added script to combine all files in one
This commit is contained in:
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4 changed files with 435 additions and 748 deletions
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#!/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 4 (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 4 dfs with aa position as linking column
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# This is done in 2 steps:
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# merge 1: of 3 dfs (filenames in lowercase)
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# <gene.lower()>_dssp.csv
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# <gene.lower()>_kd.csv
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# <gene.lower()>_rd.csv
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# merge 2: of 2 dfs
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# gene.lower() + '_complex_mcsm_norm.csv' (!fix name)
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# output df from merge1
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# Input: 3 dfs
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# <gene.lower()>_dssp.csv
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# <gene.lower()>_kd.csv
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# <gene.lower()>_rd.csv
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# gene.lower() + '_complex_mcsm_norm.csv' (!fix name)
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# Output: .csv of all 4 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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import argparse
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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 = None)
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arg_parser.add_argument('-g', '--gene', help='gene name', default = None) # 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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# data dir
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#==========
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datadir = homedir + '/' + 'git/Data'
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#=======
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# input
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#=======
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indir = datadir + '/' + drug + '/' + 'output'
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in_filename1 = gene.lower() + '_dssp.csv'
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in_filename2 = gene.lower() + '_kd.csv'
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in_filename3 = gene.lower() + '_rd.csv'
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#in_filename4 = 'mcsm_complex1_normalised.csv' # FIXNAME
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in_filename4 = gene.lower() + '_complex_mcsm_norm.csv'
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infile1 = indir + '/' + in_filename1
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infile2 = indir + '/' + in_filename2
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infile3 = indir + '/' + in_filename3
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infile4 = indir + '/' + in_filename4
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print('\nInput path:', indir
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, '\nInput filename1:', in_filename1
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, '\nInput filename2:', in_filename2
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, '\nInput filename3:', in_filename3
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, '\nInput filename4:', in_filename4
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, '\n===================================================================')
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#=======
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# output
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#=======
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outdir = datadir + '/' + drug + '/' + 'output'
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out_filename = gene.lower() + '_mcsm_struct_params.csv'
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outfile = outdir + '/' + out_filename
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print('Output filename:', out_filename
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, '\nOutput path:', outdir
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, '\n===================================================================')
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#%% end of variable assignment for input and output files
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#=======================================================================
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#%% function/methd to combine 4 dfs
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def combine_dfs(dssp_csv, kd_csv, rd_csv, mcsm_csv, out_combined_csv):
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"""
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Combine 4 dfs
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@param dssp_df: csv file (output from dssp_df.py)
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@type dssp_df: string
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@param kd_df: csv file (output from kd_df.py)
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@type ks_df: string
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@param rd_df: csv file (output from rd_df.py)
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@type rd_df: string
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# FIXME
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@param mcsm_df: csv file (output of mcsm pipeline)CHECK}
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@type mcsm_df: string
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@param out_combined_csv: csv file output
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@type out_combined_csv: string
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@return: none, writes combined df as csv
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"""
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#========================
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# read input csv files to combine
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#========================
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dssp_df = pd.read_csv(dssp_csv, sep = ',')
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kd_df = pd.read_csv(kd_csv, sep = ',')
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rd_df = pd.read_csv(rd_csv, sep = ',')
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mcsm_df = pd.read_csv(mcsm_csv, sep = ',')
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print('Reading input files:'
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, '\ndssp file:', dssp_csv
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, '\nNo. of rows:', len(dssp_df)
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, '\nNo. of cols:', len(dssp_df.columns)
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, '\nColumn names:', dssp_df.columns
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, '\n==================================================================='
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, '\nkd file:', kd_csv
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, '\nNo. of rows:', len(kd_df)
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, '\nNo. of cols:', len(kd_df.columns)
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, '\nColumn names:', kd_df.columns
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, '\n==================================================================='
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, '\nrd file:', rd_csv
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, '\nNo. of rows:', len(rd_df)
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, '\nNo. of cols:', len(rd_df.columns)
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, '\nColumn names:', rd_df.columns
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, '\n==================================================================='
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, '\nrd file:', mcsm_csv
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, '\nNo. of rows:', len(mcsm_df)
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, '\nNo. of cols:', len(mcsm_df.columns)
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, '\nColumn names:', mcsm_df.columns
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, '\n===================================================================')
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#========================
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# merge 1 (combined_df1)
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# concatenating 3dfs:
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# dssp_df, kd_df, rd_df
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#========================
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print('starting first merge...\n')
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# checking no. of rows
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print('Checking if no. of rows of the 3 dfs are equal:\n'
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, len(dssp_df) == len(kd_df) == len(rd_df)
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, '\nReason: fasta files and pdb files vary since not all pos are part of the structure'
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, '\n===================================================================')
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# variables for sanity checks
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expected_rows_df1 = max(len(dssp_df), len(kd_df), len(rd_df))
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# beware of harcoding! used for sanity check
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ndfs = 3
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ncol_merge = 1
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offset = ndfs- ncol_merge
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expected_cols_df1 = len(dssp_df.columns) + len(kd_df.columns) + len(rd_df.columns) - offset
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print('Merge 1:'
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, '\ncombining 3dfs by commom col: position'
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, '\nExpected nrows in combined_df:', expected_rows_df1
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, '\nExpected ncols in combined_df:', expected_cols_df1
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, '\nResetting the common col as the index'
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, '\n===================================================================')
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#dssp_df.set_index('position', inplace = True)
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#kd_df.set_index('position', inplace = True)
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#rd_df.set_index('position', inplace =True)
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#combined_df = pd.concat([dssp_df, kd_df, rd_df], axis = 1, sort = False).reset_index()
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#combined_df.rename(columns = {'index':'position'})
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combined_df1 = pd.concat(
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(my_index.set_index('position') for my_index in [dssp_df, kd_df, rd_df])
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, axis = 1, join = 'outer').reset_index()
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# sanity check
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print('Checking dimensions of concatenated df1...')
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if len(combined_df1) == expected_rows_df1 and len(combined_df1.columns) == expected_cols_df1:
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print('PASS: combined df has expected dimensions'
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, '\nNo. of rows in combined df:', len(combined_df1)
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, '\nNo. of cols in combined df:', len(combined_df1.columns)
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, '\n===============================================================')
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else:
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print('FAIL: combined df does not have expected dimensions'
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, '\nNo. of rows in combined df:', len(combined_df1)
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, '\nNo. of cols in combined df:', len(combined_df1.columns)
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, '\n===============================================================')
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#========================
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# merge 2 (combined_df2)
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# concatenating 2dfs:
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# mcsm_df, combined_df1 (result of merge1)
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# sort the cols
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#========================
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print('starting second merge...\n')
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# rename col 'Position' in mcsm_df to lowercase 'position'
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# as it matches the combined_df1 colname to perfom merge
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#mcsm_df.columns
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#mcsm_df.rename(columns = {'Position':'position'}) # not working!
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# copy 'Position' column with the correct colname
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print('Firstly, copying \'Position\' col and renaming \'position\' to allow merging'
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, '\nNo. of cols before copying: ', len(mcsm_df.columns))
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mcsm_df['position'] = mcsm_df['Position']
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print('No. of cols after copying: ', len(mcsm_df.columns))
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# sanity check
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if mcsm_df['position'].equals(mcsm_df['Position']):
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print('PASS: Copying worked correctly'
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, '\ncopied col matches original column'
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, '\n===============================================================')
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else:
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print('FAIL: copied col does not match original column'
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, '\n================================================================')
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# variables for sanity checks
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expected_rows_df2 = len(mcsm_df)
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# beware of harcoding! used for sanity check
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ndfs = 2
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ncol_merge = 1
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offset = ndfs - ncol_merge
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expected_cols_df2 = len(mcsm_df.columns) + len(combined_df1.columns) - offset
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print('Merge 2:'
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, '\ncombining 2dfs by commom col: position'
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, '\nExpected nrows in combined_df:', expected_rows_df2
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, '\nExpected ncols in combined_df:', expected_cols_df2
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, '\n===================================================================')
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combined_df2 = mcsm_df.merge(combined_df1, on = 'position')
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# sanity check
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print('Checking dimensions of concatenated df2...')
