{{ message }}
-
Notifications
You must be signed in to change notification settings - Fork 62
Expand file tree
/
Copy pathscale.html
More file actions
901 lines (722 loc) · 107 KB
/
Copy pathscale.html
File metadata and controls
901 lines (722 loc) · 107 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
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
<!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="X-UA-Compatible" content="IE=Edge" />
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<title>matplotlib.scale — Matplotlib 3.2.0 documentation</title>
<link rel="stylesheet" href="../../_static/mpl.css?v3.2.0-3-gea62ccde8" type="text/css" />
<link rel="stylesheet" href="../../_static/pygments.css" type="text/css" />
<link rel="stylesheet" type="text/css" href="../../_static/graphviz.css" />
<link rel="stylesheet" type="text/css" href="../../_static/gallery.css" />
<link rel="stylesheet" type="text/css" href="../../_static/copybutton.css" />
<script id="documentation_options" data-url_root="../../" src="../../_static/documentation_options.js"></script>
<script src="../../_static/jquery.js"></script>
<script src="../../_static/underscore.js"></script>
<script src="../../_static/doctools.js"></script>
<script src="../../_static/language_data.js"></script>
<script src="../../_static/clipboard.min.js"></script>
<script src="../../_static/copybutton.js"></script>
<script >var copybuttonSkipText = '>>> ';</script>
<script async="async" 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 3.2.0 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 3.2.0 documentation" href="#" />
<link rel="canonical" href="https://matplotlib.org/3.4.3/_modules/matplotlib/scale.html" />
<link rel="stylesheet" href="../../_static/custom.css" type="text/css" />
<meta name="viewport" content="width=device-width, initial-scale=0.9, maximum-scale=0.9" />
<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 (v3.2.0). For the latest version see <a href="/stable/">https://matplotlib.org/stable/</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 3.2.0</span></div>
<img src="../../_static/logo2_compressed.svg" 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>
<nav class="main-nav">
<ul>
<li><a href="../../users/installing.html">Installation</a></li>
<li><a href="../../contents.html">Documentation</a></li>
<li><a href="../../gallery/index.html">Examples</a></li>
<li><a href="../../tutorials/index.html">Tutorials</a></li>
<li><a href="../../devel/index.html">Contributing</a></li>
<li class="nav-right">
<form class="search" action="../../search.html" method="get">
<input type="text" name="q" aria-labelledby="searchlabel" placeholder="Search"/>
</form>
</li>
</ul>
</nav>
<div class="related" role="navigation" aria-label="related navigation">
<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><a href="../../index.html">home</a>| </li>
<li><a href="../../contents.html">contents</a> »</li>
<li class="nav-item nav-item-1"><a href="../index.html" >Module code</a> »</li>
<li class="nav-item nav-item-2"><a href="../matplotlib.html" accesskey="U">matplotlib</a> »</li>
</ul>
</div>
<div class="sphinxsidebar" role="navigation" aria-label="main navigation">
<div class="sphinxsidebarwrapper"><div class="relations">
<h3>Related Topics</h3>
<ul>
<li><a href="../../contents.html">Documentation overview</a><ul>
<li><a href="../index.html">Module code</a><ul>
<li><a href="../matplotlib.html">matplotlib</a><ul>
</ul></li>
</ul></li>
</ul></li>
</ul>
</div>
</div>
</div>
<div class="document">
<div class="documentwrapper">
<div class="bodywrapper">
<div class="body" role="main">
<h1>Source code for matplotlib.scale</h1><div class="highlight"><pre>
<span></span><span class="sd">"""</span>
<span class="sd">Scales define the distribution of data values on an axis, e.g. a log scaling.</span>
<span class="sd">They are attached to an `~.axis.Axis` and hold a `.Transform`, which is</span>
<span class="sd">responsible for the actual data transformation.</span>
<span class="sd">See also `.axes.Axes.set_xscale` and the scales examples in the documentation.</span>
<span class="sd">"""</span>
<span class="kn">import</span> <span class="nn">inspect</span>
<span class="kn">import</span> <span class="nn">textwrap</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">numpy</span> <span class="kn">import</span> <span class="n">ma</span>
<span class="kn">from</span> <span class="nn">matplotlib</span> <span class="kn">import</span> <span class="n">cbook</span><span class="p">,</span> <span class="n">docstring</span><span class="p">,</span> <span class="n">rcParams</span>
<span class="kn">from</span> <span class="nn">matplotlib.ticker</span> <span class="kn">import</span> <span class="p">(</span>
<span class="n">NullFormatter</span><span class="p">,</span> <span class="n">ScalarFormatter</span><span class="p">,</span> <span class="n">LogFormatterSciNotation</span><span class="p">,</span> <span class="n">LogitFormatter</span><span class="p">,</span>
<span class="n">NullLocator</span><span class="p">,</span> <span class="n">LogLocator</span><span class="p">,</span> <span class="n">AutoLocator</span><span class="p">,</span> <span class="n">AutoMinorLocator</span><span class="p">,</span>
<span class="n">SymmetricalLogLocator</span><span class="p">,</span> <span class="n">LogitLocator</span><span class="p">)</span>
<span class="kn">from</span> <span class="nn">matplotlib.transforms</span> <span class="kn">import</span> <span class="n">Transform</span><span class="p">,</span> <span class="n">IdentityTransform</span>
<span class="kn">from</span> <span class="nn">matplotlib.cbook</span> <span class="kn">import</span> <span class="n">warn_deprecated</span>
<div class="viewcode-block" id="ScaleBase"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.ScaleBase">[docs]</a><span class="k">class</span> <span class="nc">ScaleBase</span><span class="p">:</span>
<span class="sd">"""</span>
<span class="sd"> The base class for all scales.</span>
<span class="sd"> Scales are separable transformations, working on a single dimension.</span>
<span class="sd"> Any subclasses will want to override:</span>
<span class="sd"> - :attr:`name`</span>
<span class="sd"> - :meth:`get_transform`</span>
<span class="sd"> - :meth:`set_default_locators_and_formatters`</span>
<span class="sd"> And optionally:</span>
<span class="sd"> - :meth:`limit_range_for_scale`</span>
<span class="sd"> """</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">axis</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="sa">r</span><span class="sd">"""</span>
<span class="sd"> Construct a new scale.</span>
<span class="sd"> Notes</span>
<span class="sd"> -----</span>
<span class="sd"> The following note is for scale implementors.</span>
<span class="sd"> For back-compatibility reasons, scales take an `~matplotlib.axis.Axis`</span>
<span class="sd"> object as first argument. However, this argument should not</span>
<span class="sd"> be used: a single scale object should be usable by multiple</span>
<span class="sd"> `~matplotlib.axis.Axis`\es at the same time.</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="n">kwargs</span><span class="p">:</span>
<span class="n">warn_deprecated</span><span class="p">(</span>
<span class="s1">'3.2.0'</span><span class="p">,</span>
<span class="n">message</span><span class="o">=</span><span class="p">(</span>
<span class="sa">f</span><span class="s2">"ScaleBase got an unexpected keyword "</span>
<span class="sa">f</span><span class="s2">"argument {next(iter(kwargs))!r}. "</span>
<span class="s1">'In the future this will raise TypeError'</span><span class="p">)</span>
<span class="p">)</span>
<div class="viewcode-block" id="ScaleBase.get_transform"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.ScaleBase.get_transform">[docs]</a> <span class="k">def</span> <span class="nf">get_transform</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Return the :class:`~matplotlib.transforms.Transform` object</span>
<span class="sd"> associated with this scale.</span>
<span class="sd"> """</span>
<span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">()</span></div>
<div class="viewcode-block" id="ScaleBase.set_default_locators_and_formatters"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.ScaleBase.set_default_locators_and_formatters">[docs]</a> <span class="k">def</span> <span class="nf">set_default_locators_and_formatters</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">axis</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Set the locators and formatters of *axis* to instances suitable for</span>
<span class="sd"> this scale.</span>
<span class="sd"> """</span>
<span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">()</span></div>
<div class="viewcode-block" id="ScaleBase.limit_range_for_scale"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.ScaleBase.limit_range_for_scale">[docs]</a> <span class="k">def</span> <span class="nf">limit_range_for_scale</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">vmin</span><span class="p">,</span> <span class="n">vmax</span><span class="p">,</span> <span class="n">minpos</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Returns the range *vmin*, *vmax*, possibly limited to the</span>
<span class="sd"> domain supported by this scale.</span>
<span class="sd"> *minpos* should be the minimum positive value in the data.</span>
<span class="sd"> This is used by log scales to determine a minimum value.</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="n">vmin</span><span class="p">,</span> <span class="n">vmax</span></div></div>
<div class="viewcode-block" id="LinearScale"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.LinearScale">[docs]</a><span class="k">class</span> <span class="nc">LinearScale</span><span class="p">(</span><span class="n">ScaleBase</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> The default linear scale.</span>
<span class="sd"> """</span>
<span class="n">name</span> <span class="o">=</span> <span class="s1">'linear'</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">axis</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="c1"># This method is present only to prevent inheritance of the base class'</span>
<span class="c1"># constructor docstring, which would otherwise end up interpolated into</span>
<span class="c1"># the docstring of Axis.set_scale.</span>
