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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 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
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|
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|
|
|
|
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|
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|
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|
| 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
|
Binary file (37.1 kB). View file
|
|
|
evalkit_tf437/lib/python3.10/site-packages/numpy/core/tests/__pycache__/test_hashtable.cpython-310.pyc
ADDED
|
Binary file (1.1 kB). View file
|
|
|
evalkit_tf437/lib/python3.10/site-packages/numpy/core/tests/__pycache__/test_scalar_ctors.cpython-310.pyc
ADDED
|
Binary file (6.8 kB). View file
|
|
|
evalkit_tf437/lib/python3.10/site-packages/numpy/core/tests/__pycache__/test_scalarmath.cpython-310.pyc
ADDED
|
Binary file (35.5 kB). View file
|
|
|
evalkit_tf437/lib/python3.10/site-packages/numpy/core/tests/__pycache__/test_ufunc.cpython-310.pyc
ADDED
|
Binary file (98.9 kB). View file
|
|
|
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 @@
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|
| 1 |
+
dtype,input,output,ulperrortol
|
| 2 |
+
np.float32,0xbe7d3a7c,0xbe7fe217,4
|
| 3 |
+
np.float32,0x3dc102f0,0x3dc14c60,4
|
| 4 |
+
np.float32,0xbe119c28,0xbe121aef,4
|
| 5 |
+
np.float32,0xbe51cd68,0xbe534c75,4
|
| 6 |
+
np.float32,0x3c04a300,0x3c04a35f,4
|
| 7 |
+
np.float32,0xbf4f0b62,0xbf712a69,4
|
| 8 |
+
np.float32,0x3ef61a5c,0x3f005cf6,4
|
| 9 |
+
np.float32,0xbf13024c,0xbf1c97df,4
|
| 10 |
+
np.float32,0x3e93b580,0x3e95d6b5,4
|
| 11 |
+
np.float32,0x3e44e7b8,0x3e4623a5,4
|
| 12 |
+
np.float32,0xbe35df20,0xbe36d773,4
|
| 13 |
+
np.float32,0x3eecd2c0,0x3ef633cf,4
|
| 14 |
+
np.float32,0x3f2772ba,0x3f36862a,4
|
| 15 |
+
np.float32,0x3e211ea8,0x3e21cac5,4
|
| 16 |
+
np.float32,0x3e3b3d90,0x3e3c4cc6,4
|
| 17 |
+
np.float32,0x3f37c962,0x3f4d018c,4
|
| 18 |
+
np.float32,0x3e92ad88,0x3e94c31a,4
|
| 19 |
+
np.float32,0x3f356ffc,0x3f49a766,4
|
| 20 |
+
np.float32,0x3f487ba2,0x3f665254,4
|
| 21 |
+
np.float32,0x3f061c46,0x3f0d27ae,4
|
| 22 |
+
np.float32,0xbee340a2,0xbeeb7722,4
|
| 23 |
+
np.float32,0xbe85aede,0xbe874026,4
|
| 24 |
+
np.float32,0x3f34cf9a,0x3f48c474,4
|
| 25 |
+
np.float32,0x3e29a690,0x3e2a6fbd,4
|
| 26 |
+
np.float32,0xbeb29428,0xbeb669d1,4
|
| 27 |
+
np.float32,0xbe606d40,0xbe624370,4
|
| 28 |
+
np.float32,0x3dae6860,0x3dae9e85,4
|
| 29 |
+
np.float32,0xbf04872b,0xbf0b4d25,4
|
| 30 |
+
np.float32,0x3f2080e2,0x3f2d7ab0,4
|
| 31 |
+
np.float32,0xbec77dcc,0xbecceb27,4
|
| 32 |
+
np.float32,0x3e0dda10,0x3e0e4f38,4
|
| 33 |
+
np.float32,0xbefaf970,0xbf03262c,4
|
| 34 |
+
np.float32,0x3f576a0c,0x3f7ffee6,4
|
| 35 |
+
np.float32,0x3f222382,0x3f2f95d6,4
|
| 36 |
+
np.float32,0x7fc00000,0x7fc00000,4
|
| 37 |
+
np.float32,0x3e41c468,0x3e42f14e,4
|
| 38 |
+
np.float32,0xbf2f64dd,0xbf4139a8,4
|
| 39 |
+
np.float32,0xbf60ef90,0xbf895956,4
|
| 40 |
+
np.float32,0xbf67c855,0xbf90eff0,4
|
| 41 |
+
np.float32,0xbed35aee,0xbed9df00,4
|
| 42 |
+
np.float32,0xbf2c7d92,0xbf3d448f,4
|
| 43 |
+
np.float32,0x3f7b1604,0x3faff122,4
|
| 44 |
+
np.float32,0xbf7c758b,0xbfb3bf87,4
|
| 45 |
+
np.float32,0x3ecda1c8,0x3ed39acf,4
|
| 46 |
+
np.float32,0x3f3af8ae,0x3f519fcb,4
|
| 47 |
+
np.float32,0xbf16e6a3,0xbf2160fd,4
|
| 48 |
+
np.float32,0x3f0c97d2,0x3f14d668,4
|
| 49 |
+
np.float32,0x3f0a8060,0x3f1257b9,4
|
| 50 |
+
np.float32,0x3f27905a,0x3f36ad57,4
|
| 51 |
+
np.float32,0x3eeaeba4,0x3ef40efe,4
|
| 52 |
+
np.float32,0x3e58dde0,0x3e5a8580,4
|
| 53 |
+
np.float32,0xbf0cabe2,0xbf14ee6b,4
|
| 54 |
+
np.float32,0xbe805ca8,0xbe81bf03,4
|
| 55 |
+
np.float32,0x3f5462ba,0x3f7a7b85,4
|
| 56 |
+
np.float32,0xbee235d0,0xbeea4d8b,4
|
| 57 |
+
np.float32,0xbe880cb0,0xbe89b426,4
|
| 58 |
+
np.float32,0x80000001,0x80000001,4
|
| 59 |
+
np.float32,0x3f208c00,0x3f2d88f6,4
|
| 60 |
+
np.float32,0xbf34f3d2,0xbf48f7a2,4
|
| 61 |
+
np.float32,0x3f629428,0x3f8b1763,4
|
| 62 |
+
np.float32,0xbf52a900,0xbf776b4a,4
|
| 63 |
+
np.float32,0xbd17f8d0,0xbd1801be,4
|
| 64 |
+
np.float32,0xbef7cada,0xbf0153d1,4
|
| 65 |
+
np.float32,0x3f7d3b90,0x3fb63967,4
|
| 66 |
+
np.float32,0xbd6a20b0,0xbd6a4160,4
|
| 67 |
+
np.float32,0x3f740496,0x3fa1beb7,4
|
| 68 |
+
np.float32,0x3ed8762c,0x3edf7dd9,4
|
| 69 |
+
np.float32,0x3f53b066,0x3f793d42,4
|
| 70 |
+
np.float32,0xbe9de718,0xbea084f9,4
|
| 71 |
+
np.float32,0x3ea3ae90,0x3ea69b4b,4
|
| 72 |
+
np.float32,0x3f1b8f00,0x3f273183,4
|
| 73 |
+
np.float32,0x3f5cd6ac,0x3f852ead,4
|
| 74 |
+
np.float32,0x3f29d510,0x3f39b169,4
|
| 75 |
+
np.float32,0x3ee2a934,0x3eeace33,4
|
| 76 |
+
np.float32,0x3eecac94,0x3ef608c2,4
|
| 77 |
+
np.float32,0xbea915e2,0xbeac5203,4
|
| 78 |
+
np.float32,0xbd316e90,0xbd317cc8,4
|
| 79 |
+
np.float32,0xbf70b495,0xbf9c97b6,4
|
| 80 |
+
np.float32,0xbe80d976,0xbe823ff3,4
|
| 81 |
+
np.float32,0x3e9205f8,0x3e94143f,4
|
| 82 |
+
np.float32,0x3f49247e,0x3f676296,4
|
| 83 |
+
np.float32,0x3d9030c0,0x3d904f50,4
|
| 84 |
+
np.float32,0x3e4df058,0x3e4f5a5c,4
|
| 85 |
+
np.float32,0xbe1fd360,0xbe207b58,4
|
| 86 |
+
np.float32,0xbf69dc7c,0xbf937006,4
|
| 87 |
+
np.float32,0x3f36babe,0x3f4b7df3,4
|
| 88 |
+
np.float32,0xbe8c9758,0xbe8e6bb7,4
|
| 89 |
+
np.float32,0xbf4de72d,0xbf6f3c20,4
|
| 90 |
+
np.float32,0xbecdad68,0xbed3a780,4
|
| 91 |
+
np.float32,0xbf73e2cf,0xbfa18702,4
|
| 92 |
+
np.float32,0xbece16a8,0xbed41a75,4
|
| 93 |
+
np.float32,0x3f618a96,0x3f89fc6d,4
|
| 94 |
+
np.float32,0xbf325853,0xbf454ea9,4
|
| 95 |
+
np.float32,0x3f138568,0x3f1d3828,4
|
| 96 |
+
np.float32,0xbf56a6e9,0xbf7e9748,4
|
| 97 |
+
np.float32,0x3ef5d594,0x3f0035bf,4
|
| 98 |
+
np.float32,0xbf408220,0xbf59dfaa,4
|
| 99 |
+
np.float32,0xbed120e6,0xbed76dd5,4
|
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| 1321 |
