argopy.xarray.ArgoAccessor.reduce_profile

argopy.xarray.ArgoAccessor.reduce_profile#

ArgoAccessor.reduce_profile(func, params=[], **kwargs) → DataArray[source]#

Apply a vectorized function for unlabeled arrays for each Argo profiles

This method allows to execute a per profile diagnostic function very efficiently. Such a diagnostic function takes vertical profiles as input and return a single value as output (see examples below).

Typical usage example would include computation of mixed layer depth or euphotic layer depth.

Parameters:

  • func (callable) – A function that takes one or more profile parameters as input, and return a single value as output.

  • params ([List, str]) – Name, or list of names, of the dataset parameters expected by func. All of these parameters must have N_LEVELS as a dimension.

  • **kwargs (dict, optional) – Keyword arguments to be passed to func.

Return type:

xarray.DataArray

Examples

Example 1#
from argopy import ArgoFloat
dsp = ArgoFloat(6901864).open_dataset('Sprof')

def max_salinity_depth(pres, psal):
    # A dummy function returning depth of the maximum salinity
    idx = ~np.logical_or(np.isnan(pres), np.isnan(psal))
    return pres[idx][np.argmax(psal[idx])]

# Apply reduce function on all profiles:
dsp.argo.reduce_profile(max_salinity_depth, params=['PRES', 'PSAL'])
Example 2: with keyword arguments#
from argopy import ArgoFloat
dsp = ArgoFloat(6901864).open_dataset('Sprof')

def max_salinity_depth(pres, psal, max_layer=1000.):
    # A dummy function returning depth of the maximum salinity above max_layer:
    idx = ~np.logical_or(np.isnan(pres), np.isnan(psal))
    idx = np.logical_and(idx, pres<=max_layer)
    if np.any(idx):
        return pres[idx][np.argmax(psal[idx])]
    else:
        return np.NaN

# Apply reduce function on all profiles:
dsp.argo.reduce_profile(max_salinity_depth, params=['PRES', 'PSAL'], max_layer=700)
Example 3: automatically parallelize func with Dask#
from argopy import ArgoFloat
dsp = ArgoFloat(6901864).open_dataset('Sprof')

# Make sure we're working with dask arrays
dsp = dsp.chunk({'N_PROF': 10})

def max_salinity_depth(pres, psal):
    # A dummy function returning depth of the maximum salinity
    idx = ~np.logical_or(np.isnan(pres), np.isnan(psal))
    return pres[idx][np.argmax(psal[idx])]

# Apply reduce function on all profiles:
da = dsp.argo.reduce_profile(max_salinity_depth, params=['PRES', 'PSAL'])  # Return a dask array
da.compute()
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