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# Module tsops

source code

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.

 Classes axis This class represents a time axis for a time series.
Functions

 time(datasets, start=None, end=None) source code

 time2(a, b) source code

 test_fig() source code

 interp_fill(a, t, fill) source code

 interpN_fill(a, t, fill) source code
numpy array.
 interp(a, t) Interpolate to a specified time axis. source code

 test_interp1() source code

 test_interp2() source code

 test_interp3() source code

 interpN(a, t) Interpolate to a specified time axis via nearest-neighbor interpolation. source code
list(numpy.ndarray) where the first ndarray is 1-D and the rest are two dimensional.
 common(data_sets, start=None, end=None) Computes a common time axis for several datasets. source code

 commonN(data_sets, start=None, end=None) Put several data sets on a common time axis. source code

 mul(a, b, hdr_op=None) source code

 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

 apply(fcn, a, hdrfcn= at 0x5e5f938>) Apply a function, point-by-point to the data in a. source code

 resample(a, dt) source code

 test() source code
 Variables __package__ = `'gmisclib'`

Imports: math, Num, NG, gpkimgclass

 Function Details

### interp(a, t)

source code

Interpolate to a specified time axis. This does a linear interpolation.

Parameters:
• `a` (gpkimgclass.gpk_img) - data to be interpolated (a time series)
• `t` (an array of times.)
Returns: numpy array.
data interpolated onto the specified time values.

### interpN(a, t)

source code

Interpolate to a specified time axis via nearest-neighbor interpolation. A is a gpkimgclass, and t is an array of times. Returns a Numeric array, not a gpkimgclass.

### common(data_sets, start=None, end=None)

source code

Computes a common time axis for several datasets. Linearly interpolate as needed. The data sets need not be the same width, and need not have the same sampling interval or a common starting time.

Parameters:
• `data_sets` (`list`(`gpkimgclass.gpk_img`)) - this is a list of the data to be put on a common time axis.
• `start` (`float` or `None`) - this allows you to restrict the output data to a smaller region.
• `end` (`float` or `None`) - this allows you to restrict the output data to a smaller region.
Returns: list(numpy.ndarray) where the first ndarray is 1-D and the rest are two dimensional.
the time_axis (as a 1-D numpy array of time values), followed by a 2-D numpy array for each of the input data sets.

### commonN(data_sets, start=None, end=None)

source code

Put several data sets on a common time axis. Interpolate by choosing nearest neighbor.

### copy_interval(a, t0, t1, hdr_op=None, mode=`'``rr``'`)

source code

This copies the part of the time-series in a where t0 < t < t1. 'a' is a gpk_img object.

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