matplotlib · efiring · Jun 24, 2012 · May 31, 2012 · May 31, 2012 · Jun 3, 2012
diff --git a/examples/mplot3d/contour3d_demo.py b/examples/mplot3d/contour3d_demo.py
@@ -1,10 +1,11 @@
 from mpl_toolkits.mplot3d import axes3d
 import matplotlib.pyplot as plt
+from matplotlib import cm
 
 fig = plt.figure()
 ax = fig.add_subplot(111, projection='3d')
 X, Y, Z = axes3d.get_test_data(0.05)
-cset = ax.contour(X, Y, Z)
+cset = ax.contour(X, Y, Z, cmap=cm.coolwarm)
 ax.clabel(cset, fontsize=9, inline=1)
 
 plt.show()

diff --git a/examples/mplot3d/contour3d_demo2.py b/examples/mplot3d/contour3d_demo2.py
@@ -1,10 +1,11 @@
 from mpl_toolkits.mplot3d import axes3d
 import matplotlib.pyplot as plt
+from matplotlib import cm
 
 fig = plt.figure()
 ax = fig.gca(projection='3d')
 X, Y, Z = axes3d.get_test_data(0.05)
-cset = ax.contour(X, Y, Z, extend3d=True)
+cset = ax.contour(X, Y, Z, extend3d=True, cmap=cm.coolwarm)
 ax.clabel(cset, fontsize=9, inline=1)
 
 plt.show()

diff --git a/examples/mplot3d/contour3d_demo3.py b/examples/mplot3d/contour3d_demo3.py
@@ -1,13 +1,14 @@
 from mpl_toolkits.mplot3d import axes3d
 import matplotlib.pyplot as plt
+from matplotlib import cm
 
 fig = plt.figure()
 ax = fig.gca(projection='3d')
 X, Y, Z = axes3d.get_test_data(0.05)
 ax.plot_surface(X, Y, Z, rstride=8, cstride=8, alpha=0.3)
-cset = ax.contour(X, Y, Z, zdir='z', offset=-100)
-cset = ax.contour(X, Y, Z, zdir='x', offset=-40)
-cset = ax.contour(X, Y, Z, zdir='y', offset=40)
+cset = ax.contour(X, Y, Z, zdir='z', offset=-100, cmap=cm.coolwarm)
+cset = ax.contour(X, Y, Z, zdir='x', offset=-40, cmap=cm.coolwarm)
+cset = ax.contour(X, Y, Z, zdir='y', offset=40, cmap=cm.coolwarm)
 
 ax.set_xlabel('X')
 ax.set_xlim(-40, 40)

diff --git a/examples/mplot3d/contourf3d_demo.py b/examples/mplot3d/contourf3d_demo.py
@@ -1,10 +1,11 @@
 from mpl_toolkits.mplot3d import axes3d
 import matplotlib.pyplot as plt
+from matplotlib import cm
 
 fig = plt.figure()
 ax = fig.gca(projection='3d')
 X, Y, Z = axes3d.get_test_data(0.05)
-cset = ax.contourf(X, Y, Z)
+cset = ax.contourf(X, Y, Z, cmap=cm.coolwarm)
 ax.clabel(cset, fontsize=9, inline=1)
 
 plt.show()

diff --git a/examples/mplot3d/contourf3d_demo2.py b/examples/mplot3d/contourf3d_demo2.py
@@ -5,14 +5,15 @@
 
 from mpl_toolkits.mplot3d import axes3d
 import matplotlib.pyplot as plt
+from matplotlib import cm
 
 fig = plt.figure()
 ax = fig.gca(projection='3d')
 X, Y, Z = axes3d.get_test_data(0.05)
 ax.plot_surface(X, Y, Z, rstride=8, cstride=8, alpha=0.3)
-cset = ax.contourf(X, Y, Z, zdir='z', offset=-100)
-cset = ax.contourf(X, Y, Z, zdir='x', offset=-40)
-cset = ax.contourf(X, Y, Z, zdir='y', offset=40)
+cset = ax.contourf(X, Y, Z, zdir='z', offset=-100, cmap=cm.coolwarm)
+cset = ax.contourf(X, Y, Z, zdir='x', offset=-40, cmap=cm.coolwarm)
+cset = ax.contourf(X, Y, Z, zdir='y', offset=40, cmap=cm.coolwarm)
 
