Neighbourhood methods

Neighbourhood methods compute for each gridpoint a statistic based on values within a neighbourhood surrounding this gridpoint.

The functions in gridpp look like this:

values = gridpp.neighbourhood(input_2d, radius, statistic)
values = gridpp.neighbourhood_ens(input_3d, radius, statistic)

where the first is used for processing a single 2D field, and the second is for processing an ensemble of 2D fields. Both return 2D fields. radius is the half-width of the neighbourhood, and statistic is one of gridpp.Mean, gridpp.Min, gridpp.Max, gridpp.Std, gridpp.Variance, or gridpp.Sum.

To compute the neighbourhood mean on a 15x15 square neighbourhood, use:

radius = 7
ovalues = gridpp.neighbourhood(ivalues, radius, gridpp.Mean)

Neighbourhood quantiles

Neighbourhoods for ensembles add the ensebmle dimension to the neighbourhood. When computing a statistic, the statistic is calculated by pooling all ensemble members together, creating a single field (instead of one field for each ensemble member).

To calculate quantiles, use the neighbourhood_quantile method:

radius = 7
quantile = 0.9
num_thresholds = 20
ovalues = gridpp.neighbourhood_quantile(ivalues, quantile, radius, num_thresholds)

To calculate ensemble neighbourhoods, use the _ens functions:

ivalues_ens = np.random.rand([100, 80, 10]) # 10 member ensemble
radius = 7
ovalues = gridpp.neighbourhood_ens(ivalues_ens, radius, gridpp.Mean)