QH3dwBORDk8Q2XCUlrZqt5Pioin7JGiVR4JUOy0XtK02+3j5pFLU1Najf0vQ42L389/p3V48XnM+up0yZsmb8e7wbdbrSCdcy6alk0WCtkNk2rJ9nDnRbbr8Vikt04RkqpAVOuFNCCGTDTUiI4SQSaQnbteDq8trAjIkyQVks2Kz3IPXs2XWzK9HXW0N5jbX4JDZ1aj2uxAXq/h1sujFPTUfxtaWM0t6ep9LggQVQrZbtWSVXnD57G3wCzKfipnP6/FCFe3ihXtc87DDdQAMwQFFp2wRQgjZX4hpq8iP4qyCxzUw50TKNM9iAq3/Ax65Gvjfz1na7bhu52QQTecFbb1OVDXMzF0+IvHEgO/iwQQ9TuTHd7e7F+eW0+GuXEM4TbAzPU0qj1C2/IZu2d3t0O2g7a6eJDqj9mUtFUVuiOTyY1r74/hM57d4Iz9Xx2vlbwAhhJAJRZm2hBAyiXTH7SyJ+oC75PvpnmpAtgrLvuE7EnPq5g15+1q/C//v3OW5y5Io4n9VZ0I3RcQdIb6u1CmUfrcDLsMONpuZTFuPzwoCM4cknhlwP3bgqDmDcMppHii+q+Yyni3CaBS0JYSQ/QLLzhTVOD/tp7uri97G6Q0i++0ot61HW9jJp4X7Zh4OzLFmpxCLEu2BZGrQBBeqPE5IrmlgcVr29eooMWCb/Y7eWncc5nY/hV6pEfqSc4DXN1jPEetCUrFKJ8jOarzjXcWDt7Xe2fQyDGN9WxR3vbYHy+oknN3YCCOvodtzDedjm9EERXBjhW5g6552rL/rh1BED46/5Go0hvxIxqP2g7mCkJyuXHkrXbYDwIQQQiYHCtoSQsgkEo90QzJNCJKHT2ssleCthhmxukO/4D8R1TNLK62Qxcor9ISWI5I3rdJbYtC2RuvCCbF7c5dNp5Vp6/EFc+sGK81ruKsAuQtuI8VLM7AsW0bV7VIPhBBCpq5YuDt7vo5/lxXjq5ue7XeJtGogrcroiMpo6epE05zx29bJ4Jjt1+H4dC9izga4pL+wb2MEvB7EkmmY1XbWbSniSz+AO9YdAEfNDHzioFnoeN1aL0e7YWa+14VgEx4TzuPLhwWGb566v7vvjb3Y0d6L7l3dOH15M8y8hm5qzXz0Rqys8rRmILX2L5gjb+SXu958GI3HnIdUwm49K7gDkFzeXPkLXR26gRkhhJDKQ0FbQgiZJEzTxIq9/8TZqfUwPDUQ5BsBj5X1OpyOhRfiUbUTCTEIRfDAX2R66XDYNMr8oK3PVWKzEiOCKnm9vSITtHW4/Tzwy6ZQshIKRbGgbcYsZTNmKNt5p2pX7ykAji77byCEEDK5RPuszveM6K8reptpM+Zg8yGfRuuOjYgqwBx5MxTBBV32omkct7XisdqmSoRnLYtOe7bOzAt/gb71T6B+xallPdyFh83G2zNqMKfOB79LRLsgQDBNOKJ7cK55C2TBA9O5CA/hEH57RaPSRsM5550v8BMPhmEgtqsBUO3sWG8gBESsDOaUoiPU8WKuYauj2wreykm7EZ/ksYK2WYZCQVtCCJlsKGhLCCGTRDStoUrt5stBIVUQ0ByOp34ueiR72qPPXVrANV+1z4naPc9jVeJ/SIteCMkPs1DqsPdz+UO5LA9GyNbXlbyY2+BHPK0VdDcu2G7WQCNjurITByef5ctmlDUio6AtIYRMdYmw9b3HOAfJ1GRT9Y8/+Ry+/Mzmbtzy7Ha+/P7hMkdZ06b19wK924AVFwA1U3v6vpIIwzSsyfIGK5uU13y0qWHosknFOB0iVs60H0d1huBSwvDI3ZgJ63VL6qx+fSZoS6WNhmaaMPLi2rHedvQJIahSM7yCAjev3WyFadOqDj2vsVtKFwYGbb0BSG67N4CRl7VLCCFkcqCgLSGETBLdsRRCutVB2wg0saPUku9b43NBMhSIMHgttIC7/I//WpeBhvQ6+I0o/0k6S3t+T7+grZjN+hBFuKatQG3HW8Ci04ve1y2ZsHJKgKXijtx66kBNCCH7hzbXHPyv+v/gN2I4tmXlsLfPPwkoa9lqnkV0vAPjxRvQ3roDCVlDXUJG7RnfwlSWjNgBcOQFbUcLmwUExZ6ezzhZoDGTLKpkmpORwXagBj1bC4S9XuFOPF11FiKSimqfCycYXViaWgeXmYYSbYSi2bftdM3gv9VktKDWs9Odl2mr5Y/GCCGETAYUtCWEkEki0t3G67oyQrCl7CzZA9Mv45jYgzAhoLb3a8CME8p6jBahBw3qrtxlp4dlzwzPHSg8MJSr5toXjv86EN4F1BbP8NGXnAdz9+tQBRew8FTgtT/w9YZul2kghBAydXWpbuxwL+bLpzUtGPb2Hqc9Q0NWB5mOv/kR6C/+Hjt6EoilrVODyvb1mOoVVxN5QVvBVzP6T+CrB6JWlnOW5PbhEzv/HxymilSSFRj+7eg/7xRhqkkYhh2IlaOdSOvWuM/rEtESfRMzonfzy3p4NaLww4MwUqIfm/yHgRW3iJsehJ3TeS+AOn9NQdDW1Kg8AgFiaRVv7olg+YwQb0ZICKlsFLQlhJBJItmzB9kwqVQ9vaz71jpVrEw+z5cFmPB4MiUKyuAJFVYGZBkcpfB7/byBGAs490hNkOsPtK90OIG6wZuiHXjQoVjr+jV8Xh/8yd2Ivmatp0xbQgjZP/Ql7SngtX7XsLd3S3b5n3SxTFvDgPLKX7C9M85rqrNvRcCEmewF0pGSa8VPRuloT27Z4R/9ELUjMLDmsMMTgMDmzJg6BIMyPYeiplO83nCWHu+GLFknHljtf4fDakLGpBIxuFWrVEJCrEJMtk4+7Ko+DA/WWmVBvtyyCC7Jznw2qREZAXDLszvwxu4w6gIuXPvuA+EpsbEwIWRiDNKvmxBCSKWR+1pzy97a8oK2PjOJkGEP3D3+0gKu+fyhwgM8l7e0TFufR0JKsILEHiMFd16d2uE4RAGHL1uA5fOmwZHXNMWkTFtCCNkv9CbUglkjw/EICi7o/R3+r+c6LNx668AbqAm8nGhEn+7iJxLf9B3OV2uGiVhHYZboVKPE7UxbV5EA675yBesHrHO6AzAdVrBd0O0APBlITicKLrOgbRZr3Opw2yfcE917+El4Ju6o4tmTfH0meMv4XRJceZm2oPII+z1WMmZdqxXs74kruPeNvfv9PiGk0lGmLSGETBJGxB5YBRuHaa7Sj+Ct4Q1D5EznZo+v9CZmWSGfC3Z7C8DtKy1oy55XkfzwKzF4zCSQN3W1HFJ+0Dav+QYhhJCpy9e9DtMVE7q3tqSMMLfLg0bVOsmppweWAJAdPtwinQ+z3kStW8dRri3A1hf4dX2tmxGcPXzd3MlKi/fllt1Vo59pa84/HjfvnoaF8jpejomRvD4YogsOJCEa9N09FDWdKf6bfY3kXlzY81vIogc+7xI4Zy9ANnc8kkxjbfBMBPQo+qT6XJmPeF7QNuCR4JK8mVxy9gag8gj7u+1dccxLvok252wkHFV4+O0OHDmvDjNry5+BRwgZHxS0JYSQSUKMt1u/BcBfZzWcKJnTgyqvE10xmTchk7zlB22rvU685DkYS9KvodM5DY1uO4g6HMFh3dZhavCK9gFFORxOO8PKpGwdQgiZ8kzTxOEdf4NbT8BIs8zQk4a9j9vthC5I/PtGKBKk2tOXAu/1JAhYPKsZM30SsBVIiz70xpKYhanLSNpBW1+oYdQfv6a6lgeCXHnBWZcnCJOVQuKZtlSPfihKv0xbpkGzTtibajUk10G59X0pDW/4jsYJ0XtwUPIFrE48BU3/a0Gmrc/lgODw4cH6S5DUJQSq63DsqLzSZLLavWMTTo/8gy+/5T0UT1S9G395YSe+fvpiCGU0OCaEjB8K2hJCyCQ5cHUlO/my6PTwzNlyTQt5+NRSNsUOLrsuWqlYR+4ng2dju3sx9rpm4zBX6TWwWrQ9SGWWgymWAVVeIzXG4cyrZUgHfoQQMuVFk2kesGVMb2FTy8GwEjyK4IZ3kKDtrl47m3F2rQ/1DQfgZ/VfRUIMYo2/AVM3zxYwU7255UD1wFIG+ypbc9hlpgtKKZmitZ5l2rLxDAWHitPkwkzbfKLLD5fXzoaUk3GwRgc1ejfqtTa+Lp6IY/XO3+PARAwJZzXc0mp+cqIzsBSRlIoa1tSV7NfiO99E9nSN4rN6VfTs3YZXXo1j9apDJ3TbCCHFUU1bQgiZBKKJNAKadbClskHWCM6GC0vO5vXNxOYDAbH8pgOSQ4TH58NWzzKkxEBZjQvWzriI/+6TGiA0LMRIuPLKI0AfWbYuIYSQySPc25VbFnylTednAUEjM7tD1PsFbQ0de3rsQj+z6nxoqQkiKVXx79XdeQHdqejxhovx99pP4Z7qS1DtH/3p0GwmDyuJlB+05aWUJOv1kEwVaqZMExlIHTJo64Pktk+4OzNN3bI9A5hENIxQciemqTsxS9+dC467M2WpFNr3+zXdMIGu9XzZ6RBwzFHHYra8Cef33ojUq3+b6M0jhAyCMm0JIWQS6E7p+H3DVajWe3DEnCocPJIHOfj/gDlHA6Hy6uHmq/G5EE9rkBxCWQ3FeutW45ZYA5JiAN/OK3NQDofbiz2uedAEJ5zu8hqxEUIImXziYWuGCSP5S6/ByoO2KiD2b7zU8RZWvXw1ao1mvOY7CjNrDoFLEtFU5UF7JI294RQPbLAmmFNRu+JGj3Ma/G5W63T0c3dYkPBQ803MUrbyyxs9K7EwWMemyuRuo6gyXE6qn1lMtGoh/ht6P2q0TnQYtZBcrtxUdofbD4/PDtrWaR3w61Fe1iMrFeuDpCZ4/VrDad/WLVkn2dNqtiIu2R+19ibRnLaaLbq9ASxYdCDOfeAqGKYGIzObjxBSeSY80/bpp5/G2WefjWnTpvEv+rvvvnvI2z/55JP8dv1/2tutWo+EEDIVdcdkyKIXHc4ZcLUsHdmDsIyL2rmAY+Tn604/sJmXSThzhfWZXSrW4CDuCMHpcqIuMLLpeU6PH3fVXIb7qi/CO7XvGtFjEEIImTySfd25ZRcL/pXIkDz8t2CoPLs2t75rMzQ5henKdjR6DXgzZX5m1FiBLxaw7YhOzWZNrCxBOGnVlK3xjezkaSlWpl5AUA/DhIBHqs6DN1jLIo656xV5au7f0RCX6rDJcxBeCJyEDe4DYeQdqkseHw+0ZbWou3BZ949xYOrl3LpkbxtM08pkNl2Ft50rr8fc5JvQdMp03l/t3LEFXiPOl8WGRXzWneyxiiU45TAMem8QUpEmPNM2kUjgoIMOwmWXXYb3vOc9Jd9v48aNqKqyG+k0NjaO0RYSQsjE647bTT3qAqU3ABtth8+rw2Fza8uuR3fqsmae2TOnzgefa2RfPU7RPnhRdd4HeeKlI8Dul4GWg4DA6Dd1IYSQ/Vk61oNseNEbLKMGq8MK2uqs4xira5up4x5vfQdshjDjal6Su/k8fxq+2P2o19qRePUo4MSLMdXEZM2aHs0ai/rGrrap4KsDwjsgwEStlILIspYl+/lUCtoOqn8mbH6ZCVYaweMLICX6ec3mkG7XJ85K9u6B9c4HhLzeBau774YjvIOthaJdxMtdkf1PZOcbCGaWg7OtpnYm65GRaIPDVBGNRVBdXX7PDELIFA/ann766fynXCxIW11dWkMCQgiZ7HoS9hTP+gkM2jIjaSDCsplOXmo1PBgpduDHfgzDhFoh2QDms7+GvOd1uOvnQDjzpxO9OYQQMqWo8Z7csr+69BNjZibTlsds5SQkFsAyTcgdG/l6FvhqaJpR0KizKfmC9ZwdpWf0TibRzj1YmXwWSTGIFseKMXseR7CeT89n6gQrq6+1+SS8lVwEHRIuzZvOTwrJ/WrOukx77Ofy+OFwunFb8zf4GOjjnf+vIKjLqH120BZuO9MWmf8HwIQsy/C5xy7TmlRupr3R8Q5fZuVf6uZaLRcFnx2kjfZ1UdCWkAo04UHbkVq5ciX/0jnwwAPxne98B0cdddREbxIhhIyZ0M6HcXAiibBUh3q/dXZ8f8QaJ8iVErSNd6J1w4s8CzoU2YC5mlKQTUQIIWTf6Mm+3HKwpozZDM5c6ApKOgmJpZdF90JJWk3I2p0zsaDOzkRsaZmObYLHCoL17ZqSL1u6fQOOiT1oLafZd9XYdIp3BxuQ7he0TYfmY3smiKig/Eao+wsh2opGtRWy4EJI3YMjk4/lrnN6g7mT4GYynQvYsnFRbvZRzC4X6HBncyoBIdMIzi5PEeBBPJZ5TVm3+4euWBp1CavWtNvjg1g3jy9LfnsGQzLMGj8eMGHbSAiZIkHblpYW3HDDDVi9ejUP2v7hD3/A8ccfjxdffBGHHHJI0fuw27GfrGg0yn8bhsF/xhp7Dn52axyea7KhfUP7hd4zwzMNHbNa/4PZWgqKFITP9cGinyf7w//TeT1/gFOJAPEqGMZvS77fWOwbZfOT6ElYZSsiKRVaMgwxUMb03QqxP7xvRor2zcTuF3pPEiR7c5lh7jJq2nbXHIy34iGogguz2PcmW9mzGanM9PM25yycWOcrKDv0insamtLbgGQPIMdY9HFKvQDpqJ217GZ1ZseIN2QHbY/r+jOADxQ0PeufTUpsLTvvwQW9L/FlTfJDysu09XitkwwepwOCbp184LcLTAMirXzZlezIrZc8/uInMeQkL8Nw7f3vIClr+OppizGt2ksvwxS3Y+cO+A0rBoL6hbn+Fq5gLevZyCWjdg1xQkjlmHRB20WLFvGfrDVr1mDr1q34xS9+gT//mQ0MBvrBD36Aa665ZsD6rq4upNNjXwyfHXREIhF+gCPm1WQktG/oPUP/T6WIduyAqVjdgOP+2fyza3/9rAkoXXCrESiQ0dlZeqfbUd83ponUaw9Bz2T8/j70OXy+V0FoEnbf3R/eNyNF+2Zi90ssZgcmyP6Hvb9kVeMNrSTJWVYQNVK7Aq/1TuPLacEKSJldm5BSrKBtIjCHN9XML/tjhGYC6W1QdAOpru3wzhi7EgITQY33IlvcyFc1dicYg9V1yOZHOwTr8yE/aKtQ0HaIFyllL/pbIEW28OUt7mVYFGrmyx6nCGc2+MZKR9XMgZ4J2rqNZG6902v3fhGk/MzzFN7eG0VHxDoGfnVXHwVt9wM727vhc81Bs7oH/pnLc+u9VXW5oK2cd2KHEFI5Jl3QtpjDDjsMzzzzzKDXX3nllfjiF79YkGk7c+ZMNDQ0FDQzG8uDGzYYZM9HB8S0b+g9Q/9P5erb+L/cZ4d/1opBGy/uD581O11eiHoMkmCU1YBy1PdN3w5si3bwx9rrnAXZ1wiXP4TGvOm2k8X+8L4ZKdo3E7tfPB470ED2P9GUhruqPwzBNHBIixNLyqinzgJb/Zs7pXp25xpxuZsWDriPs24O0PEUX+5t3YLpUyxoqyXspm6BMuoDl6umpha9HgkJWUON33pGvxbBdGU7JFOFnmAZ09TsqCjVTiZyVLUAmaDtG/41eG+1NeY5JPIoZvb9J3c7qX4O1va6ETO9mCNvwixlM1/v9NnHuKLTnaszrMkpRDU17/+DMp/3hxNgL0eq0FPzUbhFDb9YdWDuOn9NI7KnALTEwOZ2hJCJNyWCtq+//jovmzAYt9vNf/pjBxrjdYDKDm7G8/kmE9o3tF/oPTO0ZNv6XAW4mtnLh/wcmer/T6ZofW2Jplb23zia+ya5+Sl+QMps9KyEAAHRtD5p9/tUf9/sC9o3E7df6P24f+tNWuVnTEFEIFTedH635BgwHT8eDfPfuiBhRuPAxws2zwWsPj2Id+7AVGOmrL+fCdaNXdBWqJmL+QuWwOjbBcdhH+XrGnpfwXv6rBmRQi8L2lr1NEk/mpVpqwlO+Koboe22VtcK8VwT2Ca5sOayO9SM9Q0HoSeuoN05A63KbHiMFI6tnZW7jej0wjp1AahKEpFcbiUqo0cAGVMdUZm/P5j5zbVwZ+ojM8HqBrRlls1MORpCSGWZ8KBtPB7Hli3WWURm+/btPAhbW1uLWbNm8SzZ1tZW/OlPf+LXX3fddZg7dy6WLVvGSxuwmraPP/44Hn744Qn8KwghZOyIXRvtA835S/fvXS1ajb5EQ7XagpeReTVqDAPh9U/yrBVDcGCLx8pYCKfsgyBCCCH7pi8TtGVqfOU1efQ6DPj1KJymAiXRBKAK6USEX5cWfJhVa9ezzWponpXLOEuH7YZOU4WQsooW6KILQb/VFGxMiCKE034IR7KbRcKtVS47eUbPyyYl/V4jPc3HFqwWc2NtI7IhtFoxr1SMs7D+rK+6AUGPkwflOpwz+Q9zcp3129r/nlzQVpfTSCm9qNJ6kRQDFLTdD7y91/rsY5ZNK5xl7K2qx8uhUxAxAxB9M3DsBGwfIaTCg7Zr167FCSeckLucLWNwySWX4NZbb0VbWxt27bLPKCqKgi996Us8kOvz+bBixQo8+uijBY9BCCFThRzrhpjqturZBubAt59PFzYzjRNYwFbXNThYncPx1vkO4rzDLrDXORsHpl5CQI/Cu2UBcMAl4789hBAyBfVmMsOYWn95QdvGyDpc1v0rvuzZcwmw4IN4qvEi7NDaIcDEx4oEbevrG9ArOPkUfsQnX33y4TgUKyStuUK5rM2xezIpF7BlJKcH2VdTV+3mWqSQqMk8uKqKHl5r1OkQoOommh3x3G0EV2HQNljbhKDHvj4r4LYP8x1OTy63VlVSqNv7JC7puZ9f3tF0BYA59FJMYTu2bYZoSjzRYNm0UMF1guTCtsZT0BlNw63TbCtCKtGEB22PP/54XmdlMCxwm++rX/0q/yGEkP1Bx5Y3eEIpIzTYTRj3V6ZoB2lVRZ6QoG2n2IBHXCdhkfEGtgUOwbG9d/D1Uic7IKagLSGEjAatYz1OjTyAhBhEo3IWC6uWfF/JbQdlddlqzrRTmI4d7mo+QaM+MLBsGstWTDprUaV0WFmiEzWbYwyw5lNiZuq94ake9+dnQcMsQ6Og7WBEPc2DtobDDdNbyzPC+5IqFpmv2Ldx2e/tF6pOx0GhBoRccQT1PniNJHqkRuisvIIrL2jr8vKGfqrghq7p8EW35q6b1cZmq549yq84qRSapmH5hl9gmaFjV/AQzKhZPeA2tX4nD9rKqsGbNXpddnkZQsjEm/CgLSGEkMFFdr+VWw7OXLbf7yrBYQdpNdXOwhpPL7TpeNV/DP85ZWkjlCfvh8tM56aeEkIIGQXhnTgg/SZfrFUPK+uukseXmw5uZKbjJxSrDrnXJUEUBwZjWfZpW/0abIiEEZVqsEhT4XSWl+FbqaJ9eZnD3vFvAiY588sjUNC2KF1lEW2+aEgeGO4Qz5ZlJxPyiXmZtkrVDD4uWtl+Bw7utpro3VFzOXq9s+GS7KxJefYJuH7TdH4S4qyaFjTKd+euy/TmI1PU7o2vwqVZmdhz/XLRLPv88jOsLI23XzY3IWRiUQ48IYRUML1jQ265ed7U6mQ9Ig57YKkpE3Pgt70rkVs+YXET4g6rPphDDluZWYQQQvaZkbQbZ3lDpWfZMs68oIOhWhmmcdkK4wbcg2eRhWedjBcD78J6zyHoSmTDvpNfLJlGp3M6EmIVRH95+3I0SG4709akoG1xagp6JoJqOjyAKMGckcmKXMwyzQdm2ta6rCZikteuU/q+vt/jo90/LHhoD8uczATrWDalmfmf4JcxNU5MkOLCG/+XW/bOP6robWrdJmq0TsxQtiISoQQEQioNZdoSQkiFYqVjnnesQo2nCkExjYMaxv9Aq6KDthN04NebsJ7XIQpoCLqhuasBrRO6pgBKHHDbXXkJIYSMUGb2Aos1sfqe5cgvj2AqKRiJXkyLvoGU4EOVaDdo6q+pyg4udkTTmFY9NTLOusQG/KP2k3z5/YsG//vHipTXiMyk8ghFsYzwXNarlHnfHf1FINEJBFvsfen25erT1jitzFzJF8pllvN9LBXWbHZL9omKaFqFQ7fHT4Y5dU5OkH5ME2Lry7wvBmtmPHdZ8aDtou6HMa/Hyr6WO6YBs6fRriSkglDQlhBCKlR3XMFa8SAgdBCWTQ/hzClSW29fdNWtxo5oNXRIuFAaw+7XgzEMpGPdEEwPanw+Ps3M8NQACUDTTRiJHoglBm3Tqg63JI59QxhCCJmEhLSVaesURQje8uqwurx5QVs1jXT7epwe/hu/vDt4HoAji96vscoOLnZEp840fjblOavGN/614FkjshwK2hYlu2rxu8Zvw2koOKA5CF4QSxCBqsIAmsdM54K209Sd/LfLX438d6vp8hfch401srpiMlymfWuBnXAmU1Ki9R0gaZ38ioSWIBQqbEKW5Q7W5d5TqWj3OG4hIaQUFLQlhJAKtbXL7gY8v6FwAL6/itQciDc6Gvmy4hj/DKh0tAsfaP0BO8xBDw4HsALw1QE94JkMiXAXgrXDd2F+feMWvPPwrTCbluGD77+QAreEEJLHMEw4FNbcEXA4RMBdPNgwGFdepi20NNLxSO6iwxMcMtPWYWoI6mEkOlkYo3lKvC7hvKBtdV79yvHidNvf15RpW5ysG9AEFzSHC4J38Pd7c2MTEoJVi3amxwq+uv0hxIYK2uoxHBe7H05TQVydwd/jOQYFbaeqtk1r+diUEWezMWtx3lADskccSrxnXLaNEFI6CtoSQkiFenOPXc9vfsMEZJVWIGde8xhVt2q5jadIb7aZiwm3xwoKSH572m4q0oVS8mz1Z36NpckNwPaX0dvzLtTVW4FoQgghQEzW4DOsMILuqgLE8tpweDwsSMi+L0xATUNJWgFg/pntHSJo6wM+1XkNv1/aOAA4cc2UeDn6kuqEZtq68mraGgZNxy8mrRpFM2P78y86AQfsfZnXfPa967N8nTdYmInef8aP21SwIvkCX27TCjMpBdYAjUxJsb4u/inINM9cMOjtAtUN6Mos6/Hecdk2QkjpKGhLCCEVqP31h6GtWwfJewScHi8WNFLQlnGyjKsMVR//pl/xsH2w4wzUWr/zai2yTNxSVMe35LJitGgbQEFbQgjJiSYV+PXMp6SnvCxbxu2UoApOnlkILQU1aWfaSj67aVN/AX8AmuSDpCUgJqfONOF52/6KmeFWJMQgqt3Lx/35nYF6PvVfgxMLmoI4bpye962H/ghl96uYccoVaJy9BJWMlUzK8gwRtIVDguekqwpW+aus8UiW6C4cM2ZPMjNVemGjKZEybacsPR3LBXsC/d4j/YO2mVNcMDO1xAkhlYOCtoQQUmFMTUHnM7diTaIHByefRfLkH8PjHLzb9f7ELagI6n1wmDp0mTVTGfzgeywkI90FNcCs33aDODlW2rSybIdoRjHHP+uJEEIqWTzWBwGZzENvTdn3Z5mKiuDmQVtBS0NN2ZPH3b6h50PovgZI0QRcch8URYHLNf7lBEabP7YdfmUvBIcEV15TsPHCSlyYDjef0y9r4zNLJtHbBv2Nf4KFP+P/+TYaP/kvVDKjewuOiD8KVXChVjsawOyS7+v2V4NNRMoOLRyewqCt4PTkrvcb+YUUAFGn8ghTlmy/1r7g4Ce/HL4aSA6BJ0MIFLQlpOJQ0JYQQirM5hfu4w2tmERgDo5fPm+iN6litHT+Dx/u/itfljq/DMwZ33qDLCib/eL0h6xgra+mCbtdc3gG00z3TCws4XF6pUZ4lDZoghMJ/4wx3WZCCJlskpEeZE9VOnzlNSFjRFHAA/UfgawDdTUhvD/939x1bt/QmbtCoBGI7uB5Z92drZg2Yy4mM9M0ISlWprHuCgET1PzSJYlIKTqUcSptlI715up5Qkmi0gk9m3Fo4km+nJDnlRW0FVx+SKKY27fO/iVAHG6IrHGqaaLTOR33hz6Ey7p/bF1lKPw9Qk1RpyDFCtoaggN+/xAz9iQXTFcASMXgVCLQdANS3sw2QsjEoqAtIYRUEEWWEVn7L2RzL6cfcxENnPKIkmtCm5mo8V47aFvdwH8HQ3W4s+ajfPn4QMMgPckLCVqK/5YFD1zjlHVECCGTRUQVsdd7KK9ru6yulFNhAyW9zYilNThMF8y03djTGxg6aOsMNQN7reVwBQdtexMKr087XLAtkZbh1hN82fCUHwAf9aDtOH3nKXlPkze5pWJpsh1YdrjKbLQqCHCwVNpMhYUBQVuRZTq7AEPmTcgSjir8N3Q+XEaaZ/Yu1w24JZrRNdXc3/xpdDv7EBBl/NA5dNjH8NTwoK3PiCGSVFAXtOtQE0ImFgVtCSGkgmx46b9wylY9qUT9Chy0bOVEb1JFESVnLnNG18a/eYaetBs0VNc2DGjqEs5r9jIUKRO0VUT3uB3AEkLIZNFtVuOJqnfz5aULF43oMVhZIRa0ZdPxzUzGmS5ICPns+p7FeGuakc4sJ3oy0dsK89+32/HPl3djSUsVvnTKAUMGbiO9dq11wTtxQdtV8aegJCJwyCwYdNCYP5/itP9W3az8qK2uWOMCRnKXGbRlDcZqD0V1h9VszF2kbjMP2qqyVecZwCbPitx1bFq8m6ICU05UBlKOANy+wevZFnw29O3iQf1wJIy64PjOZCOEDI4+ngkhpIKk9q5Httpcy+Hvpelq/TgkF7TMsjEBmbZCOsx/i6IIX9AaBAc9LNOJTUEFwqnhg7aapsFhWNuuCF4K2hJCSD/RtP1ZGvKOrO43q2vLyKoBKFamaVrwwe8eOqMwWDctF7RNh9sq8rXZ+9bTOD66DjV93Ygc9j1U1wwelImH7Vrrkn/44M1YWRp/AUKqFyklhL3hFP7w1EbUugV88mTrBOhoS0tVUNhsFjONsGTXnq9URl4JB8k99ImFYnbOvQD3a4fBY6Tw2WnLBj4+qymMWC5om4+fPB7/UsdkDLGSF3HZGjEHhvnMYxz+Op6ozU5sxSO9wAwK2hJSKShoSwghlaSP1dGzNM9dOqGbUolEp31UYYxzpi0bAIuZoK3uquLTDRk2JbHK40QkpfIpZTCM3HXFpFNW8IBpUndD7VoHLDhhHP4CQggZR/EugHWxd5afNcg+T7PY5+tITNd2oTq5HS5TZtMkYEJAWvQiMExKYW3TdGRzU7VYJypRbXQDZqde4ss9bTuGDNqyBprZPFxnYOKCtqbDybdDNBQ8/b8nccbG69EhNKB11S8xq2HokhUjoeoGVMHJg7aiPv4nectlqNlTBYDkKT9oe8zSWVjfrWFOYwAN9UWC1JI13d1rJLCSNbkVA9jkXsFLK9CMn6mHzTDINr0NeIYP+cSWXIA/hY9EWvDiAkzc5wQhZCAK2hJCSIUwDR3u+G6+nPY0wOcfOL1tf5df09bQxzdoG0sr8OpWXUSzX13AQ+XnMbP7Cd6V2ez+BYTGxYM+TkrReCYDm4LGOGKtY7zlhBAyzlpfAZ76MeCuAs7+JeAqLwgVT6ZyJ8V8rpHV2lwQfwW1sWf48t2zv4hNqRo4BQ0nOod+PG91C68lz5rxCPHKDNp2oi7XpirWuRNYesigt5XjPchWp3RNaNDWOukqmSqWrL+Oz05p0VsRb30HaLCqwbPg4a7eBObWB6warfsctLXGDJMjaGuXR3C5/WXff15DAD98r13yoNj+z+7RY2IP8t9ddS2QTA2qvIAVFBnBVpNKlehrx5r4w0gJPtRpBwIYfFzKVFXXIS12l1XqixAyPihoSwghFSLSsQvIBCK1qlkTvTkVWx6hfyOyJzZ24ulNXXjPwTOwfMboZ+tk9cRV3Fb/Rfj1KA6dW4v8Q+Qql4CAbnXnTka64B8qaAsvHgxdiLPCf+GXDbXyDyYJIaQc6ad+gd2dMXikJGZsfwrCotPLuv+Ru3+PM1K7oLtDEPQ/svni5b8ATjsIlYjHAakWHo93+LJDDqfVsCvRC4cSRVrVeX3cSsFmfew17eBrqsc62TsYLWHXYvdUTWCZAFZTlf0ytVxteqbD0YTsvKL77/wzIh070VnvxJoPXLlPT6cpMiJSHWTRi7QjgFWocPlBWw/LTh/dOryRqgPQk3BjvvxObt3/9fyK/9YjC4DGmlF9PjKx5J49WJV4mi8nUizkc/KQt6/x2ePrcGpgCQ1CyMShoC0hhFSIvj0bc8tibWV2q55oDqc9TdbUVH7w+q+1u3nNwvve3DumQdvepIqoo4b/iI3TC7crr05gMtyFoXJkUqrOp2wWmxJJCCFTQW8kioSs8x9XVxeayuglZhgmJCXKa28GzFgu2FcuIa8sg4NlWkqAr8RuS+8s/iye2ZVGSvBjTkzGzNryp6uPlXRaRo1mNxfTw3uGvL2RtJqbMr5QHSZMkdfxbfdB8MjWa8Kmcvtan8E8ZRecrSKgf4WdqR3x03nbXsRseRNffsJzDs+cZhnUkyFo6/b6WYr0qD78rlnn4ulkO67o/M6AgLBOJ4+nnHTCSiRgRO/wM/eqvPb/WjSV7R5BCKkEFLQlhJCJYBjY2taFLX0ajlvUzLN4tgsz8GbwbDRobVgyYzm9LkVIeTVtTV3hNbua4huxKP0GtruOB7BkzPZbb8LOPKjzFx58uqrsRipy1JpeNpikouWmbOZnDBNCyFShanZQKJ5MoqmM+8bSGnxGjC+zTFve6XEfg7bZ5kvBEoO2gcbZSO2xStd0RNMVFbRNRrtxbOyB3GVHbO+Qt9/iPhBKQIJfj2FpbQsmTJFs6R3SfDQlrBlGfQmZT+VmNMOAKUchlND1fjB63ncrK0mkjDRo+/rfgPY3gUM/CtTNx2hhJ52f2dLNy3+sml0LaNYJXE1wwu2UoIzy0IA15vMarNmZ9b+ZbaDKn1Ohk8dTjZq0g7bOEoK2XkHDYamn4dGicAmNAMo400YIGVMUtCWEkAmQ7tuD5O2Xo8kA3nr7OKy+4CpsT/uxznc4v/6o2dSErBhH3kGfqSlIyRreHb6NX57fxjJqypuCW46+vKBtTb+grbvKzl5S4kMHbVmmrYa8jGHKtCWETDFapgEO0+WehXJCXdFkCh7Dyjo0PSOfPeHIK49wRuRveF1dA7F2TUkn9/KnCkfTlVXfUU5ECy575G4kUyn4vMUbvm0W56LT3wKvy4GL/IX12MeTUCRou9M5D07WwJN9N67/L+bKG/gyCyYmo73w70PQ1lCVXA1XNruFzcjJe1lLE+9C7NU7kFA01MW+D+f7/4jR8tL2Xtz67A64jBRqTl2EXuc0yE5Ah4hlTgdGe4K6W3LwJmRZLofIT3wzOo1Dphw1ZX9OuHzDf44KogPHJB/ldaV7zGzFbEJIJaCgLSGETIBYLA7dGitjW6+CVaxGXTiVy35oqaaGEMUIVc34S91noQtOrGyejhlpezphFEE+vXJfm5cMxux4B8uTm5EQg6h3Fpav8IfsTFuDdUwfgm/Xk/hg7+/tx6VMW0LIFJMQWABRRcxRjd2eA3FEGfeNR3pyy6J35HU2RXdhduzK5HNoMxeWdN/85mesxEMlScXtDDpGgImutl2YPa94Zlw26FzltU8WVkLQls04adA7EOhiNXeXIBUubPqWYEHb5pE/HzuxK+Rl2mYDlOVIhfdie3cc7ByEqnVipq7tU8mGfK2bXsUl3X9Eld6H6LoL8WzjhdgmWEHVU6XRL+PAMm19hl1ywcFmLmnWGIrKI0w9Wn7Q1l/CyS9Wy9vpB7QYXEqk8suJELIfof9EQgiZAKrMpqhZYroTW7sSaO2zBs8NQTfPiCADOV0e9EmNvK5sWvAgnbSm0DIRRy0vPTBWqrpexvGx+3Bm5HZU63ZQgQlVVfHgBOOK7CioTdefnra3maOgLSFkKjFNOFQrOJQS/ejLZFKWKhWxZyuIvpEHbSXXwMxTyRMs6b5Vei9WJ57CUbGH4Ol6E5VESRUGbZlIx47it9UMpBUr6Bya4KCtmNdIlJ2c9osa3h/7M1Z3/ouXClAiHQW3T0XtBmojYeR9t54V/iv03p1lP0ZvRysP2DKRlAqjZxtGy/aYyAO2jN6zDWnVCiq7neLwzfJGYHrnkzg3fGvusum3TzZTpu3UY6TssaYnWFqGve62Pm/9RgzxCpthQMj+jIK2hBAyAVTZDurVaR3o/O/P0JTaDI+RxPTq4lMcCeB0CAXTb5W8oK0seMY0IyrbzIVtgzNQ2Myl2uvEdpeV5aRrKtA2+EG+IdvTE607UE1bQsgUoqWx1zGdn0hjJ9jy64GXQo7bwTpXYOTT4yX3wO9Sl6+0oK1f7cOR8UdwSPIZePrsJqEjxYKSW7viCJcZwC5GTRaWR2CS3bv57yc2dOLXj21Ga2bmTjSRRLXWDZeRRlWJ9XzHiuK36+kG3BI8Tusw1KMn+Hbq/UoLyXG7gdpIM23zaf1PmJYg2WvVNWZSphO7o6MTyGLB9A2JIM8AZsTwTqRVa/ziGaOT9q5+BReEAKtbajGoEdmUY6btzwlfoMSyKJmTZKKpIxoNj9WmEULKROURCCFkgoO2c+SNEPYC5+E5fjk8+3MASpvCub9x5k3VYgc9cn7QVvTyunNjgU8TkyP2NngKB8Bs2ukOz2KsSL3ID7zM1rUQZln1ifszFDvLmhH6Zdqyg/uxyLIhhJDxIAsu/L3647nLNfEkb74JsbRcESXei2y41RXch6Cty5dpuWRzllDbkfH4qwY/0TYCL2zrxZ/+twmS24Ofnb+SNx8dKT0VK5j2/4rvGPgwC3MiafzlBSublE1r/uTx85Ho3o2Leq7j69TAiQC+homSmnsKYm89hFqtE1V+H3a7FwLJl/l10Z4OCMnugtdLSexj0FYvDFIqeeOuUsnh9tzyP2o/gaNj1RiNap97OlkgPYluqQVN6m44k50wFfY+Y03IxianSnIVlt1yVtntASloOwUp1vjYhABfYPhGZNlyNNnUh2S4C2gpp4UkIWSsUNCWEEImgKYUHjzkH6iEmgvrpRIbK/O2LPUyHKaOxnA9NH8Dsoe+y1MvIc2mUzYERn2XhVNqrps53IEBNe1YHV3/zOXQwi724qKvdTMGCzXkB207ndMR9RyQu3z3a614elMXPnD4LBw6Z+TBCkIImSjxtHXy7NDEE1iRfBF+Iw6t93eQ6ueVdH8tL1jny2vyWC6nN4BeRwgB3S4n4AmUH7Q1ZbsO6Ei1b3kVH+v+Ffoc9djT8yssaC6xwRoLdq+/h9UWABafxesKaOlorpXlXdWXos01G02aB1Vvb4bDVHnN9/ZIakCpCclXWuBmrKyeU4NbD/4mGpRWLF/owJ633syNfeK9e+FI9yK/6qyWDI9qpq3eb9xVCiNm19llWeNv7Anj3IOnY1/FNjyOy7tuKZg59H97rkGbcxZiwgoAB2K0OZx2jWd27tkRai5aSoJMDaJifW7Jog/+ErPsHf66XNA2FR26qS4hZPxQ0JYQQiaA3i/bMr8xx7Rp1LV1ME5RxInRe3mYWxbmQK09LRe0dZga1CjLipm1jy+OCrBpZX47WNATk3mNL26QxjjHLpmOx3aei7CjDrPrD8QnBnv8vNf+3uqLEKyqx/v4sbmJdWufxqrUOrz42ik4dM7x+/Z3EELIBIhlgraiafDGRywwF+/rQHWJQVszlRe0DY08aCtWT8ct9V/Be3t/j2mqlYHqzgvGDsXtDfC6q6YJCGrx7+tyrNz0a8RMDfVaO6S2V4HmE0q74/ankHz5LxAgwBtoAmYeBiNtB5F11jiInQCMynC1/hkfTe7ARs8KrHWczmdtpGN2/XWnf2JPBPpcEj51IisjZJUScu1qQzZUqHRsYlNaCm5vFKndWw6TfZfn0YfJtGU18V/bFcYBTUHeW4ARElZjUdaAlAXDd/UkeYmLap9dn3ckUu2bUazdbIu6C5IxE2OBZXlnvVZ/Do6vsRMEqCHq1NMmTYfD6YXh9JU8e8sZrM0V0WAzHgghlYFq2hJCyATQlXTR9YboRFNVsaE8YUSHCCNTA04wNOj9pq0qiX07yIOhA//5CnDPp4CdVrkKJhzp4zW+GMcgjXEOnlmNtupDePbsq7vCuY7dA+Q1KVMEDy/zwKRVDWf33oplqVdw5K4b6QUnZIr4zW9+gzlz5sDj8eDwww/HSy+9NOTt//Wvf2Hx4sX89suXL8d//vMfTCZx2QraZpszMom+wiZTQ3nVdzTurb4YT1S/B56akWc1Zht6ekzrM1cTXAh67WzDoQguPxyZcg6Cuu/lEVTTPuTS1NLr2kZe+Rc2dcSxsSOG6Bv3WStluyxQY4PVTMqvhdGc2AiXmcYcZRPimoSEokPNy1p2B0ceAB8L3mp76rXa/k7BjKOt7qXY69zHE9j9a9oOU7f1by/txs3PbMfPH9nEA97MvaEP4r+h8/F84GR+mWUyb9i6fd+2i72neodoaOYcmzGg02W/94OSAbNuAW6r+yL+WP81bGk6bUyek0ycB6ouxD9rP47/zbi85Pt4q+pzy2q/GtOEkIlDQVtCCJkARt40PVbTLCsZmMVr0ZEh9p1oTQwVDGVA0HYkjUYKdG0AYm3W8rO/zK1O5E0x7d+ELIu9bkctsAa8umHimc3d6Gzdjmdu/hqeuekLiEasrAUxEwAwBAevR6jomaBt2n5PeNR9q+VHCKkM//jHP/DFL34RV199NV599VUcdNBBOPXUU9HZaU+7zvfcc8/hAx/4AD7ykY/gtddew7nnnst/3nrrLUwW4tZH8b7em3Bc7IHcunSk+N9bzF6tCjvdB2Bv7REQWDmaEco1ujKsTNm06IXfXWItWUGAIVmVdUVt3zNtXw0cl1tWxNKDcn15jcu6E1YwXOMZqVbm3Kymen5CcU384VyhpaAexhmRv6G3fRf0RG/RgEwl8NfY0/Olnk255WcCp+E/1R/EK3n7bCTebDyLnwDIMtTiJ8uz0nvexLGxB3DMjl+jp20Xr0+/zWjBJs8KtAYP5Bnbn+j6f3CsvWmftktOJ+BOWuOMPqkBZr/DccE5Ns1onR77fRd0aHC6PIhKtUg6gkgbNPl2KmF9GLKN7YJlNCDM/4ww9rGmNCFk9FBkgBBCJjjTdmPzGUiKAR7A65h9Nr0eJQdt1QG1BvXUwK7aZTENnvnKsmSzmTYMr5VbQrbScQdYWU+CaaDtxTvQevtnEOh6HYG+d7DlpQet6zQrOCsL1gGUkpkSmk7FC7KMCCGT389//nNcfvnluPTSS7F06VLccMMN8Pl8uPnmm4ve/pe//CVOO+00fOUrX8GSJUvw3e9+F4cccgiuv/56TBZmZI81zdu0A45K1JpmPhx2wiubqRvyZiu37kOmrWnmMm3TAgvalh7AMCQrM1HSkrx8zb6Ia/Yhlz5M8DCfktdcM61b2/Bw46X4deO1+GPzN7FMX49PdX4Hi9JvFNxvvvwO4p3bYSTtwIu/urKCtlV1eU2O8mqqxhxWvd9oat8ai0YctdjjmldyTdtQfCsOSj7PS2l07XoHXTF7m5bNakYdwjxAzgLM5WRL99e+Y5NVd4P9r9QfgB6psNmTOEZB29pQFbyZBnhza5xwsSYBGdkZP2RqSMh2qZFyPvP8NU28rvIWz4FoE+2TKoSQiUWn1QghZAJsbzgBr/fMgtNUcOiSVbhVmcmn3Z07y25KRYozRbs8wg7fCgTduzFPXp/rqr0vZCmAzR0x3hRkerUXDblpYnZdQPcQjXEaqzw4pMFE9dZ7eJmDfGrUyjRz6NYBu9dI4KNdP+SBDUO7C0rC3vak6eJB41LrkBFCKo+iKHjllVdw5ZVX5taJooiTTjoJzz//fNH7sPUsMzcfy8y9++67B30eWZb5T1Y0ap28MgyD/4w19hzs8yr7XEaRBlJGvLukbYkkFRiZgFbQI+3T9rscwEnRf+dK27S654Ml35b6mKwWJAtruYw04mkFAU/5QWT2XOzEXFK3M3w1JV3yNqTygmnPTP8IFhsGD2qbAsucDKK6zod0XmEBtyRCztwn2bMXQn594KracXk/lIptv8lPwupQBSceCp2PoB5Bu3M6TJiIpVXouj7i70EWiFQEV0Gm7VB/f6vQlGv/FW/bhHjTYXw7GFbjVm9YCux9HoKuYMemNzFvySEj2q7ePeszedJAzczF2ByTUa+15ZXm8Az4nxoVuoaFTQGehensfRoR8fLc3yer2oS/N9jfu70nCZ/TgeZQ8Wz0MdkvU0T+vommldxr63OJJe8vV7AO9zZ+ArKmo9nlwfunyH6m9w3tm0p935T62BS0JYSQCRARq3nHZ+byedMApw/hpJrL1CSDMx3WgbNoqNjrnIGo//hc0NbYxy7fHUITuoU6VKMbEVXMBW1jKng2DGtGNtwU02NmuWC8VRiw5duW6OVf/g8F3gO3kcRJ0TvhNaztVZQUlLRd6kEW3PzA25PJiiGETD7d3d086NTUVJhJxy5v2LCh6H3a29uL3p6tH8wPfvADXHPNNQPWd3V1IZ0uPatzXw46IpEI/3xjQel0tAeOzIEICxuwABVrEjlYSYh8e3vjmBt9FQnRD39qFjo7S2scNljWbkt6K9++pODDC8GTcWpXaRm/jGI64eJ/h4Hde3ajrqr8Ug3sufd29YEljWYPzuLh3pL2Bc/GTPTw+/U66tHaHeX364slIGsm4AJ0qbrgoE+d8y4Ymx6xnqdzB1xJ6/6aw4O+MAvm7+NslFHEtislVcGt9KHXEcIGzAPrLBqSHJDTCm9StmNPW1mZgvkisQQU1d7vqVh40P1uGDr0RF/utum2TYh6X8CMWBsiYg1cahCOuvkw9jzLr9/99vMI1M0Y0XZFd76FYOZ5HIFmbA95ocpJLFGsEihpxeDbmf8/NRoE2YGQYmUIJ6oXI9LdgaWRZyCZKqrFOnR2WhnOE2VTZxI3Pr+XNwD80CFNOHhGkK/vTap4c28cS5v8qPdLo75fpor8z+HeLa/gg52389kFeugUdHZaDQtL4YKGqKyiM6yV9jk1CfT/jiK0byrlfROLlZZsREFbQgiZAOwsdhYLzJ2x3K5rS4Zm5pVHSCs60mJeYxnF/vJj9bxYvdiqMrKjUqqOhCOAar3bqn/HfpwevONchifq5vPX6oh5Q2fXLF26Av996xyY3ZtQu/Ic+J77sbW9qT4eiN3mXswvs0BzNtispFNQ84K2rEEZ234K2hJChsMyefOzc1mm7cyZM9HQ0ICqqpEHPcs5sGHZkOz52IFNhynDFEUYEBGW6lGndcKjx62mWcNkTfaE+3B26i6+LKaORmPjMfu0bZtYtqwag0fQedC1sbGx5Ptuq52H9qQCWfTgIH95983fNx2dHTg9/QBLs+brvG6ppMcyUlF0mBrfpylnDRTBiZq6esCxC6w0b311AC2z5iPCOr4nwgh5JNQe80Fs3/IYv79bCcOtJ/j9TU/NiLZ/LLF98+c5n8GGiARBFODOrD/D+waCXY/DZ8TgV/8fGmeuGNHjz1Ufhyz0oMM9B1vcSzGtYTGOGWQfJKJhnJG6L/ca+VJ74et+Du9JruWXQ403wNd0GNrW/YVfdqe7Rrw/dyZbrddEcGDR8kPxYnovotENELV3+PXBmnr+2Pn/U6OjETj+S0DXRriWnQePM4CT0v/l16SS89HYeBkm0nOtrXC7rczoO98Oo7mxjp+3+PfTr2N+7CU8UncIPnf+aWOwX6aG/M/h+DYF9WYvP2uWDjjKeq821fQipsXBTiuwzxvnFOiz0f87itC+qZT3DWs4WwoK2hJCyASQVWNAsxRSbtBWR0LWkM7UhuXrFCtzldWk/da/X4WuKvjK2YdgZk1pNeKSioaEaGV3aKx+YDoMOJsRSal8XVUJNRZZQ7IzP/DpXHmDV176NRxaCqLcl2sMwR9fsB9LldPQ8oK2hyaehBz/COCrrINsQkjp6uvr4XA40NHRUbCeXW5uLl4vkK0v5/aM2+3mP/2xg4zxOkBln3XZ5xOVGNgnHTuhJrvqAK0TuqZClCOAr3bIx1Hivbmp45K/dp+335TcgAqeTRhwO8p6vPYFF+CBTMOoAwTviLdFTkaRf1hmakpJjxVWDLzgexcCRgS9UgP/HkrH+nBy5E7IrKla6ECIjqVYcMZnEX3jXgQPfi9c9dNgiC6IhgJnbDf/zeiemooMVvgDQQixNITMq86yams9gE+36sin4pERb/fqnvvglHt5z4DX/UdD9A6+D5S8mvKMQ0tC7F7PA1cmBDQ0TYPHAbQJIq99L8aswGu55FQc7pT1/50OzIDH68W0ai/a8+o/O71+/tj5/1OjZu4x1k+msQ0LHAumDkFXJ/z9kdbM3PuAlZD+3ZPbUKV04H19v4fHSCEuvwVZPWVs9ssUkd03Wl6pMKcvVNa+Cvlc1utgmoinNdQFS2+cWMnofUP7phLfN6U+Ln3aEULIBKgJv4n56bcxU94C1xQ4iz2uMkFbNu02kNgBb6YzOONQrMDn+u17cOHeH+HSzh9i44a3S37olKIjmQnaqroBk2fH6jyjt9zGONk6fJrLmnLolCNI5TWVUfNq7bHyCKpsB22LHUQSQiYXl8uFVatW4bHHrMzHbOYGu3zkkUcWvQ9bn3975pFHHhn09pWA1Q7dG5ERTir8QN+hWlPwFSkAMWDVAFd1E1q8e9jHivbZt/EOUT+8VIbDDjhUO+2TZqXwuezclmTmO2Ak1GSkcJu00ppY9ShOvBQ4AW95V/PLh/Y+gNiet7Ak/RpWJp/DNHkLX++adxTqz/sR3HMOgyCKULxWCR9RjeMP9V/H7bVXYPus96ESVXslXs//gPSbaFF2YppHhstfnbtejtlNQMvFZuPknyAdqtmWnBw4RdXIfCcnnbXwe1yQXG7IXutEqjvZDtMo/z3RvnurNXhhaufyXy0hD1z5QVtX3uyhMWY4rHGIkAnuT6Rg51ocGX8EByee4aWjWGmT8/pu4QFbJqBH0Bu2+wuQwWl5TXk9Afv/qRTLw4/jw90/xae6rkGyawftZkIqAGXaEkLIBDiw416sTHRAcfggOT5Ar0EZZFc1VEctdEHCuZ03ZMK3QJ/UgF6hmme46u3vwGdYB1w1Ox4AjrQOeocT2nQHPxhmDAg8yyCd18F6JN3MN7ecjR09SZ7Be3G4jx+YKoIbkmk/rianYMh28JlR+gVxCSGTDytbcMkll2D16tU47LDDcN111yGRSODSSy/l11988cWYPn06r0vLfO5zn8Nxxx2Hn/3sZzjzzDPx97//HWvXrsVNN92ESvTKzl789omtSMsy/m+NE6cvroGhWZ9tuiuInqY1eE2ejpijGtOdTbk64YNJ9bUhW1mzuq6wtu9IzJS3wgr5APMTr7GQRMn39bMaBBlsVsdoBFCeCZyO6uYzcEQJ9+uKW83l5sobcVjiCb6caq3JXS+6i9fYNdgMjcRe3oBNgooeZzOEmn3fl2MVtA3pfTg18k9+Oe5dA3fL0TxTm5ETdiO1cuWyjAXrcDfboK0YeYgmprqvPncSVg9OB5LtvBFqX8du1LbMKWubthrN+Efjt1CjdePsA7JBWy/ucy3mYwSnKWN1YOi6+aOJZWU7kOLN1SZade8bmJl4gS8v9MXR1PcMr6bicTn4CXUm1t2K2oaR1RLen7DSKlmevJMgpQhIBkTdaiaZjLAa4FZJL0LIxKGgLSGETABRl3MDZlKeN2dehDeFMM/o+kznt/m6DucM/LP2E3z5TM1AhxmCdTgExPXS97GRsrue/7XuM/isfz6UpIwP9FzPD6jqqlltvfllbW+66RBsj1vZIbHWjXyqX3+sm7iqqqyvTI6azoYaCCGT1QUXXMAbgn3729/mzcRWrlyJhx56KNdsbNeuXQXT49asWYPbb78d3/zmN3HVVVdh4cKFuPvuu3Hggdm+9pWlzqngxOhdqJH3IvT2YhizP8wbgDGmqwqO+oXYkmkm1qfYzR0HY4T38N8sWFPVVF5ArBgxr4SuX7QyL0vlczlGJdM2P4CScATh10vrFt0TzzSNysz+YJTunbllh6d4vWKhqhnI9FtjAdGYo2ZEJxzHQ50Qw5mRv+UuOwL18FTVIHvKUkva38lD0hTEdqxFYPoSCN6aXLNS/pimxrM0Han0oAGooWa2CAE74C1WzwQ6rEajfW3byg7a7uxJQhNc6HJOQ9OMeXxdY9CNPZ6F2G0u4JePDAxdQmQ0Zceg2QD3hFLtE9cnOdchXu/n5cPisobdvdZ4KNXbBlDQtqg390Tw1o5evLemDmY6misz4wmWF7SVArW8LAiTjlFmMyGVgIK2hBAyAUTN6uhtSFOjVtR4khzWUNRtsn1oBQfkvLq27OC6S/HkgrasxEGpTMU+aFAFN/qSCsxkL+q1dtSjHR7FznIqVX4d3L5oBNnDP9Xhg1NP5oK2WxtORGpXFw5PPG5dn1fjlhAyeV1xxRX8p5gnn3xywLr3v//9/GcyaK4NYWnqFZiGAa3PiWSsNzf7G54q1PjtU1F9iaEDQ4ZhwpnYy5ddkgOO0PR930AH+/y1Aq6SL5vDW5qa2Gac33sT3EYKjj3nAMsvHNEm6LKdxcnqqw41TT9frK8LkqEg7rCDs0Jkd27Z5bWDufmcoWm55ZDegz2YhypvZR7y1bhZLVO7JIY71ARfsDYXtDVKDNq+ff/1UDf+F0L1LBz0sZus0gWmtZ+r9D5c2v0TKAlW2qB4Y7v8GqD9OUN20NbbMAfmRms50WW/FqXa1ZvMnZSYkam1z+rgN1a50RGxxoVuyT5ZMNZM/v9RGUFbQbX3jXjQ+Qit+xfgq0dHw7HAMzeza5CMjTzzeipjfRx+88QWJNMyHO69WJxryivAHyivGaU7WJebnaBS0JaQilCZ3+CEEDKVmSZEQ7YaXDgGNo8hQ3MWBG0trClL/jTWNpkFCtjtTAis+c0IgrashAFr/OKKdOWayEj+8mssVucFbWN5QVvNXQ1nMhu0TfHpf4povx+0fuUSCCGk0ng8XqQ8jfAk2+GKtyGOAJ4LnAKvkUBtzWJMywva9g4TtO2OpVGtWimipq8ekPZ9Jsorcz6KRet/g4SjCsb08uoCex0GmlQr8zeW3Ifaqmk70zYl+Hi99FLM33wzlvZuKFhnpOzvM8lXPBjjbZyLze5FqNG6MFveDA1O1Eh2ILeS+EN1sL/JAV91E4I1ddlEYRh5+24ozu2Psn5zMMO7oKky9CL1ZoXMDKdi1LSdaRtx1CJoRHh5CcZb05K7LjhrBW6ruQw9jkYcXjUXbO5NqTTdQGufFQ5rqvLA47SDsy1Vnryg7fj1OciOQR26wk+8sJrIEx20heiCsPx9wJyjAU813NEkbltfjbgjhFWBeiycsC2sXF3RNE7v+wvq5d14ZsNFWCxb7+e06EHAU97nqDdUnwva6omRf+4RQkYPBW0JIWScmZrMM4qsT2HKtC2XM9O4jWU/ZfXPtE3Hw3ywyhpYOJUIP1gq6bXJTc8TeNA2nFJRHbODts7gcJN7B6pxm7yOrd+IwZ3caD+XJwQkrawyXZWRUnUYQn7QlsojEEIqnxacyet8wlDREY7iFf+xfP3JjU2o8TnRqLYiqEcg7WkFlp876ON0dbXBmW3IFBqdupXxmmX4Y/3XoIgefNxnn9wrhcdnZ7LqmSDISAi5rDfg4OSzcHWzQMjwoSchNXBqspYdO7DvwEGCtsEZB+K+6otwSuQOLEq/jvnyOwgJx6MSOV0eSKKQ+7uq6poR9PmhC07eoEws8aSrbtr7JZ1KDlmWimGBc1EQ4MjUz9DTcWRDqOumvR+h7tewPPUSvxyoswPeDQ2N2OOyyhq0R/PDzcNr72jHu/r+iR6pCbVNKwuuO2J+HV7fHUZzyINp1ex9av89YyrTiIw9m6Yp/PWYKA7Net10KfN/Gmzmv2pCLkQlq2REzzAnfvZXau8ezJM38EaXZ7TfwEtwsCCP6gjwTO5y+EMN6B3BTDVCyNihoC0hhIwzOb9WKQVtyzYj/BLOCj+HOfKm3Lr56iYE+26Dx0wh1f4pnLv357mgrs+II5oqsZahmn1tTLw7fBumv92AiN+uWecOlR+0rVfbi9axjdSuwPPaYqiCC0cHD0AqrkPIC9oaCpVHIIPb0Z1AZ0zGIbOqyz4oI2Q0ibVzgI6X+XLnLva5PJMvBz1O1PrdOCf8J555697BGiwNHrSNdOyEL7PsqrEeY1+xrMWUw2rY5XeXd9jj8dtBW3MfPo8dqh3wXZJ+DUmeUXnJkPdhWY9S2gqYxNxN8CvduczPLPcgXeHrAlYgzm/YWaqB0Pg1txrJiVgtkxlb3dDCsz01ZxAOpRdiXsB7KKrJQq4Gb3iXdvghpQZmCDpYCQDT5J+b/++B9XBJIr5zzjIE3BIMOZEL2s5oqofQYQerahrsMh3sPc3eR2xGT3tk8MzdYrr3bMai9BvWtmjsu/5duesOnVOLhecH+LawQHLuxP4Yy5/tpcqpCQvasgayDt0afxlS9lPAwl4nVmaKjeOGy9bfX/Vv2CdlTn7pruLNCocSrLY/KwQK2hJSEWiUTwgh40xJ5R38SVQeoVxVSifmyhsg5FolAAGHitnKZj6VNdKxqyALlx3oRqKl1cUTNft+s5Qt8IU3wcg0EWN8Iwja+muKHyzrNfPxjncVNnuWIy7VYmH7g1gTf9i+XqFMWzJ4/bp777gVG+67Ds+t3067iUwoT0O2gjgQb9+WWw54JN4Ai01rZhzpMKBrgz5ObzSBPqmeFbWBv3H2qGzbomYrG9XrcmBWbWEwaDgeX1WumY+wD0FbSe0XeNSGDzyFw70QTWtfScHGgmZkWV5/8UxbFlRkfy+b3cHogoRgsLy6luMpfsjHeGZgdOaJ8PmsIJPhDuYC3rw+7TCyze/YDJm0qvM68QOYBjRVwdqdfTzoymosv7nHGhvouRO2wLxpDdAEJy+7pItOVPf7Dm8JWYHNcFLhz1WqaN7/RrDZ/p/Jqva5xv0EnOyu5Zm/nc7pULSRN9vb5+1QVDiNTINe58D/07pMmZVwUi3INieD12R+0f8u7Ko5ouxd5HK5oEjW/6FDLrERICFkTFGmLSGEjDMlr1ap4CxvuiYBBHFgF2zN2whB3sGX5e6BQaxEuAv+etaEZGhCXtCWEeUohKQVtBVGGLStqm6AVQShkJ9ncVkHSYpuYHrsDXh1O1vCoKAtGUT3zg04NnIvX9bWScCB36Z9RSZMVfN8ZHNJA31vw1N9KNKCF0GPlTWY8jYD6l6oug50bQCaDyz6OG8LC7Cp7vP8RNsvl5RTLXRwh82tRXOVB9V+J3yu8g57BJcfoihYAcG8zvblYKV5HvGcihpHCifE7rcet4SmT5GettyyFKxHKhEDUoUBFG9g8MZq9QE3n2XCt0HyQxrH5lblOvjYcyAfeSa8bvu7fUfTyWgVennjtgWyhoB38O3XNRWCYQW4FcEDWTPg0DWkRD8kU7VLbmTGX7G0PfMmmrLut7b5QrwpnwSXmca1M+bgxoaLeEB2dkjEqn6B1DneJDzJl1Crd6JnmwPTFx1S0t+p9OzMLTfMWIBKsHnW+XhRs7KSV2Yy0idCKplXfsTlH3D9QuxEc/w1VOm9iPddDDTbzeEIq8kcy2WKZ6VFL+IN5dXxRl7PBZcWh6REeXY67w5HCJkwlGlLCCHjTEnnB22ppm25BOfA7GQhaA/gxb6BQdtUdGBtwAEMo6DmHaPpGnxJqxENCz5IgfKnmHo8HqiOgZkj/rzMJ9ZNPD/L9z+hD2B76LCyn4vsH4Su9bnlxh5rWjohE6WxeVqurniDuheXd30fn+n8NkKC9ZkWrrGCtKpuQt01+Pu1LWzdPujzwOctLyt2KLPqfKjyDDzZNyzJBSFT81NUR5ZpG5c1bHUtxjrf4TwLlBH04YO2sZ7O3DKrpW54a3KXu6UW7HIvhM89eIOho5KP5macBFH5TS3zA7ZMonEV1nsPwU73AYjKRskBP9bMkwVb075p+EPDlbih8dvY6l5qX59OI5bWCmYtZGvhs4zklBhAwOvGhw6fhXkNfpx76PwBzzcH7Tghdi8OSr6A5J43S57+L0R282WnJMJfPzqZ5KPVIyB78niipJN5DeeKZNrOUzZhdeIpHJBeh1RfsdPg+zctfwYfwGcsRB3VvNzGSBjuTOkVQ4OcoGxbQiYaZdoSQsg4U1WVnwF3mipl2o6A6Bh48C2FmqFZx0Pwp1oHXM8akw1LS+WmWOZW6SZ8Rgdv0mE6/cAIg+yauwbOZOGBc9DnRZ22gWcCiXETUqaeW5/UgK2eZfBlphSTqWF9WxTrdvfhxKXNPAtuX6iqfXLByGvAQ8hEqPK6EHY1watmPoQzfEHrM0yafjDMnX/hJW16Nz2HpkM/PCBzi01XzwbTWDOmSqGz+pqqzJsk8cBbmRlnLGibxabcu0y5pEzbVKQD2ZCsr7oBWjiJzlgHEmIV/hc4Haq/CedkmmgVU+2ws0ldjsn3GcHKamRFUmqmOVdxcsoO2s6WN8Hs3gK16YDcumywnC/LKRiRPTggvQmCaULsYUHZmTxomz0563QIWLOgnv8UE2yaA61I9uxQuqJphBQrEG/6m0Y8lhht7rwMbHbyeKKkVQO7XAvgNtMIBlqKjvGy5Ih9QoNYeqUG7PIewsuJdHtm4zXf0TAFB+Z5RhbqaWs+Htu1xUg4qjBdk1D+HDNCyGiioC0hhIyzaNUB+H3DN/jyObMHDk7J0ETJzi56PnASz6K5bEYd8NY9fB1reJP1RPAcnq1zhK8Ji4Z5XBUOPFj1friNNFakXkSt1mn1b84c72oeO9OpXIanGkjawWRWs5FloX2w59f8cnr3EVaTFHZAkslYYwcxZGpgJwPW3fUzzI2/imfbLsG73/2+fXo8VbUDQRSzJZVA8U8HwnbQNi16EPRawbLDFs3EhrVzMEPZhlj3XjSxjMPqWQX3b4/aNUgrKWhrsqy/VB//XmCfyaxWbDnyszpZ0JYR9OEbYyrR7lzQNlDdhK45K/CkbNenbBhmO9xVdbmq76ZrYD3cSscaT2UN10hUThZmGbKMVrV+wYD9zpflFA7dfiOEZDe/nNrDynCciKRivU4+l2PYwHx9y2zsFRy8jIeZ954fSuveXbnmUI5RarI3GliAOotlwk+UuFSHe2o+zJfPnj1twPXemhZkPyH0OAVt+9vtOQCvVNVDlhUsnVELs8v6nxhppq3aeBA29liB8ogiUNCWkAlG5REIIWScyXnNHtxOOndWLiEvaJsWfOiTGhGqGzjIZ9qcs/iUR9a8Yjgpw4FNnoP4NNZ1Xrs0QYdzJtb6j0N3Q/kNHXLyprb+u+aj+FvzV+Dy2FMAzbxahawmH1NOgxNS2VKpJA6IvcBrKy7ZdvM+P55i2sO3TTXH7fPjEbKvkvUH8ZNoWWnRD38msDi/IYDemhW5zNPIlucH3D+y6x1+Euv0yN9xgLapYl4QM1NfkwXcEukiza2GkYz2okXbgyq9j5+sY7In6Iaix/8/e+cBHcd1n/tv+mxfLDoJkmCvkkiR6r3LKpZlufe4x3Hy4nSnvDwnfvZLnvNS7CROXOO4JK6ybMuyilUtWZ2iJBaxN/SyfafPO/fO7s4sCgGCwGIB3t85OJgt2J0dzO7e+93v//0Hq9uJ1k4kwvK4ZmOngltzfdVhenj1e7DQSEgOmq1+KvRrI15E0WQUlDY8Hbm2etkySjWuUZOTazJtjYA4aZre/2LbwN24JH8/zjFenHLfWuJhpEXPhSsU+k/ZXK/C6MmD1e1QazcahY7MTrxh9Gt408i/gxvwY3fqTUU0rwjnY4k1L61uO3lPcGdMfPyuWe/7YtvjM1sAi4fEGqc7g8GYX5hawGAwGHWGNMmooEps7ex0EUS53L6LfIl5g8lEUwpkGB/0iZDpcVHycrmmJdqWyyMJwU7dB5WNeD5yJeIrOma+z5FUdZvEIRRDLZBVfzDNk67qZWJOGp3GUcTzZKK5ecbPyWgcdFekiweCa9FO3bMRsVKRIfrU8V3IGYx6I3RswgvZDlySf5BetqVY1bFIfnduugLov5teHtzzKyR2vLXm7wsDh6hIR36S/Mya58wFA+1XYL+2Cjofwpv1019I43tfxDuyXwGf97/rBceE6zjg+Mm//92i1xyKRI6SBphNo/la0XYKp+26FUvw2ZWfhFHI4APbLsdCo1U7Wq1EMU7cAew4b9L7lhweg5K/cGsbJUgjr+GmzPepy3YgtBrfUX+Lirfvj3TDDoisnJGDa5vYlHuSjh+KInHovv+U+yYKPIxIJ5Dup6Kvm+sFlzy1e7Y4eASVUUBT5/ic3PkibGUQMzxB2S1NI/t/jigGFqkncrMnW5dgkI7qXIglJtqOpVD+bCLG6YtWplAyHZRMGztWNJ1xPEkl95nBYMwfTLRlMBiMOhN0UAbzxBjTg5eUqmgruN5WmLhWBbHW8SKpCEdiKBVMpEtTO5sqmXYEQ/KbhEWc3LhB7OkSbGAWcbIwZR6y4jttRSNT3W6yBvGm0S+hWCB/8/oZPyejsRZqNC6EiJsDb515UyDH8JvWae7kzYgYjHrRFpVqomkcpbYkf8fmdXjhiXY0m/0w+vdTUZIL+4tZ5ohfZh7vaJyFiGzHJXhxqI9uF2dQ/GAWM9UFFh8XhmVCkSfOtibZuT+IvBVheRQrowbOkUJoChtoM0/gztGv00x8LXINcIrQHzK2+J93XUybS6nSwhtnhONN8L55AbuYnnLBNeimdfQiuFwv1mlek7BSbCVe5rwoqqwBKLZ/ngpGFqVCrrrgS+MwpkN8GZDeCRKDn+k/guQUoq09eoz+FnkO8Y7Gcdryon8O2kZtI9Z6Elw0n8hpGw6pKIlxhKwMJI2JtmMp6mZV8CaLZNdtPLPF4Sino908Tk0GyJL3VtsZPR6DwTgzmGjLYDAYdSbc+zRuzPwKBicjZrwDQDP7H5wGgiiV/bXABYVHYMgJ8MIFsMUIeNsXPy01hW3WThTyPXDzLizn1O4ZLT9CO5+TktJkIg5q3SX/Lyd/xqKtGk/BAociHwEPFyFJgCyRgbDnHBGs0vjXaZ9+KS6jcRdqDF6lCwAzbWgU5HDiIuxPpmhTI1tkkynG/NMWkxFy/AUJV/EXvghNERl6xw709r2CQ8oGcAMlbAxqV1kv85v01ko2kGgblsVxbrbTwS7530lZdSlyjgQLEtaYk4u2pBy5iBCKUghLWr1qkaTK460jX6Tbsq2BF6beF57noPILT7AlRBMp9Je33UAlykQQRyEZT1VwTA2O5S/UyooClC+O5PLocv3F3ZCdR3o04DCVo9PaP6WpE/B0WGSG+pA8RWh+pmhiH7caxRCHNUoGXNwv9Z9veFmpCta2NX+ibdPRX+Cdww9B50KIlz4OVH3JHuT70gy1IpTLQLAKcI0ioE7vf3U2cNuJ/wfFzJQNBxed8eOF80fxlpF/o9tO350AJne6MxiMuYeJtgwGg1FnlMwhrNd20m3VeQM7/qdLpA2vhC7AltKzNCP0sgIpx/0gjqYuxXC2AN51cEjZiHOWRHHuyf+CWeilzb1y2qlz/cSTz+Ft5UlxX+pteDW0nT4WKbskDUfORLQVurbjX1s/iR2FRxGxc2jX9oEXNsHmZQjOxBMl0dFoAyvSzZqxOJy2BNExoBsGVCIkzJARPgWND8N2RTRpnguQwZhPmkIiklVvJBCaQCtsveSd+OKjh+i2dEyrirbkc04u9tJtXg6DD8+86eNsE1aECXMjp4sTEG1fXPZu7Mp6QtMVE/hvKwzlfcExFfXul4yGKuuIFEFZ3IJVNJ4iSh3ttMhNIdryI2RM5blqCa5ZhGP636uyolZF29xorUuTg4v0oJ+ZS86/ae1fyo9LKmUq8vLE9GZL2BfaSn+wqR0XCDMfS8w2gqSicla7gWNWb0hjONL8laBOss7gRFqB3AHafLMw0ov4krX13ckGxXFciFYBiqtBwMzHFUEEyX8c1566Uq1R0A0d/SePYNmKtaeMn2EwFhpMtGUwGIw6Y5t6tQukpHpCDuM0SCzBw/E7sFF7kWaE2qI3yTrSeQtecfwJ8jnt7eCGiUuplw5mc4VTO1dN3XeJxZpa8b3sFlyf+QGuzP2M/jRZ/0xqImf0ryITbtUpYlvxV/RyoUictXfCEXzRdr96DmRHwwpjP71MXhsR98Lq7AzCGfOHO7gPneYxf5JfyJ2RaKubNl6f/gY9p/QiceqzxR/G/EKccFeZj1cvh/nxOYhblzfR8l1SCr23zxd4h9IZRC1PmLOiSz2xrkGIiEDEzkJ2NWh5Iib7TX6mA6dnq9uhWBOQ9Y6LafvZ9mNJF32RpDkiVzNsyfodKccniOHa+InFBieIsKUoBCMHQfe/1yciPLgTW4tP+lcQp61tVsdZCV7DxtILdJFXGRj/uVscPFadEHOK13huKuKpNlTqY8ys3zRuIvoy/tijY4aNoeoh2hKH8nzhGn5khTrJuS3E2oHyGmVmuIeJtgGnORnjEpzyePhMkSR/YcENuNYbGZIT/vxXPoFw9iD6Nt2FC2//8HzvEoMxa7AlCAaDwag3pl8KL5MsVsZpIQk8BNekomYwgy4ScEQRmsJyTWZiMec1dpkMS/MbvcSjcepwjTr+hDua9HNpT5dkSKaT/gqc5E3cbN6bQBLX5H2Jt+KepvfimEwaoXhopTPPP2U0AHnPQVRBL/rn1UwnaaTBDsU5ffcfgzEXHF319nLkCzC0/OYJP7tbot5nXrZk0ZgQuj1wvGZRrpFoHXoa7x/6W7xr+J8Q7nv2tP+e0z1xmuc4KOFE9Xoj0JB0LKXBo9ha/BXNZG3hMlVRnBy/Cqq4+H03luxFQ4hmDs4pRG6HlMoHcM1SjWu0yRnF9dkf4qrcT9E2+Otxf2+O+uefOE3RNtncgQFpCa3qOSkuP+V9+7MB0TbRWKKtKPv7E4yUqDsB0VaJTLw4zjevwjF5NV5StmPEmd7/6WygUCqOGw+fKULgvFgooq1WzFPBlhA68vB87w6DMass/m98BoPBaDBqy/aYaHu6SDwPxfEnQa7kDd4jSu1XWioiQ4o2oXK09cLoKR/XJs1LKv+XUBSJkFAVbW1eQmSSicR0UCUekYDzTChPDF3Bc1ERB5D/Av1STl0jE5nGKRVmzAwr4OIm6IUzE20T2dcQLec3C87CmFAxFj/RjrX49omP05L261OrJrxPPCSBc22E7CIKmo5oSIUecCry0dNzss41Stj/3Ld1f2FvupBGVwRTikEKNB4lDcImgxvaiytyP6fbyQLJrN5At0VBoFErBDl0FlTpqEkgf5zGE+VyaSSStTmnk4m2nEUybXVUjrYQ8v+Hih7Ir62Q7aluiqHpOZhT8Qj+O/Uxur0qFMH4JQqfkeFhGotj8TLaG85p6zuPyTGbN0xftA2FJ47+EJbtwN2Hm6DrBjr4JSxltYyW98cTrjQ7nwtiTTzC+KqJRkQr+efQgHLqhRQGY6HBRFsGg8GoM1yg6ZQSYqLt6SKJXLUUjFLOoIvIIlbqe9BunsSw2I6U0g0plkJlmm3mT52LRzpOVyZ5SjiCZNhGwvbcuY4UPaN8LOKSukx/rHqZr4q23sCYuiSI64zjIBAhvzz21AODUMbCxTZK1QUBglHyS8Nnwo6B71e3Occ848ZmDMZssGVpHHfv9LqWr++YWPy6aOiHuHbAc0EVhr6A6LK10PPp6mevHGmsRSolICA5pynaEneoZHl/Y8sxLB9+Am8fvh8CTHDDfwC0bpvw78zAAmMo7jcq3bn2t7Dq1S8gLTRj2dLtWOxwId+ZXMgMTSrakgzbCqNiC3LyMnRYvtAkBhzOUmDBl1Dko7ADn8fTzQomrudEWKJNxoYDGcQT0XXsR7hk9GmUxASarL8H0DiNyEQlIPLNo2jLl/+HDi9BmqRBX0s535kwUmCLlRVKxcB4omxiOFME2ih3YWXaaoEKphLHYsUYiwsm2jIYDEa9sSsDYw5KcMDMmBaymcW7hv+xepkrd3vuHvwlutPfql6f4q6DFfcjDazSFKKtWfJF21AUl4/+B3X4EKLV9LqZs8w4XBWQFc5zS/266wM4OFSAGeh8LQXKM03qtGUsKLQscPAhoG0T0Lp+3LlF0LUzO5/4wMIPzXW2HYgBFx+DMR+saI7gU3dsputPy1ITL0hKge+8YtYTJ83iqC/aRhtMtA1UWDj66X0e54t5+v4kn/auEoPq5BG1vIZrdiCOZyxO0Y/yiSR90bZ59fn4cv+f0Hidv00t7kxbghAQ8AvpYWDF1JFT3236CBKJJN5o/KR6nRxJVIpXaHb849GbEXVyyAhN0PgILsnfjx2GF2Ejn0ZWcGtUoaJtpmTSuAtZHL+waxHhvuD9z1NcviayqRGQSJO2Mu58irbl77RKj4KJSEV8IW4qofxswgyIttwsVe9J5QgvygJx2hpF/zNVcxvL0c5gnClMtGUwGIx5ctpavAQxkFHHmB6CWNt5ueJaldQwKkNL4jmMN3dA1/yyW2cK0TaYqUbK86KCJ9gSZkMQEwXfCaly3p66ahwaD1yT/TFW6nth8CrCHeuq9zM1lmm74Hj+a8DRclOct3yDWJlo+W7lDPp+04dwXXKrd4E0fnn0b7xc2qv+GJiGy4u4agW71i1mmDrEWWpAwmCcCV1Npz4PhbAvxFUia04oa9AXfR1CbgGva+luqH9AKJKY8DtiOhSz/ncOp8bBi77oZBunaPpU/q4i5vlIwo+LuHJtKxIhCW0xFdExcUCLESnaTAVvmxNROlUOeEC0NTkFmmnXuAOVSLIq2pKF2IKQoD8VMkIzjsproboa1kenL6o2R2UcIFqv62Ikr6EjOf7cH8zrSFpD3gVy7s9S+fpsIahR7AxfSjPSW6Pr5m8/LG+sY4uTH59kSKLZ0ITRQLO+sx1SuVOZSfDy7DhtJVlecKKtXvJFW8k6s2omBqPRWPzf+AwGg9FgcGU3A2lCxUqaTx8p0CCBIKieM0YKJ6qiLSld5CUFary52nGbIw7IU1Gd+HEIhSNQFLnaVZmfhYlWrmkLUHzRu5DwyiPlsmgfcXLVHye8w98l/cwdvoz6Yhx8AgM5DVFVQrIwCCS6yKzKv51XUTDKZ9arPwQGdnvbr3wf2P6+KR9fN4xq05EKpqGTlYZZfiUMxuwjR5KoLIcZOU+07eGX4KWId/6+qaWxsghJ/jhpSmk7LrjTFG2Jk/MLbf8LvJbGzd1LsErbU73NIgs2k8BrXl41WdTlVF9cJJe3r2gsp+ZcYnVfhS8dXQqDU3FXdBnOncKlaXEyXI6HZjoYCnejoHoNmi6Ot6Ai4cjueDfp7tB2+kP4q+bpn3/r9Vewbui7iDlpFA/8D2DHjePuMzA0BNUpL77GGqvJHkFRY3g8dgvd3hr1Gr/VG9e2INg6SFtCR5xcdOR5Dm/OfA3x4nGEikTC+FFd97NRMYtZKGP6JczqOHuBxCNYmi/UriqUx9oMxiKBibYMBoNRZ/hyPIIrsvKdmSAFGiQEuz2r4SiKgdxbAhdqohNdUrrIG95EeCoHtMErkEUBpdW3QNpPshddHF/7HqzGmWGc/wEMD3wWRT6GSzZeS69TJH7cRDK/+nZ8Z/R8Gpnw9uTKM3xWRr3py2o0b4/8hHSdTqZIN/MKBiejaHiyldv/KgazGp2stg7sq7plToVWHC8cWUS0ZTAWAEq0qfo5bRY9R2lO851cUbXBpiZSmIpFVLQtuwGnS1Yz4XACSnwMcqwFvK1Q5yjBMScWQmg5vel9VzlSDODP3tiTeCwGg/cWTNPFyd1+pPEYQS/nWJq2g9fil+GVxBZ6+bpkGyo1N6JrVhuXOq6LUvmzuEJYmv7xJq5n2J6Ltpjum/A+2f5jqAQuSE2Nk2WLMWOlynGbr0xW8h0Y7FEwGSHepj0NBEsAHIcouTjbCUatiOr0MpmngjRNJJ9dxJnOLZBmp1YgToxUJDEYi4kGGxkxGAzG4uc1dQsEoUhz1hinjzQmN66SexeWJYwEnLYUJY5CuAs9moJjXCeM0ROQX/lv8O2bgA23TjjxcwSVOqC7V63DP7R8FJJVwts3XnLG/6ptG9bgudDnsEwV0Z70hOa24kHsKLyEpcZhepm4hCLxJhi890qIY4ixsCALBATi7i7ks1BI8/fyuVUp360IBcPZAnoy3m1uwUbHdB5/gpxjy2SiLWNhoEaTVdHWLnriZLZkVYU04mptKDgOjhAGrDxtljTdpn89rz2P77zof1c1hWXwBTUg2k78ns2WTIQdT4RxlLN7jBAnomgZkhs7GZW4GFKp8o7hz9NF0F8l/6B6u6oQB64AzrXRaR7DecUnIUkdiBmDkLLHIbk6fpZ8J71vSJ6+aBtpaqv0DIWeKUcgjKEwdLwq2kZalqHRqFT7EAx7foSuos3jkdjtVIztmMLp7EqeqEsWUVwjT2NHznZORjZhX/JdkJ0ibkytmZXHJJ9xX2v/JHRHwJJUHBdiYYi2lbOZjL9cxzmjBsIMRiPBRFsGg8GoI2TC92DkdtqopbslgjvY0Z/RYNLiFYiOjlGxFc2d59Hrm1eei75YK9zCEHDRR7w78zye2/gnePbIMGytgEu+/eeQiv2Iqo+gu/N8CInO6uN+q/UTdNDXHhVwMXE+xhT8j7fcDN10Jm2qczoQt9aFK2tLW9sLe9Gdf7B62RJURGT/q5lk8zEWFi+kbsa63p/SbaOcscYFnLaX5B9AqmcVgPfjcOoKSIe8kumsq05LtNVLEzltF4YThsEIx5uri2tOObJGyh9H2A4hoTZm6b9DciL1PBVFjNwglMO/BMj3TtvGCe7s4OR9f4eRVx/EssjrkQ5fgJaIhPNXJHE07UfuOJM0fcpm09UGmDQD9SyGOlkDruWJcGwHGS4OWVAQtTNotvrp9Rr9nIxUhUlbUCCWndJX5u6FyXfD5gSo2kF6HYlRcAUJygTNxCbdv5bOqmhr5/38/CBW+qR///bGymsmkEUSsgTBuybc02y0N1uUHAkvhy+i25e3+s1jJ6Qs2tK/K+YQZqIthrkkDisb4MLFrTE/A/tMcaUIbMOGQRTQBYCt+dm+BNM0IAca7TEYCxkm2jIYDEYdMW2XCrYEtVwazzh9HF4irbzpRCtcdsZwgoQtH/oSrGw/xJTfZjoZ9iZ+l5d+CdHop2V4Oc1C7/4X0LXj1qqYnrVEuEIcVtifFJCGL3MJ6QJe87qEUM15oVlMtF1oFBylpkEI4WhkC5ySinXay9hSehZa2hNze9TV1YbohjO9zwMjUAq5K3wxHo3eik+GpyP3MhjzTzThC5GcloGmG3hT/+dpDI1hkcWMf0WjUXH3qU4J+PkniUIHHHgIeMO/ks6YNffNndyL4VcepE6vy/L3w+66EHeetxSKKEAIfN67k4i2+TTpbDW+advZCDlmFxpPQdWHEbXIsftkzWKmKgnQbAffav4devm67I+wqfQ83TaIaMt7ecRkwbQkJRGyzWo8gqvE4ZJxRJkPD34GveF14Dg/U34qmlJt6KGSpwsUJ3baIttDf0kCBzXVhUZcBP/A8P9FyMrAyZFFk+/UfR9KgcXpSGDRekICjbY0Ito25jpPXSnqfsZ9aBbnFaRirQSbRrYsBJzA4nilJwQTbRmLBSbaMhgMRh0JinBkQsKYGS7vfX0R0ZZM3KpIIYjNtW6WzoRK4wfO135dk382euSlqmirW041A6siAtcDYUw+L+mcHLLzuDD/MC3ZTAxsAqqyHmMhkHP8rstWWbR9OXo5ThjnY632Cjj4DY1GDbH637WN6eVlmoF4hBIXpuXbZDGIwVgIqGqIuh5J4yHOyCKfIb7b8vnboHEAe1a+Dy9wQ7gj/Q30DfTTxbwQsoBZAITafe7tOUIFW0JmyWX4+E1bkR4dppeFQEf2yeIR8iUdWWkpjUhojs6ea26hcr72NKTSEEzdE8570iX81T2v0pikT7/hHFgk17QMyYGv8JbBf0KJj0AXSfn813Fv1+/BGjmKdw6TBQLQBm+c4H//iq6BZrfiAZ8ekiTBkBOQjTSEktdUL0jRsBDRPeevSL7rI435/6yI19w8NZwK5gqrU4y/+ECjLT3vN546m6lk5BPP9OyKtl4MzEIZX+zseDM6ek5ghbGfXrYmyQ1nMBYiTLRlMBiMOhIsdz+dMjzGBE5b+iVGnLan/iq7dEUUK6X7IUQlJCIqDg56opfdt3vCScNUjzeb8GOctq4YggoNFxUeopdzaTJYvqtu+8M4c7JOCAWeNNBRIblydVGAiKs6H6KdxCvdzkfMwLk2XdFW16oNQkjTvGCOLoOxELCkOAR7EKKRRSHrCZoELtSYom28bQVGjvPYGboE0fx9GC2aaInK6CKLL2rtPmtpv0y+Y9W5kAOLirVO24kFhX6hE/ekfpNu/86mtTjbobm+pSFIdhGapuHFvQfwjoHPweIkvHL001jR4QuhRrkRWYWQU4DgitXxluz4C158OAEIodrnkiKnv3+hZoCItmYOhq7VOPv60kUkbE8IdqPtDds0yxW87yl+nhpOaflRJKxh+v0YDjRGmwgh0GhLLy+Knu1Esgew1NAAOQoOs+fm3lR4BlZ+AAodrmzFQhCvyTkUHCsxGIsFJtoyGAxGHbEHD+IjA5+GyUmwYtcB8CZnjNMjSQb4ZCLmlKZ0xsonf43lcg56VIYSTyGrdSKeO4CsZqGQyyASS0DLDeGi/EN00tepk47Ts9PM4XSdtiSvTVED+bnm6XUrZ8wvVmEU3cWX8Zp6Lnql5biw5VJsDizW6JwKFUUIxKHnupBHDyIrNCEnJCCpk+cdEsdWZTFhqGkbvtn2KeoyrzgUg24zBqPReXHJ23B8VIcmRPDBrO9uFMJJNCI3bGqnJdyP7LsK24uPUzFwOG+gtZQnvS5rMHODqHwjRVLtNbcJ0Vb8OnodLMhYFp8gD3dMw61gputZS6gJSHubucwwUnu+iZDtuVrNww9Ba37zhE7bsVU5RLTlAqKtGEpWYy+qjL08DbhIM5DxcnHTQ/1oW+pXxvTlDPxny58gaQ/j1g3NaFScqmhr0u8lssBYT0Inn8B7hr/p7UPmE6Q+atL7CgGnbSV+6Gxn+8APENf7IIgywH1p1h53feFZhApHvcaL83BenC7kM5os5lSwJlkYYzAWIky0ZTAYjDpi6gXIrkZ/NM7PoWKcHi+13IINfT/FntB2nDeVY7llHalLhROKwr3+r5DeeQzfP5BHQUggMmJje4wYZfpxYeFhene9QAamN9TlXyLIKmpSa6UQ1JA/KQk2sGI0PnohTTNrCUpIQ8m8kU529IpoS1wgNul2XoKrpXHN8c/T0kPSUG9X0624cYLH/P7zJ/Dzl3upcPS2C5dX8/9azR6sMvbSjEZu5DZgBQv3YywM9Kb16Ct4Slxm9AQq3kQp0piirSzyeNP2Ltx2bice/K/L0dXzC7pcohVyGLPsBis/XBVtY01ttY8TSeLZyDV0WwlPLOKli+a4PPazGT7knxPFzBASI7tQlWIyPXQh/PWj/wGTVxDhx4s0pLkYQZEEiI6fBy5Hm2g+qj5JXup0EQONnzLDvTWibX9Wg8GHMMB3Iba8gV3TZdHWIcIciUgQx57Vc4ut5WkyMEEO+U7aiZBCseq2Fch3P1shsV5CuXLHnsGiw6lweLn6HK5j0b4RjQxZ3LYC0pZJ3McMxiKBibYMBoNRR4LlOoJcW5rHmD7iljvxb/Z52Nzd6bkATkWiC+5dX0F2oB9qrAPdq8MoHPYyr17tyWL7ihTMgGNDUGZ34HsqxIBoS0rqB1ovBi+HIfAA7f0wSbMaRmNilPxJJHFtk9gNR8vhIz1/BouTaW5iZRJkpHthlbPiinwUBWPiRZzH9g1Adkp4Zc8e4IJlVdduq9WL7YXH6DafO78Or47BmB2iqj/9yKWHq6KtHGnshQeSnx6OxGsaIY0NdOBKXkMql+OQbG4f19ingjFJc5+K05Z8rcXUxhZJ6oEY8ZuxpUcG4QaiYPImh2hhsJphqUWWjvt7t5xbuy7zBLpyP6ter0SbwKuxGtGWk0//u1+Jt6LyyV0Y9fJrK/Rl/EfvSDRuF/vKMaKaralBmGPR1nFcfPmJQ9jfn8eHr1wFRy9UFzqCouxEkNvNMZnxZzMkeolUnBHcGcR7nIqgSGsaOuQGd/5vGvgZNpWenjI3nMFYiDDRlsFgMOqIpRcmzTNlTJ+7zl+KK9a2oC02zckFGXyW3STr2mO0o7TtuNjdk6XXGYHmTsIMJm4zRQg4I/aGtiGU2kxn6zbJKnX0avYpY2GgjxFtOdOGoXsRFxXBtkKm/2il/RIVbSvNRIIUdAvXDnwd3fo+7zFLl1SdtjbnD+FsNjlhLCDiAdG2mPEzbUNxX6BrVMSa8uzxTj9BK5fuSwlIoggnEF1CmmdV/3aSHOpzj38L52tDsJUkBJDFmLO7Yakcba6KdL09xxDjY4g4nljnlkZrGjO6ahNQOFn7AOV4hGa99no1loIQSlSSFyiCcmqX50RIXVvxi313Is/HcZ6yrpr8SYTJk2nvs5/nOTRHxkc3NAzlsVHFWECOy1yy62QGTx8aoYuRTz/9BDaUG3MS1ClEW75pOX4eeQMcJYbtqXNwLs5uipruLwbPstO2knVMMAwDDe0zcV1syniL2CTz/z+bfxcfirImvozFAxNtGQwGo45YzGk7KxB3bXtcnbFbak1bFPv6chjM6RjIarACEz9RnV23wqkQZV90llwDIdmb1DuCCt7SwdusvGshYWp+BjGJ2ygc1KGvfNeE9y0OHa9ul7gIivp40XYgp0Pj/JlSdnQQqd6ncVV2L1YanpBLcOap6zeDMRNSXB6rtd0IOXlEtT3V60Pxxs39rCAEyreDFRoEy9AgGjm6GGOr413DssDTRoQk0kTQxr/fiQM/WTyChDUEibzv+bNbsCWocV+0HRgawfdb/hAfH/if9LKgjdCF8IoUzoWbAH8NoEZ44sbkx0fiTTSOozdwHT8D0TbZsRK7Q55436HL1TLtf3v0EDpPPoAOcBCTSyHy29GwBJy1pJnaXNsJHtrTj5sz/4212ivgR3hYqXXV2+TIqf8HaqwZu5XzoCgyVnKN7cyvB8W8ZzyYabzHdJ22xIHdyFhk4drxPlN7pWW0V4CBxnYGMxinAxNtGQwGo444hu+cFBt62XpxsyORQ/v+n2KpcRTHX34HoBf98rx6irahGIbEDtpAJcsn0VbuNO6KKqBnqGhLJvJTRkAwGk60JSRyr8EsO23Hoo+cqG5fnv85bXBkpv8JUtIv8S3tfwwbtJ01mY7JzKvoLD1X81gOa7jBWEC0a4dxS+bb466PxBtfhHFTa/Bg/E4YXAjXN23FqsBtmeGBqnsepEHVGCSBwweG/ga8Sxz4ywFcXHN7TrcQsj0hmDhtGUA4nkJFGue1NFyZR15IIGpnoBijMEqFqshoN63CU6MqbdJYyagH7wk3vBgad64JokRjKEgsAEFQT1+0TQUctEN5HSfTJfzzwwdgDB/Du/K/oFmtXbF2gHvLghBtrTnO0e9Jl2iFUxtdjHTh2DbU0X3VnOJQIH5kIiKBxrNEHD/b0Qu+aMvJp3/+npIx8QiNjFYMVjl5nwjmJBE0DMZChIm2DAaDUUcsU6s2XBAUJtrOFxtiGlLFX9PtkaO7EI1EqqKtPIOJ20wRE534SfLdCDs5OtA8h/cmIY4YovsjOzpMy0G6ZNIMxK6m+kU3zDXpooGvPHGYTnrfd2n3ohCmg45tAm8Wa4TcnJBEWmiGzqlYVdKqDjECcR3qxWyNaGsMel3JK5QyQ6Qme9zzsuw2xkKC5ImOlVtcTkAoMrdl2bOBFG+jDTAJO/jaOIchJPHllj9BzMng0hUd4/6WfMY5nERFW84Z747PZPOQ3bI4EmjAdTYTSfqNvsLlRmI5PgHZ0VDiI3ByGb+RXdNyPBdZi6id9kXbsiDJBeKo7m99L7ZKntj6Uup1OHf4597fT9EEayJITnEiLCFTNHFspIhP3fMqYsYg3jj6NYg8hxXNEcTWXIiGppxpO7YabC545vlncVPmHgiuPS4qxOIkhJRTR16FakTb8W71sw295Iu2/CyPXTlRXjARTFrRPw5kfEUgY2cGY7HARFsGg8Gos9O2MuQU69jwilFLx+rzMMJzsBwX7sAe6B0bUBmeyqFIXbuSb9RewMX5h+hlMf/HAJYRC275Hi6ODo7ixz+5G5Jdws13vgfrOxtf2JgOj+8fqmYKX7amhWYNL3Qso9ZVS5zSwWYpL0cuxvOhy+n2W+yfobZNEelGn0dw2mVlemoGalpuGLAmcO6yeATGAiIUa0JleeOAugVPRm5Am2phGx9cxmhMwrI4qWg0XDRQEqL0R20lTtrxOKRc39HATfCezae9JmYEnom2lGg8hV55BW3UScqeCT9qen810/uO4s9Q8TRHouQ7xIDoWuNKvIVAPEJc8sWcdHILHjYkKK6GK5sm/p9NxXKlgNH0CSTsYZpte23uHrQIRXS3xKC0rgZ2/AYamYH2y/BQZglMTsJHIl1z9jzEGZt57Vc4T3t5wtstIUT7DUz1/muxBhDnTERHiTjnRyucjRjFYBPdyNxlHTe401YPHIe1+isYKC4FnyHfJy3zul8MxmzBRFsGg8GoI47pi7aywhqRzRd8KA4+tQIYOoJm/QT6+0RUJEM1HK2raEvctBWk8qC7GOlCPleCySnA84/h+vT36PW9u7qwvvNOLAayhSJ2FB6lrqnh/CqMUzAXIPaYKATS7M4p+CGLiuov1JiFzLi/D048CFyur+aylR+ZsDkdi0dgLCQiiWZU5EmS75oRm5FILIxFzLAyudNvpGBMWDYfxCmX63OOl9T6/NERPLB7ADdtboeTHfYXdSON35StHvCihAeWfgz5QgnvGP48lhhHcVRZh1dDO+jteqAsOh4jpfVDKPIR/CLxFhqTsLrVC7DgZX+8lRD9/5udWI5X8t5C6PXJJTPax6tGvgdu1MtmJpJja1xBRzwKvmkFcO2fzXrW6GzjRDpxQuaqDTTniif2D2FZcTfdTkUU9GgSVNv//9ljIiwmgoi6by58C1E3D7dE3Oh34GzGLOWrya3SFE3cThc+4LRt9DGGEfgcIO/7K3M/A0aIYNvAWdIMxmnARFsGg8GoI24gzF9iTtt5pe2c63Di4a/Q7Xb9SPV6pZ6ircD75bB00O1N7o52vwmP6oN0+5rjXukmIXn0PgCLQ7RtPfkQNucfoNv5wXOA1Y3fhGgqnDFOW8d14RZ995xK/r/ljwDJDDQQCUzAqrgupNIgggV+dnEUnDW+fNW1K616GIzGJxIjYgsRiVyEHM9zG1cXRtOYiCSg2eyjn9vSKHkPL59QtG2eRLStNMbiy07bJx69HxtHfoX7eq/AxctCqEi1Umzhfx7OFuTcUDJHkLSH6Y+ihvAqPNE2+P0Zi8cRco5Acgz0i0vpYkBHoq3aQ6Div40KvmjbFPbPu5g6s2nxkiVdGBjaS7/Pu1IhRIgbO74UuObPAKXxK0jI4nEFnZSUk5Bfx6rJND1TLNvBMy/vwW3WAL2cWrEJr44k0Nn3SPU+jjQ9cZuKu2Z+wgXMsw3SOE4qf5aSHgmziRlqxYi0AhYnIjKHYv5sYGiBsVMZx2psdzCDcTow0ZbBYDDqyMHkZTiW6YTk6ngXm5TNKy3n3oyRX38LxZIngo2KreBcF6EZ5NrNFEXksaX0bPVyJU9XFX0311PqVdiS+xXdzi0ibW5Nzz2oyJZq79PjmvIsRDJyB0aU9ejW99HLpA+GG3DahsiCQFlzrYhVQSzdn3ho2cHx7pbSKARbq+ZjkiZ1BLfBXTAMRhBZlmCKYUhWoZpTGg8tDNE2rIp42+i/0lxauCsAXFe9LXLicWwtkhL5BFLhLRP+vcuXRVvHgG07OH/gR4jZaawYfA3PWLeikn6qsvFBlURIgmP5VQfRznXAqLctBSpVYtEY3j/0OfCuhWGxHd9u/m0qpNL/mz5Y/b5Zl30CwPvp9jUb2vDaQB6rWiLoiM+s+ql9221oy+8DJ4WAjnOA9i3A0u1EKcZCICjafufpo/iDpkfRxGvAVX84q3FI8ZFdVXE8uuoiqCEZKIu2x+S1ONr5Jlwxjccimf8wAc7WAZuIy2evnHE0eREealsB2dXw+8s2kVHErD32aOeVuLtvNd3+ncjMokPqhTWhaMvGRYzFw9n7KcdgMBjzwIDUhdcqwly48R0Yixo1jvDaK1Dc5bk9XwxfiteiF+FqtX6ljER4IxFuTrl7tRLxzg1F8idRGh+mDVeIyCeWhqhjRSxPRBcyJDqggq4vjsH1wdQVeGJ4Iy7J348dhcfodXbed9omZZuW+JL8xOPyanw3+hGs0V7F1bmfjHPapvuPjXt8XiOirSdS6HIThp0YdcEIkucmYzAWCrYco6ItESy3FJ9Bu3sBgJVodMKSQBvdhNwCOLPWWd/Z/whWF06QIFXE1Pec0mlLFlxyhTx9/RU2ZJ/0nyfBshiDoq1o9lQvd3V24JqjP0bEySHupLErdBFCvEmbi9mCAt6yILned4okemX/bvOa6t8bybXVbdIo7DN3nnNG5wTaNoC760tYqJy3LImf7uqBbjrYeOK7OH7gWbhNYaS2ngQSfmPMmaKZNn688ySu1/fSyx0JFVi6Ay0hHvbznkuUNGO1otOLp3BFL0qFDCGMUg5y9OyNEiE5weA4GFwI4VAY0MdX8MwU0mSvgklWoBdQE1gCE20Zi4mFP+tjMBiMBYRu+WV5QTclY35YcsGd1O1KOK/4a4QDYmm94Dmutnye/JZqz40R0RPlQnYewyMjWAyQor4KQ/ziEChKpvf+NsrdiwknIufgJ8l3eRmLzavRbPUjamcQtnMo8VH0S/6k2Nb9iUdu8Pi4x5dLpLTUE7v1UBu+n/oQ7m76DexJXTvHr4zBmF1s2W+oeE3uHiwpvrYgDjFZMCMNkwhcoCkgEWFF3bN/WkoCHC9M2dwnM+gLkYS4Peq7duOL4zNxNtiQ/RXOLZFqDKpPYdnK9bRCZaW+FwPiEjwavx1Ptb2V3u4IavVYkhgL1fFK6FdsuQwnOm/AicT5aL/6Q/P4ahqPpckQ/udtm7EsFYbJyVQMPTFaRGFo/MLhTPjFq32IZ19Dl3EIybCESFMnkOjCqqXtGBQ76X1arD7Eibt3GjiSn39dKsyeSLkQKej2hHnbs4Ek+GNTo8FF25yQwDGZLMz4+8wqkBiLCSbaMhgMRh2heWHliUdwQMSYH/iW1Qh1bqDbRExb4R6bX9FW8XLD2tI78daRf8EHBz+LVdpujAit1fuM9BzCYmBQ8DuPvRxaHM0itHJjIuLEqzBiqziibMBr6rkIt/iduVVXGyfwOgHRtjRysrr9ZPRGfDv1cXyr6beq1/GB5jam7buWGYwFwZisTznii7iNjlN2+tFMzXJESbFUgmx57183lJr0b13Bz4bMjg4iL9S+7gPqFuyNXwI57n/mn+1ERLdmsVtpXl6tNok62ZqFTkf0P0/fMfIFtI948UOiKOC29/wBbv3IZ9DWwgTxsRD365/eshHRJeu94+gCw71+1v9MyZRMPPTyMVyXvZuOezuJy3bj7XQQHFFE5JvI83EYkJYiyY0vcZ+QgGirneWiLXXaBqoAZpOg09Zq8DHGidg2/Ljpffh2yh8joZwbzmAsBlg8AoPBYNSRaO4w2kyT1MHT0njG/NO54w7kTu5BDhF0KvUf5D3dehc29/8Eu6KXYmt5kEwEvbZyOeitmW8jL/kiQG6ATKQqyYcLE+JKE0xP4CDOnozW2BOC03XaDkqdeDl0IXReRcn1BYJkWMEop9DmObLrOcD4clwKwTF80dbO9FY9I/1N2zFsRBCxs1TUkR0d0dgycAVPM2r00kUGYyxciDQj81EWUIarS7JLS4DjOCTTBJDDSA/3+3eITP5aONGvMBhw4vh2yx/i3OKvcVXup/Q6jQthb+ddgFK/bPVGJyrayJW3Q7IA8AIsOUlWthCzMzWClVt22lYQxuTKsnHXqbNtl69YBRzwLmsjJ874f/fzl3uxLf0AEvYImqMylM5NwNobq7fra27Bv+FSGHwIN8c7pvWYnOyLtiQe4Wyme+ABdOZGYYthiNzWWX3sRGYf3jryVQiuhWjPncC6Oxt+7GVzvrTlMtGWsYhgoi2DwWDUkSv6/gOikYGukAyuG9ixbwCU1Zeh6YY/xF5jFa7e6JXq1ZPwphvxb865OL/bF2YlJYJg39v2RASFIS8WwRipvxt4LhzncrlsVedDyGkWFXIX+oT6+kN/g5Ju0iY49ybfQa+LOmSoZVXdYF7nax1N1hC2FZ4A17EZT0RvhsGrWNbUjcq065XQdpQiMSTsUSxZshQnjqRREOL4eeJt9PbrlrVD2j8Iw3KYaMtYcPSteRu0/lFsKj1PL4fik7tTGw237HInbkRTy0OSw8iN+I2yxOjkTs5DnbfiZf0CWJyEbSZ5HAN71G241X0Y+XwBG7SXMKy8qS6vY6GQXHsJ8s9+hwbDaOvvoNc5oRT40ghCTp42haNiLkGqFW15sbG73jcayfblZD2CYqVr4ztmwis9GSzhI3A5Ae3JGHDRR71SszLdXUvxwCFvtBNTp9eMMFhlogdy4M8aRo8CBx4AOrdiaXYn4qWTEEUR4H5vVp9G5nS0mV7Fj6v52duNSLFc5UQ+VyuweATGYoKJtgwGg1EnXMeGYBfHlUgy5hlBQtfW6/DmeXr6t+xYhivXtaI95k82RdV3khBaL7wLhXv/wbuQHp91utAo6hYE0n2dOsvCVHQkQu7YLN8FhesiYgxBsS2YnP/+TmT3IwkHBqdAlc6FLUYA05sAXZ6/D31iG74XuZxe5gI5n7uc1RiJLkNYEXFDUwQ4UjtpUiUebxz5MgQzD7dIBK9/rNtLZTDOlKgqgXd8wSUSXzhOWz6YqVnMQYq3oZgeqGbOKfHJGwNa4TYMl7PTBwveZ6DJK4isvxZ45ec4Zidxef3XDhua5mXroV/7W9Az/ei++t30Oi6cAsrx7r818JcoChcB+Cvy5Vnzt3zA2cyYmrZUE/bwUYSdPLhc7xkfsmzJQm/kSmRS52LbJWEgXttsbPuKJjyzLIm8buHiVdNbuOEV//1nFs9C0fa5rwKDe2Htux9JLQNSZ0OctkExfDYQAvnbjd7Uq1QVbQPSlm3O3w4xGLMME20ZDAajToz0HgVXHkSYkel1yWUsfoi7tDPhNbapIKu+k4Q4iJIbrob10NeQsVUM2k0L3pVaMGz8W9uf47rsj7DMOIj3Dv0dssWvQR1zHBYUtkkXZggG74u21+R+TF21JAZCkW7zmqiUAuJVIgWM1pb4ERF7tOhNktpiClr4AtaXdtL8xj5pGU7KK6nAnbIHIVg56IFcOwZjIRAPSUBVtOUQTSycDvCcEnD6lTM19cwAKp9eoUTbKUvQKwwVfCFE2PR6rD7nWixNrkNIYdOzsSy54PU1l4VoMxWrqpfL0UIcia4IXi/XiriMU0MahWWlVoT1PKBlABLZE3C2ng6W7aCge99NbqIL6N44YW7q71y39rQeV6y+/zgYRuDL9GwhexK24+LQYJ66/QmiWpsRPhsIkrJgXKsXH/5nnF8Ypf0ECnycircWzyJmGIuHeW9E9thjj+H222/HkiVL6AT07rvvnvJvHnnkEZx//vlQFAVr1qzB17/+9brsK4PBYJwJvYdfrW6rHac3SGWcXbQk4xB5T5RtjsiAFMJjm/4a327+bTwQvhVZbWGLdBVxknT4jtlp+juXX9jZdK5ZgFOeQRFXLYFkwcVsT9ShTltRGDcBDseaoZSdd5WmIkN5vdLfiIq2zc4Qbsx+H5fm70e3/hq9PiQJcHnPRcY55fOhMAw8/Fngua9VGyQxGI1ITBWpm49giBFIpLx3gSAE3sNa0cuhtvLD1etizX6TxVM191nbcw9uyvw3Ls/9HNGmFnDtm5hgO02kWOuE7uexoq0YEJ4YU0Pm4k7Uy5Y1bOeMIhKC45QEWaSZJfSWzfjX1j/H59v+Csdar8FZhW3BLmWpYFuJBCBNjVd0zb4RRJTkBZMPG9b7kbIG0ORm8NXWP8I3Wn4Pz7TNV/0cgzH7zPsIqVAo4LzzzsP73/9+vPGNb5zy/ocPH8att96Kj370o/jWt76Fhx56CB/84AfR2dmJm266qS77zGAwGDMh27MflaKu5mUb2EFkTIrcshJrN22DOXQQkes/Sa/rTIbwao8nAPZltFmdBNWbivumyPviRzFHal0nd6g1OnqpSDMXCS4v4mMD/4uKthVICTSZXEGqFW1DsRSahAHopTQiOQtwNmO4vwdN1iAyQgptcQWRRHPVnHt+8XGsNPYilf0AjKpoWy4DfObfgd6dAKlqXbINWDK7jUkYjNkiwZVglptIxeDFBi0UhEAlhKGVI48KQ9Xr4qnJGyrFjQFsLL0A0TVxbuFJ+hlB8j5DCnOEng5qorUm951XPLGWG5NpG3QLMqYHn1gCDHvrftnB40i1zcxkkCmZtc76WSKkKPT7lANXXeg8a3BtPBq6HsfFkzAkFUlBx+s6Mohuu6vGeT4bBBc8HKuxjzNveSMkRwqB5zm6gE4WHRiMxcK8i7ave93r6M90+eIXv4iVK1fi7/7u7+jljRs34oknnsDf//3fM9GWwWA0NOag15KXVLUvXbVpvneH0chwHJRbPgOFxGmUu193JPzJaG+mhPUds18OVy8qDpEi77+GUtZ3qi1EjEBDFD4Uh6vVxlfYgupFWgRKqwmRRArXp/8ZTZndoOZq8xrgtfvwruF7aPmna/8FoslN8CUhUEFXFQC9nDknOKYXmdG7kzYmE3gOQvooE20ZDUtUlavntEgWMxYQ+aVX4Bu9y6l7/oPNa7CafCa7KWjSUiS4AuTI5FEPLfl9uD77w5rrLDkOjp/34scFRSTZBk/yr3U/j3RdC/vIq+gyDnnXB9yCjOkhN6/AwPGlSAvNCNthzLRFYGngMD44+H9Q4GOIj14HwMsjPlNInvvYscRZg6jgZ/ZFSMcMGrXyp7dsRDRVtoM4sytSCnJgwcMKLpE0Fq5tgrN1umjukqoNgYPuuKxBK2NRMe+i7eny1FNP4frrr6+5jjhsf/d3f3fSv9F1nf5UyGY9p5LjOPRnriHPQSZT9XiuhQY7Nuy4nC3nTLakI1w4RrfdcAukUHzWX8NCPTb1YMEeG16sDsTbYwrcspezd7Q4a69lPo6NM/garsn+DJtLz1Wv07PDDff/OZ1jQxoSVZCUCM1WI2666mMJKn2cbOo8xI/cX70+GglTdwi9jwvoxWygJNVFvLkT0UgMNifWOHcFOVyNR+BdC4ZpQSuZODxUoNEaay0X0hwdz3qdM412PjBmj0SyCQfabkTzyAtIn/9BLKTAoFA4Ap0PVRvgkOzOB5Qb4Mo3oLslgotOIcByYxplESwlOaf7uxiJt3YhWLgvlJ22fDiFtOAfTxaPcPooy7fjW8c8qbZJXI510/kjPQ/n6X8Hp8bAXfBBuvCsZQcRcvL0R+E0zBbhQMPSs020JVm2mZIXVbAkGcKyimA7BwQXPIgw2qiYWqGaBuXKYcgCD910YFqLPCLKtoBjTwKRVqBtfF40Y3Gx4ETbvr4+tLfXZkWRy0SILZVKCIXGNzH57Gc/i0996lPjrh8cHISmzd6XyKkmHZlMhk5weLaSzo4NO2fOyvfT/kMHELF1Wr5kRZdiYGBg1p9joR6berAYjo1ULOCm4W+i2R6EqLVjoPtPFuyxKfW+hk2FZzxnRPm67FDvnLwv6nVsSKRBRWQk5c5FV0YoIDqaEOjrG5I6kXIVqG4JGT6JluwoTFes/m3viWOwR4+CcxzaTMOGiKHhIWh8BCFz1J+s6iZMB1VhtqfnBHr5DVCdZ2E4wIHeUTS3zc3xrNc5k8st7JxjxuQQ1/lt7/pdGvWyonnuhIe5ICz7olHBsDBaJE5373KKZJBP171WxlUXThO2RkGNt+DJpjtw6eiPa5pTkQaNQmCxTJrgeDNOTXvcP2Z92enNk7PP/ReOP3M/rfJY1bQG8tproOeGq8355FjzrB124rS9oPAIQk4JzT3kvfM7OFtIF43qZ01TeG4jsqRAEz/OadxMW60QGCdIYVyR+QmE0hDEAtn/v8Wi5cCDwPNf87bv+Gcg0jLfe8SYQxacaDsTPvnJT+L3fu/3qpeJwLts2TK0trYiHo/P+fOTyQ0ZnJLnW6hiwVzBjg07LmfLObP7xcer+9vSvQVtbbOf3blQj009WAzHprXFQdY9CsHVYGiYtXNoPo6NItjjnkuGPifvi3odm9IxCWb5PslUM3KDEfCuH/kgqDH6+joGLYQ5HeB4OGoSSzrasT+WAj/s/W1Y5pE3RmHxPIpyO7YvX0r34WC4GXzOLwhu71yKkhKp7lcyEceu1DasGHieXpYVZc6OZ73OGVVlOZ+LGSKwEWfqQiMs+9Onom7TxoEVWqOnFgl5UR6fPRliou1MaJL9ygOpnDOsijwejL8Rj8Zup+Lt/4rWNixjTE1b3P/cHZimaJvb/SBMm5Sku+jb+2ssX3sNrALJqfdQ4rMn2soChx3FX0FyNFjc5PnRi5F0ehiKU6KVPKnI3C5ISEGnrdXAom3JF205OYyu7G4oej9se7yRbzHhPPdVDOe9mIzEkSeAzW+Y711izCELTrTt6OhAf39/zXXkMhFfJ3LZEhRFoT9jIRONek1QyeSmns+3kGDHhh2Xs+Gc6RktQRBSSNgjaO/eNGf7vhCPTb1Y8MeG52FGOiFkD0PRR2AaJSiBhjgL6di4emH8dVrGe37Hhj18GEKqGxDmf5gy3WNjGX4zJUmNwhbDxF7rQxtk8EgEykRdJU6vEwI5t9bgQdjlph9WbAkEoezqCyWBgKFEDcereccExzKRdwKXDW1O/5/1OGcW7HuVsaiJ8AYuKDwMxdGQ7FmFwYTfCLkldmqnrSirGCt/CJGZpoae3SREu+YzlxBx8tigvQTJ1TEodkKS5v87ZKERU0SEZIFGfwxm8l4Z9hTfxZrj51KXylWsTsFftAwnWmf1u4d8v0rkO84cP5ZYzIi7voMPD95P45Jy3F8BWD5nzyXJITwduQ42JyCR6MLFaEz0QDQVr0SBSmxUpUHrImW4YOBkugTyzlup25h7GyJjPllw32SXXHIJ7r333prrHnjgAXo9g8FgNCKaaeNRaxOclo1YHnPxl8vOm+9dYixQ3MQyIHuYRgqM9BxG56otWIi4ut+0qwKvpenvPT/+HMz9v4S4bDs2vf0zWCikY2vxePwNVCzYnlwNV3oW8Boae5SzLEMSh8PKBoSdAszI0vJNnuBAKPTsrkZG8Anvdkq4VthRw5Ea0dY0NORsv1zSmkAYZzAYZ05Y4nFx/iHvfTeShXFIwHuHfoys0IQu8wPEBz/p3wqBjuwVpCgTbWdCUvKdtqGQ9/kaMUdwbfZuur0zfCkkgS38zEQUvcx5AUuHHkR8IA2z/3OQlpx6rKHZ/nEuaZ7z3C153+mEaHJ2Hc90UdQYgWCVQPMCSJPPswAj70UkkXz7aHxuPzfIgscz0Wvo9ioy3lgITWAVP+ufcyy4pCpokS7+6qa3aEXGi6MlJtoudub9LM7n89i5cyf9IRw+fJhuHzt2rBpt8J73vKd6/49+9KM4dOgQ/uiP/gh79+7Fv/zLv+C73/0uPvGJT8zba2AwGIxTQRoDOaTDEFkNXdJGZm3sgDFmhNy0pLqdGepdsEfRNfxBdmVSzelZOsC2DzxMG3Kd7OunDX4WChmpHa+GdmBn+DJwyS64Um1OJ1++rMRb8dPku/Dd1EdwYvkd9DqBuEPKuEP7q9tKs++iEQNuPDI9lWUFw6nt+FX0JjwWuxUmH4ZY8CqRLE5GyV1w6/IMxoIgHPHfrzAKMEd7ELdH0WUcQlPo1OLRRKKtEmdZhDNhzarVUEhpcDSM1iUr6XVSuSEZQXZ1JtrOkGRYpOc0EUSzg8envL9h+d/Vv2x/X81CLMm5lSOzGwHiSp6I6DoWHNOPJ1nsOOXICRccEsnZi5yYTLwXBe/zjMReNEITtqcPDeO1/tqse0vzx5NkLOUKgcVss3FjHc4UqzyvJJgLaKzMmBnzPqJ/7rnncM013ioOoZI9+973vhdf//rX0dvbWxVwCStXrsTPfvYzKtL+4z/+I7q6uvDlL38ZN93klyYxGAxGI7F/wB9QrGkLTPYYjNNEiaZQ8RZpOb/0cKHBGZ4LlOeAwy1X41AxhLSQwnl9x6sD0TwXpU1QupoWRpMizfJLdUlpKSf7zpSfJt+Jrk6vuLA56k8oOsrZgXLI/1ywitnqdqx1RXVbjvv5tDzP0QlVtvk8vNDvZfrd6ri4eOC/6fnRIy1HoeNW7JiD18lgnO0okgiTV2mmJswSkPdj2xKtXVPGI4xFjTHRdia0bbsNbU0JILmcdCaj18kh/3N3s/Y8FQwZp4+a8s/j3OAxnFIedF0IRpZmNQ+L7RgqeGXpku6JtpYUm/WoI1f2xwWlQhaRCd5XixG3LIQX+SiWTZGfPRuQRXXLtmE0gCj45MEhfP1XR+j451Ov34wlSW+BZiS8Ek/HbofiatieWgMcfab6N6ZegqwsznODiNgVcopv6GAsTuZdtL366qtp9+HJIMLtRH/z4osvzvGeMRgMxuzw6km/edC69hg7rIwZE0o0V2NNjZzf5GOhwZVz6ASex6GuO/HSCe89cvjAblR86CSP8PhIacGItiT/L9jdGgHRlpQyViYObTEV775kBU6MlnDdRk+IlULRqhjvT444pDp9p63bfTn0p78K1SmiKHlT6GDpr1nKVQfxOh9CMbA/DAZj9iALJg6JOzE0cFYRvD3ovc9FEXKsdcqcyCAHlU24rKmxGjAuGEg8zJrraq6SAznv/FlSMj8XxFqXVbeN0ZOnvK9ZysIhubck3oePYTivQzMsKLY3WrHV5KzvHx8UbYtZRM6G95DjgNe8sVJRiCEZmvuqvQing7NLkHU/s3++OFg2wJDKxWePjOCOrV58VEZsw8vhi+j2jlQ3uIDT1iJOW6Iz7bsXysgQ0PJO2h9iuhBXb29GwyWrmmnDr0bikY73Iz0yCN61cU5s/XzvDmOxi7YMBoOxmDkwkEf7wR/gAmM/tNgKNHOkhG/uV8cZi5NooqUq2trFhSvaCmaxmksXD0w8ho7vQ2dQtB0t4pJTe3zmDbLg/MDufjiui5s2d4DL9aLJGoTJKQiJPEZTW/FCJkYF1FGhGesCA/6r19dOMOVQrCraVsiITdiU9FtLJEISvkfcJE4JyVgYZIoiBR6zmM9VM69IZ+miPvYRGQzGbOHQTM00eLMI1fUaL1lq85SCAHHalvgILE7CEXkdHom/HjfHZ1/UOltRQ76YxyTbmdPcthQjnEgXHJ1Mzynvm7Fl/EfL7yNiZ2nTKst20dPfT8Uk758yB+e37Fen6IXacvlFi5aGaXvH1FKSEOuQ1/zGgS9A0YZgZ8jxvgrz3XirwovH0lXRtmgEsq0lAblABB3J+sexX4N74RsIGQbQ1gWsvX5az5cpmvjcL/bRxXDSm4SM8xqJg/I69Ie8hf21gUovxuKEibYMBoMxh9z30jGco7+KhD2CZVwGHMuzZZwBsaY2VJJsnaLf5GMhQbK3ZKcs2kqRGtHWGDpU3Q65RZSOvwTs8B0/jcSrPVn897Ne1l8qomDVkf/CmuFX6eUQ91/gYu04pviTKoW4bydBTnbii82fgMEr0LkQFWa7QgauCoiyxFWzXz2Hbq9u9iasCmfSiTKZWOsZFxUPH3XalptUMBiM2aeSWU06lFd9XZGpmy1JkSS+3PrJ6mVSvh+WBfYvmiUUxV8UZ07bmdOeUHFYSCFlDYAreE7yyUiXLNqEj3MdLDMOYq32KnqOXYHHEm9HxMlhY4cf8zNbCIrvqNaLZ4doaxVGqSBOCc1uRvCk8J5UxDle5MV8i7ZkIWBr8Uk0ZYcwUvhfSEVklAJjHfJZmhdrnbbu/gfQk9Fgmia69t4LfpqiLTENVKqXDg02XmPX4MJ8sNKLsThhoi2DwWDMEcdHiojt/S4VbGWBR9PycwA1wY43Y8aEoknaCZc07CKui4UIKds/Iq+H6hSQjLUhLnOI2hmEnRy69IPV+5EO4CMmmezdhkZ9fwcd9RtMrervUsMRhOQC1mm70GEeo+7bmH0ngIlzx8KqgozoO4pLQhRKa+1nBXHaEoE7WzLRmfCiFjr6H8f7h/7Tu0Pkkup9txceQ//xEoBPz+ZLZjAYFcY0GiTwsamdWFK5sU8F8p4mcQuM2YEcS1tQIdgaMirLeZwpYVmEQ5p9WUT40gESfzBJLu1o0RP0OszjuCZ3D93u6enAQfU8ur1myalznmeCqEZrooHOBvLpQVQCJYVwfURbl5eri1PzXdk0ktPxhvTXsNQ4Qq878PKvceHFVwK5PiSsUei8irAkAMF4BEPDYCaPwZwOx3GgFJxqNddUjAScvSOFxmp2R45HMAJLCzQCZCxOmGjLYDAYc8TTv34U5xWfotvNiSj4iz/KjjXjjCCC7a6WWzFU4qCprbhsAR5PUsp2f+JNdPuSlc24auCX+I2hb05437A+QEvUEuG5z247XSoTVcLJdBEbLSKSAgYnQ5VEWqZHXEebSs971+N1kz5WWBk/HKsIsxVIKeT/uG4t9vRmcdlar3ERLyqoDNudUroaj0Boz+8+o9fHYDBOQSCzuoKSnI5oy1O3mOiaMDiFLsYwZpehS/8Cx3Y9ig0XT/6Zy5gaSQkDJVB3p2uVwAkT92RIl78Lidu2QmGkF1A80XYuznFSyXJCXkWjgLr56Fkj2laQY/WJjXIr1YGuA9e2wM1yQ7npktUsmI6Lveq2qmgr7/wP4IJLsf7It7F5eC+9LiT8EMXEWhwIZWgEzZVCFI7uC64le/oLZEHRNhjN0AgYehGd2kHcOfpVetnFFuDiv5vv3WLMIUy0ZTAYjNPAsp1p5UgNjIygY/fXvA9ankPTJe8BEl7+EoNxJvR2Xo99fZ6zhORsqcRZsIAo6MFSNhGK2oSxhWek4TepSlOdEk709SOxavadOmdKabQHSWsUOSGJk6MlcGXRlnaVFzhERActui+cSurkDdWIwDuWjjGiLaG7JUJ/KgiSXBVtuVJtxjFnaV4DDubiYzBmHSHQCKlCODW1s1MRebSZJ/Hm0X+HxckYVG8EsIn9h2aRmy7dAefi7bTLPOMMkLyoCeLuNPQSFGWSRro9L2BL8RC4qg8UEEtD1fYNcyHa2p3b8KMmrxrlzZHGGx/MBaXscHVbrZNoG3StmqYBeZ5E2+FsEUlrCPvU87Cl9CzazRPgsidgPvhXiBWPkrUF2JyIUCiEdOsOPNzrzbcuUFIIWf4Cu4npj5eF3hdwUf5V5IUEBs0OmPa5Nc1f5xNt6FhVsKWU+0QwFi+NceYxGAzGAuC7zx3Hx771Au5/tW/K+x56/Hu05JugLD0H8ubGLPFmLDySAddpxeGykAhmb0UUAUo0Ne4+sajvnBnq9VwVjcaSnvvx7uF/wMcGPgUldwyO4Q2aSVd5UqIb4i2aTVtBnEDkCeZabrFewW/3/zn9Oaf4NDriUzcsJKJtdVv3Pm+qODZcu7HcIWcbIyMjeOc734l4PI5kMokPfOADyOe9DtiTcfXVV9PzJ/jz0Y+yKo1Gw4otwUm5u+a6eOvUoi35f96W/Q7dFl0DquK/hxmzBxNszxxO9BcODW1yUSjR8wSNRbg695PqdSv1vVRYI+PguDr7cgMtg59gIXgxc7D5Knyz+Xdwd/J9kDo21OU5XT7Qc4A09ZonskMn6XjrNwc+VY2TIWvS6SO7YBuekzYvJCGLPP2pYNouNK6S9g8ca79u2s8ZHnwJFxYeplFdbx35IkbzjRORoBdrxxEV0wBj8cKctgwGgzFNHtk3QEPpH943iBun6CLqDPvZnK3XfIy53RizRjLsT/JHi8aEjsxGphDo9EuctpHEeNE2svZyZF64j27nBo4CuByNhlBtzuLSxl+87Q3oHcH7f6ihaMB3BMjq+HLqIOeXnqxuX1T4JToTH5l6H8pOKLoXxFkbgDy3ViwgNA3xlzE3EMG2t7cXDzzwAG2C8hu/8Rv48Ic/jG9/+9un/LsPfehD+Ku/+qvq5XB4csGfMT9kl12HHw6sR7PZhzarB0lnFOe2TK9pYtTNVx3yYqROjjkG4zTpab0cTxRX0DLzj4oJTOKzhVvy8/WLYgJhy1tAfMvIF+nvJL5OlmJn9fiHFV+0PVuabg7rPEbFNvqTSNbnc4MTfdG2Io7OB8XhkyDfgsTNrXSdh5d7O3FO6RmcTJfo+dkrr8RrrTfjKlrdGBRtHRxouRlH8msgGDm0h9ZM+zn5wkDN5dGRAbQlZr+p3kwwxuQ408oqxqKGibYMBoMxzVgE3fSC3rPaNNyNxXIZEy8g0d4YX/KMxUFKARLWMMJOHtnRFNAZR6Pz8N4BHBku4I3buiD2PIMPDn4ZGq+iZfQdCC+/CFxZZDyobMKupW/DX2wQ0fvifdRJYYwcRyN+Hqi6ny/3vqFAlljZnRRSZRQn6Wo+EXLAjFSSkoiHph6iCWJAtA0qxJXHKeYQio8XxRlzz549e3Dffffh2WefxY4dO+h1n//853HLLbfgc5/7HJYsmdyVSUTajo6p81EZ80clh3pY6qA/bXEVfKA50qkgVfsVmUmKMtGW0ZjoidU4qHrjC72SdTABvDZKf5tiGGa4A8gGqj44DpHE7J/jEdn/fsxr/kLwYmYk71fOpCJ1cuiXG5FV4hEm40fPHcKePbtx0xWXYnv37I85jLQn2hK6u1fje/olOFzYQDONB6SliIQUfOSq1dUIGorrwjQt9Mjd2B1KQOcNXOsI0270JWlDNdfRnOaVKxrSacszp+2ih4m2DAaDMQ1IZ84V+mvYpL2AF8KXwbC21pTgjPuyJ3leRNxRmmjzKAZjtliefhrvGf4S3dZ7PwJs8gaqjcrR4QK++euj5cG0gNXFLEJO3vvhbfChBI0HsByXCtFL21sgNsWhigJKpg0u1wvDciZ9v80Ho3kNsXL8yVjccld5klMbFG2nyh4Ouu/4cNO0OsoHnbYvhS/G8+ErcVP2u9VGHcRpy5gfnnrqKRqJUBFsCddffz14nsfTTz+NO++8c9K//da3voVvfvObVLi9/fbb8Rd/8RfMbdtgROTa93NrdPoiCk/f294qixpnoi2jMamKX2SsMUl3etdxIBred6EtJ+FG24DsvurtlhiBGIjxmS3iqkCdvGTMIBgdwNX/iMWA47i455ePwTRKuO266xEKNCkdKXqiKRkbJOvUwLDGaWvqky5ihx//LG4xjuHEY/uA7t+f9f1wsn4sXaJtGbZqETx1cB29fOHKFN5x0XLEVG9fm4eexscGvgjBtcCf+BAK+ubq3xr2xOfxWEqGiYg5WnvdaC8aBVOrddrSSi/Ww2BRw0RbBoPBmGYO5+vT36Dba7RXkNNuRXN0YudBuqDjpdBFVNRpSTGXG2N2Ccebq2KgkattPtWIPH3Y38fX+nPohu8QkMNxojx6QqdeoBOw7uYIEGmBrKgomQU0WUO0BG5loAHXfJMZ6QeHSQb/kpefFpYF/CR6I406eC58Jd4yhegccXLIlrflCXJ+J0IMiLaEghBHv7isKtoa2qnzUxlzR19fH9ra2mquE0URqVSK3jYZ73jHO7BixQrqxN21axf++I//GPv27cMPf/jDSf9G13X6UyGb9c4kx3Hoz1xDnoMsVtbjuRoFVeThBgJQmqPyhK9/omMTXI8JxZrOquN2tp83C+nYyAJXPcc1w5pwX4q5NDjHc7o6agJCtLXmdlNOzuprqBwX0hAq5YxAsoswNHHRnEN7X9uH7uc/Q7dfTak4/8IrvRtcF8t670fYDsGOkiqN86nAO+fnTCDTVtdLEz62phXRbhyj26sHH4TjfGL2nr+yG3nvO5N8dEabl+LNrRG0xRSsaA7jnKVeQ7rKvgm8AMf1zknb1JDXLbQYvXDNAtRMEY6zasrnGxnoGTfGM9K9DXOeWWPiEUh0n2MUgLJpYKF91jQqTh2OzXQfm4m2DAaDMQ1Kem35VVazfNG2UpdcnokNFkw8Fb2Bbl+3tp0dX8asEkm2oFK0ZRUbW7Qlg52XD5J4A2/gf2K0BEPJVMvc5JCXc2crcSraRojTtjVC30t8YgmQ34+EPYLjQ9mGEm2LIycnvY0rNxwLyQKej1yJF8OXweEEvFs6tWibTmwEX9pFt/m29dPaD1H2HUyi68W26Lwv5Bol5rSdbf7kT/4Ef/M3fzNlNMJMIZm3Fc455xx0dnbiuuuuw8GDB7F69cSu+s9+9rP41Kc+Ne76wcFBaNrcZ92RSUcmk6Hvd+IkPhuw+4/gN/r/hn5m7ZM3AeYHMTBQm4E42bF5Kn4Lzhu8B4eltVhruhP+3dnA2XjeLKRjY2X70Zw/AAkmho4DA+LycfcZ6TtaFR0MIQJXjNWIELoQmdXzO3hcDCEMwcyDK2UWzXuI2/nN6vFr2vnvGOj2Go5ZpQy2Dt9Lt0edDRgYuLAu58xrsUvwQnQlbE7CO7UQ5AmOcy6Xqe6zac3N5xmpuCLPwYkKhnI6wBm4sINUO+jjnq9Q1CGW96eYHYU8uht3DX8JruPCdLowMHDulM/Xe3gPhDFimjZysmHOs3x6CHJg/8jWwMljQDi1ID9rGhWnDscml6sV4Osi2pJyr4suumg2H5LBYDAaAm2M+JEtlAAiIlkG8NCnSN0McO1fAPFODJIBRZnWGGsCxJhdYoF8OKfoNwBpRI4M5XHD8X+CxoexK3QR9oTOx/DIKJLl29WIl5fXgVEQH0WYt7BcIgOYGELNXRjuOYS00IyhgUFgQ2fd978YaJoWRBvtRUUuJWs1lXWbnybfiTVLt1bjEchtDoRqNMSpOLrizWga7MGo2ILla66e1v6Jst+ETnS9cAWTC4i2zGlbpVQqYWRkBEuXLq05hq+++io2b/bLJ6fi93//9/G+973vlPdZtWoVjTYYO8GzLIvuw+nk1VbG1QcOHJhUtP3kJz+J3/u936tx2i5btgytra2Ix+c+85pOpjmOPt/ZMukTrAJcUvPA89ho7UWI24O2ts3TOjb9S6/HV/gtKHFh/GNXByKBEuizibPxvFlIx2bV3mewrvAfdFvWfgNtbX7US4V8/0EY5f0LN3UgtHwt7N3+/krxtnEVB7N1XI6Gm8AbQ+BhIpGIQVG8KpeFTEFRoZePpygK1WM3fDxTPQ/kSY7pXJwzkXYT2QFvTBFLkecd/33Cw0ah/HwuB/r804l3mgziGv2XRw7i2EgRv3n1anRGRcSdDI2as6KdaGs/tRkm29yCYuWc5E28YfSrcIn7FjZk3pnW+di3t0Q/24OoVmZWz+UzoZd3xkXvxaNhqC1tC/KzplFx6nBsVHV6zaRndZTw5je/GceOefZ4BoPBWEzoAfHjhLwKSb2s0hx5DBg+4G3/+p+BGz/NRFvGnCJGUhDLGbCc1tii7YGXnkCrPYq4PYq13MvYq26FUM6/I6hRr6ytdccdCO36KUKqCjniXRe5/DfxL33XwOV4rC/WqelGgB+9eAI/eakHFy4N4cPX1Q6ErWxfVbQtpjYjNPyqt892DnyoiW6TgZ4iCdAMm5ZyktzeU7Gsew3+9cjv0uzbz3Z4x2AqSCnqd1Ifg8XJ2Fb8FbYVnkDMSdPjbPAq1ootM3rti43vf//7+N3f/V20tLTQQfiXvvSlqhj67ne/Gy+88MK0H4sM3snPVFxyySVIp9N4/vnnsX37dnrdL3/5S/r8p2Nw2LlzJ/1NHLeTQZrcTdTojkwy6jUJI+d7PZ9vvglFa8WLWHPXpK997LG5eFUzDgzksa0riVio/p9tjcTZdt4spGMjqeFqAIhjaRPuh5Yb9j+HYi1IdG3G15PvqcaJCZHmWd//ynHhVP97spBNI9TeONU4ZyJYViCblWOXz/pNsUh80nQ/a84UWRTA0VACgMQaT/S4tu03ZyYL2KbtQh2T+X06vHwyjV0nvHHiL17txxtW8yBBHQQn2jHlaxMD34W8lg40aeXA2ca0jg0Z440VyeTSED2+ZyJIzxauUSz/V3xMvYjwDP/v8/1Z08hwc3xspvu4py3avuUtb5nwemIbJu4BBoPBWIwYpXzZLwdkhSbwlbiEQqC76NB++iudSYN3bVoS3XIazUkYjGkhqeBIbqpehKB7ZTuNMIgcC9kv97X7q5dXGPvxkcFPQ3IrzTQAJezFIwjnvhmJSDPQugGQvYlXJJagGaCm7dBMsnrz1EFvMvrc8Rz8gnUPN9tf3RZXXwGURdtO81jNZIU0KyKiLYlKmIrtK5rwZ7duRDwkIV5uqDEVsqxgSFoCwTWxpfQsve6k3I0fNn2Qbjepta7Ss5VPf/rTVDxtb2+nv9/73vfiT//0T2l+LDlP54KNGzfi5ptvxoc+9CF88YtfhGma+PjHP463ve1tNK+WcPLkSRp98I1vfAMXXnghjUD49re/jVtuuQXNzc000/YTn/gErrzySpx77tQlnYz6ES5/dlVItE3/vXb1+jZc0J2iudcMRqNCKjkqcpxjTByzoueGq2NjJd6ClphM89mrjxGZu74OQigo2o6gpX3hf98FK2XSoWXV7WImINrG6te8kCw4V/dtkiZeOlfrcC4WclBlb/F6Jjx7eAjXZu9Gq9WLR7m3IBv3H1+Id55W1r9THEVQEuMdb/w5FU5gjMdJKlxTQ9hKI1fSEQ9Pzxk5p5D82jIPxt9Iq9k+qLZhesv9jIXIaYu2Dz74IP7zP/8T0Wi05noy6H3sscdmc98YDAajYbACoq3BKciW/JVlh4hTLgm/94Sz5Qe/ja1Dz6PAx9DCfYFM7+ZprxmLFdLwg4i2ITtH85UTdeokfDocO34UbTkv2zOqiiiRqIHAgJnnBXBiefCrxIBNd4x7jLAiIFN0UNC90v96ohsW2syTOGmnqPulZjG8VHY4cxw6N1+J/me/TDsVE9FWDzQcu35jO+7eeRLXbZy6ZI0I76taa8dWUyEJ3meO6pSq15l8qKaBIgNUMCWCLYG4Xsl49c4776SRA3O54PGtb32LCrVEmCVuirvuugv/9E//5P+vTJM2GSsWvdaCsizTcfY//MM/oFAo0IgD8jd//ud/zv6NDQYvKfQ7n3w2kN+kNPx0OFsjERgLB1EO+6Kt6X/HBElbEiC2I+JkkUq0ICyLaOLzdREYxbAvUZHs0sVASUpWxZkjTZdiW3lby45U5yCheP1E24gxiPWlnRDJmZAl47XxIrzOh/Gaeg5W6fug8SGUigWgaWairWU7yOx/EjtKz9HLl4z8GIcPXwBvmROQm6Yj2vqiKq/Xnhe8PT3Rto9vBy+volViodQKSP07qdt3dKgP8eXdmG9+2fURHOaHoTglZETvfCiiAcRkxpxx2iOGq6++GrFYjK76j4W5ABgMxmLF1AtQAqKtqXnOPyM/jP29OViOgzVtUUQsHSiSFXEXSS4HNTrz1WYGY1LUJJDphezqyOTySJRL8hsGS8fwM98F6a9OMDfeBXnvj1Eq+W4dS/Qajp2KiCwiUzQnzZadSy4Z/j42FF/APnE9isbFSAQyab+XfD8sJYMuuYA/aG7CMCz6SknTNFMnTco8AefGzR1UuOWniEaYKUQsIodQdv3jKqq+A7AwD8etESE5dMS1WhmnplIpPPDAA9RxS66fK8jzEOfsZHR3d9c4fYlI++ijj87Z/jBmEY5DTBWRLpp00Yw0yGEwFhOyGkZFqnUncdruiVyEF5u9xpn/t8vLdG6W/MXCUGLuInqkaBMq33B6fnFU+zqmLypqjj/mMPO+ozmSnDqeZ7ZI5A7gxuz36bYwQhY+zxl3H8Ny8Iv4W6rjuT8UZp6jvrs3i4ztf5Z2GYfwneztKLX8ER1fvbPr/NNy2gp6GjVL164F2BYgnFoC+3XoCvQ3XQBF4vGmVRYeMi6kVZbvc2JYgfknb3DUXUt+KmjmxE5oxlkm2pLOZkSs/eEPfzjpfcgAmMFgMBYjluY5oQjnlJ7BgaGNpE0Dhgb6qiVDP1n+x3gjJMjllV1XiROrwrztM2PxwoebqgPRXHoIaGsQ0fbEc8jvugejR1+GmCvSjrYkJqTrwjtwcuAV4KQXI0Bwpakd6JdkfoJSegAmJ8O0t9WU6s0lxO3Rbhyn22uNPSgaNhLl3SXv9xxx/vJR2E3tEAUeA8ltaB19kd6uirUC7VwJtgTiEt2sv4TOkhfNQlDCUVROjpLhVwScjVTGrqRCjERtBCGu1u985zvUCctgzIRlN/wWml/8AcLb38oOIGPRISn+dzTJtJ2I0aL3HUP0ukrFz/HuN+Kn3FUIOzn8YevKOds/YoqoeHqNgp+Vv5AZiK7HyUgRIiyYvD+ucyrVPaRyKVm/rHpBlOk4ju4Dabw8AQaZAwUW4M+kMuq5I6M4Ka/C47HX4Yrcz+l1W/JP4ZH461EQ4mhqOXUTMoIo+fMux6odAznkxdj6KUVbspA6WvBea1NYhtq2An2yN/YcLjTGmGoiI4Nmscqqxcy0Zz9XXHEF+vpIb2cGg8E4+xhQl+PF8GV0O+QU0DSyq7r6TbA5ES8OKxjKFhC1s/Q6J8yaADHmBilaKVHjkM80iMNEz6H3vv+LAy8/jeFskTbRIGQ6LkMs2Qylgyx0eByV1+Jg201TPmR38VWs1nfTPNxiHSMSNMtBkfebmhTK5euE0aI/cUlFPEfHybXvwh51G56OXAeueTXqyVW5n2KD5jWrIsRl4EODn8FvDfwlzjn8NZzNVMauXV1d6OiYuHz9ssu8z3UG43QRNrwOsbd/GcK6G9jBYyw6ZDWQVWpOJtp634ckh70SEXbJ6ma4vEgzZpekZu66nIpwzBc1zeLiiEcYCK/FM9Fr8WT0RmSEZPV6LiCaK6H6NVwTggLoJNECpl5C2M5BdkrgXPv0KqMKw3D3/ATI9tDF8hePe+L0wdgOSIpX7r9R20kfuyKiToVY/jvC2Mh6ctGaxDVe3SXDpu5hQioio7k8ziOMlMXc+YYYCQiiY6DV7MFS4xDsTM987xajEZy227Zto91uf/GLX2DDhg01XW1JM4d77713rvaRwWAw5p0sl8DL4Utph3YCp3ur+r3qGvSTFViOw1DBwKFjJxAtl4STzu4MxlxgbLgDX+7dQkujbhO7GuIg92oivmdejivxU+SEJE6o65BYtR2XXXkzvb1l5TnIPO+V2Y2IbTDaLpryMR2ZlPoPQnWKKOgmEuH6ZPdqpg2NC5SdURePtwgzGnBapCLe/mxZuQT/evQu6gR+R3N9M6zJ5DiIFGuB4BS9TyHTF5vPRtjYlcFgMM5ctHVJ9NcYHMet9ndIBsS07StS+Me3xWmjvbnMDI+0LqeOzBIXQVtoHRbD8ptp2Xj30O07SfwAAQAASURBVD/QDFnbII3I/m7c8ZeV2sZfcwkfiH2xLQN6314MP/wFhFbsQNOl76PXh/qfwweG/pluPxq7DQV9+pmvh3/6f5E/thOhUBij5/8Wiron8m9c3gkzcjmw/0GIroGN2os40nwV5EDPgMmQ1QjuTbwdFifhvOJTdNE/iGGUTimAjeb9Y01F20BD6eEGEG1do4jzR+6Fzql0bFyZl8on3gCc40WUMM5i0fZrX/sa/vIv/xKXX3457r77bpoPRhoj/OAHP6BdbhkMBmMxQ1Y1SwHnnWBkaAnN0023Y1fBL1va9dp+XFreluNTNx9iMGZCPNGCktA/zvk5n3z32RN4Wb0QGT6JFedcjjduX4ZooNlObOkmyAJPS+lIw67BaTTi4RSvMRdpAFEq5ojNAvVAt5ya97ue99/jxpGncWXuCWSEFNqE6+l121c04U9v3YhkSEJMrW9TOIeXq1l3BD7STCMZSIMk0vH4bIaNXRkMBmNmqNSxSERXl2SEjbs9m03jbUNfoE13xdB5ADbVtdFeJNGClyKXUTelifq5T+cSw3YRd0bBuzZKpu9SzvNxmEIKEsyazNa5Rgg+l2Vg+Ed/jIGsBvHEAUS23AY53gLH0OlZUqn8SfcsB7aMbyw7DsdG7sQrVPzPFwqQHv9bvE7dgp/H34od3Skku25Hev+D9K5X5u6F0O6fX6dCkmQcVD3xcpXuNcMNYun+uezm+sGFm2viEqw99+KDg9+hGbai9W4kQyuw0tiLuDWK5hPkeKzBfFLKj+D8wuN0Oy34TekcfeJmgYzFwWl9on7qU5+Coii44YYbYNs27Yb71FNP4cILL5y7PWQwGIwGoGTaNAKBdEYlndpJKVBetzCY9778SWlKi9WHc4e8FU9CKMlEW8bckCw7PAmkEc5M2d2TxZcfP4QVcR6/fVPbGT3OrhNpgOORbjoXf3ThciiBxl0UNQ4n1gmkT6LN6kFRmEbcAcmFLqNR4XTqPLPZQC8V0G28Vr1sFP28PK5vF3VvEET+Eu86jsPqVk9grjtjnLZKmEzuVBKCB95ig3g2dmUwGIzTRxYFGJxMG57CHj/OyI4O0nFvC/pgOfX5bg5C8uTJwnBOs5ApO34XPEYevOuV5nOBOIJHWt6GfklDSBZw6Ry6l8ciSv5Y07UMWvFEsBwXuXwOzfEW6sANjkKk7FHv/q6LvTufoM3VNl1w7TjXdXbwxLjM2TXaK/iwdQTnNH8ZQng9cjwPmwbRAtsKTwC4YZoNWjn6/Krrj4H+I/5RFMJL8L8jXSAjyyNP/QiFX/0bjbTa9L4vVHN59UwvjcEjP1ZYpufZzYWfQDQyMDVS/fURzCelQiXJGdCVJqDoxfQ5Z3ll1WJn2qJtf38/PvOZz+BLX/oSNm3ahL179+J973sfE2wZDMZZgZTvQZuZgeJ4Im3EydGysOG8N6i6MvdztFi9NX8Ta+6cl31lLH5iikgHpsRNmZ6p09Yy8P1nDyNdMtCfNujjpKJ+Fti06N0FZ+QQ7tuTANwIHfTedf7S8YJtGaVpCaz0Seoi6dAO0WZ+p4IP+aKtXsdGIySrOmr7z2cXvZxqgpPzHM6ESMsSzDeOUOu6kcMx6GIIMD3Rlkxc5rJEtZFhY1cGg8GYGaRi45vtf4SSI2JJKoodY27PjgxUt4WI7/irJ6T5WUW0XQzfdTuOfbWcvApIli/CVTJWpxMPMJuIsj++cGwTpuWHxBrlbFhnTEWPo3ui4rH9u6Df/2m6vauYwXlXv7HmfoPH/dgCO9wCqTREeyHEm1ohR5J0PLlv/cewZs8X6H2KnVNHalWQRQ666eK++JuhxF4P2Sli2ApD5oTqsXSf/QpM2wUGDiDdcwDJpWu9/c/6PZzCTUvpbyvUQkVbyczB0DXIgdzcemMUfdHWDTUDxQPetsEW6Rcz0xZtV65cifXr1+N73/sebr31Vtx3331461vfimPHjuEP//AP53YvGQwGY57ZNHgvOrIvVy8LroW+oZHql/+I2Foj2pJxYyzFRFvG3MA5Fi6zfg27MArbSJBU1dN+jIFXHsLr9/4D3T7BL0FxsBWp6PrTe5CDDyG95zFcO1LEUOojUDrW0yYkk9GxYh2OHH2WljOul3zxczIE1RdtzYDbda6x9FrHgl0KOG0L3kSV5KUl69jFeTpOWxLZ0BptQlZUabmi5Gg0jmIyEX2xw8auDAaDMXN4JQJHs6CZ3lg3SO74y6ikq8bbls/LYW6RdGhmL1SjiJK5FWF57mMZ5hLO8RfhXdeuiWwi1Pu7PBiPwGkZmLa3H4PiEsTCXj8Fx6o1Drhl0TZzzJ8zic/9O7SLboIaaKKW7T9UjVWQd7wHK5MCcoeeRcvFb6+6XpdsugQ/7U1Dcg1cumLsssHkLLVPwtQLdPu4sgYlPgS37FyuHEur7OAljI4MVEVb5L0xHqmuTDR7vUncSCuQOUi304Mn0dZV34azQfSAiYCLtACe0RauyUTbxcy0P9m++tWv4m1ve1v18s0334yHH34Yt912G44cOYJ//mcvgJrBYDAWI9wEX4bczm/jw4NP0qypokBKZnxIdifPGpEx5uyEFHBp9uco6BYGzCV08eB0HRh9e5+ubi+xTsAaPQGsPA3R1rZgn3wRvZkSjQ0ZEJfgDy5YdkqnS2z727Ch0AvHLEHZ9vopn0IK+6KtVfIHqnONqY0RbbXyc7suxNIQyFQqK6SQmEYn4zlH8Pfh280fx/9uXgZI3lRack2UdOusFW3Z2JXBYDBmDvnuyMGCbo2PM+L7dlW329dNX1CbTa4Y+BYw8irdzuRuQri5CQuaQCQC5/jRAUb5+Ct1dtoKkj++4PM9ZQ8wMCS2Y2nZdTu2SR1nemKpXsyjEq5AHK27f/ltnH/rh6r3O2om4Sgb0Wz1Y92ytYh2rUJ0o9cnoMJFq5pxdOQqOsa9aNX0F8lvGPkOZH0EJT6KL7f+Sc1tlXM5oNliSBexsjrGGwT5L2SFJDZFPNFaiHcAPd59c8MTi7Ylw8aeY31Y29U2p70NjFKuui3Hmv3c6bO8h8FiZ9qibVCwrXD++efjySefxOte97rZ3i8Gg8FoGEjJFW+PF22t4YNQnBL9Qcc5wAmvROW4vBrFriuwUakVchmMWYPn4SoJQB9GxM7jZLqElS2RceftNx96HqP9R/D6G29Cd6t/PtqWBavnJQSH/4bmDwRfPpHBc0dHcNPmDixJTtKpeGA3BkeI88LFUXU9tq5oxoYOX2SdEFGGdO0fT/tlypGEv891FG2J07ZmgFQRbYsjsMsZbHqohZaPzjuCPzkQXctr/iZXGra5KJYKSJYnHmcbbOzKYDAYM0eR+Bp3YgWrmIGaPUxFvFJ4CaKpjnk5zDzJyi9v5zMjwAIXbblAdrDgmLAsGzzH4fahr8DkZIj8CgBek616IMsqND5MK4sEzR8X5YRkVfwcJ9oahWovgBrpcs9PkL/8jYgmvGqsp52NGEiuhCTw+OclVDIdB4kBe/uFp+/idsvjIlIVSWgxe7G89DLChgNuiAOWXIAcaaCHNG2k18stxQXkjqVR2KYnnBelFpohTI9Dwq+cLO5/AjjvynHP+dj3P4/24z/Hr9qvxE3v/dNxBgYyJr93Vw9s28KtW5fT1zYTLM07voRwJAGDUyC7GrhAnAZj8XHGyzXd3d1UuGUwGIzFChGlJNtbwQx+xSrlsH1Cy9rtEMqfqDofgtF1SbW8h8GYC0LlgW/YyWPX8dFxt/cOj2L9C3+NK098Ca89/v2a2w7s3Ql+TNMCq+QNBEkn3y89fghPvDaIHzx/YtLnLxx+BgM5731xJLQBb9q+DLONEk5Wt52KcFoH7DHxCKQ5CP2V7acNOChhr2xu3hF9QVbkbOrE4aWKaAtogfwzhgcbuzIYDMbUrC3uxGW5+3DpyN1wApmZAweepyIUwWw7Z94OpRAYIxSzI1jo8IF4BIJpGtB1DV3GIazU96JN9+cd9UCMpPCl1j/F11r+EM+ol1evJy7UajbsmHgEviwePp16A/6t9c9xQvZ6F/C2jr0PkMxeQDNtDGTLjZybQjMWMCfD5T2HMBEzdxQexcWFX+LS0qM4v/grCOnD9DahPK8zeLU6lnWzPdUICCPiN9fr3HQZvR+9z6FHMXzwhXHPGRrYSX939D+GYz1+Lm6FVw4eQ/L+/4HWh34PL+/3ohZmgqn5Y7pwNAaD98aAnMWctouZWfHYNzUt7FUtBoPBOBUl04biel+GZEWY5Eb2SiuqA1ZCbNk5CJWD6VPWAFpjZ6ezjVE/Wlq8ASUHBwePHBl3u9a7jw5YCSuOfK/mtuOv/nrc/a3yQLBo2jh/6B58ZPDTiPX59yMD2f986gj+/b7n8fDukzi26zHaNMIFj5VbLkFHYvYbMyhN7dinbsXO8KXolbr9GwpDQLa28d9sYgcmp8+rF+PxhBflUBj1G69w89R4ZSyWHEdeSCAttCAqeV2Tedl3RxtF35XB8GFjVwaDwTg13aVXcX7xCZxTeqamLJtkj1ZQl2+bt8NIG1aVKeXGL143OsF5xNhIBIKhl6Dr/njEHdN4dK4hc54KquMvZm/UXgQ/Um6AFYh0IAhWkb4u0hyOCJ33J98Kuyyiiod+ifTACZwY9R9rRbO/yDwXWf+X5B/AKn1v9bJt6nAdG3xFtOVUDGQ9t3B+6Dgd1xK4mO+ubW9rRWnTW+k2uf3E/Z+HG3BFk9d7QFzrGyNefGTcLnEvfoM2sQ45eUi7fzTjl1Zp9EZQwrFqM1rSeJaxeKlvMAqDwWAsQEhOkex6X+hadCm+0fJ7+H7qQ8gKTVVnbXMyCbncZTRpD2NJvAGyLhmLmlDHOoQkr3Sr+cQDyBRrB/u64ZeskTI2txzgRXJwnR7PERDELg8EC8USthafpOf8RX3frt6+uyeLR/YNonXvNxD76UegZ7xGYv2hVbhl+5q5eY1NS3F/4k14PHYLjijrvCuJWHvPbwM//V1geOZuhVPhGP6EokdchhHbE6SLmcExWWLzz6EVb6W52iU+gotKj9HrMl1X4d7E23F38n3ICn7EBIPBYDAYM6nk0CtZ70RoLOfZkmZN7au3ztsBVaK+ccwopLGQ+O5zx/Hb33kRTx4cql7HB4RAgqlrMAOVP1ygMVg9kATfAftM9Fo8EL+Lbi81jkAa9URbjIlHUBwNRd3CaHlMqsaaYK671bvRdbD3xSdwfGAUXLnR2rLU7Iu2FSGzAomWqGAbOu1bUNHLdU7BYFajwmu6/1j1fpGW2uqxi254C9LhFVVH7oFHv1O9rWjY2K9sql7WDz9VdexWEIf3+ReKQzN/beUGawQ1EoctlBfpieA/ZhGAsXhgoi1jdijn1zAYi5GSrldzkWS1nBvqunTFlN4uxBFXRSxNKmiOyuiIitgijy+NYTBmlXU3IRrxBrtbSs9hz6Hasjm94McJEGfA8MBJur3zwHG0GV7sgaoo4waCxbw/8XECA8CsZoJ3bVqmJ7r+xKJ946VejuocIAp8NVOvYHjvQbzwHzAtCwYZEO/0ReW5Em0NTqYTEDKg17L+QFuJN0Y8gsRz6DBPoNM8inbbcx9zLetwUN1MuyYXnIXdTZvBYDAY8wMnquMbdGZ7YBe8KIIeZTWWNs/fwmAo5ou25gISbS3bgfCrv8fr+74A58G/ptc5tgM+MLZ6MP5G6EKICrcVuICIXg9I5Y4YEG5J09kKdrlB83Mtb8B3Uh+DVRVGXeTyGWoQICTDMtZfegcVff+z+Xfx/exGCLt/gN8c/Gu8ffifsUocnv39DmT9EwqBxWvX0lDSDfRLnii73DiA9/f8T+SKRRSHjlfvl+qozdJVZRGt1/xWNSjvxdeO0JgHQp40BRaX0qonQmdxP3Yd8sbc1ect+edngavtQXE6pIVm9ErLMSK2UdH20SUfwL+2/k98seOvWSzfIoaN5BlnzJGH/h3my3dD2nwbum/4GDuijEWHUfJLUXg5gggn0qZIFSHXDTXRgY0Qa8eypnJ70cE9QEf9mgUwzkLUBMQNNwO//h49FzO77ga2/EH1ZquUrfmSH+4/gZaOZRg5+Dwi5R7AyurLoe1+iG675cU3rUa09f9eMx0at3BY2YBzpeMQzRy4UBNWXHrLnL7MsCxCNw0UdG9wXBrtxf7eLDUUrExqmKL12YxwA114DciwXZc2YhkW23Fc2YSok0Vr0s87m09UEJdL+R8leRMBtezArlQKMBgMBoNxunCBqJ2KeJiR2/Bvyd+nYleqtZ0urs4XkXiqum2XMlgoFIeOYrX2Kt1WC97xMwx/3EFyYPeEzqfjDz4QjxAU0evFtbl7IBh52pBsv+rnFzvl/c3wCQxJEp6JXI1mawAar4IbGsFV2Z+gIMSQsjaiqfU2lNa/EekTabICj0zmEOKuhVarFx2tc1C1JNRWOzpqE2B41WGOqdPKpO+mPoIbM9/Deu0lOoYe6TuBZ5puxfHRNbRi8o7OrnEPu3nLefjp7rvw63QTeuUV2DJYwKYlceQ0iwqmB5TNtFKNxJYdfukxbF/7Tvp3pNJNN33n7Uutt+PCGb60V5puwK4CbZuGf4g1QVCHYOULsC2HmgvGNkBjLA6YaMs4Y4o7f0TDyKWXfgJc/5tslYex6Ag28uHkMGKCCDPvuxi5cHnQuOMDwAN/AZBB1frXzceuMs4yWnfchb5nf0wzxZI9j8IsfABSxHOe2MVM9Uv+nuR7cKGwEuvJwkPfS/Q6Mq5LbboGmbJoW2m2pRf8iQ9pSlYZBJKIBY2P4BeJt2DtNauxrdkidlNAmttJREQWMJp3YZZyXm5YdAcc18sn67FicyLaGg5HY08Up4S15l4sL/SjUNyIg7EdeDDpuTP+pHkJGoEQAi4cxRNtw+WOx5WyPQaDwWAwThc+8P1ulqtxDg3mqaNwd2g7burumNeDSpy2ZCxDFnHdBSTa6qN+Jn85uQpmQLQlsRMEMr8WAtfzdY5HIKzWdtNF+pyQpP/zCkT8rOwj4fnIldXbloxkcG7pabptlUjm7W24an0rdhHRlmTKm/3VMYsSm4OqpTFOW2KuQXna5lo67VVCIK+pQmb4JPYWWtGnbqaN0T6S8Bcsgsibb0fvs8erFWj0MY8/gw8P/hMdM1YQTzyDdPHN1Gk8MjJEwn/p9cfl1Ug7Ez/2dChWqs7I+S8JUMvVaOQ9QCrQFM4BRo8ATSsBgUl9iwX2n2ScEaSMtvJhTbJbCiM9iDR7uZ4MxmLB1ArVD0teieCq4bvRNvJE9XYxWl4ljrYCd/yLp4axlU5GHeAjTch2XYnY0QepwHps/8tYvdUbODu63zSEuAp60iVaysUVvGZakhxCZNk5MDkJAnRwplf6aBazqAx3R4UWWI5Lc82CLgFFEoGo73CZS24a+DJCI/vgchwM6x7aBLCtfBsRVueCF5pvxWvmlbg58x3sKD4F3uBRyr6xmtFGIAPxRqBj+OmKzxZJ0/vfRqGj0zjqZXFnya3jHSMMBoPBYExftPXEw0ODfiTe6taZl3nPBpwah8TzVKzi9IUj2mqB+CpSyUOUW5NXacyA5JowOE+cpaaogGjLzfEi+UQ4vDcijNlpbC94ufmVmAHC2OxWQnpkABUpViw3bT1naQJNERmFXAZR2/tfObGlczJf4sY4bblIqqaSqhJrQBpLV8gO9qA/G6PbS5IhKtxORDzkC8LZkjcmNAqjVcGWjJdN28Uy4wCefu0kbtq6EiMnD1X/hlRsVSrHZkIlKoxEhxGXuyr6i/Sa4UB58V+Ao08Cyy8GLv/EjJ+H0Vgw0ZZxRgz1+oHdhJGeA0y0nUMe3N2PwbyOO7YuoSXDjPqQlpfgu21/CdnR8Z41K9He/7dVkYSgxlr8CzyLCmfUl8jWN+L5IQMvhi/FZUY3Vpevf7HlDuwtXEA7/qbFZkQzJRwdLuIHTR9E3BrB9V0mNskq+pVuWIYOQVoK4qEwi5mqaPt85ArsIJMGgUd84Blck32BNnQIm2Q4Phce1/HIAk9LzTgXKBTyGNG5qmhbcXrMNhWBuhTIHdMKaaSL/kQgGa51cswXseKxioEECrwJRCT3Gt40+iW6PTj0BmDGhXgMBoPBOFsRAvEIdjnr/eCgX322ujWKeUWOQhBFsnOAUaSL1/wkYlsjUVPRRERbS4Ph8BiSlkB2Sgg7BbSYvbC0DvBGaUIRvV64AQF0tb7bv6EcI7Us+wJadAs6r+KIsoFel0/7+f9yuVkcEUGvXwYYT/139TappXtudlqsFW2liD9PCzptKw2lCYO9x+GK66dsjhZT/fk3jUWgecr+/zOe6sDwYC+NXOjf8ySwdSXyg0fKSbjAiNhazfudCZXqqYoO0KkfgZJ/ni7SG8MJT7AlHPv1jJ+D0Xgw1YdxRqTLjW0q5PoOA+dcxY7qHEBccs899jMk7BE8rrwFN51XG5DOmDtKlgObk1ASJCjRJM0SrfBM5BpctHQLO/yMeWPDqhX41/hNdLKy60QGby/rc1mTQ0GI0x9CT0bD4SHPIZMVU4iv9gbLv+z4IPrTeXQkorgdwICyAieiN0N1ixgQO6GbNm00Fs28htbSs/RvVLylbq/PVfxJYSmfwZBvOqGD77lAs8r5ubwv2hr5EYzmvQF+VBWpkN0IiPAdG0J5oqKGPLcIwQlM+BgMBoPBmIloaxka7PRJNB25FxvdMLTE6vmvOOE4PLruz/F8nwWLk3CRbiERcEI2DIVhb+5QLle3AlEOVLM1SjBt71hvLj2Py/P30W1h8BOwTRIv4CHMh2hbdtqOozz+umj0p5CtPBVAqWjrujCyg9W7qXE/s/bSWD8OGweqxpdo+8o52edjK+5CbsCoOoOVwD6QODH5xNN488j3oQbiDNrSL2JNtJ02+lqemLxnQVy0saPwCCJOHsljy4HtH4BdSqPqd113I9T0f9I+EJGB56GZb8Nhfjl6oq/DltKziNujEEeJoHre6b8w18UbTnwOJSjQbGLROA+txhGsKTzs3dy/FcN5Hf1ZHW1xBQFLEWOBw0RbxhkxUHCQ5GSIrveFYgzXdi9nzB6Z3gO4OeOtTuYPhoDzSAdLRj0INvIhDX6McFNVJjmobMQNczToYDCmA1ltX9YUxtHhAgZzWtVpUlnJT1n9WKe9jDarByf3Xk8Kv+j13c21+aeV+w8I7Xgxcnn18UkDrrGuVlmZm1iCieAVX4A0Mv3g00eql13Ln8zMJpXSOdJ4owI3sBtvO/Jz5PkY+lsvBbANjUBq7cXIHn6BToLiG71FUyXki80ucUcdeQL6rrshb7kd3Cq2sMpgMBiM6Ym2leJ32yghc3IfLsj8gl7uaXlzQxxCOdEGa2CoWq7eaKKtvf+XyD/2BSgt3VBv/xytyLNKfnwVQdeKMFwvEoGIz9W/NTQU1Q7sC18GyTVwTmIeoo74ieUi19ZpnwGeuJzJ+MMexQcHP0uFUFIdVSEcEEzjay9DPPRPyJRMEEN069I1c7LLoiRDcvwxayjRguPiEriiClnqRHt+AB2ml0tbgUQ2vK48z47ynwEwsTkqHlZwSf5Bum0NjdDfTilTFW2F5ReB2/cw9uSbcUDdjPUDeRw0U9gbuQzr9Jewo/AY3AIHy/ogxEC0wXQwtTzi5iCtc8s7noGIl/zxuMbJOKC1ImUfw0BWRwtZEWBxfYsCJtoyzohX+XV4pfXP8KHBzyItpmDZSWxlx3ROkHpfoD3CCctO/hwAE23rRcV1Vwl9tyO+aEtWWpujjZFtyTh7qUxSyPgsb1iIq1JAtB3EBYVH6HZ/31IguoS6RElmV1C0JZlwlu0gr9t0IU5xNDpJ0HXyyRMCV84vI0j1FG1VP4bB7X8Vl/X/V9WlYbpzUwZ54eAPqSC8xPQFYmfkCATXpNUOJalxmntFt9yCDaU+0p4Y4ra76HWCHKaliLbjAnoWh+/5P3SS1HTyEFb8JhNtGQwGgzE1fLgJx+VuWJDQJqZQynoiFUGN+WLcfJIIRBWR7zmvVWjjMPjgP6Ivq0Ec2Y11I8cgt3TD0WpFW0MrwjIdrC/tRLfxWk010WhoBZ6Iec2NN7fMjcg53XiEGiwdtu3QcRGBjDlCjp93XCHWFGg0JkfQseVKSHseRyQcRrht1ZzsMxnjEudvr7QciqthZaIZ34p/CIoiY31rHDcaD1fjCiaibenksQ3RcBgGr0J2NPDlHGVO9zOK1XgLjl/9WTz4+GF6+bX+HHoz3vhZ57yxMwcXxVIe8ZhfuTkdSoXAeSN5pgKh3ICWkMnmULRFkKRe23W82BCx/s3rGLMPE20ZZ0RvpgSHE/BvrX9GV3JSsoxb2DGdE4qyH5a+q/V2rGDHuW6Ehl/FJfmXaGOAsNUJI9qMir8vzhURU9hHKWN+WYJ+iIVnEHbyyPdEEFu5EZuG7oMGFQ7nl/FfWHgYomuh2HV5tclCRbQlFE2bir1X5u7F5tJz9DonsxxoT5J6vur9FHXyvK/ZRgjFq54NY/AQFaYJT0ZvhLP09bh4lp+POEdWFndVm0pUnptL+xnuYiAfbd4RRIgXf7j2OikEgeNgw0VH5iVUCjEzuQJcswQu4MxgMBgMBmNCmtfgh00fpJu3NXWifZiYRmqzSueboLOWiLaNBKlQGi54lhvS1HVkdBgdLd1wNV/kI5h6CXy2Hzdmv19zPYlG0APGEVmch1imMaJtWmyh8yFLaIVhmVSAJHBEHAw0TSOQYWbQaUsIXfxBdJH4gfbNQCD+arZFW1IxVqka+5vmJeC4nmpzN0cv+s5YWYFt+K5cVwojnpi80S4ZOxtSHLKuQTQqoq0nptqciGgkivWy/z976XgamXIT22D1Fon7Ol3RVg80sONkT6wVFf8xB0ZGq05tx/GaxdH/C2PBw5QGxowhH3pD+fKHXNl6P1IwaFkpKSFnzC6GYVTfsEWnsUp/FjvxzD6sLTxKt8PGDdCiTai0YVjF94JjpSeMeWaJdRxr817JojG4DXpnN3bkvYyrfnUlDE6F7HqD6fOLj6M/7NdEbMj9ChsyDyPCG9D7PwWh0AfJ9Qewpu41H0E5ioCrs9NWCieqVQbuqOdcIOSEBKRydMNsQhzHxGFcGZyTgS99bs3PoJPGTEIaDiLaktnSGEMwMd5mhnqR7JwbdwuDwWAwFg+qxNc06DTzvtM2FGsM0bZNO4oL848g5OShj7wRKCd5ksqhl09msDQZQlu8/lmwhKP7d8G0/dbFpeww/e0afjM332k7PqPfIQ3KAuMcZR5EW07w55wiz+HupX9AG3CRKsNLNX8x31abAKO39o9FdfwicTgFbH/vnO5zonQUl+XuhwAL+5VzEJG3QhY8rYKI4K7hO4Kt+ApwQ7672Yl2TBkpYCtJQB+gDeRIQzbB8MRUQ4hAlgT6QzJlSUTB6FA/2p0MRoVWaGWnLaGUrxXup4MWiNXgy4K3GHDaDqcz4MuiLTnrTF2DHOjDwli4MNGWMWP6MlrV8RSElACsbPE/QBizQ7CZTI6JtvUl8OUuqxGEYr4SsqXwdJ13hsEYjxTxRUQ9P4JiLl29HI41YcDh0GUcql6X6PZF2xhKCNl94F0eWmEUV/V8GRHDm1gQrLIDoRKP4AgSOL5+C3NSOF4VbWkzjzI5PonIHIi2mm6Ad733uKG2gMv30+3gxCsUD5T7NSKCDI4nk7vxMQ7pwRNMtGUwGAzGlCiBzE2Sb28HGmiFT+FGrCeJ3Gu4qPAQ3R7Nkdobb3xz/+5+/OD5E4ipIv7PXedObSgiY33bBELJWdu3fUdPoCNwWct5YxjeyFereJ6I3owb1TbY6T3j/t41dZikEVk5mzT4/6gXNeM9OUz3IQeLislEFAyKtkKut0YboOLmPBDV+nB+8Qm6nZbaqUOZuG+J35XstxuYU2srb8C+Ygu2Fp+kl8WE1/fhVLhKvLoQrmUHIZh5OtqyJL8Hw7r2GDLpNM3J7TS9nj+a5B8PveC/l6aLXvTFfkH1tBZJDVVHeuuGvPdBBUMvgQX4LQ6YaMuYMUO9h/Heob+jmTHH49vwnOA1ZelNF5hoOwfYpJlMmbzN3rp1xfSOPTGuiWoEKVlBWhWR1y2kovOzes9gBAnF/ZxlqzBCy64qyJEE+JKBaqYHgBUdvsjLB1bp9UIOklmbSWbp3mXO8R7A4etbaqVEklVne9BxkhfiEOfCaVvyX78dboVYFm2DRJJtaGg4znNf6J54HyWfV5qXcZwbHv96GAwGg8EYixJw2pJKSrfkfae44BCNNYZoK4YDwmBAVD485H2XE1fo0eEi1nf4gto4yOv6ye94ou1N/xtInXk1CmkKe1+mG4mm9+PO0a/S66yyU3lvaBtsexgmJ9MS/svlFvCBEv3qY1gGVh/5L5wz8DgtvQ+VPk9q/FBPtFA7KqM+I7K0ek5oxHkdcAfbcgwyabJme+Oyk3I3kvGgZF0/eFGujomjokMrIq/P34Ok3otQ2gEC0R7xNRej57VRbIUn2oaap9HsLeT/fXHoGBzbeza7LOYSdnD7sHXwH6rxEQQruRIYfJFuG2Oa0U0HM/A3FYetpEYnWJ73RVvG4oApP4wZkxk4jiZ7lHaLbImtRGfPf6LZ6kf82XXAmr9mR3aWcUx/NXPLKFlJezs7xvWifOxpubEUAqcmsOqi2+D0vAThst9m/wfGvBOOt6AylLOKaWh532nLKzFk2lZiSf4Venln4lq8N1AqSAZ8FQrZkWqMQrB7MX0cu+K0ra9oq0b90q5gccdF+V8i4pDXsfnMnkDLYvSJL0NJdCC84+3QA6Itr8ZQ5GPUXRy3vWNKctzjyQaPRwCwtC0FxS15mdsc8FPpdXhNPRdXx1binPneOQaDwWA0PArn4G3D/0IjgyR0A+WYoBIfQSIiN5xo6wQaQpUMX8rqSZdOLdruuQeWQSpIXUhPfgG47f+d8X7t689RwVjk/eetiLZPhK9HQfAWUgm6aUMpR1AFIY3ISC4pQXAtSHL980kzyU1I4KdV0bES0WDaDsyA0MyJIdhiGLDz1NBFspCvW9eOy+q+xwAv+cfpsuzPAHwMKXsEzVYvBIdEDnoSmAsey1qTSNpD1fsnO5ZP/fgk4qFMOpPBT5Lvok3YOlv8Bf0lqzfh+OO1JclSx8aqaGsVTz8ewSr5Tlsp5J1XshpG7agdGBVb8UL4crw90A+HsbBhoi1jxhSHe1BZZ+pYsRHikcdpx3FkjrOjOhcEnLYt+nG6kikK8xBIfxbCW2WnLRVtvZVN7uLfhFAuV2Iw5ptIorkq2rrFNIyA24Q08kLHhXi59xXIroGRFTfX5DCLarRapqene8eVUlUaNAi299uts2gbirfg3sTbaQOHN45+pXr9Ru1FcPaZO91PPvIVDL70C5CP01VtG2Aa/mOSLLZvdfwhdFfAhwc/C9UposhH0RRt/MYOihqhWX5QYhi64tN4+WfH6OfVQHbs8J7BYDAYjAm+R2QZrRbJKXVh6THw5ezOkhBtmCa85LuughuYK2099h84P3scUTuLnpO/D2yYvEKmVMjjQG+GZtivcXsxGyF/zx7xBNo8H8dL4YvpAnBrdCO2ui6Khi/YEjTLgWTpGDurc4mQa/nCaD37CQQbXr0a2k4Xrpc0r8L5J36ObSP7ILkmSvnf9O8nSnCkCDg9D7XcyDUZnp8eLBLPV2O1Kk133XI2L/kfS2aWjntNQUU8JKOdG/VeAwek2qdu9S0FnLrpTBqHlQ10O9rii6TN7cuxT+1AWOsrPzaHZNsyVLyv1phmdNOB/E1l9C6HPVevHBp/tj4UewN65RXQ+DM/k3cdH8Xew8dxzbb1aI01/th3sdIYn7aMBYmZ7a1+wHUtX4n9UjtajeMQigOeM1FiZeOzCen4HaSk64iFWQfwuYasuvN2+djzMu3UXoUJtowGIRGN4BinQCYNxPQMzEBWFsmE7UhG8M346+nlm1prV97lULS6Su9kvcFlEMcs0kWig/J6KvqGou2oJ2FVxUF1M3WZjIW3/ay3meIe/CX9TSr6hg7uhNl6rv/4chghS4ClW1SwJRSFBCLyAmi2eeOnATJJ4QWkHBeCcAK246I/O74Ek8FgMBiMsSiSAJOXIDkGZCMNx/K+h2053jBNeJVIwEEbEG3b8nvglE0XzYfuAa67ctLHOOa20jEAYVTHGYu25Lv2+aOeEOhKKh6L3Ua3N0gxFAzby311XTpmIy5mq5iBY44XbalgGxBtFbX+8z47vgy/jN9Jtz++eg2aDj8JtZLRWiqgwMchwIQkReAS0ZaMK8lY1HWRDM2TaBvIAxO48lEtGw7IoRdNL1PYFjyt4nz5GMioORWWp5VpK0X9cXQ+TVy6a+l2TK19vVzrWuC4N65WRA6haKIq2tqBqq7TcdpWnkEtn/chNYx+aRnaTd80Nyy2V3OozwQy9u/50Z9jXekAnh9+B26+c24byDEmh4m2jBlB82ryA3RbFQWI8U5YsaXA8HEYpg1z9DikNu8DjDE7jIpt8IuYAa2QY6JtHSBfeLJTdhiKbCGC0bgdnjUxBtnUIRDRtpStDv7lcALnLUvi+8+fgGE7uHhlbWk/Wa2viLaVpltByESCvA9+kXgrvbxlaQI3oX4Qh3tIFiAX/SZkFVzX8TLoxJmXaVqkk0SZkmHBLmf4Vpy2YYmHE8gRI802GmWyekoCC6fkGJJOxr1pDQM5rwR00tdAnD1HnwBIB+X2TfXbXwaDwWA0FOR7gubYOwYVuo4JXQghDzPUOM04SYPgClwgk1+HVBW4hNyJU37v9bdcjDi+TLdH5U5MI9X0lOzty+KCgR+gyR6C0rkJP8F25G0Z6aKJfFEH5zroMI/jTaNf8v7g6C0wA8GkD8XvxFF5LVa2N+OiI1/0XhtH1mHrPw/ZvqIJv9w7gHhIxOYlCbwg+m7LEaEN32r9I7r9ppVdaB85WL7FheJqSMyT01Ysu2vptlDrtCXsVHagxIUQCkdxCYCVKRWmEvcqWOXwlI+vxttwVO6m7mnNbqleH1FqF/SlNdcAxx+n2z0d12NLJEGziTUuDNM5/WrZ47Hz0BMVoLol3Br3hFlVFvHdpg/jNwf/mlY8k2gKg1fH9YGYCcXsEDpKB+j2mmPfBcBE2/mCibaMGTGY15GwvAm0JMtAOAW+aTkw/Gu6gpXuPYhWJtrOKrsS16A4eAzrtZ30slbIAq0N3gxnEUAysRTHk7RcaeovcgZjPiATEeJ8gTkEzizBDQicpBFZKiLj/775XOr+GOsEUMK+S0XRvMW4II5RrFmtDzYmqRcRWYSUJ1leZPDti6xEbyV536TpxEyx7IBoqxPR269qEJQQwpaAAS6CHyffi4iTQ6q1EwuRjfwJLCnspjn0w6Mr0ZKaIJc3fQzpBz+H/mMHIEkiut/77+DnqZEIg8FgMOYfmmNv5VBwJHyv5cPVxdtb0RiQ8n1SAk/GN1y5cTBBqFTJkYodcxTpTBZNST8jP0jRERHhRK+iR/crlWbK/v48Vhj7EbPTWF4cwcOJy5HP2ciUTFjHnsbHB/62dixD8nQDPtshsR0FIY6iK4OrRFORar95WDBelgrj795yHhVCyViTC+TFFqlb1NsnWeTRt/QmyMODaLH68MbRr6LZ+GNSC1b3fY6tuRgnHwhBdkro2/h+T4QPRHvtUi/AkLSEvjbKVX8Maf/9wKqrp/X4avMymtlL6NSHscQ4QnOe43Lt+HDV5gvwvedvRtLoRfvG26C2LMe/tP0vets5qQSm92w+J6RuvBTxMpzfnPDGcKok0HEdjagkGcRCCnFrBBJMWEWyuBJo1HeamIbvWCaLHoz5g4m2jBnRM1qiHxAELtpGv0TUlm7AW4xBvv8wGmcNdnFQMm3o5ZUzgl70w8gZc3jcDcsr86FOWxZHwWhcXDUBFDznqJjrqU4HSDkWISxP/JWvBkRbOPaEjTBI1+gKilj/aIAObgh5t4Rfxu/ASakb12d/iE7zGL3NMDSoJLd3hpyUliOhH6LbdjGNkpDAUXUbLVlcF+vCmuFXsC77AkJuEY9Fb0Gs9Qwbn80TG7WXwOcfptuj/cfHi7YHHkTxqa/geN8onfyS75yjrz6FlZd4ZZEMBoPBOPuo5NgL5aozQmKeyt4nRAqDJwvXcMGVm3Y5tu3FJwUYPHkQTcnzJ3wI8n1H8uqJyMrpfmXNTNGzg/SxCELrWkQFFUpmCGoxBy1DHr9WAHOtEgwhAYcP0axYi5OqTsmKaOsQ0XaekAI9VPiA21crEZE8UhVt862bkJaWUtG2xeqlebHzQTwaQ/NbP4+h/pPYtvUC78rA4j45xgRSxUVpXg00/+b0Hz/kj6fXZZ7EuaWn6TZv/G8g4NMmhokb3/xh2kuAOJaDTu9ioFHedCnofkxYJaaLLFh0uH6VXMzO4L3DXiM9+eR7gfXdmCkmcdmXYZrt/MJEW8aMGBrsRXs5X1BKetkvic6V1duNYS/rhjF76KYDnfO/KA3tzAcVjKkhzrtj8mooro6W6JkWTDEYc4cR6UJfdohOPKJODJCW0hzWleVV+ckIR+Jjpg8ez0SuwT71PKxtWYKuoNO23Dm4nmzPPIh4+nm6/fWW30dWSFVFW5OItmfw2D9OvAvvyf8V3XZLoxgJr8CDibvo5VUdq9F0aBda9d30cszJoCncGB2zTxelqRPmYW87N9RD2rT4Nw4fhParL+LQQJ4KthUyx14BmGjLYDAYZy1uOfeTd23643BCY4m2guiVvtt6tXGwFog0qpDtPQhsnkS0NRzqlCRCK2/kvW5V/MzHOuLIfn+7fSMuOfATvG7Qy883ey8cd3/X1LF76evxdPHymutJpFVFfHaExhh7cIF4BF0r+aKtwFNzgOt4piKSUBCK1/ZQqCerly+jPwSH/D8DTluxLNqGpZmZEOKBirWw60dyyDG/QVmFlS0R+lNBlQVoho18QICdLoVyAztS8RZsRt7p+lVyvdIyJO2hciPhM2s8a/AR6gDn4GBYYXPg+YSJtozTgljjnz0yipf37kelFU045ZUCtLV24ASnQnY1OJmecX9HVmhIrh5jZpDOojrnOz3NEnPa1oOSzePHTe+j27evW4LtdXlWBuP0Gei+DY/qF1UdD4boCa3/Fj+1aEsGkL8KXwdXCuG84jPVwd4JeRXSYgtyrgJn6AA+MPh/YHIypKEbAfxGff9Fiu8GDjlFmGUXCsEMxBmcLsTFkjEl+lo1PgxIq8CZtQI1r0Rrnnu+uiGfKZFUJzzfD1Ac8RqJVtBPvIRDgwXq0t6rbqV5aL3ScnDShqC0y2AwGIyzjGA/B1KCbXChxhJtSbZqeDWy0JARm+mcUyuMnyNpQ0cm/fs1+78C2TxJt3uFTmwi7lZ+5tV1Ys5vCqV0rId40t8fJ+3fVsXSavJHN2ovQrULCNkKeMeocTzPN7zkH5fm4efwumwaFkREC2+BHV4B2J5gLggiOKX+0QiTwQWctnFnlEZhVJ22pwkZGxL3sWk7CJVFakI4Ol60HUukLNoWZyDaivk+RGwekXDtcd1a+BVtrEYoRpcB2ot02yaN4c8AwyaLNDwE0j/COf39ZcweTLRlTJuRgoF/emg/jo8UsbHUX7Xkx1uW0u22uIpXpBa0GSeA4pDXzESUaX7PZ362h97nT2/ZOG+h5AudO3o/j5bygIJgMadtXSAlUxVIbhCD0agEV/4rg/+xq/ETQcq1XolfCYsTsVvdQXNbSZODUaGl+liWlke4PDB13TMbBM4ETvVFW+IeFmUFlRa8xGk7U3KaSeN9ftT0fnq5OSTjPKv2PS+ofvQCyS1rCi/MoVOyrasq2prZ2oZzx08cpY4ewmDn1eiXu2jTMi4H6gaJKgvzNTMYDAbjzHADzsp3jnwBg2InUtxvA1X7zvzz7MqPYndPlm7fZTnQaNZqLe5EYmkZSfP7AHwn8WFcBvmMKng4rRyNwHMQoy0Qo34ckVDoh+fzDGBqMAL5+leUHoJipKFrMYgV0Tbwf5hPBEmpVmdFC0fRoXnHVTVvhMN1IGx5xi1ZoG4tNAyB43dt9sf0Jx+/HcDHT/uhyLj5Wu0XWJLZiajtZSCTSItoNNgyfGIuKDxKypigpHW4zj+Cm+Yxch0Hb+j5f2QDmrYcwI7qbS+3vA6be36AfmkZ1I71gOe9oD0fzgTDcmFDhAALPBNt5xU2CmdMm0f2DVDBlnBCXom93e/C9csAscPrLk2FgdgSYPgEDJK/OnoSSutKPHVwGEN5L49n54k0rlrH0m5PF7KSl7L6aq6ztfEDEsbcNCKrMNMVWQajHkzkfJmu2BaSBOQswOJlZPhmBNtwkCZkVkAY5eX6ZzvzAeF0e/FxoGUtKjtp6eV9I+Uco0eAWCcwzQ7LZFFx7GVtjNNWUKM17peoujArRhItS2i5Im3elq9tOKenfRH3ugu24JUhoDfdRw/pvr4stq+YvxJHBoPBYMwfXOD7lAhU5CcyT1mlkxE0VZAM/omMLXL+JBzHnbjq0/RWgS1OhsvxyGrmGRk1OM0TkEWBA9QElFiqJu5u3P1tnc71qpeJwGiQ6w38sOn91OHc3daE8cEK9UeQQ6h4LgXDP86irKIpt7eiF9bk4DYCpVg3nopeh259fzVei7yWmRIXzKpgSx+fj0xrzL3MOABV20e3dZ30ZJhek2tdK1LBluDKftwC4UTrlXjGWIkCH8O7WqSanhRngmF5Tluq0runn8HLmD2YaMuYNsHJ7Vuv3o6LV6VqArUJ2rIr8KC5FGmhBe+z4lgD4NBQvqapE+P00XWd5khVOCqvRZhvnJKTxUzQaUuELQajUQk2RqgwWfOxsYQkHjnLd3mQj/ZzrVfg6AWEEIbd3Dqvoq0QcNp2GYfQl7oeB4c3UWdDXCgPXvf+DHjxP4H4EuDW/zdhl+V9fTn0pEu4bE0LjZCwjj2Ldw99ieYAvxC5HIeVjVi1/+s4Z3AnDF5ByPx7iGPK0JKxqZ0UjQinJiDIKhxdg1gcpNm1xAVEcAqD9LfNiWhpbsEGSccvXvUWCnf35phoy2AwGGcpQ6nzsTObwOX5n1eviyUaayEvKLCScbtBG2TVoth5DA0Poq21bdxtnOWJtjrnuTGzJQttgR6tpwOpTpItT8wUeYGKtqFYMypL38EeAhofguqUwFk6NvT9BN2ZIZik4Vg5CkGEhZOy1zOmOTl16X29RVvJ8g1EoqSiSXUwUl4cnmle7Fyhx1bg2UgTCnzCF22VWvHztFBr/x+6EKXjyimR/TFkMZeetmhbyPsCMRd4DEJrVMFrQpJW161ob6oaL0hW8pmg9D0HON57KWr6bnRG/WGiLWPakC6HJP+FTOo2dMTGCbaEWPdW7Pn/7L0HmCRXefV/Knfunhw256TdVc4I5QBCgSTABgMGbIztz3+cwDZ8Boxxtj/b2GCbZGNsbHKSiJKQkFDO2pzD7OTOXbn+z73V3VU9aWcn7PTMvr995unq6lRbXV1177nvPafPP4kdHrWxvge48Lk/xpW63yHM9b0XOO8u2utniBGa5nNE24hvZd6GK0JTbYj5Q+t/Dr84/B88BK5t+E3AmhtpdxNNSUbS8fqRf0Gb3Q/VM3BCXQ1X3QFg22lfm5BsVJw8Iq6OUbkDES2Ka0a+DZgllM0OOMadqJ3xpWlWsc4lES2Y1saERmPZ5fjeIJseBmyJ+eEIxhOfx6mcgWTpMFrzJ4B0Y2hCrmzhr3+wh4uVrBLntu09MHP9yDjD/O8l92JerWuVc9ybXXV0aJoGNdrYOM40mZfftBEEuLEOwDiGpDOK4YKOzrQvwH+96zeRFfsQRwl/nIjwxOe4W0S3eQTSrkeBy39zobeeIAiCWAAKbTvwzGA3dlR+jpQzCkOMIhU/++2AqQgXVbDZMgW1Ez9L3MrbQhvsPWgxTqIopdHf3zehaCs6OvcEtWqiLbNOmiHstcxmiuEycU2UkEi310XbGqYQgS3FALfCP7+n+BJUvQ+OqMJJ++0bVrAjeA48QVqQENgJSa/AU/FrYEPBanMvuizfHoHZVsXicazrSPA2FrNkaiZqlb+sfVdDjsxctJVijXkRrjo9lV8MCcWV0vRDxfWiX73NEEIzwBh3XbCMV/lu7U0hoeiBaDvLSttwkNmj6VdNozdBzBck2hLTxi4N463Df4efJO9ETJs4fXNVazBadHSkjGyxjEhVsGXERvfSHp8BemjEmAUB1UR0Yv5hafIttn8MRzF+5J4gmoVkPFGvHmAsMw+j5Pie46fjiux3eKAE4/nY5cgp58GVNIgoQXQMOGa53mCQF6DSlgmnZmjaoCZL4/x7+/MGRssmchUTMdMZ50d3PFvmgi3jwKA/A8QqjdbF6JvyX8UNhW9AYh2k6jotGocWTyMnJnkn7EjifJzfZFP+zgQx2Qln9BjvCA4N9qMzvZqHtvSXHOhyG7RULxfFJVHCW40vQcmz40lANvtWZJqkyocgCII4e9TEwpjrF5DoUqLpZp6tHfwxuobv5yKtNfLHyCvteCZ+NX/M7LoZL/UVYYpR3Ol0YPvYF3seRLvCRVsWxHrPyD9B2XcTsPItM9qWQsVCtLqvvIg/UyeRacUQb20EdbamHGOqIZjBLbNBEMSqd62oNoSOyZ4NS5CmV8V5NmhdjUcSLJAWSOazddFWUSNAxzrEN9+A+PB+4Kr/g2ZCk/3WnuYGQqY8i0pbKd5Ybe5qgY3XVAih5xmlWtLA6dFLgWgbDshltMZVvPGSFXw5lwuK6mYt2rJ8oiqVWbk8E7OFRFti2iwffBhJJ4tX5b8MNXc50LZ2/HNaYrwCl3UCmWh7/OjBhsddi0SvmWCF/GvN6ihweNo+MT/BezFVgmsGx6wSneFcKYI4C6QScRwWIg1VBOEArylRgwG3HeWfw5BP1TsNTLS1LaPeYFAiZ1+0RfcO9Ckr0W6fwvNrfgUrQp2XmmhbC9JiumypUhrXvCwPHsEFpYdRkNIojWwAsAFOebShIcTEzFqXSmCJubLKLSa+kHkbVloHUey5Endi8aKme+oBKLnBE8D61TxojCUZM9oToYrmri0AF209HN37HDKXXrtAW00QBEEspPUAC8NivqoMR01NONtyIYl5JURs35vdKudRkYNBxpW93Xim3w9yPjJU4jkrTACth7faBlw38JPttE7CzflhWjMhXzHx08SreHjr+T29fF