cleaned up some pyplots examples that got funkily duplicated

jdh2358 · jdh2358 · commit 316ae24336ff · 2008-06-27T15:42:44.000Z
svn path=/trunk/matplotlib/; revision=5693
diff --git a/doc/pyplots/boxplot_demo.py b/doc/pyplots/boxplot_demo.py
@@ -27,119 +27,3 @@
 plt.boxplot(data)
 plt.show()
 
-import numpy as np
-import matplotlib.pyplot as plt
-
-spread = np.random.rand(50) * 100
-center = np.ones(25) * 50
-flier_high = np.random.rand(10) * 100 + 100
-flier_low = np.random.rand(10) * -100
-data = np.concatenate((spread, center, flier_high, flier_low), 0)
-
-# fake up some more data
-spread = np.random.rand(50) * 100
-center = np.ones(25) * 40
-flier_high = np.random.rand(10) * 100 + 100
-flier_low = np.random.rand(10) * -100
-d2 = np.concatenate( (spread, center, flier_high, flier_low), 0 )
-data.shape = (-1, 1)
-d2.shape = (-1, 1)
-
-#data = concatenate( (data, d2), 1 )
-# Making a 2-D array only works if all the columns are the
-# same length.  If they are not, then use a list instead.
-# This is actually more efficient because boxplot converts
-# a 2-D array into a list of vectors internally anyway.
-data = [data, d2, d2[::2,0]]
-# multiple box plots on one figure
-
-plt.boxplot(data)
-plt.show()
-
-import numpy as np
-import matplotlib.pyplot as plt
-
-spread = np.random.rand(50) * 100
-center = np.ones(25) * 50
-flier_high = np.random.rand(10) * 100 + 100
-flier_low = np.random.rand(10) * -100
-data = np.concatenate((spread, center, flier_high, flier_low), 0)
-
-# fake up some more data
-spread = np.random.rand(50) * 100
-center = np.ones(25) * 40
-flier_high = np.random.rand(10) * 100 + 100
-flier_low = np.random.rand(10) * -100
-d2 = np.concatenate( (spread, center, flier_high, flier_low), 0 )
-data.shape = (-1, 1)
-d2.shape = (-1, 1)
-
-#data = concatenate( (data, d2), 1 )
-# Making a 2-D array only works if all the columns are the
-# same length.  If they are not, then use a list instead.
-# This is actually more efficient because boxplot converts
-# a 2-D array into a list of vectors internally anyway.
-data = [data, d2, d2[::2,0]]
-# multiple box plots on one figure
-
-plt.boxplot(data)
-plt.show()
-
-import numpy as np
-import matplotlib.pyplot as plt
-
-spread = np.random.rand(50) * 100
-center = np.ones(25) * 50
-flier_high = np.random.rand(10) * 100 + 100
-flier_low = np.random.rand(10) * -100
-data = np.concatenate((spread, center, flier_high, flier_low), 0)
-
-# fake up some more data
-spread = np.random.rand(50) * 100
-center = np.ones(25) * 40
-flier_high = np.random.rand(10) * 100 + 100
-flier_low = np.random.rand(10) * -100
-d2 = np.concatenate( (spread, center, flier_high, flier_low), 0 )
-data.shape = (-1, 1)
-d2.shape = (-1, 1)
-
-#data = concatenate( (data, d2), 1 )
-# Making a 2-D array only works if all the columns are the
-# same length.  If they are not, then use a list instead.
-# This is actually more efficient because boxplot converts
-# a 2-D array into a list of vectors internally anyway.
-data = [data, d2, d2[::2,0]]
-# multiple box plots on one figure
-
-plt.boxplot(data)
-plt.show()
-
-import numpy as np
-import matplotlib.pyplot as plt
-
-spread = np.random.rand(50) * 100
-center = np.ones(25) * 50
-flier_high = np.random.rand(10) * 100 + 100
-flier_low = np.random.rand(10) * -100
-data = np.concatenate((spread, center, flier_high, flier_low), 0)
-
-# fake up some more data
-spread = np.random.rand(50) * 100
-center = np.ones(25) * 40
-flier_high = np.random.rand(10) * 100 + 100
-flier_low = np.random.rand(10) * -100
-d2 = np.concatenate( (spread, center, flier_high, flier_low), 0 )
-data.shape = (-1, 1)
-d2.shape = (-1, 1)
-
-#data = concatenate( (data, d2), 1 )
-# Making a 2-D array only works if all the columns are the
-# same length.  If they are not, then use a list instead.
