#include "Python.h"
#include "npy_config.h"
#include "npy_pycompat.h"
#include "numpy/ufuncobject.h"
#include "ufunc_type_resolution.h"
#include "common.h"

Data Structures
struct	_ufunc_masker_data
Defines
#define	_UMATHMODULE
#define	NPY_NO_DEPRECATED_API NPY_API_VERSION
#define	PY_ARRAY_UNIQUE_SYMBOL _npy_umathmodule_ARRAY_API
#define	NO_IMPORT_ARRAY
Functions
static const char *	npy_casting_to_string (NPY_CASTING casting)
NPY_NO_EXPORT int	PyUFunc_ValidateCasting (PyUFuncObject ufunc, NPY_CASTING casting, PyArrayObject operands, PyArray_Descr *dtypes)
static PyArray_Descr *	ensure_dtype_nbo (PyArray_Descr *type)
NPY_NO_EXPORT int	PyUFunc_DefaultTypeResolver (PyUFuncObject ufunc, NPY_CASTING casting, PyArrayObject operands, PyObject type_tup, PyArray_Descr **out_dtypes)
NPY_NO_EXPORT int	PyUFunc_SimpleBinaryComparisonTypeResolver (PyUFuncObject ufunc, NPY_CASTING casting, PyArrayObject operands, PyObject type_tup, PyArray_Descr **out_dtypes)
NPY_NO_EXPORT int	PyUFunc_SimpleUnaryOperationTypeResolver (PyUFuncObject ufunc, NPY_CASTING casting, PyArrayObject operands, PyObject type_tup, PyArray_Descr **out_dtypes)
NPY_NO_EXPORT int	PyUFunc_NegativeTypeResolver (PyUFuncObject ufunc, NPY_CASTING casting, PyArrayObject operands, PyObject type_tup, PyArray_Descr **out_dtypes)
NPY_NO_EXPORT int	PyUFunc_OnesLikeTypeResolver (PyUFuncObject ufunc, NPY_CASTING NPY_UNUSED(casting), PyArrayObject operands, PyObject type_tup, PyArray_Descr **out_dtypes)
NPY_NO_EXPORT int	PyUFunc_SimpleBinaryOperationTypeResolver (PyUFuncObject ufunc, NPY_CASTING casting, PyArrayObject operands, PyObject type_tup, PyArray_Descr **out_dtypes)
NPY_NO_EXPORT int	PyUFunc_AbsoluteTypeResolver (PyUFuncObject ufunc, NPY_CASTING casting, PyArrayObject operands, PyObject type_tup, PyArray_Descr **out_dtypes)
static PyArray_Descr *	timedelta_dtype_with_copied_meta (PyArray_Descr *dtype)
NPY_NO_EXPORT int	PyUFunc_AdditionTypeResolver (PyUFuncObject ufunc, NPY_CASTING casting, PyArrayObject operands, PyObject type_tup, PyArray_Descr **out_dtypes)
NPY_NO_EXPORT int	PyUFunc_SubtractionTypeResolver (PyUFuncObject ufunc, NPY_CASTING casting, PyArrayObject operands, PyObject type_tup, PyArray_Descr **out_dtypes)
NPY_NO_EXPORT int	PyUFunc_MultiplicationTypeResolver (PyUFuncObject ufunc, NPY_CASTING casting, PyArrayObject operands, PyObject type_tup, PyArray_Descr **out_dtypes)
NPY_NO_EXPORT int	PyUFunc_DivisionTypeResolver (PyUFuncObject ufunc, NPY_CASTING casting, PyArrayObject operands, PyObject type_tup, PyArray_Descr **out_dtypes)
NPY_NO_EXPORT int	PyUFunc_MixedDivisionTypeResolver (PyUFuncObject ufunc, NPY_CASTING casting, PyArrayObject operands, PyObject type_tup, PyArray_Descr **out_dtypes)
static int	find_userloop (PyUFuncObject ufunc, PyArray_Descr dtypes, PyUFuncGenericFunction out_innerloop, void **out_innerloopdata)
NPY_NO_EXPORT int	PyUFunc_DefaultLegacyInnerLoopSelector (PyUFuncObject ufunc, PyArray_Descr dtypes, PyUFuncGenericFunction out_innerloop, void *out_innerloopdata, int out_needs_api)
static NpyAuxData *	ufunc_masker_data_clone (NpyAuxData *data)
static void	unmasked_ufunc_loop_as_masked (char *dataptrs, npy_intp strides, char mask, npy_intp mask_stride, npy_intp loopsize, NpyAuxData innerloopdata)
NPY_NO_EXPORT int	PyUFunc_DefaultMaskedInnerLoopSelector (PyUFuncObject ufunc, PyArray_Descr dtypes, PyArray_Descr mask_dtype, npy_intp NPY_UNUSED(fixed_strides), npy_intp NPY_UNUSED(fixed_mask_stride), PyUFunc_MaskedStridedInnerLoopFunc out_innerloop, NpyAuxData out_innerloopdata, int out_needs_api)
static int	ufunc_loop_matches (PyUFuncObject self, PyArrayObject op, NPY_CASTING input_casting, NPY_CASTING output_casting, int any_object, int use_min_scalar, int types, PyArray_Descr *dtypes, int out_no_castable_output, char out_err_src_typecode, char out_err_dst_typecode)
static int	set_ufunc_loop_data_types (PyUFuncObject self, PyArrayObject op, PyArray_Descr out_dtypes, int type_nums, PyArray_Descr **dtypes)
static int	linear_search_userloop_type_resolver (PyUFuncObject self, PyArrayObject op, NPY_CASTING input_casting, NPY_CASTING output_casting, int any_object, int use_min_scalar, PyArray_Descr out_dtype, int out_no_castable_output, char out_err_src_typecode, char out_err_dst_typecode)
static int	type_tuple_userloop_type_resolver (PyUFuncObject self, int n_specified, int specified_types, PyArrayObject op, NPY_CASTING casting, int any_object, int use_min_scalar, PyArray_Descr out_dtype)
static int	dtype_kind_to_simplified_ordering (char kind)
static int	should_use_min_scalar (PyArrayObject **op, int nop)
NPY_NO_EXPORT int	linear_search_type_resolver (PyUFuncObject self, PyArrayObject op, NPY_CASTING input_casting, NPY_CASTING output_casting, int any_object, PyArray_Descr *out_dtype)
NPY_NO_EXPORT int	type_tuple_type_resolver (PyUFuncObject self, PyObject type_tup, PyArrayObject op, NPY_CASTING casting, int any_object, PyArray_Descr out_dtype)

