#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Jun 25 08:46:36 2019

@author: tanushree
"""
############################################
# load libraries
import os
import pandas as pd
from Bio import SeqIO
############################################
#********************************************************************
# TASK: Read in fasta files and create mutant sequences akin to a MSA,
# to allow generation of logo plots

# Requirements:
# input: Fasta file of protein/target for which mut seqs will be created
	# path: "Data/<drug>/input/original/<filename>"
# output: MSA for mutant sequences
	# path: "Data/<drug>/input/processed/<filename>"
#***********************************************************************
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
############# specify variables for input and output paths and filenames
homedir = os.path.expanduser('~') # spyder/python doesn't recognise tilde
basedir = "/git/Data/pyrazinamide/input"

# input
inpath = "/original"
in_filename_fasta = "/3pl1.fasta.txt"
infile_fasta = homedir + basedir + inpath + in_filename_fasta
print("Input file is:", infile_fasta)

inpath_p = "/processed"
in_filename_meta_data = "/meta_data_with_AFandOR.csv"
infile_meta_data = homedir + basedir + inpath_p + in_filename_meta_data
print("Input file is:", infile_meta_data)

# output: only path specified, filenames in respective sections
outpath = "/processed"

################## end of variable assignment for input and output files
#==========
#read files
#==========
#############
#fasta file
#############
#my_file = infile_fasta

my_fasta = str()
for seq_record in SeqIO.parse(infile_fasta, "fasta"):
    my_seq = seq_record.seq
    my_fasta = str(my_seq) #convert to a string
    print(my_fasta)
#    print( len(my_fasta) )
#    print( type(my_fasta) )

len(my_fasta)

#############
# SNP info
#############
# read mutant_info file and extract cols with positions and mutant_info
# This should be all samples with pncA muts
#my_data = pd.read_csv('mcsm_complex1_normalised.csv') #335, 15
#my_data = pd.read_csv('meta_data_with_AFandOR.csv') #3093, 22
my_data = pd.read_csv(infile_meta_data) #3093, 22
list(my_data.columns)

#FIXME: You need a better way to identify this
# remove positions not in the structure
#pos_remove = 186
my_data = my_data[my_data.position != 186] #3092, 22

# if multiple positions, then try the example below;
# https://stackoverflow.com/questions/29017525/deleting-rows-based-on-multiple-conditions-python-pandas
#df = df[(df.one > 0) | (df.two > 0) | (df.three > 0) & (df.four < 1)]

#mut_info1 = my_data[['Position', 'Mutant_type']] #335, 2
mut_info1 = my_data[['position', 'mutant_type']] #3092, 2

#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
###############
# data cleaning
################
# extract only those positions that have a frequency count of pos>1
###mut_info['freq_pos'] = mut_info.groupby('Position').count()#### dodgy

# add a column of frequency for each position
#mut_info1['freq_pos'] = mut_info1.groupby('Position')['Position'].transform('count') #335,3
mut_info1['freq_pos'] = mut_info1.groupby('position')['position'].transform('count') #3092,3

# sort by position
mut_info2 = mut_info1.sort_values(by=['position'])

#FIXME
#__main__:1: SettingWithCopyWarning: 
#A value is trying to be set on a copy of a slice from a DataFrame.
#Try using .loc[row_indexer,col_indexer] = value instead

#See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy

#sort dataframe by freq values so the row indices are in order!
#mut_info2 = mut_info1.sort_values(by = 'freq_pos'
#                      , axis = 0
#                      , ascending = False
#                      , inplace = False
#                      , na_position = 'last')

#mut_info2 = mut_info2.reset_index( drop = True)


# count how many pos have freq 1 as you will need to exclude those
mut_info2[mut_info2.freq_pos == 1].sum() #20

# extract entries with freq_pos>1
# should be 3093-211 = 3072
mut_info3 = mut_info2.loc[mut_info2['freq_pos'] >1] #3072

# reset index to allow iteration <<<<<<<< IMPORTANT
mut_info = mut_info3.reset_index(drop = True)

del(mut_info1, mut_info2, mut_info3, my_data)

#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
###################
# generate mut seqs
###################
mut_seqsL = [] * len(mut_info) 

