{{ message }}
-
Notifications
You must be signed in to change notification settings - Fork 62
Expand file tree
/
Copy pathimage_api.html
More file actions
854 lines (790 loc) · 53.8 KB
/
Copy pathimage_api.html
File metadata and controls
854 lines (790 loc) · 53.8 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
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
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
"http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
<html xmlns="http://www.w3.org/1999/xhtml">
<head>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<title>matplotlib.image — Matplotlib 3.1.0 documentation</title>
<link rel="stylesheet" href="../_static/mpl.css"
type="text/css" />
<link rel="stylesheet" href="../_static/pygments.css"
type="text/css" />
<link rel="stylesheet" href="../_static/graphviz.css" type="text/css" />
<link rel="stylesheet" href="../_static/gallery.css" type="text/css" />
<link rel="stylesheet" href="../_static/copybutton.css" type="text/css" />
<script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
<script type="text/javascript" src="../_static/jquery.js"></script>
<script type="text/javascript" src="../_static/underscore.js"></script>
<script type="text/javascript" src="../_static/doctools.js"></script>
<script type="text/javascript" src="../_static/language_data.js"></script>
<script type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.0/clipboard.min.js"></script>
<script type="text/javascript" src="../_static/copybutton.js"></script>
<script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
<link rel="search" type="application/opensearchdescription+xml"
title="Search within Matplotlib 3.1.0 documentation"
href="../_static/opensearch.xml"/>
<link rel="shortcut icon" href="../_static/favicon.ico"/>
<link rel="index" title="Index" href="../genindex.html"
/>
<link rel="search" title="Search" href="../search.html"
/>
<link rel="top" title="Matplotlib 3.1.0 documentation" href="../index.html" />
<link rel="up" title="API Overview" href="index.html" />
<link rel="next" title="matplotlib.legend" href="legend_api.html" />
<link rel="prev" title="matplotlib.gridspec.GridSpecFromSubplotSpec" href="_as_gen/matplotlib.gridspec.GridSpecFromSubplotSpec.html" />
<link rel="canonical" href="https://matplotlib.org/stable/api/image_api.html" />
<script data-domain="matplotlib.org" defer="defer" src="https://views.scientific-python.org/js/script.js"></script>
</head>
<body>
<div id="unreleased-message"> You are reading an old version of the documentation (v3.1.0). For the latest version see <a href="https://matplotlib.org/stable/api/image_api.html">https://matplotlib.org/stable/api/image_api.html</a></div>
<div style="background-color: white; text-align: left; padding: 10px 10px 15px 15px; position: relative;">
<a href="../index.html">
<div style="float: left; position: absolute; width: 496px; bottom: 0; padding-bottom: 24px"><span style="float: right; color: #789; background: white">Version 3.1.0</span></div>
<img src="../_static/logo2.png" height="125px" border="0" alt="matplotlib"/></a>
<!-- The "Fork me on github" ribbon -->
<div id="forkongithub"><a href="https://github.com/matplotlib/matplotlib">Fork me on GitHub</a></div>
</div>
<div class="related">
<h3>Navigation</h3>
<ul>
<li class="right" style="margin-right: 10px">
<a href="../genindex.html" title="General Index"
accesskey="I">index</a></li>
<li class="right" >
<a href="../py-modindex.html" title="Python Module Index"
>modules</a> |</li>
<li class="right" >
<a href="legend_api.html" title="matplotlib.legend"
accesskey="N">next</a> |</li>
<li class="right" >
<a href="_as_gen/matplotlib.gridspec.GridSpecFromSubplotSpec.html" title="matplotlib.gridspec.GridSpecFromSubplotSpec"
accesskey="P">previous</a> |</li>
<li><a href="../index.html">home</a>| </li>
<li><a href="../gallery/index.html">examples</a>| </li>
<li><a href="../tutorials/index.html">tutorials</a>| </li>
<li><a href="index.html">API</a>| </li>
<li><a href="../contents.html">contents</a> »</li>
<li><a href="index.html" accesskey="U">API Overview</a> »</li>
</ul>
</div>
<div class="sphinxsidebar">
<div class="sphinxsidebarwrapper">
<div id="searchbox" style="display: none" role="search">
<h3>Quick search</h3>
<div class="searchformwrapper">
<form class="search" action="../search.html" method="get">
<input type="text" name="q" />
<input type="submit" value="Go" />
<input type="hidden" name="check_keywords" value="yes" />
<input type="hidden" name="area" value="default" />
</form>
</div>
</div>
<script type="text/javascript">$('#searchbox').show(0);</script><div class="relations">
<h3>Related Topics</h3>
<ul>
<li><a href="../contents.html">Documentation overview</a><ul>
<li><a href="index.html">API Overview</a><ul>
<li>Previous: <a href="_as_gen/matplotlib.gridspec.GridSpecFromSubplotSpec.html" title="previous chapter">matplotlib.gridspec.GridSpecFromSubplotSpec</a></li>
<li>Next: <a href="legend_api.html" title="next chapter"><code class="docutils literal notranslate"><span class="pre">matplotlib.legend</span></code></a></li>
</ul></li>
</ul></li>
</ul>
</div>
<div id="sidebar-pagesource" role="note" aria-label="source link"
style="margin-top: 1.5em; padding-top: 0.1em; border-top: 1px solid #86989b">
<a href="../_sources/api/image_api.rst.txt"
style="color: #c0c0c0" rel="nofollow">Show Page Source</a>
</div>
</div>
</div>
<div class="document">
<div class="documentwrapper">
<div class="bodywrapper">
<div class="body">
<div class="section" id="module-matplotlib.image">
<span id="matplotlib-image"></span><h1><code class="docutils literal notranslate"><span class="pre">matplotlib.image</span></code><a class="headerlink" href="#module-matplotlib.image" title="Permalink to this headline">¶</a></h1>
<p>The image module supports basic image loading, rescaling and display
