# 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¶

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 Pythonwarningsmodule).- ‘raise’ : Raise a
FloatingPointError.- ‘call’ : Call a function specified using the
seterrcallfunction.- ‘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.
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.

- 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!

- 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

- 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_asMinuses:

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

- 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

- scipy.weave

- Plusses:

- 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 very uncertain: it’s the only part of Scipy not ported to Python 3 and is effectively deprecated in favor of Cython.

- 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++:¶

- Cython
- CXX
- Boost.python
- SWIG
- SIP (used mainly in PyQT)