+
np.float64,0x3fd1609fc9a2c140,0x3fd1992efa539b9f,3
|
| 1322 |
+
np.float64,0xbfac1291bc382520,0xbfac162cc7334b4d,3
|
| 1323 |
+
np.float64,0xbfedb461ea7b68c4,0xbff309247850455d,3
|
| 1324 |
+
np.float64,0xbfe8d2adf8f1a55c,0xbfec6947be90ba92,3
|
| 1325 |
+
np.float64,0xbfd7128965ae2512,0xbfd79a9855bcfc5a,3
|
| 1326 |
+
np.float64,0x3fe8deb09471bd62,0x3fec7c56b3aee531,3
|
| 1327 |
+
np.float64,0xbfe5f4d329ebe9a6,0xbfe8327ea8189af8,3
|
| 1328 |
+
np.float64,0xbfd3b46ac9a768d6,0xbfd407b80b12ff17,3
|
| 1329 |
+
np.float64,0x3fec899d7cf9133a,0x3ff19ef26baca36f,3
|
| 1330 |
+
np.float64,0xbfec192fd5783260,0xbff126306e507fd0,3
|
| 1331 |
+
np.float64,0x3fe945bdaef28b7c,0x3fed222f787310bf,3
|
| 1332 |
+
np.float64,0xbfeff9635d7ff2c7,0xbff87d6773f318eb,3
|
| 1333 |
+
np.float64,0xbfd604b81cac0970,0xbfd67a4aa852559a,3
|
| 1334 |
+
np.float64,0x3fcd1cc9d53a3990,0x3fcd5e962e237c24,3
|
| 1335 |
+
np.float64,0xbfed77b0fffaef62,0xbff2b97a1c9b6483,3
|
| 1336 |
+
np.float64,0xbfc9c69325338d28,0xbfc9f401500402fb,3
|
| 1337 |
+
np.float64,0xbfdf97e246bf2fc4,0xbfe0855601ea9db3,3
|
| 1338 |
+
np.float64,0x3fc7e6304f2fcc60,0x3fc80a4e718504cd,3
|
| 1339 |
+
np.float64,0x3fec3b599e7876b4,0x3ff14a2d1b9c68e6,3
|
| 1340 |
+
np.float64,0xbfe98618e1f30c32,0xbfed8bfbb31c394a,3
|
| 1341 |
+
np.float64,0xbfe59b3c0feb3678,0xbfe7b832d6df81de,3
|
| 1342 |
+
np.float64,0xbfe54ce2fe6a99c6,0xbfe74e9a85be4116,3
|
| 1343 |
+
np.float64,0x3fc9db49cb33b690,0x3fca092737ef500a,3
|
| 1344 |
+
np.float64,0xbfb4a922ae295248,0xbfb4aee4e39078a9,3
|
| 1345 |
+
np.float64,0xbfd0e542e0a1ca86,0xbfd11925208d66af,3
|
| 1346 |
+
np.float64,0x3fd70543f2ae0a88,0x3fd78c5e9238a3ee,3
|
| 1347 |
+
np.float64,0x3fd67f7a7facfef4,0x3fd6fd3998df8545,3
|
| 1348 |
+
np.float64,0xbfe40b643d6816c8,0xbfe5a947e427f298,3
|
| 1349 |
+
np.float64,0xbfcd85f69b3b0bec,0xbfcdcaa24b75f1a3,3
|
| 1350 |
+
np.float64,0x3fec705fb4f8e0c0,0x3ff1833c82163ee2,3
|
| 1351 |
+
np.float64,0x3fb37650ea26eca0,0x3fb37b20c16fb717,3
|
| 1352 |
+
np.float64,0x3fe5ebfa55ebd7f4,0x3fe826578d716e70,3
|
| 1353 |
+
np.float64,0x3fe991dfe5f323c0,0x3fed9f8a4bf1f588,3
|
| 1354 |
+
np.float64,0xbfd658bd0aacb17a,0xbfd6d3dd06e54900,3
|
| 1355 |
+
np.float64,0xbfc24860252490c0,0xbfc258701a0b9290,3
|
| 1356 |
+
np.float64,0xbfefb8d763ff71af,0xbff705b6ea4a569d,3
|
| 1357 |
+
np.float64,0x3fb8fcb4ae31f970,0x3fb906e809e7899f,3
|
| 1358 |
+
np.float64,0x3fce6343cb3cc688,0x3fceae41d1629625,3
|
| 1359 |
+
np.float64,0xbfd43d5a11a87ab4,0xbfd497da25687e07,3
|
| 1360 |
+
np.float64,0xbfe9568851f2ad11,0xbfed3d9e5fe83a76,3
|
| 1361 |
+
np.float64,0x3fe1b66153e36cc2,0x3fe2c53c7e016271,3
|
| 1362 |
+
np.float64,0x3fef27452bfe4e8a,0x3ff571b3486ed416,3
|
| 1363 |
+
np.float64,0x3fca87c0a7350f80,0x3fcab958a7bb82d4,3
|
| 1364 |
+
np.float64,0xbfd8776a8fb0eed6,0xbfd91afaf2f50edf,3
|
| 1365 |
+
np.float64,0x3fe9522a76f2a454,0x3fed3679264e1525,3
|
| 1366 |
+
np.float64,0x3fea14ff2cf429fe,0x3fee7da6431cc316,3
|
| 1367 |
+
np.float64,0x3fe970618bf2e0c4,0x3fed68154d54dd97,3
|
| 1368 |
+
np.float64,0x3fd3410cfca68218,0x3fd38e9b21792240,3
|
| 1369 |
+
np.float64,0xbf6a8070c0350100,0xbf6a8073c7c34517,3
|
| 1370 |
+
np.float64,0xbfbe449de23c8938,0xbfbe56c8e5e4d98b,3
|
| 1371 |
+
np.float64,0x3fedbc92e27b7926,0x3ff314313216d8e6,3
|
| 1372 |
+
np.float64,0xbfe3be4706677c8e,0xbfe546d3ceb85aea,3
|
| 1373 |
+
np.float64,0x3fe30cd6d76619ae,0x3fe467b6f2664a8d,3
|
| 1374 |
+
np.float64,0x3fd7d69b21afad38,0x3fd86d54284d05ad,3
|
| 1375 |
+
np.float64,0xbfe501001fea0200,0xbfe6e978afcff4d9,3
|
| 1376 |
+
np.float64,0xbfe44ba3d8e89748,0xbfe5fc0a31cd1e3e,3
|
| 1377 |
+
np.float64,0x3fec52f7c078a5f0,0x3ff16367acb209b2,3
|
| 1378 |
+
np.float64,0xbfcb19efcb3633e0,0xbfcb4ed9235a7d47,3
|
| 1379 |
+
np.float64,0xbfab86796c370cf0,0xbfab89df7bf15710,3
|
| 1380 |
+
np.float64,0xbfb962feda32c600,0xbfb96db1e1679c98,3
|
| 1381 |
+
np.float64,0x3fe0dd14e861ba2a,0x3fe1c2fc72810567,3
|
| 1382 |
+
np.float64,0x3fe41bcc6de83798,0x3fe5be59b7f9003b,3
|
| 1383 |
+
np.float64,0x3fc82f4c4f305e98,0x3fc854bd9798939f,3
|
| 1384 |
+
np.float64,0xbfcd143a613a2874,0xbfcd55cbd1619d84,3
|
| 1385 |
+
np.float64,0xbfd52da61baa5b4c,0xbfd595d0b3543439,3
|
| 1386 |
+
np.float64,0xbfb71b4a8e2e3698,0xbfb7235a4ab8432f,3
|
| 1387 |
+
np.float64,0xbfec141a19782834,0xbff120e1e39fc856,3
|
| 1388 |
+
np.float64,0xbfdba9319db75264,0xbfdc9a8ca2578bb2,3
|
| 1389 |
+
np.float64,0xbfbce5d74639cbb0,0xbfbcf5a4878cfa51,3
|
| 1390 |
+
np.float64,0x3fde67f7b3bccff0,0x3fdfaf45a9f843ad,3
|
| 1391 |
+
np.float64,0xbfe12d87bc625b10,0xbfe221fd4476eb71,3
|
| 1392 |
+
np.float64,0x3fe35b8f6be6b71e,0x3fe4ca20f65179e1,3
|
| 1393 |
+
np.float64,0xbfdbada1d3b75b44,0xbfdc9f78b19f93d1,3
|
| 1394 |
+
np.float64,0xbfc60159c52c02b4,0xbfc61d79b879f598,3
|
| 1395 |
+
np.float64,0x3fd6b81c38ad7038,0x3fd739c27bfa16d8,3
|
| 1396 |
+
np.float64,0xbfd646a253ac8d44,0xbfd6c08c19612bbb,3
|
| 1397 |
+
np.float64,0xbfe6babef0ed757e,0xbfe94703d0bfa311,3
|
| 1398 |
+
np.float64,0xbfed5671f1faace4,0xbff28f5a3f3683d0,3
|
| 1399 |
+
np.float64,0x3fc01d1e85203a40,0x3fc02817ec0dfd38,3
|
| 1400 |
+
np.float64,0xbfe9188a61f23115,0xbfecd8eb5da84223,3
|
| 1401 |
+
np.float64,0x3fdca3bab9b94774,0x3fddb1868660c239,3
|
| 1402 |
+
np.float64,0xbfa255750c24aaf0,0xbfa25675f7b36343,3
|
| 1403 |
+
np.float64,0x3fb3602db626c060,0x3fb364ed2d5b2876,3
|
| 1404 |
+
np.float64,0xbfd30a14bda6142a,0xbfd354ff703b8862,3
|
| 1405 |
+
np.float64,0xbfe1cfe381639fc7,0xbfe2e3e720b968c8,3
|
| 1406 |
+
np.float64,0xbfd2af6a4fa55ed4,0xbfd2f61e190bcd1f,3
|
| 1407 |
+
np.float64,0xbfe93c50937278a1,0xbfed12d64bb10d73,3
|
| 1408 |
+
np.float64,0x3fddd8bc44bbb178,0x3fdf0ced7f9005cc,3
|
| 1409 |
+
np.float64,0x3fdb2bc73cb65790,0x3fdc0fc0e18e425e,3
|
| 1410 |
+
np.float64,0xbfd073f6aba0e7ee,0xbfd0a3cb5468a961,3
|
| 1411 |
+
np.float64,0x3fed4bad7b7a975a,0x3ff281ebeb75e414,3
|
| 1412 |
+