 ax.set_xlabel('X')
 ax.set_xlim(-40, 40)

diff --git a/examples/mplot3d/subplot3d_demo.py b/examples/mplot3d/subplot3d_demo.py
@@ -17,7 +17,7 @@
 X, Y = np.meshgrid(X, Y)
 R = np.sqrt(X**2 + Y**2)
 Z = np.sin(R)
-surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.jet,
+surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.coolwarm,
         linewidth=0, antialiased=False)
 ax.set_zlim3d(-1.01, 1.01)
 

diff --git a/examples/mplot3d/surface3d_demo.py b/examples/mplot3d/surface3d_demo.py
@@ -11,7 +11,7 @@
 X, Y = np.meshgrid(X, Y)
 R = np.sqrt(X**2 + Y**2)
 Z = np.sin(R)
-surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.jet,
+surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.coolwarm,
         linewidth=0, antialiased=False)
 ax.set_zlim(-1.01, 1.01)
 

diff --git a/examples/mplot3d/surface3d_radial_demo.py b/examples/mplot3d/surface3d_radial_demo.py
@@ -18,7 +18,7 @@
 X,Y = R*np.cos(P),R*np.sin(P)
 
 Z = ((R**2 - 1)**2)
-ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.jet)
+ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.YlGnBu_r)
 ax.set_zlim3d(0, 1)
 ax.set_xlabel(r'$\phi_\mathrm{real}$')
 ax.set_ylabel(r'$\phi_\mathrm{im}$')

diff --git a/examples/pylab_examples/agg_buffer_to_array.py b/examples/pylab_examples/agg_buffer_to_array.py
@@ -9,7 +9,7 @@
 ax.set_title('a simple figure')
 fig.canvas.draw()
 
-# grab rhe pixel buffer and dumpy it into a numpy array
+# grab the pixel buffer and dump it into a numpy array
 buf = fig.canvas.buffer_rgba()
 l, b, w, h = fig.bbox.bounds
 X = np.frombuffer(buf, np.uint8)

diff --git a/examples/pylab_examples/anchored_artists.py b/examples/pylab_examples/anchored_artists.py
@@ -22,7 +22,7 @@ def __init__(self, transform, size, label, loc,
                  pad=0.1, borderpad=0.1, sep=2, prop=None, frameon=True):
         """
         Draw a horizontal bar with the size in data coordinate of the give axes.
-        A label will be drawn underneath (center-alinged).
+        A label will be drawn underneath (center-aligned).
 
         pad, borderpad in fraction of the legend font size (or prop)
         sep in points.

diff --git a/examples/pylab_examples/annotation_demo.py b/examples/pylab_examples/annotation_demo.py
@@ -113,7 +113,7 @@
 
 
 if 1:
-    # You can also use polar notation on a catesian axes.  Here the
+    # You can also use polar notation on a cartesian axes.  Here the
     # native coordinate system ('data') is cartesian, so you need to
     # specify the xycoords and textcoords as 'polar' if you want to
     # use (theta, radius)

diff --git a/examples/pylab_examples/axhspan_demo.py b/examples/pylab_examples/axhspan_demo.py
@@ -14,7 +14,7 @@
 # draw a default vline at x=1 that spans the yrange
 l = plt.axvline(x=1)
 
-# draw a thick blue vline at x=0 that spans the the upper quadrant of
+# draw a thick blue vline at x=0 that spans the upper quadrant of
 # the yrange
 l = plt.axvline(x=0, ymin=0.75, linewidth=4, color='b')
 

diff --git a/examples/pylab_examples/boxplot_demo2.py b/examples/pylab_examples/boxplot_demo2.py
@@ -80,7 +80,7 @@
       medianY.append(med.get_ydata()[j])
       plt.plot(medianX, medianY, 'k')
       medians[i] = medianY[0]
-  # Finally, overplot the sample averages, with horixzontal alignment
+  # Finally, overplot the sample averages, with horizontal alignment
   # in the center of each box
   plt.plot([np.average(med.get_xdata())], [np.average(data[i])],
            color='w', marker='*', markeredgecolor='k')

