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1
+ #ifndef NUMPY_CORE_INCLUDE_NUMPY_NDARRAYTYPES_H_
2
+ #define NUMPY_CORE_INCLUDE_NUMPY_NDARRAYTYPES_H_
3
+
4
+ #include "npy_common.h"
5
+ #include "npy_endian.h"
6
+ #include "npy_cpu.h"
7
+ #include "utils.h"
8
+
9
+ #define NPY_NO_EXPORT NPY_VISIBILITY_HIDDEN
10
+
11
+ /* Only use thread if configured in config and python supports it */
12
+ #if defined WITH_THREAD && !NPY_NO_SMP
13
+ #define NPY_ALLOW_THREADS 1
14
+ #else
15
+ #define NPY_ALLOW_THREADS 0
16
+ #endif
17
+
18
+ #ifndef __has_extension
19
+ #define __has_extension(x) 0
20
+ #endif
21
+
22
+ #if !defined(_NPY_NO_DEPRECATIONS) && \
23
+ ((defined(__GNUC__)&& __GNUC__ >= 6) || \
24
+ __has_extension(attribute_deprecated_with_message))
25
+ #define NPY_ATTR_DEPRECATE(text) __attribute__ ((deprecated (text)))
26
+ #else
27
+ #define NPY_ATTR_DEPRECATE(text)
28
+ #endif
29
+
30
+ /*
31
+ * There are several places in the code where an array of dimensions
32
+ * is allocated statically. This is the size of that static
33
+ * allocation.
34
+ *
35
+ * The array creation itself could have arbitrary dimensions but all
36
+ * the places where static allocation is used would need to be changed
37
+ * to dynamic (including inside of several structures)
38
+ */
39
+
40
+ #define NPY_MAXDIMS 32
41
+ #define NPY_MAXARGS 32
42
+
43
+ /* Used for Converter Functions "O&" code in ParseTuple */
44
+ #define NPY_FAIL 0
45
+ #define NPY_SUCCEED 1
46
+
47
+
48
+ enum NPY_TYPES { NPY_BOOL=0,
49
+ NPY_BYTE, NPY_UBYTE,
50
+ NPY_SHORT, NPY_USHORT,
51
+ NPY_INT, NPY_UINT,
52
+ NPY_LONG, NPY_ULONG,
53
+ NPY_LONGLONG, NPY_ULONGLONG,
54
+ NPY_FLOAT, NPY_DOUBLE, NPY_LONGDOUBLE,
55
+ NPY_CFLOAT, NPY_CDOUBLE, NPY_CLONGDOUBLE,
56
+ NPY_OBJECT=17,
57
+ NPY_STRING, NPY_UNICODE,
58
+ NPY_VOID,
59
+ /*
60
+ * New 1.6 types appended, may be integrated
61
+ * into the above in 2.0.
62
+ */
63
+ NPY_DATETIME, NPY_TIMEDELTA, NPY_HALF,
64
+
65
+ NPY_NTYPES,
66
+ NPY_NOTYPE,
67
+ NPY_CHAR NPY_ATTR_DEPRECATE("Use NPY_STRING"),
68
+ NPY_USERDEF=256, /* leave room for characters */
69
+
70
+ /* The number of types not including the new 1.6 types */
71
+ NPY_NTYPES_ABI_COMPATIBLE=21
72
+ };
73
+ #if defined(_MSC_VER) && !defined(__clang__)
74
+ #pragma deprecated(NPY_CHAR)
75
+ #endif
76
+
77
+ /* basetype array priority */
78
+ #define NPY_PRIORITY 0.0
79
+
80
+ /* default subtype priority */
81
+ #define NPY_SUBTYPE_PRIORITY 1.0
82
+
83
+ /* default scalar priority */
84
+ #define NPY_SCALAR_PRIORITY -1000000.0
85
+
86
+ /* How many floating point types are there (excluding half) */
87
+ #define NPY_NUM_FLOATTYPE 3
88
+
89
+ /*
90
+ * These characters correspond to the array type and the struct
91
+ * module
92
+ */
93
+
94
+ enum NPY_TYPECHAR {
95
+ NPY_BOOLLTR = '?',
96
+ NPY_BYTELTR = 'b',
97
+ NPY_UBYTELTR = 'B',
98
+ NPY_SHORTLTR = 'h',
99
+ NPY_USHORTLTR = 'H',
100
+ NPY_INTLTR = 'i',
101
+ NPY_UINTLTR = 'I',
102
+ NPY_LONGLTR = 'l',
103
+ NPY_ULONGLTR = 'L',
104
+ NPY_LONGLONGLTR = 'q',
105
+ NPY_ULONGLONGLTR = 'Q',
106
+ NPY_HALFLTR = 'e',
107
+ NPY_FLOATLTR = 'f',
108
+ NPY_DOUBLELTR = 'd',
109
+ NPY_LONGDOUBLELTR = 'g',
110
+ NPY_CFLOATLTR = 'F',
111
+ NPY_CDOUBLELTR = 'D',
112
+ NPY_CLONGDOUBLELTR = 'G',
113
+ NPY_OBJECTLTR = 'O',
114
+ NPY_STRINGLTR = 'S',
115
+ NPY_STRINGLTR2 = 'a',
116
+ NPY_UNICODELTR = 'U',
117
+ NPY_VOIDLTR = 'V',
118
+ NPY_DATETIMELTR = 'M',
119
+ NPY_TIMEDELTALTR = 'm',
120
+ NPY_CHARLTR = 'c',
121
+
122
+ /*
123
+ * No Descriptor, just a define -- this let's
124
+ * Python users specify an array of integers
125
+ * large enough to hold a pointer on the
126
+ * platform
127
+ */
128
+ NPY_INTPLTR = 'p',
129
+ NPY_UINTPLTR = 'P',
130
+
131
+ /*
132
+ * These are for dtype 'kinds', not dtype 'typecodes'
133
+ * as the above are for.
134
+ */
135
+ NPY_GENBOOLLTR ='b',
136
+ NPY_SIGNEDLTR = 'i',
137
+ NPY_UNSIGNEDLTR = 'u',
138
+ NPY_FLOATINGLTR = 'f',
139
+ NPY_COMPLEXLTR = 'c'
140
+ };
141
+
142
+ /*
143
+ * Changing this may break Numpy API compatibility
144
+ * due to changing offsets in PyArray_ArrFuncs, so be
145
+ * careful. Here we have reused the mergesort slot for
146
+ * any kind of stable sort, the actual implementation will
147
+ * depend on the data type.
148
+ */
149
+ typedef enum {
150
+ NPY_QUICKSORT=0,
151
+ NPY_HEAPSORT=1,
152
+ NPY_MERGESORT=2,
153
+ NPY_STABLESORT=2,
154
+ } NPY_SORTKIND;
155
+ #define NPY_NSORTS (NPY_STABLESORT + 1)
156
+
157
+
158
+ typedef enum {
159
+ NPY_INTROSELECT=0
160
+ } NPY_SELECTKIND;
161
+ #define NPY_NSELECTS (NPY_INTROSELECT + 1)
162
+
163
+
164
+ typedef enum {
165
+ NPY_SEARCHLEFT=0,
166
+ NPY_SEARCHRIGHT=1
167
+ } NPY_SEARCHSIDE;
168
+ #define NPY_NSEARCHSIDES (NPY_SEARCHRIGHT + 1)
169
+
170
+
171
+ typedef enum {
172
+ NPY_NOSCALAR=-1,
173
+ NPY_BOOL_SCALAR,
174
+ NPY_INTPOS_SCALAR,
175
+ NPY_INTNEG_SCALAR,
176
+ NPY_FLOAT_SCALAR,
177
+ NPY_COMPLEX_SCALAR,
178
+ NPY_OBJECT_SCALAR
179
+ } NPY_SCALARKIND;
180
+ #define NPY_NSCALARKINDS (NPY_OBJECT_SCALAR + 1)
181
+
182
+ /* For specifying array memory layout or iteration order */
183
+ typedef enum {
184
+ /* Fortran order if inputs are all Fortran, C otherwise */
185
+ NPY_ANYORDER=-1,
186
+ /* C order */
187
+ NPY_CORDER=0,
188
+ /* Fortran order */
189
+ NPY_FORTRANORDER=1,
190
+ /* An order as close to the inputs as possible */
191
+ NPY_KEEPORDER=2
192
+ } NPY_ORDER;
193
+
194
+ /* For specifying allowed casting in operations which support it */
195
+ typedef enum {
196
+ _NPY_ERROR_OCCURRED_IN_CAST = -1,
197
+ /* Only allow identical types */
198
+ NPY_NO_CASTING=0,
199
+ /* Allow identical and byte swapped types */
200
+ NPY_EQUIV_CASTING=1,
201
+ /* Only allow safe casts */
202
+ NPY_SAFE_CASTING=2,
203
+ /* Allow safe casts or casts within the same kind */
204
+ NPY_SAME_KIND_CASTING=3,
205
+ /* Allow any casts */
206
+ NPY_UNSAFE_CASTING=4,
207
+ } NPY_CASTING;
208
+
209
+ typedef enum {
210
+ NPY_CLIP=0,
211
+ NPY_WRAP=1,
212
+ NPY_RAISE=2
213
+ } NPY_CLIPMODE;
214
+
215
+ typedef enum {
216
+ NPY_VALID=0,
217
+ NPY_SAME=1,
218
+ NPY_FULL=2
219
+ } NPY_CORRELATEMODE;
220
+
221
+ /* The special not-a-time (NaT) value */
222
+ #define NPY_DATETIME_NAT NPY_MIN_INT64
223
+
224
+ /*
225
+ * Upper bound on the length of a DATETIME ISO 8601 string
226
+ * YEAR: 21 (64-bit year)
227
+ * MONTH: 3
228
+ * DAY: 3
229
+ * HOURS: 3
230
+ * MINUTES: 3
231
+ * SECONDS: 3
232
+ * ATTOSECONDS: 1 + 3*6
233
+ * TIMEZONE: 5
234
+ * NULL TERMINATOR: 1
235
+ */
236
+ #define NPY_DATETIME_MAX_ISO8601_STRLEN (21 + 3*5 + 1 + 3*6 + 6 + 1)
237
+
238
+ /* The FR in the unit names stands for frequency */
239
+ typedef enum {
240
+ /* Force signed enum type, must be -1 for code compatibility */
241
+ NPY_FR_ERROR = -1, /* error or undetermined */
242
+
243
+ /* Start of valid units */
244
+ NPY_FR_Y = 0, /* Years */
245
+ NPY_FR_M = 1, /* Months */
246
+ NPY_FR_W = 2, /* Weeks */
247
+ /* Gap where 1.6 NPY_FR_B (value 3) was */
248
+ NPY_FR_D = 4, /* Days */
249
+ NPY_FR_h = 5, /* hours */
250
+ NPY_FR_m = 6, /* minutes */
251
+ NPY_FR_s = 7, /* seconds */
252
+ NPY_FR_ms = 8, /* milliseconds */
253
+ NPY_FR_us = 9, /* microseconds */
254
+ NPY_FR_ns = 10, /* nanoseconds */
255
+ NPY_FR_ps = 11, /* picoseconds */
256
+ NPY_FR_fs = 12, /* femtoseconds */
257
+ NPY_FR_as = 13, /* attoseconds */
258
+ NPY_FR_GENERIC = 14 /* unbound units, can convert to anything */
259
+ } NPY_DATETIMEUNIT;
260
+
261
+ /*
262
+ * NOTE: With the NPY_FR_B gap for 1.6 ABI compatibility, NPY_DATETIME_NUMUNITS
263
+ * is technically one more than the actual number of units.
264
+ */
265
+ #define NPY_DATETIME_NUMUNITS (NPY_FR_GENERIC + 1)
266
+ #define NPY_DATETIME_DEFAULTUNIT NPY_FR_GENERIC
267
+
268
+ /*
269
+ * Business day conventions for mapping invalid business
270
+ * days to valid business days.
271
+ */
272
+ typedef enum {
273
+ /* Go forward in time to the following business day. */
274
+ NPY_BUSDAY_FORWARD,
275
+ NPY_BUSDAY_FOLLOWING = NPY_BUSDAY_FORWARD,
276
+ /* Go backward in time to the preceding business day. */
277
+ NPY_BUSDAY_BACKWARD,
278
+ NPY_BUSDAY_PRECEDING = NPY_BUSDAY_BACKWARD,
279
+ /*
280
+ * Go forward in time to the following business day, unless it
281
+ * crosses a month boundary, in which case go backward
282
+ */
283
+ NPY_BUSDAY_MODIFIEDFOLLOWING,
284
+ /*
285
+ * Go backward in time to the preceding business day, unless it
286
+ * crosses a month boundary, in which case go forward.
287
+ */
288
+ NPY_BUSDAY_MODIFIEDPRECEDING,
289
+ /* Produce a NaT for non-business days. */
290
+ NPY_BUSDAY_NAT,
291
+ /* Raise an exception for non-business days. */
292
+ NPY_BUSDAY_RAISE
293
+ } NPY_BUSDAY_ROLL;
294
+
295
+ /************************************************************
296
+ * NumPy Auxiliary Data for inner loops, sort functions, etc.
297
+ ************************************************************/
298
+
299
+ /*
300
+ * When creating an auxiliary data struct, this should always appear
301
+ * as the first member, like this:
302
+ *
303
+ * typedef struct {
304
+ * NpyAuxData base;
305
+ * double constant;
306
+ * } constant_multiplier_aux_data;
307
+ */
308
+ typedef struct NpyAuxData_tag NpyAuxData;
309
+
310
+ /* Function pointers for freeing or cloning auxiliary data */
311
+ typedef void (NpyAuxData_FreeFunc) (NpyAuxData *);
312
+ typedef NpyAuxData *(NpyAuxData_CloneFunc) (NpyAuxData *);
313
+
314
+ struct NpyAuxData_tag {
315
+ NpyAuxData_FreeFunc *free;
316
+ NpyAuxData_CloneFunc *clone;
317
+ /* To allow for a bit of expansion without breaking the ABI */
318
+ void *reserved[2];
319
+ };
320
+
321
+ /* Macros to use for freeing and cloning auxiliary data */
322
+ #define NPY_AUXDATA_FREE(auxdata) \
323
+ do { \
324
+ if ((auxdata) != NULL) { \
325
+ (auxdata)->free(auxdata); \
326
+ } \
327
+ } while(0)
328
+ #define NPY_AUXDATA_CLONE(auxdata) \
329
+ ((auxdata)->clone(auxdata))
330
+
331
+ #define NPY_ERR(str) fprintf(stderr, #str); fflush(stderr);
332
+ #define NPY_ERR2(str) fprintf(stderr, str); fflush(stderr);
333
+
334
+ /*
335
+ * Macros to define how array, and dimension/strides data is
336
+ * allocated. These should be made private
337
+ */
338
+
339
+ #define NPY_USE_PYMEM 1
340
+
341
+
342
+ #if NPY_USE_PYMEM == 1
343
+ /* use the Raw versions which are safe to call with the GIL released */
344
+ #define PyArray_malloc PyMem_RawMalloc
345
+ #define PyArray_free PyMem_RawFree
346
+ #define PyArray_realloc PyMem_RawRealloc
347
+ #else
348
+ #define PyArray_malloc malloc
349
+ #define PyArray_free free
350
+ #define PyArray_realloc realloc
351
+ #endif
352
+
353
+ /* Dimensions and strides */
354
+ #define PyDimMem_NEW(size) \
355
+ ((npy_intp *)PyArray_malloc(size*sizeof(npy_intp)))
356
+
357
+ #define PyDimMem_FREE(ptr) PyArray_free(ptr)
358
+
359
+ #define PyDimMem_RENEW(ptr,size) \
360
+ ((npy_intp *)PyArray_realloc(ptr,size*sizeof(npy_intp)))
361
+
362
+ /* forward declaration */
363
+ struct _PyArray_Descr;
364
+
365
+ /* These must deal with unaligned and swapped data if necessary */
366
+ typedef PyObject * (PyArray_GetItemFunc) (void *, void *);
367
+ typedef int (PyArray_SetItemFunc)(PyObject *, void *, void *);
368
+
369
+ typedef void (PyArray_CopySwapNFunc)(void *, npy_intp, void *, npy_intp,
370
+ npy_intp, int, void *);
371
+
372
+ typedef void (PyArray_CopySwapFunc)(void *, void *, int, void *);
373
+ typedef npy_bool (PyArray_NonzeroFunc)(void *, void *);
374
+
375
+
376
+ /*
377
+ * These assume aligned and notswapped data -- a buffer will be used
378
+ * before or contiguous data will be obtained
379
+ */
380
+
381
+ typedef int (PyArray_CompareFunc)(const void *, const void *, void *);
382
+ typedef int (PyArray_ArgFunc)(void*, npy_intp, npy_intp*, void *);
383
+
384
+ typedef void (PyArray_DotFunc)(void *, npy_intp, void *, npy_intp, void *,
385
+ npy_intp, void *);
386
+
387
+ typedef void (PyArray_VectorUnaryFunc)(void *, void *, npy_intp, void *,
388
+ void *);
389
+
390
+ /*
391
+ * XXX the ignore argument should be removed next time the API version
392
+ * is bumped. It used to be the separator.
393
+ */
394
+ typedef int (PyArray_ScanFunc)(FILE *fp, void *dptr,
395
+ char *ignore, struct _PyArray_Descr *);
396
+ typedef int (PyArray_FromStrFunc)(char *s, void *dptr, char **endptr,
397
+ struct _PyArray_Descr *);
398
+
399
+ typedef int (PyArray_FillFunc)(void *, npy_intp, void *);
400
+
401
+ typedef int (PyArray_SortFunc)(void *, npy_intp, void *);
402
+ typedef int (PyArray_ArgSortFunc)(void *, npy_intp *, npy_intp, void *);
403
+ typedef int (PyArray_PartitionFunc)(void *, npy_intp, npy_intp,
404
+ npy_intp *, npy_intp *,
405
+ void *);
406
+ typedef int (PyArray_ArgPartitionFunc)(void *, npy_intp *, npy_intp, npy_intp,
407
+ npy_intp *, npy_intp *,
408
+ void *);
409
+
410
+ typedef int (PyArray_FillWithScalarFunc)(void *, npy_intp, void *, void *);
411
+
412
+ typedef int (PyArray_ScalarKindFunc)(void *);
413
+
414
+ typedef void (PyArray_FastClipFunc)(void *in, npy_intp n_in, void *min,
415
+ void *max, void *out);
416
+ typedef void (PyArray_FastPutmaskFunc)(void *in, void *mask, npy_intp n_in,
417
+ void *values, npy_intp nv);
418
+ typedef int (PyArray_FastTakeFunc)(void *dest, void *src, npy_intp *indarray,
419
+ npy_intp nindarray, npy_intp n_outer,
420
+ npy_intp m_middle, npy_intp nelem,
421
+ NPY_CLIPMODE clipmode);
422
+
423
+ typedef struct {
424
+ npy_intp *ptr;
425
+ int len;
426
+ } PyArray_Dims;
427
+
428
+ typedef struct {
429
+ /*
430
+ * Functions to cast to most other standard types
431
+ * Can have some NULL entries. The types
432
+ * DATETIME, TIMEDELTA, and HALF go into the castdict
433
+ * even though they are built-in.
434
+ */
435
+ PyArray_VectorUnaryFunc *cast[NPY_NTYPES_ABI_COMPATIBLE];
436
+
437
+ /* The next four functions *cannot* be NULL */
438
+
439
+ /*
440
+ * Functions to get and set items with standard Python types
441
+ * -- not array scalars
442
+ */
443
+ PyArray_GetItemFunc *getitem;
444
+ PyArray_SetItemFunc *setitem;
445
+
446
+ /*
447
+ * Copy and/or swap data. Memory areas may not overlap
448
+ * Use memmove first if they might
449
+ */
450
+ PyArray_CopySwapNFunc *copyswapn;
451
+ PyArray_CopySwapFunc *copyswap;
452
+
453
+ /*
454
+ * Function to compare items
455
+ * Can be NULL
456
+ */
457
+ PyArray_CompareFunc *compare;
458
+
459
+ /*
460
+ * Function to select largest
461
+ * Can be NULL
462
+ */
463
+ PyArray_ArgFunc *argmax;
464
+
465
+ /*
466
+ * Function to compute dot product
467
+ * Can be NULL
468
+ */
469
+ PyArray_DotFunc *dotfunc;
470
+
471
+ /*
472
+ * Function to scan an ASCII file and
473
+ * place a single value plus possible separator
474
+ * Can be NULL
475
+ */
476
+ PyArray_ScanFunc *scanfunc;
477
+
478
+ /*
479
+ * Function to read a single value from a string
480
+ * and adjust the pointer; Can be NULL
481
+ */
482
+ PyArray_FromStrFunc *fromstr;
483
+
484
+ /*
485
+ * Function to determine if data is zero or not
486
+ * If NULL a default version is
487
+ * used at Registration time.
488
+ */
489
+ PyArray_NonzeroFunc *nonzero;
490
+
491
+ /*
492
+ * Used for arange. Should return 0 on success
493
+ * and -1 on failure.
494
+ * Can be NULL.
495
+ */
496
+ PyArray_FillFunc *fill;
497
+
498
+ /*
499
+ * Function to fill arrays with scalar values
500
+ * Can be NULL
501
+ */
502
+ PyArray_FillWithScalarFunc *fillwithscalar;
503
+
504
+ /*
505
+ * Sorting functions
506
+ * Can be NULL
507
+ */
508
+ PyArray_SortFunc *sort[NPY_NSORTS];
509
+ PyArray_ArgSortFunc *argsort[NPY_NSORTS];
510
+
511
+ /*
512
+ * Dictionary of additional casting functions
513
+ * PyArray_VectorUnaryFuncs
514
+ * which can be populated to support casting
515
+ * to other registered types. Can be NULL
516
+ */
517
+ PyObject *castdict;
518
+
519
+ /*
520
+ * Functions useful for generalizing
521
+ * the casting rules.
522
+ * Can be NULL;
523
+ */
524
+ PyArray_ScalarKindFunc *scalarkind;
525
+ int **cancastscalarkindto;
526
+ int *cancastto;
527
+
528
+ PyArray_FastClipFunc *fastclip;
529
+ PyArray_FastPutmaskFunc *fastputmask;
530
+ PyArray_FastTakeFunc *fasttake;
531
+
532
+ /*
533
+ * Function to select smallest
534
+ * Can be NULL
535
+ */
536
+ PyArray_ArgFunc *argmin;
537
+
538
+ } PyArray_ArrFuncs;
539
+
540
+ /* The item must be reference counted when it is inserted or extracted. */
541
+ #define NPY_ITEM_REFCOUNT 0x01
542
+ /* Same as needing REFCOUNT */
543
+ #define NPY_ITEM_HASOBJECT 0x01
544
+ /* Convert to list for pickling */
545
+ #define NPY_LIST_PICKLE 0x02
546
+ /* The item is a POINTER */
547
+ #define NPY_ITEM_IS_POINTER 0x04
548
+ /* memory needs to be initialized for this data-type */
549
+ #define NPY_NEEDS_INIT 0x08
550
+ /* operations need Python C-API so don't give-up thread. */
551
+ #define NPY_NEEDS_PYAPI 0x10
552
+ /* Use f.getitem when extracting elements of this data-type */
553
+ #define NPY_USE_GETITEM 0x20
554
+ /* Use f.setitem when setting creating 0-d array from this data-type.*/
555
+ #define NPY_USE_SETITEM 0x40
556
+ /* A sticky flag specifically for structured arrays */
557
+ #define NPY_ALIGNED_STRUCT 0x80
558
+
559
+ /*
560
+ *These are inherited for global data-type if any data-types in the
561
+ * field have them
562
+ */
563
+ #define NPY_FROM_FIELDS (NPY_NEEDS_INIT | NPY_LIST_PICKLE | \
564
+ NPY_ITEM_REFCOUNT | NPY_NEEDS_PYAPI)
565
+
566
+ #define NPY_OBJECT_DTYPE_FLAGS (NPY_LIST_PICKLE | NPY_USE_GETITEM | \
567
+ NPY_ITEM_IS_POINTER | NPY_ITEM_REFCOUNT | \
568
+ NPY_NEEDS_INIT | NPY_NEEDS_PYAPI)
569
+
570
+ #define PyDataType_FLAGCHK(dtype, flag) \
571
+ (((dtype)->flags & (flag)) == (flag))
572
+
573
+ #define PyDataType_REFCHK(dtype) \
574
+ PyDataType_FLAGCHK(dtype, NPY_ITEM_REFCOUNT)
575
+
576
+ typedef struct _PyArray_Descr {
577
+ PyObject_HEAD
578
+ /*
579
+ * the type object representing an
580
+ * instance of this type -- should not
581
+ * be two type_numbers with the same type
582
+ * object.
583
+ */
584
+ PyTypeObject *typeobj;
585
+ /* kind for this type */
586
+ char kind;
587
+ /* unique-character representing this type */
588
+ char type;
589
+ /*
590
+ * '>' (big), '<' (little), '|'
591
+ * (not-applicable), or '=' (native).
592
+ */
593
+ char byteorder;
594
+ /* flags describing data type */
595
+ char flags;
596
+ /* number representing this type */
597
+ int type_num;
598
+ /* element size (itemsize) for this type */
599
+ int elsize;
600
+ /* alignment needed for this type */
601
+ int alignment;
602
+ /*
603
+ * Non-NULL if this type is
604
+ * is an array (C-contiguous)
605
+ * of some other type
606
+ */
607
+ struct _arr_descr *subarray;
608
+ /*
609
+ * The fields dictionary for this type
610
+ * For statically defined descr this
611
+ * is always Py_None
612
+ */
613
+ PyObject *fields;
614
+ /*
615
+ * An ordered tuple of field names or NULL
616
+ * if no fields are defined
617
+ */
618
+ PyObject *names;
619
+ /*
620
+ * a table of functions specific for each
621
+ * basic data descriptor
622
+ */
623
+ PyArray_ArrFuncs *f;
624
+ /* Metadata about this dtype */
625
+ PyObject *metadata;
626
+ /*
627
+ * Metadata specific to the C implementation
628
+ * of the particular dtype. This was added
629
+ * for NumPy 1.7.0.
630
+ */
631
+ NpyAuxData *c_metadata;
632
+ /* Cached hash value (-1 if not yet computed).
633
+ * This was added for NumPy 2.0.0.
634
+ */
635
+ npy_hash_t hash;
636
+ } PyArray_Descr;
637
+
638
+ typedef struct _arr_descr {
639
+ PyArray_Descr *base;
640
+ PyObject *shape; /* a tuple */
641
+ } PyArray_ArrayDescr;
642
+
643
+ /*
644
+ * Memory handler structure for array data.
645
+ */
646
+ /* The declaration of free differs from PyMemAllocatorEx */
647
+ typedef struct {
648
+ void *ctx;
649
+ void* (*malloc) (void *ctx, size_t size);
650
+ void* (*calloc) (void *ctx, size_t nelem, size_t elsize);
651
+ void* (*realloc) (void *ctx, void *ptr, size_t new_size);
652
+ void (*free) (void *ctx, void *ptr, size_t size);
653
+ /*
654
+ * This is the end of the version=1 struct. Only add new fields after
655
+ * this line
656
+ */
657
+ } PyDataMemAllocator;
658
+
659
+ typedef struct {
660
+ char name[127]; /* multiple of 64 to keep the struct aligned */
661
+ uint8_t version; /* currently 1 */
662
+ PyDataMemAllocator allocator;
663
+ } PyDataMem_Handler;
664
+
665
+
666
+ /*
667
+ * The main array object structure.
668
+ *
669
+ * It has been recommended to use the inline functions defined below
670
+ * (PyArray_DATA and friends) to access fields here for a number of
671
+ * releases. Direct access to the members themselves is deprecated.
672
+ * To ensure that your code does not use deprecated access,
673
+ * #define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
674
+ * (or NPY_1_8_API_VERSION or higher as required).
675
+ */
676
+ /* This struct will be moved to a private header in a future release */
677
+ typedef struct tagPyArrayObject_fields {
678
+ PyObject_HEAD
679
+ /* Pointer to the raw data buffer */
680
+ char *data;
681
+ /* The number of dimensions, also called 'ndim' */
682
+ int nd;
683
+ /* The size in each dimension, also called 'shape' */
684
+ npy_intp *dimensions;
685
+ /*
686
+ * Number of bytes to jump to get to the
687
+ * next element in each dimension
688
+ */
689
+ npy_intp *strides;
690
+ /*
691
+ * This object is decref'd upon
692
+ * deletion of array. Except in the
693
+ * case of WRITEBACKIFCOPY which has
694
+ * special handling.
695
+ *
696
+ * For views it points to the original
697
+ * array, collapsed so no chains of
698
+ * views occur.
699
+ *
700
+ * For creation from buffer object it
701
+ * points to an object that should be
702
+ * decref'd on deletion
703
+ *
704
+ * For WRITEBACKIFCOPY flag this is an
705
+ * array to-be-updated upon calling
706
+ * PyArray_ResolveWritebackIfCopy
707
+ */
708
+ PyObject *base;
709
+ /* Pointer to type structure */
710
+ PyArray_Descr *descr;
711
+ /* Flags describing array -- see below */
712
+ int flags;
713
+ /* For weak references */
714
+ PyObject *weakreflist;
715
+ #if NPY_FEATURE_VERSION >= NPY_1_20_API_VERSION
716
+ void *_buffer_info; /* private buffer info, tagged to allow warning */
717
+ #endif
718
+ /*
719
+ * For malloc/calloc/realloc/free per object
720
+ */
721
+ #if NPY_FEATURE_VERSION >= NPY_1_22_API_VERSION
722
+ PyObject *mem_handler;
723
+ #endif
724
+ } PyArrayObject_fields;
725
+
726
+ /*
727
+ * To hide the implementation details, we only expose
728
+ * the Python struct HEAD.
729
+ */
730
+ #if !defined(NPY_NO_DEPRECATED_API) || \
731
+ (NPY_NO_DEPRECATED_API < NPY_1_7_API_VERSION)
732
+ /*
733
+ * Can't put this in npy_deprecated_api.h like the others.
734
+ * PyArrayObject field access is deprecated as of NumPy 1.7.
735
+ */
736
+ typedef PyArrayObject_fields PyArrayObject;
737
+ #else
738
+ typedef struct tagPyArrayObject {
739
+ PyObject_HEAD
740
+ } PyArrayObject;
741
+ #endif
742
+
743
+ /*
744
+ * Removed 2020-Nov-25, NumPy 1.20
745
+ * #define NPY_SIZEOF_PYARRAYOBJECT (sizeof(PyArrayObject_fields))
746
+ *
747
+ * The above macro was removed as it gave a false sense of a stable ABI
748
+ * with respect to the structures size. If you require a runtime constant,
749
+ * you can use `PyArray_Type.tp_basicsize` instead. Otherwise, please
750
+ * see the PyArrayObject documentation or ask the NumPy developers for
751
+ * information on how to correctly replace the macro in a way that is
752
+ * compatible with multiple NumPy versions.
753
+ */
754
+
755
+
756
+ /* Array Flags Object */
757
+ typedef struct PyArrayFlagsObject {
758
+ PyObject_HEAD
759
+ PyObject *arr;
760
+ int flags;
761
+ } PyArrayFlagsObject;
762
+
763
+ /* Mirrors buffer object to ptr */
764
+
765
+ typedef struct {
766
+ PyObject_HEAD
767
+ PyObject *base;
768
+ void *ptr;
769
+ npy_intp len;
770
+ int flags;
771
+ } PyArray_Chunk;
772
+
773
+ typedef struct {
774
+ NPY_DATETIMEUNIT base;
775
+ int num;
776
+ } PyArray_DatetimeMetaData;
777
+
778
+ typedef struct {
779
+ NpyAuxData base;
780
+ PyArray_DatetimeMetaData meta;
781
+ } PyArray_DatetimeDTypeMetaData;
782
+
783
+ /*
784
+ * This structure contains an exploded view of a date-time value.
785
+ * NaT is represented by year == NPY_DATETIME_NAT.
786
+ */
787
+ typedef struct {
788
+ npy_int64 year;
789
+ npy_int32 month, day, hour, min, sec, us, ps, as;
790
+ } npy_datetimestruct;
791
+
792
+ /* This is not used internally. */
793
+ typedef struct {
794
+ npy_int64 day;
795
+ npy_int32 sec, us, ps, as;
796
+ } npy_timedeltastruct;
797
+
798
+ typedef int (PyArray_FinalizeFunc)(PyArrayObject *, PyObject *);
799
+
800
+ /*
801
+ * Means c-style contiguous (last index varies the fastest). The data
802
+ * elements right after each other.
803
+ *
804
+ * This flag may be requested in constructor functions.
805
+ * This flag may be tested for in PyArray_FLAGS(arr).
806
+ */
807
+ #define NPY_ARRAY_C_CONTIGUOUS 0x0001
808
+
809
+ /*
810
+ * Set if array is a contiguous Fortran array: the first index varies
811
+ * the fastest in memory (strides array is reverse of C-contiguous
812
+ * array)
813
+ *
814
+ * This flag may be requested in constructor functions.
815
+ * This flag may be tested for in PyArray_FLAGS(arr).
816
+ */
817
+ #define NPY_ARRAY_F_CONTIGUOUS 0x0002
818
+
819
+ /*
820
+ * Note: all 0-d arrays are C_CONTIGUOUS and F_CONTIGUOUS. If a
821
+ * 1-d array is C_CONTIGUOUS it is also F_CONTIGUOUS. Arrays with
822
+ * more then one dimension can be C_CONTIGUOUS and F_CONTIGUOUS
823
+ * at the same time if they have either zero or one element.
824
+ * A higher dimensional array always has the same contiguity flags as
825
+ * `array.squeeze()`; dimensions with `array.shape[dimension] == 1` are
826
+ * effectively ignored when checking for contiguity.
827
+ */
828
+
829
+ /*
830
+ * If set, the array owns the data: it will be free'd when the array
831
+ * is deleted.
832
+ *
833
+ * This flag may be tested for in PyArray_FLAGS(arr).
834
+ */
835
+ #define NPY_ARRAY_OWNDATA 0x0004
836
+
837
+ /*
838
+ * An array never has the next four set; they're only used as parameter
839
+ * flags to the various FromAny functions
840
+ *
841
+ * This flag may be requested in constructor functions.
842
+ */
843
+
844
+ /* Cause a cast to occur regardless of whether or not it is safe. */
845
+ #define NPY_ARRAY_FORCECAST 0x0010
846
+
847
+ /*
848
+ * Always copy the array. Returned arrays are always CONTIGUOUS,
849
+ * ALIGNED, and WRITEABLE. See also: NPY_ARRAY_ENSURENOCOPY = 0x4000.
850
+ *
851
+ * This flag may be requested in constructor functions.
852
+ */
853
+ #define NPY_ARRAY_ENSURECOPY 0x0020
854
+
855
+ /*
856
+ * Make sure the returned array is a base-class ndarray
857
+ *
858
+ * This flag may be requested in constructor functions.
859
+ */
860
+ #define NPY_ARRAY_ENSUREARRAY 0x0040
861
+
862
+ /*
863
+ * Make sure that the strides are in units of the element size Needed
864
+ * for some operations with record-arrays.
865
+ *
866
+ * This flag may be requested in constructor functions.
867
+ */
868
+ #define NPY_ARRAY_ELEMENTSTRIDES 0x0080
869
+
870
+ /*
871
+ * Array data is aligned on the appropriate memory address for the type
872
+ * stored according to how the compiler would align things (e.g., an
873
+ * array of integers (4 bytes each) starts on a memory address that's
874
+ * a multiple of 4)
875
+ *
876
+ * This flag may be requested in constructor functions.
877
+ * This flag may be tested for in PyArray_FLAGS(arr).
878
+ */
879
+ #define NPY_ARRAY_ALIGNED 0x0100
880
+
881
+ /*
882
+ * Array data has the native endianness
883
+ *
884
+ * This flag may be requested in constructor functions.
885
+ */
886
+ #define NPY_ARRAY_NOTSWAPPED 0x0200
887
+
888
+ /*
889
+ * Array data is writeable
890
+ *
891
+ * This flag may be requested in constructor functions.
892
+ * This flag may be tested for in PyArray_FLAGS(arr).
