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r"""
Annotations
===========
Annotations are graphical elements, often pieces of text, that explain, add
context to, or otherwise highlight some portion of the visualized data.
`~.Axes.annotate` supports a number of coordinate systems for flexibly
positioning data and annotations relative to each other and a variety of
options of for styling the text. Axes.annotate also provides an optional arrow
from the text to the data and this arrow can be styled in various ways.
`~.Axes.text` can also be used for simple text annotation, but does not
provide as much flexibility in positioning and styling as `~.Axes.annotate`.
.. contents:: Table of Contents
:depth: 3
"""
# %%
# .. _annotations-tutorial:
#
# Basic annotation
# ----------------
#
# In an annotation, there are two points to consider: the location of the data
# being annotated *xy* and the location of the annotation text *xytext*. Both
# of these arguments are ``(x, y)`` tuples:
import numpy as np
import matplotlib.pyplot as plt
fig, ax = plt.subplots(figsize=(3, 3))
t = np.arange(0.0, 5.0, 0.01)
s = np.cos(2*np.pi*t)
line, = ax.plot(t, s, lw=2)
ax.annotate('local max', xy=(2, 1), xytext=(3, 1.5),
arrowprops=dict(facecolor='black', shrink=0.05))
ax.set_ylim(-2, 2)
# %%
# In this example, both the *xy* (arrow tip) and *xytext* locations
# (text location) are in data coordinates. There are a variety of other
# coordinate systems one can choose -- you can specify the coordinate
# system of *xy* and *xytext* with one of the following strings for
# *xycoords* and *textcoords* (default is 'data')
#
# ================== ========================================================
# argument coordinate system
# ================== ========================================================
# 'figure points' points from the lower left corner of the figure
# 'figure pixels' pixels from the lower left corner of the figure
# 'figure fraction' (0, 0) is lower left of figure and (1, 1) is upper right
# 'axes points' points from lower left corner of axes
# 'axes pixels' pixels from lower left corner of axes
# 'axes fraction' (0, 0) is lower left of axes and (1, 1) is upper right
# 'data' use the axes data coordinate system
# ================== ========================================================
#
# The following strings are also valid arguments for *textcoords*
#
# ================== ========================================================
# argument coordinate system
# ================== ========================================================
# 'offset points' offset (in points) from the xy value
# 'offset pixels' offset (in pixels) from the xy value
# ================== ========================================================
#
# For physical coordinate systems (points or pixels) the origin is the
# bottom-left of the figure or axes. Points are
# `typographic points <https://en.wikipedia.org/wiki/Point_(typography)>`_
# meaning that they are a physical unit measuring 1/72 of an inch. Points and
# pixels are discussed in further detail in :ref:`transforms-fig-scale-dpi`.
#
# .. _annotation-data:
#
# Annotating data
# ~~~~~~~~~~~~~~~
#
# This example places the text coordinates in fractional axes coordinates:
fig, ax = plt.subplots(figsize=(3, 3))
t = np.arange(0.0, 5.0, 0.01)
s = np.cos(2*np.pi*t)
line, = ax.plot(t, s, lw=2)
ax.annotate('local max', xy=(2, 1), xycoords='data',
xytext=(0.01, .99), textcoords='axes fraction',
va='top', ha='left',
arrowprops=dict(facecolor='black', shrink=0.05))
ax.set_ylim(-2, 2)
# %%
#
# Annotating an Artist
# ~~~~~~~~~~~~~~~~~~~~
#
# Annotations can be positioned relative to an `.Artist` instance by passing
# that Artist in as *xycoords*. Then *xy* is interpreted as a fraction of the
# Artist's bounding box.
import matplotlib.patches as mpatches
fig, ax = plt.subplots(figsize=(3, 3))
arr = mpatches.FancyArrowPatch((1.25, 1.25), (1.75, 1.75),
arrowstyle='->,head_width=.15', mutation_scale=20)
ax.add_patch(arr)
ax.annotate("label", (.5, .5), xycoords=arr, ha='center', va='center')
ax.set(xlim=(1, 2), ylim=(1, 2))