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if len(combined_df2) == expected_rows_df2 and len(combined_df2.columns) == expected_cols_df2:
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print('PASS: combined df2 has expected dimensions'
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, '\nNo. of rows in combined df:', len(combined_df2)
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, '\nNo. of cols in combined df:', len(combined_df2.columns)
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, '\n===============================================================')
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else:
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print('FAIL: combined df2 does not have expected dimensions'
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, '\nNo. of rows in combined df:', len(combined_df2)
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, '\nNo. of cols in combined df:', len(combined_df2.columns)
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, '\n===============================================================')
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#===============
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# writing file
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#===============
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print('Writing file:'
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, '\nFilename:', out_combined_csv
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# , '\nPath:', outdir
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, '\nExpected no. of rows:', len(combined_df2)
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, '\nExpected no. of cols:', len(combined_df2.columns)
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, '\n=========================================================')
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combined_df2.to_csv(out_combined_csv, header = True, index = False)
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#%% end of function
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#=======================================================================
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#%% call function
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#combine_dfs(infile1, infile2, infile3, infile4, outfile)
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#=======================================================================
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def main():
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print('Combining 4 dfs:\n'
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, in_filename1, '\n'
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, in_filename2, '\n'
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, in_filename3, '\n'
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, in_filename4, '\n'
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, 'output csv:', out_filename)
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combine_dfs(infile1, infile2, infile3, infile4, outfile)
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print('Finished Writing file:'
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, '\nFilename:', outfile
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## , '\nNo. of rows:', ''
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## , '\nNo. of cols:', ''
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, '\n===========================================================')
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if __name__ == '__main__':
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main()
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#%% end of script
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#=======================================================================
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177
scripts/combining_FIXME.py
Executable file
177
scripts/combining_FIXME.py
Executable file
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#!/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 on comm_valson cols by detecting them
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# includes sainity checks
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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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import re
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#from varname import nameof
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#%% end of variable assignment for input and output files
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#=======================================================================
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#%% function/methd to combine dfs
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def detect_common_cols (df1, df2):
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"""
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Detect comm_valson cols
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@param df1: df
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@type df1: pandas df
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@param df2: df
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@type df2: pandas df
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@return: comm_valson cols
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@type: list
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"""
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common_cols = np.intersect1d(df1.columns, df2.columns).tolist()
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print('Length of comm_cols:', len(common_cols)
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, '\nmerging column/s:', common_cols
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, '\ntype:', type(common_cols)
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, '\ndtypes in merging columns:\n', df1[common_cols].dtypes)
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return common_cols
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#%% Function to combine 2 dfs by detecting commom cols and performing
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# sanity checks on the output df
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def combine_dfs_with_checks(df1, df2, my_join = 'outer'):
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"""
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Combine 2 dfs by finding merging columns automatically
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@param df1: data frame
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@type df1: pandas df
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@param df2: data frame
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@type df2: pandas df
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@my_join: join type for merging
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@type my_join: string
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@return: combined_df
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@type: pandas df
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"""
