https://github.com/zmoon/uscrn

Easily load U.S. CRN data
https://github.com/zmoon/uscrn

climate-data ncei noaa-data

Last synced: about 1 year ago
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Easily load U.S. CRN data

• Host: GitHub
• URL: https://github.com/zmoon/uscrn
• Owner: zmoon
• License: mit
• Created: 2023-07-25T14:59:52.000Z (almost 3 years ago)
• Default Branch: main
• Last Pushed: 2025-03-03T20:51:14.000Z (about 1 year ago)
• Last Synced: 2025-04-04T19:06:02.739Z (about 1 year ago)
• Topics: climate-data, ncei, noaa-data
• Language: Python
• Homepage: https://uscrn.readthedocs.io
• Size: 1.98 MB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 9
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

          
# uscrn

Easily load [U.S. Climate Reference Network](https://www.ncei.noaa.gov/access/crn/) (USCRN) data.

[![Version on PyPI](https://img.shields.io/pypi/v/uscrn.svg)](https://pypi.org/project/uscrn/)

[![CI status](https://github.com/zmoon/uscrn/actions/workflows/ci.yml/badge.svg)](https://github.com/zmoon/uscrn/actions/workflows/ci.yml)

[![Documentation status](https://readthedocs.org/projects/uscrn/badge/?version=latest)](https://uscrn.readthedocs.io/en/latest/)

[![Test coverage](https://codecov.io/gh/zmoon/uscrn/branch/main/graph/badge.svg)](https://app.codecov.io/gh/zmoon/uscrn)

[![pre-commit.ci status](https://results.pre-commit.ci/badge/github/zmoon/uscrn/main.svg)](https://results.pre-commit.ci/latest/github/zmoon/uscrn/main)

[![Project Status: WIP – Initial development is in progress, but there has not yet been a stable, usable release suitable for the public.](https://www.repostatus.org/badges/latest/wip.svg)](https://www.repostatus.org/#wip)

With `uscrn`, fetching and loading years of [data](https://www.ncei.noaa.gov/access/crn/qcdatasets.html) for all USCRN sites[^a] takes just one line of code[^b].

Example:

```python

import uscrn

df = uscrn.get_data(2019, "hourly", n_jobs=6)  # pandas.DataFrame

ds = uscrn.to_xarray(df)  # xarray.Dataset, with soil depth dimension if applicable (hourly, daily)

```

Both `df` (pandas) and `ds` (xarray) include dataset and variable metadata.

For `df`, these are in `df.attrs` and can be preserved by

writing to Parquet with the PyArrow engine[^d] with

[pandas v2.1+](https://pandas.pydata.org/docs/whatsnew/v2.1.0.html#other-enhancements).

```python

df.to_parquet("uscrn_2019_hourly.parquet", engine="pyarrow")

```

Conda install example[^c]:

```sh

conda create -n crn -c conda-forge python=3.11 joblib numpy pandas pyyaml requests xarray pyarrow netcdf4

conda activate crn

pip install --no-deps uscrn

```

[^a]: Use `uscrn.load_meta()` to load the site metadata table.

[^b]: Not counting the `import` statement...

[^c]: `uscrn` is not yet on conda-forge.

[^d]: Or the fastparquet engine with [fastparquet v2024.2.0+](https://github.com/dask/fastparquet/commit/9d7ee90e38103fef3dd1bd2f5eb0654b8bd3fdff).