operations.</p>
<dl class="class">
<dt id="matplotlib.image.AxesImage">
<em class="property">class </em><code class="descclassname">matplotlib.image.</code><code class="descname">AxesImage</code><span class="sig-paren">(</span><em>ax</em>, <em>cmap=None</em>, <em>norm=None</em>, <em>interpolation=None</em>, <em>origin=None</em>, <em>extent=None</em>, <em>filternorm=1</em>, <em>filterrad=4.0</em>, <em>resample=False</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#AxesImage"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.AxesImage" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">matplotlib.image._ImageBase</span></code></p>
<p>interpolation and cmap default to their rc settings</p>
<p>cmap is a colors.Colormap instance
norm is a colors.Normalize instance to map luminance to 0-1</p>
<p>extent is data axes (left, right, bottom, top) for making image plots
registered with data plots. Default is to label the pixel
centers with the zero-based row and column indices.</p>
<p>Additional kwargs are matplotlib.artist properties</p>
<dl class="method">
<dt id="matplotlib.image.AxesImage.format_cursor_data">
<code class="descname">format_cursor_data</code><span class="sig-paren">(</span><em>self</em>, <em>data</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#AxesImage.format_cursor_data"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.AxesImage.format_cursor_data" title="Permalink to this definition">¶</a></dt>
<dd><p>Return a string representation of <em>data</em>.</p>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">This method is intended to be overridden by artist subclasses.
As an end-user of Matplotlib you will most likely not call this
method yourself.</p>
</div>
<p>The default implementation converts ints and floats and arrays of ints
and floats into a comma-separated string enclosed in square brackets.</p>
<div class="admonition seealso">
<p class="first admonition-title">See also</p>
<dl class="last docutils">
<dt><a class="reference internal" href="#matplotlib.image.AxesImage.get_cursor_data" title="matplotlib.image.AxesImage.get_cursor_data"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_cursor_data</span></code></a></dt>
<dd></dd>
</dl>
</div>
</dd></dl>
<dl class="method">
<dt id="matplotlib.image.AxesImage.get_cursor_data">
<code class="descname">get_cursor_data</code><span class="sig-paren">(</span><em>self</em>, <em>event</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#AxesImage.get_cursor_data"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.AxesImage.get_cursor_data" title="Permalink to this definition">¶</a></dt>
<dd><p>Return the image value at the event position or <em>None</em> if the event is
outside the image.</p>
<div class="admonition seealso">
<p class="first admonition-title">See also</p>
<dl class="last docutils">
<dt><a class="reference internal" href="_as_gen/matplotlib.artist.Artist.get_cursor_data.html#matplotlib.artist.Artist.get_cursor_data" title="matplotlib.artist.Artist.get_cursor_data"><code class="xref py py-obj docutils literal notranslate"><span class="pre">matplotlib.artist.Artist.get_cursor_data</span></code></a></dt>
<dd></dd>
</dl>
</div>
</dd></dl>
<dl class="method">
<dt id="matplotlib.image.AxesImage.get_extent">
<code class="descname">get_extent</code><span class="sig-paren">(</span><em>self</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#AxesImage.get_extent"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.AxesImage.get_extent" title="Permalink to this definition">¶</a></dt>
<dd><p>Get the image extent: left, right, bottom, top</p>
</dd></dl>
<dl class="method">
<dt id="matplotlib.image.AxesImage.get_window_extent">
<code class="descname">get_window_extent</code><span class="sig-paren">(</span><em>self</em>, <em>renderer=None</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#AxesImage.get_window_extent"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.AxesImage.get_window_extent" title="Permalink to this definition">¶</a></dt>
<dd><p>Get the axes bounding box in display space.</p>
<p>The bounding box' width and height are nonnegative.</p>
<p>Subclasses should override for inclusion in the bounding box
"tight" calculation. Default is to return an empty bounding
box at 0, 0.</p>
<p>Be careful when using this function, the results will not update
if the artist window extent of the artist changes. The extent
can change due to any changes in the transform stack, such as
changing the axes limits, the figure size, or the canvas used
(as is done when saving a figure). This can lead to unexpected
behavior where interactive figures will look fine on the screen,
but will save incorrectly.</p>
</dd></dl>
<dl class="method">
<dt id="matplotlib.image.AxesImage.make_image">
<code class="descname">make_image</code><span class="sig-paren">(</span><em>self</em>, <em>renderer</em>, <em>magnification=1.0</em>, <em>unsampled=False</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#AxesImage.make_image"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.AxesImage.make_image" title="Permalink to this definition">¶</a></dt>
<dd><p>Normalize, rescale, and colormap this image's data for rendering using
<em>renderer</em>, with the given <em>magnification</em>.</p>
<p>If <em>unsampled</em> is True, the image will not be scaled, but an
appropriate affine transformation will be returned instead.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
<dt><strong>image</strong> <span class="classifier-delimiter">:</span> <span class="classifier">(M, N, 4) uint8 array</span></dt>
<dd><p class="first last">The RGBA image, resampled unless <em>unsampled</em> is True.</p>
</dd>
<dt><strong>x, y</strong> <span class="classifier-delimiter">:</span> <span class="classifier">float</span></dt>