0yFoUuxhCpirkMR0ngyc43YiBfhqhG8NqRf+PrXUmFIAWewRuN55EXW5CyWLtnNRaacMVv7Zjgyywglh0XV/wamhFFZIJtBS1O4OmvzKLSVk2MGciuCvSnQw5VyZrlQIg9HWYlP+F7jEWLhtrozuxEWxZkVvulmx7JhgsJ7X1i+geLPsJvY4LBDdMngl0E1yd0mENHkDk5igG1Fw3jUCEBjJidaFs2yB94vnji8Ag+/eABPpXmyvIwakYU6hQXSYJoho6VISehhtJixeg0R/6V+LhpXl71FKN4Jop6IIIuRKVtJq7hr1veDQkOXtnT21BxwoLSGE4oedmqhZOFcAd24+rifXz5YfEueN7VcCvhyLVGHCZaCwL3+410rMGz2R7cvLx50rJnQrRzDQ4qK5Bj3mfVXTRYCK7nnclAtG1dvQPZfd/ny9kjLwAk2hIEQZxzREQbHXZfaEXzZWpIaoxXytaCmnXNaZgFymaQsJk2Qweexmf2PYh+dSVuvWwHbt7WDdso8cHeMFO1DU5H3nDwYsyPDNu4ZpW/faIAS00hUu3PfS/9FrRlktATyzDELPA8QHT9IVVPUiDIgWh7ff6b/jaNvJpFsWKh0UJV1qwKuIaqLcCA/hmQ0vvwK4N/2rBurP3VmRBJtTfcF0KBuVMhRZP1Y9Ws+PYIbPA8rkrjB0McG5B8uc4sB1YKyhTbrSkyt7JklpbMK3k2uLaJ2rdteM1VXX+uQaItMW0kyz9Z8OCS1ORTbm8ufANi9gW+fODgpY2ibTWdc0Ici5kl0jcyAZYZ7Lfzy49glbkPXpFdHP5pVvvr8FCJf58rQrYWBPDcsSxPTWcVfI4RDDSwqdIE0cw4bNqVFVjSyNMUbUWt8RzAp155fgdCgAdHL85JZcJM6c1EcfN53Tg8XMZNW7rQf/A5vH3obyB7FqKHbgc2vxMu+9FWKUfGi6tWYRA1SXLES6Fg2BD0yTtmruTL1KwR/bu3bMLRkQr3c1/MJLfegv950d83Wy3/2CgfegKXFR9DXsqgR7u+/tze9ecj/yO/ctnu37Vg20wQBEEsHJ2jz+H1o//a1KJt2CfUMopwnFHEnTzPo2Bi2Or2OA4MFLG9/BjWGy/x5+166i5g23thhApjanhTtA1OR0G3JwyIdbUMoPvi93F1DbTWLmghsZhZJPDPFlWIIdG2/n9cgDyBySpt2awkNqBf8+6t2yM0MZISDErPhWgbS7XWbbtYFsRo7yum9To1lqr7G9uVPB7cO4h/f+QwNvck8Ts3bwqE20f/CTj6CHDprwBrXgG7UqgLqMqYgNww7PX/2/EbqLgSOlvSuGRWlbZG/TMvLf4EnvtWCGKT2HScY5BoS0wL22FJmH6nXWI/1jGphWEibStg9vui7fKSf1tDsCeotHUd4PF/AQ79FNh2F7Djjef8t/LkkVF868njeN1lKi5Y2Qo7JBwymMeqMc7S/sxgno5/+t1dfCbLH9+xjVtbEBjnF8z8sRjMxpJEW6LZ8bQ0UGxsHE63SqUBVjEQPl/r2QWvprjnEj+YgzEiCtyuh+Ex2x3XRbXgFv3KCijS+GuUVwzE7GsL38bw4DUQjClEWzn4f7KAh/OWNV9H9UxhHchkROadyiPDZW5l5J54GpeW7uePy3JQxaOm2uHFO4HiANLlIyjrOmKR5u6UEQRBEHOLpEYRNmSTxwQwNQMsUKpWU+gYJaw8/hW8c+hRfj9ufQq/eNkq3PtiH7a+PABm08kGI5dnnwDwXhjl8aIt9OlPWx9LvhKEmNUtGADsWf5a7JULqIgJX0zWZFjVhovguVwIrVXaivJ4gXGidQuBZmXxvoH/27CO9SUnEkWbCUmNhByFfSKzEG2T8Tj6qpZk7LuLxqY3qK/G0vUePCuI+NHLvq3H7r4CLybgxwybmXzoQf9J+3/ki7Z6INpqp2nbV6JdKOo2ot7svpOwJy6zJ7MdG4o4fkCBmH9IKiemRdlyEHH9DrzLptFOMcqS7gw61sw7puGAs8dX2mpHH4Rw4CcAu1jt/g43hD/X+dlPf4Tzj3wWDz/8AL8/VrRlyE6Fd7hnyoGTQ7gt99+4KfsV7DkxOqvtXWqE/YJvUF/igg2r9BOmGKwgiKYg2uixpU6zcyWNCWOQmBcuC8ioskvdjp+k7sJDydugxBe+w8a906p4lgFPEPAPnX+Mf+34A3w7/Qs8PXosQnm4vswSsAsDRyBXZ5CUNJYm3TgtzQv9/5cKrAKDVRwxSoaNwaIBJ+93GBiZjsa0Zz3u+/GxaXZGOTQaQBAEQZwTKFrjtVCON18oZdjjk82Q44O5VSKxJFa2xfAr16zF1jt/B9B8cU0287wfVVYyvH3zcOK2+mskc+airV4YRtoe5j7AyZBoK2VWYFTu5N62TOVkfQtmO7Gp8iyfRRk8UYMTaeXVm2NFx2ZAGzNwvytyAQ7GdjBVGc3MRJXAWnzm/bpUVEGpWiAQd4v8+5wOYcG1mM/iZDbQRmr5ArB1WI6LXMWCrfif4RpBGywST03Ld7hmHzYbe4Qwjk3WjAtFc/+6iKahbDj1JEz3NMJVe+8aBPVMOK1oa6y8BjjwDf8OG9EpnAJSPThXYVPyrzv1WW6Kv/H4AQCvRTa6Ci8mX8WrPi8uPcSN31knuqJXEIvOrEI2c+zH6NRf5MulA98FznvPHP9PFi81v+Bt5nPoSkXQVbs2Ks3t10QQ0hiRNpKYZjDCmGoDZqvglIIG7mG3E0PRjmmN8J/txjerBGAhZI4nwmGdId5QHR/UKFV92WtUTh2Axmx5GNEW6HYFEaewpEVbxpr2OF44nkPUKeLwQB5y2c+0Zh5o7W2NnUQhdM4zmO8eGj3cCIIgiKWNPGZQV5wgiHqhUUO2Ta5ZghSy46vPkmPloL3nw9YyEPUCNLcMw3JQFhN4KXoxf8oG40V0Wccg2qUGP9EzofPUg3jb8L18OZ37UyDtz2BJRxUu5rY6AzCEKDJiGi2Fp7E57z+3jqRgaNWr8NWhHTiv/DiuK3yLrxabpJJV0aK8zcDKhk4qq/Cj9Ov4/+3uJgunG4ukBhWirL3zrcxb8UfqzG0ZmUj7WOIGCEz4FxN4lTo9z9doqg17I9uhCzH0V3qxGrvRZZ3gfXyjuAJIdcOzKtg/UOSiq5swcCGz7mt7NfYWz4fmVfD7rY0D7GOp5T5M1BY+E7wxoq1tB1XkxNmFRFtiWpQqFe5dw/DU04zutC7nIzwTje5Iju5X0oZP7KKMIz23AC99HW1xDcnRw+e0aJstBaPDbPoOO+FmlU48F7uSr1vu9WF56WW+XCkVZizaRrN769OdymUKiAuz89RXsLOSxVrnIJCoihZMvCDRlmhylDFpttMVbdVI43mE+WV5If80iYVPin6lpiIJTdFpqOFZOiqmw6taUu4oFM+CW0g0CIyO40IzG2cUWP0v1z1uhWgGtlkCQkEP3hL9vZ+nP4PM0H8i7Yyg//Dvoac8yEMxdLUVEVWeXLTV6TpBEARxLlfavhS9CJs71qPZ0MJT080yhGqlLRPnImMqhT3Vfy6b0l4qFVEJ6VJl0Rd4HRcwylloyTMfqPQqvnUT6+pGEkGyCxM21xosENUXae3Kb8CcoJ3hSRoPQmbICEQyeUz2wEIhsPwZUeL2hqy9xahtbzOjygrYUcFasMNyJ/oi62e13SwTpi99AbchYCSi0xOAY+k2fD99j3/H87gof16FWXUAdul1ALpRLvuCLeNE0eOibdZWkJP9aOxYbOpsibX6bnSVT/Dvx3N2QpjB4ANnTPA8VdouHM3/CyOaAqMYmiZSnVYyKbE2aCHfO3ZyjFaTJj3XhRNKNmfTUu7dNYwv7pORLVs4OlKGN3oI5zL5kWAKL6NQKjdM9Q2PeOulQGA4U0ZVf9orY7+w5vQvYN7DpcZtc10PP3jpFO7fMzArq4Zmo7O8D2uNXf5F7ro/BHovBC55V+NgA0E0IeqYaYux5PSsDJTQ1EL+Pky01VIoSmmMyu11HzBNEcen2y5wJ5JV2jIf6m77GH5h+B/wxpFPIXWi6gVWpZAdqvvF1SgV8vhx6m48mrgJuc6L4UaCfcVsIPo6X4mlSHcmwgVbhn308fr0NyfWWGU7NvjEItGWIAjinEMJiYWsgIeJj81caeuZpfrMTkeKQGQB2mFC/dhyMdtgiVapiraMYm5m1nGC4feZFVGEEApta1VMXFD+WbDN8TSk0IweW1CxL7IdxeTaeqVkrWCKITVJEBnDkbSG7attbzOjyBLfx3yZhdhOszJ2KlhGQH1ZU6Yf5FY9Ju/MfqEu2DIs3T9uzVB7y6rk4ZllbmlV29en29+bCz/DKwr34vLij2CGwszPlD2tN8AVgv3kjKm8Jc4eVGlLTAujNFp3+xNYQM1UCAJEVilbZFP7gf0dN6AHQxjMV2AIGjYYJuJqFLrl4F8ePIDHDoyiXfYra5l/S/bEPrScf+5+McXsQMP9Un6U76saSjRobBiVmXsMskZNjX79NBdbz8Oh/3o/nMH9SF3zXnReeDtf/dTRUXz5iWN8uTsVwZaehZ82PVuY+CxXA5g8OQb07PD/CGIRoLatQr/cA8UzuB/a9mkGR0WiyVA9h1+he2TNa/Bfgzv5/TbrFFrsAUTVqUf3zxYNYWiOAWv4MC4r/iRYNaaRWhgNfFtryHYJL1enLS7rWQZh8Ln6Y8eVtWhp2YylSLx3G1RJ5JYSbUOP1deLCebrO4ZoK7JSO0xRg+IuvFhPEARBnF200Iw+JnY1o2gb9iYVrApEW6+LtmMRo4GQqhezsCyTt29MQYPD2v1VyvlhtGHDGfchpKpoK7EqzpBomxEqKLj5Bl9SM9SWeSD5auyKXoRrujuwsVZpW61knchbeEGRVMAq1yuBF4NoK4sCHFGF4phcbK4VlM0GFhrWV40VS4QE3KlghQ9xVYJZLmCF6WslNaxqho1ZCUTbtaXnUTryNEpm1T93Ot65ocEAQ69AC2kHZ8Kw0o0D2lZs0P1geYdZhhALAom2xLRgo5Cj6mpE3TJ6kxN07MYQbVuB7En/RGStvAbPyR148rBf2XM9S8wEcPB//wir+3OIey34Wcsd0MUoIm4FxVP70XwW92ePSm4Q4ckyRcOBVxlBwsnCEjSoIe9Jszxzo3yE/J7ytoKyaSM2ZmpsjdLoKeSP7+bVdtLDnwKqou2hoUD4ZVXSS0G0rZg2NFcPQvcIYhERa+3Bf7W9jy9HVAl3ja0wmQQ2O+I5bTOWm4chwuWireYGjXBWDRB3C3BKbKrftWimIAzBMeGMHkWvdaTBMiFMeQLRNu2M1u16UhEZoz1X4dFiN0pSCjmpFZE5aNA3JcluSLE0U7L5NbeGmh5/bc+uuhlfOuWL178cC0JGCYIgiHOD8CCp6ulIRZtPPlAjCTyWuJEXB0WTy3HZ6Evc9scNibA1pGiKP1YTbVP9j+AXh7/J7x/quglPutfwittrpTasOMPtKBo2Ym4hFNwdtCPimXaE50dG4hmUQvYIarVqlU3ZTxX24c7RL2Klua/+uNwkQWQMV1T5dO2Ek8OvDPwJLIv5HP8tmh1B8Nu1bLt7nZMAZleUc96yNPacKqAnE0FLbPqDGTFNRk92H4T6PDY0BI9bRtC/ZhRGTmH9yHGYroyo2sWM/KZ8f0EO/I8to7E9fCawwX0X4Urbxe1pWzRsHB0uY1N3kttbLCaa76xLNCXDSg++0fIuvvwbG08/6tjRuwalfQ9DZ6FavQ7uHQ06/8x7kHWUpcGXsNI0oAnt+KUr12Doez1Ybh5EJT8MVEbHpaCfKxiFkbpo+8PU3bgSSWw6/q+4aOgpvk5oe1X9FG/NptI2lKyqeTqGRvNY2eV7L7HKXjZ9ozYNulwp1z9TD5maL3/5X/GW4cN8uol16heBbd1Y7LD/q1BtznlKc/hHEcR0CVfAJCYZhJkIWRLxw/a38SBExt9nuqCFBoVYQALDrU6JW2hUWeJedSyQkQVYjj0XetUqmxp508OIsoIPfiXcAj+fsUDHmFtEWUry/Vbo2Iw9x4NqhIjS/JUjM4Kd1zs2AYWfN6yOtoz3kg8L10bIpocgCII4NwgPkq60j0CTm29Ak/msvtByA+9j9krApW61HS+P94xVomnUnDqtUg6Cwdr9PpXui/HoKb9gYzumlwkQJl+xeLuCf3aoypaRSaVwKnQ/lsogq0XqAnLYaiDilhsEW/54E4m2XkgUZEK+0DBXq3lRhaBSdGfhAQC3zur9bjuvG9t6Uzy0+kysw24Z+nekc8+PW28blQabhBr54VO4Mncfj3+zRGZp+IYp318IVdpa1fecCaxP4ITsEdxFXGnreR4+/t2XMZA38JqdvbjrgmVYTCzRHgkx17AqzBqspP90iOlerO1uwdYt29GV0hp8Y/hUf9uAV03ttqQYXrGhHU56dfWzHBT69nNht/TEl5D94V8C5cbU76XMsBPFEXUDBqUu5KUWFHSLnb3rj6upoBrK0mcu2rLpQzXuGflnVI49zZeZP+2v/+dT+JefHqw/boYEEX4CL2f599M29CTa7H6etNp1xE83XewYpUCoErRGn0+CaHbYVK0a052qVSNWPU+zdidbZgM3HM+rh014oYbgQiJWp7nVKm3tMefCsZW2x6Jb8D+tv4LPdvw+jnZcU1+/xtwDza3wSlsm3LLqi2XmIXRYJxGtd+uWHrFl28atS7ZOJNoGzcSwTQ9BEARxbqDIIvoVv+b0SOpiNCu16e6VcjGoX1QnCPrq3o4fpV6Lb2d+Eadi6+GGilha0oG3fb4aMHUmFAoFfzCZ2wk2iraaIiFc3JeIxSGHti8s2kpKIIo+Hr8On+z8CJRWv5/cFIwdwGd2CYuAx9vvDu7MwWxKJtSuaouf8cwsTZw4B8atWhfaZmPwqz50mAu2HPX0BUVC6PiZjWjbUtyPXvPIkggiKxg2F2wZBwZnrp8sFFRpS0yLkuE0lPSflpVXAqtfUQ9uigydqD/EDd9NE7bjNUxBb12xGaOjL2BQ7oE14mJt6Uc4+cC/w3E9JMU2rLvhnefEt7VL3oKDLStgGCY0VeWNhmS1aozvzu5teCRxMwwhgm2xdTP+nJpJfw196Ci/3f/ED/Cuwa9gd+ECGFf9ER9RDwfQsNnE2f5DaO1eAyNUdSuUh8eNaDVDYNGZopeDE7kwjQsjQTQTrLHPBsEeOTDMb8+E3kyUB0Iuy0R9z63yMbwq+yVEvHLQWAxVVyw0j2VeDcNyEE224BVm4/mHDQyGGS0HVSCp9hXAoL98ff4buB7fQBqfRzEa5QGEryx8hz8m5pjNxCosRdrX7MDwQ8H9rNSGNZ3jqw7CFVW1JGOCIAji3IG1Bw5tex+eOvQ8Nu68Cs0KLxBimldI8PImEOa0thXYFb2QL6/zkugMPb81w4TWIb6cq1gTTq9OTNEPrhRGggyYMTNG2X58sf1WbB28D7til2CnIkGJxFCLdrq49CC2Vp6CMvgmyCuY3QDqvrYsDEqbg+CsucJbpKKtqfkzShnCBIL+2YJ5KNd64cwOg2X6MByz6sU8Njws6/fRGcLpAuFZ+zVcaWvOvADh8oEvQ3X89vUJdTXSoarbxYZpObwog/2eUnn2O9+ExQSJtsS8VNpCajy0uvMv4K1D/8GnUChH3g4vdh4XYxmu4lczLjv/Bnz8RC9f3p5NQ9zzT5Cqz+nLllGTJ4eKBr7wyGH0pKN486UrFqUwOBUj5cZkxkLFguBUT96iAqVtDZ6K+5ViPdL4tO+ZirZO9gSvpLr0xH9weeb88iPQKzq0ZHxcRe9IyYYycATVr4ej6CN1f8gnDo/giz8/gqvWteONl5ypI9TCEvYJFqnSlliEvP2qNXjLZavOOBjiFy9biccOj+KiVX5HI+pVsM54ufFJTSTaHk5dgmzZREZVcaX+w3pHiTPGHoE9r0bbio3Ye3ALF2hrJDOtSOfMBl9cWVu6ntbRzvWQVQ22aaAgZfDfXb+NV7aMF/kTlZO4Y/QLvAIofuIa4IJfXJDtJQiCIBaO99x0PvoLm3nocLOSFE0knVF+vfqPtt/itzt7x9u2JUOiK5/NGBLI2lvSEL1+RN0irJzbMHD77edO4hvPnMDVG9rxjqvYFPUqL30dGNwLXPxO6PnhusWdHA+qdmskLroH//LURbhkk99/VcYIh9xaQXAbrBBqYWQsQLRZOLj8bhyxtuGW3P/w+4K8OETbmGA2RWFOJbmKVUDxZafrPODk8w1ibX/LRXgoI+P27H/6L9Bz9deKkdPPAhXVoK1ujxWAzwDR8fdXUUrjay3vwrrY+BlZiwXLcfHa0c/yIpSyw6rW/XyexQKJtsS0WH/sK9g4sg8VIYaY+zF26TijPReRXGiOP3Lp6HnoxVxo6op/0lzTHkcyIqOg2zh+eB+kou/8wwJh9nXchqurT39wzyBePpnnf1evb8fKtsVbDZnXLXz96RO8su3GrV2wHZf7IbFGxy/kPosEdOjCBRDswE8yGvKpZFYSM4EJ5sfkVUgLSfRY/uidUDiJvhOHGyzR9XIB6WQcFURxXF3LfS2fil2NS6WViA483vCenmPCKGehxVvww+ePoKjb+MHLp3DH+b2LKtAn7I0pRWaWtkkQC81Mknw7khru2OkPnDHC0/aClZGm+z+yin/XKYeiEgBhjGg7WhVtWUVD57rt+NvnJPzS0F8j5Yxyix5N1ZDWrHpCLn+utnivLadFkmFn1gEDLyPpZLEqWplwAFQTLKyq+uoVS+PD3AiCIIilD7MkYsUyzcwr+v8DkWF/oPnTHX8IU2yHl2ChTY3EQ6JtybAh2H6lrQcRXekY3jfwf/l93WLlQpfVn/vIAb8fy2Yyvf3K1f41szgA77n/4uuFh0ZgyFfVRVs1Pj6bhbWxrt/cWa/WVSLxehB3DUmNQNGCtpYCC7Ik8O+gWTBTKzAoh+zkmiTv4HTEBaMpCnNiF74eIydegq61Ye0lb8LIN6v+tlWrjpzSgUPaFgwovei0TvKaqBrSNLZbDBVY1HxyZ4Lg+gMGNnzrtVrB3WLEYu6cgsLzLJit2mKDRFtiWsTKJ5GyTnAvHjV0IZkuihavT/9wjBIqpWDECKp/8mEXvx3LM/jZ/iHsLAcBKc/HLgNCASjh6SqsI76YRduHntsH9Yl/x265G5t73oOYKvMTMwv2anFGIIoidD0P0amJthHEQgIot5qYAex1P0y/ni/XhAu11IfsgSfr1WpMpE3DP+kPJzfi6y2BPcWqvIGVI8fGvW9u6BQ6YhnctPejuM4T+VSKwcI2rGhdPN8Rqyqu7WE5snQr7QjidCih8JEaotI8om3Nc5eHp3mNSbtC9ZzJcSzcdugTyCKFfHojWuMX8OtNLSzE0VL8NhmP8fNfrUkqRxbPeWsmyN1bYA+8zKddrlKyEz5HC++DMbMzCIIgCKJpCFkhsIBlE1FE1fED2Myzv80ZQMQpIZqN1mfmmGIE8YgGR45CsisQzXyD5Ruzj2K4rseLZpj46xVO4dBgCSXTwRpjP45ufA++2v77iLkF/MaaiybczLC9gpruwr92/CEuKP0MVxfv5etEWYUcqrTdUnkalsqqdpvHT5hV/cpV796xHqrNTE8lCHfTxIWzfNq4eSdGl38JUU2BPnoKu5VlMAUNotzekCFQFNPoxMmG18rR0xcUMeG/9r9zrJnbI4g10VbwRduateVixB09zAVbRsrow2KDRFtiWoiW37kVRHlGlVZsJLEu2pplGMWggyiFyvx3rkjjib3HsL3yBL/PxMuXIxdiTUic7Oq7H7fkXobouTCz7wZWjJ9+sljIHPgmlunP8OVTB69Ay7INePfgx1EWQ6NoZgGyq9dDgKKKyKftaK4OscA60etZnCP7cuoewqcjHCgzIndy0da1dCiHH0DtEvxw4lasc335kvlGhunP67Cyx8e9b2H4FJRkJxSnzMfkWPrpQMFYVKJtVm7HsdiVvMF3YXrlQm8OQSwYWnT871ZoItE24xVQtgYhw4KHRguX2kAXQ88NIG4OI45hxL1WyJKIrqhXn3Loqn5YCFsviQLsaiWBupQrbQGkt96ITx9r5ZUctyyf+FynReOThrsRBEEQRNMQmu6uugZYBUYtnCwMG7R9S/bT3Pu+rHdDrFa5unKEP2arKS7aSmahodhle+4BPvOEDfgWc3+DeGcnBkdG64FlfbkKzPwgylKS/yVag+DoydCqYZ8SgoIkUdGgjhk0315+DM0E2+5aG2ox2SOsyj2OmgIRq7vKLgwtCb897WW68eXW9/LlrekUXhXqdxekxjA7hjIN0VaMpjEitXEdJX2Gs6PDoWOC52+HVRVtnXDJ7yLDMYLijsVYMEyiLTG9A6Uq2lpKctrCYBg1luTe8AzPKDf4hkohQ+3zxKP4teE/hQOPj4Q+rlzAp6u4IS+XzSe/yi0UGOLxR4DtWxbtt9gz+DPU9oRx5EkUYhk+RYb91UbIZCNbTyJloi2rLnvX0J9z/9iKuRrDh1IY/PZH4aZXYMsv/hUEKUiPn4xKyFZhVGrHauzhUx6UUZZOCVTEOA+Eq1Xy6qFKZ8ZAvgIhf2L8+2b7MdJ3sH5/ROqAXFhcCexD2go8lGSXTOCSns0LvTkEsWCM9VpjiErzTI+8eOhrSIw8x5eFaKZeIfuvHR+EGonh/Or90mhoWn+sjd8sj5oTTpFj9gk2GwRjnRJ1cXRCZsqKVWtx5RVRnBit4MYt46eQMtRI6PseYzlBEARBEM2CoAaDjBeXH8RhdRMSLhO9xvtw2koCsm3w/q3ombz9wGYzMjw1CZT7ITk6TMOAqmnIlky02IM8yIhRKQwDnZ08eKzGferNOJRn7+GLmVMFltWIVMM+lZAAKisRKCFP0po9XjORsoawWX92wun4zYyptvLvdqKguIWeNRYuqlKzB7DMHEbULY1/fuz0oq3VczH+o90vanttZvmMtsuqhqIxmJXim4c/CXXwXcDKmmHl4sIJVRy7i1B8JtGWOC227UB1/JOGW7UyOFPC1TquWYYVFm1DlbbseWs74twPtS2hwex7HOeVHoNVYB3tL43zUzFmkYjYDIRHrMrZQSA7hLET8uNOsK9YlbMginCkCB8FFuwKRu79U+iVElDZjeO7n8CKbVee9nMrYypta9TSK4+qG7g4r1fF3drzN1WexY7KY+gYOAVRHj+txMwPwKmOxjHYcwcPbwPOuweLhbBPcNg/mCDORXuEsF1As9kjhEPR+oUO2EoaHjtviXHYTjC4WKqGPfCXJP3wxitHv1H/fyURVNPs7r0bq458BcNyF9a2LN7Ahenyqu1T/x8VNcbHafmliiptCYIgiCZFDFXabtRf4H+SsYHdG/dclwmzlWGoNgv+8td5sj9IKUSC6sZCbhhtnb0o9R/AlurMSEY5P8pvjWJQVJST2uoWfsw6gc3eOR2KJPBrbLhqlVkjSLIWXHvZtknNNYjcMfos2qqzYmvVwYuB3eveid6n/wolMYn0quvQDDCvYpbRwKy+akVSG49/BZtG/SKor7S8m/enN+q+760W8y29pkKrDgbUch8Yj+wfwsmcjldt7+Z2jGNhth8HhwpYlokhqkowx2gs7XYfYIQ0iUWGU80HYnArStuBHNpPzQ4pEsRpKVfYKKQTXORmgBoNib1WGQPJzXgpnofmltGbCCVWZ1YiHk8irlaAnvNRGn4Rmj4E0dEnFDodo3FK7GKjKCQgVCdqmMVhCLnBumgri0K92rZOtcrNlWNctI0ZA6jowf4YOb53WqKtM3SQe9maQgR5ZXxiOJsGsdrYDYePGLdj5aEv4y3DL6DNDsSPmrVwv7ICXZbvb+sUh/hFJyzrRAZZJdwiFW0XUYAaQcw1Aus0iCI810VByuBrLb+M161gHaDm2b4aP4rdimEpqBZlvlss2JF1mnQ2IFZFSfmibUwR67M/NDloCq244nX4X30ZVq9aixuiKlx34TzPmgJJgSBK8BwHQug6TBAEQRDNhKiNz6FQQoVBDYT6s//e+ltwIWJNR5yHXrOp5TWK+REu2pbzQTuCYRT9Clu7HNj9lcU4dpR/DslzoEms2OiC024zs2O4rnQftpUfqa+TFJWHhb6YvBrb8g9XVzaXaMuCamvzlQ5rm9DbUZvb1Nx4bevxb+0fgC3I+EC8eXJLWGC3L9pW+6DVQXLmc9unrkKvdQTt8ilobDZuIjNt2w2GYbsYyOv4zMN+lXhEEXH7jiB0uMZDD/8E0pOfwXOtF+K17/hdWBMEmHlO4GO82HDGiNCmZUCWF48NGom2RJ1c2cLBoSK29aYbksf1Qig0LGRlcCZEuWjr12wJVhmnYpvw80QKHjy8PdEWOiI14JrfA049D2y8Bd7Lv8NXh0VbNhJUw9MXt2ire3I9ZTRlDuDEYD9qEio7qZYt///KQsCYZcFlK7pxCdsHzGxfH27YF4xK/4Fpfa5VyXMfW77csg5jbX3OqzzB/+x+Jlhsh1oZRDwk2NZMyV+KXgSpbS26jn2Jh9mUDROK0WibIJQaGzrNjqHr/hAcCypSSbQlzmEEAa6oAK7Bf+95qQVK9PQj/GeNkGgrsbCEMT9Xsyra2oWB+rpoxp9Z4F3wFuCoP7XP2PbG+uMXrmrFzrfdxr1tidoxoAFOGWKoSoEgCIIgmglpAh/6hqKhMGP6s3m5FW686m8fC0Tbcm6Y3xr5kQZnUKvs942dSiDaVoQ4Liw/jKSTheqyttJbp7Xda83dDfdr1gjPtN4WiLZNZj8ghewbDmqb0ZNegcXAlevasOtUEe0JFes6mke0vXXki5DLA/ByTBX4dH2Q3BL8/fxU/Br+x/inzjXTCoqrwfxxT2SDjj6zxJqIZc/8LcqOg9Tgj5HPvxv2BLOr3EUs2rp2YIvGMCoVxCbI7mhWSLQl6qmYf3bfLgzkDdy4tQtvvjQIJdFLvrjHqaZsnykRVYIlqlBcg0/pLxrBj36cMNa11f8LT1VxHVimwS9kYXsEz1zcom3Bi9ZF2ydj1yCdDcQFJ94NZPvqla9suq6X7Ob3vUl8JYVRfxTtdFh6qa5vRFp68E3rl8DqelvtQS7WZhy/keIY5eoLxp/gTyir8dPk7Xjlmg580VmDUTuCjlQEdx//84bnMcGXicts+sdi4NIjn8L1uf0wRQ2a8D88HoAgzlU8SYNgG1CqiataE1Wfi6FQzNr2rTD2o8s+zv3hjMIqxNo6+QyAGokW/xy6YdMOPH7t/4Vjm7h8x2UN70uCbSMsnEW0yg2DpwRBEATRTLCq2sBkYLw9XxgxEvRnWWhyeHadluqoV5GWq/ZKVnG4QbR1qhW2biWYLr7a3MsFW75eGx8gNRk1L92wPQIjJjkNbbFmQgr1Q1n7K1zs1cww68UP3NZ8eSVtziBkux+W63/3oq3z2baerEGWBD57rJa7ELY+mIyIncNrsv/BbTciJ7ajmLgRy82D3BaiUJr4WGIVuTV0RGCb7NfUaJLmOmN/YYvL01YI3bfMhQ2iO1NItCU4zINnMFdBwi3g6SNqg2hrhvx6xBlWWfE0TilaF21rQWKMuDr5iV4IXRSYb6sgyg2Jf8IiFm3ZNIgvt/yKf6da2Xlz7iv8Lk8wTy6vi7axqhE5q77lsErbKmzK8mtHP8OXNX0QuVwW6fTUUyccvViXIjPpNI7mVvPlI9omnFRX4Y0jn/Y3y/Q/l1VHM1g1rQOJX6BbHb+CtjMZQTzVitHRCrLFEjTDF3xrqG4FI9lRtLe2YjHAxAl2gdIEG0KTTUciiLPNkfTFyBfLvIJkbGDCQiOEPNRqfnBrjV3c+4thFe4E2jqBsi/aOoKMZLqtfk267LLTW8kQQWdRdAw+wMv2HUEQBEE0E3IkPk60VaMTzxCVQv3ZqFcVbatFRIm2XtTixYzsKX7rlILAMX6/4veNH+z9ZRxwTmK5eQi3Fr+KmszqhXxxTwcT5mrcn7wD76hW+saEQLRFM+UJ8MLfSEP7i4mJxMxhQeMMppNYtsMHyZmE6shRbJeOwimcgOZVsLfj5mm9nyYJWG3s4cvlcivsvudx9+hn+f3n5LtY6kzD8yuGDtv1uKjZryxH2nLhJJbjHzo/yr2cb8x/zd9OdxFX2lpmQxmWZSyu2WMk2hKcfNnC3dnP8VTMh/VbkStvRTrmB0qZIb8eKTQyeaY833IzSroBmV0oS8Pc84d5ykw5BT0s2paLEKTGQ7YmJi5GymboxFftBMfdfH1aQyXFppr4Ju+19MhayqgQMttnHkovxS/DtpIvVJw8tAvp86+Y8rPrFbRV0ZaJxLUK5vB7u9X9W5umYTAPXKmVe9iy0WTZNdGR1NAa13B8tIKUORS45ofIDp5YZKItu1DG6t8LQZyr7Oq6A6b5DDrtk9hZfhRRm1WqNodFQjj44o7sf2BAWYZO62R9nan7o+iyPsI7UkUxhUycBmLOlBMtl2LIHYEhatjBPMubqNqaIAiCIGpWCI21cwKisYkrbZVYGrVe2NWF+5Bwcmi1Lmb1ski3L6uLtk7BL1Dxyo2iLXRftM2ZAopSBnui5+Mt7ncwWDTGicKnJTRraG9kO5SqzUNECCRoQVabLqi2xjLzMCJYXAJYs4q2rGjIKOfhedWqV0nDlcUfQSww+0MBp5ZNT7RVQ6I6bJ1XitcYtGPjBuALI/31KtSc1IKK6XALS9YPZr669e10QgMJiwzPCewRvpP5RbwpGgSxLwZoWITgFPKjXLBlXF28j3vb1hiJrMRDydvwZPyV8FrXzXiPHWu5FC9FL8EeeRNu3vsR/NrAR3DP6L9ClYRpVdqalTIqpSDlmyFZi7fSNhx4VaMm2rIKMqtrJ36UuguPJG7mdgVbKk8jYfsVY6IWeDRF3Aq6tlzFvW7uTd+DvcbpxVHHqEXwAGokwaeL1FjRGQomM8v1aRo1u4qCFpzkWLVtJxNtE35joqVafctfE2pgFIcCIaWZYRcxyanUw94I4lyHVdauMXbjiuIPcU3hu4jazZMcK42pPOm0mJ92MGhks/OXpcOr2ruYWgtVg8yAYz0342fJW/Bk/NogJIOYFh//+Mdx5ZVXIhaLIZM5fXhI7Tr04Q9/GD09PYhGo7jxxhuxb98+2uMEQRBTILeswJfafoPPqmEwsWmyQUal6l/LyDhDuLbwbXSV/fNsqr0H32p7Nz7X/jv4Qfp1fJ2oBwVMDEH3+6P5aipzIqKgtbOHF8EwLaxXOYOiolClreqZ9XbKbcf/ZkI7qGYJIqux0tyPaOHIgm7PoqdqB8nQC0Nwak4FSgRiPbDdw3XD/z2tt9Miwft5tgEnNOiQ0Y9DtxpDdssjfkU5Iye1oWLZsKqWDK4QyIXeIrZHcK1gYMGG3GAHsRhoCtH2k5/8JFavXo1IJILLLrsMjz/++KTP/fznP89HBsJ/7HXE7CjnAs8/xoljh+vLI3Inno1dhUcTN0HsmHlyeM0riAmBLNW7JspONdVSDFV9mnqJC7dhFEeHadmLv9LW8xB1imipirJepAVqphcvRy/i1a0XlX7KpyZkigf542LIbL875uLCy6/j4u7+yHbszY6vdB1LrYKWoUbj6KiKroy1vX66OoeJHZ5X9zJkI4FaLBg9vmfkn3ml7XKvH1cX7sUW/Vm+Hfel70F2ZTAaWBn1bR6aHcOyobjV/6tCoi1BMA/bml/s2OqKhUYKVdrWsIXgXGYbOpdwfxh/DR5N3MgrRokzJ2yJMbahT0yNaZp4wxvegPe+973T3lV/8Rd/gb//+7/Hpz71KTz22GOIx+O45ZZboLOQTIIgCGLia1UkwvM/TMHXBSwpwsNIJyISEm1rSKpflStICoy2TbyCdrDo8IE02WgUbUWTVUN6yOu+iJWOKohueCW2dCexpSeFzAo/m2U6CCHBTgUTbf1+sSMHVcJWzPfjbxYaKjlDPrzE7O2+auF3/o6NQk0Gge0rSy9O7/0kFWJVXxEcE145yCe6uPRTbmcYxswHou1640V4w4dgVbUaN2Qq4LHQ30XKQOo8PJB8DR5O3IZRuWPRibYLbo/w5S9/Ge9///t545QJtn/3d3/HG6d79uxBZ+fEZcupVIo/XoP81WaPXhiBEr5/7BkAF/Dlcig0LKFJsxZtmeE7801heOokqZ4TJIEy0dbR0jikbeaVXz4eisU8WlsWx9T7MNbAPm4SbghRbNafrdeHsRPKpSs70FXd16oXjAwp1ZHNiIS6b9P2niha4yoyMRXZsonDQ+XTBn95IXsELZpAZ0rGSyf9CroNvW3IsVE1z+VWAXw6QdXDxpWjEDIrgWpe2mB8A85XJHSKObSUf8bX9SkrsC+yHa/ZCFgHv+P/X3PBxaCZCVdyn+7YJIhzRbALi7ZskKeZKm3H1n3qShoJ06/4t4wySq6M5yOX8PvbuqbvMUcEhMPnqNL2zPjIRz5SLziYDkwEYO3gP/qjP8Kdd97J1/37v/87urq68I1vfANvetOb6NAkCIKYop+pev4AlyNNPsisdG/Bpzv+EOdVnsRVxe/zdXIk6HO2JzScyulcuOrLlhFxgv7BY/EbUFIy2JkfwkW5H0EXY4hnNgPbXgt5aC/A8kC2vGZGgl2Pc7Kua5Siy6AUfAEv23tVU33namhfMZQxIi5xZgihQqFydrDBJjIeT9SD8aYdlCsIcFkui21AcAxI1mjjDOvsEHpagyKsmg0IgxWQecN74HkWrinci27reP0xdxHbI4xEV+KFWGgGN4m2Z8bf/M3f4N3vfjfe8Y538PtMvP3ud7+Lz372s/jABz4w4WvYyay7u7lGnBY7Q14KQyExVBl8qS78lULT+GPqzHX+pGShxR7k0yjqaBMbxNeQ1FhdzLT0EsrtbdyHJOWM8gpQXYziD1wNi0+yBZz8qbpJOPP1re3nPZGduHDNJsQUh4+SNVS5VS+SPRe+Cod2/5D725y/k3kwAWs74nj6iMk71SdzFSxvmbxStDZdmBGJJXDT1jT3pF3VFsPq9jiekqKQ7RIPjbP0cmBTq0ThrLwKxw8/xL+DPSveiJuYmN/ahdrlIOnk+NSgtavXYG91srJXrKq8TY5RDtlvUKUtQWD7yf+BZrxU3xNaEzXMmYXM59t/F5v05+udLifSAlRFW8fUUQyFXqYiCz5OvWg7wqLn8GuRbrJBxOYR7pcahw4dwqlTp7glQo10Os2LGh599NFJRVvDMPhfjXzeH4R1XZf/zTfsM5jgfDY+a7FB+4b2DR03Z+f3xO32PId7crKCF1NNT3pOikVUGGIESqgwRtZi9ee3xRXf05Odl4+fQLy2rG3CY4lr+fJI32FcWrqfL2d1C650I3D9h8MbO63/kxCyPrgp9xW47jv5shcKQ1Zhn/b8ejbPNWPtqSRZberzf7Ofh4XQ/tSrQr2/XkNbOomcAB7EzsLypvt/cEUVIgy4lo5INWyvRnF0EK7rh5Cz9xNKA+MCy5XsEews/5zfz0mteCl6IVYnNjXtPjwdhuWg0zqGjfoLvD0rDt4Gd/WVC37cTPe95YWeNvbUU0/hgx/8YH2dKIq8scoap5NRLBaxatUq/p+88MIL8ad/+qfYtm3bpM+nxuzp6XPTeCr9Zrxn6BPQXB3d+n4cHylgeWsCXr4PMafMRxIjsjDjA3fz8A9xyfB9jSvVxJQ/hkrXhfhui8ynutya3ATVsPlFNCcF3nBsaspiPIFY5Xzd9Jv51pVM35+3xRlAOsKOZwe9wghWm0FVuaRG+f8107EMO37tPyBKMgTRP4Gvbo1i/4F96LT7cPRIDL3pLVN9OL8Rqu/Zoan4vVs28nX8+2ANCLsEyS6hEhIymadteyqGz7S8g4vmV7V28M9OtHSHRNssr/zVEi0Yja3BoBvDqLAar5ij72g+T6CVYjD9iQWyLbbjqtkbJQsJ7ZuZ7RspZF/DlkQl0jTHl6RGUJRSIRdbZi2TAaq2u45ZRq5s1Dte8TNo7Nag4wZYd/xreN/Ad/39MfQxuN0Xn7X90izH2tmCCbYMVlkbht2vPTYRn/jEJ+pVvWEGBwfPiq0C+55yuRw/Jlg7nqB9Q8cN/aYW4lyzpvQ8npV38uX+1mtx3sDERSMsfNkwTIhWqX6dYTNLB6rPT1WOYVPuaaTdLPbvvRAvx9+KuFeELkT56xhHjvQhWn2tCbX+2jNFtz1o1feRINTfx3Q8yLX3L4yc9v3P5nmYfQbLqNlg7uL3c4UiKl7zFug0+zWK1RfUjqWjlTj+N/1HUGDiqkwaW5URrMyo/HiItK+d9nFmeRIUJshaFWieARdBe2q47zAGlq2q7xux2A/XZcVj1Srv3Ahk261v0wOx67BX3gq46Rkf5wvNaK6AZOUkdpYe5veLfWxfrl/w46ZQaMxrakrRdmhoCI7jTNg43b27Nv29kU2bNvEq3B07dvCd+Fd/9Vc85OGll17C8uXLJ3wNNWZPT99wDrpp47C4ChvsXVDcMvY89xjUbduxc98/4cJyP0xBRSn3rzCLMztoDUdAfEwHzISEbDY76Y+hYCs46C3j5Zp9WQOabNUvljWOnRpCm7T4vN4KIwNI1C7GqdXwss/zitZU5QTcSh7ZUgWvGfoXSEa+fpotVoxJT5arhh7EWwc/x5dPvqxjoDvwwBnLk/JFQGQFUpKJrmwREBpH4AwovKFg2Q5OjpTxo8gtfNS6LbIC54s6LNPkI34dmt/A4R149seCzPQ96HKHMDA4iPt73439Q8wXF7jpRF/gazwL5vMEOth/st5AMlxp0V2Ymr1RspDQvpnZvtEdIFbrTAjA4HDjFKuFpJir8OuBEOp0WVIw2FLJjWDkyH5Ey32oCDG4euKMf9N03PjVCWp1n44O9tfP+WfjXDPdxuzZhM0C+/M///Mpn7Nr1y5s3rz5rG0TK35gVmPhStsVK1ago6ODW4rNN7xSRxD459G1h/YNHTf0m1qoc8211k+5RRILIbs/ffukVouMlkQfYoZdf5+2rt7687cc+DlW6/fy5WcLPRhMXAA2h0cWxbrA6thG/bWptu4pP2sq9nZthnjE/yxZFuvvczyeqr9/Zzp22vc/2+fhhOJBtP3P6elZBik6/9eapXqN6s+0watuF7vRIswyQ0Nrexdat10AFPdByB2Fd8VvIJ6a3nF2XItCtETEYFS12OD/zfr0tePJtmw8JSSREoPCJVV0oLJZVtVtEtQoNE1FLJ6Y8XG+0GSkw7Akvf5/YlXLzfCbmm4216KbK3jFFVfwvxpMsN2yZQs+/elP42Mf+9iEr6HG7OmxhQFomosTziZssPfhlLICzFudHcwDng5XFGHJKSzrmbktRTzdNu6AT7R08UTlyX4MvU4EmuYnHqrRBFRZhKblGp6jxpLo7AyFZy0SjohBQyG68nzY/btQNh1scA9h/bIOFAt5lKMZiJZfgcvo7l2GZNvEJ5iMdxH2PPkFLpzKxZNTnoiOxrehKG/ink2dXeO/0/9Y//9h94AOTxDxBy29eDl9NV9/zYoObFmzDH/0mhRGyxYuWtVS99fJKjLMqmn5VdKL6Oy8Dqu6yjhWqNprRFLobJv9tNr5PIEOHRJhV98z0dK+6C5Mzd4oWUho38xs35xKZvwWJDuvSEJT/SZKYgmaNoi46dS3O962DGKfv6zJAlpGn8S7C1/j923tA+jsPDMhjY4bYDjdCqu6fyOazI+Bs7VfmjFo9rd/+7fx9re/fcrnrF27dkbvXbP+6u/vR09PT309u3/++edP+jpN0/jfWNh3c7auBex4OJuft5igfUP7ho6bs/N74iHCJhOlTMRCotNEXGQ9ia0VluHiE00k689Pti9DzUjOK/RDSPp9nd50BP3Do4i6JeijfajJlFqydcbnPrPrAngQIMBDNrK8/j7do0+hFhPeOvosRHHya8BCnGsigt+/YxOyFJZB0+Tn/mY+DzvtzHbjRliCBgVd/GhgRFUZoiQBV76P35+mo61PyHZjLHZptL4fsoaD/06+He1yCe8Y+pvqEyp+pk1t+wSVb5Pj+e2KxWr3Fi0Gdm9eaNBlIY+b6b7vgoq27e3tkCSJN0bDsPvT9axVFAUXXHAB9u8P+aSOgRqzp8cpDUNzZQy0XYJ/iV4AS1CwzIjitay6yinxSk9HSczqgJWj44OdtHh6yh9DTFPqJy7ddtHTfz9+aeheRLwKDqubMCq3QRy+DKLYWK29GHCNILlR6t6GrlQEx0fLuFw9gHjxMEpCG7xIGsgHBuDRWNCgGEukcy1UVYVhmtAKh8H8tZnIPREsAZztV+ZRPNH7qVocECy+53O6Xf8Oas/f1DM+0Id9Vk20jWf8jnxXKlp/7XDJwpqOuTnhzdcJdCSxAT9q+WUeYnBLz4WL8sLUzI2ShYb2zZnvGxYGWXM1Zx7nzXRcxQQTF5YewfnlwE5Jy3RX06M1xJRWaPpQqEM2s07VuX7cKNFEPfiSeaPVKy/Own5pxn3OhGr2Nx+sWbOGt39//OMf10VaVjX72GOP4b3vfe+8fCZBEMRSgQUm+3hIylMn3W+pPN1wX4sEhSWZ9t5a5jK3fatxpfskOgf/y/8EJXh+NB7Y9p0pmsT6ZN44H9u04nLRltXGdEaaLwBKE+3ARksKx5kTZ0zbejxZPZy6HSa2+jOIZzND9VTLRRg0e3mQWLd1DELIHsEr+wVxjMGC7+tsCCGR1yzDswPRluUaRN0iBLP5BtKnTUiErrVnFxMLKtoygemiiy7ijdO77rqLr2PVG+z+r//6r0/rPZi9wgsvvIBXvepV87y1SxcWXHXb4Gd4SJhSSuN/V/1fHBsp42S2gkoxV59q6qjjRdczQWZC4Bi02NRp3lHBwhpjF1TXQHR0JYTyMNKOf6LZpD/Lb8tDbJzzlVh0VD1sGVrrSqSjClLRFBc53WgL2BCvGAmmmjDpU9EmT0JlF0wrsQwYOcSTH7PFEjoz44PeWBIq83KqTQ2YiKgadJRHS0GjJzLJ8xlGvBcwjvLlROdKftuR9Ct/BM/FYI5Nc23uyLiCF8UJdQ1fljLLFnpzCKIpfGProm3I37YZUGHh6uK9jes6N+BLbb/Bl69sbcdFRz9bfyzKqoaJM0ZWg+uObQQhlsTpOXr0KEZGRvgta68++6zfblm/fj0SCb9NxWwUmI3X3XffzYXw3/qt38Kf/MmfYMOGDVzE/dCHPoTe3t56O5kgCIKYmDbrFGrxoysKrIp28swbb0wYdjQW9HNjmS5IogjHdbHeeAmrjd0oiSm0pIN+K7Nmqr82Pbkl3emIiiHrwJD4mbzyndj82Of4jEZl43VoNrYoA+gTBb+v12Ttw8WGFiqySo++iA2lvbz4IGHdwcwxZvSeJ7qux+M1cdbzuH7ytuG/5XdFPbA6Gyr6YibLD/LVBg+CVYYbEm3vzH6B31bky1jNKhYjgjNmEMcOQggXAwtuj8A8uH7pl34JF198MS699FL83d/9HUqlEt7xjnfwx9/2trdh2bJlvEHL+OhHP4rLL7+cN3iZF+pf/uVf4siRI3jXu961wP+TxQsL8oq5voAoqjGs60xw0Zb5qx47vA/V4kl46uy8apRIvH4hraElphZtY24Zt2f/ky+X+i+By6a9jMHRm8/zbjoIZrDdsWSKqdoQaieQWCtQyUJklbahKjchNAI7EV6ik4u27ISbHxmcULStmDZv1JiihkR4VC1EeGSvkB9F0hnlUzYiUwz4CVe+D6V7/xTF+Eps3exbmPRaR/BLQ3+NpJND4cAdwM7mrhSqWMFIdmwKgZogzhVkNcJmGo4LJWsGFK3x/PV4/DrckmbCrF9da9gOPD0IfIynWhZgKxc/cmiw0DUXV2XCQvPhD38YX/iC39lhsJlhjPvvvx/XXusnkO/Zs4f7A9f4vd/7Pd4Ofs973sPbuVdffTXuu+++prSKIAiCaCYino5aSUyrcXLqJ2uhwhg2xV8OtfslGXakhRcLMV6T/SK/TV/wfjSa9M2+fRER7KB/LIYqVjfcjIggAayQp3VmljvzSVv3SrQmTkGYYho+MT0ioX53W/kgzi8/wpc155oZ78KG2baCgJzU6g8KOBZE3c8TYgPFQ8Wq9iAIPL+I+d2KNqu0HS9qeu5YJWcR4Tb+f1x7cbVnF1y0veeee3i6LWvYsmRcNh2MNU5r4WSsOiE8PW50dBTvfve7+XNbWlp4pe4jjzyCrVu3LuD/YnGTL5ahudXqmWgGa9vjeIAFwlnHoP30c6hHVI0ZkTxTmCdt7aferyzHz+M34BeSbGRy8ukrWiwk0loV7vkzFs8IKlYXE4Lp71lH1KAoKnD9h4BnvwSsupKVt/HH5Himvs94ldtpRBM50V7fm6Usm9gz/iKvlwt4y8g/8mVXOg/AX497TmflAK4u/IzbBPTuU/D2oZ/7DxR+m0mxE3721m3nI7fmi4hrMmTJ/822pFNIOf5onp1v/lCvshlcjGLKgp8eCWLBkdTgHLyr6zWYOmf17KKGxMSTyio8lrgB9ySDShmD2cAY/uCYLahIxscP+hGnh3vVVXEtqrQ9Ez7/+c/zv6lgHacwrBPFChTYH0EQBDF9ajkbDKXaF5mM8GzG73b+CnaO6WO5sQ5IVdGWv7ckId6xapxo6wgyUvGZz0ZNFg+iVvfYU9oVPMAEtk23omm57g8hHH4IWHHpQm/Joicqi4i4ZSiegYw91FDwNlO08CAEa1uw4q9YK/dohuvwQiVme9i+50t4Q34/ilqnrzN4gGBX4I6xE+AsYtFWcBq33bOo0vaMYVYIk9khPPAAkw8D/vZv/5b/EXNHKR9ckMRoBpu7k9xb5xWFezFqhX6wsxRtlUhwQWOjPUe1DUjEosyfYdLXqJG4f/5gfRp7ks5itVO+2JBtX2y2a55I7RuAG/+vv1y1pFBigWj7RMdrp5jk46OlAtG2kmM5p+PRy8F0HqgTXwzajOPoKv+suqGBX7Ac6rxPRDrWWAkca+nmDShmxyAUG72rmxFtdC/WGKf4FJGoTANBBCGHqlmj4tT+cGcbTVXhQeQ+XTIs3kGLhyrkdduBaBa4i5cuxWblDXYuExbHSbQlCIIgmpVsZiuE0lN82Ukvn/K5ciwQbdPi+Ko7MdkFDO2u37fVFGLp9nHPM+UE1Fm0L6R4YB1nqotoRhDbP9tfv9BbsSSI6gN49+CfjluvRmKzslxg9oSSZ8MRJKRiETy78rfx8OEKXEHCxrLFRdtIdh9W2kcguMeRTa5DqZKFLqbQOkGlreA2n7fydBHdRhFacEi0JRYZldwIat1yOd6CtoSGO3Z04cQja9Bpn+Q/doaXmF3YlxbyCmKl94xEREZxiup0gY0ysqkijgXB1iFUC1LYQCqrRuHerGZIhFxEPKddDFUqIj7F6Gwk0VJPL00Kp69wiqQ66tOCzEIwUhfGqASVyeIEdhN8fch/WNZH68Kxop3ZSLLARGHmhawXoBjD/PsKj4I3Gyv7fohN2ef5cky4iU14WuhNIoimqbJUxxncLCz8GiCqkF0discanxKv8n9N7j8RdYpQjTREq8hFW1tJ8ucTZ44SDZ0HF1lwA0EQBHHucGTNmxEb6ENFiKNjhW9BMxlSNBBtU+L4Ppaa7mqYC+pGWhBPpquunz5Dco9vXzAL0iu24sHYpegyj8Ld/j5smtW7EYsRNdzOmiPRdt3Jb+HXB77JlweUXmTlyyCmbocr9PF1o2UTvekIlLI/E1bXWvHz1e/D/gFfJ7hbehiuWkbULaPDPrno7REElzxtiUWOURiui7ZKdbTv9vNX4i8HXot/P3EJLig/wm0J2jr9JOOZEomGRFtX5x1oVvl0OnMDV9K4eTQr1Req0wg9UYUnyTzdUAoZwS8WWBjYz2I38OX1XZMLoSw4pzZlJoHT/z+TLZ1VN0fALg5P/Nl68D7Mw3gi5EiinjEZNiJXo2d+8XBjbVy0jdt5jJZ0tCenCFNbaMKhArHZVZYTxFKgc9lafL3n7RisAHfv8P04mwmXWcm4Og9YiMt+BUCPcxKalYNcGuAhIhyVfs8zRQt1GjyyRyAIgiCaFDnZhi+3/hpf/s3T+ICrsUzdsz8pjB+QjGYaRVswj1tJ4TMkWd+TzRr9r7b3YX1nAjfOYpuTEQU77v4dHB0p47rNHbN4J2Kxok0i2kYmWT8dJFmpBwl3WiexovgwTkTvrj+eK1soF0YhOgbv87vxzgZv3Z8nb0SfczVEz8H7BmozgRevaCuGRNvD2ia4qh+avlgg08ZzkP7j+3Hox59DYu2lOO8Vd8IsBQmCkVRbPfTq3a9Yiz/+VgUPSa/i694cmZ3YFolo+GLbb2Kj/gLyUgtWC30QhAtP+zpXjkIyi/ykUvN+c2WNKYvcF5bZDNTMtBcLZSOYXhBXJ/8ZJjNtvKrY8QSkQt70kxHPdPARYEOIoGRMfGK1QpW20iR2B7IWrzdkWDFz2K7iTBHjbXBGDvMpzCPDA2hPrkKzIlZFWzZQIMp0eiQIOZbGu99yDwq6xWdhNBuC6DcwWaPytsHPArgIHhNyLda2ZNeM6hMjJNrOFDXRim+2/BIsqGht6cbUtUsEQRAEsTBcuroVD+4Z5DM5t/RMHaAdU4R6X2fH0L0A3tfweKJjBfKh+2K1sMlRkly0rYV4p6LT6KCdhu3L0/yPODeR1SjXMcZ63M9KtFW0umjL8GItaIkHx2q2YiE7ELJSTHQ1hHDnKtWZ1oJUt6oUvMVpj+C5bl207VdW4NuZt6IzFcFrsXggVeJcw3Vx6lt/gkihD1b/0yjseAXskGgbS/uiLaM1ruKXr16DT96/H67nYWvv1Be/0xGRJe4Vemnpfn5/SGAhWK8+7eu8aiql4ursV+f/N+QYBCUGAYPQXJ0LlInI7C+aZ4uyFQq8Cp0gx6JleuC89t/w4oCN23ZMHAAWRk514z9XfRTDuoh0TMFrJqm0rf3wxUlEWBYaZ57G4mK6yInO+kWjMNwPrG5i0bbqm+zIFFhEEOEE2mYUbBmyUJsTAHhVuxc26MJvQ21fIZQSTZwZgqxhILEZuunAq8/LIQiCIIjmggkxf/H6HXz5dMU8asuy+nI5HizXyKzcjn/J/AJuz/6n//yEL9q6rD1RPgXFMyF5FpIRklOIWSIIfOaYEPKRtQUF2ixCsSWlsb0mxVrR6gzjiuIPEHcKkE9ciUIm0CCUdA+iIU2iXC3+UhWRC7fwHAiLtNLWCmU02YK/Tw17cQnQdJY5xyjueQAo+F4mzJz6xKHd8CpZ/z4TEJOBaMvYuSKDj9+9nVd7zrbTzqp301JwMvKmG2xW7YizSirNq3oOsUrbuu+qh1Ipj0SkcdubmVLF4P8fZgQe16b4GQoiLli/AhdMN7JdFJFKJDGsl5CvWLAdl3s88uExNq1VjcExAtFWCXnXnt5bR0BkBt460UwHat96afQUmhnJLtcHBQiCaH5kuPVBIVHxZ4N4cjAr5HPtv8vTeK9YNr5DRpzZoCsTbXUrEMkJgiAIotmY7szLnlUb8Vj3rTyEePXVvznhgHWnXA7up6ohZKFBYOb3mVpERUNE8+JIEcgh0ZbbQ85iFrGkNoq2cqINGaGAi0s/5feHRzuhu0ExVrS1F8tzz+DO0R9D83Q8mHwVr0pVJRGuIENkre1FGkRmehLvD8iexbUXvs5eXO1ZEm3PMZ4vtyEsR40c3wvovmgrSwLE2Hgz9Y7k3FVYpULpnMJ0PQarlbaMnyVu4QmIKzva0eucREEehC5Gka4ssnCU40/gfQN/BUtQYff8AovAmrO3bomrODRU4jptrmKhLa4CP/kTYOBl4LJfhW2UUPtG1cjElbPaBH6upqA1eN1Ml3hrD/wjDDByvtl5M2KZen3qhKNQABlBLAZMNQ1JL/BlUdGCQb1QpUJRSkNNdy7UJi4JNEXkt/oiq0wgCIIgiIlggthdb/stVCwHsUms6rqUIKAsWhVtj6+6E/l8FFv0Z3BT/qvoMN4MgAaGidnhSUzvyDWIuLNBHiPaask2JDKBZ7JXHoWZ3YXakEOyez3M0QfQau7j99848mmMyh3Qo928iMx/UbUN6Hk4+rgfcrby0jt5pXAzY7se7wuEIdGWaGoeGtQw0vo+vHnkk/y+3r8P98buQFR5JdYmLWyLzK+fzlb9mfqyrE3PI9fTEtxWwRQ17I3s4D+6eFc7xKSGb5ZP8Ocs9xaXyGaVfZGBT61R1Dl975ZY8H4sGbLNHQb6X/RX/Pyf4GqvmDgVPIQWCo2rYUuaX7V7hqTaunGiHo4W8s5pMirl4rjqboIgmpuXel6LHfm/9+/Url+hgT7ZY1OiYjR9cZastA8jqQ9BMDx4XvMF0hEEQRDETITbyQRbRmtc4X1QDToybf7gr5JZxotuom4Ry80i1GoIKkHMhvAssePqWojR8YV0Z8JYnSWW7oASb4MsClzETIzuguL4ekQ+tgI7V6xE9mCjLtBiD6LiqLiv810o6DZi8TguB3Di+Z9g5IF/5s+RIgks2zmbKL75xwxV1d6c+wq6raO86ta2vwxZPvOCtIWAKm3PIbJlE/v6CxDkTl4azqbni9kjKLdFUVai6GlLs6jBed2GjaWn4Ec9YdpVm4c2vAPfN25rWBdVJCS04PWThW41K7YeWNvLE1S1zoY1xi5cl38ICTeP4uCvAukyPHhwXPATNQz/BD25DQIQjUbhQeThYTUcaWZBdFrrSjzcfg8GnDg8tReN32TzYJSC70ScxDaCIIjmIiYEPlViNViRebDWYANjDPKcmx2XD36VWyvxAVTnl6CwawlBEARBLGG23foefOf527GpXUWis6venshVQ8gYkcTsxDWCCGf4ML6d+UUs72iBHwU/M5QxlbbxTCer1oLAvG6NCiJVwZahrbmCD2DIkQTGKiqepCAbXYFhx0RK8utynZe/U3/cefl7QLOLtk6gZ8TdPNLOiL/erECWzzyvZyEg0fZcwfPw1JFRPmWemUkPy13osE4iYw9Bdk3Yojon6ZfT8bWtoanT+7yJgrqiqoh4aGS0uNhE20qBewgztOjcBuR0mMcRrTzBl/WRE/C8Ig4MlLiwvbw1isOdO/FU+0VQXQMfals94XuosoQj2kawHB9HkPFY/Dq0xxUENbpngJZAtutynBgqgekrluNCmUHF7nxjVKufOSqJtgSxGIh6gTWOXB1s4Q3SKpeXfoLD6gak5InPdcT0OxPsmsX8gQ3LgTKVFztBEARBLAFYKPfbrmhsPyQjCmJerQQJSKT8gDKCmBWhtisrOIhUbalm/HZqUGwliQK3R2A2Bo6W4aKtLahcHN5uPY8rtl3FnzeRaAtJgyT628IqdBlOKOk3vNysOKURXFB6mFumtdtBvo5p6IjNIGR9IaBW97lAoR944BPoq1wOeGv5Dza9bBNw+CQP8eqw+9CnrkL6LIi2ptoC6P4UeSlRNXSfRgAKQ3UrSLp5GIKGmNjVEOA1VrRlnq57ThVw9YZ2JJqwc+kaRdSkaC0xt6KtlupAbfzXKAwha/TV989AwYCW6ENB2gG2AdHoxDYAbLTtx11vrydHMuLxmVsGtCe1us/uSMlEV6r5Esh1Q4chRqG5OlXaEsQiISoEoQ21YEWhGkjGWK+/yP+Syt0Lsn1LrTMhwIOu60hoi6ORSxAEQRBzSUrQebuiRjxNoi0xew6seB2esV/BrTd0IVbXP2aKEgnawrxYKuYfpx6zXcj3cfuwU/Jy7LjoSkhxpT4Dd1xKkKT4M3W5QOtXrGaTGyFgF1+uqBk0O16+D1cX7xu33jQCz+pmp/nK3Yi559kvwsyewPZDn8P2yuPoyUSwev22+sO35P8XmyrPosud/5Col9e9i0+vHJR74ay4YlqviVYrbZdZh/GW4X/AO4b+CisGfoJM5RjuHP087hn5Z2SO/6T+fCZQ/sv3HsFzD38XX3v8IJoRL2RRoMXn1kc43hKYjFuFYezOXIsXo5fw+4blQhw5UD+BT+VRGx0zwlcLopkJ7YlguvJgIRBZmolsYgP+peMP8Q+dH8Xo6tsXenMIgpgGqwfvry/H7WF+W2ndgsfj1zU8L5Fs/kZlMyOEpu0Z+uJp5BIEQRDEXJIWgipblr8Um6QAhiDOBC/RhRG5CwWpBZ4g1vWPmRJt6UF/ZC0qYhxerK2e1xIOnU+hgOs2BbqBFh1v2ehJGpbpe7lWtK74NF83nNhYf7ysTq8IbyGxrYm1B2sRtWebrwSRmFOsk88jv+shDBQNlMUE9kR24tbVrWhblcG9qWsh2Dp2lH+Om/NfQWqUGalfOK/fgNe2Hv/W8QE4kPCH8el5pKZLh3Bj7qs8pTM8DTaueFhp7uf3s8VAcH5s70m8pv9TiLlFHNt3CnjFH6LpMIILfjQ+t5W2iYzvucRwS8N4oZzGk8lXYav+NPcxzugngXgghk8G8w2e6v6Z0KMUscrYi4Sbw+hIK7BsfgPvZkLZrAYJCAIikUBkJgiiecmvuB4Y/hJf1lazeARAb9uGxxJpbNKf475VrII+HpnbwMdzDVa9XJsAZ+nlBd4agiAIglgYYiE7BFkUIVSnjhPEbGCVtcy68rbcf8MSNEit1wJYO+P3k7UY2u/6OJ49lsOrt/f4IwysaCvehlp03uU9IjIxFQPVKbrqRFYBsoYLRu6FVjjK8248710wQhKiawfZEs2KM4loa5sk2hJNQK6o46Wv/C1iFf+A/FnqJvZrxBVr2yClIhhc+1rYh36GHfg5f1xLzv/0jms2tGNXXx496QjWtMfhsfnypyHu5BoEW4YciSMashVwTb9ylb3f8We+j51Vg/jVww+hGREsf/uYt8pcj9CqySAZEpUR7B8owhEUfD/1euSkNiyzDmFn+VFoIhtpO3/S92FBcQknh/XGi7w6usPeyqLkZrRNq4Yewh3Zr/DlwaFNANah2Sib9pQ+ygRBNB8br3kDHjc8aOlu9K7e1BByGXV9cdFWEtzyhZg5zCe4drU2SbQlCIIgzlHYzB3WpGBdWEWitgUxN7C2a8QtByFZ1TbsbLhoVSv/C7M8o+KwyMyugOuWNz5/okIygYWPib5IywLKXdeD4QWirWc35wzaMK4VEpbZ/8X1+/zWIrJHoErbJczeZx5ErMJ8a4EBZRnc1a/Eb1+0HJ1VP9H1nQkcPRBM048yg+p5hn32h25n4p/PdERbNTI+FEqJxKDFUvWLZq1ylQmUPUOPNphjMzEuFgotawakqmhrSjGo8hyP0GopSLIC2zQhGaMYLvonqv2R7fz2zuy/I+oW4blsOsNbJ32b80d/gJ6h79Xv54psXO6aGW1SvLULQ9VlPduPZiRfCUTbVGT+/Z0Jgpg9bNDr2jve3rBOk0U+q0CthpS56vjpXsSZIaox1LJ3LZMqbQmCIIhzE1GSYOx4G8y9P4J2VWP7gyBmSsocwM6KX0jHkLTpzUg+UxIbr8HWwz/ky2JbR71tx4hobFaVwPMLagiyVhdtGbZjwbat+n1vMVTa2oGwzAo5ZCPrL5vjHHybluZSsog5RT+1GzXpaeXVb8bNl25peHxdRwLD1YpURjQ9/6LtTFAj4ytR1WgSgprkFaWW40Ew/f/H0889g03WiYbnDo9kEetuLr8V0Srzk6Qjz4MPkiDAjWQAcwBtVj9WGvswInfCjrRAt9y6kOHJU18MYmLjyJmkzXxbk63dPHmcXQLsgh9E12y0HfkubswdRkVMIKWsX+jNIQhihmiygKTjN8g4JNrOGkmJ1BOFyR6BIAiCOJe59NZfANgfQcwRreUDSBq76/fFUKjunNK+EeL1f+Qv9+wAquFiDFmW8ELianQah9FtHePrBFmFFxJtHdvCmhPfqd8/lroIvjlZ8+KG7BGckGjrkGhLNAPOyFF+y8SyNZv8Kssw6zpiOG4e5sssFDByFuwRZoIWHe+vojFLAUmGJ0UApwLRKmG0ZOK543nI2lasM16uPzc7dAIrmkm09Tx8rfWdEM0S2pJRvGI+PoMlROZ9n987s1/gt9lrPor/3O1B8uxpibaC2ljhLFaT2WeCnOjgoWeW48It1mpuF44jwyUcHCzh8rVtdW/f1MgLaNN9j+Rk9P0LvIUEQcyUVH4f3jb8t/X7QmRufcPPRSQtCCKzF9F0MoIgCIIgiGZH1qKwxswqnhfYNGUm1k7Cs+23wykOYa2xC7JnYVPreUhmj9QfZ1W2tifUCwMHlWVodtxQNbCnBrqSbVGlLbHA2I4LtVgVbbU4tFQQTlUjlt2L9eIJMGMBJlwJ0SBNsJngAu24df4PzmXColmB6pTwe199Hq7Yhe9l3oJXGd/Duuwj/DnlYVZ5uxPNAvOaPSYsBzRAyUxg+D0HiEy0bUDAJTvOw4v7vhGsUae+GEhjHmfTY2dMrA2qzKqiAcUYgW45dd/Js41pu/jL7+9BxXQwVDTwhotX8PWikfcfFyOIR+dpdJMgiHlHjUT5da2GSKLtrJHVGBxBhilocJ1ahAVBEARBEAQx63bWmOIoWV2YvmhEldAvpfF8zK+fXdmyDAnx+/XHXcsGnKByVQ/52zYrrqWjZkZZyGzBE9YaWIKKi2NrsFho/r1MzIj+oSHEbF+EslMr64mBDbSsxqq2OHJlC+moAoRGHpoJ5mlb966tVg5HqqJtJtOKkeIQN+7eWnwML8Yu5evXb70Q3iOPwBUklAs5NBNlplxWmS+vXa9zK/acLPH0dIYdbUc8HserzB/CPyqYZhwezxuPqDUeD8oE3sLTRktCVjTAKCPp5jBSMtGbWZiLUa5iQa4MocfJo3LkOFAVbSXTP05slXklU7AAQSxWlDE+YHIsvWDbslQw196If9q3ki/flupZ6M0hCIIgCIJYum3X2fS7Z0FsTFGVIokQwvYIjgXBqWkIAgx3jrN55gHPCXJrzMxaPFvyCxU3qh1YLJBou0QZOL6fi5sMpW3VxE9S41AjCXRIZSDRNbGw2wQISgySIMCuqraiKNSrRFf2dKPNOsnDtnrcPrzIkhJXt2Dl9g34471RlMQktkdacR2ah7IRiLZxbX6qTYWNt+CR/Zm6aCu3+PGQme7VyA/38eVWZ2pv2bEG6KzSauYbJADxdqB4FEknh8G8vmCirV3O4e1Df82XtSK70NwNz9IhOAb33HVUEngIYkk1fBPN6de+mNCUoLlYCQ08EgRBEARBEHNbaavMIktmNrDZ18xKUXV16GKUh/tCDPQKx7Z5n9nHQ7rIrAWDkPlmRBciMOV2yJ4NLRpvmH27WCDRdomyz12BBzv+AO32Kbx543mTP/GGDwEHfgKsbSZZcwzcu1Zmte38riCKbO6+/9im2xAfOYi4WkTX9otxWc9WLG+J8oCyipKB53oYLjUGap1NPM9DwbCRitScXwA924f1+ovQxRjSQuATOJe0xFS02b6nLSPeuZrftq67GOh7Dqbjom3V1CdYOTKHlbbsa0x0AP1HIXsmRrOjbL4FFgKvcKq+bDseS9VBuTBSr+RGhERbgljMsNkZNY5oG9G78soF3Z6lQDRUeWGQaEsQBEEQBDFnhMVE//7CiLaX9v8Pbhh4mC9/veWdUIVVQEMQmQmhqskwLhr4KoA70Mwc6rge9w/7etibW9msMd9ClERbYsE5OlLmouBxdS16Vm2c/Imta/2/JocHjlnsBCHg+yvfj/NqVcHLLgRe929AaQiarGJNqLPeltAwkNe5bykTT8/2lHf2mX/1gz3Yc6qAX7pyNV6xwS/B9/pexG25/+bL5eI7AIwPiZstmZiCVjuopG1fVv2ON96C1uNPAMUB4MKpU0+ZSOtMcTE5U7RUMAWhOMqE04U57mwrMCN3XA96fhCl3GjwhGhmQbaLIIi5QYsElbZskCgZofHp2RJRgulvxiKqTCAIgiAIglgMeQwN9xeo0lZUg4Kyu0c/CyXbhoIS41W3HiS4rgfBCfrS4eW55uVDJ/DyM49g20VXYcuq3hm/DwtCr5FUgaQzykPWUGYFCb71V7NDPZklCBMLj42W+XIqqiAdC6o8Fyt96QswggJKUhJmfExKIRNjWRXnGDoSKhdtDctF0bCRDFW7ng2yZQu7+wp8+bGDI3XR1q5k68+RovOTas5CvnqFIb6sSAIy3VWBVFKAmz7iGwSfRsRWo0mEM8KZlcZsiLV0wRZUFKQ0soVwTNDZxQ4lSDKKowMol/zviSGRaEsQixpN1eAJAgTPg8JF28V/DVxoNCuP6/LfhOJZUAY2LdigG0EQBEEQxFKjFrJev59YmCKiscHjkqrh4Mo34Mela/j9P4j3QHSDvnR4ea45dd9fYn1+F/oGHsWWX/2bORFt085o3SaxHL0KwIVYDJBouwRhYmFR9w2XV7QuzCjNXLN7+RvxPHyx87wxBtmTsdE9gEzhaaScEYwML0Ny2cxHaGZCQbdxfvlnWGvsxv7YXczLoe6pWkONz9NUfMfGK5UXMaiI6EpFIKR9T9s606g6VqPxBtF2ttM04he8Af+2ayMs10O3NT+2ENPBDVXaMsq5IRgVf5CDocQXxraBIIi5gVnoOGIEslOB6lKl7VygCTbOqzzBl4cL8+PFThAEQRAEcS6isArXavJ6v7Ic22c5w3WmSGOCyFmQuGwLDbNUgyAyNFglzDUdxd181m9n4eUzet3RwTx+9shPsWbjebh8y2pYzA6xSiwWg167Yy2cheaZQqLtEuTk8cO8ImZI7saG6GVYalMzY+r0OoyrjT1YXn6IL+cHjwNnW7TNjeAVhXv5cteJfwNwK192KoFoG0nMk0AoyWiLa/yPo5y5SKrF0ngoeRtMIYKc1IrfiVR9hGeIKCvozkRxbKSM/rwB23EhS+KCV9pWcoMwzeCkrSVaz/o2EQQxt0ShgzUjW5whJFRq6swWUY1CFACXtXvtxdPIJQiCIAiCWAwFByWljfePy3IGEmt0LQDSmEA0WY1ANoNtsWwHoucXBzLEeRJt2cxxZsXAcOvBM9Nj7w8/gy3H7kPuQCfM9Z/HhhPfwPLRw7AFBfHI72Gk9kS7Lt82PdSTWYLkTuyqV8TETTb9/hIsduKSjUtK93MBsVdnHr3rTvsaNd2N2imlMnICZ5uC6aImlSpWoe6r61Ty9edEU/MoEF75m8Ce7wHb7p7Ry6ORCJ6NsWkDPhF59tVVPekIF23ZvhgoGOjNNPr3nA28MaNqVnEIw4mNOBG9CDG3hK0tPWd9mwiCmFvKkW4opT4+YCUuUMN3SSFHeAfCZdUK9dRggiAIgiAIYi74zqrfx0DeQEtcxcx677NHjsQRlkglxW//1aiYDr7W+qt448in+H3Bc2FbFmRlbq3ILMuqb4frAZ7r+mH002Bl/w/B5tCmrQEUsgNIlw6j09zHH4vHk8ET59GPd64h0XYJUhk4iNpE9pZl67EUSAoVnFf8MV8WR0fqVatTEWvr4T9YhpHrx9kma6tIiwlE3SIKYhoVy0FMleEZNf9UAYnkPPrVrL7K/5sh0VBFMxM9mDfubOkJibQns5UFEW0du3FE0CmN4Ej7Vvy0GpR2Ufuqs75NBEHMLalbPoiDj9+LFRfcQLt2LpBUCAJrLDsQF1FlAkEQBEEQxGLg1dt78a3nTuBV2xeugEiJJmCOsW1oy76Am3L3Q4QDb/RN3L7hmLoOK8wD/DmWacy5aKvrjfk3hmUholVnEJ+GYakTUfTx5XIhD9R9dwVokRgvouPFdIuoCIFE2yWIN3KE37JBkdZlrCp18bPl2P/UBdiEXS9qn5JU+zL4UVyAkz/7om2+YkER41y0jblFFCsWF21FIw9mh10RY0hGmzcgR5PFmrUOoorET3CzZXP5KTjZH6HTOoH+4T8DVp99KwJ3zNRerzzCv6saqSidFglisbNpwyb+R8wRgoDlHS0Q7Arc1NkfbCMIgiAIgljKXL2hnf8tJEqkUbRl9ghxvQ+b9Wf5fb14E4BWbjVQw7QMRDG7wPKxmJUgWeeYug5rXaE+g/l07Fc3YXvFF231wijEakWtIyp+7oWk8QIEEm2JBUO3HERLx/myFElCjLctiW9Dcyt10VaaZml8srWn7sEnlAZwtsnrFqJiAm3oh+g5KBQL6ExHIZoFLtpachzaHFgOzBdMpNUUCbrpNHgKz4ZO8zjKxm6+fPIUm6Zw9ivB3TGVtoZl8++qRkIj0ZYgCGIsqbUXsxMoEO/k1zCCIAiCIAhi6aDFkgjXuCqqBkEM+sa1HBhHCNZZRji6fG4wQ5W2RTEFMxQmNhWsgjbnBsUFRmmEpZDzZVf0hWZX1CCCRFtiATk1OIi460+/d9Mr/BTCJUAqqmA0tDwdBC0BT00ARhFyZajuKXu2sApDkL1ADNQLI4Cdroe4OErIU6VJuXR1K366dxAXz1FFbHLZZrBvgJ12vaH9WAgsSLDEBK9+/nbmF1Fq3wmx4k/3jWvygoSjEQRBND1X/3/BskuyLUEQBEEQxFJCizZWzKpaBKIk1wfrvUoWG/XBujWCBwG2NffesJVoN/6548NQPQOeIOCV9vTancyOsiwEYWpmKQvB8fUYryraepIfrl6rwF0MUEnZEqM8dKy+zEXbJUImpkBq93+AqZj/Q5sOTqwDolFE3M4hXyghfeoRX8hef+O8C9prTnwb3dbR+n29OALH6EReSCAilOFpzS/avu2KVbjz/F5kzmCfT4XUsQGaIkK3XKi5QzwVkidQPvNF7pmIHW9kjueYT050XotvdwS2IdFSCb/S/8eoeBEMZc4DcMG8fj5BEARBEARBEARBNBOReLrhvqIoEEJ9c6V4ArfkHuDLz8Uux0+Tt+NDka453w7ddmGLKmz4GoQxTdG2XC6jy/ZnnTPscg5y1dOWVdjyW8m/lUi0JRYKozDMKxkZ8hKxRmAIyy5Carhambnsoum/MNEJjB7i40CVl76N9KFvVNd3AT07MJ8w79owRnEURTGJz7T/PjeKPX9ZAtejuWGVyXMl2HJSyyBrMcAqosM4hqGigc5j98HZfS9/WJI1YPvrMZ9YTuNJX7EKYKtU6IhJ05t6QRAEQRAEQRAEQRBLhWhEw2fbfw+KZ0KGgz+XRF5p61Qf98zAtsAStAn71nMBE2l7zCPoto5B8Sw4hXagfc3pX5ftx87yz+v33UoWYtUewZOq9aq1SlvPgm07kJvYrrIGVdouMazCcHU8AlBTC2tkPadsvh3InwyWp4mS6qqfZPqOHYRSNBBRJMTzJ+ZVtGVWDJKZa1hnl7Io1LxTBQHx2DkY5iIIsDNrgeLziLt5DPT3Ibrrxzh0MscLn3vN/0Hbmmt8sX2esMZ44sQc306EE83M2+cSBEEQBEEQBEEQRDOiSAJ0JY2S6/HZsQwm2taxAv9aqxpGZs6DaMtymlabe3Fx6UF+38u/gs1jPv3ryo1Fc8zOQXBtbs1YE2ufXv427FEKPEztLxyXRFvi7GOXAtE2klw6lbaQVeDKXz/jlykd63FA24Sc1ILyoAah6J9olo2MoAPzR9l0EA2JgQNKL2xHQUG36+um68271JA7NgDHn+fLzp77MNx3iIfFsbPpscEcjn3z/2Hbmz4GZZ68ZWujgW12PzZXnsGOyuP1x6QYibYEQRAEQRAEQRDEuQWbZRtVJRR1G6o0XrQVrFo0PLjoyTCnaV1wJogj+3FR6afBZ5nTCzszx4i2huWiVibnib5K5sY7UKj+n1jAWQzND1XaLjEGxU7Y2lYk3AJWZLpxrtO29Vp8Z3cbr3ztNQ/X1w+NZudVtC1UTMTcUl2w/XLrr2F7LI32kGib0M7Nn1+8dxOKz/jLyQPfQc6o7ZNqRNnxJ/HFb/8Qb7/z5nkJjus59RPcknsZK8yDiFa/oxpyrGXOP48gCIIgCIIgCIIgmp0NnQk8czSLdR1+KJkQEm1FOxBtLyv+BGlnFOLoHcDKM7CvnAbqyH4Ifn0sx7b8IPfTYVWCormfJl+NY+krsMVIsxhypNPL+fqaGH0mXrkLzbmpGi1hdkfPx97MOr58befSCSKbKR1JDf/nhg3YfSoPZ8QARv31tt4o1s01xTz7IP9EUxH9Ex6rspUPP4Bbco+iIsbQhjcDOPeE9daVWzFQ3Tv5qojtCDIOrngtek/+AA/Gb8OB0RZcP1LGqrYg/XGuSBUOolN/YcLH1ASJtgRBEARBEARBEMS5x7tesRb7+ovY0OVrGGLVVoAhhOwRVM/AjvLPIeaZYDu3oq1rVRCec+uY+rReZ1WKqDnU6kIUozrws+Qt/P7ODn9Gbc32Yb6qhOcDEm2XGLmKVT8YtUVgqnw22L48zf8OHrKRr1Z4esb8irbl3HB9uSQm+S3zs5WMfdio+9YAEekNOBfRUh2wtDRkI/D8PahtwXW3/wIe23cDDjw/yNcNFox5EW0xRVJkJNk6959HEARBEARBEARBEE0Oy/9h2kmdWAv2RrbDhYS0m0UPGnN7XHvyvvVMccfYITjTrLR1jEC0NcQIy36vU7NebKscwc7yc5A9C1ahE2hdhWaHRNslRr4q2qbPUb/UqdDivnjKcEPJh/OBURxB7Rsoi77wWDRsuF6OmwAw4qlz1z/1eM+tODiiY1Du5RYFy5f1oD0ZQXuGfUe+aDtUnPsLAMOrJkhORDS1hHygCYIgCIIgCIIgCGKGeJnV+H76Hr58Sel+9IQsJxmONb0q2DPBHfOe7jQ/w9GL9WVDiI4LWWN05V/AssJ3/ffNXwWARFviLGLZDirMH1QQkIqQaDuWWCwZ+KaGTLTnAzMk2l5Uehg91jEongknGauPlMRT525Vp7n2JrxUOVW/f8fODfy2LRFMvxgqTm9E7Yxx7IYq6LgbeN8kUmSPQBAEQRAEQRAEQRCiGGTMsOrUscxHpS1CNgxnItp6RiDaXlX8PgwxiocTt2BU7oQqV4PVlMgZ2y4sNFRpu4QojpzCewc/yoUoM3IFgC0LvUlNRVSTYQoq91+Zb9HWKo3UlzUJWGYeqq73RUFTiCARDU4Y5xo9meD/3hJXsWOZPwWjPSqi3epDws3BGcjOy8iX4FYvLIIAXWtDvGpYznx1U4l5sGMgCIIgCIIgCIIgiEWGIgYesMwiwRQ0X0+p4s2DaOvZjaKtZ0+vmMsLzabusY7y29XGHngQoKdfA+B9ENVAh7DH2DA0KyTaLiFKuSE++pF2RqCL8zO1fDGjySIsMQrVMSDMs2j7cupq7G5fjbhbxF3CA9CGd/H1iuEnoZlKvD7acy5SS6Nk3LC5sz6Cl3ZH8ZbRT3L/mVPiJQBumvPPFhx/hNATZLiRFqDiT/F4MnkjLjyHvxOCIAiCIAiCIAiCqCFVbQUYjyVu4H+95mG8bvTf+Dp3mn6zZ8QYkdadrqetPbENogAPoui73UpKBE7t+VRpS5xtKjnfC5QfjHGa5j0WQRDwUMc9KBouookUds7jd5EzPJSkNP+TE8uAqmhbw1UCf91zkd5MFL9xwwaMlk28ckNHfb0Qa4MqiTBYkmN5GJ7n8e9tTql62rqiAiHWCvg6OgrJtXP/WQRBEARBEARBEASxCFFK/fjlwT+DCAf7tO14IHUHbCGo/fScuRdthZA9wpDcA1cKBaNNwX0d78CAUsL2yhN4ZeE7DY+JisZvZTUk2s6H4DwPUKXtEoKFX9VQ4hSoNBH51Ab053RE67mC80O+4vumRlUJSqIV7pjHPS2Fc53zV0wQxKZEIahxwC4gamVRMh0ktLk9TQmu/924ogwpHvgKd0jzW31NEARBEARBEARBEIsFWRIQc32vWNXzZ3PbgjJn9gisSGv3qQLa4io6U751gVAVgtnn/Ffb+3B+awY3T+O9yoYNT5AwIgdFYTVEyc/OCdsjeEaBeSQwJRfNDIm2SwirOMxjthhq8twNuZqKuOqLtbrlzHkV58jgKaRbOyFJIgq6X82ZjChQYhmMG8Mh0XZyWPVruYCEm8dQXkciZKUwF4g1T1tWaduyCoe0zdwHWki0z+nnEARBEARBEARBEMRiRRQDyZBV2zLseuQ6N4ad1fs/enAYn3noEDRFxF+8ficv2BqUu6AoIvfQZfBZuBNgO/56WRK5tlM2/e2rCONzasSqMMsqbWtEdn0Nu/RBLHv17yIVCf2fmgwSbZcQbmm0Xj8aS5MANRFR1T/kmWcq+1HH56iK85kffgnC019AObMJF7/zb3He6I+4SXcqthxqomWcaCtGqdJ2MkRWJT50BJJnYzQ3gtVzLNrujeyAJJWhJVLI9OzEdw77Fb9Xt9JvhiAIgiAIgiAIgiB431wJxMwN+guwBBWKZ6JPWQlL0CCpPbPaUQcG/fAww3JxbKSMLT0p/DBzD4oRf3Ysw6yKs2FGSiY+/ZXvAYKI977+Vj7D2XE9/lgk2QIEk9A5ourbI7S096IQet89x3P4x6++gA/dvhXd6eYMiifRdgnhVUaDitL0+JJwAuh0h6Dre6F5Oir5lYh3dM3Jbkm89EWw000suwf7dz+Hy4s/Yd8IoKxDNPme+omBcUDbirb0avo6JkEJVbwWRvoBrJzTffVA4tX8hL6yLYbXxv2TNyMTa97RNYIgCIIgCIIgCII4m8hyo2S4tfIUv/2Hzo+x0CBsaUnh1bN4fzNURVsy7LqAG8awai60AS8++zhuO/XPfHnXS13YvH4jrs9/A4YQRSy+btzzJdnv97f2rkXhql/GiV2PYLhoYlju5LOwf7p3EG+8ZAWaERJtlxBCVbR1BQnJNAWRTcSawlM4L+ebUpvDFwJzJNrWRnUYJ5//MdqZYMuIphFNBVYVeyPb8f30PbhnWXOeEJqBaKYDuepyeTQI15sLXNerf1cs8GxLTxIbupIYLZm4ch1V2hIEQRAEQRAEQRAEQ5LHFzaxQG8m2DKsCapgZyraFg2b99Vr73l79ovQ3AqgMz3lzxpe17rri6jV4sb3fw96Tye2VZ7039O0YYgxaG6QWSMpgW/tqqvfyP9OZiv41rdeAlwPjx8ewRsuXt6UweQk2i4hJNOXunQpiUjVBoAYs4+0WH3ZKPuG2nMt2iZPPlJfFqMZxFNBKFzMLdW9bomJiWcCId3ID8zpbrJct8FUnfnffOC2zXPub0wQBEEQBEEQBEEQixl5opAuUYEkClwDmcxvdmaVtg4MO6iq7bGOIOJWUNF9DSVMWddR27LnM9fi6lI+2DwtAUNNQNPDou1464PeTBRbe1J48UQOo0UDh44ewdpVzTcjmpS9BaRiOtx7Y05wbMhmgY822Krv0UmMR4oE/qiWHjYtmB171O1YW3meL4dHdOR4K9RIDC8mr0LRi2BU8m0rUlH66U1GoqWTD9wx32G7ODSnh7HlBOK6Ion1ZRJsCYIgCIIgCIIgCCJAlMbrFq6kQpFFOKYzod/smdA2+gxeO/pjXtxmD/wizGUX4s3D/8i9c5lg2xAkXsW2Hbi6L9LqYgyH7E5cUh5tEG0HU+chrjPLSh9JCWwRw1y6phXa3m/zKl3xPg941xdZeTGaiebamnOIbz++B7ufehCrt1+BN1y9fdbvZ7suvpp6G+JuAe0taVwzJ1u59FCiYdF2/IjNTGDl+99NvRHXQcN5lScaPy+e4VMHXui8CyOFMreuYCQ1qrSdDCHexq0LCo6KomHNaRWsUxrFewc+CluQYEkXAvjwnLwvQRAEQRAEQRAEQSwlBFHifXHWJ6/hiQruGv4MZDMLr5gG8E8zfn/RLGKZeZgv95eGYVaKaLdPNT7HaRRt+/tPIuL4Ws6A3IuhkgmzEhTkyZE4Diy7Dj91diLljEKGhTenl034+ReszKDPOYW0M4JSQYB78lmIKy5GM0Gi7QKhPvHPuKm4C/1PPAHrik83VP3NhIIJHNU28OXzO6jSdtL9Hkmg9pO350i0LZt+Cf9Rdf040VZL+tYIyYiMa459Hl3WcVTEOFLqZ+fks5ckqWX4/pY/wwsD/jf1etNBXJubU5Vp6ZA9E7IHCLDm5D0JgiAIgiAIgiAIYiniscIzr+YgC3iShhZrGLI9ClOYXZ9asCvB+1aysIzxGo3kWbAdl1sbMoaP7ak/Nqj0oqjbqBTzdbsEOZJEwpWRk9v4H0OJJCf8/Jgqw1l5FbDneT4rd/jFH6OjyUTb2SmFxIxgoxQ9pV18ucs8ihP9s58CnteDH0s6RlWck6FE4/VlZ4ITwkwom/4J7Ji6lguyYaJVP9tEREbULUH2LH6biEXn5LOXJKKEdDpVv8tSHecKxwpdVCT6nRAEQRAEQRAEQRDEZPy09XX4Qer19fuepMKr9qXHWhecKduH76svi5VhmHog4tbXew7MUD++dOpAfTnilrGt8gSKI331dWosOS5DSJUnlz5XnXcFdNHXZ0pHnmaiAZoJEm0XAMO0WEBdnYEjL836PfOVYOQjRSFXkxKJBWKgY8xNEBkzzGaYYhT/1v4B7I0EdhfxVGvVDkHmJfccSamPEhET054IPGcGi8ac7SbHDL2XNLGvDUEQBEEQBEEQBEEQwKHEhTgQ2RrqR6vwRL+uVXRnJ3Caocn/jlWBbVYmfp6h15fd4UC0ZV601+e/CXF4X4Noy4rmGILnQvJsHkI+GeevasdTyRtwf/IO/HPyN+EIzWVI0Fxbc45QKgQmyYzCCVbefd2s3tMYOojl5gGUxCTSWs8st3DpEokFnraeEQSGzQa37wW8bejvYYgR9HVey020GcyGtVZpe97QvfxkwYgjOOEQE9OWCFIqh+dQtLXtYCRQlOn0RxAEQRAEQRAEQRCTIYkC5JA9Ahdtpapo69k8GEyW/eyeM8X0FESqy4JZhj2JRmMZTMxl/rmAkj8y7vF4+Wh9WYul0JPdjd/o/3N+/4i6ASrPs5mYiCIBm27Di0dGwRwUd5/KY0v3xHYKCwGpFgtAOd8o2j7trMUNs3zP6KEf4O5RPx1PdNjBuXyW77g0iSaCSlvPmhvR1ipn/SpaB8i0idiX7YDq6UhJFgTFL7OPqApqxdVTjfIQPstLL+O6/A+QcPMoD78NQPec7BrHCgRgsXqhIQiCIAiCIAiCIAhiPLIowIOAPZHzeT5MKrkWqezu+uOWaUCWYzOyDbU8sS7aymahQbRlqok3ptI2X65gv7QOnfJJdDp99RnsSsimIRpLIRoZRU1mXmXugypOrcFcuqYVTx8Zxer2OEKZa00BibYLgF6oTpMH8GT8GuyutHBfVGaCPFPccra+HEu3z3oblyqxSIyXu1uCCt2bG09Tu5IP3j+RQvIVv4qvv9CHO89f5pfbstEeRarX18oiWSOcjlb9WD3UbffIyTn5nvh3ZYU8d2QSbQmCIAiCIAiCIAhiMlqcIQhOHs/GLseAshxX97ZjayGwKGCCajR2etHWeunbsEdPIHrxW4BICo7r8UrdGppbgl4po6bSeLIG2H7RlWX6asrxrIUfp+7my9e05bDz5b+sv/6wtgmaW8HWRBrRZCsKoc+WTzPLdufyDD7xuu3oTPoSsuu6TXNAkGi7ABiloNK2Flx1eKiMrb1BFegZU/Hfk42AJKpT8onxiJKIzy77KHTLRVc6gmvnYCdZeqn+Q1KiSdyxsxev2dEDoSrYMqTMsuCrSq6ir+Y0xDKdXO9mo1xWcfZBfTWc6kmffycUREYQBEEQBEEQBEEQk3L9wBeglE7BFDR8uvNDUFg+DxNUq9QE1akwT+3Cvnv/CbbjoqtYQvetvw3DdqF4oQpZt4RyuVgXbU+mzkefocGGgleKvih8fDSoxF2zZg2El/1q3KPqenw781a+bbdF44glW+qiLVNlTpcpxILKaoJts0ElfwvAiNyFnyduwHOxy9Ev+zYGh4Z8H9SZIup+pW1ZTCIVm5sK0qVKraK5YvoBYrPF1YNAMy3me5+EBVtGy5ZX4qi2ATmpFdkd75mTz13KiPFWqNUTq1McnLP3dUOpkwJV2hIEQRAEQRAEQRDEpHjVYC6J+UFWBU4hZDVohywIJ2PwwLOwHJcLrM6BB/k6k4u2Qf9c9iyYpVz9fn/7ZXgkcTMeT1yHiuTrLMdGg6CyFV1dgOILrSnH18Nimu+tG09l6s9b7BOdqdJ2ARiSu/FE3A8eY+FUndZxjB7NAjtmGCBmm5DMPPNMRkVOIsqMlIlJiWsyRkomSobNfVTGCqxnimsEgrsam7hauj0Zxbo3fhyDBQOXrCf7itMSa4Mii3z0TTVzXGCPqrM/rp2GIDKyRyAIgiAIgiAIgiCISRF92VD0fNFWk1mlbUi0NU8v2ppWYIPgVE1jTcuEgMCG4P7kHdCkTuxLSLwCV0l0Abnqc23/eaeG/RnmTMJZ1hLDSLQdknUcKXeUT9ONVTWDhKbUPXEFvrR4IdF2ASga/gEbdYt459Bf8oN/0NgA4Cbs6y/gVF7H5Wvb/LLz6XDiKV5mzt87umzWIuRSp/ZDZh4qluNBlWe3vzwzqLSNxidPGdzW66cdEtP5ktrqlbYJJ4fRsomo6oe6zYZ8cgMeSt8DyXNwfdsW+ioIgiAIgiAIgiAIYhI80ddPWBzZOwf/AurA6zDadhH2DkVhCzJeI0+ugdQY7LgCKr7oL8fWYSUTYvXAVuGougEvxi6F5okw4qv5uhvTncCJfr5sOi4cvYjb9/wBcmIG/S0XQZUvAWLtQP4419TibgFx1d8WURTwYMebsWX0Qexrvx7nLeJvd5EXCi9OCrov2lbEBKD5B1W6fBRfe+oY/uze3fj8zw7jJ7sHpv1+pT0/4QIko7/1knna6qXDpuITuDn3Fdye/SLKhcBfeKZ4ZlBpG43PwpeYCFAT9UrYpOuLtnNBSWnB/sh27ImeDy8V+AwTBEEQxFzz8Y9/HFdeeSVisRgymWCa3lS8/e1v54Pv4b9bb72VvhyCIAiCIBa00pYRd/NQYaHUthXPxK/CC7HLoMuJ075F0Q0qcy3XL5qzzMCf1hJ8i0/DCipvk5rEZ6azcDEm8A6fPMBnSqecUXRFfE1NTHXVn//Oob/ApcPfrN9fc9HN+Grnr2PNhTdiMUOVtguAUxyC6lrcyFnuWA/32JNQPQM/e+ZFQPanzu8fKOKWbad/L6s0iuMvP8rLvotSGsmVO+f/P7DI6TYOQdOf5ct6MQu0zC64TbCqJxtBhBY5fWoiMZ2dKkCItQG5IuJOHtly4HVzJjAbjIf2DWLH8gzWtMe5j06NaVeyEwRBEMQMME0Tb3jDG3DFFVfgM5/5zLRfx0Taz33uc/X7mhaEfRAEQRAEQZxNPKHRplBUIg196Zp1wVQUHQmt1WXB9itsK1IK/9bxAe5l605QT7oy9yR+beCf+LLV9w4U2jrrj2msCpffdiNc3pVEUFD3mp29eNX2Hkji4p6JTqLtAnDloX/ANZUh6HIKyYvuQO7Yk3z9ReWHeGIe8+/YnXwTgPX11zBPT2b4PPaAe/jH30SL4QtaJzMX464LqXrwdIhavL5slGuZgjNHtPwTgyNHISx2l+smQkqwAYwjfEBjOM8qos/cC/i/Hj+Kp4+M4qd7h/BXb9gxRrRd3CdvgiAIorn5yEc+wm8///nPn9HrmEjb3d09T1tFEARBEAQxfYRQpS1DUrW6leF0RVvdcmELKmTPrIu2puP5s889D6qnI2MP8ceLUgaWoCIajdUlWNcyoJeCWdJqosVf2Hgr/vtAHG8a8cVdQWus+l3sgi2DRNuF2OmWLxS6Sgztq7Yi95i/fmvlqbpZcqH/fnjeNXxaHPO5/asf7EFLTMUf37ENkWrQ2GMHh3H/qSh2alux1tqDq268GzGVvtLTIUaCH7JRDvxoZ8oj8RshKzlkYioumvW7ETWUdNBhNUb6ADDf5zPj8FCJXwSyZZOHmomlQXSbR7n3jur5XjkEQRAE0Uw88MAD6OzsREtLC66//nr8yZ/8CdraZjcriCAIgiAIYkZIY0RbWYMm2Dx7hlXJOgazKJi6nZI69SgXZBmalW0Qe1ea+3Fn9gsNz3cFBWrk9wPR1tZhFu16pFgk7ucFtadi0LzAG1ccI9ouBUjhO8t4VgWCbXBh1lWTSPZuQltCQ7ZsoCsZQcGwueetauZQMh0kNBmPHhyG7XgYLBh46UQWF632fxDfePYkBtS1OKGuxTsv68Sy1avO9n9nUSKrMb7/GYY+O9HWdT28KJ/Hf0lrO4IKXmL2aL3bsDdyFHmpFZ4VeOBMFxbO1zn8ON6Y/xZejl6EsrkTnacexBtG7+WPR0vM/ryDviqCIAiiaWDWCK997WuxZs0aHDhwAH/wB3+A2267DY8++igkqXF6Yg3DMPhfjXw+z29d1+V/8w37DOYxdzY+a7FB+4b2DR039Huic83CQufh2e+b8fYIKtoGH8M7hv7Ff/zUu+FuWj7le7QMP1VffjZ2Bba7LnTLgQcPZXG8jmJLGqRqxg3DMXVYpo3aGi2e4dudicrc87aGqMXmpD1yNo6b6b43ibZnmXIhWxcMPS0NaAmsuO5dWH74YQhdW/HiceB/h9fCFKO4sGBw0bbSvw+35e5Dp3UC+u47gdVvRtGwMZD3RxSYWHjlZiZAEdNBjiZQc0i1KrMTbcuWU1+OUpXznBLbdD1+8HianyxXWLEZ+dnelPsKX95ZfhSlig7PCbxxZdk3OycIgiCI6fKBD3wAf/7nfz7lc3bt2oXNmzfPaKe+6U3MHstn+/bt2LFjB9atW8erb2+44YYJX/OJT3yibsUQZnBwEHoomXk+Ox25XI5fr0WyiaJ9Q8cN/aboXHPWofMw7Zv5PG5My4EaEhhZv9rUTcjVdcXsCAYGBuqPH3nkK1AO3Adz4+1YfdldfJ1VzkOpPv8h8VLcMjCAwok92Jl9FBGvMk7AtCCjVNbr6/VSHo5drH+mYaP+mczHtvY8wxEatqWZf1OFwvSsOkm0PcuU8yP1ZSGS8he23gFh6x180VD7YI4e58usspaFJ43kirhMf5GvGxk4EEz7rrK2I8FtFIjpoUUC0dY2SrP7PtnZokpcnbgChpgZoiggE1MwWjK5vcGZMlho7KjqpQIboqvfl5Uzr94lCIIgzm1++7d/G29/+9unfM7atWvn7PPYe7W3t2P//v2TirYf/OAH8f73v7+h0nbFihXo6OhAKlVta84jrGPD2qHs80i0pX1Dxw39puhcc/ah8zDtm/k8bn604a2Qiw4268/x+61tXdBVGeXqa6Kqwm2dGPqJFxHd/224HiAf/Ak6X/Mevv4obP4ZHgS4chRt7R1o9R7DcuN+/0PGfL6oxtDe2Y1KdT2rj1Mso76dK9esRzSe5MuvtB6CW13Ptq22Lc3+m4pEItN6Hom2Zxm9EBJto5lxj3ckg4RgJtqyitrDDjvofLdbafQgV/uP9Q+gxzyCAaUXq9rOvArxXEaNBT4nziztEcrlEtrsfhhCBAmlaoZNzBktVdGWWYawELFwSuXpyA71ITzRQq8UGiptJXV6J0mCIAiCqMEa7+zvbHH8+HEMDw+jp6dnyuAy9jcW1sk4WyIq69iczc9bTNC+oX1Dxw39nuhcs7DQeXiW+0aN8xCxGlo01lD85jmm/3rbRO7Bf+KCLX+ZleMqFgtrF2zfwsAQIxAEkYeQeXZg7TQWT44gEgv15m0TguHbP0GUEIsn6yHwaZRQiyiLSe6ctUXm+7iZ7vtSy+osYxSDxDs55psnTybaDhR0DAz0o8s+Uff5SOl9yBZKqBx9Fq8f/Vf86uCfYHPh52dp65cGWswfkWG4RnlW72UP7sNbhv8B7xj6S2wa8L1SibmDhbuJnoOkM4pcJRBcp0N52K9Yr2GVG0VbhSptCYIgiHnk6NGjePbZZ/mt4zh8mf0Vi8GAMbNR+PrXv86X2frf/d3fxc9//nMcPnwYP/7xj3HnnXdi/fr1uOWWW+i7IgiCIAjirCOLAg8cq99XtIZZq65dnc1qlXCoHOhZTLw1DF+slWxfd2EiLgswM0wTrjW5aAtZg6JGg/u2AdH020+WkqoLtgxv5RX+Z4hA28qZ2VM1M1Rpe5axyn5SHkOJTVBpqxi4oPQwWpxBaMfWo+B14bWjn6k/LsDFqaN74fbv9t9DcNDSsewsbf3SIBISbb3qD3+mmCFPXCmy9JIKF5pXnPwMrhp4hvtAjxYuQXtifCXRZFgjgWh7TF2HhNQKNexpq0z/vQiCIAjiTPnwhz+ML3whSEO+4IIL+O3999+Pa6+9li/v2bOHe6YxWNDY888/z1+TzWbR29uLm2++GR/72McmrKQlCIIgCIKYbyRRwLPRS3FcXQvJs/HOeAvkcuDHWquYHbCi+IzyC/gNfKj+WKWYQyQSg+j41oUsNIwVvJnZT8K19QZh2K6V6PJK2xgUVavON/c/43uJ1yLmFNCTjuCS0PatuuV9iMbiiHasQqxrHZYaJNqeZexSINpqyfHT6WOig2vL3+cHbP+IDl2rYGwz/eShl5EuHuTLEVWB0LFx3rd7KRFNZLA/ch4MQYOsze5HzQy1a8iRQAwm5gZNi6DCnW+A4kg/0DN9CwqvcLK+/Hj8OlyCKNpCnraKSp62BEEQxPzx+c9/nv9NBbO8qhGNRvH973+fvhKCIAiCIJqGltJ+LLcOQvRc7I6cDzWahKEGKpVXrbR95MAw8xTAi9FLcF7lCb6uXMghmW4F3CDAnWHqZXhmSLSVZdhmUGAlKBoEOcKtbh0XsAwdx9K+dhPpaix+FCJpdN30f7BUIdH2LONU/GoKhpZoHf+EeDv32rT1CiKVU7BGlXGibeHIs1ht9/l3Usu5xwgxfZRYCj9oeTMc18OK6Oz8gG29hFr8mBqlStu5Rk5115crWXbMT3+6w7PCNhxJRNBiD2JE7uChcW2uHxzHRGBJpqolgiAIgiAIgiAIgpiMlsJeXFH8EV/uU1ZCkQTIoXwYZkHIBqEfOTDE7+tiYGugl3LQy+NnN1t6BV6o0taKtANmVeNi/XU5wjrsuLfjXciZInQx0G3SUeWc+rJItD3LPN91N17Mn4+oV8J7MhOk2gkCrFg3oB9Cyh5BZdQXmURRAPvnuC5Wl16oP13u3nI2N39JwAyl45qMfMVCyfD370xxjGJdtFWi85/QfK6hZoLgFZOLttOjYjo4iGVAPLAOKZsOULVHEESBG5gTBEEQBEEQBEEQBDExYqjfzOw5ZUnk1gU1BKuMvYePYrhgcD1LFwKB1Swz0TawUqhhmeXGILJ4G5AP+vti1c92KLERw8VgtiwjFT23ZMxz63/bBIzaGoYVv3owEZukyjPVC4wcggAPMduvzC0nVkERXEi5ow1PTa/cPv8bvQSJqRIXbctWY5n+aTEKwAN/xlQ/4NoPctG2hhajStu5Jt7ai+Hqspvvn/brhor+BYB57iSdLDRPh1C0Ibj+Cd8VFH5BIQiCIAiCIAiCIAhiYgQpkA07PL93roQqbVOV44h+99fxK2URz8SuRHT5drx4dJhXx26T25G0/YyZFeaB+mtsFggfsi4cWf1q3GdcDdUz4UDClT3r+XpV9gPHWHhZqz2AshhHRm4/p74qEm3PMkUj8OmIqxNX+sktK2AeblznpZZDVGQgJNoyQ+iWVSTazlS0ZeiGDdesQDz2KNC+CUifJtRtz73A8H5/+bkvwTNK9Yci8fSMtoWYnER78H2IpemLtgNslA9Am30K94x8ii8Pqtfgm53vQ75URmtUwvm04wmCIAiCIAiCIAhiUgRR5mFgjKsK9wL4P9zX9kutvw5HkHF59BjOO/V1qJ7LBd7NOy/Hp7Md/PndSg8ySgLfaHkHtlaewg35r/P1jlFhyq3//kxLaVuBASWUEZTo4jea7Os2q409uK7wLb4czb8XwJpz5hsj0fYsU6xOx49pMi8rn4ho+wo0FoADSusqSJ1r8eBQFK/gPxT2Jq0QEv6PgTgzrjv1Odw8vBuyZ+HYVzZDGDmARLodrW/+NCBPEVA1cihYPvIoYAbBWNEEBZHNNWqyg6dpO44DuTIw7dflhvvQbp2EWzevADyzDF2TYIpRuJTCTRAEQRAEQRAEQRBTIkoynJDVpL9OQlbr4TlBZvlFGLbL12ttK9ESCzxnmR1lpTq72RQCncU2KsiLaXhyFzTBRirKcpqy9ce1aoVtr3kYsn4KF5QfqT8WSUw/nHwpQKLt2cTWsWbwfrS4MYja8kmflupchSCuzCfetRqZdRfh5NOD9XUuqwwlZkREdCB6vjQ+emyXf1s+CfXI00isu3zyrzDair7RMh8NWpbxANP3cmH3ozESbeccSYaltUIsD0LTh+G5/sXgdChHfoo3j/ijeGGvHYtFT7ITH4uhJAiCIAiCIAiCIAhiUgQpEGHFkMWgIotwTAdaKfCibelZzfODahQNh+fNMKyQaOtYFTyavh1D0k1IRmT8+pgCuIjiF1+dP/wdSLnAVoERS7WeU98WibZnEbswiEuz3+PLQ+qlAG6b8HmZrpU4xn4MXq0IHWjpXYvOZARJ0UBOakXaGUG0d9tZ2/alhqCykZxGnoi9EiPlHlw3xeuOD+WRrRphx1QZsuXL646kNZzMiLnDinVAKw9CcXXk81kkU5lxzymbNj73s8P85P7Wy1fBy51ssBFhI4CwSrAc/zelTFLlThAEQRAEQRAEQRDEeE9bluddQ5VE6HDQ5vgzYl1BQs+y1UhE/OdLngWjmEPF9LOc7JBoyywqDdup+9bGJRvX5r+F5dYhjEgdiLn/B0A7IAWBZzXiaaq0JeaJSmE0uBOZ3P9UUjSYkXaoFb+q1hY1tLf3QBQFCL078e/SOsScAj68ZSd9VzOkt7MN/Sf88v5UVMF/KG/AIW0LBo6Vcd0UNsEHW69BSrif2yoUdAs/Sd6F/lgKXTHgQvo25gUv3gUMvcyXC0MnJhRtHzs0gqeP+L+vnnQEqeIpvsy+X1WRUTEsyHYZl+Z/yC0TkhoLA9xK3xhBEARBEARBEARBTILEZr9OUGm7UX8RVnkYLbavW41K7djZmUJMFvDegY9C9kzoxjqI3hV4y/APkXFqEeOAa+kwq7NguWgre9heeZzfZ+8X01mezWpAarSuZKJx9ByzR2iKcrNPfvKTWL16NSKRCC677DI8/rj/ZU3G//7v/2Lz5s38+du3b8f3vudXrzY7eki0FaYQbRnZlu3YE9mJJ+LX4v6VvwG5asB889ZuXk24fcMqtLedW2Xhc0lX72ps603jvN4UVl3/bljdvuR6YKCIgYI+6euG5G48mriRLxd0G5o+hLzcCj0xud0FMTsKq27AV1rejc+2/x4GlYmD4oarwWOM77/Yh2jFF22tSBssJcWXVVfHJaUHcFnpx9iQ/zl9LQRBEARBEARBEAQxBeHq2vDyJbn7cE0h0OIqkW60xVXIssyUXr5OsIoQy0Nos/sheTYeTtzKA8x2td0Cs+aDK0uIjQl1V7WI/3rFv61/vqRAUPzK3XOFBbdH+PKXv4z3v//9+NSnPsUF27/7u7/DLbfcgj179qCzs3Pc8x955BG8+c1vxic+8Qncfvvt+NKXvoS77roLTz/9NM477zw0M0YpMFaW4uOrBcOcWn8PHtzjj1hc2BmMJFy0qgUXrMjwqltiFmy4CZJVBpI9wNprcZlzCsefOs4fevzQCG7f0TtpkFy/vBwDSi8G5GUYldr4+rgWBF4Rc0ukfTX6qgNs2Yof5DeWnv3/jXcNPoqymMBPk6+G5Poirh3vARz2uxtGzC0GLyArC4IgCIIgCIIgCIKYEiEU1F4LImN4Y/rUcuuK+uOuHAPsCmSrCMcI+uGHtY0YlTsx4kbwmpF/gwQHKnohyx8KbA2ZnWHEt7MU5EZ7BFtNsY04p76xBa+0/Zu/+Ru8+93vxjve8Q5s3bqVi7exWAyf/exnJ3z+//t//w+33norfvd3fxdbtmzBxz72MVx44YX4x3/8RzQ7VimotFViU1fadiSCg5NN9w5Dgu0cwDxtz38LsO46/qO/dE0rL99fp78M9/F/hefW8hEbYemHfeoqfLn113B/6k6cUNfU/W2J+aElFlwkRsu+n/A4KiOIuiU+gpfkIq2PkO71v2seFhd4REOk74sgCIIgCIIgCIIgpsJp3VBfHswEFp3eGOuCVJevjTBcxQ8Wk+wyPFYsV8UUfJ0rX7G5f+1y8yA6jKP+c0OFiWokNqFo66nnXvj7gioXpmniqaeewgc/+MH6OlEUceONN+LRRx+d8DVsPavMDcMqc7/xjW9M+jmGYfC/Gvl8nt+6rsv/5hv2GSz1ngWR1VBimSk/e31HHF5VZNrYmTgr27kQ8H3jeQv+/2uNKXiT82205J7h9/sOPI/udeM9g+PDL6DbdFASk7AFBdv0p2AIUfSYW+G6a5fcfmkG0hG5/lsYKRkT7xvdD4Rj3Jj/Wn1ZTvfCM4PfXR1JXZL7lo4b2jd03NBvaimda5bieZogCIIgCGIxwQLF6oRF1DGibcfydcGdWvC750EoD9VXM+2Eka+Y3C4h/D6yKKBWoqWqfuGiqGgItwaFiG99eC6xoKLt0NAQHMdBV1dXw3p2f/fu3RO+5tSpUxM+n62fDGal8JGPfGTc+sHBQej65P6lc0HJdHDvT36CW4wfImYMoVTtgOiOgIEBP2VvItj4wS9d2M7Lw9tlHQMD87udCwXrkOVyOd75Y4L9QiJ3nwd38Cm+vP/x+yAy64Qwro3Ljnwa59suTsor8GD0RlxW+CF/qDhYwMDAtiW5XxYay3QQL5/ASjYS9/IgBtb+OnL5fOO+KQ/zfcZmSnihglpLSQOuBGFMx9+w3Sl/f4sVOm5o39BxQ7+ppXSuKRQK8/beBEEQBEEQxOnRYKNUXZZCVgkQA3uEH6fvxq+tDBWxaYn6olz2i6g8CFjvHYJQKUGsBP3zWjXty6veipX7/wOHI1uxNenbiYpyo2grnmbG+lLknJgjzCp5w9W5rNJ2xYoV6OjoQCo1v0r9N549gSFdRGH0JEqixDs3I3IHVqzciM7OqUu7J7D0XXL4QpvAv4uFFiejV92Ggy//JwTPhTb00nhPZT2HIQh8Oy0ljqTi1Lc5nmmf0IN5KeyXhYaJAjfZD2KlvguqJaJTMyFkMvV9w6rYT7glvmzGenDq/2fvPuDkKMs/gP+2971ec+k9IYQUiITee7EACkpTlKYiigoqCCrFioJipfxFFBQQC71LDQFCSEgC6e363d72Pv/P8+5tu36X6/f7fj73udnZ2dm5udmdd5553uctORBTdv0LEb0VUxcciE1Fs/D36DGwJ3w4r+XXap1Wu3NQ/1+jBY8b7hseN/xMjafvGhlwloiIiIhGzqRpc/C3+TejxevHpYfO7TLrNlK2P2zW7GOdJRtn08dD6nfCYMMx3n9CH/WpXssd17P0iDPwL+f+WDy9HGZjqn1pMEvIOOVp96dw4JxjMNGMaNC2tLQUBoMB9fX1efPlcWVlZZevkfn9WV5YLBb105FcaAx1QGx3axi7LDNUd3q/qRSbrIvVz/cdlgkfjEuTC7/h+F/0pqCoBOGCGbB5NsMSbkTQ2wxnYVnmeS0WQKI9jdOV8OF0z/2Z50w216Bv/2jZL6NBvHQesGsDookk/Lvfh67yoMy+CYb8MCRTXTaS1gIcfOrn8fiLs1BaUYMV5aXY4dchrPfAkcgWQJc7duN1v/K44b7hccPP1Hj5rhmv39NEREREY4XE7K45banqBW40ZNtmupyByKYX5ZdKMFizmbZpCaMVkERG+GDMKbmQzrSdUmLHlcfn917Wm2yI68yI6Uwq0OssSGXgTiQj2ho2m81YtmwZnnvuubzsDXl88MEHd/kamZ+7vHjmmWe6XX6kfeWY2fjGCQvw8syv4+/Fl+B9+wrAbEeBLX+kPRolyuZnJhu3rsl7KhLwZbrehxzVec+ZuvhSosFTMDVbX7h1+3t5z/k9zdkHtiKUua244PQTcMqK1Be+3WxQvw2Ze3SqXwf/PURERERERER9uFmfG7BVckolTC/Kj28ZuihjkDTaoXUYWEytW4K53QhNPw53lV+PP5Zdiy3WhXBbJ14cbcQjF1K24IILLsDy5ctx0EEH4fbbb0cgEMBFF12knj///PMxadIkVZdWfPWrX8URRxyBn/3sZzjllFPwt7/9DatXr8bvf/97jFZzK134/KGz4IMdb+/0YH6VG1ZTKpBEo4t90gJoH/1bTft3rQeWHpd5LhRIDWAnbEXVMPvfVZmfwmRj0HYoTZm5AK2vWmDWIojXrs8rXBtoyxY219uLOr02HbQ1pgudq0zb/DuBRERERERERNQ3k8JbIKPEyLgyc1w5g5W190TOSZlKzwR0OQPQtNOZOgdy08wdAsVu24iHMIfdiP/F55xzjhoQ7Prrr1eDiR1wwAF48sknM4ON7dy5M6973MqVK/HAAw/gu9/9Lq677jrMnj0b//znP7HffvthtJtZ7sTsyok32t1YUjZ9fzS8qFNlspMN+YPhRXKCtgabE06rES2B1PiGzvbAIA2N6WUubLJOQ01oE6IBD3T+OhmBUD0X9maDtsYugrbOWAuWBV5GTXSretxgmoQSWyn/VUREREREREQDUHbgJ2B+9U+wGA1wlWTLSgp9xSI8VngBYjozJke3IiljIRRXYarv3U7r0feQaWs26jE/9DYqYnsQ0jtQoJPBzmwT6v814kFbceWVV6qfrrz44oud5p111lnqh2iwVZaWYJO5AkXROpi8O4BYKHVHSIK2wbbMcgarC85jrkHoyVthsBWgYu6B/GcMIemKoZUtAHZuQiyhIbj3A2BmqmRCzN+SWc7kLO70Wke0CSv9T6vp1Y4j8LrzOJxbM4X/LyIiIiIiIqIBMM05DqW6JOCqBoqm5j1nKSjBTstsNV1rTj13UHkxpoY3dFqP3tR90NZi1GNKdDPmhN9XGb02XDDh/lejImhLNFro9TpEimYD9XWIxpMI1m6EfcoS9Vw0lB3IymxzoXj+4SieNAuwFmQCuzR0CqYuAnY+qqb9e7Jf9rudi7Cm4FzYkz6cUD6n0+ssDld2OpkaudLUsR4PEREREREREfWNBFsXnNHlUy5L51CjTXon58RNNlsWwm8owLLiWd2+hTXarAK26u30euisE6/nOoO2RB0YKhehtmUn9pqnwhq2Y177/HjYl1nGYm//snDnD0hGQ2fyrP3Q/IoZRi0KXdOHmbq2TZobW6wL1PTHSyZ1ep3J6lJ35WRxixZOzWPQloiIiIiIiGjQOboI2trNRhhygrZvOw5Dg6kGi8u7D9pa2pOuhNGgA0z2CfffYtCWqAPH7EPwp7pUMLYy7M4EbWPRVMAvL2hLw2ZqqRsbrNNQHfoQiVArNF8tUFiDtmC26HlXo0nqzA4YdDrENS0TtDUbpW4xEREREREREQ0mGQx8cmwrCmJN6hp8vW05ZAwxXU7Q1qRFM3Vru2O12mHQ65BIarCaDKlRzyYYBm2JOphW4shMb28OZKbXVZ+D5z2HqS+drxV1zuikoS9dkahaii17DNimm4yigB5TCgFvOBu0dVm7+Eoz2WEw6BBPapga+RCfavkDnK0XAFMP4b+MiIiIiIiIaBDpdDqc6n0IxniqxKSMMaNr/oLULkRIb1cDlOXWre2O2WLD1BI7fOE4ypyWCfk/YtCWqINKtxUWkx6RWBLbm7JB22A0Dk1nQFjngNM2Mb8wRppt4cl4NLAQkUgUi1p1mDIJKGh5HxUxEzRrkRqwrBODEZrBkhpUDkBVbAfMyWzWNBERERERERENnoTZkQnaqstyqxOtk4/EvbULYNBi0EFTNQzNBkP3KzFaVG/arnrUThQM2hJ1kdE5pdiBj+q8SLbtha/eBVfFDHV3J81h6eGLhYbM/OpsWYoP6304YX4Zjqi7G4kk4HNOA3BUl6/TjFL7Jqcejil7Z4+IiIiIiIiIBk/SlO3BLMxWR6rEAYCP+Z/D0uAratruuQ0oOaDrlVicwKKzgF1vAssumpD/HgZtibowxxXBEetuhS0ZQOCtw+E69Tsq01ZIGRWHmR+dkVBdYIXVaEAkAuxoCSLsb1UBW6FZC7t9naYKljdnHhuMDNoSERERERERDQmzK/+hzaGu5YVRy5Y4NFl66cW86FOpnwmKkSeiLlRVVmcGrQq37FK/F+79O6rCBgRtFdDrD+R+G6HaODXFNvj9Xpg9W9Cyy5t90lrU/QvN+Xf5DGYGbYmIiIiIiIiGgk6yZHOYbU5YTfq8QcjUtEUSrKg7DNoSdWF6mQsbDMUoijci4a0H4hHM9LyOaUkNHv1M7rMRtEz3EU5o/bUaRTK4dlFmvsHefdA26qyBvn5j5rHRyJrERERERERERENBb83PtLXYndBCjTi+7R+YG16TmW+yWPkP6EH3w7QRTWAVbgu8plI1HY2GkWzZgWRSSz3ZoTYLDa/CismpouVS9qDhg8x8o7P7oG3tnM9ivW15dlnWtCUiIiIiIiIaEnprdjwaYbM7YUUkL2CrU9fmTKjqCYO2RN10w4ezSk1HY0mE6ja1hwnlFhGDtiOpomYm4rrU6JHBSCIz3+Qo7vY1drMBRi07kJyR5RGIiIiIiIiIhoTJlp9pa7W5YLY7Og0CrzPZ+B/oAYO2RN0wFaaCthKsbd2d7Vqv71CbhYZXdZEDjcZKNa3lngRcPQdtDcgGbQ0sj0BEREREREQ0JEz2guz1t14HvUEPsyU/QKuXVFsDx5vpCYO2RN2wl07OTEfqNmWmDZb8O0Y0vIwGPULOKZ3m2wpS5Sy6YjcbYcjJtDUx05aIiIiIiIhoSJgd2fIIBunJLPMsNlUSIS2pN0s3Z/4HesCByIi6UVCeDdomvXsz0wY7g7YjTVc4DWh7PW+eq7Ck2+ULPesxPZLKlvYZi2Awc4RKIiIiIiIioqFgrtofd5d+E2YtjCqXCQvkOt5kUyUREu3jBSWZZdsrZtoSdaOsvAoJXeq+RjCaUzu1wyiINPysFTPzHof1drjs3Y86adPHMtMfOD8GsDwCERERERER0ZCwW40IGNxoNZYj6mpPiNMboOlT49MIzcBByHrDoC1RNyoKbGgzpOqkajnFU83MtB1xxeWTENOlat+E9E78veIqWIyGbpe32LJ1iO2IDMs2EhEREREREU1EBTYTbObUNXq5O5tglcwJ1L5Tfe6IbNtYwvIIRN0wGfRYPemz2BvQYVngf1gUWqXmW+zZ2iw0MqoLrFhnmoRJ0W2wJf1wW3v+KrPk1NNh0JaIiIiIiIhoaOMpVxw1C+v3enH0vPLMfM1ogS7iQ0jvgM89h/+CXjBoS9QDW9kM+MIeNBkrsd0yF5ZkCAtcqexbGjkWox4h11SgeRu8hiKUG4M9Lm+yOlV9c8mYdmi+YdtOIiIiIiIioolofpVb/eTSDFY1GJlZi8BsZOf/3jBoS9SDqkIb1uzyYJ39IPUjbi2q4j4bBbxTT8Qf9CtVPdulRUU9LqszO+CwGOEPxzE/lhqQjIiIiIiIiIiGT1Ph/miKVahyh2aDhG+pJwzaEvWgqqDz4FZOCz82o0FFRQXCe+JqurfyCDDZMb3EgUAkDmdvyxIRERERERHRoNtdeTwa215DTGfCglgdgBncyz1g9IKoB5UuIxaE3kZhohlRnQXvuI6E1cQU/tFgZpkjM13m6hxcz2MwwqDXwW3LjlRJRERERERERMPHBS+Wtj2UetB4GICV3P09YNCWqAdVhQ4c4fsPjFoMHkMpNpUcA50UR6URN7vciTOXTEKzP4LDZpf2/oIFZwIb/wMccN5wbB4RERERERER5bDrE5lpnamX5Cti0JaoJ3aLCSFLKVzhWhQmmnCCV+4ILeFOGwUkeH7a4uq+v+CAzwCLzlJZt0REREREREQ0vGz6VIlDoTeauft7wX7eRL0wOEoy09XRndxfYxkDtkREREREREQjYtruf2WmXaFd/C/0gkFbol5YnIWZaYcW5P4iIiIiIiIiIuonuy6WjbUYGJLsDfsJE/XC4SpEuH3arMum8hMRERERERERUd+UuW2I2FMDhJe7WB6hNwxrE/XCNnVpZrq14mDuLyIiIiIiIiKifjJU74+pJQ71Y6haxP3XC2baEvViyoKDsWvjqUi07cF+R17M/UVERERERERE1F8LPwG07UlNLziD+68XDNoS9UKn1+PQT32Z+4mIiIiIiIiIaKBMVuCIa7j/+ojlEYiIiIiIiIiIiIhGEQZtiYiIiIiIiIiIiEYRBm2JiIiIiIiIiIiIRhEGbYmIiIiIiIiIiIhGEQZtiYiIiIiIiIiIiEYRBm2JiIiIiIiIiIiIRhEGbYmIiIiIiIiIiIhGEQZtiYiIiIiIiIiIiEYRBm2JiIiIiIiIiIiIRhEGbYmIiIiIiIiIiIhGEQZtiYiIiIiIiIiIiEYRBm2JiIiIiIiIiIiIRhEGbYmIiIiIiIiIiIhGEQZtiYiIiIiIiIiIiEYRBm2JiIiIiIiIiIiIRhEjJiBN09Rvr9c7LO+XTCbh8/lgtVqh1zNOzn3DY4afJ37XDDd+D3Pf8LgZe5+ndDst3W6jvmE7d/TguYf7hscNP0/8rhlZ/B7mvhmtx01f27kTMmgrO19Mnjx5pDeFiIiIiHpptxUUFHAf9RHbuURERETjo52r0yZg+oJEzffu3QuXywWdTjfk7ycRdAkQ79q1C263e8jfbyzhvuF+4THDzxO/a0YWv4e5b0brMSNNVGnIVldXs6dSP7CdO3rw+5X7hscNP0/8rhlZ/B7mvhmtx01f27kTMtNWdkhNTc2wv6/8sxm05b7hMcPPE79rRg6/h7lveNyMrc8TM2z7j+3c0YfnHu4bHjf8PPG7ZmTxe5j7ZjQeN31p57LAKhEREREREREREdEowqAtERERERERERER0SjCoO0wsFgsuOGGG9Rv4r7hMcPPE79rhh+/h7lveNzw80T8fuW5Z/TgeZn7hscMP0/8rhlZ/B4eG/tmQg5ERkRERERERERERDRaMdOWiIiIiIiIiIiIaBRh0JaIiIiIiIiIiIhoFGHQloiIiIiIiIiIiGgUYdCWiIiIiIiIiIiIaBRh0HaI/frXv8a0adNgtVqxYsUKrFq1ChPNLbfcggMPPBAulwvl5eU488wzsWnTprxljjzySOh0uryfSy+9FOPd97///U5/97x58zLPh8NhXHHFFSgpKYHT6cQnP/lJ1NfXYyKQz03HfSM/sj8m0jHz8ssv47TTTkN1dbX6G//5z3/mPS9jSV5//fWoqqqCzWbDsccei48++ihvmZaWFpx33nlwu90oLCzE5z//efj9foznfROLxfCtb30LixYtgsPhUMucf/752Lt3b6/H2a233orxftxceOGFnf7uE088ERP9uBFdfe/Iz09+8pNxfdz05Vzdl3PSzp07ccopp8But6v1XHPNNYjH48P819BwmuhtXbZzu8d2bvfYzs1iW7d7bOv2f78ItnPZzh1PbV0GbYfQgw8+iKuvvho33HAD3nnnHSxevBgnnHACGhoaMJG89NJL6sB/44038Mwzz6hgyvHHH49AIJC33CWXXILa2trMz49//GNMBAsXLsz7u1955ZXMc1/72tfw73//G3//+9/VfpSA0yc+8QlMBG+99VbefpFjR5x11lkT6piRz4l8d8hFcVfkb/7Vr36F3/72t3jzzTdVgFK+Z+SEkyaBt/Xr16t9+J///Ec1dL74xS9iPO+bYDCovne/973vqd+PPPKIOimffvrpnZa96aab8o6jL3/5yxjvx42QIG3u3/3Xv/417/mJeNyI3H0iP3fffbe6IJBG23g+bvpyru7tnJRIJFQjNhqN4rXXXsN9992He++9V91YovGJbV22c3vDdm7X2M7NYlu3e2zr9n+/pLGdy3buuGnrajRkDjroIO2KK67IPE4kElp1dbV2yy23TOi93tDQoMmh99JLL2XmHXHEEdpXv/pVbaK54YYbtMWLF3f5nMfj0Uwmk/b3v/89M2/Dhg1q373++uvaRCPHx8yZM7VkMjlhjxn53z/66KOZx7IvKisrtZ/85Cd5x43FYtH++te/qscffPCBet1bb72VWeaJJ57QdDqdtmfPHm287puurFq1Si23Y8eOzLypU6dqv/jFL7TxrKt9c8EFF2hnnHFGt6/hcZMl++noo4/O2z8T4bjpeK7uyznp8ccf1/R6vVZXV5dZ5q677tLcbrcWiURG4K+goca2bmds52axndt3bOemsK3bPbZ1+75f2M7t+zEzUdu5Y6mty0zbISKR97ffflt1VU7T6/Xq8euvv46JrK2tTf0uLi7Om/+Xv/wFpaWl2G+//XDttdeqTLmJQLqyS9eOGTNmqMw2SbcXcvzI3Z/cY0hKJ0yZMmXCHUPyebr//vtx8cUXq4y3iX7MpG3btg11dXV5x0hBQYHqnpo+RuS3dG1fvnx5ZhlZXr6PJDN3on33yPEj+yOXdGuXLjBLlixRXeAnSlfuF198UXXpmTt3Li677DI0NzdnnuNxkyLdof773/+q0hAdjffjpuO5ui/nJPktJUkqKioyy0jmv9frVVnbNL6wrds1tnPzsZ3bt88S27ldY1u3f9jWzWI7t3cTuZ07ltq6xiFZK6GpqUmlTuf+M4U83rhx44TdQ8lkEldddRUOOeQQFWhLO/fcczF16lQVvFy7dq2qRSldmaVL83gmwTVJp5egiXSvvfHGG3HYYYdh3bp1KhhnNps7BZjkGJLnJhKpU+TxeFR9ool+zORKHwddfc+kn5PfEpjLZTQa1clpIh1HUi5CjpHPfOYzqkZr2le+8hUsXbpU7Q/p4iLBf/ks/vznP8d4Jl3GpKvP9OnTsWXLFlx33XU46aSTVEPEYDDwuGknXZ6k7lXHsjTj/bjp6lzdl3OS/O7q+yj9HI0vbOt2xnZuPrZz+4bt3O6xrdt3bOtmsZ3bNxO1nTvW2roM2tKwkhoiEpDMrdsqcuskyp0LGVTpmGOOUcGEmTNnjtv/kgRJ0vbff3/VuJVA5EMPPaQGlaKUP/3pT2pfSYB2oh8z1H9yx/Tss89Wg7bdddddec9J3fHcz6CcqL/0pS+pQvUWi2Xc7u5Pf/rTeZ8f+dvlcyNZCfI5ohSpZys9IGSApYl03HR3riaigX12Jmqbhe3cvmE7l/YV27r52M7tm4nazh1rbV2WRxgi0mVbspU6jjQnjysrKzERXXnllWowmxdeeAE1NTU9LivBS7F582ZMJHJXZ86cOervluNEuktJhulEPoZ27NiBZ599Fl/4whd6XG4iHjPp46Cn7xn53XHwQ+ne0tLSMiGOo3QjVo4jKTifm2Xb3XEk+2f79u2YSKQ8i5y30p+fiX7ciP/9738qe7+3757xdtx0d67uyzlJfnf1fZR+jsYXtnXzsZ3bO7ZzO2M7t2ds6/aObd3esZ3b2URt547Fti6DtkNE7kYsW7YMzz33XF4Ktjw++OCDMZFIdpt8MB599FE8//zzqjtub9asWaN+SybCROL3+1XWhfzdcvyYTKa8Y0i+WKXm7UQ6hu655x7VvV9GaezJRDxm5LMkJ4fcY0Tq6Uit2vQxIr/lxCM1etLkcyjfR+lA93hvxEo9PQn8S12m3shxJPV+O5aUGO92796tatqmPz8T+bjJzXyS72EZnXgiHDe9nav7ck6S3++//35ewD99s2TBggXD+NfQcGBbN4Xt3L5jO7cztnN7xrZuz9jW7Ru2czubaO3cMd3WHZLhzUj529/+pkZxv/fee9VI3F/84he1wsLCvJHmJoLLLrtMKygo0F588UWttrY28xMMBtXzmzdv1m666SZt9erV2rZt27THHntMmzFjhnb44Ydr493Xv/51tV/k73711Ve1Y489VistLVUjGYpLL71UmzJlivb888+r/XPwwQern4kikUiov/9b3/pW3vyJdMz4fD7t3XffVT/ylf3zn/9cTe/YsUM9f+utt6rvFdkHa9euVSOATp8+XQuFQpl1nHjiidqSJUu0N998U3vllVe02bNna5/5zGe08bxvotGodvrpp2s1NTXamjVr8r570iN7vvbaa2pkVHl+y5Yt2v3336+VlZVp559/vjae9408941vfEONgiqfn2effVZbunSpOi7C4fCEPm7S2traNLvdrkaD7Wi8Hje9nav7ck6Kx+Pafvvtpx1//PFq/zz55JNq31x77bUj9FfRUGNbl+3cnrCd2zO2c1PY1u0e27r93y9s57KdO97augzaDrE77rhD/dPNZrN20EEHaW+88YY20cgXaVc/99xzj3p+586dKthWXFysgtyzZs3SrrnmGnXRPN6dc845WlVVlTo+Jk2apB5LQDJNAm+XX365VlRUpAIIH//4x9UXy0Tx1FNPqWNl06ZNefMn0jHzwgsvdPn5ueCCC9TzyWRS+973vqdVVFSofXHMMcd02l/Nzc0q2OZ0OjW3261ddNFFqkEznveNBCO7++6R14m3335bW7FihTp5W61Wbf78+drNN9+cF7gcj/tGGibS0JAGhslk0qZOnapdcsklnW4oTsTjJu13v/udZrPZNI/H0+n14/W46e1c3ddz0vbt27WTTjpJ7T+5CSlBm1gsNgJ/EQ2Xid7WZTu3e2zn9ozt3BS2dbvHtm7/9wvbuWznjre2rq5944mIiIiIiIiIiIhoFGBNWyIiIiIiIiIiIqJRhEFbIiIiIiIiIiIiolGEQVsiIiIiIiIiIiKiUYRBWyIiIiIiIiIiIqJRhEFbIiIiIiIiIiIiolGEQVsiIiIiIiIiIiKiUYRBWyIiIiIiIiIiIqJRhEFbIqIx4sILL8SZZ5450ptBRERERDSo2M4lIurM2MU8IiIaZjqdrsfnb7jhBvzyl7+EpmnDtk1ERERERPuK7VwiooHRaYwAEBGNuLq6usz0gw8+iOuvvx6bNm3KzHM6neqHiIiIiGgsYTuXiGhgWB6BiGgUqKyszPwUFBSojITceRKw7dht7Mgjj8SXv/xlXHXVVSgqKkJFRQX+8Ic/IBAI4KKLLoLL5cKsWbPwxBNP5L3XunXrcNJJJ6l1yms+97nPoampaQT+aiIiIiIa79jOJSIaGAZtiYjGsPvuuw+lpaVYtWqVCuBedtllOOuss7By5Uq88847OP7441VQNhgMquU9Hg+OPvpoLFmyBKtXr8aTTz6J+vp6nH322SP9pxARERERZbCdS0QTHYO2RERj2OLFi/Hd734Xs2fPxrXXXgur1aqCuJdccomaJ2UWmpubsXbtWrX8nXfeqQK2N998M+bNm6em7777brzwwgv48MMPR/rPISIiIiJS2M4loomOA5EREY1h+++/f2baYDCgpKQEixYtysyT8geioaFB/X7vvfdUgLar+rhbtmzBnDlzhmW7iYiIiIh6wnYuEU10DNoSEY1hJpMp77HUws2dlx6tN5lMqt9+vx+nnXYabrvttk7rqqqqGvLtJSIiIiLqC7ZziWiiY9CWiGgCWbp0KR5++GFMmzYNRiNPAUREREQ0PrCdS0TjDWvaEhFNIFdccQVaWlrwmc98Bm+99ZYqifDUU0/hoosuQiKRGOnNIyIiIiIaELZziWi8YdCWiGgCqa6uxquvvqoCtMcff7yqf3vVVVehsLAQej1PCUREREQ0NrGdS0TjjU7TNG2kN4KIiIiIiIiIiIiIUphWRURERERERERERDSKMGhLRERERERERERENIowaEtEREREREREREQ0ijBoS0RERERERERERDSKMGhLRERERERERERENIowaEtEREREREREREQ0ijBoS0RERER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", 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", "text/plain": [ "
" ] @@ -549,17 +549,17 @@ { "data": { "text/plain": [ - "{'MAE': 0.0080215854461753,\n", - " 'MASE': 0.03452452148384243,\n", - " 'NMSE': 2.7266879462026973e-06,\n", - " 'MSE': 0.00010147284380389543,\n", - " 'R2': 0.9999937456506395,\n", - " 'Correl': 0.9999968409538269,\n", - " 'AIC': -9.175519344010073,\n", - " 'logMSE': -9.195719344010072}" + "{'MAE': 0.007963265021941862,\n", + " 'MASE': 0.028261797795356862,\n", + " 'NMSE': 5.2669074036781514e-05,\n", + " 'MSE': 0.00010040799290876968,\n", + " 'R2': 0.999892774142037,\n", + " 'Correl': 0.9999464154243469,\n", + " 'AIC': -9.186068743230287,\n", + " 'logMSE': -9.206268743230288}" ] }, - "execution_count": 32, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -612,7 +612,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -623,18 +623,18 @@ "Matrix Reconstruction Errors:\n", "============================================================\n", "A Matrix:\n", - " Frobenius Norm Error: 0.023059\n", - " Relative Error: 0.008991 (0.90%)\n", + " Frobenius Norm Error: 46.949720\n", + " Relative Error: 17.623318 (1762.33%)\n", "\n", "B Matrix:\n", - " Frobenius Norm Error: 0.008771\n", - " Relative Error: 0.001946 (0.19%)\n", + " Frobenius Norm Error: 0.007565\n", + " Relative Error: 0.001337 (0.13%)\n", "============================================================\n" ] }, { "data": { - "image/png": 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v+jc27iNrLczyr1qurLvuupU5c+Z07Hf22Wdnj//pT3/Kfn777bcrq622Wna+US51lwMGkiu689hjj2W/89xzz53vuep9euSa2GLfM844o9LS0lLZYIMNOs7htddeqyy55JKVQw45pNPrZ8yYkb3v2se7+5y6K1MnTpzYKZ+E+CzjM+2q6/19nNdKK61U2WuvvTrtF3kj9rvtttsGfN7dqf7enrbnnnuuz+9M9bsedRHxeVc9//zzWQ7ZaaedsrqKqu985zvZ/hdffHG/6o7iM4znom6mN1FnE/t1V0e0xRZbVLbeeuteX7/77rtn17KaO6sij8Zx43sT4t9016xUddRRR2XPVb/Hf/zjH7NMV72e8XnGtXj88ccrI0eOzHJXf6y99tqVXXbZpV/7wkCZ+olSieF93U3T0F/RozJ6YkbLdrTwV7dokY+hjrfddluvr4/5mmMahOiRUO0JUB2SOdBpFaKHRPQEjLksq70U99hjjx7nTazONVrbQyHeS/RwiKGL/RW9I2qP1ZPoyRKjOKLHTfREiCGW0fugu5Z+AOhJlLHRIy6mi4hRetGrMUZAVIfxv/TSS1mPuphOqFq+xRY94aI3XwyNj95mIXpBxjRO3fUAq5bL0fMseldGr/yq6GEXPfmjTIupBKrld/Tei3K96uWXX856akY5X5sdohdp9F6rzQ7VUQYxjUatGLlZO8933IvHecc81/H/a48R7y96jMYUANVzjx6itfM6Ry6ozk3cH5FHotdgTI1UvS7xfmLqx9ppHfo7qiJGK8SUSjHFQfS27Kn3XW22iN6o8fnFVCExF3r1/fVHjICJ69IfcV1i3+hRGj0IY8qn6FEIUEbRsz7K0XpZmOVfVdzr145yrE4XWZ1SKXqxR0/4GInRda2Nag4YSK7oTuwXuusNH6J3f+Sa2KKci9EfMXojpmqunkNkiRjFGVmk9n1HORpTSnW9dr2VqXGd4rVxfeM6xM8DFecVIykiZ0QWqooMFL3wY7REPc67KkZBxLG6bl2nuO76nakVI4OqozzCr3/96yyPxGcfdRW1+8W6ETFapD91R1HHEd/HvkaGxCjX6nG6iqnEqs/3JEZ8xLWMDBbf2xiFFOd+7733djp+X7+ndp+Y3jK+vzGCJP4b2SyuRYyE2muvvbLRS5HbIitHnoqZMN5pE+qsWh8GQ8HUT5RKFKLdTVXUX/HHPBYcilDRndp5jrsTQ2lfeOGFbAhe7VyZURkQDQ4xNLK20OxPBUAsyB1DbWO6hN6mj4rhq7G2RISuGCZbayBhJQqs/oob/hheGVNAxRyPMfcpAAxETDUQc+xGWRXT9USngNqbsShP4yYqypieypkonyMDxLoWcSPWm5ijO4bSdy2Pq9MLxfMhplKIY0VHgRieH+cUN3dRyV7bUBHZIaaU6G926FrORm6IG9ULL7ww23o7RpxbVHrUTosQupv+qDvREBENEpFLoiKnKioXIm/cfPPNaaeddkr9FdMlRAVMTNMRDSAx1UbMVd2duImOqURiasyoAKq9MR6qnBK+973vZXklPqfIUv3pjAHQjKICurup+QZrYZZ/VbVz9Nc2FkRHgtr1reLetCcDyRW96a6Ct1p5HNMlhZh2MqaniuPVlj/V9QeqjTpdRcV6b2JanpjO8M4775xvXYkoU2OdzIGKbBPTRUVnkaiHiO9LNFzEVIvV3LGg510VFerRUWVByvyuz1XzW9dMFPVDq6++esfz9ao7qn6e3a0T9uabb/aZN6Kz57nnnptNPRZTlYbIeDH9WqzzUp2iu6/fU7tPqK63UhX1Q1FPFdNgxRbZLaY1jYaYaHCKjkJdG2ziu901a0K9aKigVAZ689m152DM6RiLB0XB0J2oSOlNddREd4tIhuilWe3B2B9RcMQohegFEPM19lR5EAEvehtEMIhW8bghjwIqeqDEwtYDWQh8oNcwCr3w7LPPZr1LopcqAPRXNO5Xb6hibujotRc3yHEzFTdp1TIsKsR76kkfN3ZDoXozFxXxcW4xT3L0HI2eaFVxfnHDHXMCdyduAHsrZ6vvL+ZHjlGN3YkRlvUQN6ux5lY0VsTWXY4ZSENFjO6INSaikSMqTaJnbE9iVEM0UkRvwQkTJmSVKHETHNd4KHNKrEdRvbmPudPjdwOUTVSYRwV2PcvLPMq/2h70/Wk06Om8FyRXxH15beNIV3GOtZXw8TsiO0SFf3Wx7uo5xHoP3d0/1y7O3FU0xsR6UHHMuPZxnaPCPRoVYn2QgZSptaK3fVReR9aJHBaNLdHJoLZzxoKc92D0VuYvaMeDBX19ZKAQuarrdz0ei3zbl1h7IxoJorNsfIabbLJJ1sGitu6p9vd0FY/FKJTuRltU67tiXZZoDImGmVhndJtttulomIjvZGS/rg0V8d2OTj0wFDRUwL96WkRlfq0YFtj1j31U8EfPgf607nc3xDOGc0ZBXjslQ1VMKRGFwEAaKqLHSAwTjZvsGBrYU8Efz0cjQfT0jIUoq2p7S1bVs2X8ggsuyIZoRqt/9JKMgi6uAQAMRtzcR3kSZeV3vvOd7MYqesFVp2fqq3yOcvzBBx/sdZ9VVlkluyGMm+3aURXVaRLj+aooU+MGMaY+iAaUqOivLkhZ+zsfeOCBrNJgMGVs9ESNUQhxM9nX+4tzi/fXtadbNOr0R+SQ6E0bo1i6igxx/fXXZ2X7QG7eozLjE5/4RDbFxq677trjfjH9QFRERaNGbU/Arvmsnjklcl40kETjS1QAVCulaj9jgDKIiuXQ36nz+mNhln8DOacQZWVPxxxIrujpHj3Kye7utbsTOSJmSIhFnu+6666sQaB6nlEmD/QcogEhGuCj0aN2hEl30y4N9HOJDpdnn312NkNDZJ9ouIjzrVqQ8x5q1bI9MlH1M67W+8RnVe/zjUaFEFM11TZKRAfOaBjs77ScMeVpbSeKmMIqvl9RDxSigSH+rVSnhKo1derUjvPoTiwkHtObRf6pnlss9l0V/7/rNGcxnfn06dOzhdphKFijAv5VoHZdXyKGl3YdUREFcwyfvOGGG+a7bnEjHX+0exI399FYcdhhh2UNFV23mA4hehp2N2SvN6eccko2rDNutHtS7VlS25MkCuRYN6K7gnAw81Z2FYV9TPkU02LElFRnnHFGFpYuvfTSBT42AOUVPfTjhi+mH4iK7LgZjsdiZEN3vcli6oiqKJOi0iTK5K6qZWRUps+YMaPT2hNRvsfw+xjBESMUq6IhI8rwqBSISp7Yr7ZnYTU7xE3eRRddNN/vjJ6IkQ16E2V4nHdkhO4aWWrfX5x73GRGpX9VTPnQ05QZXc8lGiMij3SXU6JXX9zMVnt79le8NnJKZI7eplCI99m1x2tc865ZLHJK6NqAMRgxIjUapKJ3Ylyj6PBx8MEHD6jnLUCji0b26EkdU+Xst99+dTvuwiz/+ium0In3GRmiazlS/ds/kFzRnWjgiJGg3VUc9yTu5WNNqW9+85sdDUYxG0KsmxRTSg7kHLq794/7+xi12FWUqQMpTyPjRH3FD37wgzRlypT5ZopYkPMeatEQETnknHPO6XRtIgPE9dltt936dZz4TkTnle7eX9c1RWNUS9d6pWgciAai2s6r8fvjmH3Vw8QUlZHVIqvUTt8V/05+8YtfZA0IVTFdZ6xrEWuLdCfWYol8dvrpp3esZRFrlNWuXxpTt3UdGfOXv/wly98x8gKGghEVkFLW0+/Tn/509gc+pnaKSoxojIhFMmtFxXvcoMdN/EEHHZQ222yzLGDFVAFRKfDkk0/O95raXooxDLSnP+jRIh0hLhZx+n//7//1+3OJCpPaSpPuxO+MUSPRUzFGbkTBGBUq3d2Ix3uKypnJkyenLbbYIquUiQXMBiKO+/GPfzxr6Y+COMRoigiZMbQwQkJtSz0ADESUx3HjFQsaRvkdIwBiRENMMRGVz9FTbubMmVnngui1FuV69XVRXsdro5yKMi9u1KJsj5ECMWVT9HCLyoko56dNm5b1FozXxNRFUbHRdY2FuGmPCvW42YvfX13LoioWaY5pEuI8ozdj9ICLG9a4EYzHI2/UzhXcnai4iNfGWhHx/mLhyDjvmMIxetbF/w/xXIw0OeCAA7Jzj16aUd5H5Udf4hpEQ0RPPeSix2T02Is807UxpjdxIx3rVfUlslWca+wf7y8+u3hv1Sk0qqJnYFTCxLpecUMf0xnEXNgDnVc9Kmwic8V3qLowe3yOMcVIZJdDDz10QMcDaAQxVWGUP9GwHuVkNFLECPjobR7lQLXCsh4WZvnXX9HBIP7Gx/1tlCcxpU2UlXFOsaZjtUNif3NFT/bYY49shGWMPOjPugxR1sW5RKN+VA5HlojzjGsYjSsxDWKUwU8//XRWdsW1jPK+O9VRgvEe4x48ZoSIeoYoJ7s2vEQOit8TnR9jOqvYp6f1JUKcS+wX7y0aLLrmgXivgz3vWr/73e861lfoOtXXYKe7jPOIabNj5MrOO++c5Z0YXRHXPOo9ovzvjzhGNNREx8y+FtSORoD4PfGZxLWIBrd4/1H/VJsXowNNfP6RTSJ/hlgzIxqC4vXRWBDfz8iq8f6jIahWdAyNxetjxHHUt8RnHr87vr/dLQgeYv2VeL62ISPqw2Kq8JitI/4mRB7uOnVb/L2IXBn1ZjAkKtCEDjvssKiB7/TYdtttV1l//fW73X/evHmVo48+urLssstWFltsscrEiRMrjz32WGWVVVapHHjggZ32fe211yrHHntsZc0116wMGzYse80222xTOeOMMypz587t9vgzZ86sLLLIIpWPfexjPZ7zG2+8kf3u//iP/+hxnyeeeCJ7X6effnqv7z/OefHFF+/02B133FHZeuutKyNHjqyMGzeu8qUvfalyww03ZMf77W9/27Hf66+/XvnoRz9aWXLJJbPn4hqE2Cd+vvrqq+f7fdXnqsc5++yzs5+vvfbaTvs9/fTTldGjR1d23XXXXs8fAC655JKsLLnnnnu6LbfXWGONbHv77bezx/72t79VDjjggMrYsWMriy66aGWllVaqfPCDH6xcc801nV77j3/8o3L44Ydnz0c5vvLKK2fl5osvvtip3J40aVJWxsc+G264YXY+3Wlvb6+MHz8+O9dTTjml230iH5x22mlZDhk+fHhlqaWWqmy22WaVr33ta5VXX321Y784RmSY7sQ5xXPxu+L9xfvcYYcdKhdeeGGn/Z566qnKhz70oSxTxPkfccQRlSlTpsxX3ne1++67V0aMGFGZPXt2j/scdNBB2e+uvVZd9Za3qrrLFC+//HLHNV9iiSWyLPbwww93m8Uuuuiiyuqrr15pa2vr9L5i3912263b31l7nOnTp1fGjBmTveeuIodFhnr88cd7fQ8AjVimVrco26Ic+cAHPpDdu82aNWu+15xwwgn9vqfu6e/vwir/erpXrd4/dy3Db7/99uy9jxo1Kvubv9FGG1XOPffcTvv0N1f0dv//wx/+sM/79NrfF+VabZkX7yvKwyizooyO3BNl8b333tvr5/Szn/0se0/xmlVXXTX7DC6++OJsv7gmVTNmzMg+t7gO8Vx8vrXXs7vc8JWvfCV7LupDetKf8+7pdbXf065bvNe+vjO95cfwne98p7LOOutkn+kKK6xQ+cxnPpNlkP5mmfh8ul7H3lx//fWVTTbZJPv+R+b86le/Ol+9UfWca7+nL730UmWPPfbIvn/x73W11VbL6qy6+7caHnzwwcpOO+2U5b+oy9lvv/2yz7c7f/zjH7Nj3n///fM99/3vfz/7ziyzzDKVyZMnd+Tsqq222qqy//779+u9w2C0xP8MTRMIAAAAAJRLTM8TU+/E6ABoBn/4wx+ykTIxoqm3tS9gQWioAAAAAIA6iemO1l577WytgOrCx9DIYvqqWNsrpm6DoaKhAgAAAAAAyE1rfr8aAAAAAAAoOw0VAAAAAABAbjRUAAAAAAAAudFQAQAAAAAA5GaR1MBitflnn302jRo1KrW0tOR9OgD0U6VSSa+99loaN25cam0dmjbzN998M82dO7duxxs2bFgaMWJE3Y5HfuQHgMY11BlCfqA3MgRAY2q0/FDWOoiGbqiIRorx48fnfRoADNL06dPTyiuvPCQhYZmRS6Q30ry6HXPs2LHpiSeeKF1QaEbyA0DjG4oMIT/QFxkCoLE1Sn4oax1EQzdUxEiKcPN9f05LLPHO/y+ClUcNS0WzyB1XpKJZZL1tUtHMWepdqUj+/tpbqWhWf/bOVDTtq22Wiqb1n6+koqkMXzwVxWuvvZ5W32jzjr/j9RY9GSIk7JdWSsPqMMvh3NSeLpvxTHbcMoWEZlXU/DC+gPmh9XeXpaJZdIP3paIpWn549vXi5YdVn5Ef+qP1jZdS0VRGFOfv5FBnCPmBvlS/d489+uiQ5djBqOR9Ag3CPBx9812iWf/NxWiKNddaqyHyQ5nrIBq6oaI63VNUMiwxanQqitGji1fRsMjiI1PRLDJqiVQ0c0YX53sUlkjFq2gYvcRiqWjaRxfnJqGqdZG3U9FUhhfv39xQT9s3MrWmYS0LHhTaJPamIj/0X+tixcsPi8oPfZrVIj80bH5oq++UAc3YULEwMoT8QF/fu6joGl2ge0dRtfEqTYvKd4lm/zfXCPmhzHUQFtMGAAAAAABy09AjKgCgN20tLdm2oNqiL0hJezQAQNnIDwBAXvmhzHUQGioAaFqtLREW6nCc+J8ShgQAKCP5AQDIKz+UuQ7C1E8AAAAAAEBujKgAoGnVdeoGAKAU5AcAIK/8UOY6CA0VADSttjoNvWyrx8kAAA1BfgAA8soPZa6DMPUTAAAAAACQGyMqAGhapm4AAOQHAKBR6h/KPPWTERUAAAAAAEBujKgAoGmZYxoAkB8AgEapfyjzGhUaKgBoWqZ+AgDkBwCgUeofgqmfAAAAAAAAFjIjKgBoWi11WoypnMtYAUA5yQ8AQF75ocx1EIVYTPu8885Lq666ahoxYkTaaqut0tSpU/M+JQCaaOhlPTaKR34AYCjID81NfgCg6PmhraR1ELk3VFx55ZVp8uTJ6YQTTkj33Xdf2njjjdPEiRPT888/n/epAQAFJT8AAPIDADSP3BsqzjzzzHTIIYekSZMmpfXWWy9dcMEFabHFFksXX3xx3qcGQINra6nfRrHIDwAMFfmheckPADRCfmgraR1Erg0Vc+fOTdOmTUs77rjj/51Qa2v285133pnnqQEABSU/AADyAwA0l1wX037xxRfTvHnz0gorrNDp8fj54Ycfnm//OXPmZFvVrFmzFsp5AtCY3umJsOBdEdrqcjbUi/wAwFCSH5rTQPNDUAcBwMLOD2Wug8h96qeBOPXUU9OYMWM6tvHjx+d9SgAUmGGXBPkBAPmBwZAhAMij/qHN1E8L37LLLpva2trSzJkzOz0eP48dO3a+/Y899tj06quvdmzTp09fiGcLABSB/AAADHV+COogAKAkIyqGDRuWNttss3TzzTd3PNbe3p79PGHChPn2Hz58eBo9enSnDQB6EsMu67VRHPIDAENJfmhOA80PQR0EAHnkh7aS1kHkukZFmDx5cjrwwAPT5ptvnrbccst01llnpdmzZ6dJkyblfWoANLjWOg2ZbKh5EktCfgBgqMgPzUt+AKDo+aHMdRC5N1Tss88+6YUXXkjHH398mjFjRtpkk03SlClT5lvgCgBAfgAA1D8AQPPJvaEiHH744dkGAPVUryGTbamcwy6LTn4AYCjID81NfgBgKNRzyqa2ktZBlHUkCQAAAAAAUACFGFEBAEOhrU5zRLbV42QAgIYgPwAAeeWHMtdBaKgAoGmpaAAA5AcAoFHqH8rcUGHqJwAAAAAAIDdGVADQtCyGCQDIDwBAo9Q/BItpAwAAAAAALGRGVADQtGJex7ospl2px9kAAI1AfgAA8soPZa6DsEYFAE2r9V9DLxd0i+MMxnnnnZdWXXXVNGLEiLTVVlulqVOn9rjv9ttvn1paWubbdtttt459DjrooPme33nnnQd1bgBA9+QHACCv/NA2yDqIZqh/MKICAIbAlVdemSZPnpwuuOCCLCScddZZaeLEiemRRx5Jyy+//Hz7X3fddWnu3LkdP//jH/9IG2+8cdp777077RfB4JJLLun4efjw4T4/AGgS8gMAUNb8YEQFAE0rhl3WaxuoM888Mx1yyCFp0qRJab311ssCw2KLLZYuvvjibvdfeuml09ixYzu2m266Kdu/a1CIYFC731JLLTXYywMAdEN+AADyzA9tLeWsf9BQAUDTqtewy9jCrFmzOm1z5szp9vdGz4Rp06alHXfcseOx1tbW7Oc777yzX+f+ve99L+27775p8cUX7/T4LbfckvWIePe7350+85nPZD0fAID6kR8AgDzzQ9sA6iCaqf5BQwUA9NP48ePTmDFjOrZTTz212/1efPHFNG/evLTCCit0ejx+njFjRp+/J+aSfPDBB9MnPvGJ+YZdXnrppenmm29Op512Wrr11lvTLrvskv0uAKCY5AcAYKgyxItNVP9gjQoAmtZgp23q7jhh+vTpafTo0UM+P2P0Zthwww3Tlltu2enx6OFQFc9vtNFGaY011sh6Oeywww5Dci4AUDbyAwCQV35Y2HUQRap/MKICAPopAkLt1lNIWHbZZVNbW1uaOXNmp8fj55jXsTezZ89OV1xxRTr44IP7PJ/VV189+12PPfaYzxAACkp+AACGKkMs20T1D00xomLN5+9No9/oPIdWnk564F2paL78vo+korlzZvdzu+dp2VlvpSKZOXtuKpo1V3p3Kpq5P/1OKprWxRZLRTNigwmpKNpen71wfk/N3I4LepyBGDZsWNpss82yIZJ77rln9lh7e3v28+GHH97ra6+++ups3sn999+/z9/z97//PZsjcsUVVxzQ+fEO+aFvX96+7+/hwiY/NGZ+WL2A+WHO9eekomlbYolUNEXKDwsrQ8gPQFnVqTN406uk4vHZ5a9e+SEM5DjNVP9gRAUATau1paVu20BNnjw5XXTRRekHP/hBeuihh7KFp6K3wqRJk7LnDzjggHTsscd2O+wywsUyyyzT6fHXX389HXXUUemuu+5KTz75ZBY69thjj7TmmmumiRMnLsBVAgBqyQ8AQJ75oXWAdRDNUv/QFCMqAKBo9tlnn/TCCy+k448/PlvAapNNNklTpkzpWODq6aefTq2tnfsLPPLII+n2229PN95443zHi6Gcf/zjH7Pg8corr6Rx48alnXbaKZ188slDtlYGALBwyQ8AQFnzg4YKAJpWS1tLamld8KGXLYMcvhnDLHsaahkLUHX17ne/O1Uq3Q8kHjlyZLrhhhsGdR4AQP/JDwBAXvlhsHUQzVD/oKECgKbV2taSWusQFAYz9RMA0JjkBwAgr/xQ5joIa1QAAAAAAAC5MaICgObV1ppauszDOCgt3Q+HBACakPwAAOSVH0pcB2FEBQAAAAAAkBsjKgBoWrGQVSxotcDHSeWcHxIAykh+AADyyg9lroPQUAFAcy9mVYeg0FrSkAAAZSQ/AAB55Ycy10GY+gkAAAAAAMiNERUANK1YyKoei1m1VMq5kBUAlJH8AADklR/KXAehoQKApmXqBgBAfgAAGqX+IZj6CQAAAAAAYCEzogKAptXS1pJtC3ycki5kBQBlJD8AAHnlhzLXQVhMGwAAAAAAyI0RFQA0eY+GOiymndrrcj4AQPHJDwBAXvmhzHUQGioAaFoW0wYA5AcAoFHqH4LFtAEAAAAAABYyIyoAaFotLS2ppbUOi2m3l3MhKwAoI/kBAMgrP5S5DkJDBQBNq7WtNdsW+DiV+swzCQAUn/wAAOSVH8pcB1HOdw0AAAAAABSCERUANK2WtpZsW+DjVMo57BIAykh+AADyyg9lroMwogIAAAAAAMiNERUANC09IgEA+QEAaJT6hzKPqNBQAUDTshgmACA/AACNUv8QLKYNAAAAAACwkBlRAUDzqtfQy5IOuwSAUpIfAIC88kOJ6yAspg0AAAAAAOTGiAoAmlZrS0tqbW2py3EAgHKQHwCAvPJDmesgNFQA0LRa2lqzbYGP024AIgCUhfwAAOSVH8pcB1HOdw0AAAAAABSCERUANK3WtpZsW+DjtJdz2CUAlJH8AADklR/KXAehoQKAptXS1pJtC3yckoYEACgj+QEAyCs/lLkOwtRPAAAAAABAboyoAKBpWQwTAJAfAIBGqX8IFtMGAAAAAABYyIyoAKBptba9s6DVAh+nvS6nAwA0APkBAMgrP5S5DkJDBQBNq6W1JdvqcRwAoBzkBwAgr/xQ5joIi2kDAAAAAAC5MaICgKbV2tqaWuuwmFXrPO36AFAW8gMAkFd+KHMdRFM0VLxy201p3ojhqSiO/fQ3UtH8bvprqWg2W3GJVDSjZj2dimS1J+7M+xTm89ar/0hFc+wS/y8VzePPz05Fc8m4jVNRvDZr1kL5PS1tLdlWj+PQfF767Q3prSLlh8O+mYpGfmjQ/PC321PRvPXaK6lojlty71Q0j854PRXND1YqTn5YWBlCfqAv8yrvbEVRxKhaoMtT6HMq2kdXxGtE//js8r8+9coPZa6DKGfzDAAAAAAAUAhNMaICALrT0taabQuqHscAABqD/AAA5JUfylwHUc53DQAAAAAAFIIRFQA0rZbW1myrx3EAgHKQHwCAvPJDmesgNFQA0LRa21qzrR7HAQDKQX4AAPLKD2WugyjnuwYAAAAAAArBiAoAmle9FrMqaW8GACgl+QEAyCs/lLgOopzvGoDyzBHZVodtkPNDnnfeeWnVVVdNI0aMSFtttVWaOnVqj/t+//vfTy0tLZ22eF2tSqWSjj/++LTiiiumkSNHph133DE9+uijgzo3AKB78gMAkFt+aBtcHUQz1D9oqACAIXDllVemyZMnpxNOOCHdd999aeONN04TJ05Mzz//fI+vGT16dHruuec6tqeeeqrT89/61rfSOeecky644IJ09913p8UXXzw75ptvvukzBIAmID8AAGXNDxoqAGjuHg112gbqzDPPTIccckiaNGlSWm+99bLCfbHFFksXX3xxz+fb0pLGjh3bsa2wwgqdejOcddZZ6atf/WraY4890kYbbZQuvfTS9Oyzz6af/OQng75GAECX8lh+AAByzA8tA6yDaJb6Bw0VANBPs2bN6rTNmTOn2/3mzp2bpk2blg2N7ChwW1uzn++8884ej//666+nVVZZJY0fPz4LA3/+8587nnviiSfSjBkzOh1zzJgx2ZDO3o4JAORLfgAAhipDzG2i+gcNFQA0rXfmd2yrw/ZOcRkFeBTO1e3UU0/t9ve++OKLad68eZ16JIT4OQr77rz73e/Oejv89Kc/TT/60Y9Se3t72mabbdLf//737Pnq6wZyTABg4OQHACC//NA2oDqIZqp/WGTIjgwAOasuRFWP44Tp06dn8zhWDR8+PNXLhAkTsq0qQsK6666bvvvd76aTTz65br8HAOid/AAA5JUfFkYdRFHrH4yoAIB+ioBQu/UUEpZddtnU1taWZs6c2enx+DnmfuyPRRddNG266abpsccey36uvm5BjgkALHzyAwAwVBli2Saqf9BQAUDTinkZ67UNxLBhw9Jmm22Wbr755o7HYihl/Fzba6E3MXTzT3/6U1pxxRWzn1dbbbUsENQeM+aovPvuu/t9TACgb/IDAJBnfmgdQB1EM9U/mPoJgKZV76kbBmLy5MnpwAMPTJtvvnnacsst01lnnZVmz56dJk2alD1/wAEHpJVWWqljjsmTTjopbb311mnNNddMr7zySjr99NPTU089lT7xiU+8cw4tLenII49Mp5xySlprrbWy4HDcccelcePGpT333HOB3yMA8A75AQAowtRPZat/yLWhIi7Oddddlx5++OE0cuTIbD6s0047LVvQAwAa2T777JNeeOGFdPzxx2eLTW2yySZpypQpHYtRPf300516Sbz88svpkEMOyfZdaqmlsh4Rv//979N6663Xsc+XvvSlLGx88pOfzMLEe9/73uyYI0aMSGUiPwDQrOSHoSM/ANCs9mmS+oeWSqVSSTnZeeed07777pu22GKL9Pbbb6cvf/nL6cEHH0x/+ctf0uKLL97n62PISax4/rdvfCaNGlG/BU0X1BKf/kYqmtunv5aKZrMVl0hFM2rW06lI5j10Zyqa9lf/kYrmS+kDqWgef352KppLPrpxKorXZs1Ka71rXHr11Vc7LQxVL9Xy4S9HH5BGDR+2wMd7bc7ctN5plw7Z+ZJPfnj05E8VKj+MPuybqWjkhwbND3++PRVN+2uvpKI5tnWnVDSPzng9Fc0P9itOfqhmiDXGD02GkB+a24Lmh9rvyLPPzShUJmxrSYWTW0VTgynaR+dzo1nF3++YyqgR8kOZ6yByHVERrTC1vv/976fll18+TZs2Lb3//e/P7bwAgOKSHwAA+QEAmkuh1qiIVqKw9NJLd/v8nDlzsq22xQoAetLS0ppaBrgQdk/HobjkBwDqSX4oh77yQ1AHAcDCzg9lroMozLuO1chjkY5tt902bbDBBj3OKRlDaarb+PHjF/p5AtB4i1nVY6OY5AcA6k1+aH79yQ9BHQQAeeSHlpLWQRTmXR922GHZ/JBXXHFFj/sce+yxWa+H6jZ9+vSFeo4AQLHIDwDAUOSHoA4CAEo29dPhhx+efvGLX6Tbbrstrbzyyj3uN3z48GwDgP6oV0+EsvZmKDr5AYChID80t/7mh6AOAoD+qudIiJaS1kHk2lBRqVTSZz/72XT99denW265Ja222mp5ng4A0ADkBwBAfgCA5rJI3sMtL7/88vTTn/40jRo1Ks2YMSN7PNafGDlyZJ6nBkATaG1rzbZ6HIfikB8AGEryQ3OSHwBohPxQ5jqIXBsqzj///Oy/22+/fafHL7nkknTQQQfldFYANIuW1pbU0lqHqZ9aW+pyPtSH/ADAUJIfmpP8AEAj5Icy10HkPvUTAID8AACofwCA8irEYtoAMBQshgkAyA8AwFCzmPaC01ABQNPSUAEAyA8AQKPUP4R6HafRlPNdAwAAAAAAhWBEBQBNq6WltT6Labdo1weAspAfAIC88kOZ6yDK+a4BAAAAAIBCMKICgKbV0taWWtva6nIcAKAc5AcAIK/8UOY6CA0VADQti2kDAPIDANAo9Q/BYtoAAAAAAAALmREVADQtIyoAAPkBAGiU+ocyj6jQUAFA02ppbc22ehwHACgH+QEAyCs/lLkOopzvGgAAAAAAKAQjKgBoWqZ+AgDkBwCgUeofyjz1UznfNQAAAAAAUAhGVADQtFpaW+rSEyGOAwCUg/wAAOSVH8pcB9EUDRWjt9w2jV58sVQUz8x+OxXN6kuNTEUzs4DXadaIlVKRrLTB+1LR7Pe//0hF8+N1H01F07bBiqlozvzDc6ko3pz92kL5PRbDpDdLTnif/NCH1ZYcUbgvkfzQt5U22j4VTTHzw19T0bStV8D8cH9x8sPCyhDyA32J+qOS1iH1WxEvTyUVTxHPicb8fhdR2b7fFtNecKZ+AgAAAAAActMUIyoAoDstrW3ZtqDqcQwAoDHIDwBAXvmhzHUQGioAaF5RuNejgC9pSACAUpIfAIC88kOJ6yBM/QQAAAAAAOTGiAoAmldr6ztbPY4DAJSD/AAA5JUfSlwHUc53DQAAAAAAFIIRFQA0rZa2tmyrx3EAgHKQHwCAvPJDmesgNFQA0LwshgkAyA8AQKPUPwSLaQMAAAAAACxcRlQA0OSLWdWhR0NJF7ICgFKSHwCAvPJDiesgNFQA0LRaWluzrR7HAQDKQX4AAPLKD2WugyjnuwYAAAAAAArBiAoAmldLnRaziuMAAOUgPwAAeeWHEtdBGFEBAAAAAADkxogKAJpX9Gaoy2La5ezNAAClJD8AAHnlhxLXQWioAKBpWQwTAJAfAIBGqX8IFtMGAAAAAABYyIyoAKB5mboBAJAfAIBGqX8o8dRPFtMGAAAAAAByY0QFAM0r5oesy2La2vUBoDTkBwAgr/xQ4joIDRUANK2WtrZsq8dxAIBykB8AgLzyQ5nrIMrZPAMAC8F5552XVl111TRixIi01VZbpalTp/a470UXXZTe9773paWWWirbdtxxx/n2P+igg1JLS0unbeedd14I7wQAWFjkBwCgjPlBQwUATT70sk7bAF155ZVp8uTJ6YQTTkj33Xdf2njjjdPEiRPT888/3+3+t9xyS/rIRz6Sfvvb36Y777wzjR8/Pu20007pmWee6bRfBIPnnnuuY/vxj3886MsDAHRDfgAA8swPra2lrH/QUAFA84r5Ieu1DdCZZ56ZDjnkkDRp0qS03nrrpQsuuCAttthi6eKLL+52/8suuywdeuihaZNNNknrrLNO+p//+Z/U3t6ebr755k77DR8+PI0dO7Zji94PAEAdyQ8AQJ75obWtlPUPGioAoJ9mzZrVaZszZ063+82dOzdNmzYtGz7ZUeC2tmY/R2+F/njjjTfSW2+9lZZeeun5ej4sv/zy6d3vfnf6zGc+k/7xj3/4/ACgwOQHAGCoMsTcJqp/0FABQNNqaW2r2xZiOOSYMWM6tlNPPbXb3/viiy+mefPmpRVWWKHT4/HzjBkz+nXuRx99dBo3blynsBHDLi+99NKsl8Npp52Wbr311rTLLrtkvwsAqA/5AQDIMz+0DKAOopnqHxYZsiMDQJOZPn16Gj16dKdhkEPhm9/8Zrriiiuy3guxEFbVvvvu2/H/N9xww7TRRhulNdZYI9tvhx12GJJzAQAWjPwAABQ1Q3yzQPUPRlQA0Lxa6rSIVRwnpSwg1G49hYRll102tbW1pZkzZ3Z6PH6OeR17c8YZZ2RB4cYbb8yCQG9WX3317Hc99thjA740AEAP5AcAIK/80DqwOohmqn/QUAFA06r3sMv+GjZsWNpss806LURVXZhqwoQJPb7uW9/6Vjr55JPTlClT0uabb97n7/n73/+ezRG54oorDuj8AICeyQ8AQBGmfipb/YOGCgAYApMnT04XXXRR+sEPfpAeeuihbOGp2bNnp0mTJmXPH3DAAenYY4/t2D/mfDzuuOPSxRdfnFZdddVsLsnYXn/99ez5+O9RRx2V7rrrrvTkk09moWOPPfZIa665Zpo4caLPEACagPwAAJQ1P1ijAoDmlQ2bbKvPcQZon332SS+88EI6/vjjswJ/k002yXoqVBe4evrpp1NrzXHPP//8NHfu3PSf//mfnY5zwgknpBNPPDEbyvnHP/4xCx6vvPJKttDVTjvtlPWAGKq1MgCglOQHACCv/DCIOohmqX/QUAFA86rO71iP4wzC4Ycfnm3diQWoakUvhd6MHDky3XDDDYM6DwBgAOQHACCv/BAGcZxmqH8w9RMAAAAAAJAbIyoAaFotbW3ZVo/jAADlID8AAHnlhzLXQRhRAQAAAAAA5MaICgCaVyxkVZfFtMvZmwEASkl+AADyyg8lroPQUAFA81LRAADIDwBAo9Q/lLihwtRPAAAAAABAboyoAKBptbS2Zls9jgMAlIP8AADklR/KXAehoQKA5tVSp6GXcRwAoBzkBwAgr/xQ4jqI5mioWGPzlEaNSkWx8tsvpKK56eXFU9GsvfTIVDTz2lOh7P3zF1PRnLzbuqloWp6ZmfcpzOfXac1UNJ/dfLFUFLNmjUzH530SID80ZH5YU37ok/zQP/JD4+WHIENQBC3/2oqikvcJNIgifWZF/exco8b83IqqSN+nIp0Lzd5QAQDdaWmJMZP1OQ4AUA7yAwCQV34ocR1EOSe8AgAAAAAACsGICgCaV/RmqMuICu36AFAa8gMAkFd+KHEdhIYKAJpWpaU12+pxHACgHOQHACCv/FDmOohyvmsAAAAAAKAQjKgAoHmZugEAkB8AgEapfwglHVGhoQKA5tXS8s5Wj+MAAOUgPwAAeeWHEtdBlLN5BgAAAAAAKAQjKgBoXq2t72z1OA4AUA7yAwCQV34ocR1EOd81AAAAAABQCEZUANC0Ki2t2VaP4wAA5SA/AAB55Ycy10FoqACgeUXhXo8CvqQhAQBKSX4AAPLKDyWugyjnuwYAAAAAAArBiAoAmpcekQCA/AAANEr9QzCiAgAAAAAAoKRTP33zm99MLS0t6cgjj8z7VABoth4N9dgoLBkCgLqSH0pBfgCgsPmhpZx1EIWY+umee+5J3/3ud9NGG22U96kA0EQqLS2pUocCPo5DMckQANSb/ND85AcAipofylwHkXvzzOuvv57222+/dNFFF6Wllloq79MBABqEDAEAyA8A0Bxyb6g47LDD0m677ZZ23HHHvE8FgGZj2GVTkyEAGBLyQ1OTHwAYEqZ+auypn6644op03333ZcMu+2POnDnZVjVr1qwhPDsAGl4Ml6zHkMmSDrsssoFkCPkBgAGRH5qWOggACp8fSlwHkduIiunTp6cjjjgiXXbZZWnEiBH9es2pp56axowZ07GNHz9+yM8TACiWgWYI+QEAUAcBAMWWW0PFtGnT0vPPP5/e8573pEUWWSTbbr311nTOOedk/3/evHnzvebYY49Nr776ascWQQMAemTqhqY00AwhPwAwIPJDU1IHAcCQMvVT4079tMMOO6Q//elPnR6bNGlSWmedddLRRx+d2tra5nvN8OHDsw0AKK+BZgj5AQBQBwEAxZZbQ8WoUaPSBhts0OmxxRdfPC2zzDLzPQ4Ag1Fpac22BVWPY1A/MgQAQ0l+aE7yAwCNkB/KXAeR62LaADCkonBvrUMBX9KQAAClJD8AAHnlhxLXQRSqoeKWW27J+xQAgAYkQwAA8gMANK5CNVQAwJAsZlWP4wAA5SA/AAB55YcS10FoqACgealoAADkBwCgUeofStxQUc53DQAAAAAANG5DxeOPP17/MwGAoerRUI+NupAhACg8+aFw5AcASpUfWspZBzGoqZ/WXHPNtPLKK6ftttsubb/99tl/4zEAABkCAKgndRAA0PwG1Twzffr0dOqpp6aRI0emb33rW2nttdfOGi7222+/9D//8z/1P0sAGIRKS0uqtLTWYWtx/etEhgCg6OSH4pEfAChPfmgtbR3EoBoqVlpppaxR4sILL0yPPPJItu24447pqquuSp/61Kfqf5YAMBiGXRaODAFA4ckPhSM/AFB4pn7KZ+qnN954I91+++3plltuybb7778/rbPOOunwww/PpoICAJAhAIB6UAcBAM1vUA0VSy65ZFpqqaWyURXHHHNMet/73pf9DACFEsMl6zFksqTDLoeCDAFA4ckPhSM/AFCa/FDiOohBNVTsuuuu2YiKK664Is2YMSPbYiRFrFUBAIUbelmP41AXMgQAhSc/FI78AEBp8kOJ6yAG9a5/8pOfpBdffDFNmTIlTZgwId14443ZqIrqvJEAQErnnXdeWnXVVdOIESPSVlttlaZOndrrZbn66quzqRRj/w033DD98pe/7PR8pVJJxx9/fFpxxRXTyJEjs/WhHn300Ya61DIEAPROfpAfAKCM+WGBmmfiTWy77bZZY8UWW2yRnn/++XTllVfW7+wAYAFUWlrrtg1UlIeTJ09OJ5xwQrrvvvvSxhtvnCZOnJiVld35/e9/nz7ykY+kgw8+OFv7ac8998y2Bx98sGOfb33rW+mcc85JF1xwQbr77rvT4osvnh3zzTffbLjviQwBQFHJD8UlPwBQhvxQGWAdRLPUPwyqoeLMM89MH/rQh9IyyyyTtdD8+Mc/zqZ9uvbaa9MLL7xQ/7MEgAYTZeUhhxySJk2alNZbb72scF9sscXSxRdf3O3+Z599dtp5553TUUcdldZdd9108sknp/e85z3pO9/5TkdvhrPOOit99atfTXvssUfaaKON0qWXXpqeffbZbJRCo5AhAKD3clJ+kB8AoIz5YVBrVETDxHbbbZc++clPZlM+jRkzpv5nBgAFm2N61qxZnR4ePnx4tnU1d+7cNG3atHTsscd2PNba2poNlbzzzju7/RXxePSAqBW9Faoh4IknnsjWhIpjVEX5Gx0G4rX77rtvagQyBACFJz8UjvwAQBnXqJjVjzqIZqp/GFRDxT333JOKpGXO7NQyrDirof9u9lKpaHaYcUMqmpnL7Zn3KRTeFR9ZNxXNkaM2TkVz7r3npaLZbvwSqWiemDUvFcXrr1cWyu+ptLRkWz2OE8aPH9/p8RhWeeKJJ863f6zjNG/evLTCCit0ejx+fvjhh7v9HRECuts/Hq8+X32sp30aQZEyRNHywy2vL5mKZocZN6aikR/6Jj/0z7lTz0lFIz8UI0PID8VTpPwQ4lu4cNJs/xQnzfyfIl2fIp9T0T67Il4jGlelZOdSr/ww0DqIZqp/GFRDRXjllVfS9773vfTQQw9lP8ewkpjXyugKAJrV9OnT0+jRozt+7m40BX2TIQAoE/mhPuQHAMpmesnqIAY1HuXee+9Na6yxRvqv//qv9NJLL2Vb/P94LBbsAIAiqFTqt4UICLVbTyFh2WWXTW1tbWnmzJmdHo+fx44d2+1r4vHe9q/+dyDHLCIZAoCikx+KR34AoEz5oTKAOohmqn8YVEPF5z//+Wwx7SeffDJdd9112RZzV33wgx9MRx55ZP3PEgAGob1Sqds2EMOGDUubbbZZuvnmm//vXNrbs58nTJjQ7Wvi8dr9w0033dSx/2qrrZYFgtp9Yr7Ku+++u8djFpEMAUDRyQ/FIz8AUKb80D6AOohmqn9YZLC9GS666KK0yCL/9/L4/1/60pfS5ptvXs/zA4CGFAtTHXjggVm5uOWWW6azzjorzZ49O02aNCl7/oADDkgrrbRSOvXUU7OfjzjiiLTddtulb3/722m33XZLV1xxRVbeXnjhhdnzLS0tWWeAU045Ja211lpZcDjuuOPSuHHj0p57Ns6aPzIEAPRMfpAfAKCs+WFQDRUx1OTpp59O66yzznzzZo0aNape5wYAhVjocDDH2GeffdILL7yQjj/++GyxqU022SRNmTKlYzGqKEdbW/9vYOM222yTLr/88vTVr341ffnLX87CwE9+8pO0wQYbdOwTHQIibHzyk5/M5ml+73vfmx1zxIgRqVHIEAAUnfxQPPIDAGXJD2kQx2mW+oeWSmWA81mklD73uc+l66+/Pp1xxhnZGwt33HFH+uIXv5j22muvdPbZZ6eFIYacxOLdLz48LY0etUQqit/NXioVzbbP3JCKZuaGjdMDOC/LD5+XiubIURunojn33vNS0cxbd/tUNE/MKs736fXXZqUt1x6fXn311U4LQ9W7fPj7czPqcvw43sorjh2y8y2TImSIouaHW15fMhXN+5+9MRWN/NA3+aF/zp16TiqaeevvkIqmSPlhqDOE/FBcRcgPtd+RqAQqUiZsScVTr8q6Zle0z87nRrOKv98xlVEj5Icy10EMakRFhIMYAhLDRt5+++0UbR0xH9ahhx6avv71r9f/LAFgENor72wLqh7H4B0yBABFJz8Uj/wAQFnyQ5nrIAa1mHY0SkSPhZdffjn94Q9/SA888EB66aWXsrmuYs4qACiCaEiv10Z9yBAAFJ38UDzyAwBlyg+VktZBDKihYs6cOenYY4/NFubYdttt04033pg23HDDbLGNmMsqGi8+//nPD93ZAgANSYYAAOQHAKAuUz/Fghzf/e5304477ph+//vfp7333jtbPfyuu+7KVgmPn9va2gZySAAYMqZuKA4ZAoBGIT8Uh/wAQKMw9dNCbqi4+uqr06WXXpo+9KEPpQcffDBttNFG2RoVMfVTrFkBACBDAAD1oA4CAMpjQA0Vf//739Nmm22W/f8NNtggDR8+PJvqSSMFAEVVzpkdi0eGAKCRyA/FID8A0Ejkh4XYUDFv3rxsEauOFy+ySFpiiSUW8BQAYGiYuqE4ZAgAGoX8UBzyAwCNwtRPC7mhIlYcP+igg7KRFOHNN99Mn/70p9Piiy/eab/rrruuDqcGADQLGQIAkB8AgLo0VBx44IGdft5///0H8nIAWOiV47HV4zgsGBkCgEYhPxSH/ABA2fJDmesgBtRQcckllwzdmQBAnbX/a6vHcVgwMgQAjUJ+KA75AYCy5Ycy10G05n0CAAAAAABAeQ1oRAUANJIYLVmPEZMlHXUJAKUkPwAAeeWHMtdBGFEBAAAAAADkxogKAJpWe+WdrR7HAQDKQX4AAPLKD2Wug9BQAUDTqlQq2VaP4wAA5SA/AAB55Ycy10GY+gkAAAAAAMiNERUANK32f231OA4AUA7yAwCQV34ocx2EhgoAmlYMlqzHiMlyDroEgHKSHwCAvPJDmesgTP0EAAAAAADkxogKAJpWe6WSbfU4DgBQDvIDAJBXfihzHYQRFQAAAAAAQG6MqACgueeIrNNxAIBykB8AgLzyQ5nrIDRUANC02ivvbPU4DgBQDvIDAJBXfihzHYSpnwAAAAAAgNwYUQFA86qkVJc1qEramwEASkl+AADyyg8lroPQUAFA02pPlWyrx3EAgHKQHwCAvPJDmesgTP0EAAAAAADkxogKAJpWpU5DL+s2fBMAKDz5AQDIKz+UuQ7CiAoAAAAAACA3RlQA0LTaK+9s9TgOAFAO8gMAkFd+KHMdhIYKAJqWqRsAAPkBAGiU+odg6icAAAAAAICFzIgKAJpWe6pkWz2OAwCUg/wAAOSVH8pcB2ExbQAAAAAAIDdNMaJi3oO/S/MWH5mKYpMn/paKZpGJ/5mKZvnh81LRtNz3y1QkbWOWSUVz/EsPpqJp+0fxzunRWW+nolmz7dVUFLPaXlsov8caFTRSfnjPY4+mollk1w+nopEf+tY6aslUNPJD/8gPxcgQ8gN9afnXVhRF7HdbpOtT5OtUxHOib77fjXedFsa5WKNiwTVFQwUAdKe9Usm2BVWPYwAAjUF+AADyyg9lroMw9RMAAAAAAJAbIyoAaFrz2t/Z6nEcAKAc5AcAIK/8UOY6CA0VADQtUzcAAPIDANAo9Q/B1E8AAAAAAAALmREVADSt6IUwz2LaAID8AAA0QP1DMKICAAAAAABgITOiAoCm1V6pT0+EOA4AUA7yAwCQV34ocx2EhgoAmta89ne2ehwHACgH+QEAyCs/lLkOojXvEwAAAAAAAMpLQwUATSuGXdZrGyovvfRS2m+//dLo0aPTkksumQ4++OD0+uuv97r/Zz/72fTud787jRw5Mr3rXe9Kn/vc59Krr77aab+Wlpb5tiuuuGLI3gcANItGyA9BhgCA5swP7SWtgzD1EwBNa16lkm31OM5QiYDw3HPPpZtuuim99dZbadKkSemTn/xkuvzyy7vd/9lnn822