-
-
Notifications
You must be signed in to change notification settings - Fork 8.4k
Expand file tree
/
Copy pathscale.py
More file actions
1001 lines (808 loc) · 34.7 KB
/
Copy pathscale.py
File metadata and controls
1001 lines (808 loc) · 34.7 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
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
"""
Scales define the distribution of data values on an axis, e.g. a log scaling.
The mapping is implemented through `.Transform` subclasses.
The following scales are built-in:
.. _builtin_scales:
============= ===================== ================================ =================================
Name Class Transform Inverted transform
============= ===================== ================================ =================================
"asinh" `AsinhScale` `AsinhTransform` `InvertedAsinhTransform`
"function" `FuncScale` `FuncTransform` `FuncTransform`
"functionlog" `FuncScaleLog` `FuncTransform` + `LogTransform` `InvertedLogTransform` + `FuncTransform`
"linear" `LinearScale` `.IdentityTransform` `.IdentityTransform`
"log" `LogScale` `LogTransform` `InvertedLogTransform`
"logit" `LogitScale` `LogitTransform` `LogisticTransform`
"symlog" `SymmetricalLogScale` `SymmetricalLogTransform` `InvertedSymmetricalLogTransform`
============= ===================== ================================ =================================
A user will often only use the scale name, e.g. when setting the scale through
`~.Axes.set_xscale`: ``ax.set_xscale("log")``.
See also the :ref:`scales examples <sphx_glr_gallery_scales>` in the documentation.
Custom scaling can be achieved through `FuncScale`, or by creating your own
`ScaleBase` subclass and corresponding transforms (see :doc:`/gallery/scales/custom_scale`).
Third parties can register their scales by name through `register_scale`.
""" # noqa: E501
import inspect
import textwrap
from functools import wraps
import numpy as np
import matplotlib as mpl
from matplotlib import _api, _docstring
from matplotlib.ticker import (
NullFormatter, ScalarFormatter, LogFormatterSciNotation, LogitFormatter,
NullLocator, LogLocator, AutoLocator, AutoMinorLocator,
SymmetricalLogLocator, AsinhLocator, LogitLocator)
from matplotlib.transforms import Transform, IdentityTransform
class ScaleBase:
"""
The base class for all scales.
Scales are separable transformations, working on a single dimension.
Subclasses should override
:attr:`!name`
The scale's name.
:meth:`get_transform`
A method returning a `.Transform`, which converts data coordinates to
scaled coordinates. This transform should be invertible, so that e.g.
mouse positions can be converted back to data coordinates.
:meth:`set_default_locators_and_formatters`
A method that sets default locators and formatters for an `~.axis.Axis`
that uses this scale.
:meth:`limit_range_for_scale`
An optional method that "fixes" the axis range to acceptable values,
e.g. restricting log-scaled axes to positive values.
"""
def __init__(self, axis):
r"""
Construct a new scale.
Notes
-----
The following note is for scale implementers.
For back-compatibility reasons, scales take an `~matplotlib.axis.Axis`
object as the first argument.
.. deprecated:: 3.11
The *axis* parameter is now optional, i.e. matplotlib is compatible
with `.ScaleBase` subclasses that do not take an *axis* parameter.
The *axis* parameter is pending-deprecated. It will be deprecated
in matplotlib 3.13, and removed in matplotlib 3.15.
3rd-party scales are recommended to remove the *axis* parameter now
if they can afford to restrict compatibility to matplotlib >= 3.11
already. Otherwise, they may keep the *axis* parameter and remove it
in time for matplotlib 3.13.
"""
def get_transform(self):
"""
Return the `.Transform` object associated with this scale.
"""
raise NotImplementedError()
def set_default_locators_and_formatters(self, axis):
"""
Set the locators and formatters of *axis* to instances suitable for
this scale.
"""
raise NotImplementedError()
def limit_range_for_scale(self, vmin, vmax, minpos):
"""
Return the range *vmin*, *vmax*, restricted to the
domain supported by this scale (if any).
*minpos* should be the minimum positive value in the data.
This is used by log scales to determine a minimum value.
"""
return vmin, vmax
def val_in_range(self, val):
"""
Return whether the value(s) are within the valid range for this scale.
Accepts a scalar or array-like ``val``. For a scalar, returns a
Python ``bool``. For an array, returns a bool ndarray of the same
shape. This is a generic implementation, and subclasses may implement
more efficient solutions for their domain.
