231 lines
7.8 KiB
Python
Executable file
231 lines
7.8 KiB
Python
Executable file
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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'''
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Created on Tue Aug 6 12:56:03 2019
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@author: tanu
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'''
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# FIXME: change filename 2(mcsm normalised data)
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# to be consistent like (pnca_complex_mcsm_norm.csv) : changed manually, but ensure this is done in the mcsm pipeline
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#=======================================================================
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# Task: combine 2 dfs with aa position as linking column
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# Input: 2 dfs
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# <gene.lower()>_complex_mcsm_norm.csv
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# <gene.lower()>_foldx.csv
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# Output: .csv of all 2 dfs combined
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# useful link
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# https://stackoverflow.com/questions/23668427/pandas-three-way-joining-multiple-dataframes-on-columns
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#=======================================================================
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#%% load packages
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import sys, os
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import pandas as pd
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import numpy as np
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#from varname import nameof
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import argparse
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from combining import combine_stability_dfs
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from combining import detect_common_cols
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#=======================================================================
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#%% specify input and curr dir
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homedir = os.path.expanduser('~')
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# set working dir
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os.getcwd()
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os.chdir(homedir + '/git/LSHTM_analysis/scripts')
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os.getcwd()
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#=======================================================================
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#%% command line args
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#arg_parser = argparse.ArgumentParser()
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#arg_parser.add_argument('-d', '--drug', help='drug name', default = 'pyrazinamide')
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#arg_parser.add_argument('-g', '--gene', help='gene name', default = 'pncA') # case sensitive
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#args = arg_parser.parse_args()
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#=======================================================================
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#%% variable assignment: input and output
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drug = 'pyrazinamide'
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gene = 'pncA'
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gene_match = gene + '_p.'
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#drug = args.drug
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#gene = args.gene
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#======
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# dirs
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#======
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datadir = homedir + '/' + 'git/Data'
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indir = datadir + '/' + drug + '/' + 'input'
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outdir = datadir + '/' + drug + '/' + 'output'
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#=======
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# input
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#=======
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#in_filename_linking = gene.lower() + '_linking_df.csv'
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#in_filename_mcsm = gene.lower() + '_complex_mcsm_norm.csv'
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#in_filename_foldx = gene.lower() + '_foldx.csv'
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in_filename_dssp = gene.lower() + '_dssp.csv'
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in_filename_kd = gene.lower() + '_kd.csv'
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#in_filename_rd = gene.lower() + '_rd.csv'
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in_filename_snpinfo = 'ns' + gene.lower() + '_snp_info.csv'
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in_filename_afor = gene.lower() + '_af_or.csv'
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in_filename_afor_kin = gene.lower() + '_af_or_kinship.csv'
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#infile_linking = outdir + '/' + in_filename_linking
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#infile_mcsm = outdir + '/' + in_filename_mcsm
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#infile_foldx = outdir + '/' + in_filename_foldx
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infile_dssp = outdir + '/' + in_filename_dssp
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infile_kd = outdir + '/' + in_filename_kd
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#infile_rd = outdir + '/' + in_filename_rd
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infile_snpinfo = indir + '/' + in_filename_snpinfo
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infile_afor = outdir + '/' + in_filename_afor
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infile_afor_kin = outdir + '/' + in_filename_afor_kin
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print('\nInput path:', outdir
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# , '\nInput filename1:', infile_mcsm
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# , '\nInput filename2:', infile_foldx
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, '\nInput filename2:', infile_dssp
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, '\nInput filename2:', infile_kd
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# , '\nInput filename2:', infile_rd
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, '\nInput filename snp info:', infile_snpinfo
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, '\nInput filename af or:', infile_afor
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, '\nInput filename afor kinship:', infile_afor_kin
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, '\n============================================================')
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#=======
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# output
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#=======
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#out_filename_comb = gene.lower() + '_struct_params_TEST.csv'
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#outfile_comb = outdir + '/' + out_filename_comb
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#print('Output filename:', outfile_comb
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# , '\n============================================================')
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o_join = 'outer'
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l_join = 'left'
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r_join = 'right'
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i_join = 'inner'
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#del(in_filename_dssp, in_filename_foldx)
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# end of variable assignment for input and output files
