renamed files that combine dfs
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a220288c5f
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5addb85851
5 changed files with 187 additions and 622 deletions
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@ -25,8 +25,7 @@ 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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@ -35,6 +34,10 @@ homedir = os.path.expanduser('~')
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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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# local imports
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from combining_dfs import combine_dfs_with_checks
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from combining_dfs import detect_common_cols
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#=======================================================================
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#%% command line args
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#arg_parser = argparse.ArgumentParser()
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@ -60,33 +63,33 @@ outdir = datadir + '/' + drug + '/' + 'output'
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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_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_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_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_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 mcsm:', infile_mcsm
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, '\nInput filename foldx:', infile_foldx
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, '\nInput filename dssp:', infile_dssp
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, '\nInput filename kd:', infile_kd
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, '\nInput filename rd', 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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@ -95,10 +98,10 @@ print('\nInput path:', outdir
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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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out_filename_comb = gene.lower() + '_all_params.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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@ -111,6 +114,8 @@ i_join = 'inner'
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#=======================================================================
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# call function to detect common cols
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# FIXME: do the OR combining in the end to iron out any problems
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# Couldn't run the function combin
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#=======================================================================
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def main():
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@ -133,99 +138,166 @@ def main():
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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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#combined_df1 = combine_dfs_with_checks(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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#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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#hardcoded test
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mcsm_df = pd.read_csv(infile_mcsm, sep = ',')
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mcsm_df.columns = mcsm_df.columns.str.lower()
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foldx_df = pd.read_csv(infile_foldx , sep = ',')
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print('==================================='
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, '\nFirst merge: mcsm + foldx'
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, '\n===================================')
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#mcsm_foldx_dfs = combine_dfs_with_checks(mcsm_df, foldx_df, my_join = o_join)
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merging_cols_m1 = detect_common_cols(mcsm_df, foldx_df)
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mcsm_foldx_dfs = pd.merge(mcsm_df, foldx_df, on = merging_cols_m1, how = 'outer')
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ncols_m1 = len(mcsm_foldx_dfs.columns)
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print('==================================='
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, '\nSecond merge: dssp + kd'
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, '\n===================================')
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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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rd_df = pd.read_csv(infile_rd, sep = ',')
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#dssp_kd_dfs = combine_dfs_with_checks(dssp_df, kd_df, my_join = o_join)
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merging_cols_m2 = detect_common_cols(dssp_df, kd_df)
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dssp_kd_dfs = pd.merge(dssp_df, kd_df, on = merging_cols_m2, how = 'outer')
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print('==================================='
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, '\nThird merge: dssp_kd_dfs + rd_df'
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, '\n===================================')
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#dssp_kd_rd_dfs = combine_dfs_with_checks(dssp_kd_dfs, rd_df, my_join = o_join)
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merging_cols_m3 = detect_common_cols(dssp_df, kd_df)
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dssp_kd_rd_dfs = pd.merge(dssp_kd_dfs, rd_df, on = merging_cols_m3, how = 'outer')
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ncols_m3 = len(dssp_kd_rd_dfs.columns)
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print('==================================='
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, '\nFourth merge: First merge + Third merge'
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, '\n===================================')
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#combined_dfs = combine_dfs_with_checks(mcsm_foldx_dfs, dssp_kd_rd_dfs, my_join = i_join)# gives wrong!
