Skip to content
Navigation Menu
{{ message }}
-
Notifications
You must be signed in to change notification settings - Fork 13
Expand file tree
/
Copy path_array_object.py
More file actions
1289 lines (1146 loc) · 50.1 KB
/
Copy path_array_object.py
File metadata and controls
1289 lines (1146 loc) · 50.1 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
"""
Wrapper class around the ndarray object for the array API standard.
The array API standard defines some behaviors differently than ndarray, in
particular, type promotion rules are different (the standard has no
value-based casting). The standard also specifies a more limited subset of
array methods and functionalities than are implemented on ndarray. Since the
goal of the array_api_strict namespace is to be a minimal implementation of the array
API standard, we need to define a separate wrapper class for the array_api_strict
namespace.
The standard compliant class is only a wrapper class. It is *not* a subclass
of ndarray.
"""
from __future__ import annotations
import operator
from enum import IntEnum
from ._creation_functions import asarray
from ._dtypes import (
_DType,
_all_dtypes,
_boolean_dtypes,
_integer_dtypes,
_integer_or_boolean_dtypes,
_floating_dtypes,
_complex_floating_dtypes,
_numeric_dtypes,
_result_type,
_dtype_categories,
)
from ._flags import get_array_api_strict_flags, set_array_api_strict_flags
from typing import TYPE_CHECKING, SupportsIndex
import types
if TYPE_CHECKING:
from typing import Optional, Tuple, Union, Any
from ._typing import PyCapsule, Device, Dtype
import numpy.typing as npt
import numpy as np
class Device:
def __init__(self, device="CPU_DEVICE"):
if device not in ("CPU_DEVICE", "device1", "device2"):
raise ValueError(f"The device '{device}' is not a valid choice.")
self._device = device
def __repr__(self):
return f"array_api_strict.Device('{self._device}')"
def __eq__(self, other):
if not isinstance(other, Device):
return False
return self._device == other._device
def __hash__(self):
return hash(("Device", self._device))
CPU_DEVICE = Device()
ALL_DEVICES = (CPU_DEVICE, Device("device1"), Device("device2"))
_default = object()
class Array:
"""
n-d array object for the array API namespace.
See the docstring of :py:obj:`np.ndarray <numpy.ndarray>` for more
information.
This is a wrapper around numpy.ndarray that restricts the usage to only
those things that are required by the array API namespace. Note,
attributes on this object that start with a single underscore are not part
of the API specification and should only be used internally. This object
should not be constructed directly. Rather, use one of the creation
functions, such as asarray().
"""
_array: npt.NDArray[Any]
# Use a custom constructor instead of __init__, as manually initializing
# this class is not supported API.
@classmethod
def _new(cls, x, /, device):
"""
This is a private method for initializing the array API Array
object.
Functions outside of the array_api_strict module should not use this
method. Use one of the creation functions instead, such as
``asarray``.
"""
obj = super().__new__(cls)
# Note: The spec does not have array scalars, only 0-D arrays.
if isinstance(x, np.generic):
# Convert the array scalar to a 0-D array
x = np.asarray(x)
_dtype = _DType(x.dtype)
if _dtype not in _all_dtypes:
raise TypeError(
f"The array_api_strict namespace does not support the dtype '{x.dtype}'"
)
obj._array = x
obj._dtype = _dtype
if device is None:
device = CPU_DEVICE
obj._device = device
return obj
# Prevent Array() from working
def __new__(cls, *args, **kwargs):
raise TypeError(
"The array_api_strict Array object should not be instantiated directly. Use an array creation function, such as asarray(), instead."
)
# These functions are not required by the spec, but are implemented for
# the sake of usability.
def __str__(self: Array, /) -> str:
"""
Performs the operation __str__.
"""
return self._array.__str__().replace("array", "Array")
def __repr__(self: Array, /) -> str:
"""
Performs the operation __repr__.
