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#!/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
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,

#Thicken the zero contour.
zc = CS.collections[6]
plt.setp(zc, linewidth=4)

plt.clabel(CS, levels[1::2],  # label every second level

# 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

# 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])


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