"""

============================

Affine transform of an image

============================

Prepending an affine transformation (:class:`~.transforms.Affine2D`)

to the :ref:`data transform <data-coords>`

of an image allows to manipulate the image's shape and orientation.

This is an example of the concept of

:ref:`transform chaining <transformation-pipeline>`.

For the backends that support draw_image with optional affine

transform (e.g., agg, ps backend), the image of the output should

have its boundary match the dashed yellow rectangle.

"""

import numpy as np

import matplotlib.pyplot as plt

import matplotlib.transforms as mtransforms

def get_image():

delta = 0.25

x = y = np.arange(-3.0, 3.0, delta)

X, Y = np.meshgrid(x, y)

Z1 = np.exp(-X**2 - Y**2)

Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)

Z = (Z1 - Z2)

return Z

def do_plot(ax, Z, transform):

im = ax.imshow(Z, interpolation='none',

origin='lower',

extent=[-2, 4, -3, 2], clip_on=True)

trans_data = transform + ax.transData

im.set_transform(trans_data)

# display intended extent of the image

x1, x2, y1, y2 = im.get_extent()

ax.plot([x1, x2, x2, x1, x1], [y1, y1, y2, y2, y1], "y--",

transform=trans_data)

ax.set_xlim(-5, 5)

ax.set_ylim(-4, 4)

# prepare image and figure

fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2)

Z = get_image()

# image rotation

do_plot(ax1, Z, mtransforms.Affine2D().rotate_deg(30))

# image skew

do_plot(ax2, Z, mtransforms.Affine2D().skew_deg(30, 15))

# scale and reflection

do_plot(ax3, Z, mtransforms.Affine2D().scale(-1, .5))

# everything and a translation

do_plot(ax4, Z, mtransforms.Affine2D().

rotate_deg(30).skew_deg(30, 15).scale(-1, .5).translate(.5, -1))

plt.show()

#############################################################################

#

# ------------

#

# References

# """"""""""

#

# The use of the following functions, methods and classes is shown

# in this example:

import matplotlib

matplotlib.axes.Axes.imshow

matplotlib.pyplot.imshow

matplotlib.transforms.Affine2D