import matplotlib.pyplot as plt

import numpy as np

from matplotlib.cbook import get_sample_data

from mpl_toolkits import axes_grid1

import example_utils

def main():

fig, axes = setup_axes()

plot(axes, *load_data())

example_utils.title(fig, '"ax.imshow(data, ...)": Colormapped or RGB arrays')

fig.savefig('imshow_example.png', facecolor='none')

plt.show()

def plot(axes, img_data, scalar_data, ny):

# Note: I'm defining the extent so I can cheat a bit when using ImageGrid

# to make all of the axes the same height...

# Default: Linear interpolation

axes[0].imshow(scalar_data, cmap='gist_earth', extent=[0, ny, ny, 0])

# Use nearest interpolation instead.

axes[1].imshow(scalar_data, cmap='gist_earth', interpolation='nearest',

extent=[0, ny, ny, 0])

# Show RGB/RGBA data instead of colormapping a scalar.

axes[2].imshow(img_data)

def load_data():

img_data = plt.imread(get_sample_data('grace_hopper.png'))

ny, nx, nbands = img_data.shape

scalar_data = np.load(get_sample_data('axes_grid/bivariate_normal.npy'))

return img_data, scalar_data, ny

def setup_axes():

# We'll set up the axes a bit differently here so that they'll all be the

# same height even though the aspect will be set and adjustable is "box".

fig = plt.figure(figsize=(6,3))

axes = axes_grid1.ImageGrid(fig, [0, 0, .93, 1], (1, 3), axes_pad=0)

for ax in axes:

ax.set(xticks=[], yticks=[])

return fig, axes

main()