diff --git a/examples/pylab_examples/centered_ticklabels.py b/examples/pylab_examples/centered_ticklabels.py
@@ -1,10 +1,10 @@
 # sometimes it is nice to have ticklabels centered.  mpl currently
 # associates a label with a tick, and the label can be aligned
-# 'center', 'feft', or 'right' using the horizontal alignment property:
+# 'center', 'left', or 'right' using the horizontal alignment property:
 #
 #
 #   for label in ax.xaxis.get_xticklabels():
-#       label.set_horizntal_alignment('right')
+#       label.set_horizontalalignment('right')
 #
 #
 # but this doesn't help center the label between ticks.  One solution

diff --git a/examples/pylab_examples/colorbar_tick_labelling_demo.py b/examples/pylab_examples/colorbar_tick_labelling_demo.py
@@ -5,7 +5,7 @@
 
 import matplotlib.pyplot as plt
 import numpy as np
-
+from matplotlib import cm
 from numpy.random import randn
 
 # Make plot with vertical (default) colorbar
@@ -14,7 +14,7 @@
 
 data = np.clip(randn(250, 250), -1, 1)
 
-cax = ax.imshow(data, interpolation='nearest')
+cax = ax.imshow(data, interpolation='nearest', cmap=cm.coolwarm)
 ax.set_title('Gaussian noise with vertical colorbar')
 
 # Add colorbar, make sure to specify tick locations to match desired ticklabels
@@ -27,7 +27,7 @@
 
 data = np.clip(randn(250, 250), -1, 1)
 
-cax = ax.imshow(data, interpolation='nearest')
+cax = ax.imshow(data, interpolation='nearest', cmap=cm.afmhot)
 ax.set_title('Gaussian noise with horizontal colorbar')
 
 cbar = fig.colorbar(cax, ticks=[-1, 0, 1], orientation='horizontal')

diff --git a/examples/pylab_examples/contour_image.py b/examples/pylab_examples/contour_image.py
@@ -26,13 +26,17 @@
 levels = arange(-2.0, 1.601, 0.4) # Boost the upper limit to avoid truncation
                                   # errors.
 
+norm = cm.colors.Normalize(vmax=abs(Z).max(), vmin=-abs(Z).max())
+cmap = cm.PRGn
+
 figure()
 
 
 subplot(2,2,1)
 
 cset1 = contourf(X, Y, Z, levels,
-                        cmap=cm.get_cmap('jet', len(levels)-1),
+                        cmap=cm.get_cmap(cmap, len(levels)-1),
+                        norm=norm,
                         )
 # It is not necessary, but for the colormap, we need only the
 # number of levels minus 1.  To avoid discretization error, use
@@ -65,7 +69,7 @@
 
 subplot(2,2,2)
 
-imshow(Z, extent=extent)
+imshow(Z, extent=extent, cmap=cmap, norm=norm)
 v = axis()
 contour(Z, levels, hold='on', colors = 'k',
         origin='upper', extent=extent)
@@ -74,7 +78,7 @@
 
 subplot(2,2,3)
 
-imshow(Z, origin='lower', extent=extent)
+imshow(Z, origin='lower', extent=extent, cmap=cmap, norm=norm)
 v = axis()
 contour(Z, levels, hold='on', colors = 'k',
         origin='lower', extent=extent)
@@ -89,7 +93,7 @@
 # This is intentional. The Z values are defined at the center of each
 # image pixel (each color block on the following subplot), so the
 # domain that is contoured does not extend beyond these pixel centers.
-im = imshow(Z, interpolation='nearest', extent=extent)
+im = imshow(Z, interpolation='nearest', extent=extent, cmap=cmap, norm=norm)
 v = axis()
 contour(Z, levels, hold='on', colors = 'k',
         origin='image', extent=extent)

diff --git a/examples/pylab_examples/contourf_log.py b/examples/pylab_examples/contourf_log.py
@@ -5,7 +5,7 @@
 from matplotlib import pyplot as P
 import numpy as np
 from numpy import ma
-from matplotlib import colors, ticker
+from matplotlib import colors, ticker, cm
 from matplotlib.mlab import bivariate_normal
 