Define Documentation

#define _UMATHMODULE

#define NO_IMPORT_ARRAY

#define NPY_NO_DEPRECATED_API NPY_API_VERSION

#define PY_ARRAY_UNIQUE_SYMBOL _npy_umathmodule_ARRAY_API

Function Documentation

static int dtype_kind_to_simplified_ordering ( char kind ) [static]

Provides an ordering for the dtype 'kind' character codes, to help determine when to use the min_scalar_type function. This groups 'kind' into boolean, integer, floating point, and everything else.

Boolean kind

Unsigned int kind

Signed int kind

Float kind

Complex kind

Anything else

Referenced by set_ufunc_loop_data_types().

static PyArray_Descr* ensure_dtype_nbo ( PyArray_Descr * type ) [static]

Returns a new reference to type if it is already NBO, otherwise returns a copy converted to NBO.

Referenced by PyUFunc_MultiplicationTypeResolver().

static int find_userloop	(	PyUFuncObject *	ufunc,
		PyArray_Descr **	dtypes,
		PyUFuncGenericFunction *	out_innerloop,
		void **	out_innerloopdata
	)		`[static]`

Use this to try to avoid repeating the same userdef loop search

no more ufunc arguments to check

It matched

Didn't find a match

NPY_NO_EXPORT int linear_search_type_resolver	(	PyUFuncObject *	self,
		PyArrayObject **	op,
		NPY_CASTING	input_casting,
		NPY_CASTING	output_casting,
		int	any_object,
		PyArray_Descr **	out_dtype
	)

Does a linear search for the best inner loop of the ufunc.

