matplotlib/tutorials/text/annotations.py at v3.7.x · matplotlib/matplotlib · GitHub

Repository navigation

History

100

101

102

103

104

105

106

107

108

109

110

111

112

113

114

115

116

117

118

119

120

121

122

123

124

125

126

127

128

129

130

131

132

133

134

135

136

137

138

139

140

141

142

143

144

145

146

147

148

149

150

151

152

153

154

155

156

157

158

159

160

161

162

163

164

165

166

167

168

169

170

171

172

173

174

175

176

177

178

179

180

181

182

183

184

185

186

187

188

189

190

191

192

193

194

195

196

197

198

199

200

201

202

203

204

205

206

207

208

209

210

211

212

213

214

215

216

217

218

219

220

221

222

223

224

225

226

227

228

229

230

231

232

233

234

235

236

237

238

239

240

241

242

243

244

245

246

247

248

249

250

251

252

253

254

255

256

257

258

259

260

261

262

263

264

265

266

267

268

269

270

271

272

273

274

275

276

277

278

279

280

281

282

283

284

285

286

287

288

289

290

291

292

293

294

295

296

297

298

299

300

301

302

303

304

305

306

307

308

309

310

311

312

313

314

315

316

317

318

319

320

321

322

323

324

325

326

327

328

329

330

331

332

333

334

335

336

337

338

339

340

341

342

343

344

345

346

347

348

349

350

351

352

353

354

355

356

357

358

359

360

361

362

363

364

365

366

367

368

369

370

371

372

373

374

375

376

377

378

379

380

381

382

383

384

385

386

387

388

389

390

391

392

393

394

395

396

397

398

399

400

401

402

403

404

405

406

407

408

409

410

411

412

413

414

415

416

417

418

419

420

421

422

423

424

425

426

427

428

429

430

431

432

433

434

435

436

437

438

439

440

441

442

443

444

445

446

447

448

449

450

451

452

453

454

455

456

457

458

459

460

461

462

463

464

465

466

467

468

469

470

471

472

473

474

475

476

477

478

479

480

481

482

483

484

485

486

487

488

489

490

491

492

493

494

495

496

497

498

499

500

501

502

503

504

505

506

507

508

509

510

511

512

513

514

515

516

517

518

519

520

521

522

523

524

525

526

527

528

529

530

531

532

533

534

535

536

537

538

539

540

541

542

543

544

545

546

547

548

549

550

551

552

553

554

555

556

557

558

559

560

561

562

563

564

565

566

567

568

569

570

571

572

573

574

575

576

577

578

579

580

581

582

583

584

585

586

587

588

589

590

591

592

593

594

595

596

597

598

599

600

601

602

603

604

605

606

607

608

609

610

611

612

613

614

615

616

617

618

619

620

621

622

623

624

625

626

627

628

629

630

631

632

633

634

635

636

637

638

639

640

641

642

643

644

645

646

647

648

649

650

651

652

653

654

655

656

657

658

659

660

661

662

663

664

665

666

667

668

669

670

671

672

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)

###################################################################

# .. _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="->"))

#############################################################################

# 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.

matplotlib/tutorials/text/annotations.py at v3.7.x · matplotlib/matplotlib · GitHub

Repository navigation

Expand file tree

Breadcrumbs

annotations.py

Latest commit

History

Breadcrumbs

annotations.py

File metadata and controls