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Change","sub_category":"Climate Data Access and Visualization","monthly_downloads":19115,"total_dependent_repos":86,"total_dependent_packages":18,"readme":"# Intake-esm\n\n- [Intake-esm](#intake-esm)\n  - [Badges](#badges)\n  - [Motivation](#motivation)\n  - [Overview](#overview)\n  - [Installation](#installation)\n\n## Badges\n\n| CI           | [![GitHub Workflow Status][github-ci-badge]][github-ci-link] [![Code Coverage Status][codecov-badge]][codecov-link] [![pre-commit.ci status][pre-commit.ci-badge]][pre-commit.ci-link] |\n| :----------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |\n| **Docs**     |                                                                     [![Documentation Status][rtd-badge]][rtd-link]                                                                     |\n| **Package**  |                                     [![Conda][conda-badge]][conda-link] [![PyPI][pypi-badge]][pypi-link] [![Versions][versions-badge]][pypi-link]                                      |\n| **License**  |                                                                         [![License][license-badge]][repo-link]                                                                         |\n| **Citation** |                                                                         [![Zenodo][zenodo-badge]][zenodo-link]                                                                         |\n\n## Motivation\n\nComputer simulations of the Earth’s climate and weather generate huge amounts of data.\nThese data are often persisted on HPC systems or in the cloud across multiple data\nassets of a variety of formats ([netCDF](https://www.unidata.ucar.edu/software/netcdf/), [zarr](https://zarr.readthedocs.io/en/stable/), etc...). Finding, investigating,\nloading these data assets into compute-ready data containers costs time and effort.\nThe data user needs to know what data sets are available, the attributes describing\neach data set, before loading a specific data set and analyzing it.\n\nFinding, investigating, loading these assets into data array containers\nsuch as xarray can be a daunting task due to the large number of files\na user may be interested in. Intake-esm aims to address these issues by\nproviding necessary functionality for searching, discovering, data access/loading.\n\n## Overview\n\n`intake-esm` is a data cataloging utility built on top of [intake](https://github.com/intake/intake), [pandas](https://pandas.pydata.org/), [polars](https://pola.rs/) and [xarray](https://xarray.pydata.org/en/stable/), and it's pretty awesome!\n\n- Opening an ESM catalog definition file: An Earth System Model (ESM) catalog file is a JSON file that conforms\n  to the [ESM Collection Specification](./docs/source/reference/esm-catalog-spec.md). When provided a link/path to an esm catalog file, `intake-esm` establishes\n  a link to a database (CSV file) that contains data assets locations and associated metadata\n  (i.e., which experiment, model, the come from). The catalog JSON file can be stored on a local filesystem\n  or can be hosted on a remote server.\n\n  ```python\n\n  In [1]: import intake\n\n  In [2]: import intake_esm\n\n  In [3]: cat_url = intake_esm.tutorial.get_url(\"google_cmip6\")\n\n  In [4]: cat = intake.open_esm_datastore(cat_url)\n\n  In [5]: cat\n  Out[5]: \u003cGOOGLE-CMIP6 catalog with 4 dataset(s) from 261 asset(s\u003e\n  ```\n\n- Search and Discovery: `intake-esm` provides functionality to execute queries against the catalog:\n\n  ```python\n  In [5]: cat_subset = cat.search(\n     ...:     experiment_id=[\"historical\", \"ssp585\"],\n     ...:     table_id=\"Oyr\",\n     ...:     variable_id=\"o2\",\n     ...:     grid_label=\"gn\",\n     ...: )\n\n  In [6]: cat_subset\n  Out[6]: \u003cGOOGLE-CMIP6 catalog with 2 dataset(s) from 67 asset(s)\u003e\n  ```\n\n- 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:\n\n  ```python\n\n    In [7]: dset_dict = cat_subset.to_dataset_dict()\n\n    --\u003e The keys in the returned dictionary of datasets are constructed as follows:\n            'activity_id.institution_id.source_id.experiment_id.table_id.grid_label'\n    |███████████████████████████████████████████████████████████████| 100.00% [2/2 00:18\u003c00:00]\n  ```\n\nSee [documentation](https://intake-esm.readthedocs.io/en/latest/) for more information.\n\n## Installation\n\nIntake-esm can be installed from PyPI with pip:\n\n```bash\npython -m pip install intake-esm\n```\n\nIt is also available from `conda-forge` for conda installations:\n\n```bash\nconda install -c conda-forge intake-esm\n```\n\n[github-ci-badge]: https://github.com/intake/intake-esm/actions/workflows/ci.yaml/badge.svg\n[github-ci-link]: https://github.com/intake/intake-esm/actions/workflows/ci.yaml\n[codecov-badge]: https://img.shields.io/codecov/c/github/intake/intake-esm.svg?logo=codecov\n[codecov-link]: https://codecov.io/gh/intake/intake-esm\n[rtd-badge]: https://readthedocs.org/projects/intake-esm/badge/?version=latest\n[rtd-link]: https://intake-esm.readthedocs.io/en/latest/?badge=latest\n[pypi-badge]: https://img.shields.io/pypi/v/intake-esm?logo=pypi\n[pypi-link]: https://pypi.org/project/intake-esm\n[conda-badge]: https://img.shields.io/conda/vn/conda-forge/intake-esm?logo=anaconda\n[conda-link]: https://anaconda.org/conda-forge/intake-esm\n[zenodo-badge]: https://img.shields.io/badge/DOI-10.5281%20%2F%20zenodo.3491062-blue.svg\n[zenodo-link]: https://doi.org/10.5281/zenodo.3491062\n[license-badge]: https://img.shields.io/github/license/intake/intake-esm\n[repo-link]: https://github.com/intake/intake-esm\n[pre-commit.ci-badge]: https://results.pre-commit.ci/badge/github/intake/intake-esm/main.svg\n[pre-commit.ci-link]: https://results.pre-commit.ci/latest/github/intake/intake-esm/main\n[versions-badge]: https://img.shields.io/pypi/pyversions/intake-esm\n","funding_links":[],"readme_doi_urls":["https://doi.org/10.5281/zenodo.3491062"],"works":{"https://doi.org/10.5281/zenodo.3491062":null},"citation_counts":{},"total_citations":0,"keywords_from_contributors":["climate-science","climate","climate-analysis","zarr","climate-model","climate-data","cf-conventions","intake-esm","opendap","earth-science"],"project_url":"https://ost.ecosyste.ms/api/v1/projects/20630","html_url":"https://ost.ecosyste.ms/projects/20630"}