Repository navigation
Expand file tree
/
Copy patharray.py
More file actions
1406 lines (1151 loc) · 39.9 KB
/
Copy patharray.py
File metadata and controls
1406 lines (1151 loc) · 39.9 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
#######################################################
# Copyright (c) 2015, ArrayFire
# All rights reserved.
#
# This file is distributed under 3-clause BSD license.
# The complete license agreement can be obtained at:
# http://arrayfire.com/licenses/BSD-3-Clause
########################################################
"""
Array class and helper functions.
"""
import inspect
import os
from .library import *
from .util import *
from .util import _is_number
from .bcast import _bcast_var
from .base import *
from .index import *
from .index import _Index4
_is_running_in_py_charm = "PYCHARM_HOSTED" in os.environ
_display_dims_limit = None
def set_display_dims_limit(*dims):
"""
Sets the dimension limit after which array's data won't get
presented to the result of str(arr).
Default is None, which means there is no limit.
Parameters
----------
*dims : dimension limit args
Example
-------
set_display_dims_limit(10, 10, 10, 10)
"""
global _display_dims_limit
_display_dims_limit = dims
def get_display_dims_limit():
"""
Gets the dimension limit after which array's data won't get
presented to the result of str(arr).
Default is None, which means there is no limit.
Returns
-----------
- tuple of the current limit
- None is there is no limit
Example
-------
get_display_dims_limit()
# None
set_display_dims_limit(10, 10, 10, 10)
get_display_dims_limit()
# (10, 10, 10, 10)
"""
return _display_dims_limit
def _in_display_dims_limit(dims):
if _is_running_in_py_charm:
return False
if _display_dims_limit is not None:
limit_len = len(_display_dims_limit)
dim_len = len(dims)
if dim_len > limit_len:
return False
for i in range(dim_len):
if dims[i] > _display_dims_limit[i]:
return False
return True
def _create_array(buf, numdims, idims, dtype, is_device):
out_arr = c_void_ptr_t(0)
c_dims = dim4(idims[0], idims[1], idims[2], idims[3])
if (not is_device):
safe_call(backend.get().af_create_array(c_pointer(out_arr), c_void_ptr_t(buf),
numdims, c_pointer(c_dims), dtype.value))
else:
safe_call(backend.get().af_device_array(c_pointer(out_arr), c_void_ptr_t(buf),
numdims, c_pointer(c_dims), dtype.value))
return out_arr
def _create_strided_array(buf, numdims, idims, dtype, is_device, offset, strides):
out_arr = c_void_ptr_t(0)
c_dims = dim4(idims[0], idims[1], idims[2], idims[3])
if offset is None:
offset = 0
offset = c_dim_t(offset)
if strides is None:
strides = (1, idims[0], idims[0]*idims[1], idims[0]*idims[1]*idims[2])
while len(strides) < 4:
strides = strides + (strides[-1],)
strides = dim4(strides[0], strides[1], strides[2], strides[3])
if is_device:
location = Source.device
else:
location = Source.host
safe_call(backend.get().af_create_strided_array(c_pointer(out_arr), c_void_ptr_t(buf),
offset, numdims, c_pointer(c_dims),
c_pointer(strides), dtype.value,
location.value))
return out_arr
def _create_empty_array(numdims, idims, dtype):
out_arr = c_void_ptr_t(0)
if numdims == 0: return out_arr
c_dims = dim4(idims[0], idims[1], idims[2], idims[3])
safe_call(backend.get().af_create_handle(c_pointer(out_arr),
numdims, c_pointer(c_dims), dtype.value))
return out_arr
def constant_array(val, d0, d1=None, d2=None, d3=None, dtype=Dtype.f32):
"""
Internal function to create a C array. Should not be used externall.