 N = 100
@@ -30,7 +30,7 @@
 
 # Automatic selection of levels works; setting the
 # log locator tells contourf to use a log scale:
-cs = P.contourf(X, Y, z, locator=ticker.LogLocator())
+cs = P.contourf(X, Y, z, locator=ticker.LogLocator(), cmap=cm.PuBu_r)
 
 # Alternatively, you can manually set the levels
 # and the norm:

diff --git a/examples/pylab_examples/custom_figure_class.py b/examples/pylab_examples/custom_figure_class.py
@@ -1,5 +1,5 @@
 """
-You can pass a custom Figure constructor to figure if youy want to derive from the default Figure.  This simple example creates a figure with a figure title
+You can pass a custom Figure constructor to figure if you want to derive from the default Figure.  This simple example creates a figure with a figure title
 """
 from matplotlib.pyplot import figure, show
 from matplotlib.figure import Figure

diff --git a/examples/pylab_examples/custom_ticker1.py b/examples/pylab_examples/custom_ticker1.py
@@ -1,7 +1,7 @@
 #!/usr/bin/env python
 
 """
-The new ticker code was designed to explicity support user customized
+The new ticker code was designed to explicitly support user customized
 ticking.  The documentation
 http://matplotlib.sourceforge.net/matplotlib.ticker.html details this
 process.  That code defines a lot of preset tickers but was primarily

diff --git a/examples/pylab_examples/customize_rc.py b/examples/pylab_examples/customize_rc.py
@@ -1,6 +1,6 @@
 
 """
-I'm not trying to make a good liking figure here, but just to show
+I'm not trying to make a good looking figure here, but just to show
 some examples of customizing rc params on the fly
 
 If you like to work interactively, and need to create different sets
@@ -36,7 +36,7 @@ def set_pub():
 rc('xtick.major', size=5, pad=7)
 rc('xtick', labelsize=15)
 
-# using aliases for color, linestyle and linewith; gray, solid, thick
+# using aliases for color, linestyle and linewidth; gray, solid, thick
 rc('grid', c='0.5', ls='-', lw=5)
 rc('lines', lw=2, color='g')
 subplot(312)

diff --git a/examples/pylab_examples/date_demo_convert.py b/examples/pylab_examples/date_demo_convert.py
@@ -17,7 +17,7 @@
 ax.plot_date(dates, y*y)
 
 # this is superfluous, since the autoscaler should get it right, but
-# use date2num and num2date to to convert between dates and floats if
+# use date2num and num2date to convert between dates and floats if
 # you want; both date2num and num2date convert an instance or sequence
 ax.set_xlim( dates[0], dates[-1] )
 

diff --git a/examples/pylab_examples/demo_agg_filter.py b/examples/pylab_examples/demo_agg_filter.py
@@ -221,7 +221,7 @@ def filtered_text(ax):
 
 
 def drop_shadow_line(ax):
-    # copyed from examples/misc/svg_filter_line.py
+    # copied from examples/misc/svg_filter_line.py
 
     # draw lines
     l1, = ax.plot([0.1, 0.5, 0.9], [0.1, 0.9, 0.5], "bo-",

diff --git a/examples/pylab_examples/fancybox_demo.py b/examples/pylab_examples/fancybox_demo.py
@@ -42,7 +42,7 @@ def test1(ax):
 def test2(ax):
 
     # bbox=round has two optional argument. pad and rounding_size.
-    # They can be set during the initiallization.
+    # They can be set during the initialization.
     p_fancy = FancyBboxPatch((bb.xmin, bb.ymin),
                              abs(bb.width), abs(bb.height),
                              boxstyle="round,pad=0.1",

diff --git a/examples/pylab_examples/finance_work2.py b/examples/pylab_examples/finance_work2.py
@@ -95,7 +95,7 @@ def moving_average_convergence(x, nslow=26, nfast=12):
 
 
 fig = plt.figure(facecolor='white')
-axescolor  = '#f6f6f6'  # the axies background color
+axescolor  = '#f6f6f6'  # the axes background color
 