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print('Finding comm_cols and merging cols:'
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,'\n=========================================================')
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||||||
|
common_cols = np.intersect1d(df1.columns, df2.columns).tolist()
|
||||||
|
print('Length of comm_cols:', len(common_cols)
|
||||||
|
, '\nmerging column/s:', common_cols
|
||||||
|
, '\ntype:', type(common_cols))
|
||||||
|
|
||||||
|
#print('\ndtypes in merging columns:\n', df1[common_cols].dtypes)
|
||||||
|
|
||||||
|
print('selecting consistent dtypes for merging (object i.e string)')
|
||||||
|
#merging_cols = df1[comm_valson_cols].select_dtypes(include = [object]).columns.tolist()
|
||||||
|
#merging_cols = df1[comm_valson_cols].select_dtypes(include = ['int64']).columns.tolist()
|
||||||
|
merging_cols = common_cols.copy()
|
||||||
|
|
||||||
|
nmerging_cols = len(merging_cols)
|
||||||
|
print(' length of merging cols:', nmerging_cols
|
||||||
|
, '\nmerging cols:', merging_cols, 'type:', type(merging_cols)
|
||||||
|
, '\n=========================================================')
|
||||||
|
|
||||||
|
#========================
|
||||||
|
# merge 1 (combined_df)
|
||||||
|
# concatenating 2dfs:
|
||||||
|
# df1, df2
|
||||||
|
#========================
|
||||||
|
# checking cross-over of mutations in the two dfs to merge
|
||||||
|
ndiff_1 = df1[merging_cols].squeeze().isin(df2[merging_cols].squeeze()).sum()
|
||||||
|
ndiff1 = df1.shape[0] - ndiff_1
|
||||||
|
print('There are', ndiff1, 'unmatched mutations in left df')
|
||||||
|
|
||||||
|
#missing_mutinfo = df1[~left_df['mutationinformation'].isin(df2['mutationinformation'])]
|
||||||
|
#missing_mutinfo.to_csv('infoless_muts.csv')
|
||||||
|
|
||||||
|
ndiff_2 = df2[merging_cols].squeeze().isin(df1[merging_cols].squeeze()).sum()
|
||||||
|
ndiff2 = df2.shape[0] - ndiff_2
|
||||||
|
print('There are', ndiff2, 'unmatched mutations in right_df')
|
||||||
|
|
||||||
|
#comm_vals = np.intersect1d(df1[merging_cols], df2[merging_cols])
|
||||||
|
#comm_vals_count = len(comm_vals)
|
||||||
|
#print('length of comm_valson values:', comm_vals_count , '\ntype:', type(comm_vals_count))
|
||||||
|
|
||||||
|
#========================
|
||||||
|
# merging dfs & sanity checks
|
||||||
|
#========================
|
||||||
|
fail = False
|
||||||
|
print('combing with:', my_join)
|
||||||
|
comb_df = pd.merge(df1, df2, on = merging_cols, how = my_join)
|
||||||
|
|
||||||
|
expected_cols = df1.shape[1] + df2.shape[1] - nmerging_cols
|
||||||
|
|
||||||
|
|
||||||
|
if my_join == 'right':
|
||||||
|
df2_nd = df2.drop_duplicates(merging_cols, keep = 'first')
|
||||||
|
expected_rows = df2_nd.shape[0]
|
||||||
|
|
||||||
|
if my_join == 'left':
|
||||||
|
expected_rows = df1.shape[0]
|
||||||
|
|
||||||
|
|
||||||
|
#if my_join == 'inner':
|
||||||
|
# expected_rows = comm_vals_count
|
||||||
|
|
||||||
|
#if my_join == 'outer':
|
||||||
|
# df1_nd = df1.drop_duplicates(merging_cols, keep = 'first')
|
||||||
|
# df2_nd = df2.drop_duplicates(merging_cols, keep = 'first')
|
||||||
|
# expected_rows = df1_nd.shape[0] + df2_nd.shape[0] - comm_vals_count
|
||||||
|
|
||||||
|
|
||||||
|
if my_join == ('inner' or 'outer') and len(merging_cols) > 1:
|
||||||
|
#comm_vals = np.intersect1d(df1['mutationinformation'], df2['mutationinformation'])
|
||||||
|
print('length of merging_cols > 1, therefore omitting row checks')
|
||||||
|
combined_df = comb_df.copy()
|
||||||
|
expected_rows = len(combined_df)
|
||||||
|
|
||||||
|
else:
|
||||||
|
comm_vals = np.intersect1d(df1[merging_cols], df2[merging_cols])
|
||||||
|
print('length of merging_cols == 1, calculating expected rows in merged_df')
|
||||||
|
combined_df = comb_df.drop_duplicates(subset = merging_cols, keep ='first')
|
||||||
|
if my_join == 'inner':
|
||||||
|
expected_rows = len(comm_vals)
|
||||||
|
if my_join == 'outer':
|
||||||
|
df1_nd = df1.drop_duplicates(merging_cols, keep = 'first')
|
||||||
|
df2_nd = df2.drop_duplicates(merging_cols, keep = 'first')
|
||||||
|
expected_rows = df1_nd.shape[0] + df2_nd.shape[0] - len(comm_vals)
|
||||||
|
|
||||||
|
if len(combined_df) == expected_rows and len(combined_df.columns) == expected_cols:
|
||||||
|
print('PASS: successfully combined dfs with:', my_join, 'join')
|
||||||
|
else:
|
||||||
|
print('FAIL: combined_df\'s expected rows and cols not matched')
|
||||||
|
fail = True
|
||||||
|
print('\nExpected no. of rows:', expected_rows
|
||||||
|
, '\nGot:', len(combined_df)
|
||||||
|
, '\nExpected no. of cols:', expected_cols
|
||||||
|
, '\nGot:', len(combined_df.columns))
|
||||||
|
if fail:
|
||||||
|
sys.exit()
|
||||||
|
|
||||||
|
#if clean:
|
||||||
|
#foo = combined_df2.filter(regex = r'.*_x|_y', axis = 1)
|
||||||
|
#print(foo.columns)
|
||||||
|
#print('Detected duplicate cols with suffix: _x _y'
|
||||||
|
# , '\Dropping duplicate cols and cleaning')
|
||||||
|
|
||||||
|
# drop position col containing suffix '_y' and then rename col without suffix
|
||||||
|
combined_df_clean = combined_df.drop(combined_df.filter(regex = r'.*_y').columns, axis = 1)
|
||||||
|
combined_df_clean.rename(columns=lambda x: re.sub('_x$','', x), inplace = True)
|
||||||
|
|
||||||
|
return combined_df_clean
|
||||||
|
|
||||||
|
#%% end of function
|
||||||
|
#=======================================================================
|
||||||
|
|
||||||
|
|
@ -8,169 +8,280 @@ Created on Tue Aug 6 12:56:03 2019
|
||||||
# FIXME: change filename 2(mcsm normalised data)
|
# FIXME: change filename 2(mcsm normalised data)
|
||||||
# to be consistent like (pnca_complex_mcsm_norm.csv) : changed manually, but ensure this is done in the mcsm pipeline
|
# to be consistent like (pnca_complex_mcsm_norm.csv) : changed manually, but ensure this is done in the mcsm pipeline
|
||||||
#=======================================================================
|
#=======================================================================
|
||||||
# Task: combine 2 dfs on comm_valson cols by detecting them
|
# Task: combine 2 dfs with aa position as linking column
|
||||||
# includes sainity checks
|
|
||||||
|
|
||||||
|
# Input: 2 dfs
|
||||||
|
# <gene.lower()>_complex_mcsm_norm.csv
|
||||||
|
# <gene.lower()>_foldx.csv
|
||||||
|
|
||||||
|
# Output: .csv of all 2 dfs combined
|
||||||
|
|
||||||
|
# useful link
|
||||||
|
# https://stackoverflow.com/questions/23668427/pandas-three-way-joining-multiple-dataframes-on-columns
|
||||||
#=======================================================================
|
#=======================================================================
|
||||||
#%% load packages
|
#%% load packages
|
||||||
import sys, os
|
import sys, os
|
||||||
import pandas as pd
|
import pandas as pd
|
||||||
import numpy as np
|
import numpy as np
|
||||||
import re
|
|
||||||
#from varname import nameof
|
#from varname import nameof
|
||||||
|
import argparse
|
||||||
|
|
||||||
#%% end of variable assignment for input and output files
|
|
||||||
#=======================================================================
|
#=======================================================================
|
||||||
#%% function/methd to combine dfs
|
#%% specify input and curr dir
|
||||||
|
homedir = os.path.expanduser('~')
|
||||||
|
|
