#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed May 25 02:35:41 2022

@author: tanu
"""
import sys, os
import pandas as pd
from pandas import DataFrame
import numpy as np
import argparse
from functools import reduce
from sklearn.preprocessing import MinMaxScaler
#%%
mmcsm_lig_raw = pd.read_csv('/home/tanu/git/Data/pyrazinamide/output/mmcsm_lig/single_muts/pnca_mmcsm_results.csv'
                        , index_col = False)
mmcsm_lig_raw = pd.read_csv('/home/tanu/git/Data/cycloserine/output/mmcsm_lig/single_muts/alr_mmcsm_results.csv'
                        , index_col = False)
mmcsm_lig_raw = pd.read_csv('/home/tanu/git/Data/ethambutol/output/mmcsm_lig/single_muts/embb_mmcsm_results.csv'
                        , index_col = False)
mmcsm_lig_raw = pd.read_csv('/home/tanu/git/Data/streptomycin/output/mmcsm_lig/single_muts/gid_mmcsm_results.csv'
                        , index_col = False)
mmcsm_lig_raw = pd.read_csv('/home/tanu/git/Data/isoniazid/output/mmcsm_lig/single_muts/katg_mmcsm_results.csv'
                        , index_col = False)
mmcsm_lig_raw = pd.read_csv('/home/tanu/git/Data/rifampicin/output/mmcsm_lig/single_muts/rpob_mmcsm_results.csv'
                        , index_col = False)
mmcsm_lig_raw.columns
# drop 1st column
#mmcsm_lig = mmcsm_lig_raw.iloc[:,1:]

# extract specific columns: might be simplers
mmcsm_lig_df = mmcsm_lig_raw[['MUTATION', 'CHAIN', 'DDG']]
mmcsm_lig_df['CHAIN'].value_counts()

# Drop the chain column
mmcsm_lig_df.drop(['CHAIN'], axis = 1, inplace = True)

# Rename columns using lower case and consistently to allow merge later on
mmcsm_lig_df.rename({'MUTATION': 'mutationinformation'
                  , 'DDG': 'mmcsm_lig'}, axis = 1, inplace = True)

#----------------------------------------
# Rescale values in mmcsm_lig_affinity
# col b/w -1 and 1 so negative numbers
# stay neg and pos numbers stay positive
#-----------------------------------------
mmcsm_lig_min = mmcsm_lig_df['mmcsm_lig'].min()
mmcsm_lig_max = mmcsm_lig_df['mmcsm_lig'].max() 
print('\nmmcsm_lig (MIN):', mmcsm_lig_min
      , '\nmmcsm_lig (MAX):', mmcsm_lig_max)

# quick check
print('\nNo. of Stabilising mmCSM mutations:', len(mmcsm_lig_df.loc[mmcsm_lig_df['mmcsm_lig'] >= 0]))
print('\nNo. of Destabilising mmCSM mutations:', len(mmcsm_lig_df.loc[mmcsm_lig_df['mmcsm_lig'] < 0]))

mmcsm_ligscale = lambda x : x/abs(mmcsm_lig_min) if x < 0 else (x/mmcsm_lig_max if x >= 0 else 'failed')

mmcsm_lig_df['mmcsm_lig_scaled'] = mmcsm_lig_df.loc[:,'mmcsm_lig'].apply(mmcsm_ligscale)
print('\nRaw mmcsm_lig scores:\n', mmcsm_lig_df['mmcsm_lig']
    , '\n---------------------------------------------------------------'
    , '\nScaled mmcsm_lig scores:\n', mmcsm_lig_df['mmcsm_lig_scaled'])

print('\nmmCSM lig raw (Max):', mmcsm_lig_df['mmcsm_lig'].max()
      , '\nmmCSM lig scaled (Max):', mmcsm_lig_df['mmcsm_lig_scaled'].max())

print('\nmmCSM lig raw (Min):', mmcsm_lig_df['mmcsm_lig'].min()
      , '\nmmCSM lig scaled (Min):', mmcsm_lig_df['mmcsm_lig_scaled'].min())

mmcsm_lig_df['mmcsm_lig_scaled'].hist(bins = 30)
mmcsm_lig_df['mmcsm_lig'].hist(bins = 30)