"""
suffix = f", dtype={self.dtype}"
if self.device != CPU_DEVICE:
suffix += f", device={self.device})"
else:
suffix += ")"
if 0 in self.shape:
prefix = "empty("
mid = str(self.shape)
else:
prefix = "Array("
mid = np.array2string(self._array, separator=', ', prefix=prefix, suffix=suffix)
return prefix + mid + suffix
# This function is not required by the spec, but we implement it here for
# convenience so that np.asarray(array_api_strict.Array) will work.
def __array__(self, dtype: None | np.dtype[Any] = None, copy: None | bool = None) -> npt.NDArray[Any]:
"""
Warning: this method is NOT part of the array API spec. Implementers
of other libraries need not include it, and users should not assume it
will be present in other implementations.
"""
if self._device != CPU_DEVICE:
raise RuntimeError(f"Can not convert array on the '{self._device}' device to a Numpy array.")
# copy keyword is new in 2.0.0; for older versions don't use it
# retry without that keyword.
if np.__version__[0] < '2':
return np.asarray(self._array, dtype=dtype)
elif np.__version__.startswith('2.0.0-dev0'):
# Handle dev version for which we can't know based on version
# number whether or not the copy keyword is supported.
try:
return np.asarray(self._array, dtype=dtype, copy=copy)
except TypeError:
return np.asarray(self._array, dtype=dtype)
else:
return np.asarray(self._array, dtype=dtype, copy=copy)
# These are various helper functions to make the array behavior match the
# spec in places where it either deviates from or is more strict than
# NumPy behavior
def _check_allowed_dtypes(self, other: bool | int | float | Array, dtype_category: str, op: str) -> Array:
"""
Helper function for operators to only allow specific input dtypes
Use like
other = self._check_allowed_dtypes(other, 'numeric', '__add__')
if other is NotImplemented:
return other
"""
if self.dtype not in _dtype_categories[dtype_category]:
raise TypeError(f"Only {dtype_category} dtypes are allowed in {op}")
if isinstance(other, (int, complex, float, bool)):
other = self._promote_scalar(other)
elif isinstance(other, Array):
if other.dtype not in _dtype_categories[dtype_category]:
raise TypeError(f"Only {dtype_category} dtypes are allowed in {op}")
else:
return NotImplemented
# This will raise TypeError for type combinations that are not allowed
# to promote in the spec (even if the NumPy array operator would
# promote them).
res_dtype = _result_type(self.dtype, other.dtype)
if op.startswith("__i"):
# Note: NumPy will allow in-place operators in some cases where
# the type promoted operator does not match the left-hand side
# operand. For example,
# >>> a = np.array(1, dtype=np.int8)
# >>> a += np.array(1, dtype=np.int16)
# The spec explicitly disallows this.
if res_dtype != self.dtype:
raise TypeError(
f"Cannot perform {op} with dtypes {self.dtype} and {other.dtype}"
)
return other
def _check_device(self, other):
"""Check that other is on a device compatible with the current array"""
if isinstance(other, (int, complex, float, bool)):
return
elif isinstance(other, Array):
if self.device != other.device:
raise ValueError(f"Arrays from two different devices ({self.device} and {other.device}) can not be combined.")
# Helper function to match the type promotion rules in the spec
def _promote_scalar(self, scalar):
"""
Returns a promoted version of a Python scalar appropriate for use with
operations on self.
This may raise an OverflowError in cases where the scalar is an
integer that is too large to fit in a NumPy integer dtype, or
TypeError when the scalar type is incompatible with the dtype of self.
"""
from ._data_type_functions import iinfo
# Note: Only Python scalar types that match the array dtype are
# allowed.
if isinstance(scalar, bool):
if self.dtype not in _boolean_dtypes:
raise TypeError(
"Python bool scalars can only be promoted with bool arrays"
)
elif isinstance(scalar, int):
if self.dtype in _boolean_dtypes:
raise TypeError(
"Python int scalars cannot be promoted with bool arrays"
)
if self.dtype in _integer_dtypes:
info = iinfo(self.dtype)
if not (info.min <= scalar <= info.max):
raise OverflowError(
"Python int scalars must be within the bounds of the dtype for integer arrays"
)
# int + array(floating) is allowed
elif isinstance(scalar, float):
if self.dtype not in _floating_dtypes:
raise TypeError(
"Python float scalars can only be promoted with floating-point arrays."
