matplotlib.github.com/examples/api/skewt.html at master · Redwa/matplotlib.github.com · GitHub

History

583 lines (490 loc) · 44.7 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

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

<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"

"http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">

<head>

<title>api example code: skewt.py — Matplotlib 1.5.1 documentation</title>

type="text/css" />

type="text/css" />

var DOCUMENTATION_OPTIONS = {

URL_ROOT: '../../',

VERSION: '1.5.1',

COLLAPSE_INDEX: false,

FILE_SUFFIX: '.html',

HAS_SOURCE: true

};

</script>

title="Search within Matplotlib 1.5.1 documentation"

href="../../_static/opensearch.xml"/>

</head>

<body>

<div class="alert" style="background-color: #FEA46C; padding: 10px 0;

<h3>We're updating the default styles for Matplotlib 2.0</h3>

Learn what to expect in the <a href="/style_changes.html" style="font-weight: bold;

color: #11557C;">new updates</a>

</div>

</map>

</div>

<h3>Navigation</h3>

<ul>

<a href="../../genindex.html" title="General Index"

accesskey="I">index</a></li>

<a href="../../py-modindex.html" title="Python Module Index"

>modules</a> |</li>

<a href="span_regions.html" title="api example code: span_regions.py"

accesskey="N">next</a> |</li>

<a href="scatter_piecharts.html" title="api example code: scatter_piecharts.py"

accesskey="P">previous</a> |</li>

<li><a href="../index.html">examples</a>| </li>

<li><a href="../../gallery.html">gallery</a>| </li>

<li><a href="../../api/pyplot_summary.html">pyplot</a>| </li>

<li><a href="../index.html" >Matplotlib Examples</a> »</li>

<li><a href="index.html" accesskey="U">api Examples</a> »</li>

</ul>

</div>

</a>

Travis-CI: <a href="https://travis-ci.org/matplotlib/matplotlib">

</a>

<h3>Related Topics</h3>

<ul>

<li><a href="../../contents.html">Documentation overview</a><ul>

<li><a href="../index.html">Matplotlib Examples</a><ul>

<li><a href="index.html">api Examples</a><ul>

<li>Previous: <a href="scatter_piecharts.html" title="previous chapter">api example code: scatter_piecharts.py</a></li>

<li>Next: <a href="span_regions.html" title="next chapter">api example code: span_regions.py</a></li>

</ul></li>

</ul>

</div>

<li><a href="../../_sources/examples/api/skewt.txt"

rel="nofollow">Show Source</a></li>

</ul>

</div>

<h3>Quick search</h3>

</form>

Enter search terms or a module, class or function name.

</div>

</div>

<h1>api example code: skewt.py<a class="headerlink" href="#api-example-code-skewt-py" title="Permalink to this headline">¶</a></h1>

(<a class="reference external" href="../../mpl_examples/api/skewt.py">Source code</a>, <a class="reference external" href="../../mpl_examples/api/skewt.png">png</a>, <a class="reference external" href="../../mpl_examples/api/skewt.hires.png">hires.png</a>, <a class="reference external" href="../../mpl_examples/api/skewt.pdf">pdf</a>)

</div>

<div class="highlight-python"><div class="highlight"><pre># This serves as an intensive exercise of matplotlib's transforms

# and custom projection API. This example produces a so-called

# SkewT-logP diagram, which is a common plot in meteorology for

# displaying vertical profiles of temperature. As far as matplotlib is

# concerned, the complexity comes from having X and Y axes that are

# not orthogonal. This is handled by including a skew component to the

# basic Axes transforms. Additional complexity comes in handling the

# fact that the upper and lower X-axes have different data ranges, which

# necessitates a bunch of custom classes for ticks,spines, and the axis

# to handle this.

from matplotlib.axes import Axes

import matplotlib.transforms as transforms

import matplotlib.axis as maxis

import matplotlib.spines as mspines

import matplotlib.path as mpath

from matplotlib.projections import register_projection

# The sole purpose of this class is to look at the upper, lower, or total

# interval as appropriate and see what parts of the tick to draw, if any.

class SkewXTick(maxis.XTick):

def draw(self, renderer):

if not self.get_visible():

return

renderer.open_group(self.__name__)

lower_interval = self.axes.xaxis.lower_interval

upper_interval = self.axes.xaxis.upper_interval

if self.gridOn and transforms.interval_contains(

self.axes.xaxis.get_view_interval(), self.get_loc()):

self.gridline.draw(renderer)

if transforms.interval_contains(lower_interval, self.get_loc()):

if self.tick1On:

self.tick1line.draw(renderer)

if self.label1On:

self.label1.draw(renderer)

if transforms.interval_contains(upper_interval, self.get_loc()):

if self.tick2On:

self.tick2line.draw(renderer)

if self.label2On:

self.label2.draw(renderer)

renderer.close_group(self.__name__)

