# 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:
• 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

Thus:

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
• 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.