893
+ */
894
+ #define NPY_ARRAY_WRITEABLE 0x0400
895
+
896
+ /*
897
+ * If this flag is set, then base contains a pointer to an array of
898
+ * the same size that should be updated with the current contents of
899
+ * this array when PyArray_ResolveWritebackIfCopy is called.
900
+ *
901
+ * This flag may be requested in constructor functions.
902
+ * This flag may be tested for in PyArray_FLAGS(arr).
903
+ */
904
+ #define NPY_ARRAY_WRITEBACKIFCOPY 0x2000
905
+
906
+ /*
907
+ * No copy may be made while converting from an object/array (result is a view)
908
+ *
909
+ * This flag may be requested in constructor functions.
910
+ */
911
+ #define NPY_ARRAY_ENSURENOCOPY 0x4000
912
+
913
+ /*
914
+ * NOTE: there are also internal flags defined in multiarray/arrayobject.h,
915
+ * which start at bit 31 and work down.
916
+ */
917
+
918
+ #define NPY_ARRAY_BEHAVED (NPY_ARRAY_ALIGNED | \
919
+ NPY_ARRAY_WRITEABLE)
920
+ #define NPY_ARRAY_BEHAVED_NS (NPY_ARRAY_ALIGNED | \
921
+ NPY_ARRAY_WRITEABLE | \
922
+ NPY_ARRAY_NOTSWAPPED)
923
+ #define NPY_ARRAY_CARRAY (NPY_ARRAY_C_CONTIGUOUS | \
924
+ NPY_ARRAY_BEHAVED)
925
+ #define NPY_ARRAY_CARRAY_RO (NPY_ARRAY_C_CONTIGUOUS | \
926
+ NPY_ARRAY_ALIGNED)
927
+ #define NPY_ARRAY_FARRAY (NPY_ARRAY_F_CONTIGUOUS | \
928
+ NPY_ARRAY_BEHAVED)
929
+ #define NPY_ARRAY_FARRAY_RO (NPY_ARRAY_F_CONTIGUOUS | \
930
+ NPY_ARRAY_ALIGNED)
931
+ #define NPY_ARRAY_DEFAULT (NPY_ARRAY_CARRAY)
932
+ #define NPY_ARRAY_IN_ARRAY (NPY_ARRAY_CARRAY_RO)
933
+ #define NPY_ARRAY_OUT_ARRAY (NPY_ARRAY_CARRAY)
934
+ #define NPY_ARRAY_INOUT_ARRAY (NPY_ARRAY_CARRAY)
935
+ #define NPY_ARRAY_INOUT_ARRAY2 (NPY_ARRAY_CARRAY | \
936
+ NPY_ARRAY_WRITEBACKIFCOPY)
937
+ #define NPY_ARRAY_IN_FARRAY (NPY_ARRAY_FARRAY_RO)
938
+ #define NPY_ARRAY_OUT_FARRAY (NPY_ARRAY_FARRAY)
939
+ #define NPY_ARRAY_INOUT_FARRAY (NPY_ARRAY_FARRAY)
940
+ #define NPY_ARRAY_INOUT_FARRAY2 (NPY_ARRAY_FARRAY | \
941
+ NPY_ARRAY_WRITEBACKIFCOPY)
942
+
943
+ #define NPY_ARRAY_UPDATE_ALL (NPY_ARRAY_C_CONTIGUOUS | \
944
+ NPY_ARRAY_F_CONTIGUOUS | \
945
+ NPY_ARRAY_ALIGNED)
946
+
947
+ /* This flag is for the array interface, not PyArrayObject */
948
+ #define NPY_ARR_HAS_DESCR 0x0800
949
+
950
+
951
+
952
+
953
+ /*
954
+ * Size of internal buffers used for alignment Make BUFSIZE a multiple
955
+ * of sizeof(npy_cdouble) -- usually 16 so that ufunc buffers are aligned
956
+ */
957
+ #define NPY_MIN_BUFSIZE ((int)sizeof(npy_cdouble))
958
+ #define NPY_MAX_BUFSIZE (((int)sizeof(npy_cdouble))*1000000)
959
+ #define NPY_BUFSIZE 8192
960
+ /* buffer stress test size: */
961
+ /*#define NPY_BUFSIZE 17*/
962
+
963
+ #define PyArray_MAX(a,b) (((a)>(b))?(a):(b))
964
+ #define PyArray_MIN(a,b) (((a)<(b))?(a):(b))
965
+ #define PyArray_CLT(p,q) ((((p).real==(q).real) ? ((p).imag < (q).imag) : \
966
+ ((p).real < (q).real)))
967
+ #define PyArray_CGT(p,q) ((((p).real==(q).real) ? ((p).imag > (q).imag) : \
968
+ ((p).real > (q).real)))
969
+ #define PyArray_CLE(p,q) ((((p).real==(q).real) ? ((p).imag <= (q).imag) : \
970
+ ((p).real <= (q).real)))
971
+ #define PyArray_CGE(p,q) ((((p).real==(q).real) ? ((p).imag >= (q).imag) : \
972
+ ((p).real >= (q).real)))
973
+ #define PyArray_CEQ(p,q) (((p).real==(q).real) && ((p).imag == (q).imag))
974
+ #define PyArray_CNE(p,q) (((p).real!=(q).real) || ((p).imag != (q).imag))
975
+
976
+ /*
977
+ * C API: consists of Macros and functions. The MACROS are defined
978
+ * here.
979
+ */
980
+
981
+
982
+ #define PyArray_ISCONTIGUOUS(m) PyArray_CHKFLAGS((m), NPY_ARRAY_C_CONTIGUOUS)
983
+ #define PyArray_ISWRITEABLE(m) PyArray_CHKFLAGS((m), NPY_ARRAY_WRITEABLE)
984
+ #define PyArray_ISALIGNED(m) PyArray_CHKFLAGS((m), NPY_ARRAY_ALIGNED)
985
+
986
+ #define PyArray_IS_C_CONTIGUOUS(m) PyArray_CHKFLAGS((m), NPY_ARRAY_C_CONTIGUOUS)
987
+ #define PyArray_IS_F_CONTIGUOUS(m) PyArray_CHKFLAGS((m), NPY_ARRAY_F_CONTIGUOUS)
988
+
989
+ /* the variable is used in some places, so always define it */
990
+ #define NPY_BEGIN_THREADS_DEF PyThreadState *_save=NULL;
991
+ #if NPY_ALLOW_THREADS
992
+ #define NPY_BEGIN_ALLOW_THREADS Py_BEGIN_ALLOW_THREADS
993
+ #define NPY_END_ALLOW_THREADS Py_END_ALLOW_THREADS
994
+ #define NPY_BEGIN_THREADS do {_save = PyEval_SaveThread();} while (0);
995
+ #define NPY_END_THREADS do { if (_save) \
996
+ { PyEval_RestoreThread(_save); _save = NULL;} } while (0);
997
+ #define NPY_BEGIN_THREADS_THRESHOLDED(loop_size) do { if ((loop_size) > 500) \
998
+ { _save = PyEval_SaveThread();} } while (0);
999
+
1000
+ #define NPY_BEGIN_THREADS_DESCR(dtype) \
1001
+ do {if (!(PyDataType_FLAGCHK((dtype), NPY_NEEDS_PYAPI))) \
1002
+ NPY_BEGIN_THREADS;} while (0);
1003
+
1004
+ #define NPY_END_THREADS_DESCR(dtype) \
1005
+ do {if (!(PyDataType_FLAGCHK((dtype), NPY_NEEDS_PYAPI))) \
1006
+ NPY_END_THREADS; } while (0);
1007
+
1008
+ #define NPY_ALLOW_C_API_DEF PyGILState_STATE __save__;
1009
+ #define NPY_ALLOW_C_API do {__save__ = PyGILState_Ensure();} while (0);
1010
+ #define NPY_DISABLE_C_API do {PyGILState_Release(__save__);} while (0);
1011
+ #else
1012
+ #define NPY_BEGIN_ALLOW_THREADS
1013
+ #define NPY_END_ALLOW_THREADS
1014
+ #define NPY_BEGIN_THREADS
1015
+ #define NPY_END_THREADS
1016
+ #define NPY_BEGIN_THREADS_THRESHOLDED(loop_size)
1017
+ #define NPY_BEGIN_THREADS_DESCR(dtype)
1018
+ #define NPY_END_THREADS_DESCR(dtype)
1019
+ #define NPY_ALLOW_C_API_DEF
1020
+ #define NPY_ALLOW_C_API
1021
+ #define NPY_DISABLE_C_API
1022
+ #endif
1023
+
1024
+ /**********************************
1025
+ * The nditer object, added in 1.6
1026
+ **********************************/
1027
+
1028
+ /* The actual structure of the iterator is an internal detail */
1029
+ typedef struct NpyIter_InternalOnly NpyIter;
1030
+
1031
+ /* Iterator function pointers that may be specialized */
1032
+ typedef int (NpyIter_IterNextFunc)(NpyIter *iter);
1033
+ typedef void (NpyIter_GetMultiIndexFunc)(NpyIter *iter,
1034
+ npy_intp *outcoords);
1035
+
1036
+ /*** Global flags that may be passed to the iterator constructors ***/
1037
+
1038
+ /* Track an index representing C order */
1039
+ #define NPY_ITER_C_INDEX 0x00000001
1040
+ /* Track an index representing Fortran order */
1041
+ #define NPY_ITER_F_INDEX 0x00000002
1042
+ /* Track a multi-index */
1043
+ #define NPY_ITER_MULTI_INDEX 0x00000004
1044
+ /* User code external to the iterator does the 1-dimensional innermost loop */
1045
+ #define NPY_ITER_EXTERNAL_LOOP 0x00000008
1046
+ /* Convert all the operands to a common data type */
1047
+ #define NPY_ITER_COMMON_DTYPE 0x00000010
1048
+ /* Operands may hold references, requiring API access during iteration */
1049
+ #define NPY_ITER_REFS_OK 0x00000020
1050
+ /* Zero-sized operands should be permitted, iteration checks IterSize for 0 */
1051
+ #define NPY_ITER_ZEROSIZE_OK 0x00000040
1052
+ /* Permits reductions (size-0 stride with dimension size > 1) */
1053
+ #define NPY_ITER_REDUCE_OK 0x00000080
1054
+ /* Enables sub-range iteration */
1055
+ #define NPY_ITER_RANGED 0x00000100
1056
+ /* Enables buffering */
1057
+ #define NPY_ITER_BUFFERED 0x00000200
1058
+ /* When buffering is enabled, grows the inner loop if possible */
1059
+ #define NPY_ITER_GROWINNER 0x00000400
1060
+ /* Delay allocation of buffers until first Reset* call */
1061
+ #define NPY_ITER_DELAY_BUFALLOC 0x00000800
1062
+ /* When NPY_KEEPORDER is specified, disable reversing negative-stride axes */
1063
+ #define NPY_ITER_DONT_NEGATE_STRIDES 0x00001000
1064
+ /*
1065
+ * If output operands overlap with other operands (based on heuristics that
1066
+ * has false positives but no false negatives), make temporary copies to
1067
+ * eliminate overlap.
1068
+ */
1069
+ #define NPY_ITER_COPY_IF_OVERLAP 0x00002000
1070
+
1071
+ /*** Per-operand flags that may be passed to the iterator constructors ***/
1072
+
1073
+ /* The operand will be read from and written to */
1074
+ #define NPY_ITER_READWRITE 0x00010000
1075
+ /* The operand will only be read from */
1076
+ #define NPY_ITER_READONLY 0x00020000
1077
+ /* The operand will only be written to */
1078
+ #define NPY_ITER_WRITEONLY 0x00040000
1079
+ /* The operand's data must be in native byte order */
1080
+ #define NPY_ITER_NBO 0x00080000
1081
+ /* The operand's data must be aligned */
1082
+ #define NPY_ITER_ALIGNED 0x00100000
1083
+ /* The operand's data must be contiguous (within the inner loop) */
1084
+ #define NPY_ITER_CONTIG 0x00200000
1085
+ /* The operand may be copied to satisfy requirements */
1086
+ #define NPY_ITER_COPY 0x00400000
1087
+ /* The operand may be copied with WRITEBACKIFCOPY to satisfy requirements */
1088
+ #define NPY_ITER_UPDATEIFCOPY 0x00800000
1089
+ /* Allocate the operand if it is NULL */
1090
+ #define NPY_ITER_ALLOCATE 0x01000000
1091
+ /* If an operand is allocated, don't use any subtype */
1092
+ #define NPY_ITER_NO_SUBTYPE 0x02000000
1093
+ /* This is a virtual array slot, operand is NULL but temporary data is there */
1094
+ #define NPY_ITER_VIRTUAL 0x04000000
1095
+ /* Require that the dimension match the iterator dimensions exactly */
1096
+ #define NPY_ITER_NO_BROADCAST 0x08000000
1097
+ /* A mask is being used on this array, affects buffer -> array copy */
1098
+ #define NPY_ITER_WRITEMASKED 0x10000000
1099
+ /* This array is the mask for all WRITEMASKED operands */
1100
+ #define NPY_ITER_ARRAYMASK 0x20000000
1101
+ /* Assume iterator order data access for COPY_IF_OVERLAP */
1102
+ #define NPY_ITER_OVERLAP_ASSUME_ELEMENTWISE 0x40000000
1103
+
1104
+ #define NPY_ITER_GLOBAL_FLAGS 0x0000ffff
1105
+ #define NPY_ITER_PER_OP_FLAGS 0xffff0000
1106
+
1107
+
1108
+ /*****************************
1109
+ * Basic iterator object
1110
+ *****************************/
1111
+
1112
+ /* FWD declaration */
1113
+ typedef struct PyArrayIterObject_tag PyArrayIterObject;
1114
+
1115
+ /*
1116
+ * type of the function which translates a set of coordinates to a
1117
+ * pointer to the data
1118
+ */
1119
+ typedef char* (*npy_iter_get_dataptr_t)(
1120
+ PyArrayIterObject* iter, const npy_intp*);
1121
+
1122
+ struct PyArrayIterObject_tag {
1123
+ PyObject_HEAD
1124
+ int nd_m1; /* number of dimensions - 1 */
1125
+ npy_intp index, size;
1126
+ npy_intp coordinates[NPY_MAXDIMS];/* N-dimensional loop */
1127
+ npy_intp dims_m1[NPY_MAXDIMS]; /* ao->dimensions - 1 */
1128
+ npy_intp strides[NPY_MAXDIMS]; /* ao->strides or fake */
1129
+ npy_intp backstrides[NPY_MAXDIMS];/* how far to jump back */
1130
+ npy_intp factors[NPY_MAXDIMS]; /* shape factors */
1131
+ PyArrayObject *ao;
1132
+ char *dataptr; /* pointer to current item*/
1133
+ npy_bool contiguous;
1134
+
1135
+ npy_intp bounds[NPY_MAXDIMS][2];
1136
+ npy_intp limits[NPY_MAXDIMS][2];
1137
+ npy_intp limits_sizes[NPY_MAXDIMS];
1138
+ npy_iter_get_dataptr_t translate;
1139
+ } ;
1140
+
1141
+
1142
+ /* Iterator API */
1143
+ #define PyArrayIter_Check(op) PyObject_TypeCheck((op), &PyArrayIter_Type)
1144
+
1145
+ #define _PyAIT(it) ((PyArrayIterObject *)(it))
1146
+ #define PyArray_ITER_RESET(it) do { \
1147
+ _PyAIT(it)->index = 0; \
1148
+ _PyAIT(it)->dataptr = PyArray_BYTES(_PyAIT(it)->ao); \
1149
+ memset(_PyAIT(it)->coordinates, 0, \
1150
+ (_PyAIT(it)->nd_m1+1)*sizeof(npy_intp)); \
1151
+ } while (0)
1152
+
1153
+ #define _PyArray_ITER_NEXT1(it) do { \
1154
+ (it)->dataptr += _PyAIT(it)->strides[0]; \
1155
+ (it)->coordinates[0]++; \
1156
+ } while (0)
1157
+
1158
+ #define _PyArray_ITER_NEXT2(it) do { \
1159
+ if ((it)->coordinates[1] < (it)->dims_m1[1]) { \
1160
+ (it)->coordinates[1]++; \
1161
+ (it)->dataptr += (it)->strides[1]; \
1162
+ } \
1163
+ else { \
1164
+ (it)->coordinates[1] = 0; \
1165
+ (it)->coordinates[0]++; \
1166
+ (it)->dataptr += (it)->strides[0] - \
1167
+ (it)->backstrides[1]; \
1168
+ } \
1169
+ } while (0)
1170
+
1171
+ #define PyArray_ITER_NEXT(it) do { \
1172
+ _PyAIT(it)->index++; \
1173
+ if (_PyAIT(it)->nd_m1 == 0) { \
1174
+ _PyArray_ITER_NEXT1(_PyAIT(it)); \
1175
+ } \
1176
+ else if (_PyAIT(it)->contiguous) \
1177
+ _PyAIT(it)->dataptr += PyArray_DESCR(_PyAIT(it)->ao)->elsize; \
1178
+ else if (_PyAIT(it)->nd_m1 == 1) { \
1179
+ _PyArray_ITER_NEXT2(_PyAIT(it)); \
1180
+ } \
1181
+ else { \
1182
+ int __npy_i; \
1183
+ for (__npy_i=_PyAIT(it)->nd_m1; __npy_i >= 0; __npy_i--) { \
1184
+ if (_PyAIT(it)->coordinates[__npy_i] < \
1185
+ _PyAIT(it)->dims_m1[__npy_i]) { \
1186
+ _PyAIT(it)->coordinates[__npy_i]++; \
1187
+ _PyAIT(it)->dataptr += \
1188
+ _PyAIT(it)->strides[__npy_i]; \
1189
+ break; \
1190
+ } \
1191
+ else { \
1192
+ _PyAIT(it)->coordinates[__npy_i] = 0; \
1193
+ _PyAIT(it)->dataptr -= \
1194
+ _PyAIT(it)->backstrides[__npy_i]; \
1195
+ } \
1196
+ } \
1197
+ } \
1198
+ } while (0)
1199
+
1200
+ #define PyArray_ITER_GOTO(it, destination) do { \
1201
+ int __npy_i; \
1202
+ _PyAIT(it)->index = 0; \
1203
+ _PyAIT(it)->dataptr = PyArray_BYTES(_PyAIT(it)->ao); \
1204
+ for (__npy_i = _PyAIT(it)->nd_m1; __npy_i>=0; __npy_i--) { \
1205
+ if (destination[__npy_i] < 0) { \
1206
+ destination[__npy_i] += \
1207
+ _PyAIT(it)->dims_m1[__npy_i]+1; \
1208
+ } \
1209
+ _PyAIT(it)->dataptr += destination[__npy_i] * \
1210
+ _PyAIT(it)->strides[__npy_i]; \
1211
+ _PyAIT(it)->coordinates[__npy_i] = \
1212
+ destination[__npy_i]; \
1213
+ _PyAIT(it)->index += destination[__npy_i] * \
1214
+ ( __npy_i==_PyAIT(it)->nd_m1 ? 1 : \
1215
+ _PyAIT(it)->dims_m1[__npy_i+1]+1) ; \
1216
+ } \
1217
+ } while (0)
1218
+
1219
+ #define PyArray_ITER_GOTO1D(it, ind) do { \
1220
+ int __npy_i; \
1221
+ npy_intp __npy_ind = (npy_intp)(ind); \
1222
+ if (__npy_ind < 0) __npy_ind += _PyAIT(it)->size; \
1223
+ _PyAIT(it)->index = __npy_ind; \
1224
+ if (_PyAIT(it)->nd_m1 == 0) { \
1225
+ _PyAIT(it)->dataptr = PyArray_BYTES(_PyAIT(it)->ao) + \
1226
+ __npy_ind * _PyAIT(it)->strides[0]; \
1227
+ } \
1228
+ else if (_PyAIT(it)->contiguous) \
1229
+ _PyAIT(it)->dataptr = PyArray_BYTES(_PyAIT(it)->ao) + \
1230
+ __npy_ind * PyArray_DESCR(_PyAIT(it)->ao)->elsize; \
1231
+ else { \
1232
+ _PyAIT(it)->dataptr = PyArray_BYTES(_PyAIT(it)->ao); \
1233
+ for (__npy_i = 0; __npy_i<=_PyAIT(it)->nd_m1; \
1234
+ __npy_i++) { \
1235
+ _PyAIT(it)->coordinates[__npy_i] = \
1236
+ (__npy_ind / _PyAIT(it)->factors[__npy_i]); \
1237
+ _PyAIT(it)->dataptr += \
1238
+ (__npy_ind / _PyAIT(it)->factors[__npy_i]) \
1239
+ * _PyAIT(it)->strides[__npy_i]; \
1240
+ __npy_ind %= _PyAIT(it)->factors[__npy_i]; \
1241
+ } \
1242
+ } \
1243
+ } while (0)
1244
+
1245
+ #define PyArray_ITER_DATA(it) ((void *)(_PyAIT(it)->dataptr))
1246
+
1247
+ #define PyArray_ITER_NOTDONE(it) (_PyAIT(it)->index < _PyAIT(it)->size)
1248
+
1249
+
1250
+ /*
1251
+ * Any object passed to PyArray_Broadcast must be binary compatible
1252
+ * with this structure.
1253
+ */
1254
+
1255
+ typedef struct {
1256
+ PyObject_HEAD
1257
+ int numiter; /* number of iters */
1258
+ npy_intp size; /* broadcasted size */
1259
+ npy_intp index; /* current index */
1260
+ int nd; /* number of dims */
1261
+ npy_intp dimensions[NPY_MAXDIMS]; /* dimensions */
1262
+ PyArrayIterObject *iters[NPY_MAXARGS]; /* iterators */
1263
+ } PyArrayMultiIterObject;
1264
+
1265
+ #define _PyMIT(m) ((PyArrayMultiIterObject *)(m))
1266
+ #define PyArray_MultiIter_RESET(multi) do { \
1267
+ int __npy_mi; \
1268
+ _PyMIT(multi)->index = 0; \
1269
+ for (__npy_mi=0; __npy_mi < _PyMIT(multi)->numiter; __npy_mi++) { \
1270
+ PyArray_ITER_RESET(_PyMIT(multi)->iters[__npy_mi]); \
1271
+ } \
1272
+ } while (0)
1273
+
1274
+ #define PyArray_MultiIter_NEXT(multi) do { \
1275
+ int __npy_mi; \
1276
+ _PyMIT(multi)->index++; \
1277
+ for (__npy_mi=0; __npy_mi < _PyMIT(multi)->numiter; __npy_mi++) { \
1278
+ PyArray_ITER_NEXT(_PyMIT(multi)->iters[__npy_mi]); \
1279
+ } \
1280
+ } while (0)
1281
+
1282
+ #define PyArray_MultiIter_GOTO(multi, dest) do { \
1283
+ int __npy_mi; \
1284
+ for (__npy_mi=0; __npy_mi < _PyMIT(multi)->numiter; __npy_mi++) { \
1285
+ PyArray_ITER_GOTO(_PyMIT(multi)->iters[__npy_mi], dest); \
1286
+ } \
1287
+ _PyMIT(multi)->index = _PyMIT(multi)->iters[0]->index; \
1288
+ } while (0)
1289
+
1290
+ #define PyArray_MultiIter_GOTO1D(multi, ind) do { \
1291
+ int __npy_mi; \
1292
+ for (__npy_mi=0; __npy_mi < _PyMIT(multi)->numiter; __npy_mi++) { \
1293
+ PyArray_ITER_GOTO1D(_PyMIT(multi)->iters[__npy_mi], ind); \
1294
+ } \
1295
+ _PyMIT(multi)->index = _PyMIT(multi)->iters[0]->index; \
1296
+ } while (0)
1297
+
1298
+ #define PyArray_MultiIter_DATA(multi, i) \
1299
+ ((void *)(_PyMIT(multi)->iters[i]->dataptr))
1300
+
1301
+ #define PyArray_MultiIter_NEXTi(multi, i) \
1302
+ PyArray_ITER_NEXT(_PyMIT(multi)->iters[i])
1303
+
1304
+ #define PyArray_MultiIter_NOTDONE(multi) \
1305
+ (_PyMIT(multi)->index < _PyMIT(multi)->size)
1306
+
1307
+ /*
1308
+ * Store the information needed for fancy-indexing over an array. The
1309
+ * fields are slightly unordered to keep consec, dataptr and subspace
1310
+ * where they were originally.
1311
+ */
1312
+ typedef struct {
1313
+ PyObject_HEAD
1314
+ /*
1315
+ * Multi-iterator portion --- needs to be present in this
1316
+ * order to work with PyArray_Broadcast
1317
+ */
1318
+
1319
+ int numiter; /* number of index-array
1320
+ iterators */
1321
+ npy_intp size; /* size of broadcasted
1322
+ result */
1323
+ npy_intp index; /* current index */
1324
+ int nd; /* number of dims */
1325
+ npy_intp dimensions[NPY_MAXDIMS]; /* dimensions */
1326
+ NpyIter *outer; /* index objects
1327
+ iterator */
1328
+ void *unused[NPY_MAXDIMS - 2];
1329
+ PyArrayObject *array;
1330
+ /* Flat iterator for the indexed array. For compatibility solely. */
1331
+ PyArrayIterObject *ait;
1332
+
1333
+ /*
1334
+ * Subspace array. For binary compatibility (was an iterator,
1335
+ * but only the check for NULL should be used).
1336
+ */
1337
+ PyArrayObject *subspace;
1338
+
1339
+ /*
1340
+ * if subspace iteration, then this is the array of axes in
1341
+ * the underlying array represented by the index objects
1342
+ */
1343
+ int iteraxes[NPY_MAXDIMS];
1344
+ npy_intp fancy_strides[NPY_MAXDIMS];
1345
+
1346
+ /* pointer when all fancy indices are 0 */
1347
+ char *baseoffset;
1348
+
1349
+ /*
1350
+ * after binding consec denotes at which axis the fancy axes
1351
+ * are inserted.
1352
+ */
1353
+ int consec;
1354
+ char *dataptr;
1355
+
1356
+ int nd_fancy;
1357
+ npy_intp fancy_dims[NPY_MAXDIMS];
1358
+
1359
+ /*
1360
+ * Whether the iterator (any of the iterators) requires API. This is
1361
+ * unused by NumPy itself; ArrayMethod flags are more precise.
1362
+ */
1363
+ int needs_api;
1364
+
1365
+ /*
1366
+ * Extra op information.
1367
+ */
1368
+ PyArrayObject *extra_op;
1369
+ PyArray_Descr *extra_op_dtype; /* desired dtype */
1370
+ npy_uint32 *extra_op_flags; /* Iterator flags */
1371
+
1372
+ NpyIter *extra_op_iter;
1373
+ NpyIter_IterNextFunc *extra_op_next;
1374
+ char **extra_op_ptrs;
1375
+
1376
+ /*
1377
+ * Information about the iteration state.
1378
+ */
1379
+ NpyIter_IterNextFunc *outer_next;
1380
+ char **outer_ptrs;
1381
+ npy_intp *outer_strides;
1382
+
1383
+ /*
1384
+ * Information about the subspace iterator.
1385
+ */
1386
+ NpyIter *subspace_iter;
1387
+ NpyIter_IterNextFunc *subspace_next;
1388
+ char **subspace_ptrs;
1389
+ npy_intp *subspace_strides;
1390
+
1391
+ /* Count for the external loop (which ever it is) for API iteration */
1392
+ npy_intp iter_count;
1393
+
1394
+ } PyArrayMapIterObject;
1395
+
1396
+ enum {
1397
+ NPY_NEIGHBORHOOD_ITER_ZERO_PADDING,
1398
+ NPY_NEIGHBORHOOD_ITER_ONE_PADDING,
1399
+ NPY_NEIGHBORHOOD_ITER_CONSTANT_PADDING,
1400
+ NPY_NEIGHBORHOOD_ITER_CIRCULAR_PADDING,
1401
+ NPY_NEIGHBORHOOD_ITER_MIRROR_PADDING
1402
+ };
1403
+
1404
+ typedef struct {
1405
+ PyObject_HEAD
1406
+
1407
+ /*
1408
+ * PyArrayIterObject part: keep this in this exact order
1409
+ */
1410
+ int nd_m1; /* number of dimensions - 1 */
1411
+ npy_intp index, size;
1412
+ npy_intp coordinates[NPY_MAXDIMS];/* N-dimensional loop */
1413
+ npy_intp dims_m1[NPY_MAXDIMS]; /* ao->dimensions - 1 */
1414
+ npy_intp strides[NPY_MAXDIMS]; /* ao->strides or fake */
1415
+ npy_intp backstrides[NPY_MAXDIMS];/* how far to jump back */
1416
+ npy_intp factors[NPY_MAXDIMS]; /* shape factors */
1417
+ PyArrayObject *ao;
1418
+ char *dataptr; /* pointer to current item*/
1419
+ npy_bool contiguous;
1420
+
1421
+ npy_intp bounds[NPY_MAXDIMS][2];
1422
+ npy_intp limits[NPY_MAXDIMS][2];
1423
+ npy_intp limits_sizes[NPY_MAXDIMS];
1424
+ npy_iter_get_dataptr_t translate;
1425
+
1426
+ /*
1427
+ * New members
1428
+ */
1429
+ npy_intp nd;
1430
+
1431
+ /* Dimensions is the dimension of the array */
1432
+ npy_intp dimensions[NPY_MAXDIMS];
1433
+
1434
+ /*
1435
+ * Neighborhood points coordinates are computed relatively to the
1436
+ * point pointed by _internal_iter
1437
+ */
1438
+ PyArrayIterObject* _internal_iter;
1439
+ /*
1440
+ * To keep a reference to the representation of the constant value
1441
+ * for constant padding
1442
+ */
1443
+ char* constant;
1444
+
1445
+ int mode;
1446
+ } PyArrayNeighborhoodIterObject;
1447
+
1448
+ /*
1449
+ * Neighborhood iterator API
1450
+ */
1451
+
1452
+ /* General: those work for any mode */
1453
+ static inline int
1454
+ PyArrayNeighborhoodIter_Reset(PyArrayNeighborhoodIterObject* iter);
1455
+ static inline int
1456
+ PyArrayNeighborhoodIter_Next(PyArrayNeighborhoodIterObject* iter);
1457
+ #if 0
1458
+ static inline int
1459
+ PyArrayNeighborhoodIter_Next2D(PyArrayNeighborhoodIterObject* iter);
1460
+ #endif
1461
+
1462
+ /*
1463
+ * Include inline implementations - functions defined there are not
1464
+ * considered public API
1465
+ */
1466
+ #define NUMPY_CORE_INCLUDE_NUMPY__NEIGHBORHOOD_IMP_H_
1467
+ #include "_neighborhood_iterator_imp.h"
1468
+ #undef NUMPY_CORE_INCLUDE_NUMPY__NEIGHBORHOOD_IMP_H_
1469
+
1470
+
1471
+
1472
+ /* The default array type */
1473
+ #define NPY_DEFAULT_TYPE NPY_DOUBLE
1474
+
1475
+ /*
1476
+ * All sorts of useful ways to look into a PyArrayObject. It is recommended
1477
+ * to use PyArrayObject * objects instead of always casting from PyObject *,
1478
+ * for improved type checking.
1479
+ *
1480
+ * In many cases here the macro versions of the accessors are deprecated,
1481
+ * but can't be immediately changed to inline functions because the
1482
+ * preexisting macros accept PyObject * and do automatic casts. Inline
1483
+ * functions accepting PyArrayObject * provides for some compile-time
1484
+ * checking of correctness when working with these objects in C.
1485
+ */
1486
+
1487
+ #define PyArray_ISONESEGMENT(m) (PyArray_CHKFLAGS(m, NPY_ARRAY_C_CONTIGUOUS) || \
1488
+ PyArray_CHKFLAGS(m, NPY_ARRAY_F_CONTIGUOUS))
1489
+
1490
+ #define PyArray_ISFORTRAN(m) (PyArray_CHKFLAGS(m, NPY_ARRAY_F_CONTIGUOUS) && \
1491
+ (!PyArray_CHKFLAGS(m, NPY_ARRAY_C_CONTIGUOUS)))
1492
+
1493
+ #define PyArray_FORTRAN_IF(m) ((PyArray_CHKFLAGS(m, NPY_ARRAY_F_CONTIGUOUS) ? \
1494
+ NPY_ARRAY_F_CONTIGUOUS : 0))
1495
+
1496
+ #if (defined(NPY_NO_DEPRECATED_API) && (NPY_1_7_API_VERSION <= NPY_NO_DEPRECATED_API))
1497
+ /*
1498
+ * Changing access macros into functions, to allow for future hiding
1499
+ * of the internal memory layout. This later hiding will allow the 2.x series
1500
+ * to change the internal representation of arrays without affecting
1501
+ * ABI compatibility.
1502
+ */
1503
+
1504
+ static inline int
1505
+ PyArray_NDIM(const PyArrayObject *arr)
1506
+ {
1507
+ return ((PyArrayObject_fields *)arr)->nd;
1508
+ }
1509
+
1510
+ static inline void *
1511
+ PyArray_DATA(PyArrayObject *arr)
1512
+ {
1513
+ return ((PyArrayObject_fields *)arr)->data;
1514
+ }
1515
+
1516
+ static inline char *
1517
+ PyArray_BYTES(PyArrayObject *arr)
1518
+ {
1519
+ return ((PyArrayObject_fields *)arr)->data;
1520
+ }
1521
+
1522
+ static inline npy_intp *
1523
+ PyArray_DIMS(PyArrayObject *arr)
1524
+ {
1525
+ return ((PyArrayObject_fields *)arr)->dimensions;
1526
+ }
1527
+
1528
+ static inline npy_intp *
1529
+ PyArray_STRIDES(PyArrayObject *arr)
1530
+ {
1531
+ return ((PyArrayObject_fields *)arr)->strides;
1532
+ }
1533
+
1534
+ static inline npy_intp
1535
+ PyArray_DIM(const PyArrayObject *arr, int idim)
1536
+ {
1537
+ return ((PyArrayObject_fields *)arr)->dimensions[idim];
1538
+ }
1539
+
1540
+ static inline npy_intp
1541
+ PyArray_STRIDE(const PyArrayObject *arr, int istride)
1542
+ {
1543
+ return ((PyArrayObject_fields *)arr)->strides[istride];
1544
+ }
1545
+
1546
+ static inline NPY_RETURNS_BORROWED_REF PyObject *
1547
+ PyArray_BASE(PyArrayObject *arr)
1548
+ {
1549
+ return ((PyArrayObject_fields *)arr)->base;
1550
+ }
1551
+
1552
+ static inline NPY_RETURNS_BORROWED_REF PyArray_Descr *
1553
+ PyArray_DESCR(PyArrayObject *arr)
1554
+ {
1555
+ return ((PyArrayObject_fields *)arr)->descr;
1556
+ }
1557
+
1558
+ static inline int
1559
+ PyArray_FLAGS(const PyArrayObject *arr)
1560
+ {
1561
+ return ((PyArrayObject_fields *)arr)->flags;
1562
+ }
1563
+
1564
+ static inline npy_intp
1565
+ PyArray_ITEMSIZE(const PyArrayObject *arr)
1566
+ {
1567
+ return ((PyArrayObject_fields *)arr)->descr->elsize;
1568
+ }
1569
+
1570
+ static inline int
1571
+ PyArray_TYPE(const PyArrayObject *arr)
1572
+ {
1573
+ return ((PyArrayObject_fields *)arr)->descr->type_num;
1574
+ }
1575
+
1576
+ static inline int
1577
+ PyArray_CHKFLAGS(const PyArrayObject *arr, int flags)
1578
+ {
1579
+ return (PyArray_FLAGS(arr) & flags) == flags;
1580
+ }
1581
+
1582
+ static inline PyObject *
1583
+ PyArray_GETITEM(const PyArrayObject *arr, const char *itemptr)
1584
+ {
1585
+ return ((PyArrayObject_fields *)arr)->descr->f->getitem(
1586
+ (void *)itemptr, (PyArrayObject *)arr);
1587
+ }
1588
+
1589
+ /*
1590
+ * SETITEM should only be used if it is known that the value is a scalar
1591
+ * and of a type understood by the arrays dtype.
1592
+ * Use `PyArray_Pack` if the value may be of a different dtype.
