diff --git "a/data/dataset_Pseudotime_analysis.csv" "b/data/dataset_Pseudotime_analysis.csv" new file mode 100644--- /dev/null +++ "b/data/dataset_Pseudotime_analysis.csv" @@ -0,0 +1,5227 @@ +"keyword","repo_name","file_path","file_extension","file_size","line_count","content","language" +"Pseudotime analysis","linnarsson-lab/ipynb-lamanno2016","pystan_bayesmodel.py",".py","4300","143","#!/usr/bin/env python +from Cef_tools import CEF_obj +import cPickle as pickle +import pystan +import numpy as np +from itertools import izip +from multiprocessing import Pool +import getopt +import sys, os + +if __name__ == '__main__': + n_processes = 2 + clustering_attribute = 'Cell_type' + outfiles_path = '' + + optlist, args = getopt.gnu_getopt(sys.argv[1:], ""hi:o:c:p:"", [""help"", ""input="",""output="",""clus_attr="",""processes=""]) + + if optlist== [] and args == []: + print 'pystancef -i [INPUTFILE] -o [OUTPUTFOLDER] -c [CLUSTER_ATTRIBUTE] -p [THREADS]' + sys.exit() + for opt, a in optlist: + if opt in (""-h"", ""--help""): + usage() + sys.exit() + elif opt in ('-i', '--input'): + input_path = a + elif opt in (""-o"", ""--output""): + outfiles_path = a + elif opt in ('-c','--clus_attr'): + clustering_attribute = str(a) + elif opt in ('-p', '--processes'): + n_processes = int(a) + else: + assert False, ""%s option is not supported"" % opt + + cef = CEF_obj() + + try: + print 'Loading CEF' + cef.readCEF(input_path) + except: + print 'Error in loading CEF file' + + try: + print 'Loading Model' + sm = pickle.load(open(os.path.join(outfiles_path,'pystan_model_compiled.pickle'),'rb')) + except IOError: + print 'Compiled model was not found. Defining and compiling model.' + sten_code = ''' + # Bayesian Negative Binomial regression for single-cell RNA-seq + + data { + int N; # number of outcomes + int K; # number of predictors + matrix[N,K] x; # predictor matrix + int y[N]; # outcomes + } + + parameters { + vector[K] beta; # coefficients + real r; # overdispersion + } + + model { + vector[N] mu; + vector[N] rv; + + # priors + r ~ cauchy(0, 1); + beta ~ pareto(1, 1.5); + + # vectorize the scale + for (n in 1:N) { + rv[n] <- square(r + 1) - 1; + } + + # regression + mu <- x * (beta - 1) + 0.001; + y ~ neg_binomial(mu ./ rv, 1 / rv[1]); + } + ''' + + sm = pystan.StanModel(model_code=sten_code) + + print 'Saving model for future use.' + pickle.dump(sm, open(os.path.join(outfiles_path,'pystan_model_compiled.pickle'),'wb')) + + + + + print 'Formating the model input' + for i,v in izip(cef.col_attr_names, cef.col_attr_values): + if i == clustering_attribute: + predictor_list = v + if 'total' in i.lower(): + total_molecules = [float(j) for j in v ] + + for i,v in izip(cef.row_attr_names, cef.row_attr_values): + if 'gene' in i.lower(): + gene_names = v + + total = sum(total_molecules)/len(total_molecules) + total_molecules_norm = [j/total for j in total_molecules] + predictors = ['Size'] + for i in predictor_list: + if i not in predictors: + predictors.append(i) + + predictor_matrix = [] + for i, c_p in enumerate( predictor_list ): + predictor_matrix.append( [total_molecules_norm[i]] + [float(c_p==p) for p in predictors[1:]] ) + + + N = len( predictor_matrix ) + K = len( predictors ) + + def one_gene_model(name_gene, gene_vector, predictor_matrix, N, K): + path = os.path.join( outfiles_path, ""beta_%s.npy"" % name_gene ) + my_data = {'N': N, 'K': K, 'x': predictor_matrix, 'y': gene_vector} + + fit = sm.sampling(data = my_data, iter=3000, chains=1, seed='19900715',warmup=2000, n_jobs=1) + + # extract the traces + traces = fit.extract(permuted=True) + + # save the traces with numpy + + np.save(path, traces['beta']) + print path + + print 'Passing the inputs to mutliple threads' + + try: + p = Pool(processes=n_processes) + for name_gene, gene_vector in izip(gene_names, cef.matrix): + p.apply_async(one_gene_model,(name_gene, gene_vector, predictor_matrix, N, K)) + p.close() + p.join() + except KeyboardInterrupt: + p.terminate() + + +","Python" +"Pseudotime analysis","linnarsson-lab/ipynb-lamanno2016","ipynb-lamanno2016-pseudotime.ipynb",".ipynb","183745","950","{ + ""cells"": [ + { + ""cell_type"": ""markdown"", + ""metadata"": { + ""toc"": ""true"" + }, + ""source"": [ + ""# Table of Contents\n"", + ""

"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""# Imports"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 1, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [ + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Populating the interactive namespace from numpy and matplotlib\n"" + ] + } + ], + ""source"": [ + ""%pylab inline"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 2, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""import pandas as pd\n"", + ""from Cef_tools import *\n"", + ""from backSPIN import fit_CV\n"", + ""import statsmodels.formula.api as smf\n"", + ""import statsmodels.api as sm\n"", + ""from statsmodels.sandbox.stats.multicomp import multipletests\n"", + ""from scipy import stats\n"", + ""import rpy2.robjects as robj\n"", + ""from rpy2.robjects.packages import importr\n"", + ""from rpy2.robjects.vectors import DataFrame, FloatVector\n"", + ""from sklearn.linear_model import Lasso\n"", + ""from sklearn.grid_search import GridSearchCV\n"", + ""from sklearn.cross_validation import ShuffleSplit\n"", + ""from sklearn.cluster import AffinityPropagation\n"", + ""from sklearn.svm import SVR"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 3, + ""metadata"": { + ""collapsed"": true, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""def principal_curve(X, pca=True):\n"", + "" \""\""\""\n"", + "" input : numpy.array\n"", + "" returns:\n"", + "" Result::Object\n"", + "" Methods:\n"", + "" projections - the matrix of the projectiond\n"", + "" ixsort - the order ot the points (as in argsort)\n"", + "" arclength - the lenght of the arc from the beginning to the point\n"", + "" \""\""\""\n"", + "" # convert array to R matrix\n"", + "" xr = array_to_rmatrix(X)\n"", + "" \n"", + "" if pca:\n"", + "" #perform pca\n"", + "" t = robj.r.prcomp(xr)\n"", + "" #determine dimensionality reduction\n"", + "" usedcomp = max( sum( np.array(t[t.names.index('sdev')]) > 1.1) , 4)\n"", + "" usedcomp = min([usedcomp, sum( np.array(t[t.names.index('sdev')]) > 0.25), X.shape[0]])\n"", + "" Xpc = np.array(t[t.names.index('x')])[:,:usedcomp]\n"", + "" # convert array to R matrix\n"", + "" xr = array_to_rmatrix(Xpc)\n"", + ""\n"", + "" #import the correct namespace\n"", + "" princurve = importr(\""princurve\"")\n"", + "" \n"", + "" #call the function\n"", + "" fit1 = princurve.principal_curve(xr)\n"", + "" \n"", + "" #extract the outputs\n"", + "" class Results:\n"", + "" pass\n"", + "" results = Results()\n"", + "" results.projections = np.array( fit1[0] )\n"", + "" results.ixsort = np.array( fit1[1] ) - 1 # R is 1 indexed\n"", + "" results.arclength = np.array( fit1[2] )\n"", + "" results.dist = np.array( fit1[3] )\n"", + "" \n"", + "" if pca:\n"", + "" results.PCs = np.array(xr) #only the used components\n"", + "" \n"", + "" return results\n"", + ""\n"", + ""def array_to_rmatrix(X):\n"", + "" nr, nc = X.shape\n"", + "" xvec = robj.FloatVector(X.transpose().reshape((X.size)))\n"", + "" xr = robj.r.matrix(xvec, nrow=nr, ncol=nc)\n"", + "" return xr"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""# Load dataset with tine annotations (E-day)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 4, + ""metadata"": { + ""collapsed"": true, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""df, rows_annot, cols_annot, _ = cef2df('data/Mouse_Embryo_fulldataset.cef')"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 5, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + ""array(['E11.5', 'E12.5', 'E13.5', 'E14.5', 'E15.5', 'E18.5'], dtype=object)"" + ] + }, + ""execution_count"": 5, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""order_eday, Eday_ix = unique( cols_annot.ix['Timepoint'].values, return_inverse=True )\n"", + ""order_eday"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""# Select mOMTN"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 6, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""bool_sel = (cols_annot.ix['Cell_type'] == 'mOMTN').values\n"", + ""df = df.ix[:, bool_sel]\n"", + ""Eday_ix = Eday_ix[bool_sel]"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 7, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""df = df.ix[df.sum(1) > 9, :]"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""# Select variable genes"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 8, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [ + { + ""data"": { + ""image/png"": 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Lr0ksQbhdev06+pKNrEpkvYNoaVQvqCV+0FAJjy20HnCwc00REwifZSJJ5eTPF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\nSUVORK5CYII=\n"", + ""text/plain"": [ + """" + ] + }, + ""metadata"": {}, + ""output_type"": ""display_data"" + } + ], + ""source"": [ + ""thrs = 5000\n"", + ""\n"", + ""#Pre-filtering\n"", + ""df_f = df.copy()\n"", + ""df_f = df_f.ix[sum(df_f>=1, 1)>=5,:] # is at least 1 in X cells\n"", + ""df_f = df_f.ix[sum(df_f>=2, 1)>=2,:] # is at least 2 in X cells\n"", + ""\n"", + ""#Fitting\n"", + ""mu = df_f.mean(1).values\n"", + ""sigma = df_f.std(1, ddof=1).values\n"", + ""cv = sigma/mu\n"", + ""score, mu_linspace, cv_fit , params = fit_CV(mu,cv, 'SVR')\n"", + ""\n"", + ""#Plotting\n"", + ""figure()\n"", + ""scatter(log2(mu),log2(cv), marker='o', edgecolor ='none',alpha=0.1, s=5)\n"", + ""mu_sorted = mu[argsort(score)[::-1]]\n"", + ""cv_sorted = cv[argsort(score)[::-1]]\n"", + ""scatter(log2(mu_sorted[:thrs]),log2(cv_sorted[:thrs]), marker='o', edgecolor ='none',alpha=0.15, s=8, c='r')\n"", + ""plot(mu_linspace, cv_fit,'-k', linewidth=1, label='$Fit$')\n"", + ""plot(linspace(-9,7), -0.5*linspace(-9,7), '-r', label='$Poisson$')\n"", + ""#Adjusting plot\n"", + ""ylabel('log2 CV')\n"", + ""xlabel('log2 mean')\n"", + ""grid(alpha=0.3)\n"", + ""xlim(-5.,7)\n"", + ""ylim(-2,6.5)\n"", + ""legend(loc=1, fontsize='small')\n"", + ""gca().set_aspect(1.2)\n"", + ""\n"", + ""#Confirm Selection\n"", + ""df = df_f.ix[argsort(score)[::-1],:].ix[:thrs,:]\n"", + ""del df_f"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 9, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""# normalize\n"", + ""df_log = log2( df +1)\n"", + ""df_norm = df_log.subtract(df_log.mean(1), axis='rows')"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""# Perform a likelihood ratio test\n"", + "" To test wether a gene has some variability over the time points comparing:\n"", + "" - A GLM with a Negative binomial link function and time (E-day) as a categorical predictor \n"", + "" \n"", + "" against\n"", + "" \n"", + "" \n"", + "" - The null model that fits the data to a single nagative binomial distribution without taking time into account"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""## Define functions"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 10, + ""metadata"": { + ""collapsed"": true, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""def test_significance(df, group, family='NegativeBinomial', FDR_corrected=True):\n"", + "" \""\""\""\n"", + "" df is a standard [genes,cells] pd.Dataframe\n"", + "" groups is a vector of len = df.shape[1]\n"", + "" \""\""\""\n"", + "" dfk = df.copy()\n"", + "" \n"", + "" p_values = []\n"", + "" dfk.index = [ 'var'+str(i).zfill(5) for i in range(len(dfk.index))]\n"", + "" for i, gene in enumerate(dfk.index):\n"", + "" p_values.append( likelihood_ratio_test(dfk, group, gene, family=getattr(sm.families,family)() ) )\n"", + "" if i % 100 == 0:\n"", + "" print i,\n"", + "" \n"", + "" p_values = array(p_values)\n"", + "" if FDR_corrected:\n"", + "" q_values = multipletests(p_values, 0.5, 'fdr_bh')[1]\n"", + "" print 'Returning q-values'\n"", + "" return q_values\n"", + "" else: \n"", + "" return p_values\n"", + "" \n"", + ""def likelihood_ratio_test(df, group, gene, family):\n"", + "" \""\""\"" Performs a likelihood ratio test\n"", + "" df is a standard [genes,cells] pd.Dataframe\n"", + "" groups is a vector of len = df.shape[1]\n"", + "" \""\""\""\n"", + "" assert len(group) == df.shape[1], 'groups and df have not compatible shape'\n"", + "" expression = df.T\n"", + "" expression['Groups'] = group\n"", + "" \n"", + "" #Fit the full and the restricted model\n"", + ""\n"", + "" model_full = smf.glm('%s ~ C(Groups)' % gene , family=family, data=expression)\n"", + "" model_res = smf.glm('%s ~ 1' % gene , family=family, data=expression)\n"", + "" results_full = model_full.fit()\n"", + "" results_res = model_res.fit()\n"", + "" \n"", + "" lrdf = (results_res.df_resid - results_full.df_resid) #degrees of freedom\n"", + "" lrstat = -2*(results_res.llf - results_full.llf) # test statistics, using the log likelihood\n"", + "" lr_pvalue = stats.chi2.sf(lrstat, df=lrdf) #perform statistic test\n"", + "" \n"", + "" return lr_pvalue"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""## Test Significance"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 11, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [ + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 2500 2600 2700 2800 2900 3000 3100 3200 3300 3400 3500 3600 3700 3800 3900 4000 4100 4200 4300 4400 4500 4600 4700 4800 4900 Returning q-values\n"" + ] + } + ], + ""source"": [ + ""q_values = test_significance(df, Eday_ix, family='NegativeBinomial', FDR_corrected=True)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 12, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + ""380"" + ] + }, + ""execution_count"": 12, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""sum( q_values<0.1 )"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""# Consider the subspace\n"", + ""- that includes only the genes that have significant variation over time\n"", + ""- excluding genes that are marker of other cell types\n"", + ""(to avoid the genes variable because of a contamination event)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 13, + ""metadata"": { + ""collapsed"": true, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""df_sign_genes = df.ix[q_values<0.1,:]\n"", + ""df_sign_genes_norm = df_norm.ix[q_values<0.1,:]"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 14, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""pattern_df = pd.read_csv('data/Mouse_binary.tsv',sep='\\t', index_col=0)\n"", + ""\n"", + ""bool1 = (pattern_df.ix[:,'mOMTN'] & (pattern_df.sum(1) < 4) & \n"", + "" ~(pattern_df.ix[:,'mEndo'])).values\n"", + ""\n"", + ""cleaned_list = list(set(df_sign_genes.index.tolist()) & set(pattern_df.index[bool1]) )"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 15, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""df_sign_genes = df_sign_genes.ix[cleaned_list,:]\n"", + ""df_sign_genes_norm = df_sign_genes_norm.ix[cleaned_list,:]"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""# Pseudotime using principal curve"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 16, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [ + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""4\n"" + ] + } + ], + ""source"": [ + ""res = principal_curve(df_sign_genes_norm.T.values)\n"", + ""print res.PCs.shape[1]"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""## Infer missing values using the correlation structure\n"", + ""(from the rest of the transcriptome)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 17, + ""metadata"": { + ""collapsed"": true, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""def infer_gene(df, gene,verbose=False, **kwargs):\n"", + "" lasso = Lasso(**kwargs)\n"", + "" X = df.ix[df.index !=gene,:].values.T.astype(float)\n"", + "" y = df.ix[gene].values.T.astype(float)\n"", + "" results = lasso.fit(X, y)\n"", + "" if verbose:\n"", + "" print sum( abs( results.coef_ ) > 0.05),\n"", + "" return lasso.predict(X)\n"", + ""\n"", + ""def gene_inference(df,gene_list,verbose,**kwargs):\n"", + "" return pd.DataFrame(data=[infer_gene(df,gene, verbose, **kwargs) for gene in gene_list ], index = gene_list, columns=df.columns)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 18, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + }, + ""scrolled"": true + }, + ""outputs"": [ + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""11 6 5 4 21 12 7 3 11 6 8 14 2 13 10 21 12 16 6 10 12 9 1 8 2 5 4 7 5 10 2 17 17 2 4 4 11 12 15 6 25 2 3 2 2 4 6 7 5 2 10 10 9 4 5 4 5 3 1 11 5 3 7 6 5 11 10 14 11 3 11 7 22 8 10 13 2 7 4 7 26 8 9 13 5 9 5 9 1 2 1 2 4 5 11 0 8 6 11 8 9 5 11 1 12 2 1 3 2 9 11 13 6 1 13 1 4 5 2 13 8 16 13 5 6 4 11 4 6 3 2 1 4 4 5 2 6 4 4 12 16 4 4 7 14 2 5 15 9 2 11 2 6 2 9 4 5 1 4 2 17 11 1 4 1 6 2 1 5 4 2 2 5 5 3 2 4 15 2 7 3 2 10 15 8 8 2 4 6 5 5 2 1 6 3 6 7 1 3 3 5 4 12 11 9 9 15 7 10 5 6 3 13 4 3 2 6 3 4 2 7 9 3 6 7 3 4 5 12 7 12 3 4 0 8 3 1 1 7 6 6 9 4 2 4 7 10 6 3 5 2 4 4 4 2 5 5 13 2 4 6 4 4 3 12 1 0 2 0 4 10 3 10 3 5 3 1 3 3 8 7 2 2 8 10 4 3 5 18 6 5 11 5 1 2 2 2 8 5 2 0 9 1 5 10 6 2 3 3 2 1 4 0 1 3 5 0 6 3 9 4 13 1 1 1 2 11 5 3 6 2 3 4 2 1 2 5 3 6 4 3 6 1 3 3 7 2 4 5 10 1 1 8 8 0 1 2 2 2 3 4 5 1 11 4 0 2 0 9 1 4 7 2 0 7 3 11 4 3 3\n"" + ] + } + ], + ""source"": [ + ""#Include all the genes and repeat inference\n"", + ""df_sign_genes_norm = df_norm.ix[q_values<0.1,:]\n"", + ""\n"", + ""df_sign_genes_norm_inferred =gene_inference(df_sign_genes_norm,\\\n"", + "" df_sign_genes_norm.index.values.tolist(),verbose=True, alpha=0.1)\n"", + ""#WITHOUT INCLUDING\n"", + ""sorted_df_norm_temp = df_sign_genes_norm_inferred.ix[:,res.ixsort[::-1]].T\n"", + ""sorted_df_norm_temp['Pseudotime'] = max(res.arclength) - res.arclength[res.ixsort[::-1]]\n"", + ""sorted_df_norm_pseudotime = sorted_df_norm_temp"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""source"": [ + ""## Fit a curve for every gene"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 19, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + ""Counter({0: 19, 1: 13, 2: 17, 3: 7, 4: 9, 5: 1})"" + ] + }, + ""execution_count"": 19, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""from collections import Counter\n"", + ""Counter(Eday_ix)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 21, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""lin = linspace(0,max(sorted_df_norm_pseudotime.Pseudotime),400)\n"", + ""predictions_df = pd.DataFrame()\n"", + ""R2_df = {}\n"", + ""for i in sorted_df_norm_pseudotime:\n"", + "" svr = GridSearchCV(SVR(kernel='rbf'), cv=ShuffleSplit(len(Eday_ix), test_size=0.5,n_iter=8),\\\n"", + "" param_grid={\""C\"": [1,2,5,7],\\\n"", + "" \""gamma\"": linspace(0.001,0.004,6)})\n"", + "" svr.fit(sorted_df_norm_pseudotime.ix[:, 'Pseudotime'].values[:,newaxis], sorted_df_norm_pseudotime.ix[:, i].values)\n"", + "" R2 = svr.score(sorted_df_norm_pseudotime.ix[:, 'Pseudotime'].values[:,newaxis], sorted_df_norm_pseudotime.ix[:, i].values)\n"", + "" if R2 >0.35:\n"", + "" predictions_df[i] = svr.predict(lin[:,newaxis])\n"", + "" R2_df[i] = R2"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 22, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""predictions = predictions_df.values"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 23, + ""metadata"": { + ""collapsed"": true, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""predictions = predictions - predictions.min(0)\n"", + ""predictions = predictions / predictions.mean(0)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 24, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [ + { + ""data"": { + ""image/png"": 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S2TT9JcHQdS8aAxLVcFe6Konw8Jd0uO9/5U65ca7DWFBwfBtyzvExlvsdWYwju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fkfu/wPwU0KIX+XNJqhnhR8H/ichxM8DDvgT7G38CSGEA34hhPA3n6aBb+HHgJ98q21CiP9VCPFr\n7BvJ/u2naeANPwr8JSFExf7C/xMhhFII8Zefkp0/BLwM/KzYh0YB+Hd49nz5JDufNV8C/MfAfyuE\n+FPs9ePfYJ9G+Lrr5in780l2BuCnnyV/hhB+9OGxEOIn2a8TXPIteH8empgOHDhw4EPIoYnpwIED\nBz6EHMT9wIEDBz6EHMT9wIEDBz6EHMT9wIEDBz6EHMT9wIEDBz6EHMT9wIEDBz6EHMT9wIEDBz6E\nHMT9wIEDBz6E/P8BjfHvLYyp/AAAAABJRU5ErkJggg==\n"", + ""text/plain"": [ + """" + ] + }, + ""metadata"": {}, + ""output_type"": ""display_data"" + } + ], + ""source"": [ + ""# quick visualization of 100 exaples of fit\n"", + ""for i in range(100):\n"", + "" plot( predictions[:,i], alpha=0.1 )"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""# Cluster the profiles to obtain prototypical dynamics"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 25, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + ""2"" + ] + }, + ""execution_count"": 25, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""ap = AffinityPropagation(damping=0.8, max_iter=500,\\\n"", + "" convergence_iter=30, copy=True,\\\n"", + "" preference=-400, affinity='euclidean')\n"", + ""labels = ap.fit_predict(predictions.T )\n"", + ""len( set( labels ) )"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 26, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [ + { + ""data"": { + ""image/png"": 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ZqFc888wzLli0zy8P5/X7q9bo+K26WjWxXz825ROmsotOYWJhGSsnw8DBdXV0A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\nGgwGg8EwRfx/QwilXcEGJu8AAAAASUVORK5CYII=\n"", + ""text/plain"": [ + """" + ] + }, + ""metadata"": {}, + ""output_type"": ""display_data"" + } + ], + ""source"": [ + ""from scipy import stats\n"", + ""fig = figure(figsize(10,4.5*len(set(labels)) /3.))\n"", + ""l_set = array( list(set(labels)) )\n"", + ""for n,i in enumerate( l_set[argsort([sum(labels==k) for k in l_set])[::-1]] ):\n"", + "" subplot(int( ceil( len(set(labels)) /3.)), 3, n+1)\n"", + "" y = predictions[:,labels == i].flatten()\n"", + "" mean_y = mean( predictions[:,labels == i], 1)\n"", + "" x = repeat(lin,sum(labels == i) )\n"", + "" X, Y = mgrid[min(x)-1:max(x)+1:40j, min(y)-0.1:max(y)+0.1:40j]\n"", + "" positions = vstack([X.ravel(), Y.ravel()])\n"", + "" values = vstack([x,y])\n"", + "" kernel = stats.gaussian_kde(values)\n"", + "" Z = reshape(kernel(positions).T, X.shape)\n"", + "" contour(X,Y,Z,10, colors=[(j,)*3 for j in linspace(0.6,0.9,8)[::-1]])\n"", + "" plot(lin, mean_y, c='r', lw=2.5)\n"", + "" xlabel('Pseudotime (a.u.)')\n"", + "" ylabel('Expression (a.u.)')\n"", + "" grid(alpha=0.2)\n"", + "" title('Pattern %i' % (n+1), fontdict={'size':14,'weight':'semibold'})\n"", + ""tight_layout()"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 27, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""genes_by_pattern = [predictions_df.columns[ labels==i ].tolist() for i in l_set[argsort([sum(labels==k) for k in l_set])[::-1]]]"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""# Plot some examples"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 28, + ""metadata"": { + ""collapsed"": true, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""def adjust_spines(ax, spines):\n"", + "" for loc, spine in ax.spines.items():\n"", + "" if loc in spines:\n"", + "" #spine.set_position(('outward', 10)) # outward by 10 points\n"", + "" #spine.set_smart_bounds(True)\n"", + "" pass\n"", + "" else:\n"", + "" spine.set_color('none') # don't draw spine\n"", + ""\n"", + "" # turn off ticks where there is no spine\n"", + "" if 'left' in spines:\n"", + "" ax.yaxis.set_ticks_position('left')\n"", + "" else:\n"", + "" # no yaxis ticks\n"", + "" ax.yaxis.set_ticks([])\n"", + ""\n"", + "" if 'bottom' in spines:\n"", + "" ax.xaxis.set_ticks_position('bottom')\n"", + "" else:\n"", + "" # no xaxis ticks\n"", + "" ax.xaxis.set_ticks([])"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 29, + ""metadata"": { + ""collapsed"": true, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""color_list = [(231,47,39),(255,200,8),(19,166,50),(1,124,141),(1,84,201), (43,56,86)]"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 31, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [ + { + ""data"": { + ""image/png"": 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fu3dmzZ49qoIpB1R0VgD4BPRlq6I1dsvmzJlXSV+nIiG4PWNXekfAMNFqzgcrM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\n"", + ""text/plain"": [ + """" + ] + }, + ""metadata"": {}, + ""output_type"": ""display_data"" + } + ], + ""source"": [ + ""gene_toplot = [ 'Pvalb', 'Esrrb'] \n"", + ""figure(figsize=(6,2.4))\n"", + ""for n, gene in enumerate(array(gene_toplot)[argsort([R2_df[j] for j in gene_toplot])[::-1]]):\n"", + "" subplot(int(ceil( len(gene_toplot) / 2.)), 2, n+1)\n"", + "" plot(lin, predictions_df.ix[:,gene], '-k', linewidth=2.5, zorder=-1)\n"", + "" scatter(sorted_df_norm_pseudotime.Pseudotime,\\\n"", + "" sorted_df_norm_pseudotime[gene],\n"", + "" c=array(color_list)[Eday_ix[res.ixsort[::-1]],:]/255., s=40,alpha=0.7,lw=0.2, zorder=100)\n"", + "" text(0.2,0.92,r'$R^2 = %.2f$' % R2_df[gene], ha='center', va='center',transform = gca().transAxes,\\\n"", + "" fontdict={'weight':'semibold', 'size':'12', 'family':'Times'})\n"", + "" title(gene, size=15, weight='semibold')\n"", + "" adjust_spines(gca(), ['left', 'bottom'])\n"", + "" gca().yaxis.set_tick_params(labelsize=8)\n"", + "" gca().xaxis.set_tick_params(labelbottom='off')\n"", + "" xlim([0-0.5,ceil(max(lin))])\n"", + "" ylim(percentile(sorted_df_norm_pseudotime[gene], 0.5)-0.15,percentile(sorted_df_norm_pseudotime[gene]*1.1, 99.9))\n"", + "" xlabel('Pseudotime')\n"", + "" ylabel('Expression ($log_2$)')\n"", + "" #grid(alpha=0.2)\n"", + ""tight_layout()"" + ] + } + ], + ""metadata"": { + ""kernelspec"": { + ""display_name"": ""Python 2"", + ""language"": ""python"", + ""name"": ""python2"" + }, + ""language_info"": { + ""codemirror_mode"": { + ""name"": ""ipython"", + ""version"": 2 + }, + ""file_extension"": "".py"", + ""mimetype"": ""text/x-python"", + ""name"": ""python"", + ""nbconvert_exporter"": ""python"", + ""pygments_lexer"": ""ipython2"", + ""version"": ""2.7.12"" + }, + ""toc"": { + ""toc_cell"": true, + ""toc_number_sections"": true, + ""toc_section_display"": ""none"", + ""toc_threshold"": 6, + ""toc_window_display"": true + } + }, + ""nbformat"": 4, + ""nbformat_minor"": 0 +} +","Unknown" +"Pseudotime analysis","linnarsson-lab/ipynb-lamanno2016","ipynb-lamanno2016-cellscoring.ipynb",".ipynb","476184","1290","{ + ""cells"": [ + { + ""cell_type"": ""markdown"", + ""metadata"": { + ""toc"": ""true"" + }, + ""source"": [ + ""# Table of Contents\n"", + ""

"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""# Imports"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 1, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [ + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Populating the interactive namespace from numpy and matplotlib\n"" + ] + } + ], + ""source"": [ + ""%pylab inline"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 2, + ""metadata"": { + ""code_folding"": [], + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""#imports\n"", + ""from __future__ import division\n"", + ""import pandas as pd\n"", + ""from backSPIN import fit_CV\n"", + ""from Cef_tools import *\n"", + ""from sklearn.linear_model import LogisticRegression, LogisticRegressionCV\n"", + ""from sklearn.cross_validation import StratifiedShuffleSplit \n"", + ""from collections import defaultdict"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""# Load data"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""## Load Reference dataset"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 3, + ""metadata"": { + ""collapsed"": true, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""df_all, rows_annot, cols_annot, _ = cef2df('data/Human_Embryo_fulldataset.cef')"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 4, + ""metadata"": { + ""code_folding"": [], + ""collapsed"": true, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""# For this analysis we masked the following genes\n"", + ""panther_cellcycle = open('data/PANTHER_cell_cycle_genes.txt').read().split('\\n')\n"", + ""blood_genes = ['HBG1','HBA1','HBA2','HBE1','HBZ','BLVRB','S100A6']\n"", + ""SNAR_genes = ['SNAR-E', 'SNAR-A13_loc1', 'SNAR-C1_loc1', 'SNAR-A1_loc2', 'SNAR-A8_loc1', 'SNAR-C1_loc2', 'SNAR-A2_loc2', 'SNAR-C4', \n"", + "" 'SNAR-A12_loc1', 'SNAR-C3', 'SNAR-C1_loc3', 'SNAR-G2', 'SNAR-G1', 'SNAR-A11_loc9', 'SNAR-A6_loc3', 'SNAR-A14_loc7', 'SNAR-A6_loc5', \n"", + "" 'SNAR-A10_loc6', 'SNAR-A5_loc9', 'SNAR-A14_loc3', 'SNAR-A9_loc9', 'SNAR-A11_loc7', 'SNAR-B1_loc1', 'SNAR-B1_loc2', 'SNAR-D', 'SNAR-F']\n"", + ""\n"", + ""df = df_all.ix[~in1d(df_all.index, panther_cellcycle) &\\\n"", + "" ~in1d(df_all.index, blood_genes)&\\\n"", + "" ~in1d(df_all.index, SNAR_genes),:]"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""## Load iPSC dataset"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 5, + ""metadata"": { + ""collapsed"": true, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""df_ips, rows_annot_ips, cols_annot_ips, _ = cef2df('data/iPSC_fulldataset.cef')"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""## Load ES dataset"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 6, + ""metadata"": { + ""collapsed"": true, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""df_es, rows_annot_es, cols_annot_es, _ = cef2df('data/ES_fulldataset.cef')"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""# Annotations"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""## Describe the prototypic cell types"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 7, + ""metadata"": { + ""collapsed"": true, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""# prototype dictionary\n"", + ""proto = pd.Series({'hEndo': 'Endo',\n"", + "" \n"", + "" 'hPeric': 'Peric',\n"", + "" \n"", + "" 'hMgl': 'Mgl',\n"", + "" \n"", + "" 'hDA1': 'DA',\n"", + "" 'hDA2': 'DA',\n"", + "" 'hDA0': 'DA',\n"", + "" \n"", + "" 'hSert': 'Sert',\n"", + "" \n"", + "" 'hOMTN': 'OMTN',\n"", + "" \n"", + "" 'hRgl1': 'Rgl',\n"", + "" 'hRgl3': 'Rgl',\n"", + "" 'hRgl2c': 'Rgl',\n"", + "" 'hRgl2b': 'Rgl',\n"", + "" 'hRgl2a': 'Rgl',\n"", + "" \n"", + "" 'hOPC': 'OPC',\n"", + "" \n"", + "" 'hProgFPM':'ProgFP',\n"", + "" 'hProgFPL':'ProgFP',\n"", + "" 'hProgM': 'ProgFP',\n"", + "" \n"", + "" 'hProgBP': 'ProgBP',\n"", + "" \n"", + "" 'hNbML5': 'Gaba',\n"", + "" 'hGaba': 'Gaba',\n"", + "" 'hNbGaba': 'Gaba',\n"", + "" \n"", + "" 'hNbML1': 'NbML1',\n"", + "" \n"", + "" 'hNProg': 'NProg',\n"", + "" 'hNbM': 'NbM',\n"", + "" \n"", + "" 'hRN': 'RN',\n"", + "" \n"", + "" 'eSCa': 'eES',\n"", + "" 'eSCb': 'eES',\n"", + "" 'eSCc': 'eES',\n"", + ""\n"", + "" 'Unk': 'none'})\n"", + ""\n"", + ""# Colors of the prototypes\n"", + ""protocol = {'NbML1':(100, 100, 240) ,\n"", + "" 'ProgFP':(160, 235, 235),\n"", + "" 'Gaba':(7,121,61),\n"", + "" 'ProgBP': (230, 140, 120),\n"", + "" 'NProg':(255, 195, 25),\n"", + "" 'Rgl':(175,175,35),\n"", + "" 'NbM':(180,140,130),\n"", + "" 'DA':(190,200,190),\n"", + "" 'OMTN':(0,240,0),\n"", + "" 'RN':(200,120,0),\n"", + "" 'Mgl': (217,245,7),\n"", + "" 'Peric': (225,160,30),\n"", + "" 'Endo': (190,10,10),\n"", + "" 'OPC':(255, 95, 105),\n"", + "" 'Sert':( 50, 180, 180),\n"", + "" 'eES':(240,255,250)}"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": { + ""collapsed"": true, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""source"": [ + ""# Prepare to train the model"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""Since we merge ES cell in culture with in vivo cell, feature selection is performed taking extra precautions to avoid picking up signature of the cell culture condition rather than the ES cell signature."" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""## Add day0 ES to the Reference Dataset"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 8, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + ""(16229, 2298)"" + ] + }, + ""execution_count"": 8, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""df_merge = pd.concat([df, df_es.ix[:,in1d(cols_annot_es.ix['Cell_type'], ['eSCa','eSCb','eSCc'])] ], axis=1)\n"", + ""df_merge = df_merge.dropna(axis=0)\n"", + ""df_merge.shape"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""## Filter for Development genes"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 9, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [ + { + ""data"": { + ""image/png"": 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r+iL/MPcQXvEEGRCO69IFuIhQVr1VG9WqIiy5GWb4c/0Ur0Eb6GHnmEEoqiW9p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\nu38piFYbv9FGz6Ve1gB0S2vFbGADNy10HS4qblove8Z1WS10GhqSj5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+ ""text/plain"": [ + """" + ] + }, + ""metadata"": {}, + ""output_type"": ""display_data"" + } + ], + ""source"": [ + ""thrs = 4500\n"", + ""\n"", + ""#Pre-filtering\n"", + ""df_f = df_merge.copy()\n"", + ""df_f = df_f.ix[sum(df_f>=1, 1)>=5,:] # is at least 1 in X cells\n"", + ""df_f = df_f.ix[sum(df_f>=2, 1)>=2,:] # is at least 2 in X cells\n"", + ""df_f = df_f.ix[sum(df_f>=3, 1)>=1,:] # is at least 2 in X cells\n"", + ""\n"", + ""#Fitting\n"", + ""mu = df_f.mean(1).values\n"", + ""sigma = df_f.std(1, ddof=1).values\n"", + ""cv = sigma/mu\n"", + ""score, mu_linspace, cv_fit , params = fit_CV(mu,cv, 'SVR', svr_gamma=0.005)\n"", + ""\n"", + ""#Plotting\n"", + ""def plot_cvmean():\n"", + "" figure()\n"", + "" scatter(log2(mu),log2(cv), marker='o', edgecolor ='none',alpha=0.1, s=5)\n"", + "" mu_sorted = mu[argsort(score)[::-1]]\n"", + "" cv_sorted = cv[argsort(score)[::-1]]\n"", + "" scatter(log2(mu_sorted[:thrs]),log2(cv_sorted[:thrs]), marker='o', edgecolor ='none',alpha=0.15, s=8, c='r')\n"", + "" plot(mu_linspace, cv_fit,'-k', linewidth=1, label='$Fit$')\n"", + "" plot(linspace(-9,7), -0.5*linspace(-9,7), '-r', label='$Poisson$')\n"", + "" ylabel('log2 CV')\n"", + "" xlabel('log2 mean')\n"", + "" grid(alpha=0.3)\n"", + "" xlim(-8.6,6.5)\n"", + "" ylim(-2,6.5)\n"", + "" legend(loc=1, fontsize='small')\n"", + "" gca().set_aspect(1.2)\n"", + "" \n"", + ""plot_cvmean()\n"", + ""\n"", + ""#Adjusting plot\n"", + ""\n"", + ""\n"", + ""#Confirm Selection\n"", + ""df_dev = df_f.ix[argsort(score)[::-1],:].ix[:thrs,:]"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""## Variable genes in the whole ES dataset"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 10, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [ + { + ""data"": { + ""image/png"": 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s88sgjbN26lVtv/RhC2KmqUtmVmqZicCCgYqHdPqFZfdd7B+n92V7sDo2Gu288d3fEbJaR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+ ""text/plain"": [ + """" + ] + }, + ""metadata"": {}, + ""output_type"": ""display_data"" + } + ], + ""source"": [ + ""thrs = 800\n"", + ""\n"", + ""#Pre-filtering\n"", + ""df_f = df_es.copy()\n"", + ""df_f = df_f.ix[sum(df_f>=1, 1)>=7,:] # is at least 1 in X cells\n"", + ""df_f = df_f.ix[sum(df_f>=2, 1)>=3,:] # is at least 2 in X cells\n"", + ""df_f = df_f.ix[sum(df_f>=3, 1)>=1,:] # is at least 2 in X cells\n"", + ""\n"", + ""#Fitting\n"", + ""mu = df_f.mean(1).values\n"", + ""sigma = df_f.std(1, ddof=1).values\n"", + ""cv = sigma/mu\n"", + ""score, mu_linspace, cv_fit , params = fit_CV(mu,cv, 'SVR', svr_gamma=0.005)\n"", + ""\n"", + ""#Plotting\n"", + ""plot_cvmean()\n"", + ""\n"", + ""#Confirm Selection\n"", + ""variable_es = df_f.ix[argsort(score)[::-1],:].ix[:thrs,:].index"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""## Avoid to select genes that are cell-culture specific\n"", + ""The selected genes coming from ES must have a minimal variabiliry in the full ES dataset"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 11, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [ + { + ""data"": { + ""image/png"": 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wqwUMPudUM7r/fzfhra3NH8aXS05RKbyepKHzY18+6gEEiUCOgNkhM7cFGwepb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\n3RhjQsYSuzHGhMz/A24gZ6LenMrqAAAAAElFTkSuQmCC\n"", + ""text/plain"": [ + """" + ] + }, + ""metadata"": {}, + ""output_type"": ""display_data"" + } + ], + ""source"": [ + ""thrs = 4500\n"", + ""\n"", + ""#Pre-filtering\n"", + ""df_f = df.copy()\n"", + ""df_f = df_f.ix[sum(df_f>=1, 1)>=5,:] # is at least 1 in X cells\n"", + ""df_f = df_f.ix[sum(df_f>=2, 1)>=2,:] # is at least 2 in X cells\n"", + ""df_f = df_f.ix[sum(df_f>=3, 1)>=1,:] # is at least 2 in X cells\n"", + ""\n"", + ""#Fitting\n"", + ""mu = df_f.mean(1).values\n"", + ""sigma = df_f.std(1, ddof=1).values\n"", + ""cv = sigma/mu\n"", + ""score, mu_linspace, cv_fit , params = fit_CV(mu,cv, 'SVR', svr_gamma=0.005)\n"", + ""\n"", + ""#Plotting\n"", + ""plot_cvmean()\n"", + ""\n"", + ""#Confirm Selection\n"", + ""variable_emb = df_f.ix[argsort(score)[::-1],:].ix[:thrs,:].index"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 12, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + ""(3789, 2298)"" + ] + }, + ""execution_count"": 12, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""es_batch_genes = setdiff1d( setdiff1d(df_dev.index, variable_emb), variable_es )\n"", + ""df_dev = df_dev.ix[~in1d(df_dev.index,es_batch_genes),:]\n"", + ""df_dev.shape"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""## Enrichment: Select for cell type positive markers (optional)\n"", + ""This is not essential since the regularization in the logistic regression works as a feature selection.\n"", + ""However we reasoned filtering genes that are not enriched in any cell-type (e.g. methabolic, early immediate, cell cycle or others are orthogonal to cell type) may produce a model that can generalize better when applied on cells in different environment (e.g. cell cultures, differentiation experiments)."" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 13, + ""metadata"": { + ""code_folding"": [], + ""collapsed"": true, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""# for some single cell types\n"", + ""enrichment_order = ['hEndo', 'hPeric','hMgl','hSert','hOMTN','hOPC','hProgBP','hNbML1',\n"", + "" 'hNProg','hNbM','hRN','eSCa','eSCb','eSCc','hNbML5','hGaba','hNbGaba',\n"", + "" 'hDA1','hDA2','hDA0','hRgl2b','hRgl2a','hRgl3','hRgl1','hRgl2c',\n"", + "" 'hProgFPM','hProgFPL','hProgM','hProgBP']\n"", + ""# for prototypes\n"", + ""higher_order = [['hDA1','hDA2','hDA0'],['hRgl2c','hRgl2b','hRgl2a','hRgl3','hRgl1'],\n"", + "" ['hProgFPM','hProgFPL','hProgM'],['hNbML5','hGaba','hNbGaba'],\n"", + "" ['eSCa','eSCb','eSCc']]"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 14, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""cols_annot_all = pd.concat( [cols_annot, cols_annot_ips, cols_annot_es], 1)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 15, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""ct_dev = cols_annot_all[df_dev.columns].ix['Cell_type']\n"", + ""protogruop = proto.ix[ct_dev].values"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 16, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""df_fall = df_dev.ix[:,protogruop != 'none']\n"", + ""df_fall = df_fall.ix[(df_fall.sum(1)>6) & ((df_fall>0).sum(1)>4),:]\n"", + ""cell_types = cols_annot_all[df_fall.columns].ix['Cell_type'].values\n"", + ""df_means = df_fall.mean(1) + 1e-5\n"", + ""df_bin = df_fall>0\n"", + ""df_fold = pd.DataFrame()\n"", + ""df_avgpos = pd.DataFrame()\n"", + ""for ct in enrichment_order:\n"", + "" df_fold[ct] = df_fall.ix[:,cell_types == ct].mean(1) / df_means\n"", + "" df_avgpos[ct] = df_bin.ix[:,cell_types == ct].mean(1)\n"", + "" \n"", + ""for ct in higher_order:\n"", + "" df_fold['-'.join(ct)] = df_fall.ix[:,in1d(cell_types, ct)].mean(1) / df_means\n"", + "" df_avgpos['-'.join(ct)] = df_bin.ix[:,in1d(cell_types, ct)].mean(1)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 17, + ""metadata"": { + ""collapsed"": true, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""score00 = df_fold\n"", + ""score05 = df_fold * df_avgpos**0.5\n"", + ""score10 = df_fold * df_avgpos\n"", + ""\n"", + ""ix00 = argsort( score00 , 0)\n"", + ""ix05 = argsort( score05 , 0)\n"", + ""ix10 = argsort( score10 , 0)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 18, + ""metadata"": { + ""collapsed"": true, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""markers = defaultdict(set)\n"", + ""N = 145\n"", + ""for ct in df_fold.columns:\n"", + "" markers[ct] |= set( df_fold.index[ix00.ix[:,ct][::-1]][:N] )\n"", + "" markers[ct] |= set( df_fold.index[ix05.ix[:,ct][::-1]][:N] )\n"", + "" markers[ct] |= set( df_fold.index[ix10.ix[:,ct][::-1]][:N] )"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 19, + ""metadata"": { + ""collapsed"": true, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""for ct in df_fold.columns:\n"", + "" for mk in markers[ct]:\n"", + "" for ct2 in list( set(df_fold.columns) - set([ct])):\n"", + "" if score10.ix[mk,ct] >= score10.ix[mk,ct2]:\n"", + "" markers[ct2] -= set([mk])\n"", + "" for mk in list(markers[ct]):\n"", + "" if df_avgpos.ix[mk,ct] < 0.15:\n"", + "" markers[ct] -= set([mk])"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 20, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + ""1969"" + ] + }, + ""execution_count"": 20, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""list_genes = sum([list(markers[ct]) for ct in df_fold.columns])\n"", + ""len(list_genes)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 21, + ""metadata"": { + ""collapsed"": true, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""df_dev = df_dev.ix[list(set(list_genes)),:]"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""# Train model"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": { + ""collapsed"": true, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""source"": [ + ""## Prepare the reference dataset"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 22, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""# Log normalization\n"", + ""df_dev_log = log2(df_dev+1)\n"", + ""df_ips_log = log2( df_ips+1 )\n"", + ""df_es_log = log2( df_es+1 )"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 23, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""# Check if this is repeated\n"", + ""ct_dev = cols_annot_all[df_dev.columns].ix['Cell_type']\n"", + ""protogruop = proto.ix[ct_dev].values"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 24, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""bool1 = protogruop != 'none'\n"", + ""classes_names, classes_index = unique(protogruop[bool1], return_inverse=True, return_counts=False)\n"", + ""train_index = classes_index\n"", + ""df_train_set = df_dev_log.ix[:,bool1].copy()\n"", + ""train_index = classes_index"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""## Regularization Path"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": null, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""LR = LogisticRegressionCV(Cs=logspace(-3.25,0.8,30), refit=True, penalty='l2',\n"", + "" solver='newton-cg', fit_intercept=False, multi_class='multinomial',class_weight='balanced',\n"", + "" cv=StratifiedShuffleSplit(train_index, n_iter=35,test_size=0.15, random_state=150790))\n"", + ""normalizer = 0.9*df_train_set.values.max(1)[:,newaxis]\n"", + ""LR.fit((df_train_set.values/normalizer).T, train_index)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 26, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [ + { + ""data"": { + ""image/png"": 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GqDz6o2qGDatOmqlWrqkZEqF5yieqNN6pWqaLaqpXqZ5+pnjp1pp/jx1Ufe0y1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\nQJUrB3hw7x07Zo6aNYMgmMVSNPn999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+ ""text/plain"": [ + """" + ] + }, + ""metadata"": {}, + ""output_type"": ""display_data"" + } + ], + ""source"": [ + ""figure(figsize=(6,3))\n"", + "" \n"", + ""chos = 0.2\n"", + ""\n"", + ""subplot(122)\n"", + ""CMA = array([abs(LR.coefs_paths_[i]) for i in range(len(set(classes_index)))])\n"", + ""val_ = array( [[sum( sum( CMA[:,i,j,:],0) ,0) for i in range(35)] for j in range(30) ] )\n"", + ""plot( log10(LR.Cs_),\\\n"", + "" val_.mean(1), c='k' , lw=2 )\n"", + ""xlabel('-log( Regularization strength )')\n"", + ""ylabel('# Sum abs(coeffs)')\n"", + ""xlim(-3.25,0.8)\n"", + ""axvline( log10(chos) )\n"", + ""\n"", + ""subplot(121)\n"", + ""plot(log10(LR.Cs_), mean([LR.scores_[i].mean(0) for i in range(len(set(classes_index)))],0), c='k', lw=2 )\n"", + ""plot(log10(LR.Cs_), percentile( LR.scores_[1],97.5,0), c='r')\n"", + ""plot(log10(LR.Cs_), percentile( LR.scores_[1],2.5,0), c='r')\n"", + ""axvline( log10(chos) )\n"", + ""ylabel('Accuracy Score')\n"", + ""xlabel('-log( Regularization strength )')\n"", + ""xlim(-3.25,0.8)\n"", + ""ylim(0.70,0.95)\n"", + ""\n"", + ""tight_layout()"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""## Final model"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 25, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + ""LogisticRegression(C=0.2, class_weight='balanced', dual=False,\n"", + "" fit_intercept=False, intercept_scaling=1, max_iter=100,\n"", + "" multi_class='multinomial', n_jobs=1, penalty='l2',\n"", + "" random_state=150790, solver='newton-cg', tol=0.0001, verbose=0,\n"", + "" warm_start=False)"" + ] + }, + ""execution_count"": 25, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""# Normalized by the max\n"", + ""LR = LogisticRegression(C=0.2, penalty='l2', solver='newton-cg', fit_intercept=False,\n"", + "" multi_class='multinomial',class_weight='balanced',random_state=150790)\n"", + ""normalizer = 0.9*df_train_set.values.max(1)[:,newaxis]\n"", + ""LR.fit((df_train_set.values / normalizer).T, train_index)"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""# Score with the model"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 26, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""hist_order = ['Endo', 'Peric', 'Mgl', 'OPC', 'eES','Rgl','ProgBP','ProgFP','NProg', \n"", + "" 'RN', 'NbM','NbML1','DA','Gaba','Sert','OMTN',]\n"", + ""hist_ixes = [list(classes_names).index(i) for i in hist_order]"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 27, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + ""array(['DA', 'Endo', 'Gaba', 'Mgl', 'NProg', 'NbM', 'NbML1', 'OMTN', 'OPC',\n"", + "" 'Peric', 'ProgBP', 'ProgFP', 'RN', 'Rgl', 'Sert', 'eES'], dtype=object)"" + ] + }, + ""execution_count"": 27, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""classes_names"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 28, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [ + { + ""data"": { + ""image/png"": 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H7sIi4YdzBomG0zp2j7tVrBrZb5sxWV8/qUe9TTGdnpzo7O4fcJ//8rNx0i8/mfbyrctPj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\n/h6Uo7aX2N5ie9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Ii9kvaLanL9tyI4NFudeyK71+RdQhjcaOkb9jeN28e3Jcl7STpnyR9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\njd4AAAAASUVORK5CYII=\n"", + ""text/plain"": [ + """" + ] + }, + ""metadata"": {}, + ""output_type"": ""display_data"" + } + ], + ""source"": [ + ""figure(figsize=(10,4))\n"", + ""pobs_list = []\n"", + ""for z,i in enumerate(hist_ixes):\n"", + "" subplot(len(set(classes_index))/4,4,z+1)\n"", + "" prob = LR.predict_proba((df_train_set.values[:,classes_index==i]/ normalizer).T)[:,i]\n"", + "" amounts,_,_ = hist(prob, color=array(protocol[classes_names[i]])/255.,bins=linspace(0,1,25) )\n"", + "" pobs_list.append([ classes_names[i], mean(prob)])\n"", + "" \n"", + "" if z == len(hist_ixes)-1:\n"", + "" tick_params('both',which='both', right='on',left='off',top='off',\n"", + "" labelleft='off',labelbottom='on',labelright='on')\n"", + "" axvline(0.5)\n"", + "" xlim(0.,1)\n"", + "" ylabel(classes_names[i],rotation='horizontal',horizontalalignment = 'right')\n"", + "" yticks(linspace(0,max(amounts), 4 )[1:],['','','%d' % ( max(amounts) )])\n"", + "" ylim(0,1.2*max(amounts))\n"", + "" tick_params('both',which='both', right='on',left='off',top='off',\n"", + "" labelleft='off',labelbottom='off',labelright='on')\n"", + ""tight_layout(pad=1,h_pad=0,w_pad=1)"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""# Viualize the top positive coefficients for DA neurons"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 29, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""sel_class = 'DA'\n"", + ""nonzero_coef_bool = LR.coef_[ where( classes_names == sel_class )[0][0],: ] > 0\n"", + ""values_nonzero_coef = LR.coef_[ where( classes_names == sel_class )[0][0],: ][nonzero_coef_bool]\n"", + ""names_nonzero_coef= df_dev_log.index[nonzero_coef_bool]\n"", + ""\n"", + ""ix_nonzero_coef = argsort(values_nonzero_coef)[::-1]\n"", + ""values_sorted_coef = values_nonzero_coef[ix_nonzero_coef]\n"", + ""names_sorted_coef = names_nonzero_coef[ix_nonzero_coef]\n"", + ""\n"", + ""avg_expr = df_dev.ix[names_sorted_coef, protogruop == sel_class].mean(1)\n"", + ""std_expr = df_dev.ix[names_sorted_coef, protogruop == sel_class].std(1)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 30, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [ + { + ""data"": { + ""image/png"": 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DPnj3PXefTZwmRkZH4+++/0ahRI7Rt2xb169fHt99+q/F+6ULPnj3x6NEjYVix0gcffAAi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5V6r1Wr65JNPyMLCgpRKJfF4PDpz5gwREWk0Gvrjjz/omWeeIRsbG7p161ar66ytrSU/Pz966qmn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\n"", + ""text/plain"": [ + """" + ] + }, + ""metadata"": {}, + ""output_type"": ""display_data"" + } + ], + ""source"": [ + ""# Plot in a matplotlib table\n"", + ""rcParams['savefig.dpi'] = 160\n"", + ""colLabels=(\""Gene\"", \""Weight\"", 'Avg %s' % sel_class, 'Std.Dev.')\n"", + ""rows_list = zip( list(names_sorted_coef)[:20],\\\n"", + "" map(lambda x: '%.3f' % x, values_sorted_coef)[:20],\\\n"", + "" map(lambda x: '%.3f' % x, avg_expr[:20]),\\\n"", + "" map(lambda x: '%.3f' % x, std_expr[:20]))\n"", + ""nrows, ncols = len(rows_list)+1, len(colLabels)\n"", + ""hcell, wcell = 0.02, 0.7\n"", + ""hpad, wpad = 0, 0\n"", + ""fig = plt.figure(figsize=(ncols*wcell+wpad, nrows*hcell+hpad))\n"", + ""ax = fig.add_subplot(111)\n"", + ""ax.axis('off')\n"", + ""the_table = ax.table(cellText=rows_list, colLabels=colLabels, cellLoc='center')"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""# Wheel/Polygonal plot"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""## Prepare "" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 31, + ""metadata"": { + ""collapsed"": true, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""def polygonalPlot(data, scaling=True,start_angle=90, rotate_labels=True, labels=('one','two','three'),\\\n"", + "" sides=3, label_offset=0.10, edge_args={'color':'black','linewidth':2},\\\n"", + "" fig_args = {'figsize':(8,8),'facecolor':'white','edgecolor':'white'},):\n"", + "" '''\n"", + "" This will create a basic polygonal plot\n"", + ""\n"", + "" # Scale data for plot (i.e. a + b + c = 1)\n"", + "" scaling=True,\n"", + "" # Direction of first vertex.\n"", + "" start_angle=90,\n"", + "" # Orient labels perpendicular to vertices.\n"", + "" rotate_labels=True,\n"", + "" # Labels for vertices.\n"", + "" labels=('one','two','three')\n"", + "" # Offset for label from vertex (percent of distance from origin).\n"", + "" label_offset=0.10,\n"", + "" # Any matplotlib keyword args for plots.\n"", + "" edge_args={'color':'black','linewidth':2},\n"", + "" # Any matplotlib keyword args for figures.\n"", + "" fig_args = {'figsize':(8,8),'facecolor':'white','edgecolor':'white'},\n"", + "" '''\n"", + "" basis = array([[cos(2*i*pi/sides + start_angle*pi/180),\n"", + "" sin(2*i*pi/sides + start_angle*pi/180)] for i in range(sides)])\n"", + ""\n"", + "" # If data is Nxsides, newdata is Nx2.\n"", + "" if scaling:\n"", + "" # Scales data\n"", + "" newdata = dot((data.T / data.sum(-1)).T,basis)\n"", + "" else:\n"", + "" # Assumes data already sums to 1.\n"", + "" newdata = dot(data,basis)\n"", + ""\n"", + "" fig = figure(**fig_args)\n"", + "" ax = fig.add_subplot(111)\n"", + ""\n"", + "" for i,l in enumerate(labels):\n"", + "" if i >= sides:\n"", + "" break\n"", + "" x = basis[i,0]\n"", + "" y = basis[i,1]\n"", + "" if rotate_labels:\n"", + "" angle = 180*arctan(y/x)/pi + 90\n"", + "" if angle > 90 and angle <= 270:\n"", + "" angle = mod(angle + 180,360)\n"", + "" else:\n"", + "" angle = 0\n"", + "" ax.text(\n"", + "" x*(1 + label_offset),\n"", + "" y*(1 + label_offset),\n"", + "" l,\n"", + "" horizontalalignment='center',\n"", + "" verticalalignment='center',\n"", + "" rotation=angle\n"", + "" )\n"", + ""\n"", + "" # Clear normal matplotlib axes graphics\n"", + "" ax.set_xticks(())\n"", + "" ax.set_yticks(())\n"", + "" ax.set_frame_on(False)\n"", + ""\n"", + "" # Plot borders\n"", + "" ax.plot([basis[_,0] for _ in range(sides) + [0,]],\n"", + "" [basis[_,1] for _ in range(sides) + [0,]],\n"", + "" **edge_args)\n"", + ""\n"", + "" return newdata,ax"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""## Plot wheel plot"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 32, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""wanted_order = ['NProg', 'ProgFP','eES', 'ProgBP', 'Rgl', \n"", + "" 'Gaba', 'Sert','DA','OMTN','RN', 'NbM','NbML1',]\n"", + ""reorder_ix = [list(classes_names).index(i) for i in wanted_order]\n"", + ""bool00 = in1d( classes_names[classes_index], wanted_order )"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 33, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""color_dict = pd.Series({'hEndo': (190, 10, 10),'hPeric': (225, 160, 30),'hMgl': (217, 245, 7),\n"", + "" 'hDA1': (170, 180, 170),'hDA2': (130, 140, 140),'hNbM': (180, 140, 130),\n"", + "" 'hNbML1': (100, 100, 240),'hProgM': ( 80, 235, 255),'hProgFPM':(190, 235, 255),\n"", + "" 'hProgFPL':(210, 255, 215),'hProgBP':(230, 140, 120),'hNProg': (255, 195, 28),\n"", + "" 'hNbML5': (139, 101, 100),'hRgl1': (252, 183, 26),'hRgl3': (214, 194, 39),\n"", + "" 'hRgl2c': (255, 120, 155),'hRgl2b': (250, 145, 45),'hRgl2a': (250, 125, 25),\n"", + "" 'hDA0': (190, 200, 190),'hOPC': (255, 35, 155),'hRN': (199, 121, 41),\n"", + "" 'hNbGaba': ( 40, 55, 130),'hGaba': ( 7, 121, 61),'hOMTN': ( 95, 186, 70),\n"", + "" 'hSert': ( 50, 180, 180),'eSCa': (245, 205, 170),'eSCb': (205, 245, 170),\n"", + "" 'eSCc':(205,205,220)})\n"", + ""color_dict = color_dict.map(lambda x: map(lambda y: y/255., x))"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 35, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + """" + ] + }, + ""execution_count"": 35, + ""metadata"": {}, + ""output_type"": ""execute_result"" + }, + { + ""data"": { + ""image/png"": 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84r3rmZqYQaX0RB8PJ+pjMNo7wILGJC85r25eT4uLiHv2fGhoaepWRJIn4+Hh7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\nrus+3K0EBdMlXOqC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Q0NDyZ43n0hZYDV/le7GSilMhYuCaWhoiMrKSpYsWTL5SXchs3ZL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bXl8kpihxu7mLu6sn5pZosSJLE7h0vMS2njOQ6O/G5lez+6wuDBvPLyMjIXCoX3qsvvIdPJial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\nLgdBzUBDt4DBlIHRUozNUcTJoWI+9oXf0R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zsG3z59r/H7Tp3clOyHMrYJA2md5g7dy4p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8qcn+c+azzp/YWT6PHv27SP0nDImYRER7Nq9u8OxPp+PHdt2UFJUwuG8w9TX1VN8shgBgaGD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\nfLLv8PHimBqKAhFVxXS8EOanJFgS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+ ""text/plain"": [ + """" + ] + }, + ""metadata"": {}, + ""output_type"": ""display_data"" + } + ], + ""source"": [ + ""rcParams['savefig.dpi'] = 90\n"", + ""newcolors = array(list(color_dict[cols_annot_all.ix[:,df_train_set.columns].ix['Cell_type']].values))\n"", + ""\n"", + ""newdata,ax = polygonalPlot(LR.predict_proba((df_train_set.values/ df_train_set.values.max(1)[:,newaxis]).T)[:,reorder_ix],\\\n"", + "" scaling=False, sides=len(reorder_ix), labels=classes_names[reorder_ix])\n"", + ""\n"", + ""ax.scatter( newdata[bool00,0]*0.99, newdata[bool00,1]*0.99, alpha=0.8,\\\n"", + "" c=newcolors[bool00,:],\\\n"", + "" s =20, lw=0.2)"" + ] + } + ], + ""metadata"": { + ""kernelspec"": { + ""display_name"": ""Python 2"", + ""language"": ""python"", + ""name"": ""python2"" + }, + ""language_info"": { + ""codemirror_mode"": { + ""name"": ""ipython"", + ""version"": 2 + }, + ""file_extension"": "".py"", + ""mimetype"": ""text/x-python"", + ""name"": ""python"", + ""nbconvert_exporter"": ""python"", + ""pygments_lexer"": ""ipython2"", + ""version"": ""2.7.12"" + }, + ""toc"": { + ""toc_cell"": true, + ""toc_number_sections"": true, + ""toc_section_display"": ""none"", + ""toc_threshold"": 6, + ""toc_window_display"": false + }, + ""toc_position"": { + ""height"": ""432px"", + ""left"": ""880.734375px"", + ""right"": ""20px"", + ""top"": ""120px"", + ""width"": ""467px"" + } + }, + ""nbformat"": 4, + ""nbformat_minor"": 0 +} +","Unknown" +"Pseudotime analysis","linnarsson-lab/ipynb-lamanno2016","backSPIN.py",".py","37182","940","#!/usr/bin/env python + +# Copyright (c) 2015, Amit Zeisel, Gioele La Manno and Sten Linnarsson +# All rights reserved. +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: +# +# * Redistributions of source code must retain the above copyright notice, this +# list of conditions and the following disclaimer. +# +# * Redistributions in binary form must reproduce the above copyright notice, +# this list of conditions and the following disclaimer in the documentation +# and/or other materials provided with the distribution. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS ""AS IS"" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +# This .py file can be used as a library or a command-line version of BackSPIN, +# This version of BackSPIN was implemented by Gioele La Manno. +# The BackSPIN biclustering algorithm was developed by Amit Zeisel and is described +# in Zeisel et al. Cell types in the mouse cortex and hippocampus revealed by +# single-cell RNA-seq Science 2015 (PMID: 25700174, doi: 10.1126/science.aaa1934). +# +# Building using pyinstaller: +# pyinstaller -F backSPIN.py -n backspin-mac-64-bit +# + +from __future__ import division +from numpy import * +import getopt +import sys +import os +from Cef_tools import CEF_obj + +class Results: + pass + +def calc_loccenter(x, lin_log_flag): + M,N = x.shape + if N==1 and M>1: + x = x.T + M,N = x.shape + loc_center = zeros(M) + min_x = x.min(1) + x = x - min_x[:,newaxis] + for i in range(M): + ind = where(x[i,:]>0)[0] + if len(ind) != 0: + if lin_log_flag == 1: + w = x[i,ind]/sum(x[i,ind], 0) + else: + w = (2**x[i,ind])/sum(2**x[i,ind], 0) + loc_center[i] = sum(w*ind, 0) + else: + loc_center[i] = 0 + + return loc_center + +def _calc_weights_matrix(mat_size, wid): + '''Calculate Weight Matrix + Parameters + ---------- + mat_size: int + dimension of the distance matrix + wid: int + parameter that controls the width of the neighbourood + Returns + ------- + weights_mat: 2-D array + the weights matrix to multiply with the distance matrix + + ''' + #calculate square distance from the diagonal + sqd = (arange(1,mat_size+1)[newaxis,:] - arange(1,mat_size+1)[:,newaxis])**2 + #make the distance relative to the mat_size + norm_sqd = sqd/wid + #evaluate a normal pdf + weights_mat = exp(-norm_sqd/mat_size) + #avoid useless precision that would slow down the matrix multiplication + weights_mat -= 1e-6 + weights_mat[weights_mat<0] = 0 + #normalize row and column sum + weights_mat /= sum(weights_mat,0)[newaxis,:] + weights_mat /= sum(weights_mat,1)[:, newaxis] + #fix asimmetries + weights_mat = (weights_mat + weights_mat.T) / 2. + return weights_mat + + +def _sort_neighbourhood( dist_matrix, wid ): + '''Perform a single iteration of SPIN + Parameters + ---------- + dist_matrix: 2-D array + distance matrix + wid: int + parameter that controls the width of the neighbourood + Returns + ------- + sorted_ind: 1-D array + indexes that order the matrix + + ''' + assert wid > 0, 'Parameter wid < 0 is not allowed' + mat_size = dist_matrix.shape[0] + #assert mat_size>2, 'Matrix is too small to be sorted' + weights_mat = _calc_weights_matrix(mat_size, wid) + #Calculate the dot product (can be very slow for big mat_size) + mismatch_score = dot(dist_matrix, weights_mat) + energy, target_permutation = mismatch_score.min(1), mismatch_score.argmin(1) + max_energy = max(energy) + #Avoid points that have the same target_permutation value + sort_score = target_permutation - 0.1 * sign( (mat_size/2 - target_permutation) ) * energy/max_energy + #sort_score = target_permutation - 0.1 * sign( 1-2*(int(1000*energy/max_energy) % 2) ) * energy/max_energy # Alternative + # Sorting the matrix + sorted_ind = sort_score.argsort(0)[::-1] + return sorted_ind + + +def sort_mat_by_neighborhood(dist_matrix, wid, times): + '''Perform several iterations of SPIN using a fixed wid parameter + Parameters + ---------- + dist_matrix: 2-D array + distance matrix + wid: int + parameter that controls the width of the neighbourood + times: int + number of repetitions + verbose: bool + print the progress + Returns + ------- + indexes: 1-D array + indexes that order the matrix + + ''' + # original indexes + indexes = arange(dist_matrix.shape[0]) + for i in range(times): + #sort the sitance matrix according the previous iteration + tmpmat = dist_matrix[indexes,:] + tmpmat = tmpmat[:,indexes] + sorted_ind = _sort_neighbourhood(tmpmat, wid); + #resort the original indexes + indexes = indexes[sorted_ind] + return indexes + + +def _generate_widlist(data, axis=1, step=0.6): + '''Generate a list of wid parameters to execute sort_mat_by_neighborhood + Parameters + ---------- + data: 2-D array + the data matrix + axis: int + the axis to take in consideration + step: float + the increment between two successive wid parameters + Returns + ------- + wid_list: list of int + list of wid parameters to run SPIN + + ''' + max_wid = data.shape[axis]*0.6 + new_wid = 1 + wid_list = [] + while new_wid < (1+step)*max_wid: + wid_list.append( new_wid ) + new_wid = int(ceil( new_wid + new_wid*(step) +1)) + return wid_list[::-1] + + + +def SPIN(dt, widlist=[10,1], iters=30, axis='both', verbose=False): + """"""Run the original SPIN algorithm + Parameters + ---------- + dt: 2-D array + the data matrix + widlist: float or list of int + If float is passed, it is used as step parameted of _generate_widlist, + and widlist is generated to run SPIN. + If list is passed it is used directly to run SPIN. + iters: int + number of repetitions for every wid in widlist + axis: int + the axis to take in consideration (must be 0, 1 or 'both') + step: float + the increment between two successive wid parameters + Returns + ------- + indexes: 1-D array (if axis in [0,1]) or tuple of 1-D array (if axis = 'both') + indexes that sort the data matrix + Notes + ----- + Typical usage + sorted_dt0 = SPIN(dt, iters=30, axis=0) + sorted_dt1 = SPIN(dt, iters=30, axis=1) + dt = dt[sorted_dt0,:] + dt = dt[:,sorted_dt1] + """""" + IXc = arange(dt.shape[1]) + IXr = arange(dt.shape[0]) + assert axis in ['both', 0,1], 'axis must be 0, 1 or \'both\' ' + #Sort both axis + if axis == 'both': + CCc = 1 - corrcoef(dt.T) + CCr = 1 - corrcoef(dt) + if type(widlist) != list: + widlist_r = _generate_widlist(dt, axis=0, step=widlist) + widlist_c = _generate_widlist(dt, axis=1, step=widlist) + if verbose: + print '\nSorting genes.' + print 'Neighbourood=', + for wid in widlist_r: + if verbose: + print ('%i, ' % wid), + sys.stdout.flush() + INDr = sort_mat_by_neighborhood(CCr, wid, iters) + CCr = CCr[INDr,:][:,INDr] + IXr = IXr[INDr] + if verbose: + print '\nSorting cells.' + print 'Neighbourood=', + for wid in widlist_c: + if verbose: + print ('%i, ' % wid), + sys.stdout.flush() + INDc = sort_mat_by_neighborhood(CCc, wid, iters) + CCc = CCc[:,INDc][INDc,:] + IXc= IXc[INDc] + return IXr, IXc + #Sort rows + elif axis == 0: + CCr = 1 - corrcoef(dt) + if type(widlist) != list: + widlist = _generate_widlist(dt, axis=0, step=widlist) + if verbose: + print '\nSorting genes.\nNeighbourood=', + for wid in widlist: + if verbose: + print '%i, ' % wid, + sys.stdout.flush() + INDr = sort_mat_by_neighborhood(CCr, wid, iters) + CCr = CCr[INDr,:][:,INDr] + IXr = IXr[INDr] + return IXr + #Sort columns + elif axis == 1: + CCc = 1 - corrcoef(dt.T) + if type(widlist) != list: + widlist = _generate_widlist(dt, axis=1, step=widlist) + if verbose: + print '\nSorting cells.