#!/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
#=======================================================================