Using intake together with CDO#
These examples were provided by Florian Ziemen (ziemen@dkrz.de) for use on the Levante Supercomputer of DKRZ. The notebook is based on the icon-datashader notebook, and gives a hint on how to process data from the intake catalog with cdo.
Some of the ideas were contributed by Lukas Kluft, Tobi Kölling, and others. The examples are by no means meant to be perfect. They should just provide some input on how things can be done.
Copyright 2022 Florian Ziemen / DKRZ
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Preparations#
[1]:
# basics
import intake
import xarray as xr
import dask # memory-efficient parallel computation and delayed execution (lazy evaluation).
import subprocess as sp
Paths for storing images and a data cache#
[2]:
%run gem_helpers.ipynb
uid = getpass.getuser()
image_path = make_tempdir("intake_demo_plots")
data_cache_path = make_tempdir("intake_demo_data")
Helper functions#
[3]:
# this function is also part of the gem helpers
def get_list_from_cat(catalog, column):
"""A helper function for getting the contents of a column in an intake catalog.
Call with the catalog to be inspected and the column of interest."""
return sorted(catalog.unique(column)[column]["values"])
The catalog containing all the data#
[4]:
catalog_file = "/work/ka1081/Catalogs/dyamond-nextgems.json"
[5]:
cat = intake.open_esm_datastore(catalog_file)
cat
ICON-ESM catalog with 130 dataset(s) from 88823 asset(s):
| unique | |
|---|---|
| variable_id | 546 |
| project | 2 |
| institution_id | 12 |
| source_id | 19 |
| experiment_id | 4 |
| simulation_id | 12 |
| realm | 5 |
| frequency | 12 |
| time_reduction | 4 |
| grid_label | 7 |
| level_type | 9 |
| time_min | 918 |
| time_max | 1094 |
| grid_id | 3 |
| format | 1 |
| uri | 88813 |
[6]:
# a somewhat closer look using get_from_cat
get_from_cat(cat, ["experiment_id", "source_id", "simulation_id"])
# print ("\n".join(get_from_cat (cat, "variable_id")))
[6]:
| experiment_id | source_id | simulation_id | |
|---|---|---|---|
| 0 | Cycle1 | ICON-SAP-5km | dpp0052 |
| 1 | Cycle1 | ICON-SAP-5km | dpp0054 |
| 2 | Cycle1 | ICON-SAP-5km | dpp0065 |
| 3 | Cycle1 | IFS-FESOM2-4km | hlq0 |
| 4 | Cycle1 | IFS-NEMO-4km | hmrt |
| 5 | Cycle1 | IFS-NEMO-9km | hmt0 |
| 6 | Cycle1 | IFS-NEMO-DEEPon-4km | hmwz |
| 7 | Cycle2-alpha | ICON-ESM | dpp0066 |
| 8 | Cycle2-alpha | ICON-ESM | dpp0067 |
| 9 | DW-ATM | ARPEGE-NH-2km | r1i1p1f1 |
| 10 | DW-ATM | GEM | r1i1p1f1 |
| 11 | DW-ATM | GEOS-1km | r1i1p1f1 |
| 12 | DW-ATM | GEOS-3km | r1i1p1f1 |
| 13 | DW-ATM | ICON-NWP-2km | r1i1p1f1 |
| 14 | DW-ATM | ICON-SAP-5km | dpp0014 |
| 15 | DW-ATM | NICAM-3km | r1i1p1f1 |
| 16 | DW-ATM | SAM2-4km | r1i1p1f1 |
| 17 | DW-ATM | SCREAM-3km | r1i1p1f1 |
| 18 | DW-ATM | SHiELD-3km | r1i1p1f1 |
| 19 | DW-ATM | UM-5km | r1i1p1f1 |
| 20 | DW-CPL | GEOS-6km | r1i1p1f1 |
| 21 | DW-CPL | ICON-SAP-5km | dpp0029 |
| 22 | DW-CPL | IFS-4km | r1i1p1f1 |
| 23 | DW-CPL | IFS-9km | r1i1p1f1 |
| 24 | DW-CPL | NICAM-3km | r1i1p1f1 |
Selecting ‘tas’ from the catalog#
[7]:
var = "tas"
hits = cat.search(simulation_id=["dpp0066", "dpp0067"], variable_id=[var])
hits
ICON-ESM catalog with 2 dataset(s) from 479 asset(s):
| unique | |
|---|---|
| variable_id | 37 |
| project | 1 |
| institution_id | 1 |
| source_id | 1 |
| experiment_id | 1 |
| simulation_id | 2 |
| realm | 1 |
| frequency | 1 |
| time_reduction | 1 |
| grid_label | 1 |
| level_type | 1 |
| time_min | 406 |
| time_max | 406 |
| grid_id | 1 |
| format | 1 |
| uri | 479 |
The CDO-relevant part#
Getting the file names#
[8]:
file_cat = {}
for simulation_id in ("dpp0066", "dpp0067"):
file_cat[simulation_id] = get_list_from_cat(
hits.search(simulation_id=simulation_id), "uri"
)
Feeding things into CDO#
[9]:
outfile_dict = {}
for simulation_id, files in file_cat.items():
outfile = f"{data_cache_path}/{var}_monstd_{simulation_id}.nc"
if not os.access(outfile, os.R_OK):
query = (
[
"cdo",
"-P",
"8",
"-monstd",
f"-select,name={var}",
"[",
]
+ files[:10]
+ ["]", outfile]
)
# Note, we only use the first 10 files to save time (the [:10] in files[:10]). Remove the [:10] to compute over the whole experiment.
