"""
=============
Masked Arrays
=============
Arrays sometimes contain invalid or missing data. When doing operations
on such arrays, we wish to suppress invalid values, which is the purpose masked
arrays fulfill (an example of typical use is given below).
For example, examine the following array:
>>> x = np.array([2, 1, 3, np.nan, 5, 2, 3, np.nan])
When we try to calculate the mean of the data, the result is undetermined:
>>> np.mean(x)
nan
The mean is calculated using roughly ``np.sum(x)/len(x)``, but since
any number added to ``NaN`` [1]_ produces ``NaN``, this doesn't work. Enter
masked arrays:
>>> m = np.ma.masked_array(x, np.isnan(x))
>>> m
masked_array(data = [2.0 1.0 3.0 -- 5.0 2.0 3.0 --],
mask = [False False False True False False False True],
fill_value=1e+20)
Here, we construct a masked array that suppress all ``NaN`` values. We
may now proceed to calculate the mean of the other values:
>>> np.mean(m)
2.6666666666666665
.. [1] Not-a-Number, a floating point value that is the result of an
invalid operation.
"""
from __future__ import division, absolute_import, print_function
__author__ = "Pierre GF Gerard-Marchant ($Author: jarrod.millman $)"
__version__ = '1.0'
__revision__ = "$Revision: 3473 $"
__date__ = '$Date: 2007-10-29 17:18:13 +0200 (Mon, 29 Oct 2007) $'
from . import core
from .core import *
from . import extras
from .extras import *
__all__ = ['core', 'extras']
__all__ += core.__all__
__all__ += extras.__all__
from numpy.testing import Tester
test = Tester().test
bench = Tester().bench