1593
+ */
1594
+ static inline int
1595
+ PyArray_SETITEM(PyArrayObject *arr, char *itemptr, PyObject *v)
1596
+ {
1597
+ return ((PyArrayObject_fields *)arr)->descr->f->setitem(v, itemptr, arr);
1598
+ }
1599
+
1600
+ #else
1601
+
1602
+ /* These macros are deprecated as of NumPy 1.7. */
1603
+ #define PyArray_NDIM(obj) (((PyArrayObject_fields *)(obj))->nd)
1604
+ #define PyArray_BYTES(obj) (((PyArrayObject_fields *)(obj))->data)
1605
+ #define PyArray_DATA(obj) ((void *)((PyArrayObject_fields *)(obj))->data)
1606
+ #define PyArray_DIMS(obj) (((PyArrayObject_fields *)(obj))->dimensions)
1607
+ #define PyArray_STRIDES(obj) (((PyArrayObject_fields *)(obj))->strides)
1608
+ #define PyArray_DIM(obj,n) (PyArray_DIMS(obj)[n])
1609
+ #define PyArray_STRIDE(obj,n) (PyArray_STRIDES(obj)[n])
1610
+ #define PyArray_BASE(obj) (((PyArrayObject_fields *)(obj))->base)
1611
+ #define PyArray_DESCR(obj) (((PyArrayObject_fields *)(obj))->descr)
1612
+ #define PyArray_FLAGS(obj) (((PyArrayObject_fields *)(obj))->flags)
1613
+ #define PyArray_CHKFLAGS(m, FLAGS) \
1614
+ ((((PyArrayObject_fields *)(m))->flags & (FLAGS)) == (FLAGS))
1615
+ #define PyArray_ITEMSIZE(obj) \
1616
+ (((PyArrayObject_fields *)(obj))->descr->elsize)
1617
+ #define PyArray_TYPE(obj) \
1618
+ (((PyArrayObject_fields *)(obj))->descr->type_num)
1619
+ #define PyArray_GETITEM(obj,itemptr) \
1620
+ PyArray_DESCR(obj)->f->getitem((char *)(itemptr), \
1621
+ (PyArrayObject *)(obj))
1622
+
1623
+ #define PyArray_SETITEM(obj,itemptr,v) \
1624
+ PyArray_DESCR(obj)->f->setitem((PyObject *)(v), \
1625
+ (char *)(itemptr), \
1626
+ (PyArrayObject *)(obj))
1627
+ #endif
1628
+
1629
+ static inline PyArray_Descr *
1630
+ PyArray_DTYPE(PyArrayObject *arr)
1631
+ {
1632
+ return ((PyArrayObject_fields *)arr)->descr;
1633
+ }
1634
+
1635
+ static inline npy_intp *
1636
+ PyArray_SHAPE(PyArrayObject *arr)
1637
+ {
1638
+ return ((PyArrayObject_fields *)arr)->dimensions;
1639
+ }
1640
+
1641
+ /*
1642
+ * Enables the specified array flags. Does no checking,
1643
+ * assumes you know what you're doing.
1644
+ */
1645
+ static inline void
1646
+ PyArray_ENABLEFLAGS(PyArrayObject *arr, int flags)
1647
+ {
1648
+ ((PyArrayObject_fields *)arr)->flags |= flags;
1649
+ }
1650
+
1651
+ /*
1652
+ * Clears the specified array flags. Does no checking,
1653
+ * assumes you know what you're doing.
1654
+ */
1655
+ static inline void
1656
+ PyArray_CLEARFLAGS(PyArrayObject *arr, int flags)
1657
+ {
1658
+ ((PyArrayObject_fields *)arr)->flags &= ~flags;
1659
+ }
1660
+
1661
+ #if NPY_FEATURE_VERSION >= NPY_1_22_API_VERSION
1662
+ static inline NPY_RETURNS_BORROWED_REF PyObject *
1663
+ PyArray_HANDLER(PyArrayObject *arr)
1664
+ {
1665
+ return ((PyArrayObject_fields *)arr)->mem_handler;
1666
+ }
1667
+ #endif
1668
+
1669
+ #define PyTypeNum_ISBOOL(type) ((type) == NPY_BOOL)
1670
+
1671
+ #define PyTypeNum_ISUNSIGNED(type) (((type) == NPY_UBYTE) || \
1672
+ ((type) == NPY_USHORT) || \
1673
+ ((type) == NPY_UINT) || \
1674
+ ((type) == NPY_ULONG) || \
1675
+ ((type) == NPY_ULONGLONG))
1676
+
1677
+ #define PyTypeNum_ISSIGNED(type) (((type) == NPY_BYTE) || \
1678
+ ((type) == NPY_SHORT) || \
1679
+ ((type) == NPY_INT) || \
1680
+ ((type) == NPY_LONG) || \
1681
+ ((type) == NPY_LONGLONG))
1682
+
1683
+ #define PyTypeNum_ISINTEGER(type) (((type) >= NPY_BYTE) && \
1684
+ ((type) <= NPY_ULONGLONG))
1685
+
1686
+ #define PyTypeNum_ISFLOAT(type) ((((type) >= NPY_FLOAT) && \
1687
+ ((type) <= NPY_LONGDOUBLE)) || \
1688
+ ((type) == NPY_HALF))
1689
+
1690
+ #define PyTypeNum_ISNUMBER(type) (((type) <= NPY_CLONGDOUBLE) || \
1691
+ ((type) == NPY_HALF))
1692
+
1693
+ #define PyTypeNum_ISSTRING(type) (((type) == NPY_STRING) || \
1694
+ ((type) == NPY_UNICODE))
1695
+
1696
+ #define PyTypeNum_ISCOMPLEX(type) (((type) >= NPY_CFLOAT) && \
1697
+ ((type) <= NPY_CLONGDOUBLE))
1698
+
1699
+ #define PyTypeNum_ISPYTHON(type) (((type) == NPY_LONG) || \
1700
+ ((type) == NPY_DOUBLE) || \
1701
+ ((type) == NPY_CDOUBLE) || \
1702
+ ((type) == NPY_BOOL) || \
1703
+ ((type) == NPY_OBJECT ))
1704
+
1705
+ #define PyTypeNum_ISFLEXIBLE(type) (((type) >=NPY_STRING) && \
1706
+ ((type) <=NPY_VOID))
1707
+
1708
+ #define PyTypeNum_ISDATETIME(type) (((type) >=NPY_DATETIME) && \
1709
+ ((type) <=NPY_TIMEDELTA))
1710
+
1711
+ #define PyTypeNum_ISUSERDEF(type) (((type) >= NPY_USERDEF) && \
1712
+ ((type) < NPY_USERDEF+ \
1713
+ NPY_NUMUSERTYPES))
1714
+
1715
+ #define PyTypeNum_ISEXTENDED(type) (PyTypeNum_ISFLEXIBLE(type) || \
1716
+ PyTypeNum_ISUSERDEF(type))
1717
+
1718
+ #define PyTypeNum_ISOBJECT(type) ((type) == NPY_OBJECT)
1719
+
1720
+
1721
+ #define PyDataType_ISBOOL(obj) PyTypeNum_ISBOOL(((PyArray_Descr*)(obj))->type_num)
1722
+ #define PyDataType_ISUNSIGNED(obj) PyTypeNum_ISUNSIGNED(((PyArray_Descr*)(obj))->type_num)
1723
+ #define PyDataType_ISSIGNED(obj) PyTypeNum_ISSIGNED(((PyArray_Descr*)(obj))->type_num)
1724
+ #define PyDataType_ISINTEGER(obj) PyTypeNum_ISINTEGER(((PyArray_Descr*)(obj))->type_num )
1725
+ #define PyDataType_ISFLOAT(obj) PyTypeNum_ISFLOAT(((PyArray_Descr*)(obj))->type_num)
1726
+ #define PyDataType_ISNUMBER(obj) PyTypeNum_ISNUMBER(((PyArray_Descr*)(obj))->type_num)
1727
+ #define PyDataType_ISSTRING(obj) PyTypeNum_ISSTRING(((PyArray_Descr*)(obj))->type_num)
1728
+ #define PyDataType_ISCOMPLEX(obj) PyTypeNum_ISCOMPLEX(((PyArray_Descr*)(obj))->type_num)
1729
+ #define PyDataType_ISPYTHON(obj) PyTypeNum_ISPYTHON(((PyArray_Descr*)(obj))->type_num)
1730
+ #define PyDataType_ISFLEXIBLE(obj) PyTypeNum_ISFLEXIBLE(((PyArray_Descr*)(obj))->type_num)
1731
+ #define PyDataType_ISDATETIME(obj) PyTypeNum_ISDATETIME(((PyArray_Descr*)(obj))->type_num)
1732
+ #define PyDataType_ISUSERDEF(obj) PyTypeNum_ISUSERDEF(((PyArray_Descr*)(obj))->type_num)
1733
+ #define PyDataType_ISEXTENDED(obj) PyTypeNum_ISEXTENDED(((PyArray_Descr*)(obj))->type_num)
1734
+ #define PyDataType_ISOBJECT(obj) PyTypeNum_ISOBJECT(((PyArray_Descr*)(obj))->type_num)
1735
+ #define PyDataType_HASFIELDS(obj) (((PyArray_Descr *)(obj))->names != NULL)
1736
+ #define PyDataType_HASSUBARRAY(dtype) ((dtype)->subarray != NULL)
1737
+ #define PyDataType_ISUNSIZED(dtype) ((dtype)->elsize == 0 && \
1738
+ !PyDataType_HASFIELDS(dtype))
1739
+ #define PyDataType_MAKEUNSIZED(dtype) ((dtype)->elsize = 0)
1740
+
1741
+ #define PyArray_ISBOOL(obj) PyTypeNum_ISBOOL(PyArray_TYPE(obj))
1742
+ #define PyArray_ISUNSIGNED(obj) PyTypeNum_ISUNSIGNED(PyArray_TYPE(obj))
1743
+ #define PyArray_ISSIGNED(obj) PyTypeNum_ISSIGNED(PyArray_TYPE(obj))
1744
+ #define PyArray_ISINTEGER(obj) PyTypeNum_ISINTEGER(PyArray_TYPE(obj))
1745
+ #define PyArray_ISFLOAT(obj) PyTypeNum_ISFLOAT(PyArray_TYPE(obj))
1746
+ #define PyArray_ISNUMBER(obj) PyTypeNum_ISNUMBER(PyArray_TYPE(obj))
1747
+ #define PyArray_ISSTRING(obj) PyTypeNum_ISSTRING(PyArray_TYPE(obj))
1748
+ #define PyArray_ISCOMPLEX(obj) PyTypeNum_ISCOMPLEX(PyArray_TYPE(obj))
1749
+ #define PyArray_ISPYTHON(obj) PyTypeNum_ISPYTHON(PyArray_TYPE(obj))
1750
+ #define PyArray_ISFLEXIBLE(obj) PyTypeNum_ISFLEXIBLE(PyArray_TYPE(obj))
1751
+ #define PyArray_ISDATETIME(obj) PyTypeNum_ISDATETIME(PyArray_TYPE(obj))
1752
+ #define PyArray_ISUSERDEF(obj) PyTypeNum_ISUSERDEF(PyArray_TYPE(obj))
1753
+ #define PyArray_ISEXTENDED(obj) PyTypeNum_ISEXTENDED(PyArray_TYPE(obj))
1754
+ #define PyArray_ISOBJECT(obj) PyTypeNum_ISOBJECT(PyArray_TYPE(obj))
1755
+ #define PyArray_HASFIELDS(obj) PyDataType_HASFIELDS(PyArray_DESCR(obj))
1756
+
1757
+ /*
1758
+ * FIXME: This should check for a flag on the data-type that
1759
+ * states whether or not it is variable length. Because the
1760
+ * ISFLEXIBLE check is hard-coded to the built-in data-types.
1761
+ */
1762
+ #define PyArray_ISVARIABLE(obj) PyTypeNum_ISFLEXIBLE(PyArray_TYPE(obj))
1763
+
1764
+ #define PyArray_SAFEALIGNEDCOPY(obj) (PyArray_ISALIGNED(obj) && !PyArray_ISVARIABLE(obj))
1765
+
1766
+
1767
+ #define NPY_LITTLE '<'
1768
+ #define NPY_BIG '>'
1769
+ #define NPY_NATIVE '='
1770
+ #define NPY_SWAP 's'
1771
+ #define NPY_IGNORE '|'
1772
+
1773
+ #if NPY_BYTE_ORDER == NPY_BIG_ENDIAN
1774
+ #define NPY_NATBYTE NPY_BIG
1775
+ #define NPY_OPPBYTE NPY_LITTLE
1776
+ #else
1777
+ #define NPY_NATBYTE NPY_LITTLE
1778
+ #define NPY_OPPBYTE NPY_BIG
1779
+ #endif
1780
+
1781
+ #define PyArray_ISNBO(arg) ((arg) != NPY_OPPBYTE)
1782
+ #define PyArray_IsNativeByteOrder PyArray_ISNBO
1783
+ #define PyArray_ISNOTSWAPPED(m) PyArray_ISNBO(PyArray_DESCR(m)->byteorder)
1784
+ #define PyArray_ISBYTESWAPPED(m) (!PyArray_ISNOTSWAPPED(m))
1785
+
1786
+ #define PyArray_FLAGSWAP(m, flags) (PyArray_CHKFLAGS(m, flags) && \
1787
+ PyArray_ISNOTSWAPPED(m))
1788
+
1789
+ #define PyArray_ISCARRAY(m) PyArray_FLAGSWAP(m, NPY_ARRAY_CARRAY)
1790
+ #define PyArray_ISCARRAY_RO(m) PyArray_FLAGSWAP(m, NPY_ARRAY_CARRAY_RO)
1791
+ #define PyArray_ISFARRAY(m) PyArray_FLAGSWAP(m, NPY_ARRAY_FARRAY)
1792
+ #define PyArray_ISFARRAY_RO(m) PyArray_FLAGSWAP(m, NPY_ARRAY_FARRAY_RO)
1793
+ #define PyArray_ISBEHAVED(m) PyArray_FLAGSWAP(m, NPY_ARRAY_BEHAVED)
1794
+ #define PyArray_ISBEHAVED_RO(m) PyArray_FLAGSWAP(m, NPY_ARRAY_ALIGNED)
1795
+
1796
+
1797
+ #define PyDataType_ISNOTSWAPPED(d) PyArray_ISNBO(((PyArray_Descr *)(d))->byteorder)
1798
+ #define PyDataType_ISBYTESWAPPED(d) (!PyDataType_ISNOTSWAPPED(d))
1799
+
1800
+ /************************************************************
1801
+ * A struct used by PyArray_CreateSortedStridePerm, new in 1.7.
1802
+ ************************************************************/
1803
+
1804
+ typedef struct {
1805
+ npy_intp perm, stride;
1806
+ } npy_stride_sort_item;
1807
+
1808
+ /************************************************************
1809
+ * This is the form of the struct that's stored in the
1810
+ * PyCapsule returned by an array's __array_struct__ attribute. See
1811
+ * https://docs.scipy.org/doc/numpy/reference/arrays.interface.html for the full
1812
+ * documentation.
1813
+ ************************************************************/
1814
+ typedef struct {
1815
+ int two; /*
1816
+ * contains the integer 2 as a sanity
1817
+ * check
1818
+ */
1819
+
1820
+ int nd; /* number of dimensions */
1821
+
1822
+ char typekind; /*
1823
+ * kind in array --- character code of
1824
+ * typestr
1825
+ */
1826
+
1827
+ int itemsize; /* size of each element */
1828
+
1829
+ int flags; /*
1830
+ * how should be data interpreted. Valid
1831
+ * flags are CONTIGUOUS (1), F_CONTIGUOUS (2),
1832
+ * ALIGNED (0x100), NOTSWAPPED (0x200), and
1833
+ * WRITEABLE (0x400). ARR_HAS_DESCR (0x800)
1834
+ * states that arrdescr field is present in
1835
+ * structure
1836
+ */
1837
+
1838
+ npy_intp *shape; /*
1839
+ * A length-nd array of shape
1840
+ * information
1841
+ */
1842
+
1843
+ npy_intp *strides; /* A length-nd array of stride information */
1844
+
1845
+ void *data; /* A pointer to the first element of the array */
1846
+
1847
+ PyObject *descr; /*
1848
+ * A list of fields or NULL (ignored if flags
1849
+ * does not have ARR_HAS_DESCR flag set)
1850
+ */
1851
+ } PyArrayInterface;
1852
+
1853
+ /*
1854
+ * This is a function for hooking into the PyDataMem_NEW/FREE/RENEW functions.
1855
+ * See the documentation for PyDataMem_SetEventHook.
1856
+ */
1857
+ typedef void (PyDataMem_EventHookFunc)(void *inp, void *outp, size_t size,
1858
+ void *user_data);
1859
+
1860
+
1861
+ /*
1862
+ * PyArray_DTypeMeta related definitions.
1863
+ *
1864
+ * As of now, this API is preliminary and will be extended as necessary.
1865
+ */
1866
+ #if defined(NPY_INTERNAL_BUILD) && NPY_INTERNAL_BUILD
1867
+ /*
1868
+ * The Structures defined in this block are currently considered
1869
+ * private API and may change without warning!
1870
+ * Part of this (at least the size) is expected to be public API without
1871
+ * further modifications.
1872
+ */
1873
+ /* TODO: Make this definition public in the API, as soon as its settled */
1874
+ NPY_NO_EXPORT extern PyTypeObject PyArrayDTypeMeta_Type;
1875
+
1876
+ /*
1877
+ * While NumPy DTypes would not need to be heap types the plan is to
1878
+ * make DTypes available in Python at which point they will be heap types.
1879
+ * Since we also wish to add fields to the DType class, this looks like
1880
+ * a typical instance definition, but with PyHeapTypeObject instead of
1881
+ * only the PyObject_HEAD.
1882
+ * This must only be exposed very extremely careful consideration, since
1883
+ * it is a fairly complex construct which may be better to allow
1884
+ * refactoring of.
1885
+ */
1886
+ typedef struct {
1887
+ PyHeapTypeObject super;
1888
+
1889
+ /*
1890
+ * Most DTypes will have a singleton default instance, for the
1891
+ * parametric legacy DTypes (bytes, string, void, datetime) this
1892
+ * may be a pointer to the *prototype* instance?
1893
+ */
1894
+ PyArray_Descr *singleton;
1895
+ /* Copy of the legacy DTypes type number, usually invalid. */
1896
+ int type_num;
1897
+
1898
+ /* The type object of the scalar instances (may be NULL?) */
1899
+ PyTypeObject *scalar_type;
1900
+ /*
1901
+ * DType flags to signal legacy, parametric, or
1902
+ * abstract. But plenty of space for additional information/flags.
1903
+ */
1904
+ npy_uint64 flags;
1905
+
1906
+ /*
1907
+ * Use indirection in order to allow a fixed size for this struct.
1908
+ * A stable ABI size makes creating a static DType less painful
1909
+ * while also ensuring flexibility for all opaque API (with one
1910
+ * indirection due the pointer lookup).
1911
+ */
1912
+ void *dt_slots;
1913
+ void *reserved[3];
1914
+ } PyArray_DTypeMeta;
1915
+
1916
+ #endif /* NPY_INTERNAL_BUILD */
1917
+
1918
+
1919
+ /*
1920
+ * Use the keyword NPY_DEPRECATED_INCLUDES to ensure that the header files
1921
+ * npy_*_*_deprecated_api.h are only included from here and nowhere else.
1922
+ */
1923
+ #ifdef NPY_DEPRECATED_INCLUDES
1924
+ #error "Do not use the reserved keyword NPY_DEPRECATED_INCLUDES."
1925
+ #endif
1926
+ #define NPY_DEPRECATED_INCLUDES
1927
+ #if !defined(NPY_NO_DEPRECATED_API) || \
1928
+ (NPY_NO_DEPRECATED_API < NPY_1_7_API_VERSION)
1929
+ #include "npy_1_7_deprecated_api.h"
1930
+ #endif
1931
+ /*
1932
+ * There is no file npy_1_8_deprecated_api.h since there are no additional
1933
+ * deprecated API features in NumPy 1.8.
1934
+ *
1935
+ * Note to maintainers: insert code like the following in future NumPy
1936
+ * versions.
1937
+ *
1938
+ * #if !defined(NPY_NO_DEPRECATED_API) || \
1939
+ * (NPY_NO_DEPRECATED_API < NPY_1_9_API_VERSION)
1940
+ * #include "npy_1_9_deprecated_api.h"
1941
+ * #endif
1942
+ */
1943
+ #undef NPY_DEPRECATED_INCLUDES
1944
+
1945
+ #endif /* NUMPY_CORE_INCLUDE_NUMPY_NDARRAYTYPES_H_ */
evalkit_tf437/lib/python3.10/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h ADDED
@@ -0,0 +1,124 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #ifndef NPY_DEPRECATED_INCLUDES
2
+ #error "Should never include npy_*_*_deprecated_api directly."
3
+ #endif
4
+
5
+ #ifndef NUMPY_CORE_INCLUDE_NUMPY_NPY_1_7_DEPRECATED_API_H_
6
+ #define NUMPY_CORE_INCLUDE_NUMPY_NPY_1_7_DEPRECATED_API_H_
7
+
8
+ /* Emit a warning if the user did not specifically request the old API */
9
+ #ifndef NPY_NO_DEPRECATED_API
10
+ #if defined(_WIN32)
11
+ #define _WARN___STR2__(x) #x
12
+ #define _WARN___STR1__(x) _WARN___STR2__(x)
13
+ #define _WARN___LOC__ __FILE__ "(" _WARN___STR1__(__LINE__) ") : Warning Msg: "
14
+ #pragma message(_WARN___LOC__"Using deprecated NumPy API, disable it with " \
15
+ "#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION")
16
+ #else
17
+ #warning "Using deprecated NumPy API, disable it with " \
18
+ "#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION"
19
+ #endif
20
+ #endif
21
+
22
+ /*
23
+ * This header exists to collect all dangerous/deprecated NumPy API
24
+ * as of NumPy 1.7.
25
+ *
26
+ * This is an attempt to remove bad API, the proliferation of macros,
27
+ * and namespace pollution currently produced by the NumPy headers.
28
+ */
29
+
30
+ /* These array flags are deprecated as of NumPy 1.7 */
31
+ #define NPY_CONTIGUOUS NPY_ARRAY_C_CONTIGUOUS
32
+ #define NPY_FORTRAN NPY_ARRAY_F_CONTIGUOUS
33
+
34
+ /*
35
+ * The consistent NPY_ARRAY_* names which don't pollute the NPY_*
36
+ * namespace were added in NumPy 1.7.
37
+ *
38
+ * These versions of the carray flags are deprecated, but
39
+ * probably should only be removed after two releases instead of one.
40
+ */
41
+ #define NPY_C_CONTIGUOUS NPY_ARRAY_C_CONTIGUOUS
42
+ #define NPY_F_CONTIGUOUS NPY_ARRAY_F_CONTIGUOUS
43
+ #define NPY_OWNDATA NPY_ARRAY_OWNDATA
44
+ #define NPY_FORCECAST NPY_ARRAY_FORCECAST
45
+ #define NPY_ENSURECOPY NPY_ARRAY_ENSURECOPY
46
+ #define NPY_ENSUREARRAY NPY_ARRAY_ENSUREARRAY
47
+ #define NPY_ELEMENTSTRIDES NPY_ARRAY_ELEMENTSTRIDES
48
+ #define NPY_ALIGNED NPY_ARRAY_ALIGNED
49
+ #define NPY_NOTSWAPPED NPY_ARRAY_NOTSWAPPED
50
+ #define NPY_WRITEABLE NPY_ARRAY_WRITEABLE
51
+ #define NPY_BEHAVED NPY_ARRAY_BEHAVED
52
+ #define NPY_BEHAVED_NS NPY_ARRAY_BEHAVED_NS
53
+ #define NPY_CARRAY NPY_ARRAY_CARRAY
54
+ #define NPY_CARRAY_RO NPY_ARRAY_CARRAY_RO
55
+ #define NPY_FARRAY NPY_ARRAY_FARRAY
56
+ #define NPY_FARRAY_RO NPY_ARRAY_FARRAY_RO
57
+ #define NPY_DEFAULT NPY_ARRAY_DEFAULT
58
+ #define NPY_IN_ARRAY NPY_ARRAY_IN_ARRAY
59
+ #define NPY_OUT_ARRAY NPY_ARRAY_OUT_ARRAY
60
+ #define NPY_INOUT_ARRAY NPY_ARRAY_INOUT_ARRAY
61
+ #define NPY_IN_FARRAY NPY_ARRAY_IN_FARRAY
62
+ #define NPY_OUT_FARRAY NPY_ARRAY_OUT_FARRAY
63
+ #define NPY_INOUT_FARRAY NPY_ARRAY_INOUT_FARRAY
64
+ #define NPY_UPDATE_ALL NPY_ARRAY_UPDATE_ALL
65
+
66
+ /* This way of accessing the default type is deprecated as of NumPy 1.7 */
67
+ #define PyArray_DEFAULT NPY_DEFAULT_TYPE
68
+
69
+ /* These DATETIME bits aren't used internally */
70
+ #define PyDataType_GetDatetimeMetaData(descr) \
71
+ ((descr->metadata == NULL) ? NULL : \
72
+ ((PyArray_DatetimeMetaData *)(PyCapsule_GetPointer( \
73
+ PyDict_GetItemString( \
74
+ descr->metadata, NPY_METADATA_DTSTR), NULL))))
75
+
76
+ /*
77
+ * Deprecated as of NumPy 1.7, this kind of shortcut doesn't
78
+ * belong in the public API.
79
+ */
80
+ #define NPY_AO PyArrayObject
81
+
82
+ /*
83
+ * Deprecated as of NumPy 1.7, an all-lowercase macro doesn't
84
+ * belong in the public API.
85
+ */
86
+ #define fortran fortran_
87
+
88
+ /*
89
+ * Deprecated as of NumPy 1.7, as it is a namespace-polluting
90
+ * macro.
91
+ */
92
+ #define FORTRAN_IF PyArray_FORTRAN_IF
93
+
94
+ /* Deprecated as of NumPy 1.7, datetime64 uses c_metadata instead */
95
+ #define NPY_METADATA_DTSTR "__timeunit__"
96
+
97
+ /*
98
+ * Deprecated as of NumPy 1.7.
99
+ * The reasoning:
100
+ * - These are for datetime, but there's no datetime "namespace".
101
+ * - They just turn NPY_STR_<x> into "<x>", which is just
102
+ * making something simple be indirected.
103
+ */
104
+ #define NPY_STR_Y "Y"
105
+ #define NPY_STR_M "M"
106
+ #define NPY_STR_W "W"
107
+ #define NPY_STR_D "D"
108
+ #define NPY_STR_h "h"
109
+ #define NPY_STR_m "m"
110
+ #define NPY_STR_s "s"
111
+ #define NPY_STR_ms "ms"
112
+ #define NPY_STR_us "us"
113
+ #define NPY_STR_ns "ns"
114
+ #define NPY_STR_ps "ps"
115
+ #define NPY_STR_fs "fs"
116
+ #define NPY_STR_as "as"
117
+
118
+ /*
119
+ * The macros in old_defines.h are Deprecated as of NumPy 1.7 and will be
120
+ * removed in the next major release.
121
+ */
122
+ #include "old_defines.h"
123
+
124
+ #endif /* NUMPY_CORE_INCLUDE_NUMPY_NPY_1_7_DEPRECATED_API_H_ */
evalkit_tf437/lib/python3.10/site-packages/numpy/core/include/numpy/npy_common.h ADDED
@@ -0,0 +1,1086 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #ifndef NUMPY_CORE_INCLUDE_NUMPY_NPY_COMMON_H_
2
+ #define NUMPY_CORE_INCLUDE_NUMPY_NPY_COMMON_H_
3
+
4
+ /* need Python.h for npy_intp, npy_uintp */
5
+ #include <Python.h>
6
+
7
+ /* numpconfig.h is auto-generated */
8
+ #include "numpyconfig.h"
9
+ #ifdef HAVE_NPY_CONFIG_H
10
+ #include <npy_config.h>
11
+ #endif
12
+
13
+ /*
14
+ * using static inline modifiers when defining npy_math functions
15
+ * allows the compiler to make optimizations when possible
16
+ */
17
+ #ifndef NPY_INLINE_MATH
18
+ #if defined(NPY_INTERNAL_BUILD) && NPY_INTERNAL_BUILD
19
+ #define NPY_INLINE_MATH 1
20
+ #else
21
+ #define NPY_INLINE_MATH 0
22
+ #endif
23
+ #endif
24
+
25
+ /*
26
+ * gcc does not unroll even with -O3
27
+ * use with care, unrolling on modern cpus rarely speeds things up
28
+ */
29
+ #ifdef HAVE_ATTRIBUTE_OPTIMIZE_UNROLL_LOOPS
30
+ #define NPY_GCC_UNROLL_LOOPS \
31
+ __attribute__((optimize("unroll-loops")))
32
+ #else
33
+ #define NPY_GCC_UNROLL_LOOPS
34
+ #endif
35
+
36
+ /* highest gcc optimization level, enabled autovectorizer */
37
+ #ifdef HAVE_ATTRIBUTE_OPTIMIZE_OPT_3
38
+ #define NPY_GCC_OPT_3 __attribute__((optimize("O3")))
39
+ #else
40
+ #define NPY_GCC_OPT_3
41
+ #endif
42
+
43
+ /*
44
+ * mark an argument (starting from 1) that must not be NULL and is not checked
45
+ * DO NOT USE IF FUNCTION CHECKS FOR NULL!! the compiler will remove the check
46
+ */
47
+ #ifdef HAVE_ATTRIBUTE_NONNULL
48
+ #define NPY_GCC_NONNULL(n) __attribute__((nonnull(n)))
49
+ #else
50
+ #define NPY_GCC_NONNULL(n)
51
+ #endif
52
+
53
+ /*
54
+ * give a hint to the compiler which branch is more likely or unlikely
55
+ * to occur, e.g. rare error cases:
56
+ *
57
+ * if (NPY_UNLIKELY(failure == 0))
58
+ * return NULL;
59
+ *
60
+ * the double !! is to cast the expression (e.g. NULL) to a boolean required by
61
+ * the intrinsic
62
+ */
63
+ #ifdef HAVE___BUILTIN_EXPECT
64
+ #define NPY_LIKELY(x) __builtin_expect(!!(x), 1)
65
+ #define NPY_UNLIKELY(x) __builtin_expect(!!(x), 0)
66
+ #else
67
+ #define NPY_LIKELY(x) (x)
68
+ #define NPY_UNLIKELY(x) (x)
69
+ #endif
70
+
71
+ #ifdef HAVE___BUILTIN_PREFETCH
72
+ /* unlike _mm_prefetch also works on non-x86 */
73
+ #define NPY_PREFETCH(x, rw, loc) __builtin_prefetch((x), (rw), (loc))
74
+ #else
75
+ #ifdef NPY_HAVE_SSE
76
+ /* _MM_HINT_ET[01] (rw = 1) unsupported, only available in gcc >= 4.9 */
77
+ #define NPY_PREFETCH(x, rw, loc) _mm_prefetch((x), loc == 0 ? _MM_HINT_NTA : \
78
+ (loc == 1 ? _MM_HINT_T2 : \
79
+ (loc == 2 ? _MM_HINT_T1 : \
80
+ (loc == 3 ? _MM_HINT_T0 : -1))))
81
+ #else
82
+ #define NPY_PREFETCH(x, rw,loc)
83
+ #endif
84
+ #endif
85
+
86
+ /* `NPY_INLINE` kept for backwards compatibility; use `inline` instead */
87
+ #if defined(_MSC_VER) && !defined(__clang__)
88
+ #define NPY_INLINE __inline
89
+ /* clang included here to handle clang-cl on Windows */
90
+ #elif defined(__GNUC__) || defined(__clang__)
91
+ #if defined(__STRICT_ANSI__)
92
+ #define NPY_INLINE __inline__
93
+ #else
94
+ #define NPY_INLINE inline
95
+ #endif
96
+ #else
97
+ #define NPY_INLINE
98
+ #endif
99
+
100
+ #ifdef _MSC_VER
101
+ #define NPY_FINLINE static __forceinline
102
+ #elif defined(__GNUC__)
103
+ #define NPY_FINLINE static inline __attribute__((always_inline))
104
+ #else
105
+ #define NPY_FINLINE static
106
+ #endif
107
+
108
+ #if defined(_MSC_VER)
109
+ #define NPY_NOINLINE static __declspec(noinline)
110
+ #elif defined(__GNUC__) || defined(__clang__)
111
+ #define NPY_NOINLINE static __attribute__((noinline))
112
+ #else
113
+ #define NPY_NOINLINE static
114
+ #endif
115
+
116
+ #ifdef HAVE___THREAD
117
+ #define NPY_TLS __thread
118
+ #else
119
+ #ifdef HAVE___DECLSPEC_THREAD_
120
+ #define NPY_TLS __declspec(thread)
121
+ #else
122
+ #define NPY_TLS
123
+ #endif
124
+ #endif
125
+
126
+ #ifdef WITH_CPYCHECKER_RETURNS_BORROWED_REF_ATTRIBUTE
127
+ #define NPY_RETURNS_BORROWED_REF \
128
+ __attribute__((cpychecker_returns_borrowed_ref))
129
+ #else
130
+ #define NPY_RETURNS_BORROWED_REF
131
+ #endif
132
+
133
+ #ifdef WITH_CPYCHECKER_STEALS_REFERENCE_TO_ARG_ATTRIBUTE
134
+ #define NPY_STEALS_REF_TO_ARG(n) \
135
+ __attribute__((cpychecker_steals_reference_to_arg(n)))
136
+ #else
137
+ #define NPY_STEALS_REF_TO_ARG(n)
138
+ #endif
139
+
140
+ /* 64 bit file position support, also on win-amd64. Issue gh-2256 */
141
+ #if defined(_MSC_VER) && defined(_WIN64) && (_MSC_VER > 1400) || \
142
+ defined(__MINGW32__) || defined(__MINGW64__)
143
+ #include <io.h>
144
+
145
+ #define npy_fseek _fseeki64
146
+ #define npy_ftell _ftelli64
147
+ #define npy_lseek _lseeki64
148
+ #define npy_off_t npy_int64
149
+
150
+ #if NPY_SIZEOF_INT == 8
151
+ #define NPY_OFF_T_PYFMT "i"
152
+ #elif NPY_SIZEOF_LONG == 8
153
+ #define NPY_OFF_T_PYFMT "l"
154
+ #elif NPY_SIZEOF_LONGLONG == 8
155
+ #define NPY_OFF_T_PYFMT "L"
156
+ #else
157
+ #error Unsupported size for type off_t
158
+ #endif
159
+ #else
160
+ #ifdef HAVE_FSEEKO
161
+ #define npy_fseek fseeko
162
+ #else
163
+ #define npy_fseek fseek
164
+ #endif
165
+ #ifdef HAVE_FTELLO
166
+ #define npy_ftell ftello
167
+ #else
168
+ #define npy_ftell ftell
169
+ #endif
170
+ #include <sys/types.h>
171
+ #ifndef _WIN32
172
+ #include <unistd.h>
173
+ #endif
174
+ #define npy_lseek lseek
175
+ #define npy_off_t off_t
176
+
177
+ #if NPY_SIZEOF_OFF_T == NPY_SIZEOF_SHORT
178
+ #define NPY_OFF_T_PYFMT "h"
179
+ #elif NPY_SIZEOF_OFF_T == NPY_SIZEOF_INT
180
+ #define NPY_OFF_T_PYFMT "i"
181
+ #elif NPY_SIZEOF_OFF_T == NPY_SIZEOF_LONG
182
+ #define NPY_OFF_T_PYFMT "l"
183
+ #elif NPY_SIZEOF_OFF_T == NPY_SIZEOF_LONGLONG
184
+ #define NPY_OFF_T_PYFMT "L"
185
+ #else
186
+ #error Unsupported size for type off_t
187
+ #endif
188
+ #endif
189
+
190
+ /* enums for detected endianness */
191
+ enum {
192
+ NPY_CPU_UNKNOWN_ENDIAN,
193
+ NPY_CPU_LITTLE,
194
+ NPY_CPU_BIG
195
+ };
196
+
197
+ /*
198
+ * This is to typedef npy_intp to the appropriate pointer size for this
199
+ * platform. Py_intptr_t, Py_uintptr_t are defined in pyport.h.
