Note

Click here to download the full example code

# 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.

## axes_grid1¶

### ImageGrid¶

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.

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).

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.

### 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¶

#### 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)
axScatter.set_aspect(1.)
# 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.

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.

### ParasiteAxes¶

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¶

#### 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"$2\pi$"])
```

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.

### AnchoredArtists¶

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.

### InsetLocator¶

`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.

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.

#### 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)
```

## AxesDivider¶

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:

```
ax.set_axes_locator(locator)
```

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).

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