#!/usr/bin/env python3 # -*- coding: utf-8 -*- ''' Created on Tue Aug 6 12:56:03 2019 @author: tanu ''' # FIXME: include error checking to enure you only # concentrate on positions that have structural info? # FIXME: import dirs.py to get the basic dir paths available #======================================================================= # TASK: extract ALL matched mutations from GWAS data # Input data file has the following format: each row = unique sample id # id,country,lineage,sublineage,drtype,drug,dr_muts_col,other_muts_col... # 0,sampleID,USA,lineage2,lineage2.2.1,Drug-resistant,0.0,WT,gene_matchPOS; pncA_c.POS... # where multiple mutations and multiple mutation types are separated by ';'. # We are interested in the protein coding region i.e mutation with the_'p.' format. # This script splits the mutations on the ';' and extracts protein coding muts only # where each row is a separate mutation # sample ids AND mutations are NOT unique, but the COMBINATION (sample id + mutation) = unique # output files: all lower case # 1) _gwas.csv # 2) _common_ids.csv # 3) _ambiguous_muts.csv # 4) _mcsm_snps.csv # 5) _metadata_poscounts.csv # 6) _metadata.csv # 7) _all_muts_msa.csv # 8) _mutational_positons.csv #------------ # NOTE #----------- # drtype is renamed to 'resistance' in the 35k dataset # all colnames in the ouput files lowercase #------------- # requires #------------- #reference_dict.py #tidy_split.py #======================================================================= #%% load libraries import os, sys import re import pandas as pd import numpy as np import argparse #======================================================================= #%% dir and local imports homedir = os.path.expanduser('~') # set working dir os.getcwd() os.chdir(homedir + '/git/LSHTM_analysis/scripts') os.getcwd() #======================================================================= #%% command line args arg_parser = argparse.ArgumentParser() arg_parser.add_argument('-d', '--drug', help='drug name (case sensitive)', default = None) arg_parser.add_argument('-g', '--gene', help='gene name (case sensitive)', default = None) arg_parser.add_argument('--datadir', help = 'Data Directory. By default, it assmumes homedir + git/Data') arg_parser.add_argument('-i', '--input_dir', help = 'Input dir containing pdb files. By default, it assmumes homedir + + input') arg_parser.add_argument('-o', '--output_dir', help = 'Output dir for results. By default, it assmes homedir + + output') arg_parser.add_argument('-m', '--make_dirs', help = 'Make dir for input and output', action='store_true') arg_parser.add_argument('--debug', action ='store_true', help = 'Debug Mode') args = arg_parser.parse_args() #======================================================================= #%% variable assignment: input and output paths & filenames drug = args.drug gene = args.gene datadir = args.datadir indir = args.input_dir outdir = args.output_dir make_dirs = args.make_dirs #drug = 'ethambutol' #gene = 'embB' #%% input and output dirs and files #======= # dirs #======= if not datadir: datadir = homedir + '/' + 'git/Data' if not indir: indir = datadir + '/' + drug + '/input' if not outdir: outdir = datadir + '/' + drug + '/output' if make_dirs: print('make_dirs is turned on, creating data dir:', datadir) try: os.makedirs(datadir, exist_ok = True) print("Directory '%s' created successfully" %datadir) except OSError as error: print("Directory '%s' can not be created") print('make_dirs is turned on, creating indir:', indir) try: os.makedirs(indir, exist_ok = True) print("Directory '%s' created successfully" %indir) except OSError as error: print("Directory '%s' can not be created") print('make_dirs is turned on, creating outdir:', outdir) try: os.makedirs(outdir, exist_ok = True) print("Directory '%s' created successfully" %outdir) except OSError as error: print("Directory '%s' can not be created") # handle missing dirs here if not os.path.isdir(datadir): print('ERROR: Data directory does not exist:', datadir , '\nPlease create and ensure gwas data is present and then rerun\nelse specify cmd option ---make_dirs') sys.exit() if not os.path.isdir(indir): print('ERROR: Input directory does not exist:', indir , '\nPlease either create or specify indir and rerun\nelse specify cmd option ---make_dirs') sys.exit() if not os.path.isdir(outdir): print('ERROR: Output directory does not exist:', outdir , '\nPlease create or specify outdir and rerun\nelse specify cmd option ---make_dirs') sys.exit() # Requires from reference_dict import my_aa_dict # CHECK DIR STRUC THERE! from tidy_split import tidy_split #======================================================================= gene_match = gene + '_p.' print('mut pattern for gene', gene, ':', gene_match) nssnp_match = gene_match +'[A-Za-z]{3}[0-9]+[A-Za-z]{3}' print('nsSNP for gene', gene, ':', nssnp_match) wt_regex = gene_match.lower()+'([A-Za-z]{3})' print('wt regex:', wt_regex) mut_regex = r'[0-9]+(\w{3})$' print('mt regex:', mut_regex) pos_regex = r'([0-9]+)' print('position regex:', pos_regex) # building cols to extract dr_muts_col = 'dr_mutations_' + drug other_muts_col = 'other_mutations_' + drug resistance_col = 'drtype' print('Extracting columns based on variables:\n' , drug , '\n' , dr_muts_col , '\n' , other_muts_col , '\n' , resistance_col , '\n===============================================================') #======================================================================= #======= # input #======= #in_filename_master_master = 'original_tanushree_data_v2.csv' #19k in_filename_master = 'mtb_gwas_meta_v6.csv' #35k infile_master = datadir + '/' + in_filename_master print('Input file: ', infile_master , '\n============================================================') #======= # output #======= # several output files: in respective sections at the time of outputting files print('Output filename: in the respective sections' , '\nOutput path: ', outdir , '\n=============================================================') #%%end of variable assignment for input and output files #======================================================================= #%% Read input file master_data = pd.read_csv(infile_master, sep = ',') # column names #list(master_data.columns) # extract elevant columns to extract from meta data related to the