mpas_xarray.py

Wrapper to handle importing MPAS files into xarray (https://github.com/pydata/xarray). Module can be installed via

pip -v install git+ssh://git@github.com/pwolfram/mpas_xarray

The module does the following:

1. Converts MPAS "xtime" to xarray time. Time dimension is assigned via preprocess_mpas.
2. Converts MPAS "timeSinceStartOfSim" to xarray time for MPAS fields coming from the timeSeriesStatsAM. Time dimension is assigned via preprocess_mpas(...,timeSeriesStats=True).
3. Allows generalized selection of variables via preprocess_mpas(..., onlyvars=['var1','var2']) and slicing via preprocess_mpas(..., iselvals={'nVertLevels':1}) and preprocess_mpas(..., selvals={'lonCell':180.0}).
4. Provides capability to remove redundant time entries from reading of multiple netCDF datasets via remove_repeated_time_index.

Example Usage:

import xarray
from mpas_xarray import preprocess_mpas, remove_repeated_time_index

ds = xarray.open_mfdataset('globalStats*nc', preprocess=preprocess_mpas)
ds = remove_repeated_time_index(ds)

To test:

tar xzvf globalStatsShort.tgz
python mpas_xarray.py -f "globalStats*nc"

This outputs a simple time-series plot of Time vs. Time to test functionality.

Example Usage for timeSeriesStatsAM fields:

import xarray
from mpas_xarray import preprocess_mpas, remove_repeated_time_index

def preprocess(x, timestr='timeSeriesStatsMonthly_avg_daysSinceStartOfSim_1'):
  return preprocess_mpas(x, timeSeriesStats=True, timestr=timestr)

ds = xarray.open_mfdataset('am.mpas-cice*nc', preprocess=preprocess)
ds = remove_repeated_time_index(ds)

To test:

tar xzvf am.mpas-ciceShort.tgz
python mpas_xarray.py -f "am.mpas-cice*nc" --istimeavg "true"

This plots a short time series of global average ice concentration, showing the correctly centered curve (derived using preprocess_mpas_timeSeriesStats) and the curve incorrectly shifted toward the end of the time averaging period (derived using preprocess_mpas).