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Example: remove overlapping text from image_masked.py #7682

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Dec 25, 2016
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Example: remove overlapping text from image_masked.py #7682

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Example: remove overlapping text from image_masked.py
efiring Dec 25, 2016
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minor code cleanups
efiring Dec 25, 2016
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43 changes: 27 additions & 16 deletions examples/pylab_examples/image_masked.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,15 +5,17 @@
get a filled contour effect.
"""
from copy import copy
from numpy import ma
import matplotlib.colors as colors

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as colors
import matplotlib.mlab as mlab
import numpy as np

# compute some interesting data
delta = 0.025
x = y = np.arange(-3.0, 3.0, delta)
x0, x1 = -5, 5
y0, y1 = -3, 3
x = np.linspace(x0, x1, 500)
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👍

y = np.linspace(y0, y1, 500)
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)
Expand All @@ -31,31 +33,40 @@
# If you comment out all the palette.set* lines, you will see
# all the defaults; under and over will be colored with the
# first and last colors in the palette, respectively.
Zm = ma.masked_where(Z > 1.2, Z)
Zm = np.ma.masked_where(Z > 1.2, Z)

# By setting vmin and vmax in the norm, we establish the
# range to which the regular palette color scale is applied.
# Anything above that range is colored based on palette.set_over, etc.

# set up the axes
fig, (ax1, ax2) = plt.subplots(1, 2)
fig, (ax1, ax2) = plt.subplots(nrows=2, figsize=(6, 5.4))

# plot using 'continuous' color map
im = ax1.imshow(Zm, interpolation='bilinear',
cmap=palette,
norm=colors.Normalize(vmin=-1.0, vmax=1.0, clip=False),
origin='lower', extent=[-3, 3, -3, 3])
ax1.set_title('Green=low, Red=high, Blue=bad')
fig.colorbar(im, extend='both', orientation='horizontal', shrink=0.8, ax=ax1)
norm=colors.Normalize(vmin=-1.0, vmax=1.0),
aspect='auto',
origin='lower',
extent=[x0, x1, y0, y1])
ax1.set_title('Green=low, Red=high, Blue=masked')
cbar = fig.colorbar(im, extend='both', shrink=0.9, ax=ax1)
cbar.set_label('uniform')
for ticklabel in ax1.xaxis.get_ticklabels():
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The x-axis could be shared, which renders these two lines unecessary.

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Actually, both axis could be shared.

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On the contrary, axis sharing is inconsistent with the default aspect-ratio handling of imshow. This is a fundamental limitation (to avoid an over-determined system), not a matter of implementation. Since imshow is typically used in cases where unit aspect ratio is desired, I decided to leave that default and forgo the axis sharing.

ticklabel.set_visible(False)

# plot using 'discrete' color map
# Plot using a small number of colors, with unevenly spaced boundaries.
im = ax2.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])
ncolors=palette.N),
aspect='auto',
origin='lower',
extent=[x0, x1, y0, y1])
ax2.set_title('With BoundaryNorm')
fig.colorbar(im, extend='both', spacing='proportional',
orientation='horizontal', shrink=0.8, ax=ax2)
cbar = fig.colorbar(im, extend='both', spacing='proportional',
shrink=0.9, ax=ax2)
cbar.set_label('proportional')

fig.suptitle('imshow, with out-of-range and masked data')
plt.show()