<span class="sd">"""</span>
<span class="sd"> """</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">axis</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<div class="viewcode-block" id="LinearScale.set_default_locators_and_formatters"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.LinearScale.set_default_locators_and_formatters">[docs]</a> <span class="k">def</span> <span class="nf">set_default_locators_and_formatters</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">axis</span><span class="p">):</span>
<span class="c1"># docstring inherited</span>
<span class="n">axis</span><span class="o">.</span><span class="n">set_major_locator</span><span class="p">(</span><span class="n">AutoLocator</span><span class="p">())</span>
<span class="n">axis</span><span class="o">.</span><span class="n">set_major_formatter</span><span class="p">(</span><span class="n">ScalarFormatter</span><span class="p">())</span>
<span class="n">axis</span><span class="o">.</span><span class="n">set_minor_formatter</span><span class="p">(</span><span class="n">NullFormatter</span><span class="p">())</span>
<span class="c1"># update the minor locator for x and y axis based on rcParams</span>
<span class="k">if</span> <span class="p">(</span><span class="n">axis</span><span class="o">.</span><span class="n">axis_name</span> <span class="o">==</span> <span class="s1">'x'</span> <span class="ow">and</span> <span class="n">rcParams</span><span class="p">[</span><span class="s1">'xtick.minor.visible'</span><span class="p">]</span>
<span class="ow">or</span> <span class="n">axis</span><span class="o">.</span><span class="n">axis_name</span> <span class="o">==</span> <span class="s1">'y'</span> <span class="ow">and</span> <span class="n">rcParams</span><span class="p">[</span><span class="s1">'ytick.minor.visible'</span><span class="p">]):</span>
<span class="n">axis</span><span class="o">.</span><span class="n">set_minor_locator</span><span class="p">(</span><span class="n">AutoMinorLocator</span><span class="p">())</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">axis</span><span class="o">.</span><span class="n">set_minor_locator</span><span class="p">(</span><span class="n">NullLocator</span><span class="p">())</span></div>
<div class="viewcode-block" id="LinearScale.get_transform"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.LinearScale.get_transform">[docs]</a> <span class="k">def</span> <span class="nf">get_transform</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Return the transform for linear scaling, which is just the</span>
<span class="sd"> `~matplotlib.transforms.IdentityTransform`.</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="n">IdentityTransform</span><span class="p">()</span></div></div>
<div class="viewcode-block" id="FuncTransform"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.FuncTransform">[docs]</a><span class="k">class</span> <span class="nc">FuncTransform</span><span class="p">(</span><span class="n">Transform</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> A simple transform that takes and arbitrary function for the</span>
<span class="sd"> forward and inverse transform.</span>
<span class="sd"> """</span>
<span class="n">input_dims</span> <span class="o">=</span> <span class="n">output_dims</span> <span class="o">=</span> <span class="mi">1</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">forward</span><span class="p">,</span> <span class="n">inverse</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> forward : callable</span>
<span class="sd"> The forward function for the transform. This function must have</span>
<span class="sd"> an inverse and, for best behavior, be monotonic.</span>
<span class="sd"> It must have the signature::</span>
<span class="sd"> def forward(values: array-like) -> array-like</span>
<span class="sd"> inverse : callable</span>
<span class="sd"> The inverse of the forward function. Signature as ``forward``.</span>
<span class="sd"> """</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="k">if</span> <span class="n">callable</span><span class="p">(</span><span class="n">forward</span><span class="p">)</span> <span class="ow">and</span> <span class="n">callable</span><span class="p">(</span><span class="n">inverse</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_forward</span> <span class="o">=</span> <span class="n">forward</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_inverse</span> <span class="o">=</span> <span class="n">inverse</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'arguments to FuncTransform must be functions'</span><span class="p">)</span>
<div class="viewcode-block" id="FuncTransform.transform_non_affine"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.FuncTransform.transform_non_affine">[docs]</a> <span class="k">def</span> <span class="nf">transform_non_affine</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">values</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_forward</span><span class="p">(</span><span class="n">values</span><span class="p">)</span></div>
<div class="viewcode-block" id="FuncTransform.inverted"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.FuncTransform.inverted">[docs]</a> <span class="k">def</span> <span class="nf">inverted</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="n">FuncTransform</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_inverse</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_forward</span><span class="p">)</span></div></div>
<div class="viewcode-block" id="FuncScale"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.FuncScale">[docs]</a><span class="k">class</span> <span class="nc">FuncScale</span><span class="p">(</span><span class="n">ScaleBase</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Provide an arbitrary scale with user-supplied function for the axis.</span>
<span class="sd"> """</span>
<span class="n">name</span> <span class="o">=</span> <span class="s1">'function'</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">axis</span><span class="p">,</span> <span class="n">functions</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> axis : `~matplotlib.axis.Axis`</span>
<span class="sd"> The axis for the scale.</span>
<span class="sd"> functions : (callable, callable)</span>
<span class="sd"> two-tuple of the forward and inverse functions for the scale.</span>
<span class="sd"> The forward function must be monotonic.</span>
<span class="sd"> Both functions must have the signature::</span>
<span class="sd"> def forward(values: array-like) -> array-like</span>
<span class="sd"> """</span>
<span class="n">forward</span><span class="p">,</span> <span class="n">inverse</span> <span class="o">=</span> <span class="n">functions</span>
<span class="n">transform</span> <span class="o">=</span> <span class="n">FuncTransform</span><span class="p">(</span><span class="n">forward</span><span class="p">,</span> <span class="n">inverse</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_transform</span> <span class="o">=</span> <span class="n">transform</span>
<div class="viewcode-block" id="FuncScale.get_transform"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.FuncScale.get_transform">[docs]</a> <span class="k">def</span> <span class="nf">get_transform</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""Return the `.FuncTransform` associated with this scale."""</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_transform</span></div>
<div class="viewcode-block" id="FuncScale.set_default_locators_and_formatters"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.FuncScale.set_default_locators_and_formatters">[docs]</a> <span class="k">def</span> <span class="nf">set_default_locators_and_formatters</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">axis</span><span class="p">):</span>
<span class="c1"># docstring inherited</span>
<span class="n">axis</span><span class="o">.</span><span class="n">set_major_locator</span><span class="p">(</span><span class="n">AutoLocator</span><span class="p">())</span>
<span class="n">axis</span><span class="o">.</span><span class="n">set_major_formatter</span><span class="p">(</span><span class="n">ScalarFormatter</span><span class="p">())</span>
<span class="n">axis</span><span class="o">.</span><span class="n">set_minor_formatter</span><span class="p">(</span><span class="n">NullFormatter</span><span class="p">())</span>
<span class="c1"># update the minor locator for x and y axis based on rcParams</span>
<span class="k">if</span> <span class="p">(</span><span class="n">axis</span><span class="o">.</span><span class="n">axis_name</span> <span class="o">==</span> <span class="s1">'x'</span> <span class="ow">and</span> <span class="n">rcParams</span><span class="p">[</span><span class="s1">'xtick.minor.visible'</span><span class="p">]</span>
<span class="ow">or</span> <span class="n">axis</span><span class="o">.</span><span class="n">axis_name</span> <span class="o">==</span> <span class="s1">'y'</span> <span class="ow">and</span> <span class="n">rcParams</span><span class="p">[</span><span class="s1">'ytick.minor.visible'</span><span class="p">]):</span>
<span class="n">axis</span><span class="o">.</span><span class="n">set_minor_locator</span><span class="p">(</span><span class="n">AutoMinorLocator</span><span class="p">())</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">axis</span><span class="o">.</span><span class="n">set_minor_locator</span><span class="p">(</span><span class="n">NullLocator</span><span class="p">())</span></div></div>
<div class="viewcode-block" id="LogTransformBase"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.LogTransformBase">[docs]</a><span class="nd">@cbook</span><span class="o">.</span><span class="n">deprecated</span><span class="p">(</span><span class="s2">"3.1"</span><span class="p">,</span> <span class="n">alternative</span><span class="o">=</span><span class="s2">"LogTransform"</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">LogTransformBase</span><span class="p">(</span><span class="n">Transform</span><span class="p">):</span>
<span class="n">input_dims</span> <span class="o">=</span> <span class="n">output_dims</span> <span class="o">=</span> <span class="mi">1</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">nonpos</span><span class="o">=</span><span class="s1">'clip'</span><span class="p">):</span>
