FM / ma

/opt/cloudlinux/venv/lib/python3.11/site-packages/numpy/ma/

/opt/cloudlinux/venv/lib/python3.11/site-packages/numpy/ma

Name	Size	Mode	Actions
tests/	-	0755	rm
__pycache__/	-	0755	rm
bench.py	4859	0644	edit dl rm
core.py	272643	0644	edit dl rm
core.pyi	14277	0644	edit dl rm
extras.py	61910	0644	edit dl rm
extras.pyi	2646	0644	edit dl rm
mrecords.py	27232	0644	edit dl rm
mrecords.pyi	1934	0644	edit dl rm
setup.py	418	0644	edit dl rm
testutils.py	10239	0644	edit dl rm
timer_comparison.py	15658	0644	edit dl rm
__init__.py	1404	0644	edit dl rm
__init__.pyi	6085	0644	edit dl rm

Edit: /opt/cloudlinux/venv/lib/python3.11/site-packages/numpy/ma/__init__.py (1404B)

"""
=============
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.

.. moduleauthor:: Pierre Gerard-Marchant
.. moduleauthor:: Jarrod Millman

"""
from . import core
from .core import *

from . import extras
from .extras import *

__all__ = ['core', 'extras']
__all__ += core.__all__
__all__ += extras.__all__

from numpy._pytesttester import PytestTester
test = PytestTester(__name__)
del PytestTester

""" ============= 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. .. moduleauthor:: Pierre Gerard-Marchant .. moduleauthor:: Jarrod Millman """ from . import core from .core import * from . import extras from .extras import * __all__ = ['core', 'extras'] __all__ += core.__all__ __all__ += extras.__all__ from numpy._pytesttester import PytestTester test = PytestTester(__name__) del PytestTester