np.float64,0xbfdc75b50bb8eb6a,0xbfdd7e1a7631cb22,3
|
| 1413 |
+
np.float64,0x3fd458a90fa8b154,0x3fd4b4a5817248ce,3
|
| 1414 |
+
np.float64,0x3feead5db57d5abc,0x3ff484286fab55ff,3
|
| 1415 |
+
np.float64,0x3fb3894382271280,0x3fb38e217b4e7905,3
|
| 1416 |
+
np.float64,0xffefffffffffffff,0x7ff8000000000000,3
|
| 1417 |
+
np.float64,0xbfe428212ae85042,0xbfe5ce36f226bea8,3
|
| 1418 |
+
np.float64,0xbfc08b39f7211674,0xbfc0971b93ebc7ad,3
|
| 1419 |
+
np.float64,0xbfc2e7cf5525cfa0,0xbfc2f994eb72b623,3
|
| 1420 |
+
np.float64,0xbfdb0d85afb61b0c,0xbfdbee5a2de3c5db,3
|
| 1421 |
+
np.float64,0xfff0000000000000,0x7ff8000000000000,3
|
| 1422 |
+
np.float64,0xbfd0d36af7a1a6d6,0xbfd106a5f05ef6ff,3
|
| 1423 |
+
np.float64,0xbfc333d0912667a0,0xbfc3467162b7289a,3
|
| 1424 |
+
np.float64,0x3fcdababc53b5758,0x3fcdf16458c20fa8,3
|
| 1425 |
+
np.float64,0x3fd0821b38a10438,0x3fd0b26e3e0b9185,3
|
| 1426 |
+
np.float64,0x0,0x0,3
|
| 1427 |
+
np.float64,0x3feb7f70edf6fee2,0x3ff08ae81854bf20,3
|
| 1428 |
+
np.float64,0x3fe6e075716dc0ea,0x3fe97cc5254be6ff,3
|
| 1429 |
+
np.float64,0x3fea13b682f4276e,0x3fee7b6f18073b5b,3
|
evalkit_tf437/lib/python3.10/site-packages/numpy/core/tests/data/umath-validation-set-cos.csv
ADDED
|
@@ -0,0 +1,1375 @@
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|
| 1 |
+
dtype,input,output,ulperrortol
|
| 2 |
+
## +ve denormals ##
|
| 3 |
+
np.float32,0x004b4716,0x3f800000,2
|
| 4 |
+
np.float32,0x007b2490,0x3f800000,2
|
| 5 |
+
np.float32,0x007c99fa,0x3f800000,2
|
| 6 |
+
np.float32,0x00734a0c,0x3f800000,2
|
| 7 |
+
np.float32,0x0070de24,0x3f800000,2
|
| 8 |
+
np.float32,0x007fffff,0x3f800000,2
|
| 9 |
+
np.float32,0x00000001,0x3f800000,2
|
| 10 |
+
## -ve denormals ##
|
| 11 |
+
np.float32,0x80495d65,0x3f800000,2
|
| 12 |
+
np.float32,0x806894f6,0x3f800000,2
|
| 13 |
+
np.float32,0x80555a76,0x3f800000,2
|
| 14 |
+
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 |
+
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np.float64,0x7fe56482dd6ac905,0x3fe7189de6a50668,1
|
| 1267 |
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np.float64,0x7fec67a2e3f8cf45,0xbfef86d0b940f37f,1
|
| 1268 |
+
np.float64,0x7fe38b202fe7163f,0x3feb90b75caa2030,1
|
| 1269 |
+
np.float64,0x7fdcbc64883978c8,0x3fed4f758fbf64d4,1
|
| 1270 |
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np.float64,0x7fea5f0598f4be0a,0x3fdd503a417b3d4d,1
|
| 1271 |
+
np.float64,0x7fda3b6bcf3476d7,0x3fea6e9af3f7f9f5,1
|
| 1272 |
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np.float64,0x7fc7d7896c2faf12,0x3fda2bebc36a2363,1
|
| 1273 |
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np.float64,0x7fe7e8e2626fd1c4,0xbfe7d5e390c4cc3f,1
|
| 1274 |
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np.float64,0x7fde0f3d7abc1e7a,0xbfede7a0ecfa3606,1
|
| 1275 |
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np.float64,0x7fc692b8f52d2571,0x3feff0cd7ab6f61b,1
|
| 1276 |
+
np.float64,0xff92d1fce825a400,0xbfc921c36fc014fa,1
|
| 1277 |
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np.float64,0xffdec3af2fbd875e,0xbfed6a77e6a0364e,1
|
| 1278 |
+
np.float64,0xffef46e7d9be8dcf,0xbfed7d39476f7e27,1
|
| 1279 |
+
np.float64,0xffe2c2ce4525859c,0x3fe1757261316bc9,1
|
| 1280 |
+
np.float64,0xffe27c8b5864f916,0xbfefe017c0d43457,1
|
| 1281 |
+
np.float64,0xffe184d7442309ae,0x3fa1fb8c49dba596,1
|
| 1282 |
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np.float64,0xffddf5f98d3bebf4,0x3fee4f8eaa5f847e,1
|
| 1283 |
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np.float64,0xffee3ef354fc7de6,0xbfebfd60fa51b2ba,1
|
| 1284 |
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np.float64,0xffdecb3e85bd967e,0x3fbfad2667a8b468,1
|
| 1285 |
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np.float64,0xffe4ee900b29dd20,0xbfdc02dc626f91cd,1
|
| 1286 |
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np.float64,0xffd3179f6da62f3e,0xbfe2cfe442511776,1
|
| 1287 |
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np.float64,0xffe99ef7cef33def,0x3f50994542a7f303,1
|
| 1288 |
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np.float64,0xffe2b66b1ae56cd6,0xbfefe3e066eb6329,1
|
| 1289 |
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np.float64,0xff8f72aff03ee540,0x3fe9c46224cf5003,1
|
| 1290 |
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np.float64,0xffd29beb85a537d8,0x3fefcb0b6166be71,1
|
| 1291 |
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np.float64,0xffaef02d4c3de060,0xbfef5fb71028fc72,1
|
| 1292 |
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np.float64,0xffd39a2a89273456,0x3fe6d4b183205dca,1
|
| 1293 |
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np.float64,0xffef8a9392ff1526,0x3fedb99fbf402468,1
|
| 1294 |
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np.float64,0xffb9b3f31e3367e8,0x3fee1005270fcf80,1
|
| 1295 |
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np.float64,0xffed9d5c693b3ab8,0x3fd110f4b02365d5,1
|
| 1296 |
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np.float64,0xffeaba45f9f5748b,0x3fe499e0a6f4afb2,1
|
| 1297 |
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np.float64,0xffdba3f70d3747ee,0xbfca0c30493ae519,1
|
| 1298 |
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np.float64,0xffa35b985426b730,0xbfdb625df56bcf45,1
|
| 1299 |
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np.float64,0xffccbc9728397930,0x3fc53cbc59020704,1
|
| 1300 |
+
np.float64,0xffef73c942bee792,0xbfdc647a7a5e08be,1
|
| 1301 |
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np.float64,0xffcb5acfb236b5a0,0x3feeb4ec038c39fc,1
|
| 1302 |
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np.float64,0xffea116fe2b422df,0x3fefe03b6ae0b435,1
|
| 1303 |
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np.float64,0xffe97de6e7b2fbcd,0xbfd2025698fab9eb,1
|
| 1304 |