\nNeighbourood=', + for wid in widlist: + if verbose: + print '%i, ' % wid, + sys.stdout.flush() + INDc = sort_mat_by_neighborhood(CCc, wid, iters) + CCc = CCc[:,INDc][INDc,:] + IXc = IXc[INDc] + return IXc + + +def backSPIN(data, numLevels=2, first_run_iters=10, first_run_step=0.05, runs_iters=8 ,runs_step=0.25,\ + split_limit_g=2, split_limit_c=2, stop_const = 1.15, low_thrs=0.2, verbose=False): + '''Run the backSPIN algorithm + Parameters + ---------- + data: 2-D array + the data matrix, rows should be genes and columns single cells/samples + numLevels: int + the number of splits that will be tried + first_run_iters: float + the iterations of the preparatory SPIN + first_run_step: float + the step parameter passed to _generate_widlist for the preparatory SPIN + runs_iters: int + the iterations parameter passed to the _divide_to_2and_resort. + influences all the SPIN iterations except the first + runs_step: float + the step parameter passed to the _divide_to_2and_resort. + influences all the SPIN iterations except the first + wid: float + the wid of every iteration of the splitting and resorting + split_limit_g: int + If the number of specific genes in a subgroup is smaller than this number + splitting of that subgrup is not allowed + split_limit_c: int + If the number cells in a subgroup is smaller than this number splitting of + that subgrup is not allowed + stop_const: float + minimum score that a breaking point has to reach to be suitable for splitting + low_thrs: float + genes with average lower than this threshold are assigned to either of the + splitting group reling on genes that are higly correlated with them + + Returns + ------- + results: Result object + The results object contain the following attributes + genes_order: 1-D array + indexes (a permutation) sorting the genes + cells_order: 1-D array + indexes (a permutation) sorting the cells + genes_gr_level: 2-D array + for each depth level contains the cluster indexes for each gene + cells_gr_level: + for each depth level contains the cluster indexes for each cell + cells_gr_level_sc: + score of the splitting + genes_bor_level: + the border index between gene clusters + cells_bor_level: + the border index between cell clusters + + Notes + ----- + Typical usage + + ''' + assert numLevels>0, '0 is not an available depth for backSPIN, use SPIN instead' + #initialize some varaibles + genes_bor_level = [[] for i in range(numLevels)] + cells_bor_level = [[] for i in range(numLevels)] + N,M = data.shape + genes_order = arange(N) + cells_order = arange(M) + genes_gr_level = zeros((N,numLevels+1)) + cells_gr_level = zeros((M,numLevels+1)) + cells_gr_level_sc = zeros((M,numLevels+1)) + + # Do a Preparatory SPIN on cells + if verbose: + print '\nPreparatory SPIN' + ix1 = SPIN(data, widlist=_generate_widlist(data, axis=1, step=first_run_step), iters=first_run_iters, axis=1, verbose=verbose) + cells_order = cells_order[ix1] + + #For every level of depth DO: + for i in range(numLevels): + k=0 # initialize group id counter + # For every group generated at the parent level DO: + for j in range( len( set(cells_gr_level[:,i]) ) ): + # Extract the a data matrix of the genes at that level + g_settmp = nonzero(genes_gr_level[:,i]==j)[0] #indexes of genes in the level j + c_settmp = nonzero(cells_gr_level[:,i]==j)[0] #indexes of cells in the level j + datatmp = data[ ix_(genes_order[g_settmp], cells_order[c_settmp]) ] + # If we are not below the splitting limit for both genes and cells DO: + if (len(g_settmp)>split_limit_g) & (len(c_settmp)>split_limit_c): + # Split and SPINsort the two halves + if i == numLevels-1: + divided = _divide_to_2and_resort(datatmp, wid=runs_step, iters_spin=runs_iters,\ + stop_const=stop_const, low_thrs=low_thrs, sort_genes=True, verbose=verbose) + else: + divided = _divide_to_2and_resort(datatmp, wid=runs_step, iters_spin=runs_iters,\ + stop_const=stop_const, low_thrs=low_thrs, sort_genes=False,verbose=verbose) + # _divide_to_2and_resort retruns an empty array in gr2 if the splitting condition was not satisfied + if divided: + sorted_data_resort1, genes_resort1, cells_resort1,\ + gr1, gr2, genesgr1, genesgr2, score1, score2 = divided + # Resort from the previous level + genes_order[g_settmp] = genes_order[g_settmp[genes_resort1]] + cells_order[c_settmp] = cells_order[c_settmp[cells_resort1]] + # Assign a numerical identifier to the groups + genes_gr_level[g_settmp[genesgr1],i+1] = k + genes_gr_level[g_settmp[genesgr2],i+1] = k+1 + cells_gr_level[c_settmp[gr1],i+1] = k + cells_gr_level[c_settmp[gr2],i+1] = k+1 + # Not really clear what sc is + cells_gr_level_sc[c_settmp[gr1],i+1] = score1 + cells_gr_level_sc[c_settmp[gr2],i+1] = score2 + # Augment the counter of 2 becouse two groups were generated from one + k = k+2 + else: + # The split is not convenient, keep everithing the same + genes_gr_level[g_settmp,i+1] = k + # if it is the deepest level: perform gene sorting + if i == numLevels-1: + if (datatmp.shape[0] > 2 )and (datatmp.shape[1] > 2): + genes_resort1 = SPIN(datatmp, widlist=runs_step, iters=runs_iters, axis=0, verbose=verbose) + genes_order[g_settmp] = genes_order[g_settmp[genes_resort1]] + cells_gr_level[c_settmp,i+1] = k + cells_gr_level_sc[c_settmp,i+1] = cells_gr_level_sc[c_settmp,i] + # Augment of 1 becouse no new group was generated + k = k+1 + else: + # Below the splitting limit: the split is not convenient, keep everithing the same + genes_gr_level[g_settmp,i+1] = k + cells_gr_level[c_settmp,i+1] = k + cells_gr_level_sc[c_settmp,i+1] = cells_gr_level_sc[c_settmp,i] + # Augment of 1 becouse no new group was generated + k = k+1 + + # Find boundaries + genes_bor_level[i] = r_[0, nonzero(diff(genes_gr_level[:,i+1])>0)[0]+1, data.shape[0] ] + cells_bor_level[i] = r_[0, nonzero(diff(cells_gr_level[:,i+1])>0)[0]+1, data.shape[1] ] + + #dataout_sorted = data[ ix_(genes_order,cells_order) ] + + results = Results() + results.genes_order = genes_order + results.cells_order = cells_order + results.genes_gr_level = genes_gr_level + results.cells_gr_level = cells_gr_level + results.cells_gr_level_sc = cells_gr_level_sc + results.genes_bor_level = genes_bor_level + results.cells_bor_level = cells_bor_level + + return results + + + +def _divide_to_2and_resort(sorted_data, wid, iters_spin=8, stop_const = 1.15, low_thrs=0.2 , sort_genes=True, verbose=False): + '''Core function of backSPIN: split the datamatrix in two and resort the two halves + + Parameters + ---------- + sorted_data: 2-D array + the data matrix, rows should be genes and columns single cells/samples + wid: float + wid parameter to give to widlist parameter of th SPIN fucntion + stop_const: float + minimum score that a breaking point has to reach to be suitable for splitting + low_thrs: float + if the difference between the average expression of two groups is lower than threshold the algorythm + uses higly correlated gens to assign the gene to one of the two groups + verbose: bool + information about the split is printed + + Returns + ------- + ''' + + # Calculate correlation matrix for cells and genes + Rcells = corrcoef(sorted_data.T) + Rgenes = corrcoef(sorted_data) + # Look for the optimal breaking point + N = Rcells.shape[0] + score = zeros(N) + for i in range(2,N-2): + if i == 2: + tmp1 = sum( Rcells[:i,:i] ) + tmp2 = sum( Rcells[i:,i:] ) + score[i] = (tmp1+tmp2) / float(i**2 + (N-i)**2) + else: + tmp1 += sum(Rcells[i-1,:i]) + sum(Rcells[:i-1,i-1]); + tmp2 -= sum(Rcells[i-1:,i-1]) + sum(Rcells[i-1,i:]); + score[i] = (tmp1+tmp2) / float(i**2 + (N-i)**2) + + breakp1 = argmax(score) + score1 = Rcells[:breakp1,:breakp1] + score1 = triu(score1) + score1 = mean( score1[score1 != 0] ) + score2 = Rcells[breakp1:,breakp1:] + score2 = triu(score2) + score2 = mean( score2[score2 != 0] ) + avg_tot = triu(Rcells) + avg_tot = mean( avg_tot[avg_tot != 0] ) + + # If it is convenient to break + if (max([score1,score2])/avg_tot) > stop_const: + # Divide in two groups + gr1 = arange(N)[:breakp1] + gr2 = arange(N)[breakp1:] + # and assign the genes into the two groups + mean_gr1 = sorted_data[:,gr1].mean(1) + mean_gr2 = sorted_data[:,gr2].mean(1) + concat_loccenter_gr1 = c_[ calc_loccenter(sorted_data[:,gr1], 2), calc_loccenter(sorted_data[:,gr1][...,::-1], 2) ] + concat_loccenter_gr2 = c_[ calc_loccenter(sorted_data[:,gr2], 2), calc_loccenter(sorted_data[:,gr2][...,::-1], 2) ] + center_gr1, flip_flag1 = concat_loccenter_gr1.min(1), concat_loccenter_gr1.argmin(1) + center_gr2, flip_flag2 = concat_loccenter_gr2.max(1), concat_loccenter_gr2.argmax(1) + sorted_data_tmp = array( sorted_data ) + sorted_data_tmp[ix_(flip_flag1==1,gr1)] = sorted_data[ix_(flip_flag1==1,gr1)][...,::-1] + sorted_data_tmp[ix_(flip_flag2==1,gr2)] = sorted_data[ix_(flip_flag2==1,gr2)][...,::-1] + loc_center = calc_loccenter(sorted_data_tmp, 2) + + imax = zeros(loc_center.shape) + imax[loc_center<=breakp1] = 1 + imax[loc_center>breakp1] = 2 + + genesgr1 = where(imax==1)[0] + genesgr2 = where(imax==2)[0] + if size(genesgr1) == 0: + IN = argmax(mean_gr1) + genesgr1 = array([IN]) + genesgr2 = setdiff1d(genesgr2, IN) + elif size(genesgr2) == 0: + IN = argmax(mean_gr2) + genesgr2 = array([IN]) + genesgr1 = setdiff1d(genesgr1, IN) + + if verbose: + print '\nSplitting (%i, %i) ' % sorted_data.shape + print 'in (%i,%i) ' % (genesgr1.shape[0],gr1.shape[0]) + print 'and (%i,%i)' % (genesgr2.shape[0],gr2.shape[0]), + sys.stdout.flush() + + # Data of group1 + datagr1 = sorted_data[ix_(genesgr1,gr1)] + # zero center + datagr1 = datagr1 - datagr1.mean(1)[:,newaxis] + # Resort group1 + if min( datagr1.shape ) > 1: + if sort_genes: + genesorder1,cellorder1 = SPIN(datagr1, widlist=wid, iters=iters_spin, axis='both', verbose=verbose) + else: + cellorder1 = SPIN(datagr1, widlist=wid, iters=iters_spin, axis=1, verbose=verbose) + genesorder1 = arange(datagr1.shape[0]) + elif len(genesgr1) == 1: + genesorder1 = 0 + cellorder1 = argsort( datagr1[0,:] ) + elif len(gr1) == 1: + cellorder1 = 0 + genesorder1 = argsort( datagr1[:,0] ) + + # Data of group2 + datagr2 = sorted_data[ix_(genesgr2,gr2)] + # zero center + datagr2 = datagr2 - datagr2.mean(1)[:,newaxis] + # Resort group2 + if min( datagr2.shape )>1: + if sort_genes: + genesorder2, cellorder2 = SPIN(datagr2, widlist=wid, iters=iters_spin, axis='both',verbose=verbose) + else: + cellorder2 = SPIN(datagr2, widlist=wid, iters=iters_spin, axis=1,verbose=verbose) + genesorder2 = arange(datagr2.shape[0]) + elif len(genesgr2) == 1: + genesorder2 = 0 + cellorder2 = argsort(datagr2[0,:]) + elif len(gr2) == 1: + cellorder2 = 0 + genesorder2 = argsort(datagr2[:,0]) + + # contcatenate cells and genes indexes + genes_resort1 = r_[genesgr1[genesorder1], genesgr2[genesorder2] ] + cells_resort1 = r_[gr1[cellorder1], gr2[cellorder2] ] + genesgr1 = arange(len(genesgr1)) + genesgr2 = arange(len(genesgr1), len(sorted_data[:,0])) + # resort + sorted_data_resort1 = sorted_data[ix_(genes_resort1,cells_resort1)] + + return sorted_data_resort1, genes_resort1, cells_resort1, gr1, gr2, genesgr1, genesgr2, score1, score2 + + else: + if verbose: + print 'Low splitting score was : %.4f' % (max([score1,score2])/avg_tot) + return False + + +def fit_CV(mu, cv, fit_method='Exp', svr_gamma=0.06, x0=[0.5,0.5], verbose=False): + '''Fits a noise model (CV vs mean) + Parameters + ---------- + mu: 1-D array + mean of the genes (raw counts) + cv: 1-D array + coefficient of variation for each gene + fit_method: string + allowed: 'SVR', 'Exp', 'binSVR', 'binExp' + default: 'SVR'(requires scikit learn) + SVR: uses Support vector regression to fit the noise model + Exp: Parametric fit to cv = mu^(-a) + b + bin: before fitting the distribution of mean is normalized to be + uniform by downsampling and resampling. + Returns + ------- + score: 1-D array + Score is the relative position with respect of the fitted curve + mu_linspace: 1-D array + x coordiantes to plot (min(log2(mu)) -> max(log2(mu))) + cv_fit: 1-D array + y=f(x) coordinates to plot + pars: tuple or None + + ''' + log2_m = log2(mu) + log2_cv = log2(cv) + + if len(mu)>1000 and 'bin' in fit_method: + #histogram with 30 bins + n,xi = histogram(log2_m,30) + med_n = percentile(n,50) + for i in range(0,len(n)): + # index of genes within the ith bin + ind = where( (log2_m >= xi[i]) & (log2_m < xi[i+1]) )[0] + if len(ind)>med_n: + #Downsample if count is more than median + ind = ind[random.permutation(len(ind))] + ind = ind[:len(ind)-med_n] + mask = ones(len(log2_m), dtype=bool) + mask[ind] = False + log2_m = log2_m[mask] + log2_cv = log2_cv[mask] + elif (around(med_n/len(ind))>1) and (len(ind)>5): + #Duplicate if count is less than median + log2_m = r_[ log2_m, tile(log2_m[ind], around(med_n/len(ind))-1) ] + log2_cv = r_[ log2_cv, tile(log2_cv[ind], around(med_n/len(ind))-1) ] + else: + if 'bin' in fit_method: + print 'More than 1000 input feature needed for bin correction.' + pass + + if 'SVR' in fit_method: + try: + from sklearn.svm import SVR + if svr_gamma == 'auto': + svr_gamma = 1000./len(mu) + #Fit the Support Vector Regression + clf = SVR(gamma=svr_gamma) + clf.fit(log2_m[:,newaxis], log2_cv) + fitted_fun = clf.predict + score = log2(cv) - fitted_fun(log2(mu)[:,newaxis]) + params = None + #The coordinates of the fitted curve + mu_linspace = linspace(min(log2_m),max(log2_m)) + cv_fit = fitted_fun(mu_linspace[:,newaxis]) + return score, mu_linspace, cv_fit , params + + except ImportError: + if verbose: + print 'SVR fit requires scikit-learn python library. Using exponential instead.' + if 'bin' in fit_method: + return fit_CV(mu, cv, fit_method='binExp', x0=x0) + else: + return fit_CV(mu, cv, fit_method='Exp', x0=x0) + elif 'Exp' in fit_method: + from scipy.optimize import minimize + #Define the objective function to fit (least squares) + fun = lambda x, log2_m, log2_cv: sum(abs( log2( (2.**log2_m)**(-x[0])+x[1]) - log2_cv )) + #Fit using Nelder-Mead algorythm + optimization = minimize(fun, x0, args=(log2_m,log2_cv), method='Nelder-Mead') + params = optimization.x + #The fitted function + fitted_fun = lambda log_mu: log2( (2.**log_mu)**(-params[0]) + params[1]) + # Score is the relative position with respect of the fitted curve + score = log2(cv) - fitted_fun(log2(mu)) + #The coordinates of the fitted curve + mu_linspace = linspace(min(log2_m),max(log2_m)) + cv_fit = fitted_fun(mu_linspace) + return score, mu_linspace, cv_fit , params + + + +def feature_selection(data,thrs, verbose=False): + if thrs>= data.shape[0]: + if verbose: + print ""Trying to select %i features but only %i genes available."" %( thrs, data.shape[0]) + print ""Skipping feature selection"" + return arange(data.shape[0]) + ix_genes = arange(data.shape[0]) + threeperK = int(ceil(3*data.shape[1]/1000.)) + zerotwoperK = int(floor(0.3*data.shape[1]/1000.)) + # is at least 1 molecule in 0.3% of thecells, is at least 2 molecules in 0.03% of the cells + condition = (sum(data>=1, 1)>= threeperK) & (sum(data>=2, 1)>=zerotwoperK) + ix_genes = ix_genes[condition] + + mu = data[ix_genes,:].mean(1) + sigma = data[ix_genes,:].std(1, ddof=1) + cv = sigma/mu + + try: + score, mu_linspace, cv_fit , params = fit_CV(mu,cv,fit_method='SVR', verbose=verbose) + except ImportError: + print ""WARNING: Feature selection was skipped becouse scipy is required. Install scipy to run feature selection."" + return arange(data.shape[0]) + + return ix_genes[argsort(score)[::-1]][:thrs] + +def usage_quick(): + + message ='''usage: backSPIN [-hbv] [-i inputfile] [-o outputfolder] [-d int] [-f int] [-t int] [-s float] [-T int] [-S float] [-g int] [-c int] [-k float] [-r float] + manual: backSPIN -h + ''' + print message + +def usage(): + + message=''' + backSPIN commandline tool + ------------------------- + + The options are as follows: + + -i [inputfile] + --input=[inputfile] + Path of the cef formatted tab delimited file. + Rows should be genes and columns single cells/samples. + For further information on the cef format visit: + https://github.com/linnarsson-lab/ceftools + + -o [outputfile] + --output=[outputfile] + The name of the file to which the output will be written + + -d [int] + Depth/Number of levels: The number of nested splits that will be tried by the algorithm + -t [int] + Number of the iterations used in the preparatory SPIN. + Defaults to 10 + -f [int] + Feature selection is performed before BackSPIN. Argument controls how many genes are seleceted. + Selection is based on expected noise (a curve fit to the CV-vs-mean plot). + -s [float] + Controls the decrease rate of the width parameter used in the preparatory SPIN. + Smaller values will increase the number of SPIN iterations and result in higher + precision in the first step but longer execution time. + Defaults to 0.1 + -T [int] + Number of the iterations used for every width parameter. + Does not apply on the first run (use -t instead) + Defaults to 8 + -S [float] + Controls the decrease rate of the width parameter. + Smaller values will increase the number of SPIN iterations and result in higher + precision but longer execution time. + Does not apply on the first run (use -s instead) + Defaults to 0.3 + -g [int] + Minimal number of genes that a group must contain for splitting to be allowed. + Defaults to 2 + -c [int] + Minimal number of cells that a group must contain for splitting to be allowed. + Defaults to 2 + -k [float] + Minimum score that a breaking point has to reach to be suitable for splitting. + Defaults to 1.15 + -r [float] + If the difference between the average expression of two groups is lower than threshold the algorythm + uses higly correlated genes to assign the gene to one of the two groups + Defaults to 0.2 + -b [axisvalue] + Run normal SPIN instead of backSPIN. + Normal spin accepts the parameters -T -S + An axis value 0 to only sort genes (rows), 1 to only sort cells (columns) or 'both' for both + must be passed + -v + Verbose. Print to the stdoutput extra details of what is happening + + ''' + + print message + + + +if __name__ == '__main__': + print + #defaults arguments + input_path = None + outfiles_path = None + numLevels=2 # -d + feature_fit = False # -f + feature_genes = 2000 + first_run_iters=10 # -t + first_run_step=0.1 # -s + runs_iters=8 # -T + runs_step=0.3 # -S + split_limit_g=2 # -g + split_limit_c=2 # -c + stop_const = 1.15 # -k + low_thrs=0.2 # -r + normal_spin = False #-b + normal_spin_axis = 'both' + verbose=False # -v + + optlist, args = getopt.gnu_getopt(sys.argv[1:], ""hvi:o:f:d:t:s:T:S:g:c:k:r:b:"", [""help"", ""input="",""output=""]) + + if optlist== [] and args == []: + usage_quick() + sys.exit() + for opt, a in optlist: + if opt in (""-h"", ""--help""): + usage() + sys.exit() + elif opt in ('-i', '--input'): + input_path = a + elif opt in (""-o"", ""--output""): + outfiles_path = a + elif opt == '-d': + numLevels = int(a) + elif opt == '-f': + feature_fit = True + if a != '': + feature_genes = int(a) + elif opt == '-t': + first_run_iters = int(a) + elif opt == '-s': + first_run_step = float(a) + elif opt == '-T': + runs_iters = int(a) + elif opt == '-S': + runs_step = float(a) + elif opt == '-g': + split_limit_g = int(a) + elif opt == '-c': + split_limit_c = int(a) + elif opt == '-k': + stop_const = float(a) + elif opt == '-r': + low_thrs = float(a) + elif opt == '-v': + verbose = True + elif opt == '-b': + normal_spin = True + if a != '': + if a == 'both': + normal_spin_axis = a + else: + normal_spin_axis = int(a) + else: + assert False, ""%s option is not supported"" % opt + + if input_path == None: + print 'No input file was provided.\nYou need to specify an input file\n(e.g. backSPIN -i path/to/your/file/foo.cef)\n' + sys.exit() + if outfiles_path == None: + print 'No output file was provided.\nYou need to specify an output file\n(e.g. backSPIN -o path/to/your/file/bar.cef)\n' + sys.exit() + + try: + if verbose: + print 'Loading file.' + input_cef = CEF_obj() + input_cef.readCEF(input_path) + + data = array(input_cef.matrix) + + if feature_fit: + if verbose: + print ""Performing feature selection"" + ix_features = feature_selection(data, feature_genes, verbose=verbose) + if verbose: + print ""Selected %i genes"" % len(ix_features) + data = data[ix_features, :] + input_cef.matrix = data.tolist() + input_cef.row_attr_values = atleast_2d( array( input_cef.row_attr_values ))[:,ix_features].tolist() + input_cef.update() + + data = log2(data+1) + data = data - data.mean(1)[:,newaxis] + if data.shape[0] <= 3 and data.shape[1] <= 3: + print 'Input file is not correctly formatted.\n' + sys.exit() + except Exception, err: + import traceback + print 'There was an error' + print traceback.format_exc() + print 'Error occurred in parsing the input file.' + print 'Please check that your input file is a correctly formatted cef file.\n' + sys.exit() + + if normal_spin == False: + + print 'backSPIN started\n----------------\n' + print 'Input file:\n%s\n' % input_path + print 'Output file:\n%s\n' % outfiles_path + print 'numLevels: %i\nfirst_run_iters: %i\nfirst_run_step: %.3f\nruns_iters: %i\nruns_step: %.3f\nsplit_limit_g: %i\nsplit_limit_c: %i\nstop_const: %.3f\nlow_thrs: %.3f\n' % (numLevels, first_run_iters, first_run_step, runs_iters,\ + runs_step, split_limit_g, split_limit_c, stop_const, low_thrs) + + + results = backSPIN(data, numLevels, first_run_iters, first_run_step, runs_iters, runs_step,\ + split_limit_g, split_limit_c, stop_const, low_thrs, verbose) + + sys.stdout.flush() + print '\nWriting output.\n' + + output_cef = CEF_obj() + + for h_name, h_val in zip( input_cef.header_names, input_cef.header_values): + output_cef.add_header(h_name, h_val ) + for c_name, c_val in zip( input_cef.col_attr_names, input_cef.col_attr_values): + output_cef.add_col_attr(c_name, array(c_val)[results.cells_order]) + for r_name, r_val in zip( input_cef.row_attr_names, input_cef.row_attr_values): + output_cef.add_row_attr(r_name, array(r_val)[results.genes_order]) + + for level, groups in enumerate( results.genes_gr_level.T ): + output_cef.add_row_attr('Level_%i_group' % level, [int(el) for el in groups]) + for level, groups in enumerate( results.cells_gr_level.T ): + output_cef.add_col_attr('Level_%i_group' % level, [int(el) for el in groups]) + + output_cef.set_matrix(array(input_cef.matrix)[results.genes_order,:][:,results.cells_order]) + if sum(type(i)==float for i in input_cef.matrix[0]) + sum(type(i)==float for i in input_cef.matrix[-1]) == 0: + fmt = '%i' + else: + fmt ='%.6g' + output_cef.writeCEF( outfiles_path, matrix_str_fmt=fmt ) + else: + + print 'normal SPIN started\n----------------\n' + print 'Input file:\n%s\n' % input_path + print 'Output file:\n%s\n' % outfiles_path + + results = SPIN(data, widlist=runs_step, iters=runs_iters, axis=normal_spin_axis, verbose=verbose) + + print '\nWriting output.\n' + + output_cef = CEF_obj() + + for h_name, h_val in zip( input_cef.header_names, input_cef.header_values): + output_cef.add_header(h_name, h_val ) + + if normal_spin_axis == 'both': + for c_name, c_val in zip( input_cef.col_attr_names, input_cef.col_attr_values): + output_cef.add_col_attr(c_name, array(c_val)[results[1]]) + for r_name, r_val in zip( input_cef.row_attr_names, input_cef.row_attr_values): + output_cef.add_row_attr(r_name, array(r_val)[results[0]]) + output_cef.set_matrix(array(input_cef.matrix)[results[0],:][:,results[1]]) + + if normal_spin_axis == 0: + for r_name, r_val in zip( input_cef.row_attr_names, input_cef.row_attr_values): + output_cef.add_row_attr(r_name, array(r_val)[results]) + output_cef.set_matrix(array(input_cef.matrix)[results,:]) + + if normal_spin_axis == 1: + for c_name, c_val in zip( input_cef.col_attr_names, input_cef.col_attr_values): + output_cef.add_col_attr(c_name, array(c_val)[results]) + output_cef.set_matrix(array(input_cef.matrix)[:,results]) + + + + output_cef.writeCEF( outfiles_path ) + + + + + + + + +","Python" +"Pseudotime analysis","linnarsson-lab/ipynb-lamanno2016","ipynb-lamanno2016-speciescomparison.ipynb",".ipynb","88578","396","{ + ""cells"": [ + { + ""cell_type"": ""markdown"", + ""metadata"": { + ""toc"": ""true"" + }, + ""source"": [ + ""# Table of Contents\n"", + ""

"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""# Import libraries"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 1, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [ + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Populating the interactive namespace from numpy and matplotlib\n"" + ] + } + ], + ""source"": [ + ""%pylab inline"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 2, + ""metadata"": { + ""code_folding"": [], + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""#imports\n"", + ""import pandas as pd\n"", + ""import cPickle as pickle\n"", + ""from scipy.spatial.distance import cdist, pdist, squareform"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""# Load data and annotations"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""## Load Homologene mappings"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 3, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""mouse2human_dict = pickle.load(open('data/Homologene_mouse2human_dict.pickle'))"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""## Load cell type pattern and median tables"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 4, + ""metadata"": { + ""collapsed"": true, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""pattern_df = pd.read_csv('data/Human_binary.tsv',sep='\\t', index_col=0)\n"", + ""pattern_df_m = pd.read_csv('data/Mouse_binary.tsv',sep='\\t', index_col=0)\n"", + ""median_df = pd.read_csv('data/Human_medians.tsv',sep='\\t', index_col=0)\n"", + ""median_df_m = pd.read_csv('data/Mouse_medians.tsv',sep='\\t', index_col=0)"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""## Restrict the genes to the one that have a clear homologue"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 5, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""# For easier handling convert the mouse name in the human name\n"", + ""mouse_translable = [i for i in pattern_df_m.index if i in mouse2human_dict]\n"", + ""pattern_df_m = pattern_df_m.ix[mouse_translable,:]\n"", + ""median_df_m = median_df_m.ix[mouse_translable,:]\n"", + ""pattern_df_m.index = [mouse2human_dict[i] for i in pattern_df_m.index]\n"", + ""median_df_m.index = [mouse2human_dict[i] for i in median_df_m.index]"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 6, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""# Take genes that have an homologue\n"", + ""common_genes = intersect1d(pattern_df_m.index, pattern_df.index)\n"", + ""pattern_df_m = pattern_df_m.ix[common_genes, :]\n"", + ""pattern_df = pattern_df.ix[common_genes, :]\n"", + ""median_df_m = median_df_m.ix[common_genes, :]\n"", + ""median_df = median_df.ix[common_genes, :]"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""# Filters"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""## Filter the binary patterns so that the distance is useful"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 7, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [ + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""13100 are not significant as basal in human\n"", + ""12969 are not significant as basal in mouse\n"", + ""2805 are significant in at least one cell type in both mouse and human\n"", + ""13044 are significant in less than 6 cell types type in either mouse or human\n"", + ""5998 max median is more than 1.5 in either mouse or human\n"", + ""10261 min median is less than 0.25 in either mouse or human\n"", + ""1405 satisfy all the conditions above\n"" + ] + } + ], + ""source"": [ + ""# Exclude house-keeping and noisy genes genes\n"", + ""bool1 = (pattern_df.ix[:,0] == 0)\n"", + ""print '%s are not significant as basal in human' % sum(bool1)\n"", + ""bool2 = (pattern_df_m.ix[:,0] == 0)\n"", + ""print '%s are not significant as basal in mouse' % sum(bool2)\n"", + ""bool3 = (pattern_df.ix[:,1:].sum(1) > 0) & (pattern_df_m.ix[:,1:].sum(1) > 0) \n"", + ""print '%s are significant in at least one cell type in both mouse and human' % sum(bool3)\n"", + ""bool4 = (pattern_df.ix[:,1:].sum(1) < 6) | (pattern_df_m.ix[:,1:].sum(1) < 6) \n"", + ""print '%s are significant in less than 6 cell types type in either mouse or human' % sum(bool4)\n"", + ""\n"", + ""# Exclude gene whose signal-to-noise ration is low\n"", + ""bool5 = (median_df.ix[:,1:].max(1) > 1.5) | (median_df_m.ix[:,1:].max(1) > 1.5) \n"", + ""print '%s max median is more than 1.5 in either mouse or human' % sum(bool5)\n"", + ""bool6 = (median_df.ix[:,1:].min(1) < 0.25) | (median_df_m.ix[:,1:].min(1) < 0.25) \n"", + ""print '%s min median is less than 0.25 in either mouse or human' % sum(bool6)\n"", + ""\n"", + ""selected = (bool1 & bool2 & bool3 & bool4 & bool5 & bool6) \n"", + ""print '%s satisfy all the conditions above' % sum(selected)\n"", + ""\n"", + ""#Apply the filter\n"", + ""pattern_df_m = pattern_df_m.ix[selected,:]\n"", + ""pattern_df = pattern_df.ix[selected,:]\n"", + ""median_df_m = median_df_m.ix[selected,:]\n"", + ""median_df = median_df.ix[selected,:]"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""# Calculate correlation"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 8, + ""metadata"": { + ""collapsed"": true, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""# These values were calculated in ipynb-lamanno2016-bayesGLM.ipynb here, for simplicity is just loaded\n"", + ""total_relative = array([1. , 1.257, 1.076, 0.973, 1.001, 1.085, 0.868, 1.237, 1.255,\n"", + "" 1.056, 1.003, 0.887, 1.269, 0.868, 0.712, 0.847, 0.871, 0.788,\n"", + "" 0.738, 0.641, 1.078, 0.826, 0.816, 0.703, 1.423,1.228])\n"", + ""total_relative__m = array([ 1., 1.164, 0.852, 1.203, 0.935, 0.941, 1.019, 0.972, 0.927, 0.6496,\n"", + "" 0.629, 0.908, 0.645, 0.692, 0.9322, 0.838, 0.781, 1.205, 1.025,\n"", + "" 1.3181, 1.402, 0.875, 0.873,1.615, 1.3315, 1.450, 0.664])"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 9, + ""metadata"": { + ""collapsed"": true, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""# Normalize\n"", + ""median_df_m = (median_df_m/total_relative__m)\n"", + ""median_df = (median_df/ total_relative)\n"", + ""median_df_logn = log2(median_df+1).subtract( log2(median_df+1).mean(1), 'rows' )\n"", + ""median_df_m_logn = log2(median_df_m+1).subtract( log2(median_df_m+1).mean(1), 'rows' )"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 10, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""# Calculate Correlation\n"", + ""metric = 'correlation'\n"", + ""D = 1-cdist(median_df_m.ix[:,1:].T, median_df.ix[:,1:].T, metric) # this is faster and equivalent at np.corrcoeff"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""# Visualize by heat map"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 11, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [ + { + ""data"": { + ""image/png"": 