--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/peak_calling_script.py	Thu Mar 29 12:06:11 2018 -0400
@@ -0,0 +1,90 @@
+
+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]
+inName = sys.argv[4]
+
+with open(toolInput, '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]))
+
+csvfile.close()
+
+# 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 = inName
+
+#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(toolOutput, "w") as wr:
+        wr.write(pystache.render(wt.read(), template_dir))
+
+wt.close()
+wr.close()