"""
arr = np.asarray(val)
with np.errstate(invalid='ignore'):
try:
vmin, vmax = self.limit_range_for_scale(arr, arr, minpos=1e-300)
except (TypeError, ValueError):
result = np.zeros(arr.shape, dtype=bool)
else:
result = np.isfinite(arr) & (vmin == arr) & (vmax == arr)
return bool(result) if arr.ndim == 0 else result
def _make_axis_parameter_optional(init_func):
"""
Decorator to allow leaving out the *axis* parameter in scale constructors.
This decorator ensures backward compatibility for scale classes that
previously required an *axis* parameter. It allows constructors to be
called with or without the *axis* parameter.
For simplicity, this does not handle the case when *axis*
is passed as a keyword. However,
scanning GitHub, there's no evidence that that is used anywhere.
Parameters
----------
init_func : callable
The original __init__ method of a scale class.
Returns
-------
callable
A wrapped version of *init_func* that handles the optional *axis*.
Notes
-----
If the wrapped constructor defines *axis* as its first argument, the
parameter is preserved when present. Otherwise, the value `None` is injected
as the first argument.
Examples
--------
>>> from matplotlib.scale import ScaleBase
>>> class CustomScale(ScaleBase):
... @_make_axis_parameter_optional
... def __init__(self, axis, custom_param=1):
... self.custom_param = custom_param
"""
@wraps(init_func)
def wrapper(self, *args, **kwargs):
sig = inspect.signature(init_func)
try:
# Try old signature.
sig.bind(self, *args, **kwargs)
except TypeError:
# Use the new signature and pass in an unused axis=None.
init_func(self, None, *args, **kwargs)
else:
# Use the old signature.
init_func(self, *args, **kwargs)
return wrapper
class LinearScale(ScaleBase):
"""
The default linear scale.
"""
name = 'linear'
@_make_axis_parameter_optional
def __init__(self, axis):
# This method is present only to prevent inheritance of the base class'
# constructor docstring, which would otherwise end up interpolated into
# the docstring of Axis.set_scale.
"""
""" # noqa: D419
def set_default_locators_and_formatters(self, axis):
# docstring inherited
axis.set_major_locator(AutoLocator())
axis.set_major_formatter(ScalarFormatter())
axis.set_minor_formatter(NullFormatter())
# update the minor locator for x and y axis based on rcParams
if (axis.axis_name == 'x' and mpl.rcParams['xtick.minor.visible'] or
axis.axis_name == 'y' and mpl.rcParams['ytick.minor.visible']):
axis.set_minor_locator(AutoMinorLocator())
else:
axis.set_minor_locator(NullLocator())
def get_transform(self):
"""
Return the transform for linear scaling, which is just the
`~matplotlib.transforms.IdentityTransform`.
"""
return IdentityTransform()
def val_in_range(self, val):
"""
Return whether the value(s) are within the valid range for this scale.
This is True for all values, except +-inf and NaN.
"""
arr = np.asarray(val)
result = np.isfinite(arr)
return bool(result) if arr.ndim == 0 else result
class FuncTransform(Transform):
"""
A simple transform that takes and arbitrary function for the
forward and inverse transform.
"""
input_dims = output_dims = 1
def __init__(self, forward, inverse):
"""
Parameters
----------
forward : callable
The forward function for the transform. This function must have
an inverse and, for best behavior, be monotonic.
It must have the signature::
def forward(values: array-like) -> array-like
inverse : callable
The inverse of the forward function. Signature as ``forward``.
"""
super().__init__()
if callable(forward) and callable(inverse):
self._forward = forward
self._inverse = inverse
else:
raise ValueError('arguments to FuncTransform must be functions')
def transform_non_affine(self, values):
return self._forward(values)
def inverted(self):
return FuncTransform(self._inverse, self._forward)
class FuncScale(ScaleBase):
"""
Provide an arbitrary scale with user-supplied function for the axis.
"""
name = 'function'
@_make_axis_parameter_optional
def __init__(self, axis, functions):
"""
Parameters
----------
axis : `~matplotlib.axis.Axis`
The axis for the scale.
.. note::
This parameter is unused and will be removed in an imminent release.