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RMasI1E1s2pQ9m1e0djulNzMlJI/BlFRPX1PJFeHtgrvClmBLyDqYUX4C5gT7A\nrIpx24lginXKG5ByAeX6nvXFlKXH0va/Ft7xozAlKGNwNObO+zs2Ua413gmrn7SEDFZnLDnhCUyx\nGR/+34r1OftTk7X9mfB79fKlce+OxVeWpu5hDvetd+CoxuJAn8EmsB9j/ei/saWYzq7rXsT+rSQb\nelvdqFGYjgkv+P61HHMettp2HL6UiXN4aPS3JShrHjYAnI6Zj/+NzdKmYIN+pkKBu2MztDcJC5PW\nckzKLRenkvmgwDMZs5itx0za3yaSUo+Zqc+KId+uxpcqxvcdYpYQqIevKiJfTnxp7bk7NqtcEjqi\nraGd/F/0WKCX89XJ3RlzT/8M68TnY2njL2GujTjvyFAsgytlgdgSeP6KDXpZBRk3At8PMTfKn8L5\nDYp5Slq+WvjbYYP7nZhFJLXO55/JIea0jrYkmAUn52Ka1IwDYzAX30ZgQxbnF2F9+gZMybkKs3od\nGtreAuBnMbm6BDkuwJTMJ4H3sMndw9gYMhu4OstrG4VN4u+r7RhiGhjCe3YRptD9Egs/GRHe58eC\nrNOBhxvyfH0tuQQhIntgJsFtWK2HMlVdERbNvRtba+3MGDypxQ3/gAUbb8KKJj6MLd67rl6Cnfna\nq+qWsB7dnthLVBl+oxPWQR2MBSf+OrXuUB1c92IWpQ+w2fahWI2PHc7LZg05EemGxSgMwGYsR4S/\nfbCZ+Uvh+h/RGGv/7OJ8S7D4mZeAf6pqxsV7M/AtC1xPY51FHL52qrpVRK7BTPD/w6yHvTHLy0nA\nzap6Z+Sc+tpMm+ELC/hegE125gP3qur8yPe9MEvB6dj6VufUJ1tURmyy0geb8ByEres3CosdLMMs\nxj9R1Sn1cSXNJyJnYJWVhwSujtgA/xrWll/WHRe7zohGuN7Usjdb6vi+m6quTR0bp+9KtZtszskg\nX3tgS+iXd2gTYSH3r2ExkrdmwdsXy+g8AbMU9cQUxkpMYbxVaxY1z0berph7+kBMMRkW+G9Q1fuz\n4PkT5o1QLGD8MeA5DYvL5yDXKVjSRn/gp8CdamuQDsL6xU2q+lAu3IBbmJLesMbzKmYOvBcrpLUQ\nMw0eniXXWGyhxJvD+WuwWe9kbNmRPbLk+yc22yjDBvjvhEbUJ+24utxJPTALxqnYy90uXN9sEqic\nS43fPi80+JMwd+ELmLn16tT3rZjvp40g3/PZ8FET4zCdtIDVwP07zJ0RKx25LfFh8YKrMRfKKuyd\nPZq04OJW/buNAAAgAElEQVS0c3JeAxKb7JyNufwrgZNz5WoIH2aBGIopTt/CrEHvhftQgVmyv08D\nlwDKRb5oe8esiCkXWiGmgA3F+sEridGnZuAbgSmOx2L9a8b4mZh8h2CJO7HDJqLtKvCOxQp+jiO3\nivWCZa4dFjh6Rr7bE5sExM76DOftjcVpXYrFwC6kpnDvE+RYUBNTEl/HrFeJVE1XdQtTo0BE+mCK\nzqmYWfYVbHY/qQGc+VgjHYk12MMx19/amOe3w2IBrsCUpULsBeqFKWMfAI+q6rP1cJyDZYQURPYd\nilW77pLDZUW5O2O1PT7EZo4fA+Vqq2J3DPLOU9VVcWZzzpcI32QsIPTJtO8KMQvM8ao6xfm2H1eC\nuaovwxSEfthSJfMw17UGS0IegKatnh4HIlKAWYP3wwaV51R1poh0wQafuZrBWtWYfBHeDlixydMw\nxf3I8FWPuH1WEvKFZ3I25p7qjFm/+mNWKrCBvjOmDCg28atTvnr4uoZD9mpEvmpMScnm/g3CrHOj\nMeX1FVWdHb6La0lLeTz6Y7F7Z2Bej7cC58vYxOAwbFKWVbuOyhHGzhLMCnsc8CmN4VGpxSLXDvN+\nXIdZ22/GMrYXZCPbTkhK82qrGzWz032wzLMbgf/DTPK5BLCllNiOWGfzF8ylcjw1ft8uxC9PkDrn\nbGwwHZf2/XepWX+nGht067IwPQr8JSVD5Py3G3D/ogHz72LuzLmYKf9eTME7iixryzhfbnwR3pQC\n9i5pMTvY7Hm98+3EcQ3wZtq+y4GK8H9OWYuRd/gYLA6qClPMJmAxOBmznBqTL427G6YoXYFluM0M\n/P+Ly5ukfJjbqRoLQ/g3Nri/gg38H2BK2DexvvZTLZzvhAbcv7ewDMFJZJEFGHhSVdVvwLIISzEF\nZBE2MavG3M9vxuRL9VldsZIdL2Jej88TSRQixuoERGpcYWPwYGx8LMAMDOOwcJhtwK9ybdfbf6Oh\nBG19o0ZhegCrj1SBKSaTsAC027AA4Vhr10T4rg6N8O7QIH+AFb27Mkv5Ui/Pn4EHw//tqXHf7IMN\nFPtjsUnTgUPr4FpJWoos5n6MVfgsg3wXYlkY38esc9eEjqMam/n/k3jp0s7XAL7AlerQ9sMU6Vex\nVPpx1GQT3Rv9fedTsAH9+vB/6v26A/hPru9H2jN+B/gb5kLqg1ltfoHN9jOmmjcGH2ZBz8OsSL/C\nBuZt1GTLXoxZTLJJBElSvgsxF+kawlJE1BRgfBKLccnmWbRovizu37ez4EuNSZOBb4b/b6BmRYO7\nsMlZ3El8iu8WbLy8HstavArzgtxJ/AzS2zFl8wPMpfc2piBWYuPwGiyovzKba67z9xpK4Nt2a9A2\nLAOhKxYI9y1MEXkVU3xiZ1+ETmglYc05LLD4SGyWsZ4sFzYMHN8MDWinOIzwYn0n/P8G8L1ajulD\nTZrvPzAr2oDwsg+LHJcKrowrV2qwWgqcU8v3DwaZHsDity5zvsbji5z3DaxkwYGYspWqYF8ROrpB\n4bi48VWtng/L5hqatm8mkTUDc92wjKEl1LIUEuZ2eIkssseS4MMCiO/EqtGXY33dXVjMVklcWRr7\nerF0/XuDjL+kRiFZCXwh+h61Br5GbC+TgC+Hz78N70w+Njbdgblb4/J1wJSZE8LnpaHdHBT+vzAG\nR7/wjm7Exrb/YePcSMwAsB8Wu7UXpjRmLHOT8TcbStCWN2oGq9GYWXGndGPMR75TYbM6+FKa9wmY\nbx4sKHENIcAOC0C9NwsZ8yNyTMIKv10WGlB7LH13GTUDwoLa5MWUo1uoKSS3LLzQ1VgneTpZLLmR\nxr035kY6NHUfIvfiGCxuAcxq8gQZAimdr8F8vbBOa3Davv7kMBC2BT7MTVGNlXj4Zni/OmNlI/aM\nHJdNqvSxmMVwDBb4ew9hXTF2DOg9inguw6T5zgvXvBoLsD2LWtLoQ3uMY+lLVL7Ub4e//bEFdzdj\nVvQbMY9AVrXnWjJfY9y/tGf4C0wpLsTGgtTi06XAspg8qTHzOGrGuNGhDe0RPv+YGIuOBzn+GWR6\nA7PIPYyV8DgVi4XKuCxNVvchSbK2tkUa+6GY++3chHg/GRpAT8wVF11L6DrgtRgcO2V7YGmff6Um\ng2crpvh8A0vhvZ0Qb1EP726hgX8uvOBPYsX5VmDBrROJUX08jbN7aPgvkqZ0YVkdq8L/xwJLnK9x\n+CLteTgWT1Hrkg7Er2LeZviwJIwnMJfAovBezcNmv+eRQ1HY8O5XYy6GaYHzIyxZIxVX0hvLrIzT\nJyTNdzAWcvAQltk5OVz/A1hs4yFkMatPWr56ZE6tTlBGTSHMXItNthi+xr5/2OT9Nmwy8C2srz8W\ns+y8kSXXAeE6B4S2Eh3jrgHeyoKrBzZm/ghTmCZisVVvYF6eX5BhfcDYv5UESVvfME1+A2YC/QVW\nFK2QLKp6R7gEc+s9j2nKHwDXhu/GYDEC18bgeR2bqdyJddgDIt8Nw2aDnwcODvtuwkzrp9TBl1fb\nZ0yBOhDzPf82vKBfy+Z6w98DMffFx5hSeAKWEj+VsFYUFtdVb/FP58udL/JMr8XcwEuwme4x5FA2\noq3wRXg6Ye6AL2CD0iOYq2BZ4H4dW3g3qwW0sUDW72IxGmswK8Qb2Ls9BZsExa5g3Ah83TFXykXY\nkhSpooYfYMkHD2VzzY0gX3rf1Sc85xWBN6tilbsAX6L3rxb+fMyqNAlT0MYDh2VxvmAlaR7DXJHT\nsCDy3bCJx3vEKIBJHaU6MG/KqZh78wnMa3Jerte7A3cSJG1tw0zte0Ue/uexwLUnQyfxATbj+jM2\nSGWM9q/lN0qpyUD4GJu1LccsCfXOVjH/8Kpw7kRMySoPf3+O1RzpnHZOF+oJTMeUuLPDi/c28Dg2\nIB+adty+2VwvERM+ltlwKzaz2hY6jKsxc/UNWDxJvb5t52sYX+D5LBbA+xQWyPwBVoTwL1gQdOxY\nhbbAhw0gO63LiFmID8YKDt6OFQ9dT5iUUH881ACsaO1Obm5MOfllkHMT9p5n6hMS5QvndcMG9/RV\n4vti9Zi+i1X/nkBw+TelfGkce2ADaXQdta9SU0G7z67M10jPtwNmCboCW14rn7RlcMLzz6mye2gn\nTwV5KjHlugpT7DJW02fHMekdTDn6CXBI2nHDiSxC3JCtwQRtccOKbNWapQEUY6mSfwgPMqOpMjTE\nA7FA6m8SZhSYMnYCFlD3cGgcGQu/YTPdCzDNfTk26/sBNgC8h7kKyrEFXm+kno478PXDZiXRjKtJ\n1CwV8odsOrDQ0L8eZHg7yJb+InajJq5qEJZue276cc7XcL5wzB5Y5/gpQoYK1oEfirls/4Ct0TQ1\nZhtsM3xYsGo15op7FPgStbvE+2KxGx1iyHd24NyCLdPyZ2wNup0svcRIN28EvmLMsl6NxTL+g9oT\nSgTYp6nlC8fuhxW4nIGVi3gkvCulkWN6YdWud2m+Rni+vbBxoxqbPHTD3G/VWGmBnxBjUdxUGwh/\n9wI+gQVkd0xrS5cFzsOItz5qomNS3C1RsrayhQb5w/B/yjdcUMtx7YmRaokpYKkKp5uxmUCtC6lG\nG2AM3gGh0b+GVZ3dK2ynYCmcjwH/zsSJafwTwwvYKbJ/L8yytgr4fdgXJ7jzKkxpex6znG3AAv1G\nYkHox0SvIYZ8ztcwvsMxhSoVwPs4aQs+R9rT4c6303G9sGJ+12LZRynrbjk2Sdlh4KqPK3JMD8zK\n/HnsHZ6I9Q2p9e1uIuKqIfMahknz/QcbqG7ErHALQxv8MmY1+DHm+h9EButSI8n3Wayfnoe5fe7H\nwgWqMRfpzwnWGOLV+2npfEnfv99i49DpBGsPpoR8iMUFVUe2m2PINxKrpVWNBYynMgHHYRO5Q8ii\nThnZjUmxs7Yz/m5SRG1lw6xBVQSfPDUm1PnAZ6KNEYsdqldhCnzLwwuyO6Z9T8UC6VKrQDdkpfmu\nWIDegtDYT6NG4+9PWFm6Lv6IfCeyY6ZF6rq7YKbjzcQo1BbOqcTiHXpgZt9LQyexIPzdiGUXrSdG\nkTXnazDfRCwO4XSsLswyzFKSylrJquBiW+OL8LbHlKdSLEbwDiwQf2N4FpOAo3Lg7UzNOoFfx9yH\n86kZsLLibCgf1idsAg6IfP5SOHc5NkGbSY375wvNIF85FqDcmciAibmQrsesYrfEedYtna+R7t8y\nLA4o2ufPxca0npgVdk5o55ncwXmYN+NFbKmXlAL2PSxsYBEWb3s98ayviY9Jse9tkmRtYcMW41yM\ndY4pxaM3NkANjBy3D9ZJ1muODnwL2bHC6VnAxgbImJIrOqvths083sLMvnGtVMdjs6B6ZznYLP1n\nmV5wLMZpa/S+YGb7zdisdD/MLXIRZvIfF46pq/q48zWcbzORNaCwemJVpLmV4rSZtsaX/p6l7d8N\nc8//L/xmNaEDj/Ge7DQrDnxF2ED7EWYdqyaeyysxPqzPWsKO8XP7hHt4AlbmYijWd3yDGPEjCct3\nEtanpmeMpgbUjkGuauqx5O8qfI1w/z6F9fnRKtq9sDEumjz0ATEyogPfHKAwsm8IZhl/FLMufTPI\ndkAMvkTHpGy2REja0ob5TVNurJQ77v8w601UQfkssDIm38Ph/9SiixcAU8P/OS/MWcfvXYjV95hK\nvErSP8RiOeqM48AG7G8C42PwXQpMSbve/bAZVfc0zji+bOdrOF+qraXM5Adg2TVZt722xlcLfxE2\nIN6FzZrnU2NNyGq2i83MdwPODOdPwhSvD7GCpF8gu+KDifBh1aP/ldYGv4q5fiTt2GwKOCYl3/Y+\ntS4Zwm+NB365q/M1wv37HmYRiipMI7AM8JRS1x4LDXgoBt/twAORz10x9145NZ6afMzV9/0YfImO\nSdls7XBki+Ox2TvYQ96KNdAXdMdFIE/Gov4z4ZNYNkkUZ6bO1fD040JExmKLPnbDXAOpquFDMUtY\nJZZN8HlMMXsowyKMM7G4hP7ARyLSXlW3RA9QVRWRUszylgmnY9lCYJlcYPEf76nqGhHJC5SKmfSd\nr3H5zsBmYmDtBqwsxjvZtr22yCcie2EB5J/E1pIciLlVJmCB0G+o6rORBUx3WCS0Ds6x2AB1PPa8\nFXNbjMeCY2dpZBHRTIuoJs2HBblfF/5PXctngOdT1wigqtVx7mkjyLdDn5p+bOoZiMhKaha5rQ8t\nmq8R7t9ELLNzKPCBiHRU1alYUcyUzFtEpAizEmVCd2CJiLRT1a1Y4cwzsbXdpoT20h4bT/eMwZf0\nmBQfSWpfrX3DYoyqsQDqvSP7l2ArmYPFG7XDFJN6LTgRvpsxf3OXCF9OvlfMz5xa0uFBLDB4Glby\n4J+YIvZX7IVNBbTWGRSHZfYspJbaT0QC3rFU9jNjyFeNuQZHUePLfp+wNEsO1+t8DePbis3YukX2\nTSSLtcnaON+T1GTI/RqztPQm4i6gJqYxbrD3CmoKF55NxH0YOW6nitpNxJfqs+7BrOglmGVjKXBi\nDvevseRL9ak7Fc7ErA955NZHtzS+RO9fOLYnNmb8vpbvUn1+f8zN9tkYfKdjmYD9w+eXMWtkqihn\nO8wNOTsmX6JjUlbtNUmy1r5h5vYXqMloK8OyRbYRSanF/L3VZFhAMPA9jwXrVgfeF8L/WWUNBL59\nMWVrITVlA86idv92Nuu9XRFkuh8r6teHmjipwVhhutfI4ALCZg/vYC7BVDbgHdggdjpmTs4mU8L5\nGsbXO3RkmwPnC5iZvYpQ0DTL9tfW+Ppjrryl2OD2B6zGWW0lBeIuh1KKufImYRX0nwOuxKw6sQe9\nRuTbF8vq+ghL4V4V2mE1ZrUeQHbVvZOWL71PXYT10ReG71IupQHk1ke3NL5E71+E96zw+y8C52CF\nmKOV8R/CSiFkfNZYTNuzWEzvaqy/+kLaMWdiY1asGn4kNCZlu6V+wBEDItIOU4b2xmZWh1Kzbk8v\nrBN5FHOHnaGqg7LkOwQrYz8U06LLsE79RWw9sGUZ+LpjKdODsJdveODKw16qNwPXa5pmwoxx7Zdj\nweJghTRXY66IAZjr56uq+noGjjxs0O+Fza6OweJHBmJK5yQsLft1LE5nnvM1Kl8nrN31DTyHYW1m\nT2rWB3samxHO0AydRRvk60bN+zYc6wsGYC6R2cAr2AD2Vtz3TUQ6Yu/uECwtf3T4uyf2nk3FBtyX\nVXVyDHdconyBsxPWxw3B6uocjPVbQ7AJ29tYcsmTqjq5ia83vU89GOunR2DZY2WYq2o3zMI+JIN8\nLZ0v8ecb4T4Vy1zrhWWlVWEhHnthE4Wvq+orMbm6Ym60XsBEVX1MRHph7+AhmJvyKVX9URy+wNng\nMSlbuMLUAIhIF+wB7Yu5SI7AAnBLgJ+p6tU58vXH6lYcFviGA9er6o1ZcPXAGk4B9jKOxQqE7Y51\ndr9S1Zti8Gx/wURkP2wmcEzgqMTcGfep6sdxZUvxYi/f7tiA8wlsVjQOG9D+rqrnx33Bna/BfB0D\nV1+snRwSOAuxGIQ/qurFmXjaMF8P7L0dhL1vYwJvT7J432rh3Rsr0leM9TFjMAV5GFYy4vfNzJdH\nzSA6DFu5/tDw90ZVvb6Z5dsNu/8DMWXiMGyyMYzc+uiWzpfI/YvE3JVgxVYPw55xan26B1V1ejZc\ntfx/FqbwdAbuxpZwitX3NcaYFOtaXGFqGESks6puDP93wDTo0diaXVkHnNXBNwKYpKqLMpwrUHug\neLA+DcSUucOx1aCfE5F8Vd2WfnwMOdthJtqqbM9NlznS+PMw02opsEBVJ2Yrn/Nlx1eXQiUiqQDM\ngdgAOEFVX3W+Hbkg+fctnVdqgmVTQeaDMQvZ/1S1sj6uxuCrhb8bsCF1XUEZLcAWel7ZAuRrij66\n2fga+/5FfiensSKNIw+bkGzArOLDgbmquqohvIE7kTGp3t9whSk+pCZ7oT8WNH0y5nd9EzNBTwMW\nqmqczIHE+SK8nbCAzBOxeKYJmLumQlXXh2PimN6j8l2JVQifgy358hpmPl4BrNAMmT+1yHcmVrNl\nETYj+AirrbEq25fS+RLjOzHwTcCCMitVdXU2XG2Yr8HvWzguvU84BesT3sD6hY+A+RrfxdfYfCdj\n7se3MFfcDOyerm0h19ui+ugm4Ev6/p0ake91rM9fTsw+vxa+T1PTXt7ESh6sw9pLxn6rscak2NAE\nA6Ja+0ZN3ZY/YKmNd2CZZ0uxALQ5WMxCrMj8RuBL1UT5CjZ4TsUCAVPrPb2AVRQ/JkH5xmPxWg2R\nL7VQ7PNYCf9jna9F8GXbXpyvAe9bzHeuIjznpPqYpPnmYAG+LVm+5uyjm5ov6ftXTnZ9fhz5kuSb\nkw1ftlvihG1hwzTk89P2FWJLkEwFLm5mvleAOyKfe2FLF/wzNKo/hf2xMqp2Afmcz/laDV84tqX3\nMc7nfK2GL/bvNgZpa9tCB3giNUXpHgNObUF8+ZgvuGf4/DT11O+gpt5TXUs6tHT5nM/5Wg1f+K6l\n9zHO53ythi/XzWOYYkBELsWKBc7CZpM9sUyErwJzVHVdM/OdgjWgx7GU6L2x7IivAos0+5iWli6f\n8zlfq+ELnC29j3E+52s1fLnCFaaYEJHOWMr2uWHrivlznwZexepALAKWxekwk+QLmQfDsYC6i7Fy\nAmA1l/6NVYKuxALhNjb19SYtn/M5X2vii/C22D7G+ZyvtfHlhKY2abWWDavhcirwCBY0ugzznY5q\nbj6gE5ZG+lssA2EzVpX1HmB0a5PP+ZyvNfFFeFtsH+N8ztfa+GL9ZmMRt6UNEKxo3SXA7i2QryeW\nfvkScGjqN1qxfM7nfK2GL3V+C+9jnM/5Wg1fXZu75BwOh8PhcDgyIK+5BXA4HA6Hw+Fo6XCFyeFw\nOBwOhyMDXGFyOBwOh8PhyABXmBwOh8PhcDgywBUmh8PhcDgcjgxwhcnhcDgcDocjA1xhcjgcDofD\n4ciAds0tQEtHhw4dOgCjEqScBpS0YL5Um9jqfM7nfM7nfM7XjHyQ/Bj3YVVVVVUuJ7rClBmjfnTb\nHe8NLCxqMFHFzBn87IpLzr3uN3feNygBvvKZM7jhmxede+ddP7mvqHhQg/kAnn3mNfoPHEBRyeBE\n+J57+lX6DhhCYXFhInzPj3+BffoXMaS4OBG+l8aPZ2BBv8Tke2H88xQU7JPo9fbpP4whRclc78vP\njmef/gOS43tuPAMLClq0fIMK+jG0uOHvG8CL459jQMG+ibaXUXsvpGRo70T4nn7xIwa220LJgJ7J\n8L1TQUGv3SjpnxDfuxXsNW05hd06ZTz2y2/OAuDvhwyt85jnF61mxFcOo2RIr2Tke3kaAztWJ3q9\ngw4eR0lh32T4np9E38kzKN5zt0T4xs9exuBBe1Dcu1sifM98vIghnzogsfY8bdZizr/i3nP/evNZ\n9yXxjKeVLeH87z20PzAhl/NdYYqBgYVFFI8akxjfoMIiSkaPTYyvqHgQY0uHJ8I1Y1o5Q0sGM3bc\niIT4ZjOosJDR45Ix0s2cPosBhcWMGpvM/Zs1fTqFxYMYNTaZ5ztr+kwKiwsSvd7+Q4sZOXZcInxl\nM6ZTMLSIEQnyDSlq2fINLR6cYHuZQWHxkETbS0nfakpHDch8cAxMm7WI4vZVlBYlo0BMq1xBSb8e\nlBbunQzf3JXsM389o3tmHvCXbNoCUO+xM9dupGRIL0pH9EtGvrIlFHfaluj1lhT2pXRMQTJ8Mxcw\naP58xvbungjf9OXrKe7djXH9d0+Gb/FaSob2Tqw9p1AypBelI/dNlDMXNGoMk4h8WUSqReTqWr67\nV0TuDv9fJyKv1sNzXeD5fS3f5YvIyvD9gLDvxRS3w+FwOBwOR0PRVEHf3xeRTDbJTIvabcNWJk7H\nkUDXGOdvh4hcIyIbI9sGEflj3PMdDofD4XC0LTSFwrQEmAfc0kCeScDuIrJ/2v7TgNez5BoO3ASM\nBsYAY4FrGyifw+FwOByOVoqsFSYRGRjcX18SkSkislZE7hSRQ0RkcrDYPCkiXcMpVcC3gLNF5OD6\nZBGRn4nIChGpFJEbREQi328EngFOTzvvVOCxLC+jEHhZVWeq6oywLc6Sw+FwOBwORxtBQyxM3wcu\nBS4ALgT+g1lpjsYsN19PHaiqzwD/A35TD98hQBFwXOD+duDfToMpRtsVJhEZC+wNPAtElatMKAQu\nC4pZmYj8RETaZ3G+w+FwOByONoSGKEzXqepLqvovYCFwt6o+qqpvAS+xc92EK4BRInJ+HXzLgC+q\n6gRVfQBTrr6adszjQLGIpHLeT8WUpY1xhRaRPljM0+Jw/g8wpe/WuBwOh8PhcDjaFhqiMH0U+b8K\nKI983gJ0jB6sqrOBXwE/FZHaij5MVdVoMal3gB2KC6nqSuBV4DNh1+nAo1nKvRQYqKrfUNUPgsJ3\nOfA1EcnPksvhcDgcDkcbQK51mBTYlLavOsZ5PwPOA66p5bv089sB62s57lHgsyLyT6wC9xNAjxi/\nDYCqbsOC0KOYArQH9sAUKofD4XA0Au4rX8r9L34c+/h+/3mv3u//76G3uePGMxoqlsOREU1auFJV\nN4jI94C/Ax+wo5VqrIi0V9Ut4fMngA9roXkMc59dCLypqstEpAcxywqIyAXApapaGtm9P7BSVV1Z\ncjgcjkbEuYP25ubSgozHpRSl+WekJ0bX4N+VyzjgrIOSEs3hqBe5KkzZBFjvAFV9SEQuBo5gR4Wp\nO/BnEbkVS/P/OvDZWs6vEJEPgSuBq+qRqa+IHJu2bxnwAvBrEfkt8DdgIHAzHsPkcDgcDoejDjTE\nJVff50y4DFvLJXreC0AqRmkdcJWqPl3H+Y9imXj/rUeG48IWxTOqepKIfBpTki4AVgB3YXWZHA6H\nw+FwOHZC1gqTqlYA+Wn7Bqd9jmbC/b0Wjg+BDpHPN0S+vqKW428Abqjnc1lUJlU9OsM1vIqVMXA4\nHA6Hw+HIiKZaGsXhcDgcDodjl4UrTA6Hw+FwOBwZ4AqTw+FwOBwORwY0aVmBXRUVM2ckylOeEF+K\nZ8b08gxHxkdFxXyqSa5+Z+Wc+Wyt7pQcX0UlWxLkm1dRQb5sTYyvsqKSPNmS+cAs+Kqqd0uMb15F\nBduqs83RqIevsoL8vJyTZnfmawT52ucl93znVlQiEqfkXDxUVlTSY0Nyy1jOmbsCbZdc+5uzaC1U\nJ3e9cxatYd3a9BJ+9WPyytrK8Rkq11fRrWxJQ8XajjnzVqAdk71embkgOb7KpVQtr/t+ZIuK1RvJ\nX7w2Mb45K9aTPyu59jwtcE1L6Bk3lEdUk+ucWiM6dOiwGzsv+NsQjAeOb8F8HbASDZudz/mcz/ma\nke/X4e+3EuKLA+drWXyQ/Bj3aFVVVU5apytMGdChQ4f9rr39D+8VFBY1mGvOzBncePnF5/7kd3fe\nN6iwuMF85TOnc/WlF5376z/99r6hRYUN5gN48dnnKSjoQ2HxkET4nh//En37D6WwOBn5Xhj/PH36\nD2VIUcPvH8DLz45n0KB9EpVvyOC9KCoenPngGHjumVfovW8JQ4sb3v4AXhz/HHvvO5jBRcnwvfrc\nswwsGJjo8+g7YGBi8r3y3LP0H5CgfM+NZ2BBQaLXO6ZgFcOK902E76nxEylov5GSgj0T4Xv6jTIK\n+vVMlm/YQEqG9k6G78WPKBiyLyVFfZPhe24SBd2gZNDeyfC9NoOC0rEJPt8JDFw7n5J9Yy9uUS+e\nnjCPQaOHUDI4oet9dQYFIwoZltDz+HjGAs6/5M5zf17Q574hnTrUe+wls+YDcMfQfnUeU7apih/M\nWbR/VVXVhFzkcZdcDBQUFlEyemxifIMKixk+Jjm+oUWFjB43OhGuWdNnUljcnzHjRiXCN3P6LAYV\nFjJq7JhE+GZNn0n/ocWMHDsuEb6yGdMpLB6YqHxFxX0YM25kInwzppfRf0gRo8Ym015mTZ9B3yFF\njBiTzP2bPWM6Q4qSfR4FhcnLNyJB+ZK+3mHFXSkdm4yCPW36PIo7rKO0ZJ9k+OYso6RgT0qHJcg3\ntA9d71cAACAASURBVDelowYkwzdrESVFfSkdMyjzwXH4ZiygZHcoHZ7MgD+tfCnDiveldNzQZPim\nz6No1TpKh+yVDN+8VZQM3pvSEXUrGVnxzV7KsASfRwpDOnVgxG71h2Is3boNIONxDUGzBH2LyMUi\n8r6IrBeRFSLyooh8Ju2Yl0SkWkQ+V8v5+4XvZofP5eFzbds2ETlCRK4Ln8+the9VEbm28a7Y4XA4\nHA7HrowmV5hE5C9YVe17gIMx3+Q7wL9E5AeRQxXYSu3xQ6cB2yKfjwCGhu232OK6Q8LnQuDtyLE3\niUiXRC7G4XA4HA5Hm0CTuuTCkiRfAo5U1TcjX70nIiuB60XkXlWdH/a/AZwoIvmqGlWQTgNew9aB\nQ1XnRn5jBbBVVXdIHRMRsLXregE/Aq5O9OIcDofD4XC0WjTYwiQiA4Or60siMkVE1orInSJyiIhM\nFpGNIvKkiHTDFtR9Kk1ZSuE2YA22vlsKbwBbgO1LnYhIATAceCIHcVcA1wDfEZGBOZzvcDgcDoej\nDSJJl9z3gUsxhedC4D/AtZiyMwpTlg7CFtfdCaq6GZgMRNNjtgKPYxalFE4LHCtzEVJV/wh8DPwq\nl/MdDofD4XC0PSSpMF2nqi+p6r+AhcDdqvqoqr4FvAyUAD0xK09dWAak5zc+xs4K02MNlPUy4AwR\nOaqBPA6Hw+FwONoAkoxh+ijyfxUQjSHaAnQEVgP1FeDoB8xJ2zce2ENE9gNmA4dhVqxjcxVUVV8X\nkX8At4tIMvnBDofD4XA4Escls+ZvLxuQCcMnZFxJ439ATnUjkrIwKZBe7z69vrwCE7HMuJ0gIj2A\nUixuqeYk1U2Y0nQacDLwsarOabjIfA8YBFyUAJfD4XA4HI5WjKYuXPlX4H4RGa2qk9O+uxRTuh6o\n5bzHgO8C02m4Ow4AVV0gIj8BbsRciA6Hw+FwOFoY7hjaL2NBypRl6aP96l4lYOr6TXxuWuUpucqR\nlIUp1uqbqvog8F/gaRH5uoiMEpFxQXG5Cvg/VV1Vy6n/w2KgPk1CClPAbVhM1YgEOR0Oh8PhcLQy\nJOmSq+9zFJ8FbgEuwQpKPgeMBY5W1Udr41DVFVjdpaWqmtMaMLVBVbdgCztqBpkdDofD4XC0YTTY\nJaeqFUB+2r7BaZ/Pj/y/DbPs3JaB95i0z0enfb4LuKuW824Abshi/5Pp8jscDofD4XBE0SxryTkc\nDofD4XDsSnCFyeFwOBwOhyMDXGFyOBwOh8PhyICmLiuwK6LdnJkZC2HFQuDpWD5zeiJ8gafjrBkz\nE+EDmFNeTn5eVWJ85WUVbK2uPx00K77Z5VRVd0iMr2L2bNoneb2zy2mfvyE5vrJKNm/tlhjfnNmz\n2bQtude+srycdnmxkmRjoWL2bKoTTL+omF2OxEvijck3m/wEr3fO7DL2lHWJ8c2avQjttDkxvrJ5\nq0CSm1eXzV0JPRYnxzdnGXTokhxf+WLYPcHrrVwB0+clxjerbCHVG1cnxle2cC0ye2lyfJXLYfcF\nifF9PGMBQMeyTfH76Knr00tC1iDw5NwBusIUA6sWzmNZh4bHha9aaC/Osvlz6dSu4Z3usvlzAZhf\nWUa+JNNJLlk0j07tqsiXZJSIpYsWkJffnnzZmgjfkkXzad9O6ZSQUrJiyRzmd9hKHknJt4BO7beQ\nVNLlooVLqJbdyJP0OrC5YcmihXRoX03X9sncv9VL57C042Y65ycz6K9cUk6XDpvZrV0yfKuXlrOy\n0zqWdFieCN/aZTNZ0XkV3dotSoRvzdLpzNm6GTbUVk0leyyomE/+nl2gY8dk+FZtJL/jOmjfPhm+\nlRvIe38GujCZ0nfzZywgb+FC9OOuyfB9tBjdqlT16JwI39zKleQLsKA847FxsGBaBXmyFd2YTP+8\nYNEa8j8sQ5csSYRvftkipv7ufd7UzKrFP9rZKmlf3LpHnccsla3QDjaeNJx1/XrWy7fnLcsAWHfe\nAXUes3H+Srgj93fXFabM2HrkccczcmzDV1CZ8sH7/ObnP9181HHHJcb365t+uvnY449izLhRDeZL\nYdiw/owdl1xpqkGFoxidoHyFxUMT5RtaVMjocaMT4xs+fF/GjBuZGN/AoWMZNXZMYnzFJQWJ3r+i\n4sEt+vkOG94v0fY8vKQ/Y0uHJcY3uvdqSkcNSIyvZJ9ulI7olxzfoL0oHZ7TShK1orhHPqVFvRLj\nK9mjA6WD90yMb/D6bYzbp3tifKMPHUrpsH0S4yvSjZQW1K1kZIthRb0oHbpXYnyVT66jgMwK+5/E\nFJxPULcFfY5u5hFWbT7h8KKMbfCLJ2fuIyd+tIAb7ngh59nxLhfDJCLDROS/IrJYRFaKyKsiclrm\nMx0Oh8PhcDhywy6lMInInsCL2MK+nwaOB14A/iUi5zSA91URuTYZKR0Oh8PhcLQ27GouudOAFap6\neWTfuyLSG7gAuD8bMhFpp6rJBK84HA6Hw+FotWh2C5OIDBSRahH5kohMEZG1InKniBwiIpNFZKOI\nPCki3YAeQA8RSY/w+wlwdYRzPxF5LZw7U0QuiXz3VxH5k4j8DVgpIi8ChwLXicjdjX/FDofD4XA4\ndjW0JAvT94FvAHsDD2HWpIuBRcDDwIXA48CNwMci8gjwMvCKqs4D5gGISE/gaeAXwEXAfsDtIrJU\nVf8Vfutc4HbgQGAVtiDw65ji5XA4HA6Hw7EDmt3CFMF1qvpSUGoWAner6qOq+hbwElCiqtOBw7EF\nez8L/BNYJCLPi8iQwHMJ8LKq/lJVp6jq34HfAl+J/NY0Vf2hqn6sqguBTZirb1lTXKjD4XA4HI5d\nCy3JwvRR5P8qLLA7hS1geYqq+gFwPkBQkk4BvospT/sBY4DTRGRj5Pw8YHbk89SkhXc4HA6Hw9F6\n0VIUJsWsPFHsVKlPRH4NPK2qTwOoahnwaxH5EBgvIj2A9sB9wM9ghxK/0UpfCdYSdjgcDofDAfBi\n3hpezqKQ7VfaZyzqeUaDBEoQLUVhiotBwHlYjFIUW4BtmFI0DThYVbevFyIiV4V/f9oUQjocDofD\n0RZxdHV3zq/eO+NxKUXpb1sG1XnMHDZzffsF/6GFKE0tRWGKu07IL4AXRWQR8CCwFRgLXAXcp6ob\nReQO4DIRuQYL5j4c+BHwqXp4NwOFItJLVZOpEe9wOBwOh6PVoKUEfae7yGp1manqG8AngGGYMvQS\nFuT9K+Cr4Zi5wMnAmcBbWKbcBar6Wj2//yBwOuDFKx0Oh8PhcOyEZrcwqWoFkJ+2b3Da5/Mj/08A\nTsrA+RJmeartu/Nr2fcX4C+xhXY4HA6Hw9Gm0FIsTA6Hw+FwOBwtFq4wORwOh8PhcGSAK0wOh8Ph\ncDgcGdDsMUy7ANqVzZieCFHg6ThrxoxE+AJPx5nTyxLhAygvqyBftiTGN7usgq3VnRLjK59dTpJl\ntMpnl6OxkzRj8JXPoV1+VeYD4/KVVVK1bbfk+GaXk5fg8y2fXY7sXDKtAXxzEuNK8eXnbU6Mb3ZZ\nBfmS3Hrds2dX0mFtegm63FE2ZxlsTJCvcjlocu9bWeVytEf75PgWrIYNHZLjW7SGrVUJ9i8r19Oh\nPLkFJMrmrkDzEuyfF69FuiR4/xauYalsyaqLnkPd7+cC66s6Tpu9tMGyAQSenPUeV5hiYOLHM1mw\ntuGd7qJ5lQBMmTGb5Rsb3ukunGt8M8tWsHFrtwbzAcydv4b27QRNyPi4aOFyNusyNm5NZtCfO38V\nm6qXsKaqcyJ8FXNXgHRmW3UynfjC+cvJy+vAlm1dEuGbN38Va7fOZ3VVMq9qxdwlrK5qx4J1yXSS\n08uXs2ZLZxZvSEYpnlmxkpVVXViYEN/0OStZvaULC9YtT4hvPau2rGfu2rWJ8H1cvolOm1aAJKO0\nL1i8hrzVq9A1axLhm1+5mLw1q9Flydy/+eULkY75VC9ZlQjfgvKltBu2L7I+mf5qYZXAig1oQnOA\nhWuq6LhyM+y2IRG+BWuq2DS5krW7dcx47A0fzgXgulH96zxm9rK1VC9cT1X3zNV0fvTaLAB+dtjQ\nOo+pnL+KwVfuz8D+e2bk2/PHjwAw8Jpj6jxG5y6H256ifMpsdGnDlaY5ixv23rrClBlb9z/yWIaM\nHNNgorIpk3jwN7dsPvjoT1I0quF8Mz6cxN9u/fnmI487npFjxzWYL4VxI/dgzLiRifH1LjiYEQnK\nt++QQoaPqTUJMieMHjGUUWOT4ysqGcKosQ1/vin0HDicYaOTk6/XwCGJtL8UhhQVU5ygfP2HFlGc\noHyDhhYlKl9BYTElCfKdMLSc0lEDEuMr7lBFaVGvxPhKenWhdOheifEVdcqndGDPxPhGHDKE0pI+\nifENqlzKuD7dE+MbfVQJpSPrVlqyRe8lqxm9e+YJ6OUT5gBwZgblZfTAPRjbK/OE+6LnpgFwVnHv\neo8bc/QISutR0lL44mcOyHjMxA/ncsNtT20+Yb/+ibTBibOWceMD7+dsrWiWGCYR+YKIvC0i60Vk\nuYiMF5ETsjj/SBGpFpGc5ReR80TkYxHZJCKzROTLuXI5HA6Hw+Fo3WhyhUlErgb+jC2WeyBWZHI6\n8ISIfD4LqpwdzSJyKPBX4A5gf6wG090iklnldTgcDofD0ebQpC45ERkNXA+coqpPRb56M1iLbsIU\nqcbGefw/e/ceH2dZ5n/8c+XYllJAKC30kPPMpIccpoogKltOouuKB3yx8kO0rMK6oqsryu6K1rrK\nQQXxxLKiHJRFWRQLq9iWUyuKXRUopdAkTWYyk3PSNG3TJM3x+v3xTOp0mmQmmbtt2l7v1yuvzsxz\nP9+5n5lp5spzuG94UlW/F7u/TUTehTda+J+PwPMbY4wx5hiS9h4mEcmLHR67RkS2iUi3iNwjIueJ\nyFYR6RORJ0XkZLyC5OWEYmnUV4HPi0hmLHeuiDwqIl2xn9+KSOLZZh8SkVDssN7DInLgwHjcIbe+\n2CG3z8WtdxLwh4SsDsDdgXBjjDHGHDdc7mG6CfgkMBd4BLgc+ATQCjwKXA+8CfjjWCurahvwWNxD\n3wZOBS7CmzrlNuA/gUtiywX4AvAPwDDwX3iH1j4gIj68w37XA38CzgV+KCKbVfUPqvrh+OcWkTzg\n4thzGmOMMcYcxOU5TKtVdaOqPgq0APep6lpV3QxsAgLA6cCe0RVEZHFsD1Bv7N8+Ebkgtngz8GlV\nfUlV/4xXhMXPMafAKlV9TlV/B3waeG9sL1Mm8HlVfVBVt6vq/UB7wvqjfbgI+D2wCyuYjDHGGDMG\nl3uYXo+7PQCE4+4PArlADxB/vWszMHr9cDawlb9OxPsj4CoRuQEoAN5MXLGFt1dpS9z9P+HtdSpQ\n1ZdEZKaIfAUoAUqBebHlAIhILvAd4Dq8gu5qVY3PN8YYY4wB3BVMCiQOL5s49JcCrwEHBuRR1SGg\nBkBE/MQKGhHJAl4A9gE/wTsRfBnwuYMC9aDhxUa3pSd2td19eHuMfgm8DPxutGGsWNoILAE+oar/\nNZmNNcYYY46Gh8IdPBypSrn9wrUvTrj8I337uetMf7rdOiEc6YErfw48ISLnq2riSdfX89ehApYB\nFcAZqroLQEQuTmifKSIrVHX00/AWvAIrDNwO3K+qX4qtOxvvcOCofwOWAm9V1a1uNs0YY4w5vK4u\nmMs3KvOTthstlBrfu2LcNr9s6KSs8A2uunbcc1UwpTSuv6r+WkQeB34pIl/C24s0F7gG+BtgdPz9\nNrzDeNeJyK/xTvT+GJAhIvFDiH5PRG4EZgJ3Aner6oCINAErReQcYDbw77G8AhGZAXwAeBhvb1RR\nXN5eVXUzaY0xxhhjjhuuTvpOHERyokElrwDuAm4EXsQrXADOJ3YFnaq24O1xugF4Hm+P04V4h/1u\njbXvB+7BuwLvMeA54MuxZV8BGoBnY8/1PbyC6iYgH++cqI/jHQ6M/7k99U02xhhjzIki7T1Mqhrh\nrydqjz5WmHB/VdztYbwhAm4bI+7yuHYPAA8kLM+Luz06u+lPxuhTB/DOhIcfB9bEbs8e47mNMcYY\nY8Z0VOaSM8YYY4w5lljBZIwxxhiThBVMxhhjjDFJHOlhBY5JDXU7nOZEamuc5I3m1NVUO8kDaIxE\nOCl7t7O8aH0T+wYc9i8aYXAkcYivqWuKRpiV5S6vIRJFxF1eNBJlz4C7/6Yt0Qj7B4ed5bU2RMnK\nSOki2ZS0NEQZmeiSkcnmRaNO/ypsaYgi4m57WxsjVGmrs7z6aCeaM+gur3Uv9Pe7y2vbi+ZmJm+Y\nokhnD5mRzuQNU1TfspvBzh5nedHdfeTUtjvLq2/sYm934pCHE9u6e/ztaejpJ7erd1J5W9q7x10W\n2buf3Fp3n+eqWFZVo5vvpHRzRNXhb6fjUE5OzknAex1GbgAuncZ5OXjDRLj6LWl5lmd5lmd5Rz7v\nrti/n5mmealy/R23dmBgYEpVsRVMSeTk5Kz4/r3f/EuJvyh54yR2VNdxw8c/f/X37r3jIVd5n/r4\n567+rx//x0N+f0HaeQAb1v+eRXmL8AUOmXZvSp5a9zyL8xfjc7C9AE+v38TCvDxcvH4Az2zYRH7+\nfGfb+/S65ykomIfL92NhXp7T/i1YXEiJv9hJ3jMbnmNx/kKH78dGFi9e7LR/eflnO/28LMpb7HR7\nlywaotS/0Enebze8RH7+fEp9C9zkPfUy+QtOc5f39Bbyz5xBoHi+k7x1z71Gfkkepb6zneT99ulX\nyD8tk0DRmckbp2Ddpiryly9x+37MGiRQONdJ3rrna8g/+xQCBY7yfl9DfmWZs+3dXtPEquu+e/X9\n37rqoUBx+u9JVW07q258+I0DAwMTD38+Djskl4ISfxHllcsc5y13luf3F1ARLHWSVV0VpjhQSEXl\nEid5NVWh2PYudZNXXUeRz937saO6Dl9gMRWu+lcVwu9f5PT9KAoUOu1fYUkxZY4+fzuqaynxFzr7\nPO+orqXE57p/+U4/L8UO///uqK6ltGSIYIWbgriqupGAbwHBSjcFXVV1I4GieQQr3PwBUFXThH/h\nSQSXL0reOJW82lZKfWcTLHfVv2b8Z2YRXOrmC7+qrp1Sx++Hf84gwSVuCsSqUAeB/NPd5YU7nG7v\nqEDxmQSXufnMpOOwnPQtIn8vIv8nIj0i0ikiG0Tkskmsf4GIjIhI2v0TkSdE5Np0c4wxxhhz4nJe\nMInIzcC9eBPmngO8G6gGfhObFDdVaR0rFJEsEfkQkHKhZowxxhgzFqeH5ESkDG9akr9T1d/GLfpj\nbG/RrXiF1GElIj5gK5B9uJ/LGGOMMce/pHuYRCQvdnjsGhHZJiLdInKPiJwnIltFpE9EnhSRk/Em\nyH05oVga9VXg8yKSGcudKyKPikhX7Oe3IpJ4pueHRCQUO6z3sIicFtevD4vI9tjz14rI5+LWi+Dt\n3aoEDrnGMcXnNsYYY4wBJndI7ia8yXCvBa7Dm/D2y8BKYDneZLlvIjaBbiJVbVPVx2JzyQF8GzgV\nuAjvksEc4D/jVhHgC8A/AO/DK35+BAf2IN2LNx9dEPg6cJuInB97rn5V3aqqW4GBMbqT7LmNMcYY\nYw6YzCG51aq6EUBE7gIeUNW1sfubgABwOrBndAURWYx3/pLiFUAAl6nqJmAz8Iyqbo+1fQSvKBul\nwCpVfSm2/NPAuthepkzg86r6YKztdhH5GlAI/CGFbUn23MYYY4wxB0ymYHo97vYAEI67PwjkAj1A\n/GAJzUB57HY23nlFo8O8/gi4SkRuAAqANxNXbAHDwJa4+3/CK7oKVPUlEZkpIl8BSoBSYB5/LcqS\nSfbcxhhjzHHvff/8MK079znLmz9vI9Gae53lTSepHpJTIHE89sT5HxR4De/QmfeA6pCq1qhqDTBE\nrKARkSzgBeCjwMvA7XiH9w4OVI1/jtHirid2td3v8IqvXwIfAFpS2ZBUn9sYY4wxZpTrgSt/Djwh\nIuerauKhsev561ABy4AK4AxV3QUgIhcntM8UkRWqOjoi51uAfXh7tm4H7lfVL8XWnY13ODAVy/D2\nes2d4LmNMcaY496vvnOVs4ErH/7NK5S+9VwnWdNRqgVTSoe6VPXXIvI48EsR+RLenpy5wDXA3wCj\nsyS24R3Gu05Efg1cgneFXYaIxA/n+T0RuRGYCdwJ3K2qAyLSBKwUkXOA2cC/x/IKRCRXVSeax6YN\nb2/XmM+tqg2pbKsxxhhjThyTOSQ30f14V+BN0ncj8CLwcOzx84ldQaeqLXh7nG4Ansfb63Mh3mG/\nW2Pt+4F7gEfxrsh7jr8eOvsK0AA8G3uu7+EVVDfhnZM0bl9TfG5jjDHGmAOS7mFS1Qh/PVF79LHC\nhPur4m4P413uf9sYcZfHtXsAeCBheV7c7Vmxf38yRp86gHcmPPw4sGaMtodM0pTCcxtjjDHGHHBY\n5pIzxhhjjDmeWMFkjDHGGJOEFUzGGGOMMUm4HlbgeJS1o7rOSVAsJ9d1XnV1OFnTlIVCUUYc1tGh\nuih68ClwaeZFGFF3eeG6CBky5CwvVBch02VeKMqww/+moboIwyO5zvLCoXqQia4BmWReXQTU3ecv\nHKpHZDh5w1Tz6iKow/8f4boIs0fcvX61oVZU3PWvNtyKDrv7PNeGWtH9JznLq6vvgBmzneXVhlrR\n7hxneXWRnXBak7O82nArenLiEIhTVxftBHWZtwtq3G3vdi8rt6q23UleLGfKv1CtYErB5tc6CO+Z\nmXZOW2MHAFuqOmjrST+vpcHLq67bR++Qm4HKI419DMlu+od3OslrbO5mkH30Du12khdt7KNneBe7\n+092khdu2INmnORue5v2QcYe9g+5yYs27WPXwF7ae7uc5NXU99A10E5b7wwneTsiXewdmkVbz6zk\njVNQG9nNnkH3/WvvdfOluiOyhz1DHXT0utvek4e7YSBxXOCpaW5oJ7OzFRpCbvJej5LR1og2uvmj\nrGl7AyO/72XgFDfvb7RhN5kD/dB5yBzrU9JcF0Gq+xne7qaoa9rRQebMWdC710lec6SJoepG9p/s\n5vWLtO5BzzqZwVPdfJ4bIp1knjQL9u1yklff4I1EFN5ej+5O/3dgfUt635NWMCU39MYLLqJ4WXny\nlknUbnuFh+76Rv+5Ky/GX1aRdl711i38+I7b+ldeejHLK9Lv36gSfxFllWXO8vJKyllWUZm8YYrO\nKihmSXn6r9+o8mVFTl8/vz/f6et3yuIyAg4+L6MWFBY7+fyNyi/xO+1fXnGJ0/4V+tz2L7/E5zTv\nMn8zwfJ8Z3mBWYMEA/Od5fkXziHon+csrzC8k4p5c5zllb21mGCpm4EXAUp6uwnmneYsb+nKZQTL\nFjvLW9zbS8UZ7vaqLSk5g8qzT3GWt3zlUoLLFyVvmIKXXm1gzbd/23/ZuYVOPoMvVbfx1R+/MOVd\npoflHCYR+XsR+T8R6RGRThHZICKXTWL9C0RkRCT9fcsi8oSIXJvw2IiIXJhutjHGGGNODM4LJhG5\nGbgX+B/gHODdQDXwm9gccKlK68C+iGSJyIeAlAs1Y4wxxpixOD0kJyJleKNw/52q/jZu0R9je4tu\nxSukDisR8QFbgezD/VzGGGOMOf4l3cMkInmxQ1jXiMg2EekWkXtE5DwR2SoifSLypIicjDcn28sJ\nxdKorwKfF5HMWO5cEXlURLpiP78VkeKEdT4kIqHYYb2HReTAgWUR+bCIbI89f62IfC5uvQje3q1K\nYLyzAStjhw27ReR3IrI82WthjDHGmBPTZA7J3YQ3/9q1wHV487t9GVgJLMebn+1NxOaLS6Sqbar6\nWGzqFIBvA6cCFwGXAjnAf8atIsAXgH8A3odX/PwIDuxBuhdv+pUg8HXgNhE5P/Zc/aq6VVW3AgPj\nbM+/Ad/FmxR4J/BbEXF3vbUxxhhjjhuTKZhWq+pGVX0UaAHuU9W1qroZ2AQEgNOBA9fticji2B6g\n3ti/fSJyQWzxZuDTqvqSqv4ZeASIn/dNgVWq+pyq/g74NPDe2F6mTODzqvqgqm5X1fuB9oT1k7lF\nVf9bVV8EVgGnAe+axPrGGGOMOUFM5hym1+NuDwDxA3MMArlAD3Bm3OPNwOj12tl45xWNjjr4I+Aq\nEbkBKADeTFyxBQwDW+Lu/wlvr1OBqr4kIjNF5CtACVAKzIstT9VfRm+o6h4RqYn1wxhjjDHmIKkW\nTAokjqyWODyoAq/hHTrzHlAdAmoARMRPrKARkSzgBWAf8BO8E8GXAZ87KFAPGoJ0tK89savt7sM7\nrPdL4GXgdyluy3j9z8Ir+Iwxxphp6cGqFi6qbnOW99HuXirPtlN4U+F64MqfA0+IyPmq+oeEZdfz\n16EClgEVwBmqugtARC5OaJ8pIitih8wA3oJXYIWB24H7VfVLsXVn4x0OnIw3A7+PrX8G3p6qVyeZ\nYYwxxhwxHwmcxbffWuIk639q21niO8NJ1okg1YIppUNdqvprEXkc+KWIfAlvL9Jc4Bq8k6s7Y03b\n8A7jXScivwYuwbvCLkNE4ocI/Z6I3AjMBO4E7lbVARFpAlaKyDnAbODfY3kFIpKrqv0pdPdzIlKL\nd9jwy8AWVX0hle00xhhjzIkl1ZO+EweRnGhQySuAu4AbgReBh2OPn0/sCjpVbcHb43QD8DzeHqcL\n8Q773Rpr3w/cAzyKd0Xec3iFDXhjPTUAz8ae63t4BdVNHHoe0lh9Vbwr6/4D74T1GcBkBtU0xhhj\nzAkk6R4mVY3AwdPNq2phwv1VcbeH8S73v22MuMvj2j0APJCwPC/u9uhsgD8Zo08dwDsTHn4cWDNG\n20OunFM9MN39D8boozHGGGPMQQ7LXHLGGGOMMccTK5iMMcYYY5KwgskYY4wxJgnXwwocj7Ia6nY4\nCYrl5EZqa5zkxXJya6vd5AHUh8KIJA5RlUZeOMyQznCXF6pj/9Bw8oYpioZDnJTtLq8+FCZDhpzl\nhcP1nNLv7vVrCIcYGnH3/jbWhxCZzHixE2uqDzHxNSWT01gfIsNh/xrDIWdZo3lVGV3O8urCOzqY\nxwAAIABJREFUbTDb3etX19SFDrv7PNc17WZwV6+zvNDuPnLCO53l1UV3MTLU5ywv1NFNZm2Ls7y6\n+jb273b4+u3pI3PnPnd5XT1k1443fevkVXlZuVWRzmRNU8vzcqZc91jBlILO5kZyMtPfGdfZ3AhA\nS0M07az4nMZoPXLIOJxT097aTGZmBqibL5nW5jaGmOnsS7qlpYVBFYYcfSe0t7SQm53NwHC2k7ym\n5g4yMzP463UF6Wlt7mD/8EwyHX3n72prISsrkywHn2eA3e1tzMzJJtdRB/d0tDIjJ4sZWW76t7ej\njY6cbHIc9W93RyuzZ+SwM8fN+9vd2U49bdDv5ku6ubGdjBmg+xPHGZ6appYuNNTG0Ouzkrb9t/Xb\nAbj1HaXjtmmMdJJZsZjss05x0r/2vX3M6NwPMx29fnv6ycgYRPalMjJNci39w2Q37YZMN1+1ze37\nYFYG2Se7+fx1zMhgZraQlevm/0dbppDb1gO5e53k1bd5Y0nX17bAnu708zrSKw6tYEpu6NyVF+Nb\nXp68ZRI1r77CA3fe1n/uyovxl1WknVe9dQs/vuO2/pWXXERZZVnaeaOK/T6WV6S/vaPOLlrG0vLK\n5A1TdFZhCaUOXr9Ry0pLWVrhrn9Ll+Q5ff1Oz1tKabm77c0r8Tl9/Up8fpY47F9ByfTOKy0tZZnD\nz8vbzqoiuHxR8oYp8s8YJOg7M3nDFBV19VJ5dvIC57q1WwH4+/IFE7Zbdm4BQf88J30DKD1nidvX\nb3gPwaLJjoM8vtKLlhMsy3eWVxLJpHLRac7yliybR7DQ4fZeXE6w3M0sYy+9EmbN7b/qv6xyoZP3\n5KW6Tr768y1T3mV6zJ/DJCIXiMhIws+giIRjc80ltvvRGBn/ISLPHdGOG2OMMeaYcbzsYVLgArxR\nu8EbN+ptwD0i0qGq8eMtfVREfqCqL4+RYYwxxhhziGN+D1OcqKqGYj87VPU+vNHB35HQ7vfAd498\n94wxxhhzrJqWBZOI5MUOn10jIttEpFtE7hGR80Rkq4j0iciTsUl3J9IHDMTdV+DTwLki8veHbQOM\nMcYYc1yZlgVTnJvw5pu7FrgOb065LwMrgTK8+egOISIZInIJ3qS+jyQs3gbcDdwuIu6u1zbGGGPM\ncWu6F0yrVXWjqj4KtAD3qepaVd0MbAQCsXYCVMf2PPXhTdy7Hng6tm6iLwMzgX877FtgjDHGmGPe\ndC+YXo+7PQCE4+4PArlx998JlMf9/DPwbhFJPIcJVd0D/Dtwo4i4ux7VGGOMMcel6XyVnAKJo6+N\nN/qhAiFVjR8R8nURuR4I4u1tSvRjvMN8dwDVafbVGGOOS1f+7EVaJzGQ45w1v51w+fzn66j/5XXp\ndsuYI246F0wudAFjDuGsqioinwb+EPtxN/6/McYYY44r07lgmsxY7eO1VcYpmABUdbOIPAR8GO+c\nKGOMMXEe+dCKlEb6Ht2ztHf1O8dt8/NXmlj2zmXO+mbMkTSdz2FKHEhyooElx1u2C7g8ydVwNwF7\nk+QbY4wx5gQ2LfcwqWoEb7Tu+McKE+6virs75kyEqvreuLubxmqnqq3AqVPurDHGGGOOe9N5D5Mx\nxhhjzLRgBZMxxhhjTBJWMBljjDHGJDEtz2GabiK1NU5zXOfV1uxwkgfQEI2ik7pAcWLRSJT9wznO\n8hqjUfqH3Z2f3xKNMDPT3d8NjdEIOZkDyRumKBqJsnfA3X/T5mjE6dUNLdEIWe4+LjRFI+7CDlNe\njsvPSyRCVW+bs7z6xk40Z9hdXms3Q919k1rn5eY94y6L7O4lK7Ir3W4dUN+yB6lz/PqN7HOX17EP\n2dHiLi+6k+HWbnd5nT1kNI7/fk06r30fUtPsLK8qllXVtNtNXpo5omoXh00kJyfnJOC9SRumbgNw\n6TTOy8EbpiH1keosz/Isz/Lgrti/n3GUlwrLO77zwP133NqBgYGeqaxoBVMSOTk5K/71zh/8ZXGx\nL+2saG0Nt/3LJ6+++a67H8pzkBepreFrn/mnq7/zw+88VOIrTjsP4NmnnmNR3iJc5r3hbB+FvvS3\nF+D5p5/ijAWLKChxk/eHZ56iqLDAWf9+9/RTlBSeOa1fv7MWLaKgxO8k7/fPbGBRnrvX7/mnn+Ls\nxYspdNS/55/ZwOK8fKfvb0H+Ior8bvq3ccMGys9sJFA8z0neuudeZ3F3J/6z5jjJW7+1hYLFpxJY\n4OZC4nUvN5JffBaBvNPd5G0OUVC6mECRo9dv03byZmUQyHuDm7zNYQreVEGpb4GTvN8+vYV5L7yC\n75RZTvKebtyF77IlBPJOc5K3bnM9BedUOtve7TVNrPqn/7z6vi9c8lBgcfp9rIp2ce03nnrjwMDA\ni1NZ3w7JpWBxsQ/f8nJneXnFPvxlFc7ySnzFlFWWOcnaUb3Ded68Qh9Lyyud5IVqqjm7sISAo9ev\nfkcNhT63/SvxLZzWr19esY8l5W5ev/COauevX0GJ31n/QjXu+1fk97G8wk3/aqurCSwYJLh8sZO8\nqtpWSroGCea7+cKvat5LYMGpBIvcFDhVjbsJ5J1O0O+mwKmKdBIomkdw2UI3eXVt+E/OcNi/XZT6\nFhCsKHCTV9PEwtdmUXHGbCd5Nbt7CeSdRtA3Pbd3VGDxaQR9ZzrNnIrDetK3iHxEREZE5OYxlv1U\nRO6L3V4tIs9PkLM6lvODMZZlikhXbPni2GPPjWan0McvikhD6ltljDHGmBPNkbpK7iYROTtJm2TH\nBoeB94zx+AXA7BTWP4SIlAI3T2VdY4wxxpw4jkTB1A40At9MM+cV4FQReWPC45fjTZ47KSIiwL3A\n5jT7ZYwxxpjj3KQLJhHJix3+ukZEtolIt4jcIyLnichWEekTkSdFZPQg6wDeVRMfEpFzJ+qLiNwi\nIrtEJCoia2JFzag+YD2HXrH2HuDxyW4H8ClgP/DAFNY1xhhjzAkknT1MNwE3ANcC1wGPAV8GVgJl\nwPWjDVV1PfC/wHcnyDsP8AGXxLL/JZZ/IAavMDpQMIlIBTAXeApSHzxIRPLwDsVdn6ytMcYYY0w6\nBdNqVd2oqo8CLcB9qrpWVTcDG4FAQvvPAstFZBVj2wlcpaovqurP8IqrjyW0+TXgF5HRiXjfg1cs\nTW5kNfgv4FuqWjfJ9YwxxhhzAkpnWIHX424PAOG4+4NAbnxjVQ2JyB3A10XkF2Pkvaaq8UMk/wnv\nsFl8Rlfsarr3AXfg7W36zmQ6LSLXAPOAb40+NJn1jTHGmKPl/z39Om19g87y5r/WRP0vPu4s73g2\n1T1Minf+T7yRFNa7Ba+Y+tIYyxLXzwLGGo1zLXC5iCwClgO/SeF5460ElgI9ItIH/BBYKCK9IvK3\nk8wyxhhjzAngiA5cqaq9IvIF4EFgCwfvpaoQkWxVHS2d3wK8OkbM48CdeOdN/VFVd4rIKaQ+NMC/\nArfG3X8/8Gm84QmaUt4YY4wx5gj774uXOBu48n9q26n4fyucZJ0IplowTfkwlqo+IiKfAN7OwQXT\nHOBeEbkTqMA7IfuKMdaPiMirwOeBL07Qp7NF5KKEx3aq6ivAgdkaRaQFGFJVdzPYGmOMMea4MtWC\nKXFvzmQHfvwU8GLCes8CXcDzwD7gi6q6bpz11+JdiffEBH24JPYTbz3wrkn21RhjjDEnuEkXTKoa\nATITHitMuB9/JdyDY2S8ijer8ej9NXGLPztG+zXAmgnu18X3SVVXprApo20fHKuPxhhjjDGjjtTU\nKMYYY4wxxywrmIwxxhhjkrCCyRhjjDEmiSM6rMAxKitaW+MkKJaTG3GUF8vJ3VFT6yQPIByuB3E3\nlmc4XE/P0CxnedFwmP7hyV5jML6GcIgZWe7+boiEwszMShyibOoOx+s3ou5ev2g4RIa4e/2i4fCk\nryCZMK8+RGaGu89zJBQmO8NdDyOhEKf3tSVvmKK6+p2M9O51lhdq7yZjVrazvLrWvXBKp7u8xi7k\nVLevn85x97VY17QbqXE3Wk1tqJXePb3O8kLdfcyIdDnLq2va43R7t3tZuVVRN32M5Uz5DbaCKQX9\nHQ3sn5X+L93+jgYAetqidM9IO46etigALdFqcjL2pR8IdLZGyc4UMhh2ktfe0kJmZjazsvqd5O3p\niDIrR+nKddO/ns5mOmYpMzLd9G93R5TmGb1kZbgpmna2NpGRkcvMLDcj++5ub2RWzgizs4ec5O3t\naGJnLszKGkjeOAW72xuYlaPMynLTvz3tTXTkjjh9f1tyB8h29f62RanX/ZCVm7xxCpo7eqFjN7rX\nzfY2t+wj69STYYab7W3eN0RWfy6y38EvQKBlIIesLpAOJ3E0d0PmjGxkf07yxiloGcgkq3kPkuNm\ne5t39pC5bBEzF5zmJG9XdiaR/RlIr5tSoKVfyGrqQrLcvH71TV6hFOnaj8xMv1CMdKX3ObaCKbmh\nlZdezPKKirSDXt2yhW/felu/67xLLnsbFZVL0s4bVVCynLLK5e7yfEsoqyxzllfkD7C8otxZXom/\nyGn/li45i/LKZc7yFhcHHW9vsdO8Yn+J07wiv39av7/+QIHT/x9vWtxJsDzfWV5xjVK58FRneUvO\nySPoO9NZXumFy51ub2mZn2BlkbM8/8JZBMvynOWVrigjWFnsLM9/Uh/BJWc7ywssLyC4fJGzvNIV\n5c7ej5dermPN13/ef9lbiggGzko/r6qFNfc+P+W/xqbVOUwi8hERGRGRm8dY9lMRuS92e3VsTrnx\ncj4qIqlM1YKIbBWRC6fea2OMMcYc76ZVwRTnJhFJVkJPdCKBJlmOiMwQkc/gzStnjDHGGDOu6Vgw\ntQONwDcP1xPEpkzpBu44XM9hjDHGmOPHYS+YRCQvdpjtGhHZJiLdInKPiJwXOxzWJyJPisjobIID\nwGeAD4nIuRP1XURuEZFdIhIVkTUiKV/e9X/ACqCSNObFM8YYY8yJ4Uie9H0T8ElgLvAIcDnwCaAV\n+AXeZLs7AVR1vYj8L/Bd4Jxx8s6LrXsJ4AN+GFv/e8k6oqr7gK0AqddYxhhjjDlRHclDcqtVdaOq\nPgq0APep6lpV3QxsBAIJ7T8LLBeRVYxtJ3CVqr6oqj/DK64+dpj6bowxxpgT2JEsmF6Puz0AhOPu\nDwIHDUSiqiG8c4y+LiInj5H3mqrGD/7yJ6DAUV+NMcYYYw44UofkFEgcMSqVy/5vAT4MfGmMZYnr\nZwE9k++aMcYcP+77vwj3f2/cUVcm7dq9A9zts5FXjJnWA1eqaq+IfAF4ENjCwXupKkQkW1VHh0B+\nC/Dqke6jMcZMJ9e+OY/vfcDNwJ8/f6mBJW/zOcky5lh3pAqmKZ9ZraqPiMgngLdzcME0B7hXRO4E\nKvBOGr8i/jljwwck2qSqbuZdMMYYY8wJ4UgekpvofjKfAl5MWO9ZoAt4HtgHfFFV1yU8x4Yxss7C\nG+tpqn0xxhhjzAnmsBdMqhoBMhMeK0y4H38l3INjZLwK5MTdXxO3+LNjtH9wrJxx+peZvJUxxhhj\nTmTTcaRvY4wxxphpxQomY4wxxpgkrGAyxhhjjEliWg8rME1k1VbXOAmK5eS6zqupCjnJAwjVRRka\nmeksLxyqZ5hsZ3n14TDqsM6vD4URSWVIsBTzwmGyM/uc5YXrogwMz07eMEX1oTAur3MIh8JOr5rw\n8txNV3Q43t8McXeRbTgU5uR+d8PH1YXbGG7f5y5vZw8Z0S53ec17kB0t7vLCbZB7krO82lAL2ucu\nry7cASc1OMurrWtBZw8mb5iiumgnzHT3+76uvgNmNzrL217dCJBbVd/pJC+WM+W6xwqmFLxUVU/T\nvvR/6bY1RgHYUh2htSf9r5nWWN6O0B76h918oBqa9jFIB/0js5zkNTZ10TPczO5+N0VTfbSD7sEc\nOvvcfKmGom0MkkPPQE7yximINu4kIyOLwWFXr99ueoYb6Rl0c21CQ6O3vb2Dbt6PhqYO9o/ksGfA\nza+SSEM7g+TSO+gmr6GpnSFy6R108/42NHaCzGJgxE0R29S0hzk9jdC710lec7QFunudHTto6e4n\nu2EnDPQ7yWtu20PmltfRtiYneU07Gsgc6Ye9bU7ymkP1ZEQG0fBYk0tMXtNrrWTOmIEMuPkjqrmx\nFXa2MrzdTVHXWLuTjK4uNBRO3jgFTa+3kTlzFgy62d76aIf3b1s3ZKX/O7C+rTut9a1gSm7ojRdc\nRPGy9AeCq932Cg/d9Y3+c/7mYnzL08+refUVfvLt2/svvPQille4GagOIN+3lOUVFc7y5ub7KC1z\nl7egoBi/w7ylS5awpNxdXvmyfKfvx9z8pU77V+QLOM1bWFzi9P31+932r8jvZ2l5pbO80tJSllW4\nyzv/jFcILl3gLK+4pZXKhac6y1uyYhHB4jOc5QWWLibon+csr3SFj+Cyhc7y/PQ63d4lF5cTLHc3\na1dxCCoXneYsb8myeQQLT3eWV+pwe196Jcya23/Vf9lbiwmWnp1+3vZm1tyzccq7iO0cJmOMMcaY\nJA5rwSQiHxGRERG5eYxlPxWR+2K3V4vIuJMfxZaPiMgPxliWKSJdseWLY489N5o9Tl6JiGwUkR4R\niYjIrSJi4zEZY4wxZkxHag/TTSKSbH9aspN6hoH3jPH4BcDsFNYHvPlSgCeAXcD5wKeBjwM3pbK+\nMcYYY048R6JgagcagW+mmfMKcKqIvDHh8cuBP0wi501AMXCtqm5R1ceB7wDvTrN/xhhjjDlOTbpg\nEpG82OGva0Rkm4h0i8g9InKeiGwVkT4ReVJERi8jGQA+A3xIRM6dqC8icouI7BKRqIisie0NGtUH\nrAfem7Dee4DHJ7EJs4ENqro77rFBwN21lcYYY4w5rqSzh+km4AbgWuA64DHgy8BKoAy4frShqq4H\n/hf47gR55wE+4JJY9r/E8g/E4BVGBwomEakA5gJPQWqDt6jqs6r6t3EZBcA/4B2mM8YYY4w5RDoF\n02pV3aiqjwItwH2qulZVNwMbgUBC+88Cy0VkFWPbCVylqi+q6s/wiquPJbT5NeAXkdHJe9+DVyxN\nadAHEYkCdXh7l344lQxjjDHGHP/SGYfp9bjbA0D8yFeDQG58Y1UNicgdwNdF5Bdj5L2mqgNx9/8E\nfCohoyt2Nd37gDvw9jZ9Z+qbwIVAEfBVvD1gwTSyjDHmqPvgj/+P1m43A00CzF9XRf39H3KWZ8yx\naqp7mBTYn/BYKkNh34JXTH1pjGWJ62cBY80ZsBa4XEQWAcuB36TwvAeISLmInAegqrWxw4WfAspF\nxN1oasYYY4w5bhzRkb5VtVdEvgA8CGzh4L1UFSKSraqjE+W8BXh1jJjHgTvxzpv6o6ruFJFTSH2C\nrPcCVwH+uMdygSG8oQaMMeaY9eg/vNnZSN8/f7GBJSsWOcky5lg31YJpyhN5qeojIvIJ4O0cXDDN\nAe4VkTuBCryTxq8YY/2IiLwKfB744gR9OltELkp4bCfwCPBFEfkm8DBwJvAt4L/jijVjjDHGmAOm\nWjAl7s2Z7EyynwJeTFjvWaALeB7YB3xRVdeNs/5avCvx4q9sS+zDJbGfeOtV9V0i8h7g68A/Ah3A\nL4DVk9wGY4wxxpwgJl0wqWoEyEx4rDDhfvyVcA+OkfEqkBN3f03c4s+O0X4NsGaC+3XxfVLVlUm2\nYR0wXjFmjDHGGHMQm3zXGGOMMSYJK5iMMcYYY5I4olfJHasa6nY4zYnW1TjJG83ZUe2mfwDRSJRh\ndfexaIxE6HF4Kn1LNMLQcCojWKSY1xBlZnZm8oYpamqIMCvb3QZHI1H2Dbp7P5qiEQ6ecSjNvIYI\nQzrZUxjH1xKNkJ3htn9OtzcaJSfT3d+ZjZEIVXvaneXVN+5ieGe3u7xdvWQ07k7eMNW8tm6Y3eku\nr2UPUtfmLq9xF3rIiDlp5LV1IzXNzvLC0Q6GWh2+v509ZDTucZfXvs/p9lbFsqrCO93kpZkj6vCX\n3fEoJyfnJA6dvy4dG4BLp3FeDt4Vh65GvrM8y7M8y7M8y5sq199xawcGBsYa4zEp28OUXODmu+5+\nKK/Yl3ZQpLaGr33mn65e/d17HiooST8vvKOGNZ/+x6t/eN9XHvL789POA9iw/gXmL/JT4i9ykvfM\nho3MXxigyO9P3jgFGzds4KxFBRT53ORtenoDefl5FPvSfz8ANj71FL6ik/E5ev2eXr+JeYtKKfa7\n6d9zG54mP38+Jf4SJ3nPbHiWhXkFFPvc5D331DPk5Z3ttH/5+Wc5/DxvorBgLj5/gZO8p9b/Hl9m\nE4H8053krXuhjgWNnfhOPylp2ysf2wLAI++vGL9/oZ0U+k7HP3+Ok/6t39ZCYXm+u+39Y4h83wIC\nRWe6ydtURV7uCIFFp7nJ+3OEgjeXEyg5y03eM6+S19tBYIGb92Pdy83kF59FIM/R9m6up+DcFZT6\nFjjJ217TxKp//MHV96+5/KFAwRlp51WFd7Jq9eNvxLtKf9KsYEpBXrEPf9n4v1Qmq6DER8Bhnt+f\nT2Ww1ElWdVU9i0uKKK9c7iRvR3Uti4r9LK9ws7211dXkF/tZWlHpJK+upppin49ljvJqa6rx+d9A\neeVSJ3k11XUsKvaxvKLcSV5tdQ0l/jzKHL6/hb4SyirLnOTVVu+gxF/gtH8l/sWUVy5zlFeHz382\nFcElTvJqqsIEsvYTLHXzhVpVv5OCvv1UplDgtPV4M1FN1La6swf//DkEHX2hVrXsJZB/OkH/fDd5\n9Z0Eis4kuNTNF3RVXTv+GcMES+a6yWvoIlByFsGyfDd5O1rwd/cTLHRTcFY17SGQdxpBn5tJLqoi\nuyj1LSBYUZi88SQECs5w9n8kHYf1pG8R+YiIjIjIzWMs+6mI3Be7vTo2R9x4OatjOT8YY1mmiHTF\nli+OPfbcaHaS/q0QkYbJbZUxxhhjTjRH6iq5m0Tk7CRtkp1MNQy8Z4zHLwBmp7D+QWLF1W2TXc8Y\nY4wxJ54jUTC1A43AN9PMeQU4VUTemPD45cAfJhMkIvcD9cCFafbJGGOMMSeASRdMIpIXO/x1jYhs\nE5FuEblHRM4Tka0i0iciT4rI7NgqA8BngA+JyLkT9UVEbhGRXSISFZE1cvD1wH3Aeg69Yu09eBPy\nTsZqvPnqvjLJ9YwxxhhzAkpnD9NNwA3AtcB1wGPAl4GVePO8XT/aUFXXA/8LfHeCvPMAH978bzcB\n/xLLPxCDVxgdKJhEpAKYCzzFJCYEVtWoqm4FoqmuY4wxxpgTVzoF02pV3aiqjwItwH2qulZVNwMb\ngUBC+88Cy0VkFWPbCVylqi+q6s/wiquPJbT5NeAXkdFT8N+DVyz1pbEdxhhjjDETSmdYgdfjbg8A\n4bj7g0BufGNVDYnIHcDXReQXY+S9pqoDcff/BHwqIaMrdjXd+4A78PY2fWfqm2CMMceXKx/bcmDI\ngFSc8s2nJ1w+/w91hG5/d7rdMuaYN9U9TAqHjB+fynwVt+AVU18aY1ni+lnAWKNxrgUuF5FFwHLg\nNyk8rzHGGGPMlB3RgStVtVdEvgA8CGzh4L1UFSKSraqjE3G9BXh1jJjHgTvxzpv6o6ruFJFTsOEB\njDGGR95fkdLAlaN7lvZ8/uJx2/z8tRaWlrsZZNKYY91UC6Ypz2apqo+IyCeAt3NwwTQHuFdE7sS7\ngu164Iox1o+IyKvA54EvTtCns0XkooTHdqrqK1PtuzHGGGNOTFMtmBL35kx2786n8OZyiV/vWaAL\neB7YB3xRVdeNs/5avCvxnpigD5fEfuKtB941yb4aY4wx5gQ36YJJVSNAZsJjhQn346+Ee3CMjFfx\nZjUevb8mbvFnx2i/Blgzwf26+D6p6soUNgVVfXCs/hljjDHGxDtSU6MYY4wxxhyzrGAyxhhjjEnC\nCiZjjDHGmCSO6LACx6isSG2Nk6BYTm54h5u8WE5udXW9kzyAUKiB/pGTneWF6yIMDM9O3jBFkVCI\n4RF3dX4kFCLT4Z8N9aEws7K6nOWF6iL0D7t7P+pDYTIl9UENkwmHwowcfEpjWurDYTJkyFleOBQm\nQwaTN0w1ry5CVka/s7xQKEpuTqezvLqGLgY7xxq+bnwvt+4dd1m4q4esCZZPVqijm8x6h9vb2AUn\nt7vLi+xEZzmLo655D7KjxV1euA3td/d+1LV2wxx3v6/qmvYgNU3O8rZ7WblV4Z1O8mI5U657rGBK\nwb62BvbMSP9bdV9bAwDdrSF256b/pdDd6k2FFw63MTLi5q1sau5iWBpwNaxVa2sbGZnZZGckjnM6\nNTvbomRnQW6mmy/9Xe0NnDRjP7mZbn4J7WoPEcmZj0595I2DtLbsRDJ3kJXhZvafjtjrp+qqf+1I\nRi64ymtuIyszw2n/sh3+lmttbSMzK5OhkdzkjVPQ1NzFzAWzkIHk4yalonloBpkVS8nJOzNp29Of\n3A5A/VvPGbdNu7xO7pmnkLFwrpP+tTYMkj18EjLkaHuHZ5IZbocBN78Pmlu6EB1kZPfkis5x8xq7\neP0Tv+SPmvxD+HDWLgCuGnrDuG2qpI/MG89Hck510r+WzF1k9Smyz0kcLfuVrHAzDLv5I6U+2uH9\n25eF7MtOP68vvV8GVjAlN/Q3l17K8oqKtINe3bKFu269tf/CSy9ieUW5g7xXuPOWb/Rf8o63UhFc\nknbeqHzfMsorlzvLK/YFKHOYV+grdfL6jQqULnS6vYFAPuWVy5zlFfn8TvtXVFJKWWWZuzy/2/fD\n5y9w2r9A6WKn70eJv8hp3ht9EKwscpYX8J1FsKIwaburrnx7Snn+wjMIlhek260DSkvzU+pfqvyz\n+wmWnu0sr2Swh2D++EXLZDU+s598khfYPxRvL8pbmGCPssJlF5QSXLbQVfcIFM1zm1cecPZ5eemV\nMGtu/1X/Oy8ud5L50ith1tz2yynvrTgmzmESkY+IyEjcz7CINIrIHSIy5g5UEVkba3tIpSMihSKy\nXkS6RWSXiNwvIjMP/5YYY4wx5lh0TBRMMQoUAcVAKd54Te8HfpXYUETmAO/Am7fuioQ+ngulAAAg\nAElEQVRlAjwZW/Y24CrgQuCbh7HvxhhjjDmGHVOH5FQ1HHe3RkSqgS0i8j5VjS+c3g/0A/8DfBC4\nOW7ZuXiF1/mq2gkgImuA74vIp1TV5qQzxhhjzEGO2h4mEcmLHTK7RkS2xQ6P3SMi54nIVhHpE5En\nRWTcS6xUdSuwGa8oinclsA5vot4SEYk/IeJM4JXRYimmA8gF3JxJZ4wxxpjjynTYw3QT8ElgLvAI\ncDnwCaAV+AXeJLwTXVO4DXjT6B0ROR24CPgw3vx0+/EOy20FUNXH8QqpeB8DGlXV3fWVxhhjjDlu\nTIdzmFar6kZVfRRoAe5T1bWquhnYCASSrN8BzIi7/0G8852eVNU+YBOH7oECQETmisgTwN8yxhx2\nxhhjjDEwPQqm1+NuDwDx5ykNQtLrMefgFU2jrgQ2qWp37P46wCciB10HLCIfiD33ecAVqvrYFPpu\njDHGmBPA0T4kp3iHzOKNTDJjBfBnABE5C+/KN0QOGd73g3iH7xCRzwB3Aj8DPqOqHRhjjDGOPZex\nl02ZqY8M+dHs8ITLh3++mbu/dsWEbczhcbQLprSIyAq8q95ujD10JTAEvBXojWv6fbzzmFaLSAlw\nO/A1Vf3yEeyuMcaYE8zKkTmsGkk+UvpoofTA4PgDNL5AN6v+/lxnfTOTc7QLpsnMfyAiMjocbgYQ\nBL4FPKKqL8QevxJ4QlX/krDi/cADIrIEeBewG/hpXB4Aqlo3hW0wxhhjzHHuaBdMiWMejTcGksZ+\nauLuNwIPAl8Bb5gC4Bxg9RjrPwbcjXdYbi5wBlAVt1xime5mETXGGGPMceOoFUyqGiGhQFHVwoT7\nq+Lu/mSyeXHLeuCgCXpumFRnjTHGGHNCmw5XyRljjDHGTGtWMBljjDHGJGEFkzHGGGNMEkf7pO9j\nQVZddbWToFhO7o7qHU7yYjm5NdUTj9sxGaFQlEGd6SwvXBdB1d259OFQmGF197ENh8JkZvS7y6uL\nkCGTHUpsorwoIy63ty7CiGa7ywvXM+Lw10g4FEZk2F1euJ7MjMQh2dLIq4s4yxrNO8nh/4/aUAuq\n7l6/2lArOjTgLK8u3AaZ7j5/taFW9GR321sX3cUI7n4fhNq72SmD41/ONIb6CZ6/TYaoqmtz0DNP\nXf1Es45NMW9ms7O8qppmgNztNW4yYzlT/oVlBVMKdtRF6HHwO7c56v2y3RFqpncw/V8aTQ3eh6g+\n3MqIo1+6Lc2djEgLIyOO8lp2MsQc9g+f5CSvsWk3/erm9QNoaOpAMnLod9i/jMxshkdynOQ1NXcy\nxKn0D487B/WkNDbtJSOzAXQSv8En0NbSBnISw46KsJbmnWRnidP+zcgaIFPcfOm3tzaTmeWuwGlt\nbWP20H7o2+MkrznSROb+vdDtZlrM5nADmT27oXuXo7wmMof6Yd9uN3n1jWQM9zDS1Ookr2lHO/tb\n+9h3UrIJJmDNqw0ArF6+aNw24Z3d5L/lZObPmTFum1Fv+H07APMvnjNumzlNI4S31aOdbt6PplAz\nGX296G43n7+mSCuZObkwmDge9dTURzu9f2vqoTf9PtY3pvf/wgqm5IbOv+hSSssr0g7a/soW7vnm\nrf1vu+hSljjIe/2VLdx9+y39F1/2Nioql6adNyq/uIyyyjJneQuLK1lWUeksb1GRz8nrN2rpUh/L\nKxzmlS6krHK5s7zFxRVOX78lgYVO39+8kundv2VL51FRucRZXqGvlPLKZckbpmjFgl0Ey/Kc5QXy\n3kBwgi/xSectPoXg0gXu8gKLnfbP17uTYMEbnOWdxW7KTk3+B9Q/v1gPwAcWnT5hu7K8N1Bx5skT\ntgG40j8vpf6VnZtP0Jda21QECs8kmOJzp6L03KUEly92kvXSq1HW3Pmb/sv+JkBwWfqfmZe2NbDm\nrnVDU13fzmEyxhhjjEnimCiYROQjIjIS9zMsIo0icoeIzBpnnbWxtuPuOhCRD4jI84ev58YYY4w5\nHhxLh+QUKMYblTsLKAe+ASwD3hHfUETmxB4bxJtDbktimIiUAjcD3Ye118YYY4w55h0Te5hGqWpY\nVUOqWqOqjwKXA5eIyPsSmr4f6MebOuWDiTki8hzwGuDuRAljjDHGHLeOWsEkInmxQ2bXiMg2EekW\nkXtE5DwR2SoifSLypIiMe3mQqm4FNnNoUXQlsA54HCgRkcTC6ONABfBDh5tkjDHGmOPUdDgkdxPw\nSbxJcR/B22v0CaAV+AVwPTDRYBHbgDeN3hGR04GLgA8DzwL78Q7LbR1to6q1sbatgLvLy4wxxhhz\nXJoOh+RWq+rG2CG2FuA+VV2rqpuBjUAgyfodQPwgFx/EO9/pSVXtAzYxxmE5Y4wxxphUTYc9TK/H\n3R4A4oetHgSSjSA2B69oGnUlsElVR0/mXgfcKSLLVHVbup01xhhz/ProH2tp7099qJ6Fa1+ccPm8\n7c1UrTov3W6ZaeBoF0yKd8gs3mTnlVgB/BlARM4C3ha7nTg29wfxDt8ZY4wxxkzK0S6Y0iIiK4Bz\ngRtjD10JDAFvBXrjmn4f7zym1Ue0g8YYY44pD5xXnNJI36N7lhrfu2LcNr9s6KSs0N0o5OboOtoF\nk0ymrYgUxW5nAEHgW8AjqvpC7PErgSdU9S8JK94PPCgiS1Q1/hCgMcYYY0xSR/uk78QZNsebcVNj\nPzWxn+3A7XjjLH0YvGEKgHOAH42x/mNAD3bytzHGGGOm4KjtYVLVCJCZ8Fhhwv1VcXd/Mtm8uGU9\nwCGzH6rqGmBNil02xhhjzAnqaO9hMsYYY4yZ9qxgMsYYY4xJ4mif9H1MCO+odpoTcpQ3mlNTFXKS\nBxCtb2JwZJazvIZoI/0O8xojEQZHxjvVbfKaGiLMyHKX1xiJkJPR5ywvGoky4PT1i5KdkTiSx9Q1\nRBsZ1Ondv5zMHmd5kfomhjXHWV400shJvXud5dU3dMJQ4ogqaeQ1dsLQgMO83ZCdbGi9yeR1ovv3\nOMuLdOxjX/fkPn9bd4//+Wro6Se3q3fc5ZMV2bufnEiXs7z6lr2QOeaZLFPM24PsaHWWVxXLqqpt\nd5OXZo6ouvuyOB7l5OScBLzXYeQG4NJpnJeDd/Viv+VZnuVZnuWN667Yv59xlJeKEy0P3H/HrR0Y\nGJjSX1FWMCWRk5Oz4mvfv+cvBSX+tLPCO6q5+YZ/vPq2u3/4UKHPl3ZeqKaGf/2n667+4Y/XPOTz\nF6SdB/DU+j8wd9Eyin0lTvKee+oZ5i0sociX/usHsOmpDcxflIeL1w/g+aefoqAg32n/CgrmUuIr\ndpL37FPPsWBxPiV+N+/HsxueobjwNHyBwuSNU/D0uueZtygwrfvnK5qDP5DnJG/9us0syluE35/v\nJG/D+hcomrWHQOFcJ3nrfldD/rzZBArOcJP3+1ryF5zqNq94PoGieW7yNm1n8Z4u/PPnJG17xd1/\nAOAX/3T+uG3Wb2thftcgvlNmOunf001d+C8uIbDoNCd56/4cIX/R6QTyHOVtrqeg+CwCBY4+f7+v\nId+3iECxm/e3qraNVZ/96dX3f/uahwIl89PP29HKqs/+5I0DAwMTD88+Djskl4KCEj9Lyiuc5RX6\nfCwtr3SW5/MXUBksdZJVXRVmQXEJZZVlTvJqq3ewqNjPsgo321tXU83iYnevX6immiKf2/6V+M52\n9vrtqN5Bkb+E5RXlTvJqq3fgC8ylotLNnNM1VSEWF0/v/vkDbyAYdFMQV1VFKPHlUxlMNsVlaqqr\n6gmcnENw6QIneVWhdgILTyVYerabvPBOAgVnuM0rmkdw2UI3eXVtlMwcJphCAdG6xzvUNlHbqpa9\n5I30U376bCf9q9nTS2DRaQRL3BQkVQ1dBPJOI+hzVJBEdhEomEtwiav3t4NA8TyCyxc7yRsVKJnv\nPHMqjomTvkXkIyIyEvczLCKNInKHiIx5AoWIrI21PaTSEZFiEXlWRHpE5HURueLwb4UxxhhjjlXH\nRMEUo0ARUAyUAp8F3g/8KrGhiMwB3oE3ee8VCcsygF8DDXjTqnwT+OlYhZUxxhhjDBxbBROqGlbV\nkKrWqOqjwOXAJSLyvoSm78c76exBDh3d+93AGcDHVPVVVb0feAL42GHuvjHGGGOOUUetYBKRvNgh\ns2tEZJuIdIvIPSJynohsFZE+EXlSRMY9mKyqW4HNHFoUXQmsAx4HSkQk/oSStwObVDX+2tvngZVu\ntswYY4wxx5vpcNL3TcAngbnAI3h7jT4BtAK/AK4Hdk6w/jbgTaN3ROR04CK8OeaeBfbjHZbbGmtS\nCIQTMpoBN2fRGWOMMea4Mx0Oya1W1Y2xQ2wtwH2qulZVNwMbgWSXo3QAM+LufxDvfKcnVbUP2MTB\ne6BmA4kjC+6LPW6MMcYYc4jpsIfp9bjbAxy892cQSDYs7By8omnUlXiH3Lpj99cBd4rIMlXdBuwB\nEq+smwG4Gz7VGGPMMenqZ7fT1udutPT5rzdT/9A1zvLM0XO0CybFO2QWb2SSGSuAPwOIyFnA22K3\nEz/xH8Q7fNcCJA7osBCITvJ5jTHGGHOCONoFU1pEZAXe0AA3xh66EhgC3grET+DzfbzzmFYDTwE/\nFpHsuBO/LwaePiKdNsYYM209dGGps4ErHw21U/lBN4O6mqPvaBdMMpm2IlIUu50BBIFvAY+o6gux\nx68EnlDVvySseD/woIgsAdYD3cAPReQuvHni3oE3rpMxxhhjzCGO9knfiRPZjTexncZ+amI/24Hb\n8cZZ+jB4wxQA5wA/GmP9x4Ae4IOqOgD8LVACvAB8APg7VY2ktSXGmP/P3p2Hx3XWd/9/fzRavDuL\nnXiXLEuakW3Z0hBokjakIWSB8pC0pT8e+EGIKSVNWUpoIU8fFuM0CTukpKEp0JCUtBDgogFKmgUS\nQ0qgkDjBTmJJlmY0WkaSJUuWZUvWNvfzx4zCeJA1I+l2JMvf13Xpks6c+3zmPprtO2e5jzHGzFuz\ntoUpVaAEMm4rzZjekTb5r1PNS5t3DFiaNl1LcredMcYYY0xWs72FyRhjjDFmzrOCyRhjjDEmCyuY\njDHGGGOymO2z5E4H+dEDdV6CUjlFkfp6L3mpnKL6uswrvUxfNNLCYOIcb3lN0SjDiWxjj+YuFokw\nOtWRuibRHI1SGPD3vaEp0khB3kD2hjmKRptIeHyZRiNRCgP92RvmKNIYY2hsafaGOToV/VvgNa+N\nhPP3eDRGWslf6q9/jbEeGBn1l9fc4y3rpbyFC7I3zDWvqZvEsSNTWmZP7ORjFEe6+hnqG5tpt14S\nPXKchS3+xkRujPdBQYG/vLY+tLgre8Nc85p7YLG/i2bUNnQCFNUe6PCTl8yZ9gvYCqYctDX7OYFu\nPKejJUp+3sw/9TtaknmN0UOMOD9vQq3xPkbUfPLzFaeos70DtJA8+Qns6minID+fwsBURqQ4uZ7O\nDuKFjoCGveR1d7SysPA4eb8zbur0HOyIU5AP+XmZ47tOT1dnC0sKjxLAz/p2dXSgwAK//SvqJ19D\nfvI62okVHCPP+Vnf9rZO8vPGKHDHvOQdjLdRmNePO3rUS15b60ECY6Mw5udDP36wjwAORv0UYfHO\nwwQW+yuY4gePkJc/ho5nX99zlyW/uNVP0rZ9xJFXBIUL/XyJ6i6A2JFR1Ovp+XdslEB7H77eoOPd\n/eQv6QP5Wd94dz+Btj4I+Cnqmtr6kr9jHTA28/9hU+vMvgBYwZTd6B9cfiWbt1fPOOjF3zzHP33m\nk0N/eOWVVFXPPG/fc89xxyc/OfTaq17N9pqtM84bV1y2nW0127zllZRXsbW6xlvepopKr3nBUAlV\n1f4Gl6usXMv2mipveeXBMq95VZXnUROu9JZXXLHda/+2bV7htX/bKs8hHC73lhcKrvOaV5HoJBz0\nd+3vyrJVhCtXe8sLFZ9LOOQxr3oT4a3rvOUF844TLluRtd1bLyvLKa/s+Cg1a5fPtFsv2XrxJq+P\nR3DVYsIVHp8v20oJb17jLS8UDhHeVuwla8/eGLs+94Ohqy+t9PKc2fN8K7vueHTa1f+sHsMk6R2S\nEpI+OsG8b0i6J/X3TklP5pAz/jMmqVXS5yVlXjdufJkHU21nXrkYY4wxZl6bKwd93ywpW4mbbRuk\nAzYBZUAlyZG7/wT4j8yGkpaRHN17hOQlU4wxxhhjTmouFEwHgVbgszMNcs5FnXMR51y9c+47wDXA\nFZL+OKPpnwBDJEcK/7OZ3q8xxhhj5jfvBZOk4tSuruskPS+pX9Ldki6StFfSoKSHJI0fSj8MfAB4\ni6QLJ+urpNsl9UhqlrRL0qRH/jrn9gK/5HeLojcDDwPfB8ol+TtgxxhjjDHzzqk86Ptm4D3ASuAB\nklt7bgQ6gO8CNwDdAM65RyT9EPgSyevBTeSi1LJXABXAV1LL35mlH88DrxyfkHQucDnJa9A9Dhwn\nuVtu71RX0BhjjDFnhlO5S26nc253atdYO3CPc+5B59wvgd1AKKP9TUCVpB1MrBt4q3PuGefcN0kW\nV+/KoR9dQPp5rH9G8ninh5xzg8BPsd1yxhhjjJnEqSyYXkz7exhIH11xBDhhNEPnXAT4PHCbpIlG\nwnvBuRMGU/kVsDGHfiwjWTSNezPwU+fc+GhxDwMVkvydl2+MMcaYeeVU7ZJzJHd1pctlpMbbSe4q\n+9gE8zKXzwdyGT3uFcCvASStBi5J/Z05suCfkdx9Z4wx5gx1z69j3PtPLd7y/nwEvuxxHCYze+bU\nwJXOuQFJHyZ59tpznLiVqlpSgXNuvNC5GNg3WZ6kVwAXAn+buunNwCjwB0D69Sv+keRxTDtnvBLG\nGGNOW+98ZTF3XuvnPKBvPdfG1jf4G2TXzK5TVTBN+7oVzrkHJN0IvJoTC6ZlwFclfQGoJnnQ+Alj\nKEnalPozDwgDnwMecM49lbr9zcAPnHNPZyz3deA+SZudc+n3aYwxxhhzyo5hyhxkcqoXvnkfyS1B\n6cs9DvQCTwKfBD7inHs4Y7n61M9+4NMkt1S9HZLDHZA8A+9rE9zf90ju3rODv40xxhjzO7xvYXLO\nxYBAxm2lGdPpZ8LdN0HGPqAwbXpX2uybJmh/30Q52fqVNu8Y4O+S68YYY4yZV+bCSN/GGGOMMXOa\nFUzGGGOMMVlYwWSMMcYYk8WcGlZgjsqPHqjzEpTKKWqs85OXyimqr4t4yQOINjYzkljkLa8pGmXU\nFWVvmGteJIrPOr8p0khAw9kb5pwXJT8vcwiy6Ys2xpjBSacT5hXl5TJ8WY55kRaG3ZLsDXPNa4xR\nFDjqLS8SaaFA/ta3obEdN+VzWCbJi8RJ6LC3vMbWXpRf4C+vpXfqp+xMltd6GJZ2+str6sYVZA6p\nN4O89sOMjfpb4cZDR8lvOuQvr6UXN+Tv/aqxrQ8t6creMNe85kOwtN1bXu2BdoCi2kY/z5lUzrTr\nHiuYctDZ2kJB3sw/pDtbk4OhNURiDI7OOI625hgA0WgHY4nCLK1zzIwfwuU1e3uT7GzvQCokDw8r\nDHS1t5EfEDDmJe9gexv5gQBjzs9LoT3eRWFB5gme09fR0Ql5ixhNLMjeOAfxeC8JLeHIyED2xjmI\ntAwxTCdjCT9FcTzeA3kLGRjxUzQ1tRxnlDH6R3MZNze7xtYxEnnHGE70Z2+cg+b4cfJcL4z6eT7H\nDx6Bpm5GzvLzpacldgjauhk7EPeS19rYTV7PYRL1TV7y2moPEthaggYnPJ9nytqH8wjkjxEI+Hn9\ndgqKBgNwzE8RGx/Kwz3ZwMi+hV7yWpt7UVcfY/sX+8lr6CZw9gqU7+f9oCl+JPn7QDscG/SQN7Mv\nJ1YwZTd66WuvZEv1zAcfe+G5Z7nzU7cN/cHlV7J5e/WM8178zXP802c+OXT5la9hW03VjPPGlVaE\n2FbjZ+C2ZF4lVdUzX9+X8oJ+88oqQmz18PiO27r5fLZ7fDxKyrdTVb3dW97Gis1e/3+h0Eav/SsL\nlnvNC4ZKvL4+tm1eQXW40lte1fALhCvO85a36dBRatYs95ZXufEcataf5S+v4lzCJed4y9t8SQXh\nzWu85QUDg4RLz/WWV/na7YS353IVr9xsDAxRs2qZt7wtoRXUrD/bW97W11YTrvazvnuei7LrU98d\nuvr3ywh7GC19z/52dv3zT6f97d2OYTLGGGOMyWJOFUyS3iEpIemjE8z7hqR7Un/vlPTkJDnXS5p0\nG7ykv5EUlXRc0vOSXj/zNTDGGGPMfDSnCqY0N0vKto11sp3MbrL5kt4K/D3wUeCVwI+B70paN9WO\nGmOMMWb+m4sF00GgFfjsKbyP64CvOef+zTm3zzn3gdT9vv0U3qcxxhhjTlOnvGCSVJzazXZdatdX\nv6S7JV0kaa+kQUkPSRo/N3kY+ADwFkkXTtZ3SbdL6pHULGmXpFzPv14A/DLjtkPAqqmtnTHGGGPO\nBC/nWXI3A+8BVgIPANcANwIdwHeBG4BuAOfcI5J+CHyJ5AVzJ3JRatkrgArgK6nl78zWEefcH6ZP\nS3oVsC2XZY0xxhhz5nk5d8ntdM7tds59B2gH7nHOPeic+yWwGwhltL8JqJK0g4l1A291zj3jnPsm\nyeLqXVPtlKT/DTwCPAd8Y6rLG2OMMWb+ezm3ML2Y9vcwEE2bHgFOGOnKOReR9HngNknfnSDvBedc\n+pCnvwLel2tnJJ0F3ANcC3wP+HPnnJ/R44wxxphT4M3ffZbOY/5G+171syIa/95OEs/Fy7WFyQGZ\n14vIZejd20kWUx+bYF7m8vlATtdAkLSCZIF1MfBnzrk3Oef6clnWGGOMMWeeOT3St3NuQNKHgftI\n7jJL30pVLanAOTd+IaGLgX05Rn8OWAS8wjnX5q3DxhhjzCn0wJtqvI30/a0X2tkSWuEl60zwchVM\n0756qHPuAUk3Aq/mxIJpGfBVSV8AqkkeNP6m9PuUdPkEkT8juRvu88ACSZvS5vU453qn21djjDHG\nzE8vV8GUOYjkVK9s+D7gmYzlHgd6gSeBo8BHnHMPZ9zHoxNkFZMstj6R+km3C7hlin0zxhhjzDx3\nygsm51wMCGTcVpoxnX4m3H0TZOwDCtOmd6XNvmmC9vdNlJNmLg7YaYwxxpg5ygoHY4wxxpgsrGAy\nxhhjjMliTp8lN1c01td5zYke8JM3nnOg7oCXPIDmWDOJE/egzkhLczMJ5zEv1ozzWOe3xmL4/N7Q\nGmumKHDUW15zrJXRxEJveS0xv49HayxGQKPe8ppjzVM+wDFbXp5GsjecQl5RoN9bXizWRsFIj7e8\npo4jjPYNeMuLHR4gr7PAW15TzzHy4v7yYl1HyYt0ectrauvF5Q35yzt4FNXH/eU1dzFyKKfRc3LS\nfHiQQIe/53PToWME6v2deL4/lVUb7faSN9McOefz7Wn+KSwsXEzyrDpfHgWunMN5hSTPavT1rmF5\nlmd5lmd5ljddvj/jHhweHp5W1WlbmLIL7fzS3fdvLK+YcVD0QD273v+Xb/vUl79yf2nFzPMi9fX8\nn79699v+8aufvb88uCn7Ajn4yaM/ZfX6csqD5V7yHn/0J6zbUEJZcObrC/DEoz9mbXEpZR7+fwC7\nH3uM9cXr2RQM+sl79FFKN57n7f/3k0cfZ+2GUq+Px4aS9ZRV+Ml74rGfsG5D8ZzuX3HxGq+PR0Xp\nciqCG73kPfbIz9k0EiW04RwveQ//qomSxXmE1voZp+fhZ+OsdyK4cqmXvEfrOyk5bwnB8/3kPbK/\nk01XvZJQmZ/rpj/8xAuULMsnVHKun7ynGim5YDuVwXVe8v7r0T0UD3YSWn+2l7yHn26mZM1Z/vJ+\nHWPjqy+msmKtl7z99W3sePeX3vb1f9hxf6h85o9x7YEOdvz11y8gedb9lFnBlION5RWEtlV7yyut\nqGDL9hpveeXBTWyv2eol60BdIyXl5VRVb/eS11B3gNKKCqqq/fz/GurqKa2oYGu1n/9fQ30dm4I+\n+1dHeXAd22qqvOQdqGugtMLv41FWUca2mm3+8oJzu3/lwY1eH4+K4DnUhCu95NXVRgkN9xGuOM9L\nXm1zD6FleYRL/Xzg17b1UZYIULN2uZe8uq6jBM9fSs26s/zkdfYTKltFuGq9l7zahg5CZ+cTDq32\nk9fUTWVwHeHq0uyNc8mra6Xi2DDhMj+DTda2HCa0/mzC5Ss95fVSWbGWcI2fL/DjQuWrCG8r9po5\nHaf0oG9J75CUkPTRCeZ9Q9I9qb93SnpykpydqZy7JpgXkNSbmr8hddsT49knyVsp6duSDkvql/Qf\nkvy8YxljjDFm3nm5zpK7WdKaLG2yHUw1BrxxgtsvBZbksHy6fwM2AFcBrwdKSV6I1xhjjDHmd7wc\nBdNBoBX47AxzfgOcJemCjNuvAX6ea4ik1cBrgRudc//jnHuS5OCXr0vNM8YYY4w5wZQLJknFqd1f\n10l6PrVL625JF0naK2lQ0kOSlqQWGQY+ALxF0oWT9UXS7ZJ6JDVL2iUp/Rp0g8Aj/O4Za28Evj+F\nVVhNsoB7Pu22LpJH9vs5ctAYY4wx88pMtjDdDLwXeCfwbuB7wMeBy4BtJC+GC4Bz7hHgh8CXJsm7\nCKgArkhlfzCV/1IMycLopYJJUjWwEniMHC/w65zb45zb4JxLH5zlL4ABoD6XDGOMMcacWWZSMO10\nzu12zn0HaAfucc496Jz7JbAbCGW0vwmokrSDiXUDb3XOPeOc+ybJ4updGW3+EwhKGj/l4I0ki6XB\n6ayApMWSvgq8B/ioc87fiGDGGGOMmTdmMqzAi2l/DwPRtOkRoCi9sXMuIunzwG2SvjtB3gvOueG0\n6V8B78vI6E2dTffHwOdJbm36h+l0XtKlwL8C5wJ/5Zz75+nkGGPMfPb/3f9rOvr9jUO4amkRDTuv\n8pZnzMtluluYHHA847ZEDsvdTrKY+tgE8zKXzwcm2uLzIHCNpPVAFfCjHO73BJLeRHL00DpgqxVL\nxhhjjJnMyzpwpXNuQNKHgfuA5zhxK1W1pIK0Y4suBvZNEPN94Askj5v6hXOuW3FNEBMAACAASURB\nVNJychxWQNIy4CvAN51z109vTYwx5szw7be90tvAld96ro3QOj+jkBvzcptuwZTTAdYTcc49IOlG\n4NWcWDAtA74q6QtANcmDxt80wfIxSfuADwEfmaRPayRdnnFbN1AGLAbukJQ5HGmTc25squtkjDHG\nmPltugVT5tacqV7B930kr+WSvtzjQC/wJHAU+Ihz7uGTLP8gyTPxfjBJH65I/aR7BPgJyfVOv5aM\nUstvBJpzXgtjjDHGnBGmXDA552JAIOO20ozp9DPh7psgYx/JqxqPT+9Km33TBO13AbsmmW5M75Nz\n7rIsq/H5LPONMcYYY17ycl0axRhjjDHmtGUFkzHGGGNMFlYwGWOMMcZk8bIOK3Cayo8e8HPFlFRO\nUaTeT14qp+hAXaOXPIBoY4yRxEJ/eZEoCeevLm+KREh4fNo2RaJoyucsnFwsEqEwb1oDz08oGoky\n5vytbzQSBflb36ZoFDf9k2Z/x6noX55GveVFI1EWBPo85rVQkOjxltfYdhj6/T1fGjv6GVMge8Nc\n8w4dRQX+ni+NXUcJNHT4y2vqgr7C7A1zzWs9DHWt3vIaIh0khnu95TW2H4aAv/fnxngfqm/zlrc/\nmVVUe8DPY5zKmfYLxAqmHByKt7Awf+Yv8kPxFgDammNePmLammMAtMaiBDScpXVuDnbEycvPJz8v\nc1zS6enqbKGoYJTCgJ+rzhw6GKOoYIyivKNe8no6oywoGKbA0/p2dzazuHDA2/+vu6OVxYXHWJJ/\n0Ete38EDdBf1sSS/00ve4c4GFhccY1HgkJ+8g40cKjrsrX99nQfoLuphSYGn9T14gLZjwxR0v+Al\n72B9BNfQyeCSoqxtP/brJgD+/pUlJ20T6zxCoGY1yE9REu89TuC8JQQ8fVJ0BkTRWcsJrDjbT96y\nfgpb/Dz3AOKdfQRGFkCBnxWO9x4j0NSJnJ8vAfF4D4ElQgN+Ht/2YZE/HEBDfta3fSSP/OYuJD9F\nWFNzV/J3rBMSI1la55DXMrMvJ1YwZTf66tdewZbtNTMOeuE3z/KPn7pt6NLXXsmWag95zz3LnZ+6\nbeiKqy+humbzjPPGbazYwvaaKm95FcFSttds9ZZXVhFkm8f+lQUrvOZt3rya6pot3vK2Vp5PTTjz\n0ozTV1m5wWteWUUFNeFKb3lbNq8hHA56y9tcuZZwuMJbXuXSo4S3rPWWt25wmO3nLsna7j3/3QDA\nn5WeN2m7mvBawqXneukbQGj92YQ3+curvLCC8OY13vJC2zYRrlrvL+/8RV77V3lxDeHq0uwNcxRc\nOuL3/1e5jvDWdd7yKi98BeGaMi9Ze55tYNet3xy6+rLNhKs2zDxvXzO7vvDQtDc5z+ljmCS9Q1JC\n0kcnmPcNSfek/t6ZusbcyXJ2pnLummBeQFKvpDFJM39EjDHGGDPvzOmCKc3NkrKV1Nm2eY4Bb5zg\n9kuB7F/vjDHGGHPGOh0KpoNAK/DZGeb8BjhL0gUZt18D/HyG2cYYY4yZx172gklScWr32HWSnpfU\nL+luSRdJ2itpUNJDksa3+gwDHwDeIunCSaLzJN0uqUdSs6Rd0glHPg6SvDTKtRnLvZHkBX2NMcYY\nYyY0m1uYbgbeC7wTeDfwPeDjwGUkrxN3w3hD59wjwA+BL02SdxFQQfL6cTcDH0zlvxRDsjB6qWCS\nVA2sBB5jBhcUNsYYY8z8NpsF007n3G7n3HeAduAe59yDzrlfAruBzNN4bgKqJO1gYt3AW51zzzjn\nvkmyuHpXRpv/BIKSxk9ZeCPJYsnfwDnGGGOMmXdms2B6Me3vYSCaNj0CnDAwiXMuQvKiubdJWjpB\n3gvOufTBiH4FbMzI6AWeBP44ddO1wIPT6r0xxhhjzhizNQ6TAzJH9kvksNztwNuBj00wL3P5fGCi\n0RIfBN4k6dtAFfAjYDnZz7Izxhjv/rW+g28cyH1g0vP+9alJ57+zCL58w0Uz7ZYxJsNpNXClc25A\n0oeB+4DnOHErVbWkAufc+HCgFwP7Joj5PvAFksdN/cI51y1p+anstzHGnMx1Fav4/EXZB/obL5QO\nXnfxSdt8J3KQmiv8DdJpjPmt2dolN+0DrJ1zDwC/BF6VMWsZ8FVJ2yRdR/Kg8S9MsHyMZCH1IU48\nO84O+jbGGGPMhGZzl9xk09m8D3gmY7nHgfFjlI4CH3HOPXyS5R8keSbeD2bQB2OMMcacIV72gim1\nhSeQcVtpxnT6mXD3TZCxDyhMm96VNvumCdrvAnZNMt2Y2SdjjDHGmHGnw0jfxhhjjDGzygomY4wx\nxpgsrGAyxhhjjMnitBpWYJbkR+rrvQSlcooa6+u85KVyiuprI17yACKNzYy6Bd7yoo0x5PF4+mhj\nDOf8HW4WjTThPJ4gGY1Eyc/zN3B8pDFGgce8xkgrkr/Ho7GxjYQr8JYXjbSQnzeSvWGOIo2t5GnU\nW15jYxtaOuQvL9bNQN/UHt/fHDp60nnRI8dZ2No30269pLHjCOT5e300dhxB0S5/ec09sGixv7ym\nLhhY5C+vuQdWtHnLa4h04JblMmRhbhqbe6CwMHvDXPNi3XB2q7e8/XWtAEW1DZ1e8lI50657rGDK\nwaG2/SzOP/mbVO45LQAMtD3O0fwXZpw30JZ8EkWauhh1RVla56atvZeiff/CwhV+3jR66rsZvvcI\nR4v8fKg29Q+g85fiFvt5kbd39bP0ys0sbfMzFFffi810tBWxoG6Zl7xD++LU/6CP44v9PL7NXUeo\nOzDGr1z2l/7Tece4IDH5h1GtBqmlgKc95tVRwP94zNtPAb/0mPciBfwih7x/z+8B4K2j50ya94fv\n3MrCVRNdvOBE5z6Z/CLTsuW8k7Y5lBgl0nCIRE/muMDT09ZyhMDSJdDnp+iMD4yR39EP+X6+9MQP\nHSNw8DhaMNEYxdPI6x0mUFgEhzytb/8YgXgfKvDzJTTedZRA3lLU5+n/N5hH4GgBOuzn/TQ+kE9e\n62Fcvp/1bWo9DEB0bwOua+aFdlP7zL5MWMGU3ejlV17GtpqqGQftfXYfn73ti0Ovu/IVhGs2zThv\nz7ON7LrtW0OXX/mHbPfQv3E1jd3UbDjbW96yQ2NsWeRvq9X2VcvZdra/b5WvvHgT4dBqb3nBFQsI\nl5/8Q22q1ozK6/oeqh+ihOwF2FcC3bw/sWryRg7WUGh5E+WpG4CLmaQYcnD1q4oJV2R/vrz1tZmX\n15xY+cgoNevPyqltLjZfuJFw8HxveZXVpYS3rPWXd0E14eqN2RvmKLjxHMLbir3lVb5iG+Fwube8\nUPHZXtc3tD3otX8Vm8sIh/0MnrpnTz237Lp36GpPz8E9dZ3c8vVfTLsatmOYjDHGGGOymJWCSdI7\nJCUkfXSCed+QdE/adCLjZ0jSs5Kuenl7bYwxxpgz1WxvYbpZ0poc2n0IKEv9vAL4FfAfkvxtlzTG\nGGOMOYnZLJgOAq3AZ3No2+Wci6R+ngf+KnX7FZkNJdlxWcYYY4zxylvBJKk4tcvsOknPS+qXdLek\niyTtlTQo6SFJS1KLDAMfAN4i6cKp3Jdzbiy1fEHqvhOS/lzSc8A/pm57fep+ByTtkXRNWl8l6VOS\neiR1SPobSfslvdrH/8IYY4wx88up2BpzM/AeYCXwAHANcCPQAXwXuAHoBnDOPSLph8CXgFflEi6p\nCPggsBj4cdqsnSQvyvuUpErgP0heL+6HqexvSbrSOfck8NfA24G3kLxg72eAmZ+2Zowxxph56VQU\nTDudc7sBJN0B3OucezA1vRsIAf+d1v4m4AVJO5xzXz9J5lcl/XPq70JgEHivcy59BMjPOue+n7qf\nTwIPO+duT83bJ+kCkoXWk6n7/IRz7pFU+7cBTdNfZWOMMcbMZ6eiYHox7e9hIJo2PQInDmDinItI\n+jxwm6TvniTzY8CDaRkx51zmcKfp91sFfDtj/nPA+yQtAtYD/5PWh1ZJHSdfJWPM6eqL+Z30aSyn\nttcXRLO2We4C/MPohpl2yxhzmvF90LcDMoeYzWUc99tJFkIfO8n8DudcfeonOkGxNH7f4xaQLNbS\nLQQGgPEhnXN7BzXGGGPMGW9OnFHmnBuQ9GHgPpJbgl7Mskg29cDvAXem3XYJsBfoIlnUVQEvAEja\nBOQyvIEx5jRz0+j5OY3MfX1BlHtHJh+p5Cn6WYO/a28ZY04fvgumaV+l0Tn3gKQbgVcz84LpS8CP\nJe0BHic5/MAbgAucc6OSvg3cKqmL5PFQnyW5hcsYY4wx5necil1yk01n8z5gNGO5XDJOaJM6E+4G\nkuM1/RJ4B/AW59y+VJP3AI8B3yJ5Nt13SJ4t5++y8MYYY4yZN7xtYXLOxYBAxm2lGdM70ibvmyBj\nH5y4vds5l/WyzBO1cc7dC9x7kkWCwC7n3I0Aks4CPgc0Z7svY4wxxpx55sQxTLPg/wJrJf01yQPE\n/w74qXOuc3a7ZYwxxpi5aLavJTdb3gO0A48APwCOAm+d1R4ZY4wxZs46I7cwpbYk/Umu7Q/UNXi5\n3/Gc/XWtXvLGcw7UNXrJA2iOtbKwvd9bXlP3AEuOZ47wMH2tQyMs6s8cuWL6mo8Ns7TpkLe8pngf\nbsDfoXBNHUc4dsRfXvOxIfo0kvPRhU0MTTq/S6OALG+amV0apba5N/ewbPfV0U9idNRfXs8x8mI9\n/vLa+9CyLn95rYdhWZu3vGhzF2508ufAVDQ1d8OSFm950VgnbmjAX15zF27BIn95sU4SeQXe8mpr\nY8nfnp6DM82Rc1M9LvvMUlhYuBi41mPko8CVczivkOTZjr7eNSzv9M37E+B7ljftvDtSvz/gKS8X\nlmd58ykP/H/GPTg8PHxsOgtawZRFYWHhK+786uefLg/O/FJzB+oaed9f/M3bvn7nu+4Pla+ecV7t\ngXZ2vO9rb7vra5+5vyJYmn2BHPz4kZ+x8alHCJ7t51vHo7Ee+p4eYA1+vnU8xwBrCwpYJz95zyQG\nCJ5dQEmBn7F1nho4RvnKRWxa5Cfvpz1HKVm2kE2Ls48j9M3WQ7xl3bmTttnd3Q9tCdZ7+hb49OgA\nSwYLWOP85P1GA6xkbudtXllAcb6fx/eXx49xyUeuIFS60kvewz+rp7hojND6s/zkPd3CxqpSQpvO\n85P301pKStcQKl/lJ+/xFygJlVEZXOcl778efYaSdedQWbHWT95jz1ISLKUy5Gdk+P96+FeUrF7q\nt38VG732r7hkHcFQsZe8utoY79xx+9u+/pk33+/jOVjbeJAdH37gguHh4Wems/wZuUtuqsqDm9he\nU+UtL1S+mvD2Em95FcFSttds9ZJVX9dIcP8iqlcu9ZJX1ztANyOUKvsHfi7a3DDrVMCmPD95rYlh\nSgoKCRX5yWsaHmbTokK2LlnoJa9xYIhNi4uoWpa9gL0m3sDtm9dP2qbh2HGUl6As4Gd9WxLDLHcF\nOQ0MmYu4G2YNczuvOL+QYKGfvNjIMKHSlYS3+PkArI0cJLhgjHCZnwKstqWX0Kbz/PWv8SCh8lWE\nq/x8QNc2dFAZXEe4xs+102vrWpPrW+3nC2htfRuh0AbC4XI/ebXNhIrPmtP9Kw8VEw4HveSNC206\nj/BWP0XxTMz5g74lXSopkfEzIikq6RNTyHlC0i0T3P4DSe/02mljjDHGzCunyxYmB1wKxFPTAZKX\nOrlbUpdz7q6pBkrKB/4MuJrfXtjXGGOMMeZ3nC4FE0Czcy59YMkDkt4MXAVMqWCSVEHyunL+Duc3\nxhhjzLw1K7vkJBWndq1dJ+l5Sf2S7pZ0kaS9kgYlPSRpSZaoQeClc9Yl3SSpXVKPpM9IeljSdRMs\nFwNeBdQAHf7WzBhjjDHz0Wwfw3Qz8F7gncC7SZ7S+3HgMmAbyevB/Q5JeZKuIHlR3QdSt10L7ALe\nD1wOFAOvmWh559yQc26vc24vaQWXMcYYY8xEZnuX3E7n3G4ASXcA9zrnHkxN7wZCwNMkx3WokzS+\nXD7J45h+6Jz7Tuq2m4A7x6cl7SBZUBljjDHGzMhsF0wvpv09DETTpkfghHOBX8dvD/rOJ7kV6Q5J\nVznnHgEqgc+PN3bODUhKzzfGnAJfzO+kT2Pe8pa7AP8w6ue0c2OM8WU2CyYHZF7jIjFJ20jGQd8v\nSroBCJO8JtxywN+7tjHGGGNMymxvYZqpXn57plszUAX8CEDSUqB6lvplzBnjptHzvQ0M+RT9rMHP\nKNrGGOPTbBZMyt4ka1vHbwumfwM+KKkWaCF58PjpXhAaY4wxZg6Y7V1yk03nMq8HuEbSbcCtJNfn\nLmAR8BVgFcmhBybLsIvpGWOMMWZSs1IwOediJM9yS7+tNGN6R9rkCW3T2lw7/rekLcBXnXMfTU3n\nAZ0kd9XhnDvZEAN+LspjjDHGmHlrtsdh8mkH8JikSyRdCNxL8hinX81qr4wxxhhz2ptPx/h8HFhB\ncvDLBcAvgD9yztkuN2OMMcbMyLwpmJxzA8D1s90PY4wxxsw/86ZgOoXyD9Q1eglK5RTVHmj3kpfK\nKaqvi3jJA4g2NhPoHfCWF+kb5Cgj3g6t72SUQqeTj9g1Re1ulLNGpnLC5uRaR0dYPODvajuxwRHy\nC4dybr/vyOSPXezYMIGEv42u7WOjHJP8Pb4aBeZ2XmzU3/OlbXSE2kiXt7zGWA9ukb/HtzF+BDUe\n9JcX64bCBf7yogdhUau3vIZIO25sxGNeB25BtkuiTiGvMY4bOuYvL9KBK1rsL68xTsJjWVFXGwMo\nqvX0HEzlTLuDVjDloDUWI0+jHnLaAGiKtsPYzD9Um5oPvZSXSPh5KOPxbgqOZI4nOrEfRrr5X6Ur\nJm3TfmyIo6MjjOawZ/TpvGNckJj8xdutEdwoDHrK69AII+3Q7invgEZIjA7Qm+/nTbfp+CBnV61g\n2bnZ39T+1+BquiqWT9rmSN4I67cvZdmKRV76t7C+m5V5eaw5y8+HYDQmVm06m/Ur/XzIxGoPsmrd\nOWxYtcxP3r44+SXnU7DmLC95ec/GaBpYgI76eTzixwsJnL2MvIXneclrL+gj/1gROuLn8Y0PFhI4\nLOj2U3TG+/NQ61HG8vu85LV1DkLhMGNFg9kb56C1exQX6WN47JCXvJa2owQCeajosJe8ePcARHsY\nTfh5fFvbjkDeQcY8fR7FYslCqenYAuif+Wuk6djM1tMKpuxGX3vVq9les3XGQb959nk+feuXhq5+\nzRbCVTO/9MOefc3s+sKPhi6/8jK21VTNOG/c1qP1VK9cmrXdXz5ex/1Xb8narmNff04DG34l0M37\nE6smb+RgDYVzOi+8oIhggZ+BHAGu3rKKmvVnZ233llfm9pwKlZxNuPicmXbrJeXkUbN68kJtKrZs\nX0t4o7/+VVauJVy+0l/eto2EK1f7y7vilYTD5d7yQqUrCFf7O/k3VFXhtX/BzRsJhyu85ZVXlBEO\nB73lhUIl1IRD3vLKKiqoCVd6y9u+5Vyvj0dpRcjr+gY9/v+e3VPLrbd8bejq173Ky3Nmz556du26\nb9pbP+b8WXKSLpWUyPgZkRSV9Ikp5Dwh6Za06bdL2i/puKQGSe84JStgjDHGmNPe6bKFyQGX8tuL\n7waAS4C7JXU55+6aSpik3we+DtwEPAG8AbhH0ovOuV/767Yxxhhj5oPTpWACaM64+O4BSW8GriI5\nuvdUvB14yDl3Z2r6eUmvB94FWMFkjDHGmBPMyi45ScWpXWvXSXpeUr+kuyVdJGmvpEFJD0nKduTn\nIPDS0dOSbpLULqlH0mckPSzpugmWWwz8POO2LpKXUjHGGGOMOcFsH8N0M/Be4J3Au0kOOvlx4DJg\nG3DDRAtJypN0BXAF8EDqtmuBXcD7gcuBYuBkl0N5u3Pu02l5xcBrgWe9rJUxxhhj5pXZ3iW30zm3\nG0DSHcC9zrkHU9O7gRDwNCCgTnrpVNR8kscx/dA5953UbTcBd45PS9pBsqCalKTLSV5GpQf4oo+V\nMsYYY8z8MttbmF5M+3sYiKZNj8AJ53q/Dtie9vPXwBskXZWaXwn8z3jj1Mjf6fknkFQk6W7gMaAB\n+APnnJ/BPIwxxhgzr8zmFiYHZI6QeLLxmx0QyTjo+0VJNwBh4BFgOTCWyx1LKgJ2A5uBG51z/zyF\nfpt55om8I/w0cDSnttcXRLO2+aNjSwme5W/cH2OMMbNvtnfJzVQvUJD6uxmoAn4EIGkpUH2S5f4O\n2EJyq9LeU91JM7ddlljGjkT2Auf6gij3jmyctM1T9BNe4W/QSmOMMXPDbBZMUxkb/2RtHb8tmP4N\n+KCkWqCF5MHjJ1u/PwX+HTgmaVPa7Uecc/4u7GSMMcaYeWG2d8lNNp3LvB7gGkm3AbeSXJ+7gEXA\nV0gOEzB+USCXlrOR5O64v8jIu4/kGXvGGGOMMS+ZlYLJORcjeZZb+m2lGdM70iZPaJvW5trxvyVt\nAb7qnPtoajoP6CS5qw7n3GvSlvN3+WhjjDHGzHuzfZacTzuAxyRdIulCkkMF9AK/mtVeGWOMMea0\nd7of9J3u48AKkoNfLgB+AfyRc26yXX3GGGOMMVnNm4IpNe7S9bPdD2OMMcbMP/OmYDqF8uvrIl6C\nUjlFtQc6vOSlcooO1DV4yQOIRpoo6B3Iuf1zXf2Tzo/0DXJYI5Mf0p+miaFJ53dqFNCczmsemcoJ\noJOLj4xQ2zH5/3gqGruOoiJ/L/tI51ESgQkPMZxeXs8A+XF/48dGOvrRkl5veY3xPrS0219eSw/U\ntnjLa2hsx41kDm83g7xIB65gob+8xnac/D1fGhvaSCQKsjfMUaSxlamdwD25xsY2Es5f/6KRFgoD\nx7zlNTTGGXELvOU1Rlp9/vuoq2sCKKrd35ylZW5SOdN+A7SCKQfNTa3k/Ik6aU4bAE1tvZA388PH\nmtqSHwTNzc2gk435OTUdHR0srVhN0dqzsra9pmeApuriSdt0J2D1kn7WnJX9RfmG2hHWhM6ZtE00\nJlYsLGT18uxv4m84MMzq8snXI9Li6HlhkEAOj+8FLKSzYHjSNkcYYf8h6M5hT/DTece4ILF40jZR\njbChbxDy/Rxu2HF0iIKeYxDw867WfuQ4BeEKCtad6yXvYL+jqGAJKlruJa89/xD5hSvIW3Cep7xe\n8seWopHsr49cxMcWQVMvo85PUdIa70eBBYwV5DYQazZtB4dIFPYznPBTxDbHBxjLO8LQmJ8iNtY2\nwJgOMZLw86HfEj9CQr0MJxZ5yWttO0JeXhxfB4a0t3cTCAQYHjvkJa+l7SgurwPn/Ly/tMcPkR/I\n8/FxCUAsFgegrrGf42OHZ57XNLMvn1YwZTf62qtezfaarTMO+s2zz/PpW780dPVrthLeNnmhkYs9\ne2Ps+twPhy6/8g/ZXlM147xxFyysJVy5Jmu7t75+W055pU0d1KzO/gH4v6vW5pRXsWQh1ecvy9ru\nzZtX55TX9YIoVfbBJi9hafYwB+e7QkrInveVQDfvT6zKmnfV1tWEi8/Oft85qiw5m3DJ5IXpVGz+\nw82Eq9Z7ywuVnE14S27PhVxU/l6YcHVp9oY5Cm0tIxwu95ZXGtpCTTjkLa+ycj3hcNBbXllFiOpw\npbe88lAp1eHN3vIqQhu95pWFyqmu2eItb3NoLTUe/3/B0EaveaHKYr95oQ3ens/P7qnl1lu+NnTF\n1ZdQXTPzx/i5Z1/kU7d+eXS6y8+ns+SMMcYYY06JOV8wSbpUUiLjZ0RSVNInppDzhKRb0qb/TlKb\npCOSfibpladkBYwxxhhz2jtddsk54FIgnpoOAJcAd0vqcs7dNZUwSW8heT25dwMvAn8L/EhSuXPO\n3xGnxhhjjJkX5vwWpjTNzrlI6ueAc+4e4AngqmlkvR34mnPuW6mL7/45sBT4PY/9NcYYY8w8MSsF\nk6Ti1K616yQ9L6lf0t2SLpK0V9KgpIckZbuEySDw0mlLkm6S1C6pR9JnJD0s6boJlusFnkybHgMS\ngL/zZ40xxhgzb8z2LrmbgfcAK4EHgGuAG4EO4LvADcDTmQulrhN3OXAFqcEqJV0L7CK5tagB+D/A\na4B/z1zeOff/p2UFgA8DQ8DPfK2YMcYYY+aP2S6YdjrndgNIugO41zn3YGp6NxAiWTAJqJNeGjsm\nn+RxTD90zn0nddtNwJ3j05J2kCyoTkrSXwD/lMq/1Tnnb4Q7Y4wxxswbs10wvZj29zAQTZsegRMG\ns3kdvz3oO5/kFqY7JF3lnHsEqAQ+P97YOTcgKT1/Ig+QLMiuAG6X9Ixz7gfTWhNjUr6Y30mfxnJq\ne31BNGubH3z550Q+/YaZdssYY8wMzOZB3w7IHMP/ZMNVOyDinKtP/bzonLsT2A+EU22WkzwWaVKS\nCiVdI+ks59wR59yzzrnPAI+SLMqMMcYYY04w21uYZqoXGL9QTzNQBfwIQNJSoHqCZRLAd4DrgG+l\n3V4IHDxlPTVnjJtGz89ppO/rC6LcO7Jx0jZP0c91f7XdV9eMMcZM02wWTFO5mNXJ2jp+WzD9G/BB\nSbVAC/BxJlg/59yopG8Dt0k6ArQCfwpcTPIAdGOMMcaYE8xmwZR5eb7JLtd3snk9wDWSbgNuJbk+\ndwGLgK8Aq0gOPZCZcSPwhVSbxcA+4ErnXN1UVsAYY4wxZ4ZZKZicczGSZ7ml31aaMb0jbfKEtmlt\nrh3/W9IW4KvOuY+mpvOATpK76nDOvSZtuX7gL2a2FsYYY4w5U5xOI31nswN4TNIlki4E7iV5jNOv\nZrVXxhhjjDntne4Hfaf7OLAC+B6wAPgF8EfOucl29RljjDHGZDVvCibn3ACpUb99q6+LeM2pPdDh\nJW8850Bdo5c8gOZYK0uKur3lNbUdZqT7mLe82OHjuKGTjT4xjby+QQ4zMvkRdFPQxShjUs55TQxN\nnqdR6jqOeOhZUuzQMfIKJ9zDPb28rqPkNXZ6y2tqPQRjIx7zeuCsVm950dhBXEH2MyBzz+tkRIu9\n5cVicfLk8fXR1M6Y87e+sVgbTlM53yd73pROH8ohLzHxESDT0tzURr6G6lXLRgAAGKtJREFUszfM\nNS8Wx+O/L5mX52+bQiwWRx6ff3V1TQDU13r6DJ5hjmwDzOQKCwsXA9dmbZi7R4Er53BeIcm3oMk/\nyS3PR96fkNwi6isvF5ZneZZneadLHvj/jHtweHh4Wt/irWDKorCw8BV3fe0zT1cES7M3zqK+LsJ7\n3vXht939L7feXxGcfPyd3PKi/OWff/Rt//S12+6vCM28fwCPPfwkpaXnEQyWeMl79JGnKFu/iMrg\nOi95//XoM5SUrqEyuN5P3iNPU7JslNCm873kPfzT/ZSUriNUvtpP3k/2UVK80m9esJTKkKf/38NP\ns6pkC16ffxvPx8frA+CxR37O+pL1HvP+m1XrQ5RXlHnJe/yxJ1i7oYTyYLmfvEd/wtoNpWwKBr3k\n7X70UYpL1nrt3/riDV7zNpSsp6zCT94Tj/2EdRtKKAtW+Ml79MeUlZ7l7fXx44efZPWGcq/Pv3XF\nxV7/f8HSJd5eb/V1Ud795zvf9oW7v3y/j8ekoa6eD/7lX10wPDz8zHSWnze75E6limAp22u2eszb\nSHV4s7+8UCnVNX7y6msjBINrqQlXesmrq22isnwJ4Ro/L/DaulZCwfWEw35e4LW1LQTPGSW81U9B\nV9vYSah8NeFtxX7yDrSn8kq85VWG1vt7PGpbWO/5+VcRXO/x+Rel3OPrrb42yoayMrbVbPOSd6Du\nAJuC5VRV+xmctKHuACUVQaqqJxqzdzp5dZQHN3rtX5nn9S2r8Pd4NNQdYFOwwmP/6qkIraC6ZouX\nvPraCCW+n38V5V7/fxXB5d5ev+PKghXentMz8bKfJSfpUkkJSWOp3+k/Y6nhAF7uPhXL545XY4wx\nxswrs7WFyQFBJrh2nHNutgoX2zdpjDHGmAnN5i65yCwWR8YYY4wxOfOy+2t8l5ak6yQ9L6lf0t2S\nLpK0V9KgpIckLckx7x2SDkh6l6RmSQcl3SGpIK3NFZKeTWXvlfSmtHlPSPqEpHtTfYlLeldGf38s\naSB17bnX+fg/GGOMMWZ+8r2F6WaSF7BdCTwAXEPyum0dwHeBG4CnU22zjSaxAXhzKmMlcA8wAPxf\nSRXAd4C/AX4NXAZ8Q1Kzc258ZO8PA7eQvKjuDcBdkr7tnDuS6kt/arlzSV5/zhhjjDFmQr4Lpp3O\nud0Aku4A7nXOPZia3g2ESBZMAvqlE4bgcsCdzrmb0/q2wznXmlr+48DngP8LfAj4mnPuX1Jt90q6\nAHgHv70Uym7n3KfSlv0rICipENgCrHfOHUrN/zDwLZ//CGOMMcbMH74LphfT/h4GomnTI8D4kLEO\nCPO7B333pP0dHy+WUv4HWC7pHGA7UC3pPWnzA8BP0qafTfu7L/V7IVAFHBgvllJ+fdI1MvPeH7/7\nHjq6+r3lrTpvObFnP+stzxhjzOzzWTA54HjGbZMd1F2f5aDvzJFCF6bdTwHwGeBfM9qkj945dpLc\nAn73jDh/Y+EbY4wxZt6ZywNXrpd0jnNufKvT7wOdzrne1IHaa51z9eONJd0JPAf8ywRZ6fYDFRnZ\nl/juvDl9/MdX3ult4Mp///4zhLZs8pJljDFm7vBZME3lkoACyiRNtBWoKfU7ANwraSewCfgYcGdq\n3heAn0r6NfAU8EZgB5DLUKCPAgeAb6WyVwAfmULfjTHGGHOG8b1LbrLpzHn7M25T6vbxi9DEgZ+R\nLHASwFdJnvWGc+7Xkt4B3EbyQPAXgDc65xomuW+XWtZJuobklqjHSRZP7wUeyrJ+xhhjjDlDeSmY\nnHMxMo4Dcs6VZkzvSJuc9Jih8ZPnnHOfI1kQTXSf3yE5tMBE816TMT2Wfp/OuSbg8ozF7DgmY4wx\nxkzoZb9umzHGGGPM6cYKJmOMMcaYLOZkweScu885t2G2+2GMMcYYA3N7WIG5Ir++LuIlKJVTVF8X\nzdY0x7xoMq/WT/8AIo3NBPJG/OVFWihILPWW19DYjgv4O9ysIRLH9U52fsLUNDZ1Q+Eif3nRg6Cp\nnICaLa8TFi7zltfQ2M4gnp9/GvWWF4204OTv+RKJNDPk8fkcjTaR8Pg2HI1EGXP+8mKRiOfHIzrp\n2UDTyUP+EpuiUZzH7QhNkShFgSPe8iKNMUYS/t5fotEmr6+PpmiURfnLveWNf8Y11NVna5qTVM60\nXyBWMOWgvbmeQh31kBNP/o7VsUCHZ54X6wTg0es/y7M5vEk+nXeMCxKLJ21Tq0EW3rCd/IifJ33n\n3haK8sMov9BLXvzgEfKKekioKHvjHLS1HyGQfy467CcvPlCA685nZHFB9sY5aDkcgC4xusjPm3hr\nbx6B5i4kP3nxjl4ChfUsyPfzoXCos4WigjHy5Kdo72g/SFHgOAtcr5e87vYYC3WEgy+dkDszR9r3\nczjQQTd1XvL6O/ZzJL+Nw3rBS96xzv0c1gG6Rvd6yetvq+VwfpvX9e0t6qQ70OQl70jnCxwqPMLS\ngnYveX0H6+lYcC5FAT9XEjjU2cLigqMc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+ ""text/plain"": [ + """" + ] + }, + ""metadata"": {}, + ""output_type"": ""display_data"" + } + ], + ""source"": [ + ""# the code below just takes D and plots it\n"", + ""rcParams['pdf.fonttype'] = 42\n"", + ""figure(figsize=(5,5))\n"", + ""\n"", + ""hh = 0.02\n"", + ""ww = 0.065\n"", + ""color_name = 'RdYlBu_r'\n"", + ""for i, ct_x in enumerate( median_df_logn.columns[1:] ):\n"", + "" for j, ct_y in enumerate( median_df_m_logn.columns[1:] ):\n"", + "" colorvalue = clip( (D[j,i] + 0.425) / 0.85 ,0,1)\n"", + "" colormap = plt.cm.get_cmap(color_name)\n"", + "" edc = '0.02'\n"", + "" lww = 0.4\n"", + "" zzor = -1\n"", + "" gca().add_patch(Rectangle((i*ww,j*hh),0.98*ww,0.99*hh,ec=edc,lw=lww,\\\n"", + "" fc=colormap(colorvalue),clip_on=0, zorder=zzor))\n"", + "" \n"", + "" # It adds horizontal and vertical lines to the max horizontal of row- and columns-wise respectivelly\n"", + "" if D[j,i]==max(D[j,:]):\n"", + "" gca().add_patch(Rectangle((i*ww,(j+0.485)*hh),0.98*ww,0.03*hh,ec=edc,lw=1,\\\n"", + "" fc='none',clip_on=0, zorder=1000))\n"", + "" if D[j,i]==max(D[:,i]):\n"", + "" gca().add_patch(Rectangle(((i+0.485)*ww,j*hh),0.03*ww,0.99*hh,ec=edc,lw=1,\\\n"", + "" fc='none',clip_on=0, zorder=1000)) \n"", + ""\n"", + ""for j, ct_y in enumerate( median_df_m.columns[1:] ):\n"", + "" gca().text(-ww+0.05,hh*(j+0.5), ct_y,\\\n"", + "" fontdict={'fontsize':14}, va='center', ha='right')\n"", + ""for i, ct_x in enumerate( median_df_logn.columns[1:]):\n"", + "" gca().text(ww*(i+0.5)-0.03,hh*len(median_df_m.columns[1:])+0.01,ct_x,\\\n"", + "" fontdict={'fontsize':15}, va='bottom', ha='left',rotation=75)\n"", + ""axis('off')\n"", + ""ylim(0.,0.35)\n"", + ""tight_layout()"" + ] + } + ], + ""metadata"": { + ""kernelspec"": { + ""display_name"": ""Python 2"", + ""language"": ""python"", + ""name"": ""python2"" + }, + ""language_info"": { + ""codemirror_mode"": { + ""name"": ""ipython"", + ""version"": 2 + }, + ""file_extension"": "".py"", + ""mimetype"": ""text/x-python"", + ""name"": ""python"", + ""nbconvert_exporter"": ""python"", + ""pygments_lexer"": ""ipython2"", + ""version"": ""2.7.12"" + }, + ""toc"": { + ""toc_cell"": true, + ""toc_number_sections"": true, + ""toc_section_display"": ""none"", + ""toc_threshold"": 6, + ""toc_window_display"": true + }, + ""toc_position"": { + ""height"": ""306px"", + ""left"": ""1131.015625px"", + ""right"": ""20px"", + ""top"": ""113px"", + ""width"": ""189px"" + } + }, + ""nbformat"": 4, + ""nbformat_minor"": 0 +} +","Unknown" +"Pseudotime analysis","linnarsson-lab/ipynb-lamanno2016","Cef_tools.py",".py","7877","224","# Copyright (c) 2015 Gioele La Manno and Sten Linnarsson +# All rights reserved. +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: +# +# * Redistributions of source code must retain the above copyright notice, this +# list of conditions and the following disclaimer. +# +# * Redistributions in binary form must reproduce the above copyright notice, +# this list of conditions and the following disclaimer in the documentation +# and/or other materials provided with the distribution. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS ""AS IS"" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + +# This code contains a simple parser reader for Cef files +# typical usage to write a cef_file: +# +# cef = CEF_obj() +# cef.add_header('Mynote', 'This is my message') +# cef.set_matrix(your_array) +# cef.add_col_attr(attribute_name, attribute_values_list) +# cef.add_row_attr(attribute_name, attribute_values_list) +# cef.writeCEF('path/to/your/file.cef') +# +# to read: +# cef = CEF_obj() +# cef.readCEF('path/to/your/file.cef') +# your_array = cef.matrix +# attribute_values_list2 = cef.col_attr_values[2] +# ... + +class CEF_obj(object): + def __init__(self): + self.headers = 0 + self.row_attr = 0 + self.col_attr = 0 + self.rows = 0 + self.cols = 0 + self.flags = 0 + self.tab_fields = 7 + + self.header_names = [] + self.header_values = [] + + self.row_attr_names = [] + self.row_attr_values = [] + + self.col_attr_names = [] + self.col_attr_values = [] + + self.matrix = [] + + + def update(self): + self.headers = len( self.header_names) + self.row_attr = len(self.row_attr_names) + self.col_attr = len(self.col_attr_names) + self.rows = len(self.matrix) + self.cols = len(self.matrix[0]) + self.tab_fields = max(7, self.cols + self.row_attr + 1) + self.linestr = u'%s' + u'\t%s' * (self.tab_fields-1) + u'\n' + + def add_header(self, name, value): + self.header_names.append(name) + self.header_values.append(value) + + def add_row_attr(self, name, value): + self.row_attr_names.append(name) + self.row_attr_values.append(list(value)) + + def add_col_attr(self, name, value): + self.col_attr_names.append(name) + self.col_attr_values.append(list(value)) + + def set_matrix(self, matrix): + for row in matrix: + self.matrix.append(list(row)) + + def readCEF(self, filepath, matrix_dtype = 'auto'): + #Delete all the stored information + self.__init__() + #Start parsing + with open(filepath, 'rbU') as fin: + # Read cef file first line + self.header, self.row_attr, self.col_attr, self.rows,\ + self.cols, self.flags = fin.readline().rstrip('\n').split('\t')[1:7] + self.header = int(self.header) + self.row_attr = int( self.row_attr ) + self.col_attr = int(self.col_attr) + self.rows = int(self.rows) + self.cols = int(self.cols) + self.flags = int(self.flags) + self.row_attr_values = [[] for _ in xrange(self.row_attr)] + # Read header + for i in range(self.header): + name, value = fin.readline().rstrip('\n').split('\t')[:2] + self.header_names.append(name) + self.header_values.append(value) + # Read col attr + for i in range(self.col_attr): + line_col_attr = fin.readline().rstrip('\n').split('\t')[self.row_attr:] + self.col_attr_names.append( line_col_attr[0] ) + self.col_attr_values.append( line_col_attr[1:] ) + #Read row attr and matrix + self.row_attr_names += fin.readline().rstrip('\n').split('\t')[:self.row_attr] + for _ in xrange(self.rows): + linelist = fin.readline().rstrip('\n').split('\t') + for n, entry in enumerate( linelist[:self.row_attr] ): + self.row_attr_values[n].append( entry ) + if matrix_dtype == 'auto': + if sum(('.' in i) or ('e' in i) for i in linelist[self.row_attr+1:]) != 0: + matrix_dtype = float + else: + matrix_dtype = int + try: + self.matrix.append( [matrix_dtype(el) for el in linelist[self.row_attr+1:] ]) + except ValueError: + print repr(el), ' is invalid' + + + + def writeCEF(self, filepath, matrix_str_fmt = '%i'): + self.update() + with open(filepath, 'wb') as fout: + #Write cef file first line + fout.write( self.linestr % ( ('CEF', unicode(self.headers), unicode(self.row_attr),\ + unicode(self.col_attr) , unicode(self.rows), unicode(self.cols), unicode(self.flags) ) +\ + ('',) * (self.tab_fields - 7) ) ) + #Write header + for i in range(self.headers): + fout.write(self.linestr % ( (unicode( self.header_names[i]), unicode( self.header_values[i]) ) + ('',) * (self.tab_fields - 2) )) + #Write col attributes + for i in range( self.col_attr ): + fout.write( self.linestr % ( ('',) * (self.row_attr) + (unicode( self.col_attr_names[i] ),) + tuple( unicode(el) for el in self.col_attr_values[i] ) )) + #Write headers of row attributes + fout.write( self.linestr % ( tuple(unicode(el) for el in self.row_attr_names) + ('',)*(self.tab_fields-self.row_attr) ) ) + #Write rows + for i in range(self.rows): + for j in range(self.row_attr): + fout.write( unicode(self.row_attr_values[j][i]) + u'\t') + fout.write(u'\t') + fout.write(u'\t'.join( [unicode(matrix_str_fmt % el) for el in self.matrix[i]] ) ) + fout.write(u'\n') + + +def cef2df(filepath, index_ix=0, columns_ix=0): + '''Reads a cef file and returns a `data`, a `rows_annotations` and a `col_annotations` pandas dataframes. + + Inputs + ------ + + filename: str + + Returns + ------- + dataset: pandas.Dataframe + Dataframe containing the data (cef.matrix) + `indexes` will be cef. row_attr_values[index_ix] and `columns` cef.col_attr_values[columns_ix] + + rows_annotations: pandas.Dataframe + A pandas.Dataframe containing all the annotations for the rows (genes) + + cols_annotations: pandas.Dataframe + A pandas.Dataframe containing all the annotations for the columns (cells) + + headers: dict + a dictionary key (header name) : value (header text) + + ''' + try: + import pandas as pd + except: + print 'To run simple_cef3df you need to have pandas installed. This can be achieve by doing: pip install pandas' + return None + + cef = CEF_obj() + cef.readCEF(filepath) + df = pd.DataFrame( data= cef.matrix, index=cef.row_attr_values[index_ix], columns=cef.col_attr_values[columns_ix]) + + cols_annotations = pd.DataFrame(data = cef.col_attr_values,index=cef.col_attr_names, columns=df.columns, dtype=object) + rows_annotations = pd.DataFrame(data = cef.row_attr_values,index=cef.row_attr_names, columns=df.index, dtype=object) + + headers = dict(zip(cef.header_names, cef.header_values)) + + return df, rows_annotations, cols_annotations, headers + +def cef2df_simple(filepath): + '''Reads a cef file and returns a `data`, pandas dataframe. + It dorps the rest information. For all the info use cef2df + + Inputs + ------ + + filename: str + + Returns + ------- + dataset: pandas.Dataframe + Dataframe containing the data (cef.matrix) + `indexes` will be cef. row_attr_values[index_ix] and `columns` cef.col_attr_values[columns_ix] + + ''' + try: + import pandas as pd + except: + print 'To run simple_cef3df you need to have pandas installed. This can be achieve by doing: pip install pandas' + return None + + cef = CEF_obj() + cef.readCEF(filepath) + return pd.DataFrame( data= cef.matrix, index=cef.row_attr_values[0], columns=cef.col_attr_values[0]) + +","Python" +"Pseudotime analysis","linnarsson-lab/ipynb-lamanno2016","ipynb-lamanno2016-bayesGLM.ipynb",".ipynb","47435","474","{ + ""cells"": [ + { + ""cell_type"": ""markdown"", + ""metadata"": { + ""toc"": ""true"" + }, + ""source"": [ + ""# Table of Contents\n"", + ""

"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""# Imports"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 1, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [ + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Populating the interactive namespace from numpy and matplotlib\n"" + ] + } + ], + ""source"": [ + ""%pylab inline"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 2, + ""metadata"": { + ""code_folding"": [], + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""#imports\n"", + ""import pandas as pd\n"", + ""from Cef_tools import *\n"", + ""from itertools import izip\n"", + ""import pystan"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""# Define the model in Stan"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""\n"", + "" # Bayesian Negative Binomial regression for single-cell RNA-seq\n"", + ""\n"", + "" data {\n"", + "" int N; # number of outcomes\n"", + "" int K; # number of predictors\n"", + "" matrix[N,K] x; # predictor matrix \n"", + "" int y[N]; # outcomes\n"", + "" }\n"", + ""\n"", + "" parameters {\n"", + "" vector[K] beta; # coefficients\n"", + "" real r; # overdispersion\n"", + "" }\n"", + ""\n"", + "" model {\t\n"", + "" vector[N] mu;\n"", + "" vector[N] rv;\n"", + ""\n"", + "" # priors\n"", + "" r ~ cauchy(0, 1);\n"", + "" beta ~ pareto(1, 1.5);\n"", + ""\n"", + "" # vectorize the scale\n"", + "" for (n in 1:N) {\n"", + "" rv[n] <- square(r + 1) - 1;\n"", + "" }\n"", + ""\n"", + "" # regression\n"", + "" mu <- x * (beta - 1) + 0.001;\n"", + "" y ~ neg_binomial(mu ./ rv, 1 / rv[1]);\n"", + "" }\n"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""# Run the script to do MCMC sampling (using pyStan)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": null, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""%run pystan_bayesmodel.py -i data/Mouse_Embryo_nound.cef -o output_folder/ -p 20"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""# Load the Cef file\n"", + ""This is just reading the annotation, actual data is not used here. Only posteriors will be considered"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 3, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""cef = CEF_obj()\n"", + ""cef.readCEF('data/Mouse_Embryo_nound.cef')"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 4, + ""metadata"": { + ""code_folding"": [], + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""clustering_attribute = 'Cell_type'\n"", + ""for i,v in izip(cef.col_attr_names, cef.col_attr_values):\n"", + "" if i == clustering_attribute:\n"", + "" predictor_list = v\n"", + "" if 'total' in i.lower():\n"", + "" total_molecules = [float(j) for j in v ]\n"", + ""\n"", + ""for i,v in izip(cef.row_attr_names, cef.row_attr_values):\n"", + "" if 'gene' in i.lower():\n"", + "" gene_names = v\n"", + ""\n"", + ""total = sum(total_molecules)/len(total_molecules)\n"", + ""total_molecules_norm = [j/total for j in total_molecules]\n"", + ""predictors = ['Size']\n"", + ""for i in predictor_list:\n"", + "" if i not in predictors:\n"", + "" predictors.append(i)\n"", + ""\n"", + ""predictor_matrix = []\n"", + ""for i, c_p in enumerate( predictor_list ):\n"", + "" predictor_matrix.append( [total_molecules_norm[i]] + [float(c_p==p) for p in predictors[1:]] )"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""# Load the posteriors and define Binary Patterns"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 5, + ""metadata"": { + ""code_folding"": [], + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""#Load regression filenames and genes names\n"", + ""gene_names = cef.row_attr_values[0]\n"", + ""get_path_from_name = lambda x: 'output_folder/beta_%s.npy' % x\n"", + ""file_paths = map(get_path_from_name, gene_names )\n"", + ""\n"", + ""# !!!! For a quick test uncomment the following two lines !!!!!\n"", + ""file_paths = ['output_folder/beta_Th.npy','output_folder/beta_Ednrb.npy']\n"", + ""gene_names = ['Th','Ednrb']"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 6, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""order_predictors = ['Size','mEndo','mPeric', 'mMgl','mEpen', 'mRgl3', 'mRgl2', 'mRgl1',\n"", + "" 'mNProg', 'mNbM', 'mNbML2', 'mNbL2', 'mNbDA', 'mDA0', 'mDA1', 'mDA2',\n"", + "" 'mNbML1', 'mNbML3', 'mNbL1', 'mNbML4', 'mRN', 'mGaba1b', 'mGaba1a', 'mGaba2', 'mSert',\n"", + "" 'mOMTN', 'mNbML5']\n"", + ""reordering = [predictors.index(i) for i in order_predictors ]"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 7, + ""metadata"": { + ""code_folding"": [ + 1 + ], + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""x = array(predictor_matrix, dtype=float)\n"", + ""total_relative_to_basal = ( (x>0) * x[:,(array(predictors) == 'Size')] ).sum(0) / (x>0).sum(0)\n"", + ""reordered_total_relative_to_basal = total_relative_to_basal[reordering]"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 8, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""# Define Binary patterns\n"", + ""pattern_dict = {}\n"", + ""\n"", + ""for path, gene in zip(file_paths, gene_names):\n"", + "" d = load(path)[:,reordering] - 1 #because in the mode 1 is added\n"", + "" \n"", + "" # Condition 1 to define binary pattern\n"", + "" difference_basal = d[:,0:1]*reordered_total_relative_to_basal - d + 1e-12\n"", + "" #Check that the differece with the bassal is positive for almost all the realizations\n"", + "" bigger_condition = (difference_basal < 0).sum(0) >= 998 # For max strictness do == 1000\n"", + "" #Check if the gene is \""basal\""\n"", + "" bigger_condition[0] = (difference_basal > 0).mean() > 0.999\n"", + "" \n"", + "" #Condition 2 to define binary pattern\n"", + "" median_categories = median(d/reordered_total_relative_to_basal, axis=0)\n"", + "" median_condition = median_categories >= 0.35 *max(median_categories)\n"", + "" \n"", + "" #Condition 3 to define binary pattern\n"", + "" high_enough = (max(median_categories) > 0.4 )*ones_like(median_condition)\n"", + "" \n"", + "" #Logic and: all the condition must be satisfied\n"", + "" binary_pattern = bigger_condition & median_condition & high_enough\n"", + "" \n"", + "" #Store in the dictionary\n"", + "" pattern_dict[gene] = binary_pattern"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 9, + ""metadata"": { + ""code_folding"": [], + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""median_dict = {}\n"", + ""\n"", + ""for path, gene in zip(file_paths, gene_names):\n"", + "" d = load(path)[:,reordering] - 1 #because in the mode 1 is added\n"", + ""\n"", + "" medians_expression = d[:,0:1]*reordered_total_relative_to_basal + d + 1e-12\n"", + "" median_dict[gene] = median(medians_expression, 0)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 10, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""# Make a pattern dataframe\n"", + ""pattern_df = pd.DataFrame( pattern_dict, index=order_predictors, dtype=int).T\n"", + ""median_df = pd.DataFrame( median_dict, index=order_predictors, dtype=float).T"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""# Plot violins\n"", + ""of the posterior probability distribution for the NB mean parameters"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 11, + ""metadata"": { + ""collapsed"": true, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""gene = 'Ednrb' # the name of the gene to plot"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 12, + ""metadata"": { + ""collapsed"": true, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""from matplotlib.pyplot import violinplot"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 13, + ""metadata"": { + ""collapsed"": false, + ""hide_input"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [ + { + ""data"": { + ""image/png"": 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nW2o8Bv/E8737g/k7pFdbaD3qe96t+iri42/MiYDbwMeDRbNqUwquBPzjnGgCM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\nlo63flt4FDBEESvsmtmOeE2JbSUNXNb/vwLtR40zKeksdl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+ ""text/plain"": [ + """" + ] + }, + ""metadata"": {}, + ""output_type"": ""display_data"" + } + ], + ""source"": [ + ""rcParams['savefig.dpi'] = 100\n"", + ""\n"", + ""\n"", + ""d = load(get_path_from_name(gene))[:,reordering] - 1\n"", + ""binary_pattern = pattern_dict[gene]\n"", + ""maxy = 1.3*percentile(d[:,argmax(d.mean(0))],91.)\n"", + ""\n"", + ""fig = figure(figsize=(8,2.5))\n"", + ""\n"", + ""r = violinplot(d,widths=0.9, showmeans=True)\n"", + ""\n"", + ""for b in r['bodies']:\n"", + "" b.set_color(cm.rainbow(random.rand()))\n"", + "" b.set_zorder(100)\n"", + "" b.set_alpha(1)\n"", + ""for b in [r[u'cbars'],r[u'cmins'],r[u'cmaxes'],r[u'cmeans']]:\n"", + "" b.set_color('k')\n"", + "" b.set_alpha(1)\n"", + "" b.set_linewidth(0.3)\n"", + "" b.set_zorder(100)\n"", + ""\n"", + ""xticks(arange(len(order_predictors))+1, order_predictors, rotation=90)\n"", + ""\n"", + ""bar(arange(binary_pattern.size)+1, (2.01*maxy * binary_pattern),color='0.1',\\\n"", + "" width=1 , alpha=0.2, linewidth=0, align='center',zorder=-10)\n"", + ""\n"", + ""xticks(rotation=90,horizontalalignment='center', verticalalignment='top',\n"", + "" position=(0,0.02), fontsize='small')\n"", + ""yticks(fontsize='small')\n"", + ""\n"", + ""ylabel('Posterior\\n(Molecules/cell)', fontsize='medium', position=(-0.5,0.5), color=(0.2,0.2,0.2), alpha=0.8)\n"", + ""title(gene, position=(0.46,1), fontsize='xx-large')\n"", + ""\n"", + ""ylim(0, maxy)\n"", + ""\n"", + ""tight_layout()"" + ] + } + ], + ""metadata"": { + ""kernelspec"": { + ""display_name"": ""Python 2"", + ""language"": ""python"", + ""name"": ""python2"" + }, + ""language_info"": { + ""codemirror_mode"": { + ""name"": ""ipython"", + ""version"": 2 + }, + ""file_extension"": "".py"", + ""mimetype"": ""text/x-python"", + ""name"": ""python"", + ""nbconvert_exporter"": ""python"", + ""pygments_lexer"": ""ipython2"", + ""version"": ""2.7.12"" + }, + ""toc"": { + ""toc_cell"": true, + ""toc_number_sections"": true, + ""toc_section_display"": ""none"", + ""toc_threshold"": 6, + ""toc_window_display"": true + }, + ""toc_position"": { + ""height"": ""306px"", + ""left"": ""1131.015625px"", + ""right"": ""20px"", + ""top"": ""113px"", + ""width"": ""189px"" + } + }, + ""nbformat"": 4, + ""nbformat_minor"": 0 +} +","Unknown" +"Pseudotime analysis","linnarsson-lab/ipynb-lamanno2016","ipynb-lamanno2016-proliferation.ipynb",".ipynb","238521","731","{ + ""cells"": [ + { + ""cell_type"": ""markdown"", + ""metadata"": { + ""toc"": ""true"" + }, + ""source"": [ + ""# Table of Contents\n"", + ""

"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""# Imports"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 1, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [ + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Populating the interactive namespace from numpy and matplotlib\n"" + ] + } + ], + ""source"": [ + ""%pylab inline"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 2, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""from __future__ import division\n"", + ""from itertools import izip\n"", + ""import pandas as pd\n"", + ""from backSPIN import backSPIN, fit_CV\n"", + ""from Cef_tools import *\n"", + ""from scipy.spatial.distance import cdist, pdist, squareform\n"", + ""from sklearn.decomposition import PCA\n"", + ""from sklearn.manifold import TSNE\n"", + ""from sklearn.cross_validation import StratifiedShuffleSplit\n"", + ""from sklearn.linear_model import Lasso, LassoCV"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""# Load data with annotations"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""## Load cef file"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 3, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""df, rows_annot, cols_annot, headers = cef2df('data/Human_Embryo_fulldataset.cef')"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""## Load genes from Panther GO annotation"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 4, + ""metadata"": { + ""collapsed"": true, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""cell_cycle_putative = open('data/PANTHER_cell_cycle_genes.txt').read().split('\\n')"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""# Calculate correlation"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""## Prepare the dataset"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 5, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""#Limit the dataset to the putative cell_cycle genes\n"", + ""cell_cycle_putative = [i for i in cell_cycle_putative if i in df.index]\n"", + ""df = df.ix[cell_cycle_putative, :]\n"", + ""#Use only classified cells\n"", + ""df = df.ix[:, cols_annot.ix['Cell_type'] != 'Unk']\n"", + ""\n"", + ""# Filter away lowly expressed\n"", + ""df = df.ix[df.sum(1)>8, :]\n"", + ""df = df.ix[(df>1).sum(1)>5, :]\n"", + ""df = df.ix[(df>2).sum(1)>2, :]"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 6, + ""metadata"": { + ""collapsed"": true, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""# Log-transform and zero-center\n"", + ""df_log = log2(df + 1)\n"", + ""df_norm = df_log.subtract(df_log.mean(1), axis='rows')"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""## Calculate correlation and filter core genes"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 7, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""# Correlation between genes\n"", + ""CCg = corrcoef(df_norm)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 8, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""# Set a threshold for pairwise correlation\n"", + ""thrsh = percentile( CCg[ triu_indices(CCg.shape[0],1,CCg.shape[1]) ], 99.25)\n"", + ""# Select the genes that correlate above threshold with at least other 12\n"", + ""bool_ix = (CCg > thrsh).sum(0) > 12\n"", + ""df = df.ix[bool_ix,:]\n"", + ""df_log = df_log.ix[bool_ix,:]\n"", + ""df_norm = df_norm.ix[bool_ix,:]"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 9, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""from sklearn.cluster import KMeans\n"", + ""km = KMeans(3)\n"", + ""res = km.fit_predict(df_norm.T)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 10, + ""metadata"": { + ""code_folding"": [ + 0 + ], + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""colors_types = {'Sz': ( 0, 0, 0),\n"", + "" 'hEndo': (190, 10, 10),\n"", + "" 'hPeric': (225, 160, 30),\n"", + "" 'hMgl': (217, 245, 7),\n"", + "" 'hDA1': (170, 180, 170),\n"", + "" 'hDA2': (130, 140, 140),\n"", + "" 'hNbM': (180, 140, 130),\n"", + "" 'hNbML1': (100, 100, 240),\n"", + "" \n"", + "" 'hProgM': (80, 235, 255),\n"", + "" 'hProgFPM':(190, 235, 255),\n"", + "" 'hProgFPL':(210, 255, 215),\n"", + "" 'hProgBP': (230, 140, 120),\n"", + "" \n"", + "" 'hNProg': (255, 195, 28),\n"", + "" 'hNbML5': (139, 101, 100),\n"", + "" \n"", + "" 'hRgl1': (252, 183, 26),\n"", + "" 'hRgl3': (214, 194, 39),\n"", + "" \n"", + "" 'hRgl2c': (255, 120, 155),\n"", + "" 'hRgl2b': (250, 145, 45),\n"", + "" 'hRgl2a': (250, 125, 25),\n"", + "" \n"", + "" \n"", + "" 'hDA0': (190, 200, 190),\n"", + "" \n"", + "" \n"", + "" 'hOPC': (255, 35, 155),\n"", + "" \n"", + "" 'hRN': (199, 121, 41),\n"", + "" 'hNbGaba': (40, 55, 130),\n"", + "" 'hGaba': (7, 121, 61),\n"", + "" 'hOMTN': ( 95, 186, 70),\n"", + "" 'hSert': ( 50, 180, 180),\n"", + "" 'Unk':(255,255,255)}"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""# Identify the activelly proliferating cells"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""## Visualy check expression of well known cell cycle genes"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 12, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [ + { + ""data"": { + ""image/png"": 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GQurqpY9cbGqo4fnyfdT7ZwVuBblW9GrgdJ2u1J/T39zMw0Ept7fQDV319AUeO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\nmhqqq6s9kau/v5+2tjbGxs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+ ""text/plain"": [ + """" + ] + }, + ""metadata"": {}, + ""output_type"": ""display_data"" + } + ], + ""source"": [ + ""# Violin plot of gene expression of some genes\n"", + ""figure(figsize=(9.5,1.5))\n"", + ""for n,g in enumerate(['CDK1', 'TOP2A', 'PCNA', 'CKS2']):\n"", + "" subplot(1,4,n+1)\n"", + "" Y,ymeans = [df.ix[g, res == i].values for i in set(res)], [mean(df.ix[g, res == i]) for i in set(res)]\n"", + "" violinplot(Y, )\n"", + "" title(g)\n"", + "" ylim(0)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 13, + ""metadata"": { + ""collapsed"": true, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""cycling_cells_target = (res == argmax(ymeans)).astype(float) # Cluster 3, in red, is chosen as the cycling cells cluster"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""## Visualy check the separation in PCA space"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 14, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + """" + ] + }, + ""execution_count"": 14, + ""metadata"": {}, + ""output_type"": ""execute_result"" + }, + { + ""data"": { + ""image/png"": 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CBbP3GlCn4R48+6NcxvWQJ3ssvxxk9msztt+N9Zz2Ly7U/ecdj9lQjveUoOeMPezfR9W+Q\nfu6R7MXGwsvJHT4KVdPIrRoDQHzXkxQ2XQXADudPpPVTCPj3HS4eGDIxEqgtSxGAd9JnCA0einPu\nJVixCB2Dx9GW9JGfzlAUb8XobMOfasNX1kjAaCdQ8XkSc04iVV6E4/MTfmkll9sKDcUVnJyroygK\nJQUFOLEUhi0oCXpojFhEyAEbcowOCEFbLM2OtIpXKUItvZDchtvw1ozHRMWDzcYoZIRguC9BwhZI\nBIMDGhalyKQBu7bjoZ6qgkoqYl2kV79Az6xz8Ez6BcUBhUAwZ7/PpW3b9KxcjrlpNb5Jx1MwacY+\nccS2TNKbngXbwjd2HoFJnyKVX4n0hfE88v/QWlfjrLkbY8iL+PNLD/p17G14mtiK61ELp1A4axF6\nuOygH/th4yYf75PKykouvfRSbNsmfARXum9NvZVS7ncabjqdxnEcALxe78AwzD7bvfQSvtNPR1gW\n6TPPRH/kEdKlpYhvfAP/7NmYU6civvhFBOBZsgRPTg7B++8ntXAhpNOY/cdQdu0i+Je/IIDkBReg\n33cfyT/+keDZZw8cS587F+bOJbN9O5ZtI1UVGQjAWx/Qnh7sYcMIPPooAMlzz8W+/nrikydj+3zk\n/uAHAJgPPYT++98DkBICZeFC7J07kV4vjs+H4/NBJoMxdy6Bu+9G3HEHqVtuQVx77X6fAyuZxHvj\njWi7dqH86EeYF1+Mt/Dt3bfGc8/hP/dchOOQuvVW9Kuuwj91avaKY0w2UFsTJqDmHdoS1mL5ctT2\ndtT2dtLr14ObfLgOgl48k3TofnAkvsDhL5sujf6pt46DNPatPVLsPgTZz7hu9+DR9l/nEanfmB1e\nEaAPkQRbH8MMl+Gd/GlyRo4hbaSJRBUQgl7Hi+UvIjaskrKWDXj0IahyF7qiIErjxAcNB0BMGs/U\nB+/j+NPOo6hievZ3QlCeEwDAiNSDbYAyDpUMqtgMlGJKgQeHmKKDT8dXMpvhspNkdzumJ8zO3GoA\nOowMnWT3pSTT5I+ajrVuFQiB9PnpC+Qydcc6klNOYefQqSAEwkox9AA9EplYFPO158DKYKx6Cat2\nyj4rVKc3PYt23xUIwLBvITj1AgLDpuA4DsmScdC6GnvQJLz+wMG/iIDR/hqYfdjtz2H0bnOTj4+i\njo4OotEolZWVByzMlFIOjAceatVzLBbjySefpLm5mdNPP53a2sOrTq6trcU0TVRV3e9smOrqak49\n9VQSiQQTJkwgENj/m1k0N6O8tXCPbSM9HmT/VNX0s8/iufZaMmefTfr88zGnTcN3553ZpMFxUDs7\nYepU0r/7HdLvJ1Nbi3fDBpyTTsK48Ub0/bRLSon5xz8ienpIn346IpnESSRwHAfvmWeS7OhAW70a\nta8P85RTUOrqCP/kJ2SmTME46STUpibsqVORHg+YJuTlEZgxA+Pmm0lccQV2KoV6++0Ix8EeMQL5\n3HMkrrgC2dJC9M478VgW3osuQtH17PkLgarrGF/6EurPf07my1/Gt1cC8Vb9CJYF/YkWprnn+RMC\n5ZpriE2ciDZuHHpJybu+bnYmQ+a55xB5efhmzoQFCzCXLMEZORJ1+vR3fazro0FKSaJ7K3a6l1D5\nVFR1/+H0SOJIJtGG2PZ9lORmUsMXESg7vBqE0LjJSEDx+QmNHLPPv3tKzyBpxrAch3DZfDzvnObe\nz472gpMdD7FlEKn6IGcwAJ2pLjq0HvLUfDx2mCAmnfhBSsxwPiKnF5EZjbHjL/gtG8WwcLwavvwy\nci65hmDpvj2Wtm3T0/IgjYW1jFTvR1HaUPtmIPNnUx7QMMw0ScdGCoHuG4p/4zPkPf8DomPOp/uU\nX2CrXgKKA0524UZNSHJHjMFbUEK6dhpOupOz1/8er+2lN2c8wrEpttsxjDS9O7fixcFfMx9FzSYX\nQgi8oTBa7RSsdSvx1ExC2+u5GogjtsnAK23vWUtEURTEqf9FZNpnCQaL8ejvfmFqpZNkGl5GhEvw\nV0xAr5iL2bECrXACetHhLWvwYfGJXF69q6uLO++8k0gkwqxZs8jJyWHEiBEU7TWTQUrJ6tWrWb16\nNTU1NRx//PGHFDgaGhq48847AZg8eTLnnnvuUT+Pt8TjcTZs2ICu69TW1u63yMrs6sL64x8hncZe\nsAAtEMA3ZQpCCNLXX4//f/4HgMRTT0FzM57f/x5j/nyc0aPxDh8ODQ3oV14JQOwPf0AdNw4RDiOX\nLcMZMgT/ggVoOXu6KaWUJH7wA3x/+xv2xIlY5eXYoRDa/PkEzzmHTCRC+le/wvvqq2hvvknqkksI\n/fnPkMmQuP9+xNixOI8+igREYWE2GamoQNuxA6mqOJEIvsWLMU48EfHpT2Pt3EnOV7+KME3i//mf\nhP72N5J33QWZDOrjj2N/7nPoCxciLQuzrQ1PeTmKxzMwFKMsW4b83OfwnnYamYcegr4+PBdeiNZ/\nsz/HcTC++U303/6W9Be/iOcPf0B9l/vxJG67jcCVV4Kuk3rsMQKnnoplmiiq+qFbOtpdXv3wxDo2\n07P0EjC68NZ+B28gn8CQk9Fz9nyJSinpqruH9I4H8Y+4mKKaCw4pjqTal+LfejkA6ZKvoFd/72if\nxoB0pJdEfR1qKIfcMfuuj/HWNrHVryKFgn/oYDw+D/6y7NpE21NNxP3Z2V1l8WL6DD9djoewkyas\nJskJ+dHb7sXf8lMkEBl6K07RLPR4I07X60ilEn3YHLS9egNs22bX1j/xvfwrmaO8yiArzZQ1reRM\nGkqw/GSMeA/ta9cguzvx7tiG7htGoO272bZ+9l+Y5ZPoTAPSIUiUgs5/kfFUYvQ0ItQAenMT2q5V\nWOWTYfJFdIYraFaHgBBURl6jYvGVmPN/jXijEPW1dpwLRqHPGYFtW2SiEXy5eaiqhm1ZpF67A1pX\no0y6CO+waRjrHwfbxDf+HDQ9OzvPsix2WDtJ+tLkpEJU+ge/azyIvnAr/qU/wfaGsS+7m2DlZCzL\nQv0YxJGj3vMhhHgBeGvd2o1Syv33gR9D8XicSCQCQGtrKy+//DJTpkyhoKAAXdeZOHEijuOwfPly\nent72b17NzU1NeTnH/y9EkpLSxk3bhw7d+5kxAG62A+5aOkdMpkMqVSKtWvX8vzzzwOg6zqj91OL\noOTnk7r6asLhMPo7vjTl3LlY995LZs4cnLVrCfz619khgt27MZYuRR87llRXFxJAUdAqK/FMn44z\ndy7e114jM2ECifp6vLaNevHFePsXStP+8z9JGQbe9etRW1pwJk1C2bgR56yzyNxwA56tW/E/+yx2\nIIBn40aiP/oRSiqF7OhAbtqE/u9/Y40ejT1qFIFFi0idfjr6XvUe+HzIwYNRKythx45srwUgMhkc\nwPJ6CV15JUo8TlJK0jNmoA8Zgm+v6cRGYyP6VVchLIvMzp0wfz76RRft8/zZ6TSeRx4BwPPww9g3\n3YRauG/F/YDNmxGATKWw33wTTj0Vzb154EH7KMQRO9UJRvZ2Ambn62R2P4cR/Tpxfxg1OJj8qjPI\nJHtJrvsfyPSQjO/EHHkG3kMoFFTDYzFyTkdNb0HmnrjfbY40jliGgWmkia9ZgbnyBRAKSjBETuW+\nccsTChOYOR6/twxVffv7OSQDJO0UoV4D2fwSLRXZ2SogqVIS6Ho+SfWtCxQNv78QFQdnw1fxGjsw\n/DPobkmieHMJjZuMXlCEqqoUl5zKFc3raNKKKW2qQ801UYxGbPtEjOd/hy+Wg9O6Gzx+vK/5iF54\nO73VxfhUh3jSJqLo+DAJxLfi3/lbEtpcMi3ZpQo0/4konlyswklQNhHinQPn4ygCENh9OuEfrwQJ\n8UEWqalhAjklaAV7LlaN9i14lnwfAWSkgzpqFsGpF+7z/GXsDClPdvg84Uli2/a7JhF2dAcASiZO\nvKeO0NB9h3g+qo5q8iGECABRKeX+51N+SFRUVLBgwQK6u7sxDIPhw4dj2zZLly6lpqYGwzCYNm0a\ntbW1vPTSS9TW1hIKhbBtm82bN5NOpxk7diyWZSGlJBwO7/OhD4VCnHfeeViWNbDOh5SSlpYWAMrK\nynjllVdYt24dM2fOZNq0g19IByCZTLJ48WIaGhqYOHEiwMC0071Z0SiZv/yFlWVlvLR9OzNmzGD0\n6NGUrFqFsno1LFyIf/58rLVr4amn8H/ve6TnziVw332k582Dxx6DsWPxXXABiTvuQG7ahLJxI9aM\nGShv1aAoCoG//hVPYyOpaBR5880YL7+M7O5Gu+ACnFQKKzcXGQpl6zZsG3XDBoSUpM44g8zs2fiW\nL0dtaSH0+99jh0IkP/MZvOvW4Vm3jsit2SlqMi8Pc/RorDFjyNTWou/eTfhnPyPR2EjwmWeIXX89\nSjKJPW0axsKF6PPmkbngAmhtxbtiBRx/POn77sN/3HEDz49WXIx51ll4H3kE84wzONCKBpquk7r5\nZqyHHsI56yz0/h6RA5GzZpHasgXp8aC49R2H5KMSR0Ll08lMvoFMsh0rvxXKTsTq68Tc8gcM+1P0\nbYhScfYFeIedT6b+r/iGLUTz6NiWSaRxCdI2yRk2D8tMgaLiD+TtEwO8wXKscX/AcTLonmyPwFtF\nn0JV0YuLMRpvRe1dTLr8awSHnH1IcSQd7aNvycM4bc2I6mwcQQjEO+JIKmPSHDXRlF9heX5NsOOH\nhH3zUTu2Q18jWvXpFJeMIsfMwWpZjLX1ccKDTiOqBgmZEaz6x2H2l/EOOp+oCZlIM3bLRnJHDEPI\n7EWD1PzYazZiGynijoN/9nx60r04ehFTcnuo2bQaOz8Xp8yDUjiZTDKN2rMJXS8mUzkIPNMQmzO0\njaulK1iCamcIYpEWHjKolGrZIQ7hy0cWTcWq/QIJM5fM8y/DmnrSvlW0jjqBIZl6FCzyrBjW6b/B\nXzIRc24fydJmopU/QCzJp+Ck3xEqHTfw/Gg5JViVx6M2v4ozZPoBF2bTvTpFyTwSaoqwE0TT3/0r\n2Bw1k4jZihUI4CmtOOjX9aPgaPd8VAPDhRBLAQv4vpRy1VE+xrvq6elh165dlJWVUbKfcXkpJVJK\nZsyYwbp163ik/2p25syZTJgwgR07drBx40Z6e3sJh8NcdtllmKZJJpOhpaWF+++/H4BEIsGbb74J\nwAknnMD06dP3+dBrmva28cCtW7dy7733oigKc+bMGWhnXV0dBQUFjHyP5cKllEQiEYQQRCIRNm7c\nCGSXVZ83bx55eXn77MN65hl8//u/bPyv/2LIkCGsWrWKxMaNnHf99YhMhnRTE9xxB56cHOxRo7Cq\nq/G99BLGrFmomzZhnnYaxvr1yNZWaGpCtLYi29rI3HorzoUXYtbU4OTkoO7cmX18RQXGa6/hmzcP\nkU6T/M1vsAMBwosW4YTDmAsWQDSKNWYMIhbDrKkheNVVaNddh3311QAohoFdVYXUtGy9yKhRpJ99\nFqW8HOOxxwh95zuIVArPunUAqE1NKOk0vjVr8Cxe/LYETPzf/2Hceiv+Z57JPh+rV8NeyYcaCiH/\n8hfiV1yBdtttmHV12D/5Cd6hQ9/2PAohCFx4IfKCg+s2DyxciJGXh7As/Kce/lLXn1DHPI5Etm0j\ntn49udOmEa7YN+hLKUHRKBp/OZ2Jf5JQ/hsk6Lt/RGrzZ+j41T1YHX8m9aNtFCwYTmjBfRBIYNkJ\nEk0vE3s5+17PJG8gs+WPCE8Yf+3XKRmz7/CspnmAPVe7kc0bSD55D3j8dJ53KUIMJ98/FW/nA6QD\nQ9ELJ7zruUkpMfraUTQfmc4unB1bAHAyfXhPXoA3v4zQ4Le//3cnLbocm2Lf7XjaT8d+9VaS+esp\naHwTIR0ymQS+U7+D3+sjmT8MM28kScVPUGYwJBAsIbarGSedJNneid07ilXDx+OPOowt/TH+xAai\nubPwFtXhhPMRuQV0p3to9WWXKygsyCFRdRK78svQHJsJqoWm2LSe8WNMJUCorZPyUWNRz9Vwdndn\nz0fRCFp9RBWd/HQHIaGSmfgvwv4hJIVFMs9Bb0ngiUYBUPs6sBSNlG8oE4r8CLGnTs/8xRyMDX+B\nbb3IeC9G75a3JR+ecBHywlvpjidp8Zbh681QFVYJ+N5eTyiEoDxYtqc25D0UjjqXvrxCvMJDQcnx\n77n9R8mj+l+sAAAgAElEQVTRTj4s4BYp5Z+EEDXA40KIUVJK5ygfZ78SiQSPPfYYO3bsoKKigksu\nueRtq4tKKVm3bh2bNm3CcRwK9rp6VVWVQCAwMBwTiUTYvn07u3fvZsuWLYwdO5axe923JBqNUlxc\njGVZdHR00NzczNB3fGG9U19fH7ZtM2TIEJYuXYrjOIwYMYKmpiaKioreM/lobGzkwQcfRFEUTjvt\nNEaPHk1HRwe2bePz+d7WvgGDBkE0yoxIhN2lpUhVZfTu3STOO4/Qvfci9xqC8E2ahHHLLaQWL0Z7\n9lmsCy9EnTwZbd481N27if7wh3g6OwemziYuvhh98WIy06djnHUWOdddh9LTg1lRAf2recqdO9E2\nbkQASiyG7OzE6u1Fa2lBf/hhzGHDkJdcgpKXh/qNb5DIz8epqEB6vfTedBNqayssW4aVn49+/PHI\nqiqkEHhfeYX4z3+O1tCANWMG7NwJZ5wxkHhIKck0N6OEw3jOOw9j9WoAxNy5A+drtrdj3XQTorMT\nZcQI/I8/DkB6wQI4wGt5sFeViqKgu0nH4Tq2caSnjc1XfYnEq6+T96lPMfGOO952FSulpGvTA9g7\nXyOQymBOKYC3RuA0P2ZXBqutDQBjSyOZsatJFYUx1QdJxr9OgD1fIlZiJ874aqQvTcpcQzQ1nBz9\n3afU2pEecByS42bS7BsK/mEMVbyMaPky6e5x8B7JR7LhFdRnrscOFGJP/hoMrURGojhyM1p+ycD0\n2735FDBlAMv4Pv6uBsziAMmKT6P4a8jfeAcEigY+G3rlZNALKHeiRPFSRAKneCKJ+/8KVgZf7Uxe\nKh3HP0qrQEq+1tdA9RsxvCN2kpizkDY1n5A0GGJH31o6BDuVwjKzxeOWUMjYkgxJYkopEZlDpLSY\nEiHwqioVQR++2E78wiS4cwV5ycEQ00mW2ShKK8HJJ+DLdIPsJV0aJHTifJSebpyRtQxWDUr8YuBc\npJS0pwzCAQ+hUadhJdYhvGECZXuKxlN9O+ld9weknaFv7A+JCR8xCYVGmsABFrc+2DiiqhqFh1lk\n/GF3tJOPDVLK9QBSyk1CiC6gDGjde6Mbb7xx4Oc5c+Yw5yit/LhhwwZisexdGPv6+qirq2PChAms\nXr2anp4exo8fz9q1a3Ech6amJnRd57jjjiM3N5eenh66urqYMmUK3d3d5PcvBV5fX8+ECRNIp9MU\nFhZyyimn0NbWRk5ODtu3bycnJwfHcXj66af5whe+MDAel8lk2LFjB6FQiPLycoQQVFdXE4lEUFWV\n5uZmgIFx2rKy954y1dbWNnC32ra2NkzTJBgMEo1GGbSf5cgBfMcdR/y++6j53e+oiMXQ2tspuOsu\nrEGDiD30EP7Zs0n9+9+I+nrEuefinzQJ37XXwrXX4tg2xqpVKP2LpeHz4YRC2doPIbDLy8lMm4b0\nePCsXIlwHLyrVpHq6yP9j39ARwfMmgWbNpE680zsGTPQTzkFq6MDp78+Q9u1CyMSgYoK/NXVsGgR\nqaYmlG9/G6esDG3rVnxLlpC65BLM117Dd955pJ95BlSV0Mknk161Cs+zzyJPPhn/jBkD551++GF8\nl1yCPX483H03nn9lbzG+d6+IvXw5+m9/C0Ds5z/HLi5GFhYixh/+egofBcuWLWPZsmXHuhnv5pjG\nkUzLk9n6HyC9aycr+xJMDXvp23wvTqqDwMiFpDb9kRw5DH/L66g9RcjTfoIWHI7cYpBfFcT3sx8T\nWbYc//gqhLoVdenraLXfwA7F8RVPpyfz/0i8uYFwYTFO9eNonZU4gQTd5k8J+e4eeJ9m4nG6X30V\nvaKCvP6p3oHRtdjJGMqgPRcOjhJE4kEGD+L+UB11KJkoZKIoXRvwUkeiqBNFqvgKv7Lfh5SF/TjR\ndoy1pagE6Rg/l6ivmJ6ikxk78mQCpWOIr30IGd+NVjWPQOlIqvoLgm1bJ964FazsrA/Hl4+3/98U\nIBTKp+W8r6IKsBUJQhDHh9OZpEjJxxEOOZYkVLcV4dh4cwMUFuaTyPixHBUEGGiYtokOFObmUpib\nS7R9C07La2yuvRhrcCk5ehKxzkJr30RJ+Tg8hgcFhfyZY6iLpdhoCcZ5IT+4Z3HD5/tS3Gr6qREm\nX8utYtC827Lt3iuOJFtfwdyejS/+YZeiBCfgFzYh7cNVEHq0HWkcOdrJx3eFEF4p5Y1CiHIgDLS9\nc6O9g8bRlE6nCYVChEIh8vPzefLJJwkEAjzxxBNAtnJ69OjRdHV10dXVhaZpNDY2MmrUKFb2r6pp\nWRZDhgxB0zT8fj/Dhw+nra2N9vZ2Bg0axKuvvko6naajo4MhQ4awfv164vE4VVVVZDKZgeTj5Zdf\n5sUXXyQQCHDppZeiaRr19fVUVlYihGD8+PFYloWu61xxxRVvSx4Mw+CVV14hlUpRU1PD8OHZ+fDh\ncJhRo0YRCoUoLCykra2NcDjMoEGDKC8vR0rJ5s2b6evrY8yYMQMJVGj+fNLJJMG6OmR/zYmIxfBO\nnIi9axfi9ttRkkmsrVtJXHNNtjh00CC0730PNRAg/qc/4Wluxnf++TgXXURi3jyU0lLk66+Dzweq\nSqayEuXUU7EHD0bp6MAZMgRl/XrE6NEot92GjMUI9heiqhUVJL73PSInnIAoL8ezaRNWeTlafwGn\n2d2NKCsj9Ic/IL1ejAULwDAQlZUoqoq3/0vGcRzU667Du2wZ9uDBmG++ibd/qE289hpKKoXy+uuk\nt2zZp+4i09iI3dxM4tOfRl+8GHXiRKw1a7L3jSkuxorFsNrb8Y4YgfKOsdu3Zll8WG46daje+UX9\n4x//+Ng1Zv+OaRzx+boZ9aMZRNYbNM+5hLvtAD9re4PMmz/MbiAUvMM/Q6q1E4+nHqkUoj1jQ1UY\n1r2BAgSHDUO7fBZaySBE2oDSEGxfiYhtoWf4NHZe92MwTYyWNsryJkHDEpygiTNoFubkNL5AACkl\nDbf8hrafLsI7fDgTFy9GCRokW17CN2ocirmTiu5mHE+YfLseo/Yp9LxRA+eRSSWJrHwWaSYJjp1M\nsDy77oXtL8EumoksGE4mtwbPjjWEc0ZhVZ5KIL8yO7y7eT12IkaouhZfODc7XDColL6eMjKxKI6a\n7YVwFBXvoInYHXVk6ldhpAoRfa8QO76QjOpDxabJ8OLJG8agBRej9bQTrK1hVkYQsHoJ+r0ECkpI\n+IJYQNhpBtLoTidaWx/J3BlEbQU1R1Jw6iSGmxa+0kKEEOToYSpSUfrMKOG+duzOFOaI0Xj0bG2M\nFe/m6Slf577gcPSkw5W0kh/MRwuXoKoq+d5sjU3SNPlj2kOL0BiaNFnkMweK8jfbAksI1uOl3UxT\n5H/7MMrOpMG6gtmUjvwixc33UaCmKAmbaIqC7vNhJiJY8W78JcP3iRef9DhytJOPW4C7hBAvka1U\n//IHMR/urfGzyZMnoygKvb291NXVMWTIEEKhEMFgkEQiMdBLsXv3bsrLy7Ftm9bWVgzDoLa2lk2b\nNqHrOvF4nI0bN+L3+8nLyyMYDCKlpLu7m4r+8d9MJoPX60XXdUKhECtWrMAwDE455RTS6TS7du0C\nsoWhyWSSuro6Vq9ejaZpHHfccazrr1eYPXs2vb296LrOzp07KS4uZteuXQM3jkulUgPH3LhxI1u3\nbkUIwYgRI2hra8NxnIHhnpaWFh544AFs2yYajbJgQfb+CkII9IUL2TZ+PGsffJAxBQUokyeT7uuj\n2u8Hjwff8uX4li8nmUzif/BBElddhWfZsuyMjQsuwP+Tn2DF48jmZvRLL8WyLMRvfoN/6VLsggLS\ns2ZhDhuGPXgwcupUci+4ACUWI/PGGyirV6MNHjzweqVefRXuuQclkUCpq0NmMhhbt6J+97vZWTKa\nhmhoQNg2IpXCGj8e9bOfxVdVRezxx+Hpp3FyctDOOw+tP9lwysoQe6/Xcu65GGvXYo8fj+ztzU67\nHT0az4gRqPn5OD/9KYG77sKqqCD50ksEJ0/OjoU/8ADJhgbkzp0Ebr2V1C23oF999UCAyOzYgf2r\nXyGTSayaGhSfD+/ChXiPYBVb1z6OaRzRBn2O4MwwzceV8+fwOUwnjd8bJKOFwYohg4NI9nTTfc9S\nPOExFB83FmFE8OnbcSaVY67djfRF8PrDKK9tRIZ07KIu8A+BaB3SbCJ00kmoF1+Gc8Js0pl6vE3r\n6b7XIPrEf2Ncn2LEt75OJtZGatsaADJNTRiRCOn6P2G3PE7SW4A24hK8m28BFMxJPyLd14Ij/Rhd\na/EV1pDcsgPz9VcBSNop/CVV2YR91e14dq1EajrOhfNIRish4uAfnU3+Y00NJJ+4B6TESacoPvE0\nIHu1XzBhGj3bn6Vg8yL04lMIqB7S0TQ+7xAyTimyow3Z0UnH8OPoKq6iSCZIimzNSmllNeXjJ5FJ\nRXCc3ZxUMIJUJMp6T5iU4sfrZCiMvkYhazDkaGTxWeywdBACK5GitDTnbcOq0catWLuaoTKIkfc4\nThuYa06j5PhTAGgtGs8WQ0UKQRKVTMpDfvFUPDmltPbUkZJrUZIhcrTR5KnDaQFykahiT4/F8V5o\nTWeodVKUbt9Kt6rhVIwirPvwqAq3J2CDUkrF6G/znZovkFc4GMd2MB7bQqIziu17CF/j3STO/B9C\nMz4zsN9kRzuxlS/haArGlImofj9FvkJ0r/4+v8s/PI5q8iGljAHv34IW+x6PTZs2sX79eoYNG8b0\n6dMH7nVSXl5OXl4eL7/8MjNnzqSoqIhIJMLTTz9NIBAgPz+fiRMnUlJSwooVKxBCMGXKFGKxGJZl\nUVhYSEdHB0VFRQSDQSZOnEhraysTJkzgjTfeoKenh9bWVk4++WReeeUVALZt20ZfXx8TJ04kmUwy\nbNgwcnJy8Hg8KIrCiBEjkFIO1KHouk5vby+bNm2ipKSEuro6cnNzOf74PWPCmqbx6KOPEolEBmpU\nSktLqaio4OKLLyadTg8stR6Lxd42VvnW3+3t7RiGkV0PJJViQ0kJwzwemh9/nMKqKgoqKpA+H9aY\nMaj9Y9Uik8EcPRqhaZiZDGo8jv21r+G//XZSN9yAcs01yP5FumQgQOD++zFOO42cm24iefnlZKZO\nxb9sGda0aW+7K6YZj6N96Ut46upIXnABgQceQALx/nMA0HJzsTMZUmedhT1sGP7rr0fLzye+fTvK\n/fcT/Mc/MKuqSAwejHbTTaTPOw8xceLbFg3zz5qF88QT2RvDXXABAkj131fG/Mc/sAIBOPnk7DTF\npiaSDzxAZvRofIsXo8RimJWVCClRn3wSudcy7M6SJei/+x324MGoW7fie+kl0s8/j33vvagHWJjp\nYGS2bcN55RXElCn4DnNBuo+LYxFHUq1PoHQ/gZ13KoGKhdSV/gd/TWmMkQb5WNymjuTUWf+kSkmS\nzF2DOfS/yPnVUPquSSOGf5ZgzhpyYj9H+r10nnQ1fXoNjr+anI5n0dp2IErKyCRz0HI+S379Urzf\nvZb6qrOwhUraKWDoyG8Seza7jk7fU0/QOX07/mELyTnORvEvQBkzBqskBzpGIYrOQ7HzEWr/sIte\njtmzCXPHgwi9DKdtCfG8ifjLv77nJFWNtue/irQscvJH4tm1EqtiOsFh1Sjn5oJlERqZnaKfSXRk\nVyOWEtgTR3qTaRwJMtmJp+kuPE13IUpOJt69ksyoX0OoGMRuZOlgksH+Ihgh8DgWHieD6Gkm7Smn\n++XrsFqexj/xB/hHX4pqZGdUa06CcN8dZPT55LzwZ+I1CuGKzxDLDxPsiyPK9nwxp6MREk/dh5aM\n4x/Whxm6HbtKoDXtqVcJez2YBkxy0gwSNrMKivGrKrHuRpLa77E9f0N4TiDd8W2uHDScBjPNKK+C\nd6/P8eQcnQlBm8j6etLPPpR9feZeTGvlGEYFbcKORTUOGpLemMC7ZgXpUC6Z/BR2hZ/w+hn4nTtg\n50rk9D33g0o1bMLe9CZOUSmJ0Dhsf4xMymK4VnlE63ckuupJd67DXzKZYOG71xAeax/ZFU4jkQhN\nTU1s3ryZzZs3s2XLFkaMGEFubi6hUIimpiby8/NpamrCsiy6u7sHisaSySTjx4+noKCAnp4eIPvh\nUlWV+vp6VFVlwoQJ5OfnEwwGicfjdHd3U11dTXt7OyUlJfT09FBWVkY0Gh1ITAYPHoxlWbS1tTFi\nxAg6Ojro7e3ljTfeYN26dQwePJhQKERLSwvnnnsuvb29LF++nNzcXAKBAEOHDs12B+bnM3/+fAzD\nIBwO83h/IaTP52PGjBlMmTKFcDhMbm7uwPPx1jTecePGEYvFKCoqYtu2bWiaxj333IN0HC4PBvlK\nNMrWKVN4s6eHgoICfNu2EfzLXzBOOonMddehtLVhjx+POXMmVlkZyu7dBL/yFRJVVQT//W8A1Ecf\nxY5EUBsaiF91FZ5Nm3A8HpT2dowzz0RbuRJr2jRiNTWIvYpAAYSiYBcUkDz/fKTXS2rePHyvvII4\n6aSBD6Z36FCMX/8auX07/pNOQsvPJ3bPPXh/8hOc/l4ebceO7Mybn/0M5YYb8O5nRoKiKOA4AysN\n2mVleJ54gvg//4kWi+FfsiT7++HD8axahbJmDf4nnkAA9qWXkjn1VOwrr8QrBJnGRpwdO3Cqq7EL\nCzErKlCbmrLPx+bNOJnMYSUfUkqS996LeOABlK4ulFtuwXz2WbScHKSUBwxEZksL9pIlMGoUvlmz\nPrJdt8daa1sP6zftpFo8ylDtCZzYC2SKZlOihvEKqMNHsWPTZFsoW18GowtzVme2EDLYROEXv0y4\nPACpeHaHMoMTKqW96HQEkvrZF9CXSlCz+hk8i1/A6WpDue5sMDcRNo6jz19CONYBooPi//omyZWr\nCP3H53GGWHhSjYRqzyQy7A1e86c4+9UXUBp2Q2UtzqBOMDoJjL+bVOIN5Pb/hXANPfnTqS8/h8HJ\nnVSXF+CZPQ9hpbHDCew3soXi8aL/xJx9E4GJc/DoYbwj3r44oNPRhKipwjaasSqL6Un1YmS8bDb8\nKLZFVbQcb+lPsAOd2B2PoOTV4vRFEfV1UD4UfebJlOoG6UwDeal2SlsacGImcst24p86D6slezPK\nTOdq2oZciq1oDEmtpbj3djLGBFK783HKz0TtWseQ7gJQcghWjEaIPbMXFUVB+AJ4qyWBSIKodw7p\nwHr00j01L5W6jy9Ig24Lxgf96JpGtOEe1F1/h0n9BfHKWmRHFO+2xRw/6zR8+r73c1FVFfaqdfaL\nJN22QnzNPZQUTOHFguysl2nJTpzpYfB4yPQP/cj80fjlbMS4cxFCkOjcjRWLIApLQfMiC4twvNkE\nz1QsDreDz3Ecdjc9SLLzedSudpKNj+Cb/zcURR14vvYnYSSIOnF0fOTph798/+H4SCYfjuOwdOlS\n1q9fT1FREfn5+RQWFhIKhdB1nYsvvphoNEpzczO9vb1s27YNgOOPP56pU6cSDofx+XzcddddVFdX\nM2rUqIHCT6/XS3l5OWvWrEFKSVVVFZ2dncTjcVpbW9m1axcTJkxg5MiRaJpGX1/fwFTctWvXUlJS\nQmNjI6qqMn36dFRVHTi+ruvU19dn6zBCIWKxGFJK+vr6iMfjNDU14fF4yM3NJRwOM2nSJHbt2kVB\nQQGJRAKrv0jznnvuYfTo0cydO3egF2Xbtm20trZi2zZFRUU899xzpFIpZs2ahWEYHO/xUPaNb2DM\nmEFtbi7Dpk9Hqakh76KLSM+fj5OTg/e445Df/z6mrkNjI2pjIzgOsW99C295Oenf/hb10Ucxr7gC\nzx/+gG/NGpRkktRnPoP3xRcJ3XEHdjhM4utfJ+en2VUM4+efT2r7dshk8AwejBYOk1q0iNC8eYhU\nivT8+aRfeAF/dTWpP/0pu92ll6LV1JBYtgz7m98kXVKCEwjg27iRZFUVicsuwx45kpxFixCGQerU\nU+Gzn93ve8V7zjnEf/Qj1DfeQF+8mOTVVxP4859xBg/GKirKLs8+ciTe227DmD0bu7gYtbMzey+a\nhx9GDwYx29pQzjsP77p1JH/2M8zXX8eKxTBXrMDcvBlx1ln49cPrLjW2bCHw+c9npz3PnQvxOGZX\nF/YPfoDYsQP5gx/gnznzbY+RUmL99rfoN9+Mk59PZsUKfKNGHeAIrgMxTYuf/e+TPPdSA7OmTeaW\nz7yJyJ2N5s1hsOblR4pB1LJ4Iy0Y1Lic2uWLso8b+m3E0KtQndH4wkE8/7oIY9rFxMPnINFJZULZ\nex9Jgzt9BUh/IekxJ1P7/ZuQlom9exnCbKQ0PYqS/gJyJ9hB/PmXEDUT6TzuLEq7fkpu4h+EyKHH\n+SGTcwRiw3YA7NHNpKtuBvx4Xvo9VmQNKg4kG1ky+Hxe9RaS45h8Z8efyfPr5NScT7rpRdCHgJ1B\npv0YuTESz38O34iLKJ589cDS8N31TWyzqimJRdCrBtE1yIdU2vEmSvj/7L15YNx1nf//+Jxzz2SS\nyX0fzdmQpk1PWqEUyn0WUI6CgKwgKqK7surCqqv7XRdXf66C4AXLV1QOAREFBFEOaaGlLWnTpmnS\n3Pdkkrk/M5/r+8ekAQR3xWUP/fH8azL5ZM533p/X5/V6HrbgpmCmH+Glp9AaO8lWXoS77kx8gkXq\nqSehtBrcPryVtbgO3kgq20p2oQw7oSFLMYzWDhz+KqwVnyc7/SLOZZeQtCUyokJUKSHorkPfbcD8\nUdIeL9SvgO5XQRDIrG9lIZlCEHTcqhuHz4/75HX4Ri5BWDCR7fMw67+Cw9/EeHoSwRYoVAuoUSWc\nvbvpD1ahuFwUzU/iTu0nMH46iaIWpHgH4o7XMG0brbYRR9Pbdx39LStIWRq6YpFsyqMsEcb329to\nWXEV7kAjqihQ4rTI+kVUzUYwbWxRwBZncFzxQ2RZJhWeIfrwPRBfQN14GoHLPkLWNLGTFoZskif4\n39Yb5I9BbP4A86O3AhZq/vGI02FS2TSzQgRLsCgmhN/55sLKsiymrBmSTg3JFFEzKm7HO8ua+c/g\nz7L4OOZ3ATnJ60UXXURpaSmuxRNAIBAgEAhQVlZGQUEB8/PzxGIxfD4fBQUF7N+/H1mWsW0bRVFI\nJBLouk5VVRVdXV1YlsXc3ByJRAKHw0FFRQVOp5OFhQXKysrYu3cvtm1TVVW11Gk5Zib2xgpTFEXC\n4TAFBQU4nU7ciySyY46EgUCAxsZGgsEg4XDOKVEQBBRF4fnnn2diYgK3240kSWzcuBFVVZeKkb17\n91JcXEw0GsXv9zM5Ocng4CCKolBeXk56MccllUpRV1eHyzDQly3DaGvD+81v4pEkMvffj/nJTyI+\n+ijC+eej+v3Ei4tRX3kFbBvXL3+JLcskrrqK7PPPI+/ZQ/b978/Zo+fnkyothUQCz9e/TubsnB+U\nVVeHXVlJ+rzzMEMhHH/918h9fWinnYbhcpG58UaQZYy6OpSeHiyfD/P550nffz/K736H86WXSDsc\nGOXlKL/4xZKjaeLqq9G2bEGanSV73XVw+DCW04lVUkJmfh45HEYJhTA0jdRddyGOjyNeeCHuNWuQ\nGhtxfvGLoKpIGzaQTSZx3XknyY98BGn7dpyhEMmZGaQ9e0h84hNIY2OgaWR/9zus/Pxcou2BA7nv\ntK8PR20tTkGARXO3/wzEQACzoQH54MGct8kXvoDY04PzW98CQOvogN8rPgDILuZFZDLYpvnW37+H\n/xC6YTI8ugDAyESGZN3dBAqqkeUcd6jQ6aAQqHQazOQXYboKELV5HNlK5ORq9JlnkFMZBFPHdjiR\nlaMIooXfuY7quWdZkEL4/K3EJIU8JY3w6RtYWLWReOSH5KUrEPbvznGqapsQRAXPhk6ETSfjcMQQ\nFscdCDZFAjgHfkM8XIQzLw/Ls9h5QSNdNkymr4