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SF.net SVN: matplotlib: [5221] trunk/matplotlib
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From: <mme...@us...> - 2008-05-23 07:51:24
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Revision: 5221
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=5221&view=rev
Author: mmetz_bn
Date: 2008-05-23 00:51:20 -0700 (Fri, 23 May 2008)
Log Message:
-----------
Major hist() revision
Modified Paths:
--------------
trunk/matplotlib/CHANGELOG
trunk/matplotlib/examples/pylab/histogram_demo.py
trunk/matplotlib/lib/matplotlib/axes.py
Added Paths:
-----------
trunk/matplotlib/examples/pylab/histogram_demo_extended.py
Removed Paths:
-------------
trunk/matplotlib/examples/pylab/histogram_demo_cumulative.py
trunk/matplotlib/examples/pylab/histogram_demo_step.py
Modified: trunk/matplotlib/CHANGELOG
===================================================================
--- trunk/matplotlib/CHANGELOG 2008-05-22 21:49:25 UTC (rev 5220)
+++ trunk/matplotlib/CHANGELOG 2008-05-23 07:51:20 UTC (rev 5221)
@@ -1,3 +1,8 @@
+2008-05-23 Major revision of hist(). Can handle 2D arrays and create
+ stacked histogram plots; keyword 'width' deprecated and
+ rwidth (relative width) introduced; align='edge' changed
+ to center of bin - MM
+
2008-05-22 Added support for ReST-based doumentation using Sphinx.
Documents are located in doc/, and are broken up into
a users guide and an API reference. To build, run the
Modified: trunk/matplotlib/examples/pylab/histogram_demo.py
===================================================================
--- trunk/matplotlib/examples/pylab/histogram_demo.py 2008-05-22 21:49:25 UTC (rev 5220)
+++ trunk/matplotlib/examples/pylab/histogram_demo.py 2008-05-23 07:51:20 UTC (rev 5221)
@@ -1,23 +1,23 @@
#!/usr/bin/env python
-from pylab import *
+import pylab as P
mu, sigma = 100, 15
-x = mu + sigma*randn(10000)
+x = mu + sigma*P.randn(10000)
# the histogram of the data
-n, bins, patches = hist(x, 50, normed=1)
-setp(patches, 'facecolor', 'g', 'alpha', 0.75)
+n, bins, patches = P.hist(x, 50, normed=1)
+P.setp(patches, 'facecolor', 'g', 'alpha', 0.75)
# add a 'best fit' line
-y = normpdf( bins, mu, sigma)
-l = plot(bins, y, 'r--')
-setp(l, 'linewidth', 1)
+y = P.normpdf( bins, mu, sigma)
+l = P.plot(bins, y, 'r--')
+P.setp(l, 'linewidth', 1)
-xlabel('Smarts')
-ylabel('Probability')
-title(r'$\mathrm{Histogram\ of\ IQ:}\ \mu=100,\ \sigma=15$')
-axis([40, 160, 0, 0.03])
-grid(True)
+P.xlabel('Smarts')
+P.ylabel('Probability')
+P.title(r'$\mathrm{Histogram\ of\ IQ:}\ \mu=100,\ \sigma=15$')
+P.axis([40, 160, 0, 0.03])
+P.grid(True)
-#savefig('histogram_demo',dpi=72)
-show()
+#P.savefig('histogram_demo',dpi=72)
+P.show()
Deleted: trunk/matplotlib/examples/pylab/histogram_demo_cumulative.py
===================================================================
--- trunk/matplotlib/examples/pylab/histogram_demo_cumulative.py 2008-05-22 21:49:25 UTC (rev 5220)
+++ trunk/matplotlib/examples/pylab/histogram_demo_cumulative.py 2008-05-23 07:51:20 UTC (rev 5221)
@@ -1,33 +0,0 @@
-#!/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