||||||
def detect_common_cols (df1, df2):
|
# set working dir
|
||||||
"""
|
os.getcwd()
|
||||||
Detect comm_valson cols
|
os.chdir(homedir + '/git/LSHTM_analysis/scripts')
|
||||||
|
os.getcwd()
|
||||||
|
|
||||||
@param df1: df
|
# FIXME: local imports
|
||||||
@type df1: pandas df
|
#from combining import combine_dfs_with_checks
|
||||||
|
from combining_FIXME import detect_common_cols
|
||||||
@param df2: df
|
|
||||||
@type df2: pandas df
|
|
||||||
|
|
||||||
@return: comm_valson cols
|
|
||||||
@type: list
|
|
||||||
"""
|
|
||||||
common_cols = np.intersect1d(df1.columns, df2.columns).tolist()
|
|
||||||
print('Length of comm_cols:', len(common_cols)
|
|
||||||
, '\nmerging column/s:', common_cols
|
|
||||||
, '\ntype:', type(common_cols)
|
|
||||||
, '\ndtypes in merging columns:\n', df1[common_cols].dtypes)
|
|
||||||
|
|
||||||
return common_cols
|
|
||||||
|
|
||||||
|
|
||||||
def combine_dfs_with_checks(df1, df2, my_join = 'outer'):
|
|
||||||
"""
|
|
||||||
Combine 2 dfs by finding merging columns automatically
|
|
||||||
|
|
||||||
@param df1: data frame
|
|
||||||
@type df1: pandas df
|
|
||||||
|
|
||||||
@param df2: data frame
|
|
||||||
@type df2: pandas df
|
|
||||||
|
|
||||||
@my_join: join type for merging
|
|
||||||
@type my_join: string
|
|
||||||
|
|
||||||
@return: combined_df
|
|
||||||
@type: pandas df
|
|
||||||
"""
|
|
||||||
|
|
||||||
print('Finding comm_cols and merging cols:'
|
|
||||||
,'\n=========================================================')
|
|
||||||
|
|
||||||
common_cols = np.intersect1d(df1.columns, df2.columns).tolist()
|
|
||||||
print('Length of comm_cols:', len(common_cols)
|
|
||||||
, '\nmerging column/s:', common_cols
|
|
||||||
, '\ntype:', type(common_cols))
|
|
||||||
|
|
||||||
#print('\ndtypes in merging columns:\n', df1[common_cols].dtypes)
|
|
||||||
|
|
||||||
print('selecting consistent dtypes for merging (object i.e string)')
|
|
||||||
#merging_cols = df1[comm_valson_cols].select_dtypes(include = [object]).columns.tolist()
|
|
||||||
#merging_cols = df1[comm_valson_cols].select_dtypes(include = ['int64']).columns.tolist()
|
|
||||||
merging_cols = common_cols.copy()
|
|
||||||
|
|
||||||
nmerging_cols = len(merging_cols)
|
|
||||||
print(' length of merging cols:', nmerging_cols
|
|
||||||
, '\nmerging cols:', merging_cols, 'type:', type(merging_cols)
|
|
||||||
, '\n=========================================================')
|
|
||||||
|
|
||||||
#========================
|
|
||||||
# merge 1 (combined_df)
|
|
||||||
# concatenating 2dfs:
|
|
||||||
# df1, df2
|
|
||||||
#========================
|
|
||||||
# checking cross-over of mutations in the two dfs to merge
|
|
||||||
ndiff_1 = df1[merging_cols].squeeze().isin(df2[merging_cols].squeeze()).sum()
|
|
||||||
ndiff1 = df1.shape[0] - ndiff_1
|
|
||||||
print('There are', ndiff1, 'unmatched mutations in left df')
|
|
||||||
|
|
||||||
#missing_mutinfo = df1[~left_df['mutationinformation'].isin(df2['mutationinformation'])]
|
|
||||||
#missing_mutinfo.to_csv('infoless_muts.csv')
|
|
||||||
|
|
||||||
ndiff_2 = df2[merging_cols].squeeze().isin(df1[merging_cols].squeeze()).sum()
|
|
||||||
ndiff2 = df2.shape[0] - ndiff_2
|
|
||||||
print('There are', ndiff2, 'unmatched mutations in right_df')
|
|
||||||
|
|
||||||
#comm_vals = np.intersect1d(df1[merging_cols], df2[merging_cols])
|
|
||||||
#comm_vals_count = len(comm_vals)
|
|
||||||
#print('length of comm_valson values:', comm_vals_count , '\ntype:', type(comm_vals_count))
|
|
||||||
|
|
||||||
#========================
|
|
||||||
# merging dfs & sanity checks
|
|
||||||
#========================
|
|
||||||
fail = False
|
|
||||||
print('combing with:', my_join)
|
|
||||||
comb_df = pd.merge(df1, df2, on = merging_cols, how = my_join)
|
|
||||||
|
|
||||||
expected_cols = df1.shape[1] + df2.shape[1] - nmerging_cols
|
|
||||||
|
|
||||||
|
|
||||||
if my_join == 'right':
|
|
||||||
df2_nd = df2.drop_duplicates(merging_cols, keep = 'first')
|
|
||||||
expected_rows = df2_nd.shape[0]
|
|
||||||
|
|
||||||
if my_join == 'left':
|
|
||||||
expected_rows = df1.shape[0]
|
|
||||||
|
|
||||||
|
|
||||||
#if my_join == 'inner':
|
|
||||||
# expected_rows = comm_vals_count
|
|
||||||
|
|
||||||
#if my_join == 'outer':
|
|
||||||
# df1_nd = df1.drop_duplicates(merging_cols, keep = 'first')
|
|
||||||
# df2_nd = df2.drop_duplicates(merging_cols, keep = 'first')
|
|
||||||
# expected_rows = df1_nd.shape[0] + df2_nd.shape[0] - comm_vals_count
|
|
||||||
|
|
||||||
|
|
||||||
if my_join == ('inner' or 'outer') and len(merging_cols) > 1:
|
|
||||||
#comm_vals = np.intersect1d(df1['mutationinformation'], df2['mutationinformation'])
|
|
||||||
print('length of merging_cols > 1, therefore omitting row checks')
|
|
||||||
combined_df = comb_df.copy()
|
|
||||||
expected_rows = len(combined_df)
|
|
||||||
|
|
||||||
else:
|
|
||||||
comm_vals = np.intersect1d(df1[merging_cols], df2[merging_cols])
|
|
||||||
print('length of merging_cols == 1, calculating expected rows in merged_df')
|
|
||||||
combined_df = comb_df.drop_duplicates(subset = merging_cols, keep ='first')
|
|
||||||
if my_join == 'inner':
|
|
||||||
expected_rows = len(comm_vals)
|
|
||||||
if my_join == 'outer':
|
|
||||||
df1_nd = df1.drop_duplicates(merging_cols, keep = 'first')
|
|
||||||
df2_nd = df2.drop_duplicates(merging_cols, keep = 'first')
|
|
||||||
expected_rows = df1_nd.shape[0] + df2_nd.shape[0] - len(comm_vals)
|
|
||||||
|
|
||||||
if len(combined_df) == expected_rows and len(combined_df.columns) == expected_cols:
|
|
||||||
print('PASS: successfully combined dfs with:', my_join, 'join')
|
|
||||||
else:
|
|
||||||
print('FAIL: combined_df\'s expected rows and cols not matched')
|
|
||||||
fail = True
|
|
||||||
print('\nExpected no. of rows:', expected_rows
|
|
||||||
, '\nGot:', len(combined_df)
|
|
||||||
, '\nExpected no. of cols:', expected_cols
|
|
||||||
, '\nGot:', len(combined_df.columns))
|
|
||||||
if fail:
|
|
||||||
sys.exit()
|
|
||||||
|
|
||||||
#if clean:
|
|
||||||
#foo = combined_df2.filter(regex = r'.*_x|_y', axis = 1)
|
|
||||||
#print(foo.columns)
|
|
||||||
#print('Detected duplicate cols with suffix: _x _y'
|
|
||||||
# , '\Dropping duplicate cols and cleaning')
|
|
||||||
|
|
||||||
# drop position col containing suffix '_y' and then rename col without suffix
|
|
||||||
combined_df_clean = combined_df.drop(combined_df.filter(regex = r'.*_y').columns, axis = 1)
|
|
||||||
combined_df_clean.rename(columns=lambda x: re.sub('_x$','', x), inplace = True)
|
|
||||||
|
|
||||||
return combined_df_clean
|
|
||||||
|
|
||||||
#%% end of function
|
|
||||||
#=======================================================================
|
#=======================================================================
|
||||||
|
#%% command line args
|
||||||
|
#arg_parser = argparse.ArgumentParser()
|
||||||
|
#arg_parser.add_argument('-d', '--drug', help='drug name', default = 'pyrazinamide')
|
||||||
|
#arg_parser.add_argument('-g', '--gene', help='gene name', default = 'pncA') # case sensitive
|
||||||
|
#args = arg_parser.parse_args()
|
||||||
|
#=======================================================================
|
||||||
|
#%% variable assignment: input and output
|
||||||
|
drug = 'pyrazinamide'
|
||||||
|
gene = 'pncA'
|
||||||
|
gene_match = gene + '_p.'
