| Name | herbie-data JSON |
| Version |
2024.3.1
JSON |
| download |
| home_page | None |
| Summary | Download numerical weather prediction GRIB2 model data. |
| upload_time | 2024-03-26 22:49:27 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | MIT License Copyright (c) 2019-2022 Brian K. Blaylock Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
weather
meteorology
forecast
atmosphere
grib2
xarray
hrrr
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
<div
align="center"
>
# Herbie: Retrieve NWP Model Data 🏁
<!-- Badges -->
[](https://pypi.python.org/pypi/herbie-data/)
[
](https://anaconda.org/conda-forge/herbie-data)
[](https://zenodo.org/badge/latestdoi/275214142)
[
](https://pypi.org/project/herbie-data/)
[](https://github.com/astral-sh/ruff)
[](https://github.com/blaylockbk/Herbie/actions/workflows/tests-conda.yml)
[](https://github.com/blaylockbk/Herbie/actions/workflows/tests-python.yml)
[](https://herbie.readthedocs.io/?badge=latest)
[](https://anaconda.org/conda-forge/herbie-data)
<!-- (Badges) -->
</div>
**Herbie** is a python package that downloads recent and archived numerical weather prediction (NWP) model output from different cloud archive sources. **Its most popular capability is to download HRRR model data.** NWP data in GRIB2 format can be read with xarray+cfgrib. Much of this data is made available through the [NOAA Open Data Dissemination](https://www.noaa.gov/information-technology/open-data-dissemination) (NODD) Program (formerly the Big Data Program) which has made weather data more accessible than ever before.
Herbie helps you discover, download, and read data from:
- [High Resolution Rapid Refresh (HRRR)](https://herbie.readthedocs.io/en/latest/user_guide/_model_notebooks/hrrr.html) | [HRRR-Alaska](https://herbie.readthedocs.io/en/latest/user_guide/_model_notebooks/hrrrak.html)
- [Rapid Refresh (RAP)](https://herbie.readthedocs.io/en/latest/user_guide/_model_notebooks/rap.html)
- [Global Forecast System (GFS)](https://herbie.readthedocs.io/en/latest/user_guide/_model_notebooks/gfs.html)
- [Global Ensemble Forecast System (GEFS)](https://herbie.readthedocs.io/en/latest/user_guide/_model_notebooks/gefs.html)
- [ECMWF Open Data Forecasts](https://herbie.readthedocs.io/en/latest/user_guide/_model_notebooks/ecmwf.html) (IFS and AIFS)
- [Navy Global Environmental Model (NAVGEM)](https://herbie.readthedocs.io/en/latest/user_guide/_model_notebooks/navgem.html)
- [North American Mesoscale Model (NAM)](https://github.com/blaylockbk/Herbie/blob/main/docs/user_guide/_model_notebooks/nam.ipynb)
- [National Blend of Models (NBM)](https://herbie.readthedocs.io/en/latest/user_guide/_model_notebooks/nbm.html)
- [Rapid Refresh Forecast System (RRFS)](https://herbie.readthedocs.io/en/latest/user_guide/_model_notebooks/rrfs.html) _prototype_
- [Real-Time/Un-Restricted Mesoscale Analysis (RTMA/URMA)](https://herbie.readthedocs.io/en/latest/user_guide/_model_notebooks/rtma.html)
- [Hurricane Analysis And Forecast System (HAFS)](https://herbie.readthedocs.io/en/latest/user_guide/_model_notebooks/hafs.html)
- [High Resolution Deterministic Prediction System (HRDPS)](https://herbie.readthedocs.io/en/latest/user_guide/_model_notebooks/hrdps.html)
# 📓 [Herbie Documentation](https://herbie.readthedocs.io/)
## Installation
The easiest way to instal Herbie and its dependencies is with [Conda](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html) from conda-forge.
```bash
conda install -c conda-forge herbie-data
```
You may also create the provided Conda environment, **[`environment.yml`](https://github.com/blaylockbk/Herbie/blob/main/environment.yml)**.
