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.autoscale_view() Function
The Axes.autoscale_view() function in axes module of matplotlib library is used to autoscale the view limits using the data limits.
Syntax: Axes.autoscale_view(self, tight=None, scalex=True, scaley=True)
Parameters: This method accepts the following parameters.
- scalex: This parameter is used to whether to autoscale the x axis.
- scaley: This parameter is used to whether to autoscale the y axis.
- tight: This parameter is used to expand the axis limits using the margins.
Return value: This method does not return any value.
Below examples illustrate the matplotlib.axes.Axes.autoscale() function in matplotlib.axes:
Example 1:
# ImpleIn Reviewtation of matplotlib function import numpy as np from basic_units import cm, inch import matplotlib.pyplot as plt N = 5val1 = [150 * cm, 160 * cm, 146 * cm, 172 * cm, 155 * cm] val2 = [20 * cm, 30 * cm, 32 * cm, 10 * cm, 20 * cm] fig, ax = plt.subplots() ind = np.arange(N) width = 0.35 ax.bar(ind, val1, width, bottom = 0 * cm, yerr = val2, label ='In Review') woval1 = (145 * cm, 149 * cm, 172 * cm, 165 * cm, 200 * cm) woval2 = (30 * cm, 25 * cm, 20 * cm, 31 * cm, 22 * cm) ax.bar(ind + width, woval1, width, bottom = 0 * cm, yerr = woval2, label ='Published') ax.set_title('Scores by group and gender') ax.set_xticks(ind + width / 2) ax.set_xticklabels(('Geek1', 'Geek2', 'Geek3', 'Geek4', 'Geek5')) ax.legend() ax.set_ylabel("Articles") ax.autoscale_view() fig.suptitle('matplotlib.axes.Axes.autoscale_view()\ function Example\n', fontweight ="bold") fig.canvas.draw() 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 = 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, ax2] = plt.subplots(1, 2) 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) ax1.set_title("Without autoscale_view() function") col = collections.RegularPolyCollection( 7, sizes = np.abs(xx) * 10.0, offsets = xyo, transOffset = ax2.transData) trans = transforms.Affine2D().scale(fig.dpi / 72.0) col.set_transform(trans) ax2.add_collection(col, autolim = True) col.set_color(colors) ax2.autoscale_view() ax2.set_title("Using autoscale_view() function") fig.suptitle('matplotlib.axes.Axes.autoscale_view()\ function Example\n', fontweight ="bold") fig.canvas.draw() plt.show() |
Output:
