diff --git a/scripts/ml/ml_data_dissected.py b/scripts/ml/ml_data_dissected.py index 218cd30..d1daa2c 100644 --- a/scripts/ml/ml_data_dissected.py +++ b/scripts/ml/ml_data_dissected.py @@ -419,7 +419,7 @@ def setvars(gene,drug): #--------------------------------------- #!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! - #%% Data for ML ############################################################### + #%% Data for ML #========================== # Data for ML #========================== @@ -428,7 +428,7 @@ def setvars(gene,drug): # Build column names to mask for affinity chanhes if gene.lower() in geneL_basic: #X_stabilityN = common_cols_stabiltyN - gene_affinity_colnames = []# not needed as its a common one + gene_affinity_colnames = []# not needed as its the common ones cols_to_mask = ['ligand_affinity_change'] if gene.lower() in geneL_ppi2: @@ -487,7 +487,6 @@ def setvars(gene,drug): , 'ddg_foldx' , 'deepddg' , 'ddg_dynamut2' - , 'mmcsm_lig' , 'contacts'] #-------- # FoldX @@ -506,7 +505,8 @@ def setvars(gene,drug): # FG3: Affinity features #=================== common_affinity_Fnum = ['ligand_distance' - , 'ligand_affinity_change'] + , 'ligand_affinity_change' + , 'mmcsm_lig'] # if gene.lower() in geneL_basic: # X_affinityFN = common_affinity_Fnum diff --git a/scripts/ml/run_fg.py b/scripts/ml/run_fg.py new file mode 100755 index 0000000..d9a504a --- /dev/null +++ b/scripts/ml/run_fg.py @@ -0,0 +1,558 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Sat May 28 05:25:30 2022 + +@author: tanu +""" + +import os +import re +import argparse + +############################################################################### +# gene = 'pncA' +# drug = 'pyrazinamide' +#total_mtblineage_uc = 8 + +#%% command line args: case sensitive +arg_parser = argparse.ArgumentParser() +arg_parser.add_argument('-d', '--drug', help = 'drug name', default = '') +arg_parser.add_argument('-g', '--gene', help = 'gene name', default = '') +args = arg_parser.parse_args() + +drug = args.drug +gene = args.gene +############################################################################### +homedir = os.path.expanduser("~") +os.chdir( homedir + '/git/LSHTM_analysis/scripts/ml/') + +#================== +# Import data +#================== +from ml_data_dissected import * +setvars(gene,drug) +from ml_data_dissected import * + +# from YC run_all_ML: run locally +#from UQ_yc_RunAllClfs import run_all_ML + +#==================== +# Import ML function +#==================== +# TT run all ML clfs: baseline model +from MultModelsCl import MultModelsCl + +############################################################################ +print('\n#####################################################################\n' + , '\nRunning ML analysis: feature groups ' + , '\nGene name:', gene + , '\nDrug name:', drug) + +############################################################################### +#================== +# Specify outdir +#================== +outdir_ml = outdir + 'ml/uq_v1/fgs/' +print('\nOutput directory:', outdir_ml) +outFile = outdir_ml + gene.lower() + '_baseline_FG.csv' + +#================== +# other vars +#================== +tts_split_name = 'original' +resampling = 'none' + +############################################################################### +score_type_ordermapD = { 'mcc' : 1 + , 'fscore' : 2 + , 'jcc' : 3 + , 'precision' : 4 + , 'recall' : 5 + , 'accuracy' : 6 + , 'roc_auc' : 7 + , 'TN' : 8 + , 'FP' : 9 + , 'FN' : 10 + , 'TP' : 11 + , 'trainingY_neg': 12 + , 'trainingY_pos': 13 + , 'blindY_neg' : 14 + , 'blindY_pos' : 15 + , 'fit_time' : 16 + , 'score_time' : 17 + } +#%%########################################################################### +print('\n================================================================\n') + +#all_featuresN = X_evolFN + X_structural_FN + X_genomicFN +# X_structural_FN = X_stability_FN + X_affinityFN + X_resprop_FN +# X_resprop_FN = X_aaindex_Fnum + X_str_Fnum + X_aap_Fcat + +print('\n================================================================' + + , '\nTotal Evolutionary features (n):' , len(X_evolFN) + , '\n--------------Evol. feature colnames:', X_evolFN + + , '\n================================================================' + + , '\n\nTotal structural features (n):', len(X_structural_FN) + + , '\n--------Stability ncols:' , len(X_stability_FN) + , '\n--------------Common stability colnames:' , X_common_stability_Fnum + , '\n--------------Foldx colnames:' , X_foldX_Fnum + + , '\n--------Affinity ncols:' , len(X_affinityFN) + , '\n--------------Common affinity colnames:' , common_affinity_Fnum + , '\n--------------Gene specific affinity colnames:', gene_affinity_colnames + + , '\n--------Residue prop ncols:' , len(X_resprop_FN) + , '\n--------------Residue Prop cols:' , X_str_Fnum + , '\n--------------AA change Prop cols:' , X_aap_Fcat + , '\n--------------AA index cols:' , X_aaindex_Fnum + + , '\n================================================================' + + , '\n\nTotal Genomic features (n):' , len(X_genomicFN) + , '\n--------MAF+OR cols:' , len(X_gn_mafor_Fnum) + , '\n--------------MAF+OR colnames:' , X_gn_mafor_Fnum + + , '\n--------Lineage cols:' , len(X_gn_linegae_Fnum) + , '\n--------------Lineage cols:' , X_gn_linegae_Fnum + + , '\n--------Other cols:' , len(X_gn_Fcat) + , '\n--------------Other cols:' , X_gn_Fcat + + , '\n================================================================') + +# Sanity check +if ( len(X.columns) == len(X_evolFN) + len(X_structural_FN) + len(X_genomicFN)): + print('\nPass: No. of features match') +else: + print('\nFail: Count of feature mismatch' + , '\nExpected:', len(X_evolFN) + len(X_structural_FN) + len(X_genomicFN) + , '\nGot:', len(X.columns)) + sys.exit() + +print('\n#####################################################################\n') +############################################################################### +#================ +# Evolutionary +# X_evolFN +#================ +feature_gp_nameEV = 'evolutionary' +n_featuresEV = len(X_evolFN) + +scores_mmEV = MultModelsCl(input_df = X[X_evolFN] + , target = y + , var_type = 'mixed' + , skf_cv = skf_cv + , blind_test_input_df = X_bts[X_evolFN] + , blind_test_target = y_bts + , add_cm = True + , add_yn = True) + +baseline_allEV = pd.DataFrame(scores_mmEV) + +baseline_EV = baseline_allEV.filter(regex = 'bts_.*|test_.*|.*_time|TN|FP|FN|TP|.*_neg|.*_pos', axis = 0) +baseline_EV = baseline_EV.reset_index() +baseline_EV.rename(columns = {'index': 'original_names'}, inplace = True) + +# Indicate whether BT or CT +bt_pattern = re.compile(r'bts_.*') +baseline_EV['data_source'] = baseline_EV.apply(lambda row: 'BT' if bt_pattern.search(row.original_names) else 'CV' , axis = 1) + +baseline_EV['score_type'] = baseline_EV['original_names'].str.replace('bts_|test_', '', regex = True) + +score_type_uniqueN = set(baseline_EV['score_type']) +cL1 = list(score_type_ordermapD.keys()) +cL2 = list(score_type_uniqueN) + +if set(cL1).issubset(cL2): + print('\nPASS: sorting df by score that is mapped onto the order I want') + baseline_EV['score_order'] = baseline_EV['score_type'].map(score_type_ordermapD) + baseline_EV.sort_values(by = ['data_source', 'score_order'], ascending = [True, True], inplace = True) +else: + sys.exit('\nFAIL: could not sort df as score mapping for ordering failed') + +baseline_EV['feature_group'] = feature_gp_nameEV +baseline_EV['resampling'] = resampling +baseline_EV['tts_split'] = tts_split_name +baseline_EV['n_features'] = n_featuresEV +############################################################################### +#================ +# Genomics +# X_genomicFN +#================ +feature_gp_nameGN = 'genomics' +n_featuresGN = len(X_genomicFN) + +scores_mmGN = MultModelsCl(input_df = X[X_genomicFN] + , target = y + , var_type = 'mixed' + , skf_cv = skf_cv + , blind_test_input_df = X_bts[X_genomicFN] + , blind_test_target = y_bts + , add_cm = True + , add_yn = True) + +baseline_allGN = pd.DataFrame(scores_mmGN) + +baseline_GN = baseline_allGN.filter(regex = 'bts_.*|test_.*|.*_time|TN|FP|FN|TP|.*_neg|.*_pos', axis = 0) +baseline_GN = baseline_GN.reset_index() +baseline_GN.rename(columns = {'index': 'original_names'}, inplace = True) + +# Indicate whether BT or CT +bt_pattern = re.compile(r'bts_.*') +baseline_GN['data_source'] = baseline_GN.apply(lambda row: 'BT' if bt_pattern.search(row.original_names) else 'CV' , axis = 1) + +baseline_GN['score_type'] = baseline_GN['original_names'].str.replace('bts_|test_', '', regex = True) + +score_type_uniqueN = set(baseline_GN['score_type']) +cL1 = list(score_type_ordermapD.keys()) +cL2 = list(score_type_uniqueN) + +if set(cL1).issubset(cL2): + print('\nPASS: sorting df by score that is mapped onto the order I want') + baseline_GN['score_order'] = baseline_GN['score_type'].map(score_type_ordermapD) + baseline_GN.sort_values(by = ['data_source', 'score_order'], ascending = [True, True], inplace = True) +else: + sys.exit('\nFAIL: could not sort df as score mapping for ordering failed') + +baseline_GN['feature_group'] = feature_gp_nameGN +baseline_GN['resampling'] = resampling +baseline_GN['tts_split'] = tts_split_name +baseline_GN['n_features'] = n_featuresGN +############################################################################### +#all_featuresN = X_evolFN + X_structural_FN + X_genomicFN +# X_structural_FN = X_stability_FN + X_affinityFN + X_resprop_FN +# X_resprop_FN = X_aaindex_Fnum + X_str_Fnum + X_aap_Fcat +#================ +# Structural cols +# X_structural_FN +#================ +feature_gp_nameSTR = 'structural' +n_featuresSTR = len(X_structural_FN) + +scores_mmSTR = MultModelsCl(input_df = X[X_structural_FN] + , target = y + , var_type = 'mixed' + , skf_cv = skf_cv + , blind_test_input_df = X_bts[X_structural_FN] + , blind_test_target = y_bts + , add_cm = True + , add_yn = True) + +baseline_allSTR = pd.DataFrame(scores_mmSTR) + +baseline_STR = baseline_allSTR.filter(regex = 'bts_.*|test_.*|.*_time|TN|FP|FN|TP|.*_neg|.*_pos', axis = 0) +baseline_STR = baseline_STR.reset_index() +baseline_STR.rename(columns = {'index': 'original_names'}, inplace = True) + +# Indicate whether BT or CT +bt_pattern = re.compile(r'bts_.*') +baseline_STR['data_source'] = baseline_STR.apply(lambda row: 'BT' if bt_pattern.search(row.original_names) else 'CV' , axis = 1) + +baseline_STR['score_type'] = baseline_STR['original_names'].str.replace('bts_|test_', '', regex = True) + +score_type_uniqueN = set(baseline_STR['score_type']) +cL1 = list(score_type_ordermapD.keys()) +cL2 = list(score_type_uniqueN) + +if set(cL1).issubset(cL2): + print('\nPASS: sorting df by score that is mapped onto the order I want') + baseline_STR['score_order'] = baseline_STR['score_type'].map(score_type_ordermapD) + baseline_STR.sort_values(by = ['data_source', 'score_order'], ascending = [True, True], inplace = True) +else: + sys.exit('\nFAIL: could not sort df as score mapping for ordering failed') + +baseline_STR['feature_group'] = feature_gp_nameSTR +baseline_STR['resampling'] = resampling +baseline_STR['tts_split'] = tts_split_name +baseline_STR['n_features'] = n_featuresSTR +############################################################################## +#================ +# Stability cols +# X_stability_FN +#================ +feature_gp_nameSTB = 'stability' +n_featuresSTB = len(X_stability_FN) + +scores_mmSTB = MultModelsCl(input_df = X[X_stability_FN] + , target = y + , var_type = 'mixed' + , skf_cv = skf_cv + , blind_test_input_df = X_bts[X_stability_FN] + , blind_test_target = y_bts + , add_cm = True + , add_yn = True) + +baseline_allSTB = pd.DataFrame(scores_mmSTB) + +baseline_STB = baseline_allSTB.filter(regex = 'bts_.