-# This is actually more efficient because boxplot converts
-# a 2-D array into a list of vectors internally anyway.
-data = [data, d2, d2[::2,0]]
-# multiple box plots on one figure
-
-plt.boxplot(data)
-plt.show()
-
diff --git a/doc/pyplots/contour_demo.py b/doc/pyplots/contour_demo.py
@@ -62,269 +62,5 @@
 CB.ax.set_position([ll, b+0.1*h, ww, h*0.8])
 
 
-#savefig('contour_demo')
-plt.show()
-#!/usr/bin/env python
-"""
-Illustrate simple contour plotting, contours on an image with
-a colorbar for the contours, and labelled contours.
-
-See also contour_image.py.
-"""
-import matplotlib
-import numpy as np
-import matplotlib.cm as cm
-import matplotlib.mlab as mlab
-import matplotlib.pyplot as plt
-
-matplotlib.rcParams['xtick.direction'] = 'out'
-matplotlib.rcParams['ytick.direction'] = 'out'
-
-delta = 0.025
-x = np.arange(-3.0, 3.0, delta)
-y = np.arange(-2.0, 2.0, 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)
-# difference of Gaussians
-Z = 10.0 * (Z2 - Z1)
-
-
-# You can use a colormap to specify the colors; the default
-# colormap will be used for the contour lines
-plt.figure()
-im = plt.imshow(Z, interpolation='bilinear', origin='lower',
-                cmap=cm.gray, extent=(-3,3,-2,2))
-levels = np.arange(-1.2, 1.6, 0.2)
-CS = plt.contour(Z, levels,
-                 origin='lower',
-                 linewidths=2,
-                 extent=(-3,3,-2,2))
-
-#Thicken the zero contour.
-zc = CS.collections[6]
-plt.setp(zc, linewidth=4)
-
-plt.clabel(CS, levels[1::2],  # label every second level
-           inline=1,
-           fmt='%1.1f',
-           fontsize=14)
-
-# make a colorbar for the contour lines
-CB = plt.colorbar(CS, shrink=0.8, extend='both')
-
-plt.title('Lines with colorbar')
-#plt.hot()  # Now change the colormap for the contour lines and colorbar
-plt.flag()
-
-# We can still add a colorbar for the image, too.
-CBI = plt.colorbar(im, orientation='horizontal', shrink=0.8)
-
-# This makes the original colorbar look a bit out of place,
-# so let's improve its position.
-
-l,b,w,h = plt.gca().get_position().bounds
-ll,bb,ww,hh = CB.ax.get_position().bounds
-CB.ax.set_position([ll, b+0.1*h, ww, h*0.8])
-
-
-#savefig('contour_demo')
-plt.show()
-#!/usr/bin/env python
-"""
-Illustrate simple contour plotting, contours on an image with
-a colorbar for the contours, and labelled contours.
-
-See also contour_image.py.
-"""
-import matplotlib
-import numpy as np
-import matplotlib.cm as cm
-import matplotlib.mlab as mlab
-import matplotlib.pyplot as plt
-
-matplotlib.rcParams['xtick.direction'] = 'out'
-matplotlib.rcParams['ytick.direction'] = 'out'
-
-delta = 0.025
-x = np.arange(-3.0, 3.0, delta)
-y = np.arange(-2.0, 2.0, 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)
-# difference of Gaussians
-Z = 10.0 * (Z2 - Z1)
-
-
-# You can use a colormap to specify the colors; the default
-# colormap will be used for the contour lines
-plt.figure()
-im = plt.imshow(Z, interpolation='bilinear', origin='lower',
-                cmap=cm.gray, extent=(-3,3,-2,2))
-levels = np.arange(-1.2, 1.6, 0.2)
-CS = plt.contour(Z, levels,
-                 origin='lower',
-                 linewidths=2,
-                 extent=(-3,3,-2,2))
-
-#Thicken the zero contour.
-zc = CS.collections[6]
-plt.setp(zc, linewidth=4)
-
-plt.clabel(CS, levels[1::2],  # label every second level
-           inline=1,
-           fmt='%1.1f',
-           fontsize=14)
-
-# make a colorbar for the contour lines
-CB = plt.colorbar(CS, shrink=0.8, extend='both')
-
-plt.title('Lines with colorbar')
-#plt.hot()  # Now change the colormap for the contour lines and colorbar
-plt.flag()
-
-# We can still add a colorbar for the image, too.
-CBI = plt.colorbar(im, orientation='horizontal', shrink=0.8)
-
-# This makes the original colorbar look a bit out of place,
-# so let's improve its position.