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np.float64,0xffdddba314bbb746,0x3fd31f0fdb8f93be,1
|
| 1305 |
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np.float64,0xffd613a24a2c2744,0xbfebbb1efae884b3,1
|
| 1306 |
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np.float64,0xffe3d938aa67b271,0xbfc2099cead3d3be,1
|
| 1307 |
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np.float64,0xffdf08c2e33e1186,0xbfefd236839b900d,1
|
| 1308 |
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np.float64,0xffea6ba8bd34d751,0x3fe8dfc032114719,1
|
| 1309 |
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np.float64,0xffe3202083e64040,0x3fed513b81432a22,1
|
| 1310 |
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np.float64,0xffb2397db62472f8,0xbfee7d7fe1c3f76c,1
|
| 1311 |
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np.float64,0xffd9d0682ab3a0d0,0x3fe0bcf9e531ad79,1
|
| 1312 |
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np.float64,0xffc293df202527c0,0xbfe58d0bdece5e64,1
|
| 1313 |
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np.float64,0xffe1422c7da28458,0xbf81bd72595f2341,1
|
| 1314 |
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np.float64,0xffd64e4ed4ac9c9e,0x3fa4334cc011c703,1
|
| 1315 |
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np.float64,0xffe40a970ae8152e,0x3fead3d258b55b7d,1
|
| 1316 |
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np.float64,0xffc8c2f2223185e4,0xbfef685f07c8b9fd,1
|
| 1317 |
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np.float64,0xffe4b2f7216965ee,0x3fe3861d3d896a83,1
|
| 1318 |
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np.float64,0xffdb531db3b6a63c,0x3fe18cb8332dd59d,1
|
| 1319 |
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np.float64,0xffe8e727a3b1ce4e,0xbfe57b15abb677b9,1
|
| 1320 |
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np.float64,0xffe530c1e12a6184,0xbfb973ea5535e48f,1
|
| 1321 |
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np.float64,0xffe6f7849cedef08,0x3fd39a37ec5af4b6,1
|
| 1322 |
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np.float64,0xffead62a78b5ac54,0x3fe69b3f6c7aa24b,1
|
| 1323 |
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np.float64,0xffeefdd725fdfbad,0xbfc08a456111fdd5,1
|
| 1324 |
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np.float64,0xffe682182fed0430,0x3fecc7c1292761d2,1
|
| 1325 |
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np.float64,0xffee0ca8dcbc1951,0x3fef6cc361ef2c19,1
|
| 1326 |
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np.float64,0xffec9b338f393666,0x3fefa9ab8e0471b5,1
|
| 1327 |
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np.float64,0xffe13c5e29a278bc,0xbfef8da74ad83398,1
|
| 1328 |
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np.float64,0xffd7bd48c62f7a92,0x3fe3468cd4ac9d34,1
|
| 1329 |
+
np.float64,0xffedd0ed14bba1d9,0xbfd563a83477077b,1
|
| 1330 |
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np.float64,0xffe86b83f3f0d707,0x3fe9eb3c658e4b2d,1
|
| 1331 |
+
np.float64,0xffd6a4db4bad49b6,0xbfc7e11276166e17,1
|
| 1332 |
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np.float64,0xffc29e8404253d08,0x3fd35971961c789f,1
|
| 1333 |
+
np.float64,0xffe27cf3d664f9e7,0xbfeca0f73c72f810,1
|
| 1334 |
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np.float64,0xffc34152352682a4,0x3fef384e564c002c,1
|
| 1335 |
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np.float64,0xffe395728ba72ae4,0x3f8fe18c2de86eba,1
|
| 1336 |
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np.float64,0xffed86c4fbbb0d89,0x3fef709db881c672,1
|
| 1337 |
+
np.float64,0xffe8a98d37f1531a,0x3fd4879c8f73c3dc,1
|
| 1338 |
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np.float64,0xffb8ce9fea319d40,0xbfb853c8fe46b08d,1
|
| 1339 |
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np.float64,0xffe7f26db8efe4db,0xbfec1cfd3e5c2ac1,1
|
| 1340 |
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np.float64,0xffd7935b77af26b6,0x3fb7368c89b2a460,1
|
| 1341 |
+
np.float64,0xffc5840ed02b081c,0x3fd92220b56631f3,1
|
| 1342 |
+
np.float64,0xffc36a873926d510,0x3fa84d61baf61811,1
|
| 1343 |
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np.float64,0xffe06ea583e0dd4a,0x3feb647e348b9e39,1
|
| 1344 |
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np.float64,0xffe6a33031ed4660,0xbfe096b851dc1a0a,1
|
| 1345 |
+
np.float64,0xffe001c938e00392,0x3fe4eece77623e7a,1
|
| 1346 |
+
np.float64,0xffc1e4f23b23c9e4,0xbfdb9bb1f83f6ac4,1
|
| 1347 |
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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 @@
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|
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|
|
|
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|
|
|
|
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|
|
|
|
|
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|
|
|
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|
| 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 @@
|
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|
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|
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|
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|
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|
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|
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|
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|
|
|
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|
|
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|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
| 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 |
+
np.float32,0xff52c052,0xbf800000,3
|
| 22 |
+
np.float32,0x7fc00000,0x7fc00000,3
|
| 23 |
+
np.float32,0xff4f65f9,0xbf800000,3
|
| 24 |
