Overview of axes_grid1 toolkit

Controlling the layout of plots with the mpl_toolkits.axes_grid1 toolkit.

What is axes_grid1 toolkit?

mpl_toolkits.axes_grid1 is a collection of helper classes to ease displaying (multiple) images with matplotlib. In matplotlib, the axes location (and size) is specified in the normalized figure coordinates, which may not be ideal for displaying images that needs to have a given aspect ratio. For example, it helps if you have a colorbar whose height always matches that of the image. ImageGrid, RGB Axes and AxesDivider are helper classes that deal with adjusting the location of (multiple) Axes. They provides a framework to adjust the position of multiple axes at the drawing time. ParasiteAxes provides twinx(or twiny)-like features so that you can plot different data (e.g., different y-scale) in a same Axes. AnchoredArtists includes custom artists which are placed at some anchored position, like the legend.


Demo Axes Grid



A grid of Axes.

In Matplotlib, the axes location (and size) is specified in normalized figure coordinates. This may not be ideal for images that needs to be displayed with a given aspect ratio; for example, it is difficult to display multiple images of a same size with some fixed padding between them. ImageGrid can be used in such a case; see its docs for a detailed list of the parameters it accepts.


Simple Axesgrid

  • The position of each axes is determined at the drawing time (see AxesDivider), so that the size of the entire grid fits in the given rectangle (like the aspect of axes). Note that in this example, the paddings between axes are fixed even if you changes the figure size.

  • axes in the same column has a same axes width (in figure coordinate), and similarly, axes in the same row has a same height. The widths (height) of the axes in the same row (column) are scaled according to their view limits (xlim or ylim).


    Simple Axes Grid

  • xaxis are shared among axes in a same column. Similarly, yaxis are shared among axes in a same row. Therefore, changing axis properties (view limits, tick location, etc. either by plot commands or using your mouse in interactive backends) of one axes will affect all other shared axes.

The examples below show what you can do with ImageGrid.


Demo Axes Grid

AxesDivider Class

Behind the scene, the ImageGrid class and the RGBAxes class utilize the AxesDivider class, whose role is to calculate the location of the axes at drawing time. Direct use of the AxesDivider class will not be necessary for most users. The axes_divider module provides a helper function make_axes_locatable, which can be useful. It takes a existing axes instance and create a divider for it.

ax = subplot(1, 1, 1)
divider = make_axes_locatable(ax)

make_axes_locatable returns an instance of the AxesDivider class. It provides an append_axes method that creates a new axes on the given side of ("top", "right", "bottom" and "left") of the original axes.

colorbar whose height (or width) in sync with the master axes


Simple Colorbar

scatter_hist.py with AxesDivider

The Scatter plot with histograms example can be rewritten using make_axes_locatable:

axScatter = subplot(111)
axScatter.scatter(x, y)

# create new axes on the right and on the top of the current axes.
divider = make_axes_locatable(axScatter)
axHistx = divider.append_axes("top", size=1.2, pad=0.1, sharex=axScatter)
axHisty = divider.append_axes("right", size=1.2, pad=0.1, sharey=axScatter)

# the scatter plot:
# histograms
bins = np.arange(-lim, lim + binwidth, binwidth)
axHistx.hist(x, bins=bins)
axHisty.hist(y, bins=bins, orientation='horizontal')

See the full source code below.


Scatter Hist

The Scatter Histogram (Locatable Axes) using the AxesDivider has some advantage over the original Scatter plot with histograms in Matplotlib. For example, you can set the aspect ratio of the scatter plot, even with the x-axis or y-axis is shared accordingly.


The ParasiteAxes is an axes whose location is identical to its host axes. The location is adjusted in the drawing time, thus it works even if the host change its location (e.g., images).

In most cases, you first create a host axes, which provides a few method that can be used to create parasite axes. They are twinx, twiny (which are similar to twinx and twiny in the matplotlib) and twin. twin takes an arbitrary transformation that maps between the data coordinates of the host axes and the parasite axes. draw method of the parasite axes are never called. Instead, host axes collects artists in parasite axes and draw them as if they belong to the host axes, i.e., artists in parasite axes are merged to those of the host axes and then drawn according to their zorder. The host and parasite axes modifies some of the axes behavior. For example, color cycle for plot lines are shared between host and parasites. Also, the legend command in host, creates a legend that includes lines in the parasite axes. To create a host axes, you may use host_subplot or host_axes command.

Example 1. twinx


Parasite Simple

Example 2. twin

twin without a transform argument assumes that the parasite axes has the same data transform as the host. This can be useful when you want the top(or right)-axis to have different tick-locations, tick-labels, or tick-formatter for bottom(or left)-axis.

ax2 = ax.twin() # now, ax2 is responsible for "top" axis and "right" axis
ax2.set_xticks([0., .5*np.pi, np.pi, 1.5*np.pi, 2*np.pi])
ax2.set_xticklabels(["0", r"$\frac{1}{2}\pi$",
                     r"$\pi$", r"$\frac{3}{2}\pi$", r"$2\pi$"])

Simple Axisline4

A more sophisticated example using twin. Note that if you change the x-limit in the host axes, the x-limit of the parasite axes will change accordingly.


