Matplotlib.axis.Axis.get_transformed_clip_path_and_affine() function in Python

• Last Updated : 08 Jun, 2020

Matplotlib is a library in Python and it is numerical – mathematical extension for NumPy library. It is an amazing visualization library in Python for 2D plots of arrays and used for working with the broader SciPy stack.

matplotlib.axis.Axis.get_transformed_clip_path_and_affine() Function

The Axis.get_transformed_clip_path_and_affine() function in axis module of matplotlib library is used to get the clip path with the non-affine part of its transformation applied, and the remaining affine part of its transformation.

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Syntax: Axis.get_transformed_clip_path_and_affine(self)

Parameters: This method does not accepts any parameter.

Return value: This method return the clip path with the non-affine part of its transformation applied, and the remaining affine part of its transformation.

Below examples illustrate the matplotlib.axis.Axis.get_transformed_clip_path_and_affine() function in matplotlib.axis:

Example 1:

Image used:

Python3

 `# Implementation of matplotlib function``from` `matplotlib.axis ``import` `Axis``import` `matplotlib.pyplot as plt  ``import` `matplotlib.patches as patches  ``import` `matplotlib.cbook as cbook  ``        ` `     ` `with cbook.get_sample_data(``'image.PNG'``) as image_file:  ``    ``image ``=` `plt.imread(image_file)  ``        ` `fig, ax ``=` `plt.subplots()  ``im ``=` `ax.imshow(image)  ``patch ``=` `patches.Rectangle((``50``, ``50``), ``200``, ``200``,   ``                          ``transform ``=` `ax.transData)  ``   ` `# use of get_transformed_clip_path_and_affine() method ``val ``=` `Axis.get_transformed_clip_path_and_affine(im) ``ax.set_title(``"Value Return by get_transformed_clip_path_and_affine(): "` `             ``+` `str``(val)) `` ` `fig.suptitle(``"""matplotlib.axis.Axis.get_transformed_clip_path_and_affine()``function Example\n"""``, fontweight ``=``"bold")  ``   ` `plt.show()`

Output:

Example 2:

Python3

 `# Implementation of matplotlib function``from` `matplotlib.axis ``import` `Axis``import` `numpy as np  ``import` `matplotlib.cm as cm  ``import` `matplotlib.pyplot as plt  ``from` `matplotlib.path ``import` `Path  ``from` `matplotlib.patches ``import` `PathPatch  ``        ` `     ` `delta ``=` `0.025``     ` `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) ``*` `2``        ` `path ``=` `Path([[``0``, ``1``], [``1``, ``0``], [``0``, ``-``1``], [``-``1``, ``0``], [``0``, ``1``]])  ``patch ``=` `PathPatch(path, facecolor ``=``'none'``)  ``        ` `fig, ax ``=` `plt.subplots()  ``ax.add_patch(patch)  ``im ``=` `ax.imshow(Z,  ``               ``interpolation ``=``'bilinear'``,   ``               ``cmap ``=` `cm.gray,  ``               ``origin ``=``'lower'``,  ``               ``extent ``=``[``-``3``, ``3``, ``-``3``, ``3``],  ``               ``clip_path ``=` `patch,  ``               ``clip_on ``=` `True``) ``   ` `# use of get_transformed_clip_path_and_affine() method ``val ``=` `Axis.get_transformed_clip_path_and_affine(im) ``print``(``"Value Return by get_transformed_clip_path_and_affine(): "``) ``for` `i ``in` `val:  ``    ``print``(i)``     ` `fig.suptitle(``"""matplotlib.axis.Axis.get_transformed_clip_path_and_affine()``function Example\n"""``, fontweight ``=``"bold")  ``   ` `plt.show()`

Output:

```Value Return by get_transformed_clip_path_and_affine():
Path(array([[ 0.,  1.],
[ 1.,  0.],
[ 0., -1.],
[-1.,  0.],
[ 0.,  1.]]), None)
Affine2D(
[[ 82.66666667   0.         328.        ]
[  0.          61.6        237.6       ]
[  0.           0.           1.        ]])
```

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