+
np.float32,0xfe0f6c20,0xbf800000,3
|
| 25 |
+
np.float32,0x80322b30,0x80322b30,3
|
| 26 |
+
np.float32,0xfb757000,0xbf800000,3
|
| 27 |
+
np.float32,0x3c81e0,0x3c81e0,3
|
| 28 |
+
np.float32,0x79d56a,0x79d56a,3
|
| 29 |
+
np.float32,0x8029d7af,0x8029d7af,3
|
| 30 |
+
np.float32,0x8058a593,0x8058a593,3
|
| 31 |
+
np.float32,0x3f3a13c7,0x3f88c75c,3
|
| 32 |
+
np.float32,0x2a6b05,0x2a6b05,3
|
| 33 |
+
np.float32,0xbd64c960,0xbd5e83ae,3
|
| 34 |
+
np.float32,0x80471052,0x80471052,3
|
| 35 |
+
np.float32,0xbe5dd950,0xbe47766c,3
|
| 36 |
+
np.float32,0xfd8f88f0,0xbf800000,3
|
| 37 |
+
np.float32,0x75a4b7,0x75a4b7,3
|
| 38 |
+
np.float32,0x3f726f2e,0x3fc9fb7d,3
|
| 39 |
+
np.float32,0x3ed6795c,0x3f053115,3
|
| 40 |
+
np.float32,0x17d7f5,0x17d7f5,3
|
| 41 |
+
np.float32,0xbf4cf19b,0xbf0d094f,3
|
| 42 |
+
np.float32,0x3e0ec532,0x3e1933c6,3
|
| 43 |
+
np.float32,0xff084016,0xbf800000,3
|
| 44 |
+
np.float32,0x800829aa,0x800829aa,3
|
| 45 |
+
np.float32,0x806d7302,0x806d7302,3
|
| 46 |
+
np.float32,0x7f59d9da,0x7f800000,3
|
| 47 |
+
np.float32,0x15f8b9,0x15f8b9,3
|
| 48 |
+
np.float32,0x803befb3,0x803befb3,3
|
| 49 |
+
np.float32,0x525043,0x525043,3
|
| 50 |
+
np.float32,0x51a647,0x51a647,3
|
| 51 |
+
np.float32,0xbf1cfce4,0xbeeab3d9,3
|
| 52 |
+
np.float32,0x3f1f27a4,0x3f5cb1d2,3
|
| 53 |
+
np.float32,0xbebc3a04,0xbe9d8142,3
|
| 54 |
+
np.float32,0xbeea548c,0xbebc07e5,3
|
| 55 |
+
np.float32,0x3f47401c,0x3f96c2a3,3
|
| 56 |
+
np.float32,0x806b1ea3,0x806b1ea3,3
|
| 57 |
+
np.float32,0x3ea56bb8,0x3ec3450c,3
|
| 58 |
+
np.float32,0x3f7b4963,0x3fd597b5,3
|
| 59 |
+
np.float32,0x7f051fa0,0x7f800000,3
|
| 60 |
+
np.float32,0x1d411c,0x1d411c,3
|
| 61 |
+
np.float32,0xff0b6a35,0xbf800000,3
|
| 62 |
+
np.float32,0xbead63c0,0xbe9314f7,3
|
| 63 |
+
np.float32,0x3738be,0x3738be,3
|
| 64 |
+
np.float32,0x3f138cc8,0x3f479155,3
|
| 65 |
+
np.float32,0x800a539f,0x800a539f,3
|
| 66 |
+
np.float32,0x801b0ebd,0x801b0ebd,3
|
| 67 |
+
np.float32,0x318fcd,0x318fcd,3
|
| 68 |
+
np.float32,0x3ed67556,0x3f052e06,3
|
| 69 |
+
np.float32,0x702886,0x702886,3
|
| 70 |
+
np.float32,0x80000001,0x80000001,3
|
| 71 |
+
np.float32,0x70a174,0x70a174,3
|
| 72 |
+
np.float32,0x4f9c66,0x4f9c66,3
|
| 73 |
+
np.float32,0x3e3e1927,0x3e50e351,3
|
| 74 |
+
np.float32,0x7eac9a4d,0x7f800000,3
|
| 75 |
+
np.float32,0x4b7407,0x4b7407,3
|
| 76 |
+
np.float32,0x7f5bd2fd,0x7f800000,3
|
| 77 |
+
np.float32,0x3eaafc58,0x3ecaffbd,3
|
| 78 |
+
np.float32,0xbc989360,0xbc9729e2,3
|
| 79 |
+
np.float32,0x3f470e5c,0x3f968c7b,3
|
| 80 |
+
np.float32,0x4c5672,0x4c5672,3
|
| 81 |
+
np.float32,0xff2b2ee2,0xbf800000,3
|
| 82 |
+
np.float32,0xbf28a104,0xbef7079b,3
|
| 83 |
+
np.float32,0x2c6175,0x2c6175,3
|
| 84 |
+
np.float32,0x3d7e4fb0,0x3d832f9f,3
|
| 85 |
+
np.float32,0x763276,0x763276,3
|
| 86 |
+
np.float32,0x3cf364,0x3cf364,3
|
| 87 |
+
np.float32,0xbf7ace75,0xbf1fe48c,3
|
| 88 |
+
np.float32,0xff19e858,0xbf800000,3
|
| 89 |
+
np.float32,0x80504c70,0x80504c70,3
|
| 90 |
+
np.float32,0xff390210,0xbf800000,3
|
| 91 |
+
np.float32,0x8046a743,0x8046a743,3
|
| 92 |
+
np.float32,0x80000000,0x80000000,3
|
| 93 |
+
np.float32,0x806c51da,0x806c51da,3
|
| 94 |
+
np.float32,0x806ab38f,0x806ab38f,3
|
| 95 |
+
np.float32,0x3f3de863,0x3f8cc538,3
|
| 96 |
+
np.float32,0x7f6d45bb,0x7f800000,3
|
| 97 |
+
np.float32,0xfd16ec60,0xbf800000,3
|
| 98 |
+
np.float32,0x80513cba,0x80513cba,3
|
| 99 |
+
np.float32,0xbf68996b,0xbf18cefa,3
|
| 100 |
+
np.float32,0xfe039f2c,0xbf800000,3
|
| 101 |
+
np.float32,0x3f013207,0x3f280c55,3
|
| 102 |
+
np.float32,0x7ef4bc07,0x7f800000,3
|
| 103 |
+
np.float32,0xbe8b65ac,0xbe741069,3
|
| 104 |
+
np.float32,0xbf7a8186,0xbf1fc7a6,3
|
| 105 |
+
np.float32,0x802532e5,0x802532e5,3
|
| 106 |
+
np.float32,0x32c7df,0x32c7df,3
|
| 107 |
+
np.float32,0x3ce4dceb,0x3ce81701,3
|
| 108 |
+
np.float32,0xfe801118,0xbf800000,3
|
| 109 |
+
np.float32,0x3d905f20,0x3d9594fb,3
|
| 110 |
+
np.float32,0xbe11ed28,0xbe080168,3
|
| 111 |
+
np.float32,0x59e773,0x59e773,3
|
| 112 |
+
np.float32,0x3e9a2547,0x3eb3dd57,3
|
| 113 |
+
np.float32,0x7ecb7c67,0x7f800000,3
|
| 114 |
+
np.float32,0x7f69a67e,0x7f800000,3
|
| 115 |
+
np.float32,0xff121e11,0xbf800000,3
|
| 116 |
+
np.float32,0x3f7917cb,0x3fd2ad8c,3
|
| 117 |
+
np.float32,0xbf1a7da8,0xbee7fc0c,3
|
| 118 |
+
np.float32,0x3f077e66,0x3f329c40,3
|
| 119 |
+
np.float32,0x3ce8e040,0x3cec37b3,3
|
| 120 |
+
np.float32,0xbf3f0b8e,0xbf069f4d,3
|
| 121 |
+
np.float32,0x3f52f194,0x3fa3c9d6,3
|
| 122 |
+
np.float32,0xbf0e7422,0xbeda80f2,3
|
| 123 |
+
np.float32,0xfd67e230,0xbf800000,3
|
| 124 |
+
np.float32,0xff14d9a9,0xbf800000,3
|
| 125 |
+
np.float32,0x3f3546e3,0x3f83dc2b,3
|
| 126 |
+
np.float32,0x3e152e3a,0x3e20983d,3
|
| 127 |
+
np.float32,0x4a89a3,0x4a89a3,3
|
| 128 |
+
np.float32,0x63217,0x63217,3
|
| 129 |
+
np.float32,0xbeb9e2a8,0xbe9be153,3
|
| 130 |
+
np.float32,0x7e9fa049,0x7f800000,3
|
| 131 |
+
np.float32,0x7f58110c,0x7f800000,3
|
| 132 |
+
np.float32,0x3e88290c,0x3e9bfba9,3
|
| 133 |
+
np.float32,0xbf2cb206,0xbefb3494,3
|
| 134 |
+
np.float32,0xff5880c4,0xbf800000,3
|
| 135 |
+
np.float32,0x7ecff3ac,0x7f800000,3
|
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np.float64,0xffe0c7993fa18f32,0xbff0000000000000,3
|
| 1400 |
+
np.float64,0x3fce5210fd3ca422,0x3fd11b40a1270a95,3
|
| 1401 |
+
np.float64,0x6c0534a8d80a7,0x6c0534a8d80a7,3
|
| 1402 |
+
np.float64,0x23c1823647831,0x23c1823647831,3
|
| 1403 |
+
np.float64,0x3fc901253732024a,0x3fcb9d264accb07c,3
|
| 1404 |
+
np.float64,0x3fe42b8997685714,0x3fec1a39e207b6e4,3
|
| 1405 |
+
np.float64,0x3fec4fd00fb89fa0,0x3ff6c1fdd0c262c8,3
|
| 1406 |
+
np.float64,0x8007b333caaf6668,0x8007b333caaf6668,3
|
| 1407 |
+
np.float64,0x800f9275141f24ea,0x800f9275141f24ea,3
|
| 1408 |
+
np.float64,0xffbba361a23746c0,0xbff0000000000000,3
|
| 1409 |
+
np.float64,0xbfee4effa9fc9dff,0xbfe396c11d0cd524,3
|
| 1410 |
+
