Revision: 5147
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=5147&view=rev
Author: mmetz_bn
Date: 2008-05-16 07:52:15 -0700 (Fri, 16 May 2008)
Log Message:
-----------
added cumulative histograms
Modified Paths:
--------------
trunk/matplotlib/CHANGELOG
trunk/matplotlib/lib/matplotlib/axes.py
Added Paths:
-----------
trunk/matplotlib/examples/histogram_demo_cumulative.py
Modified: trunk/matplotlib/CHANGELOG
===================================================================
--- trunk/matplotlib/CHANGELOG 2008-05-16 13:41:25 UTC (rev 5146)
+++ trunk/matplotlib/CHANGELOG 2008-05-16 14:52:15 UTC (rev 5147)
@@ -1,3 +1,7 @@
+2008-05-16 Added 'cumulative' keyword arg to hist to plot cumulative
+ histograms. For normed hists, this is normalized to one
+ assuming equally spaced bins - MM
+
2008-05-15 Fix Tk backend segfault on some machines - MGD
2008-05-14 Don't use stat on Windows (fixes font embedding problem) - MGD
Added: trunk/matplotlib/examples/histogram_demo_cumulative.py
===================================================================
--- trunk/matplotlib/examples/histogram_demo_cumulative.py
(rev 0)
+++ trunk/matplotlib/examples/histogram_demo_cumulative.py 2008-05-16
14:52:15 UTC (rev 5147)
@@ -0,0 +1,33 @@
+#!/usr/bin/env python
+from pylab import *
+
+mu, sigma = 100, 25
+x = mu + sigma*randn(10000)
+
+# the histogram of the data
+n, bins, patches = hist(x, 50, normed=1, histtype='step', cumulative=True)
+setp(patches, 'facecolor', 'g', 'alpha', 0.75)
+
+# add a 'best fit' line
+y = normpdf( bins, mu, sigma).cumsum()
+y /= y[-1]
+l = plot(bins, y, 'k--', linewidth=1.5)
+
+# overlay the first histogram with a second one
+# were the data has a smaller standard deviation
+mu, sigma = 100, 10
+x = mu + sigma*randn(10000)
+
+n, bins, patches = hist(x, bins=bins, normed=1, histtype='step',
cumulative=True)
+setp(patches, 'facecolor', 'r', 'alpha', 0.25)
+
+# add a 'best fit' line
+y = normpdf( bins, mu, sigma).cumsum()
+y /= y[-1]
+l = plot(bins, y, 'k--', linewidth=1.5)
+
+grid(True)
+ylim(0, 1.1)
+
+#savefig('histogram_demo',dpi=72)
+show()
\ No newline at end of file
Modified: trunk/matplotlib/lib/matplotlib/axes.py
===================================================================
--- trunk/matplotlib/lib/matplotlib/axes.py 2008-05-16 13:41:25 UTC (rev
5146)
+++ trunk/matplotlib/lib/matplotlib/axes.py 2008-05-16 14:52:15 UTC (rev
5147)
@@ -5416,9 +5416,9 @@
#### Data analysis
- def hist(self, x, bins=10, normed=False, bottom=None, histtype='bar',
- align='edge', orientation='vertical', width=None,
- log=False, **kwargs):
+ def hist(self, x, bins=10, normed=False, cumulative=False,
+ bottom=None, histtype='bar', align='edge',
+ orientation='vertical', width=None, log=False, **kwargs):
"""
HIST(x, bins=10, normed=False, bottom=None, histtype='bar',
align='edge', orientation='vertical', width=None,
@@ -5439,6 +5439,12 @@
pdf, bins, patches = ax.hist(...)
print np.trapz(pdf, bins)
+ If cumulative is True then histogram is computed where each bin
+ gives the counts in that bin plus all bins for smaller values.
+ The last bins gives the total number of datapoints. If normed is
+ also True then the histogram is normalized such that the last bin
+ equals one (assuming equally spaced bins).
+
histtype = 'bar' | 'step'. The type of histogram to draw.
'bar' is a traditional bar-type histogram, 'step' generates
a lineplot.
@@ -5461,6 +5467,12 @@
if not self._hold: self.cla()
n, bins = np.histogram(x, bins, range=None,
normed=bool(normed), new=True)
+
+ if cumulative:
+ n = n.cumsum()
+ if normed:
+ # normalize to 1
+ n *= (bins[1]-bins[0])
if histtype == 'bar':
if width is None:
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