--- a/galaxy_wrapper.xml	Tue Jan 16 15:15:36 2018 -0500
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -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>

--- a/generate_data.py	Tue Jan 16 15:15:36 2018 -0500
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,24 +0,0 @@
-from __future__ import print_function
-import math
-import random
-# that's our x data, i.e. reference
-x = range(1, 101)
-
-# generate a gaussian
-def gaussian(x, amp, cen, wid):
-    return amp * math.exp(-(x - cen) ** 2 / wid)
-
-read1 = 18
-read2 = 66
-
-# that's our y data, i.e. reads
-y = [int(round(gaussian(i, 20000, read1, 0.5) + gaussian(i, 20000, read2, 0.5) + random.gauss(200, 90))) for i in x]
-
-# that's our data printed in pairs (x_i, y_i)
-with open("input.txt", "w") as f:
-    for pair in zip(x, y):
-        for p in pair:
-            print(p, end="\t", file=f)
-        print(file=f)
-
-# you have to set this manually to weed out all the noise. Every bit of noise should be below it.

--- a/peak_calling_script.py	Tue Jan 16 15:15:36 2018 -0500
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -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))