--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/pairend_distro.py	Tue Dec 06 05:46:28 2016 -0500
@@ -0,0 +1,140 @@
+#!/usr/bin/env python
+#  (c) 2012 - Ryan M. Layer
+#  Hall Laboratory
+#  Quinlan Laboratory
+#  Department of Computer Science
+#  Department of Biochemistry and Molecular Genetics
+#  Department of Public Health Sciences and Center for Public Health Genomics,
+#  University of Virginia
+#  rl6sf@virginia.edu
+
+import sys
+import numpy as np
+from operator import itemgetter
+from optparse import OptionParser
+
+# some constants for sam/bam field ids
+SAM_FLAG = 1
+SAM_REFNAME = 2
+SAM_MATE_REFNAME = 6
+SAM_ISIZE = 8
+
+parser = OptionParser()
+
+parser.add_option("-r",
+    "--read_length",
+    type="int",
+    dest="read_length",
+    help="Read length")
+
+parser.add_option("-X",
+    dest="X",
+    type="int",
+    help="Number of stdevs from mean to extend")
+
+parser.add_option("-N",
+    dest="N",
+    type="int",
+    help="Number to sample")
+
+parser.add_option("-o",
+    dest="output_file",
+    help="Output file")
+
+parser.add_option("-m",
+    dest="mads",
+    type="int",
+    default=10,
+    help="Outlier cutoff in # of median absolute deviations (unscaled, upper only)")
+
+def unscaled_upper_mad(xs):
+    """Return a tuple consisting of the median of xs followed by the
+    unscaled median absolute deviation of the values in xs that lie
+    above the median.
+    """
+    med = np.median(xs)
+    return med, np.median(xs[xs > med] - med)
+
+
+(options, args) = parser.parse_args()
+
+if not options.read_length:
+    parser.error('Read length not given')
+
+if not options.X:
+    parser.error('X not given')
+
+if not options.N:
+    parser.error('N not given')
+
+if not options.output_file:
+    parser.error('Output file not given')
+
+
+required = 97
+restricted = 3484
+flag_mask = required | restricted
+
+L = []
+c = 0
+
+for l in sys.stdin:
+    if c >= options.N:
+        break
+
+    A = l.rstrip().split('\t')
+    flag = int(A[SAM_FLAG])
+    refname = A[SAM_REFNAME]
+    mate_refname = A[SAM_MATE_REFNAME]
+    isize = int(A[SAM_ISIZE])
+
+    want = mate_refname == "=" and flag & flag_mask == required and isize >= 0
+    if want:
+        c += 1
+        L.append(isize)
+
+# warn if very few elements in distribution
+min_elements = 1000
+if len(L) < min_elements:
+    sys.stderr.write("Warning: only %s elements in distribution (min: %s)\n" % (len(L), min_elements))
+    mean = "NA"
+    stdev = "NA"
+
+else:
+    # Remove outliers
+    L = np.array(L)
+    L.sort()
+    med, umad = unscaled_upper_mad(L)
+    upper_cutoff = med + options.mads * umad
+    L = L[L < upper_cutoff]
+    new_len = len(L)
+    removed = c - new_len
+    sys.stderr.write("Removed %d outliers with isize >= %d\n" %
+        (removed, upper_cutoff))
+    c = new_len
+
+    mean = np.mean(L)
+    stdev = np.std(L)
+
+    start = options.read_length
+    end = int(mean + options.X*stdev)
+
+    H = [0] * (end - start + 1)
+    s = 0
+
+    for x in L:
+        if (x >= start) and (x <= end):
+            j = int(x - start)
+            H[j] = H[ int(x - start) ] + 1
+            s += 1
+
+    f = open(options.output_file, 'w')
+
+    for i in range(end - start):
+        o = str(i) + "\t" + str(float(H[i])/float(s)) + "\n"
+        f.write(o)
+
+
+    f.close()
+
+print('mean:' + str(mean) + '\tstdev:' + str(stdev))