 ax1 = fig.add_axes(rect1, axisbg=axescolor)  #left, bottom, width, height
 ax2 = fig.add_axes(rect2, axisbg=axescolor, sharex=ax1)

diff --git a/examples/pylab_examples/ganged_plots.py b/examples/pylab_examples/ganged_plots.py
@@ -1,7 +1,7 @@
 #!/usr/bin/env python
 """
 To create plots that share a common axes (visually) you can set the
-hspace bewtween the subplots close to zero (do not use zero itself).
+hspace between the subplots close to zero (do not use zero itself).
 Normally you'll want to turn off the tick labels on all but one of the
 axes.
 

diff --git a/examples/pylab_examples/griddata_demo.py b/examples/pylab_examples/griddata_demo.py
@@ -16,7 +16,8 @@
 zi = griddata(x,y,z,xi,yi,interp='linear')
 # contour the gridded data, plotting dots at the nonuniform data points.
 CS = plt.contour(xi,yi,zi,15,linewidths=0.5,colors='k')
-CS = plt.contourf(xi,yi,zi,15,cmap=plt.cm.jet)
+CS = plt.contourf(xi,yi,zi,15,cmap=plt.cm.rainbow, 
+                  vmax=abs(zi).max(), vmin=-abs(zi).max())
 plt.colorbar() # draw colorbar
 # plot data points.
 plt.scatter(x,y,marker='o',c='b',s=5,zorder=10)

diff --git a/examples/pylab_examples/hexbin_demo.py b/examples/pylab_examples/hexbin_demo.py
@@ -6,8 +6,7 @@
 """
 
 import numpy as np
-import matplotlib.cm as cm
-import  matplotlib.pyplot as plt
+import matplotlib.pyplot as plt
 
 np.random.seed(0)
 n = 100000
@@ -20,14 +19,14 @@
 
 plt.subplots_adjust(hspace=0.5)
 plt.subplot(121)
-plt.hexbin(x,y, cmap=cm.jet)
+plt.hexbin(x,y, cmap=plt.cm.YlOrRd_r)
 plt.axis([xmin, xmax, ymin, ymax])
 plt.title("Hexagon binning")
 cb = plt.colorbar()
 cb.set_label('counts')
 
 plt.subplot(122)
-plt.hexbin(x,y,bins='log', cmap=cm.jet)
+plt.hexbin(x,y,bins='log', cmap=plt.cm.YlOrRd_r)
 plt.axis([xmin, xmax, ymin, ymax])
 plt.title("With a log color scale")
 cb = plt.colorbar()

diff --git a/examples/pylab_examples/hexbin_demo2.py b/examples/pylab_examples/hexbin_demo2.py
@@ -39,14 +39,15 @@
 gridsize=30
 
 plt.subplot(211)
-plt.hexbin(x,y, C=z, gridsize=gridsize, marginals=True)
+plt.hexbin(x,y, C=z, gridsize=gridsize, marginals=True, cmap=plt.cm.RdBu, 
+           vmax=abs(z).max(), vmin=-abs(z).max())
 plt.axis([xmin, xmax, ymin, ymax])
 cb = plt.colorbar()
 cb.set_label('mean value')
 
 
 plt.subplot(212)
-plt.hexbin(x,y, gridsize=gridsize)
+plt.hexbin(x,y, gridsize=gridsize, cmap=plt.cm.Blues_r)
 plt.axis([xmin, xmax, ymin, ymax])
 cb = plt.colorbar()
 cb.set_label('N observations')

diff --git a/examples/pylab_examples/image_demo.py b/examples/pylab_examples/image_demo.py
@@ -11,8 +11,9 @@
 Z2 = mlab.bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
 Z = Z2-Z1  # difference of Gaussians
 
-im = plt.imshow(Z, interpolation='bilinear', cmap=cm.gray,
-                origin='lower', extent=[-3,3,-3,3])
+im = plt.imshow(Z, interpolation='bilinear', cmap=cm.RdYlGn,
+                origin='lower', extent=[-3,3,-3,3],
+                vmax=abs(Z).max(), vmin=-abs(Z).max())
 
 plt.show()