)
elif isinstance(scalar, complex):
if self.dtype not in _complex_floating_dtypes:
raise TypeError(
"Python complex scalars can only be promoted with complex floating-point arrays."
)
else:
raise TypeError("'scalar' must be a Python scalar")
# Note: scalars are unconditionally cast to the same dtype as the
# array.
# Note: the spec only specifies integer-dtype/int promotion
# behavior for integers within the bounds of the integer dtype.
# Outside of those bounds we use the default NumPy behavior (either
# cast or raise OverflowError).
return Array._new(np.array(scalar, dtype=self.dtype._np_dtype), device=CPU_DEVICE)
@staticmethod
def _normalize_two_args(x1, x2) -> Tuple[Array, Array]:
"""
Normalize inputs to two arg functions to fix type promotion rules
NumPy deviates from the spec type promotion rules in cases where one
argument is 0-dimensional and the other is not. For example:
>>> import numpy as np
>>> a = np.array([1.0], dtype=np.float32)
>>> b = np.array(1.0, dtype=np.float64)
>>> np.add(a, b) # The spec says this should be float64
array([2.], dtype=float32)
To fix this, we add a dimension to the 0-dimension array before passing it
through. This works because a dimension would be added anyway from
broadcasting, so the resulting shape is the same, but this prevents NumPy
from not promoting the dtype.
"""
# Another option would be to use signature=(x1.dtype, x2.dtype, None),
# but that only works for ufuncs, so we would have to call the ufuncs
# directly in the operator methods. One should also note that this
# sort of trick wouldn't work for functions like searchsorted, which
# don't do normal broadcasting, but there aren't any functions like
# that in the array API namespace.
if x1.ndim == 0 and x2.ndim != 0:
# The _array[None] workaround was chosen because it is relatively
# performant. broadcast_to(x1._array, x2.shape) is much slower. We
# could also manually type promote x2, but that is more complicated
# and about the same performance as this.
x1 = Array._new(x1._array[None], device=x1.device)
elif x2.ndim == 0 and x1.ndim != 0:
x2 = Array._new(x2._array[None], device=x2.device)
return (x1, x2)
# Note: A large fraction of allowed indices are disallowed here (see the
# docstring below)
def _validate_index(self, key):
"""
Validate an index according to the array API.
The array API specification only requires a subset of indices that are
supported by NumPy. This function will reject any index that is
allowed by NumPy but not required by the array API specification. We
always raise ``IndexError`` on such indices (the spec does not require
any specific behavior on them, but this makes the NumPy array API
namespace a minimal implementation of the spec). See
https://data-apis.org/array-api/latest/API_specification/indexing.html
for the full list of required indexing behavior
This function raises IndexError if the index ``key`` is invalid. It
only raises ``IndexError`` on indices that are not already rejected by
NumPy, as NumPy will already raise the appropriate error on such
indices. ``shape`` may be None, in which case, only cases that are
independent of the array shape are checked.
The following cases are allowed by NumPy, but not specified by the array
API specification:
- Indices to not include an implicit ellipsis at the end. That is,
every axis of an array must be explicitly indexed or an ellipsis
included. This behaviour is sometimes referred to as flat indexing.
- The start and stop of a slice may not be out of bounds. In
particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
following are allowed:
- ``i`` or ``j`` omitted (``None``).
- ``-n <= i <= max(0, n - 1)``.
- For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
- For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
- Boolean array indices are not allowed as part of a larger tuple
index.
- Integer array indices are not allowed (with the exception of 0-D
arrays, which are treated the same as scalars).
Additionally, it should be noted that indices that would return a
scalar in NumPy will return a 0-D array. Array scalars are not allowed
in the specification, only 0-D arrays. This is done in the
``Array._new`` constructor, not this function.