*|test_.*|.*_time|TN|FP|FN|TP|.*_neg|.*_pos', axis = 0) +baseline_STB = baseline_STB.reset_index() +baseline_STB.rename(columns = {'index': 'original_names'}, inplace = True) + +# Indicate whether BT or CT +bt_pattern = re.compile(r'bts_.*') +baseline_STB['data_source'] = baseline_STB.apply(lambda row: 'BT' if bt_pattern.search(row.original_names) else 'CV' , axis = 1) + +baseline_STB['score_type'] = baseline_STB['original_names'].str.replace('bts_|test_', '', regex = True) + +score_type_uniqueN = set(baseline_STB['score_type']) +cL1 = list(score_type_ordermapD.keys()) +cL2 = list(score_type_uniqueN) + +if set(cL1).issubset(cL2): + print('\nPASS: sorting df by score that is mapped onto the order I want') + baseline_STB['score_order'] = baseline_STB['score_type'].map(score_type_ordermapD) + baseline_STB.sort_values(by = ['data_source', 'score_order'], ascending = [True, True], inplace = True) +else: + sys.exit('\nFAIL: could not sort df as score mapping for ordering failed') + +baseline_STB['feature_group'] = feature_gp_nameSTB +baseline_STB['resampling'] = resampling +baseline_STB['tts_split'] = tts_split_name +baseline_STB['n_features'] = n_featuresSTB +############################################################################### +#================ +# Affinity cols +# X_affinityFN +#================ +feature_gp_nameAFF = 'affinity' +n_featuresAFF = len(X_affinityFN) + +scores_mmAFF = MultModelsCl(input_df = X[X_affinityFN] + , target = y + , var_type = 'mixed' + , skf_cv = skf_cv + , blind_test_input_df = X_bts[X_affinityFN] + , blind_test_target = y_bts + , add_cm = True + , add_yn = True) + +baseline_allAFF = pd.DataFrame(scores_mmAFF) + +baseline_AFF = baseline_allAFF.filter(regex = 'bts_.*|test_.*|.*_time|TN|FP|FN|TP|.*_neg|.*_pos', axis = 0) +baseline_AFF = baseline_AFF.reset_index() +baseline_AFF.rename(columns = {'index': 'original_names'}, inplace = True) + +# Indicate whether BT or CT +bt_pattern = re.compile(r'bts_.*') +baseline_AFF['data_source'] = baseline_AFF.apply(lambda row: 'BT' if bt_pattern.search(row.original_names) else 'CV' , axis = 1) + +baseline_AFF['score_type'] = baseline_AFF['original_names'].str.replace('bts_|test_', '', regex = True) + +score_type_uniqueN = set(baseline_AFF['score_type']) +cL1 = list(score_type_ordermapD.keys()) +cL2 = list(score_type_uniqueN) + +if set(cL1).issubset(cL2): + print('\nPASS: sorting df by score that is mapped onto the order I want') + baseline_AFF['score_order'] = baseline_AFF['score_type'].map(score_type_ordermapD) + baseline_AFF.sort_values(by = ['data_source', 'score_order'], ascending = [True, True], inplace = True) +else: + sys.exit('\nFAIL: could not sort df as score mapping for ordering failed') + +baseline_AFF['feature_group'] = feature_gp_nameAFF +baseline_AFF['resampling'] = resampling +baseline_AFF['tts_split'] = tts_split_name +baseline_AFF['n_features'] = n_featuresAFF +############################################################################### +#================ +# Residue level +# X_resprop_FN +#================ +feature_gp_nameRES = 'residue_prop' +n_featuresRES = len(X_resprop_FN) + +scores_mmRES = MultModelsCl(input_df = X[X_resprop_FN] + , target = y + , var_type = 'mixed' + , skf_cv = skf_cv + , blind_test_input_df = X_bts[X_resprop_FN] + , blind_test_target = y_bts + , add_cm = True + , add_yn = True) + +baseline_allRES = pd.DataFrame(scores_mmRES) + +baseline_RES = baseline_allRES.filter(regex = 'bts_.