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""" |
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============= |
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Masked Arrays |
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============= |
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Arrays sometimes contain invalid or missing data. When doing operations |
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on such arrays, we wish to suppress invalid values, which is the purpose masked |
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arrays fulfill (an example of typical use is given below). |
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For example, examine the following array: |
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>>> x = np.array([2, 1, 3, np.nan, 5, 2, 3, np.nan]) |
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When we try to calculate the mean of the data, the result is undetermined: |
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>>> np.mean(x) |
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nan |
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The mean is calculated using roughly ``np.sum(x)/len(x)``, but since |
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any number added to ``NaN`` [1]_ produces ``NaN``, this doesn't work. Enter |
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masked arrays: |
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>>> m = np.ma.masked_array(x, np.isnan(x)) |
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>>> m |
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masked_array(data=[2.0, 1.0, 3.0, --, 5.0, 2.0, 3.0, --], |
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mask=[False, False, False, True, False, False, False, True], |
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fill_value=1e+20) |
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Here, we construct a masked array that suppress all ``NaN`` values. We |
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may now proceed to calculate the mean of the other values: |
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>>> np.mean(m) |
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2.6666666666666665 |
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.. [1] Not-a-Number, a floating point value that is the result of an |
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invalid operation. |
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.. moduleauthor:: Pierre Gerard-Marchant |
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.. moduleauthor:: Jarrod Millman |
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""" |
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from . import core |
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from .core import * |
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from . import extras |
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from .extras import * |
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__all__ = ['core', 'extras'] |
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__all__ += core.__all__ |
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__all__ += extras.__all__ |
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from numpy._pytesttester import PytestTester |
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test = PytestTester(__name__) |
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del PytestTester |
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