iframe-proxy | Sunbelt Computer Software

History

434 lines (351 loc) · 15.4 KB

Raw

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

"""

========================

Text in Matplotlib Plots

========================

Introduction to plotting and working with text in Matplotlib.

Matplotlib has extensive text support, including support for

mathematical expressions, truetype support for raster and

vector outputs, newline separated text with arbitrary

rotations, and unicode support.

Because it embeds fonts directly in output documents, e.g., for postscript

or PDF, what you see on the screen is what you get in the hardcopy.

`FreeType <https://www.freetype.org/>`_ support

produces very nice, antialiased fonts, that look good even at small

raster sizes. Matplotlib includes its own

:mod:`matplotlib.font_manager` (thanks to Paul Barrett), which

implements a cross platform, `W3C <http://www.w3.org/>`

compliant font finding algorithm.

The user has a great deal of control over text properties (font size, font

weight, text location and color, etc.) with sensible defaults set in

the :doc:`rc file </tutorials/introductory/customizing>`.

And significantly, for those interested in mathematical

or scientific figures, Matplotlib implements a large number of TeX

math symbols and commands, supporting :doc:`mathematical expressions

</tutorials/text/mathtext>` anywhere in your figure.

Basic text commands

===================

The following commands are used to create text in the pyplot

interface and the object-oriented API:

=================== =================== ======================================

`.pyplot` API OO API description

=================== =================== ======================================

`~.pyplot.text` `~.Axes.text` Add text at an arbitrary location of

the `~matplotlib.axes.Axes`.

`~.pyplot.annotate` `~.Axes.annotate` Add an annotation, with an optional

arrow, at an arbitrary location of the

`~matplotlib.axes.Axes`.

`~.pyplot.xlabel` `~.Axes.set_xlabel` Add a label to the

`~matplotlib.axes.Axes`\\'s x-axis.

`~.pyplot.ylabel` `~.Axes.set_ylabel` Add a label to the

`~matplotlib.axes.Axes`\\'s y-axis.

`~.pyplot.title` `~.Axes.set_title` Add a title to the

`~matplotlib.axes.Axes`.

`~.pyplot.figtext` `~.Figure.text` Add text at an arbitrary location of

the `.Figure`.

`~.pyplot.suptitle` `~.Figure.suptitle` Add a title to the `.Figure`.

=================== =================== ======================================

All of these functions create and return a `.Text` instance, which can be

configured with a variety of font and other properties. The example below

shows all of these commands in action, and more detail is provided in the

sections that follow.

"""

import matplotlib

import matplotlib.pyplot as plt

fig = plt.figure()

fig.suptitle('bold figure suptitle', fontsize=14, fontweight='bold')

ax = fig.add_subplot(111)

fig.subplots_adjust(top=0.85)

ax.set_title('axes title')

ax.set_xlabel('xlabel')

ax.set_ylabel('ylabel')

ax.text(3, 8, 'boxed italics text in data coords', style='italic',

bbox={'facecolor': 'red', 'alpha': 0.5, 'pad': 10})

ax.text(2, 6, r'an equation: $E=mc^2$', fontsize=15)

ax.text(3, 2, 'unicode: Institut für Festkörperphysik')

ax.text(0.95, 0.01, 'colored text in axes coords',

verticalalignment='bottom', horizontalalignment='right',

transform=ax.transAxes,

color='green', fontsize=15)

ax.plot([2], [1], 'o')

ax.annotate('annotate', xy=(2, 1), xytext=(3, 4),

arrowprops=dict(facecolor='black', shrink=0.05))

ax.axis([0, 10, 0, 10])

plt.show()

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

# Labels for x- and y-axis

# ========================

# Specifying the labels for the x- and y-axis is strightforward, via the

# `~matplotlib.axes.Axes.set_xlabel` and

# `~matplotlib.axes.Axes.set_ylabel` methods.

import matplotlib.pyplot as plt

import numpy as np

x1 = np.linspace(0.0, 5.0, 100)

y1 = np.cos(2 * np.pi * x1) * np.exp(-x1)

fig, ax = plt.subplots(figsize=(5, 3))

fig.subplots_adjust(bottom=0.15, left=0.2)

ax.plot(x1, y1)

ax.set_xlabel('time [s]')

ax.set_ylabel('Damped oscillation [V]')

plt.show()

