1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
|
#ifndef _NPY_PRIVATE__REDUCTION_H_
#define _NPY_PRIVATE__REDUCTION_H_
/************************************************************
* Typedefs used by PyArray_ReduceWrapper, new in 1.7.
************************************************************/
/*
* This is a function for assigning a reduction identity to the result,
* before doing the reduction computation. The
* value in 'data' is passed through from PyArray_ReduceWrapper.
*
* This function could, for example, simply be a call like
* return PyArray_AssignZero(result, NULL, NULL);
*
* It should return -1 on failure, or 0 on success.
*/
typedef int (PyArray_AssignReduceIdentityFunc)(PyArrayObject *result,
void *data);
/*
* Inner definition of the reduce loop, only used for a static function.
* At some point around NumPy 1.6, there was probably an intention to make
* the reduce loop customizable at this level (per ufunc?).
*
* TODO: This should be refactored/removed.
*/
typedef int (PyArray_ReduceLoopFunc)(PyArrayMethod_Context *context,
PyArrayMethod_StridedLoop *strided_loop, NpyAuxData *auxdata,
NpyIter *iter, char **dataptrs, npy_intp const *strides,
npy_intp const *countptr, NpyIter_IterNextFunc *iternext,
int needs_api, npy_intp skip_first_count);
/*
* This function executes all the standard NumPy reduction function
* boilerplate code, just calling the appropriate inner loop function where
* necessary.
*
* operand : The array to be reduced.
* out : NULL, or the array into which to place the result.
* wheremask : NOT YET SUPPORTED, but this parameter is placed here
* so that support can be added in the future without breaking
* API compatibility. Pass in NULL.
* operand_dtype : The dtype the inner loop expects for the operand.
* result_dtype : The dtype the inner loop expects for the result.
* casting : The casting rule to apply to the operands.
* axis_flags : Flags indicating the reduction axes of 'operand'.
* reorderable : If True, the reduction being done is reorderable, which
* means specifying multiple axes of reduction at once is ok,
* and the reduction code may calculate the reduction in an
* arbitrary order. The calculation may be reordered because
* of cache behavior or multithreading requirements.
* keepdims : If true, leaves the reduction dimensions in the result
* with size one.
* identity : If Py_None, PyArray_CopyInitialReduceValues is used, otherwise
* this value is used to initialize the result to
* the reduction's unit.
* loop : The loop which does the reduction.
* data : Data which is passed to the inner loop.
* buffersize : Buffer size for the iterator. For the default, pass in 0.
* funcname : The name of the reduction function, for error messages.
* errormask : forwarded from _get_bufsize_errmask
*/
NPY_NO_EXPORT PyArrayObject *
PyUFunc_ReduceWrapper(PyArrayMethod_Context *context,
PyArrayObject *operand, PyArrayObject *out, PyArrayObject *wheremask,
npy_bool *axis_flags, int keepdims,
PyObject *initial, PyArray_ReduceLoopFunc *loop,
npy_intp buffersize, const char *funcname, int errormask);
#endif
|
