Getting Started with MAUD

One can use MAUD inside Python or in the shell.

Shell script

MAUD provides two shell commands, maud4nc for 1D filter and maud4latlonnc for 2D filter on geographic coordinates.

maud4nc

To check the available options:

>>> maud4nc -h

In the example below, the variable temperature (–var), at the netCDF file model_output.nc, is filtered along the time (–scalevar) using a hann window (-w), and the output will be saved at model_highpass.nc (-o). This is a bandpass filter (–highpasswindowlength together with –lowpasswindowlength), preserving scales between 120 and 10 units of the scalevar (on this case: time).:

>>> maud4nc --highpasswindowlength=120 --lowpasswindowlength=10 --scalevar=time \
>>> --var='temperature' -w hann -o model_highpass.nc model_output.nc

maud4latlonnc

To check the available options:

>>> maud4latlonnc -h

In the example below, the variable temperature (–var), at the netCDF file model_output.nc, is filtered along the space (lat x lon). The variables latitude and longitude must exist in the same file. This is a lowpass filter (–largerpasslength), hence it attenuates eveything with spatial scale smaller than 600e3 meters. The weights are defined by a hamming function (-w). The npes define the number of parallel process to be used, in this case 18. The option –interp defines that any missing value will be replaced in the output as the filtered result of the valid values around it, inside the window lenght.:

>>> maud4latlonnc --largerpasslength=600e3 --var='temperature' \
>>> -w hamming --interp --npes=18 -o model_highpass model_output.nc

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)