--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/fsd.py	Mon Oct 08 05:50:18 2018 -0400
@@ -0,0 +1,372 @@
+#!/usr/bin/env python
+
+# Family size distribution of SSCSs
+#
+# 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, but up to 4 files can be provided, as input.
+# The program produces a plot which shows the distribution of family sizes of the all SSCSs from the input files and
+# a tabular file with the data of the plot, as well as a TXT file with all tags of the DCS and their family sizes.
+# If only one file is provided, then a family size distribution, which is separated after SSCSs without a partner and DCSs, is produced.
+# Whereas a family size distribution with multiple data in one plot is produced, when more than one file (up to 4) is given.
+
+# USAGE: python FSD_Galaxy_1.4_commandLine_FINAL.py --inputFile1 filename --inputName1 filename --inputFile2 filename2 --inputName2 filename2 --inputFile3 filename3 --inputName3 filename3 --inputFile4 filename4 --inputName4 filename4 --output_tabular outptufile_name_tabular --output_pdf outptufile_name_pdf
+
+import argparse
+import sys
+
+import matplotlib.pyplot as plt
+import numpy
+from matplotlib.backends.backend_pdf import PdfPages
+
+plt.switch_backend('agg')
+
+
+def readFileReferenceFree(file):
+    with open(file, 'r') as dest_f:
+        data_array = numpy.genfromtxt(dest_f, skip_header=0, delimiter='\t', comments='#', dtype='string')
+        return(data_array)
+
+
+def make_argparser():
+    parser = argparse.ArgumentParser(description='Family Size Distribution of duplex sequencing data')
+    parser.add_argument('--inputFile1', help='Tabular File with three columns: ab or ba, tag and family size.')
+    parser.add_argument('--inputName1')
+    parser.add_argument('--inputFile2', default=None, help='Tabular File with three columns: ab or ba, tag and family size.')
+    parser.add_argument('--inputName2')
+    parser.add_argument('--inputFile3', default=None, help='Tabular File with three columns: ab or ba, tag and family size.')
+    parser.add_argument('--inputName3')
+    parser.add_argument('--inputFile4', default=None, help='Tabular File with three columns: ab or ba, tag and family size.')
+    parser.add_argument('--inputName4')
+    parser.add_argument('--output_pdf', default="data.pdf", type=str, help='Name of the pdf file.')
+    parser.add_argument('--output_tabular', default="data.tabular", type=str, help='Name of the tabular file.')
+    return parser
+
+
+def compare_read_families(argv):
+    parser = make_argparser()
+    args = parser.parse_args(argv[1:])
+
+    firstFile = args.inputFile1
+    name1 = args.inputName1
+
+    secondFile = args.inputFile2
+    name2 = args.inputName2
+    thirdFile = args.inputFile3
+    name3 = args.inputName3
+    fourthFile = args.inputFile4
+    name4 = args.inputName4
+
+    title_file = args.output_tabular
+    title_file2 = args.output_pdf
+
+    sep = "\t"
+
+    plt.rc('figure', figsize=(11.69, 8.27))  # A4 format
+    plt.rcParams['patch.edgecolor'] = "black"
+    plt.rcParams['axes.facecolor'] = "E0E0E0"  # grey background color
+    plt.rcParams['xtick.labelsize'] = 14
+    plt.rcParams['ytick.labelsize'] = 14
+
+    list_to_plot = []
+    label = []
+    data_array_list = []
+    with open(title_file, "w") as output_file, PdfPages(title_file2) as pdf:
+        fig = plt.figure()
+        plt.subplots_adjust(bottom=0.25)
+        if firstFile != str(None):
+            file1 = readFileReferenceFree(firstFile)
+            integers = numpy.array(file1[:, 0]).astype(int)  # keep original family sizes
+
+            # for plot: replace all big family sizes by 22
+            data1 = numpy.array(file1[:, 0]).astype(int)
+            bigFamilies = numpy.where(data1 > 20)[0]
+            data1[bigFamilies] = 22
+
+            name1 = name1.split(".tabular")[0]
+            list_to_plot.append(data1)
+            label.append(name1)
+            data_array_list.append(file1)
