Autoscaling

The limits on an axis can be set manually (e.g. ax.set_xlim(xmin, xmax)) or Matplotlib can set them automatically based on the data already on the axes. There are a number of options to this autoscaling behaviour, discussed below.

We will start with a simple line plot showing that autoscaling extends the axis limits 5% beyond the data limits (-2π, 2π).

import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt

x = np.linspace(-2 * np.pi, 2 * np.pi, 100)
y = np.sinc(x)

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

Out:

[<matplotlib.lines.Line2D object at 0x7f7f029a6430>]

Margins

The default margin around the data limits is 5%:

Out:

(0.05, 0.05)

The margins can be made larger using margins:

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

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.

autoscale

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)")
default margins, margins(0.2)

Out:

Text(0.5, 1.0, '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)")
default use_sticky_edges margins(0.2), use_sticky_edges=False margins(0.2), default use_sticky_edges margins(-0.2)

Out:

Text(0.5, 1.0, '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")
Single curve, Two curves

Out:

Text(0.5, 1.0, 'Two curves')

However, there are cases when you don't want to automatically adjust the viewport to new data.

One way to disable autoscaling is to manually set the axis limit. 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. To recalculate the new limits calling Axes.autoscale will toggle the functionality manually.

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()")
set_xlim(left=-1, right=1) , set_xlim(left=-1, right=1) autoscale()

Out:

Text(0.5, 1.0, '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

Out:

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())
autoscale

Out:

(0, 0)

Working with collections

Autoscale works out of the box for all lines, patches, and images added to the axes. One of the artists that it won't work with is a Collection. After adding a collection to the axes, one has to manually trigger the autoscale_view() to recalculate axes limits.

fig, ax = plt.subplots()
collection = mpl.collections.StarPolygonCollection(
    5, 0, [250, ],  # five point star, zero angle, size 250px
    offsets=np.column_stack([x, y]),  # Set the positions
    transOffset=ax.transData,  # Propagate transformations of the Axes
)
ax.add_collection(collection)
ax.autoscale_view()
autoscale

Total running time of the script: ( 0 minutes 6.979 seconds)

Keywords: matplotlib code example, codex, python plot, pyplot Gallery generated by Sphinx-Gallery