# This class exists to provide two separate sets of intervals to the tick,

# as well as create instances of the custom tick

class SkewXAxis(maxis.XAxis):

def __init__(self, *args, **kwargs):

maxis.XAxis.__init__(self, *args, **kwargs)

self.upper_interval = 0.0, 1.0

def _get_tick(self, major):

return SkewXTick(self.axes, 0, '', major=major)

@property

def lower_interval(self):

return self.axes.viewLim.intervalx

def get_view_interval(self):

return self.upper_interval[0], self.axes.viewLim.intervalx[1]

# This class exists to calculate the separate data range of the

# upper X-axis and draw the spine there. It also provides this range

# to the X-axis artist for ticking and gridlines

class SkewSpine(mspines.Spine):

def _adjust_location(self):

trans = self.axes.transDataToAxes.inverted()

if self.spine_type == 'top':

yloc = 1.0

else:

yloc = 0.0

left = trans.transform_point((0.0, yloc))[0]

right = trans.transform_point((1.0, yloc))[0]

pts = self._path.vertices

pts[0, 0] = left

pts[1, 0] = right

self.axis.upper_interval = (left, right)

# This class handles registration of the skew-xaxes as a projection as well

# as setting up the appropriate transformations. It also overrides standard

# spines and axes instances as appropriate.

class SkewXAxes(Axes):

# The projection must specify a name. This will be used be the

# user to select the projection, i.e. ``subplot(111,

# projection='skewx')``.

name = 'skewx'

def _init_axis(self):

# Taken from Axes and modified to use our modified X-axis

self.xaxis = SkewXAxis(self)

self.spines['top'].register_axis(self.xaxis)

self.spines['bottom'].register_axis(self.xaxis)

self.yaxis = maxis.YAxis(self)

self.spines['left'].register_axis(self.yaxis)

self.spines['right'].register_axis(self.yaxis)

def _gen_axes_spines(self):

spines = {'top': SkewSpine.linear_spine(self, 'top'),

'bottom': mspines.Spine.linear_spine(self, 'bottom'),

'left': mspines.Spine.linear_spine(self, 'left'),

'right': mspines.Spine.linear_spine(self, 'right')}

return spines

def _set_lim_and_transforms(self):

"""

This is called once when the plot is created to set up all the

transforms for the data, text and grids.

"""

rot = 30

# Get the standard transform setup from the Axes base class

Axes._set_lim_and_transforms(self)

# Need to put the skew in the middle, after the scale and limits,

# but before the transAxes. This way, the skew is done in Axes

# coordinates thus performing the transform around the proper origin

# We keep the pre-transAxes transform around for other users, like the

# spines for finding bounds

self.transDataToAxes = self.transScale + \

self.transLimits + transforms.Affine2D().skew_deg(rot, 0)

# Create the full transform from Data to Pixels

self.transData = self.transDataToAxes + self.transAxes

# Blended transforms like this need to have the skewing applied using

# both axes, in axes coords like before.

self._xaxis_transform = (transforms.blended_transform_factory(

self.transScale + self.transLimits,

transforms.IdentityTransform()) +

transforms.Affine2D().skew_deg(rot, 0)) + self.transAxes

# Now register the projection with matplotlib so the user can select

# it.

register_projection(SkewXAxes)

if __name__ == '__main__':

# Now make a simple example using the custom projection.

from matplotlib.ticker import ScalarFormatter, MultipleLocator

import matplotlib.pyplot as plt

from six import StringIO

import numpy as np

# Some examples data

data_txt = '''