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

# The x- and y-labels are automatically placed so that they clear the x- and

# y-ticklabels. Compare the plot below with that above, and note the y-label

# is to the left of the one above.

fig, ax = plt.subplots(figsize=(5, 3))

fig.subplots_adjust(bottom=0.15, left=0.2)

ax.plot(x1, y1*10000)

ax.set_xlabel('time [s]')

ax.set_ylabel('Damped oscillation [V]')

plt.show()

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

# If you want to move the labels, you can specify the *labelpad* kyeword

# argument, where the value is points (1/72", the same unit used to specify

# fontsizes).

fig, ax = plt.subplots(figsize=(5, 3))

fig.subplots_adjust(bottom=0.15, left=0.2)

ax.plot(x1, y1*10000)

ax.set_xlabel('time [s]')

ax.set_ylabel('Damped oscillation [V]', labelpad=18)

plt.show()

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

# Or, the labels accept all the `.Text` keyword arguments, including

# *position*, via which we can manually specify the label positions. Here we

# put the xlabel to the far left of the axis. Note, that the y-coordinate of

# this position has no effect - to adjust the y-position we need to use the

# *labelpad* kwarg.

fig, ax = plt.subplots(figsize=(5, 3))

fig.subplots_adjust(bottom=0.15, left=0.2)

ax.plot(x1, y1)

ax.set_xlabel('time [s]', position=(0., 1e6),

horizontalalignment='left')

ax.set_ylabel('Damped oscillation [V]')

plt.show()

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

# All the labelling in this tutorial can be changed by manipulating the

# `matplotlib.font_manager.FontProperties` method, or by named kwargs to

# `~matplotlib.axes.Axes.set_xlabel`

from matplotlib.font_manager import FontProperties

font = FontProperties()

font.set_family('serif')

font.set_name('Times New Roman')

font.set_style('italic')

fig, ax = plt.subplots(figsize=(5, 3))

fig.subplots_adjust(bottom=0.15, left=0.2)

ax.plot(x1, y1)

ax.set_xlabel('time [s]', fontsize='large', fontweight='bold')

ax.set_ylabel('Damped oscillation [V]', fontproperties=font)

plt.show()

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

# Finally, we can use native TeX rendering in all text objects and have

# multiple lines:

fig, ax = plt.subplots(figsize=(5, 3))

fig.subplots_adjust(bottom=0.2, left=0.2)

ax.plot(x1, np.cumsum(y1**2))

ax.set_xlabel('time [s] \n This was a long experiment')

ax.set_ylabel(r'$\int\ Y^2\ dt\ \ [V^2 s]$')

plt.show()

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

# Titles

# ======

# Subplot titles are set in much the same way as labels, but there is

# the *loc* keyword arguments that can change the position and justification

# from the default value of ``loc=center``.

fig, axs = plt.subplots(3, 1, figsize=(5, 6), tight_layout=True)

locs = ['center', 'left', 'right']

for ax, loc in zip(axs, locs):

ax.plot(x1, y1)

ax.set_title('Title with loc at '+loc, loc=loc)

plt.show()

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

# Vertical spacing for titles is controlled via :rc:`axes.titlepad`, which

# defaults to 5 points. Setting to a different value moves the title.

fig, ax = plt.subplots(figsize=(5, 3))

fig.subplots_adjust(top=0.8)

ax.plot(x1, y1)

ax.set_title('Vertically offset title', pad=30)

plt.show()

