""" ============= 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