8000 contourf_demo: add a figure to illustrate extend options with cmap · matplotlib/matplotlib@bca3321 · GitHub
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contourf_demo: add a figure to illustrate extend options with cmap
Also removed "from pylab import *".
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‎examples/pylab_examples/contourf_demo.py

Lines changed: 45 additions & 25 deletions
Original file line numberDiff line numberDiff line change
@@ -1,68 +1,70 @@
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#!/usr/bin/env python
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from pylab import *
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import numpy as np
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import matplotlib.pyplot as plt
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origin = 'lower'
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#origin = 'upper'
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delta = 0.025
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8-
x = y = arange(-3.0, 3.01, delta)
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X, Y = meshgrid(x, y)
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Z1 = bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)
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Z2 = bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
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x = y = np.arange(-3.0, 3.01, delta)
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X, Y = np.meshgrid(x, y)
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Z1 = plt.mlab.bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)
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Z2 = plt.mlab.bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
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Z = 10 * (Z1 - Z2)
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nr, nc = Z.shape
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# put NaNs in one corner:
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Z[-nr//6:, -nc//6:] = nan
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Z[-nr//6:, -nc//6:] = np.nan
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# contourf will convert these to masked
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Z = ma.array(Z)
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Z = np.ma.array(Z)
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# mask another corner:
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Z[:nr//6, :nc//6] = ma.masked
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Z[:nr//6, :nc//6] = np.ma.masked
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# mask a circle in the middle:
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interior = sqrt((X**2) + (Y**2)) < 0.5
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Z[interior] = ma.masked
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interior = np.sqrt((X**2) + (Y**2)) < 0.5
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Z[interior] = np.ma.masked
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# We are using automatic selection of contour levels;
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# this is usually not such a good idea, because they don't
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# occur on nice boundaries, but we do it here for purposes
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# of illustration.
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CS = contourf(X, Y, Z, 10, # [-1, -0.1, 0, 0.1],
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CS = plt.contourf(X, Y, Z, 10, # [-1, -0.1, 0, 0.1],
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#alpha=0.5,
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cmap=cm.bone,
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cmap=plt.cm.bone,
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origin=origin)
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# Note that in the following, we explicitly pass in a subset of
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# the contour levels used for the filled contours. Alternatively,
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# We could pass in additional levels to provide extra resolution,
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# or leave out the levels kwarg to use all of the original levels.
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CS2 = contour(CS, levels=CS.levels[::2],
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CS2 = plt.contour(CS, levels=CS.levels[::2],
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colors = 'r',
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origin=origin,
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hold='on')
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title('Nonsense (3 masked regions)')
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xlabel('word length anomaly')
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ylabel('sentence length anomaly')
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plt.title('Nonsense (3 masked regions)')
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plt.xlabel('word length anomaly')
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plt.ylabel('sentence length anomaly')
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# Make a colorbar for the ContourSet returned by the contourf call.
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cbar = colorbar(CS)
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cbar = plt.colorbar(CS)
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cbar.ax.set_ylabel('verbosity coefficient')
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# Add the contour line levels to the colorbar
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cbar.add_lines(CS2)
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figure()
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plt.figure()
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# Now make a contour plot with the levels specified,
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# and with the colormap generated automatically from a list
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# of colors.
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levels = [-1.5, -1, -0.5, 0, 0.5, 1]
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CS3 = contourf(X, Y, Z, levels,
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CS3 = plt.contourf(X, Y, Z, levels,
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colors = ('r', 'g', 'b'),
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origin=origin,
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extend='both')
@@ -72,16 +74,34 @@
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CS3.cmap.set_under('yellow')
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CS3.cmap.set_over('cyan')
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CS4 = contour(X, Y, Z, levels,
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CS4 = plt.contour(X, Y, Z, levels,
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colors = ('k',),
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linewidths = (3,),
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origin = origin)
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title('Listed colors (3 masked regions)')
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clabel(CS4, fmt = '%2.1f', colors = 'w', fontsize=14)
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plt.title('Listed colors (3 masked regions)')
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plt.clabel(CS4, fmt = '%2.1f', colors = 'w', fontsize=14)
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# Notice that the colorbar command gets all the information it
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# needs from the ContourSet object, CS3.
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colorbar(CS3)
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show()
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plt.colorbar(CS3)
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# Illustrate all 4 possible "extend" settings:
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extends = ["neither", "both", "min", "max"]
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cmap = plt.cm.get_cmap("winter")
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cmap.set_under("magenta")
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cmap.set_over("yellow")
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# Note: contouring simply excludes masked or nan regions, so
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# instead of using the "bad" colormap value for them, it draws
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# nothing at all in them. Therefore the following would have
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# no effect:
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#cmap.set_bad("red")
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fig, axs = plt.subplots(2,2)
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for ax, extend in zip(axs.ravel(), extends):
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cs = ax.contourf(X, Y, Z, levels, cmap=cmap, extend=extend, origin=origin)
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fig.colorbar(cs, ax=ax, shrink=0.9)
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ax.set_title("extend = %s" % extend)
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ax.locator_params(nbins=4)
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plt.show()
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