Note that if an error is returned, the caller must free the non-zero references in out_dtype. This function does not do its own clean-up.

For making a better error message on coercion error

If the ufunc has userloops, search for them.

Error

A loop was found

Determine the UFunc loop. This could in general be much faster, and a better way to implement it might be for the ufunc to provide a function which gives back the result type and inner loop function.

A default fast mechanism could be provided for functions which follow the most typical pattern, when all functions have signatures "xx...x -> x" for some built-in data type x, as follows.

System Message: ERROR/3 (<string>, line 9) Unexpected indentation.

Use PyArray_ResultType to get the output type
Look up the inner loop in a table based on the output type_num

</blockquote>

The method for finding the loop in the previous code did not appear consistent (as noted by some asymmetry in the generated coercion tables for np.add).

Copy the types into an int array for matching

Error

Found a match

If no function was found, throw an error

TODO: We should try again if the casting rule is same_kind: or unsafe, and look for a function more liberally.

Referenced by PyUFunc_DefaultTypeResolver().

static int linear_search_userloop_type_resolver	(	PyUFuncObject *	self,
		PyArrayObject **	op,
		NPY_CASTING	input_casting,
		NPY_CASTING	output_casting,
		int	any_object,
		int	use_min_scalar,
		PyArray_Descr **	out_dtype,
		int *	out_no_castable_output,
		char *	out_err_src_typecode,
		char *	out_err_dst_typecode
	)		`[static]`

Does a search through the arguments and the loops

Use this to try to avoid repeating the same userdef loop search

no more ufunc arguments to check

Error

Found a match

Didn't find a match

static const char* npy_casting_to_string ( NPY_CASTING casting ) [static]

References NPY_EQUIV_CASTING, NPY_NO_CASTING, NPY_SAFE_CASTING, NPY_SAME_KIND_CASTING, and NPY_UNSAFE_CASTING.

NPY_NO_EXPORT int PyUFunc_AbsoluteTypeResolver	(	PyUFuncObject *	ufunc,
		NPY_CASTING	casting,
		PyArrayObject **	operands,
		PyObject *	type_tup,
		PyArray_Descr **	out_dtypes
	)

This function applies special type resolution rules for the absolute ufunc. This ufunc converts complex -> float, so isn't covered by the simple unary type resolution.

Returns 0 on success, -1 on error.

Use the default for complex types, to find the loop producing float

NPY_NO_EXPORT int PyUFunc_AdditionTypeResolver	(	PyUFuncObject *	ufunc,
		NPY_CASTING	casting,
		PyArrayObject **	operands,
		PyObject *	type_tup,
		PyArray_Descr **	out_dtypes
	)

This function applies the type resolution rules for addition. In particular, there are a number of special cases with datetime:

System Message: ERROR/3 (<string>, line 3) Unexpected indentation.

System Message: WARNING/2 (<string>, line 10) Block quote ends without a blank line; unexpected unindent.

TODO: Non-linear time unit cases require highly special-cased loops: M8[<A>] + m8[Y|M|B] m8[Y|M|B] + M8[<A>]

Use the default when datetime and timedelta are not involved

m8[<A>] + m8[] => m8[gcd(<A>,)] + m8[gcd(<A>,)]

m8[<A>] + M8[] => m8[gcd(<A>,)] + M8[gcd(<A>,)]

Make a new NPY_TIMEDELTA, and copy the datetime's metadata

m8[<A>] + int => m8[<A>] + m8[<A>]

M8[<A>] + m8[] => M8[gcd(<A>,)] + m8[gcd(<A>,)]

Make a new NPY_TIMEDELTA, and copy the datetime's metadata

M8[<A>] + int => M8[<A>] + m8[<A>]

Make a new NPY_TIMEDELTA, and copy type1's metadata

int + m8[<A>] => m8[<A>] + m8[<A>]

Make a new NPY_TIMEDELTA, and copy type2's metadata

Check against the casting rules

NPY_NO_EXPORT int PyUFunc_DefaultLegacyInnerLoopSelector	(	PyUFuncObject *	ufunc,
		PyArray_Descr **	dtypes,
		PyUFuncGenericFunction *	out_innerloop,
		void **	out_innerloopdata,
		int *	out_needs_api
	)

If there are user-loops search them first. TODO: There needs to be a loop selection acceleration structure,

System Message: ERROR/3 (<string>, line 3) Unexpected indentation.