# %%
# Here the annotation is placed at position (.5,.5) relative to the arrow's
# lower left corner and is vertically and horizontally centered at that
# position. For an example of chaining annotation Artists, see the
# :ref:`Artist section <artist_annotation_coord>` of
# :ref:`annotating_coordinate_systems`.
#
#
# .. _annotation-with-arrow:
#
# Annotating with arrows
# ~~~~~~~~~~~~~~~~~~~~~~
#
# You can enable drawing of an arrow from the text to the annotated point
# by giving a dictionary of arrow properties in the optional keyword
# argument *arrowprops*.
#
# ==================== =====================================================
# *arrowprops* key description
# ==================== =====================================================
# width the width of the arrow in points
# frac the fraction of the arrow length occupied by the head
# headwidth the width of the base of the arrow head in points
# shrink move the tip and base some percent away from
# the annotated point and text
#
# \*\*kwargs any key for :class:`matplotlib.patches.Polygon`,
# e.g., ``facecolor``
# ==================== =====================================================
#
# In the example below, the *xy* point is in the data coordinate system
# since *xycoords* defaults to 'data'. For a polar axes, this is in
# (theta, radius) space. The text in this example is placed in the
# fractional figure coordinate system. :class:`matplotlib.text.Text`
# keyword arguments like *horizontalalignment*, *verticalalignment* and
# *fontsize* are passed from `~matplotlib.axes.Axes.annotate` to the
# ``Text`` instance.
fig = plt.figure()
ax = fig.add_subplot(projection='polar')
r = np.arange(0, 1, 0.001)
theta = 2 * 2*np.pi * r
line, = ax.plot(theta, r, color='#ee8d18', lw=3)
ind = 800
thisr, thistheta = r[ind], theta[ind]
ax.plot([thistheta], [thisr], 'o')
ax.annotate('a polar annotation',
xy=(thistheta, thisr), # theta, radius
xytext=(0.05, 0.05), # fraction, fraction
textcoords='figure fraction',
arrowprops=dict(facecolor='black', shrink=0.05),
horizontalalignment='left',
verticalalignment='bottom')
# %%
# For more on plotting with arrows, see :ref:`annotation_with_custom_arrow`
#
# .. _annotations-offset-text:
#
# Placing text annotations relative to data
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Annotations can be positioned at a relative offset to the *xy* input to
# annotation by setting the *textcoords* keyword argument to ``'offset points'``
# or ``'offset pixels'``.
fig, ax = plt.subplots(figsize=(3, 3))
x = [1, 3, 5, 7, 9]
y = [2, 4, 6, 8, 10]
annotations = ["A", "B", "C", "D", "E"]
ax.scatter(x, y, s=20)
for xi, yi, text in zip(x, y, annotations):
ax.annotate(text,
xy=(xi, yi), xycoords='data',
xytext=(1.5, 1.5), textcoords='offset points')
# %%
# The annotations are offset 1.5 points (1.5*1/72 inches) from the *xy* values.
#
# .. _plotting-guide-annotation:
#
# Advanced annotation
# -------------------
#
# We recommend reading :ref:`annotations-tutorial`, :func:`~matplotlib.pyplot.text`
# and :func:`~matplotlib.pyplot.annotate` before reading this section.
#
# Annotating with boxed text
# ~~~~~~~~~~~~~~~~~~~~~~~~~~
#
# `~.Axes.text` takes a *bbox* keyword argument, which draws a box around the
# text:
fig, ax = plt.subplots(figsize=(5, 5))
t = ax.text(0.5, 0.5, "Direction",
ha="center", va="center", rotation=45, size=15,
bbox=dict(boxstyle="rarrow,pad=0.3",
fc="lightblue", ec="steelblue", lw=2))
