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Editing: ndarraytypes.h
#ifndef NUMPY_CORE_INCLUDE_NUMPY_NDARRAYTYPES_H_ #define NUMPY_CORE_INCLUDE_NUMPY_NDARRAYTYPES_H_ #include "npy_common.h" #include "npy_endian.h" #include "npy_cpu.h" #include "utils.h" #define NPY_NO_EXPORT NPY_VISIBILITY_HIDDEN /* Only use thread if configured in config and python supports it */ #if defined WITH_THREAD && !NPY_NO_SMP #define NPY_ALLOW_THREADS 1 #else #define NPY_ALLOW_THREADS 0 #endif #ifndef __has_extension #define __has_extension(x) 0 #endif #if !defined(_NPY_NO_DEPRECATIONS) && \ ((defined(__GNUC__)&& __GNUC__ >= 6) || \ __has_extension(attribute_deprecated_with_message)) #define NPY_ATTR_DEPRECATE(text) __attribute__ ((deprecated (text))) #else #define NPY_ATTR_DEPRECATE(text) #endif /* * There are several places in the code where an array of dimensions * is allocated statically. This is the size of that static * allocation. * * The array creation itself could have arbitrary dimensions but all * the places where static allocation is used would need to be changed * to dynamic (including inside of several structures) */ #define NPY_MAXDIMS 32 #define NPY_MAXARGS 32 /* Used for Converter Functions "O&" code in ParseTuple */ #define NPY_FAIL 0 #define NPY_SUCCEED 1 enum NPY_TYPES { NPY_BOOL=0, NPY_BYTE, NPY_UBYTE, NPY_SHORT, NPY_USHORT, NPY_INT, NPY_UINT, NPY_LONG, NPY_ULONG, NPY_LONGLONG, NPY_ULONGLONG, NPY_FLOAT, NPY_DOUBLE, NPY_LONGDOUBLE, NPY_CFLOAT, NPY_CDOUBLE, NPY_CLONGDOUBLE, NPY_OBJECT=17, NPY_STRING, NPY_UNICODE, NPY_VOID, /* * New 1.6 types appended, may be integrated * into the above in 2.0. */ NPY_DATETIME, NPY_TIMEDELTA, NPY_HALF, NPY_NTYPES, NPY_NOTYPE, NPY_CHAR NPY_ATTR_DEPRECATE("Use NPY_STRING"), NPY_USERDEF=256, /* leave room for characters */ /* The number of types not including the new 1.6 types */ NPY_NTYPES_ABI_COMPATIBLE=21 }; #if defined(_MSC_VER) && !defined(__clang__) #pragma deprecated(NPY_CHAR) #endif /* basetype array priority */ #define NPY_PRIORITY 0.0 /* default subtype priority */ #define NPY_SUBTYPE_PRIORITY 1.0 /* default scalar priority */ #define NPY_SCALAR_PRIORITY -1000000.0 /* How many floating point types are there (excluding half) */ #define NPY_NUM_FLOATTYPE 3 /* * These characters correspond to the array type and the struct * module */ enum NPY_TYPECHAR { NPY_BOOLLTR = '?', NPY_BYTELTR = 'b', NPY_UBYTELTR = 'B', NPY_SHORTLTR = 'h', NPY_USHORTLTR = 'H', NPY_INTLTR = 'i', NPY_UINTLTR = 'I', NPY_LONGLTR = 'l', NPY_ULONGLTR = 'L', NPY_LONGLONGLTR = 'q', NPY_ULONGLONGLTR = 'Q', NPY_HALFLTR = 'e', NPY_FLOATLTR = 'f', NPY_DOUBLELTR = 'd', NPY_LONGDOUBLELTR = 'g', NPY_CFLOATLTR = 'F', NPY_CDOUBLELTR = 'D', NPY_CLONGDOUBLELTR = 'G', NPY_OBJECTLTR = 'O', NPY_STRINGLTR = 'S', NPY_STRINGLTR2 = 'a', NPY_UNICODELTR = 'U', NPY_VOIDLTR = 'V', NPY_DATETIMELTR = 'M', NPY_TIMEDELTALTR = 'm', NPY_CHARLTR = 'c', /* * No Descriptor, just a define -- this let's * Python users specify an array of integers * large enough to hold a pointer on the * platform */ NPY_INTPLTR = 'p', NPY_UINTPLTR = 'P', /* * These are for dtype 'kinds', not dtype 'typecodes' * as the above are for. */ NPY_GENBOOLLTR ='b', NPY_SIGNEDLTR = 'i', NPY_UNSIGNEDLTR = 'u', NPY_FLOATINGLTR = 'f', NPY_COMPLEXLTR = 'c' }; /* * Changing this may break Numpy API compatibility * due to changing offsets in PyArray_ArrFuncs, so be * careful. Here we have reused the mergesort slot for * any kind of stable sort, the actual implementation will * depend on the data type. */ typedef enum { NPY_QUICKSORT=0, NPY_HEAPSORT=1, NPY_MERGESORT=2, NPY_STABLESORT=2, } NPY_SORTKIND; #define NPY_NSORTS (NPY_STABLESORT + 1) typedef enum { NPY_INTROSELECT=0 } NPY_SELECTKIND; #define NPY_NSELECTS (NPY_INTROSELECT + 1) typedef enum { NPY_SEARCHLEFT=0, NPY_SEARCHRIGHT=1 } NPY_SEARCHSIDE; #define NPY_NSEARCHSIDES (NPY_SEARCHRIGHT + 1) typedef enum { NPY_NOSCALAR=-1, NPY_BOOL_SCALAR, NPY_INTPOS_SCALAR, NPY_INTNEG_SCALAR, NPY_FLOAT_SCALAR, NPY_COMPLEX_SCALAR, NPY_OBJECT_SCALAR } NPY_SCALARKIND; #define NPY_NSCALARKINDS (NPY_OBJECT_SCALAR + 1) /* For specifying array memory layout or iteration order */ typedef enum { /* Fortran order if inputs are all Fortran, C otherwise */ NPY_ANYORDER=-1, /* C order */ NPY_CORDER=0, /* Fortran order */ NPY_FORTRANORDER=1, /* An order as close to the inputs as possible */ NPY_KEEPORDER=2 } NPY_ORDER; /* For specifying allowed casting in operations which support it */ typedef enum { _NPY_ERROR_OCCURRED_IN_CAST = -1, /* Only allow identical types */ NPY_NO_CASTING=0, /* Allow identical and byte swapped types */ NPY_EQUIV_CASTING=1, /* Only allow safe casts */ NPY_SAFE_CASTING=2, /* Allow safe casts or casts within the same kind */ NPY_SAME_KIND_CASTING=3, /* Allow any casts */ NPY_UNSAFE_CASTING=4, } NPY_CASTING; typedef enum { NPY_CLIP=0, NPY_WRAP=1, NPY_RAISE=2 } NPY_CLIPMODE; typedef enum { NPY_VALID=0, NPY_SAME=1, NPY_FULL=2 } NPY_CORRELATEMODE; /* The special not-a-time (NaT) value */ #define NPY_DATETIME_NAT NPY_MIN_INT64 /* * Upper bound on the length of a DATETIME ISO 8601 string * YEAR: 21 (64-bit year) * MONTH: 3 * DAY: 3 * HOURS: 3 * MINUTES: 3 * SECONDS: 3 * ATTOSECONDS: 1 + 3*6 * TIMEZONE: 5 * NULL TERMINATOR: 1 */ #define NPY_DATETIME_MAX_ISO8601_STRLEN (21 + 3*5 + 1 + 3*6 + 6 + 1) /* The FR in the unit names stands for frequency */ typedef enum { /* Force signed enum type, must be -1 for code compatibility */ NPY_FR_ERROR = -1, /* error or undetermined */ /* Start of valid units */ NPY_FR_Y = 0, /* Years */ NPY_FR_M = 1, /* Months */ NPY_FR_W = 2, /* Weeks */ /* Gap where 1.6 NPY_FR_B (value 3) was */ NPY_FR_D = 4, /* Days */ NPY_FR_h = 5, /* hours */ NPY_FR_m = 6, /* minutes */ NPY_FR_s = 7, /* seconds */ NPY_FR_ms = 8, /* milliseconds */ NPY_FR_us = 9, /* microseconds */ NPY_FR_ns = 10, /* nanoseconds */ NPY_FR_ps = 11, /* picoseconds */ NPY_FR_fs = 12, /* femtoseconds */ NPY_FR_as = 13, /* attoseconds */ NPY_FR_GENERIC = 14 /* unbound units, can convert to anything */ } NPY_DATETIMEUNIT; /* * NOTE: With the NPY_FR_B gap for 1.6 ABI compatibility, NPY_DATETIME_NUMUNITS * is technically one more than the actual number of units. */ #define NPY_DATETIME_NUMUNITS (NPY_FR_GENERIC + 1) #define NPY_DATETIME_DEFAULTUNIT NPY_FR_GENERIC /* * Business day conventions for mapping invalid business * days to valid business days. */ typedef enum { /* Go forward in time to the following business day. */ NPY_BUSDAY_FORWARD, NPY_BUSDAY_FOLLOWING = NPY_BUSDAY_FORWARD, /* Go backward in time to the preceding business day. */ NPY_BUSDAY_BACKWARD, NPY_BUSDAY_PRECEDING = NPY_BUSDAY_BACKWARD, /* * Go forward in time to the following business day, unless it * crosses a month boundary, in which case go backward */ NPY_BUSDAY_MODIFIEDFOLLOWING, /* * Go backward in time to the preceding business day, unless it * crosses a month boundary, in which case go forward. */ NPY_BUSDAY_MODIFIEDPRECEDING, /* Produce a NaT for non-business days. */ NPY_BUSDAY_NAT, /* Raise