978.0 345 7.8 0.8 61 4.16 325 14 282.7 294.6 283.4

971.0 404 7.2 0.2 61 4.01 327 17 282.7 294.2 283.4

946.7 610 5.2 -1.8 61 3.56 335 26 282.8 293.0 283.4

944.0 634 5.0 -2.0 61 3.51 336 27 282.8 292.9 283.4

925.0 798 3.4 -2.6 65 3.43 340 32 282.8 292.7 283.4

911.8 914 2.4 -2.7 69 3.46 345 37 282.9 292.9 283.5

906.0 966 2.0 -2.7 71 3.47 348 39 283.0 293.0 283.6

877.9 1219 0.4 -3.2 77 3.46 0 48 283.9 293.9 284.5

850.0 1478 -1.3 -3.7 84 3.44 0 47 284.8 294.8 285.4

841.0 1563 -1.9 -3.8 87 3.45 358 45 285.0 295.0 285.6

823.0 1736 1.4 -0.7 86 4.44 353 42 290.3 303.3 291.0

813.6 1829 4.5 1.2 80 5.17 350 40 294.5 309.8 295.4

809.0 1875 6.0 2.2 77 5.57 347 39 296.6 313.2 297.6

798.0 1988 7.4 -0.6 57 4.61 340 35 299.2 313.3 300.1

791.0 2061 7.6 -1.4 53 4.39 335 33 300.2 313.6 301.0

783.9 2134 7.0 -1.7 54 4.32 330 31 300.4 313.6 301.2

755.1 2438 4.8 -3.1 57 4.06 300 24 301.2 313.7 301.9

727.3 2743 2.5 -4.4 60 3.81 285 29 301.9 313.8 302.6

700.5 3048 0.2 -5.8 64 3.57 275 31 302.7 313.8 303.3

700.0 3054 0.2 -5.8 64 3.56 280 31 302.7 313.8 303.3

698.0 3077 0.0 -6.0 64 3.52 280 31 302.7 313.7 303.4

687.0 3204 -0.1 -7.1 59 3.28 281 31 304.0 314.3 304.6

648.9 3658 -3.2 -10.9 55 2.59 285 30 305.5 313.8 305.9

631.0 3881 -4.7 -12.7 54 2.29 289 33 306.2 313.6 306.6

600.7 4267 -6.4 -16.7 44 1.73 295 39 308.6 314.3 308.9

592.0 4381 -6.9 -17.9 41 1.59 297 41 309.3 314.6 309.6

577.6 4572 -8.1 -19.6 39 1.41 300 44 310.1 314.9 310.3

555.3 4877 -10.0 -22.3 36 1.16 295 39 311.3 315.3 311.5

536.0 5151 -11.7 -24.7 33 0.97 304 39 312.4 315.8 312.6

533.8 5182 -11.9 -25.0 33 0.95 305 39 312.5 315.8 312.7

500.0 5680 -15.9 -29.9 29 0.64 290 44 313.6 315.9 313.7

472.3 6096 -19.7 -33.4 28 0.49 285 46 314.1 315.8 314.1

453.0 6401 -22.4 -36.0 28 0.39 300 50 314.4 315.8 314.4

400.0 7310 -30.7 -43.7 27 0.20 285 44 315.0 315.8 315.0

399.7 7315 -30.8 -43.8 27 0.20 285 44 315.0 315.8 315.0

387.0 7543 -33.1 -46.1 26 0.16 281 47 314.9 315.5 314.9

382.7 7620 -33.8 -46.8 26 0.15 280 48 315.0 315.6 315.0

342.0 8398 -40.5 -53.5 23 0.08 293 52 316.1 316.4 316.1

320.4 8839 -43.7 -56.7 22 0.06 300 54 317.6 317.8 317.6

318.0 8890 -44.1 -57.1 22 0.05 301 55 317.8 318.0 317.8

310.0 9060 -44.7 -58.7 19 0.04 304 61 319.2 319.4 319.2

306.1 9144 -43.9 -57.9 20 0.05 305 63 321.5 321.7 321.5

305.0 9169 -43.7 -57.7 20 0.05 303 63 322.1 322.4 322.1

300.0 9280 -43.5 -57.5 20 0.05 295 64 323.9 324.2 323.9

292.0 9462 -43.7 -58.7 17 0.05 293 67 326.2 326.4 326.2

276.0 9838 -47.1 -62.1 16 0.03 290 74 326.6 326.7 326.6

264.0 10132 -47.5 -62.5 16 0.03 288 79 330.1 330.3 330.1

251.0 10464 -49.7 -64.7 16 0.03 285 85 331.7 331.8 331.7

250.0 10490 -49.7 -64.7 16 0.03 285 85 332.1 332.2 332.1

247.0 10569 -48.7 -63.7 16 0.03 283 88 334.7 334.8 334.7

244.0 10649 -48.9 -63.9 16 0.03 280 91 335.6 335.7 335.6

243.3 10668 -48.9 -63.9 16 0.03 280 91 335.8 335.9 335.8

220.0 11327 -50.3 -65.3 15 0.03 280 85 343.5 343.6 343.5

212.0 11569 -50.5 -65.5 15 0.03 280 83 346.8 346.9 346.8

210.0 11631 -49.7 -64.7 16 0.03 280 83 349.0 349.1 349.0

200.0 11950 -49.9 -64.9 15 0.03 280 80 353.6 353.7 353.6

194.0 12149 -49.9 -64.9 15 0.03 279 78 356.7 356.8 356.7

183.0 12529 -51.3 -66.3 15 0.03 278 75 360.4 360.5 360.4