200
+ */
201
+ typedef Py_intptr_t npy_intp;
202
+ typedef Py_uintptr_t npy_uintp;
203
+
204
+ /*
205
+ * Define sizes that were not defined in numpyconfig.h.
206
+ */
207
+ #define NPY_SIZEOF_CHAR 1
208
+ #define NPY_SIZEOF_BYTE 1
209
+ #define NPY_SIZEOF_DATETIME 8
210
+ #define NPY_SIZEOF_TIMEDELTA 8
211
+ #define NPY_SIZEOF_INTP NPY_SIZEOF_PY_INTPTR_T
212
+ #define NPY_SIZEOF_UINTP NPY_SIZEOF_PY_INTPTR_T
213
+ #define NPY_SIZEOF_HALF 2
214
+ #define NPY_SIZEOF_CFLOAT NPY_SIZEOF_COMPLEX_FLOAT
215
+ #define NPY_SIZEOF_CDOUBLE NPY_SIZEOF_COMPLEX_DOUBLE
216
+ #define NPY_SIZEOF_CLONGDOUBLE NPY_SIZEOF_COMPLEX_LONGDOUBLE
217
+
218
+ #ifdef constchar
219
+ #undef constchar
220
+ #endif
221
+
222
+ #define NPY_SSIZE_T_PYFMT "n"
223
+ #define constchar char
224
+
225
+ /* NPY_INTP_FMT Note:
226
+ * Unlike the other NPY_*_FMT macros, which are used with PyOS_snprintf,
227
+ * NPY_INTP_FMT is used with PyErr_Format and PyUnicode_FromFormat. Those
228
+ * functions use different formatting codes that are portably specified
229
+ * according to the Python documentation. See issue gh-2388.
230
+ */
231
+ #if NPY_SIZEOF_PY_INTPTR_T == NPY_SIZEOF_INT
232
+ #define NPY_INTP NPY_INT
233
+ #define NPY_UINTP NPY_UINT
234
+ #define PyIntpArrType_Type PyIntArrType_Type
235
+ #define PyUIntpArrType_Type PyUIntArrType_Type
236
+ #define NPY_MAX_INTP NPY_MAX_INT
237
+ #define NPY_MIN_INTP NPY_MIN_INT
238
+ #define NPY_MAX_UINTP NPY_MAX_UINT
239
+ #define NPY_INTP_FMT "d"
240
+ #elif NPY_SIZEOF_PY_INTPTR_T == NPY_SIZEOF_LONG
241
+ #define NPY_INTP NPY_LONG
242
+ #define NPY_UINTP NPY_ULONG
243
+ #define PyIntpArrType_Type PyLongArrType_Type
244
+ #define PyUIntpArrType_Type PyULongArrType_Type
245
+ #define NPY_MAX_INTP NPY_MAX_LONG
246
+ #define NPY_MIN_INTP NPY_MIN_LONG
247
+ #define NPY_MAX_UINTP NPY_MAX_ULONG
248
+ #define NPY_INTP_FMT "ld"
249
+ #elif defined(PY_LONG_LONG) && (NPY_SIZEOF_PY_INTPTR_T == NPY_SIZEOF_LONGLONG)
250
+ #define NPY_INTP NPY_LONGLONG
251
+ #define NPY_UINTP NPY_ULONGLONG
252
+ #define PyIntpArrType_Type PyLongLongArrType_Type
253
+ #define PyUIntpArrType_Type PyULongLongArrType_Type
254
+ #define NPY_MAX_INTP NPY_MAX_LONGLONG
255
+ #define NPY_MIN_INTP NPY_MIN_LONGLONG
256
+ #define NPY_MAX_UINTP NPY_MAX_ULONGLONG
257
+ #define NPY_INTP_FMT "lld"
258
+ #endif
259
+
260
+ /*
261
+ * We can only use C99 formats for npy_int_p if it is the same as
262
+ * intp_t, hence the condition on HAVE_UNITPTR_T
263
+ */
264
+ #if (NPY_USE_C99_FORMATS) == 1 \
265
+ && (defined HAVE_UINTPTR_T) \
266
+ && (defined HAVE_INTTYPES_H)
267
+ #include <inttypes.h>
268
+ #undef NPY_INTP_FMT
269
+ #define NPY_INTP_FMT PRIdPTR
270
+ #endif
271
+
272
+
273
+ /*
274
+ * Some platforms don't define bool, long long, or long double.
275
+ * Handle that here.
276
+ */
277
+ #define NPY_BYTE_FMT "hhd"
278
+ #define NPY_UBYTE_FMT "hhu"
279
+ #define NPY_SHORT_FMT "hd"
280
+ #define NPY_USHORT_FMT "hu"
281
+ #define NPY_INT_FMT "d"
282
+ #define NPY_UINT_FMT "u"
283
+ #define NPY_LONG_FMT "ld"
284
+ #define NPY_ULONG_FMT "lu"
285
+ #define NPY_HALF_FMT "g"
286
+ #define NPY_FLOAT_FMT "g"
287
+ #define NPY_DOUBLE_FMT "g"
288
+
289
+
290
+ #ifdef PY_LONG_LONG
291
+ typedef PY_LONG_LONG npy_longlong;
292
+ typedef unsigned PY_LONG_LONG npy_ulonglong;
293
+ # ifdef _MSC_VER
294
+ # define NPY_LONGLONG_FMT "I64d"
295
+ # define NPY_ULONGLONG_FMT "I64u"
296
+ # else
297
+ # define NPY_LONGLONG_FMT "lld"
298
+ # define NPY_ULONGLONG_FMT "llu"
299
+ # endif
300
+ # ifdef _MSC_VER
301
+ # define NPY_LONGLONG_SUFFIX(x) (x##i64)
302
+ # define NPY_ULONGLONG_SUFFIX(x) (x##Ui64)
303
+ # else
304
+ # define NPY_LONGLONG_SUFFIX(x) (x##LL)
305
+ # define NPY_ULONGLONG_SUFFIX(x) (x##ULL)
306
+ # endif
307
+ #else
308
+ typedef long npy_longlong;
309
+ typedef unsigned long npy_ulonglong;
310
+ # define NPY_LONGLONG_SUFFIX(x) (x##L)
311
+ # define NPY_ULONGLONG_SUFFIX(x) (x##UL)
312
+ #endif
313
+
314
+
315
+ typedef unsigned char npy_bool;
316
+ #define NPY_FALSE 0
317
+ #define NPY_TRUE 1
318
+ /*
319
+ * `NPY_SIZEOF_LONGDOUBLE` isn't usually equal to sizeof(long double).
320
+ * In some certain cases, it may forced to be equal to sizeof(double)
321
+ * even against the compiler implementation and the same goes for
322
+ * `complex long double`.
323
+ *
324
+ * Therefore, avoid `long double`, use `npy_longdouble` instead,
325
+ * and when it comes to standard math functions make sure of using
326
+ * the double version when `NPY_SIZEOF_LONGDOUBLE` == `NPY_SIZEOF_DOUBLE`.
327
+ * For example:
328
+ * npy_longdouble *ptr, x;
329
+ * #if NPY_SIZEOF_LONGDOUBLE == NPY_SIZEOF_DOUBLE
330
+ * npy_longdouble r = modf(x, ptr);
331
+ * #else
332
+ * npy_longdouble r = modfl(x, ptr);
333
+ * #endif
334
+ *
335
+ * See https://github.com/numpy/numpy/issues/20348
336
+ */
337
+ #if NPY_SIZEOF_LONGDOUBLE == NPY_SIZEOF_DOUBLE
338
+ #define NPY_LONGDOUBLE_FMT "g"
339
+ typedef double npy_longdouble;
340
+ #else
341
+ #define NPY_LONGDOUBLE_FMT "Lg"
342
+ typedef long double npy_longdouble;
343
+ #endif
344
+
345
+ #ifndef Py_USING_UNICODE
346
+ #error Must use Python with unicode enabled.
347
+ #endif
348
+
349
+
350
+ typedef signed char npy_byte;
351
+ typedef unsigned char npy_ubyte;
352
+ typedef unsigned short npy_ushort;
353
+ typedef unsigned int npy_uint;
354
+ typedef unsigned long npy_ulong;
355
+
356
+ /* These are for completeness */
357
+ typedef char npy_char;
358
+ typedef short npy_short;
359
+ typedef int npy_int;
360
+ typedef long npy_long;
361
+ typedef float npy_float;
362
+ typedef double npy_double;
363
+
364
+ typedef Py_hash_t npy_hash_t;
365
+ #define NPY_SIZEOF_HASH_T NPY_SIZEOF_INTP
366
+
367
+ /*
368
+ * Disabling C99 complex usage: a lot of C code in numpy/scipy rely on being
369
+ * able to do .real/.imag. Will have to convert code first.
370
+ */
371
+ #if 0
372
+ #if defined(NPY_USE_C99_COMPLEX) && defined(NPY_HAVE_COMPLEX_DOUBLE)
373
+ typedef complex npy_cdouble;
374
+ #else
375
+ typedef struct { double real, imag; } npy_cdouble;
376
+ #endif
377
+
378
+ #if defined(NPY_USE_C99_COMPLEX) && defined(NPY_HAVE_COMPLEX_FLOAT)
379
+ typedef complex float npy_cfloat;
380
+ #else
381
+ typedef struct { float real, imag; } npy_cfloat;
382
+ #endif
383
+
384
+ #if defined(NPY_USE_C99_COMPLEX) && defined(NPY_HAVE_COMPLEX_LONG_DOUBLE)
385
+ typedef complex long double npy_clongdouble;
386
+ #else
387
+ typedef struct {npy_longdouble real, imag;} npy_clongdouble;
388
+ #endif
389
+ #endif
390
+ #if NPY_SIZEOF_COMPLEX_DOUBLE != 2 * NPY_SIZEOF_DOUBLE
391
+ #error npy_cdouble definition is not compatible with C99 complex definition ! \
392
+ Please contact NumPy maintainers and give detailed information about your \
393
+ compiler and platform
394
+ #endif
395
+ typedef struct { double real, imag; } npy_cdouble;
396
+
397
+ #if NPY_SIZEOF_COMPLEX_FLOAT != 2 * NPY_SIZEOF_FLOAT
398
+ #error npy_cfloat definition is not compatible with C99 complex definition ! \
399
+ Please contact NumPy maintainers and give detailed information about your \
400
+ compiler and platform
401
+ #endif
402
+ typedef struct { float real, imag; } npy_cfloat;
403
+
404
+ #if NPY_SIZEOF_COMPLEX_LONGDOUBLE != 2 * NPY_SIZEOF_LONGDOUBLE
405
+ #error npy_clongdouble definition is not compatible with C99 complex definition ! \
406
+ Please contact NumPy maintainers and give detailed information about your \
407
+ compiler and platform
408
+ #endif
409
+ typedef struct { npy_longdouble real, imag; } npy_clongdouble;
410
+
411
+ /*
412
+ * numarray-style bit-width typedefs
413
+ */
414
+ #define NPY_MAX_INT8 127
415
+ #define NPY_MIN_INT8 -128
416
+ #define NPY_MAX_UINT8 255
417
+ #define NPY_MAX_INT16 32767
418
+ #define NPY_MIN_INT16 -32768
419
+ #define NPY_MAX_UINT16 65535
420
+ #define NPY_MAX_INT32 2147483647
421
+ #define NPY_MIN_INT32 (-NPY_MAX_INT32 - 1)
422
+ #define NPY_MAX_UINT32 4294967295U
423
+ #define NPY_MAX_INT64 NPY_LONGLONG_SUFFIX(9223372036854775807)
424
+ #define NPY_MIN_INT64 (-NPY_MAX_INT64 - NPY_LONGLONG_SUFFIX(1))
425
+ #define NPY_MAX_UINT64 NPY_ULONGLONG_SUFFIX(18446744073709551615)
426
+ #define NPY_MAX_INT128 NPY_LONGLONG_SUFFIX(85070591730234615865843651857942052864)
427
+ #define NPY_MIN_INT128 (-NPY_MAX_INT128 - NPY_LONGLONG_SUFFIX(1))
428
+ #define NPY_MAX_UINT128 NPY_ULONGLONG_SUFFIX(170141183460469231731687303715884105728)
429
+ #define NPY_MAX_INT256 NPY_LONGLONG_SUFFIX(57896044618658097711785492504343953926634992332820282019728792003956564819967)
430
+ #define NPY_MIN_INT256 (-NPY_MAX_INT256 - NPY_LONGLONG_SUFFIX(1))
431
+ #define NPY_MAX_UINT256 NPY_ULONGLONG_SUFFIX(115792089237316195423570985008687907853269984665640564039457584007913129639935)
432
+ #define NPY_MIN_DATETIME NPY_MIN_INT64
433
+ #define NPY_MAX_DATETIME NPY_MAX_INT64
434
+ #define NPY_MIN_TIMEDELTA NPY_MIN_INT64
435
+ #define NPY_MAX_TIMEDELTA NPY_MAX_INT64
436
+
437
+ /* Need to find the number of bits for each type and
438
+ make definitions accordingly.
439
+
440
+ C states that sizeof(char) == 1 by definition
441
+
442
+ So, just using the sizeof keyword won't help.
443
+
444
+ It also looks like Python itself uses sizeof(char) quite a
445
+ bit, which by definition should be 1 all the time.
446
+
447
+ Idea: Make Use of CHAR_BIT which should tell us how many
448
+ BITS per CHARACTER
449
+ */
450
+
451
+ /* Include platform definitions -- These are in the C89/90 standard */
452
+ #include <limits.h>
453
+ #define NPY_MAX_BYTE SCHAR_MAX
454
+ #define NPY_MIN_BYTE SCHAR_MIN
455
+ #define NPY_MAX_UBYTE UCHAR_MAX
456
+ #define NPY_MAX_SHORT SHRT_MAX
457
+ #define NPY_MIN_SHORT SHRT_MIN
458
+ #define NPY_MAX_USHORT USHRT_MAX
459
+ #define NPY_MAX_INT INT_MAX
460
+ #ifndef INT_MIN
461
+ #define INT_MIN (-INT_MAX - 1)
462
+ #endif
463
+ #define NPY_MIN_INT INT_MIN
464
+ #define NPY_MAX_UINT UINT_MAX
465
+ #define NPY_MAX_LONG LONG_MAX
466
+ #define NPY_MIN_LONG LONG_MIN
467
+ #define NPY_MAX_ULONG ULONG_MAX
468
+
469
+ #define NPY_BITSOF_BOOL (sizeof(npy_bool) * CHAR_BIT)
470
+ #define NPY_BITSOF_CHAR CHAR_BIT
471
+ #define NPY_BITSOF_BYTE (NPY_SIZEOF_BYTE * CHAR_BIT)
472
+ #define NPY_BITSOF_SHORT (NPY_SIZEOF_SHORT * CHAR_BIT)
473
+ #define NPY_BITSOF_INT (NPY_SIZEOF_INT * CHAR_BIT)
474
+ #define NPY_BITSOF_LONG (NPY_SIZEOF_LONG * CHAR_BIT)
475
+ #define NPY_BITSOF_LONGLONG (NPY_SIZEOF_LONGLONG * CHAR_BIT)
476
+ #define NPY_BITSOF_INTP (NPY_SIZEOF_INTP * CHAR_BIT)
477
+ #define NPY_BITSOF_HALF (NPY_SIZEOF_HALF * CHAR_BIT)
478
+ #define NPY_BITSOF_FLOAT (NPY_SIZEOF_FLOAT * CHAR_BIT)
479
+ #define NPY_BITSOF_DOUBLE (NPY_SIZEOF_DOUBLE * CHAR_BIT)
480
+ #define NPY_BITSOF_LONGDOUBLE (NPY_SIZEOF_LONGDOUBLE * CHAR_BIT)
481
+ #define NPY_BITSOF_CFLOAT (NPY_SIZEOF_CFLOAT * CHAR_BIT)
482
+ #define NPY_BITSOF_CDOUBLE (NPY_SIZEOF_CDOUBLE * CHAR_BIT)
483
+ #define NPY_BITSOF_CLONGDOUBLE (NPY_SIZEOF_CLONGDOUBLE * CHAR_BIT)
484
+ #define NPY_BITSOF_DATETIME (NPY_SIZEOF_DATETIME * CHAR_BIT)
485
+ #define NPY_BITSOF_TIMEDELTA (NPY_SIZEOF_TIMEDELTA * CHAR_BIT)
486
+
487
+ #if NPY_BITSOF_LONG == 8
488
+ #define NPY_INT8 NPY_LONG
489
+ #define NPY_UINT8 NPY_ULONG
490
+ typedef long npy_int8;
491
+ typedef unsigned long npy_uint8;
492
+ #define PyInt8ScalarObject PyLongScalarObject
493
+ #define PyInt8ArrType_Type PyLongArrType_Type
494
+ #define PyUInt8ScalarObject PyULongScalarObject
495
+ #define PyUInt8ArrType_Type PyULongArrType_Type
496
+ #define NPY_INT8_FMT NPY_LONG_FMT
497
+ #define NPY_UINT8_FMT NPY_ULONG_FMT
498
+ #elif NPY_BITSOF_LONG == 16
499
+ #define NPY_INT16 NPY_LONG
500
+ #define NPY_UINT16 NPY_ULONG
501
+ typedef long npy_int16;
502
+ typedef unsigned long npy_uint16;
503
+ #define PyInt16ScalarObject PyLongScalarObject
504
+ #define PyInt16ArrType_Type PyLongArrType_Type
505
+ #define PyUInt16ScalarObject PyULongScalarObject
506
+ #define PyUInt16ArrType_Type PyULongArrType_Type
507
+ #define NPY_INT16_FMT NPY_LONG_FMT
508
+ #define NPY_UINT16_FMT NPY_ULONG_FMT
509
+ #elif NPY_BITSOF_LONG == 32
510
+ #define NPY_INT32 NPY_LONG
511
+ #define NPY_UINT32 NPY_ULONG
512
+ typedef long npy_int32;
513
+ typedef unsigned long npy_uint32;
514
+ typedef unsigned long npy_ucs4;
515
+ #define PyInt32ScalarObject PyLongScalarObject
516
+ #define PyInt32ArrType_Type PyLongArrType_Type
517
+ #define PyUInt32ScalarObject PyULongScalarObject
518
+ #define PyUInt32ArrType_Type PyULongArrType_Type
519
+ #define NPY_INT32_FMT NPY_LONG_FMT
520
+ #define NPY_UINT32_FMT NPY_ULONG_FMT
521
+ #elif NPY_BITSOF_LONG == 64
522
+ #define NPY_INT64 NPY_LONG
523
+ #define NPY_UINT64 NPY_ULONG
524
+ typedef long npy_int64;
525
+ typedef unsigned long npy_uint64;
526
+ #define PyInt64ScalarObject PyLongScalarObject
527
+ #define PyInt64ArrType_Type PyLongArrType_Type
528
+ #define PyUInt64ScalarObject PyULongScalarObject
529
+ #define PyUInt64ArrType_Type PyULongArrType_Type
530
+ #define NPY_INT64_FMT NPY_LONG_FMT
531
+ #define NPY_UINT64_FMT NPY_ULONG_FMT
532
+ #define MyPyLong_FromInt64 PyLong_FromLong
533
+ #define MyPyLong_AsInt64 PyLong_AsLong
534
+ #elif NPY_BITSOF_LONG == 128
535
+ #define NPY_INT128 NPY_LONG
536
+ #define NPY_UINT128 NPY_ULONG
537
+ typedef long npy_int128;
538
+ typedef unsigned long npy_uint128;
539
+ #define PyInt128ScalarObject PyLongScalarObject
540
+ #define PyInt128ArrType_Type PyLongArrType_Type
541
+ #define PyUInt128ScalarObject PyULongScalarObject
542
+ #define PyUInt128ArrType_Type PyULongArrType_Type
543
+ #define NPY_INT128_FMT NPY_LONG_FMT
544
+ #define NPY_UINT128_FMT NPY_ULONG_FMT
545
+ #endif
546
+
547
+ #if NPY_BITSOF_LONGLONG == 8
548
+ # ifndef NPY_INT8
549
+ # define NPY_INT8 NPY_LONGLONG
550
+ # define NPY_UINT8 NPY_ULONGLONG
551
+ typedef npy_longlong npy_int8;
552
+ typedef npy_ulonglong npy_uint8;
553
+ # define PyInt8ScalarObject PyLongLongScalarObject
554
+ # define PyInt8ArrType_Type PyLongLongArrType_Type
555
+ # define PyUInt8ScalarObject PyULongLongScalarObject
556
+ # define PyUInt8ArrType_Type PyULongLongArrType_Type
557
+ #define NPY_INT8_FMT NPY_LONGLONG_FMT
558
+ #define NPY_UINT8_FMT NPY_ULONGLONG_FMT
559
+ # endif
560
+ # define NPY_MAX_LONGLONG NPY_MAX_INT8
561
+ # define NPY_MIN_LONGLONG NPY_MIN_INT8
562
+ # define NPY_MAX_ULONGLONG NPY_MAX_UINT8
563
+ #elif NPY_BITSOF_LONGLONG == 16
564
+ # ifndef NPY_INT16
565
+ # define NPY_INT16 NPY_LONGLONG
566
+ # define NPY_UINT16 NPY_ULONGLONG
567
+ typedef npy_longlong npy_int16;
568
+ typedef npy_ulonglong npy_uint16;
569
+ # define PyInt16ScalarObject PyLongLongScalarObject
570
+ # define PyInt16ArrType_Type PyLongLongArrType_Type
571
+ # define PyUInt16ScalarObject PyULongLongScalarObject
572
+ # define PyUInt16ArrType_Type PyULongLongArrType_Type
573
+ #define NPY_INT16_FMT NPY_LONGLONG_FMT
574
+ #define NPY_UINT16_FMT NPY_ULONGLONG_FMT
575
+ # endif
576
+ # define NPY_MAX_LONGLONG NPY_MAX_INT16
577
+ # define NPY_MIN_LONGLONG NPY_MIN_INT16
578
+ # define NPY_MAX_ULONGLONG NPY_MAX_UINT16
579
+ #elif NPY_BITSOF_LONGLONG == 32
580
+ # ifndef NPY_INT32
581
+ # define NPY_INT32 NPY_LONGLONG
582
+ # define NPY_UINT32 NPY_ULONGLONG
583
+ typedef npy_longlong npy_int32;
584
+ typedef npy_ulonglong npy_uint32;
585
+ typedef npy_ulonglong npy_ucs4;
586
+ # define PyInt32ScalarObject PyLongLongScalarObject
587
+ # define PyInt32ArrType_Type PyLongLongArrType_Type
588
+ # define PyUInt32ScalarObject PyULongLongScalarObject
589
+ # define PyUInt32ArrType_Type PyULongLongArrType_Type
590
+ #define NPY_INT32_FMT NPY_LONGLONG_FMT
591
+ #define NPY_UINT32_FMT NPY_ULONGLONG_FMT
592
+ # endif
593
+ # define NPY_MAX_LONGLONG NPY_MAX_INT32
594
+ # define NPY_MIN_LONGLONG NPY_MIN_INT32
595
+ # define NPY_MAX_ULONGLONG NPY_MAX_UINT32
596
+ #elif NPY_BITSOF_LONGLONG == 64
597
+ # ifndef NPY_INT64
598
+ # define NPY_INT64 NPY_LONGLONG
599
+ # define NPY_UINT64 NPY_ULONGLONG
600
+ typedef npy_longlong npy_int64;
601
+ typedef npy_ulonglong npy_uint64;
602
+ # define PyInt64ScalarObject PyLongLongScalarObject
603
+ # define PyInt64ArrType_Type PyLongLongArrType_Type
604
+ # define PyUInt64ScalarObject PyULongLongScalarObject
605
+ # define PyUInt64ArrType_Type PyULongLongArrType_Type
606
+ #define NPY_INT64_FMT NPY_LONGLONG_FMT
607
+ #define NPY_UINT64_FMT NPY_ULONGLONG_FMT
608
+ # define MyPyLong_FromInt64 PyLong_FromLongLong
609
+ # define MyPyLong_AsInt64 PyLong_AsLongLong
610
+ # endif
611
+ # define NPY_MAX_LONGLONG NPY_MAX_INT64
612
+ # define NPY_MIN_LONGLONG NPY_MIN_INT64
613
+ # define NPY_MAX_ULONGLONG NPY_MAX_UINT64
614
+ #elif NPY_BITSOF_LONGLONG == 128
615
+ # ifndef NPY_INT128
616
+ # define NPY_INT128 NPY_LONGLONG
617
+ # define NPY_UINT128 NPY_ULONGLONG
618
+ typedef npy_longlong npy_int128;
619
+ typedef npy_ulonglong npy_uint128;
620
+ # define PyInt128ScalarObject PyLongLongScalarObject
621
+ # define PyInt128ArrType_Type PyLongLongArrType_Type
622
+ # define PyUInt128ScalarObject PyULongLongScalarObject
623
+ # define PyUInt128ArrType_Type PyULongLongArrType_Type
624
+ #define NPY_INT128_FMT NPY_LONGLONG_FMT
625
+ #define NPY_UINT128_FMT NPY_ULONGLONG_FMT
626
+ # endif
627
+ # define NPY_MAX_LONGLONG NPY_MAX_INT128
628
+ # define NPY_MIN_LONGLONG NPY_MIN_INT128
629
+ # define NPY_MAX_ULONGLONG NPY_MAX_UINT128
630
+ #elif NPY_BITSOF_LONGLONG == 256
631
+ # define NPY_INT256 NPY_LONGLONG
632
+ # define NPY_UINT256 NPY_ULONGLONG
633
+ typedef npy_longlong npy_int256;
634
+ typedef npy_ulonglong npy_uint256;
635
+ # define PyInt256ScalarObject PyLongLongScalarObject
636
+ # define PyInt256ArrType_Type PyLongLongArrType_Type
637
+ # define PyUInt256ScalarObject PyULongLongScalarObject
638
+ # define PyUInt256ArrType_Type PyULongLongArrType_Type
639
+ #define NPY_INT256_FMT NPY_LONGLONG_FMT
640
+ #define NPY_UINT256_FMT NPY_ULONGLONG_FMT
641
+ # define NPY_MAX_LONGLONG NPY_MAX_INT256
642
+ # define NPY_MIN_LONGLONG NPY_MIN_INT256
643
+ # define NPY_MAX_ULONGLONG NPY_MAX_UINT256
644
+ #endif
645
+
646
+ #if NPY_BITSOF_INT == 8
647
+ #ifndef NPY_INT8
648
+ #define NPY_INT8 NPY_INT
649
+ #define NPY_UINT8 NPY_UINT
650
+ typedef int npy_int8;
651
+ typedef unsigned int npy_uint8;
652
+ # define PyInt8ScalarObject PyIntScalarObject
653
+ # define PyInt8ArrType_Type PyIntArrType_Type
654
+ # define PyUInt8ScalarObject PyUIntScalarObject
655
+ # define PyUInt8ArrType_Type PyUIntArrType_Type
656
+ #define NPY_INT8_FMT NPY_INT_FMT
657
+ #define NPY_UINT8_FMT NPY_UINT_FMT
658
+ #endif
659
+ #elif NPY_BITSOF_INT == 16
660
+ #ifndef NPY_INT16
661
+ #define NPY_INT16 NPY_INT
662
+ #define NPY_UINT16 NPY_UINT
663
+ typedef int npy_int16;
664
+ typedef unsigned int npy_uint16;
665
+ # define PyInt16ScalarObject PyIntScalarObject
666
+ # define PyInt16ArrType_Type PyIntArrType_Type
667
+ # define PyUInt16ScalarObject PyIntUScalarObject
668
+ # define PyUInt16ArrType_Type PyIntUArrType_Type
669
+ #define NPY_INT16_FMT NPY_INT_FMT
670
+ #define NPY_UINT16_FMT NPY_UINT_FMT
671
+ #endif
672
+ #elif NPY_BITSOF_INT == 32
673
+ #ifndef NPY_INT32
674
+ #define NPY_INT32 NPY_INT
675
+ #define NPY_UINT32 NPY_UINT
676
+ typedef int npy_int32;
677
+ typedef unsigned int npy_uint32;
678
+ typedef unsigned int npy_ucs4;
679
+ # define PyInt32ScalarObject PyIntScalarObject
680
+ # define PyInt32ArrType_Type PyIntArrType_Type
681
+ # define PyUInt32ScalarObject PyUIntScalarObject
682
+ # define PyUInt32ArrType_Type PyUIntArrType_Type
683
+ #define NPY_INT32_FMT NPY_INT_FMT
684
+ #define NPY_UINT32_FMT NPY_UINT_FMT
685
+ #endif
686
+ #elif NPY_BITSOF_INT == 64
687
+ #ifndef NPY_INT64
688
+ #define NPY_INT64 NPY_INT
689
+ #define NPY_UINT64 NPY_UINT
690
+ typedef int npy_int64;
691
+ typedef unsigned int npy_uint64;
692
+ # define PyInt64ScalarObject PyIntScalarObject
693
+ # define PyInt64ArrType_Type PyIntArrType_Type
694
+ # define PyUInt64ScalarObject PyUIntScalarObject
695
+ # define PyUInt64ArrType_Type PyUIntArrType_Type
696
+ #define NPY_INT64_FMT NPY_INT_FMT
697
+ #define NPY_UINT64_FMT NPY_UINT_FMT
698
+ # define MyPyLong_FromInt64 PyLong_FromLong
699
+ # define MyPyLong_AsInt64 PyLong_AsLong
700
+ #endif
701
+ #elif NPY_BITSOF_INT == 128
702
+ #ifndef NPY_INT128
703
+ #define NPY_INT128 NPY_INT
704
+ #define NPY_UINT128 NPY_UINT
705
+ typedef int npy_int128;
706
+ typedef unsigned int npy_uint128;
707
+ # define PyInt128ScalarObject PyIntScalarObject
708
+ # define PyInt128ArrType_Type PyIntArrType_Type
709
+ # define PyUInt128ScalarObject PyUIntScalarObject
710
+ # define PyUInt128ArrType_Type PyUIntArrType_Type
711
+ #define NPY_INT128_FMT NPY_INT_FMT
712
+ #define NPY_UINT128_FMT NPY_UINT_FMT
713
+ #endif
714
+ #endif
715
+
716
+ #if NPY_BITSOF_SHORT == 8
717
+ #ifndef NPY_INT8
718
+ #define NPY_INT8 NPY_SHORT
719
+ #define NPY_UINT8 NPY_USHORT
720
+ typedef short npy_int8;
721
+ typedef unsigned short npy_uint8;
722
+ # define PyInt8ScalarObject PyShortScalarObject
723
+ # define PyInt8ArrType_Type PyShortArrType_Type
724
+ # define PyUInt8ScalarObject PyUShortScalarObject
725
+ # define PyUInt8ArrType_Type PyUShortArrType_Type
726
+ #define NPY_INT8_FMT NPY_SHORT_FMT
727
+ #define NPY_UINT8_FMT NPY_USHORT_FMT
728
+ #endif
729
+ #elif NPY_BITSOF_SHORT == 16
730
+ #ifndef NPY_INT16
731
+ #define NPY_INT16 NPY_SHORT
732
+ #define NPY_UINT16 NPY_USHORT
733
+ typedef short npy_int16;
734
+ typedef unsigned short npy_uint16;
735
+ # define PyInt16ScalarObject PyShortScalarObject
736
+ # define PyInt16ArrType_Type PyShortArrType_Type
737
+ # define PyUInt16ScalarObject PyUShortScalarObject
738
+ # define PyUInt16ArrType_Type PyUShortArrType_Type
739
+ #define NPY_INT16_FMT NPY_SHORT_FMT
740
+ #define NPY_UINT16_FMT NPY_USHORT_FMT
741
+ #endif
742
+ #elif NPY_BITSOF_SHORT == 32
743
+ #ifndef NPY_INT32
744
+ #define NPY_INT32 NPY_SHORT
745
+ #define NPY_UINT32 NPY_USHORT
746
+ typedef short npy_int32;
747
+ typedef unsigned short npy_uint32;
748
+ typedef unsigned short npy_ucs4;
749
+ # define PyInt32ScalarObject PyShortScalarObject
750
+ # define PyInt32ArrType_Type PyShortArrType_Type
751
+ # define PyUInt32ScalarObject PyUShortScalarObject
752
+ # define PyUInt32ArrType_Type PyUShortArrType_Type
753
+ #define NPY_INT32_FMT NPY_SHORT_FMT
754
+ #define NPY_UINT32_FMT NPY_USHORT_FMT
755
+ #endif
756
+ #elif NPY_BITSOF_SHORT == 64
757
+ #ifndef NPY_INT64
758
+ #define NPY_INT64 NPY_SHORT
759
+ #define NPY_UINT64 NPY_USHORT
760
+ typedef short npy_int64;
761
+ typedef unsigned short npy_uint64;
762
+ # define PyInt64ScalarObject PyShortScalarObject
763
+ # define PyInt64ArrType_Type PyShortArrType_Type
764
+ # define PyUInt64ScalarObject PyUShortScalarObject
765
+ # define PyUInt64ArrType_Type PyUShortArrType_Type
766
+ #define NPY_INT64_FMT NPY_SHORT_FMT
767
+ #define NPY_UINT64_FMT NPY_USHORT_FMT
768
+ # define MyPyLong_FromInt64 PyLong_FromLong
769
+ # define MyPyLong_AsInt64 PyLong_AsLong
770
+ #endif
771
+ #elif NPY_BITSOF_SHORT == 128
772
+ #ifndef NPY_INT128
773
+ #define NPY_INT128 NPY_SHORT
774
+ #define NPY_UINT128 NPY_USHORT
775
+ typedef short npy_int128;
776
+ typedef unsigned short npy_uint128;
777
+ # define PyInt128ScalarObject PyShortScalarObject
778
+ # define PyInt128ArrType_Type PyShortArrType_Type
779
+ # define PyUInt128ScalarObject PyUShortScalarObject
780
+ # define PyUInt128ArrType_Type PyUShortArrType_Type
781
+ #define NPY_INT128_FMT NPY_SHORT_FMT
782
+ #define NPY_UINT128_FMT NPY_USHORT_FMT
783
+ #endif
784
+ #endif
785
+
786
+
787
+ #if NPY_BITSOF_CHAR == 8
788
+ #ifndef NPY_INT8
789
+ #define NPY_INT8 NPY_BYTE
790
+ #define NPY_UINT8 NPY_UBYTE
791
+ typedef signed char npy_int8;
792
+ typedef unsigned char npy_uint8;
793
+ # define PyInt8ScalarObject PyByteScalarObject
794
+ # define PyInt8ArrType_Type PyByteArrType_Type
795
+ # define PyUInt8ScalarObject PyUByteScalarObject
796
+ # define PyUInt8ArrType_Type PyUByteArrType_Type
797
+ #define NPY_INT8_FMT NPY_BYTE_FMT
798
+ #define NPY_UINT8_FMT NPY_UBYTE_FMT
799
+ #endif
800
+ #elif NPY_BITSOF_CHAR == 16
801
+ #ifndef NPY_INT16
802
+ #define NPY_INT16 NPY_BYTE
803
+ #define NPY_UINT16 NPY_UBYTE
804
+ typedef signed char npy_int16;
805
+ typedef unsigned char npy_uint16;
806
+ # define PyInt16ScalarObject PyByteScalarObject
807
+ # define PyInt16ArrType_Type PyByteArrType_Type
808
+ # define PyUInt16ScalarObject PyUByteScalarObject
809
+ # define PyUInt16ArrType_Type PyUByteArrType_Type
810
+ #define NPY_INT16_FMT NPY_BYTE_FMT
811
+ #define NPY_UINT16_FMT NPY_UBYTE_FMT
812
+ #endif
813
+ #elif NPY_BITSOF_CHAR == 32
814
+ #ifndef NPY_INT32
815
+ #define NPY_INT32 NPY_BYTE
816
+ #define NPY_UINT32 NPY_UBYTE
817
+ typedef signed char npy_int32;
818
+ typedef unsigned char npy_uint32;
819
+ typedef unsigned char npy_ucs4;
820
+ # define PyInt32ScalarObject PyByteScalarObject
821
+ # define PyInt32ArrType_Type PyByteArrType_Type
822
+ # define PyUInt32ScalarObject PyUByteScalarObject
823
+ # define PyUInt32ArrType_Type PyUByteArrType_Type
824
+ #define NPY_INT32_FMT NPY_BYTE_FMT
825
+ #define NPY_UINT32_FMT NPY_UBYTE_FMT
826
+ #endif
827
+ #elif NPY_BITSOF_CHAR == 64
828
+ #ifndef NPY_INT64
829
+ #define NPY_INT64 NPY_BYTE
830
+ #define NPY_UINT64 NPY_UBYTE
831
+ typedef signed char npy_int64;
832
+ typedef unsigned char npy_uint64;
833
+ # define PyInt64ScalarObject PyByteScalarObject
834
+ # define PyInt64ArrType_Type PyByteArrType_Type
835
+ # define PyUInt64ScalarObject PyUByteScalarObject
836
+ # define PyUInt64ArrType_Type PyUByteArrType_Type
837
+ #define NPY_INT64_FMT NPY_BYTE_FMT
838
+ #define NPY_UINT64_FMT NPY_UBYTE_FMT
839
+ # define MyPyLong_FromInt64 PyLong_FromLong
840
+ # define MyPyLong_AsInt64 PyLong_AsLong
841
+ #endif
842
+ #elif NPY_BITSOF_CHAR == 128
843
+ #ifndef NPY_INT128
844
+ #define NPY_INT128 NPY_BYTE
845
+ #define NPY_UINT128 NPY_UBYTE
846
+ typedef signed char npy_int128;
847
+ typedef unsigned char npy_uint128;
848
+ # define PyInt128ScalarObject PyByteScalarObject
849
+ # define PyInt128ArrType_Type PyByteArrType_Type
850
+ # define PyUInt128ScalarObject PyUByteScalarObject
851
+ # define PyUInt128ArrType_Type PyUByteArrType_Type
852
+ #define NPY_INT128_FMT NPY_BYTE_FMT
853
+ #define NPY_UINT128_FMT NPY_UBYTE_FMT
854
+ #endif
855
+ #endif
856
+
857
+
858
+
859
+ #if NPY_BITSOF_DOUBLE == 32
860
+ #ifndef NPY_FLOAT32
861
+ #define NPY_FLOAT32 NPY_DOUBLE
862
+ #define NPY_COMPLEX64 NPY_CDOUBLE
863
+ typedef double npy_float32;
864
+ typedef npy_cdouble npy_complex64;
865
