## SF.net SVN: matplotlib: [5683] trunk/matplotlib

 SF.net SVN: matplotlib: [5683] trunk/matplotlib From: - 2008-06-26 20:15:52 Revision: 5683 http://matplotlib.svn.sourceforge.net/matplotlib/?rev=5683&view=rev Author: jdh2358 Date: 2008-06-26 13:15:45 -0700 (Thu, 26 Jun 2008) Log Message: ----------- cleaned some more examples Modified Paths: -------------- trunk/matplotlib/examples/pylab_examples/cohere_demo.py trunk/matplotlib/examples/pylab_examples/csd_demo.py trunk/matplotlib/examples/pylab_examples/fill_demo.py trunk/matplotlib/examples/pylab_examples/hexbin_demo.py trunk/matplotlib/examples/pylab_examples/histogram_demo.py trunk/matplotlib/examples/pylab_examples/image_demo.py Added Paths: ----------- trunk/matplotlib/lib/mpl_examples Modified: trunk/matplotlib/examples/pylab_examples/cohere_demo.py =================================================================== --- trunk/matplotlib/examples/pylab_examples/cohere_demo.py 2008-06-26 19:08:48 UTC (rev 5682) +++ trunk/matplotlib/examples/pylab_examples/cohere_demo.py 2008-06-26 20:15:45 UTC (rev 5683) @@ -2,36 +2,35 @@ """ Compute the coherence of two signals """ -import numpy as n +import numpy as np +import matplotlib.pyplot as plt -from pylab import figure, show +# make a little extra space between the subplots +plt.subplots_adjust(wspace=0.5) dt = 0.01 -t = n.arange(0, 30, dt) -Nt = len(t) -nse1 = n.random.randn(Nt) # white noise 1 -nse2 = n.random.randn(Nt) # white noise 2 -r = n.exp(-t/0.05) +t = np.arange(0, 30, dt) +nse1 = np.random.randn(len(t)) # white noise 1 +nse2 = np.random.randn(len(t)) # white noise 2 +r = np.exp(-t/0.05) -cnse1 = n.convolve(nse1, r)*dt # colored noise 1 -cnse1 = cnse1[:Nt] -cnse2 = n.convolve(nse2, r)*dt # colored noise 2 -cnse2 = cnse2[:Nt] +cnse1 = np.convolve(nse1, r, mode='same')*dt # colored noise 1 +cnse2 = np.convolve(nse2, r, mode='same')*dt # colored noise 2 # two signals with a coherent part and a random part -s1 = 0.01*n.sin(2*n.pi*10*t) + cnse1 -s2 = 0.01*n.sin(2*n.pi*10*t) + cnse2 +s1 = 0.01*np.sin(2*np.pi*10*t) + cnse1 +s2 = 0.01*np.sin(2*np.pi*10*t) + cnse2 -fig = figure() -ax = fig.add_subplot(211) -ax.plot(t, s1, 'b-', t, s2, 'g-') -ax.set_xlim(0,5) -ax.set_xlabel('time') -ax.set_ylabel('s1 and s2') +plt.subplot(211) +plt.plot(t, s1, 'b-', t, s2, 'g-') +plt.xlim(0,5) +plt.xlabel('time') +plt.ylabel('s1 and s2') +plt.grid(True) -ax = fig.add_subplot(212) -cxy, f = ax.cohere(s1, s2, 256, 1./dt) +plt.subplot(212) +cxy, f = plt.cohere(s1, s2, 256, 1./dt) +plt.ylabel('coherence') +plt.show() -show() - Modified: trunk/matplotlib/examples/pylab_examples/csd_demo.py =================================================================== --- trunk/matplotlib/examples/pylab_examples/csd_demo.py 2008-06-26 19:08:48 UTC (rev 5682) +++ trunk/matplotlib/examples/pylab_examples/csd_demo.py 2008-06-26 20:15:45 UTC (rev 5683) @@ -2,32 +2,35 @@ """ Compute the cross spectral density of two signals """ -from __future__ import division -from pylab import * +import numpy as np +import matplotlib.pyplot as plt +# make a little extra space between