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"""
============================
Affine transform of an image
============================
Prepending an affine transformation (`~.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>`.
The image of the output should have its boundary match the dashed yellow
rectangle.
"""
import matplotlib.pyplot as plt
import numpy as np
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()
# %%
#
# .. admonition:: References
#
# The use of the following functions, methods, classes and modules is shown
# in this example:
#
# - `matplotlib.axes.Axes.imshow` / `matplotlib.pyplot.imshow`
# - `matplotlib.transforms.Affine2D`
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