Mercurial > repos > mheinzl > hd
changeset 1:7414792e1cb8 draft
planemo upload for repository https://github.com/monikaheinzl/galaxyProject/tree/master/tools/hd commit 90ee23e393deb06fa5c15e3778fa23c39a25f7ce
| author | mheinzl |
|---|---|
| date | Sat, 12 May 2018 04:52:34 -0400 |
| parents | 239c4448a163 |
| children | 316fbf91dd12 |
| files | hd.py hd.xml |
| diffstat | 2 files changed, 394 insertions(+), 6 deletions(-) [+] |
line wrap: on
line diff
--- a/hd.py Thu May 10 07:30:27 2018 -0400 +++ b/hd.py Sat May 12 04:52:34 2018 -0400 @@ -24,16 +24,404 @@ import cPickle as pickle from multiprocessing.pool import Pool from functools import partial -from HDAnalysis_plots.plot_HDwithFSD import plotHDwithFSD -from HDAnalysis_plots.plot_FSDwithHD2 import plotFSDwithHD2 -from HDAnalysis_plots.plot_HDwithinSeq_Sum2 import plotHDwithinSeq_Sum2 -from HDAnalysis_plots.table_HD import createTableHD, createFileHD, createTableHDwithTags, createFileHDwithinTag -from HDAnalysis_plots.table_FSD import createTableFSD2, createFileFSD2 +#from HDAnalysis_plots.plot_HDwithFSD import plotHDwithFSD +#from HDAnalysis_plots.plot_FSDwithHD2 import plotFSDwithHD2 +#from HDAnalysis_plots.plot_HDwithinSeq_Sum2 import plotHDwithinSeq_Sum2 +#from HDAnalysis_plots.table_HD import createTableHD, createFileHD, createTableHDwithTags, createFileHDwithinTag +#from HDAnalysis_plots.table_FSD import createTableFSD2, createFileFSD2 import argparse import sys import os from matplotlib.backends.backend_pdf import PdfPages +from collections import Counter +def plotFSDwithHD2(familySizeList1,maximumXFS,minimumXFS, quant, + title_file1, subtitle, pdf, relative=False, diff = True): + if diff is False: + colors = ["#e6194b", "#3cb44b", "#ffe119", "#0082c8", "#f58231", "#911eb4"] + labels = ["HD=1", "HD=2", "HD=3", "HD=4", "HD=5-8","HD>8"] + else: + colors = ["#93A6AB", "#403C14", "#731E41", "#BAB591", "#085B6F", "#E8AA35", "#726C66"] + if relative is True: + labels = ["d=0", "d=0.1", "d=0.2", "d=0.3", "d=0.4", "d=0.5-0.8", "d>0.8"] + else: + labels = ["d=0","d=1", "d=2", "d=3", "d=4", "d=5-8","d>8"] + + fig = plt.figure(figsize=(6, 7)) + ax = fig.add_subplot(111) + plt.subplots_adjust(bottom=0.1) + p1 = numpy.bincount(numpy.concatenate((familySizeList1))) + maximumY = numpy.amax(p1) + + if len(range(minimumXFS, maximumXFS)) == 0: + range1 = range(minimumXFS - 1, minimumXFS + 2) + else: + range1 = range(0, maximumXFS + 2) + counts = plt.hist(familySizeList1, label=labels, + color=colors, stacked=True, + rwidth=0.8,alpha=1, align="left", + edgecolor="None",bins=range1) + plt.legend(loc='upper right', fontsize=14, frameon=True, bbox_to_anchor=(1.45, 1)) + + plt.title(title_file1, fontsize=12) + plt.suptitle(subtitle, y=1, x=0.5, fontsize=14) + plt.xlabel("No. of Family Members", fontsize=12) + plt.ylabel("Absolute Frequency", fontsize=12) + + ticks = numpy.arange(0, maximumXFS + 1, 1) + ticks1 = map(str, ticks) + if maximumXFS >= 20: + ticks1[len(ticks1) - 1] = ">=20" + plt.xticks(numpy.array(ticks), ticks1) + [l.set_visible(False) for (i, l) in enumerate(ax.get_xticklabels()) if i % 5 != 0] + + plt.xlim((0, maximumXFS + 1)) + if len(numpy.concatenate(familySizeList1)) != 0: + plt.ylim((0, max(numpy.bincount(numpy.concatenate(familySizeList1))) * 1.1)) + + plt.ylim((0, maximumY * 1.2)) + legend = "\nmax. family size: \nabsolute frequency: \nrelative frequency: " + plt.text(0.15, -0.08, legend, size=12, transform=plt.gcf().transFigure) + + count = numpy.bincount(quant) # original counts + legend1 = "{}\n{}\n{:.5f}" \ + .format(max(quant), count[len(count) - 