"""
if not isinstance(dtype, c_int_t):
if isinstance(dtype, int):
dtype = c_int_t(dtype)
elif isinstance(dtype, Dtype):
dtype = c_int_t(dtype.value)
else:
raise TypeError("Invalid dtype")
out = c_void_ptr_t(0)
dims = dim4(d0, d1, d2, d3)
if isinstance(val, complex):
c_real = c_double_t(val.real)
c_imag = c_double_t(val.imag)
if (dtype.value != Dtype.c32.value and dtype.value != Dtype.c64.value):
dtype = Dtype.c32.value
safe_call(backend.get().af_constant_complex(c_pointer(out), c_real, c_imag,
4, c_pointer(dims), dtype))
elif dtype.value == Dtype.s64.value:
c_val = c_longlong_t(val.real)
safe_call(backend.get().af_constant_long(c_pointer(out), c_val, 4, c_pointer(dims)))
elif dtype.value == Dtype.u64.value:
c_val = c_ulonglong_t(val.real)
safe_call(backend.get().af_constant_ulong(c_pointer(out), c_val, 4, c_pointer(dims)))
else:
c_val = c_double_t(val)
safe_call(backend.get().af_constant(c_pointer(out), c_val, 4, c_pointer(dims), dtype))
return out
def _binary_func(lhs, rhs, c_func):
out = Array()
other = rhs
if (_is_number(rhs)):
ldims = dim4_to_tuple(lhs.dims())
rty = implicit_dtype(rhs, lhs.type())
other = Array()
other.arr = constant_array(rhs, ldims[0], ldims[1], ldims[2], ldims[3], rty.value)
elif not isinstance(rhs, Array):
raise TypeError("Invalid parameter to binary function")
safe_call(c_func(c_pointer(out.arr), lhs.arr, other.arr, _bcast_var.get()))
return out
def _binary_funcr(lhs, rhs, c_func):
out = Array()
other = lhs
if (_is_number(lhs)):
rdims = dim4_to_tuple(rhs.dims())
lty = implicit_dtype(lhs, rhs.type())
other = Array()
other.arr = constant_array(lhs, rdims[0], rdims[1], rdims[2], rdims[3], lty.value)
elif not isinstance(lhs, Array):
raise TypeError("Invalid parameter to binary function")
c_func(c_pointer(out.arr), other.arr, rhs.arr, _bcast_var.get())
return out
def _ctype_to_lists(ctype_arr, dim, shape, offset=0):
if (dim == 0):
return list(ctype_arr[offset : offset + shape[0]])
else:
dim_len = shape[dim]
res = [[]] * dim_len
for n in range(dim_len):
res[n] = _ctype_to_lists(ctype_arr, dim - 1, shape, offset)
offset += shape[0]
return res
def _slice_to_length(key, dim):
tkey = [key.start, key.stop, key.step]
if tkey[0] is None:
tkey[0] = 0
elif tkey[0] < 0:
tkey[0] = dim - tkey[0]
if tkey[1] is None:
tkey[1] = dim
elif tkey[1] < 0:
tkey[1] = dim - tkey[1]
if tkey[2] is None:
tkey[2] = 1
return int(((tkey[1] - tkey[0] - 1) / tkey[2]) + 1)
def _get_info(dims, buf_len):
elements = 1
numdims = 0
if dims:
numdims = len(dims)
idims = [1]*4
for i in range(numdims):
elements *= dims[i]
idims[i] = dims[i]
elif (buf_len != 0):
idims = [buf_len, 1, 1, 1]
numdims = 1
else:
raise RuntimeError("Invalid size")
return numdims, idims
def _get_indices(key):
inds = _Index4()
if isinstance(key, tuple):
n_idx = len(key)
for n in range(n_idx):
inds[n] = Index(key[n])
else:
inds[0] = Index(key)
return inds
def _get_assign_dims(key, idims):
dims = [1]*4
for n in range(len(idims)):
dims[n] = idims[n]
if _is_number(key):
dims[0] = 1
return dims
elif isinstance(key, slice):
dims[0] = _slice_to_length(key, idims[0])
return dims
elif isinstance(key, ParallelRange):
dims[0] = _slice_to_length(key.S, idims[0])
return dims
elif isinstance(key, BaseArray):
# If the array is boolean take only the number of nonzeros
if(key.dtype() is Dtype.b8):
dims[0] = int(sum(key))
else:
dims[0] = key.elements()
return dims
elif isinstance(key, tuple):
n_inds = len(key)
for n in range(n_inds):
if (_is_number(key[n])):
dims[n] = 1
elif (isinstance(key[n], BaseArray)):
# If the array is boolean take only the number of nonzeros
if(key[n].dtype() is Dtype.b8):
dims[n] = int(sum(key[n]))
else:
dims[n] = key[n].elements()
elif (isinstance(key[n], slice)):
dims[n] = _slice_to_length(key[n], idims[n])
elif (isinstance(key[n], ParallelRange)):
dims[n] = _slice_to_length(key[n].S, idims[n])
else:
raise IndexError("Invalid type while assigning to arrayfire.array")
return dims
else:
raise IndexError("Invalid type while assigning to arrayfire.array")
def transpose(a, conj=False):
"""
Perform the transpose on an input.
