PYZARR¶

—– CLEO —– File: pyzarr.py Project: sdmout_src Created Date: Tuesday 24th October 2023 Author: Clara Bayley (CB) Additional Contributors: —– License: BSD 3-Clause “New” or “Revised” License https://opensource.org/licenses/BSD-3-Clause —– File Description: functions to return zarr data in useful formats for plotting e.g. for handling ragged arrays of superdroplet attributes

cleopy.sdmout_src.pyzarr.get_thermodata(dataset, ntime, ndims, consts, getwinds=False)[source]¶: returns a thermodynamic data in a dictionary. The value under each key is the thermodynamics data in a 2D array with dimensions [time, gridbox]. E.g. thermo[“qvap”][:,0] gives the timeseries of qvap for the 0th gridbox. thermo[“qvap][0] gives the qvap of all gridboxes at the 0th output time

cleopy.sdmout_src.pyzarr.raggedvar_fromzarr(ds, raggedcount, var)[source]¶: returns ragged ak.Array dims [time, ragged] for a variable “var” in zarr ds

cleopy.sdmout_src.pyzarr.surfaceprecip_estimate(dataset, gbxs)[source]¶: use last radius of SDs before they leave the domain to estimate the volume of precipitation at each timestep. Values should be approx. equal to sum over gbxs (multiplied by area_gbx/area_domain) of logbook values for precip

cleopy.sdmout_src.pyzarr.var3d_fromzarr(ds, ndims, key)[source]¶: ‘ returns 3D variable with dims [y, x, z] from zarr dataset “ds”

cleopy.sdmout_src.pyzarr.var4d_fromzarr(ds, ntime, ndims, key)[source]¶: ‘ returns 4D variable with dims [time, y, x, z] from zarr dataset “ds”