Getting Started with MAUD

One can use MAUD inside Python or in the shell.

Inside Python

>>> from maud import window_1Dmean, window_mean_2D_latlon
>>> window_1Dmean(x, l=200e3, t=None, method='hann', axis=0, parallel=True)

>>> window_mean_2D_latlon(Lat, Lon, data, l, method='hamming', interp=False)

The faster version

There is a Cython version of each filter. If you’re able to, use cmaud instead of maud to gain at least one order of magnitude on the speed.

>>> from cmaud import window_1Dmean
>>> window_1Dmean(x, l=200e3)

>>> from cmaud import window_mean_2D_latlon
>>> window_mean_2D_latlon(Lat, Lon, data, l)

Shell script

To check the available options

>>> maud4latlonnc -h

On this example, it will filter the variable ‘temperature’ at the netCDF file model_output.nc. The weight function used is hamming, and the window scale is 600e3. The npes define the number of parallel process to be used, in this case 18. The option –iterp defines that any masked value will be replaced in the output as the filtered result of the valid values around it, inside the window lenght.

>>> maud4latlonnc -l 600e3 --var='temperature' -w hamming --interp --npes=18 model_output.nc