Parameters
-----------
a : af.Array
Multi dimensional arrayfire array.
conj : optional: bool. default: False.
Flag to specify if a complex conjugate needs to applied for complex inputs.
Returns
--------
out : af.Array
Containing the tranpose of `a` for all batches.
"""
out = Array()
safe_call(backend.get().af_transpose(c_pointer(out.arr), a.arr, conj))
return out
def transpose_inplace(a, conj=False):
"""
Perform inplace transpose on an input.
Parameters
-----------
a : af.Array
- Multi dimensional arrayfire array.
- Contains transposed values on exit.
conj : optional: bool. default: False.
Flag to specify if a complex conjugate needs to applied for complex inputs.
Note
-------
Input `a` needs to be a square matrix or a batch of square matrices.
"""
safe_call(backend.get().af_transpose_inplace(a.arr, conj))
class Array(BaseArray):
"""
A multi dimensional array container.
Parameters
----------
src : optional: array.array, list or C buffer. default: None.
- When `src` is `array.array` or `list`, the data is copied to create the Array()
- When `src` is None, an empty buffer is created.
dims : optional: tuple of ints. default: (0,)
- When using the default values of `dims`, the dims are caclulated as `len(src)`
dtype: optional: str or arrayfire.Dtype. default: None.
- if str, must be one of the following:
- 'f' for float
- 'd' for double
- 'b' for bool
- 'B' for unsigned char
- 'h' for signed 16 bit integer
- 'H' for unsigned 16 bit integer
- 'i' for signed 32 bit integer
- 'I' for unsigned 32 bit integer
- 'l' for signed 64 bit integer
- 'L' for unsigned 64 bit integer
- 'F' for 32 bit complex number
- 'D' for 64 bit complex number
- if arrayfire.Dtype, must be one of the following:
- Dtype.f32 for float
- Dtype.f64 for double
- Dtype.b8 for bool
- Dtype.u8 for unsigned char
- Dtype.s16 for signed 16 bit integer
- Dtype.u16 for unsigned 16 bit integer
- Dtype.s32 for signed 32 bit integer
- Dtype.u32 for unsigned 32 bit integer
- Dtype.s64 for signed 64 bit integer
- Dtype.u64 for unsigned 64 bit integer
- Dtype.c32 for 32 bit complex number
- Dtype.c64 for 64 bit complex number
- if None, Dtype.f32 is assumed
Attributes
-----------
arr: ctypes.c_void_p
ctypes variable containing af_array from arrayfire library.
Examples
--------
Creating an af.Array() from array.array()
>>> import arrayfire as af
>>> import array
>>> a = array.array('f', (1, 2, 3, 4))
>>> b = af.Array(a, (2,2))
>>> af.display(b)
[2 2 1 1]
1.0000 3.0000
2.0000 4.0000
Creating an af.Array() from a list
>>> import arrayfire as af
>>> import array
>>> a = [1, 2, 3, 4]
>>> b = af.Array(a)
>>> af.display(b)
[4 1 1 1]
1.0000
2.0000
3.0000
4.0000
Creating an af.Array() from numpy.array()
>>> import numpy as np
>>> import arrayfire as af
>>> a = np.random.random((2,2))
>>> a
array([[ 0.33042524, 0.36135449],
[ 0.86748649, 0.42199135]])
>>> b = af.Array(a.ctypes.data, a.shape, a.dtype.char)
>>> af.display(b)
[2 2 1 1]
0.3304 0.8675
0.3614 0.4220
Note
-----
- The class is currently limited to 4 dimensions.
- arrayfire.Array() uses column major format.
