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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.:

    >>> myarr = np.array([1., 0., np.nan, 3.])
    >>> np.where(myarr == np.nan)
    >>> 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

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-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. 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.

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