"""
_key = key if isinstance(key, tuple) else (key,)
for i in _key:
if isinstance(i, bool) or not (
isinstance(i, SupportsIndex) # i.e. ints
or isinstance(i, slice)
or i == Ellipsis
or i is None
or isinstance(i, Array)
or isinstance(i, np.ndarray)
):
raise IndexError(
f"Single-axes index {i} has {type(i)=}, but only "
"integers, slices (:), ellipsis (...), newaxis (None), "
"zero-dimensional integer arrays and boolean arrays "
"are specified in the Array API."
)
nonexpanding_key = []
single_axes = []
n_ellipsis = 0
key_has_mask = False
for i in _key:
if i is not None:
nonexpanding_key.append(i)
if isinstance(i, np.ndarray):
raise IndexError("Index arrays for array_api_strict must be array_api_strict arrays")
if isinstance(i, Array):
if i.dtype in _boolean_dtypes:
key_has_mask = True
single_axes.append(i)
else:
# i must not be an array here, to avoid elementwise equals
if i == Ellipsis:
n_ellipsis += 1
else:
single_axes.append(i)
n_single_axes = len(single_axes)
if n_ellipsis > 1:
return # handled by ndarray
elif n_ellipsis == 0:
# Note boolean masks must be the sole index, which we check for
# later on.
if not key_has_mask and n_single_axes < self.ndim:
raise IndexError(
f"{self.ndim=}, but the multi-axes index only specifies "
f"{n_single_axes} dimensions. If this was intentional, "
"add a trailing ellipsis (...) which expands into as many "
"slices (:) as necessary - this is what np.ndarray arrays "
"implicitly do, but such flat indexing behaviour is not "
"specified in the Array API."
)
if n_ellipsis == 0:
indexed_shape = self.shape
else:
ellipsis_start = None
for pos, i in enumerate(nonexpanding_key):
if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
if i == Ellipsis:
ellipsis_start = pos
break
assert ellipsis_start is not None # sanity check
ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
indexed_shape = (
self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
)
for i, side in zip(single_axes, indexed_shape):
if isinstance(i, slice):
if side == 0:
f_range = "0 (or None)"
else:
f_range = f"between -{side} and {side - 1} (or None)"
if i.start is not None:
try:
start = operator.index(i.start)
except TypeError:
pass # handled by ndarray
else:
if not (-side <= start <= side):
raise IndexError(
f"Slice {i} contains {start=}, but should be "
f"{f_range} for an axis of size {side} "
"(out-of-bounds starts are not specified in "
"the Array API)"
)
if i.stop is not None:
try:
stop = operator.index(i.stop)
except TypeError:
pass # handled by ndarray
else:
if not (-side <= stop <= side):
raise IndexError(
f"Slice {i} contains {stop=}, but should be "
f"{f_range} for an axis of size {side} "
"(out-of-bounds stops are not specified in "
"the Array API)"
)
elif isinstance(i, Array):
if i.dtype in _boolean_dtypes:
if len(_key) != 1:
assert isinstance(key, tuple) # sanity check
raise IndexError(
f"Single-axes index {i} is a boolean array and "
f"{len(key)=}, but masking is only specified in the "
"Array API when the array is the sole index."
)
if not get_array_api_strict_flags()['boolean_indexing']:
raise RuntimeError("The boolean_indexing flag has been disabled for array-api-strict")
elif i.dtype in _integer_dtypes and i.ndim != 0:
raise IndexError(
f"Single-axes index {i} is a non-zero-dimensional "
"integer array, but advanced integer indexing is not "
"specified in the Array API."
)
elif isinstance(i, tuple):
raise IndexError(
f"Single-axes index {i} is a tuple, but nested tuple "
"indices are not specified in the Array API."
)
# Everything below this line is required by the spec.
def __abs__(self: Array, /) -> Array:
"""
Performs the operation __abs__.
"""
if self.dtype not in _numeric_dtypes:
raise TypeError("Only numeric dtypes are allowed in __abs__")
res = self._array.__abs__()
return self.__class__._new(res, device=self.device)
def __add__(self: Array, other: Union[int, float, Array], /) -> Array:
"""
Performs the operation __add__.