Parasite Simple2


It's a collection of artists whose location is anchored to the (axes) bbox, like the legend. It is derived from OffsetBox in Matplotlib, and artist need to be drawn in the canvas coordinate. But, there is a limited support for an arbitrary transform. For example, the ellipse in the example below will have width and height in the data coordinate.


Simple Anchored Artists


mpl_toolkits.axes_grid1.inset_locator provides helper classes and functions to place your (inset) axes at the anchored position of the parent axes, similarly to AnchoredArtist.

Using mpl_toolkits.axes_grid1.inset_locator.inset_axes(), you can have inset axes whose size is either fixed, or a fixed proportion of the parent axes:

inset_axes = inset_axes(parent_axes,
                        width="30%", # width = 30% of parent_bbox
                        height=1., # height : 1 inch
                        loc='lower left')

creates an inset axes whose width is 30% of the parent axes and whose height is fixed at 1 inch.

You may creates your inset whose size is determined so that the data scale of the inset axes to be that of the parent axes multiplied by some factor. For example,

inset_axes = zoomed_inset_axes(ax,
                               0.5, # zoom = 0.5
                               loc='upper right')

creates an inset axes whose data scale is half of the parent axes. Here is complete examples.


Inset Locator Demo

For example, zoomed_inset_axes() can be used when you want the inset represents the zoom-up of the small portion in the parent axes. And inset_locator provides a helper function mark_inset() to mark the location of the area represented by the inset axes.


Inset Locator Demo2

RGB Axes

RGBAxes is a helper class to conveniently show RGB composite images. Like ImageGrid, the location of axes are adjusted so that the area occupied by them fits in a given rectangle. Also, the xaxis and yaxis of each axes are shared.

from mpl_toolkits.axes_grid1.axes_rgb import RGBAxes

fig = plt.figure()
ax = RGBAxes(fig, [0.1, 0.1, 0.8, 0.8], pad=0.0)
r, g, b = get_rgb()  # r, g, b are 2D images.
ax.imshow_rgb(r, g, b)


The mpl_toolkits.axes_grid1.axes_divider module provides helper classes to adjust the axes positions of a set of images at drawing time.

  • axes_size provides a class of units that are used to determine the size of each axes. For example, you can specify a fixed size.
  • Divider is the class that calculates the axes position. It divides the given rectangular area into several areas. The divider is initialized by setting the lists of horizontal and vertical sizes on which the division will be based. Then use new_locator(), which returns a callable object that can be used to set the axes_locator of the axes.

Here, we demonstrate how to achieve the following layout: we want to position axes in a 3x4 grid (note that Divider makes row indices start from the bottom(!) of the grid):

| (2, 0) | (2, 1) | (2, 2) | (2, 3) |
| (1, 0) | (1, 1) | (1, 2) | (1, 3) |
| (0, 0) | (0, 1) | (0, 2) | (0, 3) |

such that the bottom row has a fixed height of 2 (inches) and the top two rows have a height ratio of 2 (middle) to 3 (top). (For example, if the grid has a size of 7 inches, the bottom row will be 2 inches, the middle row also 2 inches, and the top row 3 inches.)

These constraints are specified using classes from the axes_size module, namely:

from mpl_toolkits.axes_grid1.axes_size import Fixed, Scaled
vert = [Fixed(2), Scaled(2), Scaled(3)]

(More generally, axes_size classes define a get_size(renderer) method that returns a pair of floats -- a relative size, and an absolute size. Fixed(2).get_size(renderer) returns (0, 2); Scaled(2).get_size(renderer) returns (2, 0).)

We use these constraints to initialize a Divider object:

rect = [0.2, 0.2, 0.6, 0.6]  # Position of the grid in the figure.
vert = [Fixed(2), Scaled(2), Scaled(3)]  # As above.
horiz = [...]  # Some other horizontal constraints.
divider = Divider(fig, rect, horiz, vert)

then use Divider.new_locator to create an AxesLocator instance for a given grid entry:

locator = divider.new_locator(nx=0, ny=1)  # Grid entry (1, 0).

and make it responsible for locating the axes:


The AxesLocator is a callable object that returns the location and size of the cell at the first column and the second row.

Locators that spans over multiple cells can be created with, e.g.:

# Columns #0 and #1 ("0-2 range"), row #1.
locator = divider.new_locator(nx=0, nx1=2, ny=1)

See the example,


You can also adjust the size of each axes according to its x or y data limits (AxesX and AxesY).


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