gmpas

Fast plotting of MPAS output on its own native mesh — no regridding, so variable resolution is preserved exactly as the model carries it. Plus the other end of the pipeline: designing a mesh, building it with JIGSAW, and looking at either before or after it exists.

import gmpas

ds = gmpas.open_mpas("diag.2019-09-01_00.00.00.nc", mesh="maritime.region.nc")

ds.mpas.plot("mslp")        # cell field, filled Voronoi polygons
ds.mpas.plot("u")           # edge field, drawn on the cell faces themselves
ds.mpas.plot_mesh()         # where the mesh actually refines, in km

mesh= may be omitted when the file carries its own mesh information, or when a mesh file with a matching cell count sits beside it.

Installation

conda env create -f environment.yml && conda activate gmpas && pip install -e . --no-deps

Pure pip works too where wheels exist:

pip install -e ".[dev]"

Two workflows need external programs, which pip cannot provide. Conservative remapping needs ESMF (conda install -c conda-forge esmf nco); mesh generation needs JIGSAW and, for the final step, MPI and PnetCDF. Skip both if you only plot and view.

Check it landed:

gmpas --version && pytest -q

Full detail, including the extras and what each one pulls in: docs/installation.md.

Usage

gmpas info          history.2012-02-25_12.00.00.nc
gmpas plot          history.2012-02-25_12.00.00.nc precipw -o pw.png
gmpas view          /path/to/run/
gmpas remap         history.*.nc -o out/
gmpas prep view     mesh.nc
gmpas prep hfun     hfun.py --check
gmpas prep generate hfun.py -o mesh/     # needs $JIGSAWDIR and $MKGRIDFILE

Any path may be a file, a directory, or a glob; a directory or glob is read as one time series across files, which is how MPAS writes output. Running gmpas with no arguments prints the whole list with examples.

Everything except prep is postprocessing — it opens a run and renders, remaps or exports it. prep is the other end, for work that happens before there is any output.

Documentation