It can already be left out because of special preprocessing,
so that ``FuncScale(functions)`` is valid.
functions : (callable, callable)
two-tuple of the forward and inverse functions for the scale.
The forward function must be monotonic.
Both functions must have the signature::
def forward(values: array-like) -> array-like
"""
forward, inverse = functions
transform = FuncTransform(forward, inverse)
self._transform = transform
def get_transform(self):
"""Return the `.FuncTransform` associated with this scale."""
return self._transform
def set_default_locators_and_formatters(self, axis):
# docstring inherited
axis.set_major_locator(AutoLocator())
axis.set_major_formatter(ScalarFormatter())
axis.set_minor_formatter(NullFormatter())
# update the minor locator for x and y axis based on rcParams
if (axis.axis_name == 'x' and mpl.rcParams['xtick.minor.visible'] or
axis.axis_name == 'y' and mpl.rcParams['ytick.minor.visible']):
axis.set_minor_locator(AutoMinorLocator())
else:
axis.set_minor_locator(NullLocator())
class LogTransform(Transform):
input_dims = output_dims = 1
def __init__(self, base, nonpositive='clip'):
super().__init__()
if base <= 0 or base == 1:
raise ValueError('The log base cannot be <= 0 or == 1')
self.base = base
self._clip = _api.getitem_checked(
{"clip": True, "mask": False}, nonpositive=nonpositive)
self._log_funcs = {np.e: np.log, 2: np.log2, 10: np.log10}
def __str__(self):
return "{}(base={}, nonpositive={!r})".format(
type(self).__name__, self.base, "clip" if self._clip else "mask")
def transform_non_affine(self, values):
# Ignore invalid values due to nans being passed to the transform.
with np.errstate(divide="ignore", invalid="ignore"):
log_func = self._log_funcs.get(self.base)
if log_func:
out = log_func(values)
else:
out = np.log(values) / np.log(self.base)
if self._clip:
# SVG spec says that conforming viewers must support values up
# to 3.4e38 (C float); however experiments suggest that
# Inkscape (which uses cairo for rendering) runs into cairo's
# 24-bit limit (which is apparently shared by Agg).
# Ghostscript (used for pdf rendering appears to overflow even
# earlier, with the max value around 2 ** 15 for the tests to
# pass. On the other hand, in practice, we want to clip beyond
# np.log10(np.nextafter(0, 1)) ~ -323
# so 1000 seems safe.
out[values <= 0] = -1000
return out
def inverted(self):
return InvertedLogTransform(self.base)
class InvertedLogTransform(Transform):
input_dims = output_dims = 1
def __init__(self, base):
super().__init__()
self.base = base
self._exp_funcs = {np.e: np.exp, 2: np.exp2}
def __str__(self):
return f"{type(self).__name__}(base={self.base})"
def transform_non_affine(self, values):
exp_func = self._exp_funcs.get(self.base)
if exp_func:
return exp_func(values)
else:
return np.exp(values * np.log(self.base))
def inverted(self):
return LogTransform(self.base)
class LogScale(ScaleBase):
"""
A standard logarithmic scale. Care is taken to only plot positive values.
"""
name = 'log'
@_make_axis_parameter_optional
def __init__(self, axis=None, *, base=10, subs=None, nonpositive="clip"):
"""
Parameters
----------
axis : `~matplotlib.axis.Axis`
The axis for the scale.
.. note::
This parameter is unused and about to be removed in the future.
It can already now be left out because of special preprocessing,
so that ``LogScale(base=2)`` is valid.
base : float, default: 10
The base of the logarithm.
nonpositive : {'clip', 'mask'}, default: 'clip'
Determines the behavior for non-positive values. They can either
be masked as invalid, or clipped to a very small positive number.
subs : sequence of int, default: None
Where to place the subticks between each major tick. For example,
in a log10 scale, ``[2, 3, 4, 5, 6, 7, 8, 9]`` will place 8
logarithmically spaced minor ticks between each major tick.
