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

@author: tanu
"""

# FIXME: include error checking to enure you only
# concentrate on positions that have structural info?

# FIXME: import dirs.py to get the basic dir paths available
#=======================================================================
# TASK: extract ALL pncA_p. mutations from GWAS data
# Input data file has the following format: each row = unique sample id 
# id,country,lineage,sublineage,drtype,pyrazinamide,dr_mutations_pyrazinamide,other_mutations_pyrazinamide...
# 0,sampleID,USA,lineage2,lineage2.2.1,Drug-resistant,0.0,WT,pncA_p.<wt>POS<mut>; pncA_c.<wt>POS<mut>...
# where multiple mutations and multiple mutation types are separated by ';'. We are interested in the 
# protein coding region i.e mutation with the 'p.' format.

# the script splits the mutations on the ';' and extracts protein coding muts only
# where each row is a separate mutation
# sample ids AND mutations are NOT unique, but the COMBINATION (sample id + mutation) = unique

# output files:
# 0) pnca_common_ids.csv
# 1) pnca_ambiguous_muts.csv
# 2) pnca_mcsm_snps.csv
# 3) pnca_metadata.csv
# 4) pnca_all_muts_msa.csv
# 5) pnca_mutational_positons.csv
#=======================================================================
#%% load libraries
import os, sys
import pandas as pd
#import numpy as np

#from pandas.api.types import is_string_dtype
#from pandas.api.types import is_numeric_dtype

#%% specify homedir as python doesn't recognise tilde
homedir = os.path.expanduser('~') 

# set working dir
os.getcwd()
os.chdir(homedir + '/git/LSHTM_analysis/meta_data_analysis')
os.getcwd()

# import aa dict
from reference_dict import my_aa_dict #CHECK DIR STRUC THERE!
#=======================================================================
#%% variable assignment: input and output paths & filenames
drug = 'pyrazinamide'
gene = 'pncA'
gene_match = gene + '_p.'

#=======
# data dir
#=======
#indir = 'git/Data/pyrazinamide/input/original'
datadir = homedir + '/' + 'git/Data'

#=======
# input
#=======
#indir = 'git/Data/pyrazinamide/input/original'
in_filename  = 'original_tanushree_data_v2.csv'
infile = datadir + '/' + in_filename
print('Input filename: ', in_filename
      , '\nInput path: ', indir)

#=======
# output
#=======
# several output files
# output filenames in respective sections at the time of outputting files
outdir = datadir + '/' + drug + '/' + 'output'
print('Output filename: in the respective sections'
      , '\nOutput path: ', outdir)

#%%end of variable assignment for input and output files
#=======================================================================
#%% Read input file
master_data  = pd.read_csv(infile, sep = ',')  

# column names
#list(master_data.columns)

# extract elevant columns to extract from meta data related to the pyrazinamide
meta_data = master_data[['id'
       ,'country'
       ,'lineage'
       ,'sublineage'
       ,'drtype'
       , 'pyrazinamide'
       , 'dr_mutations_pyrazinamide'
       , 'other_mutations_pyrazinamide'
        ]] 

del(master_data)

# checks and results
total_samples = meta_data['id'].nunique() 
print('RESULT: Total samples:', total_samples)
print('======================================================================')

# counts NA per column
meta_data.isna().sum()
print('No. of NAs/column:' + '\n', meta_data.isna().sum())
print('======================================================================')
# glance
meta_data.head()

# equivalent of table in R
# pyrazinamide counts
meta_data.pyrazinamide.value_counts() 
print('RESULT: Sus and Res samples:\n', meta_data.pyrazinamide.value_counts()) 
print('======================================================================')

# clear variables
del(indir, in_filename,infile)
#del(outdir)
#%%
# !!!IMPORTANT!!! sanity check:
# This is to find out how many samples have 1 and more than 1 mutation,so you
# can use it to check if your data extraction process for dr_muts 
# and other_muts has worked correctly AND also to check the dim of the 
# final formatted data.
# This will have: unique COMBINATION of sample id and pncA_p.mutations

#========
# First: counting pncA_p. mutations in dr_mutations_pyrazinamide column
#========
print('Now counting WT & pncA_p. muts within the column: dr_mutations_pyrazinamide')

# drop na and extract a clean df
clean_df =  meta_data.dropna(subset=['dr_mutations_pyrazinamide'])

# sanity check: count na
na_count = meta_data['dr_mutations_pyrazinamide'].isna().sum()

if len(clean_df) == (total_samples - na_count):
   print('PASS: clean_df extracted: length is', len(clean_df),
         '\nNo.of NA s=', na_count, '/', total_samples)
else:
    print('FAIL: dropping NA failed')
print('======================================================================')

dr_pnca_count = 0
wt = 0
id_dr = []
id2_dr = []