rM4RPxnNCBraVALUC0bdwegeL5z5PoPYw1nEcg\nGiLdeQmms4GYMIfovBJ6v8m8uhYrFkf05/Gkv4qfrqgmzzD4ZHIAW1rMRDFGKIz24QCsUBnzq08l\nJnmRbIsWl46jcz36cD+OZa3IskxabCQ7YmLrYezxISxJgnqdeGqEbEkb7tINFBQ1UhdPM6NNoaiP\nM1/0VbyBv4P5MAQKEDwF9Bx/FsnSOvKVAsykSIFjHg8LFE1GEUQZQ6lE0YfQ5CqEkV0k2MVc9Vpw\nyUiaiHzoAAtyEWFfjtDvpAGnug739ByO4OeIxw2QJrCC+WhM485UoTj8ZFNxYrsex9bSuNvW4alo\nRWxpJu6cBxvynE70486lZc93+YdgMe728/C5QkzHZsjKFp45A0vQESOHiNoSqieErbkgnltvZmwe\nT2ExHuCP983+w5CVAKJchGVMIbtqCK75GClBI+nMnQdiWgI/b+3qHDNDtwT7T+66/Kn4syw+JEli\n8+bNHDlyhPLycvx+P7FYDLfb/ab2syRJNDc3oygKBw8eZGJigr179zI7O4vX66WrqyuXHbBIDp2e\nnubss8/m7rvvxu/3U1BQQDAY5OjRo0xMTFBaWkp7ezuapqHrOl6vl6GhIRoaGujt7SWbzbJy5Uqa\nm5vJZDJEIhGcTufSSMfr9bJq1aolQuixkVBNTQ1VVVXk5eXhdrvp6ekBcr4e5eXlOJ1OBgYG8Pv9\n7N+/H8iZoj3xxBMYhkFtbe1S4aXrOrZtc/LJJxOLxXKZC0ePEi0ooO673yXvjjuQFgud7IEDuG+4\nAfv9r88ipbm53Kx3kbxpBQKYJSW4vvtd5JkZtIUF4tksqseDtW0brquuwvZ4MK64gnhLC9LOnYjx\nOJbHA5KE1NeHWVGB7XZjR6MoH/sYViCQ6zKUl2MEg/g/+UkA4n/1VwiZDObcHJbXi1lUlAuWE0Xs\n8nLU++8nfcEFOdfQ3btJXn01YjSKtHcvWiaDctNNpH/5Szy33IIUj5NIp2HNGhzvfz9acTG4XKhr\n18KmTWSuvx53ScmS5bl6222k+/sJLF+OkM2SPuss1PPPRxBFMr/9Lem77845wn7gA+9oxGEmk2Qf\nfdrkojUAACAASURBVBQcDhznnov4e9bIamkpmUcewRgYwLF+PXJeHpm+PvQVK5D6+rDfpvAQBAHp\nxhvR6uqwW1pyEuX38I7hdjn4u0+dysuvDrK6s5p5wUFkPsKyYu+bvmNVlimv6yTx/juwpn+NHj2M\nT/86qjFCOtBOZN3HcXsTOPWXATDSK/C13kzRjy7ic8WriPhrKFIUvnn2tQzJHtaW1nPF9Asw04sl\nOhBkFXHYge/s44itrEUU54hmzkU2NKTMFLa0CyhEyNOI7OzFV7Ueh+uvsTUT8/Agk7fcD6aJ4FQ4\ntXE3y8QEZZkZymLfR8DGrT9NUrsCB07E4V8wt+JvmAlsBdum2reC7C8fzYUpVjUQW5cba8YkCV1L\nU5guwrRNpLFfoRz4OoL/OKQN/4gpyliCiCWIJC2d6pXrsTvXvb6PGBGwgnAsbM/lgjwf86VurKRO\n/JfPoqmHCXmakNb0Mq/eCnYIeX0no/VdPJdXTqPbxbAtU42FgYjTSGMho06+QmL/r7AdQcY770Iy\npnBrk5QMfZoAEBP+GbmkBjXajY0DVfeBbSPaFqphIO3Yj9Z2AVZCJFPoZ+iia5hJ2Syf6EHNPkOw\n/QLiB18mu6cHTJMkCp6KVorUEGpGQULC5/Bib/17suuup9JftKRyrLW9zKcWGCucAkHBpZ5A4tGz\nQA0S3PITHCeehTk/h3t55ztaq9lUkkTfAUSHC19j21s6JF5/NeWt3yWrTeLPX4WierC0GJIhYok2\nLt5qOCKKIsVCiJiWwIkT9ztM2P3P4s+u+DgmJ00kEqxfv55IJMK//du/kclkOP/882lra3vL3yST\nSaLRKOPj40vhcce6FCUlJQwODi7JdF966SWWLVtGJpNhfn4eWZbRFg2zUqkUo6OjhEIhxsfHiUaj\nqKq61Pk49lzBYHCpMzM9PU0wGGRwcJCxsTHWrVuHYRgMDg4SDAaJx+MEg0FGRkZQFAXTNGloaGB4\neJhAIMDIyAjNzc1LniLH8MYkXlVVcTqdrFq1CkVRaGtr4+DBg/T29rJ27VrWr1/PgQMHePLIEc7Y\ntg1R07BVFWHLFvTpacyeHqT2dpTCQuzTT8d88EEsvx/jhhsQZ2fJ1NTg9PvJVlTgeegh7IcfJn3O\nOVjLlpH84AdRursRnnsOx5NPYgWDuXyYBx/E9PtJ3Hgj7kcfRRoeRjv3XLzf/Cbi4CCZM85ADARy\n6bqL3Azb5yN7/PH4PvtZ9PZ2kh/7GLHlyxGPOw7nhg0kg0Fct9+OPjeHVVCA5957kebm0DZvxvB6\n0bNZBFFEXPR6ERa/E1GWcZx0Etqdd2J99rPoN9yActZZb+JoCIKA7PeTPu88xIUF9JYWnE88AaYJ\nc3O4r7gCrrjiHa/VzP33477mGmxy3iOuc899y3GOxkZ4Qx6Po7ER/emn0ZNJnG8wgXsj1Npa+PjH\n39HreQ+vwzRNRsZHyC+w+PBVm9g1cpDjv/NxbBsevORLnNjU9abjcyqsCXRzAc/wIbLthajGCAm5\nkL7qLaywBtBjZQgYGFYxqVcfI7HmevLoozTzGjFhLVEhd4KK2wLeiSeZP+FsRvwn40lFKHz+QVTH\n61lBpqiT1ZdhLlSj21OIwijFviyuhh1YvYeZr/gk2uQ0grYbZ1MtlqaT/MBJaNlpVvY/hyh7iQcu\nxa0/R0pbjR1LoNW0oy38DN7odZN5fVxoIrFpegBfmUVJJsayojwSe76LNfksStN1WHVfggPD2PpO\nCjYWgiwiYxJUJbLJSczkALK/HcUZwPS9D1fFDrRsAUKoBtU5Qlr1ImZN9HufIPK5LxNWVRwX3oYY\nL8ZT9o+YqaeIlg5yb8npVJgL2JaHFsmFbOu0jPwAz/5H0fMaGG04l/9v49cQsfm4Pg3OLtwL31ns\nFQmEMpO4Bl/Ak/gFWfc6rOxFOKbBWVJNcPlmFowb0UbvxbFfxgidz3eam8jkiYQlBxcNPYeezWIL\njtz/PmDbue9NlmWCBIi8/Dyzw304OjfgrW9+k4u1IAiosopDU7AFG1FfjKDILmDrcQq6jn/Ha9W2\nbeL7d5N5IdcFFpQr39YIzpe3DHh9/Bpw+lEzCpZp/cHCwufw4ePfT9b9r8KfXfExODjIgw8+mEtN\nTSYJBAKkUrkkxWOji99HRUUFg4ODFBYWEgwGl0YubW1tlJeXI0kSmqZhGAbT09MMDQ2h6zqlpaVM\nT0/j9/sJhUI4nU4Mw0DXdaLRKNFolI6ODnbv3s3GjRuJxWJLNuyjo6M4nU7WrVvH0NDQUktraGiI\n/Px8mhY9LxRFYXh4eKlr4/F4yGaztLS0EIlElgqKhoYGxsbGaGxsJC8vj2g0SmdnJ5qmEQwGWbZs\nGcXFxSiKwvT0NDt25CR2hw8fJj8/n1QqRTweZ7KpCV9REYgi5r33IvT14fzpT9EuvxzxnntwdHZi\n33wztt9PtrmZZ9esYf/UFB+8+Wb8iwRNLAuzvBzfV78Kto129tkov/1tTpny85+jf/SjYBjI4TDy\n8DBKby8KkD35ZIymJsyiIqSDBzH9fsTZWbTTTwdAGhnBWgzvk8bHcZ15Jo6yMoxIBON3v0MwDOSB\nASxZJrNpE45nnwVyahujpgZnayvCtdeSvP12hJER7AsvJDM8jFpVhT4xgfNv/gacTvRHHkH8m78h\n/YUv4LryyqWrNcHhQDl8GOW117BdLlL/9E+IgQDq5s1vWk+2bZN58UUYGkI69VSUt7Hxz+zdi33X\nXdguF0ZlJdLo6KJ64I+DEgrBG1KW38O7i/HJcXZ1v+7t0zc3SiyT20cOh4ffUnwAiIEuLG8vcV8N\nU/Jmxks/yFHHcs7Kr8CrdpDscaKbFqacQZX3UhX9JTYihqMRn7Gbjx6O0le0mrXTv8WQ8llwtaKL\nCgveYgpWNlIc/Sx23y2kLS8F9otofV7sqTkEvw9x+RaEyCMItgkImBO/xuWpR9NPoPizbszQcszx\nWQYrV7OvvJHj+g4QzyRI+q/AmppDavFheucQfOfg6fkHius/jO1tIZU1cdS2I5g6olemprKKllAB\nslxMbKqbbP/dAJjhXYiJNdhaGkYH8E4+R1A9TNS9hmmthqq5W3EmX0ILXYPUdCtK/goWnt2DkDeL\nUmYxfPsuYi/+kGVPPchcMndCtg0Dq8uLOfYS4hiwrBl19jluTf8K/8Kvman6MocLL8UQVJS5EeT4\nGHJ8DOrOZIOaokFM8bIUJD+ZoiiVIRvYgg04UgMI6uKFhzGDf+XphJw+MrEo6YkxDFHCzkyRUfsw\nXBaqbZERRESHi/SCwMsrV1L6iU/gW38SlpZCXbGOZCaFW3WRnptF3/E0ti/AXD5MMEgoFaTE8wYy\nLAK6YmDJNrLpxdF2E7KnAm/pyjetJ9u2SQ2+DMkwav37UNxvHYvMJtJMpkEpacLvehHhGLH5j4TL\n8b9XuvtnV3z8Purr61m3bh2aptHc/NZqcGhoiO7ubkKhEBUVFUxPT1NRUUEymaSnp4fR0VFisRjj\n4+NEIhGam5spKysjk8ksyWSP+Xx0d3cjyzIdHR1MTU0RCAQYGxujrKyMl156iUAgQElJCS6Xi5qa\nGqanp3OZIG43K1aswOPxkEqlGB4eZmFhgY0bN3Lo0CFmZ2eRJImuri72799PQ0MDyWRy6XVv2rSJ\nyclJZmZmcLlcjIyM4Pf7SaVSjI2N0draymuvvcb4+DinnHIKlmVx3HHH0dfXR1tbG3Nzc5SXl1NU\nVER4dJTi2VnKnnqK1IUXIu/cCYC8cydWJoNaXEz6+9/HfPxxBurr6R4ZIejxMFFYSNEtt5BubsZM\nJiEeR1g8mVrFxRiXXor/kksQAHXfPpLbtyPYNkZJCXpDA1ZeHlZtLdaBA4iRCK777gMgetNNWJoG\nioItScjDw6SuuQbhAx/AuWicpv/TP+G67Tb0jg4Sd9yBLUkEPvxhMps3o518MnZREa4f/hB5YADp\n1ltJ3Hcf1NTguvBChGgU7aGHUDduJHvlldh9fbgfeADBsrDuugtr+/alFqat60iL6icpHke96aa3\nJYBlDh5EPeMMxESC9K23oryNuY79wAM477oLgMTXvoZUUYHjzDP/8wv+Pbz7sGFr01o+PnMRNhan\nNr01Q2O272XC3U/gLm9jtvkCIrqGN5LhhPEHyaouZkqKSJkJRg+a6OkkTSvLUd1rEKwkhlyJYS5D\ndBVwnN1LtXw7el4JorkNySrHY81TmP4GureL0rlPkXG0YjpaEGuTZHwF6NMWJiKJ8irSteuxHB70\nmTAjN91Pdnyamh9+B9eB3ZCMs2JikL1bL2T0Yx+k6NPXYpt7mPrcN/CcsJGar34c5n+BOLcTJbSe\nhNRA4VQvkpWBiUHshjoSrz1HYi6K98QzILGAVHEO5uxLSOVnEHavINOxBbehgTJHaeS3VEz/Nf2V\nP0ZO5XKuxNQBbNvGlR8iu3Ub2mAfs68eYuGZ1xDr6jB7J6i75nrcheVIphu5MA89vvghuyrwVq4k\ncDTX0fMnduLLPw+XOYMdDGB6S7H8Ifx+DyudUfZlAjwuBMAbIFC8jdUzd2IpMno8hDUrYpZXolSd\nisvpw7IsFp5/ErN3H0JZFc6OL4FegPzcw9zQPsVETTsV/gAjn/4cxuQk41/6Ei0PP4DQ3MJY3jzY\n81RoxXj8AaRly8k4JNJFCmARt5IUvyGrxbItbHHxQkOWKFr18bdVm6THupF/cQOCpZNJfgJl3dVv\nOWZKgzBOcDnwbH0/fiOJt/Yvg1z+Z1d81NbWsm3bNhKJBK2trfh8PrZu3Qq82SnuWBdi3759vPba\nawCsWbOGXbt2UV1dzfCiTLK5uZl0Or2UNquqKnsXc0COjT+am5uJxWLouo6u68RisaWxR3FxMZIk\nYZommqYRDoeZnp7G4/HQ1dW1NJoZGBigrKxsSUrrcDjYs2cPDQ0NuN1uAoEA0WgUwzCW+CDNzc1M\nTU1x+PBhNE1bMk0TRZG+vj4AVq9ezZEjRwiFQsTjcfr7+3n55ZdpbGykra2N2dlZdu/eTWVlJXv3\n7s3xTU48kXN378ZyONC+/nWkF18ks3kzZjqNX1EY9npRtm+nwuvlmq9+ldC3v01KVbFKS3NZMF4v\nak0Nibw87LExTFlGfuUVUpddhtLdTeakk5AOHkRIJpEcDhBFpMlJlGefxTrhBMRnnslFbcsyOByY\nlZWweTPql76E8uqrZH/8Y5TVq9HuugtblhGmpwGQe3sxt26FVAorLw/nb35D4qqroKsLs6oK68gR\ntLPPxn3ddWQ2b4Z0GrO0FPbuRdqyBf7lX8gMD6N973uod9+N8cEPoogi2dFRrHgctbmZ7A9/iL5z\nJ8KWLX+Yea7rCG8gfL4drPZ2bEnCrKlBOf10HG8ojPWpKcwXX0RoasLxF27l/r8V5aXldJld6IZO\ndUU1ToeTr5zzUeDN+8jkwiy6YRDbeR/x3mdY2Cvi3PSPZA/sIy9kEO97iDhgbDyDjOImEc5tqYLo\nxxV/AoCE5yICj8Gu605gVfweBDuDqg+T0Mf4mWs5tUaMOnE5im2DrSNZCaTUSyjWMJniZqbU7ZA5\nhFZ6POmWNtyDEZTZfgKbN5OZmkYd7EVpzUe1Z0jIjQS+eQf4vai+PiRlP4UfvYDkK6No/fchqF6E\n4CokOUB+/26kwzkTs+5zruVngXLWxKY4uf//ovc8gXvHP+NYfgMETiezUMFMfgivpTEphECoxMz/\nCA2Zw5iin3T1vyAmdmPknYRpZFAFEdsextVUjFCxjN9s3cbuUDmXkGS5z8XcxZdSrY/gUYIki2ys\n1DyuvH5ImiTcl6BaPRjORhrCX2HSdw62V0NszSJZh+gVBZpsFZ9ggG2jYpPxBXnc/fd0CBrBJ36C\nnYhit76frKeU5/sfxiG6aYhFEAF7bhZ/3SXo0QgJaQeV+18i5HChuJpIf/jDTH7ta+R/4GL05x9H\nO+M08Ocj2CIaWYKuIPmnXkAqtoCuZUgpGl4z113QZufBMHAXF1CqFaEJGfyC7w/7dpg62Obrt98G\nHslmVrdxCSbBykp8zte5G9lIFHN4CrGkAEfpn1+XVPjvZrgKgvBfblY4NTXFQw89RCwWY/369Tz/\n/PMUFhbS1NTEiy++SF1dHaOjo1iWxfLly8lms2iahtPpJJVKkUwmCYfDNDY2EolEKCsrY2ZmZolb\n4fV6SafT+Hw+NE1b8vhIpVKk02nGxsYIhUK0tLQgiiJzc3PMz89j2zYTExOoqkpnZydHjhxZUsR4\nPB6am5tRVZWJiQmSySQ1NTUUFRUxMzPD7Owsc3NzVFdX4/F4mJqayhEPJYmjR3MSvMbGRgoLCxkf\nH2dkZATLsmhra1saRx0bvXQ1NbEpPx/XmjWYvb0Y3/0uGa+Xifx8EiedxK+efRZRFNl+2mlUn3AC\nQjpN+n3vQwgEcP7856S3bsUqK8NS1Rxb3+XCEgR8t99O+rzzcD3yCKJhoJ1zDsYXvoDwta8hxmIo\nu3ejv/gime5uhD17kMbGEMfGcD/1FEZNDdppp2F3deG56ioyd9+N60MfAiDxr/+KNDaGXlaGGAoh\nn3IK+iOPID/6KNLkZG58VFCQK2g+8xlc995Lcts2xFgMub+fzJe/jPcNUlzLMDCiUZT8fLIHDyKf\ncw7i2Bjaj36Ea9u2peOyk5Poe/dCXh5ycTFqXR2CIGDbNtovfoEwMJCT4L5NCJ1t22S6uxHz8t70\ne9u20W66Cdc3voFRX4/1wguopaX/Ff8GfzQW39OflVHIf8c+smekl0t+fCuxTJJvrzuf4t9+DWdl\nJ1bL5Uzu3kVhhR+75x4QFfI3n0pGKmFhzI3DG0Awpmkvvh/VHCStfYzM0UYeOmsTujnOBZG7UMQ8\njuZdwWtigGXJCVYlf4xqvEbGtQoSaRwcQjV60OQNxOeuZnboIOaqVlw1xbh/8QzCwhy210/C8uD3\n6oQaHkQ2x9GUVgZ+fTyeLhXJOICVnSdT9hGczkqkuZ9hpQ9jJ4dJT6/GseFcLNVEnl7ge8tP44Dk\nAtvmi91PUqUfxJybQptWc+PC47Yy1L4OUYSMoGAKEqXGJCXiHJ78ZtKjgyQP7EZ3iiSDNr5QOdZz\n20Fyom15nL9VciaCm4wYM7KTw6icmhnkstnvYUzbxOvWIZk6jugRsi8lkZqLCfluQ8QmkXc5evGH\nGJt7khmljIe9Z/Blv0kqOsph2YcoWvzcKOCo6KTWznKZpVMg6lSFAjw18Aj3D+byWD5c8QnqJnSi\njgrCUg3t7R7C+3cSHT3KfDpDxxnn4ZAVjEyW5G9/DuODZNafzMyy9ejI1ChpKoKveysZhoFuGjhV\nB9rwJMoPn0HIGGQ+cALuttcNvhJagpSVRo3N45KdOEI5HpdlWaQP/xqSsyiNp6D6C9+yBk3TZCGd\nxSmLeN5QeFiWRfbHz+A4MIxemg/XnI7q+e8ljP4+3uk+8mfX+fiPYNv20okacgtk+/bt7N+/n127\ndrF27VoWFhY4/vjjKSgoYGRkhEOHDiEIAjU1NUsJsxs3blyyOo9EItTU1CyZlSUSCaqqqpZs3E3T\nxDRN2tvbicViFBcXMzc3RyaTYWpqivn5eZqamphZDGjzer1ks1kaGhqYmpoCwO12k8lksG2b4eFh\nWltbefXVV5FlmeOPP55AIJevkM1mmZ6eJhwOEwqFKC0tXZID19fXU1ubyxAYGhoCcgVHV1fXUvZL\nJBKhtrYWn8/H5OQk7u9/n8APfoAHcJ92GoOLPAPLsogrCsnLL8f1yCOkN23Cf889AMhHj5Lq6kKK\nRPDceSeoKrFPfpL45z+PODKCsXw56r592CtXIqsqximnwMGDZK++Gj0SQXryScT5ebR16/Asvn+y\nWdRXXkF67DEyJ50Ebwiwk6qrsTs64Kc/RXjpJcynn8bx9a9jtbdj5uVh33cfjm98A+3WWxFvuglt\n2TLsUAjX9bmQLHPPnjf5gIiyjLqYI2MPDCAtFm/Ca6/BYvGhp1Lot96K3NOD3tKC8OqrZO64A+eG\nDbljy8tzRckfsFUXBAHn20hxbdtGWFwH4sQExiJf6T3874Jt2/RMHWU4mlufR2Una6/8AU9EnuFA\n6j42dZzFY+ERNpz8GdpLlpGITzPywh4kOYWsOpgbDJNNnkdNazlGQQe25yW27fsK+5ddxCDn03rk\nEZqUH1BT0omiDeERfpwr5AUnMfsMEkonDjWMNNlPJKjzaPt2Rhw+LokN0Xhsju/24g15ybiL0KUS\nZHMcQ/fhbl5AdrqxBl8gte5HTOWvR7ItKjwm0tFawj99AKlKQP9QCNspItdW0p4wGbAt1hsR8ssF\n1NCFJPa9AFMHAXDMTtE8PwWVxViCjmbqFAYKUZVS4tMHSe3ZCUP9KIAVTJJZeSIKgKkRyIbZqjvZ\n6SqlPTnK9/0tIMAYAfRMgNmmLYx7VyHYFo3yzxE7FWx9DlOuQTQGkdy16J5ykuIVjCFxtWJiRIbI\n6hq16SyO8SxCR+7EbdkQFx1kbJnixDwOKRcSJyLiDRYzkWlgyk5SWB9lJB2jbuVaXGWVVDlc6Hte\nQut9DfX4U/AefwqZoSNoVa0khdznnbTfLGmXZfl1smk4irTIZRGm52FR95DSUoyb01iShaJkKbvv\netIXfANXxXIEQSCdDGJbAUK+t+9cSJJEgfetvA3bthFiub1DjCUx9T8/uf1fVPFx5MgRenp6KC0t\npbOzk3g8TlNTEw6HY8mzo6+vj4svvphAIIAsyySTSSRJoqKiYikzxTAMli9fTjKZpLe3F4/HQzgc\nRtM0urq6iEQiDA0NUVRURH19Pf39/ZSWlmJZFvv27SMvL4+mpiZ0XV9Kr43H41iWRUdHB8FgkOee\ne46KigpKSkooLCxEEAReffXVpRwXw8i53em6zuzs7JJk1+PxsLCQ04qLoshxxx1HUVERO3bsYHR0\nlBNOOIG2tjYKCgqIRqNUVlZSUFCA2+1GURQqFk+WCwsLPPDAA5ySn48fMBsaECMR3IWFbG5vz5Fc\nm5qwh4YwKytx7tlD4tprUffsQbBt5CNHsCorsQoKyK5ejV1bi+eb30Q5cIDMhg0kfv5zxPp61FNP\nxTk6SvorXwFRxH3ppZi1tcgHDzKbn8/EFVdQf9ppmNPTOH/6U5IXXYT6qU8h3HEH2mOPYSWTiIKA\nMTyM5777EGyb7Jo1WIcO4dywgezMDMIjj2CVlqLedRf2jTeirFiBPjFB5le/Qty3j0xZGdYttyBd\neimOljenfsqbNpH+u79DmJxEuOCCpfv17m483/seAJbfn5P8Hj4MGzaQ2bED9ZRTEDQN7b77cH3g\nA/yxEEUR4eab0VpbsTs7cf5e4N17+J+FbdtMTk8SXgjTklfBB1ecTiKb5tTm9SRlnch8Gku0OOTa\nw03HfQqv348tCsz2pJAUhaa2DD7vARxSGdmsH3/dKdiT+1H33UU0uBr9md+QQURb14En24Nz+Bmy\nhZ1ovi04U8+TdbQRKTqZCVctLj1GAz9mWipgtztXLB+WAzSKIlJXE5YviP3bV1ho8qF7r8RvriSr\ng6Q9iD2fC6PMOotzCdSIJNUQ7sgRPJ1OhLJCDNHGBgRR4H1Om1WDDyP0/hOGbRFr/zTulZuQCpsw\n0ymUsnqEUAiXQ3mTuiM5d5T531yJ7L405wwcCKAnj+JzFeE47tPIjgK8Rcu5/KGruVhxkS3dwJX1\nBYxl46wefgrDjmCoRYhmmmSqlwW1luDBn2Onk0TqL8S7biUZTzsHkgomKptkjQnb5halgU5nkrUV\n0/ico1wWTjJa0kXAMlHSGgULhzCGf87aLZ/Fo/hQMgL1sz5e8unc6y/Ek7HY7p6iws5SUlNHYnaa\n5NDhHCG9r5vgyvX4q2pxaBnm4lmytoCsRxiJ6xSohXgcnjetG6mhAm19C2R1xNaapfvjRpKMJzei\nFUwHmGnsuUGoWM7Us8/Se+GF2JZF049/TPkZZ/zR61SSJLKnrUY7MgZVxTgD3j9twf8P4i+m+LAs\ni1deeYX+/n66u7u59tprKS4uJpPJ8MADDzA0NERzczOVlZXcc889hEIh2traeP7552lvb2f37t3Y\ntk1jYyNnnHEGeXl5/OpXv1pKhtU0jerqavr7+3G73ViWhW3bBINBNm3axMGDB8lkMrS0tCAIAlNT\nUzidTpqamhgfHycYDOJ2u5mamqKnp4eVK1fS3d3NxMQEZ555JvF4nMrKSvLz85Ekiby8POoW2/ym\nadLf3080GiWbzeLxeCgqKqKgoABN05ifn0fXdWpqaujp6WFgYICVK1dSXV1Nb28v+/fvp7S0lHXr\n1lFaWorb7SadTqPrOvva28necw+eWIzhWIxX+vv5QFcXdXV1WJZF3zXXEEkmWX3rrbifeIL0eeeB\nZSEPD5O94QZSgPdrX0M5fJj0VVflzLgqK3GddBL6wYOIi1waxsYQ+/tzapLDh0lv3Uo4GOSXvb1c\nd911uAYHsZ56Ct+3vkVq2zbMnh6soSGEsTGkcBg7FMp5kABGcfGSo6cViSDk5yPMzaHdcAO+RS6O\nUlaG+ZOfEHvgAQJXX42o66TCYazbbyf7yivw2muIW7agNjQgffGLwJtn/UpdHZkTT0R9/nn0zk5k\nXUdZ5BYxMYG42LEQFqPX3wkcHR3vikHZe3j3YZomwxPDpLNpREHkttM/isfjIaot8I29tzKUPMLG\nglNpjrfwwy/9A0W19dSvq6fT+iwz6z9CWepbCJqNVLMdx7KrUWSRzIv/iumvIykWIDo9+AJejhxM\n0Fil4y03EdISidga5ssvZcS7ioLMIZZFdyPYJh5hB+3iCCemqznoKKAtM4mrdRp3dgeSNsaRLV/i\nH8vORbYtPsXxhIxhtM6TUWMxxAM/wjk1iK+mNLe2zTSy8QhKSRQh6MLck8Ys2QAJkMV+3LO9ZPUE\nYslmhuYOMiIV0VbcROHsOJP6DFPJUgKxBJWM4w9WIzs8GNk4CC5sKY69uoOMYqIWryRWXUCl8EzD\nSAAAIABJREFUsZ08px/DMIg2nkJaFBmqvhKHKLPO1ChL9jAfnKZAFTgw9RBPTN5HuauGq1pOxb1n\nF4KzDHfxBpLJDCYCCAJZG57XJaZFhSftPNozUfyJQfL330XTOc+yEJ1m4pG/JRIZwd2+BWV6Eo0y\nxrOHmMwMsuDbiiZIaEgsZFy4/LnOyAwL3Ns6BrbFhfkXUrLI0/A7HayQDcJDrzBbFwQBrOQMbrWG\nQzOvMZkc47jC1RTmFWOfnZPRvnEf8Spu5rIShmLijCWxWs7GUZfrnmYmJ7EXuWOZY/vkO4Crthxq\ny//jA/+X4i+m+BAEgdLSUvr7+ykvL8fnyxF9NE1bIpNGo1EKCgrIZDKMj4/j8/moq6tb4m/EYjEs\ny2LPnj10dHTg8/kYGxujvb0d0zQ5dOgQ4XCYhoYGHA4Hu3fvpr6+fimDZWxsjEwmw+rVq+nr68Mw\nDDo6Oli1ahUHDx5kdnaWsrIyFEUhm80uyXyPcTRmZ2cZHx9nzZo1SwZqx2TBS+Y9koSqqmiaxq5d\nu2hpaWF0dJSGhoYl0urRo0dZWFiguLh4KVV3YGAAt9vNnj17KC4uZmRkhBUrVjA/P8/jhw/T0tLC\ngUWlxzFfk0x3N3umpsCyWHNsPCBJ2Pn5mCedhGTbCIcPgyShNzVhB4OkXn45d0J3uxE6O3PmXL29\niJdcgnnoENZPf0qmq4ux88/nqbk5WpuacA8MIH71qzh25aSPgqIgPvAAvu98B7OmBisUIrN6Ndop\np+Q8Qa6/HqVwcT66ezeO3/0OyBl6vclkTlVR+vuxAwEIh7EXrdKVbduQJibQrrwS6wc/eFtCmFJU\nhP7ww2RmZvA0NOQ6FouPLW/dSvq22yAWQ7r44nd5Jb+H/0mIoojf6ycdSeP3+HE4c35AyUyMkeQA\nACk7QWIsjJ7JMN57kGkKkTadQ4E8iSEVoJhhNAJMHT1AeVUjkjuIFZsm3ng6peVzTPYeJb0QYTp/\nOe6yQYLWbcxIVxCOr8KnTlI/fhWirRELXo8z+SIudK603cTmV1GsfhnVHEFzb0LRB1DcGtvtDGls\nJqancLT4sGQbPS8fz+86id1+G97LLkdYswLnwu8QxEVCrKxANM4LewvpPlLL1lMW8B+3AW/VZXgW\nnuK+6ssYlX00azEu/dkPyFz5OUxBIiIH8A08RubIT4j9zk16bJTQVf8A0zMIe/fDZRdghpxgg7k4\nptDGusmO/xQzrwN7MTHWcvmJVnvw+N6PYz5GZL4PWVBo16uQA/l4Lr4WV2EJoigScqvUGBpZS6DE\nJbFGM+gxLNZqMxRP7UPa+3XUukuJpXUmB7vRw7nvKSm6ecX08EheFeWuArzzX6ROr6bNysOJRafD\nharkLlYGYr0MabkYjAl3guVv2EdkSUIKH0GoXo0tCwi6wUR0lH/d/wU0K8UpyfO5dPmH39Z80OPw\nUJetIpvR8RZ7EE7tWDqu+LTTyP7932NbFiWLDtH/f8K7WnwIgiADd5NzOjGAq23b7ns3n+PfeW6O\nP/546urqCAaDeL25NlReXh5nnnkmw8PDuFwuwuEwTU1NS/bjhw4dAmDjxo2MjY3hdDp5+eWXiUQi\nFBUVYVkWk5OT9Pf34/P5KC8vJz8/H9u2KS8vZ3Jykmg0ulSgHHstx26bpkk6nV46oeu6TmNjI5Ik\nLfEyjuXNzM7OUlRUhKqqWJZFd3c3xx13HIIgUF9fTzKZRFEUCgsLee655zBNk8HBQQzD4NChQ2za\ntOl12ehieq7P52NhYYFAIEAkEkHX9aXCZMeOHQSDQZqamqirq6OgoACXy8Wyxa6C4PWSJwjsisXY\n9dnP0pZKIabTOQXK+ecjPfYYYixG8rrr8PzgB7iefJL0nXcir1kDgChJOC+/HEvXkVQVli8n2dKC\num0bdZ/5DFfefDP5Z5yB+MgjKDt2kD7/fIRkEvvqqxHvuAMBkIaH0ZubQRBwvPACemcnUmMj6R/9\nCGFwEGvTJrKbNyPMzSG8731vWRfS6aeTymQQVBXluutyxkHH7Mj/A1tyJRhECb7V/Fj2+5H/+q//\nqHX5Ht45/if3EVEUWVazjKKCIrxu79LJqTyvmg8uu4mBaC+puJdHhWE6V7QgxBTCRXFun9+DKIh8\novxzOLMa2sgkiVf+Cn3VpehV7ycTjiLEhpk7ehh3XhBPUQneoBuDAiLy6ezf6yCTfIqKlZ0ISk75\nYGYBcrdFM4tTGkGycxcI2CbzvmtIu9bjEZ14bJuq7DjCrIdkaSHO8ByOqhLKz9uMMNeHPZpEkKYR\nCi+G1AjmfAHJ0o28tDc39jvQ62d1l4e5fC+OaDNwTH5uEC3pIC8bJe0owRedQh1Jo6v7mXtoiJL7\nvs18Yx7qQj5eLPwpD6rmRrFl8hw53wrB4UZQC5FHH6QitBaj6ES0gYMkzdMIVa0k27OX1XMFdAS3\nUfNqP9hPYW69ALUq99okSaIm4MQwTBRF5mSXyvKJAbz3X4psJEmt+xj+NdcyNj1Nyl2Id+UliIkZ\n8jvOZdyZM9CalAKsdtagy16OCAqttk6xQyA58BPQw9T611HryRn9NeS1vmlNCIKAJ7SMotf2YRZU\nkV+2irn0DMcSf603hM69HZyqEyfOt9zvCoVo/PSn/8iV+ZeHd7vzcQUQtm17uyAIm4CvAWe9y8/x\nB+FwOKipqXnTfYIg0NHRgcPh4P7FVNampiZKSkqWCJ4AkUhkKQn22JjD4/GgaRrZxdaYJElUV1eT\nzWY5cuQI9fX1S9bsyWSSFStWEIvFME2TVatWkUgkmJ+fJ5vNEgqFKCwspGjRkOqYMdqxLsjMzAwt\nLS1UVVWRTCaX0nZjsRjBYHCpgCksLGTnzp10dHQQj8fx+Xzs37+fyspK5ubmaGhoQFVVSkpK6Onp\nIRQKUVxczPDwMB6Ph/Xr1zM6OkoikVjKsGloaMAwjKXP5Vhl7qivZ92mTVRNTBBqa0N9+GEyjz/O\nE5dcwszDD3POihUUv/hiLltksZhj8XUDGAsLGJ/5DPKzz6L9y7/gPPNMxKEhzPZ2Mq2thM44A6fb\njX7SSWQvuwwSCfj4xxEdDrJdXaQUBaOmBtu2UV9+meQtt6Cccw52by/O7dsRLIv0zTcjPvEEtmni\nXMzOsSxrqVPhWrsWe82aHFl3YgLLMMg++CBCdzfiKaf8p+Kr38N/Gf5H9xFVVSkseLPyQBAE3le7\nlZk9Itc/kRvT0XYGrVILxZWj9GVsTNtkIJWhztmE1n0nWDraoV/iXWmip1zoUj6iKGFaEqV1pSxE\nDQa6nZQs20wmlYtU0KbmCNd+ENNMMjldS6nyIbx583B0gpF1NxDVm/Cn96JbTozZLIq6AM5qJMtE\nio9T+ND/JbnyUmx3KVPuaoqS3QDYsQRmWQnRklMxZDeB8f1U/uZv2XL6YyQTDvIrbWwb/KkZio4u\ncKXxGpPCFLKrmn8751o69ATnvvBDpPFh7PwgStuHcLfejx7yggDZoALVZQjxPvI9LXhDrxOx3SVN\nmMs/TmZ+KwVlKxg+tI/E4D7Kfa+w8OsF3C23UD4ZxI67ltJj7WP7CbCwkOGBB5KMjcHFFyu0tXkZ\nMsZ49X2n0y5VsKZiM4rDTSg/RCKdgOB5VJZUYqWSLBu1UIriVEkRGkw3gV33sWx9AS2hUsy5l1H7\nPoUAlFZ/mptX3wbkioVjY/VjF3Oehg247Zz/Szg+gyI4uKHtFiZTY6woXPNeqvSfgHe7+DgZ+DaA\nbdsvCILw43f58f9kHAuASyaT+Hw++vv7KS4upr29fcmVtKWlhfLy8iXb9e7ubgRBoLW1lUAgwMDA\nwNL4JZlMcujQIdauXbsUHjc2NoYsy7z66qvU1tZSXl6ek1ZqGsPDw8iyTH5+PrFYjFQqxerVq5ce\np62tDY/HQyQSIR6PU19fTzabxevN5UwcPJhjnSuKQmtrK7Ozs4yMjNDe3k57ezuqqrJz5046OzsR\nBIFEIkFBQUGujez3U11dTV1dHV1dXbS3t9PT00M6ncbr9TIzM0M4HKa3t5eNGzdi2zaxWAzDMCgr\nK2N5Zy6HIL18ORNr1nA4FgPgiK4zeeON1Hk8iO3tiIkE8qWXLn3mRk8PzjvvBCD7k5+gn3AC8v/5\nP6g7dmA//jiZa67JvafSUuR//mcyU1PoDz6I9PTTBB5/HIDkxz6G+uyzyIcPkznpJBzLl6P19GAH\nAgjz89ihEMbAAILLhVFUhP7lLyM/8wzZz38e1yKBSxAEtH37kM87DykSIf3tb+O94Yb/noX3Hv4U\n/K/dR2qDpYRcARYyCTa4ykgffAF3sIn1NechzSeYfGw/1efW4+y6nOyhJ3GWthB98X5kfzHS8hsY\nzRzPnv35nFoeRu97CMswmB4QaVjXTnImSkjNJz31M54e+zwvd1ezZtUmzmr5Ja7SQ+iSn1HHicju\n4ykN347L7qdkdw+03cAroRq+3fpBrvfW0JgeRFkY4Tf163hf+3pcqQRKYAbR38astwYAR7CYVNvl\nVK32ookeNCuFte85CifHCR7WsDxearO/ZWfzZbzf1slTHETWnUn+vn9DrlpBSduJ5H/7RGbiU8ST\nOo5YAunIEwzo9Sj949StE8l6StDCYdjxAnldXRSuzMUTeGfTGOEUUmwnNgL23F7UwiBUrsAsLEay\nwdu6YukzHx7W2bUrd5Hw2u44tbXw4NHvE9Fn6JUDrPBfjAvweX0sb1yONjeJvvte+vpVntr9AcCi\ncYtBY/hBlNmDlJS14Km6llTCB4ID7Ay2kk/GzCKLMnEtxiOH72UocYTz666gvXQVkNtHemf2860D\n/4Bt22xv+gxb698al/Ae/ji828VHATD3hp///X7UfyNKS0u58MILCYfDS/LZSCTCmjVrmJiYIBAI\nMDMzgyzLSwRTXdfJz88nPz+fV199NUecikapq6ujt7eXqqoqZFnGNE3C4TBVVVVLY5yxsTHc7pzu\n2uFw0NHRgdvtXnouWZaxLGupqLAsi8HBQSYnJ+nq6mJgYIDq6moyiyZWgUCAWCyGy+UiEAiwe/du\nGhsbl/JqNE2jtbUVv9+PaZq43W5isRgTExMcPXqUjo4OVFVdGsd0dnZSVlbG008/jSRJ/4+9946T\n467v/59Ttpfbdre713s/6e5kVVu2ZGFb7hXsYGNsB5IYCAkhhARICISSfCHO4wcmYAgh+AemuGLj\nIiN3y+rSSaeTTtL1Xva2952d+f6xp3WTDY5N+Rq9/lrdjWbn5jPznve836/368XQUKHf6fF42Ldv\nH6qq0tDQwP79+7n55psxmUwYL7iAkooKeoeHyeZyDA4OFlVhr7j1VkwmE7m5OVL//u9QUoJ46aWk\nLr8c/UsvQSaD8otfIFx4IezcSfaKK5BfIR+ePXoUPvIRhIYGMBhIbt2Kft8+tEwGIRwm/olPoLv6\nanJLS4heL9lt22BmBk1RMPT2orrdJL/9bWxfLdie5++9F+3ii19WHRwYQF4WlhMfeIDcpZeiczh+\nB1feGfwv8AcbR3pr27nv/V9mMjiPrm+M+cVFotsXabr6vSzseh63p5zsvh9hqO0hd97fIfV9D9Qc\nosPNtL6eXfvd6HUK2UgAZ00L4YkT+GpM2Epy5G0+9EMvkHNfzbEXC1Nvx4fM9HrNNKBQbfsZYce5\nWNN9mNMHyLobUUOLLClhHjQV9CdClmq0I31kgyG2Gg7zje6LOEcJsnrqXxEUJzplFYpkxBydJFd/\nDmnJSmPkfsqC/0Uo1kR4wEb4nDVk3SqLTzupLcsSLxWICQY0gwmp7iJK1YVCNdJqpdxcT3J+GF74\nDsPSWQQmZgDQVYQI1NSApQJbIMripz9N9733Issy1W0d6E0aKW0Vgqph/sW/oItMEKm7AfcH/g+S\nLJNdCpN6ai+azUJ5bR1tzUmmZyVqhWNkJzx0u9by9PwjdLvWYdG/rG+RnjhE+un/w46aOsItRhrS\ns0QWyvCf7eOg4VEqAgcod5eSiQaQTHXken4OuSAxRxeLhmmkvIgUVHh6/hEADi7uKiYfAOPREeJK\nwbdrT3ScVcoKdPK7hjr5O8U7fdaCQMkr/v27VTD7NUgkEvT19VFaWorD4ShWIdLpNHV1dSwtLaFp\nGoFAAFEUWbNmDdlslrm5OXw+H/l8nsHBwWL1IR6PEwwGGRoaKu7zFAHUaDRy4sQJWltbkSSJxcVF\n9Ho94+PjGAwGNmzYQCqVYsWKFaiqWmyfQMGjprOzk3A4zODgIJqmsXHjRhRFIRAIkEwm6erqKuqZ\nLC0tcd5557F3716OHz9eFBZzu91MLk9jpNNp+vv7sVqtlJaWsmPHDjKZDOeffz5zc3OMjY0hyzLJ\nZBJVLcT6fD5PJpMhNzCAoCjEGxvZfeIEhw8XSrn19fUEg0FSqRSxWAyTyYRyzz2YlvkQqYcfRr31\nVpTZWYwPPEDmuuvQ//3fk77qKuTqamTry+Nh6vHjaGYzlh/8AAFI3HQT8a99jZJbby1Itu/dS/76\n65E3bEDQNNQf/QjjlVeS+spXEDIZpJkZtIEB0ps3Y9i1i/zmza8qhYobN5K86irESKQgmvQ7Ftc7\ng7eEP+g4Eh7VePBn46zr9mFxOPG01jNZsp/hTaNsFJtgIEPK7CGVTSO0vw9T/UZ2hVNYIks0N/vx\nWsbQprcT0RuQKjdhMfyKgFLLUlokVXMJ9fvmuOL8OfpPWql0TROYmaeqxsezjvOYFO18LPoTLKnd\nZOWTBOs+QlV0iItSbbTOj1OfWITlqTDn3Am+wAhqYoSg8UmUzDZqVQdSKo1lZDuZik2Uy3584TvR\n5UYo842hXPRp5gxfRVOzGKq2Ypw8hlLbQnxZ4Eq0aKhDdxPzlJKylPHY6H0AXHjh57EMjyFMvICs\nN5BbCkPt8v1nLyGfSrE0M4UoitgkE2o8R7yzMA5s3PBB3I//C2p4kUw4jNnjIb9vEOOz/YVR4Fst\n3PieRZIH9mBYnEQQb+S6tls5p/JCvNZyZPllp2h18QQnHHbuU/YBsHWNiW7P1cwYC/yRaOkKvJE+\nxLvfh2ZyIlzzDUyVZ7GQnAAB8rJKVjRRa13JTPI4zSWdr1r7Fmc37e4LUNDI2Xp+5zb07ya808nH\nU8B1wE5BELYCL5xuo3/+538uft60aRObNm16hw/j9dA0jf7+fqamppiamuKcc85hfn6ewcFBAKan\np6msrERRFJqampBlGZ1OV5RaX7t2LZqmMTw8TC6XI5PJFEdWBUHAaDQyNzfHxMQEvb29LC4uFrkm\nO3bswGazUbo8oSFJEpFIhAMHDhRVVCORCM3NzSSTSSRJYmBggJqaGnp6ekgmk5jNZp588kkaGhpY\nWloiGAxSVVXF4uIiVVVVZLPZIo9EURREUWRwcJCVK1eSyWRYWloinU6TTCYZGRlh377Czel2u6mv\nr2f9+vUIgsDBgwdpaWnBarXicDhoiMWwnH8+YiJB6DvfIWZ4WWXP4/EU9ECcTqwPPkiivBw1Eik8\nKXQ6sNsxbdhARhDIqCqGyy5D1OtRvV5y//3f5JxO9DfdhBoOo+7dS3bjRuSTJ9END0M2iwCk3vte\nDM89h3LTTYh79iAvy8or+/fDmjXke3tJbd2KZjYjDA+jOhwkr74abcWKV62/sbqa9D//M9qLLyKu\nWvU6IumpIPLH0Lt99tlnefbZZ3/fh/Fm+IONI6qq8qN793Gwf4ZDA7N87XNbWchPsD/+LABDxmFy\nnm6i8SVqXA5SlBGVXCyNvkRM7qe3wY5ZzrC0BPlsBkHJE89WkJyOg9uOIQbm9h9zDvspX/kFgrMJ\nSlpqmHEYuN9YS4WWIycuv+kLBoyTx3Dv/yHvXZwik3CCzgB1rQi5KKbSExh0o2RcHXilm0ikIuhj\nMRzb/47kyvcTa61DtUeJpjbhyk0S0V2AwAyaWmir6qsF8kciuB/7PvpzrwFzjMqlf0O2RkikY/Sn\nJnlu/lEAKq21rFTKqHN40ZQ8wX/6B0q+egfGujoMJQaEz3+Offf/GDSNjtWbMehEwAQaZJwdLLku\nJ9+2HiH+GJFkFclkHh+g6SQw6HHXdKHXSQjyRmyNbQU5gryDg796BqvDSde6deSjc+Rnj+L0duNI\nhQnnQ/j0ATz5Y2SSeiImPy6dClMHkGIzEJtBmTkC5W1YghEUOYqUyfFQuoeS0U/jNeapeY1qaK27\nhiv1H+VkTqPVIKLX6V71+1dOJr7b8XbjyDudfPwQuFsQhL1AHLjpdBu9Mmj8rnBKwfT48eP4/X7C\n4TDxeJy6urqi2dzCwgJerxe9Xk80GqWkpISVK1diNBoZHR3F4/HQ1dWF0WhkdnaWyclJGhoaaG5u\nxmg0cujQIRwOB4lEAofDQW1tLaOjo6xYsaLYbunp6UFVVaLLvIloNIrD4WBoaAij0YjP52N4eBhZ\nlllcXGRqaorNmzezatUqQqEQ+Xye/fv3A2Cz2fD7/ZhMJlRVpa2tDUVR0Ol0+Hw+crkcmqYRiUSK\nkzTj4+NFifZMJoPBYGD37t0MDAwgiiKXXXYZmUyGJ598kpKSEuqMRqRli3ppYgKammhpacHlcqEo\nCmNjY5z30kuYv/lNNIOB1NatpC+5BOXSS7Gee26B9HnVVa9aC+XHP8b0qU8VbOa9XtS5OYTxcezb\ntpG5+GJSX/oSUlsbxk9+siDJXleHeO65iLkcmUsvBU0rTrbo6uqQ9+9HiERI/umfYn7wQQCS550H\n3d2v+l7jG+hrpPftg7vuQm1owPDxjyOZf78yxb9tvPZB/YXTGOP9nvEHG0dEUWTLuc0c7J9h0xo/\n2fAC6bxKZ/U6JvLHqbW08KI2RXXeTmDsWWrD41gb1tG8rhSLaOXbe3/IVY5OKmtbMVhdxGZPMj45\njanaR06ronL8MPoVe1gyr2OvpwdzpUiFmoL4Hv4pfA8N4Z+RsGwhQAs5QcMaLLw8ydExMtYqmBwC\nmx1n90sYsn0oShlGOYMuNYho/QrWjmtJxIdJl9aT8BUS8MWyi5g7OELc3457ehFj51YQwBoWybd5\nMc3sxHTyR1g8L6FXT5A2rEMcep7SxnMwSzY0TcWdlxn/138l9qtfIZjNNPzP/2AwpMk/eCc4PCyV\nugvEdCCmZSgdiKPpJdJlVvRDz+NIPI/q1mOcfAidYGVX8BbGKjxUdHdSVV0gwTs7e161FodffJG9\nTzwOgoDd5cIe2ovROkeDdh//5L6CqZLN1NoqkR/+W5qzMXLOFvRbv4xWv5Hc8DlgtCPXrAbAqoHt\ne7eiCWBpe5G9z+oBie6KPK+1TWmxmWg5zbXRH0vxdBpqRI1LHQZ0b+QN9S7B240j72jyoWlaDviT\nX7vh7wmrV6+mvr6eZDJJKBSip6eHaDTK4cOHCYfDTE5O4na7mZmZIRQKMTs7S1lZGbOzs3R0dCBJ\nEqOjo4RCIXp7ezGbzcTjcWy2gmviunXriEajRR7Hqd/Nzc0RCAQIBAJcdtllnDx5ElVViyO2lZWV\n6PV6AoEAZWVlCIKAy+XiwIEDmM1mQqEQfX196PV6EokEFRUVzM/PU1ZWVhQLC4VCRW+aYDCI2+1m\naWkJo9GIwWAoTsEcPHgQo9FYHD8+duxY8TvtdjuVlZW8+OKLaJpGOBymv7OTxEc/ij6V4kR9PUZj\ngQl+6NAhZFmmtLSU7DKXAkBIJjFu3076L/7ijd8AXK5CdUSvh5IS1EAA/dgYUjCI4eGHET77WYyt\nraQuvRT9U09BW1uBlGq1kv/FLwCKLHR9czPpRx9Fm55GUxS0738fJAmhsfE3vzB+8hOMy2qm6TVr\nkM4//y1fW2fwzuEPOY4IgsAHrt/A+RtbUNMx4sEFVnp9JFJJJhbHcOLi6ho/C3NZ6oe+Sz6xhBYc\n4dD6RhaVAC7PShZMIuHnd5FOJFh36eWk5XoODNdT5YWAQUdG/jwvuHu419wFZtAO/5h2e5z24I/R\n5SYx54Y5Zryf53a6kclyYffDuMpMmHzrSckSxCLEtQvJyC40SyP2yHdRpGqYPEISK6pkgeQ8hlCc\nbIkVlRIWWq7Bl55Cmp4ko2vBXjmBxbqbrKsVw/4XSZsvIDtZTtbdiBazYjn5M2rtPv7K+RF0c7Po\n9w+TveZiYtu3Y2xtxdW7ktiepwrtzdAimmTB1XM2aBpiCajivUzGLuLO8vfh7v04H4/O4lVeNmnM\npFLsOLKHqzetfcM4Ylpu28o6HXqTiQWticbofyMqEWzJJ2hq+EsMFi+JpovRH/guorMG2eJELikj\nf8tPEAShOO1mrOwkeeOP0BJBOqIC+0UNvR487t/sEalpGs+l4VmMkNdoS2VoOY0s+hm8jHelsdxb\nwcDAAPfddx+iKNLb21usagwPD1NbW8vi4iImk4nOzk6CwSDj4+NEIhF8Ph/19fVMTU3hdDrJ5XJE\nIhFsNhvHjx8HoLa2FoPBgF6vp7+/n4aGBlwuF3v27AFgy5YtnHPOOQBF8umePXtwOBzIsoxer38V\nz2Pr1q0MDg6Sz+exWq2oqsquXbtQFIXe3l6Gh4dJJBK0t7eTy+VobGzkmWeewePx0NLSwrPPPouq\nqmzatIkTJ04UJ2tMJhMtLS0IgkBZWRljY2PkcjkEQUBRlKJWidlsxufzEYvFyGazxapOh9dLZX9/\nwfEWECQJw/nno8ZiKGNj6FpakEwv34j5TIbs9u0IdjuGc84h1d+Pcs89yP39CKqK4HCgfeYzsHMn\najyO/Cd/guENzNfy2SyZr38d+ZFHUP7u7xBqagrfv6yP8psg+cMfYrrtNvItLeQffhjDW0lc3gU4\nYyz39jE0OsSBgQOIoojL5Wc+PU/51JPojz6PsOJi/sczQo2pEb+2irSySOYXQ2QTSZytjTSt3ojO\nLBIPusgtLWKXx5jtPYefWKvQAbftuROrJtBcMYUn8TNClhvYPX4jDz5WIKXeeKPIpk0FTQ1VVYkc\nfBThyM8Jb7wFJCPW6T1IwRi6oXk06wL5zX9HdHE7Ql6H6KlClwhTsuO7CGqOE/Vf5AfnE3l4AAAg\nAElEQVTPbiSXg1s3P02DuZ90eTepw19EdK3D6N2Ebed/oIl6wp0fZeY7D+Nf0wS5LJrdhVK9hGi0\nY7FXoo2Mkkx4yEYSBKfC5D/xGVDTlJ74d2xSNeMt53JE9vFI3snH5nZwljGJzrCAaqhlcsaC3mik\nrq2NXCJONhrGVOZHegW5M5NOMzIwgMVup6qxkWOhKOaT30eRT/Bw1orV4OXy6uuwHn8GTVUxdF+L\nwfZ63R4AJZsgffwbSJHdqLUfZya1BoNBoKrK/BvHkV8GE3xfMdEoKHzaBh6j/m1dU/+v4a3GkT/6\n5COVSrFz507i8ThWq5VoNFrU/zj1gI/FYsiyzMLCAhUVFQiCQCwWKyYWOp2u+P+hQO602wvB4JRj\n7qkEZXFxkfn5eRRF4aKLLiIYDBKNRunu7i6Mcg0Osnv3bgDa29tRFIUTJ07Q09ODwWBgcXERWZYJ\nBAIEg0FWrlxZdNWdn59Hp9NRUlJCf38/a9asKRJdzWYziqJgNBpZWloqtpiGhoaKiq2jo6PU1dUR\njUaRZRmTyUR1dTUNDQ309fWRTCYRRZH9+/cjSRIdHR1FGffXIp9MonzoQxh+8hNSn/scxi9+8bQ3\nsaqqpKenMfT0kDnvPMwPPABA/LOfxfrlLwOQvPtujDfeeFpNjti2bVi3bkUA0pdcgv6RR95UuyNz\n4ADaI4+g9fZivOwyBEEoOEQePgwOB7qqqmJV5beN7NgY6tQUutWrkV7Bpfld40zy8faRSCY4Pnyc\nXC6HJBmZSy1yLL0DnyCQEfxU2XyYBTuD2T6mwsfpSnajJSTujxzltp5L0ccdhMIO5gMWasqDeN1z\nZBNJNJ0TFSOzARc5NU1r4wySDPLiPr736K3oZI0P3pBGDh8glc5Q3r0enSFANJEkUlPwg3ENHMNx\n7wLmn8rEPm5B7Z5FbzpMXnJiTO5GVmaJSregGz7GTvlj7ItYEHzHaNcZuXDkS8Q2XE8+MUYuWoNo\nrMCQGCFv1pEIjDD1lV3UffLPEAOzqDVNJBb2Y7A9j63+IgxHTxBLelgcNWBfuxHXRRuJn/wZgpom\nseIGkpYc5OHEooWzLQ78yzHzlcgm4gQf/SnqxDC6DRfgWb/5DePIwswUP/7KV7FcUcEusUATusV5\nDRseKbQDstf+F+auraeND+Mjj1Fx/MMApMtuwNL79TdNOqLT+0nNPIe+dBWOmkJ7WcnnGU1lKREF\n3Abd7yyOzEQmiWbCNLrbkKXf3+TNH72r7VuFyWTi/PPPL5q4BQIBjh8/TiQSwWg0EgqFig/oUw91\nq9VKfX09NpuNTCbDoUOHyOVyVFdXFydg6uvrmZiYoLOzk4GBAVpaWjCZTEiSRH19PS6Xi4mJCSKR\nCJFIpDgW29HR8apj0zSN8vLyouhYLBZDEASsVitLS0tks1lqamqIRqOIolhUcu3u7ubAgQNUVlYW\nSbVtbW0cPnyYiooKfD4fRqORtWvXMj8/j6Zp1NXVIYpicUKmp6eHtWvXAoUqDcD27dsBiloiJtPp\nS4tKMIj+oYcAkH/5S/Kf/3zRkCq9bx+88ALq6tUIDz2E/OSTpG+8EWF0FMXvB1FEi8VIXH890vAw\nwmOPkX3iCYTPfe51xnBiXx/ZjRvR79xJbv16DG8SMFRVRfva1zD+9KeoJhOZ/n6MDQ2IoohosaB9\n4hPkYzGUr38dw2v4Iu80spOTiJdfju7IEVJ33IH5E5/4rX7fGfx2YTFb6FnmJOSUHNUhL5P7FuiL\nLdLuLMVZ4iOeiuHSu5EHKxk9she7z0tFuw+rpEPJpnjy6ToURUDEBhMPk89kcDe2EhkdQi6/mm1P\n+dHnEvQ29+Fxj/KPt/wnIa2bYwuLBKUQnjmR3P7nsTi/jKnqDtAKyUdS5yOzJk7ZrIJuKAKd+yET\nQqdNkjfWoVMmyYtpVEcnjfFneLZ1jqnsECF9BWtXXIsneRcR5XrygxEgQr7eh+OFf0NuPp/s7Rsx\nGGZJbNxIssyJNu5CPWrEnlXRT+3DDdi2fgzrxQX9H5vrk2iaxlh6EsghaCpnHb8Tx6q/BU6TfMSj\nqBMFuXRlZvxVhM7kxAG0uX40XxeB6IOkYidpW7eFQDCC2WvFJlrxL82Rbr6UaDjK3heH0O3/ARsu\nuxzXstjjKQwkZjFYVuFO9hMztWJ9kziSz+eJHPhX1KU9pA1ujO4nMNnKkCUJfXaG/znxPSRR5vrm\nD1PpeP2L2TuJ6fA4/973OYLZBT7Q8JdsafidafG9bfzRJx+ncMrTZX5+noqKiiKnweFwYDAYyGQy\n6JdNy9LpdMHvIZFAUZSCvkUuh06no6mpicHBwWLLJP8KCe+5uTnm5uaQJImVK1fS19cHQF1dHQaD\nAUVRihWLfD7PkSNH0DSNhoYG8vk8JpOJcDiMpmlUVlbidDqx2WyEQiFGRkbwL7cmTlUtXin3rmka\niqIAEAqFqKioIBaLEQ6HizLva9euJZ/P09zczMLCAo2NjWiaRj6fR5IkBEFg7dq16PV6stksXq+3\nOMEDhSmbyclJbDYb7ooKUv/5n4hPPIFy9dVIhw4hdHUVVAM/8hF0e/eS7epCSKfRnTyJ0tuLeNdd\npJNJpI9+FNs3vkHe7yf5kY9g+8d/LJz3NWvgFcmHpmmofj+5ujqUpiZ01133pm8rgiCgLZ+jfFUV\nyokTKE4nssuFumMHxuVkKf30068jq77TUOfn0Q0MIADi8eN/VCz5dysEQSCby/LdnQ8xvDTFSm8j\n+04+izcoEdJZMZhl/GIDifTy+HssTrOhFuYCkDdhMeeJRGVMcoKqGhczY2FEnR41ryAJOURRw2JI\nUK7ejT4xQl5y81iqh+3CvaDCe6ovwK8IgEZq5ktYhc+TjFsZ2v4cok4kf6tK+Z4Zgso6ThzLIOkk\n6rv9ZLQt6NMyJvkAZRPPYSkvtIJ16AmKzXhQEEQNKFyfQqagc2FYmkLq/QA/q7mY8wxBtDIJahzo\nBRHZfIhs0wWIoQnEhvNeFUdEUcQnlxIIjkFwBEvVhRitr4gjmRTZuaNI9nJMHi/p8y5BmR6HhpWM\njaWoqTGST0URtn8WOTZN1r+KVMUJtPwSbb0rcVX8Be9JX4bukU9RMvUrcp42xis/zNATTwFQ3tj4\nquQjn8/jd27kBVUH1o2cW/nmD3BBEBAtFahLIJgryUUHkQ1mdHorx4KHOBotTEj2BNf91pOPcDpI\nMLsAwHzyrZvT/T5xJvlYxuzsLNu2bQMoEj4XFhYYHh6mpaWFTCZDKpWivb29WMFYXFzEbrfj8Xio\nqKggnU4XNUGcTicrVqzAarWyfv16hoeHKSkpYW5uDqfTWRypzefzOBwO/H4/NpuNVCrFyZMnqa6u\nJpvNIooiFosFk8lEJpMhEinc+KFQiKNHj2K1WmltbaWiogKn04nD4SjqbjQ1NRW5LPl8vlgl8Xg8\nqKrKsWPHilwPm81GVVUVDQ0N7Ny5sxgkdu7cWRy/3bRpEzabjfPOKwSTRCLB6OgoMzMz1NXVMTY2\nxvbt23G5XLz//e/HfcstZC+5BMN734v8/POk7rgD3Uc+gnZK38NqJXvhhYg/+AH5Cy5AfOklpMbG\ngtAYkLvySuT3vIf8XXchpFJoPa9mu6cfewzrbbcBkPjpTzG2tr7pGguCgPz3f09y7Vq0bduwXnIJ\nqY9/HPE//qNggnf11WiAWlf3Dl5Zp4e+p4fUXXch9vfDjTeeSTzeJdg7fpS/feJOAD61/gYanOUo\nSzn2HDhEV1cnB4z34ugqodK9lqTBSpksE5k4gq+mjgtW7SaDi7LoT1Gm4/i9jaTNDpwrzkcwwC3X\nHSU1NUBCakWvjLCkrCIStCOWiKioeGwuSks95LM3k8vFyczeSSJwE2peQRBlTBkruY42wlkP6Xih\nGrowXc38iSnMdjv1HZWYPQ1sVbqotDeRVdJEo0skdJcjGHJIXS3kBQld6AjZ2nNJWDuYsDbxlMlP\ni6ji1WJIiRyu+laMznNJB+5C1dmQRJHY9v9GOnYP+Y73Y918KyadiWp3O5qrjVQsSHhiF0p4AFPF\nJrQTz2DY+00UTwfCFd/AfdY5LNacxbe/nWJ6OssNN+Q4Z60A8nLVVTZjsJ1HJvYMqq2bY/HDlJv8\n2G2VwH60pvfgr1mB4YVdBW+sispXrdlMLENI34jH30iDLobXXsKbQRRFXGd9mkT5uejj27CN3Egq\n8Qnkpk9QX9JCR+kVCNYO7Jbffhxpcndwfd2fE0jPsaHi/y2i/JnkYxlWqxWv18v8/DxOpxO/309F\nRQWRSASHw0EymSQQCBRbLacIoYcOHSraz58igoZCIXw+H5OTkywtLTE9PU1TUxMWi4WNGzcSCASI\nRqOsXr2aXC6HxWIpKpAePXoUvV7P2NgY3d3dyLJc1OQ4++yzKS8vJ5/PF6swp5IYl8uFyWRix7LD\na2NjI0NDQ7S1tbGwsEBVVVWxenOKwOrxeBgdHaW3txe9Xs/AwADj4+MsLCwwPj5OSUlJkZgaCoXI\n5XLo9XpWrVrF0NAQTz/9ND6fj8XFRYaGhnC5XABFIqvb7Uadm8PwwguFN/y9e5H1erJ33kl61y6E\nNWswNDeTvOIKxPvuw/jNb5KvqyO7bRvpD30IubER2WYju3s3mqpirHiNffT8PMJydUdaTspOh3wq\nhRKJoPd60ZWVoZ59NrrlpEU6dAhN09B1dZHV6TD9/Ocog4PkNm5E5/G84T7fLkRJwvzhD//W9n8G\nvx/47W4aXRUMBaepc5XT4qlGrgQ1DUaPm242MKYbxGRxMXpAQy8aKdcLpF/6Cv7WraiaBnIS0eQi\nH53DWCUgHvwBMV0X8WAUT1s7j6c302q8nCNHmjDGE1zv/yAYYzRYTfjrtiCKF7Iw9SRq3kKd53ss\n+i4jG7JR9vTnQNJjP+/fsZd6kZHRTjnPJlNkSi4iXpMhbYGnogX9Dqt5Dd1PP0is+way0jNIZevJ\nT6ZIpZ2YYnupy81Q4+3hHtHDJwaOkZYMPGCvwBcYoWPhAJXDL5Dw9GI88E2kxALivm8QS0wg2r2I\nK69n8cg25p+9E2t9NxZtB9ngcUriBU6GtHQMJRFCbyslFMozNSUAAtPTGnqLk9QFXyY3fwxdZS9V\ndi8n4p9k29h/syewnSpzPX+z9XOIZ9+OsayROoOJD3zmswiiiMPtftWaZTUBBKEgaCa+MUk0m8mQ\nTaex2O2Y7H5QOzH0/w0AYmYEgFpnCxGtgQHBwJKWozOXw/gaLZB3Enqdnoubrvmt7f+3iTPJxzIc\nDgfve9/7iEQiTExM8Nhjj9HW1sYVV1zBgQMHeOGFF5AkCavVik6nIxQK4Xa7Wb16NUtLS4iiSCwW\nY3p6mhUrVjA0NFQgU6bTdHV1Icsyhw4dwm63s2rVKqanp9m1axcNDQ14vV5GR0eJRqNUVVXx+OOP\nE4/Hi5WWUyX5RCKB3+8nnU5jtVppaWkhn8+zd+9eqqurKSkpwWg0kk6nMRqNVFZW4vF42LJlCw6H\ng/vvvx9JkojH48RiseK+I5EIkiRx/PhxBEFg3bp1TExMYLfb8fl8hMNh6urq6O/vJ5VKEY/HWVpa\nIpVKMTo6Sk1NDTqdrujAe+o4NE1D39FB6lvfQty7F+0DH0AQBAzt7dBecI5MPf44luuvJ7NuHQDi\n7CyCpmF8RZVDX15+2jXTXXEFqX/7t8LnK0/vsaCEw+T/8i/RPfwwqW99C/NNN6GvqCB1992IO3ag\nXXEF+uUKlBCPAyBEIqjLLaozOIO3gvqyKh754NdZjAV5fHAnX33xR/zFWVfytSs+zrETxxgc0VEl\nrEBXKlDSPIwYWiJStwaH93YUTUF015Ld8S3UZBhTz21ET/RjFPVks1DW1sVMoo3nfuHlZGWWi7Yc\nQ3jmc2iPBcl13EBixTUsLMxCZgaLtQFL8i8RtRSlZAkrWxAATVOxxRdprFKxBEoJ2kHrOgu8tUyX\ntuAY/iXZnAmDbCKjpbCINjI1G9E87bhXfgqd0Ulw758Rj5RQop/EPzbBp3/5HErWik19gbve+3F2\nyW5kvcbtaz6If2wPaq4ZxX4JYurH5Ko3YTh0D0IuwWTfApH8QdRUmOixHZjPakMQZcSua8kabKjO\nBkS9aZmPZuTqq3PMzamsXVtIEEz+NvAX2rAvhpP8V85OTSYAwFJmkbysx1z9stigs7T0desF4DNJ\n5JNpRDS85tMnColohMPbfkl4doq2LRdT27ECY0kTqdo7EBJHwHNpgbyuaaQoJE9J/oB8Af4AcSb5\neAVcLhclJSU8/fTTABw7doxNmzbhcrmQJAmfz1dUEvV4PBw8eJDy8nIikUix4gGFlsjMzEzR5C0S\niZDL5VBVlXA4zPz8PPHlB53dbuell16iqqqK/v5+ampqaG9vZ//+/dTW1jI/P09nZyeiKBZFw9Lp\nNPl8/lXuu6faLaWlpUiShE6nY35+HqvVitPpRBAEHA4HO3fupKOjg9ra2qJYWU9PD4lEAqDYUlq5\nciWLi4vk83m6urowGAzF1lE2m8VisWCz2WhoaKC8vJyamhrKysqwWCzcd999PP/881xxxRU0NDQQ\nufJKyj784SLh9JUQxsYQYzHEdJrkRz+KcOGFGJuafqP1kj0e5F9jSa2MjGD80Y8AELdtQ1tucZiv\nvRauvba4nSRJKF/5CunNm2HNGgxe7290DGdwBq9FXWkF5SUebrm/MK11T/92/uH8W7BaCiaRSVnj\ngWN7yeYVOkrruO/gz1nta+G25s1YlwbJRwr+KNnwLOGggeqWVbjkUlKhMLMLejRNYHrSwHg0RE0q\nDKJMWLqMX/3cx0cuvwtf5jtErFcTLnk/zsj/T8iwiZh9EGnNB9FlKskKM+TkNEF/P8ns+Qj+euK+\nZvT5DILezOj4OhqETiRznArLBPrp7yCUNmGy+9A0DXNVBY7AD0m134wWq8H3pVk0SSD2zTLKlHmQ\n3XjVOEY1y9K5N2IYHkJDT9r3GXLGIIZckoylkdTkIpKrHNkeo6TzYqx1tVgr1mFyVBE3Wwk/9+do\ng/9C7pxvYbR2sr4ugP2i5tNOkcyrAhFRRvN/kE22Nrpd7bitv1nl0mLU0/x6x/tXIboUYGm8QHwN\nTo1T21F40TJXXQO8XHkw6nR82JzmaC5Nmw7MujNaH2+EM8nHa3DK00WSJBobG3G73RiNRi644AJC\noVBxDPbU6KxOp6OiooJ8Pk9bWxvpdJqKigqqq6sRBIFwOAwUCK3V1dWYTCYmJydZv349er2ewcFB\nUqkUCwsF0tD4+DiSJLF27VoikQgWiwVVVTEYDOh0OmZnZ9Hr9SSTSSYmJqipqcHn87Fnzx56e3uZ\nnZ3FYrFgNpuLRnPZbLbIFWlra8NmszExMUFpaSnJZLKo6dHa2lpsPdlsNnK5HCaTib6+PlasWFFI\nJCIR4vE4paWltLa2UlVVRWdnZ5GzEAgEin/LwsIChw4dYmRkhC1btnD22We/jtsgXXklqdlZkCR0\nt9+O7jUs9DeDpmlkJiaQ3e5X+cS8ErqWFlKf+hTyo4+inoaQqmkaqqoiiiKGN1BAPYMzeKvQ6/R8\nfvNt3H3wca7t2ExZiYv5jIieGg4k9/Dg8RcBaPPUMhsLkC1rYDYwTwVuSjouQ8slkWu6qW10kzfp\nyE/PIkoSlfYTZFs7MfmX2KP7BbWXfBKzZOEXD1UDAk7lSQBsyV/xbelSuks+T03sGDqdD01JkjGm\nSNnciOY+jLKT1PwMwefHsLWsxGNWKdn9TdrWdHH02VU0VM1j6JzmwarbcVmq2ZxXSMWGWOpdg7Lx\nKgyhOOLe/8HUuoWcbGN3tARv3xJ/3fgATsGGv/8pjtXWQ0eYrqNJLONfIGX7MCfnziE+MIJUtYT9\n6luxddZj1TnwmD3F+zMbPomWGAMgMTnIiS99lcTOndR+4xvU33rr6873BrNIPJHGZK/l4vImbPrf\nvNWRz+cJxOcpMTsxvkGy4PT68HesJDo3g7f+9S9Hr4wjrVYjb84+OwM4o/NxWmia9qrpg8cff5y9\ne/fS2trK8PAwOp2uKGUej8cRBAGv18tLL72EKIpUVVXRukx+7OvrIxaL4fF4qK+vZ3p6GqfTybFj\nx0ilUvT29nLw4EG6urpYWFjAZDIxOjpKZWUllZWVTExM4Ha7GR4eprS0lKWlJUKhEM3Nzaiqik6n\nw2azEQgEUBQFRVFIpVJ4vd7iiO0111xDIBBgbGyMiYkJRFGkubmZ+fl5Ghsb2bt3LwCtra3Mzs7i\ncrkYHR0FClURnU5HVVUVIyMjKIpCe3s7TzzxBJFIBLPZzO23317UOEkkEjz33HOEQiFWrlzJ/fff\nD0BzczNNTU1Eo1E6OzspewtJxhsh/q1vYfmbvyGzdSvyD36AvMw5eS1UVUXN55GWHYtfuc6pn/wE\n+a67UN7/fkx/9md/lMTPMzofvx28Mo5omsZnvvQQv3jsCBff7Of7sz+l3OZhS90q2nROhGSaWsmG\n166R3XEHIKJrvATnqusRdFGGnzsImobJ5cbiryKyNEHSr+OJxEOomsZG3Z/x4r293H7Dk1TpH+KY\nVM13l46yrmQVlVMr2b17ivesdpNYnKO0soQOz3+hU6aJmK5jYsqFpHfjVSMYgifQrHkEfxpJCPOQ\ntI4nQ4UXrk90fRln9CXileeimA0IebC/8F+I00McN36Wn21rBuDqC4+zeeTDHGg/j+8ZCl5Mt5ou\npz0Fsr+J8MHnyc5msV95E4EmA5qoIedEmoR6dMv8iHRsjlDfneTTQQTpEo5ffj0ApR+9nbprehCS\nAXTd16J3Vb7+xL/FNXr06M94YPpu1rjO44MrPo5Jf/oE5BSvTz5NHInv/jFi/wOo3X+C9aw3n7p7\nt+KMzsc7AEEQihfPqakOgJMnT3LDDTfgdDo5ePAgc3NzxWTEYrFgsViIx+OYzWZ2797NVVddRSAQ\nYHR0FL1ez+zsLCdPnqS5uRlZlqmsrGR+fp76+vpiZUIURcrLy7FYLIyOjjI1NQUUPGBsNhuxZZ+V\neDxOWVkZZrO5OAp86NAhvF4v9fX1xYqLJEmYTCbsdnux7WG1WrHb7RgMBkRRRJZlFEVBr9cTj8eL\nxFGgSDIdGRnh4MGDyLJMb29vcRzX4/EUya8AFouFiy++uBh4t2zZwsTEBI2NjTz6aIHEls1m2bp1\n69taI1VVkR5/HCGbxfjww8T7+7Ged97rttM0jcwzzyBs20Zu82aMW7cW1zafy6H//OeRh4aQDx0i\ne9VVZ9otZ/CO4ZVxRFVV5uYLfk57fhnm6f/vW3hdTr7+3I8xpDL4ZkIIhhQn4n7q9Ha0TATJ5mf4\npWfpueIaJJ+EstBPOFdDaiJIZm6cpMOFXanHMfIBZjUjrVc+wouLs5QFVkN3kA22Vtr0JXz1vllO\njoRpqy/Bkkqh5mRkZa5wkPkkOksHFlklZ6xDTi1h6rsHJdRLuH4LZmvBIdcomrDKVmRdOWI2DmYD\nUiaHXqgnWO0gl4ohSRr5PFgUE2IyRE54OTnMqzE0QUc8uYNU7f0I9SUYSj/E/C4b46MSrV0KUtfL\n7RSjzYfvnH8pjOhmsyS++EXiBw/iv7Ab3WOfQgBymoZ+y1+/rTVSFIUDSy+hkmd38BkuCV9HdVnD\n67bTNI3oyaNkp0Yx1DZT0vCyu0s2toTu6a8gpsPkI1PkVlyO3vBr+jhncCb5+HUQBIH169fjdDrx\n+Xw0NDQgCAIbNmxgbGysONVy6NAhVq1ahdlsZnBwEFVVmZqaIhaLkU6nicViVFdX09nZSTwep7Ky\nsmhNX1NTg9frZWZmBofDgSiKuN1uJElCkiTKysqKScLZZ5/NwsICer2ekpIS9uzZg81mo6amhu7u\n7mLlw2QysWbNGmpqahgZGSm64BqNRsLhMIqiMDExgcPh4PzzzycSiTA3N0dHRweqqhaJtBMTE9hs\nNszLZmuKopBIJBgYGCCZTBYN8l57zk4F3bPPPpsNGzYwNzdXTJLM75Bxm9LeTnZyEqW5Gamq6rTb\n5DMZ5L/6K3QDA+TvuYfc4cPol5MrUZbJ3HQT0he+QPbmm9G9QeXkDM7g7UKSJP7qzzfx4q5hVnZW\n0NtYGHH/7AW30jd2DMUeZfuzMzz2wij/8Ne301NmYn5kFlEncXxyhBMTfk6OvQ+fT6G7ZQq/bozM\n7AgR3XXs2V8gZLeWVNFcY2b+5Aid+VYapGkkuYv3XBakKZhFqrcxZMyyKKm47V/ClhpgIdbMeNMF\n3G3205xJcHPaiavbTByJwaSPRrvCbf6r8DjPwr37Z6jpKPmms7CODmM99jSavZ66k9uoLJug/P1l\n5Bcl6hLHCK99P02axA0ll6FbGuasQz8H30pSOhlE0NQIkZDC/T8yoSgCSzMy6zpfXck6FUdEo5Gm\nvylMlaTGD8BLRjQljWZ2v+48v1UIgkCv2EtOn6VVasdpPD1PJBOPkfzVQ5BOoEwMY66uQ6crvHTJ\nZgepruvQ7f0++a5rMej+uGTV/7c4k3z8BqisrMTv9zM8PMyJEydobGwscirKysoIh8M0NzdTU1ND\nQ0MDVquV8fFxEokE+Xy+2G7Ys2cPNTU1zM7OUlNTU9T18Hg8BAIBSktLmZqawm63k0gkmJubY35+\nvtiKgQIh1Gw209fXR3NzM6lUilQqhc1mK1ZeTCYTR48eRVVVSkpKmJycJB6Po2kaFosFu91OJBIh\nFAoRCoWora2ltbUVTdM4dOgQAGvWrCEYDKIoCi6Xi1wux5o1aygpKaG2tpaKigpOnjyJ1+s9LQHs\nlKiZLMuIoojf7+fGG28kGo3S0PD6N4u3ClEU0d12G0pjI7S2YnwDbQ5BpyO/YgUMDJBfuRLxFYqs\noiii/4d/IHPjjegqKpB+iyNxZ3AGPStq6Wyv4OkTe3n+5AHObeql1Obkgq4NDFvm2L4rwmVbW6hs\naaahsZGo7jDPHBpl7VwEk3ma3vUdTA/7+Pkj9WzsztE89wOa2noYsDSj5gXaLRdQ8rcAACAASURB\nVBbM0zM0r+4lJAtMCQlMmWmmy19gyTPFXMkmDoYLLRRD4hM4YteTH32EkUYTKUHkkNHGmHcGW7Ka\nWVlPmSmF7cj3sWl5xJ5v0mfoIGnU0z7aj11IgtGHbuYAcngMKTxGTeP5GLo2EJvIkJ59GGQznd4P\n4Tz6PJreyuKmPyVv1jAtrcZocGN21FNXl+LkSYGKiuxpWxWvjCOCIGCq6SV1871oyTDGpnPe9prI\nsszahgtonm7FXubD9gYaH5Jej+Dxok2NILm9SK+QMZdkGeOFnyG35haMrso3tXc4g5dxJvn4DXHs\n2LEif+Gaa66hq6sLAI/Hw7XXXoumacWLLhaLkUqlmJ2dRZIkotFosV0SCoWwWCw4nU6y2Sz5fJ5Y\nLMb4+Dj5fJ7Ozk6OHDkCQGdnJ3q9HoPBgCRJGAwGgsEg09PTVC+buHm93qK/TCqVwuFwoNPpUNXC\nkFculyu2bk6pkQ4ODtLc3Ixer6eyspKmpib8fj9zc3NYrVaCwSA6nY5Vq1aRSCTYtWsXUOBtbN68\nmampKerq6lAUhbm5OY4fP0778ugsFAJGX18fL7zwAu3t7WzatAlZlql6g+rE/xbG1lb4NcJikiSh\n3nEH6ZtvRuzsRH6NHLyk1yP9kRnJncHvD/f3Pc0tD3wZWZR46MZ/5T1tBfuChnofX/vCe19VNdx1\nJIw2n2Q4McXj3T1UCCnU0YIH0MRSNa3GEiz2HKsq70PJKWhBPaGlWZgYwdzbzFPKgwCc495KScaJ\nVbYjIuGSfAwfrmX6hJer1zXTsDjBWKWZmuwELlFmsvsyLOkw0vxTsKwDEsfKQiYApDlc0smG0G4s\nh35EpvVyVL2VbP0WDE0bMbkriSZyCNEKtOgQOoON6IUfJetoIlZZqCqY9XHcxvcwGDjMxq0x1q04\nhNPyDOHAZ3CVrS2eK03TGLv7bqbvuAPvbbdR/7GPIUkS5tpV7+ialPorKPVXvOk2OoMR50XXkgnM\nYfRWvC7BkA1GZG/9O3pc73a8Y8mHIAg64ACwuPyjpzVN+9I7tf/fN9Lp9Gk/w6vbDFAY2Z2amsJo\nNBIMBslms3R2dpJOpwsPQ1XFarVy4MABFEVh5cqVVFZWksvlihe13+9nZmaGSCSC0+ksTpT09fVR\nUlKC1+vFZrNRWVlJNBolnU4XqxbXXHMNPp8PVVWx2+1UV1cTCAQwGAxF/srQ0BCrVq2ipaUFv99P\nJpNhYWGBdDrNWWedhSRJ9PT0MDMzw/79+8nlcsiyzMDAQJG7UVdXRzgcJhKJ8Pzzz2M0Gunp6UGS\nJPr6+giFQuzYsYOuri68v0cuhc7nQ/c2OSZn8LvBuz2OhFOFlxBFzRNJx1/1u9c+0Kp8pRydmUW2\nWFhQFOYJctvFYfKTEiXyMQLSNej0ThaGn0BVVeq6L0fzl6EtLSGqhX21WFYyGO0jmg/hNVTw3tKL\nSI2t5vGjPqqrMrj8pXiys6yeOcZcLkeq7GISeg/o3DTZSzA7nCAZMJqdWJdSpLIZFtNBdIkFBDT0\nJ7cxdN5fYWq7iAZ3Jbl0BJt0GJcvSrj5YyhmPZ6q6wimA0S0wt8u5GPsHvsV3x/9BpIg8QFPPVLy\nOEpimsTgnWDwYay+ClXVmL7jDrJDQ0x/5Sv43/c+rG/gbv27gNHpwug805p9p/BOVj4agec1Tfvo\nO7jPPxi0t7cX3W5faf52OqxYURC2WVpaoq6ujtnZWaLRKE6nE6vVytzcHP39/axevZpEIsHJkydJ\nJpNUV1djNBppa2srqqcCReGzTZs2sXXrVux2O3v27GF2dpbS0lL6+vpwu92UlpaSzWbJZDKcddZZ\nxGIxfvjDH+J2uykpKSEWizE/P09dXR0Wi4V0Ol3UJgkGgxw8WPAkmJqaoq+vD1EUWbt2LRdddFFR\nm2R6eppTUwZGo5H6+nri8TgvvfQSUCCztrW10dbWxuzsLB0dHTgcjv/1eVcVhcwDD8DSEvJ116F7\nA6Gg10JZWkI5cQK5tRXZeXob7TP4g8S7Oo5cu/J8MvkcZr2RrW0b3nTbqy/rxV0iI8Rm+GuXkyFF\nwaoN0+ZOEUzIHDxxA6l9Iuu3CuiUMAEtQz6v4q2thWSSsszZGEw2ArlCHAlk59gVH+Bj9c3c/qEA\nosHJ9ic7cBiClPsVfvFUJXWNeda8N4OgTxBklqr268nEpmD3VViFLXx9KMpGk4w+sI9M/RbyNj/m\ndIDF7CINtKHEhzBFCs7UhsxBFoP/t707D2/7KhM9/j3aZcmSLMv7vi9x7MROnD11m9KNLmkZCrRl\n2IcOMMPA8MDwDAOdeeaWyzA7y7BcuBQupRQKtHRJ27RN2rSpkzibEyfxEq/xvsqytevcP+SIbG0S\nx1uS83mePLHln3TOT5Zevzq/c97zf9DojCRn3U1wuJ7IdA9xh7dzzBWdsBmWYcaD5Wh9y8mabMZ4\n6jtIBH5TOubUdaR8/OOcevRRkj/9acxXUHE4FAzibjqIDAWJL1+BwXxp884CQx7CXePoSpLQWxdv\n5+lr0VwmH6VArRDiNWAC+JKUsnUOH39RxcXFsWHDhks69vSoAUSXnj755JN0dXWRkZGBVqultTX6\ntDgcDuLj42OrUEwmE36/HyC2nHdqagqn04lOp6OhoYG6ujreeusttFotU1NTGI1GIpEIQ0NDVFVV\nxeZ27N+/n9TUVKamphgZGWHFihUsX76crq4uOjs7GR8fJzExkfz86FCh0+mkpqaG48ePk5qaGntc\nIURsxMNoNLJ8+XLWr19PMBjE6/VSUvKnWd8ajQaj0RjbgK60tBSLxRJbPnchpzffM77DlvL+HTsw\nffCDCCnxTk+j/9u/vejzHw4ECH3hC5h+8Qt8n/wkmh/+UF2HvXpc03Ekyebk83UfuqRj9XodW26q\nBqqZ9Ezy5r43cXvc6ArK6J+yc+JwdEWFLW056zMPoQlJwkTQGcyM9VhY60wnqEnAEHcbPjFBKpkk\nhPLY3dBH9WYLL/b9iLiVObTsuBefTkskImhr1pHXXc+45SDVcSt5K/Aq5ZYULOEx1kV+xbdq/hpN\n6m342vLRdTegG2nB4FpJuStaH0dnLcCf8H60k2/g0xRhjhiJBKNxxDbShbHrHwmb01im30Ag/X6C\n3niGGjeRXBqHxvhk9MQ1JoQuWpAt/3OfI+3++zG7XO86JytaxBGMxgsf42lpwvdyNClCShJXXTyW\nBz1+5Fd3YtrRi/dT5ei+vP66XEI7X+Yy+RgDvi2lfFIIUQc8DtTO4eNflSwWC/feey+jo6M4HA7a\n2tpISUlhfHwcp9NJeXk5lZWVdHV14fV6GR8fZ3x8HJ/PFys45nQ66e/vp6ioiObm5tgcjuLiYux2\nOxkZGSQmJmKz2TCZTLzwwguUlpbS09NDbW0tgUB0Mld7ezslJSWxcvA6nQ63243T6cRoNHL77beT\nk5PDgQMHYhVVT5dSn56exmKxcOzYMYaGhrjlllu4bWbZaigUwmq1YjQaY8nM6Yqq72Z4eJjnn38e\nj8fDbbfdFrvvWQwG0GohFIKZBOXMgj4XCgbh6Wn027cDoNu+nbDXi8ZiuZJfo7JwVBy5gHhrPOtq\n1jE9PY3Ua7FO9JOcZcIzqiM7RxJXXMEanYXh0WGmp4MELWb26OtJOvp5WvbfhMEgyas7Su/AITJy\nk3h74A3avIeAQ6ytXk62PgmPJ5Gc7DBOTYTk0UIGXt2BviqHF1zHucH5d8RJN9kaiEy9RaRyC4PO\nAYQwExrXYRkbBLMdvcmBpuxf6Graw6F9I1iNhRQ5vUx63WiDLWjD4/jNBQSCv6J0uhtnzqMkfzQF\nIQTh4AME4rLA4MLsis6p02q1F73U0t09zeOPewkGBQ88YCI///xRDXHGpHgxM1n0onFkYhrD7uiS\nZN2+gejy/gtMrldmZ1bJhxDia8AHgNP7LEvg+1LKHwBIKXcIIRbv4twS43A4MJvN/O53v6O5uZna\n2lpWrVpFdnY2ycnJeL1e6uvrOXbsGDqdjvz8fMLhcOzFbjabyc3NpbGxkZqaGvR6PVarlUgkwt69\ne6mqqmJ8fJzDhw+j1WpjG+KFw2EOHDiA1WolPj6evr4+PB5PbEJraWkpIyMjOJ1OTpw4wcjICJFI\nhPb2dpKTkzlx4gR79+5l48aNZGRkkJKSwv79+wEYGBiIzXXR6/VnTTi9VN6XXuLWp59msLSUzs7O\nCyYfpk2b8D33HIyOon/ve6OFwX78Y3T/7//h/9SnMD/00HmBQ2+34/3ud9G8+CKRO+7AfMbSXikl\ngf5+tBYLOpvtsvuszB0VRy6PPd5OSEg+/PgjvNqxn3+56a+5L3MVRRlJ2OJtuMfHaRrsZ9QzjkFv\nwKXPQGv0EQ4LNNoI2lQ9zVMjfPvt5/jX93wMvXibFEMWee6TBJt38J5VtdR39tKyexSzychKVzKa\nyQjLQ+24ml8kFJ/JcImRwPQJTJH34ff8AQCn7za0I+0E7Bbcg7vw+rvZ33QP294qJ6fiFG+Mf4+p\nt7fxYPr9lFqq8NpWE574JQAB74nYqKTOYEGXdXlzs6SUHD7sRaMBpzNCa2vggsmHtbAMeeeD0csu\nxeWEwyFG9+widLIJ44p1JJSvOC+OGNMdTP+vdWj29SNvyUV/xuiplJLJ8XFMZjMGk6rpMRuzSj5m\nJoCdNQlMCPEjIcRHpZQ/E0JUAp3vdP9HHnkk9nVdXR11dXWz6caSNzo6ysDAABkZGYRCIdraonsD\ndHZ2csstt8Sy6FOnThEIBIDo0q+CggIGBwepra1laGiIt99+m9LSUnJycti7dy/p6elkZWVx8OBB\nIHpp5/Qb2OVykZCQgM1mi5U593g8uGaul+p0ulhxMqfTydTUFK+//jrDw8N0dnZSVhbdqMnhcNDc\nHK1O2NHRwUc+8hEikQgOh4PBwcHYJnKzFYlESHrsMUwvvUSyVkvPe997weOEEJhvuSX2fWBoCNNX\nv4pmdBRNVxeh978f/TlvfiEEcffdB/edvdujr6GB4MsvY/nmNwmtWkXgZz/DMMcrcK4GO3bsYMeO\nHYvdDRVHLlFn9xAdXSNUVWQxODXKax3RuVlvntrP5266L/be72lrJTw9MxlewnrrVtpTprnxrlH2\n6kb51tgIy6wpPJD2Cd76xU0UlldTXD5GuCc6SugNBoi3WhgeHSXJmYjFZsTicpPe5wVA6+lFRw0B\nIoAJs3k5Fo0Vf3whY75hXCf/nQlfN37vITSadYCL5IJW3vJF417b6Ekq1j2NI+wn2O0i5O/BnnT7\nFT03oVCI3bsFAwMaTCbJjTde+DitVoujtCL2/dTQAMG3XgIp8UckkdLK80Y1hBBYtpbD1rM/WB04\n3IG7p42p5j0kZOZQddtdxFnjr+g8rkZXGkfmrLy6ECITeAzQAiHgr6SUxy5w3JIvizwXfD4fTzzx\nBJ2dnVRUVLB161YOHDhAR0cHZWVllJeXxy5ZtLS00NzcHFtR0tnZSSAQoLCwMFaIbNmyZQghYqMW\neXl5hMNhkpKSaGpqwmAwUFlZyYoVK7Db7Tz77LNEIhFCoRAulwu/349eryctLY2GhgZOnjyJyWTC\n5XLFapV4vV7C4TBWq5WsrCza2trwer1kZGRw9913A9Eqr0IICgsLr2gIUkqJ99FHifva1/Ddeiu6\nJ55AdwkTUyOhEP4vfxnTf/4nvq99DeMjj1zSfI7Q1BTyhhsIJyZieim6/4XvpZcwvec9sz6Ha8VS\nKq+u4sjZRsYm+YvP/5Km5kE+9sBqvviZ9/Cz+j9S393Eh1bcwk2lqwHw+UIcPzZMR9M+xrzl6NL6\n2W/+PkgdBXyM/zuzymZrgsDU6qR5V/RDxu13DZKe1EfYq2PA149GqyM1MZ2SwiLirVY6t/8dfRPZ\n6KUkPycBr7EPrSURnS4P6+gPiPO9jVebyXciNdxmTcIWbiMoIRgIcmr4Luwpabzh+yNT0stdms2s\n3PRBIqEg3uadCEMccYVXNo8iEonwxBMTvPaaoKYmwsc+ZsNovPhn6pDfx8jLTxM+cQj9+vfgWlt3\nSf0YGXFz/yd+yufvdKAZ7wJg9f1/Tkp27qzP4VqxaOXVpZQ9wJa5eryrnd/vZ2BgAIChoSEikQir\nVq2ipqbmrBf5m2++SWtrK2azOTb6ceZmdGvWrMHr9aLT6WhpaaGkpASbzYbVasXj8TA4OEhJSQkG\ngwGv10sgEGB4eJiRkRECgQA6nY6cnBxunPlIMDY2FksaTl/z1Ov1ZGZmEggEePvttxkcHIxVVjUa\njZSWljI0NMTBgwfp6+ujq6uLP/uzP7voqp93I4TA+Ld/i+/WW9Hm5FxS4gHRqqSGRx/F/9nPYsjK\nuvSJpEKATofU6wmsXEl4zRp0M5OClaVDxZGzeTw+WtqjJc47u8fQaDR8csO9fEJuPWsLiBdfHOfY\nMT2OhDWMDGvQO/cypo+uVraYh/jzhAICES9r9IJQkSQnbhyzNkxero+JESsHjrioqDSisY7TrWui\nQJPF+KCHrsFiIuEInYNjWPPXsKz6zwEYHx5CjEbjiCBCUPrpMWSzQZfEeGiK0PRPyHQcQKv/CPeF\n6jCGjTiKK3CPH2Oy44+Yj+3D2lyP94HHiSuefbEwjUbD1q3xrFnjIznZcEmJB0TrciS+5x78a+ow\nORMvPQESoNVpaB3Vs9zpIik3F0eS2pJhNlSRsXlis9m466676OzspKioKLbi49wNifr6+mITSFes\nWEFfXx/5+flIKXE4HLS0tDA5OUlycjKTk5OxvWFaWlqYmJhg3bp1HD58mHA4TGFhIc8//zy5ubl0\nd3cD0dLt5jMKa3V1deF2uyksLMRut+Pz+aisrCQ9PZ2JiQm8Xi9jY2MEg0H279/P2rVryczM5Kmn\nnmJ6ehqj0UhKSgp+v5+2tjaamppITU1lxYoVdHZ2Eh8fT3Jycuw8JyYm8Pl8Z90G0QRrcHCQ9NJS\nTO+wI+070ZpMaC+zSqouLg7/D3+IqK8ntGEDprIytQJGWfKyM138yzfuorGpl5vrSmOv2XPjSFcX\ntLVpAS2bNvvpObWMsurVGPRalgW8jAx3ENEa0DkchDR+UnI6cbTv5NQbJkL+AGtrahluPITQakjL\nzuJo8ws4XFlMDUYnXDqcSRjj/vQ+HenuZLR9NUlpxXTFOUklwsqMm3HYs9BPdjMYHiEYGMcweBDR\n/wM0y/8eQ+KNdDZ+k+4Q2IvzqBg8hfBNMnV8J/L4NshZi2HZe/CMHcJgTsNqy421N+wZJBgOkmpL\nP+vcByf7GPD0kZ9ZjMV4eWXNdUYTuqTLm6+R6LTxr/+4laYTfRRW55KTnaTiyCyp5GOeCCEoLy9/\n14mYQgiysrJoaWnBbreTmZlJcXExDoeD1tZWBgcHY6MnKSkpWK3WWLnyQCBAdXU1g4ODeDzRYkVu\nt5vu7m4yMzOx2+1EIhFSU1PPqq46MjISuwxjNBqpq6uL7Uhrt9u5++67aW9v55e/jE4IO10pVaPR\n0NHRgV6v54YbbqCkpIRnnnmG5uZmhBB4PB5ef/114uPjefDBB0lJSWFgYIAnn3ySiYkJ7rnnnlhV\nWJ/PxzPPPEN7eztVVVXcfffdC/IGNlZVQVXVvLejKHNFCMFtN1dy282V73iMRqMhP19w5IgkPT1C\navEIyywOElM/SnPfPgLjHiYDRiBIZmomZmMckXCQSGIp4d5W4tYtw9vdT8gXndsh3dN4TnVjT8pD\nG2dHqxPY8ssQ+ugf/ZOD3TwzdJQUvYOTPj02RxofKrodiykaRyzxWeQse5TRjreYanwo2kcZfezD\nkWS2jb+JRWvlb+q+RE7xZkI/ux997wEijb9hwPwFJge/g9ZQQFbFj4mzpnNyuJnvHPknpkMe/rL8\nq6xIj1ZBnZge4/uNj9Lhaeb2jPu5v/zjC7IUtnJZNpXLsue9nWudSj4W2fr16yksLIxtRKfT6Xjl\nlVdoa2sjIyOD4uJiRkdHkVIyOjpKb28vOp2OG2+8keHhYUKhEJmZmbFVJ0ajEbPZzIoVK+jo6KC+\nvh6Xy8WqVauor6+nvr6e/Px8Tp48SWtr63n1S6KBLJ977rmH8fFxysvL0Wg0sc3ggsEgVquV4eFh\n0tPTaWlpweVyxTaYm5ycjFVRHR4eZnR0FIC+vr6zko/e3t7Y7aeXuy0l4UCAwKuvIhwOjGvWqPX9\nypJ2xx1Oqqq8eOUEb/S0YjVl8diJn9I+1UytdRPZgTSCoRBNo13EeSP4A34MegNlGzZwgl7s4TDx\nKeloDQYiUqIzxxG0RzCX34rBMsx0OMDw9CS54TD/8cYT/Ljhjzy8bhPjch/0gdlipS7/T5NHNRoN\nCTlrYe2/E/EOEl9wD3q9gYAuuneKNzyNz5HN2PAIppxNaPsOE0yrIhiIjtiGA+0E/WNgTadvqoux\nQPQSUvdkOyuIJh9TAQ/dU9HJrL1TXUgpl9z71Bvw0jR0EIcxgfzEkiXXv8Wkko9FJoQgJSWF7du3\n8+abb2KxWEhMjO7W6Ha7SU5OZnR0lKNHj1JTU0Nvby+FhYVUV1fzwgsvkJCQQDgcRq/Xk5CQQDAY\n5NVXX2Xt2rWxgmWny6pLKamqqsLv95OUlMTQ0NBZk0allLjdbuLi4vD7/UxNTeF2u0lKSqK2thaz\n2Yxer2fnzp04nU68Xi9lZWVMTEwQHx9PUVERBoOBnp4eEhMTycnJoba2FrfbTWlpKX19fbHy8Hfc\ncQddXV0UFxfHiqwtJYGf/xzTpz4FZjP+V17BtG7dYndJUd6REIKMDDN/98ef8F9v/4aqlHxuqY5e\nbu0L9XBsUM+OrkPE6Y1894bP0N7TToorhdyKatoPbmcqW2Lz6ogL+zEmuHg77ShNw9/i3oyP4JyO\nrgiTCCamxylPt/Cfme/DH/bSF8qi39eNTvun4l7hcBi3b5w4nQXfuIkptwOdawqzDbbk3ovN5MKO\nnd7ndmJMChM39jZTKz/EVOgoxlAeGm0dOunC17EPszmd0sQqbky5C294iqrkWk4On2B332ukW3L4\nWNEX6XA3U5Oyccl9gAF4reM5nuz4MWZNHF+p/ldynVe+qea1YulF/evU6Zn7Xq+X1atXY7fb8fv9\nHDhwgOXLl5Ofn09paSmFhYU4nU5MJhNVVVUcPXqUyclJ2tvb0Wg0sQ3nwuEw6enpOJ1OIpEIhw8f\nZs+ePQghyM3NJSsri/Xr11M6szGblJL6+np27NjBmjVrOHToEBMTEwwMDJCYmEhiYiJ1dXUcP36c\niYkJrFYrvb299Pb2kpOTEysTD9GlvUIINm3axG233RbbdG/btm3s2bMHgAcffJA777xzEZ7pSzQ0\nFC084fUiZyYAK8pSF5HRy6RtY33cnv512nt2srPPzc8PPcenq+/m5uJalhcvJzsjG4fNES0MmFFA\nf/8Apo4X8B99hoDehPmGGwAIiSDTk34s8RaGBkfpN7xI/eTTaIWOYutyKu21bM39MNXp0eRcSskL\nrU/xXM8TPGy9n/69nQT9fkYmQlTakshwZJPheJDOo4c5Jo+RMrgD4+BBDL37mF65CUtPJ3H7ngFg\nbHkdbqMOV8WD/Hnln6rt/+zwd9g58BwCwd+v/A825t68wM/ypZsMTgDgi3iZDnoucvT1RSUfS8Tq\n1auJj4/HbrdTWlqKXq/n17/+NUBsxOL0HJHTk0Lz8vLIzs7mxRdfpL29HSEEy5YtIzc3l7KyMurr\n62lqasJqtcZ2tD2d5OTm5p61WiUSicSW8Z6+nAMQCARobGxk06ZNsftt3rwZj8dDT08Pubm5rFq1\nCqPRSHt7OxBde2+aqb1x5qZ7dnt0yNVisWBZ4hVHdQ89hBfA5cJw002L3R1FuSghBJ/d8H4KEjMo\nceWwLn8Vfk+An7/4DSBarv2uys1AdCsHn8+HZ8pDUUYJOUl5tHbtIggIrYENqbdQoFvP6rRNdIZO\n4Z6ewOG0Mki0dlBEhtEJPRXJ1VSkVMfe44FggNf6nkUgGAwMx+aM+bw+ju3bx+ot0YVMyXkFTI5U\n4xsPYxo8RKD0LuKqHkAfgFDzdiImKyHhQW+0x87ttCRTdHVJkjGNeOPs941aCJszb0WvMZBoSqbE\nVXHxO1xH5qzOxyU3eJ2sz79S4XCYhoYGBgYGqKioIC8vD4gWLvvDH/5Af38/d911F8uXL2dycpIT\nJ05gNBrJy8ujvb2dnp4eJiYmOHHiBEIIHnroIQYGBpBSYrVaCYVCHDt2jLi4OG644QYcDgc7d+6k\nubmZzMxMILpEOBQKUV5ezvr1Z2+CdfLkSdra2rDZbLFdcIeGhujq6kKr1VJRUXHe5ZRQKERHRwcW\ni4XU1FR1/XOJWEp1Pi6ViiOXJhgK8pPdT9M60sOD1bexMjs60jnhnuBY2zH8QT/FOcWkpaQxNdbP\n6IldaK1JJOStpGFoD72TXWRFSvF6fdH6PvkFNE00oJU6EkOJ+GWANye34zKlsLXow8SbbDx19Occ\nmdhHuXUlSSPxBDpGGR0ep2zDBlZu3HRW/4b73sTX34DWkUNqbrSW0PhQL56hRkxGPc68uvPqCfmC\nXlpGjpFoSiLdcf0VCVyqFq3OhzK3tFottbXnb2kxNDQUW0bb1dXF8uXLiY+Px2w288wzz1BQUEBv\nby9WqxUpJfn5+ZSUlJCXl0deXh67du1i7969WK3W2AZ3ubm5xMfH09jYiF6vp6Wlhbi4OIxGY2x+\nyZmklDQ0NNDU1ARARkYG6enpvPHGGzQ3N1NcXExPTw8rVqyIJTIQra5aWFg4X0+Zoijn0Ov0PLzp\nz867fXJqkmn/NABuj5u0lDQsCamMG+Lo+s0X6Fv9fn5vacKsszBiGKLIVEWhq4RMVxaZriwO73yV\nka6jtOaM0OjfCxNQ4VpFoaOU1wdfwGl0cdi9B/SQnpJBZdEqlp0TzyKRCOO9/4fA1G6Y1GFNXInJ\nnMahthbc4x5qIu1Mn3gLfc0HMaUWxe5n0ptZnnp2TFKuPktvho7yrjIyzcqIewAAHQtJREFUMlix\nYgVpaWkkJSXFLqN0d3cTCATo7OyMjSr09vYyODhIWloaQgimpqbYvXs3PT09SCkxGAw4HA6SkpLQ\narUUFxczOTmJzWZDr9djMpnQarVs27YtlvCclpycDEBqairx8fGEw2EGBgZIS0ujsbGRhoYGDh06\nRDgcZmJiIjb8eiUmJyeZmJhAfeJVlCuTYE8g0ZaIxWhBr9PH3lOTHfuQQR+B46+RYy5AI7TUT7zK\naxN/QKM1IYRgYnSYnv27mR7sIzXswqAxkmHOJ82SicUUz9rEmxjy95NoSMEozBjsWXgcER4/9kO6\nxtpjfRBCYLRGlxAbrKswGOwEg0Em3BPkyGGS6v8DY/3/EDr8e0KhIP7xwTl5749PjTIxPXbFj6Nc\nGTXycZWxWq1kZ2fT3NzMtm3bYjU6kpKSYrvbrl+/nv7+fqxWK1NTU7z55pvcf//9mEwmCgsLaWxs\nRKPRUFBQwPDwMIODg2RmZsbqgeh0OpKSkjh8+DAjIyNYrVYaGxvJmtkHRQjBunXryM7OxuFwYLfb\nkVKyZcsWOjo6GBkZYXJyEofDwcsvv8yBAwfYuHEjGzdunNWlFiklp06d4ve//z2BQICtW7dScJlF\nxhRF+RNLnAWD3sDJrpN09XZh0BkIhoMYc9di6j1KXNYKPlD8IM0jrbxx8AgnDwoe27OPr33pTuIs\n8Thz8hlpb8XlSyLJ8Vd0+sKccEvS7FqGfL0YpA3Zs4plmhpee9XMePY04do97DW8QXZC9BKyEILU\nvL8g3rkZkyULoykaR6rKqwj0GgibE9F4R5G2THzPfh3dkd/hufkbWNd8aNZxpGWwiR8c+99ohZaH\ny/+OgqTSuX5qlUukko+rkM/nY3o6OmTa09PD7t270Wq1PPTQQ2RnZ6PRaDCbzWzfvh2Px0NFRQXP\nP/88EN0TZvXq1Xg8Hn7zm98gpYwVMisrKyMcDnPixAna29tj80fS09MJBAL09/eTmpoKgMFgiM1D\ngWggKS4upri4mKqqKjweD3a7nR/+8IeEQiEOHDjA2rVrY5VeL5WUEu9Pf0riL37Bphtu4BmtllOn\nTqnkQ1GuUDAYJBgKAtA31Ed7Tzs6rY5NH/wuic5ENBoN/uk4vvA/9UxNB6n8RAW//P1RDHFuSgsL\nKFy7mSOBIC8dPUoEaJ6c5EYhWJ2yGV2XnkMvV3JCSMrKIjQ1WXiw5Cbig2OMpozidDoB0BssOJJq\nYn0SQpCblYvMzMGXnU/Y60ZjtqN7/ksIQHPsGWTtBy87+QiHw/zyN2/z4s4mSjfW0pb0LJ2TrSr5\nWEQq+bgKLV++nHA4jE6nixX0CofD+P3+2Fr3+Ph47rnnHk6dOoXb7aahoQEhBOFwmNTUVDo7Oyko\nKMDpdOLz+Th8+DAVFRVMT09z4sQJAMxmM6WlpZw8eZLJyUni4uJiRc3OJaUkFAqh1WpJSUkhJSWF\ncDjMxo0bOXDgAGvWrJlVPY/gyAjGr3wF7cgIFe3tnPz2t1XioShzID87H6ERGPQGJj3RUc9QOEQo\nHIrFkayMRP7jf93H8ZYBdJpsdr7gQKeTRO58k/S0PMwDvfxzoo0hcz8B/6sc7XCzLudGPM0THAKk\nBJstxE0bJ5nu2ofHP810vJ68kuVkJuSc16cz44g5rSTap6Af34bPoz36DLLqA7Ma9TjVN8q3/vtV\nIhGJ3+fiPZ+7mRLnO1eNVeafWu1ylRocHGR8fByHw0FTUxMWi4WVK1de8A/8tm3bqK+vx2w2U1lZ\nidPp5IUXXiArK4ve3l7C4TBpaWlUV1czPDyMTqcjMTGRgoICdu3axb59+0hISMDhcODxeHj/+9+P\ny+U6q43m5mZefvllUlNTueWWW4iPj24xfTqY6HS6WQWNSCiE/0tfwvRf/4X37/8e/Te+cdmjJ8q7\nU6tdrk9SSvpntmdISHBwqq8HS5yF/Jz8Cxbs+sUTPbz+ipUkV4BbtxwiOdlFY1cLRoeOp6d/TIQI\nqw2VlAfu4nBbkNTEInKy7VRU6Oiuf4q2Ax2YkpwcKRjAjZvPrfw6TkviWW10Hz9K867XSMjKYdnm\nmzHO7EsViUQIh0Lo9PpZxZHpaT//9O1nefqFo3zxMzfw8Qc3XtGu3Mr51GqX60BrayvPPvssExMT\nbN68mbq6d98OesOGDbFdb+vr62NzP0ZHR0lISGB4eBiTyURzczMtLS0IIfjUpz6F2+1mamqKVatW\nodFoaG5uxmAwMDw8fF7y0drayvDwMMPDw1RUVFBSEv3Ucrrs+2xpdDoM3/wm/s9+FmN2NlqVeCjK\nnGg92crT215i2utly6ZNbFiz5l2Pf+8tScTH95KsOc7owb34LVYSS8vxhLwkGtIYCpzCLs385Lcn\naDjch8lYz7NPfJojg528MNbB7bVZdFhD9Lg7sekTGJrsOyv5kFIy0NaCd3wU7/goGWXLY1vVazQa\nNIbL2zjuTHFxRv7hS+/l0x/dSFaGSyUeS8AVJR9CiK8AY1LKH818vwX4FyAEvCyl/NqVd1E5Uzgc\n5siRI2ftpdLQ0IBWq2X58uUXHPkIBAKEQiF8Ph8QnTPicrmIj4+PlVnXaDSxeSQOhwOTyURjY2Ns\nOe2KFSsYG4vOEG9qasLlcpGYmEhbWxsTExNkZGRw5MgRUlNTYyth5orWbEZbVHTxA5WrkoojC8/n\n99HR3cW0N7rhm9s7wasnn8Oij2dV+oYL/nHWBU6xjN8z5k8DIDA9RWK8DqPeyQd0DyHadmMIWOnI\njqPhMJQXJWKNM7H98F6+tX873wL+6957GA4MMBwY4JXeVhKsuSRZLGw/Vs/w9ARVmZlo206QkJVL\nvNN1Xh+uhMViIs9yebvYKvNnVsmHECIZ+A2wDvjcGT/6HnCDlHJACLFdCFEjpWyYg34qZ/B4PBQW\nFhIOh7FarTz33HNAdJXK6c3bznT06FF2795NSkoKK1asIC4ujoaGBvx+PzU1NRw4cACITjjNyckh\nJSWF9vZ2kpKSsNlsGI1GcnJyYrvaDg4O0tnZic/n44knnojN7Xj44YcxGo0YjcYFfT6Uq5OKI4tH\nIJAiTNWyMoLBED7XML9ufQyB4Au6f6YybdV59xk8+CzDb/4EffZm0stvIegw8ZPx/yYQ8fNw5Cbk\n278kANyy8i/JcWSTW5jJya6T1KSXkmJ1kmNPodS5kuapQ5h1qbw5rqNsdBRjfxv3Pv5VQpEw3771\nM3zsow9jNJnRX8FIh7L0zSr5kFIOCiFuBL5++jYhRDFwSko5MHPTC8AmQAWNOaTVarn55pvp7Owk\nPT2d/v7+2M/eaWMlm82GEILR0VFuvfVW7HY7IyMjeDwebDZb7Din00l2djaNjY3s2bOH2tpaPvGJ\nT6DVarFYLMTFxfHKK68QiURIS0sjGAzG1t2Hw2HcbjdHjx4lKSmJlStXqgqmyrtScWTxGI1GVi5b\nycjYCC6ni32jr8d+Jrjw+9boyAAgPHSIgvd+nul4CyXNVYQiAaw+F5Mz99alJVBcnMG+Pa2MHDnI\nxjVr2PuZn2A0GLCZrRj0ufxbcyvxeiMFNhvtg4OEZ+oA+cMhev097O9+i9z4YtZkbVZx5Bo168su\nUsqIEOLMGV+JwMgZ308A5y+LUK5YampqbMlrcnIyOp0OrVYbm2dxrsrKSmw2GwaDgYyMDIQQ3H//\n/UgpkVJisVgYGBjgyJEjpKSkkJqayvDwMMFgkLi4uNilnKKiIlJTU9FoNLEKqlu3bsXtdlNeXs7O\nnTs5dOgQQgiSk5NxOp00NDQgpaS6ujpWk0RRTlNxZPEkuZJIckX3fNpovYU4vRWL3sqylJUXPD59\n1T2YXNnozXbs6UUkCMHf1DwCQCQUpDsuga7pbp4KvkReoJSU4iLG93sIhUK4bAmxD0drs/L4H2cy\nJp2OeJOJdOsKfvG+f2DEO8HWiht4vPk7HBp/G53Qk2MrwKZPoK2rDa1GS2FuoRpZvUZcNPkQQnwN\n+AAgIbrRJ/B9KeUPzjl0FDhzlx8nMHShx3zkkUdiX9fV1VFXV3c5fVbOYDQaWbnywsHiNI1GQ35+\n/nm3nVZUVMTLL7+M3+8nEomQn59PbW0tfr+fP/zhDxQVFZGfn8/g4CDJycmxJKKlpSW2PwwQG0Wx\n2WyYzWaOHTvGq6++GuvnmotMaFMWxo4dO9ixY8eCtqniyNJmNVrZnHvLux6j1elIKlx99m0zc0O0\nWi3m4loe2/MjJBKzz4bbsJ7iujx6xW5+vfcYK1xrSEnJoX+qh2x7PtaZzSdbGxuZaBvF4XBg0RhI\nNqfBOKSaszAbLHT2dHK87TgQjSOFuWqLhqXgSuPIFS21FUJ8A+iTUv5IRMfGjgBbiAaL14BPSimb\nz7mPWiK3xITDYV599VUOHjxIQUFBbJJpWVkZJ0+exO/3s2LFChoaGigrK+O+++5Dp9Px0ksvUV9f\nTyQS4cYbb2T16tV0dHSQkJBAamoqTU1N/Pa3vwXg3nvvveB8FGXxLfZSWxVHrg2BUIAnm37CnuGd\nFNney8/7E7BotTxs7SH+wAhEJJFVaTw1/StuTLmTDy//LBqNhl+88gr/PTPx9es5OdxQmE3b2DFS\nLZmk2TNpa2+j4WgDAsHalWvJylCbyS1FC73UNvbul1JKIcTngW1AEPjVuQFDWZq0Wi1btmxh7dq1\nvP7664TDYZxOJzqdLrYCxu12k5OTQ39/P4FAAJ1Oh9FojO3ZEolEMJvNlJWVxR63rKyMBx54ACml\nKgymvBsVR64BBp2BByo+zZ1TH+Q/m5qJMERunBmb30AkEIgeNBlkpX0jHZ5WIpEIGo2GiM1GZCb5\n8On12Mx2VprXxh43NzsXg8GAVqslLSVtMU5NmQeqyJhylqmpKTo7OwmFQrS3txMKhZBSotFoaGxs\nZN26ddx8882xpbn79u0jFApRU1OD3W5f7O4rs7DYIx+zoeLI0jY0NcXR0VHsQT9Tg13ohsfQCi1e\nm4X+iTHycnNYtawWIQRjXi+/b21Fq9WyNT8fu0kth70aXW4cUcmHckFPP/00Bw8eBOB973sfTz31\nFAD5+fk8+OCD562sOb35W29vLxaLhZKSklmVU1cWnko+lPkgpWTXnl30DfUBUFNRQ8OR6KKlzJRM\n1q1ad95KFiklrUPH6XF3kGBOZHlqjSoIdpVQFU6VOZGZmcnhw4fJyMggPT2dLVu20N7eTnV19QWX\nvg0MDLBjxw46OjoIh8Pcfvvt1NbWLkLPFUVZKhITEukb6iPZmUxSYhLFecWMTYyRlZ51wThycuQE\nf2z/FUfd+4jICJ8KfYX1OTcuQs+V+aaSj+tcT08Phw8fJjExkVWrVsU+ZVRXV5OdnU1cXBwWi4X1\n69dTWFiIXq+nra2N4eFhioqKSEyMlkf2+XyEw2HC4TBAbK5IJBJh37599PX1sWzZMgoL1Ux1RbnW\ndHR1cfTECVKTkqiuqkIIgRCC0sJSUpNTiTPFYTKZqCyrpHe8mziDiYFju/CPncJVdiNxCdGqyFNB\nD2EZIiyjcWQyOAFAKBRiz/79DI+OUrVsGTlZatLp1U4lH9e5AwcOsH//fgBSUlLIzc0FokNoSUlJ\nseOOHz/O7373OywWCw6Hg66uLoaGhrjzzjsRQpCdnc3y5ctxuVzodDqqqqqA6IjItm3bkFLi9Xop\nKChQRYMU5RoipWTv/v0ca21FCEF6aippM3WINBoNToczduzb3Tv5SfO3STGmc3tLAF3bPgITfRTe\n/jcAlCdXMTI9RIo5A4veypq0zQD09Pay/fU/FUJTycfVTyUf1zmHI1pSwW63v2sRsP7+/lgV09Oj\nHWfSaDRUV1efd7vFYiEtLY3e3l5SUlLmruOKoiwZTmc0wUhKTIzV/bmQzskWwjJMr68bj73krIIu\nADqtnhsLbj/vfvFWK4kJCYyMjeFKSJjLriuLRE04vc4FAgG6urqIj49/1+Tg1KlT7N69G7PZTH5+\nPuPj4xQXF5+XiPT09NDR0UFmZmZsFGVsbIzx8XEyMjIwXGS/BiklJ06cYGxsjNLSUhJUoJl3asKp\ncqV8Ph/dvb0k2O24LvDh5LSWoSZe6vo9SaZU1mqKYawPV1ld7LLLaSPtB5jsOoQ9bzUJ2cuit42O\nMunxkJmeftHJ7FJK9p16i3H/KKtTN+CwON/1eOXKqdUuyrw5/Xt7p8smoVCIxx57jJ6eHpxOJx//\n+MexWCyX1UZPTw8/+9nPCIfDrFmzhttuu+2K+628O5V8KAvpYnHEN+XmyA8eIDhyEnNWDRWf/Ck6\nvf6y2jg+0Mi/HPoyESJszf5ztpY+eMX9Vt7d5caRC+9EpigXcHoS2bs5/YlEp9O940Z370aj0cTa\nmM39FUVZ2i4WR4QQaPXR/Vs0OiPMYo6YODOOqD9zS5Ia+VDm1MDAAF1dXaSnp5ORkXHWz6SUhMNh\ntFrtOwYfKSWtra2MjY1RUlKiCpctADXyoSw14z3H8Zw6gi17Jba0s6sjX2ocOdS3l3H/KNWp67CZ\nVRyZb+qyi7IkSSmpr6+noaGBmpoaioqKEEJgtVppb2/HarWSnp6uVsIsApV8KFeLcDjM483NPN/f\nz4PZ2WxIjG4DYTAYGBgcwGKxkJjwznNOlPmjkg9lSZqenuZ73/se09PTxMXFkZCQwMjICKtWrWLX\nrl2YTCY+/OEPk56evthdve6o5EO5WvS53dz3xhsEpKQwLo4PR6aJyAgZyRm0dbdhMprYvHpzbBWf\nsnDUnA9lSTKZTFRVVaHRaCgqKmJwcBCfz8fERLSIkM/nixUmUxRFuRCn2cy96enohKDOYcfr9+Lz\n+/D6Zjam8/vwB/2L3EvlUqiRD2XBhEIhJiYmCAQC7N69G4DKykra29ux2WzU1NSo/WAWgRr5UK4m\n/mCQ0elptAE/zSeb0el0ZKZlMjA8QLw1nsLcQjVZfRGoyy7KkuZ2u9m5cycej4cNGzaQnZ292F26\n7qnkQ7najI6OsnP3bkKhEBtqa0lPS1vsLl33FnRjOSHEV4AxKeWPZr7/APBFYGrmkC9LKfddSRvK\ntaWrqytWzt3lcqnkQ1FxRLls7d3dNB47BkBKcrJKPq5Cs0o+hBDJwG+AdcDnzvhRNfA5KeXeOeib\ncg1KSkrC5XIxPj6uJpde51QcUWYrxeXCbrPh9/tJPWMPKuXqMevLLkIIDfB1oPeMTyxPE53Eagfe\nAr567tioGi5V3G43gUCAxMREtbR2CVjMyy4qjiizNTExQTgSIcHhUHFkCViw1S5Syghw7rt/F/BX\nUsrNQBLwl7N9fOXaZbPZcLlclxwwpJSxksxSSnp6emhpaSEUCs1nN5UFoOKIMlt2ux1nQsKs48jA\n0ACn+k4RiUTms5vKO7joZRchxNeADxANEGLm/+9LKX9wgcP/bSaYAPwWuPdCj/nII4/Evq6rq6Ou\nru6yOq1cP/r7+3nrrbewWCxkZGRgNBr57W9/SyAQ4I477mD16tWL3cWrzo4dO9ixY8eCtqniiLKY\njg0P8/jJk2SaTJQ7HGRoYP+hPURkhJplNRTkFVz8QZSzXGkcuaLVLkKIbwB9UsofCSH0QDuwTEo5\nIYT4NtByeij1jPuo4VLlku3cuZP6+nqcTienTp1i5cqVHDhwAICNGzeyZcuWRe7h1W+xV7uoOKLM\nt+83NvJCfz82nY7jU1P8Q1oygb4OAMoLy6korVjU/l0LFnS1C2cMl0opg0KILwKvCCHcQAvw0yt8\nfOU6l5KSQnx8PG63G4BTp05x00034fV6qaysXOTeKXNExRFlXpXYbBwYHeXYVHQB1etePx8rKCMc\nDpOTkbPIvbs+qTofypImpWRwcJDe3l56enooKCigrKxMTTCbQ4s98jEbKo4ol0NKScfYGEdGR2ma\nnOSG5GTWnrPxpXJlVJExRVEui0o+FEW5UmpvF0VRFEVRljSVfCiKoiiKsqBU8qEoiqIoyoJSyYei\nKIqiKAtKJR+KoiiKoiwolXwoiqIoirKgVPKhKIqiKMqCUsmHoiiKoigLSiUfiqIoiqIsKJV8KIqi\nKIqyoFTyoSiKoijKglLJh6IoiqIoC2pWyYcQIkEIsU0I8ZYQ4nUhRNXM7VuEEA1CiHohxD/PbVcV\nRbmWqDiiKNev2Y58/DXwipRyPfCXwHdnbv8ecIeUcg2wVghRMwd9vGI7duy4pttbjDavh3NcjDYX\n4xwXkYojS6i9xWhTneO10+blmm3ycRB4YubrKcAhhCgGTkkpB2ZufwHYdIX9mxPqxXb1t3e9tHk1\nBI05pOLIEmpvMdpU53jttHm5ZpV8SCmfllJ2CyE2AH8E/hFIBEbOOGwCcFx5FxVFuRapOKIo1y/d\nxQ4QQnwN+AAgATHz/4+BEmAF8JCU8pAQooSzg4QTGJrzHiuKctVRcURRlDMJKeXl30mIvwOypZSf\nOeM2ARwBthANFq8Bn5RSNp9z38tvUFGUeSWlFAvdpoojinJtuZw4ctGRj3dwO2AWQrw2832flPIB\nIcTfANuAIPCrcwPG5XZOUZRrmoojinKdmtXIh6IoiqIoymypImOKoiiKoiyoBU0+hBBfEUL8xRnf\nf2CmkNCrM/9WLUCb817ASAihF0I0nnFeX5undnRCiF8IId4WQuyaWaY474QQO884t+9e/B5X1NYH\nhBDfnPm6cuZc3xZC/HiB2tww83o5fb53zmE7BiHEEzOvxbeEEO8RQiyfr3N8h/bm7fzmi4ojc96O\niiPz36aKI+eSUs77PyAZ2AkEgL844/ZvAasXuM3jQMrM19uBmnlouwz43gI8rx8H/mPm603AswvQ\nZhzwxwVoRwAvAdPAozO3vQ5Uznz9E+B9C9DmXwLvn6dz/Mjp1wnRJabNM6/ZeTnHc9pzzbT38Hyd\n3zw8XyqOzM85qjgy/22qOHLOvwUZ+ZBSDgI3Ao+e86NS4OsiWlr5fwsh5mwS2YXaFAtXwKgUqBVC\nvCaE+IMQonAe2gC4GfgdgJTyDaJLFudbCZAnhHhFCPHifHzKBJDRV/ZtwGcAhBBmIE1KeXjmkOeZ\n49/duW3OKAX+YuZT2g+FEHFz2GQH8IOZr/1E38jp83iOZ7bnA6xE/8DN1/nNKRVHVBy5XCqOAEs0\njizYZRcpZYTo2v4z7QL+Skq5GUgimh3OZ5sLVcBoDPi2lPJG4D+Bx+ehDTj/fCLz1M6ZQsB3pJRb\ngL8Bfi2EmJfX0Tm/PwfR5/W0efndXeA10wh8WUp5A9BLtBDWXLW1U0rZKISoIPpJ6d+Zx3O8QHv/\nyjye33xQcWReqDgyv22CiiPnme1S23ckLlxM6PtSyh9c4PB/m/klAfwWuHee2xxljgsYXaxtKeUO\nIUTalbTxLkYB+xnfL8TSpSNSykYAKeUxIcQwkEr0BTefzj3XhSo+9dMzXqNPAv81lw8uhPg6cB/R\nALwb+PAZP57zczyzvZnXpmY+z2+2VBxRcWSeqDgyx+3NNo7MeaYppfxnKeVyKWXlGf+fFzCEEHqg\nSwhx+oVwE7BvPtskem0qQwiRKoTQAncCL8+mzXdrG6gWQnwUopObgM4raeNdvAL82Uw7twFvzFM7\nZ/qqEOKRmTbTgHigb74blVL6gYGZ5xOif2BenO92gSYhRN7M17N+jV6IEOJDwCqi8xV2zPc5ntve\nzM3zdn5XQsURFUfmg4ojc9/ezM2XfX5zPvJxEbGMWkoZFEJ8EXhFCOEGWoCfznObUgjxeS5SwGgO\n/BPw2EzgCAGfmoc2AB4Dfi6E2At4gIfmqZ0zfQf4pRDiDSBMdBLeQhWL+TzwUyFEGNglpdy+AG0+\nDDwhhPAQ/fQwl7/L24Bc4MWZeQqS6E6v83WOF2pvPs9vvqg4MrdUHJl/Ko6cQxUZUxRFURRlQaki\nY4qiKIqiLCiVfCiKoiiKsqBU8qEoiqIoyoJSyYeiKIqiKAtKJR+KoiiKoiwolXwoiqIoirKgVPKh\nKIqiKMqCUsmHoiiKoigL6v8Dwl+Exr+MylgAAAAASUVORK5CYII=\n"", + ""text/plain"": [ + """" + ] + }, + ""metadata"": {}, + ""output_type"": ""display_data"" + } + ], + ""source"": [ + ""# Visualize on PCA the cluster and the types\n"", + ""figure(figsize=(9,6))\n"", + ""subplot(221)\n"", + ""pca = PCA()\n"", + ""pca.fit(df_norm.T)\n"", + ""pcs = pca.transform(df_norm.T)\n"", + ""scatter(pcs[:,0],pcs[:,1],\n"", + "" c=['r' if i==argmax(ymeans) else '0.5' for i in res] ,\n"", + "" s=10, edgecolor='0.9', lw = 0.3)\n"", + ""\n"", + ""colors_cells = [map(lambda x: x/256., colors_types[i]) for i in cols_annot.ix['Cell_type',df_norm.columns]]\n"", + ""subplot(222)\n"", + ""scatter(pcs[:,0],pcs[:,1], color=colors_cells, s=10, edgecolor='0.9', lw = 0.3 )"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""# Fit a L1 Regularized Regression Model"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""## Evaluate a grid of parameters by Crossvalidation"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 15, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + ""LassoCV(alphas=array([ 0.00032, 0.00032, ..., 0.31188, 0.31623]),\n"", + "" copy_X=True,\n"", + "" cv=StratifiedShuffleSplit(labels=[ 5 20 ..., 6 6], n_iter=30, test_size=0.15, random_state=None),\n"", + "" eps=0.001, fit_intercept=False, max_iter=1000, n_alphas=100, n_jobs=1,\n"", + "" normalize=False, positive=True, precompute='auto', random_state=None,\n"", + "" selection='cyclic', tol=0.0001, verbose=False)"" + ] + }, + ""execution_count"": 15, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""_, type_indexes = unique(cols_annot.ix['Cell_type',df_log.columns], return_inverse=1)\n"", + ""\n"", + ""lassocv = LassoCV(alphas=logspace(-3.5,-0.5,500),fit_intercept=False, positive=True,\n"", + "" cv=StratifiedShuffleSplit(type_indexes, n_iter=30, test_size=0.15))\n"", + ""lassocv.fit( df_log.T, cycling_cells_target)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 16, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + ""[]"" + ] + }, + ""execution_count"": 16, + ""metadata"": {}, + ""output_type"": ""execute_result"" + }, + { + ""data"": { + ""image/png"": 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+ ""text/plain"": [ + """" + ] + }, + ""metadata"": {}, + ""output_type"": ""display_data"" + } + ], + ""source"": [ + ""figure(figsize=(3,2))\n"", + ""chos = 0.002 # The reg parameter is chosen to generate a very general model \n"", + ""axvline(log10(chos))\n"", + ""plot(log10(lassocv.alphas_), lassocv.mse_path_.mean(1))"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 17, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [], + ""source"": [ + ""lasso = Lasso(alpha=chos,max_iter=2000)\n"", + ""lasso.fit(df_log.T, cycling_cells_target)\n"", + ""y1 = lasso.predict(df_log.T)"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""# Inspect the results"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""## Visualize the coefficients distribution"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 18, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [ + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""26\n"" + ] + }, + { + ""data"": { + ""text/plain"": [ + ""(-10, 230)"" + ] + }, + ""execution_count"": 18, + ""metadata"": {}, + ""output_type"": ""execute_result"" + }, + { + ""data"": { + ""image/png"": 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C4qJXPyiQF+qibnKbNSeDuqnoX8BHgc8D/DdS/x0vvEWheVZ3ih/c8bGTx8Ri+6W3hZrhm\nDY0LwDeAP6iqdwDfBuaYonGpqv1V9Y0kl9H718tuGvqsTFKgT8UNS6fp36vqzwGq6kngKL0P4IJN\nwFPr0bGzxPBnZfCmt+Gb4aZtnO6tqsf721+k9y/dqRqXJB8HPg98DPgUDX1WJinQHwJuAOjfsHRg\nfbuzru5IMgeQ5LXABcB3krypX38P8OA69W3dVdUP6I3H5f2XFsbjYeBXAZL8OPBTVfUf69PLdfOf\nSS7ub78TOEJvXH4N2h+XJDcCbwWurKpua5+VmfXuwDLcB+xJcoQf3uQ0re4GvpDkAPAC8AF6Y/JX\nSV4ADlbVP61nB88CvwfcOzweSf4mydfp3Qz3u+vZwXXyQeCBJM/Sm3F+oKqeSfKlKRmXHcDrgAfT\n+6MHBXyYRj4r3lgkSY2YpCUXSdIIBrokNcJAl6RGGOiS1AgDXZIaYaBLUiMMdElqhIEuSY34f+xV\nsbOlIhTPAAAAAElFTkSuQmCC\n"", + ""text/plain"": [ + """" + ] + }, + ""metadata"": {}, + ""output_type"": ""display_data"" + } + ], + ""source"": [ + ""figure(figsize=(6,4))\n"", + ""print (abs(lassocv.coef_)>0).sum()\n"", + ""vlines(arange(lassocv.coef_.shape[0]), 0, sort(lassocv.coef_)[::-1] )\n"", + ""xlim(-10,230)"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""## Tabulate the top coefficients"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 19, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [ + { + ""data"": { + ""image/png"": 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LcUKIJqa6uJlNTU3JyciKRSEQ9e/ak8ePH08aNGxUeEzt06ECFhYVKhxICgYCu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\ngYODQ4r/AUIILqIyjTvuAAAAAElFTkSuQmCC\n"", + ""text/plain"": [ + """" + ] + }, + ""metadata"": {}, + ""output_type"": ""display_data"" + } + ], + ""source"": [ + ""colLabels=(\""Gene\"", \""Weight\"")\n"", + ""rows_list = zip( df_norm.index[ argsort( lassocv.coef_ )[::-1] ].tolist()[:15],\\\n"", + "" lassocv.coef_ [argsort( lassocv.coef_ )[::-1] ][:15])\n"", + ""nrows, ncols = len(rows_list)+1, len(colLabels)\n"", + ""hcell, wcell = 0.001, 2\n"", + ""hpad, wpad = 0, 0 \n"", + ""fig=plt.figure(figsize=(ncols*wcell+wpad, nrows*hcell+hpad))\n"", + ""ax = fig.add_subplot(111)\n"", + ""ax.axis('off')\n"", + ""the_table = ax.table(cellText=rows_list, colLabels=colLabels)"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""## Visualize the scoring"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 22, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + """" + ] + }, + ""execution_count"": 22, + ""metadata"": {}, + ""output_type"": ""execute_result"" + }, + { + ""data"": { + ""image/png"": 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QAKQVSTrJhg8/NN3vcf/86hBDzUqt+8AOfXK6DRKJGT4/F9u1F8nmHJ56oUamkgAjxeIXb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\n7N5dpVhcTaXSQ6WyjgcfzNis+pYVi/WRHie+7/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+ ""text/plain"": [ + """" + ] + }, + ""metadata"": {}, + ""output_type"": ""display_data"" + } + ], + ""source"": [ + ""figure(figsize=(5,4))\n"", + ""scatter( cycling_cells_target+random.normal(0,0.1, size=y1.shape), y1, alpha=0.7, s=25, lw=0.1 )\n"", + ""xlabel('Target Groups')\n"", + ""ylabel('Score')"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""# Overlay the scoring on the PCA"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 21, + ""metadata"": { + ""collapsed"": false, + ""run_control"": { + ""frozen"": false, + ""read_only"": false + } + }, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + """" + ] + }, + ""execution_count"": 21, + ""metadata"": {}, + ""output_type"": ""execute_result"" + }, + { + ""data"": { + ""image/png"": 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WNLYQDPhaWTPt99tXVY0pKIeiyEXa7cDhdDAwOYEhKZ7SvE51x+kYM+8ZIzE4D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+ ""text/plain"": [ + """" + ] + }, + ""metadata"": {}, + ""output_type"": ""display_data"" + } + ], + ""source"": [ + ""# Visualize the score on the PCA\n"", + ""figure(figsize=(4.5,3))\n"", + ""scatter(pcs[:,0],pcs[:,1], c=cm.Reds(y1),s=10, edgecolor='0.5', lw = 0.3)"" + ] + } + ], + ""metadata"": { + ""kernelspec"": { + ""display_name"": ""Python 2"", + ""language"": ""python"", + ""name"": ""python2"" + }, + ""language_info"": { + ""codemirror_mode"": { + ""name"": ""ipython"", + ""version"": 2 + }, + ""file_extension"": "".py"", + ""mimetype"": ""text/x-python"", + ""name"": ""python"", + ""nbconvert_exporter"": ""python"", + ""pygments_lexer"": ""ipython2"", + ""version"": ""2.7.12"" + }, + ""toc"": { + ""toc_cell"": true, + ""toc_number_sections"": true, + ""toc_threshold"": 6, + ""toc_window_display"": false + } + }, + ""nbformat"": 4, + ""nbformat_minor"": 0 +} +","Unknown" +"Pseudotime analysis","linnarsson-lab/ipynb-lamanno2016","scoringtool/VMscore.py",".py","2904","78","#!/usr/bin/env python + +# Copyright (c) 2015 Gioele La Manno and Sten Linnarsson +# All rights reserved. +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: +# +# * Redistributions of source code must retain the above copyright notice, this +# list of conditions and the following disclaimer. +# +# * Redistributions in binary form must reproduce the above copyright notice, +# this list of conditions and the following disclaimer in the documentation +# and/or other materials provided with the distribution. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS ""AS IS"" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +import sys +import cPickle as pickle +import getopt +import numpy as np +import pandas as pd + +optlist, args = getopt.gnu_getopt(sys.argv[1:], ""hi:o:"", [""help"", ""input="",""output=""]) +if (optlist== [] and args == []): + print ''' + usage: + VMscore.py -i inputpath -o outputpath + Input file containing single cell transcriptomes (UMI) + formatted as a tab-delimited text file with + a row header (official gene symbol) and + a column header (unique_cell_id) + ''' + sys.exit() +for opt, a in optlist: + if opt in (""-h"", ""--help""): + print ''' + usage: + VMscore.py -i inputpath -o outputpath + Input file containing single cell transcriptomes (UMI) + formatted as a tab-delimited text file with + a row header (official gene symbol) and + a column header (unique_cell_id) + ''' + sys.exit() + elif opt in ('-i', '--input'): + input_path = a + elif opt in (""-o"", ""--output""): + output_path = a + +try: + input_path == output_path +except NameError: + sys.exit() + +dict_model = pickle.load(open('VMmodel.pickle')) +normalizer = dict_model['normalizer'] +genenames = dict_model['genenames'] +classesnames = dict_model['classesnames'] +Logistic = dict_model['Logistic'] + +df_in = pd.read_csv(input_path, sep='\t', index_col=0) +df_in = np.log2( df_in.ix[genenames,:].fillna(0) +1) + +prob = Logistic.predict_proba((df_in.values/normalizer).T) +df_out = pd.DataFrame(prob.T,index=classesnames,columns=df_in.columns) + +df_out.to_csv(output_path,sep='\t') +","Python"