<span class="n">Transform</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_clip</span> <span class="o">=</span> <span class="p">{</span><span class="s2">"clip"</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span> <span class="s2">"mask"</span><span class="p">:</span> <span class="kc">False</span><span class="p">}[</span><span class="n">nonpos</span><span class="p">]</span>
<div class="viewcode-block" id="LogTransformBase.transform_non_affine"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.LogTransformBase.transform_non_affine">[docs]</a> <span class="k">def</span> <span class="nf">transform_non_affine</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">a</span><span class="p">):</span>
<span class="k">return</span> <span class="n">LogTransform</span><span class="o">.</span><span class="n">transform_non_affine</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">a</span><span class="p">)</span></div>
<span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="s2">"</span><span class="si">{}</span><span class="s2">(</span><span class="si">{!r}</span><span class="s2">)"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
<span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> <span class="s2">"clip"</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_clip</span> <span class="k">else</span> <span class="s2">"mask"</span><span class="p">)</span></div>
<div class="viewcode-block" id="InvertedLogTransformBase"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.InvertedLogTransformBase">[docs]</a><span class="nd">@cbook</span><span class="o">.</span><span class="n">deprecated</span><span class="p">(</span><span class="s2">"3.1"</span><span class="p">,</span> <span class="n">alternative</span><span class="o">=</span><span class="s2">"InvertedLogTransform"</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">InvertedLogTransformBase</span><span class="p">(</span><span class="n">Transform</span><span class="p">):</span>
<span class="n">input_dims</span> <span class="o">=</span> <span class="n">output_dims</span> <span class="o">=</span> <span class="mi">1</span>
<div class="viewcode-block" id="InvertedLogTransformBase.transform_non_affine"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.InvertedLogTransformBase.transform_non_affine">[docs]</a> <span class="k">def</span> <span class="nf">transform_non_affine</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">a</span><span class="p">):</span>
<span class="k">return</span> <span class="n">ma</span><span class="o">.</span><span class="n">power</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">base</span><span class="p">,</span> <span class="n">a</span><span class="p">)</span></div>
<span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="s2">"</span><span class="si">{}</span><span class="s2">()"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="vm">__name__</span><span class="p">)</span></div>
<div class="viewcode-block" id="Log10Transform"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.Log10Transform">[docs]</a><span class="nd">@cbook</span><span class="o">.</span><span class="n">deprecated</span><span class="p">(</span><span class="s2">"3.1"</span><span class="p">,</span> <span class="n">alternative</span><span class="o">=</span><span class="s2">"LogTransform"</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">Log10Transform</span><span class="p">(</span><span class="n">LogTransformBase</span><span class="p">):</span>
<span class="n">base</span> <span class="o">=</span> <span class="mf">10.0</span>
<div class="viewcode-block" id="Log10Transform.inverted"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.Log10Transform.inverted">[docs]</a> <span class="k">def</span> <span class="nf">inverted</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="n">InvertedLog10Transform</span><span class="p">()</span></div></div>
<div class="viewcode-block" id="InvertedLog10Transform"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.InvertedLog10Transform">[docs]</a><span class="nd">@cbook</span><span class="o">.</span><span class="n">deprecated</span><span class="p">(</span><span class="s2">"3.1"</span><span class="p">,</span> <span class="n">alternative</span><span class="o">=</span><span class="s2">"InvertedLogTransform"</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">InvertedLog10Transform</span><span class="p">(</span><span class="n">InvertedLogTransformBase</span><span class="p">):</span>
<span class="n">base</span> <span class="o">=</span> <span class="mf">10.0</span>
<div class="viewcode-block" id="InvertedLog10Transform.inverted"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.InvertedLog10Transform.inverted">[docs]</a> <span class="k">def</span> <span class="nf">inverted</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="n">Log10Transform</span><span class="p">()</span></div></div>
<div class="viewcode-block" id="Log2Transform"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.Log2Transform">[docs]</a><span class="nd">@cbook</span><span class="o">.</span><span class="n">deprecated</span><span class="p">(</span><span class="s2">"3.1"</span><span class="p">,</span> <span class="n">alternative</span><span class="o">=</span><span class="s2">"LogTransform"</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">Log2Transform</span><span class="p">(</span><span class="n">LogTransformBase</span><span class="p">):</span>
<span class="n">base</span> <span class="o">=</span> <span class="mf">2.0</span>
<div class="viewcode-block" id="Log2Transform.inverted"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.Log2Transform.inverted">[docs]</a> <span class="k">def</span> <span class="nf">inverted</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="n">InvertedLog2Transform</span><span class="p">()</span></div></div>
<div class="viewcode-block" id="InvertedLog2Transform"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.InvertedLog2Transform">[docs]</a><span class="nd">@cbook</span><span class="o">.</span><span class="n">deprecated</span><span class="p">(</span><span class="s2">"3.1"</span><span class="p">,</span> <span class="n">alternative</span><span class="o">=</span><span class="s2">"InvertedLogTransform"</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">InvertedLog2Transform</span><span class="p">(</span><span class="n">InvertedLogTransformBase</span><span class="p">):</span>
<span class="n">base</span> <span class="o">=</span> <span class="mf">2.0</span>
<div class="viewcode-block" id="InvertedLog2Transform.inverted"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.InvertedLog2Transform.inverted">[docs]</a> <span class="k">def</span> <span class="nf">inverted</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="n">Log2Transform</span><span class="p">()</span></div></div>
<div class="viewcode-block" id="NaturalLogTransform"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.NaturalLogTransform">[docs]</a><span class="nd">@cbook</span><span class="o">.</span><span class="n">deprecated</span><span class="p">(</span><span class="s2">"3.1"</span><span class="p">,</span> <span class="n">alternative</span><span class="o">=</span><span class="s2">"LogTransform"</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">NaturalLogTransform</span><span class="p">(</span><span class="n">LogTransformBase</span><span class="p">):</span>
<span class="n">base</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">e</span>
<div class="viewcode-block" id="NaturalLogTransform.inverted"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.NaturalLogTransform.inverted">[docs]</a> <span class="k">def</span> <span class="nf">inverted</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="n">InvertedNaturalLogTransform</span><span class="p">()</span></div></div>
<div class="viewcode-block" id="InvertedNaturalLogTransform"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.InvertedNaturalLogTransform">[docs]</a><span class="nd">@cbook</span><span class="o">.</span><span class="n">deprecated</span><span class="p">(</span><span class="s2">"3.1"</span><span class="p">,</span> <span class="n">alternative</span><span class="o">=</span><span class="s2">"InvertedLogTransform"</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">InvertedNaturalLogTransform</span><span class="p">(</span><span class="n">InvertedLogTransformBase</span><span class="p">):</span>
<span class="n">base</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">e</span>
<div class="viewcode-block" id="InvertedNaturalLogTransform.inverted"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.InvertedNaturalLogTransform.inverted">[docs]</a> <span class="k">def</span> <span class="nf">inverted</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="n">NaturalLogTransform</span><span class="p">()</span></div></div>
<div class="viewcode-block" id="LogTransform"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.LogTransform">[docs]</a><span class="k">class</span> <span class="nc">LogTransform</span><span class="p">(</span><span class="n">Transform</span><span class="p">):</span>
<span class="n">input_dims</span> <span class="o">=</span> <span class="n">output_dims</span> <span class="o">=</span> <span class="mi">1</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">base</span><span class="p">,</span> <span class="n">nonpos</span><span class="o">=</span><span class="s1">'clip'</span><span class="p">):</span>
<span class="n">Transform</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">base</span> <span class="o">=</span> <span class="n">base</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_clip</span> <span class="o">=</span> <span class="p">{</span><span class="s2">"clip"</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span> <span class="s2">"mask"</span><span class="p">:</span> <span class="kc">False</span><span class="p">}[</span><span class="n">nonpos</span><span class="p">]</span>
<span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="s2">"</span><span class="si">{}</span><span class="s2">(base=</span><span class="si">{}</span><span class="s2">, nonpos=</span><span class="si">{!r}</span><span class="s2">)"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
<span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">base</span><span class="p">,</span> <span class="s2">"clip"</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_clip</span> <span class="k">else</span> <span class="s2">"mask"</span><span class="p">)</span>
