add imports where needed

endolith · endolith · commit e1b17ec1dab7 · 2012-06-07T23:17:10.000-04:00
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
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
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
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.YlOrRd_r)
+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.YlOrRd_r)
+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 +39,14 @@
 gridsize=30
 
 plt.subplot(211)
-plt.hexbin(x,y, C=z, gridsize=gridsize, marginals=True, cmap=cm.RdBu, vmax=abs(z).max(), vmin=-abs(z).max()) # NEEDS NORM
+plt.hexbin(x,y, C=z, gridsize=gridsize, marginals=True, cmap=plt.cm.RdBu, vmax=abs(z).max(), vmin=-abs(z).max()) # NEEDS NORM
 plt.axis([xmin, xmax, ymin, ymax])
 cb = plt.colorbar()
 cb.set_label('mean value')
 
 
 plt.subplot(212)
-plt.hexbin(x,y, gridsize=gridsize, cmap=cm.Blues_r)
+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_nonuniform.py b/examples/pylab_examples/image_nonuniform.py
@@ -7,6 +7,7 @@
 from matplotlib.pyplot import figure, show
 import numpy as np
 from matplotlib.image import NonUniformImage
+from matplotlib import cm
 
 interp='nearest'
 
diff --git a/examples/pylab_examples/tricontour_vs_griddata.py b/examples/pylab_examples/tricontour_vs_griddata.py
@@ -24,7 +24,7 @@
 yi = np.linspace(-2.1,2.1,ngridy)
 zi = griddata(x,y,z,xi,yi,interp='linear')
 plt.contour(xi,yi,zi,15,linewidths=0.5,colors='k')
-plt.contourf(xi,yi,zi,15,cmap=plt.cm.rainbow, norm = Normalize(vmax=abs(zi).max(), vmin=-abs(zi).max()))
+plt.contourf(xi,yi,zi,15,cmap=plt.cm.rainbow, norm=plt.normalize(vmax=abs(zi).max(), vmin=-abs(zi).max()))
 plt.colorbar() # draw colorbar
 plt.plot(x, y, 'ko', ms=3)
 plt.xlim(-2,2)
@@ -37,7 +37,7 @@
 plt.subplot(212)
 triang = tri.Triangulation(x, y)
 plt.tricontour(x, y, z, 15, linewidths=0.5, colors='k')
-plt.tricontourf(x, y, z, 15, cmap=plt.cm.rainbow, norm = Normalize(vmax=abs(zi).max(), vmin=-abs(zi).max()))
+plt.tricontourf(x, y, z, 15, cmap=plt.cm.rainbow, norm=plt.normalize(vmax=abs(zi).max(), vmin=-abs(zi).max()))
 plt.colorbar()
 plt.plot(x, y, 'ko', ms=3)
 plt.xlim(-2,2)
diff --git a/examples/pylab_examples/tripcolor_demo.py b/examples/pylab_examples/tripcolor_demo.py
@@ -35,14 +35,14 @@
 # pcolor plot.
 plt.figure()
 plt.gca().set_aspect('equal')
-plt.tripcolor(triang, z, shading='flat', cmap = cm.rainbow)
+plt.tripcolor(triang, z, shading='flat', cmap = plt.cm.rainbow)
 plt.colorbar()
 plt.title('tripcolor of Delaunay triangulation: flat')
 
 # Illustrate Gouraud shading.
 plt.figure()
 plt.gca().set_aspect('equal')
-plt.tripcolor(triang, z, shading='gouraud', cmap = cm.rainbow)
+plt.tripcolor(triang, z, shading='gouraud', cmap = plt.cm.rainbow)
 plt.colorbar()
 plt.title('tripcolor with Gouraud shading')
 
