Miscellaneous

Note

XXX: This section is not yet written.

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)

Methods vs. Functions

Placeholder for Methods vs. Functions documentation.