Axis autoscaling

Basic concept

Autoscaling ensures that data is visible within the Axes by automatically adjusting the axis limits. When you plot data, Matplotlib's autoscaling mechanism updates the axis limits accordingly.

import matplotlib.pyplot as plt
import numpy as np


x = np.linspace(-6, 6, 201)
y = np.sinc(x)

fig, ax = plt.subplots()
ax.plot(x, y)

Margins

To ensure that the data is not at the very edge of the plot, Matplotlib adds a margin around the data limits. Note that the x data range in the above plot is [-6, 6], but the x-axis limits are slightly wider due to the margin.

The default margin is 5%, defined via

• rcParams["axes.xmargin"] (default: 0.05)

• rcParams["axes.ymargin"] (default: 0.05)

• rcParams["axes.zmargin"] (default: 0.05)

print(ax.get_xmargin(), ax.get_ymargin())

0.05 0.05

The margin size can be overridden to make them smaller or larger using margins:

fig, ax = plt.subplots()
ax.plot(x, y)
ax.margins(0.2, 0.2)

In general, margins can be in the range (-0.5, ∞), where negative margins set the axes limits to a subrange of the data range, i.e. they clip data. Using a single number for margins affects both axes, a single margin can be customized using keyword arguments x or y, but positional and keyword interface cannot be combined.

fig, ax = plt.subplots()
ax.plot(x, y)
ax.margins(y=-0.2)

Sticky edges

There are plot elements (Artists) that are usually used without margins. For example false-color images (e.g. created with Axes.imshow) are not considered in the margins calculation.

xx, yy = np.meshgrid(x, x)
zz = np.sinc(np.sqrt((xx - 1)**2 + (yy - 1)**2))

fig, ax = plt.subplots(ncols=2, figsize=(12, 8))
ax[0].imshow(zz)
ax[0].set_title("default margins")
ax[1].imshow(zz)
ax[1].margins(0.2)
ax[1].set_title("margins(0.2)")

This override of margins is determined by "sticky edges", a property of Artist class that can suppress adding margins to axis limits. The effect of sticky edges can be disabled on an Axes by changing use_sticky_edges. Artists have a property Artist.sticky_edges, and the values of sticky edges can be changed by writing to Artist.sticky_edges.x or Artist.sticky_edges.y.

The following example shows how overriding works and when it is needed.

fig, ax = plt.subplots(ncols=3, figsize=(16, 10))
ax[0].imshow(zz)
ax[0].margins(0.2)
ax[0].set_title("default use_sticky_edges\nmargins(0.2)")
ax[1].imshow(zz)
ax[1].margins(0.2)
ax[1].use_sticky_edges = False
ax[1].set_title("use_sticky_edges=False\nmargins(0.2)")
ax[2].imshow(zz)
ax[2].margins(-0.2)
ax[2].set_title("default use_sticky_edges\nmargins(-0.2)")

We can see that setting use_sticky_edges to False renders the image with requested margins.

While sticky edges don't increase the axis limits through extra margins, negative margins are still taken into account. This can be seen in the reduced limits of the third image.

Controlling autoscale

By default, the limits are recalculated every time you add a new curve to the plot:

fig, ax = plt.subplots(ncols=2, figsize=(12, 8))
ax[0].plot(x, y)
ax[0].set_title("Single curve")
ax[1].plot(x, y)
ax[1].plot(x * 2.0, y)
ax[1].set_title("Two curves")

If you don't want automatic updates of the axis limits, either deactivate autoscaling with autoscale or set the limits manually with set_xlim / set_ylim.

Let's say that we want to see only a part of the data in greater detail. Setting the xlim persists even if we add more curves to the data. Calling Axes.autoscale will re-enable the autoscaling and recalculate the limits to fit all the data.

fig, ax = plt.subplots(ncols=2, figsize=(12, 8))
ax[0].plot(x, y)
ax[0].set_xlim(left=-1, right=1)
ax[0].plot(x + np.pi * 0.5, y)
ax[0].set_title("set_xlim(left=-1, right=1)\n")
ax[1].plot(x, y)
ax[1].set_xlim(left=-1, right=1)
ax[1].plot(x + np.pi * 0.5, y)
ax[1].autoscale()
ax[1].set_title("set_xlim(left=-1, right=1)\nautoscale()")

We can check that the first plot has autoscale disabled and that the second plot has it enabled again by using Axes.get_autoscale_on():

print(ax[0].get_autoscale_on())  # False means disabled
print(ax[1].get_autoscale_on())  # True means enabled -> recalculated

False
True

Arguments of the autoscale function give us precise control over the process of autoscaling. A combination of arguments enable, and axis sets the autoscaling feature for the selected axis (or both). The argument tight sets the margin of the selected axis to zero. To preserve settings of either enable or tight you can set the opposite one to None, that way it should not be modified. However, setting enable to None and tight to True affects both axes regardless of the axis argument.

fig, ax = plt.subplots()
ax.plot(x, y)
ax.margins(0.2, 0.2)
ax.autoscale(enable=None, axis="x", tight=True)

print(ax.margins())

(0, 0)