"""

Generating useful files

    -- all_ALS_var.txt

    -- promoter1.csv

    -- var_in_file.json

"""

import os

import sys

import vcf

import pandas as pd

import numpy as np

import bisect

import json

import csv

def Used_variant_pos(promoters):

    used_variant = []

    for pro in promoters:

        for p in pro:

            if p not in used_variant: used_variant.append(p)

    return used_variant

fold_path = ''#'./chr22new/'

files = ['xaa.vcf']

# read all ALS variants position

all_pos = []

for f_str in files:

    file_name = fold_path+f_str

    with open(file_name, 'r') as f:

        lines = [l.replace('\n','').split("\t") for l in f if not l.startswith('##')]

    info = lines[2:]

    l = [i[1] for i in info]

    all_pos += l

print "Total number of variant pos: ", len(all_pos)

all_pos = map(int, all_pos)

all_pos_min = min(all_pos)/10000*10000

all_pos_max = max(all_pos)/10000*10000

# all_pos is the list of poistion of variants from ALS dataset

ALS_var_pos = map(int, all_pos) # variant position in ALS

# read refS dataset of this chromsome

csv_folder = './csv_files/'

refs = pd.read_csv(csv_folder+'chr22.csv', sep="\t")

refs = refs.drop_duplicates(subset=['txStart', 'txEnd']).sort_values('txStart')

# scale the refS dataset

refs_ = refs[refs.txStart>all_pos_min]

refs_ = refs_[refs_.txStart < all_pos_max]

temp = refs_.txStart.tolist()

ref_start = map(int, temp) # start posotion

temp = refs_.txEnd.tolist()

ref_end = map(int, temp) # end position

temp = refs_.strand.tolist()

strand_f = lambda x: 1 if x == "+" else 0

ref_strand = map(strand_f, temp) # strand

# generate promoter ddictionary

# {start_RefS_pos: ALS_promoter_vars}

promoter_dict = {}

sim_promoter_dict = {}

unique_promoter = []

for i in range(len(ref_start)):

    promoter_pos = []

    strand_ = ref_strand[i]

    if strand_:

        start_p = ref_start[i]

        p = bisect.bisect_left(ALS_var_pos, start_p + .5)

        promoter_pos = ALS_var_pos[p - 55:p] + ALS_var_pos[p:p + 9]

    else:

        start_p = ref_end[i]

        p = bisect.bisect_left(ALS_var_pos, start_p - .5)

        promoter_pos = ALS_var_pos[p - 8:p] + ALS_var_pos[p:p + 56]

    promoter_dict[start_p] = promoter_pos

    if promoter_pos not in unique_promoter:

        unique_promoter.append(promoter_pos)

print "    There are ", len(promoter_dict.values()), " genes"

print "    There are unique", len(unique_promoter), " genes"

print "   ",len(promoter_dict.values()) - len(unique_promoter), "genes are repeated"

# used ALS variants position

promoters = promoter_dict.values()

used_var = Used_variant_pos(promoters)

# generate lisit of used ALS position

# because there are some repeat pos

# this process will generate all necessary information for sequencing

q = []

for i in all_pos:

    if i in used_var:

        q.append(i)

thefile = open('all_ALS_var.txt', 'w')

for item in q:

    thefile.write("%s\n" % item)

# generate used variants in each .vcf file

all_pos_dic = {}

for f_str in files:

    l = []

    file_name = fold_path+f_str

    with open(file_name, 'r') as f:

        lines = [s.replace('\n','').split("\t") for s in f if not s.startswith('##')]

    info = lines[2:]

    l = [i[1] for i in info]

    all_pos_dic[f_str] = l

print "Total number of variant file: ", len(all_pos_dic)

for k,v in all_pos_dic.items():

    q = []

    for i in v:

        if int(i) in used_var:

            q.append(i)

    all_pos_dic[k] = q

with open('var_in_file.json', 'w') as fp:

    json.dump(all_pos_dic, fp)

# save promoter information

promotersx = []

for ps in promoters:

    if ps not in promotersx:

        promotersx.append(ps)

promotersx = sorted(promotersx)

with open("promoter1.csv", "wb") as f:

    writer = csv.writer(f)

    writer.writerows(promotersx)

print "Files generated: \n \t --var_in_file.json \n \t -- promoter1.csv \n \t -- all_ALS_var.txt"