"""
self._transform = LogTransform(base, nonpositive)
self.subs = subs
base = property(lambda self: self._transform.base)
def set_default_locators_and_formatters(self, axis):
# docstring inherited
axis.set_major_locator(LogLocator(self.base))
axis.set_major_formatter(LogFormatterSciNotation(self.base))
axis.set_minor_locator(LogLocator(self.base, self.subs))
axis.set_minor_formatter(
LogFormatterSciNotation(self.base,
labelOnlyBase=(self.subs is not None)))
def get_transform(self):
"""Return the `.LogTransform` associated with this scale."""
return self._transform
def limit_range_for_scale(self, vmin, vmax, minpos):
"""Limit the domain to positive values."""
if not np.isfinite(minpos):
minpos = 1e-300 # Should rarely (if ever) have a visible effect.
return (minpos if vmin <= 0 else vmin,
minpos if vmax <= 0 else vmax)
def val_in_range(self, val):
"""
Return whether the value(s) are within the valid range for this scale.
This is True for value(s) > 0 except +inf and NaN.
"""
arr = np.asarray(val)
with np.errstate(invalid='ignore'):
result = np.isfinite(arr) & (arr > 0)
return bool(result) if arr.ndim == 0 else result
class FuncScaleLog(LogScale):
"""
Provide an arbitrary scale with user-supplied function for the axis and
then put on a logarithmic axes.
"""
name = 'functionlog'
@_make_axis_parameter_optional
def __init__(self, axis, functions, base=10):
"""
Parameters
----------
axis : `~matplotlib.axis.Axis`
The axis for the scale.
.. note::
This parameter is unused and about to be removed in the future.
It can already now be left out because of special preprocessing,
so that ``FuncScaleLog(functions=(forward, inverse))`` is valid.
functions : (callable, callable)
two-tuple of the forward and inverse functions for the scale.
The forward function must be monotonic.
Both functions must have the signature::
def forward(values: array-like) -> array-like
base : float, default: 10
Logarithmic base of the scale.
"""
forward, inverse = functions
self.subs = None
self._transform = FuncTransform(forward, inverse) + LogTransform(base)
@property
def base(self):
return self._transform._b.base # Base of the LogTransform.
def get_transform(self):
"""Return the `.Transform` associated with this scale."""
return self._transform
class SymmetricalLogTransform(Transform):
input_dims = output_dims = 1
def __init__(self, base, linthresh, linscale):
super().__init__()
if base <= 1.0:
raise ValueError("'base' must be larger than 1")
if linthresh <= 0.0:
raise ValueError("'linthresh' must be positive")
if linscale <= 0.0:
raise ValueError("'linscale' must be positive")
self.base = base
self.linthresh = linthresh
self.linscale = linscale
def transform_non_affine(self, values):
linscale_adj = self.linscale / (1.0 - 1.0 / self.base)
log_base = np.log(self.base)
abs_a = np.abs(values)
inside = abs_a <= self.linthresh
if np.all(inside): # Fast path: all values in linear region
return values * linscale_adj
with np.errstate(divide="ignore", invalid="ignore"):
out = np.sign(values) * self.linthresh * (
linscale_adj - np.log(self.linthresh) / log_base +
np.log(abs_a) / log_base)
out[inside] = values[inside] * linscale_adj
return out
def inverted(self):
return InvertedSymmetricalLogTransform(self.base, self.linthresh,
self.linscale)
class InvertedSymmetricalLogTransform(Transform):
input_dims = output_dims = 1
def __init__(self, base, linthresh, linscale):
super().__init__()
if base <= 1.0:
raise ValueError("'base' must be larger than 1")
if linthresh <= 0.0:
raise ValueError("'linthresh' must be positive")
if linscale <= 0.0:
raise ValueError("'linscale' must be positive")
self.base = base
self.linthresh = linthresh
self.linscale = linscale
@_api.deprecated("3.11", name="invlinthresh", obj_type="attribute",
alternative=".inverted().transform(linthresh)")
@property
def invlinthresh(self):
invlinthresh = self.inverted().transform(self.linthresh)
return invlinthresh
def transform_non_affine(self, values):
linscale_adj = self.linscale / (1.0 - 1.0 / self.base)
invlinthresh = self.inverted().transform(self.linthresh)
abs_a = np.abs(values)
inside = abs_a <= invlinthresh
if np.all(inside): # Fast path: all values in linear region
return values / linscale_adj
with np.errstate(divide="ignore", invalid="ignore"):
out = np.sign(values) * self.linthresh * np.exp(
(abs_a / self.linthresh - linscale_adj) * np.log(self.base))
out[inside] = values[inside] / linscale_adj
return out
def inverted(self):
return SymmetricalLogTransform(self.base,
self.linthresh, self.linscale)
class SymmetricalLogScale(ScaleBase):
"""
The symmetrical logarithmic scale is logarithmic in both the
positive and negative directions from the origin.