M844I6233nrpqaeeSp/+9Kezx6655ppO+15yySVp55137vg5QggA0Pj5IcgQANB8+aHMdRAaKgAgJw899FCaMmVKuueee9Lmm2+ePXbuueemXXfdNQsB48aNm+81G2ywQbr22ms7fl5jjTXS17/+9bT//vunt99+Oy2yyCKdQsHYsWMX0rsBABYWGQIAaLb8YOonAJpWrD/VXqnD9q/jzZo1q9M2Z86cBTq/O++8MyvIqwEh7Ljjjqm1tTXdfffd/T5ODLmMYZu1ASEcdthhadlll01bbrlluvjii1NliHt2AkAzKHp+CDIEADRpfqiUtw5CQwUA9NP48ePTmDFjOrZTTz11ga7djBkz0vLLL9/psSjol1566ey5/njxxRfTySefnA3VrHXSSSelq666KhvOuddee6VDDz006ykBADR2fggyBAA0v/Elq4Mw9RMATWteeyXb6nGcMH369KzXQNXw4cO73f+YY45Jp512Wp9DLhdU9KjYbbfdsnkiTzzxxE7PHXfccR3/f9NNN02zZ89Op59+erboFQBQvPwQZAgAKHd+KHMdRO4NFc8880w6+uij069+9av0xhtvpDXXXDNbeKN2CAoADEYMM2yvw3RH1eGKERBqQ0JPvvCFL6SDDjqo131WX331bO7G559/vtPjMcfjSy+91Oe8jq+99lq2SNWoUaPS9ddfnxZddNFe999qq62yXg8xVLS3CpJGIT8A0Gz5IcgQQ0t+AKDo+aHMdRC5NlS8/PLLadttt03/9m//ljVULLfccunRRx9NSy21VJ6nBQALJMqz2PoyYcKE9Morr6Rp06alzTbbLHvsN7/5TWpvb88K9d56MUycODEr7H/2s5+lESNG9Pm7/vCHP2TlazM0UsgPADQrGWLoyA8ANKvlmqQOIteGihiSEnNtxQiKqtVWWy3PUwKgicyrvLPV4zhDYd111816JBxyyCHpggsuSG+99VY6/PDD07777pvGjRvX0fNvhx12SJdeemm2IFUEhJ122ikbhfijH/2oY1GtEMGkra0t/fznP08zZ85MW2+9dRYgYo7Ib3zjG+mLX/xiagbyAwBlzg9Bhhg4+QGARsgPZa6DyLWhIlpgojVm7733TrfeemtaaaWVsoU24mJ1J4aK1K5uXr0oANCd9joNvazX8M3uXHbZZVkwiCDQ2tqaLTp1zjnndDwfweGRRx7JQkG477770t133539/5gusdYTTzyRVl111WwI5nnnnZc+//nPZ0NGY78zzzyzx/K10cgPAJQ9PwQZYmjzQ1AHAcDCzg9lroPItaHi8ccfT+eff36aPHly+vKXv5zuueeebIGNYcOGpQMPPHC+/WNl86997Wu5nCsADIWll146XX755T0+H4V+dX7KsP3223f6uTvRQyK2ZiU/AIAMMdT5IaiDAKDZLF3gOohcGypi/qtYNDuGglRXBH/wwQezoSfdBYVjjz02CxW1Iypi6igA6M689kq2Lah6HIP6kR8AGEryQ3MaaH4I6iAAWNj5ocx1EK15/vIVV1wxrbfeevPNlfX00093u38svlFd7by/q54DAM1FfgAAhjo/BHUQALDw5DqiYtttt83mvKr117/+Na2yyiq5nRMAzaNR5phmYOQHAIaS/NCc5AcAhlKjrFFRZLk2VMQCG9tss0029PLDH/5wmjp1arrwwguzDQAW1LzKO1s9jkNxyA8ADCX5oTnJDwA0Qn4ocx1ErlM/bbHFFun6669PP/7xj9MGG2yQTj755HTWWWel/fbbL8/TAgAKTH4AAOQHAGguuY6oCB/84AezDQDqzdQNzUt+AGCoyA/NS34AYKiY+qkJGioAYKi0t1eyrR7HAQDKQX4AAPLKD2Wug8h16icAAAAAAKDcjKgAoGlFJ4R6LEJV0s4MAFBK8gMAkFd+KHMdhBEVAAAAAABAboyoAKBpWQwTAJAfAIBGqX8I9TpOo9FQAUDTmlepZFs9jgMAlIP8AADklR/KXAdh6icAAAAAACA3RlQA0LTa2yvZVo/jAADlID8AAHnlhzLXQRhRAQAAAAAA5MaICgCa1rxsbsf6HAcAKAf5AQDIKz+UuQ5CQwUATau9Usm2ehwHACgH+QEAyCs/lLkOwtRPAAAAAABAboyoAKBpzatUsq0exwEAykF+AADyyg9lroNoioaKU9snpOHzlkhFcdKmy6Wi+c3bq6Si+be/3Z2K5ql1P5iKZPqsOalonnnylVQ0e717w1Q0735qWiqa818en4rin7MXzoyL7e2VNK+9DlM/1eEYFE/h8sPm8kOj5ocn19ktFcnfX5ubikZ+6J+1n5iaiuaCV1dNRbIwMoT8QF8iGRYpHbak4inS9SnydSoan1vjXqciqpTsXOqVH8pcB2HqJwAAAAAAIDdNMaICALozr049GurVKwIAKD75AQDIKz+UuQ7CiAoAAAAAACA3RlQA0LT0iAQA5AcAoFHqH8o8okJDBQBNa157fQr4OA4AUA7yAwCQV34ocx2EqZ8AAAAAAIDcGFEBQNMy9RMAID8AAI1S/xBM/QQATUZDBQAgPwAAjVL/UOaGClM/AQAAAAAAuTH1EwBNq71OPRriOABAOcgPAEBe+aHMdRBGVAAAAAAAALkxogKApjWvUp8eDXEcAKAc5AcAIK/8UOY6CA0VADQti2kDAPIDANAo9Q/BYtoAAAAAAAALmREVADQtIyoAAPkBAGiU+ocyj6jQUAFA03q7vZLa6lDAx3EAgHKQHwCAvPJDmesgWvM+AQAAAAAAoLyMqACgaZn6CQCQHwCARql/KPPUT0ZUAAAAAAAAuTGiAoCm1V6nHg1xHACgHOQHACCv/FDmOggNFQA0rXmVSrbV4zgAQDnIDwBAXvmhzHUQpn4CAAAAAAByY0QFAE3LYtoAgPwAADRK/UOZF9PWUAFA09JQAQDIDwBAo9Q/lLmhwtRPAAAAAABAbjRUAND0PRrqsQ2Vl156Ke23335p9OjRackll0wHH3xwev3113t9zfbbb59aWlo6bZ/+9Kc77fP000+n3XbbLS222GJp+eWXT0cddVR6++23h+x9AECzaIT8EGQIAGjO/DCvpHUQpn4CgBxFQHjuuefSTTfdlN566600adKk9MlPfjJdfvnlvb7ukEMOSSeddFLHzxEGqubNm5cFhLFjx6bf//732fEPOOCAtOiii6ZvfOMbQ/p+AICFQ4YAAJopP2ioAKBpzau0p3nt7XU5zlB46KGH0pQpU9I999yTNt988+yxc889N+26667pjDPOSOPGjevxtREKIgR058Ybb0x/+ctf0q9//eu0wgorpE022SSdfPLJ6eijj04nnnhiGjZs2JC8HwBoBkXPD0GGAIDmzA9lroMw9RMATau9TkMu4zhh1qxZnbY5c+Ys0Pndeeed2VDLakAIO+64Y2ptbU133313r6+97LLL0rLLLps22GCDdOyxx6Y33nij03E33HDDLCBUTZw4MTvnP//5zwt0zgDQ7IqeH4IMAQDNmR/mlbgOwogKAOin8ePHd/r5hBNOyHoHDNaMGTOyuRs7FcyLLJKWXnrp7LmefPSjH02rrLJK1tvhj3/8Y9ZL4ZFHHknXXXddx3FrA0Ko/tzbcQGA4ueHIEMAQPMbX7I6CA0VADSt6InQWodFqKoLWU2fPj1bcKpq+PDh3e5/zDHHpNNOO63PIZeDFfNHVkWvhRVXXDHtsMMO6W9/+1taY401Bn1cACC//BBkCAAod34ocx2EhgoA6KcICLUhoSdf+MIX0kEHHdTrPquvvno2v+Pzzz/f6fG33347vfTSSz3O/didrbbaKvvvY489loWEeO3UqVM77TNz5szsvwM5LgCw8PJDkCEAgLLWQWioAKBpvd2eUksdejTEcQZiueWWy7a+TJgwIb3yyitp2rRpabPNNsse+81vfpPa29s7Cv7++MMf/pD9N3o1VI/79a9/PQsg1WGdN910UxZw1ltvvYG9GQAombzyQ5AhAKDc+aHMdRAW0wagadVrIavqsMt6W3fdddPOO++cDjnkkKz3wR133JEOP/zwtO+++2ZzP4ZnnnkmrbPOOh29E2Jo5cknn5wFiyeffDL97Gc/SwcccEB6//vfnzbaaKNsn5122ikLAx/72MfSAw88kG644Yb01a9+NR122GG9TjcBABQ/PwQZAgCaNz/MK2kdhIYKAMjRZZddloWAmN9x1113Te9973vThRde2PH8W2+9lS1S9cYbb2Q/Dxs2LP3617/OgkC8LoZ47rXXXunnP/95x2va2trSL37xi+y/0bNh//33z4LESSedlMt7BADqT4YAAJopP5j6CYCmVe/FMIfC0ksvnS6//PIen1911VVTpfJ/v3/8+PHp1ltv7fO4q6yySvrlL39Zt/MEgLJohPwQZAgAaO7FtMuWHzRUANC0GqWiAQAoDvkBAMgrP5S5DsLUTwAAAAAAQG6MqACgabXXaRGqOA4AUA7yAwCQV34ocx2EERUAAAAAAEBujKgAoGlFb4YWa1QAAPIDANAA9Q9lXqOiKRoqPrrJuLTEqNGpKNrenJOKZs0lRqaiuW+/b6aiefSbl6Yi2ftdxRv01PLSfalobnp6q1Q0E277VSqaT6y2RiqKWbP/mb60EH5PpVJJlToU8HEcmo/80Le1RhUwP3xUfujL3uNT4RQxP/xmevHyw5a335iKpkj5YWFlCPmBvrT8awOAeueHMtdBFK8WFAAAAAAAKI2mGFEBAD0tQFWPRajKupAVAJSR/AAA5JUfylwHoaECgOYeelmHIZNlHXYJAGUkPwAAeeWHMtdBmPoJAAAAAADIjREVADStWMiqLotpl3TYJQCUkfwAAOSVH8pcB2FEBQAAAAAAkBsjKgBoWhbDBADkBwCgUeofgsW0AaDJVNrf2epxHACgHOQHACCv/FDmOghTPwEAAAAAALkx9RMATatSqWRbPY4DAJSD/AAA5JUfylwHoaECgKZljQoAQH4AABql/qHMa1SY+gkAAAAAAMiNERUANK1KeyXb6nEcAKAc5AcAIK/8UOY6CCMqAAAAAACA3BhRAUDzqlePhpL2ZgCAUpIfAIC88kOJ6yByHVExb968dNxxx6XVVlstjRw5Mq2xxhrp5JNPLu3K5gDUV3ulUreN4pAfABhK8kNzkh8AaJT80F7SOohcR1Scdtpp6fzzz08/+MEP0vrrr5/uvffeNGnSpDRmzJj0uc99Ls9TAwAKSn4AAOQHAGguuTZU/P73v0977LFH2m233bKfV1111fTjH/84TZ06Nc/TAqBJxAi9uiymXdLeDEUlPwAwlOSH5iQ/ANAI+aHMdRC5Tv20zTbbpJtvvjn99a9/zX5+4IEH0u2335522WWXbvefM2dOmjVrVqcNAHoSIaFeG8UhPwAwlOSH5jTQ/BDUQQCQR36olLQOItcRFcccc0zW2LDOOuuktra2bM7Ir3/962m//fbrdv9TTz01fe1rX1vo5wkAFIf8AAAMdX4I6iAAoCQjKq666qp02WWXpcsvvzzdd9992VoVZ5xxRvbf7hx77LHp1Vdf7dimT5++0M8ZgMbR3h5bpQ5b3u+EWvIDAENJfmhOA80PQR0EAAs/P1RKWweR64iKo446KuvVsO+++2Y/b7jhhumpp57Kei0ceOCB8+0/fPjwbAMAykt+AACGOj8EdRAAUJKGijfeeCO1tnYe1BFDMNvL2mwEQP0Xs6rDIlRlXciqqOQHAIaS/NCc5AcAGiE/lLkOIteGit133z2bE/Jd73pXWn/99dP999+fzjzzzPTxj388z9MCoElU2t/Z6nEcikN+AGAoyQ/NSX4AoBHyQ5nrIHJtqDj33HPTcccdlw499ND0/PPPp3HjxqVPfepT6fjjj8/ztACAApMfAAD5AQCaS64NFaNGjUpnnXVWtgFAvcUiVC3tlboch+KQHwAYSvJDc5IfAGiE/FDmOojOC0QAAAAAAACUZUQFAAylSnsl2+pxHACgHOQHACCv/FDmOggNFQA0LRUNAID8AAA0Sv1DmRsqTP0EAAAAAADkxogKAJpWe6WSWip1WEy7DscAABqD/AAA5JUfylwHoaECgKZl6icAQH4AABql/iGY+gkAAAAAAGAhM6ICgKZVqdSnR0McBwAoB/kBAMgrP5S5DsJi2gAAAAAAQG6MqACgaUVvhvZ6jKioU68IAKD45AcAIK/8UOY6CA0VADT30Ms6DJks67BLACgj+QEAyCs/lLkOwtRPAAAAAABAboyoAKBpxXDJuiymXdJhlwBQRvIDAJBXfihzHYQRFQA0rZgfsl7bUHnppZfSfvvtl0aPHp2WXHLJdPDBB6fXX3+9x/2ffPLJ1NLS0u129dVXd+zX3fNXXHHFkL0PAGgWjZAfggwBAM2ZH9pLWgfRFCMqWlve2Yqi8uoLqWhaRq2ZiubO4/8nFc3hiz2ViuTtYeulonlzvQ+kovn3Aja5tuwzORXN1JeK84dy9muvpZQ+l/dpFEIEhOeeey7ddNNN6a233kqTJk1Kn/zkJ9Pll1/e7f7jx4/P9q914YUXptNPPz3tsssunR6/5JJL0s4779zxc4QQ/o/80A/yQ2Pmh+HFyw//XHfHVDTbtRWnXKySH/omQ/wfGYIiK95f2GIqWp9pn5vr1Kx8txsjPzRFQwUAdKfSPi/bFlQ9jtGdhx56KE2ZMiXdc889afPNN88eO/fcc9Ouu+6azjjjjDRu3Lj5XtPW1pbGjh3b6bHrr78+ffjDH05LLLFEp8cjFHTdFwBo7PwQZAgAaM78UOY6iAL2QwaAYpo1a1anbc6cOQt0vDvvvDMryKsBIey4446ptbU13X333f06xrRp09If/vCHbLhmV4cddlhadtll05ZbbpkuvvjiVKkUrc8WADS/eueHIEMAQPObVbI6CCMqAGha9e4RGUMea51wwgnpxBNPHPRxZ8yYkZZffvlOjy2yyCJp6aWXzp7rj+9973tp3XXXTdtss02nx0866aT07//+72mxxRZLN954Yzr00EOzeSc/9zlTbgFAI+eHIEMAQPOPqBhfsjoIDRUANK1Ke3udKhras/9Onz49W3Cqavjw4d3uf8wxx6TTTjutzyGXC+qf//xnNo/kcccdN99ztY9tuummafbs2dkckhoqAKCY+SHIEABQ7vxQ5joIDRUA0E8REGpDQk++8IUvpIMOOqjXfVZfffVs7sbnn3++0+Nvv/12eumll/o1r+M111yT3njjjXTAAQf0ue9WW22VTj755GyoaG8VJABAPvkhyBAAQFnrIDRUANC0KvPmZVs9jjMQyy23XLb1ZcKECemVV17J5njcbLPNssd+85vfpPb29qxQ78+Qyw996EP9+l0xh+RSSy2lkQIACpofggwBAOXOD2Wug9BQAUDTqlTqNMd0pT5ho6uY13HnnXdOhxxySLrgggvSW2+9lQ4//PC07777pnHjxmX7PPPMM2mHHXZIl156abYgVdVjjz2WbrvttvTLX/5yvuP+/Oc/TzNnzkxbb711GjFiRLrpppvSN77xjfTFL35xSN4HADSToueHIEMAQHPmhzLXQWioAIAcXXbZZVkwiCDQ2tqa9tprr3TOOed0PB/B4ZFHHsmGV9a6+OKL08orr5x22mmn+Y656KKLpvPOOy99/vOfT5VKJa255prpzDPPzMIIANAcZAgAoJnyQ0slXt2gZs2alcaMGZPufXR6WmJU/+b8XBjWmHFXKpq/rzwhFc1PH34hFc3hyz6XiuTtceulopnTOiIVzbDWVDgtc15PRTP1pZZUFLNfey3tssnq6dVXX+33nM2DKR/G7nVWal105AIfr/2tf6YZ1x45ZOfLwiU/9J/80D/yQ9/ebCne2jjD24pTLlbJD/lmCPmB/n5HZs6YIRNSF0WrkCteyQj1+/u9wtixDZEfylwHUcDqPQAAAAAAoCxM/QRA04r5Iesyx3Sd5pkEAIpPfgAA8soPZa6D0FABQNNS0QAAyA8AQKPUP5S5ocLUTwAAAAAAQG6MqACgaVXa2+s09VN7Xc4HACg++QEAyCs/lLkOQkMFAE2rPUJCHYJCdhwAoBTkBwAgr/xQ5joIUz8BAAAAAAC5MaICgKZlMW0AQH4AABql/iFYTBsAAAAAAGAhM6ICgKZlRAUAID8AAI1S/1DmERUaKgBoXvPmpUprHQr4eeUMCQBQSvIDAJBXfihxHYTFtAEAAAAAgNwYUQFA06pU5qVUhyGT2XEAgFKQHwCAvPJDmesgjKgAAAAAAAByY0QFAE2r0t5enxEVcRwAoBTkBwAgr/xQ5joIDRUANK1KhIS6NFSUc9glAJSR/AAA5JUfylwHYeonAAAAAAAgN0ZUANDkQy8XfMhkWYddAkAZyQ8AQF75ocx1EBoqAGhapm4AAOQHAKBR6h+CqZ8AAAAAAAAWMiMqAGhaRlQAAPIDANAo9Q/BiAoAAAAAAICFzIgKAJpWe/u81FKHHg1l7c0AAGUkPwAAeeWHMtdBaKgAoGlV5rWn1DKvPscBAEpBfgAA8soPZa6DaM37BAAAAAAAgPIyogKAplWp1Gcxq+w4AEApyA8AQF75ocx1EBoqAGha2byO9Zj6qaTzQwJAGckPAEBe+aHMdRCmfgIAAAAAAHJjRAUATUuPSABAfgAAGqX+IRhRAQAAAAAAsJA19IiKSqWS/ff1115LRTLr9dmpaF57bVYqmjdnF+tzC7NGFOuze3tW8T63Oa1zU9EMK+Akdi1zivVdCrNfa0lFMfv11zr9HR8qlbferE9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+juf+5je/6ffco48+urrGGmtk5Wm9Qw45pDplypS+crl27N/61rf6nhO5YKONNmp4WqQ777wze+51113Xd17WXnvt6nHHHVcdzhRPYfvtt8+OPTz99NPVcePGZVMDDDYFxGA55dZbb82ed/nllzc0xVPkl/jerFmzlpjpwoUXXth3rW+77bYsyw2clgqg6FM81UR5sO222y526qD6z+iBn5EDp3hqZflVKxc23XTTftPafOELX8gev++++7KvFy5cmE1HFccb5cpg5fhwcsFgHn744ex3nnvuuYt8r3afHbkktnhuTFkT0zttscUWfccQ09csv/zy1Xe84x39fj6mgIrXXf/4YNdpsDIxpuWpzxeLm3Zo4P15HNdaa62VTbFYL/JCPO+mm24a9nEPpvZ7h9r+9re/LfE9U3uvR11CXO+aJ554IssRe++9d79ppr/4xS9mz7/kkksaqvuJaxjfa3Sa7ZrhTPEU2TB+x5e+9KV+j0f+jMdXWmml7H0YrzW2+P94bffee+9i9xt/k/HzX/7ylxs+7njf1M5//I6oAxpsKq24NvXXKrLfwOlUf/3rX2eP154T76e4FjE1aEyJFrmtES972cuq++67b8OvgfIxxRMdL4YBDjYdQ6OiB2X0vIyeEdECXduiBT+GRN50002L/flojY7pDqIHQ63nQG3o5nCnT4hW82h5j8Wya70SDzzwwKyH4WCilXpgj4Z4LdEjIoY4Nip6U9TvayjR8yVGbUQPnei5EEMxo7dCDK0FgEZFGRs96GKYfYzKi16QMeKhNtz/H//4R9YDL6YNqpVvsUUvsOj9F73AYuRjiF6TMV3TYD22auXy//7v/2a9MaMXfk30yIue+1GmxZQDtfI7evtFuV4TCxBGz84o5+uzQ/Q6jd5m9dmhNqogptuoFyM1o3dZTdzDx3EfcMAB2f/X7yNeX/Qwvfvuu/uOPXqUxnmqiVwQvfQaFXkkehnWevHFeYnXE1M81k//0OgoihidED33vvOd72Q98YbqLVefLaLnYVy/mFJk+eWX73t9jYgRL3FeGhHnJZ4bPVCjx19M7RQ9AAHaUfSkj3KwWVpZftXEvXr9qMTatI61qZOiB3/0fI+RF1E+DFaODycXDKa2GHL9aIh60Zs/cklsUU7FaI8YrRFTKteOIbJAjLqMLFH/uqMcjKmjBp67xZWJcZ7iZ+P8xnmIr4crjitGTkROiCxTExkmer3H6IhmHHdNjHqIfQ3cBk5FPfA9Uy9GAtVGdYSf/OQnWZ6Ia18/7VI8b/LkydnokEbqfqKOIt6PYzV6olY3E9lx4JSWtXMf78vrr78+G7kSW7y2OKYYMTSUqLOJ0TMx3XfUyTQqRiXFyKovf/nL2WimOIdRLzVQXJu4RjGiJma8iIxb/14JMdIj/n5ixEr8G9kurkWMvHrzm9+c7T9yX2TtyGOxn3+1RfVXq0+jc5niiY4Xhe9gUxI1Kj6Ef/3rX2dhZDD18xgPJgqGJ598MptvsH4uzagEiIaGGEI5nDkO48b/c5/7XDYkN6ZFWNw0UTHMNdaOiLAWw/zqDSfkREHTqLjRj2GYMdVTzAEZc6MCwHDElAQve9nLsrIqpuWJzgBx01kT5Wnc/EQZM1Q5E+VzZIBYtyJuoBYn5vCOOX4Hlse1aYTi+yGmXIh9xU1oTOkUxxQ3ZVG5Xt9AEdkhpp5oNDsMLGcjN0RlwUUXXZRti9tHHFtUltRPnxAGm+ZoMNEAEQ0RkUuiAqgmKiUib8TN9N57750aFdMqRMVNTOcRDR8xJUfMZT2YmJ4qphyJKTCj4qj+hnasckqIG/bIK3GdIks10gkDoIiiMnGwKfRGqpXlV01MoVyv1kgQHQDq15+Ke8uhDCcXLM5gFash1rOIStwQ00NGpXLsr778iHMXao05A0WF+uLE9Dcx7eCtt966yBoFUSbGOpbDFdkkpoWKTh5RjxDvl2iwiCkRa7lhtMddX5EdHUxGU2YP/F4tfw3MNFG/s8EGG/R9v1l1PyMV5zUaq6IxLKa0qld7j0SDVnS8qX/fRyNR5JDBzJkzJ1sHJK57rcNHo7bZZpu+/4+prmJ602gUif3Ui3NVu2aR12JKzjjO+EyJr2tq67nURP1S1HPFdFuxRfaL6U+jASgauuJ1Dmwoir+tgVmVzqKBgo433JvOgT0FY87HWNQnFgYbTFSgLE5tlMRgi0OG6JW5pHkH68UHfoxKiF4DUfgNVWkQwTB6J0SgiFbsuBGPgiV6rMSC1cNZ4Hu45zAKqxBzLUZvlOiVCgCNikb92o1QzB0dN3BxYx03QdFbtFaGRUX4UD3no9J+LNRuwqICPo4t5huOnqbRc6wmji9u1GPO3sHU36AOVs7WXl/cVA7VYy5GVDZD3GTGmlrRSBHbYDlmOA0UMZoj5i6Oxo2obImetEOJUQzROBE9I6N3YNyEx81rnOOxzCmx3kRtsdeYWz1+N0C7iYryqLhuZnmXR/k1VMXrUI0FQx33aHJBrVK51igyUBxjfeV7/I4o+6Oiv7YId+0YYj2Hwe5/o5PDUKIRJiqHY59x7uM8R+VxNCbE+h/DKRPrRe/2qDSOrBI5KhpZonNAfaeK0Rz3SCyuzB5th4G8OhzE2izRqBRrpwwUa4MNtVZINATECKGB4u86ZqSIOp2f//znffsYiXgfxbomMaoirv3izlGsnxE5rtbBZKj6slj3JdariAahWIc0fq7WIBF/E/HzAxso4m8rOgPRuTRQwBCiZ0Z84NeLoW9xk14vKvajRbyR3gCDDQWNlvQIAPVTL9TE1BHx4T2cBopoaY9W7bi5jkWihgoM8f1oHIienbHAZE1978iaZrZkX3DBBdkwwViMLXpFRgEV5wAARiIqBaI8ibLyi1/8YnZDFL3mQgylX1L5HOV4LA64OOuuu242WjJu0utHUdSmQ4zv10SZGjdvMUVCNJxEBX9tocr633nvvfdmlQ0jKWOj52qMOoibwCW9vji2eH0De6ZFY04jIofEDXKMWhkoMsTVV1+dle3DuemPSpD//M//zKbi2G+//YZ8XvTkiwqsaMyoeeGFFxbJZ83MKZHzomEkGl3ipr1WmVV/jQHaQVQoh0anuGtEK8uv4RxTiLJuqH0OJxcMdY8d5dxg98qDiRwQMxrE4s233XZb1hBQO84oU4d7DNFwEA3n0dhRP6JksOmVhntdoqNkLMYcMypEdokGizjemtEc91irlc2RaWrXuFZvE9eqKMcbWSo60ERDwEDR4Bfvy8GmGIsOnQNHK0UOiinSYlH6mAZqqOmwhiMaJiInxjRTS8pz8fsXN4o1FiiP/UR+qr2G+gaU+P+BrzWml5o9e/ag54fOYQ0KGEIUxAPXj4hhqANHUESBHsMsr7322kX2ETfQg83lVxM39dFIEfMGRgPFwC1apaNnYa0XX6NOPfXUbPhn3GAPpdYTpb7nSRTksS7EQDG390jmtRwoQkJM7RTTX8TUU2eeeWYWsi6//PJR7xuAzhU98mNURUxTEDdOcRMdj8VIhoEdC2pTTNREmRSVLVEmD1QrI6MSPYbS168tEeX7ueeem91wxojEmmjAiDI8KhOiciieV98TsZYd4ubs4osvHvQmMbLB4kQZHscdGWGwxpX61xfHHjeH9cP2oxffUFNrDDyWaISIPDJYTjnmmGOym9Ba79BGxc9GTonMsbipFuJ1DuwhG+d8YBaLnBIGNlyMRIxAjYaomOYpzlF09Dj66KOH1VMXIG/ROB49l2NKnMF6bY9UK8uvRsX0NPE6IwMMLAdqn93DyQWDiQrkGLl55513NnxccS8eaz5Fz/RaQ1HMXhDrGsXUj8M5hsHu3eP+PEYZDhRl4nDKw8goUd/wla98Jc2aNWuRmR1Gc9xjLRogIkecc845/c5NlOFxfmIKpEbEeyI6nQz2+kYrzk80JMR6W4OtDRoNdpHVYiqn+nVAYyq1eCxm66iJ/BPXK+qfYj2YxY3wHOw1DTb9eLxX4u8xRuXUpoOLv+OB04iFeF6MdBhqDdFY6yXy3RlnnJHNzlEbGTLwdQ0cifPb3/42y+8x0oLOZQQFDCF69r373e/OAlQUClF5EY0QsTBQvahwjxvzuHmPefu222677AM9pgSIyoA//vGPi/xMfUt6DBcd6oM4WpAj/MXiTm9605savlZRUVJfWTKY+J0xSiR6JsZIjehpERUpg92Ax2uKSpkZM2akHXbYIauMiVb74Yj9HnXUUVmLfLSqhxg9EYVcDAGMcDGaoYkAdLYoj2Oxx1joMMrv6PEfIxiiZ1pUOkfPuscffzy7qYtpL6Jcr/1clNfxs1FORZkXN1hRtsfIgJiaKRZOjkqNKOfvuuuurHdh/ExMURQVIgPXUIibx6hIj5u0+P21tSpqYvHlmE4hjjN6P8bIx7jpjBu4eDzyxlA3fzVR4RE/G2tBxOuLHnRx3DFVY9wIx/+H+F6MLDn88MOzY49enVHeD3aTPFCcg2iAGKpHW/SwjJ59kWcGNsIsTkzVFOtRLUlkqzjWeH68vrh28doGzt8ccylH5U2s2xUVErH2R8yVPdx516OiJzJXvIdqC67HdYypSCK7vPe97x3W/gBaIaYUjPIjGsSjnIvGiRixHr3L43O8VlHYDK0svxoVHQPiMzruT6M8iKljoqyLY4o1F2sdCRvNBUM58MADsxGRMdKgkXUXoqyKY4nG+KiUjSwQxxnnMBpVYrrCKEMfe+yxrOyJcxnl9WBqo/riNcY9dMzgEPUEUc4NbHCJHBO/JzotxrRV8Zyh1o8IcSzxvHht0VAxsDyP1zrS464XUxFFJfRgU3qNdFrKOI6Y3jpGqrzuda/L8kqMpohzHvUWUX43IvYRDTTRoXJJC2VHJ9ZaR9ZofIi6nzjXtVG09bNThKhHib/NxTUURuNPrOkV1ynqZkI0usQi1fVrisbC0/E3He+D+Dv52te+1m8/9a93sNcU00JFvom/vXhfxDWM7BMdWeo74cS6I1E/E++FmFYs/saicS5+X+wr6m8GE+u7xN9XZOqaqE+LKcVjdo/4TIo8PXCKuPi8ilxa3xhDB6pCh5g+fXrUvPd7bPfdd69uvvnmgz6/p6en+pGPfKS68sorV5dZZpnqPvvsU3344Yer6667bnXatGn9nvvss89WTzzxxOpGG21UHTduXPYzu+yyS/XMM8+sLliwYND9P/7449Wlllqq+va3v33IY37++eez3/3GN75xyOc8+uij2es644wzFvv645iXXXbZfo/dcsst1Ve+8pXViRMnVtdcc83qhz/84eq1116b7e+GG27oe95zzz1Xfdvb3lZdfvnls+/FOQjxnPj629/+9iK/r/a92n6+8IUvZF9fddVV/Z732GOPVSdPnlzdb7/9Fnv8AHDppZdmZckdd9wxaLm94YYbZtvChQuzx/7whz9UDz/88Orqq69eXXrppatrrbVW9fWvf331O9/5Tr+f/fvf/1495phjsu9HOb722mtn5eZTTz3Vr9w+8sgjszI+nrPllltmxzOY3t7e6tSpU7NjPfXUUwd9TuSD008/Pcsh48ePr66wwgrV7bbbrvrJT36y+swzz/Q9L/YRGWYwcUzxvfhd8fride65557Viy66qN/z/vSnP1Xf8IY3ZJkijv+4446rzpo1a5HyfqADDjigOmHChOq8efOGfM4RRxyR/e76czXQ4vJWzWCZ4umnn+4758stt1yWxR544IFBs9jFF19c3WCDDard3d39Xlc8d//99x/0d9bvZ/bs2dUpU6Zkr3mgyGGRoR555JHFvgaAPMrE2hZlU5QDr33ta7N7r7lz5y7yMyeffHLD98RDfX62qvwa6l6zdv87sAy++eabs9c+adKk7DN7q622qp577rn9ntNoLljc/ftXv/rVJd5n1/++KJfqy6x4XVGeRZkTZWzklihL77zzzsVep+9///vZa4qfWW+99bJrcMkll2TPi3NSM2fOnOy6xXmI78X1rT+fg5X7H/3oR7PvRX3GUBo57qF+rv59OnCL17qk98zi8l/44he/WN1kk02ya7raaqtV3/Oe92QZotEsEtdn4HkcSu3aLOm11ER9y6qrrtqXTYdy1113Vffaa6/svRTX7sADD6w+9NBDi7yGxZ3LJb2mOE+vetWrslwV7+VVVlklyz033XRTv5998sknq+985zuzcxrHE58tG2+8cfX444/PvjeYX//619nzfvWrXy3yvcsuuyx7z6600krVGTNmLHIudtppp+phhx222PND+VXiP3k3kgAAAABAUcWUfzH3f4wGAEbvnnvuyUbmxAiqGAFF59JAAQAAAACLEVPivOxlL8um44mpjYDRiSnDYu2vmCKOzqaBAgAAAAAAaLmu1v9KAAAAAACg02mgAAAAAAAAWk4DBQAAAAAA0HIaKAAAAAAAgJZbKrWxWOn9r3/9a5o0aVKqVCp5Hw4AY6BaraZnn302rbnmmqmra2za1V944YW0YMGCpu1v3LhxacKECU3bH80lPwCUn/zAWJAhAMpvrDNEs+sfylAH0dYNFNE4MXXq1LwPA4AWmD17dlp77bXHJBysNHG59Hzqado+V1999fToo4+2dUAoM/kBoHPIDzSTDAHQOcYiQ4xF/UMZ6iDauoEiRk6Eh3//+77/L4pqKqbCjjPpXZiKqNrV1n8iLVUp6DVMlWLOZFct6HEV8TMiei5stPHGY/Y5Hz0XIhwcmtZK45ow8+GC1Ju+Pucv2X7bNRyUXV9+eOjBwuWHwn5m5X0AbaaIn6VFvYZFPFdFPl8MLz9sLD/QZLXc8PsC1kEUVaXamwqpoMelDqJx6iCG+d4q6H1Gp9VBNLv+oSx1EG1d+1qb1ineMJMnT05FUtSbmiL+4WcKWrktHDROOBjme0s4GLaxnspvYupK45pwXbqLWgDQFvlBA0U5FDFvFfWjqYjnqsjni+GTH+iYDFFQGiiGRx3EMN5bBa1HKmyeL+hxFTULjnWGaFb9Q1nqIIr57gQAAAAAAEqtrUdQAECzdFcq2Tbq/UQfkBL0YAAAlkx+AADyyg9lqYPQQAEAMaSwEiGhSUMT2zwcAACNkR8AgLzyQ1nqIEzxBAAAAAAAtJwRFADQ7CkaAICOID8AAHnlh7LUQWigAIAsIDRniGW3swkAHUN+AADyyg9lqYMwxRMAAAAAANByRlAAgCkaAIARMMUTAJBXfijLFE9GUAAAAAAAAC1nBAUAmEMaABgBa1AAAHnlh7KsQaGBAgBM0QAAjIApngCAvPJDMMUTAAAAAADACBhBAQApZctKNWNhpvZfngoAaJT8AADklR/KUgdRiEWyzzvvvLTeeuulCRMmpJ122indfvvteR8SAB06xLIZG60hPwCQN/mh/cgPAJQpP3SXoA4i9waKK6+8Ms2YMSOdfPLJ6e67705bb7112meffdITTzyR96EBAAUlPwAA8gMAtL/cGyjOOuus9I53vCMdeeSRabPNNksXXHBBWmaZZdIll1yS96EB0EG6K83bGHvyAwBFID+0F/kBgLLlh+4S1EHk2kCxYMGCdNddd6W99trr/x9QV1f29a233prnoQEABSU/AADyAwCUQ66LZD/11FOpp6cnrbbaav0ej68feOCBRZ4/f/78bKuZO3duS44TgPL7V8+D0Xc96G7K0bA48gMARSE/lDc/BHUQABQ5P5SlDiL3KZ6GY+bMmWnKlCl929SpU/M+JABKwvDK8pIfABgr8kO5yRAAjAVTPBWogWLllVdO3d3d6fHHH+/3eHy9+uqrL/L8E088MT3zzDN92+zZs1t4tABAEcgPAMBY54egDgIASt5AMW7cuLTddtul66+/vu+x3t7e7Oudd955keePHz8+TZ48ud8GAM0QwyubtTG25AcAikJ+KG9+COogACh6fuguQR1ErmtQhBkzZqRp06al7bffPu24447p7LPPTvPmzUtHHnlk3ocGQAfpyuaAbMJ+mnEwLJH8AEARyA/tRX4AoEz5oSx1ELk3ULz1rW9NTz75ZDrppJPSnDlz0jbbbJNmzZq1yMJVAADyAwCg/gEAyiP3BopwzDHHZBsA5KVZQyO7U/sPr2wX8gMAeZMf2o/8AEDemjk1U3cJ6iDKMAoEAAAAAABoM4UYQQEAeetu0hyQ3c04GACgLcgPAEBe+aEsdRAaKABABQMAMAIaKACAvPJDWRooTPEEAAAAAAC0nBEUAGCRSwBgBCySDQDklR+CRbIBgFGZOXNm2mGHHdKkSZPSqquumg466KD04IMP9nvOCy+8kKZPn55WWmmltNxyy6U3v/nN6fHHH3fmAaBDyQ8AQFkyhCmeAOCleRtr80COahvm2bzxxhuzgv+2225L1113XXrxxRfT3nvvnebNm9f3nPe///3pBz/4Qfr2t7+dPf+vf/1retOb3uS6AUDO5AcAILf8UClHHYQpngAgWuybNMQy9jMcs2bN6vf1ZZddlvViuOuuu9Juu+2WnnnmmfTlL385XXHFFek1r3lN9pxLL700bbrpplmgeOUrX+n6AUBO5AcAIK/8UJY6CCMoAKBAIgyEFVdcMfs3QkL0aNhrr736nrPJJpukddZZJ9166625HScAUBzyAwDQrhnCCAoAqBteOVq1fcydO7ff4+PHj8+2xent7U3HH3982nXXXdMWW2yRPTZnzpw0bty4tPzyy/d77mqrrZZ9DwDIj/wAAOSVH8pSB2EEBQBkhXqlaVuYOnVqmjJlSt8WC1EtScwDef/996dvfvObrgkAtAH5AQDIMz90l6AOwggKABgDs2fPTpMnT+77ekk9F4455pj0wx/+MN10001p7bXX7nt89dVXTwsWLEj//Oc/+/VgePzxx7PvAQDlIT8AAJ2WIYygAIC6IZbN2EIEg/ptqHBQrVazYHD11Venn/70p2n99dfv9/3tttsuLb300un666/ve+zBBx9Mjz32WNp5551dOwDIkfwAAOSZH7pLUAdhBAUA5CiGVF5xxRXpmmuuSZMmTeqb0zGGZE6cODH79+ijj04zZszIFq2KoHHsscdmweCVr3ylawcAHUh+AADKkiFK0UBRfWmjfS2sFPStWMA3VrMW0Wm2noJew6Ker94CvrdCEQ9rYYsOqn7uxtHuZzjOP//87N899tij3+OXXnppOuKII7L///znP5+6urrSm9/85jR//vy0zz77pC996UujPtZON7+3km1FMq47FVJRP7O6inX5+hTxdBX0VBXyXBWZ61g88kNnqvQsyLZC6R6X9xG0FXUQ7X9PrQ6iHHm+U+sgmpUfylIHUcwaRQBosa5KJduasZ/hiOGVSzJhwoR03nnnZRsAUBzyAwCQV34oSx2ENSgAAAAAAICWM4ICAGKofnclVZowX0ylSb0gAIDikx8AgLzyQ1nqIDRQAEAMKeyupK4mBIRmDdMEAIpPfgAA8soPZamDMMUTAAAAAADQckZQAEDo7kqVria021eWvOAUAFAS8gMAkFd+KEkdhBEUAAAAAABAyxlBAQDR6aCrki1UNVqV1P7zPwIAjZEfAIC88kNZ6iA0UABAbZGqJgSErhKEAwCgMfIDAJBXfihLHYQpngAAAAAAgJYzggIAsiGWzVmkqlJt/wWqAIDGyA8AQF75oSx1EBooAMAUDQDACJjiCQDIKz8EUzwBAAAAAACMgBEUABDDIrsr2TZalRIsUAUANEZ+AADyyg9lqYOwSDYAAAAAANByRlAAQF8PhiYskp16nU8A6BDyAwCQV34oSx2EBgoAsMglADACFskGAPLKD8Ei2QAAAAAAACNgBAUAxLDISiVVupqwSHZv+y9QBQA0Rn4AAPLKD2Wpg9BAAQDZEMuubButrmpz5pEEAIpPfgAA8soPZamDaP9XAAAAAAAAtB0jKAAghkV2V7JttCrV9h9eCQA0Rn4AAPLKD2WpgzCCAgAAAAAAaDkjKABAD0gAYASMoAAA8soPZRlBoYECACxyCQCMgEWyAYC88kOwSDYAAAAAAMAIGEEBAKFZQyxLMLwSAGi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" ] @@ -724,28 +724,13 @@ "\n", "The results show that DMDc can accurately recover the dynamics of a controlled linear system from data, with the eigenvalues and singular values closely matching the ground truth values.\n" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "dsa_test_env", + "display_name": "DSA (uv)", "language": "python", - "name": "python3" + "name": "dsa-uv" }, "language_info": { "codemirror_mode": { @@ -757,7 +742,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.18" + "version": "3.10.19" } }, "nbformat": 4, diff --git a/examples/dsa_fig3_tutorial.ipynb b/examples/dsa_fig3_tutorial.ipynb index 3489faa..5da6125 100644 --- a/examples/dsa_fig3_tutorial.ipynb +++ b/examples/dsa_fig3_tutorial.ipynb @@ -7,19 +7,6 @@ "This is the first tutorial on DSA! We'll recreate the third figure from the paper (except the Procrustes analysis). In doing so, we'll learn about how to structure our data matrices to fit into DSA, how to apply the DSA to data, and how to select various paramters for DSA." ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#install the packages that we haven't added in setup.py\n", - "! pip install matplotlib\n", - "! pip install scikit-learn\n", - "! pip install seaborn\n", - "! pip install pandas" - ] - }, { "cell_type": "code", "execution_count": 1, @@ -30,8 +17,6 @@ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from sklearn.manifold import MDS\n", - "import seaborn as sns\n", - "import pandas as pd\n", "\n", "rng = np.random.default_rng(2023)\n", "np.random.seed(2023)" @@ -200,7 +185,7 @@ }, { "data": { - "image/png": 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c2rVnyjCyzV6Xvi8fSFYPJt8H4nB1CzpLUrtF9yzqqdJJf+2BPvdI7edjAYtYGIOCImtyGi+99FIXisQLh+cC4aP/cyhCRKjZXCmApFUcxU4o9aRQhKgGhD75CD89qxFm2sHRezq0lwywiNFmkrQFWr2FdrPBs3Aw8KBQUEZBJ8YR3laUI7wP3B96ZQ8G2szhcaUdHX3p+T5oT0k+KJsCi9xQQJEsHuMwlI/czoFqT4zxzW233ebaKmZyOG2PDwYbKjKNzFJThazjHMCjSprTTTfd5BSMgcCLyaZO84eBQP7JM0cp4TfNbx1oC0lRK+/DmoLsH6kcEIWgTSwNJ8gNp7aBKcVhVkA6tKjlMT5raE/NusXno2U1ue2kUqHk0AoaxYS6q9Bbn9aWREZYq/isRC+Ya0HaaPqQTcM4FMxOYPgcex0KOL9JnGu0n8UApgX7UIA8sY9TJM7vFOUZWRsI5JPf/4FSiPnNoyvQEIHheRybFrG0lSd6gIwgKweToQNxuLoF8k5qFK16+Txhpg2ZC+gwYR7FgT73SO3nY4LRbktl5Db/+7//61qiLlq0yLVco63iggULkh/5yEeSe/bs6ffcu+66y7VipY3k3LlzkzfeeKNrtZjZPpKWjNddd11y2rRprsXeOeeck1y3bp1rAZfZTvLZZ591bes45owZM5LXX3998nvf+94h282GVnGcA63vCgsLk9XV1a6NJu/d2Nh4WO1mDwXfwRVXXJGcOHGi+26WLl3ar6XnodrN0nJwoHaE6Z9tw4YNybPOOst9V5kteQ0j/GYOdKGl5YHazQ70Gw+/zUxoLYn88DukrSO/ddpP79y584D/CDxn9uzZB/1HQv4nT56c7O7udreRcWSd9q/psnAgOTiQLAHtO2nhXFVV5dpUv+Md73DnmymPsGXLFtd2dtKkSW69oJXlhz/84X5tO7/73e+6+2m/ndk2k/ayrJP5+fnJKVOmJD/0oQ8l6+vr+70HaxTrkTE+OZzW0IAssE/NmzfP/Z5mzZqVvOaaa5IdHR39nnegdrO0dU1nIPlvaWlJvuc973GywWMHaj27Zs0a9/g//dM/HfB8N2/e7J7zd3/3d+727t27XVtX1gnuTz/HA8kQ789rBuJwdYvw3NNPP92tE7SEXbVqlWsxfTif+3D2802p75LW2OOVCP8bbePGMAzDMAzDMIzcxmosDMMwDMMwDMMYNGZYGIZhGIZhGIYxaMywMAzDMAzDMAxjfBsWTKekMp/psHT/SG87msn69evdiHWeT5/igdqXfv7zn3ePpV/odGQYhmEYhmEYxhg1LEI7MIYq0cKMFmwXXnih9u7dO+DzaSfGcKIvfelLmjp16gGPe8IJJ7gWZeFCK0HDMAzDMAzDMMaoYUGv8quuusrNSVi8eLFuueUWlZSUuF7tA0E/4q985Suu9zm9kQ8EPdMxPMIlc3aCYRiGYRiGYRhjZEAeQ9gYUMLQofTpqEw4feSRRwZ1bIY6MVyJ9KrTTjvNDX1KHxOfCaPkuQQY915XV6eampqjGk9vGMahoUt2c3Ozk1Vk/0gxuTWM0cFk1zDGttzmpGFRW1vrpiRPmTKl3/3cZnLy0UKdxn/8x39o4cKFLg2KSamve93rtG7dugNOVcTw4HmGYYw827Zt08yZM4/4dSa3hjG6mOwaxtiU25wckLdz507NmDFDDz/8sIsqBD796U/rgQce0GOPPXbQ11PA/fGPf9xdDkZDQ4PmzJnj0q6uvPLKw/J8MoaeCAdfPiPkDcMYepqamjRr1iwno5WVlUf8epNbwzgKejqkP1zsh7bHSqSCSql6uXTCPxz2IUx2DSP3OBK5zcmIBXUPsVhMe/bs6Xc/tw9WmH2kVFVV6bjjjtPGjRsP+BzqNQaq2cCoMMPCMIaXo003NLk1jKNg95NSSSoNIpaUigqksiI2vCM+lMmuYeQehyO3OVm8XVBQoJUrV+ree+/tV9vA7fQIxmBpaWnRK6+8omnTpg3ZMQ3DMAwjJ4m3911PJnJVhTAMYxjJyYgF0Gr28ssv18knn6xVq1a5uRStra2uSxS8733vc+lS5FKHgu/nn3++9/qOHTu0du1alZWVacGCBe7+T33qU3rLW97i0p9It6KVLZGRd7/73aP4SQ3DMAwjG0jzViZ6/N9YwaidjWEY2UfOGhbvfOc7tW/fPn32s5/V7t27tXz5ct199929Bd1bt27tV7mOobBixYre21/96lfd5eyzz9b999/v7tu+fbszIvbv369JkybpzDPP1KOPPuquG4ZhGMa4Ztv/ZNwRkUrmjtLJGIaRjeSsYQFXX321uwxEMBbSC7YPVad+xx13DOn5GYZhGMaYoWN3xh1JqeK4UToZwzCyEUuQNAzDMAzj0PS0ZNyRkIqHrmGKYRi5jxkWhmEYhmEcmsyof7xTilqNhWEYfZhhYRiGYRjGwaFY27WaTDMuInlSYY19c4Zh9GKGhWEYhmEYhybRJfW0ST2t/nZ+lX1rhmH0wwwLwzAMwzAOTleT1JM2x4LIReks+9YMw+iHGRaGYRiGYRyc5leleIeU6Pa3k3GpZLZ9a4Zh9MMMC8MwDMMwDk7dE96YSPakirgjUplFLAzD6I8ZFoZhjCx0kjEMI7do2y4lOlI3MCySvt7CMAwjDTMsDMMYWeWkcZ3UttO+dcPIBZIJqWGd1NMsJTpTEYuEfyyvfLTPzjCMg0H6Yt0aqT1zuOXwYYaFYRgjR8ee1N9d9q0bRi7Q1eANiq5mf9ulQ1FnEfXtZg3DyF4a1/u/7TtG7C1tVTAMY2R74TO9N7/CvnXDyGYo0m7dKnVjWPT4v+lgXEw+c7TOzjCMw6FurdS+U5p4qq+NcrNohheLWBiGMXK0bJZ2PyA1vWzfumFkI93NUstWad+fpM5af19XXV+0sd/APFMhDCOraXlFat8l7XvYRxtHAItYGIYxcrx0i9SxXcqrkOa9x755w8gm4l1S80s+H7ur3t9XMrN/m9lArCilqOSPyqkahnEIiFBQz9jV6GfQ4DQorNZwY4aFYRgjR+tGr4yEyb2GYYyu4oE3s7tJiqc6PHXW9xkVBVXeqKADFHUW6RRNlSJmVBhG1tK5T6p9xDdciBZI+x6VZl487G9rcUzDMEaOEQrFGoZxGHTu940U4q2p9rHtUsduKa/UT9UuniYVTfIGSHdj/9fi+RyBfG3DMI6S9v1Sost3caMmatdvNRJYxMIwjNGhp1PKK7Rv3zBGY5YMUYq2rf3vo7FCySwpv0wqqJG69vvHQgQjnZK5I3e+hmEcOV17Uu2h41IiVeM4AphhYRjG6EA6lBkWhjGyYFA0pzVPcLUTESlW6C9QdaIUzZd6pkhNz/vi7e52qXWfVFQuFRf6iIYVbxtG9tLwQv8sgZaNUjddGcuG9W0tFcowjNHBhuQZxuChIDPBbIlk2n0tUmddqj4iRcsmqe4pqf7Z/p2diFRE03yMkZhU+6i09wFfV0EtRcc+b1RAR7MUK5HK5tm/nmFkM/sf7X+b9aDphWF/W4tYGIYxOoQ0C8MwDh8iDBgTseJUG9i0ibrRolSkITXMLkzHJs+azjCtm70RUXaMf15XrU9/yiuWimdI+ZXStl+6AIajp0OacNJrZ1hQj8FzDcPIXppf6X8b3wNOgWHGDAvDMEaHDjMsDOPwZGWvNwDQ+DEmHGj/aVEKSNAWtqN/HUXXi754s/c53b5moqetzwDB6Mir9D3vIwN0lWnbNcBJWeG2YWQ1PalOb4Fkp5RfPuxva4aFYRijQ+sW++YN42AQmehqkDr39r+fyEQwIAonSoWTfYcm6pZ4PgYFkF8djApXQ1Hsr6cPu4uV+tuZA/ACGB3x9v73cdyYqQ+GkdUku1/rDCAKOczYymAYxshAPnc66fnfhmF4IwA5iRV4+WBYXTr0oi+ZLRVUphT+NqloSl8RNfUReWVSK92eElLxVG94kP5EDQZRj2B00P2JQZXtO6TMLtCuhmKO1ES0o/u1c2cYpFdqXaEMI6tJZEQsiHDSuKHyuGF925wu3r755ps1d+5cFRUVafXq1Xr88ccP+Nz169frsssuc8+PRCL6xje+MehjGoZxCFz/7ERf8ai7L/UY+d2GYaTkoVFqeFZqfM4XWDdv7PtmUOKrT5KqlnqjAvjLnAmMChoh1K9NvX6d1BMiFkmpYIJUsUgqmuzTIEpmSFXLpJpTpUjCD8HDk4mhQfSi8nhp2vlS5RKpdJ43KjLnzxRPlgpr7F/OMLKZRGqz7WGWReq+kmnD/rY5a1j89Kc/1Sc+8Ql97nOf01NPPaVly5bpwgsv1N69GSHjFG1tbZo/f76+9KUvaerUqUNyTMMwDpLCseseacevpM0/lvav8d7X5lqpaZfUuMvnbxuG4SMMtIIMCjxRAjfpOiJVHO+V+MxhdEQN3OTs3X7IXT/lP+KLtl16VJPUuF5qeM4bCYWTfEenpnX+qXklUvkC34Jy0qnShJVSfoVPnSqbn0qfyJi6XTy9f92GYRijS8c+P6ei6SWpbo2/Tboke23rfr/vjlCiUs4aFl/72td01VVX6YorrtDixYt1yy23qKSkRLfddtuAzz/llFP0la98Re9617tUWFg4JMc0DGMAOmr9wta+U+qs9wpP4/Peq+p65qewVChjvENb2LYdUvsun6aEsp5X5RsbRAu9UYHiP1B90r4HpV3/66MUFGpTP1F+nFRGmkO0f2cojk/BNmlQbo7Fi32Plc7xkRCiIkRA0ime4s9h17qM+2f2b29rGMbokej2wy7ptBjk3g2/jL32ucmuYT+dnKyx6Orq0po1a3TNNdf03heNRnX++efrkUceyZpjGsa4g376pHJ0ptpg4lUF0i3woKST3ibTMMYyKOGd+30qUjcdmEq9MUC+M4o+XZ+IEJCO1NMgFdWkWsTW+y5QvI4oArRs8elOPMYxwqwJIg0cw03XpWg77g37vCIpVuZf71rPpkUfeF1IaUo3YDhuZ6P0y/dL++6VStNSGCfMtRkWhpFNtKTSjAM4Jlz3uPjAe/Qwk5OGRW1treLxuKZMmdLvfm5v2LBhRI/Z2dnpLoGmplRuq2GMN/C21j7sPbAoJrSvLCxK5X2vl6KpjjQBCk1HCZNbY8RAFhhKFVKVKM7GkEBegnexcIJUONUXbYdC7JZt0t4/+rRC5kxQtI1xgDFClyYKr0l1CN2gdt/rFX4UCt4rkicVTTzwTl9+bJ+xks6D10sP3eivR5JSdTxlVUSkCXP8+1GnMUpTt012DSPNYcH6gLMCgyHe6h0HOPYOmKqY0URlGMhJwyKbuOGGG3TdddeN9mkYxuiDskSKBp5WOtLQiYYCUzrZ0KmGdIx0RjEVyuTWGBHY4Nu2eUXfDbZjrkR9X0ogskF9A8oBMkPdEYYHCkPzBv98N/CuTWpKc3ARAcRoKJnpb/MeGBR1qRkXyFvpLH/dDbKL+A5RITpCyhTGSjpbH5XueLcUJ7KYlPKSUiwhRVJGSnXqvSKFUtGk19Z8jBAmu8a4I5n08hb+kglAZgC0bvfOC6ITpbP9+uE6vR0g5ali8bCfbk4aFhMnTlQsFtOePf37bnP7QIXZw3VMUqco+E6PWMyalVrQDWO8QJpH86veWKCYNEzmJe8bxYhLZklXcvg9JwfC5NYYUjAUUNgpjCbqgLcQQzqk+6EQYFBwfyRf6ia9qVIqSKUhIR/IUHpDA1KUMCCqTvSPueJulP1CadpF/i8KBUo+tREMt8NwQLEItRK8B4ZLgBSs8BjHWv8L6cXfSi/9TkrW9828i6aiFYvPk7pTaRZhlgWKDR2mRgmTXWPckMTBsDEV2UyraWJWTYhMuNv7/bqCA4O0qIIKqTuz1WwK1o1hJicNi4KCAq1cuVL33nuvLrnkEndfIpFwt6+++uoRPSaF4AcqBjeMMQnKEelOaB90kkkkpF2/8wWh1FGUH+MVGpQeClJJz0DZiWf05McLOkqY3BpDQkg5YIAdijcbPulFbPQuStHjowcUYgcPPzJC9ADPP6kLTbv7F0LTcYk0I6IZlYu9ceEMlROkzt1SyRyfOpUOtRkYJ0QL86tTKVWxA8tYR5N004lSO8YE0QkijKnHyqdKb7pRKpsmvfhtqT7jta4WZPRmWJjsGuOm/XTLqz6SmXk/jVDCeuJqrTqkwkqpM9XCvatp4KYPwHMP9Nh4NiyAKMHll1+uk08+WatWrXJzKVpbW11HJ3jf+96nGTNmuLBpKM5+/vnne6/v2LFDa9euVVlZmRYsWHBYxzSMcQ8GBUOziES4Iu39vlUmRgRpUHSRaXpZKqj2EQlCs0QweH5TWpea4D01jFyOUrRuSst1bpLie71yj+GA55AIRCiaJjWQwXMuqtAlxeJ96YAuJeoYH/FAZjLB0KD//IF60KNkZHZ0yuTV+6WHvikVlkn7XvRGUUFCKimWVr5HmrhMmrjQn3c5hkrMGyu8t+tWVeaNJWo7CsoG++0ZRvbgCpoTqch6ynnGvuUiiBFv3JN2OFJ1RcmEj0ASpYjTerq9z4lBSiTrBjJPnRTzZrrrvdy6NGReFpfatkjNA7R0J2Uy0zExxOSsYfHOd75T+/bt02c/+1nt3r1by5cv1913391bfL1161bX1Smwc+dOrVixovf2V7/6VXc5++yzdf/99x/WMQ1jWEE5x/vAIhYrGp0vm/enXSy1EYUZedSEVvf9yXtMKMZ2w+96fOEoixpeUnLBUaI4/8LJ3sMaqZQ2/Fra+7QUHCUl5IgP/6AewxgW8BiSx8zvnSiF64DW4WslUMhDVyYMbECMMK479vSlCZYwCwIXfLWPMvC6gYqpB0vdJum5O6X7v9RXQB7lHOLS8rdKCy/2ChPnWveUl9sg96RZcd7IODLNBQPJMMYKOAVQ1lHKXdpg0u9n6am6GBjIAI+z/wHK+ZEYGslUfcTh7sOcRwcGw8v+NulQOCAwgki7ZI92NVOp1OMJJ/v1aP+T0s610sbfSKVpnyGcq6vnGl4iyaQ1ox5KqLGorKxUY2OjKiqGYZMwxiYoJUQCWMxIm6g8Qepu8AsFmzlpDc7reZSdlJzS0+KVB47BcVksEX88MSgLPAeDIcAiVrbAF3kSmah/Rmrb7gfwhAWSvE6UJRZdUkE4LpN7OebOx6WXfiu1tkoFqXSLorhUTG55iTTvA9KKL2SFnJncGgcFI7p1m5dJHAB4E4OSjqIRy/fRCGSA1EBXaJnyglIozWvo3OIaGkz0kT1qKFDiD0emXW3GYSox3R3S5j/54s0XfyM9/UOveET6mjtp5iLp1L9gwIX30jJhm7oPFBbSt2DSGdK+h6Q990vxNt/Vjc9VOEV63Y+P+gdjsmtkDcgIHnycBJmQRohMs/+6TmsZNYE8xgDJQ9UsdNRKXbU+4sAxK471acREO9nX2SuDAyKcE13kGl/0f92ak9bohNfgjIgU+Ntu6OVkqaVOevpHUssuKT/hGy+UpNaowmKpqMpfP+On0pTXDavcmuvBMLKiHeWGtHaUndL+J1JeijavULCQxEr936IpfWlEKAEoKxgIzqtY6FOTGE7nFka8jIV9hZeER0lpoPYhPaUDTymLq2tRF/FKEPmanAMLGQVkeDo4Ny4cg5SIigV+qrZbIOdKPd3Snuelnf8tdWz1ne0KmQKMYpSQSidKealwc9kAKR+GkY3UPprKd041KkBm8OSjbLgi62r/+48z8G67l6Voka+RQFYc07ycVC49MgcBDgGm1vM+DLMDjJeX7pT++HmpsU6qmC9NWi7tWSvtxjmQ7hklOpGQFpwtnXF1Kq2p0Ley5fyJnDBMC+9nLxG/hoCL/KflZFMYahi5DPLLnobBPBA48YhOBGOeLAJksBc6M7X5eTLsg0Qv8qu84d3dmKpjKPcGCe2hSSF23eB6fD1W605fhwUYFqQxOsMFB1ypfz5t2l1tYkfKsVjmo/xlc70DgONyX/Meaf1dUu0rUifPS3MgQGm1n2UTSG8QMUyYYWEYI4nr5tDjvRBs8EQF3PW4v44yQmSA/vQQ0hBCv3sGWgVln5XDRTgIj3Z5TweGSAiN8jipDCHdIiySwahwRaf7paY/9ilJGC8scix6rTukurV95wrdrV7RKEhFVjAqXv4fqXGfVE9nitTndIZExKdcTFkszTpVasEDkzbxN3hcDCOb6WmXWjd7ZSS0TKb4kZoIjHZkBhlMdngjn43fFWfHUp5IpmCjtFMHMePIjAocBq6OIym175VefVB67i5p1+9ZTPxzkLnmtf7Sw3ugPMSkRNQb8a//qJe/aDxVVJ6U6l9NOSwm+bSsUCMSCK1q3WelGL0hK7q5GcZRwe8bBR05RbFmjwyOPGD/Y8I8+yH3u3bKaRFCBlTSIMEVSU/0TrvQ7hXZdvKdRoIaB2oPO/ze7I7LHsqsp8f6iq9dN7g9UnKZd9wR2SD64AZfkmbc4m9XL5Oqlnq9oHi2dO9npXX/7gdrs9cmU+daiNMg7ltDT5zpO8llpl+NQJKSGRaGMRKglDARN3MB6iXqlQ4UGDZ1FhQ8HigwTM9lQWJ9YINvbfHKf8h1ZhFC4cFT0u+QeT7fEuWHxanmZO/9JDrBgsZ7kUtKKkdyn1cgWNySFJaW9L0fHljuS2LIdHtliYW2aaO0+V6pcZvUE5Xi5IgnpdJKb0zMPlMqo4j1VW/wFAxvwZhhDCn8ZvFqum5nqYF2U16f6raUTKUkTO7rAOWI+lRAIoQujaKw/7TbIzEqkOudf5K2POhTmpq39DkFMNiRxyknSgvfJLXukmpflsomS1OW+MggTohilJQ2fz4oSpxTa+o4eGFZc1B8kHXWB9YpDKP6Z/1a4Gq9MhST0tHrCGUYR0wwzl3dQjoRb0wgFxgKro1yaqDkQGS2WMbQYH7MQHRSk9Hu04TZw3kf1gIam7Rs8ZFN9tuiGVKyU6p/2p8P+3Jyd+p8mHJ/ipe38sVS8URpx5PSz97uI6i9Hdx5XUKavEiafbL/PB07UzUd6Ajpk7YjvhZymDHDwjBGREF5ub+nD6FnUXKb+3Tfzx4PB55JFhe6PbDJ8xqKtFxaAsbHtFR//H1S9YneW0q+d+gGQTiWhYhFrP4pv/ih9KBYsBixuBJqRdFp357KD2eAXXlqkBbpUru88cBzQm53wSTfnWL3GqnhcSmR570n0W7vJa05U7rgSikakTopfCO/c7r3unBO3A7HD1Qtsd+ekZ2QakR0rfF578lHycZgQK4qFnqFm992bwoj06hnpWa3FPRvvkDE0UU0mGXR3FcrRYSRaGDwkuI1ZS3Yt0Fq3y+t+Za05fdewYinlHsm2c9eJk3lcoZUMc+vL3hRcQAg265LW7vUvlXq2ObfzylFGBYFvsUtjoGyY6SaU3xEc/9jqaF6c72ChcxjkAzUlrJ6+cj9OxjGYCEtsZ9REfXpxMhA5pDIIwGZ4oIBAfVrvRyWzZe6/ihFGvwe5xx5ZCOwLpT4PR3ZmrDSOw94HXVXrC0YAhj9yB9pT9FS6ff/LG160jsziDoSlShOSuVTpHmvk4pS2QZ0jiLd2HWn6/Ipiy7tOc3hiCNkBBwDZlgYxnCCx5+oQOhFjZcCwSZfk7aSKBQoFg2EVSOpxa7itXmQhGaB6AWLDwsiigT1DkQh3HPI4Y54Q8OFWYtSqVd4JKNSZ1Oqg9Or3jBBuSmeKeWTI04Bd4/UtinV6abFh4s7Kdosl7au9woPBgJKTg9pU0mp8hjp7GulyUtSxaFJqbPKn2fNaT7M6xbOCineI+26O+0zWVcoIwvhN773QV+PhHwhoyj8TKzFEUBkkPvcJTVBG4M/yGg6FGyGlrQBUggxACjqrH3RF1vzd+/z0o6nvZwCdRGA0nHsm6VJC6XKGilBM4QKqXyel2tmyWAdoPDjgMAAwihCfkNUs73HrwWune1cvybgsAh53tRKufPdm7q90OeY73v4tZ8Jp4dhZHX9BK1akR8M5FZ/P3ud69RWeXitztkrUdKRmQN1QnP7IbUW672sc71jlzdmQiokawPTsV1WQZMv3m5rkfZvl0qqpfJFUgc1WXlSG7pAanbFzs3SS8/498lLNT8piEtTF0pzT5OKq/zncEPxcEpGUh0Zi9Ochdv6GxbMlML4GWbMsDCM4SJEJVgk8BzQTYXNvvFZ/ziKhRtok5bzyEJBXiSeQhSKkhmphaLQGxLUSvCXHM3wHi5fNOmVnpAX6gq3C323F/7yXiUFUsM6qX2f1LxTamVq53pp12apM2V80O2BNpQuL5SFFSOCUHGqpQwRkpM/IFXO8MrNrNd7A4nnuzqRVGE279G5yy9weIcIx7JApzNSPcEN41CESdnOENguNVKv0OZ/02H4HNEFvIKZ4AXMnCHhDOxaP42eFKVWBuh1S1se9opPYbm061mpIZVKEYotnVcyXyop8TMlll0hLXxXqvAzNWkXWaNY1NVE8MKof4wIZADlheegYHEeKBMUf4duVaRAHSztA1CMOFdwkY5UVBRHiGFkK8yfCMZEAGcdndgOhSuu5vcd9lUi+zt9WiF7savVaE81N9jtnYZu4n2zly2MkWTc10S62qxNUikRjCZp3X9Ljez3BdK25/pkHgfg3BV+Bk7tRgoZfapje56UH5FKi6TXf96nVWGwVM/xa1PFIr+HuvdOpVjiPMirlKa9ybd6pwYyneI5KUfE8GKGhWEMF2EQFkKPl490qLDgsUljPBQu9IsBNQwsMORyE14NBVakR9EJxhVQ9/j73cJF2DM/ld+dSLWkJYUhL1UcjiJC27pX/XvSio7uEbuflVq2+QUtkZQS5FwmpeKYP357ndSZyt/ujnkvbLRDmrpcmnysVLPA53GT8oFXsyzVpSa95R4RFIyKAAs6BpNLhcKYSHli0+bMGMaogUyhIKBI1K3p61OPQR+G1ZHOMFBbSVIbglHhOrh0S9sflupflrbcL219RGrd79MFncyl5M2lNyEDyAWGxCRpwZnSzBOkiim+NsIZAeorEg0gS6QxhSFZHIx6Kdfkoc0bDS7a0uOjgkQ3eYzPR+SFLjRhXgbpGVyn/onrLrWrx7e75nvZeFvqcxb2GRZ4fA0jGwldlwLshzjXDtbJjP0T+cD556L5u/ze6mZE7PHpSshZ1QqfacA+7uoht0nt21KzXQr6hsJ20jWOCGGP9Mp/eSdeW4M3FuIYAqQ0s6YQ8UxFWPY96OskYqmIJnUXc+dLE9hvq6XODX4vrjrZO/eIOpJxwHuzDhBZREaJSLBuTTvXyzK3qbdw30W+VFCU1qVu+DDDwjAGS+jEhFeQTRrjAG9GSJOgfsJt+qHwstjXL+AFxOigaJJNnNZ1dJPBI8IxiErgvax/znv9eb5Le0pN7I3XesUGPZ3Fzy1uVf51jZt9ysb+9dL+DVIPz2cRTSnzsTJp+hJvNBSQNz7d10LEi6U8WmfioZkgxfellI78lNc2labh2tEyB2OBT6tybStn+MdCekUIP/O9hKLVoCy57yEV3TCM0QK5o60jRdrIJ55Fdz8RtlSzgaCIE7lwUcGYj24gb/y2cQxsu1/a9rD04t1SfVrqE791FImSKT7Vgbzo4mofjZh5slRFKgOdY9r7Oq+R6oSnE4OF90sH5YAcbuQoPT8cx4UzjvZ5DyqRTdaZVro/5fnzrDqxf80EBaWZNRR0n0mnt9lESlVgvbEmDEa24WY/PJ/RRGGplyPXGSmvb+ZEMkTkSV3q8MZCiAhSAwWuycFmb8TjpGM/3vm7PsOa1CdXt5GaeN/F/tvj0wf3vCy98huf+uui/DjqUm2aaWrS1iqV1UhzV0sNG6W6zVI+jkFm3syRpp7p06EK6Ri1L9XmvdC3riW9iXUIYwFHHfLLZeJqb1Dw3JpVfalec9/l17cw6yLUUA0zZlgYxmAhNYnQZ8hhbnjGK/oIPsozU3WpMyAdiUXAGR+1fiHDYGiln3w8lbJEd6aSVH5mpR9GxyJY/4LUhQJC1IKFlILSFqmnS2qhfR4D72g3i7c19RwIi1q0RKqplirnS9NfJ1XMTk3Qpji1MfV+Vb7gGhhgF2GxxJvJlOxJ3tPBQkx6lytILUx1s0hBfQifLXh8GQzGff1WnHKvwLnvJeMxwxhJQjc1PJxuuGOzTxOIkTYxKRU5jHtPJU0JnHcwNEmokVr3SX+6Rnr5LqmBHOcU/LYnzPeG+sLLpKXvkMpSaYCsESFtge5MtJYOOREc37V23e8dDMgSa4gr/izs84wGxYBzQRZZH7hQtO06w0W8o8N5UFFWFrzWiHezcAYozM7EdZVJwXpUtczk1sg+SANMNypQ9um8FAxjnHz7H0+1Zu/sm83kmhzs9cMh0xug8HgbqUkbUm1fGVbL4zQywehgxlSn1MG+n4q+p7rJyrWBT/qhdJMWSFMXSaVE+XHalfr9z7WTjfsaxQrm31A3Rapinm85W32cz1JAbqsmejlGtlkDgnMvff+kCUNm3QjpW50NUs3q1GDOmsOrLRkCzLAwjMHgJloHZSGZ8n6ker67AskJqcIuiqULfHSD4Xd48Ek3wKOCEoAnksIv12auQNqzwU/eZJFhEWvbl1q48FSSChX16Uox6hpSikYq3dqJdRFzK8ilrpGmny5VpwrFeY8iCryKfK4mi07D2lR3qKJUG8oJfcqLS3miaLXcKyrBYEIJCwtxgBBxL9GBF7Hpb5JqH5YKpx79FHHDGCzte7wiXve0727mOrKlGgoU0JmFtssTfCSAqdrOuN8gPX2r9OitfalNFFUCG/e0lVLNPOnEd/h2r8BmHowKHAFEE3rTryjSTPrp9pWLUkYHTRdKvNyj/IeoSYBjkFft2ldvSk3EZu1gjk1bqjNVWSodstG3rsZTi7JExJPXORkuS+WLt/V1qmFtwruL57N84QCDtFDYiGwOfyqFYRzRHtzb9SnqI3PsLciHmxVFm9cNPurgIhBpw1+d3LRLbXVS03bfSCER8w0U2HPbkDWMCYq+U93fUjNk1UHkMt93aHJONdq4JqTq6al262f7VCpX+M2LSDeOp5wExamaJyKQc1J6RMohx3OQWfQFMh9w6pXM9vtrmADuohVlfk8+UBQCWcVRsfVO/zhyS13GCGCGhWEcLWzSdHMiqkC6hEuR2J1SKGjX2uUXA1cfQUi0qG+6JpEN6h9oCcuiQ8ShpVHau05qpetMyojgCgWd6OBEDgpS7V9Z/EjULC3zC1ol0zunSoUlfqFyxg6FX/Okkjl+8iZKBMoDi69raYvCQZeq6VJHndSVUiRieEJYSKukyiX9DQCXx5lqV8dnpZhsINyiOUANxaTVPiTMsB/DGE0Pp+vesj3VVrW8L3URnIeQzmh7fd/5J74rPXunl3nEknktgHG+4gPSsW+gqvm170P0oXZvqi4h0ud5RHZYLzAeSIV0HsW0tCdkhPUDgx/Fh+gJRaQoKq6gdJdP2+IzcExSnXBUYIi4iCDpTzV9nWKIjiDHznER7Ts31zwiA96P+4nEklqRjqUvGtkAM6GcYyvmU6CQF5qdVJ3gH0c22Jt5Xoii55d6GWLoK3LUQd1Rm1RP23UaI0RSzjoOm7IeJs9LdWqM+rqpeFyqmCqVzZDmXCxNPcVnCDBjonu/l8tYoXfOIT84DGhdTUQidGfsDpFRaiwnS4VESPJ8lIWW8EQkOA6GQenMVE1Fan93w2+jqQGch6hRZP/HIUidhctKiI7YLCkzLAzjaMDTQRpQ8NKHjkikF7g0oSKfw4kxkT8r1bmBOoU8qXm39PL/pSIZDLxrkLpT3n88oRgUpZOkKcf7Is6quVIpE3ybvDKAcUBRqat7KEwVdS1LdW7BcEjhvBm0pEyFZtNTKDBmQq99FBC8HigTLGosxJnDgAaCxS08j89PLmfve2fkhgdmvk2qXuEXTcMYDZBF0hhwCIQNl2hB0QSvmPD7J2KBsv7y3dLTP5T2b0z95idLkxZLS98mVVJgzZTeKX6qPDLlHAIdPg0BxYT8bCIEgPcw0eZTI0kTdO2emcSdN8Dcm5f6Ujt4PYO1qKlAhjFKQiolSoeb/k3jh10+wsKxMfhdo4ekV7wyQbE6GBhcTAjOJH0+h2GMNBj2/P7dPtjR18o5pACS2osi/8p3/W+cPZfmJ/tf8R3Y6PgmDGdSiYnepfbE7kKpeoZUPUsqwkgplcpn+RbPoYAbWcpLyRsRA9cFaqffy7qoq9yTavNa6tcU1hIMFec45DFSocr65sUg93wO52BI7aPc72otp6dqqKI+6gg8HwfmkYJhQftpV7M1Mo0XzLAwjCOF3EgMimBUsIgQtmRxYwEKQ+ycx6DaK+54P7f+r/Tyr6RG2uGlusNw6cCTUCZNWiTNPFuac55Pdwqt7TBSXNRjspSgtWuiL/LBosl0TnLCgfd34dd8r/AEYyI9VDrQ5E03kCc18OdI6G1vG/XpE0wpdhPDD1A/4SIlxxzZexjGkE7h3eLroJi14gY3okhQUH2q39CRX2omHvma9Ox/ekM/WSK9+Sbp+Iu90k1UoJtOMqWpDkxEMmJ9E66RQYwGPKTdbammCtt9WmFo80pnp3QDPLSodUZBMCo6fMoU6wfHxnvK+4TIoesGhzzjOIimUrpS0ZbMlpsDgWHiar7o4hbWiFQ0BgMpE1I0DGPUZkK90nedPTaAQo4ijkzvvkdq2+GLp5s2Sxvu8Z3Z2KqiqZ+3K2uaKE1fLNXMlGqO89E+ZMy1c8ZpkDISSG0KjRVCeiDrhGuxTgphS1+qEbejqfpBZ9iTihUiDKQ9ze3ryOaOk9Gmmkg+ukSQXeRtsHUR7LduXUrN1RgBzLAwjCM1KvAo4AFwt7tT7R63pDo2zPYFlSwsRC7wbDTtkp74sg/N4sGgJoLFqmKmVHK8NHWVVEOeZNK3ke0hnDrJLwLRid5b6eZBzPHeBxQIvK0oFeRUB6MC3OyKVIvMkYaFlcjJgYYJGcZoQ6cXCqZJb0IZoL6BNMGK41Jd3aqkh78tPfBlL4uxiLTiCj8Eknqm1pRig/JACoPrIkNkgonaxV4pCGAgYGyT5uC6SNFVrVEqOd17DtPTilBCKCalkNzVZtX4yIPzyG7y+eCcH15PhbRI6iZK/PNYE5wB0pCanZNOqpgVR4NrWY1jhAgM03uPTUVOUgoO3wkGEIaRS5vMgHQrwxiNfZc9FlDWaXKCYU9xNuk91B70FEq1D0kNz0stu6Rdr0j1jVJRapirSzeq8ZGEWZdIM5f5WQ+0emcfd8PlaOHe0NdqOjgNifzzF7lBXrjdO9W7rq8e0Q2+3O8NfGTdpQ0XeAPhcJR65M+lT/WkzmkQk8HTYQK4y14YmRbvpgEYxpHAQubay6aKsYlGuLQeUosKvQJBjQNKQ8Ek6dEbpRe/n8q/xqCYK81cLVWkIg3kTjpjgQKzF/pSFPCM4DFkMaJTEwtWutJ+pJGFkcKMCiMbcZ7/TVLjBu+9wyNIigEKPvKHvJFvfd/7/fAqnPfTjpFW/qU0/xype4ebW9ULdUsuurChT0FHYSfNAWXCpTIl+6ceENlgnaB5AU0MSEkM0UOUeSIhpHm4idiN0s7fpCIaEWnS6d5REWZpVC33aZcuYpFSFoheNrakopkRv0awbqQrNHhNld9nIAwU0STSwvrmH8gY4HkYnaQMYygg+o7MhuGUAWe80/lsl1e+UehDy9jGV6XtT0j1KPzULZVIc8+QJpC2SypiUpp2QWpOTJ6Ul+9lkPREV/s4IWW0pIwLUo9C9J33wtgIw2pR/olEMJAuxjA7jh/36wKzm8JAPteiufrI99Gh2ktxbrDmZTaCGEbMsDCMI4EahNCdgQWm5qRUC7pKr7y46dJR6bmfS+tIoyD6wKY/VVr0TmnOW/wCxuKYR33ENL8wEs0gZEpI1xVcZuQyu+iFiathHBUMrWLDp+sRtU+ukwrF10u9Mf/kD6UX/88/l6nXp/2tdNzFXmFIBycCnn8iFKFVrIsaLPDrAYo7qU6kUuCAwLtJHRQGg+uTX+/XkC13pIyCmDcYwLXBLPDnp5QXtDPuzxUlhfdIr5XKhPWhcrE3SjIjIpkcrJe9Gwa2VKp93H+WXiPjEK8zjKEEY4HoXTw9VZC5SjXeMeBaIadqHbtapM1/khppO8s+HZPmnCOdcIlvFRtkCPl0UQDSlJhwzXyLY/q3biX1uC3mHXqxlDwSgehtZ5t+jqnWr64oGmMnFXEM9xFdGe16QjIJRriTm2kqhnG4sLDtf7ov1SC0bnNhTobmvCy99BvpmTul9k2+TqIoX5q2SlrwZ1LNslS+JBOvycUs8h5J11kplcNZfaL3fGCkgBuOQ85nWrqTYRhHBjnXpC+GwXco9BXH+sfW/VR68R5fS8F0+fM+L1XN9EY/hgHeTBQZGi00veLTIYh8IJcoKzPe1H8qt+uWtjMl56kWzxgfIT0Sw4JLMChCFID1oJzObpNS6R57vIGSl0p5PBzF3tVWpWo4BgPvnz7B2DBGEmQjs505DRXYL0lVwtjlORRM71ov7XxGyu/xzgKyCFZ/SJq4QCpkXgtOvDTFGvliH8aL71qr53k5dfJKG+dXvJHfWO/XifRucSGC59rLR1LRQewbuiqmnAED1U+NM8ywMIzDxbV73NVXNE36A3+DYvD4jdKzP/K1EvkRaQa5nGf74TVVx/dPK8BTQqoT6U8BjhcKu0L3FevCYhiDo2Wr936iSAC1EwyvwsBY+yPpqZ952Tz27dIlN3vZxFBwk6xJiSDK0Czt+2OfMRCYsNwXbxKldHnezV7ZQTFB1vFgho5xpCy5TmqptpFENFzhdcoTWjbbpyGRfoETg/MgHYTXphsuI4EZFcZogVHL3Il+UOtAu/aIVPekj1xg8K/7ldTS5IuyMdznnictepuXbeZZ4KAjfZl0JuqoXJOFPC+T1FPhMHDvlZbu1wtNUlJtZ6lvcsY9dZCppg3cz/FxHOC4CA1VYsU+C2EcY4aFYRwubiBVqkvTpDP6dzda/5/S8//tF5ZZS6WJ06XCSl+IxXNZ9FykIykVz+zr3OImg4bBU2ndIgzDGDwo5hRAuzQKFH/SFav93Jb7v+TbPqMgzDlXevM3fN93jAim7ob5M/uf7GtrCSgVKBGkZJDmtP8x3zef6xgUKC3UN6DcOAUD5STaP9pQuMQ/jic1DPfC05pZwzBacyMGmm9hGCNBw7r+0QXqGojw7f2jb1pCJAC5XvczqanJpyvSYGHWah+tqzk9lVYYTRnIkb6GJq5eI5XOfCCQSZqwIMfILIZCb9FzpH/aFGlVXNzMmNR6E2FWzPhOGTTDwjAOBzpSsKjhrZhwkvc8Qne79PsPSxt+4nvaT5wqTZrqIw0Tz5CmX+Q9kC4ykfC97FE6WHhCw4eD5U0bhnH0uCYI3X2pR7Rqrn9OeuUR39c+FpNmnyn92c+9goKxX/9squtbPFXInGqogEJRfbJUscAbAygnGBxhTgXtLQEnQSjaxlDgtqtTSPq1gPqq9HqpbJxkjZH0mvtMXTCGGeQ0PfUo1CfgEGjf62WtfpO04S6pdreUH5NO+Stp1hlSyVy/Nzc+1/+YyDBy3fyK35fTC8H9Dzs1sZ7i605vWBztfjzSkcUsxVYKwzgUeCP3POBrIVh08FTixdj+sPS7D0oNLFgJadpiadL0VL95+uKv9h2iGl/wCxYKBfUYmYuWGRWGMfSg/FPvQGMEBmehVNRtkF59RGqn7WpUOund0uI/lxrW+IgG3kbSlLi4CdekXDDNfok08TTvnaTzG+kPJTP8+5AKxfu49s+zfccllHDaTeJIoE4j1/Ktw6DN9IJVS8s0hpv0SELpfC9HGBW1j0jNm6Tn75D2MKuFYXUxadl7pBmr/Z7MXhta0gbILmDPbt6QMZsl4o0JN9S2rM9oHmjGk3HEmGFhGAeDaMKOu1J51Kk+7oRVH75eeuhLvuMTxZUnXCCVVfg867zJ0pw/9wYDE3LDJk03GZQMwzCGF+SO9q5Mtw8zZ1r3SNsf9VPuSyql5R+W5r/eF2IyZZpiUdKc6KqE5xGl2k3cJdJYJnXQZCENjIbQepboJM9LV0xGaBjVsOKKWsfQ5zFyaG7Fq1Jjuy+m3vektP4nUhuyGfUzoJb9hVRNXWKlNPl1qXkxNE1IQWE29UyZk+cxKEixMofesGGGhWEcbHGrX+uv04mCtIaiGunVP0iPfkkqjEtV06W5p/uQbHhO9TLfLYY8bNe7PgUFYIZhDD9EC0h/YBBeT6OfGbHzcamjw0+bfuMPpco5XkZJjSIFgzzqZKFPV0KJRmHBUYDhUTThtTnYQzW8KhsJg/jSla+pbxi10zHGAW7YbCDZF3VEhp/8N1+gzc+xvFI68TKpKjWkztVPRKW6NT4zgH2WtstApDLgplinIojGsDIyY/iGkZtvvllz585VUVGRVq9erccff/ygz7/zzju1aNEi9/ylS5fqt7/9bb/H3//+9ysSifS7XHTRRcP8KYyshE4y1EYQtcAbyaCZhnrpDx+WinukyVOlxW/zg3dc9xb6yC+XZl3qvZgBWk3SoWKEpl4axrgHY6B1m2+Y0N0mvfKwtG+3FM+TVv+dVDbRRyGZVk0tBe0hK46Ximel2kgyeKvED7AkpbH6JGnCSqlyiVSxcGwbFYBxBW5WQAo3Y8cwhgmM+5C+5Lom9vi6iMe+LeWlDI3j3iydfLVUPs+3gyayiANw+y9SNU9bfS0VBeDIdugER4TCzYIyo2IkyGlN56c//ak+8YlP6HOf+5yeeuopLVu2TBdeeKH27h24//bDDz+sd7/73bryyiv19NNP65JLLnGXdevSuhBIzpDYtWtX7+UnP/nJCH0iI2ugTWQIqzolo1yq3Sj96q1SjKm9E6TpK33dBR1U8OxNPV+acaH3oNCdonKpV1aYdzFa3V0MY9w2W2CS/W5p34tS835fU3HCe6VF7/eDsijsRpkhAkHRJwPoCiqkyuPSusgUpnrdR8ZXcWboUEfnKgpoGZhHFMgwhouQbhyiFe210m+v8VEL2szWLJfmXOR/k8gq8kv9BalOIdrhuqol+4ZRpneWMkaMnDYsvva1r+mqq67SFVdcocWLF+uWW25RSUmJbrvttgGff9NNNzmj4e///u91/PHH6/rrr9dJJ52kb3/72/2eV1hYqKlTp/Zeqqstt3Tc4SZoprq8uL7XRdKj10kFXVLpRGnGEm9ssJDRaq7mVGn6hVLVkr7IBPnWPG65nIYxciC3FFjj7WzaJe15ReqOSNULpcXv8vJKYShtZVGgKcJmhgzRCLyabvhV1KdMocCMZy8nKVFFk/x3kDmwzDCGEuQROvf7v3+6Xsrr8Y1RpqyQ5lNHEZOmXJjqHpXWTZG/yC9zpZz8psBpUDzN/p1GmJytsejq6tKaNWt0zTXX9N4XjUZ1/vnn65FHHhnwNdxPhCMdIhy//OUv+913//33a/Lkyc6geP3rX68vfvGLqqlJ9SnOoLOz010CTfRVNnIb0p5cP/qUF4RQ66NflxLNUnGFNPsUqXiyT5/Ai0dOJ+kSIX3AyHpMbscwpEKQBoFHc+/LUldUmnaKdNrf+DQL581s8HNoiFww7R6PJ4XezluKEbLMUheJVISobcHE/mlRo4jJ7hgEeXRDJVOzIXY85Du1Eeivmi1VTfX7MXUSnTt8ihPGBE6CqmX+tW72RLcfVkdLd6KN1uVpVMjZiEVtba3i8bimTJnS735u7969e8DXcP+hnk9E4wc/+IHuvfde3XjjjXrggQd08cUXu/caiBtuuEGVlZW9l1mzyNE1cpqwmXa3+tqJ3U/6zhR0o5i1yisis98hlc6SJp0mVS323aKMnMHkdgzTstkPs2ytlTrbpI6YtPzP+trEtm7u62WPNx6oxQitVfFwWj2Ub4/dbxBYkbIBk90xXF+RTBn26273cyXLq6TJpBMv8HKJoRAiGrRwJv24ZLpUNs9H1WiJjMOAbAIzKkaNnDUshot3vetdeutb3+oKu6m/+PWvf60nnnjCRTEGgohJY2Nj72XbNqYrGzlNUDDatkttu6X1P5AKE1LFZGnCMmnCCv84dRNEKSaf0zcQy8gJTG7HMHv+4Au39zHQUtKSi6R4c2o4ZVrkma5PKCsoM7SYBRo0WOqExw3xnOaHgZJeQq1JFmCyOwYhuuj+1kprviPVr5NiSam00stsfpUv1qZ2imgFRu/UC6x2MUvJjtjmUTBx4kTFYjHt2ZOaipqC29RFDAT3H8nzYf78+e69Nm7cqPPOO+81j1OPwcUYQ5AWQZ42k3jpnZ0MC9kpPlWClICepr4CR1cwZuQSJrdjFDpAEV2sw6jokMqmSXOW+sfo8BTyrhloh0HRsD5VkJ3wynM2TsEeDZznOGMoXpa0yzbZHWP0pDo3QbxbeulH3uVdXCRVzPUGBRFE0ptKpvlUR+qfLCKRteRsxKKgoEArV650KUuBRCLhbp922mkDvob7058P99xzzwGfD9u3b9f+/fs1bZoVAI0b4h1S206pEQVlo++dXTNfKq7xm2tPszc+WOhoI2sYRnZA20nytZvpGBOVZp/tW1dSlE2HNqIUdJ/BgMAjzyTe0O0Ig8PwMN8jE/t+jOEgpCUit68+mDLyk9LkJb5ugsGys/9MmnJW6ndY6tOQjawlZyMWQCH25ZdfrpNPPlmrVq3SN77xDbW2trouUfC+971PM2bMcDmZ8LGPfUxnn322/vVf/1VvetObdMcdd+jJJ5/Urbfe6h5vaWnRddddp8suu8xFMV555RV9+tOf1oIFC1yRtzEOoFDbDdZ6Vdr+mBRLTeOd82aplFzOBVJ3vS8CxWtibWQNI3tkl/qouhd9fZRKpClEK+JeTt2wu7SBb+mEoXdGXwOLdPAYW3c7YzggtYlUxLZd0oY7pPykVHOM329jNFdYJlUe759btTyVLVBl/xZZTE4bFu985zu1b98+ffazn3UF2MuXL9fdd9/dW6C9detW1ykqcPrpp+vHP/6x/vEf/1HXXnutjj32WNcRasmSJe5xUqueffZZff/731dDQ4OmT5+uCy64wLWltXSncQKTssn37NrvB+Ql6BDD4KwaacLJUusm/zza2lEwZhhGdkBRJ7nZtet9bcWkeVIkLhVO9n3s0xVj6i0qju1Toq1Yuz+Z3wcd8AxjOGDeBO3dN/9J6twlFSelshrv0CPdOL3miQJtoo9GVpPThgVcffXV7jIQAxVcv+Md73CXgSguLtbvfve7IT9HI4egfqJ5o7TnSamn0W+w1cdIxTN9CgV97V07yuXju7+9YWQbRBqbXvY52zEGVi73naCIRuSV9xkUNFpIL0Q2o+K1ZEZiSSczjKEmDLZjRsq6/ycVJ/pSF2Mlfr6MOfByDlstDCOzvoJ0iv1P+5b25TOlqa/zEQza2LHosdiZUWEY2UX9U1LrXl94XDxDKirzfe4rFvp2qRgTpqQcPjSsCEPxKKA1jOFIgyKFsfZpKS/Vapb2sm5w5Sxfx8iQRiOnMMPCMNJp2uA7y7TVe8Niyirv4YyVeaOCtIrQDcowjOyBSdvNO73cTpzvjf/JZ/Z1M2Lgm3H4YISFmT7WgtcYtmG03dKeZ3x7WSiskMrnS1Nf750CVtuTc5hhYRjpkO5UTyeouBQplqa9TiplQm9q0TPlxDCyk/1P+oijizROlUrneicA07Wdc8Dagh8RpIgxp8ddP0DRu2EMBowKnHkNL0lkFldMkUrn+RkVyCsRRyPnMMPCMNIhLNv4vL9ePNlP5iUNipZ3YClQhpGdCkrjJu8AKCqSKo+Tiib6moqqE0b77AzDGAhazJIhwFwZIhZExmpO8amL1qUtZzHDwjDSh/PQ174jlQZVNU8qnCLlFXqPXZYMiDIMI4OWLVL7Hj9zpnCarw9AXisW2VdlGNkKw+72r/UZAqijlfOkggqfdoxTz8hJzLAwjAB97ruafFeZJJ2flkoF5X1pAHSGMgwj+6hbI3U0++slqehi5YkWYRwKaFZhGMMBrd1pD01340JmycySKk+QJqy07zuHMcPCMNK9J7XP+MmfsQJp+rl9RkX5QlNSDCNbad0uxVv89dJJvr6iZMZon1VuY8qdMdy0bZPqX/IZAjjx6D5W5eeKGblLxhQcwxjHNDwn7X3CXy+qSJtTEZXyy0bzzAzDOBA9HdLOP/gJ25GYNGGRlF9qjgDDyHba9/ssASisliaulmJFo31WxiAxw8IwAvsflbpqfZ42xdp5qQUudEYxDCM7vZ7Nr/rrGBRMiZ7+xtE+K8MwDgbzZvbRZjaeipAtlkqsJfRYwAwLwwgkElJng8uEUsV87/3EyrAiMmMY9lRjiOjYK3XU+ev5lT4FKmqtZQ0jq+lpk+o3eEeeS4WqsL12jGA1FoYRaHhBSvRIyZhUNl3KK5eqV9j3Ywwpu3dLO3ZIZWXSwoX25Q6allek7kZ/vSxVW2FzFwwju+lpljpSGQJlU32WAI4BI+exiIVhQHe71F7vF7mCImlCqqOMKSjGEINRAS2pWmNjkPR0SfFOf500KBdyNAwjq+mq9xf23OJqafpFfiijkfPYv6JhhLZ3nalFjkLtSIF9L4aRC9AH3+VSxKRJS/ykbcMwspuGdVKi1V8nykhHKGNMYIaFYUC8oy+dIlbpB/RY/3ZjmOnpsa940OxjwBZyWyhVn2xyaxi5QANtZlOF29Q0FlSP9hkZQ4QZFoYB3fW+D34y1faubL6UX2HfjTGs1KVqjo1B0L7V/80vkQpKpLxi+zoNI9up3+gdAtF83w2qsGq0z8gYIsywMAzoaZG6O/x3UVwllc+378UYcrq7+9+27lCDJBGX4u3+Og6BqqWDPaJhGCNBy9aUYVEoTT7bvvMxhBkWhgFdjVIylZdScZzNrjCGraNxOtUW/R9804XuNn+9cJoN1zKMXKF9j/8bK7Y2s2MMMywMA/Y/3WdYTHmdfSfGsJDZCSrPGn4Pjs49UqLLXy9fMMiDGYYxIsS7pO4Gfz3GUEvLEBhLmGFhGNC6O/U9RKSJJ9l3YgwLVlMxxDS+kOYQWDXURzcMYzjophtUKoWxoMrqosYYZlgYBrTX+u+BNrM2adsYIazGYpA0vpT6IiVV2rRBw8gJupv6Uhhtvx1zmGFhGND6qv8e8op8zqdhDANNTQevuTCOkNY6XwAaiUnFU+zrM4xcIN4mJVKdLArLR/tsjGwzLNrb27UjjJJNY/369YM9tGGMHJ2piEWswM+wMIxhNiog32a5DY7Wnf4vLSsLKgd5MMMwRoSOfVgX/vqEZfaljzEGZVj87Gc/07HHHqs3velNOvHEE/XYY4/1PvaXf/mXGgluvvlmzZ07V0VFRVq9erUef/zxgz7/zjvv1KJFi9zzly5dqt/+9rf9Hk8mk/rsZz+radOmqbi4WOeff75efvnlYf4UxqgTT00ALZgkRXCBGsbQsnevfaNDTmuYYVFgc2cMI1fY82jf9ZKpo3kmRrYZFl/84he1Zs0arV27VrfffruuvPJK/fjHP+5V0Iebn/70p/rEJz6hz33uc3rqqae0bNkyXXjhhdp7gB384Ycf1rvf/W53nk8//bQuueQSd1m3bl3vc7785S/rm9/8pm655RZnKJWWlrpjdnSkZhwYY49kQuru9NeLJo/22RhjlMbUYHdjCGnflrpSKOWX2VdrGLlAQ8pZS3ZAtc2eGWsMyrDo7u7WlCk+r3XlypV68MEH9e///u/6whe+oMgIeH2/9rWv6aqrrtIVV1yhxYsXO2OgpKREt91224DPv+mmm3TRRRfp7//+73X88cfr+uuv10knnaRvf/vbvcbQN77xDf3jP/6j3va2t7kozA9+8APt3LlTv/zlL4f98xijRA9GRaqzTJWFZQ0jZ+gJLSuLRvtMDMM4XFpeSV1h6vYM+97GGIMyLCZPnqxnn3229/aECRN0zz336IUXXuh3/3DQ1dXloiWkKgWi0ai7/cgjjwz4Gu5Pfz4QjQjP37Rpk3bv3t3vOZWVlS7F6kDHNHKf9sZadSe8YZGYbC0rDSNX6Ojwg0E6UFAMw8gJmmo3ub/dyG2RNV0YawyqSvWHP/yh8jImPBUUFOgnP/mJrr76ag0ntbW1isfjvRGTALc3bNgw4GswGgZ6PveHx8N9B3pOJp2dne4SaBqoQtPIap75/d06NVVK1l62QpZQMfYxuR0jKYzJLtcVasf+Kh0z2udjjAgmu7lPZ2uTVCw1teWpJlY42qdjZEPE4oknntB5552niy++WB/60Idc6tNdd92lrVtThXSSzjjjDI0HbrjhBhfVCJdZs2aN9ikZR8jv/zhF7fF8dcXzVVpthWTjgZGW23iqAYoxlN9pRD09+epORrUvvsS+2nGC7bm5z87909QZj2lLvU3cHosclWFBx6dYLKYPfvCDmjdvnh544AFX50B3ppqaGo0EEydOdOewZ8+efvdze+rUgZVD7j/Y88PfIznmNddco8bGxt7Ltm2hmNDIFR7adZ5WfeVBnf+tu6XCqtE+HWMEGGm5tXkVQ8+rm6Q//94P9Lnf/qP2zP7iMLyDkY3Ynpv7nHXLI6r5h536zJP3jPapGNmSCsUm/Jvf/EbHHNM/+LxlyxbXIWokIOWKgvF7773XdXaCRCLhbh8oDeu0005zj3/84x/vvY+aEO4HjCQMCJ6zfPny3tQmukMRmRmIwsJCdzFyl7b2pJ7fuVQlpUlFotZqdjww0nJrrWaHnvv/2KPfb7hQ9798rp7/hs2wGC/Ynpv70ISxs6tUU6emmqYYY4qjMixIc9q+fftrDIs5c+a4y0hBq9nLL79cJ598slatWuU6OrW2trroCbzvfe/TjBkzXOgUPvaxj+nss8/Wv/7rv7rZG3fccYeefPJJ3Xrrre5xOllhdNBGl/kcGBr/9E//pOnTp/caL8bYAk9yYwOtkSMqKrV8FWN4KC+nhsu+3aHksSfjSiaj6koUauZUG2ppGLlAW5vUnbIn5s2OjfbpGMPAYa/Gb3/72137VWZF/M3f/I1r1crt6upqjRbvfOc7tW/fPjfQjuJqogx33313b/E1NR90igqcfvrpbs4G7WSvvfZaZzzQRnbJkr783E9/+tPOOCHNq6GhQWeeeaY7JgP1jLFHd7fU1umjFFUTzLAwhgersRh6WjvQTiLKi0YUM/3EMHKCx55I9KaGVpcWjPbpGKNpWBCdeOihh/Sd73zHdWSC4447zs17OPXUU7VixQo3yZoUpZGEtKcDpT7df//9r7nvHe94h7scCKIWFKNzMcaH96Sj3UcsKqqGf6ijMT559dXX3me+isGxebN3COQXJTQCY5MMwxgC1q7rlpJeYI+Za22ix7Vh8ZWvfKX3+o4dO1wtRbjceOONevXVV13r2YULFw77DAvDGCqee05qbfWLXHn5oMa6GMYRsWiRfWFHS1eX1FDvrxcUmkPAMHKFnbvJDIiIZJJFi8wjMBY5qsRU6ha4UKcQaGlpcUbGM888M5TnZxjDSmur1JVKhaqusEXOGDksfefoqauTmluQ16SKS8ywMIxc2W9f3YZhEVV+oUUaxypDVvFWVlbm6hG4GEaueD1JhUooKUWTmjvHDAvDyAXI0Y67RO2IqiZabZRh5ALMLt610zsCCgto+DPaZ2QMB5b7YYxbXn5ZamyUenqkSDSpY+ZYBahh5Irns7Od7SupqTPNsDCMXIAO3/V13oFXUpFUvpVYjEnMsDDGNZu3xpXE8ekMC+tQYRi5wM6dUlfct5aZVGEOAcPIBTo6pNZ2Ao1JlZVZCuNYxQwLY9ymQbHI7djbJUUTisUSmjXF3CfGyFBaat/00UKEcc8eL8NwwiKTW8PIBfbvl9o7vEFRXjLaZ2MMF2ZYGOOSTZv62oAmkxEVFCfV02M1FsbIpfIYR0dzs7RtZ7cIWETyElp6nM0YMoxcYNs2DAufenzcQttvxypmWBjjkrw8H7HYvScplrfCaEzthGgNw8hqkklp3Ys9lFe4HO1ZUy1iYRi5YFQ8/3xSXZ1JKS+uWVOGrHeQkWWYYWGMW7ZuZeq2L/wsK5emTx/tMzLGIhiwmVg3lMFNMd+01cttXiyisrJBHMwwjBHhpZekXft6FO+KKRpJauqEQvvmxyhmWBjjEqITjz8uNbfHXSHZcQuTKi8f7bMyxothMXHiaJzJ2KCzU6qtT/TWqphhYRjZz/btyG3cNUopKIlrQrU1XRirmGFhjDu6u32bWVfA3ZNwC92COQVuEqhhDDX8zrgQIbPaiqFRUBoafAFoZZVvYWkYRnbD2lfXmMoQKIlp6dLRPiNjuDBVyhh37Nsn7d0rbXy1Wz09SUWLu3TsrAKbhGwMW24xzQLYWDEujMHxyitSY4Mv/JxQEXX1UoZhZC/MsmQN3FvnDYvK8qhmzx7tszKGCzMsjHFHS4t0773S3v1x1xGKdIqKsjwzLIxhww2JNgYNxtlLL8XV3hZVJJZQVak5BAwjF+SWNtG7a+OK5Mc1uSam4uLRPitjuDDDwhhXkJKye7dUVyd1JLsViSQ0dVLEpUEV2Hw8YxhgQzWGrl7lpS2dSiaTiuUlNX92nsmtYWQ5TU1Sba33rkTzezSpJmqpx2MYMyyMcbfA1df7zjLNHT2KFnVr5oyYKwCNWS2ZMQysW+dToJi/AMcea1/zYKKN6zYkpUTUyezrXx8xw8IwspyXX5a27+mWsC2iSa1YVKSIjbEYs5hhYYw7jyfRCjyeTZ1dipV0aXpNocv3tIXOGK65CxiytbXStGlSRYV9z0fL2rXSpp0MnEmqvCSqqir7Lg0j2yFLYF99j9M4J02IavKkqO23YxgrezPGDXSC2rPHXxrbutQdj6uwNKmZNYWuzsIwhgOUYQyLyZN9xMwYXEeozo6YmGo5a7Y0d659m4aRzbS1+QYWdc10YIxr5oyIqqstQ2AsYxELY9zkuW/c6BU8OkI1dXUpoYgmVhWouDBm+Z7GsEB0jN8c8LuzdrNHD6lkm7d3K9kTc4XbSxcUqaRkqP6lDMMYDugG5VJBW+OKRJKaPbXAya11cxu7mGFhjIu5Fc8846/v3+8VvYb2LkXzEppaWeBSU2yGhTEc/OY3/W/bYLyjZ/Nm6fmNXWRBqag4qeqKfEsrM4wcqIvavqNbHV1JRaJJzZpYpEmTRvusjOHEDAtjzLNrV9/1F17wnaFqG+KKROOaVF5kaVDGsJGZ+jRhgn3Zg5m4vbu2R4okVVaa1AknyFpWGkaWRSco1KauLLTZbmiQtu/1YdvCoogqy/LdfcbYxWosjDELBgSKHVEKIDJBOkVLW7frCBUrTqgsr1ALF1qKijF8KXhsriEiVlRk3/TR8tJL0q59zJ7J04SqPFe4bZFGw8ie7ncY//DUU33379wp1TZ2O4cAhdu0dafGwhi7mGFhjEnwmKCIhIWOnE6MCgq4t+ztUl51i8ojpZo7M98tdNOnj/YZG2PRqCB9B8M2hP4XLRrts8pNMM721yVUuzfqUqEmV+WpsnK0z8owDCC9OOy1mTz8sFRbn3BdoGZPLnRyO3++fW9jGUuFMsakUeG6x6QtdHTkue8+H8HY3dimSCKqKSUlzqhgfoUVgRpDDUYsbRbTMQ/70UGr3t892KlEd1T5+dKkqgJNmTIU/0qGYQx2v6Xz3UCQ8vTqjnZ1dGNYJDV7UqHba60L49gmZw2Luro6vfe971VFRYWqqqp05ZVXqoUqoYPQ0dGhD3/4w6qpqVFZWZkuu+wy7aH3aBqRSOQ1lzvuuGOYP40xlJ5NwrB04KHN3YYN3nB49VVpxw7ppY3d2tXSpmRXno6fW9w7XMtSVIyhZv163zggbL50QbFOKEcHcvzMhg4lExGVF8W0YkVExxwzlP9ahmEcLjjtSH1as6Z/2hPzoBYvlubN84NAy8ulzbu6XD1jUVWnZk7Pd44B22/HNjlrWGBUrF+/Xvfcc49+/etf68EHH9QHP/jBg77m7/7u7/SrX/1Kd955px544AHt3LlTb3/721/zvNtvv127du3qvVxyySXD+EmMoSS088RTsmWLj1QQpiWCwWPN3d2KJyMqyy/UjJoiZ1ig7BUW2r+DMfRGbgDDgo5QoajRODL2NXVqN00YItL8uXk6/3wbaGkYI7mW0TqbPZTWsen1FAFSnUj5LC72TSr4SxpoXQvelahWzKtwKVDMnrFhtGObnKyxeOGFF3T33XfriSee0Mknn+zu+9a3vqU3vvGN+upXv6rpAyTMNzY26nvf+55+/OMf6/Wvf32vAXH88cfr0Ucf1amnntr7XCIgU6dOHcFPZAwVpDphVNAJCo8JRgO57gzoIZ2iuavTaXdzpviQLDnvS5bY928MPfzeAhgUpEHZhnrkEPX59T0dSnbnqSBfmj+zUDNmDOW/lGEYh4pOHIiQ1kS0Ip1XXpHuvb9bbR1JRSIJrVpaqLIyi1aMB3LSsHjkkUec8h+MCjj//PMVjUb12GOP6dJLL33Na9asWaPu7m73vMCiRYs0e/Zsd7x0w4J0qQ984AOaP3++/uZv/kZXXHGFS4kaiM7OTncJNNlo3VEFDwlpUORfh7QTFBOG46HctURbpJ6YplcXupAsYVtSpYzxxUjIbfohQ0qUcWjPKF1kiDKS2cp1slUfuA9hTqioMKoLLrCaqPGM7bmj1649UFPjOzsRlQjpxIDMYmh0dPi6iyeeb1OyJ6LKCQlVV+a71xljn5w0LHbv3q3J5LikkZeXpwkTJrjHDvSagoICZ5CkM2XKlH6v+cIXvuAiGiUlJfr973+vv/3bv3W1Gx/96EcHPO4NN9yg6667bkg+lzE4hYQZFShzXOefmRxscjz/93+916Up3qqWrm5FY/maWl3gohVz5ti3Ph4ZCblNj1iE6dvGwaEOCscAUUfkub6e7m5J7dobVzJCD/yIcwZY8ef4xfbcoYW1iUYTGAih4QTONlI3cdQR8Q/gX6U9+0Dyx3Ppggd0YNywsUtPvdqoZLxcE8vyXYYAEYvjjhviD2BkHVlVY/GZz3xmwOLp9MsGqviGkX/6p3/SGWecoRUrVugf/uEf9OlPf1pf+cpXDvj8a665xqVZhcs2cm6MEQWPJq1l8ZKwMGJU4BlhbgVeE6IVdS2devzlBvU0FuukEwq06Nh8rVxp/1DjlZGQ27DJAo0EjIHBiCDVghzuffv8feRy4wxob5ceWdei7h6fTnH26XkuEpnuJTXGF7bnDq1RgUrFYLsXX+zrYsf97KvpRsXSpT5t+FBGBdHZLVuSuvOB/epsLHbyevysEmeskCVgHRjHPlkVsfjkJz+p97///Qd9DulJ1D/sxa2VRk9Pj+sUdaDaCO7v6upSQ0NDv6gFXaEOVk+xevVqXX/99S78WjhAhS/3DXS/MTKgfFCYTZoTIVtuUxxGPvvTT/vQ7KbNCf3h2Tr1xKVpMxM674QpboEzxi8jIbcuStbklWC6oBA9M14Ludjw6KNebnEOEK3g72NrO7RtD11lopoyKabzzzWLYrxje+7QwZ6JQw7CnsiyiFMO4x4ZBAyKTGOe12H4kyFA18XQSpv9+Js/26OXnitUJJbQkmOKNG1Kfu8MH0s9HvtklWExadIkdzkUp512mjMQqJtYmXI733fffUokEs4QGAiel5+fr3vvvde1mYUXX3xRW7dudcc7EGvXrlV1dbUZD1kKqRLAAshCSAEZ15m6TSrKY0/Edc+z+9XWmVBBaY/eumq6q8U5/njfucIwhgM2W7zveP7YfNmsLb+4P3w3RBoBeSW6yH3UR2GQvfJqj9ZsblC0NC61FevS88pdl7dp0+w3axhDsUYRJQTShjOyxA8KAd7g22UPJoWRyMSsWdLPHtqr55+n/V1SE8sKNKOm2MksNRk0VDHGPlllWBwudHK66KKLdNVVV+mWW25xRdlXX3213vWud/V2hNqxY4fOO+88/eAHP9CqVatUWVnpZl184hOfcLUYzL/4yEc+4oyKULhNK1oiGNwuKipyrWz/5V/+RZ/61KdG+RMbmRChYHF78kmvsOE9oV6CkC7KCh7iPfWd+s0T+7WjsVWJrgqtXlKpiZX5OuMM6eyzbViZMXyQYxxaH4ffq3WE6oNIIikYfCekWwSjgtt4P7dvT+qpLY0u/SnRla+3nF2qmpqIy9Em99swjMGBMUDaEhEEIhRHIrsYFcgtacbsvRxr+85u/ebZ3arbH1VPS7Ei0YRmVnujYuZMX1thUdvxQU4aFvCjH/3IGRMYD3igiUJ885vf7H0cY4OIRFtacvPXv/713ueS2nThhRfqO9/5Tu/jRDRuvvlmN+8imUxqwYIF+trXvuYMGCO7YCF7+WWfu44Cx4JFSgWGBYvlsy916MGX9qm9K65Ea7lOO75Mx04rFZ2GCcnaBGRjOCHfmI4pRCvG4+yOg8kX6Rd/+IOP4uAMwFPKBc8naRTPvtSuJ19udt1kIgVRzZtUolXLC51xhgJk9RWGMTgw4kNzCaL8QV4xFthHWbeC0Y/cYTxgGLCmMRQPpwDPYS4Fz2vv6tGv121XY0Oe4m0+Z2rlvGotPI5hllZbMd7IWcOCqAMzKQ7E3LlznXGQDlEIDAcuA0EUhIuRPWA0EK4lNQLlg3IYFrS77vKeTcKrpE6E4Twsihs2dei+dbWKx3pUVpin00+o1NSqYtf7ftky35nCMIaT557r32K2stKnCYxVUDxCNyeW3RNO8MoLCgvKCQoJ4OGklgJQbDASqEUhFcqlLzZ1as3WOsUqevxxZpfqna8vcxFIgtHWDcowBgfyxt6JEwC5Sk8JpmCbiEQAGablM/eF4uz1672ck5bIENpErFs/f36TmruiireVqDJWrEXHlerYYyM66SS/L594otVWjCdy1rAwxhYoEaFeAiUMY4IJn+lQ0HnvvdLzz/s6CnI6WfBQSPC64Amlg8zDGxqkslZNLS3RKVOnakJVvotovOc9ZlQYIxdRS++oggI9Fg1alBOUEZSPTMMqffI4qRA4AJ591t/GUYBSgjGCo4C/0fxuPbp9j2KlcSU783TOyjItn1XtnAkhfxtFyDCMozcq2D+RTWRq3rz+e3CIYuCEQy6feMLvrzzGXzICMCpIP2af3tnapO8+9IoSTUWKFca1clGFSqLFzlhhzBhyf/HFfYXhxvjADAtj1GHRItIQOlAMBN5fWuFhPITnsTCyUBLJ2LS3VQ+sr9MzO9qUyMvXjMpCnVQzVfPn5ruUCxZQm9ZrjBSZXbHHYkoUjgA8nwORblQAaYuPPea9naQzIYu0qAzfTUNLlx5+Zb8aO6RoYVwffe9knbSw1CkxOBiYa0r0wxrwGcbg6hKRTeRowYK+iAQRRYwKnCGhnTMOA1KdkDsMfxwBZBDgIOkqq9f3n96jbfs61V1boURnTMumTtRxs4vdscgMePOb/d5sRsX4wwwLY9RhsUs3FtI9veR1UlxGkTYLIJ128IbgMWGBu/9PXfq/Zxu1uaNWEUUUby7WKUsKtXLWRFWW5fWmSIU8T8MYCTI9+NQCEVUbKwYFEYdgGKSDwUBUIbSi5LkoNHg6H3/cyzaPIec4C3p64np+Z4seeqZVybxuFRVLV1w8XRecUeKikcg+NVGhRaXJsDGewRAIs15Q4DHSQ1MIGkZgMAxUg4QMMqcCw4Dn0wWKCASpiRkZ405+kVfguaxbPAeDpLG5R8837NV9e59Vd0m+ehpmS/Go3nDCZE2rLnGvJZJB6hN7tLWWHZ+YYWGMGiyEeEGIOgAeFBZFQrVBSSFCQVrJQw/5xY0ZFXgxV65M6ns/b9Tdzzepoz0iJcs0qSpPy5aU66yVZe6YIYf0zDN9zrdhjBTpxnHgSNo5ZiMYAkQW07tdUeOE8oC3E9nMbKnLfTgOiFTwnVAjgXHQ0JDUftXpFy9sVfPmSiUi+ZpcnafLzq5WUaLEFYii/PDc9LqKsZhOZhgHgj2MaAEGA5EDmhuEuRPAsDmMCwx1HkMWccZlDqEL7diDTOIY4PnpkC6MUUCkIkAHNtelbVePvvt/O7Rm9y7FO/NVODOmxKvTdfaxkzS1qELV1VEnq6wR553nz9WMivGLGRbGqICRwILFIolXE68IixqQS81C+NRT3ojA+AjtOnlufaJJV39nr3Y2timeX6zp1QVaUDrZhWFZHFFkOB4LKPmdNunTGOnfdvDwscEGj+JwetvxYoYhfLw3Gzzvi5LAOdDy8Wjb3eIlDamI6UYFhhIphrwf9RPp9Q989jCjAq8oj2GEINubNye1Zv823fdso5IdxVJ+QjOnJPTXb5iuxsaY86RiQIQiUq6jOOEFtZa9xnASvPcj8TsLdYXUE2IcUAyd+TgF06H2MBBSAokIEglNa3zpZJVoA93WMMjD5wjRReSUNYJJ90D0n7UBmUNGcerhJOA2I8JefjWu3z7UqP96bJcau9vU3VCuaE+BTp08T7MWV6urK+LWAaKKOA6YU8F+y/sY4xczLIzDgk2eBS1dSccoYPHDM4HSFPIzWfBYpAZqOYnCwfN8GoRfVFnceA0KCJ1fWAxZ+FCW8NYQfaCwc1tzk57aUavNO3sUSUZVUJHUpW8u0LEFM7VjW8wtqiyyKDt4WsgPNaPCGGn4/YZOR2Hq9pEQUoW4DARRPBR8lBHkiOtE/4K3EoM9RAEDQXlB1pDVw61V4PikUKQfD6UBZSR4JEn7Qu6QN+5DDolscB4oNEQcUY4owt7T2agfrdup3Y1tUrJAhSrUqbNnaNWKApeXzRqQHpUgjREHAa81o8IYTtjL2HfYtw43wo18sE/xOz1SxwEOs6Dw40gjtSk4BpCpUPMQCGm9yALvxfqCXLEeIHMhYsCxiBDiCOA1HJP7eQ6PYTywDiBn7JWcP3LLZ0eOcUbkFXfp+n9r0o8f3a6eunIl48WK5OcrGY/qHWdOVWnSZwWwpjBslu8MQ4b3sg5QhhkWxmtgcWHRwMPIgoTXhNApixTeCRQfFqL0tnQBHmMxZMGhriGA4cAQLAg5oMHICHUVLGhcCNGSj40ytKOpRet2NKqjsFHd7flSZ6EieQVadmyR/vyciW7gHeexfatfmFkwUXw4d7wohjHShNap4fqRGBYYCSjyIe2Pv8gDGzcdVlAMwrTczGYHKA50ThsI5BkFIuRnhy5NBwO5QkEJBg5yy2vSPaucLwoMBPkGlKLQUQblZ/qMhB7Zu1W/eaJeyXhEkfYSnTV/so6pnOAG35EGiWKEshMUJJwCpFYFZ4NhjJTcIkuHSuVBppA3nsvriBIc7u8U2QpGBb9z9lwaHOAQQ9ZDOhLnwDqA7CFnGBbpeyrvy/NZCziPsI+GFGIeZ0/kdeHceC/uxzlw993+uRzLGe9FHfqvJ7Zpa0OruvaXSj2VihZ3qrJGunjBHBVHi1RVEeldR5gtTEvZ9G5SlgJlmGFh9IKiTweWsODhgQipDyxa3E96A0oJiwcKB0oOixmLG17TsDCH17AgsmCGrjgsbij9vBZFhIWUhQ5PBy0qCeNu2ZLU7vpOtfR0amd7kyL5PVJrqYpVpGNnFmnpMSWaPS1fxXk+osH7LF/u3xvvCcdCmQu98w1jJMlMXTgS8FSGdIzQNz49xSEdZIv0QaIPPI7iwWtDa1bu43E2fRT14M0E5Jj3QpZRPJAdZBO54XmsA3S24rNwDOodiEikG0kcnwgDsAagoGAIsA7QAaq+PqmWjh5taa3TXZvr1FnUokhxXHNiE3X8tBmaNbnYnTvKCAYLKYyAwrNwoT8/DA4zKoyRgD0KY4HfZGZBcxgoh6yE7kqk6QbYG3G+8Rhywr7J8Xg+z+O1GBDcDkY+oPDz+yd6gayF+wGZQPZYBzBgkFFkG+cexj0yxz7MX4wILqH+gmNxzuyFvDfyHIx23oO9PTgEmPe1s7Fdf3i+RbsbOtWRzFMkv0xVecWaOqlQKxZO1PSKMu3ZE1FxpV8zOC+OfcopfUYQ+21mOpcxPjHDIothwWCxSh9gcziwoIRUicMFBZ2FKb1NJMdgUUQZCQoD58TCFIqzWEiCx4RFkut4Y1jYUEy4zTFRVngPFlcWUbywLHgskigSHBtF5LkNnXplX5vaezoVyZdipT2aPztPq2dO1oI5he49WXA5HxZzFmmUD86Dugr+Zm4KhjGS8Fs+VPvVgUBGkPdMUAh4fcilRjlA8cawSFe6WSeC3POc8J7IC2sCcoE8oqRgxHOb1KlwTAx+lAWUkLCGAEoE8sxQSlJEiCAg48gzhgWKBrL4yCNemeL29oZm3fXEfrV3JRTNj6tgerPKS6K6eNYilfZUuXPk/fDG8p7B8YD8oqygpFietjGSfO97XsbwwvP7T09FxBDnscyC53TCQMxQWwQhugiZ9RDIHLLEnoYxgjwF4xoDnn2RuS845tIjoEQ0iQYiyxj8YW9lb2YPDJ2cSHPic1CriLyyHoQIB/sml5e2t+jV+mY1dnQq2Z0nxaRJ05O6/OyZisWL3JqCrLK/h45PpCxyzshoeqF3ZuMGY/xihkUWc9NNfiE691zf2ehAsLCQzxwWraBQoLSz8BwKPCIhSsGihKLOwkFEAS8k3k+UGxYVFqaBQp0oCSySLEQsNiHHmsUreB85Bn9RvDhXFlaMiT11Xdofb9aGjXE1N/jCjGhJQqef06Uzl1Tq1AXV2rUr4t7/jDO8twWjhUWbSAULXnpqh3k4jdGEDmaZEJk7EPyO8R4G5Ro5QWaQEX7LIfKGIYA8uRTBHV5BSE+/QA5CRAHPJQoJjoHQ+hbPKGtFaAGLIoKSggKFTLNe8BflCdlEgUC5Qd5YC5D9p5/214OnlHMmksJ5cd5Nne36yUP79dKWHkUjScXKujStslBvOmWB3nh6uZ56PN+tC7wXhZ6AcUPeN69HwbJIozHSYNAjFyEVkTRaZAA5Y/9LNwjS4beL/PGaQ5Hu6EMJx8mGwo6sImeh7gEZxvn28MN+b+Y6ssv5YCCEqAT7ITLI+/N4qEdCtoi6ILcY+8Fw5z17kt3avLtDOzta9ejGVrW0SoVTGlRMjURHtRZOqtKpi8pVoHwlUwYI+3WY0H3ppalhltH+7abNEWCkY4ZFFoM3kAWPBeJAhgUKQnqqUTooFSGVAWWEBSezIBTvJK0dya/ECGHxCUVoLHzBqGBRTE9LwODgwrmF/E8UBhR+FjJew0LH+fEYnhiUGha8rp4e7d3doVdrm7R9X48S8Yjrha28uAon9WjR/AK9ccUkxRKFqon5z8F78F58HgwIjoNXhgUPBeVIojOGMZwQEQgpFYEDRR1RDFAe0qNs4bmZjQc4JgpMekEnHkm8/Mgc13kOGz/RQGSe6GJmahVGDo6AkCKFbAVDgQvyFYrOWRM4Bk6DIMPBKEEWUXL4nK9s69R379muzu6k+C8ai2nezDz97dvmKd5eoKqqiNY85o/BubKu8P68D4YMaxOXsTLrw8gt2GOQDxR9DOjTTut7LEQR+N1jeCAXoUtZ2CuJ5IW6Q37X7Lnsf8gJchkMfpR0joHjDYOB/Z1jcDyehzyhtHMfe3pILwyD6th/kRkuyCVOPGQeueF8WDOQe45FVPKVV5La19ypp/bUq3ZjtzbsbFE8v1OiQ3tpVCWVCZ27bILetHiGqssK3NrDueJAYN/mOnLOexPJCelcIQUSWEts+KyRjhkWWQqLGwoKi8vB0ihCLieLWlAW0kHZD4slCsjSpX2LFt4YQqosQqF4K8BiwvuyQC5Z0tfticWMvyyaLGQPPvjaYWC8D0oCx6PTBIpDa3u3NtTXaXd3g5uS3dFQpGQ7uRz5iuUlNO+YiFYtqtCpJ5Qpmshzn4dFloUtLMwsaHiOCO0ChgYXMyqMbCLMbEARCQoFyvRAoIikGxVs4OlGiGuvXO9lFg9hSNFANrmgEHEhwoGygpwjI8gpCkfwdHJhHUAxIEUC+UW+UKZ4DeeLosN1lBQcA2GoFmA8MP2a88UZ4euiktraWq8f/bFFj67tVKSoSyVzGlWwf6ImRyt08Yk1qiyOaNJs6Xe/869BVjEqQmMFFKXQPQ6DZ6DhXoYx3CCfRN5C1AIDOr3LE7KAfCDPyEjmwNUg5+zBXAJB9gCZ/OMfvUzxO+e9kCX2WGSa90VmuR85DQYHMocBwnUi9MH45vnsf+yz/MUIYb9Fljs7E/rVmj1as32/GptRICKKFfUoUhrXtClJTasq0iWrJ2te2QR1NOe7PR3544JzEOcEcCyiKWGeTNh7AwPNrjEMMyyyGISbBe5ghkV6+gRpEVxCygQKDspDUPxD6JJFEE9HKBRl0WBx4zaLFwsbj3PsxYu9QsLihpeC+0P41XWASsRVUhRRRUVE+5u7tLepS9tae9TaGFNHslvbdse1e7v08u42tapdsYp2xetKVVmWrwWzS7T6uApNKi9UdVWst6NTaKWHQcJ7oQiRd423B+UpwCLI5zWMbCIUSGMEBNlF2eC3yyYciipRqEOhN/djkCNXGNW8DkUmPUcb2NzxXqKYIA8hnztEMjAUQlEnRgXRPBQD3ivUWYSoJXKGoyGcK9EJZJ7nczs4JXgf5JK1iJSll3Y264V9DXpme6M27mlVtLBLscoKRQu7deGCWTphxQzV13lrgTWDSxi+xTnRH5+/fPbwXfH9WLTCGC1Cei7gqed3HlIHUdyJWrDfhFbImQMwkSsUe+SWtCCMfWSIVCmcfVz/1a/8c9jbMGI4Nnsxzw3yyR7NGoCMhvfq6kqouzuieDyip9Z1aV9rt9Zs6ND+hrgS0bimT8zX5GkJPbehS9v2dWtnQ5vaWyNqbE5KBXHlFyU0uapApy2v0PI5VfqLCyaostKnHjzzjLQ7dc7IO5f0qd6kQ4YIZWY73eDMNIxMzLDIUghj4pEILeQgDJNjEWKjRvjZtMPiFwjdXfBi3nuvvx2GdrFAcpvFkwWN561a1VfUyYLH+4b8bhbFwpK4ntvUqrUvt2rz7k71JJLuWM1NETU0x1UytUXKS6i7I6ZINKmu2lIluvKU7KD6OqFoYY87VmFxgVZNrtFxSyo0tbpQs2ZF3MIUFKp0UJxe97q+4nAgCoKCg3ETenobRjYXb6e3rwzRBQiez2B4YDCEnGvkkN98aCeJ/CPrPI5yHmqcWANQxkOxNYY4yhAKCY+hCPF8HsNIQabS14l0OGaoA0G2kX+MdtYXN8irqUf/84c2PfxynZ7aWq9Eng/JJFvKVVNeoPzJXVpcNE8TWyvUme9fEzrgoJBwbNYjrnMOGFIu57vHr2s2UdsYTZA5nGf8HpETupqx/0BIP0r/jbIvsnchV8gmv3cKrdmfiS6E2RD85pEjbuNYwHBA9nEOsH9xHOScfY7XFhYl1NmV1NNbGrWxvkV18VbVd7cqb2tM3S356motUCSSdPtsoifmrsfK21Q4o1Hx5iIlExFF8hJK9BSppCKqty6ZrTMWV2jO7JiLFLKHcq5kK3Au4f1ZW4hKhnk1pHbhzMO4CI4O1gKeS1qkdX8yDoYZFlkKqQOhy0Qo2CREiZCz0LGgpQ+tCt7AzHxsiprD43gnUGy4P0QvqJtwkYv8Lt11b7vqm7sVK46rvqVTiUhC8Zd6tGVHXC0d5F6waMUV78xzhoO6YoqWxNXeUKhkR6EUSaqsIE/FeTF198SUXxJTcVFUZRUJTa8p1CnHFyumfGe4oHTwOVAu8M6EQlQULD5rSLfKHLLHZ2RBZxE80AAxwxgtQrQwgFIPmel6oQATguKNQRJyl5ERog/IL8pAOBayygUlAeMBWcAZgFGB0Y2CgLERFAIUh+AlBV6L7IT6D46JYsQ5oPDwOMfj+RgArd1duul/tmnNxmb1KKlILK78Kc2aGCnX5O5pOnlpjSryi51BxDF4f2QYxYO/oUCb8w3TyNML2dM9s4YxWuCp5zcfBreSUoRhEQzhANHz0KyAwmgMDvYrIhzcx+vZY/nL/oYjDzkMg/RmzkyqvbROLzb16Kk9Sb3aWK+20gbF9kfU3pFUe7RDeZVtikeL1BMtUaSwxylpsdIudW+rVrQjT3k9BSos7daEyqgKuorUES9Sd2OhJk6KaFpZsU5dWOneu0ylOmFhvpNrzg3DJtRVpYOcsg+zzgSnCE1S0ltLByPJMA4HU82ylMwUCHIzUToQ9uB1SAdFHMODRZDrLGh4QFgseT6LGkPn3NyHRLcKyntU296uPz7YrH2/b1O8J6FENKlInp9qrWREsbJOJdoKFG/LV0lhTHOn5+u4mgrV7y1QQaFUUS6V5xdpz76ET4cqzXOGRCj65ByDN5WoCIsd50ieaJgySq41Rk86B/OGcAwWQsPIRjJTJAIHGxqFMo7RH4o/OQZ/yV9GltI9iZkFpTwPJwMyz5qB/Ie2slx4DEJRaFCSOB5KPoYEMuV7+CfV3NmtLftbtWV/m17Z3aYtu7qVKOhUMhFVaUFMJy+s0OuOO04zysq1ZUvEGS1Nqfqr4KjgfIl44Bgg7ckcAEa2g9IdFO4Q4UM+Qg0UMoJijRGMck5Ugt8+1zEk2GeJpLOvYWzMmp3Uzp0JvfBcQrUNPeruSapDnXp4R7M6k91K0qhkXq0inflK7C1UtLhLyc4CxcpILZAmFJTo4rOmaV51uWZPLFZLZ7cS3VHV7ytQeWG+cziwl9O4JbRx54KMd+/zDjhkns+Bo4H1IlNn4Fy5YOiH2qcjGeZpGAfCDIscGbLFwhf6YLMA4mFg0WNDZ0EgDMuiwqIWhtXt2BlXe3dc6zd1aGdtlzrbYqpr6VFrV6fyJ7aqaHaduruK1V1XIUUTmjk7oROX56t+Z5GmVxc6b+iuTYXqqC/UsbOK1N0VVWeHVFzhF1gWJAq4WYBZmEL6RZgkSrpVaMeHQsMCePrpfZEKcrnTO+cYRq6T2TwBkIegXKOEIC9E7IJ3NHNaNl5RnAHIMY0TQuF28PZTVxFqnrhwf0hz4vgYDSGVKqRaTZqUVFV1UvGeiGKlndqyPaH1L8T1u7W12tbWqHzlq76lW/GuqBLdeYp3R5Voz1c0L08VZQW6cPF0Ta8sVkFBRK07pY2pVpQ4AUJzBVIkcCiEtEaMDDMqjFwgGOCBUOME7FMMXg3RR9KYaFrC40Q6iC66IbEVzart6FTNxE797Lltqn1mkuLNJUr2kGaQVP7EZkWLuhVRRPmxiOZ2zdb8yaXqiieUH4tqyTFFmjwhT109cc2YWKjS0ohbJzBiKiJEMKR5S/xagBGDQ+6CC7zzjnpK9lL2ZS6sD6wJ7MM8DhgjGPuhMUPoBMdebA1QjKHEDIssBUMhndAxItRX4GlEAWHRw+igPZyrY6B/dUVcD720X0+u63SKgiOWcFEI8i/zJnSqpCKuGVXFmlE6U7OWl2jyhJiOnV3olJi9NV5RcF7IQqmlTOrq9AsRShLviRIR0hhCD22UGhYooiNc53w4Z14TukxwX0jXyExzMoxcJ70NYwC54fcecppDwwE298yBW8gUXkQMC5SKUDAZBlTiGUX+SMMgQoG8hSm4KBEcE+PDy11S9d1t+tWGzXpu/T7V78lTaWGeGtu9a7anqcgPxYJIhyIFceWV92hmZYmKGiaqri6i6SXlOnVJiSrK8pwhhMzWpNYHFB7OB+dCeicrzp/bVthp5AohVTAd9rRQp4SscQkRSZxmrvVsaVxl1T3a3NykBzZtV4KAw4YCxVuY1BpRpLBb0dIOVU5r16pjq1WuUs2dWKKCWJ6Tk9DMIbRTZ2/s7MzvLYxmPchsVc05IX88B2dCGJSJwyG0Zg9ZAKE2hAvpTQeLnBrGUGGGRY5ELFAquLBgoGygzIdBOigndfVxbatv076GHnWUNKTaxcYUrWzThGppyZxSTZoW19KFBSpvn6o8FfROrmaBC11gQliU2yyehFNJZ8CDw/NZyEL3FpQkDAbuR6Hh3MKCRs518Iakw+PpXk3DGEuEYuX0POYgW6T9BXnAaA9zJcLzUSxIGUTGMhUA8rrT0yPxSGJMYKQQBeQ9nljbrd9t2KE1L3Zrb11C29rrtKW9TpFgwFfkqbmlyKVc5PcUuXqo/FiByid26awTq3TOwkku9XHH9qhbP0JEhb/I/HnneZkP83VQfFgvWIt4nHUI+cbwMYxcbbgQICrAb5loAAY1jjyceB0dST36NCmDzXqxcb+6E3HlV7VL+QnFd1eqNFakRbMqXApT+cRuXXZhiUrLpFg04mQYGUFuKKDmmKE4OtRIhvkVyDRrAWsKUYcwR4PHcDxwHPbnML+GDo6cb+jiFLIXwpwJMyqMkcIMiywlhF0DYeI1ygfK/JTpPXripTbNnZfUAy+365ntLWrdX6R4IqK8Lqkwmq9TT6jWhWeW6IQTIr0t7/BqsiixaLLgcJvFiGPj8SDaEKb9cl+ITnAJ3aXwonDBAMFDwmIZFj3eh3MN3h7gtaGo01IjjLEMchO6PYWNHBmhiBn5QRngsZD+hIGA4Y6BgDKAIpFexI38odinGxXI8a79XeosaNWWhj36hy/vcOkU+1u7evOokwk5g4LLqnkT9Mk3HKfpVcV6dmO7SroqlJfMd06JMAeGv84ZEOlLbeScQstnLvfc89qZG3wuHAUQZlMYRq6BoZwJeyNy6NKPE3H95N567W5p1dZdPa6JSSSWVF5VmwqLu1UzpVurChZr9vIKdXXGnEww6yKZLOhtuILssxci49wmpepAawiPhVqr9Fk3yCkphpkGEfUfHBtDg/UGgyVkFNhsGGOkMcMiR3CpRXlxba/rcFNsb/vtq9rT1KGuOysVK+6mYZMmzo1r2awqJdtLVRUtlRL5rv6CtCmMkdCnG48Hixd/UVxYAFESUP5DMSfPD7mXXEKEIRgZwbNCVAOChxMFiHSsELmgU415MI3xwtq1fR2fkDFAHkKtAwoDQyl5LLSJRvZQ4MnjxrDHkxlARqGnJ6ENu5v1fMtObd3bpT8+06xELK6CSS393n/B5DK97tiJenprg46fVq5PX7hI1aUFTi7xtpZ3lDiFgyYKrAecB39JvQwpGcg615Fpaih4PopXmHmDwwDHATnbB5oobhi5RGZzBGhtjeuWP2xWXXOPeroiSnTlK5KPUcGjES1aENElZ81SS12+KvOK1dlCK1jprNdJb3iDP0aIHvD3SBV8jBqcdQH2VAwLZBC5Q56RS9aPUD9hGNmAGRY5ErHYviOuNfU7lCxmwERS8fZiqadUkYIeTYpV6sT55Xrr2WWqr4/2RhBQYliISFUKE33xnoT6CVIpwjRNlBzeE28rF6IZof1raEWJRzI9nIpSgmcE+ItSFBQmjBK62hjGeDMs0pUVjIqQQkG6ELVQyBCylz5/BnmlaxtKvkuXiiS1cV+z/vR0m7bWdujV1lrVdbQ7xaWnuUjRgpgWHBfVtGnVes/q2TpmUpmmVhapsqBQtbURlZ/lFRlkl9aZGBLIPIoIzgBXi5WKUoRBnIDRwH38RX5xNuAhpasM0UjqKTj34IU1jLFA6GrmITwQ121/2KlYCdejUk+ekknCeXmKVbfpg+fP1HkrK53M0kgJu4Q9k3Skc87xe2lIV0RW0usJ2Z+RwTBjhtfxvHR4TrpRwf6bHqkA5BPjn33dMgGMbCInDYu6ujp95CMf0a9+9StFo1Fddtlluummm1R2kIbot956q3784x/rqaeeUnNzs+rr61UVGsQP4rjDhcttjnerpbNH3fGkatd1KH9yXPmlCcXbClUeLdKFKyerqCjiWsHOnx9xYVGUkuAdZTFDaQjDqYheoGjgUUXZCGHUoCCEVraA94OFC8UHhYL7D5WjyWu4hPZ8hjHeSJ9PEeQwdFBjfgyKAAoDSj2PE4lEYcAIQXnf0dCmjet368XubXr5hZh6WgsUzY/jIFUkGtPMkiotWTRBC6aU6bzVZe44KChTJnvjZXtKQSLVEeWE2ozQOhOQzeAswMuJEyHMykA5CcXfRBkxTHAm8FwGZhnGWKYr3q36tr4q7vwJCUU6Y4o3lSi/qk3nHj9RZywrc/LCXknkkf0xGOko+Kee2tfmOb1DHDUOPI6Tj6yAdGMGhxxOQGSQ/Zp9OX0iPccbqAU7cmmD6oxsJCcNi/e+973atWuX7rnnHnV3d+uKK67QBz/4QWc4HIi2tjZddNFF7nLNNdcM2XGHk7q0RY4QbKK9UGeeWKkJFXlq31fm+lqfe4FfxAibhqLq9MUG5YDUJVKSQjca4L7gUQndXfCW4kHBe8lzQzrTkS5eZlQYRh/BaMBLicKPjIV0QmQ3vyiuvfsS+vkj+/RM7R7FinuU6KxQQV5UJ0wu17TJUZ04u0ynLqxSXiTPFYKjzPAXgwF5wxBAhjEkiBqitJBSxYX3RxFiDUDBwVgg7YpzQdZ5bOlSXyDKfWEoWPrATcMYyyBH6UaFu4/IYElcbz55ik46yRdxIx/IDhFADAyMcZx5oWMazgM3SyJjCF2QxXRCzSIgt8hdencq9mainbafGrlGzhkWL7zwgu6++2498cQTOvnkk9193/rWt/TGN75RX/3qVzU97NgZfPzjH3d/77///iE97khRUxnT6sWVmlpWqDzmVVT4BQ6vCDUQpCwEQ4CFjfkRKAwYCxgXmXUOYTgO2FAcwxgaMpUHwEtJDUVINwwDHhOJhJ5t2KWv/UeqFzOyWxDXiZMn6vip5Tp+WqVmTc13RghRDpQWDAYcCHg3iUgg30Q9eA+MFNaBMI07DNsjIkLUBI8pl5CewXE4H6KMdKjiOCg3oWGDYYwXMofHQVl+gV5/QrUzHDAoMOaRD6IJoZkJxjf7bng98pSexsxjIfoAIbKBzAXDAlkNw2yRS16D3CKfZlQYuUjOGRaPPPKIS2EKyj+cf/75LnXpscce06WXXppVxx0qTl9Yo+oKr1iwcJFywYW0BQwJFiTCqSxY6T3keW7o5GQYxsi2rUT2iBiEeotJMztdhOKhzXv01J49rrOMogWi+0JxZbf+9rz5WlA2yR0HjygXDAjSJ4IzIEz5RfnAKYBSgmHAa3if0Oo5vH7ZMj/tnmOENtYhHYO1I90YIl3S0iuM8c7ksgItnV/qup6FttDIb5glhcLPXAiM8cx6yGDcY5CEOTRkBfD6zIGwoTsUsovMYvTbXm3kOjmnbu7evVuTM3a+vLw8TZgwwT020sft7Ox0l0DTQH3rhgAWqtBqloWM0GzwPAJeSab0sqilY72rDWPk5LZ/EahXFiZOTLh2lW1FdbrzxZeUN6FFyouoeHbSDax824JjNLdgkpKNFTp+ar5TVlA2SM8ghYocbMCoSB+ORbSC9YD7UVhQUqil4nGMEaKWRDVDgwWOiaHDa8JgvlB7ATzPujwZ41F2gwEQiETyXeQOJR9nHfsvcsNf5CxE+dmLkUWM9zDDZqA991CTrTOLtw0jl8kaw+Izn/mMbrzxxoM+h3SlbOOGG27QddddN+zvQ84zSkUooiYUy3VSHAiZ4pkMqU2GYYy83GJEoOCnd3OJJ7v10zVeiy+Y0aD8qqhTTGL7azRjQpHevGimls+sdkZCW9SnNAEzZUhzxOuJMcDfiy7yigxGATVVRC3QqTgenWhYE1B8UHpIf2Q9eK3C5GsteBxFiHQpjoFRYZOyjfEquwOlQuHIQ07OPNPLHYY5ezBRP+QxyCp7cLqxYRjjnawxLD75yU/q/e9//0GfM3/+fE2dOlV7cR2k0dPT4zo68djRcrTHpRD8E5/4RD/vySzyCYaYkN7Egsbp4EUhQoHHMVN5MAzj4AyX3IaizUSiW3Vt3ero6VbxxB5FYgklWorUFY/qc+esUnlRfu/zKZoOkUaUfSIWGBo4DHAiEAUh7fFPf/IKThiWh+HA6wi00myBoVyHG6FMNyKC19UwcoGR2nOpdcCwoDsiRgXGA2lRGBchXWmg+kXDGO9kjWExadIkdzkUp512mhoaGrRmzRqtZGStpPvuu88VQq5evfqo3/9oj1tYWOguww2lH3hIUCZIgyK0ijJgCoFhHDnDIbfUKiTVrW31fT1nY+Wdrl3s1LmdKle5PnT+cSpICS1eUrIsiXTgHT3rLOm557wTIUyrJ6JAhIHnhJQo0ia4TeEoxgdrwWA6YtsaYox32aVuojAvps6evoIJDArkMAyFDIZ/GFppndMMI8sNi8Pl+OOPdy1jr7rqKt1yyy2uLezVV1+td73rXb2dm3bs2KHzzjtPP/jBD7Rq1Sp3H3USXDaSQyA28OdUXl6u2bNnuzqKwznuSFNTUqD27oTKiwqdksElZfMYhpFlYCRs2NXSb1mNFMR11QUzdPJx5S46gdeTFCccBBgERA5QVjAQaB8boo88lhkoJZKBAoTCg5fUUi8MY2gg8lBVXOQcA5GU/CJnGBTpNgyqAP5P0h0zxmAZhpEibR5k7vCjH/1IixYtcsYD7WDPPPNMNwAvgFHw4osvutkVAYyFFStWOMMBzjrrLHf7rrvuOuzjjiR4EfNi+c6oCKRP7zQMI7twOdfRpJvbG/jz101USU+5iyigjLAkhWncNFwg6kDhJ+lMoYc9aU2ZRgVF2Aypo0sNj5tRYRhDR0htigjL3k93Da1eMydeY/ybUWEYYyhiAUQYDja0bu7cuUpmVGN9/vOfd5fBHHckwYhIb2OHBxOlwjCM7GTNGvKbIlI8qmQkocllRardm6/Fi7zRQQlXaLJA4TUXIhakNpHyFFIuiEySDkXaOG1hUWJCxNIwjKFnIKcdzoATT7QaRsMYF4bFePGgpE/vNKPCMHKAlJdTyahi0XxnFCDHDK/DUECuQ952uqGAEoNnlEiGb1HrlR1SpAzDGF7SJ14HkFtrjGIYR44ZFlkKEYrMnviGYWQ3kSiRUi4RZyyQ6gShwxOGBWlOQbYp2g7taTEkbI6EYYw8mUPpgqwahnHkmOhkKeRckyphGEb2EybyQrTQ5zBSB4GhQKtKUqC4vXixT4Vi0i4GBUYGk7KZTUHthGEYIw/DJNP3W4q5R6DZo2GMSawcOEvJGAJuGEYWs2GDnzUxs6q4X3oFHWSQZTrMUIAdhlhSVxEKtOmXT2H2YFrGGoZx9Cxa1P8286EwNgzDOHLMsMhSli8f7TMwDONwoRWsm5RdVqwlsyp07ORKl84YohSkRFnxtWFkJ8yQSYeIBbVOhmEcOZYKlaUwfoPCMTwnlutpGNlNuhKSlxdxMhtaUhKdMAwje8mUUSKLGY0lDcM4TCxikaXg4SRnGwXFcj0NI7thGi+pUKH3PSC/zKiwSIVhZDekIabXOC1caPuuYRwtZlhkKeRlE44lalFTM9pnYxjG4RRwhxoKQG6pnTAMI7thr50yxUcZceRxPV2WDcM4fCwVKkshBYqWdzBt2mifjWEYh5JX+t4HmQ3OgYEGbxmGkV3QXIHoIp3aqIu66KL+smwYxuFjhkWWwuJ2zDH+uvW2N4zspalJ2rrV52Sn52WvXj2aZ2UYxuFCyvEpp/jWz9RLhfoowzCOHDMssniho90d/e4JyxqGkZ28/HJfZCK9xoIJ24Zh5E46VEg7tonbhnH0mGGRpZBWgdeEi3k+DSO7YYJ2pmHBUDzDMHKD6mrvDKBttMmuYRw9ZlhkcV/8c8/1odlzzhntszEM40jSGOPxvgF4hmFkP0Qdw/wok13DOHqstDDLO8ywwBGiNQwje9m5s+868spwPCvcNozcYcGCvuvpkUfDMI4Mi1hkKdb73jByq4A7XRmhS5Q5BAwjd6islObP9xFHwzCOHjMsshQ6Qe3Z4z2fhmFkP+kdocwxYBi5WWdhGMbgMMMiSyGNwoZrGUZuYu0qDcMwjPGIGRaGYRhDFK0gnYI0qBkz7Cs1DMMwxh9mWBiGYQyyviI90shlxQr7Sg3DMIzxh3WFMgzDGATPPPPa+970JvtKDcMwjPGHGRaGYRhDjE3uNQzDMMYjZlgYhmEMgoGGaVkffMMwDGM8YoaFYRjGIOjpkTo6+m7TIjoWs6/UMAzDGH/kpGFRV1en9773vaqoqFBVVZWuvPJKtbS0HPQ1t956q8455xz3mkgkooaGhtc8Z+7cue6x9MuXvvSlYfwkhmGMBcrL+64XFo7mmRiGYRjG6JGThgVGxfr163XPPffo17/+tR588EF98IMfPOhr2tradNFFF+naa6896PO+8IUvaNeuXb2Xj3zkI0N89oZhjCXicT+tl6F46QaGYRiGYYw3cq7d7AsvvKC7775bTzzxhE4++WR337e+9S298Y1v1Fe/+lVNnz59wNd9/OMfd3/vv//+gx6/vLxcUwdKmjYMwxiA9nZvVJSW+tuzZtnXZBiGYYxPcs6weOSRR1z6UzAq4Pzzz1c0GtVjjz2mSy+9dFDHJ/Xp+uuv1+zZs/We97xHf/d3f6e8vAN/TZ2dne4SaGxsdH+b0pvbG4YxpAT5SobJdEfIUMotL8G46Oryt6dM6T/bwjCM7JRdwzCGXm5zzrDYvXu3Jk+e3O8+FP8JEya4xwbDRz/6UZ100knuWA8//LCuueYalw71ta997YCvueGGG3Tddde95v5Z5rY0jGGnublZlYy7PkKGU25vu036wAcGfRjDGNNko+wahjF4uY0kj9ZtMMR85jOf0Y033njINKif//zn+v73v68XX3yx32MYGyw2H/rQhw56DFKhzj33XNXX17vIx8G47bbb9Nd//deuMLzwABWZmd6TRCLhistrampc8ffhWIEsiNu2bXOF5bmIfYbsYDz9O7BsscCR+ki08kgxuR1fv5dsZrx9htGU3fH2XWcr9hnG9p6bNRGLT37yk3r/+99/0OfMnz/f1T/s3bu33/09PT1uYRnq2ojVq1e7Y2/evFkLFy4c8DkYHJlGx6EMloHgHzRXF4mAfYbsYLz8OxyNtzNgcntk33W2Y58ht/4dRlt27feSHdi/w9jcc7PGsJg0aZK7HIrTTjvNtYpds2aNVq5c6e677777nNcCQ2AoWbt2rbPMMlOvDMMwDMMwDMPIUsPicDn++ONd29irrrpKt9xyi7q7u3X11VfrXe96V29HqB07dui8887TD37wA61atcrdR/0Fl40bN7rbzz33nOsARZE2NRUUhVP8TZoU93Obwu2/+Iu/UHV19ah+ZsMwDMMwDMPIdnJyjsWPfvQjLVq0yBkPtJk988wz3QC8AMYGNRjMrghghKxYscIZJHDWWWe523fddZe7TWj1jjvu0Nlnn60TTjhB//zP/+wMi/TjDge87+c+97kD1nDkAvYZsgP7d7Dv2n4vuYfJrX3X9nvJPcaC3A7X58ia4m3DMAzDMAzDMHKXnIxYGIZhGIZhGIaRXZhhYRiGYRiGYRjGoDHDwjAMwzAMwzCMQWOGhWEYhmEYhmEYg8YMiyyBIXxXXnml5s2bp+LiYh1zzDGuUr+rq0u5BN20Tj/9dJWUlBzVoMDR4Oabb9bcuXNVVFTkZqE8/vjjyiUefPBBveUtb3Htlpk8+8tf/lK5xg033KBTTjnFtXpmbswll1ziOrtlOya3o0suy67J7ehisjt65LLcjgXZvWGY91szLLKEDRs2uCF///7v/67169fr61//umuRe+211yqXwBB6xzveoQ996EPKBX7605/qE5/4hDPinnrqKS1btkwXXnjha6a7ZzOtra3uvFmsc5UHHnhAH/7wh/Xoo4/qnnvucS2jL7jgAvfZshmT29Ej12XX5HZ0MdkdHXJdbseC7D4w3Pst7WaN7OTLX/5yct68eclc5Pbbb09WVlYms51Vq1YlP/zhD/fejsfjyenTpydvuOGGZC6CSP/iF79I5jp79+51n+WBBx5I5homtyPDWJJdk9vswGR3+BlLcjtWZHfvEO+3FrHIYhobG91UcGP4oitr1qzR+eef33tfNBp1t5m8bozubx9y8fdvcjv8mOxmJ7kst2CyO7yY3I4PuTXDIkvZuHGjvvWtb+mv//qvR/tUxiy1tbWKx+OaMmVKv/u5vXv37lE7r/EOKYEf//jHdcYZZ2jJkiXKJUxuRwaT3ewjl+UWTHaHH5Pb8SG3ZlgMM5/5zGdccc/BLuR6prNjxw5ddNFFrlbhqquuUi5+BsM4Wsj9XLdune64445R+xJNbg0j9+QWTHYNY3TlNm/IjmQMyCc/+Um9//3vP+i3M3/+/N7rO3fu1Lnnnus6K9166605+RlyhYkTJyoWi2nPnj397uf21KlTR+28xjNXX321fv3rX7uuGzNnzhy18zC5zW5MdrOLbJFbMNnNXkxux4fcmmExzEyaNMldDgciFRgVK1eu1O233+7y/XPtM+QSBQUF7ru+9957Xbu1EBbkNgJnjBzUwH3kIx/RL37xC91///2u7fJoYnKb3ZjsZgfZJrdgspu9mNyOD7k1wyJLwKg455xzNGfOHH31q1/Vvn37eh/LJe/51q1bVVdX5/5Sv7B27Vp3/4IFC1RWVqZsg7Z3l19+uU4++WStWrVK3/jGN1zLtSuuuEK5QktLi8sPDmzatMl97xRizZ49W7kSjv3xj3+s//mf/3G9tUONS2VlpZvrkq2Y3I4euS67Jreji8nu6JDrcjsWZPfDw73fDklvKWNI2rPyzzHQJZe4/PLLB/wMf/jDH5LZyre+9a3k7NmzkwUFBa4V3qOPPprMJfhuB/rO+bfIFQ7020cushmT29Ell2XX5HZ0MdkdPXJZbseC7GqY99tI6k0MwzAMwzAMwzCOmuxI4jcMwzAMwzAMI6cxw8IwDMMwDMMwjEFjhoVhGIZhGIZhGIPGDAvDMAzDMAzDMAaNGRaGYRiGYRiGYQwaMywMwzAMwzAMwxg0ZlgYhmEYhmEYhjFozLAwDMMwDMMwDGPQmGFhGIZhGIZhGMagMcPCGHd86lOf0iWXXDLap2EYxhFismsYuYfJ7fjCDAtj3LF27VqdeOKJo30ahmEcISa7hpF7mNyOL8ywMMYdzzzzjJYtWzbap2EYxhFismsYuYfJ7fjCDAtjXLF9+3bV1ta66294wxtUUlKihQsX6rHHHhvtUzMM4yCY7BpG7mFyO/4ww8IYdyFZuPnmm3Xttdc6T8rs2bP1mc98ZrRPzTCMg2Cyaxi5h8nt+MMMC2PcLXITJkzQf/3Xf+ncc8/Vscceq7e+9a3at2+fe/zSSy9VdXW1/uzP/my0T9UwjDRMdg0j9zC5HX+YYWGMu0XubW97myZOnNh736ZNm7RgwQJ3/WMf+5h+8IMfjOIZGoYxECa7hpF7mNyOP8ywMMbdInfqqae+5r7ly5e76+ecc47Ky8tH6ewMwzgQJruGkXuY3I4/zLAwxg3Nzc169dVXtWLFigMaFoZhZB8mu4aRe5jcjk/MsDDGDRRqx2IxLV26tPe+LVu2qL6+3gwLw8hiTHYNI/cwuR2fmGFhjBuITNBatqioqPe+p59+WlVVVZo7d+6onpthGAfGZNcwcg+T2/GJGRbGuOHqq6/WunXr+t13ySWXuIiFYRjZi8muYeQeJrfjk0gymUyO9kkYRrZw/vnnu/Bta2ura0t755136rTTThvt0zIM4xCY7BpG7mFyO/Yww8IwDMMwDMMwjEFjqVCGYRiGYRiGYQwaMywMwzAMwzAMwxg0ZlgYhmEYhmEYhjFozLAwDMMwDMMwDGPQmGFhGIZhGIZhGMagMcPCMAzDMAzDMIxBY4aFYRiGYRiGYRiDxgwLwzAMwzAMwzAGjRkWhmEYhmEYhmEMGjMsDMMwDMMwDMMYNGZYGIZhGIZhGIYxaMywMAzDMAzDMAxDg+X/BzRppIagqsidAAAAAElFTkSuQmCC", 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", 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" ] @@ -349,7 +334,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Fitting DMDs: 100%|██████████| 12/12 [00:02<00:00, 4.23it/s]\n" + "Fitting DMDs: 100%|██████████| 12/12 [00:03<00:00, 3.32it/s]\n" ] }, { @@ -363,7 +348,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "Computing DMD similarities: 100%|██████████| 66/66 [00:00<00:00, 646.60it/s]\n" + "Caching compare objects X: 100%|██████████| 12/12 [00:00<00:00, 5717.56it/s]\n", + "Computing DMD similarities: 100%|██████████| 66/66 [00:00<00:00, 1368.35it/s]\n" ] } ], @@ -396,7 +382,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 8, @@ -405,7 +391,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -415,7 +401,8 @@ } ], "source": [ - "sns.heatmap(similarities)" + "plt.imshow(similarities)\n", + "plt.colorbar()" ] }, { @@ -427,20 +414,20 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/Users/mitchellostrow/opt/anaconda3/envs/dsa_test_env/lib/python3.10/site-packages/sklearn/manifold/_mds.py:677: FutureWarning: The default value of `n_init` will change from 4 to 1 in 1.9.\n", + "/Users/mitchellostrow/Desktop/Projects/dsa_test/DSA/.venv/lib/python3.10/site-packages/sklearn/manifold/_mds.py:677: FutureWarning: The default value of `n_init` will change from 4 to 1 in 1.9.\n", " warnings.warn(\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -450,16 +437,22 @@ } ], "source": [ - "df = pd.DataFrame()\n", - "df['Model Type'] = model_names\n", "reduced = MDS(dissimilarity='precomputed').fit_transform(similarities)\n", - "df[\"0\"] = reduced[:,0] \n", - "df[\"1\"] = reduced[:,1]\n", "\n", - "palette = 'plasma'\n", - "sns.scatterplot(data=df,x=\"0\",y=\"1\",hue=\"Model Type\",palette=palette)\n", + "# Create a colormap for the different model types\n", + "unique_types = list(set(model_names))\n", + "colors = plt.cm.plasma(np.linspace(0, 0.7, len(unique_types)))\n", + "color_map = {model_type: colors[i] for i, model_type in enumerate(unique_types)}\n", + "\n", + "# Plot each model type with its corresponding color\n", + "for model_type in unique_types:\n", + " mask = [name == model_type for name in model_names]\n", + " plt.scatter(reduced[mask, 0], reduced[mask, 1], \n", + " label=model_type, color=color_map[model_type], alpha=0.7)\n", + "\n", "plt.xlabel(f\"MDS 1\")\n", "plt.ylabel(f\"MDS 2\")\n", + "plt.legend()\n", "plt.tight_layout()\n" ] }, @@ -480,7 +473,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -518,7 +511,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 17, "metadata": { "scrolled": true }, @@ -534,7 +527,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/sh/r0d61tjn42s4mc3nxqb0hhq00000gn/T/ipykernel_15994/1281545838.py:28: RuntimeWarning: CUDA device 'cuda' requested but CUDA is not available. Falling back to CPU with NumPy. To use GPU acceleration, ensure PyTorch with CUDA support is installed.\n", + "/var/folders/sh/r0d61tjn42s4mc3nxqb0hhq00000gn/T/ipykernel_79107/1281545838.py:28: RuntimeWarning: CUDA device 'cuda' requested but CUDA is not available. Falling back to CPU. To use GPU acceleration, ensure PyTorch with CUDA support is installed.\n", " dmd = DMD(x,n_delays=n_delays,rank=rank,delay_interval=delay_interval,device='cuda')\n" ] }, @@ -577,7 +570,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 19, "metadata": { "scrolled": true }, @@ -607,20 +600,20 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/Users/mitchellostrow/opt/anaconda3/envs/dsa_test_env/lib/python3.10/site-packages/sklearn/manifold/_mds.py:677: FutureWarning: The default value of `n_init` will change from 4 to 1 in 1.9.\n", + "/Users/mitchellostrow/Desktop/Projects/dsa_test/DSA/.venv/lib/python3.10/site-packages/sklearn/manifold/_mds.py:677: FutureWarning: The default value of `n_init` will change from 4 to 1 in 1.9.\n", " warnings.warn(\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -630,17 +623,22 @@ } ], "source": [ - "df = pd.DataFrame()\n", - "df['Model Type'] = model_types\n", - "lowd_embedding = MDS(dissimilarity='precomputed').fit_transform(sims_dmd)\n", - "df[f\"DMD:0\"] = lowd_embedding[:,0] \n", - "df[f\"DMD:1\"] = lowd_embedding[:,1]\n", - " \n", - "\n", - "fig,ax = plt.subplots(1,1,sharex=True,sharey=True)\n", - "sns.scatterplot(data=df,x=f\"DMD:0\",y=f\"DMD:1\",hue=\"Model Type\",ax=ax,palette=palette)\n", - "ax.set_xlabel(f\"MDS 1\")\n", - "ax.set_ylabel(f\"MDS 2\")\n", + "reduced = MDS(dissimilarity='precomputed').fit_transform(sims_dmd)\n", + "\n", + "# Create a colormap for the different model types\n", + "unique_types = list(set(model_types))\n", + "colors = plt.cm.plasma(np.linspace(0, 0.7, len(unique_types)))\n", + "color_map = {model_type: colors[i] for i, model_type in enumerate(unique_types)}\n", + "\n", + "# Plot each model type with its corresponding color\n", + "for model_type in unique_types:\n", + " mask = [name == model_type for name in model_types]\n", + " plt.scatter(reduced[mask, 0], reduced[mask, 1], \n", + " label=model_type, color=color_map[model_type], alpha=0.7)\n", + "\n", + "plt.xlabel(f\"MDS 1\")\n", + "plt.ylabel(f\"MDS 2\")\n", + "plt.legend()\n", "plt.tight_layout()" ] }, @@ -654,9 +652,9 @@ ], "metadata": { "kernelspec": { - "display_name": "dsa_test_env", + "display_name": "DSA (uv)", "language": "python", - "name": "python3" + "name": "dsa-uv" }, "language_info": { "codemirror_mode": { @@ -668,7 +666,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.18" + "version": "3.10.19" } }, "nbformat": 4, diff --git a/examples/dsa_koopstd_fix.ipynb b/examples/dsa_koopstd_fix.ipynb index 98bfc7b..89973b2 100644 --- a/examples/dsa_koopstd_fix.ipynb +++ b/examples/dsa_koopstd_fix.ipynb @@ -34,7 +34,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "35270a13", "metadata": {}, "outputs": [], @@ -214,13 +214,13 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 3, "id": "e75af356", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -251,7 +251,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 4, "id": "90e66ece", "metadata": {}, "outputs": [ @@ -261,7 +261,7 @@ "((2000, 3), 100)" ] }, - "execution_count": 45, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -280,7 +280,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 5, "id": "0eb18f19", "metadata": {}, "outputs": [ @@ -288,7 +288,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Fitting DMDs: 100%|██████████| 100/100 [00:03<00:00, 30.85it/s]\n" + "Fitting DMDs: 100%|██████████| 100/100 [00:01<00:00, 57.16it/s]\n" ] }, { @@ -302,7 +302,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "Computing DMD similarities: 100%|██████████| 4950/4950 [00:03<00:00, 1617.36it/s]\n" + "Caching compare objects X: 100%|██████████| 100/100 [00:00<00:00, 25628.16it/s]\n", + "Computing DMD similarities: 100%|██████████| 4950/4950 [00:01<00:00, 3277.61it/s]\n" ] } ], @@ -318,7 +319,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 7, "id": "e7a0aecd", "metadata": {}, "outputs": [ @@ -326,7 +327,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/mitchellostrow/opt/anaconda3/envs/dsa_test_env/lib/python3.10/site-packages/sklearn/manifold/_mds.py:677: FutureWarning: The default value of `n_init` will change from 4 to 1 in 1.9.\n", + "/Users/mitchellostrow/Desktop/Projects/dsa_test/DSA/.venv/lib/python3.10/site-packages/sklearn/manifold/_mds.py:677: FutureWarning: The default value of `n_init` will change from 4 to 1 in 1.9.\n", " warnings.warn(\n" ] }, @@ -334,12 +335,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "5 [1. 1. 1. 1. 1.]\n" + "5 [0. 1. 1. 1. 1.]