|
||||||
|
|
||||||
|
#drug = args.drug
|
||||||
|
#gene = args.gene
|
||||||
|
#======
|
||||||
|
# dirs
|
||||||
|
#======
|
||||||
|
datadir = homedir + '/' + 'git/Data'
|
||||||
|
indir = datadir + '/' + drug + '/' + 'input'
|
||||||
|
outdir = datadir + '/' + drug + '/' + 'output'
|
||||||
|
|
||||||
|
#=======
|
||||||
|
# input
|
||||||
|
#=======
|
||||||
|
in_filename_mcsm = gene.lower() + '_complex_mcsm_norm.csv'
|
||||||
|
in_filename_foldx = gene.lower() + '_foldx.csv'
|
||||||
|
in_filename_dssp = gene.lower() + '_dssp.csv'
|
||||||
|
in_filename_kd = gene.lower() + '_kd.csv'
|
||||||
|
in_filename_rd = gene.lower() + '_rd.csv'
|
||||||
|
in_filename_snpinfo = 'ns' + gene.lower() + '_snp_info.csv'
|
||||||
|
in_filename_afor = gene.lower() + '_af_or.csv'
|
||||||
|
in_filename_afor_kin = gene.lower() + '_af_or_kinship.csv'
|
||||||
|
|
||||||
|
|
||||||
|
infile_mcsm = outdir + '/' + in_filename_mcsm
|
||||||
|
infile_foldx = outdir + '/' + in_filename_foldx
|
||||||
|
infile_dssp = outdir + '/' + in_filename_dssp
|
||||||
|
infile_kd = outdir + '/' + in_filename_kd
|
||||||
|
infile_rd = outdir + '/' + in_filename_rd
|
||||||
|
infile_snpinfo = indir + '/' + in_filename_snpinfo
|
||||||
|
infile_afor = outdir + '/' + in_filename_afor
|
||||||
|
infile_afor_kin = outdir + '/' + in_filename_afor_kin
|
||||||
|
|
||||||
|
|
||||||
|
print('\nInput path:', outdir
|
||||||
|
, '\nInput filename mcsm:', infile_mcsm
|
||||||
|
, '\nInput filename foldx:', infile_foldx
|
||||||
|
, '\nInput filename dssp:', infile_dssp
|
||||||
|
, '\nInput filename kd:', infile_kd
|
||||||
|
, '\nInput filename rd', infile_rd
|
||||||
|
, '\nInput filename snp info:', infile_snpinfo
|
||||||
|
, '\nInput filename af or:', infile_afor
|
||||||
|
, '\nInput filename afor kinship:', infile_afor_kin
|
||||||
|
, '\n============================================================')
|
||||||
|
|
||||||
|
#=======
|
||||||
|
# output
|
||||||
|
#=======
|
||||||
|
out_filename_comb = gene.lower() + '_all_params.csv'
|
||||||
|
outfile_comb = outdir + '/' + out_filename_comb
|
||||||
|
print('Output filename:', outfile_comb
|
||||||
|
, '\n============================================================')
|
||||||
|
|
||||||
|
o_join = 'outer'
|
||||||
|
l_join = 'left'
|
||||||
|
r_join = 'right'
|
||||||
|
i_join = 'inner'
|
||||||
|
|
||||||
|
# end of variable assignment for input and output files
|
||||||
|
#&%%====================================================================
|
||||||
|
mcsm_df = pd.read_csv(infile_mcsm, sep = ',')
|
||||||
|
mcsm_df.columns = mcsm_df.columns.str.lower()
|
||||||
|
foldx_df = pd.read_csv(infile_foldx , sep = ',')
|
||||||
|
|
||||||
|
print('==================================='
|
||||||
|
, '\nFirst merge: mcsm + foldx'
|
||||||
|
, '\n===================================')
|
||||||
|
#mcsm_foldx_dfs = combine_dfs_with_checks(mcsm_df, foldx_df, my_join = o_join)
|
||||||
|
merging_cols_m1 = detect_common_cols(mcsm_df, foldx_df)
|
||||||
|
|
||||||
|
mcsm_foldx_dfs = pd.merge(mcsm_df, foldx_df, on = merging_cols_m1, how = 'outer')
|
||||||
|
ncols_m1 = len(mcsm_foldx_dfs.columns)
|
||||||
|
#%%~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||||
|
print('==================================='
|
||||||
|
, '\nSecond merge: dssp + kd'
|
||||||
|
, '\n===================================')
|
||||||
|
|
||||||
|
dssp_df = pd.read_csv(infile_dssp, sep = ',')
|
||||||
|
kd_df = pd.read_csv(infile_kd, sep = ',')
|
||||||
|
rd_df = pd.read_csv(infile_rd, sep = ',')
|
||||||
|
|
||||||
|
#dssp_kd_dfs = combine_dfs_with_checks(dssp_df, kd_df, my_join = o_join)
|
||||||
|
merging_cols_m2 = detect_common_cols(dssp_df, kd_df)
|
||||||
|
|
||||||
|
dssp_kd_dfs = pd.merge(dssp_df, kd_df, on = merging_cols_m2, how = 'outer')
|
||||||
|
|
||||||
|
print('==================================='
|
||||||
|
, '\nThird merge: dssp_kd_dfs + rd_df'
|
||||||
|
, '\n===================================')
|
||||||
|
#dssp_kd_rd_dfs = combine_dfs_with_checks(dssp_kd_dfs, rd_df, my_join = o_join)
|
||||||
|
merging_cols_m3 = detect_common_cols(dssp_df, kd_df)
|
||||||
|
dssp_kd_rd_dfs = pd.merge(dssp_kd_dfs, rd_df, on = merging_cols_m3, how = 'outer')
|
||||||
|
|
||||||
|
ncols_m3 = len(dssp_kd_rd_dfs.columns)
|
||||||
|
#%%~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||||
|
print('==================================='
|
||||||
|
, '\nFourth merge: First merge + Third merge'
|
||||||
|
, '\n===================================')
|
||||||
|
#combined_dfs = combine_dfs_with_checks(mcsm_foldx_dfs, dssp_kd_rd_dfs, my_join = i_join)# gives wrong!
|
||||||
|
merging_cols_m4 = detect_common_cols(mcsm_foldx_dfs, dssp_kd_rd_dfs)
|
||||||
|
combined_df_expected_cols = ncols_m1 + ncols_m3 - len(merging_cols_m4)
|
||||||
|
|
||||||
|
combined_df = pd.merge(mcsm_foldx_dfs, dssp_kd_rd_dfs, on = merging_cols_m4, how = 'inner')
|
||||||
|
|
||||||
|
|
||||||
|
if len(combined_df) == len(mcsm_df) and len(combined_df.columns) == combined_df_expected_cols:
|
||||||
|
print('PASS: successfully combined 5 dfs'
|
||||||
|
, '\nnrows combined_df:', len(combined_df)
|
||||||
|
, '\ncols combined_df:', len(combined_df.columns))
|
||||||
|
else:
|
||||||
|
sys.exit('FAIL: check individual df merges')
|
||||||
|
#%%~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||||
|
|
||||||
|
#%%~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||||
|
#%% OR combining
|
||||||
|
afor_df = pd.read_csv(infile_afor, sep = ',')
|
||||||
|
afor_df.columns = afor_df.columns.str.lower()
|
||||||
|
|
||||||
|
if afor_df['mutation'].shape[0] == afor_df['mutation'].nunique():
|
||||||
|
print('No duplicate muts detected in afor_df')
|
||||||
|
else:
|
||||||
|
print('Dropping duplicate muts detected in afor_df')
|
||||||
|
afor_df = afor_df.drop_duplicates(subset = 'mutation', keep = 'first')
|
||||||
|
|
||||||
|
|
||||||
|
snpinfo_df_all = pd.read_csv(infile_snpinfo, sep = ',')
|
||||||
|
snpinfo_df = snpinfo_df_all[['mutation', 'mutationinformation']]
|
||||||
|
|
||||||
|
|
||||||
|
if snpinfo_df['mutation'].shape[0] == snpinfo_df['mutation'].nunique():
|
||||||
|
print('No duplicate muts detected in snpinfo_df')
|
||||||
|
else:
|
||||||
|
dups = snpinfo_df['mutation'].duplicated().sum()
|
||||||
|
print( dups, 'Duplicate muts detected in snpinfo_df'
|
||||||
|
, '\nDim:', snpinfo_df.shape)
|
||||||
|
print('Dropping duplicate muts')
|
||||||
|
snpinfo_df = snpinfo_df.drop_duplicates(subset = 'mutation', keep = 'first')
|
||||||
|
print('Dim:', snpinfo_df.shape)
|
||||||
|
|
||||||
|
print('==================================='
|
||||||
|