# %%
# The arguments are the name of the box style with its attributes as
# keyword arguments. Currently, following box styles are implemented.
#
# ========== ============== ==========================
# Class Name Attrs
# ========== ============== ==========================
# Circle ``circle`` pad=0.3
# DArrow ``darrow`` pad=0.3
# Ellipse ``ellipse`` pad=0.3
# LArrow ``larrow`` pad=0.3
# RArrow ``rarrow`` pad=0.3
# Round ``round`` pad=0.3,rounding_size=None
# Round4 ``round4`` pad=0.3,rounding_size=None
# Roundtooth ``roundtooth`` pad=0.3,tooth_size=None
# Sawtooth ``sawtooth`` pad=0.3,tooth_size=None
# Square ``square`` pad=0.3
# ========== ============== ==========================
#
# .. figure:: ../../gallery/shapes_and_collections/images/sphx_glr_fancybox_demo_001.png
# :target: ../../gallery/shapes_and_collections/fancybox_demo.html
# :align: center
#
# The patch object (box) associated with the text can be accessed using::
#
# bb = t.get_bbox_patch()
#
# The return value is a `.FancyBboxPatch`; patch properties
# (facecolor, edgewidth, etc.) can be accessed and modified as usual.
# `.FancyBboxPatch.set_boxstyle` sets the box shape::
#
# bb.set_boxstyle("rarrow", pad=0.6)
#
# The attribute arguments can also be specified within the style
# name with separating comma::
#
# bb.set_boxstyle("rarrow, pad=0.6")
#
#
# Defining custom box styles
# ~~~~~~~~~~~~~~~~~~~~~~~~~~
#
# You can use a custom box style. The value for the ``boxstyle`` can be a
# callable object in the following forms:
from matplotlib.path import Path
def custom_box_style(x0, y0, width, height, mutation_size):
"""
Given the location and size of the box, return the path of the box around
it. Rotation is automatically taken care of.
Parameters
----------
x0, y0, width, height : float
Box location and size.
mutation_size : float
Mutation reference scale, typically the text font size.
"""
# padding
mypad = 0.3
pad = mutation_size * mypad
# width and height with padding added.
width = width + 2 * pad
height = height + 2 * pad
# boundary of the padded box
x0, y0 = x0 - pad, y0 - pad
x1, y1 = x0 + width, y0 + height
# return the new path
return Path([(x0, y0), (x1, y0), (x1, y1), (x0, y1),
(x0-pad, (y0+y1)/2), (x0, y0), (x0, y0)],
closed=True)
fig, ax = plt.subplots(figsize=(3, 3))
ax.text(0.5, 0.5, "Test", size=30, va="center", ha="center", rotation=30,
bbox=dict(boxstyle=custom_box_style, alpha=0.2))
# %%
# See also :doc:`/gallery/userdemo/custom_boxstyle01`. Similarly, you can define a
# custom `.ConnectionStyle` and a custom `.ArrowStyle`. View the source code at
# `.patches` to learn how each class is defined.
#
# .. _annotation_with_custom_arrow:
#
# Customizing annotation arrows
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#
# An arrow connecting *xy* to *xytext* can be optionally drawn by
# specifying the *arrowprops* argument. To draw only an arrow, use
# empty string as the first argument:
fig, ax = plt.subplots(figsize=(3, 3))
ax.annotate("",
xy=(0.2, 0.2), xycoords='data',
xytext=(0.8, 0.8), textcoords='data',
arrowprops=dict(arrowstyle="->", connectionstyle="arc3"))