\n" ] }, { "data": { - "image/png": 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", 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PP/5Y+xjwMmCeWz/i4fRS4EseYeSGG27Qx8226S/OwE4va7ewbLszLS9/tBdoeNiHVdvihrmWTn4l+E3F82/wA0I5/iVMOExPmjRJCzjG98kvfs+DH5ycvcGqBQCuB20J0Xbaf6L3hdl2ove/Fzy/Tto88zzHe375CEHIxScRvz6uExN+PAgeRjtjhbZopNAmG42UHxDU+YgIG84tgh6DuNt+zfOJRg/NE478H330kfZ/RCP1ySefJJQSgQ8DzjWCk5PjPO8h/CczhfX9bvdtY56XH6kRqnlf2H2VeL7xI+Td1rRp07jjCO8nJy2UkDgiNKUQHlxeyNzodvjy4auGaDKr6Q7VO4Oy3ZxnhxcsDooMZtbBiTw9VowDI4KPPX9UPDBTYNLAfMKXsIEvOycw0aE9whGTYz/ooINUs2bNirTDTMbEVxTmS4RKXnx8iYYBWhG+NNFoIcDZtQ7JgHYPcxqCCQIY+yGppBcMLAxyToMsDuJh5LmxwosWoYmXZlChKR5me/b7DDDXokHEAdkI0gjedoy2ynqPch+jCTRaiqCCmNv8ZO5/LxDAEAIw31qdwTFRm+V+NXtGu8e5QmuINtaJ4cOH6/PkFjXnBO0RyngWw4Zza3JFuZ1/q1aUdiYvFQEUOPZzDrku8da3w/uFDw60vnZ4X2HOGjJkSDQpJO/KeMfCtcNtIIhA6oa5/rzfrQISzzvO53Y3Cjtoy+wfF2DcGkyCTEx4vD/Yjx1yNKUrZ1khIea5FEH0DypxTFtOkU18afKywC8GXxcDEXCY9YiI8gLVPg+gNXQVrRYRYlaIGOKFQLIzp0zjXlljzRegVcuAUOfmH4HJgEGTiDMGbauWyQgzdo2FeaiTNdHF6zc4ZZJOBPx38N3BdIQ5JZ7PAEIV14uvQ6sfGOszD1+goKxatSqqorfC9UGQY5BKRZI/vmy5ZvjKWAUiBiW0BhyngfsO0xdRPwYEOQY0K0Q/cc0Qcu3XjN/WNBEIZE7mNCOo2YW0oPe/35QD+BIyqFmfN+5hzM2YiKzaBbZnve5uEJnFYEhovBN8YBiTrhNOzzLRZMxHwA8bPuz4EHjhhReKLOOZMGZaJy2X/bl3u35OsG0EIzTbXAf7hM8h71Si4wAhFOHOft+Bud9og2kZDbJbmyApB/hYxNeL+8Mq/HA9EBCtvqjcz2zTel/z8YDgZPevQ3AGqxBM33GFsKa54B1ABGO8cURIgBCcyQUHnnnmGR258NFHH7men1mzZkVKliwZOeiggyKDBw/Wic9KlSoV6dSpU9xzaiLlaH/TTTfpyJlWrVpFDjjggCKRUvxNu7p16+rIpeeff17/T3K9E0880TV6jggeIrOImOnfv7+OLKGvJGxzS6BJ9A7LiIZasWJFzDKiaIjkufHGG3XSNaK9GjduHNlzzz11krqgESpeyfw4NiL8OKdELvbo0SPab9aLtw2nSEJ7csvPPvtMX78uXbpEduzY4dlXoqrKlSunk5k+9NBDeuJvEvItW7YscAQXUTNEYHXs2DHy4IMP6uifhx9+OHqMV199dULRc07JLe3njGglorKaNGmi29977736OLhXrNeR+6ds2bKRfffdV9+f9LNOnTqRgw8+OCZ6DjgfzDv88MMjjz76qH4euE+4X6x9Yhntrrnmmsibb74Zef/99/V8zj+RRtxPRB0RKWn64vf+93vurVGunAeilLif6Tu/J02aFNOO7Tkd79lnn60TWXJ/8szT5v7773fcF5GELL/55ptd+0OCw/PPP18/qwMHDtRRpdwjRDvyvojXp6DRc//884++99lHr1699DuP63zppZdGKleuHI2SJZqN98btt9+uk/cSDVmrVi0d6bZhw4a418/OiBEjdN/fe+89x+X0i/uxW7du+vemTZt0okneSSSDJbqPe5EItjlz5ug2u3btinTo0EFvl2Ph/HGvcV2s+/H7bgIiTjk3RFlyz1155ZU62tCekNa8a6yRg0RS8uzwzrjlllt0n7metLNHfBKhR9QriVW5nzg2nsUWLVpI5FwKEKEpRfBAEpbNw+gF4bq8bHmp86AzKP/111++9kEG2JNOOkkLB1WrVtUvJ4Q0J4Hm22+/jZx88sn64WKg5+VH2DIDv5egQNgwx8ILuWbNmnogI1u5m9A0c+ZMvcxJ8COLLQ8+gxd94PwwaBE6bOWUU07R+/vzzz8TFpoQRAjD50VcoUIFPcAhxIUpNJmQfAbKM844Q7+svUBIPvbYY/XLkIzO3bt3j/z0008xbXjBmxe3F9wjTz31lA6lZ/Ah2zjbJPUDA5M1TDpsoQkmTJgQOeKII/R1QuhlgFqwYEGRdT/55JNI8+bNdZg8A+Lrr79eJOWA4Z133om0a9dOnx8mhDLO98KFC2Myvp911lnRUGzrMXAtGBy5HvZByM/9H1RoIgP49ddfr9ODsE1CyZ0+kpwEFDI9t27dWl8znl+eMWv6EDsIS2xj7ty5rm0uuugiffxsk/uBjyo+qJzeJ3xgxUtrYscp3QPCDtnuScHAOWCwZtv33HNPNM0B55h7nfcH9wH/8x6w3/te188K9xrvS7sgaAXhkXNgUjeQqoEPOoQ1+sAzwzNgTe1AGgo+skhFwbqcn1NPPTXyyy+/JCQ0mTQrCK2cG/aJ4Gj/wHISmozgxP750KA/nHvuN6fjnj9/vn7nci/xbCCQUzVACJ/d+CcRDZUgOIEaHNU7UWYkvUwEHCDxjzLJMwsJzH6YHTiP+EIJQthgusJBGHM10ZeCIPhHfJqEUMG/Aed3t2zF8SCEGZ8FovMKERxjCckWgUlIFUSy4UDslcVeEARnRNMkhAIpBHBMphQGjphuYciCIAiCkKuI0CSEAhlyifYgkScRgPHC8AVBEAQh1xChSRAEQRAEwQfi0yQIgiAIguADEZoEQRAEQRB8IEKTIAiCIAiCD0RoEgRBEARB8IEITYIgCIIgCD4QoUkQBEEQBMEHIjQJgiAIgiD4QIQmQRAEQRAEH4jQJAiCIAiC4AMRmgRBEARBEHwgQpMgCIIgCIIPRGgSBEEQBEHwgQhNgiAIgiAIPhChSRAEQRAEwQciNAmCIAiCIPhAhCZBEARBEAQfiNAkCIIgCILgAxGaBEEQBEEQfCBCkyAIgiAIgg9EaBIEQRAEQfCBCE2CIAiCIAg+EKFJEARBEATBByI0CYIgCIIg+ECEJkEQBEEQBB+I0CQIgpBBBg4cqPbZZx9VqlQp1aZNGzVz5kzP9iNHjlRNmjTR7Vu0aKHGjx8fs3z06NGqU6dOqkqVKmq33XZTc+bMSfERCELhIEKTIAhChnjrrbfUtddeq+666y41e/Zs1bJlS9W5c2e1evVqx/bTp09XZ555purdu7f69ttvVY8ePfQ0f/78aJstW7aodu3aqUceeSSNRyIIhcFukUgkkulOCIIgFCJolg499FD17LPP6t///vuvqlOnjurXr5+6+eabi7Q/44wztFA0duzY6LzDDjtMHXjggWrIkCExbZcsWaLq16+vhSuWC4KQPKJpEgRByAA7duxQs2bNUscee+z/Xsi7765/z5gxw3Ed5lvbA5opt/aCIIRLsZC3l/fwJbhixQpVvnx57S8gCGGC4nfTpk2qZs2aegB1Y9u2bXrQ9aJEiRLa7yWof81jjz2mVq1apU1FzzzzjGrdurWnf80dd9yhtRoNGzbUJqEuXbpEl999991qxIgR6vfff9f9adWqlXrggQe0hqXQWbt2rfrnn3/UXnvtFTOf3z/++KPjOlwXp/bMT4bt27fryfqeW79+fdQvShDynYjPd68ITQFBYEJ9LgipBCGjdu3argITZpd4A2WNGjXU4sWLfQtOxr8GMw9CzZNPPqm1GAsXLlTVq1d39a956KGH1IknnqjefPNN7V+Db07z5s11m0aNGmnT07777qu2bt2qnnjiCe2kvGjRIlWtWjVf/RJSD9fwnnvukVMtFDy/e7x7QXyaArJx40ZVsWLFjNxYSL9eEnCqycQX5xFHHKEqVKjguw8s9+OmZ7bj1pbr/Mcff6g99thDt2GyrsN880Vu5pnJbN86Oe2fifWZzHVF87BgwQK1YcOGmOO28tdff+llaHf23HNP1zZEZHEcbm3S6V9j7/uECRNUx44dVSGDprBMmTJq1KhRWtg0nHfeefr6jxkzpsg6devW1YLt1VdfHZ2HE/l7772nvvvuu4R9muyaJu4b9sUA4vf+EYRchncT7zuvdy+IpikgmVRVuw3AyWIVCOLtP90UK1ZMFS9ePO1CE/tFMIonNFnnJSI0mckuDPs515iImZww/eBFYKVkyZJ6cvOvueWWWwL51zCAW0EzxQDuBPt4/vnn9QsJ01+hY8yVn332WVRoQlDl9xVXXOG4Ttu2bfVyq9D06aef6vnJ4HZfIDCJ0CQUErvFefeKI3iOYh3sgwZAWgd3JwEimYBKr3XzJVDT6zj8CLZhCb9GS+U2AV9OCClmwgwT1L/GzQzo178GTVS5cuW0mRDzHIN81apVkzz6/ACh84UXXlCvvPKK+uGHH9Rll12mtXcXXHCBXn7uuefGCLJXXXWV+uijj1T//v213xM+Y998802MkIUvErmZ0FgC5lV+J+v3JAiCaJpyFuugG3QAdmtv5iczoHutm4sOpW4Ckpt2zqppcmtnbWMXYIP2zat/YDevOGkTUs3RRx+tB20EMwSE008/XX311VeOflKFBibONWvWqDvvvFMLNZjREIqMMPrbb7/FaCEPP/xw7Tt2++23q1tvvVU736PZMz5k8P7770eFLujVq1fUjIeQlSwI1zt37kx6O4UCmnKjmRZyHzHP5RDJDLDJYgb4IIKPl2DhtD27CcxLKLH2y2t5UIIeY9BtQ1jbt2qUnJYFMa+g+eHFjh+XFX7jUO4E8/20L1u2rNpvv/30hM8TA/1LL70Uo0EpZNASuZnjvvjiiyLzTjvtND25cf755+spFWzevFktW7Ysb7TG6YDnHcditK1C7iNCk+CbVGu0ktGehUEmB4JEhDU/mqZs9K9hu1anYyE3QMOEwITzOpGPuag5Tjc8h2gSOW98LIjGKfcRoUnIaRLVDKVSo5Qu/GiagvrXELl1yCGH6NxMpByw+9fUqlUr6heFf0379u21f03Xrl11Pib8a3D2BtYlJ9NJJ52k9t57b22eIw/U8uXLPTUlQnaCSY7nBoGpdOnSme5OzsD5IpKR8ydCU+4jQpOQNpz8fay/0ynoJKLVCVvICsOUGJamKRX+NQwQOCvj5IzARKJEUhpMmTJFNWvWLOHjFjJLrn9spBs5X/mFCE1C2vAyxyWyfroII6owFzRNYfvXEC03evTohPohCIKQjUjKASFlTul+2/htm2vEO55kfbisuaGcJkEQ4jNu3Djtz0dkqdU/z3x8kNy1QYMGOpDBJH4VChfRNAkJEVYyzDDSHKQae6SfX4Ek1ceUCk2TIITNjl3/qnHzVqhxc1eqlRu3qb0rlFJdD9hbdW1RU5UolvnvdszKQ4cO1XUUiQ608vrrr+t8Vz/99JPOkn7QQQfpFBpiXi5cMn/HCgVHLmtBsq3vomUSsl1guv29eerh8T+qucs2qo1bd+r/+c18lifLunXrdO3Dxo0b64mAhCBQH5EM9VQBcKrH2KdPH+2fV7lyZe33N3z48KT7LOQueSU0TZ48WXXr1k1XKeYr362cAxE81OXC54J6WzNnzkx7XwuZRE1RYWI1YcUzZ6XKpymZrO5+M4ILQiZBwzRp4RpVvlRxtdeepVSlMiX0//xm/vh5K5Pex+WXX641QGQ+//DDD1Xfvn3V0qVL9TKEHAIanCYSv8aD4Id69epFfzNuME8oXPJKaCLEmS8GhKJ4ldzJjks1dtpTL2v16tVp7asQjFQnnPSz77D7ID5NQr6DSY4nrHSJ2IzY/Gb+2Lkrkt4HEZ4XXXSR/nvfffdVRx55pP6ANu97stE7TZQYEoSsEpqmTp2q872g0SHj8FNPPZXK3akTTjhB3X///apnz56ubQYMGKDVreSeadq0qa7GTrI2bNpC+ggrVD5dKQjCKjETpuAlmiYh28GHyc1vifksTxZ7UWoryWqa6tatG9VaAfmWmCcULikTmsaPH6+FF1Snc+fOVZdccom65ppr9E1n5cEHH9Tp5b2msNShppI7ldv9VnInczEPpXXKFJlylk5mv25CkL18SpB9han5sQoyTtuLl9Xca3t++piMICXRc0K2g9O3m98S81keBsOGDdP/M76QBwxtUxiaJtJpUC+RbOgUQmZ7CGJC4ZKS6Llt27ZpIQnN0llnnaXn3Xvvverpp5/WalPswoZLL71UF/D0Ah+lMPCq5E4SPifIfnzPPfeobMHPAOsnEWM2ZcT2qk/n1tZLaAkS3Ravll1QEknYmaivlETPCdkOUXLzlm1UW3f8E2Oi4zdPyIkHhPNu572OXxMuGowz1jEmHpQCIhM+H8Q8i6NGjVKDBg3SmezPOecc9fXXX+sIO55pXDtatGgRSp+F3CQlQtPEiRPV1q1bYyRyog+46exV1olIYMpWKCrKg2LgwcqkLdzvAOunXZih8/GEHL/t/a7nt6Bv0O373Z5foTPVBYDDzAguCGFDWoEZv6zTTt9/bdupTXJomHgi2jeuprq02DuU/Vx33XXqvvvuS2jdjh076tpwTjBuefnICoVHSoSmzz//XNuMrXV2Fi1apDZt2qS/BuzmOSYvyJMRhh05kUruCHl2QS9TZEo75Ge/yWT3Dnpc1mi2MM9HUC1RphFNk5DtICTd36OFjpLD6dvkaULDhMCUDXmaBCHjQtO3336r/YesoO4k6yo5Mayk0zyXSCV3IfVCW7I+U7ki5KSiHp5omoRsB8Gox0G19JQKRKsq5IXQxI386quv6jxIZFodPHiwmj59epG2YZrnyOaKRsuwePFi7fDH9o2mKl4ldyH7s3YH8VtK1b78Ooi7mSyt5zRopnGDaJoyA6acihUr6iAVK1SxJ6DkqKOOylDPBEHIOaGJSDeiDMaOHatuvvlmnX7+gAMO0Lk07Ka5sPnmm290inuD8UVCSHr55Zd9VXIXvP2IwMunyA17+2Q0RJn4srSfA6924Mffymwv0eMRTVN6WblyperevbuOwOXaEeSCBt0IT7z3eP/glCwIQn4SutBkNDtdu3bVUzrp0KGDrwHIq5K7kJpkjH40MNmIPXN4mI7giWzPimia0gsfgaQo+eqrr9SGDRv0b4SkTz75RFWqVEm3EVORIOQ3u6fCNCchmYIT2TCgeOU2csLNlOYlBPpdhwE4WdOn1J5LHxMmTNDh7Jj2ye02bdo0tffee6tjjjlGa5ly6WNAEIQsEpowxwn5QyIOyk5/p3tAsQtCYQttiSbDDJoA0w3JCJ5eqHJvNEpAVO3o0aN1TiA0TlKKKTdBEG7evLn+2Gfsev3112OWU2WiQYMGerrtttsy1k8hT81zbkVyhdwlqOkoDIEgDJy0QfZjieen5FVU12mZ3afJqSiwfT0zSfRcdkNdM6obkOjQUKxYMR3oQuboE088MaP9y1Z2/rNTfbTkI/XJkk/Uqr9XqRplaqhO+3RSx+9zvCq+R/FMd081a9ZMaw0rVKigS6vge9u2bVstJJGMefjw4fq6c62POOIIdfjhh6fd9UTI8+g5QchWvLRDQTRR8YQcu9N8Iv2KRyH4NHEcRMSixbEfU7qj1Kht+fzzz6tTTjklZr4RnJjvliSxkAWm+768T01dPlX/LrFHCbVu6zr1/brv1dervlZ3HHZH0oLTunXrdLDPzz//rH9TwaFXr16BklsaSFxMzj6EJ4QmyqaQFbxs2bJ6+YUXXqiFKBGaChcRmgTBB4mkBki1li3fo+e+/PJLHaFGwVSnyMN0R6k98MAD6u+//3ZchuD0zjvvqOXLl6e1T9kOGiYEpvIlyqtSxf5XZ27brm16/sdLP1Yn7pucho76pmiHiNj+9ddf1aGHHqo1RfXq1dPR0gsXLnRc74MPPihS3QG/tT///FNvw0SDt2vXLrocU+yIESOS6q+Q24jQJKSMRGrgZVuyyqB5mhJtlwj5rmki8S1O1+PGjdMO15m+LxCM9txzT8/lDNTC/8AkB1aByfzetGOT+nhx8kITKWMeffTRqAmVYr2Y1dAQoSnyy7x583S+PtYxmiVBsCNCUw6RqUHDqmGxa1vi1Y/zqhFnd4iOtz37dv3i1Acnn6MwSCQVQ7KRc27Lch3MLRRP3W+//TLdFSFB8GHCJOcEZjmWJwv1QN3wq2miVBc+aUOHDo3RLJEUGU2nYcmSJaGU9BJyFyn8k0NkaiB0EpD8DvbJRN4l0yZoH/zkUvI738t5PF77IOR79BzVBKwZ/oXcA6fvHf/EltSy+juxPAyGDRsWFWqmTJmitU2A1ojcgU6TEZh++OEH1aVLF+2vdtxxx8VsFwf/1157TVeN2L59uxaqgvhLCfmHaJqSwE/WZz/rOOEVhZVu6IO1+HI62bVrly5PYU9d4KXp8jKpuZ1X69/kT0LowNzC38Bva04lltn3bxVUrG3NNqxRcma+8c0x5zeIkJnv5rl+/frp6vVk7iccvHjxWIdhSW2S/RAlh9M3Pkx2nyboXL9zKPvhGcKvCeGGFAL4Hvnlyiuv1OkkbrrpJj3BI488ojp37qwTJqOtMrkH+VuiJAub3SL5oMdPI6iCCU1lkEu30MRAmInLxbEaG7/d/8g+zwu39k5mP9OO8O4yZcoEEpoSxSo02dMA2FMGmOtvFU7cclJZBU5e7lYtnbmmVgFq6tSp+iXu5j9j7sEvvviiSP0zax1GXvhe28l2jLDpZvLNVLkSnIPRUjjdx0Rd5YP5xtxj1vtn27Ztup5n/fr1ValSsT5KfqPnMMkxD9rVahdK9BzXAedt6gFmI4mcNyE77nknRNOUIPZMzkEGcT8+OdlWdsQrVD+RciJ+j88uLNrPXSqESKNVsmbstgtNZkC3DuzWNkbwMn22+4XZNVRmf0GOJ981TQw02QiDH3XoqlevHjOfrOAsk9pz/wOBCMGIKDmcvk2eJjRMnet1zoo8TYIQBBGaEsQ6cMZrl2mBJywScXJ2EyadnMud9uPHR8iv0Oa2f7draxearEJiED8pa3unbOBmP0GdwvPdETxbI9Hcnmm0e6JJKAqCERFyyUbJeV0PQUgXIjQlSDaEnOcjqTpfQSPuUuGcHvbL3Wjh0rGvdPH+++/rJJL4L/G3FyeddJJKJ9dee230Xrrjjju02diAdolCvgceeGBa+yQIQnoRoSnF5JOmyauobbz2bj5J8XyazBSGT5Of9AjWdl5lU9yO0X4sbo78drOf2X/QVAr5pmnq0aOHdvzG9MXfbmTCp4m6mubcktOnRIn/hdLzd8uWLdX111+f1j4JgpBeRGhKMfkkMHkJR0HNSn6iAp38f4L4UfnB6lhs/dvs303wMxoee//NOlZncnv0nBWrv5b4NMX6YmWbX9bnn3+u/ycB4lNPPZWzDvaCICSOCE1CKFqaVJnNnASXVEXPufXP7Ndrvl3gcupbss70haBp8opAyhZ/IZMTSBCEwkOEpiTIJ9ObH5IVjhJZZhcMrGasZPyUnPZj/dvJMdtJWIu33Pxt12jZ10/EETzfo+cwvz344INqyJAh6o8//lA//fSTLpOBPxF5eHr37p2RfpEL6OGHH1afffaZYyFh6p8JgpCfSEZwIWdJhWN1LmloTH/dplyHArkvv/yyritm9R9q3ry5evHFFzPWr4suuki99NJLOuv0FVdcoa666qqYScgtqG3YqlUrVbJkSXX11VfHLLv77rtVtWrVtIM/09lnnx1dRhJN7kUSX5Jo9fXXX89A74V0I5qmBLH7qghCPDOjISztZL5rml599VVd2qJjx466eK8Bh+sff/wxY/368MMP9UB7xBFHZKwPucQ/u3aqhTOmqp9mTFGb1q1V5atUVY3aHqkat22n9iiW+TxNJNClPMrIkSN12gg7CEpPPvlkkfnNmjVT06ZN0wkRSWpKRvK2bduqBg0apKnnQiYQTVOCePmuCOHhZrIKasryuy879qSWTvOty+zt3do47Sco+a5pWr58uWOxXgRCSutkikqVKqnKlStnbP+5JjBNeHGQmvzGMLXql5/Vti1b9P/8Zj7Lk2XdunW6tEnjxo31NGLEiEDrN2rUSAvipjSSXxDmEZiADPE1atTQwpOQ34jQJOQkYQgGfhNUBklkmcg+grQrpIK9TZs21cVX7YwaNUp/1WeK++67T915553q77//zlgfcgU0TIvnzFIly5RV5SpXUaXLl9f/85v5P834b3mVZLj88sv1/bBw4UKtBezbt69aunRptFacMa3ZJ78CDhoohKpjjjkmGkFpZ8KECbqUy6GHHpr08QjZjZjnkiDRCK580AKkwtxk3a5b2L9T+0T276ZVirdOvFxO8aLnvISwRO6jfI6eQzA577zztMYJIXD06NF6YMRsN3bs2Iz1q3///uqXX35Re+21l3ZItxcSnj17dsb6lm1gklORiCpesmTMfH5v37JZLZwxRe1/5NFJ7eOjjz7Sfm9AoAC+ZpMnT1bnnHOOeuutt5LaNmbh2267TV9jTHE9e/ZUX3/9dUy2enJ2kYaCfZkanUL+IkJTEiQqLIQZ9ZVPUYRO58TLPGf+dxJevVIEOEW8OQk7bkkn7SY4P/s1y0ydOetxuZnzCt2nqXv37uqDDz5Q9957rx6MEKIOPvhgPe+4447LWL+8km4KseDDtEfx/znxW2E+y8MotOoGmiYEbSe4jzCreYHJzYAPGxqtb775Jio0LViwQJsG8Ylq165dwscg5A55JTQ99NBD+msUJ9HSpUurww8/XD3yyCPazm1l4MCB6rHHHtOZh1G7PvPMM6p169aB9lVo6QbCSmppiLeeddtWwcZpG25CkJsQZm3ntR17n51+e82Ll+XbzV/KL/muaQK0Bp9++qnKJu66665MdyFnwOl7y4Y/HZf9s3OHKl+lXmi5s4h0W7JkiTbpGsftZDVNy5YtU7Vr19Z///zzz2rOnDk6Wg5++OEH1aVLFx2skEkhXkgveSU0TZo0SduzsSvv2rVL3XrrrapTp076a8CoTXmIqCFF7pc2bdroh6tz5876a8RetdyLQheY0nkOkhEs0q3xS1fSzXytPWcFUwumkCpVqsTM37Bhg9Y4ZTIfEn3Atwoz3Q033KAdwzHLYbKrVatWxvqVbRAlt+rXRWrn9u0xJjp+q912U43bHhlaTi+0QOTQIhUAZlO/kG8LMzAaK54bruugQYN0bUNMc7NmzdJO4nvssYf+4MZxHK688kq1ceNGddNNN+kJ+EhnPBGyA57Pd999NyYqsly5ctrMmmiU426RFL5dp06dqgWUuXPn6o6SlC6deUzWrFmjBSGEqaOOOkrPQ1BCqHr22Wf1bwYdVLT9+vVTN998c9xt8mARMcFD5GWWSQW8GDIxGPKy4Pp54UdzFK+907ERPUVhVL/CiF9ByE2TZF9u/rcLJ07pJqyaH+v6nD/rNbSa5kwZFX6zTZbzpczL2K1Mh7kH33777ZiisVZwUj799NM9t5PtcD5MHTorJLqsW7eu2s7AmwF4nx177LH6GqDZ4IMLAe/2229Xv/32m/a5ynXMPWa9f8jKvnjxYlW/fn3f2dlN9BxO3/g2YZJDw4TAVP/AVurYiy5POu0Azw5O2BUrVlTZSCLnTVBJawhfeeUVzyhb/NQQlo0m0emeT6umafz48bpDmMEwk3EA11xzjfZTsH4FkPGXyQs0Rbwkg8LBgwkP3rFjh/5quOWWW2JezLwAZ8yY4bgNXszWl7OX/bxQfZpSSRCNTTZq/6zZy8MmX32a3n///ejfH3/8cTSsGxAq0QwE0SSEDR+C559/vnY+Ll++fHQ+ppqzzjorY/3KRhCIEIyIklsYzdNUT2uYGmVJniYh/wSmF30kv0WgIkktlQWM4OSHYqmSrC+55BJd1NK8RHDmRG1KVIP1hUd0Al/EXtSsWTNwHxg0yO6K8x5ZW2Ht2rX6pYsK3Qq/3ZLl4Sd1zz33BN5/IeE3JD+etshJmPTKOxTPCdsJJ38n63asfyNQW/8O4pNlbW+0UmY9pzIqZl9BfcTy0afJOFpzLvjwsn8d8v4ggi1TYDJ87rnniszHLIdmTIgFwYgIuWSj5NzI5XtdCJ8gfmzcO/hBY2rNqNA0ceJEtXXrVh25YMBEwUuQVPVW0AKlIlEcvk3z58/XJsJkQCvFl6VV04Q5TxzBi5LoOXFbzy1yLVlNnV1Iss536oPTOvH+tq7r9++g5KumyfQdcwYCStWqVVU2wTvMSeNMbTxKbgiCkDkfpk2bNgVaZ/369Xo9v89uSpJbkgCM5GFWX45Fixbpg7EnpcM0h7+M14SfQBCoB0UeF/phVbvx8qVP+ERY4bc1tNT+gsS+aZ2y1RSUKZz8erwyVPv52/xGA2OfjP+P9bfbPKdl3APx/mYdI5SgnYyXSNL4JtHW3t44bJv55n/7/KBJKb3OdT58feMHYheYcMDONDgIozk3/hLcK7yjcAY+5ZRTMt09QShYpiaoJPnuu+98t02J0PTtt99q/yErRCNQFNFEHljNc4Rxek1+zXMMFAhMeMuj7eJL1QpFP+kDPhEGBil+UzMoKH7NQfmAl4nM+n8y23f62w9OZi270GSd77S+n3659S2ekOIn5UAipCIjONFBmL9wWCVoYubMmXGzJTdp0kS3JxQbX0YDQgWCBPOJXuU5Pvfcc9WKFSt89YVIJKuq/bTTTtNaacxgQV5yYYNpkGgcHNTRqLdv314HLODfRJFhQRAyAy44iboUZdQ8h9DEYEAUCS9eXqyDBw9W06dPL9I2TPMcJrk333xTjRkzRr/AjH8BjqTkbQJMbfhJHHLIITo3EykHCFMlo2tQkimvkYsEFTjC3E868BPhFw8/0X3ZmhE8aDoOnuczzzxT+/2R4I9nD38kQu/xIySCj7+JmiUfGhFORM+iqSFBYDzoxxtvvKH/JlcTpSrI/kzUIGH+n3zyicoEvE/oD1+1RNIhQJECgYASQRByjyBRjaELTaipsRFiHiOEHzv/AQccoF92qa4XhWAGHTp0KJL4jGgXwM+KVARkF0aowoxI3+zO4YL/Ad3PYO8V2u/mY2R+O4Xyx9uuWx/jtTcmQScNlZMjuFPqgSCO4NbjMmbETPk0DRgwQPXp0yf6AYHQMm7cOJ3t2CkdB4Eexx9/vBZgTE02hAnSebCuES6ssIyPFd4T8SJieT5NxmbeJwSMkHcNTRhCXaYhA7Rkgc59CFAiQaV5Bm+88Ub1n//8Ry+j+O/DDz+s8/4Bz8Z1110XXZfoK5bzvFGbDouKvayOkD7I6RbUpwmQAzImNGFOQ3PUtWtXPaUTvwMoJjymZPeVT1okPyRzvE7ms3jO02a5X2ftZO8L044XoF1witdPJ5zau5kLnUyJYWia7A7L+OjZgzESTcfBfGuQBKCZeu+99zzTgHCMfnLqVKpUSRdVRXDiw+b++++PHht+YJkEkz7T6tWriwioCJnC/0Dg+P777/XE/YhfaLNmzfREvrtMQz+oK4eQz/3Gxz3uGiQ/NPcePq/cu7h3MPFhjs8dWlST1JR0OghfWDyEzFUQIHdaELgHybPmN53Q7qkwzZk08/lMoQlMhXStUnltw3TS9uPTxEufwcBMmNKc8ErH4RZGz/wg7fEbwMcJk56fhJsnn3yyTllCiYp169apE044IfqOwYcoU5CCBI0XQhPnDbOjdRJiBSa0hJhW8WXjHuB/fjPfaHCSgXsD8zDlspjQDgWhY8eO0VxgPC8ISAhPQMoaEyREG/z3zKBM5nBMzSznnYF/7vDhw+XyZxAE3aBFk8kOHoTQxXxeaJjjBMGLRAUHuzkvGawmt3jbdFueSUd/P5omXv5WAcVJy5QOcArHvEa/jBk9Hk888YQO5sCURyJJk5V+5cqV6vLLL1eZAtPjyy+/rM4555yM9SFXQLtEODf3ndVsxf3AfBIXJztecC+gHUIIo7QOFR/QFFFUN2jBXoQ5BF+2YYe+ol3l+gP3pSncC5iNg0Z6C+HDR5mf5JbGnIemMaNCk5dqXhDCICxNUJBkldmoWTQpC9yWgTVNhheJpONgvp/2RmBaunSpjmr10x/WIUEu5g97FCyVBTIJpkyqHAj+hCbuRbufD7/ROpFLL1mhCfMZQjVgZsFEQxJlhNogiQ7nzZunfZZYx66tIMs05jcEpiDZo4X0w/Xp1auXvo5eH7VoDonmDUpKUg4IQlgE9fMJYzt2J227M7pbLqR4uaniaYWCmu7CzNOUSDoO5lvbA47f1vZGYKJCPF/x9uK7bjCovvPOOyobueiii3SkoBAf/ETc/JYQ0sMoS+W1DTRNOPk6TcYEZ7RImPjwR7M792NOxLeP2oKkvTAQyMCHgAGzXSLlvoTwwYxKRRDMdfbgGrSeXP8LL7wwpkSTXzLvhZfDFKIzeK4TNJu4NbotSIi/tb3XfeLXPJiO6Ll46Tj4KiNHkvGLIn0AOYrIW0TQB74kpBLAGdYITKeeeqp2lMV0gs+U8XciWARBzQvSF6C5zrRmyQ4aEo4RIRAtiV2LQhSi8F/QKlorzFvhfgirmDQR0nfffbcWXCh4zb0LfjRNP/zwg64byDXFf84KpmB8nvDFs5f0IZEpAhb7xZcPLRQaDiE7QCBC24jPG/cFNWQRmNBcJ5PmSIQmIeuxa2z81LHz2obTfKeUAm6pAaz/29d3i4xz66vXsnTnaYqXjgN/DetXGyYqNC58gd96662qYcOGWsgxtR6XL18eLb5rD+klW789NYgdtkfmbSKb0ILZTSZB6kWFCbmZzPFgXrIiH1Gx4C+C4IEAbfdp4lyZeyVZEMDwa0LIJ4VAkILO3EdExiEYMZnEqkSC8ixw35Neg8l8LPAhgSmQoACcxYH7GZOykF2g3far4fbDbpF8SVmdJlAFI8Gicra+IIOcxkRrp/FiyMTlQo2OE24iUWhe+Zkg3vHwYjKJSf3u02zX/reTD5Pb/k1ZFYNTHibrfPv/1nNlzBPGB8kqjJlyKiZHE9d40qRJ+iXu9hVu7kFyHrmdGzJVk1bDazvZjt2XyQrnC6dfITWYe8x6/6BhI8ye6+I3GaCJnsPpm/ucZ4p7nOuH6QSTWLJpB9gWztt+0lhkgkTOm5Ad97wTomkS8pKwiuK6CVdumia/fQqDfC3Ya2CgyXZwEAZxDnYGgQjBCJ8htHImTxMapqZNm2ZFniZBCILcsSHhV3tkNcc4Dbhu5iB+27Vb6YJ92msJpgsisexfkPHOnRdOvkNOJj/s4AyIRqNkNESmvdFCWYUTp8zl/E9ba+JMq+nPCD7W/WTKPJetcO8hQKGZyIZBlmtEok1Tgw4o20Sm6Ntuuy1QVvdCgGuG71eqUtHk070uZD+ZfwPlKE7RWF7Ovl7+LG7r2ufhOGsGDTdzk9s+nSK9/PrToGJ3c+ZMNai0cT52w+kaBBFe3c4LBWEpVutm7otnnjP7cLuuTuY5piCZrvNd00Ttun79+qlXXnlF/6YkE+Za5nFPOJV2SQcIRqZ8hvFnoQ4dDsGYYqRoryDkL/JJlAYS0Q75FcjC3Gehkw1frImUUQkj5UA2QkkXhNcvvvgixheE8O8g+XfCBiGO5HmXXXZZVINCgsUXXnhBJ70UBCF/EU1TivAyA3n97bSdVAhBIlSFQyaFk3zXNBGJh3B02GGHxdyvRGThWJwpKEhOHhg7zGOZIAj5i2iaUkQQc5yXAGM15YigE/8cJ0O6zm9YmqB81zSR/qB69epF5hNWnslnoWXLljpy0Q7zWCYIQv4imqaQ8ZukMJFkhkEHwnwYOL2OwcmBO94xO6UfSCZJaaLpI8Ig3zVNJNkcN26c9mECc40wjbllKU8HlOwgmSfJLU0/qElGhunx48dnrF+CIKQeEZpCJmipDiE5ggg/Ttm3nRy4g2pqggpOQUq6FIJg7MaDDz6oTjjhBB2uTjACyQX5e/r06TqXVaYgCzpO6QMHDlQ//vijnnfyySdrv6aaNWtmrF9CYvTt21cnUDVwTRGMSXqJjxrJLE3OsEqVKunErELhIkKTkPV4RQmmYr1cId81TZSomDNnjo5Sa9Gihfrkk0/UwQcfrLU6/M4kCEcSJeePHf/+q95fvUFPK7bvVDVLFlcnVa+opxJZkJ4B4ddAJnwEJOolGo4++mgpRC9EEaEpQfxqNZyyUjuV3Aiy/XwUABLFT54mp9xYfrbj11k/LK1PUA1XIeRpIjcTUWnZBhmoSTtA3TIgUSOlNZKpaZWvAtNNC5epiev/W1S3xO67qTU7dqrvNv2tpv25WT3SuHbSghM51agLR1FooLRJojXgiIykfAr54QTBicyL+TlKvAHUboKxO3P7WT/evHjbipcjyE8//LZJFaa8iPV/++Q132mZdZ7b31ZhyWrCcxJGggovfrYZRNPkNuU6FPqdN29e9PeYMWN0EV/q3GUq2SpMnjxZ1zajxhnCExN/o6FgmfA/0C4hMO1ZbA+1V8niqlLxYvp/fjP/g9Ubkj5dmEWpO7dw4UL14YcfanPb0qVLo/UUqRPoNOGDZmfo0KGqd+/eMfPIwUV7ai2OHDlSLm+BI5qmFBLEwThe2gE3ASkR4UtQofkrJavRScaRPN81TRQ/JYElpjjqzDEA4jvEwEXiS1PJPt0wKNOXwYMHRzPDk5SUwZtlVkGv0EFogtJ7xH6f8/uvXf+oMas3qFNqJKedo6g0PkhA8tMjjzxSC69UuA+Sz2vKlClq06ZNqkuXLtF5lIDBVFemTBmtVezUqZOqU6eOToMhFCYiNKWQIAJLvLQDYZJrA6qTA7ef5XafpiCO4GYdp/+DOn2H0aYQfZpwtuYLHxCUcMB+8803tdMu5pdMCU2LFi1So0aNiinozN/XXnutevXVVzPSp2wFHyZMck4wn+XJQj07NxBu0UA58cEHH2gByIC5FTOf9bpWrVo1+vf++++vBSruPxGaChcRmgqQTIbJh022HEciApXTNhLJCO62LNfhGIzwR3g/X/3AQLd27dqM9QtndLQOjRs3jpnPPMnTFAtO3/gwObHj34heHgbDhg3TZWyWLFmiNUZGoParaULwQhD+9ttvY+YvX748Wsbpjz/+UBMnTtSCmFC4iNAUEonm+vHSnohpLbk0Dn4c7hO5NokSz2E9qH9TIeRpojAuZVNIMYA5DCjeu9dee2WsX4SiE4aOxsloHL788ksdhUWk39y5c6NtU1WkNlcgQg6n763//BtjouM3dK8eW4w7UTCP4tdE4lP8y/A5C8KIESNUq1atVMOGDWPmc03xpStevLh+pq655hp1zDHHhNJnITcRoSkkkkmOGOb2ChU/59FJiHLSusUraRMEP4WRkxG481nThLbg7LPP1uHeFMndb7/99Hw0AjjlZoozzzxT/3/jjTc6LrPeZ0EKMOer0ESUHE7f+DBhkkPDBMdU3lN1C0louu6669R9992X8PoXX3yxnpxyhTEJQs4JTTj2PfbYY2rWrFlq5cqV6t1339WRNFZQzxJuagUVuklA99BDD6nRo0fr36VLl9Yv3kceeaSImj1sUqE1SpeJLReEt3hCkNd6qUximWryXdOElsbJqZr3gNXvJN2g6RL8QToB0goQJTfGkqcJDVO3LMnTJAh5KTShdsVf4MILL9QRNG5QzBP/B0OxYv87RFT8RLcceuihOsMwoctEQ5BluGzZsgUteBQa2VQ2JRnzYT5rmggJ59zUrl1b/545c6Z2BCcnkpNWIF3Uq1cvY/vORRCMiJBLNkrOjXy414UCEJrIXUG0CPb7cuXKqTvuuEPb+VMF5RSY4oGQ5JaYjNBUK6TIpyAo2qujjjpK5RLpelFkywvJyx/IrfacW0ZwtzaZJqgjeL5rms466ywtHBE6Tqbm4447Tn8UvfHGG/r3nXfembG+rVixQr8DV69eXeRc4/MkCEJ+kpDQRFFKQjNRk2PiIosqDnLdu3ePccDzYw9Gy1O3bl0VFmSFpcRBqVKldDFNTHJu29+4caP+3yuL7/bt2/XkJ7w1nqOvaZOM5slNQPCTrTpXHc6d6ss59dupGK/TNvzuL4zzkupzm01CX9jMnz9ftW7dWv/99ttvq+bNm+twb8qpXHrppRkTmvjYIodUiRIlVJUqVYqYh/NdaMrney4VyPkqcKFp27Zt+oVB8Uy+BOHee+/VEQsmU66BF5u1ho8TYRa4bNOmjX6h4aOE3xP+TSQ64+Vbvnz5mLZ8HV599dXqiCOO0C9jNxC67H5SbqQ6u3YYZToSMS9l20MfL29TItvzU44lLMLafr5rmnbu3KlKliyp/8bkftJJJ+m/mzRpop/vTIFWHYHtlltu0VnkCwUiyLh316xZo6pVq5b1H1vZAO8UzhfnivMnFKDQRJ6KrVu3xuSqwCmTm8K84AxocNJZi8lqvsOJFCEK/wO+Uu2p8fFtQphCxe4FL0bMkFZNkzUhmpvmw64Rsi9z+x32wG0XeLJNAEoWp3px8drbE136WS/s/ExhCXv57NOEKW7IkCGqa9eu6tNPP41GR2EaQ8OTKchGTnLNQhKYzHse/7Jly5bpfEiCP4xfXiaDF4QMCk2ff/65ztJrvQHIV0L6efJkWMmEec5KxYoVVaNGjXT/rFxxxRVq7NixWjNmnEzdQBC0C4NW/JQ3iVf6xKut4I8gBXVzwSTph3zXNBHZ2rNnT+0GgDuASRz5/vvvR812mYAPMDKUU+Kl0MB/lVxGaAEFf6BhEoGpgIUmMqbai2UOGjRIJwZDQLGSbvOcnc2bN6tffvlFO5KawbJfv346XcEXX3yhC2zmIlZBLVENRy5pIpz8txLRyHnlaXLapl/hKl5tuni+ZNb9B/W9ymdNU4cOHXTmb7S7lSpVis7HOZxaYJkCkz3ZyQksoS6e3ewyYMAAlc8gAIgQIBQqCQlNvJCpsYT5iy8uMvVOnz69SNswzXMIQFaNEblS5syZo7dvNFXXX3+96tatmzbJocK/66679MNtktFhkiNkmQyv+DgRgQMVKlTQeZuC4jWQew24Xk7Mbts2/9sFpniDut+kj/Z9Wpdlg1bGTTvnlgU83naCCkamrdu8oMJOGOc03zVNwPNrFZggaLbnVAhNH3/8cTS/m2iKBaFwCCQ0/fbbb2r9+vXatIVqmoKa+A7xxWU3zYXNN998o44++ujob+NnhNoe52/A1o6AtG7dOu2o2K5dO13egL/BlGHgC9Zet+j8888P3KdEzWxOy+KZ8FJJvpirksVJ4Eq1SS8ZjVC+a5pM9m98Enn32DXcs2fPzkif+vfvr4YOHZrQO0MQhAISmoxmB8dMpnSCoBNvIKB+kBdhDyROJpYg60LQgdjJkTnREhzW/Wf74JuoqczN6d4pjUGqCXs/+a5pIiKX8ikIJ2iHL7jgAm1u//rrr7XWOFPg40jUrSAIhcfuQU1z2PAFlbRaPhGzl5cWJNHt+Fk304KT9Tjj/W39bV/udL6c2thNo07m0WTMoH636ee8eE25Dr6Szz//vHrmmWd0TiRqvRFFRx4kk2MtE5DElz4JglB4FAsqNBV61W4/BPVnSpREHMGTWS9TBO2vW3uv7aQqT5Ob/5p1P4neE/muacIkZwrz4nNIhC4Q2HHYYYepZ599NiP9opwLqVdwUyAtgt0RnPqWgiDkJ4GEJqqNC//DbbAL6s/kFyetRDyBwsmhPGh/ssnfyckB3O243Jzpnbbnth2/Gr1MCKH57tNEOSR8KAnsINgD/0TSDhAEksnjI5WJV/1LQRDyl5wp2JuNJKspSHSQdIuyc2pv/52IP08mBygn05tTG/A6Di8fMLtvmh17pKJT1GQQbVhY90q+a5qOOeYYnZOJIBP8mSjVhGM4QSGZFFoIHBEEoTARoSkJzGCZqCN2vEE+nokvEWEmHzQQVoIIgE6Ck5ufk31b8bSHbtckqK9ToV9PK/gzGeEPx2+ygJPahHIqlHLKNJTHWLhwof6b9AMmSlcQhPxFhKYkSNbUlqptC+7n1I+QkcncVEH2m6+apvbt26uOHTvqFCP4LhkoXcKUabZs2aKT5JKrzpxn8kmde+652kE8k4k3BUFILYVVPClFuJmPgkSpuUV0+V0/XwUvP6ZII+T4PV5r+2S0PJnGCE1uU65Cpn5MYAhP+A8de+yx6oEHHtA+Tf/880+mu6dzxE2aNEl98MEHasOGDXoiJQLzrrvuukx3TxCEFCJCUwi4mXGCpCSIZ67JxUE9bMI4B17O8Ll2jvM15QDJanH2/vXXX7XmplatWtpURyQd2cEpzE09ukzxzjvvqJdeekn3Y88999RTly5d1AsvvKB9rgRByF/EPJcg5KtKd/0lMiLbC2UGdUD2m9TSSqlSpeIWVXaLVnPbR7yEk9aEqiQzTEU6ACdMX/bff3/18MMP62vMPDQ3Zt/8bcLMrZoPq7Bi1WSZ+4RltN999931ZNZnKlasmG67bds2dfnllxe0ec5aLuXCCy/UEyBEkYkbQeqTTz5RN9xwQ0b69ffff6u99tqryPzq1avrZYIg5C8iNCVInTp1iuRnSTUULt26dWuMo7IRNtyECrfIL2s7+wBrb4uJxKmqvFluBAAvwcYr9YFX9NrAgQPV8uXLVSaEYvIBGaHJKhxxvsgKDbt27YqZbz8n/G+9TxB6mWeOl/VZD6GJ+eQi8is05XvKAVi6dKkurm2m1atXaz8nTHeZom3btrquJT5NfFAAz+U999yjlwmCkL+I0JRj2DNbuwkgXoNpPC2Q27xEE3a6pT6wL3Nql0mcTF3mb6vmyTrf3tYqkFp/WwWnRE1q+appQhgxQtLatWu1WQ4hqU+fPurQQw9N+8eKnaeeekp17txZ1a5dW+eNgu+++04LUBTyFQQhfxGhKYsIKnzEm+ekiUq0X0HbBzH/JbKPXMJJoApru/moaaLWHOZgioL37t0740KSnebNm6uff/5ZvfHGG+rHH3/U8ygUfvbZZ+vM5YIg5C8iNGURqfDXScfgaXWkDproM9dKuhj8ZGZ3is7zivQLev3zVdNEzTm0TJi7brnlFtWuXTtdsBttU6tWrbLCYZ+0Ami+BEEoLCR6LkGcwtaDrJvMPp22Ze+PUzs/+3Va32uQd+tPIvvNVvwkqAxyTrzWCXIe8jV67tJLL1UjRoxQK1euVNOmTdORadR769q1q46e4//HH3887f2aNWuWzh2Fb6EdCgizDDOdIAj5iwhNOZwY0+5T4xal5pW6IBPCil8hJNOClJfwGSQnlNe6yRxjvuZpstK0aVN12WWXqbfeeksXDL/iiivU1KlT1U033ZT2vvTv31+XdiHFgJ0KFSqo4447LqOpEARBSD1inssQiWoCEnUEt/oYuTmCezmIBzW7xTuGeBF2Tr+zxRHcSUB181tyWzdZJ3Drum7Lch0i5T7//POoU/hPP/2k/ZuInkOrk26++uor7WflRrdu3dSLL76Y1j4JgpBeRGgKiaC+OWH78sTzqfHT3kswsmtHnPYV9Hhy1Z8pHolqoYJij86zL8tVSLmAkERdN1IxkO7i1FNP1YISkXQmzD/dkPqifPnyrsvLlSunTYqC4MZln1ympq6cGjNvvwr7qXd7vCsnLUcQ81yIBPXdSWSgdHMudlsWb323/riZlZy278cfx81Hyrpdk9vIaZ1MYPpk0gOY307nwsy3L/ea7OsGPd5U+DSRF4ukkggmbdq00b5EXowcOVI1adJEtye31fjx42OWjx49WnXq1EkX2+X4SFYaD8xwPXr0UB999JH6888/1ZQpU9R9992nTWOZEpiAgrymQK8TRNJVrVo1rX0ScoPbJt+mWrzSoojABIs2LtLLen/YOyN9E4IhQlPI+Bkog7R3WzcZgcKvRspqQvLahptpKhEhINF1U0U8k6FXP93MeGE5aoft04TfEHXVSNw4e/ZsnYOIfESYyZyYPn26DrUnLYARdJjmz58fU9yW6LdHHnnEdz9mzJihHnzwQe0jlE3Fb00NPCe4niyjjSDYBab3F78f96TMXD1TBKccQISmLCLIQOqVFDIVxNtHsn3PZnOSm9DodhxB/Z0SFaTC1jQNGDBAh9FfcMEF2gF7yJAhWmihdIlbksfjjz9elzOh7AzaoIMPPlg9++yz0TZkVb/zzjvzQpi4/fbb1bx587QG7u2339aRckwIm8xDWLztttsy3U0hy/AjMFkFJyG7EaEpiwiiXQkrAiuR/QX5Hc+M52YqdJuXbtxMmHZzopuJ1O23mykvbE0T4fHWafv27a51DQmptwo39I/faH6cYL5dGEIz5dY+12nQoIGaMGGC1p716tVLC4hMaNuoOffpp5+q/fbbL+MmUQRmBNW9995bJ9vkGpGMU8iMD1NQer7XMyV9EcJBhKYESWawS1QQSMYHKlEhy68Q4PbbaRtu8+zzM008Hy+nv+1t3AQmt2MMcux+NE3USCQc3kwPPfSQ47YoV0J9PXshWn6vWrXKcR3mB2mfDxxyyCFao4T5klxSw4cP138zjxIv2WASffTRR9XTTz+tNYVE/JUtW1Zvk2LQQnqZvnJ64HXwcRKyF4meEwSfINC4mb28lgXZftgZwX///feYvEKm0HCug4BHMeVMceCBB+opWawmUUDQGTdunDaJOqU3sJpEAZMoGi5MoqzLPfjkk09qU2L37t2jtfwQZt977z2tIRPSx78qP/KlCTmoaeILmS85Qn6rV6+uv67skSx+2gRVhbuRjP9IOv133HINhbWteO3d9mnPNxVv/UzglmPJqe9eTt/29e3bdtpfWJomBCbr5CY0EfWFEPLHH3/EzOd3jRo1HNdhfpD2YUCuphtvvFEXy811UmESXbx4sdb0WdugYeRdl69m00zT9I7xap+bx0WnhreOU0vWbtHLdlPZoTUXClBomjRpkurbt6/68ssv9ZfVzp07dSgz/gV+2wRVhWcbiQgXfoSceAO522Dv5fTstG68PrjtI5N49d/rnHidJydBKpHjDDN6rkSJErqu22effRazfX63bdvWcR3mW9sDz51b+0TBX2jYsGHqyCOP1A7qkydP1s9xrpMKk6j5P6jZFF83u/+b4E2rez7WQtLfO2Of3Z3/KtXh8S9Uo1vHqTrl6gc+jY0rNJZTn4/mOUoZ8OKaO3euTup2xx13qKuuukqlCnK2WHn55Ze1NokvtaOOOspXm6Cq8ELBySfHvsztNzDgO5mn/JisnJZni09TmIRhvktEoAsCz/N5552n/XZIKImZhw8O87yce+65qlatWlG/KJ53iuhSXoR6cPj4fPPNN+r555+PbnP9+vXqt99+UytWrNC/jeYXbVQ8jRQfP2TYxvG5bt266ocfftAZwhGehHDhmlIgWfAvMK3busuzzY5/lVo452RVer9gdRJH9RgllyHfNE1Ea/Ts2VNn7kVouuSSS9Q111yjlixZEtOOXCsIVF4TL9REoEAmVK5c2VebRFThfr/AvLQFfrUsbr8T1ULkswAipCZP0xlnnKEL4RJ5hb8OiSj5EDFaC55Va8ZrsnO/+eabWkhCaztq1CjtN9O8efNom/fff18ddNBBWqgCfGr4zQeLGwhhzZo101nAKdCLZolQf+5lkmTmC6kwiZr/g5pNb7nlFv2+NBO+cII78QQmw66dVVVJVc33qWxdvbWc9nzTNBGBgZCEQ+JZZ52l59177706WoOXG/5C1mrlp59+uuf2atasGbjTDAhXX321OuKII2Je0F5t+NJ1U4WTyTeZLzAvwcSP0OKlzUlW6EmHeSuZPqZCA5PLBDkXYWuagIK4TE5Q2sTOaaedpic3zj//fD0FgWK8TLxXMuns7WROQ/NWr1696Lzvv/9eC5rMx4fSvBODmkRZ12oSdbsGxiTKu83JJFq/fn0tHNHGOKrzoUcUHYWP3cDXLV+CBNLhwxSEdT/3VjWbDlZ/7fzvR7wbLau0VC+d8FKSvROyTmiaOHGi2rp1q/4qNfBiY/CzP3RoeLw0QYmC3xIhtpgIk2njB77ArP4TvIAI484EXmHqXlFdie7Huk0/IfJBs4rb2+Wqqc7ed5NSwMwLcu6CphzIx9pzRIThw/Taa6/p8HoSZLp9HKWTfv366Y88NGGALySmQuaRwwnhkA8z+pspkyj3DwLV/fffrxo2bKiFKFwn6KMRzITksPswxSOyq6I6t95Tapl6T33464dq+7+xudLKFy+vRncfrWqUTV0AhZBBoQmfAr5grF+AixYtUps2bdJqd7t5jsmLBQsWaH8Fv/AFNnbsWK3VcougcWqTiCo8lV9g6dCw+B2s4wlHTpow48cUZj/99CWduCXcdPvfKR+WW+6meHmeMqVpygb4UGEisAN/QyK/SBrJMVGLLlPgY4WfpIFQfj4KMWNSWBiNE9G5QYQmPj7XrFmjTaI4avNutZtErUlUjUmUlAK33nqrFozsJlGiCxG8Lr74YrVhwwZdxoZtZrJuX6Gzx7+V1H1H3qf6tOijZq6aqbbs3KLKFi+r2tRoo+rsmZmPcCFNQhMJ1fAPsjJo0CCtZm7UqFHM/DDNc7ww+dJ79913tZmAL6ggbRJRhacSr8Eu3uAZT4PhtS37uk7+VNZ14oXSG9wEHj/bt66fTYM99wcDlld0HG2sx+rmFG+Wmfbmf6e0BGHmacpl0KgwkYMIQQEBit9oZPB3SncEHUKN1f0ArfvJJ5+sBSY46aSTXJOHptMkyn2FaZNJyA7KlvzvPVJ3z7p6EgpMaOLFzlcWX4BEtgwePFhnqrUTpnkOcxsvzjFjxug8TCZ8lhwklArw0yaeKjzTWAfPoGaaVPTDPs+rT0Gc4L2EOb/bSBd++uXmyO+WysF6/MmkHchXTZMdnmX8KJlwCH/ppZfUww8/nHahiTxXaG6MTxM53sjMbeCaupWpEfKHMsV3C2yiO7xB/gQwFDqBoudQFRNCjK/BY489pg444AAtoKD6tZvmwgbBjKiODh066JpKZiL3kt828aKDMk2iZpogJTkSNXnFc1Z321e8PriZqeKVG0kH1vpw9v4y3ywzf9v7bm1jn+/VJlPRc7kAtdb42Fm+fHna933YYYfpgBfOLZGCuCQcc8wxMYk3M+XvKKSPBfd1CdT+tFa1Vb0qZVPWHyGLNU0IGWiOcEA0IcTpIpHQ/aCq8CC4DXBupjM/vjpu2hw/uZK8+hmvb9a2TmY3u8+Rm09TPN8n699O86zzsyGqzqlfbv3165DvJRgHFZryVdMUdpRamA7qHTt2VK+//rratWuX9ikiJYIBp2zMh0L+U6V0MV9pB/D8vea4WLcVoYCEJkxzfOkJ7mTaeTkZ/Ay0uXx8+Ua++jSlIkotDNCsk2Bz2rRpOngE9wQr5KAiY7mQ/8y6q3PcBJcldlfqixuPUTUr/td9RChQoYkXh5BY0kk3J2GnNvZ5br+9tuflZ+RH+PHyzfHjCG4/R3anb+t8r/bZ4Aju5rDt5Ahu/rZinMDjOYIHEXbyVdOUiii1sCAK1xTCtZNu7buQOB8/94yaP/HjmHllKldWlw1+NZDgZPI2WX2cSuyh1KfXdhCTXJ4SSGgitFVQSQlKXg7Vbm28hK0gg2OyEXtBHMG9fttNdl7tMz34uwlybsJdvLZhOoLnq6YpVVFqYcB5RaAbPXq0roDAdSRKl2g+hDjRxGY3U958Rc0cM9Jx2d/r16v+Z5yoKtWqqy4cMChlPk5CbpMzBXtzlWT8keKtE9R52I9jdpiO4F7t7X/bt5FNjuBWJ2+7w7f9bzencHt7+z4SOVa7wJWMAJZNmCg1A1FqVlNYpqLUOKcIbBdddJF2RMdVgXIvS5cu1SZDSksJuSkwWflz+W9q6LWXp6VPQgEV7BWKkshAlYx5xU0j5bTMax9BTXZO5sNEBvwgZshMkY19yndNk4lSe+GFF7RGJ1ui1NAwkTCX3G5HH310zDK0YTioY0oki7eQffgRmKyCkyA4IZqmkPA7mCbrs+M0iLv527i19dNvez8ZhL36Hu9Y3MxWZnLafqY1Jva+uWlzrG2MION0bE4+YtZ17OegUFMOEKVGoV9yq5EmhAzX2RClNnz4cB0xZxeYAKHu5ptvVm+88Uba+yX482EKyuDLRPgViiKappCw++jEa2f+DioUuDlc+zWx2dc1fXZLF2D+9gqV93Msbttz0pbZ+5cpnExmbiY2+7E4me7cTHVWM2AiGrt8I1uj1ObOnaseffRR1+UnnHCC1pAJ2ceP04pmVo8HPk6CYEeEppAJOugFae/m5+O1DSdByc0fyY+jt5PGyGzDTeCJ58BudXS3C1E4/xoH4HRCH8jFY9UGWfvNMiD03WDX8JhjsR4P61n9nvjNNozgZLbrB2vkntOyXCYbo9RI7OuVCJdlmayNJ7jzr+U5FYRkEKEpQX7//XddADgRbVE8LYzb9ooXL673aYol+91vPK0I23PalhnYccrFJOJFUI2R3Y/KTdt1991364LOfjRkXoKaWWYEDXv0mjW1AH/jfNypUydXDaK9D17Xwlpw1Sro8L8Rbsw8qxAWDy/zZa5roLIxSo1r4yXA8xwFEXqF9LH7Hnuof+XaCCEgQlOCbN26NSo0JTtI+RWaWGbdp3W/XoKJFatwYN2u03pm/s6dOz3LVjj1wc/2rcudtsHf+++/v2rZsqWjSdKpvdc+gghNCxYsUDNmzFDZTL5qmkyU2vjx4/W1J0qNeZjsiFJDkMpE+hP6wP5LlizpuFzqzmUvTY7oUCQvUzzKVpZ6cUJRRGhKEKvGJxGtT7yINSdNjFdouts8N+HB7X+nv405yUuY8zMvaHuzb6smzM006aXpsh+nH6HJqh3KVvJV05StUWoU+46HRM5lJ50v6RdYaLp08Csp64+Qu4jQlCDxIrucNEdOQozdV8jL7OMWgeWkyXHTWPkZTO39DXKsQXA7R/Y2Tn5Cpr2bMOd0nt3Ov9uU7eSrpslvlFq6BZRhw4aldX9CuLTufprvtAMkuBQEJ0RoSvFXvt/B1886Zr59QPQSGqzlOuzaFa++WAUMv0KTkwAXbx23/hvihc97mfrc+mM/F1ZfolwSmvJV0yRRakIqOPKs/2oK4wlOQTOCC4WFCE0J4mYmM8v8aFHc1nX62+BkhnLzcYrX1mt9LydrP30PIkD58VFKhCACXCYci5MlXzVN2RqlduGFF/pqN3To0JT3RUhccGJyqj2HD5OY5IR4iNCUQoIIAH6dwa3YTXV+l1nne+3LrzCRTASh33Pk5Vhu7avbcbuZPY22xmqKzBUBKl81TdkapYavVb169dRBBx2U0+dX+K+PE5MgBEWEpgRx8i9yauP0tx0n3yUv3yK/A7ubMBXvhe/kXxVEiPPav9c6bv110pp4+TS5bcdJMLILT7lknstXTVO2Rqlddtll2t9q8eLF6oILLlD/+c9/VOXKlTPSF0EQMoMITSGY54JoWpxSFNhNfV7mKhNR5rZd+zwnjYs9P5CbM7VTcdp04jd6zmlZPE2TFSfncomeyxzZGqU2cOBANWDAAJ3yABPcLbfcohNt9u7dOyanlyAI+YsITQmSioHVa9C3pjmwC2t+zVr2fZjf8fyz2KdXyoFUwT4x05DU049fmJMw5Fdosm8nF4SmfNU0ZXOUGtqvM888U09Lly7VJrvLL79cmwu///57Va5cuUx3URCEFCJCU4KkwoRj13i4RZI5ZZh2csJ2Mn05Jbd06geYbThFsMUT8IJo2uzLrZF/TPi4+BGAkkluaf3NOcoVoSMXzIj5ismbxjUIksldEITcJfs/p7MUJx8Ye3V7t3l+5jstd+qDU1+clnmtYz8mp7Zex+02P2g7+76sgpP9HHmda7dlbufcvtz8n+2YPrtNQvjgT4Vf03HHHacaNWqk5s2bp5599ln122+/iZZJEAoA0TSFiJtfkd+2QZYnSiKRbn5zHfnFrX08U6Nf3Hy5vPZt117lgn+Kl8YwF4S+XAMzHDUY69Spo9MPIDxRWFgQhMJBhKYsMs/5SQFg/9vNRyfIoG/fn91/KJ6my2kdv/13SwXgtq5d+PGbqsHPtYqn2cs2vLRJomkKnyFDhuji0fvuu6+aNGmSnpzAUVwQhPwkr8xz++yzT4xjs5n69u0bbXP33XcXWd6kSZPA+/JjjvI7Bd2eGRD9mOzs/bWbnuIJB34EiGQEDC/THNjNavFMbFZTm9PktNy+3Vwxz/m5r4TwIGKP0i4VK1ZUFSpUcJ0EQchf8krT9PXXX8c4ZM6fP1/7Hpx22mkx7Zo1a6YmTJgQ/e2VSM8NMzAFNXe5mYncBBmnfEJeSRydtC727RnBwAiNXn2xHqsfkh2s3fbtpk2yCpCJJLe0z2PKRKRgIoimKb0QKScIQmGTUk3T1KlTVevWrVWpUqW07f+pp55K5e5UtWrVVI0aNaLT2LFjVYMGDVT79u1j2iEkWdsl45cQdHANqgXw0sQkMrA7aZ+yHSeBMp6Gys82rNsJon3LFkTTJAiCkCdC0/jx41XPnj218yQFOC+55BJ1zTXXqCVLlsS0e/DBB3XUiddEZEpQduzYoV5//XXtsGn3s/n5559VzZo1tW/C2Wef7bl9omX++uuvmAmczICJTvbtmVBm+2Sfb/1ttmFv47Vtk2vKq32mnaJNckvzv/nbbTJt3CavNmYbCNW5kqdJoucEQRBy3Dy3bds2LSShWTrrrLP0vHvvvVc9/fTTavLkydr3yHDppZeq008/3XN7CDhBee+999SGDRt0OQYrbdq00Wr2xo0bq5UrV6p77rlHHXnkkdqUV758+SLbeeihh3QbO6kWJoJu2ytKz0lzEiR6L1OCk99Eln5MpH77nwtRc340hbmiLRMEQVCFLjRNnDhRbd26VZ1xxhnReSaTtb2eFLWbUlG/6aWXXlInnHBCEYGLeYYDDjhAC1EU4Xz77bd1OQQ7lEq49tpro7/RNBFynEm8fKm8/Jjs863bcttHvP2lEr9RgX7MlkGc2XNF4BCfJkEQhDwQmj7//HN14IEHakHJsGjRIrVp0yZdIdxunmPyYsGCBTrU1y+UN8DR20/oL5EwJKmjf04g5DkVDrWWUUl2kPUbNm81Q5n9GkHCKxTfPt/eZ7spyt7WWoPOCXt7v/hZj3sIc5mbEGh12rZux8sR3O231RE8F8xzomkSso3Ff29X763+U32/eav+3axcadWjeiVVv4xz8WVByDVSIjR9++232qfIyqBBg1SrVq20gGIlFeY5aldVr15dF9OMx+bNm9Uvv/yizjnnHJUoYSVktPoUuZnUrD5IXkKTfTvWbdvbWQUEuzbHuk83YS7R4/djIjT+Rn634Vcj5hXFaI452xFNk5AtLN+2Q50+Z5H6ZWvse3/cmo3q2aV/qGOq7Knu3q+WqlWqRMb6KAhZLTQx+Lz66qva/DVy5Eg1ePBgNX369CJtwzbPMZAgNFEp3SmVwPXXX6+6deumTXIrVqxQd911lx6UKcCZafyawZIR0hI1s2XCZJWKfTqZ+NzMfrlArpgShfzlhh+WqtdW/em4jLtzy78RLTwt3bZDDWteXwQnIacJXWgiEm39+vU63P/mm29WP/30k/Yd+uijj4qY5lIBZjn6QNScE8uWLdMC0rp163SKgnbt2qkvv/xS/x0Eq5bH/PZDPBOSVxi9U5i9nzxNTsu92iVqbksFTnmanNqYZfHyNLkV7LUWMs6lPE3xTKaCkErtUs/ZP6vftu+M25Zsat9v2qoeW7xKPbm/f1cLQch7oWnOnDlac4RpzI95LGw6derkOWBQOyoMEhEonExJTkKYV0SU1SRjFyacfKO8hAfz201IMG0zWcF9165daufOnXGP08nMaIe2bkKTfZu5ULXe6z4XoUlIJbM2blEnz/5ZbQ+wDk/UpPWbtN+T+DgJuUqxVJjmWrRooQqBIGHvQUP8nRyW7aH/TtuM51RuX+7m6+SUcsDuL2UIQ8vmdkzGKdsu7DidBz8pCtxyT1mP0fhwZTuiaRIy6b8URGAyrNu5U03bsFmEJiFnSYnQhDku33EbeBPdll3QcRvU3XImuQlTdodmp4g4L0HMvl83wcnvccbzIXJyQrcKMV6O3059dNq+k6bJ3iYXHMFF0yRkgjt+Xq79lBJhR0SpzbuyX4srCGkTmkgqWQi4CS/xwtq9tmdfJ1XajqDO4H61W377m+gxxhO0rP/HSzkQVp8yiWiahHSDae3z9f+tipAo5Yq5R8MKQraTVwV7swF7biS/A7CfPE1O2/SKBnMLp48nMHnldPJqnwjxzpHdEdxKPP8vP/Pt2/bKfZRtiKZJSDdTN2xS2xLUMhnaVSoXWn8EId2I0JSEg7JXpJsbToKIXz8ke0004xjupvXyip6zOpS7Jbc083GK9nKMdtKk+Yl2czt+63ycwK2O4Mkmt/QbPcf1zXZE0ySkmy27/vfeSIT9y5ZU+5SWRJdC7pL9jhtZit3XJ52TVx+cltn77fcYnNbxOhfx5rnt16u9va3xb7L6OjkVHvYqeuxWtNiacT0XTHSpKNg7cOBAXRuyVKlSOsfazJkzPduTg61Jkya6PQEgFOq2ghB65513qr333luVLl1aHXvssbpgtpCblC2W3JDx+gENQuuLIGQCEZoSxDrokhzTOuDGm6zrmZp8boO324Du1jZeGzfhw09/vZa7CSvxhBv7OvZtm3OElo3J/G3m2yfrMrOOfZ6f7eWKI7jXFJS33npL11kk4evs2bNVy5YtVefOndXq1asd25Oslpxn1GwkAKRHjx56ovi14dFHH9WFuocMGaK++uorVbZsWb1NinoLuUe7iuVVhQQFp351qkliSyHnyf6RIcsJMkDZTW+JDG52gcdJW+NX0+ImxHhpnRLFaxv2c+J0vG6TVWA1fzsJsVZhyK+gWGiapgEDBqg+ffqoCy64QDVt2lQLOmXKlFFDhw51bP/UU0+p448/Xt1www1q//33V/fdd586+OCD1bPPPhu9lk8++aS6/fbbVffu3XVULVUCyMRfKAEj+Qb5lTpVrRB4vdvr11C37VcrJX0ShHQiPk0BMYO6m49PKqOvjG+R8WWJlynb2l8D69rX80oKyXKzT7Pcvi83fyO3fsVLCWDt25YtW3ShZ6/t2df3wp7vyskRnPORaU2IH2Haj9D9119/+SpATa3IWbNmqVtuuSU6j/OAOW3GjBmO22Y+mikraJGMQLR48WK1atUqvQ1DhQoVtNmPdXv16hX3GIXMYO4r+/0Dl1cpq6Yv+0P9tj22zpwTB5UtpUa2auS6LUHIFsz9Ge+dKkJTQMwAvmDBgkSvjRCADh06FOx9hoDhRIkSJVSNGjW0QOJFuXLlVJ06dWLmYXq7++67i7Rdu3atFo732muvmPn8/vHHHx23z/6d2pt+mf+92gjZCWWmwH7/BOUTBOWQ+iQImX73gghNAalZs6b6/fffVfny5RPSKCHN8iJiG3vuuadKJ5nad6HtN5l985XDQ8t95gZO12hx0A7F25b9HnXSMgmCHVNEnTqeXgOIIKSTVL7T/bx7QYSmgGCyqF27tkoWLni6B/JM77vQ9pvovv0MUghOTGFRtWpV7fP1xx9/xMznN1otJ5jv1d78zzyi56xtDjzwwND6LoSPMdlzL2bq2RGEdL/T/bx7xRFcEARt8mvVqpX67LPPomcDPzZ+t23b1vEMMd/aHj799NNo+/r162vBydqGL0Wi6Ny2KQiCkM2IpkkQBA1O3eedd5465JBDVOvWrXXkG474RNPBueeeq2rVqqUeeugh/fuqq65S7du3V/3791ddu3ZVI0aMUN988416/vnn9XJMg1dffbW6//77VcOGDbUQdccdd2j1N6kJBEEQcg0RmtIMPiU442bCtyRT+y60/WZ634lyxhlnqDVr1uhklDhqY0L76KOPoo7c+LdYIy0PP/xw9eabb+qUArfeeqsWjIica968ebTNjTfeqAWviy++WG3YsEG1a9dObzNM06IQPrl4/wr5T8ksuC93i+RKoS1BEARBEIQMIj5NgiAIgiAIPhChSRAEQRAEwQciNAmCIAiCIPhAhCZBEARBEAQfiNAkCIJQYKxfv16dffbZOkFgxYoVVe/evdXmzZs916EeY9++fVWVKlV0iZ5TTjmlSHLTK6+8Uuf7IrpJEpgKfhg4cKDaZ599dEQtdSlnzpzp2X7kyJGqSZMmun2LFi3U+PHjY5aPHj1aderUSd+npD2ZM2eOChMRmgTBJ6ZosSDkOghM33//vU5GOnbsWDV58mSdFsKLa665Rn3wwQd60Jo0aZJasWKFOvnkk4u0u/DCC3X6CkGIx1tvvaXzw5FGYPbs2aply5a66Pfq1asd20+fPl2deeaZWsj/9ttvdb43pvnz50fbkOKE1CaPPPKISgmkHBCEXOPff/9N277mzp0bWb9+fdr2JwipZMGCBaSZiXz99dfReR9++GFkt912iyxfvtxxnQ0bNkSKFy8eGTlyZHTeDz/8oLczY8aMIu3vuuuuSMuWLVN0BEK+0Lp160jfvn2jv//5559IzZo1Iw899JBj+9NPPz3StWvXmHlt2rSJXHLJJUXaLl68WN+f3377bah9Fk1TEiDpku34oosuUu+//75atGhRXu+XhIfsK2x1ZzyWLl2qZs2apaZNm6a2bt2q56F2TUeKMb6o+fqxlwsRhFxlxowZ2iRH5nfDscceqxOXUuLGCZ6/nTt36nYGTCR169bV2xOEoFBwnPvKek9xD/Lb7Z5ivrU9oJlK5z0oQlOCoCY86qij9MX68ccf1SWXXKLt+aiv83W/J5xwgurWrZs6+OCDVb9+/dT27dtVqpkyZYquU3b++eerI488UnXv3l0NHjw4Kjil0mSGwMQDioB66qmnpmw/gpBO+PipXr16zLxixYqpypUr62Vu61CfEGHLCtni3dYRBC/Wrl2r/vnnn2jFAT/3FPODtE8FIjQlwN9//60eeOABdfnll6tx48apqVOnqpdfflm/UG677TbtiJZP+/36669Vly5dVK9evdRrr72m9z1o0CD9dypZtmyZOuuss/TxUnoDDVe1atXUiy++qG6++ebol0kqNE7UUDv66KN1/TVKhGAnnzhxoho6dKj67rvvtCOtIGQTPBN8SHhNfGgJgpA4UnsuQZYsWaI1H1YVYdWqVdVTTz2lHnvsMf0lhzNaru933bp1WtOCRuumm27SAgov3z59+mhBBhMh2h5rTbIwzXKlS5fWjqUUeaVYLMf4wgsvqFGjRqmyZcvqArD0J2zhdMiQIXrf5lyfeOKJ2jmRaCHUyhSvveyyy1SzZs1C3bcgJMp1112nNbJe7LvvvqpGjRpFHG137dqlPwRY5gTzue+pH2jVNvE8uK0jCF4wbu2xxx5FIjC97inmB2mfCkTTlADFixfXRUkpYMoAayDUlggUboQxY8boeWFpQdhOJvaLD9Fff/2li7OCEVDq1Kmjfv/9d5VKCGvetGlTNDKCY0J4QlhB80WoKdq2sClTpoy64IILdAQQU7169bTANnz4cK1SRkCdMGGCGjFiRLRfgpBp0MLiZ+Q1YWLD3I3wgz+JAS0qHz+EfDvBO4b3j9W3b+HChfpdxPYEISjci9xX1nuKe5DfbvcU8+3+pUSApvUeDNWtvIB46qmnIuXLl4+MGTOmyLL+/ftHKlWqlJKIqyeffDIt+yVibO3atfrvRYsWRefv3LlT///cc89FOnbsGLPOihUrImFCJM9BBx0U6dOnT+Svv/6KWbZs2bLIPvvsE7nnnntCjSoaPXp09PfMmTMjvXr1ipx00kmRJUuWFIkOqlq1amTjxo2h7V8Q0sXxxx+vn62vvvoqMnXq1EjDhg0jZ555Zszz1bhxY73ccOmll0bq1q0bmThxYuSbb76JtG3bVk9Wfv75Zx2tRDRTo0aN9N9M27dvl4srFGHEiBGRkiVLRl5++WX9/r344osjFStWjKxatUovP+eccyI333xztP20adMixYoVizz++OM6epP3MFGd8+bNi7ZZt26dvufGjRuno+fYB79XrlwZCQMRmnywdevWmJBIwwUXXKCFlI8//jgqTMCECRN0uO2aNWuSujh//PFH5Ndff43Mnz8/Zv7555+f0v1+8cUXOvz47bffjpnPsZtQ/+effz5y6KGHRpcRNnrFFVdEduzYkfB+eWgQxm688cbIl19+qeeNHz9e9wXhaNu2bTHtOf+nnnpqJAymTJkSKVOmTKRnz54x8+fMmaPPh7nuu3bt0v8PHjw4csghhyR1vIKQKRhYEJLKlSsX2XPPPfWztGnTpiLh2p9//nnMe/Dyyy/X7x7zrNgHovbt2+v17BPbEwQnnnnmGS2MlyhRQqcgMO9+cz+dd955Me0ZlxDIad+sWTMtHFkZNmyY4z2IgBUGIjTFgS+t3r17xwguVkHlrLPOipQtWzYycOBA3Wbz5s2Riy66SA+o/J0oSNQHH3xwpEmTJpHdd989cv3110f+/vtvvYwBPFX7RUBAkn/ggQc82w0ZMiTSvHlz/Xe/fv3018JPP/2UlNBSq1Yt/ZDsv//++oF45513ogIK5+Cmm26K0fh06dIlcuWVV0aShWNmfyeccIIeRCZPnhw3J9SFF16oc4bIF7QgCELhIEKTB999912kWrVqWtOBicYqFFg1Tgg0TZs2jVSoUCHSqlUrPfj/8ssvCSdhxCzEtkjwReI4hAf68Prrr8e0C3u/JLtjP88++6z+jfD12WefRV588UWtceHr1PDCCy9oIeOGG27Q5kJjwrMKlH5BtUpCs4cffjiq1bv66qsjDRo0iGzZskX/Hj58uN4PJkGSm6FhqlKlShGzWaJC4iOPPKK1RkceeaT+muY4jFbJCl/MaNVq1KgRWbp0adoTbQqCIAiZQ4QmFxAYrrrqKq1RQOvDAN25c+cYwck6qOIDhJpw7NixkdWrVxdZ7hf8iBAK0KpYB2TU5/jXgNUkFNZ+EU7QqKF2R1iEDh06aKGMY0dgQVhgfzB06FAtYNWpU0f7MSS6X0xumOMwOdIHc7z4Wey7774xghpCHYLVGWecoQVGIzAlsl9AIOUYHn300ei822+/Xfsqmf1aBaLp06dHjjrqKK0SNsJpovsWBEEQcg9JOeACIfSE7hORRuQYeXvw9CeR5NNPP60aNmyol5Gci/8pHMhkjQJgflAoikm0mgl1t0arEeECbNeE+Ye1XyLGTPoAIsaIzqMPRIodcMABatiwYbqWT6VKlfT+GjdurJOKkXyS6DJzHoJCYc/99ttPFw6lDwYKOG7cuFEnLStfvrxOvkcGY5PF2Bx/ovsFQqgpFkk0ntne9ddfr95++2314IMPqscffzwmnQERGuTDohApqR2S2bcgCIKQe+yG5JTpTmQrCA7WgfzXX39VrVu31gP3M888owUn8puQ/DHMkEcS0BEeDGwfgYHBncKaH374YbQdAkUY+SmseZY4lgEDBuhjRzhEIDLcfffduh8//fSTFp7ICI7QY/oYJitXrtRC6hdffKEaNWqk5yE0Nm3aNJRjNvmm7FAqAsGYBJaffPKJTnvw/xrZmFxUqcpNJQiCIGQv8tb3wCowoVUgMRy1mdA6MbDOnTtXJ15E+0DG6LAwApNda0RuFQP7f/TRR/UgnyhotYDB35QjOfTQQ3VmYSpPo90yxw4IK2iASCpp8mxAUIGJxHqLFy/WVdatGPmd/+lPqVKltJYJSK5JZWvmhYGTwGRyYV111VW64vbrr78ebWsXkERgEgRBKDxEaPIJwgsalQYNGqiZM2fqormY7SiYS4ZqI0iEenF23z06uCNEmFpvCEzPPfecLi/CIJ8IHMMVV1wRFVysghMFaqlvZwQDI7ihhUJwNCSSidvUsCM5JWa/G264ISr4me2Zkg8IdUwc78iRI9XHH3+ssxGnSjlqatlhejz77LPVu+++GyOoCoIgCIWN+DQFOVnFikU1TocddpjWRpCRmkrfqfJvMWYghKPatWtrLRD15hYsWKAFuERMY2jIKAtCdus1a9Zo3x1MjaaOmxFarKVbaEMGbjRtaJjczFt+athxDB06dFArVqzQhXDxEUJIsUJfMI2hXWI9hFR8n1JhCrTvFzg/p5xyis567JYlWRAEQSgwMu2JnmuQaoAwe6Ku0hlBRdJH9lm7du2kotX8RAWmImLMT1SgSePAMlIYcLykUSDBZ6L7TRT6QFI1MiMLgiAIAoh5LgHQllBMFo1TuiKoMBlVqVJFa7bQuCS6XxMViCbFRAWiycEE9vPPP+s29ogxNEM4YSdzvF5RgcuWLYtpyzK0aBQKnjx5sqpfv37aI9Xow4svvqhrzhmfLkEQBKHAEdkxcdKl+TBaGZP4MZEEklZMwkgDGiS7xol9kKXbijWhZyJQK8hgjoFEmtTBsmLX7iR7vIIg5A5oeBmaqF9nh8SzLLOW1jDtmUhUW7169cixxx4beemll4q8s0jS261bN520mCoG9erV05n9KVnlBdp9csmh+aZ6AHUv0ZCTO074H1R0wJIRDxI2H3fccZHKlSvr60ZtuFyhoDVN+MckQ6Kaj6D7NVoZwvshWZ8ev1GBd955p9q8eXNoEWN+owL79+8fExWYzPEme40FQUg/aKBHjBihtm7dGqOtfvPNN7UPqZ3jjz9epynB/5K0LEcffbSOgkWjbt4B+G927NhRVa5cWQeV/PDDDzr/XM2aNT2jn02OPlKtEICDPylBIrzPrrvuuhSdgfxmy5Yt2uJB7r+cI1KgGO3Fhg0bdHZpa1HefNyvnz6hcdprr710TTtKs5jSKKnk6aef1tXWTQ07vuKMz1Y+nmtBELxBc9S9e3dd29JaOuqNN96IHHDAAXqZXdPEPDuUgGKIo+QTvPvuu1oTFURzjZYff07KVDlp2v/888/o31RLOProoyOlSpXSGpQ+ffrEFEE2/aSuJ9ow3rEUIqc/vJ8ohIwmi2oL9sLJlJFq27at1o7RH8o/WeE3BdR5f1LiCd9R63GiAeL9ij8u++E9by9gy7FQFYKKCFSA4FjQzBlo37Jly8irr76qNXQUeqY6w19//RU9vqCFms3xiaYpyzERWJs2bdJRcLNmzQot/0827jdoVCBfYmib8CtKlT+PSW/gFBVoouTy8VwLguAPtN1oggxDhw5VF1xwge/Td8wxx+j0KaNHj47mmeO9gJbIb9qSOXPm6LQsaJScNO2kQDGak86dO+ukv/iIkiJlwoQJOq2LFXxDiRrGV5MkwnfddZfWhrEe2v5LL71U56Sz+3mSmoU+EEWMn2m3bt3UunXr9LLly5drP1ty7JGUd/Dgweqll17SPqFWXnnlFZ0ah/2Q4+/ee+9Vn376aXT5aaedpnPooanjfXnwwQdrzdz69eujbX755Rf13nvv6UTLTJMmTVIPP/ywXkb1CPrWp08frfVjMrn+8opIgWGkb6TjJk2aRE4++eTosieeeKKIv0+u77dQogJz6VwLguCO0chQSxPNCjUmmdDgrFmzxremCdCE7L///tHft956q9Y2oQnCl5K6k6tWrXLty1tvvaU1IbNnz/a8ZM8//7zW4BCdbKAm6O677x7dPv1EQ2PVWDVu3FgXCTfw7kPTj2bJqomh5qb1/cb7kiLj5pjYjjXqeeDAgZFy5cpF94WmqV27djF9RjNlopnxX0VzRC1QKxRN5x1tNE1lypSJapaAMaNNmzaBfZoMomnKcqzaB8qhYJN+55139DJy8iB9Uz4kX/ZbSFGBuXauBUHwplq1aqpr165aA43Gib+rVq0a6LTZ88k98MADuvzUkCFDVLNmzfT/vCPmzZvnur4f8I9Cq2VNcnzEEUdojTq53gzs06qxon6ntXYo7z7eh2h8rFjLdJk6nOzT7Jvl1uNk3/ijWjVWJBO2svfee0f3g4aK9uyb/HhmonID2iXDPvvsE63SYN9GoVAwyS0ZjM1gSrJCHhTUtECCxUWLFqkZM2YEfiizdb9B4UEm4aTpczoEJl5I7du3V7///rs2nSWbuDJXzrUgCP5NdMbERd3LoCBQkLLECoIBpigmCnMfdNBBOnkv5is7pu4l9UBplyz2Cg4IOk7zjPtCmHjtB4EJAYhan24myHjbKBQKJnoOIQC7M1K9fTDlgaAcirU4bS7vt1CjAjN1jQVBSA1Exe3YsUNH0+IzFAT8h9AgoWF2g+oG+G66Rc9RrYAi4UT0OgkHJvJ3//3319oa63amTZumP0bRpifLl19+GfOexeeIfZp98zFo1YqxbzRC+Iv6Af8lNHC8g9H4W6cgH5klSpTI+7x2BSM0wVtvvaUfQuMYaAbTcePGpXQwTed+jbZm48aN2nnQFOVNNcnsN5Eadtl2jQVBCB8+hNAWESDi9TFHXU4GfZyiKW+FBql79+7ayfrcc8/VbXBc/s9//qP/J30AZjNTHoq2bu8mTIO0JzEvbX/99VcdKIOpz6xHGSi05eedd56aP3+++vzzz1W/fv3UOeeco01wyYKWjY9A3mV9+/ZVf/75p9bCATVI0dazP5aPGTNGO5hTdN1vmphjjz1Wm/h69OihPvnkE526gTqhFKMn5YJf9tlnH+1ozvqU6XLTQuFcjpM91xW4FvzmGmY9kQLCGv5JSQ9CWnEwzJf92h2gCRlNB5nabzZdY0EQwsHLsRucHMGtyS1JXElyS0L3rU7XBLiQBqBRo0aR0qVLRypWrKidoYcNGxa3TwsXLoyce+65kZo1a+qwfhy6zzzzzBgHcb8pB6w4OU6zbQJWrI7Sb775ZqR169Z6302bNo1MnDgxcMoB+37s55H3N2kJOMbixYtH6tSpEzn77LMjv/32W0zKASv0k/5az9Nhhx2mz69XygHOuT09AZM9DUI2kndCkzWCwPq3lffeey/SqVOnuDkkcmG/hRgVmOlzLQiCkA5yMbos39mNf1SeYI2UQMVIvgzsvccdd1yMxz810GhnnZeL+/WKGDP+PNjzsXejTg7bAToT+830uRYEQUgXmLlwZCc/E/5VQhYQyUOoOUTunxYtWuj/zznnnMivv/7qqZnI1f2anEZoeshH0qNHj+iyU045RWfQTYV5KlP7zfQ1FgRBSBeiaco+8k5oYuBs2LBhZMSIEdqePXXqVJ107LbbbkvpgJqp/QIJ1bAr9+zZM0ZwId1+KgWXTO03k+daEARBKFzyLnqOkE/KgBCeinmGJF8UWSSpIV79YUZqZcN+CyUqMFvOtSAIglC45J3QROgpeSLKlCmj/V/I70EYJXWICBXNt/3C6aefrjPbAiG2hG+mI8Q+U/vN5LkWBEEQCpe8E5pI/U5ODcpykKOCDKZMOAaTvyNX92v117f77rNvID8HAsQHH3wQmuCSqf1m4zUWBEEQCpu8E5rIvnrRRRfppFymZg4DOlWkie7Kxf3aI8ZI3IZJjKg1K0cffbQaNWqUTjAWBpnab7ZeY0EQBKGwyauUAwY0EHfeeafWQvTq1Uv//vDDD7UWolatWjm736FDh2phoXnz5jrrLNlt77nnHh2Sai9MGSaZ2m82XmNBEAShcMk7TRO0a9dOdenSRdceImV+hQoV1JQpU/Rgmsq6OKncL9WmH374YTV8+HCdbp7tso+XXnoptP5n036z9RoLgiAIhUveaZqsmg+qWOMYTHFD40BsisPm2n7R8KDt+eijj7SAwL5Gjhypax4hLLRp0yaU48iW/WbjNRYEQRAKm7zTNDGYmiKBgwYN0hWy+/Tpo3+ncjBN9X4LMSow266xIAiCUNjkndAEprIz5Ttuv/12nYL+5Zdfzun95mtUYK5dY0EQBKFwyUuhyaqRILKrUaNGqlmzZjm933yMCszlaywIgiAUHnnn0+TEtm3bVKlSpXJ+v/kaFZjL11gQBEEoHPJa02TIlJ9L2PvNx6jAsBBfJkEQBCHVFISmKR/I16hAQRAEQcgVCkLTlA/ka1SgIAiCIOQKIjTlEPkYFSgIgiAIuYIITTlIPkUFCoIgCEKuID5NOUy+RAUKgiAIQi4gQlMOk6liuZnaryAIgiBkEhGaBEEQBEEQfCA+TYIgCIIgCD4QoUkQBEEQBMEHIjQJgiAIgiD4QIQmQRAEQRAEH4jQJAiCIAiC4AMRmgRBEARBEHwgQpMgCIIgCIIPRGgSBEEQBEHwgQhNgiAIgiAIKj7/ByAiUMucujTxAAAAAElFTkSuQmCC", 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" ] @@ -354,15 +355,15 @@ "from sklearn.metrics import silhouette_score, accuracy_score\n", "from sklearn.svm import LinearSVC\n", "from sklearn.model_selection import StratifiedKFold\n", - "import seaborn as sns\n", - "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "fig, axes = plt.subplots(1, 2, figsize=(6,3)) # Add a third axis for decoding accuracy\n", "\n", "# Plot heatmap\n", - "sns.heatmap(sims, cmap='gist_gray', ax=axes[0], cbar_kws={'label': 'DSA Wasserstein'})\n", + "im = axes[0].imshow(sims, cmap='gist_gray', aspect='auto')\n", + "cbar = plt.colorbar(im, ax=axes[0])\n", + "cbar.set_label('DSA Wasserstein')\n", "rho_values = [10, 20, 152, 220, 75]\n", "\n", "lorenz_names = [rf\"$\\rho=$ {rho_values[i]}\" for i in range(len(rho_values))]\n", @@ -376,22 +377,30 @@ "vis = MDS(n_components=2, dissimilarity='precomputed', random_state=42)\n", "embedding = vis.fit_transform(sims)\n", "\n", - "# Create DataFrame for scatter plot\n", - "df = pd.DataFrame()\n", - "df[\"x\"] = embedding[:, 0]\n", - "df[\"y\"] = embedding[:, 1]\n", - "df[\"System\"] = rho_labels\n", + "# Create data for scatter plot\n", + "x = embedding[:, 0]\n", + "y = embedding[:, 1]\n", "silhouette_score_ = round(silhouette_score(sims, rho_labels),3)\n", "\n", "# Plot scatter with improved styling\n", - "sns.scatterplot(data=df, x=\"x\", y=\"y\", hue=\"System\", ax=axes[1], s=60, alpha=0.8)\n", + "# Create a color map for each unique rho value\n", + "unique_labels = []\n", + "for label in rho_labels:\n", + " if label not in unique_labels:\n", + " unique_labels.append(label)\n", + "\n", + "colors = plt.cm.tab10(np.linspace(0, 1, len(unique_labels)))\n", + "label_to_color = {label: colors[i] for i, label in enumerate(unique_labels)}\n", + "\n", + "for label in unique_labels:\n", + " mask = [rho_labels[i] == label for i in range(len(rho_labels))]\n", + " axes[1].scatter(x[mask], y[mask], label=label, s=60, alpha=0.8, color=label_to_color[label])\n", + "\n", "axes[1].set_xlabel('MDS Component 1')\n", "axes[1].set_ylabel('MDS Component 2')\n", - "axes[1].set_xticks([round(min(df['x']),2), round(max(df['x']),2)])\n", - "axes[1].set_yticks([round(min(df['y']),2), round(max(df['y']),2)])\n", - "handles, labels = axes[1].get_legend_handles_labels()\n", - "# axes[1].legend(handles, labels, bbox_to_anchor=(1.05, 1), loc='upper left', frameon=True, fontsize=8, markerscale=0.7)\n", - "axes[1].legend(handles, labels, frameon=True, fontsize=8, markerscale=0.7)\n", + "axes[1].set_xticks([round(min(x),2), round(max(x),2)])\n", + "axes[1].set_yticks([round(min(y),2), round(max(y),2)])\n", + "axes[1].legend(frameon=True, fontsize=8, markerscale=0.7)\n", "\n", "def compute_decoding_accuracy(sims, rho_labels, n_splits=5, random_state=42):\n", " \"\"\"\n", @@ -483,9 +492,9 @@ ], "metadata": { "kernelspec": { - "display_name": "dsa_test_env", + "display_name": "DSA (uv)", "language": "python", - "name": "python3" + "name": "dsa-uv" }, "language_info": { "codemirror_mode": { @@ -497,7 +506,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.18" + "version": "3.10.19" } }, "nbformat": 4, diff --git a/examples/how_to_use_dsa_tutorial.ipynb b/examples/how_to_use_dsa_tutorial.ipynb index c5b004d..653bafe 100644 --- a/examples/how_to_use_dsa_tutorial.ipynb +++ b/examples/how_to_use_dsa_tutorial.ipynb @@ -36,16 +36,7 @@ "cell_type": "code", "execution_count": 1, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/orcd/data/fiete/001/om2/ostrow/dmrsa/DSApublic/DSA/DSA/pykoopman/__init__.py:3: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", - " from pkg_resources import get_distribution\n" - ] - } - ], + "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", @@ -226,9 +217,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "Fitting DMDs: 100%|██████████| 2/2 [00:00<00:00, 114.21it/s]\n", - "Caching compare objects X: 100%|██████████| 2/2 [00:00<00:00, 26715.31it/s]\n", - "Computing DMD similarities: 100%|██████████| 1/1 [00:02<00:00, 2.31s/it]" + "Fitting DMDs: 100%|██████████| 2/2 [00:00<00:00, 1053.18it/s]\n", + "Caching compare objects X: 100%|██████████| 2/2 [00:00<00:00, 27060.03it/s]\n", + "Computing DMD similarities: 100%|██████████| 1/1 [00:02<00:00, 2.09s/it]" ] }, { @@ -249,7 +240,7 @@ }, { "data": { - "image/png": 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", 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" ] @@ -303,16 +294,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "Fitting DMDs: 100%|██████████| 2/2 [00:00<00:00, 934.14it/s]\n", - "Caching compare objects X: 100%|██████████| 2/2 [00:00<00:00, 32513.98it/s]\n", - "Computing DMD similarities: 0%| | 0/1 [00:00" ] @@ -761,7 +739,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -771,9 +749,9 @@ "Components shape: (2, 2, 3)\n", "\n", "Distance components between systems 0 and 1:\n", - " Controllability distance: 3.8842\n", - " State similarity: 2.2045\n", - " Control similarity: 0.0599\n" + " Controllability distance: 3.0558\n", + " State similarity: 0.9661\n", + " Control similarity: 0.0629\n" ] } ], @@ -817,23 +795,23 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "Fitting DMDs: 100%|██████████| 2/2 [00:00<00:00, 489.16it/s]\n", - "Caching compare objects X: 100%|██████████| 2/2 [00:00<00:00, 31536.12it/s]\n", - "Computing DMD similarities: 0%| | 0/1 [00:00" + "" ] }, - "execution_count": 16, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -957,7 +928,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -970,13 +941,13 @@ " ], dtype=object))" ] }, - "execution_count": 18, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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" ] @@ -998,23 +969,23 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "Sweeping: 100%|██████████| 5/5 [00:10<00:00, 2.20s/it]\n" + "Sweeping: 100%|██████████| 5/5 [00:15<00:00, 3.15s/it]\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 22, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -1044,7 +1015,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -1057,13 +1028,13 @@ " ], dtype=object))" ] }, - "execution_count": 23, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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M+BAlrUzFwWTPZAeaNveVPT4cSnSy5BXUQLmvCiHNfM09NCIiIiIiIqLLysgpwKFTSXK7P/t9kDUGP7xc5Tog1A833NEHm37ZhiVzVuB/89lniojsw6hRo+osI65Wq2HJVUGMgZkfjUiUthp4QxSUGi0U5fqm5806B7DkFRGRGcydOxdt27ZFjx49ePyJiIiIGmDT3tMQ5yXatQhCoK8HjxlZjcIcQ9krffBDuO3Zm+V6409bkZ2aY7axERGZkoeHB6KiompdwsLC7O6PweBHI0pPyMS6T/4GElKh1Oj3HTsdJ/cTEZFpTZo0CUePHpUplURERERUv42V/T4GMOuDrDTzw91LX/ZKaNMrGq17RUNTVo5/5q014+iIiMhcGPxoRImnkmXPD0VRKZRl+syPMj8nJJ1OacynISIiIiIiImpU2XlF2Hc8QW4PZPCDrLznx8XZH8vmrUZZaeUsVSIishtWF/woLS1F586doVAosH///svetqSkRM789fPzk01dxo4di9TUVKONrWl0sOz5AU05lKX6fVpvNUKigoz2nERERERERETXakvsGVRodWgZFoCmTbx5QG2gBGx4eDicnZ1l7ffdu3fXedvFixeje/fu8Pb2hpubmzzn8v3339e4jajTPn36dNms1sXFBYMHD8apU/pMIUtQaMj8uCj40W9sL/g39UV2ai42/7rdTKMjIiJzsbrgx9SpUxESEtKg206ePBnLli3D77//js2bNyMpKQm33Xab0cYmGmo99+VjQLkGqmJ9Axd1Eze5n4iIiIiIiMjiS151jzb3UOga/frrr5gyZQpmzJiB2NhYdOrUCUOHDkVaWlqtt/f19cUrr7yCHTt24ODBg5gwYYJcVq9eXXWb999/H3PmzMG8efOwa9cuGSQRjykmnVqCgtxLe34IDmoHjHpymNxe/MlyszTbJSIi87Gq4MfKlSuxZs0afPjhh/XeNjc3F99++y1mzZqFgQMHolu3bliwYAG2b9+OnTt3Gm2Mwx8ehFufGAqVftIBNI78YCUiIiIiIiLLlV9Ygj1Hzstt9vuwfuI8yMSJE2UAo23btjJg4erqivnz59d6+/79++PWW29FmzZt0KJFCzz77LPo2LEjtm7dKq8XAYPZs2fj1VdfxejRo+V1ixYtkhNMly5dCksueyXc/OhgODqrcXpfHA5vPW6G0RER2bdnnnlGZiM2pJKT3QY/RLkq8eEtUi/Fh3Z9YmJioNFoZCqmQevWrdG8eXM5m8GYIto3gzpPK7fLFBVGfS4iIiIiIiKia/HvvrMor9AioqmfXMh6lZWVyfMh1c+FKJVKebkh50JEoGP9+vU4ceIEbrjhBrkvLi4OKSkpNR7Ty8tLltO63GOKsuV5eXk1FlOXvRI8/Tww5IEbq7I/iIjoUukJmdi/8bBcN7bbb79dBtTDwsJgag6wAuLDd/z48Xj88cdlHcr4+Ph67yM+mB0dHWXNyuoCAwPldZf7cBaLwdV8OIt6kuos0UjLEVqVDlqdDkqFvgE6ERERERERkUWWvOoRZe6h0DXKyMhARUWFPPdRnbh8/Pjxy1bPaNq0qTwfolKp8Pnnn2PIkCHyOsM5lNoe83LnV2bOnIk33ngDplCQU3vZK4Mxz4zA8q/XYfvS3UiJT0NQeBOTjIuIyJzn00uK/jvHfTlrFm7C3GfmQ6fVQaFUYNKch3DTg/0bdF9nVyeZ0XE5hmC6OZg1+PHSSy/hvffeu+xtjh07Jktd5efnY9q0aUYfU2N8OPuH+sExrQzQOQIKIK2wAEHuHo02RiIiIiIiIqLGUFRShl2H9BMMB3RvyYNqpzw8PGQpkoKCApn5IXqGREZGypJYV0ucwxGPU31yabNmzWDqsldCeLtm6DqkI2LXHsRfn63CYx+OM8o4iIgshQh8jPJ44IrvJwIgnz31rVwa4u/87+Hi5gxLZdayV88//7wMblxuER+2GzZskKmUTk5OcHBwQFSUfjaKyAJ58MEHa33soKAgme6Zk5NzSfkscd3lPpzFjAfDcuHChavK/HDILgH0la9wKivjih+DiIiIiIiIyNi2H4hDqaYCoU28EN3cnwfcyvn7+8vMDXHu40rOhYjSWOJcS+fOneW5GlGiREwOFQz3u9LHFOdwPD09ayzGmt18ubJXBrc9M0KuV367HsUFxUYZCxERWRazZn4EBATIpT5z5szBW2+9VXVZNNUaOnQofv31V1ljsjaiwblarZYzFsaOHSv3iZqV58+fR58+fS774SyWayE+bB3LKqAoV0Cn0uF0dib6NY+4psckIiIiIiIiMl7Jq+h6y1aQ5RPlv8X5EHEuZMyYMXKfVquVl5966qkGP464j6EkeEREhAxyiMcQwRFDFseuXbvwxBNPwNyKC0qg1eouW/ZK6DG8C5pGByPxVDLWLNyM0ZOGmXCURESmJcpR/Z3/fb23y0jMwsNtn5MZHwZKlRLfHPlYTvBvyPNYMqvo+SGalFfn7u4u1y1atEBoaKjcTkxMxKBBg7Bo0SL07NlTNt96+OGHZYqlr6+vnGHw9NNPy8BH7969jTpe8YXR01EFpQaocAJOZzLzg4jI1ObOnSsXUfOYiIiIiC5VUqbBtv1xVcEPsg3iPIiokiGqZYjzI7Nnz0ZhYSEmTJggrx83bpzs72HI7BBrcVtxjkUEPFasWIHvv/8eX3zxRdU5jueee05OSo2OjpbBkNdeew0hISFVARZzMpS8Ujs6wNHZ8bLZLbc+MwKfPf0tlsxZgZFP3CT3ERHZIvHe7dKAclTNWoZg8pePYfbjX0FboZWBj+fmPSr32wKrCH40hEajkZkdRUX6JlfCxx9/LD/IROaH+AAX2SKiaZcp+Pm6QVEmZs3ocO6i0ltERGR8kyZNkouYlSYC4kRERERU065D51BcqkGgnwfaRtZdvoisy1133YX09HRMnz5dNiQX2RqrVq2qalguKmJUP+kvAiNPPvkkEhIS4OLigtatW+OHH36Qj2MwdepUebtHH31Ulhfv27evfExnZ/PXeS/M1Z8HcvNyrTd76aYHb8SCV3+W2R97Vu5Dr5u7mWiURESWa/jDg9B9aGcknU5BSFQQAkL9GvXxH3vsMSxfvlx+Jonz86LP1OnTp2EKCp0ojkh1Mpw0E/0/rqQ+5at3zsJv3cpRFqxFtMofa+97hEeZiMiK3seJiMhy8L2cyDhen7cSK7cdw11Du2DK/QN4mMkq38sPbz2GyTdMlyWtvjsxp97bf/nCIvwxa5lsgP7e6tcabRxERMZQUlKCuLg4mXVnCQFnazsmzO8zkuBQP6iK9XGl7NL/slGIiIiIiIiIzE1TXoF/952V2yx5RdasIKeo3n4f1Y1+ahiUSgVi1x5E/JELRh4dERGZE4MfRhISEQAHfdlJFGrLjPU0RERERERERFds79HzKCgqha+XKzpG20Zdb7JPhp4fbt5uDbp9UHgTXDemp9xeOmeFUcdGRETmxeCHkYjaaA55WrldpmCzXSIiIiIiIrIcG/eckuv+3aKgYtNnsoHgh3sDgx/Cbc/eLNdrv9+MvMx8o42NiIjMi8EPIwY/1NkauV2h0oGtVYiIiIiIiMgSlFdosTnmjNxmySuydoWGsldeDSt7JbTv2xrRXSNQVqLB8q/WGXF0RERkTgx+GIlfU184ppZWHeXc0hJjPRURERERERFRg+0/kYCc/GJ4ujuja+tQHjmyu8wPhUKBW5/RZ3/8/fkqlGvKjTY+IiIyHwY/jMQn0AuOWaVAZcWrU1mZxnoqIiIiIiIioisueXVD1xZwcFDxyJFd9fwwuPGu6+S5m4zELPz75y4jjY6IiMyJwQ8jUalUcIMChnYfp7MyjPVURERERERERA2i1eqwae9puc2SV2QLCnOvPPNDcHRSY+TjQ+X24k+WG2VsRET2rqSkBGPGjEHLli3RqVMnDBkyBKdP67+HmAKDH0bk4+YIhUYht09mMPhBRERERERE5nX4dDIycgrh5uKInu2a889BVq/A0PPDu+E9PwxueXwI1I4OOL7rFI7uPGmE0RERWYf01Fzs3xMn143t0UcfxYkTJ3DgwAGMHj0ajzzyCEzFwWTPZIcC/D2g1CighQ7xOVnmHg4RERERERHZuY179SWv+naOhKOapwTIfsteCT6B3hhwb1+s+W4TlsxZgba9WxphhEREpqfT6VBaomnQbdf+sx9z318BnVYHhVKBSVNHYMgtnRt0XydnteyjVBdnZ2eMGDGi6nLv3r3x4YcfwlT4TceIApv6QlmaI7eTC/ON+VRERERERERE9Z4IMfT7YMkrshWFV9HwvLpbnxkhgx///rET6e9nIiDUr5FHSERkeiLwMbrv21d8PxEA+ezd5XJpiL+2vgJnF8cGP/4nn3wisz9MhWWvjCg0PACqEv12VmmxMZ+KiIguMnfuXLRt2xY9evTgsSEiIiICcDwuFckZeXB2dECfjuE8JgR7L3slRHWOQMcb26KivAJ/f766kUdHREQG77zzjuz3MXPmTJgKMz+MKEgEPw7oa0YWVJQa86mIiOgikyZNkkteXh68vLx4fIiIiMjubajM+riuUwScndR2fzzINrKZrqXslcFtz96Mg5uPYvlXa3Hfq2Ph7OrUiKMkIjI9UY7qr62v1Hu7jLQ8PHL7ZzLjw0CpVODrP56CfxPPBj1PQ4hSV4sXL8a6devg6np1weqrwcwPI/Jv6guHvAq5XabQr4mIiIiIiIjMUvKqst8HS16RrSgpLIG2Qiu33byu/mRa75HdEBTRBPlZBVg4/RekJ2Q24iiJiExP9OFwdnGsdwkN88dzr4yUAQ9BrJ99ZaTc35D7X67fh8GsWbPw888/Y+3atfD29oYpMfPDiESdSIescgCOKFfpP4yJiIiIiIiITO1MQgYupOTAUa3C9Z0j+Qcgm1CYqy95pXJQXVO2hkqlQqseUUiJS8Mfs/7B4tnL8dyXj2H4w4MacbRERJZp2Jhu6NYnCkkXshDSzBcBgY1XPSMhIQHPP/88IiMjMWDAALnPyckJu3btgikw+GFEfiE+cEoTTT8cARVQUq6BswNTi4mIiIiIiMi0DI3Oe7UPg9sVNCYlspZ+Hw2ZfVwXkemx5Y8dVZe1Wh1mP/4Vug/tzAboRGQXAgK9GjXoYRAaGiqzT82FZa+MyNHZEW65FUBl0seZ7CxjPh0RERERERHRZYMfLHlFtqQx+n0IiaeSa9S7F0Q5rUNbjl7T4xIRkXkx+GFkXioVDO0+TmalG/vpiIiIiIiIiGo4n5yNMwmZUKmU6Ne1BY8O2YzCyuCH+zUGP5pGB1fVu6/u48e+wqZft13TYxMRkfkw+GFkfp4uUJTrP0CPpzH4QURERERERKa1oTLro3vbZvB0c+bhJ5sse3WtPVtFjw+lSn+aTKxDogJlQ/W375mNWRPnoaSotFHGTEREpsOeH0YWFOgNRVkR4ALEZWUb++mIiIiIiIiIati4t7LkVfdoHhmyKY1V9koQzc1Fj4+k0ykIiQqCb5A3vn/jd/z0zmKs/HY9jmw/jld/mYyIDmGNMHIiIjIFZn4YWXBzPyjL9JkfSQW5xn46IiIiIiIioipJ6bk4HpcKpUKBG7tF8ciQTQY/3L2uPfhhyADp1L+dXKscVBj/f3fjvbWvwTfYB+ePJeKpXtOwbN6aq2reK5qq7994WK6JiMg0GPwwsmYtAqEq1m9nlOg/lImIiIiIiIhMYdPe03LduVVT+HpdW2kgIsvt+WG813aXgR3w5f4P0GN4F5SVaDDnya/xf3d+hPzsggYFNLRaLX7/6G/cF/4E/jfoDdwf/oTMJCEiIuNj2SsjC2jmB9VZrdzOrygz9tMRERERERERVdlY2e9jQA+WvCLb7fnRGGWvLsc7wAtvLXsJi2cvx7fTfsS/f+7CgY1HkJ9TCJ1WB4VSgbumjkZE++ZIiU9HanwaUs6JtX67XFNR9VharQ6zH/9KltgSGSZERGQ8DH4Ymfggc8gTwQ8lShXlxn46IiIiIiIiIik9uwAHTyXJ7f7dWfKKbE9BriHzw7jBD0GpVOL2KSPRoV8bvHnHR0g7n1F1nQiA/PLu0gY/lrZCK3uLMPhBRPbgpptuQkpKinwf9fDwwJw5c9ClSxeTPDeDH0bm39QX6hwR9HBEhUqfAUJERERERERkqpJX7aOC0cTXgwecbLjslfGDHwatekTh6c8ewWuj3r3kushOYWjRORxBYU0QGB6AoPAmUDs54Ll+r8kAiYFSpZRN1YmILEVqVj4upGSjWZAPAhv5O8Nvv/0Gb29vub1kyRKMHz8eBw4cgCkw+GFkbl6ucMrQyOCHTgWUV1TAQaUy9tMSEdm9uXPnyqWi4r8UcyIiIiK7LHnVnSWvyLbLXhmz50dtRIBDqVTIElbVAxpvLZtWazbH5C8fw8ePfVkVAHlu3qPM+iAio9LpdCgpbVgVouX/HsFH32+EVqeDUqHA8w8MwM392jXovs5ODlAoFJe9jSHwIeTm5tZ7+8bE4IeRiT+mV74WieLzTQFcyMtBhA9rOhIRGdukSZPkkpeXBy8vLx5wIiIisivZeUXYdzxBbg9kvw+yUQWVmR/G7vlxMRHgeO7Lx2TvDlHCSgQ+LhfQGP7wIJkV8lTPafLy9bf2NOl4icj+iMBH/4mfXvH9RADkg0Ub5NIQm75+Gi7O6npvN27cOGzcuFFur1ixAqbC4IcJ+KkdATHx2AE4npHB4AcREREREREZ1ZbYM/IERquwJghpwokgZNtlr0TVDVMTAQ3RtFz07hAlrOrr39GqexTC2obi3NEE7N9wGDfc3sdkYyUiMrdFixbJ9cKFC/Hiiy+aLADC4IcJBPq6Q1FeDp2DDsfT0zA8upUpnpaIiIiIiIjsveQVsz7Ihku6/Ff2yrSZHwYi4HElTcu7Dekkgx8xaw4w+EFERiXKUW36+ul6b5eWVYC7X/pOTpgwEGX9fpk5Hk183Rv0PFfiwQcfxOOPP47MzEz4+Rm/OpLS6M9ACGnqC6Vo+wHgdGYmjwgRERER2ZXXX39dloOtvrRu3drcwyKyWXmFJdh95LzcZvCDbFVpcRkqyivM0vPjanUd0lGuY9YelMEbIiJjEd+3XZzV9S5hIT6Y9tBgGfAQxHrahMFyf0PuX1//jpycHCQlJVVdXrp0qQx6+Pr6muSPz8wPE2gaEQBlaRoqPICEvFxTPCURERERkUVp164d1q1bV3XZwYE/RYiMZeu+s6io0CKyqR/CQ0xzcoHIXP0+RL8NZzdnq/gDdLyxLRzUKqSeS0fi6RSERgebe0hERBjVvwN6dQxHQmoOQgO9Eejr0WhHRTQ4v+OOO1BcXAylUomAgAD8888/Jmt6zl8cJhAa2QTKo0fkdnpxgSmekoiIiIjIoohgR1BQkLmHQWQXNrDkFdlRvw9R8spUJ9GulYubM9pd3xoHNh1B7NqDDH4QkcUI9PVo1KCHQVhYGHbv3g1zsbqyV6WlpejcubP8YNu/f/9lb9u/f/9L0utFTTFT8w/1g6pIn86YX1Fq8ucnIiIiIjK3U6dOISQkBJGRkbjvvvtw/ry+JA8RNa7C4jLsOhQvt1nyimzZf/0+rKPklUHXwYbSVwfMPRQiIptndcGPqVOnyh9NDTVx4kQkJydXLe+//z5MLSDUFw75+jqUJYpykz8/EREREZE59erVC9999x1WrVqFL774AnFxcejXrx/y8/PrnPCUl5dXYyGihtl+IA5lmgpZtiKqmT8PG9l82Ss3MzU7v1rdbuok1/s3Hq7qWUJERMZhVcGPlStXYs2aNfjwww8bfB9XV1eZXm9YPD09YWreTbygztEHPcpVbGhFRERERPZl+PDhstZvx44dMXToUKxYsUI2P/ztt99qvf3MmTPh5eVVtTRr1szkYyayVhsrS14N7BFtNaWAiK617JU1ieoSDg9fdxTlFeP47tPmHg4RkU2zmuBHamqqzOL4/vvvZUCjoX788Uf4+/ujffv2mDZtGoqK9GmRpiSaubjm6IMeOgcddDoGQIiIiIjIfnl7e6Nly5Y4fbr2kz7ie7tojmhYLly4YPIxElmjkjKNzPwQWPKKbJ21lr1SqVToOriD3I5Zw9JXRESw9+CHCBaMHz9e9uvo3r17g+9377334ocffsDGjRvlDygROLn//vsvex9jpdj7GmIuCiC1kE3PiYiIiMh+FRQU4MyZMwgODq71eicnJ5mxXX0hovrtOngOxaUaBPl5oE1EIA8Z2UfZKy/ryvwQug7Wl76KWXfQ3EMhIrJpZg1+vPTSS5c0JL94OX78OD799FNZD1gEMK7Eo48+KtPqO3ToIJsqLlq0CEuWLJE/tOpirBT7QEdnoLKU44mM9EZ5TCIiIiIia/DCCy9g8+bNiI+Px/bt23HrrbfKma/33HOPuYdGZFM27NWXvOrfnSWv7N3cuXMRHh4OZ2dn2Xdp9+7ddd7266+/ln2YfHx85DJ48OBLbi8mpF58vmbYsGEwJ2steyV0G6Jven581ykU5ur/HUREZGPBj+effx7Hjh277BIZGYkNGzZgx44dcgaYg4MDoqKi5P1FFsiDDz7Y4OcTH/hCXen1xkyxb9rEC4pyfb3VIympjfKYRERERETWICEhQQY6WrVqhTvvvBN+fn7YuXMnAgICzD00IpuhKa/A1n1n5TZLXtm3X3/9FVOmTMGMGTMQGxuLTp06yYmhaWlptd5+06ZN8j1aVM0Q517EJNCbbroJiYmJNW4ngh3JyclVy88//wxLyPywxuBHYFgAQlsGQ1uhxf6NR8w9HCIioxLBePE7oHPnznIRn1Om4gAzEj92GvKDZ86cOXjrrbeqLiclJckPbnGgDAGNhti/f79c15VeL4gAi1gaW2hzfyg0edA5ASczmflBRERERPbjl19+MfcQiGzeniPnUVBUCj8vN3SMDjH3cMiMZs2aJXumTpgwQV6eN28eli9fjvnz58sKHLX1Sq3um2++wZ9//on169dj3LhxVfvFuZKgoCBYioJcfX1xNyvr+WHQbUgnJJxMln0/rh/T09zDISI7l5Kbj/isHIT7eiPIy6PRH1+cxxeBD1Mza/CjoZo3b17jsru7u1y3aNECoaGhclvMSBg0aJAsbdWzZ09Z2uqnn37CiBEj5MyygwcPYvLkybjhhhvQsaM+vdCUmkUFQpl5FloA53JyTP78REREREREZLs27jGUvIqCUqmvOkD2p6ysDDExMTXKhiuVSlnKSmR1NERRURE0Gg18fX0vyRBp0qSJLI01cOBAOUlVnG8xf88P6wx+dB3SEX/NXcW+H0RktB7axZryBt126f6jeGvlRmh1OigVCrw6fADGdG7boPu6qB1kKURLZRXBj4YQH8wnTpyQH9KCo6Mj1q1bh9mzZ6OwsFCmbY4dOxavvvqqWcYXHB4AVSIgXnJpRWx4TkRERERERI2jvEKLzTH68s4seWXfMjIyUFFRgcDAmg3vxWXRU7UhXnzxRYSEhMiASfWSV7fddhsiIiLkZNOXX34Zw4cPlwEV0cOpNqWlpXIxyMvLQ2Oy5p4fQqf+7aByUCHpdAqS41IRHFHzb0ZEdC1E4KPrO59d8f1EAOTNFRvk0hCxLz8FV0d1vbcTmYQiICOSFt59912Tlb91sNY6YeJgXW6fCHaIpoqWwj/UF8pi/fjyK/778CciIuM1eRSL+PFHREREZMv2H09AbkEJvNyd0aW1vjoC0dUQJ6REqUKR5SGapRvcfffdVdsdOnSQFTVENQ5xO1GFozYzZ87EG2+8YbQ/REFOkVUHP9w8XdGmdzQObz2O2LUHcfOjQ8w9JCIio9iyZYus7CSSF0RigujhvWLFCpiCVQY/rJFfiC8c8kTRKyVKFA1LOSIioqs3adIkuYgZZl5eXjyUREREZLM2VJa8uqFrCziolOYeDpmRv7+/zMRITU2tsV9crq9fx4cffiiDH6KKRn3lwiMjI+VznT59us7ghyi9JRqvG4jv5WKiauNnflhn2Suh6+COMvgRs/YAgx9E1KhEOarYl5+q93apeQW4ee5CmfFhIEpfLZ/0IAI93Rv0PA1taaFWq/Hcc8+hZcuWMBV+KzIRRyc1nPL0s4/LVSIIQkRERERERHRttFodS17Rf+ceHB3RrVs32az8v9eIVl7u06dPnUfq/fffx//93/9h1apV6N69e71HNCEhAZmZmQgODq7zNqJBuqenZ42lsYjKH4VVDc+tM/ND6HZTJ7nev+EwM9aJqFGJPhyujup6lwh/H7w5crAMeAhiLS6L/Q25f339PkQ7ipxq/a9//vlndOnSxWR/bWZ+mJB7gQ4Z4ouHqmbJLiIiIiIiIqKrceh0EjJyCuHm4oge7fQzK8m+iWwLUVJEBDFEbXVDL9QJEyZU1V1v2rSpLEslvPfee5g+fTp++uknWVI8JSVF7nd3d5dLQUGBLF8l+qiK7BHR82Pq1KmIiorC0KFDzfJvLCspg6as3KrLXgmtureQDdvzswtxKuYsWveMNveQiMgO3d61Pfq2CMO5rByE+XojyMuj0R5bZB6Kzw9RklwErkXm4KJFi2AqDH6YkF+JA+LFhgrILSmBV7X6mURERERERERXamNlyat+XSLh2IDSE2T77rrrLqSnp8uAhghkdO7cWWZ0GJqgnz9/Hkrlf4VAvvjiC5SVleH222+v8TgzZszA66+/LstoHTx4EAsXLpSzd0Uz9JtuuklmiojsDnMw9PtQKhVwcbfecyui4XmXQR2wdfEuxKw5yOAHEZlNkJdHowY9DESwY9++fTAXfjMyoVBnZ8Roy2SxseMZ6egV2ni1LomIiIiIiMi+iBmUm/aeltsDenDGOP3nqaeekkttRJPy6uLj5TTNOrm4uGD16tUWdXgLKvt9iJJX9ZVcsYa+HzL4se4A7nt1rLmHQ0RkU9jzw4TCm/rD0Ov8UHKyKZ+aiIiIiIiIbMzxuFQkZ+TB2dEBvTuEm3s4ViklNx874y7INVmP/5qdW2/JK4PulX0/jm4/iaL8YnMPh4jIpjD4YULNIptAodHPSDiRnm7KpyYiIiIiIiIbs6Gy5NX1nSPh7KQ293Cszh+xhzFg9jcYv/APDJz9rbxM1sFQ9srd2xXWLjgyUC4V5RU4uPmouYdDRGRTGPwwobDoICg1+u24rCxTPjURERERERHZWMkrQ/CDJa+unMj0eG3ZWuh0+stanQ7Tl61jBoiVqF72yhZ0G9JRrmPWHDD3UIiIbAqDHybUpJkflCX67dSiAlM+NREREREREdmQ0xcykJCaA0e1Ctd1ijD3cKzOydSMqsCHgQiAnMvKMdeQyE7LXgldh+hLX8WsO2juoRAR2RQGP0zIv6kvVEX6b1d55ZVRECIiIiIiIqIrtLEy66NXh3C4uTjy+F2hvw4eu2SfUqFAmK83j6U1lb3ysv6yV0KXge2hVCpw4Xgi0i5kmHs4REQ2g8EPE3L1dIVDoVZuFxs6nxMRERERERFdZfBjYI9oHrsrtOLwCSw/fKIq4GFYvzlyMIK8PHg8rYCtlb0SGSytekbJ7di1zP4gImosDo32SFQvhUIBp3wd8gGUq/RBECIiIiIiIqIrcS45C2cTM6FSKdG3SyQP3hW4kJUje3sIj/fribu7d5SlrkTGBwMf1lf2ys1GMj+EroM74tjOU7L01bCHBpp7OEREjSIzMxODBg2qulxUVISzZ88iLS0Nvr6+MDYGP0zMo1gBkcCodbiouCgRERERERHRFWR99GjbDJ5uzjxmDVRWXoHJf6xAQWkZujUPwVP9+8BBpWTQwwoV5NpWzw+h+02d8ONbf2LfuoPQarVQKlmshYhMJ7kwD3H52Yjw8EGwm2ejPa6fnx/2799fdfnDDz/E5s2bTRL4EBj8MLHAcjXiUCGPfEm5Bs4OalMPgYiIiIiIiKzYhsrgxwCWvLois9ZvxeGkVHi5OOPDsSNk4IOsvOeHDQU/WveKhquHC3Iz8nFmfzyiuzKri4iunk6nQ3G5pkG3/fPMIczYvQ5a6KCEAm/0HIyxLTo06L4uDmpZ7aihvv32W8ycOROmwuCHiUW6umOnLhdQACczMtExKMjUQyAiIiIiIiIrlZSWixPxabJHxY3d9D0CqH6bTp7Fdzti5fbM0TchmL09bKPslbftlL1yUDug04B22PH3XsSsOcDgBxFdExH4aPvzrCu+nwiAvLZ7rVwa4ug9U+CqdmzQbbdv347s7GzccsstMBVOczCxqLBAoLLX+YGkJFM/PREREREREVmxjXv1WR+dWzeFj6ftnPg1ppTcfLy0ZLXcHterCwa2bmHuIVEjNTy3pcwPQ98PQfT9ICKyNd9++y3GjRsHBwfT5WMw88PEwlsGQZl9Clq1DkeSU0z99ERERERERGQD/T4GsuRVg1RotXhh8UrkFJegbXATvDCkr3H/QGQShbm2V/bK0PdDOLL1OEqKSuHs6mTuIRGRlRLlqI7eM6Xe26UU5WPwX9/IjA8DkV26btQjCHL1aNDzNERBQQF+++037NmzB6bEzA8TaxrZBIoy/fbZzCxTPz0RERERERFZqbSsfBw6nSy3WfKqYT7fvAt7zyXC1VGNWbePgKMJZ5uSKXp+2Fb2U9PoYDRp7g9NWTkObTlq7uEQkRUTfThc1Y71LpFefpjZZxhUlX07xHpm72Fyf0Pu39B+H7/++is6deqE1q1bw5T4qW9iTZr5Q7UJouU5UovyTf30REREREREZKU2x5yW6w5RwWjiW/9sTHu3K+4CvtiyS26/cctghPv5mHtI1AjKSsqgKdU38XWzscwPcRJRlL5aNX+D7PvRY1gXcw+JiOzAXdGdcENIBOLzsxHu4YNgN0+jlLyaOHEiTI3BDxPzCvCEslifRpRTXmLqpyciIiIiIiIrtaGy5NUAlryqV1ZhEV74cyW0Oh3GdmmHkR1NO9OUjN/vQ6lUwMXd2eYOtSh9JYMf7PtBRCYU7OZplKBH9Wbn5sCyV6Y+4Eol1IX64EeJorLzORERNbq5c+eibdu26NGjB48uERERWb3svCLsP54otwd0jzb3cCyaVquTDc7TCwrRwt8XrwwfYO4hkRFKXrl5ucpzLLamy6AOMgMk/vAFZCSxXDoR0bWwvU8JK+BcpA9+lDtozT0UIiKbNWnSJBw9etTkzbSIiIiIjFXySmQxtApvgpAmXjzIl7FgRwy2nI6Hk4MKs+64Wfb7INvL/LC1klcGnn4eiO4WKbf3rTtk7uEQEVk1Bj/MwKtUJddaB30QhIiIiIiIiOhyNlaWvBrIkleXdTAhBR+v3ya3pw3rj1aB/nxh2ZjCyuCHu40GPwTR90OIWXfA3EMhIrJqDH6YQRAc5VrnAFRomf1BREREREREdcsrLMGeoxfkNvt91C2/pBRT/liOcq0Ww9pG465uHfiysuGyV+7errBVou+HELv2IHQ6TpwlIvC9oJoreV9kw3MzaO3li61IBhTA2ewsRPtxJgoRERERERHVbuu+s6io0CKyqR/Cgn15mOo4EfLa32uRkJOHUG9P/N+oIbJvAtlw2Ssv2w1+tOnTEs6uTshOzUXcofOI7Bgm96en5iLxfBaaNvdFQCDL3xHZA7VaLT/P0tPTERAQYPefbTqdTh4LcUzEsakPgx9m0DYyGChPlkd/34VEBj+IiIiIiIioTht2n5TrgT3Z6Lwuv8Ycwqqjp+CgVGLW7TfDw9mJrygbL3tlqz0/BEcnNTr2b4vdK/YhZs0BGfxYtTQGs99aJk/8KZUKPPvKSAwb083cQyUiI1OpVAgNDUVCQgLi4+N5vAEZ+BDHRByb+jD4YQaRrUOgOLkPOgcdDiQk4c7O+nRGIiIiIiIiouoKi8uw6/A5uc2SV7U7kZqBmas2ye0pg/uiY2gQX0R2kPnh7mW7wQ+h2+BO+uDHuoPo/8CNmP22PvAhaLU6fPL2MnTrE8UMECI74O7ujujoaGg0GnMPxSKIjI+GBD4EBj/MICi8CZRHgApn4ExmpjmGQERERERERFZg2/6zKNNUoFmQN1qEsmTyxYrKNJjy+3KUllfgxugIjO/d1Sx/JzJHzw8bD37cpG96fmjLUZw7nQqdVgeNO6DxVkKdo4W6QIekC1kMfhDZCXGyv6En/Ok/DH6YgV+ID5SlQIUHkFyQZ44hEBERERERkRXYuOeUXA/oHm33db5r8/bKjTiTkYUmHm6YOeYmWQ6IbFthrqHsle32/BCatwmV548yk7KRn5yF3HYqpAxUA+I1rtUheGM5QpqxBxAR0eUoL3stGYXaUQ1VsT5VMbe8hEeZiIiIiIiILlFSqsH2A3Fye2AP9vu42LKDx/HnviNQKhT44Lbh8HWz7ZPhdFHZKxvP/BA17bsO0Wd/xMYeR+ogR33gQ1AqkDpIjXJ3BvuIiC6HwQ8zURfr18XKCnMNgYiIiIiIiCzYzkPxKCkrR7C/J1pHBJp7OBYlPjMbM/5ZJ7efuKEXekU0M/eQyETspeyV0H2IvkfszsMnoLsozqEV/w/ys80zMCIiK8Hgh5m4VAY/yh3ExxURERERERFR7SWv+nePYsmrasrKyzHljxWy30ePsFA8eWMvvnTsSGFukV2UvRK6DNZnfqRvOwfoC4hUUSkUCPfwMc/AiIishNUEP8LDw+WXverLu+++e9n7lJSUYNKkSfDz84O7uzvGjh2L1NRUWALvcn27Fa36ok8vIiIiIiIisntlmnL8u++sPA4DWPKqhg/W/oujyWnwdnHGh2OHQ6W0mlMb1AgK7aTsleDTxAstOofDIUcDlabaFTodXu04AMFunmYcHRGR5bOqbwhvvvkmkpOTq5ann376srefPHkyli1bht9//x2bN29GUlISbrvtNliCpg76GQo6B0CrZfYHERERERER/WfPkfMoLC6Dv7cbOkSF8NBU2nD8DL7ftV9uv3vrUAR6uvPY2Bl76flh0G1wRxS38UCF43/7nFO06FLqb85hERFZBasKfnh4eCAoKKhqcXOr+4MuNzcX3377LWbNmoWBAweiW7duWLBgAbZv346dO3fC3Nr5Vn5IKYGk/DxzD4eIiC6SXJiH7Snn5JqIiIjIXCWvbuwWBaWhybGdS87Nx7S/1sjt8X26on/LSHMPiUysrKQMZSX6FAh3Oyh7JXS7qRPy+/jJbdcL+r6xpQFKnDyZZOaRERFZPqsKfogyV6KEVZcuXfDBBx+gvLy8ztvGxMRAo9Fg8ODBVftat26N5s2bY8eOHXXer7S0FHl5eTUWY+jWJkzfnUrM6IlPMMpzEBHR1fn11AFc/+cXuHfNz3ItLhMRERGZSnmFFltiz8jtgSx5VXVMnv9jBXKLS9AhJBBTBvXlC9KO+32IUuguHi6wB9F9olDQ3VtuhxxQwFXnAJ2DAnsunDf30IiILJ7VBD+eeeYZ/PLLL9i4cSMee+wxvPPOO5g6dWqdt09JSYGjoyO8vfUfEAaBgYHyurrMnDkTXl5eVUuzZs1gDOGtQqCorNe47zyDH0RElkJkery0YyW0lR0FxfqlHauYAUJEdkl89yYi09t3PAG5BSXwcndG59ah/BMA+GzTDsReSIK7kyNm3T4Cjg4qHhc7Lnnl5uUKpZ30etmacR5aFxUc8rVokq1EKxd9FsiRvDRzD42IyOKZ9ZPipZdeuqSJ+cXL8ePH5W2nTJmC/v37o2PHjnj88cfx0Ucf4dNPP5WZGo1p2rRpsmSWYblw4QKMIbC5PxQaferyqVR+YBERWYrY5MTKsMd/dNAhNoVp5URkf4YNG4YWLVrgrbfeMtr3YiK6fMkrB5V9nOC9nO1nzuHLf3fL7f8bORjNfGtOciT7UZBTZFclr4SlcUfl2vNEBSoKS9ErpLm8nKAuREW5vgwWERHVzgFm9Pzzz2P8+PGXvU1kZO01PHv16iXLXsXHx6NVq1aXXC96gpSVlSEnJ6dG9kdqaqq8ri5OTk5yMTZXDxeoyvSVr5IK8o3+fERE1EDlShHtAKqX1tahKmBNRGRPEhMT8f3332PhwoV44403ZC+9hx9+GGPGjJFZ1kTU+LRaHTbtPS23B7DkFTIKCvHiklXy69md3TpgePtLf/9bKx8fHznpsyGysrKMPh5ryvxw9bKP4EduWQk2JuhL4HmcqEBecjb6NAvHvLN7UBSowIVzmQhv0cTcwyQislhmDX4EBATI5Wrs379fpjg2aVL7m7xocK5Wq7F+/XqMHTtW7jtx4gTOnz+PPn36wBKoSnQQla9ytSXmHgoREVVq7uIFh3QVygMq9AEQHeCQ7oBmLl48RkRkd/z9/TF58mS5xMbGYsGCBXjyySflcu+998pASKdOncw9TCKbcvBUEjJzC+Hu6oQe7fQzvO05EPTiktVILyhCdIAfpg29EbZk9uzZ5h6C1SmsDH64e7vBHqw6dwJl2gp4l6jhlFGM8qISJC4+AoUPUO6hxN7j8Qx+EBFZavCjoUSD8l27dmHAgAHw8PCQl8UPsPvvv1/OlDDMShs0aBAWLVqEnj17yn4d4seYKJfl6+sLT09PPP300zLw0bt3b1gCdYkCJdChRMU0RSIiS1FaWAaPsyrkeFRA5wI4ZCrhcVaJsqLKRk1ERHaqa9euMoPaz88P7777LubPn4/PP/9cfr+eN28e2rVrZ+4hEtlUyau+XSKhtvO+Ft9u34ttZ87B2cEBH99xM1wc1bAlDz74oLmHYMVlr+wj+PFXZcmrwAv69wJdaRkWPP89HN/ugNKmjth+Pg63o6eZR0lEZLmsonioKEMlmp3feOON8kfV22+/LYMfX331VdVtNBqNzOwoKtJ/EAoff/wxbrnlFpn5ccMNN8gfa4sXL4alcC3Vp7dq1BdXlyciInNxUznAMU8Hh0L9R6SyHPKyq9K+Tz4Qkf0S37P/+OMPjBgxAmFhYVi9ejU+++wzWU729OnTct8dd9zR4McTgRNR5uW5554z6riJrJFOJ0pe6YMfA7tHw57tu5CE2eu3ye1XRwxAVBN9k2d7UFJSgry8vBrLlZo7dy7Cw8Ph7Owsy4bv3q3vmVKbr7/+Gv369ZOTS8UyePDgS24vXpvTp09HcHAwXFxc5G1OndK/Vs3S8NwOen6kFOVjR8o5ua3eV6zfWaafkOUcp798KCfFfAMkIrICSmuZZbZz507Zv6O4uBhHjx6Vjcmr9+YQH+riw1g0RTcQH/LiA1/UxiwsLJSBj8v1+zA1nwp9nWStbU1eISKyaqV5pXBJLoaqQH+5wkUnL5fll5p7aEREJicyp8WJrsceewwtW7bEvn37ZBb2I488Ajc3N/kd/MMPP8Tx48cb9Hh79uzBl19+iY4dOxp97ETW6FhcKlIy8+HipEavjmGwV7nFJXj+jxWo0Olwc/tWGNvF9jPLxDmLp556Spb2Fu+vhkCEYbkSv/76q6yCMWPGDFmyUJQnHDp0KNLS0mq9/aZNm3DPPfdg48aN8j2+WbNmuOmmm2SFDYP3338fc+bMkZl+ojKHGKN4TBGoMUvZKy/bz/z4J/6Y7HXTxS8YmuSSGsEPl5P63rFJjpVBESIist7gh61q5qz/sNY5MPODiMhSNG3uC+dcDZzStfKy1kUnL4c08zX30IiITE5MOvr000+RlJQka9O3b9++1r4g4oRZfQoKCnDffffJGcZXeiKPyN5KXl3fOQLONlbiqaHEpMZX/1qLpNx8NPfxwhu3DGpwU3BrNnXqVGzYsAFffPGFnOj5zTff4I033kBISIgs730lZs2ahYkTJ2LChAlo27atDFi4urrKcoW1+fHHH2Uvp86dO6N169byubVareyhavibiM+AV199FaNHj5YBbDEm8dmwdOlSmCPzwx7KXi09e0Sub/SsDISWV4hGOHLT+ViuXBf66pCaod8mIqJLMfhhRp0CK7NQVEBmkf4DnIiIzExTDu35JLgk6Psx6RyB8tQUuZ+IyN6IWcOipFX1jGuhvLwcW7ZskdsODg6yPG19Jk2ahJtvvlmWSiGiS4kTzBsqgx8Dethvyauf9xzE2uOnoVYq8dHtI+DuXPP9x1YtW7ZM9lESZbvF+6ooQyWCDe+8844MTjRUWVkZYmJiarzXKpVKeVlkdTSEKCcuSh6K/qlCXFwcUlJSajym6LMqymk19DEbS0GuffT8OJ2bicNZqVApFGhbrv87BAR5QanSn8ZzzCqDukgHqBRYd7hh2ZdERPaIwQ8zuq59JGQOI4Cdp+LNORQiIqqUeCoZuowcuMTnA5XxjkJ/LZJOs54uEdmfAQMGyBKyF8vNzZXXNZTo3ydKr8ycObNBty8tLb3mevdE1ubU+QwkpObASa3CdZ0iYI+OJafh3dWb5fYLQ/qhQ1PLKVttbOK9NjIyUm57enpWvff27du3KtjcEBkZGaioqEBgYGCN/eKyCGA0xIsvvigzTgzBDsP9rvQxjfFeXmgnPT/+jtNnfdwQEoniNH1pq5YdmuGHuM/x4YbX8WPc5wgp0weAtifwfBIRUV0Y/DCjiDahUFSeWNtz9rw5h0JERJWaRgdDoVRAlVkAZam+xEJJa2+ERNnPj28iouoz0WsrN5OZmSnrvTfEhQsX8Oyzz8qZy6InX0OIIImYVWxYRP15Ilu3ce9Jue7VIRyuzvr+kPaksLQMk/9YgbKKCgxoGYlxvbvAnojAh8iwEETpqd9++60qI8Tb29tk43j33XdlwHrJkiUNfs825Xt5oR1kfojP3r/ijsrt0RFtkZqUI7cDQ7wREOqHTv3byXUbN3+5/2h+ulnHS0RkyRj8MCMvfw8oNPofk3sSEpCSq29YRURE5iN+SEz+8jEo8ougKtK/R3v0bS73ExHZi9tuu00uIvAxfvz4qstiEfXeRZPb6667rkGPJcqviCa7Xbt2laVcxLJ582bZOFdsixnKF5s2bZrMLjEsIoBCZOs27jlt1yWv3lyxAfGZ2QjydMc7Y26yiz4f1Yn+HAcOHJDbL730EubOnSuDD5MnT8b//ve/Bj+O6MOkUqmQmppaY7+4HBR0+ck8H374oQx+rFmzRvb1MDDc70of0xjv5fbQ82N/RjLO5efAxUGNIc2ikZpcGfwIrhkE6xXSXK4THQplwISIiC7lUMs+MhFRd1Oh0W8fQzb6z/kab918E27vemkjSSIiujLiB6NYajupVp/hDw/CpqV7kJingyYAyHW58scgIrJmYoauIE6meHh4wMXFpeo6R0dH9O7dWzbTbYhBgwbh0KFDl5zkEzObRWkVcZLuYqLHyMV9RohsWXxSFuISM+GgUqJfF33pI3uydP9R/HXgGJQKBT4cOxw+rv+959gLEeQwEOWmjh8/LoPHUVFRNQIR9RHv0d26dZPNyseMGSP3GZqXP/XUU3Xe7/3338fbb7+N1atXo3v37jWui4iIkEEO8RiiKbogSljt2rULTzzxRJ2PaYz38oKcIpsve/VXZcmrIaHRcFM71sj8qG5A21Z4c/2/KHMC4nIyEemjzwQhIqL/MPhhRiLTQ6vWb2u9tCjxLMO0javQt0UYgrw8zDk0IiKrJxrrikX8MDOcxLsSXfu1xuq44yhqAeSj1ChjJCKyVAsWLJDr8PBwvPDCCw0ucVUbETxp377m5B7xeH5+fpfsJ7JXGysbnfdo1xwebtdWasjanM3IwpvLN8jtp/v3QfewUHMPySKEhYXJ5WpMmTIFDz74oAxi9OzZE7Nnz0ZhYaEMPAvjxo1D06ZNq/owvffee5g+fTp++ukn+b5v6OPh7u4uF5GF89xzz+Gtt95CdHS0DIa89tprsi+IIcBiKoU2nvlRrtXin3h9A/MxkW3luq7gR/NmfnDJBIoCgXUnTuLR3gx+EBFdjMEPM/o75jB0LtVSExWAxr8cy2KOYOLA3uYcGhGR3YvqEgHXjfuR08cZWgcdckqK4e1sf7MQici+zZgxw9xDILKr4Ie9lbwq1ZRjyu8rUKTRoHdEMzzarwfs1ZtvvnnZ60VwoqHuuusupKeny/uIQIbI1li1alVVw/Lz58/LShQGX3zxBcrKynD77bdf8hnw+uuvy+2pU6fKAMqjjz6KnJwc2YhdPOa19gW5EmWlGpQWl9l08GN7yjlklBTCx8kF/UIiUFRYirzKPieBQTWDH+JvGFLqitMoxo4L8Xi0d8PKURIR2RMGP8zoxNlE4OI+dgrgRFyimUZEREQGkZ3C4Xw+Hyh3lp+W2y7E4+boNjxARGTzRG8OUdrEx8cHXbp0uWzd/djY2Kt6jk2bNl3DCIlsS2JaDk6cS5Mln27o2gL25L01W3A8NR2+ri54/7ZhUFU7IW9vRIPx6jQajWyALnojtWjR4oqCH4IocVVXmauL34Pj4+PrfTzxWSACNPUFaYzJ0OxccPFwtumSVzeHtYZaqUJicoa87O7pArda/s2t3fxxGhdwtIBNz4mIasPghxl1cPbGn6KMfPXfkzr9fiIiMi+fJl7wKNdCWaqQmR+bz8Ux+EFEdkE0NDfUaDd1ORMie2503qV1KHw8bbePwcXWHD2Fn/boG3y/d+swNPFwhz3bt2/fJftE+dbx48fj1ltvNcuYLI2h5JWrp0ut/aKsXUm5BqvPn5TboyP0Ja9SKkteBV3U7Nygd9Pm+KfkAtKUxSjQlMJdzX5ZRETVMfhhRjf374KPPtiG/G6VH046wG9tAUa8q28gRkRE5hXW3A/HChUoc9PhYJq+9jERkT2VumLZKyLj27jX/kpeJWTn4tW/18rtR67vjn7R4eYekkXy9PTEG2+8gZEjR+KBBx6AvSuw8X4f6xPOoEBThqZunujWRN/7JjW59n4fBl1ahsFh678o91TiQEYyrg/m/yUioursN6fUAgSE+uFht8iqy8FfJOKtAQPkfiIiMr82ncPgkKvvzZRYlGvu4RAREZGNSc3Kx+HTyXK7f/co2ANNRQWe/3Ml8kpK0Sk0GM8OZJ+Cy8nNzZULieBHkU0HP5ZWlrwaFdFWlsG7XLNzg/CoJnBJ1srt7efrL19GRGRvmPlhZg//71bMXvKZ/EsMfHIQho8bZO4hERFRpbY9WsBp/QUURStRqNQ3VyQisnWi18fl+nxUl5WVZfTxENmyzXv1Ja86RocgwMc+yj7N2bgDBxKS4eHkhI/GDofaBssXXY05c+bUuKzT6ZCcnIzvv/8ew4cPN9u4LLHslZu37ZWHyy0twabEM3J7TES7qv1VmR91lL1yc3dGYJEz8lGOHQnxQC8TDZiIyEow+GFmYsaCUgNoHYAT2fzxSERkSVp0DofL10XIvt5d9v3IKi6Gr4uLuYdFRGRUs2fP5hEmMpGNe+yr5NXW0+fw9dY9cvut0UMQ6uNl7iFZjI8//rjGZaVSiYCAADz44IOYNm2a2cZlSWy57NWKc8eh0WrR2jsArXwCqvbXl/khtJFNz1NwrDADWp2uKmuEiIgY/DA7MatOWaKA1kWHpOI8cw+HiIiqCW4RCLeEIqDcXX5i/nsuDqNb65sPEhHZKnGijYiMLyu3CPtPJNpNyav0/EK8uGSV3L6ne0cMbWsfAZ+GiouLM/cQLJ4tl736K+6oXI+O/C/ro6HBj66hofhHk4xidTnO5GYi2tvfyKMlIrIe7PlhAdQl+nWeSAEhIiKLoVKp0NTLVQaphc3x+lR0IiJ7VFJSgry8vBoLEV29zTGn5Szt1hGBCAmw7QyICq0WUxevRGZhEVoF+uOloTeae0hkxZkfbl62VfYquTAPu1LPy+2R4W2q9hcVliIvVx/wCQyqO/gR3TIEzin6vh+x6fqAKhER6bHslQVwKVWiGBUoc9Q31SUiIsvRqm0IthcWoMxdh0PpqeYeDhGRSRUWFuLFF1/Eb7/9hszMzEuur6io4F+E6Cpt3FtZ8qq77WdAiFJXO+IuwEXtgI9vvxlOap6KEG677bYGH8PFixfD3hl6ftha5sey+GMQZ4N6NglFqPt/gdC0yn4f7p4ucPNwrvP+EdGBcFmiRXEzFfamJuCu6E4mGTcRkTVg5ocF8NM6ynW5E4MfRESWpl2PKKhz9e/PSSWc5UxE9mXq1KnYsGEDvvjiCzg5OeGbb77BG2+8gZCQECxatMjcwyOyWnmFJdh79ILcHmjj/T5iziXKJufC9JsHIjLA19xDshheXl5Vi6enJ9avX4+9e/dWXR8TEyP3iesJKMi1zeDH0rNH5HpUtUbnQkplyaugOpqdGwQ39YFXpv703q5kfQYJERHpcbqFBWjh7o1TKIaOmR9ERBYnumsEHGN2o7ClA4pYnpCI7MyyZctkkKN///6YMGEC+vXrh6ioKISFheHHH3/EfffdZ+4hElmlf2PPoKJCixahfmge7ANblVNUguf/XCHLe43q2BpjOrF3WnULFiyo2hZZdnfeeSfmzZsnS68asuuefPJJGRghoLCyBJSbDQU/TuVk4Gh2GhwUStwc1rrGdWkp9ff7EJRKJVq7+SMO2ThfnIuc0mJ4O7kYddxERNaCmR8WoG9UhFzrHIDULM4qJiKyJBEdmsPtlH6WmU6tQ1axfpuIyB5kZWUhMjJSbouTb+Ky0LdvX2zZssXMoyOyXhv36EteDezR0txDMRqdToeX/1qNlLwChPl6Y/rNg6BQ6Puo0aXmz5+PF154oSrwIYjtKVOmyOuoesNzV5trdH5j0wj4OLtccbNzg9YRIVBn6/t+7EtPMspYiYisEYMfFmBIz3aQBR4VwMpYfbojERFZBmdXJwTk64By/eWNcWfNPSQiIpMRgY+4uDi53bp1a9n7w5AR4u1d/8kYIrpUQXEpdh0+J7cH2HDJq+937ceGE2ehVqkw+46b4e6kL/dMtSsvL8fx48cv2S/2abX6k9r2ztZ6fogA4V9x+nNAoy8qeVUj+FFP2SshMioQLslsek5EdDEGPyxAk2BfKCpPqm07wZNqRESWJqq5H5Ql+pmKm87yfZqI7IcodXXgwAG5/dJLL2Hu3LlwdnbG5MmT8b///c/cwyOyStv2x6FMU4HmQT6IDPWDLTqSlIoP1v4rt1+86Qa0CW5i7iFZxfvtww8/jFmzZmHr1q1y+eijj/DII4/I60hkfthW8CM2IwkXCnLh6qDG4NCoS65PTW545kdky6Cq4EdMeqIRRktEZJ3Y88MCiNRfRalCllOJy8s293CIiOgi7btF4K/Csyhz1+FwRgqPDxHZDRHkMBg8eLCcgSwa8Iq+Hx07djTr2IisveSVyPqwxTJQBaVlmPLHCmgqKjCkdRTu69nJ3EOyCh9++CGCgoJkwCM5OVnuCw4OloHm559/3tzDswiFlWWv3Lxso+zV35WNzoc2bwlX9aWZUVdS9io8qklV8EOUvSrXauGg5HxnIiIGPyyEQylQ5g5k6UrNPRQisnMi/To9PR1NmnCGnkHbnlFQbz6NskAgtazArH8fIiJzEo3OxUJEV6ekVIMdB+JstuSV+B75+j/rcS4rByFeHnhr9BCbDPAYg2haPXXqVLnk5el7gbLR+X80ZRqUFOnPl7jZQM8PjbYC/5zTlzkbFdH2kuuLi0qRWxnsCQyqP/jh5u6M5s5eOF9agmInDU7kpKOdb6ARRk5EZF0Y/LAQTqUKlEGHIscKcw+FiGycq6srzp07h4CAAHn55ptvxjfffCNnlglpaWkICQlBRQXfjwxadAqD088aFLZWo1hVWaeQiMhO7NmzBxs3bpSfDxfXnRflWYio4XYcjEdJWTmC/T3ROtz2Jpos3n8U/xw6DpVCgQ/HjoCXi7O5h2SVGPS4VGGuPhAguHlaf/BjW/I5ZJYUwc/ZFX2Dwy+5PjU5V67dPV3g5tGw/0eRUUHYm3IWRWEqxKYlMvhBRMTgh+XwLHdAPspQ7iQ6nxMRGU9JSYmclWewZcsWFBcX17hN9esJ8A3ygde5YmRBLUsUphcWIsDNNmoNExFdzjvvvINXX30VrVq1QmBgYI0Z3JzNTXTlbLnk1Zn0TLy1YoPcfmbgdejaPMTcQ7J4Xbt2xfr16+Hj44MuXbpc9jURGxsLe1ZQmQXh6uEClYMK1s7Q6PzmsNZQK1V1lrwKakCzc4PIloFwOXhaBj9E348HWndtxBETEVknZn5YiFBHdyQiC9pLyzwSEZmcrf0YbwwRSmfEiaQPB2DdmdO4pyPrVxOR7fvkk08wf/58jB8/3txDIbJ6ZZpybN1/Vm4P6G5bJa9KNOV47vflKNaU47rI5ph4fQ9zD8kqjB49Gk5OTnJ7zJgx5h6ORSusbHZuCyWviss1WH3+pNweHdmu1tukJmc3uN+HQUR0EFxW6zM0Y9n0nIhIYvDDQnQKCcEubRa0ah3KNBo4qtXmHhIREVXToW0oNpWkQ+uuw6azDH4Qkf3UoL/++uvNPQwim7D7yHkUFpchwMcN7aP05UZtxcxVm3AqLRP+bq54/7ZhUCo5kaYhZsyYUes2XaqgMvjh7m392ddrL5xCUbkGoe5e6Opfe4bUlTQ7r5754ZyiFWn8OF+Qg/TiQgS4WP/xIiK6Fsprujc1mpt7tNdvOAA7D+pnAxERGSur4+KyJcz0qF/HPi3hUNnr/EhGKl+cRGQXJk+ejLlz55p7GEQ2VfLqxm5RNhUcWHXkJH6NOQTxLxKBD393nmy9GhcuXEBCQkLV5d27d+O5557DV1991Yh/Lesve2ULwY+/447K9eiItnX+DqsKflxB2avgpj5wUznCMVNfwpjZH0REzPywGB2imwM79OGo1fuO4YZurcw9JCKyUaKfR8uWLau+aBcUFMgaw2J2r+F6ulR01wg4HtyKsiAgo/y/hotERLbshRdewM0334wWLVqgbdu2UF+Unbx48WKzjY3ImpSXV2BL7Jmqfh+2IiE7F6/+vVZuT+zbA9e1CDP3kKzWvffei0cffRQPPPAAUlJSMHjwYLRv3x4//vijvDx9+nTYM1spe5VdUoxNifoJr2Miai95JaQmX3nmh/g9FxHVBPHJqSjzV8rgx9DmLRth1EREdlD2Kjs7Gz/88AMefPBBeHp61rguNzcXixYtqvU6aviHlEID6JyAw2mcUUxExrNgwQIe3qvQNDoIzomlKGjrhFK1aP5BRGT7nnnmGWzcuBEDBgyAn58fMwWJrlLs8QTkFZTA28MFnVuF2sRxLCuvwJQ/VqCgtAxdmgXjmQHXmXtIVu3w4cPo2bOn3P7tt9/QoUMHbNu2DWvWrMHjjz9u98EPWyl7teL8cZTrtGjj0wTR3v513u5qyl4JEdGB2HksGbkdgJi0xGseLxGR3QQ/PvvsMxw8eBBPP/30Jdd5eXnh33//RV5eHl555RUYQ3h4OM6dO1dj38yZM/HSSy/VeZ/+/ftj8+bNNfY99thjmDdvHiyRqlSBcicdUsv1H+pERMYgAtV05VQqFYLSdcgQ2TFqIKUgH0HuHjyURGTTFi5ciD///FNmfxBRY5S8agEHlW1Un569YRsOJqbAy9kJH40dYTP/LnPRaDRVzc/XrVuHUaNGye3WrVsjOTkZ9q4q+OFl3cGPv84eqTfro7ioFLmVZb4Cg64s+BEpmp5v0Dc9P5iZjLKKCjiqVNc0ZiIia9bgbyfiR4+YbVAXEVT4448/YExvvvmm/NA3LLUFYi42ceLEGvd5//33YanUpfp1gUOFuYdCRHampKREnuD6/PPPceqU/sc5XaqDrw9QmfSx5uRJHiIisnm+vr6y5BURXb0KrRabYk7bVMmrLafiMH97jNx+e/RNCPFmBYhr1a5dOzlRU0wsXbt2LYYNGyb3JyUlycw7e2fo+WHNZa/2pSVid5q+r8vIiDZ13i41OVeu3T1d4ObhfMWZH+ocHRxKgTJtBY5ksbIIEdm3Bgc/zpw5g+jour+oievEbYzJw8MDQUFBVYubW/0Rf1dX1xr3seSyXG4afTS+zJn19onIeKZMmVIjeFxWVoY+ffrIYPHLL78s+3/s2CGaENHFuvaMgqpY3ytlw2n9SQwiIlv2+uuvY8aMGSgqYq8joqt18FQSsnKL4O7qhO5tm1v9gUzNK8CLS1bL7ft7dsbgNlGwZsmFedieck6uzem9997Dl19+KStY3HPPPejUqZPc//fff1eVw7JnRXnW3fD811MHcNuq76su/5sUV2/Jq6AraHZuEBEVCPFrxSlRP6mWTc+JyN4pr6Tch5hxUBdxnaFZrrG8++67csaDODH3wQcfoLy8/prrojmYv7+/bBQ2bdq0en+4lZaWyvJd1RdT8Ve6yHWFE4MfRGQ8om7wkCFDarxPirKCIuND9He644478NZbb/FPUIt2vaLgUKDfPp6dzmNERDZvzpw5WLlyJQIDA2X9+a5du9ZYiKjhJa/6dYmE2kFl9Vks/1u8EtlFxWgb1ARTb+oHayZOSF//5xe4d83PuH7xF/KyuYigR0ZGhlzmz59ftV80QbfU0t2mZM09P0RgbdrOVah+puflnavqDLilJmdfVb8PQWSKiPu5JOtLXzH4QUT2rsE9P0TAYenSpejdu3et1y9ZskTexpjNFsUPLJF6v337dhnIEGWsZs2aVed97r33XoSFhSEkJET2K3nxxRdx4sQJLF68uM77iD4ib7zxBsyhpa8fjiMfWkdAq9UaPZhERPbp/PnzaNu2bY1gyO233y7fL4Vnn30WI0aMMOMILVdEh+ZQL6lAabASWdpicw+HiMjoxowZw6NMdA10Oh027bWdklfztuzG7vgEuKrV+Oj2EXB0aPApBYtzIT8XL+1YWXVCWqvTyRPSN4REINjN02yvl5iYGFlVQ5zPENUvHB0dZUULe2cIfrhZYfAjLj9bvr6qq9DpEJ+fXetr7WqbnVfv+xF3Wl+iNyadTc+JyL41+JvKU089hbvvvhuhoaF44oknZCaIUFFRIWvEf/zxx/jpp5+u6MlFs3KR2nk5x44dkw2+RJkWg44dO8ovAKLPiAhWGJqCXUzMkDAQM9WCg4MxaNAg+UWirtrFIqhS/blE5kezZs1gCgPbt8LfJ+Ohc9Dh3IUMRIQ1McnzEpF9EYFV8cPKYOfOnXjttdeqLnt7e8sMELqUi7sLPFIrUNBeCY0T+zMRkW0TWdYKhQIPPfSQ/A1ARFfu6NkUpGbmw8VJjV4d9BNNrJUIeszdvFNuv37LIET4+8BqA1JJZ/HaztU1ZuLXd0La2EQmtujzISYqiYoUIlNbBD/EORNx2d6zPwoNPT+8rC8QFOHhAyUU0FZ7xakUCoR71P5/qCr4cRVlr+TzRQfCedtxKHRASlE+kgrzEGKmgB4Rkbk1OLVg7NixmDp1qszAENkXIstDLGL7ueeekwEDMXP4Sjz//PMyuHG5JTIystb79urVS/4gi4+Pb/DzifsIpy9Tp10EUkRfkOqLqfTv3Fq/oQJWbDtosuclIvvSpk0bLFu2TG4fOXJE/sAaMGBAjR9eorwJ1S6yWC3XOgcgMU//w4SIyBY5ODg0uNQsEV2+5NX1nSPg7Kj/DmGNsguL8cKfK+Ts9Vs7t8WoTnU3a7ZkovnzA+t+xYT1vyOhlpJDlzshbWwi+7p79+5yEpKLi74ktnDrrbdi/fr1sHf/lb2yvuCHCKY90rZHjdfZO72H1RlkS02+xsyPlkFQlgOeefpJyzFpzP4gIvt1RTmqb7/9NkaPHi3rw4sAgpgxceONN8p0zKtpwBUQECCXq7F//345e7lJkyZXdB9BZIBYIm9nF0BMJFYBe85fMPdwiMhGiUC2yORbvny5DH6IElcRERFV169YsYJNFS+jd0QzbNNcANTAP0eP47E6ykESEdmCgQMHYvPmzQgPDzf3UIisjvi9bAh+DOzREtb873hp6Wqk5Rci0t8Xr40YCGsjZr9/uG8L/jxzSM69d1SqMKFNdwS5euCtvetlxkd9J6SN7d9//5UlvkWVi+rE+29iIk9eW3PPD6GTf4hct/L2x3eD7rzs6+zay17pJ7I5nC8DOqgQm5GIkRHWGbAkIrpWV1ygUwQ5ribQcS127NiBXbt2yZnJIu1TXJ48eTLuv/9++PjoZ2WILwOipNWiRYvk+ERpK1GGS5zUE03SRc8PcZ8bbrhBls2yVMoyQOsCnCsyXaN1IrIvYvaYCHD8888/uOmmm/D000/XuF7UFH7yySfNNj5L161vK6jOJ6BCrcPGU6cZ/CAimzZ8+HBZqvbQoUPo1q0b3NxqnnQaNWqU2cZGZOlOnc9AQlounNQq9OlknQHElNx8zPt3NzafioOjSoWPbx8BVyvKYCnQlOKrI7vw1ZHdKKnQZ7GNDG+DqV1uRDMP/YnlYc1bylJXIuPDXIEPQfT9FGXFL5aQkCDPg9izck05SgpLrTr4kVWiL9sV7ul72ddZcVEpcitLfAUGXV3wIzjUB07OajgmVOiDH8z8ICI71uDghwgeNIQxAguiFNUvv/yC119/Xda6FDOURSCjem8OjUYjm5kXFek/JMRsiXXr1mH27NkoLCyUfTtE6a5XX30VlsyhVIEyFx2ylRpzD4WIbJgIFoulNjNmzMDhw4dNPiZr0bJbJByOrEOFB3A6L9PcwyEiMipDMHzWrFmXXCf6gdR2oo6I9Dbu1Tcc7t0xHK7ONWfzW4M/Yg/jtWVrYWgVN6xtNFoFXV3lBlMr12rx2+mDmLX/X2SU6DMGugeE4pXuA9ElQD8D30CciDZn0MNATEoS5y+++uqrqvfYgoIC+d1cTOq0Z4W5+vM81trzQ8gs1f8bfJ3+K2lWm9TkXLl293SBm4fzVT2XqJISERWIgnOJVeXeSso1cHawnsAlEZHJgx+dO3eWH77Vm+Sa6gdQ165dZUPeyxGpoNXHJoIdIkXf2jhplChDBUqcteYeChHZmfz8fPz888/49ttvsXfvXp7QqoNfsA8cM7UoDVEiV6GfgUZEZKvETGQiujobdutLXg3oEW2VGR/Tl62rCnwI/xw+gSmD+yLIy8Pim5nPjNmIkzkZcp/I6Hipa38Mbd5SnrOwVB999BGGDh2Ktm3boqSkRJYXP3XqFPz9/eV3dHtmKHnl4u4MlYO+j4W1Zn74Ol8+eGMoeRV0lc3ODSJbBuLY4QS4a9UoUGpwMDMFPQObXdNjEhHZdPAjLi6uQSfO6Np465yQjyKUO9cdZCIiakxbtmyRAY8///wTISEhuO222/DZZ5/xINdB/Gj2y1ZAfOKVO/GkIBEREV0qLjET8UlZcFAp0bdzpNUdotgLSbK5eXXi8rmsHIsNfojZ7SLosTU5Xl72dnTGs5364r6WXWTJLksXGhqKAwcO4Ndff5VrkfXx8MMP47777qvRAN0eFVSWgbLWkldCVmmxXPs61RP8SM6+pn4fBhHRgRChPt9cBxT4aBCTnsjgBxHZpQYHP8LCwmrdz5nCjau5mycuoAhap0Z+YCKialJSUvDdd9/JoEdeXh7uvPNOWVZw6dKlcraZLZg7d65cjJGR2EnliXjkQ6cGzufkoLn3tf04ISKyZCKb+sMPP8SxY8fkZfE58b///Q/9+vUz99CILJah0XmPds3h4XZ1pWvMJTWvAB+u/feS/UqFAmG+3lbTzPzJDn3g5Whdx97BwUEGO8RikJycLN9z7XlyUmFl5oebt3WWvKqe+eHXwMyPaw1+REYHybUyvhTwAWLT9SWwiIjsjfJaZgo/+OCDCA4Olj+GRDPy+kpTUf16RujTEHVqHbIrP+CJiBrTyJEj0apVK9nLSdQVTkpKwqeffmpzB3nSpEk4evQo9uzZ0+iPPaRHKygqWzP9Hnug0R+fiMhS/PDDDxg8eDBcXV3xzDPPyEXMQBZ9o3766SdzD4/IYm3cqw9+DLSyklfp+YUYv/APJOXmw8fFWQY8BLF+c+Rgi8r6EM3MZ+3fgv5LvsQflYEP0cx8/eiJmNZtgFUFPo4cOSKDG6LfR06O/uR3RkaG7HUaGRmJjRs3wp4Zyl5ZdebHFZa9CrzGslei54egPanPOBHBj8uVsScigr1nftjLTGFzG9y1LT5etQc6B2DL1qMYfUsPcw+JiGzMypUr5cmrJ554AtHR1vWD3FK079MKyo0xqFDr8O+ZODw/8EZzD4mIyCjefvttvP/++/IEnIH4DBEN0P/v//5P1qQnopoS03Jw8lw6VEoFbugWZTWHJ7OgCBMW/YG4zGyEeHlg0fg74KBUylJXIuPDUgIfV9LM3Br8/fffuP3221FeXi4vi/fcr7/+Wp5v6datG5YsWYJhw4bBntlC2auGNzxvnMwP0SxdPEZyag4cFEpklhThXH4Owj19rulxiYhsNvPDXmYKm1u0XxPIKStKYMvR0+YeDhHZoK1bt8qSheLHVK9eveQsMzGzjBquWasQOOTrZ07FF2Xx0BGRzTp79qz8HXCxUaNGNagnIJE92lBZ8qpL61B4e1hHr4bsomJMWPQnTqdnIdDDHQsfvB2hPl4y4NEroplFBD7ErPWNiWcw4p/5eHnnKhn4EM3M5914K34fdp9VBj6Et956S2YsiwmmIrAs3ndFkHnFihVYtWrVVQc+RPnX8PBwODs7y+/8u3fvvmzmydixY+XtRX87cc7nYq+//rq8rvrSunVrmIK1l70Sr93skmKTlr0y9P1QVgChcJeXWfqKiOyR8kpmCotmW2+88QZuvvlmqKygYZg1Eo3YFPoJHziRk2nu4RCRDerdu7ecTSbqBz/22GP45ZdfZKNzrVaLtWvXysAIXZ7KQQWXHH3wo0BdWf+KiMgGNWvWDOvXr79k/7p16+R1RFR3v48BVlLyKre4BA8vWoyTaRkIcHfDwvG3o5mF9fYQzcwfWPcrJqz/HSdzMmQz8xk9BmPNqEcwLKyVPBFvrU6cOCGDH+7u7nj66aehVCrx8ccfo0ePq68CIZqmT5kyBTNmzEBsbCw6deqEoUOHIi0trdbbFxUVyfJa7777LoKC9L0iatOuXTv5G8KwiElVJi175WWdmR95ZaUo12nlts9lgh/FRaXIrcxyCQy69v+Dhr4fPjn6oi+i6TkRkb1pcNkr8aEmyl2JmcJt2rTBAw88gLvvvtu4o7NTyjKFLKWSrig191CIyIa5ubnhoYcekov40SXe48UPnpdeeglDhgyRKfhUt5BCNXJQjnJn1s4lItv1/PPPyxnI+/fvx3XXXSf3bdu2TZbC/eSTT8w9PCKLk5qVjyNnUiDOxffvbvklr/JLSvHI94txNCUNfm6u+O7BsQj3s5yyOLbUzLwuYuKRp6en3BaTTEVfJRGIuBYig2TixImYMGGCvDxv3jwsX74c8+fPl9/1LyYCLYZgS23XV2/IfrngiLEU5lp32ausypJXbg6OcFbVfRouNTlXrt09XWTZqmsVGa3v+6ESTc87MfODiOyTw5XMFBaLSH8UswjEh6aYSWCYKSxmfnl4mD8V1hY4lilQ7KZDoVOFuYdCRHZClDUU9YVnzpyJf/75R77H0+X1CwjBUZyXn6QnUtPQKrAJDxkR2RzRH0qc6Proo4/w22+/yX1iIpT4PTB69GhzD4/I4myqzProEB0Cf299qRlLVVBahok/LMGhpFR4uzhjwbixaBHgB0tpZv7l4V34+uhulFToSyOIZuZTu9yIZh6WlZXSGFavXg0vLy+5Lc6xiIy7w4cPX1JusCHKysoQExODadOmVe0T2SSDBw/Gjh07rmmcp06dkhnjopRWnz595G+H5s2b13l70SNWLAaitNfVKMitLHvl5Wrlzc5dGlTyKugam50bRLbUB6oKDuQAndQ4kZOO/LJSeDg6NcrjExHZXMNzgTOFjc+twgHFKEOZbUxkISILIzI96uPnZxk/fC3Z8P6d8NXp89CpgZ93xeL1UfbdiJKIbNett94qFyJqeMmrgd0tu+RVUZkGj/24BPsTkuHl7CQDHy0D/c09LJtrZt5QDz74YI3LojRtdaKsV0VFwyZHil5+4raBgfpZ/wbi8vHjx696jKJviMj6E5OmRMkrURK9X79+MkhT10RYERwRt7tWhZWloNysNPNDNBsXfOvr95Gc3Wj9PoSgpj5wclajNFuDICcfpJQW4EBGMvqGhDfK4xMR2WTwo66ZwsuWLeNM4UYS6OiGDJRBy+AHERmB+NESFhaGLl26yOZ7tbHmusmm0rJrJJSH9GUKt587Z+7hEBEZlZhJLGrFixnJ1V1uxi+RvcnMLcT+k/qa+v0tuN9HcZkGT/z0F2LOJ8HDyQnfjhuLNsHmzWAV30k3JZ3FzJiNsqeHIJqZv9S1P4Y2b2nT300vfl+1VMOHD6/a7tixowyGiN8UIitQ9Ietjcg+ERVDqmd+XE2/qKqeH1ba8DyrtLLZuZPpmp0LKpUSEVGBOH44AeEKT6SgALEZiQx+EJFduabgh4GoSzlmzBi50LVrGxiII8XZqHDSoaSkDM7OjjysRNSoJUx+/vlnxMXFyTrA999/P3x9fXmEr5CrhwvU+TpUeAJJGjaJJyLbJEqciIzB7du3X3Ki8kpmIhPZg80xZyDmlbSJCESwv76Hg6Up1ZRj0i9/Y1f8Bbg5OuKbB25F+5CaGQLmaGYugh5bk+PlZdHM/NlOfXFfyy5wVKnMOjZr5O/vL8/RpKam1tgvLjdmvw5vb2+0bNkSp0+frvM2Tk5OcrlW/wU/bDzzwxD8aKSyV0JEtD74IZueOwMxaWx6TkT2RWnuAdClBnZsKdc6tQ4xe87wEBFRo5o7d65MVZ86darM2hOzr+68805Za7iuTBCqnUeefhZisTNP/hGRbRo/frysFS/6QYka8rGxsXLZt2+fXBPRpSWvBlho1kdZeTme/nUZtp89D1e1Gl/fPwadQoPN2sz8hW3Lccs/C2TgQzQzf6xdL2y+7XHZ1JyBj6vj6OiIbt26yb4hBoY+IqJPR2MpKCjAmTNnEBxs/NeQtZe9MvT88Ku37FXjZn4IkS31wU3FWX3vlX0ZidDyNx8R2ZFGyfygxtUlNBTYJVJqgC0xJ3B9vzY8xETUqMQMrHvuuUcu586dk6WwnnzySZSXl+PIkSNwd7fsBp2WIhLuSEc+KpwZNCIi27R//34Z9GjduvU1Pc4XX3whl/h4/czudu3aYfr06TXKqBBZs9yCYsQcu2CxwY+y8go8+9tybDkdD2cHB8y7bwy6Nm9qlrHYWzNzcxClpkQfke7du6Nnz56YPXs2CgsLZda3MG7cODRt2lSWMDeUNjx69GjVdmJionz/F78JoqKi5P4XXngBI0eOlKWukpKSMGPGDJlhIn5PGJv1l73SBz98nBrW8Lwxgx8R0fpsn9xDWXDpqEZeWSnO5GYi2tv8PX6IiEyBwQ8L1MTVAxDn0RTAwYtSVYmIGpuY0StKl4isD5YvuTKj2rTELsTIT9O9Z8+jeyRr3xORbWnbtq1snnutQkND8e677yI6Olp+3ixcuBCjR4+WGSQiEEJk7f6NPYuKCi2imvmjeZAPLImmogLP/7kCG0+ehZODCl/cOxo9w0Mtopl5jyaheLmbbTczN4e77roL6enpMsickpKCzp07Y9WqVVVN0M+fPy9/AxiIYIboB2jw4YcfyuXGG2/Epk2b5L6EhAQZ6MjMzERAQAD69u2LnTt3ym1jqiivQHFBiVWXvcoqKa637FVxUSlyKzNcAoMaMfMjSv83z0jJQ3vvKOzJSERMeiKDH0RkNxj8sEBKhQIKDaBzBBIr9F8KiYgaU2lpKRYvXoz58+dj69atuOWWW/DZZ59h2LBhNX4I0eXdNLArXl0dA50a+HH7XgY/iMjmvPfee7JM4jvvvIMOHTpArVbXuN7Ts2F9DcRs4erefvttmQkiTpwx+EG2wFJLXpVXaPHiktVYe+w01CoVPrt7FPqYeLKGbGaeeBbvxGzEqVz7amZuTk899ZRcamMIaBiEh4fXW/72l19+gTkU5uoDAoKbl3Vnflyu7FVqcq5cu3u6wM3DudGeWzyW6CEiSmqFwRN7kIjY9ETcHd2p0Z6DiMiSMfhhoRzKFNA46pCn1qcBExE1FlHeSvx4Eb0+RBNb0fxcNEakK+ff1BfKIgUqvHSISWbzQCKyPYMHD5brQYMGNVrDc3Gf33//XZZgqav+vAjSi8UgLy/vip+HyFQKikux6/A5uT3QgoIfFVotXv5rNVYcPgG1Uok5d96CflHhJh0Dm5lTY5W8cnZzgoPawap7fvg6udZb8iqoEZudG0S0DJTBD59slbwsMj+IiOyFdX5y2AHnchU0KEepdU5sICILNm/ePDRv3hyRkZHYvHmzXGojMkPo8sSJP6cCoMgLyFDo0/GJiGzJxo0bG+2xDh06JIMdJSUlso78kiVLZFmt2og69G+88UajPTeRMW3bHwdNeQXCgn0Q0dTPIg62VqvDa3+vw98Hj8NBqcTHd9yMAa0iTdrM/MN9W/DnmUOyorNoZi6amD/ZoQ+8HBtvVrs9EL07Lly4gA0bNsAe/dfvwzpLXgmZDWh4npqc3ej9PqqXvtq5+QQUcaVAc8ieHzmlxfCupwcJEZEtYPDDQvkqnJCPcpTzeyERNTLR4JDlBRqPT7EKRShHsUcF/tp9EKN7dmzERyciMi9R772xtGrVSjbQzc3NxR9//CFP6IkAfG0BkGnTpsmGvdUzP0TGIpGll7yyhO9YIjPr9eXrsXj/EagUCnw4djgGt9E3rTY2NjNvfKIxuT2XpS2o7INhrcGPIk0ZSir0FT18nV1M2uzcILKlvul58okMRLb3xdm8LOxLT8KA0BaN/lxERJaGwQ8LFe7ti3O6QmidddBqtXb9ZYeIGtd3333HQ9qICtz1a50L8OyxFfh85w6sfuYxHmMisjmi58eKFSuuOgjh6OiIqCj9Cdhu3bphz549+OSTT/Dll19eclsnJye5EFm64hINth+Is5h+HyLw8dbKTfgt5pDsJfnurcMwrF1Loz8vm5kbj+i5ZM8KKzM/3LytsyxGZmW/D5H95ObgWH/wwxhlr6L1Tc/jz6Shi38XGfwQpa8Y/CAie8Dgh4XqFR2BzScvQKsGjh86j7adTFublYiI6icyPXIDq/VmUgAnvLKZAUJENik+Ph4ajabRHk9M8Kne14PIGu04FIfSsnIE+3uiVVgTswc+3l29BT/u3i++kuDt0UMwsmNr8zQz79YfQ5uxmTldO2sve5VdUizXvs6ul80MEz05jJX5ERzqCydnNUpLNIhQesl9ouk5EZE9YPDDQvUKDwNOAjq1Djt2n2Lwg4jIAi3fdwS4OHtdAazYd5Tlr4iILipjNXz4cNlzKj8/Hz/99BM2bdqE1atX8ziRVbOUklciCDFr3VYs3BkrL785cjBu7dzO6M3M39m7AdtS9M3evR2d8WynvrivZRc4qvSNlanhqpf6q068rpydnWXm3OjRo+Hr62tXh9Xay14ZMj986+mvYcyyVyqVEuEtmuDEkcSqpuf7M5JkxpboCUREZMsY/LBQUb6VjfJUQMzJBDxs7gEREdEl2jt5Y43unAx4VNEB7Zwb/0cLEZG59evXDy4uV9ccNS0tTfacSk5OhpeXFzp27CgDH0OGDGn0cRKZSkJqNjbvPSO3B5q55NWnG3fg62175faMmwfijm4djPZcbGZuHPv27UNsbCwqKipkjyTh5MmTUKlUaN26NT7//HM8//zz2Lp1a629kmxVYa4+88PV0zrLXmU1oNl5cVEpciuDPIFBxvkdIfp+iOBHaXwhPHydkK8pxfHsNLT30/cDISKyVQx+WChPtRNQoQ9+nC3OM/dwiIioFvcM7omvP9iN/K5O+gCIDvCILcM9/+vB40VENkf0+7ha3377baOOhcjc/t50CO/MXwudTn/5TEImOkSHmGUsn2/eic+37JLbLw/rj3t6dDLK87CZuXEZsjoWLFgAT09PuS83NxePPPII+vbti4kTJ+Lee+/F5MmT7SprrrAq88O6gx+i7FVdUpNz5drd0wVuHs5GGUdV349Taeh8cwj+TY6TTc8Z/CAiW8fgh4USqa0qjQIVKh0y1WXmHg4REdUiINQP73fsi//t2YKCni5AKfB+x+vlfiIiW3Hq1Cls3LhRZm+IPh3VTZ8+3WzjIjKX1Kx8zJy/rirwIbz33Tr06RSOQF8Pk47l6617MGfjDrk99aYbMK53l0Z9/OTCPJzOzcThzFR8e2wPMkr0s/B7NAnFy90GokuAeQI+tuiDDz7A2rVrqwIfgsiUe/3113HTTTfh2Wefle+5YtueFFRmflht2atqPT/qK3kVZIRm59UzP4SzJ1PRfUJPGfwQTc8faN3VaM9JRGQJGPywYGqNEhXOFSh2qfatmoiILMrwhwfh9Hcl+AiHACcg8kbj1tcmIjKlr7/+Gk888QT8/f0RFBRUo6eB2Gbwg+zRhZRsaKtHPgBotTokpOaYNPjx3Y5YfLRuq9yePOh6PHRdt0Z9/F9PHcBLO1aKxNYqbGZuPCLLQwSZLy5plZ6ejrw8fTUIb29vlJXZ1+RIa294nlXZ88PP6XKZH9lG6/dhEBHVRK7TU3PRxs1fbrPpORHZA3Y2smAeOrVca1wZ/CAismQP3T5QX6pQAXy1Zpu5h0NE1GjeeustvP3220hJScH+/ftlTXrDImrTE9mj1Mz8S/YplQqEBpqu59cPu/bj3dWb5fZT/XvjsX49G/XxD2Yk48WLAh8i9Llo8F0Y1ryVWZu723LZq4ceeghLlixBQkKCXMT2ww8/jDFjxsjb7N69Gy1btoQ9MZS9crPW4Edl2SsfZxezNDs3cPdwQWBlZolnllL+fz5fkIO04gKjPScRkSVg8MOCBbu4y3WFM6C7aGYRERFZDjd3F6iK9ScBdqclmHs4RESNJjs7G3fccQePKFG1klef/KQPOiiqBT6mTRhssqyPX/YexFsrN8rtx/v1xKQbezfaY1dotVhwbC/uXP3jJdeJX6SJhfreBNT4vvzySwwaNAh33303wsLC5CK2xb558+bJ24jG5998842dZn5Yac+P0uL6Mz8MwQ8jlr2q3vcj7WwWWnkHyG1mfxCRrWPZKwvWITQEB1MzoHUCEuLS0CxS/0FFRESWx7lAgUJ3HTIcS809FCKiRiMCH2vWrMHjjz/Oo0p2r7xCi1fnLkduQQlahTfBO0/djNSsApnxYarAx5+xh/H6P+vl9sPXdcOzA69rtCwMke3x8s5VOJyVWuv1KoVClr0i43B3d5elBj/++GOcPXtW7ouMjJT7DTp37mx3h9/qy141qOG58TM/hMjoQOzcckL2/egypCmO56TLpucim4uIyFYx+GHBekSE4cfUg9Cpddi14ySDH0REFixQ44SzKEaZOzP1iMh2REVF4bXXXsPOnTvRoUMHqNX6sqwGzzzzjNnGRmRqX/6xDQdPJsHNxRHvPHWLDHqEBpouGPDXgaN49e+1cntcry54YUi/Rgl85JWV4KN9W7DoRKzM7vB0dMKLXftDFMZ5dddqVOh0MvDxTu9hCHb7rxk3Na4ffvgBt912mwx2dOzYkYe3Un6WPvhRWmKdvU4yGxL8MEHZKyEiurLp+elU3HhfH/x8aj9i0hKN+pxERObG4IcFaxegz/TQOQCxx8/hdvQz95CIiKgO/ZuG4SyOo8JFh+ycfPh4m67hKRGRsXz11VfyRNzmzZvlUp046crgB9mL7QfisOifPXL7lUduMml/D2H5oROYtnSNDE7c070jpg278ZoDH6K08j/xx/Hm3nVIL9afYB4T0Q6vdB+IABf9LPv+TSMRn58tMz4Y+DCuyZMnyyy7UaNG4f7778fQoUOhUqlgz5Z/vRYlhSVy+4UBr2Pyl49h+MODYC3KKiqQrym9bNmr4qJS5Fb2NQkMMnLmR0v9OaZzp9PQ2TdYbh/MTJbjdLTz1xoR2S72/LBgzTy89BtK4GSOfiYAERFZpkdH3KAvhq0C5q/YYe7hEBE1iri4uDoXQ1kWInvo8/H6vJVy+44hnTGop2kbTq8+egpTF6+EVqfDHV3b47URA6858BGfl41x637D0//+JQMfkZ6++HHI3Zjdb2RV4EMQAY8+QWEMfJhAcnIyfvnlF/m3vfPOOxEcHIxJkyZh+/btsEfpCZn45Imvqy7rtDrMfvwrud9aZJfqgxpKhQJeTs613iY1Wd9Hx93TBW4etd+msQSH+sLJSY3SUg0cc3TwcXJBmbYCR+oodUdEZAusKvixfPly9OrVCy4uLvDx8cGYMWPqnckyffp0+aVB3Gfw4ME4deoUrIWzgxqKcv12ikI/24GIiCxTUBNfKCvbfaw5az2fNUREDSW+W4uFyN76fLw2d0VVn49n7rnBpM+/4fgZPP/HCll66tbObfHGLYNlg/WrVVpRjjkHt+Gmv7/Bv8lxcFSqMLlTX6wc+RCuDw5v1LHTlXFwcMAtt9yCH3/8EWlpabL3R3x8PAYMGIAWLVrY3eFMPJUsAx7VaSu0SDqdAmsreSWCDCIAcrmSV0FGbnYuqFRKhEc1kdtxp1LRLaCp3I5JTzD6cxMRmYvVBD/+/PNPPPDAA5gwYQIOHDiAbdu24d57773sfd5//33MmTMH8+bNw65du+Dm5iZTR0tKrCeQoNLoPyALnLXmHgoREdVDXah/z05S6n/oEBHZgkWLFsl+H2IykVhELfrvv//e3MMiMomv/tyOAycTq/p8OKpNVzl688k4PPvbPyjXanFLh9Z4a9SQawp8bE85h+HL5mPW/n/lbO9+weFYPephPNupL5xUrIhtSVxdXeW5i+HDhyM6OloGQexN0+hgKC56vStVSoRE6ftWWIPs0mK59q2j5JWQmpxtkn4fBhHR+tJXcadT0aUy+CGanhMR2Sqr+IZTXl6OZ599Fh988AEefvjhqv1t27at8z5iVtrs2bPx6quvYvTo0VU/3AIDA7F06VLcfffdsAYuFQ7IhwalbpxlR0Rk6XxK1EhBGYrdGbAmItswa9Ys2fD8qaeewvXXXy/3bd26Vdalz8jIkDXqiWzVjoNxWLhst9x+5WHT9vnYevocnv51GTRaLYa1jca7Y4ZCpby6uYsZxYV4J2YDFp89Ii/7O7theo9BGBneplEaplPjKSoqwpIlS2T2x/r169GsWTPcc889+OOPP+zuMAeE+skeH6LUlcj4EIGP5+Y9KvdbX7NzF7M3OzeIbFnZ9PxkCkbcpu8rG5POpudEZLusIvgRGxuLxMREKJVKdOnSBSkpKejcubMMhrRv377W+4g6xOJ2otSVgZeXlyybtWPHDqsJfvg5uMjgR4WrQgZ0+OWUiMhydfUOxApcQIWrDmVlGjg6qs09JCKia/Lpp5/iiy++wLhx46r2iWa87dq1w+uvv87gB9l4n49Vcvv2wZ0wqJfp+nzsPHsek375SzYhHtI6Ch+MHQ4H1ZUHPkSPkF9OHcC7sRuRV1YKEeZ4oFVXPN/lBng5Gre3AF05cY7in3/+kVkfoueHCDz36dPHrg+laG7efWhnWepKZHxYU+BDyKoMfvg5u9Yf/DBB2Ssh0pD5cSoVnfyDoVIokFKUj6TCPIS4eZpkDEREpmQVZa8MzRTFDyyRySG+EIieH/3790dWVlat9xGBD0FkelQnLhuuq01paSny8vJqLObUwl//4V7hDGSmsuk5EZElmzhYPytapwaWbthn7uEQETVKA97rrrvukv1in7iOyJb7fOTkF1f2+bjRZM+991wCnvj5L5SWV6B/ywh8dPsIqFWqK36co1mpuH3VD3h55yoZ+GjnG4ilIx7Em71uYuDDQqlUKvz222/yvfWzzz6rEfg4fPgw7JUIeHTq387qAh9CVmXD88uXvcoxS9mrtJRclBeVo62v/nJMGrM/iMg2mTX48dJLL8lMhsstx48fh1arLx/yyiuvYOzYsejWrRsWLFggr//9998bdUwzZ86UGSKGRaSZmlOXZqFyrVPrELOTDXSJiCxZl+hwQAOIqZW/xxww93CIiK5ZVFSUPBl3sV9//VXWoSeyRV8v1vf5cHV2xNtP3QwnR9MUTIg9n4RHf1yKYk05+kWFY86dt8DR4coCH4WaMry9dwNGLv8OsemJcHNwlCWu/hrxoJzlTZZLlLoaMWKEDIII+fn5+Oqrr9CzZ0906tTJ3MOjq5BZUtnzoyGZHyYKfrh7uKBJkJfcjj/NpudEZPvMWvbq+eefx/jx4y97m8jIyKpZZdV7fDg5Ocnrzp8/X+v9goL0dQxTU1MRHPzflzxxWZTMqsu0adMwZcqUqssi88OcAZB2gUHAMUCr1mH/ofMYOqan2cZCRET1UxcpoPHS4Ux5Lg8XEVm9N954A3fddRe2bNlS1fNj27ZtshZ9bUERImu382A8vvu7ss/HI0PQLNDHJM97MCEFj/64BEVlGvSJaIZP7xoJR4cr+7m+5vxJvL57HZKK9NULRoS1wvQegxHk6mGkUZMxiPfbb7/9Fn/++SdCQkJw2223Ye7cuTzYVqi+slfFRaXIzdHfJjDIdD2FRN8Pkfkh+n506dUU3x2PYdNzIrJZZg1+BAQEyKU+ItNDBDtOnDiBvn37yn0ajQbx8fEICwur9T4REREyACJ+mBmCHSKQsWvXLjzxxBN1Ppd4HrFYila+/voNB+B4cpq5h0NERPVwL1Yh26scBWx6TkQ2QGRdi+/PovH50qVL5b42bdpg9+7dshcfkS1Jy8rHjHkr5fbYQZ0wuFcrkzzvkaRUPPLDYhSUlqFHWCg+v2c0nNUN/6meUJCL13evxbqE0/JyqLsX/q/nTRgQ2sKIo6bGJEpzf/fddzLoIc5biJ4foiS3eN+tPgmUrEtmZdkrH6faG56nJusnS7l7usDNw3R9eETpq51bTuDsqVTccUsbue9IVipKyjVwdmDPQiKyLVbR8NzT0xOPP/44ZsyYIbMwRMBDNDsX7rjjjqrbtW7dWpatuvXWW2VJrOeeew5vvfWWTMkXwRDRMEzMnBgzZgysRaCYpaPTl1C5UK5PmSQiIsvV0tEbu5CBcjedLNuoVFpFey0iostORBLlWIhsvs/H55V9PsKa4Nl7TdPn40RKOh76fjHySkrRtVkI5t07Gi6ODTv5qNFWYP7RvZh9cCuKyzVQK5V4tF0vPNXhOrjwBKbVGDlypMz2uPnmmzF79mwMGzZMlr6aN2+euYdG1yi7suxVXZkfhpJXQSZqdm4QGa2vlCKCH03dPBHo4o7U4gIczExBz8CalU9Ss/JxISUbzYJ8EOjLLDIisj5WEfwQRLDDwcEBDzzwAIqLi9GrVy9s2LBBNj43EJkhubn/lRmZOnUqCgsL8eijjyInJ0dmjaxatQrOzqaLqF8rlVIJpQbQOgI5LhXmHg4REdXj7p5dsOv0Wmidddgdewa9u7MmPhFZHxG4FZOJLkdcX15ebrIxERm7z8f+E5V9Pp42TZ+PU2kZGL/oT+QWl6BT0yB8dd8YuDk5Nui+e9MS8MrO1TiRky4v92wSird7D0O0d2XlALIaK1euxDPPPCMrVLCXkm2panheV/AjOduk/T4ubnp+7nQatFodugY0xcrzJxCTnlgj+PH3pkOYOX8dtDodlAoFpj00GKP6dzDpWImI7Cb4oVar8eGHH8qlLjqdSJGo+YPszTfflIs1cyxXocSxAiVuNf99RERkeUb26ozJJ9cCSmDBhp0MfhCRVVqyZEmd1+3YsQNz5syR2W1EttLnY+EyfZ+Plx82TZ+Ps+lZmLDwT2QXFaNdcBN8ff+tcHd2atBM8vdiN+GX0weqyum83G0Abm/Rod6AJVmmrVu3ynJXIstOlBUUEz7vvvtucw+LrpEIGGSXVmZ+OF0+88PUwY+QZr5wclKjtFSDpAtZ6NakMviRlvDf2LLyqwIfgljPXLAOvTqGMwOEiKyK1QQ/7JmHwhElKEa5G7/MEhFZOgeVCqpiBSrcdDhYoJ+NSURkbUaPHn3JPpFl/dJLL2HZsmW47777rH6CEVH1Ph/i/J7o8zGkt/H7fMRnZuPBhX8go7AIrQMD8O0DY+HpcvnqBGKi359nD+OdvRuQVXlC9a6ojnip6wD4ONfeT4CsQ+/eveUiSl79+uuvmD9/PqZMmSIDzGvXrpWlvz08WG7I2uSUFlcFDur6P1oV/DBx2SuVSomwqCY4eSQRcadS0aVzU7l/X0aSfK8RgVRR6sowfgORJXL2QgaDH0RkVViI3Ao09fCS6woXIC+7wNzDISKiejgX6YPV2a4sB0NE1i8pKQkTJ05Ehw4dZJmr/fv3Y+HChbIPH5G19/mYXtnno2VYgEn6fCRk52L8wj+QXlCI6CZ+WDBuLLxdLx/4OJ2TgbvX/IQXti2XgY+W3v74feh9eO+6EQx82BA3Nzc89NBDMhPk0KFDeP755/Huu++iSZMmGDVqlLmHR1coq0Rf8srT0QlqparW26QmmyfzQ4isLH119lQK2vsGwlGpQmZJEc7l68fULLD2Mb31zRqs33XyksorRESWisEPK9A2WP+hpHXS4UjMWXMPh4iI6hGqcJNrjQd/FBCR9RK99F588UVERUXhyJEjWL9+vcz6aN++vbmHRtQovlm8A/sMfT6eusXofT4Sc/Iw7rs/kJJXgBb+vvhu3O3wcas7a6OkXIMP923B8H/mY1fqBTirHPBS1/5YfssE9LioKTHZllatWuH9999HQkICfv75Z3MPh65CZmW/j7pKXpmz7FX14IfI/HBSOaCDn74Jeky6vvTVrkPnatxeZIN4uTsjI6cQL3/2DybN/ANnEzJMPm4ioivFsldWoG2TQOACoFUDsbFx6DO4o7mHRERElzGiVWucyNyDChcd4uNTER6u/3FBRGQtxEm39957D0FBQfLEW21lsIis2c5D8fhu2a6qPh/Ng4zb5yMlN19mfCTl5iHM1xsLHhwLP/e6T4puSjyL6bvW4HyB/uTooNAWeL3nEDRzN/1JUjIflUqFMWPGyIWsM/PDp45m58VFpcjN0d8mMMj7it5L4rNyEO7rjSCvqy+HFtlSH+w4ezJFrkXTc9HwPDY9Cb09muHjHzfJ/eNH9UTP9mEIDfSWwY8f/tmLRf/sRsyxC7j/le9x+5DOmHhrH3i4XT6DjYjIXBj8sAJt/fUnzXQOOhw/n2bu4RARUT3GDeyDj3/fIz9lv/5rK95+diyPGRFZFdHbw8XFRWZ9iBJXYqnN4sWLTT42omuVnl2A17/Q9/m4bWDHq+7zkZ6ai8TzWWja3BcBgfpSxbVJyy+QPT4uZOeimY8XFj54O5p4uNd629SifLy5Zz2WnzsuLwe7emBGz8EY2qwlG5oTWRFDbx5fpzr6fSTnyrW7pwvcPBoWOPgj9jCmL9M3IVcqFHhz5GDc3vXqsjHDo/TnmdJSclGQX4xuAU3xtcj8SEvAG2tzUFSiQedWTfHo2OugUv5XNOaR2/pgRL+2+OSnzdi09zR+Xb0Pa7Yfx5N39cMt/dpBqWSvWiKyLAx+WIFwr8pZSA7AuYJ8cw+HiIjq4ePiCkUpoHMCtqfpU8eJiKzJuHHjeKKVbLrPR3Z+MaKbB+C5+/pf1eOsWhqD2W8t0zcHVirw3CsjMWxMt0tul1FQiPEL/8S5rByEeHniuwdvr3W2doVWi0UnYvHR/i0o0JRBpVBgQpvueK5TX7irna5qjERk/swPvzoyPwwlr4Ia2OxcZHwYAh+CWIvLfVuEXVUGiIenC5oEecnghyh91bW1vun5iZx0ZJ7JgbuzI2Y8OqxG4MMgJMAL7z07SpbGmvXDRsQnZeHtb9ZgyYYDeGHcQLRrEXzF4yEiMhYGP6yAj5gpoNV3aMlw1Jh7OERE1ABORQqUOOmQ7lzK40VEVue7774z9xCIjOKbJTsQezwBrs5qvPP01fX5EBkfs9/WBz4EnVaHT95ehm59ompkgGQXFmPCoj9xNiMLQZ7uWPjgWDT19rzk8Q5mJOPlnatwOCtVXu7sH4K3ew9FO1+WzSSyVqJ5uOBbV/AjOfuK+n2IUleGwIeBuHwmPeuqy19FRAfK4MfZU6no0DUcQc7uSCkpgMZLh8m3DUBIk7oz2oReHcLw49sP4Le1+/H14h04ejYVD73+M265oR2evLMv/Lz0fRCJiMyJDc+tgGgs5aDRpw4W1p4dTUREFsa/XJ++XsKm50RERJbT5+NvfZ+PaQ9dfZ+P83HpMuBRnVarQ9KFrKrLOUUlMvBxKi0TAe5uWPjgHWjmW/MkZ15ZCWbsWoPRKxbKwIeno5MMeiwe/gADH0RWLqukuEGZHw0NfogeH7UVlHp12VqsPHyiKhh7tX0/yjTlKE+tkJdD2nhj5A3tGvQYDg4q3Du8G/74YAJu7ttW7vtnyxHc8b8F+HlVLMrL9Y9JRGQuDH5YCRedfkaSxp31E4mIrEGfkOZyXeEG5GQXmHs4REREdq16n49bB3bETX1aX9XjiBOMa5btv2S/qHMf0sxXbucVl+Dh7//E8dR0+Lu5yh4fYX7eNR5jWdwxDPrrayw8EQtxynJMRDusH/0o7mvZRdbyJyLrllVamfnhVE/wo4Flr0R2R3Pf/zIxxNuEu5MjknPzMfmPFbj7m18Qcy7xisYYUdn3Q5S9+urP7ShNLpOXvcPdrrj0pZ+3G6Y/NgzfTL8brSMCUVhchtk/bsIDr/6AvUfOX9FjERE1JgY/rISfkz5dsNwVKCrQzyAgIiLL9fDA6+Ra56jDT0t3mHs4REREduvSPh83XvVj/bZwKzatOiS3DScHReDj2VdGypJXBSWlmPjDEhxJToOPqwsWPDgWkQH6oIgQn5eNcet+w9P//oX04kJEePjgxyF3Y3a/kQhwYYkYajxz585FeHg4nJ2d0atXL+zevbvO2x45cgRjx46Vtxev69mzZ1/zY9o7Q9krH+e6Gp5fWeZHVmERLmTnye2Pbx+Bjc89gs1TJuKp/r3hqlbjQGIK7lvwG5765W9Zau9KMj9OJmTghxV7oc7Rv6cdyk65pMRWQ3WIDsH81+/BtIcGw9vDBWcTMzHp3T8wbc4ypGTox09EZEoMfliJSF8/udY6A6cOMGpORGTpWjVpAogsbwWw8tRJcw+HiIjIbn27tFqfj6dugbOj+qoeZ/Oaw5j/6Tq5/cQLw/H98sl4/8vxWPTPZNnsvLC0DI/+uFSehPRyccaCcWMR3cRf3r60ohxzDm7DTX9/g3+T4+CoVGFyp75YOephXB8c3qj/XqJff/0VU6ZMwYwZMxAbG4tOnTph6NChSEtLq/XgFBUVITIyEu+++y6CgvQnxK/1Me1ddmll2av6Mj8aGPzYfCpOBiTaBjXB8PatZCaIm5MjnurfB6ueGY87u3WQWWPrjp/ByLmL8ObyDcgs0Adg6iKy1dTOauQGOMqsuNFd2sPFQY18TSnO5Gbiaokm6WMGdMTvH0zAnUM6y3Ft2HMKd774Hb5duhOlZeVX/dhERFeKwQ8r0UacRBPBDycd9sfGmXs4RERUDzFrTl2knz11weHyPzyIiIjIOHYdOocFf+n7fLwk+nwEX12fjyMHzuODGUvk9pi7e2HMPb1lpken7hFyXVSmwWM/LUXshSR4Ojth/gO3oXVQgLz99uR4DF82H7P2/4sybQX6Bodj9aiH8WynvnBWXXnDdaL6zJo1CxMnTsSECRPQtm1bzJs3D66urpg/f36tt+/Rowc++OAD3H333XBycmqUx7RnorRd1mUanhcXlSI3R399YFDDgh8bTpyV6wGtIi+5romHO94cORh/P/EA+reMQIVOh5/2HMBNcxZg3pZdKC7T1PqYKpUSiPSE1lEJH3cXPH//AHTyC5bXxaRfWQmt2ni6OeP5cQPx/dv3o2vrUBn0EOW17n5pITbHnL6qPiVERFeKwQ8r0dpPH/zQqYFjp5PMPRwiImoAz1L9zNIiT36xJyIiMkefjxlfrJAzmscM6IChV9nnQzQyf2PKz9CUlaP3ja3w6JRhNa4v0ZRj0s9/Ye+5RFmD/9sHbkO7kEBkFBdi8tZluHftLziblwV/ZzfM6TcK3w++CxGe/5XCImpMZWVliImJweDBg6v2KZVKeXnHjh0mfczS0lLk5eXVWOxBgaZMBjrraniempwr1+6eLnDzcK738Uo15dh2+pzcHlhL8MMgqokf5t07RvYZahfcBIVlZZi9YTuGfroAf8YeRoVWW+P2W2LPIB3lIlqDG8NC4O7ihG5NmsrrYtIS0FiimgXg85fvwFuTbkYTX3ckpedi6uy/8dwHixGf1LASXUREV4vBDyvRyk+fLq1z0CE+yz6+MBARWbsO3vrAdbm7DiUl+gaCREREZHziJN/0L/R9PqKa+WPy/f2v6nHycorw2rM/yFna0W1CMO3t2/WzpQGk5Obj31PxmPjDYuyIuwBXRzW+uu9WGfj46eR+DPzrKyw5e0RUwMQDrbpi/ZiJGBXR9oobCRNdiYyMDFRUVCAwUN/M2kBcTklJMeljzpw5E15eXlVLs2bNYE/NzkUJKbHUVfIqqIHNznfFX0CRRoNAD3e0Ddb/vricXhHN8PvEe/Hh2OFo6u2JtPxCvPL3Wtw670f5niUyLrLzivDOt2vl7Z0yy1CSWii3uwbogx+xGY076Va87w3p3Qq/vTcB40f1hNpBhZ2HzuHelxdhzs+bUVBc2qjPR0RkwBxbK9HU3Uu/oQRSwQ8FIiJrcH/fHti0M0GWLFy5ch9uvbWXuYdERERkF75dshOxxyr7fDx9dX0+ysrK8cYLvyDhXCaaBHnhjY/vhbOLo7zuj9jDmL5sXVVTYLVKiS/vHQNndxXGrvwe+ypPHLbzDcTbvYeis39II/8LiSzftGnTZJ8QA5H5YQ8BEEOzc1+nupqdZ19Rv4/qJa8aGjxVKhW4pUNr3NQmCj/sPiDLX51My8DEH5egT2RzOKZVyABIUz9PFBy7gDhVqrxfl8r3KtHzI6e0GN51/BuulouzGk/c0Re33NAOs3/YjK37z+LHFTFYte04nrq7H4Zf34YBYiJqVMz8sBJuakco9FmTyHNn+RQiImtwQ4soQLxlq4A/Yg6aezhERER2Yffhc5j/186qPh9hwVdeYkrMjJ71xlIc3ncOrm5O+L9P7oNfgEdVxsdry9ZWBT6Ecp0WSxIPY+Ty72Tgw83BEdN7DMJfIx5k4INMyt/fHyqVCqmp+pPZBuJyXc3MjfWYon+Ip6dnjcUeGPp91Fby6kqbnYv3oo2VwY/Llbyqi6ODAx66rhvWPPMQxvfpCrVKhR1nz2NzfgJKAhV48v5+UOhEQCYHBfnFskdJZGVZvn3pxiu53izQBx89Pwaznh+D0EBvZOYW4o0vV+HR//sVx+Nrvs6IiK4Fgx9WxLFcJdelnkyTJiKyBo4qFVQl+u1TFfravkRERGQ8GTkFmP7Fymvu87Fo3kZsXHVIlrh69f27EB6lL/cjGge/t2aLfHydSocKZy3K3ctRHFqKn87sl42GR4S1kiWuHmrTAw5K/uQm03J0dES3bt2wfv36qn1arVZe7tOnj8U8pj0EP3yc6gl+NKDs1dHkNKTmF8BVrUav8KvPmvF2dcZLQ2/Ed/feBucihahDhVJ34PlVq1HazQtaByDudFqN0leN0fS8Ptd3jsTPM8fhyTv7wsVJjYOnkjB++o94d8E65OQXG/35icj28ZuYFfFQOcl1uZsCpayHSERkFVyL9RUm8zxrNhgkIiKixiX6fLz2+QpZyuVa+nys/nsffvpms9x+5uVb0K13C7m99fQ5jPx8EVYeOYlyjwqUNi+DJkSD8iYVsqB0sKsHFgy8A5/feCuCXPVZIkTmIEpNff3111i4cCGOHTuGJ554AoWFhZgwYYK8fty4cbIkVfWG5vv375eL2E5MTJTbp0+fbvBj0n+ySvUn7f2c6yp71fDMD0PJq+ujwuCkvrbK9VqtDt/8sh1OyVp0VvihR1hTlJZXICVEgcSBnvh+5z78P3tnAd/E+YfxJ566uxstUGihWHEbMsbGmDCFMebK9D/mznxMYD4Y2xiMIcPdHVq0Rkvd3Zs09v+8b5q0oS02oE36+273udzlktwl9L2793l/z9Oo1iDGKH5cvdDzCyGViDFj8gD8/ckDXLBm4vKqHadwx0u/YvnWE1Br6D6KIIgrhzI/zAgfO3uUNtRDYwVkJeejW5+gjt4lgiCI68att96KXbt2YcyYMfjnn3/M5psPkjngFMqgstNBo9EaQ1IJgiAIgrg2OR9s9PCV5nwcP5KOr95fwx9PmzkME6bEoKKuAXM378KaU8l8vaujFXKd9Z2XLVky7m4ENdnFEERHMm3aNJSUlODNN9/kgeTR0dHYtGmTMbA8OzsbwhZVSfn5+ejTp49x+bPPPuPTiBEj+PX3pbwn0bryg1lI/Vfbq/9ieXU+f22KQ3yyvo387LGb4ePugJ1n0/H635tRLlNiTVEGTi5YjDsHR/LtT5YWQK3VXrcKNndnO7z7xI24dUxvfL54J1KzS/DZ4h34d9dpvDB9FPqE+16X/SAIwrKgHhgzItTZlc+1MuBknP4ESBAE0VV49tlnsXjxYpgbt/TpxedaKx2OHEzp6N0hCIIgCIvkaEJ2c87HzLFXlPORlV6M915axgcrjBgXiRmPj8K/JxMx8dtFXPhg5sPj+4ZC4Nd2BmNhfc1/Pg6CuFo89dRTyMrKglKpxOHDhzFw4EDjc0zQWLRokXE5MDCQZ0ucPxmEj0t5T6KZMqUh8Ly1+NFQr0RVpf55D88Lix8FVTVILCyGUCDAiLD/Nvj1XE4pvlu+nz9+9p4RPGeDhaePDg/B+wOGwvlUPaRqAbLKK/HJur0Q6QSoV6uwPycThzJyeNbR9YKJHIvevRcvzRgNexsZF0Eee/9vvLFgPYrLqZ0lCOLyIPHDjOjm4sbnOqkOSckFHb07BEEQ15WRI0fCzs78LCRuidKLHzoJ8Ne2ox29OwRBEARhcZRV1uHN7zZwq5RbRvbChCHdL/s9yktr8MYzf6KuVoGeUf6469lReGTJv/jfqs2obFAgxN0ZI4cFYk1lAnLqWud4iQQCBNo5XaUjIgjCnClXGGyvWosfRQX69sPW3go2dvJLqvqI9vWCs03bVSSXgkqtwVvfb+TzIdFBPA+pJaHdvGCX1Qj/PbV4fNgAni+ia4rbmLViJR747R+MnvcL/ok/g+uFWCTE7WOjsfzTB3Hr6N4sogRbDqbgzpcX4be1R9CoUl+3fSEIwrwh8cOM6O7aJH5IdEgvLuvo3SEIgjCyZ88eTJ48Gd7e3nwE0erVq1t9O/Pnz+ejyuRyOR8lduTIkS7xDbpa2UCg0j+Oryvt6N0hCIIgCIvM+Siv0ud8PH//5ed8KBoa8dbzf3Effi8/J4Tc0x1Tf/4L+89lQSISYsLAMBS7VmNjXgpYzcfU4Ei80W8MFzwYbP7hoAnwsrG/BkdIEIQl2V4ZLK88LyHsfEfKuatiefXTyoO8esLBVo7XZo3j92st8fF3gVQmhqpOhdtCw7Hp6ZkId3Dnz2ll+rwNrU6HN9duu64VIAxHOytezccqQXqHeaNBqcKCv/fhnlcXY/8JckQhCOLiUOaHGRHkqC/d1omBQqWio3eHIAjCCAs7jIqKwoMPPoipU6e2+maWLVvGQxK///57LnzMmzcP48ePR0pKCtzd9RfWzDdYrW49gmfLli1cVDFnZPVCKBy0qLClEUoEQRAEcTX5dfUhxCXlXHHOB7O4+vj1FTibkAeJjw1Kxzjhm32H+XO9Aj0g9NBhdYl+tHOQnRM+GDQeg70C+fKNAeHIrKngFR8kfBAE0dr2qnXgeVFBxSXlfdQqG3E4Ux84zqyprpRTZ/Px+zp99fkrD46Fi6NNq21YJmFgiDvOJuYj/WwRht/QE7eG90TSmSLo5M1h40wAWR53Gk+Nim0loFxrIgI98OMb07BxfxK+XboXOYWVeP7z1RgaHYzZ942AnwdV3hEE0TYkfpgRntZ24EONBEClVds+swRBEB3BxIkT+dQeX3zxBR5++GHMnDmTLzMRZP369fj111/xyiuv8HUnTpy4KvvCPIjZZKC6uhodjZfABhmogdJe7598vW8WCIIgCMJScz5+Wf3fcj5+/moL9u1NRk2kNaqDJNCWlcNeLkX/Pj7YXpoGRYkaUqEIj0cOwuO9YiEXNd9CM8GDRA+CIM6nosn26kKVHxcTP1jlmUqjQYCzI4Jcr6xjv17RiHd+2MhFi4lDumN0/27tbhsc5snFj4zUQi5+jPEPwYend3DrXp1IB4FGf/8yf89hxOfk440bRyPY7fLb3P8Cu4e6cWgPDI8J4cL30s3Hse9EOg6fycK9N8bggckDYSW/PAGcIAjLh2yvzAiJUARR0wmnwfxs7wmC6KI0NjYiLi4OY8eONa4TCoV8+eDBg1f98+bOnQsHBwfj5Ofnh45mZJh+tJbGBkhNzuvo3SEIgiAIC8v5iLyinI81fx/Gn1uOomCEHSqDpGDjmwdG+MAuQor1RclQaNQY5OGPjZMfxHPRw0yED4IgiLZg7UaduvHi4sdFbK+aLa9Crnjg1Nd/7UFucRU8XOzw4vTRF9w2KMyDz9NTi/g8xNUFXnI7o/UVC11n9lsysQgHM3Jwy3e/4/Ote1Gn1B/r9cTWSoZn7h6BJR9Ox6BeATzLZNGaI7jj5YXYcjCZDzYjCIIwQOKHmWHFPK9YYJW9EI3KJhN5giCITkxpaSk0Gg08PPQX1AbYcmFh4SW/DxNL7rjjDmzYsAG+vr7tCidz5sxBVVWVccrJyUFHM61fXz7XSXVY+q9+hCpBEERXggnT/fv3h52dHbc7nDJlCrc+JIgrzflgwkdzzseoy36PLdtO4Z0tu1A8yBZqGxHcHWwwaJAPdjem41x1GZxkVvh8yCT8Ne5uhDi40A9FEMRl5X1IhELYS2StnmfZQher/FBrtNh9NuM/5X2wPIxVO07xx28+PB621q33pSXB3Tz5PKNJ/GCMCtAP4JrQLww7Zs/CgrtvwbonpmNUt2CotFr8tP8YJs3/DZsSznaI4BDo7Yx5L03FJ7NvhrebA0oqankG1BMfLucZJwRBEAwSP8wMF7mNcfRwfnrzSYkgCMLS2bZtG0pKSlBfX4/c3FzExsa2uZ1MJoO9vb3J1NF0c3YDNPqz7r4iqvwgCKLrsXv3bjz55JM4dOgQtm7dCpVKhXHjxvHMKIK4XBauPoxjifqcjw+eurycD9ZB98PGA3huxzbU+Uqhgw6xUb6o9VZgV7E+PPeOkF7YfsvDuC2kF1lVEgRxReKHk8y6zfbjUmyvTuTmo7JBAQe5DH38Lj/7sLKmAR/8vJU/vmt8X/Tr6X/R1xgqP5g4U1ujt+3q6+bD5yk1JdCJ9eKGn7MjvrvnFiy4+2b4OtqjsLoWs5evx6zfVyK9tBzXG/Ydj4gJxV8fTccjtw2GTCpGfHIupr/+Bz5bvAPVdZSXSxBdHRI/zAx/e/0JUiMHEuMzO3p3CIIgLoqrqytEIhGKikwFW7bs6akfYWTpsDJxiUJ/81No3ZxHQhAE0VXYtGkTHnjgAfTs2RNRUVFYtGgRsrOzuS0iQVwOxxKy8fNqffXn/2aO4SN/L5Wc8krc/8vf+PLwYWikAlgLBOge44KdNedQrqznFR5Lx92DT4dMatOuhiAI4mKUNYkfzvLWYecN9UpUVeqf9/BsX/zYkaIXYkd0C4JYdHnddkzg/XjhNpRV1fH28fE7h1zS6+zsreDm4cAfZ6QV83lhXQ2fn60qxZAV32FZ6knj9syOa92TM/DkiEGQikQ4kJ6NWxb8ji+27UN94/V3KWEi+Kwpg7Ds4wcwun+YPpx96wnc8dJCrN55ilcMEgTRNSHxw8wIdXblc50UOJPY8VYuBEEQF0MqlSImJgbbt283rtNqtXy5veoNS8RJrS81V1zY3pcgCKJLwGwJGc7ObXdcK5VKVFdXm0wEwXI+3mjK+bh5RCQmDulxSV8Ks5D5Zf8x3LTgdxzLzYdOo4WtWIu6EBVOVBTwQPMXoodhw00zMcjz4iOkCYIg2oMJqQwXWRt5HwX6c5+tvRVs7OQXFT+YwHC5bDqQhB1HUyESCfHO4xMvqzLOmPtxthAFddX44sRe43Na6DDn0Cbk1+qPgSGXiPH0qFise3I6RoQFcSusH/cdxaRvf8OWxNQOscLycrXH3Gcm49tXbkeQjwuvgpn76zY8+PZfOJ2Wf933hyCIjofEDzOjW5P4oZXqkJlb1tG7QxAEwamtrcWJEyf4xMjIyOCP2ahexvPPP4+ffvoJv/32G5KSkvD4449zq5OZM2d2mW8wxtOXz9U2OhTmUftNEETXhQngs2fPxpAhQxAZGdluRoiDg4Nx8vPzu+77SXQu2Kjdt77fyHM+Qnxd8MIl5nycyS/CnT/9hU+37oVSrYZI0QiBuwql/iootRoM8QzA5ptn4eneQyCjQHOCIK5S5YfTBcLOPS8Qds6sozLLKnhmyNCQgMv67KKyGny2eCd//NCUQYgINM1cvBjB3TyMuR8ZNRVc8GgJq6a4ecNvePPwFuzKS+fh7gx/Z0d8z6yw7roZ3g72KKiuwTN/r8PDf6zix9IR9O/pjz/evw/P3TcSNlZSJGcU4aF3luLdHzZxIZ0giK4DiR9mRriLG58zv8W8Wv1JlSAI86SwqgaHMnL43Nw5duwY+vTpwyeD2MEev/nmm3x52rRp+Oyzz/hydHQ0F0aYBcr5IeiWzB39o/lca6XDytVHOnp3CIIgOgyW/XHmzBksXbq03W3mzJnDq0MMU04OVTx3dRb+exhHE7J5zseHT0+GXHbh0czMduXjzXu48JFYWAyJUAChoAF1PXRQOLAsRWvMGzoZf9xwF4LsL906iyAI4kJUKPV5GS6y1rZXRQUVF8372NlU9TEgyA+28guHlLdEq9Xh3R83obZeichQL0yfPOCyf6igME9j5UeQnRO37j2fUkU9FqfE44Htf6Pvsq/wyM4V+Dv1JF8/OiIE65+cjieGD4REJMK+c1mYvOB3zNu+Hw0dYIUlFot45sk/nz6IycN78nXr9yXi9pcWYsnGOKjVLJSRIAhLRwwzYv369Xj33Xdx6tQpyOVyjBgxAqtXr253e+YrzEYZt2T8+PG8w81c8bNrOkmKgEoRNdQEYa78E38Gb67dxkfPsIvKdyePxe192x79ag6MHDnyomXNTz31FJ+6KrG+AeCDp0TAttQ0PNHRO0QQBNEBsPPAunXrsGfPHvj66ivi2kImk/GJIIw5H6v0OR8vP3DxnI99aVl4a9025FVW80BzbxcrZEsrobHSj/27OywK/+s7Eo5tdE4SBEFcjcDztnKDLiXs3CB+jA4PvqzP/XvrcRxLzIFcKsZbj0647KwQRnCT7VXmuWK4y20xd9AEvHpoEzQ6HUQCAd4ecAO8beyxLScNO3LTUNRQiy05qXxiRLl6YaxvKMZEh2Jy7wh8sHEXF0C+33sEa04l49UJIzAmIqTNIPhribODNV5/eDymjOqNz3/fgcT0Iny1ZDf+3XUaz983CoE+zsgprICfpxM8nO2u674RBHHtMRvxY8WKFXj44Yfx4YcfYvTo0VCr1XzE2MWYMGECFi5caFw295soNkKJd54JgFr763vCIAji6rAnNROvr9lqXGYCCBNCWFmzpwNdbFkqVmIJREoBNHIdsiT6EWEEQRBdBSaQP/3001i1ahV27dqFoKCgjt4lwgxzPtjI3RuHtp/zUV5Xj4827+adbAxXJ2sInTXI0DGPegE8YI1vx9+K/h5ko0YQxDVqsy5F/GjH9qqirgHxOfpcilHdLl38yMgrw4Jl+nyOZ+4eAX9Ppyvadx8/Z0hlYigVKhTklmNaWBSGewchs6YCgXZO8LKx59uN8Q3l5/WE8iJsz03j06myQpwsLeDT5yf2wsvaDqPDQvBIaD+sOZSM/KpqPLVsLYaHBuK1iaMQ4HL9gxBZRcwvb92DdXsT+PeVmV+OZz5ZYXyeDUqc8+BY3Dyy13XfN4Igurj4wYSOZ599Fp9++ilmzZplXN+jx8UD7pjY4empL92zBFhjLNEIoRJr0egghFqlhlhiFj8jQXRp2MXhwfRs/Lz/GA6k63MwWsIEkKzyShI/LBzbRjGq5CrUUeg5QRBd0OpqyZIl+Pfff2FnZ4fCwkK+nuV5WFnR6Hvi4jkfwT4ueHH66Havs/49mcSFj8oGBVuDnpHuONWQD5VOC4Fah2ENXvj5sfshFYno6yYI4ppRrmiyvWpD/MjN1uf+ya2lbb52d2oGvy+M8HCDt6NeaLgYzLrp7R82QanSILZ3IKaO6X3F+y4SixAY4o6zifk898M3wJULHgbRoyWseiPSxZNPz0YNRXF9LXbkncO2nFTsK8hEQX0N/jyrz4O0chfD28ceebk12J2RgYMLcvDwkH54eGh/WF1GIPvVQCgU4OYRkRjVLxRf/bUba3cnGJ9j3/3chdswsHcgVYAQhAVhFpkf8fHxyMvLg1Ao5B7yXl5emDhx4iVVfrCRZe7u7ggPD+cBu2Vl5h8yay+S87naVoCirJKO3h2CIC6AWqPF+tMpuO2HJXjw95Vc+DA0vDqRDhq5ls+ZsBngTD3iV5P58+dzkbx///6d5t9oN3sXPlfZAzXVlNtEEETX4bvvvuPZHcwmkV3LG6Zly5Z19K4RnZhFa47wnA9m4/Lh0ze1mfORU16JWb+vxCurN3Phw9vTDi6RcsTV53LhwzpTgykp3lj42HQSPgiCuOaUK5sqP2Sm4sem1XFcUGDMe28NXz6fHSnnLtvy6pd/D/Ewb3tbOV57aNx/tpQKarK+Yrkfl4O7tS3uCovCz6Nvx4lpz2Lh6DtwX7c+vAKkQaNGuqIcSlcVlAGNqPVowNen9uOGH37B9qQ0dAR2NnJMGNy9zeyU3CJ9hQ5BEJaBWZQMpKfrPQ/ffvttfPHFFwgMDMTnn3/Ob57Onj0LZ2fndi2vpk6dysvqz507h1dffZWLJgcPHoSonRE/SqWSTwaqq6vR2fC0tkNZQz001kDKqWz4hHp19C4RBHEeLNBtxfEELDwYx/2mGVYSMc/1mDGoL76K24/leae4hR2zsrvDpzdVfVyDUcZsYu04G1ncGbg5OhJHTxRCa6XFxjXHcOd9wzt6lwiCIK4LF8uFIojzOZaYjZ9X6nM+/jdzLIJ89AMIWg4wWXQwDt/uOgSFmlXDCxHU3QGnawuBekCqEMBlpxJ9hG748Ke7ILoC/3uCIIgrtb1qWflRUlSFeR+sNTknfvXBWsTEhsLNQ3+f0qhW83wMxqhLFD/OpBXgtzVH+OP/PTAGbk62//kHCzaEnqfphZorQS6WYJRvCJ/e041DUkVxkz3WOZwszYdOroNarkEuKjHr4D/wPeaIp/rF4pawHtwq+HrBMj7YIERW8dGStOwSxHQne0SCsBQ69ArwlVde4ar0habk5GRotVq+/WuvvYbbbrsNMTExPMeDPb98+fJ23/+uu+7CzTffjF69emHKlCk8XPHo0aO8GqQ95s6dyzvJDJOfX+dr8AIc9f6NWpkOCWdyOnp3CII4z6f1210HMerLn/H+xp1c+HCytsIzo2Kx47mHuL+pSqTBP/lNwgdDAKwsOI2Cus4nthJXl7HB3fhcJwE2Hkuir5cgCIIg2qCsqg5vLtjIO6RuaiPn40x+Ee786S98tm0fGtQqBAQ6QBis0wsfrPOuwAr+C+sRUmWL9+bdBytr8859JAjCPFBpNahqZNZ7gJOs2dIxL7scOq2uVYVBfk65cflwZi7qG1Vwt7NBTy999cWFaFCo8PYPG6HR6jA+NgJjB4ZflWMwVH5knL1y8aMlrN+uh7MHnu49BKtvnI4jdzyNT2In8mB0iUDIh2Tn6irxytGN6PXXl3hg29/48+xxFNbX4FrDws1ZxgezwmrJF3/swt9bjl/zzycIogtUfrzwwgt44IEHLrhNcHAwCgoKWmV8sCwP9lx2dmvv/Au9l6urK9LS0jBmzJg2t5kzZw6ef/554zIbMdzZBJBQRxegANBKdUjPJNsrgugM5FZUYeHBeKyIP8NHHzL8nBwwMzYGt0b34F6mVUoFvjm1Hz8mHGbFHiZodDoeJNeWnyphOXja2EGgBnRiIFlHYhdBEARBtJXz8fZ3G7kAwnI+XmqR88E6Br/eeQCLDx3nwoiNrRiOQXKk1BUDKiDc0Q09T8uQsDIV1jYyvPfVvXBxs6MvmSCI60KlUi98CM4TP3z8W7uVsA53b7/m9TtT0o1B5+d3xrfFN0v3IKewkld7vDij7Tyk/yJ+FBVUoq5GARs7ve361cLNygZ3hkXxSaFR49+UBHx97ADyVFVQS7TYlZ/Op9ewGT2dPXi4Opt6uXjyKo2rDQs3ZxkfzOrKx80Bf26M48LH57/vRHWdArOmDPrPVmIEQXRh8cPNzY1PF4NVejCxIyUlBUOHDuXrVCoVMjMzERAQcMmfl5ubyzM/mL9we7DPYVNnJtzF1ThyOL+itqN3hyC6NAn5RfjlQBw2JZw1lsv29HLHQ0P64YbuYRCLhCiqr8GXp/bywLc6dWOb7yMSCBBop6/qIiwXduEsV4jQYKtBlSNZwBAEQRDE+TALlyNNOR8ftMj52JuWibfXbedVtTqBDkHdHHFOW4qyujrIRWLMjhoK2b5aLF25l1tcvf7JNGMnHkEQxPWgvMnyylFmBZGw2WjF1d0eNjYy1NXpLdaZuPHsa5ONllfMBqs57yPkgp9RVF6DLQeSsWL7Sb785iPjYW9z9QQKewdruHrYo7Somltf9epz6X1ulwtru6f1iMKd3Xvz++l3t+1ACWqhsdZya6yE8iI+sXwQJpqM8QnFaN8QDPUKhLWk7dD4K60AYRPj+ftGwsFWjp9WHuRTVa0Cz9078pIEKYIgOidmkflhb2+Pxx57DG+99RavwmCCx6effsqfu+OOO4zbRUREcNuqW2+9FbW1tXjnnXe4TZanpyfP/Hj55ZcRGhqK8ePHw5wJYZUf7AQp1qGUDXEiCOK6wi5OWXD5L/uP8bmBoSEBXPQYGOTHO7kzqyvwQ8IhrDh3Bo1aDd8mwskNj0fGok7ViDcOb+YVH0z4+HDQBKr66CL4Su2RigqoHABVoxoSqVmcigmCIAjimhOXlMM7mww5H6zyo7yuHnM37cba08l8vZOLDFoPLZIVxXx5lE8I3h1wA5J3ZeKznzbydc+8ehNiBl24A5EgCOJqU2YIO2+R98FISy7gwodMLsFbn98F/yA3o/DBSCosQWF1Lc+IHBTUvvPIml2nMffXbcZBd/16+GFA5NUXJ1juBxM/MlILr6n4YYDdO0+MDMfwsCB8t+cwFh2MhwoaCO0AP18HXhVS0lCHpWkn+SQVijDYK8BYFeJ9Fd0T2L48dGssF5RY9QerAqmpU+D1h8ZBLG47O5ggiM6N2fS4MLFDLBbj/vvvR0NDAwYOHIgdO3bAyal5pDSrDKmqquKPWaD5qVOn8Ntvv6GyshLe3t4YN24c3nvvvU5f2XExvG2bGnYRUNtcSUkQxDWGBWtuSjzLRQ92gcr/DAUC3BgZjllD+iHCU1/JdqasEN+dOYSN2SnGC9P+7r54IjIWI32CjWWzo3yCudUVq/ggu6uuw9iIbkhNPwyNjQ67t57C2El9O3qXCIIgCKLDYTZXb8zfYMz5mDikO1adSMBHm/egqkEBgQjwDbdDqqIUUADuVrZ4e8BYTPQPx8ljmZj3/hr+PtNmDsOEKTEdfTgEQXThyg+XFpZXjCP7zvJ534EhiBkU2up1hqqPISEBkEnE7VZ8tBQ+GPHJuXy9oWrhasGq5tg+p1+l3I9LxUYmxYs3DOO20e9t2IlDGTnIrqrmdtK3Du6BYl0tD07Pqa3Crrx0Pr1xeAu6O7nzDJHRvqGIcvW6KvZYd47rAztmn/jjZmzcn4TahkZ88OQkyGjgGkGYHWYjfkgkEnz22Wd8utBobANWVlbYvHkzLBF7qRwCLaATAkpHETRqDUSkQBPENYP5S688noCFB+O41QKDjcq5o28vzIjtCx9He97+HCrMxoIzB7EnP8P42tE+IXg8chD6e7QewcMEDxI9uh4TwyPwXfphaGU6rN9F4gdBEARBtMz5CPJxwbSbYjDr95W8wlYHHdx8rFFpU8+FD9alNT28L17oM5zfF2VnlODdF5dCrdZgxLhIPPDE1fO+JwiCuBLx4/zKjyP7Uvl8wNCwNl+3w5D3cQHLq5zCChPhwxCazrIqrrb4EdzNk88zUq+v+GEgxM0FC6ffho0JZ/HR5t3IqajG1+sPYWxECJaMuxsKoRrbctOwIzcNcSV5SKoo5tM3pw/AVW7NKwLH+IVimFcQbP6DPdbEIT1gayXDa9+uw974c5j92Up8+twtfB1BEOaD2YgfhClWOgnqoYLKQYDSvDJ4BLjTV0QQVxlms/DnkZP488gJVDbow+ucrK1w/8Bo3N0/ij9mF6Bbc1J5pUd8SR7fho00mRzYHY9FDuKjUAiiJT1cPAAt+4cCnKgroy+HIAiC6PIYcj5kUhH6DA/Enb/8BYVaDbFcwAPNc5WVgBr8umpu7AREu3rz76yirBavP/MH6moV6BnljxffngJhC599giCI60lZk/jhJGsWPyor6pCSoL9PHDC0W6vXFFbVILGgmAu7I8OC2n1vT5fWAgfLofD1cMTVJrgpLykjrQgajZbnKF1vBE0OC8wKa8HuQ1h86Di2JZ/DvrQsPDZ8AGYN7scHGTLBaXd+OrblpPFBiKWKeiw/d5pPzB5rkKe/0R7L17bZauxSGdY3BPNemooXvvgX8Um5eHLuP5j34q1wsjcVuAiC6LyQ+GGmOEmtUK9RQWMtQHpCHokfBHEVySmvxMKD8bzag914M1ip7YODY3BrdE/IJWKotBqsSj/DRY+zlaV8G3ZxdUdobzzacyD87a7+RShhGYiFQkiUQqistChz0GfBEARBEERXJb4p50MjBcTdbbDwSDwPNPcIskG+sAq5SgWsxBK8ED0MD0T04+dRhqKhEW8+twRF+ZXw9nPmPvrSpnB0giCIjqBC2cDnLi0qP47uT+UuASHhnjz4/Hx2ndVXfUT7ecHFtv0O9e1H9NUjLYWPOTPHXvWqD4aPnzOkMjGUChUK8yrg46/Pne0IbGVSvDxuuNEK60hmLubtOIBVJxLx+sRRGBYWiFuDI/nUqNHgWHEOrwph9lhZNZVcEGHTW0e2IsLRjVtjsaqQaBcvYyh9QV01MmoqENSOHXXf7n747rU7MPuTlUjOKMJjH/yNr1++DR5tCFIEQXQ+SPwwU3zsHLj9jsZKh8RT2Yi9sU9H7xJBmD1n8ovw6/5j2JSYaiwp7unljoeH9scN3UP5xZFCrcLi5Dj8kHAYeXV6CyxbiRT3deuLB3v04/7TBHExXGGFAtRB4cTK1bU0SpUgCILokpRX1eO1BetR56yDylGA2toayB1EEHkB2Y0VgA7cx/2dATfAp8WIXTYS+ZM3VuJsQh7sHKzw/tf3wcHJpkOPhSAIwlD54Sy3MhE/2qv6aGl5NfoCllcs1+PX1Yf44+fuG4kwfzde8XEthA8Gs1UPCHZHalI+0s8Wdqj4YSDM3RW/zbgd68+k4OPNe5BVXomH/1zF79PnjB8Bb0d7SEUsCD2QT2/0G4Nz1eXcGouJIXHFuUiuLOETs6pmAhXL45SLxPjr7ElooeMODnMHTcC0sKhWnx8R6IEf3piGpz9egcz8cjz83lJ887/bEODl3CHfB0EQlw6JH2ZKkIMzjlTmQCsFUjvIh5EgLAE2Cmf/uWz8sv8oDmbkGNcPCw3kIeYDA315yW1VowJ/pMTj16Rjxota5ic6s3t/3BfeBw5SeQceBdEW8+fP55NG0/mqKwb6B2B1SSIPPT9xKA19B7d9M0QQBEEQlgrzqn9y/kpk2tdDJxFAJ9LBPdgaOZpKoBHwtLbjgebj/brxa7GW/PzVFuzfmQSJRIS3P7+7U3TMEQRBlCsNgef6Cg6Wzxp3MI0/7j+kdd5HnbLReA86qltwu1/g10v2QNGoRlQ3H0wb16dVm3gtCO7moRc/UoswbGxPdAbYcd/UK4Lbg327+xB+P3QcW5PSsDc1E4+PGIiZsX0hFYuN24Y6uPDpkZ4DUalswO68dC6EsKB0dk+/4twZk/dnAyBfPbQJw72D2qwAYULHj69PwzOfrEBWQQUefW8Z5r08lQsjBEF0Xkj8MFNCnZyBLEAn1SGvuKqjd4cgzA61RssD1H7ZfwzJRSV8nUggwKRe4XhwcD9EeLrxdcUNtfgl8Sj+PHsctapGvo55hT7aYyDuCO0FuZjsFTorTz75JJ+qq6vh4HD5/q7XkhsjIvTih1yHdZviSPwgCIIguhRltfV4+JcVSGwshU4CWLuKoHRSceGDjbydERHDba5sJa1DZdf+fQQr/zzIH7/w9q2I7BPQAUdAEATRmnJFg0ngeeKpHNTWKGDvYI2ISN9W2+8/lwWVRgN/JweEuLVdQXAsMRvbDqfwtvHF6aOui/DBCArVd+inpxais2Erl+GV8SMwNbon3t2wA8ey8vDl9v1GK6yhoa3PC44yK9wS3JNPzML6WHEu/kg5jvVZySbbaXQ6LDh9CC/2Hd7mAEdPV3v88Po0PPvpSqRkFuOJD5fj8+enoE9E69+XIIjOAYkfZkq4iz5EWScGSpo6ZAmCuDj1jSqsOH4GCw/EI79Kb1tlLZHgjphITB/UFz6O+hEeWTUV3Nrqn7TTaNTqKwfCHd14qNpNgd2NftMEcSUM8vHXP5AAhwoK6EskCIIgukzF7eqTifhgwy7UNjZCK9HAykeMCmE9oAEinT0wN3Yierl4tvn6w3vPYsGnG/jjGU+MxqgJva7zERAEQVyK7ZVe/Di87yyf9xsc2mZouMHyalR4SJuihlqtweeLd/LHU8f0RrcAfT/Q9SC4m74dzujETiPdPFzx+wN3YO2pZHyyZQ8yyyrw0B8rMb5HGBdHvBzatgWTCEWI9QxAoJ0TNmanGC2vDfx+Nh4r0k/zHJEZEX3RzVE/MNIACztf8OodeImFoCfn4tlPVuDDpydjaJ/2q3cIgug4SPwwUwxhyjqxDjWS66P8E4Q5U15Xjz+OnMCfR06iqkHB1zlbW+H+gX1wd/8oOFrrR3UklhfxEHM2AsRwEdTXzQdPRA7i4WhsxA1B/FfspXIIGwXQSnUosFfRF0oQBEFYPNnllXh73XYcSM/mgeYaezU0Llo0CtSwEUvxQp9hmB4e0+4Ak7TkAnw4Zzm3yxp/Sx/c/eDw634MBEEQ7cHuHSsMtldN4seRfe3nfWi0WmPY+ejwtjvNl287ifS8MjjaWeGR2wZf1y8/KExf+VGUX4m6GgVs7DqnzTMTjW6O6o5R4cH4ZudBfs+/OTEVe1Iz8MSIQZgxiFlhidp8LbO2YhkfzOqKVXywe/0pQT2RUF6ElMoS7v7AplhPf16RONY3zHiOsrWS4cuXbsXr367H3uPpeHnev3jzkQmYMKT7df4GCIK4GCR+mCmeNk0KthBQOAgpMJcgLnCjvehgPK/2UKr1FRysrPjBwTGYEt0Tcom+GTxSlMNFj51554yvZQForNJjgLvfdSsvJroO9mopKqVKNDjRvy2CIAjCMimsqsG50jIcycjFb4eOQ6FWQ2elhcpZBW2To9V4/254u//YNv3VDZQUVeHN2X9C0dCI6AHBeGbOZLo2IwiiU1HdqOAd6AwnmRWKCiqRda4YQqEAMbGtw8xP5BSgskEBB7kMff29Wz1fVlmHn1Ye4I+fuHMoHGybQ9SvB8yqy9XDHqVF1chIK+r0FoN2chlenTgSt/XRW2HFZefj8237sPJ4At64cRQGh7S9/yzcnGV8ZNZU8EoQdi5iVYqHirLxW3IctuSk4mBhNp98bOxxb7c+uCssilf3yKUSfPTMZLz38xZs2p+Et77fiJp6Be64oc91P36CINqHxA8zRS4SQ6wVQi3UQuUoRHlBBVx9KOiPIAycyS/ieR5s1IehgiPS2wMPDemHG7qHQiQU8oua7blp+O70IRwryeXbsNEeNwZEcNGjpzMFlxHXjkhXD+yrz4baHshIzkUQ+cQSBEEQFsQ/8WfwxtqtMLiJsEBzqbcANXJ9xaO7zAYfDJ6AG/xahwC3pK5WgTee/RNlJTXwD3bDG5/cCbGk7VG8BEEQHUW5Up/3YSuRQiYS40iT5VX3Xn5cSDifnU1VH8PDgiARtW7T5i/bi7qGRvQI9sDk4ZHoCIJDPbj4wXI/Orv4YSDc0w1/zLwT/55Mwqdb9yKjrAIP/r4SE3t2w//GDYdnG1ZYTPBoKcCzgY/MFotNebVV+PPsCSxNPYG8ump8cnw35p3ch1uCevBqkEgXT7z1yATYW8vw99YT+GzxTlTVKjBryiAS6Qmik0DihxljK5SiEgqo7QTISskn8YPo8jAxY9+5LC56HMrIMX4fw0MDMWtIPwwI9OUXIGqtFv+mJ/BKj+RKfdi5VCjCbSG98GjPgQi0d+ry3yVx7RkXEY598dnQWOmw9t84PEPiB0EQBGEhxGfl4fU1W7ngoZVooZXroHHUQMncQrTAKMdgfDtpCmwk0gu+j0at4VZXzHPeycUW7399H2ztru/oZ4IgiEuh3JD3ITvf8qptgdeQ99GW5dWps/lYvy+RP35x+mhePdIRBHXzxJH9qZ0696Mt2D3/lOge/Lv9eudBLDl6EhsTzmL32Qw8OXIQt75uzwrrfHxsHfBy3xF4JmoI1mUm8WqQ02WFWH7uNJ9i3Hy4CPL0vcN5dc5Pqw7ip5UHUV2nwOx7RnbYb0cQRDMkfpgx7la2vExSbQ2cPZWDmNEU+Ed0TVQaDTYlpHLRI7lIL2YwL85JkeHc3oqN/mAo1Cp+gfJjwmHk1Fbxdcxj+r7wPpjVvT/crW079DiIrsUI/2AgHtBJddibmoVnOnqHCIIgCOI/UlRdi+/3HMayuNNQ22mgdlUDLfp9RDUCTJKE4atbbr3oiFg2qOXbTzbg2IE0yGQSvPPlPfDw0uceEgRBdOawc6VChZNHM/jygGGt8z4ySiuQXlrO71mHhga2ygL5bPEO/vjmEZHoGeKFjiK4Kfcj/ax5iR8G7K3keP3GUUYrrOM5BbwaxGCFNSjY/7LcV24P6YXbgiMRX5qPxclx2JCVjLiSPD6x/rl7ukXj4btj8dNfB7Fs83HU1Cnx2kPjIG4j7J4giOsHiR9mjK+dA842lEIn0yE5taCjd4cgripF5TXIKayAn6cTPJxbl6Yy6htV3FKBZXrkV1XzddYSCe6IieTBZt6O9kb/1T9SjuPXpGMoVdTxdc4yKzzYvT/uD+8LB1nnDG8jLBt/O0cINMwGBMiS6cvkCYIgCMIcqahvwM/7jvKgWZaxphVrWwkf0AHh+U74+I1Ly+v45/f92LDiGN/2lQ9vR3hPn2t6DARBEFej8sNFboWTxzKgVKp4ZkZQqEe7llfMmYBlVbTk352nkZJVDDtrGc/66EiCu3ny+bmUAp5hYq4CdHcvd/w5cxpWn0zEZ1v34lxpOR5YvAI3RoZzKywmtmeWVyLQ2bFNW6yWsHMSq/Zg02sxo7Ek9QQPRS9uqOV2WBKhENG3eiFjZzHW70tATb0SHzw5CTIpdb8SREdBf31mTJCDM1B8DlopkFtY2dG7QxBXjTW7TmPur9t4VgfL4Jjz4FjcPLK5sqmsth5/HjmBP4+eRFWDgq9zsbHG/QOjcVe/KDha68WMkoY6/Jp0lAsfNSolX8dCyh7pORB3hvaGlVhCvxrRYbALZ6tGMeqt1Kh1oXJogiAIwvyoVTZi8aF4/Hogjj9mhPo7odCmBqVKfbaHEQEwa9ogWMkvfv21d1sCfv5qK3/8yPPjMXhkxLU5AIIgiKtEubLZ9qrZ8qpbm2LvjpRzfD7qPMurqpoGfPfPfv740dsHw8m+dVbI9eTM8Sw+V6k0mDF5Hma/PhkTpsTAHGH2U1P79MSYiBB8teMAlh47hQ1nUrAtKY07SbB4Ktb38O7ksbi976VlrDDniNlRQ/FEZCw2ZadwSyxWBXK0Ng/oD0hqBNiSdRY1nynw+XNTYGtlKnQRBHF9IPHDjOnm7MrnWqkOxXX6DmCCMGfUGi22HkrGB7/ob3YZTACZu3AbBvYOhBIaLDwQh5UnEvioQkaAsyNmDo7BlKgekEv0TVpOTSV+SDiMv9NOoVGr3y7MwRWPRQ7EzUE9IBFSSCbROQi0cUKitgQqR6A4vxzu3s4dvUsEQRAEcVGUKjXvOPph7xGU1+urF7t5uMAr1BZbCs5Cpx9zYoJABwwOMbV3aYuk0zn45M2V/PHN0wbg1rsH0S9CEITZ2F45yaxwZN9J/njAkLA2K+Xis/P541HdTMUPJnxU1yoQ6ueKW0dHoSMpKarCN3PXGZdZdcS899fAxs6KC9IiM7VycrCS481Jo7kV1utrtiGpsNik7+HNtdswNCTgohUgLZGKRLyfgU1nygq5CPJvRiIa7TRQRWqwpTEDE3/4GT/edTu6e7auBCII4tpinq0VwQl1cuFzFiRYLWY6NUGYH+wi6nRaPvc1venpH/D295v4eq0IUMv180aJDi+v2oQJ3yzCX8dOceGjl7cHvrrzJmx4agbu6tebCx/JFcWYvXctRq7+AX+cPc6Fj2hXb/w4cio23zyLB5qT8EF0JkaGhvK5xlqHTauPdfTuEARBEMRFB6osjzuN8d8sxNzNu7nwwQaiPD5+AKo8G7CZCR8AJvqEwy5ZxK2uODrAPkkMofLClY4FueV467m/0KhUY+CwbnjshYmXZJFFEERr5s+fj8DAQMjlcgwcOBBHjhy54Ne0fPlyRERE8O179eqFDRs2mDz/wAMP8L/HltOECRPoq2+iQqkXgoX1OhTlV0IiFaPPgNZh5ntSM3gne7iHK3ydHIzrkzKKsHrnKf74xRmjOzwnIi+7HFqtaT+TTge8//Iy3Dfxc3z70Tpu76XRaGGO9PT2wMs3DGu1nv02y+PPtDr2SyXSxROfDpmEQ7c/iZf7jICbzAY6KZDjXIOJmxdi5pblOFiYxftBCIK4PlDlhxnjY9t0ohQDDdZCaLVaCIWkZxHmQWZ+OTYfTMKWA8nILdaHjzPsbeQoFSrQwDLK2c0uuygQCHA4J5c/PyIsCLOG9EP/AB/jzfCx4lwsOH0QO/L05cOMYV5BeKLXIAzy8KebZqLTMjowBAvOHoRWpsOuA2mYjnEdvUsEQRCEhVKSW4a81AL4hHnBzVc/iOpSYZ1AGxPO4uudB5BVrrfb9bS3xayh/ZCiLcaXZ/fq11nb4cNB45FxtATxORmQFQu5wC+qF0CkFCC3qLLdLLfqqnq8/syfqKqoQ2iEF+Z8eLvZjiwmiI5m2bJleP755/H9999z4WPevHkYP348UlJS4O7u3mr7AwcO4O6778bcuXNx0003YcmSJZgyZQri4+MRGdlsAcTEjoULFxqXZTKy8Tm/8qM8U99G9o4JhNxK2uq73pmiz/sYHR5i0saywYDs1nfC4Aj0CfdFR+Pj78ytokxEAAFgZSVDeVkt1i4/yidHZxsMHtUdw8b0QFRMIERi83FZCHJ14lZXTPBoyfzdh7AtOQ1PjYzF2IiQK+pPYMH3T/SK5Zbbf506jo/270SdnRo7C8/xKcLRDdMjYjAlqAesJa3/nRAEcfUg8cOMcZPb6EdTCQCVswiVxVVw9nTq6N0iiHYpqajF1kMp2HQgCSmZzeWlcqkYI/uFYlxsBOycrXDnr0ubX9R0oTGueyieHBnLR8gw2EiJnXnn8N3pgzhSrBdG2JY3BkTg8chBfMQF0bVho93YpNHorc86I5Funvp2XASc1dV09O4QBEEQFihcMDb+sh3zHv2Bd2KxzqzZPzyKibPGXPR17Hprd2oG5m0/gOSiEr7OydoKjw0bAD8fe7xxdAvy6qr5+rvDovBy1Aj8/u9R/Lkhjq9jggebGOxzfT3aDsttbFTjvZeWITerFG4eDnh33r2wsqZOVYK4Ur744gs8/PDDmDlzJl9mIsj69evx66+/4pVXXmm1/VdffcWFjZdeeokvv/fee9i6dSu+/fZb/tqWYoenJ91nXSjwPD9B31YOHNra8qpRrcbetKxWllcb9iXiTFoBrOUSPHXX8E7xD5+1xc++NhlffbDWeO5gy2MmReHEkQzs3Z6AA7uSUVlehw0rjvHJ3sEag0dFYNiYnojuHwSxpHMLIczaimV8MKsrQ97o8LBAHM3MQ0pRKZ5ethY9PN3x1KhB/Pe6EhFELBTi/ugY3OAbhofmLcM5qwoovLVIrizBq4c24aP4nTyPdHp4DPztzDNQniA6OyR+mDEioRByiKGAGio7IXLTCkn8IDodtfVK7Diaii0Hk3EsMZuPZmGIhAIM7BXIR7YM6BWIA5nZWHTyJPalZbb5PvcOiObCh1qrxYasZHx35hCSKvQCikQoxG3BvfBI5EAE21NmAqHnySef5FN1dTUcHJpLyjsTcpEYkkYhVDItqij0nCAIAl1dpLiYcCEQCvDY5zMwZMoA1Nc0oKFWgQbjXMHnbL2iac6WK4srcWhdvPH92PvMe+xH9BsffcF9PJKZiy+378PxnAK+bCuT4sHBMbi1b098dXof3tq1ha/3tXXAR4MmItzKFf/7fC1Ons3j6wdGBuBoYrax02zOzLFtVn0YfORPxWXC2kaG9766Fy5ul+61ThCEKY2NjYiLi8OcOXOM65hDxNixY3Hw4ME2vy62nlWKtIRViqxevdpk3a5du3jliJOTE0aPHo33338fLi5Xp60zd8oVetur3IRiyJrCzs9nU0Iq6hob4WwtR6S3Pvuhpk6Bb5fpq+dmTYmFm5MtOgss3DwmNhT5OeXw9nPmggij/5AwPj0zR8Otr/ZuT8SBnUmoqqzHptXxfLK1t0LsiHAMH9sTfQYGQ9KUz9nZYOHmLOODVTUyG0cmiFTWK7DoYBwWHz6OxMJiPPHXGv57PT0ylosjVyKCeLraY/FL92L2ZyuRtKcImgABpN2lKFTW4ufEo/gl8ShG+YRgRkQMhnkHcSGGIIirQ+dsfYhLxkEih0JdC7UtsHPFEXgFul+1Gy2CuFIaVWocOJmBTQeSsf9EOhpVzSPve4d5Y/zgCIzuH4bMqkqsOpGI17/diRplG8mYTbATv6eDLf48exw/nDmM7Fp9KbG1WIJ7u/XBrB79uc0CQZgjHmIb5KIGSmegpqoedg7WHb1LBEEQxBWw/qet+Oqxn3hnPhMpnrvE6or2qKuqw/7VR/DFI98bszN0Wh2+e24Rn/4LWo0W+WmFbd43nMkvwrzt+7HvnH50skwswv0D+3Db0fjyPNy6eTEK6/XVijPC++LlviORlFqI+z/4AxXV9bCWS/HmI+Mxqn8YispruNUVq/hoz+7qjx93Yfv6kxCKhHj94zsRFEZhsATxXygtLeWVzx4epn9LbDk5ObnN1xQWFra5PVtvgFWGTJ06FUFBQTh37hxeffVVTJw4kQsnIlHbI/yVSiWfDLBBSZYIa/fLlPrKD0GdFr4BrvDyNR2U90/8Gby+Zit/XF6vwMoTCbzj/adVB3nbGejtjGnj+6CzwQQPg+hxPqyyg4kjbHr6lUk4HZ/FhZD9O5NQUVaLrWtP8MnGVo5BI8K5NVbMoBBIZRJ0Jpjg0TLg3NFajtljhmDGoL749WAc/jx8gp8bH12yGlE+nnhqVCwXTC5XBHF2sMaCV+/Ai5+vxvGUPEiy1XjxgWE42piH3fnp3MabTWxA5/Twvjyz1E5KVZAE8V8h8cPM8bK2Q1F1LbRyYM3i3djwzfpLLmMniKsJG9UXn5yDzQeSeaUHq/gwEOTjwis8xg2KgFYCrDmVhLt/+9voGc3wdrDDLVE9cEtUdxzLysPrG7ZCI9ZCpBFgVL8g3L79D5Q01PFtnWRWeCAiho+KcJRZ0Q9JmDWD/ALwT/4ZaGx02Lk+HjffM7Sjd4kgCIK4TDJOZ2Heoz8al5lI8cXD32Pn0v2IHBKBsJhghPUNgou3s7Gz5PwqkeryGpzZm4xTuxNwak8izp3IbDdwlXU42TraQG4rh7WdVdNcDitbuf6xrRV/bGVnBY1ag9/fWW4SrsqEBu9QU+uacyVl+GrHAWxJStN/hlCIO2N64dFhAyCVivDusW1YlZ7Anwu0c8Ing29EPzdfLF53FD/8s59bhoT6uWLuM5Ph32TFywSP9kQPxtZ1J7j4wXhmzk28A40giM7JXXfdZXzMAtF79+6NkJAQXg0yZkzb/Q8sQ+Sdd96BpVOvVkGpUfPH4gYdBkw2tbwqrKrh1kotYcu+1rb4Z+sJvvz8faMgMaO8jPNhWR/RA4L59MTLNyLhRDa3xtq3PQnlpTVc5GYTq/AbOKwbt8bqNzgUMnnnEkJa4mRjhRfGDsUDg/rilwPHsOTISZzMK8TDf6xCHz8vXgkSG3x5GaO2VjLMe3kqXvtmPfadSMeSX4/izYfH460pY/F7Sjz+STuN9OpyvH10Gz49vge3hUTybJBQBxrkTBBXCokfZo67WD9CmIXl6lwdoK1tuKQydoK4GrCb6LNZJTzDg2V5sEwPA6xcd1xsOCYM7g5vDwdsTkrD/9ZvwdEsfT4Hw1oiwfieYZgS1QP9A3y5JQLjcHk2lP6N0EEHFRtJWZRsFPse7jkAd4VGUSgYYTGMCg7h4odWrsOO/UkkfhAEQZgZaScy8OrED9p87vj203wy4OThgNC+wfya58iG43pBQgC4+jijLK/CRKBgeAS6oSirxFj5YRAuFp+bf1nX+uz92T0Cq/hgr5/9/SPG1+dWVOHbXYf44BQmYLCrsZt7d8dTIwfBz9kRG7OS8cbhLShV1PNq3Ie698dz0cOgUmrw0pf/8s4bxqShPfDyA2Mgv8QRvcwqZd57a/jjaQ8MxcRbYy75eAiCuMDfu6srr8QoKioyWc+W28vrYOsvZ3tGcHAw/6y0tLR2xQ9mvdXSTotVfvj5+Vls3odAAwhULO/D1PIqs7yyVag2W563fC80Wh2vlBvYKwCWgkgk5IHvbHr8xYlIOpWLPdsSsG9HIkqLqrFz02k+sUD4AUPDuBDC5m0FxHcGXGyt8fK44ZgZG4Of9x/D0mMnuSXkg7+vRD9/Hzw9KhYDgy7937VcKsHHz07Gez9vwab9SXjr+414cfpovHXDWLwQPQyrziXgt5Q4pFWVYXFKPJ+GeQVyEWS0Twi3wCcI4tIh8cPMcVLqGz2tVAdVoCuEDvbQpue3W8ZOEFeDvOJKXuGx+WAyMvPLjevtrGUYPSAM4wd35/ZWTOj4OS4OW5PS0KDSj4RhN9SDgvwwJboHbugeBmtp8w2ySqvByrTT+N+hTa0+87WYUZgR0Q/SdkqqCcJcGeStv9HRSYEzNRUdvTsEQRDEZbDjr3344qHvoGxobPUcs766/807UJBRhNS4dGQn5qKiqApHNx433VAHlObqr6f8InzQe3gP9B7RA72Hd4erj4s+86Md4eJSYVXhbHAUu0dgFR/s9SU1dfh+7xH8fewUVFot3+6GiFA8MzoWYe6uvOL2id2rsCErhT8X5uDKqz36uHkjObMIc75eh/ySKkglIrxw/yjcMrLXJY9+zc4owbsvLoVarcGIcZF44EmqWieIq4VUKkVMTAy2b9+OKVOm8HVarZYvP/XUU22+JjY2lj8/e/Zs4zoWeM7Wt0dubi7Kysrg5eXV7jYsIJ1Nlk65Up/3IarXwsZGhp59/E2eD3RuHWTNWstz50pgJRXj2XtGwFJheTM9o/359Ojz45GSkIe92xKxb3siigoqsWdrAp9kMgn6cyGkB89LYRUinQ03OxvMmTACs4bE4Kd9R7Hs2Gkcy87DjN/+wYBAX14J0j/Q95LeSywW4a1HJsDeWoa/t57AZ4t3oLpOgQdvGYj7I/rivvA+OFCYhUXJcdiem4a9BZl88rN1wP3hfXlIOrlgEMSlQeKHmRPi7A7UpUInAZSeVmh0Y+XuEsgcbTp61wgLg/mQbjt8FpsPJOF0mj74ksFueIdGB/MKj9ioQORWVWP1iUS8sHETCqubK0ECXZx4hcfNvSPg7Whv8t4J5UVYce40/s1IRFnTqJnziXTxJOGDsEhc5NYQqgTQSnQocdIh+UQmIqIDO3q3CIIgiAvArKR+fuVP/PPFWr7cb3wUBtzYF98//5uJSNHSilZRr0T6qSzs/GsfVn+zsdV7vrXiRQy9deAlCRdXgsZOioYAe1RIdPh92z78fvi4cXDK4GB/zB49BL19PXn1yb/pCdxyo0LZAJFAgCciY/FU78GQCkVYteMUvvhjJ89083ZzwNxnbkJE4KXndFSW1+KNZ/9EbY0CPaL88OLbU3jnGEEQVw9WbTFjxgz069cPAwYMwLx581BXV4eZM2fy56dPnw4fHx9uS8V49tlnMWLECHz++eeYNGkSli5dimPHjuHHH/V2frW1tdy+6rbbbuPVICzz4+WXX0ZoaCgPRu/qGCo/RA1A30EhrcK9xSIhb0s1TdUfrIrOtUYCpUaJB6YMgJer6f2xpcLa+u69/Pj08OxxOJuYr7fG2paIgrwKLoiwSSoTo19sKK8IYRZZNnZydCbc7Wzx2sRReGhIf/yw9wiWx5/Bkcxc3L9oOWKD/PD0qMHo6+990fdhVaDP3z8K9rZy/LzqEH5ccQDVtQouhrHnhngF8imnthJ/pBzHstSTyKmtwodxO/HFib2YEtQTM7rHoLuT+3U5boIwV0j8MHNkSv1PqBProBUDIq0ADd5WKKyoR0RH7xxh9tQrGrEn7hy3tTpyJouX5Bou1vr18MO4wREY1S8MauiwMSGFn+yZB6YBe7kMN0aGc9EjytfTZDRgaUMdFzv+OXcaSRXFxvUsz6NS2dDS3YFfKDJvaYKwVOT1AtQ76KB0E+CpRxZi/LAwvPT5fR29WwRBEEQbVJfV4IO7v0T8Nr2d1V2v3IoH3pvGbWaYeNGeSCG3lqHHoG58/Zr5m0zyPJhYEt6//bwL9pr/UtXNgnaZv/z5titRvl54bswQXpXLYEHmrx/ajG25+twP1qHy6eAb+SAUhVKFdxdtxoZ9ify5YX2C8eajE2Bvc+mdUkqFCm899xcK8yp4GPDbn9/d6YJvCcISmDZtGkpKSvDmm2/y0PLo6Ghs2rTJGGqenZ1tIjoOHjwYS5Ysweuvv86DzMPCwrB69WpERkby51n7durUKfz222+orKyEt7c3xo0bh/fee++aV3aUFFUhL7scPv7O7QZvdzSGsHMRy/s4z/KK8U98Ahc+unu645Xxw7Fr/1ms3HwSPu4OuPfGfuiKsL6B8J4+fJr19A04l1LIrbH2bktAfk45DuxK5pNEIuKCEhNCWGi6nX3nyfz0sLfFm5NG4+GhehFkRfwZHMzIwcGMZTwQndlhsfPsxb6Hh6cOhp2NHF/+sQtLN8ejpk6BVx8ax0Uzhp+tI+bEjMJzUUOxJiORV4MkVhRjadpJPg1w9+UuGeP8wyARklMGQZyPQHe+sSxhAvOkdHBwQFVVFeztO58a/++ek3g2Uz9yzP6YBLJqAYQa4M37R2PSuOiO3j3CDGH2A4fOZGHLgWTsjkuDolE/IpAREeSBCbERuCE2HA52VtiXloXVJxOxIyUdKo3GKFQMDQ3ErdE9MKpbMGQtRr00ajTYkZfGQ7x25aVDrdNbLLBRhGP9QnF7SC8M9w7mVSCvHtrELxDZ+304aAKmhUV1wLdBWAKdvR1nlR63rfwLdaECiCuEcIoXwSanAd9/cR9VgBAEQXSytvzcyUy8fesnKMwsgdxGhpcWPonht7dvC9MebVlZtawSuZqwoN3R835pJXx8cMsNmBrdk3e8sFvC5Wmn8N6xHahRKSERCvFM7yF4LHIQ70jJLqjAnG/WIi2nlA+CefzOIbjvxv7GvLZLgdnufPDKcj6q187BCvMWPgTfANdrcMQEQVhKW75pdRzmfbAWOq2OtzfPvjYZE6Z0vnygeUf2YF7yAdglq7H7+Wfh7GpnfE6j1eKGr35FflUNPpoyHn08PHHPnMVQa7T4/PkpGNonuEP3vbPBzkcZaUXcGosJITmZpSZWUX0GBnNrrNgREbB31GfgdhZYhhYTQVadSIS6yU5yRFgQz9Dq5dN+fo4BNrjg/Z8280Gnw/uG4P0nJ0EmFbf5HR0rzsVvKfHYlJVi7FfxtLbDvd2icXdYNFytyA2GIAyQ+GEmN1rtUVhWjUFrFwBCQJothVAFWBXrcJfQHv+b/3BH7x5hJrCT5+nUAl7hsf3IWVTW6D1LGb7uDjzDY/zgCAR4OSOpoJgLHutOp6CsrtmiKtzDlVd4TO4dAVdbG5P3ZrZWrMKDVXow+wQDUS5eXPCYHNS9lV9lQV01MmsqeMWHl03n+9sjzIfO3o4v+nYTPi4+jYZQDdAIyPIlsC4Ano1kHuhkI0AQBNFZ2vKdS/fj81kLeL6Hd4gH3l71MoIiTX3dL4eS3LL/bGV1KSw9dgpvr9veav1vM27nAa25tVWYc3AT9hZkGK/PWLZHuJMbX95x9Cze+3ELrwh2drDmnTEx3S8/sPineZvxz+8H+Cjeud/NQK8+lhPuSxDE1W/LWcXH/ZO+5PeTBpgAsnjdc52uAuSRZUuxRZmJoCwZdr7xnMlzO1PS8fhf/8JBLsPymXfhjfkbkZxRhCHRQfjihVs7bJ/Nhaz0Yi6CMDEk81yzYwQbOBDdLwjDxvbA4FHd4ejUeTr7c8or8d2eI/j3ZKLR6owNDGUiSE/vC9tE7ok/h9e+XcetJdm59tPnboHNBYLgWcXmkrPHseTsCZQ22a+xwaU3BUbwapAoVy9j/0pGTQWCqH+F6IKQ7ZW5IxZAoBZAJ9Vx6yuohWhwB7Z8uxd3PDoOwb3ppoJon/S8Mp7hwcLLC0qrjeud7K1xw8BuXPToGeLJRY61p5KxevV6pBQ1j7xwsbHGTb0ieJVHhKf+BtkAC8n8NyOBV3kkV5YY17tb2eLW4J5c9AhzbH+0HxM8SPQgugSetmhUNT2WAsoAFTTWIsCz81zAEwRBdPV8j1/m/Inlnzfne7y6ZDbsnGz/0/v+VyurS2FLYirmbtzVaj2r3vBzcsDvKfH4KG4X6tSNvLPkhejhmNWjP8RCIa8G/nbZXvy1KZ6/JjrcBx88NQmujpd/3GuXH+HCB+P5t6aQ8EEQxEVhVlfnG5Uwu0BmidTZxI+MwlLACQj2Mr0nNgjQjN6uHrjzpUUwHFLvsItnQhBAQLA7Ah5xx32PjEJ2Rgn27WAVIYlIP1uI+MPn+PTN3HXoHROIoWN6YOjoHnBy+W/n5/+Kn7MjPpwyDo8OG4AFew7xvpSdZ9P5NDYiBE+NjG3Vf2KAVXzMe2kqXvziX8Ql5eDJucv5sqNd23ZfrNrj+ejheLLXYGzISsZvyfE4UZqPlekJfGLiR4SjG5annYYWOn7+n0vOGkQXgyo/zGCU2YU4lJGDezYvgdZaB1GFEOJqMQQaAdzX5aKfrQ0+3/mOSc4CQRSV12DrwRRsPpiEs1nNooS1XIIRMaE8uLxfT39enstOzqxkc19apnHEgkQkwujwYF7lMTQ0gC8bUGrU2JGbxqs8mK2V4TXsZnqcfzfcFhKJYV5B/IaaIK4Xnb0dX3L0BF5N3AS0bKp1wDNWffD8nVT5QRAE0ZFt+YXyPTozrMPw5/3H8Pm2fXw5zN0F50rKufUV6/h4Zlwsdlan4XBRDn++n5svPh48ESEOejGmuLwGr81fj1Nn8/nyfTf2w+N3DjX6j18qbOT29g0nsWj+dt7hN+Px0bjnoRFX/XgJgrDMyo/pN31pmo/UCSs/VCo1Yj76FNUBAjzjPxDPjxxlYoPELK/YEdhlA0KV6bGs/vIheDg3W2QRl05edhm3Udy7PRGpSfpzFYP1f/XqG9AkhHSHi1vH3/9llFZgwe5DWHc62ZhtOq57KBdBunm0PSA0KaMIsz9dyV05Ar2d8fXLt8HD5dL+rZwsLcBvycewLjMZjVq9PXlL2G0ny/Pq7eoFHxsH2EjarywhCEuAxA8z7zRjHr6xyxZAJ29qQnWAuEQEvzW1EB8+i1d/ewqj7hrS0btJdDDVdQrsPJrKba2OJ+caR5uIREIM7h3IKzxYaCXzkzyZW8htrTacSUG1Qml8jygfT0yJ7oGJPcPhaC03tcwqK+SCBwvfqmxUGJ/r4+qN25itVWB3OMguPQyTILpSO74+LQlPHvi31fqQ9QpsWfJGp+9gIwiCsNS23CTfw1qGFxc+iRF3XH6+x/WmUa3BO+u3Y8XxBL5874BozBk/AoklRTiSn4N8VTWWpJ2AQqOGlViCl/uMwIyIGC6KMI4mZOONBRtQUV3PrTbeemQCRvRrP4z9gl797681jtzuEeWHL36ZRQOzCKILcyWZH199sNYogEy+oz+eeuUmdCZOHEnHXTuXQuElxHfDp2BiYITxuS+27cOP+44i3MUFhUeaHRQMLHj1jiuyESRMKcyr4CIIs8dKScgzEUJ69PbD0LH6ihB3z44Vzc6VlGH+7sPYeCaFiyDsrDuhZzc8OWIQQt1bV4Jm5pfj6Y//QXF5LTxd7PDN/26Hv5fTJX9eaUMdPonfjb/P6auP2sNJZgVfWwf42Njzua+Ng3656bGdVHZFx0sQnQUSP8y804z59sWuWGC6Ugd4rQPsjhbARaPAr0nzYGXbdokcYbkoG9XYdyKd21odOJkJlbpZ8We2BUzwGNM/jAeXF1TV4N+TSVz0yCyrMG7naW+Lm3t351UewW7OJu9fXF+LVekJWJF+Gmcrmy/kPKxsMTUkkoseoU2jBwmiIzGHdnzwigXGUUDGdny1Fk8O7IH7XpnScTtHEATRRdvylvkeXsEeeGfVSwjq1fntZCvrFXjm77U4kpnLxYxXJ4zEfQOjsSz1JF45uAm6FmebWE9/fBx7I/ztHPky61z8be0R/LjiAK8QCfN3w9xnboKfx6V3tDCho7iwCof3pmD+xxtMnuuMI7YJguj8bTmrAPnlm63YufE0AkPc8d3SxyHsRE4CP365GZ+K4qByFGL5+HvR38PPKESP+vJnbiHtVSNDfXHzIEEGVX5cG4oKKrGfWWNtT0TiSX11o4HuvXz1FSFjesDT28nk3xizWfPxd74u56jU4lLM33UImxJT+TITQSb1isATIwYi2NW036WwtBpPf7wC2YUVcLKzwlcv34bwQPfLutccsuI7bnnVkjAHFxQ11KK6sXnAa3s4SOXNwkjTxCpGDI/Z8wTRmSHxw8w7zQ4UZuGeLX+1Wu90VASXo3UQnE7DXc/eiFkf3tMh+0dcG9o7OTOrqrjEHGw+mMwrPeoaGo3Phfi6cMFjXGwEvFztUd+o4j7QLISL2acZToVWEjFu6B7KBQ8WgilqcWHJRghuz9HbWu3OT+c3xgyZSIzxft1we2gvDPEMMHkNQXQ0nb0dZ7BOqTmHNhn/pljwueNRMdz3F2PJ5v/B1YeERIIgujbXoy1nAeQ5yXnYu+IQ1v2w1ZjvMefPZ2FvBrYkzFbjsSWrkVVeCRupFF/ecSOGhQZifVYyntpjWmHIOlr2T30c3rb668iq2ga8/f0mHDipDz2/aXhPvDRjNORSyQU/U9HQiNTkAiSdykHy6Vw+Ly+rbXf7T354AFH9gq7K8RIE0XXa8prqBsyYPA91tQrMmXsHRo6LRGfhgVvmYc9NDdDKBNh+y8NG+8D1p1PwwooNEGkFsMnQwtneClU1Cr39oFCAOTPH4uaRvTp69y2+32T/jiQuhCScyDbJkOnW0wfDxvSAVqvFbwt28AEA7Hd59rXJmDAl5rrsX0phCb7dfQhbk9L4Mhu0MLlXBB4fMRCBLs3iTHlVPbfASskq5hWZn78wBX3CfS/rXvPVQ5u4LblIIMCHLTI/qhsVyKurRm5tFZ/y6qpaPK5GhbLhou9vJ5HBx9beWDFiUj1i4wBHmfyaV30yV5zM8koEOjvC06HzX7MR1xezED927dqFUaOafRNbcuTIEfTv37/N5xQKBV544QUsXboUSqUS48ePx4IFC+Dh4WExnWZcxV35XXOHGUMHuOyVwKpEDevEYohy8vHzmS/gE+rVkbtKXCVY6e8XH6+DWiyAWK3Dcy9PQmC0H6/w2HooBaWVdcZtmSfk+NgIjB8cgVA/N35CP5KVi9UnErnwUa8ypCwDAwJ9cUtUD4zvEQZbWbPnI2siTpYV8ODyNZmJJiMDYtx8eHD5pMAI2JPaT3Qy5s+fzyeNRoOzZ8922na8ZXseX5KPZ/b8Cw10kOWI4XpIjViBEJ+tebmjd48gCKJDudbX5Bt/2Y4vH/0Buhbe8nf9bwoeeP8us7AfPJyRg2eWrUWVQglvB3t8d/fNyFFVYsGZQ4gvabYAaclf4+5GrGcAktILMeebdSgorYZMIsKLM8bg5hGtOxbZNWFBbgWSTucg6XQuFztY4KxGozXZjtmq+ge7ISO1yGQ9VX4QBPFf2vI/ftyF33/YCd8AV/z49xMQiTu+bf7jx5347eddSH1K77Txud1I3HbrIP74jh+W4HRBEWTlOkRYOePbV27n/Ta5RZXw9XCkrI/rTFlJDQ7s1Ashp+MzTbJkOvpclVhQjG93HcSOlHS+zASKm6O644nhA3l4OqO2XokXv1iN4yl5/Fz94TOTMTQ6+LLuNTNrKhBo5wQvm0v/26tVKZFXW43cuirkNYki+sf6dWWK+ou+h41Y2iSK2JtUjBgeO8us/pM48k/8Gby5dpsx1+zdyWNxe9/OI5ASHY9ZiB+NjY0oLy83WffGG29g+/btOHfuXLt/JI8//jjWr1+PRYsW8RPsU089xcsj9+/fbzHih3HE8MFN+jI2lvlRLoL9WREktYBtei2EZ9IxcHg43l87p6N3lbhCNGoNH1V3YFcSFq86jAYvK2Zgye5CIVDroJM0V1rY28gwZmA4Fz2iuvnwk3dWWSW3tGLWVvlV1cZt/Z0ceI4Hs7bydTI9uRfW1+htrc6dRlpVmXG9l7Udt7Ri1lbB9qYlmQTRGTGHdrwlbx/eikUpcRAoBbA/wQSQcnzw7m0YNKlvR+8aQRCExbXlTCA/vD4eb936Cb+ONiAQCvBn5ndw8728yrvrbZ3BWBF/Bm+t2w61VovePh64eWgE/kg9juTKEv68RCCESneeQCEQYO+tj+HQ4Ux88ccubo/q6+6Auc9MRrcAvZ1GQ70SKQn5XOzgVR2nc1FV0TzIxoCLmx269/ZDRKQv91YPjfCCTC4x8eq/3qNpCYKwvLacVX3MuHkeaqoa8OI7t+KGm6LREdTWNPBqgk1rjiPxRDZUNkD6Q1aAVoeI+Ur8vu45nC4qwRMr1/L79chGJ/zwyp1wcbDpkP0lWlNRVsv7VjasjENackGnqVI8nVeIb3cdwu7UDOO5+tbonnhs+ADeX6NoVOG1b9Zze3M20OCtR8Zzd4+OpF7VqK8cOa9ixPC4VNH6uuF8WP5Yq7yRFiKJq9y6Vb+vWqNFdnkljmbl4u1126EV6aCV6CBUCSDSCrFj9iyqACHMS/w4H5VKBR8fHzz99NNcBGkLdjJ1c3PDkiVLcPvtt/N1ycnJ6N69Ow4ePIhBg/RqvKV0mjEVd+b2v5FcWQpRpQiu1TbQpSggqVXB9mwFtEnn8P7aVzBwEt1wmANsBB0bSXfiaAZOHcvAmRPZqGtQotFOggbvJuGjBWKhACP7h/ETX2zvQEjEIlQ3KLAx4SwXPY7nNJ/QWVXHxJ7dcGt0D/Tx8zY5iSjUKmzJScWKc2ewtyDDWFEkF4kxwT+cV3kwf2iytSLMCXNpxw2UK+oR+898KLUaSPPFcD2qhVdCIf6Mn8sDdwmCILoiV7Mtry6rwbHNJ3Bk43Ec3XSCL7fFZzveRtTInpf8vte7s599zhfb9+Hn/cegE+jQvZsrymR1yKmt4s/bSqS4t1sfzOrRHztzzxkHSwkhwDv9bkDaniJs2p/Etx0RE4IHx/dHTloxEpvEjsy0olYjYyUSERc3DGIHm7t52Lc7GI2JQfk55fD2u35iEEEQltuWL1u0F79+sw1ePk74ecXTEEuuT/VHo1KFw/tSsXPTKRzZlwpVo9r4nMJVgKx75RDV6xD6kwJPvz8Fb+/cg1prLRy1Umx66UE42lEGa2eEnaPuv+lLk6rPzlCleCq3EN/sOoi9aZl8WSwUYmqfnnhs2AC429rgvZ82Y9OBZN4t9ML9o3HHDR0jBF4KrI/JKIYYKkaaHrN5cUP7VpkGpEIRnKXWsIIEUAMNdWpUViugbWSDgQXQWGugdtXoPT3ZgPBSMf6cOo3buBMEQ2yOX8OaNWtQVlaGmTNntrtNXFwcF0nGjh1rXBcREQF/f//LEj/MBVa29krMaDyw/W9o7DWorFTAzk4ANSRQOVtD5OyIBc8tQp+xvSGVXdi7l7j+MJ9JZg1w8lgGTh7LxOn4LNTWKqCxFkFlI4baTQyNlayV6GHgtQfG4sZRvbj6fSA9i9tabUs+h0aNPuSclf4NCQngOR5jIkIglzT/6TP983hpPs/xWJuRhBpVs61Vf3dfLnjcGBABOyl1uhLE9cBZbo1Heg7EN6cPQOWqQZ23GOVF9lj45jI8/tl0+hEIgiAuI8MjL7UAXiEeqCqpxpENx3FkYzySD6eZ+H5b2cnRUHNeEK1ICO9Qz0v+rrPSi/Hl+2ugFQmgkYsgatRi3vtrERTmifCePv/5Nzu/ooRlt/1v5SZsSUmFxkEDmZsIJ1T5gArcPmJm936YHh4DB5k+hNQhQY3AxQo0OAshqwaWHjyC0tp63k8QamWDtH+T8Ozv8a0+193TARG99CIHC4oNCfeCVHrpt5BsX0n0IAjianHLtIFY+edBFORVYMva47hxar+r/uUa2lsvb0fk5pRzwYNVetTXNd8nB4S4Y+DQMCxffAAaK/09OhM/tNYifL56H2rd2X24AO/ceQMJH50Ydn6a/drkVgMXOvq81dvXEz/ddyuO5+Tjm50HcSA9G3/Hncaq4wm4I6YXHr17KOxs5Fi+9QQ+W7wDNXUKzLxl4DXP1bgS5GIJz8ExZOGcD8uVZYO5s2sqcbq4EEllJcisKkdhQw2q1UqoBBo0ajUoVLQYqMK6ptxavAm7pDMcugBQu6phZWWW3d3ENcIs/zX88ssvPL/D17f9gJ/CwkJIpVI4Our98QywvA/2XHuwbBA2tRyZYC6M8A5CLxdPnC4rhNpBA4nYFpqkBig85LCr8UD+mbNY+eU63PXKrR29q10eJnZknStpEjsycCo+i4e4aWVCvdjhJIbG1x46oenJy9PFDoVlNdCKAK0EEKoAkRZw8rXHJ1v2YO2pJJTUNnsuhrm5cFurm3pFwMPe1uS92AlmZZOtVXp1s60cKze8LSQSU4N7IdC+OWSLIIjrBxM/fk08hjppI+r8tZAX22DV8mOY8MBIBEX6009BEARxKRkej/xgInK0JLh3AAZM7IMBN/ZFj9hu2PLbLsx77EdoNVoufMz+/pFLsrxio4HX/XMUv/+wC0oHCapD5FDbA+JqwP6cAs9M/5FXPfTuG4je/QLRq28gFxQuxSKLhYmXFFVjw8pjWPH3IaglQohVWvQZGoqDLtXIs66Dxl8DiAC1VgN7rQSDGz0RXeUCSVE9lu/aw0ez1tUqsXrnKTR42fKBNA2uOjQw4UOlhU1ePUrq9ZUiUpkYYd29uchhEDtc3Dp/xSRBEF0HuZUU02YOww+fb8KSn/dg7E3RlyXIXkoFHxOu2zp3sHZ61IReGDWxF4JCPXhHs4+/C979ez1/XtQIKEMdUCNiN+kC+Dra44YeYVdt34hrA6vQjIkN7ZRVisyp49fptyEuK49XghzKyMGSoyexPP4MpsX0wrRJfbFsfTx+WHEAVbUKPHvPCC7gdGaYyMSs2FOLy5BWUobU4lKkFZcjvbQcCnVzRRVDBDGEEEEqF8HDxRZO9nLIrETQinWo1zaiWFnH+7V05x+yAKhD43U9LqJz06G2V6+88go+/vjjC26TlJTEKzYM5ObmIiAgAH///Tduu+22dl/H7K5YZUhLIYMxYMAAHp7e3ue+/fbbeOedd1qtNxe7lE1ZKXhs9yoItIA0SwqnEhF0VRpY59RBfDYf8poaLEz+Cq4+l+dfTPw32J9ZdoZB7MjE6bhMVFXWQysR6MUOGzE0thI+WrAlTnZW6NfTH/3Z1MMf3u4OeP2vTfgnOclY0ucol6Gyxb9zRys5FzuY6NHTy91E/W9gtlbZZ3mVx76CTKO1NfNYnMhtrSIxyDOAV4oQhKVgbrZXBn44cwhz43fxUbz2pyVwPt2ACIUCC/a+y/OrCIIguhKX05azio/7Ah9vZdnUb0I0hk0dhP4TotsUNtjr8tMKecXHxYQPlse2bf1JLnowEUMjESB/vA3qg5ttF6zTRfDaXg9xg2neBrNrcXK1Q9KpbGYHzwt7Y0dEwNHFFqVF1fz9mOhRW93At1c6Nosq0OhQ76+D2kkLNJ0KJJU6OJ7SwCYT0AkE0IlMJ3Z9qZWLTCuIdTp4VWgxKDrIKHYEhXlA0qI6mCAIojNelzPReeaUr1FaXI0nXr6RV4NcDYoKKjFj8pe8XW7J6Im9Mem2fugR5dfmNfhHu7fj+6yjsC4WwfakCNoQKWqhwks3DMOsIVe/MoXouhzJzMXXOw/gWFYeX5aJRejj5omEg7kQaoBJw3ri1Vk3QCwSdoo+sPyqGqQZRQ79/FxJGRpUpiKHAalIhBA3Z4S6uSDU3YUP5mVzH0f7dq3XWcXIyFU/6DOQm2BZKfumPn5Zwe6EZdOhV7cvvPACHnjggQtuExwcbLK8cOFCuLi44Oabb77g6zw9PXlQemVlpUn1R1FREX+uPebMmYPnn3/e5OTs52c+PnHj/Lsh1MGFB1SzEnixzAaNVXVQuMlhX+MGRWkFvn7qZ0x9ZhJ8wrwuO8SRuPSGPjerrLmyIy4TleV1/OZTbS3Six1hdtC0CCpnyKVi9InwxYDIAPTr4Y9QP1ejcs8srbYnn8OKlCbhgyEAFz7EAgFGhgdzW6vhYUGQikUm+xJXkscrPNZlJpvYWg3w8GuytQqHrYRsrQiiMzEjIgbfnz6MCjSg3k8Leakc545VYeMvOzDp4WZLR4IgCMIUZnV1vvDBuOvlKRfM8GDXxRe7NmbXVft3JmHR/O3IySzl62x87FESKEF9cL3JNRoTQioKbeEpsoGbTAZVaT3K0sq4XQubtGIBNFIht8g6sCu5+TPYxAQLaxFgJ0HRQBEaAtVGUcXwGeIaAWzSRZAVC8D+a/C6jH8JAgGefnUyxgxpHmRGEARhDjAb77seHIZvP1qPpb/swfib+/CKkP9CcUEl3nnhr1bCB2PClL6I7BPQ5uvW7DqN33fFAcGATqmDu48d0lDLO3FZRgNBXE0GBPri9wfuwOGMHHy98yDic/JxqCAPkmARRBVarD2QgNp6Bd57YhJkV7Ei6mLXRYXVtVzkSC0pM4odbGIWnW0hEYkQ7OrERY4wdxej2OHn5HDZ+bL+do6YGzsBrx7aBI1Ox4WPDwdNIOGD6DziBwskZ9Pl/FEx8WP69OmQSC6cWxETE8O32b59u7FCJCUlBdnZ2YiNjW33dTKZjE/mChux/0RkLJ7fvw4aBy1Kqurg6iyBuhxodJBC4uWOg/8e4xPrVJ/9w6OYOGtMR++22cNV7dxynDyqr+xgYkd5aQ0vv1NbN1V2hNhCLTMNZBMJBegZ4qWv7Ojpj8hQLx5WbnhPpo4fysjGwfQcHM3KRa2y7dK9r+68CWO6h5qsy6utwqomW6uMmgrjel9bB9wWHInbQnrxEwVBEJ0T5o/6Yt/heO3wZqidNGjwkMAqwAk/vLsCg2/pDyf3zlOSTRAEcTH27NmDTz/9lOfyFRQUYNWqVZgyZco1+eLYAB92ndtSALncDI+2OHEkHb98uw1nE/QjLm0creDS3xcn84pR6aVoFj4MCIDyfirUVlUju0IIqZUAklBbuNhYobKoGgo3kdEiy6pIAy8vR6ikQJGoHrXWajQ66aCy10Inb1E50iSA2CaJYJMvhL2NFezdZbC3lcPeRs49wNncsMwmrU6HD3/e0mJMpL4IJLL7f88iIQiC6AiYILH8t/28WoNZD95+/5Areh92z7117Ql899lGk0wPA+xcwqyQzoedX3YdS8UHv2yFtnvTto1AtqoWkAMTe3aDkzWFnBNXH+bsMSjYn4d5sywQJoKczC2AygFQ2AmwKeMcyj/9B189PxU2/1EUPP9vpbimzljBYRA7WCVHe/1UEqEQga5OxgoOg8jh7+R4VatTpoVFYbh3EDJrKhBo50TCB9EKs6pr3rFjBzIyMvDQQw+1ei4vLw9jxozB4sWLubUVK6WcNWsWr+JwdnbmJZVPP/00Fz4sLez8fCYHdccXJ/cit7YKGjsNxFa2UJWr0OAmh7jKESgpAxSN/ITNvI37jW+79J+4MIV5FcbKjpNxmdymgN1UspBLta0YmkBbXuVx/uCRYB8X9I/U21j16e4LWxZk3kRuRRX3cTyYns3V/NK65vwOhq1M2urEwgSvnt4eRlurTdkp+CftNA4UZhk/21os4aHlrMqDVXuQrRVBmAd3hvXGNycP8IC3Bl8t6spkkBQ54bsXfsOrvz/T0btHEARxydTV1SEqKgoPPvggpk6dek2/OXZdywb4XEmGR1ucTczDr99uw/HD6XxZZiVF7/EROFZQjKzSIlTFaKB2NrW2MiIDGt21fEI4IFABZTVqIAJQOzVXc4hqBSgQVkJrzXywLrJDAmDG+P54YcRleHvrdJj76zYuhLDrwDkPjoWHs91lfhMEQRCdA2bRd+/DI/DFu//i70X7uC2VlfXlDWKtKKvFvA/W4NDuFL7M7P8GDuuGxd/taDP8mg9OzC7F5gNJ2HIoGcXltXy9VqK/6xaoBWi00Zfn3dW/91U/ZoI4XwQZEhKAwcH+2JeWxTEvuAAAADhOSURBVO2wTucXodFJgH3KfEz+aCEWPnknAtwvL8eV/Tsvra03WlXxTA4ucpSjWtFaIGSIhUIEODsaBQ5DNUeAiyOv8rgeMIsrsrkiLEL8YEHngwcPNskAMaBSqXhlR319c2fxl19+yT0ZWeUHy/5gIekLFiyApSMRivBYz0F4/fBmaB21KMyuhbe7DPXFSqicZZD6eEJXVAooG6FVqbF3xSFMfXZSR+92p4eVwrKqDoONFRtlwi5ttFKhvrLD3wYaJnqc9zp3Z1v07xmAAT390a+nH1wdm4PHy+vqseFMChc7mOiRU6EPnDQgF4vR198bscH+GBTkhx5e7lh1IhGvb9gKjVgLkVqIdyaOQbaiEl8k7MWGrGTUqprFkVhPf17hwfI8bCRXT/UnCOL6tedz+o3Es/vWQu2ogcJDCKWvHXZuS0LYF2sw8s4hJF4TBGEWTJw4kU/X7fNmjeEDfC41w6MtWF4b6wTbuz2RL4vFIoy4uTeyoMKW5CzUe2lR100DnVTf8eUhskWRutYoaIxxDUWAvRMOFmQho64cCqEaOglaCyUCQGPXYriMGhAqhbDVSeFpZYezwhJTQUQHjO8eflmhpjeP7IWBvQORW1QJXw9HEj4IgjB7xk6KwtKFe3lQ9eqlh3H3g8Mv+bWsXf/mw7U8h5O17dMfG4Xbpw+BSCRE9JBQnErMRe8evuge7o384ipsPpjMp4y8MuN7WMklaFCooG26zdayPl6hAKGuzoj2vRwfQoL4byLIsLBADA0NwO7UDHyyaQ/SyyuQj3pMnL8I9w3sgydHDUJDowqZ5ZUIdHaEp4N+8ENZbb2pVVVTNUdVg6LNz2KWUv5NIkfLao5AFycT63WC6Gx0aOC5OWCuQbkKjRrDV36P4oZaiEvECNA5ouZkNYRqLexTayDQ6RVdXXYBUF6JQTfFYNbcexHY03zyTa41LEDNWNlxLJNXejCYP7MhoFznIEXjebUdttYy9OvhZ7Sy8vd0MoaO1ykbeTjVwYxsHErPQXJRSauTSS8fT6PY0cfPC1KxqUa5LPUk5hzcxAOd2Ls6yaxQrtQHYjL8bR1xW0gkpoZEws+WbK0IwlzbcQNslO6YVT8io7YCogoRnE4J4ZBQDeHZHAgaFHju21lkX0gQhFnBrosuZnvFBi6x6fwcvmvZlrOg8bzscsitJNi4Mg5b1h7no3/Z/o6a2At2PdywdNsJ1GtVqOmhhdJdH24uhQgfDZqIqd0icaqwAMcK8tDPywe9PU07v7IrKrEi+QyWnT2JQkFNq88P0DjxQSv9fHwQ7uEGF1trvv6lbRuwPO+UUVS5w6c3Ph174zX5DgiCIDrbdXlReQ1yCivg5+nUSrjdseEUPn5jBWztrbB4zWzY2Mkv+F411Q1Y8MkG7Nh4ii8Hd/PES+/eiuAwT2OGh6FKjt3Ce7s5IK+4eYAis6geEh2E8YMjMCQqmFeBPJ+0AWobHWTZEgjUQrx90xjc1Y8qP4iOgfXz/XXgBD7auAeNYv1gC5ZBo9JojD1Xgc5OqFIoUFHf3I/UElYhyvI3WuZxMLEjyJWJHGY1hp4gOCR+WHCn2U8Jh/FB3E4IVQJIciTwq7NGTWE95MUKyEtblKslpUGraOSjx8bNGInp70zrkiOJy0pqeEWHQfBgI0gYOmFTboedBEInORpgOlqPXQBFdfPm1R39e/ohIsjDGNLUqNZw/0WD2HEqrxBqrenru7m7IjbYD4OC/NE/wAe2clnbAep1Vdiek4a3j25r9byVSMLtzpitVX93X6PYQhCEebfjBrbmpOLhnSvAmh/rsxI4Japhm1PP2wZBbiH+iJ/bJdttgiAsV/x4++238c4777Raf63a8k2r4zDvg7XQnReUPmhEOIbdEoVFW+JxNrsEShcdarproLXSX8/1sHXH4onT4Gplc8mfxQSSmzf/1qqaY834Ga0Ek5avaU9UIQiCsNTrciZGfPjrNn7Na7DsY5VsBjQaLR67awGy00tw3yMjcf+jo9p9r2MH0vDle//yQY6s72PazGHcOotZaBlElimzf+bCx/mwQY1M8BgZE8qzlVoSvXQeKhsVkOZIYCuQYc8Lj3C7aoLoSPJKqvDA50uRK6iDTtZ2/xBby0SO8+2qglydIW/6uyAIS4DEDwvuNKtTNWLIigX8RCwpEsNHbYf6xDoINDpY59VDrNBAqNbhyeduwPHVh7Bv5WH+Oqlcwm2w7nplCuprFMhLLeDBkZbWscY8PpvFjkzkZpXy9SykXGPFcjskELtaoY7XWDTDdIXwQA9uY8Uugnp384ZcKuHPsdGBSYXFxtyOuOw8NKjUJp/r62jfVNnBQqp84Wrb+maZCSSJ5UU4VpKLuOI8HCvORVGD3lO0LRaNvgMjfUOu2ndDEJaEObfjBtgN36S1C5FYWQxRlRCOp8WwzWiAtFoFgUqLcQN88fyCh7jVI0EQRGens1V+sIqP+yd9wWIxTPjf3DtwvLAYy7edgEYI1IXrUO+pAkSAUCfAi72H4/HoQVc06ISqOQiC6IpcznU5EyNumf2TSdvMRIvVXz5kUgGyd1sC3v/f37C2keG3NbNh76ivmjPQUK/ET/O2YP2KY3zZN8AFL70zFRG9fE2223IwGW8s2NBqP+Y+cxNG9+/W5j5qtFqE/vEJ7y+QZUlxT58ovHXTmEv7MgjiGlNeVY/7P/4D56R1rZ57cdQQ3BvbB1ZNfVkEYcmQlGfBsIyHmd374cuT+6B10qAgtw62Ui3EjULU+9vw4EOrggYs/GkvZj09Frc9Nwm/zFmCM/uSsfTj1Vj97UYo6xv1oyyEAh4cyfyTzQGDbYGPv7MxoKyqog6n4rOMlR1sdAiDh5TLhNC4yCBxt0a9SAe1cdSfflQf80bmYkekP2K6+8HB1kr/Wp0OmWWVOJSRrQ8pz8xt5Y/obG2FQcH+iA3y46KHr5N+f1pS3ahAfEk+4opzueBxorSAh5e3RCwQIszBFcmVxSZiDLPKCndyu6rfH0EQnQvWsfZ6/9G4Z+tSaOy1qPfQQdRgBYWHnLfjm5cdRF5iDl745Qn4htGIYIIgzB+ZTMan68G65UdbCR+NdmJ8vGoPKmoVaHTQoa6HFipbfbKbt8wei8ffiVBH1yv+TGZbdX9hH6rmIAiCaAdmdXV+28wGG7Lsopbix5DR3RES7olzKYX45/f9ePDpG4zPnTmehc/eXo2CXL2rw5S7BmLmU2MhtzKtzDh0KhMfL9reah9YP0jPkPavrdlAU+MuaoC7+kfR70l0GpwdrPHITYPxv81b9KN4Deh08Ld1IOGD6DKQ+GHhPBDRDz8mHEEdGiG01kLhJoRNXlOVvUCABi8rSFJr8M3cdejWwxtPLngU5ekF+OHFxTwgsuVFxpeP/AAHVzvEjIuCzOr63IxeqW3BVx+sbfJoBqL6BaGqoh4ZaUXGbTQSAdSOEsg8baGQCKDU6G9muaWVFnCyt+YWVnorK394uTaPSimqrsWuk4k4mJ6Dwxk5KKg29Wy2lkrQP8CXCx1M8AhzdzUJpOQWVrVVXOQ4VpzHBY+UypLzkkMAe6kMfd180M/NFzHuPoh29YaVWMIzP149tAkanY4LHx8OmgAvG/MczU4QxKUz2CsQkbYeOFNbhEZPDZRVYkirm9rxEF+cScjHI1EvYOZ7d2Pq7BshElHoHEEQxMVYueQgD8w15rpZidDoKOV2p7X1CqjChaj0UgBsYKQOuC+0D96KHQuJ8L+3scy+iiysCIIg2oZlfDCrq/NtqNydbE2WWeXz9MdG463nlmDVX4fQrYcPgrt5YMPKOPzz+wF+/+3u6YAX3pqC6AHBJq9lfQYL1xzGTyvZduD3/UVlNfwz2T38nJljW+WMtKRcUa9/oAF6eXki3OPKRXGCuBYMigiA9Z8C1Lvq9AKITgfrUgGigrzpCye6DGR71QXsUubG7cQPCYchUgohzhPDJh+QtMg16uXrhtL9OWioU/LRxROnxiAm0gvvTPm4zfcTiUUIjgpARP9QhA8IRcSAUPhF+HSo3YqioRHpqUU4ceQcfvtuJ7951UiFEDVqubWXVqQPKZd72aLRSoTaRtOqCiuZBH0ifI3VHSG+rkYLg+oGBY5k5uJgk5VVeql+1IgBiUiEaF8vY25HLx8Pvs6ASqtBYnkxt66KaxI8WBD9+QTYOSLGzRf93Nnkg1AHV36x1xYFddXIrKlAoJ0TCR8E0QXacQN/HYnHnOQtvANOXCKCqF4I60IB7FMaIK9shK6qBrqcAkT0CcSLvz6BgB5+Hb3LBEEQnNraWqSlpfHHffr0wRdffIFRo0bB2dkZ/v7+170tZ51hv36zDX//to8vu0d746yy1jgyUmWjQ31PQGGv4qOGHERy/DRmKgZ4XnxfCYIgiKvTlvMA8oXbuEhhYERMCD58ejLEIqFJmz7j5nkoyq9s9R7jbu6Dx56f0CoMvaq2AW9/vwkHTmbw5SmjeuH5+0ahsraBV5cw94cLCR+Mubt34oesw4AKkOdL8f6kcbi9byT9/ESngv0dfbB4G9QiHcQaAV6bbpqdQxCWDokfXaDTrKShDkNXfgelRg1JvgSSCgHk5YBIBQj1BQ/wcbNHkFCOMztT+bKdvRw1CenQVtUCLKxL2Qio1bB3tkN1mWmlA8Pazgrd+oc0CyIDw+Dq7az//Nyyq5obUl1Vz0ta05LzkZZciHMpBcjNKuMXPAylo4SPhDao2gKVDjqpqTAjYuWroV5NuR0B6BniyYPLGQqVGvHZeVzsOJSejYSCYpPRJuyWuIeXuzG3I8bf26RcsErJLKzymio7cnGytAAKjWnuh0QoRE9nTy5ysMqOvu4+cLcyHcFCEMTVwRLacQOnswp5SK5O3tQmNYkgTmdEsClRQlqkgECtgTavCOKaWtz3xu2Y9vItqGBWgBaa30QQhHmwa9cuLnacz4wZM7Bo0aLr2pZr1Boebr5lzXG+fOuDQ7Hw0GmorHVQOeigkgNKPw10Mn1bO9YrDPNG3gRbSeetfCYIgjAHrqQtZ9kfTIyoqK7HOz9sQqNKg5tHROLVWTcYByy2l9303Ju3YMItfVu9Z3JmEV75ai0KSqshk4jw8gNjcdPwnpd1LPvPZWH6hmXQOGiN1+XSUjH2PPQoPB0uLJoQxPXG8Hd0KaIeQVgaZHvVBXCzssG00N5YnBIPjZMGIoUE9T76k/NgF28UpVYir6QaeahGv1t6oP5MCfLPlQB+3hD6NpfGjR3VDS9+di9KckqRfCQNyYdTkXw0DanH0lFf04ATO87wyYCrjzMc3R1w7kQGvwgRCAV47jJyQ5iYUVpc3UroKC6sMtmOVXVorIWQOVtD6CBDpaax2c9QIIBOqn8c4uuit7GK9EefcF/YNPl8qjVanMkvasrtyMHxnHw0NtlgGQhycWoSO/wwINAPjtZy4z5m11biWE6ThVVJLlIrS1tZWDlI5Yhx80FMU1VHlIsX5GIKliKIa8n8+fP5pDnv79mcyW6oMnbGcQSA2k2DWj9AJ5LC2lkGSUYtxP7e0FTXYtF7K7D+x60oqK9Bo5sUspJGvPTRg2aT30QQhOUwcuRI40CVjoRVC8999R8c2pPCLU0eeWkiVpxMQU2wBvXBGv0oF7abAkAKET4bOgk3B/fo6N0mCILosrCOWkNnLXNYeOXrtViz+wwc7azw5LRhfD3L+2zrFOPl49TmKPhPF+/gIoqPuwM+emYyugW4X/L+nMotxPd7D2NrTio0Hk3CB0MANLqqEV+Qjxsdwq/oWAnievwdEURXgyo/usiIYZYxMWLVD9DotJDmSSBU6ishmK3S2sfux79bT2H5lhO8wsFaLkW0hysSNiZBd5591LAxPdCtpw/8g9wQEOwGD29H6LQ6ZCXmckEk5Ugqn2eeyTYpTTUgFAnxR8aCViOPtVot8nPKkdYkdJxrEjqqKps8NJvuQ7VSITRyEaxc9UJHvU6LuvMsrNqCXdCM6h+mfx+dDqnFZUax42hWLmpZZUsLPOxsMSjYD7FBesHDMHKDiSIJ5UVN9lV6waNUUdfq85gdVUxTVQezsQpxcGnXwoogiGuLpbTjjPVpSXjywL/tPi9QA6J6AaSVOsgLVZCWa6GUNKBilANr8AGtDh6/ZWPd7+9TBQhBEF2uLa+pbsBbs5cg4WQ2pDIxHn/tJizeewpnK0pRPlhvb2VEB3zR9yZM7UX2JQRBEJ2pLecWPr9s5Y+fvWcE7pkYwys/pt/0pUkfBBO4F697Dm4eDnxZ2ajGZ4t3cOGEMTQ6GG89NgH2NqZ2WG3B+hCYFfYPe49gf2YW1M5qaOxbCB8tmD9kCiaFRFzRsREEQRBXH6r86CL42jpgqHsgdhelQ+WkhrhSDKFKAK0GSCgs5t6WNw3riY8XbceZtAIcyMqHINQOOonAWPlhVdCAvdsT+WRAJpPAL9AV/sFufBp49wjc8drtcHS0xpZFO/Hdc4sAidhonaVVqZGdnIfqukYToSM9tRAN9c0ChE4ILnJoXWSQu1pDZyVGrVoFddPFDJdElMrm4/NwRJi/Gw8o+2tTHDRCQCsBhCpArBPA2c0GK+LP4EB6Ng8pL61rFlUY9nIZBgb6GQWPIFcnXkJbqWzgFlaLz8VzGytmYcXsw863sIrkFlb6YHKW28GqbQiCIK42fb18jIOSTWAasBjQiQG1vQ5qe6De31Bd1sKmRShA0Qx/PDr+DQwb0htRoyIRNbInXLxaj4ojCIKwJFg18atP/Y6sc8WwsZVj1qs3Yv66QyitrIO6G7vePe8FAqBSreigvSUIgiDag2UVsFyO+cv24aslu3kFyI1De+DZ1ybjqw/WcgGECR9s2SB85BdX4ZVv1iIls5gPSnz09sGYftMAvt35FFbVILO8EoHOjvCwt8Xu1AwuesTn5ENro4XaV82vudtCAAH6elKQNEEQRGeCKj+60Ijhw7nZmLZ9SfPNHfOKLxVDVivGtH698eSIQXCytsKa3af5RUS94ryKCp0OY4L9IFRoUZJTjrzMMqgaTYUAAxKJCJ7ejshJzIHG3hpamQhCpQaiBhXE1jKo1drmag6JgAsdsJFA5myFRokAdaq231cuFSPU340LHWF++nmIn6vRworx+l+b8E9yktG2wEYiQZ3a9FjkYjH6+nsbraxYhge7CMqqqeQiR1xTVUdqVWmrfXCSWRktrNi8t4snWVgRRCfGktpxxrLUk5hzaBOv1GPt1txBExBl543X127FqeIC6CQ6iFiEkUoLNbtB09/zmeBwpA6uq4ogrKgHFEr4hXtzESR6VCR6j+wJJ/c2XkQQBGFmbTkbCcysUNg14edvreLWqc6udrhz9hh8vXIfv9a1CbJGum8lcP7AXx2wZvwM9Pb0uibHQxAE0RW5WtflrBLj67/2YMnGOJ7n+cnsWzC0TzBv95mjhLefs1H42H8iHW9/vxHVdUoulLz3xI0YEBnQ5vv+E38Gb67dxq+zWXeCp70tCqproRPpoHHTQG2tt9MNtnfGh4MmIKumotV1+bSwqCs+LoIgCOLqQ+JHF+o0K6irRuyKBaYrdYCkQMxtsGwkUjw4OAYPxMbg2Oks/O+rtdDIdNBY67iNikhpOirCwVYOR1srWIvFEGl00NSrUF/RgKqiav5YoNZBZSc2CR+XlSohVGkhtJNB5CBDA7RQadsuF/VwsTMKHIbJx8MBImFzeDkb1ZFbWYXkwhI+ncgt4NUd58Ne0dvXyyh29PHz4vt0prxQL3SU5PF5qcK0IsRwYcNEDn1lhy9C7J2NwWoEQXR+LKkdb9meZ9ZUcIs9Lxv9MWm0Wiw5ehJfbt+P+kYVhBBAVKNBTe/zrFyaECp1cDythmNcI8QlDUBdA3T1+nlAN89mMWREDzi42iP5RCYSjqajZ/9gREQHXvNjNHRa+vg337xaGl3hGAmio9ryTavjjCOADfgGuGDk9IH4btUBaLQ6uEY4INGtFDq5jreZrPPKMHjmDp/e+HTsjfQDEgRBdNLrcta+v/fTZmzYl8hDy7/+3+2IDmfhpjBeG/+86iB+XX2YL/cM9sTcZybzfoa2YBUfo+f9oj8XNKGDDkJHQO2ihkqn5a4Pj0cOwhO9BkMuErd7XU4QBEF0Hkj86EKdZgcKs3DPlr/afpKd39XMJkoAK0gw0NsPB09loMFPa7wJtE8UIUjpiIrqeihVlxggzG8iLywUSMQiBPu4NFd0+LvyuYOtlcl2CpUaqcWlXORIahI7UopKUdfY2PpjRTpoJTp+PAKNAN/ffQuiAryasjr0weTMwqqR+X61QCoUoZdLk4VVU3WHi9z60o6VIIhOiSW145dCQVUN3tuwAztS0vmyxloNlUdziK+0RACtDaC2abqx0+hgd1YL52MqyMv163QsS6mFGGLr6YQ6O3su/LKRdjeM6oaXPr/vmh1Dy05Lg23BhCkxsCTYMX758TqoxAJI1Do897+bLO4YCaKj2vK2vN/Zo9GPDsGqvXqv94BoNxy1zYfOSgeZQIx/Jt4LnRo4VpCHfl4+VPFBEARhBtflarUGL3+1BvtPZMDOWoYPnp7EB0uyvoRvlu7B4dNZfLvbxkRh9r0jIGWW3G1Qp2zE51v3YsmxU8a+BHbmUDtruEDO6Ovmg49iJ6Cbo9t/3m+CIAji+kHiRxfqNGMjEoas/M5kJAPDRixFnbq1gNAWB6Y+zkczVNcpUFJRq5/Ka1FseNxiXUVNQ5vvEerrgoG9A7nA0c3fHQFeThCLmUdLMyU1dUgu0gschimjrKLVvjMkIhHC3F3Q3dMNXg52+CpuP1SuamNHn0ApgL+rI7JqK1q91llmxS9imNjBpkhmYdU0goMgCMvAktrxS4UJFFuT0vD6v1tRrVSaCsJqQF6kg9ZWhwY/DVROze2qVbEQtglaWOVqIFZoIFJqIdTo+PvpJEJopEKIGrW8gi+8pzds7Kz5iDhj08y20+k/v+W+GBb5ev5/k8jC7ytNn29UaZBxthBasaD589Q6BHfz5JaKTIDhmnqTsM5mzF+Z/99yXQvhvb3XGDdpet74+jbW4bzX8PXtvMbkM5vKbgzvxeYKhQo74tNMKiNtChVY9udTFlcBUlReg5zCCvh5OsHDue2RlgRxtdvyE0cz8L/HFhmXWRNT720FlaPeJrXnEF/sFGQ0CR8iLJ94H3q7kr0VQRCEOV6XK5QqPPPJSpw8m9fqOZlUjDkPjsXEIT3afG1FXQN+P3Icfx4+gSqFEmo7DdQt+hLY3FoswZy+o3BveB9ubUUQBEGYFyR+dEGv+FcPbYJGp4NIIOA+lXeG9kaZop6XaqZVlmFT6lnsz8+ESta6usNRJMdkvx64NbgnQlxceFB4exZQuUUVuP3FhdCImsPHRVrg33kPGztA1BotMssqeCVHSpG+oiOlsKRVILkBlknCRI4ITzc+D/dw4+Hk5cp6nCorxIGCTCxMjmv3+EMcXPQWVk1VHczSiiysCMKysbR2/HJYeSwBr67b0u7zEoEQIilQZ6uEyr6p0o9lp1cJYJMpgqxYABGLYFIzj2MRtFIBhI06SCtUXBxpRWt9uhWCS3iN2koEpbPE+HmychXEDRqTN2jzowQXWXmB+1Xdxe5lBRdZ0cbrde08zz6Ld8K2PH/qdBjUPQC+vk56kcT4uhaCyvniS/MmegGoHeHn/G30os15rzF5rlnQafl6UyGneZ/Qzv4lnCvA9sNn+ffAOgtY5wMLKbUkWoaiejqQuNPZKj/UQkAjF0LhIofGRswryfqNCcb6hmRorXSQNgkfUSR8EARBmPV1+bncUtwzZ3Gr9V+9PBWDerW2a82rrMbCA3E840OhVvOBOU4echTaVLfa9t+J0xHlRiHmBEEQ5goNce9isPCt4d5BrTwpXa1s+MSqH+7qFoWVp8/g+fh1pp05OqBSo8DvmfH4PT0eolohbBpk8Ld2hJeDPa+68Haw43O2zB6PntAdK88mG0dOjPQPwNbUc0ahg9lYKdWtO9DY5oEuTnqBo4XQ4W5nw3M5TpcVcLHj34QEPi9uqL3gcb8YPRz3dIuGM1lYEQTRhRgc5m8cuNYSP0cH5FdVc+9ilRIQKSUQVGuhcdBAY8tC0nWoilJDoARkhSJIykVodAK0Uh2EjYBVkRTSmhZveN4H/NcxcY12QANzFOBVEcyi67zPsxC0LQYHCDUCHErOBthkkceow9yF23jlp6VUgLQMRWXizruTx+L2vpEdvVsEwCuoht7TD2viUoxKHRv0M/TGcPxTedoofCybcC8JHwRBEBYw0IBZc7dnsd0SZpv9y/6jWH86BWqdFjqpDo7+MijkKhSqWwsfjHqN6iocBUEQBNFRkPjRBWGCx8WCuAb7B0C6RYzGFiWf4jIRgl2dkaWtgFKohsZei2r7BiQoFNyiSnROCEFbQ2eNtiLArpwsPrXEWiJBNw9XE6EjzN0V1lIJr0g5XVaIk2X5+CMunj8uqG/dA8Y6Hbo5uPLKjg1ZySb9cOxm97aQSBI+CILocrAbxPduvqHNDtpGtRqZZZU4V1KGtJJypJeWI62kDOm55VDYqaCx10AnAxQBGij8mjJDms4HansRxDUivejRNLWoVTiv6W9+xlA90OK0YFJ9wJa0Wi0adRroxKzTnFlsCfRCiLsYUpHIWF3A5ux4DNUJbC5s9VzL5davYY9bv0Z/Tml+vnlbPm96L6FAaPLebFlkeL1Q/9n89S0m/fsAykY11p9MgdLJIPDoIKsAJkeFQyaT6q3BmB1YC0sxg+1j8zr9XKv3EdP/DMb1TfZjJtsb3rPFa8+3JzP8nIbHzX5mRjuz5m2MzzR/vtHOTIfaeiVSy8uhsms+RnGtDr/uOwYfdwewKAYWRMq21baYWAg1W6dpWuaPtYbntfx1+nXscYvXGrdpvU7DOjeajtfwmZrztmlrneHz9OtMP69RrUE5y8Rpgq1jf2dDQwKoAqST2K2tO55qUl1V56TDPxWnobXWCx9LJ9yDPjSSlyAIotMPNHh9wxZoxDqI1EK8f+MNbQ40YPaa7Fqr5bUNux7z9XDkj+Oy8vDjvqPYnZoBrVgLrYMWUmch6qFCMVQ8/9ReIkM1GxXUAnZtxwaNEgRBEOYLiR9Eux1mH46agNc3bIVGrDW50GAdBIeKsvF78nFsyTkLtVwLtVwNobsI/mJnODbKUV3VyEtJ1Vptq/eO8vFEbLA/t65ik7+TI78wqVIquLixvywT36cdwqmyAuTVtR59wW5jQx1ceTB5bxdPPu/h7AErsaRda6+LiT0EQRCWCmu3WYdsVnklAlqMmJOKxVx4ZlNLmB1hdkUlEguL8W9mInaXnUOjsEWFngDQOGv4ZIK2hRDC5lqB6TITx7UtHrP1xteYLmvlOl6FYhBbROUi6GqBeq3aWGVyvthidji3tOMSQOkM/JNzFhaFvaApb0YvYqntBPgt/gQsiZZ5OloN+N8Z2V91PCxnhgtVTZVHjTY61EWojcLHX+Pv5plvBEEQROeu+Hh15yY0+ukHZKp0wJydm1BZ34BhYUEIdXPm4eYMVlXK7DU/WLwNapEOYo0A/7t/DBJKivHcqg2Iy8+DxkYDjbfWGGCuhobnfd7gF4ZbgnpguHcwVqWfob4EgiAIC4MyPy5CV/aKN1xwnN9h1hJWmfFP2mksST2OrJpK43qWq3Gjbzg+W7UfWkFzx4BIK8SO2bNgbSXBmfIinC4t5CIHEz2ya5tf3xKWy9HbxQuRLh583tPZAzYSfWDlhcLdz7f2Igiia9LV2/H/yprURDxzcE2r9UxcZiJzR8CED6lQCIlQBKlQBIlABLFABAlbxx8L+SRqmouhf8zqRticVW+wdex9eC2Jjq1jlSOsgpFdHLF1TRUiWv0oQkPVgKE6gc2ZwM8qAnjFgFZfYcCeM91OCzWbt9iuQaVCZYPCNIheI+A5Wry6xVAh0yJkvTlPw7DcXPHSnMthWgljEgbfxvsZ4jrOfx3PGTkvGL7992n786vqGxBfk28SGiouFSPW2R9utjYQCoXGihljdYywdbWMYR2vqmFzob7iRr9O2LSuRXWNYf/1fSFNy3p9zfjY8O+IP+ZlSxymvQkEzdUthlqW5nqZpkoa1q4olPg+7hA0dk1ZOTpAWirGnoceJfGjE7TlrPJj4hs/o85DC61MB7WjGjorfc7RknF3o7+H37XaTYIgCOIqteXrk1Pw5OFVray4JYViCJVCHkTe29sLvX09+QBLNnjnkx179YM3VQK4WlujUFcLra2G2x0a3ofbIHoFYUpwDy582EpkJp9LfQkEQRCWBVV+EBeECR4XGsHoIrfGo5ED8XDPAThQkMVFkC3ZqYgryeOTJEgEFRsK2dQx4G/tgHt2/oX06vI234+JFZEtKjqY0GEvlV8Tay+CIAji4vT39m2VG8K6mPdNfRzuVrZQaNR8alCr9I+Nc7ZeZfKc0mQ7NRrY82rT9SUNdUitKr3gPrHuZ6VWw6frARNQ5GIxHx0ol0j0cz5JmteLpc3rxWJYNT0na7GdYV29ohHPbVgPdYvqFnGlCB/fcjOcbK2MNkxcdIHejsnEHgrtLaPZrgnNz/PnmqyZmpdbWkQ12Tuh7edbb3+x/dBBW6+GOq9J+ND/o+FCSKO7ClWyer0gxMQiJiKxzzeIR03rmIDEhSS+b+dv07ytUYQyrruOglzLyww2ItWN2bV1jCBImKITATXBGqgM4htDC3wz/BYSPgiCIMwFcdMAA5x3vvVS84dKbSP2qM5hb2o6BIlN1aZ+WmOVSK6ukY1sMcIq/liFx6SACJ532h7Ul0AQBGFZkPhBXBXYaMuh3oF8YuHjy9NO4feU4yhk+RwtOj6yG6qAJotsX1uHJpHDi88jnT3hILt8oYMgCIK4drAbwI9iJ2LOoU3G3JC5LewEbYTSi1bjXQ5stN2Qld+ZeDazEXp7b30MLlY2bQgsepGloUlsUTatM4osLYWYFtubiDLG9aZCjQHW8V6rauTTVUNvQd0sDDhpMHPPclgUbXRYHC7JQUfC/i3xKiChPqOF/Xtm4pYhs0X/XFOFEHuOV6jol/Xb6R/XqpVIrihpJcqxqlMafNHxxBXkmwofDOa8dxX/hAmCIDo78+fPx6efforCwkJERUXhm2++wYABA9rdfvny5XjjjTeQmZmJsLAwfPzxx7jxxhuNz7MBDm+99RZ++uknVFZWYsiQIfjuu+/4tteCvl4+rQbgGAZgMgcKJmzoZDo+taIpq85DZoP7u8fg5qAe8LdrefFFEARBdBVI/CCuOmwk8JO9BiPKxQv3bVvW6vk5fUfhztDecJJb0bdPEARhBkwLi8Jw76DrYifI3puJK+dnN3nbOvDnWSWFwxVUBF4uPNSaiyHNYknLihW9YNK6iqWtCpfm7fTzckU9ctvItHKRWfPKEKPlEwtNZ53vhjB2ZvfUIki9OYC92RbK+Lp2lg0d/ucHspss888VtPtZLbc3+VxjgLwAtSolvj9z6LyKIeDlPiPhLLdqIT4YLMoELcSGJiGiaRujEGEQH4SmQoRBrDCKF+w5vs5U2DAcx7UU6SgUtXOPFtZJqDKHIIiuwbJly/D888/j+++/x8CBAzFv3jyMHz8eKSkpcHd3b7X9gQMHcPfdd2Pu3Lm46aabsGTJEkyZMgXx8fGIjNQHjH/yySf4+uuv8dtvvyEoKIgLJew9ExMTIZfLr9sAHHZdyq6pcmuruHV2Tm0VduWmY2f+uVbv8WbMWEwK7X7V940gCIIwHyjz4yKQV/zV7xhgVik0KpIgiOsFtePmiSX7LXeV8+Oy1JOtRCzWYWEpWPrxmXNbzv7GBq9Y0Mqu78BtlvU3RhAE0R5M8Ojfvz++/fZbvsyyx/z8/PD000/jlVdeabX9tGnTUFdXh3Xr1hnXDRo0CNHR0VxAYVUf3t7eeOGFF/Diiy/y51l77OHhgUWLFuGuu+66Ztfll3JNSO0+QRAE0R4tHBAJ4tqM3mUdAgxDxwDddBIEQRCXcg6J9QywyHNGVzk/MiGACTp/jbubzy1NGLD04zNnDKOF2ShhBpt/FGt5f2MEQRBt0djYiLi4OIwdO9a4jlWSsuWDBw+2+Rq2vuX2DFbVYdg+IyOD22e13IaJGExkae89r+c1IbX7BEEQRHuQ7RVhMVYpBEEQBGEudJXzo6WHhlr68ZkzXeVvjCAI4nxKS0uh0Wh4VUZL2HJycnKbXxgTNtranq03PG9Y1942baFUKvnUsvLjWkHtPkEQBNEWJH4Q1xzqGCAIgiAIOj8SxPWGrkEJgiA6FpYh8s4771y3z6N2nyAIgjgfsr0iCIIgCIIgCIIgCIKwAFxdXSESiVBUVGSyni17enq2+Rq2/kLbG+aX856MOXPm8HwPw5STk3PFx0UQBEEQVwKJHwRBEARBEARBEARBEBaAVCpFTEwMtm/fblzHAs/ZcmxsbJuvYetbbs/YunWrcfugoCAucrTchllYHT58uN33ZMhkMh5s3nIiCIIgiOsJ2V4RBEEQFsn8+fP5xDyPCYIgCIIgCKKr8Pzzz2PGjBno168fBgwYgHnz5qGurg4zZ87kz0+fPh0+Pj7clorx7LPPYsSIEfj8888xadIkLF26FMeOHcOPP/7InxcIBJg9ezbef/99hIWFcTHkjTfegLe3N6ZMmdKhx0oQBEEQF4LED4IgCMIiefLJJ/nERqU5ODh09O4QBEEQBEEQxHVh2rRpKCkpwZtvvskDyaOjo7Fp0yZjYHl2djaEwmYjkMGDB2PJkiV4/fXX8eqrr3KBY/Xq1YiMjDRu8/LLL3MB5ZFHHkFlZSWGDh3K31Mul9OvShAEQXRaBDqdTtfRO9GZMXSaMX9KKtEkCIIwP6gdJwiCMH+oLScIgjB/qC0nCIIgrjeU+UEQBEEQBEEQBEEQBEEQBEEQhEVB4gdBEARBEARBEARBEARBEARBEBYFiR8EQRAEQRAEQRAEQRAEQRAEQVgUJH4QBEEQBEEQBEEQBEEQBEEQBGFRiDt6Bzo7hjx4FsxFEATRWbCzs4NAIOjo3TALqB0nCKKzQm35pUNtOUEQnRFqxy8PassJguiMUFtu2ZD4cRFqamr43M/P73r8HgRBEJdEVVUV7O3t6du6BKgdJwiis0Jt+aVDbTlBEJ0RascvD2rLCYLojFBbbtkIdAbpnWgTrVaL/Px8i1IBWRULE3NycnIstvPU0o/R0o+PQcd4YSypTbrWUDtunlAbYBlY+u/4X4+P2vJLh9py84TaAPPH0n/D/3qM1I5fHtSWmx/UBlgGlv470jU5cSGo8uMiCIVC+Pr6whJhDZ4lNnpd6Rgt/fgYdIzEf4XacfOG2gDLwNJ/R0s/vs4AteXmjaX/jVj68THoGImrAbXl5gu1AZaBpf+Oln58xJVBgecEQRAEQRAEQRAEQRAEQRAEQVgUJH4QBEEQBEEQBEEQBEEQBEEQBGFRkPjRBZHJZHjrrbf43FKx9GO09ONj0DESBP19UDtn/lh6W27px0dcW7rCvx9LP0ZLPz4GHSNBdO2/EUs/PgYdo/nTFX5D4sqhwHOCIAiCIAiCIAiCIAiCIAiCICwKqvwgCIIgCIIgCIIgCIIgCIIgCMKiIPGDIAiCIAiCIAiCIAiCIAiCIAiLgsQPgiAIgiAIgiAIgiAIgiAIgiAsChI/LJg9e/Zg8uTJ8Pb2hkAgwOrVq02e1+l0ePPNN+Hl5QUrKyuMHTsWqampMBfmzp2L/v37w87ODu7u7pgyZQpSUlJMtlEoFHjyySfh4uICW1tb3HbbbSgqKoK58N1336F3796wt7fnU2xsLDZu3Ggxx3c+H330Ef+3Onv2bIs5xrfffpsfU8spIiLCYo6PuLZQO27+fyNdrR1nUFtuGb8jcfWgttz827qu1pZTO27+vyFxdbH0drwr9K90tXacQW25ZfyOxH+HxA8Lpq6uDlFRUZg/f36bz3/yySf4+uuv8f333+Pw4cOwsbHB+PHjeaNvDuzevZufnA4dOoStW7dCpVJh3Lhx/LgNPPfcc1i7di2WL1/Ot8/Pz8fUqVNhLvj6+vITVlxcHI4dO4bRo0fjlltuQUJCgkUcX0uOHj2KH374gV+QtMQSjrFnz54oKCgwTvv27bOo4yOuHdSOm//fSFdqxxnUllvG70hcXagtN/+2riu15dSOm/9vSFx9LL0d7wr9K12pHWdQW24ZvyNxldARXQL2U69atcq4rNVqdZ6enrpPP/3UuK6yslInk8l0f/31l84cKS4u5se5e/du4/FIJBLd8uXLjdskJSXxbQ4ePKgzV5ycnHQ///yzRR1fTU2NLiwsTLd161bdiBEjdM8++yxfbwnH+NZbb+mioqLafM4Sjo+4flA7bjl/I5bYjjOoLbeM35G4tlBbbjl/I5bYllM7bv6/IXHt6QrteFfpX7HEdpxBbbll/I7E1YMqP7ooGRkZKCws5OWYBhwcHDBw4EAcPHjw/+3dX2jV5R8H8GehM1bgEv9shJrlv8IMWhdGhIFGhUF6o8jARVcmwoJEvfHCKJaig4rECzEjgghjBN4Utqnghagl/gEHq9QgaVe1xMp03x/P98dGM4Pfz53jep7zesHpnO8529Gnj9/39/D9nOf7hBT98ssv5f2kSZPK+9jRj99W+OsY4+WGZsyYkeQYb9y4ET755JPymxdximZO44vfMFm2bNmIsUS5jDFOeY5TpB988MHQ2toaLl26lNX4GBtyPL19JOccj2R5HnXkzpLl6e0jOWe5HE+/htx5OeZ47udXcs7xSJbnUUcqZ1wF34uExINzNG3atBHPx+2h11IyODhYrhPx1FNPhQULFpTPxXHU19eHxsbGpMd45syZ8oAcp8zGa092dXWFRx55JJw6dSqL8cUPHV9//XU5LfNmOdQwfujdt29fmDdvXnnJq61bt4ann346nD17NovxMXbkeDr7SO45HsnyPOrInSfL09lHcs9yOZ5+DRkbueV4zudXcs/xSJbnUUcqS/ODLMTOdjyZ/Ne1FHIRT5rHg3H85sX+/ftDW1tbee3CHPzwww+hvb29vKbo3XffHXL0wgsvDD+O65nEZsjMmTPDp59+Wi6GB/yXHE+XLJflMESWp0mOy3GohSzP+dxKJMtlObfmslc1qqmpqbz/6aefRjwft4deS8X69evDgQMHQk9PT7mI1ZA4jmvXroWff/456THGbyDMnj07tLS0hI6OjnKhtXfeeSeL8cXppf39/eHxxx8P48aNK2/xw0dcLC4+jh361Md4s/gthLlz54a+vr4sasjYkePp7CM553gky/OoI2NDlqezj+Sc5XI8/RoydnLK8dzPr+Sc45Esz6OOVJ7mR42aNWtWufN/9dVXw88NDAyEY8eOldMAUxDXGosH5jhVsbu7uxzTX8UD2vjx40eMsbe3t1xvIZUx/tMU1D/++COL8S1ZsqScehq/fTF0e+KJJ8p1MYYepz7Gm125ciV8++23obm5OYsaMnbkeLr7SE45Hsn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" ] @@ -1085,7 +1056,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -1094,14 +1065,14 @@ "text": [ "DMD Matrix Statistics:\n", "==================================================\n", - "MAE : 0.0078\n", - "MASE : 0.2682\n", - "NMSE : 0.0728\n", + "MAE : 0.0079\n", + "MASE : 0.1737\n", + "NMSE : 0.0400\n", "MSE : 0.0001\n", - "R2 : 0.8992\n", - "Correl : 0.9481\n", - "AIC : -9.1745\n", - "logMSE : -9.2149\n" + "R2 : 0.9479\n", + "Correl : 0.9736\n", + "AIC : -9.1654\n", + "logMSE : -9.2058\n" ] } ], @@ -1186,9 +1157,9 @@ ], "metadata": { "kernelspec": { - "display_name": "dsapublic-env", + "display_name": "DSA (uv)", "language": "python", - "name": "python3" + "name": "dsa-uv" }, "language_info": { "codemirror_mode": { diff --git a/examples/inputdsa_fig2_tutorial.ipynb b/examples/inputdsa_fig2_tutorial.ipynb index 72cdd0c..fce56c4 100644 --- a/examples/inputdsa_fig2_tutorial.ipynb +++ b/examples/inputdsa_fig2_tutorial.ipynb @@ -386,7 +386,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -428,13 +428,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/sh/r0d61tjn42s4mc3nxqb0hhq00000gn/T/ipykernel_89440/1787994131.py:36: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n", + "/var/folders/sh/r0d61tjn42s4mc3nxqb0hhq00000gn/T/ipykernel_79424/1787994131.py:36: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n", " ax[1].legend(title=\"Delays\", loc='upper right', bbox_to_anchor=(1.5, 1),\n" ] }, { "data": { - "image/png": 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", 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", 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", 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", 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" ] @@ -538,25 +538,18 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 16/16 [00:00<00:00, 196.85it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 42.53it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 120.82it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 49.00it/s]" + "100%|██████████| 16/16 [00:00<00:00, 615.48it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 164.51it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 626.13it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 220.96it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Custom backend silhouette score: 0.975\n", - "N4SID backend silhouette score: 0.956\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" + "Custom backend silhouette score: 0.980\n", + "N4SID backend silhouette score: 0.963\n" ] } ], @@ -609,19 +602,19 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 16/16 [00:00<00:00, 50.42it/s]\n", - "100%|██████████| 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nADYiNgGwFfEJCUtiyixRBx10kFqwYIFq1qxZ9P6hQ4eq0aNHl2sbOsNbe0Snxsb0B/zLirwdRhv+GolbA7PpJG+NzJ0n9Yxe3z1ztWfZ7/neOlC7NVznud2pQex2jzRvXRW/1eFl0ethX/06ADUjEbGpGHe5EX+pplI+6v4amEVLl3mXd9w1er2wjE7xBb7lEdeOI6qw3AdVqCsV8gFYEJ/apHprWaa6arj56zhlBfO9Dw61i1sDs1iNzIYNo9fbpXjbPvm69HqZQVezMpJWRlVMV6jSwYpU0ARgfdsp6PpeV1bdNnc9zVJqYPrtiKSVunx72Ls87KpZl+t7bGHAO/lBXjhWB7Mw7J1DAUDyxKddUzbGrZHprzWeE9zpuR1q0iNuDczwZl9N3mCoAm2n+DGxKMc770ppXxELs+vv97pKnfmsWbPUnDlzVFqa9w9Ax44d1e+//56oYwOACiE2AbAV8QmAjYhNAGxFfEJJKtVlMBKJqHC4+C9lK1asMMPKAaA2EJsA2Ir4BMBGxCYAtiI+IWFJzAEDBqgHHnjAM0xAJvQZP368GjRoUGU2CQBVRmwCYCviEwAbEZsA2Ir4hIQNJ584caIaOHCg6t69u8rLy1NnnHGGWrRokWrevLmaMmVK+TbiLwUQCZR8XfhKJe1oETvsnN8K4tbAFA3e+Cz2uFu8NQa6ZKz13J6zpYvndn4kVg9lU3iHZ1mury7ChqJWsWMKeespAKgZiYhNvtJynvijQ6XXddKuuk+BonDcGpii6Nfl0eupyrvTiC9ApvmWhwOxgwr6fosK+WpPuWu/pPpqPgFInviUEfTWVHJ/nv11nTIC3nUD6bH2jy4ojFsDU4S3bnXto2K14ILu2FXGz+Q65DrmMkrmAag+CfleVxHBxMwdkB4svU2T5lvuriee6YunGa4aw+Z2KLY8NVT+Op0ALGs7+b77BF3focpqO6mQu15vIG4NTCMSjtt2ivi+x/m/u7nplPJ/19T1uFxvpZKY7dq1M8VVX375ZfO/9MIcNWqUOvPMM1WDBqVPgAMA1YXYBMBWxCcANiI2AbAV8QkJS2LOnDlTHXbYYSZpKRdHUVGRWXbEEUdUZrMAUCXEJgC2Ij4BsBGxCYCtiE8oSUBr7RusXbZQKKRWrVqlWrRo4bl/w4YN5r6SJv3x63b9/Z7b7h7/vh79qtA3V1C/47+MO+xy98zVnts7wrEhVB/v4+0lGuq+u3fDviFWKhTro7tofLZnUWZmvud27nLXcCxf195fL7rKeweAapGI2LTot9ae2wWu4UfrIpmeZc2C3tIR27Wr1IVvSEKhb+iAewj55R0P8yxLadc2/nAGGT6QHTuOxWc1LXU4fPoG17AD3+iF7+++wnsHAGvjU//et3luB0ppvmlfzHjin4/EXbddirdt5B4GNbDNft59pqap8mo9y1vCpzRrdnobeu/1fbDcjwVQ+22nFxYd4rm9ORxrp8zb0smzrEf2Ks/thdtjJblap2/xLNsRSYs7hHz+/qrSympnqXCsMaW3bfMsem/j05XfMYAajU/Htb/Me0fE9UXJ347K8LZbHvr4n9Hrwaq0nVK8/QZ1JH777ZCv81Rpwq4vc6vyGnmWTeo5SdUXlSpKInlPmczHT95MWVlZiTguAKgwYhMAWxGfANiI2ATAVsQnVHk4+cknn2z+lwTmyJEjVbqrULxkwb/55hszzBwAahKxCYCtiE8AbERsAmAr4hMSlsRs1KhRNCOek5PjmcQnLS1NHXLIIWr06NEV2SQAVBmxCYCtiE8AbERsAmAr4hMSlsScNOmPcfYdO3ZUV111VZWGjocKvLddpStVim+ZX8OUWB26Qt/c8r/nN/Hc7pKxNrbP7vt69/nDz57bwf26e24HCmJ1VyIR7/D5iPYNp28cq3+n8+vxfPdALUhkbKqKiKtOSaSEkhuedV31fP21mYpW/O65ndK+XfwN+Xbjr3tZ2roAkic+BVw12sztUiqaa9/CvFICQ7721QMvpQamLvQ10HxxLuCqJV4YCZXebnIJRypV3QiAJW2nsO/z7WkP+eKPu6ZbSY+tFf4adRWfMgKAhfFJF7kmXvF/1rWvXVXkTY3l+fJMpbWdIq65DorVwPQfQynyIqm+w40fH3eGvevWJ5VqNV599dWempjLli1TDzzwgHr//fcTeWwAUCHEJgC2Ij4BsBGxCYCtiE9IWBJzyJAh6vnnnzfXN2/erHr27KkmTpxo7n/88ccrs0kAqDJiEwBbEZ8A2IjYBMBWxCckLIn51VdfqT59+pjrr7zyimrVqpXpjSmJzYceeqgymwSAKiM2AbAV8QmAjYhNAGxFfEKVa2I6duzYYSb2ETKEXGaPCgaDZmIfSWaWh7sGpijKitUniKR4x/4HfSWYtrke/OOWVp5luzVc57k9Z0uX2I2CwlJrYEbm/+C5HeraKXpdb/LWhdruu60zw64HUkcFqA2JiE3++idhVyFJf52SvIA3OBUoVz04X82nAt9vRmmu2ikqFCy1BmbRbyvixiblCzcBVygy67oO0YayU0B9VdX4VFoNzBrjr/VbgbpxQStOAEB1tJ22hL316raEY5O/bitMj7vM7L8o9p1qa5F32faw9/tWWtBdWy42R0LCRby18gAkadvJ127xfD3zfWcqTdD/hcs30UDQ9T1P+2vsVoG/7eSukRmsx+2qSvXE7Nq1q3rjjTfUb7/9pqZPn64GDBhg7l+7dq1q2LBhoo8RAIhNAJIabScANiI2AbAV8QkJS2LeeOONZpYomS2qV69e6tBDD41mx/fff//KbBIAqozYBMBWxCcANiI2AbAV8QkJG05+yimnqMMPP1ytWrVK7bvvvtH7+/Xrp4YOHVqZTQJAlRGbANiK+ATARsQmALYiPiFhScxJkyap4cOHmwl93GSW8nLzDeFP3xAb3+8f3l/QyHs7PxI77M456z3LOjXw1sTMd9WwWx9q4VkWKCiKX2dOyiQsXhq9nrLLH7UYHFkNvLXwtvzaOHpdp1JHBagNiYhN7hqY/tojkQp0Xi++He9jw65Ap7MzS91WabFJae+5+naj8pu4jiG1/tZOAZI9Pvlr2lakFFLItXLI1wAL+pqCQV/s8uwzFCr9GIu87SoA9aPtVOirJ+6+XeRrmBRF/Lfj1xMP69LbUpXmr+frr/fr3Wli9gmgdvJOdUh9roPpVqm/BNdee61q2bKlGjVqlJozZ05lNgEACUdsAmAr4hMAGxGbANiK+ISEJTF///139dxzz6n169erI488Uu25557qrrvuUqtXr67M5gAgIYhNAGxFfAJgI2ITAFsRn1CSgNb+/vQVs2bNGvXiiy+apObChQvVsccea3ponnDCCSoYjJ8j7TzlDs/t9IzY8OyC/NgQcJGdlee5/dGBk6LXV/hGLvVIa+C5vSm8I3q956y/eZZFIt6hA3pTmud2yi47o9c7Df/Gs2zr6Yd4bm8Zti1242vvDO0Lb73Ce5AAql1lY1Nk9W6e2/m6MHp9RyR2XWQH0z23N0VisSo74I1jEeUtMxF0/Ya03wuXeQ/CP6pJx7/d6bq53kW99/Pc3tgtFhO3dvFuZvE1V/o2DMDW+HTUh2PjDikK+oJEftg7RHzlV63jhpNImu8e1+6POOR7z6JC17DPsqw7bLPndiAlJe7tQIY3lr638ely7wdA7bed8ld19txeH459h5qX7x0Guluqt/TXb0WxklztU7xxIzfi/W6WGYy1w8YdPkxViOsrb9HvK0sdTh5qHDumQGPv97p3l9xbsf0CqLX4dNInf4vfdvINzd5W6G2L6MsbxY0RRTm+dVNiy3s/MM+zLM9V3rAs8yswR3aoSRNv22nDk6q+qHJhERlWLpP8yAzl8ub59ttv1YgRI1SXLl3URx99lJijBABiE4A6grYTABsRmwDYiviEKicxJRN+7733qh49epgh5Vu3blVvv/22Wrp0qen2e9ppp5lkJgDUJGITAFsRnwDYiNgEwFbEJyQkiSlddtu3b68mT56sRo8ebZKWU6ZMUf379zfLs7Ky1NixY9Vvv/1Wmc0DQKUQmwDYivgEwEbEJgC2Ij6hJN4CReXUokUL9fHHH5sh5PHssssupldmPMGgtz5cOBw/nxrw1SuIuGqa5Ea89Qj8cnVsP5mZ+b7teGsbbPfVxMxqUBC3BmbDKZ96bne/LDt6fVaRty4MgJqRiNjk565dWeirJle8zmVMyFc7RWlvjHMvD0RKXVUFwvGX+2tgBmbP99wO73uYa+UqlUAGYFl8qmn+dpO/nlRpNTB1kbeIeaCBq4Z5oMrVjQDUYmxKDXjr5aa62jiZAe/3r1RfoycjEKtzmeFr8BQGvHHDszzkixuRMto47nZZsTYa7SPARrXadnLFCe2PGcHqaTtViPZ9gaxHKtRqnDt3rhky/swzz0TfSM8//7zq1KmTeYNdcMEFKj//jz9UgUBAdejQoXqOGgCITQCSAG0nADYiNgGwFfEJCUti3nLLLer772MzVcokPjIjlAwjv/baa9W///1vNWHChIpsEgCqjNgEwFbEJwA2IjYBsBXxCQlLYs6fP1/169cvevtf//qX6tWrl3rqqafUlVdeqR566CE1derUimwSAKqM2ATAVsQnADYiNgGwFfEJCauJuWnTJjO1vUPqExx33HHR2wcffHClJ/PJ3+aqbVngza3uTPXWR5mX3yR6fe72rp5lq8PLPLc3FLWKXs9d3tC708axGixCZ3r3s+XXxrHjG7Ytbg1MseKQ2PJdTsv07uc0700AiZXI2PR9wU7P7Twdq/O0XWd5ljUO5nlubwznlFjjqSRBV02o9A2+Ois+oVh5XsMVAtXGbg3i18CUWjKPzYleX/fX+PVkAFSP6mo7uWss+esr+esv+evseviW6VBiajX5a2J6amDKMebmRq+HGjdKyD4B1E5s+r+1e3tubyyMtZcWbd3Fs6xZxnbP7a0FGdHrzTO837fywqme2xkhV9sq7C8o7otdkfj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" ] @@ -715,12 +708,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 16/16 [00:00<00:00, 39.36it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 55.38it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 44.85it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 55.31it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 173.94it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 48.12it/s]\n" + "100%|██████████| 16/16 [00:00<00:00, 47.98it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 74.16it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 42.14it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 202.52it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 418.82it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 132.72it/s]\n" ] }, { @@ -731,13 +724,13 @@ "DMD State: 0.901\n", "DMDc State: 0.887\n", "DMDc Control: 0.064\n", - "SubspaceDMDc State: 0.956\n", - "SubspaceDMDc Control: 0.747\n" + "SubspaceDMDc State: 0.963\n", + "SubspaceDMDc Control: 0.746\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -864,10 +857,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 16/16 [00:00<00:00, 52.96it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 52.17it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 234.84it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 49.00it/s]\n" + "100%|██████████| 16/16 [00:00<00:00, 39.69it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 134.52it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 619.13it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 197.54it/s]\n" ] }, { @@ -875,15 +868,15 @@ "output_type": "stream", "text": [ "Silhouette Scores:\n", - "DMDc Full (state): 0.036\n", - "SubspaceDMDc Full (state): 0.342\n", - "DMDc Full (control): 0.05\n", - "SubspaceDMDc Full (control): 0.218\n" + "DMDc Full (state): 0.037\n", + "SubspaceDMDc Full (state): 0.293\n", + "DMDc Full (control): 0.051\n", + "SubspaceDMDc Full (control): 0.251\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -1051,19 +1044,19 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 16/16 [00:00<00:00, 52.49it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 51.19it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 237.84it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 47.02it/s]\n", - " 10%|█ | 1/10 [02:13<19:58, 133.17s/it]" + "100%|██████████| 16/16 [00:02<00:00, 5.72it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 81.21it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 313.65it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 229.41it/s]\n", + " 10%|█ | 1/10 [00:34<05:07, 34.15s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "0.978293692065396 0.8581800888829048\n", - "0.756474386212644 0.03636748940373985\n", + "0.9691272825017969 0.8582303653068575\n", + "0.7565087127808146 0.03803439019336802\n", "dmdc: [(20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20)]\n", "subspacedmdc: [(20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20)]\n", "dmd: [(20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20)]\n" @@ -1073,19 +1066,19 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 16/16 [00:00<00:00, 87.44it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 119.92it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 306.02it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 120.36it/s]\n", - " 20%|██ | 2/10 [03:07<11:32, 86.58s/it] " + "100%|██████████| 16/16 [00:00<00:00, 69.48it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 226.30it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 484.20it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 122.87it/s]\n", + " 20%|██ | 2/10 [01:05<04:17, 32.22s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "0.9816189763655433 0.9410053615361056\n", - "0.6531657035335597 0.008316243011282809\n", + "0.9816171028048734 0.9408706396597346\n", + "0.6532221986198002 0.006820164804708317\n", "dmdc: [(20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20)]\n", "subspacedmdc: [(20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20)]\n", "dmd: [(20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20)]\n" @@ -1095,19 +1088,19 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 16/16 [00:00<00:00, 79.39it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 121.26it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 309.88it/s]\n", - 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(20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20)]\n" @@ -1117,19 +1110,19 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 16/16 [00:00<00:00, 119.86it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 124.62it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 305.49it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 125.13it/s]\n", - " 40%|████ | 4/10 [04:43<06:04, 60.80s/it]" + "100%|██████████| 16/16 [00:02<00:00, 6.32it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 122.91it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 232.24it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 157.78it/s]\n", + " 40%|████ | 4/10 [02:08<03:12, 32.00s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "0.958468708116758 0.5258048513628725\n", - "0.7118416212469749 0.0271533429911993\n", + "0.9657347175367499 0.5254964762925847\n", + "0.7118533916252097 0.025776671998498396\n", "dmdc: [(20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20)]\n", "subspacedmdc: [(20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20)]\n", "dmd: [(20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20)]\n" @@ -1139,19 +1132,19 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 16/16 [00:00<00:00, 84.12it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 117.57it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 305.96it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 117.21it/s]\n", - " 50%|█████ | 5/10 [05:32<04:43, 56.74s/it]" + "100%|██████████| 16/16 [00:02<00:00, 5.51it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 112.90it/s]\n", + "100%|██████████| 16/16 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20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20)]\n", "dmd: [(20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20)]\n" @@ -1183,19 +1176,19 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 16/16 [00:00<00:00, 147.31it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 119.07it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 303.65it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 118.65it/s]\n", - " 70%|███████ | 7/10 [07:13<02:39, 53.21s/it]" + "100%|██████████| 16/16 [00:00<00:00, 35.26it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 119.99it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 437.30it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 164.11it/s]\n", + " 70%|███████ | 7/10 [03:48<01:38, 32.70s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "0.18007869134174986 0.28041849415995196\n", - "0.7016796892537245 0.023003658175830666\n", + "0.15213020884633638 0.28032833751779207\n", + "0.7017545866785109 0.022449185478241485\n", "dmdc: [(20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20)]\n", "subspacedmdc: [(20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20)]\n", "dmd: [(20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20)]\n" @@ -1205,19 +1198,19 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 16/16 [00:00<00:00, 113.02it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 120.38it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 300.66it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 119.74it/s]\n", - " 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20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20)]\n" @@ -1227,19 +1220,19 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 16/16 [00:00<00:00, 124.88it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 120.38it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 296.70it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 118.16it/s]\n", - " 90%|█████████ | 9/10 [08:42<00:48, 48.64s/it]" + "100%|██████████| 16/16 [00:00<00:00, 46.39it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 113.97it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 249.65it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 217.27it/s]\n", + " 90%|█████████ | 9/10 [04:50<00:31, 31.79s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "0.6148711827247706 0.30871393332041147\n", - "0.7892348986304961 0.04847692242475707\n", + "0.5497244328142894 0.30866882882957425\n", + "0.7893131703514245 0.04832453849166829\n", "dmdc: [(20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20)]\n", "subspacedmdc: [(20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20)]\n", "dmd: [(20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20), (20, 20)]\n" @@ -1249,26 +1242,19 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 16/16 [00:00<00:00, 115.87it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 123.19it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 300.93it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 118.83it/s]\n", - "100%|██████████| 10/10 [09:29<00:00, 56.96s/it]" + "100%|██████████| 16/16 [00:00<00:00, 32.29it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 195.26it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 552.80it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 177.09it/s]\n", + "100%|██████████| 10/10 [05:21<00:00, 32.13s/it]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "0.6697496840242342 0.03562441959227844\n", - "0.8604693896682944 0.022974132723392215\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" + "0.7319381371318918 0.03569785229639352\n", + "0.860447067466593 0.022725976669829284\n" ] } ], @@ -1283,13 +1269,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "id": "b73f3851", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -1362,7 +1348,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 18, "id": "5f1c041a", "metadata": {}, "outputs": [ @@ -1386,19 +1372,19 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 16/16 [00:00<00:00, 895.32it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 132.73it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 1119.58it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 157.36it/s]\n", - " 50%|█████ | 1/2 [00:09<00:09, 9.29s/it]" + "100%|██████████| 16/16 [00:00<00:00, 1572.04it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 191.10it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 2053.95it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 283.01it/s]\n", + " 50%|█████ | 1/2 [00:05<00:05, 5.09s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "0.9531274943624406 0.751483744775471\n", - "0.7473233814335034 0.12052441390167412\n", + "0.9531408210695748 0.751644714397508\n", + "0.7464886400711467 0.12159508515977946\n", "dmdc: [(2, 2), (2, 2), (2, 2), (2, 2), (2, 2), (2, 2), (2, 2), (2, 2), (2, 2), (2, 2), (2, 2), (2, 2), (2, 2), (2, 2), (2, 2), (2, 2)]\n", "subspacedmdc: [(2, 2), (2, 2), (2, 2), (2, 2), (2, 2), (2, 2), (2, 2), (2, 2), (2, 2), (2, 2), (2, 2), (2, 2), (2, 2), (2, 2), (2, 2), (2, 2)]\n", "dmd: [(2, 2), (2, 2), (2, 2), (2, 2), (2, 2), (2, 2), (2, 2), (2, 2), (2, 2), (2, 2), (2, 2), (2, 2), (2, 2), (2, 2), (2, 2), (2, 2)]\n" @@ -1408,19 +1394,19 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 16/16 [00:00<00:00, 878.65it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 140.76it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 904.01it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 157.45it/s]\n", - "100%|██████████| 2/2 [00:17<00:00, 8.91s/it]\n" + "100%|██████████| 16/16 [00:00<00:00, 1995.68it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 311.00it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 2360.91it/s]\n", + "100%|██████████| 16/16 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"text": [ - "0.8746353570856221 0.8248834494677864\n", - "0.7199616579840207 0.11537041002887422\n", + "0.8745472829437568 0.8250370191204569\n", + "0.7200099972664604 0.11478322546537062\n", "dmdc: [(3, 3), (3, 3), (3, 3), (3, 3), (3, 3), (3, 3), (3, 3), (3, 3), (3, 3), (3, 3), (3, 3), (3, 3), (3, 3), (3, 3), (3, 3), (3, 3)]\n", "subspacedmdc: [(3, 3), (3, 3), (3, 3), (3, 3), (3, 3), (3, 3), (3, 3), (3, 3), (3, 3), (3, 3), (3, 3), (3, 3), (3, 3), (3, 3), (3, 3), (3, 3)]\n", "dmd: [(3, 3), (3, 3), (3, 3), (3, 3), (3, 3), (3, 3), (3, 3), (3, 3), (3, 3), (3, 3), (3, 3), (3, 3), (3, 3), (3, 3), (3, 3), (3, 3)]\n" @@ -1465,19 +1451,33 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 16/16 [00:00<00:00, 801.28it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 149.10it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 997.28it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 154.73it/s]\n", - "100%|██████████| 2/2 [00:17<00:00, 8.59s/it]\n" + "100%|██████████| 16/16 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5), (5, 5), (5, 5), (5, 5), (5, 5), (5, 5), (5, 5), (5, 5), (5, 5), (5, 5), (5, 5), (5, 5), (5, 5), (5, 5)]\n" @@ -1579,19 +1579,19 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 16/16 [00:00<00:00, 851.02it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 137.85it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 915.96it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 146.10it/s]\n", - "100%|██████████| 2/2 [00:18<00:00, 9.07s/it]\n" + "100%|██████████| 16/16 [00:00<00:00, 1593.47it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 291.99it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 1692.40it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 318.78it/s]\n", + "100%|██████████| 2/2 [00:09<00:00, 4.89s/it]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "0.7423448817172416 0.9654296133435506\n", - "0.640654934185919 0.007281464511442931\n" + "0.7257133126510311 0.9664609022104687\n", + "0.639872495928252 0.006430861597669257\n" ] }, { @@ -1614,19 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[(6, 6), (6, 6), (6, 6), (6, 6), (6, 6), (6, 6), (6, 6), (6, 6), (6, 6), (6, 6), (6, 6), (6, 6), (6, 6), (6, 6), (6, 6), (6, 6)]\n", "dmd: [(6, 6), (6, 6), (6, 6), (6, 6), (6, 6), (6, 6), (6, 6), (6, 6), (6, 6), (6, 6), (6, 6), (6, 6), (6, 6), (6, 6), (6, 6), (6, 6)]\n" @@ -1636,19 +1636,19 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 16/16 [00:00<00:00, 785.12it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 150.14it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 833.02it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 155.17it/s]\n", - "100%|██████████| 2/2 [00:17<00:00, 8.70s/it]\n" + "100%|██████████| 16/16 [00:00<00:00, 1600.31it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 315.96it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 1575.62it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 316.99it/s]\n", + "100%|██████████| 2/2 [00:09<00:00, 4.81s/it]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "0.5348287984609177 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0.12109375240490969\n", "dmdc: [(7, 7), (7, 7), (7, 7), (7, 7), (7, 7), (7, 7), (7, 7), (7, 7), (7, 7), (7, 7), (7, 7), (7, 7), (7, 7), (7, 7), (7, 7), (7, 7)]\n", "subspacedmdc: [(7, 7), (7, 7), (7, 7), (7, 7), (7, 7), (7, 7), (7, 7), (7, 7), (7, 7), (7, 7), (7, 7), (7, 7), (7, 7), (7, 7), (7, 7), (7, 7)]\n", "dmd: [(7, 7), (7, 7), (7, 7), (7, 7), (7, 7), (7, 7), (7, 7), (7, 7), (7, 7), (7, 7), (7, 7), (7, 7), (7, 7), (7, 7), (7, 7), (7, 7)]\n" @@ -1693,19 +1693,19 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 16/16 [00:00<00:00, 681.49it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 149.58it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 760.79it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 151.46it/s]\n", - "100%|██████████| 2/2 [00:17<00:00, 8.95s/it]\n" + "100%|██████████| 16/16 [00:00<00:00, 1396.27it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 315.21it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 1422.70it/s]\n", + "100%|██████████| 16/16 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"text": [ - "0.8714690495124766 0.8448611573708045\n", - "0.7552656897491047 0.019415401054816914\n", + "0.8828967988777602 0.8444937232760863\n", + "0.7539226861848893 0.05843904778576672\n", "dmdc: [(8, 8), (8, 8), (8, 8), (8, 8), (8, 8), (8, 8), (8, 8), (8, 8), (8, 8), (8, 8), (8, 8), (8, 8), (8, 8), (8, 8), (8, 8), (8, 8)]\n", "subspacedmdc: [(8, 8), (8, 8), (8, 8), (8, 8), (8, 8), (8, 8), (8, 8), (8, 8), (8, 8), (8, 8), (8, 8), (8, 8), (8, 8), (8, 8), (8, 8), (8, 8)]\n", "dmd: [(8, 8), (8, 8), (8, 8), (8, 8), (8, 8), (8, 8), (8, 8), (8, 8), (8, 8), (8, 8), (8, 8), (8, 8), (8, 8), (8, 8), (8, 8), (8, 8)]\n" @@ -1750,19 +1750,19 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 16/16 [00:00<00:00, 539.72it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 147.59it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 684.90it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 140.66it/s]\n", - "100%|██████████| 2/2 [00:17<00:00, 8.82s/it]\n" + "100%|██████████| 16/16 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(10, 10), (10, 10), (10, 10)]\n", "dmd: [(10, 10), (10, 10), (10, 10), (10, 10), (10, 10), (10, 10), (10, 10), (10, 10), (10, 10), (10, 10), (10, 10), (10, 10), (10, 10), (10, 10), (10, 10), (10, 10)]\n" @@ -1864,19 +1878,19 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 16/16 [00:00<00:00, 509.98it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 144.08it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 581.01it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 148.18it/s]\n", - "100%|██████████| 2/2 [00:17<00:00, 8.99s/it]\n" + "100%|██████████| 16/16 [00:00<00:00, 964.50it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 268.37it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 547.08it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 238.02it/s]\n", + "100%|██████████| 2/2 [00:10<00:00, 5.03s/it]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "0.8687752632844028 0.9576492778168073\n", - "0.6417669454534949 -0.028015569297223653\n" + "0.8649797841261901 0.9577234009193044\n", + "0.6397380023776962 -0.026284979283893103\n" ] }, { @@ -1899,19 +1913,19 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 16/16 [00:00<00:00, 199.94it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 127.52it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 560.84it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 146.10it/s]\n", - " 50%|█████ | 1/2 [00:08<00:08, 8.81s/it]" + "100%|██████████| 16/16 [00:00<00:00, 927.38it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 297.61it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 1103.36it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 293.81it/s]\n", + " 50%|█████ | 1/2 [00:04<00:04, 4.83s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "0.9396480434698776 0.8701038645802609\n", - "0.7529067449758837 0.07081924118478469\n", + "0.9500566028807026 0.8705500032529395\n", + "0.7522356204788497 0.06912449482788813\n", "dmdc: [(11, 11), (11, 11), (11, 11), (11, 11), (11, 11), (11, 11), (11, 11), (11, 11), (11, 11), (11, 11), (11, 11), (11, 11), (11, 11), (11, 11), (11, 11), (11, 11)]\n", "subspacedmdc: [(11, 11), (11, 11), (11, 11), (11, 11), (11, 11), (11, 11), (11, 11), (11, 11), (11, 11), (11, 11), (11, 11), (11, 11), (11, 11), (11, 11), (11, 11), (11, 11)]\n", "dmd: [(11, 11), (11, 11), (11, 11), (11, 11), (11, 11), (11, 11), (11, 11), (11, 11), (11, 11), (11, 11), (11, 11), (11, 11), (11, 11), (11, 11), (11, 11), (11, 11)]\n" @@ -1921,19 +1935,19 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 16/16 [00:00<00:00, 466.06it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 139.02it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 540.54it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 141.85it/s]\n", - "100%|██████████| 2/2 [00:18<00:00, 9.17s/it]\n" + "100%|██████████| 16/16 [00:00<00:00, 911.68it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 297.69it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 1079.70it/s]\n", 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"output_type": "stream", "text": [ - "0.9550022785988987 0.8773003527326414\n", - "0.7517910279535488 0.04395134642658022\n", + "0.9680151944271322 0.8772439094084834\n", + "0.7520958229741626 0.04291183156419753\n", "dmdc: [(12, 12), (12, 12), (12, 12), (12, 12), (12, 