def calc_mean_time_deviation(timepoints, weights, mean_time=None):
"""Return the weighted deviation of the given timepoints from their mean
time.
With equal given weights, the is simply the standard deviation of the
given time points:
>>> from hydpy.auxs.statstools import calc_mean_time_deviation
>>> calc_mean_time_deviation(timepoints=[3., 7.],
... weights=[2., 2.])
2.0
One can pass a precalculated or alternate mean time:
>>> from hydpy.core.objecttools import round_
>>> round_(calc_mean_time_deviation(timepoints=[3., 7.],
... weights=[2., 2.],
... mean_time=4.))
2.236068
>>> round_(calc_mean_time_deviation(timepoints=[3., 7.],
... weights=[1., 3.]))
1.732051
Or, in the most extreme case:
>>> calc_mean_time_deviation(timepoints=[3., 7.],
... weights=[0., 4.])
0.0
There will be some checks for input plausibility perfomed, e.g.:
>>> calc_mean_time_deviation(timepoints=[3., 7.],
... weights=[-2., 2.])
Traceback (most recent call last):
...
ValueError: While trying to calculate the weighted time deviation from mean time, the following error occured: For the following objects, at least one value is negative: weights.
"""
try:
timepoints = numpy.array(timepoints)
weights = numpy.array(weights)
validtools.test_equal_shape(timepoints=timepoints, weights=weights)
validtools.test_non_negative(weights=weights)
if mean_time is None:
mean_time = calc_mean_time(timepoints, weights)
return (numpy.sqrt(
numpy.dot(weights,
(timepoints - mean_time)**2) / numpy.sum(weights)))
except BaseException:
objecttools.augmentexcmessage('While trying to calculate the weighted '
'time deviation from mean time')
def calc_mean_time(timepoints, weights):
"""Return the weighted mean of the given timepoints.
With equal given weights, the result is simply the mean of the given
time points:
>>> from hydpy.auxs.statstools import calc_mean_time
>>> calc_mean_time(timepoints=[3., 7.],
... weights=[2., 2.])
5.0
With different weights, the resulting mean time is shifted to the larger
ones:
>>> calc_mean_time(timepoints=[3., 7.],
... weights=[1., 3.])
6.0
Or, in the most extreme case:
>>> calc_mean_time(timepoints=[3., 7.],
... weights=[0., 4.])
7.0
There will be some checks for input plausibility perfomed, e.g.:
>>> calc_mean_time(timepoints=[3., 7.],
... weights=[-2., 2.])
Traceback (most recent call last):
...
ValueError: While trying to calculate the weighted mean time, the following error occured: For the following objects, at least one value is negative: weights.
"""
try:
timepoints = numpy.array(timepoints)
weights = numpy.array(weights)
validtools.test_equal_shape(timepoints=timepoints, weights=weights)
validtools.test_non_negative(weights=weights)
return numpy.dot(timepoints, weights) / numpy.sum(weights)
except BaseException:
objecttools.augmentexcmessage(
'While trying to calculate the weighted mean time')