- numpy uses row major format by default which can cause issues during conversion
"""
# Numpy checks this attribute to know which class handles binary builtin operations, such as __add__.
# Setting to such a high value should make sure that arrayfire has priority over
# other classes, ensuring that e.g. numpy.float32(1)*arrayfire.randu(3) is handled by
# arrayfire's __radd__() instead of numpy's __add__()
__array_priority__ = 30
def __init__(self, src=None, dims=None, dtype=None, is_device=False, offset=None, strides=None):
super(Array, self).__init__()
buf=None
buf_len=0
if dtype is not None:
if isinstance(dtype, str):
type_char = dtype
else:
type_char = to_typecode[dtype.value]
else:
type_char = None
_type_char='f'
if src is not None:
if (isinstance(src, Array)):
safe_call(backend.get().af_retain_array(c_pointer(self.arr), src.arr))
return
host = __import__("array")
if isinstance(src, host.array):
buf,buf_len = src.buffer_info()
_type_char = src.typecode
numdims, idims = _get_info(dims, buf_len)
elif isinstance(src, list):
tmp = host.array('f', src)
buf,buf_len = tmp.buffer_info()
_type_char = tmp.typecode
numdims, idims = _get_info(dims, buf_len)
elif isinstance(src, int) or isinstance(src, c_void_ptr_t):
buf = src if not isinstance(src, c_void_ptr_t) else src.value
numdims, idims = _get_info(dims, buf_len)
elements = 1
for dim in idims:
elements *= dim
if (elements == 0):
raise RuntimeError("Expected dims when src is data pointer")
if (type_char is None):
raise TypeError("Expected type_char when src is data pointer")
_type_char = type_char
else:
raise TypeError("src is an object of unsupported class")
if (type_char is not None and
type_char != _type_char):
raise TypeError("Can not create array of requested type from input data type")
if(offset is None and strides is None):
self.arr = _create_array(buf, numdims, idims, to_dtype[_type_char], is_device)
else:
self.arr = _create_strided_array(buf, numdims, idims, to_dtype[_type_char], is_device, offset, strides)
else:
if type_char is None:
type_char = 'f'
numdims = len(dims) if dims else 0
idims = [1] * 4
for n in range(numdims):
idims[n] = dims[n]
self.arr = _create_empty_array(numdims, idims, to_dtype[type_char])
def as_type(self, ty):
"""
Cast current array to a specified data type
Parameters
----------
ty : Return data type
"""
return cast(self, ty)
def copy(self):
"""
Performs a deep copy of the array.
Returns
-------
out: af.Array()
An identical copy of self.
"""
out = Array()
safe_call(backend.get().af_copy_array(c_pointer(out.arr), self.arr))
return out
def __del__(self):
"""
Release the C array when going out of scope
"""
if self.arr.value:
backend.get().af_release_array(self.arr)
self.arr.value = 0
def device_ptr(self):
"""
Return the device pointer exclusively held by the array.
Returns
--------
ptr : int
Contains location of the device pointer
Note
----
- This can be used to integrate with custom C code and / or PyCUDA or PyOpenCL.
- No other arrays will share the same device pointer.
- A copy of the memory is done if multiple arrays share the same memory or the array is not the owner of the memory.
- In case of a copy the return value points to the newly allocated memory which is now exclusively owned by the array.
"""
ptr = c_void_ptr_t(0)
backend.get().af_get_device_ptr(c_pointer(ptr), self.arr)
return ptr.value
def raw_ptr(self):
"""
Return the device pointer held by the array.
Returns
--------
ptr : int
Contains location of the device pointer
Note
----
- This can be used to integrate with custom C code and / or PyCUDA or PyOpenCL.
- No mem copy is peformed, this function returns the raw device pointer.
- This pointer may be shared with other arrays. Use this function with caution.
- In particular the JIT compiler will not be aware of the shared arrays.
- This results in JITed operations not being immediately visible through the other array.
"""
ptr = c_void_ptr_t(0)
backend.get().af_get_raw_ptr(c_pointer(ptr), self.arr)
return ptr.value
def offset(self):
"""
Return the offset, of the first element relative to the raw pointer.
Returns
--------
offset : int
The offset in number of elements
"""
offset = c_dim_t(0)
safe_call(backend.get().af_get_offset(c_pointer(offset), self.arr))
return offset.value
def strides(self):
"""
Return the distance in bytes between consecutive elements for each dimension.