Added: trunk/matplotlib/examples/pylab/histogram_demo_extended.py
===================================================================
--- trunk/matplotlib/examples/pylab/histogram_demo_extended.py (rev 0)
+++ trunk/matplotlib/examples/pylab/histogram_demo_extended.py 2008-05-23 07:51:20 UTC (rev 5221)
@@ -0,0 +1,79 @@
+#!/usr/bin/env python
+import pylab as P
+
+#
+# The hist() function now has a lot more options
+#
+
+#
+# first create a single histogram
+#
+mu, sigma = 200, 25
+x = mu + sigma*P.randn(10000)
+
+# the histogram of the data with histtype='step'
+n, bins, patches = P.hist(x, 50, normed=1, histtype='step')
+P.setp(patches, 'facecolor', 'g', 'alpha', 0.75)
+
+# add a line showing the expected distribution
+y = P.normpdf( bins, mu, sigma)
+l = P.plot(bins, y, 'k--', linewidth=1.5)
+
+
+#
+# create a histogram by providing the bin edges (unequally spaced)
+#
+P.figure()
+
+bins = [100,125,150,160,170,180,190,200,210,220,230,240,250,275,300]
+# the histogram of the data with histtype='step'
+n, bins, patches = P.hist(x, bins, normed=1, histtype='bar', rwidth=0.8)
+
+#
+# now we create a cumulative histogram of the data
+#
+P.figure()
+
+n, bins, patches = P.hist(x, 50, normed=1, histtype='step', cumulative=True)
+P.setp(patches, 'facecolor', 'b', 'alpha', 0.75)
+
+# add a line showing the expected distribution
+y = P.normpdf( bins, mu, sigma).cumsum()
+y /= y[-1]
+l = P.plot(bins, y, 'k--', linewidth=1.5)
+
+# create a second data-set with a smaller standard deviation
+sigma2 = 15.
+x = mu + sigma2*P.randn(10000)
+
+n, bins, patches = P.hist(x, bins=bins, normed=1, histtype='step', cumulative=True)
+P.setp(patches, 'facecolor', 'r', 'alpha', 0.5)
+
+# add a line showing the expected distribution
+y = P.normpdf( bins, mu, sigma2).cumsum()
+y /= y[-1]
+l = P.plot(bins, y, 'r--', linewidth=1.5)
+
+P.grid(True)
+P.ylim(0, 1.05)
+
+
+#
+# histogram has the ability to plot multiple data in parallel ...
+#
+P.figure()
+
+# create a new data-set
+x = mu + sigma*P.randn(1000,3)
+
+n, bins, patches = P.hist(x, 10, normed=1, histtype='bar')
+
+#
+# ... or we can stack the data
+#
+P.figure()
+
+n, bins, patches = P.hist(x, 10, normed=1, histtype='barstacked')
+
+
+P.show()
\ No newline at end of file
Deleted: trunk/matplotlib/examples/pylab/histogram_demo_step.py
===================================================================
--- trunk/matplotlib/examples/pylab/histogram_demo_step.py 2008-05-22 21:49:25 UTC (rev 5220)
+++ trunk/matplotlib/examples/pylab/histogram_demo_step.py 2008-05-23 07:51:20 UTC (rev 5221)
@@ -1,31 +0,0 @@
-#!/usr/bin/env python
-from pylab import *
-
-mu, sigma = 100, 15
-x = mu + sigma*randn(10000)
-
-# the histogram of the data
-n, bins, patches = hist(x, 50, normed=1, histtype='step')
-setp(patches, 'facecolor', 'g', 'alpha', 0.75)
-
-# add a 'best fit' line
-y = normpdf( bins, mu, sigma)
-l = plot(bins, y, 'k--', linewidth=1)
-
-
-# overlay the first histogram with a second one
-# were the data has a smaller standard deviation
-mu, sigma = 100, 5
-x = mu + sigma*randn(10000)
-
-n, bins, patches = hist(x, 50, normed=1, histtype='step')
-setp(patches, 'facecolor', 'r', 'alpha', 0.25)
-
-y = normpdf( bins, mu, sigma)
