Usage

To use a module from grid-process in a project:

import grid_process.<module_name>

Manipulating data

Use case: remove ‘forecast_reference_time’ and ‘lead_time’ dimensions and set ‘time’ dimension with numpy.datetime64 coordinate labels:

import grid_process.timeseries as tms
ds_validtime = tms.setindex_validtime(ds)

# same but with accessor:
ds_validtime = ds.timeseries.setindex_validtime()

Returns a xarray.Dataset with dimension ‘forecast_reference_time’ squeezed and dimension ‘time’ as coordinates of dtype numpy.datetime64. Also works when the initial dataset has dimension ‘forecast_reference_time’ and ‘lead_time’ of length greater than 1. In such cases, same validtimes are not duplicated but the data from the most recent run is used.

Use case: temporal interpolation:

ds_1hr = tms.interp2granularity(ds, '1h', method='cubic')

# same but with accessor:
ds_10min = ds.interp2granularity('10min', method='back_cum')

Returns a xarray.Dataset interpolated to the specified granularity in the ‘lead_time’ or ‘time’ dimension. There are many interpolation methods available for specific variables such as ‘back_cst’ (e.g. for mean rainfall rate over last timestamp), ‘back_cum’ (e.g. for precipitation amount accumulated since previous timestamp), ‘sundur’ for ‘duration of sunshine’, ‘linear’ (e.g. for windspeed, windgust), ‘nearest’ (e.g. for wind direction), ‘cubic’ (e.g. for temperature)

Use case: POI interpolation:

import geopandas as gpd
import grid_process.grid_interp as gri

# geopandas.GeoDataFrame with POI metadata (lat, lon, altitude, type, name)
poi_def = gpd.read_file("poi.geojson")

ds_poi = gri.interp2poi(ds, poi_def, method='linear')

# same but with accessor:
ds_poi = ds.interp2poi(poi_def, method='temp', k=50, z_fact=15)

Returns a xarray.Dataset with dimension ‘x’ and ‘y’ replaced by ‘poi’. There are many interpolation methods available for specific variables such as temperature, snowlimit etc. There are also methods to perform local smoothing, or taking local extremas.

Use case: regridding:

import grid_process.grid_interp as gri

ds_regridded = gri.interp2grid(ds, ds_dst, method='linear')

# same but with accessor:
ds_regridded = ds.interp2grid(ds_dst, method='temp_fixgrad', r_search=1.7)

Returns a xarray.Dataset with the data of ds remapped to the coordinates of ds_dst. Same interpolation methods exist as for POI interpolation