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merging_cols_m4 = detect_common_cols(mcsm_foldx_dfs, dssp_kd_rd_dfs)
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combined_df_expected_cols = ncols_m1 + ncols_m3 - len(merging_cols_m4)
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combined_df = pd.merge(mcsm_foldx_dfs, dssp_kd_rd_dfs, on = merging_cols_m4, how = 'inner')
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if len(combined_df) == len(mcsm_df) and len(combined_df.columns) == combined_df_expected_cols:
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print('PASS: successfully combined 5 dfs'
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, '\nnrows combined_df:', len(combined_df)
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, '\ncols combined_df:', len(combined_df.columns))
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else:
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sys.exit('FAIL: check individual df merges')
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#%% OR combining
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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_df.columns = afor_df.columns.str.lower()
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if afor_df['mutation'].shape[0] == afor_df['mutation'].nunique():
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print('No duplicate muts detected in afor_df')
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else:
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print('Dropping duplicate muts detected in afor_df')
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afor_df = afor_df.drop_duplicates(subset = 'mutation', keep = 'first')
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snpinfo_df_all = pd.read_csv(infile_snpinfo, sep = ',')
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snpinfo_df = snpinfo_df_all[['mutation', 'mutationinformation']]
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if snpinfo_df['mutation'].shape[0] == snpinfo_df['mutation'].nunique():
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print('No duplicate muts detected in snpinfo_df')
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else:
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dups = snpinfo_df['mutation'].duplicated().sum()
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print( dups, 'Duplicate muts detected in snpinfo_df'
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, '\nDim:', snpinfo_df.shape)
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print('Dropping duplicate muts')
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snpinfo_df = snpinfo_df.drop_duplicates(subset = 'mutation', keep = 'first')
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print('Dim:', snpinfo_df.shape)
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print('==================================='
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, '\nFifth merge: afor_df + snpinfo_df'
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, '\n===================================')
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merging_cols_m5 = detect_common_cols(afor_df, snpinfo_df)
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afor_snpinfo_dfs = pd.merge(afor_df, snpinfo_df, on = merging_cols_m5, how = 'left')
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#afor_df.shape
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#snpinfo_df.shape
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if len(afor_snpinfo_dfs) == afor_df.shape[0]:
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print('PASS: succesfully combined with left join')
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else:
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sys.exit('FAIL: unsuccessful merge')
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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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print('==================================='
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, '\nSixth merge: afor_snpinfo_dfs + afor_kin_df'
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, '\n===================================')
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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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merging_cols_m6 = detect_common_cols(afor_snpinfo_dfs, afor_kin_df)
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print('doing thing')
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print('Dim of df1:', afor_snpinfo_dfs.shape
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, '\nDim of df2:', afor_kin_df.shape
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, '\nno. of merging_cols:', len(merging_cols_m6))
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ors_df = pd.merge(afor_snpinfo_dfs, afor_kin_df, on = merging_cols_m6, how = 'outer')
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print('Dim of ors_df:', ors_df.shape)
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#%%
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print('==================================='
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, '\nSeventh merge: combined_df + ors_df'
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, '\n===================================')
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merging_cols_m7 = detect_common_cols(combined_df, ors_df)
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print('Dim of df1:', combined_df.shape
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, '\nDim of df2:', ors_df.shape
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, '\nno. of merging_cols:', len(merging_cols_m7))
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print('checking mutations in the two dfs:'
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, '\nmuts in df1 but NOT in df2:'
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, combined_df['mutationinformation'].isin(ors_df['mutationinformation']).sum()
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, 'muts in df2 but NOT in df1:'
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, ors_df['mutationinformation'].isin(combined_df['mutationinformation']).sum())
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#print('\nNo. of common muts:', np.intersect1d(combined_df['mutationinformation'], ors_df['mutationinformation']) )
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#combined_df_all = pd.merge(combined_df, ors_df, on = merging_cols_m7, how = 'outer') # FIXME
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combined_df_all = pd.merge(combined_df, ors_df, on = merging_cols_m7, how = 'left')
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outdf_expected_rows = len(combined_df)
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outdf_expected_cols = len(combined_df.columns) + len(ors_df.columns) - len(merging_cols_m7)
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print('\nDim of combined_df_all:', combined_df_all.shape)
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if combined_df_all.shape[1] == outdf_expected_cols:
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print('combined_df has expected no. of cols')
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if combined_df_all.shape[0] == outdf_expected_rows:
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print('combined_df has expected no. of rows')
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else:
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print('WARNING: nrows discrepancy noted'
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, '\nFIX IT')
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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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#%% write csv
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combined_df_all.to_csv(outfile_comb, index = False)
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