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users guide updates
svn path=/trunk/matplotlib/; revision=585
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‎TODO‎

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@@ -587,4 +587,6 @@ ZeroDivisionError: SeparableTransformation::eval_scalars yin interval is zero; c
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-- Humufr errobar
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-- why is tkagg loading init 2x
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-- why is tkagg loading init 2x
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-- add ishold and spy

‎examples/anscombe.py‎

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#!/usr/bin/env python
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"""
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Edward Tufte uses this example from Anscombe to show 4 datasets of x
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and y that have the same mean, standard deviation, and regression
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line, but which are qualitatively different.
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matplotlib fun for a rainy day
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"""
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from matplotlib.matlab import *
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x = array([10, 8, 13, 9, 11, 14, 6, 4, 12, 7, 5])
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y1 = array([8.04, 6.95, 7.58, 8.81, 8.33, 9.96, 7.24, 4.26, 10.84, 4.82, 5.68])
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y2 = array([9.14, 8.14, 8.74, 8.77, 9.26, 8.10, 6.13, 3.10, 9.13, 7.26, 4.74])
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y3 = array([7.46, 6.77, 12.74, 7.11, 7.81, 8.84, 6.08, 5.39, 8.15, 6.42, 5.73])
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x4 = array([8,8,8,8,8,8,8,19,8,8,8])
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y4 = array([6.58,5.76,7.71,8.84,8.47,7.04,5.25,12.50,5.56,7.91,6.89])
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def fit(x):
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return 3+0.5*x
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xfit = array( [min(x), max(x) ] )
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subplot(221)
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plot(x,y1,'ks', xfit, fit(xfit), 'r-', lw=2)
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axis([2,20,2,14])
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set(gca(), xticklabels=[], yticks=(4,8,12), xticks=(0,10,20))
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text(3,12, 'I', fontsize=20)
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subplot(222)
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plot(x,y2,'ks', xfit, fit(xfit), 'r-', lw=2)
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axis([2,20,2,14])
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set(gca(), xticklabels=[], yticks=(4,8,12), yticklabels=[], xticks=(0,10,20))
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text(3,12, 'II', fontsize=20)
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subplot(223)
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plot(x,y3,'ks', xfit, fit(xfit), 'r-', lw=2)
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axis([2,20,2,14])
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text(3,12, 'IIII', fontsize=20)
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set(gca(), yticks=(4,8,12), xticks=(0,10,20))
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subplot(224)
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xfit = array([min(x4),max(x4)])
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plot(x4,y4,'ks', xfit, fit(xfit), 'r-', lw=2)
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axis([2,20,2,14])
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set(gca(), yticklabels=[], yticks=(4,8,12), xticks=(0,10,20))
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text(3,12, 'IV', fontsize=20)
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#verify the stats
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pairs = (x,y1), (x,y2), (x,y3), (x4,y4)
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for x,y in pairs:
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print 'mean=%1.2f, std=%1.2f, r=%1.2f'%(mean(y), std(y), corrcoef(x,y)[0][1])
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show()

‎examples/specgram_demo.py‎

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#!/usr/bin/env python
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from matplotlib.matlab import *
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4-
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dt = 0.0005
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t = arange(0.0, 20.0, dt)
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s1 = sin(2*pi*100*t)
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s2 = 2*sin(2*pi*400*t)
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# create a transient "chirp"
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mask = where(logical_and(t>10, t<12), 1.0, 0.0)
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s2 = s2 * mask
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# add some noise into the mix
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nse = 0.01*randn(len(t))
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x = s1 + s2 + nse
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NFFT = 1024
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Fs = int(1.0/dt)
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Noverlap = 0
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x = s1 + s2 + nse # the signal
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NFFT = 1024 # the length of the windowing segments
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Fs = int(1.0/dt) # the sampling frequency
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# Pxx is the segments x freqs array of instantaneous power, freqs is
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# the frequency vector, bins are the centers of the time bins in which
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# the power is computed, and im is the matplotlib.image.AxesImage
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# instance
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Pxx, freqs, bins, im = specgram(x, NFFT=NFFT, Fs=Fs, noverlap=900)
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colorbar()
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show()

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