Since the values close to zero tend toward infinity, there is a
need to have a range around zero that is linear. The parameter
*linthresh* allows the user to specify the size of this range
(-*linthresh*, *linthresh*).
See :doc:`/gallery/scales/symlog_demo` for a detailed description.
Parameters
----------
axis : `~matplotlib.axis.Axis`
The axis for the scale.
.. note::
This parameter is unused and about to be removed in the future.
It can already now be left out because of special preprocessing,
so that ``SymmetricalLocSacle(base=2)`` is valid.
base : float, default: 10
The base of the logarithm.
linthresh : float, default: 2
Defines the range ``(-x, x)``, within which the plot is linear.
This avoids having the plot go to infinity around zero.
subs : sequence of int
Where to place the subticks between each major tick.
For example, in a log10 scale: ``[2, 3, 4, 5, 6, 7, 8, 9]`` will place
8 logarithmically spaced minor ticks between each major tick.
linscale : float, optional
This allows the linear range ``(-linthresh, linthresh)`` to be
stretched relative to the logarithmic range. Its value is the number of
decades to use for each half of the linear range. For example, when
*linscale* == 1.0 (the default), the space used for the positive and
negative halves of the linear range will be equal to one decade in
the logarithmic range.
"""
name = 'symlog'
@_make_axis_parameter_optional
def __init__(self, axis=None, *, base=10, linthresh=2, subs=None, linscale=1):
self._transform = SymmetricalLogTransform(base, linthresh, linscale)
self.subs = subs
base = property(lambda self: self._transform.base)
linthresh = property(lambda self: self._transform.linthresh)
linscale = property(lambda self: self._transform.linscale)
def set_default_locators_and_formatters(self, axis):
# docstring inherited
axis.set_major_locator(SymmetricalLogLocator(self.get_transform()))
axis.set_major_formatter(LogFormatterSciNotation(self.base))
axis.set_minor_locator(SymmetricalLogLocator(self.get_transform(),
self.subs))
axis.set_minor_formatter(NullFormatter())
def get_transform(self):
"""Return the `.SymmetricalLogTransform` associated with this scale."""
return self._transform
def val_in_range(self, val):
"""
Return whether the value(s) are within the valid range for this scale.
This is True for all values, except +-inf and NaN.
"""
arr = np.asarray(val)
result = np.isfinite(arr)
return bool(result) if arr.ndim == 0 else result
class AsinhTransform(Transform):
"""Inverse hyperbolic-sine transformation used by `.AsinhScale`"""
input_dims = output_dims = 1
def __init__(self, linear_width):
super().__init__()
if linear_width <= 0.0:
raise ValueError("Scale parameter 'linear_width' " +
"must be strictly positive")
self.linear_width = linear_width
def transform_non_affine(self, values):
return self.linear_width * np.arcsinh(values / self.linear_width)
def inverted(self):
return InvertedAsinhTransform(self.linear_width)
class InvertedAsinhTransform(Transform):
"""Hyperbolic sine transformation used by `.AsinhScale`"""
input_dims = output_dims = 1
def __init__(self, linear_width):
super().__init__()
self.linear_width = linear_width
def transform_non_affine(self, values):
return self.linear_width * np.sinh(values / self.linear_width)
def inverted(self):
return AsinhTransform(self.linear_width)
class AsinhScale(ScaleBase):
"""
A quasi-logarithmic scale based on the inverse hyperbolic sine (asinh)
For values close to zero, this is essentially a linear scale,
but for large magnitude values (either positive or negative)
it is asymptotically logarithmic. The transition between these
linear and logarithmic regimes is smooth, and has no discontinuities
in the function gradient in contrast to
the `.SymmetricalLogScale` ("symlog") scale.
Specifically, the transformation of an axis coordinate :math:`a` is
:math:`a \\rightarrow a_0 \\sinh^{-1} (a / a_0)` where :math:`a_0`
is the effective width of the linear region of the transformation.
In that region, the transformation is
:math:`a \\rightarrow a + \\mathcal{O}(a^3)`.