for i, id in enumerate(clean_df.id):
#    print (i, id)
#    id_dr.append(id)
#    count_pnca_dr = clean_df.dr_mutations_pyrazinamide.iloc[i].count('pncA_p.') #works 2201
    count_pnca_dr = clean_df.dr_mutations_pyrazinamide.iloc[i].count(gene_match) #works 2201
    if count_pnca_dr > 0:
        id_dr.append(id)
    if count_pnca_dr > 1:
        id2_dr.append(id)
#        print(id, count_pnca_dr)    
    dr_pnca_count = dr_pnca_count + count_pnca_dr
    count_wt = clean_df.dr_mutations_pyrazinamide.iloc[i].count('WT')
    wt = wt + count_wt
    
print('RESULTS:')        
print('Total WT in dr_mutations_pyrazinamide:', wt)
print('Total matches of', gene_match, 'in dr_mutations_pyrazinamide:', dr_pnca_count)
print('Total samples with > 1', gene_match, 'muts in dr_mutations_pyrazinamide:', len(id2_dr) )
print('======================================================================')

del(i, id, wt, id2_dr, clean_df, na_count, count_pnca_dr, count_wt)

#========
# Second: counting pncA_p. mutations in dr_mutations_pyrazinamide column
#========
print('Now counting WT & pncA_p. muts within the column: other_mutations_pyrazinamide')
      
# drop na and extract a clean df
clean_df =  meta_data.dropna(subset=['other_mutations_pyrazinamide'])

# sanity check: count na
na_count = meta_data['other_mutations_pyrazinamide'].isna().sum()

if len(clean_df) == (total_samples - na_count):
   print('PASS: clean_df extracted: length is', len(clean_df),
         '\nNo.of NA s=', na_count, '/', total_samples)
else:
    print('FAIL: dropping NA failed')
print('======================================================================')

other_pnca_count = 0
wt_other = 0
id_other = []
id2_other = []

for i, id in enumerate(clean_df.id):
#    print (i, id)
#    id_other.append(id)
#    count_pnca_other = clean_df.other_mutations_pyrazinamide.iloc[i].count('pncA_p.')
    count_pnca_other = clean_df.other_mutations_pyrazinamide.iloc[i].count(gene_match)
    if count_pnca_other > 0:
        id_other.append(id)            
    if count_pnca_other > 1:
        id2_other.append(id)
#        print(id, count_pnca_other)    
    other_pnca_count = other_pnca_count + count_pnca_other    
    count_wt = clean_df.other_mutations_pyrazinamide.iloc[i].count('WT')
    wt_other = wt_other + count_wt   
print('RESULTS:')
print('Total WT in other_mutations_pyrazinamide:', wt_other)
print('Total matches of', gene_match, 'in other_mutations_pyrazinamide:', other_pnca_count)
print('Total samples with > 1', gene_match, 'muts in other_mutations_pyrazinamide:', len(id2_other) )
print('======================================================================')

print('Predicting total no. of rows in your curated df:', dr_pnca_count + other_pnca_count )
expected_rows = dr_pnca_count + other_pnca_count

del(i, id, wt_other, clean_df, na_count, id2_other, count_pnca_other, count_wt)

#%%
############
# extracting dr and other muts separately along with the common cols
#############
print('======================================================================')
print('Extracting dr_muts in a dr_mutations_pyrazinamide with other meta_data')
print('gene to extract:', gene_match )

#===============
# dr mutations: extract pncA_p. entries with meta data and ONLY dr_muts col
#===============
# FIXME: replace pyrazinamide with variable containing the drug name
# !!! important !!!
meta_data_dr = meta_data[['id'
       ,'country'
       ,'lineage'
       ,'sublineage'
       ,'drtype'
       , 'pyrazinamide'
       , 'dr_mutations_pyrazinamide'
        ]] 
print("expected dim should be:", len(meta_data), (len(meta_data.columns)-1) )
print("actual dim:", meta_data_dr.shape )
print('======================================================================')

# Extract within this the gene of interest using string match
#meta_pnca_dr = meta_data.loc[meta_data.dr_mutations_pyrazinamide.str.contains('pncA_p.*', na = False)] 
meta_pnca_dr = meta_data_dr.loc[meta_data_dr.dr_mutations_pyrazinamide.str.contains(gene_match, na = False)] 

dr_id = meta_pnca_dr['id'].unique()
print('RESULT: No. of samples with dr muts in pncA:', len(dr_id))
print('checking RESULT:',
      '\nexpected len =', len(id_dr),
      '\nactual len =', len(meta_pnca_dr) )

if len(id_dr) == len(meta_pnca_dr):
    print('PASS: lengths match')
else:
    print('FAIL: length mismatch')
print('======================================================================')

dr_id = pd.Series(dr_id)

#=================
# other mutations: extract pncA_p. entries
#==================
print('======================================================================')
print('Extracting dr_muts in a other_mutations_pyrazinamide with other meta_data')
# FIXME: replace pyrazinamide with variable containing the drug name
# !!! important !!!
meta_data_other = meta_data[['id'
       ,'country'
       ,'lineage'
       ,'sublineage'
       ,'drtype'
       , 'pyrazinamide'
       , 'other_mutations_pyrazinamide'
        ]] 

print("expected dim should be:", len(meta_data), (len(meta_data.columns)-1) )
print("actual dim:", meta_data_other.shape )
print('======================================================================')