12), (12, 12), (12, 12), (12, 12), (12, 12), (12, 12), (12, 12), (12, 12), (12, 12), (12, 12), (12, 12), (12, 12)]\n", "subspacedmdc: [(12, 12), (12, 12), (12, 12), (12, 12), (12, 12), (12, 12), (12, 12), (12, 12), (12, 12), (12, 12), (12, 12), (12, 12), (12, 12), (12, 12), (12, 12), (12, 12)]\n", "dmd: [(12, 12), (12, 12), (12, 12), (12, 12), (12, 12), (12, 12), (12, 12), (12, 12), (12, 12), (12, 12), (12, 12), (12, 12), (12, 12), (12, 12), (12, 12), (12, 12)]\n" @@ -1978,19 +1992,19 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 16/16 [00:00<00:00, 402.31it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 124.10it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 445.39it/s]\n", - 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(13, 13)]\n" @@ -2035,19 +2049,19 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 16/16 [00:00<00:00, 366.23it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 122.24it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 459.65it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 125.37it/s]\n", - "100%|██████████| 2/2 [00:18<00:00, 9.49s/it]\n" + "100%|██████████| 16/16 [00:00<00:00, 725.66it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 275.77it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 943.45it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 252.61it/s]\n", + "100%|██████████| 2/2 [00:09<00:00, 4.87s/it]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "0.8818706569178612 0.9652677305664412\n", - "0.6317697377504198 0.05880957903351308\n" + "0.8356993897113918 0.9650813091493078\n", + "0.6326676014385092 0.06057712777980473\n" ] }, { @@ -2070,19 +2084,19 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 16/16 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(14, 14), (14, 14), (14, 14), (14, 14), (14, 14), (14, 14), (14, 14), (14, 14), (14, 14), (14, 14)]\n", "dmd: [(14, 14), (14, 14), (14, 14), (14, 14), (14, 14), (14, 14), (14, 14), (14, 14), (14, 14), (14, 14), (14, 14), (14, 14), (14, 14), (14, 14), (14, 14), (14, 14)]\n" @@ -2092,19 +2106,19 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 16/16 [00:00<00:00, 346.42it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 123.34it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 424.92it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 123.11it/s]\n", - "100%|██████████| 2/2 [00:19<00:00, 9.61s/it]\n" + "100%|██████████| 16/16 [00:00<00:00, 663.16it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 245.54it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 816.58it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 251.56it/s]\n", + "100%|██████████| 2/2 [00:09<00:00, 4.88s/it]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "0.9299475477974051 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0.1333125512924334\n", "dmdc: [(15, 15), (15, 15), (15, 15), (15, 15), (15, 15), (15, 15), (15, 15), (15, 15), (15, 15), (15, 15), (15, 15), (15, 15), (15, 15), (15, 15), (15, 15), (15, 15)]\n", "subspacedmdc: [(15, 15), (15, 15), (15, 15), (15, 15), (15, 15), (15, 15), (15, 15), (15, 15), (15, 15), (15, 15), (15, 15), (15, 15), (15, 15), (15, 15), (15, 15), (15, 15)]\n", "dmd: [(15, 15), (15, 15), (15, 15), (15, 15), (15, 15), (15, 15), (15, 15), (15, 15), (15, 15), (15, 15), (15, 15), (15, 15), (15, 15), (15, 15), (15, 15), (15, 15)]\n" @@ -2149,19 +2163,19 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 16/16 [00:00<00:00, 307.37it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 124.43it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 410.00it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 127.09it/s]\n", - "100%|██████████| 2/2 [00:17<00:00, 8.80s/it]\n" + "100%|██████████| 16/16 [00:00<00:00, 638.04it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 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(17, 17), (17, 17), (17, 17), (17, 17), (17, 17), (17, 17), (17, 17)]\n" @@ -2263,19 +2277,19 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 16/16 [00:00<00:00, 257.00it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 120.49it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 369.70it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 119.09it/s]\n", - "100%|██████████| 2/2 [00:17<00:00, 8.94s/it]\n" + "100%|██████████| 16/16 [00:00<00:00, 553.69it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 261.00it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 760.60it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 256.93it/s]\n", + "100%|██████████| 2/2 [00:09<00:00, 4.86s/it]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "0.9247215807940506 0.9461185140876085\n", - "0.6279705380949225 0.03897688577269154\n" + "0.9139374358001568 0.9459394527492089\n", + "0.6296836866963601 0.03688086533729029\n" ] }, { @@ -2298,19 +2312,19 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 16/16 [00:00<00:00, 241.12it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 114.59it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 340.90it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 74.02it/s]\n", - " 50%|█████ | 1/2 [00:09<00:09, 9.56s/it]" + "100%|██████████| 16/16 [00:00<00:00, 430.35it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 233.42it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 723.43it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 250.47it/s]\n", + " 50%|█████ | 1/2 [00:04<00:04, 4.86s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "0.9675442836976859 0.8830632687543813\n", - "0.7478361409399403 0.07717033130155881\n", + "0.9524378731389866 0.8828488862813867\n", + "0.7474470478293957 0.0750155231031836\n", "dmdc: [(18, 18), (18, 18), (18, 18), (18, 18), (18, 18), (18, 18), (18, 18), (18, 18), (18, 18), (18, 18), (18, 18), (18, 18), (18, 18), (18, 18), (18, 18), (18, 18)]\n", "subspacedmdc: [(18, 18), (18, 18), (18, 18), (18, 18), (18, 18), (18, 18), (18, 18), (18, 18), (18, 18), (18, 18), (18, 18), (18, 18), (18, 18), (18, 18), (18, 18), (18, 18)]\n", "dmd: [(18, 18), (18, 18), (18, 18), (18, 18), (18, 18), (18, 18), (18, 18), (18, 18), (18, 18), (18, 18), (18, 18), (18, 18), (18, 18), (18, 18), (18, 18), (18, 18)]\n" @@ -2320,19 +2334,19 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 16/16 [00:00<00:00, 208.12it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 117.09it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 352.15it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 119.01it/s]\n", - "100%|██████████| 2/2 [00:18<00:00, 9.38s/it]\n" + "100%|██████████| 16/16 [00:00<00:00, 478.83it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 256.97it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 690.88it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 120.30it/s]\n", + "100%|██████████| 2/2 [00:09<00:00, 4.89s/it]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "0.8865734158388725 0.9304078136075623\n", - "0.6304518782587429 0.011593066098459295\n" + "0.9137941908361018 0.930338122405584\n", + "0.6301716317211821 0.014117342103063063\n" ] }, { @@ -2355,19 +2369,19 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 16/16 [00:00<00:00, 213.71it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 115.54it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 318.63it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 117.14it/s]\n", - " 50%|█████ | 1/2 [00:08<00:08, 8.83s/it]" + "100%|██████████| 16/16 [00:00<00:00, 419.02it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 232.33it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 650.65it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 238.08it/s]\n", + " 50%|█████ | 1/2 [00:04<00:04, 4.81s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "0.9667392237852279 0.8807069582669418\n", - "0.7474672678045506 0.05260565979092585\n", + "0.9579200163534667 0.8810645336112748\n", + "0.7459380519255265 0.05181165752834982\n", "dmdc: [(19, 19), (19, 19), (19, 19), (19, 19), (19, 19), (19, 19), (19, 19), (19, 19), (19, 19), (19, 19), (19, 19), (19, 19), (19, 19), (19, 19), (19, 19), (19, 19)]\n", "subspacedmdc: [(19, 19), (19, 19), (19, 19), (19, 19), (19, 19), (19, 19), (19, 19), (19, 19), (19, 19), (19, 19), (19, 19), (19, 19), (19, 19), (19, 19), (19, 19), (19, 19)]\n", "dmd: [(19, 19), (19, 19), (19, 19), (19, 19), (19, 19), (19, 19), (19, 19), (19, 19), (19, 19), (19, 19), (19, 19), (19, 19), (19, 19), (19, 19), (19, 19), (19, 19)]\n" @@ -2377,19 +2391,19 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 16/16 [00:00<00:00, 217.32it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 116.04it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 319.67it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 120.35it/s]\n", - "100%|██████████| 2/2 [00:17<00:00, 8.78s/it]\n" + "100%|██████████| 16/16 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21), (21, 21), (21, 21), (21, 21), (21, 21), (21, 21), (21, 21), (21, 21), (21, 21), (21, 21), (21, 21), (21, 21), (21, 21), (21, 21), (21, 21)]\n" @@ -2491,19 +2505,19 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 16/16 [00:00<00:00, 100.36it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 109.17it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 277.92it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 110.97it/s]\n", - "100%|██████████| 2/2 [00:19<00:00, 9.82s/it]\n" + "100%|██████████| 16/16 [00:00<00:00, 45.33it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 170.81it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 237.47it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 177.56it/s]\n", + "100%|██████████| 2/2 [00:26<00:00, 13.31s/it]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "0.9250469590643454 0.9152269040658624\n", - "0.6309100199430768 0.002663213012101956\n" + "0.9260369370450757 0.9151553231502526\n", + "0.6295512806610911 -0.0015256924894253288\n" ] }, { @@ -2526,19 +2540,19 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 16/16 [00:00<00:00, 86.32it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 107.44it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 269.00it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 104.51it/s]\n", - " 50%|█████ | 1/2 [00:09<00:09, 9.10s/it]" + "100%|██████████| 16/16 [00:00<00:00, 36.36it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 119.43it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 336.30it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 202.86it/s]\n", + " 50%|█████ | 1/2 [00:07<00:07, 7.04s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "0.9519706704841875 0.9016432260691306\n", - "0.7457949937266393 0.04179394545733671\n", + "0.9621496235334114 0.9003190536716277\n", + "0.7462753634875992 0.04347795871798134\n", "dmdc: [(22, 22), (22, 22), (22, 22), (22, 22), (22, 22), (22, 22), (22, 22), (22, 22), (22, 22), (22, 22), (22, 22), (22, 22), (22, 22), (22, 22), (22, 22), (22, 22)]\n", "subspacedmdc: [(22, 22), (22, 22), (22, 22), (22, 22), (22, 22), (22, 22), (22, 22), (22, 22), (22, 22), (22, 22), (22, 22), (22, 22), (22, 22), (22, 22), (22, 22), (22, 22)]\n", "dmd: [(22, 22), (22, 22), (22, 22), (22, 22), (22, 22), (22, 22), (22, 22), (22, 22), (22, 22), (22, 22), (22, 22), (22, 22), (22, 22), (22, 22), (22, 22), (22, 22)]\n" @@ -2548,19 +2562,19 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 16/16 [00:00<00:00, 124.24it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 109.03it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 260.99it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 106.36it/s]\n", - "100%|██████████| 2/2 [00:18<00:00, 9.07s/it]\n" + "100%|██████████| 16/16 [00:05<00:00, 3.09it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 119.60it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 377.97it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 139.85it/s]\n", + 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0.8937918216961871\n", - "0.7471392250450317 0.007713818544397788\n", + "0.957875392307344 0.8935756915347725\n", + "0.7452794073343041 0.013108771938180747\n", "dmdc: [(23, 23), (23, 23), (23, 23), (23, 23), (23, 23), (23, 23), (23, 23), (23, 23), (23, 23), (23, 23), (23, 23), (23, 23), (23, 23), (23, 23), (23, 23), (23, 23)]\n", "subspacedmdc: [(23, 23), (23, 23), (23, 23), (23, 23), (23, 23), (23, 23), (23, 23), (23, 23), (23, 23), (23, 23), (23, 23), (23, 23), (23, 23), (23, 23), (23, 23), (23, 23)]\n", "dmd: [(23, 23), (23, 23), (23, 23), (23, 23), (23, 23), (23, 23), (23, 23), (23, 23), (23, 23), (23, 23), (23, 23), (23, 23), (23, 23), (23, 23), (23, 23), (23, 23)]\n" @@ -2605,19 +2619,19 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 16/16 [00:00<00:00, 114.84it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 103.09it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 247.55it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 98.24it/s]\n", - 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"output_type": "stream", "text": [ - "100%|██████████| 16/16 [00:00<00:00, 100.06it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 103.66it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 237.98it/s]\n", - "100%|██████████| 16/16 [00:00<00:00, 104.52it/s]\n", - "100%|██████████| 2/2 [00:18<00:00, 9.36s/it]" + "100%|██████████| 16/16 [00:00<00:00, 38.02it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 113.12it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 431.27it/s]\n", + "100%|██████████| 16/16 [00:00<00:00, 153.04it/s]\n", + "100%|██████████| 2/2 [00:13<00:00, 6.89s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "0.9351310376022378 0.8893785252050508\n", - "0.6293174428283013 -0.006050551393489173\n" + "0.9228377313102021 0.8891951391322248\n", + "0.6318162920324164 -0.008518802270398834\n" ] }, { @@ -2711,13 +2725,13 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 19, "id": "0a65665b", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -2767,9 +2781,9 @@ ], "metadata": { "kernelspec": { - "display_name": "dsa_test_env", + "display_name": "DSA (uv)", "language": "python", - "name": "python3" + "name": "dsa-uv" }, "language_info": { "codemirror_mode": { @@ -2781,7 +2795,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.18" + "version": "3.10.19" } }, "nbformat": 4, diff --git a/examples/ring_attractors.ipynb b/examples/ring_attractors.ipynb index 88e5258..8b1ddf4 100644 --- a/examples/ring_attractors.ipynb +++ b/examples/ring_attractors.ipynb @@ -9,26 +9,33 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fatal: destination path 'netrep' already exists and is not an empty directory.\n", + "\u001b[2mUsing Python 3.10.19 environment at: /Users/mitchellostrow/Desktop/Projects/dsa_test/DSA/.venv\u001b[0m\n", + "\u001b[2K\u001b[2mResolved \u001b[1m7 packages\u001b[0m \u001b[2min 623ms\u001b[0m\u001b[0m \u001b[0m\u001b[37m⠋\u001b[0m \u001b[2mResolving dependencies... \u001b[0m\n", + "\u001b[2K \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m netrep\u001b[2m @ file:///Users/mitchellostrow/Desktop/Projects/dsa_test/DSA/\u001b[0m\n", + "\u001b[2K\u001b[1A \u001b[32m\u001b[1mBuilt\u001b[0m\u001b[39m netrep\u001b[2m @ file:///Users/mitchellostrow/Desktop/Projects/dsa_test/DSA/\u001b[0m\n", + "\u001b[2K\u001b[2mPrepared \u001b[1m1 package\u001b[0m \u001b[2min 187ms\u001b[0m\u001b[0m \n", + "\u001b[2K\u001b[2mInstalled \u001b[1m1 package\u001b[0m \u001b[2min 1ms\u001b[0m\u001b[0m file:///Users/mitchellostrow/Desk\u001b[0m\n", + " \u001b[32m+\u001b[39m \u001b[1mnetrep\u001b[0m\u001b[2m==0.0.2 (from file:///Users/mitchellostrow/Desktop/Projects/dsa_test/DSA/examples/netrep)\u001b[0m\n" + ] + } + ], "source": [ - "# #install the packages that we haven't added in setup.py\n", - "# ! pip install matplotlib\n", - "# ! pip install scikit-learn\n", - "# ! pip install seaborn\n", - "# ! pip install pandas\n", - "\n", - "# #install netrep\n", - "# ! git clone https://github.com/ahwillia/netrep\n", - "# ! cd netrep #if this does not work, install it via the terminal\n", - "# ! pip install -e .\n", - "# ! cd .." + "# Clone and install netrep\n", + "!git clone https://github.com/ahwillia/netrep.git\n", + "!uv pip install -e netrep\n" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -36,14 +43,12 @@ "import matplotlib.pyplot as plt\n", "import torch\n", "from DSA import DSA\n", - "import seaborn as sns\n", - "import pandas as pd \n", "from netrep.metrics import LinearMetric" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": { "tags": [ "{hidden}" @@ -490,28 +495,41 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "def plot_results(ts,nruns,diffs_dsa,diffs_proc,parameter): \n", - " df = pd.DataFrame(dict(ts=np.tile(ts,nruns),\n", - " diffs_dsa=diffs_dsa.flatten(),diffs_proc=diffs_proc.flatten()))\n", - " palette = None \n", - " errorbar = 'se'\n", - " hue = None\n", - " g = sns.lineplot(data=df, x=\"ts\", y=\"diffs_dsa\", hue=hue,\n", - " palette=palette,errorbar=errorbar,c=\"blue\",marker='o',label=\"DSA\")\n", - " k = sns.lineplot(data=df, x=\"ts\", y=\"diffs_proc\", hue=hue,\n", - " palette=palette,errorbar=errorbar,c=\"orange\",marker='o',label=\"Procrustes\")\n", - "\n", + " # Reshape data for plotting\n", + " diffs_dsa_flat = diffs_dsa.flatten()\n", + " diffs_proc_flat = diffs_proc.flatten()\n", + " ts_repeated = np.tile(ts, nruns)\n", + " \n", + " # Compute mean and standard error for each time point\n", + " diffs_dsa_mean = np.array([diffs_dsa[:, i].mean() for i in range(len(ts))])\n", + " diffs_dsa_se = np.array([diffs_dsa[:, i].std() / np.sqrt(nruns) for i in range(len(ts))])\n", + " \n", + " diffs_proc_mean = np.array([diffs_proc[:, i].mean() for i in range(len(ts))])\n", + " diffs_proc_se = np.array([diffs_proc[:, i].std() / np.sqrt(nruns) for i in range(len(ts))])\n", + " \n", + " # Plot DSA\n", + " plt.plot(ts, diffs_dsa_mean, c=\"blue\", marker='o', label=\"DSA\")\n", + " plt.fill_between(ts, diffs_dsa_mean - diffs_dsa_se, diffs_dsa_mean + diffs_dsa_se, \n", + " color=\"blue\", alpha=0.2)\n", + " \n", + " # Plot Procrustes\n", + " plt.plot(ts, diffs_proc_mean, c=\"orange\", marker='o', label=\"Procrustes\")\n", + " plt.fill_between(ts, diffs_proc_mean - diffs_proc_se, diffs_proc_mean + diffs_proc_se, \n", + " color=\"orange\", alpha=0.2)\n", + " \n", " plt.xlabel(parameter)\n", - " plt.ylabel(rf\"$D({parameter}={ts[0]},\\cdot)$\")\n" + " plt.ylabel(rf\"$D({parameter}={ts[0]},\\cdot)$\")\n", + " plt.legend()\n" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -556,7 +574,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "scrolled": true }, @@ -573,8 +591,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "Fitting DMDs: 100%|██████████| 1/1 [00:00<00:00, 25.36it/s]\n", - "Fitting DMDs: 100%|██████████| 1/1 [00:00<00:00, 26.63it/s]\n" + "Fitting DMDs: 100%|██████████| 1/1 [00:00<00:00, 72.98it/s]\n", + "Fitting DMDs: 100%|██████████| 1/1 [00:00<00:00, 73.04it/s]\n" ] }, { @@ -588,7 +606,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "Computing DMD similarities: 100%|██████████| 1/1 [00:00<00:00, 199.13it/s]" + "Caching compare objects X: 100%|██████████| 1/1 [00:00<00:00, 123.29it/s]\n", + "Caching compare objects Y: 100%|██████████| 1/1 [00:00<00:00, 132.43it/s]\n", + "Computing DMD similarities: 100%|██████████| 1/1 [00:00<00:00, 43.98it/s]" ] }, { @@ -602,7 +622,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "\n" + "\n", + "/var/folders/sh/r0d61tjn42s4mc3nxqb0hhq00000gn/T/ipykernel_79963/1197694459.py:120: RuntimeWarning: invalid value encountered in divide\n", + " centers = np.sum(\n" ] }, { @@ -616,8 +638,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "Fitting DMDs: 100%|██████████| 1/1 [00:00<00:00, 24.53it/s]\n", - "Fitting DMDs: 100%|██████████| 1/1 [00:00<00:00, 25.73it/s]\n" + "Fitting DMDs: 100%|██████████| 1/1 [00:00<00:00, 59.80it/s]\n", + "Fitting DMDs: 100%|██████████| 1/1 [00:00<00:00, 45.35it/s]\n" ] }, { @@ -631,7 +653,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "Computing DMD similarities: 100%|██████████| 1/1 [00:00<00:00, 238.37it/s]" + "Caching compare objects X: 100%|██████████| 1/1 [00:00<00:00, 133.92it/s]\n", + "Caching compare objects Y: 100%|██████████| 1/1 [00:00<00:00, 122.02it/s]\n", + "Computing DMD similarities: 100%|██████████| 1/1 [00:00<00:00, 121.85it/s]" ] }, { @@ -645,9 +669,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "\n", - "/var/folders/sh/r0d61tjn42s4mc3nxqb0hhq00000gn/T/ipykernel_74501/1197694459.py:120: RuntimeWarning: invalid value encountered in divide\n", - " centers = np.sum(\n" + "\n" ] }, { @@ -661,8 +683,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "Fitting DMDs: 100%|██████████| 1/1 [00:00<00:00, 25.23it/s]\n", - "Fitting DMDs: 100%|██████████| 1/1 [00:00<00:00, 21.33it/s]\n" + "Fitting DMDs: 100%|██████████| 1/1 [00:00<00:00, 53.82it/s]\n", + "Fitting DMDs: 100%|██████████| 1/1 [00:00<00:00, 47.38it/s]\n" ] }, { @@ -676,7 +698,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "Computing DMD similarities: 100%|██████████| 1/1 [00:00<00:00, 258.48it/s]" + "Caching compare objects X: 100%|██████████| 1/1 [00:00<00:00, 123.26it/s]\n", + "Caching compare objects Y: 100%|██████████| 1/1 [00:00<00:00, 100.87it/s]\n", + "Computing DMD similarities: 100%|██████████| 1/1 [00:00<00:00, 233.97it/s]" ] }, { @@ -690,9 +714,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "\n", - "/var/folders/sh/r0d61tjn42s4mc3nxqb0hhq00000gn/T/ipykernel_74501/1197694459.py:120: RuntimeWarning: invalid value encountered in divide\n", - " centers = np.sum(\n" + "\n" ] }, { @@ -706,8 +728,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "Fitting DMDs: 100%|██████████| 1/1 [00:00<00:00, 15.86it/s]\n", - "Fitting DMDs: 100%|██████████| 1/1 [00:00<00:00, 18.51it/s]\n" + "Fitting DMDs: 100%|██████████| 1/1 [00:00<00:00, 41.10it/s]\n", + "Fitting DMDs: 100%|██████████| 1/1 [00:00<00:00, 65.12it/s]\n" ] }, { @@ -721,7 +743,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "Computing DMD similarities: 100%|██████████| 1/1 [00:00<00:00, 220.03it/s]" + "Caching compare objects X: 100%|██████████| 1/1 [00:00<00:00, 126.42it/s]\n", + "Caching compare objects Y: 100%|██████████| 1/1 [00:00<00:00, 117.30it/s]\n", + "Computing DMD similarities: 100%|██████████| 1/1 [00:00<00:00, 140.00it/s]" ] }, { @@ -736,7 +760,7 @@ "output_type": "stream", "text": [ "\n", - "/var/folders/sh/r0d61tjn42s4mc3nxqb0hhq00000gn/T/ipykernel_74501/1197694459.py:120: RuntimeWarning: invalid value encountered in divide\n", + "/var/folders/sh/r0d61tjn42s4mc3nxqb0hhq00000gn/T/ipykernel_79963/1197694459.py:120: RuntimeWarning: invalid value encountered in divide\n", " centers = np.sum(\n" ] }, @@ -751,8 +775,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "Fitting DMDs: 100%|██████████| 1/1 [00:00<00:00, 17.86it/s]\n", - "Fitting DMDs: 100%|██████████| 1/1 [00:00<00:00, 21.92it/s]\n" + "Fitting DMDs: 100%|██████████| 1/1 [00:00<00:00, 10.86it/s]\n", + "Fitting DMDs: 100%|██████████| 1/1 [00:00<00:00, 43.30it/s]\n" ] }, { @@ -766,7 +790,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "Computing DMD similarities: 100%|██████████| 1/1 [00:00<00:00, 252.79it/s]" + "Caching compare objects X: 100%|██████████| 1/1 [00:00<00:00, 102.10it/s]\n", + "Caching compare objects Y: 100%|██████████| 1/1 [00:00<00:00, 118.44it/s]\n", + "Computing DMD similarities: 100%|██████████| 1/1 [00:00<00:00, 282.31it/s]" ] }, { @@ -780,9 +806,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "\n", - "/var/folders/sh/r0d61tjn42s4mc3nxqb0hhq00000gn/T/ipykernel_74501/1197694459.py:120: RuntimeWarning: invalid value encountered in divide\n", - " centers = np.sum(\n" + "\n" ] }, { @@ -796,8 +820,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "Fitting DMDs: 100%|██████████| 1/1 [00:00<00:00, 20.98it/s]\n", - "Fitting DMDs: 100%|██████████| 1/1 [00:00<00:00, 22.40it/s]\n" + "Fitting DMDs: 100%|██████████| 1/1 [00:00<00:00, 36.12it/s]\n", + "Fitting DMDs: 100%|██████████| 1/1 [00:00<00:00, 38.00it/s]\n" ] }, { @@ -811,7 +835,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "Computing DMD similarities: 100%|██████████| 1/1 [00:00<00:00, 226.50it/s]" + "Caching compare objects X: 100%|██████████| 1/1 [00:00<00:00, 100.94it/s]\n", + "Caching compare objects Y: 100%|██████████| 1/1 [00:00<00:00, 103.39it/s]\n", + "Computing DMD similarities: 100%|██████████| 1/1 [00:00<00:00, 312.87it/s]" ] }, { @@ -825,7 +851,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "\n" + "\n", + "/var/folders/sh/r0d61tjn42s4mc3nxqb0hhq00000gn/T/ipykernel_79963/1197694459.py:120: RuntimeWarning: invalid value encountered in divide\n", + " centers = np.sum(\n" ] }, { @@ -839,8 +867,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "Fitting DMDs: 100%|██████████| 1/1 [00:00<00:00, 13.66it/s]\n", - "Fitting DMDs: 100%|██████████| 1/1 [00:00<00:00, 10.81it/s]" + "Fitting DMDs: 100%|██████████| 1/1 [00:00<00:00, 46.91it/s]\n", + "Fitting DMDs: 100%|██████████| 1/1 [00:00<00:00, 38.93it/s]\n" ] }, { @@ -854,8 +882,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "\n", - "Computing DMD similarities: 100%|██████████| 1/1 [00:00<00:00, 286.05it/s]\n" + "Caching compare objects X: 100%|██████████| 1/1 [00:00<00:00, 100.67it/s]\n", + "Caching compare objects Y: 100%|██████████| 1/1 [00:00<00:00, 101.61it/s]\n", + "Computing DMD similarities: 100%|██████████| 1/1 [00:00<00:00, 282.33it/s]" ] }, { @@ -869,8 +898,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/sh/r0d61tjn42s4mc3nxqb0hhq00000gn/T/ipykernel_74501/1197694459.py:120: RuntimeWarning: invalid value encountered in divide\n", - " centers = np.sum(\n" + "\n" ] }, { @@ -884,8 +912,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "Fitting DMDs: 100%|██████████| 1/1 [00:00<00:00, 23.87it/s]\n", - "Fitting DMDs: 100%|██████████| 1/1 [00:00<00:00, 26.60it/s]\n" + "Fitting DMDs: 100%|██████████| 1/1 [00:00<00:00, 41.24it/s]\n", + "Fitting DMDs: 100%|██████████| 1/1 [00:00<00:00, 50.05it/s]\n" ] }, { @@ -899,7 +927,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "Computing DMD similarities: 100%|██████████| 1/1 [00:00<00:00, 220.12it/s]" + "Caching compare objects X: 100%|██████████| 1/1 [00:00<00:00, 113.74it/s]\n", + "Caching compare objects Y: 100%|██████████| 1/1 [00:00<00:00, 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[00:00<00:00, 101.04it/s]\n", + "Computing DMD similarities: 100%|██████████| 1/1 [00:00<00:00, 147.08it/s]" ] }, { @@ -958,9 +988,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "\n", - "/var/folders/sh/r0d61tjn42s4mc3nxqb0hhq00000gn/T/ipykernel_74501/1197694459.py:120: RuntimeWarning: invalid value encountered in divide\n", - " centers = np.sum(\n" + "\n" ] }, { @@ -974,8 +1002,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "Fitting DMDs: 100%|██████████| 1/1 [00:00<00:00, 18.95it/s]\n", - "Fitting DMDs: 100%|██████████| 1/1 [00:00<00:00, 20.08it/s]\n" + "Fitting DMDs: 100%|██████████| 1/1 [00:00<00:00, 46.63it/s]\n", + "Fitting DMDs: 100%|██████████| 1/1 [00:00<00:00, 21.53it/s]\n" ] }, { @@ -989,7 +1017,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "Computing DMD similarities: 100%|██████████| 1/1 [00:00<00:00, 241.30it/s]\n" + "Caching compare objects X: 100%|██████████| 1/1 [00:00<00:00, 130.92it/s]\n", + "Caching compare objects Y: 100%|██████████| 1/1 [00:00<00:00, 112.87it/s]\n", + "Computing DMD similarities: 100%|██████████| 1/1 [00:00<00:00, 158.80it/s]\n" ] } ], @@ -1028,12 +1058,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { - "image/png": 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YuU+h4oGTaRnJYWTKe3um5PiTIocwurN06VKz/s477zSPfeMb38Do0aPN4yeccILf502IDsHOxPS48ILNC2w4iBsKr6L11lS8/z/WC+SBIxWMqfhLQGrf0OwfRz0M+C7Q5wJg2+NW9BS8AxS+D/T7BjD4J9aY3eH3SbTHWFcJlG23oodiJ5UjJVKCcSQizAgLoRMIDQ0Nxp/zxz/+0UQCmPLYu3cv7r333laFDlMuXLqE5Dz7F0NbjbOqCux27RE6fvLKK6+YKAaFH88VRQgjLox2UAB4+3I+/fRTk/LziBzCdCCfx2gKxca+fftwzjnntPmeEydO9Hs/KTwZPfrrX/9qInjf+ta3MGjQoFa3Z9SGopjGc2+YzmLKjVx77bUmXfef//zHvCZFz5gxQShhFaI9sOqo5FPbRI8pqVBfPJky27cU2PdvoHJv0/rEnkDvGVZYpA8NnyZ7FDPDbwT6fdOm0Sh2dv3dRp8G/cj6eiiKOgo/l3iOlCgFijYCFfTvDAJS+mikhMMIC6GTnZ1txMqBAweared977/mvaFHhCkO7zTV8OHDkZ+f32kGW5M+YmSlvdCA3FbjLP6HmvFB+9/bD+h7YTqI54GRD6aSPHgLmvamvdpDy9dl+pERJW8ovLyh+KIIe/XVV/Haa68ZkcqU1MUXX+zzPegb4mfOVGfLFKUnPUXhxFQcX5NihynL3//+9yaiJUTnpqr2AcWf2gIERg0YzQkF9J+wX83+f9u5UB6YOss9y4ob9r0J1f61B0ZZJtwPHFppDculnwNbFgK7n7Ol6vQPBUOc0awcn2Z/T7PqjSMlTIflPI2UcAhhYUbmxZgRGaZYPDCawPv0jfiCEQemq7idh88++8wIoE4ROYT/qZg+au/C6ir+h/QF13uqr9qz+PkfmqKDZeXHH398M5HjCwpEmoLp1fFA/w2FCs3KNArT7O39+bQH+qZKS0ubvS49OC1hdOYXv/iFESVf//rXTQqK8HNk9MYb+nm4jj4iHp/34i2KmZK88sor8fzzz5uUG9NzQnSq54UCh/1mUG/7tnS1iGAqhpGbVdcCyy8ANt9nRQ59gPS/jPmdNRWPmQdknxzeIsebnpOBqU8Do26zUSgKkY9vAlb+FCj2bW0IbKREDzsglCKVIyUOr7JRdxmWI56wEDqeFAYvRn/+859NKoWpB14gPZ6Pyy67rJlZmY+z6oqeEQoc/vVOfwZTM2EDBcrIOcCouTaCQ/iT97m+k0vM2wt9TuxdNGvWLGzYsAFvvvmmiX5ceumljb4pRl4YFXnwwQfx+eefY82aNXjooYfafF1WQ7Fy7tZbb8W2bdvwzDPPNJqbSWVlpTEWs2KKlVIUVzRDU3gRiitGcCiw6OOqqKgwooj7y+8DRcyOHTtMvyVGbfgdINdff73xJPEx7iePx/OaQgQdVlRR4JRstmkX+vO6CvaFKVgBfDIXePM84JPbrKeFhRAZI20KaNprwIQHgD5fCn0aLVAo1o77GnDG88AJPwRik2z0ZcWlwPrfWEESrPdh6XtKrrUesP/OkXVATSsWBBERhI2knzlzpjHKssqG6adx48YZg6nnQkuDKSMM3n+x82LGSAD9Fyw1puhh1VVYwZJRmo5H/qqpERgjOWHUpZNihOeS548GY96nr4Ul/x4ogmhYvv/++435l+nGb37zm22+blZWFp5++mljNqaIpceHgoml64SpJ1aCUbQwTcnXZETHUxnHyitGZfjd4HZMa/H5jPh4zNH0ZfF5J598Mi666CLzPEZ8KHj37NljqstYkcX9FqJTqqpMqqqs61JVfF+KKnpuaCqusdWUhpS+Ni1FUcOy9VBCEUYRWF8JJGYFR2Txj8MTfw70+zrw2cPA/qXA3n8B+f+1TQzZvDAYv1vpAWI5Oivl6HGqco+USOsfNn+givYT42ppoogiWHXFyh6WSvOC6A0v6owIDBw40EQ7hHPRZy0CSlWVbrP9Zjh2oCuiOBxnwAs7TbnlXzSt5x9PHMHAkvDM0aE1FZuGiOX2jzreNhPGu1vvEu/TnB1MMVi0Adi8wHY/Jkk5wIlX2QqyYDYEpJCtOmTL0NlwkCkufu4iLK/fYRvREUKIiIBVOvS+UHhwcnZn/oXPlAknhFPcFK1rWs/UTc4ZNnqTfUro/TbsTcN9pU+Ixl5Gkzi7ipEcpoNYus1Se0bAGNkx64MgRDJHAVP+ZCM6HFfByMv624GdS+zA0KzxwTg69zF1swKOqSxToXWCPcZQn3txTPQJCSFEOKWqzITwd9wTwt9rPiG85yR3M7+z7MU3lHDYJ1NTFH48DzQK0xfE6FZ8iypRCkKafSlE2F+IhmKKHUZ7OgojWIxo5ZwJ7PwbsO1JoGQTsPIKIPdsW/jBCEww3sdEqNJtA0h2V2ZvJDNSIkcjJcIYCR0hhGh3qurzpoZzwUwRHXNC+AXuCeFHz/3rchi1YSk2PTgUKhzjwP2iCGgrShPLyM5xVgjR98IBn4wCBWtwKVNJJ/zADgvd+gdg94vAgTeAg+8A/b8DDPphkIQVR0pkAwl1tgfaoQLrjaLgScrq+OuLoCOhI4QQbcGIBc2/5bv9S1VxVhQjHRQErc16YnSDkRt6b6q8+ogxUsDIDU3F3QeH/vMxxuJioJYd2lOA5N42bcMLfpyf7TwoatigkH1qynfYqeJMb/G1ghEh41yrkbcCx3/b9t/hfLEv/mpNy0N+Chx3cXDexzNSghG4yj12cClTdGakRBAElQgaEjrHIIq92lGDPmPRyhfDXrzox6HYaW+qKjbZ/mXP1AyHSDJlw1RH9WE71ZuCpnFC+OdNz2MqKu9cK256jA99KsQYi8vcoyJirOm5x2Ar9oIxG8pMFh9rRRNHMbCTdEKaPV/BiJZRIE58GCh8D9i80Bq4N90N7Pw7MOx621soGDCSZPrvVLh9SPusf4frIrWc32FI6LQCuy4T9m5pb2dgEZnwM/b+zIUwEYwyd6qKkRletNpz8aXI4V/0m+6x8+yY4mHvLPpEhs0GPrwC2P1Pqgi7vZkQfrqN3vDCGw6VPIxQcL/5M64b0I2m2zwr3Jh+CiY8p+xZwygMx1MwwsUIT3LP4HiQ+Pq9TgN6ngzsft6mtBhFWn2dNXFzYGiwImb0JcUfb31LxeutD6m7e6REMEZWiIBReXkb5Wn79+9HUVGRmanF3jKc+SScFcmhyGGX5czMTNNVWwiTnjGzqnbbC7A/F1xe1D5fDGyYd/Rj7OzbYwLwzsU2YsMS6NxzumZC+LFgg0FGrXiRpgeJx03fiTl+/8bPdAgTFfkCqNhpB6OmsBw9iJ3ueYzb/gTsfNadTowF+l0MDP5pcP01jIYxmseIGFNySb2s2GGKruVC8ehrfagjeg4qL5fQaeNE8ULI5oUUO8K5UORwfISErEClJ1VV4n/pMKMzNOY+n9f6fLuL9wEH37bpn3CAwoKGYAodpqOYnqOxmPsayj/sKBJKmc7aA8QlWLHAi3+w4OtyYOiBN+19+q7YcZmm5WBG1XheTcqyujGI536g6SYFTaOw8bptRBDFUYIVe7xNP5RPUdTKEuwIXBghoRPkE8VOuy2HUQpn0HIwrIhSWqaqzIU1xn+TLUusX2gjMnhxPlBTaDvuhtRYzJ435XZYcFIukMrUVM/wmtrNSjT6mVjtVn3QeoRY2RVMWOnGhoM0mxNGsYZeYyNtXSH0jAe0wR4rRZHL6zZ8rONPX7QqlmKPFkvmZ3xzQXXMJfyyGWoYGGR4IdTFUAgnp6o2AxW7/E9VeVNXBXSjwTaz9YgOL9Q0OIfEWFwK1JQYX7Hx23Qfao83XCuEeBE2TQc5yHOPNSzTv+OrT0+gZJ0EnPIXO07js0XWJ7T2FiBzrG04yIaEnYkREG4xgQ74eI4SQx6BVA/U1bR4jLddvvelUSx5CSWzjhWErYmluLAXS0pdtTPHJ4RwaKqKIofpGxpuAzWN8nXW3Wong3Pq9YbfHb0Nh/kOudJW5XQVjByxaorGYgo4eohYus7KpkhLa1CQlu2wgpQXbPbfCabJl/2BWIa+4y9NETeaxDlSgt8Np+Pyjia5vKJJXlElbwHlsyI5prlA8kSX+Fn1GBPU3VXqqhNOlBDCaamqHXZWlWkA1yvwvzgPLLfmY/p6epwETH8T+PT3R1ddcbgvjbYsMe90YzE7FpdZIcCoDavGGBlxQrlz9SGbzmKXaqYLaSIOpnGXU8s/ewTY90rTuI2B37dDQ7vSmB2JuNwCyTsdV1Nso3C9TgnqW0nodMKJEkI4BHpTit2pKjOGIC3wbskUMxw7QDJGAGPvBNIGe/XRKbLpKu8+Op15XMZYzGGa6UDKcfYvaXpbwtBj0SEa6m0KkOXoFD5JPYLT28cbjvpgw8Eja+x9CsYhPwf6XhRcY7TTqS4E4tMldEKFhI4QUUblAVs6zqoeU1UVYOqDnpG1t9qZSqT/d62J1fv12tMZuaNQbPEv5voKayxmWoppFl6Uo6F3C1Ny7FhNI3l9ZfDGSXigaGRlFiu06N/xjORgT6SeE4P3Pk6mOvRCRw0DhRDOhxEAk6raYqMbqf0Cj3JwmviG39oICqMIo2+3AyVbQnFT3wkChykB9mcxxmIOmswCMobZ1FSg0alIhaXg6YNtvx1jVt5tz4npWxOEyxtfK+9sIOc021F52+M23fnRlXZ6PBsOclK7CGtkRlbqSojoSFVxkGRSB8QAowdMZZjOxmzANMamqrrKqEqDLFNTbKTHSikai9lV2IxMUHM5E32pLrD+HZal009DERjMtB3PvxkY+rz1nzCFxZlag34cHo0fw5Hq0Ed0JHQkdIRwLjSW0mtBj0xHUlU0Ea+b0zSb6oTLbTfdYEQNjtnzpsRGcBi9SMy2Tf0o2IKZonESPGc0KtO/U3ukcyJdjA5uWQgUvGfvM7I36CfA8d9s/p3oivRluFNbDCT0BLLGBvVlJXQ64UQJISI4VZWUE/hf9uyxsnG+9YAwejJ6XtD/Oj0qMlFPY3Gx21icaRvZGWNxuvOMxZ0Fy8UZxeMwT0bjjH8nyLPECj+wUT56hAinl3NgKIezUoy2Ntg1Goj1DLflsbsFp6vWdqEOAhI6nXCihBARAscaFG+xFzhTjdM98Avlp/cAe/9l72dNAMb8zl4wO4OGGitu+L5MuzAtxcnexlgsO2WH0k2lO4DK3U09XYJZNcVozd6X7YwzihmOATn3XWtg3vJQaFoMhJrYNobbjpwTlGikhE4nnCghRCSlqg65U1UBjjSgz4OpKhpc2QRt8BXAoB8Fv6yYxmLPME2+tqfnjenQHJy/fIU7StZsnET34M/zYnpx21NAv68Dh1eGT9PIUNDmcNu5VvB18PstodMJJ0oIEeapKkZwmKriRY1l1oFcxPjcvS8Bm+61gxgpOMbcEfxSYkZt6F1geXh8d/cwzV4yFnc2PN8Ve63g4fnncNVgCkoz2HUo8HzvNga77gFeGmhNumYeh6/X8fe729r2/r6+H+/b2muw4u2iT4EXjmv9HHz9QIfnqqm8XAgRpamqzMAbx7E6i16c/Uvt/Z4n25EO9BkEzVhcDNSWA/HJ7p43fdzG4iB7R4RvaEZnSoWikimkip02tWXGSQRhoClTjHw9Xxd4wvVVBfazZ4VY85HmTbSyOmi4OvG12aCSkdW2zgH/H8R1UgrYB0r8CiEiF140ijdZbwQncAd6sSrZAqydY7slM4U05GfAwMs6XrZthmmW21/uvLjQWNxjkL2wMpIjY3FoYBQnc6RNb9JIXLHPRhjM1PrYjolZmm/bGuzKdgQTH7DRpXYJkNZUia/1rQknV5u7HdjzXa2LSZ7Xts4BxVAXIqEjhIjgVNVn1udCX0sgFyj+AmdfHFbO0AzMv7TH3gH0GBecvjfsxMxhmt0G2gscq3BkLA4fTFWQu6qNgocNB9kPh+sCgSXkFN003fryp3A9K5C6+ELf5fAY2zoHrL5CECJo7URCRwgRWdDfYmZV7bQXpUBTVTQB0zB6YJm93+t02+U40IucNyZ9UQZ0HwR0HyxjcThDgZzKFGI2ULHb3WF5l/WaBDIElSXkNNuS1ga7Op3qNs5BkKqu/EENA2VGFiJyqCq0s6po5EzpQKqqeKOdVcX5RTSQck4V51V1NJXEbrmV+XbiNccymFET6locUVAAswcTRQ+jhck5/kfhGnvIdPFg17Dto1Nkz0WI+ugooiOECH94wSnzVFV1MFW18xnb34RpBqYsmKrKHBWcSBNLmGkwNrOnenT8NUXXw9LzzNH2c2Q6i12WGdkx4yTa+Z2jmGEJOU25FEmctB5tnZEb3OeAf5iwM7IZldJ16SpvJHSEEOENBQQFDv/Kprch0JlC/Kty/W+Agnfs/dyzgVG/DryhoDeMMLH7LpvFMVWlKqrIhpE9lp5TrFa5x0kwwkOx48/3pbMGu0YSDbVAfUVId0FCRwgR/qkqVlfxL8JABcSRtcC6X9mIS0wCMHw20O+bHU9VmblK+dZwnDXKRgFUSeUcYuPcDRyzrW+HYttTjq5ZYxGDhI4QIvxgeooXlpLN9q/ibgF6Xfg6O/4CfP6o9c9wFtG4+bapWzA64dJzwfQXU1WBmqJF+ENRk36ircor32G/m6aTdbaq6CIACR0hRHjBsmwKHHpymCZIDLCxGEXIJ3OBQx/Y+72/ZCs+OmqGpM+Hzd4onDJGAGknBD4VXUQWpspvrJ1Bxuos9t/hZHSmuBTJC1skdIQQ4UP1ITuryqSqcgNPVR1aBXxym/XOsAJqxE1A368GIVVVY1NVvLDRj2MMliKq4HeI3012tGbVHv07jPAkcz5ZWqj3LnxwuewfA4yqhhgJHSFE6OEvQ5o92R+HYqJboFVV9cC2PwFbH6cqsdGWsXdag3BH4eDN6iKgW38gY6h640Q7rKbid4H9dih0mNLi9yOF5eihqS4KGq76FkLF/dMsntuex3jbPKnFi8TY/8Nc+MdGCJHQEUKEQarqM3dVVZqtdgkERoEYxTm82t7v+2Vg+M2BNX3zhr/MjYk5DugxBug2wJpUhSDxqdajxShPKdNZe4C4BPc4iS7+nnjECFoRJa6WosUjTlqIFCNQ4tx/bHhuU7jEWdHCVC3FXOPPePc2Xgufx/8n5jld2yCwJRI6QojQQR8NZ1Wx34hJVQX4C7FgBbB+rm09H5divTh9LgjeGAdW2aQPD1yECefDdGbWeNtlmdPRKXjYDoHLsVKmnugJBYe3KDHrWggVz3ae53njESNGoLh/xniiKm5hwqpDb6HSUqA0ipy4Vh7rYPo3BEjoCCFCnKqqDbyqiuXdny8Gdjxl73cfAoydbydUdxSKJk4aZ9qLFTcqJxbHgt9hDrRM7GmFjjEs77aiolmah7h8p3k80ZDGKEoCEJfqFihuccIBpK0KkdZESkzUfn4SOkKIyExV0RTM3jhF6+x99sUZdn3HBUnjGIdk91/oAfqFRPRCIdL9BBulLNtluwS3N83jS7yIDiGhI4ToOhglKdrkHpXQgVTVwbdtl+PaYmsKZofjvOlBGuPANFpvjXEQHYffzczhOpMhRkJHCNFFqao9tnS8saoqAKMm01ycU8V5VSR9BDDuTht1CfoYhwivnBFCGCR0hBCdC8WDSVVt71iqqmIvsG6ONS+T/pfYqeMdLeVtNsaBwxx7R7WfQQinIaEjhOjcVJVpAJhv2+cHmqrK/x+w4Xd27AJHLYy+Hcg5M0hjHA4Bqf1sJCcYAz6FEGFF2LmcFi1ahAEDBiA5ORlTpkzBypUrW932qaeeQkxMTLOFzxNChAEsy2aHYqaEmFoKROQwGrTpbmDtLVaUZI4Bpj7TcZHDslz6hGrLgIxRQI/xEjlCOJSwiugsWbIEs2fPxuLFi43IWbhwIWbMmIEtW7YgJyfH53PS09PN4x4odoQQIaZyP3DkE1tOm9o3sNco3wmsnQOUfmbvD5wFDPlZx4coNo5xyHKPccjt2OsJIcKasIroLFiwAFdccQUuv/xyjBgxwgie1NRUPPHEE60+h8ImLy+vccnN1S8tIUIKvTRH1prWIEj2/QfKMdm3FHj/UityEjKBCQ+6/TgdFDk1xTbSlDoA6DlRIkeIKCBshE5NTQ1Wr16N6dObSkRjY2PN/RUrVrT6vLKyMvTv3x/9+vXDV7/6VWzcuLHVbaurq1FSUtJsEUIEkfLdbpETb1vgB9JjZ8Nv7SiH+gqgx0nAqX8Dek3teNUXo0wN1Tb9xVEOHZ1iLoSICMJG6BQWFqK+vv6oiAzv5+fn+3zO0KFDTbTnpZdewtNPP42GhgZMnToVe/bs8bn9/PnzkZGR0bhQHAkhggA9L0w1sXkfvThJWf6/BquyVlwG7HnJdooddAUw6RE7fqEjUDxRgDEy1HOybeSmWVVCRA1hI3QC4ZRTTsFll12GcePG4cwzz8Tzzz+PXr164Q9/+IPP7efMmYPi4uLGZffu3V2+z0I4UuSwyzE9OWxVn5jp//P3vGxTVRQ7ST2twBny0yCkqo4AVYW2Lw5TVXxtIURUETZm5OzsbMTFxeHAgQPN1vM+vTftISEhAePHj8fWrVt9Pp6UlGQWIUSQoEjhAMPijUBihv/l2XUVwKb5wL7X7P2eU4Ax8zouSMwYh/1AbIrGOAgR5YRNRCcxMRETJkzAsmXLGtcxFcX7jNy0B6a+1q9fj969e3fingohGn0vNAsbkZPpv8hhE8H3v29FDrskD7kKmPhQx0UOxVP5HiApB8ieDHQ7XvOChIhiwiaiQ1haPmvWLEycOBGTJ0825eXl5eWmCoswTdW3b1/jtSHz5s3DySefjMGDB6OoqAj33nsvdu7ciR//+MchPhIhHE5DvRU5JVusH8cfYy+jQLufAzYvsKXeFCQc49BjXMf2ia/L5n98TY1xEEKEo9CZOXMmCgoKMHfuXGNApvdm6dKljQblXbt2mUosD0eOHDHl6Ny2R48eJiL0/vvvm9J0IUQnipySzTYiw3EO8antfy4b9G38ne10THqdBoz+P/99PT7HOOy3UaVMjXEQQjQR43Lxz6DohOXlrL6iMZmNB4UQ7RAUnDVVts32yGmr2zFLzGkm5nNc7uexAWDlXpuqOvEaYMD3Oj5XiuKpRmMchIgmSvy4fodVREcIEcZwcjj9OKXbbaO91kROrLu8nJ2HWfWU2MOKnE33WJGT0gcYeyeQOSoIqaqD9mfGaCBtYMertIQQjkO/FYQQx6a+BijaCJTvAFJ7tz4xnCInbYAVNVseBGqLbP+aoVcD57wBfHwTcMIPOj5XSmMchBDtREJHCHHswZpFG4CKnUBqHyA2ofVtGcmhyNkwr2kdxQ4nj7MJ4Nj5QNW+jo9xqC22YxwyhvrnERJCRB1hU14uhAhD6iqBok9s1+OUvm2LHHpymK5iJMcXW1g6nmW3C7ScvWKfjeZkjnWPcZDIEUK0jSI6QojW+9FQ5FBccAL5sfwvfJyeHEZwfMH1NUV2u/o6/8c4cBgnDdAZw9XhWAjRbiR0hBBHU1duRzpU5QPdjrNVUseC1VWmcWCmb7HD9Xycr+kP1Yet6Oo+BEgf0nallxBCtECpKyFEc2pLgcMfA5X5NpLTHpFDyr8ADrxhjce+GHotUHPYlpq3BwqnCveA3qyTgMyREjlCCL9RREcI0dzoy3QVoygmktPOv4WqDgIf/RxISAfOfcf+DdWs6upaYMTNQNkX7Xs9RnCqCqz5mV2OO9pQUAgRtUjoCCEs9M8cWWfFiYnktLORH305H10FVO6zwqh4MzDkSmDkrfY1KVIYyaHIaahq5xiHao1xEEIEBQkdIYSN4FDk1JXZ6qr2ihx2JV51je2vk5wLTHwEiE+xoodRHhqP6clpT7pKYxyEEJ2AhI4Q0U5VoRU59RVASu/2ixxWQq253s69YvfjiYtsqskDxU17q6vMGIfDQOpxNpLT0YaCQgjhRkJHiGiGUReKHPam8RYpx4Lbs8vxkbVAfBow8WHbEdlfmKqqOmBvZ4zSGAchRNCR0BEiWmFVFUUOGoCUvPY/jymmdbcBhStsFdSEB4D0oR0b48DeOOyRI4QQQUZCR4hopGIvULTejmXwR2CwO/HGO2wZeUwCMP4+oMdY/99fYxyEEF2EhI4Q0Ub5bqB4vR3FkNTTvzTT5gXA3n/Z3jrj7gSyT/bvvSmU2OGYJuXMcUC344HYdvbpEUKIAJDQESJaoFCp2GUHdMYlWQOxP2z9A7DzWXt71Fwg9yz/nk/zMj1BSRzjMExjHIQQXYKEjhDRInLKdgDFG+0gTH8b8O34K7DtcXt7xC+BvhcGNsYhbbDGOAghuhQJHSGiQeSUbgNKNtmybXYv9ofdzwNbHrC3h1wFHP+t9j/X9MbJt+KKYxxYPt7e8nUhhAgCEjpCOBl6Ykq3AsWfugdupvn3/H1LgY3z7e2Bs4BBl/tXVVWx3z3GYTiQmOHfewshRBCQ0BHCySKn5DPb0C+ph+134w8H3wHW384XAvp9Ezjxaj/eu96KnG4D3cM4E/3efSGECAYSOkI4kYZ6K3BKP7emX6aO/OHQKmDtL61g6XO+HcjZ3pSTMT3vs5GczBESOUKIkCKhI4TToC+GqSqKHPbI4ewpf2BV1prZNvWUcyYw6vb2TzEnlfttRRc7HbO6SwghQoiEjhBOoqEWKN4ElG0DUnJt52J/oDhafa2de9VzMjD2Ttvzxp+5WXzPzNH++4GEEKIT8OPPNCFEWFNfAxzZYCuskvP8Fznlu4CPrgZqS4DMMbbrsT8RGXY75iDPzFFAUpbfuy+EEJ2BhI4QTqC+2o50KN8OpPb2P2XEEvCPfg7UHAK6nwhMWOifr4c9cmpLgfQRdgK6EEKECUpdCRHpsOOwETm7gdS+QGyC/838KHKq8oHU44GJD/nXa4denupCIH1YYBPMhRCiE5HQESKSYSSFIodDOo3I8fO/NKMwq662oyGY7pr0iH+jGWh8Zhl59xOA7kPUDFAIEXZI6AgRqdSVA0c+cUdiAhA5FEmrrwNKPwMSe1qRk5LX/uezjLzSqyGgv+8vhBBdgH4zCRGJ1JYBR9YB1QVW5HCauL+eno9vBIo+AeK7A5MetpPE/fX1qIxcCBHmSOgIEWmwKsqInENukeNnTQHTTet+BRxaCcSlABMftGknfzBl5EkqIxdChD2quhIikqgpAg5/bA3EgYgcjoXYMA84uByITQROWmDFil/7oDJyIUTkIKEjRKRAcXNkLVBbHKDIcQGf3gvs+7dNdY27C+g5yb/XUBm5ECKaUle1tbXIz89HRUUFevXqhawsNQkTolNgmooip64SSOkTWHXT548Au/4BIAYY/Rsg5wz/nt9YRj5cZeRCCOdGdEpLS/Hoo4/izDPPRHp6OgYMGIDhw4cbodO/f39cccUV+Oijjzpnb0VkUXnALrw4i8CpKrDpKvbLYTPAQETO9qeA7U/a2yPnAH2+5N/zTRn5PiBtINB9sMrIhRDOjOgsWLAAd9xxBwYNGoQvf/nLuPXWW9GnTx+kpKTg8OHD2LBhA9555x2cd955mDJlCh566CEMGeKnyVE4ZxxB8Ubr52CH3cQMIDnXVviwGV1cYqj3MDJgZRNLyFHnX+m3N4zifPawvT30WqDf1/17fuM08r4qIxdCRBwxLhd/i7WPSy65BLfddhtGjhzZ5nbV1dV48sknkZiYiB/+8IcIV0pKSpCRkYHi4mITnRJBjkIUvG8vzg3VtucLS5oZjYhLs2XJydlAQncrftSD5WgoLtgMEC4guVdgnwP9OJ/MtbcH/QgY8rPA9oPiNGuCBnUKISLu+u2X0HEaEjqdeXI/txGdbv2ar3fVW0MrhQ8nbVPgxHcDErPtIEgjfNL8N9o6jYo9VuTwPCRlB/YaB5YDa39pz/nxM4HhN/qfcmIZOfeBIkeDOoUQEXj9Vh8dEXxYwlx1AIhPOfoxVvtQzHAhFDsUPeU7gLKttjcLhU5SDpCY6U5zpUSXJ4RTxClyWP4dqLgo/BBYO8eKnL5fBobf4P859JSR9zhJIkcIEbFI6IjgU1cG1JUACRnH3pYDKClouBAabhnxKdliUzYUORQ7yTn2pxE+fk7mjhQYXC3faUWO8TW5z4m/sJngxzcArlog92xg5K/8j5B5ysh7jNU0ciFERCOhI4IPIwF11UBysv/PjUu2CyMZvPDXV9q+MYwQsSzaCAD6e3q5jc3d/Z/WHY7wWMu2A8Wb3Km8dohEX1Agcn4VBWPPk4Gxv/Pf/+RdRt6tf2D7IYQQYYKEjugcI3Iwqqpi3MKGS6O/p9KKnvLdbn9PqvWwcOK2R/hEmr+Hqb7SbVbkUOB40nr+UvaFnUTOiFqPccD4e236y98ycg7qTNM0ciGEM5DQEcGFkYSaQ9ZnE2yMvyetqfKHF2Xj79kFlG634sr4e3oBST2s8GF0JJz9PUbk0Li9GUjKDPy8UZysugqoOQKkDwNOWujbI+VXGbmfg0KFECIM6fCfvlOnTkVubm5w9gbAokWLTBPC5ORk04tn5cqV7Xres88+i5iYGHzta18L2r6IQNNW5U1RmM6EER1GQFL7AGnH25QWfSllnwMFHwAF7wKFK2wFWNXB8Gtc2FBvBQ4jOUaYBShymGb66Oc20tVtADDxocDKwCmWmDLMGOlcH5QQIurocERn5syZKCwsDMrOLFmyBLNnz8bixYuNyFm4cCFmzJiBLVu2ICcnp9XnffHFF7jxxhtx+umnB2U/RAdgRIFemlCkj3hx5mIEj8ttbC61Iof7RGMzDb7099DUzIhPqBoXMhpFkcNojvEb+Rl98RaWH10FVOy2oyEmPWKPP6B0YwrQY4x65QghHEVY9dGhuJk0aRIefth2cW1oaEC/fv1wzTXX4JZbbvH5nPr6epxxxhmmMSG7MhcVFeHFF19stZEhF+86fL6+GgYGMULBKArNrOHWc4UpovoKr8aFcUBcN+vt4dLYuLAL0jUsqS/+1JbTs1s0zdeBwGNhJIf9iuhTmvI4kHpcYGKJpm/2ygm0+7IQQoRpH52wcW3W1NRg9erVmD59euO62NhYc3/FihWtPm/evHkm2vOjH/3omO8xf/58c2I8C0WOCCKMntAIG0japLNhhImpIQqLbsfbn7GxQOUe4PAqoOA9oOBtoGij9amwtLoz/gbwjMYopcjJC1zkUKytucG+Fsv4Jy4KTOR4ysiZrpLIEUI4kLAxIzP9xehMS78P72/evNnnc95991386U9/wtq1a9v1HnPmzDGpsZYRHREkWAbOaI6/lT6hgP6eWHdfHsL95kWfURZGfyhAjDBi/55Md8Sng74jipOiTbY5IodzBnqemPZae4sVaIxK0ZPTfVBg+6MyciGEwwkboRPIFPVLL70Ujz32GLKz29ciPykpySyik+Ck8kg1sVJ0JCa2aFxYbn00nsaFZjBpDhBPgdTdv2Pl6xVtACp2WfN0oL1/WGK//nag4B0gNgmYcD+QMSIwsVSVrzJyIYTjCRuhQ7ESFxeHAwfYGK4J3s/LO9o3sG3bNmNC5hR1D/T0kPj4eGNg5pR10UUwGkIjcmeUlYeCxsaF7rJr+ntqimxlEjO+pqlfFpDcs8nY3FpjPlZ7FX0CVOy1pduBDjDlfmy8C9j/uvUYjb8byDopsNfhcaSojFwI4XzCRuhw0vmECROwbNmyxhJxChfev/rqq4/aftiwYVi/npOdm+BkdUZ6HnjgAaWkupraEmtopbHXaZjGhd3s4j2YtGo/ULHTRmfiUpv69xjh4x5Myu04koHRk46KnC0PAntesEJrzO+AXqcF9loUOazMyhwVuRE4IYSINKFD6J+ZNWsWJk6ciMmTJ5vy8vLyclx++eXm8csuuwx9+/Y1pmL22Rk1alSz52dm2rRDy/WiC6g+bAVBODfnCxbtGUxK7wwNzzWHbX8bihw+L1C2/wn44q/29qhfAb3PDex1vMvIPcJNCCEcTKcIHVZLTZs2Dffee6+J0vjTk6egoABz585Ffn4+xo0bh6VLlzYalHft2mVeW4QZ9HtUH3BO2spf2hpMSnHDaqiO9BX64lng88X29rDZwHFfDex1mHpDg43kBNJrRwghIpBO6aPz1FNPGf8MRcoHH3wAJ9Thi2NEc1ieTaNuoKkZ4Zs9/wI2/MbeHvwTuwQCI07VR4CscRrUKYSIePy5fodVw8CuRkInSJTtsD6Ubl6l+jEs34630R5XXbDeKbrI/x+w9lYbhen/XWDYLwJLDbKMvDLfVmelD42O9KIQwtGU+CF09Oe36BjUyfSgeBrfxbJSKctWJLEKiykS+lQY9Wmo0tluLwXvA+tusyKHqapARY6njLy7ppELIaKToBtedu/ebcYxiCjBlF0XW38ORU7aAOsneT4PeKG3/cn7XM/HxbE5/DHw8U02EpZ3LjDy1sBEDhsfqoxcCBHlBF3oHD58GH/+85+D/bIiXPHMSTI9Z7KATfcAG+YBtUXusvMie5/rw23+VTjCBoVrrgcaqoFepwJj5gVercV0Fcv9VUYuhIhi/E5dvfzyy20+vn379o7sj4g0qg/ZiiJTeZRle734gutHzgEq9oRmsnmkeJ1WXW2Nwz1OAsbdHXgHZU8ZOUWOysiFEFGM30KHzfxiYmLQloeZj4sogP1jqg+601bx1pPjieS0hOsZYfjwpzYlwwtw5mi7cLhltH9n2DWZk8h5nmganrAg8IGfKiMXQojAhU7v3r3xyCOP4Ktf9d3LgwM2/emdIyI8bcVp5RQqNL3SeMwBmL7EDtcn9wIqd9tBksXrgZ1/s48xvZIx2i1+xgDpw4H4FEQNjL5Q5FQX2NlTEx4MvCcRo0G1ZbaMXNPIhRDCf6FDEbN69epWhc6xoj3CQTBywM+a0RxGaVhdNfRa68lpCdfXlABTHgeK1ruXDUDpFpv+OrjcLoSelO6DvcTPaCD1eGdGfXgOV10FVO61s6cmPtLUeDCQMvKqQiBzpD1fQggh/Bc6N910kxnL0BqDBw/Gm2++qVMbLWXl3pEXlpCPuMmWRG952EZ2GMmhyBlxM1D2he0SzKXP+U1dhEs2e4mf9Taywa7CXHb/026XkGFFj0f8ZIwCEiK8EzOjYauuAcq2A0k5wKRHgOTswF7Lu4w8bbAzRaEQQgSAGgaqM3Jg1JYCBe/aAZbeXpJDK4Hk3nYWE1MojE7420eHAsoT8eHPkk+BhpqWX10gbaAVPB6vD+93ZJ5UV0KBt+pa4MgaKwYZ6WIJfiCwjJwen5TeQI/xQFxisPdWCCHCCjUMFJ1PLcvKq+zYB292/t2moIZeDwz+sY0y+NsZmcMw87hMbzI9l37WXPww1cNICJe97kpADtJk2qZR/ITpTCcez8c3W5HDiqiJDwcucgh75TSWkUvkCCGEN+qMLAKDXpCWpc8cZFm4wt7uOdkKoWDA98mggBkJ9HevY4So2C16uBRvBOrLbUSJiwemyUy6y710HxLaeVyueuCTXwOF7wOxScCEB4CMYYG/nikjp8BTGbkQQvgi6L/xp0+fbnrpqJ+Og6HplZVTLSuDONiTje5S+1lB0Zmw+WDOGXbxCAhGd7yNzuU7bN8eLvtfs9tRXGQMb17lxWqwroAppg132BlWnAU2/j6gx7jAX09l5EII0fVC5+KLL0ZhYWGwX1aEW9qKZcypvZuvP7DM/sw9p+vNsKZSa4hd+n3dvZ8lQNFGW8ruSXnVlQJH1trFO1XGaI8n5ZU+DIhLCr55e/P97jRbLDD2TqDXKYG/XrMy8txg7qkQQjiKoAudq666KtgvKcINNgYk3sZfpqloTiZ5ZyMsoFGaYsIjKBhRKd/VPOVVutWan/O5/M9ux2hL+onNU14pffwXb94T3D9f1NQ3aPTcjp0jU0Z+CMgcoTJyIYToDKHDiM0TTzyBFStWID8/36zLy8vD1KlT8YMf/AC9enVRKkB0PRQLLcvKPdO2jTm5t234F45w9ARNv1z6XtTkKyre1Fz8sErMrNsE7Fpit+N4i8bydkZ/RgDxqb7fp+UEd5bG97kAKHgH6PvlpvfuUBn5IJWRCyFEZ5SXf/TRR5gxYwZSU1ONHyc314bNDxw4gGXLlqGiogKvv/46Jk6cCCeVpwmvbsiM3LCayTu9s+42YP9SYMD3gWHXR+7p4n8HVjE1prs+sf18jqoci7VNDb3FT7fjgbhUK6Q4xJTzvRp7CV0NDLvBloG3t8zeZxn5HttYkN4eVVgJIaKUEj+u334LnZNPPhljx47F4sWLj5ppxZe68sor8cknn5hoT7gjoRMATP0cXm0v6t6plDfOs1VPJz9pL/pOgsdHsePt9WFUpSXx3YEzXwYOvAFs+O3Rj4+aCwy5EqjcF9h+UCRRYGadpEGdQoiopsQPoeN36mrdunV46qmnfA7u5Lpf/OIXGD9+vL8vKyKFqoNAbIteLYc+tCKH3X1ZAu40GLnqMcYu3mXdpqzdLX6Y5uJ2PScBb1/cxgT3W+059Le3EJ9jyshHS+QIIYQf+C106MVZuXIlhg3z3fuDj3nSWcJh1FXa/jUtRy/kv2F/0mBLH0w0wJJ0Hq/HVGy8M4XHnuDOknAalOvr/Cwjd7kbIAY4B0sIIaIUv4XOjTfeiJ/85CdmsOc555xzlEfnsccew3333dcZ+yrCohtyBZDUo3mX34NvNZWVRysUL92OA5J6tT3BnULFV9qrrTJyLvTkqIxcCCE6X+iwfDw7Oxv3338/HnnkEdTX15v1cXFxZrI501rf/va3/d8TEf4wmsMZU95Rm0Mf2d40iT2bp3aikXZNcD/c/rRVszLyfkHfXSGEiAYCKi+fOXOmWWpraxubA1L8JCS0GAkgnENDvZ0qntCtlSaBZ0XOQM3OxExwv9neblZ15TXBvT0wFcbqL1Z2aRq5EEKEpmEghU3v3i264wpnUldiIzdJ2c0vxgeW29t5UZy28oal4xQzrK6i8Zj+Gs8Ed65vT2k5y8hZmcXKNvYkipWAFEKIQNFQT9H+/jn1tc0HeXL6Nn07jFj0UKVdIxQzFCqmQi3e/wnuZhp5tq1gU68cIYToEBI6on3wYh2f3HxdvidtNS20E8HDFYobf6qrjiojb6XzshBCiHYTJbXAokOw6ocRHe9p5ZwW7klbRXO1VTAxZeRQGbkQQoSL0NmzZw8aGhqOui0cBkUOZ0LFec23OrIOqDlkB2eySZ7oGJ4ycqarVEYuhBDhIXRGjBiBL7744qjbwmHQSBsb23x6tydt1esMpa06iqeMPH2oysiFECLIdMhY4T0my8+RWSJSYENA+kaapa0a7Dyntqqt+H2o2G0a+pqqIY5H4FRv81NtCI4uIx+iMnIhhOgE5CAVbVPLsvIyINlrrAdnO7GnDk2z2VN8P4+zrzjJO/1EOzqC/WSY/uJrUTxRAVHwxCa5xQ+XKPs6NisjH6YyciGE6ASi7Moi/Ibl47wge4sQT5PAnDOOHvDpoabEXsDTBjatq68B6iubltpStwCqAhr4PqxQirECKC65SQQ5dX6WKSPvpTJyIYToRCR0ROsw/VSZ39yEzHXeQzx9Pq/eLsl5zdezJ4zpC5PR/PUaqq3wYeSHP435ucRd7XXIbkMBxOd6R4C8PUORRmMZ+SiVkQshRCcioSNah2km0xAwvWldyadA1X4rfrJP8f08RmoSM4CkrGOfXYoVRm+4JHoNC2UUqb6qKfrDtBfLr80+ldjoENNfjPZ4xI8RQK1EmMIJlZELIUSXIaEjWscIimogOdlHtdVpVpy09jyWSXfEdEwBw4Z5LZvmceZWs/QXhQ8FEMXQYbf/xz1NnKLHY4IOF/8PhZqmkQshRJcRJr/9RVhSXdh8UCdTSI1DPFtJW1EYUWAk9+qcfWIFV2wakOBVBUYocLzTXxRbjEYxKsQIE6ubzPM9/h+3COrKQaTcl6rDQOZIlZELIUQkCJ1bb70VWVlZR90WDoCpoarC5oKi9HOgYo9NFfU61ffzKC4Ss4AELx9OV2AquBKap9mM/6eFAZr+H1NJxlTYEZsi6wr/jykjP+CeRj4osv1FQggRLUJnzpw5Pm8LB0DBwjRLqtd0ek80p9dU3wZaCgtWUGX0DY9KKeP/cYsXZLbw/1T78P+UtvD/xDT1/jHRn4TABEpjGXk/lZELIUQXo9SVaMMw62pK7Zhqq/8dI21VbgVQUs/wPqvG/5NiF5/+H7cJml4a+n9qy5v7f3hOGs3P7fD/qIxcCCFChoSO8B2BMNPKvaI2ZduB8p02qtHrdN9njWmh1OOB+G6ReVaP5f/xCCB6figEPbfra93Rn4Sm1JfH/2O6SquMXAghQoWEjmilMqgUSPBK93hGPmSffLQQaOyd4wJSWvTOcQK+/D/EO/1FEcQmiZ4O0Mb/U2/L8DNHA4le51IIIUTkCJ1NmzZh165dqKmhr6GJr3zlKwG93qJFi3DvvfciPz8fY8eOxUMPPYTJkyf73Pb555/HnXfeia1bt6K2thZDhgzBDTfcgEsvvTSg9xZuTMM+lpXT29KirLy12VaMbCR0t0bkaMGn/8fVvP9PZ1agCSGE6Dyhs337dlx88cVYv349YmJiGod68japr6/3+zWXLFmC2bNnY/HixZgyZQoWLlyIGTNmYMuWLcjJyTlqe1Z5/epXv8KwYcOQmJiIV155BZdffrnZls8TAVLFOVZejfeYsirbalMxHPvQmtDJGN78edEIv/++/D9CCCFCQsClMddddx0GDhyIgwcPIjU1FRs3bsTbb7+NiRMnYvny5QG95oIFC3DFFVcYsTJixAgjePjaTzzxhM/tp02bZsTW8OHDMWjQILNPY8aMwbvvvhvoYQmTgjnUfFq5Z+RDz8lHp28IS7hpyOXcJiGEEMIJQmfFihWYN28esrOzERsba5bTTjsN8+fPx7XXXuv36zH1tXr1akyfPr1p52JjzX2+17FgRGnZsmUm+nPGGb6jDtXV1SgpKWm2CF9pq4rmRuTGJoHntP4cpqzkQxFCCOEUocPUVPfu3c1tip19+/aZ2/379zdiw18KCwvNa+bm5jZbz/v067RGcXEx0tLSTOrqwgsvNJ6ec8891+e2FGEZGRmNS79+/fzeT8dDEy3x9MFhg8CSzfarknum7+dQGKWGSe8cIYQQIhgenVGjRmHdunUmfUU/zT333GPExh//+EeccMIJ6CoottauXYuysjIT0aHHh+/PtFZL2NSQj3tgREdip0UfmaoDQEK3o6utsiY0H7rpgb1mWD6dGOa9c4QQQkQlAQud2267DeXl5eY2U1gXXXQRTj/9dPTs2dOYiv2FUaG4uDgcOHCg2Xrez8trvWSZ6a3Bgweb2+PGjcOnn35qIje+hE5SUpJZRCuwpJyl5d5Txz3+nLw20lYpx/kuORdCCCFCTMC5BhqAv/a1r5nbFBqbN2826Seakz3Cwx8YDZowYYKJynhoaGgw90855ZR2vw6fQy+OCHDsgzEWuyunKvOB4g12FlTuNN+NBdkrJtWBvXOEEEJEd0SHKav9+/c3K/tmufehQ4fMY4GUlzOtNGvWLFO5xd45LC9n1IhVWOSyyy5D3759TcSG8Ce3ZcUVxc2///1v/PWvf8Wjjz4a6GFFNxw6afrCtEhb9RgHJGUfvT3nQpneOUpbCSGEcJjQ8fTNaQm9MsnJyQG95syZM1FQUIC5c+caAzJTUUuXLm00KLMxIVNVHiiCfv7zn2PPnj1ISUkx/XSefvpp8zrCTzzdfL3Lyj1Cp7VqK/bOSR+m3jlCCCHClhhXa4qlFTxm3gceeMD0vGGfGw+M4nz44YfGa/Pee+8h3KEZmdVXrNxKT/fRHyaa4ODJwg+B1ONs0zs2DVx+gR3sOe1VIDn36PlP3IaTzMN9iKcQQghH4c/12++Izscff2x+Uh+xKzK9NR54m2MbbrzxxkD2W4SS6sO2PNzd2RoH3rQiJ2P00SKnsXdOD9+VWEIIIUSY4LfQefNNXgBhfDOM6kR9JMQJNNQB1e4p2y2bBLZWbVVfDqQPVe8cIYQQzvToPPnkk8HdExE6aCquLQOSc5qiO4dt5A65Z/v288SlKmUlhBAi7OlQK9t33nkH3//+90359969e806Vj1p1lQElpW76uy8KnKQs8oagPQRQGof39tzrhUrroQQQggnCp3nnnvOTAhntRN9O57eNTQG3XnnncHcR9GZ0IvOfjlxXtO28z1pq7N9985hqiultz4XIYQQzhU6v/vd78x08cceewwJCQmN60899VSsWbMmWPsnOhuOcGCExlNWXlMEHF7VetqKJeXx3ZW2EkII4Wyh09qUcJZ7FRUVdXS/RFdBkVNfCcS5ex8dfNt2O+5+ItDteB/bl9pojndjQSGEEMJpQofzp7Zu3XrUevpzunKop+gg1YVATJxXWfmy1qM57J3D7TymZSGEEMKpQofNAq+77jrTIDAmJgb79u3D//t//8/00PnZz34W3L0UnQOFC4WOJ23Fyis2DWytrJzRH/XOEUIIEQ3l5bfccosZoHnOOeegoqLCpLE4GZxC55prrgnuXorOgU3/OK082T2Us4Bpqzog7QQgbaDvsnKmtGLj9IkIIYRwttBhFOdXv/oVbrrpJpPC4oyrESNGIC3Na1aSCG9oPGbVlaes3FNt5Wu2VZ3bx6NxD0IIIaJB6HiPfRg+fHij+BERAgVOVT4Qn9JUfVW4ovWycqatOAqCFVdCCCFENDQM/NOf/oRRo0aZaeVcePvxxx8P3t6JzqOuFKgrafLnFLwHNNQAqccDaYOP7p1TX2OrrSRmhRBCRENEZ+7cuViwYIHx47AzMlmxYgV+8YtfYNeuXZg3b14w91N0ij+nGkhObtEk8JyjxQx9POyCrLSVEEKIaBE6jz76qGkWeMkllzSu+8pXvoIxY8YY8SOhE+ZUHwLi3I0e66uAwvdaLyuvKQG6D27qtSOEEEI4PXVVW1uLiRMnHrV+woQJqKur6+h+ic6kvrp5WXnB+1bspPQB0oc135bjHhjgoT9HCCGEiBahc+mll5qoTkv++Mc/4nvf+15H90t0JjQW03wc361Fk0AfaStum5Bp++cIIYQQTk5dzZ49u/E2K6xoPP7Pf/6Dk08+2axj80D6cy677LLg76kIHjVH7M+YWBvdOfhOG9VWZUCPweqdI4QQwvlCh1PKW6apyLZt28zP7Oxss2zcuDGY+yiCCSuoOK08PtXeP/QBUF9hU1MZI5tvy3RWfDKQnK3PQAghhPOFzptvvtmu7Y4cOWLGQfTo0QMXXHBBoPsmOoPaEltFlZRl7+e/0WRCZoSnZUNBzrVS7xwhhBDR2EfHm+3bt2PhwoU466yz0Lt3bzzyyCOmmaAIQ6HDfjmxiXbW1cG3fM+2YkNBbkeDsnrnCCGEiMbOyPTkvPTSS2bZuXMnzj77bHz/+9/HkiVLkJOjCddhSdVBK3LIoZXu6E5PIHPM0Q0FGclR7xwhhBDRJHT+9a9/4eWXX8arr75qhnpeeOGFuPPOO3HeeechJcU9TkCEJ5xXVX0YSHCXlR9oK23F3jmD1DtHCCFEdAkdDvH86le/in/84x+YOnWq5ltFEiwVp/GY/hz2xzmw3PcQT9M7J0a9c4QQQkSf0Nm8eXPn7InofBjNoYDhcmh1U4+cHuOab2fWZ6h3jhBCiOgyI7NHjj/s3bvX3/0RnUVDPVB9sKkbcmOTwLOA2BZ6t7YcSD3u6PVCCCGEk4XOpEmT8NOf/hQfffRRq9sUFxebGVicZP7cc88FYx9FMOCkchqP2Q3ZVd+UtmpZbWV65yQBSeqdI4QQIvLx60/2TZs24Y477sC5556L5ORk0zCwT58+5jZ75/BxNgs86aSTcM8996iHTrhNK6+vBWITgMOrgRqaktOBrIlH985J6mUfE0IIIaIpotOzZ08sWLAA+/fvx8MPP4whQ4agsLAQn3/+uXmcM65Wr16NFStWSOSEG1XshuyePp7vTlvlnNk8PeXpnZOq3jlCCCGcQUAmDJaRf/Ob3zSLiAA4wJMRHfpzOALiwJu+q61MaisNSOwZkt0UQgghwqIz8rJly8wgT6asunfvbrw7d999N0pLS4O+gyIIUOTUVQBxKUDReqC6wHp1sicfXW2VnAfEqx+SEEKIKBU67IZ8/vnnIykpCbfddht+/etfY8yYMbjvvvuMAfmTTz7pnD0VgVN9yE4fZ1m5J23V64ymDsme3jkuhutydaaFEEJEb+qKJmNPw0BvKioqTEUWOyWvX78emZmZwdxPESicZ2UiOExbuZq6IbestuIMrMRMINE97FMIIYSIxogOjcZXX331UetTU1Px5z//GccddxwWL14crP0TwZpWzlRVySZrSmYKK/vk5ttxm5S+6p0jhBAiuoVOQUEBBg4c6PvFYmNx3XXXmTlYIkxguTj75rC6qjFtdVrzGVbsncM0VrJ65wghhIhyoVNfX29MyK3B3jpbtmzp6H6JYMBUVdUBIC7V3vYInaPSVsV2SjnHPgghhBDRXnX1l7/8xZiSq6qqjnosPT0dRUVFwdg30VGYjjJzq9KA0s+Ayr1AbBKQPbVpGwqg+mqbtqJZWQghhIhmM/Lpp5+O3/72t6aUPD4+HkOHDjVRHHZD5s/c3FwT9RHhMq28CkjO8UpbTQXiU5v32InrZiM6QgghRLQLnbfeesv8ZDdkdkFes2aNWV5++WUTyYlRVCB8qCq0Ix9MtZVniKePtFW3/s3FjxBCCOEQAh5PzfEPXL7zne80rtuxYwdWrVqFjz/+OFj7JwKlvsb2z2G1Vdl2oHwnEJMA5JzWoneOyzYJFEIIIRxIwELHF6zG4vKtb30rmC8rAoGRGnp0UnsDB56161hSzn46HupKgcQMIEm9c4QQQjiTgMzIncmiRYswYMAAU9k1ZcoUrFy5stVtH3vsMeMZ6tGjh1mmT5/e5vZRV1bOVscxcW1UW3l65ySEZBeFEEKIqBI6S5YswezZs3H77bcb38/YsWMxY8YMHDx40Of2y5cvxyWXXII333zTNDLs168fzjvvPOzduxdRDQd3mmnlqUDZF0DZNit4cs5o2oaVVhQ46p0jhBDCwcS4XDRphAeM4HBA6MMPP2zuNzQ0GPFyzTXX4JZbbjnm81ntxcgOn3/ZZZcdc/uSkhJkZGSguLjYlMU7aohnwbtAYg/gi/8HfP6ILSmf+GDTNlUHgYRMm86SgVwIIUQE4c/1O2wiOjU1NaaKi+kn707LvM9oTXvgvK3a2lpkZfn2nFRXV5uT47041p/TUAPEJTVVW+Wd3fQ4tW1dJZCq3jlCCCGcTdgIncLCQhORYR8eb3g/Pz+/Xa/xy1/+En369GkmlryZP3++UYCehdEiR1JVYEc6VOwBSra401bTmh6vL7fVWOqdI4QQwuGEjdDpKHfddReeffZZvPDCC62OqJgzZ44Jc3mW3bt3w3GwQWDNYVtd5ZlUnjXBTib3UFMCpORasSOEEEI4mKCWl3eE7OxsxMXF4cCBA83W835eXtt9Xu677z4jdP73v/9hzJgxrW6XlJRkFkdDf05dBZCa2VRtleudtqq3ZmX1zhFCCBEFhE1EJzEx0YyQWLbMfXF2m5F5/5RTTmn1effcc48ZSbF06VJMnDixi/Y2jKk5Yn9ymGfxRvrNgVyvtFVtKZDQXWkrIYQQUUHYRHQIS8tnzZplBMvkyZOxcOFClJeX4/LLLzePs5Kqb9++xmtD7r77bsydOxfPPPOM6b3j8fKkpaWZJepoqLcCJ6EbsO/fdl2P8UBSdnOhkzFcvXOEEEJEBWEldGbOnImCggIjXihaxo0bZyI1HoPyrl27TCWWh0cffdRUa33zm99s9jrsw/N///d/iDrqSmy3Ywqb/DeOrrZiJVZsvB3yKYQQQkQBYdVHp6txXB8dNgc88jEQlwwsv8Cum/bvJmHDaqyEdHfvnLDJWgohhBDO76MjggCbAJreOW/a+5ljmkdvTO+c4yRyhBBCRA0SOk6BlVY0InuXlXtXW9Wxd06qTMhCCCGiirDy6IgOdkOur7S3D3989BBPlp2n9lPvHCGEEFGFIjpOofqwTUkdfIuuYyB9BJDS26t3Tj2Q0nY/IiGEEMJpSOg4gYY6W1bOTse+ZluZ3jnpQKLvGWBCCCGEU5HQcUraqq7M9tE5vNquyz2nudDhAM+4xJDtohBCCBEKJHScQG2JTU0Vvmt/dj8R6Navee+cpF6h3kshhBCiy5HQiXTYBqkyH4hLaZpt5Z22ogmZKSvvoZ5CCCFElCChE+mwbJypKwqeQyvtutzpXo9zwGdf9c4RQggRlai83Cll5UXrAVcdkHYCkDbAq3dONyCxZ6j3UgghhAgJiuhEOtWFQEx8U7WVtwmZaaukHCAhCgecCiGEEBI6EU59DVBFoRMLFH7QvEmgq8Eak1PVO0cIIUT0otRVpFdb1ZcDRZ/Y6qrU44G0QU2PJXRX2koIIURUo9RVJFNTZCM3niGejObExDT1zklR7xwhhBDRjYROpMIqq6p8K2wK3mvuz2moBWLigGT1zhFCCBHdSOhEKnWlQF0JULQBaKi20Zv0oV69c3rYRQghhIhiJHQiFYqZumrg4NtNTQI9aSv6dlKPU+8cIYQQUY+ETqRSVQBQ1xS82zxtxQaBcalAknrnCCGEEBI6kUh9NVBzGCj+FKivAJJzgYyRTQ0EOdeKFVdCCCFElCOhE7HTysvtEE+S605bsQKroQ5I6R3qPRRCCCHCAgmdSKTmiK2sOvhO8yaBLCmP7660lRBCCOFGQifSYNSG08pLtwB1ZUBSNpA5xqt3Tm8gLinUeymEEEKEBRI6kQY7HlPgHHKPfMg9y1ZXmd45MUByTqj3UAghhAgbJHQi0p9T0ZS28lRbcb165wghhBDNkNCJNKoOAqWfNQmbrPF2PcUPe+fExoV6D4UQQoiwQUInkqirBKqPAIc+tPdzptlRD1wflywTshBCCNECCZ2IS1uVNjUJbKy2cvfOYcWVEEIIIRqR0Ikkqg8DJZ/a8vKEdCBroq3Cqq+x1VaeERBCCCGEMEjoRApsBFh9ADi0yt7POROIjbcVWOyCrJEPQgghxFFI6ERSWXlNCVD4XvO0FdeZ3jnJId09IYQQIhyR0IkU6MMp3gRUFwLxaUDPyTbKw2yVeucIIYQQPpHQiQRcLqDqAHB4pb2fczoQm2jFT0ImkJgV6j0UQgghwhIJnUiAE8qri4CC91s0CSxX7xwhhBCiDSR0IoEapq0+AaoPAnGpQPbJQH0VEJ8EJPcK9d4JIYQQYYuETiRQfQgodM+26nWqNR7XFAGJ2eqdI4QQQrSBhE64w2Gd9Od4hA6rrejZaagBUvuqd44QQgjRBhI6EZG2Wg9U7Qdik4DsU23vHFZeqXeOEEII0SYSOuEOU1QF7zWlreJTbLWVeucIIYQQx0RCJxLKyj1pq9yzbe8cF3vn5IZ674QQQoiwR0InnGGKqugToHKv7ZuTc5rtkJzI3jk9Qr13QgghRNgjoRPOMEV1cLm9zZJy+nJqS929c+JDvXdCCCFE2COhE85UFTZPW7F3TlwSkJQd6j0TQgghIoKwEjqLFi3CgAEDkJycjClTpmDlSvfIAx9s3LgR3/jGN8z2MTExWLhwIRxFfTVweA1QsQuIiQdyzrDGZIqchPRQ750QQggREYSN0FmyZAlmz56N22+/HWvWrMHYsWMxY8YMHDx40Of2FRUVOOGEE3DXXXchLy8PjkxbHXjD3uYAz/juQEO1eucIIYQQkSh0FixYgCuuuAKXX345RowYgcWLFyM1NRVPPPGEz+0nTZqEe++9F9/5zneQlJQER/bPKXy/qUmg6Z3THUjsGeo9E0IIISKGsBA6NTU1WL16NaZPn964LjY21txfsWJF0N6nuroaJSUlzZawxNUAHF4NlO8AYuKAnDNthCc5z/bREUIIIUTkCJ3CwkLU19cjN7d5bxjez8/PD9r7zJ8/HxkZGY1Lv379EJawsmr/6/Z21gRbbcXeOSnqnSOEEEJEnNDpKubMmYPi4uLGZffu3QhLGL0peNfezj3Hq3dOVqj3TAghhIgowqIZS3Z2NuLi4nDgwIFm63k/mEZjenkiws9zZB1QthVADJA7zfpzMkard44QQggRiRGdxMRETJgwAcuWLWtc19DQYO6fcsopiCrqKoG9r9rbPcYD8d1sV+Rk9c4RQgghIjKiQ1haPmvWLEycOBGTJ082fXHKy8tNFRa57LLL0LdvX+Oz8RiYN23a1Hh77969WLt2LdLS0jB48GBELExTFbzVVG3FNBanlCdkhHrPhBBCiIgjbITOzJkzUVBQgLlz5xoD8rhx47B06dJGg/KuXbtMJZaHffv2Yfz48Y3377vvPrOceeaZWL7cPTYhEinZbBeSc5bthpzSF4iJCfWeCSGEEBFHjMvFEdnRCcvLWX1FY3J6ehh0G26oB9bMBj57EMgcA0x40HZI5jDP+NRQ750QQggRcdfvsPDoCDd1JU3dkD1pK5aUS+QIIYQQASGhE06UbAWKN9rbOdMABtvYJFAIIYQQke3REQB2/4NtkYGMETaKw2GeSeqdI4QQQgSKIjrhQl0FkP8/ryaBpdaEHJsQ6j0TQgghIhYJnXChbLttFEh6nabeOUIIIUQQUOoqXNj9PMuugO4nAgndgYRMuwghhBAiYBTRCQca6oB9rzalrdgdOVW9c4QQQoiOIqETDpTvBA6vsbd7nWLHPrAbshBCCCE6hIROOLDnBcBVB6QNsqMeTO+cbqHeKyGEECLikdAJNeyVs/slezv3LPXOEUIIIYKIhE6oqdwPHPrQ3u45xRqRlbYSQgghgoKETlikrWqBbv2BxJ7WhKzeOUIIIURQkNAJNbtfsD9zzgTiEoCkXqHeIyGEEMIxSOiEkuojQMG79nbWJCAxC0hU7xwhhBAiWEjohDpt1VBtRz0k93b3ztFHIoQQQgQLXVVDye7n7M+cM4CENOvREUIIIUTQkNAJFbXlwIE37e0eE4CkHCt2hBBCCBE0JHRCxd6XgfpKK3DSTgBS80K2K0IIIYRTkdAJFbv+YX/mnA4kpittJYQQQnQCEjqhoL4ayP+PvZ010ZqR4xJDsitCCCGEk5HQCQX7XgPqym05efpwIDknJLshhBBCOB0JnVCwc4n92etUO+5BvXOEEEKITkFCp6tpqAX2v2ZvZ00GuvVT7xwhhBCik5DQ6Wr2/w+oLQYSMoCs8TIhCyGEEJ2IhE5Xs2tJ06RydkNW7xwhhBCi05DQ6Uoa6oC9r9jb2acAqb279O2FEEKIaENCpyspeAeoOQTEp1mhw6orIYQQQnQaEjqhqLbiyIdu/YG4pC59eyGEECLakNDpKlwNwO4X7O3caUByry57ayGEECJakdDpKgreB6oPAnGpQM6ZQEJml721EEIIEa1I6HT1bKusCUDaQCA2rsveWgghhIhWJHS6LG31T3s792zbDVkIIYQQnY6ETldw6COgch8QmwzknQckdO+StxVCCCGiHQmdLk9bDeiStxRCCCGEhE6nUF4O1NQABw8CNdUuNOx63j6Qe05UpK2aHX+NvR9tRPs50PFH9+dP9B3Qd6A8TM5BfGje1rlUVQH33AM8+CBQVAScMfpjvHXLDrjiUlGd+w00VDu3d05MjP3pffyZmcC11wK33GIfc7ngaKL9HOj4o/vzJ/oO2POg7wB8noM5c4Dk5K79TkroBBGqVX6w8+Y1rZsx3JqQNxV9Cd0qh2HtB3As48YBTzwB/Pa3Tev4Bfecjx/8AFi3Do4m2s+Bjj+6P3+i74C+A+OOcQ5uvhno1q0Lv5SuKKa4uJh/W5mfwaC62uXKzOTfa56lwbXlviEu1/+D64fn/M1VVuZyZWd7P+6chcfF42t+/E0L1zv5+HUO9B3Q/wF9B/QdcLXrHPBa2ZXXb5mRgwgVKxcPo/ptwIm9P0dVTRL+/t6FKCgA8vLgSHhczMN6H783XO/k4yfRfg50/NH9+RN9B/QdyGvHOSgu7trvpVJXQYQ5SC6eD/ibk23aauknX0J8cnfzBbj7bmvKchqJiUDv3s2P3xuud/Lxk2g/Bzr+6P78ib4D+g4ktuMcZGR07fdSQieI1NZasxXzkNnZwPcuWAckZeO5j76Ba691oaYmBtOmISqOvyVcz1/uTj5+Eu3nQMcf3Z8/0XdA34HaY5wDPk5B1GW4woyHH37Y1b9/f1dSUpJr8uTJrg8//LDN7f/+97+7hg4darYfNWqU69VXXw2ZR4dUVblcWzeXuWp4o3S7q6G2zLVtc5FZHw1UVrpcc+c25Wf5k/e5PlqI9nOg44/uz5/oO6DvQGUnnwN/rt8x/KcLdVWbLFmyBJdddhkWL16MKVOmYOHChfjHP/6BLVu2ICcn56jt33//fZxxxhmYP38+LrroIjzzzDO4++67sWbNGowaNeqY71dSUoKMjAwUFxcjPT09OAdRXwXXxvmI2fIgUFtkhne6hl6LmJFzgLgurqkLYfVZQoLNwzJESfXepQ77MCDaz4GOP7o/f6LvgL4D5Z14Dvy5foeV0KG4mTRpEh5++GFzv6GhAf369cM111yDWzxNKLyYOXMmysvL8corrzSuO/nkkzFu3Dgjlrpc6NSVA5vuATb4iNeNmguMuBmIj7LfdkIIIUSQ8ef6HTZVVzU1NVi9ejWmT5/euC42NtbcX7Fihc/ncL339mTGjBmtbl9dXW1OjvcSVGISAEZyfMH1fFwIIYQQXUbYCJ3CwkLU19cjNze32Xrez8/P9/kcrvdne6a4qAA9C6NFQYWpKi6tPtbFNXVCCCFElBM2QqcrmDNnjglzeZbdu3cH9w0SMu3S6mNdXFMnhBBCRDlhI3Sys7MRFxeHAwcONFvP+3mtdNjien+2T0pKMrk87yWouGqBodf6fozr+bgQQgghok/oJCYmYsKECVi2bFnjOpqRef+UU07x+Ryu996e/Pe//211+06HRmNWV9F47Ins8Cfvc72MyEIIIUT0NgycPXs2Zs2ahYkTJ2Ly5MmmvJxVVZdffrl5nKXnffv2NV4bct111+HMM8/E73//e1x44YV49tlnsWrVKvzxj38M3UGwhJzVVSN/ZT05TFcxkhMlpeVCCCFEOBFWQofl4gUFBZg7d64xFLNMfOnSpY2G4127dplKLA9Tp041vXNuu+023HrrrRgyZAhefPHFdvXQ6VQ8kZu4Xu4VXdkCUgghhBBh2Uenq+mUhoFCCCGE6FQiso+OEEIIIUSwkdARQgghhGOR0BFCCCGEY5HQEUIIIYRjkdARQgghhGOR0BFCCCGEY5HQEUIIIYRjkdARQgghhGOR0BFCCCGEYwmrERBdjacpNDssCiGEECIy8Fy32zPcIaqFTmlpqfnZr1+/UO+KEEIIIQK4jnMURFtE9ayrhoYG7Nu3D927d0dMTEybypFiaPfu3VE5Eyvaj59E+zmI9uMn0X4OdPzR/fmH23eA0oUip0+fPs2GffsiqiM6PDnHHXdcu7fnBxvqDzeURPvxk2g/B9F+/CTaz4GOP7o//3D6DhwrkuNBZmQhhBBCOBYJHSGEEEI4FgmddpCUlITbb7/d/IxGov34SbSfg2g/fhLt50DHH92ffyR/B6LajCyEEEIIZ6OIjhBCCCEci4SOEEIIIRyLhI4QQgghHIuEjhBCCCEci4SOm0WLFmHAgAFITk7GlClTsHLlyjZP3D/+8Q8MGzbMbD969Gj8+9//RrQc/1NPPWU6SXsvfF6k8vbbb+PLX/6y6bDJY3nxxReP+Zzly5fjpJNOMtUHgwcPNuckkvH3HPD4W34HuOTn5yMSmT9/PiZNmmS6pOfk5OBrX/satmzZcsznOeX3QCDH77TfA48++ijGjBnT2AzvlFNOwWuvvRYVn38gxx9Jn7+EDoAlS5Zg9uzZpmxuzZo1GDt2LGbMmIGDBw/6PGnvv/8+LrnkEvzoRz/Cxx9/bH4pcNmwYQOi4fgJ/yPs37+/cdm5cycilfLycnPMFHvtYceOHbjwwgtx1llnYe3atbj++uvx4x//GK+//jqi5Rx44MXQ+3vAi2Qk8tZbb+Gqq67CBx98gP/+97+ora3FeeedZ85Lazjp90Agx++03wPskn/XXXdh9erVWLVqFc4++2x89atfxcaNGx3/+Qdy/BH1+bO8PNqZPHmy66qrrmq8X19f7+rTp49r/vz5Prf/9re/7brwwgubrZsyZYrrpz/9qSsajv/JJ590ZWRkuJwI/0u88MILbW5z8803u0aOHNls3cyZM10zZsxwRcs5ePPNN812R44ccTmRgwcPmuN76623Wt3Gab8H/D1+J/8e8NCjRw/X448/HnWff3uOP5I+/6iP6NTU1BgFO3369GYzsHh/xYoVPsUh13tvTxgBaW17px0/KSsrQ//+/c2At2OpfqfhpM+/o4wbNw69e/fGueeei/feew9Oobi42PzMysqKyu9Be47fyb8H6uvr8eyzz5qIFlM40fb517fj+CPp8496oVNYWGg+1Nzc3GYnhvdb8xtwvT/bO+34hw4diieeeAIvvfQSnn76aTMFfurUqdizZw+igdY+f072raysRDRAcbN48WI899xzZuEvumnTppnUZ6TD7zPTkaeeeipGjRrV6nZO+j0QyPE78ffA+vXrkZaWZrx3V155JV544QWMGDEiaj7/9X4cfyR9/lE9vVwEBhW+t8rnl3v48OH4wx/+gN/+9rc6rVEAf8lx8f4ObNu2Dffffz/++te/IpKhV4U+i3fffRfRSHuP34m/B/idpu+OEa1//vOfmDVrlvEvtXaxdxpD/Tj+SPr8o17oZGdnIy4uDgcOHGh2Yng/Ly/P50njen+2d9rxtyQhIQHjx4/H1q1bEQ209vnTmJeSkoJoZfLkyREvDq6++mq88sorpgqN5sy2cNLvgUCO34m/BxITE00VJZkwYQI++ugjPPDAA+biHQ2ff6Ifxx9Jn3/Up674wfIDXbZsWeNJYQiO91vLTXK99/aElQpt5TKddPwtYeqLIU+mM6IBJ33+wYR/CUbqd4AebF7kGap/4403MHDgwKj6HgRy/NHwe4C/C6urqx3/+Qdy/BH1+YfaDR0OPPvss66kpCTXU0895dq0aZPrJz/5iSszM9OVn59vHr/00ktdt9xyS+P27733nis+Pt513333uT799FPX7bff7kpISHCtX7/eFQ3H/5vf/Mb1+uuvu7Zt2+ZavXq16zvf+Y4rOTnZtXHjRlckUlpa6vr444/Nwv8SCxYsMLd37txpHuex8xx42L59uys1NdV10003mc9/0aJFrri4ONfSpUtdkYq/5+D+++93vfjii67PP//cfO+vu+46V2xsrOt///ufKxL52c9+ZipIli9f7tq/f3/jUlFR0biNk38PBHL8Tvs9wGNjldmOHTtcn3zyibkfExPj+s9//uP4zz+Q44+kz19Cx81DDz3kOv74412JiYmm3PqDDz5oPElnnnmma9asWc1O3N///nfXiSeeaLZnqfGrr77qipbjv/766xu3zc3NdV1wwQWuNWvWuCIVT6l0y8VzzPzJc9DyOePGjTPn4IQTTjCllpGMv+fg7rvvdg0aNMj8YsvKynJNmzbN9cYbb7giFV/HzsX7c3Xy74FAjt9pvwd++MMfuvr372+Op1evXq5zzjmn8SLv9M8/kOOPpM8/hv+EOqokhBBCCNEZRL1HRwghhBDORUJHCCGEEI5FQkcIIYQQjkVCRwghhBCORUJHCCGEEI5FQkcIIYQQjkVCRwghhBCORUJHCCGEEI5FQkcIIYQQjkVCRwghhBCORUJHCOFInnrqKYwYMQKpqakYPnw4Xn311VDvkhAiBEjoCCEcx3PPPYerr74av/71r7FhwwbMmDEDV155Zah3SwgRAjTUUwjhOE499VRMnz4dv/nNb8z9//73v/jWt76FoqKiUO+aEKKLUURHCOEoSktL8cEHH+CCCy5oXPf6669j/PjxId0vIURoiA/R+wohRKewbt06xMbGYuzYsaioqMAzzzyDBx98EC+88ILOuBBRiISOEMJRrF27FsOGDcPq1atx2mmnmXVf//rXcf7554d614QQIUCpKyGE44TOSSedhNGjR+PDDz/EggULsHTpUsybNy/UuyaECAGK6AghHCd0Lr30UqSnp2Py5Mlm2bJlixE9QojoQxEdIYRjqKurw8aNG03fnJa+HU8aSwgRXSiiI4RwDJs3b0ZVVZVJU/Xq1cs0C3z00UfxxRdf4Ec/+lGod08IEQIkdIQQjkpb9e7dGykpKTj99NPRrVs3E8l58803kZeXF+rdE0KEAAkdIYSjhM6UKVNUSi6EaEQeHSGEo4TOmDFjQr0bQogwQkJHCOEYaDqW0BFCeKNZV0IIIYRwLIroCCGEEMKxSOgIIYQQwrFI6AghhBDCsUjoCCGEEMKxSOgIIYQQwrFI6AghhBDCsUjoCCGEEMKxSOgIIYQQwrFI6AghhBDCsUjoCCGEEAJO5f8Dto1gN17VFp8AAAAASUVORK5CYII=", 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", "text/plain": [ "
" ] @@ -1055,7 +1085,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -1078,7 +1108,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -1087,45 +1117,45 @@ "text": [ "on run 0\n", "0.0\n", - "[0.25369717 0.45654168 0.61520462 0.73780483 0.82856849 0.88754471\n", - " 0.92879732 0.95389112 0.9700081 0.97930919 0.9854652 0.98951094\n", - " 0.99130669 0.99260625 0.99388587]\n", + "[0.34813535 0.51610552 0.66213145 0.76551707 0.8384662 0.89794759\n", + " 0.93656195 0.96131608 0.97581514 0.98310719 0.98793454 0.99123529\n", + " 0.99268905 0.99382806 0.9948947 ]\n", "0.1\n", - "[0.19048048 0.32985884 0.45220731 0.56204825 0.64656907 0.72225737\n", - " 0.78645379 0.8387057 0.88046408 0.91326671 0.93781133 0.95550473\n", - " 0.96743091 0.9754896 0.98078049]\n", + "[0.54582073 0.76558439 0.87481118 0.94078347 0.96911968 0.98067321\n", + " 0.98936751 0.9924271 0.99426955 0.99589332 0.9967694 0.99739081\n", + " 0.99786609 0.99818268 0.99847013]\n", "0.2\n", - "[0.45967021 0.61126547 0.72997665 0.81298848 0.87317053 0.91252379\n", - " 0.94365227 0.96314694 0.9755494 0.98291577 0.98769552 0.99125876\n", - " 0.99318308 0.99419832 0.99518581]\n", + "[0.21830951 0.38287959 0.51502781 0.61739591 0.70785376 0.78032985\n", + " 0.83866465 0.88552491 0.92136682 0.94655041 0.96349438 0.97514645\n", + " 0.98190376 0.9858382 0.98927634]\n", "0.30000000000000004\n", - "[0.23221853 0.43225934 0.59113581 0.71408954 0.80805437 0.87151854\n", - " 0.91953221 0.94998535 0.96778991 0.97754717 0.98465794 0.98913341\n", - " 0.99109576 0.99240676 0.99366563]\n", + "[0.67282294 0.87458353 0.95275757 0.97585083 0.98853111 0.99323142\n", + " 0.99521829 0.99677828 0.99758222 0.99803759 0.99846682 0.99872347\n", + " 0.99897825 0.9992007 0.99934618]\n", "0.4\n", - "[0.19048048 0.32985886 0.45220733 0.56204827 0.64656909 0.72225738\n", - " 0.7864538 0.83870572 0.8804641 0.91326672 0.93781133 0.95550473\n", - " 0.96743092 0.9754896 0.98078049]\n", + "[0.36890001 0.53236022 0.6814589 0.78470399 0.85580787 0.91136665\n", + " 0.9461438 0.96700512 0.97920082 0.98491131 0.9898081 0.99209045\n", + " 0.99328691 0.99443158 0.99542766]\n", "0.5\n", - "[0.19227735 0.33898624 0.46312195 0.57362714 0.66190565 0.7406905\n", - " 0.80478509 0.85580001 0.89642329 0.92684243 0.94889075 0.96370089\n", - " 0.97341818 0.97962759 0.98485685]\n", + "[0.15959428 0.28908879 0.41094848 0.49578087 0.57750645 0.65379548\n", + " 0.71925711 0.77567115 0.8238677 0.8647262 0.89808503 0.92386401\n", + " 0.94375256 0.95847659 0.96958638]\n", "0.6000000000000001\n", - "[0.16155528 0.28339831 0.38959138 0.49323849 0.57274959 0.64213237\n", - " 0.70436733 0.76123389 0.80881667 0.84990632 0.8839797 0.91123191\n", - " 0.93278742 0.94931456 0.96168152]\n", + "[0.26300956 0.41647857 0.53726641 0.62719734 0.71537568 0.77416938\n", + " 0.83227965 0.87786799 0.912936 0.93812491 0.9575343 0.97066461\n", + " 0.97891474 0.98405959 0.98744505]\n", "0.7000000000000001\n", - "[0.41582455 0.47106512 0.52099833 0.57023939 0.61476138 0.65558472\n", - " 0.69136598 0.7251092 0.75488265 0.78261983 0.80822032 0.83151158\n", - " 0.85187938 0.87028326 0.88678245]\n", + "[0.45429789 0.5207928 0.5836935 0.63666869 0.68222928 0.72322328\n", + " 0.76024541 0.79099829 0.82038663 0.84525522 0.86677413 0.88663519\n", + " 0.90411281 0.91922178 0.9324377 ]\n", "0.8\n", - "[0.67329322 0.70255427 0.73088931 0.75639627 0.77912073 0.79779598\n", - " 0.8133835 0.82872582 0.84370986 0.85798003 0.87175487 0.88467783\n", - " 0.89732628 0.90904598 0.92008426]\n", + "[0.6645814 0.71164052 0.75469257 0.78664353 0.81688598 0.84232286\n", + " 0.86199444 0.87971805 0.8962877 0.91122159 0.9258577 0.93918892\n", + " 0.9506958 0.96057451 0.96898793]\n", "0.9\n", - "[0.68293678 0.71278328 0.74169053 0.76762367 0.79037159 0.80888744\n", - " 0.8247635 0.84028549 0.85518636 0.86945398 0.88327483 0.89639464\n", - " 0.9088834 0.92034872 0.93094254]\n", + "[0.69288973 0.73119948 0.7672158 0.79889557 0.82703214 0.84951018\n", + " 0.86916308 0.88746149 0.904444 0.92053566 0.93514135 0.94770815\n", + " 0.95866581 0.96777368 0.97521807]\n", "1.0\n" ] }, @@ -1133,7 +1163,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/sh/r0d61tjn42s4mc3nxqb0hhq00000gn/T/ipykernel_74501/1197694459.py:120: RuntimeWarning: invalid value encountered in divide\n", + "/var/folders/sh/r0d61tjn42s4mc3nxqb0hhq00000gn/T/ipykernel_79963/1197694459.py:120: RuntimeWarning: invalid value encountered in divide\n", " centers = np.sum(\n" ] }, @@ -1141,17 +1171,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "[0.1106887 0.21309875 0.30240264 0.38665383 0.45767216 0.52288068\n", - " 0.58136988 0.6394324 0.69465593 0.74241949 0.78972251 0.82641405\n", - " 0.86284668 0.88916518 0.91540882]\n" + "[0.10351104 0.19950946 0.2815726 0.36148873 0.4332065 0.49886852\n", + " 0.55804923 0.61560174 0.66799287 0.7172119 0.76383889 0.80236109\n", + " 0.83913826 0.8672754 0.89480113]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Fitting DMDs: 100%|██████████| 1/1 [00:01<00:00, 1.17s/it]\n", - "Fitting DMDs: 100%|██████████| 11/11 [00:00<00:00, 23.75it/s]\n" + "Fitting DMDs: 100%|██████████| 1/1 [00:01<00:00, 1.49s/it]\n", + "Fitting DMDs: 100%|██████████| 11/11 [00:00<00:00, 12.74it/s]\n" ] }, { @@ -1165,7 +1195,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "Computing DMD similarities: 100%|██████████| 11/11 [00:00<00:00, 1261.34it/s]" + "Caching compare objects X: 100%|██████████| 1/1 [00:00<00:00, 1663.75it/s]\n", + "Caching compare objects Y: 100%|██████████| 11/11 [00:00<00:00, 5432.40it/s]\n", + "Computing DMD similarities: 100%|██████████| 11/11 [00:00<00:00, 1344.05it/s]" ] }, { @@ -1236,12 +1268,12 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -1264,9 +1296,9 @@ ], "metadata": { "kernelspec": { - "display_name": "dsa_test_env", + "display_name": "DSA (uv)", "language": "python", - "name": "python3" + "name": "dsa-uv" }, "language_info": { "codemirror_mode": { @@ -1278,7 +1310,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.18" + "version": "3.10.19" } }, "nbformat": 4, diff --git a/examples/sweep_resdmd_tutorial.ipynb b/examples/sweep_resdmd_tutorial.ipynb index e9751fb..e18f818 100644 --- a/examples/sweep_resdmd_tutorial.ipynb +++ b/examples/sweep_resdmd_tutorial.ipynb @@ -136,13 +136,13 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "id": "a0b65b61", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -202,7 +202,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 6, "id": "3a4db4a7", "metadata": {}, "outputs": [ @@ -210,9 +210,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "/orcd/data/fiete/001/om2/ostrow/dmrsa/DSApublic/DSA/examples/../DSA/pykoopman/__init__.py:3: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", - " from pkg_resources import get_distribution\n", - "Sweeping: 100%|██████████| 9/9 [00:01<00:00, 6.24it/s]\n" + "Sweeping: 0%| | 0/9 [00:00" ] @@ -242,7 +247,7 @@ ], "source": [ "from DSA.sweeps import PyKoopmanSweeper\n", - "import pykoopman as pk\n", + "import DSA.pykoopman as pk\n", "from pydmd import SubspaceDMD,DMD\n", "sweeper = PyKoopmanSweeper(\n", " data=X_train[0],\n", @@ -262,7 +267,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "id": "d83efaf6", "metadata": {}, "outputs": [ @@ -270,7 +275,308 @@ "name": "stderr", "output_type": "stream", "text": [ - "Sweeping: 100%|██████████| 7/7 [00:01<00:00, 6.72it/s]\n" + "Sweeping: 0%| | 0/7 [00:00" ] @@ -301,7 +607,6 @@ ], "source": [ "from DSA.sweeps import PyKoopmanSweeper\n", - "import pykoopman as pk\n", "from pydmd import SubspaceDMD,DMD\n", "#we can also sweep other parameters here! here we sweep the dimension of random fourier features\n", "sweeper = PyKoopmanSweeper(\n", @@ -325,7 +630,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "id": "2920979a", "metadata": {}, "outputs": [ @@ -333,7 +638,56 @@ "name": "stderr", "output_type": "stream", "text": [ - "Sweeping: 100%|██████████| 7/7 [00:00<00:00, 35.82it/s]\n" + "Sweeping: 0%| | 0/7 [00:00" ] @@ -385,7 +739,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "id": "57fb8535", "metadata": {}, "outputs": [ @@ -393,16 +747,16 @@ "name": "stderr", "output_type": "stream", "text": [ - "Sweeping: 100%|██████████| 9/9 [00:00<00:00, 18.85it/s]\n" + "Sweeping: 100%|██████████| 9/9 [00:00<00:00, 107.62it/s]\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 11, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -424,7 +778,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "id": "9a225798", "metadata": {}, "outputs": [ @@ -438,13 +792,13 @@ " ], dtype=object))" ] }, - "execution_count": 12, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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ymvmlz4QtZTYqhnYCRcGq6zgyK/jtxy0Mv6b+iy1CCNEchISEYDAYyMnJqdWek5NDRESEm1IJIYQQQoiG8F3KFp5J+AkdUFB4rucorm7Vu07bljjKqqezKtzBtuI9R0xn1c2/A30Du9M3qCtBHgEn3F+wOVBupjdCmdbcWiN34O81WeTvSwghTl+nTp1IS0sjKyurZq3QdevW1erTu3dv5syZQ1hYWJ2mny0oKGDfvn188sknnH/++QD88ccfR/Tr1q0bffv25ZNPPmHmzJl88MEHtb5vNBoZNmwYw4YN45lnniEgIIAVK1YwZsyYUzrXZlP88PDwqHXTzOFw8OOPP3Lvvfce8YRHfdmTmMmCdburH0OA6gJIlDefT1vO0Kv6YDDIevJCCPFvHh4e9OnTh+XLl9es+aFpGsuXL2fy5MnuDSeEEEIIIc6Y7KoSnk34uea2to7Os9t+5tywNkcdAaLrOmmVWWwq2s6moh0klh2qdVM80ORPn6Dq6ay6+3fEbPBooDMRZ1KUJQxFV9CVv/+uFV0h0nL0J42FEEKcnGHDhtG+fXtuueUWXn/9dUpLS3niiSdq9bnxxht5/fXXufLKK3n++edp0aIFKSkpzJ8/n0ceeYQWLVrU6h8YGEhwcDAff/wxkZGRpKam8thjjx31+BMmTGDy5Ml4e3tz1VVX1bQvWLCAgwcPcsEFFxAYGMivv/6Kpml06NDhlM+12RQ//u2nn36ioKCA22677bj9jrZIbl1t353+d+HjL4pCdkkFfyzczoWX9zyZyEII0WyUl5dz4MCBmq+Tk5NJSEggKCiIli1bMmXKFG655Rb69u3LOeecwzvvvENFRcUJr9knEh8fT3x8PC6X63RPQQghhBBC1LOU8kK0fz/Rr+ukVhTWFD8cmpPdpfvZVLiDTUU7yLUV1Oof5x1D38Bu9A3qRmvvGFRFHjpsblzlRopWhOI/NLdmzY+S30JxdTSCLPMnhBCnTVVVvv/+e+644w7OOeccYmNjee+997jkkktq+nh5ebFq1SoeffRRxowZQ1lZGdHR0Vx00UVHHQmiqiqzZ8/mvvvuo2vXrnTo0IH33nuPwYMHH9H3hhtu4IEHHuCGG26otVh5QEAA8+fP59lnn8VqtdKuXTtmzZpFly5dTvlcFV3X9RN3a3ouvfRSAH799dfj9nv22Wd57rnnjmgvKSk54ZCePYmZ3P70zNoFEF3Hd3sebWPDiP9lyhkbdSKEEI3ZsdZhuuWWW/j8888B+OCDD3j99dfJzs6mZ8+evPfee/Tv379ejl9aWoq/v3+druVCCCEaL7meC9G8ZFeVMGzJ2xhUFx4GF3aXAU038v2QO0mvSmVT0Q4SivdQ5bLWbGNSjNXTWR1ev0OmPWqaTuZ6vnlXKpNf+Bb/6zLwDreSV+CDY2Y48U9dR+/Osui5EOLsYrVaSU5OJi4urlahoCk7dOgQbdq0YePGjfTuXbepL/+tru9Lox/58dhjj/Hqq68et8+ePXvo2LFjzdfp6eksXryYuXPnnnD/jz/+OFOmTKn5urS0lJiYuv0w7dQ+ilEDO/Pj+t3oJgXFCaqmYPT3JHlvFutX7GbARademRJCiKZq8ODBnKi2PnnyZJnmSgghhBDiLBLh6U+0j463paDmiX5fox+P73ix1oiQAJMffQK70jeoejori0Ee9z+beJmMFLXXMJpMeGNFsWiUtNHwNBrcHU0IIcRpcDgcFBQU8OSTTzJgwIBTLnycjEZf/HjooYe49dZbj9undevWtb6eMWMGwcHBXHHFFSfcv9lsxmw+9Q9SHfu3ZHbaPlwqqE4dc7FOZRt/zIWVzI5fTv+hnWX0hxBCCCGEEEKIs96mgv14W/L+uWwm5a7qqadjvVrUjO5o49NSprM6i+3MyqaqBdgc1besTAYXFbE6O7Oy6dQ+ys3phBCi8fvmm2+4++67j/q9Vq1asWvXrgZOVG3NmjUMGTKE9u3bM2/evAY5ZqMvfoSGhhIaWvdFrXRdZ8aMGYwfPx6TyXQGk0F2cRlP/7gUeyCgALqCywMMaRpau0D2JqSS8OcBep3b7ozmEEIIIYQQQgghGruFmVuOWDYT4MH2t3NeSN+GDyQapUylEhSw1xQ/NFSDTpZS5eZkQgjRNFxxxRXHnFb8TN8vP566zBJS3xp98eNkrVixguTkZCZMmHDGj7U1JRO7D+gGHd2go7gUnL4KLrOOw9cDW6Q3c6Yuk+KHEEIIIYQQQoizmq7rrM/NwMNSe9lMFZVOvm3cF0w0OoPbt+GtfWv/Ln6oLhRV48L2rU+wpRBCCABfX198fX3dHaNRaHbjSD/99FMGDRpUaw2QM0U3gstTwx7swhFY/afLU8NgdQJgjfFl845U9mw5dMazCCGEgPj4eDp37ky/fv3cHUUIIYQQQvzDvtIckstLcbj+XrdBReXuNjfIIuailiinBwGrCrDbq4sfRlXDO7WSKKeHm5MJIYRoappd8WPmzJmsWbOmQY4VHeyH01dDMWgYTC4Ug4bTVwOXhq9iAEWhsk0AX32wrEHyCCHE2W7SpEns3r2bjRs3ujuKEEIIIYT4h0UZOzGpLjyMLgAean8H0/r8j2Hh57o5mWhsMvZn4b2jAme5AV2vHinkqbnIPJDt7mhCCCGamGZX/GhIFZoDo6cDr6AqPAOseAVVYfR0UNTTA3t+Jf5eZjSLkT/Sstm/M93dcYUQQgghhBBCiAan6zqLMnbhb6les6GHf0cGhfSRER/iqDwi/Ci/MA6DXa0ZKaTFGTGFyxQuQgghTo4UP06Dj9mI2cdeM1+pooDZx45u0sntayZE8UABHCGevPnOr27NKoQQQgghhBBCuMOu4kzSKgvxt9gAGBI20M2JRGNWblRAVVAdCnZHdfHD6O2kTO5gCSGEOEnyo+M0VGm2Wgu1weGF2ywunD4q2wJKuaBbHADbSktYtzax4UMKIYQQQgghhBButChzF54mByaDC0+DhXOCerg7kmjEWoYGoCoKqp2/Fz23OPHWlRNsKYQQ4nTkpReQ8NtO8tIL6nW/9913H7GxsSiKQkJCQr3u+0Sk+HEaWvkEoaJgVF14mewYVRe6Dk6Ljqbq2IIMrLNlE2QwgEHl2Q8XYnc43R1bCCGEEEIIIYRoEDVTXpmtAJwb3AezQRauFscWEeDL09cNQ7UrNcUPo4cLZ1GVm5MJIUTTous6VRXWOr1+nLqIG2Pv4eGLnuPG2Hv4ceqiOm2n6/oJc1xzzTX88ccftGrVqgHOujZjgx+xGYnw9Ofmdu1ZV7QaRQFdh+xyX2wWD5z+LkxFBjLNNgYOjKDwtzRKgDc+Xsp/J410d3QhhBBCCCGEEOKM21aUTnZVMe1Cqqe8GhzW382JRFMwZkBXft66ix32QgBMRo3E9Cx60t7NyYQQoumwVtq4wvfmk95O13Q+mPwpH0z+9IR9fyr7Ck9vy3H7XHDBBSedob7IyI/TUGArYkPxH7XW/IjwKcPHrwrFouHy0dB1jbUl2fi1qP5H8PMfu1mz5aAbUwshhBBCCCGEEA1jYcZOfM1WVEUnwhJKR9827o4kmojOkeE47IdHfqguDuTlujmREEKIpkZGfpyGTGsuOrWH9igKeBhdePrZqHBZUBwqBhskh7nwz7Vh1M08P3UhX792C6FBPm5KLoQQzVN8fDzx8fG4XC53RxFCCCGEOOtpusaSzN34W6qnvBoc2h/l3wtnCnEMLYIDsCcdXvPDoJFWUr9z0AshRHNn8TLzU9lXJ+yXn1HIHZ0fQNf+vs+tGlSm73qbkOigEx6jMZORH6chyhKGQu0PbgoKXqoPilHDw9uO019HVzUASrt44jS5KK2w8tzUhbg0zR2xhRCi2Zo0aRK7d+9m48aNp7T9vi3JfPfhUvZtSa7nZEIIIYQQZ58tBakU2ovxMjkAuDBUprwSdRcV5Iej0gSAUdXIsZW4OZEQQjQtiqLg6W054SumfRQPfnQ3qqG6VKAaVB6Ydhcx7aNOuG1jf6hBRn6chmBzIBPbjOOjpJloh0eAtPVpxX1tRnPPupl4eDlxOQzYg4yYc0A3qJS2NeKbrLF5Vyrf/LyR8VfKhz8hhGgMXps0gxW/7kRRFHR9EcMu78b/vX+ru2MJIYQQQjRZCzN24W+xoijQ1a89YZZgd0cSTUhkoB+6TUXTQFWhyFzm7khCCNFsjbzjIvqO6EnmgWyi2kYQ2qJ5/MyWkR+naVj4uUzr8wJ3xl2PAuwvP4SnycpNrauLGr7+LhSDhj1Ew+AC3ahQ1krBaYaP565h5/5M956AEEII9m1Jril8QPXTEcsW7JARIEIIIYQQp8ipuViatQt/8+Epr8IGuDmRaGqignxRHCp2Z/Vzu3Y/Bzarw82phBCi+QptEUyPwV3qvfBx991306JFC9LT0xkxYgRt27at1/0fjxQ/6kGwOZBLIi/giqhhAHx8cBZ3tx9Ee78wnDjx93ehm3Rs/k68dAO6h0p5KwW7qvPMB79SXmlz8xkIIcTZbef6AyiKgstiwB7sictiQFEUdm1Icnc0IYQQQogmaVNBCpWuEjyMLsyqBwOCe7o7kmhifCxmzE4Vh9MAgBKsk5tR5OZUQgghTtZHH31Eeno6TqeTnJwcDhw40GDHluJHPRobczlRljAK7SXMSvuJ1/tcg1k14jTZ8fBw4vKCcrMdk1XDZVaoaK2Snl/Ca9OXouv6iQ8ghBDijOjavy1V0b6UdQ+lsm0AZd1DqYr2pcs5bdwdTQghhBCiSVqYsRN/SxUAA4N742mwuDmRaIqCTBbsjuqRH6qvRnaaLHouhBCi7qT4UY/MBg8mtb0ZBYUVuWsp1/J5pOsIACx+DlSDC0eAjmYGQ6ULhxnKYhWWrNvHL7/vcnN6IYQ4e2k+ZmzR3vDXQl2Kgi3am5QcWVRRCCGEEOJkOTQXSzN34WuunuVgiEx5JU5RrG8gNnt18cNo0UhLy3NzIiGEEE2JFD/qWUe/NoyMvBCADw/M5IqYrgyJ6ICm6Pha7IBOZZiGyQmqTcPprVDWUuGNz5eRklno3vBCCHGW2r47HRQFpwdY/cHpASgKU99aSFlJpbvjCSGEEEI0KevyDqKpJRhUnVCPIDr7Ndzc3qJ56RASit1uAsCkahzIzXFzIiGEEE2JFD/OgBtbXkmYOZh8eyFfp/zA/3peQajZB5eXho9uAwVKY3W80qwoLh2Hn0JBiMaT7/2M3eF0d3whhDjrdO/cgoowKGmvUNFSrf4zDCozinnr4TkyNaEQQgghxElYlLELf0v1QucXhvVHVeTWgzg1McEB2O3Va34YDS5SivPdnEgIIURTIp9AzgCLwcx/2t4EwOKc1aRb03mlzxgUgDAXZocD3QhFvT3w31qGAtiCFLZXFTB11mp3RhdCiCYtPj6ezp07069fv5PazuGlYg1T0c0aup8d3axhDVNwBZtZt2wX332y8swEFkIIIYRoZuwuJ79l78DbZAdkyitxeiIDfXFWVU97ZVI1sqtK3ZxICCFEUyLFjzOkm38HLg4/H4APD3xDz6Bobmt7LgAeQXYMLg3NAoUDvGi5p/pDYVW4whfrt7Bmy0G35RZCiKZs0qRJ7N69m40bN57UdltSMiDMiqltOR4xVZjalkOYjaoeQWgGhRmvL2THhqQzlFoIIYQQovlYk5eEwViKokBH3zZEWELdHUk0YVFBfjiqqqe9Mqg6BWqZmxMJIYRoSqT4cQbd3Go0IR6B5NjymZX6M/d2GkIHn3B0C/hgRdF1HEGQ0ULnspCWAFREKTz25S/kFZa7Ob0QQpw9fINMGENt/1zvHGOojSrFjmlgDC5N4+V7v6YwT540E0KcvVatWsWoUaOIiopCURR++OGH4/afP38+w4cPJzQ0FD8/PwYOHMjixYsbJqwQwm0Wpu+omfJKRn2I0xUZ6Ac2Faer+oO6PdBJRWmVm1MJIUTzlJdVzLa1B8jLKq7X/VqtVkaPHk379u3p0aMHw4cP58CBA/V6jGOR4scZ5GX0ZGKbGwH4Jes3Dpan8Fb/azFqKq4InWhX9byVFW0MbNl+kGu7dwFFISfEwf1Tv8elae6ML4QQZ40Spaq64KG68DLZMaouFAVskRoFDgf0jKAwr4xX7/8Gl9Pl7rhCCOEWFRUV9OjRg/j4+Dr1X7VqFcOHD+fXX39l8+bNDBkyhFGjRrF169YznFQI4S5Wl4M1eTsxG10YFSODgnu7O5Jo4oJ8PDE4FBzO6qmvlDCd7PRCN6cSQoimQdd1rJW2Or0WfLWGW85/kcdunMYt57/Igq/W1Gm7uq6Retddd7Fv3z62bdvGlVdeyYQJE87w2VczNshRzmK9AjszNGwgK3LXEn/gK97o8V8ebHMRrycvpTisjHblEey3l3GoG4wpUzm3ZUvWpKay2ZXHW9+u5OGxQ919CkII0ez1DY3B71AVkb5lKAroOmSV+ZJv8sbmq1NSpuDVJpBt65L48q3F3PbIpe6OLIQQDW7kyJGMHDmyzv3feeedWl+/9NJL/Pjjj/z888/06tWrntMJIRqD1Tn7MXtUT0s0MLgXXkZPNycSTZ2iKPirFuxOA55mB0qATnZaIW06R7s7mhBCNHq2KjtXdX3ipLfTNZ34Z74n/pnvT9j3+50vYvEyH7ePxWLh0kv/vo8yYMAA3njjjZPOdSpk5EcDuCX2agJN/mRac5mb9gu3dBtEq+IAMECpuRDvKhUM8G7BVp6/fCitfQPQDQqf7k1gRcJ+d8cXQohmL9rHkyi/slrTXkX6lmEyaJS3VHF4QmWIBVuEN3OnrWDd8l3uDSyEEE2QpmmUlZURFBR0zD42m43S0tJaLyFE07EwfQd+ZpnyStSvGG8/7PbqZ3cNPhqZafluTiSEEOJ0vPvuu1x55ZUNciwZ+dEAfIxe3N3mBl7ZO42fMpcxMLgXrwwcw03rP6PCz8457SP5/UA+LgtcPecbFt5+G5e8/zklJjv3/fQLC1vcSkxIgLtPQwghmq1Ma+4RbYoC5pYVOBIDKItV8EvSsbbyQ7W7ePOh2bz/8wNExAS7Ia0QQjRNb7zxBuXl5Vx33XXH7PPyyy/z3HPPNWAqIUR9qXTa2VS0g1BfHV+jL139O7g7kmgm2geHkH64+GEyaaRkHfnZXQghxJHMnh58v/PFE/bLzy7hrotfR9f+nsJKVRU+WvIwIRH+JzzGyXjppZc4cOAAy5cvP6ntTpWM/Ggg/YK6c35IPzR0PjjwFZ3aRjIwpRVosNGaQi/NCzTIMVTx1KplzLn9ekwuBbtR59rpM6mw2d19CkII0WxFWcJQUGq16Tq4PHT0YDu6UaEsTkEzQFXbQIp0Fy9O+hK7zeGmxEII0bTMnDmT5557jrlz5xIWFnbMfo8//jglJSU1r7S0tAZMKYQ4Hb/nJOLpUQ7AsPCBGBS53SDqR2xwIHa7Caheo+9QUYGbEwkhRNOgKAoWL/MJXy1ah3H/i9egGqrvi6gGhftevIYWrcNOuK2iKCdI8bc33niD+fPns3DhQry8vM7Uadcin0Ya0O1x1+Jv8iWtKot56YuYPOoifNZX/xWkxRYTuLe6wPFLRiJ/5KTy5qWXoDh1CjUbN3w6G4dLFtkVQogzIdgcyMQ241D/UQAx4Y9LN2AKtaF5OtE8FMpaq2gGqOwQxL6kHD56/kc3phZCiKZh9uzZTJgwgblz5zJs2LDj9jWbzfj5+dV6CSGahl/St+DtUf077ZCwgW5OI5qT6CB/HDYDACZVI6uy2L2BhBCiGRoxtj+fr3qCV2dO5PNVTzBibP963f9bb73FrFmzWLp0KQEBAfW67+OR4kcD8jP5cGfcWAC+z1iMdxs4p7wVpkyFSs1BYG8jnukaAM+sW05EjB+3tO0Oms7e4gLun7cATdePdwghhBCnaFj4uUzr8wLjYq4AwKWUEmo2oBp0jNFWNFXDaQFrawOaQaG8YxALvtvA8u83uzm5EEI0XrNmzeK2225j1qxZXHbZZe6OI4Q4Q8odVnaW7kJRIMYzmmjPcHdHEs1IZKAvjqrDIz8MLvJdFehyb0QIIepdaGQA3Qe0JTQyoF73m56ezkMPPURxcTFDhgyhZ8+e9O9fv8WVY5HiRwMbGNKbAUG9cOkaU5O+5robziHgVyOqDXJ9q/AvKcdYApqic/Ov87jtinPoRQjoOsuSDvLqslXuPgUhhGi04uPj6dy5M/369Tul7YPNgVwdcwnnh/RDR6dTqBMVMJhdGKLs6OhUeuq42nigeRgo7xDEu09/x6F9WfV7IkII0QiVl5eTkJBAQkICAMnJySQkJJCamgpUT1k1fvz4mv4zZ85k/PjxvPnmm/Tv35/s7Gyys7MpKSlxR3whxBm0Imsv3uZKAC6JON/NaURzExXkh6vSiK6DqoA9xEVRfpm7YwkhhKijFi1aoOs6SUlJNb9PrF+/vkGOLcUPN7iz9Vh8jN4kV6ST3fIQbYJC8VtWPYSz4kIDITvKUa1Q6rRx+6L5vHX3lYQWVD/lMGPjFmZs2OLO+EII0WhNmjSJ3bt3s3HjxtPaz+1x1+Jn9CHXlsfIltVz0xv8bBDgQNd1Sryc6K1MaF4milr68L9JX1JZbq2PUxBCiEZr06ZN9OrVi169egEwZcoUevXqxdNPPw1AVlZWTSEE4OOPP8bpdDJp0iQiIyNrXvfff79b8gshzpyfMzZgMbpQUDkvtK+744hmJtTPB8Wu4nQdvoUVoZCTVujeUEIIIZoEKX64QYCHH3fEXQvAvPSFDL+xPZ77DPgf8EBXoOpCHf8dDnDB7uI83tr9J6/eeCle2dVTYr287Hd+3rXXnacghBDNmp/JhztbXw/AIetuegaGoihgDLeCsfpaXOTnwhlhwOlnZr/q4J3H5srweyFEszZ48GB0XT/i9fnnnwPw+eefs3Llypr+K1euPG5/IUTzUGKv4mBlIgDd/DrhY2yYBUzF2cNoUPHRTdidRgDUEI1sKX4IIYSoAyl+uMn5If3oE9gVp+5kS/g6QsJ9sSzUCcILV5CKMboSvzQVdJi3fydJniWM790LS371jbVHf17MHwdT3HwWQgjRfA0M7kX/oJ64dI0wv2J8jWZUo47SyorirC6AlITqOAIUHMGeLNmZzE9frnFzaiGEEEKIhrU0cxc+5ioALo++0M1pRHMV6eWD3VE9Y4bqp5GdLsUPIYQQJybFDzdRFIW7W4/Dy+BJUkUqXW/zRnUohC21YEChqocRn9xiLDnVf0XP/rmCfhfF0cMjGI9iHaemMfm7n9mRle3mMxFCiOZJUZTD0xR6kVqZwQ1tYwEweTrRWtpRbNXF6PKWKg5vsEX68P4ny9izVQrTQgghhDh7LMhcg1HV8VAs9Azo5O44oplqGxiMzV49HbjBUyczPd/NiYQQQjQFUvxwo2BzALfGXg3AzoBt+MYaKNlRzmXmrgCUDVPpWGXBVKygoTP5twX8544LCclTMZXrVDoc3DnnBw4VFrnzNIQQotkK9PDntthrAPizcC3j4roDYPK34QpxoFh1NF3H2taI0wIVLXz478PfUFJY4c7YQgghhBANoshWQZbtEACDgvtgUAzuDSSarTZBwTgOj/wwqS4OFeS5OZEQQoimQIofbjY0bCA9Ajrh0B2E3FwBik7Rd8V0MUegmxVSI7PpYA/EUAXFdivP7ljJ5JsuxCdFx1ilU1hZxe2z55NX3jhutGVVlPFnZipZFWXujiKEEPXiwtD+9AroglN3UsYBOvpFoKhgjLShW1yodrBrGvb2JlweCtnBHvz3gS9xuTR3RxdCCCGEOKN+Tt+Ct4cNgCujh7g5jWjOWgT7Y7dVj/wwGlxkVBS7N5AQQogmQYofbqYoChNbj8Oimin0ysfr/CoOJOZwZ+B5mGwK9hgDnkGV+Gd6oDhgb2Eey/VULu7fHt9DOianQnpxKbfMnMdv+w+SXeq+osPsfdsZNPsjbvh1DoNmf8TsfdvdlkUIIeqLoihMbHMDngYL+8sPMSYuBotqwmDS0OPsoGkoTqjSXdg7mNBMClurypn6+gJ3RxdCCCGEOKMWZ/+JooCfMYiW3lHujiOasahAPxxV1Quem1SNAq0Sl9Pl5lRCCCEaOyl+NAJhlmBujr0KAPPFJSiBTpbM2c5/Qs4DYE94Ppef2wavNAPo8OuhRAIGBBId6Id3kgsjCgfyC7n72x8ZHP8p3ybsbLDsuq6TWlrMF7u28NjqxWh69Rz4mq7z3z+WyAgQIUSzEGIOYnyr6uv0guwlPNa9+slGk6cDe6wDQ5UOLqhUXNjaGtFMKrM37GXFIikCCyGEEKJ5yreWU+zMAGB4+CA3pxHNXeQ/ih9GVcMeAHlZxW7NJIQQzU1ebikJWw6Rl1ta7/u++OKL6d69Oz179uT8889n69at9X6MozE2yFHECV0cfh5/5m9mV+l+vK4pYusnydxx52DmbdpARgcbi63bODeuPauzUqmK0nh/+1qeuuFCpk/7A6eugaIA1UWHpxYu4/zWrYjw8633nAVVlWzLz2ZbbhYJeVlsy8umyFZ11L4uXedQSRGR3vWfQwghGtqw8HNZk7+ZnaWJ7Chbz5iYXsxP24o5wIYtSsWSasTpr1DpoaHHqngmw7MfL6JNhwhaxYW5O74QQgghRL36NmUNFpMTdIVRURe4O45o5iIDfdEqDWg6qAroUZCdVkhETLC7owkhRKOl6zpWq6NOfZcs2sEH7y5B13UURWHy/Rdz8SXdTridxWJCOXxf+njmzp1LQEAAAN9//z233nor27Ztq1O20yHFj0ZCVVT+0/YmHkx4AVrb8Dingrmz1vHkBZdx78F5VIQ70NtXEZ7qTWZBOfZgnbcS13LxsLb8kpZUa1+arrMhNZ0runY6rUxVTgc783NqihwJeVmklZUc0c9DNdA2IJg9hbno/2g3KAqx/oGnlUEIIRoLVVG5p+2NPJjwArtK93NbbE82FwaRUlGIMdyGowJMBUacgQpVPqBHK5Bh4O7Hvmb+jEl4eZndfQpCCCGEEPVmZf46UCDC0gJfk4+744hmzmwy4qmZcDgNmE0ulHDISSt0dywhhGjUrFYHoy5546S303Wd999ZzPvvLD5h358X/R+enh4n7PdX4QOgpKSkTgWT+iDTXjUiEZZQbmx5JQCWkSX8sW0ncR2i6fGHLzh11hYf5OJLY7FkqxgqoNxhZ6MpG71WyaHaC0tWsjU9s87HdmoaewpymbV3O4+tXszI+Z/T5Yt3uWbBLF5Yv5KfD+6tKXy08Q/i6nZdeH7QRfx05U3svOU+Fo65hVfOH8E//9k+N/AiGfUhhGhW/nmdnp32M8/0GIFRMWA0u9BinLi8NIwloADWIIWqMChRde6c8gW6fuS1WgghhBCiKcqsLMKq5wFwRfRg94YRpyQ+Pp7Y2FgsFgv9+/dnw4YNx+3/zjvv0KFDBzw9PYmJieHBBx/EarU2UNpqoSZv7I7qZ3iVQI3sdCl+CCFEUzJ+/HhiYmJ46qmn+OqrrxrkmDLyo5EZGTmYPwu2sI+DWK4qYu7sdUwYO5ykeXMoHm3mu/xNXHZ+Txb8mUhlW4208hJMFqBKRVFU0HVUBxRj5eZv5vHaqBFc2rlDrWPouk56eSnb8v6eumpHfg5VziOHQYV5edMrNJIeh1/dQsLxN1uOmt2jSMU30UBpnAtMsONQNnQ+E++SEEIcXXx8PPHx8bhcZ27xw+rr9Gb2lSWzNG8pj3S5mJd2LsTD205VKxXPRAVDmYrTF6rCVRSnxsHCMp54cT4vPXn1GcslhBBCCNFQvj60DKOqgW7korBz3B1HnKQ5c+YwZcoUpk2bRv/+/XnnnXcYMWIE+/btIyzsyOlaZ86cyWOPPcZnn33GoEGDSExM5NZbb0VRFN56660Gyx3nH0CSwwjYMPhqZKUVNNixhRCiKbJYTPy86P9O2C8/r4zbx39c66FNVVX49Iu7CAk9/oPtFoupznm+/PJLAL744gseffRRfv311zpve6oUXR5FraW0tBR/f39KSkrw8/NzS4b0ymymJLyICxe2+cF89sjD/HfcVLYOK8fayUh733DY7EViYQGVrTU0RceSCeZiBVMJmKwKoQND2Fda/RTEf84/h56xkYfX6shmW14W+dbKI47rY/Kge2gEPUMj6XH4z4ijjNzQdZ3C8kqSc4s4lFvEodxC9mXksX5/GgC2YA1rhIahCtbcdDeRge55H4UQZ68zfS1Pr8zm/7a9hEN3MrnNzcw/dIgV2fvQnAq2LAteh0xoZgWnN6CDT6qGuRTuGHUOE8adX+95hBCiuWoMn82FEEe69o//oqnFtPHqxGs973V3HHGS+vfvT79+/fjggw8A0DSNmJgY7r33Xh577LEj+k+ePJk9e/awfPnymraHHnqI9evX88cff9TpmPVxPX/phxUsUJcSGVJCUZUnXT5tyfsz7z+lfQkhRFNhtVpJTk4mLi4Oi+XoD6TXh4W/JPD2GwvRNB1VVXjw/0Yy8rKeZ+x4np6epKenExx8ams31fV9kZEfjVALrwhuaDmKr1N/wGNkIXN+Ws3YiReR+r+55DxqJJEcRp3fg/T5JbjSHVTF6FijwBqlV99kO6RTkZqHb1sLBVoVb+/7E/bVPoZJVekUFFZT5OgRGkmbgCDUf8y3ZnM42Z+Vf7jAUV3kSM4tIiW3iDKr7Zj5TcUK1jBwecLyg0nc1KfXmXqrhBDCLVp4RXBdzKV8k/oTMw7N4/kuD7OjKJM8yjCGOLBVqpizDFhMBqweGhUxKuohjU9/2kCrliEMP+/01mQSQgghhHCX/aWZOJViVGBszHB3xxEnyW63s3nzZh5//PGaNlVVGTZsGGvXrj3qNoMGDeLrr79mw4YNnHPOORw8eJBff/2Vm2+++ZjHsdls2Gx/3zcoLS097exxwUHYc6pvYxlVF+llRae9TyGEENVGXtaTvv1ak5FRRHR0IKFh9ffgUXFxMZWVlURFRQHwww8/EBwcTFBQUL0d41ik+NFIXRF9EctS15LtmcMK42/cNPRZIt8NwD6zlLw7Pfk5Zxs3XDKQ2Qt3gQ41i20oUB4H1Y1VNau6KE4IM3tzW68+DIiKoVNQGBajEV3XyS+t4FBeEfN2p1cXOfKqix2ZhaVoxxgYpCgQGehHXFgQsWGBBPl48sHCP9F1UF0KphIFR6DOqvxD3IQUP4QQzc8VUcNZW7CVgxVpzEn/kdf6jOH2P7/A5OnEGmnAVaWgFCv4hpooUx2UtVTwS9Z5Nv5XwkP96N4h2t2nIIQQQghx0r4+tBhVAVX3pE9wR3fHEScpPz8fl8tFeHh4rfbw8HD27t171G3GjRtHfn4+5513Hrqu43Q6mThxIv/973+PeZyXX36Z5557rl6zRwb6Yk+pvo1lUjUKqcJaZcdSh4V2hRBCnFhomF+9Fj3+UlJSwrXXXktVVRWqqhIaGsqCBQsaZNFzKX40UgbFwMPdJ/DQ1pdQ21fy8aofufauwcQ/8z2hmxXy+ugstm2nQ4dwNpFzxPZxlgCK9pShFmv4e3lSEOKi2GVjVtl2yuKsfFuyrXoUR14R5Vb7MXP4WDyIDQskNjSQ2PCg6j/DgmgZEoDFo/Y/n2Bfb56fuwxN1/EoVHEEulialkRuZTlhXj71/h4JIYQ7GVUDk9rezCPbX2FD4TbOC+nL3e0vYFriKsw+NipjVPycBux5DoKiLBRipayljl8yPPDiPGa8cjOtos78Uw5CCCGEEPVpZ+kuUKGbXzd3RxENZOXKlbz00ktMnTqV/v37c+DAAe6//37+97//8dRTTx11m8cff5wpU6bUfF1aWkpMTMxp5YgK8sNZdXjkh8GFM0glJ72QVu0iTmu/QgghzqxWrVqxYcMGtxy7WRU/EhMTefjhh1mzZg12u53u3bvzv//9jyFDhrg72imJ9Ymmn7MvGw0b2eCzjpsuGE7gB77os8uI6BVBtr2clu2ckKhXD8U4TAGuj+5OiquAlQlJFFlt6HkK+EN2eTnTt23BowRUZ3V/VVGIDvYjNrR6FEdcWHWBIzYskGBfrzpX4cYM6Mqgjq1Iyy/mUG4Rj25egstT58Gff+GbsWPPwDskhBDuFevdgquiRzAvfSHTD87hzR5PsD4/ma2FaVj8bJRFgm+KBxVZVkJivMinkrJWGkqyk8nPz+XzV24mOMDb3achhBBCCFEnG/L3gFqJrsPNcSPcHUecgpCQEAwGAzk5tR+izMnJISLi6EWEp556iptvvpkJEyYA0K1bNyoqKrjrrrt44oknUFX1iG3MZjNms7les0cG+uGs+GvaKx1XmEpOmhQ/hBBCHNuRP6GasMsvvxyn08mKFSvYvHkzPXr04PLLLyc7O9vd0U7ZA+feiJJnBi8Xb237gqvvvLB6Cqtv7JhVIwnFqXg5XNWzXAHoYMlQmfrTn/ySsI8KnOhGBdUF5kIdo0sBFbQglZsv6cP3j45nw2uT+eWJ24m/azQPj76QawZ1p2/bFoT4eZ/08KOIAF/6tY3h2kHdua5tVwDWFKbyxcpN9fzOCCFE43BNi0uI8Yyk1FnOlynzea3P1fiaLBhMGsZAJ9ZwJ6BTnlFJoKcnLk+VspaQV1rBAy/No6Lq2KPvhBBCCFE/sovL2LA/jeziMndHadLmpi0DwEMJJM4n0s1pxKnw8PCgT58+tRYv1zSN5cuXM3DgwKNuU1lZeUSBw2AwAKAfY6rsM8HX04zBZsSlVd+n0CMgO72wwY4vhBCi6Wk2xY/8/Hz279/PY489Rvfu3WnXrh2vvPIKlZWV7Ny585jb2Ww2SktLa70aE4uHB5dwMboLDpmTCRrhiW+AF8VbChmtdQZAjbThkwHeySq+iQY8ilW6t4rgliF9eGbsMGZMvpYpgwfin6fgt9eFr82IU9f4ZPNmfjuUjOnwh5b69uzwYXipJnQTvLLidxZs2nNGjiOEEO5kUk1ManszKgqr8zeSYU3luZ6jqr/n6cAZ7MIUZQAXkO/Ex8MDp49KWQzsT8vniXd+wul0ufckhBBCiGZs/rqdXPL8p0yYOo9Lnv+U+euO/fuhODaXrnGwcj8A/YNkXcembMqUKXzyySd88cUX7Nmzh3vuuYeKigpuu+02AMaPH19rQfRRo0bx4YcfMnv2bJKTk1m6dClPPfUUo0aNqimCNJQgoxd2Z/UxlVCNnDQpfgghhDi2ZlP8CA4OpkOHDnz55ZdUVFTgdDr56KOPCAsLo0+fPsfc7uWXX8bf37/mdbpzUJ4JN158EawLBODT1LmMnHAOAJkfJjEwqHX132KHSlSzC8WgoyoKb9x6OQ9dcQFXD+hGnzYtuPXKAXz07PVEBvliSrTjVVS979dWrObphctxuOr/xpvFaOSWrtUfim1BOk/PWsLq3cn1fhwhhHC3dr6xXB51EQAfH5zFeWFxXNuqD4oCZl8rBYE2/KMsVFU6CHWY8VBVHP4qFVGwbnsKL3+ytEGfmhNCCCHOFtnFZTXrEgJous7zc5fJCJBTsCRrPYrixKUp3BR7sbvjiNMwduxY3njjDZ5++ml69uxJQkICixYtqlkEPTU1laysrJr+Tz75JA899BBPPvkknTt35o477mDEiBF89NFHDZ69pa8/Dkf11Feqn0a2FD+EEEIcR7MpfiiKwrJly9i6dSu+vr5YLBbeeustFi1aRGBg4DG3e/zxxykpKal5paWlNWDquvH09GBU2HBcOUaqlCoKLszG08dMSmIO11V1wawYwUtH71GJNqCMy0a1ISLA94j9dGkbyecv3cy5PePwTNfwytQAmJOwg7vm/kCZ1Vbv2W/q1BNVUXD56NiMLh76fAFbD2bU+3GEEMLdro+5nEhLGIX2Er44NJ9Hu46gjW8oqgEsvjbS/MrwDTKTlVtGd/9wFMAWrFIVBr+u2sXH3/7p7lMQQgghmp2DOQU1hY+/aLpOWn6xewI1YT9n/g6AtxpOqMXPzWnE6Zo8eTIpKSnYbDbWr19P//79a763cuVKPv/885qvjUYjzzzzDAcOHKCqqorU1FTi4+MJCAho8NytA4OwHS5+GL00MjMKGjyDEEKIpqPRFz8ee+wxFEU57mvv3r3ous6kSZMICwtj9erVbNiwgdGjRzNq1KhaTyz8m9lsxs/Pr9arMRoz+hxcP4eia7C2ZAvn3NsWgO8+X4Vdd/7dUYGfyraQXVVy1P34+3ry+sNXcc/15+FdqOB7SEPVYU1yKtd/OYeMkvqd9quFrz8XxbQBILStD1aHk8nTfyQxM79ejyOEEO5mNnjwn7Y3AbA8908Sy5N4o+81eKgGjGYXRh8nRSE2DCaVnYlZDG9VfW2sClexBsHn36/j+2Xb3HkKQgghRLPicLr4auXWI9pVRSEmJKDhAzVhFc4qsm3VDwoODTv6uhBCNIQ2wcHY7dXFD5PBRWZJkZsTCSGEaMwaffHjoYceYs+ePcd9tW7dmhUrVrBgwQJmz57NueeeS+/evZk6dSqenp588cUX7j6N0+bv78XIXgOw/+EDwP7u+zAHGknMz+bfE6Vouk5qxbGHfqqqwvgr+/P+k9cSafDG94CG6oT9+QVc+/kstmfW7wLx4zv3BCDXo4pucRGUVdm456P5pBccvUAjhBBNVWe/toyMuBCAD5O+Icbbn0e6jgDAw9tOlacN/w6e6Ois3pzE5W3bAVARpWDzgzc+W87PK3eyeVcquQUyHYcQQghxquxOJ1M+X8CavYcwqAqqUr1AsqooPH3dsKOOlBfH9kP6byiKjt1p5NqW57k7jjiLtQjyx2E7PPLDoFFmslNWUunmVEIIIepixowZKIrCDz/80GDHNDbYkU5RaGgooaGhJ+xXWVn9w05Va9dzVFVF07Qzkq2hXX3dOfx480aMna0Uh5QS81g0FS8Vgw4otfsGe3ifcH+9O8fwxcs38/T7v7BxfxplrRTyqeSmr7/ljStHcnGHtvWS+7zoWOL8AkkuLWLwhW2xWp3sz8rn7mnf8eV9Ywn2PXHWk5FbUEZadhExEYGEBcsvNUKIhnVjqyvZVLSDPFsh36T8yB1x17E29yDLs/di8bWRXFRMn56RHEjIZ93WFIZ2bsWKtBTKW4B6SOeljxYD1TdnHr1zOFcM6ebmMxJCCCGaFpvDyZQZC1i9JxmzycA7t19Bm4hg0vKLiQkJkMLHKViWsxaAYFML/Dw83ZxGnM2igvxwWA+P/FBdVAap5KQV4uvv5eZkQgjR9J3Je6qHDh3ik08+YcCAAfW63xNp9CM/6mrgwIEEBgZyyy23sG3bNhITE3n44YdJTk7msssuc3e8ehEe7s/Qwd2omhcIOmRGZ+AZpxE0x4r6r+rHe3tXoOknLvoEB3jz7n+v4fbL+uN/UMdUpmN1Orn3u5/5bP3mk16At8BWxI6SfRTY/h56qioK4ztXL3w+98BOpt41muggP9LyS7jno+8pq6q/tUZ++m0HV937CZNf+Jar7v2En37bUW/7FkKIuvA0WLinzY0ALMz+nT1lSTzf6wrCLb6oRh2zj50t9mxiWwdSbrWTnV5Kd99gMCiUxSrYvcHhDQ6DzqvTl8oIECGEEOIkWO1OHvjsZ1bvScZiMvL+HVdybsdYIgJ86dc2RgofpyCzModSVwG6DpdGnu/uOOIsFxnoh7Pyr2mvNJwhKlmpsu6HEEIcja7rVFkddXp9tySh1j3V75Yk1Gm7utw71jSNCRMm8P7772M2mxvgzP/W6Ed+1FVISAiLFi3iiSeeYOjQoTgcDrp06cKPP/5Ijx493B2v3oy9YQBLb92Bba0P5kHl+PxHw3Gfg3MCYhn7vxFUOOw8uGkuy7L28un+NdzZ/sQfTo0GlYljz6N7+yienforWXY7tmCFV5avIrWomCcvHoJRPXGdbFnOGqYlzURHR0FhYptxDAs/F4Cr23XhtU2rSSzKJ7mqmI8mXs0t789hb0Ye93/6Ex/efRVm06n/c3Q4XSxes4eXP15S06bp1TcOB3SPlREgQogG1SOgExeFDWJ57p9MPfA1b/b4L6/2uZrb1nyBydOJy2Eg2VhCcKAnB3OLGBzemsyiUvKNDsriAEUBXcc7Qyctu0iuYUIIIUQdVNkdPPDpT6xNTMXiYeSDCaM5p12Mu2M1ed+lLwegymHmsujebk4jznZBPp64Kk0AqIqOK1whJ/3Y034LIcTZzGpzMvS29056O03XeWPGct6YsfyEfVfMuA9Pi+m4fd566y3OPfdc+vTpc9JZTlezGfkB0LdvXxYvXkxBQQGlpaWsXbuWkSNHujtWvYqNC2XgoHZYF/thtnph87LhcYOD3YsPkLc4m66E80S3SwF4d89y1uQeqPO+B/VqzZcvj+ccr3C8MjXQdWZu2c5dc36g3FY9OkPXdcoc5SSVp7K2YCs/ZSxj+sE5PLvrXT5M+gb98AokOjofJc2qGQHib7ZwVdtOAHy5eystQwP48O6r8LF4sCkpnUe+/BWn6+SmJ3M6XazbdogXpi3isokf8uK0xUf00TSd9Jzik9qvEELUh1tixxDk4U+WNZc5aQvoFxLLxA4XAGD2tlGmWzF3NGM0qKzccZAB0TGg69WFDwBFoSJa4aP5a6iyOtx4JkIIIUTjV2lzcO/0H1mbmIqnh4mpd10lhY96oOkaaws2AxBjaY23qWGf1hTi3xRFwU/3xOmqvp2lhEN2mhQ/hBCisdq5cyffffcdTz75pFuO32xGfpxNrr9xIGv/3E/JbB8st1ZivMiJc52BD576DuUZhftfvIZrOvRmXsoWHt70HXMvvIsW3oF12nd4sC+v/3cU8T8uZUnSTlzRTpIMudz02yZahFoochRh1eo2TZWGRpY1j2Bz9bHHd+rFzL3bWXxoP9kVZXSMDuO9O65k4kfz+W1nEv/7dhnPjh2OoijH3KdL09i6O51l6/axcn0iJeXWmu8F+nlSVFpVq7+qKrQID6hTXiGEqE/eRi/uan0Dr+ydxs+ZyxkY3JuJ7S9gbc5BEorTsPja2FuSx4XnxpGwKp0l+w5CwL+uf4rClpRMJjw9k1emXEFMRN2u5UIIIcTZpNJmZ/InP7IpKR0vs4kP77qKXq2j3R2rWdhZkohNr8KlKVzZ8gJ3xxECgCgvX+wOA0aDhhqkkbNBih9CCHE0FrORFTPuO2G/vMIybvi/z9H+MYWVqirMev1WQoOOPxOFxXz88sLq1as5dOgQ7dq1AyA7O5u77rqLrKws7rnnnjqcxemR4kcT1KVrC7p2a8HOHenEZrckOyIVj7ts2Gd4oKervPfkPD5e+Sj7SnLYUZzB/Rvn8PV5t+Np9EDXdcqdFeTaCsm1FZBnLSDXVv3KsxWSay2oLm60hLCWtY+b9Y+aR5CHP6HmYMLMwYSag/AyevL1oR9qLbyu6AqRlr8Xq+8UHEa/8Gg25mQwc+82pvQ5j75tW/D6+Mt4cMbPfL9+FwHenjw4qvZUXZqmsz0xg6V/7uW39fspKq2s+V6gnydDzmnPRQM70KNjNL/8votXpy9F03RUVeHRCcNluhghziLx8fHEx8fjcrncHQWAfkHdOT+kH6vzNxJ/4Cte7/EYr/e7miuXTaXSw46Ht4PfCw5xUb84Nm5Oqz3y4y/BRpJS87n9iW94dtKlnNu7tXtORgghhGiEKqx2/vPx92xNzsTH4sGHd19Fj9god8dqNn7I+A2ASrsnQyM6uTmNENViAwLZ6TDiZXGg+rhk5IcQQhyDoignnJIKoGVUEI/eOfyIe6oto4JOO8M999xTq8gxePBgHnjgAUaPHn3a+64LKX40UdffOIgnH5vLoc/A4z5QQ8HyiB1dA/vXJnak7mN8+868s/sQRa793L3pBYLNHuTaCuo0ciPQ5E+YJQhvzZetW/PIsDuxuUxoNjOvXDKKSzp2pKiogt27MtizO4O1W1Oo9ArEc0zRX1PVY/0+AK2dAcL+3u/4zr0OFz+2M7nnQDwMBoZ0a8OzY4fz9OwlzFixiQBvT24d0oed+7NYtnYfK9Ynkl9UXrMPPx8Lg/u146KBHejdOQaj4e/Z264Y0o0B3WNJzymmRXiAWwsf2cVlpOYV0zI0QBZWFKKBTJo0iUmTJlFaWoq/v7+74wBwe9y1bCveQ1pVFt+lL+b6lpfzYt/RPLhxLh6eDlx2A2vt6XRpFcLurHycPnrNmh8oCqX+GgFdLJTutfLwG99zx9WDuO2qAajqsUfJCSGEEGeDcquNez76nm2HsvC1mJk2cQzdWkW4O1azUeWysqNkNwDtfTrhafRwcyIhqrUNDmaz4/Ci52aNrNwiNE1DrcNapUIIIY6uMd1TrU9S/Gii+g9oQ2xcKCkFWZi9/m5XVDCPd/CJ/g0kg69ndXuFVkXFP2aE+qu48ffojWDCLEGEmYMJMQfhof5dFbR2cvDS9CV8l7YPh6/Cfd8tJCZrCewop/atN290K3jfWIReoWDb5EVGRhGhYX41PS6JbU+opzd5VRUsPrSfUW06AjC6fxeKyit5e8EfvP3zar7+YT2VeX9PaeXjZeaCvm0ZNrAD/bq2xGg0HPO9CQv2dft/0PnrdvLc3KXoOqiKwtPXDWPMgK5uzSSEcA8/kw8TWo/lrcRPmZ+xiAHBPbk4qjNjonsxP2MrFm8rlSUquaFVGA7pGOwKmgFUl4JuAjXISLHTjndXDwz77Uyf9yd7DmbzzH9G4uttcffpCSGEEG5RWmXlno++Z0dKNn5eZj6eeDWdY8LdHatZ+TN/Cxou7E4Do2MHujuOEDXigoNwpFbfzjKqLqzeCkV5ZQSHN46Hn4QQoqlqiHuqK1euPKP7/zcpfjRRiqJw/biBvD5rLspRHm7wNngR7RlOqCWYnMoq/shJRdOMPNfzai4M64rZcPyndgoLymtGdezelUHiviyCDRpFnUzYQlTSolx4ayZ6V/rRuXMLYloG8cm0lTj3eKHbilF9dIxRTqKja89P72EwMK5jd97dupYv92zl8tYdOJCax7K1+1i+LhFThY7DRyFPteHnY+SiHm0ZNrAj/bu3wsPUNP65rktM4dk5S2u+1nSd5+cuY1DHVjICRIiz1KDg3qwJ2sT6wm3EH/iKV7o/wn97jmR9TjIZFGPxtpJSXowpSsUzQ8WgVZeWFRuQ58Qj0ECF5kRpo+KVB6sSDnLHk9XrgLSOCXHvyQkhhBANrKTCysSP5rMrLQd/Lwsf33M1nVqEnXhDcVIWZP4OQJXDh/PD27k5jRB/iwz0xZ54eOSHqlEZbCA7rVCKH0IIIY7QNO4mi6MaPLQTn80Jwqrl1yqAqCi83fOJmoXGdV3n4c3fsTBjJy9sW07PC9sS5vl38cPhcJF0IIc9uzJqCh7Z2SVHHC/Ix0J3LYRN+cUUBjupaGEk28/COxOG4Wsx4+vrydtvLMR50Iypk5XBt7eqNerjL+M69uD9hHVsyE7niic+IT+5rOZ7Ph4GfHx8SCsvwxqgMOqSHvRt26Ie37UzJz2/mKmL17Fg054jvqfpOmn5xVL8EOIspSgKd7a+nl2l+zlYkcaPGcsY02IE8effwNXLp4GnhsnpxBFgQrVqGKsUVLuC6lTACUq+C9UHNAtUhILqq7KnpJhrX/2Kc7vGMbxPezpEhxIXFoTpOCPjhBBCiKauuKKKuz6cz96MXAK9Pfn4nqvpEB164g3FScmx5pNalYauQ0//bpgNJ54vXIiGEhXkh7Oq+t+k0eDCEWIgJ72QLn3j3JxMCCFEYyPFjybMaDRw7aXn8vH3eXhdVQQqqKjc3eaGmsIHVN90e77nFSSV5ZJYmsvktbOYqJ7H/j3Z7N6Vwf592djtzlr7VhSIjQulU+doOneJplPnaGJaBqOqChVVdiZ/OJ81lZnsLy1k6NvTmX379Yy8rCd9+7Xmh5RlLHIsoyqiuNY+07KKWLZ2H8vW7cPDX8caDgc8SggzmRjYM45hAztwbq82mEwGHvp8Ab/tTOK+T3/k00nXNuonufJKyvl46Qa+W7cDp0s7ah9VUYgJCWjYYEKIRiXQw59bY6/hgwNfMjftF84J6k47v0ge6jic1xKXYPa243Kq2MIN2BQddLguuisPDTqPxIw89mbksWj/fnaW5KFZwG5U0Eth5b5kVu5LBsBoUGkTHky7qBA6RIXSISqE9tGhBPl4nSCdEEII0fgVlldy14ffkZiZT5CPF5/852raRcoIyDPht9x1AFQ6TIxq09fNaYSoLdTPB2fF3yM/nMGKLHouhBDiqKT40cSNvKwnX3/xB6Wvmrn4ho5cOXgg7cNjar5vtztJOpDD7l0ZtNsfRFLbPHaRxaMJ3+G/4u+/fl8/Tzp1jqJz52g6dYmmY6covL3NRz2mt6cHnz04lne+X8VHO7dQYrQz6uOvePOyS4gJDsBR4Q8esLcsieTsPP7YkMyytftIPJRbsw+/ShVruIYzxsDsB24nMqD2CJFXb76Uez6ez+akDO756Hu+vG8sLUMD6vfNO00lFVY+W7GRWasTsDqqi0eDOrbi3pGD2JeZz/Nzl6Hpes2aHzLqQwgxOLQ/a/I3sbV4N1OTvuZ/XR9ifMeBLD20m632dCy+NiqLPAEFFPg2cyfdMiI4r0Urzuscx10X92dTWgb3z19AXkUlriAFNVdDdYBiVnG6NPZl5rEvM48F/D0KLdTPu6Yg0v5wUSQ2LAijQRaFFEII0TQUlFVy54fzOJBVQIivF9MnXUPr8GB3x2qWNF1jSc4aAOwOPwaGtXZzIiFqMxpUPOwWdL36wU09HLJTC9wdSwghRCOk6LquuztEY1JaWoq/vz8lJSX4+R05ZVNj9PR/v+XPNfuB6lEel4/qidliqh7VsT8bh91V09cap1E02gkKXJjTmtEte9KpczQtYoJQFOVYhzim37bt574ffsVm0kDTQQEU6ND/EB6eTgp+jsB2yBsAg6rQt2srhg3swPl92nDNktkkFuXzzICh3N61zxH7LquycUf8t+zNyCM6yI8v7xtLqL/Pqb1J9ajSZufr37fy+W+bKLfaAegRG8n9l51Xa4qu7OIy0vKLiQkJkMKHEA2sMV/L82yFPJjwAlUuK7fFXsPlUUMptlcy9Ke3sJmcOKwGnFYTmktB1/4uTnibTHQKCqNLcBgtfQJYkLCXXem5KCj4FCqYMlyEhnhz/ZX9qNAc7MvIIzEzn9T84qPm8DAaaBMRTIeo0H+MFAnF//Ai6tnFZaTmFdMyVK5hQohqq1at4vXXX2fz5s1kZWXx/fffM3r06ONus3LlSqZMmcKuXbuIiYnhySef5NZbb63zMRvz9Vw0nPzSCu6cOo+knEJC/byZPuka4sKC3B2r2dpVsp+nd72NS1Po4jWUF3tf7e5Iohmo7+v5xe9/irlHAh5GF2mJgQxeHM1rs/5TD0mFEKJxsVqtJCcnExcXh8VicXecRqOu74uM/Gji8nJLWfvngZqvdV3n55+21urj5+9ZM6Kjc5doVpmS+PjgatZGpjBpwDBiAk79iakhPdqxsMUtjJn2DcWqvaa9rMiLYM9SPFpV0dW7E8MGdmBwv7YE+P099cr4Tj158s9lfLVnK7d26Y36r+KLr6eZD+++ilvem0tqfjETP/qeGZOvxc/LPf/RbQ4n3/65nU+WbaCovAqA9lEh3HvpuVzQOe6I4lFEgK/cMBRCHCHUHMTNrUbz8cHZfJP6I32DuhFhCeXJbpfz5J4fMFlcmCwudB1s5R6084riYEkRFQ4Hm3Iy2JSTUbMvNUxBd+gU+SpYwg3YU8r56JtVPHbrcO6+bQBQXbDdn5XPvow89mXmk5iZx/6sfCptDvak57InPbdWvvAAH/w8LRzIykeHmtFrYwZ0bci3SQjRCFVUVNCjRw9uv/12xowZc8L+ycnJXHbZZUycOJFvvvmG5cuXM2HCBCIjIxkxYkQDJBbNQW5JOROmzuNQbhFh/j58OukaWoUGnnhDccp+y10LQJnNzKXte7g5jRBH19I/gDSHAQ+jC9VfI0emvRJCCHEUUvxo4jLSCzna4J2B57bjggs70qlLNNHRgbVuzPfQW5JYmcPK7ETu3zCHby+8i0Cz9ylnaBEcwMQB/Xhl/ZqatvJCb4KjSgnvovPB+dcedbur2nbhlY2rOFhSxJqMFM5vEXtEn2Bfb6ZNHMMt781hf1Y+907/kWkTx+Dp0XAL7jldGj9t3M20xevILq5enL1lSACTRg5iRM/2qOrJj5gRQpzdhoefx5r8zewq3c+HB77h2S7308ErlH9eTRQFLN52DLPyucDHH49oC84QA+U+GvkmK+mucipcDjCBbtKp9HRSGQiFOLl320Le2r+WK3t3pXtoBF3CwugRG1Wzb03TySgsqZ4i6/AIkX2ZeWQWlpJTXE5OcfnffXWd5+cuY1DHVlLQFeIsN3LkSEaOHFnn/tOmTSMuLo4333wTgE6dOvHHH3/w9ttvH7P4YbPZsNlsNV+XlpaeXmjRpOUUlzNh6rek5BUTEeDLp5OukXX0zjCry8aa/C0AaK4A+ofIAtKicWodGESSwwiedoyeLvJySnA6XBhNBndHE0II0YhI8aOJi24RhKoqaNrfBRBVVbjvgRGEhh19KKmqqLzSewxjf/+YlIpC/m/TPD4aeBNG9dQ/JJzTtiWs+6P6bh1QXuyJrkGloZRcawFhliNHl/h4eHB1uy58sXsrX+7ZetTiB0CLYH8+vHsMt30wl63JmTz8xS+8ffsoTIYz+6FG03SWbkvkg4VrSckrAiDM34eJIwZw5Tmdz/jxhRDNl6qo/KftTTyY8AI7SxNZmrOGvDQD/LuWqkKpyYotyQ5Jtb8VCTi9wRak4Ig2UhKt4DDrYACXFxygmDe3/FHTP8zLmy7B4XQJrp46q2twOBd1a8uw7u1q+pRV2fhxwy5e++F3NKOO5qGj2hVwQlp+sRQ/hBAnZe3atQwbNqxW24gRI3jggQeOuc3LL7/Mc889d4aTiaYgu6iMO6Z+S1p+CVGBfkyfdA0tgv3dHavZW1+YgF23Y3epXBjW87R+RxTiTGobEsQveYcXPTe6cHip5GYWEdUqxM3JhBCi6TqT01/HxsZiNpvx9PQE4PHHH2fs2LH1eoyjkeJHExca5seD/zeSt99YiKbpqKrCg/838piFj7/4miy8e85Yblg1nXX5yby7ZwUPdRl+yjm6torg2jad+TZpNygKmtOAyeaP07OEbSV7GG4576jb3dypF1/s3sqy1CTSy0po4Xv0X2jaR4XwwYQruXvafFbtTuaZWUt4YdwlZ2TUha7r/LH3EO//8id7M6qngwnwtjBh2DlcN6gHFg/5byOEOH0RllDGtbyCzw99x5cp8/lP5J2QA/xrDfJ+t7VjrE8fCgvKycsrIy+3jPy8UvLyysjPK6M8zQppDvwNUNjZREW0Ad2oozh1VE3H6Q1Ob53cygpyKw/yW9rBmn1bFAOxlgA6BYbSMyKSfi1bMrhba15Y+RtVUVp1MUYHz0yVBZv20CsuWhZJF0LUWXZ2NuHh4bXawsPDKS0tpaqqquYXn396/PHHmTJlSs3XpaWlxMTEnPGsonHJLCzljvhvySgsJTrIj08nXUtUkKz50hBW5K4DoMTqyciO3dycRohjiwkKwJ5e/bu50aDhDDaSk1YoxQ8hhPgHXdepsjvr1Penjbt5Zf5vaLqOqig8NmYIV/TrfMLtPD2MdV5Hes6cOfTs2bNOfeuL3MVtBkZe1pO+/VqTkVFEdHTgCQsff2nnF84Lva7koU3z+OzAGroGRDEiussp53jx+ku4IaUnL6/4nY1ZmVjLfDF6lpBQtIfh4UcvfrQLDGZQVEv+zEzlm73beLTfBcfcf6/W0bxxy2Xc/9lPLNi8lwBvTx4efeEpLdR+LJuT0nnvlzVsTc4EwNvswfjBvbl5cG98LOZ6O44QQgBcGjmEP/O3kFiezIqqFVxj7ME857bqAogOKLC4Yg/mIA+eGzkKD/XIH9tVlXby88vIzy8jN6eUhckHWFScggYY7OC3X0O1a6C4cHlVjxSxBYE9QMFqcLG3qoC9VQV8n7kXtgAuIEqvGcmHAlVRGt9t3klmYSmv33IZAd5H3rAUQoj6YDabMZvlM9fZLL2ghAnx88gsKiUmxJ9P/3MtEYEy8rAh5NsK2VmyDwCDFkTfkFZuTiTEsUUG+uGoOjzyQ3VRFWogO13W/RBCiH+qsjsZ8NgHJ72dpuu89N0KXvpuxQn7rntlMl7mhlue4GTJ45vNRGiYHz17tapz4eMvl0R35ba2gwB4YusPHCjNPcEWx9e1VQQfjL0Cf4uZQ+nVbTtK9uLSXcfc5pbOvQCYvW87Vufxq5EXdGnN/26oniP661Vbmb5s42nl/cue9Fz+8/H33PbBt2xNzsRsMnDLkD78+uTt3HPJQCl8CCHOCIOiMqntTRgVI1uLdzGkfyRz+97JE2GXMLfPnTzT43IMisJPaduYuPYbSh1VR+zD08uDmJbB9Oody4iR3XnnP2OYfcv1RPj64DRDeXsDDj8VVTNxQWxb3hgykv+1vJApth5cmRlNz2RfIg8Z8MzRUe3V02bx76KyAgZPlfX70xj39iz2Z+U3zBskhGjSIiIiyMnJqdWWk5ODn5/fUUd9CJGaV8ztH3xLZlEprUID+GzSdVL4aEC/520AoNJuYlhkDwyK3C4QjVdkoC/OquqbbSZVwxlikEXPhRCikRs/fjzdunXjjjvuIC8vr0GOKSM/BA90uojdxVmsz0/mvg2zmXPhXfiaLKe8v0AvTyadN4CXlq1EcxqooIoD5Sl08G191P7DWrYlytuXzIoyfk3ex5h2xx99cnnfThRVVPH6D7/z/q9rCPS2cM2g7qeUNTm3kKkL17I4IREAg6owpn9X7rp4AOEBPqe0TyGEOBktvCIZG3Mp36T+xIxD83in51N0iRkAQBeiifT0Z8rGb1mfn8zNqz/jwwE3EuUVcNx99oyO5Ifbb2TKjwv581Aq5S1VXAU6f+5KoaC8ilemXEFkaO1pBl1OjYKCMhZs281zKatrF0A0nZevvJj3f11LekEJN70zm5duvISLuret77dDCNGMDBw4kF9//bVW29KlSxk4cKCbEonG7FBuEROmziO3pJy4sCCm/+dqQv3l83hD0XWdFblrASixWrjkNGYEEKIhmE1G1CoP4K9prxSypfghhBC1eHoYWffK5BP2yy0pZ/QrX6Dp/1hTWlH44bFbCDvB5zHPOi4PsGrVKlq2bInD4eDJJ5/klltuOeJ3hTNBHuUQGFUDb/S9hghPP1IqCnl8y/dounZa+xzXpwexQYGUFVY/1beteM9xjq8yrmMPAL7cvbVO+7/5wt5MGHYOAC/MW8HSbftPKl9WUSnPzF7KmFe/ZHFCIooCl/buyI+P3cpT1w2TwocQokFdETWcOO8Yyp2VfHDgS3YU76PAVgTA+eHt+PL82wiz+HKgLI8bVk1nd3HmCfcZ5O3Fp9dfxT2Dqq+VVcEKFe0M7EnP5db/fs2G7Ydq9TcYVcLC/bn94oHcHNAF/vrQo+vcHNiFS/t0ZtaUcZzTLoYqu4MHZ/zMtMXr0DSdMyW3oIzNu1LJLSg7Y8cQQtRdeXk5CQkJJCQkAJCcnExCQgKpqalA9Xod48ePr+k/ceJEDh48yCOPPMLevXuZOnUqc+fO5cEHH3RHfNGIJecUckf8t+SWlNMmPIjPJl8jhY8GllieTLY1D00HixJCryBZa0c0foH4ov116yJCl2mvhBDiXxRFwctsOuErNiyQp68bhnr4IUhVUXj6umHEhgWecNu6LkfQsmVLAEwmEw888ACrV68+Y+f9T1L8EAAEmb15t99YPFQDv2Xv4+PE0/sH6GEw8OjQCygr8gJgff6O4/a/vkN3TKrK1rwstudl1+kY9146iGsGdkPTdR77aiHr96eecJuCskpe/X4ll7/4Od+v34lL0xnctTXf/t9NvHLzSFqGBtTp2EIIUZ+MqoFJbW9CQSGheA/P7n6Xuzc/ybKcNQB08o9k5gUTaOcbRr6tnPF/zOD37MQT7tegqjw4+Fw+uvZK/CxmbGad8o4GCnQrD74yn69+2oCuH1m8eOHay3ipzzAAzAYj/zdqKAAB3p5Mu3sMN15QPV3h1EVreeiLBVTa7PX1VtT46bcdXHXvJ0x+4VuuuvcTfvrt+D9HhBBn3qZNm+jVqxe9elVfA6ZMmUKvXr14+umnAcjKyqophADExcXxyy+/sHTpUnr06MGbb77J9OnTGTFihFvyi8YpKbuA2+O/Ja+0gnaRIUyfdC3Bvt7ujnXWWXl4ofMym4UR0d1QZcor0QRE+wVgd1Y/cawGamSnFbg5kRBCNF1jBnRl0dN38Omka1j09B2MGdC13vZdUVFBcXFxzdezZs2q+Z3iTJNPNKJG18Bonu5+OQAf7P2N1TknN5ri34a2a007z+opUQ5VplLurDxm31Avby6N6wDUffSHoig8cc1QhnVvi8Pl4v5Pf2JX6tELJ2VVNj749U8ufeEzvlm1FYfLRb+2Lfjq/rG8d8eVtI8KPcmzE0KI+uVn9EHn70KEjs60pJmkV1Zf1yI9/fnq/NsZGNqaKpeDyetnMTu5buseDWnXmvm3jaNTeChORaestUpFiE78rNU88c7PVFQdWbwY16sXnYPDsGkuvt67rabdaFB59KrBPH/9xZgMBpZvP8BN784mPb/4tM4foKzCyq4DWcz+dTOvfLykZsitpuu8On2pjAARws0GDx6MrutHvD7//HMAPv/8c1auXHnENlu3bsVms5GUlMStt97a4LlF47U/K5/b47+loKySDlGhTP/PNQT7erk71lnH5rLzR/4moHrKq5FR9XezQ4gzKTYgALvTAIDB10VJQQVVFTY3pxJCiKYrIsCXfm1jiAio3zXXcnJyGDJkCN27d6dbt278/vvvfPnll/V6jGOR4oeo5apWvRgb2xcdeGTzd6RWnPqwUUVReGLICKwVJlDgp4Nrj9v/r4XPfzq4lyLrkYv6Ho1BVXnl5pGc0y6GSpuD/3z8AxsPpLFhfxrZxWVU2R18tnwjI1/4lI+XrqfK7qBLTDgfTRzD9P9cQ4/YqFM+PyGEqE+Z1twj2nR0Hkx4gRd2f8Ci7FXYtSo+HHAjo2N6oqHzv+2/8OaupXWaqrBlYABzxl/P1d27oAOVESoVsSrLN+1nwlPfkJJZ+3qvKAp3desHwOe7tmBzOWt9f3T/Lnw2+VpCfL04kFXADW/PYl3iiUfgOV0aaVlF/LEliZkLNvHyx0u457nZXHr3h1w8IZ4JT83k3a9W8u/xKJqmk55TfML9CyGEaBr2ZeQxIX4eReVVdGoRxvT/XEOgj6e7Y52VNhVtp9JlxeFSCTSF0i0w2t2RhKiTdkEh2O3VIz9MZheaEXJk6ishhGh0WrduzdatW9m+fTs7duzgxx9/JDY2tkGOLQueiyM81vUS9pZks60onfs3zOab8yfgZfQ4pX11Cg8l0tiSIpL47sAfXN9uaM38cf/WOyyKLsFh7CrIZU7iDiZ2P6dOx/AwGnn39iu4I/5bdqfnckf8PAAUwNvsQfnh6VjahAcx+dJzGdqtTZ3noxNCiIYSZQlDQak1+gNAQ2Nr8W62Fu/mE2bT2juGviHd8DP34MsDCXx2YA1ZVcW82Gs0ZoPpuMewmIy8fPnF9GoRyfOLf8Pm60LrYOBAciF3PPkNT/9nJBf0/XsR88tbd+DVjavIqijjx6Q9XNe+W6399YiNZNaUG3lwxs/sTM3mno/m89AVF3DjBb0oKasiJauI1MxCUrOKSM0qJCWziIycYpyuYxdrQgK8iQj1Y+f+rFrtqqrQIjygju+mEEKIxmxPei53ffgdJZXWmgeT/Lws7o511vrt8JRXJVYLV0R1ld+VRJMRFxKI/UD1bS2jquEMNpCdXkhsh0g3JxNCCNFYSPFDHMHDYOTtftdx7e8fkViay7PbfubV3mNO+UPwuA4XEn8oCbulgJ927mF0t85H7acoCuM79+LR1Yv5ek8Cd3bti0Gt2+Akb4sHz14/nOve+KamTQfKbXbCA3y499JzuaxPxzrvTwghGlqwOZCJbcbxUdIsNDRUVO5ufQMd/dqwqWg7Gwt3sK/sIAcr0jhYkQZA7whvsso1VuVu4Y4/S/ig/zgCPE48Xch1PbvROTyM++YvIL2klLK2Ks50O4+++SO3XjWACdcMpKCogrTsIq6N7cJ7u9bxyY5NXNuu9g0Ru8NJZbmN2wb24lPbJnbn5PHaD7/z/rerUQqcHOunhtnDSMvIwOpXVBCtIoNqvvb2MgPVa368On0pmqajqgqPThhOWHD9Dr0VQgjR8HalZnPXtPmUVdno1iqCD+++Cj9PKXy4S6G9mITiPQCU2CyMjO7i5kRC1F1UkB8O6+GRH6qLqjAzOWky8kMIIcTfpPghjirc04+3+13H7Wu+4Jf0HXQNiGJ8m4GntK9B4d2YekjFw+Lk3bXLGdGxHZ6moz+dfGWbTry04XfSykpYmZ7MRS3b1Pk4pZVHn9vzueuHM6hD7KlEF0KIBjUs/Fx6BXQmy5pHpCWUYHMgAC28IhgdfTEl9jI2F+9kU+F2Eor3UOmqwN8T/D2rKNe2cuu6/dwcdzHDI/rjZ/I57rG6RoYz//Yb+b+fFrIq6RAVMSpOL50ZP6xj1aYDHEovqF5zwwjmwQYSi/J5+ecVeJcYSM0sJCWriKzckpp1OXTAwwvsvgpVJg01SCFG9aJ1ZMg/ihzVf4YF+aKqxy+oXzGkGwO6x5KeU0yL8AApfAghRDOwIyWbidPmU2a10TM2kql3X4WPxezuWGe1VXkb0NGpdJiItITRyV+emBdNR2SgH46q6nsLRoMLR7CBbCl+CCGE+Acpfohj6hPcioe7XMzLOxfxxq4ldPKPpF9I7Envx2Iw09mvLbtKE7Ga8/l03WYmnz/gqH09jSaubdeV6Ts38eXurSdV/GgZGoCqKDU34gBURaF1ePBJZxZCNE3x8fHEx8fjcrncHeWUBZsDa4oe/+bv4cvQsIEMDRuIXXOwo2Qfmwq3s7YggTJnOahlzEz7jllp8+no14Z+gd3oF9SdKM/wo+4vwNPCx9eNZuof63l/9VpswQqal8L+lHwwgGYG1QbGFBe2ljBjzxZCEmqPoPPy9KguakQG0TIqkCrFxTfrt1GOnUo/lTtuPJfurU7tRkpYsK8UPYQQohnILi7jtx0HePeXNVTaHPSKi2LqXVfhbTm1qXVF/dB1nd9y1wPVU16NbdlFprwSTYqvpxm9srr4YVI1nMGqrPkhhBCiFil+iOO6sXV/dhRnsCB9Bw9t+pa5F95FhKf/Se+nd2BndpUm4hNUwSfrNnJNz65E+B79qeSbO/Vk+s5NrExP5lBJEbH+R78J+G8RAb48fd0wnp+7DE3XURWFp68bRkSA3DgT4mwxadIkJk2aRGlpKf7+J3+tako8VBN9ArvSJ7Ard7a+ni2F+3h591ysegEWo5M9pQfYU3qAL1O+J8ozvKYQ0t63NQbl7wKGqihMPn8APaIjeOjHhRRjpbidAiqgKKDrWPKq/7QHw9BLOtE3OppWUdVTVQUHeB9xo2TUeV25/9MfScop5Lb3v+Xp6y7iynNkGg0hhDgbzV+3k+fmLuWv55NahQby4d1X4WVu2MJHdmkZhwqLiQ0KIMJPfj8ASKpIJb0qC02HMpuZkdFd3R1JiJPm7fIGwKDquMIge5cUP4QQQvxNFkAQx6UoCs/2GEUHv3AKbBU8uHEudpfzpPfTM6B6nQ+/QCtWp523V645Zt9Y/0AGt4gD4Ks9CSd1nDEDurLo6Tv4dNI1LHr6DsYMkA/wQojmT1VU+gZ34suBj9HKoz8HCoLJKfMh1CMSo2IgsyqHHzOX8eTOt5iw8THe3/8l6wq2UuWy1uzj/NaxfH/7jbQJCgSDUl34AFAUrKEqF0TEAlDZSueqYT3o3TmGkECfoz4h2jI0gK8fuIEhXdvgcLl4atYSXvt+5XEXOhdCCNH8ZBeX1Sp8AKTlF1NadfTpas+UbxN2Mjj+U8bPnMfg+E/5NmFngx6/sfprofMym5lY7zDa+Ya5OZEQJy/CMxCXq/rzqBqikZ1eiP7Pi44QQoizmhQ/xAl5Gj1495yx+JksbC/K4KUdC096Hy29oggw+YGq4eVv5fsdu9mZlXPM/uM79wJgbuIOqpyOkzpWRIAv/drGyIgPIcRZx9to5r1zrufqlv0psnqxOsNFV+/hPNDudi4I6Ye3wZNSZzkr89bx+r5PuHXDI7ywO57F2asosBUT7e/HE8MHA2A0O/AOqMRodoCiMLJVewB+StpLVkXZibNYPHj7tlHcfXF/AL5etZV7PppPcUXVGTt/IYQQjUtqXjH/vgep6Tpp+cUNliG7tIynFi6rmRpX03WeWriM7NIT/yxrzhyagz/yNwFQarUwMrqrTHklmqRWfgHYndWTmqh+LqrKbZQVV7o5lRBCiH+z2WxMnjyZdu3a0a1bN2666aYGOa4UP0SdxHgH8Vqfq1GAb1M2My9l80ltryoqPQI6AtCrvScALy37/ZhPZAxuEUeMrz+ldhs/Ju05rexCCHE2MaoGnup+GQ91Hg7A7OQtzD24n7ta38hn/V7juS4PcHnkUMLNITh1J1uLd/Hxwdnctfm/PLLtFRL1rYTHFtBxwCFa98yg44BDBEWWMjS2Nf0jWuDUNT7ftaVOWVRVYdLIQbx56+V4ephYvz+NcW/PYn9W/pl8C4QQQjQSLYL9jmhTFYWYkIAGy3CosLjWmoBQXQBJKSpusAyN0aainZQ7K3C6VCocHlwSLdNTiqapdVAQdkd18cPo5UIDslIL3BtKCCGaqOzSMtYdSjsjD4k89thjKIpCYmIiO3bs4I033qj3YxyNFD9EnZ0f3o57Ow4F4IXtv7K9KP2ktu8R0AkA36BKLEYjm9IyWLLvwFH7GlSVmzr1BODL3Vtl2KoQQpwERVG4vd25vNn3GjxUAyuy93Hrms8ptlfR1b89t8VdQ3zv53in51Pc2PJKOvjGoaCQVJHKL7lLCYst/OesV0S3z8FkdnJXt34AzNy7jXK7vc55hvdox1f3jyUqyI/0ghJuemc2y7cf/fovhBCi+cgtqaj1tTvW5IsNCjhq+48792B3uRosR2Oz8vCUVyU2C+39ImjtG+rmREKcmvYhIdjsBgBMRhcuP1n0XAgh/qLrOpV2R51e32zeVmua0G82b6vTdnW5Z1tRUcGnn37Kiy++WDPSNCIi4kyfPiALnouTdGf789hVnMny7L08uGEucwffRbD56AuX/1sP/+riR7o1k5sHDOSTP7bz2orVDGkbh4fxyH+KY9t3463Na9hVkMuW3Ez6hEfX67kIIURzd0l0V8Isfty7YRY7izO5cfV0PhxwI619Q1EUhRivSGK8IhnTYgTF9lI2F+1kee6f7Cs7WHtHCqzLSmRkq3No4x9EUkkhcxN3cHvXPnXO0j4qlFkPjuP/vviFjQfSeHDGz/znkoHcNbw/qirTbAghRHO0Ykd1odtQpWOq0jG4FIxVDftQU7C3F14mExUuO7pBR3UpoCnM27aLxLwC3hl9KS0C/Bs0k7sV20vZUrQLgBKrhXHtZdSHaLpaBPvjOGgCwGjQcIZ5kJ0mxQ8hhACocjjp+cYHJ72dpus8t3gFzy1eccK+Cf83GS8P03H7JCUlERQUxEsvvcSyZcvw9PTk2Wef5aKLLjrpbCdLRn6Ik6IqKi/1Hk2sdzDZ1lIe2jQPp1a3J6YCPPyI824BQM92noT5eJNWXMKXmxKO2j/Q4skVraunyvpi99Z6yS+EEGeb3sEtmXn+BGK8A0mvLObG1Z+yKf/QEf0CPPy4KHwQD7W/A4XaxQhdh6krtuNwuZjQrS8An+7chFM7uQXMA308mTbxKsad3xOAqYvW8tAXC6i01X0UiRBCiKZB13WWbtsPgNGqY7ADLp1Xpy8lt6Dh1ttYeyiNMoMNe7ALR6CGI0Rj3Hnd8beY2Z6ZzejPvmF5YlKD5WkMFmX/joaG1WHA7jJySXRXd0cS4pRFBvrhqDo88kN14QgxkCPFDyGEaFScTicpKSl07tyZTZs28d577zF27Fhyco69HnR9kZEf4qT5mCy81/96rv/9EzbmH+Lt3ct4uOuIOm3bI6AzyRXp7ClPZMrgc3lswRKmrlnPVd06E+ztdUT/W7r04tv9O/k1eR9P9R9CqJd3fZ+OEEI0e618gpl5/gQmr5/FtqJ0Jqz9ihd7jeayFt2O6BtsDmRi63F8ePAbABQUipOjScsu5+3f/+SBCwfyxqY/SC8vZdGhRC4/XKSuK5PBwGNjhtAxOoz/fbuc5dsPkJJXxHu3X0GLBpwDXgghxJmVnFtIRmEp6IcLH4dpmk56TjFhwQ0z9dWc7Ttw+mr8VdfX0Ply/1bmXX8DLy1ZxfbMbO6Z9xO39+/DQ4PPxWQwNEgud1mWs4Zv0xcCYDa66BToQUvvIDenEuLUBfl44rJWP3FsUjWcQSrZMu2VEEIA4GkykvB/k0/YL6esnJEff1FrnTRVUVh41y2E+x5/xh9P04nLCy1btkRVVW688UYAevXqRVxcHDt27CA8PPyE258OGfkhTkkb31Be6j0agM+T1vJr+o46bdfz8Lof24r3ckXXjnSNCKPcZue9VWuP2r9bSAS9QiNxaBqz922vl+xCCHE2CjJ789m5tzAsshMOzcUjm7/jk8TVR52fc1jEuVwScSEAPQM680T/sQDMWL+Z7Rk53Hx4TaaPd2w85TWZRvfvwmeTryXE14sDWQXc8PYs1iWmntrJCSGEaHRW7KgeTWGwg/KPHxWqqtAiPKBBMlgdTn5POci/BjTi0nVsuJh583Xc2q8XAJ+t38yNX39LZklpg2RzhwJbEdOSZtZ8rSiAMYMCW5H7QglxmhRFwcNe/SCl0eDCEaLIyA8hhDhMURS8PEwnfMUFB/K/kcNQD6/HoSoK/xs5jLjgwBNu+9caHscTEhLCRRddxOLFiwFITk4mOTmZTp06ndHzByl+iNMwPKozE9qdB8DTCT+RWHrioUodfVtjVj0odpSSXpXF48Oqb67NSdhBYm7+UbcZ37n6F5Jv9m476SlWhBBC/M1iMPFWv2sZ32YAAO/sWc6z234+6vSFQ8MGArC7dD/nto7hmh5d0YFHFyxmTJvOmA1GtuVlszEn45Tz9IiNZNaUG+naMoKSSiv3fDSfb1ZtPeWCihBCiMbjt52Hix/WfzxBqCo8OmF4g436WHngIDarC47yYyXKxxcPg4H/Dh/MB1ePwtdsJiEji9GffcNvBw4euUEzsL1kH/q/3gwdnSxrnpsSCVE/gtTqdXtUBfRQyMksQpN7B0IIcVKu7dmVlZPu4Ksbr2HlpDu4tmf9Tos5bdo0Xn/9dbp168bo0aP56KOPiI4+8+s7S/FDnJb7Og1lYGhrqlwO7tswmwOlOazPSya7quSo/U2qia7+7QFIKN5Nv5YtGNGhLZqu88ryVUe94XVpXAeCLJ5kVZSxLPXAGT0fIYRo7gyKyqNdL+G/3UaiAPNStjBp/SwqHLZa/Vp7xxBmDsam2Uko3s1/h11IiwA/MkpK+fCPDVzdrjMAn+zYeFp5wgN8mDH5Wkb17YRL03n1+5U8M3spdqfztPYrhBDCfXJLytmRkg2AwQZXDOlG/FPX8f17d3LFkCOnXDxTft61DzTwUI+cyuqNzWtqpna4uENbvr99HF0jwiiusnL33B95/bfVOFx1W9uwKbC6bHyXvuiIdhWVSEuoGxIJUX9a+AbicFbf3lIDXTjtLgpymu8oLiGEOFMi/Hzp3yqGCL/6f1CldevW/Pbbb+zYsYNt27Zx9dVX1/sxjkaKH+K0GBSVN/peQ5SnP2kVRVz524fc/ucXDF/yDt+lbDnqNj0OT32VULwHgIeHno/JYOCP5BR+Tzp0RH+L0cj1HboD8KUsfC6EEPXixtb9ee+c67EYjPyRe4Dxa2aQU/X3L4mKotA/qCcA6wq24mP24NXLR6AA87fvppN3GABLUw5wsOT0phYwm4y8MG4ED4++EFVR+GHDLm7/YB67U7PZsD+N7OKGWxhXCCHE6Vv516gPB6gaXH1xT3p3jmmwER8AZVYbvycloxvBrrvwNBr5csQ1vDv4UoyKyk9Je3h146qa/i0DA5g9fiw39ekJwCdrN3HzN/PILm36P4N0Xeejg7PIsuZiVEz89byZrkO/gHMJNge6N6AQpykuIBC7s3rOeaO3Ex1k6ishhBCAFD9EPQjw8OLpHpfVatPQeXbbz0cdAdIzoPpp4T2lSVhdNloGBnBLv54AvLJ81VGfsLqxYw9URWFNZir7iwrq/ySEEOIsNDSyIzPOvZVgszd7S7IZt2p6rSkMBwT3BGBT0Q4cmpN+LVswYUBfAOJXrueCqFh04NMdm047i6Io3Hxhbz68+yr8vMxsT8ni+rdnMWHqPC55/lPmr9t52scQ4mz122+/uTuCOMv8trN62ijVqtG2ZSjtY8MaPMPSxAPYXS78A80AXBTThgtj4hjdtguvnj8CgGnbN/D5rr8f2PIwGnl6xBDeveoyvD082JKeyZWffsOqozyg1ZQsyVnNqrwNqKgcLPIhqTCY1OIAkgqD+epA4jFH7QvRVHQICcVury5+mCwuNLMsei6EEKKaFD9EvfBQTUe0abpOasWRHziiLGGEeATh1J3sKt0PwD2D+hPk5cnBgkJmbz1y8fQWvv4Ma9kGgK/2yOgPIYSoL90DWzDz/AnE+QSTbf1/9u47rKmzfeD49yQhYRM2yBQQAUVw76p1a7Vqra3a1qrddtq9tNNu6/t7a19bq91Dq611770VxcUSRIbsvVdyfn9EaSmooJCgPp/rylWSPOec+1gNJ+d+nvsu4r49S9iflUBGeSEFFRK2KhvKdBWcKowF4JnbetPexYn88nIqCg2lqX4/e4bc8rJmiad3ex/+b8addV7TyzJvL99C7AVRk1wQrsWIESPw9/fnvffeIyUlxdThCDe5kopKDp1NBkBVASP7h5gkjjVnYpCR0akNyxxG+AbWvjcxsCMvdDX0LnzrwDY2no+rs+3I4EBWzZhKsKsz+eXlPLTsT+bv3HdD9h+ML0liaeIKAIKtwymrNqNGr6SsWk2NXnnZ72yCcCNp62RPVZWhvJ1KqafaSUlGspg0KQiCIIjkh9BMfKwdUCDVeU0hSXhbOdQbK0kS4faG0lcnLpa+sjHX8Mxthua6/91zgMLyinrbPRBsaHy+8uwZSqqqmjV+QRCEW5mnlT0/9Z9JN0cfSmuqePTATwzZ/DkzD/xAcrEhwXEw15B4VqtUfDJ2BGZKJZGJ6Xha2lKpq+Gn6Mhmi6ehm0uyDPfM/5nZ365hT1QiNbob7waUIJjKhQsXePLJJ1mxYgV+fn4MHz6c5cuXUyWup4QWsDf6PDU6PVKNjEovMbxfsNFjyCkp5cD5FGQl5FeXo1YoGejVts6YJ8N7MSWoEzLw9I51HM28UOd9Hwcty6fdy5QuhvK7i/Yf5sFfVpJZXGKs07huxdUlfBq7mBq5hkDrQNYlpdcbc7nvbILp2dvb4+Dg0KjHra6Ngy3VFYYJmWYKHTVOSrHyQxAEQQBAZeoAhJuDm4Udb4WP4a3INegxzK7q4eiLm4Vdg+PDtSFszdxHZEFU7Wt3h4fy09ETnM3JZeG+Q7w2ZECdbfp6+OBnZ8+5wnz+jD/D/SGdW+6EBEEQbjFatSWLe9/P80dXsD0jpvb1okoNWotyDuWd4FF5MkpJSZCLM8/e1ptPduwlP7scrAw9mR7t1ANz1fVfWng7a1FIUm0j2kv0epmtJ+PZejIeZ1srxnYP4c4eHfB1EbXKBeFKnJyceO6553juuec4duwY3377LU888QRPPPEEU6ZMYebMmYSFhZk6TOEmseNivw9VBfQM88VRa2X0GDbEnEUvy7g4W3FBX0RfDx9s1Jo6YyRJ4t0+Q8ksLWVbSgIzN//BH2Om4q/9+0ayRqXirRGD6eblyZsbtnA4OZVxS37m0ztH0Letj7FPq0n0sp7/nP2e7Mo87M3s2ZZSQoVeh7+1E4mluehlGYUk8VbYmMt+ZxNMa8GCBaYO4YbhbGtNdfnFnh8XV36Inh+CIAgCiOSH0Izu8ulCXxd/1qee4rOorRzMSeRQdiI9ndvWGxtq1x4FEhfKM8muzMNZ44BKoeDVIQOY8dsf/Hw0kildOuHr8PcNLYUkcX9wZ94+uJ0foiO5LzgcSZLq7VsQBEG4Nmqliql+3eskP8qqzdDpJUpqSokqiifUrj0AM3p2ZUd8IkdSUlFbKcmpKOPP+CgmB3W67jjctDbMmTSEd5Zvrb05M2fSEDp4ubLq8BnWRUSTXVTKkm1HWLLtCJ3btuHOHh0YHh6Ilbn6uo8vCDezLl264ObmhqOjIx9++CFLly7lyy+/pHfv3ixatIgOHTqYOkThBlZdo2NPVCIAykrZZCWv1p4x/B7Ta2QohxG+7Rocp1Io+OL2O7h3/TJOZGcwbdMK/hgzBRdL6zrj7ujQnhA3Z575cx2xWTnM+PUPnujXkyf79UKpaJ3FFFambuR4wRlUkoroXDNKa2ro5dSWhb2mUFBVRnJpHt5WDiLx0YpNmzbN1CHcMFRKBcpKQ4LTTKGj2klBRpRIfgiCIAg3WdmrY8eOMXToULRaLY6OjjzyyCOUlNw4y5JvBm4Wdsxo1497fA0Ncd84voqS6volrKxVlrSz8QX+Ln0F0M/PhwH+banW6/lo+556293VrgMWKjPi8nM4lJHaMichCIJwC/O1dvpXGUOJkirDl8mDuZG1ryoVCj4aMxxrtRpdsWGFxuJTR+qt1rhWE3p1ZOOcmSyZNZGNc2YyoVdH2ns48/L4gWx962E+e/AO+ge3RSFJHE9M461lWxg09yve+GUTRxNSkZspDkG4WVRXV7NixQpGjRqFj48PmzZt4osvviAzM5P4+Hh8fHy4++67TR2mcIM7Ep9KSUUVkk7GRmVG/27+Ro8htaCQ4xfSQSGTXl6MQpIY6h1w2fGWZmqWDpuAj62WlOJCZmz+g9Lq+iXh/Bwd+H3aZCaFd0QGFu49xPRf/yC7pLQFz+banCiIZlnKOgCySmwpqITuTr580XMy5kozZL2Cmiolsv6muh1wy6ioqKCoqKjOQwBr2RYAlUKPzh5yM4qoqqwxcVSCIAiCqd00VztpaWkMGTKEgIAADh06xMaNGzlz5gwPPvigqUO7JT3fYSiellrSygv5+MzmBseE2Rnq//6z9BXAy4P7o5QktsUlcOB8cp337DTmTAgwzCD7PupYC0QuCIJwa7tUxvCfCZBBLj0AOJwXiV7+u9eGl9aO14YMRFkhgR4SCvPYkXKu+WLR2tA9wAs3rU2d19UqFUPD2rHwkXFsnvsQz4zui4+zPRVVNaw+EsWML37njnnf8vWWQ2QUFDdbPIJwo3rqqadwd3fn0UcfJTAwkOPHj3PgwAEeeughrKys8PX15dNPPyUmJubqOxOEK7hU8kpZCUN6BWGuNjN6DOuiYgHwdDesaOju6oGjheUVt3GysOKH4RNxMLfgVE4mT2xbTbVeV2+cuZmK90YN5dOxI7A0M+NgUgrjlvzEwfMpzX8i1yinMo8Fcd8iI1NaaUVWmYpujj582XMKFio1v8WepM9vXzF5/TL6/PYVv8WeNHXIQiOUlpby5JNP4uLigpWVFfb29nUeAriZOyDLIEkgOcnIskx2Wr6pwxIEQbihpJcWsz8tmfTS5v0enZubS3h4eO0jMDAQlUpFXl7Lr9K7aZIfa9euxczMjIULF9K+fXu6d+/OokWLWLlyJfHx8ZfdrrKyUsyaaAFWKg3vdR6HBKxMOsbuzLh6Y8LtDUmMkwUx6P5xMy3AyZHJXQx1p+dt3YXuX41vH7jY62PT+bNkNPM/RkEQBMFQxnDLsGfpqG0DQGapHgulOXlVhZwtOV9n7MSwDgwO8EdZbkiWfH3yiFFjdbGzZuaQHqx+dRrfPzWJ8T07YqkxIyWnkC/W72f4O9/w2Fd/sOl4LJXVYvafcGuKioriv//9L2lpaSxYsICOHTvWG+Pk5MSOHTtMEJ1ws9DrZbafMnzvUlbIjLrNVCWvDMkPWWNYATjCN7BR2/na2bN02ATMlSp2piby+t4tl11FOLZjMCunT6GdkyPZpWU8+OtKFu49WO97i7FV62v4LHYJRTUlVNWYkVpkSWcHL77sNQVLlZr00mJe3bO5dpWmXpZ5be/mZr/BITS/l156ie3bt/O///0PjUbDN998w9tvv02bNm344YcfTB1eq+BrZ09VjRIApU0NMoim54Ig3PJkWaasuqpRjx+ijtWZIPFD1LFGbdeYqguOjo5ERkbWPh555BFGjhyJg4PDVbe9XjdNz4/KykrUajWKf9RctbCwAGDv3r0EBDS81PmDDz7g7bffNkqMt5ruTr7c79+LHxIOMuf4albd/gRa9d+zrgKsfbBSWlCqKyehJIlAm797gzzVvxerz0QTm5XDypNnmBQeWvtekIMzPdw8OZyRyi8xJ5jdtZ9Rz0sQBOFW4GZhx/udxzFh5//YlhHHBL+2RBVHczA3kvY2frXjJEnivZFDiFhygSy5lIMZKZzKySTUydWo8UqSRGc/Dzr7efDy+AFsOXGWVYfPEJFwgf0xSeyPScLWUsPoLsHc2SOEYE8X0TdKuGXMnTuXPn36oFLVvfSvqalh//793HbbbahUKgYMGGCiCIWbQVRqJtlFpaCX8dTa0qm9h9FjiMvKITY7B6VS4nxpAQDDfC5f8urfOru04Yvbx/DI1lUsizuFh7Utz3Tp0+BYfycHVkyfzDubdrDy5Bn+s/sAR1Mu8OnYkThaXXmlSUv5IekP4koS0esVpBTaEmbvxVe97sNKZShf+cOZ4+ipe4NCJ8ucL8zH3cqmoV0KrcSaNWv44YcfGDhwINOnT6d///4EBATg4+PDzz//zNSpU00doskFODoSUalCY6ZDZVmDbKYQTc8FQbjllddUE/z9f5q8nV6WeXP/Nt7cv+2qY6OnPYOlWdN6by5ZsoQPPvigyXFdi5tm5cftt99ORkYGn3zyCVVVVeTn5/PKK68AkJ6eftntXn31VQoLC2sfKSmtZ8nyzeCZ4MG0tXYku7KEeac21HlPKSkJtQsCIPIffT8A7C0tmNWvFwALdu2npLJu3d0Hgg2rP36JOUmVrv6SdEEQBOH6Bdi6MNGnKwDReWUAHMw9Xm9mh5O1FfNGDENRaUgmfHhwl3ED/RdLjZo7e3Tg2ycnsfa16Tw8tAeuWmuKyir5dW8k987/hUmf/sxPu46RX1Ju0lgFwRgGDRrU4JLywsJCBg0aZIKIhJvRP0teje7fwSQJ5rUXS175ezqgl2U6OrriadO0ht5DfQJ4t88QAOYf28ey2FOXHWthZsYHdwzjwzuGYa5SsS8xmTuX/MSRZOP3Jtybc5T16TsBuFBsQ7CdN1/1vg8rMw0VNTW8tnczX548VG87pSThayfKJrV2eXl5+PkZJp/Y2trWfqb369eP3bt3mzK0ViPI2YmqakOS38xMR42tkgyR/BAEQWh19u/fT35+PnfccYdRjtfqkx+vvPIKkiRd8RETE0OHDh34/vvv+eyzz7C0tMTNzY22bdvi6upaZzXIv2k0Gmxtbes8hOZjrjRjXpfxKJBYl3qKTWln6rwfbt9w3w+AqV3D8HXQklNaxlcHDtd5b7hvO5wtrMguL2XT+bMtdwKCIAi3uCeDBmGt0hBTUIZSUpJVmUtiaf2bOkPbBzDUwzC7dm96EnF5OcYOtUHezlqeGtWXjW/O5H+Pjmd4eCBmSiWxadl8vGoXg9/6mtnfrmFPVCI1OtOWKxGEliLLcoM3onNzc7GysjJBRMLNaEuk4ZpcVSkzsr/xS17Jslzb70NfW/Kq3TXt677gcGaFGSZivbp3EztTEq84fkKnDqycPhl/RweySkq5/+cVLNp/uLa8VEtLLUtnYfyPAOSUWeJr2Zavet+HtZk5ycUF3LXmF36OOYEEDPb2Q3nx80ApSczrN0ys+rgB+Pn5kZho+HsYFBTE8uXLAcOKEK1We037XLhwIb6+vpibm9OzZ08OHz58xfEFBQXMmjULd3d3NBoNgYGBrF+//pqO3RI8HLRUVRiSHyqFnmonJZmi7JUgCLc4C5UZ0dOeuepjx8QZKP71fUEhSeyYOOOq21qomtbjbcmSJTzwwAP1VqW3lFZf9ur555+/atPySzMgpkyZwpQpU8jMzMTKygpJkpg/f37t+4JpdLL35KHAfnwdt4d3T6yjq4MPTubWwN9Nz88Wn6e0pgwr1d9LxNVKJS/dfhtPrFjNt4eOcU94KJ5au9r3pgaFseD4fn6IPs4Y/yDjn5ggCMItwEFjxaOBt/FZ1BbKqjRozMo4lBeJn7VXvbHzR42k6/eJVChqeHrTWjZOftD4AV+GUqGgb5AvfYN8KSytYP2xGFYdPkN0ahZbT8az9WQ8zrZWjO0ewp09OuDrYpgFm1FQTHJ2Ad7O2nqN1wWhtZswYQJgKAv34IMPotFoat/T6XScPHmSPn0aLukjCE2RnF3A+ex8kGXCvN3xcNUaPYaTaRmkFBRiYaYirigXMEyYulYvdutHemkRf8RH8fi2v1h+x72EOrlddnw7ZydWTJ/MWxu389fpaObv3MeR5At8MnYEDpYW1xzH1ZTrKng/ehFV+mpKq8xwVgWwuPf92JpZsDUpnud2raeoqhJ7jQULBo5moFdb0kuLOV+Yj6+dvUh83CCmT5/OiRMnGDBgAK+88gpjxozhiy++oLq6mvnz5zd5f8uWLWP27NksWrSInj17smDBAoYPH05sbCwuLi71xldVVTF06FBcXFxYsWIFHh4eJCUlXXPipSW429tQczH5YabUUeGkEis/BEG45UmS1KiSVH5aRz7oN4zX9m5GJ8u1EyT8tI7NGk9JSQnLly/nyBHj9Qpt9ckPZ2dnnJ2dm7SNq6uhzvjSpUsxNzdn6NChLRGa0ASPtx/Azow44ooyeefEWv7T4x4kScLF3JE2Fq6klWdyqjCWXo6d62w3uJ0fvXy8OJiUwqc79rJg/Oja96YEhfFF5EEOZ6QSnZtFsGP9izRBEATh+t3n15Pl54+SW15BGzND6avJ3mPqjbPWaHiuS18+iNxFdHE2a6JiGBPS+pLTdlbmTO4fzuT+4cReyGbV4TOsi4gmu6iUJduOsGTbETq3bYOXk5a1R6PRyzIKSWLOpCFM6FW/UbQgtFZ2doZJI7IsY2NjU9sPD0CtVtOrVy8efvhhU4Un3ES2nzY0OldUwdgBpvmcXHMmBoD23k4cKk7Fz86edtfxhV2SJD7qP4KsslL2piUxfdNK/hg7FW8b7WW3sVKr+XjMcHp4e/LO5u3sOXeecUt+4vNxo+jq1fw9UGRZ5rOYb8mqzKZap8CKAL7p8wBWKg0fHt7F/04aZvJ3dnZn4eCxeFgbqhy4W9mIpMcN5rnnnqv9eciQIcTExBAREUFAQACdOnVq8v7mz5/Pww8/zPTp0wFYtGgR69atY+nSpbXlw/9p6dKl5OXlsX//fszMDDN8fX19r+1kWojGTIVcYbjBZ6bQU+MskRGVa+KoBEEQbhz3tu/EAM+2LTpBYtmyZYSFhREUZLz7BK2+7FVTfPHFFxw7doy4uDgWLlzIk08+yQcffNCqZiPcqtQKFR90GY9KUrAtI4Y1qSdr3wu3u1T6KrredpIk8eqQAUjA+ug4jqWm1b7namVdO5vrh+jjLXsCN6iMomIOnk8ho6jY1KEIgnADUytVzO4wlJIqNbIMqeUZpJY13E/rka7dsTczBwW8tmMzeaVlRo62adp7OPPy+IFsfethPnvwDvoHt0UhSRxPTGP1kajakiV6Wead5VvJKBCfp8KN49tvv+Xbb79l7ty5LFmypPb5t99+y1dffcWrr76Kk5OTqcMUbgLrjxgSD5pqBbf3am/04+v0ejZExwEgmxteG+7T7rr7jqiVShYNuZNgB2eyy8t4cONK8iuu3CtKkiTuDu/I7w9Opq2DPRnFJdz30+8sPnCEtMKiZr02/yVpA8cLTyHLoNT5sqTPTCpr9ExZv6w28TG9QxeW3zG5NvEh3Bx8fHyYMGHCNSU+qqqqiIiIYMiQIbWvKRQKhgwZwoEDBxrcZvXq1fTu3ZtZs2bh6upKx44dmTdvHror9N+srKykqKiozqOlmesMpRxVSh01jgqK8ssoK6lo8eMKgiDcLNytbOjdxrvFJkksWbKEmTNntsi+L6fVr/xoisOHDzN37lxKSkoICgriq6++4v777zd1WMJFQXZuzAoayH+itzPv5Hp6OPniZmFHuDaY9Rk7iSyIarAmdbCrMxPDO/J75Gnmbd3F8mn31taheyCkM+sSY/kzPppXug/ATmNuilNrlX6PPM2bG7bWzlh+d+QQ7g4XM5YFQbg2Q92D6ezgS2ZVEdaaKg7mRTLR0r3eOIUk8UL3/ry+fwtFqkpeW7+F/00ca5LGt02hVqkYGtaOoWHtyCos4csN+/njUN0+VXpZJiWnQJS/Em44c+fONXUIwk0st7iMmPRsAPoH+2BtqbnKFs3vUFIq2aVl2FloOJOfCcAI38Bm2beNWsP3wycybvVPJBTmMXPzH/wyahLmV6lvHeTizMrpU5izYStro2L5ZMdePtmxF6BZrs13Z0Wy8sJaJAnkGjeW9H6c6JwcntqxhuzyMqzMzPi4/wju8Gt9KzCFpnvnnXeu+P6cOXMava+cnBx0Ol1txYxLXF1diYmJaXCbc+fOsX37dqZOncr69euJj4/niSeeoLq6+rK/Yz744APefvvtRsfVHJxUWgBUChm91jCBJTMlj7bBbYwahyAIgtCw/fv3G/2YN9XKjx9++IHc3FwqKys5ceKESHy0QjMC+hKq9aC4ppI3j69GlmU62AWikpRkV+aRXpHV4HbP3tYHK7UZJ9MyWHvm7wuynm6eBNo7UV5TzcqzZxrc9laUUVRcm/gAww27NzdsFStABEG4ZpIk8VKH4RRXGW5q7ci8fFPMiYEd0arNQQlbkuP581SUscJsFi521jw2oneDDd+8nLSmCUoQmqhLly7k5+cD0LlzZ7p06XLZR1M0tUHuggULaN++PRYWFnh5efHcc89RUSFm4d5Mdpy6WPKqWuau28NNEsPaKMP3g44+rpTWVONmaU0n58v352gqVytrvh8xEVu1hoisNJ7esQ6dXn/V7aw1aj67cyTPD+xb5/XrvTaPLkzh87ilSBLodbZ81eM5lsWcZsqG5WSXlxFo78SaOx8QiY+byJ9//lnnsXz5cj766CM+++wzVq1a1eLH1+v1uLi48PXXX9O1a1fuueceXn/9dRYtWnTZbV599VUKCwtrHykpKS0ep4eVIzq94fpNYadDBjJE03NBEIRb2k2V/BBaP5VCybwu49AoVOzPTuD3pAjMlRqCbPyBhktfAThbW/Fonx4AfLpjL+XV1YDhZtwDweEA/Bh9vPZm/62qWqdj+9kEnv9rQ70/C70sk5RfYJrABEG4KXS096CvUxdkGTIqM8koz25wnLlKxYzQrgDoLPW8s3kHqQWFxgz1urlpbZgzaUhtAuRSzw+x6kO4Udx55521Dc7HjRvHnXfeedlHY11qkDt37lyOHTtGWFgYw4cPJyur4ckrv/zyC6+88gpz584lOjqaJUuWsGzZMl577bVmOUehdfhj/2kAbDCje6iP0Y9fVVPDphhDAuZSyathvu3qJbBzK/M5VRhLbmX+NR0n0N6JxUPHo1Yo2ZR0lncO7kBuxHcPSZIIa1N/peS1XpunlOby8on/oFDUIOvVvN/xSV7ds4UPj+xGL8tMCAjhr7FT8dc6NHnfQut1/PjxOo/Tp0+Tnp7O4MGD6/QDaQwnJyeUSiWZmZl1Xs/MzMTNreGkobu7O4GBgSiVytrXgoODycjIoKqqqsFtNBoNtra2dR4tLcDekepqQ4wqqxpktUI0PRcEQbjF3VRlr4Qbg5+NM8+GDOaj05v4+PQmejv7Ea4N4XRRHJEFUYxyH9jgdtN7dGHZ8VNcKCxi6aEIZvXrBcCEdh346MhuzhXms+9CEv09fY13Mq2ALMtEZWax6lQ0a87EkFfWcB1ihSThY681bnCCINx0XggZyfTDB7Awq+LH85t4Mfi+BsfdHxzOl5GHqKCGErmSV9Zu5oepE+vdjGrNJvTqSJ8gH1JyCvBy0orEh3BD+WcZkuYqe9XUBrn79++nb9++TJkyBTA0x508eTKHDh1qlngE0yurrCYqzZD8GhwWgEpp/Ll1u88lUVxZiYuNFSfyDP2oRlzsC3jJ1sx9LEr4BRkZCYnH/KcwxLVvQ7u7ol7uXnw+cBSztq/hu6hjtLG24dFOPa66na+DFoUk1ZmcdC3X5mllBTx+5HM06jJkWeI+z3t5eNN6UksK0SiVvN17MPe279TqS00KzcPW1pa3336bMWPGNKnqhVqtpmvXrmzbto1x48YBhpUd27Zt48knn2xwm759+/LLL7+g1+tRKAz/zuPi4nB3d0etVl/3uTSX9s5OrMtSYa6pQWVeg06jJFOs/BAEQbiliZUfgknc59eT7o4+lOuqeeP4X3TSGhojnik8S7W+psFtNCoVLwzqB8DXB46QWVwCgJWZmrvaGerlfh916zQ+zywuYfGBI9yx+EfGL/2F748cJ6+sHCcrS6b36MKT/XrVmbH87sghuNmKG3eCIFwfVwtbwuw6ALAv5xiVuuoGxzmYWzIx8GItcys4nJzKt4ePGSvMZuOmtaF7gJdIfAi3vGtpkNunTx8iIiJqS2OdO3eO9evXM2rUqMsexxQNcoVrty3yLHpZRqqRmTq0q0liWHOxJG7ntu7kVZSj1ZjT082r9v3cyvzaxAeAjMxXCb9c8wqQO/yCeKPnQADmHd7FXwkNr1z/JzdbG94dOeS6rs3Tywt5+OCXqM0MN3I7mvfh5e37SS0pxMvGjpVjpjI5KEwkPm4xl0pKNdXs2bNZvHgx33//PdHR0Tz++OOUlpbWJrcfeOABXn311drxjz/+OHl5eTzzzDPExcWxbt065s2bx6xZs5rtXJqDv7MjlZWGOb5mKj01Diqx8kMQBOEWJ1Z+CCahkBS813kc43Z8ydHcJPZmXMDOzIbC6mJii8/R0a7hBoWjggP58ehxjqWm8/mufXx4x3AA7g8J57uoY2xLSSC1uBBPGztjno7RlFVVszUunlWnotl/Prl29phaqWRIoD/jQkPo5+eD6uJsnEnhHUnKL8DHXisSH4IgNJtZgXfyTORxlMoKvjm7g1lBwxoc91DHbvwcHUm1Wo+ZUmL+zn30b+tDoIuTkSMWhFuPvb19o2+C5uVd/cbQtTTInTJlCjk5OfTr1w9ZlqmpqeGxxx67YtkrUzTIFa7d8j0nAHDSWNDO18Xoxy+tqmLH2XOGJxdLXg3x9q+9FgZIq8iqTXxcokdmU8YeJnuPuaZkwcOh3UkvLWbJ6Qie37UeZwsr+rTxvuI2d4d3pL+fzzVdm2eWFzFz/zeoNReQJDCv8eT7qDQAhnoH8NmAkdhpzJt8HsKN4//+7//qPJdlmfT0dH788UdGjhzZ5P3dc889ZGdnM2fOHDIyMggPD2fjxo21n/HJycm1KzwAvLy82LRpE8899xydOnXCw8ODZ555hpdffvn6TqyZudvbUl1xMfmh0FHhpCRTJD8EQRBuaSL5IZiMp5U9L3Ucztsn1vKf6O1M8G/L8YKTRBZEXTb5IUkSrw4ZyN3f/cqfJ6O4v1s4HdxcCdA60reNN/vSkvk55gQvd7/NyGfTcvSyzJHkVFadimZjTBylVX/Psu7q2YbxnUIYEdQOW/P6X3jcbG1E0kMQhGbnaemCs9qV7KpMVqbu5N62fXDUWNcb19bOnmE+AWxKisfVzYrsC2W8sHojK6ZPRv2PmtGCIDS/BQsWmDoEdu7cybx58/jyyy/p2bMn8fHxPPPMM7z77ru8+eabDW7z6quvMnv27NrnRUVFeHl5NThWMK0anZ7TFww9A4Z1bvjavaVti0ugoqYGHwc7IrIvADDCt24sKiyQZfh3jmPlhY0klqbwiP9knDVN74/xRs9BpJcWsz4xjke3ruL3OyYT5OB8xW2u5do8u6KYGfu+BbMklAqZmipL9iWoUEoSL3W/jUdDu4vVHreAzz//vM5zhUKBs7Mz06ZNq7NCoymefPLJy5a52rlzZ73XevfuzcGDB6/pWMZiY6FBX2EGgEqpp8ZZQUZ0HrIsi38ngiAItyiR/BBM6m6frmxNj2ZfVgIncwpBBScKornPZ9xltwlr48bYDkGsPhPDvC27+Om+uw2Nz0M6sy8tmd9iT/JM5z6Yq27sv96JufmsOhXFX6ejSSsqrn3dS2vHuNBg7uwYjLfo4SHcxMaPH8/OnTsZPHgwK1asMHU4wr+MdO/DD0l/olaVsjBmJ3PC7mhw3MOh3dmUFE+WrhRbSw0xWdn8d88Bnh/Yz8gRC8KtZdq0ac26v2tpkPvmm29y//3389BDDwEQGhpKaWkpjzzyCK+//nqdWcWXaDSa2kbtQuu24XA0OmQkvcz0kVfve9ES1pyJBaC7nyc/J5/AUmVGf4+6TdfzK6vqJD9kGUqq1NhpajhWcIZnj7/LVJ+xDHcbgFJqfFVohSTx+YDRZJeVciTzAg9uWsmfY6fibtV8E48MiY/vKZeSsTerQadTEJXkgJO5NQtvH0NPd5EYvFUkJiaaOoQbhqraAjCs/Kh2kqgoq6IwrxStY/2JOoIgCMLNT/T8EExKkiTeCR+LjUpDTIGhh8e50hQKq4qvuN3zg/phrlJxJOUCm2PjARjiHUAbKxvyKsr5z7H9pJdeeR+tUUF5Bb9EnGDS978x/Kvv+N/+w6QVFWOtUTMpvCO/3D+JrY9P56n+vUXiQ7jpPfPMM/zwww+mDkO4jF6OnQGwNKtmZdIRzhZlNjium6sHnZ3dqdLr6BVsuEmz+MBRjqWmGS1WQRD+VlFRcU09Nf7ZIPeSSw1ye/fu3eA2ZWVl9RIcyourvmRZbmgT4Qby2+5IANytbHBxMP5K47yycvYlJhmeXFwAPcCzLeYqszrjqihEoYAaPSQX2JGQ58iFIi3xeVr0Ogsq9JUsSfydN059RnJZ0343matUfDN0PP52DqSXFvPgxhUUVVU2x+mRW1nCzP0/kFOdir1FOQDnLjjTxcmP9eMfEIkPQbgMW8kWAJVCj87ekPXMSMk1ZUiCIAg3jIzyQg5lJ5JR3vR+Ulezfv16unTpQnh4OB07duT7779v9mM05MaeGi/cFNws7Hit0yhePfYnlTUqNKoaThbG0N+5+2W3cbe1YWbPrizcd4iPt+9hUEBb1CoVoU5upJUW8+XJQyw6dZgP+g3j3vadjHg2TVet07E74TyrTkWxPT6Rap0OAKUk0c/Pl3GhwQxu54+5mfjnKtxaBg4c2OCSe6F1cDV3oq2VF4mlKVhpKvj49Ga+7n1fvZICkiTxcGh3nti+mv3ZydzRsT1rT8fy4uqNrH7oPqzUahOdgSDcOkpLS3n55ZdZvnw5ubn1bwDpLl57XM3s2bOZNm0a3bp1o0ePHixYsKBeg1wPDw8++OADAMaMGcP8+fPp3LlzbdmrN998kzFjxtQmQYQbk06nJzo9GyQY0aW9SWLYFBNHjV5PiKsLh7JSARju267euPgSw0Spkkpzyqo1KJDo4uBFdGE6cXlKtOYqXKxKiCtJ5PnIedzlOZy7PEdgpjCrt6+GaM0t+H7ERMav/pmY/Bwe3bqK74dPvK7yjnmVpczY9z0pZen42hsmdKVla7nP/3Zmd+1bp6eJcPOaMGFCo8f+8ccfLRjJjcXd3IlCQKmQkW31yEBGSh5B4T5X21QQBOGmI8sy5brqqw8E/kqOZN6pDeiRUSDxWuhI7vQOv+p2Fkqzq5YWlGWZ++67j507d9KpUyfOnz9PUFAQEyZMwMamZSfRiLupQqswxrMTW9OiOVl8FI2qhoj8M1dMfgA81Lsbv584TUpBIT8ejWRUaHu2JMXXvq+XZV7Zs4mz+TmEObvjr3XAz84BC1Xjvsi0JFmWOZORxapTUaw5E0t+eXnte0EuzozvFMIdIe1xtrYyYZSCcHm7d+/mk08+ISIigvT0dP7880/GjRtXZ8zChQv55JNPyMjIICwsjP/+97/06GGashhCy+jlGE5iaQq2mir2ZyewJ+sst7nWr/s+3LcdntZ2pJYUEtLJhWPJaaQUFPLh1t28O2qICSIXhFvLSy+9xI4dO/jf//7H/fffz8KFC7lw4QJfffUVH374YaP309QGuW+88QaSJPHGG29w4cIFnJ2dGTNmDO+//36zn6NgXH/tPU2NJIMM00dc+Zq9pay9WPKqV4An/zt7GDOFgtu9/OqMkWWZ/TmRAFRWW/K/nlMItHPFzcKO0upK1l04xYrzEcTmp+JqXYyNporfUzewIX0fTwTcR0/Hjo2KxcvGju+G38Wkdb+yPy2ZF3dv4POBo1FcQ4+B/MpSZu7/nnMlmfhoC1FIMmVlVrwTPp2hPvWTO8LNy87OrvZnWZb5888/sbOzo1u3bgBERERQUFDQpCTJraCtnRNHdQpUSj0q62pktUI0PRcE4ZZVrqum+7p5Td5Oj8x7p9bz3qn1Vx17ZPRrWKquPqlRkiQKCgoAQ18/R0dHo5S7FckPoVWQJIk5YXcwaXcsUMah3JNXbUpmpVYze2BfXlm7mYX7DuHpYothXsffZOCb0xF1XvOwtsXPzh5/O0f8tIb/+msdcLO0bvEmaBlFxaw+E8OqU1HE5/x9AeZkZcnYjoY+HsGuV26UKAitQWlpKWFhYcyYMaPBL1zLli1j9uzZLFq0iJ49e7JgwQKGDx9ObGwsLi4uAISHh1NTU1Nv282bN9OmTZsmxVNZWUll5d9lJhpbxkW4Pr0cwvk1eQ3W6ioUkp5PTm+mt7M/Zoq6s11VCgUPdezKWwe383NsJPNGD+PBX1eyLPIUtwf6MSjA7zJHEAShOaxZs4YffviBgQMHMn36dPr3709AQAA+Pj78/PPPTJ06tdH7akqDXJVKxdy5c5k7d+71hC+0Qst3nwDA09YGO2sLox8/vaiYoymGBueXSl71aeONnca8zriksgsU1hSil6GvUxi3uf2doLcy0zDJtxuTfLsRVZDG7+cj2J51CK1lASUU8XHsl1gr2jDddwK3uQahuEo/kI5OriwafCfTN/3BqoRo3K1seKXHgCadV0FVGQ/t/5G4okzcrYvRqHTIOjXzuz5LsL1Hk/Yl3Pi+/fbb2p9ffvllJk2axKJFi2pXzul0Op544glsbW1NFWKr1N7Rkf0lSkPyw1KHXqMiI1UkPwRBEExJkiSWLVvGhAkTsLKyIj8/nz/++AO1ESpBiOSH0Go4mVvzQofxLD6/iCoq2Jp+nKFtulxxm3GhIfx4NJIzGVlsjUpAIUno/1FDWgJGtw0io6yYhII88ivLuVBSxIWSIvZcSKqzLyszM9raOuCndSDAzrBKxF/rQFs7+2taLZJRVMz5vAJcbaw5mZ7BqlNR7E9Mrk3PaFRKhgQGMC40mL5tfcTydeGGMnLkSEaOHHnZ9+fPn8/DDz9cWwpl0aJFrFu3jqVLl/LKK68AEBkZ2WzxfPDBB7z99tvNtj+hcTwt3fG0cCO1PANXSzhXksPv5yOY4ld/hc+k9qHMP7aPc4X5lCqqeLBHF747fIzX121h7cMP4GBp/JtngnCryMvLw8/PkGS0tbUlL89wE6hfv348/vjjpgxNuAGVVVQRm5UDKhjVLcgkMayPikUGunl5sD8jGYDhDayKOJAbCUBplZrxgV0vu78QbRvmhrfhhZphrE45xsoL65EVuZTo05h/9ks+iXJnnOdtjPMOx9n88qUZbvNsy0f9h/P87g387+Rh3K1tmBZy5e8zlxRWlTN97/fEFWei1ZRjZ1EJssTbobMI1orEx61u6dKl7N27t07JQKVSyezZs+nTpw+ffPKJCaNrXYJcXajKV2FpXo2ZWofOSi1WfgiCcMuyUJpxZPRrVx2XWV7E2O0L60wqV0gSqwfNwtXiykl2C+XV75nW1NTw3nvv8ccff3Dbbbdx5MgRxo4dy6lTp3Bycrr6iVwHkfwQWpVRHp34NcWRMn0uX5xdTX/XUMyv8I9IIUm8NmQAU3/6nTWnYnluaB8WnNiPTpZRShLz/tXzI6+ijISCPM4V5pNQkMu5wnzOFeZxviif0upqTudmcjq3ftNeT2vb2mTIpf/62zng2sBqEb0ss/RQBB/v3I1eKSPpJCT932O6eXkwLjSYkUGB2Ji3/PIuQTC2qqoqIiIiePXVV2tfUygUDBkyhAMHDrTIMV999VVmz55d+7yoqAgvL9EI1Bh6OXZmReoGQuwtSS+tYGHsDkZ7hmKnrpvMsDJTMzUojP+dPMzXp47w04i72XvuPPE5eczdsJX/m3BHi6++E4RblZ+fH4mJiXh7exMUFMTy5cvp0aMHa9asQavVmjo84Qbz586T6C5+i5wyqHE39pvb2ihDyav+AT58eGY3EjDUJ6DeuJ1ZRwBQylp6OPledb9WKg2T2/ZmctverLtwgJ+SV4KyDDPlBZan/sH/YrfS3zWYiT5d6ePij7KB1SATAzuSVlrMZxF7mbt/G26WNg32Ivmnoupypu5eQmJpDhplNa42JQDM8JtIqFaUuhIMN41iYmJo375uj52YmBj0er2JomqdPBzsqKpQgQ2oFDqqnJRi5YcgCLcsSZIaVZKqrY0Tb4WP4a0Ta9DLMgpJ4q2wMbS1aZ7ERGRkJGlpadx2220AdO/eHU9PT44fP87QoUOb5RiXI5IfQqsz1qMfv6X8RaWcz3+it/FyxxFXHN/d25Nh7QPYHBvPifgM9t37KOcL8/G1s8fdqu7MLAdzSxzcLOnu5lnn9Wq9juSiQhIKczlXkEd8YR7nCvJIKMyjoLKC1JIiUkuK2H3hfJ3tzJUqHNQWWCrUSDVQVl5NXmEZVUodOkfZsPREBlWxgoc6dWVq13C87bXN8KckCK1XTk4OOp2utgb8Ja6ursTExDR6P0OGDOHEiROUlpbi6enJ77//Tu/evRscq9FojFIrUqivp0M4K1I3kF2djr9NAAnFuXwdt5sXOw6vN/bBDl345vRRDmekEp2XzadjRzLxu1/ZFBvPX6ejGRcaYoIzEISb3/Tp0zlx4gQDBgzglVdeYcyYMXzxxRdUV1czf/58U4cn3GBW7D0JgIetDQ42lkY//rncPM5kZKFSKJAvVrnq6uqBi6V1nXG5lfnkVGUjy3C7a/erlq36t9EevRni1pVfktewLn07tuaVWKlzOJJ3nK3p0bSx0HKXTxfGe3euNyPyqfBepJcW8UvMSZ7asZZfR02iq2vDqzeKqsoZt/0rMisLUMh6vO2KkSTo49iFUW4DmxSzcPOaPn06M2fOJCEhobaH3qFDh/jwww9rV1oLBg7WFtRUGiZQmil1VDoryI4tQKfTo1SKaguCIAiXc5dPF/q6+JNcmoe3lQNuFnZX36iRvLy8SE9PJzo6muDgYOLj40lISKiX1G8JIvkhtDq9HcP4LeUvLMyq+SnhALe7BdH9KjO1Xrq9PzviE9lzLom4jBwG+Ldt0jHNFErDag6tA/gYXquoriG1oJDTWZlEZqYTl59LSkkhuZWllOmrkZVQoashrbz47x0pAHsMzUYuTWCWoMZGT5iXu0h8CEITbN261dQhCI3Q1soTF40jWZW5jPPx57PTufx07hCTfLvhY+1YZ6yblQ1j/YNZefYMi08dYeHgsTzVvxef79rPO5t30MPbkzZ2om61IDS35557rvbnIUOGEBMTQ0REBAEBAXTq1OkKWwpCXZm5RZzPKwCNxOjuwSaJ4VKj8z5tvdmddh5ouOTVzqyjAJTXqLjbp+c1HUujVDO97V0McO7B/xJ+4lxpCu62xThUV5FapOO/MTv4MnYnt7kGcrdPV/q5BqCUFEiSxLt9hpJZWsq2lARmbv6TP8ZOwc/Ooc7+08uKGL9tEcX6MmS9TLBTDTplDR4WrjwRcJ9YESnU+vTTT3Fzc+Ozzz4jPT0dAHd3d1588UWef/55E0fXukiShKLKMCnKTKGn2kVBTbWO3IxCXDzsTRydIAhC6+ZmYdesSY9LXF1d+frrr5k0aRIKhQK9Xs8XX3yBt7d3sx/r30TyQ2h1PCxccVLbk1OVj4W6ijeOr+KPgY9jZXb5Wd3e9lqmdQ/nm4MRvLdpJ8oREgFOjrjZXr4mL0BJZSXJ+YUk5ReQnF9AUn4hyRd/ziguuex2alSoVQrctDbYWGsw0yiokfQU1VSSUlJIhf5fTZwlkJVywzsThFZAlmWys7Nrm5FfDycnJ5RKJZmZdUvIZWZm4ubmdt37F1oXSZLo5diZ1WlbKdJl0s8lgL1Z8cyP2sJ/etxbb/xDHbux8uwZ1p+PI6W4kId7d2dHfCKRF9J5Ze1mvptyFwpxs0cQWpSPjw8+Pj6mDkO4Af25/SS6i5UTxvXqYPTjy7LMuoslr24P9OONY1sAGNFAWaktmYcAsFW44Gt9fSUb/Ky9+LDTS6xN285vKWvBrJJAJx0KnRuncivZkRHLjoxY3CxsmeDdmQk+XXC3sOOL2+/g3vXLOJGdwQMbV/DnmKk4W1oBEJF1gRn7vqdGUYWshyFtnLigi0ajUPNC+4exUJpfJSrhVqJQKHjppZd46aWXKCoqAhCNzq/AQm/4d6ZS6tDZG1Z7ZKTmieSHIAiCCU2ePJnJkycb/bgi+SG0OpIkEa4NYWvWPlwsJBILC/g0ajNzw8ZccbvH+/Tk12MnSSooYMZvf6KQJN4ZOZghgQEXExsFtf9NuZjwyCsrv+I+rTVqfOy1eGvt8HbQXvxZi7eDFhdrqwZv0KWVFNHnt6/4Z6pDArq4i0aFgulYWlqSlJSEs7MzAKNHj+abb77B3d0dgKysLNq0aYNOp7vuY6nVarp27cq2bdsYN24cAHq9nm3btvHkk09e9/6F1qeXYzir07YSkX+K14Nf4EB2AlvTYziSc77eyr0QRxf6e/iw50IS356JYE6v2/lkzAjGLvmRg0kp/HDkOA/2ME0NeUG4mR05coQdO3aQlZVVrz68KH0lNIYsy6w6cAYkCVcbazydtEaPISozi8S8fDQqJTqNjE6WCXFwxtu2bixlNeVkVaYjSTDUrVezHFspKbnTYyg9HcNZlPALpwpj0SlSud3LHXtlO7ZeSCKjvIgvY3exKHY3/V0DmOjTla+HjGPSut9IKirgvg3Lebn7bUTlZbEwfisKMx3IEk8EdWFH/gYAHvefirdlm2aJWbg5iaTH1TmbOVCFYeVHjbWhfW9mSh709Dd1aIIgCIKRieSH0CqFaYPZmrWPNtZKEgth+fkIBrsH08+lfiPDS0qrqiirqq59rpdl3li/lTfWX7l0joOlBT72Wrzs7fCxv5jguPiwtzBv8nLzNta2fNh/OK/s2VSbAPmw//B6/UcEwZgqKiqQ5b9Tcrt376a8vG7y75/vX01JSQnx8fG1zxMTE4mMjMTBwQFvb29mz57NtGnT6NatGz169GDBggWUlpa2eE3ihQsXsnDhwmZJ4giN187aFwe1HXlVhZTqc5no05Vl54/y8elNLBvwcL066w+HdmfPhSR+iz3JM5374OOg5ZXBtzF343Y+3bGXfm19CHB2vMzRBEFoqnnz5vHGG2/Qvn17XF1d61zbiLI6QmNFJWSQWV4K5hKjugWZJIY1F0te3R7gx87UcwANNhNfm7YfSZKp0im526dPs8bgZu7M3JCn2ZF9kO/PryStIp0MMnkkeBD2yrb8mXKSIznn2ZV5ll2ZZ3Ext2FoYDC/niwitjCLh3Ysx8yyCpVaRomCD7vewbL0XwAY4TaA/s7dmzVe4cbVpUsXtm3bhr29PZ07d77i5/WxY8eMGFnr52XlTLwMkgQKmxr0aiXpKbmmDksQBEEwAZH8EFqlTtogFEjkVuUyyXcIy8+fZM7xv1h1+xPYmlk0uM35vAIud+vWzcb6YoJDi8/FJIchwWGHdQs0Sb63fScCtY6MX2P4ItOnTcvXsBOE69WUG2BHjx5l0KBBtc9nz54NwLRp0/juu++45557yM7OZs6cOWRkZBAeHs7GjRvrNUFvbrNmzWLWrFkUFRVhZ9f8dSqFhikkBT0cwtmYsYuDeZE8GTSBdamniCpM56+UE4z37lxn/G0evrS3dyI2P4dfY07wWFhP7u3ciW1nz7E74TwvrtnIsmn3olYqTXRGgnBz+c9//sPSpUt58MEHTR2KcANbs+t0bcmr4Z0DjX58vSyz/mLJqyFBATx/aL0hlgb6fWzNMJS8cjJrg526+ZuyS5LE7S696aLtwNLE39mXG8Ga9G24mTvzfMcp2CjvYEXSMVYlR5JVUcxvSYfBFiwx3IwFkPXwcfcJbM9dS0lNGe2sfXnQd0KzxyrcuO688040F7+rXlpNLTROoIMz0TVK1GY6zKxq0GuUhpUfgiAIwi1HJD+EVslaZUmAtS9xJYn0cHHiULYDSaV5fHBqIx90Gd/gNr4OWhSShP4fs9cVksTGRx/E10FrpMj/1sXVg35tfNiblsTvcad5vms/o8cgCC1l4MCBV10p8uSTT4oyV7eQXo6G5MeRvJM85j+FRwNv47OoLfwnahvD2oRgpfo70SxJEg+HdueF3Rv49swxZnTshlqpZN7oodyx+EfOZGTx5d5DPDugeWfrCsKtSqFQ0LdvX1OHIdzAqqpr2HAkBiwk7K0sCPa8/h5hTRWRcoGM4hJsNBr0aj2VOh0+tlqCHJzrjCvXVZJdlYZCAcPcrq3ReWNp1bbMbj+T/nndWXzuNzIqsnnrzH+43aU3j7W/i6eDb2dHeixfxuwmvqRuLzQk2J2zm4SyZGxUVjzf/iHMFGYtGq9wY5k7d26DPwtXF+TszO+phuSHyrwGncaMDJH8EARBuCUprj5EEEwjTBsMQFRRHPO6jEeBxOqUE2xLj25wvJutDe+OHFLbh0MhSbw7cohJEh+X3NM+FIAVcafR/au+tiAYkyRJ9cqciFInQnMKsQ3AVmVNcU0pZwrPcp9fT7ws7cmuLOHb+P31xo/1D8LZwoqMshLWnosBwMXamrdHDAZg0f7DbItL4OD5FDKKio16LoJws3nuuedYuHChqcMQbmD7IxMplg3lZYeFB5rkGmLNGcPvimHtA9iacrHklU+7erGsSNqDQqFHr1cwwcs4k4+6O3RiQfibjHAbAMD2rAM8c/wdjuadZFibEB4LvK3eNnbm5cSWnUZC4pl203HWOBglVuHGlJKSQmpqau3zw4cP8+yzz/L111+bMKrWy9/FkapKw1xfM5UenVZJZqpIfgiCINyKRPJDaLXCLyY/ThbGEGrvwfQAwwzgt0+sJa+ytMFt7g7vyM5ZM/lx6kR2zprJ3eEdjRZvQ4b5tMNOY05aaTF705JMGotwa5NlmcDAQBwcHHBwcKCkpITOnTvXPg8KMk3tbuHmoZSUdHfoBMChvEjUShWzOwwF4Nv4fWSUF9YZr1GqmN7B0Nh88akjtSuJRgYHMrZDEHpZ5vEVq3nglxUMXLiE3yNPG/FsBOHm8sILLxAbG4u/vz9jxoxhwoQJdR6CcDXrd52h5uICvsGdLt+Dr6VU6XRsjDkLwIjgAHYkJwAN9/vYmmkoeeWi8UCjVBstRkuVBQ/73cP7HZ/H08KNguoiPov7ho9ivsLX1oZ/pmg0yhrcbAyJ/bs9R9LZPsRocQo3pilTprBjxw4AMjIyGDJkCIcPH+b111/nnXfeMXF0rY+zrTXVFReTHwodVS4KcjOLqKqsvsqWgiAIws2m0cmP/Px8/vvf/1JUVFTvvcLCwsu+JwjXqp2NL5ZKC0pqykgoSeLJoEEE2DiTW1nKOyfWXrbkjputDT19vHCzNX2DcXOVinH+hiTOsthTJo5GuJV9++23LFiwgM8//5zPP/+cb7/9lv/85z+1zxcsWMDSpUtNHaZwg+vlaOjtcSj3BHpZz1D3YLo6elOhq2FB1LZ646cGhWGhMiMqL5t9acm1rz/ap26zV70s8+aGrWIFiCBco6effpodO3YQGBiIo6MjdnZ2dR6CcCUFRWXsOZ0ISgkrjZqu/h5Gj2FfYhIF5RU4WVmiM5Mprq7C2cKKLi5t6ozLrywlryYDgOFuvY0eJ0CQrT+fhr3KJM9RqCQlR/JP8n7MAu4LCECt0GGlrsDDtgCFBOHaECZ6jTJJnMKN5fTp0/To0QOA5cuXExoayv79+/n555/57rvvTBtcK6RSKpCr1Bd/1lPjakiEZF3IN2VYgiAIrV5uZT6nCmPJrWz+z8uNGzfSrVs3OnXqRK9evThx4kSzH6Mhje758cUXX3Dy5Emeeuqpeu/Z2dmxZ88eioqKeP3115s1QOHWpZSUhNq151BeJCcKogm0acsHXcYzefc3bEmPZv2F04z2DDV1mFd1T/tQvo86zuaks+RVlOFg3vxNFwXhaqZNm2bqEIRbQKhdeyyV5uRXFxJXnEiQrT8vdRjOPbsXsyb1JFP9ehJq//dNM625BZMCO/J91HG+PnWEfh4+AOSWltfbt16W2RqXwNSuYaJkmyA00ffff8/KlSsZPXq0qUMRbkBbDsRSZSYDEgM7+mGmVBo9hnVnDI3ORwYHsiU5HoDhPgG15W4v+S1pL2qlDmSJkW16GT3OS8wUZtzjfQe9nbrwv/ifiStJ5Ejhfvwc/x5jrbTkmXYPopREMQbh6qqrq2ubn2/dupWxY8cCEBQURHp6uilDa7XUOsP3bjOFDt3FqnIZKXl4+hm/Z5EgCIKpyLJMpb6qUWN3Zh3km8TlyMhISDzUdhIDXa5+PaVRqK/6HT0/P5+pU6eye/duOnTowJ49e5g6dSqnT7d8hYdGJz9WrlzJZ599dtn3H330UV544QWR/BCaVbg2mEN5kUQWRHO31yhCtG14tP1tLIzZyXsn19Hd0QcXC1tTh3lFHRxd6ejoyuncTFbFRzOjY1dThyQIAFRUVLBs2TJKS0sZOnQo7drVLx1xo1m4cCELFy5Ep9OZOpRbkplCRTf7UHbnHOFgbiRBtv50tPdgrFcYq1NO8NHpjfzYb0adC6OZHbvyY3Qku1ITic3Lpr2DM74OWhSShP5fK/ze2byDP06e4b5u4YwOaY9G1ejLGEG4pTk4OODv72/qMIQb1Ppdp6kxN/w8qKPx/x6VV1ezNc5Q5mpUSCCP7FwFNFzyalvmYVCCq6YNFkpzY4bZIG/LNrwX+jx/pG7kt5S1dd4r1ZVTrRcleITG6dChA4sWLWL06NFs2bKFd999F4C0tDQcHR2vsvWtyU5hWNmoUuipsZaRJUTTc0EQbjmV+iqmHnquydvJyCxOXMbixGVXHftzz88xV2quOCYhIQFHR0c6dOgAQP/+/UlOTubYsWN06dKlyfE1RaOnmSQkJFzxxli7du1ISEholqAE4ZJwraH+bVxxIqU1hpnAD7frTwc7d4qqK5h7Ys1ly1+1Jpcany+LPXlDxCvcfGbPnl1n5V5VVRW9e/fm4Ycf5rXXXqNz584cOHDAhBE2j1mzZhEVFcWRI0dMHcot61Lpq4N5x2s/754NHoy5UsXxvBQ2p0XVGe9ja89wH8P1xeLTRwFD+cJ3Rw6pndGrkCS6eLqjVio5nZHFK2s30/+/i/l0x17SCkXJTUG4mrfeeou5c+dSVlZm6lAEI8vOKiLy2Hmys67tszIxNZeo5CxklYSZUkHfIN/mDbARtp89R1l1NZ5aW3RKPTkVZdiqNfRy964zLr4oizJ9NgBD3XoaPc7LUUoKgmzrJ41kZNIrsk0QkXAj+uijj/jqq68YOHAgkydPJiwsDIDVq1fXlsMS6nI3dwIMyQ/ZSo9eLZqeC4IgmEq7du3Izc1l//79gOH3V3FxMefPn2/xYzd6yqRSqSQtLQ1vb+8G309LS0OhEEt2heblYu5IG3MX0iqyOF0YS0/HcMwUSuZ1Gc/du75id+ZZ/kg+zl0+LZslvF53+gfz3qEdxOTncDIngzBnd1OHJNxiNm/ezLx582qf//zzzyQlJXH27Fm8vb2ZMWMG7733HuvWrTNhlMLNIFwbgkahJrsyj3OlKfhbe+NqYcuMgL58GbuLz6K2MNAtEI3SrHabh0O7seF8HH/FR/NSt/64WFpzd3hH+vv5kJRfgI+9FjdbG/LKyvk98hS/HDtJelExXx84wjcHjzIk0J/7uobT08dTlMQShAb83//9HwkJCbi6uuLr64uZmVmd948dO2aiyISWtGFdJPM/WY8sg0Ih8dwLIxk5Orxp+9hzpnbVR69AH6zMjddA/JK1UYaSV6ND2rMpyVDyarC3P+p/ld/6PekQ5qoaAAa4dDNukFfRxtwFCQmZvydBKVDgbu5swqiEG8nAgQPJycmhqKgIe3v72tcfeeQRLC1FWeWG+Nu5kqEHhQJU1jXoNUqx8kMQhFuORqHm556fX3VcbmUBz0S+869rFYkF4XNw1GiveoyrsbOzY8WKFbz66quUlJTQu3dvQkJCUBmhmkOjj9C5c2dWrVpFr14N1/r6888/6dy5c7MFJgiXhGmDScvIIrIgip6O4QAE2LrwdPDtfHpmCx+e2kgv57Z4WNpfeUcmZKcxZ4RvIH8lRLMs9pRIfghGl5ycTEhISO3zzZs3M3HiRHx8DD0WnnnmGUaNEg03heunUarprO3AwbzjHMw9jr+1YdLE9IC+rEg6xoWyAn46d4iZ7frVbtPV1YOuLm2IyErj+6jjvNitP2BYAeJma1M7zsHSgkf79GBmr25sP3uOn45GcjAphc2x8WyOjaedkyP3dQvjzo4hWKrr3twVhFvZuHHjTB2CYGTZWUV8/ukGLi041utlPv90A926++Hs0riSsTq9no17o9FpDEnlQaHGL3lVWF7B7oTzANwR3J4ZO/4EqF0xeIlO1rMr+yhW5uCidsFBrTVypFfmqLHnMf8pfJXwK3r0KFDwqP9kHDWt9/uL0PrIskxERAQJCQlMmTIFGxsb1Gq1SH5cRrCTCzvyVJira1BaXEp+5Jo6LEEQBKOSJOmqJakAPCxdG7xW8bB0bbZYBg0axKBBgwCorKzEzc2tzn2qltLo5MeTTz7Jvffei6enJ48//jjKizNtdDodX375JZ9//jm//PJLiwUq3LrCtSFsyNhFZEE0sizXzup9wL8329JjOJ6XwhvH/2JJnwdQtOKGgfe0D+WvhGhWJ0TzZq9BWKjEjTnBeBQKRZ2SawcPHuTNN9+sfa7VasnPzzdFaMJNqJdjuCH5kRfJFO+xSJKEpUrNs8GDee34Kr6K280473AcNda12zwc2p2IbX/xY3Qks8J6Yml2+dkjKoWCYe0DGNY+gLPZOfx09ASrTkdxNieXuRu38+mOfdwV1oGpXcLwcdAa4YwFofWqqalBkiRmzJiBp6enqcMRjORCah56fd1Sq3q9zIUL+Y1OfkScSSGzoAS9iwJJgoEd/Foi1CvaHBtPtU5HoLMjNSo9qSWFaJQqBnj61hl3IPsceqkQgAEu3Y0eZ2MMce1LZ20I6RXZuJs7i8SH0CRJSUmMGDGC5ORkKisrGTp0KDY2Nnz00UdUVlayaNEiU4fY6gS7ulCdaUh+mKl16MzNxMoPQRCEK2jpa5X09HTc3Q2Twd99911uv/12AgICmvUYDWn0neK77rqLl156iaeffhoHBwc6d+5M586dcXBw4Nlnn2X27NlMnDixJWMVblEd7NqhkpRkVebWqYurlBS833kcFkozDuec59fE1l3jv7e7N142dhRXV7EhMc7U4Qi3mODgYNasWQPAmTNnSE5Ors24g+ELlatr82X0hVtbV/uOqCQVaeWZpJZn1L4+xqsTHezcKa2p4ouYHXW2GeYTgI+tlsLKCn4/e7rRx2rn7MTbIwez56mHeW3IAHzstRRXVvLd4WMMW/QtDy9bxa6ExHrN0wXhVqFSqfjkk0+oqakxdSiCEXl4OsC/qgAqFBIeHo3/Erth9xl0FycKdvJxx8nWqhkjbJy1UTEA3NEhiE3nzwIw0NO3XoL8z6QIrNRVAPS+uFK8NXLU2NPRLlAkPoQme+aZZ+jWrRv5+flYWFjUvj5+/Hi2bdtmwshaLw8HW6oqDZN2zZQ6qpwVlBSWU1pUbuLIblxZucVEnEkmK7fY1KEIgtBCWvJaZc6cOQQFBREQEEBSUhJLlixp9mM0pEnT5N9//30OHjzIgw8+SJs2bXB3d2f69OkcOHCADz/8sKViFG5xFkpz2tsYZpqdKIiu856PtSPPdxgKwPyoLZwvyTF6fI2lkCTuDuwIwLLYUyaORrjVvPTSS7z66qsMHjyYwYMHM2rUKNq2bVv7/vr160WzRKHZWKosCNMGAXAw93jt6wpJwcuhIwBYcf4YZ4sya99TKhTM7NgVgCWnI9Dp9U06pq25OQ/26MKmxx5k8T3juM3fFxnYlZDIw8tWMeKr7/j+8DGKKyqv8+wE4cZz++23s2vXLlOHIRiRs4stTo5/r6671POjsas+Ssur2HnkLDrziyWvOhq/5FVWSQmHklIBQ7+PjReTH8N965a8Kq6u4FDuSRQSaM3s8LZsY/RYBaGl7dmzhzfeeAO1um7iz9fXlwsXLpgoqtZNY6ZCV2motmCm1KNvY/g5QzQ9vyard5xi/FOLefK93xn/1GJW7xD3NARBaJrFixcTExNDfHw8P/74I1qt1ijHbXJXkR49eogbZILRhWuDOVN0lsiCKEa6D6jz3j2+3diWHsOB7HO8fmwVP/SfgbKVlr+6u11HPo/Yx8GMFM4X5uNr17KzvjLKC0kqycPH2gE3C7sWPZbQuo0fP57169ezdu1ahg0bxlNPPVXnfUtLS5544gkTRSfcjHo5dCYi/zQHcyO52+vvfjJdHX0Y6h7MlvRoPj69ma9731dbzvDudh35LGIfSUUFbE6KZ2TbwCYfVyFJDPBvywD/tiTlFfBzRCQrT0ZxPq+A97fu4vNd+xkXGsx9XcMJcHZstvMVhNZs5MiRvPLKK5w6dYquXbtiZVV3Bv/YsWNNFJnQUtIu5JOTU4JCIfH2e3cR0M6t0YkPgJ2H4yivqkFnb7imNkW/jw1RcehlmXAPd3SSntj8HJSSxGCvurFsSjuDxswwk7uvY5fa3ymCcDPR6/XodLp6r6empmJjY9PAFgKAotoCKMBMoaPawfDZkJGSh3+Ih2kDu8Fk5Rbz0eIttSup9bLMR99soVcnX1wcxd8/QRBat0YnP06ePNmocZ06dbrmYAThcsK1IfycvJrThXFU62swU/z9V1chKXgnfCzjd/yPyPxUvovfX6eRbmvSxtqW2zzbsis1keVxp3mpe/8WO9bKpGO8FbkGPTIKJN4KH8NdPl1a7HhC63dp1UdD5s6dy+nTjS811FotXLiQhQsXNvjlUDCu7g6hKBIUnC9LJaMiGzdz59r3ZncYys7MOPZnJ7An6yy3uRqSHJZmau4PDueLyIMsPnXkmpIf/+TjoOW1oQN5ZkAfVp+O5qejJzibk8svx07yy7GT9PLx4v5u4Qxq54dK0TqT5oLQHC4lt+fPn1/vPUmSxGfmTWjvnlgAwjv70Ltv0z9LN+yOqi151dbFgbYuDs0ZXqOsjTKcwx0h7dmYZFj10dvdG625RZ1xfyYfx1pjWNXX3VF8FxVuTsOGDWPBggV8/fXXgOGzu6SkhLlz5zJq1KirbH3rssKwAk6l1FNtrUeWIFP0/WiylIz8eiVk9XqZ1MwCkfwQBKHVa3TyIzw8HEmS6jTM/Tfx5UloKb5WntiqrCmqKSGu+Bwd7Op+iWtjqeWV0BG8cfwv/i96O/ZqS/q4+Jt0tcPlVl3c0z6UXamJrDh7mtld+17TDbdKXTVF1RUUX3wUXXxcep5eXsiy80drx+uReevEGvqa+M9EaH2Ki4v59ddfWbJkCUePHr3hP8NnzZrFrFmzKCoqws5O/F03JRszazrYteNUYSwHcyMZ5zG09j1vKwfu8+vJt/H7+eT0Zno7+2OmMNRknhbSma9PHiEiK42IzAt0db3+mXlWajWTu4Rxb+dOHEpK5aeISLbGJXAwKYWDSSm0sbVhStcwJoZ1xMHS4uo7FIQbjL6JZeSEG9++PYb+cn37t2/ytunZRUREpVBjd6nklfEbnSfnF3AiLQOFJDEqOJCHt68C6pe8SirJJbYoER97GQulOcE2Ld80UxBM4bPPPmP48OGEhIRQUVHBlClTOHv2LE5OTvz666+mDq/VclLbUwqYKXTIVjJ6tVKUvboGBcX1+6QoFBKerlrjByMIt7Ar3ZO/FTX2z6PRyY/ExMSrjikuFk2PhJahkBSEaYPZk3OEyILoeskPgHFe4fyYcJDYokzejFyNBIzxCqO3sx9mCiVqhfLif1WY/evnf7936bniGstnXWnVxRBvf7QaczLLi/k9/gQdnV0uJjDKr5jQ+Pu1cqr0Tb9BrZdlkkvzRPJDAGD37t0sWbKElStX0qZNGyZMmMAXX3xh6rCEm0wvx84Xkx/H6yQ/AB4NvI1VyZGcK8nh9/MRTPEzlNR0sbTmzoBgfo87zeJTR5sl+XGJJEn08vWil68XaYVF/HLsJMsjT5FWVMynO/by3z0HuCMkiPu7hRPi5tJsxxUEQTCmvNwSos4YemX07df0VR+b9kYhA1gqQJYZFGr8hMK6i6s+evl4oZNkjmWlATDMp24sq1NO1K766GYfiupiIl0Qbjaenp6cOHGCZcuWceLECUpKSpg5cyZTp06t0wBdqMvX0o0zgFIhg6UOvUYpVn40UVpWIZ8s2VrnNYVC4uWHhopVH4JgJEql4fqmqqpKfOb/Q1VVFfD3n8/lNDr54ePj0+DrN9usYaH1Cr+Y/DhREM1UnzvrvZ9ZUcTZoqza5zKGL0SrU05c8zFVkqLBRMm/kyRm/3iuk2W2Z8TU7kOPzJzI1Sw9u5fyiys2amyrsQbei/4Loi9//CuRABsz89qH7cWHjZk5Cknij6Tj/DMHqkDC28r4JQuE1iMjI4PvvvuOJUuWUFRUxKRJk6isrGTVqlWEhISYOjzhJtTTIYxvzi3jbMl5civzcdT83efIxsycJ4MG8e7JdSyM3cFoz1Ds1IYLuUdCu/N73Gk2no/jz/gz9HL3xt2qeb9ctbGz5YVB/Xiqfy/WRcXy49FIzmRksfLkGVaePEMXT3fu69qZTm1cSSssxtdBi5ut+IIn3Lh27drFp59+SnS04cIjJCSEF198kf79W64Ep2Aa+/fFIcsQFNIGJ+emfW7JssyGPVHo1KCTZZxsLAn1dmuhSC8fw5ozhmvpMR2C2Hyx5FVnF3fc/vG7QC/rDckP84slrxxCjRqnIBibSqVi6tSpTJ06tfa19PR0XnzxRTGJ6TKCHN04WSGhVMqoLGvQa8TKj6YoLa/ixU9XUVhSQVBbV955ahTZ+aV4umpF4kMQjEilUmFpaUl2djZmZmYoRMlm9Ho92dnZWFpaolJdOb3R5Ibnl4hZw4KxhWmDAThXmkJhdTF2ZnV/2SaV5KGn/pKnDnZtsDRTU62roUqvo1qvo0pfc/G/dZ/XyHXLQtTIemp0esp11dcd//nS+hdZsh5cLW3Qqi2wNbOok8ho6L82ZubYXRxnpVJfcWVKmL0Xb51YU1ub09NKi4u5uEC5VY0ZM4bdu3czevRoFixYwIgRI1AqlSxatMjUoQk3MXu1He1t/IgpTuBQXiSj3AfVeX+iTxd+STxMQnE2X8Xt5qWOwwEItHci0N6JuPwcnt25HoUk8UG/YdzbvvlruWtUKiZ06sD40BAiL6TzU8QJNkbHcSw1nWOp6bXjFJLEuyOHcHd4x2aPQRBa2k8//cT06dOZMGECTz/9NAD79u1j8ODBfPfdd0yZMsXEEQrNac9uw6qJftdQ8upMfDrJ6fmgNVxjDuzoj0Jh3Abisdk5xOfkYaZUMrS9P0/sWA3ACJ+6Ja+O5iaRXZWLv7UOpaQkXNvBqHEKgrGcOXOGHTt2oFarmTRpElqtlpycHN5//30WLVqEn5/xS9PdKDq4ulCVoMJCWY2ZRTV6jYbMlDxkWUaSjPvZdqPR62Xe+XI951JycNRa8dELd+LiYIOXu5hQKQjGJkkS7u7uJCYmkpSUZOpwWg2FQoG3t/dVP8+blPwQs4YFU7JX2+Fj6UFS2QVOFsTQ37l7nfd9rB1QINVJgCgkif/reU+jSz3pZT01ej1V+qsnSi73Xm5lKYtid9VJw0hIfNx1Aj7WjrWJjAc2rOREbgaT/W/jkU7dLxvTtbrLpwt9Xfw5npvMG8f/Irk0nxVJx5jk263ZjyW0fhs2bODpp5/m8ccfp127dlffQBCaSS/HcGKKEziYWz/5oVIoebHDMB47+DM/nzvEPb7d8LF2JL20mLP5ObXj9LLMy3s2cSo7k6E+AXRz9cBarW7WOCVJorNnGzp7tuHlwf1ZcjCCbw8fqxPDGxu20K+tN+52ts16bEFoae+//z4ff/wxzz33XO1rTz/9NPPnz+fdd98VyY+bSElxBZHHDF+K+/W/tkbnMqCwUoGuhkGh/s0c4dWtPWNI3gzw90WW4EBaCgDDfeuez1/JJ7BRG8oddLRth5VKlIEQbj6rV69m4sSJ1NTUAPDxxx+zePFiJk2aRNeuXfnzzz8ZMWKEiaNsvfycHamKVmGhqcbMTE+1VkllajX5OcU4OIvruSv5+vd97D6agNpMyUfPGxIfgiCYjlqtpl27drWlngTDn0ljVsE0OvkhZg0LrUG4NpiksgtEFkTXS364WdjxVviY2tUOCknirbAxTepxoZAUqJUK1MprXhQFgLuFXb04RnnWXYp/T2AoJ7IzWBZ3iodDu7XIzBM3CztGeoaSU1nKh6c38tmZLQx0DcTFQlzo3Wr27t3LkiVL6Nq1K8HBwdx///3ce++9pg5LuAX0dAjnu/MriS6Kp7CqGDt13S9O/V3b0c8lgL1Z8cyP2sJ/etxLYmF+A+v44KeYSH6KiUQpSXRydqOPuze93b3p5uaBhcqs2WJ2sbZmUIBfneQHgCzD4ytWM3/cKPwcxaw34cZx7tw5xowZU+/1sWPH8tprr5kgIqGlHDoYj06nx8fXCU8vxyZtW1Vdw5YDsehVUKGrwVJjRs92Xi0UacNkWa7t9zGmQxDbkhOokfUE2jvR1u7v0ollNVVsTovCyeZSyaswo8YpCMby3nvvMWvWLN59912++eYbZs+ezdNPP8369evp3r35J9DdbGwsNFRXqMDG0PS8ysMMTkNmSp5IflzBpn3RfL/qEACvPTKcDgHuJo5IEAQwrHQwNzc3dRg3nEYXCduwYQMzZ87k7bffZvTo0VdtJiIILSFMa1hhdKIgGlmuf2vsLp8ubBn6LN/2ncaWoc/WNhk3tsbEMcY/GHOliviCXI5npzewl+Yzxa8HHbVtKKmpZN6pDS16LKF16tWrF4sXLyY9PZ1HH32U3377jTZt2qDX69myZQvFxcWmDlG4SbmYO+Jv5Y0emcP5DfdgerHDMJSSxNb0GA7nJNLWzh7FvxLCEjC6bXs8re3QyTLHs9JZeOIQ9238ndAf/o+Ja37hs4i97EtLoqLm+ksV+jpo68UAEJWZzZjFP/L5zn2UV1//cQTBGLy8vNi2bVu917du3YqXl3Fvbgsta+8eQ+Kg7zWs+th37BzFpRVo7A0r6/oF+aK+Sg3l5nb8QjoXCouwUpsxKMCPjecN/T5G+NZdtbo1PZpKfQUWKsPncDfR70O4ScXGxjJr1iysra156qmnUCgUfP755yLx0RTVGgBUCj1VDoZruwzR9PyyohIymPfVZgDuH9ud4f2CTRyRIAjC9Wl08mPv3r0UFxfTtWtXevbsyRdffEFOTs7VNxSEZhRs649aYUZ+dSHJZWkNjnGzsKOHU9smrfhoCVeLw1atYbSfoRbzsthTLRqLUlLwdvhYlJLElvRotqfHXH0j4aZkZWXFjBkz2Lt3L6dOneL555/nww8/xMXFhbFjx5o6vOu2cOFCQkJCxBfCVqaXYzgAh3IjG3w/wNaFiT5dAfj49CZcLa34oJ8hIQKglCQ+7D+cLwePZd+9j7D3nkf45LYR3NWuA22sbKjW6zmSeYH/O36AKeuX0+nH/3LP2t9YcGwfh9JTqNTVNDlmN1sb3h05pDYBopAknh/YlwH+banW6/nf/sOM/voHdpw91/Q/EEEwsueff7629OGPP/7Ijz/+yGOPPcazzz7LCy+8YOrwhGZSWVnNkUOGz6Rr6fexYU8UAAprQ8JjUGhA8wXXSJdKXg0JDECWZHalJgIw/F/9Pv5KjsRKXYkkQVsrT5w1YjWecHMqLi7G1tawQkGpVGJhYSF6fDSRRm8JgJlSR7W1HllCND2/jOy8El7+dBVV1TX07eLHo/f0M3VIgiAI163RU3l69epFr169WLBgAcuWLWPp0qXMnj27dtawl5cXNjaiBqDQstQKMzrYtuN4QRSRBVH4WHmYOqTrMikwlJVnz7DmXDRzeg3Cyqx5a9j/U5CdG9MD+vLN2b28d3IdPZx8sTYTy+VuZe3bt+fjjz/mgw8+YO3atSxdutTUIV23WbNmMWvWLIqKirCzM20CVPhbT8fO/Jy8mlOFsZTWlGGlsqw35smgQaxPPUV0YQZ/pZzg3vadGeDZlvOF+fja2eNu9fc1hpeNHV42oUwKDEWWZZKLC9iflsKB9GQOpCeTVVbKwYwUDmak8Dn7MVeq6Oraht7u3vRu400nJzfUjVjBend4R/r7+ZCUX4CPvRY3WxsekWW2xCXw/padpBYW8ejvfzE40J83hg7EQ/QCEVqpxx9/HDc3Nz777DOWL18OQHBwMMuWLePOO+80cXRCc4k4mkhFRTUurra0C3Rr0rb5RWXsj0xEr4T88gpUCgX9Q3xbJtDLqNHr2RAdB8AdHdqzKzWRCl0Nnta2dHB0qR2XXl7IoZxE2tgaal53t+9k1DgFwdg2bdpUe12r1+vZtm0bp0+frjPmZpjE1FK0Sjt0GFZ+6K1k9GolmWLlRz0VVdW8PP8vcgpK8fN05O1Zo1A2opa+IAhCa9fkdcyXZg3PmDGD2NhYlixZwocffsgrr7zC0KFDWb16dUvEyfvvv8+6deuIjIxErVZTUFBQb0xycjKPP/44O3bswNrammnTpvHBBx+gMvJybaFlhWtDOF4QxYmCaO70GGrqcK5LTzdPfG21nC8qYF1iLJMCW3bJ/uPtB7Ap7Qwppfn8J3o7r3ca1aLHE1qPGTNmXHWMo2PTaoMLQmN5WLjiZeFOSnk6R/JOMdClZ70xDhorHm1/G5+e2cJ/orYxrE0I7lY2dZIeDZEkCR9be3xs7Zkc1AlZlkksyudAWjIH0lM4kJZMTkUZ+9KS2ZeWDBFgoTKju6sHvdt409vdi1AnN1SX+XLnZmuDm61NneMNax9A37befLn3EN8ePsa2uAT2nUtiVr+eTO/ZtVGJFUEwtvHjxzN+/HhThyG0oH27DYmDvv0Cm9xLbsv+GHQ6PY5eNqRWl9ItwBNbC+NOkjl4PoXcsjK0Fub08fXmpT0bARju267O+axJOQHI2KgNJa+6O4jkh3BzmzZtWp3njz76aJ3nkiSh0+mMGdINpY25EykYVn7Ilnr0GqUoe/UvsizzwdebiU7IwNbanI9fGIeVpcbUYQmCIDSL60rjXpo1nJqayq+//tpcMTWoqqqKu+++m8cff7zB93U6HaNHj6aqqor9+/fz/fff89133zFnzpwWjUswvjCtoeZkVFE8lboqE0dzfSRJqk14LG/h0lcA5koz3gozNDz9NfEwkXkpLX5MoXX47rvv2LFjBwUFBeTn5zf4aCipLAjNpZdjZwAO5UVedszUtj3xsrQnu7KEpWf3XdNxJEnCz86BqcHhfHH7GI5OfYItd03nnT6DGdU2EAdzC8prqtl94TwfHdnNuNU/E/bjf3lw00q+OnmYk9kZ6PT62v2llxazPy2Z9NK6fXGs1GpevL0/f82cSg9vTypqavhs5z7GfvMTB8+Lz1ahdaqqqiI1NZXk5OQ6D+HGp6vRc+CAoT9G32soebV+9xkAJCtD8vb2UP/mC66R1kYZyrKOCg4ECbYmJwAwwvfv/iWyLPNX8gks1VUg6XFUa2lrJfrWCDcvvV5/1YdIfFxZO7s2ACgkUFjq0GuUZIqyV3X8uPowm/fFoFQqmPfsGDxctaYOSRAEodk0y5IIpVLJuHHjGDduXHPsrkFvv/02YLiB15DNmzcTFRXF1q1bcXV1JTw8nHfffZeXX36Zt956C7W64XJClZWVVFZW1j4vKipq9tiF5uVp4YajWktuVQFRRfF0tg8xdUgNyq3MJ60iizbmLjhq7C87bmK7jnwasZcjmRdIKMjDX9uyNYt7Oftxp1cYf6Wc4K3INSwf+AhqhVgddbN7/PHH+fXXX0lMTGT69Oncd999ODiI+tiC8fRyDOf31PVEFkRRrqvAQll/RrFaqWJ2h6E8d2Q53yXsZ6JvV9yvs3+TJEkE2jsRaO/EtJAu6GWZuPyciytDkjmYkUphZQU7Us6xI8VQK99WraGHmyfmShXrEuOQkVFIEh/0G8a97evOMG7n7MSPUyey+kwMH27dzbncPB74ZQVjOwTx8uDbcLa2uq74BaE5nD17lhkzZrB///46r8uy3OQZwwsXLuSTTz4hIyODsLAw/vvf/9KjR4/Lji8oKOD111/njz/+IC8vDx8fHxYsWMCoUWL1aXM6dTKZosJybO0sCA1tWjIgISWH2MQsJJWC1ELDd6GBHYyb/KisqWFzbDwAo0PaczAthaKqShzNLenq0qZ23Mn8VM6X5uJh8/eqj6auchEE4dYS6urOpnQFZio9Kstq9BozstIK0NXoUKrEat09R+NZtGwvALOn3U7XDt4mjkgQBKF53TQF/A4cOEBoaCiurq61rw0fPpyioiLOnDlz2e0++OAD7Ozsah9eXmLmUGsnSRLhWkPCI7IgysTRNGxLxl4ejXiDt878h0cj3mBr5uVnMLtaWTPQsy0Ay+NafvUHwEsdh2OvtuRscRbfnt1/9Q2EG97ChQtJT0/npZdeYs2aNXh5eTFp0iQ2bdqELMumDk+4BfhYeuBm7kyVvprj+Zf/7B7qHkxXR28qdDV8eHIDh7ITySgvbLY4FJJEkIMz0zt25euh44m870nWj3+AN3sOYoi3P7ZqDUVVlWxNTmBtYiwyhn8felnmtb2b660AAcPvpTs7BrPpsWlM7RqGBKw+E8Pwr77jx6ORdVaSCIIpPPjggygUCtauXUtERATHjh3j2LFjHD9+nGPHjjV6P8uWLWP27NnMnTuXY8eOERYWxvDhw8nKympwfFVVFUOHDuX8+fOsWLGC2NhYFi9ejIfHjd2zrTXau8dQ8qp3n3YoVU37irfh4qoP33ZOyDKEeLrgZm/cXo474xMpqazCzcaarl4ebEwyrGIZ5hNQp+b8XxdLXmnNawBR8koQhKsLdnWlqtow2c9MrUNnrUSv05OT0XzXlzeq+ORs3lq4HlmGCUPDmDA0zNQhCYIgNLubJvmRkZFRJ/EB1D7PyMi47HavvvoqhYWFtY+UFFGq4kZwqfRVZEG0iSOpL7boHIvO/VJ7w0xG5quEX8mtzL/sNve0N5S+Wnn2NNX6ll+2rFVb8krHEQAsitvF+ZKcFj+mYHoajYbJkyezZcsWoqKi6NChA0888QS+vr6UlJSYOjzhJidJEr0cwgE4eIXSV5Ik8VKH4QBszYhhxv7vGbp5ASuTGn+DtikUkkQHR1ceCu3GkmETiLzvSdbceT9TgurfUNPJMucLL/9Zbmtuztzht7Ni+mQ6urtSUlnFu5t3cNe3v3LiQnqLxC8IjREZGclXX33FyJEjCQ8PJywsrM6jsebPn8/DDz/M9OnTCQkJYdGiRVhaWrJ06dIGxy9dupS8vDxWrVpF37598fX1ZcCAAU06pnB1siyzb68h+dGviSWvdHo9m/ZevJ62vFTyKqBZ42uMdVGxgKHROcDmi8mP4b7tasdU6qrZcOE05qoadFRhoTSng227+jsTBEH4BycbS6orLyY/lDr0bQxVQW71vh8FRWW89Okqyiqq6RrixXMPDDJ1SIIgCC3CpMmPV155BUmSrviIiYlp0Rg0Gg22trZ1HkLr18kuCAmJ1PL0KyYVjG1P9hHejvq/eq/r0ZNekX3Z7QZ7++Nkbkl2eRk7UxJbMsRaoz1D6eviT5Vex1sn1orZ/7cYhUKBJEnIsizqBAtG09MxHICIvFNU6asvO87J3LrOcz0yb51Y06wrQC5HqVDQydmNpzv3QfGvUioS4Gt3+TKGl4S6u/H7tHt5e8Tt2JpriMrMYtL3vzFnw1YKyitaKHJBuLyQkBBycq5vokNVVRUREREMGTKk9jWFQsGQIUM4cOBAg9usXr2a3r17M2vWLFxdXenYsSPz5s274u+dyspKioqK6jyEK4uLzSA7qwhzCzO6dmvbpG2Pnk4mp6AUa2sNcZmGvyODjNzvo6Sykh3xhrKDd4QEcTwrjayyUqzN1PRp83f5lZ0ZcRRVV+B2sZpguDYEM4WZUWMVBOHGI0kSuipDwsNMqafyYuXfjORcE0ZlWtU1Ol5bsIb07CI8XLW8/+wYVKIEmCAINymTJj+ef/55oqOjr/jw8/Nr1L7c3NzIzMys89ql525ubs0eu2BaNmZWBFj7AK1j9UdpTTn/ifuWBWe/pVJfvwm7AgXu5s6X3d5MoWRCuw4ALDNC43MwXATO6XQH5koVR3LO82fycaMcVzCdyspKfv31V4YOHUpgYCCnTp3iiy++IDk5GWtr66vvQBCuU4C1D45qLRX6Sk5c4bM7qaT+TDy9LJNcarwZeu5WNnzQbxjKfyRAzFUqNMrGfTFUKhRM7hLGpkcfZHxoCDLw2/FTDF/0HX+cPINeJJwFI/roo4946aWX2LlzJ7m5udeUXMjJyUGn0zW40vpyq6zPnTvHihUr0Ol0rF+/njfffJPPPvuM995777LHESVpm27fHsOqiR49/FFrmtbH7VKj86AQd6pqdHg62hHg5tjsMV7JlrgEKmt0tHWwJ9jVmU3nDas+bvfyQ6P8+3xWpUQC4GBuSJ71ECWvBEFoJJXO0GtOpdBRZa1HliDjFm16Lssy87/bzvHoVCwt1HzywjjsbCxMHZYgCEKLMWnyw9nZmaCgoCs+Lteo/N969+7NqVOn6tQc3rJlC7a2toSEtM6G2ML1uVT66ko30Iwhqiie50+8z+6cIyhQcI/XaB7zm4LE3zfMHvWffMWm5wCTAg2lr7anJJBZZpwSRJ5W9jwZZFje+smZzeRUiNJHN6snnngCd3d3PvzwQ+644w5SUlL4/fffGTVqFArFTVMBUWjlFJKCnhdLXx3KjbzsOB9rBxTUb2D7Q8JBCqvKWyi6+u5t34l99z7KzyMn0U7rQHlNDZ8c3dOkfThaWfLRmOH8fN/dtHNyJL+8nFfWbmbqj8uJzRIlBwXjGDJkCAcPHmTw4MG4uLhgb2+Pvb09Wq0We/urr2a6Vnq9HhcXF77++mu6du3KPffcw+uvv86iRYsuu40oSdt0ey8mP/o2seRVaVklu44YmozrLQzXAreH+hu9gfjaM4b4x3QIAmDTxZJXI3wDa8dkVxSzLyseM4WOMn0RChR01nYwapyCINy4rCVDHyMzpR69tYxeo7xly16t3BLJqm0nkSR458nRtPU0bsJbEATB2Jo2NciEkpOTycvLIzk5GZ1OR2RkJAABAQFYW1szbNgwQkJCuP/++/n444/JyMjgjTfeYNasWWg0GtMGL7SIcG0wK1I3cKIwBp2sRykZ9wZujV7HspS1rLqwGT0yrhonngl8kPY2htVKgTZteeHEPPTIBNlcvXxAO3tHuri04VhWGn+ePcNjYT1b+hQAuN+vF+tSTxFdmMGHpzfyabeJRjmuYFyLFi3C29sbPz8/du3axa5duxoc98cffxg5sua1cOFCFi5cKEp5tWK9HMNZn7GTI/knqdHrUCnqr6Rws7DjrfAxvHViDXpZrk2D7MiIZfyOL3mv8zj6uBinLIu7lc3FVSDDmbj2V36NOcnk9mF0cm7aqtLu3p6smjmVH44c5797DhKRmsa4JT/xQPfOPNW/N9aaxk32EIRrsWPHjuveh5OTE0qlssGV1pdbZe3u7o6ZmRnKf6yYCg4OJiMjg6qqqgYnOWk0GnHt3gQpybkkJ+WiUino2btpn4vbD8VRWVWDl7uWE8mGvkSDOhq35FVeaRn7E5MAQ7+P2PwczhcVoFEqGej1dwmvdamn0MkyQfYW6IAQ2wBszKyMGqsgtEbTpk0jJSWF7du3mzqUVs3JzJ58wEyhQ7bQo9coybwFV34cOZ3Egu8N1wRPTL6Nvl0aV2lFEAThRnbDTPedM2cOnTt3Zu7cuZSUlNC5c2c6d+7M0aNHAVAqlaxduxalUknv3r257777eOCBB3jnnXdMHLnQUtpZt8VSaU5JTSnnSpKNeuy08kxeP/0pf1zYhB6ZQc69+Cz8tdrEB4CPlQfhWsOqo/25EY3a76XG58viThmtB4dKoeTt8LEokNhw4TS7M+OMclzBuB544AEGDRqEVqutU07k348b3axZs4iKiuLIkSOmDkW4jCDbAGxV1pTUlHGm6Oxlx93l04UtQ5/l277T2DrsOX7p/xA+Vg5kVhTz8IEfef/kespr6pcZbCnd3TwZH2AoXzVn/9ZrKltlplQys1c3Njw6jeHtA9DJMt8ePsaIr75jQ3Sc6L0ktJgBAwZc8dEYarWarl27sm3bttrX9Ho927Zto3fv3g1u07dvX+Lj49Hr9bWvxcXF4e7u3ujV3cKVXVr1Ed7FF2tr8yZtu2FPFAChoV4UlVdib2VBeNs2zR7jFWOIOYtOluno5oKvg31tyat+bXyxMjP8HZFlmb8ulrxytKgBoLsoeSUIAHh4eODj42PqMFq9ttaGJL1Kob+Y/FDdcis/UtLzeX3BWnR6mRH9Q5h6RzdThyQIgmAUN8zKj++++47vvvvuimN8fHxYv369cQISTE6lUBJq155DeSc4URBNOxvfFj+mLMtszdrHt4krqNRXYa2y5DG/KfR26tLg+L5OXTlWcIZ9ORHc7TnqqmUE7mgbxNsHtnOuMJ+jmRfo7ubZEqdRTwdtGx7w78V3CQd458Q6/rrdByuVmHV5M7na56cgGItSUtDDMYytmfs4mHucMG3QZce6WdjhZmFX+/OKgY/x2Zkt/Hb+CL8kHuZAdgIfdJlAqL2HUWJ/tccANied5Xh2OivOnq4tV9hU7rY2/PeuMexKSOSdTTtIKSjkmT/X0a+tD3OGD8LXoeXKEAlCaGgo69evv6ZeGrNnz2batGl069aNHj16sGDBAkpLS5k+fTpgSLR7eHjwwQcfAPD444/zxRdf8Mwzz/DUU09x9uxZ5s2bx9NPP92s53Qr27fHMGmlX//Aq4ysKy2rkOPRqUgS1Fy85BvQwQ+lkUthrj0TA8AdF0tebbxU8qptu9oxMYUZxBVlYa6UyKkylDjuJpIfggDAvHnzTB3CDaGDoycRxSBJoLKsQa9Rkp9STGVFNRpzM1OH1+JKyip58dNVFJdW0LGdO688NNToJQ4FQRBM5YZZ+SEIDbnU9yOyIKrFj1VUXcJHsV+xKOEXKvVVhNq1Z37Y65dNfAB0dwhDJalILc8guSztqsewVqsZ7Weo12ysxueXzAoahIellvTyQr6Iuf7yGIIgCJfTy6EzAIfzTqCT9VcZ/TdLlZo3w0bzVe/7cNZYk1iSy9Q937AwZgfV+pYvdeZqac2znfsC8OHh3RRWVlzX/gb4t2Xdww/wZL9emCmV7E1MYvTiH/nP7v1UVNc0R8iCUM/58+eprq6+pm3vuecePv30U+bMmUN4eDiRkZFs3Lixtgl6cnIy6enpteO9vLzYtGkTR44coVOnTjz99NM888wzvPLKK81yLre6nOxiYqLTkCTo07dpyY+New3Xzp2DvTgUb+irMijUuCWv0gqLiEhNQwJGhbQnubiAqNwsFJLEEO+/Y7nU6LyrswN69HhbtsHN3MmosQqCcGMLc3enqsZQgtHMvAbZxpDwuBVKX+n0eub8dx1JaXk4O1jzwXNj0ahvmHnQgiAI10184gk3tEtlpWKLEymrKcdSZdEixzmeH8UX8T9QUF2ESlIy1ftO7mhzO4qr9BmxUlnQ2T6EI3kn2ZcbgY/V1Wcn3xMYyu9xp1mbGMtbvQdjbaSyEJYqNW92Gs1jB3/mp4RDjPIINdpsakEQbi0d7QKxUlpQUF1EXPE5gm0DmrR9P5cAVt3+BO+dXMeGC2f4MnYXuzPP8mGXCbS1adkbYtM7dmFZ3CniC3L5/Ng+3uo9+Lr2Z26m4unbejO2YxDvbt7BnnNJLNx7iDVnYnhz2CAG+Le9+k4EwYiefPJJnnzyyQbf27lzZ73XevfuzcGDB1s4qlvTvr2GklchHTxxcLRu9HayLNeWvOoc5sWeHQcxN1PRK9C7ReK8nHVRhvh7eHviZmPN4lOGkpU93DxxMLcEoFqvY32qYUKQg3kNuaWi5JVwa5o9e3aDr0uShLm5OQEBAdx55504ODgYObIbg5eDPVVVKjRmOszUOvSOhu/YGSl5eAe4mji6xsnOKuJCah4eng44u9g2ersvf93DgchENGoVHz8/Dif7xv++EARBuBmIlR/CDc3V3Ak3c2f06DlVGNvs+6/SV7MkcTnvRX9BQXURnhZufNjpJcZ6DLlq4uOSvo5dAdiXE9Goeu7dXD3wt3OgvKaaNediriv+purv2o7RnqHokZkbudooM6kFQbj1mClUdHMwlIw6mBt5TfvQqi35tNvdfNz1LmzNzDldkMbEXYv4+dwh9E1YTdJUZgolb/e+HYDvo44TnZvVLPv1dbDnm3vG83/jR+NqY01yfiEPL1vFkyvXkF5UTEZRMQfPp5BRVNwsxxNuXf3798fComUmiwjGtfdiyau+TSx5depsGqkZBVhozChTGK71+gT5YKE2bumXtReTH6M7GFY9X+r3McL37/PZk3mWvKoyHDWWpFYYVqh0txfJD+HWc/z4cZYsWcLXX3/Nrl272LVrF4sXL2bJkiVs27aN2bNnExAQQFRUy1dEuBGplAp0VYa5v2YKPRUOhu/lmTdI348N6yKZes9CXnjuF6bes5AN6yIbtd26XWf4Za2hT+4bjw0nyO/GSPRcTUZBMYfPppBRIK6LBUG4OpH8EG54l1Z/nCiIbtb9JpVe4OWTH7E+fScAI90G8HGnV2hr1bQa2d0cQlErzMioyOZcacpVx0uSxKTAjgAsizvZ5Liv18sdh2NnZkFsUSY/JBww+vEFQbg19HI0lL46mBt5XY2+R3uG8uegx+nj7EeFroZ5pzbwyIGfyCgvbK5Q6+nn4cuotoHoZZm5B7Y1W6NySZIYERzIhkemMaNnV5SSxObYeIb+71sGfPEND/yygoELl/B75OlmOZ5wa1q/fj3u7u6mDkO4TkVF5ZyITAKgX//2Tdp2w27DzdGBPdqxN+Y8AIM6GrfkVXxOLtGZ2agUCoa3b0d2WSlHMy8AMNzn79WAf6WcAKCfmzvlugrszezwtzbuChVBaA3uvPNOhgwZQlpaGhEREURERJCamsrQoUOZPHkyFy5c4LbbbuO5554zdaitV7U5ACqFjiprPbICMm6AslfZWUXM/2QDer3helOvl/n80w1kZxVdcbuTcRf46JstADw4vhdDel++z96N5I+Dpxn+zjc89OUKRryzhD8OiutiQRCuTCQ/hBteeG3fj+ZJfuhlPWvStvHSyY9ILkvDzsyG14Kf4CG/e9Aom16CykJpTld7QzJjX87RRm0zoV0HlJLE8ax04vJzmnzM6+GosebFjsMA+DJ2J8mlrf+CUBCEG0+YXTDmCg05VXkklCZf177cLOz4qvd9vBY6EnOligPZ5xi/43+sS2253klv9ByEuVLFoYxUVjfzKj1rjZpXBt/Gqpn30dHNlSqdjkvpFb0s8+aGrWIFiNBkZ8+e5euvv+a9997jnXfeqfMQbjwH98ej18m09XOmjYd9o7errKph20HDiovunX2IuZCNQpK4LcSvpUJt0Lozhhj6+/lgb2nBluR4ZKCTkxttrA3lXAqqytiZYRin1Rj61HRzCG306mtBuJl88sknvPvuu9ja/l3uyM7OjrfeeouPP/4YS0tL5syZQ0REhAmjbN3MZSsAzJR6dNYyerWKjORcE0d1dRdS8+pNtNHrZS5cyL/sNpm5Rbw6fzXVNToGdA/g4Yl9WjpMo8goKObtZVu49Mehl2XeWb5VrAARBOGKxJWjcMPraBeIUlKQWZlDevn1lR/Jqyrgvagv+O78SmrkGrrad+TzsDdqkxfXqq9TNwD25x5r1AxhF0trBl9s9Lg8zriNzwHGeYXT06ktFboa3j2xttlmNQuCIFyiUarpbN8BgIO5x697fwpJwVS/nqwY8BgdtW0oqq7gpYiVvHD0dwqqyq57///mYW3Lk+G9AHj/0E5Kqqqa/RjtXZx44fZ+9V7XyzJJ+QXNfjzh5rV48WKCg4OZM2cOK1as4M8//6x9rFq1ytThCddg3x5DUqCpqz72HkuguLQSV0cbcqsqAOji54G9tfFKocmyXFvy6o4OhpnIGy+WvBru26523PrU09TIeoLsXIkvjQegu32o0eIUhNaksLCQrKz633Wzs7MpKjKsANBqtVQ14Xpk4cKF+Pr6Ym5uTs+ePTl8+HCjtvvtt9+QJIlx48Y1+litgVZpBxhWfsiWevQa5Q2x8sPDs34fF4VCwuMyie/yimpe+vQv8grLCPB2Zs4TI1EopJYO0ygOn03h33cm9LJMSk6BKcIRBOEGIZIfwg3PQmlOkI0hUXA9pa8O5h7nucj3OVEYg1phxiN+9/Jq0OPYqW2uO8Yu2g6YKzRkV+ZxtuR8o7a5J9Dw5e6Ps1FU6Yzbe0OSJOaG3YFGoWJ/9jnWpBq//JYgCDe/3o7hwPWXvvqntjZO/NR/JrPaD0QpSWy4cIbxO/7H3qz4Ztn/Pz0c2h0fWy2ZZSX8N7JlygT6OdijkOp+YVVIEj722hY5nnBzeu+993j//ffJyMggMjKS48eP1z6OHTtm6vCEJqqoqObokXMA9G1qyauLjc6H9wtmx2nDPoxd8upUeiZJ+QWYq1Tc3s6PoqpK9qcZSniN+Efy46+USAAGunmTW1WARqEmVHtzlG0RhKa68847mTFjBn/++Sepqamkpqby559/MnPmzNokxOHDhwkMbFwPoGXLljF79mzmzp3LsWPHCAsLY/jw4Q0mWP7p/PnzvPDCC/Tv3/96T8no2lg4AWCm1CFbyOg1yhui54ednSUqM2Xtc4VC4rkXRjbY9Fyvl3l30Ubizmdhb2vBxy+Mw9K86dUrWqOKqhqWbjtS73WFJOHlpDV+QIIg3DBE8kO4KYRdR+mrcl0FX8b/xCexiympKcXPyotPOr3KcLfbkKTmmSGhUarp7mBozrgvp3FLkQd6+eFsYUVuRRnbkhOaJY6m8LF25PH2AwD46PRG8itLjR6DIAg3ty72HTGTVKRXZJFSlt5s+zVTKHkiaCA/93+IttaOZFUU8+iBn3j3xDrKappvhYa5SsXcXobm50tOHyW+oPlLJ7jZ2vDuyCG1CRCFJPHuyCG42V5/Yl64deTn53P33XebOgyhmRw9fI7Kyhrc3OzwD3Bp9HZ5BaUcjEwEoF83f46dSwVgUEfjlry6tOpjcKA/Vmo125MTqNbr8bdzIEDrCEBCcTanC9JQSQpsNIbP7XBtMGqFcZuyC0Jr8dVXXzF48GDuvfdefHx88PHx4d5772Xw4MEsWrQIgKCgIL755ptG7W/+/Pk8/PDDTJ8+nZCQEBYtWoSlpSVLly697DY6nY6pU6fy9ttv4+dn3M+N5hCk9QBApZDBQodeo6S0uILiwuZfIdycIiOTqKnWobW35NPPp/DzslmMHB3e4Nhv/zjAjkNxqJQKPnhuLO7O9RMkN6pPVu3kXGYelhqzOtfFcyYNwU0rrosFQbg8kfwQbgqXmp6fKoylRt/4VRJni8/zwokP2Ja1HwmJcR7DmBf6Ip6Wbs0eY1+nrgDsz41AL+uvOl6lUDCxnaEkzDITlL4CeDCgD4G2LhRUlfPx6U0miUEQhJuXhdK8Nnl9MO/6S1/9W6i9B78PeJQpbXsA8Nv5I0zcuYiTeanNdozB3v4M9vKnWq/nrWZsfv5Pd4d3ZOesmfw4dSI7Z83k7vDrK8Uo3HruvvtuNm/ebOowhGay92LJq7792zdpos7m/THo9DIh/m6czy9Ap5cJbOOEpxFnzOr0etZfKnkVYli1sinJUPKqzqqP5EgA+ru243SRoa9Sd4cwo8UpCK2NtbU1ixcvJjc3t3blXm5uLl9//TVWVoZeFuHh4YSHh191X1VVVURERDBkyJDa1xQKBUOGDOHAgcuvZH3nnXdwcXFh5syZjYq5srKSoqKiOg9TCnPxRq83fGaamdcg2WkAWv3qj/174wBDmcPwLr4NrvgA2HEojm9WGv7/vThzCGFBnkaLsaWtPxbD7wdOIUkw/8ExbJwzkyWzJrJxzkwm9BLXxYIgXJlIfgg3hbZWntiqrKnQVxJbfO6q43Wyjt9T1vPaqU/JqMjGSW3PWx2e4X6fcZgpVC0SY7g2GEulBXlVhUQXNW4lx6T2htJXu1ITySg1fhMvM4WSt8PGIgGrU0+yP8v4K1AEQbi59XLsDMCBZuj70RALlZrXO41ice/7cTW3Iak0j/v2LuG/0dupbkKy/Erm9h6EWqFkz4Wk2pt4zc3N1oaePl5ixYdwTQICAnjzzTd58MEH+eyzz/i///u/Og+hZWSnF3DiQDzZ6QXNts+aGh0H9xvK+PXr37jyNpes330GgJG3hbD9tOGaztglr44kXyCrpBRbcw39/XyoqKlmZ4phNcoIX8P56GR9bcnV2939SCxNQYFEl4t9ogThVvTTTz9RVlaG9f+zd9/hVZTZA8e/M7en3fTeQw0BAqE3AREQBVEQ7G11retvLbu2XeuqWNburoqiqKyAqAhIR3onECAhtIQ00nu/beb3xw2RSAuQBr6f57nPys2UM+jezJ3znnPc3OjVqxe9evXCzc3tgo5VXFyMw+EgICCgyfsBAQHk5+efdp9NmzbxxRdfMHPmzGaf5/XXX8dsNje+wsLCLijeltIjKACrzdk+Smew4/B0VpLld+Dkh6qqbN3ivLccMvTMn/mHMwp5+b/LAJg2vi+TRl0+85GOFZby8vzVANw3ZiBDukUQ6OlO/05houJDEIRmEckP4bIgSzK9GnoAJ5UfOOu2BfXFPJ/8HnOzl6CgMNQngX/HP0ec+fy+QJ4vnaxjQMOKtS0lzWt9FW32ZkBgKIqqsuBISmuGd0a9vEO5NXogAC/vXUJdC7aMEYSW9vHHHxMbG0v//v3bOxShmfp59UQjyWTV5pJbd/Y+0xdjiH8MP416iAkhcThUlU8Ob+DWDZ+TVlV00ceO8PDi/l7O6pJXtq2lzm676GMKQkv67LPPcHNzY/369Xz00Ue8++67ja/33nuvvcO7LC2bu407h7/K07d+wp3DX2XFvO0tcty9SVlUV9fj6elCbFzzV/UezSziSGYRWo3MiH6d2HwwA2j75MeSA84qjnHdOqPXatl4PJNau41gV3d6+jofxG4rSqewvgqzzoRe42y72tU9GrNOPOQS/rgee+wx/P39ueWWW1i6dCmONpwJWVVVxe23387MmTPx9fVt9n7PPPMMFRUVja/s7OxWjPLcTHodNqtzoaNO66Dew1mt25GHnh85nE9JcTVGk474PhGn3aa0vIa/v72Qeoudgb0i+MttV7RxlK2n3mrnb7N/odZio3+nUB4cP6i9QxIE4RIkkh/CZeNE66szDT1XVZV1hdt5Yu9rHKxKw6Qx8pdOd/JYl3tw07q0SYwnWl9tLdmDQ23eDeu0hsHn8w/vR2mFdirN8Wi30QSaPMiuLeM/h9a3SwyC0BwPP/wwBw4cYOfOU4fhCR2Tu86VOA9n8nl7SVKrnsusN/FWv6m83W8qHjojKRV53LjuU75J29asdoRn83D8QELcPMipruS/e1vmIacgtJRjx46d8ZWefu6KWeH8FOWV8+FzP6Aqzvs2VVH54B8LWqQCZHNDy6vBQ7ug0TT/q9zSjc5FNEP7RpOaV0S91U6gpzvdQ5s/M+RiWR0OVhx0rmBubHmV4fzz2IjOjS28Fja0vJoQGsfu8mSAxtl5gvBHlZeXx9y5c5EkiWnTphEUFMTDDz/Mli1bzvtYvr6+aDQaCgoKmrxfUFBAYOCp7Z/T0tLIyMhg4sSJaLVatFotX3/9NYsWLUKr1ZKWdvruAAaDAQ8Pjyav9qbYnMO/dRoFi7uCKksduu3V1s3Oz8j+A6LRG07tUGG12Xn63UUUlFQRHuTFK49ei/Y8fjd0dG8uXMfh3GK83VyYcdvVaOTL59oEQWg74pNDuGz0bqj8SK/JptJW3eRn1fZa3j08iw+PzqbOUU839xj+3ftZRvoPbLGh5s3Ry9wNN60rFbYqUiqa1xrlmqguuOn0ZFaWsz2vfVbLuOoM/KPXNQDMTttCannLDSYWBEE40fpqe2lSm5zv6pA4Fo56iKH+MVgUOzOSl3Pflm/Iq6u44GOatDr+MW3tHeAAALuLSURBVHAkAJ/s20FWZXnLBCsILUxV1VaZTSP8Jjej+JS/Y8WhkpdZfFHHVRSVzSd6v49ofsWy3aGwcrOz4uLq4bH8uv+3lldteR+8KT2DinoL/m6uDAgPxa4orMpytvA6Me+jylbPmryG6pDgbqRUNjz4E8kP4Q9Oq9Vy7bXXMmfOHAoLC3n33XfJyMhg1KhRxMScXwWXXq8nISGBNWvWNL6nKApr1qxh8ODBp2zfrVs39u/fT1JSUuNr0qRJjBo1iqSkpHZvZ3U+tA6T839lB4qrimLQdOi2V1sakh+Dh5z6ma+qKm9+sZr9h3NxczHw5pOTcXc1tnWIreaXxIMsaJjz8fpt4/EzX1ibN0EQBJH8EC4b3npPwl2CUVHZV3Gw8f3kisM8kfQqm0sSkZG5OWwiL8f9lQBj80t2W4pW1jDIJx6Azc1sfeWi0zMx2pnYmd9Og88BRgV2ZVxwLA5V5YWkRTgucpW0IAjCCf29eyEhcaQ6g2JL23wBDTB58Omg2/hHrwkYNVq2FR/j+l//w+LsvRf8YPjqyC4MC47A4nDw0rZfWzhiQbg4X3/9NT179sRkMmEymejVqxfffPNNe4d1WQqOPPUeU9ZIBEVc3L3noYO5lBRX4+Kip0/fyGbvt3N/JiXlNZjdjAzsHcn6FGe1z6iebdzyKsVZtTKhexc0ssz2/GzKLfV4GUz0D3S28FqRm4JFsRPt5otFLcOuOggxBRBsCjjboQXhD8XFxYVx48Zx9dVX07lzZzIyMs77GI8//jgzZ85k9uzZpKam8uCDD1JTU8Pdd98NwB133MEzzzwDgNFoJC4ursnL09MTd3d34uLi0Ov1LXl5rcpNcrbP02kUFFcFxaChoIO2vSooqCDtaAGyLDFw0Kmf13OXJvLL+hRkSeJf/3ctEcHe7RBl6zhWWMor3zvnfPz5qoEM7nr6ll+CIAjNIZIfwmUl3rM7AGsLtpJfV8Q3mQt5MeV9iq1lBBr9eLXnE0wNuxqNpGm3GIf6OFtfbStJwt7MYbvTGwafLz12mEqrpdViO5dnel6Nu9ZASkUec9JFWxdBEFqGl95MNw/nl7ptrdz66mSSJHFz1AB+GPkAPT1DqLJbeHr3Tzyx63vKrbUXdLwXB1+JVpJZnZXGmqzTt4EQhLb2zjvv8OCDDzJhwgTmz5/P/PnzGT9+PA888ADvvvtue4d32dFom37FkjUSj/5rKn5Bnhd13E0bnVUfAwbFoNef2v7kTJY1DDq/akg3UnMKKa2uxd1kICEm5KLiOR+1Vhtrjjg/E6/p4VzUc6Ll1ZiIGLQNrUx+ztoLwHXh8ewqcy766e8lqj4EAaC2tpY5c+YwYcIEQkJCeO+997j++utJSTn/2ZDTp0/n7bff5vnnnyc+Pp6kpCSWL1/eOAQ9KyuLvLzLr9rfT+9MEOhkB6rpt8oPRel4C/tOtLzqEReK2dOFvJoqtuRmkVdTxdakY3w0ZwMAf7ntCgb2imzHSFtWvdXOk1/9NufjgXFizocgCBen+XfNgnAJOLFaN6kilYf3vND4/pX+Q7g7aiomTfuXgfYwd8asc6fCVsX+ioP08epxzn3i/YLo7OnDkfISFqWlclv3+NYP9DT8jO480WMsL+5dzAepvzImqDvBLp7tEosgCJeXQd7xpFYeZXtpEtcGj27Tc0e6+fLt8HuYeWQTnxxaz4rcAySWZPGvPtfhazSwv/wYPT2j6G4OP+exOnv5cE9cAp/t38lL235laHAERq243RLa14cffsh///tf7rjjjsb3Jk2aRI8ePXjxxRd57LHH2jG6y8+udc4K5MiugTz4wmSCInwvOvGhqmrjvI+hw7s2e79jOSWs3eF8gDZhRA+WJjsTKCNio9Bp2m4x0K9H0qiz2Qn3MtMrKABVVVmZ6Wx5dXWks51LVk0pu0uzkJGYENKDp/f/BIiWV4IAcNNNN7FkyRJcXFyYNm0a//znP0/boup8PPLIIzzyyCOn/dm6devOuu9XX311UeduL5FuARQCWllBNSmoRi02ay1lRVX4BJjbO7wmTiQ/Bg/tzNxD+3hm40oUVCTA76gWrapy7cg4pl/dt30DbWFv/LSWI3lizocgCC1HfIoIl40SSxlL8tae8v4D0TfzUKfbOkTiA0AjaRjc0N9+c3HzWl9JktRY/dGera8ApkT0oZ9PBHUOGy/vXSL6hguC0CIGNrQETK1Mo9xa2ebn18oaHux6BXOG/4loN1+KLdU8lfQ5zyXPYN7xeTyXPIM3D3zXrGP9X58h+Lu4kllZzufJu1o5ckE4t7y8PIYMGXLK+0OGDLksV/a2tx1rUwEYMrYnvQZ1uujEB0BWZjE52aXodBoGDGxeu6pFa/dz69+/wu5wrmg+nFHA2pPmfbSlBXudK9NHxkQhSRL7ivPJq6nCVadjaLCzncmibGfVxyC/aEpshdQ46vDQutHZPapNYxWEjkij0TB//nzy8vL46KOPmiQ+kpOT2zGyS0u8XyQAGllFNtjRmJ3PCDra3I/q6nr2JmUC0CkhmKc3rkDB+b1bBQpj7HSJ9edv91zZprObWtsvian8sC0ZSYIZYs6HIAgtRCQ/hMtGbn0hKqc+iA/qgD2ChzS0vtpeuhebYmvWPjd06oFWktlblM/B0qLWDO+sZEnmhd7XopM1bCw8yrLj577ZPrlEVxAE4XT8DN50cotARWVH6d52iyPOK4Rvht/FyCBvAt2qOPF9UpJgW9lGUiuyznkMN72e5waMBODDPVs5Xt32yRxBOFmnTp2YP3/+Ke/PmzePzp07t0NEly+b1c7uhqHkA0Z3b7Hjnmh51SchEldXwzm3Lyyp4o2Zqzh5jcrrs9eQVVyOXqthaLfIFovtXGbv3MOWDOdn5zeJe/k+KZnlDS2vRoZGY9RqUVSlMflxXXhvdpbuA6Cfd080kvjKKggn2l1pGiq2qqqq+OyzzxgwYAC9e/du5+guHX2Dw7E7nJ8peqMDu1kH0OHmfuzakY7drhAe7kOuWnvqUw4JJkzsjV53+VQXHyss5eX5awDnnI9BYs6HIAgtRNxJCpeNYKM/Ek1XPcjIBBn92imiM+vuEYO33kyto46k8tRm7eNjcmFMhHOV3rxD7Vv9Ee3ux/1dRgAwI3n5WXvjzz20jyFzP+XmpfMYMvdT5h7a11ZhCoJwiRnk7ayK21aa1ObnrrHXsaFoB28e/JQHdz9Hvv0gv19IJ0nw98TvmHVkMzk1ZWc93nUx3RkQGEq9w86r29e1XuCC0AwvvfQSzz//POPHj+eVV17hlVdeYfz48bz00ku8/PLL7R3eZSVl1zHqqi14+rjRuWdoix33RMurYc1seZVxvATld9W5Nr3zzwM7h+NqbJsBxcfLK3h11brGP6uqyj+XreaXdOf1jIt0Jt8SS7I4XluOq1bP6ICujckP0fJKEJrasGEDd955J0FBQbz99tuMHj2abdu2tXdYlww/d1esVmcCSWewU2dyfi52tMqPrVt+a3mlsUqckv1Qwaw9dyL8UlFntfHkV79QZ7XRv1OYmPMhCEKLEskP4bLhY/DigZhbkBv+s5aRuT/mZnwMXu0c2alkSW6s/mhu6yuA6V2dXwB/PJqCxWFvldia60+dhxLj7keJpYZ/p6w67TZ5NVXOEt2GL9+KqvLsppWiAkQQhNM60foqueIQ1fbzHzh+vqrttawt3MZrqf/hnp1P8f6Rr9heuherYsNb78nvu/qpKmTVVPPvA6sYt/p9pq37lJmHN5JZXXLKsSVJ4uXBVyJLEr8cO8Sm45mtfj2CcCZTpkxh+/bt+Pj4sHDhQhYuXIivry87duzg+uuvb+/wLis7fnUuauk3shtyC/UpLyio4PChfGRZYvDQc1fqOBSFBSuTTn3f6MzojurZNi2vaqxWHv952Snv22WFzKpy9LKG0WHRAPycnQTA+OAeFFmLKbSUoJd19DJ3a5NYBaEjy8/PZ8aMGXTu3Jkbb7wRDw8PLBYLCxcuZMaMGfTv37+9Q7ykOKzOag+drGBxdaDKUodKftjtDrZvdbYoHDy0My4aHU3WeKpgypUwKpdP1ccbP63jSF4xPu4uvHG7mPMhCELLunw+LQUBGBMwlD6eseTVFxFk9OuQiY8ThvomsCTvV3aW7sPisGLQnHsF3hUhkQS6uJFfW83qzDSuiW7+wMuWppe1vNh7IrdvmsWPWXu4NrQXA/2a9mT+7uC+UxapOFSVjIoyglzd2y5YQRAuCcEmf8JdgsmqzWVn6T5G+bf8qq9KWzU7SveytWQP+ysO4lCVxp+FmAIY7NOXwT59iHAJ4a3UuWwr24gkORMfkgR/6tyfpJIydhZnkFKRR0pFHu+lrqGbOZCxQbGMC4kl0s0XgO4+/tzRvQ9fHdjNC1vXsPyGO9HJbTdgWBBOlpCQwJw5c9o7jMveiXkfA0a1XMurLQ0tr3rEheLl5XrWbVVV5c3PV7MxMa2xD7yqqqCVUHQSkgQje0S3WGxnUlBVzQPf/0xKfuGpMRqdd4dDgsNx1xuotVtZcfwAANeFxzdWffQyd8OouXxWNgvChZg4cSIbNmzgmmuu4b333mP8+PFoNBo++eST9g7tkiU5jEAtWtmBw11FMWg6VNur5H3ZVFfXYzab6B4bwlNLnUlkyQJqw0eitlrmxfmr+CrQm0j/jvvMozmW7Erlx4Y5H6/fdjW+Hmf/PScIgnC+RPJDuOz4GLw6dNLjhM5ukfgZvCmylLK7PKVxCPrZaGSZqV3i+ChpG/MO72vX5AdAX59wpkf2Y17GLl7au5gfRz2IUaNDVVX+u28H7+/Zcso+Gkki0tzx//0IgtA+Bvv0Ias2l+0lSS2W/CizVrC9dC/bSvaQUnEEhd8SHuEuwY0JjzCXoCb7/T32ZlIrhpJScYy0mkPsKEviSN0ePh38LJU2C2vyDrIy9wA7io9xsCKfgxX5fHDwV7p4+DM2uAdjg2N5PGEoi9MPcrS8hK9SdnNfT7E6U2g7siyfcxCqJEnY7e1bTXq5yM0o5vixIjRamb7DurTYcTc1tLwaeo6WV6qq8sG361m0dj+yJPHyo9fQs3MwOQXl7M7J5YNlW+gVEdTqD5YOFRbz5/kLyauswstkYlp8HDO37UJRVWRJws/PjZzaSsY3tLxak5dKrcNKmKsXfb3DmX98LiBaXgkCwLJly3j00Ud58MEHxYymFmJUXYFSdBoFxVVBMWg6VOXHloaWV4MGd0ajkdlS7JyZZCySsLur2Mxgd1MoLa/jrg/n8+kDN9A1pOO1+m6OYwWlvPK9c87H/WMHMahLeDtHJAjC5UgkPwShnUiSxBCfvvycu5rNxYnNSn4ATGtIfmzIyeB4dSUhbh6tHOnZPRY7hrX5h8isKeXTwxt4qOtIntu0inmHnXNJhgaHsy0vG4eqopEkXhs2VlR9CIJwRgO945mX/QtJ5Qeoc9Rj0hgv6DgllnK2l+5ha8keUivTUE+qQ4t2DWOwTx8G+fQh2BRw1uN0N4fT3RxOjb0/f9mTRm5dAT/nrmZq6NVMi+zHtMh+lFlqWJPvTIRsLzrG4cpCDlcW8tHBtcS4+zEw0pflRzJ4b/dmJsV0J8DF7YKuSRDO108//XTGn23dupUPPvgARVHOuI1wfk5UfcT1j8LVw9Qix6wor2X/vmwAhg0/e0Llix+2Mneps53qM38ey5WDnMkSfx93Ptu4E4BRca3b8mpTeiZ/+XEJNVYrUd5ezJw+mXAvT25N6E1mWTkGvYbrfpmDBFwV0QmAnxsGnU8K602ZrYKj1ZlISPTz6tmqsQrCpWDTpk188cUXJCQk0L17d26//XZuuumm9g7rkuapNVNNNlrZgerirPwozivHbnOg1bVvha6qqmzZ9Nu8j/L6Oo7bq0ECtyo7GpONEq0W70gvQovcOHi8iHs+/p7//Pl6ekcGnePoHUud1cYTs5c0zvm4f+zA9g5JEITLlEh+CEI7Gurbj59zV5NYtr/ZD/kiPLwYFBTGtrxsFhxO5v/6DmmDSM/MXWfkuZ4T+L+d8/jiyGbWpeewO68QWZJ4YdBo7urRl7yaKjIqyog0e4nEhyAIZxXuEkyQ0Z+8+kJ2l6Uw1Deh2fsW1pewrTSJbSW7OVR1rMnPOrtFMqgh4RFo9D3vuFy1LtwVOYX3j3zFDznLGebbv/E4XgZXpkYkMDUigXJrLWvzDrEy7wBbCtNIqyoijSJcvEGx13Pvhjm8Nfg6unoEnHNFviBcrOuuu+6U9w4dOsTTTz/N4sWLufXWW8XA8xZ0IvnRvwVbXm3dcgRFUYnpFEBgkOcZt/vfkl188cNWAB67cxTXjoxr/FlVnYUdR5wJlNE9O7VYbL83b89+Xly+BoeqMiA8lI+mTMTT5Ly3DfRwJ9DDnVnJzuRM/8BQfE2u5NVVsK0oHYDrwnqzq9S5eKazWySe+vZd4CMIHcGgQYMYNGgQ7733HvPmzWPWrFk8/vjjKIrCqlWrCAsLw91dfL86HyEmPw4BOo2CalTARYei1FKUV05QuE+7xpaZUUx+Xjk6vYaE/lF8mbwbJPBzqSLihmKQIESFzLw6/jn+Id6au46kjDz+/N8feP9Pky6pyokZP67jaF6JmPMhCEKrE58ugtCOol3DCDT6YVVsjV/2mmN6F+dKuPmHkxuHibenMcHdGewbg0NVSK3LxFWn5fOrrueuHn0BCHJ1Z3BwuEh8CIJwTpIkNQ4+31ay55zb59cXsfD4Sp7a9wYP7v4nszN+aEx8dHOP4e7IqXyS8C9m9Po7k0OuuqDExwnDffvT09wVq2Lj8/R5zj76v+Opd+H6iD78d9CtbLz6b7zWZzIjA7uglWRkrUq6JY8p6z7hmjUf8t6B1Rwozz3tcQShpeXm5nLffffRs2dP7HY7SUlJzJ49m4iIiPYO7bJQW13P/u3OAbUDRsW22HE3N8z7OFvVx8I1+/hwznoA7p82lGnj+zb5+abUDOwOhSj/1ukNr6gqb63dyD+XrcahqlwX151ZN13fmPg42YoM54rmcRHO9j1Lsp3z4fr7RBDi4tU476O/t6j6EISTubq6cs8997Bp0yb279/PE088wYwZM/D392fSpEntHd4lpatnCABa2QEGB1pPZ6VeflZJe4YFwJbNzs/IvglRmEx65uzdi05rJzyiuHHouSRBRFAxy47v55MHpjC4Szh1VhsPf7aQtclp7Rh98y3eeYCftjvnfMy4Xcz5EAShdYnkhyC0oxOtrwA2lyQ2e78JUV3w0BvIqa5gS25Wa4XXbIkFx0nMKENVQKNTuLdvL64Mb922CoIgXL4Ge8cDsLssBYvDesrPj9cVsCBnGU/ufY2Hd7/AN5kLOVqdiYxED4/O/ClqGp8lvMarPZ/g2uDR+Bm8WyQuSZK4L/omtJKWPeUpbCtNOuv2HjoT14XH8/HAW9h89d/p49oJu0UDKmTWlDLzyCZuXP8ZV6/+gH+nrCK57LhIhAgtrqKigqeeeopOnTqRkpLCmjVrWLx4MXFxcefeWWi2PZuPYLc5CIrwITS6ZXqv19Va2bXLWRVxpnkfKzan8uYXqwC4fVJ/7px8atuQEw/DRvds+XuzepudxxYuZebWXQA8MmwQb04ch157aoOBkrpadhTkADAusjOqqvJzdhIA14XFU+eoZ3+Fc75Jf+/eLR6rIFwuunbtyptvvklOTg7fffdde4dzyRkQHI2qgiyBxuDA5u5sdZXfAYaeb21Ifgwe0plam5UcRyVGvY3fFwtLEqzMTsak1/LhfddxZc9O2BwOHv9yMb8kHmyHyJsvvaCEVxY453w8MHYQAztfOtUqgiBcmkTyQxDa2TDffgDsKTtAjb2uWfsYtTomRXcDaJyt0V6WpB/k5qXzKK2z4IOzTHhu1nby6yraNS7hj+njjz8mNjaW/v3FUOlLWYxbBL56L+oVC4tz11BcX0pWTS7zspbwWNK/eHTPS3yXtZhjNTnIyPQyd+P+6JuZ2e91Xo57jAlBI/ExeLZKbCGmACaHXAXArGPzqXPUN2s/N52RD4bdgMHiSXWJCxMDE7gqqDtGjZbs2jJmHd3M9A0zGbvqPd5KXsG+0pwmiZD8ugq2Fx0Tn63CeXnzzTeJjo5myZIlfPfdd2zZsoXhw4e3d1iXpZ0NLa8GjOreYi3tdu5Iw2Z1EBziRdRpEiobdh3llf8sQ1Xhhqt68+BNw085t9VuZ+MBZzXcqBZOfpTW1HLn/xawLPUwOlnmjYnjeHTE4DNe/+qsNBRVpYePP2HuZvaV5XCsugSTRsfY4Fj2lqdiU+0EGv0INQW2aKyCcDnSaDRMnjyZRYsWtXcol5Quvv7Y7M6Eh95gp1bvvN8qaOeh56Ul1RxMPQ7AoCGdmLUjEWSw1sn8fm2MqkJmRS2ppUXotVreuvMaru3XHYei8uycZXy/ZV87XMG51VltPDn7F+qtdgZ0DuPPYs6HIAhtQMz8EIR2Fu4STKgpkJy6fHaW7mWk/6Bm7Te9ay++PbiXFRmHqbDUYzZc2FDgC6WqKv/Zu503d20EYEx4DO+NvIYHtn9LUmk2/9q3lA8H3CR62gtt6uGHH+bhhx+msrISs9nc3uEIF0iSJIJM/hRby/guezHfZS9u8nOtpKGnuRuDffrQ37sXHrq2HSB+Q8g4NhXvIr++iLlZS7g7amqz9vM2uvBkwjD+uWU1vxw6xrob78Wo07Cp4Cgrcw+wvuAwuXUVfJW2la/SthJo8mBscCx6WcusI5tRUJGReDF+IlMi+p77hMIf3tNPP43JZKJTp07Mnj2b2bNnn3a7H3/8sY0ju7yoqsrOdc6Vti0572NTQ8urocO7nHI/tWNfBv94fwkOReXq4bE8cdeVp73nWpl0hBqLFW83E3FhLZdQSC8p5b55C8kur8DDaODjKRMZGBF21n2WZzivZ1yks+XViUHnY4K646oz/NbyyquXuH8UBKHVaGQZm1WLXudAp3NgMSqoGon8dk5+bNt6FFWFrt2C8PV159vv94AOXKFJ5YeqQn61Ow5Vw6K0VGJ9/NFqZP518zhcDXrmbd7LK9+voarOwj1XdqwFYSfP+Zhxm5jzIQhC2xCfNILQziRJahzou7m4+a2vevoG0N3bD4vDwcK0A60V3mlZHQ7+vnFFY+Ljnh4JfDZmMu56Ay/1nohWklmbf4hVealtGpcgCJeHEksZyRWHT3m/l7kbf+l0B1/0f4N/xD7MlQFD2jzxAWDQ6Lk3ajoAS/PWkl6d3ex9b+3Wm1gffyqtFt7atRFXrYFxIT34d/8b2Tj+b7zXfxoTQuJw0ejJr6vk67RtfH5kEwrOJX8KKi/uXSwqQIRmueOOO5g2bRre3t6YzeYzvoSLk5ZynNLCSowuenoOaJnqCpvNwfZtRwEY9ruWV3sPHeepd37GZncwckBnnr1/HLJ8arLgx23JPDtnOQCl1XUs3JHSIrHtzMph+uy5ZJdXEOrpwdw7pp8z8VFttbLpeCYA4yM6Y3HYWHY8GXAOOneoDhLLnH8W8z4EQWhtql0POIeeK+4qikHT7m2vGlteDe1MrcVKvlwDQKiPA4A6m6Zx2xqLAcUhsTj9YGOlsCxLPDtlFH8a40x4vLdkEx8u3dxhWqqemPMhSxJv3D5BzPkQBKHNiMoPQegAhvgkMC/7F/ZWpFJlq8Fdd+4bAUmSmN6lJy9u+5V5h/ZzZ2zbrAKusNTzwJqf2ZKbhSxJvDh4dJNzd/Lw597Ow/jk8AZe27eUQX5ReOhMbRKbIAiXh9z6QlRO/aI2JXQ8ceYzD/1tS328Yhni05ctJbv5LP07Xu35JBrp3GtKNLLMy4OvZOqS75h7aB83d+tFb78gAExaPVcFx3JVcCz1DhtbCtP4X/oOthanNzmGoqpk1ZQSaBIPrYWz++qrr9o7hD+E7b86F6H0GdoZvaFlvl4l7cmkptqCt7cr3WNDGt8/dKyAJ974kXqLnUG9I3npkQloNad+9uSXV/Hy/NVN3nt5/mqGdIsg0NP9guNalJzKM7+swuZw0Ds4kE9uvA4fV5dz7rcuJx2r4iDKw4suXr6szD1Apa2eQKMHA/yiOFSVTpW9BjetK908xNw4QRBal85hAqrQyg4crgqKQdOuba/q620k7nK2KBwytAvvrNyAqgHJpqLxLAOgtM4VH2ox6uz0N/uxpcxBTnUliYW59Atw/p6QJIn/u2YYbgY97/+ymZmrdlBTb+Xvk0eeNkneVprM+Rg3iAGdz54wFwRBaEmi8kMQOoBQl0AiXEJwqArbzzFA92STO8WilzWklBSSXFzQegE2yKos5/pFc9iSm4WrTscXY284bdLlz12GE+XmQ5GlmncPrD7NkQRBEM4s2OiPRNMvaDIyQcaWGSLcUu6OmopJY+RIdQarCzY1e7/+gaHc0CkWFXh+yxqU06zIM2p0jA7qxr/6XofMqV9WvfXnftgoCELbaJz3MTq2xY65eaNz8PeQYV0aH1gdyynh/15bQE2dlfhuIbz+2CT0utMnW7KKyk/5bFFUlezi8guKR1VVPtq4jScXLcfmcDCuaye+ufXGZiU+AH466kwQDQ2OQJKkxkHnE8N6oZFkdpY6W2AlePVAI2nOdBhBEIQW4SY7k8A6WUF1VVH0GspLqqmvtbRLPLsTj2G12gkMNBMV7cePR5yfmT4aBY3eiqJCiCGMSosBgCLLccaGN7QQTDu128Kfxgzg2SmjAfjfxiSen7sSu0Npo6tpqs5q44mvnHM+BnYO476rBrRLHIIg/HGJ5IcgdBAX0vrKy2hibEQnoPUHnycWHGfyojmkVZQS6OLGgmtvYXRY9Gm3NWh0vNB7IgDzMxJZlXtADOoVBKHZfAxePBBzC3LDbYqMzP0xN+Nj8GrnyJry1ntyc7jzs25O5s+UWyubve/TA67ATacnqSiP7w8nn3G7QJOZF+MnIv+u//3juxZwvLbswgIXBKHFlBdXcXhfDgD9R3ZrkWMqisqWTSfmfThbXh0vKOfR1xZQUV1P9+gA3v7b9RgNujMeI7+s6pT3ZEkizNfzvOOxOhw8vWQlH2zcCsC9gxJ4/4ZrMZ4h8fJ736YmsTorDYA5B5P4PGUHmwqdLb2uC4tHVVV2nDTvQxAEobX5GbwB0GkcqCYF2d2ZVCjIaZ97q5NbXh3LLaHU5EzCBPjXA1BtNfBkj/EE6p0VExrXSqLdna1fl6Yfwq6cmti4aVhvXrt1PBpZYtHOA/z961+w2u1tcTlNzPhxLWn5Jfi6uzDjdjHnQxCEtic+dQShgziR/EiuOESF9dQvrGcyvauzL/LCoweob6WbmcVpB7l56TxK6mvp4ePPz9fdRqyP/1n36e8b2TiQ968753PPltlctfI9fsjc3SoxCoJweRkTMJRPEl7hpR5/5ZOEVxgTMLS9Qzqt8YEjiHYNo8ZRx+zM5g+NDnBx4699hwDwxs4NVFjqz7jtlIi+rLrqr3w59E4+HXQrAUZ30qqKuHnD5+wry7noaxAE4cLtXO/stx7TIwSfgJZpRZd64DilpTW4uhmI7xNBYUkVf3n1e4rLqokO9eHdp6fg6mI44/419VY+WrYFoLFuTJYknp825rxbXlXU1XPv3J/4af8BNJLES+NH8/fRI05JyJ6gqCrHqyvZdDyTOalJPL1pBc9tXtX4cxV4M2k1DlWll1cIUe6+HK/LJ7++CK2kJd6r5apnBEEQziTKLRAArayAQUFjNgK0y9Bzh0Nh65bfkh/vLduIqgMcKhqzMx7VbmaAXxTXhQ2g3qZFkiCp6ABeBhPF9bVsyc087bGv7dedf991LTqNhtX7jvLoF4uos9ra6tJYtPMAP21PQZYkZtw+AR93MedDEIS2J5IfgtBBBBr9iHENR0Fla+meZu83NDiCEDcPKq0WVmQeadGYVFXlo6RtPLJ2MRaHgzHhMXx/7c0Eujbvi/PtUQOb/FkM6hUE4Xz4GLyIM3fpcBUfJ9NIGu6PvhkJiQ1FO9hfcajZ+97Voy+dPH0oqa/lncTNZ9020GRmgG8UwwI6878R99LNHEiJpYa7Nn3FytwDF3sZgiBcoMaWV6O6t9gxNzW0vBo4qBNVtRYefW0BeUWVhAZ68v6zUzG7n32W2ge/bCa/vIoQbw9+fuYuvnh4Ksuf/xM3DIo7rziyyyuY/vU8tmVm46rX8em0ydzct3eTBMe3qUn8a/ta7l35E2MWzKLbV+8yZO6n3LpsPs9uXsV3B/edclzZ6Fysc11YPAA7S53Vyz3NXTBpjOcVoyAIwoXo7R8BNCQ/dAo2V+ejsfYYen4oNZfyslpc3Qx07R7MmgLn7A8fnYLeYEdRYULIYDSSzPiQOKqtzs/J42oao8OiAFiUdvCMxx/dsxMf3XcdRr2WLQczeeCTH6mqa/32Xmn5JfyrYc7Hg+PFnA9BENqPSH4IQgdyovpjy3m0vtLIMlM79wBg3qFTv2BeKKvDwd82LuetXRsB+FNcAp+NmYyrTt/sY5Raa095T1FV/nNwPaWWmhaLVRAEoT11co9kXOBwAD5Ln4tNad6KOp2s4aXBVwLwdeoeUksKm7VfoMnM10Pv5oqAzlgUO4/vnM+sI5tRTzM7RBCE1mO3OUjc4GxP1b+Fkh+qqrJ5o/OYCQOi+OvrC8jMLSXAx50Pnr0RXy+3s+6fdCyXuZuTAHhh2hgi/b3o3ynsvCs+9uTkcv3sORytLMbFU8eovtF8nbbnlATHc5tXMXP/LlZlHeVIeQkWhwOtJBNt9mJ0WDTTu/RsMrVI1jjQaBV0kobxIc77151lznkf/b1FyytBENpG/6AoFBUkCXRGO7UNX3HbY+j5loaqjwEDYli38wi1ng4APL2c3SCqLQamhPcDwNfoRidXZ9trV3MtWquz3dXyjMNn7QIxuGsEnz0wBXejgT3Hcrn3PwsorT71u3pLqbXYePKrJdRb7QzqEs69Y1p2zkdeTRVbcrPIq2l+xwxBEP64RPJDEDqQIQ3JjwOVRym1ljd7vxsbvlhuzs0iq6r5+51JhaWeO5Yv4PvDyciSxCtDxvD8oNHn3Z8zws37tIN6f8jazZUr3+GZ3T+xv+z4RccrCILQ3m4Jn4SnzoPcugJ+Pr662fsNC4ngmqiuKKrK81vXNDuB4aoz8MGAm7glagAq8O8Dq3h53xLsiuMCr0AQmufjjz8mMjISo9HIwIED2bFjR7P2mzt3LpIkMXny5NYNsA2l7DpGbXU9Zh9XuvRqmRWtx9KLyD1ehk6vZf7G/RzJLMLLw4UPnptKkJ/HWfe12u28OG8VqgrXDYhlUNeIsz4gUlSVnKqKUyo4Bs35hMnL5lDkWoPNS6FMX8cP6SlNEhw6+bcExz09Enh5yJV8M34qG6fdx6G7H2Ptjffy5bgpvDliPDOGj0PT0CZLb3J+Ro0K6oqn3oVyayWHqzIA6CfmfQiC0EY8jCZsNufcIp3BTr3OgaKRyM8uafNYTsz7GDKsM7PW7UAxAIqKq5dzlpy3LpgIN5/G7SeFDqDe7mx9tbt4P4GublTZrKzLST/reeKjgvn84al4uZlIzSnkno++p6C8ulWuacaPa0krKMXX3YXXbxvfonM+/ndwL4O/+4Sbl85jyNxPmduCC0AFQbg8NW9KnSAIbcLP4E1X9ygOVR1ja8kergka1az9wtzNDA2OYFNuJgsOJ/N4wrALjiGrspy7VvxAWkUprjodH4+exKgzDDY/lxODel/cuxhFVZElievCenO4spCU8lwWZe9lUfZe4jyDuSVqAONDemDQnHl4pyAIQkflqnXhrsgpvHfkS344vpxhfv0INPo1a9/nBo7k1+x0duTnsDAtles7Na/nvVbW8FyvCUS4ejMjeTnzMxI5XlvOO/1uxE0nWscILW/evHk8/vjjfPLJJwwcOJD33nuPcePGcejQIfz9zzwLLCMjgyeffJLhw4e3YbStb0dDy6t+V3RDo2mZBzubNx5CBaRQFw6k5ePuauSDZ6cSHuR9zn1nrtpBekEpPu4uPDnpCuYe2sczm1aiqCoSMLlTLD5GFzIryzlWWUZ2VTkWxxkSppJzXkikhxfRZm8izZ5EeHgS5eFFpIcXwW4eaJv5MOumrr24IjSKo2UlPL1vHuU2G5MbWl4lliWjohLjGo6PwbNZxxMEQWgJDqsO9Hb0GoUaV1AMmjaf+XE8p5TMjGI0GhnvIDMHFGfyxVNnQ693oKgS14eNaLLPmKDuvH/YhFFbhexZQi85lvyaIyxKO8j4yC5nPV/3UH++emQaf/7kB9ILSrnrw3nMfHAKob6eLXZNP+9IYeGO1pnzkVdTxbObVnJiqZCiqjy7aSVXhEYR1MzW3IIg/PGIyg9B6GCG+DirPzafR+srgGkNg8+/P5yMQ1Eu6Ny7Co5z3aJvSasoJcjVnQXX3nLBiY8TTh7Uu+qqv/KvPpOZf8Wf+W7EvUwK7YVO1pBcnsuzexZy5cp3effAanJryy/qnIIgCO1hmG8/epq7YlVsfJ4+r9lVHCFuHjwSPwiAV7evo8p6fn2Yb4sZxAcDbsKk0bG5MI3bN80Sn6NCq3jnnXe47777uPvuu4mNjeWTTz7BxcWFWbNmnXEfh8PBrbfeyksvvUR09LnvKSwWC5WVlU1eHVVrzPvYuOEgFm8tZfUWXIw63n36BjpFnDuReiSvmC/W7ATgmRtGUYutMfEBzkHjPx09wOfJzhZVR0+q4IgxezM6LJpYV3+0VTK6Mpkbg+I4dNdjrJt2L7PG3cDzg0ZzZ2xfRoRGEe7h2ezExwlBru7YtRbKbXX4GFwZ6h8DwM5S54pd0fJKEIS2JjmcC0W0sgPFTXEmP3JK27SN6NYtRwHoFR/Oiq0Hsfg63zc3tLyqtRq5JqR3k31cdQbizM6FMm7mOkpLnb8nV2elUW21nvOcUQHefPWXaYT5mjleWsmdH87naF5xi1xPWn4Jr/7wK9A6cz6Olpfw+387DlUlo6KsRc8jCMLlRSQ/BKGDGeLbFwmJQ1XpFFmav/JkXERnzAYjuTVVbMrNPO/zLkpL5Zal8yitryPOJ4CfJ91GrM+ZV3GejxODegNN5sb3enmF8nrCDfw69nH+2v1Kgkxmyqy1fH5kE+NWvc8j279jS2EainphiRxBEIS2JkkSf46+Ca2kZU/5AbaW7Gn2vvf17EekhydFdTV8sGfreZ97dFA3Zg+7G1+DG4crC7l5w+eklOee93EE4UysViuJiYmMGTOm8T1ZlhkzZgxbt575v9mXX34Zf39//vSnPzXrPK+//jpms7nxFRbWMQek5mWVkJ1WiKyR6Tu8a4scM+d4KQdKynAYNeh1Gt762/X06BR0zv0cisKL81ZhdyiMjIvmqt6dSS8vbUx8nGxCZBdeGTKGb8ffyMbp93HorsdYNOl2NOUyacdK0dbJPHfFKN6aMB6DtmWbBPyclQTAtaG90MoaLA4reyucCSSR/BAEoa0ZcQFAp1FQXFQUg5a6agtV5a03C+P3tmxumPHUP4pFick4TICq4unlnI/ZybUz7qep5p0cOpB6uwZJguz6NELdPLA47KzMPNKs84Z4m/nqkWl0CvKhqLKGez7+ngPZBRd1LSfP+RjcCnM+wNml4vckINLs1eLnEgTh8iGSH4LQwXjrPenu4VwNdz6Dz41aLZNjnCsP5x/a3+z9VFXlwz1b+cvaJVgcDq4K78T3195EgOvZB2q2FG+DK/d1Gc7yMY/ywYDpDPaLRkFlbf4h7tv6DRPXfMy3aduostW3STyCIAgXI9gUwPUhYwH4MuN7au11zdrPoNHyQsPw81nJiRwpO/+e0z08g/luxL108fCn2FLNnZu+5Ne8g+d9HEE4neLiYhwOBwEBAU3eDwgIID8//7T7bNq0iS+++IKZM2c2+zzPPPMMFRUVja/s7OyLiru1nGh51aNfJG4epos+nqqqvPzhUhwuGiTg9ccm0Te2eYmf7zYmsT8zHzejnuemXIkkSazKPHrKdhpJ4vnBo7kjtg/DQyMJd/ekqLqGW76Zz4a0DIxaLR9OmcjdA/oiSafObLsY5dZa1hU4H/JdF+Zcxbyv4iBWxYafwZsIl5AWPZ8gCMK5eGk9AWflh+qioPd0JhnaqvVVRUUtyfudv+Pq9VDq7RxY7qGzoNM5cCgSN4Wfvg32sIBO2GzO7+uegdUEaJ0tnxalN/++z8/sxqyHbyQuPIDymnr+9PECdqXlXPD1vP7jr6QVlOLn4cprt13donM+Tvjp6AEA9Fo77i516LR2tLKmWRUvgiD8cYnkhyB0QEN9+wHn3/pqekPrq5WZRymrP/cDN6vDwd82LOftxE0A/CkugU/HXIeLTn+eEV88razhyqDufD7kDhaPfphbowbgqtWTUVPC68nLGbXi37y8dwlHKi9uRYogCEJruyF0HIFGP0qtFczNXtLs/UaHRTMmPAa7qvDieQw/P1mwiyffDLuHof4x1DlsPLpjLt+kbWvTFg6CAFBVVcXtt9/OzJkz8fX1bfZ+BoMBDw+PJq+OaMevzgcwLdHySlVV3v9mHfszC0BVmTioO0P6NK/t6PHSCj5cugWAxyYOJ8DTjR+OJPPlgd2Ac0UsOBMfrw0b26Qn+oGCQqZ+9R0HC4vwcXHhm9tuZGzXThd9Paez7HgyNsVBN3MgXc2BwEktr7x6tXiyRRAE4VxCXZwtBXWygmpUkN0NABTktE3yY+f2dBSHSmSUL7/uPEK9r/NezcvbOYTc4XBjSEDn0+6rl7UkNFTMuZnryMwrAmBjTkazngOc4OlqYuaDU+kXE0qNxcqDn/7IxtRj530tP+9I4ecdBxrmfFyNj7vLeR/jXPYX57Oz4Dj+ntX07pJD18h8enfOxuxRzkO/LqLObmvxcwqCcHm4ZJIfr776KkOGDMHFxQVPT8/TbvPoo4+SkJCAwWAgPj6+TeMThJY02LsPMhJpNVnk1xc1e78ePgH08PHHqjgaV0WcSYWlnjuWL+D7I8nIksS/hozh+UGjW2WFxvmKdvfj2V4TWDv2Cf7Z6xpi3P2oc9iYl7GLyWv/y52bvmTF8RRsyhmGdAqCILQjvazjvujpACzLW0d6dVaz931+0CgMGg2bcjNZlnH4gs7vpjPyn4G3MC0