+ # define PyFloat32ScalarObject PyDoubleScalarObject
866
+ # define PyComplex64ScalarObject PyCDoubleScalarObject
867
+ # define PyFloat32ArrType_Type PyDoubleArrType_Type
868
+ # define PyComplex64ArrType_Type PyCDoubleArrType_Type
869
+ #define NPY_FLOAT32_FMT NPY_DOUBLE_FMT
870
+ #define NPY_COMPLEX64_FMT NPY_CDOUBLE_FMT
871
+ #endif
872
+ #elif NPY_BITSOF_DOUBLE == 64
873
+ #ifndef NPY_FLOAT64
874
+ #define NPY_FLOAT64 NPY_DOUBLE
875
+ #define NPY_COMPLEX128 NPY_CDOUBLE
876
+ typedef double npy_float64;
877
+ typedef npy_cdouble npy_complex128;
878
+ # define PyFloat64ScalarObject PyDoubleScalarObject
879
+ # define PyComplex128ScalarObject PyCDoubleScalarObject
880
+ # define PyFloat64ArrType_Type PyDoubleArrType_Type
881
+ # define PyComplex128ArrType_Type PyCDoubleArrType_Type
882
+ #define NPY_FLOAT64_FMT NPY_DOUBLE_FMT
883
+ #define NPY_COMPLEX128_FMT NPY_CDOUBLE_FMT
884
+ #endif
885
+ #elif NPY_BITSOF_DOUBLE == 80
886
+ #ifndef NPY_FLOAT80
887
+ #define NPY_FLOAT80 NPY_DOUBLE
888
+ #define NPY_COMPLEX160 NPY_CDOUBLE
889
+ typedef double npy_float80;
890
+ typedef npy_cdouble npy_complex160;
891
+ # define PyFloat80ScalarObject PyDoubleScalarObject
892
+ # define PyComplex160ScalarObject PyCDoubleScalarObject
893
+ # define PyFloat80ArrType_Type PyDoubleArrType_Type
894
+ # define PyComplex160ArrType_Type PyCDoubleArrType_Type
895
+ #define NPY_FLOAT80_FMT NPY_DOUBLE_FMT
896
+ #define NPY_COMPLEX160_FMT NPY_CDOUBLE_FMT
897
+ #endif
898
+ #elif NPY_BITSOF_DOUBLE == 96
899
+ #ifndef NPY_FLOAT96
900
+ #define NPY_FLOAT96 NPY_DOUBLE
901
+ #define NPY_COMPLEX192 NPY_CDOUBLE
902
+ typedef double npy_float96;
903
+ typedef npy_cdouble npy_complex192;
904
+ # define PyFloat96ScalarObject PyDoubleScalarObject
905
+ # define PyComplex192ScalarObject PyCDoubleScalarObject
906
+ # define PyFloat96ArrType_Type PyDoubleArrType_Type
907
+ # define PyComplex192ArrType_Type PyCDoubleArrType_Type
908
+ #define NPY_FLOAT96_FMT NPY_DOUBLE_FMT
909
+ #define NPY_COMPLEX192_FMT NPY_CDOUBLE_FMT
910
+ #endif
911
+ #elif NPY_BITSOF_DOUBLE == 128
912
+ #ifndef NPY_FLOAT128
913
+ #define NPY_FLOAT128 NPY_DOUBLE
914
+ #define NPY_COMPLEX256 NPY_CDOUBLE
915
+ typedef double npy_float128;
916
+ typedef npy_cdouble npy_complex256;
917
+ # define PyFloat128ScalarObject PyDoubleScalarObject
918
+ # define PyComplex256ScalarObject PyCDoubleScalarObject
919
+ # define PyFloat128ArrType_Type PyDoubleArrType_Type
920
+ # define PyComplex256ArrType_Type PyCDoubleArrType_Type
921
+ #define NPY_FLOAT128_FMT NPY_DOUBLE_FMT
922
+ #define NPY_COMPLEX256_FMT NPY_CDOUBLE_FMT
923
+ #endif
924
+ #endif
925
+
926
+
927
+
928
+ #if NPY_BITSOF_FLOAT == 32
929
+ #ifndef NPY_FLOAT32
930
+ #define NPY_FLOAT32 NPY_FLOAT
931
+ #define NPY_COMPLEX64 NPY_CFLOAT
932
+ typedef float npy_float32;
933
+ typedef npy_cfloat npy_complex64;
934
+ # define PyFloat32ScalarObject PyFloatScalarObject
935
+ # define PyComplex64ScalarObject PyCFloatScalarObject
936
+ # define PyFloat32ArrType_Type PyFloatArrType_Type
937
+ # define PyComplex64ArrType_Type PyCFloatArrType_Type
938
+ #define NPY_FLOAT32_FMT NPY_FLOAT_FMT
939
+ #define NPY_COMPLEX64_FMT NPY_CFLOAT_FMT
940
+ #endif
941
+ #elif NPY_BITSOF_FLOAT == 64
942
+ #ifndef NPY_FLOAT64
943
+ #define NPY_FLOAT64 NPY_FLOAT
944
+ #define NPY_COMPLEX128 NPY_CFLOAT
945
+ typedef float npy_float64;
946
+ typedef npy_cfloat npy_complex128;
947
+ # define PyFloat64ScalarObject PyFloatScalarObject
948
+ # define PyComplex128ScalarObject PyCFloatScalarObject
949
+ # define PyFloat64ArrType_Type PyFloatArrType_Type
950
+ # define PyComplex128ArrType_Type PyCFloatArrType_Type
951
+ #define NPY_FLOAT64_FMT NPY_FLOAT_FMT
952
+ #define NPY_COMPLEX128_FMT NPY_CFLOAT_FMT
953
+ #endif
954
+ #elif NPY_BITSOF_FLOAT == 80
955
+ #ifndef NPY_FLOAT80
956
+ #define NPY_FLOAT80 NPY_FLOAT
957
+ #define NPY_COMPLEX160 NPY_CFLOAT
958
+ typedef float npy_float80;
959
+ typedef npy_cfloat npy_complex160;
960
+ # define PyFloat80ScalarObject PyFloatScalarObject
961
+ # define PyComplex160ScalarObject PyCFloatScalarObject
962
+ # define PyFloat80ArrType_Type PyFloatArrType_Type
963
+ # define PyComplex160ArrType_Type PyCFloatArrType_Type
964
+ #define NPY_FLOAT80_FMT NPY_FLOAT_FMT
965
+ #define NPY_COMPLEX160_FMT NPY_CFLOAT_FMT
966
+ #endif
967
+ #elif NPY_BITSOF_FLOAT == 96
968
+ #ifndef NPY_FLOAT96
969
+ #define NPY_FLOAT96 NPY_FLOAT
970
+ #define NPY_COMPLEX192 NPY_CFLOAT
971
+ typedef float npy_float96;
972
+ typedef npy_cfloat npy_complex192;
973
+ # define PyFloat96ScalarObject PyFloatScalarObject
974
+ # define PyComplex192ScalarObject PyCFloatScalarObject
975
+ # define PyFloat96ArrType_Type PyFloatArrType_Type
976
+ # define PyComplex192ArrType_Type PyCFloatArrType_Type
977
+ #define NPY_FLOAT96_FMT NPY_FLOAT_FMT
978
+ #define NPY_COMPLEX192_FMT NPY_CFLOAT_FMT
979
+ #endif
980
+ #elif NPY_BITSOF_FLOAT == 128
981
+ #ifndef NPY_FLOAT128
982
+ #define NPY_FLOAT128 NPY_FLOAT
983
+ #define NPY_COMPLEX256 NPY_CFLOAT
984
+ typedef float npy_float128;
985
+ typedef npy_cfloat npy_complex256;
986
+ # define PyFloat128ScalarObject PyFloatScalarObject
987
+ # define PyComplex256ScalarObject PyCFloatScalarObject
988
+ # define PyFloat128ArrType_Type PyFloatArrType_Type
989
+ # define PyComplex256ArrType_Type PyCFloatArrType_Type
990
+ #define NPY_FLOAT128_FMT NPY_FLOAT_FMT
991
+ #define NPY_COMPLEX256_FMT NPY_CFLOAT_FMT
992
+ #endif
993
+ #endif
994
+
995
+ /* half/float16 isn't a floating-point type in C */
996
+ #define NPY_FLOAT16 NPY_HALF
997
+ typedef npy_uint16 npy_half;
998
+ typedef npy_half npy_float16;
999
+
1000
+ #if NPY_BITSOF_LONGDOUBLE == 32
1001
+ #ifndef NPY_FLOAT32
1002
+ #define NPY_FLOAT32 NPY_LONGDOUBLE
1003
+ #define NPY_COMPLEX64 NPY_CLONGDOUBLE
1004
+ typedef npy_longdouble npy_float32;
1005
+ typedef npy_clongdouble npy_complex64;
1006
+ # define PyFloat32ScalarObject PyLongDoubleScalarObject
1007
+ # define PyComplex64ScalarObject PyCLongDoubleScalarObject
1008
+ # define PyFloat32ArrType_Type PyLongDoubleArrType_Type
1009
+ # define PyComplex64ArrType_Type PyCLongDoubleArrType_Type
1010
+ #define NPY_FLOAT32_FMT NPY_LONGDOUBLE_FMT
1011
+ #define NPY_COMPLEX64_FMT NPY_CLONGDOUBLE_FMT
1012
+ #endif
1013
+ #elif NPY_BITSOF_LONGDOUBLE == 64
1014
+ #ifndef NPY_FLOAT64
1015
+ #define NPY_FLOAT64 NPY_LONGDOUBLE
1016
+ #define NPY_COMPLEX128 NPY_CLONGDOUBLE
1017
+ typedef npy_longdouble npy_float64;
1018
+ typedef npy_clongdouble npy_complex128;
1019
+ # define PyFloat64ScalarObject PyLongDoubleScalarObject
1020
+ # define PyComplex128ScalarObject PyCLongDoubleScalarObject
1021
+ # define PyFloat64ArrType_Type PyLongDoubleArrType_Type
1022
+ # define PyComplex128ArrType_Type PyCLongDoubleArrType_Type
1023
+ #define NPY_FLOAT64_FMT NPY_LONGDOUBLE_FMT
1024
+ #define NPY_COMPLEX128_FMT NPY_CLONGDOUBLE_FMT
1025
+ #endif
1026
+ #elif NPY_BITSOF_LONGDOUBLE == 80
1027
+ #ifndef NPY_FLOAT80
1028
+ #define NPY_FLOAT80 NPY_LONGDOUBLE
1029
+ #define NPY_COMPLEX160 NPY_CLONGDOUBLE
1030
+ typedef npy_longdouble npy_float80;
1031
+ typedef npy_clongdouble npy_complex160;
1032
+ # define PyFloat80ScalarObject PyLongDoubleScalarObject
1033
+ # define PyComplex160ScalarObject PyCLongDoubleScalarObject
1034
+ # define PyFloat80ArrType_Type PyLongDoubleArrType_Type
1035
+ # define PyComplex160ArrType_Type PyCLongDoubleArrType_Type
1036
+ #define NPY_FLOAT80_FMT NPY_LONGDOUBLE_FMT
1037
+ #define NPY_COMPLEX160_FMT NPY_CLONGDOUBLE_FMT
1038
+ #endif
1039
+ #elif NPY_BITSOF_LONGDOUBLE == 96
1040
+ #ifndef NPY_FLOAT96
1041
+ #define NPY_FLOAT96 NPY_LONGDOUBLE
1042
+ #define NPY_COMPLEX192 NPY_CLONGDOUBLE
1043
+ typedef npy_longdouble npy_float96;
1044
+ typedef npy_clongdouble npy_complex192;
1045
+ # define PyFloat96ScalarObject PyLongDoubleScalarObject
1046
+ # define PyComplex192ScalarObject PyCLongDoubleScalarObject
1047
+ # define PyFloat96ArrType_Type PyLongDoubleArrType_Type
1048
+ # define PyComplex192ArrType_Type PyCLongDoubleArrType_Type
1049
+ #define NPY_FLOAT96_FMT NPY_LONGDOUBLE_FMT
1050
+ #define NPY_COMPLEX192_FMT NPY_CLONGDOUBLE_FMT
1051
+ #endif
1052
+ #elif NPY_BITSOF_LONGDOUBLE == 128
1053
+ #ifndef NPY_FLOAT128
1054
+ #define NPY_FLOAT128 NPY_LONGDOUBLE
1055
+ #define NPY_COMPLEX256 NPY_CLONGDOUBLE
1056
+ typedef npy_longdouble npy_float128;
1057
+ typedef npy_clongdouble npy_complex256;
1058
+ # define PyFloat128ScalarObject PyLongDoubleScalarObject
1059
+ # define PyComplex256ScalarObject PyCLongDoubleScalarObject
1060
+ # define PyFloat128ArrType_Type PyLongDoubleArrType_Type
1061
+ # define PyComplex256ArrType_Type PyCLongDoubleArrType_Type
1062
+ #define NPY_FLOAT128_FMT NPY_LONGDOUBLE_FMT
1063
+ #define NPY_COMPLEX256_FMT NPY_CLONGDOUBLE_FMT
1064
+ #endif
1065
+ #elif NPY_BITSOF_LONGDOUBLE == 256
1066
+ #define NPY_FLOAT256 NPY_LONGDOUBLE
1067
+ #define NPY_COMPLEX512 NPY_CLONGDOUBLE
1068
+ typedef npy_longdouble npy_float256;
1069
+ typedef npy_clongdouble npy_complex512;
1070
+ # define PyFloat256ScalarObject PyLongDoubleScalarObject
1071
+ # define PyComplex512ScalarObject PyCLongDoubleScalarObject
1072
+ # define PyFloat256ArrType_Type PyLongDoubleArrType_Type
1073
+ # define PyComplex512ArrType_Type PyCLongDoubleArrType_Type
1074
+ #define NPY_FLOAT256_FMT NPY_LONGDOUBLE_FMT
1075
+ #define NPY_COMPLEX512_FMT NPY_CLONGDOUBLE_FMT
1076
+ #endif
1077
+
1078
+ /* datetime typedefs */
1079
+ typedef npy_int64 npy_timedelta;
1080
+ typedef npy_int64 npy_datetime;
1081
+ #define NPY_DATETIME_FMT NPY_INT64_FMT
1082
+ #define NPY_TIMEDELTA_FMT NPY_INT64_FMT
1083
+
1084
+ /* End of typedefs for numarray style bit-width names */
1085
+
1086
+ #endif /* NUMPY_CORE_INCLUDE_NUMPY_NPY_COMMON_H_ */
evalkit_tf437/lib/python3.10/site-packages/numpy/core/include/numpy/utils.h ADDED
@@ -0,0 +1,37 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #ifndef NUMPY_CORE_INCLUDE_NUMPY_UTILS_H_
2
+ #define NUMPY_CORE_INCLUDE_NUMPY_UTILS_H_
3
+
4
+ #ifndef __COMP_NPY_UNUSED
5
+ #if defined(__GNUC__)
6
+ #define __COMP_NPY_UNUSED __attribute__ ((__unused__))
7
+ #elif defined(__ICC)
8
+ #define __COMP_NPY_UNUSED __attribute__ ((__unused__))
9
+ #elif defined(__clang__)
10
+ #define __COMP_NPY_UNUSED __attribute__ ((unused))
11
+ #else
12
+ #define __COMP_NPY_UNUSED
13
+ #endif
14
+ #endif
15
+
16
+ #if defined(__GNUC__) || defined(__ICC) || defined(__clang__)
17
+ #define NPY_DECL_ALIGNED(x) __attribute__ ((aligned (x)))
18
+ #elif defined(_MSC_VER)
19
+ #define NPY_DECL_ALIGNED(x) __declspec(align(x))
20
+ #else
21
+ #define NPY_DECL_ALIGNED(x)
22
+ #endif
23
+
24
+ /* Use this to tag a variable as not used. It will remove unused variable
25
+ * warning on support platforms (see __COM_NPY_UNUSED) and mangle the variable
26
+ * to avoid accidental use */
27
+ #define NPY_UNUSED(x) __NPY_UNUSED_TAGGED ## x __COMP_NPY_UNUSED
28
+ #define NPY_EXPAND(x) x
29
+
30
+ #define NPY_STRINGIFY(x) #x
31
+ #define NPY_TOSTRING(x) NPY_STRINGIFY(x)
32
+
33
+ #define NPY_CAT__(a, b) a ## b
34
+ #define NPY_CAT_(a, b) NPY_CAT__(a, b)
35
+ #define NPY_CAT(a, b) NPY_CAT_(a, b)
36
+
37
+ #endif /* NUMPY_CORE_INCLUDE_NUMPY_UTILS_H_ */
evalkit_tf437/lib/python3.10/site-packages/numpy/core/lib/npy-pkg-config/mlib.ini ADDED
@@ -0,0 +1,12 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ [meta]
2
+ Name = mlib
3
+ Description = Math library used with this version of numpy
4
+ Version = 1.0
5
+
6
+ [default]
7
+ Libs=-lm
8
+ Cflags=
9
+
10
+ [msvc]
11
+ Libs=m.lib
12
+ Cflags=
evalkit_tf437/lib/python3.10/site-packages/numpy/core/tests/__pycache__/test_arrayprint.cpython-310.pyc ADDED
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evalkit_tf437/lib/python3.10/site-packages/numpy/core/tests/__pycache__/test_hashtable.cpython-310.pyc ADDED
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evalkit_tf437/lib/python3.10/site-packages/numpy/core/tests/__pycache__/test_scalar_ctors.cpython-310.pyc ADDED
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evalkit_tf437/lib/python3.10/site-packages/numpy/core/tests/__pycache__/test_scalarmath.cpython-310.pyc ADDED
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evalkit_tf437/lib/python3.10/site-packages/numpy/core/tests/__pycache__/test_ufunc.cpython-310.pyc ADDED
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evalkit_tf437/lib/python3.10/site-packages/numpy/core/tests/data/generate_umath_validation_data.cpp ADDED
@@ -0,0 +1,170 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #include <algorithm>
2
+ #include <fstream>
3
+ #include <iostream>
4
+ #include <math.h>
5
+ #include <random>
6
+ #include <cstdio>
7
+ #include <ctime>
8
+ #include <vector>
9
+
10
+ struct ufunc {
11
+ std::string name;
12
+ double (*f32func)(double);
13
+ long double (*f64func)(long double);
14
+ float f32ulp;
15
+ float f64ulp;
16
+ };
17
+
18
+ template <typename T>
19
+ T
20
+ RandomFloat(T a, T b)
21
+ {
22
+ T random = ((T)rand()) / (T)RAND_MAX;
23
+ T diff = b - a;
24
+ T r = random * diff;
25
+ return a + r;
26
+ }
27
+
28
+ template <typename T>
29
+ void
30
+ append_random_array(std::vector<T> &arr, T min, T max, size_t N)
31
+ {
32
+ for (size_t ii = 0; ii < N; ++ii)
33
+ arr.emplace_back(RandomFloat<T>(min, max));
34
+ }
35
+
36
+ template <typename T1, typename T2>
37
+ std::vector<T1>
38
+ computeTrueVal(const std::vector<T1> &in, T2 (*mathfunc)(T2))
39
+ {
40
+ std::vector<T1> out;
41
+ for (T1 elem : in) {
42
+ T2 elem_d = (T2)elem;
43
+ T1 out_elem = (T1)mathfunc(elem_d);
44
+ out.emplace_back(out_elem);
45
+ }
46
+ return out;
47
+ }
48
+
49
+ /*
50
+ * FP range:
51
+ * [-inf, -maxflt, -1., -minflt, -minden, 0., minden, minflt, 1., maxflt, inf]
52
+ */
53
+
54
+ #define MINDEN std::numeric_limits<T>::denorm_min()
55
+ #define MINFLT std::numeric_limits<T>::min()
56
+ #define MAXFLT std::numeric_limits<T>::max()
57
+ #define INF std::numeric_limits<T>::infinity()
58
+ #define qNAN std::numeric_limits<T>::quiet_NaN()
59
+ #define sNAN std::numeric_limits<T>::signaling_NaN()
60
+
61
+ template <typename T>
62
+ std::vector<T>
63
+ generate_input_vector(std::string func)
64
+ {
65
+ std::vector<T> input = {MINDEN, -MINDEN, MINFLT, -MINFLT, MAXFLT,
66
+ -MAXFLT, INF, -INF, qNAN, sNAN,
67
+ -1.0, 1.0, 0.0, -0.0};
68
+
69
+ // [-1.0, 1.0]
70
+ if ((func == "arcsin") || (func == "arccos") || (func == "arctanh")) {
71
+ append_random_array<T>(input, -1.0, 1.0, 700);
72
+ }
73
+ // (0.0, INF]
74
+ else if ((func == "log2") || (func == "log10")) {
75
+ append_random_array<T>(input, 0.0, 1.0, 200);
76
+ append_random_array<T>(input, MINDEN, MINFLT, 200);
77
+ append_random_array<T>(input, MINFLT, 1.0, 200);
78
+ append_random_array<T>(input, 1.0, MAXFLT, 200);
79
+ }
80
+ // (-1.0, INF]
81
+ else if (func == "log1p") {
82
+ append_random_array<T>(input, -1.0, 1.0, 200);
83
+ append_random_array<T>(input, -MINFLT, -MINDEN, 100);
84
+ append_random_array<T>(input, -1.0, -MINFLT, 100);
85
+ append_random_array<T>(input, MINDEN, MINFLT, 100);
86
+ append_random_array<T>(input, MINFLT, 1.0, 100);
87
+ append_random_array<T>(input, 1.0, MAXFLT, 100);
88
+ }
89
+ // [1.0, INF]
90
+ else if (func == "arccosh") {
91
+ append_random_array<T>(input, 1.0, 2.0, 400);
92
+ append_random_array<T>(input, 2.0, MAXFLT, 300);
93
+ }
94
+ // [-INF, INF]
95
+ else {
96
+ append_random_array<T>(input, -1.0, 1.0, 100);
97
+ append_random_array<T>(input, MINDEN, MINFLT, 100);
98
+ append_random_array<T>(input, -MINFLT, -MINDEN, 100);
99
+ append_random_array<T>(input, MINFLT, 1.0, 100);
100
+ append_random_array<T>(input, -1.0, -MINFLT, 100);
101
+ append_random_array<T>(input, 1.0, MAXFLT, 100);
102
+ append_random_array<T>(input, -MAXFLT, -100.0, 100);
103
+ }
104
+
105
+ std::random_shuffle(input.begin(), input.end());
106
+ return input;
107
+ }
108
+
109
+ int
110
+ main()
111
+ {
112
+ srand(42);
113
+ std::vector<struct ufunc> umathfunc = {
114
+ {"sin", sin, sin, 1.49, 1.00},
115
+ {"cos", cos, cos, 1.49, 1.00},
116
+ {"tan", tan, tan, 3.91, 3.93},
117
+ {"arcsin", asin, asin, 3.12, 2.55},
118
+ {"arccos", acos, acos, 2.1, 1.67},
119
+ {"arctan", atan, atan, 2.3, 2.52},
120
+ {"sinh", sinh, sinh, 1.55, 1.89},
121
+ {"cosh", cosh, cosh, 2.48, 1.97},
122
+ {"tanh", tanh, tanh, 1.38, 1.19},
123
+ {"arcsinh", asinh, asinh, 1.01, 1.48},
124
+ {"arccosh", acosh, acosh, 1.16, 1.05},
125
+ {"arctanh", atanh, atanh, 1.45, 1.46},
126
+ {"cbrt", cbrt, cbrt, 1.94, 1.82},
127
+ //{"exp",exp,exp,3.76,1.53},
128
+ {"exp2", exp2, exp2, 1.01, 1.04},
129
+ {"expm1", expm1, expm1, 2.62, 2.1},
130
+ //{"log",log,log,1.84,1.67},
131
+ {"log10", log10, log10, 3.5, 1.92},
132
+ {"log1p", log1p, log1p, 1.96, 1.93},
133
+ {"log2", log2, log2, 2.12, 1.84},
134
+ };
135
+
136
+ for (int ii = 0; ii < umathfunc.size(); ++ii) {
137
+ // ignore sin/cos
138
+ if ((umathfunc[ii].name != "sin") && (umathfunc[ii].name != "cos")) {
139
+ std::string fileName =
140
+ "umath-validation-set-" + umathfunc[ii].name + ".csv";
141
+ std::ofstream txtOut;
142
+ txtOut.open(fileName, std::ofstream::trunc);
143
+ txtOut << "dtype,input,output,ulperrortol" << std::endl;
144
+
145
+ // Single Precision
146
+ auto f32in = generate_input_vector<float>(umathfunc[ii].name);
147
+ auto f32out = computeTrueVal<float, double>(f32in,
148
+ umathfunc[ii].f32func);
149
+ for (int jj = 0; jj < f32in.size(); ++jj) {
150
+ txtOut << "np.float32" << std::hex << ",0x"
151
+ << *reinterpret_cast<uint32_t *>(&f32in[jj]) << ",0x"
152
+ << *reinterpret_cast<uint32_t *>(&f32out[jj]) << ","
153
+ << ceil(umathfunc[ii].f32ulp) << std::endl;
154
+ }
155
+
156
+ // Double Precision
157
+ auto f64in = generate_input_vector<double>(umathfunc[ii].name);
158
+ auto f64out = computeTrueVal<double, long double>(
159
+ f64in, umathfunc[ii].f64func);
160
+ for (int jj = 0; jj < f64in.size(); ++jj) {
161
+ txtOut << "np.float64" << std::hex << ",0x"
162
+ << *reinterpret_cast<uint64_t *>(&f64in[jj]) << ",0x"
163
+ << *reinterpret_cast<uint64_t *>(&f64out[jj]) << ","
164
+ << ceil(umathfunc[ii].f64ulp) << std::endl;
165
+ }
166
+ txtOut.close();
167
+ }
168
+ }
169
+ return 0;
170
+ }
evalkit_tf437/lib/python3.10/site-packages/numpy/core/tests/data/umath-validation-set-arcsin.csv ADDED
@@ -0,0 +1,1429 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ dtype,input,output,ulperrortol
2
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+ np.float32,0x804e1fb8,0x3f800000,2
15
+ np.float32,0x80687de9,0x3f800000,2
16
+ np.float32,0x807fffff,0x3f800000,2
17
+ np.float32,0x80000001,0x3f800000,2
18
+ ## +/-0.0f, +/-FLT_MIN +/-FLT_MAX ##
19
+ np.float32,0x00000000,0x3f800000,2
20
+ np.float32,0x80000000,0x3f800000,2
21
+ np.float32,0x00800000,0x3f800000,2
22
+ np.float32,0x80800000,0x3f800000,2
23
+ ## 1.00f + 0x00000001 ##
24
+ np.float32,0x3f800000,0x3f0a5140,2
25
+ np.float32,0x3f800001,0x3f0a513f,2
26
+ np.float32,0x3f800002,0x3f0a513d,2
27
+ np.float32,0xc090a8b0,0xbe4332ce,2
28
+ np.float32,0x41ce3184,0x3f4d1de1,2
29
+ np.float32,0xc1d85848,0xbeaa8980,2
30
+ np.float32,0x402b8820,0xbf653aa3,2
31
+ np.float32,0x42b4e454,0xbf4a338b,2
32
+ np.float32,0x42a67a60,0x3c58202e,2
33
+ np.float32,0x41d92388,0xbed987c7,2
34
+ np.float32,0x422dd66c,0x3f5dcab3,2
35
+ np.float32,0xc28f5be6,0xbf5688d8,2
36
+ np.float32,0x41ab2674,0xbf53aa3b,2
37
+ np.float32,0x3f490fdb,0x3f3504f3,2
38
+ np.float32,0xbf490fdb,0x3f3504f3,2
39
+ np.float32,0x3fc90fdb,0xb33bbd2e,2
40
+ np.float32,0xbfc90fdb,0xb33bbd2e,2
41
+ np.float32,0x40490fdb,0xbf800000,2
42
+ np.float32,0xc0490fdb,0xbf800000,2
43
+ np.float32,0x3fc90fdb,0xb33bbd2e,2
44
+ np.float32,0xbfc90fdb,0xb33bbd2e,2
45
+ np.float32,0x40490fdb,0xbf800000,2
46
+ np.float32,0xc0490fdb,0xbf800000,2
47
+ np.float32,0x40c90fdb,0x3f800000,2
48
+ np.float32,0xc0c90fdb,0x3f800000,2
49
+ np.float32,0x4016cbe4,0xbf3504f3,2
50
+ np.float32,0xc016cbe4,0xbf3504f3,2
51
+ np.float32,0x4096cbe4,0x324cde2e,2
52
+ np.float32,0xc096cbe4,0x324cde2e,2
53
+ np.float32,0x4116cbe4,0xbf800000,2
54
+ np.float32,0xc116cbe4,0xbf800000,2
55
+ np.float32,0x40490fdb,0xbf800000,2
56
+ np.float32,0xc0490fdb,0xbf800000,2
57
+ np.float32,0x40c90fdb,0x3f800000,2
58
+ np.float32,0xc0c90fdb,0x3f800000,2
59
+ np.float32,0x41490fdb,0x3f800000,2
60
+ np.float32,0xc1490fdb,0x3f800000,2
61
+ np.float32,0x407b53d2,0xbf3504f1,2
62
+ np.float32,0xc07b53d2,0xbf3504f1,2
63
+ np.float32,0x40fb53d2,0xb4b5563d,2
64
+ np.float32,0xc0fb53d2,0xb4b5563d,2
65
+ np.float32,0x417b53d2,0xbf800000,2
66
+ np.float32,0xc17b53d2,0xbf800000,2
67
+ np.float32,0x4096cbe4,0x324cde2e,2
68
+ np.float32,0xc096cbe4,0x324cde2e,2
69
+ np.float32,0x4116cbe4,0xbf800000,2
70
+ np.float32,0xc116cbe4,0xbf800000,2
71
+ np.float32,0x4196cbe4,0x3f800000,2
72
+ np.float32,0xc196cbe4,0x3f800000,2
73
+ np.float32,0x40afede0,0x3f3504f7,2
74
+ np.float32,0xc0afede0,0x3f3504f7,2
75
+ np.float32,0x412fede0,0x353222c4,2
76
+ np.float32,0xc12fede0,0x353222c4,2
77
+ np.float32,0x41afede0,0xbf800000,2
78
+ np.float32,0xc1afede0,0xbf800000,2
79
+ np.float32,0x40c90fdb,0x3f800000,2
80
+ np.float32,0xc0c90fdb,0x3f800000,2
81
+ np.float32,0x41490fdb,0x3f800000,2
82
+ np.float32,0xc1490fdb,0x3f800000,2
83
+ np.float32,0x41c90fdb,0x3f800000,2
84
+ np.float32,0xc1c90fdb,0x3f800000,2
85
+ np.float32,0x40e231d6,0x3f3504f3,2
86
+ np.float32,0xc0e231d6,0x3f3504f3,2
87
+ np.float32,0x416231d6,0xb319a6a2,2
88
+ np.float32,0xc16231d6,0xb319a6a2,2
89
+ np.float32,0x41e231d6,0xbf800000,2
90
+ np.float32,0xc1e231d6,0xbf800000,2
91
+ np.float32,0x40fb53d2,0xb4b5563d,2
92
+ np.float32,0xc0fb53d2,0xb4b5563d,2
93
+ np.float32,0x417b53d2,0xbf800000,2
94
+ np.float32,0xc17b53d2,0xbf800000,2
95
+ np.float32,0x41fb53d2,0x3f800000,2
96
+ np.float32,0xc1fb53d2,0x3f800000,2
97
+ np.float32,0x410a3ae7,0xbf3504fb,2
98
+ np.float32,0xc10a3ae7,0xbf3504fb,2
99
+ np.float32,0x418a3ae7,0x35b08908,2
100
+ np.float32,0xc18a3ae7,0x35b08908,2
101
+ np.float32,0x420a3ae7,0xbf800000,2
102
+ np.float32,0xc20a3ae7,0xbf800000,2
103
+ np.float32,0x4116cbe4,0xbf800000,2
104
+ np.float32,0xc116cbe4,0xbf800000,2
105
+ np.float32,0x4196cbe4,0x3f800000,2
106
+ np.float32,0xc196cbe4,0x3f800000,2
107
+ np.float32,0x4216cbe4,0x3f800000,2
108
+ np.float32,0xc216cbe4,0x3f800000,2
109
+ np.float32,0x41235ce2,0xbf3504ef,2
110
+ np.float32,0xc1235ce2,0xbf3504ef,2
111
+ np.float32,0x41a35ce2,0xb53889b6,2
112
+ np.float32,0xc1a35ce2,0xb53889b6,2
113
+ np.float32,0x42235ce2,0xbf800000,2
114
+ np.float32,0xc2235ce2,0xbf800000,2
115
+ np.float32,0x412fede0,0x353222c4,2
116
+ np.float32,0xc12fede0,0x353222c4,2
117
+ np.float32,0x41afede0,0xbf800000,2
118
+ np.float32,0xc1afede0,0xbf800000,2
119
+ np.float32,0x422fede0,0x3f800000,2
120
+ np.float32,0xc22fede0,0x3f800000,2
121
+ np.float32,0x413c7edd,0x3f3504f4,2
122
+ np.float32,0xc13c7edd,0x3f3504f4,2
123
+ np.float32,0x41bc7edd,0x33800add,2
124
+ np.float32,0xc1bc7edd,0x33800add,2
125
+ np.float32,0x423c7edd,0xbf800000,2
126
+ np.float32,0xc23c7edd,0xbf800000,2
127
+ np.float32,0x41490fdb,0x3f800000,2
128
+ np.float32,0xc1490fdb,0x3f800000,2
129
+ np.float32,0x41c90fdb,0x3f800000,2
130
+ np.float32,0xc1c90fdb,0x3f800000,2
131
+ np.float32,0x42490fdb,0x3f800000,2
132
+ np.float32,0xc2490fdb,0x3f800000,2
133
+ np.float32,0x4155a0d9,0x3f3504eb,2
134
+ np.float32,0xc155a0d9,0x3f3504eb,2
135
+ np.float32,0x41d5a0d9,0xb5b3bc81,2
136
+ np.float32,0xc1d5a0d9,0xb5b3bc81,2
137
+ np.float32,0x4255a0d9,0xbf800000,2
138
+ np.float32,0xc255a0d9,0xbf800000,2
139
+ np.float32,0x416231d6,0xb319a6a2,2
140
+ np.float32,0xc16231d6,0xb319a6a2,2
141
+ np.float32,0x41e231d6,0xbf800000,2
142
+ np.float32,0xc1e231d6,0xbf800000,2
143
+ np.float32,0x426231d6,0x3f800000,2
144
+ np.float32,0xc26231d6,0x3f800000,2
145
+ np.float32,0x416ec2d4,0xbf3504f7,2
146
+ np.float32,0xc16ec2d4,0xbf3504f7,2
147
+ np.float32,0x41eec2d4,0x353ef0a7,2
148
+ np.float32,0xc1eec2d4,0x353ef0a7,2
149
+ np.float32,0x426ec2d4,0xbf800000,2
150
+ np.float32,0xc26ec2d4,0xbf800000,2
151
+ np.float32,0x417b53d2,0xbf800000,2
152
+ np.float32,0xc17b53d2,0xbf800000,2
153
+ np.float32,0x41fb53d2,0x3f800000,2
154
+ np.float32,0xc1fb53d2,0x3f800000,2
155
+ np.float32,0x427b53d2,0x3f800000,2
156
+ np.float32,0xc27b53d2,0x3f800000,2
157
+ np.float32,0x4183f268,0xbf3504e7,2
158
+ np.float32,0xc183f268,0xbf3504e7,2
159
+ np.float32,0x4203f268,0xb6059a13,2
160
+ np.float32,0xc203f268,0xb6059a13,2
161
+ np.float32,0x4283f268,0xbf800000,2
162
+ np.float32,0xc283f268,0xbf800000,2
163
+ np.float32,0x418a3ae7,0x35b08908,2
164
+ np.float32,0xc18a3ae7,0x35b08908,2
165
+ np.float32,0x420a3ae7,0xbf800000,2
166
+ np.float32,0xc20a3ae7,0xbf800000,2
167
+ np.float32,0x428a3ae7,0x3f800000,2
168
+ np.float32,0xc28a3ae7,0x3f800000,2
169
+ np.float32,0x41908365,0x3f3504f0,2
170
+ np.float32,0xc1908365,0x3f3504f0,2
171
+ np.float32,0x42108365,0xb512200d,2
172
+ np.float32,0xc2108365,0xb512200d,2
173
+ np.float32,0x42908365,0xbf800000,2
174
+ np.float32,0xc2908365,0xbf800000,2
175
+ np.float32,0x4196cbe4,0x3f800000,2
176
+ np.float32,0xc196cbe4,0x3f800000,2
177
+ np.float32,0x4216cbe4,0x3f800000,2
178
+ np.float32,0xc216cbe4,0x3f800000,2
179
+ np.float32,0x4296cbe4,0x3f800000,2
180
+ np.float32,0xc296cbe4,0x3f800000,2
181
+ np.float32,0x419d1463,0x3f3504ef,2
182
+ np.float32,0xc19d1463,0x3f3504ef,2
183
+ np.float32,0x421d1463,0xb5455799,2
184
+ np.float32,0xc21d1463,0xb5455799,2
185
+ np.float32,0x429d1463,0xbf800000,2
186
+ np.float32,0xc29d1463,0xbf800000,2
187
+ np.float32,0x41a35ce2,0xb53889b6,2
188
+ np.float32,0xc1a35ce2,0xb53889b6,2
189
+ np.float32,0x42235ce2,0xbf800000,2
190
+ np.float32,0xc2235ce2,0xbf800000,2
191
+ np.float32,0x42a35ce2,0x3f800000,2
192
+ np.float32,0xc2a35ce2,0x3f800000,2
193
+ np.float32,0x41a9a561,0xbf3504ff,2
194
+ np.float32,0xc1a9a561,0xbf3504ff,2
195
+ np.float32,0x4229a561,0x360733d0,2
196
+ np.float32,0xc229a561,0x360733d0,2
197
+ np.float32,0x42a9a561,0xbf800000,2
198
+ np.float32,0xc2a9a561,0xbf800000,2
199
+ np.float32,0x41afede0,0xbf800000,2
200
+ np.float32,0xc1afede0,0xbf800000,2
201
+ np.float32,0x422fede0,0x3f800000,2
202
+ np.float32,0xc22fede0,0x3f800000,2
203
+ np.float32,0x42afede0,0x3f800000,2
204
+ np.float32,0xc2afede0,0x3f800000,2
205
+ np.float32,0x41b6365e,0xbf3504f6,2
206
+ np.float32,0xc1b6365e,0xbf3504f6,2
207
+ np.float32,0x4236365e,0x350bb91c,2
208
+ np.float32,0xc236365e,0x350bb91c,2
209
+ np.float32,0x42b6365e,0xbf800000,2
210
+ np.float32,0xc2b6365e,0xbf800000,2
211
+ np.float32,0x41bc7edd,0x33800add,2