an exception for non-business days. */ NPY_BUSDAY_RAISE } NPY_BUSDAY_ROLL; /************************************************************ * NumPy Auxiliary Data for inner loops, sort functions, etc. ************************************************************/ /* * When creating an auxiliary data struct, this should always appear * as the first member, like this: * * typedef struct { * NpyAuxData base; * double constant; * } constant_multiplier_aux_data; */ typedef struct NpyAuxData_tag NpyAuxData; /* Function pointers for freeing or cloning auxiliary data */ typedef void (NpyAuxData_FreeFunc) (NpyAuxData *); typedef NpyAuxData *(NpyAuxData_CloneFunc) (NpyAuxData *); struct NpyAuxData_tag { NpyAuxData_FreeFunc *free; NpyAuxData_CloneFunc *clone; /* To allow for a bit of expansion without breaking the ABI */ void *reserved[2]; }; /* Macros to use for freeing and cloning auxiliary data */ #define NPY_AUXDATA_FREE(auxdata) \ do { \ if ((auxdata) != NULL) { \ (auxdata)->free(auxdata); \ } \ } while(0) #define NPY_AUXDATA_CLONE(auxdata) \ ((auxdata)->clone(auxdata)) #define NPY_ERR(str) fprintf(stderr, #str); fflush(stderr); #define NPY_ERR2(str) fprintf(stderr, str); fflush(stderr); /* * Macros to define how array, and dimension/strides data is * allocated. These should be made private */ #define NPY_USE_PYMEM 1 #if NPY_USE_PYMEM == 1 /* use the Raw versions which are safe to call with the GIL released */ #define PyArray_malloc PyMem_RawMalloc #define PyArray_free PyMem_RawFree #define PyArray_realloc PyMem_RawRealloc #else #define PyArray_malloc malloc #define PyArray_free free #define PyArray_realloc realloc #endif /* Dimensions and strides */ #define PyDimMem_NEW(size) \ ((npy_intp *)PyArray_malloc(size*sizeof(npy_intp))) #define PyDimMem_FREE(ptr) PyArray_free(ptr) #define PyDimMem_RENEW(ptr,size) \ ((npy_intp *)PyArray_realloc(ptr,size*sizeof(npy_intp))) /* forward declaration */ struct _PyArray_Descr; /* These must deal with unaligned and swapped data if necessary */ typedef PyObject * (PyArray_GetItemFunc) (void *, void *); typedef int (PyArray_SetItemFunc)(PyObject *, void *, void *); typedef void (PyArray_CopySwapNFunc)(void *, npy_intp, void *, npy_intp, npy_intp, int, void *); typedef void (PyArray_CopySwapFunc)(void *, void *, int, void *); typedef npy_bool (PyArray_NonzeroFunc)(void *, void *); /* * These assume aligned and notswapped data -- a buffer will be used * before or contiguous data will be obtained */ typedef int (PyArray_CompareFunc)(const void *, const void *, void *); typedef int (PyArray_ArgFunc)(void*, npy_intp, npy_intp*, void *); typedef void (PyArray_DotFunc)(void *, npy_intp, void *, npy_intp, void *, npy_intp, void *); typedef void (PyArray_VectorUnaryFunc)(void *, void *, npy_intp, void *, void *); /* * XXX the ignore argument should be removed next time the API version * is bumped. It used to be the separator. */ typedef int (PyArray_ScanFunc)(FILE *fp, void *dptr, char *ignore, struct _PyArray_Descr *); typedef int (PyArray_FromStrFunc)(char *s, void *dptr, char **endptr, struct _PyArray_Descr *); typedef int (PyArray_FillFunc)(void *, npy_intp, void *); typedef int (PyArray_SortFunc)(void *, npy_intp, void *); typedef int (PyArray_ArgSortFunc)(void *, npy_intp *, npy_intp, void *); typedef int (PyArray_PartitionFunc)(void *, npy_intp, npy_intp, npy_intp *, npy_intp *, void *); typedef int (PyArray_ArgPartitionFunc)(void *, npy_intp *, npy_intp, npy_intp, npy_intp *, npy_intp *, void *); typedef int (PyArray_FillWithScalarFunc)(void *, npy_intp, void *, void *); typedef int (PyArray_ScalarKindFunc)(void *); typedef void (PyArray_FastClipFunc)(void *in, npy_intp n_in, void *min, void *max, void *out); typedef void (PyArray_FastPutmaskFunc)(void *in, void *mask, npy_intp n_in, void *values, npy_intp nv); typedef int (PyArray_FastTakeFunc)(void *dest, void *src, npy_intp *indarray, npy_intp nindarray, npy_intp n_outer, npy_intp m_middle, npy_intp nelem, NPY_CLIPMODE clipmode); typedef struct { npy_intp *ptr; int len; } PyArray_Dims; typedef struct { /* * Functions to cast to most other standard types * Can have some NULL entries. The types * DATETIME, TIMEDELTA, and HALF go into the castdict * even though they are built-in. */ PyArray_VectorUnaryFunc *cast[NPY_NTYPES_ABI_COMPATIBLE]; /* The next four functions *cannot* be NULL */ /* * Functions to get and set items with standard Python types * -- not array scalars */ PyArray_GetItemFunc *getitem; PyArray_SetItemFunc *setitem; /* * Copy and/or swap data. Memory areas may not overlap * Use memmove first if they might */ PyArray_CopySwapNFunc *copyswapn; PyArray_CopySwapFunc *copyswap; /* * Function to compare items * Can be NULL */ PyArray_CompareFunc *compare; /* * Function to select largest * Can be NULL */ PyArray_ArgFunc *argmax; /* * Function to compute dot product * Can be NULL */ PyArray_DotFunc *dotfunc; /* * Function to scan an ASCII file and * place a single value plus possible separator * Can be NULL */ PyArray_ScanFunc *scanfunc; /* * Function to read a single value from a string * and adjust the pointer; Can be NULL */ PyArray_FromStrFunc *fromstr; /* * Function to determine if data is zero or not * If NULL a default version is * used at Registration time. */ PyArray_NonzeroFunc *nonzero; /* * Used for arange. Should return 0 on success * and -1 on failure. * Can be NULL. */ PyArray_FillFunc *fill; /* * Function to fill arrays with scalar values * Can be NULL */ PyArray_FillWithScalarFunc *fillwithscalar; /* * Sorting functions * Can be NULL */ PyArray_SortFunc *sort[NPY_NSORTS]; PyArray_ArgSortFunc *argsort[NPY_NSORTS]; /* * Dictionary of additional casting functions * PyArray_VectorUnaryFuncs * which can be populated to support casting * to other registered types. Can be NULL */ PyObject *castdict; /* * Functions useful for generalizing * the casting rules. * Can be NULL; */ PyArray_ScalarKindFunc *scalarkind; int **cancastscalarkindto; int *cancastto; PyArray_FastClipFunc *fastclip; PyArray_FastPutmaskFunc *fastputmask; PyArray_FastTakeFunc *fasttake; /* * Function to select smallest * Can be NULL */ PyArray_ArgFunc *argmin; } PyArray_ArrFuncs; /* The item must be reference counted when it is inserted or extracted. */ #define NPY_ITEM_REFCOUNT 0x01 /* Same as needing REFCOUNT */ #define NPY_ITEM_HASOBJECT 0x01 /* Convert to list for pickling */ #define NPY_LIST_PICKLE 0x02 /* The item is a POINTER */ #define NPY_ITEM_IS_POINTER 0x04 /* memory needs to be initialized for this data-type */ #define NPY_NEEDS_INIT 0x08 /* operations need Python C-API so don't give-up thread. */ #define NPY_NEEDS_PYAPI 0x10 /* Use f.getitem when extracting elements of this data-type */ #define NPY_USE_GETITEM 0x20 /* Use f.setitem when setting creating 0-d array from this data-type.