drug if in_filename_master == 'original_tanushree_data_v2.csv': meta_data = master_data[['id' , 'country' , 'lineage' , 'sublineage' , 'drtype' , drug , dr_muts_col , other_muts_col]] else: core_cols = ['id' , 'sample' #, 'patient_id' #, 'strain' , 'lineage' , 'sublineage' #, 'country' , 'country_code' , 'geographic_source' #, 'region' #, 'location' #, 'host_body_site' #, 'environment_material' #, 'host_status' #, 'host_sex' #, 'submitted_host_sex' #, 'hiv_status' #, 'HIV_status' #, 'tissue_type' #, 'isolation_source' , resistance_col] variable_based_cols = [drug , dr_muts_col , other_muts_col] cols_to_extract = core_cols + variable_based_cols print('Extracting', len(cols_to_extract), 'columns from master data') meta_data = master_data[cols_to_extract] del(master_data, variable_based_cols, cols_to_extract) print('Extracted meta data from filename:', in_filename_master , '\nDim:', meta_data.shape) # checks and results total_samples = meta_data['id'].nunique() print('RESULT: Total samples:', total_samples , '\n===========================================================') # counts NA per column meta_data.isna().sum() print('No. of NAs/column:' + '\n', meta_data.isna().sum() , '\n===========================================================\n') #%% Write check file check_file = outdir + '/' + gene.lower() + '_gwas.csv' meta_data.to_csv(check_file, index = False) print('\n----------------------------------' , '\nWriting subsetted gwas data:', gene , '\n----------------------------------' , '\nFile', check_file , '\nDim:', meta_data.shape) # equivalent of table in R # drug counts: complete samples for OR calcs meta_data[drug].value_counts() print('===========================================================\n' , 'RESULT: No. of Sus and Res', drug, 'samples:\n', meta_data[drug].value_counts() , '\n===========================================================\n' , 'RESULT: Percentage of Sus and Res', drug, 'samples:\n', meta_data[drug].value_counts(normalize = True)*100 , '\n===========================================================') #%% # IMPORTANT sanity check: # This is to find out how many samples have 1 and more than 1 mutation,so you # can use it to check if your data extraction process for dr_muts # and other_muts has worked correctly AND also to check the dim of the # final formatted data. # This will have: unique COMBINATION of sample id and mutations #======== # First: counting mutations in dr_muts_col column #======== print('Now counting WT &', nssnp_match, 'muts within the column:', dr_muts_col) # drop na and extract a clean df clean_df = meta_data.dropna(subset=[dr_muts_col]) # sanity check: count na na_count = meta_data[dr_muts_col].isna().sum() if len(clean_df) == (total_samples - na_count): print('PASS: clean_df extracted: length is', len(clean_df) , '\nNo.of NAs in', dr_muts_col, '=', na_count, '/', total_samples , '\n==========================================================') else: sys.exit('FAIL: Could not drop NAs') dr_gene_count = 0 wt = 0 id_dr = [] id2_dr = [] #nssnp_match_regex = re.compile(nssnp_match) for i, id in enumerate(clean_df.id): #print (i, id) #id_dr.append(id) #count_gene_dr = clean_df[dr_muts_col].iloc[i].count(gene_match) # can include stop muts count_gene_dr = len(re.findall(nssnp_match, clean_df[dr_muts_col].iloc[i], re.IGNORECASE)) #print(count_gene_dr) if count_gene_dr > 0: id_dr.append(id) if count_gene_dr > 1: id2_dr.append(id) #print(id, count_gene_dr) dr_gene_count = dr_gene_count + count_gene_dr count_wt = clean_df[dr_muts_col].iloc[i].count('WT') wt = wt + count_wt print('RESULTS:') print('Total WT in dr_muts_col:', wt) print('Total matches of', gene, 'SNP matches in', dr_muts_col, ':', dr_gene_count) print('Total samples with > 1', gene, 'muts in dr_muts_col:', len(id2_dr) ) print('=================================================================') del(clean_df, na_count, i, id, wt, id2_dr, count_gene_dr, count_wt) #======== # Second: counting mutations in other_muts_col column #======== print('Now counting WT &', nssnp_match, 'muts within the column:', other_muts_col) # drop na and extract a clean df clean_df = meta_data.dropna(subset=[other_muts_col]) # sanity check: count na na_count = meta_data[other_muts_col].isna().sum() if len(clean_df) == (total_samples - na_count): print('PASS: clean_df extracted: length is', len(clean_df) , '\nNo.of NAs =', na_count, '/', total_samples , '\n=========================================================') else: sys.exit('FAIL: Could not drop NAs') other_gene_count = 0 wt_other = 0 id_other = [] id2_other = [] for i, id in enumerate(clean_df.id): #print (i, id) #id_other.append(id) #count_gene_other = clean_df[other_muts_col].iloc[i].count(gene_match) # can include stop muts count_gene_other = len(re.findall(nssnp_match, clean_df[other_muts_col].iloc[i], re.IGNORECASE)) if count_gene_other > 0: id_other.append(id) if count_gene_other > 1: id2_other.append(id) #print(id, count_gene_other) other_gene_count = other_gene_count + count_gene_other count_wt = clean_df[other_muts_col].iloc[i].count('WT') wt_other = wt_other + count_wt print('RESULTS:') print('Total WT in other_muts_col:', wt_other) print('Total matches of', gene, 'SNP matches in', other_muts_col, ':', other_gene_count) print('Total samples with > 1', gene, 'muts in other_muts_col:', len(id2_other) ) print('=================================================================') print('Predicting total no. of rows in the curated df:', dr_gene_count + other_gene_count , '\n===================================================================') expected_rows = dr_gene_count + other_gene_count #del( wt_other, clean_df, i, id, na_count, id2_other, count_gene_other, count_wt) del(clean_df, na_count, i, id, wt_other, id2_other, count_gene_other, count_wt ) #%% ############ # extracting dr and other muts separately along with the common cols ############# print('Extracting dr_muts from col:', dr_muts_col, 'with other meta_data') print('muts to extract:', nssnp_match ) #=============== # dr mutations: extract gene_match entries with meta data and ONLY dr_muts col #=============== if in_filename_master == 'original_tanushree_data_v2.csv': meta_data_dr = meta_data[['id' ,'country' ,'lineage' ,'sublineage' ,'drtype' , drug , dr_muts_col]] else: dr_based_cols = [drug, dr_muts_col] cols_to_extract = core_cols + dr_based_cols