mirror of
https://github.com/PiBrewing/craftbeerpi4.git
synced 2024-12-29 00:41:45 +01:00
226 lines
6 KiB
Python
226 lines
6 KiB
Python
"""
|
|
=============
|
|
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.
|
|
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) 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 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.
|
|
|
|
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) 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.
|
|
|
|
6) 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 <https://docs.scipy.org/doc/numpy/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)
|
|
|
|
"""
|