From 658214d5d13d98a8654937b90407039bb01f5a16 Mon Sep 17 00:00:00 2001
From: Leonid Boytsov <leo@Leonids-MBP.fios-router.home>
Date: Fri, 12 Apr 2019 21:08:36 -0400
Subject: [PATCH] xrange -> range (for Python3)

---
 data/genhist_nonunif.py | 35 -----------------------------------
 data/genhist_unif.py    |  4 ++--
 2 files changed, 2 insertions(+), 37 deletions(-)
 delete mode 100755 data/genhist_nonunif.py

diff --git a/data/genhist_nonunif.py b/data/genhist_nonunif.py
deleted file mode 100755
index 3e25ded..0000000
--- a/data/genhist_nonunif.py
+++ /dev/null
@@ -1,35 +0,0 @@
-#!/usr/bin/env python
-#
-# This script is released under the
-# Apache License Version 2.0 http://www.apache.org/licenses/.
-#
-# It generates vectors with coordinates sampled randomly and uniformly from [0,1]
-# If one specifies the flag -b or --binary, the script generates only binary data
-#
-
-import random
-import argparse
-import sys
-import numpy
-
-parser = argparse.ArgumentParser(description='vector generator (uniform)')
-
-parser.add_argument('-d','--dim',   required=True, type=int, help='dimensionality (# of vector elements)')
-parser.add_argument('-n','--ndata', required=True, type=int, help='# of data points')
-parser.add_argument('-o','--outf',  required=True, help='output file')
-parser.add_argument('--min_val', default=1e-5, help='the minimum possible vector element')
-
-args = vars(parser.parse_args())
-
-nd   = args['ndata']
-outf = args['outf']
-dim  = args['dim']
-minVal=args['min_val']
-
-f=open(outf, 'w')
-for i in xrange(nd):
-  arr = numpy.array([random.random() for _ in xrange(dim)])
-  arr /= sum(arr)
-  f.write("\t".join([("%g" % max(v,minVal)) for v in arr]) + "\n")
-f.close()
-
diff --git a/data/genhist_unif.py b/data/genhist_unif.py
index c3dc40a..71a3060 100755
--- a/data/genhist_unif.py
+++ b/data/genhist_unif.py
@@ -28,10 +28,10 @@ dim  = args['dim']
 minVal=args['min_val']
 
 f=open(outf, 'w')
-for i in xrange(nd):
+for i in range(nd):
   # See an explanation from this blog post http://blog.geomblog.org/2005/10/sampling-from-simplex.html
   # There's more on sampling from the simplex:  Luc Devroye's book, 'Non-Uniform Random Variate Generation'.
-  arr = numpy.array([-math.log(random.random()) for _ in xrange(dim)])
+  arr = numpy.array([-math.log(random.random()) for _ in range(dim)])
   arr /= sum(arr)
   f.write("\t".join([("%g" % max(v,minVal)) for v in arr]) + "\n")
 f.close()