-l = plot(bins, y, 'k--', linewidth=1)
-
-axis([40, 160, 0, 0.09])
-grid(True)
-
-#savefig('histogram_demo',dpi=72)
-show()
Modified: trunk/matplotlib/lib/matplotlib/axes.py
===================================================================
--- trunk/matplotlib/lib/matplotlib/axes.py 2008-05-22 21:49:25 UTC (rev 5220)
+++ trunk/matplotlib/lib/matplotlib/axes.py 2008-05-23 07:51:20 UTC (rev 5221)
@@ -5392,7 +5392,8 @@
right
"""
- ax2 = self.figure.add_axes(self.get_position(True), sharex=self, frameon=False)
+ ax2 = self.figure.add_axes(self.get_position(True), sharex=self,
+ frameon=False)
ax2.yaxis.tick_right()
ax2.yaxis.set_label_position('right')
self.yaxis.tick_left()
@@ -5408,7 +5409,8 @@
top
"""
- ax2 = self.figure.add_axes(self.get_position(True), sharey=self, frameon=False)
+ ax2 = self.figure.add_axes(self.get_position(True), sharey=self,
+ frameon=False)
ax2.xaxis.tick_top()
ax2.xaxis.set_label_position('top')
self.xaxis.tick_bottom()
@@ -5420,16 +5422,19 @@
def hist(self, x, bins=10, normed=False, cumulative=False,
bottom=None, histtype='bar', align='edge',
- orientation='vertical', width=None, log=False, **kwargs):
+ orientation='vertical', rwidth=None, log=False, **kwargs):
"""
HIST(x, bins=10, normed=False, cumulative=False,
bottom=None, histtype='bar', align='edge',
- orientation='vertical', width=None, log=False, **kwargs)
+ orientation='vertical', rwidth=None, log=False, **kwargs)
Compute the histogram of x. bins is either an integer number of
bins or a sequence giving the bins. x are the data to be binned.
+ x can be an array or a 2D array with multiple data in its columns.
- The return values is (n, bins, patches)
+ The return values is (n, bins, patches) or
+ ([n0,n1,...], bins, [patches0,patches1,...]) if the input
+ contains multiple data.
If normed is true, the first element of the return tuple will
be the counts normalized to form a probability density, ie,
@@ -5442,22 +5447,25 @@
If cumulative is True then a 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
+ The last bin gives the total number of datapoints. If normed is
also True then the histogram is normalized such that the last bin
equals one.
- histtype = 'bar' | 'step'. The type of histogram to draw.
- 'bar' is a traditional bar-type histogram, 'step' generates
- a lineplot.
+ histtype = 'bar' | 'barstacked' | 'step'. The type of histogram
+ to draw. 'bar' is a traditional bar-type histogram, 'barstacked'
+ is a bar-type histogram where multiple data are stacked on top
+ of each other, and 'step' generates a lineplot.
- align = 'edge' | 'center'. Interprets bins either as edge
- or center values
+ align controles how the histogram is plotted
+ - 'edge' : bars are centered between the bin edges
+ - 'center': bars are centered on the left bin edges
orientation = 'horizontal' | 'vertical'. If horizontal, barh
will be used and the "bottom" kwarg will be the left edges.
- width: the width of the bars. If None, automatically compute
- the width. Ignored for 'step' histtype.
+ rwidth: the relative width of the bars as fraction of the bin
+ width. If None, automatically compute the width. Ignored
+ for 'step' histtype.