# %%
# The arrow is drawn as follows:
#
# 1. A path connecting the two points is created, as specified by the
# *connectionstyle* parameter.
# 2. The path is clipped to avoid patches *patchA* and *patchB*, if these are
# set.
# 3. The path is further shrunk by *shrinkA* and *shrinkB* (in pixels).
# 4. The path is transmuted to an arrow patch, as specified by the *arrowstyle*
# parameter.
#
# .. figure:: ../../gallery/userdemo/images/sphx_glr_annotate_explain_001.png
# :target: ../../gallery/userdemo/annotate_explain.html
# :align: center
#
# The creation of the connecting path between two points is controlled by
# ``connectionstyle`` key and the following styles are available.
#
# ========== =============================================
# Name Attrs
# ========== =============================================
# ``angle`` angleA=90,angleB=0,rad=0.0
# ``angle3`` angleA=90,angleB=0
# ``arc`` angleA=0,angleB=0,armA=None,armB=None,rad=0.0
# ``arc3`` rad=0.0
# ``bar`` armA=0.0,armB=0.0,fraction=0.3,angle=None
# ========== =============================================
#
# Note that "3" in ``angle3`` and ``arc3`` is meant to indicate that the
# resulting path is a quadratic spline segment (three control
# points). As will be discussed below, some arrow style options can only
# be used when the connecting path is a quadratic spline.
#
# The behavior of each connection style is (limitedly) demonstrated in the
# example below. (Warning: The behavior of the ``bar`` style is currently not
# well-defined and may be changed in the future).
#
# .. figure:: ../../gallery/userdemo/images/sphx_glr_connectionstyle_demo_001.png
# :target: ../../gallery/userdemo/connectionstyle_demo.html
# :align: center
#
# The connecting path (after clipping and shrinking) is then mutated to
# an arrow patch, according to the given ``arrowstyle``.
#
# ========== =============================================
# Name Attrs
# ========== =============================================
# ``-`` None
# ``->`` head_length=0.4,head_width=0.2
# ``-[`` widthB=1.0,lengthB=0.2,angleB=None
# ``|-|`` widthA=1.0,widthB=1.0
# ``-|>`` head_length=0.4,head_width=0.2
# ``<-`` head_length=0.4,head_width=0.2
# ``<->`` head_length=0.4,head_width=0.2
# ``<|-`` head_length=0.4,head_width=0.2
# ``<|-|>`` head_length=0.4,head_width=0.2
# ``fancy`` head_length=0.4,head_width=0.4,tail_width=0.4
# ``simple`` head_length=0.5,head_width=0.5,tail_width=0.2
# ``wedge`` tail_width=0.3,shrink_factor=0.5
# ========== =============================================
#
# .. figure:: ../../gallery/text_labels_and_annotations/images/sphx_glr_fancyarrow_demo_001.png
# :target: ../../gallery/text_labels_and_annotations/fancyarrow_demo.html
# :align: center
#
# Some arrowstyles only work with connection styles that generate a
# quadratic-spline segment. They are ``fancy``, ``simple``, and ``wedge``.
# For these arrow styles, you must use the "angle3" or "arc3" connection
# style.
#
# If the annotation string is given, the patch is set to the bbox patch
# of the text by default.
fig, ax = plt.subplots(figsize=(3, 3))
ax.annotate("Test",
xy=(0.2, 0.2), xycoords='data',
xytext=(0.8, 0.8), textcoords='data',
size=20, va="center", ha="center",
arrowprops=dict(arrowstyle="simple",
connectionstyle="arc3,rad=-0.2"))
# %%
# As with `~.Axes.text`, a box around the text can be drawn using the *bbox*
# argument.
fig, ax = plt.subplots(figsize=(3, 3))
ann = ax.annotate("Test",
xy=(0.2, 0.2), xycoords='data',
xytext=(0.8, 0.8), textcoords='data',
size=20, va="center", ha="center",
bbox=dict(boxstyle="round4", fc="w"),
arrowprops=dict(arrowstyle="-|>",
connectionstyle="arc3,rad=-0.2",
fc="w"))