# Extract within this the gene of interest using string match
meta_pnca_other  = meta_data_other.loc[meta_data_other.other_mutations_pyrazinamide.str.contains(gene_match, na = False)] 

other_id = meta_pnca_other['id'].unique()
print('RESULT: No. of samples with other muts:', len(other_id))
print('checking RESULT:',
      '\nexpected len =', len(id_other),
      '\nactual len =', len(meta_pnca_other) )

if len(id_other) == len(meta_pnca_other):
    print('PASS: lengths match')
else:
    print('FAIL: length mismatch')
print('======================================================================')

other_id = pd.Series(other_id)
#%% Find common IDs
print('Now extracting common_ids...')
common_mut_ids = dr_id.isin(other_id).sum() 
print('RESULT: No. of common Ids:', common_mut_ids)

# sanity checks
# check if True: should be since these are common
dr_id.isin(other_id).sum() == other_id.isin(dr_id).sum()

# check if the 24 Ids that are common are indeed the same!
# bit of a tautology, but better safe than sorry!
common_ids = dr_id[dr_id.isin(other_id)]
common_ids = common_ids.reset_index()
common_ids.columns = ['index', 'id']

common_ids2 = other_id[other_id.isin(dr_id)]
common_ids2 = common_ids2.reset_index()
common_ids2.columns = ['index', 'id2']

# should be True
print(common_ids['id'].equals(common_ids2['id2']))

# good sanity check: use it later to check pnca_sample_counts
expected_pnca_samples = ( len(meta_pnca_dr) + len(meta_pnca_other) - common_mut_ids ) 
print("expected no. of pnca samples:", expected_pnca_samples)
print('======================================================================')
#%% write file
#print(outdir)
out_filename0 = gene.lower() + '_' + 'common_ids.csv'
outfile0 =  outdir + '/' + out_filename0

#FIXME: CHECK line len(common_ids)
print('Writing file: common ids:',
      '\nFilename:', out_filename0,
      '\nPath:', outdir,
      '\nExpected no. of rows:', len(common_ids) )

common_ids.to_csv(outfile0)
print('======================================================================')
del(out_filename0)


# clear variables
del(dr_id, other_id, meta_data_dr, meta_data_other, common_ids, common_mut_ids, common_ids2)

#%% Now extract "all" pncA mutations: i.e 'pncA_p.*'
print("extracting all pncA mutations from dr_ and other_ cols using string match:", gene_match)
#meta_pnca_all = meta_data.loc[meta_data.dr_mutations_pyrazinamide.str.contains(gene_match) | meta_data.other_mutations_pyrazinamide.str.contains(gene_match) ]
meta_pnca_all = meta_data.loc[meta_data.dr_mutations_pyrazinamide.str.contains(gene_match, na = False) | meta_data.other_mutations_pyrazinamide.str.contains(gene_match, na = False) ]
print('======================================================================')

extracted_pnca_samples = meta_pnca_all['id'].nunique()
print("RESULT: actual no. of pnca samples extracted:", extracted_pnca_samples)
print('======================================================================')

# sanity check: length of pnca samples
print('Performing sanity check:')
if extracted_pnca_samples == expected_pnca_samples:
    print('No. of pnca samples:', len(meta_pnca_all),
          '\nPASS: expected & actual no. of pnca samples match')
else:
    print("FAIL: Debug please!")
print('======================================================================')

# count NA  in pyrazinamide column
pnca_na = meta_pnca_all['pyrazinamide'].isna().sum() 
print("No. of pnca samples without pza testing i.e NA in pza column:",pnca_na)

# use it later to check number of complete samples from LF data
comp_pnca_samples = len(meta_pnca_all) - pnca_na
print("comp pnca samples tested for pza:", comp_pnca_samples)
print('======================================================================')

# Comment: This is still dirty data since these
# are samples that have pncA_p. muts, but can have others as well
# since the format for mutations is mut1; mut2, etc.
print('This is still dirty data: samples have pncA_p. muts, but may have others as well',
      '\nsince the format for mutations is mut1; mut2, etc.')
print('======================================================================')

#%% tidy_split():Function to split mutations on specified delimiter: ';'
#https://stackoverflow.com/questions/41476150/removing-space-from-dataframe-columns-in-pandas

print('Performing tidy_spllit(): to separate the mutations into indivdual rows')
# define the split function
def tidy_split(df, column, sep='|', keep=False):
    """
    Split the values of a column and expand so the new DataFrame has one split
    value per row. Filters rows where the column is missing.

