testbed/matplotlib__matplotlib/galleries/users_explain/axes/mosaic.py

"""
.. redirect-from:: /tutorials/provisional/mosaic
.. redirect-from:: /gallery/subplots_axes_and_figures/mosaic

.. _mosaic:

========================================================
Complex and semantic figure composition (subplot_mosaic)
========================================================

Laying out Axes in a Figure in a non-uniform grid can be both tedious
and verbose.  For dense, even grids we have `.Figure.subplots` but for
more complex layouts, such as Axes that span multiple columns / rows
of the layout or leave some areas of the Figure blank, you can use
`.gridspec.GridSpec` (see :ref:`arranging_axes`) or
manually place your axes.  `.Figure.subplot_mosaic` aims to provide an
interface to visually lay out your axes (as either ASCII art or nested
lists) to streamline this process.

This interface naturally supports naming your axes.
`.Figure.subplot_mosaic` returns a dictionary keyed on the
labels used to lay out the Figure.  By returning data structures with
names, it is easier to write plotting code that is independent of the
Figure layout.


This is inspired by a `proposed MEP
<https://github.com/matplotlib/matplotlib/pull/4384>`__ and the
`patchwork <https://github.com/thomasp85/patchwork>`__ library for R.
While we do not implement the operator overloading style, we do
provide a Pythonic API for specifying (nested) Axes layouts.

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


# Helper function used for visualization in the following examples
def identify_axes(ax_dict, fontsize=48):
    """
    Helper to identify the Axes in the examples below.

    Draws the label in a large font in the center of the Axes.

    Parameters
    ----------
    ax_dict : dict[str, Axes]
        Mapping between the title / label and the Axes.
    fontsize : int, optional
        How big the label should be.
    """
    kw = dict(ha="center", va="center", fontsize=fontsize, color="darkgrey")
    for k, ax in ax_dict.items():
        ax.text(0.5, 0.5, k, transform=ax.transAxes, **kw)


# %%
# If we want a 2x2 grid we can use `.Figure.subplots` which returns a 2D array
# of `.axes.Axes` which we can index into to do our plotting.
np.random.seed(19680801)
hist_data = np.random.randn(1_500)


fig = plt.figure(layout="constrained")
ax_array = fig.subplots(2, 2, squeeze=False)

ax_array[0, 0].bar(["a", "b", "c"], [5, 7, 9])
ax_array[0, 1].plot([1, 2, 3])
ax_array[1, 0].hist(hist_data, bins="auto")
ax_array[1, 1].imshow([[1, 2], [2, 1]])

identify_axes(
    {(j, k): a for j, r in enumerate(ax_array) for k, a in enumerate(r)},
)

# %%
# Using `.Figure.subplot_mosaic` we can produce the same mosaic but give the
# axes semantic names

fig = plt.figure(layout="constrained")
ax_dict = fig.subplot_mosaic(
    [
        ["bar", "plot"],
        ["hist", "image"],
    ],
)
ax_dict["bar"].bar(["a", "b", "c"], [5, 7, 9])
ax_dict["plot"].plot([1, 2, 3])
ax_dict["hist"].hist(hist_data)
ax_dict["image"].imshow([[1, 2], [2, 1]])
identify_axes(ax_dict)

# %%
# A key difference between `.Figure.subplots` and
# `.Figure.subplot_mosaic` is the return value. While the former
# returns an array for index access, the latter returns a dictionary
# mapping the labels to the `.axes.Axes` instances created

print(ax_dict)


# %%
# String short-hand
# =================
#
# By restricting our axes labels to single characters we can
# "draw" the Axes we want as "ASCII art".  The following


mosaic = """
    AB
    CD
    """

# %%
# will give us 4 Axes laid out in a 2x2 grid and generates the same
# figure mosaic as above (but now labeled with ``{"A", "B", "C",
# "D"}`` rather than ``{"bar", "plot", "hist", "image"}``).

fig = plt.figure(layout="constrained")
ax_dict = fig.subplot_mosaic(mosaic)
identify_axes(ax_dict)

# %%
# Alternatively, you can use the more compact string notation
mosaic = "AB;CD"

# %%
# will give you the same composition, where the ``";"`` is used
# as the row separator instead of newline.

fig = plt.figure(layout="constrained")
ax_dict = fig.subplot_mosaic(mosaic)
identify_axes(ax_dict)

# %%
# Axes spanning multiple rows/columns
# ===================================
#
# Something we can do with `.Figure.subplot_mosaic`, that we cannot
# do with `.Figure.subplots`, is to specify that an Axes should span
# several rows or columns.


# %%
# If we want to re-arrange our four Axes to have ``"C"`` be a horizontal
# span on the bottom and ``"D"`` be a vertical span on the right we would do

axd = plt.figure(layout="constrained").subplot_mosaic(
    """
    ABD
    CCD
    """
)
identify_axes(axd)

# %%
# If we do not want to fill in all the spaces in the Figure with Axes,
# we can specify some spaces in the grid to be blank


axd = plt.figure(layout="constrained").subplot_mosaic(
    """
    A.C
    BBB
    .D.
    """
)
identify_axes(axd)


# %%
# If we prefer to use another character (rather than a period ``"."``)
# to mark the empty space, we can use *empty_sentinel* to specify the
# character to use.

axd = plt.figure(layout="constrained").subplot_mosaic(
    """
    aX
    Xb
    """,
    empty_sentinel="X",
)
identify_axes(axd)


# %%
#
# Internally there is no meaning attached to the letters we use, any
# Unicode code point is valid!

axd = plt.figure(layout="constrained").subplot_mosaic(
    """αб
       ℝ☢"""
)
identify_axes(axd)

# %%
# It is not recommended to use white space as either a label or an
# empty sentinel with the string shorthand because it may be stripped
# while processing the input.
#
# Controlling mosaic creation
# ===========================
#
# This feature is built on top of `.gridspec` and you can pass the
# keyword arguments through to the underlying `.gridspec.GridSpec`
# (the same as `.Figure.subplots`).
#
# In this case we want to use the input to specify the arrangement,
# but set the relative widths of the rows / columns.  For convenience,
# `.gridspec.GridSpec`'s *height_ratios* and *width_ratios* are exposed in the
# `.Figure.subplot_mosaic` calling sequence.


axd = plt.figure(layout="constrained").subplot_mosaic(
    """
    .a.
    bAc
    .d.
    """,
    # set the height ratios between the rows
    height_ratios=[1, 3.5, 1],
    # set the width ratios between the columns
    width_ratios=[1, 3.5, 1],
)
identify_axes(axd)

# %%
# Other `.gridspec.GridSpec` keywords can be passed via *gridspec_kw*.  For
# example, use the {*left*, *right*, *bottom*, *top*} keyword arguments to
# position the overall mosaic to put multiple versions of the same
# mosaic in a figure.

mosaic = """AA
            BC"""
fig = plt.figure()
axd = fig.subplot_mosaic(
    mosaic,
    gridspec_kw={
        "bottom": 0.25,
        "top": 0.95,
        "left": 0.1,
        "right": 0.5,
        "wspace": 0.5,
        "hspace": 0.5,
    },
)
identify_axes(axd)

axd = fig.subplot_mosaic(
    mosaic,
    gridspec_kw={
        "bottom": 0.05,
        "top": 0.75,
        "left": 0.6,
        "right": 0.95,
        "wspace": 0.5,
        "hspace": 0.5,
    },
)
identify_axes(axd)

# %%
# Alternatively, you can use the sub-Figure functionality:

mosaic = """AA
            BC"""
fig = plt.figure(layout="constrained")
left, right = fig.subfigures(nrows=1, ncols=2)
axd = left.subplot_mosaic(mosaic)
identify_axes(axd)

axd = right.subplot_mosaic(mosaic)
identify_axes(axd)


# %%
# Controlling subplot creation
# ============================
#
# We can also pass through arguments used to create the subplots
# (again, the same as `.Figure.subplots`) which will apply to all
# of the Axes created.


axd = plt.figure(layout="constrained").subplot_mosaic(
    "AB", subplot_kw={"projection": "polar"}
)
identify_axes(axd)

# %%
# Per-Axes subplot keyword arguments
# ----------------------------------
#
# If you need to control the parameters passed to each subplot individually use
# *per_subplot_kw* to pass a mapping between the Axes identifiers (or
# tuples of Axes identifiers) to dictionaries of keywords to be passed.
#
# .. versionadded:: 3.7
#


fig, axd = plt.subplot_mosaic(
    "AB;CD",
    per_subplot_kw={
        "A": {"projection": "polar"},
        ("C", "D"): {"xscale": "log"}
    },
)
identify_axes(axd)

# %%
# If the layout is specified with the string short-hand, then we know the
# Axes labels will be one character and can unambiguously interpret longer
# strings in *per_subplot_kw* to specify a set of Axes to apply the
# keywords to:


fig, axd = plt.subplot_mosaic(
    "AB;CD",
    per_subplot_kw={
        "AD": {"projection": "polar"},
        "BC": {"facecolor": ".9"}
    },
)
identify_axes(axd)

# %%
# If *subplot_kw* and *per_subplot_kw* are used together, then they are
# merged with *per_subplot_kw* taking priority:


axd = plt.figure(layout="constrained").subplot_mosaic(
    "AB;CD",
    subplot_kw={"facecolor": "xkcd:tangerine"},
    per_subplot_kw={
        "B": {"facecolor": "xkcd:water blue"},
        "D": {"projection": "polar", "facecolor": "w"},
    }
)
identify_axes(axd)


# %%
# Nested list input
# =================
#
# Everything we can do with the string shorthand we can also do when
# passing in a list (internally we convert the string shorthand to a nested
# list), for example using spans, blanks, and *gridspec_kw*:

axd = plt.figure(layout="constrained").subplot_mosaic(
    [
        ["main", "zoom"],
        ["main", "BLANK"],
    ],
    empty_sentinel="BLANK",
    width_ratios=[2, 1],
)
identify_axes(axd)


# %%
# In addition, using the list input we can specify nested mosaics.  Any element
# of the inner list can be another set of nested lists:

inner = [
    ["inner A"],
    ["inner B"],
]

outer_nested_mosaic = [
    ["main", inner],
    ["bottom", "bottom"],
]
axd = plt.figure(layout="constrained").subplot_mosaic(
    outer_nested_mosaic, empty_sentinel=None
)
identify_axes(axd, fontsize=36)


# %%
# We can also pass in a 2D NumPy array to do things like
mosaic = np.zeros((4, 4), dtype=int)
for j in range(4):
    mosaic[j, j] = j + 1
axd = plt.figure(layout="constrained").subplot_mosaic(
    mosaic,
    empty_sentinel=0,
)
identify_axes(axd)