<div class="viewcode-block" id="LogTransform.transform_non_affine"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.LogTransform.transform_non_affine">[docs]</a> <span class="k">def</span> <span class="nf">transform_non_affine</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">a</span><span class="p">):</span>
<span class="c1"># Ignore invalid values due to nans being passed to the transform.</span>
<span class="k">with</span> <span class="n">np</span><span class="o">.</span><span class="n">errstate</span><span class="p">(</span><span class="n">divide</span><span class="o">=</span><span class="s2">"ignore"</span><span class="p">,</span> <span class="n">invalid</span><span class="o">=</span><span class="s2">"ignore"</span><span class="p">):</span>
<span class="n">log</span> <span class="o">=</span> <span class="p">{</span><span class="n">np</span><span class="o">.</span><span class="n">e</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">,</span> <span class="mi">2</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">log2</span><span class="p">,</span> <span class="mi">10</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">log10</span><span class="p">}</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">base</span><span class="p">)</span>
<span class="k">if</span> <span class="n">log</span><span class="p">:</span> <span class="c1"># If possible, do everything in a single call to NumPy.</span>
<span class="n">out</span> <span class="o">=</span> <span class="n">log</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">out</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="n">out</span> <span class="o">/=</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">base</span><span class="p">)</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_clip</span><span class="p">:</span>
<span class="c1"># SVG spec says that conforming viewers must support values up</span>
<span class="c1"># to 3.4e38 (C float); however experiments suggest that</span>
<span class="c1"># Inkscape (which uses cairo for rendering) runs into cairo's</span>
<span class="c1"># 24-bit limit (which is apparently shared by Agg).</span>
<span class="c1"># Ghostscript (used for pdf rendering appears to overflow even</span>
<span class="c1"># earlier, with the max value around 2 ** 15 for the tests to</span>
<span class="c1"># pass. On the other hand, in practice, we want to clip beyond</span>
<span class="c1"># np.log10(np.nextafter(0, 1)) ~ -323</span>
<span class="c1"># so 1000 seems safe.</span>
<span class="n">out</span><span class="p">[</span><span class="n">a</span> <span class="o"><=</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1000</span>
<span class="k">return</span> <span class="n">out</span></div>
<div class="viewcode-block" id="LogTransform.inverted"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.LogTransform.inverted">[docs]</a> <span class="k">def</span> <span class="nf">inverted</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="n">InvertedLogTransform</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">base</span><span class="p">)</span></div></div>
<div class="viewcode-block" id="InvertedLogTransform"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.InvertedLogTransform">[docs]</a><span class="k">class</span> <span class="nc">InvertedLogTransform</span><span class="p">(</span><span class="n">Transform</span><span class="p">):</span>
<span class="n">input_dims</span> <span class="o">=</span> <span class="n">output_dims</span> <span class="o">=</span> <span class="mi">1</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">base</span><span class="p">):</span>
<span class="n">Transform</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">base</span> <span class="o">=</span> <span class="n">base</span>
<span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="s2">"</span><span class="si">{}</span><span class="s2">(base=</span><span class="si">{}</span><span class="s2">)"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">base</span><span class="p">)</span>
<div class="viewcode-block" id="InvertedLogTransform.transform_non_affine"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.InvertedLogTransform.transform_non_affine">[docs]</a> <span class="k">def</span> <span class="nf">transform_non_affine</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">a</span><span class="p">):</span>
<span class="k">return</span> <span class="n">ma</span><span class="o">.</span><span class="n">power</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">base</span><span class="p">,</span> <span class="n">a</span><span class="p">)</span></div>
<div class="viewcode-block" id="InvertedLogTransform.inverted"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.InvertedLogTransform.inverted">[docs]</a> <span class="k">def</span> <span class="nf">inverted</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="n">LogTransform</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">base</span><span class="p">)</span></div></div>
<div class="viewcode-block" id="LogScale"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.LogScale">[docs]</a><span class="k">class</span> <span class="nc">LogScale</span><span class="p">(</span><span class="n">ScaleBase</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> A standard logarithmic scale. Care is taken to only plot positive values.</span>
<span class="sd"> """</span>
<span class="n">name</span> <span class="o">=</span> <span class="s1">'log'</span>
<span class="c1"># compatibility shim</span>
<span class="n">LogTransformBase</span> <span class="o">=</span> <span class="n">LogTransformBase</span>
<span class="n">Log10Transform</span> <span class="o">=</span> <span class="n">Log10Transform</span>
<span class="n">InvertedLog10Transform</span> <span class="o">=</span> <span class="n">InvertedLog10Transform</span>
<span class="n">Log2Transform</span> <span class="o">=</span> <span class="n">Log2Transform</span>
<span class="n">InvertedLog2Transform</span> <span class="o">=</span> <span class="n">InvertedLog2Transform</span>
<span class="n">NaturalLogTransform</span> <span class="o">=</span> <span class="n">NaturalLogTransform</span>
<span class="n">InvertedNaturalLogTransform</span> <span class="o">=</span> <span class="n">InvertedNaturalLogTransform</span>
<span class="n">LogTransform</span> <span class="o">=</span> <span class="n">LogTransform</span>
<span class="n">InvertedLogTransform</span> <span class="o">=</span> <span class="n">InvertedLogTransform</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">axis</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> axis : `~matplotlib.axis.Axis`</span>
<span class="sd"> The axis for the scale.</span>
<span class="sd"> basex, basey : float, default: 10</span>
<span class="sd"> The base of the logarithm.</span>
<span class="sd"> nonposx, nonposy : {'clip', 'mask'}, default: 'clip'</span>
<span class="sd"> Determines the behavior for non-positive values. They can either</span>
<span class="sd"> be masked as invalid, or clipped to a very small positive number.</span>
<span class="sd"> subsx, subsy : sequence of int, default: None</span>
<span class="sd"> Where to place the subticks between each major tick.</span>
<span class="sd"> For example, in a log10 scale: ``[2, 3, 4, 5, 6, 7, 8, 9]``</span>
<span class="sd"> will place 8 logarithmically spaced minor ticks between</span>
<span class="sd"> each major tick.</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="n">axis</span><span class="o">.</span><span class="n">axis_name</span> <span class="o">==</span> <span class="s1">'x'</span><span class="p">:</span>
<span class="n">base</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'basex'</span><span class="p">,</span> <span class="mf">10.0</span><span class="p">)</span>
<span class="n">subs</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'subsx'</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
<span class="n">nonpos</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'nonposx'</span><span class="p">,</span> <span class="s1">'clip'</span><span class="p">)</span>
<span class="n">cbook</span><span class="o">.</span><span class="n">_check_in_list</span><span class="p">([</span><span class="s1">'mask'</span><span class="p">,</span> <span class="s1">'clip'</span><span class="p">],</span> <span class="n">nonposx</span><span class="o">=</span><span class="n">nonpos</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">base</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'basey'</span><span class="p">,</span> <span class="mf">10.0</span><span class="p">)</span>
<span class="n">subs</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'subsy'</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
<span class="n">nonpos</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'nonposy'</span><span class="p">,</span> <span class="s1">'clip'</span><span class="p">)</span>
<span class="n">cbook</span><span class="o">.</span><span class="n">_check_in_list</span><span class="p">([</span><span class="s1">'mask'</span><span class="p">,</span> <span class="s1">'clip'</span><span class="p">],</span> <span class="n">nonposy</span><span class="o">=</span><span class="n">nonpos</span><span class="p">)</span>
<span class="k">if</span> <span class="n">kwargs</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="sa">f</span><span class="s2">"LogScale got an unexpected keyword "</span>
<span class="sa">f</span><span class="s2">"argument {next(iter(kwargs))!r}"</span><span class="p">)</span>
<span class="k">if</span> <span class="n">base</span> <span class="o"><=</span> <span class="mi">0</span> <span class="ow">or</span> <span class="n">base</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'The log base cannot be <= 0 or == 1'</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_transform</span> <span class="o">=</span> <span class="n">LogTransform</span><span class="p">(</span><span class="n">base</span><span class="p">,</span> <span class="n">nonpos</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">subs</span> <span class="o">=</span> <span class="n">subs</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">base</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_transform</span><span class="o">.</span><span class="n">base</span>