<blockquote> like a hash table.</blockquote>

Error

Found a loop

Copy the types into an int array for matching

References NPY_OBJECT, NPY_VOID, PyArray_CanCastArrayTo(), PyArray_CanCastTypeTo(), PyArray_DESCR, and PyArray_DescrFromType().

NPY_NO_EXPORT int PyUFunc_DefaultMaskedInnerLoopSelector	(	PyUFuncObject *	ufunc,
		PyArray_Descr **	dtypes,
		PyArray_Descr *	mask_dtype,
		npy_intp *	NPY_UNUSEDfixed_strides,
		npy_intp	NPY_UNUSEDfixed_mask_stride,
		PyUFunc_MaskedStridedInnerLoopFunc **	out_innerloop,
		NpyAuxData **	out_innerloopdata,
		int *	out_needs_api
	)

This function wraps a legacy inner loop so it becomes masked.

Returns 0 on success, -1 on error.

Create a new NpyAuxData object for the masker data

Get the unmasked ufunc inner loop

Return the loop function + aux data

NPY_NO_EXPORT int PyUFunc_DefaultTypeResolver	(	PyUFuncObject *	ufunc,
		NPY_CASTING	casting,
		PyArrayObject **	operands,
		PyObject *	type_tup,
		PyArray_Descr **	out_dtypes
	)

This function applies the default type resolution rules

for the provided ufunc.

Returns 0 on success, -1 on error.

Decide the casting rules for inputs and outputs. We want NPY_SAFE_CASTING or stricter, so that the loop selection code doesn't choose an integer loop for float inputs, or a float32 loop for float64 inputs.

Find the best ufunc inner loop, and fill in the dtypes

Find the specified ufunc inner loop, and fill in the dtypes

References linear_search_type_resolver(), _tagPyUFuncObject::nin, _tagPyUFuncObject::nout, NPY_SAFE_CASTING, PyArray_DESCR, PyTypeNum_ISOBJECT, and type_tuple_type_resolver().

Referenced by PyUFunc_MultiplicationTypeResolver(), and PyUFunc_SimpleUnaryOperationTypeResolver().

NPY_NO_EXPORT int PyUFunc_DivisionTypeResolver	(	PyUFuncObject *	ufunc,
		NPY_CASTING	casting,
		PyArrayObject **	operands,
		PyObject *	type_tup,
		PyArray_Descr **	out_dtypes
	)

This function applies the type resolution rules for division. In particular, there are a number of special cases with datetime:

System Message: ERROR/3 (<string>, line 3) Unexpected indentation.

<blockquote> m8[<A>] / m8[] to m8[gcd(<A>,)] / m8[gcd(<A>,)] -> float64 m8[<A>] / int## to m8[<A>] / int64 -> m8[<A>] m8[<A>] / float## to m8[<A>] / float64 -> m8[<A>]</blockquote>

Use the default when datetime and timedelta are not involved

m8[<A>] / m8[] to m8[gcd(<A>,)] / m8[gcd(<A>,)] -> float64

m8[<A>] / int## => m8[<A>] / int64

m8[<A>] / float## => m8[<A>] / float64

Check against the casting rules

NPY_NO_EXPORT int PyUFunc_MixedDivisionTypeResolver	(	PyUFuncObject *	ufunc,
		NPY_CASTING	casting,
		PyArrayObject **	operands,
		PyObject *	type_tup,
		PyArray_Descr **	out_dtypes
	)

Function to check and report floor division warning when python2.x is invoked with -3 switch See PEP238 and #7949 for numpy This function will not be hit for py3 or when __future__ imports division. See generate_umath.py for reason

Depreciation checks needed only on python 2

If both types are integer, warn the user, same as python does

References data, _ufunc_masker_data::nargs, npy_memchr(), _ufunc_masker_data::unmasked_innerloop, and _ufunc_masker_data::unmasked_innerloopdata.