For large values of :math:`a` the transformation behaves as
:math:`a \\rightarrow a_0 \\, \\mathrm{sgn}(a) \\ln |a| + \\mathcal{O}(1)`.
.. note::
This API is provisional and may be revised in the future
based on early user feedback.
"""
name = 'asinh'
auto_tick_multipliers = {
3: (2, ),
4: (2, ),
5: (2, ),
8: (2, 4),
10: (2, 5),
16: (2, 4, 8),
64: (4, 16),
1024: (256, 512)
}
@_make_axis_parameter_optional
def __init__(self, axis=None, *, linear_width=1.0,
base=10, subs='auto', **kwargs):
"""
Parameters
----------
axis : `~matplotlib.axis.Axis`
The axis for the scale.
.. note::
This parameter is unused and about to be removed in the future.
It can already now be left out because of special preprocessing,
so that ``AsinhScale()`` is valid.
linear_width : float, default: 1
The scale parameter (elsewhere referred to as :math:`a_0`)
defining the extent of the quasi-linear region,
and the coordinate values beyond which the transformation
becomes asymptotically logarithmic.
base : int, default: 10
The number base used for rounding tick locations
on a logarithmic scale. If this is less than one,
then rounding is to the nearest integer multiple
of powers of ten.
subs : sequence of int
Multiples of the number base used for minor ticks.
If set to 'auto', this will use built-in defaults,
e.g. (2, 5) for base=10.
"""
super().__init__(axis)
self._transform = AsinhTransform(linear_width)
self._base = int(base)
if subs == 'auto':
self._subs = self.auto_tick_multipliers.get(self._base)
else:
self._subs = subs
linear_width = property(lambda self: self._transform.linear_width)
def get_transform(self):
return self._transform
def set_default_locators_and_formatters(self, axis):
axis.set(major_locator=AsinhLocator(self.linear_width,
base=self._base),
minor_locator=AsinhLocator(self.linear_width,
base=self._base,
subs=self._subs),
minor_formatter=NullFormatter())
if self._base > 1:
axis.set_major_formatter(LogFormatterSciNotation(self._base))
else:
axis.set_major_formatter('{x:.3g}')
def val_in_range(self, val):
"""
Return whether the value(s) are within the valid range for this scale.
This is True for all values, except +-inf and NaN.
"""
arr = np.asarray(val)
result = np.isfinite(arr)
return bool(result) if arr.ndim == 0 else result
class LogitTransform(Transform):
input_dims = output_dims = 1
def __init__(self, nonpositive='mask'):
super().__init__()
_api.check_in_list(['mask', 'clip'], nonpositive=nonpositive)
self._nonpositive = nonpositive
self._clip = {"clip": True, "mask": False}[nonpositive]
def transform_non_affine(self, values):
"""logit transform (base 10), masked or clipped"""
with np.errstate(divide="ignore", invalid="ignore"):
out = np.log10(values / (1 - values))
if self._clip: # See LogTransform for choice of clip value.
out[values <= 0] = -1000
out[1 <= values] = 1000
return out
def inverted(self):
return LogisticTransform(self._nonpositive)
def __str__(self):
return f"{type(self).__name__}({self._nonpositive!r})"
class LogisticTransform(Transform):
input_dims = output_dims = 1
def __init__(self, nonpositive='mask'):
super().__init__()
self._nonpositive = nonpositive
def transform_non_affine(self, values):
"""logistic transform (base 10)"""
return 1.0 / (1 + 10**(-values))
def inverted(self):
return LogitTransform(self._nonpositive)
def __str__(self):
return f"{type(self).__name__}({self._nonpositive!r})"
class LogitScale(ScaleBase):
"""
Logit scale for data between zero and one, both excluded.
This scale is similar to a log scale close to zero and to one, and almost
linear around 0.5. It maps the interval ]0, 1[ onto ]-infty, +infty[.
"""
name = 'logit'
@_make_axis_parameter_optional
def __init__(self, axis=None, nonpositive='mask', *,
one_half=r"\frac{1}{2}", use_overline=False):
r"""
Parameters
----------
axis : `~matplotlib.axis.Axis`
The axis for the scale.
.. note::
This parameter is unused and about to be removed in the future.
It can already now be left out because of special preprocessing,
so that ``LogitScale()`` is valid.
nonpositive : {'mask', 'clip'}
Determines the behavior for values beyond the open interval ]0, 1[.