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

# Ticks and ticklabels

# ====================

# Placing ticks and ticklabels is a very tricky aspect of making a figure.

# Matplotlib does the best it can automatically, but it also offers a very

# flexible framework for determining the choices for tick locations, and

# how they are labelled.

# Terminology

# ~~~~~~~~~~~

# *Axes* have an `matplotlib.axis` object for the ``ax.xaxis``

# and ``ax.yaxis`` that

# contain the information about how the labels in the axis are laid out.

# The axis API is explained in detail in the documentation to

# `~matplotlib.axis`.

# An Axis object has major and minor ticks. The Axis has a

# `matplotlib.xaxis.set_major_locator` and

# `matplotlib.xaxis.set_minor_locator` methods that use the data being plotted

# to determine

# the location of major and minor ticks. There are also

# `matplotlib.xaxis.set_major_formatter` and

# `matplotlib.xaxis.set_minor_formatters` methods that format the tick labels.

# Simple ticks

# ~~~~~~~~~~~~

# It often is convenient to simply define the

# tick values, and sometimes the tick labels, overriding the default

# locators and formatters. This is discouraged because it breaks itneractive

# navigation of the plot. It also can reset the axis limits: note that

# the second plot has the ticks we asked for, including ones that are

# well outside the automatic view limits.

fig, axs = plt.subplots(2, 1, figsize=(5, 3), tight_layout=True)

axs[0].plot(x1, y1)

axs[1].plot(x1, y1)

axs[1].xaxis.set_ticks(np.arange(0., 8.1, 2.))

plt.show()

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

# We can of course fix this after the fact, but it does highlight a

# weakness of hard-coding the ticks. This example also changes the format

# of the ticks:

fig, axs = plt.subplots(2, 1, figsize=(5, 3), tight_layout=True)

axs[0].plot(x1, y1)

axs[1].plot(x1, y1)

ticks = np.arange(0., 8.1, 2.)

# list comprehension to get all tick labels...

tickla = ['%1.2f' % tick for tick in ticks]

axs[1].xaxis.set_ticks(ticks)

axs[1].xaxis.set_ticklabels(tickla)

axs[1].set_xlim(axs[0].get_xlim())

plt.show()

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

# Tick Locators and Formatters

# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~

# Instead of making a list of all the tickalbels, we could have

# used a `matplotlib.ticker.FormatStrFormatter` and passed it to the

# ``ax.xaxis``

fig, axs = plt.subplots(2, 1, figsize=(5, 3), tight_layout=True)

axs[0].plot(x1, y1)

axs[1].plot(x1, y1)

ticks = np.arange(0., 8.1, 2.)

# list comprehension to get all tick labels...

formatter = matplotlib.ticker.StrMethodFormatter('{x:1.1f}')

axs[1].xaxis.set_ticks(ticks)

axs[1].xaxis.set_major_formatter(formatter)

axs[1].set_xlim(axs[0].get_xlim())

plt.show()

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

# And of course we could have used a non-default locator to set the

# tick locations. Note we still pass in the tick values, but the

# x-limit fix used above is *not* needed.

fig, axs = plt.subplots(2, 1, figsize=(5, 3), tight_layout=True)

axs[0].plot(x1, y1)

axs[1].plot(x1, y1)

formatter = matplotlib.ticker.FormatStrFormatter('%1.1f')

locator = matplotlib.ticker.FixedLocator(ticks)

axs[1].xaxis.set_major_locator(locator)

axs[1].xaxis.set_major_formatter(formatter)

plt.show()

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

# The default formatter is the `matplotlib.ticker.MaxNLocator` called as

# ``ticker.MaxNLocator(self, nbins='auto', steps=[1, 2, 2.5, 5, 10])``

# The *steps* keyword contains a list of multiples that can be used for

# tick values. i.e. in this case, 2, 4, 6 would be acceptable ticks,

# as would 20, 40, 60 or 0.2, 0.4, 0.6. However, 3, 6, 9 would not be

# acceptable because 3 doesn't appear in the list of steps.

# ``nbins=auto`` uses an algorithm to determine how many ticks will

# be acceptable based on how long the axis is. The fontsize of the

# ticklabel is taken into account, but the length of the tick string

# is not (because its not yet known.) In the bottom row, the

# ticklabels are quite large, so we set ``nbins=4`` to make the

# labels fit in the right-hand plot.

fig, axs = plt.subplots(2, 2, figsize=(8, 5), tight_layout=True)