+
+            legend = "\n\n\n{}".format(name1)
+            plt.text(0.1, 0.11, legend, size=12, transform=plt.gcf().transFigure)
+            legend1 = "singletons:\nabsolute nr.\n{:,}".format(numpy.bincount(data1)[1])
+            plt.text(0.4, 0.11, legend1, size=12, transform=plt.gcf().transFigure)
+
+            legend3 = "rel. freq\n{:.3f}".format(float(numpy.bincount(data1)[1]) / len(data1))
+            plt.text(0.5, 0.11, legend3, size=12, transform=plt.gcf().transFigure)
+
+            legend4 = "family size > 20:\nabsolute nr.\n{:,}".format(numpy.bincount(data1)[len(numpy.bincount(data1)) - 1].astype(int))
+            plt.text(0.6, 0.11, legend4, size=12, transform=plt.gcf().transFigure)
+
+            legend5 = "rel. freq\n{:.3f}".format(float(numpy.bincount(data1)[len(numpy.bincount(data1)) - 1]) / len(data1))
+            plt.text(0.7, 0.11, legend5, size=12, transform=plt.gcf().transFigure)
+
+            legend6 = "total length\n{:,}".format(len(data1))
+            plt.text(0.8, 0.11, legend6, size=12, transform=plt.gcf().transFigure)
+
+        if secondFile != str(None):
+            file2 = readFileReferenceFree(secondFile)
+            data2 = numpy.asarray(file2[:, 0]).astype(int)
+            bigFamilies2 = numpy.where(data2 > 20)[0]
+            data2[bigFamilies2] = 22
+
+            list_to_plot.append(data2)
+            name2 = name2.split(".tabular")[0]
+            label.append(name2)
+            data_array_list.append(file2)
+
+            plt.text(0.1, 0.09, name2, size=12, transform=plt.gcf().transFigure)
+
+            legend1 = "{:,}".format(numpy.bincount(data2)[1])
+            plt.text(0.4, 0.09, legend1, size=12, transform=plt.gcf().transFigure)
+
+            legend3 = "{:.3f}".format(float(numpy.bincount(data2)[1]) / len(data2))
+            plt.text(0.5, 0.09, legend3, size=12, transform=plt.gcf().transFigure)
+
+            legend4 = "{:,}".format(numpy.bincount(data2)[len(numpy.bincount(data2)) - 1].astype(int))
+            plt.text(0.6, 0.09, legend4, size=12, transform=plt.gcf().transFigure)
+
+            legend5 = "{:.3f}".format(float(numpy.bincount(data2)[len(numpy.bincount(data2)) - 1]) / len(data2))
+            plt.text(0.7, 0.09, legend5, size=12, transform=plt.gcf().transFigure)
+
+            legend6 = "{:,}".format(len(data2))
+            plt.text(0.8, 0.09, legend6, size=12, transform=plt.gcf().transFigure)
+
+        if thirdFile != str(None):
+            file3 = readFileReferenceFree(thirdFile)
+
+            data3 = numpy.asarray(file3[:, 0]).astype(int)
+            bigFamilies3 = numpy.where(data3 > 20)[0]
+            data3[bigFamilies3] = 22
+
+            list_to_plot.append(data3)
+            name3 = name3.split(".tabular")[0]
+            label.append(name3)
+            data_array_list.append(file3)
+
+            plt.text(0.1, 0.07, name3, size=12, transform=plt.gcf().transFigure)
+
+            legend1 = "{:,}".format(numpy.bincount(data3)[1])
+            plt.text(0.4, 0.07, legend1, size=12, transform=plt.gcf().transFigure)
+
+            legend3 = "{:.3f}".format(float(numpy.bincount(data3)[1]) / len(data3))
+            plt.text(0.5, 0.07, legend3, size=12, transform=plt.gcf().transFigure)
+
+            legend4 = "{:,}".format(numpy.bincount(data3)[len(numpy.bincount(data3)) - 1].astype(int))
+            plt.text(0.6, 0.07, legend4, size=12, transform=plt.gcf().transFigure)
+
+            legend5 = "{:.3f}".format(float(numpy.bincount(data3)[len(numpy.bincount(data3)) - 1]) / len(data3))
+            plt.text(0.7, 0.07, legend5, size=12, transform=plt.gcf().transFigure)
+
+            legend6 = "{:,}".format(len(data3))
+            plt.text(0.8, 0.07, legend6, size=12, transform=plt.gcf().transFigure)
+
+        if fourthFile != str(None):
+            file4 = readFileReferenceFree(fourthFile)
+
+            data4 = numpy.asarray(file4[:, 0]).astype(int)
+
+            bigFamilies4 = numpy.where(data4 > 20)[0]