They can either be masked as invalid, or clipped to a number very
close to 0 or 1.
use_overline : bool, default: False
Indicate the usage of survival notation (\overline{x}) in place of
standard notation (1-x) for probability close to one.
one_half : str, default: r"\frac{1}{2}"
The string used for ticks formatter to represent 1/2.
"""
self._transform = LogitTransform(nonpositive)
self._use_overline = use_overline
self._one_half = one_half
def get_transform(self):
"""Return the `.LogitTransform` associated with this scale."""
return self._transform
def set_default_locators_and_formatters(self, axis):
# docstring inherited
# ..., 0.01, 0.1, 0.5, 0.9, 0.99, ...
axis.set_major_locator(LogitLocator())
axis.set_major_formatter(
LogitFormatter(
one_half=self._one_half,
use_overline=self._use_overline
)
)
axis.set_minor_locator(LogitLocator(minor=True))
axis.set_minor_formatter(
LogitFormatter(
minor=True,
one_half=self._one_half,
use_overline=self._use_overline
)
)
def limit_range_for_scale(self, vmin, vmax, minpos):
"""
Limit the domain to values between 0 and 1 (excluded).
"""
if not np.isfinite(minpos):
minpos = 1e-7 # Should rarely (if ever) have a visible effect.
return (minpos if vmin <= 0 else vmin,
1 - minpos if vmax >= 1 else vmax)
def val_in_range(self, val):
"""
Return whether the value(s) are within the valid range for this scale.
This is True for value(s) which are between 0 and 1 (excluded).
"""
arr = np.asarray(val)
with np.errstate(invalid='ignore'):
result = (0 < arr) & (arr < 1)
return bool(result) if arr.ndim == 0 else result
_scale_mapping = {
'linear': LinearScale,
'log': LogScale,
'symlog': SymmetricalLogScale,
'asinh': AsinhScale,
'logit': LogitScale,
'function': FuncScale,
'functionlog': FuncScaleLog,
}
# caching of signature info
# For backward compatibility, the built-in scales will keep the *axis* parameter
# in their constructors until matplotlib 3.15, i.e. as long as the *axis* parameter
# is still supported.
_scale_has_axis_parameter = {
'linear': True,
'log': True,
'symlog': True,
'asinh': True,
'logit': True,
'function': True,
'functionlog': True,
}
def get_scale_names():
"""Return the names of the available scales."""
return sorted(_scale_mapping)
def scale_factory(scale, axis, **kwargs):
"""
Return a scale class by name.
Parameters
----------
scale : {%(names)s}
axis : `~matplotlib.axis.Axis`
"""
scale_cls = _api.getitem_checked(_scale_mapping, scale=scale)
if _scale_has_axis_parameter[scale]:
return scale_cls(axis, **kwargs)
else:
return scale_cls(**kwargs)
if scale_factory.__doc__:
scale_factory.__doc__ = scale_factory.__doc__ % {
"names": ", ".join(map(repr, get_scale_names()))}
def register_scale(scale_class):
"""
Register a new kind of scale.
Parameters
----------
scale_class : subclass of `ScaleBase`
The scale to register.
"""
_scale_mapping[scale_class.name] = scale_class
# migration code to handle the *axis* parameter
has_axis_parameter = "axis" in inspect.signature(scale_class).parameters
_scale_has_axis_parameter[scale_class.name] = has_axis_parameter
if has_axis_parameter:
_api.warn_deprecated(
"3.11",
message=f"The scale {scale_class.__qualname__!r} uses an 'axis' parameter "
"in the constructors. This parameter is pending-deprecated since "
"matplotlib 3.11. It will be fully deprecated in 3.13 and removed "
"in 3.15. Starting with 3.11, 'register_scale()' accepts scales "
"without the *axis* parameter.",
pending=True,
)
def _get_scale_docs():
"""
Helper function for generating docstrings related to scales.
"""
docs = []
for name, scale_class in _scale_mapping.items():
docstring = inspect.getdoc(scale_class.__init__) or ""
docs.extend([
f" {name!r}",
"",
textwrap.indent(docstring, " " * 8),
""
])
return "\n".join(docs)
_docstring.interpd.register(
scale_type='{%s}' % ', '.join([repr(x) for x in get_scale_names()]),
scale_docs=_get_scale_docs().rstrip(),