<div class="viewcode-block" id="LogScale.set_default_locators_and_formatters"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.LogScale.set_default_locators_and_formatters">[docs]</a> <span class="k">def</span> <span class="nf">set_default_locators_and_formatters</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">axis</span><span class="p">):</span>
<span class="c1"># docstring inherited</span>
<span class="n">axis</span><span class="o">.</span><span class="n">set_major_locator</span><span class="p">(</span><span class="n">LogLocator</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">base</span><span class="p">))</span>
<span class="n">axis</span><span class="o">.</span><span class="n">set_major_formatter</span><span class="p">(</span><span class="n">LogFormatterSciNotation</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">base</span><span class="p">))</span>
<span class="n">axis</span><span class="o">.</span><span class="n">set_minor_locator</span><span class="p">(</span><span class="n">LogLocator</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">base</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">subs</span><span class="p">))</span>
<span class="n">axis</span><span class="o">.</span><span class="n">set_minor_formatter</span><span class="p">(</span>
<span class="n">LogFormatterSciNotation</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">base</span><span class="p">,</span>
<span class="n">labelOnlyBase</span><span class="o">=</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">subs</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">)))</span></div>
<div class="viewcode-block" id="LogScale.get_transform"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.LogScale.get_transform">[docs]</a> <span class="k">def</span> <span class="nf">get_transform</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""Return the `.LogTransform` associated with this scale."""</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_transform</span></div>
<div class="viewcode-block" id="LogScale.limit_range_for_scale"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.LogScale.limit_range_for_scale">[docs]</a> <span class="k">def</span> <span class="nf">limit_range_for_scale</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">vmin</span><span class="p">,</span> <span class="n">vmax</span><span class="p">,</span> <span class="n">minpos</span><span class="p">):</span>
<span class="sd">"""Limit the domain to positive values."""</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">isfinite</span><span class="p">(</span><span class="n">minpos</span><span class="p">):</span>
<span class="n">minpos</span> <span class="o">=</span> <span class="mf">1e-300</span> <span class="c1"># Should rarely (if ever) have a visible effect.</span>
<span class="k">return</span> <span class="p">(</span><span class="n">minpos</span> <span class="k">if</span> <span class="n">vmin</span> <span class="o"><=</span> <span class="mi">0</span> <span class="k">else</span> <span class="n">vmin</span><span class="p">,</span>
<span class="n">minpos</span> <span class="k">if</span> <span class="n">vmax</span> <span class="o"><=</span> <span class="mi">0</span> <span class="k">else</span> <span class="n">vmax</span><span class="p">)</span></div></div>
<div class="viewcode-block" id="FuncScaleLog"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.FuncScaleLog">[docs]</a><span class="k">class</span> <span class="nc">FuncScaleLog</span><span class="p">(</span><span class="n">LogScale</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Provide an arbitrary scale with user-supplied function for the axis and</span>
<span class="sd"> then put on a logarithmic axes.</span>
<span class="sd"> """</span>
<span class="n">name</span> <span class="o">=</span> <span class="s1">'functionlog'</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">axis</span><span class="p">,</span> <span class="n">functions</span><span class="p">,</span> <span class="n">base</span><span class="o">=</span><span class="mi">10</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> axis : `matplotlib.axis.Axis`</span>
<span class="sd"> The axis for the scale.</span>
<span class="sd"> functions : (callable, callable)</span>
<span class="sd"> two-tuple of the forward and inverse functions for the scale.</span>
<span class="sd"> The forward function must be monotonic.</span>
<span class="sd"> Both functions must have the signature::</span>
<span class="sd"> def forward(values: array-like) -> array-like</span>
<span class="sd"> base : float</span>
<span class="sd"> logarithmic base of the scale (default = 10)</span>
<span class="sd"> """</span>
<span class="n">forward</span><span class="p">,</span> <span class="n">inverse</span> <span class="o">=</span> <span class="n">functions</span>
<span class="bp">self</span><span class="o">.</span><span class="n">subs</span> <span class="o">=</span> <span class="kc">None</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_transform</span> <span class="o">=</span> <span class="n">FuncTransform</span><span class="p">(</span><span class="n">forward</span><span class="p">,</span> <span class="n">inverse</span><span class="p">)</span> <span class="o">+</span> <span class="n">LogTransform</span><span class="p">(</span><span class="n">base</span><span class="p">)</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">base</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_transform</span><span class="o">.</span><span class="n">_b</span><span class="o">.</span><span class="n">base</span> <span class="c1"># Base of the LogTransform.</span>
<div class="viewcode-block" id="FuncScaleLog.get_transform"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.FuncScaleLog.get_transform">[docs]</a> <span class="k">def</span> <span class="nf">get_transform</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""Return the `.Transform` associated with this scale."""</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_transform</span></div></div>
<div class="viewcode-block" id="SymmetricalLogTransform"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.SymmetricalLogTransform">[docs]</a><span class="k">class</span> <span class="nc">SymmetricalLogTransform</span><span class="p">(</span><span class="n">Transform</span><span class="p">):</span>
<span class="n">input_dims</span> <span class="o">=</span> <span class="n">output_dims</span> <span class="o">=</span> <span class="mi">1</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">base</span><span class="p">,</span> <span class="n">linthresh</span><span class="p">,</span> <span class="n">linscale</span><span class="p">):</span>
<span class="n">Transform</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">base</span> <span class="o">=</span> <span class="n">base</span>
<span class="bp">self</span><span class="o">.</span><span class="n">linthresh</span> <span class="o">=</span> <span class="n">linthresh</span>
<span class="bp">self</span><span class="o">.</span><span class="n">linscale</span> <span class="o">=</span> <span class="n">linscale</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_linscale_adj</span> <span class="o">=</span> <span class="p">(</span><span class="n">linscale</span> <span class="o">/</span> <span class="p">(</span><span class="mf">1.0</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">base</span> <span class="o">**</span> <span class="o">-</span><span class="mi">1</span><span class="p">))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_log_base</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">base</span><span class="p">)</span>
<div class="viewcode-block" id="SymmetricalLogTransform.transform_non_affine"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.SymmetricalLogTransform.transform_non_affine">[docs]</a> <span class="k">def</span> <span class="nf">transform_non_affine</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">a</span><span class="p">):</span>
<span class="n">abs_a</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="k">with</span> <span class="n">np</span><span class="o">.</span><span class="n">errstate</span><span class="p">(</span><span class="n">divide</span><span class="o">=</span><span class="s2">"ignore"</span><span class="p">,</span> <span class="n">invalid</span><span class="o">=</span><span class="s2">"ignore"</span><span class="p">):</span>
<span class="n">out</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sign</span><span class="p">(</span><span class="n">a</span><span class="p">)</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">linthresh</span> <span class="o">*</span> <span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_linscale_adj</span> <span class="o">+</span>
<span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">abs_a</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">linthresh</span><span class="p">)</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">_log_base</span><span class="p">)</span>
<span class="n">inside</span> <span class="o">=</span> <span class="n">abs_a</span> <span class="o"><=</span> <span class="bp">self</span><span class="o">.</span><span class="n">linthresh</span>
<span class="n">out</span><span class="p">[</span><span class="n">inside</span><span class="p">]</span> <span class="o">=</span> <span class="n">a</span><span class="p">[</span><span class="n">inside</span><span class="p">]</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">_linscale_adj</span>
<span class="k">return</span> <span class="n">out</span></div>
<div class="viewcode-block" id="SymmetricalLogTransform.inverted"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.SymmetricalLogTransform.inverted">[docs]</a> <span class="k">def</span> <span class="nf">inverted</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="n">InvertedSymmetricalLogTransform</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">base</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">linthresh</span><span class="p">,</span>
<span class="bp">self</span><span class="o">.</span><span class="n">linscale</span><span class="p">)</span></div></div>
<div class="viewcode-block" id="InvertedSymmetricalLogTransform"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.InvertedSymmetricalLogTransform">[docs]</a><span class="k">class</span> <span class="nc">InvertedSymmetricalLogTransform</span><span class="p">(</span><span class="n">Transform</span><span class="p">):</span>