<dd><p class="first last">The upper left corner where the image should be drawn, in pixel
space.</p>
</dd>
<dt><strong>trans</strong> <span class="classifier-delimiter">:</span> <span class="classifier">Affine2D</span></dt>
<dd><p class="first last">The affine transformation from image to pixel space.</p>
</dd>
</dl>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="matplotlib.image.AxesImage.set_extent">
<code class="descname">set_extent</code><span class="sig-paren">(</span><em>self</em>, <em>extent</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#AxesImage.set_extent"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.AxesImage.set_extent" title="Permalink to this definition">¶</a></dt>
<dd><p>extent is data axes (left, right, bottom, top) for making image plots</p>
<p>This updates ax.dataLim, and, if autoscaling, sets viewLim
to tightly fit the image, regardless of dataLim. Autoscaling
state is not changed, so following this with ax.autoscale_view
will redo the autoscaling in accord with dataLim.</p>
</dd></dl>
</dd></dl>
<dl class="class">
<dt id="matplotlib.image.BboxImage">
<em class="property">class </em><code class="descclassname">matplotlib.image.</code><code class="descname">BboxImage</code><span class="sig-paren">(</span><em>bbox</em>, <em>cmap=None</em>, <em>norm=None</em>, <em>interpolation=None</em>, <em>origin=None</em>, <em>filternorm=1</em>, <em>filterrad=4.0</em>, <em>resample=False</em>, <em>interp_at_native=<deprecated parameter></em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#BboxImage"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.BboxImage" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">matplotlib.image._ImageBase</span></code></p>
<p>The Image class whose size is determined by the given bbox.</p>
<p>cmap is a colors.Colormap instance
norm is a colors.Normalize instance to map luminance to 0-1</p>
<p>kwargs are an optional list of Artist keyword args</p>
<dl class="method">
<dt id="matplotlib.image.BboxImage.contains">
<code class="descname">contains</code><span class="sig-paren">(</span><em>self</em>, <em>mouseevent</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#BboxImage.contains"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.BboxImage.contains" title="Permalink to this definition">¶</a></dt>
<dd><p>Test whether the mouse event occurred within the image.</p>
</dd></dl>
<dl class="method">
<dt id="matplotlib.image.BboxImage.get_transform">
<code class="descname">get_transform</code><span class="sig-paren">(</span><em>self</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#BboxImage.get_transform"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.BboxImage.get_transform" title="Permalink to this definition">¶</a></dt>
<dd><p>Return the <a class="reference internal" href="transformations.html#matplotlib.transforms.Transform" title="matplotlib.transforms.Transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Transform</span></code></a> instance used by this artist.</p>
</dd></dl>
<dl class="method">
<dt id="matplotlib.image.BboxImage.get_window_extent">
<code class="descname">get_window_extent</code><span class="sig-paren">(</span><em>self</em>, <em>renderer=None</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#BboxImage.get_window_extent"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.BboxImage.get_window_extent" title="Permalink to this definition">¶</a></dt>
<dd><p>Get the axes bounding box in display space.</p>
<p>The bounding box' width and height are nonnegative.</p>
<p>Subclasses should override for inclusion in the bounding box
"tight" calculation. Default is to return an empty bounding
box at 0, 0.</p>
<p>Be careful when using this function, the results will not update
if the artist window extent of the artist changes. The extent
can change due to any changes in the transform stack, such as
changing the axes limits, the figure size, or the canvas used
(as is done when saving a figure). This can lead to unexpected
behavior where interactive figures will look fine on the screen,
but will save incorrectly.</p>
</dd></dl>
<dl class="attribute">
<dt id="matplotlib.image.BboxImage.interp_at_native">
<code class="descname">interp_at_native</code><a class="headerlink" href="#matplotlib.image.BboxImage.interp_at_native" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="matplotlib.image.BboxImage.make_image">
<code class="descname">make_image</code><span class="sig-paren">(</span><em>self</em>, <em>renderer</em>, <em>magnification=1.0</em>, <em>unsampled=False</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#BboxImage.make_image"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.BboxImage.make_image" title="Permalink to this definition">¶</a></dt>
<dd><p>Normalize, rescale, and colormap this image's data for rendering using
<em>renderer</em>, with the given <em>magnification</em>.</p>
<p>If <em>unsampled</em> is True, the image will not be scaled, but an
appropriate affine transformation will be returned instead.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
<dt><strong>image</strong> <span class="classifier-delimiter">:</span> <span class="classifier">(M, N, 4) uint8 array</span></dt>
<dd><p class="first last">The RGBA image, resampled unless <em>unsampled</em> is True.</p>
</dd>
<dt><strong>x, y</strong> <span class="classifier-delimiter">:</span> <span class="classifier">float</span></dt>
<dd><p class="first last">The upper left corner where the image should be drawn, in pixel
space.</p>
</dd>
<dt><strong>trans</strong> <span class="classifier-delimiter">:</span> <span class="classifier">Affine2D</span></dt>
<dd><p class="first last">The affine transformation from image to pixel space.</p>
</dd>
</dl>
</td>
</tr>
</tbody>
</table>
</dd></dl>
</dd></dl>
<dl class="class">
<dt id="matplotlib.image.FigureImage">