Returns
--------
strides : tuple
The strides for each dimension
"""
s0 = c_dim_t(0)
s1 = c_dim_t(0)
s2 = c_dim_t(0)
s3 = c_dim_t(0)
safe_call(backend.get().af_get_strides(c_pointer(s0), c_pointer(s1),
c_pointer(s2), c_pointer(s3), self.arr))
strides = (s0.value,s1.value,s2.value,s3.value)
return strides[:self.numdims()]
def elements(self):
"""
Return the number of elements in the array.
"""
num = c_dim_t(0)
safe_call(backend.get().af_get_elements(c_pointer(num), self.arr))
return num.value
def allocated(self):
"""
Returns the number of bytes allocated by the memory manager for the array.
"""
num = c_size_t(0)
safe_call(backend.get().af_get_allocated_bytes(c_pointer(num), self.arr))
return num.value
def dtype(self):
"""
Return the data type as a arrayfire.Dtype enum value.
"""
dty = c_int_t(Dtype.f32.value)
safe_call(backend.get().af_get_type(c_pointer(dty), self.arr))
return to_dtype[to_typecode[dty.value]]
def type(self):
"""
Return the data type as an int.
"""
return self.dtype().value
@property
def T(self):
"""
Return the transpose of the array
"""
return transpose(self, False)
@property
def H(self):
"""
Return the hermitian transpose of the array
"""
return transpose(self, True)
def dims(self):
"""
Return the shape of the array as a tuple.
"""
d0 = c_dim_t(0)
d1 = c_dim_t(0)
d2 = c_dim_t(0)
d3 = c_dim_t(0)
safe_call(backend.get().af_get_dims(c_pointer(d0), c_pointer(d1),
c_pointer(d2), c_pointer(d3), self.arr))
dims = (d0.value,d1.value,d2.value,d3.value)
return dims[:self.numdims()]
@property
def shape(self):
"""
The shape of the array
"""
return self.dims()
def numdims(self):
"""
Return the number of dimensions of the array.
"""
nd = c_uint_t(0)
safe_call(backend.get().af_get_numdims(c_pointer(nd), self.arr))
return nd.value
def is_empty(self):
"""
Check if the array is empty i.e. it has no elements.
"""
res = c_bool_t(False)
safe_call(backend.get().af_is_empty(c_pointer(res), self.arr))
return res.value
def is_scalar(self):
"""
Check if the array is scalar i.e. it has only one element.
"""
res = c_bool_t(False)
safe_call(backend.get().af_is_scalar(c_pointer(res), self.arr))
return res.value
def is_row(self):
"""
Check if the array is a row i.e. it has a shape of (1, cols).
"""
res = c_bool_t(False)
safe_call(backend.get().af_is_row(c_pointer(res), self.arr))
return res.value
def is_column(self):
"""
Check if the array is a column i.e. it has a shape of (rows, 1).
"""
res = c_bool_t(False)
safe_call(backend.get().af_is_column(c_pointer(res), self.arr))
return res.value
def is_vector(self):
"""
Check if the array is a vector i.e. it has a shape of one of the following:
- (rows, 1)
- (1, cols)
- (1, 1, vols)
- (1, 1, 1, batch)
"""
res = c_bool_t(False)
safe_call(backend.get().af_is_vector(c_pointer(res), self.arr))
return res.value
def is_sparse(self):
"""
Check if the array is a sparse matrix.
"""
res = c_bool_t(False)
safe_call(backend.get().af_is_sparse(c_pointer(res), self.arr))
return res.value
def is_complex(self):
"""
Check if the array is of complex type.
"""
res = c_bool_t(False)
safe_call(backend.get().af_is_complex(c_pointer(res), self.arr))
return res.value
def is_real(self):
"""
Check if the array is not of complex type.
"""
res = c_bool_t(False)
safe_call(backend.get().af_is_real(c_pointer(res), self.arr))
return res.value
def is_double(self):
"""
Check if the array is of double precision floating point type.
"""
res = c_bool_t(False)
safe_call(backend.get().af_is_double(c_pointer(res), self.arr))
return res.value
def is_single(self):
"""
Check if the array is of single precision floating point type.
"""
res = c_bool_t(False)
safe_call(backend.get().af_is_single(c_pointer(res), self.arr))
return res.value
def is_real_floating(self):
"""
Check if the array is real and of floating point type.
"""
res = c_bool_t(False)
safe_call(backend.get().af_is_realfloating(c_pointer(res), self.arr))
return res.value
def is_floating(self):
"""
Check if the array is of floating point type.