*|test_.*|.*_time|TN|FP|FN|TP|.*_neg|.*_pos', axis = 0) +baseline_RES = baseline_RES.reset_index() +baseline_RES.rename(columns = {'index': 'original_names'}, inplace = True) + +# Indicate whether BT or CT +bt_pattern = re.compile(r'bts_.*') +baseline_RES['data_source'] = baseline_RES.apply(lambda row: 'BT' if bt_pattern.search(row.original_names) else 'CV' , axis = 1) + +baseline_RES['score_type'] = baseline_RES['original_names'].str.replace('bts_|test_', '', regex = True) + +score_type_uniqueN = set(baseline_RES['score_type']) +cL1 = list(score_type_ordermapD.keys()) +cL2 = list(score_type_uniqueN) + +if set(cL1).issubset(cL2): + print('\nPASS: sorting df by score that is mapped onto the order I want') + baseline_RES['score_order'] = baseline_RES['score_type'].map(score_type_ordermapD) + baseline_RES.sort_values(by = ['data_source', 'score_order'], ascending = [True, True], inplace = True) +else: + sys.exit('\nFAIL: could not sort df as score mapping for ordering failed') + +baseline_RES['feature_group'] = feature_gp_nameRES +baseline_RES['resampling'] = resampling +baseline_RES['tts_split'] = tts_split_name +baseline_RES['n_features'] = n_featuresRES +############################################################################### +#================ +# Residue level-AAindex +#X_resprop_FN - X_aaindex_Fnum +#================ +X_respropNOaaFN = list(set(X_resprop_FN) - set(X_aaindex_Fnum)) + +feature_gp_nameRNAA = 'ResPropNoAA' +n_featuresRNAA = len(X_respropNOaaFN) + +scores_mmRNAA = MultModelsCl(input_df = X[X_respropNOaaFN] + , target = y + , var_type = 'mixed' + , skf_cv = skf_cv + , blind_test_input_df = X_bts[X_respropNOaaFN] + , blind_test_target = y_bts + , add_cm = True + , add_yn = True) + +baseline_allRNAA = pd.DataFrame(scores_mmRNAA) + +baseline_RNAA = baseline_allRNAA.filter(regex = 'bts_.*|test_.*|.*_time|TN|FP|FN|TP|.*_neg|.*_pos', axis = 0) +baseline_RNAA = baseline_RNAA.reset_index() +baseline_RNAA.rename(columns = {'index': 'original_names'}, inplace = True) + +# Indicate whether BT or CT +bt_pattern = re.compile(r'bts_.*') +baseline_RNAA['data_source'] = baseline_RNAA.apply(lambda row: 'BT' if bt_pattern.search(row.original_names) else 'CV' , axis = 1) + +baseline_RNAA['score_type'] = baseline_RNAA['original_names'].str.replace('bts_|test_', '', regex = True) + +score_type_uniqueN = set(baseline_RNAA['score_type']) +cL1 = list(score_type_ordermapD.keys()) +cL2 = list(score_type_uniqueN) + +if set(cL1).issubset(cL2): + print('\nPASS: sorting df by score that is mapped onto the order I want') + baseline_RNAA['score_order'] = baseline_RNAA['score_type'].map(score_type_ordermapD) + baseline_RNAA.sort_values(by = ['data_source', 'score_order'], ascending = [True, True], inplace = True) +else: + sys.exit('\nFAIL: could not sort df as score mapping for ordering failed') + +baseline_RNAA['feature_group'] = feature_gp_nameRNAA +baseline_RNAA['resampling'] = resampling +baseline_RNAA['tts_split'] = tts_split_name +baseline_RNAA['n_features'] = n_featuresRNAA +############################################################################### +#================ +# Structural cols-AAindex +#X_structural_FN - X_aaindex_Fnum +#================ +X_strNOaaFN = list(set(X_structural_FN) - set(X_aaindex_Fnum)) + +feature_gp_nameSNAA = 'StrNoAA' +n_featuresSNAA = len(X_strNOaaFN) + +scores_mmSNAA = MultModelsCl(input_df = X[X_strNOaaFN] + , target = y + , var_type = 'mixed' + , skf_cv = skf_cv + , blind_test_input_df = X_bts[X_strNOaaFN] + , blind_test_target = y_bts + , add_cm = True + , add_yn = True) + +baseline_allSNAA = pd.DataFrame(scores_mmSNAA) + +baseline_SNAA = baseline_allSNAA.filter(regex = 'bts_.