• Intake-esm
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Computer simulations of the Earth’s climate and weather generate huge amounts of data. These data are often persisted on HPC systems or in the cloud across multiple data assets of a variety of formats (netCDF, zarr, etc...). Finding, investigating, loading these data assets into compute-ready data containers costs time and effort. The data user needs to know what data sets are available, the attributes describing each data set, before loading a specific data set and analyzing it.

Finding, investigating, loading these assets into data array containers such as xarray can be a daunting task due to the large number of files a user may be interested in. Intake-esm aims to address these issues by providing necessary functionality for searching, discovering, data access/loading.

intake-esm is a data cataloging utility built on top of intake, pandas, and xarray, and it's pretty awesome!

• Opening an ESM catalog definition file: An Earth System Model (ESM) catalog file is a JSON file that conforms to the ESM Collection Specification. When provided a link/path to an esm catalog file, intake-esm establishes a link to a database (CSV file) that contains data assets locations and associated metadata (i.e., which experiment, model, the come from). The catalog JSON file can be stored on a local filesystem or can be hosted on a remote server.

  In [1]: import intake
  
  In [2]: import intake_esm
  
  In [3]: cat_url = intake_esm.tutorial.get_url("google_cmip6")
  
  In [4]: cat = intake.open_esm_datastore(cat_url)
  
  In [5]: cat
  Out[5]: <GOOGLE-CMIP6 catalog with 4 dataset(s) from 261 asset(s>

• Search and Discovery: intake-esm provides functionality to execute queries against the catalog:

  In [5]: cat_subset = cat.search(
     ...:     experiment_id=["historical", "ssp585"],
     ...:     table_id="Oyr",
     ...:     variable_id="o2",
     ...:     grid_label="gn",
     ...: )
  
  In [6]: cat_subset
  Out[6]: <GOOGLE-CMIP6 catalog with 4 dataset(s) from 261 asset(s)>

• Access: when the user is satisfied with the results of their query, they can load data assets (netCDF and/or Zarr stores) into xarray datasets:

    In [7]: dset_dict = cat_subset.to_dataset_dict()
  
    --> The keys in the returned dictionary of datasets are constructed as follows:
            'activity_id.institution_id.source_id.experiment_id.table_id.grid_label'
    |███████████████████████████████████████████████████████████████| 100.00% [2/2 00:18<00:00]

See documentation for more information.

Intake-esm can be installed from PyPI with pip:

python -m pip install intake-esm

It is also available from conda-forge for conda installations:

conda install -c conda-forge intake-esm