np.float64,0x3e47e84c7c8fe,0x3e47e84c7c8fe,3
|
| 1411 |
+
np.float64,0x3fe80eb7b1301d6f,0x3ff1eed318a00153,3
|
| 1412 |
+
np.float64,0x7fd3f4c5b4a7e98a,0x7ff0000000000000,3
|
| 1413 |
+
np.float64,0x158abab02b158,0x158abab02b158,3
|
| 1414 |
+
np.float64,0x1,0x1,3
|
| 1415 |
+
np.float64,0x1f1797883e2f4,0x1f1797883e2f4,3
|
| 1416 |
+
np.float64,0x3feec055d03d80ac,0x3ff9d3fb0394de33,3
|
| 1417 |
+
np.float64,0x8010000000000000,0x8010000000000000,3
|
| 1418 |
+
np.float64,0xbfd070860ea0e10c,0xbfccfeec2828efef,3
|
| 1419 |
+
np.float64,0x80015c8b3e82b917,0x80015c8b3e82b917,3
|
| 1420 |
+
np.float64,0xffef9956d9ff32ad,0xbff0000000000000,3
|
| 1421 |
+
np.float64,0x7fe7f087dd2fe10f,0x7ff0000000000000,3
|
| 1422 |
+
np.float64,0x8002e7718665cee4,0x8002e7718665cee4,3
|
| 1423 |
+
np.float64,0x3fdfb9adb2bf735c,0x3fe4887a86214c1e,3
|
| 1424 |
+
np.float64,0xffc7747dfb2ee8fc,0xbff0000000000000,3
|
| 1425 |
+
np.float64,0x3fec309bb5386137,0x3ff69c44e1738547,3
|
| 1426 |
+
np.float64,0xffdbe2bf9ab7c580,0xbff0000000000000,3
|
| 1427 |
+
np.float64,0xbfe6a274daed44ea,0xbfe039aff2be9d48,3
|
| 1428 |
+
np.float64,0x7fd5a4e4efab49c9,0x7ff0000000000000,3
|
| 1429 |
+
np.float64,0xffbe6aaeb03cd560,0xbff0000000000000,3
|
evalkit_tf437/lib/python3.10/site-packages/numpy/core/tests/data/umath-validation-set-log10.csv
ADDED
|
@@ -0,0 +1,1629 @@
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|
| 1 |
+
dtype,input,output,ulperrortol
|
| 2 |
+
np.float32,0x3f6fd5c8,0xbce80e8e,4
|
| 3 |
+
np.float32,0x3ea4ab17,0xbefc3deb,4
|
| 4 |
+
np.float32,0x3e87a133,0xbf13b0b7,4
|
| 5 |
+
np.float32,0x3f0d9069,0xbe83bb19,4
|
| 6 |
+
np.float32,0x3f7b9269,0xbbf84f47,4
|
| 7 |
+
np.float32,0x3f7a9ffa,0xbc16fd97,4
|
| 8 |
+
np.float32,0x7f535d34,0x4219cb66,4
|
| 9 |
+
np.float32,0x3e79ad7c,0xbf1ce857,4
|
| 10 |
+
np.float32,0x7e8bfd3b,0x4217dfe9,4
|
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np.float64,0x3feb5af671f6b5ed,0xbfb16f88d739477f,2
|
| 1477 |
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np.float64,0x3feea400163d4800,0xbf934e071c64fd0b,2
|
| 1478 |
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np.float64,0x3fefd6bcf17fad7a,0xbf61f711c392b119,2
|
| 1479 |
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np.float64,0x3fe148d43da291a8,0xbfd11e9cd3f91cd3,2
|
| 1480 |
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np.float64,0x7fedf1308b7be260,0x4073439d135656da,2
|
| 1481 |
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np.float64,0x3fe614c99c6c2993,0xbfc49fd1984dfd6d,2
|
| 1482 |
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np.float64,0xd6e8d4e5add1b,0xc0733ba88256026e,2
|
| 1483 |
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np.float64,0xfff0000000000000,0x7ff8000000000000,2
|
| 1484 |
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np.float64,0x3fb530b5562a616b,0xbff1504bcc5c8f73,2
|
| 1485 |
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np.float64,0xb7da68396fb4d,0xc0733cbe2790f52e,2
|
| 1486 |
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np.float64,0x7fad78e26c3af1c4,0x4073303cdbfb0a15,2
|
| 1487 |
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np.float64,0x7fee5698447cad30,0x407343b474573a8b,2
|
| 1488 |
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np.float64,0x3fd488325c291065,0xbfdf999296d901e7,2
|
| 1489 |
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np.float64,0x2669283a4cd26,0xc073479f823109a4,2
|
| 1490 |
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np.float64,0x7fef3b090afe7611,0x407343e805a3b264,2
|
| 1491 |
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np.float64,0x7fe8b96ae0f172d5,0x4073424874a342ab,2
|
| 1492 |
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np.float64,0x7fef409f56fe813e,0x407343e943c3cd44,2
|
| 1493 |
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np.float64,0x3fed28073dfa500e,0xbfa4b17e4cd31a3a,2
|
| 1494 |
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np.float64,0x7f87ecc4802fd988,0x40732527e027b24b,2
|
| 1495 |
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np.float64,0x3fdda24da0bb449b,0xbfd566a43ac035af,2
|
| 1496 |
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np.float64,0x179fc9e62f3fa,0xc0734b0028c80fc1,2
|
| 1497 |
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np.float64,0x3fef85b0927f0b61,0xbf7ac27565d5ab4f,2
|
| 1498 |
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np.float64,0x5631501aac62b,0xc0734201be12c5d4,2
|
| 1499 |
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np.float64,0x3fd782e424af05c8,0xbfdbd57544f8a7c3,2
|
| 1500 |
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np.float64,0x3fe603a9a6ac0753,0xbfc4caff04dc3caf,2
|
| 1501 |
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np.float64,0x7fbd5225163aa449,0x40733504b88f0a56,2
|
| 1502 |
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np.float64,0x3fecd27506b9a4ea,0xbfa741dd70e6b08c,2
|
| 1503 |
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np.float64,0x9c99603b3932c,0xc0733ddb922dc5db,2
|
| 1504 |
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np.float64,0x3fbeb57f1a3d6afe,0xbfed789ff217aa08,2
|
| 1505 |
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np.float64,0x3fef9c0f85bf381f,0xbf75d5c3d6cb281a,2
|
| 1506 |
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np.float64,0x3fde4afb613c95f7,0xbfd4ca2a231c9005,2
|
| 1507 |
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np.float64,0x396233d472c47,0xc07344d56ee70631,2
|
| 1508 |
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np.float64,0x3fb31ea1c6263d44,0xbff207356152138d,2
|
| 1509 |
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np.float64,0x3fe50bdf78aa17bf,0xbfc74ae0cbffb735,2
|
| 1510 |
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np.float64,0xef74c701dee99,0xc0733ae81e4bb443,2
|
| 1511 |