, '\nFifth merge: afor_df + snpinfo_df'
|
||||||
|
, '\n===================================')
|
||||||
|
|
||||||
|
merging_cols_m5 = detect_common_cols(afor_df, snpinfo_df)
|
||||||
|
|
||||||
|
afor_snpinfo_dfs = pd.merge(afor_df, snpinfo_df, on = merging_cols_m5, how = 'left')
|
||||||
|
if len(afor_snpinfo_dfs) == afor_df.shape[0]:
|
||||||
|
print('PASS: succesfully combined with left join'
|
||||||
|
, '\nDim of df1:', afor_df.shape
|
||||||
|
, '\nDim of df2:', snpinfo_df.shape)
|
||||||
|
else:
|
||||||
|
sys.exit('FAIL: unsuccessful merge')
|
||||||
|
|
||||||
|
#%%~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||||
|
afor_kin_df = pd.read_csv(infile_afor_kin, sep = ',')
|
||||||
|
afor_kin_df.columns = afor_kin_df.columns.str.lower()
|
||||||
|
|
||||||
|
print('==================================='
|
||||||
|
, '\nSixth merge: afor_snpinfo_dfs + afor_kin_df'
|
||||||
|
, '\n===================================')
|
||||||
|
|
||||||
|
merging_cols_m6 = detect_common_cols(afor_snpinfo_dfs, afor_kin_df)
|
||||||
|
|
||||||
|
print('Dim of df1:', afor_snpinfo_dfs.shape
|
||||||
|
, '\nDim of df2:', afor_kin_df.shape
|
||||||
|
, '\nno. of merging_cols:', len(merging_cols_m6))
|
||||||
|
|
||||||
|
ors_df = pd.merge(afor_snpinfo_dfs, afor_kin_df, on = merging_cols_m6, how = 'outer')
|
||||||
|
|
||||||
|
print('Dim of ors_df:', ors_df.shape)
|
||||||
|
|
||||||
|
#%%~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||||
|
|
||||||
|
print('==================================='
|
||||||
|
, '\nSeventh merge: combined_df + ors_df'
|
||||||
|
, '\n===================================')
|
||||||
|
|
||||||
|
merging_cols_m7 = detect_common_cols(combined_df, ors_df)
|
||||||
|
|
||||||
|
print('Dim of df1:', combined_df.shape
|
||||||
|
, '\nDim of df2:', ors_df.shape
|
||||||
|
, '\nno. of merging_cols:', len(merging_cols_m7))
|
||||||
|
|
||||||
|
print('checking mutations in the two dfs:'
|
||||||
|
, '\nmuts in df1 but NOT in df2:'
|
||||||
|
, combined_df['mutationinformation'].isin(ors_df['mutationinformation']).sum()
|
||||||
|
, '\nmuts in df2 but NOT in df1:'
|
||||||
|
, ors_df['mutationinformation'].isin(combined_df['mutationinformation']).sum())
|
||||||
|
|
||||||
|
#print('\nNo. of common muts:', np.intersect1d(combined_df['mutationinformation'], ors_df['mutationinformation']) )
|
||||||
|
|
||||||
|
#combined_df_all = pd.merge(combined_df, ors_df, on = merging_cols_m7, how = 'outer') # FIXME
|
||||||
|
combined_df_all = pd.merge(combined_df, ors_df, on = merging_cols_m7, how = 'left')
|
||||||
|
|
||||||
|
outdf_expected_rows = len(combined_df)
|
||||||
|
outdf_expected_cols = len(combined_df.columns) + len(ors_df.columns) - len(merging_cols_m7)
|
||||||
|
|
||||||
|
print('\nDim of combined_df_all:', combined_df_all.shape
|
||||||
|
, '\nwith join type: ????')
|
||||||
|
|
||||||
|
if combined_df_all.shape[1] == outdf_expected_cols:
|
||||||
|
print('combined_df has expected no. of cols')
|
||||||
|
if combined_df_all.shape[0] == outdf_expected_rows:
|
||||||
|
print('combined_df has expected no. of rows')
|
||||||
|
else:
|
||||||
|
print('WARNING: nrows discrepancy noted'
|
||||||
|
, '\nFIX IT')
|
||||||
|
print ('thing finished')
|
||||||
|
#%%~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||||
|
# write csv
|
||||||
|
|
||||||
|
combined_df_all.to_csv(outfile_comb, index = False)
|
||||||
|
|
||||||
|
#=======================================================================
|
||||||
|
#%% incase you FIX the the function: combine_dfs_with_checks
|
||||||
|
#def main():
|
||||||
|
|
||||||
|
# print('Reading input files:')
|
||||||
|
#mcsm_df = pd.read_csv(infile_mcsm, sep = ',')
|
||||||
|
#mcsm_df.columns = mcsm_df.columns.str.lower()
|
||||||
|
|
||||||
|
#foldx_df = pd.read_csv(infile_foldx , sep = ',')
|
||||||
|
|
||||||
|
#dssp_df = pd.read_csv(infile_dssp, sep = ',')
|
||||||
|
#dssp_df.columns = dssp_df.columns.str.lower()
|
||||||
|
|
||||||
|
#kd_df = pd.read_csv(infile_kd, sep = ',')
|
||||||
|
#kd_df.columns = kd_df.columns.str.lower()
|
||||||
|
|
||||||
|
#rd_df = pd.read_csv(infile_kd, sep = ',')
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
#if __name__ == '__main__':
|
||||||
|
# main()
|
||||||
|
#=======================================================================
|
||||||
|
#%% end of script
|
||||||
|
|
|
||||||
|
|
@ -1,303 +0,0 @@
|
||||||
#!/usr/bin/env python3
|
|
||||||
# -*- coding: utf-8 -*-
|
|
||||||
'''
|
|
||||||
Created on Tue Aug 6 12:56:03 2019
|
|
||||||
|
|
||||||
@author: tanu
|
|
||||||
'''
|
|
||||||
# FIXME: change filename 2(mcsm normalised data)
|
|
||||||
# to be consistent like (pnca_complex_mcsm_norm.csv) : changed manually, but ensure this is done in the mcsm pipeline
|
|
||||||
#=======================================================================
|
|
||||||
# Task: combine 2 dfs with aa position as linking column
|
|
||||||
|
|
||||||
# Input: 2 dfs
|
|
||||||
# <gene.lower()>_complex_mcsm_norm.csv
|
|
||||||
# <gene.lower()>_foldx.csv
|
|
||||||
|
|
||||||
# Output: .csv of all 2 dfs combined
|
|
||||||
|
|
||||||
# useful link
|
|
||||||
# https://stackoverflow.com/questions/23668427/pandas-three-way-joining-multiple-dataframes-on-columns
|
|
||||||
#=======================================================================
|
|
||||||
#%% load packages
|
|
||||||
import sys, os
|
|
||||||
import pandas as pd
|
|
||||||
import numpy as np
|
|
||||||
#from varname import nameof
|
|
||||||
import argparse
|
|
||||||
|
|
||||||
#=======================================================================
|
|
||||||
#%% specify input and curr dir
|
|
||||||
homedir = os.path.expanduser('~')
|
|
||||||
|
|
||||||
# set working dir
|
|
||||||
os.getcwd()
|
|
||||||
os.chdir(homedir + '/git/LSHTM_analysis/scripts')
|
|
||||||
os.getcwd()
|
|
||||||
|
|
||||||
# local imports
|
|
||||||
from combining_dfs import combine_dfs_with_checks
|
|
||||||
from combining_dfs import detect_common_cols
|
|
||||||
#=======================================================================
|
|
||||||
#%% command line args
|
|
||||||
#arg_parser = argparse.ArgumentParser()
|
|
||||||
#arg_parser.add_argument('-d', '--drug', help='drug name', default = 'pyrazinamide')
|
|
||||||
#arg_parser.add_argument('-g', '--gene', help='gene name', default = 'pncA') # case sensitive
|
|
||||||
#args = arg_parser.parse_args()
|
|
||||||
#=======================================================================
|
|
||||||
#%% variable assignment: input and output
|
|
||||||
drug = 'pyrazinamide'
|
|
||||||
gene = 'pncA'
|
|
||||||
gene_match = gene + '_p.'