1], float(count[len(count) - 1]) / sum(count)) + plt.text(0.5, -0.08, legend1, size=12, transform=plt.gcf().transFigure) + legend3 = "singletons\n{:,}\n{:.5f}".format(int(counts[0][len(counts[0]) - 1][1]), + float(counts[0][len(counts[0]) - 1][1]) / sum( + counts[0][len(counts[0]) - 1])) + plt.text(0.7, -0.08, legend3, transform=plt.gcf().transFigure, size=12) + plt.grid(b=True, which='major', color='#424242', linestyle=':') + + pdf.savefig(fig, bbox_inches="tight") + plt.close("all") + +def plotHDwithFSD(list1,maximumX,minimumX, subtitle, lenTags, title_file1,pdf, + xlabel,relative=False): + if relative is True: + step = 0.1 + else: + step = 1 + + fig = plt.figure(figsize=(6, 8)) + plt.subplots_adjust(bottom=0.1) + con_list1 = numpy.concatenate(list1) + p1 = numpy.array([v for k, v in sorted(Counter(con_list1).iteritems())]) + maximumY = numpy.amax(p1) + + if relative is True: # relative difference + bin1 = numpy.arange(-1, maximumX + 0.2, 0.1) + else: + bin1 = maximumX + 1 + + counts = plt.hist(list1, bins=bin1, edgecolor='black', linewidth=1, + label=["FS=1", "FS=2", "FS=3", "FS=4", "FS=5-10", + "FS>10"], rwidth=0.8, + color=["#808080", "#FFFFCC", "#FFBF00", "#DF0101", "#0431B4", "#86B404"], + stacked=True, alpha=1, + align="left", + range=(0, maximumX + 1)) + plt.legend(loc='upper right', fontsize=14, frameon=True, bbox_to_anchor=(1.45, 1)) + bins = counts[1] # width of bins + counts = numpy.array(map(int, counts[0][5])) + plt.suptitle(subtitle, y=1, x=0.5, fontsize=14) + plt.title(title_file1, fontsize=12) + plt.xlabel(xlabel, fontsize=12) + plt.ylabel("Absolute Frequency", fontsize=12) + + plt.grid(b=True, which='major', color='#424242', linestyle=':') + plt.axis((minimumX - step, maximumX + step, 0, numpy.amax(counts) + sum(counts) * 0.1)) + plt.xticks(numpy.arange(0, maximumX + step, step)) + + plt.ylim((0, maximumY * 1.2)) + + bin_centers = -0.4 * numpy.diff(bins) + bins[:-1] + for x_label, label in zip(counts, bin_centers): # labels for values + if x_label == 0: + continue + else: + plt.annotate("{:,}\n{:.3f}".format(x_label, float(x_label) / sum(counts), 1), + xy=(label, x_label + len(con_list1) * 0.01), + xycoords="data", color="#000066",fontsize=10) + + legend = "sample size= {:,} against {:,}".format(sum(counts), lenTags) + plt.text(0.14, -0.01, legend, size=12, transform=plt.gcf().transFigure) + + pdf.savefig(fig, bbox_inches="tight") + plt.close("all") + plt.clf() + +def plotHDwithinSeq_Sum2(sum1, sum2,min_value, lenTags, title_file1, pdf): + fig = plt.figure(figsize=(6, 8)) + plt.subplots_adjust(bottom=0.1) + + ham = [numpy.array(min_value), sum1, sum2] # new hd within tags + + maximumX = numpy.amax(numpy.concatenate(ham)) + minimumX = numpy.amin(numpy.concatenate(ham)) + maximumY = numpy.amax(numpy.concatenate(map(lambda (x): numpy.bincount(x), ham))) + + if len(range(minimumX, maximumX)) == 0: + range1 = minimumX + else: + range1 = range(minimumX, maximumX + 2) + + counts = plt.hist(ham, align="left", rwidth=0.8, stacked=False, + label=["HD of whole tag", "tag1 - a\nvs. tag2 - a", "tag1 - b\nvs. tag2 - b"], + bins=range1, color=["#585858", "#58ACFA", "#FA5858"], edgecolor='black', linewidth=1) + plt.legend(loc='upper right', fontsize=14, frameon=True, bbox_to_anchor=(1.55, 1)) + plt.suptitle('Hamming distances within tags', fontsize=14) + plt.title(title_file1, fontsize=12) + plt.xlabel("Hamming Distance", fontsize=12) + plt.ylabel("Absolute Frequency", fontsize=12) + plt.grid(b=True, which='major', color='#424242', linestyle=':') + + + plt.axis((minimumX - 1, maximumX + 1, 0, maximumY * 1.1)) + plt.xticks(numpy.arange(minimumX - 1, maximumX + 1, 1.0)) + plt.ylim((0, maximumY * 1.1)) + + legend = "sample size= {:,} against {:,}".format(len(ham[0]), lenTags, lenTags) + plt.text(0.14, -0.01, legend, size=12, transform=plt.gcf().transFigure) + pdf.savefig(fig, bbox_inches="tight") + plt.close("all") + plt.clf() + +def createTableFSD2(list1, diff=True): + selfAB = numpy.concatenate(list1) + uniqueFS = numpy.unique(selfAB) + nr = numpy.arange(0, len(uniqueFS), 1) + if diff is False: + count = numpy.zeros((len(uniqueFS), 6)) + else: + count = numpy.zeros((len(uniqueFS), 7)) + + state = 1 + for i in list1: + counts = list(Counter(i).items()) + hd = [item[0] for item in counts] + c = [item[1] for item in counts] + table = numpy.column_stack((hd, c)) + if len(table) == 0: + state = state + 1 + continue + else: + if state == 1: + for i, l in zip(uniqueFS, nr): + for j in table: + if j[0] == uniqueFS[l]: + count[l, 0] = j[1] + if state == 2: + for i, l in zip(uniqueFS, nr): + for j in table: + if j[0] == uniqueFS[l]: + count[l, 1] = j[1] + + if state == 3: + for i, l in zip(uniqueFS, nr): + for j in table: + if j[0] == uniqueFS[l]: + count[l, 2] = j[1] + + if state == 4: + for i, l in zip(uniqueFS, nr): + for j in table: + if j[0] == uniqueFS[l]: + count[l, 3] = j[1] + + if state == 5: + for i, l in zip(uniqueFS, nr): + for j in table: + if j[0] == uniqueFS[l]: + count[l, 4] = j[1] + + if state == 6: + for i, l in zip(uniqueFS, nr): + for j in table: + if j[0] == uniqueFS[l]: + count[l, 5] = j[1] + + if state == 7: + for i, l in zip(uniqueFS, nr): + for j in table: + if j[0] == uniqueFS[l]: + count[l, 6] = j[1] + state = state + 1 + + sumRow = count.sum(axis=1) + sumCol = count.sum(axis=0) + + uniqueFS = uniqueFS.astype(str) + if uniqueFS[len(uniqueFS) - 1] == "20": + uniqueFS[len(uniqueFS) - 1] = ">20" + + first = ["FS={}".format(i) for i in uniqueFS] + final = numpy.column_stack((first, count, sumRow)) + + return (final, sumCol) + +def createFileFSD2(summary, sumCol, overallSum, output_file, name,sep, rel=False, diff=True): + output_file.write(name) + output_file.write("\n") + if diff is False: + output_file.write("{}HD=1{}HD=2{}HD=3{}HD=4{}HD=5-8{}HD>8{}sum{}\n".format(sep,sep,sep,sep,sep,sep,sep,sep)) + else: + if rel is False: + output_file.write("{}diff=0{}diff=1{}diff=2{}diff=3{}diff=4{}diff=5-8{}diff>8{}sum{}\n".format(sep,sep,sep,sep,sep,sep,sep,sep,sep)) + else: + output_file.write("{}diff=0{}diff=0.1{}diff=0.2{}diff=0.3{}diff=0.4{}diff=0.5-0.8{}diff>0.8{}sum{}\n".format(sep,sep,sep,sep,sep,sep,sep,sep,sep)) + + for item in summary: + for nr in item: + if "FS" not in nr and "diff" not in nr: + nr = nr.astype(float) + nr = nr.astype(int) + output_file.write("{}{}".format(nr,sep)) + output_file.write("\n") + output_file.write("sum{}".format(sep)) + sumCol = map(int, sumCol) + for el in sumCol: + output_file.write("{}{}".format(el,sep)) + output_file.write("{}{}".format(overallSum.astype(int),sep)) + output_file.write("\n\n") + +def createTableHD(list1, row_label): + selfAB = numpy.concatenate(list1) + uniqueHD = numpy.unique(selfAB) + nr = numpy.arange(0, len(uniqueHD), 1) + count = numpy.zeros((len(uniqueHD), 6)) + state = 1 + for i in list1: + counts = list(Counter(i).items()) + hd = [item[0] for item in counts] + c = [item[1] for item in counts] + table = numpy.column_stack((hd, c)) + if len(table) == 0: + state = state + 1 + continue + else: + if state == 1: + for i, l in zip(uniqueHD, nr): + for j in table: + if j[0] == uniqueHD[l]: + count[l, 0] = j[1] + if state == 2: + for i, l