User guide

NumPy User Guide, Release 1.9.0
– Efficient
– No dependencies on other tools
• Minuses:
– Lots of learning overhead:
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need to learn basics of Python C API
*
need to learn basics of numpy C API
*
need to learn how to handle reference counting and love it.
– Reference counting often difficult to get right.
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getting it wrong leads to memory leaks, and worse, segfaults
– API will change for Python 3.0!
2. Cython
• Plusses:
– avoid learning C API’s
– no dealing with reference counting
– can code in pseudo python and generate C code
– can also interface to existing C code
– should shield you from changes to Python C api
– has become the de-facto standard within the scientific Python community
– fast indexing support for arrays
• Minuses:
– Can write code in non-standard form which may become obsolete
– Not as flexible as manual wrapping
4. ctypes
• Plusses:
– part of Python standard library
– good for interfacing to existing sharable libraries, particularly Windows DLLs
– avoids API/reference counting issues
– good numpy support: arrays have all these in their ctypes attribute:
a.ctypes.data a.ctypes.get_strides
a.ctypes.data_as a.ctypes.shape
a.ctypes.get_as_parameter a.ctypes.shape_as
a.ctypes.get_data a.ctypes.strides
a.ctypes.get_shape a.ctypes.strides_as
• Minuses:
– can’t use for writing code to be turned into C extensions, only a wrapper tool.
5. SWIG (automatic wrapper generator)
• Plusses:
4.4. Interfacing to C 49