164.0 13233 -55.3 -68.3 18 0.02 277 69 365.2 365.3 365.2

152.0 13716 -56.5 -69.5 18 0.02 275 65 371.1 371.2 371.1

150.0 13800 -57.1 -70.1 18 0.02 275 64 371.5 371.6 371.5

136.0 14414 -60.5 -72.5 19 0.02 268 54 376.0 376.1 376.0

132.0 14600 -60.1 -72.1 19 0.02 265 51 380.0 380.1 380.0

131.4 14630 -60.2 -72.2 19 0.02 265 51 380.3 380.4 380.3

128.0 14792 -60.9 -72.9 19 0.02 266 50 381.9 382.0 381.9

125.0 14939 -60.1 -72.1 19 0.02 268 49 385.9 386.0 385.9

119.0 15240 -62.2 -73.8 20 0.01 270 48 387.4 387.5 387.4

112.0 15616 -64.9 -75.9 21 0.01 265 53 389.3 389.3 389.3

108.0 15838 -64.1 -75.1 21 0.01 265 58 394.8 394.9 394.8

107.8 15850 -64.1 -75.1 21 0.01 265 58 395.0 395.1 395.0

105.0 16010 -64.7 -75.7 21 0.01 272 50 396.9 396.9 396.9

103.0 16128 -62.9 -73.9 21 0.02 277 45 402.5 402.6 402.5

100.0 16310 -62.5 -73.5 21 0.02 285 36 406.7 406.8 406.7

'''

# Parse the data

sound_data = StringIO(data_txt)

p, h, T, Td = np.loadtxt(sound_data, usecols=range(0, 4), unpack=True)

# Create a new figure. The dimensions here give a good aspect ratio

fig = plt.figure(figsize=(6.5875, 6.2125))

ax = fig.add_subplot(111, projection='skewx')

plt.grid(True)

# Plot the data using normal plotting functions, in this case using

# log scaling in Y, as dicatated by the typical meteorological plot

ax.semilogy(T, p, 'r')

ax.semilogy(Td, p, 'g')

# An example of a slanted line at constant X

l = ax.axvline(0, color='b')

# Disables the log-formatting that comes with semilogy

ax.yaxis.set_major_formatter(ScalarFormatter())

ax.set_yticks(np.linspace(100, 1000, 10))

ax.set_ylim(1050, 100)

ax.xaxis.set_major_locator(MultipleLocator(10))

ax.set_xlim(-50, 50)

plt.show()

</pre></div>

</div>

Keywords: python, matplotlib, pylab, example, codex (see <a class="reference internal" href="../../faq/howto_faq.html#how-to-search-examples">Search examples</a>)

</div>

</div>

Last updated on Jun 18, 2016.

Created using <a

href="http://sphinx-doc.org/">Sphinx</a> 1.3.5.

</div>

(function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){

(i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),

m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)

})(window,document,'script','//www.google-analytics.com/analytics.js','ga');

ga('create', 'UA-55954603-1', 'auto');

ga('send', 'pageview');

</script>

</body>

<!--Flipcause Integration v3.0// Flipcause Integration Instructions:

Install the following code block once in the website Header (after <head> tag) -->

<style>

.fc-black_overlay{

display:none; position: fixed; z-index:1000001; top: 0%;left: 0%;width: 100%;height: 100%;

background-color: black; filter: alpha(opacity=50); cursor:pointer; opacity:0.5;

}

.fc-white_content {

opacity:1; display:none; margin-top: -320px; margin-left: -485px; width:970px; height:640px;

position:fixed; top:50%; left:50%; border: none;z-index:1000002;overflow: auto;

}

.fc-main-box{

opacity:1; display:none; margin:15px auto 0 auto; width:930px; position:relative; z-index:1000003;

}

.fc-widget_close{

opacity:1; content:url(http://i1338.photobucket.com/albums/o691/WeCause/X_zpse4a7e538.png);

position:absolute; z-index=1000004; right:-16px; top:-16px; display:block; cursor:pointer;

