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View xarray_laplace2d_numba_example.ipynb
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View cubed_sphere.py
from itertools import product
import numpy as np
INV_SQRT_3 = 1.0 / np.sqrt(3.0)
ASIN_INV_SQRT_3 = np.arcsin(INV_SQRT_3)
def gaussian_bell(xs, ys, xc=0., yc=0., xsigma=1., ysigma=1.):
""" Compute a 2D Gaussian with asymmetric standard deviations and
View .gitattributes
sample_data.nc filter=lfs diff=lfs merge=lfs -text
View Example.ipynb
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View README.md

This is a simple example for using snakemake to automate a basic work pipeline.

Motivation

Makefiles and GNU Make are awesome for many reasons, and it's unforgivable for any scientist working with data processing pipelines to use them throughout their projects. But Makefiles, while feature-rich, are not really an ideal tool for automating complex data processing pipelines. If, by some chance, your analyses simply require you to collect different data, process them with identical procedures, collate them, and produce a plot, then sure - Makefiles will do. But in analyzing climate model output, I've found that I have to do a lot of quirky hacks to fit this sort of workflow model.

A perfect example is the analysis of hierarchical climate model output. It's quite common to run a climate model multiple times in a factorial factor, changing 2-3 parameters (say, an emissions dataset and a parameterization in the model). While you can pigeon-hole linear da

View cartopy_wrap_example.ipynb
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View WRF_example.ipynb
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View dataarray_to_cube.py
def dataset_to_cube(ds, field):
""" Construct an iris Cube from a field of a given
xray DataSet. """
raise NotImplementedError("`iris` deprecated for Python 3")
dsf = ds[field]
## Attach coordinates to the cube, using full dataset for lookup
# dim_coords_and_dims = []
View xray_dask_test.ipynb
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