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

@author: tanu
'''
#=======================================================================
# Task: format deep ddg df to allow easy merging

# Input:  2 dfs
#1) <gene>.lower()'_mcsm_formatted_snps.csv'
#2) <gene>.lower()_complex_ddg_results.csv'
#=======================================================================
#%% load packages
import sys, os
import pandas as pd
from pandas import DataFrame
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()
#=======================================================================#%% command line args: case sensitive
arg_parser = argparse.ArgumentParser()
arg_parser.add_argument('-d', '--drug', help = 'drug name', default = '')
arg_parser.add_argument('-g', '--gene', help = 'gene name', default = '') 

arg_parser.add_argument('--datadir', help = 'Data Directory. By default, it assmumes homedir + git/Data')
arg_parser.add_argument('-i', '--input_dir', help = 'Input dir containing pdb files. By default, it assmumes homedir + <drug> + input')
arg_parser.add_argument('-o', '--output_dir', help = 'Output dir for results. By default, it assmes homedir + <drug> + output')

arg_parser.add_argument('--debug', action ='store_true', help = 'Debug Mode')

args = arg_parser.parse_args()
#=======================================================================
#%% variable assignment: input and output 
drug    = args.drug
gene    = args.gene
datadir = args.datadir
indir   = args.input_dir
outdir  = args.output_dir
#%%=======================================================================
#==============
# directories
#==============
if not datadir:
    datadir = homedir + '/git/Data/'
    
if not indir:
    indir = datadir + drug + '/input/'
    
if not outdir:
    outdir = datadir + drug + '/output/'

#=======
# input
#=======
in_filename_mcsm_snps = gene.lower() + '_mcsm_formatted_snps.csv'
infile_mcsm_snps = outdir + in_filename_mcsm_snps

in_filename_deepddg = gene.lower() + '_complex_ddg_results.csv' 
infile_deepddg = outdir + 'deep_ddg/' + in_filename_deepddg

print('\nInput path:', indir
      , '\nOutput path:', outdir, '\n'
      , '\nInput filename mcsm snps', infile_mcsm_snps , '\n'
      , '\nInput filename deepddg', infile_deepddg , '\n'
      , '\n============================================================')

#=======
# output 
#=======
#out_filename_deepddg = gene.lower() + '_complex_ni_deepddg.txt'
out_filename_deepddg = gene.lower() + '_complex_ni_deepddg.csv'
outfile_deepddg_f =  outdir + out_filename_deepddg

print('Output filename:', outfile_deepddg_f
        , '\n===================================================================')
# end of variable assignment for input and output files
#%%============================================================================  
print('==================================='
      , '\nmcsm muts'
      , '\n===================================')

mcsm_muts_df =  pd.read_csv(infile_mcsm_snps , header = None, sep = ',', names = ['mutationinformation'])
mcsm_muts_df.columns

#%%============================================================================  
print('==================================='
      , '\nDeep ddg'
      , '\n===================================')

deepddg_df =  pd.read_csv(infile_deepddg, sep = ',')
deepddg_df.columns

deepddg_df.rename(columns = {'#chain'  : 'chain_id'
                             , 'WT'    : 'wild_type_deepddg'
                             , 'ResID' : 'position'
                             , 'Mut'   : 'mutant_type_deepddg'}
                             , inplace = True)
deepddg_df.columns
deepddg_df['mutationinformation'] = deepddg_df['wild_type_deepddg'] + deepddg_df['position'].map(str) + deepddg_df['mutant_type_deepddg']
deepddg_df.columns

# add deepddg outcome column: <0--> Destabilising, >0 --> Stabilising
deepddg_df['deepddg_outcome'] = np.where(deepddg_df['deepddg'] < 0, 'Destabilising', 'Stabilising')
deepddg_df['deepddg_outcome'].value_counts()

# should be identical in count ot Destabilising and stabilising respectively
len(deepddg_df.loc[deepddg_df['deepddg'] < 0])
len(deepddg_df.loc[deepddg_df['deepddg'] >= 0])
               
# drop extra columns to allow clean merging
deepddg_short_df = deepddg_df.drop(['chain_id', 'wild_type_deepddg', 'position', 'mutant_type_deepddg'], axis = 1)

# rearrange columns
deepddg_short_df.columns
deepddg_short_df = deepddg_short_df[["mutationinformation", "deepddg", "deepddg_outcome"]]

#%% combine with mcsm snps 
deepddg_mcsm_muts_dfs = pd.merge(deepddg_short_df
                                 , mcsm_muts_df
                                 , on = 'mutationinformation'
                                 , how = 'right')
deepddg_mcsm_muts_dfs ['deepddg_outcome'].value_counts()

#%%============================================================================
# write csv
print('Writing file: formatted deepddg and only mcsm muts')
deepddg_mcsm_muts_dfs.to_csv(outfile_deepddg_f, index = False)
print('\nFinished writing file:'
      , '\nNo. of rows:', deepddg_mcsm_muts_dfs.shape[0]
      , '\nNo. of cols:', deepddg_mcsm_muts_dfs.shape[1])
#%% end of script