*/ #define NPY_USE_SETITEM 0x40 /* A sticky flag specifically for structured arrays */ #define NPY_ALIGNED_STRUCT 0x80 /* *These are inherited for global data-type if any data-types in the * field have them */ #define NPY_FROM_FIELDS (NPY_NEEDS_INIT | NPY_LIST_PICKLE | \ NPY_ITEM_REFCOUNT | NPY_NEEDS_PYAPI) #define NPY_OBJECT_DTYPE_FLAGS (NPY_LIST_PICKLE | NPY_USE_GETITEM | \ NPY_ITEM_IS_POINTER | NPY_ITEM_REFCOUNT | \ NPY_NEEDS_INIT | NPY_NEEDS_PYAPI) #define PyDataType_FLAGCHK(dtype, flag) \ (((dtype)->flags & (flag)) == (flag)) #define PyDataType_REFCHK(dtype) \ PyDataType_FLAGCHK(dtype, NPY_ITEM_REFCOUNT) typedef struct _PyArray_Descr { PyObject_HEAD /* * the type object representing an * instance of this type -- should not * be two type_numbers with the same type * object. */ PyTypeObject *typeobj; /* kind for this type */ char kind; /* unique-character representing this type */ char type; /* * '>' (big), '<' (little), '|' * (not-applicable), or '=' (native). */ char byteorder; /* flags describing data type */ char flags; /* number representing this type */ int type_num; /* element size (itemsize) for this type */ int elsize; /* alignment needed for this type */ int alignment; /* * Non-NULL if this type is * is an array (C-contiguous) * of some other type */ struct _arr_descr *subarray; /* * The fields dictionary for this type * For statically defined descr this * is always Py_None */ PyObject *fields; /* * An ordered tuple of field names or NULL * if no fields are defined */ PyObject *names; /* * a table of functions specific for each * basic data descriptor */ PyArray_ArrFuncs *f; /* Metadata about this dtype */ PyObject *metadata; /* * Metadata specific to the C implementation * of the particular dtype. This was added * for NumPy 1.7.0. */ NpyAuxData *c_metadata; /* Cached hash value (-1 if not yet computed). * This was added for NumPy 2.0.0. */ npy_hash_t hash; } PyArray_Descr; typedef struct _arr_descr { PyArray_Descr *base; PyObject *shape; /* a tuple */ } PyArray_ArrayDescr; /* * Memory handler structure for array data. */ /* The declaration of free differs from PyMemAllocatorEx */ typedef struct { void *ctx; void* (*malloc) (void *ctx, size_t size); void* (*calloc) (void *ctx, size_t nelem, size_t elsize); void* (*realloc) (void *ctx, void *ptr, size_t new_size); void (*free) (void *ctx, void *ptr, size_t size); /* * This is the end of the version=1 struct. Only add new fields after * this line */ } PyDataMemAllocator; typedef struct { char name[127]; /* multiple of 64 to keep the struct aligned */ uint8_t version; /* currently 1 */ PyDataMemAllocator allocator; } PyDataMem_Handler; /* * The main array object structure. * * It has been recommended to use the inline functions defined below * (PyArray_DATA and friends) to access fields here for a number of * releases. Direct access to the members themselves is deprecated. * To ensure that your code does not use deprecated access, * #define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION * (or NPY_1_8_API_VERSION or higher as required). */ /* This struct will be moved to a private header in a future release */ typedef struct tagPyArrayObject_fields { PyObject_HEAD /* Pointer to the raw data buffer */ char *data; /* The number of dimensions, also called 'ndim' */ int nd; /* The size in each dimension, also called 'shape' */ npy_intp *dimensions; /* * Number of bytes to jump to get to the * next element in each dimension */ npy_intp *strides; /* * This object is decref'd upon * deletion of array. Except in the * case of WRITEBACKIFCOPY which has * special handling. * * For views it points to the original * array, collapsed so no chains of * views occur. * * For creation from buffer object it * points to an object that should be * decref'd on deletion * * For WRITEBACKIFCOPY flag this is an * array to-be-updated upon calling * PyArray_ResolveWritebackIfCopy */ PyObject *base; /* Pointer to type structure */ PyArray_Descr *descr; /* Flags describing array -- see below */ int flags; /* For weak references */ PyObject *weakreflist; #if NPY_FEATURE_VERSION >= NPY_1_20_API_VERSION void *_buffer_info; /* private buffer info, tagged to allow warning */ #endif /* * For malloc/calloc/realloc/free per object */ #if NPY_FEATURE_VERSION >= NPY_1_22_API_VERSION PyObject *mem_handler; #endif } PyArrayObject_fields; /* * To hide the implementation details, we only expose * the Python struct HEAD. */ #if !defined(NPY_NO_DEPRECATED_API) || \ (NPY_NO_DEPRECATED_API < NPY_1_7_API_VERSION) /* * Can't put this in npy_deprecated_api.h like the others. * PyArrayObject field access is deprecated as of NumPy 1.7. */ typedef PyArrayObject_fields PyArrayObject; #else typedef struct tagPyArrayObject { PyObject_HEAD } PyArrayObject; #endif /* * Removed 2020-Nov-25, NumPy 1.20 * #define NPY_SIZEOF_PYARRAYOBJECT (sizeof(PyArrayObject_fields)) * * The above macro was removed as it gave a false sense of a stable ABI * with respect to the structures size. If you require a runtime constant, * you can use `PyArray_Type.tp_basicsize` instead. Otherwise, please * see the PyArrayObject documentation or ask the NumPy developers for * information on how to correctly replace the macro in a way that is * compatible with multiple NumPy versions. */ /* Array Flags Object */ typedef struct PyArrayFlagsObject { PyObject_HEAD PyObject *arr; int flags; } PyArrayFlagsObject; /* Mirrors buffer object to ptr */ typedef struct { PyObject_HEAD PyObject *base; void *ptr; npy_intp len; int flags; } PyArray_Chunk; typedef struct { NPY_DATETIMEUNIT base; int num; } PyArray_DatetimeMetaData; typedef struct { NpyAuxData base; PyArray_DatetimeMetaData meta; } PyArray_DatetimeDTypeMetaData; /* * This structure contains an exploded view of a date-time value. * NaT is represented by year == NPY_DATETIME_NAT. */ typedef struct { npy_int64 year; npy_int32 month, day, hour, min, sec, us, ps, as; } npy_datetimestruct; /* This is not used internally. */ typedef struct { npy_int64 day; npy_int32 sec, us, ps, as; } npy_timedeltastruct; typedef int (PyArray_FinalizeFunc)(PyArrayObject *, PyObject *); /* * Means c-style contiguous (last index varies the fastest). The data * elements right after each other. * * This flag may be requested in constructor functions. * This flag may be tested for in PyArray_FLAGS(arr). */ #define NPY_ARRAY_C_CONTIGUOUS 0x0001 /* * Set if array is a contiguous Fortran array: the first index varies * the fastest in memory (strides array is reverse of C-contiguous * array) * * This flag may be requested in constructor functions. * This flag may be tested for in PyArray_FLAGS(arr). */ #define NPY_ARRAY_F_CONTIGUOUS 0x0002 /* * Note: all 0-d arrays are C_CONTIGUOUS and F_CONTIGUOUS. If a * 1-d array is C_CONTIGUOUS it is also F_CONTIGUOUS. Arrays with * more then one dimension can be C_CONTIGUOUS and F_CONTIGUOUS * at the same time if they have either zero or one element. * A higher dimensional array always has the same contiguity flags as * `array.squeeze()`; dimensions with `array.shape[dimension] == 1` are * effectively ignored when checking for contiguity. */ /* * If set, the array owns the data: it will be free'd when the array * is deleted. * * This flag may be tested for in PyArray_FLAGS(arr). */ #define NPY_ARRAY_OWNDATA 0x0004 /* * An array never has the next four set; they're only used as parameter * flags to the various FromAny functions * * This flag may be requested in constructor functions. */ /* Cause a cast to occur regardless of whether or not it is safe. */ #define NPY_ARRAY_FORCECAST 0x0010 /* * Always copy the array. Returned arrays are always CONTIGUOUS, * ALIGNED, and WRITEABLE. See also: NPY_ARRAY_ENSURENOCOPY = 0x4000. * * This flag may be requested in constructor functions. */ #define NPY_ARRAY_ENSURECOPY 0x0020 /* * Make sure the returned array is a base-class ndarray * * This flag may be requested in constructor functions. */ #define NPY_ARRAY_ENSUREARRAY 0x0040 /* * Make sure that the strides are in units of the element size Needed * for some operations with record-arrays. * * This flag may be requested in constructor functions. */ #define NPY_ARRAY_ELEMENTSTRIDES 0x0080 /* * Array data is aligned on the appropriate memory address for the type * stored according to how the compiler would align things (e.g., an * array of integers (4 bytes each) starts on a memory address that's * a multiple of 4) * * This flag may be requested in constructor functions. * This flag may be tested for in PyArray_FLAGS(arr). */ #define NPY_ARRAY_ALIGNED 0x0100 /* * Array data has the native endianness * * This flag may be requested in constructor functions. */ #define NPY_ARRAY_NOTSWAPPED 0x0200 /* * Array data is writeable * * This flag may be requested in constructor functions. * This flag may be tested for in PyArray_FLAGS(arr). */ #define NPY_ARRAY_WRITEABLE 0x0400 /* * If this flag is set, then base contains a pointer to an array of * the same size that should be updated with the current contents of * this array when PyArray_ResolveWritebackIfCopy is called. * * This flag may be requested in constructor functions. * This flag may be tested for in PyArray_FLAGS(arr). */ #define NPY_ARRAY_WRITEBACKIFCOPY 0x2000 /* * No copy may be made while converting from an object/array (result is a view) * * This flag may be requested in constructor functions. */ #define NPY_ARRAY_ENSURENOCOPY 0x4000 /* * NOTE: there are also internal flags defined in multiarray/arrayobject.h, * which start at bit 31 and work down. */ #define NPY_ARRAY_BEHAVED (NPY_ARRAY_ALIGNED | \ NPY_ARRAY_WRITEABLE) #define NPY_ARRAY_BEHAVED_NS (NPY_ARRAY_ALIGNED | \ NPY_ARRAY_WRITEABLE | \ NPY_ARRAY_NOTSWAPPED) #define NPY_ARRAY_CARRAY (NPY_ARRAY_C_CONTIGUOUS | \ NPY_ARRAY_BEHAVED) #define NPY_ARRAY_CARRAY_RO (NPY_ARRAY_C_CONTIGUOUS | \ NPY_ARRAY_ALIGNED) #define NPY_ARRAY_FARRAY (NPY_ARRAY_F_CONTIGUOUS | \ NPY_ARRAY_BEHAVED) #define NPY_ARRAY_FARRAY_RO (NPY_ARRAY_F_CONTIGUOUS | \ NPY_ARRAY_ALIGNED) #define NPY_ARRAY_DEFAULT (NPY_ARRAY_CARRAY) #define NPY_ARRAY_IN_ARRAY (NPY_ARRAY_CARRAY_RO) #define NPY_ARRAY_OUT_ARRAY (NPY_ARRAY_CARRAY) #define NPY_ARRAY_INOUT_ARRAY (NPY_ARRAY_CARRAY) #define NPY_ARRAY_INOUT_ARRAY2 (NPY_ARRAY_CARRAY | \ NPY_ARRAY_WRITEBACKIFCOPY) #define NPY_ARRAY_IN_FARRAY (NPY_ARRAY_FARRAY_RO) #define NPY_ARRAY_OUT_FARRAY (NPY_ARRAY_FARRAY) #define NPY_ARRAY_INOUT_FARRAY (NPY_ARRAY_FARRAY) #define NPY_ARRAY_INOUT_FARRAY2 (NPY_ARRAY_FARRAY | \ NPY_ARRAY_WRITEBACKIFCOPY) #define NPY_ARRAY_UPDATE_ALL (NPY_ARRAY_C_CONTIGUOUS | \ NPY_ARRAY_F_CONTIGUOUS | \ NPY_ARRAY_ALIGNED) /* This flag is for the array interface, not PyArrayObject */ #define NPY_ARR_HAS_DESCR 0x0800 /* * Size of internal buffers used for alignment Make BUFSIZE a multiple * of sizeof(npy_cdouble) -- usually 16 so that ufunc buffers are aligned */ #define NPY_MIN_BUFSIZE ((int)sizeof(npy_cdouble)) #define NPY_MAX_BUFSIZE (((int)sizeof(npy_cdouble))*1000000) #define NPY_BUFSIZE 8192 /* buffer stress test size: */ /*#define NPY_BUFSIZE 17*/ #define PyArray_MAX(a,b) (((a)>(b))?(a):(b)) #define PyArray_MIN(a,b) (((a)<(b))?(a):(b)) #define PyArray_CLT(p,q) ((((p).real==(q).real) ? ((p).imag < (q).imag) : \ ((p).real < (q).real))) #define PyArray_CGT(p,q) ((((p).real==(q).real) ? ((p).imag > (q).imag) : \ ((p).real > (q).real))) #define PyArray_CLE(p,q) ((((p).real==(q).real) ? ((p).imag <= (q).imag) : \ ((p).real <= (q).real))) #define PyArray_CGE(p,q) ((((p).real==(q).real) ? ((p).imag >= (q).imag) : \ ((p).real >= (q).real))) #define PyArray_CEQ(p,q) (((p).real==(q).real) && ((p).imag == (q).imag)) #define PyArray_CNE(p,q) (((p).real!=(q).real) || ((p).imag != (q).imag)) /* * C API: consists of Macros and functions. The MACROS are defined * here. */ #define PyArray_ISCONTIGUOUS(m) PyArray_CHKFLAGS((m), NPY_ARRAY_C_CONTIGUOUS) #define PyArray_ISWRITEABLE(m) PyArray_CHKFLAGS((m), NPY_ARRAY_WRITEABLE) #define PyArray_ISALIGNED(m) PyArray_CHKFLAGS((m), NPY_ARRAY_ALIGNED) #define PyArray_IS_C_CONTIGUOUS(m) PyArray_CHKFLAGS((m), NPY_ARRAY_C_CONTIGUOUS) #define PyArray_IS_F_CONTIGUOUS(m) PyArray_CHKFLAGS((m), NPY_ARRAY_F_CONTIGUOUS) /* the variable is used in some places, so always define it */ #define NPY_BEGIN_THREADS_DEF PyThreadState *_save=NULL; #if NPY_ALLOW_THREADS #define NPY_BEGIN_ALLOW_THREADS Py_BEGIN_ALLOW_THREADS #define NPY_END_ALLOW_THREADS Py_END_ALLOW_THREADS #define NPY_BEGIN_THREADS do {_save = PyEval_SaveThread();} while (0); #define NPY_END_THREADS do { if (_save) \ { PyEval_RestoreThread(_save); _save = NULL;} } while (0); #define NPY_BEGIN_THREADS_THRESHOLDED(loop_size) do { if ((loop_size) > 500) \ { _save = PyEval_SaveThread();} } while (0); #define NPY_BEGIN_THREADS_DESCR(dtype) \ do {if (!(PyDataType_FLAGCHK((dtype), NPY_NEEDS_PYAPI))) \ NPY_BEGIN_THREADS;} while (0); #define NPY_END_THREADS_DESCR(dtype) \ do {if (!(PyDataType_FLAGCHK((dtype), NPY_NEEDS_PYAPI))) \ NPY_END_THREADS; } while (0); #define NPY_ALLOW_C_API_DEF PyGILState_STATE __save__; #define NPY_ALLOW_C_API do {__save__ = PyGILState_Ensure();} while (0); #define NPY_DISABLE_C_API do {PyGILState_Release(__save__);} while (0); #else #define NPY_BEGIN_ALLOW_THREADS #define NPY_END_ALLOW_THREADS #define NPY_BEGIN_THREADS #define NPY_END_THREADS #define NPY_BEGIN_THREADS_THRESHOLDED(loop_size) #define NPY_BEGIN_THREADS_DESCR(dtype) #define NPY_END_THREADS_DESCR(dtype) #define NPY_ALLOW_C_API_DEF #define NPY_ALLOW_C_API #define NPY_DISABLE_C_API #endif /********************************** * The nditer object, added in 1.6 **********************************/ /* The actual structure of the iterator is an internal detail */ typedef struct NpyIter_InternalOnly NpyIter; /* Iterator function pointers that may be specialized */ typedef int (NpyIter_IterNextFunc)(NpyIter *iter); typedef void (NpyIter_GetMultiIndexFunc)(NpyIter *iter, npy_intp *outcoords); /*** Global flags that may be passed to the iterator constructors ***/ /* Track an index representing C order */ #define NPY_ITER_C_INDEX 0x00000001 /* Track an index representing Fortran order */ #define NPY_ITER_F_INDEX 0x00000002 /* Track a multi-index */ #define NPY_ITER_MULTI_INDEX 0x00000004 /* User code external to the iterator does the 1-dimensional innermost loop */ #define NPY_ITER_EXTERNAL_LOOP 0x00000008 /* Convert all the operands to a common data type */ #define NPY_ITER_COMMON_DTYPE 0x00000010 /* Operands may hold references, requiring API access during iteration */ #define NPY_ITER_REFS_OK 0x00000020 /* Zero-sized operands should be permitted, iteration checks IterSize for 0 */ #define NPY_ITER_ZEROSIZE_OK 0x00000040 /* Permits reductions (size-0 stride with dimension size > 1) */ #define NPY_ITER_REDUCE_OK 0x00000080 /* Enables sub-range iteration */ #define NPY_ITER_RANGED 0x00000100 /* Enables buffering */ #define NPY_ITER_BUFFERED 0x00000200 /* When buffering is enabled, grows the inner loop if possible */ #define NPY_ITER_GROWINNER 0x00000400 /* Delay allocation of buffers until first Reset* call */ #define NPY_ITER_DELAY_BUFALLOC 0x00000800 /* When NPY_KEEPORDER is specified, disable reversing negative-stride axes */ #define NPY_ITER_DONT_NEGATE_STRIDES 0x00001000 /* * If output operands overlap with other operands (based on heuristics that * has false positives but no false negatives), make temporary copies to * eliminate overlap. */ #define NPY_ITER_COPY_IF_OVERLAP 0x00002000 /*** Per-operand flags that may be passed to the iterator constructors ***/ /* The operand will be read from and written to */ #define NPY_ITER_READWRITE 0x00010000 /* The operand will only be read from */ #define NPY_ITER_READONLY 0x00020000 /* The operand will only be written to */ #define NPY_ITER_WRITEONLY 0x00040000 /* The operand's data must be in native byte order */ #define NPY_ITER_NBO 0x00080000 /* The operand's data must be aligned */ #define NPY_ITER_ALIGNED 0x00100000 /* The operand's data must be contiguous (within the inner loop) */ #define NPY_ITER_CONTIG 0x00200000 /* The operand may be copied to satisfy requirements */ #define NPY_ITER_COPY 0x00400000 /* The operand may be copied with WRITEBACKIFCOPY to satisfy requirements */ #define NPY_ITER_UPDATEIFCOPY 0x00800000 /* Allocate the operand if it is NULL */ #define NPY_ITER_ALLOCATE 0x01000000 /* If an operand is allocated, don't use any subtype */ #define NPY_ITER_NO_SUBTYPE 0x02000000 /* This is a virtual array slot, operand is NULL but temporary data is there */ #define NPY_ITER_VIRTUAL 0x04000000 /* Require that the dimension match the iterator dimensions exactly */ #define NPY_ITER_NO_BROADCAST 0x08000000 /* A mask is being used on this array, affects buffer -> array copy */ #define NPY_ITER_WRITEMASKED 0x10000000 /* This array is the mask for all WRITEMASKED operands */ #define NPY_ITER_ARRAYMASK 0x20000000 /* Assume iterator order data access for COPY_IF_OVERLAP */ #define NPY_ITER_OVERLAP_ASSUME_ELEMENTWISE 0x40000000 #define NPY_ITER_GLOBAL_FLAGS 0x0000ffff #define NPY_ITER_PER_OP_FLAGS 0xffff0000 /***************************** * Basic iterator object *****************************/ /* FWD declaration */ typedef struct PyArrayIterObject_tag PyArrayIterObject; /* * type of the function which translates a set of coordinates to a * pointer to the data */ typedef char* (*npy_iter_get_dataptr_t)( PyArrayIterObject* iter, const npy_intp*); struct PyArrayIterObject_tag { PyObject_HEAD int nd_m1; /* number of dimensions - 1 */ npy_intp index, size; npy_intp coordinates[NPY_MAXDIMS];/* N-dimensional loop */ npy_intp dims_m1[NPY_MAXDIMS]; /* ao->dimensions - 1 */ npy_intp strides[NPY_MAXDIMS]; /* ao->strides or fake */ npy_intp backstrides[NPY_MAXDIMS];/* how far to jump back */ npy_intp factors[NPY_MAXDIMS]; /* shape factors */ PyArrayObject *ao; char *dataptr; /* pointer to current item*/ npy_bool contiguous; npy_intp bounds[NPY_MAXDIMS][2]; npy_intp limits[NPY_MAXDIMS][2]; npy_intp limits_sizes[NPY_MAXDIMS]; npy_iter_get_dataptr_t translate; } ; /* Iterator API */ #define PyArrayIter_Check(op) PyObject_TypeCheck((op), &PyArrayIter_Type) #define _PyAIT(it) ((PyArrayIterObject *)(it)) #define PyArray_ITER_RESET(it) do { \ _PyAIT(it)->index = 0; \ _PyAIT(it)->dataptr = PyArray_BYTES(_PyAIT(it)->ao); \ memset(_PyAIT(it)->coordinates, 0, \ (_PyAIT(it)->nd_m1+1)*sizeof(npy_intp)); \ } while (0) #define _PyArray_ITER_NEXT1(it) do { \ (it)->dataptr += _PyAIT(it)->strides[0]; \ (it)->coordinates[0]++; \ } while (0) #define _PyArray_ITER_NEXT2(it) do { \ if ((it)->coordinates[1] < (it)->dims_m1[1]) { \ (it)->coordinates[1]++; \ (it)->dataptr += (it)->strides[1]; \ } \ else { \ (it)->coordinates[1] = 0; \ (it)->coordinates[0]++; \ (it)->dataptr += (it)->strides[0] - \ (it)->backstrides[1]; \ } \ } while (0) #define PyArray_ITER_NEXT(it) do { \ _PyAIT(it)->index++; \ if (_PyAIT(it)->nd_m1 == 0) { \ _PyArray_ITER_NEXT1(_PyAIT(it)); \ } \ else if (_PyAIT(it)->contiguous) \ _PyAIT(it)->dataptr += PyArray_DESCR(_PyAIT(it)->ao)->elsize; \ else if (_PyAIT(it)->nd_m1 == 1) { \ _PyArray_ITER_NEXT2(_PyAIT(it)); \ } \ else { \ int __npy_i; \ for (__npy_i=_PyAIT(it)->nd_m1; __npy_i >= 0; __npy_i--) { \ if (_PyAIT(it)->coordinates[__npy_i] < \ _PyAIT(it)->dims_m1[__npy_i]) { \ _PyAIT(it)->coordinates[__npy_i]++; \ _PyAIT(it)->dataptr += \ _PyAIT(it)->strides[__npy_i]; \ break; \ } \ else { \ _PyAIT(it)->coordinates[__npy_i] = 0; \ _PyAIT(it)->dataptr -= \ _PyAIT(it)->backstrides[__npy_i]; \ } \ } \ } \ } while (0) #define PyArray_ITER_GOTO(it, destination) do { \ int __npy_i; \ _PyAIT(it)->index = 0; \ _PyAIT(it)->dataptr = PyArray_BYTES(_PyAIT(it)->ao); \ for (__npy_i = _PyAIT(it)->nd_m1; __npy_i>=0; __npy_i--) { \ if (destination[__npy_i] < 0) { \ destination[__npy_i] += \ _PyAIT(it)->dims_m1[__npy_i]+1; \ } \ _PyAIT(it)->dataptr += destination[__npy_i] * \ _PyAIT(it)->strides[__npy_i]; \ _PyAIT(it)->coordinates[__npy_i] = \ destination[__npy_i]; \ _PyAIT(it)->index += destination[__npy_i] * \ ( __npy_i==_PyAIT(it)->nd_m1 ? 1 : \ _PyAIT(it)->dims_m1[__npy_i+1]+1) ; \ } \ } while (0) #define PyArray_ITER_GOTO1D(it, ind) do { \ int __npy_i; \ npy_intp __npy_ind = (npy_intp)(ind); \ if (__npy_ind < 0) __npy_ind += _PyAIT(it)->size; \ _PyAIT(it)->index = __npy_ind; \ if (_PyAIT(it)->nd_m1 == 0) { \ _PyAIT(it)->dataptr = PyArray_BYTES(_PyAIT(it)->ao) + \ __npy_ind * _PyAIT(it)->strides[0]; \ } \ else if (_PyAIT(it)->contiguous) \ _PyAIT(it)->dataptr = PyArray_BYTES(_PyAIT(it)->ao) + \ __npy_ind * PyArray_DESCR(_PyAIT(it)->ao)->elsize; \ else { \ _PyAIT(it)->dataptr = PyArray_BYTES(_PyAIT(it)->ao); \ for (__npy_i = 0; __npy_i<=_PyAIT(it)->nd_m1; \ __npy_i++) { \ _PyAIT(it)->coordinates[__npy_i] = \ (__npy_ind / _PyAIT(it)->factors[__npy_i]); \ _PyAIT(it)->dataptr += \ (__npy_ind / _PyAIT(it)->factors[__npy_i]) \ * _PyAIT(it)->strides[__npy_i]; \ __npy_ind %= _PyAIT(it)->factors[__npy_i]; \ } \ } \ } while (0) #define PyArray_ITER_DATA(it) ((void *)(_PyAIT(it)->dataptr)) #define PyArray_ITER_NOTDONE(it) (_PyAIT(it)->index < _PyAIT(it)->size) /* * Any object passed to PyArray_Broadcast must be binary compatible * with this structure. */ typedef struct { PyObject_HEAD int numiter; /* number of iters */ npy_intp size; /* broadcasted size */ npy_intp index; /* current index */ int nd; /* number of dims */ npy_intp dimensions[NPY_MAXDIMS]; /* dimensions */ PyArrayIterObject *iters[NPY_MAXARGS]; /* iterators */ } PyArrayMultiIterObject; #define _PyMIT(m) ((PyArrayMultiIterObject *)(m)) #define PyArray_MultiIter_RESET(multi) do { \ int __npy_mi; \ _PyMIT(multi)->index = 0; \ for (__npy_mi=0; __npy_mi < _PyMIT(multi)->numiter; __npy_mi++) { \ PyArray_ITER_RESET(_PyMIT(multi)->iters[__npy_mi]); \ } \ } while (0) #define PyArray_MultiIter_NEXT(multi) do { \ int __npy_mi; \ _PyMIT(multi)->index++; \ for (__npy_mi=0; __npy_mi < _PyMIT(multi)->numiter; __npy_mi++) { \ PyArray_ITER_NEXT(_PyMIT(multi)->iters[__npy_mi]); \ } \ } while (0) #define PyArray_MultiIter_GOTO(multi, dest) do { \ int __npy_mi; \ for (__npy_mi=0; __npy_mi < _PyMIT(multi)->numiter; __npy_mi++) { \ PyArray_ITER_GOTO(_PyMIT(multi)->iters[__npy_mi], dest); \ } \ _PyMIT(multi)->index = _PyMIT(multi)->iters[0]->index; \ } while (0) #define PyArray_MultiIter_GOTO1D(multi, ind) do { \ int __npy_mi; \ for (__npy_mi=0; __npy_mi < _PyMIT(multi)->numiter; __npy_mi++) { \ PyArray_ITER_GOTO1D(_PyMIT(multi)->iters[__npy_mi], ind); \ } \ _PyMIT(multi)->index = _PyMIT(multi)->iters[0]->index; \ } while (0) #define PyArray_MultiIter_DATA(multi, i) \ ((void *)(_PyMIT(multi)->iters[i]->dataptr)) #define PyArray_MultiIter_NEXTi(multi, i) \ PyArray_ITER_NEXT(_PyMIT(multi)->iters[i]) #define PyArray_MultiIter_NOTDONE(multi) \ (_PyMIT(multi)->index < _PyMIT(multi)->size) /* * Store the information needed for fancy-indexing over an array. The * fields are slightly unordered to keep consec, dataptr and subspace * where they were originally. */ typedef struct { PyObject_HEAD /* * Multi-iterator portion --- needs to be present in this * order to work with PyArray_Broadcast */ int numiter; /* number of index-array iterators */ npy_intp size; /* size of broadcasted result */ npy_intp index; /* current index */ int nd; /* number of dims */ npy_intp dimensions[NPY_MAXDIMS]; /* dimensions */ NpyIter *outer; /* index objects iterator */ void *unused[NPY_MAXDIMS - 2]; PyArrayObject *array; /* Flat iterator for the indexed array. For compatibility solely. */ PyArrayIterObject *ait; /* * Subspace array. For binary compatibility (was an iterator, * but only the check for NULL should be used). */ PyArrayObject *subspace; /* * if subspace iteration, then this is the array of axes in * the underlying array represented by the index objects */ int iteraxes[NPY_MAXDIMS]; npy_intp fancy_strides[NPY_MAXDIMS]; /* pointer when all fancy indices are 0 */ char *baseoffset; /* * after binding consec denotes at which axis the fancy axes * are inserted. */ int consec; char *dataptr; int nd_fancy; npy_intp fancy_dims[NPY_MAXDIMS]; /* * Whether the iterator (any of the iterators) requires API. This is * unused by NumPy itself; ArrayMethod flags are more precise. */ int needs_api; /* * Extra op information. */ PyArrayObject *extra_op; PyArray_Descr *extra_op_dtype; /* desired dtype */ npy_uint32 *extra_op_flags; /* Iterator flags */ NpyIter *extra_op_iter; NpyIter_IterNextFunc *extra_op_next; char **extra_op_ptrs; /* * Information about the iteration state. */ NpyIter_IterNextFunc *outer_next; char **outer_ptrs; npy_intp *outer_strides; /* * Information about the subspace iterator. */ NpyIter *subspace_iter; NpyIter_IterNextFunc *subspace_next; char **subspace_ptrs; npy_intp *subspace_strides; /* Count for the external loop (which ever it is) for API iteration */ npy_intp iter_count; } PyArrayMapIterObject; enum { NPY_NEIGHBORHOOD_ITER_ZERO_PADDING, NPY_NEIGHBORHOOD_ITER_ONE_PADDING, NPY_NEIGHBORHOOD_ITER_CONSTANT_PADDING, NPY_NEIGHBORHOOD_ITER_CIRCULAR_PADDING, NPY_NEIGHBORHOOD_ITER_MIRROR_PADDING }; typedef struct { PyObject_HEAD /* * PyArrayIterObject part: keep this in this exact order */ int nd_m1; /* number of dimensions - 1 */ npy_intp index, size; npy_intp coordinates[NPY_MAXDIMS];/* N-dimensional loop */ npy_intp dims_m1[NPY_MAXDIMS]; /* ao->dimensions - 1 */ npy_intp strides[NPY_MAXDIMS]; /* ao->strides or fake */ npy_intp backstrides[NPY_MAXDIMS];/* how far to jump back */ npy_intp factors[NPY_MAXDIMS]; /* shape factors */ PyArrayObject *ao; char *dataptr; /* pointer to current item*/ npy_bool contiguous; npy_intp bounds[NPY_MAXDIMS][2]; npy_intp limits[NPY_MAXDIMS][2]; npy_intp limits_sizes[NPY_MAXDIMS]; npy_iter_get_dataptr_t translate; /* * New members */ npy_intp nd; /* Dimensions is the dimension of the array */ npy_intp dimensions[NPY_MAXDIMS]; /* * Neighborhood points coordinates are computed relatively to the * point pointed by _internal_iter */ PyArrayIterObject* _internal_iter; /* * To keep a reference to the representation of the constant value * for constant padding */ char* constant; int mode; } PyArrayNeighborhoodIterObject; /* * Neighborhood iterator API */ /* General: those work for any mode */ static inline int PyArrayNeighborhoodIter_Reset(PyArrayNeighborhoodIterObject* iter); static inline int PyArrayNeighborhoodIter_Next(PyArrayNeighborhoodIterObject* iter); #if 0 static inline int PyArrayNeighborhoodIter_Next2D(PyArrayNeighborhoodIterObject* iter); #endif /* * Include inline implementations - functions defined there are not * considered public API */ #define NUMPY_CORE_INCLUDE_NUMPY__NEIGHBORHOOD_IMP_H_ #include "_neighborhood_iterator_imp.h" #undef NUMPY_CORE_INCLUDE_NUMPY__NEIGHBORHOOD_IMP_H_ /* The default array type */ #define NPY_DEFAULT_TYPE NPY_DOUBLE /* * All sorts of useful ways to look into a PyArrayObject. It is recommended * to use PyArrayObject * objects instead of always casting from PyObject *, * for improved type checking. * * In many cases here the macro versions of the accessors are deprecated, * but can't be immediately changed to inline functions because the * preexisting macros accept PyObject * and do automatic casts. Inline * functions accepting PyArrayObject * provides for some compile-time * checking of correctness when working with these objects in C. */ #define PyArray_ISONESEGMENT(m) (PyArray_CHKFLAGS(m, NPY_ARRAY_C_CONTIGUOUS) || \ PyArray_CHKFLAGS(m, NPY_ARRAY_F_CONTIGUOUS)) #define PyArray_ISFORTRAN(m) (PyArray_CHKFLAGS(m, NPY_ARRAY_F_CONTIGUOUS) && \ (!PyArray_CHKFLAGS(m, NPY_ARRAY_C_CONTIGUOUS))) #define PyArray_FORTRAN_IF(m) ((PyArray_CHKFLAGS(m, NPY_ARRAY_F_CONTIGUOUS) ? \ NPY_ARRAY_F_CONTIGUOUS : 0)) #if (defined(NPY_NO_DEPRECATED_API) && (NPY_1_7_API_VERSION <= NPY_NO_DEPRECATED_API)) /* * Changing access macros into functions, to allow for future hiding * of the internal memory layout. This later hiding will allow the 2.x series * to change the internal representation of arrays without affecting * ABI compatibility. */ static inline int PyArray_NDIM(const PyArrayObject *arr) { return ((PyArrayObject_fields *)arr)->nd; } static inline void * PyArray_DATA(PyArrayObject *arr) { return ((PyArrayObject_fields *)arr)->data; } static inline char * PyArray_BYTES(PyArrayObject *arr) { return ((PyArrayObject_fields *)arr)->data; } static inline npy_intp * PyArray_DIMS(PyArrayObject *arr) { return ((PyArrayObject_fields *)arr)->dimensions; } static inline npy_intp * PyArray_STRIDES(PyArrayObject *arr) { return ((PyArrayObject_fields *)arr)->strides; } static inline npy_intp PyArray_DIM(const PyArrayObject *arr, int idim) { return ((PyArrayObject_fields *)arr)->dimensions[idim]; } static inline npy_intp PyArray_STRIDE(const PyArrayObject *arr, int istride) { return ((PyArrayObject_fields *)arr)->strides[istride]; } static inline NPY_RETURNS_BORROWED_REF PyObject * PyArray_BASE(PyArrayObject *arr) { return ((PyArrayObject_fields *)arr)->base; } static inline NPY_RETURNS_BORROWED_REF PyArray_Descr * PyArray_DESCR(PyArrayObject *arr) { return ((PyArrayObject_fields *)arr)->descr; } static inline int PyArray_FLAGS(const PyArrayObject *arr) { return ((PyArrayObject_fields *)arr)->flags; } static inline npy_intp PyArray_ITEMSIZE(const PyArrayObject *arr) { return ((PyArrayObject_fields *)arr)->descr->elsize; } static inline int PyArray_TYPE(const PyArrayObject *arr) { return ((PyArrayObject_fields *)arr)->descr->type_num; } static inline int PyArray_CHKFLAGS(const PyArrayObject *arr, int flags) { return (PyArray_FLAGS(arr) & flags) == flags; } static inline PyObject * PyArray_GETITEM(const PyArrayObject *arr, const char *itemptr) { return ((PyArrayObject_fields *)arr)->descr->f->getitem( (void *)itemptr, (PyArrayObject *)arr); } /* * SETITEM should only be used if it is known that the value is a scalar * and of a type understood by the arrays dtype. * Use `PyArray_Pack` if the value may be of a different dtype. */ static inline int PyArray_SETITEM(PyArrayObject *arr, char *itemptr, PyObject *v) { return ((PyArrayObject_fields *)arr)->descr->f->setitem(v, itemptr, arr); } #else /* These macros are deprecated as of NumPy 1.7. */ #define PyArray_NDIM(obj) (((PyArrayObject_fields *)(obj))->nd) #define PyArray_BYTES(obj) (((PyArrayObject_fields *)(obj))->data) #define PyArray_DATA(obj) ((void *)((PyArrayObject_fields *)(obj))->data) #define PyArray_DIMS(obj) (((PyArrayObject_fields *)(obj))->dimensions) #define PyArray_STRIDES(obj) (((PyArrayObject_fields *)(obj))->strides) #define PyArray_DIM(obj,n) (PyArray_DIMS(obj)[n]) #define PyArray_STRIDE(obj,n) (PyArray_STRIDES(obj)[n]) #define PyArray_BASE(obj) (((PyArrayObject_fields *)(obj))->base) #define PyArray_DESCR(obj) (((PyArrayObject_fields *)(obj))->descr) #define PyArray_FLAGS(obj) (((PyArrayObject_fields *)(obj))->flags) #define PyArray_CHKFLAGS(m, FLAGS) \ ((((PyArrayObject_fields *)(m))->flags & (FLAGS)) == (FLAGS)) #define PyArray_ITEMSIZE(obj) \ (((PyArrayObject_fields *)(obj))->descr->elsize) #define PyArray_TYPE(obj) \ (((PyArrayObject_fields *)(obj))->descr->type_num) #define PyArray_GETITEM(obj,itemptr) \ PyArray_DESCR(obj)->f->getitem((char *)(itemptr), \ (PyArrayObject *)(obj)) #define PyArray_SETITEM(obj,itemptr,v) \ PyArray_DESCR(obj)->f->setitem((PyObject *)(v), \ (char *)(itemptr), \ (PyArrayObject *)(obj)) #endif static inline PyArray_Descr * PyArray_DTYPE(PyArrayObject *arr) { return ((PyArrayObject_fields *)arr)->descr; } static inline npy_intp * PyArray_SHAPE(PyArrayObject *arr) { return ((PyArrayObject_fields *)arr)->dimensions; } /* * Enables the specified array flags. Does no checking, * assumes you know what you're doing. */ static inline void PyArray_ENABLEFLAGS(PyArrayObject *arr, int flags) { ((PyArrayObject_fields *)arr)->flags |= flags; } /* * Clears the specified array flags. Does no checking, * assumes you know what you're doing. */ static inline void PyArray_CLEARFLAGS(PyArrayObject *arr, int flags) { ((PyArrayObject_fields *)arr)->flags &= ~flags; } #if NPY_FEATURE_VERSION >= NPY_1_22_API_VERSION static inline NPY_RETURNS_BORROWED_REF PyObject * PyArray_HANDLER(PyArrayObject *arr) { return ((PyArrayObject_fields *)arr)->mem_handler; } #endif #define PyTypeNum_ISBOOL(type) ((type) == NPY_BOOL) #define PyTypeNum_ISUNSIGNED(type) (((type) == NPY_UBYTE) || \ ((type) == NPY_USHORT) || \ ((type) == NPY_UINT) || \ ((type) == NPY_ULONG) || \ ((type) == NPY_ULONGLONG)) #define PyTypeNum_ISSIGNED(type) (((type) == NPY_BYTE) || \ ((type) == NPY_SHORT) || \ ((type) == NPY_INT) || \ ((type) == NPY_LONG) || \ ((type) == NPY_LONGLONG)) #define PyTypeNum_ISINTEGER(type) (((type) >= NPY_BYTE) && \ ((type) <= NPY_ULONGLONG)) #define PyTypeNum_ISFLOAT(type) ((((type) >= NPY_FLOAT) && \ ((type) <= NPY_LONGDOUBLE)) || \ ((type) == NPY_HALF)) #define PyTypeNum_ISNUMBER(type) (((type) <= NPY_CLONGDOUBLE) || \ ((type) == NPY_HALF)) #define PyTypeNum_ISSTRING(type) (((type) == NPY_STRING) || \ ((type) == NPY_UNICODE)) #define PyTypeNum_ISCOMPLEX(type) (((type) >= NPY_CFLOAT) && \ ((type) <= NPY_CLONGDOUBLE)) #define PyTypeNum_ISPYTHON(type) (((type) == NPY_LONG) || \ ((type) == NPY_DOUBLE) || \ ((type) == NPY_CDOUBLE) || \ ((type) == NPY_BOOL) || \ ((type) == NPY_OBJECT )) #define PyTypeNum_ISFLEXIBLE(type) (((type) >=NPY_STRING) && \ ((type) <=NPY_VOID)) #define PyTypeNum_ISDATETIME(type) (((type) >=NPY_DATETIME) && \ ((type) <=NPY_TIMEDELTA)) #define PyTypeNum_ISUSERDEF(type) (((type) >= NPY_USERDEF) && \ ((type) < NPY_USERDEF+ \ NPY_NUMUSERTYPES)) #define PyTypeNum_ISEXTENDED(type) (PyTypeNum_ISFLEXIBLE(type) || \ PyTypeNum_ISUSERDEF(type)) #define PyTypeNum_ISOBJECT(type) ((type) == NPY_OBJECT) #define PyDataType_ISBOOL(obj) PyTypeNum_ISBOOL(((PyArray_Descr*)(obj))->type_num) #define PyDataType_ISUNSIGNED(obj) PyTypeNum_ISUNSIGNED(((PyArray_Descr*)(obj))->type_num) #define PyDataType_ISSIGNED(obj) PyTypeNum_ISSIGNED(((PyArray_Descr*)(obj))->type_num) #define PyDataType_ISINTEGER(obj) PyTypeNum_ISINTEGER(((PyArray_Descr*)(obj))->type_num ) #define PyDataType_ISFLOAT(obj) PyTypeNum_ISFLOAT(((PyArray_Descr*)(obj))->type_num) #define PyDataType_ISNUMBER(obj) PyTypeNum_ISNUMBER(((PyArray_Descr*)(obj))->type_num) #define PyDataType_ISSTRING(obj) PyTypeNum_ISSTRING(((PyArray_Descr*)(obj))->type_num) #define PyDataType_ISCOMPLEX(obj) PyTypeNum_ISCOMPLEX(((PyArray_Descr*)(obj))->type_num) #define PyDataType_ISPYTHON(obj) PyTypeNum_ISPYTHON(((PyArray_Descr*)(obj))->type_num) #define PyDataType_ISFLEXIBLE(obj) PyTypeNum_ISFLEXIBLE(((PyArray_Descr*)(obj))->type_num) #define PyDataType_ISDATETIME(obj) PyTypeNum_ISDATETIME(((PyArray_Descr*)(obj))->type_num) #define PyDataType_ISUSERDEF(obj) PyTypeNum_ISUSERDEF(((PyArray_Descr*)(obj))->type_num) #define PyDataType_ISEXTENDED(obj) PyTypeNum_ISEXTENDED(((PyArray_Descr*)(obj))->type_num) #define PyDataType_ISOBJECT(obj) PyTypeNum_ISOBJECT(((PyArray_Descr*)(obj))->type_num) #define PyDataType_HASFIELDS(obj) (((PyArray_Descr *)(obj))->names != NULL) #define PyDataType_HASSUBARRAY(dtype) ((dtype)->subarray != NULL) #define PyDataType_ISUNSIZED(dtype) ((dtype)->elsize == 0 && \ !PyDataType_HASFIELDS(dtype)) #define