print('Extracting', len(cols_to_extract), 'columns from meta data') meta_data_dr = meta_data[cols_to_extract] del(dr_based_cols, cols_to_extract) if meta_data_dr.shape[0] == len(meta_data) and meta_data_dr.shape[1] == (len(meta_data.columns)-1): print('PASS: Dimensions match' , '\n===============================================================') else: print('FAIL: Dimensions mismatch:' , 'Expected dim:', len(meta_data), (len(meta_data.columns)-1) , '\nGot:', meta_data_dr.shape , '\n===============================================================') sys.exit() # Extract within this the nsSNPs for gene of interest using string match #meta_gene_dr = meta_data_dr.loc[meta_data_dr[dr_muts_col].str.contains(gene_match, na = False)] meta_gene_dr = meta_data_dr.loc[meta_data_dr[dr_muts_col].str.contains(nssnp_match, na = False, regex = True, case = False)] print('gene_snp_match in dr:', len(meta_gene_dr)) dr_id = meta_gene_dr['id'].unique() print('RESULT: No. of samples with dr muts in', gene, ':', len(dr_id)) if len(id_dr) == len(meta_gene_dr): print('PASS: lengths match' , '\n===============================================================') else: print('FAIL: length mismatch' , '\nExpected len:', len(id_dr) , '\nGot:', len(meta_gene_dr)) sys.exit() dr_id = pd.Series(dr_id) #================= # other mutations: extract nssnp_match entries from other_muts_col #================== print('Extracting other_muts from:', other_muts_col,'with other meta_data') print('muts to extract:', nssnp_match) if in_filename_master == 'original_tanushree_data_v2.csv': meta_data_other = meta_data[['id' , 'country' , 'lineage' , 'sublineage' , 'drtype' , drug , other_muts_col]] else: other_based_cols = [drug, other_muts_col] cols_to_extract = core_cols + other_based_cols print('Extracting', len(cols_to_extract), 'columns from meta data') meta_data_other = meta_data[cols_to_extract] del(other_based_cols, cols_to_extract) if meta_data_other.shape[0] == len(meta_data) and meta_data_other.shape[1] == (len(meta_data.columns)-1): print('PASS: Dimensions match' , '\n===============================================================') else: print('FAIL: Dimensions mismatch:' , 'Expected dim:', len(meta_data), (len(meta_data.columns)-1) , '\nGot:', meta_data_other.shape , '\n===============================================================') sys.exit() # Extract within this nSSNP for gene of interest using string match #meta_gene_other = meta_data_other.loc[meta_data_other[other_muts_col].str.contains(gene_match, na = False)] meta_gene_other = meta_data_other.loc[meta_data_other[other_muts_col].str.contains(nssnp_match, na = False, regex = True, case = False)] print('gene_snp_match in other:', len(meta_gene_other)) other_id = meta_gene_other['id'].unique() print('RESULT: No. of samples with other muts in', gene, ':', len(other_id)) if len(id_other) == len(meta_gene_other): print('PASS: lengths match' , '\n==============================================================') else: print('FAIL: length mismatch' , '\nExpected len:', len(id_other) , '\nGot:', len(meta_gene_other)) sys.exit() other_id = pd.Series(other_id) #%% Find common IDs print('Now extracting common_ids...') common_mut_ids = dr_id.isin(other_id).sum() print('RESULT: No. of common ids:', common_mut_ids) # sanity checks # check if True: should be since these are common if dr_id.isin(other_id).sum() == other_id.isin(dr_id).sum(): print('PASS: Cross check on no. of common ids') else: sys.exit('FAIL: Cross check on no. of common ids failed') # check if the common are indeed the same! # bit of a tautology, but better safe than sorry! common_ids = dr_id[dr_id.isin(other_id)] common_ids = common_ids.reset_index() common_ids.columns = ['index', 'id'] common_ids2 = other_id[other_id.isin(dr_id)] common_ids2 = common_ids2.reset_index() common_ids2.columns = ['index', 'id2'] # should be True if common_ids['id'].equals(common_ids2['id2']): print('PASS: Further cross checks on common ids') nu_common_ids = common_ids.nunique() else: sys.exit('FAIL: Further cross checks on common ids') # good sanity check: use it later to check gene_sample_counts expected_gene_samples = (len(meta_gene_dr) + len(meta_gene_other) - common_mut_ids) print('Expected no. of gene samples:', expected_gene_samples , '\n=================================================================') #%% write file #print(outdir) out_filename_cid = gene.lower() + '_common_ids.csv' outfile_cid = outdir + '/' + out_filename_cid print('\n----------------------------------' '\nWriting file: common_ids' '\n----------------------------------' , '\nFile:', outfile_cid , '\nNo. of rows:', len(common_ids) , '\n=============================================================') common_ids.to_csv(outfile_cid, index = False) del(out_filename_cid) # clear variables del(dr_id, other_id, meta_data_dr, meta_data_other, common_ids, common_mut_ids, common_ids2) #%% Now extract 'all' gene specific nsSNP mutations: i.e 'nssnp_match' print('Extracting nsSNP match:', gene, 'mutations from cols:\n' , dr_muts_col, 'and', other_muts_col, 'using string match:' , '\n===================================================================') #meta_gene_all = meta_data.loc[meta_data[dr_muts_col].str.contains(gene_match, na = False) | meta_data[other_muts_col].str.contains(gene_match, na = False) ] meta_gene_all = meta_data.loc[meta_data[dr_muts_col].str.contains(nssnp_match, na = False, regex = True, case = False) | meta_data[other_muts_col].str.contains(nssnp_match, na = False, regex = True, case = False) ] extracted_gene_samples = meta_gene_all['id'].nunique() print('RESULT: No. of unique samples with', gene, 'nsSNP muts:', extracted_gene_samples , '\n===================================================================') # sanity check: length of gene samples print('Performing sanity check:') if extracted_gene_samples == expected_gene_samples: print('PASS: expected & actual no. of nssnp gene samples match' , '\nNo. of gene samples:', len(meta_gene_all) , '\n=========================================================') else: sys.exit('FAIL: Length mismatch in gene samples!') # count NA in drug column gene_na = meta_gene_all[drug].isna().sum() print('No. of gene samples without', drug, 'testing:', gene_na) # use it later to check number of complete samples from LF data comp_gene_samples = len(meta_gene_all) - gene_na print('Complete gene samples tested for', drug, ':', comp_gene_samples) print('=================================================================') # Comment: This is still dirty data since these # are samples that have nsSNP muts, but can have others as well # since the format for mutations is mut1; mut2, etc. print('This is still dirty data: samples have ', nssnp_match, 'muts but may have others as well' , '\nsince the format for mutations is mut1; mut2, etc.' , '\n=============================================================') print('Performing tidy_split(): to separate the mutations into indivdual rows') #========= # DF1: dr_muts_col #========= ######## # tidy_split(): on dr_muts_col column and remove leading white spaces ######## col_to_split1 = dr_muts_col print ('Firstly, applying tidy split on dr muts df', meta_gene_dr.shape , '\ncolumn name to apply tidy_split():', col_to_split1 , '\n============================================================') # apply tidy_split() dr_WF0 = tidy_split(meta_gene_dr, col_to_split1, sep = ';') # remove leading white space else these are counted as distinct mutations as well dr_WF0[dr_muts_col] = dr_WF0[dr_muts_col].str.lstrip() # extract only the samples/rows with nssnp_match #dr_gene_WF0 = dr_WF0.loc[dr_WF0[dr_muts_col].str.contains(gene_match)] dr_gene_WF0 = dr_WF0.loc[dr_WF0[dr_muts_col].str.contains(nssnp_match, regex = True, case = False)] print('Lengths after tidy split and extracting', nssnp_match, 'muts:' , '\nOld length:' , len(meta_gene_dr) , '\nLength after split:', len(dr_WF0) , '\nLength of nssnp df:', len(dr_gene_WF0) , '\nExpected len:', dr_gene_count , '\n=============================================================') if len(dr_gene_WF0) == dr_gene_count: print('PASS: length matches expected length' , '\n===============================================================') else: sys.exit('FAIL: lengths mismatch') # count the freq of 'dr_muts' samples dr_muts_df = dr_gene_WF0 [['id', dr_muts_col]] print('Dim of dr_muts_df:', dr_muts_df.shape) # add freq column dr_muts_df['dr_sample_freq'] = dr_muts_df.groupby('id')['id'].transform('count') #dr_muts_df['dr_sample_freq'] = dr_muts_df.loc[dr_muts_df.groupby('id')].transform('count') print('Revised dim of dr_muts_df:', dr_muts_df.shape) c1 = dr_muts_df.dr_sample_freq.value_counts() print('Counting no. of sample frequency:\n', c1 , '\n===================================================================') # sanity check: length of gene samples if len(dr_gene_WF0) == c1.sum(): print('PASS: WF data has expected length' , '\nLength of dr_gene WFO:', c1.sum() , '\n===============================================================') else: sys.exit('FAIL: length mismatch!') # Important: Assign 'column name' on which split was performed as an extra column # This is so you can identify if mutations are dr_type or other in the final df dr_df = dr_gene_WF0.assign(mutation_info = dr_muts_col) print('Dim of dr_df:', dr_df.shape , '\n==============================================================' , '\nEnd of tidy split() on dr_muts, and added an extra column relecting mut_category' , '\n===============================================================') #%% #========= # DF2: other_mutations_pyrazinamdie #========= ######## # tidy_split(): on other_muts_col column and remove leading white spaces ######## col_to_split2 = other_muts_col print ('applying second tidy split() separately on other muts df', meta_gene_other.shape , '\ncolumn name to apply tidy_split():', col_to_split2 , '\n============================================================') # apply tidy_split() other_WF1 = tidy_split(meta_gene_other, col_to_split2, sep = ';') # remove the leading white spaces in the column other_WF1[other_muts_col] = other_WF1[other_muts_col].str.strip() # extract only the samples/rows with nssnp_match #other_gene_WF1 = other_WF1.loc[other_WF1[other_muts_col].str.contains(gene_match)] other_gene_WF1 = other_WF1.loc[other_WF1[other_muts_col].str.contains(nssnp_match, regex = True, case = False)] print('Lengths after tidy split and extracting', gene_match, 'muts:', '\nOld length:' , len(meta_gene_other), '\nLength after split:', len(other_WF1), '\nLength of nssnp df:', len(other_gene_WF1), '\nExpected len:', other_gene_count , '\n=============================================================') if len(other_gene_WF1) == other_gene_count: print('PASS: length matches expected length' , '\n===============================================================') else: sys.exit('FAIL: lengths mismatch') # count the freq of 'other muts' samples other_muts_df = other_gene_WF1 [['id', other_muts_col]] print('Dim of other_muts_df:', other_muts_df.shape) # add freq column other_muts_df['other_sample_freq'] = other_muts_df.groupby('id')['id'].transform('count') print('Revised dim of other_muts_df:', other_muts_df.shape) c2 = other_muts_df.other_sample_freq.value_counts() print('Counting no. of sample frequency:\n', c2) print('=================================================================') # sanity check: length of gene samples if len(other_gene_WF1) == c2.sum(): print('PASS: WF data has expected length' , '\nLength of other_gene WFO:', c2.sum() , '\n===============================================================') else: sys.exit('FAIL: Length mismatch') # Important: Assign 'column name' on which split was performed as an extra column # This is so you can identify if mutations are dr_type or other in the final df other_df = other_gene_WF1.assign(mutation_info = other_muts_col) print('dim of other_df:', other_df.shape , '\n===============================================================' , '\nEnd of tidy split() on other_muts, and added an extra column relecting mut_category' , '\n===============================================================') #%% #========== # Concatentating the two dfs: equivalent of rbind in R #========== # Important: Change column names to allow concat: # dr_muts.. & other_muts : 'mutation' print('Now concatenating the two dfs by row' , '\nFirst assigning a common colname: "mutation" to the col containing muts' , '\nThis is done for both dfs' , '\n===================================================================') dr_df.columns dr_df.rename(columns = {dr_muts_col: 'mutation'}, inplace = True) dr_df.columns other_df.columns other_df.rename(columns = {other_muts_col: 'mutation'}, inplace = True) other_df.columns if len(dr_df.columns) == len(other_df.columns): print('Checking dfs for concatening by rows:' , '\nDim of dr_df:', dr_df.shape , '\nDim of other_df:', other_df.shape , '\nExpected nrows:', len(dr_df) + len(other_df) , '\n=============================================================') else: sys.exit('FAIL: No. of cols mismatch for concatenating') # checking colnames before concat print('Checking colnames BEFORE concatenating the two dfs...') if (set(dr_df.columns) == set(other_df.columns)): print('PASS: column names match necessary for merging two dfs') else: sys.exit('FAIL: Colnames mismatch for concatenating!') # concatenate (axis = 0): Rbind print('Now appending the two dfs: Rbind') gene_LF_comb = pd.concat([dr_df, other_df], ignore_index = True, axis = 0) print('Finding stop mutations in concatenated df') stop_muts = gene_LF_comb['mutation'].str.contains('\*').sum() if stop_muts == 0: print('PASS: No stop mutations detected') else: print('stop mutations detected' , '\nNo. of stop muts:', stop_muts, '\n' , gene_LF_comb.groupby(['mutation_info'])['mutation'].apply(lambda x: x[x.str.contains('\*')].count()) , '\nNow removing them') gene_LF0_nssnp = gene_LF_comb[~gene_LF_comb['mutation'].str.contains('\*')] print('snps only: by subtracting stop muts:', len(gene_LF0_nssnp)) gene_LF0 = gene_LF_comb[gene_LF_comb['mutation'].str.contains(nssnp_match, case = False)] print('snps only by direct regex:', len(gene_LF0)) if gene_LF0_nssnp.equals(gene_LF0): print('PASS: regex for extracting nssnp worked correctly & stop mutations successfully removed' , '\nUsing the regex extracted df') else: sys.exit('FAIL: posssibly regex or no of stop mutations' , 'Regex being used:', nssnp_match) #sys.exit() # checking colnames and length after concat print('Checking colnames AFTER concatenating the two dfs...') if (set(dr_df.columns) == set(gene_LF0.columns)): print('PASS: column names match' , '\n=============================================================') else: sys.exit('FAIL: Colnames mismatch AFTER concatenating') print('Checking concatenated df') if len(gene_LF0) == (len(dr_df) + len(other_df))- stop_muts : print('PASS:length of df after concat match' , '\n===============================================================') else: sys.exit('FAIL: length mismatch') #%% ########### # This is hopefully clean data, but just double check # Filter LF data so that you only have # mutations corresponding to nssnp_match (case insensitive) # this will be your list you run OR calcs ########### print('Length of gene_LF0:', len(gene_LF0) , '\nThis should be what we need. But just double checking and extracting nsSNP for', gene , '\nfrom LF0 (concatenated data) using case insensitive regex match:', nssnp_match) gene_LF1 = gene_LF0[gene_LF0['mutation'].str.contains(nssnp_match, regex = True, case = False)] if len(gene_LF0) == len(gene_LF1): print('PASS: length of gene_LF0 and gene_LF1 match', '\nConfirming extraction and concatenating worked correctly' , '\n==========================================================') else: print('FAIL: BUT NOT FATAL!' , '\ngene_LF0 and gene_LF1 lengths differ' , '\nsuggesting error in extraction process' , ' use gene_LF1 for downstreama analysis' , '\n=========================================================') print('Length of dfs pre and post processing...' , '\ngene WF data (unique samples in each row):', extracted_gene_samples , '\ngene LF data (unique mutation in each row):', len(gene_LF1) , '\n=============================================================') #%% sanity check for extraction # This ought to pass if nsnsp_match happens right at the beginning of creating 'expected_rows' print('Verifying whether extraction process worked correctly...') if len(gene_LF1) == expected_rows: print('PASS: extraction process performed correctly' , '\nExpected length:', expected_rows , '\nGot:', len(gene_LF1) , '\nRESULT: Total no. of mutant sequences for logo plot:', len(gene_LF1) , '\n=========================================================') else: print('FAIL: extraction process has bugs' , '\nExpected length:', expected_rows , '\nGot:', len(gene_LF1) , '\nDebug please' , '\n=========================================================') #%% print('Performing some more sanity checks...') # From LF1: useful for OR counts # no. of unique muts distinct_muts = gene_LF1.mutation.value_counts() print('Distinct genomic mutations:', len(distinct_muts)) # no. of samples contributing the unique muts gene_LF1.id.nunique() print('No.of samples contributing to distinct genomic muts:', gene_LF1.id.nunique()) # no. of dr and other muts mut_grouped = gene_LF1.groupby('mutation_info').mutation.nunique() print('No.of unique dr and other muts:\n', gene_LF1.groupby('mutation_info').mutation.nunique()) # sanity check if len(distinct_muts) == mut_grouped.sum() : print('PASS:length of LF1 is as expected' , '\n===============================================================') else: print('FAIL: Mistmatch in count of muts' , '\nExpected count:', len(distinct_muts) , '\nActual count:', mut_grouped.sum() , '\nMuts should be distinct within dr* and other* type' , '\nInspecting...possibly ambiguous muts' , '\nNo. of possible ambiguous muts:', mut_grouped.sum() - len(distinct_muts) , '\n=========================================================') muts_split = list(gene_LF1.groupby('mutation_info')) dr_muts = muts_split[0][1].mutation other_muts = muts_split[1][1].mutation print('splitting muts by mut_info:', muts_split) print('no.of dr_muts samples:', len(dr_muts)) print('no. of other_muts samples', len(other_muts)) #%% # IMPORTANT: The same mutation cannot be classed as a drug AND 'others' if dr_muts.isin(other_muts).sum() & other_muts.isin(dr_muts).sum() > 0: print('WARNING: Ambiguous muts detected in dr_ and other_ mutation category' , '\n===============================================================') else: print('PASS: NO ambiguous muts detected' , '\nMuts are unique to dr_ and other_ mutation class' , '\n=========================================================') # inspect dr_muts and other muts if dr_muts.isin(other_muts).sum() & other_muts.isin(dr_muts).sum() > 0: print('Finding ambiguous muts...' , '\n=========================================================' , '\nTotal no. of samples in dr_muts