XXX: This section is not yet written.
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.:
>>> np.where(myarr == np.nan) >>> nan == nan # is always False! Use special numpy functions instead. >>> np.nan == np.nan False >>> myarr = np.array([1., 0., np.nan, 3.]) >>> 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 = np.nan >>> x.sum() nan >>> np.nansum(x) 42.0
How numpy handles numerical exceptions
Default is to “warn” But this can be changed, and it can be set individually for different kinds of exceptions. The different behaviors are:
'ignore' : ignore completely 'warn' : print a warning (once only) 'raise' : raise an exception 'call' : call a user-supplied function (set using 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-thead basis.
>>> 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) >>> 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
Only a survey of the choices. Little detail on how each works.
- No dependencies on other tools
- 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!
- avoid learning C API’s
- no dealing with reference counting
- can code in psuedo python and generate C code
- can also interface to existing C code
- should shield you from changes to Python C api
- become pretty popular within Python community
- Can write code in non-standard form which may become obsolete
- Not as flexible as manual wrapping
- Maintainers not easily adaptable to new features
- being considered as the standard scipy/numpy wrapping tool
- fast indexing support for arrays
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
- can’t use for writing code to be turned into C extensions, only a wrapper tool.
- around a long time
- multiple scripting language support
- C++ support
- Good for wrapping large (many functions) existing C libraries
- 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
- Phenomenal tool
- 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.
- Future uncertain–lacks a champion
- Turns pure python into efficient machine code through jit-like optimizations
- very fast when it optimizes well
- Only on intel (windows?)
- Doesn’t do much for numpy?
Fortran: Clear choice is f2py. (Pyfort is an older alternative, but not supported any longer)
Placeholder for Methods vs. Functions documentation.