numba.npyufunc package

Submodules

numba.npyufunc.decorators module

class numba.npyufunc.decorators.GUVectorize

Bases: object

target_registry = {'gpu': <class 'numbapro.cudavec.vectorizers.CudaGUFuncVectorize'>, 'cpu': <class 'numba.npyufunc.ufuncbuilder.GUFuncBuilder'>}

class numba.npyufunc.decorators.Vectorize

Bases: object

target_registry = {'gpu': <class 'numbapro.cudavec.vectorizers.CudaVectorize'>, 'cpu': <class 'numba.npyufunc.ufuncbuilder.UFuncBuilder'>, 'stream': <class 'numba.npyufunc.ufuncbuilder.UFuncBuilder'>, 'parallel': <class 'numbapro.vectorizers.parallel.ParallelUFuncBuilder'>}

numba.npyufunc.decorators.guvectorize(ftylist, signature[, target='cpu'[, **kws]])

A decorator to create numpy generialized-ufunc object from Numba compiled code.

ftylist: iterable

An iterable of type signatures, which are either function type object or a string describing the function type.

signature: str

A NumPy generialized-ufunc signature. e.g. “(m, n), (n, p)->(m, p)”

target: str

A string for code generation target. Defaults to “cpu”.

A NumPy generialized universal-function

@guvectorize([‘void(int32[:,:], int32[:,:], int32[:,:])’,

‘void(float32[:,:], float32[:,:], float32[:,:])’], ‘(x, y),(x, y)->(x, y)’)

def add_2d_array(a, b):

for i in range(c.shape[0]):

for j in range(c.shape[1]):

c[i, j] = a[i, j] + b[i, j]

numba.npyufunc.decorators.vectorize(ftylist[, target='cpu'[, **kws]])

A decorator to create numpy ufunc object from Numba compiled code.

ftylist: iterable

An iterable of type signatures, which are either function type object or a string describing the function type.

target: str

A string for code generation target. Default to “cpu”.

A NumPy universal function

@vectorize([‘float32(float32, float32)’,

‘float64(float64, float64)’])

def sum(a, b):

return a + b

numba.npyufunc.gufunc module

numba.npyufunc.sigparse module

numba.npyufunc.sigparse.parse_signature(sig)

Parse generalized ufunc signature.

NOTE: ‘,’ (COMMA) is a delimiter; not separator.

This means trailing comma is legal.

numba.npyufunc.ufuncbuilder module

class numba.npyufunc.ufuncbuilder.GUFuncBuilder(py_func, signature, targetoptions={})

Bases: object

add(sig=None, argtypes=None, restype=None)

build(cres)

build_ufunc()

class numba.npyufunc.ufuncbuilder.UFuncBuilder(py_func, targetoptions={})

Bases: object

add(sig=None, argtypes=None, restype=None)

build(cres)

build_ufunc()

class numba.npyufunc.ufuncbuilder.UFuncDispatcher(py_func, locals={}, targetoptions={})

Bases: object

compile(sig, locals={}, **targetoptions)

targetdescr = <numba.npyufunc.ufuncbuilder.UFuncTarget object at 0x1085c0a90>

class numba.npyufunc.ufuncbuilder.UFuncTarget

Bases: numba.targets.registry.CPUTarget

options

alias of UFuncTargetOptions

class numba.npyufunc.ufuncbuilder.UFuncTargetOptions

Bases: numba.targets.options.TargetOptions

OPTIONS = {'nopython': <type 'bool'>}

numba.npyufunc.wrappers module

class numba.npyufunc.wrappers.GUArrayArg(context, builder, args, dims, steps, i, step_offset, typ, syms, sym_dim)

Bases: object

next(i)

class numba.npyufunc.wrappers.UArrayArg(context, builder, args, steps, i, argtype)

Bases: object

load(ind)

load_aligned(ind)

load_direct(offset)

store(value, ind)

store_aligned(value, ind)

store_direct(value, offset)

numba.npyufunc.wrappers.build_fast_loop_body(context, func, builder, arrays, out, offsets, store_offset, signature, ind)

numba.npyufunc.wrappers.build_gufunc_wrapper(context, func, signature, sin, sout)

numba.npyufunc.wrappers.build_slow_loop_body(context, func, builder, arrays, out, offsets, store_offset, signature)

numba.npyufunc.wrappers.build_ufunc_wrapper(context, func, signature)

Wrap the scalar function with a loop that iterates over the arguments

Module contents