"""
self._check_device(other)
other = self._check_allowed_dtypes(other, "numeric", "__add__")
if other is NotImplemented:
return other
self, other = self._normalize_two_args(self, other)
res = self._array.__add__(other._array)
return self.__class__._new(res, device=self.device)
def __and__(self: Array, other: Union[int, bool, Array], /) -> Array:
"""
Performs the operation __and__.
"""
self._check_device(other)
other = self._check_allowed_dtypes(other, "integer or boolean", "__and__")
if other is NotImplemented:
return other
self, other = self._normalize_two_args(self, other)
res = self._array.__and__(other._array)
return self.__class__._new(res, device=self.device)
def __array_namespace__(
self: Array, /, *, api_version: Optional[str] = None
) -> types.ModuleType:
"""
Return the array_api_strict namespace corresponding to api_version.
The default API version is '2022.12'. Note that '2021.12' is supported,
but currently identical to '2022.12'.
For array_api_strict, calling this function with api_version will set
the API version for the array_api_strict module globally. This can
also be achieved with the
{func}`array_api_strict.set_array_api_strict_flags` function. If you
want to only set the version locally, use the
{class}`array_api_strict.ArrayApiStrictFlags` context manager.
"""
set_array_api_strict_flags(api_version=api_version)
import array_api_strict
return array_api_strict
def __bool__(self: Array, /) -> bool:
"""
Performs the operation __bool__.
"""
# Note: This is an error here.
if self._array.ndim != 0:
raise TypeError("bool is only allowed on arrays with 0 dimensions")
res = self._array.__bool__()
return res
def __complex__(self: Array, /) -> complex:
"""
Performs the operation __complex__.
"""
# Note: This is an error here.
if self._array.ndim != 0:
raise TypeError("complex is only allowed on arrays with 0 dimensions")
res = self._array.__complex__()
return res
def __dlpack__(
self: Array,
/,
*,
stream: Optional[Union[int, Any]] = None,
max_version: Optional[tuple[int, int]] = _default,
dl_device: Optional[tuple[IntEnum, int]] = _default,
copy: Optional[bool] = _default,
) -> PyCapsule:
"""
Performs the operation __dlpack__.
"""
if get_array_api_strict_flags()['api_version'] < '2023.12':
if max_version is not _default:
raise ValueError("The max_version argument to __dlpack__ requires at least version 2023.12 of the array API")
if dl_device is not _default:
raise ValueError("The device argument to __dlpack__ requires at least version 2023.12 of the array API")
if copy is not _default:
raise ValueError("The copy argument to __dlpack__ requires at least version 2023.12 of the array API")
# Going to wait for upstream numpy support
if max_version not in [_default, None]:
raise NotImplementedError("The max_version argument to __dlpack__ is not yet implemented")
if dl_device not in [_default, None]:
raise NotImplementedError("The device argument to __dlpack__ is not yet implemented")
if copy not in [_default, None]:
raise NotImplementedError("The copy argument to __dlpack__ is not yet implemented")
return self._array.__dlpack__(stream=stream)
def __dlpack_device__(self: Array, /) -> Tuple[IntEnum, int]:
"""
Performs the operation __dlpack_device__.
"""
# Note: device support is required for this
return self._array.__dlpack_device__()
def __eq__(self: Array, other: Union[int, float, bool, Array], /) -> Array:
"""
Performs the operation __eq__.
"""
self._check_device(other)
# Even though "all" dtypes are allowed, we still require them to be
# promotable with each other.
other = self._check_allowed_dtypes(other, "all", "__eq__")
if other is NotImplemented:
return other
self, other = self._normalize_two_args(self, other)
res = self._array.__eq__(other._array)
return self.__class__._new(res, device=self.device)
def __float__(self: Array, /) -> float:
"""
Performs the operation __float__.
"""
# Note: This is an error here.
if self._array.ndim != 0:
raise TypeError("float is only allowed on arrays with 0 dimensions")
if self.dtype in _complex_floating_dtypes:
raise TypeError("float is not allowed on complex floating-point arrays")
res = self._array.__float__()
return res
def __floordiv__(self: Array, other: Union[int, float, Array], /) -> Array:
"""
Performs the operation __floordiv__.