    Params
    ------
    df : pandas.DataFrame
        dataframe with the column to split and expand
    column : str
        the column to split and expand
    sep : str
        the string used to split the column's values
    keep : bool
        whether to retain the presplit value as it's own row

    Returns
    -------
    pandas.DataFrame
        Returns a dataframe with the same columns as `df`.
    """
    indexes = list()
    new_values = list()
    #df = df.dropna(subset=[column])#!!!!-----see this incase you need to uncomment based on use case
    for i, presplit in enumerate(df[column].astype(str)):
        values = presplit.split(sep)
        if keep and len(values) > 1:
            indexes.append(i)
            new_values.append(presplit)
        for value in values:
            indexes.append(i)
            new_values.append(value)
    new_df = df.iloc[indexes, :].copy()
    new_df[column] = new_values
    return new_df
    
#%% end of tidy_split()
#=========
# DF1: dr_mutations_pyrazinamide
#=========
########
# tidy_split(): on 'dr_mutations_pyrazinamide' column and remove leading white spaces
########    
col_to_split1 = 'dr_mutations_pyrazinamide'
print ('Firstly, applying tidy split on dr df:', meta_pnca_dr.shape,
       '\ncolumn name:', col_to_split1)
print('======================================================================')
# apply tidy_split()
dr_WF0 = tidy_split(meta_pnca_dr, col_to_split1, sep = ';') 
# remove leading white space else these are counted as distinct mutations as well
dr_WF0['dr_mutations_pyrazinamide'] = dr_WF0['dr_mutations_pyrazinamide'].str.lstrip() 

# extract only the samples/rows with pncA_p.
dr_pnca_WF0 = dr_WF0.loc[dr_WF0.dr_mutations_pyrazinamide.str.contains(gene_match)]

print('lengths after tidy split and extracting', gene_match, 'muts:'
      , '\nold length:' , len(meta_pnca_dr)
      , '\nlen after split:', len(dr_WF0)
      , '\ndr_pnca_WF0 length:', len(dr_pnca_WF0)
      , '\nexpected len:', dr_pnca_count)

if len(dr_pnca_WF0) == dr_pnca_count:
    print('PASS: length of dr_pnca_WF0 match with expected length')
else:
    print('FAIL: lengths mismatch')

print('======================================================================')

# count the freq of "dr_muts" samples
dr_muts_df = dr_pnca_WF0 [['id', 'dr_mutations_pyrazinamide']] 
print("dim of dr_muts_df:", dr_muts_df.shape)

# add freq column
dr_muts_df['dr_sample_freq'] = dr_muts_df.groupby('id')['id'].transform('count') 
#dr_muts_df['dr_sample_freq'] = dr_muts_df.loc[dr_muts_df.groupby('id')].transform('count')
print("revised dim of dr_muts_df:", dr_muts_df.shape) 

c1 = dr_muts_df.dr_sample_freq.value_counts()
print('counting no. of sample frequency:\n', c1)
print('======================================================================')

# sanity check: length of pnca samples
if len(dr_pnca_WF0) == c1.sum():
    print('PASS: WF data has expected length',
    '\nlength of dr_pnca WFO:', c1.sum() )
else:
    print("FAIL: Debug please!")

print('======================================================================')

#!!! Important !!!
# Assign "column name" on which split was performed as an extra column
# This is so you can identify if mutations are dr_type or other in the final df
dr_df = dr_pnca_WF0.assign(mutation_info = 'dr_mutations_pyrazinamide')
print("dim of dr_df:", dr_df.shape)
print('======================================================================')
print('End of tidy split() on dr_muts, and added an extra column relecting mut_category')
print('======================================================================')

#%%
#=========
# DF2: other_mutations_pyrazinamdie
#=========
########
# tidy_split(): on 'other_mutations_pyrazinamide' column and remove leading white spaces
######## 
col_to_split2 = 'other_mutations_pyrazinamide'
print ("applying second tidy split separately on df:", meta_pnca_other.shape, '\n'
       , "column name:", col_to_split2)
print('======================================================================')

# apply tidy_split()
other_WF1 = tidy_split(meta_pnca_other, col_to_split2, sep = ';') 
# remove the leading white spaces in the column
other_WF1['other_mutations_pyrazinamide'] = other_WF1['other_mutations_pyrazinamide'].str.strip() 

# extract only the samples/rows with pncA_p.
other_pnca_WF1 = other_WF1.loc[other_WF1.other_mutations_pyrazinamide.str.contains(gene_match)]

print('lengths after tidy split and extracting', gene_match, 'muts:',
      '\nold length:' , len(meta_pnca_other),
      '\nlen after split:', len(other_WF1),
      '\nother_pnca_WF1 length:', len(other_pnca_WF1),
      '\nexpected len:', other_pnca_count)

if len(other_pnca_WF1) == other_pnca_count:
    print('PASS: length of dr_pnca_WF0 match with expected length')
else:
    print('FAIL: lengths mismatch')

print('======================================================================')    
# count the freq of "other muts" samples
other_muts_df = other_pnca_WF1 [['id', 'other_mutations_pyrazinamide']] 
print("dim of other_muts_df:", other_muts_df.shape) 