#-----------------------------
# mmCSM lig outcome category:
# -ve: Destabilising
# +ve: Stabilising
#----------------------------
mmcsm_lig_df['mmcsm_lig_outcome'] = mmcsm_lig_df.loc[:,'mmcsm_lig'].apply(lambda x: 'Destabilising' if x < 0 else 'Stabilising')
mmcsm_lig_df[mmcsm_lig_df['mmcsm_lig']<0].count()

###############################################################################
#%% Provean data
provean_df = pd.read_csv('/home/tanu/git/Data/pyrazinamide/output/provean/pnca_provean.csv'
                        , header = None)
provean_df = pd.read_csv('/home/tanu/git/Data/cycloserine/output/provean/alr_provean.csv'
                        , header = None)
provean_df = pd.read_csv('/home/tanu/git/Data/ethambutol/output/provean/embb_provean.csv'
                        , header = None)
provean_df = pd.read_csv('/home/tanu/git/Data/streptomycin/output/provean/gid_provean.csv'
                        , header = None)
provean_df = pd.read_csv('/home/tanu/git/Data/isoniazid/output/provean/katg_provean.csv'
                        , header = None)
provean_df = pd.read_csv('/home/tanu/git/Data/rifampicin/output/provean/rpob_provean.csv'
                        , header = None)

provean_df.head()
provean_df.columns = ['mutationinformation', 'provean_score', 'provean_outcome']
provean_df.head()
provean_df['provean_outcome'].value_counts()

#----------------------------------------
# Rescale values in provean_score
# col b/w -1 and 1 so negative numbers
# stay neg and pos numbers stay positive
# cut off =-2.5
# so provean scores >= (-2.5) are neutral
# and provean scores < (2.5) are deleterious
#-----------------------------------------
provean_min = provean_df['provean_score'].min()
provean_max = provean_df['provean_score'].max() 
print('\nprovean_score (MIN):', provean_min
      , '\nprovean_score (MAX):', provean_max)

# quick check
provean_cut_off = -2.5

if (provean_df['provean_score'] > provean_cut_off).sum() == provean_df['provean_outcome'].value_counts()['Neutral']:
    print('\nPASS: Provean cut off is indeed:', provean_cut_off
          , '\nNo. of values above', provean_cut_off, 'i.e classed as Neutral:'
          , (provean_df['provean_score']>provean_cut_off).sum()
          , '\nProvean outcome:'
          , '\nNeutral:', len(provean_df.loc[provean_df['provean_score'] > provean_cut_off])
          , '\nDeleterious:', len(provean_df.loc[provean_df['provean_score'] < provean_cut_off])
          ) 
else:
    sys.exit('\nFAIL: Numbers mismatch. Please check provean cut off and condition used')

    
# RECHECK logic!: CANNOT use this as it changes the data distribution, as seen from the his plot
# provean_scale = lambda x : x/abs(provean_min) if x < 0 else (x/provean_min if x >= 0 else 'failed')
# provean_df['provean_scaled1'] = provean_df.loc[:,'provean_score'].apply(provean_scale)
# print('\nRaw provean scores:\n', provean_df['provean_score']
#     , '\n---------------------------------------------------------------'
#     , '\nScaled provean scores:\n', provean_df['provean_scaled'])


# print('\nprovean raw (Max):'      , provean_df['provean_score'].max()
#       , '\nprovean scaled (Max):' , provean_df['provean_scaled1'].max())

# print('\nprovean raw (Min):'      , provean_df['provean_score'].min()
#       , '\nprovean scaled (Min):' , provean_df['provean_scaled1'].min())


scaler = MinMaxScaler()
provean_df['provean_scaled'] = scaler.fit_transform(provean_df['provean_score'].values.reshape(-1,1))

provean_df['provean_score'].hist(bins = 30)
#provean_df['provean_scaled1'].hist(bins = 10)
provean_df['provean_scaled'].hist(bins = 30)