212
+ np.float32,0xc1bc7edd,0x33800add,2
213
+ np.float32,0x423c7edd,0xbf800000,2
214
+ np.float32,0xc23c7edd,0xbf800000,2
215
+ np.float32,0x42bc7edd,0x3f800000,2
216
+ np.float32,0xc2bc7edd,0x3f800000,2
217
+ np.float32,0x41c2c75c,0x3f3504f8,2
218
+ np.float32,0xc1c2c75c,0x3f3504f8,2
219
+ np.float32,0x4242c75c,0x354bbe8a,2
220
+ np.float32,0xc242c75c,0x354bbe8a,2
221
+ np.float32,0x42c2c75c,0xbf800000,2
222
+ np.float32,0xc2c2c75c,0xbf800000,2
223
+ np.float32,0x41c90fdb,0x3f800000,2
224
+ np.float32,0xc1c90fdb,0x3f800000,2
225
+ np.float32,0x42490fdb,0x3f800000,2
226
+ np.float32,0xc2490fdb,0x3f800000,2
227
+ np.float32,0x42c90fdb,0x3f800000,2
228
+ np.float32,0xc2c90fdb,0x3f800000,2
229
+ np.float32,0x41cf585a,0x3f3504e7,2
230
+ np.float32,0xc1cf585a,0x3f3504e7,2
231
+ np.float32,0x424f585a,0xb608cd8c,2
232
+ np.float32,0xc24f585a,0xb608cd8c,2
233
+ np.float32,0x42cf585a,0xbf800000,2
234
+ np.float32,0xc2cf585a,0xbf800000,2
235
+ np.float32,0x41d5a0d9,0xb5b3bc81,2
236
+ np.float32,0xc1d5a0d9,0xb5b3bc81,2
237
+ np.float32,0x4255a0d9,0xbf800000,2
238
+ np.float32,0xc255a0d9,0xbf800000,2
239
+ np.float32,0x42d5a0d9,0x3f800000,2
240
+ np.float32,0xc2d5a0d9,0x3f800000,2
241
+ np.float32,0x41dbe958,0xbf350507,2
242
+ np.float32,0xc1dbe958,0xbf350507,2
243
+ np.float32,0x425be958,0x365eab75,2
244
+ np.float32,0xc25be958,0x365eab75,2
245
+ np.float32,0x42dbe958,0xbf800000,2
246
+ np.float32,0xc2dbe958,0xbf800000,2
247
+ np.float32,0x41e231d6,0xbf800000,2
248
+ np.float32,0xc1e231d6,0xbf800000,2
249
+ np.float32,0x426231d6,0x3f800000,2
250
+ np.float32,0xc26231d6,0x3f800000,2
251
+ np.float32,0x42e231d6,0x3f800000,2
252
+ np.float32,0xc2e231d6,0x3f800000,2
253
+ np.float32,0x41e87a55,0xbf3504ef,2
254
+ np.float32,0xc1e87a55,0xbf3504ef,2
255
+ np.float32,0x42687a55,0xb552257b,2
256
+ np.float32,0xc2687a55,0xb552257b,2
257
+ np.float32,0x42e87a55,0xbf800000,2
258
+ np.float32,0xc2e87a55,0xbf800000,2
259
+ np.float32,0x41eec2d4,0x353ef0a7,2
260
+ np.float32,0xc1eec2d4,0x353ef0a7,2
261
+ np.float32,0x426ec2d4,0xbf800000,2
262
+ np.float32,0xc26ec2d4,0xbf800000,2
263
+ np.float32,0x42eec2d4,0x3f800000,2
264
+ np.float32,0xc2eec2d4,0x3f800000,2
265
+ np.float32,0x41f50b53,0x3f3504ff,2
266
+ np.float32,0xc1f50b53,0x3f3504ff,2
267
+ np.float32,0x42750b53,0x360a6748,2
268
+ np.float32,0xc2750b53,0x360a6748,2
269
+ np.float32,0x42f50b53,0xbf800000,2
270
+ np.float32,0xc2f50b53,0xbf800000,2
271
+ np.float32,0x41fb53d2,0x3f800000,2
272
+ np.float32,0xc1fb53d2,0x3f800000,2
273
+ np.float32,0x427b53d2,0x3f800000,2
274
+ np.float32,0xc27b53d2,0x3f800000,2
275
+ np.float32,0x42fb53d2,0x3f800000,2
276
+ np.float32,0xc2fb53d2,0x3f800000,2
277
+ np.float32,0x4200ce28,0x3f3504f6,2
278
+ np.float32,0xc200ce28,0x3f3504f6,2
279
+ np.float32,0x4280ce28,0x34fdd672,2
280
+ np.float32,0xc280ce28,0x34fdd672,2
281
+ np.float32,0x4300ce28,0xbf800000,2
282
+ np.float32,0xc300ce28,0xbf800000,2
283
+ np.float32,0x4203f268,0xb6059a13,2
284
+ np.float32,0xc203f268,0xb6059a13,2
285
+ np.float32,0x4283f268,0xbf800000,2
286
+ np.float32,0xc283f268,0xbf800000,2
287
+ np.float32,0x4303f268,0x3f800000,2
288
+ np.float32,0xc303f268,0x3f800000,2
289
+ np.float32,0x420716a7,0xbf3504f8,2
290
+ np.float32,0xc20716a7,0xbf3504f8,2
291
+ np.float32,0x428716a7,0x35588c6d,2
292
+ np.float32,0xc28716a7,0x35588c6d,2
293
+ np.float32,0x430716a7,0xbf800000,2
294
+ np.float32,0xc30716a7,0xbf800000,2
295
+ np.float32,0x420a3ae7,0xbf800000,2
296
+ np.float32,0xc20a3ae7,0xbf800000,2
297
+ np.float32,0x428a3ae7,0x3f800000,2
298
+ np.float32,0xc28a3ae7,0x3f800000,2
299
+ np.float32,0x430a3ae7,0x3f800000,2
300
+ np.float32,0xc30a3ae7,0x3f800000,2
301
+ np.float32,0x420d5f26,0xbf3504e7,2
302
+ np.float32,0xc20d5f26,0xbf3504e7,2
303
+ np.float32,0x428d5f26,0xb60c0105,2
304
+ np.float32,0xc28d5f26,0xb60c0105,2
305
+ np.float32,0x430d5f26,0xbf800000,2
306
+ np.float32,0xc30d5f26,0xbf800000,2
307
+ np.float32,0x42108365,0xb512200d,2
308
+ np.float32,0xc2108365,0xb512200d,2
309
+ np.float32,0x42908365,0xbf800000,2
310
+ np.float32,0xc2908365,0xbf800000,2
311
+ np.float32,0x43108365,0x3f800000,2
312
+ np.float32,0xc3108365,0x3f800000,2
313
+ np.float32,0x4213a7a5,0x3f350507,2
314
+ np.float32,0xc213a7a5,0x3f350507,2
315
+ np.float32,0x4293a7a5,0x3661deee,2
316
+ np.float32,0xc293a7a5,0x3661deee,2
317
+ np.float32,0x4313a7a5,0xbf800000,2
318
+ np.float32,0xc313a7a5,0xbf800000,2
319
+ np.float32,0x4216cbe4,0x3f800000,2
320
+ np.float32,0xc216cbe4,0x3f800000,2
321
+ np.float32,0x4296cbe4,0x3f800000,2
322
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+ np.float64,0xffeefdd725fdfbad,0xbfc08a456111fdd5,1
1324
+ np.float64,0xffe682182fed0430,0x3fecc7c1292761d2,1
1325
+ np.float64,0xffee0ca8dcbc1951,0x3fef6cc361ef2c19,1
1326
+ np.float64,0xffec9b338f393666,0x3fefa9ab8e0471b5,1
1327
+ np.float64,0xffe13c5e29a278bc,0xbfef8da74ad83398,1
1328
+ np.float64,0xffd7bd48c62f7a92,0x3fe3468cd4ac9d34,1
1329
+ np.float64,0xffedd0ed14bba1d9,0xbfd563a83477077b,1
1330
+ np.float64,0xffe86b83f3f0d707,0x3fe9eb3c658e4b2d,1
1331
+ np.float64,0xffd6a4db4bad49b6,0xbfc7e11276166e17,1
1332
+ np.float64,0xffc29e8404253d08,0x3fd35971961c789f,1
1333
+ np.float64,0xffe27cf3d664f9e7,0xbfeca0f73c72f810,1
1334
+ np.float64,0xffc34152352682a4,0x3fef384e564c002c,1
1335
+ np.float64,0xffe395728ba72ae4,0x3f8fe18c2de86eba,1
1336
+ np.float64,0xffed86c4fbbb0d89,0x3fef709db881c672,1
1337
+ np.float64,0xffe8a98d37f1531a,0x3fd4879c8f73c3dc,1
1338
+ np.float64,0xffb8ce9fea319d40,0xbfb853c8fe46b08d,1
1339
+ np.float64,0xffe7f26db8efe4db,0xbfec1cfd3e5c2ac1,1
1340
+ np.float64,0xffd7935b77af26b6,0x3fb7368c89b2a460,1
1341
+ np.float64,0xffc5840ed02b081c,0x3fd92220b56631f3,1
1342
+ np.float64,0xffc36a873926d510,0x3fa84d61baf61811,1
1343
+ np.float64,0xffe06ea583e0dd4a,0x3feb647e348b9e39,1
1344
+ np.float64,0xffe6a33031ed4660,0xbfe096b851dc1a0a,1
1345
+ np.float64,0xffe001c938e00392,0x3fe4eece77623e7a,1
1346
+ np.float64,0xffc1e4f23b23c9e4,0xbfdb9bb1f83f6ac4,1
1347
+ np.float64,0xffecd3ecbab9a7d9,0x3fbafb1f800f177d,1
1348
+ np.float64,0xffc2d3016825a604,0xbfef650e8b0d6afb,1
1349
+ np.float64,0xffe222cb68e44596,0x3fde3690e44de5bd,1
1350
+ np.float64,0xffe5bb145e2b7628,0x3fedbb98e23c9dc1,1
1351
+ np.float64,0xffe9e5823b73cb04,0xbfee41661016c03c,1
1352
+ np.float64,0xffd234a00ba46940,0x3fda0312cda580c2,1
1353
+ np.float64,0xffe0913ed6e1227d,0xbfed508bb529bd23,1
1354
+ np.float64,0xffe8e3596171c6b2,0xbfdc33e1c1d0310e,1
1355
+ np.float64,0xffef9c6835ff38cf,0x3fea8ce6d27dfba3,1
1356
+ np.float64,0xffdd3bcf66ba779e,0x3fe50523d2b6470e,1
1357
+ np.float64,0xffe57e8cf06afd1a,0xbfee600933347247,1
1358
+ np.float64,0xffe0d8c65fa1b18c,0x3fe75091f93d5e4c,1
1359
+ np.float64,0xffea7c8c16b4f918,0x3fee681724795198,1
1360
+ np.float64,0xffe34f7a05269ef4,0xbfe3c3e179676f13,1
1361
+ np.float64,0xffd28894a6a5112a,0xbfe5d1027aee615d,1
1362
+ np.float64,0xffc73be6f22e77cc,0x3fe469bbc08b472a,1
1363
+ np.float64,0xffe7f71b066fee36,0x3fe7ed136c8fdfaa,1
1364
+ np.float64,0xffebc13e29f7827c,0x3fefcdc6e677d314,1
1365
+ np.float64,0xffd53e9c942a7d3a,0x3fea5a02c7341749,1
1366
+ np.float64,0xffd7191b23ae3236,0x3fea419b66023443,1
1367
+ np.float64,0xffe9480325b29006,0xbfefeaff5fa38cd5,1
1368
+ np.float64,0xffba46dc0e348db8,0xbfefa54f4de28eba,1
1369
+ np.float64,0xffdd4cc31eba9986,0x3fe60bb41fe1c4da,1
1370
+ np.float64,0xffe13a70dea274e1,0xbfaa9192f7bd6c9b,1
1371
+ np.float64,0xffde25127bbc4a24,0x3f7c75f45e29be7d,1
1372
+ np.float64,0xffe4076543a80eca,0x3fea5aad50d2f687,1
1373
+ np.float64,0xffe61512acec2a25,0xbfefffeb67401649,1
1374
+ np.float64,0xffef812ec1ff025d,0xbfe919c7c073c766,1
1375
+ np.float64,0xffd5552aeaaaaa56,0x3fc89d38ab047396,1
evalkit_tf437/lib/python3.10/site-packages/numpy/core/tests/data/umath-validation-set-exp.csv ADDED
@@ -0,0 +1,412 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ dtype,input,output,ulperrortol
2
+ ## +ve denormals ##
3
+ np.float32,0x004b4716,0x3f800000,3
4
+ np.float32,0x007b2490,0x3f800000,3
5
+ np.float32,0x007c99fa,0x3f800000,3
6
+ np.float32,0x00734a0c,0x3f800000,3
7
+ np.float32,0x0070de24,0x3f800000,3
8
+ np.float32,0x00495d65,0x3f800000,3
9
+ np.float32,0x006894f6,0x3f800000,3
10
+ np.float32,0x00555a76,0x3f800000,3
11
+ np.float32,0x004e1fb8,0x3f800000,3
12
+ np.float32,0x00687de9,0x3f800000,3
13
+ ## -ve denormals ##
14
+ np.float32,0x805b59af,0x3f800000,3
15
+ np.float32,0x807ed8ed,0x3f800000,3
16
+ np.float32,0x807142ad,0x3f800000,3
17
+ np.float32,0x80772002,0x3f800000,3
18
+ np.float32,0x8062abcb,0x3f800000,3
19
+ np.float32,0x8045e31c,0x3f800000,3
20
+ np.float32,0x805f01c2,0x3f800000,3
21
+ np.float32,0x80506432,0x3f800000,3
22
+ np.float32,0x8060089d,0x3f800000,3
23
+ np.float32,0x8071292f,0x3f800000,3
24
+ ## floats that output a denormal ##
25
+ np.float32,0xc2cf3fc1,0x00000001,3
26
+ np.float32,0xc2c79726,0x00000021,3
27
+ np.float32,0xc2cb295d,0x00000005,3
28
+ np.float32,0xc2b49e6b,0x00068c4c,3
29
+ np.float32,0xc2ca8116,0x00000008,3
30
+ np.float32,0xc2c23f82,0x000001d7,3
31
+ np.float32,0xc2cb69c0,0x00000005,3
32
+ np.float32,0xc2cc1f4d,0x00000003,3
33
+ np.float32,0xc2ae094e,0x00affc4c,3
34
+ np.float32,0xc2c86c44,0x00000015,3
35
+ ## random floats between -87.0f and 88.0f ##
36
+ np.float32,0x4030d7e0,0x417d9a05,3
37
+ np.float32,0x426f60e8,0x6aa1be2c,3
38
+ np.float32,0x41a1b220,0x4e0efc11,3
39
+ np.float32,0xc20cc722,0x26159da7,3
40
+ np.float32,0x41c492bc,0x512ec79d,3
41
+ np.float32,0x40980210,0x42e73a0e,3
42
+ np.float32,0xbf1f7b80,0x3f094de3,3
43
+ np.float32,0x42a678a4,0x7b87a383,3
44
+ np.float32,0xc20f3cfd,0x25a1c304,3
45
+ np.float32,0x423ff34c,0x6216467f,3
46
+ np.float32,0x00000000,0x3f800000,3
47
+ ## floats that cause an overflow ##
48
+ np.float32,0x7f06d8c1,0x7f800000,3
49
+ np.float32,0x7f451912,0x7f800000,3
50
+ np.float32,0x7ecceac3,0x7f800000,3
51
+ np.float32,0x7f643b45,0x7f800000,3
52
+ np.float32,0x7e910ea0,0x7f800000,3
53
+ np.float32,0x7eb4756b,0x7f800000,3
54
+ np.float32,0x7f4ec708,0x7f800000,3
55
+ np.float32,0x7f6b4551,0x7f800000,3
56
+ np.float32,0x7d8edbda,0x7f800000,3
57
+ np.float32,0x7f730718,0x7f800000,3
58
+ np.float32,0x42b17217,0x7f7fff84,3
59
+ np.float32,0x42b17218,0x7f800000,3
60
+ np.float32,0x42b17219,0x7f800000,3
61
+ np.float32,0xfef2b0bc,0x00000000,3
62
+ np.float32,0xff69f83e,0x00000000,3
63
+ np.float32,0xff4ecb12,0x00000000,3
64
+ np.float32,0xfeac6d86,0x00000000,3
65
+ np.float32,0xfde0cdb8,0x00000000,3
66
+ np.float32,0xff26aef4,0x00000000,3
67
+ np.float32,0xff6f9277,0x00000000,3
68
+ np.float32,0xff7adfc4,0x00000000,3
69
+ np.float32,0xff0ad40e,0x00000000,3
70
+ np.float32,0xff6fd8f3,0x00000000,3
71
+ np.float32,0xc2cff1b4,0x00000001,3
72
+ np.float32,0xc2cff1b5,0x00000000,3
73
+ np.float32,0xc2cff1b6,0x00000000,3
74
+ np.float32,0x7f800000,0x7f800000,3
75
+ np.float32,0xff800000,0x00000000,3
76
+ np.float32,0x4292f27c,0x7480000a,3
77
+ np.float32,0x42a920be,0x7c7fff94,3
78
+ np.float32,0x41c214c9,0x50ffffd9,3
79
+ np.float32,0x41abe686,0x4effffd9,3
80
+ np.float32,0x4287db5a,0x707fffd3,3
81
+ np.float32,0x41902cbb,0x4c800078,3
82
+ np.float32,0x42609466,0x67ffffeb,3
83
+ np.float32,0x41a65af5,0x4e7fffd1,3
84
+ np.float32,0x417f13ff,0x4affffc9,3
85
+ np.float32,0x426d0e6c,0x6a3504f2,3
86
+ np.float32,0x41bc8934,0x507fff51,3
87
+ np.float32,0x42a7bdde,0x7c0000d6,3
88
+ np.float32,0x4120cf66,0x46b504f6,3
89
+ np.float32,0x4244da8f,0x62ffff1a,3
90
+ np.float32,0x41a0cf69,0x4e000034,3
91
+ np.float32,0x41cd2bec,0x52000005,3
92
+ np.float32,0x42893e41,0x7100009e,3
93
+ np.float32,0x41b437e1,0x4fb50502,3
94
+ np.float32,0x41d8430f,0x5300001d,3
95
+ np.float32,0x4244da92,0x62ffffda,3
96
+ np.float32,0x41a0cf63,0x4dffffa9,3
97
+ np.float32,0x3eb17218,0x3fb504f3,3
98
+ np.float32,0x428729e8,0x703504dc,3
99
+ np.float32,0x41a0cf67,0x4e000014,3
100
+ np.float32,0x4252b77d,0x65800011,3
101
+ np.float32,0x41902cb9,0x4c800058,3
102
+ np.float32,0x42a0cf67,0x79800052,3
103
+ np.float32,0x4152b77b,0x48ffffe9,3
104
+ np.float32,0x41265af3,0x46ffffc8,3
105
+ np.float32,0x42187e0b,0x5affff9a,3
106
+ np.float32,0xc0d2b77c,0x3ab504f6,3
107
+ np.float32,0xc283b2ac,0x10000072,3
108
+ np.float32,0xc1cff1b4,0x2cb504f5,3
109
+ np.float32,0xc05dce9e,0x3d000000,3
110
+ np.float32,0xc28ec9d2,0x0bfffea5,3
111
+ np.float32,0xc23c893a,0x1d7fffde,3
112
+ np.float32,0xc2a920c0,0x027fff6c,3
113
+ np.float32,0xc1f9886f,0x2900002b,3
114
+ np.float32,0xc2c42920,0x000000b5,3
115
+ np.float32,0xc2893e41,0x0dfffec5,3
116
+ np.float32,0xc2c4da93,0x00000080,3
117
+ np.float32,0xc17f1401,0x3400000c,3
118
+ np.float32,0xc1902cb6,0x327fffaf,3
119
+ np.float32,0xc27c4e3b,0x11ffffc5,3
120
+ np.float32,0xc268e5c5,0x157ffe9d,3
121
+ np.float32,0xc2b4e953,0x0005a826,3
122
+ np.float32,0xc287db5a,0x0e800016,3
123
+ np.float32,0xc207db5a,0x2700000b,3
124
+ np.float32,0xc2b2d4fe,0x000ffff1,3
125
+ np.float32,0xc268e5c0,0x157fffdd,3
126
+ np.float32,0xc22920bd,0x2100003b,3
127
+ np.float32,0xc2902caf,0x0b80011e,3
128
+ np.float32,0xc1902cba,0x327fff2f,3
129
+ np.float32,0xc2ca6625,0x00000008,3
130
+ np.float32,0xc280ece8,0x10fffeb5,3
131
+ np.float32,0xc2918f94,0x0b0000ea,3
132
+ np.float32,0xc29b43d5,0x077ffffc,3
133
+ np.float32,0xc1e61ff7,0x2ab504f5,3
134
+ np.float32,0xc2867878,0x0effff15,3
135
+ np.float32,0xc2a2324a,0x04fffff4,3
136
+ #float64
137
+ ## near zero ##
138
+ np.float64,0x8000000000000000,0x3ff0000000000000,2
139
+ np.float64,0x8010000000000000,0x3ff0000000000000,2
140
+ np.float64,0x8000000000000001,0x3ff0000000000000,2
141
+ np.float64,0x8360000000000000,0x3ff0000000000000,2
142
+ np.float64,0x9a70000000000000,0x3ff0000000000000,2
143
+ np.float64,0xb9b0000000000000,0x3ff0000000000000,2
144
+ np.float64,0xb810000000000000,0x3ff0000000000000,2
145
+ np.float64,0xbc30000000000000,0x3ff0000000000000,2
146
+ np.float64,0xb6a0000000000000,0x3ff0000000000000,2
147
+ np.float64,0x0000000000000000,0x3ff0000000000000,2
148
+ np.float64,0x0010000000000000,0x3ff0000000000000,2
149
+ np.float64,0x0000000000000001,0x3ff0000000000000,2
150
+ np.float64,0x0360000000000000,0x3ff0000000000000,2
151
+ np.float64,0x1a70000000000000,0x3ff0000000000000,2
152
+ np.float64,0x3c30000000000000,0x3ff0000000000000,2
153
+ np.float64,0x36a0000000000000,0x3ff0000000000000,2
154
+ np.float64,0x39b0000000000000,0x3ff0000000000000,2
155
+ np.float64,0x3810000000000000,0x3ff0000000000000,2
156
+ ## underflow ##
157
+ np.float64,0xc0c6276800000000,0x0000000000000000,2
158
+ np.float64,0xc0c62d918ce2421d,0x0000000000000000,2
159
+ np.float64,0xc0c62d918ce2421e,0x0000000000000000,2
160
+ np.float64,0xc0c62d91a0000000,0x0000000000000000,2
161
+ np.float64,0xc0c62d9180000000,0x0000000000000000,2
162
+ np.float64,0xc0c62dea45ee3e06,0x0000000000000000,2
163
+ np.float64,0xc0c62dea45ee3e07,0x0000000000000000,2
164
+ np.float64,0xc0c62dea40000000,0x0000000000000000,2
165
+ np.float64,0xc0c62dea60000000,0x0000000000000000,2
166
+ np.float64,0xc0875f1120000000,0x0000000000000000,2
167
+ np.float64,0xc0875f113c30b1c8,0x0000000000000000,2
168
+ np.float64,0xc0875f1140000000,0x0000000000000000,2
169
+ np.float64,0xc093480000000000,0x0000000000000000,2
170
+ np.float64,0xffefffffffffffff,0x0000000000000000,2
171
+ np.float64,0xc7efffffe0000000,0x0000000000000000,2
172
+ ## overflow ##
173
+ np.float64,0x40862e52fefa39ef,0x7ff0000000000000,2
174
+ np.float64,0x40872e42fefa39ef,0x7ff0000000000000,2
175
+ ## +/- INF, +/- NAN ##
176
+ np.float64,0x7ff0000000000000,0x7ff0000000000000,2
177
+ np.float64,0xfff0000000000000,0x0000000000000000,2
178
+ np.float64,0x7ff8000000000000,0x7ff8000000000000,2
179
+ np.float64,0xfff8000000000000,0xfff8000000000000,2
180
+ ## output denormal ##
181
+ np.float64,0xc087438520000000,0x0000000000000001,2
182
+ np.float64,0xc08743853f2f4461,0x0000000000000001,2
183
+ np.float64,0xc08743853f2f4460,0x0000000000000001,2
184
+ np.float64,0xc087438540000000,0x0000000000000001,2
185
+ ## between -745.13321910 and 709.78271289 ##
186
+ np.float64,0xbff760cd14774bd9,0x3fcdb14ced00ceb6,2
187
+ np.float64,0xbff760cd20000000,0x3fcdb14cd7993879,2
188
+ np.float64,0xbff760cd00000000,0x3fcdb14d12fbd264,2
189
+ np.float64,0xc07f1cf360000000,0x130c1b369af14fda,2
190
+ np.float64,0xbeb0000000000000,0x3feffffe00001000,2
191
+ np.float64,0xbd70000000000000,0x3fefffffffffe000,2
192
+ np.float64,0xc084fd46e5c84952,0x0360000000000139,2
193
+ np.float64,0xc084fd46e5c84953,0x035ffffffffffe71,2
194
+ np.float64,0xc084fd46e0000000,0x0360000b9096d32c,2
195
+ np.float64,0xc084fd4700000000,0x035fff9721d12104,2
196
+ np.float64,0xc086232bc0000000,0x0010003af5e64635,2
197
+ np.float64,0xc086232bdd7abcd2,0x001000000000007c,2
198
+ np.float64,0xc086232bdd7abcd3,0x000ffffffffffe7c,2
199
+ np.float64,0xc086232be0000000,0x000ffffaf57a6fc9,2
200
+ np.float64,0xc086233920000000,0x000fe590e3b45eb0,2
201
+ np.float64,0xc086233938000000,0x000fe56133493c57,2
202
+ np.float64,0xc086233940000000,0x000fe5514deffbbc,2
203
+ np.float64,0xc086234c98000000,0x000fbf1024c32ccb,2
204
+ np.float64,0xc086234ca0000000,0x000fbf0065bae78d,2
205
+ np.float64,0xc086234c80000000,0x000fbf3f623a7724,2
206
+ np.float64,0xc086234ec0000000,0x000fbad237c846f9,2
207
+ np.float64,0xc086234ec8000000,0x000fbac27cfdec97,2
208
+ np.float64,0xc086234ee0000000,0x000fba934cfd3dc2,2
209
+ np.float64,0xc086234ef0000000,0x000fba73d7f618d9,2
210
+ np.float64,0xc086234f00000000,0x000fba54632dddc0,2
211
+ np.float64,0xc0862356e0000000,0x000faae0945b761a,2
212
+ np.float64,0xc0862356f0000000,0x000faac13eb9a310,2
213
+ np.float64,0xc086235700000000,0x000faaa1e9567b0a,2
214
+ np.float64,0xc086236020000000,0x000f98cd75c11ed7,2
215
+ np.float64,0xc086236ca0000000,0x000f8081b4d93f89,2
216
+ np.float64,0xc086236cb0000000,0x000f8062b3f4d6c5,2
217
+ np.float64,0xc086236cc0000000,0x000f8043b34e6f8c,2
218
+ np.float64,0xc086238d98000000,0x000f41220d9b0d2c,2
219
+ np.float64,0xc086238da0000000,0x000f4112cc80a01f,2
220
+ np.float64,0xc086238d80000000,0x000f414fd145db5b,2
221
+ np.float64,0xc08624fd00000000,0x000cbfce8ea1e6c4,2
222
+ np.float64,0xc086256080000000,0x000c250747fcd46e,2
223
+ np.float64,0xc08626c480000000,0x000a34f4bd975193,2
224
+ np.float64,0xbf50000000000000,0x3feff800ffeaac00,2
225
+ np.float64,0xbe10000000000000,0x3fefffffff800000,2
226
+ np.float64,0xbcd0000000000000,0x3feffffffffffff8,2
227
+ np.float64,0xc055d589e0000000,0x38100004bf94f63e,2
228
+ np.float64,0xc055d58a00000000,0x380ffff97f292ce8,2
229
+ np.float64,0xbfd962d900000000,0x3fe585a4b00110e1,2
230
+ np.float64,0x3ff4bed280000000,0x400d411e7a58a303,2
231
+ np.float64,0x3fff0b3620000000,0x401bd7737ffffcf3,2
232
+ np.float64,0x3ff0000000000000,0x4005bf0a8b145769,2
233
+ np.float64,0x3eb0000000000000,0x3ff0000100000800,2
234
+ np.float64,0x3d70000000000000,0x3ff0000000001000,2
235
+ np.float64,0x40862e42e0000000,0x7fefff841808287f,2
236
+ np.float64,0x40862e42fefa39ef,0x7fefffffffffff2a,2
237
+ np.float64,0x40862e0000000000,0x7feef85a11e73f2d,2
238
+ np.float64,0x4000000000000000,0x401d8e64b8d4ddae,2
239
+ np.float64,0x4009242920000000,0x40372a52c383a488,2
240
+ np.float64,0x4049000000000000,0x44719103e4080b45,2
241
+ np.float64,0x4008000000000000,0x403415e5bf6fb106,2
242
+ np.float64,0x3f50000000000000,0x3ff00400800aab55,2
243
+ np.float64,0x3e10000000000000,0x3ff0000000400000,2
244
+ np.float64,0x3cd0000000000000,0x3ff0000000000004,2
245
+ np.float64,0x40562e40a0000000,0x47effed088821c3f,2
246
+ np.float64,0x40562e42e0000000,0x47effff082e6c7ff,2
247
+ np.float64,0x40562e4300000000,0x47f00000417184b8,2
248
+ np.float64,0x3fe8000000000000,0x4000ef9db467dcf8,2
249
+ np.float64,0x402b12e8d4f33589,0x412718f68c71a6fe,2
250
+ np.float64,0x402b12e8d4f3358a,0x412718f68c71a70a,2
251
+ np.float64,0x402b12e8c0000000,0x412718f59a7f472e,2
252
+ np.float64,0x402b12e8e0000000,0x412718f70c0eac62,2
253
+ ##use 1th entry
254
+ np.float64,0x40631659AE147CB4,0x4db3a95025a4890f,2
255
+ np.float64,0xC061B87D2E85A4E2,0x332640c8e2de2c51,2
256
+ np.float64,0x405A4A50BE243AF4,0x496a45e4b7f0339a,2
257
+ np.float64,0xC0839898B98EC5C6,0x0764027828830df4,2
258
+ #use 2th entry
259
+ np.float64,0xC072428C44B6537C,0x2596ade838b96f3e,2
260
+ np.float64,0xC053057C5E1AE9BF,0x3912c8fad18fdadf,2
261
+ np.float64,0x407E89C78328BAA3,0x6bfe35d5b9a1a194,2
262
+ np.float64,0x4083501B6DD87112,0x77a855503a38924e,2
263
+ #use 3th entry
264
+ np.float64,0x40832C6195F24540,0x7741e73c80e5eb2f,2
265
+ np.float64,0xC083D4CD557C2EC9,0x06b61727c2d2508e,2
266
+ np.float64,0x400C48F5F67C99BD,0x404128820f02b92e,2
267
+ np.float64,0x4056E36D9B2DF26A,0x4830f52ff34a8242,2
268
+ #use 4th entry
269
+ np.float64,0x4080FF700D8CBD06,0x70fa70df9bc30f20,2
270
+ np.float64,0x406C276D39E53328,0x543eb8e20a8f4741,2
271
+ np.float64,0xC070D6159BBD8716,0x27a4a0548c904a75,2
272
+ np.float64,0xC052EBCF8ED61F83,0x391c0e92368d15e4,2
273
+ #use 5th entry
274
+ np.float64,0xC061F892A8AC5FBE,0x32f807a89efd3869,2
275
+ np.float64,0x4021D885D2DBA085,0x40bd4dc86d3e3270,2
276
+ np.float64,0x40767AEEEE7D4FCF,0x605e22851ee2afb7,2
277
+ np.float64,0xC0757C5D75D08C80,0x20f0751599b992a2,2
278
+ #use 6th entry
279
+ np.float64,0x405ACF7A284C4CE3,0x499a4e0b7a27027c,2
280
+ np.float64,0xC085A6C9E80D7AF5,0x0175914009d62ec2,2
281
+ np.float64,0xC07E4C02F86F1DAE,0x1439269b29a9231e,2
282
+ np.float64,0x4080D80F9691CC87,0x7088a6cdafb041de,2
283
+ #use 7th entry
284
+ np.float64,0x407FDFD84FBA0AC1,0x6deb1ae6f9bc4767,2
285
+ np.float64,0x40630C06A1A2213D,0x4dac7a9d51a838b7,2
286
+ np.float64,0x40685FDB30BB8B4F,0x5183f5cc2cac9e79,2
287
+ np.float64,0x408045A2208F77F4,0x6ee299e08e2aa2f0,2
288
+ #use 8th entry
289
+ np.float64,0xC08104E391F5078B,0x0ed397b7cbfbd230,2
290
+ np.float64,0xC031501CAEFAE395,0x3e6040fd1ea35085,2
291
+ np.float64,0xC079229124F6247C,0x1babf4f923306b1e,2
292
+ np.float64,0x407FB65F44600435,0x6db03beaf2512b8a,2
293
+ #use 9th entry
294
+ np.float64,0xC07EDEE8E8E8A5AC,0x136536cec9cbef48,2
295
+ np.float64,0x4072BB4086099A14,0x5af4d3c3008b56cc,2
296
+ np.float64,0x4050442A2EC42CB4,0x45cd393bd8fad357,2
297
+ np.float64,0xC06AC28FB3D419B4,0x2ca1b9d3437df85f,2
298
+ #use 10th entry
299
+ np.float64,0x40567FC6F0A68076,0x480c977fd5f3122e,2
300
+ np.float64,0x40620A2F7EDA59BB,0x4cf278e96f4ce4d7,2
301
+ np.float64,0xC085044707CD557C,0x034aad6c968a045a,2
302
+ np.float64,0xC07374EA5AC516AA,0x23dd6afdc03e83d5,2
303
+ #use 11th entry
304
+ np.float64,0x4073CC95332619C1,0x5c804b1498bbaa54,2
305
+ np.float64,0xC0799FEBBE257F31,0x1af6a954c43b87d2,2
306
+ np.float64,0x408159F19EA424F6,0x7200858efcbfc84d,2
307
+ np.float64,0x404A81F6F24C0792,0x44b664a07ce5bbfa,2
308
+ #use 12th entry
309
+ np.float64,0x40295FF1EFB9A741,0x4113c0e74c52d7b0,2
310
+ np.float64,0x4073975F4CC411DA,0x5c32be40b4fec2c1,2
311
+ np.float64,0x406E9DE52E82A77E,0x56049c9a3f1ae089,2
312
+ np.float64,0x40748C2F52560ED9,0x5d93bc14fd4cd23b,2
313
+ #use 13th entry
314
+ np.float64,0x4062A553CDC4D04C,0x4d6266bfde301318,2
315
+ np.float64,0xC079EC1D63598AB7,0x1a88cb184dab224c,2
316
+ np.float64,0xC0725C1CB3167427,0x25725b46f8a081f6,2
317
+ np.float64,0x407888771D9B45F9,0x6353b1ec6bd7ce80,2
318
+ #use 14th entry
319
+ np.float64,0xC082CBA03AA89807,0x09b383723831ce56,2
320
+ np.float64,0xC083A8961BB67DD7,0x0735b118d5275552,2
321
+ np.float64,0xC076BC6ECA12E7E3,0x1f2222679eaef615,2
322
+ np.float64,0xC072752503AA1A5B,0x254eb832242c77e1,2
323
+ #use 15th entry
324
+ np.float64,0xC058800792125DEC,0x371882372a0b48d4,2
325
+ np.float64,0x4082909FD863E81C,0x7580d5f386920142,2
326
+ np.float64,0xC071616F8FB534F9,0x26dbe20ef64a412b,2
327
+ np.float64,0x406D1AB571CAA747,0x54ee0d55cb38ac20,2
328
+ #use 16th entry
329
+ np.float64,0x406956428B7DAD09,0x52358682c271237f,2
330
+ np.float64,0xC07EFC2D9D17B621,0x133b3e77c27a4d45,2
331
+ np.float64,0xC08469BAC5BA3CCA,0x050863e5f42cc52f,2
332
+ np.float64,0x407189D9626386A5,0x593cb1c0b3b5c1d3,2
333
+ #use 17th entry
334
+ np.float64,0x4077E652E3DEB8C6,0x6269a10dcbd3c752,2
335
+ np.float64,0x407674C97DB06878,0x605485dcc2426ec2,2
336
+ np.float64,0xC07CE9969CF4268D,0x16386cf8996669f2,2
337
+ np.float64,0x40780EE32D5847C4,0x62a436bd1abe108d,2
338
+ #use 18th entry
339
+ np.float64,0x4076C3AA5E1E8DA1,0x60c62f56a5e72e24,2
340
+ np.float64,0xC0730AFC7239B9BE,0x24758ead095cec1e,2
341
+ np.float64,0xC085CC2B9C420DDB,0x0109cdaa2e5694c1,2
342
+ np.float64,0x406D0765CB6D7AA4,0x54e06f8dd91bd945,2
343
+ #use 19th entry
344
+ np.float64,0xC082D011F3B495E7,0x09a6647661d279c2,2
345
+ np.float64,0xC072826AF8F6AFBC,0x253acd3cd224507e,2
346
+ np.float64,0x404EB9C4810CEA09,0x457933dbf07e8133,2
347
+ np.float64,0x408284FBC97C58CE,0x755f6eb234aa4b98,2
348
+ #use 20th entry
349
+ np.float64,0x40856008CF6EDC63,0x7d9c0b3c03f4f73c,2
350
+ np.float64,0xC077CB2E9F013B17,0x1d9b3d3a166a55db,2
351
+ np.float64,0xC0479CA3C20AD057,0x3bad40e081555b99,2
352
+ np.float64,0x40844CD31107332A,0x7a821d70aea478e2,2
353
+ #use 21th entry
354
+ np.float64,0xC07C8FCC0BFCC844,0x16ba1cc8c539d19b,2
355
+ np.float64,0xC085C4E9A3ABA488,0x011ff675ba1a2217,2
356
+ np.float64,0x4074D538B32966E5,0x5dfd9d78043c6ad9,2
357
+ np.float64,0xC0630CA16902AD46,0x3231a446074cede6,2
358
+ #use 22th entry
359
+ np.float64,0xC06C826733D7D0B7,0x2b5f1078314d41e1,2
360
+ np.float64,0xC0520DF55B2B907F,0x396c13a6ce8e833e,2
361
+ np.float64,0xC080712072B0F437,0x107eae02d11d98ea,2
362
+ np.float64,0x40528A6150E19EFB,0x469fdabda02228c5,2
363
+ #use 23th entry
364
+ np.float64,0xC07B1D74B6586451,0x18d1253883ae3b48,2
365
+ np.float64,0x4045AFD7867DAEC0,0x43d7d634fc4c5d98,2
366
+ np.float64,0xC07A08B91F9ED3E2,0x1a60973e6397fc37,2
367
+ np.float64,0x407B3ECF0AE21C8C,0x673e03e9d98d7235,2
368
+ #use 24th entry
369
+ np.float64,0xC078AEB6F30CEABF,0x1c530b93ab54a1b3,2
370
+ np.float64,0x4084495006A41672,0x7a775b6dc7e63064,2
371
+ np.float64,0x40830B1C0EBF95DD,0x76e1e6eed77cfb89,2
372
+ np.float64,0x407D93E8F33D8470,0x6a9adbc9e1e4f1e5,2
373
+ #use 25th entry
374
+ np.float64,0x4066B11A09EFD9E8,0x504dd528065c28a7,2
375
+ np.float64,0x408545823723AEEB,0x7d504a9b1844f594,2
376
+ np.float64,0xC068C711F2CA3362,0x2e104f3496ea118e,2
377
+ np.float64,0x407F317FCC3CA873,0x6cf0732c9948ebf4,2
378
+ #use 26th entry
379
+ np.float64,0x407AFB3EBA2ED50F,0x66dc28a129c868d5,2
380
+ np.float64,0xC075377037708ADE,0x21531a329f3d793e,2
381
+ np.float64,0xC07C30066A1F3246,0x174448baa16ded2b,2
382
+ np.float64,0xC06689A75DE2ABD3,0x2fad70662fae230b,2
383
+ #use 27th entry
384
+ np.float64,0x4081514E9FCCF1E0,0x71e673b9efd15f44,2
385
