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.Tick.set_transform() Function
The Tick.set_transform() function in axis module of matplotlib library is used to set the artist transform.
Syntax: Tick.set_transform(self, t)
Parameters: This method accepts the following parameters.
- t: This parameter is the Transform.
Return value: This method does not return any value.
Below examples illustrate the matplotlib.axis.Tick.set_transform() function in matplotlib.axis:
Example 1:
Python3
# Implementation of matplotlib function from matplotlib.axis import Tick import numpy as np import matplotlib.pyplot as plt import matplotlib.transforms as mtransforms delta = 0.5 x = y = np.arange(-2.0, 4.0, delta) X, Y = np.meshgrid(x**2, 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 Tick.set_transform(im, 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(-3, 6) ax.set_ylim(-5, 5) fig.suptitle('matplotlib.axis.Tick.set_transform() \ function Example', fontweight ="bold") plt.show() |
Output:
Example 2:
Python3
# Implementation of matplotlib function from matplotlib.axis import Tick import matplotlib.pyplot as plt from matplotlib import collections, colors, transforms import numpy as np nverts = 50npts = 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) Tick.set_transform(col, trans) ax1.add_collection(col, autolim = True) col.set_color(colors) fig.suptitle('matplotlib.axis.Tick.set_transform() \ function Example', fontweight ="bold") plt.show() |
Output:
