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diff --git a/doc/source/user/misc.rst b/doc/source/user/misc.rst index c10aea486..031ce4efa 100644 --- a/doc/source/user/misc.rst +++ b/doc/source/user/misc.rst @@ -2,4 +2,224 @@ Miscellaneous ************* -.. automodule:: numpy.doc.misc +IEEE 754 Floating Point Special Values +-------------------------------------- + +Special values defined in numpy: nan, inf, + +NaNs can be used as a poor-man's mask (if you don't care what the +original value was) + +Note: cannot use equality to test NaNs. E.g.: :: + + >>> myarr = np.array([1., 0., np.nan, 3.]) + >>> np.nonzero(myarr == np.nan) + (array([], dtype=int64),) + >>> np.nan == np.nan # is always False! Use special numpy functions instead. + False + >>> myarr[myarr == np.nan] = 0. # doesn't work + >>> myarr + array([ 1., 0., NaN, 3.]) + >>> myarr[np.isnan(myarr)] = 0. # use this instead find + >>> myarr + array([ 1., 0., 0., 3.]) + +Other related special value functions: :: + + isinf(): True if value is inf + isfinite(): True if not nan or inf + nan_to_num(): Map nan to 0, inf to max float, -inf to min float + +The following corresponds to the usual functions except that nans are excluded +from the results: :: + + nansum() + nanmax() + nanmin() + nanargmax() + nanargmin() + + >>> x = np.arange(10.) + >>> x[3] = np.nan + >>> x.sum() + nan + >>> np.nansum(x) + 42.0 + +How numpy handles numerical exceptions +-------------------------------------- + +The default is to ``'warn'`` for ``invalid``, ``divide``, and ``overflow`` +and ``'ignore'`` for ``underflow``. But this can be changed, and it can be +set individually for different kinds of exceptions. The different behaviors +are: + + - 'ignore' : Take no action when the exception occurs. + - 'warn' : Print a `RuntimeWarning` (via the Python `warnings` module). + - 'raise' : Raise a `FloatingPointError`. + - 'call' : Call a function specified using the `seterrcall` function. + - 'print' : Print a warning directly to ``stdout``. + - 'log' : Record error in a Log object specified by `seterrcall`. + +These behaviors can be set for all kinds of errors or specific ones: + + - all : apply to all numeric exceptions + - invalid : when NaNs are generated + - divide : divide by zero (for integers as well!) + - overflow : floating point overflows + - underflow : floating point underflows + +Note that integer divide-by-zero is handled by the same machinery. +These behaviors are set on a per-thread basis. + +Examples +-------- + +:: + + >>> oldsettings = np.seterr(all='warn') + >>> np.zeros(5,dtype=np.float32)/0. + invalid value encountered in divide + >>> j = np.seterr(under='ignore') + >>> np.array([1.e-100])**10 + >>> j = np.seterr(invalid='raise') + >>> np.sqrt(np.array([-1.])) + FloatingPointError: invalid value encountered in sqrt + >>> def errorhandler(errstr, errflag): + ... print("saw stupid error!") + >>> np.seterrcall(errorhandler) + <function err_handler at 0x...> + >>> j = np.seterr(all='call') + >>> np.zeros(5, dtype=np.int32)/0 + FloatingPointError: invalid value encountered in divide + saw stupid error! + >>> j = np.seterr(**oldsettings) # restore previous + ... # error-handling settings + +Interfacing to C +---------------- +Only a survey of the choices. Little detail on how each works. + +1) Bare metal, wrap your own C-code manually. + + - Plusses: + + - Efficient + - No dependencies on other tools + + - Minuses: + + - Lots of learning overhead: + + - 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. + + - 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 + +3) 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. + +4) SWIG (automatic wrapper generator) + + - Plusses: + + - around a long time + - multiple scripting language support + - C++ support + - Good for wrapping large (many functions) existing C libraries + + - Minuses: + + - generates lots of code between Python and the C code + - can cause performance problems that are nearly impossible to optimize + out + - interface files can be hard to write + - doesn't necessarily avoid reference counting issues or needing to know + API's + +5) scipy.weave + + - Plusses: + + - can turn many numpy expressions into C code + - dynamic compiling and loading of generated C code + - can embed pure C code in Python module and have weave extract, generate + interfaces and compile, etc. + + - Minuses: + + - Future very uncertain: it's the only part of Scipy not ported to Python 3 + and is effectively deprecated in favor of Cython. + +6) Psyco + + - Plusses: + + - Turns pure python into efficient machine code through jit-like + optimizations + - very fast when it optimizes well + + - Minuses: + + - Only on intel (windows?) + - Doesn't do much for numpy? + +Interfacing to Fortran: +----------------------- +The clear choice to wrap Fortran code is +`f2py <https://docs.scipy.org/doc/numpy/f2py/>`_. + +Pyfort is an older alternative, but not supported any longer. +Fwrap is a newer project that looked promising but isn't being developed any +longer. + +Interfacing to C++: +------------------- + 1) Cython + 2) CXX + 3) Boost.python + 4) SWIG + 5) SIP (used mainly in PyQT) + + |