+ np.float64,0xC0762C710AF68460,0x1ff1ed7d8947fe43,2
386
+ np.float64,0xC0468102FF70D9C4,0x3be0c3a8ff3419a3,2
387
+ np.float64,0xC07EA4CEEF02A83E,0x13b908f085102c61,2
388
+ #use 28th entry
389
+ np.float64,0xC06290B04AE823C4,0x328a83da3c2e3351,2
390
+ np.float64,0xC0770EB1D1C395FB,0x1eab281c1f1db5fe,2
391
+ np.float64,0xC06F5D4D838A5BAE,0x29500ea32fb474ea,2
392
+ np.float64,0x40723B3133B54C5D,0x5a3c82c7c3a2b848,2
393
+ #use 29th entry
394
+ np.float64,0x4085E6454CE3B4AA,0x7f20319b9638d06a,2
395
+ np.float64,0x408389F2A0585D4B,0x7850667c58aab3d0,2
396
+ np.float64,0xC0382798F9C8AE69,0x3dc1c79fe8739d6d,2
397
+ np.float64,0xC08299D827608418,0x0a4335f76cdbaeb5,2
398
+ #use 30th entry
399
+ np.float64,0xC06F3DED43301BF1,0x2965670ae46750a8,2
400
+ np.float64,0xC070CAF6BDD577D9,0x27b4aa4ffdd29981,2
401
+ np.float64,0x4078529AD4B2D9F2,0x6305c12755d5e0a6,2
402
+ np.float64,0xC055B14E75A31B96,0x381c2eda6d111e5d,2
403
+ #use 31th entry
404
+ np.float64,0x407B13EE414FA931,0x6700772c7544564d,2
405
+ np.float64,0x407EAFDE9DE3EC54,0x6c346a0e49724a3c,2
406
+ np.float64,0xC08362F398B9530D,0x07ffeddbadf980cb,2
407
+ np.float64,0x407E865CDD9EEB86,0x6bf866cac5e0d126,2
408
+ #use 32th entry
409
+ np.float64,0x407FB62DBC794C86,0x6db009f708ac62cb,2
410
+ np.float64,0xC063D0BAA68CDDDE,0x31a3b2a51ce50430,2
411
+ np.float64,0xC05E7706A2231394,0x34f24bead6fab5c9,2
412
+ np.float64,0x4083E3A06FDE444E,0x79527b7a386d1937,2
evalkit_tf437/lib/python3.10/site-packages/numpy/core/tests/data/umath-validation-set-expm1.csv ADDED
@@ -0,0 +1,1429 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ dtype,input,output,ulperrortol
2
+ np.float32,0x80606724,0x80606724,3
3
+ np.float32,0xbf16790f,0xbee38e14,3
4
+ np.float32,0xbf1778a1,0xbee4a97f,3
5
+ np.float32,0x7d4fc610,0x7f800000,3
6
+ np.float32,0xbec30a20,0xbea230d5,3
7
+ np.float32,0x3eae8a36,0x3ecffac5,3
8
+ np.float32,0xbf1f08f1,0xbeece93c,3
9
+ np.float32,0x80374376,0x80374376,3
10
+ np.float32,0x3f2e04ca,0x3f793115,3
11
+ np.float32,0x7e2c7e36,0x7f800000,3
12
+ np.float32,0xbf686cae,0xbf18bcf0,3
13
+ np.float32,0xbf5518cd,0xbf10a3da,3
14
+ np.float32,0x807e233c,0x807e233c,3
15
+ np.float32,0x7f4edd54,0x7f800000,3
16
+ np.float32,0x7ed70088,0x7f800000,3
17
+ np.float32,0x801675da,0x801675da,3
18
+ np.float32,0x806735d5,0x806735d5,3
19
+ np.float32,0xfe635fec,0xbf800000,3
20
+ np.float32,0xfed88a0a,0xbf800000,3
21
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+ np.float64,0x3feb0bcbf7b61798,0xbfb2b31e5d952869,2
1616
+ np.float64,0xad60654b5ac0d,0xc0733d26860df676,2
1617
+ np.float64,0x3fe154e1ff22a9c4,0xbfd10b416e58c867,2
1618
+ np.float64,0x7fb20e9c8a241d38,0x407331a66453b8bc,2
1619
+ np.float64,0x7fcbbaaf7d37755e,0x4073397274f28008,2
1620
+ np.float64,0x187d0fbc30fa3,0xc0734ac03cc98cc9,2
1621
+ np.float64,0x7fd153afeaa2a75f,0x40733aff00b4311d,2
1622
+ np.float64,0x3fe05310a5e0a621,0xbfd2b5386aeecaac,2
1623
+ np.float64,0x7fea863b2b750c75,0x407342c57807f700,2
1624
+ np.float64,0x3fed5f0c633abe19,0xbfa30f6cfbc4bf94,2
1625
+ np.float64,0xf227c8b3e44f9,0xc0733ad42daaec9f,2
1626
+ np.float64,0x3fe956524772aca5,0xbfb9f4cabed7081d,2
1627
+ np.float64,0xefd11af7dfa24,0xc0733ae570ed2552,2
1628
+ np.float64,0x1690fff02d221,0xc0734b51a56c2980,2
1629
+ np.float64,0x7fd2e547a825ca8e,0x40733b992d6d9635,2
evalkit_tf437/lib/python3.10/site-packages/numpy/core/tests/test_overrides.py ADDED
@@ -0,0 +1,759 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import inspect
2
+ import sys
3
+ import os
4
+ import tempfile
5
+ from io import StringIO
6
+ from unittest import mock
7
+
8
+ import numpy as np
9
+ from numpy.testing import (
10
+ assert_, assert_equal, assert_raises, assert_raises_regex)
11
+ from numpy.core.overrides import (
12
+ _get_implementing_args, array_function_dispatch,
13
+ verify_matching_signatures)
14
+ from numpy.compat import pickle
15
+ import pytest
16
+
17
+
18
+ def _return_not_implemented(self, *args, **kwargs):
19
+ return NotImplemented
20
+
21
+
22
+ # need to define this at the top level to test pickling
23
+ @array_function_dispatch(lambda array: (array,))
24
+ def dispatched_one_arg(array):
25
+ """Docstring."""
26
+ return 'original'
27
+
28
+
29
+ @array_function_dispatch(lambda array1, array2: (array1, array2))
30
+ def dispatched_two_arg(array1, array2):
31
+ """Docstring."""
32
+ return 'original'
33
+
34
+
35
+ class TestGetImplementingArgs:
36
+
37
+ def test_ndarray(self):
38
+ array = np.array(1)
39
+
40
+ args = _get_implementing_args([array])
41
+ assert_equal(list(args), [array])
42
+
43
+ args = _get_implementing_args([array, array])
44
+ assert_equal(list(args), [array])
45
+
46
+ args = _get_implementing_args([array, 1])
47
+ assert_equal(list(args), [array])
48
+
49
+ args = _get_implementing_args([1, array])
50
+ assert_equal(list(args), [array])
51
+
52
+ def test_ndarray_subclasses(self):
53
+
54
+ class OverrideSub(np.ndarray):
55
+ __array_function__ = _return_not_implemented
56
+
57
+ class NoOverrideSub(np.ndarray):
58
+ pass
59
+
60
+ array = np.array(1).view(np.ndarray)
61
+ override_sub = np.array(1).view(OverrideSub)
62
+ no_override_sub = np.array(1).view(NoOverrideSub)
63
+
64
+ args = _get_implementing_args([array, override_sub])
65
+ assert_equal(list(args), [override_sub, array])
66
+
67
+ args = _get_implementing_args([array, no_override_sub])
68
+ assert_equal(list(args), [no_override_sub, array])
69
+
70
+ args = _get_implementing_args(
71
+ [override_sub, no_override_sub])
72
+ assert_equal(list(args), [override_sub, no_override_sub])
73
+
74
+ def test_ndarray_and_duck_array(self):
75
+
76
+ class Other:
77
+ __array_function__ = _return_not_implemented
78
+
79
+ array = np.array(1)
80
+ other = Other()
81
+
82
+ args = _get_implementing_args([other, array])
83
+ assert_equal(list(args), [other, array])
84
+
85
+ args = _get_implementing_args([array, other])
86
+ assert_equal(list(args), [array, other])
87
+
88
+ def test_ndarray_subclass_and_duck_array(self):
89
+
90
+ class OverrideSub(np.ndarray):
91
+ __array_function__ = _return_not_implemented
92
+
93
+ class Other:
94
+ __array_function__ = _return_not_implemented
95
+
96
+ array = np.array(1)
97
+ subarray = np.array(1).view(OverrideSub)
98
+ other = Other()
99
+
100
+ assert_equal(_get_implementing_args([array, subarray, other]),
101
+ [subarray, array, other])
102
+ assert_equal(_get_implementing_args([array, other, subarray]),
103
+ [subarray, array, other])
104
+
105
+ def test_many_duck_arrays(self):
106
+
107
+ class A:
108
+ __array_function__ = _return_not_implemented
109
+
110
+ class B(A):
111
+ __array_function__ = _return_not_implemented
112
+
113
+ class C(A):
114
+ __array_function__ = _return_not_implemented
115
+
116
+ class D:
117
+ __array_function__ = _return_not_implemented
118
+
119
+ a = A()
120
+ b = B()
121
+ c = C()
122
+ d = D()
123
+
124
+ assert_equal(_get_implementing_args([1]), [])
125
+ assert_equal(_get_implementing_args([a]), [a])
126
+ assert_equal(_get_implementing_args([a, 1]), [a])
127
+ assert_equal(_get_implementing_args([a, a, a]), [a])
128
+ assert_equal(_get_implementing_args([a, d, a]), [a, d])
129
+ assert_equal(_get_implementing_args([a, b]), [b, a])
130
+ assert_equal(_get_implementing_args([b, a]), [b, a])
131
+ assert_equal(_get_implementing_args([a, b, c]), [b, c, a])
132
+ assert_equal(_get_implementing_args([a, c, b]), [c, b, a])
133
+
134
+ def test_too_many_duck_arrays(self):
135
+ namespace = dict(__array_function__=_return_not_implemented)
136
+ types = [type('A' + str(i), (object,), namespace) for i in range(33)]
137
+ relevant_args = [t() for t in types]
138
+
139
+ actual = _get_implementing_args(relevant_args[:32])
140
+ assert_equal(actual, relevant_args[:32])
141
+
142
+ with assert_raises_regex(TypeError, 'distinct argument types'):
143
+ _get_implementing_args(relevant_args)
144
+
145
+
146
+ class TestNDArrayArrayFunction:
147
+
148
+ def test_method(self):
149
+
150
+ class Other:
151
+ __array_function__ = _return_not_implemented
152
+
153
+ class NoOverrideSub(np.ndarray):
154
+ pass
155
+
156
+ class OverrideSub(np.ndarray):
157
+ __array_function__ = _return_not_implemented
158
+
159
+ array = np.array([1])
160
+ other = Other()
161
+ no_override_sub = array.view(NoOverrideSub)
162
+ override_sub = array.view(OverrideSub)
163
+
164
+ result = array.__array_function__(func=dispatched_two_arg,
165
+ types=(np.ndarray,),
166
+ args=(array, 1.), kwargs={})
167
+ assert_equal(result, 'original')
168
+
169
+ result = array.__array_function__(func=dispatched_two_arg,
170
+ types=(np.ndarray, Other),
171
+ args=(array, other), kwargs={})
172
+ assert_(result is NotImplemented)
173
+
174
+ result = array.__array_function__(func=dispatched_two_arg,
175
+ types=(np.ndarray, NoOverrideSub),
176
+ args=(array, no_override_sub),
177
+ kwargs={})
178
+ assert_equal(result, 'original')
179
+
180
+ result = array.__array_function__(func=dispatched_two_arg,
181
+ types=(np.ndarray, OverrideSub),
182
+ args=(array, override_sub),
183
+ kwargs={})
184
+ assert_equal(result, 'original')
185
+
186
+ with assert_raises_regex(TypeError, 'no implementation found'):
187
+ np.concatenate((array, other))
188
+
189
+ expected = np.concatenate((array, array))
190
+ result = np.concatenate((array, no_override_sub))
191
+ assert_equal(result, expected.view(NoOverrideSub))
192
+ result = np.concatenate((array, override_sub))
193
+ assert_equal(result, expected.view(OverrideSub))
194
+
195
+ def test_no_wrapper(self):
196
+ # This shouldn't happen unless a user intentionally calls
197
+ # __array_function__ with invalid arguments, but check that we raise
198
+ # an appropriate error all the same.
199
+ array = np.array(1)
200
+ func = lambda x: x
201
+ with assert_raises_regex(AttributeError, '_implementation'):
202
+ array.__array_function__(func=func, types=(np.ndarray,),
203
+ args=(array,), kwargs={})
204
+
205
+
206
+ class TestArrayFunctionDispatch:
207
+
208
+ def test_pickle(self):
209
+ for proto in range(2, pickle.HIGHEST_PROTOCOL + 1):
210
+ roundtripped = pickle.loads(
211
+ pickle.dumps(dispatched_one_arg, protocol=proto))
212
+ assert_(roundtripped is dispatched_one_arg)
213
+
214
+ def test_name_and_docstring(self):
215
+ assert_equal(dispatched_one_arg.__name__, 'dispatched_one_arg')
216
+ if sys.flags.optimize < 2:
217
+ assert_equal(dispatched_one_arg.__doc__, 'Docstring.')
218
+
219
+ def test_interface(self):
220
+
221
+ class MyArray:
222
+ def __array_function__(self, func, types, args, kwargs):
223
+ return (self, func, types, args, kwargs)
224
+
225
+ original = MyArray()
226
+ (obj, func, types, args, kwargs) = dispatched_one_arg(original)
227
+ assert_(obj is original)
228
+ assert_(func is dispatched_one_arg)
229
+ assert_equal(set(types), {MyArray})
230
+ # assert_equal uses the overloaded np.iscomplexobj() internally
231
+ assert_(args == (original,))
232
+ assert_equal(kwargs, {})
233
+
234
+ def test_not_implemented(self):
235
+
236
+ class MyArray:
237
+ def __array_function__(self, func, types, args, kwargs):
238
+ return NotImplemented
239
+
240
+ array = MyArray()
241
+ with assert_raises_regex(TypeError, 'no implementation found'):
242
+ dispatched_one_arg(array)
243
+
244
+ def test_where_dispatch(self):
245
+
246
+ class DuckArray:
247
+ def __array_function__(self, ufunc, method, *inputs, **kwargs):
248
+ return "overridden"
249
+
250
+ array = np.array(1)
251
+ duck_array = DuckArray()
252
+
253
+ result = np.std(array, where=duck_array)
254
+
255
+ assert_equal(result, "overridden")
256
+
257
+
258
+ class TestVerifyMatchingSignatures:
259
+
260
+ def test_verify_matching_signatures(self):
261
+
262
+ verify_matching_signatures(lambda x: 0, lambda x: 0)
263
+ verify_matching_signatures(lambda x=None: 0, lambda x=None: 0)
264
+ verify_matching_signatures(lambda x=1: 0, lambda x=None: 0)
265
+
266
+ with assert_raises(RuntimeError):
267
+ verify_matching_signatures(lambda a: 0, lambda b: 0)
268
+ with assert_raises(RuntimeError):
269
+ verify_matching_signatures(lambda x: 0, lambda x=None: 0)
270
+ with assert_raises(RuntimeError):
271
+ verify_matching_signatures(lambda x=None: 0, lambda y=None: 0)
272
+ with assert_raises(RuntimeError):
273
+ verify_matching_signatures(lambda x=1: 0, lambda y=1: 0)
274
+
275
+ def test_array_function_dispatch(self):
276
+
277
+ with assert_raises(RuntimeError):
278
+ @array_function_dispatch(lambda x: (x,))
279
+ def f(y):
280
+ pass
281
+
282
+ # should not raise
283
+ @array_function_dispatch(lambda x: (x,), verify=False)
284
+ def f(y):
285
+ pass
286
+
287
+
288
+ def _new_duck_type_and_implements():
289
+ """Create a duck array type and implements functions."""
290
+ HANDLED_FUNCTIONS = {}
291
+
292
+ class MyArray:
293
+ def __array_function__(self, func, types, args, kwargs):
294
+ if func not in HANDLED_FUNCTIONS:
295
+ return NotImplemented
296
+ if not all(issubclass(t, MyArray) for t in types):
297
+ return NotImplemented
298
+ return HANDLED_FUNCTIONS[func](*args, **kwargs)
299
+
300
+ def implements(numpy_function):
301
+ """Register an __array_function__ implementations."""
302
+ def decorator(func):
303
+ HANDLED_FUNCTIONS[numpy_function] = func
304
+ return func
305
+ return decorator
306
+
307
+ return (MyArray, implements)
308
+
309
+
310
+ class TestArrayFunctionImplementation:
311
+
312
+ def test_one_arg(self):
313
+ MyArray, implements = _new_duck_type_and_implements()
314
+
315
+ @implements(dispatched_one_arg)
316
+ def _(array):
317
+ return 'myarray'
318
+
319
+ assert_equal(dispatched_one_arg(1), 'original')
320
+ assert_equal(dispatched_one_arg(MyArray()), 'myarray')
321
+
322
+ def test_optional_args(self):
323
+ MyArray, implements = _new_duck_type_and_implements()
324
+
325
+ @array_function_dispatch(lambda array, option=None: (array,))
326
+ def func_with_option(array, option='default'):
327
+ return option
328
+
329
+ @implements(func_with_option)
330
+ def my_array_func_with_option(array, new_option='myarray'):
331
+ return new_option
332
+
333
+ # we don't need to implement every option on __array_function__
334
+ # implementations
335
+ assert_equal(func_with_option(1), 'default')
336
+ assert_equal(func_with_option(1, option='extra'), 'extra')
337
+ assert_equal(func_with_option(MyArray()), 'myarray')
338
+ with assert_raises(TypeError):
339
+ func_with_option(MyArray(), option='extra')
340
+
341
+ # but new options on implementations can't be used
342
+ result = my_array_func_with_option(MyArray(), new_option='yes')
343
+ assert_equal(result, 'yes')
344
+ with assert_raises(TypeError):
345
+ func_with_option(MyArray(), new_option='no')
346
+
347
+ def test_not_implemented(self):
348
+ MyArray, implements = _new_duck_type_and_implements()
349
+
350
+ @array_function_dispatch(lambda array: (array,), module='my')
351
+ def func(array):
352
+ return array
353
+
354
+ array = np.array(1)
355
+ assert_(func(array) is array)
356
+ assert_equal(func.__module__, 'my')
357
+
358
+ with assert_raises_regex(
359
+ TypeError, "no implementation found for 'my.func'"):
360
+ func(MyArray())
361
+
362
+ @pytest.mark.parametrize("name", ["concatenate", "mean", "asarray"])
363
+ def test_signature_error_message_simple(self, name):
364
+ func = getattr(np, name)
365
+ try:
366
+ # all of these functions need an argument:
367
+ func()
368
+ except TypeError as e:
369
+ exc = e
370
+
371
+ assert exc.args[0].startswith(f"{name}()")
372
+
373
+ def test_signature_error_message(self):
374
+ # The lambda function will be named "<lambda>", but the TypeError
375
+ # should show the name as "func"
376
+ def _dispatcher():
377
+ return ()
378
+
379
+ @array_function_dispatch(_dispatcher)
380
+ def func():
381
+ pass
382
+
383
+ try:
384
+ func._implementation(bad_arg=3)
385
+ except TypeError as e:
386
+ expected_exception = e
387
+
388
+ try:
389
+ func(bad_arg=3)
390
+ raise AssertionError("must fail")
391
+ except TypeError as exc:
392
+ if exc.args[0].startswith("_dispatcher"):
393
+ # We replace the qualname currently, but it used `__name__`
394
+ # (relevant functions have the same name and qualname anyway)
395
+ pytest.skip("Python version is not using __qualname__ for "
396
+ "TypeError formatting.")
397
+
398
+ assert exc.args == expected_exception.args
399
+
400
+ @pytest.mark.parametrize("value", [234, "this func is not replaced"])
401
+ def test_dispatcher_error(self, value):
402
+ # If the dispatcher raises an error, we must not attempt to mutate it
403
+ error = TypeError(value)
404
+
405
+ def dispatcher():
406
+ raise error
407
+
408
+ @array_function_dispatch(dispatcher)
409
+ def func():
410
+ return 3
411
+
412
+ try:
413
+ func()
414
+ raise AssertionError("must fail")
415
+ except TypeError as exc:
416
+ assert exc is error # unmodified exception
417
+
418
+ def test_properties(self):
419
+ # Check that str and repr are sensible
420
+ func = dispatched_two_arg
421
+ assert str(func) == str(func._implementation)
422
+ repr_no_id = repr(func).split("at ")[0]
423
+ repr_no_id_impl = repr(func._implementation).split("at ")[0]
424
+ assert repr_no_id == repr_no_id_impl
425
+
426
+ @pytest.mark.parametrize("func", [
427
+ lambda x, y: 0, # no like argument
428
+ lambda like=None: 0, # not keyword only
429
+ lambda *, like=None, a=3: 0, # not last (not that it matters)
430
+ ])
431
+ def test_bad_like_sig(self, func):
432
+ # We sanity check the signature, and these should fail.
433
+ with pytest.raises(RuntimeError):
434
+ array_function_dispatch()(func)
435
+
436
+ def test_bad_like_passing(self):
437
+ # Cover internal sanity check for passing like as first positional arg
438
+ def func(*, like=None):
439
+ pass
440
+
441
+ func_with_like = array_function_dispatch()(func)
442
+ with pytest.raises(TypeError):
443
+ func_with_like()
444
+ with pytest.raises(TypeError):
445
+ func_with_like(like=234)
446
+
447
+ def test_too_many_args(self):
448
+ # Mainly a unit-test to increase coverage
449
+ objs = []
450
+ for i in range(40):
451
+ class MyArr:
452
+ def __array_function__(self, *args, **kwargs):
453
+ return NotImplemented
454
+
455
+ objs.append(MyArr())
456
+
457
+ def _dispatch(*args):
458
+ return args
459
+
460
+ @array_function_dispatch(_dispatch)
461
+ def func(*args):
462
+ pass
463
+
464
+ with pytest.raises(TypeError, match="maximum number"):
465
+ func(*objs)
466
+
467
+
468
+
469
+ class TestNDArrayMethods:
470
+
471
+ def test_repr(self):
472
+ # gh-12162: should still be defined even if __array_function__ doesn't
473
+ # implement np.array_repr()
474
+
475
+ class MyArray(np.ndarray):
476
+ def __array_function__(*args, **kwargs):
477
+ return NotImplemented
478
+
479
+ array = np.array(1).view(MyArray)
480
+ assert_equal(repr(array), 'MyArray(1)')
481
+ assert_equal(str(array), '1')
482
+
483
+
484
+ class TestNumPyFunctions:
485
+
486
+ def test_set_module(self):
487
+ assert_equal(np.sum.__module__, 'numpy')
488
+ assert_equal(np.char.equal.__module__, 'numpy.char')
489
+ assert_equal(np.fft.fft.__module__, 'numpy.fft')
490
+ assert_equal(np.linalg.solve.__module__, 'numpy.linalg')
491
+
492
+ def test_inspect_sum(self):
493
+ signature = inspect.signature(np.sum)
494
+ assert_('axis' in signature.parameters)
495
+
496
+ def test_override_sum(self):
497
+ MyArray, implements = _new_duck_type_and_implements()
498
+
499
+ @implements(np.sum)
500
+ def _(array):
501
+ return 'yes'
502
+
503
+ assert_equal(np.sum(MyArray()), 'yes')
504
+
505
+ def test_sum_on_mock_array(self):
506
+
507
+ # We need a proxy for mocks because __array_function__ is only looked
508
+ # up in the class dict
509
+ class ArrayProxy:
510
+ def __init__(self, value):
511
+ self.value = value
512
+ def __array_function__(self, *args, **kwargs):
513
+ return self.value.__array_function__(*args, **kwargs)
514
+ def __array__(self, *args, **kwargs):
515
+ return self.value.__array__(*args, **kwargs)
516
+
517
+ proxy = ArrayProxy(mock.Mock(spec=ArrayProxy))
518
+ proxy.value.__array_function__.return_value = 1
519
+ result = np.sum(proxy)
520
+ assert_equal(result, 1)
521
+ proxy.value.__array_function__.assert_called_once_with(
522
+ np.sum, (ArrayProxy,), (proxy,), {})
523
+ proxy.value.__array__.assert_not_called()
524
+
525
+ def test_sum_forwarding_implementation(self):
526
+
527
+ class MyArray(np.ndarray):
528
+
529
+ def sum(self, axis, out):
530
+ return 'summed'
531
+
532
+ def __array_function__(self, func, types, args, kwargs):
533
+ return super().__array_function__(func, types, args, kwargs)
534
+
535
+ # note: the internal implementation of np.sum() calls the .sum() method
536
+ array = np.array(1).view(MyArray)
537
+ assert_equal(np.sum(array), 'summed')
538
+
539
+
540
+ class TestArrayLike:
541
+ def setup_method(self):
542
+ class MyArray():
543
+ def __init__(self, function=None):
544
+ self.function = function
545
+
546
+ def __array_function__(self, func, types, args, kwargs):
547
+ assert func is getattr(np, func.__name__)
548
+ try:
549
+ my_func = getattr(self, func.__name__)
550
+ except AttributeError:
551
+ return NotImplemented
552
+ return my_func(*args, **kwargs)
553
+
554
+ self.MyArray = MyArray
555
+
556
+ class MyNoArrayFunctionArray():
557
+ def __init__(self, function=None):
558
+ self.function = function
559
+
560
+ self.MyNoArrayFunctionArray = MyNoArrayFunctionArray
561
+
562
+ def add_method(self, name, arr_class, enable_value_error=False):
563
+ def _definition(*args, **kwargs):
564
+ # Check that `like=` isn't propagated downstream
565
+ assert 'like' not in kwargs
566
+
567
+ if enable_value_error and 'value_error' in kwargs:
568
+ raise ValueError
569
+
570
+ return arr_class(getattr(arr_class, name))
571
+ setattr(arr_class, name, _definition)
572
+
573
+ def func_args(*args, **kwargs):
574
+ return args, kwargs
575
+
576
+ def test_array_like_not_implemented(self):
577
+ self.add_method('array', self.MyArray)
578
+
579
+ ref = self.MyArray.array()
580
+
581
+ with assert_raises_regex(TypeError, 'no implementation found'):
582
+ array_like = np.asarray(1, like=ref)
583
+
584
+ _array_tests = [
585
+ ('array', *func_args((1,))),
586
+ ('asarray', *func_args((1,))),
587
+ ('asanyarray', *func_args((1,))),
588
+ ('ascontiguousarray', *func_args((2, 3))),
589
+ ('asfortranarray', *func_args((2, 3))),
590
+ ('require', *func_args((np.arange(6).reshape(2, 3),),
591
+ requirements=['A', 'F'])),
592
+ ('empty', *func_args((1,))),
593
+ ('full', *func_args((1,), 2)),
594
+ ('ones', *func_args((1,))),
595
+ ('zeros', *func_args((1,))),
596
+ ('arange', *func_args(3)),
597
+ ('frombuffer', *func_args(b'\x00' * 8, dtype=int)),
598
+ ('fromiter', *func_args(range(3), dtype=int)),
599
+ ('fromstring', *func_args('1,2', dtype=int, sep=',')),
600
+ ('loadtxt', *func_args(lambda: StringIO('0 1\n2 3'))),
601
+ ('genfromtxt', *func_args(lambda: StringIO('1,2.1'),
602
+ dtype=[('int', 'i8'), ('float', 'f8')],
603
+ delimiter=',')),
604
+ ]
605
+
606
+ @pytest.mark.parametrize('function, args, kwargs', _array_tests)
607
+ @pytest.mark.parametrize('numpy_ref', [True, False])
608
+ def test_array_like(self, function, args, kwargs, numpy_ref):
609
+ self.add_method('array', self.MyArray)
610
+ self.add_method(function, self.MyArray)
611
+ np_func = getattr(np, function)
612
+ my_func = getattr(self.MyArray, function)
613
+
614
+ if numpy_ref is True:
615
+ ref = np.array(1)
616
+ else:
617
+ ref = self.MyArray.array()
618
+
619
+ like_args = tuple(a() if callable(a) else a for a in args)
620
+ array_like = np_func(*like_args, **kwargs, like=ref)
621
+
622
+ if numpy_ref is True:
623
+ assert type(array_like) is np.ndarray
624
+
625
+ np_args = tuple(a() if callable(a) else a for a in args)
626
+ np_arr = np_func(*np_args, **kwargs)
627
+
628
+ # Special-case np.empty to ensure values match
629
+ if function == "empty":
630
+ np_arr.fill(1)
631
+ array_like.fill(1)
632
+
633
+ assert_equal(array_like, np_arr)
634
+ else:
635
+ assert type(array_like) is self.MyArray
636
+ assert array_like.function is my_func
637
+
638
+ @pytest.mark.parametrize('function, args, kwargs', _array_tests)
639
+ @pytest.mark.parametrize('ref', [1, [1], "MyNoArrayFunctionArray"])
640
+ def test_no_array_function_like(self, function, args, kwargs, ref):
641
+ self.add_method('array', self.MyNoArrayFunctionArray)
642
+ self.add_method(function, self.MyNoArrayFunctionArray)
643
+ np_func = getattr(np, function)
644
+
645
+ # Instantiate ref if it's the MyNoArrayFunctionArray class
646
+ if ref == "MyNoArrayFunctionArray":
647
+ ref = self.MyNoArrayFunctionArray.array()
648
+
649
+ like_args = tuple(a() if callable(a) else a for a in args)
650
+
651
+ with assert_raises_regex(TypeError,
652
+ 'The `like` argument must be an array-like that implements'):
653
+ np_func(*like_args, **kwargs, like=ref)
654
+
655
+ @pytest.mark.parametrize('numpy_ref', [True, False])
656
+ def test_array_like_fromfile(self, numpy_ref):
657
+ self.add_method('array', self.MyArray)
658
+ self.add_method("fromfile", self.MyArray)
659
+
660
+ if numpy_ref is True:
661
+ ref = np.array(1)
662
+ else:
663
+ ref = self.MyArray.array()
664
+
665
+ data = np.random.random(5)
666
+
667
+ with tempfile.TemporaryDirectory() as tmpdir:
668
+ fname = os.path.join(tmpdir, "testfile")
669
+ data.tofile(fname)
670
+
671
+ array_like = np.fromfile(fname, like=ref)
672
+ if numpy_ref is True:
673
+ assert type(array_like) is np.ndarray
674
+ np_res = np.fromfile(fname, like=ref)
675
+ assert_equal(np_res, data)
676
+ assert_equal(array_like, np_res)
677
+ else:
678
+ assert type(array_like) is self.MyArray
679
+ assert array_like.function is self.MyArray.fromfile
680
+
681
+ def test_exception_handling(self):
682
+ self.add_method('array', self.MyArray, enable_value_error=True)
683
+
684
+ ref = self.MyArray.array()
685
+
686
+ with assert_raises(TypeError):
687
+ # Raises the error about `value_error` being invalid first
688
+ np.array(1, value_error=True, like=ref)
689
+
690
+ @pytest.mark.parametrize('function, args, kwargs', _array_tests)
691
+ def test_like_as_none(self, function, args, kwargs):
692
+ self.add_method('array', self.MyArray)
693
+ self.add_method(function, self.MyArray)
694
+ np_func = getattr(np, function)
695
+
696
+ like_args = tuple(a() if callable(a) else a for a in args)
697
+ # required for loadtxt and genfromtxt to init w/o error.