NPY_NO_EXPORT int PyUFunc_MultiplicationTypeResolver	(	PyUFuncObject *	ufunc,
		NPY_CASTING	casting,
		PyArrayObject **	operands,
		PyObject *	type_tup,
		PyArray_Descr **	out_dtypes
	)

This function applies the type resolution rules for multiplication. In particular, there are a number of special cases with datetime:

System Message: ERROR/3 (<string>, line 3) Unexpected indentation.

<blockquote> int## * m8[<A>] => int64 * m8[<A>] m8[<A>] * int## => m8[<A>] * int64 float## * m8[<A>] => float64 * m8[<A>] m8[<A>] * float## => m8[<A>] * float64</blockquote>

Use the default when datetime and timedelta are not involved

m8[<A>] * int## => m8[<A>] * int64

m8[<A>] * float## => m8[<A>] * float64

int## * m8[<A>] => int64 * m8[<A>]

float## * m8[<A>] => float64 * m8[<A>]

Check against the casting rules

References ensure_dtype_nbo(), _tagPyUFuncObject::name, NPY_DOUBLE, NPY_LONGLONG, NPY_TIMEDELTA, PyArray_DESCR, PyArray_DescrFromType(), PyArray_DescrNewFromType(), PyArray_PromoteTypes(), PyTypeNum_ISDATETIME, PyTypeNum_ISFLOAT, PyTypeNum_ISINTEGER, PyUFunc_DefaultTypeResolver(), PyUFunc_ValidateCasting(), PyUString_ConcatAndDel, PyUString_FromFormat, and PyUString_FromString.

NPY_NO_EXPORT int PyUFunc_NegativeTypeResolver	(	PyUFuncObject *	ufunc,
		NPY_CASTING	casting,
		PyArrayObject **	operands,
		PyObject *	type_tup,
		PyArray_Descr **	out_dtypes
	)

The type resolver would have upcast already

2013-12-05, 1.9

References NPY_UNSAFE_CASTING, and PyUFunc_SimpleUnaryOperationTypeResolver().

NPY_NO_EXPORT int PyUFunc_OnesLikeTypeResolver	(	PyUFuncObject *	ufunc,
		NPY_CASTING	NPY_UNUSEDcasting,
		PyArrayObject **	operands,
		PyObject *	type_tup,
		PyArray_Descr **	out_dtypes
	)

The ones_like function shouldn't really be a ufunc, but while it still is, this provides type resolution that always forces UNSAFE casting.

NPY_NO_EXPORT int PyUFunc_SimpleBinaryComparisonTypeResolver	(	PyUFuncObject *	ufunc,
		NPY_CASTING	casting,
		PyArrayObject **	operands,
		PyObject *	type_tup,
		PyArray_Descr **	out_dtypes
	)

This function applies special type resolution rules for the case where all the functions have the pattern XX->bool, using PyArray_ResultType instead of a linear search to get the best loop.

Returns 0 on success, -1 on error.

Use the default type resolution if there's a custom data type or object arrays.

Input types are the result type

If the type tuple isn't a single-element tuple, let the default type resolution handle this one.

Output type is always boolean

Check against the casting rules

NPY_NO_EXPORT int PyUFunc_SimpleBinaryOperationTypeResolver	(	PyUFuncObject *	ufunc,
		NPY_CASTING	casting,
		PyArrayObject **	operands,
		PyObject *	type_tup,
		PyArray_Descr **	out_dtypes
	)

This function applies special type resolution rules for the case where all the functions have the pattern XX->X, using PyArray_ResultType instead of a linear search to get the best loop.

Note that a simpler linear search through the functions loop is still done, but switching to a simple array lookup for built-in types would be better at some point.

Returns 0 on success, -1 on error.

Use the default type resolution if there's a custom data type or object arrays.

Input types are the result type

If the type tuple isn't a single-element tuple, let the default type resolution handle this one.

Check against the casting rules

Referenced by timedelta_dtype_with_copied_meta().