<span class="n">input_dims</span> <span class="o">=</span> <span class="n">output_dims</span> <span class="o">=</span> <span class="mi">1</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">base</span><span class="p">,</span> <span class="n">linthresh</span><span class="p">,</span> <span class="n">linscale</span><span class="p">):</span>
<span class="n">Transform</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
<span class="n">symlog</span> <span class="o">=</span> <span class="n">SymmetricalLogTransform</span><span class="p">(</span><span class="n">base</span><span class="p">,</span> <span class="n">linthresh</span><span class="p">,</span> <span class="n">linscale</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">base</span> <span class="o">=</span> <span class="n">base</span>
<span class="bp">self</span><span class="o">.</span><span class="n">linthresh</span> <span class="o">=</span> <span class="n">linthresh</span>
<span class="bp">self</span><span class="o">.</span><span class="n">invlinthresh</span> <span class="o">=</span> <span class="n">symlog</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">linthresh</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">linscale</span> <span class="o">=</span> <span class="n">linscale</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_linscale_adj</span> <span class="o">=</span> <span class="p">(</span><span class="n">linscale</span> <span class="o">/</span> <span class="p">(</span><span class="mf">1.0</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">base</span> <span class="o">**</span> <span class="o">-</span><span class="mi">1</span><span class="p">))</span>
<div class="viewcode-block" id="InvertedSymmetricalLogTransform.transform_non_affine"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.InvertedSymmetricalLogTransform.transform_non_affine">[docs]</a> <span class="k">def</span> <span class="nf">transform_non_affine</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">a</span><span class="p">):</span>
<span class="n">abs_a</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="k">with</span> <span class="n">np</span><span class="o">.</span><span class="n">errstate</span><span class="p">(</span><span class="n">divide</span><span class="o">=</span><span class="s2">"ignore"</span><span class="p">,</span> <span class="n">invalid</span><span class="o">=</span><span class="s2">"ignore"</span><span class="p">):</span>
<span class="n">out</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sign</span><span class="p">(</span><span class="n">a</span><span class="p">)</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">linthresh</span> <span class="o">*</span> <span class="p">(</span>
<span class="n">np</span><span class="o">.</span><span class="n">power</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">base</span><span class="p">,</span>
<span class="n">abs_a</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">linthresh</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">_linscale_adj</span><span class="p">))</span>
<span class="n">inside</span> <span class="o">=</span> <span class="n">abs_a</span> <span class="o"><=</span> <span class="bp">self</span><span class="o">.</span><span class="n">invlinthresh</span>
<span class="n">out</span><span class="p">[</span><span class="n">inside</span><span class="p">]</span> <span class="o">=</span> <span class="n">a</span><span class="p">[</span><span class="n">inside</span><span class="p">]</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">_linscale_adj</span>
<span class="k">return</span> <span class="n">out</span></div>
<div class="viewcode-block" id="InvertedSymmetricalLogTransform.inverted"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.InvertedSymmetricalLogTransform.inverted">[docs]</a> <span class="k">def</span> <span class="nf">inverted</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="n">SymmetricalLogTransform</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">base</span><span class="p">,</span>
<span class="bp">self</span><span class="o">.</span><span class="n">linthresh</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">linscale</span><span class="p">)</span></div></div>
<div class="viewcode-block" id="SymmetricalLogScale"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.SymmetricalLogScale">[docs]</a><span class="k">class</span> <span class="nc">SymmetricalLogScale</span><span class="p">(</span><span class="n">ScaleBase</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> The symmetrical logarithmic scale is logarithmic in both the</span>
<span class="sd"> positive and negative directions from the origin.</span>
<span class="sd"> Since the values close to zero tend toward infinity, there is a</span>
<span class="sd"> need to have a range around zero that is linear. The parameter</span>
<span class="sd"> *linthresh* allows the user to specify the size of this range</span>
<span class="sd"> (-*linthresh*, *linthresh*).</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> basex, basey : float</span>
<span class="sd"> The base of the logarithm. Defaults to 10.</span>
<span class="sd"> linthreshx, linthreshy : float</span>
<span class="sd"> Defines the range ``(-x, x)``, within which the plot is linear.</span>
<span class="sd"> This avoids having the plot go to infinity around zero. Defaults to 2.</span>
<span class="sd"> subsx, subsy : sequence of int</span>
<span class="sd"> Where to place the subticks between each major tick.</span>
<span class="sd"> For example, in a log10 scale: ``[2, 3, 4, 5, 6, 7, 8, 9]`` will place</span>
<span class="sd"> 8 logarithmically spaced minor ticks between each major tick.</span>
<span class="sd"> linscalex, linscaley : float, optional</span>
<span class="sd"> This allows the linear range ``(-linthresh, linthresh)`` to be</span>
<span class="sd"> stretched relative to the logarithmic range. Its value is the number of</span>
<span class="sd"> decades to use for each half of the linear range. For example, when</span>
<span class="sd"> *linscale* == 1.0 (the default), the space used for the positive and</span>
<span class="sd"> negative halves of the linear range will be equal to one decade in</span>
<span class="sd"> the logarithmic range.</span>
<span class="sd"> """</span>
<span class="n">name</span> <span class="o">=</span> <span class="s1">'symlog'</span>
<span class="c1"># compatibility shim</span>
<span class="n">SymmetricalLogTransform</span> <span class="o">=</span> <span class="n">SymmetricalLogTransform</span>
<span class="n">InvertedSymmetricalLogTransform</span> <span class="o">=</span> <span class="n">InvertedSymmetricalLogTransform</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">axis</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="k">if</span> <span class="n">axis</span><span class="o">.</span><span class="n">axis_name</span> <span class="o">==</span> <span class="s1">'x'</span><span class="p">:</span>
<span class="n">base</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'basex'</span><span class="p">,</span> <span class="mf">10.0</span><span class="p">)</span>
<span class="n">linthresh</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'linthreshx'</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">)</span>
<span class="n">subs</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'subsx'</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
<span class="n">linscale</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'linscalex'</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">base</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'basey'</span><span class="p">,</span> <span class="mf">10.0</span><span class="p">)</span>
<span class="n">linthresh</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'linthreshy'</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">)</span>
<span class="n">subs</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'subsy'</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
<span class="n">linscale</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'linscaley'</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)</span>
<span class="k">if</span> <span class="n">kwargs</span><span class="p">:</span>
<span class="n">warn_deprecated</span><span class="p">(</span>
<span class="s1">'3.2.0'</span><span class="p">,</span>
<span class="n">message</span><span class="o">=</span><span class="p">(</span>
<span class="sa">f</span><span class="s2">"SymmetricalLogScale got an unexpected keyword "</span>
<span class="sa">f</span><span class="s2">"argument {next(iter(kwargs))!r}. "</span>
<span class="s1">'In the future this will raise TypeError'</span><span class="p">)</span>
<span class="p">)</span>
<span class="c1"># raise TypeError(f"SymmetricalLogScale got an unexpected keyword "</span>
<span class="c1"># f"argument {next(iter(kwargs))!r}")</span>
<span class="k">if</span> <span class="n">base</span> <span class="o"><=</span> <span class="mf">1.0</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"'basex/basey' must be larger than 1"</span><span class="p">)</span>
<span class="k">if</span> <span class="n">linthresh</span> <span class="o"><=</span> <span class="mf">0.0</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"'linthreshx/linthreshy' must be positive"</span><span class="p">)</span>
<span class="k">if</span> <span class="n">linscale</span> <span class="o"><=</span> <span class="mf">0.0</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"'linscalex/linthreshy' must be positive"</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_transform</span> <span class="o">=</span> <span class="n">SymmetricalLogTransform</span><span class="p">(</span><span class="n">base</span><span class="p">,</span> <span class="n">linthresh</span><span class="p">,</span> <span class="n">linscale</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">base</span> <span class="o">=</span> <span class="n">base</span>
<span class="bp">self</span><span class="o">.</span><span class="n">linthresh</span> <span class="o">=</span> <span class="n">linthresh</span>
<span class="bp">self</span><span class="o">.</span><span class="n">linscale</span> <span class="o">=</span> <span class="n">linscale</span>