698
+ like_args_exp = tuple(a() if callable(a) else a for a in args)
699
+
700
+ array_like = np_func(*like_args, **kwargs, like=None)
701
+ expected = np_func(*like_args_exp, **kwargs)
702
+ # Special-case np.empty to ensure values match
703
+ if function == "empty":
704
+ array_like.fill(1)
705
+ expected.fill(1)
706
+ assert_equal(array_like, expected)
707
+
708
+
709
+ def test_function_like():
710
+ # We provide a `__get__` implementation, make sure it works
711
+ assert type(np.mean) is np.core._multiarray_umath._ArrayFunctionDispatcher
712
+
713
+ class MyClass:
714
+ def __array__(self):
715
+ # valid argument to mean:
716
+ return np.arange(3)
717
+
718
+ func1 = staticmethod(np.mean)
719
+ func2 = np.mean
720
+ func3 = classmethod(np.mean)
721
+
722
+ m = MyClass()
723
+ assert m.func1([10]) == 10
724
+ assert m.func2() == 1 # mean of the arange
725
+ with pytest.raises(TypeError, match="unsupported operand type"):
726
+ # Tries to operate on the class
727
+ m.func3()
728
+
729
+ # Manual binding also works (the above may shortcut):
730
+ bound = np.mean.__get__(m, MyClass)
731
+ assert bound() == 1
732
+
733
+ bound = np.mean.__get__(None, MyClass) # unbound actually
734
+ assert bound([10]) == 10
735
+
736
+ bound = np.mean.__get__(MyClass) # classmethod
737
+ with pytest.raises(TypeError, match="unsupported operand type"):
738
+ bound()
739
+
740
+
741
+ def test_scipy_trapz_support_shim():
742
+ # SciPy 1.10 and earlier "clone" trapz in this way, so we have a
743
+ # support shim in place: https://github.com/scipy/scipy/issues/17811
744
+ # That should be removed eventually. This test copies what SciPy does.
745
+ # Hopefully removable 1 year after SciPy 1.11; shim added to NumPy 1.25.
746
+ import types
747
+ import functools
748
+
749
+ def _copy_func(f):
750
+ # Based on http://stackoverflow.com/a/6528148/190597 (Glenn Maynard)
751
+ g = types.FunctionType(f.__code__, f.__globals__, name=f.__name__,
752
+ argdefs=f.__defaults__, closure=f.__closure__)
753
+ g = functools.update_wrapper(g, f)
754
+ g.__kwdefaults__ = f.__kwdefaults__
755
+ return g
756
+
757
+ trapezoid = _copy_func(np.trapz)
758
+
759
+ assert np.trapz([1, 2]) == trapezoid([1, 2])
evalkit_tf437/lib/python3.10/site-packages/numpy/core/tests/test_scalarprint.py ADDED
@@ -0,0 +1,382 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """ Test printing of scalar types.
2
+
3
+ """
4
+ import code
5
+ import platform
6
+ import pytest
7
+ import sys
8
+
9
+ from tempfile import TemporaryFile
10
+ import numpy as np
11
+ from numpy.testing import assert_, assert_equal, assert_raises, IS_MUSL
12
+
13
+ class TestRealScalars:
14
+ def test_str(self):
15
+ svals = [0.0, -0.0, 1, -1, np.inf, -np.inf, np.nan]
16
+ styps = [np.float16, np.float32, np.float64, np.longdouble]
17
+ wanted = [
18
+ ['0.0', '0.0', '0.0', '0.0' ],
19
+ ['-0.0', '-0.0', '-0.0', '-0.0'],
20
+ ['1.0', '1.0', '1.0', '1.0' ],
21
+ ['-1.0', '-1.0', '-1.0', '-1.0'],
22
+ ['inf', 'inf', 'inf', 'inf' ],
23
+ ['-inf', '-inf', '-inf', '-inf'],
24
+ ['nan', 'nan', 'nan', 'nan']]
25
+
26
+ for wants, val in zip(wanted, svals):
27
+ for want, styp in zip(wants, styps):
28
+ msg = 'for str({}({}))'.format(np.dtype(styp).name, repr(val))
29
+ assert_equal(str(styp(val)), want, err_msg=msg)
30
+
31
+ def test_scalar_cutoffs(self):
32
+ # test that both the str and repr of np.float64 behaves
33
+ # like python floats in python3.
34
+ def check(v):
35
+ assert_equal(str(np.float64(v)), str(v))
36
+ assert_equal(str(np.float64(v)), repr(v))
37
+ assert_equal(repr(np.float64(v)), repr(v))
38
+ assert_equal(repr(np.float64(v)), str(v))
39
+
40
+ # check we use the same number of significant digits
41
+ check(1.12345678901234567890)
42
+ check(0.0112345678901234567890)
43
+
44
+ # check switch from scientific output to positional and back
45
+ check(1e-5)
46
+ check(1e-4)
47
+ check(1e15)
48
+ check(1e16)
49
+
50
+ def test_py2_float_print(self):
51
+ # gh-10753
52
+ # In python2, the python float type implements an obsolete method
53
+ # tp_print, which overrides tp_repr and tp_str when using "print" to
54
+ # output to a "real file" (ie, not a StringIO). Make sure we don't
55
+ # inherit it.
56
+ x = np.double(0.1999999999999)
57
+ with TemporaryFile('r+t') as f:
58
+ print(x, file=f)
59
+ f.seek(0)
60
+ output = f.read()
61
+ assert_equal(output, str(x) + '\n')
62
+ # In python2 the value float('0.1999999999999') prints with reduced
63
+ # precision as '0.2', but we want numpy's np.double('0.1999999999999')
64
+ # to print the unique value, '0.1999999999999'.
65
+
66
+ # gh-11031
67
+ # Only in the python2 interactive shell and when stdout is a "real"
68
+ # file, the output of the last command is printed to stdout without
69
+ # Py_PRINT_RAW (unlike the print statement) so `>>> x` and `>>> print
70
+ # x` are potentially different. Make sure they are the same. The only
71
+ # way I found to get prompt-like output is using an actual prompt from
72
+ # the 'code' module. Again, must use tempfile to get a "real" file.
73
+
74
+ # dummy user-input which enters one line and then ctrl-Ds.
75
+ def userinput():
76
+ yield 'np.sqrt(2)'
77
+ raise EOFError
78
+ gen = userinput()
79
+ input_func = lambda prompt="": next(gen)
80
+
81
+ with TemporaryFile('r+t') as fo, TemporaryFile('r+t') as fe:
82
+ orig_stdout, orig_stderr = sys.stdout, sys.stderr
83
+ sys.stdout, sys.stderr = fo, fe
84
+
85
+ code.interact(local={'np': np}, readfunc=input_func, banner='')
86
+
87
+ sys.stdout, sys.stderr = orig_stdout, orig_stderr
88
+
89
+ fo.seek(0)
90
+ capture = fo.read().strip()
91
+
92
+ assert_equal(capture, repr(np.sqrt(2)))
93
+
94
+ def test_dragon4(self):
95
+ # these tests are adapted from Ryan Juckett's dragon4 implementation,
96
+ # see dragon4.c for details.
97
+
98
+ fpos32 = lambda x, **k: np.format_float_positional(np.float32(x), **k)
99
+ fsci32 = lambda x, **k: np.format_float_scientific(np.float32(x), **k)
100
+ fpos64 = lambda x, **k: np.format_float_positional(np.float64(x), **k)
101
+ fsci64 = lambda x, **k: np.format_float_scientific(np.float64(x), **k)
102
+
103
+ preckwd = lambda prec: {'unique': False, 'precision': prec}
104
+
105
+ assert_equal(fpos32('1.0'), "1.")
106
+ assert_equal(fsci32('1.0'), "1.e+00")
107
+ assert_equal(fpos32('10.234'), "10.234")
108
+ assert_equal(fpos32('-10.234'), "-10.234")
109
+ assert_equal(fsci32('10.234'), "1.0234e+01")
110
+ assert_equal(fsci32('-10.234'), "-1.0234e+01")
111
+ assert_equal(fpos32('1000.0'), "1000.")
112
+ assert_equal(fpos32('1.0', precision=0), "1.")
113
+ assert_equal(fsci32('1.0', precision=0), "1.e+00")
114
+ assert_equal(fpos32('10.234', precision=0), "10.")
115
+ assert_equal(fpos32('-10.234', precision=0), "-10.")
116
+ assert_equal(fsci32('10.234', precision=0), "1.e+01")
117
+ assert_equal(fsci32('-10.234', precision=0), "-1.e+01")
118
+ assert_equal(fpos32('10.234', precision=2), "10.23")
119
+ assert_equal(fsci32('-10.234', precision=2), "-1.02e+01")
120
+ assert_equal(fsci64('9.9999999999999995e-08', **preckwd(16)),
121
+ '9.9999999999999995e-08')
122
+ assert_equal(fsci64('9.8813129168249309e-324', **preckwd(16)),
123
+ '9.8813129168249309e-324')
124
+ assert_equal(fsci64('9.9999999999999694e-311', **preckwd(16)),
125
+ '9.9999999999999694e-311')
126
+
127
+
128
+ # test rounding
129
+ # 3.1415927410 is closest float32 to np.pi
130
+ assert_equal(fpos32('3.14159265358979323846', **preckwd(10)),
131
+ "3.1415927410")
132
+ assert_equal(fsci32('3.14159265358979323846', **preckwd(10)),
133
+ "3.1415927410e+00")
134
+ assert_equal(fpos64('3.14159265358979323846', **preckwd(10)),
135
+ "3.1415926536")
136
+ assert_equal(fsci64('3.14159265358979323846', **preckwd(10)),
137
+ "3.1415926536e+00")
138
+ # 299792448 is closest float32 to 299792458
139
+ assert_equal(fpos32('299792458.0', **preckwd(5)), "299792448.00000")
140
+ assert_equal(fsci32('299792458.0', **preckwd(5)), "2.99792e+08")
141
+ assert_equal(fpos64('299792458.0', **preckwd(5)), "299792458.00000")
142
+ assert_equal(fsci64('299792458.0', **preckwd(5)), "2.99792e+08")
143
+
144
+ assert_equal(fpos32('3.14159265358979323846', **preckwd(25)),
145
+ "3.1415927410125732421875000")
146
+ assert_equal(fpos64('3.14159265358979323846', **preckwd(50)),
147
+ "3.14159265358979311599796346854418516159057617187500")
148
+ assert_equal(fpos64('3.14159265358979323846'), "3.141592653589793")
149
+
150
+
151
+ # smallest numbers
152
+ assert_equal(fpos32(0.5**(126 + 23), unique=False, precision=149),
153
+ "0.00000000000000000000000000000000000000000000140129846432"
154
+ "4817070923729583289916131280261941876515771757068283889791"
155
+ "08268586060148663818836212158203125")
156
+
157
+ assert_equal(fpos64(5e-324, unique=False, precision=1074),
158
+ "0.00000000000000000000000000000000000000000000000000000000"
159
+ "0000000000000000000000000000000000000000000000000000000000"
160
+ "0000000000000000000000000000000000000000000000000000000000"
161
+ "0000000000000000000000000000000000000000000000000000000000"
162
+ "0000000000000000000000000000000000000000000000000000000000"
163
+ "0000000000000000000000000000000000049406564584124654417656"
164
+ "8792868221372365059802614324764425585682500675507270208751"
165
+ "8652998363616359923797965646954457177309266567103559397963"
166
+ "9877479601078187812630071319031140452784581716784898210368"
167
+ "8718636056998730723050006387409153564984387312473397273169"
168
+ "6151400317153853980741262385655911710266585566867681870395"
169
+ "6031062493194527159149245532930545654440112748012970999954"
170
+ "1931989409080416563324524757147869014726780159355238611550"
171
+ "1348035264934720193790268107107491703332226844753335720832"
172
+ "4319360923828934583680601060115061698097530783422773183292"
173
+ "4790498252473077637592724787465608477820373446969953364701"
174
+ "7972677717585125660551199131504891101451037862738167250955"
175
+ "8373897335989936648099411642057026370902792427675445652290"
176
+ "87538682506419718265533447265625")
177
+
178
+ # largest numbers
179
+ f32x = np.finfo(np.float32).max
180
+ assert_equal(fpos32(f32x, **preckwd(0)),
181
+ "340282346638528859811704183484516925440.")
182
+ assert_equal(fpos64(np.finfo(np.float64).max, **preckwd(0)),
183
+ "1797693134862315708145274237317043567980705675258449965989"
184
+ "1747680315726078002853876058955863276687817154045895351438"
185
+ "2464234321326889464182768467546703537516986049910576551282"
186
+ "0762454900903893289440758685084551339423045832369032229481"
187
+ "6580855933212334827479782620414472316873817718091929988125"
188
+ "0404026184124858368.")
189
+ # Warning: In unique mode only the integer digits necessary for
190
+ # uniqueness are computed, the rest are 0.
191
+ assert_equal(fpos32(f32x),
192
+ "340282350000000000000000000000000000000.")
193
+
194
+ # Further tests of zero-padding vs rounding in different combinations
195
+ # of unique, fractional, precision, min_digits
196
+ # precision can only reduce digits, not add them.
197
+ # min_digits can only extend digits, not reduce them.
198
+ assert_equal(fpos32(f32x, unique=True, fractional=True, precision=0),
199
+ "340282350000000000000000000000000000000.")
200
+ assert_equal(fpos32(f32x, unique=True, fractional=True, precision=4),
201
+ "340282350000000000000000000000000000000.")
202
+ assert_equal(fpos32(f32x, unique=True, fractional=True, min_digits=0),
203
+ "340282346638528859811704183484516925440.")
204
+ assert_equal(fpos32(f32x, unique=True, fractional=True, min_digits=4),
205
+ "340282346638528859811704183484516925440.0000")
206
+ assert_equal(fpos32(f32x, unique=True, fractional=True,
207
+ min_digits=4, precision=4),
208
+ "340282346638528859811704183484516925440.0000")
209
+ assert_raises(ValueError, fpos32, f32x, unique=True, fractional=False,
210
+ precision=0)
211
+ assert_equal(fpos32(f32x, unique=True, fractional=False, precision=4),
212
+ "340300000000000000000000000000000000000.")
213
+ assert_equal(fpos32(f32x, unique=True, fractional=False, precision=20),
214
+ "340282350000000000000000000000000000000.")
215
+ assert_equal(fpos32(f32x, unique=True, fractional=False, min_digits=4),
216
+ "340282350000000000000000000000000000000.")
217
+ assert_equal(fpos32(f32x, unique=True, fractional=False,
218
+ min_digits=20),
219
+ "340282346638528859810000000000000000000.")
220
+ assert_equal(fpos32(f32x, unique=True, fractional=False,
221
+ min_digits=15),
222
+ "340282346638529000000000000000000000000.")
223
+ assert_equal(fpos32(f32x, unique=False, fractional=False, precision=4),
224
+ "340300000000000000000000000000000000000.")
225
+ # test that unique rounding is preserved when precision is supplied
226
+ # but no extra digits need to be printed (gh-18609)
227
+ a = np.float64.fromhex('-1p-97')
228
+ assert_equal(fsci64(a, unique=True), '-6.310887241768095e-30')
229
+ assert_equal(fsci64(a, unique=False, precision=15),
230
+ '-6.310887241768094e-30')
231
+ assert_equal(fsci64(a, unique=True, precision=15),
232
+ '-6.310887241768095e-30')
233
+ assert_equal(fsci64(a, unique=True, min_digits=15),
234
+ '-6.310887241768095e-30')
235
+ assert_equal(fsci64(a, unique=True, precision=15, min_digits=15),
236
+ '-6.310887241768095e-30')
237
+ # adds/remove digits in unique mode with unbiased rnding
238
+ assert_equal(fsci64(a, unique=True, precision=14),
239
+ '-6.31088724176809e-30')
240
+ assert_equal(fsci64(a, unique=True, min_digits=16),
241
+ '-6.3108872417680944e-30')
242
+ assert_equal(fsci64(a, unique=True, precision=16),
243
+ '-6.310887241768095e-30')
244
+ assert_equal(fsci64(a, unique=True, min_digits=14),
245
+ '-6.310887241768095e-30')
246
+ # test min_digits in unique mode with different rounding cases
247
+ assert_equal(fsci64('1e120', min_digits=3), '1.000e+120')
248
+ assert_equal(fsci64('1e100', min_digits=3), '1.000e+100')
249
+
250
+ # test trailing zeros
251
+ assert_equal(fpos32('1.0', unique=False, precision=3), "1.000")
252
+ assert_equal(fpos64('1.0', unique=False, precision=3), "1.000")
253
+ assert_equal(fsci32('1.0', unique=False, precision=3), "1.000e+00")
254
+ assert_equal(fsci64('1.0', unique=False, precision=3), "1.000e+00")
255
+ assert_equal(fpos32('1.5', unique=False, precision=3), "1.500")
256
+ assert_equal(fpos64('1.5', unique=False, precision=3), "1.500")
257
+ assert_equal(fsci32('1.5', unique=False, precision=3), "1.500e+00")
258
+ assert_equal(fsci64('1.5', unique=False, precision=3), "1.500e+00")
259
+ # gh-10713
260
+ assert_equal(fpos64('324', unique=False, precision=5,
261
+ fractional=False), "324.00")
262
+
263
+ def test_dragon4_interface(self):
264
+ tps = [np.float16, np.float32, np.float64]
265
+ # test is flaky for musllinux on np.float128
266
+ if hasattr(np, 'float128') and not IS_MUSL:
267
+ tps.append(np.float128)
268
+
269
+ fpos = np.format_float_positional
270
+ fsci = np.format_float_scientific
271
+
272
+ for tp in tps:
273
+ # test padding
274
+ assert_equal(fpos(tp('1.0'), pad_left=4, pad_right=4), " 1. ")
275
+ assert_equal(fpos(tp('-1.0'), pad_left=4, pad_right=4), " -1. ")
276
+ assert_equal(fpos(tp('-10.2'),
277
+ pad_left=4, pad_right=4), " -10.2 ")
278
+
279
+ # test exp_digits
280
+ assert_equal(fsci(tp('1.23e1'), exp_digits=5), "1.23e+00001")
281
+
282
+ # test fixed (non-unique) mode
283
+ assert_equal(fpos(tp('1.0'), unique=False, precision=4), "1.0000")
284
+ assert_equal(fsci(tp('1.0'), unique=False, precision=4),
285
+ "1.0000e+00")
286
+
287
+ # test trimming
288
+ # trim of 'k' or '.' only affects non-unique mode, since unique
289
+ # mode will not output trailing 0s.
290
+ assert_equal(fpos(tp('1.'), unique=False, precision=4, trim='k'),
291
+ "1.0000")
292
+
293
+ assert_equal(fpos(tp('1.'), unique=False, precision=4, trim='.'),
294
+ "1.")
295
+ assert_equal(fpos(tp('1.2'), unique=False, precision=4, trim='.'),
296
+ "1.2" if tp != np.float16 else "1.2002")
297
+
298
+ assert_equal(fpos(tp('1.'), unique=False, precision=4, trim='0'),
299
+ "1.0")
300
+ assert_equal(fpos(tp('1.2'), unique=False, precision=4, trim='0'),
301
+ "1.2" if tp != np.float16 else "1.2002")
302
+ assert_equal(fpos(tp('1.'), trim='0'), "1.0")
303
+
304
+ assert_equal(fpos(tp('1.'), unique=False, precision=4, trim='-'),
305
+ "1")
306
+ assert_equal(fpos(tp('1.2'), unique=False, precision=4, trim='-'),
307
+ "1.2" if tp != np.float16 else "1.2002")
308
+ assert_equal(fpos(tp('1.'), trim='-'), "1")
309
+ assert_equal(fpos(tp('1.001'), precision=1, trim='-'), "1")
310
+
311
+ @pytest.mark.skipif(not platform.machine().startswith("ppc64"),
312
+ reason="only applies to ppc float128 values")
313
+ def test_ppc64_ibm_double_double128(self):
314
+ # check that the precision decreases once we get into the subnormal
315
+ # range. Unlike float64, this starts around 1e-292 instead of 1e-308,
316
+ # which happens when the first double is normal and the second is
317
+ # subnormal.
318
+ x = np.float128('2.123123123123123123123123123123123e-286')
319
+ got = [str(x/np.float128('2e' + str(i))) for i in range(0,40)]
320
+ expected = [
321
+ "1.06156156156156156156156156156157e-286",
322
+ "1.06156156156156156156156156156158e-287",
323
+ "1.06156156156156156156156156156159e-288",
324
+ "1.0615615615615615615615615615616e-289",
325
+ "1.06156156156156156156156156156157e-290",
326
+ "1.06156156156156156156156156156156e-291",
327
+ "1.0615615615615615615615615615616e-292",
328
+ "1.0615615615615615615615615615615e-293",
329
+ "1.061561561561561561561561561562e-294",
330
+ "1.06156156156156156156156156155e-295",
331
+ "1.0615615615615615615615615616e-296",
332
+ "1.06156156156156156156156156e-297",
333
+ "1.06156156156156156156156157e-298",
334
+ "1.0615615615615615615615616e-299",
335
+ "1.06156156156156156156156e-300",
336
+ "1.06156156156156156156155e-301",
337
+ "1.0615615615615615615616e-302",
338
+ "1.061561561561561561562e-303",
339
+ "1.06156156156156156156e-304",
340
+ "1.0615615615615615618e-305",
341
+ "1.06156156156156156e-306",
342
+ "1.06156156156156157e-307",
343
+ "1.0615615615615616e-308",
344
+ "1.06156156156156e-309",
345
+ "1.06156156156157e-310",
346
+ "1.0615615615616e-311",
347
+ "1.06156156156e-312",
348
+ "1.06156156154e-313",
349
+ "1.0615615616e-314",
350
+ "1.06156156e-315",
351
+ "1.06156155e-316",
352
+ "1.061562e-317",
353
+ "1.06156e-318",
354
+ "1.06155e-319",
355
+ "1.0617e-320",
356
+ "1.06e-321",
357
+ "1.04e-322",
358
+ "1e-323",
359
+ "0.0",
360
+ "0.0"]
361
+ assert_equal(got, expected)
362
+
363
+ # Note: we follow glibc behavior, but it (or gcc) might not be right.
364
+ # In particular we can get two values that print the same but are not
365
+ # equal:
366
+ a = np.float128('2')/np.float128('3')
367
+ b = np.float128(str(a))
368
+ assert_equal(str(a), str(b))
369
+ assert_(a != b)
370
+
371
+ def float32_roundtrip(self):
372
+ # gh-9360
373
+ x = np.float32(1024 - 2**-14)
374
+ y = np.float32(1024 - 2**-13)
375
+ assert_(repr(x) != repr(y))
376
+ assert_equal(np.float32(repr(x)), x)
377
+ assert_equal(np.float32(repr(y)), y)
378
+
379
+ def float64_vs_python(self):
380
+ # gh-2643, gh-6136, gh-6908
381
+ assert_equal(repr(np.float64(0.1)), repr(0.1))
382
+ assert_(repr(np.float64(0.20000000000000004)) != repr(0.2))
evalkit_tf437/lib/python3.10/site-packages/numpy/fft/__init__.pyi ADDED
@@ -0,0 +1,29 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from numpy._pytesttester import PytestTester
2
+
3
+ from numpy.fft._pocketfft import (
4
+ fft as fft,
5
+ ifft as ifft,
6
+ rfft as rfft,
7
+ irfft as irfft,
8
+ hfft as hfft,
9
+ ihfft as ihfft,
10
+ rfftn as rfftn,
11
+ irfftn as irfftn,
12
+ rfft2 as rfft2,
13
+ irfft2 as irfft2,
14
+ fft2 as fft2,
15
+ ifft2 as ifft2,
16
+ fftn as fftn,
17
+ ifftn as ifftn,
18
+ )
19
+
20
+ from numpy.fft.helper import (
21
+ fftshift as fftshift,
22
+ ifftshift as ifftshift,
23
+ fftfreq as fftfreq,
24
+ rfftfreq as rfftfreq,
25
+ )
26
+
27
+ __all__: list[str]
28
+ __path__: list[str]
29
+ test: PytestTester
evalkit_tf437/lib/python3.10/site-packages/numpy/fft/tests/__init__.py ADDED
File without changes
evalkit_tf437/lib/python3.10/site-packages/numpy/tests/__pycache__/test_lazyloading.cpython-310.pyc ADDED
Binary file (968 Bytes). View file
 
evalkit_tf437/lib/python3.10/site-packages/numpy/tests/__pycache__/test_numpy_version.cpython-310.pyc ADDED
Binary file (1.55 kB). View file
 
evalkit_tf437/lib/python3.10/site-packages/numpy/tests/__pycache__/test_scripts.cpython-310.pyc ADDED
Binary file (1.6 kB). View file
 
evalkit_tf437/lib/python3.10/site-packages/numpy/tests/test__all__.py ADDED
@@ -0,0 +1,9 @@
 
 
 
 
 
 
 
 
 
 
1
+
2
+ import collections
3
+ import numpy as np
4
+
5
+
6
+ def test_no_duplicates_in_np__all__():
7
+ # Regression test for gh-10198.
8
+ dups = {k: v for k, v in collections.Counter(np.__all__).items() if v > 1}
9
+ assert len(dups) == 0
evalkit_tf437/lib/python3.10/site-packages/numpy/tests/test_numpy_config.py ADDED
@@ -0,0 +1,44 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """
2
+ Check the numpy config is valid.
3
+ """
4
+ import numpy as np
5
+ import pytest
6
+ from unittest.mock import Mock, patch
7
+
8
+ pytestmark = pytest.mark.skipif(
9
+ not hasattr(np.__config__, "_built_with_meson"),
10
+ reason="Requires Meson builds",
11
+ )
12
+
13
+
14
+ class TestNumPyConfigs:
15
+ REQUIRED_CONFIG_KEYS = [
16
+ "Compilers",
17
+ "Machine Information",
18
+ "Python Information",
19
+ ]
20
+
21
+ @patch("numpy.__config__._check_pyyaml")
22
+ def test_pyyaml_not_found(self, mock_yaml_importer):
23
+ mock_yaml_importer.side_effect = ModuleNotFoundError()
24
+ with pytest.warns(UserWarning):
25
+ np.show_config()
26
+
27
+ def test_dict_mode(self):
28
+ config = np.show_config(mode="dicts")
29
+
30
+ assert isinstance(config, dict)
31
+ assert all([key in config for key in self.REQUIRED_CONFIG_KEYS]), (
32
+ "Required key missing,"
33
+ " see index of `False` with `REQUIRED_CONFIG_KEYS`"
34
+ )
35
+
36
+ def test_invalid_mode(self):
37
+ with pytest.raises(AttributeError):
38
+ np.show_config(mode="foo")
39
+
40
+ def test_warn_to_add_tests(self):
41
+ assert len(np.__config__.DisplayModes) == 2, (
42
+ "New mode detected,"
43
+ " please add UT if applicable and increment this count"
44
+ )
evalkit_tf437/lib/python3.10/site-packages/numpy/tests/test_numpy_version.py ADDED
@@ -0,0 +1,41 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """
2
+ Check the numpy version is valid.
3
+
4
+ Note that a development version is marked by the presence of 'dev0' or '+'
5
+ in the version string, all else is treated as a release. The version string
6
+ itself is set from the output of ``git describe`` which relies on tags.
7
+
8
+ Examples
9
+ --------
10
+
11
+ Valid Development: 1.22.0.dev0 1.22.0.dev0+5-g7999db4df2 1.22.0+5-g7999db4df2
12
+ Valid Release: 1.21.0.rc1, 1.21.0.b1, 1.21.0
13
+ Invalid: 1.22.0.dev, 1.22.0.dev0-5-g7999db4dfB, 1.21.0.d1, 1.21.a
14
+
15
+ Note that a release is determined by the version string, which in turn
16
+ is controlled by the result of the ``git describe`` command.
17
+ """
18
+ import re
19
+
20
+ import numpy as np
21
+ from numpy.testing import assert_
22
+
23
+
24
+ def test_valid_numpy_version():
25
+ # Verify that the numpy version is a valid one (no .post suffix or other
26
+ # nonsense). See gh-6431 for an issue caused by an invalid version.
27
+ version_pattern = r"^[0-9]+\.[0-9]+\.[0-9]+(a[0-9]|b[0-9]|rc[0-9])?"
28
+ dev_suffix = r"(\.dev[0-9]+(\+git[0-9]+\.[0-9a-f]+)?)?"
29
+ res = re.match(version_pattern + dev_suffix + '$', np.__version__)
30
+
31
+ assert_(res is not None, np.__version__)
32
+
33
+
34
+ def test_short_version():
35
+ # Check numpy.short_version actually exists
36
+ if np.version.release:
37
+ assert_(np.__version__ == np.version.short_version,
38
+ "short_version mismatch in release version")
39
+ else:
40
+ assert_(np.__version__.split("+")[0] == np.version.short_version,
41
+ "short_version mismatch in development version")
evalkit_tf437/lib/python3.10/site-packages/numpy/tests/test_warnings.py ADDED
@@ -0,0 +1,74 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """
2
+ Tests which scan for certain occurrences in the code, they may not find
3
+ all of these occurrences but should catch almost all.
4
+ """
5
+ import pytest
6
+
7
+ from pathlib import Path
8
+ import ast
9
+ import tokenize
10
+ import numpy
11
+
12
+ class ParseCall(ast.NodeVisitor):
13
+ def __init__(self):
14
+ self.ls = []
15
+
16
+ def visit_Attribute(self, node):
17
+ ast.NodeVisitor.generic_visit(self, node)
18
+ self.ls.append(node.attr)
19
+
20
+ def visit_Name(self, node):
21
+ self.ls.append(node.id)
22
+
23
+
24
+ class FindFuncs(ast.NodeVisitor):
25
+ def __init__(self, filename):
26
+ super().__init__()
27
+ self.__filename = filename
28
+
29
+ def visit_Call(self, node):
30
+ p = ParseCall()
31
+ p.visit(node.func)
32
+ ast.NodeVisitor.generic_visit(self, node)
33
+
34
+ if p.ls[-1] == 'simplefilter' or p.ls[-1] == 'filterwarnings':
35
+ if node.args[0].value == "ignore":
36
+ raise AssertionError(
37
+ "warnings should have an appropriate stacklevel; found in "
38
+ "{} on line {}".format(self.__filename, node.lineno))
39
+
40
+ if p.ls[-1] == 'warn' and (
41
+ len(p.ls) == 1 or p.ls[-2] == 'warnings'):
42
+
43
+ if "testing/tests/test_warnings.py" == self.__filename:
44
+ # This file
45
+ return
46
+
47
+ # See if stacklevel exists:
48
+ if len(node.args) == 3:
49
+ return
50
+ args = {kw.arg for kw in node.keywords}
51
+ if "stacklevel" in args:
52
+ return
53
+ raise AssertionError(
54
+ "warnings should have an appropriate stacklevel; found in "
55
+ "{} on line {}".format(self.__filename, node.lineno))
56
+
57
+
58
+ @pytest.mark.slow
59
+ def test_warning_calls():
60
+ # combined "ignore" and stacklevel error
61
+ base = Path(numpy.__file__).parent
62
+
63
+ for path in base.rglob("*.py"):
64
+ if base / "testing" in path.parents:
65
+ continue
66
+ if path == base / "__init__.py":
67
+ continue
68
+ if path == base / "random" / "__init__.py":
69
+ continue
70
+ # use tokenize to auto-detect encoding on systems where no
71
+ # default encoding is defined (e.g. LANG='C')
72
+ with tokenize.open(str(path)) as file:
73
+ tree = ast.parse(file.read())
74
+ FindFuncs(path).visit(tree)
evalkit_tf437/lib/python3.10/site-packages/pandas/tests/tools/__pycache__/test_to_datetime.cpython-310.pyc ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:5f5606939b9d2d4be91d70bd5564739c64ff81c1bef843e300ff34639ffe8b7a
3
+ size 111134
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