the subplots +plt.subplots_adjust(wspace=0.5) + dt = 0.01 -t = arange(0, 30, dt) -nse1 = randn(len(t)) # white noise 1 -nse2 = randn(len(t)) # white noise 2 -r = exp(divide(-t,0.05)) +t = np.arange(0, 30, dt) +nse1 = np.random.randn(len(t)) # white noise 1 +nse2 = np.random.randn(len(t)) # white noise 2 +r = np.exp(-t/0.05) -cnse1 = convolve(nse1, r, mode=2)*dt # colored noise 1 -cnse1 = cnse1[:len(t)] -cnse2 = convolve(nse2, r, mode=2)*dt # colored noise 2 -cnse2 = cnse2[:len(t)] +cnse1 = np.convolve(nse1, r, mode='same')*dt # colored noise 1 +cnse2 = np.convolve(nse2, r, mode='same')*dt # colored noise 2 # two signals with a coherent part and a random part -s1 = 0.01*sin(2*pi*10*t) + cnse1 -s2 = 0.01*sin(2*pi*10*t) + cnse2 +s1 = 0.01*np.sin(2*np.pi*10*t) + cnse1 +s2 = 0.01*np.sin(2*np.pi*10*t) + cnse2 -subplot(211) -plot(t, s1, 'b-', t, s2, 'g-') -xlim(0,5) -xlabel('time') -ylabel('s1 and s2') +plt.subplot(211) +plt.plot(t, s1, 'b-', t, s2, 'g-') +plt.xlim(0,5) +plt.xlabel('time') +plt.ylabel('s1 and s2') +plt.grid(True) -subplot(212) -cxy, f = csd(s1, s2, 256, 1/dt) -show() +plt.subplot(212) +cxy, f = plt.csd(s1, s2, 256, 1./dt) +plt.ylabel('CSD (db)') +plt.show() Modified: trunk/matplotlib/examples/pylab_examples/fill_demo.py =================================================================== --- trunk/matplotlib/examples/pylab_examples/fill_demo.py 2008-06-26 19:08:48 UTC (rev 5682) +++ trunk/matplotlib/examples/pylab_examples/fill_demo.py 2008-06-26 20:15:45 UTC (rev 5683) @@ -1,8 +1,10 @@ #!/usr/bin/env python -from pylab import * -t = arange(0.0, 1.01, 0.01) -s = sin(2*2*pi*t) +import numpy as np +import matplotlib.pyplot as plt -fill(t, s*exp(-5*t), 'r') -grid(True) -show() +t = np.arange(0.0, 1.01, 0.01) +s = np.sin(2*2*np.pi*t) + +plt.fill(t, s*np.exp(-5*t), 'r') +plt.grid(True) +plt.show() Modified: trunk/matplotlib/examples/pylab_examples/hexbin_demo.py =================================================================== --- trunk/matplotlib/examples/pylab_examples/hexbin_demo.py 2008-06-26 19:08:48 UTC (rev 5682) +++ trunk/matplotlib/examples/pylab_examples/hexbin_demo.py 2008-06-26 20:15:45 UTC (rev 5683) @@ -1,13 +1,12 @@ -''' +""" hexbin is an axes method or pyplot function that is essentially a pcolor of a 2-D histogram with hexagonal cells. It can be much more informative than a scatter plot; in the first subplot below, try substituting 'scatter' for 'hexbin'. -''' +""" -from matplotlib.pyplot import * import numpy as np - +import matplotlib.pyplot as plt n = 100000 x = np.random.standard_normal(n) y = 2.0 + 3.0 * x + 4.0 * np.random.standard_normal(n) @@ -16,19 +15,20 @@ ymin = y.min() ymax = y.max() -subplot(121) -hexbin(x,y) -axis([xmin, xmax, ymin, ymax]) -title("Hexagon binning") -cb = colorbar() +plt.subplots_adjust(hspace=0.5) +plt.subplot(121) +plt.hexbin(x,y) +plt.axis([xmin, xmax, ymin, ymax]) +plt.title("Hexagon binning") +cb = plt.colorbar() cb.set_label('counts') -subplot(122) -hexbin(x,y,bins='log') -axis([xmin, xmax, ymin, ymax]) -title("With a log color scale") -cb = colorbar() +plt.subplot(122) +plt.hexbin(x,y,bins='log') +plt.axis([xmin, xmax, ymin, ymax]) +plt.title("With a log color scale") +cb = plt.colorbar() cb.set_label('log10(N)') -show() +plt.show() Modified: trunk/matplotlib/examples/pylab_examples/histogram_demo.py =================================================================== --- trunk/matplotlib/examples/pylab_examples/histogram_demo.py 2008-06-26 19:08:48 UTC (rev 5682) +++ trunk/matplotlib/examples/pylab_examples/histogram_demo.py 2008-06-26 20:15:45 UTC (rev 5683) @@ -1,23 +1,22 @@ #!/usr/bin/env python -import pylab as P +import numpy as np +import matplotlib.mlab as mlab +import matplotlib.pyplot as plt mu, sigma = 100, 15 -x = mu + sigma*P.randn(10000) +x = mu + sigma*np.random.randn(10000) # the histogram of the data -n, bins, patches = P.hist(x, 50, normed=1) -P.setp(patches, 'facecolor', 'g', 'alpha', 0.75) +n, bins, patches = plt.hist(x, 50, normed=1, facecolor='green', alpha=0.75) # add a 'best fit' line -y = P.normpdf( bins, mu, sigma) -l = P.plot(bins, y, 'r--') -P.setp(l, 'linewidth', 1) +y = mlab.normpdf( bins, mu, sigma) +l = plt.plot(bins, y, 'r--', linewidth=1) -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) +plt.xlabel('Smarts') +plt.ylabel('Probability') +plt.title(r'$\mathrm{Histogram\ of\ IQ:}\ \mu=100,\ \sigma=15$') +plt.axis([40, 160, 0, 0.03]) +plt.grid(True) -#P.savefig('histogram_demo',dpi=72) -P.show() +plt.show() Modified: trunk/matplotlib/examples/pylab_examples/image_demo.py =================================================================== --- trunk/matplotlib/examples/pylab_examples/image_demo.py 2008-06-26 19:08:48 UTC (rev 5682) +++ trunk/matplotlib/examples/pylab_examples/image_demo.py 2008-06-26 20:15:45 UTC (rev 5683) @@ -1,16 +1,18 @@ #!/usr/bin/env python -from pylab import * +import numpy as np +import matplotlib.cm as cm +import matplotlib.mlab as mlab +import matplotlib.pyplot as plt delta = 0.025 -x = y = arange(-3.0, 3.0, delta) -X, Y = meshgrid(x, y) -Z1 = bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0) -Z2 = bivariate_normal(X, Y, 1.5, 0.5, 1, 1) +x = y = np.arange(-3.0, 3.0, delta) +X, Y = np.meshgrid(x, y) +Z1 = mlab.bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0) +Z2 = mlab.bivariate_normal(X, Y, 1.5, 0.5, 1, 1) Z = Z2-Z1 # difference of Gaussians -im = imshow(Z, interpolation='bilinear', cmap=cm.gray, - origin='lower', extent=[-3,3,-3,3]) +im = plt.imshow(Z, interpolation='bilinear', cmap=cm.gray, + origin='lower', extent=[-3,3,-3,3]) -savefig('image_demo') -show() +plt.show() Added: trunk/matplotlib/lib/mpl_examples =================================================================== --- trunk/matplotlib/lib/mpl_examples (rev 0) +++ trunk/matplotlib/lib/mpl_examples 2008-06-26 20:15:45 UTC (rev 5683) @@ -0,0 +1 @@ +link ../examples \ No newline at end of file Property changes on: trunk/matplotlib/lib/mpl_examples ___________________________________________________________________ Name: svn:special + * This was sent by the SourceForge.net collaborative development platform, the world's largest Open Source development site.