xrsigproc - Data and Signal Processing Tools for Xarray Datasets

Package primarily focuses on filtering signals into large and small-scale components using convolution kernels of various types (currently 2D tophat, 2D boxcar, 2D cone, and Gaussian). This package is created specifically for processing of model output of physical oceanography simulation model output---in particular from LLC4320 MITgcm---so there are tools related to processing these datasets.

Installation

pip install xrsigproc

plot_spectrum

Wrapper for matplotlib with commonly used options for plotting spectra.

d2k_tangent_plane

LLC4320 data has uneven grids and xrft will complain about this. This function takes an input grid dataset and evens out the spacing calculating latitude distance by taking the midpoint of the plane and calculating a common arc length from there.

Examples on applying a convolution kernel

There's a variety of kernels included in this package including:

• gaussian_smooth
• boxcar2D_smooth
• cone2D_smooth
• tophat2D_smooth

Simply choose the size of the kernel and apply it to your dataset:

import xrsigproc as sp

sp.gaussian_smooth(dataset, scale=5)

For the boxcar kernel, scale refers to the total width, for the round kernels, it refers to the radius, and for the gaussian kernel, it refers to sigma. The functions will use Dask parallelization where it can.

There's a helper function to compute small-scale variance according to M. Germano's 1990 paper, Turbulence: the filtering approach, where he defines small-scale variance as tau_ss = <f*g> - <f> * <g>. Angle brackets here denote a convolution operator.

sp.germano_tau(dataset, dataset, gaussian_smooth, scale=5)