# Matplotlib.axes.Axes.set_transform() in Python

• Last Updated : 30 Apr, 2020

Matplotlib is a library in Python and it is numerical – mathematical extension for NumPy library. The Axes Class contains most of the figure elements: Axis, Tick, Line2D, Text, Polygon, etc., and sets the coordinate system. And the instances of Axes supports callbacks through a callbacks attribute.

## matplotlib.axes.Axes.set_transform() Function

The Axes.set_transform() function in axes module of matplotlib library is used to set the artist transform.

Syntax: Axes.set_transform(self, t)

Parameters: This method accepts only one parameters.

• t : This parameter is the Transform.

Returns: This method does not return any value.

Below examples illustrate the matplotlib.axes.Axes.set_transform() function in matplotlib.axes:

Example 1:

 `# Implementation of matplotlib function``import` `numpy as np``import` `matplotlib.pyplot as plt``import` `matplotlib.transforms as mtransforms`` ` `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)`` ` `transform ``=` `mtransforms.Affine2D().rotate_deg(``30``)``fig, ax ``=` `plt.subplots()``     ` `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)`` ` `x1, x2, y1, y2 ``=` `im.get_extent()``ax.plot([x1, x2, x2, x1, x1], ``        ``[y1, y1, y2, y2, y1],``        ``"ro-"``,``        ``transform ``=` `trans_data)`` ` `ax.set_xlim(``-``5``, ``5``)``ax.set_ylim(``-``4``, ``4``)`` ` `fig.suptitle('matplotlib.axes.Axes.set_transform() \``function Example\n\n', fontweight ``=``"bold"``)`` ` `plt.show()`

Output:

Example 2:

 `# Implementation of matplotlib function  ``import` `matplotlib.pyplot as plt``from` `matplotlib ``import` `collections, colors, transforms``import` `numpy as np``  ` `nverts ``=` `50``npts ``=` `100``  ` `r ``=` `np.arange(nverts)``theta ``=` `np.linspace(``0``, ``2` `*` `np.pi, nverts)``xx ``=` `r ``*` `np.sin(theta)``yy ``=` `r ``*` `np.cos(theta)``spiral ``=` `np.column_stack([xx, yy])``  ` `rs ``=` `np.random.RandomState(``19680801``)``  ` `xyo ``=` `rs.randn(npts, ``2``)``  ` `colors ``=` `[colors.to_rgba(c)``          ``for` `c ``in` `plt.rcParams[``'axes.prop_cycle'``].by_key()[``'color'``]]``  ` `fig, ax1 ``=` `plt.subplots()``  ` `col ``=` `collections.RegularPolyCollection(``    ``7``, sizes ``=` `np.``abs``(xx) ``*` `10.0``, ``    ``offsets ``=` `xyo, ``    ``transOffset ``=` `ax1.transData)`` ` `trans ``=` `transforms.Affine2D().scale(fig.dpi ``/` `72.0``)``col.set_transform(trans) `` ` `ax1.add_collection(col, autolim ``=` `True``)``col.set_color(colors)``  ` `fig.suptitle('matplotlib.axes.Axes.set_transform() function\`` ``Example\n', fontweight ``=``"bold"``)`` ` `fig.canvas.draw()`` ` `plt.show()`

Output:

My Personal Notes arrow_drop_up