# add freq column
other_muts_df['other_sample_freq'] = other_muts_df.groupby('id')['id'].transform('count')
print("revised dim of other_muts_df:", other_muts_df.shape) 

c2 = other_muts_df.other_sample_freq.value_counts()
print('counting no. of sample frequency:\n', c2)
print('======================================================================')
# sanity check: length of pnca samples
if len(other_pnca_WF1) == c2.sum():
    print('PASS: WF data has expected length',
    '\nlength of other_pnca WFO:', c2.sum() )
else:
    print("FAIL: Debug please!")
print('======================================================================')

#!!! Important !!!
# Assign "column name" on which split was performed as an extra column
# This is so you can identify if mutations are dr_type or other in the final df
other_df = other_pnca_WF1.assign(mutation_info = 'other_mutations_pyrazinamide')
print("dim of other_df:", other_df.shape)
print('======================================================================')
print('End of tidy split() on other_muts, and added an extra column relecting mut_category')
print('======================================================================')
#%%
#==========
# Concatentating the two dfs: equivalent of rbind in R
#==========
#!!! important !!!
# change column names to allow concat:
# dr_muts.. & other_muts : "mutation"
print('Now concatenating the two dfs by row')

dr_df.columns
dr_df.rename(columns = {'dr_mutations_pyrazinamide': 'mutation'}, inplace = True)
dr_df.columns

other_df.columns
other_df.rename(columns = {'other_mutations_pyrazinamide': 'mutation'}, inplace = True)
other_df.columns

print('======================================================================')
print('Now appending the two dfs:',
      '\ndr_df dim:', dr_df.shape,
      '\nother_df dim:', other_df.shape,
      '\ndr_df length:', len(dr_df),
      '\nother_df length:', len(other_df),
      '\nexpected length:', len(dr_df) +  len(other_df) )
print('======================================================================')

# checking colnames before concat
print("checking colnames BEFORE concatenating the two dfs...")
if (set(dr_df.columns) == set(other_df.columns)):
    print('PASS: column names match necessary for merging two dfs')
else:
    print("FAIL: Debug please!")

# concatenate (axis = 0): Rbind
pnca_LF0 = pd.concat([dr_df, other_df], ignore_index = True, axis = 0)

# checking colnames and length after concat
print("checking colnames AFTER concatenating the two dfs...")
if (set(dr_df.columns) == set(pnca_LF0.columns)):
    print('PASS: column names match')
else:
    print("FAIL: Debug please!")
    
print("checking length AFTER concatenating the two dfs...")

if len(pnca_LF0) == len(dr_df) +  len(other_df):
    print("PASS:length of df after concat match")
else:
    print("FAIL: length mismatch")
print('======================================================================')
#%%
###########
# This is hopefully clean data, but just double check
# Filter LF data so that you only have
# mutations corresponding to pncA_p.* (string match pattern) 
# this will be your list you run OR calcs 
###########
print('length of pnca_LF0:', len(pnca_LF0),
      '\nThis should be what you need. But just double check and extract', gene_match, 
      '\nfrom LF0 (concatenated data)')

print('using string match:', gene_match)

print('Double checking and creating df: pnca_LF1')
pnca_LF1 = pnca_LF0[pnca_LF0['mutation'].str.contains(gene_match)]   

if len(pnca_LF0) == len(pnca_LF1):
    print('PASS: length of pnca_LF0 and pnca_LF1 match',
          '\nconfirming extraction and concatenating worked correctly')
else:
    print('FAIL: BUT NOT FATAL!',
          '\npnca_LF0 and pnca_LF1 lengths differ',
          '\nsuggesting error in extraction process'
          ' use pnca_LF1 for downstreama analysis')
print('======================================================================')    
print('length of dfs pre and post processing...',
      '\npnca WF data (unique samples in each row):', extracted_pnca_samples,
      '\npnca LF data (unique mutation in each row):', len(pnca_LF1))
print('======================================================================')
#%%
# final sanity check
print('Verifying whether extraction process worked correctly...')
if len(pnca_LF1) == expected_rows:
    print('PASS: extraction process performed correctly',
          '\nexpected length:', expected_rows,
          '\ngot:', len(pnca_LF1),
          '\nRESULT: Total no. of mutant sequences for logo plot:', len(pnca_LF1) )

else:
    print('FAIL: extraction process has bugs',
          '\nexpected length:', expected_rows,
          '\ngot:', len(pnca_LF1),
          '\Debug please')
#%%
print('Perfmorning some more sanity checks...')
# From LF1:
# no. of unique muts
distinct_muts = pnca_LF1.mutation.value_counts()
print("Distinct mutations:", len(distinct_muts))

# no. of samples contributing the unique muta
pnca_LF1.id.nunique()
print("No.of samples contributing to distinct muts:", pnca_LF1.id.nunique() )

# no. of dr and other muts
mut_grouped = pnca_LF1.groupby('mutation_info').mutation.nunique()
print("No.of unique dr and other muts:", pnca_LF1.groupby('mutation_info').mutation.nunique() )