<em class="property">class </em><code class="descclassname">matplotlib.image.</code><code class="descname">FigureImage</code><span class="sig-paren">(</span><em>fig</em>, <em>cmap=None</em>, <em>norm=None</em>, <em>offsetx=0</em>, <em>offsety=0</em>, <em>origin=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#FigureImage"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.FigureImage" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">matplotlib.image._ImageBase</span></code></p>
<p>cmap is a colors.Colormap instance
norm is a colors.Normalize instance to map luminance to 0-1</p>
<p>kwargs are an optional list of Artist keyword args</p>
<dl class="method">
<dt id="matplotlib.image.FigureImage.get_extent">
<code class="descname">get_extent</code><span class="sig-paren">(</span><em>self</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#FigureImage.get_extent"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.FigureImage.get_extent" title="Permalink to this definition">¶</a></dt>
<dd><p>Get the image extent: left, right, bottom, top</p>
</dd></dl>
<dl class="method">
<dt id="matplotlib.image.FigureImage.make_image">
<code class="descname">make_image</code><span class="sig-paren">(</span><em>self</em>, <em>renderer</em>, <em>magnification=1.0</em>, <em>unsampled=False</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#FigureImage.make_image"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.FigureImage.make_image" title="Permalink to this definition">¶</a></dt>
<dd><p>Normalize, rescale, and colormap this image's data for rendering using
<em>renderer</em>, with the given <em>magnification</em>.</p>
<p>If <em>unsampled</em> is True, the image will not be scaled, but an
appropriate affine transformation will be returned instead.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
<dt><strong>image</strong> <span class="classifier-delimiter">:</span> <span class="classifier">(M, N, 4) uint8 array</span></dt>
<dd><p class="first last">The RGBA image, resampled unless <em>unsampled</em> is True.</p>
</dd>
<dt><strong>x, y</strong> <span class="classifier-delimiter">:</span> <span class="classifier">float</span></dt>
<dd><p class="first last">The upper left corner where the image should be drawn, in pixel
space.</p>
</dd>
<dt><strong>trans</strong> <span class="classifier-delimiter">:</span> <span class="classifier">Affine2D</span></dt>
<dd><p class="first last">The affine transformation from image to pixel space.</p>
</dd>
</dl>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="matplotlib.image.FigureImage.set_data">
<code class="descname">set_data</code><span class="sig-paren">(</span><em>self</em>, <em>A</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#FigureImage.set_data"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.FigureImage.set_data" title="Permalink to this definition">¶</a></dt>
<dd><p>Set the image array.</p>
</dd></dl>
<dl class="attribute">
<dt id="matplotlib.image.FigureImage.zorder">
<code class="descname">zorder</code><em class="property"> = 0</em><a class="headerlink" href="#matplotlib.image.FigureImage.zorder" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
</dd></dl>
<dl class="class">
<dt id="matplotlib.image.NonUniformImage">
<em class="property">class </em><code class="descclassname">matplotlib.image.</code><code class="descname">NonUniformImage</code><span class="sig-paren">(</span><em>ax</em>, <em>*</em>, <em>interpolation='nearest'</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#NonUniformImage"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.NonUniformImage" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <a class="reference internal" href="#matplotlib.image.AxesImage" title="matplotlib.image.AxesImage"><code class="xref py py-class docutils literal notranslate"><span class="pre">matplotlib.image.AxesImage</span></code></a></p>
<p>kwargs are identical to those for AxesImage, except
that 'nearest' and 'bilinear' are the only supported 'interpolation'
options.</p>
<dl class="method">
<dt id="matplotlib.image.NonUniformImage.get_extent">
<code class="descname">get_extent</code><span class="sig-paren">(</span><em>self</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#NonUniformImage.get_extent"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.NonUniformImage.get_extent" title="Permalink to this definition">¶</a></dt>
<dd><p>Get the image extent: left, right, bottom, top</p>
</dd></dl>
<dl class="method">
<dt id="matplotlib.image.NonUniformImage.make_image">
<code class="descname">make_image</code><span class="sig-paren">(</span><em>self</em>, <em>renderer</em>, <em>magnification=1.0</em>, <em>unsampled=False</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#NonUniformImage.make_image"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.NonUniformImage.make_image" title="Permalink to this definition">¶</a></dt>
<dd><p>Normalize, rescale, and colormap this image's data for rendering using
<em>renderer</em>, with the given <em>magnification</em>.</p>
<p>If <em>unsampled</em> is True, the image will not be scaled, but an
appropriate affine transformation will be returned instead.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
<dt><strong>image</strong> <span class="classifier-delimiter">:</span> <span class="classifier">(M, N, 4) uint8 array</span></dt>
<dd><p class="first last">The RGBA image, resampled unless <em>unsampled</em> is True.</p>
</dd>
<dt><strong>x, y</strong> <span class="classifier-delimiter">:</span> <span class="classifier">float</span></dt>
<dd><p class="first last">The upper left corner where the image should be drawn, in pixel
space.</p>
</dd>
<dt><strong>trans</strong> <span class="classifier-delimiter">:</span> <span class="classifier">Affine2D</span></dt>
<dd><p class="first last">The affine transformation from image to pixel space.</p>
