#!/usr/bin/env python

# Family size distribution of tags which were aligned to the reference genome
#
# Author: Monika Heinzl, Johannes-Kepler University Linz (Austria)
# Contact: monika.heinzl@edumail.at
#
# Takes at least one TABULAR file with tags before the alignment to the SSCS
# and a TXT with tags of reads that overlap the regions of the reference genome as input.
# The program produces a plot which shows the distribution of family sizes of the tags from the input files and
# a tabular file with the data of the plot.

# USAGE: python FSD_regions_1.6_FINAL.py --inputFile filenameSSCS --inputName1 filenameSSCS --ref_genome  filenameRefGenome --output_tabular outptufile_name_tabular --output_pdf outptufile_name_pdf

import argparse
import re
import sys
from collections import OrderedDict

import matplotlib.pyplot as plt
import numpy
from matplotlib.backends.backend_pdf import PdfPages

plt.switch_backend('agg')


def readFileReferenceFree(file, delim):
    with open(file, 'r') as dest_f:
        data_array = numpy.genfromtxt(dest_f, skip_header=0, delimiter=delim, comments='#', dtype='string')
        return(data_array)


def make_argparser():
    parser = argparse.ArgumentParser(description='Family Size Distribution of tags which were aligned to regions of the reference genome')
    parser.add_argument('--inputFile', help='Tabular File with three columns: ab or ba, tag and family size.')
    parser.add_argument('--inputName1')
    parser.add_argument('--ref_genome', help='TXT File with tags of reads that overlap the region.')
    parser.add_argument('--output_pdf', default="data.pdf", type=str, help='Name of the pdf and tabular file.')
    parser.add_argument('--output_tabular', default="data.tabular", type=str, help='Name of the pdf and tabular file.')
    return parser


def compare_read_families_refGenome(argv):
    parser = make_argparser()
    args = parser.parse_args(argv[1:])

    firstFile = args.inputFile
    name1 = args.inputName1
    name1 = name1.split(".tabular")[0]
    refGenome = args.ref_genome
    title_file = args.output_pdf
    title_file2 = args.output_tabular
    sep = "\t"

    with open(title_file2, "w") as output_file, PdfPages(title_file) as pdf:
        data_array = readFileReferenceFree(firstFile, "\t")

        mut_array = readFileReferenceFree(refGenome, " ")
        length_regions = len(mut_array)
        group = numpy.array(mut_array[:, 0])
        seq_mut = numpy.array(mut_array[:, 1])
        alt_group = numpy.array(mut_array[:, 2])

        seq = numpy.array(data_array[:, 1])
        tags = numpy.array(data_array[:, 2])
        quant = numpy.array(data_array[:, 0]).astype(int)

        all_ab = seq[numpy.where(tags == "ab")[0]]
        all_ba = seq[numpy.where(tags == "ba")[0]]
        quant_ab = quant[numpy.where(tags == "ab")[0]]
        quant_ba = quant[numpy.where(tags == "ba")[0]]

        seqDic_ab = dict(zip(all_ab, quant_ab))
        seqDic_ba = dict(zip(all_ba, quant_ba))

        if re.search('^(\d)+_(\d)+', str(mut_array[0,0])) is None:
            seq_mut, seqMut_index = numpy.unique(numpy.array(mut_array[:, 1]), return_index=True)
            group = mut_array[seqMut_index,0]
            alt_group = mut_array[seqMut_index,2]
            mut_array = mut_array[seqMut_index,:]

        groupUnique, group_index = numpy.unique(group, return_index=True)
        groupUnique = groupUnique[numpy.argsort(group_index)]
        lst_ab = []
        lst_ba = []
        for i in seq_mut:
            lst_ab.append(seqDic_ab.get(i))
            lst_ba.append(seqDic_ba.get(i))

        quant_ab = numpy.array(lst_ab)
        quant_ba = numpy.array(lst_ba)

        quantAfterRegion = OrderedDict()
        for key in groupUnique:
            quantAfterRegion[key] = []

        for i in groupUnique:
            index_of_current_region = numpy.where(group == i)[0]
            quant_ba_i = quant_ba[index_of_current_region]
            alt_group_i = alt_group[index_of_current_region]
            index_alternative_refs = numpy.where(alt_group_i != "=")[0]

            dataAB = quant_ab[index_of_current_region]
            bigFamilies = numpy.where(dataAB > 20)[0]
            dataAB[bigFamilies] = 22
            for el in dataAB:
                quantAfterRegion[i].append(el)