|
|
||||||
|
|
||||||
#drug = args.drug
|
|
||||||
#gene = args.gene
|
|
||||||
#======
|
|
||||||
# dirs
|
|
||||||
#======
|
|
||||||
datadir = homedir + '/' + 'git/Data'
|
|
||||||
indir = datadir + '/' + drug + '/' + 'input'
|
|
||||||
outdir = datadir + '/' + drug + '/' + 'output'
|
|
||||||
|
|
||||||
#=======
|
|
||||||
# input
|
|
||||||
#=======
|
|
||||||
#in_filename_linking = gene.lower() + '_linking_df.csv'
|
|
||||||
in_filename_mcsm = gene.lower() + '_complex_mcsm_norm.csv'
|
|
||||||
in_filename_foldx = gene.lower() + '_foldx.csv'
|
|
||||||
in_filename_dssp = gene.lower() + '_dssp.csv'
|
|
||||||
in_filename_kd = gene.lower() + '_kd.csv'
|
|
||||||
in_filename_rd = gene.lower() + '_rd.csv'
|
|
||||||
in_filename_snpinfo = 'ns' + gene.lower() + '_snp_info.csv'
|
|
||||||
in_filename_afor = gene.lower() + '_af_or.csv'
|
|
||||||
in_filename_afor_kin = gene.lower() + '_af_or_kinship.csv'
|
|
||||||
|
|
||||||
|
|
||||||
#infile_linking = outdir + '/' + in_filename_linking
|
|
||||||
infile_mcsm = outdir + '/' + in_filename_mcsm
|
|
||||||
infile_foldx = outdir + '/' + in_filename_foldx
|
|
||||||
infile_dssp = outdir + '/' + in_filename_dssp
|
|
||||||
infile_kd = outdir + '/' + in_filename_kd
|
|
||||||
infile_rd = outdir + '/' + in_filename_rd
|
|
||||||
infile_snpinfo = indir + '/' + in_filename_snpinfo
|
|
||||||
infile_afor = outdir + '/' + in_filename_afor
|
|
||||||
infile_afor_kin = outdir + '/' + in_filename_afor_kin
|
|
||||||
|
|
||||||
|
|
||||||
print('\nInput path:', outdir
|
|
||||||
, '\nInput filename mcsm:', infile_mcsm
|
|
||||||
, '\nInput filename foldx:', infile_foldx
|
|
||||||
, '\nInput filename dssp:', infile_dssp
|
|
||||||
, '\nInput filename kd:', infile_kd
|
|
||||||
, '\nInput filename rd', infile_rd
|
|
||||||
, '\nInput filename snp info:', infile_snpinfo
|
|
||||||
, '\nInput filename af or:', infile_afor
|
|
||||||
, '\nInput filename afor kinship:', infile_afor_kin
|
|
||||||
, '\n============================================================')
|
|
||||||
|
|
||||||
#=======
|
|
||||||
# output
|
|
||||||
#=======
|
|
||||||
out_filename_comb = gene.lower() + '_all_params.csv'
|
|
||||||
outfile_comb = outdir + '/' + out_filename_comb
|
|
||||||
print('Output filename:', outfile_comb
|
|
||||||
, '\n============================================================')
|
|
||||||
|
|
||||||
o_join = 'outer'
|
|
||||||
l_join = 'left'
|
|
||||||
r_join = 'right'
|
|
||||||
i_join = 'inner'
|
|
||||||
|
|
||||||
|
|
||||||
#del(in_filename_dssp, in_filename_foldx)
|
|
||||||
# end of variable assignment for input and output files
|
|
||||||
|
|
||||||
#=======================================================================
|
|
||||||
# call function to detect common cols
|
|
||||||
# FIXME: do the OR combining in the end to iron out any problems
|
|
||||||
# Couldn't run the function combin
|
|
||||||
#=======================================================================
|
|
||||||
def main():
|
|
||||||
|
|
||||||
print('Reading input files:')
|
|
||||||
|
|
||||||
#dssp_df = pd.read_csv(infile_dssp, sep = ',')
|
|
||||||
#dssp_df.columns = dssp_df.columns.str.lower()
|
|
||||||
|
|
||||||
#kd_df = pd.read_csv(infile_kd, sep = ',')
|
|
||||||
#kd_df.columns = kd_df.columns.str.lower()
|
|
||||||
|
|
||||||
# print('Dimension left df:', dssp_df.shape
|
|
||||||
# , '\nDimension right_df:', kd_df.shape
|
|
||||||
# , '\njoin type:', o_join
|
|
||||||
# , '\n=========================================================')
|
|
||||||
|
|
||||||
# detect common cols
|
|
||||||
#merging_cols = detect_common_cols(dssp_df, kd_df)
|
|
||||||
#print('Length of common cols:', len(merging_cols)
|
|
||||||
# , '\nmerging column/s:', merging_cols, 'type:', type(merging_cols)
|
|
||||||
# , '\ndtypes in merging columns:', dssp_df[merging_cols].dtypes)
|
|
||||||
|
|
||||||
#combined_df1 = combine_dfs_with_checks(dssp_df, kd_df, my_join = o_join)
|
|
||||||
#print('Dimensions of combined df:', combined_df1.shape
|
|
||||||
# , '\nsneak peak:', combined_df1.head()
|
|
||||||
# , '\ndtypes in cols:\n', combined_df1.dtypes)
|
|
||||||
|
|
||||||
#if __name__ == '__main__':
|
|
||||||
# main()
|
|
||||||
#=======================================================================
|
|
||||||
#%% end of script
|
|
||||||
#hardcoded test
|
|
||||||
|
|
||||||
mcsm_df = pd.read_csv(infile_mcsm, sep = ',')
|
|
||||||
mcsm_df.columns = mcsm_df.columns.str.lower()
|
|
||||||
foldx_df = pd.read_csv(infile_foldx , sep = ',')
|
|
||||||
|
|
||||||
print('==================================='
|
|
||||||
, '\nFirst merge: mcsm + foldx'
|
|
||||||
, '\n===================================')
|
|
||||||
#mcsm_foldx_dfs = combine_dfs_with_checks(mcsm_df, foldx_df, my_join = o_join)
|
|
||||||
merging_cols_m1 = detect_common_cols(mcsm_df, foldx_df)
|
|
||||||
|
|
||||||
mcsm_foldx_dfs = pd.merge(mcsm_df, foldx_df, on = merging_cols_m1, how = 'outer')
|
|
||||||
ncols_m1 = len(mcsm_foldx_dfs.columns)
|
|
||||||
|
|
||||||
print('==================================='
|
|
||||||
, '\nSecond merge: dssp + kd'
|
|
||||||
, '\n===================================')
|
|
||||||
|
|
||||||
dssp_df = pd.read_csv(infile_dssp, sep = ',')
|
|
||||||
kd_df = pd.read_csv(infile_kd, sep = ',')
|
|
||||||
rd_df = pd.read_csv(infile_rd, sep = ',')
|
|
||||||
|
|
||||||
#dssp_kd_dfs = combine_dfs_with_checks(dssp_df, kd_df, my_join = o_join)
|
|
||||||
merging_cols_m2 = detect_common_cols(dssp_df, kd_df)
|
|
||||||
|
|
||||||
dssp_kd_dfs = pd.merge(dssp_df, kd_df, on = merging_cols_m2, how = 'outer')
|
|
||||||
|
|
||||||
print('==================================='
|
|
||||||
, '\nThird merge: dssp_kd_dfs + rd_df'
|
|
||||||
, '\n===================================')
|
|
||||||
#dssp_kd_rd_dfs = combine_dfs_with_checks(dssp_kd_dfs, rd_df, my_join = o_join)
|
|
||||||
merging_cols_m3 = detect_common_cols(dssp_df, kd_df)
|
|
||||||
dssp_kd_rd_dfs = pd.merge(dssp_kd_dfs, rd_df, on = merging_cols_m3, how = 'outer')
|
|
||||||
|
|
||||||
ncols_m3 = len(dssp_kd_rd_dfs.columns)
|
|
||||||
|
|
||||||
print('==================================='
|
|
||||||
, '\nFourth merge: First merge + Third merge'
|
|
||||||
, '\n===================================')
|
|
||||||
#combined_dfs = combine_dfs_with_checks(mcsm_foldx_dfs, dssp_kd_rd_dfs, my_join = i_join)# gives wrong!