PyDataType_MAKEUNSIZED(dtype) ((dtype)->elsize = 0) #define PyArray_ISBOOL(obj) PyTypeNum_ISBOOL(PyArray_TYPE(obj)) #define PyArray_ISUNSIGNED(obj) PyTypeNum_ISUNSIGNED(PyArray_TYPE(obj)) #define PyArray_ISSIGNED(obj) PyTypeNum_ISSIGNED(PyArray_TYPE(obj)) #define PyArray_ISINTEGER(obj) PyTypeNum_ISINTEGER(PyArray_TYPE(obj)) #define PyArray_ISFLOAT(obj) PyTypeNum_ISFLOAT(PyArray_TYPE(obj)) #define PyArray_ISNUMBER(obj) PyTypeNum_ISNUMBER(PyArray_TYPE(obj)) #define PyArray_ISSTRING(obj) PyTypeNum_ISSTRING(PyArray_TYPE(obj)) #define PyArray_ISCOMPLEX(obj) PyTypeNum_ISCOMPLEX(PyArray_TYPE(obj)) #define PyArray_ISPYTHON(obj) PyTypeNum_ISPYTHON(PyArray_TYPE(obj)) #define PyArray_ISFLEXIBLE(obj) PyTypeNum_ISFLEXIBLE(PyArray_TYPE(obj)) #define PyArray_ISDATETIME(obj) PyTypeNum_ISDATETIME(PyArray_TYPE(obj)) #define PyArray_ISUSERDEF(obj) PyTypeNum_ISUSERDEF(PyArray_TYPE(obj)) #define PyArray_ISEXTENDED(obj) PyTypeNum_ISEXTENDED(PyArray_TYPE(obj)) #define PyArray_ISOBJECT(obj) PyTypeNum_ISOBJECT(PyArray_TYPE(obj)) #define PyArray_HASFIELDS(obj) PyDataType_HASFIELDS(PyArray_DESCR(obj)) /* * FIXME: This should check for a flag on the data-type that * states whether or not it is variable length. Because the * ISFLEXIBLE check is hard-coded to the built-in data-types. */ #define PyArray_ISVARIABLE(obj) PyTypeNum_ISFLEXIBLE(PyArray_TYPE(obj)) #define PyArray_SAFEALIGNEDCOPY(obj) (PyArray_ISALIGNED(obj) && !PyArray_ISVARIABLE(obj)) #define NPY_LITTLE '<' #define NPY_BIG '>' #define NPY_NATIVE '=' #define NPY_SWAP 's' #define NPY_IGNORE '|' #if NPY_BYTE_ORDER == NPY_BIG_ENDIAN #define NPY_NATBYTE NPY_BIG #define NPY_OPPBYTE NPY_LITTLE #else #define NPY_NATBYTE NPY_LITTLE #define NPY_OPPBYTE NPY_BIG #endif #define PyArray_ISNBO(arg) ((arg) != NPY_OPPBYTE) #define PyArray_IsNativeByteOrder PyArray_ISNBO #define PyArray_ISNOTSWAPPED(m) PyArray_ISNBO(PyArray_DESCR(m)->byteorder) #define PyArray_ISBYTESWAPPED(m) (!PyArray_ISNOTSWAPPED(m)) #define PyArray_FLAGSWAP(m, flags) (PyArray_CHKFLAGS(m, flags) && \ PyArray_ISNOTSWAPPED(m)) #define PyArray_ISCARRAY(m) PyArray_FLAGSWAP(m, NPY_ARRAY_CARRAY) #define PyArray_ISCARRAY_RO(m) PyArray_FLAGSWAP(m, NPY_ARRAY_CARRAY_RO) #define PyArray_ISFARRAY(m) PyArray_FLAGSWAP(m, NPY_ARRAY_FARRAY) #define PyArray_ISFARRAY_RO(m) PyArray_FLAGSWAP(m, NPY_ARRAY_FARRAY_RO) #define PyArray_ISBEHAVED(m) PyArray_FLAGSWAP(m, NPY_ARRAY_BEHAVED) #define PyArray_ISBEHAVED_RO(m) PyArray_FLAGSWAP(m, NPY_ARRAY_ALIGNED) #define PyDataType_ISNOTSWAPPED(d) PyArray_ISNBO(((PyArray_Descr *)(d))->byteorder) #define PyDataType_ISBYTESWAPPED(d) (!PyDataType_ISNOTSWAPPED(d)) /************************************************************ * A struct used by PyArray_CreateSortedStridePerm, new in 1.7. ************************************************************/ typedef struct { npy_intp perm, stride; } npy_stride_sort_item; /************************************************************ * This is the form of the struct that's stored in the * PyCapsule returned by an array's __array_struct__ attribute. See * https://docs.scipy.org/doc/numpy/reference/arrays.interface.html for the full * documentation. ************************************************************/ typedef struct { int two; /* * contains the integer 2 as a sanity * check */ int nd; /* number of dimensions */ char typekind; /* * kind in array --- character code of * typestr */ int itemsize; /* size of each element */ int flags; /* * how should be data interpreted. Valid * flags are CONTIGUOUS (1), F_CONTIGUOUS (2), * ALIGNED (0x100), NOTSWAPPED (0x200), and * WRITEABLE (0x400). ARR_HAS_DESCR (0x800) * states that arrdescr field is present in * structure */ npy_intp *shape; /* * A length-nd array of shape * information */ npy_intp *strides; /* A length-nd array of stride information */ void *data; /* A pointer to the first element of the array */ PyObject *descr; /* * A list of fields or NULL (ignored if flags * does not have ARR_HAS_DESCR flag set) */ } PyArrayInterface; /* * This is a function for hooking into the PyDataMem_NEW/FREE/RENEW functions. * See the documentation for PyDataMem_SetEventHook. */ typedef void (PyDataMem_EventHookFunc)(void *inp, void *outp, size_t size, void *user_data); /* * PyArray_DTypeMeta related definitions. * * As of now, this API is preliminary and will be extended as necessary. */ #if defined(NPY_INTERNAL_BUILD) && NPY_INTERNAL_BUILD /* * The Structures defined in this block are currently considered * private API and may change without warning! * Part of this (at least the size) is expected to be public API without * further modifications. */ /* TODO: Make this definition public in the API, as soon as its settled */ NPY_NO_EXPORT extern PyTypeObject PyArrayDTypeMeta_Type; /* * While NumPy DTypes would not need to be heap types the plan is to * make DTypes available in Python at which point they will be heap types. * Since we also wish to add fields to the DType class, this looks like * a typical instance definition, but with PyHeapTypeObject instead of * only the PyObject_HEAD. * This must only be exposed very extremely careful consideration, since * it is a fairly complex construct which may be better to allow * refactoring of. */ typedef struct { PyHeapTypeObject super; /* * Most DTypes will have a singleton default instance, for the * parametric legacy DTypes (bytes, string, void, datetime) this * may be a pointer to the *prototype* instance? */ PyArray_Descr *singleton; /* Copy of the legacy DTypes type number, usually invalid. */ int type_num; /* The type object of the scalar instances (may be NULL?) */ PyTypeObject *scalar_type; /* * DType flags to signal legacy, parametric, or * abstract. But plenty of space for additional information/flags. */ npy_uint64 flags; /* * Use indirection in order to allow a fixed size for this struct. * A stable ABI size makes creating a static DType less painful * while also ensuring flexibility for all opaque API (with one * indirection due the pointer lookup). */ void *dt_slots; void *reserved[3]; } PyArray_DTypeMeta; #endif /* NPY_INTERNAL_BUILD */ /* * Use the keyword NPY_DEPRECATED_INCLUDES to ensure that the header files * npy_*_*_deprecated_api.h are only included from here and nowhere else. */ #ifdef NPY_DEPRECATED_INCLUDES #error "Do not use the reserved keyword NPY_DEPRECATED_INCLUDES." #endif #define NPY_DEPRECATED_INCLUDES #if !defined(NPY_NO_DEPRECATED_API) || \ (NPY_NO_DEPRECATED_API < NPY_1_7_API_VERSION) #include "npy_1_7_deprecated_api.h" #endif /* * There is no file npy_1_8_deprecated_api.h since there are no additional * deprecated API features in NumPy 1.8. * * Note to maintainers: insert code like the following in future NumPy * versions. * * #if !defined(NPY_NO_DEPRECATED_API) || \ * (NPY_NO_DEPRECATED_API < NPY_1_9_API_VERSION) * #include "npy_1_9_deprecated_api.h" * #endif */ #undef NPY_DEPRECATED_INCLUDES #endif /* NUMPY_CORE_INCLUDE_NUMPY_NDARRAYTYPES_H_ */
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