From: W. Trevor King <wking@drexel.edu>
Date: Fri, 5 Nov 2010 17:30:49 +0000 (-0700)
Subject: Force bin_edges and counts to be numy arrays in Histogram.
X-Git-Url: http://git.tremily.us/?a=commitdiff_plain;h=f9351d21b34df6170f0e97f57caa3cbc13d91e84;p=sawsim.git

Force bin_edges and counts to be numy arrays in Histogram.
---

diff --git a/pysawsim/histogram.py b/pysawsim/histogram.py
index 1b743ee..c0d73b7 100644
--- a/pysawsim/histogram.py
+++ b/pysawsim/histogram.py
@@ -59,17 +59,17 @@ class Histogram (object):
 
         All bins should be of equal width (so we can calculate which
         bin a data point belongs to).
-
-        `data` should be a numpy array.
         """
         self.headings = None
-        self.bin_edges = bin_edges
+        data = numpy.array(data)
+        self.bin_edges = numpy.array(bin_edges)
         bin_width = self.bin_edges[1] - self.bin_edges[0]
 
         bin_is = numpy.floor((data - self.bin_edges[0])/bin_width)
-        self.counts = []
-        for i in range(len(self.bin_edges)-1):
-            self.counts.append(sum(bin_is == i).sum())
+        self.counts = numpy.zeros((len(self.bin_edges)-1,), dtype=numpy.int)
+        for i in range(len(self.counts)):
+            self.counts[i] = (bin_is == i).sum()
+        self.counts = numpy.array(self.counts)
         self.total = float(len(data)) # some data might be outside the bins
         self.mean = data.mean()
         self.std_dev = data.std()
@@ -180,14 +180,14 @@ class Histogram (object):
         return abs(other.std_dev - self.std_dev)
 
     def chi_squared_residual(self, other):
-        assert self.bin_edges == other.bin_edges
+        assert (self.bin_edges == other.bin_edges).all()
         residual = 0
         for probA,probB in zip(self.probabilities, other.probabilities):
             residual += (probA-probB)**2 / probB
         return residual
 
     def jensen_shannon_residual(self, other):
-        assert self.bin_edges == other.bin_edges
+        assert (self.bin_edges == other.bin_edges).all()
         def d_KL_term(p,q):
             """
             Kullback-Leibler divergence for a single bin, with the