Merge remote-tracking branch 'matplotlib/master'

ericmjl · ericmjl · commit 8fa70115e0c0 · 2015-08-18T21:54:32.000-04:00
diff --git a/examples/pylab_examples/image_masked.py b/examples/pylab_examples/image_masked.py
@@ -1,23 +1,25 @@
-#!/usr/bin/env python
-'''imshow with masked array input and out-of-range colors.
+"""
+imshow with masked array input and out-of-range colors.
 
-    The second subplot illustrates the use of BoundaryNorm to
-    get a filled contour effect.
-'''
+The second subplot illustrates the use of BoundaryNorm to
+get a filled contour effect.
+"""
 
-from pylab import *
 from numpy import ma
 import matplotlib.colors as colors
+import matplotlib.pyplot as plt
+import matplotlib.mlab as mlab
+import numpy as np
 
 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 = 10*(Z2 - Z1)  # difference of Gaussians
 
 # Set up a colormap:
-palette = cm.gray
+palette = plt.cm.gray
 palette.set_over('r', 1.0)
 palette.set_under('g', 1.0)
 palette.set_bad('b', 1.0)
@@ -33,22 +35,22 @@
 # range to which the regular palette color scale is applied.
 # Anything above that range is colored based on palette.set_over, etc.
 
-subplot(1, 2, 1)
-im = imshow(Zm, interpolation='bilinear',
-            cmap=palette,
-            norm=colors.Normalize(vmin=-1.0, vmax=1.0, clip=False),
-            origin='lower', extent=[-3, 3, -3, 3])
-title('Green=low, Red=high, Blue=bad')
-colorbar(im, extend='both', orientation='horizontal', shrink=0.8)
-
-subplot(1, 2, 2)
-im = imshow(Zm, interpolation='nearest',
-            cmap=palette,
-            norm=colors.BoundaryNorm([-1, -0.5, -0.2, 0, 0.2, 0.5, 1],
-                                     ncolors=256, clip=False),
-            origin='lower', extent=[-3, 3, -3, 3])
-title('With BoundaryNorm')
-colorbar(im, extend='both', spacing='proportional',
-         orientation='horizontal', shrink=0.8)
-
-show()
+plt.subplot(1, 2, 1)
+im = plt.imshow(Zm, interpolation='bilinear',
+                cmap=palette,
+                norm=colors.Normalize(vmin=-1.0, vmax=1.0, clip=False),
+                origin='lower', extent=[-3, 3, -3, 3])
+plt.title('Green=low, Red=high, Blue=bad')
+plt.colorbar(im, extend='both', orientation='horizontal', shrink=0.8)
+
+plt.subplot(1, 2, 2)
+im = plt.imshow(Zm, interpolation='nearest',
+                cmap=palette,
+                norm=colors.BoundaryNorm([-1, -0.5, -0.2, 0, 0.2, 0.5, 1],
+                                         ncolors=256, clip=False),
+                origin='lower', extent=[-3, 3, -3, 3])
+plt.title('With BoundaryNorm')
+plt.colorbar(im, extend='both', spacing='proportional',
+             orientation='horizontal', shrink=0.8)
+
+plt.show()
diff --git a/examples/pylab_examples/image_origin.py b/examples/pylab_examples/image_origin.py
@@ -1,25 +1,25 @@
-#!/usr/bin/env python
 """
 You can specify whether images should be plotted with the array origin
 x[0,0] in the upper left or upper right by using the origin parameter.
 You can also control the default be setting image.origin in your
 matplotlibrc file; see http://matplotlib.org/matplotlibrc
 """
-from pylab import *
+import matplotlib.pyplot as plt
+import numpy as np
 
-x = arange(100.0)
+x = np.arange(100.0)
 x.shape = (10, 10)
 
 interp = 'bilinear'
 #interp = 'nearest'
 lim = -2, 11, -2, 6
-subplot(211, axisbg='g')
-title('blue should be up')
-imshow(x, origin='upper', interpolation=interp)
-#axis(lim)
+plt.subplot(211, axisbg='g')
+plt.title('blue should be up')
+plt.imshow(x, origin='upper', interpolation=interp, cmap='jet')
+#plt.axis(lim)
 
-subplot(212, axisbg='y')
-title('blue should be down')
-imshow(x, origin='lower', interpolation=interp)
-#axis(lim)
-show()
+plt.subplot(212, axisbg='y')
+plt.title('blue should be down')
+plt.imshow(x, origin='lower', interpolation=interp, cmap='jet')
+#plt.axis(lim)
+plt.show()