            if len(index_alternative_refs) == 0:
                dataBA = quant_ba_i
                bigFamilies = numpy.where(dataBA > 20)[0]
                dataBA[bigFamilies] = 22
                for el2 in dataBA:
                    quantAfterRegion[i].append(el2)
            else:  # get tags where 2nd mate is aligned to a different ref genome
                unique_alt = numpy.unique(alt_group_i[index_alternative_refs])
                for alt in unique_alt:
                    ind_alt_tags = numpy.where(alt_group_i == alt)[0]
                    dataBA = quant_ba_i[ind_alt_tags]

                    bigFamilies = numpy.where(dataBA > 20)[0]
                    if len(bigFamilies) != 0:
                        if len(bigFamilies) == 1 and type(dataBA) != list:
                            dataBA = 22
                            quantAfterRegion[alt].append(dataBA)
                        else:
                            dataBA[bigFamilies] = 22
                            for el3 in dataBA:
                                quantAfterRegion[alt].append(el3)

                index_inverse = [x for x in range(0, len(index_of_current_region)) if x not in index_alternative_refs]
                data_BA_other = quant_ba_i[index_inverse]
                bigFamilies_other = numpy.where(data_BA_other > 20)[0]

                if len(bigFamilies_other) != 0:
                    if len(bigFamilies_other) == 1 and type(data_BA_other) != list:
                        data_BA_other = 22
                        quantAfterRegion[i].append(data_BA_other)
                    else:
                        data_BA_other[bigFamilies_other] = 22
                        for el3 in data_BA_other:
                            quantAfterRegion[i].append(el3)

        quantAfterRegion = quantAfterRegion.values()
        maximumX = numpy.amax(numpy.concatenate(quantAfterRegion))
        minimumX = numpy.amin(numpy.concatenate(quantAfterRegion))

        # PLOT
        plt.rc('figure', figsize=(11.69, 8.27))  # A4 format
        plt.rcParams['axes.facecolor'] = "E0E0E0"  # grey background color
        plt.rcParams['xtick.labelsize'] = 14
        plt.rcParams['ytick.labelsize'] = 14
        plt.rcParams['patch.edgecolor'] = "black"
        fig = plt.figure()
        plt.subplots_adjust(bottom=0.3)

        colors = ["#6E6E6E", "#0431B4", "#5FB404", "#B40431", "#F4FA58", "#DF7401", "#81DAF5"]

        col = []
        for i in range(0, len(groupUnique)):
            col.append(colors[i])

        counts = plt.hist(quantAfterRegion, bins=range(minimumX, maximumX + 1), stacked=False, label=groupUnique,
                          align="left", alpha=1, color=col, edgecolor="black", linewidth=1)
        ticks = numpy.arange(minimumX - 1, maximumX, 1)

        ticks1 = map(str, ticks)
        ticks1[len(ticks1) - 1] = ">20"
        plt.xticks(numpy.array(ticks), ticks1)
        count = numpy.bincount(map(int, quant_ab))  # original counts

        legend = "max. family size =\nabsolute frequency=\nrelative frequency=\n\ntotal nr. of reads="
        plt.text(0.15, 0.105, legend, size=11, transform=plt.gcf().transFigure)

        legend = "AB\n{}\n{}\n{:.5f}\n\n{:,}" \
            .format(max(map(int, quant_ab)), count[len(count) - 1], float(count[len(count) - 1]) / sum(count),
                    sum(numpy.array(data_array[:, 0]).astype(int)))
        plt.text(0.35, 0.105, legend, size=11, transform=plt.gcf().transFigure)

        count2 = numpy.bincount(map(int, quant_ba))  # original counts

        legend = "BA\n{}\n{}\n{:.5f}" \
            .format(max(map(int, quant_ba)), count2[len(count2) - 1], float(count2[len(count2) - 1]) / sum(count2))
        plt.text(0.45, 0.15, legend, size=11, transform=plt.gcf().transFigure)

        plt.text(0.55, 0.22, "total nr. of tags=", size=11, transform=plt.gcf().transFigure)
        plt.text(0.7, 0.22, "{:,}".format(length_regions), size=11, transform=plt.gcf().transFigure)

        legend4 = '* The total numbers indicate the count of the ab and ba tags per region.\nAn equal sign ("=") is used in the column ba tags, if the counts and the region are identical to the ab tags.'
        plt.text(0.1, 0.02, legend4, size=11, transform=plt.gcf().transFigure)

        space = numpy.arange(0, len(groupUnique), 0.02)
        plt.text(0.7, 0.18, "total number of *\nab", size=11, transform=plt.gcf().transFigure)
        plt.text(0.78, 0.18, "ba tags", size=11, transform=plt.gcf().transFigure)
        lengths_array_ab = []
        lengths_array_ba = []