# %%
# By default, the starting point is set to the center of the text
# extent. This can be adjusted with ``relpos`` key value. The values
# are normalized to the extent of the text. For example, (0, 0) means
# lower-left corner and (1, 1) means top-right.
fig, ax = plt.subplots(figsize=(3, 3))
ann = ax.annotate("Test",
xy=(0.2, 0.2), xycoords='data',
xytext=(0.8, 0.8), textcoords='data',
size=20, va="center", ha="center",
bbox=dict(boxstyle="round4", fc="w"),
arrowprops=dict(arrowstyle="-|>",
connectionstyle="arc3,rad=0.2",
relpos=(0., 0.),
fc="w"))
ann = ax.annotate("Test",
xy=(0.2, 0.2), xycoords='data',
xytext=(0.8, 0.8), textcoords='data',
size=20, va="center", ha="center",
bbox=dict(boxstyle="round4", fc="w"),
arrowprops=dict(arrowstyle="-|>",
connectionstyle="arc3,rad=-0.2",
relpos=(1., 0.),
fc="w"))
# %%
# Placing Artist at anchored Axes locations
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#
# There are classes of artists that can be placed at an anchored
# location in the Axes. A common example is the legend. This type
# of artist can be created by using the `.OffsetBox` class. A few
# predefined classes are available in :mod:`matplotlib.offsetbox` and in
# :mod:`mpl_toolkits.axes_grid1.anchored_artists`.
from matplotlib.offsetbox import AnchoredText
fig, ax = plt.subplots(figsize=(3, 3))
at = AnchoredText("Figure 1a",
prop=dict(size=15), frameon=True, loc='upper left')
at.patch.set_boxstyle("round,pad=0.,rounding_size=0.2")
ax.add_artist(at)
# %%
# The *loc* keyword has same meaning as in the legend command.
#
# A simple application is when the size of the artist (or collection of
# artists) is known in pixel size during the time of creation. For
# example, If you want to draw a circle with fixed size of 20 pixel x 20
# pixel (radius = 10 pixel), you can utilize
# `~mpl_toolkits.axes_grid1.anchored_artists.AnchoredDrawingArea`. The instance
# is created with a size of the drawing area (in pixels), and arbitrary artists
# can be added to the drawing area. Note that the extents of the artists that are
# added to the drawing area are not related to the placement of the drawing
# area itself. Only the initial size matters.
#
# The artists that are added to the drawing area should not have a
# transform set (it will be overridden) and the dimensions of those
# artists are interpreted as a pixel coordinate, i.e., the radius of the
# circles in above example are 10 pixels and 5 pixels, respectively.
from matplotlib.patches import Circle
from mpl_toolkits.axes_grid1.anchored_artists import AnchoredDrawingArea
fig, ax = plt.subplots(figsize=(3, 3))
ada = AnchoredDrawingArea(40, 20, 0, 0,
loc='upper right', pad=0., frameon=False)
p1 = Circle((10, 10), 10)
ada.drawing_area.add_artist(p1)
p2 = Circle((30, 10), 5, fc="r")
ada.drawing_area.add_artist(p2)
ax.add_artist(ada)
# %%
# Sometimes, you want your artists to scale with the data coordinate (or
# coordinates other than canvas pixels). You can use
# `~mpl_toolkits.axes_grid1.anchored_artists.AnchoredAuxTransformBox` class.
# This is similar to
# `~mpl_toolkits.axes_grid1.anchored_artists.AnchoredDrawingArea` except that
# the extent of the artist is determined during the drawing time respecting the
# specified transform.
#
# The ellipse in the example below will have width and height
# corresponding to 0.1 and 0.4 in data coordinates and will be
# automatically scaled when the view limits of the axes change.
from matplotlib.patches import Ellipse
from mpl_toolkits.axes_grid1.anchored_artists import AnchoredAuxTransformBox
fig, ax = plt.subplots(figsize=(3, 3))
box = AnchoredAuxTransformBox(ax.transData, loc='upper left')
el = Ellipse((0, 0), width=0.1, height=0.4, angle=30) # in data coordinates!
