marutils - Utilities for working with MAR RCM outputs¶
This package contains utilities/tools that make it easier to work with outputs of the regional climate model “MAR” (Modèle Atmosphérique Régional, http://mar.cnrs.fr), which is commonly used in studies of the Greenland and Antarctic ice sheets as well as other land areas.
Load outputs straight into an xarray Dataset with standard dimensions names (time, y, x).
Provide a path with a wildcard expression (*) to automatically load several years of MAR files at once, concatenating them along the time axis.
Plays nicely with Dask - chunking options default to 365-366 days (but can be changed at will).
Loaded Datasets are automatically “geo-aware” using the rioxarray .rio accessor - no more trying to work out MAR’s geo-referencing manually!
Helper functions for turning X-hourly variables into continuous time series.
Helper functions for masking.
Quick Start¶
To open a MAR dataset:
import marutils
# Open a time series for the 21st century.
mar_outputs = marutils.open_dataset('MARv3.11.2/MAR*20*.nc')
print(mar_outputs.coords)
Yields:
Coordinates:
* x (x) float32 -760000.0 -740000.0 -720000.0 ... 660000.0 680000.0
* y (y) float32 -1180000.0 -1160000.0 ... 1480000.0 1500000.0
* SECTOR (SECTOR) float32 1.0 2.0
* SECTOR1_1 (SECTOR1_1) float32 1.0
* time (time) datetime64[ns] 2019-01-01T12:00:00 ... 2019-12-31T12:...
* ATMLAY3_3 (ATMLAY3_3) float32 0.99974793
spatial_ref int64 0
What is proj.4 string for the geo-referencing of this dataset?:
print(mar_outputs.rio.crs.to_proj4())
Yields:
+proj=stere +lat_0=70.5 +lon_0=-40 +k=1 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs=True
To return an X-hourly variable with a single, continuous hourly time variable:
import marutils.xhourly
hourly_air_temperatures = marutils.xhourly.xhourly_to_time(mar_outputs.TTH)
Installation¶
At the moment, manually:
git clone <repository>
cd <repository>
pip install -e .
Soon to be available via pyPI.
API