</dd>
</dl>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="matplotlib.image.NonUniformImage.set_array">
<code class="descname">set_array</code><span class="sig-paren">(</span><em>self</em>, <em>*args</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#NonUniformImage.set_array"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.NonUniformImage.set_array" title="Permalink to this definition">¶</a></dt>
<dd><p>Retained for backwards compatibility - use set_data instead.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first last docutils">
<dt><strong>A</strong> <span class="classifier-delimiter">:</span> <span class="classifier">array-like</span></dt>
<dd></dd>
</dl>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="matplotlib.image.NonUniformImage.set_cmap">
<code class="descname">set_cmap</code><span class="sig-paren">(</span><em>self</em>, <em>cmap</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#NonUniformImage.set_cmap"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.NonUniformImage.set_cmap" title="Permalink to this definition">¶</a></dt>
<dd><p>set the colormap for luminance data</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first last docutils">
<dt><strong>cmap</strong> <span class="classifier-delimiter">:</span> <span class="classifier">colormap or registered colormap name</span></dt>
<dd></dd>
</dl>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="matplotlib.image.NonUniformImage.set_data">
<code class="descname">set_data</code><span class="sig-paren">(</span><em>self</em>, <em>x</em>, <em>y</em>, <em>A</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#NonUniformImage.set_data"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.NonUniformImage.set_data" title="Permalink to this definition">¶</a></dt>
<dd><p>Set the grid for the pixel centers, and the pixel values.</p>
<blockquote>
<div><dl class="docutils">
<dt><em>x</em> and <em>y</em> are monotonic 1-D ndarrays of lengths N and M,</dt>
<dd>respectively, specifying pixel centers</dd>
<dt><em>A</em> is an (M,N) ndarray or masked array of values to be</dt>
<dd>colormapped, or a (M,N,3) RGB array, or a (M,N,4) RGBA
array.</dd>
</dl>
</div></blockquote>
</dd></dl>
<dl class="method">
<dt id="matplotlib.image.NonUniformImage.set_filternorm">
<code class="descname">set_filternorm</code><span class="sig-paren">(</span><em>self</em>, <em>s</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#NonUniformImage.set_filternorm"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.NonUniformImage.set_filternorm" title="Permalink to this definition">¶</a></dt>
<dd><p>Set whether the resize filter normalizes the weights.</p>
<p>See help for <a class="reference internal" href="_as_gen/matplotlib.axes.Axes.imshow.html#matplotlib.axes.Axes.imshow" title="matplotlib.axes.Axes.imshow"><code class="xref py py-obj docutils literal notranslate"><span class="pre">imshow</span></code></a>.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first last docutils">
<dt><strong>filternorm</strong> <span class="classifier-delimiter">:</span> <span class="classifier">bool</span></dt>
<dd></dd>
</dl>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="matplotlib.image.NonUniformImage.set_filterrad">
<code class="descname">set_filterrad</code><span class="sig-paren">(</span><em>self</em>, <em>s</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#NonUniformImage.set_filterrad"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.NonUniformImage.set_filterrad" title="Permalink to this definition">¶</a></dt>
<dd><p>Set the resize filter radius only applicable to some
interpolation schemes -- see help for imshow</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first last docutils">
<dt><strong>filterrad</strong> <span class="classifier-delimiter">:</span> <span class="classifier">positive float</span></dt>
<dd></dd>
</dl>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="matplotlib.image.NonUniformImage.set_interpolation">
<code class="descname">set_interpolation</code><span class="sig-paren">(</span><em>self</em>, <em>s</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#NonUniformImage.set_interpolation"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.NonUniformImage.set_interpolation" title="Permalink to this definition">¶</a></dt>
<dd><table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first last docutils">
<dt><strong>s</strong> <span class="classifier-delimiter">:</span> <span class="classifier">str, None</span></dt>
<dd><p class="first last">Either 'nearest', 'bilinear', or <code class="docutils literal notranslate"><span class="pre">None</span></code>.</p>
</dd>
</dl>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="matplotlib.image.NonUniformImage.set_norm">
<code class="descname">set_norm</code><span class="sig-paren">(</span><em>self</em>, <em>norm</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#NonUniformImage.set_norm"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.NonUniformImage.set_norm" title="Permalink to this definition">¶</a></dt>
<dd><p>Set the normalization instance.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first last docutils">
<dt><strong>norm</strong> <span class="classifier-delimiter">:</span> <span class="classifier"><a class="reference internal" href="_as_gen/matplotlib.colors.Normalize.html#matplotlib.colors.Normalize" title="matplotlib.colors.Normalize"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Normalize</span></code></a></span></dt>
<dd></dd>
</dl>
</td>
</tr>
</tbody>
</table>
<p class="rubric">Notes</p>
<p>If there are any colorbars using the mappable for this norm, setting
the norm of the mappable will reset the norm, locator, and formatters
on the colorbar to default.</p>
</dd></dl>
</dd></dl>
<dl class="class">