yARWYkbyc1/Yvwy4+M4WL4Ovri0ajoaCg6SKEgoICAgMDT9k+LS2NjIwMJk6ciFarRavV8vXXX7No0SK0Wi1paWltFXqLq6uxsG+7M/4Bo2Mv+nifL9jCvGXOZIW+3M6fbh7WrP1UVeWV79dQZ7WREBPClEE92Z6XzVMbVwDwcO+BbL35AeZOmM7mm+7npq6/tZZad/QYt3w9n8LqGmJ8vPn+rpvoHXzqv8eWsiDDeX2jAp0twhyqwq4yZ6WyaHklCEJ76OoVCoBO40DSqVhcnd+D26ry40TLq069QkgtKMThBqDi6elsedXHMw6NdObv5lPCBmNpaH2lcS0jxMUDu6qw9Nih84rD1ajnP3++nhGxUVhsDv7vi0WsTGr+PejRvGJeXeCc8/HQ+MH079Q67SpnJSei09oJDypCPTH5Q4KIoGKOVeXz/JY1rXJeQRAufe3/lLOZrFYrN954Iw8++OBZt7vnnnuYPn16G0UlCK3DrHcnzuz8cng+ra/gt+qPeYf2nXGlb2ZlGdcvmsPWvCxcdTpmjb2B22P7XFzQrcBVZ+CmqP78POohvhx6J2ODY9FIErtKMnl81/eMXfUe/zm0jqL6qvYOVRAEoYl4z1iG+iSgoPJp+nc4mjm/KMLDi/t7OXskv7Jt7QWvYtPKGp7vdS1P9rgKCfjfsR38ZcdcamznN0xdEE7Q6/UkJCSwZs1vDxcURWHNmjUMHjz4lO27devG/v37SUpKanxNmjSJUaNGkZSU1GFneTRH0pYj2K0OAsO8CYu5uPloc5bsZNaP2wDQV9joER6Av3/zql2W7Eply8FM9FoNz08bQ1ZVOX9evRCbojAhqgtP9htOkKs7g4PDm1R8zEncywPf/0ytzcagiDDm3TmdUE/zWc504RRV4fX9yzhY6WyN9umhDfyQuZuj1RlU2Kpw0ZiI9Tj9ymZBEITWNCDI2WpaI6tIegc1Gucj9bZIflitdnbtSAegyFJPbUPu2d1Qh06r4FAk7oi66qzH6OsTjuJwfnabA6uQ6p0VdIvSzr/lqVGv5d27JzK+TxfsDoW/f72UhdtTzrlfrcXG32b/Qr2t9eZ8ABTUVrM4/SBGve23ksYGkgQmg535h/fzw5HkVjm/IAiXtksm+fHSSy/x2GOP0bNnzzNu88EHH/Dwww8THd28MnEAi8VCZWVlk5cgdARDfRMA2Fy8+7z2mxwTi0Gj4WBZMfuLT20RtTM/h8mL5pBWUUqQqzsLrr2FUWHN//9Me5AkiQG+Ubzbfxorr/orD3QZgY/BlcL6Kj4+uI4xK9/lyV0LSCzJFK1dBEHoMO6KmoKLxsjR6kxWFWxq9n4P9R5IqJsHuTVVfJy0/YLPL0kSd3cayrv9p2HUaNlQcIQ7Nn9JQZ241xEuzOOPP87MmTOZPXs2qampPPjgg9TU1HD33XcDcMcdd/DMM88AYDQaiYuLa/Ly9PTE3d2duLg49Pq2b7HZUk7M++g/qvtFtWv6afVePpqzAYAoswe6GoVhw7s0a9+SqlreXLgegPvHDsLb7MLdK3+g3FJPb79A3rliAvLvYlNUlRlrNvDSil9RVJUbesXy+U3X42E8dZjuxVBUhcSSTF7fv4xRK/7Nt+m/fY4pqLy4dzHrCncC0NerB1pZ06LnFwRBaI5osx8Oh/NzUm+0YdUqqBqpTdpe7dubRW2tFU8fV3alZlPv5/wO6+PtrPrQ4U0nj7NX48mSzBAf5wJGN3MdxSUVAGzPzya/5vwXB+q0Gl6/7WpuGBSHoqo8P3cl364/+7OI135oOudDllunheG3qUnYFIUuHsGn/fmtXZxJl+c2r+ZwWXGrxCAIwqXrkkl+tJbXX38ds9nc+LqUV6EJl5eB3vFoJJmM2hxyak8/SPR0zAYj4yOdX5znHdrX5Gc/p6Vy67L5lNbXEecTwM+TbiPW5+JWLLa1QJOZv3Qfzeqxj/FmwhTivcOwqwrLjidzx6YvuWHdJ3yfkUitXQw9EwShfXnrPbk5fBIAczJ/psxa0az9TFod/xzkHHD56b4dZFSUXVQcVwXH8uXQu/AxuHKwIp+bN8wktSLvvI+TV1PFltws8i7gC7VweZg+fTpvv/02zz//PPHx8SQlJbF8+fLGIehZWVnk5Z3/f1uXElVV2dmQ/LiYeR/LNx7grVmrAbjp6r6UHXU+bBs6vGuz9n9z4ToqauvpGuzHLVfEc//qn0mvKCPEzYPPr7oek1bXZPs6m42//LCEWdudFcWPXTGE168Zi17TMokHh6qwqziDV/ctZfSKd7hj05d8m76dYkvNKdsqqkpimXN1rmh5JQhCe5FlGZtNC4BOp+AwgWLQtEnlx5ZNzrZSvjHe1GHH7g6gYPaoBWBYw0LIc7kxYigWuwZZArNPDX46V1Rg8XkMPj+ZRpZ5YdoY7hjZF4A3F67n05Wnnx33844UFu10zvl44/YJrTLnA6Debufb1CQABoV7A3AinBP/W6hJZGhIIHV2Gw+tWUStTTwLEAThN3/45MczzzxDRUVF4ys7O7u9QxIEANx1rvQyO79Ubyk5v9ZX07o4K6R+Tkulzm5DVVU+2LOVR9cuweJwMDaiE99fexMBrm4tHndb0ctargntyZzhf2LBFfczJbwPRo2Ww5UFvLh3MaNX/Js3kpeTWV0CQH5dBduLjpFf17yHj4IgCC1hXOAIYlzDqXXU8XXGj83fL6IzI0IisSoOXt629qLj6OUVyncj7iXG3Y+C+ipu3ziL9fnN7+c899A+hsz9lJuXzmPI3E+Z+7vkuvDH8cgjj5CZmYnFYmH79u0MHDiw8Wfr1q3jq6++OuO+X331FQsXLmz9IFtR2oFcSgoqMZh09BoUc0HHWL/zCP/6ZDmqClPGxtMz2A+bzUFYuDcRkeceDr8hJZ1luw8hSxIvTLuSl7b92tjK9Iurrsff5bf7u/zKKlakHmH61/NYdfgoOo2Gd667mgeHDrzoIeMOVWFncQb/2vcLo1e8w52bv+J/x3ZQZKnGTWtgUmgvXom/Dvl3PUoMGgcl1hI0kkwfzx4XFYMgCMLFUO3OIec6jQPFTUExaCk4XoqiNK9d6QWdU1XZuvkIKpBbXUNtECCB2bUOneZEy6sxzTpWV3MgOpwJAXNQFVXlzvamF9L66gRJknhi0ggeGu9safnxsq28s3hjkwRIkzkfVw+mX6fQCz7fufyclkppfR3Bru7kO44AUFZvJKvck9xKDxyKRGZtDgafZEI8JY6Ul/D8VjH/QxCE32jb8+RPP/00b7zxxlm3SU1NpVu3bq0Wg8FgwGAwtNrxBeFiDPVNYE95CpuLE7kxdEKzv6QOCQ4nzN1MdlUF7yRuIqeqkqUNg3PvjevHswOu6BCDzVtKd88gXu5zHY/3uIqFWUnMPbaT7Noyvk7bxtdp2+jk7k9aVSEqICPxYvxEpkT0be+wBUH4A9BIMn+OuZmn973JhuKdjPIfTC/Pc9/XSJLEi4OvZNyPX7ImO43vD+8nxM1MlNmrSe/+8xHi4sW3w+/h8Z3fs7UonUe2f8d9nUYw1LcLFVYL5fV1VFjqqbDWU25xvios9RTW1pBc8lsbRUVVeXbTSq4IjbrgWAThUnWi6qPP0C7oDbpzbH2qHfsy+OcHv+BQVK4eHsvjd47m1ZcXAs2r+qipt/LKAudDnduu6Mum8izmHd6PLEl8NHoS3U+q6P0+KZl/LFvVuDLWpNPy+fTr6R9+4Q+pnBUemazITWF1XiolJ1V2uGsNjA7qxrjgHgz2i0avcX7VVBtaXSmqiixJ3BAZw57KEnp4dMFVa7rgWARBEC6WTjEB1WhlBcVVRTVpsZfVU1JQiV+QZ6uc8+iRAoqKqtC66Sgqr8HSkEf3bhh07qkNxMvQ/EWKw337sbl8Ge4edWCxI7tK7CvO51hFGVFmrwuKUZIkHhg3CFejnrcWrmf22kRq6q3cO6Y/afmlvPHTOuptdoZ0jeDeK1tnzgc4E0VfpjgXgt7YvTOba38AoKzWFZvirFysL9MxLERDXn0hocG1WPDm+8PJDAoMY2qXuFaLTRCES0e7Jj+eeOIJ7rrrrrNucz7zOwThcjPAuzdaSUtOXT5ZtblEuIY0az9Zkujm7Ud2VQWf7d8FOOeCvTJkTIccbN5SPPUu3NVpCHfEDGJT4VG+O7aTDQVHOFpV2LjNiV7TQ/1jCDS1znBPQRCEk3Vyi2Bc4AiW569nZvpc3ol/Dp187oemMZ7e/CmuH5/s28GTG5YDzs/314eN5aauvVBVlVq7jQpL02SF83/rnAkNS12Tnzl/XofNoEVnsvPp0fV8uG8z1ho9p0yQPAuHqpJRUSaSH8IfzsnzPs7X3oM5/P3fP2OzOxg1oDPP3j8Ou83Bjm1pAAxrRvLj/V82UVBeTaiPmc6xfjy6fgkALwwazeiGGW6qqrLi4BGeW7qqyb4Wu4OwCxhsblcc7CrJZGXugVMSHh46I6MDuzEuJJZBftHo5VO/Xk6J6MtQ/xiyakoJd/Xm47QvAOjvfeZZjoIgCG3BTXanniJ0sgPVRcHg5QK51RRkl7Za8mPrFmf1ginIjfL6GqweKqBgdnO2vLoqYPB5HW96xDB+LV6JQevA7FdDtc2TGtnGovRU/q/PkIuK9fYr+uJq0PPS/FUs2LqfBVv3N/7M3aTn1VvHt9qcD4CtedmklhZh0urw9CiHWqi26rErv7VstCkaZFsXBngHsaN0L5HBRZgMFv6xZRW9/ALp4nXuikpBEC5v7Zr88PPzw8/Prz1DEIQOzVVroo9XLDtL97G5JLHZyY+8mirWZKY1eU9CYkxEp9YIs8ORJZkRAV0YEdCFxdl7eXr3T01+rqgqWTWlIvkhCEKbuSV8EttK9pBbX8jC46u4MWxCs/a7sXMcn+zb0fhnRVV5auMK3ti5niqrFduFtmWw6VEcEgY3G3oXO2aTnk66MHyMLngajJgbXp4GE4qq8MymlZzc7VkjSURe4GpCQbhUlZdUcygpC4D+I8+vMv1gegFPvPkTFqudQb0jeekv16DVyGzbkU5dnRVfP3e6dA066zH2pB9n3ua9ANx6dV/+vsmZFL0ztg939eiLzeFgxcGjzNqRSHJewSn7K6pKZlk5gR7nTlraFQc7izNYkXuANXmplFprG39m1pm4MqgbY4NjGegXddqEx+8FmswEmsxU2qo5WOm8R+3nJeZ9CILQvvwMXmTjbHulGhUkNwMqkJ9TStyA1lmIu3XzERQZimrrqA0GZDC716LVqCiqzNTwK87reCGuXrjK/tjJwyuoipID7uDhbH31aPzgi25xeMOgOGx2O6/+0LQNa029DZvDcVHHPpdZyc6qjxs6d2d9sXNOVmW9iQ/73kxmRQUGg8QbB5ezJv8wU/V9mR4ayrycXwjwqcRktPLw2h/4eeLduOj0rRqnIAgdW7smP85HVlYWpaWlZGVl4XA4SEpKAqBTp064uTlLAo8ePUp1dTX5+fnU1dU1bhMbG4teLz7shEvTUJ8EZ/KjOJGbwyY26+blWEUZCk2Hkin8MVfp9veNREZq8vchSxLhrt7tGJXQWj7++GM+/vhjHK18Iy4I58tVa+LuqKm8e3gWP+QsZ5hvP4JM/ufcr7Du1GHBAKX19Y3/rJPlhoSFyfm/eiOeRuNvSQy98589DSbMBgOeDdu56w2szjvAM7t/ok6pRXGt4MVB1+BnPPX3hCRJPLtpJQ5VRSNJvDZs7B/u94kgJG44hKqqRHcPPq8VwenZxfz19QXU1FmJ7xbK649NQqd1rlrdvPEQAEOHdTnr6lmr3c6L81ajqnBVv868e3AL9Q47I0OjeCx+KF9s28XXu5LIq6wCQK+RsTqaJkdlSSLC68xx2xoTHimsyTtI2UkJD0+9iSuDujsTHr5R6OQLG5S+uywZBZVIl1D8jT4XdAxBEISWEu0eRHY9aGUFSadSb5IwAAWtNPS8qLCSI4fzcbhrUFQVe5AMOPBpaHkVbAjHoDn/loqj/QewsvhnXN3r0dpVFCSOlpeQWlpErM+57zfPJSrg1M9rRVXJLi4n0LN17gczKspYnXUUgP5hJg7l1GB3yIRqonnw22WNrRRvHtGf74u3sSBrNz7GEfy96595/8hsPFzrsehSeHrLT3xwxfRWiVEQhEvDJZP8eP7555k9e3bjn/v0cbbuWbt2LSNHjgTg3nvvZf369adsc+zYMSIjI9ssVkFoSf28e6KXdeTXF3GsJptot/Bz7hNl9kKWJJSThpL9UVfpBprMvBg/sUmv6Rd7TxRVH5ephx9+mIcffpjKykrMZvHvWOhYhvoksMa8hX0VB5l5bB7/7P7IORPap/s8l5GYPX4KnTx98DQYMWl1F7yqb3xIHIEmM49s/46Uijxu2jCT/w66lS4eAU22u6lrL64IjSKjoozIi5g7IgiXsvNteVVYUsXu1Gw++GYdFdX1dI8J5O2/TcbYMCvE4VDYutnZ/mTo8C5nPdbMVTs4VliKl4eJfYZCispriDF7EyV5Mfo/X1BjtQHg4+LCrQm9uaVvL9YcSeelNcvQmazY6vS8cOXVp1R92BQH24uPsfJ4CmvyD1JurWv8mZfehSsbZnj094284ITHyXaWOlum9PcWVR+CILS/3n6RrM92Vn6gc1CrUdHjrPxoDVsaBp1j1qPgoNZNQZIdeLg6P3snhYy4oOPeGD6UJflL0GsdePhXU1LjBgZYlJbaIsmPcD/PU+9HJYkwX8+LPvaZfHVgNyowMjSK3RXOCpDyeiP5KeWoqvNRpqKqfLfhIE9MHsN7h1fz6eENeOvHM6PX33gp+SPKKSdL2cA7+4083vO6VotVEISO7ZJJfnz11Vd89dVXZ91m3bp1bRKLILQlk8ZIglccW0v2sLk4sVnJjyBXd14fNlas0m3w+17TIvEhCEJ7kCSJ+6Jv4vGkf7G3PJUtJbsZ6ptw1n3O9Hk+IjSqxeKK9w7jfyPu5aFtczhWXcJtG7/gnf7TGObftFVikKv7H/b3iCDYbQ4S1x8EYODocyc/Fq3dz4yZq1AbHhT5ernx7lM34OpiaNwmJTmH8vJa3N2N9Op95vu7w7nFfLF6Jyoq7nEu7C8vwCBpyEmv5H+OfQB08vXm7gEJTIrrhkHr/IrnFVRBt8EZqKhISHgFVQBgVexsLzrGytwDrMk7SIXtt4SHt96FMcHdGRfcg34+EWhbIOFxglWxkVR+ABDJD0EQOoY+AZGQDbIEGoMDq1ZB1Urkt1Llx9YtR3AYZKyKgiNMCxobnh41aGUVVC1jAvtf0HG9jW54aYOpIZvA0DpK97qDQWVx+kGe6j/ioltfBXq68/y0Mbw8f3XjgsLnp41ptaqPKquF7w8nAzC1Wwxf5f6KqoI7weTVNt1WUVV6uUTwl26j+PDgWl5PXo6n4Qbe6/McjyW+Qxl5bK5agf5QPQ91uRFZklslZkEQOq5LJvkhCH9kQ30SnMmPkkRui5jcrJsXsUq3qRO9pgVBENpTsMmfG0LHMS/7F748toA+nrG4aE1n3actPs/DXb2ZM/xe/m/nPHYWZ/DQtjn8o9c1TIvs1+LnEoRL0YHdGdRU1ePh7UqXsyQqwFnx8cZJiQ+A0ooaLFZ7k+02NbS8GjSkM1rt6ZMMDkXhxXmrsCkKLl2N7K8oABWUUpAdMDQqnLsHJDA8OqLJ/WGJpYxP0v6H2tD2U0Xlv2lzWJyVyo6iIiptlsZtfQyujGloadXSCY+TJVccpl6x4K33JNo1rFXOIQiCcD489C7Y7DI6rYLOYMeuV1EM2lZpe1VTYyFpdwZ2D+fDd2uwBKh4m51P82NcO6GRLvzz96rAQSzMz0ZnqkZr98WhQk51JYmFufQLaN7s0LO5YVAcQ7pFkF1cTpivZ6slPgDmH95Ptc1KJ08fStVjANTY9HQhgsNkNdlWAsI9zQzwGEGZtZZv07fz3O6FfDTwZv7T/2lu3/AWdkMOa0vWU55azONd7jnnvbcgCJcXkfIUhEtAX684jLKBIkspR6ozmr1fkKs7g4PD//CJD0EQhI5kcshYAo1+lNkq+C57cbP2aYvPc7PexGeDb+O6sN44VJWX9i7h7ZSVKOoFDlUXhMvIzoaWV/1GdEWjOftXqOz8siatQQAURSWnoLzxz6qqsnnjYQCGnaXl1VfrEkkqzKcuVCFf6+wJb6zSMjW2B4vuvY0vb57CiJjIUxbGHKnObEx8nCzHtptAczYx3uUMDTLyfz3i+XzoVJ7uOY5BftGtlvgA2FnqHNbe36vnRa9CFgRBaCkOm7MVoV6roLiAYtBQnF+B7XcJ64uVuDMdq6rgMGpQJJVKkxVZ68DD5JzjNiVs1EUd/4awIdgcGiRJJTDcgmRxfs4uSku96NhPCPR0p3+nsFZNfDgUhS9TdgNwd494VhZsdr5v82TTvjzAmfA4QQU+3+5si/VU3DgmhMRhVxUe2zmf1Ip8Ph70fxQXhaAoEnvKU3hm/1vk1hW2WvyCIHQ8IvkhCJcAg0bf2B5gc3FiO0cjCIIgXAy9rOPP0TcBsDxvPenVWefYo+3oZS2v9pnMX7o5v4B/eXQLj+/8njq7tZ0jE4T2dT7zPvy83E55T5YlQgM8G/989EgBBfkVGAxaEvpHn7J9fmUVz/+ymje3bsLqo2DzcCYhh3iHs/HP9zHj2nF08/c77fmP1WTzadp3p7yvqiChQZZAp7FSYs9iReFKntn/Frdtf4wn977Gx0e/YVneeg5VpVPvsJzm6BdGURV2lYl5H4IgdDyywwiAVnaguKnIbnpUVaUot7xFz7Nl8xHsLs4Es2esGVUDnuYaNLKKBh39vGMv6viuOgP+emdVnW9QLXK9M0XwS/oh7Mqls5BldVYa2VUVeBqMhHrbqVfqsDtk3GtDqLHY6BkUwNqH/8Q3t07lb6OGAfDNriReWbkOCYlX+05mqH8MdQ4bD277H+W2Gt4YcAeHM4Ow2jTk1OXz9L432FN2oJ2vVBCEtiLaXgnCJWKobwIbi3eypWQ3d0beIHpVCoIgXMJ6e3ZnmG8/NhXv4tP073it59/QdJDPdUmSeKDrFYS6evGPPT+zKi+V/M2VPN/7Wqps9US4idlJwh9LfnYJWUcKkDUyCSO6nnP77fszm/xZliWeuvcq/H1+Wym7uaHlVf8B0RiNusb3D+QXMmt7IktTD2NXFBSt6kx8SHB9TCzvjpxw1qqJzcWJfHBkNnbVjt0hoZFVJMmZ+MivdufdvvcT7uZGek026TXZHKvOIr0mmyp7DcdqcjhWk8OvbHXGjUSwKZBotzCiXZ2vSNcwXC+gXUh6dRal1gqMsoE489mHuwuCILQlI64oVKDTKCguCgYvFxxZleRllxAc6dsi53DYFbZtO9qY/Cg01wMq3h7OlldxHrEX1fLqhKuDhvK/nAxUXQU6hxd2BYrra9mam8Xw0MiLPn5bmJXsXOx5S7fe/JK3DoAqi4mcg5VIyLwwbjTBZg+CzR4MjAjD02TiH0tX8W1iEioqz48dxXv9p/OnLbPZV3ac+7d+yzfD7+GB2Ct5L2kdXcKLwFTHa6kfc1vEZCYFjxHViIJwmRPJD0G4RMR7dsdFY6LUWs7BqnRiPTqdeydBEAShw7orcgq7y5I5Wp3JqvyNjA+6or1DauLa0F4Emcz8Zftc9pcf58b1nwLOB6Ivxk9kSkTfdo5QENrGiaqP2IRI3M0uZ93W7lD47pddADwwfSg9u4QQGuDZJPEBsKmh5dXQ4V1RVJX1R48xa3si27NyTjqYit3LATIMCgzjzRHjz/iARlEVvkj/geUFawGoturJrfRAllT0GgdWhwZF1RLh5kOgyUyQyZ+hvgmAswVXsbWM9IZEyLGabNKrsymzVZBTl0dOXR4binY0nivQ6NeQDAkn2i2MKNcwPHSnVrucbGdD1Ucfr1h0su6s2wqCILQlT62ZUnLRyg4wKeBiAGjRuR/JydmUW62objo8zUZSdbVo9XbcDc4Ku+tDR7bIeSaFDOTrzHloNXbiemhJTHeguKj8nJ56SSQ/UkoK2JafjUaSuDomnJcOzkVVob7MBxwyN8bH0Ss4sMk+N8bHIUnw3C+rmJO4F1WFF8aN4r+DbuX2jbNIry7mz1u/YfbQe9ien82mDA1x4dUYXIv5OvMnMmpyeCDmVgwafTtdtSAIrU0kPwThEqGTdQzw7s26om1sLk4UyQ9BEIRLnJfezC3h1/H5sXnMyfqZgT7xeOk7VkVFgk8EHw64iTs2f9n4noLKi0mLGeofIypAhD+EE/M+BjSj5dXa7YfJK6rEy8PE9AkJGPWnPug/nlNKxrEiJJ3McQ8rV386m2OlZQBoJIkrO8ewc18mRb51qDqI8vDikzHXodecflVwla2Gp/a+T4HVmTgpr3Pl+pCr8Ytw51/7l1Jr0yBLEi/2nnja/89KkoSfwRs/gzcDfeIb3y+zVjRUh2STVpNFenU2xdZS8uuLyK8vYkvJ7sZtffXeTSpEot3Cm3yebS3eA0A395hz/h0KgiC0pVBXf0odqehkBfQq9UYJHZDfgsmPrZuPYHN1foZ79DCjamvx9HS2vNJLRmLNnVvkPDqNlhBjBAW2NDSeJWgs3iguDpYfO8y/hlyFUduxHwF+mez8vXJNVFe2lG4HnIPOi7O0eJqMPD5y2Gn3m9o7DlmSeGbJSv63ey+g8vy40Xw25HZu2/gFx6pLeGj7HN4adiNTFn/H/kwY2zWAck0qG4p3cryugL93+zO+Bu+2ulRBENpQx/7kEwShiaG+Cawr2sa2kj3cE3Vjh2mRIgiCIFyYsYHDWVu4lbSaLGZn/Mhfu9zd3iGdwn6agecKKv+3Yx4PdR3JsIBO4veRcNmqr7Wwd2sacO7kh6qqfLt4JwBTx/U5beIDYNnaZMo7aamP0vPa2g0AuBsMTO8Tx+39+vDOTxsoMdfjcAFPg5Evx03By3j6VlMbC5N57/AskOtRVDCpUcwccBfR7s55IFcEdiGrppRw1/NvV+elN5OgN5PgFdf4XqWturEyJL3GWSmSX19EsbWU4tJSdjQMNQfw1HkQ7RqGQ1U4Xp8PwFcZP2DUGBgTMPS8YhEEQWgt3bxC2FcMOo0DSadQJSt4AQU5LZP8UFWVdZsOoRhkJAkOSWVIsoKXax0A/b17t+h91KSQEczMSMMqlRHqHsIxRzVVWFmXk874yI7bdrCotoafG4az39kjnrfT3wGgotwNLBoeHz8Ub5czt128oVcPgIYEyD4UVeXF8Vcyc/Dt3L5pFsnlubyyfzHvXDGBO5YvYOWhWv42aCK7aleTVpPF3/e9wd+63kd3schUEC47IvkhCJeQXuZuuGldKbdVcqDyCD3N5+47LQiCIHRcGknm/phbeHrfG2ws3sko/8H09uzW3mE1EeHmjYyEgtrk/eTyXB7a/j/CXLyYHtWP68P74Kk/e0sgQbjUJG09is1qxz/Ei/DOAWfddmdyFoczCjEatEy5Kv6Unx8uLObLHbv5MSsFNUYHqISaPbizfx+m9I7DzaBnfUo6P+emYvNT0Uoyn46ZTJTZ65Rj1dgtvJY8n5TqbWhkFYei4eqAa7iv09gmc+ECTeYWrdDy0LnR27M7vT1/SwTV2OvIaJghkt6QGMmty6fcVsnu8pQm+6uofJr2HX08Y/ExnHpdgiAIba2PfxTzi0ErK6B1YNMqqFq5xSo/sjJLOF5dDW5a4uPCWKrJRGew4dbQ8mp8UMsmg8cGJvBp+rfIso1u3SWy9ko4XFQWpR3s0MmPOQeTsCoO+vgHUS3lYVMt2B0yZdnuxAX6c2N83DmPcXICZO6e/agqvHT1lfx30K3cs3k2W4rSMetN/LXPEN7ZvZkPdx7g6wl/Zl7efDJrj/NiyvvcGz2dqwJOX2EiCMKlSSQ/BOESopU1DPKOZ3XhZjYV7xLJD0EQhMtAjFs44wOvYGn+Omamz+Wd+OfQd6Ce+IEmMy/GT+TFvYtRVBVZkni022gqbHX8kLmb7Noy3k5ZxYepa7k2tCc3Rw2gu2dQe4ctCC1i56+/tbw610DUOQ1VH5NG9cTsbiK/sopjpWUUVdewcH8qm441DEKXQF/m4PmpY7mhfxxa2ZmsqK638PdflmHxcyYaZwwfx6CgsFPOsz7/EG+kzsGoL0Yjg0Hy4OVef6GTR0hLXfZ5cdWa6GHuQo+TBpnXOyxk1h5nU9Euluava7K9gkJefZFIfgiC0CFEewSgqiBJoDM6cOhBMWjIb6HKj40bDzYOOq8IUFBVFbO5Fo2sYpJd6OYe3SLnOUGWZSJMUWRbDlOuzUFv9aXOxcaqzKNUW6246TvebAuLw843B5IAuKdHAvOzfgCgvMaEWqXjhRtGo5GbVx1zQ68eSJLE04tXMC9pPyoqL189hg8G3MSD2+aw7HgKN0W6MDw4go25mTy7cSPfX/soszLnsbVkN5+k/Y9j1dncHXUjOlk8MhWEy4H4f7IgXGKG+iawunAz20qSuC/qJrTy6fs/C4IgCJeOm8InsqVkN3n1hSw8voppYRPaO6QmpkT0Zah/zCntcx7uOpKlx5P537EdHKzI54esPfyQtYd47zBuiRrAVcHd0YsvjsIlSlVVdqxzJj/6n6Pl1aFjBezYn4lGlrhpQgLzk/bzz6Wrm9RLyZJEnLsPBSuP0zskkGkDezU5xtMLl5PvWQvA/T37c2OXpqtci+qreH3fUvZWbcfTVA9Ad7fuPB/3QIdKmAIYNQa6ukfjq/diWf561JP+JmRkgox+7RidIAjCb7SyFrtNi05vR6d3YGlIflSW1lBXY8Hkario4y9dlwKyhIeLgd01+cgeCp4uzpZXw/wSmlTrtZTrQ0fyQdphLJRxZae+/JKfhlXrYGXmEW7o3KPFz3exFqcdpLi+lkAXN+IDPZldkIuqQmmumam9e9I75PwW1VzfMxZZknhq8QrmJyWjAq9cPYYZCTfwt10LmJuxk7tjhnC4vISj5SW8sm0D/x5xDz+5hvJd1mJWFGwkuy6PJ7vch1nv3joXLQhCmxENmgXhEtPD3Bmzzp1qew37Kw62dziCIAhCC3DVmrgnaioAP+YsJ7eusJ0jOlWgycwA36gmLXRMWj1TIvqy4Ir7+WbYPUwIiUMrySSVZvP3xB8Ys/JdPkz9lYK6ynaMXBAuzLGDeRTnVWAw6ug9+Ow9wOcs2QXAlYO7IhnkUxIfEjDnthuJydBiqFAYNrxp9e4vyQdZUn4YZBjkG8rTA65o/JmiKnyfkcjktR+QWre5MfFxS/hkXun5SIdLfJzMx+DFAzG3IDd87ZSRuT/mZlH1IQhCh6I4nNUQOtmB6qaiNRuBix96XlZaTWa58x6o7+BI6rQ2dEYbbnorACP9BlzU8c9khH9vUPXIkop7SCWyxVm5+OORlHPs2fZUVeWLlEQA7uzRl+8ylwNQY9Wjr/TkyZEX1hbsurjuvDlxHLIk8X1SMv9ctppxwT14rpdzgdGXaVu4PjYaWZL48egBFhxNYUroeJ7u9gAmjZEDlUf5+743SK/ObpkLFQSh3YjkhyBcYjSShsE+fQDYXJzYztEIgiAILWWITwK9PbtjU+18nj4XVVXPvVMHIUkSfX3CeavfVFaPfYyHu43E3+hOiaWGTw5v4KpV7/L4zvnsKs64pK5L+GPb0dDyqveQThiMZ04w5BZW8Ou2QwDcem1/jhaV8Pv/ylWgqrqepN3O1lfDhv/WIqqguprHNi1F1YKv7MLsa6ciN7TYSqsq4s5NXzEj5Ud83PJw0dkxykb+GfsIU0LHnrMVV0cwJmAonyS8wks9/sonCa+IYeeCIHQ4OsU5SFunUXC4KBi8nDPMLnbo+cJfklD0zsduh6QKVJ2Kp4ez5ZWb1o0u7lEXF/gZSJJEjGsMAOmWI8S4+gCwOTeLsvq6VjnnhdqRn8OBkkKMGi03do5lZ1kSAKVF7jw+fDjerhc+T25SXHfemjS+MQHyj6WrmB7Zn4e6OhcYzMncynXdnH9P/9y8mkOlRfTz7smMnn8jyOhPsbWU55LfZlPxrou+TkEQ2o9IfgjCJWiITwIA20v3YlNs7RyNIAiC0BIkSeK+qOnoJC17Kw6ypeTSTHD7Gd15qOtIVl71V97pdyP9fCJwqCorcg9w5+avuH7tf5mfsYsau6W9QxWEs9q57rd5H2czd2kiDkVlYK8IukT6k5Sbd8o2siRRnF6Gw6EQEelLaJjzQZTV4WDKj3OwaB1o7RLfX3cTRq0Oq8POxwfXcsPa/3K05jDhnmXoNAohpkDe6v008Z6xLX/BrcjH4EWcuYuo+BAEoUNyk52tjbSyA9WkoDbM6LjYyo+lG52VFuFBnuyryEdjcOBhdFbvjfQb0Cotr06YEjoaAJtUznU9Y5BsoKDyy7FDrXbOC/FFsvN+d0rnHmwp3Y0q2bA5ZPxqIrmpT8+LPv7EHt0aEyAL9qbw3C+reKDLFdwU2R8VWFuyn/ggX+oddh5cs4gam5VQlyDe6PV3+njGYlVsvHt4Ft9mLsShKhcdjyAIbU8kPwThEtTdIwZvvZlaRx1J5antHY4gCILQQoJM/twQOh6AWccWUGPvWKvzzodO1jAupAezh93NT6MeZFpkAiaNjiNVhby0dwmjV7zD6/uXkVFd3N6hCsIpKstqOLjHWaXRf+SZkx/llbUsWrsfgFsn9qfaYuXbxL2As9UVOBMfr1w9hpTtzuMNbaj6UFWVv6xaTLa1Ehzwj94jifbxYWdxBjes+y//ObQOL5dKgj0qkSVI8IpjRs+/EWzyb6WrFgRB+GPyN3gDzsoPyaBSp3d+gudnl1zwMcvKa8iprAIguLcfdoOK3mjD3eBseTXUN+Eioz67AT6xSA2tr4oMWRjtzgrGb1OSWvW85yOrspyVmUcAuKtHX+ZlrQSgvNKFV6+6utlDzs9lYo9uvD3pamRJ4od9KTy3dBVPxY1jfHAP7KrCcTUXXzcDaRWl/GPzKlRVxVXrwjPdH2Jy8FUA/HR8JTNS/3tJ35sLwh+VSH4IwiVIlmQG+/QFYItofSUIgnBZuT7kKoKM/pTbKpmbtbi9w2kRXTwCeKH3RH4d9zhPxY0j3NWbaruFb9O3c82aj/jz1m9Ym39IrKgTOoxd6w+hKCqRXYPwDzlztcIPq/ZisdrpGuVPvx7hzN65m9LaOiK8PFn94N18c+tU1j38JyZ178rO7ekAjfM+/rN3O8tzjoAKg7UhXN83lueTFnHX5q/IrCki2rMaHxfnAPQbQsbxVLcHcNGaWv/iBUEQ/mCizc6B2lrZgaRTqMKOysVVfsyatwUkCa0qsb08D4wOzO61yJKKl85MZ7fIlgn+DCRJoqu78/fNvqoUrol0/nNqeRH5NVWteu7m+urAblRgREgkqlyLhTJUFWKIo09ocIue69oeXfn3dVejkSR+3HeAfy5dw7/ir2OwXzR1Dhta9xo0WpUfjx5g/uFkADSSzO2R1/PXznejl3XsLk/h6X1vkFOb36KxCYLQukTyQxAuUSdWiuwo3YfFYW3naARBEISWopN13Bd9EwDL89dztDqznSNqOR46E3fEDOaXKx/h08G3cUVAFyRgc2Eaj2z/jqtXf8CsI5spt9a2d6jCH9yOXw8AMHD0mas+6i02FqzYA8BtE/tTVlfP59uci1L+b8QQwrw8GRgRRqCHO4m7jlFfb8M/wIPOXQJZeuwQb+7aCIC5WMfk4V2YtPZjfsjcjV5jJ86vDr2uDr2s4/Eu93BrxHVoWrE9iiAIwh9ZnE844Kz8QKdg06moOpmCC0x+qKrKqu3O9lKh0V4U2KvRGu14GJwtP4f79W+TmU1Tw0Y545EqubJHGFLDY4P/pe5r9XOfS7XVyvxDzsrJe+ISeG3fdwDUWAy8dcWUVjnnNbG/JUB+2n+AF5b/yjv9biTOM5hquwU/XwVJVvjnltUcLC1q3G+4X3/+FfcEPnpPcusLeWb/m6wt2Mr+ikOUWMpaJVZBEFqOuIMWhEtUF7co/Aze1CsWdpentHc4giAIQgvq7dmN4b79UVD5LO27y64iQpZkhvl34j+DbmHZmEe5u9MQPHRGjteW8+8Dqxi94h3+sWchB8pz2ztU4Q/IYXeQuMH50Kr/WeZ9/LI+hfKqOoL9zYwc0IXPtu6kxmqle4AfE2K7NNl284bDAAwd1oV9xfn8dd0vAOgrVaLiDbySuoQSSw0xHga6+9ZgVWvw1XvxatwTDPXt10pXKgjCH9nHH39MZGQkRqORgQMHsmPHjjNuO3PmTIYPH46XlxdeXl6MGTPmrNtfamK9wgDQygqyTsGhA8WgIT+nFFVVz/t4+w/nUlFnAVWFCCOKQUFnsuHWkPwY2tDFobXFe3ZFgwGNrLKpMpEoF2d7r7kdIPnx/ZFkqmxWYszedPHwopxsAKK03fF1c221806I7co7kyegkSQW7k/lleXr+WjAzUS5+VDtqMfHV8Gi2HioYf7HCTFu4bzZ62m6u8dQ66jno7RveDHlfe5P/AerCza3WryCIFw8kfwQhEuUJEkMabhp2ixaXwmCIFx27oy8AReNibSaLH7KWXHZri4Lc/XmyR5j+XXs47wSP4lu5kAsip2fspK4cf1n3Lrhc5Zk78PqsAOQX1fB9qJj5NdVtHPkwuUqdXcm1ZV1uHu60K1PxGm3sTsUvvtlFwA3X5NAcU0N3+5KAuDxkUORT1rR67ArbN3q7GneZWAof1r5ExaHHYNswxhVx1FrATpZ5tqIIPSGHCyKhW7uMbzR6ymi3cJb92IFQfhDmjdvHo8//jgvvPACu3fvpnfv3owbN47CwsLTbr9u3Tpuvvlm1q5dy9atWwkLC2Ps2LEcP368jSNvHb5GM4ri/NzWGm049KDoNdTXWqkorTnv483+cZvzWArsLs0HVwdmUz2yBH4Gb2LcTv+7paVJkkSsRzcA9lekcF/vfqBCgaWaI6XtN3NNUVW+THE+w7i7R1+e2vYtWo2C3SEzY+BtrX7+q7t3aUyA/JycyhurNvHfgbcSYHSnXrXg4WUjraKE5xrmf5zgqffgkU53NDmWisqnad9dlvfognC5EMkPQbiEnVgJmFi2nzpHfTtHIwiCILQkL72ZW8MnAfBd9uLLfnWZSavnhoi+LLjifr4dfg/XhPZEK8kkleXw1O4fGbPqXR7aNoerVr7HPVtmc9XK9/ghc3d7hy1chnasTQUgYURXNJrTf11at+MIxwsrMLsZufaKOD7ctA2rw0G/sBBGREc22Xb/viwqK+pw9TbyVs52SmyVuJjr0flYUWSVfj5h3Bjjz9HavaiojAkYyos9/g9PvUdrX6ogCH9Q77zzDvfddx933303sbGxfPLJJ7i4uDBr1qzTbj9nzhweeugh4uPj6datG59//jmKorBmzZo2jrx1SJKE3eYcCK7XKqhGMPo6qw8Kcs6v9VVFdR3bk50tS726mbHrFLQmO+7GhqoP335t0vLqhCmhztZXGk0NoUEmDA4NAB/s2tpmMfzer1lpZFaW46E30MXVlyJtGgB+mgg8DS5tEsPV3bvw3vXXoJVlFiUf5P012/nvoFvx0BlRNDZMZgs/HU1pnP9xQpH11P8eFBTy6otOeV8QhI5BJD8E4RIW7RpGoNEPq2IjsSz53DsIgiAIl5S+XnFN/qyi8kna/yisL2mniFqfJEn08Q7nzYQprB77GI90G4W/0Z0SSw3rC46g4FyBp6Dy4t7FogJEaHE7G5IfA87Q8kpVVeYs3gnAjeP6kFddzY97nS1Inxg59JSHWps2HkaVoHSsnmPW45g865H1CkZ0PNPzSgLci9hdvg8ZmXujpvNA9C3oZG0rXqEgCH9kVquVxMRExowZ0/ieLMuMGTOGrVub90C8trYWm82Gt7f3GbexWCxUVlY2eXVkssMAOIeeO9wUdGYjcP5Dz5euT8GhqEhWhUIPO4pBQW+04qZv25ZXJ/Qwd0IrGdHIKgtzNjAiOAqA1dlpF9TSqyV80VD1cVPXXry8eTFuJiuqCo/1uLFN4xjXrTPvTp7gTICkHOSzdXv4aMDNmDQ6NHoHBncL/9yyqsn8j2CjPxJNf89LQJDRr01jFwSh+UTyQxAuYaL1lSAIwuWtwHJqSwIVlceS/sW/D33OrwVbKbWWt31gbcTP6M6DXa9g5VV/5cGuV5zyc0VVyaq5sGGkgnA6BcdLyTicjyxL9Lui22m3SUzJ5uCxAgx6LVPGxvPehi04VJVRnaJICAtpsq2qqmzedJjCq1SKA0rQu9iRJDBXuvHhkEmsLfmFtJos3LWuPN/jL1wddEWbrggWBOGPp7i4GIfDQUBAQJP3AwICyM/Pb9YxnnrqKYKDg5skUH7v9ddfx2w2N77CwsIuKu7WZpSclR46jYJqUlCMzgqJ8xl6rqoq3y/fA4BiUimx1iF52HE3WpElCDD4EuXatn8PsiTT2xwLQGr1Qf46aDCoUKvaWJV+tE1jAUgtKWRLbhYaScJbNVHtkQGAUfKihzmyzeMZ161zYwXI4pSDzNmUwtsJU9FKMjqjA9VYy4Nrfm6c/+Fj8OKBmFuQT3qcKiFRKzpxCEKHJZIfgnCJG+qbAMCeshRq7HXtHI0gCILQkk63ugygXrGwpWQ3H6d9w327nuWxpH/xdcaP7Cs/iE2xtUOkrUsna5ga0Rf5d38XsiQR7nrmVaeCcL52/uqs+ujeNxJ3z9O33vi2oepj4sg4jtdWsSz1MBLw2MihTbbLr6tg9q4tJI8pQYmzIWtUVIuEdr8rD/aL4/2jMym3VRLuEswbvZ6ip7lrq16bIAhCS5gxYwZz587lp59+wmg0nnG7Z555hoqKisZXdnZ2G0Z5/jx1ZsBZ+SEZVOqcXbDIP4+2V4kp2eQVV4Kioo12RdWp6Ex2PE4MOvdNaJcE93UhzgUkBm0defZiAnRuAHyUuK3NY5mV4mxZOjoshlk7duDp6ZypMilkVJvHcsLYrp0aEyBLDhzi5+1pvBx/HQB6FzvZtoIm8z/GBAzlk4RXeDH2/+hl7oaCyqfp37VbJY0gCGcnkh+CcImLcAkhxBSATbWzs3Rve4cjCIIgtKDfry6TkXkg+hZe6/kk00In0NktEgmJrNpcfs5dzUsHPuDOHX/jtdT/sDRvHXl1px9ceikKNJl5MX5i4yBpWZJ4sfdEAk3mdo5MuJzsOEfLqyOZhWzfl4EsSdx8TT/eWeecwTOxRze6+f/W8mJBRiJjVr7LW7mrUAJVVBU0ZUY0O9wYMVjHwrJF2FU7A71781rPJwkw+rb+xQmCIAC+vr5oNBoKCgqavF9QUEBgYOBZ93377beZMWMGK1eupFevXmfd1mAw4OHh0eTVkYW6+AOgkxXQKVRiR+X82l79uNr5fVxyKBQZrThcHOgMNlz1vyU/2kN3jxh0Da2vfszeyI1dnW1V95cXUGOxtlkcJXW1/Jx2AABNrYTkX4hWVkHVMTVsZJvFcTpju3bi/YYEyC8HDrE2MYe/9xgHgMHVxuKcvcw7vL9xex+DFz09u/JgzK0YZD2plUdZW9j2ySRBEM5NNJMVhEucJEkM8+3HvOxf2FycyEj/Qe0dkiAIgtCCxgQMpY9nLHn1RQQZ/fAxeAHQ1T2a6eHXUmWrZm/FQZLKDrCn/ADltkoSy5IbZ0EFGv2I9+xOvGcsceYumDRnXqXZ0U2J6MtQ/xiyakoJd/UWiQ+hRdXXWdm71dkCpP8Zkh9zFu8C4MpBXciuq2RjeiZaWebREYMbt8mvq+CFvYub7CcBpEnEjCwk11QFwPSwa5gaejWyJNajCYLQdvR6PQkJCaxZs4bJkycDNA4vf+SRR86435tvvsmrr77KihUr6NevXxtF23a6eYeQlA9ajQNJp2LTqqg6mfzs5s1ZKy6rZsNO5+8Qi5eEgorGw467wdnyKtjoT4RLyDmO0jpkSSbBqyfbSneSVnOUlwbdxsfJ21Bklf/s3M7fhg1vkzjmHNyLxeGgs9mHtalpRA9y/j7sbe6FtgPMurqqayc+uOFa/u/HJSxNPYxKF+7rOoyZRzZhcLPyws7lxPsF0c37t8UO/kYfpoVdwzeZP/F15o/08+6JR0NljSAIHYO40xaEy8AQH+cKkr0VqVTZato5GkEQBKGl+Ri8iDN3aUx8nMxd58Yw33480vkOPu/3Ov/u/Sy3R0wmzqMLWklDfn0Ry/M3MOPgJ9y142+8kPweC4+vJKMm55Iszw80mRngGyUSH0KL27f1KFaLHb8gTyK7nrr6Oa+oktVbDwJwy7X9+HdD1ce0+J6Ee3k2brfm+KFT9tVr7USMPI7GuwqjbOBvXe9jWtg1IvEhCEK7ePzxx5k5cyazZ88mNTWVBx98kJqaGu6++24A7rjjDp555pnG7d944w3++c9/MmvWLCIjI8nPzyc/P5/q6ur2uoQW190rFPit8kPRg2LQUJRbjsOhnHP/JeuScSgKklXBEqZD1apoXWy4G5yzIIa0U8urE64JGgaAi76ejUWHifN0znyZd3B/m9wPWh0Ovj7gnIdir1Qw+NXhanAOOv9TzMRWP39zjekSw4dTrkUnyyxLPUz6wTquD+uDJIHGrZZ7f53fOP/jhGuDRhPuEkyVvYZvMn9qp8gFQTgTcbctCJeBUJdAIlxCcKgK20uT2jscQRAEoZ1IkkSkayiTQ8byUtxf+WrAWzzd7QHGB44gwOCLXXWQXHmYbzIX8sTe17hv17N8dORrNhfvosp2+TzAEIQLsb1h3seA0d1P+4Bq7tJEHIpK/7hwjttrSDqeh1Gr5aFhAxq3UVWVeRnO6hCt7MBFZ8VsqCPCswy9yY6/wYfXej7JIJ8+bXNRgiAIpzF9+nTefvttnn/+eeLj40lKSmL58uWNQ9CzsrLIy8tr3P6///0vVquVqVOnEhQU1Ph6++232+sSWly0exAAGllFo7fj0EngosNuc1CSX3HWfR2Kws+/7nP+s1HFqlXB3Y5O58BV73xQPtSnfVpendDNIwZ9Q+urRcc38+c+zt9dRUoNe47nnWPvi7ck/SBFdTV46AwcL6jAN9T5d+qnDyLE5N/q5z8fozv/lgBZfvAoFRkyw/06I0lQqiniLxsWNkkYaWUND8TcAsCvhVtJqTjSXqELgnAa7V9XJghCixjqm0Bm1nG2FCcyJmDouXcQBEEQLnsmjZH+3r3o7+3sy51XV8ie8gMklR8gueIwZbYK1hZtY23RNiQkOrlF0MczlnivWDq5RaI5aVV6iaWM3PpCgo3+p61AEYRLmaqq7DzLvI+KqjoWrXU+2Lrl2n68sm4jAHf074O/22/tLb7PTCStphAPQx1B7lWcnEOJNkXyz7iHRDsMQRA6hEceeeSMba7WrVvX5M8ZGRmtH1A7c9GacDhkNBoFrd6BTa9i8nXFVlhLfk4p/iFnvvfZmnSM/OIqJAVqg2RUVGSzDTe9BVmCUFMgYS5BbXg1p5IlmQHe8Wwq2cbx+gz6BwehlzRYZQfvb9/C7NCprXZuVVX5IjkRAEe1isbFgYdbLQDTw8e22nkvxujOMXw0ZSKP/LiEFQePMkaNpptXEAer8thaeYDnd6wgyuxFP78wevkE09U9mqsChrGqYBOfpX/H272fRdcBWnkJgiAqPwThsnFieNr+isNU2KraORpBEAShIwoy+TMhaCTPdn+I2QPe4oXYR5kUPIZwl2BUVI5UZzA/ZynP7n+be3b+nX8f+pw1BVv4+fhq7k/8By+mvM/9if9gdcHm9r4UoZ18/PHHREZGYjQaGThwIDt27DjjtjNnzmT48OF4eXnh5eXFmDFjzrp9e8o4lE9RXjl6g5Zegzud8vMfV+2l3mKnc4QfeVIdR4pL8DAauG/Qb33vc2vLeTN5BVrZcUriQ1XhHv9pIvEhCILQgSl2PQB6jQOHq4rWwzknreAcQ89/Wu1MjqsOOxZfjbPllcmOh8E56HyIT992bXl1wthA53wqN72FlbnJjA6NBmBzfibldfWtdt5dBcdJLilAg4St0kFAdA1aWUWLgSv8O+78mFGdo/loyrXoNBpWH0rHvywAX507sgw/5G3j34eWcdPGz3hq+yIAbouYjIfWjZy6fBblrm7n6AVBOEEkPwThMhFo9CPGNRwFhW0le9o7HEEQBKGD08k6enl2487IG3g3/h98lvAqD8XcxmCfvrhqTFTba9lSspv/pH3L15k/ouIs71dR+TTtf5RYytr5CoS2Nm/ePB5//HFeeOEFdu/eTe/evRk3bhyFhYWn3X7dunXcfPPNrF27lq1btxIWFsbYsWM5fvx4G0d+bieqPnoP7oTRpG/ys3qrje9X7AZg+jUJfLhxKwD3DeqH2eR8MKaqKv/cs4g6hw2NqvD7Z1ySBIfyslv5KgRBEISLoVNNAGg1CqqLgsOgASD/LMmPvKJKtialA2Dxdj5i03ja0WiUxpZXQ3zbt+XVCd08YjDIJjSyyi+5W7m7Z18AbDqF+Un7W+28s1KcVR/UgaRRcfFwtrwa7jcAjaRptfO2hFGdovl4ykR0Gg1rDh0joMofVaXx97wkweK83ewrycVN68JdUVMAWJCzjPz6onaMXBCEE0TyQxAuIyduqjYXJ7ZzJIIgCMKlxsfgxZUBQ3iy6718OeBNXuv5JDeGTiDEGHDKtgoqrxz4iO+yFrO/4hAWh/U0RxQuN++88w733Xcfd999N7GxsXzyySe4uLgwa9as024/Z84cHnroIeLj4+nWrRuff/45iqKwZs2aM57DYrFQWVnZ5NUWdpyl5dXS9Qcoq6wj0NeDYoOFnIpK/N1cuaP/b3M7vs9MZFtxOqoKWtupX7FUBQLcO1ZPc0EQBKEpN9kdAJ3sAINKrcY56Lwg58zJj59/3YeqggGJ2mDng3zJw4q73oIkQbhLcLu3vDpBI8kM8o4HoMSWS4C7C2adEWT4cm8iSisMPs+pqmB5hnMGhlwr0zVWh4veCipMDx/X4udrDSM7RfGfqRPRazTsKco97QKHxCLnAocRvgPoae6KVbHxefq8NhkmLwjC2YnkhyBcRob4OFduHKg8Sqm1vH2DEQRBEC5ZGklDV/dobgq/lhd6PIrEqa0asuvyWJCzjBdT3ufOHU/yQvJ7LMhexsHKNOyKox2iFlqT1WolMTGRMWPGNL4nyzJjxoxh69atzTpGbW0tNpsNb2/vM27z+uuvYzabG19hYWEXHfu5VJXXkro7A4D+o5smPxyKwv9+cQ4wv2F8PJ9u3QnAQ0MHYtLpAGe7qzeSVwCgWmSCfZxVUSeed6gKFCb6o9W4tPalCIIgCBfB3+j8/aTTKEg6hUrsqJy58sNmd7B4rbNiolZjR9FL6E0SstGB+0ktrzqS0QEDAXA3WPglZx/Xd44FIN9ezdaMrBY/3+wDe1BUFckq4abVYXVxVn+Gu0TgZzjz/UBHc0VMFP+ZOgmdTc/v8xmqCgl+zvsVSZL4c/RNaCUte8oPsKVkdztEKwjCyUTyQxAuI/5GH7q6R6GislW0vhIuYdnZ2YwcOZLY2Fh69erF999/394hCcIflo/BiwdibkFuuG2Ukbk1/DoeirmNEX4D8Nabsal2kisP8132Yp5L/jd37niSfx34iIXHV5FWnYVDVdr5KoSLVVxcjMPhICCgaSVQQEAA+fn5zTrGU089RXBwcJMEyu8988wzVFRUNL6ys1u/VdSu9QdRFJXILoEEhDR9ELN+51GOF5Tj4WakzM1OcU0tYZ5mpsbHAc52V//Y8zP1DhsOm0SUuQyNXqG+xEDG0khy1oWQsTSS6kxPwnw9W/1aBEEQhAsX5R4IgFZ2gE7FplVR9TL5Z6j82LDrKKUVtbgYdNQFOu+TVI96NPJvLa8Gd7DkR3ePThgbWl+tyNvOlIbkh2JQmb2rZZ8h1NisfHdwLwCaWokr+gZh1FcDcNMlUvVxshExkbx+1XjsRYYmCRBJggVpSY1/DjYFcEOIc5D7rGPfU2Ova+NIBUE4mba9AxAEoWUN8UngUNUxNhcnck3QqPYORxAuiFar5b333iM+Pp78/HwSEhKYMGECrq6u7R2aIPwhjQkYSh/PWPLqiwgy+uFj8ALgyoAhqKpKbn0h+ysOkVxxmJSKw1Taq9lTfoA95QcAcNWYiDV3pqe5Kz09uhLmEtQhBn8KbWfGjBnMnTuXdevWYTQaz7idwWDAYDC0YWS/tbzq/7uWV6qq8u1iZ6XHhCvjGh8K/fWKIeg1ztYm32cmsr34GKoKfvp6TB4WHDYZa0o0Sp2DulodsiTx/LQxBHq6t+FVCYIgCOerq3coaypAJzsrPxQ9KHoNJfkV5GYWExzh22T7n1Y5H+zrdTIWbw0SKoqHBbPB2fIqwiWEUJfA9riUM9JIMkN8+vJr0WZq1SIkrUKIqwfHayr5NTud/MoqAj1a5vfVgv9v787jo6qvPo5/7uzJJJlsZIOwryFAkE3AKipKrdrSVqVqC2Jb6yNuxbaiVqwLRVq1VEURn1p9VNyrrRsuUDdAVtn3NWHJwpY9k8zMff6YkBABBSG5meT7fr3mBbm5M3PmvuBkMuf3O2fzGkprqjEC0D0uiR3GJhw2E48tmoGJfU7LczS11JgYKPJQU+wETxB8NbjiAry+ZzHD8jrx/czwe4kftxvF5/uWsreqkJdy/8OvOo+xOHKR1kvFD5EWZljyGTy74w02lm5jn/8AyRG0lVTksPT0dNLTw71x09LSSE5O5sCBAyp+iFgoyZ1QV/Q4kmEYtI1KpW1UKt9PO5uQGSK3Yg9rijeFiyElmygPVrLkwCqWHFgFQJwjhmxfd/r4epDt60G6p42KIc1ccnIydrudgoKCBscLCgpIS/vmD3YeeughHnzwQT7++GP69u3bmGGetGAwxLLPNgJHz/v4av0u1m/Nx+V0UOwLUrrNT4+UZC7O6gHUtrtaHW535QqGaNMmPMD14PJ0nrv253hcDvL2HSIzOV6FDxGRCNA9ri0ADnsQnEGCToOQ2wFlNfzq/GncMuUyRo0Jt43asXs/y9blYTMMCuyVgIPkBDeHXCXEumpbXiU3r10fh52dMpB5RfOJdft5O28ll3Xvzd+/WkjQHeKVr1ZzyznDTvk5QqbJUyvDCwjslTYuv6Anr+77CgcwKu2sZj/o/Hg6JsZjMwxC1XaotmOWOAh2qcDuCfL7Za/Tw3cDneKScNmcXNf5Z9y77lHm5H/GiDZD6Brb0erwRVoltb0SaWESXfH0iusCwIJ96i8pjeOzzz7j0ksvJSMjA8MweOutt446Z8aMGXTs2BGPx8OQIUNYvHjxd3quZcuWEQwGm6Tvu4icOptho6O3HZdknMekXtfz7OC/Mq3PH/h5+9H0i++F2+aiJFDGgv3LeWrbS9z01Z+4btldPLr5WeYVLqTIf/yhomIdl8vFgAEDGgwrPzy8fOjQoce931/+8hfuv/9+5syZw8CBA5si1JOy4audlB6qIMYXRa8zOjT43uFdH+ee1Z3XVq8BYOKI4dgMA9M0ueurf1MVqsEImnRIOoBhQPHWOO4YcRkdUxJIi49lUNdMFT5ERCJEWlQSpgk2AxzuYHjnhzv8Ib0ZMnn0j69TtPcQAP+eF5710TEjkfL08Dkl0SXYjfqWV8OSBjT9izgBWXFdiaptfTWvYCkXdw4X9UMuk5dWrKQmeOqz2/6bt43d5SUQgh907M6XpWvxumoAuCj97FN+fKukxcVy/0UjsdUu2rEbdm7ueAFGyE7ICPKzT/9BZSD8OvvG9+Ts5EGYmMzc9hJBUzPxRKygnR8iLdDwpAGsK9nC/P3L+GHb4/fVbk32+w+yp6qQDE/KMVcuy8kpLy+nX79+XHvttfzkJz856vuvvPIKEydOZObMmQwZMoTp06czatQoNm7cSEpKCgA5OTkEAoGj7vvhhx+SkZEBwIEDBxg7dixPP/30N8bj9/vx+/11X5eUlJzKyxOR08hu2Oka25GusR35MRdSEwqwpWwHq4s3saZ4IxtLt3Og+hCfFi3m06JwkTTN04bsuO5k+8K3BJev7vGUz60zceJExo0bx8CBAxk8eDDTp0+nvLyc8ePHAzB27Fjatm3L1KlTAZg2bRqTJ09m9uzZdOzYsW42SExMDDExMZa9jiMtqW15NeB7PbA76lehbtlZxMIV27EZBiVJIfz7gpzRLoMRXToB4XZXi/dtxzRN2saU4nCF8Be7ONt9Ht/v38OS1yIiIqfGaXMQDDpwOAI4nUEq3WZd8QMgFDTZu3MfsUle3v10LQClNj+m0yDW7qAy1k98bcurTt5MMqJSrHop38hu2BmWfAZzC+dj2g5xoKaEXoltWH+giKJgBR9v2spFvbqf0nM8+OVnAHiqHYwblsPvVv+dpGjoFds9ogadH8vlOdl8r3MHdh48RIeEeNLiYum008dty1+hjAqu/O8/eXPkrzEMg3Edf8qyg2vYXp7H+3s/5ZKM86wOX6TVUfFDpAU6M6k//9j+KlvKdpJfVUSap43VIVnq44L5zNw6GxMTA4Pru1zFyNThVocV0S666CIuuuii437/kUce4de//nXdB2IzZ87k3Xff5ZlnnmHSpEkArFix4hufw+/3M3r0aCZNmsSwYd+89Xrq1Knce++9J/ciRMQSTpuDXnFd6RXXlSsyf4A/WM3G0m11M0MO/+zKryri48L5ALSLSifb1x3TDPFhwRfK5xYZM2YMRUVFTJ48mfz8fHJycpgzZ07dEPTc3FxstvqN5U8++STV1dVcdtllDR7nnnvu4U9/+lNThn5ci+aFix+Dz8tqcPzFd5cCMGBgB97ftBmA20YMxzAM9lQc4sHVcwCId/iJjakiFDBwb+nJpP/RhxoiIpHMFnSDI4DTFqLMGyJ4RPHDZjdI75DM3IWbKC2vIi05ji1GCWAjsY2DPXbqW141s0HnX3dW8gDmFs4nprb11eguvVh/oIigJ8RLy1edUvFj2d7dbCrZByb8uu9A5uSvwOcJD/3+UduW8XMyLS62wWyUizr0YsX+s3g+9ws2V+zhrqVv8+dBPyTeFcfPO4zmqW0v8VLu2wxN6q/FOyJNTG2vRFqgeFcc2b7wqsN/7fqA/f6DFkfU9CqDVXx1cC2ztr7Ek1tfxMQEwMTkqa0vtcpr0lSqq6tZtmwZI0fW7zqy2WyMHDmShQsXntBjmKbJNddcw3nnnccvfvGLbz3/jjvuoLi4uO6Wl5f3neMXkabltrvoG9+Tqzv8iKl9f89zg//Knb1u4IcZ59PJm4mBwa7KvczJ/5QPCj5XPrfYjTfeyM6dO/H7/SxatIghQ4bUfe+TTz7h2Wefrft6x44dmKZ51K25FD4Kdx9kx8a92GwGA8+p362Rv6+EjxZsAKA8BYKmyTldOjGofTtM0+TO5W/hDwVwESAtPrzT8NDqNP5+5c9wObS2TEQkkrkJzxh02oMYbpOgN1z8sNkNbn7gMtqkx/PW3PCg8/adEvD7bBghKHAfbNjyKrl5trw6rLevG1H2KBw2k8/3reDCjl0BMJ2wMC+XLfv2f+fHvuOTDwGIxc34wWfwUcEiHDaTGHsMZyT0Pi3xN0d3nDGSvjHhHaL/3r2cV7eF25CPTB1Oj9hOVIX8PLP9dStDFGmV9O5cpIVKrG0RMrdwAfMKF7b41bGHVw6vKdlUu3J4B0EzdMxzQ4TYW1WkFReNZN++fQSDwbqVwIelpqayYcOGE3qM+fPn88orr9C3b9+6eSLPP/88ffr0Oeb5brcbt9t9SnGLSPMQ7YhiQEI2AxKyASitKWddyWY+KfySxQdXNThX+VxOxZJPwrs+evbvQFyCt+74K+8vJxgM0S0rlc9zcwH47YjwDsTXdy5nyf4dGIRo5yvGsEFpbgx3nfUz2iXHN/lrEBGR0yvB6aOQQhy2EDhNQj4nU1/4DW07taFNejybdhSyZvNeHHYbmysOgg3aeaLY6y0mobblVRdve9I8yVa/lG9kN+wMS+rP3MIFOB1lbC7PZ2BqW5YW7CboNnlp+SruvvDck37chbm5bCzbBwbcNmg47+xeRZSrFAjP+ojUQecn6rkRV3Puu49SbJRw36q3yYpPIzsxg+s6X8nvVz7Ilwe+YtmB1QxIPPbvtSJy+qn4IdIC7fcfrOubDuHVsU9ufZENpVvJjutOZ2972kanRvQbj5pQgM1lO1hzRM/4gNlwfkSKO4muMR1YuP+rupXCh0Xbo5oyXDlJZ511FqHQsYtXItK6xDq9DEnKoWtMB5YsW90gnxsYpLfy1o7y3S2unfcxaESvumMlZVX8e264yFaRZoMiuCSrB1mpKeypOMTU1e8DJqnRZbhcQarLnJztPJ+R/bpZ8RJEROQ0a+ttQ2HlZpy2IIYzRI3DJK17Km2S4wB48+Pwro9hAzrz77JtALhSQhgGxBxuedXMd30cNjx5AHMLFxDr9vNO7ip+2KUnSwt2E/KEeHP1Om4bcRbRLucJP17INPnD3DlggM/u4Rd9c7hk3sN4o2swMBiZ+s2tjFsCt8PBa+f/ku9/+Dghew3jvniOj0fdQkdvOy7JOI//7PmYp7e/Qm9fdzx2Ld4TaQoqfoi0QHuqCo/6sB/gv4Vf8t/CLwFw2Zx0jG5H55j2dPZm0iWmPe2i0nHYmmdBJGgG2VqWy5ranvDrS7dSHappcE6iy0e2r0fdkNzU2tU2HxfM56mtLxGi/sP0J7Y+z729f4vXoSLI6ZacnIzdbqegoKDB8YKCAtLS0iyKSkQiXZI7geu7XNUgn5uYrCzewHkpQy2OTiKNv6qGlQu2ADD4vPrix78+Xkmlv4aUjj5WFRXgsNm45exhmKbJHcvexB8K4HNWEe+twgyBa3NPbv/NyOM9jYiIRJiuvgy+qgSHPVz8CDlhb1EJaclxlFdW8+H8cOE8mGAjVAnOihC7PPuxG8H6llfNfN7HYdm+7kTbo6igkqUH1zIx60LshkHQaVIS8PP22g2M6X/iOxTeWLmG3OpisMOtA4bxWcFmqijAC+TEZ5Ec4YPOT1TbGB+PDvkZExa9QJXdz88++QfvXnADYzIvZsG+5RT5D/Darvf5RYfRVocq0ipETPFjypQpvPvuu6xYsQKXy8WhQ4cafH/lypU8+OCDfPHFF+zbt4+OHTty/fXXc8stt1gTsIiFMjwpGBhHrY49L2Uoe6oK2V6WR1XIz6ay7Wwq2153jtNw0MHbls7ezNqiSHvaR6fjtJ34ao/TJWiG2Fm+izXFm1hdvJH1pVupDFY1OCfOEUO2rzt9fD3I9nUn3ZOCYRhHPdbI1OH0j89ib1URBgYPb/pftpfvYuqGJ7m714247a6melmtgsvlYsCAAcydO5fRo0cDEAqFmDt3LjfeeKO1wYlIRDucz/dUFvL5viXMLVzAk1tewGuPYkhSjtXhSQRZuXAL/qoaktN9dOqZDoC/OsCr7y/HBPxt7VACl/XrTYfEeF7bsZSlB3bisteQGhdu31G8NpXnr7wap6N5LhwREZGT183XFvLBaQuBM0TQZbC3qJj+vdrxwRfrqaiqoX1GAgvydwHQ0RfNDncp8bUtr7rFdCTFk2Txqzgx4dZXZ/Bx4XyiXJUsO7iD4Rkd+Gz3DkJuk9nLV3JFTvYxf8f+upKqKh744lPwgNfh4ure/fjNl/+HzxP+HX5U2vca++U0K+e27cKvO5/L0zvmsatqP7cuep3Hh47hV52v4MENM3l7z8eckzyY9t4Mq0MVafEipvhRXV3N5ZdfztChQ/nHP/5x1PeXLVtGSkoKL7zwApmZmSxYsIDrrrsOu92uD9uk1fn66lgbNn7T5cq6mR8hM9wjfVtZLtvKc9lWlse28jwqgpVsKdvJlrKdULto32HYyYzOoIu3PZ1iMunibU/76IzTXjAwTZO8ir2sLgnv7FhbvInyYGWDc2Ic0fSO60a2rzvZvh5kRqWf0Buxw9fkcE/4u7NuZPKa6awv2cJDG5/m9p7XN9sdL81VWVkZW7Zsqft6+/btrFixgsTERNq3b8/EiRMZN24cAwcOZPDgwUyfPp3y8nLGjx/fqHHNmDGDGTNmEAwGG/V5RMQ6h/N5tq87JibzChfyyKZnuLPXDfSL72l1eBIhltS2vBo8olfde4n3P1/HwZIKvBlRbCs5hNthZ8JZZ7Kn4hB/Xj0HgxAZMaXYbFC2x8sfh11NRmKclS9DREROs04x4bmFDlsIwxkk5Arv/DBNs67l1cChnfhq0wqMoElN+3A3ghhnZAw6/7ozk/rzceF8Yt1VvL1rFT/sMojPdu/A9JisKyhk5Z58ctqmf+vj/P3TBRTbw7+//6rPQHaU7WND6SbaxpnEO30tetD58fw252xW7t/NkrKN/LdwPU9t+ILf9DyLwYn9WHxgJTO3zeaB7InYDJvVoYq0aBFT/Lj33nsBePbZZ4/5/WuvvbbB1507d2bhwoX861//+sbih9/vx+/3131dUlJy6sGKNANH7nZI97RpMAzWZthoG5VK26hUvtdmEBAuiBT697O1LJdt5Xm1hZE8ygLlbC/PY3t5HhTW3h8bmdHpR+wQyaSjt91xe1bu9x9kT1UhGZ6UujhM02RPVSFrijeyurbYURIoa3C/KLuHrLiutW2setDB2xb7aXhj0MmbyZ29/of71j3G8kNreWzLc9zS7Rq96TgJS5cu5dxz6wfgTZw4EYBx48bx7LPPMmbMGIqKipg8eTL5+fnk5OQwZ86co4agn24TJkxgwoQJlJSU4PP5GvW5RMRahmFwfZerKA9UsujACv6y4Snu6X0z3WM7WR2aNHOmadbP+6hteRUMhZj9zlJMoLqtA6r8jB3Yn5QYL7/4/J9UhwKkecvwuALUVDg42z6Sc/t0tfBViIhIY4h3xWGaYBjgcAepcZrsLSpmzea9bMktwu1ysKXqEADRRUH29C7FYQvidYeLH0OT+lsY/cnr4+tR1/pq3aHN3J19CW67HT9BTAfMXr7yW4sfGwqL+L9VKzATwGmzMTYrh79v+Jh4T7gYcmHq8IieN3oqZo24gpHvzGA/B3h0w8dkJ6Tzy06Xs+rQBjaWbmNe4cK6Raoi0jgipvjxXRQXF5OY+M09BadOnVpXWBFpaY7c7fBtbIaNNE8b0jxtGF67WsU0TYr8B9hWnsvWsly2l+extSyXkkAZOyt2s7NiN/8tCs8QsWGQEZVGl5hMOnvb0zmmPZ287Zi/bxkzt87GxMTA4Jw2gwmZIdaUbOJAdXGDGFw2J71iu4Tndvi60yWmfaO9SeoV15Xf97iOBzc8yRf7lhLjiOZXncac8E6S1m7EiBGY5tFzZY504403auediDQqu2Hnt93HM3X9k6ws3sCU9TO4r/dv6eBta3Vo0ozlbi6gcPdBnC4HOUPDBYzPl24lL/8gthQnBVXlxLrd/HroIF7buYyvDuYS564kProK0wTX5h784TrN+RARaYlsho1QwIXdWY3LHqQq2mRPUXHdro/vDenKG3mbAchoF8UOezleZzWGAT1iO9EmwuZaOGx2zkzKYV7hQmLdfj4v2sSIdp35YOdmQu4Q763bxB3nn0NC9LFnZZqmyb1z5hGICs9j+3HX3ths8MGe5WQmhAedn98KBp0fj8tu55WR4xk153GCDj8TvnyJ90beyJj2l/Dcjjd4fuebDEroi88Va3WoIi1Wiy1+LFiwgFdeeYV33333G8+744476lYsQ3jnR2ZmZmOHJxIRDMMgxZNEiieJM2tXsJimyYHqQ2yta5cV/vNgTTG7Kveyq3IvnxYtPubjmZh8UrSo7muH4aBHbCeyfT3o4+tO15iOOG1Nl5bOSOjNzd2uYfqmfzIn/zNiHF6ubH9pkz2/iIicOqfNye97Xsd9ax9jU9l27lv3GFP63Eaap43VoUkzdXjXR7+hXfBEuzFNkxfeXoJpQKCtAwJBfnXmQCrMKqaumoPLHiA1Jrw7tXR9Ms9fORanvXWuYBURaQ0cZhQm1TjsIQy3yZYd+1i7eS8A3o5eAitMnCUhSvuG51nEucLdRIYlRVbLq8OGJZ1RV/x4Z9cqru1yHh/s3Izda8NfHuBfq9byyzMHHvO+/1m7gaV7dhNKCi+Mu7b3Gby6YynR7vDPzTMSereaQefHk+6NZebQK/nlwv+jxhHgys+e4d2R/8NnRYvYXr6L/9v5L27qNs7qMEVaLEuLH5MmTWLatGnfeM769evp2fPk+jevWbOGH/3oR9xzzz1ceOGF33iu2+3G7T52qx4ROZphGHU7SgYn9qs7frC6mG1luWwtP1wQyWV/9aFjPsbZyYM5L3Uo3WM6WT5s/KzkgZQHKpi17WVe3/U+MY5oLs0439KYRETk5ETZPdyVNYG71zxCbsUe7l37KFP63EaiK97q0KQZOlz8GHxuFgArN+5m7Za9BNrYKAn4SfZGM3ZgDr/+8nlqzBrax5Zgt5lUFEZx99BxpMVrdaaISEvmtcVQRjFOWxCcJgerq3DUQPdObZi7YxsA0UU17I+uCre88hxueXWGlWF/Z318PetaX+2syKNTQixep5PymhqcDjsvfbWK8UMGYPtal4TSKj/T5n5GMCoEBgxNb0+XhET+Z8kifDHhwtCFqa1r0PnxDMvoyM3dL+DRLR+wr7qE/1nwMn/s/zPuWvMwnxQtYkTKmfTx9bA6TJEWydIG97fddhvr16//xlvnzp1P6jHXrVvH+eefz3XXXccf//jHRopcRL4uweVjQGIfrsj8AZN6Xs+sgX/mob53YNDwDZINGz/v8CP6+HpYXvg4bFTa2VzV/ocAPLvjDeYVLrQ4IhEROVkxjmgmZ91EmqcNhf793Lv2MUpryr79jtKqlBZXsG7ZDgAG1877OLzroyYjvC7shuFDeHvPKlYeyqONt4woZ4CA38ZZjOTs3l2sCl1ERJpIqicJAKc9hOEMEXSGj/cb1IGdh4oxAibx3ZxggNcRLnz0iu1CkjveoohPjcNmZ0jtwsY4t5+P8tdzYYduANi9BrkHi5m/fedR93vs84UUlZdjRoe//mX2AD7cs44qDuCwmSS64umfkNVkr6O5+58+Q/lefG9ME5Yf2smb2zdzYepZAMza+hI1oRqLIxRpmSwtfrRp04aePXt+483lOvEPR9euXcu5557LuHHjmDJlSiNGLiInolNMJtd3uQpbbaqxYeM3Xa484TkkTeknbUfxw9odH09ueYFF+1dYG5CIiJy0BJePe7JuJtHlY1flXqasf4LKYJXVYUkzsuyzjYSCIdp3SyW1XSLb8vYxf/k2qpKhMhSgXXwc3+vZngdXv0+My09idHhYq2tzd26/dJTF0YuISFPoEJsGgMMWBGeIUO3HUvPz8wCI3hPgUKdwq6s4d23Lq+TI3PVx2NDa+GNdft7btYofdg53YDGiwu2rZy9b2eD8jYX7eH7pCkIek5Bh0iEunnPbdeL5rV+SUDvo/IJWPOj8eB47+8ekGikAPL9jIUmObsQ749hTVchbuz+yODqRlsnS4sfJyM3NZcWKFeTm5hIMBlmxYgUrVqygrCy8om/NmjWce+65XHjhhUycOJH8/Hzy8/MpKiqyOHKR1m1k6nBmDrife3vfyswB9zMydbjVIR2TYRiM7fATzksZSgiTRzY9w6pDG6wOS07SjBkzyMrKYtCgQVaHIiIWSfEkMTnrZmIcXjaX7WDahqeo1ko6qbWkruVVeNfH7HeXErJBTVp418fN3xvK7Uv/hWlUkx5bAkDp5kRmjLkWhz1ifnUSEZFT0Lm2+OG01e78cBmEHLB6fyEAUVU1VHiqsRvhllcGRt2MzEjVt7b1lcMeYn9NEbHRdhLcUVSGAphOk/9u2c6e4vDPRdM0ue+DeQTMEJ748M/P8VlnsKZ4D5tKc4l21Q46T2m9g86Px2138PIFY3HUhAfI/2nFe4xKHQnAG7vmsKey0MrwRFqkiHkHP3nyZPr3788999xDWVkZ/fv3p3///ixduhSA119/naKiIl544QXS09PrbvoATMR6Se4Esn3dm+WOjyMZhsH1Xa5iSGIOATPAtA1Psbl0h9VhyUmYMGEC69atY8mSJVaHIiIWyoxO5+5eE/DY3Kwu3sjfNj1D0AxaHZZYLBgMsfTT8MKGQef2onB/KR98sZ7KNgY1hOjeJony6FJWF+eRUTvno2q/m7sHjyfFF2Nx9CIi0lS6+tIBcNhrd344wZ8A2AycxSGM3uGP0mJqW15lxXWN+DljTpujbqZnrMvPnN1rubhTdwCSkqMJmSavfLUagHfWbWRJ3m4cUTbKQtXEOl1c3r0PL2z7kvjaXR8DErKb/e//VkmNjuGp4VcRqrETIsQjq5eSFduNGjPA09texjRNq0MUaVEipvjx7LPPYprmUbcRI0YA8Kc//emY39+xY4elcYtIZLEbdn7bfTx9fD2oCvl5YP0M8ir2Wh2WiIicpK6xHZnU63qchoPFB1byxJYXCJkhq8MSC21amUvJwQq8sR6yzujIK3OWU22EqG4T/pVo3LAc/rL2Q5Kjy4l21RCssTE0dD7f66U5HyIirUl6VBsAHDYTW23bq6rE8CxL7+4aSjuHd5TGeWpbXkXooPOvG1q7eyXW7eeDPWv4QafwAO5iqjAxeW3lGg5WVDJt7mcApKWFFwZc0aMPZcFKPtqzljjP4UHnZ1nwCiLHmemZ3NZrFKGgQWmgitVFBk7DwariDXyxb6nV4Ym0KBFT/BARaSpOm5Pbe/6GbjEdKQuUc9+6Ryms2m91WCIicpL6+HpwW49fYcPGJ0WL+OeO17WarhVbNC/c8mrAOT2prK7hrbmrqEgxCBkmOW3TeLXwS1yOSpKiKwBwbu7CHZf+wMqQRUTEAl57FKFQ+OMypztAMApCLgNbABxRAYKOEHZCdS2vhiTlWBvwadIvvidRdg9Oe4iqUDE1dj/p3lgqgwFifW72lVdw2bMvUVhWTlpCDNvKD2IA12SdwUvblxDtqsJhM0l2JZCT0Nvql9Ps/ar3YM5L6IsZgs2lh4gx2gPw7I43KA9UWBydSMuh4oeIyDFE2T3c1esGMqPSOVBdzL3rHuVQdYnVYYmIyEkalNiXG7v9AoD39n7Cq3nvWhyRWOXIeR9vzV1FabCa6tqVvH2zEtlYuouM2BIMA8q3xzNzzHXYbfp1SUSktTEMAyPoBsJzP3CGd456dwXw9w0vojjc8qp3XDcSXD5rAj3NnDYngxP7AuHdH+/tXs0lncO7P5ze8M/DvEPFAEQnOAG4sENX2kRH89qOZXUtr85PHY7d0M/PE/HI2ZeSYUvHNOHLglJi7HEcqinhxZ3/tjo0kRZD2UhE5DhinTFM7n0TKe4k8quKuH/d41qBISISgc5pM4RfdroCgFd3vcc7e+ZZHJE0taK9h9i2fg+GYdBnaFdeeX85FSkGpgGDu6Tz6p4vSY8rwWEP4S92cdcZ15IU67U6bBERsYibaACctiCGM1zwMGwhqjLDM8TiosLtnYYlt4yWV4cNrW3hFev2M2/vekZ17ApAfk0ZphG+DqZhsr60CIBrswfyzq7VVIZKjxh0PtSa4COQ2+7gxZFX46z2YmKwbn/4Y9oPC75gU+l2i6MTaRlU/BAR+QaJrngmZ91EvDOOHRW7+PP6J/EHq60OS0RETtIP0kfws8xLAPjnjteZV7jQ4oikKS35JDzovEdOe75cn0tBVTnV8QZgsj9+H76oMmJc1YSCBmdWn8f3enW1NmAREbFUjD0OAIe9fudHWU4IDLCF6ltenVk7J6OlyInvVdf6CqOSguqDpEfHggEhV7j4EYwywYCOsfEMTm3bYND5wIQ+GnR+klK9MTz9vZ8R9DuoqHFRVhWFiclTW18iaAatDk8k4qn4ISLyLdKjUrg760a89ig2lG7lrxufpiYUsDosOYYZM2aQlZXFoEGDrA5FRJqhy9pdxCXp5wHw5JYXWLR/hbUBSZM53PJq0IievPjOEipSDTCgV08fhYG9tPGWA+DY3Ik7L73UylBFRKQZiLHHAod3foQAE1tCeND54ZZXfXw98DljrQqxUThtTgYl9AEg1l3Fu7tX84OO3QEIeUxMTIJR4WLQ2F79WbRvO1tLC/HVDn+/IHW4NYFHuMHpmdze+yKCNTb2lnkxTRs7Knbx7t5PrA5NJOKp+CEicgI6ettxZ68bcNmcfHVoLY9tfo6gGbI6LPmaCRMmsG7dOpYsWWJ1KCLSDBmGwTUdf8p5KUMJYfLIpmdYeWiD1WFJI6v21/DV/E0AONrFsfXQQWriDGzOEDvsebSNK8YwoCIvlqcuvwGbzbA4YhERsVp3XwYQ3vlhOEPgCWF4Qpgh8HlbZsurw4Ym17e+ml+4mYu71hY/XCahKBPsEONw8fPeOTy/bRGxbj92W0iDzk/RNVlncGGbHGoCdvLLwq03X859myL/AYsjE4lsKn6IiJygnnFd+EOP3+Aw7Mzfv4z/3fYKpmlaHZaIiJwEwzC4vstVDEnMIWAG+MuGp9RTuYVb9eVW/JU1JKX5+GjFVirSwu2ufF2DpMYV47SHqC53MKnvL0mMjbY6XBERaQb6JXcAwjs/cIawxYd3NtiCJl5PNTZsDEnMsTDCxpMTn1XX+sppr2FT+R6yklLAADMu/Pvvr/oMJL+ymM8KNmnQ+WliGAZ/PesHdLC35VClh4oaJ/5QNf/Y/qrVoYlENGUlEZGT0D8hi5u7XYOBwYcFnzM79z9WhyQiIifJbtj5bffx9PP1pCrkZ8r6GeSW77E6LGkki2tbXnU8sxPL9+wl4DVwJAVwRO8j1u0nFIKB5SM4p1d3iyMVEZHmokNMKgBOewibLYjDG255FesKt7zqG9+TOGeMZfE1JpfNycDa1ldx7ire2bWajrE+AIK1i/9iXC5e3LYIpz1AtKsGGwbnpwyzLOaWwm138OzIMbj8ceSXxmKasOTAKhYfWGl1aMeVX1nMoqLt5FcWWx2KyDGp+CEicpKGJw/gus5XAvCv3R/wn90fWxyRiIicLKfNye97Xkf3mE6UBSq4b91j5FftszosOc1M06yb97HXGaQizcB0BPFklJASUwaAfWt77v7hj60MU0REmplkdyIANsPE5g1hesAMGMTHVAAwLKlltrw6bGjtIPdYt58VB3L5IHdTg+9PXfIJ/8r9qm7Xx4CEPiS545s6zBYpzRvL0+eMoaI0igOV4R2pM7e8RGWwyuLIjvbGzuVc8OF0rl3wHBd8OJ03di63OiSRo6j4ISLyHVyYdhY/bz8agOd2/ou5BQusDUhERE5alN3DXVkTaB+dwcGaYu5d+3cOVB+yOiw5jfK2FpKfdwBiXXxVvI9gFLg6VJDhK8ZmQEW+l6d/crPmfIiISAMl+6sI1IQ/MnPawrMebUGTaE8NNmwMTuxnZXiNLic+C4/NjdMewuMIYPPUNPi+zV1DVbCahKjwTpgL0s6yIswWa1BaO+7sexH5h+KoDtooDpTw7PY3rQ6rTnnAz/u71nDPiv8QIrwbKITJn1a+rR0g0uyo+CEi8h39uN2F/ChjJAAzt77Il/u/sjgiERE5WTGOaCZn3USapw2F/v3ct/YxSmvKrA5LTpPDLa882alUpBqQWE16m0O4HUFq/HZ+3308CZrzISIiX7N71wEC1XYAHPYgAD5veJdDv/iexDq9lsXWFNx2FwMSw62vYt1VON1B4PC8SxNnVA2xbj+GESTZlUhOfJZlsbZUY7P6c1HyGeQXxwHwccHnbC7Z2eRxmKbJnopDvLdrNVNWvcflnzzF0Pce5HfLXufrE1BDpkluuQa0S/Oi4oeIyCn4RYcfc37KMEKY/G3TP1l5aIPVIYmIyElKcPmYnHUTiS4feZV7mbL+iWbZWkBO3uJ56wg5beyMDxL0hkjscBCfpwrThH4Hz+K8bH1YIyIiR3O2cVLjCO8KdNrCxY/42HDLq+HJAy2LqykNq219FeeuxuYI4nCEP+p2ukPY7CZJ0eEh8CNTh2nQeSMwDIOpZ32f5GBXSqrcYMA9a2cSDAUb9XlrQkFWH9zN81u/ZOKSVzn/w0e44KPp/H7ZG8zevph1xXsJmiZtPEfPvLEZBu29iY0an8jJclgdgIhIJDMMg990uYqKYCUL93/FXzY8xT29b6Z7bCerQxMRkZOQ6knm7qybuHvN39hctoNpG57izl434LI5rQ5NvqOykkrWLt1BVTsv5SkmUZ1LSY8L7+oJbU/nvquvsDhCERFprso81QRC4Z0fTnsIlz2AxxHEwMbgxL4WR9c0+sf3xmNzA348jgBX5PTmwpS+PL7lI9aVbMft8GPDxnkadN5oPA4H/xx5OZe8tx9v+g78tmIeWPs89/S55rQ9x6HqClYcyGPFgTy+OpDHmkO7qQoGGpxjNwx6+tLpn5hJ/8RM+iVmkh7l442dy/nTyrcJmSY2w+BP/S4lLcp32mITOR1U/BAROUV2w8Yt3a6hIlDJyuINTFk/g/uzJ9I+OsPq0FqdGTNmMGPGDILBxl0NIyItU/voDO7uNYF71v6d1cUb+dumZ/hdj19hN+xWhybfwfLPNxLEpKR3DKRVk9nmIDbDpOKghxdHT8QwNOdDRESOrUNMIoFgbdsrW5A4d3hHaFZcd7yO1tEu0W13cUZCbxbsX06s289nBZu4otMAVhzMIzUmfD0GJGZr0HkjS/PG8sT3ruSWJU+QkXSIr4qX8MGe/ozKOPm5MyEzxPay/UcUO3LZXrb/qPPinB76J2aSk5hJ/8T29I7PINrhOuq8n3Y4g+EpXcgtP0B7b6IKH9IsqfghInIaOG1Oft/zOu5b+xibyrZz/7rHeCD7NlI9yVaH1qpMmDCBCRMmUFJSgs+nN14icvK6xnZkUq/rmbJuBosPrOSJLS8woesvsKmdQ8RZ/N/1VGZ4KW8Xom2XfXicAQIBGxMzx5MY07J7tYuIyKlJi/LRw9GBA6zFaQsP/QY4L2WIxZE1rWFJZ7Bg/3J8bj9bDhRz5/I3MTBJiqomBFyQqkHnTWFwWjt+0+knvLrveaJcNfxt4//RI+4OOsZ88+cNFYFq1hzaXVfsWHFgF8U1lUed1zkmmZy6YkcmHWOSTvi9r9MWIspZjdMW+k6vTaSxqfghInKaRNk93NnrBiav/Ru5FXu4b91jPJA9kQSXPoQXEYkkfXw9uK3Hr/jLhll8UrSIaEcU13a8XDsFIkgwGGLJpxs4eGYMvl6HSIwO/6LftWgQF5zdx+LoRESkucs/VMqqhcW0Oxc8jgA2m0koaNDB3tnq0JrUGQnZuG0uoBqPI8CmkkLi3H5CBDTovImN6z2AxZ9vJN+cj9ddyQ2LZvHooF+y319Bh5jwrou9lcUNWlhtqJ3PcSSP3UGf+LZ1xY6cxEziXd9tN9PHBfOZuXU2JiYGBtd3uYqRqcNPx8sVOW1U/BAROY1inV4mZ93EXasfJr+qiPvXPc592b8lppVsjRYRaSkGJfblxm6/4NHNz/He3k+IsUczpv0lVoclJ2jTqjzyPSbBHD9tkw4BULk7iYcuG2ttYCIiEhFyiw5R4QivfLfZwh8el+2PYt/BKjolWRlZ0wq3vspmYW3rq6qAk3hPeEHBBanDNei8CRmGwSPDxjDm0804ogqxO/dy2aeP4bSHqA7aibZ7KampOup+qZ5Y+ie2ryt09PSl4bQd3dLVNE2qQn5Ka8opDZRTGiir+3tZoPyI4+WU1pRRXFPKvuqD9ffH5MmtL/JB/mckuuKJdXiJdXqJdcQQ6/QS54ghxuElzhlDrMNLjMOL4xhxiJxuKn6IiJxmCS4fk3vfzF2rH2JnxW7+vP4JJmfdhMfutjo0ERE5Cee0GUJ5oJJ/bH+VV3e9h9cRzSUZ51kdlpyAxfPWse+saNp32YvdZlJZ4eL/Rv1eu3dEROSEuL0OShJtmCYc/tGxr9qLK7r1fYzWK7ZHbfGjiuIqD9GuGkwTsn3ZVofW6ngcDv426AZ+u+J+XI4gnRMPYBhgmpBfVo2tJoqevnRyEtrSM74NHWPj8dht4eJFoJytFWtZUby4vqBxRFGjLFBOwDz12ZnbyvPYVp53QudG26OIrS2IhAsj9cWSWEfDvx8umjhtzqMeZ7//IHuqCsnwpJDkTjjl1yAtS+vL2iIiTSDNk8zkrJu4e80jbCzdxl83Ps2kntfjtCntiohEkh+kj6A8UMHLee/wzx2v43VEc27KmVaHZZkZM2bw17/+lfz8fPr168djjz3G4MGDj3v+a6+9xt13382OHTvo1q0b06ZN4wc/+EGjx/n2onUkjT9AtKuGYMjgl/FXkhwX1+jPKyIiLUM5NSQnlNV9bZpgtwepoMbCqKwRZ08hZILLHiI1phSAsmoXJdUBiyNrnTrHpdDL25/N/qV1hTnDgLSYUrr4QmBsZHn5SpaXf7fHd9mcxDgOFx/CuzeO/DqmthgRMkP8dePTmNS31TIwuL7zlZgG9TtFasoa7CQpCZRTHqjAxKQiWElFsJIC/74Tjs9jc9fvJHF6KQ9UsKVsZ/3zq/WWfI0+hRMRaSQdvG25K2sC9659lBWH1vHXDbO4OOM82kWlajWCiEgEuazdRZQFKnhn7zye2PICwVCA1Kg2rW512SuvvMLEiROZOXMmQ4YMYfr06YwaNYqNGzeSkpJy1PkLFizgyiuvZOrUqVxyySXMnj2b0aNHs3z5crKzG2+16O+ee5E9VxaRFhf+0CquoAc//WnrGlArIiKnJj7KoEP6vgYfLrdPO4AvqvXtIOwWl0Z5tZtYtx+vK1z8Ka6Kpr030eLIWq/O3nZs9i9tcMwwoCJUX/EwMPA6ohsUMQ63mzqyiHHk92IdMbjtrhOO4/ouV/HU1pcIEcKGjd90ufKECg9BM0RFoIKSI4sjNeWUBMJ/Lzvi70d+P0SIqpCfKr+fIv+Box7XxOSprS/RPz6rVb1Hl29mmObXJt+0ciUlJfh8PoqLi4nT6jAROQ1WHFrHlHUzCNWuiNBqhManXC4ip5tpmjyx9QXmFS6sO9ba8vmQIUMYNGgQjz/+OAChUIjMzExuuukmJk2adNT5Y8aMoby8nHfeeafu2JlnnklOTg4zZ848oec82Xw+fPZdpHY4WPdhVVmlkznnTVe7KxERi0Xa+/PVxRv509q/H3X83t63ku3rbkFE1rp9xd/ZUrERCO+CGRx/FpN6X2VxVK3XuoN5/HHdVI58e2OacF2nsfRN6ESsw0u0I7pJZrLs9x9kb1UR6Z42jVpwMM3wTpHSQDkltQWR9cVbeHPPh0ed21r/n8qxaTKRiEgjy4xK58gq8+HVCPv9B497HxERaV4Mw+Dydhc1ONaa8nl1dTXLli1j5MiRdcdsNhsjR45k4cKFx7zPwoULG5wPMGrUqOOeD+D3+ykpKWlwO1F3vfxqg8IHgNdTwx9fee2EH0NERAQgw5OCQcPCuQ0b6Z42FkVknf3+g2yt2FT3tWHAsuIFreL9T3OVlZDJoJhzOLyc3TRhUMw5fD/jTDKiUol1xjTZMPokdwLZvu6NvtPCMMI7WdI8bege24kBCdlclH6O/p/Kt1LxQ0Skke2pKmzQBxMgRIi9VUUWRSQiIt9FgX//UcdaSz7ft28fwWCQ1NTUBsdTU1PJz88/5n3y8/NP6nyAqVOn4vP56m6ZmZknHOOi0i18fYOHYYSPi4iInIwkdwLXd7kKW+3HZodb+rTGVjr6fbZ5uqPfGB7IuoMrUq/kgaw7uKPfGKtDanL6fyonQjM/REQa2eFVQ0e+YdRqhMYxY8YMZsyYQTAYtDoUEWmBlM8b3x133MHEiRPrvi4pKTnhAsiQ2K6sN3cd1QJiSGzX0x2miIi0AiNTh9M/PqtJWvo0Z3r/03xlJWSSlXDiC0VaIv0/lW+jnR8iIo1MqxGazoQJE1i3bh1LliyxOhQRaYFacz5PTk7GbrdTUFDQ4HhBQQFpaWnHvE9aWtpJnQ/gdruJi4trcDtRU352BQU7Exq0gCjYmcCUn11xwo8hIiJypKZq6dOcteb3PxIZ9P9Uvol2foiINAGtRhARaRlaaz53uVwMGDCAuXPnMnr0aCA88Hzu3LnceOONx7zP0KFDmTt3LrfeemvdsY8++oihQ4c2Wpzzr5rCXS+/yqLSLQyJ7cq/rlLhQ0RE5FS11vc/IhL5VPwQEWkiSe4EvUkUEWkBWms+nzhxIuPGjWPgwIEMHjyY6dOnU15ezvjx4wEYO3Ysbdu2ZerUqQDccsstnHPOOTz88MNcfPHFvPzyyyxdupRZs2Y1apza6SEiInL6tdb3PyIS2dT2SkREREREvtWYMWN46KGHmDx5Mjk5OaxYsYI5c+bUDTXPzc1l7969decPGzaM2bNnM2vWLPr168frr7/OW2+9RXZ2tlUvQUREas2YMYOOHTvi8XgYMmQIixcv/sbzX3vtNXr27InH46FPnz689957TRSpiIjId2eYpml++2mtR0lJCT6fj+Li4pPqMSwiIs2HcrmISMugfC4icvq98sorjB07lpkzZzJkyBCmT5/Oa6+9xsaNG0lJSTnq/AULFnD22WczdepULrnkEmbPns20adNYvnz5CRe0lc9FRMQKKn58jX4gi4hEPuVyEZGWQflcROT0GzJkCIMGDeLxxx8HwjOcMjMzuemmm5g0adJR548ZM4by8nLeeeedumNnnnkmOTk5zJw584SeU/lcRESsoLZXIiIiIiIiIiKtQHV1NcuWLWPkyJF1x2w2GyNHjmThwoXHvM/ChQsbnA8watSo454P4Pf7KSkpaXATERFpaip+iIiIiIiIiIi0Avv27SMYDNbNazosNTWV/Pz8Y94nPz//pM4HmDp1Kj6fr+6WmZl56sGLiIicJBU/RERERERERETktLnjjjsoLi6uu+Xl5VkdkoiItEIOqwMQEREREREREZHGl5ycjN1up6CgoMHxgoIC0tLSjnmftLS0kzofwO1243a7Tz1gERGRU6CdHyIiIiIiIiIirYDL5WLAgAHMnTu37lgoFGLu3LkMHTr0mPcZOnRog/MBPvroo+OeLyIi0lxo54eIiIiIiIiISCsxceJExo0bx8CBAxk8eDDTp0+nvLyc8ePHAzB27Fjatm3L1KlTAbjllls455xzePjhh7n44ot5+eWXWbp0KbNmzbLyZYiIiHwrFT++xjRNAEpKSiyORESkodjYWAzDsDqMZm3GjBnMmDGDQCAAKJeLSPOjXH5y9N5cRJqrSM7nY8aMoaioiMmTJ5Ofn09OTg5z5sypG2qem5uLzVbfKGTYsGHMnj2bP/7xj9x5551069aNt956i+zs7BN+TuVzEWmuIjmfy7czzMM/gQSAXbt2kZmZaXUYIiJHKS4uJi4uzuowIoJyuYg0V8rlJ0f5XESaK+Xzk6N8LiLNlfJ5y6bix9eEQiH27NkTcVW/kpISMjMzycvLa/X/YXUt6ula1GsJ1yLS8pKVIjWXQ8v4t3q66FrU07WoF+nXIhLzkpWUz1sGXYswXYd6LeFaRGJeslKk5vOW8G/1dNG1qKdrUa8lXItIy0tyctT26mtsNhvt2rWzOozvLC4uLmKTzemma1FP16KerkXrEOm5HPRv9Ui6FvV0LerpWrQOyucti65FmK5DPV2L1iPS87n+rdbTtaina1FP10KaK9u3nyIiIiIiIiIiIiIiIhI5VPwQEREREREREREREZEWRcWPFsLtdnPPPffgdrutDsVyuhb1dC3q6VpIpNC/1Xq6FvV0LerpWkik0L/VeroWYboO9XQtJFLo32o9XYt6uhb1dC2kudPAcxERERERERERERERaVG080NERERERERERERERFoUFT9ERERERERERERERKRFUfFDRERERERERERERERaFBU/RERERERERERERESkRVHxI8JNnTqVQYMGERsbS0pKCqNHj2bjxo1Wh9UsPPjggxiGwa233mp1KJbYvXs3P//5z0lKSiIqKoo+ffqwdOlSq8NqcsFgkLvvvptOnToRFRVFly5duP/++zFN0+rQRBpQPj825XLlclAul8ihXH58yufK56B8LpFD+fz4lM+Vz5XLJZI4rA5ATs2nn37KhAkTGDRoEIFAgDvvvJMLL7yQdevW4fV6rQ7PMkuWLOGpp56ib9++VodiiYMHDzJ8+HDOPfdc3n//fdq0acPmzZtJSEiwOrQmN23aNJ588kmee+45evfuzdKlSxk/fjw+n4+bb77Z6vBE6iifH025XLn8MOVyiRTK5cemfK58fpjyuUQK5fNjUz5XPgflcokshqmyXItSVFRESkoKn376KWeffbbV4ViirKyMM844gyeeeIIHHniAnJwcpk+fbnVYTWrSpEnMnz+fzz//3OpQLHfJJZeQmprKP/7xj7pjP/3pT4mKiuKFF16wMDKRb9ba87lyuXL5kZTLJVK19lwOyuegfH4k5XOJVMrnyuegfH6YcrlEErW9amGKi4sBSExMtDgS60yYMIGLL76YkSNHWh2KZf7zn/8wcOBALr/8clJSUujfvz9PP/201WFZYtiwYcydO5dNmzYBsHLlSr744gsuuugiiyMT+WatPZ8rlyuXH0m5XCJVa8/loHwOyudHUj6XSKV8rnwOyueHKZdLJFHbqxYkFApx6623Mnz4cLKzs60OxxIvv/wyy5cvZ8mSJVaHYqlt27bx5JNPMnHiRO68806WLFnCzTffjMvlYty4cVaH16QmTZpESUkJPXv2xG63EwwGmTJlCldffbXVoYkcV2vP58rlYcrl9ZTLJRK19lwOyueHKZ/XUz6XSKR8rnx+mPJ5mHK5RBIVP1qQCRMmsGbNGr744gurQ7FEXl4et9xyCx999BEej8fqcCwVCoUYOHAgf/7znwHo378/a9asYebMma3qBzLAq6++yosvvsjs2bPp3bs3K1as4NZbbyUjI6PVXQuJHK05nyuX11Mur6dcLpGoNedyUD4/kvJ5PeVziUTK58rnhymfhymXS0QxpUWYMGGC2a5dO3Pbtm1Wh2KZN9980wRMu91edwNMwzBMu91uBgIBq0NsMu3btzd/+ctfNjj2xBNPmBkZGRZFZJ127dqZjz/+eINj999/v9mjRw+LIhL5Zq09nyuX11Mur6dcLpGmtedy01Q+P5LyeT3lc4k0yufK50dSPg9TLpdIop0fEc40TW666SbefPNNPvnkEzp16mR1SJY5//zzWb16dYNj48ePp2fPntx+++3Y7XaLImt6w4cPZ+PGjQ2Obdq0iQ4dOlgUkXUqKiqw2RqON7Lb7YRCIYsiEjk25fMw5fJ6yuX1lMslUiiX11M+r6d8Xk/5XCKF8nk95fN6yudhyuUSSVT8iHATJkxg9uzZ/Pvf/yY2Npb8/HwAfD4fUVFRFkfXtGJjY4/qv+n1eklKSmp1fTl/+9vfMmzYMP785z9zxRVXsHjxYmbNmsWsWbOsDq3JXXrppUyZMoX27dvTu3dvvvrqKx555BGuvfZaq0MTaUD5PEy5vJ5yeT3lcokUyuX1lM/rKZ/XUz6XSKF8Xk/5vJ7yeZhyuUQUazeeyKkCjnn75z//aXVozcI555xj3nLLLVaHYYm3337bzM7ONt1ut9mzZ09z1qxZVodkiZKSEvOWW24x27dvb3o8HrNz587mXXfdZfr9fqtDE2lA+fz4lMuVy5XLJVIol38z5XPlc+VziRTK599M+bx153PlcokkhmmaZtOVWkRERERERERERERERBqX7dtPERERERERERERERERiRwqfoiIiIiIiIiIiIiISIui4oeIiIiIiIiIiIiIiLQoKn6IiIiIiIiIiIiIiEiLouKHiIiIiIiIiIiIiIi0KCp+iIiIiIiIiIiIiIhIi6Lih4iIiIiIiIiIiIiItCgqfoiIiIiIiIiIiIiISIui4oeIRa655hpGjx5tdRgiInKKlM9FRFoG5XMRkcinXC4iR1LxQ0REREREREREREREWhQVP0ROUnV1tdUhiIjIaaB8LiLSMiifi4hEPuVyEWkMKn6IfIsRI0Zw4403cuutt5KcnMyoUaN45JFH6NOnD16vl8zMTG644QbKysrq7vPss88SHx/PBx98QK9evYiJieH73/8+e/fuPe7zLFmyhDZt2jBt2rSmeFkiIq2O8rmISMugfC4iEvmUy0WkKaj4IXICnnvuOVwuF/Pnz2fmzJnYbDYeffRR1q5dy3PPPce8efP4wx/+0OA+FRUVPPTQQzz//PN89tln5Obm8rvf/e6Yjz9v3jwuuOACpkyZwu23394UL0lEpFVSPhcRaRmUz0VEIp9yuYg0NofVAYhEgm7duvGXv/yl7usePXrU/b1jx4488MADXH/99TzxxBN1x2tqapg5cyZdunQB4MYbb+S+++476rHffPNNxo4dy//+7/8yZsyYRnwVIiKifC4i0jIon4uIRD7lchFpbCp+iJyAAQMGNPj6448/ZurUqWzYsIGSkhICgQBVVVVUVFQQHR0NQHR0dN0PY4D09HQKCwsbPM6iRYt45513eP311xk9enSjvw4RkdZO+VxEpGVQPhcRiXzK5SLS2NT2SuQEeL3eur/v2LGDSy65hL59+/LGG2+wbNkyZsyYATQc0OV0Ohs8hmEYmKbZ4FiXLl3o2bMnzzzzDDU1NY34CkREBJTPRURaCuVzEZHIp1wuIo1NxQ+Rk7Rs2TJCoRAPP/wwZ555Jt27d2fPnj3f6bGSk5OZN28eW7Zs4YorrtAPZRGRJqR8LiLSMiifi4hEPuVyEWkMKn6InKSuXbtSU1PDY489xrZt23j++eeZOXPmd368lJQU5s2bx4YNG7jyyisJBAKnMVoRETke5XMRkZZB+VxEJPIpl4tIY1DxQ+Qk9evXj0ceeYRp06aRnZ3Niy++yNSpU0/pMdPS0pg3bx6rV6/m6quvJhgMnqZoRUTkeJTPRURaBuVzEZHIp1wuIo3BML/eGE9ERERERERERERERCSCaeeHiIiIiIiIiIiIiIi0KCp+iIiIiIiIiIiIiIhIi6Lih4iIiIiIiIiIiIiItCgqfoiIiIiIiIiIiIiISIui4oeIiIiIiIiIiIiIiLQoKn6IiIiIiIiIiIiIiEiLouKHiIiIiIiIiIiIiIi0KCp+iIiIiIiIiIiIiIhIi6Lih4iIiIiIiIiIiIiItCgqfoiIiIiIiIiIiIiISIui4oeIiIiIiIiIiIiIiLQo/w95oBecnIRgDwAAAABJRU5ErkJggg==", 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", 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" ] @@ -459,7 +813,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "id": "afc7a96a", "metadata": {}, "outputs": [ @@ -486,7 +840,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "id": "fb5f1e37", "metadata": {}, "outputs": [ @@ -494,7 +848,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "Sweeping: 100%|██████████| 4/4 [00:00<00:00, 9.02it/s]\n" + "Sweeping: 0%| | 0/4 [00:00], dtype=object))" ] }, - "execution_count": 14, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -541,7 +902,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "id": "094fe74c", "metadata": {}, "outputs": [ @@ -549,7 +910,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Sweeping: 100%|██████████| 9/9 [00:11<00:00, 1.26s/it]\n" + "Sweeping: 100%|██████████| 9/9 [00:03<00:00, 2.87it/s]\n" ] }, { @@ -561,13 +922,13 @@ " ], dtype=object))" ] }, - "execution_count": 15, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -606,7 +967,7 @@ }, { "cell_type": "code", - "execution_count": 145, + "execution_count": 15, "id": "0cfe5a52", "metadata": {}, "outputs": [ @@ -620,16 +981,16 @@ { "data": { "text/plain": [ - "1.7968946616506403e-16" + "1.1975627478369772e-16" ] }, - "execution_count": 145, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -672,25 +1033,7 @@ }, { "cell_type": "code", - "execution_count": 22, - "id": "99bdd082", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Automatic pdb calling has been turned ON\n" - ] - } - ], - "source": [ - "%pdb" - ] - }, - { - "cell_type": "code", - "execution_count": 141, + "execution_count": 16, "id": "b7e7784b", "metadata": {}, "outputs": [ @@ -704,16 +1047,16 @@ { "data": { "text/plain": [ - "2.2266363006886167e-13" + "3.489429412696055e-13" ] }, - "execution_count": 141, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -767,9 +1110,9 @@ ], "metadata": { "kernelspec": { - "display_name": "dsapublic-env", + "display_name": "DSA (uv)", "language": "python", - "name": "python3" + "name": "dsa-uv" }, "language_info": { "codemirror_mode": { diff --git a/setup.py b/setup.py index b4e0697..2b05d52 100644 --- a/setup.py +++ b/setup.py @@ -17,7 +17,10 @@ "tqdm", "optht", #for havok in pykoopman "derivative", #for pykoopman - "prettytable" + "prettytable", + "scipy", + "scikit-learn", + "matplotlib", ], extras_require={ "dev": ["pytest>=3.7"],

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zbd~TEJ1_1K{&X{uFMQcaW%3Cn1#;{bZKU?8}@=eHdxD7?OuE6-yoR?vNuubKM~$S_!k03 zqL^@Y6)AKiLkBQ4oMD$NioH$RA}A5Xm^#lA{=)8rbRN5Z+)BvF;e6XBKH8tv) 27\u001b[0m sim \u001b[38;5;241m=\u001b[39m \u001b[43mdsa\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit_score\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 28\u001b[0m sim\u001b[38;5;241m.\u001b[39mshape\n\u001b[1;32m 30\u001b[0m \u001b[38;5;66;03m#TODO: check generalized dsa with other data structures for data and inputs\u001b[39;00m\n\u001b[1;32m 31\u001b[0m \u001b[38;5;66;03m#TODO: check generalized dsa with the other comparison metric and changing the config\u001b[39;00m\n", - "File \u001b[0;32m~/Desktop/Projects/DSAv2/DSAPublic2/DSA/DSA/dsa.py:692\u001b[0m, in \u001b[0;36mGeneralizedDSA.fit_score\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 679\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 680\u001b[0m \u001b[38;5;124;03mStandard fitting function for both DMDs and PAVF\u001b[39;00m\n\u001b[1;32m 681\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 689\u001b[0m \u001b[38;5;124;03m data matrix of the similarity scores between the specific sets of data\u001b[39;00m\n\u001b[1;32m 690\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 691\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfit_dmds()\n\u001b[0;32m--> 692\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscore\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Desktop/Projects/DSAv2/DSAPublic2/DSA/DSA/dsa.py:797\u001b[0m, in \u001b[0;36mGeneralizedDSA.score\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 791\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 792\u001b[0m loop \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 793\u001b[0m pairs\n\u001b[1;32m 794\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mverbose\n\u001b[1;32m 795\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m tqdm\u001b[38;5;241m.\u001b[39mtqdm(pairs, desc\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mComputing DMD similarities\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 796\u001b[0m )\n\u001b[0;32m--> 797\u001b[0m results \u001b[38;5;241m=\u001b[39m [compute_similarity(i, j) \u001b[38;5;28;01mfor\u001b[39;00m i, j \u001b[38;5;129;01min\u001b[39;00m loop]\n\u001b[1;32m 799\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m result \u001b[38;5;129;01min\u001b[39;00m results:\n\u001b[1;32m 800\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m result \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", - "File \u001b[0;32m~/Desktop/Projects/DSAv2/DSAPublic2/DSA/DSA/dsa.py:797\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 791\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 792\u001b[0m loop \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 793\u001b[0m pairs\n\u001b[1;32m 794\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mverbose\n\u001b[1;32m 795\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m tqdm\u001b[38;5;241m.\u001b[39mtqdm(pairs, desc\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mComputing DMD similarities\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 796\u001b[0m )\n\u001b[0;32m--> 797\u001b[0m results \u001b[38;5;241m=\u001b[39m [\u001b[43mcompute_similarity\u001b[49m\u001b[43m(\u001b[49m\u001b[43mi\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mj\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m i, j \u001b[38;5;129;01min\u001b[39;00m loop]\n\u001b[1;32m 799\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m result \u001b[38;5;129;01min\u001b[39;00m results:\n\u001b[1;32m 800\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m result \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", - "File \u001b[0;32m~/Desktop/Projects/DSAv2/DSAPublic2/DSA/DSA/dsa.py:768\u001b[0m, in \u001b[0;36mGeneralizedDSA.score..compute_similarity\u001b[0;34m(i, j)\u001b[0m\n\u001b[1;32m 761\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msimdist_has_control \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdmd_has_control:\n\u001b[1;32m 762\u001b[0m simdist_args\u001b[38;5;241m.\u001b[39mextend(\n\u001b[1;32m 763\u001b[0m [\n\u001b[1;32m 764\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_dmd_control_matrix(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdmds[\u001b[38;5;241m0\u001b[39m][i]),\n\u001b[1;32m 765\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_dmd_control_matrix(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdmds[ind2][j]),\n\u001b[1;32m 766\u001b[0m ]\n\u001b[1;32m 767\u001b[0m )\n\u001b[0;32m--> 768\u001b[0m sim \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msimdist\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit_score\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43msimdist_args\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 770\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mverbose \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_jobs \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m 771\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcomputing similarity between DMDs \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mi\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m and \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mj\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", - "File \u001b[0;32m~/Desktop/Projects/DSAv2/DSAPublic2/DSA/DSA/simdist.py:471\u001b[0m, in \u001b[0;36mSimilarityTransformDist.fit_score\u001b[0;34m(self, A, B, iters, lr, score_method, wasserstein_weightings)\u001b[0m\n\u001b[1;32m 468\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 469\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[0;32m--> 471\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 472\u001b[0m \u001b[43m \u001b[49m\u001b[43mA\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 473\u001b[0m \u001b[43m \u001b[49m\u001b[43mB\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 474\u001b[0m \u001b[43m \u001b[49m\u001b[43miters\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 475\u001b[0m \u001b[43m \u001b[49m\u001b[43mlr\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 476\u001b[0m \u001b[43m \u001b[49m\u001b[43mwasserstein_weightings\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwasserstein_weightings\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 477\u001b[0m \u001b[43m \u001b[49m\u001b[43mscore_method\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscore_method\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 478\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 480\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mscore(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mA, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mB, score_method\u001b[38;5;241m=\u001b[39mscore_method)\n", - "File \u001b[0;32m~/Desktop/Projects/DSAv2/DSAPublic2/DSA/DSA/simdist.py:254\u001b[0m, in \u001b[0;36mSimilarityTransformDist.fit\u001b[0;34m(self, A, B, iters, lr, score_method, wasserstein_weightings)\u001b[0m\n\u001b[1;32m 248\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mC_star \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mC_star \u001b[38;5;241m/\u001b[39m torch\u001b[38;5;241m.\u001b[39mlinalg\u001b[38;5;241m.\u001b[39mnorm(\n\u001b[1;32m 249\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mC_star, dim\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m, keepdim\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m 250\u001b[0m )\n\u001b[1;32m 251\u001b[0m \u001b[38;5;66;03m# wasserstein_distance(A.cpu().numpy(),B.cpu().numpy())\u001b[39;00m\n\u001b[1;32m 252\u001b[0m \n\u001b[1;32m 253\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 254\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlosses, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mC_star, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msim_net \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptimize_C\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 255\u001b[0m \u001b[43m \u001b[49m\u001b[43mA\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mB\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43miters\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morthog\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mverbose\u001b[49m\n\u001b[1;32m 256\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 257\u001b[0m \u001b[38;5;66;03m# permute the first row and column of B then rerun the optimization\u001b[39;00m\n\u001b[1;32m 258\u001b[0m P \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39meye(B\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m], device\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdevice)\n", - "File \u001b[0;32m~/Desktop/Projects/DSAv2/DSAPublic2/DSA/DSA/simdist.py:334\u001b[0m, in \u001b[0;36mSimilarityTransformDist.optimize_C\u001b[0;34m(self, A, B, lr, iters, orthog, verbose)\u001b[0m\n\u001b[1;32m 332\u001b[0m optimizer\u001b[38;5;241m.\u001b[39mzero_grad()\n\u001b[1;32m 333\u001b[0m \u001b[38;5;66;03m# Compute the Frobenius norm between A and the product.\u001b[39;00m\n\u001b[0;32m--> 334\u001b[0m loss \u001b[38;5;241m=\u001b[39m simdist_loss(A, \u001b[43msim_net\u001b[49m\u001b[43m(\u001b[49m\u001b[43mB\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 336\u001b[0m loss\u001b[38;5;241m.\u001b[39mbackward()\n\u001b[1;32m 338\u001b[0m optimizer\u001b[38;5;241m.\u001b[39mstep()\n", - "File \u001b[0;32m~/opt/anaconda3/envs/dsa_test_env/lib/python3.10/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1509\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/opt/anaconda3/envs/dsa_test_env/lib/python3.10/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1518\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1519\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1523\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", - "File \u001b[0;32m~/Desktop/Projects/DSAv2/DSAPublic2/DSA/DSA/simdist.py:64\u001b[0m, in \u001b[0;36mLearnableSimilarityTransform.forward\u001b[0;34m(self, B)\u001b[0m\n\u001b[1;32m 62\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, B):\n\u001b[1;32m 63\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39morthog:\n\u001b[0;32m---> 64\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mC\u001b[49m \u001b[38;5;241m@\u001b[39m B \u001b[38;5;241m@\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mC\u001b[38;5;241m.\u001b[39mtranspose(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m2\u001b[39m)\n\u001b[1;32m 65\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 66\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mC \u001b[38;5;241m@\u001b[39m B \u001b[38;5;241m@\u001b[39m torch\u001b[38;5;241m.\u001b[39mlinalg\u001b[38;5;241m.\u001b[39minv(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mC)\n", - "File \u001b[0;32m~/opt/anaconda3/envs/dsa_test_env/lib/python3.10/site-packages/torch/nn/utils/parametrize.py:368\u001b[0m, in \u001b[0;36m_inject_property..get_parametrized\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 365\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m get_cached_parametrization(parametrization)\n\u001b[1;32m 366\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 367\u001b[0m \u001b[38;5;66;03m# If caching is not active, this function just evaluates the parametrization\u001b[39;00m\n\u001b[0;32m--> 368\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mparametrization\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/opt/anaconda3/envs/dsa_test_env/lib/python3.10/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1509\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/opt/anaconda3/envs/dsa_test_env/lib/python3.10/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1518\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1519\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1523\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", - "File \u001b[0;32m~/opt/anaconda3/envs/dsa_test_env/lib/python3.10/site-packages/torch/nn/utils/parametrize.py:273\u001b[0m, in \u001b[0;36mParametrizationList.forward\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 271\u001b[0m curr_idx \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m 272\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28mstr\u001b[39m(curr_idx)):\n\u001b[0;32m--> 273\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mcurr_idx\u001b[49m\u001b[43m]\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 274\u001b[0m curr_idx \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m 275\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m x\n", - "File \u001b[0;32m~/opt/anaconda3/envs/dsa_test_env/lib/python3.10/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1509\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/opt/anaconda3/envs/dsa_test_env/lib/python3.10/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1518\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1519\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1523\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", - "File \u001b[0;32m~/Desktop/Projects/DSAv2/DSAPublic2/DSA/DSA/simdist.py:128\u001b[0m, in \u001b[0;36mCayleyMap.forward\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m 126\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, X):\n\u001b[1;32m 127\u001b[0m \u001b[38;5;66;03m# (I + X)(I - X)^{-1}\u001b[39;00m\n\u001b[0;32m--> 128\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlinalg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msolve\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mId\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mId\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> \u001b[0;32m/Users/mitchellostrow/Desktop/Projects/DSAv2/DSAPublic2/DSA/DSA/simdist.py\u001b[0m(128)\u001b[0;36mforward\u001b[0;34m()\u001b[0m\n", - "\u001b[0;32m 126 \u001b[0;31m \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\u001b[0;32m 127 \u001b[0;31m \u001b[0;31m# (I + X)(I - X)^{-1}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0m\u001b[0;32m--> 128 \u001b[0;31m \u001b[0;32mreturn\u001b[0m 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for data and inputs\n", - "#TODO: check generalized dsa with the other comparison metric and changing the config\n" + "sim.shape" ] }, { "cell_type": "code", - "execution_count": null, - "id": "ab0dbe0a", + "execution_count": 46, + "id": "2ea88dc2", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/n/home00/ahuang/DSA/DSA/dsa.py:618: UserWarning: When using cross-comparison with a list of arrays, gDSA treats each array as its own system.\n", + "If arrays within X (and Y) are samples from the same system, switch to using X=[X,Y], X_control=[X_control,Y_control], Y=None, and Y_control=None.\n", + " warnings.warn(\n", + "/n/home00/ahuang/DSA/DSA/dsa.py:408: UserWarning: Warning: You are using a DMD model that fits a control operator but comparing with a DSA metric that does not compare control operators\n", + " warnings.warn(\n", + "\n", + "Fitting DMDs: 100%|██████████| 9/9 [00:00<00:00, 2031.58it/s]\n", + "\n", + "Fitting DMDs: 100%|██████████| 9/9 [00:00<00:00, 2208.82it/s]\n", + "\n", + "\u001b[A\n", + "\u001b[A\n", + "\u001b[A\n", + "\u001b[A" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[46], line 35\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Check generalized dsa with other data structures for data and inputs\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# Self-comparison (using X and X_control)\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 29\u001b[0m \u001b[38;5;66;03m# Should return a 3x3 distance matrix\u001b[39;00m\n\u001b[1;32m 30\u001b[0m \u001b[38;5;66;03m#TODO: when doing cross-comparison and using a list of arrays, gDSA treats each array as its own system\u001b[39;00m\n\u001b[1;32m 31\u001b[0m dsa \u001b[38;5;241m=\u001b[39m GeneralizedDSA(X\u001b[38;5;241m=\u001b[39md3, X_control\u001b[38;5;241m=\u001b[39mu3,\n\u001b[1;32m 32\u001b[0m Y\u001b[38;5;241m=\u001b[39md3, Y_control\u001b[38;5;241m=\u001b[39mu3,\n\u001b[1;32m 33\u001b[0m dmd_class\u001b[38;5;241m=\u001b[39mDMDc,dmd_config\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mdict\u001b[39m(n_delays\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m,rank_input\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m,rank_output\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m),\n\u001b[1;32m 34\u001b[0m verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m---> 35\u001b[0m sim \u001b[38;5;241m=\u001b[39m \u001b[43mdsa\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit_score\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 36\u001b[0m sim\u001b[38;5;241m.\u001b[39mshape\n", + "File \u001b[0;32m~/DSA/DSA/dsa.py:704\u001b[0m, in \u001b[0;36mGeneralizedDSA.fit_score\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 691\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 692\u001b[0m \u001b[38;5;124;03mStandard fitting function for both DMDs and PAVF\u001b[39;00m\n\u001b[1;32m 693\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 701\u001b[0m \u001b[38;5;124;03m data matrix of the similarity scores between the specific sets of data\u001b[39;00m\n\u001b[1;32m 702\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 703\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfit_dmds()\n\u001b[0;32m--> 704\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscore\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/DSA/DSA/dsa.py:809\u001b[0m, in \u001b[0;36mGeneralizedDSA.score\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 803\u001b[0m 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\u001b[38;5;28;01mNone\u001b[39;00m:\n", + "File \u001b[0;32m~/DSA/DSA/dsa.py:809\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 803\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 804\u001b[0m loop \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 805\u001b[0m pairs\n\u001b[1;32m 806\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mverbose\n\u001b[1;32m 807\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m tqdm\u001b[38;5;241m.\u001b[39mtqdm(pairs, desc\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mComputing DMD similarities\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 808\u001b[0m )\n\u001b[0;32m--> 809\u001b[0m results \u001b[38;5;241m=\u001b[39m [\u001b[43mcompute_similarity\u001b[49m\u001b[43m(\u001b[49m\u001b[43mi\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mj\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m i, j \u001b[38;5;129;01min\u001b[39;00m loop]\n\u001b[1;32m 811\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m result \u001b[38;5;129;01min\u001b[39;00m results:\n\u001b[1;32m 812\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m result \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "File \u001b[0;32m~/DSA/DSA/dsa.py:780\u001b[0m, in \u001b[0;36mGeneralizedDSA.score..compute_similarity\u001b[0;34m(i, j)\u001b[0m\n\u001b[1;32m 773\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msimdist_has_control \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdmd_has_control:\n\u001b[1;32m 774\u001b[0m simdist_args\u001b[38;5;241m.\u001b[39mextend(\n\u001b[1;32m 775\u001b[0m [\n\u001b[1;32m 776\u001b[0m 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\u001b[49m\u001b[43mB\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 474\u001b[0m \u001b[43m \u001b[49m\u001b[43miters\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 475\u001b[0m \u001b[43m \u001b[49m\u001b[43mlr\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 476\u001b[0m \u001b[43m \u001b[49m\u001b[43mwasserstein_weightings\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwasserstein_weightings\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 477\u001b[0m \u001b[43m \u001b[49m\u001b[43mscore_method\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscore_method\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 478\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 480\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mscore(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mA, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mB, score_method\u001b[38;5;241m=\u001b[39mscore_method)\n", + "File \u001b[0;32m~/DSA/DSA/simdist.py:254\u001b[0m, in \u001b[0;36mSimilarityTransformDist.fit\u001b[0;34m(self, A, B, iters, lr, score_method, wasserstein_weightings)\u001b[0m\n\u001b[1;32m 248\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mC_star \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mC_star \u001b[38;5;241m/\u001b[39m torch\u001b[38;5;241m.\u001b[39mlinalg\u001b[38;5;241m.\u001b[39mnorm(\n\u001b[1;32m 249\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mC_star, dim\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m, keepdim\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m 250\u001b[0m )\n\u001b[1;32m 251\u001b[0m \u001b[38;5;66;03m# wasserstein_distance(A.cpu().numpy(),B.cpu().numpy())\u001b[39;00m\n\u001b[1;32m 252\u001b[0m \n\u001b[1;32m 253\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 254\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlosses, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mC_star, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msim_net \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptimize_C\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 255\u001b[0m \u001b[43m \u001b[49m\u001b[43mA\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mB\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43miters\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morthog\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mverbose\u001b[49m\n\u001b[1;32m 256\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 257\u001b[0m \u001b[38;5;66;03m# permute the first row and column of B then rerun the optimization\u001b[39;00m\n\u001b[1;32m 258\u001b[0m P \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39meye(B\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m], device\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdevice)\n", + "File \u001b[0;32m~/DSA/DSA/simdist.py:338\u001b[0m, in \u001b[0;36mSimilarityTransformDist.optimize_C\u001b[0;34m(self, A, B, lr, iters, orthog, verbose)\u001b[0m\n\u001b[1;32m 334\u001b[0m loss \u001b[38;5;241m=\u001b[39m simdist_loss(A, sim_net(B))\n\u001b[1;32m 336\u001b[0m loss\u001b[38;5;241m.\u001b[39mbackward()\n\u001b[0;32m--> 338\u001b[0m \u001b[43moptimizer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 339\u001b[0m \u001b[38;5;66;03m# if _ % 99:\u001b[39;00m\n\u001b[1;32m 340\u001b[0m \u001b[38;5;66;03m# scheduler.step()\u001b[39;00m\n\u001b[1;32m 341\u001b[0m losses\u001b[38;5;241m.\u001b[39mappend(loss\u001b[38;5;241m.\u001b[39mitem())\n", + "File \u001b[0;32m~/.conda/envs/py39/lib/python3.9/site-packages/torch/optim/optimizer.py:493\u001b[0m, in \u001b[0;36mOptimizer.profile_hook_step..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 488\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 489\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[1;32m 490\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must return None or a tuple of (new_args, new_kwargs), but got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mresult\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 491\u001b[0m )\n\u001b[0;32m--> 493\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 494\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_optimizer_step_code()\n\u001b[1;32m 496\u001b[0m \u001b[38;5;66;03m# call optimizer step post hooks\u001b[39;00m\n", + "File \u001b[0;32m~/.conda/envs/py39/lib/python3.9/site-packages/torch/optim/optimizer.py:91\u001b[0m, in \u001b[0;36m_use_grad_for_differentiable.._use_grad\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 89\u001b[0m torch\u001b[38;5;241m.\u001b[39mset_grad_enabled(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdefaults[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdifferentiable\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[1;32m 90\u001b[0m torch\u001b[38;5;241m.\u001b[39m_dynamo\u001b[38;5;241m.\u001b[39mgraph_break()\n\u001b[0;32m---> 91\u001b[0m ret \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 92\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 93\u001b[0m torch\u001b[38;5;241m.\u001b[39m_dynamo\u001b[38;5;241m.\u001b[39mgraph_break()\n", + "File \u001b[0;32m~/.conda/envs/py39/lib/python3.9/site-packages/torch/optim/adam.py:218\u001b[0m, in \u001b[0;36mAdam.step\u001b[0;34m(self, closure)\u001b[0m\n\u001b[1;32m 210\u001b[0m \u001b[38;5;129m@_use_grad_for_differentiable\u001b[39m\n\u001b[1;32m 211\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mstep\u001b[39m(\u001b[38;5;28mself\u001b[39m, closure\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 212\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Perform a single optimization step.\u001b[39;00m\n\u001b[1;32m 213\u001b[0m \n\u001b[1;32m 214\u001b[0m \u001b[38;5;124;03m Args:\u001b[39;00m\n\u001b[1;32m 215\u001b[0m \u001b[38;5;124;03m closure (Callable, optional): A closure that reevaluates the model\u001b[39;00m\n\u001b[1;32m 216\u001b[0m \u001b[38;5;124;03m and returns the loss.\u001b[39;00m\n\u001b[1;32m 217\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 218\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_cuda_graph_capture_health_check\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 220\u001b[0m loss \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 221\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m closure \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "File \u001b[0;32m~/.conda/envs/py39/lib/python3.9/site-packages/torch/optim/optimizer.py:436\u001b[0m, in \u001b[0;36mOptimizer._cuda_graph_capture_health_check\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 420\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_cuda_graph_capture_health_check\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 421\u001b[0m \u001b[38;5;66;03m# Note [torch.compile x capturable]\u001b[39;00m\n\u001b[1;32m 422\u001b[0m \u001b[38;5;66;03m# If we are compiling, we try to take the capturable path automatically by\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 429\u001b[0m \u001b[38;5;66;03m# Thus, when compiling, inductor will determine if cudagraphs\u001b[39;00m\n\u001b[1;32m 430\u001b[0m \u001b[38;5;66;03m# can be enabled based on whether there is input mutation or CPU tensors.\u001b[39;00m\n\u001b[1;32m 431\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m 432\u001b[0m \u001b[38;5;129;01mnot\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mcompiler\u001b[38;5;241m.\u001b[39mis_compiling()\n\u001b[1;32m 433\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mbackends\u001b[38;5;241m.\u001b[39mcuda\u001b[38;5;241m.\u001b[39mis_built()\n\u001b[1;32m 434\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mcuda\u001b[38;5;241m.\u001b[39mis_available()\n\u001b[1;32m 435\u001b[0m ):\n\u001b[0;32m--> 436\u001b[0m capturing \u001b[38;5;241m=\u001b[39m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcuda\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mis_current_stream_capturing\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 438\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m capturing \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mall\u001b[39m(\n\u001b[1;32m 439\u001b[0m group[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcapturable\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m group \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mparam_groups\n\u001b[1;32m 440\u001b[0m ):\n\u001b[1;32m 441\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[1;32m 442\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAttempting CUDA graph capture of step() for an instance of \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 443\u001b[0m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\n\u001b[1;32m 444\u001b[0m \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m but param_groups\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m capturable is False.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 445\u001b[0m )\n", + "File \u001b[0;32m~/.conda/envs/py39/lib/python3.9/site-packages/torch/cuda/graphs.py:30\u001b[0m, in \u001b[0;36mis_current_stream_capturing\u001b[0;34m()\u001b[0m\n\u001b[1;32m 25\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mis_current_stream_capturing\u001b[39m():\n\u001b[1;32m 26\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"Return True if CUDA graph capture is underway on the current CUDA stream, False otherwise.\u001b[39;00m\n\u001b[1;32m 27\u001b[0m \n\u001b[1;32m 28\u001b[0m \u001b[38;5;124;03m If a CUDA context does not exist on the current device, returns False without initializing the context.\u001b[39;00m\n\u001b[1;32m 29\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m---> 30\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_cuda_isCurrentStreamCapturing\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], "source": [ - "\n" + "# Check generalized dsa with other data structures for data and inputs\n", + "\n", + "# Self-comparison (using X and X_control)\n", + "# d4s = [d4 for _ in range(3)]\n", + "# u4s = [u4 for _ in range(3)]\n", + "# dsa = GeneralizedDSA(d4s,X_control=u4s,\n", + "# dmd_class=DMDc,dmd_config=dict(n_delays=5,rank_input=5,rank_output=5),\n", + "# verbose=True)\n", + "# sim = dsa.fit_score()\n", + "# sim.shape\n", + "\n", + "# d5s = [d5 for _ in range(3)]\n", + "# u5s = [u5 for _ in range(3)]\n", + "# dsa = GeneralizedDSA(d5s,X_control=u5s,\n", + "# dmd_class=DMDc,dmd_config=dict(n_delays=5,rank_input=5,rank_output=5),\n", + "# verbose=True)\n", + "# sim = dsa.fit_score()\n", + "# sim.shape\n", + "\n", + "# Cross-comparison (using X and X_control, Y and Y_control)\n", + "# Should return a 3x3 distance matrix\n", + "# dsa = GeneralizedDSA(X=d3s, X_control=u3s,\n", + "# Y=d3s, Y_control=u3s,\n", + "# dmd_class=DMDc,dmd_config=dict(n_delays=5,rank_input=5,rank_output=5),\n", + "# verbose=True)\n", + "# sim = dsa.fit_score()\n", + "# sim.shape\n", + "\n", + "# Should return a 3x3 distance matrix\n", + "#TODO: when doing cross-comparison and using a list of arrays, gDSA treats each array as its own system\n", + "dsa = GeneralizedDSA(X=d3, X_control=u3,\n", + " Y=d3, Y_control=u3,\n", + " dmd_class=DMDc,dmd_config=dict(n_delays=5,rank_input=5,rank_output=5),\n", + " verbose=True)\n", + "sim = dsa.fit_score()\n", + "sim.shape" ] }, { "cell_type": "code", - "execution_count": null, - "id": "57132dea", + "execution_count": 39, + "id": "997a05d3", "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/n/home00/ahuang/DSA/DSA/dsa.py:408: UserWarning: Warning: You are using a DMD model that fits a control operator but comparing with a DSA metric that does not compare control operators\n", + " warnings.warn(\n", + "\n", + "Fitting DMDs: 100%|██████████| 1/1 [00:00<00:00, 1006.55it/s]\n", + "\n", + "Fitting DMDs: 100%|██████████| 1/1 [00:00<00:00, 1508.20it/s]\n", + "\n", + "\u001b[A\n", + "Computing DMD similarities: 100%|██████████| 1/1 [00:01<00:00, 1.72s/it]\n" + ] + }, + { + "data": { + "text/plain": [ + "()" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dsa = GeneralizedDSA(X=d2, X_control=u2,\n", + " Y=d2, Y_control=u2,\n", + " dmd_class=DMDc,dmd_config=dict(n_delays=5,rank_input=5,rank_output=5),\n", + " verbose=True)\n", + "sim = dsa.fit_score()\n", + "sim.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "6a5de212", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/n/home00/ahuang/DSA/DSA/dsa.py:408: UserWarning: Warning: You are using a DMD model that fits a control operator but comparing with a DSA metric that does not compare control operators\n", + " warnings.warn(\n", + "\n", + "Fitting DMDs: 100%|██████████| 3/3 [00:00<00:00, 190.57it/s]\n", + "\n", + "Computing DMD similarities: 100%|██████████| 3/3 [00:00<00:00, 1423.73it/s]\n" + ] + }, + { + "data": { + "text/plain": [ + "(3, 3)" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Check generalized dsa with the other comparison metric and changing the config\n", + "dmdconfig = DMDConfig(n_delays=20)\n", + "dmdcConfig = DMDcConfig()\n", + "subspaceDmdcConfig = SubspaceDMDcConfig()\n", + "\n", + "simdistconfig = SimilarityTransformDistConfig(score_method='wasserstein')\n", + "csimdistconfig = ControllabilitySimilarityTransformDistConfig(compare='joint',\n", + " score_method='euclidean', align_inputs=False,return_distance_components=True)\n", + "\n", + "\n", + "dsa = GeneralizedDSA(d3s,X_control=u3s,\n", + " dmd_class=SubspaceDMDc,\n", + " dmd_config=subspaceDmdcConfig,\n", + " simdist_config=simdistconfig,\n", + " verbose=True)\n", + "sim = dsa.fit_score()\n", + "sim.shape" + ] } ], "metadata": { From 00bffc18cc03bf24ba9921abfeb894d987c9a26b Mon Sep 17 00:00:00 2001 From: Ann Huang Date: Mon, 3 Nov 2025 19:51:28 -0500 Subject: [PATCH 29/90] updated import section --- examples/all_dsa_types.ipynb | 23 +---------------------- 1 file changed, 1 insertion(+), 22 deletions(-) diff --git a/examples/all_dsa_types.ipynb b/examples/all_dsa_types.ipynb index af6e2cd..ad52778 100644 --- a/examples/all_dsa_types.ipynb +++ b/examples/all_dsa_types.ipynb @@ -2,34 +2,13 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "773aa0fd", "metadata": {}, "outputs": [], "source": [ "import numpy as np \n", "import matplotlib.pyplot as plt\n", - "import sys\n", - "import importlib\n", - "\n", - "# Remove any local DSA packages from sys.path to force using the installed package\n", - "paths_to_remove = [p for p in sys.path if 'Degeneracy' in p and 'DSA' in p]\n", - "for p in paths_to_remove:\n", - " sys.path.remove(p)\n", - "\n", - "# Also remove the current directory's parent if it contains a DSA package\n", - "# This prevents importing from /n/home00/ahuang/DSA if it's in the path\n", - "# Uncomment the next lines if needed:\n", - "# if '/n/home00/ahuang/DSA' in sys.path:\n", - "# sys.path.remove('/n/home00/ahuang/DSA')\n", - "\n", - "# Force reload to ensure we get the installed package\n", - "if 'DSA' in sys.modules:\n", - " del sys.modules['DSA']\n", - " # Also remove any submodules\n", - " modules_to_remove = [k for k in sys.modules.keys() if k.startswith('DSA.')]\n", - " for k in modules_to_remove:\n", - " del sys.modules[k]\n", "\n", "# Now import from the installed package\n", "from DSA import DSA, GeneralizedDSA, InputDSA\n", From 6fa708d598627993b1d1d971cbff29ec571239cc Mon Sep 17 00:00:00 2001 From: Ann Huang Date: Mon, 3 Nov 2025 20:59:37 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