print(query)
sp.run(query)
outfile_dict[simulation_id] = outfile
['cdo', '-P', '8', '-monstd', '-select,name=tas', '[', '/work/mh0287/m300083/experiments/dpp0066/dpp0066_atm_2d_ml_20200120T000000Z.nc', '/work/mh0287/m300083/experiments/dpp0066/dpp0066_atm_2d_ml_20200121T000000Z.nc', '/work/mh0287/m300083/experiments/dpp0066/dpp0066_atm_2d_ml_20200122T000000Z.nc', '/work/mh0287/m300083/experiments/dpp0066/dpp0066_atm_2d_ml_20200123T000000Z.nc', '/work/mh0287/m300083/experiments/dpp0066/dpp0066_atm_2d_ml_20200124T000000Z.nc', '/work/mh0287/m300083/experiments/dpp0066/dpp0066_atm_2d_ml_20200125T000000Z.nc', '/work/mh0287/m300083/experiments/dpp0066/dpp0066_atm_2d_ml_20200126T000000Z.nc', '/work/mh0287/m300083/experiments/dpp0066/dpp0066_atm_2d_ml_20200127T000000Z.nc', '/work/mh0287/m300083/experiments/dpp0066/dpp0066_atm_2d_ml_20200128T000000Z.nc', '/work/mh0287/m300083/experiments/dpp0066/dpp0066_atm_2d_ml_20200129T000000Z.nc', ']', '/scratch/k/k202134/intake_demo_data/tas_monstd_dpp0066.nc']
cdo(1) select: Process started
cdo(1) select: Processed 10066329600 values from 370 variables over 480 timesteps.
cdo monstd: Processed 10066329600 values from 1 variable over 480 timesteps [70.58s 758MB].
cdo(1) select: Process started
['cdo', '-P', '8', '-monstd', '-select,name=tas', '[', '/work/mh0287/m218027/experiments/dpp0067/dpp0067_atm_2d_ml_20200120T000000Z.nc', '/work/mh0287/m218027/experiments/dpp0067/dpp0067_atm_2d_ml_20200121T000000Z.nc', '/work/mh0287/m218027/experiments/dpp0067/dpp0067_atm_2d_ml_20200122T000000Z.nc', '/work/mh0287/m218027/experiments/dpp0067/dpp0067_atm_2d_ml_20200123T000000Z.nc', '/work/mh0287/m218027/experiments/dpp0067/dpp0067_atm_2d_ml_20200124T000000Z.nc', '/work/mh0287/m218027/experiments/dpp0067/dpp0067_atm_2d_ml_20200125T000000Z.nc', '/work/mh0287/m218027/experiments/dpp0067/dpp0067_atm_2d_ml_20200126T000000Z.nc', '/work/mh0287/m218027/experiments/dpp0067/dpp0067_atm_2d_ml_20200127T000000Z.nc', '/work/mh0287/m218027/experiments/dpp0067/dpp0067_atm_2d_ml_20200128T000000Z.nc', '/work/mh0287/m218027/experiments/dpp0067/dpp0067_atm_2d_ml_20200129T000000Z.nc', ']', '/scratch/k/k202134/intake_demo_data/tas_monstd_dpp0067.nc']
cdo(1) select: Processed 40265318400 values from 370 variables over 480 timesteps.
cdo monstd: Processed 40265318400 values from 1 variable over 480 timesteps [266.13s 2918MB].
Loading the data into xarray#
[10]:
dataset_dict = {
name: xr.open_dataset(filename) for (name, filename) in outfile_dict.items()
}