|
|
||||||
merging_cols_m4 = detect_common_cols(mcsm_foldx_dfs, dssp_kd_rd_dfs)
|
|
||||||
combined_df_expected_cols = ncols_m1 + ncols_m3 - len(merging_cols_m4)
|
|
||||||
|
|
||||||
combined_df = pd.merge(mcsm_foldx_dfs, dssp_kd_rd_dfs, on = merging_cols_m4, how = 'inner')
|
|
||||||
|
|
||||||
|
|
||||||
if len(combined_df) == len(mcsm_df) and len(combined_df.columns) == combined_df_expected_cols:
|
|
||||||
print('PASS: successfully combined 5 dfs'
|
|
||||||
, '\nnrows combined_df:', len(combined_df)
|
|
||||||
, '\ncols combined_df:', len(combined_df.columns))
|
|
||||||
else:
|
|
||||||
sys.exit('FAIL: check individual df merges')
|
|
||||||
|
|
||||||
#%% OR combining
|
|
||||||
afor_df = pd.read_csv(infile_afor, sep = ',')
|
|
||||||
afor_df.columns = afor_df.columns.str.lower()
|
|
||||||
|
|
||||||
if afor_df['mutation'].shape[0] == afor_df['mutation'].nunique():
|
|
||||||
print('No duplicate muts detected in afor_df')
|
|
||||||
else:
|
|
||||||
print('Dropping duplicate muts detected in afor_df')
|
|
||||||
afor_df = afor_df.drop_duplicates(subset = 'mutation', keep = 'first')
|
|
||||||
|
|
||||||
|
|
||||||
snpinfo_df_all = pd.read_csv(infile_snpinfo, sep = ',')
|
|
||||||
snpinfo_df = snpinfo_df_all[['mutation', 'mutationinformation']]
|
|
||||||
|
|
||||||
|
|
||||||
if snpinfo_df['mutation'].shape[0] == snpinfo_df['mutation'].nunique():
|
|
||||||
print('No duplicate muts detected in snpinfo_df')
|
|
||||||
else:
|
|
||||||
dups = snpinfo_df['mutation'].duplicated().sum()
|
|
||||||
print( dups, 'Duplicate muts detected in snpinfo_df'
|
|
||||||
, '\nDim:', snpinfo_df.shape)
|
|
||||||
print('Dropping duplicate muts')
|
|
||||||
snpinfo_df = snpinfo_df.drop_duplicates(subset = 'mutation', keep = 'first')
|
|
||||||
print('Dim:', snpinfo_df.shape)
|
|
||||||
|
|
||||||
|
|
||||||
print('==================================='
|
|
||||||
, '\nFifth merge: afor_df + snpinfo_df'
|
|
||||||
, '\n===================================')
|
|
||||||
|
|
||||||
merging_cols_m5 = detect_common_cols(afor_df, snpinfo_df)
|
|
||||||
|
|
||||||
afor_snpinfo_dfs = pd.merge(afor_df, snpinfo_df, on = merging_cols_m5, how = 'left')
|
|
||||||
#afor_df.shape
|
|
||||||
#snpinfo_df.shape
|
|
||||||
if len(afor_snpinfo_dfs) == afor_df.shape[0]:
|
|
||||||
print('PASS: succesfully combined with left join')
|
|
||||||
else:
|
|
||||||
sys.exit('FAIL: unsuccessful merge')
|
|
||||||
|
|
||||||
#%%
|
|
||||||
|
|
||||||
afor_kin_df = pd.read_csv(infile_afor_kin, sep = ',')
|
|
||||||
afor_kin_df.columns = afor_kin_df.columns.str.lower()
|
|
||||||
|
|
||||||
print('==================================='
|
|
||||||
, '\nSixth merge: afor_snpinfo_dfs + afor_kin_df'
|
|
||||||
, '\n===================================')
|
|
||||||
|
|
||||||
merging_cols_m6 = detect_common_cols(afor_snpinfo_dfs, afor_kin_df)
|
|
||||||
|
|
||||||
print('Dim of df1:', afor_snpinfo_dfs.shape
|
|
||||||
, '\nDim of df2:', afor_kin_df.shape
|
|
||||||
, '\nno. of merging_cols:', len(merging_cols_m6))
|
|
||||||
|
|
||||||
ors_df = pd.merge(afor_snpinfo_dfs, afor_kin_df, on = merging_cols_m6, how = 'outer')
|
|
||||||
|
|
||||||
print('Dim of ors_df:', ors_df.shape)
|
|
||||||
|
|
||||||
#%%
|
|
||||||
|
|
||||||
print('==================================='
|
|
||||||
, '\nSeventh merge: combined_df + ors_df'
|
|
||||||
, '\n===================================')
|
|
||||||
|
|
||||||
merging_cols_m7 = detect_common_cols(combined_df, ors_df)
|
|
||||||
|
|
||||||
print('Dim of df1:', combined_df.shape
|
|
||||||
, '\nDim of df2:', ors_df.shape
|
|
||||||
, '\nno. of merging_cols:', len(merging_cols_m7))
|
|
||||||
|
|
||||||
print('checking mutations in the two dfs:'
|
|
||||||
, '\nmuts in df1 but NOT in df2:'
|
|
||||||
, combined_df['mutationinformation'].isin(ors_df['mutationinformation']).sum()
|
|
||||||
, 'muts in df2 but NOT in df1:'
|
|
||||||
, ors_df['mutationinformation'].isin(combined_df['mutationinformation']).sum())
|
|
||||||
|
|
||||||
#print('\nNo. of common muts:', np.intersect1d(combined_df['mutationinformation'], ors_df['mutationinformation']) )
|
|
||||||
|
|
||||||
#combined_df_all = pd.merge(combined_df, ors_df, on = merging_cols_m7, how = 'outer') # FIXME
|
|
||||||
combined_df_all = pd.merge(combined_df, ors_df, on = merging_cols_m7, how = 'left')
|
|
||||||
|
|
||||||
outdf_expected_rows = len(combined_df)
|
|
||||||
outdf_expected_cols = len(combined_df.columns) + len(ors_df.columns) - len(merging_cols_m7)
|
|
||||||
|
|
||||||
print('\nDim of combined_df_all:', combined_df_all.shape)
|
|
||||||
|
|
||||||
if combined_df_all.shape[1] == outdf_expected_cols:
|
|
||||||
print('combined_df has expected no. of cols')
|
|
||||||
if combined_df_all.shape[0] == outdf_expected_rows:
|
|
||||||
print('combined_df has expected no. of rows')
|
|
||||||
else:
|
|
||||||
print('WARNING: nrows discrepancy noted'
|
|
||||||
, '\nFIX IT')
|
|
||||||
|
|
||||||
|
|
||||||
print ('thing finished')
|
|
||||||
#%% write csv
|
|
||||||
|
|
||||||
combined_df_all.to_csv(outfile_comb, index = False)
|
|
||||||
Loading…
Add table
Add a link
Reference in a new issue