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
>>> 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:


>>> x = np.arange(10.)
>>> x[3] = np.nan
>>> x.sum()
>>> np.nansum(x)

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

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!
  1. 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


  1. cython - fork of pyrex to allow needed features for SAGE
  • being considered as the standard scipy/numpy wrapping tool
  • fast indexing support for arrays
  1. 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.
  1. 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
  1. 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
  1. 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.

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