# sanity check
if len(distinct_muts) == mut_grouped.sum() :
    print("PASS:length of LF1 is as expected ")
else:
    print('FAIL: Mistmatch in count of muts',
          '\nexpected count:', len(distinct_muts),
          '\nactual count:', mut_grouped.sum(),
          '\nmuts should be distinct within dr* and other* type',
          '\ninspecting ...')
    muts_split = list(pnca_LF1.groupby('mutation_info'))
    dr_muts = muts_split[0][1].mutation 
    other_muts =  muts_split[1][1].mutation
#   print('splitting muts by mut_info:', muts_split)
    print('no.of dr_muts samples:', len(dr_muts))
    print('no. of other_muts samples', len(other_muts))        
#%%
# !!! IMPORTANT !!!!
# sanity check: There should not be any common muts
# i.e the same mutation cannot be classed as a 'drug' AND 'others'
if dr_muts.isin(other_muts).sum() & other_muts.isin(dr_muts).sum() > 0:
    print('WARNING: Ambiguous muts detected in dr_ and other_ mutation category')
else:
    print('PASS: NO ambiguous muts detected',
          '\nMuts are unique to dr_ and other_ mutation class')

# inspect dr_muts and other muts    
if dr_muts.isin(other_muts).sum() & other_muts.isin(dr_muts).sum() > 0:
    print('Finding ambiguous muts...', 
          '\n==========================================================',
          '\nTotal no. of samples in dr_muts present in other_muts:', dr_muts.isin(other_muts).sum(),
          '\nThese are:\n', dr_muts[dr_muts.isin(other_muts)],      
          '\n==========================================================',
          '\nTotal no. of samples in other_muts present in dr_muts:', other_muts.isin(dr_muts).sum(),
          '\nThese are:\n', other_muts[other_muts.isin(dr_muts)],
          '\n==========================================================')    
else:
    print('Error: ambiguous muts present, but extraction failed. Debug!')

print('======================================================================')
print('Counting no. of ambiguous muts...') 

if dr_muts[dr_muts.isin(other_muts)].nunique() == other_muts[other_muts.isin(dr_muts)].nunique(): 
    common_muts = dr_muts[dr_muts.isin(other_muts)].value_counts().keys().tolist()
    print('Distinct no. of ambigiuous muts detected:'+ str(len(common_muts)),
          'list of ambiguous mutations (see below):', *common_muts, sep = '\n')
else:
   print('Error: ambiguous muts detected, but extraction failed. Debug!',
         '\nNo. of ambiguous muts in dr:', len(dr_muts[dr_muts.isin(other_muts)].value_counts().keys().tolist() ),
         '\nNo. of ambiguous muts in other:', len(other_muts[other_muts.isin(dr_muts)].value_counts().keys().tolist()))       
print('======================================================================')
#%% clear variables
del(id_dr, id_other, meta_data, meta_pnca_dr, meta_pnca_other, mut_grouped, muts_split, other_WF1, other_df, other_muts_df, other_pnca_count, pnca_LF0, pnca_na)  

del(c1, c2, col_to_split1, col_to_split2, comp_pnca_samples, dr_WF0, dr_df, dr_muts_df, dr_pnca_WF0, dr_pnca_count, expected_pnca_samples, other_pnca_WF1)                     
 
#%% end of data extraction and some files writing. Below are some more files writing.

#%%: write file: ambiguous muts
# uncomment as necessary
#print(outdir)
#dr_muts.to_csv('dr_muts.csv', header = True)
#other_muts.to_csv('other_muts.csv', header = True)

out_filename1 = gene.lower() + '_' + 'ambiguous_muts.csv'
outfile1 = outdir + '/' + out_filename1
print('Writing file: ambiguous muts',
      '\nFilename:', out_filename1,
      '\nPath:',  outdir)

#common_muts = ['pncA_p.Val180Phe','pncA_p.Gln10Pro'] # test
inspect = pnca_LF1[pnca_LF1['mutation'].isin(common_muts)]
inspect.to_csv(outfile1)

print('Finished writing:', out_filename1,
      '\nNo. of rows:', len(inspect),
      '\nNo. of cols:', len(inspect.columns),
      '\nNo. of rows = no. of samples with the ambiguous muts present:', dr_muts.isin(other_muts).sum() + other_muts.isin(dr_muts).sum())
print('======================================================================')
del(out_filename1)