NPY_NO_EXPORT int PyUFunc_SimpleUnaryOperationTypeResolver	(	PyUFuncObject *	ufunc,
		NPY_CASTING	casting,
		PyArrayObject **	operands,
		PyObject *	type_tup,
		PyArray_Descr **	out_dtypes
	)

This function applies special type resolution rules for the case where all the functions have the pattern X->X, copying the input descr directly so that metadata is maintained.

Note that a simpler linear search through the functions loop is still done, but switching to a simple array lookup for built-in types would be better at some point.

Returns 0 on success, -1 on error.

Use the default type resolution if there's a custom data type or object arrays.

Input types are the result type

If the type tuple isn't a single-element tuple, let the default type resolution handle this one.

Check against the casting rules

References PyUFunc_DefaultTypeResolver().

Referenced by PyUFunc_NegativeTypeResolver().

NPY_NO_EXPORT int PyUFunc_SubtractionTypeResolver	(	PyUFuncObject *	ufunc,
		NPY_CASTING	casting,
		PyArrayObject **	operands,
		PyObject *	type_tup,
		PyArray_Descr **	out_dtypes
	)

This function applies the type resolution rules for subtraction. In particular, there are a number of special cases with datetime:

System Message: ERROR/3 (<string>, line 3) Unexpected indentation.

System Message: WARNING/2 (<string>, line 8) Block quote ends without a blank line; unexpected unindent.

TODO: Non-linear time unit cases require highly special-cased loops: M8[<A>] - m8[Y|M|B]

Use the default when datetime and timedelta are not involved

The type resolver would have upcast already

2013-12-05, 1.9

m8[<A>] - m8[] => m8[gcd(<A>,)] - m8[gcd(<A>,)]

m8[<A>] - int => m8[<A>] - m8[<A>]

M8[<A>] - m8[] => M8[gcd(<A>,)] - m8[gcd(<A>,)]

Make a new NPY_TIMEDELTA, and copy the datetime's metadata

M8[<A>] - int => M8[<A>] - m8[<A>]

Make a new NPY_TIMEDELTA, and copy type1's metadata

M8[<A>] - M8[] => M8[gcd(<A>,)] - M8[gcd(<A>,)]

Make a new NPY_TIMEDELTA, and copy type1's metadata

int - m8[<A>] => m8[<A>] - m8[<A>]

Check against the casting rules

References PyArray_DESCR, PyArray_PromoteTypes(), and timedelta_dtype_with_copied_meta().

NPY_NO_EXPORT int PyUFunc_ValidateCasting	(	PyUFuncObject *	ufunc,
		NPY_CASTING	casting,
		PyArrayObject **	operands,
		PyArray_Descr **	dtypes
	)

Validates that the input operands can be cast to

the input types, and the output types can be cast to the output operands where provided.

Returns 0 on success, -1 (with exception raised) on validation failure.

References _tagPyUFuncObject::name, _tagPyUFuncObject::nin, _tagPyUFuncObject::nout, npy_casting_to_string(), PyArray_CanCastArrayTo(), PyArray_CanCastTypeTo(), PyArray_DESCR, PyUString_ConcatAndDel, PyUString_FromFormat, and PyUString_FromString.

Referenced by PyUFunc_MultiplicationTypeResolver().

static int set_ufunc_loop_data_types	(	PyUFuncObject *	self,
		PyArrayObject **	op,
		PyArray_Descr **	out_dtypes,
		int *	type_nums,
		PyArray_Descr **	dtypes
	)		`[static]`

Fill the dtypes array. For outputs, also search the inputs for a matching type_num to copy instead of creating a new one, similarly to preserve metadata.

Copy the dtype from 'op' if the type_num matches, to preserve metadata.

For outputs, copy the dtype from op[0] if the type_num matches, similarly to preserve metdata.

Otherwise create a plain descr from the type number

References dtype_kind_to_simplified_ordering(), kind(), PyArray_DESCR, and PyArray_NDIM.

