Do mathematical operations on time series, where the two operands
don't necessarily have the same sampling times. It finds a common
sampling time and sampling interval, then interpolates as necessary to
bring the data onto the common time axis.
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numpy array.
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interpN(a,
t)
Interpolate to a specified time axis via nearest-neighbor
interpolation. |
source code
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list(numpy.ndarray) where the first ndarray is 1-D and the rest are
two dimensional.
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common(data_sets,
start=None,
end=None)
Computes a common time axis for several datasets. |
source code
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commonN(data_sets,
start=None,
end=None)
Put several data sets on a common time axis. |
source code
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copy_interval(a,
t0,
t1,
hdr_op=None,
mode=' rr ' )
This copies the part of the time-series in a where t0 < t < t1. |
source code
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apply(fcn,
a,
hdrfcn=<function <lambda> at 0x5e5f938>)
Apply a function, point-by-point to the data in a. |
source code
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