#%% read aa dict and pull relevant info
print('Reading aa dict and fetching1 letter aa code',
      '\nFormatting mutation in mCSM style format: {WT}<POS>{MUT}',
      '\nAdding aa properties')
#===========
# Split 'mutation' column into three:  wild_type, position and
# mutant_type separately. Then map three letter code to one using
# reference_dict.
# First: Import reference dict
# Second: convert to mutation to lowercase for compatibility with dict 
#===========
pnca_LF1['mutation'] = pnca_LF1.loc[:, 'mutation'].str.lower()
#=======
# Iterate through the dict, create a lookup dict i.e
# lookup_dict = {three_letter_code: one_letter_code}.
# lookup dict should be the key and the value (you want to create a column for)
# Then use this to perform the mapping separetly for wild type and mutant cols.
# The three letter code is extracted using a string match match from the dataframe and then converted
# to 'pandas series'since map only works in pandas series
#=======
lookup_dict = dict()
for k, v in my_aa_dict.items():
    lookup_dict[k] = v['one_letter_code']
    wt = pnca_LF1['mutation'].str.extract('pnca_p.(\w{3})').squeeze() # converts to a series that map works on
    pnca_LF1['wild_type'] = wt.map(lookup_dict)   
    mut = pnca_LF1['mutation'].str.extract('\d+(\w{3})$').squeeze()
    pnca_LF1['mutant_type'] = mut.map(lookup_dict)

# extract position info from mutation column separetly using string match
pnca_LF1['position'] = pnca_LF1['mutation'].str.extract(r'(\d+)') 

# clear variables
del(k, v, wt, mut, lookup_dict)
#print('======================================================================')
#=========
# iterate through the dict, create a lookup dict that i.e
# lookup_dict =  {three_letter_code: aa_prop_water} 
# Do this for both wild_type and mutant as above.
#=========
# initialise a sub dict that is lookup dict for three letter code to aa prop
lookup_dict = dict()

for k, v in my_aa_dict.items():
    lookup_dict[k] = v['aa_prop_water']
    #print(lookup_dict)
    wt = pnca_LF1['mutation'].str.extract('pnca_p.(\w{3})').squeeze() # converts to a series that map works on
    pnca_LF1['wt_prop_water'] = wt.map(lookup_dict)   
    mut = pnca_LF1['mutation'].str.extract('\d+(\w{3})$').squeeze()
    pnca_LF1['mut_prop_water'] = mut.map(lookup_dict)
    
# added two more cols

# clear variables
del(k, v, wt, mut, lookup_dict)
#print('======================================================================')
#========
# iterate through the dict, create a lookup dict that i.e
# lookup_dict =  {three_letter_code: aa_prop_polarity} 
# Do this for both wild_type and mutant as above.
#=========
# initialise a sub dict that is lookup dict for three letter code to aa prop
lookup_dict = dict()

for k, v in my_aa_dict.items():
    lookup_dict[k] = v['aa_prop_polarity']
    #print(lookup_dict)
    wt = pnca_LF1['mutation'].str.extract('pnca_p.(\w{3})').squeeze() # converts to a series that map works on
    pnca_LF1['wt_prop_polarity'] = wt.map(lookup_dict)   
    mut = pnca_LF1['mutation'].str.extract(r'\d+(\w{3})$').squeeze()
    pnca_LF1['mut_prop_polarity'] = mut.map(lookup_dict)
    
# added two more cols
    
# clear variables
del(k, v, wt, mut, lookup_dict)
#print('======================================================================')

#========
# iterate through the dict, create a lookup dict that i.e
# lookup_dict =  {three_letter_code: aa_taylor} 
# Do this for both wild_type and mutant as above.
# caution: taylor mapping will create a list within a column
#=========
#lookup_dict = dict()

#for k, v in my_aa_dict.items():
#    lookup_dict[k] = v['aa_taylor']
#    #print(lookup_dict)
#    wt = pnca_LF1['mutation'].str.extract('pnca_p.(\w{3})').squeeze() # converts to a series that map works on
#    pnca_LF1['wt_taylor'] = wt.map(lookup_dict)   
#    mut = pnca_LF1['mutation'].str.extract(r'\d+(\w{3})$').squeeze()
#    pnca_LF1['mut_taylor'] = mut.map(lookup_dict)
    
# added two more cols
# clear variables
#del(k, v, wt, mut, lookup_dict)
#print('======================================================================')

#========
# iterate through the dict, create a lookup dict that i.e
# lookup_dict =  {three_letter_code: aa_calcprop} 
# Do this for both wild_type and mutant as above.
#=========
lookup_dict = dict()

for k, v in my_aa_dict.items():
    lookup_dict[k] = v['aa_calcprop']
    #print(lookup_dict)
    wt = pnca_LF1['mutation'].str.extract('pnca_p.(\w{3})').squeeze() # converts to a series that map works on
    pnca_LF1['wt_calcprop'] = wt.map(lookup_dict)   
    mut = pnca_LF1['mutation'].str.extract(r'\d+(\w{3})$').squeeze()
    pnca_LF1['mut_calcprop'] = mut.map(lookup_dict)
    
# added two more cols
# clear variables
del(k, v, wt, mut, lookup_dict)
print('======================================================================')
########
# combine the wild_type+poistion+mutant_type columns to generate 
# Mutationinformation (matches mCSM output field)
# Remember to use .map(str) for int col types to allow string concatenation
#########
pnca_LF1['Mutationinformation'] = pnca_LF1['wild_type'] + pnca_LF1.position.map(str) + pnca_LF1['mutant_type']
print('Created column: Mutationinformation')
print('======================================================================')
#%% Write file: mCSM muts
snps_only = pd.DataFrame(pnca_LF1['Mutationinformation'].unique())
snps_only.head()
# assign column name
snps_only.columns = ['Mutationinformation']