<dt id="matplotlib.image.PcolorImage">
<em class="property">class </em><code class="descclassname">matplotlib.image.</code><code class="descname">PcolorImage</code><span class="sig-paren">(</span><em>ax</em>, <em>x=None</em>, <em>y=None</em>, <em>A=None</em>, <em>cmap=None</em>, <em>norm=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#PcolorImage"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.PcolorImage" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <a class="reference internal" href="#matplotlib.image.AxesImage" title="matplotlib.image.AxesImage"><code class="xref py py-class docutils literal notranslate"><span class="pre">matplotlib.image.AxesImage</span></code></a></p>
<p>Make a pcolor-style plot with an irregular rectangular grid.</p>
<p>This uses a variation of the original irregular image code,
and it is used by pcolorfast for the corresponding grid type.</p>
<p>cmap defaults to its rc setting</p>
<p>cmap is a colors.Colormap instance
norm is a colors.Normalize instance to map luminance to 0-1</p>
<p>Additional kwargs are matplotlib.artist properties</p>
<dl class="method">
<dt id="matplotlib.image.PcolorImage.get_cursor_data">
<code class="descname">get_cursor_data</code><span class="sig-paren">(</span><em>self</em>, <em>event</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#PcolorImage.get_cursor_data"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.PcolorImage.get_cursor_data" title="Permalink to this definition">¶</a></dt>
<dd><p>Return the image value at the event position or <em>None</em> if the event is
outside the image.</p>
<div class="admonition seealso">
<p class="first admonition-title">See also</p>
<dl class="last docutils">
<dt><a class="reference internal" href="_as_gen/matplotlib.artist.Artist.get_cursor_data.html#matplotlib.artist.Artist.get_cursor_data" title="matplotlib.artist.Artist.get_cursor_data"><code class="xref py py-obj docutils literal notranslate"><span class="pre">matplotlib.artist.Artist.get_cursor_data</span></code></a></dt>
<dd></dd>
</dl>
</div>
</dd></dl>
<dl class="method">
<dt id="matplotlib.image.PcolorImage.make_image">
<code class="descname">make_image</code><span class="sig-paren">(</span><em>self</em>, <em>renderer</em>, <em>magnification=1.0</em>, <em>unsampled=False</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#PcolorImage.make_image"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.PcolorImage.make_image" title="Permalink to this definition">¶</a></dt>
<dd><p>Normalize, rescale, and colormap this image's data for rendering using
<em>renderer</em>, with the given <em>magnification</em>.</p>
<p>If <em>unsampled</em> is True, the image will not be scaled, but an
appropriate affine transformation will be returned instead.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
<dt><strong>image</strong> <span class="classifier-delimiter">:</span> <span class="classifier">(M, N, 4) uint8 array</span></dt>
<dd><p class="first last">The RGBA image, resampled unless <em>unsampled</em> is True.</p>
</dd>
<dt><strong>x, y</strong> <span class="classifier-delimiter">:</span> <span class="classifier">float</span></dt>
<dd><p class="first last">The upper left corner where the image should be drawn, in pixel
space.</p>
</dd>
<dt><strong>trans</strong> <span class="classifier-delimiter">:</span> <span class="classifier">Affine2D</span></dt>
<dd><p class="first last">The affine transformation from image to pixel space.</p>
</dd>
</dl>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="matplotlib.image.PcolorImage.set_array">
<code class="descname">set_array</code><span class="sig-paren">(</span><em>self</em>, <em>*args</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#PcolorImage.set_array"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.PcolorImage.set_array" title="Permalink to this definition">¶</a></dt>
<dd><p>Retained for backwards compatibility - use set_data instead.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first last docutils">
<dt><strong>A</strong> <span class="classifier-delimiter">:</span> <span class="classifier">array-like</span></dt>
<dd></dd>
</dl>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="matplotlib.image.PcolorImage.set_data">
<code class="descname">set_data</code><span class="sig-paren">(</span><em>self</em>, <em>x</em>, <em>y</em>, <em>A</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#PcolorImage.set_data"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.PcolorImage.set_data" title="Permalink to this definition">¶</a></dt>
<dd><p>Set the grid for the rectangle boundaries, and the data values.</p>
<blockquote>
<div><dl class="docutils">
<dt><em>x</em> and <em>y</em> are monotonic 1-D ndarrays of lengths N+1 and M+1,</dt>
<dd>respectively, specifying rectangle boundaries. If None,
they will be created as uniform arrays from 0 through N
and 0 through M, respectively.</dd>
<dt><em>A</em> is an (M,N) ndarray or masked array of values to be</dt>
<dd>colormapped, or a (M,N,3) RGB array, or a (M,N,4) RGBA
array.</dd>
</dl>
</div></blockquote>
</dd></dl>
</dd></dl>
<dl class="function">
<dt id="matplotlib.image.composite_images">
<code class="descclassname">matplotlib.image.</code><code class="descname">composite_images</code><span class="sig-paren">(</span><em>images</em>, <em>renderer</em>, <em>magnification=1.0</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#composite_images"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.composite_images" title="Permalink to this definition">¶</a></dt>
<dd><p>Composite a number of RGBA images into one. The images are
composited in the order in which they appear in the <code class="xref py py-obj docutils literal notranslate"><span class="pre">images</span></code> list.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first docutils">
