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Major hist() revision
svn path=/trunk/matplotlib/; revision=5221
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‎CHANGELOG‎

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2008-05-23 Major revision of hist(). Can handle 2D arrays and create
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stacked histogram plots; keyword 'width' deprecated and
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rwidth (relative width) introduced; align='edge' changed
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to center of bin - MM
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2008-05-22 Added support for ReST-based doumentation using Sphinx.
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Documents are located in doc/, and are broken up into
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a users guide and an API reference. To build, run the

‎examples/pylab/histogram_demo.py‎

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#!/usr/bin/env python
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from pylab import *
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import pylab as P
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mu, sigma = 100, 15
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x = mu + sigma*randn(10000)
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x = mu + sigma*P.randn(10000)
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# the histogram of the data
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n, bins, patches = hist(x, 50, normed=1)
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setp(patches, 'facecolor', 'g', 'alpha', 0.75)
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n, bins, patches = P.hist(x, 50, normed=1)
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P.setp(patches, 'facecolor', 'g', 'alpha', 0.75)
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# add a 'best fit' line
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y = normpdf( bins, mu, sigma)
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l = plot(bins, y, 'r--')
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setp(l, 'linewidth', 1)
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y = P.normpdf( bins, mu, sigma)
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l = P.plot(bins, y, 'r--')
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P.setp(l, 'linewidth', 1)
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xlabel('Smarts')
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ylabel('Probability')
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title(r'$\mathrm{Histogram\ of\ IQ:}\ \mu=100,\ \sigma=15$')
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axis([40, 160, 0, 0.03])
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grid(True)
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P.xlabel('Smarts')
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P.ylabel('Probability')
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P.title(r'$\mathrm{Histogram\ of\ IQ:}\ \mu=100,\ \sigma=15$')
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P.axis([40, 160, 0, 0.03])
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P.grid(True)
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#savefig('histogram_demo',dpi=72)
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show()
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#P.savefig('histogram_demo',dpi=72)
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P.show()

‎examples/pylab/histogram_demo_cumulative.py‎

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‎examples/pylab/histogram_demo_extended.py‎

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#!/usr/bin/env python
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import pylab as P
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#
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# The hist() function now has a lot more options
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#
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#
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# first create a single histogram
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#
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mu, sigma = 200, 25
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x = mu + sigma*P.randn(10000)
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# the histogram of the data with histtype='step'
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n, bins, patches = P.hist(x, 50, normed=1, histtype='step')
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P.setp(patches, 'facecolor', 'g', 'alpha', 0.75)
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# add a line showing the expected distribution
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y = P.normpdf( bins, mu, sigma)
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l = P.plot(bins, y, 'k--', linewidth=1.5)
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#
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# create a histogram by providing the bin edges (unequally spaced)
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#
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P.figure()
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bins = [100,125,150,160,170,180,190,200,210,220,230,240,250,275,300]
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# the histogram of the data with histtype='step'
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n, bins, patches = P.hist(x, bins, normed=1, histtype='bar', rwidth=0.8)
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#
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# now we create a cumulative histogram of the data
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#
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P.figure()
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n, bins, patches = P.hist(x, 50, normed=1, histtype='step', cumulative=True)
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P.setp(patches, 'facecolor', 'b', 'alpha', 0.75)
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# add a line showing the expected distribution
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y = P.normpdf( bins, mu, sigma).cumsum()
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y /= y[-1]
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l = P.plot(bins, y, 'k--', linewidth=1.5)
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# create a second data-set with a smaller standard deviation
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sigma2 = 15.
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x = mu + sigma2*P.randn(10000)
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n, bins, patches = P.hist(x, bins=bins, normed=1, histtype='step', cumulative=True)
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P.setp(patches, 'facecolor', 'r', 'alpha', 0.5)
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# add a line showing the expected distribution
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y = P.normpdf( bins, mu, sigma2).cumsum()
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y /= y[-1]
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l = P.plot(bins, y, 'r--', linewidth=1.5)
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P.grid(True)
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P.ylim(0, 1.05)
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#
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# histogram has the ability to plot multiple data in parallel ...
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#
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P.figure()
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# create a new data-set
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x = mu + sigma*P.randn(1000,3)
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n, bins, patches = P.hist(x, 10, normed=1, histtype='bar')
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#
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# ... or we can stack the data
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#
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P.figure()
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n, bins, patches = P.hist(x, 10, normed=1, histtype='barstacked')
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P.show()

‎examples/pylab/histogram_demo_step.py‎

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