# count how many positions this corresponds to
pos_only = pd.DataFrame(pnca_LF1['position'].unique()) 

print('Checking NA in snps...')# should be 0
if snps_only.Mutationinformation.isna().sum() == 0:
    print ('PASS: NO NAs/missing entries for SNPs')
else:
    print('FAIL: SNP has NA, Possible mapping issues from dict?',
          '\nDebug please!')
print('======================================================================')

out_filename2 = gene.lower() + '_' + 'mcsm_snps.csv'
outfile2 = outdir + '/' + out_filename2

print('Writing file: mCSM style muts',
      '\nFilename:', out_filename2,
      '\nPath:', outdir,
      '\nmutation format (SNP): {WT}<POS>{MUT}',
      '\nNo. of distinct muts:', len(snps_only),
      '\nNo. of distinct positions:', len(pos_only))

snps_only.to_csv(outfile2, header = False, index = False)

print('Finished writing:', out_filename2,
      '\nNo. of rows:', len(snps_only),
      '\nNo. of cols:', len(snps_only.columns))
print('======================================================================')
del(out_filename2)


#%% Write file: pnca_metadata (i.e pnca_LF1)
# where each row has UNIQUE mutations NOT unique sample ids
out_filename3 = gene.lower() + '_' + 'metadata.csv'
outfile3 = outdir + '/' + out_filename3
print('Writing file: LF formatted data',
      '\nFilename:', out_filename3,
      '\nPath:', outdir)

pnca_LF1.to_csv(outfile3, header = True, index = False)
print('Finished writing:', out_filename3,
      '\nNo. of rows:', len(pnca_LF1),
      '\nNo. of cols:', len(pnca_LF1.columns) )
print('======================================================================')
del(out_filename3)


#%% write file: mCSM style but with repitions for MSA and logo plots
all_muts_msa = pd.DataFrame(pnca_LF1['Mutationinformation']) 
all_muts_msa.head()
# assign column name
all_muts_msa.columns = ['Mutationinformation']

# make sure it is string
all_muts_msa.columns.dtype

# sort the column
all_muts_msa_sorted = all_muts_msa.sort_values(by = 'Mutationinformation')

# create an extra column with protein name
all_muts_msa_sorted = all_muts_msa_sorted.assign(fasta_name = '3PL1') 
all_muts_msa_sorted.head()

# rearrange columns so the fasta name is the first column (required for mutate.script)
all_muts_msa_sorted = all_muts_msa_sorted[['fasta_name', 'Mutationinformation']]
all_muts_msa_sorted.head()

print('Checking NA in snps...')# should be 0
if all_muts_msa.Mutationinformation.isna().sum() == 0:
    print ('PASS: NO NAs/missing entries for SNPs')
else:
    print('FAIL: SNP has NA, Possible mapping issues from dict?',
          '\nDebug please!')
print('======================================================================')

out_filename4 = gene.lower() + '_' + 'all_muts_msa.csv'
outfile4 = outdir + '/' + out_filename4

print('Writing file: mCSM style muts for msa',
      '\nFilename:', out_filename4,
      '\nPath:', outdir,
      '\nmutation format (SNP): {WT}<POS>{MUT}',
      '\nNo.of lines of msa:', len(all_muts_msa),  
)

all_muts_msa_sorted.to_csv(outfile4, header = False, index = False)

print('Finished writing:', out_filename4,
      '\nNo. of rows:', len(all_muts_msa),
      '\nNo. of cols:', len(all_muts_msa.columns) )
print('======================================================================')
del(out_filename4)


#%% write file for mutational positions
# count how many positions this corresponds to
pos_only = pd.DataFrame(pnca_LF1['position'].unique()) 
# assign column name
pos_only.columns = ['position']
# make sure dtype of column position is int or numeric and not string
pos_only.position.dtype
pos_only['position'] = pos_only['position'].astype(int)
pos_only.position.dtype

# sort by position value
pos_only_sorted = pos_only.sort_values(by = 'position', ascending = True)

out_filename5 = gene.lower() + '_' + 'mutational_positons.csv'
outfile5 = outdir + '/' + out_filename5

print('Writing file: mutational positions',
      '\nNo. of distinct positions:', len(pos_only_sorted),
      '\nFilename:', out_filename5,
      '\nPath:', outdir)

pos_only_sorted.to_csv(outfile5, header = True, index = False)

print('Finished writing:', out_filename5,
      '\nNo. of rows:', len(pos_only_sorted),
      '\nNo. of cols:', len(pos_only_sorted.columns) )
print('======================================================================')
del(out_filename5)


#%% end of script
print(u'\u2698' * 50,
      '\nEnd of script: Data extraction and writing files'
      '\n' + u'\u2698' * 50 )
#%%