# 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.nonzero(myarr == np.nan)
    (array([], dtype=int64),)
    >>> 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

The default is to `'warn'` for `invalid`, `divide`, and `overflow` and `'ignore'` for `underflow`. But this can be changed, and it can be set individually for different kinds of exceptions. The different behaviors are:

> - ‘ignore’ : Take no action when the exception occurs.

> - ‘warn’ : Print a RuntimeWarning (via the Python `warnings` module).

> - ‘raise’ : Raise a FloatingPointError.

> - ‘call’ : Call a function specified using the seterrcall function.

> - ‘print’ : Print a warning directly to `stdout`.

> - ‘log’ : Record error in a Log object specified by 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.
    Traceback (most recent call last):
    ...
    RuntimeWarning: invalid value encountered in divide
    >>> j = np.seterr(under='ignore')
    >>> np.array([1.e-100])**10
    array([0.])
    >>> j = np.seterr(invalid='raise')
    >>> np.sqrt(np.array([-1.]))
    Traceback (most recent call last):
    ...
    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
    saw stupid error!
    array([nan, nan, nan, nan, nan])
    >>> 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

2. Cython

> - Plusses:

>   - avoid learning C API’s

>   - no dealing with reference counting

>   - can code in pseudo python and generate C code

>   - can also interface to existing C code

>   - should shield you from changes to Python C api

>   - has become the de-facto standard within the scientific Python community

>   - fast indexing support for arrays

> - Minuses:

>   - Can write code in non-standard form which may become obsolete

>   - Not as flexible as manual wrapping

3. ctypes

> - Plusses:

>   - part of Python standard library

>   - good for interfacing to existing shareable 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.data_as
>         a.ctypes.shape
>         a.ctypes.shape_as
>         a.ctypes.strides
>         a.ctypes.strides_as

> - Minuses:

>   - can’t use for writing code to be turned into C extensions, only a wrapper tool.

4. 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

5. 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:

The clear choice to wrap Fortran code is f2py.

Pyfort is an older alternative, but not supported any longer. Fwrap is a newer project that looked promising but isn’t being developed any longer.

## Interfacing to C++:

> 1. Cython

> 2. CXX

> 3. Boost.python

> 4. SWIG

> 5. SIP (used mainly in PyQT)