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np.float64,0x9a3e13a1347c3,0xc0733df68b60afc7,2
|
| 1512 |
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np.float64,0x33ba4f886774b,0xc073458e03f0c13e,2
|
| 1513 |
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np.float64,0x3fe8ba0e9931741d,0xbfbcaadf974e8f64,2
|
| 1514 |
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np.float64,0x3fe090a4cd61214a,0xbfd24d236cf365d6,2
|
| 1515 |
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np.float64,0x7fd87d992930fb31,0x40733d668b73b820,2
|
| 1516 |
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np.float64,0x3fe6422b296c8456,0xbfc42e070b695d01,2
|
| 1517 |
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np.float64,0x3febe9334677d267,0xbfae667864606cfe,2
|
| 1518 |
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np.float64,0x771a3ce4ee348,0xc0733fc274d12c97,2
|
| 1519 |
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np.float64,0x3fe0413542e0826b,0xbfd2d3b08fb5b8a6,2
|
| 1520 |
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np.float64,0x3fd00870ea2010e2,0xbfe33cc04cbd42e0,2
|
| 1521 |
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np.float64,0x3fe74fb817ae9f70,0xbfc19c45dbf919e1,2
|
| 1522 |
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np.float64,0x40382fa08071,0xc07357514ced5577,2
|
| 1523 |
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np.float64,0xa14968474292d,0xc0733da71a990f3a,2
|
| 1524 |
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np.float64,0x5487c740a90fa,0xc0734224622d5801,2
|
| 1525 |
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np.float64,0x3fed7d8d14fafb1a,0xbfa228f7ecc2ac03,2
|
| 1526 |
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np.float64,0x3fe39bb485e73769,0xbfcb3a235a722960,2
|
| 1527 |
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np.float64,0x3fd01090b2202121,0xbfe335b752589a22,2
|
| 1528 |
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np.float64,0x3fd21a3e7da4347d,0xbfe18cd435a7c582,2
|
| 1529 |
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np.float64,0x3fe7fa855a2ff50b,0xbfc00ab0665709fe,2
|
| 1530 |
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np.float64,0x3fedc0d4577b81a9,0xbfa02fef3ff553fc,2
|
| 1531 |
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np.float64,0x3fe99d4906333a92,0xbfb8bf18220e5e8e,2
|
| 1532 |
+
np.float64,0x3fd944ee3c3289dc,0xbfd9d46071675e73,2
|
| 1533 |
+
np.float64,0x3fe3ed8d52e7db1b,0xbfca53f8d4aef484,2
|
| 1534 |
+
np.float64,0x7fe748623a6e90c3,0x407341dd97c9dd79,2
|
| 1535 |
+
np.float64,0x3fea1b4b98343697,0xbfb6a1560a56927f,2
|
| 1536 |
+
np.float64,0xe1215715c242b,0xc0733b55dbf1f0a8,2
|
| 1537 |
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np.float64,0x3fd0d5bccca1ab7a,0xbfe28f1b66d7a470,2
|
| 1538 |
+
np.float64,0x881a962710353,0xc0733ed51848a30d,2
|
| 1539 |
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np.float64,0x3fcf022afe3e0456,0xbfe3b40eabf24501,2
|
| 1540 |
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np.float64,0x3fdf1ac6bbbe358d,0xbfd40e03e888288d,2
|
| 1541 |
+
np.float64,0x3fa51a5eac2a34bd,0xbff628a7c34d51b3,2
|
| 1542 |
+
np.float64,0x3fdbaf408d375e81,0xbfd74ad39d97c92a,2
|
| 1543 |
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np.float64,0x3fcd2418ea3a4832,0xbfe4910b009d8b11,2
|
| 1544 |
+
np.float64,0x3fc7b3062a2f660c,0xbfe7706dc47993e1,2
|
| 1545 |
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np.float64,0x7fb8232218304643,0x407333aaa7041a9f,2
|
| 1546 |
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np.float64,0x7fd5f186362be30b,0x40733ca32fdf9cc6,2
|
| 1547 |
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np.float64,0x3fe57ef1d6aafde4,0xbfc61e23d00210c7,2
|
| 1548 |
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np.float64,0x7c6830baf8d07,0xc0733f74f19e9dad,2
|
| 1549 |
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np.float64,0xcacbfd5595980,0xc0733c0fb49edca7,2
|
| 1550 |
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np.float64,0x3fdfdeac873fbd59,0xbfd36114c56bed03,2
|
| 1551 |
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np.float64,0x3fd31f0889263e11,0xbfe0ca0cc1250169,2
|
| 1552 |
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np.float64,0x3fe839fbe47073f8,0xbfbef0a2abc3d63f,2
|
| 1553 |
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np.float64,0x3fc36af57e26d5eb,0xbfea3553f38770b7,2
|
| 1554 |
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np.float64,0x3fe73dbc44ee7b79,0xbfc1c738f8fa6b3d,2
|
| 1555 |
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np.float64,0x3fd3760e4da6ec1d,0xbfe08b5b609d11e5,2
|
| 1556 |
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np.float64,0x3fee1cfa297c39f4,0xbf9b06d081bc9d5b,2
|
| 1557 |
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np.float64,0xdfb01561bf61,0xc0734ea55e559888,2
|
| 1558 |
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np.float64,0x687bd01cd0f7b,0xc07340ab67fe1816,2
|
| 1559 |
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np.float64,0x3fefc88f4cbf911f,0xbf6828c359cf19dc,2
|
| 1560 |
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np.float64,0x8ad34adb15a6a,0xc0733eb1e03811e5,2
|
| 1561 |
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np.float64,0x3fe2b49c12e56938,0xbfcdd8dbdbc0ce59,2
|
| 1562 |
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np.float64,0x6e05037adc0a1,0xc073404f91261635,2
|
| 1563 |
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np.float64,0x3fe2fd737fe5fae7,0xbfcd020407ef4d78,2
|
| 1564 |
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np.float64,0x3fd0f3c0dc21e782,0xbfe2766a1ab02eae,2
|
| 1565 |
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np.float64,0x28564d9850acb,0xc073474875f87c5e,2
|
| 1566 |
