import argparse
from collections import defaultdict

import numpy

import pysam


def Parser():
    the_parser = argparse.ArgumentParser()
    the_parser.add_argument(
        '--input', action="store", type=str, help="bam alignment file")
    the_parser.add_argument(
        '--minquery', type=int,
        help="Minimum readsize of query reads (nt) - must be an integer")
    the_parser.add_argument(
        '--maxquery', type=int,
        help="Maximum readsize of query reads (nt) - must be an integer")
    the_parser.add_argument(
        '--mintarget', type=int,
        help="Minimum readsize of target reads (nt) - must be an integer")
    the_parser.add_argument(
        '--maxtarget', type=int,
        help="Maximum readsize of target reads (nt) - must be an integer")
    the_parser.add_argument(
        '--minscope', type=int,
        help="Minimum overlap analyzed (nt) - must be an integer")
    the_parser.add_argument(
        '--maxscope', type=int,
        help="Maximum overlap analyzed (nt) - must be an integer")
    the_parser.add_argument(
        '--output_h', action="store", type=str,
        help="h-signature dataframe")
    the_parser.add_argument(
        '--output_z', action="store", type=str,
        help="z-signature dataframe")
    args = the_parser.parse_args()
    return args


class Map:

    def __init__(self, bam_file, minquery=23, maxquery=29, mintarget=23,
                 maxtarget=29, minscope=1, maxscope=19, output_h='',
                 output_z=''):
        self.bam_object = pysam.AlignmentFile(bam_file, 'rb')
        self.query_range = range(minquery, maxquery + 1)
        self.target_range = range(mintarget, maxtarget + 1)
        self.scope = range(minscope, maxscope + 1)
        self.H = open(output_h, 'w')
        self.Z = open(output_z, 'w')
        self.chromosomes = dict(zip(self.bam_object.references,
                                self.bam_object.lengths))
        self.map_dict = self.create_map(self.bam_object)
        self.query_positions = self.compute_query_positions()
        self.Z.write(self.compute_signature_pairs())
        self.H.write(self.compute_signature_h())
        self.H.close()
        self.Z.close()

    def create_map(self, bam_object):
        '''
        Returns a map_dictionary {(chromosome,read_position,polarity):
                                                    [read_length, ...]}
        '''
        map_dictionary = defaultdict(list)
        # get empty value for start and end of each chromosome
        for chrom in self.chromosomes:
            map_dictionary[(chrom, 1, 'F')] = []
            map_dictionary[(chrom, self.chromosomes[chrom], 'F')] = []
        for chrom in self.chromosomes:
            for read in bam_object.fetch(chrom):
                if read.is_reverse:
                    map_dictionary[(chrom, read.reference_end,
                                    'R')].append(read.query_alignment_length)
                else:
                    map_dictionary[(chrom, read.reference_start+1,
                                    'F')].append(read.query_alignment_length)
        return map_dictionary

    def compute_query_positions(self):
        ''' this method does not filter on read size, just forward reads
        that overlap reverse reads in the overlap range'''
        all_query_positions = defaultdict(list)
        for genomicKey in list(self.map_dict):
            chrom, coord, pol = genomicKey
            for i in self.scope:
                if pol == 'F' and len(self.map_dict[chrom,
                                                    coord+i-1,
                                                    'R']) > 0:
                    all_query_positions[chrom].append(coord)
                    break
        for chrom in all_query_positions:
            all_query_positions[chrom] = sorted(
                list(set(all_query_positions[chrom])))
        return all_query_positions

    def countpairs(self, uppers, lowers):
        query_range = self.query_range
        target_range = self.target_range
        uppers = [size for size in uppers if size in query_range or size in
                  target_range]
        lowers = [size for size in lowers if size in query_range or size in
                  target_range]
        paired = []
        for upread in uppers:
            for downread in lowers:
                if (upread in query_range and downread in target_range) or (
                        upread in target_range and downread in query_range):
                    paired.append(upread)
                    lowers.remove(downread)
                    break
        return len(paired)