axs = axs.flatten()

for n, ax in enumerate(axs):

ax.plot(x1*10., y1)

formatter = matplotlib.ticker.FormatStrFormatter('%1.1f')

locator = matplotlib.ticker.MaxNLocator(nbins='auto', steps=[1, 4, 10])

axs[1].xaxis.set_major_locator(locator)

axs[1].xaxis.set_major_formatter(formatter)

formatter = matplotlib.ticker.FormatStrFormatter('%1.5f')

locator = matplotlib.ticker.AutoLocator()

axs[2].xaxis.set_major_formatter(formatter)

axs[2].xaxis.set_major_locator(locator)

formatter = matplotlib.ticker.FormatStrFormatter('%1.5f')

locator = matplotlib.ticker.MaxNLocator(nbins=4)

axs[3].xaxis.set_major_formatter(formatter)

axs[3].xaxis.set_major_locator(locator)

plt.show()

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

# Finally, we can specify functions for the formatter using

# `matplotlib.ticker.FuncFormatter`.

def formatoddticks(x, pos):

"""Format odd tick positions

"""

if x % 2:

return '%1.2f' % x

else:

return ''

fig, ax = plt.subplots(figsize=(5, 3), tight_layout=True)

ax.plot(x1, y1)

formatter = matplotlib.ticker.FuncFormatter(formatoddticks)

locator = matplotlib.ticker.MaxNLocator(nbins=6)

ax.xaxis.set_major_formatter(formatter)

ax.xaxis.set_major_locator(locator)

plt.show()

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

# Dateticks

# ~~~~~~~~~

# Matplotlib can accept `datetime.datetime` and `numpy.datetime64`

# objects as plotting arguments. Dates and times require special

# formatting, which can often benefit from manual intervention. In

# order to help, dates have spectial Locators and Formatters,

# defined in the `matplotlib.dates` module.

# A simple example is as follows. Note how we have to rotate the

# tick labels so that they don't over-run each other.

import datetime

fig, ax = plt.subplots(figsize=(5, 3), tight_layout=True)

base = datetime.datetime(2017, 1, 1, 0, 0, 1)

time = [base + datetime.timedelta(days=x) for x in range(len(y1))]

ax.plot(time, y1)

ax.tick_params(axis='x', rotation=70)

plt.show()

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

# Maybe the format of the labels above is acceptable, but the choices is

# rather idiosyncratic. We can make the ticks fall on the start of the month

# by modifying `matplotlib.dates.AutoDateLocator`

import matplotlib.dates as mdates

locator = mdates.AutoDateLocator(interval_multiples=True)

fig, ax = plt.subplots(figsize=(5, 3), tight_layout=True)

ax.xaxis.set_major_locator(locator)

ax.plot(time, y1)

ax.tick_params(axis='x', rotation=70)

plt.show()

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

# However, this changes the tick labels. The easiest fix is to pass a format

# to `matplotlib.dates.DateFormatter`. Also note that the 29th and the

# next month are very close together. We can fix this by using the

# `dates.DayLocator` class, which allows us to specify a list of days of the

# month to use. Similar formatters are listed in the `matplotlib.dates` module.

locator = mdates.DayLocator(bymonthday=[1, 15])

formatter = mdates.DateFormatter('%b %d')

fig, ax = plt.subplots(figsize=(5, 3), tight_layout=True)

ax.xaxis.set_major_locator(locator)

ax.xaxis.set_major_formatter(formatter)

ax.plot(time, y1)

ax.tick_params(axis='x', rotation=70)

plt.show()

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

# Legends and Annotations

# =======================

# - Legends: :doc:`/tutorials/intermediate/legend_guide`

# - Annotations: :doc:`/tutorials/text/annotations`

Sunbelt Computer Software

PL/B Language Development and Support

Navigation Menu

Uh oh!

Uh oh!

Sunbelt Computer Software

PL/B Language Development and Support

Uh oh!

Uh oh!

FilesExpand file tree

text_intro.py

Latest commit

History

text_intro.py

File metadata and controls