}

.floating_button{

display: block; margin-top: 0px; margin-left: 0px; width:auto ; height: auto;

position:fixed; z-index:999999; overflow: auto;

}

@keyframes backfadesin {

from { opacity:0; }

to {opacity:.5;}

}

@-moz-keyframes backfadesin {

from { opacity:0; }

to {opacity:.5;}

}

@-webkit-keyframes backfadesin {

from { opacity:0; }

to {opacity:.5;}

}

@-o-keyframes backfadesin {

from { opacity:0; }

to {opacity:.5;}

}

@-ms-keyframes backfadesin {

from { opacity:0; }

to {opacity:.5;}

}

@keyframes fadesin {

0%{ opacity:0; }

50%{ opacity:0; }

75% {opacity: 0; transform: translateY(20px);}

100% {opacity: 1; transform: translateY(0);}

}

@-moz-keyframes fadesin {

0%{ opacity:0; }

50%{ opacity:0; }

75% {opacity: 0; -moz-transform: translateY(20px);}

100% {opacity: 1; -moz-transform: translateY(0);}

}

@-webkit-keyframes fadesin {

0%{ opacity:0; }

50%{ opacity:0; }

75% {opacity: 0; -webkit-transform: translateY(20px);}

100% {opacity: 1; -webkit-transform: translateY(0);}

}

@-o-keyframes fadesin {

0%{ opacity:0; }

50%{ opacity:0; }

75% {opacity: 0; -o-transform: translateY(20px);}

100% {opacity: 1; -o-transform: translateY(0);}

}

@-ms-keyframes fadesin {

0%{ opacity:0; }

50%{ opacity:0; }

75% {opacity: 0; -ms-transform: translateY(20px);}

100% {opacity: 1; -ms-transform: translateY(0);}

}

</style>

function open_window(cause_id) {

var protocol=String(document.location.protocol);

var new_url;

if( /Android|webOS|iPhone|iPad|iPod|BlackBerry|IEMobile|Opera Mini/i.test(navigator.userAgent)){

new_url="https://www.flipcause.com/widget/"+cause_id

window.open(new_url);

}

else {

document.getElementById("fc-fade").style.display = "block";

document.getElementById("fc-fade").style.webkitAnimation = "backfadesin 1s";

document.getElementById("fc-fade").style.animation = "backfadesin 1s";

document.getElementById("fc-fade").style.mozAnimation = "backfadesin 1s";

document.getElementById("fc-light").style.display = "block";

document.getElementById("fc-light").style.webkitAnimation = "fadesin 1.5s";

document.getElementById("fc-light").style.animation = "fadesin 1.5s";

document.getElementById("fc-light").style.mozAnimation = "fadesin 1.5s";

document.getElementById("fc-main").style.display = "block";

document.getElementById("fc-main").style.webkitAnimation = "fadesin 1.5s";

document.getElementById("fc-main").style.animation = "fadesin 1.5s";

document.getElementById("fc-main").style.mozAnimation = "fadesin 1.5s";

document.getElementById("fc-close").style.display = "block";

document.getElementById("fc-close").style.webkitAnimation = "fadesin 1.5s";

document.getElementById("fc-close").style.animation = "fadesin 1.5s";

document.getElementById("fc-close").style.mozAnimation = "fadesin 1.5s";

document.getElementById("fc-myFrame").style.display = "block";

document.getElementById("fc-myFrame").style.webkitAnimation = "fadesin 1.5s";

document.getElementById("fc-myFrame").style.animation = "fadesin 1.5s";

document.getElementById("fc-myFrame").style.mozAnimation = "fadesin 1.5s";

document.getElementById("fc-myFrame").src="https://www.flipcause.com/widget/"+cause_id;

}

function close_window() {

document.getElementById("fc-fade").style.display="none";

document.getElementById("fc-light").style.display="none";

document.getElementById("fc-main").style.display="none";

document.getElementById("fc-close").style.display="none";

document.getElementById("fc-myFrame").style.display="none";

}

</script>

</div><iframe id="fc-myFrame" iframe height="580" width="925" style="border: 0;

border-radius:5px 5px 5px 5px; box-shadow:0 0 8px rgba(0, 0, 0, 0.5);" scrolling="no" src=""></iframe></div>

</div>

</footer>

</html>

matplotlib.github.com/examples/api/skewt.html at master · Redwa/matplotlib.github.com · GitHub

Expand file tree

Breadcrumbs

skewt.html

Latest commit

History

Breadcrumbs

skewt.html

File metadata and controls