Referenced by ufunc_loop_matches().

static int should_use_min_scalar	(	PyArrayObject **	op,
		int	nop
	)		`[static]`

Determine if there are any scalars, and if so, whether the maximum "kind" of the scalars surpasses the maximum "kind" of the arrays

Indicate whether to use the min_scalar_type function

Referenced by type_tuple_userloop_type_resolver().

static PyArray_Descr* timedelta_dtype_with_copied_meta ( PyArray_Descr * dtype ) [static]

Creates a new NPY_TIMEDELTA dtype, copying the datetime metadata from the given dtype.

NOTE: This function is copied from datetime.c in multiarray,: because umath and multiarray are not linked together.

References PyUFunc_SimpleBinaryOperationTypeResolver().

Referenced by PyUFunc_SubtractionTypeResolver().

NPY_NO_EXPORT int type_tuple_type_resolver	(	PyUFuncObject *	self,
		PyObject *	type_tup,
		PyArrayObject **	op,
		NPY_CASTING	casting,
		int	any_object,
		PyArray_Descr **	out_dtype
	)

Does a linear search for the inner loop of the ufunc specified by type_tup.

Note that if an error is returned, the caller must free the non-zero references in out_dtype. This function does not do its own clean-up.

For making a better error message on coercion error

Fill in specified_types from the tuple or string

If the ufunc has userloops, search for them.

Error

Found matching loop

Copy the types into an int array for matching

no match

Error

Cannot cast inputs

Success

If no function was found, throw an error

Referenced by PyUFunc_DefaultTypeResolver().

static int type_tuple_userloop_type_resolver	(	PyUFuncObject *	self,
		int	n_specified,
		int *	specified_types,
		PyArrayObject **	op,
		NPY_CASTING	casting,
		int	any_object,
		int	use_min_scalar,
		PyArray_Descr **	out_dtype
	)		`[static]`

Does a search through the arguments and the loops

Use this to try to avoid repeating the same userdef loop search

It works

Didn't match

Error

Didn't find a match

References NPY_MAXARGS, NPY_NOTYPE, PyArray_DescrConverter(), PyArray_DescrFromType(), PyBytes_AsStringAndSize, PyBytes_Check, should_use_min_scalar(), and _PyArray_Descr::type_num.

static int ufunc_loop_matches	(	PyUFuncObject *	self,
		PyArrayObject **	op,
		NPY_CASTING	input_casting,
		NPY_CASTING	output_casting,
		int	any_object,
		int	use_min_scalar,
		int *	types,
		PyArray_Descr **	dtypes,
		int *	out_no_castable_output,
		char *	out_err_src_typecode,
		char *	out_err_dst_typecode
	)		`[static]`

First check if all the inputs can be safely cast to the types for this function

If no inputs are objects and there are more than one loop, don't allow conversion to object. The rationale behind this is mostly performance. Except for custom ufuncs built with just one object-parametered inner loop, only the types that are supported are implemented. Trying the object version of logical_or on float arguments doesn't seem right.

If type num is NPY_VOID and struct dtypes have been passed in, use struct dtype object. Otherwise create new dtype object from type num.

If all the inputs are scalars, use the regular promotion rules, not the special value-checking ones.

If all the inputs were ok, then check casting back to the outputs.

References _loop1d_info::arg_types, _loop1d_info::next, NPY_NOTYPE, NpyCapsule_AsVoidPtr(), PyArray_DESCR, PyTypeNum_ISUSERDEF, and set_ufunc_loop_data_types().

static NpyAuxData* ufunc_masker_data_clone ( NpyAuxData * data ) [static]

Allocate a new one

Copy the data (unmasked data doesn't have object semantics)

static void unmasked_ufunc_loop_as_masked	(	char **	dataptrs,
		npy_intp *	strides,
		char *	mask,
		npy_intp	mask_stride,
		npy_intp	loopsize,
		NpyAuxData *	innerloopdata
	)		`[static]`

This function wraps a regular unmasked ufunc inner loop as a masked ufunc inner loop, only calling the function for elements where the mask is True.

Put the aux data into local variables

Process the data as runs of unmasked values

Skip masked values

Process unmasked values (assumes unmasked loop doesn't mess with the 'args' pointer values)

Data Structures

Defines

Functions

Define Documentation

Function Documentation