"""
res = c_bool_t(False)
safe_call(backend.get().af_is_floating(c_pointer(res), self.arr))
return res.value
def is_integer(self):
"""
Check if the array is of integer type.
"""
res = c_bool_t(False)
safe_call(backend.get().af_is_integer(c_pointer(res), self.arr))
return res.value
def is_bool(self):
"""
Check if the array is of type b8.
"""
res = c_bool_t(False)
safe_call(backend.get().af_is_bool(c_pointer(res), self.arr))
return res.value
def is_linear(self):
"""
Check if all elements of the array are contiguous.
"""
res = c_bool_t(False)
safe_call(backend.get().af_is_linear(c_pointer(res), self.arr))
return res.value
def is_owner(self):
"""
Check if the array owns the raw pointer or is a derived array.
"""
res = c_bool_t(False)
safe_call(backend.get().af_is_owner(c_pointer(res), self.arr))
return res.value
def __add__(self, other):
"""
Return self + other.
"""
return _binary_func(self, other, backend.get().af_add)
def __iadd__(self, other):
"""
Perform self += other.
"""
self = _binary_func(self, other, backend.get().af_add)
return self
def __radd__(self, other):
"""
Return other + self.
"""
return _binary_funcr(other, self, backend.get().af_add)
def __sub__(self, other):
"""
Return self - other.
"""
return _binary_func(self, other, backend.get().af_sub)
def __isub__(self, other):
"""
Perform self -= other.
"""
self = _binary_func(self, other, backend.get().af_sub)
return self
def __rsub__(self, other):
"""
Return other - self.
"""
return _binary_funcr(other, self, backend.get().af_sub)
def __mul__(self, other):
"""
Return self * other.
"""
return _binary_func(self, other, backend.get().af_mul)
def __imul__(self, other):
"""
Perform self *= other.
"""
self = _binary_func(self, other, backend.get().af_mul)
return self
def __rmul__(self, other):
"""
Return other * self.
"""
return _binary_funcr(other, self, backend.get().af_mul)
def __truediv__(self, other):
"""
Return self / other.
"""
return _binary_func(self, other, backend.get().af_div)
def __itruediv__(self, other):
"""
Perform self /= other.
"""
self = _binary_func(self, other, backend.get().af_div)
return self
def __rtruediv__(self, other):
"""
Return other / self.
"""
return _binary_funcr(other, self, backend.get().af_div)
def __div__(self, other):
"""
Return self / other.
"""
return _binary_func(self, other, backend.get().af_div)
def __idiv__(self, other):
"""
Perform other / self.
"""
self = _binary_func(self, other, backend.get().af_div)
return self
def __rdiv__(self, other):
"""
Return other / self.
"""
return _binary_funcr(other, self, backend.get().af_div)
def __mod__(self, other):
"""
Return self % other.
"""
return _binary_func(self, other, backend.get().af_mod)
def __imod__(self, other):
"""
Perform self %= other.
"""
self = _binary_func(self, other, backend.get().af_mod)
return self
def __rmod__(self, other):
"""
Return other % self.
"""
return _binary_funcr(other, self, backend.get().af_mod)
def __pow__(self, other):
"""
Return self ** other.
"""
return _binary_func(self, other, backend.get().af_pow)
def __ipow__(self, other):
"""
Perform self **= other.
"""
self = _binary_func(self, other, backend.get().af_pow)
return self
def __rpow__(self, other):
"""
Return other ** self.
"""
return _binary_funcr(other, self, backend.get().af_pow)
def __lt__(self, other):
"""
Return self < other.
"""
return _binary_func(self, other, backend.get().af_lt)
def __gt__(self, other):
"""
Return self > other.
"""
return _binary_func(self, other, backend.get().af_gt)
def __le__(self, other):
"""
Return self <= other.
"""
return _binary_func(self, other, backend.get().af_le)
def __ge__(self, other):
"""
Return self >= other.
"""
return _binary_func(self, other, backend.get().af_ge)
def __eq__(self, other):
"""
Return self == other.
"""
return _binary_func(self, other, backend.get().af_eq)
def __ne__(self, other):
"""
Return self != other.
"""
return _binary_func(self, other, backend.get().af_neq)
def __and__(self, other):
"""
Return self & other.