<span class="bp">self</span><span class="o">.</span><span class="n">subs</span> <span class="o">=</span> <span class="n">subs</span>
<div class="viewcode-block" id="SymmetricalLogScale.set_default_locators_and_formatters"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.SymmetricalLogScale.set_default_locators_and_formatters">[docs]</a> <span class="k">def</span> <span class="nf">set_default_locators_and_formatters</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">axis</span><span class="p">):</span>
<span class="c1"># docstring inherited</span>
<span class="n">axis</span><span class="o">.</span><span class="n">set_major_locator</span><span class="p">(</span><span class="n">SymmetricalLogLocator</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_transform</span><span class="p">()))</span>
<span class="n">axis</span><span class="o">.</span><span class="n">set_major_formatter</span><span class="p">(</span><span class="n">LogFormatterSciNotation</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">base</span><span class="p">))</span>
<span class="n">axis</span><span class="o">.</span><span class="n">set_minor_locator</span><span class="p">(</span><span class="n">SymmetricalLogLocator</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_transform</span><span class="p">(),</span>
<span class="bp">self</span><span class="o">.</span><span class="n">subs</span><span class="p">))</span>
<span class="n">axis</span><span class="o">.</span><span class="n">set_minor_formatter</span><span class="p">(</span><span class="n">NullFormatter</span><span class="p">())</span></div>
<div class="viewcode-block" id="SymmetricalLogScale.get_transform"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.SymmetricalLogScale.get_transform">[docs]</a> <span class="k">def</span> <span class="nf">get_transform</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""Return the `.SymmetricalLogTransform` associated with this scale."""</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_transform</span></div></div>
<div class="viewcode-block" id="LogitTransform"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.LogitTransform">[docs]</a><span class="k">class</span> <span class="nc">LogitTransform</span><span class="p">(</span><span class="n">Transform</span><span class="p">):</span>
<span class="n">input_dims</span> <span class="o">=</span> <span class="n">output_dims</span> <span class="o">=</span> <span class="mi">1</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">nonpos</span><span class="o">=</span><span class="s1">'mask'</span><span class="p">):</span>
<span class="n">Transform</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
<span class="n">cbook</span><span class="o">.</span><span class="n">_check_in_list</span><span class="p">([</span><span class="s1">'mask'</span><span class="p">,</span> <span class="s1">'clip'</span><span class="p">],</span> <span class="n">nonpos</span><span class="o">=</span><span class="n">nonpos</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_nonpos</span> <span class="o">=</span> <span class="n">nonpos</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_clip</span> <span class="o">=</span> <span class="p">{</span><span class="s2">"clip"</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span> <span class="s2">"mask"</span><span class="p">:</span> <span class="kc">False</span><span class="p">}[</span><span class="n">nonpos</span><span class="p">]</span>
<div class="viewcode-block" id="LogitTransform.transform_non_affine"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.LogitTransform.transform_non_affine">[docs]</a> <span class="k">def</span> <span class="nf">transform_non_affine</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">a</span><span class="p">):</span>
<span class="sd">"""logit transform (base 10), masked or clipped"""</span>
<span class="k">with</span> <span class="n">np</span><span class="o">.</span><span class="n">errstate</span><span class="p">(</span><span class="n">divide</span><span class="o">=</span><span class="s2">"ignore"</span><span class="p">,</span> <span class="n">invalid</span><span class="o">=</span><span class="s2">"ignore"</span><span class="p">):</span>
<span class="n">out</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">log10</span><span class="p">(</span><span class="n">a</span> <span class="o">/</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">a</span><span class="p">))</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_clip</span><span class="p">:</span> <span class="c1"># See LogTransform for choice of clip value.</span>
<span class="n">out</span><span class="p">[</span><span class="n">a</span> <span class="o"><=</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1000</span>
<span class="n">out</span><span class="p">[</span><span class="mi">1</span> <span class="o"><=</span> <span class="n">a</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1000</span>
<span class="k">return</span> <span class="n">out</span></div>
<div class="viewcode-block" id="LogitTransform.inverted"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.LogitTransform.inverted">[docs]</a> <span class="k">def</span> <span class="nf">inverted</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="n">LogisticTransform</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_nonpos</span><span class="p">)</span></div>
<span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="s2">"</span><span class="si">{}</span><span class="s2">(</span><span class="si">{!r}</span><span class="s2">)"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_nonpos</span><span class="p">)</span></div>
<div class="viewcode-block" id="LogisticTransform"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.LogisticTransform">[docs]</a><span class="k">class</span> <span class="nc">LogisticTransform</span><span class="p">(</span><span class="n">Transform</span><span class="p">):</span>
<span class="n">input_dims</span> <span class="o">=</span> <span class="n">output_dims</span> <span class="o">=</span> <span class="mi">1</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">nonpos</span><span class="o">=</span><span class="s1">'mask'</span><span class="p">):</span>
<span class="n">Transform</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_nonpos</span> <span class="o">=</span> <span class="n">nonpos</span>
<div class="viewcode-block" id="LogisticTransform.transform_non_affine"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.LogisticTransform.transform_non_affine">[docs]</a> <span class="k">def</span> <span class="nf">transform_non_affine</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">a</span><span class="p">):</span>
<span class="sd">"""logistic transform (base 10)"""</span>
<span class="k">return</span> <span class="mf">1.0</span> <span class="o">/</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="mi">10</span><span class="o">**</span><span class="p">(</span><span class="o">-</span><span class="n">a</span><span class="p">))</span></div>
<div class="viewcode-block" id="LogisticTransform.inverted"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.LogisticTransform.inverted">[docs]</a> <span class="k">def</span> <span class="nf">inverted</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="n">LogitTransform</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_nonpos</span><span class="p">)</span></div>
<span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="s2">"</span><span class="si">{}</span><span class="s2">(</span><span class="si">{!r}</span><span class="s2">)"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_nonpos</span><span class="p">)</span></div>
<div class="viewcode-block" id="LogitScale"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.LogitScale">[docs]</a><span class="k">class</span> <span class="nc">LogitScale</span><span class="p">(</span><span class="n">ScaleBase</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Logit scale for data between zero and one, both excluded.</span>
<span class="sd"> This scale is similar to a log scale close to zero and to one, and almost</span>
<span class="sd"> linear around 0.5. It maps the interval ]0, 1[ onto ]-infty, +infty[.</span>
<span class="sd"> """</span>
<span class="n">name</span> <span class="o">=</span> <span class="s1">'logit'</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
<span class="bp">self</span><span class="p">,</span>
<span class="n">axis</span><span class="p">,</span>
<span class="n">nonpos</span><span class="o">=</span><span class="s1">'mask'</span><span class="p">,</span>
<span class="o">*</span><span class="p">,</span>
<span class="n">one_half</span><span class="o">=</span><span class="sa">r</span><span class="s2">"\frac</span><span class="si">{1}{2}</span><span class="s2">"</span><span class="p">,</span>
<span class="n">use_overline</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="p">):</span>
<span class="sa">r</span><span class="sd">"""</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> axis : `matplotlib.axis.Axis`</span>
<span class="sd"> Currently unused.</span>
<span class="sd"> nonpos : {'mask', 'clip'}</span>
<span class="sd"> Determines the behavior for values beyond the open interval ]0, 1[.</span>
<span class="sd"> They can either be masked as invalid, or clipped to a number very</span>
<span class="sd"> close to 0 or 1.</span>
<span class="sd"> use_overline : bool, default: False</span>
<span class="sd"> Indicate the usage of survival notation (\overline{x}) in place of</span>
<span class="sd"> standard notation (1-x) for probability close to one.</span>
<span class="sd"> one_half : str, default: r"\frac{1}{2}"</span>
<span class="sd"> The string used for ticks formatter to represent 1/2.</span>
<span class="sd"> """</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_transform</span> <span class="o">=</span> <span class="n">LogitTransform</span><span class="p">(</span><span class="n">nonpos</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_use_overline</span> <span class="o">=</span> <span class="n">use_overline</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_one_half</span> <span class="o">=</span> <span class="n">one_half</span>
<div class="viewcode-block" id="LogitScale.get_transform"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.LogitScale.get_transform">[docs]</a> <span class="k">def</span> <span class="nf">get_transform</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""Return the `.LogitTransform` associated with this scale."""</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_transform</span></div>