-
-l,b,w,h = plt.gca().get_position().bounds
-ll,bb,ww,hh = CB.ax.get_position().bounds
-CB.ax.set_position([ll, b+0.1*h, ww, h*0.8])
-
-
-#savefig('contour_demo')
-plt.show()
-#!/usr/bin/env python
-"""
-Illustrate simple contour plotting, contours on an image with
-a colorbar for the contours, and labelled contours.
-
-See also contour_image.py.
-"""
-import matplotlib
-import numpy as np
-import matplotlib.cm as cm
-import matplotlib.mlab as mlab
-import matplotlib.pyplot as plt
-
-matplotlib.rcParams['xtick.direction'] = 'out'
-matplotlib.rcParams['ytick.direction'] = 'out'
-
-delta = 0.025
-x = np.arange(-3.0, 3.0, delta)
-y = np.arange(-2.0, 2.0, 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)
-# difference of Gaussians
-Z = 10.0 * (Z2 - Z1)
-
-
-# You can use a colormap to specify the colors; the default
-# colormap will be used for the contour lines
-plt.figure()
-im = plt.imshow(Z, interpolation='bilinear', origin='lower',
-                cmap=cm.gray, extent=(-3,3,-2,2))
-levels = np.arange(-1.2, 1.6, 0.2)
-CS = plt.contour(Z, levels,
-                 origin='lower',
-                 linewidths=2,
-                 extent=(-3,3,-2,2))
-
-#Thicken the zero contour.
-zc = CS.collections[6]
-plt.setp(zc, linewidth=4)
-
-plt.clabel(CS, levels[1::2],  # label every second level
-           inline=1,
-           fmt='%1.1f',
-           fontsize=14)
-
-# make a colorbar for the contour lines
-CB = plt.colorbar(CS, shrink=0.8, extend='both')
-
-plt.title('Lines with colorbar')
-#plt.hot()  # Now change the colormap for the contour lines and colorbar
-plt.flag()
-
-# We can still add a colorbar for the image, too.
-CBI = plt.colorbar(im, orientation='horizontal', shrink=0.8)
-
-# This makes the original colorbar look a bit out of place,
-# so let's improve its position.
-
-l,b,w,h = plt.gca().get_position().bounds
-ll,bb,ww,hh = CB.ax.get_position().bounds
-CB.ax.set_position([ll, b+0.1*h, ww, h*0.8])
-
-
-#savefig('contour_demo')
-plt.show()
-#!/usr/bin/env python
-"""
-Illustrate simple contour plotting, contours on an image with
-a colorbar for the contours, and labelled contours.
-
-See also contour_image.py.
-"""
-import matplotlib
-import numpy as np
-import matplotlib.cm as cm
-import matplotlib.mlab as mlab
-import matplotlib.pyplot as plt
-
-matplotlib.rcParams['xtick.direction'] = 'out'
-matplotlib.rcParams['ytick.direction'] = 'out'
-
-delta = 0.025
-x = np.arange(-3.0, 3.0, delta)
-y = np.arange(-2.0, 2.0, 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)
-# difference of Gaussians
-Z = 10.0 * (Z2 - Z1)
-
-
-# You can use a colormap to specify the colors; the default
-# colormap will be used for the contour lines
-plt.figure()
-im = plt.imshow(Z, interpolation='bilinear', origin='lower',
-                cmap=cm.gray, extent=(-3,3,-2,2))
-levels = np.arange(-1.2, 1.6, 0.2)
-CS = plt.contour(Z, levels,
-                 origin='lower',
-                 linewidths=2,
-                 extent=(-3,3,-2,2))
-
-#Thicken the zero contour.
-zc = CS.collections[6]
-plt.setp(zc, linewidth=4)
-
-plt.clabel(CS, levels[1::2],  # label every second level
-           inline=1,
-           fmt='%1.1f',
-           fontsize=14)
-
-# make a colorbar for the contour lines
-CB = plt.colorbar(CS, shrink=0.8, extend='both')
-
-plt.title('Lines with colorbar')
-#plt.hot()  # Now change the colormap for the contour lines and colorbar
-plt.flag()
-
-# We can still add a colorbar for the image, too.
-CBI = plt.colorbar(im, orientation='horizontal', shrink=0.8)
-
-# This makes the original colorbar look a bit out of place,
-# so let's improve its position.
-
-l,b,w,h = plt.gca().get_position().bounds
-ll,bb,ww,hh = CB.ax.get_position().bounds
-CB.ax.set_position([ll, b+0.1*h, ww, h*0.8])
-
-
 #savefig('contour_demo')
 plt.show()
