Added ESP32 side of the transceiver

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Denakosg945
2026-06-04 17:01:14 +03:00
parent bcf7bede0f
commit fa830b2c37
2303 changed files with 497306 additions and 0 deletions
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"""
=============
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, extras
from .core import *
from .extras import *
__all__ = ['core', 'extras']
__all__ += core.__all__
__all__ += extras.__all__
from numpy._pytesttester import PytestTester
test = PytestTester(__name__)
del PytestTester