LowLevelCallable#
- class scipy.LowLevelCallable(function, user_data=None, signature=None)[source]#
Low-level callback function.
Some functions in SciPy take as arguments callback functions, which can either be python callables or low-level compiled functions. Using compiled callback functions can improve performance somewhat by avoiding wrapping data in Python objects.
Such low-level functions in SciPy are wrapped in
LowLevelCallable
objects, which can be constructed from function pointers obtained from ctypes, cffi, Cython, or contained in Python PyCapsule objects.See also
Functions accepting low-level callables:
scipy.integrate.quad
,scipy.ndimage.generic_filter
,scipy.ndimage.generic_filter1d
,scipy.ndimage.geometric_transform
Usage examples:
Extending scipy.ndimage in C, Faster integration using low-level callback functions
- Parameters:
- function{PyCapsule, ctypes function pointer, cffi function pointer}
Low-level callback function.
- user_data{PyCapsule, ctypes void pointer, cffi void pointer}
User data to pass on to the callback function.
- signaturestr, optional
Signature of the function. If omitted, determined from function, if possible.
Notes
The argument
function
can be one of:PyCapsule, whose name contains the C function signature
ctypes function pointer
cffi function pointer
The signature of the low-level callback must match one of those expected by the routine it is passed to.
If constructing low-level functions from a PyCapsule, the name of the capsule must be the corresponding signature, in the format:
return_type (arg1_type, arg2_type, ...)
For example:
"void (double)" "double (double, int *, void *)"
The context of a PyCapsule passed in as
function
is used asuser_data
, if an explicit value foruser_data
was not given.- Attributes:
- function
Callback function given.
- user_data
User data given.
- signature
Signature of the function.
Methods
from_cython
(module, name[, user_data, signature])Create a low-level callback function from an exported Cython function.