Intake-esm

Intake-esm

Badges

CI
Docs
Package
License
Citation

Motivation

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.

Overview

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]