+            data4[bigFamilies4] = 22
+
+            list_to_plot.append(data4)
+            name4 = name4.split(".tabular")[0]
+            label.append(name4)
+            data_array_list.append(file4)
+
+            plt.text(0.1, 0.05, name4, size=12, transform=plt.gcf().transFigure)
+
+            legend1 = "{:,}".format(numpy.bincount(data4)[1])
+            plt.text(0.4, 0.05, legend1, size=12, transform=plt.gcf().transFigure)
+
+            legend4 = "{:.3f}".format(float(numpy.bincount(data4)[1]) / len(data4))
+            plt.text(0.5, 0.05, legend4, size=12, transform=plt.gcf().transFigure)
+
+            legend4 = "{:,}".format(numpy.bincount(data4)[len(numpy.bincount(data4)) - 1].astype(int))
+            plt.text(0.6, 0.05, legend4, size=12, transform=plt.gcf().transFigure)
+
+            legend5 = "{:.3f}".format(float(numpy.bincount(data4)[len(numpy.bincount(data4)) - 1]) / len(data4))
+            plt.text(0.7, 0.05, legend5, size=12, transform=plt.gcf().transFigure)
+
+            legend6 = "{:,}".format(len(data4))
+            plt.text(0.8, 0.05, legend6, size=12, transform=plt.gcf().transFigure)
+
+        maximumX = numpy.amax(numpy.concatenate(list_to_plot))
+        minimumX = numpy.amin(numpy.concatenate(list_to_plot))
+
+        counts = plt.hist(list_to_plot, bins=range(minimumX, maximumX + 1), stacked=False, edgecolor="black",
+                          linewidth=1, label=label, align="left", alpha=0.7, rwidth=0.8)
+
+        ticks = numpy.arange(minimumX - 1, maximumX, 1)
+        ticks1 = map(str, ticks)
+        ticks1[len(ticks1) - 1] = ">20"
+        plt.xticks(numpy.array(ticks), ticks1)
+
+        plt.legend(loc='upper right', fontsize=14, frameon=True, bbox_to_anchor=(0.9, 1))
+        # plt.title("Family Size Distribution", fontsize=14)
+        plt.xlabel("Family size", fontsize=14)
+        plt.ylabel("Absolute Frequency", fontsize=14)
+        plt.margins(0.01, None)
+        plt.grid(b=True, which="major", color="#424242", linestyle=":")
+        pdf.savefig(fig)
+        plt.close()
+
+        # write data to CSV file
+        output_file.write("Values from family size distribution with all datasets\n")
+        output_file.write("\nFamily size")
+        for i in label:
+            output_file.write("{}{}".format(sep, i))
+        # output_file.write("{}sum".format(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(label) == 1:
+                output_file.write("{}{}".format(int(counts[0][j]), sep))
+            else:
+                for n in range(len(label)):
+                    output_file.write("{}{}".format(int(counts[0][n][j]), sep))
+            output_file.write("\n")
+            j += 1
+        output_file.write("sum{}".format(sep))
+        if len(label) == 1:
+            output_file.write("{}{}".format(int(sum(counts[0])), sep))
+        else:
+            for i in counts[0]:
+                output_file.write("{}{}".format(int(sum(i)), sep))
+
+        # Family size distribution after DCS and SSCS
+        for dataset, data, name_file in zip(list_to_plot, data_array_list, label):
+            maximumX = numpy.amax(dataset)
+            minimumX = numpy.amin(dataset)
+
+            tags = numpy.array(data[:, 2])
+            seq = numpy.array(data[:, 1])
+            data = numpy.array(dataset)
+
+            # find all unique tags and get the indices for ALL tags, but only once
+            u, index_unique, c = numpy.unique(numpy.array(seq), return_counts=True, return_index=True)
+            d = u[c > 1]
+
+            # get family sizes, tag for duplicates
+            duplTags_double = data[numpy.in1d(seq, d)]
+            duplTags = duplTags_double[0::2]  # ab of DCS
+            duplTagsBA = duplTags_double[1::2]  # ba of DCS
+
+            # duplTags_double_tag = tags[numpy.in1d(seq, d)]
+            # duplTags_double_seq = seq[numpy.in1d(seq, d)]
+
+            # get family sizes for SSCS with no partner