box.drawing_area.add_artist(el)
ax.add_artist(box)
# %%
# Another method of anchoring an artist relative to a parent axes or anchor
# point is via the *bbox_to_anchor* argument of `.AnchoredOffsetbox`. This
# artist can then be automatically positioned relative to another artist using
# `.HPacker` and `.VPacker`:
from matplotlib.offsetbox import (AnchoredOffsetbox, DrawingArea, HPacker,
TextArea)
fig, ax = plt.subplots(figsize=(3, 3))
box1 = TextArea(" Test: ", textprops=dict(color="k"))
box2 = DrawingArea(60, 20, 0, 0)
el1 = Ellipse((10, 10), width=16, height=5, angle=30, fc="r")
el2 = Ellipse((30, 10), width=16, height=5, angle=170, fc="g")
el3 = Ellipse((50, 10), width=16, height=5, angle=230, fc="b")
box2.add_artist(el1)
box2.add_artist(el2)
box2.add_artist(el3)
box = HPacker(children=[box1, box2],
align="center",
pad=0, sep=5)
anchored_box = AnchoredOffsetbox(loc='lower left',
child=box, pad=0.,
frameon=True,
bbox_to_anchor=(0., 1.02),
bbox_transform=ax.transAxes,
borderpad=0.,)
ax.add_artist(anchored_box)
fig.subplots_adjust(top=0.8)
# %%
# Note that, unlike in `.Legend`, the ``bbox_transform`` is set to
# `.IdentityTransform` by default
#
# .. _annotating_coordinate_systems:
#
# Coordinate systems for annotations
# ----------------------------------
#
# Matplotlib Annotations support several types of coordinate systems. The
# examples in :ref:`annotations-tutorial` used the ``data`` coordinate system;
# Some others more advanced options are:
#
# 1. A `.Transform` instance. For more information on transforms, see the
# :doc:`../advanced/transforms_tutorial` For example, the
# ``Axes.transAxes`` transform positions the annotation relative to the Axes
# coordinates and using it is therefore identical to setting the
# coordinate system to "axes fraction":
fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(6, 3))
ax1.annotate("Test", xy=(0.5, 0.5), xycoords=ax1.transAxes)
ax2.annotate("Test", xy=(0.5, 0.5), xycoords="axes fraction")
# %%
# Another commonly used `.Transform` instance is ``Axes.transData``. This
# transform is the coordinate system of the data plotted in the axes. In this
# example, it is used to draw an arrow from a point in *ax1* to text in *ax2*,
# where the point and text are positioned relative to the coordinates of *ax1*
# and *ax2* respectively:
fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(6, 3))
ax1.annotate("Test1", xy=(0.5, 0.5), xycoords="axes fraction")
ax2.annotate("Test2",
xy=(0.5, 0.5), xycoords=ax1.transData,
xytext=(0.5, 0.5), textcoords=ax2.transData,
arrowprops=dict(arrowstyle="->"))
# %%
# .. _artist_annotation_coord:
#
# 2. An `.Artist` instance. The *xy* value (or *xytext*) is interpreted as a
# fractional coordinate of the bounding box (bbox) of the artist:
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(3, 3))
an1 = ax.annotate("Test 1",
xy=(0.5, 0.5), xycoords="data",
va="center", ha="center",
bbox=dict(boxstyle="round", fc="w"))
an2 = ax.annotate("Test 2",
xy=(1, 0.5), xycoords=an1, # (1, 0.5) of an1's bbox
xytext=(30, 0), textcoords="offset points",
va="center", ha="left",
bbox=dict(boxstyle="round", fc="w"),
arrowprops=dict(arrowstyle="->"))
# %%
# Note that you must ensure that the extent of the coordinate artist (*an1* in
# this example) is determined before *an2* gets drawn. Usually, this means
# that *an2* needs to be drawn after *an1*. The base class for all bounding
# boxes is `.BboxBase`
#
# 3. A callable object that takes the renderer instance as single argument, and
# returns either a `.Transform` or a `.BboxBase`. For example, the return
# value of `.Artist.get_window_extent` is a bbox, so this method is identical
# to (2) passing in the artist:
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(3, 3))
