#!/usr/bin/env python3
# -*- coding: utf-8 -*-
'''
Created on Tue Aug  6 12:56:03 2019

@author: tanu
'''
# FIXME: change filename 4 (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
# This is done in 2 steps:
# merge 1: 

# 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
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 import
#from reference_dict import my_aa_dict # CHECK DIR STRUC THERE!
from reference_dict import low_3letter_dict
#=======================================================================
#%% 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.'

# cmd variables
#drug = args.drug
#gene = args.gene
#gene_match = gene + '_p.'

#==========
# dir
#==========
datadir = homedir + '/' + 'git/Data'
indir = datadir + '/' + drug + '/' + 'input'
outdir = datadir + '/' + drug + '/' + 'output'

#=======
# input
#=======
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'

infile0 = indir + '/' + in_filename_snpinfo 
infile1 = outdir + '/' + in_filename_afor
infile2 = outdir + '/' + in_filename_afor_kin


print('Input file0:', infile0
      , '\nInput file1:', infile1
      , '\nInput file2:', infile2
      , '\n===================================================================')

#=======
# output 
#=======
out_filename = gene.lower() + '_metadata_afs_ors.csv'
outfile =  outdir + '/' + out_filename
print('Output file:', outfile
      , '\n===================================================================')


del(in_filename_afor, in_filename_afor_kin, datadir, indir, outdir)
#%% end of variable assignment for input and output files
#=======================================================================
#%% format mutations
# mut_format: gene.abc1cde | 1A>1B

#========================
# read input csv files to combine
#========================
snpinfo_df = pd.read_csv(infile0, sep = ',')
snpinfo_ncols = len(snpinfo_df.columns) 
snpinfo_nrows = len(snpinfo_df) 
print('No. of rows in', infile0, ':', snpinfo_nrows
      , '\nNo. of cols in', infile0, ':', snpinfo_ncols)

afor_df = pd.read_csv(infile1, sep = ',')
afor_ncols = len(afor_df.columns) 
afor_nrows = len(afor_df) 
print('No. of rows in', infile1, ':', afor_nrows
      , '\nNo. of cols in', infile1, ':', afor_ncols)

afor_kin_df = pd.read_csv(infile2, sep = ',')
afor_kin_nrows = len(afor_kin_df)
afor_kin_ncols = len(afor_kin_df.columns)
print('No. of rows in', infile2, ':', afor_kin_nrows
      , '\nNo. of cols in', infile2, ':', afor_kin_ncols)

#%% Process afor_df
#1) pull all snp_info so you have ref_allele, etc
# i.e merge afor_df and snpinfo_df
# find merging column

left_df = afor_df.copy()
left_df_nrows = len(left_df)
left_df_ncols = len(left_df.columns)

right_df =  snpinfo_df.copy()
right_df_nrows = len(right_df)
right_df_ncols = len(right_df.columns)

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)')
#https://stackoverflow.com/questions/44639772/python-pandas-column-dtype-object-causing-merge-to-fail-with-dtypewarning-colu
merging_cols = left_df[common_cols].select_dtypes(include = [object]).columns.tolist()
print(merging_cols)
nmerging_cols = len(merging_cols)
print(' length of merging cols:', nmerging_cols
      , '\nmerging cols:', merging_cols, 'type:', type(merging_cols))

# drop duplicates else the expected rows don't match
print('Checking for duplicates in common col:', common_cols
      , '\nNo of duplicates:'
      , len(right_df[right_df.duplicated(common_cols)])
      , '\noriginal length:', right_df_nrows)

right_df = right_df[~right_df.duplicated(common_cols)]
right_df_nrows = len(right_df)
print('\nrevised length:', right_df_nrows)

# checking cross-over of mutations in the two dfs to merge
ndiff1 = afor_nrows - afor_df['mutation'].isin(snpinfo_df['mutation']).sum()
print('There are', ndiff1, 'mutations with OR, but no snp_info'
      , '\nExtracting and writing out file')

#afor_df[afor_df['mutation'].isin(snpinfo_df['mutation'])]
missing_mutinfo = afor_df[~afor_df['mutation'].isin(snpinfo_df['mutation'])]
#len(missing_mutinfo.duplicated(common_cols))

#missing_mutinfo.to_csv('infoless_muts.csv')

ndiff2 = snpinfo_nrows - snpinfo_df['mutation'].isin(afor_df['mutation']).sum()
print('There are', ndiff2, 'mutations that do not have OR, but have snp_info')

# Define join type
#my_join = 'inner'
#my_join = 'outer'
#my_join = 'right'
my_join = 'left'

print('combing with join:', my_join)
combined_df1 = pd.merge(left_df, right_df, on = merging_cols, how = my_join)
print('nrows:', len(combined_df1)
      , '\nshape:', combined_df1.shape)

# inner = 252
left_df_nrows - ndiff1

# outer = 331
right_df_nrows + ndiff1

# right = 290
right_df_nrows 

# left = 293
left_df_nrows


#%%
# see if you want an extra clause here!
# Define join type
#my_join = 'inner'
#my_join = 'outer'
#my_join = 'right'
my_join = 'left'
 
fail = False 
print('combing with:', my_join)
combined_df1 = pd.merge(left_df, right_df, on = merging_cols, how = my_join)
     
if my_join == 'inner':
    #expected_rows = left_df_nrows - ndiff1
    expected_rows = left_df.shape[0] - ndiff1
    
if my_join == 'outer':
    #expected_rows = right_df_nrows + ndiff1
    expected_rows = right_df.shape[0] + ndiff1     
    
if my_join == 'right':
    #expected_rows = right_df_nrows 
    expected_rows = right_df.shape[0]
    
if my_join == 'left':
    #expected_rows = left_df_nrows
    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()
    
# update nrows and ncols
afor_info_nrows = len(afor_info_df)
afor_info_ncols = len(afor_info_df.columns)
#%%