+            ab = numpy.where(tags == "ab")[0]
+            abSeq = seq[ab]
+            ab = data[ab]
+            ba = numpy.where(tags == "ba")[0]
+            baSeq = seq[ba]
+            ba = data[ba]
+
+            dataAB = ab[numpy.in1d(abSeq, d, invert=True)]
+            dataBA = ba[numpy.in1d(baSeq, d, invert=True)]
+
+            list1 = [duplTags_double, dataAB, dataBA]  # list for plotting
+
+            # information for family size >= 3
+            dataAB_FS3 = dataAB[dataAB >= 3]
+            dataBA_FS3 = dataBA[dataBA >= 3]
+            ab_FS3 = ab[ab >= 3]
+            ba_FS3 = ba[ba >= 3]
+
+            duplTags_FS3 = duplTags[(duplTags >= 3) & (duplTagsBA >= 3)]  # ab+ba with FS>=3
+            duplTags_FS3_BA = duplTagsBA[(duplTags >= 3) & (duplTagsBA >= 3)]  # ba+ab with FS>=3
+            duplTags_double_FS3 = len(duplTags_FS3) + len(duplTags_FS3_BA)  # both ab and ba strands with FS>=3
+
+            fig = plt.figure()
+
+            plt.subplots_adjust(bottom=0.3)
+            counts = plt.hist(list1, bins=range(minimumX, maximumX + 1), stacked=True, label=["duplex", "ab", "ba"], edgecolor="black", linewidth=1, align="left", color=["#FF0000", "#5FB404", "#FFBF00"])
+            # tick labels of x axis
+            ticks = numpy.arange(minimumX - 1, maximumX, 1)
+            ticks1 = map(str, ticks)
+            ticks1[len(ticks1) - 1] = ">20"
+            plt.xticks(numpy.array(ticks), ticks1)
+            singl = counts[0][2][0]  # singletons
+            last = counts[0][2][len(counts[0][0]) - 1]  # large families
+
+            plt.legend(loc='upper right', fontsize=14, bbox_to_anchor=(0.9, 1), frameon=True)
+            # plt.title(name1, fontsize=14)
+            plt.xlabel("Family size", fontsize=14)
+            plt.ylabel("Absolute Frequency", fontsize=14)
+            plt.margins(0.01, None)
+            plt.grid(b=True, which="major", color="#424242", linestyle=":")
+
+            # extra information beneath the plot
+            legend = "SSCS ab= \nSSCS ba= \nDCS (total)= \nlength of dataset="
+            plt.text(0.1, 0.09, legend, size=12, transform=plt.gcf().transFigure)
+
+            legend = "absolute numbers\n\n{:,}\n{:,}\n{:,} ({:,})\n{:,}".format(len(dataAB), len(dataBA), len(duplTags), len(duplTags_double), (len(dataAB) + len(dataBA) + len(duplTags)))
+            plt.text(0.35, 0.09, legend, size=12, transform=plt.gcf().transFigure)
+
+            legend = "relative frequencies\nunique\n{:.3f}\n{:.3f}\n{:.3f}\n{:,}".format(float(len(dataAB)) / (len(dataAB) + len(dataBA) + len(duplTags)), float(len(dataBA)) / (len(dataAB) + len(dataBA) + len(duplTags)), float(len(duplTags)) / (len(dataAB) + len(dataBA) + len(duplTags)), (len(dataAB) + len(dataBA) + len(duplTags)))
+            plt.text(0.54, 0.09, legend, size=12, transform=plt.gcf().transFigure)
+
+            legend = "total\n{:.3f}\n{:.3f}\n{:.3f} ({:.3f})\n{:,}".format(float(len(dataAB)) / (len(ab) + len(ba)), float(len(dataBA)) / (len(ab) + len(ba)), float(len(duplTags)) / (len(ab) + len(ba)), float(len(duplTags_double)) / (len(ab) + len(ba)), (len(ab) + len(ba)))
+            plt.text(0.64, 0.09, legend, size=12, transform=plt.gcf().transFigure)
+
+            legend1 = "\nsingletons:\nfamily size > 20:"
+            plt.text(0.1, 0.03, legend1, size=12, transform=plt.gcf().transFigure)
+
+            legend4 = "{:,}\n{:,}".format(singl.astype(int), last.astype(int))
+            plt.text(0.35, 0.03, legend4, size=12, transform=plt.gcf().transFigure)
+
+            legend3 = "{:.3f}\n{:.3f}".format(singl / len(data), last / len(data))
+            plt.text(0.54, 0.03, legend3, size=12, transform=plt.gcf().transFigure)
+
+            pdf.savefig(fig)
+            plt.close()
+
+            # write same information to a csv file
+            count = numpy.bincount(integers)  # original counts of family sizes
+            output_file.write("\nDataset:{}{}\n".format(sep, name_file))