<div class="viewcode-block" id="LogitScale.set_default_locators_and_formatters"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.LogitScale.set_default_locators_and_formatters">[docs]</a> <span class="k">def</span> <span class="nf">set_default_locators_and_formatters</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">axis</span><span class="p">):</span>
<span class="c1"># docstring inherited</span>
<span class="c1"># ..., 0.01, 0.1, 0.5, 0.9, 0.99, ...</span>
<span class="n">axis</span><span class="o">.</span><span class="n">set_major_locator</span><span class="p">(</span><span class="n">LogitLocator</span><span class="p">())</span>
<span class="n">axis</span><span class="o">.</span><span class="n">set_major_formatter</span><span class="p">(</span>
<span class="n">LogitFormatter</span><span class="p">(</span>
<span class="n">one_half</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_one_half</span><span class="p">,</span>
<span class="n">use_overline</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_use_overline</span>
<span class="p">)</span>
<span class="p">)</span>
<span class="n">axis</span><span class="o">.</span><span class="n">set_minor_locator</span><span class="p">(</span><span class="n">LogitLocator</span><span class="p">(</span><span class="n">minor</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
<span class="n">axis</span><span class="o">.</span><span class="n">set_minor_formatter</span><span class="p">(</span>
<span class="n">LogitFormatter</span><span class="p">(</span>
<span class="n">minor</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">one_half</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_one_half</span><span class="p">,</span>
<span class="n">use_overline</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_use_overline</span>
<span class="p">)</span>
<span class="p">)</span></div>
<div class="viewcode-block" id="LogitScale.limit_range_for_scale"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.LogitScale.limit_range_for_scale">[docs]</a> <span class="k">def</span> <span class="nf">limit_range_for_scale</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">vmin</span><span class="p">,</span> <span class="n">vmax</span><span class="p">,</span> <span class="n">minpos</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Limit the domain to values between 0 and 1 (excluded).</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">isfinite</span><span class="p">(</span><span class="n">minpos</span><span class="p">):</span>
<span class="n">minpos</span> <span class="o">=</span> <span class="mf">1e-7</span> <span class="c1"># Should rarely (if ever) have a visible effect.</span>
<span class="k">return</span> <span class="p">(</span><span class="n">minpos</span> <span class="k">if</span> <span class="n">vmin</span> <span class="o"><=</span> <span class="mi">0</span> <span class="k">else</span> <span class="n">vmin</span><span class="p">,</span>
<span class="mi">1</span> <span class="o">-</span> <span class="n">minpos</span> <span class="k">if</span> <span class="n">vmax</span> <span class="o">>=</span> <span class="mi">1</span> <span class="k">else</span> <span class="n">vmax</span><span class="p">)</span></div></div>
<span class="n">_scale_mapping</span> <span class="o">=</span> <span class="p">{</span>
<span class="s1">'linear'</span><span class="p">:</span> <span class="n">LinearScale</span><span class="p">,</span>
<span class="s1">'log'</span><span class="p">:</span> <span class="n">LogScale</span><span class="p">,</span>
<span class="s1">'symlog'</span><span class="p">:</span> <span class="n">SymmetricalLogScale</span><span class="p">,</span>
<span class="s1">'logit'</span><span class="p">:</span> <span class="n">LogitScale</span><span class="p">,</span>
<span class="s1">'function'</span><span class="p">:</span> <span class="n">FuncScale</span><span class="p">,</span>
<span class="s1">'functionlog'</span><span class="p">:</span> <span class="n">FuncScaleLog</span><span class="p">,</span>
<span class="p">}</span>
<div class="viewcode-block" id="get_scale_names"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.get_scale_names">[docs]</a><span class="k">def</span> <span class="nf">get_scale_names</span><span class="p">():</span>
<span class="sd">"""Return the names of the available scales."""</span>
<span class="k">return</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">_scale_mapping</span><span class="p">)</span></div>
<div class="viewcode-block" id="scale_factory"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.scale_factory">[docs]</a><span class="k">def</span> <span class="nf">scale_factory</span><span class="p">(</span><span class="n">scale</span><span class="p">,</span> <span class="n">axis</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Return a scale class by name.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> scale : {%(names)s}</span>
<span class="sd"> axis : `matplotlib.axis.Axis`</span>
<span class="sd"> """</span>
<span class="n">scale</span> <span class="o">=</span> <span class="n">scale</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span>
<span class="n">cbook</span><span class="o">.</span><span class="n">_check_in_list</span><span class="p">(</span><span class="n">_scale_mapping</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="n">scale</span><span class="p">)</span>
<span class="k">return</span> <span class="n">_scale_mapping</span><span class="p">[</span><span class="n">scale</span><span class="p">](</span><span class="n">axis</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
<span class="k">if</span> <span class="n">scale_factory</span><span class="o">.</span><span class="vm">__doc__</span><span class="p">:</span>
<span class="n">scale_factory</span><span class="o">.</span><span class="vm">__doc__</span> <span class="o">=</span> <span class="n">scale_factory</span><span class="o">.</span><span class="vm">__doc__</span> <span class="o">%</span> <span class="p">{</span>
<span class="s2">"names"</span><span class="p">:</span> <span class="s2">", "</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="nb">repr</span><span class="p">,</span> <span class="n">get_scale_names</span><span class="p">()))}</span>
<div class="viewcode-block" id="register_scale"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.register_scale">[docs]</a><span class="k">def</span> <span class="nf">register_scale</span><span class="p">(</span><span class="n">scale_class</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Register a new kind of scale.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> scale_class : subclass of `ScaleBase`</span>
<span class="sd"> The scale to register.</span>
<span class="sd"> """</span>
<span class="n">_scale_mapping</span><span class="p">[</span><span class="n">scale_class</span><span class="o">.</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">scale_class</span></div>
<div class="viewcode-block" id="get_scale_docs"><a class="viewcode-back" href="../../api/scale_api.html#matplotlib.scale.get_scale_docs">[docs]</a><span class="nd">@cbook</span><span class="o">.</span><span class="n">deprecated</span><span class="p">(</span>
<span class="s1">'3.1'</span><span class="p">,</span> <span class="n">message</span><span class="o">=</span><span class="s1">'get_scale_docs() is considered private API since '</span>
<span class="s1">'3.1 and will be removed from the public API in 3.3.'</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">get_scale_docs</span><span class="p">():</span>
<span class="sd">"""</span>
<span class="sd"> Helper function for generating docstrings related to scales.</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="n">_get_scale_docs</span><span class="p">()</span></div>
<span class="k">def</span> <span class="nf">_get_scale_docs</span><span class="p">():</span>
<span class="sd">"""</span>
<span class="sd"> Helper function for generating docstrings related to scales.</span>
<span class="sd"> """</span>
<span class="n">docs</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">scale_class</span> <span class="ow">in</span> <span class="n">_scale_mapping</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="n">docs</span><span class="o">.</span><span class="n">extend</span><span class="p">([</span>
<span class="sa">f</span><span class="s2">" </span><span class="si">{name!r}</span><span class="s2">"</span><span class="p">,</span>
<span class="s2">""</span><span class="p">,</span>
<span class="n">textwrap</span><span class="o">.</span><span class="n">indent</span><span class="p">(</span><span class="n">inspect</span><span class="o">.</span><span class="n">getdoc</span><span class="p">(</span><span class="n">scale_class</span><span class="o">.</span><span class="fm">__init__</span><span class="p">),</span> <span class="s2">" "</span> <span class="o">*</span> <span class="mi">8</span><span class="p">),</span>
<span class="s2">""</span>
<span class="p">])</span>
<span class="k">return</span> <span class="s2">"</span><span class="se">\n</span><span class="s2">"</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">docs</span><span class="p">)</span>
<span class="n">docstring</span><span class="o">.</span><span class="n">interpd</span><span class="o">.</span><span class="n">update</span><span class="p">(</span>
<span class="n">scale_type</span><span class="o">=</span><span class="s1">'{</span><span class="si">%s</span><span class="s1">}'</span> <span class="o">%</span> <span class="s1">', '</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="nb">repr</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">get_scale_names</span><span class="p">()]),</span>
<span class="n">scale_docs</span><span class="o">=</span><span class="n">_get_scale_docs</span><span class="p">()</span><span class="o">.</span><span class="n">rstrip</span><span class="p">(),</span>
<span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="clearer"></div>
</div>
<footer>
<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 Mar 04, 2020.
Created using
<a href="http://sphinx-doc.org/">Sphinx</a> 2.4.3.
Doc version v3.2.0-3-gea62ccde8.
</div>
</footer>
</body>
</html>
You can’t perform that action at this time.