        index_array = 0
        for i, s, count in zip(groupUnique, space, quantAfterRegion):
            index_of_current_region = numpy.where(group == i)[0]

            plt.text(0.55, 0.14 - s, "{}=\n".format(i), size=11, transform=plt.gcf().transFigure)
            if re.search('^(\d)+_(\d)+', str(mut_array[0, 0])) is None:
                nr_tags_ab = len(numpy.unique(mut_array[index_of_current_region, 1]))
            else:
                nr_tags_ab = len(mut_array[index_of_current_region, 1])

            plt.text(0.7, 0.14 - s, "{:,}\n".format(nr_tags_ab), size=11, transform=plt.gcf().transFigure)

            alt_group_i = alt_group[index_of_current_region]
            alternative = numpy.where(alt_group_i != "=")[0]
            unique_alt = numpy.unique(alt_group_i[alternative])
            lengths_of_alt_aligned_tags = []
            if len(alternative) != 0:
                for alt in unique_alt:
                    ind_alt_tags = numpy.where(alt_group_i == alt)[0]
                    name = "{:,} to {}".format(len(ind_alt_tags), alt)
                    lengths_of_alt_aligned_tags.append(name)
                ind_alt_tags_inverse = numpy.where(alt_group_i != alt)[0]
                name_inverse = "{:,} to {}".format(len(ind_alt_tags_inverse), i)
                lengths_of_alt_aligned_tags.append(name_inverse)
                plt.text(0.78, 0.14 - s, "{}\n".format(", ".join(lengths_of_alt_aligned_tags)), size=10, transform=plt.gcf().transFigure)
                lengths_array_ab.append(nr_tags_ab)
                lengths_array_ba.append(",".join(lengths_of_alt_aligned_tags))
            else:
                plt.text(0.78, 0.14 - s, "=\n", size=11,transform=plt.gcf().transFigure)
                lengths_array_ab.append(nr_tags_ab)
                lengths_array_ba.append(nr_tags_ab)
            index_array += 1

        plt.legend(loc='upper right', fontsize=14, bbox_to_anchor=(0.9, 1), frameon=True)
        plt.xlabel("Family size", fontsize=14)
        plt.ylabel("Absolute Frequency", fontsize=14)
        plt.grid(b=True, which="major", color="#424242", linestyle=":")
        plt.margins(0.01, None)

        pdf.savefig(fig, bbox_inch="tight")
        plt.close()

        output_file.write("Dataset:{}{}\n".format(sep, name1))
        output_file.write("{}AB{}BA\n".format(sep, sep))
        output_file.write("max. family size:{}{}{}{}\n".format(sep, max(map(int, quant_ab)), sep, max(map(int, quant_ba))))
        output_file.write("absolute frequency:{}{}{}{}\n".format(sep, count[len(count) - 1], sep, count2[len(count2) - 1]))
        output_file.write("relative frequency:{}{:.3f}{}{:.3f}\n\n".format(sep, float(count[len(count) - 1]) / sum(count), sep, float(count2[len(count2) - 1]) / sum(count2)))
        output_file.write("total nr. of reads{}{}\n".format(sep, sum(numpy.array(data_array[:, 0]).astype(int))))
        output_file.write("total nr. of tags{}{}\n".format(sep, length_regions))

        output_file.write("\n\nValues from family size distribution\n")
        output_file.write("{}".format(sep))
        for i in groupUnique:
            output_file.write("{}{}".format(i, sep))
        output_file.write("\n")
        j = 0
        for fs in counts[1][0:len(counts[1]) - 1]:
            if fs == 21:
                fs = ">20"
            else:
                fs = "={}".format(fs)
            output_file.write("FS{}{}".format(fs, sep))

            if len(groupUnique) == 1:
                output_file.write("{}{}".format(int(counts[0][j]), sep))
            else:
                for n in range(len(groupUnique)):
                    output_file.write("{}{}".format(int(counts[0][n][j]), sep))

            output_file.write("\n")
            j += 1
        output_file.write("sum{}".format(sep))
        if len(groupUnique) == 1:
            output_file.write("{}{}".format(int(sum(counts[0])), sep))
        else:
            for i in counts[0]:
                output_file.write("{}{}".format(int(sum(i)), sep))
        output_file.write("\n")
        output_file.write("\n\nRegion{}total nr. of ab{}ba tags\n".format(sep, sep))

        for ab, ba, i in zip(lengths_array_ab, lengths_array_ba, groupUnique):
            output_file.write("{}{}{}{}{}\n".format(i, sep, ab, sep, ba))

    print("Files successfully created!")


if __name__ == '__main__':
   sys.exit(compare_read_families_refGenome(sys.argv))