# iterate 
for i, pos in enumerate(mut_info['position']):
    print('index:', i, 'position:', pos)
    mut = mut_info['mutant_type'][i]
#    print(mut)
#    print( type(mut) )
    print('index:', i, 'position:', pos, 'mutant', mut)

    my_fastaL = list(my_fasta)
    offset_pos = pos-1 #due to counting starting from 0
    my_fastaL[offset_pos] = mut
#    print(my_fastaL)
    mut_seq = "".join(my_fastaL)
#    print(mut_seq + '\n')
    mut_seqsL.append(mut_seq)
#    print('original:', my_fasta, ',', 'replaced at', pos, 'with', mut,  mut_seq)

###############
# sanity check
################
len_orig = len(my_fasta)
#    checking if all the mutant sequences have the same length as the original fasta file sequence
for seqs in mut_seqsL:
#    print(seqs)
#    print(len(seqs))
    if len(seqs) != len_orig:
        print('sequence lengths mismatch' +'\n', 'mutant seq length:', len(seqs), 'vs original seq length:', len_orig)
    else: 
        print('**Hooray** Length of mutant and original sequences match')
 
del(i, len_orig, mut, mut_seq, my_fastaL, offset_pos, pos, seqs)       
      
############
# write file
############
#filepath = homedir +'/git/LSHTM_Y1_PNCA/combined_v3/logo_plot/snp_seqsfile'
#filepath =  homedir + '/git/LSHTM_Y1_PNCA/mcsm_analysis/pyrazinamide/Data/gene_msa.txt'

print(outpath)
out_filename_gene = "/gene_msa.txt"
outfile_gene = homedir + basedir + outpath + out_filename_gene
print("Output file is:", outfile_gene)

with open(outfile_gene, 'w') as file_handler:
    for item in mut_seqsL:
        file_handler.write("{}\n".format(item))
        
R="\n".join(mut_seqsL)
f = open('Columns.csv','w')
f.write(R)
f.close()


#################################################################################
# extracting only positions with SNPs so that when you plot only those positions
################################################################################
#mut_seqsL = mut_seqsL[:3]  #just trying with 3 seqs

# create a list of unique positions
pos = mut_info['position'] #3072
posL = list(set(list(pos))) #110
del(pos)

snp_seqsL = [] * len(mut_seqsL)

for j, mut_seq in enumerate(mut_seqsL):
    print (j, mut_seq)
#    print(mut_seq[101]) #testing, this should be P, T V (in order of the mut_info file)
    mut_seqsE = list(mut_seq)
# extract specific posistions (corres to SNPs) from list of mutant sequences
    snp_seqL1 = [mut_seqsE[i-1] for i in posL]    #should be 110
#    print(snp_seqL1)
#    print(len(snp_seqL1))
    snp_seq_clean = "".join(snp_seqL1)
    snp_seqsL.append(snp_seq_clean)

###############
# sanity check
################
no_unique_snps = len(posL)

# checking if all the mutant sequences have the same length as the original fasta file sequence
for seqs in snp_seqsL:
#    print(seqs)
#    print(len(seqs))
    if len(seqs) != no_unique_snps:
        print('sequence lengths mismatch' +'\n', 'mutant seq length:', len(seqs), 'vs original seq length:', no_unique_snps)
    else: 
        print('**Hooray** Length of mutant and original sequences match')
        
del(mut_seq, mut_seqsE, mut_seqsL, seqs, snp_seqL1, snp_seq_clean)


############
# write file
############
#filepath = homedir +'/git/LSHTM_Y1_PNCA/combined_v3/logo_plot/snp_seqsfile'
#filepath =  homedir + '/git/LSHTM_Y1_PNCA/mcsm_analysis/pyrazinamide/Data/snps_msa.txt'

print(outpath)
out_filename_snps = "/snps_msa.txt"
outfile_snps = homedir + basedir + outpath + out_filename_snps
print("Output file is:", outfile_snps)

with open(outfile_snps, 'w') as file_handler:
    for item in snp_seqsL:
        file_handler.write("{}\n".format(item))
        
R="\n".join(snp_seqsL)
f = open('Columns.csv','w')
f.write(R)
f.close()