in zip(uniqueHD, nr): + for j in table: + if j[0] == uniqueHD[l]: + count[l, 1] = j[1] + + if state == 3: + for i, l in zip(uniqueHD, nr): + for j in table: + if j[0] == uniqueHD[l]: + count[l, 2] = j[1] + + if state == 4: + for i, l in zip(uniqueHD, nr): + for j in table: + if j[0] == uniqueHD[l]: + count[l, 3] = j[1] + + if state == 5: + for i, l in zip(uniqueHD, nr): + for j in table: + if j[0] == uniqueHD[l]: + count[l, 4] = j[1] + + if state == 6: + for i, l in zip(uniqueHD, nr): + for j in table: + if j[0] == uniqueHD[l]: + count[l, 5] = j[1] + state = state + 1 + + sumRow = count.sum(axis=1) + sumCol = count.sum(axis=0) + first = ["{}{}".format(row_label,i) for i in uniqueHD] + final = numpy.column_stack((first, count, sumRow)) + + return (final, sumCol) + +def createTableHDwithTags(list1): + selfAB = numpy.concatenate(list1) + uniqueHD = numpy.unique(selfAB) + nr = numpy.arange(0, len(uniqueHD), 1) + count = numpy.zeros((len(uniqueHD), 3)) + + state = 1 + for i in list1: + counts = list(Counter(i).items()) + hd = [item[0] for item in counts] + c = [item[1] for item in counts] + table = numpy.column_stack((hd, c)) + if len(table) == 0: + state = state + 1 + continue + else: + if state == 1: + for i, l in zip(uniqueHD, nr): + for j in table: + if j[0] == uniqueHD[l]: + count[l, 0] = j[1] + if state == 2: + for i, l in zip(uniqueHD, nr): + for j in table: + if j[0] == uniqueHD[l]: + count[l, 1] = j[1] + + if state == 3: + for i, l in zip(uniqueHD, nr): + for j in table: + if j[0] == uniqueHD[l]: + count[l, 2] = j[1] + state = state + 1 + + sumRow = count.sum(axis=1) + sumCol = count.sum(axis=0) + first = ["HD={}".format(i) for i in uniqueHD] + final = numpy.column_stack((first, count, sumRow)) + + return (final, sumCol) + +def createFileHD(summary, sumCol, overallSum, output_file, name,sep): + output_file.write(name) + output_file.write("\n") + output_file.write("{}FS=1{}FS=2{}FS=3{}FS=4{}FS=5-10{}FS>10{}sum{}\n".format(sep,sep,sep,sep,sep,sep,sep,sep)) + for item in summary: + for nr in item: + if "HD" not in nr and "diff" not in nr: + nr = nr.astype(float) + nr = nr.astype(int) + output_file.write("{}{}".format(nr,sep)) + output_file.write("\n") + output_file.write("sum{}".format(sep)) + sumCol = map(int, sumCol) + for el in sumCol: + output_file.write("{}{}".format(el,sep)) + output_file.write("{}{}".format(overallSum.astype(int),sep)) + output_file.write("\n\n") + +def createFileHDwithinTag(summary, sumCol, overallSum, output_file, name,sep): + output_file.write(name) + output_file.write("\n") + output_file.write("{}HD of whole tag;tag1-half1 vs. tag2-half1{}tag1-half2 vs. tag2-half2{}sum{}\n".format(sep,sep,sep,sep)) + for item in summary: + for nr in item: + if "HD" not in nr: + nr = nr.astype(float) + nr = nr.astype(int) + output_file.write("{}{}".format(nr,sep)) + output_file.write("\n") + output_file.write("sum{}".format(sep)) + sumCol = map(int, sumCol) + for el in sumCol: + output_file.write("{}{}".format(el,sep)) + output_file.write("{}{}".format(overallSum.astype(int),sep)) + output_file.write("\n\n") + + + def hamming(array1, array2): res = 99 * numpy.ones(len(array1)) i = 0
--- a/hd.xml Thu May 10 07:30:27 2018 -0400 +++ b/hd.xml Sat May 12 04:52:34 2018 -0400 @@ -1,5 +1,5 @@ <?xml version="1.0" encoding="UTF-8"?> -<tool id="hd" name="Duplex Sequencing Analysis:" version="0.0.1"> +<tool id="hd" name="Duplex Sequencing Analysis:" version="0.0.2"> <requirements> <requirement type="package" version="2.7">python</requirement> <requirement type="package" version="1.4">matplotlib</requirement>