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"""
=============
Miscellaneous
=============
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.: ::
>>> 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[3] = 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.
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)
>>> 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) pyrex
- Plusses:
- 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
- Minuses:
- Can write code in non-standard form which may become obsolete
- Not as flexible as manual wrapping
- Maintainers not easily adaptable to new features
Thus:
3) cython - fork of pyrex to allow needed features for SAGE
- being considered as the standard scipy/numpy wrapping tool
- fast indexing support for arrays
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:
- 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
7) Weave
- Plusses:
- 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.
- Minuses:
- Future uncertain--lacks a champion
8) 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:
-----------------------
Fortran: Clear choice is f2py. (Pyfort is an older alternative, but not supported
any longer)
Interfacing to C++:
-------------------
1) CXX
2) Boost.python
3) SWIG
4) Sage has used cython to wrap C++ (not pretty, but it can be done)
5) SIP (used mainly in PyQT)
"""
|