+            output_file.write("max. family size:{}{}\n".format(sep, max(integers)))
+            output_file.write("absolute frequency:{}{}\n".format(sep, count[len(count) - 1]))
+            output_file.write("relative frequency:{}{:.3f}\n\n".format(sep, float(count[len(count) - 1]) / sum(count)))
+
+            output_file.write("{}singletons:{}{}family size > 20:\n".format(sep, sep, sep))
+            output_file.write("{}absolute nr.{}rel. freq{}absolute nr.{}rel. freq{}total length\n".format(sep, sep, sep, sep, sep))
+            output_file.write("{}{}{}{}{:.3f}{}{}{}{:.3f}{}{}\n\n".format(name_file, sep, singl.astype(int), sep, singl / len(data), sep, last.astype(int), sep, last / len(data), sep, len(data)))
+
+            # information for FS >= 1
+            output_file.write("The unique frequencies were calculated from the dataset where the tags occured only once (=ab without DCS, ba without DCS)\nWhereas the total frequencies were calculated from the whole dataset (=including the DCS).\n\n")
+            output_file.write("FS >= 1{}{}unique:{}total:\n".format(sep, sep, sep))
+            output_file.write("nr./rel. freq of ab={}{}{}{:.3f}{}{:.3f}\n".format(sep, len(dataAB), sep, float(len(dataAB)) / (len(dataAB) + len(dataBA) + len( duplTags)), sep, float(len(dataAB)) / (len(ab) + len(ba))))
+            output_file.write("nr./rel. freq of ba={}{}{}{:.3f}{}{:.3f}\n".format(sep, len(dataBA), sep, float(len(dataBA)) / (len(dataBA) + len(dataBA) + len(duplTags)), sep, float(len(dataBA)) / (len(ba) + len(ba))))
+            output_file.write("nr./rel. freq of DCS (total)={}{} ({}){}{:.3f}{}{:.3f} ({:.3f})\n".format(sep, len(duplTags), len(duplTags_double), sep, float(len(duplTags)) / (len(dataAB) + len(dataBA) + len(duplTags)), sep, float(len(duplTags)) / ( len(ab) + len(ba)), float(len(duplTags_double)) / (len(ab) + len(ba))))
+            output_file.write("length of dataset={}{}{}{}{}{}\n".format(sep, (len(dataAB) + len(dataBA) + len(duplTags)), sep, (len(dataAB) + len(dataBA) + len(duplTags)), sep, (len(ab) + len(ba))))
+            # information for FS >= 3
+            output_file.write("FS >= 3{}{}unique:{}total:\n".format(sep, sep, sep))
+            output_file.write("nr./rel. freq of ab={}{}{}{:.3f}{}{:.3f}\n".format(sep, len(dataAB_FS3), sep, float(len(dataAB_FS3)) / (len(dataAB_FS3) + len(dataBA_FS3) + len(duplTags_FS3)), sep, float(len(dataAB_FS3)) / (len(ab_FS3) + len(ba_FS3))))
+            output_file.write("nr./rel. freq of ba={}{}{}{:.3f}{}{:.3f}\n".format(sep, len(dataBA_FS3), sep, float(len(dataBA_FS3)) / (len(dataBA_FS3) + len(dataBA_FS3) + len(duplTags_FS3)), sep, float(len(dataBA_FS3)) / (len(ba_FS3) + len(ba_FS3))))
+            output_file.write("nr./rel. freq of DCS (total)={}{} ({}){}{:.3f}{}{:.3f} ({:.3f})\n".format(sep, len(duplTags_FS3), duplTags_double_FS3, sep, float(len( duplTags_FS3)) / (len(dataBA_FS3) + len(duplTags_FS3)), sep, float(len(duplTags_FS3)) / (len(ab_FS3) + len(ba_FS3)), float(duplTags_double_FS3) / (len(ab_FS3) + len(ba_FS3))))
+            output_file.write("length of dataset={}{}{}{}{}{}\n".format(sep, (len(dataAB_FS3) + len(dataBA_FS3) + len(duplTags_FS3)), sep, (len(dataAB_FS3) + len(dataBA_FS3) + len(duplTags_FS3)), sep, (len(ab_FS3) + len(ba_FS3))))
+
+            output_file.write("\nValues from family size distribution\n")
+            output_file.write("{}duplex{}ab{}ba{}sum\n".format(sep, sep, sep, sep))
+            for dx, ab, ba, fs in zip(counts[0][0], counts[0][1], counts[0][2], counts[1]):
+                if fs == 21:
+                    fs = ">20"
+                else:
+                    fs = "={}".format(fs)
+                ab1 = ab - dx
+                ba1 = ba - ab
+                output_file.write("FS{}{}{}{}{}{}{}{}{}\n".format(fs, sep, int(dx), sep, int(ab1), sep, int(ba1), sep, int(ba)))
+
+    print("Files successfully created!")
+
+
+if __name__ == '__main__':
+    sys.exit(compare_read_families(sys.argv))