an1 = ax.annotate("Test 1",
xy=(0.5, 0.5), xycoords="data",
va="center", ha="center",
bbox=dict(boxstyle="round", fc="w"))
an2 = ax.annotate("Test 2",
xy=(1, 0.5), xycoords=an1.get_window_extent,
xytext=(30, 0), textcoords="offset points",
va="center", ha="left",
bbox=dict(boxstyle="round", fc="w"),
arrowprops=dict(arrowstyle="->"))
# %%
# `.Artist.get_window_extent` is the bounding box of the Axes object and is
# therefore identical to setting the coordinate system to axes fraction:
fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(6, 3))
an1 = ax1.annotate("Test1", xy=(0.5, 0.5), xycoords="axes fraction")
an2 = ax2.annotate("Test 2", xy=(0.5, 0.5), xycoords=ax2.get_window_extent)
# %%
# 4. A blended pair of coordinate specifications -- the first for the
# x-coordinate, and the second is for the y-coordinate. For example, x=0.5 is
# in data coordinates, and y=1 is in normalized axes coordinates:
fig, ax = plt.subplots(figsize=(3, 3))
ax.annotate("Test", xy=(0.5, 1), xycoords=("data", "axes fraction"))
ax.axvline(x=.5, color='lightgray')
ax.set(xlim=(0, 2), ylim=(1, 2))
# %%
# 5. Sometimes, you want your annotation with some "offset points", not from the
# annotated point but from some other point or artist. `.text.OffsetFrom` is
# a helper for such cases.
from matplotlib.text import OffsetFrom
fig, ax = plt.subplots(figsize=(3, 3))
an1 = ax.annotate("Test 1", xy=(0.5, 0.5), xycoords="data",
va="center", ha="center",
bbox=dict(boxstyle="round", fc="w"))
offset_from = OffsetFrom(an1, (0.5, 0))
an2 = ax.annotate("Test 2", xy=(0.1, 0.1), xycoords="data",
xytext=(0, -10), textcoords=offset_from,
# xytext is offset points from "xy=(0.5, 0), xycoords=an1"
va="top", ha="center",
bbox=dict(boxstyle="round", fc="w"),
arrowprops=dict(arrowstyle="->"))
# %%
# Using ConnectionPatch
# ~~~~~~~~~~~~~~~~~~~~~
#
# `.ConnectionPatch` is like an annotation without text. While `~.Axes.annotate`
# is sufficient in most situations, `.ConnectionPatch` is useful when you want
# to connect points in different axes. For example, here we connect the point
# *xy* in the data coordinates of ``ax1`` to point *xy* in the data coordinates
# of ``ax2``:
from matplotlib.patches import ConnectionPatch
fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(6, 3))
xy = (0.3, 0.2)
con = ConnectionPatch(xyA=xy, coordsA=ax1.transData,
xyB=xy, coordsB=ax2.transData)
fig.add_artist(con)
# %%
# Here, we added the `.ConnectionPatch` to the *figure*
# (with `~.Figure.add_artist`) rather than to either axes. This ensures that
# the ConnectionPatch artist is drawn on top of both axes, and is also necessary
# when using :doc:`constrained_layout
# </tutorials/intermediate/constrainedlayout_guide>` for positioning the axes.
#
# Zoom effect between Axes
# ~~~~~~~~~~~~~~~~~~~~~~~~
#
# `mpl_toolkits.axes_grid1.inset_locator` defines some patch classes useful for
# interconnecting two axes.
#
# .. figure:: ../../gallery/subplots_axes_and_figures/images/sphx_glr_axes_zoom_effect_001.png
# :target: ../../gallery/subplots_axes_and_figures/axes_zoom_effect.html
# :align: center
#
# The code for this figure is at
# :doc:`/gallery/subplots_axes_and_figures/axes_zoom_effect` and
# familiarity with :doc:`../advanced/transforms_tutorial`
# is recommended.