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np.float64,0x3fe4758015a8eb00,0xbfc8ddb45134a1bd,2
|
| 1567 |
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np.float64,0x7fe7f19306efe325,0x4073420f626141a7,2
|
| 1568 |
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np.float64,0x7fd27f34c0a4fe69,0x40733b733d2a5b50,2
|
| 1569 |
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np.float64,0x92c2366325847,0xc0733e4f04f8195a,2
|
| 1570 |
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np.float64,0x3fc21f8441243f09,0xbfeb2ad23bc1ab0b,2
|
| 1571 |
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np.float64,0x3fc721d3e42e43a8,0xbfe7c69bb47b40c2,2
|
| 1572 |
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np.float64,0x3fe2f11a1625e234,0xbfcd26363b9c36c3,2
|
| 1573 |
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np.float64,0x3fdcb585acb96b0b,0xbfd648446237cb55,2
|
| 1574 |
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np.float64,0x3fd4060bf2280c18,0xbfe025fd4c8a658b,2
|
| 1575 |
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np.float64,0x7fb8ae2750315c4e,0x407333d23b025d08,2
|
| 1576 |
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np.float64,0x3fe3a03119a74062,0xbfcb2d6c91b38552,2
|
| 1577 |
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np.float64,0x7fdd2af92bba55f1,0x40733e9d737e16e6,2
|
| 1578 |
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np.float64,0x3fe50b05862a160b,0xbfc74d20815fe36b,2
|
| 1579 |
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np.float64,0x164409f82c882,0xc0734b6980e19c03,2
|
| 1580 |
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np.float64,0x3fe4093712a8126e,0xbfca070367fda5e3,2
|
| 1581 |
+
np.float64,0xae3049935c609,0xc0733d1e3608797b,2
|
| 1582 |
+
np.float64,0x3fd71df4b4ae3be9,0xbfdc4dcb7637600d,2
|
| 1583 |
+
np.float64,0x7fca01e8023403cf,0x407339006c521c49,2
|
| 1584 |
+
np.float64,0x3fb0c5c43e218b88,0xbff2f03211c63f25,2
|
| 1585 |
+
np.float64,0x3fee757af83ceaf6,0xbf95f33a6e56b454,2
|
| 1586 |
+
np.float64,0x3f865f1f402cbe3f,0xbfff62d9c9072bd7,2
|
| 1587 |
+
np.float64,0x89864e95130ca,0xc0733ec29f1e32c6,2
|
| 1588 |
+
np.float64,0x3fe51482bcea2905,0xbfc73414ddc8f1b7,2
|
| 1589 |
+
np.float64,0x7fd802f8fa3005f1,0x40733d43684e460a,2
|
| 1590 |
+
np.float64,0x3fbeb86ca63d70d9,0xbfed774ccca9b8f5,2
|
| 1591 |
+
np.float64,0x3fb355dcc826abba,0xbff1f33f9339e7a3,2
|
| 1592 |
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np.float64,0x3fe506c61eaa0d8c,0xbfc7585a3f7565a6,2
|
| 1593 |
+
np.float64,0x7fe393f25ba727e4,0x407340a94bcea73b,2
|
| 1594 |
+
np.float64,0xf66f532decdeb,0xc0733ab5041feb0f,2
|
| 1595 |
+
np.float64,0x3fe26e872be4dd0e,0xbfceaaab466f32e0,2
|
| 1596 |
+
np.float64,0x3fefd9e290bfb3c5,0xbf60977d24496295,2
|
| 1597 |
+
np.float64,0x7fe19c5f692338be,0x40733fecef53ad95,2
|
| 1598 |
+
np.float64,0x3fe80365ab3006cb,0xbfbfec4090ef76ec,2
|
| 1599 |
+
np.float64,0x3fe88ab39eb11567,0xbfbd8099388d054d,2
|
| 1600 |
+
np.float64,0x3fe68fb09fad1f61,0xbfc36db9de38c2c0,2
|
| 1601 |
+
np.float64,0x3fe9051883b20a31,0xbfbb5b75b8cb8f24,2
|
| 1602 |
+
np.float64,0x3fd4708683a8e10d,0xbfdfb9b085dd8a83,2
|
| 1603 |
+
np.float64,0x3fe00ac11a601582,0xbfd3316af3e43500,2
|
| 1604 |
+
np.float64,0xd16af30ba2d5f,0xc0733bd68e8252f9,2
|
| 1605 |
+
np.float64,0x3fb97d654632facb,0xbff007ac1257f575,2
|
| 1606 |
+
np.float64,0x7fd637c10fac6f81,0x40733cb949d76546,2
|
| 1607 |
+
np.float64,0x7fed2cab6dba5956,0x4073436edfc3764e,2
|
| 1608 |
+
np.float64,0x3fed04afbbba095f,0xbfa5bfaa5074b7f4,2
|
| 1609 |
+
np.float64,0x0,0xfff0000000000000,2
|
| 1610 |
+
np.float64,0x389a1dc671345,0xc07344edd4206338,2
|
| 1611 |
+
np.float64,0x3fbc9ba25a393745,0xbfee74c34f49b921,2
|
| 1612 |
+
np.float64,0x3feee749947dce93,0xbf8f032d9cf6b5ae,2
|
| 1613 |
+
np.float64,0xedc4cf89db89a,0xc0733af4b2a57920,2
|
| 1614 |
+
np.float64,0x3fe41629eba82c54,0xbfc9e321faf79e1c,2
|
| 1615 |
+
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 @@
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|
| 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 @@
|
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|
| 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
|
| 2 |
+
oid sha256:5f5606939b9d2d4be91d70bd5564739c64ff81c1bef843e300ff34639ffe8b7a
|
| 3 |
+
size 111134
|
evalkit_tf446/lib/python3.10/site-packages/networkx/readwrite/__pycache__/__init__.cpython-310.pyc
ADDED
|
Binary file (716 Bytes). View file
|
|
|
evalkit_tf446/lib/python3.10/site-packages/networkx/readwrite/__pycache__/adjlist.cpython-310.pyc
ADDED
|
Binary file (8.35 kB). View file
|
|
|
evalkit_tf446/lib/python3.10/site-packages/networkx/readwrite/__pycache__/edgelist.cpython-310.pyc
ADDED
|
Binary file (13.2 kB). View file
|
|
|
evalkit_tf446/lib/python3.10/site-packages/networkx/readwrite/__pycache__/gexf.cpython-310.pyc
ADDED
|
Binary file (25.1 kB). View file
|
|
|
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ADDED
|
Binary file (11.7 kB). View file
|
|
|
evalkit_tf446/lib/python3.10/site-packages/networkx/readwrite/__pycache__/graphml.cpython-310.pyc
ADDED
|
Binary file (28.5 kB). View file
|
|
|
evalkit_tf446/lib/python3.10/site-packages/networkx/readwrite/__pycache__/leda.cpython-310.pyc
ADDED
|
Binary file (2.88 kB). View file
|
|
|
evalkit_tf446/lib/python3.10/site-packages/networkx/readwrite/__pycache__/multiline_adjlist.cpython-310.pyc
ADDED
|
Binary file (9.53 kB). View file
|
|
|
evalkit_tf446/lib/python3.10/site-packages/networkx/readwrite/__pycache__/p2g.cpython-310.pyc
ADDED
|
Binary file (3.08 kB). View file
|
|
|
evalkit_tf446/lib/python3.10/site-packages/networkx/readwrite/__pycache__/pajek.cpython-310.pyc
ADDED
|
Binary file (6.76 kB). View file
|
|
|
evalkit_tf446/lib/python3.10/site-packages/networkx/readwrite/__pycache__/sparse6.cpython-310.pyc
ADDED
|
Binary file (9.78 kB). View file
|
|
|