<dt><strong>images</strong> <span class="classifier-delimiter">:</span> <span class="classifier">list of Images</span></dt>
<dd><p class="first last">Each must have a <code class="xref py py-obj docutils literal notranslate"><span class="pre">make_image</span></code> method. For each image,
<code class="xref py py-obj docutils literal notranslate"><span class="pre">can_composite</span></code> should return <a class="reference external" href="https://docs.python.org/3/library/constants.html#True" title="(in Python v3.7)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">True</span></code></a>, though this is not
enforced by this function. Each image must have a purely
affine transformation with no shear.</p>
</dd>
<dt><strong>renderer</strong> <span class="classifier-delimiter">:</span> <span class="classifier">RendererBase instance</span></dt>
<dd></dd>
<dt><strong>magnification</strong> <span class="classifier-delimiter">:</span> <span class="classifier">float</span></dt>
<dd><p class="first last">The additional magnification to apply for the renderer in use.</p>
</dd>
</dl>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
<dt><strong>tuple</strong> <span class="classifier-delimiter">:</span> <span class="classifier">image, offset_x, offset_y</span></dt>
<dd><p class="first">Returns the tuple:</p>
<ul class="last simple">
<li>image: A numpy array of the same type as the input images.</li>
<li>offset_x, offset_y: The offset of the image (left, bottom)
in the output figure.</li>
</ul>
</dd>
</dl>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="matplotlib.image.imread">
<code class="descclassname">matplotlib.image.</code><code class="descname">imread</code><span class="sig-paren">(</span><em>fname</em>, <em>format=None</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#imread"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.imread" title="Permalink to this definition">¶</a></dt>
<dd><p>Read an image from a file into an array.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first docutils">
<dt><strong>fname</strong> <span class="classifier-delimiter">:</span> <span class="classifier">str or file-like</span></dt>
<dd><p class="first last">The image file to read. This can be a filename, a URL or a Python
file-like object opened in read-binary mode.</p>
</dd>
<dt><strong>format</strong> <span class="classifier-delimiter">:</span> <span class="classifier">str, optional</span></dt>
<dd><p class="first last">The image file format assumed for reading the data. If not
given, the format is deduced from the filename. If nothing can
be deduced, PNG is tried.</p>
</dd>
</dl>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
<dt><strong>imagedata</strong> <span class="classifier-delimiter">:</span> <span class="classifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.array</span></code></span></dt>
<dd><p class="first">The image data. The returned array has shape</p>
<ul class="last simple">
<li>(M, N) for grayscale images.</li>
<li>(M, N, 3) for RGB images.</li>
<li>(M, N, 4) for RGBA images.</li>
</ul>
</dd>
</dl>
</td>
</tr>
</tbody>
</table>
<p class="rubric">Notes</p>
<p>Matplotlib can only read PNGs natively. Further image formats are
supported via the optional dependency on Pillow. Note, URL strings
are not compatible with Pillow. Check the <a class="reference external" href="http://pillow.readthedocs.io/en/latest/">Pillow documentation</a>
for more information.</p>
</dd></dl>
<dl class="function">
<dt id="matplotlib.image.imsave">
<code class="descclassname">matplotlib.image.</code><code class="descname">imsave</code><span class="sig-paren">(</span><em>fname</em>, <em>arr</em>, <em>vmin=None</em>, <em>vmax=None</em>, <em>cmap=None</em>, <em>format=None</em>, <em>origin=None</em>, <em>dpi=100</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#imsave"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.imsave" title="Permalink to this definition">¶</a></dt>
<dd><p>Save an array as an image file.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first last docutils">
<dt><strong>fname</strong> <span class="classifier-delimiter">:</span> <span class="classifier">str or PathLike file-like</span></dt>
<dd><p class="first last">A path or a Python file-like object to store the image in.
If <em>format</em> is not set, then the output format is inferred from the
extension of <em>fname</em>, if any, and from <code class="docutils literal notranslate"><a class="reference external" href="../tutorials/introductory/customizing.html#matplotlib-rcparams"><span class="pre">rcParams["savefig.format"]</span></a></code> otherwise.
If <em>format</em> is set, it determines the output format.</p>
</dd>
<dt><strong>arr</strong> <span class="classifier-delimiter">:</span> <span class="classifier">array-like</span></dt>
<dd><p class="first last">The image data. The shape can be one of
MxN (luminance), MxNx3 (RGB) or MxNx4 (RGBA).</p>
</dd>
<dt><strong>vmin, vmax</strong> <span class="classifier-delimiter">:</span> <span class="classifier">scalar, optional</span></dt>
<dd><p class="first last"><em>vmin</em> and <em>vmax</em> set the color scaling for the image by fixing the
values that map to the colormap color limits. If either <em>vmin</em>
or <em>vmax</em> is None, that limit is determined from the <em>arr</em>
min/max value.</p>
</dd>
<dt><strong>cmap</strong> <span class="classifier-delimiter">:</span> <span class="classifier">str or <a class="reference internal" href="_as_gen/matplotlib.colors.Colormap.html#matplotlib.colors.Colormap" title="matplotlib.colors.Colormap"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Colormap</span></code></a>, optional</span></dt>
<dd><p class="first last">A Colormap instance or registered colormap name. The colormap
maps scalar data to colors. It is ignored for RGB(A) data.