present in other_muts:', dr_muts.isin(other_muts).sum() , '\nThese are:', dr_muts[dr_muts.isin(other_muts)] , '\n=========================================================' , '\nTotal no. of samples in other_muts present in dr_muts:', other_muts.isin(dr_muts).sum() , '\nThese are:\n', other_muts[other_muts.isin(dr_muts)] , '\n=========================================================') else: sys.exit('Error: ambiguous muts present, but extraction failed. Debug!') print('Counting no. of ambiguous muts...') if dr_muts[dr_muts.isin(other_muts)].nunique() == other_muts[other_muts.isin(dr_muts)].nunique(): common_muts = dr_muts[dr_muts.isin(other_muts)].value_counts().keys().tolist() print('Distinct no. of ambigiuous muts detected:'+ str(len(common_muts)) , '\nlist of ambiguous mutations (see below):', *common_muts, sep = '\n') print('\n===========================================================') else: print('Error: ambiguous muts detected, but extraction failed. Debug!' , '\nNo. of ambiguous muts in dr:' , len(dr_muts[dr_muts.isin(other_muts)].value_counts().keys().tolist()) , '\nNo. of ambiguous muts in other:' , len(other_muts[other_muts.isin(dr_muts)].value_counts().keys().tolist()) , '\n=========================================================') #%% clear variables del(id_dr, id_other #, meta_data , meta_gene_dr, meta_gene_other #, mut_grouped , muts_split, other_WF1, other_df, other_muts_df, other_gene_count, gene_LF0, gene_na) del(c1, c2, col_to_split1, col_to_split2, comp_gene_samples, dr_WF0, dr_df, dr_muts_df, dr_gene_WF0, dr_gene_count, expected_gene_samples, other_gene_WF1) #%%: write file: ambiguous muts # uncomment as necessary #print(outdir) #dr_muts.to_csv('dr_muts.csv', header = True) #other_muts.to_csv('other_muts.csv', header = True) out_filename_ambig_muts = gene.lower() + '_ambiguous_muts.csv' outfile_ambig_muts = outdir + '/' + out_filename_ambig_muts print('\n----------------------------------' , '\nWriting file: ambiguous muts' , '\n----------------------------------' , '\nFilename:', outfile_ambig_muts) inspect = gene_LF1[gene_LF1['mutation'].isin(common_muts)] inspect.to_csv(outfile_ambig_muts, index = False) print('Finished writing:', out_filename_ambig_muts , '\nNo. of rows:', len(inspect) , '\nNo. of cols:', len(inspect.columns) , '\nNo. of rows = no. of samples with the ambiguous muts present:' , dr_muts.isin(other_muts).sum() + other_muts.isin(dr_muts).sum() , '\n=============================================================') del(out_filename_ambig_muts) #%% end of data extraction and some files writing. Below are some more files writing. #============================================================================= #%% Formatting df: read aa dict and pull relevant info print('Now some more formatting:' , '\nread aa dict and pull relevant info' , '\nformat mutations:' , '\nsplit mutation into mCSM style muts: ' , '\nFormatting mutation in mCSM style format: {WT}{MUT}' , '\nassign aa properties: adding 2 cols at a time for each prop' , '\n===================================================================') # BEWARE hardcoding : only works as we are adding aa prop once for wt and once for mut # in each lookup cycle ncol_mutf_add = 3 # mut split into 3 cols ncol_aa_add = 2 # 2 aa prop add (wt & mut) in each mapping #=========== # Split 'mutation' column into three: wild_type, position and # mutant_type separately. Then map three letter code to one using # reference_dict imported at the beginning. # After importing, convert to mutation to lowercase for compatibility with dict #=========== gene_LF1['mutation'] = gene_LF1.loc[:, 'mutation'].str.lower() print('wt regex being used:', wt_regex , '\nmut regex being used:', mut_regex , '\nposition regex being used:', pos_regex) mylen0 = len(gene_LF1.columns) #======= # Iterate through the dict, create a lookup dict i.e # lookup_dict = {three_letter_code: one_letter_code}. # lookup dict should be the key and the value (you want to create a column for) # Then use this to perform the mapping separetly for wild type and mutant cols. # The three letter code is extracted using a string match match from the dataframe and then converted # to 'pandas series'since map only works in pandas series #======= print('Adding', ncol_mutf_add, 'more cols:\n') # initialise a sub dict that is lookup dict for three letter code to 1-letter code # adding three more cols lookup_dict = dict() for k, v in my_aa_dict.items(): lookup_dict[k] = v['one_letter_code'] #wt = gene_LF1['mutation'].str.extract(gene_regex).squeeze()converts to a series that map works on wt = gene_LF1['mutation'].str.extract(wt_regex).squeeze() gene_LF1['wild_type'] = wt.map(lookup_dict) #mut = gene_LF1['mutation'].str.extract('\d+(\w{3})$').squeeze() mut = gene_LF1['mutation'].str.extract(mut_regex).squeeze() gene_LF1['mutant_type'] = mut.map(lookup_dict) # extract position info from mutation column separetly using string match #gene_LF1['position'] = gene_LF1['mutation'].str.extract(r'(\d+)') gene_LF1['position'] = gene_LF1['mutation'].str.extract(pos_regex) mylen1 = len(gene_LF1.columns) # sanity checks print('checking if 3-letter wt&mut residue extraction worked correctly') if wt.isna().sum() & mut.isna().sum() == 0: print('PASS: 3-letter wt&mut residue extraction worked correctly:' , '\nNo NAs detected:' , '\nwild-type\n', wt , '\nmutant-type\n', mut , '\ndim of df:', gene_LF1.shape) else: print('FAIL: 3-letter wt&mut residue extraction failed' , '\nNo NAs detected:' , '\nwild-type\n', wt , '\nmutant-type\n', mut , '\ndim of df:', gene_LF1.shape) if mylen1 == mylen0 + ncol_mutf_add: print('PASS: successfully added', ncol_mutf_add, 'cols' , '\nold length:', mylen0 , '\nnew len:', mylen1) else: print('FAIL: failed to add cols:' , '\nold length:', mylen0 , '\nnew len:', mylen1) # clear variables del(k, v, wt, mut, lookup_dict) #========= # iterate through the dict, create a lookup dict that i.e # lookup_dict = {three_letter_code: aa_prop_water} # Do this for both wild_type and mutant as above. #========= print('Adding', ncol_aa_add, 'more cols:\n') # initialise a sub dict that is lookup dict for three letter code to aa prop # adding two more cols lookup_dict = dict() for k, v in my_aa_dict.items(): lookup_dict[k] = v['aa_prop_water'] #print(lookup_dict) wt = gene_LF1['mutation'].str.extract(wt_regex).squeeze() gene_LF1['wt_prop_water'] = wt.map(lookup_dict) #mut = gene_LF1['mutation'].str.extract('\d+(\w{3})$').squeeze() mut = gene_LF1['mutation'].str.extract(mut_regex).squeeze() gene_LF1['mut_prop_water'] = mut.map(lookup_dict) mylen2 = len(gene_LF1.columns) # sanity checks print('checking if 3-letter wt&mut residue extraction worked correctly') if wt.isna().sum() & mut.isna().sum() == 0: print('PASS: 3-letter wt&mut residue extraction worked correctly:' , '\nNo NAs detected:' , '\nwild-type\n', wt , '\nmutant-type\n', mut , '\ndim of df:', gene_LF1.shape) else: print('FAIL: 3-letter wt&mut residue extraction failed' , '\nNo NAs detected:' , '\nwild-type\n', wt , '\nmutant-type\n', mut , '\ndim of df:', gene_LF1.shape) if mylen2 == mylen1 + ncol_aa_add: print('PASS: successfully added', ncol_aa_add, 'cols' , '\nold length:', mylen1 , '\nnew len:', mylen2) else: print('FAIL: failed to add cols:' , '\nold length:', mylen1 , '\nnew len:', mylen2) # clear variables del(k, v, wt, mut, lookup_dict) #======== # iterate through the dict, create a lookup dict that i.e # lookup_dict = {three_letter_code: aa_prop_polarity} # Do this for both wild_type and mutant as above. #========= print('Adding', ncol_aa_add, 'more cols:\n') # initialise a sub dict that is lookup dict for three letter code to aa prop # adding two more cols lookup_dict = dict() for k, v in my_aa_dict.items(): lookup_dict[k] = v['aa_prop_polarity'] #print(lookup_dict) wt = gene_LF1['mutation'].str.extract(wt_regex).squeeze() gene_LF1['wt_prop_polarity'] = wt.map(lookup_dict) #mut = gene_LF1['mutation'].str.extract(r'\d+(\w{3})$').squeeze() mut = gene_LF1['mutation'].str.extract(mut_regex).squeeze() gene_LF1['mut_prop_polarity'] = mut.map(lookup_dict) mylen3 = len(gene_LF1.columns) # sanity checks print('checking if 3-letter wt&mut residue extraction worked correctly') if wt.isna().sum() & mut.isna().sum() == 0: print('PASS: 3-letter wt&mut residue extraction worked correctly:' , '\nNo NAs detected:' , '\nwild-type\n', wt , '\nmutant-type\n', mut , '\ndim of df:', gene_LF1.shape) else: print('FAIL: 3-letter wt&mut residue extraction failed' , '\nNo NAs detected:' , '\nwild-type\n', wt , '\nmutant-type\n', mut , '\ndim of df:', gene_LF1.shape) if mylen3 == mylen2 + ncol_aa_add: print('PASS: successfully added', ncol_aa_add, 'cols' , '\nold length:', mylen1 , '\nnew len:', mylen2) else: print('FAIL: failed to add cols:' , '\nold length:', mylen1 , '\nnew len:', mylen2) # clear variables del(k, v, wt, mut, lookup_dict) #======== # iterate through the dict, create a lookup dict that i.e # lookup_dict = {three_letter_code: aa_calcprop} # Do this for both wild_type and mutant as above. #========= print('Adding', ncol_aa_add, 'more cols:\n') lookup_dict = dict() for k, v in my_aa_dict.items(): lookup_dict[k] = v['aa_calcprop'] #print(lookup_dict) wt = gene_LF1['mutation'].str.extract(wt_regex).squeeze() gene_LF1['wt_calcprop'] = wt.map(lookup_dict) mut = gene_LF1['mutation'].str.extract(mut_regex).squeeze() gene_LF1['mut_calcprop'] = mut.map(lookup_dict) mylen4 = len(gene_LF1.columns) # sanity checks print('checking if 3-letter wt&mut residue extraction worked correctly') if wt.isna().sum() & mut.isna().sum() == 0: print('PASS: 3-letter wt&mut residue extraction worked correctly:' , '\nNo NAs detected:' , '\nwild-type\n', wt , '\nmutant-type\n', mut , '\ndim of df:', gene_LF1.shape) else: print('FAIL: 3-letter wt&mut residue extraction failed' , '\nNo NAs detected:' , '\nwild-type\n', wt , '\nmutant-type\n', mut , '\ndim of df:', gene_LF1.shape) if mylen4 == mylen3 + ncol_aa_add: print('PASS: successfully added', ncol_aa_add, 'cols' , '\nold length:', mylen3 , '\nnew len:', mylen4) else: print('FAIL: failed to add cols:' , '\nold length:', mylen3 , '\nnew len:', mylen4) # clear variables del(k, v, wt, mut, lookup_dict) ######## # combine the wild_type+poistion+mutant_type columns to generate # mutationinformation (matches mCSM output field) # Remember to use .map(str) for int col types to allow string concatenation ######### gene_LF1['mutationinformation'] = gene_LF1['wild_type'] + gene_LF1.position.map(str) + gene_LF1['mutant_type'] print('Created column: mutationinformation' , '\n=====================================================================\n' , gene_LF1.mutationinformation.head(10)) #order by position for convenience gene_LF1.dtypes # converting position to numeric gene_LF1['position'] = pd.to_numeric(gene_LF1['position']) # sort by position inplace foo = gene_LF1['position'].value_counts() gene_LF1.sort_values(by = ['position'], inplace = True) bar = gene_LF1['position'].value_counts() if (foo == bar).all(): print('PASS: df ordered by position') print(gene_LF1['position'].head()) else: print('FAIL: df could not be orderd. Check source') sys.exit() #%% Write file: mCSM muts snps_only = pd.DataFrame(gene_LF1['mutationinformation'].unique()) snps_only.head() # assign column name snps_only.columns = ['mutationinformation'] # count how many positions this corresponds to pos_only = pd.DataFrame(gene_LF1['position'].unique()) print('Checking NA in snps...')# should be 0 if snps_only.mutationinformation.isna().sum() == 0: print ('PASS: NO NAs/missing entries for SNPs' , '\n===============================================================') else: sys.exit('FAIL: SNP has NA, Possible mapping issues from dict?') out_filename_mcsmsnps = gene.lower() + '_mcsm_snps.csv' outfile_mcsmsnps = outdir + '/' + out_filename_mcsmsnps print('\n----------------------------------' , '\nWriting file: mCSM style muts' , '\n----------------------------------' , '\nFile:', outfile_mcsmsnps , '\nmutation format (SNP): {WT}{MUT}' , '\nNo. of distinct muts:', len(snps_only) , '\nNo. of distinct positions:', len(pos_only) , '\n=============================================================') snps_only.to_csv(outfile_mcsmsnps, header = False, index = False) print('Finished writing:', outfile_mcsmsnps , '\nNo. of rows:', len(snps_only) , '\nNo. of cols:', len(snps_only.columns) , '\n=============================================================') del(out_filename_mcsmsnps) #%%# write frequency of position counts metadata_pos = pd.DataFrame(gene_LF1['position']) z = gene_LF1['position'].value_counts() z1 = z.to_dict() metadata_pos['meta_pos_count'] = metadata_pos['position'].map(z1) metadata_pos['meta_pos_count'].value_counts() metadata_pos.sort_values(by = ['meta_pos_count'], ascending = False, inplace = True) # Write file: gene_metadata (i.e gene_LF1) # where each row has UNIQUE mutations NOT unique sample ids out_filename_metadata_poscounts = gene.lower() + '_metadata_poscounts.csv' outfile_metadata_poscounts = outdir + '/' + out_filename_metadata_poscounts print('\n----------------------------------' , 'Writing file: Metadata poscounts' , '\n----------------------------------' , '\nFile:', outfile_metadata_poscounts , '\n============================================================') metadata_pos.to_csv(outfile_metadata_poscounts, header = True, index = False) print('Finished writing:', outfile_metadata_poscounts , '\nNo. of rows:', len(metadata_pos) , '\nNo. of cols:', len(metadata_pos.columns) , '\n=============================================================') del(out_filename_metadata_poscounts) #%% Write file: gene_metadata (i.e gene_LF1) # where each row has UNIQUE mutations NOT unique sample ids #out_filename_metadata = gene.lower() + '_metadata_raw.csv' #! rationale? out_filename_metadata = gene.lower() + '_metadata.csv' outfile_metadata = outdir + '/' + out_filename_metadata print('\n----------------------------------' , '\nWriting file: LF formatted data' , '\n----------------------------------' , '\nFile:', outfile_metadata , '\n============================================================') gene_LF1.to_csv(outfile_metadata, header = True, index = False) print('Finished writing:', outfile_metadata , '\nNo. of rows:', len(gene_LF1) , '\nNo. of cols:', len(gene_LF1.columns) , '\n=============================================================') del(out_filename_metadata) #%% write file: mCSM style but with repitions for MSA and logo plots all_muts_msa = pd.DataFrame(gene_LF1['mutationinformation']) all_muts_msa.head() # assign column name all_muts_msa.columns = ['mutationinformation'] # make sure it is string all_muts_msa.columns.dtype # sort the column all_muts_msa_sorted = all_muts_msa.sort_values(by = 'mutationinformation') # create an extra column with protein name #all_muts_msa_sorted = all_muts_msa_sorted.assign(fasta_name = '3PL1') #all_muts_msa_sorted.head() # rearrange columns so the fasta name is the first column (required for mutate.script) #all_muts_msa_sorted = all_muts_msa_sorted[['fasta_name', 'mutationinformation']] all_muts_msa_sorted.head() print('Checking NA in snps...')# should be 0 if all_muts_msa.mutationinformation.isna().sum() == 0: print ('PASS: NO NAs/missing entries for SNPs' , '\n===============================================================') else: sys.exit('FAIL: SNP has NA, Possible mapping issues from dict?') out_filename_msa = gene.lower() +'_all_muts_msa.csv' outfile_msa = outdir + '/' + out_filename_msa print('\n----------------------------------' , '\nWriting file: mCSM style muts for msa' , '\n----------------------------------' , '\nFile:', outfile_msa , '\nmutation format (SNP): {WT}{MUT}' , '\nNo.of lines of msa:', len(all_muts_msa)) all_muts_msa_sorted.to_csv(outfile_msa, header = False, index = False) print('Finished writing:', outfile_msa , '\nNo. of rows:', len(all_muts_msa) , '\nNo. of cols:', len(all_muts_msa.columns) , '\n=============================================================') del(out_filename_msa) #%% write file for mutational positions # count how many positions this corresponds to pos_only = pd.DataFrame(gene_LF1['position'].unique()) # assign column name pos_only.columns = ['position'] # make sure dtype of column position is int or numeric and not string pos_only.position.dtype pos_only['position'] = pos_only['position'].astype(int) pos_only.position.dtype # sort by position value pos_only_sorted = pos_only.sort_values(by = 'position', ascending = True) out_filename_pos = gene.lower() + '_mutational_positons.csv' outfile_pos = outdir + '/' + out_filename_pos print('\n----------------------------------' , 'Writing file: mutational positions' , '\n----------------------------------' , '\nFile:', outfile_pos , '\nNo. of distinct positions:', len(pos_only_sorted) , '\n=============================================================') pos_only_sorted.to_csv(outfile_pos, header = True, index = False) print('Finished writing:', outfile_pos , '\nNo. of rows:', len(pos_only_sorted) , '\nNo. of cols:', len(pos_only_sorted.columns) , '\n=============================================================' , '\n\n\n') del(out_filename_pos) #%% quick summary output #print('============================================' # , '\nQuick summary output for', drug, 'and' , gene.lower() # , '\n============================================' # , '\nTotal no.of unique missense muts:', gene_LF1['mutationinformation'].nunique() # , '\nTotal no.of unique positions associated with missense muts:',gene_LF1['position'].nunique() # , '\nTotal no. of samples with missense muts:', len(gene_LF1) # , '\n=============================================================' # , '\n\n\n') print('============================================' , '\nQuick summary output for', drug, 'and' , gene.lower() , '\n============================================' , '\nTotal samples:', total_samples , '\nNo. of Sus and Res', drug, 'samples:\n', meta_data[drug].value_counts() , '\n' , '\nPercentage of Sus and Res', drug, 'samples:\n', meta_data[drug].value_counts(normalize = True)*100 , '\n' , '\nTotal no.of unique dr muts:', mut_grouped[dr_muts_col] , '\nTotal no.of unique other muts:', mut_grouped[other_muts_col] , '\nTotal no.of unique missense muts:', gene_LF1['mutationinformation'].nunique() , '\nTotal no.of unique positions associated with missense muts:',gene_LF1['position'].nunique() , '\nTotal no. of samples with missense muts:', len(gene_LF1) , '\nTotal no. of unique samples with missense muts:', gene_LF1['id'].nunique() , '\n' , '\nTotal no.of samples with common_ids:', nu_common_ids['id'] , '\nTotal no.of samples with ambiguous muts:', len(inspect) #, '\nTotal no.of unique ambiguous muts:', len(common_muts) , '\nTotal no.of unique ambiguous muts:', inspect['mutation'].nunique() , '\n=============================================================' , '\n\n\n') #======================================================================= print(u'\u2698' * 50, '\nEnd of script: Data extraction and writing files' '\n' + u'\u2698' * 50 ) #%% end of script