def blazefunc_from_numpy_ufunc(uf, modname, name, acquires_gil):
"""Converts a NumPy ufunc into a Blaze ufunc.
Parameters
----------
uf : NumPy ufunc
The ufunc to convert.
modname : str
The module name to report in the ufunc's name
name : str
The ufunc's name.
acquires_gil : bool
True if the kernels in the ufunc need the GIL.
TODO: should support a dict {type -> bool} to allow per-kernel control.
"""
# Get the list of type signatures
tplist = _lowlevel.numpy_typetuples_from_ufunc(uf)
tplist = _filter_tplist(tplist)
siglist = [_make_sig(tp) for tp in tplist]
kernlist = [_lowlevel.ckernel_deferred_from_ufunc(uf, tp, acquires_gil)
for tp in tplist]
# Create the empty blaze function to start
blaze_func = function.BlazeFunc()
blaze_func.add_metadata({'elementwise': True})
# Add default dummy dispatching for 'python' mode
pydispatcher = blaze_func.get_dispatcher('python')
# Add dispatching to the kernel for each signature
ckdispatcher = blaze_func.get_dispatcher('ckernel')
for (tp, sig, kern) in zip(tplist, siglist, kernlist):
# The blaze function currently requires matching (do-nothing)
# python functions
pyfunc = _make_pyfunc(len(tp) - 1, modname, name)
datashape.overloading.overload(sig, dispatcher=pydispatcher)(pyfunc)
datashape.overloading.overload(sig, dispatcher=ckdispatcher)(kern)
return blaze_func
def blazefunc_from_numpy_ufunc(uf, modname, name, acquires_gil):
"""Converts a NumPy ufunc into a Blaze ufunc.
Parameters
----------
uf : NumPy ufunc
The ufunc to convert.
modname : str
The module name to report in the ufunc's name
name : str
The ufunc's name.
acquires_gil : bool
True if the kernels in the ufunc need the GIL.
TODO: should support a dict {type -> bool} to allow per-kernel control.
"""
# Get the list of type signatures
tplist = _lowlevel.numpy_typetuples_from_ufunc(uf)
tplist = _filter_tplist(tplist)
siglist = [_make_sig(tp) for tp in tplist]
kernlist = [_lowlevel.ckernel_deferred_from_ufunc(uf, tp, acquires_gil)
for tp in tplist]
# Create the empty blaze function to start
bf = ElementwiseBlazeFunc('blaze', name)
# TODO: specify elementwise
#bf.add_metadata({'elementwise': True})
# Add an overload to the function for each signature
for (tp, sig, kern) in zip(tplist, siglist, kernlist):
bf.add_overload(sig, kern)
return bf
def blazefunc_from_numpy_ufunc(uf, modname, name, acquires_gil):
"""Converts a NumPy ufunc into a Blaze ufunc.
Parameters
----------
uf : NumPy ufunc
The ufunc to convert.
modname : str
The module name to report in the ufunc's name
name : str
The ufunc's name.
acquires_gil : bool
True if the kernels in the ufunc need the GIL.
TODO: should support a dict {type -> bool} to allow per-kernel control.
"""
# Get the list of type signatures
tplist = _lowlevel.numpy_typetuples_from_ufunc(uf)
tplist = _filter_tplist(tplist)
siglist = [_make_sig(tp) for tp in tplist]
kernlist = [
_lowlevel.ckernel_deferred_from_ufunc(uf, tp, acquires_gil)
for tp in tplist
]
# Create the empty blaze function to start
bf = ElementwiseBlazeFunc('blaze', name)
# TODO: specify elementwise
#bf.add_metadata({'elementwise': True})
# Add an overload to the function for each signature
for (tp, sig, kern) in zip(tplist, siglist, kernlist):
bf.add_overload(sig, kern)
return bf
