added combining funct & combining_mcsm_foldx script

scripts/combining.py

@@ -0,0 +1,146 @@
+#!/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
+
+#%% end of variable assignment for input and output files
+#=======================================================================
+#%% function/methd to combine 4 dfs
+
+#def combine_stability_dfs(mcsm_df, foldx_df, out_combined_df):
+def combine_stability_dfs(mcsm_df, foldx_df, my_join = 'outer'):
+    """
+    Combine 2 dfs
+
+    @param mcsm_df: csv file (output from mcsm pipeline)
+    @type mcsm_df: string
+
+    @param foldx_df: csv file (output from runFoldx.py)
+    @type foldx_df: string
+    
+    @param out_combined_df: csv file output
+    @type out_combined_df: string
+        
+    @return:  none, writes combined df as csv
+    """
+    #========================
+    # read input csv files to combine
+    #========================
+    print('Reading input files:')
+
+    left_df = pd.read_csv(mcsm_df, sep = ',')
+    left_df.columns = left_df.columns.str.lower()
+    
+    right_df = pd.read_csv(foldx_df, sep = ',')
+    right_df.columns = right_df.columns.str.lower()
+    
+    print('Dimension left df:', left_df.shape
+          , '\nDimesnion right_df:', right_df.shape
+#          , '\njoin type:', join_type
+          , '\n=========================================================') 
+    
+    print('Finding common cols and merging cols:'
+          ,'\n=========================================================')
+
+
+    common_cols  = np.intersect1d(left_df.columns, right_df.columns).tolist()
+    print('Length of common cols:', len(common_cols)
+      , '\ncommon column/s:', common_cols, 'type:', type(common_cols))
+    
+    print('selecting consistent dtypes for merging (object i.e string)')
+    merging_cols = left_df[common_cols].select_dtypes(include = [object]).columns.tolist()
+    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:
+    # mcsm_df, foldx_df
+    #========================
+    # checking cross-over of mutations in the two dfs to merge
+    #ndiff1 = left_df.shape[0] - left_df['mutationinformation'].isin(right_df['mutationinformation']).sum()
+    ndiff_1 = left_df[merging_cols].squeeze().isin(right_df[merging_cols].squeeze()).sum()
+    print('ndiff_1:', ndiff_1)
+    
+    ndiff1 =  left_df.shape[0] - ndiff_1
+    #print('There are', ndiff1, 'unmatched mutations in left df')
+   
+    #missing_mutinfo = left_df[~left_df['mutationinformation'].isin(right_df['mutationinformation'])]
+    #missing_mutinfo.to_csv('infoless_muts.csv')
+    
+    #ndiff2 = right_df.shape[0] - right_df['mutationinformation'].isin(left_df['mutationinformation']).sum()
+    ndiff_2 = right_df[merging_cols].squeeze().isin(left_df[merging_cols].squeeze()).sum()
+    print('ndiff_2:', ndiff_2)
+    
+    ndiff2 = right_df.shape[0] - ndiff_2
+    #print('There are', ndiff2, 'unmatched mutations in right_df')
+
+    comm = np.intersect1d(left_df[merging_cols], right_df[merging_cols])
+    comm_count = len(comm)
+    print('inner:', comm, '\nlength:', comm_count , '\ntype:', type(comm_count))
+                   
+    #========================
+    # sanity checks for join type
+    #========================
+    fail = False 
+    print('combing with:', my_join)
+    combined_df = pd.merge(left_df, right_df, on = merging_cols, how = my_join)
+    combined_df1 = combined_df.drop_duplicates(subset = merging_cols, keep ='first')
+    
+    if my_join == 'inner':
+        #expected_rows = left_df.shape[0] - ndiff1
+        expected_rows = comm_count
+        
+    if my_join == 'outer':
+        #expected_rows = right_df.shape[0] + ndiff1
+        expected_rows = max(left_df.shape[0], right_df.shape[0])    
+    
+    if my_join == 'right':
+        expected_rows = right_df.shape[0]
+        
+    if my_join == 'left':
+        expected_rows = left_df.shape[0]
+    
+    expected_cols = left_df.shape[1] + right_df.shape[1] - nmerging_cols
+   
+    if len(combined_df1) == expected_rows and len(combined_df1.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_df1)
+          , '\nExpected no. of cols:', expected_cols
+          , '\nGot:', len(combined_df1.columns))
+    if fail:
+        sys.exit()
+    
+    return combined_df1
+
+#%% end of function
+#=======================================================================
+