"""
self._check_device(other)
other = self._check_allowed_dtypes(other, "real numeric", "__floordiv__")
if other is NotImplemented:
return other
self, other = self._normalize_two_args(self, other)
res = self._array.__floordiv__(other._array)
return self.__class__._new(res, device=self.device)
def __ge__(self: Array, other: Union[int, float, Array], /) -> Array:
"""
Performs the operation __ge__.
"""
self._check_device(other)
other = self._check_allowed_dtypes(other, "real numeric", "__ge__")
if other is NotImplemented:
return other
self, other = self._normalize_two_args(self, other)
res = self._array.__ge__(other._array)
return self.__class__._new(res, device=self.device)
def __getitem__(
self: Array,
key: Union[
int,
slice,
ellipsis, # noqa: F821
Tuple[Union[int, slice, ellipsis, None], ...], # noqa: F821
Array,
],
/,
) -> Array:
"""
Performs the operation __getitem__.
"""
# XXX Does key have to be on the same device? Is there an exception for CPU_DEVICE?
# Note: Only indices required by the spec are allowed. See the
# docstring of _validate_index
self._validate_index(key)
if isinstance(key, Array):
# Indexing self._array with array_api_strict arrays can be erroneous
key = key._array
res = self._array.__getitem__(key)
return self._new(res, device=self.device)
def __gt__(self: Array, other: Union[int, float, Array], /) -> Array:
"""
Performs the operation __gt__.
"""
self._check_device(other)
other = self._check_allowed_dtypes(other, "real numeric", "__gt__")
if other is NotImplemented:
return other
self, other = self._normalize_two_args(self, other)
res = self._array.__gt__(other._array)
return self.__class__._new(res, device=other.device)
def __int__(self: Array, /) -> int:
"""
Performs the operation __int__.
"""
# Note: This is an error here.
if self._array.ndim != 0:
raise TypeError("int is only allowed on arrays with 0 dimensions")
if self.dtype in _complex_floating_dtypes:
raise TypeError("int is not allowed on complex floating-point arrays")
res = self._array.__int__()
return res
def __index__(self: Array, /) -> int:
"""
Performs the operation __index__.
"""
res = self._array.__index__()
return res
def __invert__(self: Array, /) -> Array:
"""
Performs the operation __invert__.
"""
if self.dtype not in _integer_or_boolean_dtypes:
raise TypeError("Only integer or boolean dtypes are allowed in __invert__")
res = self._array.__invert__()
return self.__class__._new(res, device=self.device)
def __iter__(self: Array, /):
"""
Performs the operation __iter__.
"""
# Manually disable iteration on higher dimensional arrays, since
# __getitem__ raises IndexError on things like ones((3, 3))[0], which
# causes list(ones((3, 3))) to give [].
if self.ndim > 1:
raise TypeError("array iteration is not allowed in array-api-strict")
# Allow iteration for 1-D arrays. The array API doesn't strictly
# define __iter__, but it doesn't disallow it. The default Python
# behavior is to implement iter as a[0], a[1], ... when __getitem__ is
# implemented, which implies iteration on 1-D arrays.
return (Array._new(i, device=self.device) for i in self._array)
def __le__(self: Array, other: Union[int, float, Array], /) -> Array:
"""
Performs the operation __le__.
"""
self._check_device(other)
other = self._check_allowed_dtypes(other, "real numeric", "__le__")
if other is NotImplemented:
return other
self, other = self._normalize_two_args(self, other)
res = self._array.__le__(other._array)
return self.__class__._new(res, device=self.device)
def __lshift__(self: Array, other: Union[int, Array], /) -> Array:
"""
Performs the operation __lshift__.
