For reading time series data, that the era5_reshuffle
and eraint_reshuffle
command produces, the class ERATs
can be used. Optional arguments that are passed to the parent class (OrthoMultiTs
, as defined in pynetcf.time_series) can be passed as well:
from ecmwf_models import ERATs
# read_bulk reads full files into memory
# read_ts takes either lon, lat coordinates to perform a nearest neighbour search
# or a grid point index (from the grid.nc file) and returns a pandas.DataFrame.
ds = ERATs(ts_path, ioclass_kws={'read_bulk': True})
ts = ds.read_ts(45, 15)
Bulk reading speeds up reading multiple points from a cell file by storing the file in memory for subsequent calls. Either Longitude and Latitude can be passed to perform a nearest neighbour search on the data grid (grid.nc
in the time series path) or the grid point index (GPI) can be passed directly.