    def compute_signature_pairs(self):
        frequency_table = defaultdict(dict)
        scope = self.scope
        for chrom in self.chromosomes:
            for overlap in scope:
                frequency_table[chrom][overlap] = 0
        for chrom in self.query_positions:
            for coord in self.query_positions[chrom]:
                for overlap in scope:
                    uppers = self.map_dict[chrom, coord, 'F']
                    lowers = self.map_dict[chrom, coord+overlap-1, 'R']
                    frequency_table[chrom][overlap] += self.countpairs(uppers,
                                                                       lowers)
        # compute overlaps for all chromosomes merged
        for overlap in scope:
            accumulator = []
            for chrom in frequency_table:
                if chrom != 'all_chromosomes':
                    accumulator.append(frequency_table[chrom][overlap])
            frequency_table['all_chromosomes'][overlap] = sum(accumulator)
        return self.stringify_table(frequency_table)

    def signature_tables(self):
        query_range = self.query_range
        target_range = self.target_range
        Query_table = defaultdict(dict)
        Target_table = defaultdict(dict)
        for key in self.map_dict:
            for size in self.map_dict[key]:
                if size in query_range or size in target_range:
                    if key[2] == 'F':
                        coordinate = key[1]
                    else:
                        coordinate = -key[1]
                if size in query_range:
                    Query_table[key[0]][coordinate] = Query_table[key[0]].get(
                        coordinate, 0) + 1
                if size in target_range:
                    Target_table[key[0]][coordinate] = \
                        Target_table[key[0]].get(coordinate, 0) + 1
        return Query_table, Target_table

    def compute_signature_h(self):
        scope = self.scope
        Query_table, Target_table = self.signature_tables()
        frequency_table = defaultdict(dict)
        for chrom in self.chromosomes:
            for overlap in scope:
                frequency_table[chrom][overlap] = 0
        for chrom in Query_table:
            Total_Query_Numb = 0
            for coord in Query_table[chrom]:
                Total_Query_Numb += Query_table[chrom][coord]
            for coord in Query_table[chrom]:
                local_table = dict([(overlap, 0) for overlap in scope])
                number_of_targets = 0
                for overlap in scope:
                    local_table[overlap] += Query_table[chrom][coord] * \
                        Target_table[chrom].get(-coord - overlap + 1, 0)
                    number_of_targets += Target_table[chrom].get(
                        -coord - overlap + 1, 0)
                for overlap in scope:
                    try:
                        frequency_table[chrom][overlap] += \
                            local_table[overlap] / number_of_targets \
                            / float(Total_Query_Numb)
                    except ZeroDivisionError:
                        continue
        # compute overlap probabilities for all chromosomes merged
        general_frequency_table = dict([(overlap, 0) for overlap in scope])
        total_aligned_reads = 0
        for chrom in frequency_table:
            for overlap in frequency_table[chrom]:
                total_aligned_reads += self.bam_object.count(chrom)
        for chrom in frequency_table:
            for overlap in frequency_table[chrom]:
                try:
                    general_frequency_table[overlap] += \
                        frequency_table[chrom][overlap] / total_aligned_reads \
                        * self.bam_object.count(chrom)
                except ZeroDivisionError:
                    continue
        for overlap in general_frequency_table:
            frequency_table['all_chromosomes'][overlap] = \
                general_frequency_table[overlap]
        return self.stringify_table(frequency_table)

    def stringify_table(self, frequency_table):
        '''
        method both to compute z-score and to return a writable string
        '''
        tablestring = []
        for chrom in sorted(frequency_table):
            accumulator = []
            for overlap in frequency_table[chrom]:
                accumulator.append(frequency_table[chrom][overlap])
            z_mean = numpy.mean(accumulator)
            z_std = numpy.std(accumulator)
            if z_std == 0:
                for overlap in sorted(frequency_table[chrom]):
                    tablestring.append('%s\t%s\t%s\t%s\n' % (
                        chrom, str(overlap),
                        str(frequency_table[chrom][overlap]), str(0)))
            else:
                for overlap in sorted(frequency_table[chrom]):
                    tablestring.append('%s\t%s\t%s\t%s\n' % (
                        chrom, str(overlap),
                        str(frequency_table[chrom][overlap]),
                        str((frequency_table[chrom][overlap] - z_mean)/z_std)))
        return ''.join(tablestring)


if __name__ == "__main__":
    args = Parser()
    mapobj = Map(args.input, args.minquery, args.maxquery, args.mintarget,
                 args.maxtarget, args.minscope, args.maxscope, args.output_h,
                 args.output_z)