"""
self._check_device(other)
other = self._check_allowed_dtypes(other, "integer", "__lshift__")
if other is NotImplemented:
return other
self, other = self._normalize_two_args(self, other)
res = self._array.__lshift__(other._array)
return self.__class__._new(res, device=self.device)
def __lt__(self: Array, other: Union[int, float, Array], /) -> Array:
"""
Performs the operation __lt__.
"""
self._check_device(other)
other = self._check_allowed_dtypes(other, "real numeric", "__lt__")
if other is NotImplemented:
return other
self, other = self._normalize_two_args(self, other)
res = self._array.__lt__(other._array)
return self.__class__._new(res, device=self.device)
def __matmul__(self: Array, other: Array, /) -> Array:
"""
Performs the operation __matmul__.
"""
self._check_device(other)
# matmul is not defined for scalars, but without this, we may get
# the wrong error message from asarray.
other = self._check_allowed_dtypes(other, "numeric", "__matmul__")
if other is NotImplemented:
return other
res = self._array.__matmul__(other._array)
return self.__class__._new(res, device=self.device)
def __mod__(self: Array, other: Union[int, float, Array], /) -> Array:
"""
Performs the operation __mod__.
"""
self._check_device(other)
other = self._check_allowed_dtypes(other, "real numeric", "__mod__")
if other is NotImplemented:
return other
self, other = self._normalize_two_args(self, other)
res = self._array.__mod__(other._array)
return self.__class__._new(res, device=self.device)
def __mul__(self: Array, other: Union[int, float, Array], /) -> Array:
"""
Performs the operation __mul__.
"""
self._check_device(other)
other = self._check_allowed_dtypes(other, "numeric", "__mul__")
if other is NotImplemented:
return other
self, other = self._normalize_two_args(self, other)
res = self._array.__mul__(other._array)
return self.__class__._new(res, device=self.device)
def __ne__(self: Array, other: Union[int, float, bool, Array], /) -> Array:
"""
Performs the operation __ne__.
"""
self._check_device(other)
other = self._check_allowed_dtypes(other, "all", "__ne__")
if other is NotImplemented:
return other
self, other = self._normalize_two_args(self, other)
res = self._array.__ne__(other._array)
return self.__class__._new(res, device=self.device)
def __neg__(self: Array, /) -> Array:
"""
Performs the operation __neg__.
"""
if self.dtype not in _numeric_dtypes:
raise TypeError("Only numeric dtypes are allowed in __neg__")
res = self._array.__neg__()
return self.__class__._new(res, device=self.device)
def __or__(self: Array, other: Union[int, bool, Array], /) -> Array:
"""
Performs the operation __or__.
"""
self._check_device(other)
other = self._check_allowed_dtypes(other, "integer or boolean", "__or__")
if other is NotImplemented:
return other
self, other = self._normalize_two_args(self, other)
res = self._array.__or__(other._array)
return self.__class__._new(res, device=self.device)
def __pos__(self: Array, /) -> Array:
"""
Performs the operation __pos__.
"""
if self.dtype not in _numeric_dtypes:
raise TypeError("Only numeric dtypes are allowed in __pos__")
res = self._array.__pos__()
return self.__class__._new(res, device=self.device)
def __pow__(self: Array, other: Union[int, float, Array], /) -> Array:
"""
Performs the operation __pow__.
"""
from ._elementwise_functions import pow
self._check_device(other)
other = self._check_allowed_dtypes(other, "numeric", "__pow__")
if other is NotImplemented:
return other
# Note: NumPy's __pow__ does not follow type promotion rules for 0-d
# arrays, so we use pow() here instead.
return pow(self, other)
def __rshift__(self: Array, other: Union[int, Array], /) -> Array:
"""
Performs the operation __rshift__.
"""
self._check_device(other)
other = self._check_allowed_dtypes(other, "integer", "__rshift__")
if other is NotImplemented:
return other
self, other = self._normalize_two_args(self, other)
res = self._array.__rshift__(other._array)
return self.__class__._new(res, device=self.device)
def __setitem__(
self,
key: Union[
int, slice, ellipsis, Tuple[Union[int, slice, ellipsis], ...], Array # noqa: F821
],
value: Union[int, float, bool, Array],
/,
) -> None:
"""
Performs the operation __setitem__.
