| Previous changeset 1:adc396724afc (2017-12-31) Next changeset 3:a1cb5223ac36 (2018-01-16) |
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Commit message:
Deleted selected files |
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removed:
adams_tool.py adams_tool.xml |
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| diff -r adc396724afc -r b62d3210b814 adams_tool.py --- a/adams_tool.py Sun Dec 31 09:32:39 2017 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
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| @@ -1,83 +0,0 @@ - -from __future__ import print_function -import sys -import numpy -import math -import random -import csv -import matplotlib.pyplot as plt -import pystache -import json -from sklearn import mixture - -x = [] -y = [] - -toolInput = sys.argv[1] -toolOutput = sys.argv[2] -toolWebsite = sys.argv[3] - -with open(sys.argv[1], 'rb') as csvfile: - spamreader = csv.reader(csvfile, delimiter='\t') - for i, row in enumerate(spamreader): - if i != 0: - x.append(int(row[0])) - y.append(int(row[1])) - -# you have to set this manually to weed out all the noise. Every bit of noise should be below it. -threshold = 20 -rightLimit = 200 - -# unravelling histogram into samples. -samples = [] -for no, value in enumerate([int(round(i)) for i in y]): - if value > threshold and no < rightLimit: - for _ in range(value): - samples.append(no) - -# total number of reads -totalAmp = len(samples) - -# reshaping numpy arrays to indicate that we pass a lot of samples, not a lot of features. -xArray = numpy.array(x).reshape(1, -1) -samplesArray = numpy.array(samples).reshape(-1, 1) - -# learning a gaussian mixture model. -gmm2 = mixture.BayesianGaussianMixture(n_components=2).fit(samplesArray) - -# getting the mean of each gaussian -means = [x[int(round(i[0]))] for i in gmm2.means_] - -# rounding errors -roundErr = [i[0] - int(round(i[0])) for i in gmm2.means_] - -# getting the coverage of each gaussian -weights = gmm2.weights_ - -sampleID = toolOutput + ".html" - -with open(toolOutput, "w") as f: - print("sampleID", file=f, end="\t") - print("Al1", file=f, end="\t") - print("Al2", file=f, end="\t") - print("frac1", file=f, end="\t") - print("frac2", file=f, end="\t") - print(file=f) - print(sampleID, file=f, end="\t") - print(means[0], file=f, end="\t") - print(means[1], file=f, end="\t") - print(weights[0], file=f, end="\t") - print(weights[1], file=f, end="\t") - -template_dir = { - "sampleID": sampleID, - "al1": means[0], - "al2": means[1], - "freq1": weights[0], - "freq2": weights[1], - "x": json.dumps(x), - "y": json.dumps(y) - } -with open(toolWebsite) as wt: - with open(sampleID, "w") as wr: - wr.write(pystache.render(wt.read(), template_dir)) |
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| diff -r adc396724afc -r b62d3210b814 adams_tool.xml --- a/adams_tool.xml Sun Dec 31 09:32:39 2017 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
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| @@ -1,22 +0,0 @@ - -<tool id="adams_tool" name="Find modes of bimodal PCR-like distribution" version="0.1.0"> - <command interpreter="python"> adams_tool.py $input $output $__tool_directory__/web_template.html </command> - <inputs> - <param format="tabular" name="input" type="data" label="Source file"/> - </inputs> - <outputs> - <data format="tabular" name="output" /> - </outputs> - - <tests> - <!--test> - <param name="input" value="fa_gc_content_input.fa"/> - <output name="out_file1" file="fa_gc_content_output.txt"/> - </test--> - </tests> - - <help> - Find modes of bimodal distribution of PCR reads - </help> - -</tool> |