*|test_.*|.*_time|TN|FP|FN|TP|.*_neg|.*_pos', axis = 0) +baseline_SNAA = baseline_SNAA.reset_index() +baseline_SNAA.rename(columns = {'index': 'original_names'}, inplace = True) + +# Indicate whether BT or CT +bt_pattern = re.compile(r'bts_.*') +baseline_SNAA['data_source'] = baseline_SNAA.apply(lambda row: 'BT' if bt_pattern.search(row.original_names) else 'CV' , axis = 1) + +baseline_SNAA['score_type'] = baseline_SNAA['original_names'].str.replace('bts_|test_', '', regex = True) + +score_type_uniqueN = set(baseline_SNAA['score_type']) +cL1 = list(score_type_ordermapD.keys()) +cL2 = list(score_type_uniqueN) + +if set(cL1).issubset(cL2): + print('\nPASS: sorting df by score that is mapped onto the order I want') + baseline_SNAA['score_order'] = baseline_SNAA['score_type'].map(score_type_ordermapD) + baseline_SNAA.sort_values(by = ['data_source', 'score_order'], ascending = [True, True], inplace = True) +else: + sys.exit('\nFAIL: could not sort df as score mapping for ordering failed') + +baseline_SNAA['feature_group'] = feature_gp_nameSNAA +baseline_SNAA['resampling'] = resampling +baseline_SNAA['tts_split'] = tts_split_name +baseline_SNAA['n_features'] = n_featuresSNAA +############################################################################### +#%% COMBINING all FG dfs +#================ +# Combine all +# https://stackoverflow.com/questions/39862654/pandas-concat-of-multiple-data-frames-using-only-common-columns +#================ +dfs_combine = [baseline_EV, baseline_GN, baseline_STR, baseline_STB, baseline_AFF, baseline_RES , baseline_RNAA , baseline_SNAA] + +dfs_nrows = [] +for df in dfs_combine: + dfs_nrows = dfs_nrows + [len(df)] +dfs_nrows = max(dfs_nrows) + +dfs_ncols = [] +for df in dfs_combine: + dfs_ncols = dfs_ncols + [len(df.columns)] +dfs_ncols = max(dfs_ncols) + +# dfs_ncols = [] +# dfs_ncols2 = mode(dfs_ncols.append(len(df.columns) for df in dfs_combine) +# dfs_ncols2 + +expected_nrows = len(dfs_combine) * dfs_nrows +expected_ncols = dfs_ncols + +common_cols = list(set.intersection(*(set(df.columns) for df in dfs_combine))) + +if len(common_cols) == dfs_ncols : + combined_FG_baseline = pd.concat([df[common_cols] for df in dfs_combine], ignore_index=True) + fgs = combined_FG_baseline[['feature_group', 'n_features']] + fgs = fgs.drop_duplicates() + print('\nConcatenating dfs with feature groups after ML analysis (sampling type):' + , '\nNo. of dfs combining:', len(dfs_combine) + , '\nSampling type:', resampling + , '\nThe feature groups are:' + , '\n', fgs) + if len(combined_FG_baseline) == expected_nrows and len(combined_FG_baseline.columns) == expected_ncols: + print('\nPASS:', len(dfs_combine), 'dfs successfully combined' + , '\nnrows in combined_df:', len(combined_FG_baseline) + , '\nncols in combined_df:', len(combined_FG_baseline.columns)) + else: + print('\nFAIL: concatenating failed' + , '\nExpected nrows:', expected_nrows + , '\nGot:', len(combined_FG_baseline) + , '\nExpected ncols:', expected_ncols + , '\nGot:', len(combined_FG_baseline.columns)) + sys.exit() +else: + sys.exit('\nConcatenting dfs not possible,check numbers ') + +# # rpow bind +# if all(ll((baseline_EV.columns == baseline_GN.columns == baseline_STR.columns)): +# print('\nPASS:colnames match, proceeding to rowbind') +# comb_df = pd.concat()], axis = 0, ignore_index = True ) +############################################################################### +#==================== +# Write output file +#==================== + +combined_FG_baseline.to_csv(outFile, index = False) +print('\nFile successfully written:', outFile) +###############################################################################