"""
# Note: Only indices required by the spec are allowed. See the
# docstring of _validate_index
self._validate_index(key)
if isinstance(key, Array):
# Indexing self._array with array_api_strict arrays can be erroneous
key = key._array
self._array.__setitem__(key, asarray(value)._array)
def __sub__(self: Array, other: Union[int, float, Array], /) -> Array:
"""
Performs the operation __sub__.
"""
self._check_device(other)
other = self._check_allowed_dtypes(other, "numeric", "__sub__")
if other is NotImplemented:
return other
self, other = self._normalize_two_args(self, other)
res = self._array.__sub__(other._array)
return self.__class__._new(res, device=self.device)
# PEP 484 requires int to be a subtype of float, but __truediv__ should
# not accept int.
def __truediv__(self: Array, other: Union[float, Array], /) -> Array:
"""
Performs the operation __truediv__.
"""
self._check_device(other)
other = self._check_allowed_dtypes(other, "floating-point", "__truediv__")
if other is NotImplemented:
return other
self, other = self._normalize_two_args(self, other)
res = self._array.__truediv__(other._array)
return self.__class__._new(res, device=self.device)
def __xor__(self: Array, other: Union[int, bool, Array], /) -> Array:
"""
Performs the operation __xor__.
"""
self._check_device(other)
other = self._check_allowed_dtypes(other, "integer or boolean", "__xor__")
if other is NotImplemented:
return other
self, other = self._normalize_two_args(self, other)
res = self._array.__xor__(other._array)
return self.__class__._new(res, device=self.device)
def __iadd__(self: Array, other: Union[int, float, Array], /) -> Array:
"""
Performs the operation __iadd__.
"""
self._check_device(other)
other = self._check_allowed_dtypes(other, "numeric", "__iadd__")
if other is NotImplemented:
return other
self._array.__iadd__(other._array)
return self
def __radd__(self: Array, other: Union[int, float, Array], /) -> Array:
"""
Performs the operation __radd__.
"""
self._check_device(other)
other = self._check_allowed_dtypes(other, "numeric", "__radd__")
if other is NotImplemented:
return other
self, other = self._normalize_two_args(self, other)
res = self._array.__radd__(other._array)
return self.__class__._new(res, device=self.device)
def __iand__(self: Array, other: Union[int, bool, Array], /) -> Array:
"""
Performs the operation __iand__.
"""
self._check_device(other)
other = self._check_allowed_dtypes(other, "integer or boolean", "__iand__")
if other is NotImplemented:
return other
self._array.__iand__(other._array)
return self
def __rand__(self: Array, other: Union[int, bool, Array], /) -> Array:
"""
Performs the operation __rand__.
"""
self._check_device(other)
other = self._check_allowed_dtypes(other, "integer or boolean", "__rand__")
if other is NotImplemented:
return other
self, other = self._normalize_two_args(self, other)
res = self._array.__rand__(other._array)
return self.__class__._new(res, device=self.device)
def __ifloordiv__(self: Array, other: Union[int, float, Array], /) -> Array:
"""
Performs the operation __ifloordiv__.
"""
self._check_device(other)
other = self._check_allowed_dtypes(other, "real numeric", "__ifloordiv__")
if other is NotImplemented:
return other
self._array.__ifloordiv__(other._array)
return self
def __rfloordiv__(self: Array, other: Union[int, float, Array], /) -> Array:
"""
Performs the operation __rfloordiv__.
"""
self._check_device(other)
other = self._check_allowed_dtypes(other, "real numeric", "__rfloordiv__")
if other is NotImplemented:
return other
self, other = self._normalize_two_args(self, other)
res = self._array.__rfloordiv__(other._array)
return self.__class__._new(res, device=self.device)
def __ilshift__(self: Array, other: Union[int, Array], /) -> Array:
"""
Performs the operation __ilshift__.
"""
self._check_device(other)
other = self._check_allowed_dtypes(other, "integer", "__ilshift__")
You can’t perform that action at this time.