log: if True, the histogram axis will be set to a log scale
@@ -5466,25 +5474,86 @@
%(Rectangle)s
"""
if not self._hold: self.cla()
- n, bins = np.histogram(x, bins, range=None,
- normed=bool(normed), new=True)
+ if kwargs.get('width') is not None:
+ raise DeprecationWarning(
+ 'hist now uses the rwidth to give relative width and not absolute width')
+
+ # todo: make hist() work with list of arrays with different lengths
+ x = np.asarray(x)
+ if len(x.shape)==2:
+ n = []
+ for i in xrange(x.shape[1]):
+ # this will automatically overwrite bins,
+ # so that each histogram uses the same bins
+ m, bins = np.histogram(x[:,i], bins, range=None,
+ normed=bool(normed), new=True)
+ n.append(m)
+ else:
+ n, bins = np.histogram(x, bins, range=None,
+ normed=bool(normed), new=True)
+ n = [n,]
+
if cumulative:
if normed:
- n = (n * np.diff(bins)).cumsum()
+ n = [(m * np.diff(bins)).cumsum() for m in n]
else:
- n = n.cumsum()
+ n = [m.cumsum() for m in n]
- if histtype == 'bar':
- if width is None:
- width = 0.9*(bins[1]-bins[0])
+ ccount = 0
+ colors = _process_plot_var_args.defaultColors[:]
+ patches = []
+ if histtype.startswith('bar'):
+ totwidth = np.diff(bins)
+ stacked = False
+
+ if rwidth is not None: dr = min(1., max(0., rwidth))
+ elif len(n)>1: dr = 0.8
+ else: dr = 1.0
+
+ if histtype=='bar':
+ width = dr*totwidth/len(n)
+ dw = width
+
+ if len(n)>1:
+ boffset = -0.5*dr*totwidth*(1.-1./len(n))
+ else:
+ boffset = 0.0
+ elif histtype=='barstacked':
+ width = dr*totwidth
+ boffset, dw = 0.0, 0.0
+
+ stacked = True
+ if bottom is None: bottom = 0.0
+ else:
+ raise ValueError, 'invalid histtype: %s' % histtype
+
+ if align=='edge':
+ boffset += 0.5*totwidth
+ elif align != 'center':
+ raise ValueError, 'invalid align: %s' % align
+
if orientation == 'horizontal':
- patches = self.barh(bins[:-1], n, height=width, left=bottom,
- align=align, log=log)
+ for m in n:
+ color = colors[ccount % len(colors)]
+ patch = self.barh(bins[:-1]+boffset, m, height=width,
+ left=bottom, align='center', log=log,
+ color=color)
+ patches.append(patch)
+ if stacked: bottom += m
+ boffset += dw
+ ccount += 1
elif orientation == 'vertical':
- patches = self.bar(bins[:-1], n, width=width, bottom=bottom,
- align=align, log=log)
+ for m in n:
+ color = colors[ccount % len(colors)]
+ patch = self.bar(bins[:-1]+boffset, m, width=width,
+ bottom=bottom, align='center', log=log,
+ color=color)
+ patches.append(patch)
+ if stacked: bottom += m
+ boffset += dw
+ ccount += 1
else:
raise ValueError, 'invalid orientation: %s' % orientation
@@ -5493,22 +5562,29 @@
y = np.zeros( 2*len(bins), np.float_ )
x[0::2], x[1::2] = bins, bins
- y[1:-1:2], y[2::2] = n, n
if align == 'center':
x -= 0.5*(bins[1]-bins[0])
+ elif align != 'edge':
+ raise ValueError, 'invalid align: %s' % align
- if orientation == 'horizontal':
- x,y = y,x
- elif orientation != 'vertical':
- raise ValueError, 'invalid orientation: %s' % orientation
- patches = self.fill(x,y)
+ for m in n:
+ y[1:-1:2], y[2::2] = m, m
+ if orientation == 'horizontal':
+ x,y = y,x
+ elif orientation != 'vertical':
+ raise ValueError, 'invalid orientation: %s' % orientation
+ patches.append( self.fill(x,y) )
else:
raise ValueError, 'invalid histtype: %s' % histtype
-
- for p in patches:
- p.update(kwargs)
- return n, bins, cbook.silent_list('Patch', patches)
+
+ for patch in patches:
+ for p in patch:
+ p.update(kwargs)
+ if len(n)==1:
+ return n[0], bins, cbook.silent_list('Patch', patches[0])
+ else:
+ return n, bins, cbook.silent_list('Lists of Patches', patches)
hist.__doc__ = cbook.dedent(hist.__doc__) % martist.kwdocd
def psd(self, x, NFFT=256, Fs=2, Fc=0, detrend=mlab.detrend_none,
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