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#=======================================================================
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# call function to detect common cols
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#=======================================================================
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def main():
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print('Reading input files:')
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#dssp_df = pd.read_csv(infile_dssp, sep = ',')
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#dssp_df.columns = dssp_df.columns.str.lower()
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#kd_df = pd.read_csv(infile_kd, sep = ',')
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#kd_df.columns = kd_df.columns.str.lower()
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# print('Dimension left df:', dssp_df.shape
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# , '\nDimension right_df:', kd_df.shape
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# , '\njoin type:', o_join
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# , '\n=========================================================')
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# detect common cols
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#merging_cols = detect_common_cols(dssp_df, kd_df)
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#print('Length of common cols:', len(merging_cols)
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# , '\nmerging column/s:', merging_cols, 'type:', type(merging_cols)
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# , '\ndtypes in merging columns:', dssp_df[merging_cols].dtypes)
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#combined_df1 = combine_stability_dfs(dssp_df, kd_df, my_join = o_join)
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#print('Dimensions of combined df:', combined_df1.shape
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# , '\nsneak peak:', combined_df1.head()
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# , '\ndtypes in cols:\n', combined_df1.dtypes)
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#=============================================================================
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afor_df = pd.read_csv(infile_afor, sep = ',')
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afor_df.columns = afor_df.columns.str.lower()
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snpinfo_df = pd.read_csv(infile_snpinfo, sep = ',')
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snpinfo_df.columns = snpinfo_df.columns.str.lower()
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# print('Dimension df1:', afor_df.shape
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# , '\nDimension df2:', snpinfo_df.shape
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# , '\njoin type:', l_join
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# , '\n=========================================================')
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# detect common cols
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merging_cols = detect_common_cols(afor_df, snpinfo_df)
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#print('Length of common cols:', len(merging_cols)
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# , '\nmerging column/s:', merging_cols, 'type:', type(merging_cols)
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# , '\ndtypes in merging columns:', snpinfo_df[merging_cols].dtypes)
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comb_afor_snpinfo = combine_stability_dfs(afor_df, snpinfo_df, my_join = l_join)
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#print('Dimensions of combined df:', comb_afor_snpinfo.shape
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# , '\nsneak peak:', comb_afor_snpinfo.head()
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# , '\ndtypes in cols:\n', comb_afor_snpinfo.dtypes)
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#=============================================================================
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afor_kin_df = pd.read_csv(infile_afor_kin, sep = ',')
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afor_kin_df.columns = afor_kin_df.columns.str.lower()
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# detect common cols
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merging_cols = detect_common_cols(comb_afor_snpinfo, afor_kin_df)
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# comb2 = combine_stability_dfs(comb_afor_snpinfo, afor_kin_df, my_join = o_join)
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#print('Dimensions of combined df:', comb2.shape
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# , '\nsneak peak:', comb2.head()
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# , '\ndtypes in cols:\n', comb2.dtype)
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if __name__ == '__main__':
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main()
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#=======================================================================
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#%% end of script
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#hardocoded test
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dssp_df = pd.read_csv(infile_dssp, sep = ',')
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kd_df = pd.read_csv(infile_kd, sep = ',')
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afor_df = pd.read_csv(infile_afor, sep = ',')
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snpinfo_df = pd.read_csv(infile_snpinfo, sep = ',')
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afor_kin_df = pd.read_csv(infile_afor_kin, sep = ',')
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merging_cols = ['alt_allele',
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'chr_num_allele',
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'chromosome_number',
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'gene_id',
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'gene_number',
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'mut_info',
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'mut_region',
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'mut_type',
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'mutant_type',
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'mutationinformation',
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'position',
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'ref_allele',
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'wild_type']
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print('doing thing')
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comb_afor_snpinfo = pd.merge(afor_df, snpinfo_df, on = 'mutation', how = 'inner')
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comb2 = pd.merge(comb_afor_snpinfo, afor_kin_df, on = merging_cols, how = i_join)
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comb3 = comb2.drop_duplicates(subset=merging_cols, keep = 'first')
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common = np.intersect1d(comb_afor_snpinfo['mutationinformation'], afor_kin_df['mutationinformation'])
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print('comb3 dim:', comb3.shape
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, '\ncomb2 dim:', comb2.shape
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, '\ndim of df1:', comb_afor_snpinfo.shape
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, '\ndim of df2:', afor_kin_df.shape
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, '\ncommon vals:', len(common))
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print('expected:\n')
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bar = combine_stability_dfs(comb_afor_snpinfo, afor_kin_df, my_join = o_join)
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print('XXXXXX\n:', bar.shape)
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#bar = np.intersect1d(comb_afor_snpinfo[merging_cols[0]], afor_kin_df[merging_cols[0]])
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#print('common values:',len(bar))
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#comb2 = combine_stability_dfs(comb_afor_snpinfo, afor_kin_df, my_join = o_join)
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print ('thing finished')
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