Defaults to <code class="docutils literal notranslate"><a class="reference external" href="../tutorials/introductory/customizing.html#matplotlib-rcparams"><span class="pre">rcParams["image.cmap"]</span></a></code> ('viridis').</p>
</dd>
<dt><strong>format</strong> <span class="classifier-delimiter">:</span> <span class="classifier">str, optional</span></dt>
<dd><p class="first last">The file format, e.g. 'png', 'pdf', 'svg', ... The behavior when this
is unset is documented under <em>fname</em>.</p>
</dd>
<dt><strong>origin</strong> <span class="classifier-delimiter">:</span> <span class="classifier">{'upper', 'lower'}, optional</span></dt>
<dd><p class="first last">Indicates whether the <code class="docutils literal notranslate"><span class="pre">(0,</span> <span class="pre">0)</span></code> index of the array is in the upper
left or lower left corner of the axes. Defaults to <code class="docutils literal notranslate"><a class="reference external" href="../tutorials/introductory/customizing.html#matplotlib-rcparams"><span class="pre">rcParams["image.origin"]</span></a></code>
('upper').</p>
</dd>
<dt><strong>dpi</strong> <span class="classifier-delimiter">:</span> <span class="classifier">int</span></dt>
<dd><p class="first last">The DPI to store in the metadata of the file. This does not affect the
resolution of the output image.</p>
</dd>
</dl>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="matplotlib.image.pil_to_array">
<code class="descclassname">matplotlib.image.</code><code class="descname">pil_to_array</code><span class="sig-paren">(</span><em>pilImage</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#pil_to_array"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.pil_to_array" title="Permalink to this definition">¶</a></dt>
<dd><p>Load a <a class="reference external" href="https://pillow.readthedocs.io/en/latest/reference/Image.html">PIL image</a> and return it as a numpy array.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
<dt>numpy.array</dt>
<dd><p class="first">The array shape depends on the image type:</p>
<ul class="last simple">
<li>(M, N) for grayscale images.</li>
<li>(M, N, 3) for RGB images.</li>
<li>(M, N, 4) for RGBA images.</li>
</ul>
</dd>
</dl>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="matplotlib.image.thumbnail">
<code class="descclassname">matplotlib.image.</code><code class="descname">thumbnail</code><span class="sig-paren">(</span><em>infile</em>, <em>thumbfile</em>, <em>scale=0.1</em>, <em>interpolation='bilinear'</em>, <em>preview=False</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/matplotlib/image.html#thumbnail"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matplotlib.image.thumbnail" title="Permalink to this definition">¶</a></dt>
<dd><p>Make a thumbnail of image in <em>infile</em> with output filename <em>thumbfile</em>.</p>
<p>See <a class="reference internal" href="../gallery/misc/image_thumbnail_sgskip.html"><span class="doc">Image Thumbnail</span></a>.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first docutils">
<dt><strong>infile</strong> <span class="classifier-delimiter">:</span> <span class="classifier">str or file-like</span></dt>
<dd><p class="first last">The image file -- must be PNG, or Pillow-readable if you have <a class="reference external" href="http://python-pillow.org/">Pillow</a>
installed.</p>
</dd>
<dt><strong>thumbfile</strong> <span class="classifier-delimiter">:</span> <span class="classifier">str or file-like</span></dt>
<dd><p class="first last">The thumbnail filename.</p>
</dd>
<dt><strong>scale</strong> <span class="classifier-delimiter">:</span> <span class="classifier">float, optional</span></dt>
<dd><p class="first last">The scale factor for the thumbnail.</p>
</dd>
<dt><strong>interpolation</strong> <span class="classifier-delimiter">:</span> <span class="classifier">str, optional</span></dt>
<dd><p class="first last">The interpolation scheme used in the resampling. See the
<em>interpolation</em> parameter of <a class="reference internal" href="_as_gen/matplotlib.axes.Axes.imshow.html#matplotlib.axes.Axes.imshow" title="matplotlib.axes.Axes.imshow"><code class="xref py py-obj docutils literal notranslate"><span class="pre">imshow</span></code></a> for possible values.</p>
</dd>
<dt><strong>preview</strong> <span class="classifier-delimiter">:</span> <span class="classifier">bool, optional</span></dt>
<dd><p class="first last">If True, the default backend (presumably a user interface
backend) will be used which will cause a figure to be raised if
<a class="reference internal" href="_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show"><code class="xref py py-obj docutils literal notranslate"><span class="pre">show</span></code></a> is called. If it is False, the figure is
created using <code class="xref py py-obj docutils literal notranslate"><span class="pre">FigureCanvasBase</span></code> and the drawing backend is selected
as <code class="xref py py-obj docutils literal notranslate"><span class="pre">savefig</span></code> would normally do.</p>
</dd>
</dl>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
<dt><strong>figure</strong> <span class="classifier-delimiter">:</span> <span class="classifier"><a class="reference internal" href="_as_gen/matplotlib.figure.Figure.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Figure</span></code></a></span></dt>
<dd><p class="first last">The figure instance containing the thumbnail.</p>
</dd>
</dl>
</td>
</tr>
</tbody>
</table>
</dd></dl>
</div>
</div>
</div>
</div>
<div class="clearer"></div>
</div>
<div class="footer">
© Copyright 2002 - 2012 John Hunter, Darren Dale, Eric Firing, Michael Droettboom and the Matplotlib development team; 2012 - 2018 The Matplotlib development team.
<br />
Last updated on May 18, 2019.
Created using
<a href="http://sphinx-doc.org/">Sphinx</a> 1.8.5.
Doc version v3.1.0-4-g0078324e4.
</div>
</body>
<footer>
</footer>
</html>
You can’t perform that action at this time.