```bash
# Download environment file
wget https://github.com/blaylockbk/Herbie/raw/main/environment.yml
# Modify that file if you wish.
# Create the environment
conda env create -f environment.yml
# Activate the environment
conda activate herbie
```
Alternatively, Herbie is published on PyPI and you can install it with pip, _but_ it requires some dependencies that you will have to install yourself:
- Python 3.8 to 3.11
- cURL
- [Cartopy](https://scitools.org.uk/cartopy/docs/latest/installing.html), which requires GEOS and Proj.
- [cfgrib](https://github.com/ecmwf/cfgrib), which requires eccodes.
- _Optional:_ wgrib2
- _Optional:_ [Carpenter Workshop](https://github.com/blaylockbk/Carpenter_Workshop)
When those are installed within your environment, _then_ you can install Herbie with pip.
```bash
# Latest published version
pip install herbie-data
# ~~ or ~~
# Most recent changes
pip install git+https://github.com/blaylockbk/Herbie.git
```
## Capabilities
- Search for model output from different data sources.
- Download full GRIB2 files.
- Download subset GRIB2 files (by grib field).
- Read data with xarray.
- Read index file with Pandas.
- Plot data with Cartopy (very early development).
```mermaid
graph TD;
d1[(HRRR)] -..-> H
d2[(RAP)] -.-> H
d3[(GFS)] -..-> H
d33[(GEFS)] -.-> H
d4[(IFS)] -..-> H
d44[(AIFS)] -..-> H
d5[(NBM)] -.-> H
d6[(RRFS)] -..-> H
d7[(RTMA)] -.-> H
d8[(URMA)] -..-> H
H((Herbie))
H --- .inventory
H --- .download
H --- .xarray
style H fill:#d8c89d,stroke:#0c3576,stroke-width:4px,color:#000000
```
```python
from herbie import Herbie
# Herbie object for the HRRR model 6-hr surface forecast product
H = Herbie(
'2021-01-01 12:00',
model='hrrr',
product='sfc',
fxx=6
)
# Look at file contents
H.inventory()
# Download the full GRIB2 file
H.download()
# Download a subset, like all fields at 500 mb
H.download(":500 mb")
# Read subset with xarray, like 2-m temperature.
H.xarray("TMP:2 m")
```
## Data Sources
Herbie downloads model data from the following sources, but can be extended to include others:
- [NOMADS](https://nomads.ncep.noaa.gov/)
- [NOAA Open Data Dissemination Program (NODD)](https://www.noaa.gov/information-technology/open-data-dissemination) partners (i.e., AWS, Google, Azure).
- [ECMWF Open Data Forecasts](https://www.ecmwf.int/en/forecasts/datasets/open-data)
- University of Utah CHPC Pando archive
- Local file system
## History
During my PhD at the University of Utah, I created, at the time, the [only publicly-accessible archive of HRRR data](http://hrrr.chpc.utah.edu/). Over 1,000 research scientists and professionals used that archive.
<blockquote><cite>
<p style="padding-left: 22px ; text-indent: -22px ;"> Blaylock B., J. Horel and S. Liston, 2017: Cloud Archiving and Data Mining of High Resolution Rapid Refresh Model Output. Computers and Geosciences. 109, 43-50. <a href="https://doi.org/10.1016/j.cageo.2017.08.005">https://doi.org/10.1016/j.cageo.2017.08.005</a></p>
</cite></blockquote>
In the later half of 2020, the HRRR dataset from 2014 to present was made available through the [NODD Program](https://www.noaa.gov/information-technology/open-data-dissemination) (formerly NOAA's Big Data Program). Herbie organizes and expands my original download scripts into a more coherent package with the extended ability to download data for other models from many different archive sources.
I originally released this package under the name “HRRR-B” because it only worked with the HRRR dataset; the “B” was for my first-name initial. Since then, I have added the ability to download RAP, GFS, ECMWF, GEFS, RRFS, and others with potentially more models in the future. Thus, this package was renamed **_Herbie_**, named after one of my favorite childhood movie characters.
The University of Utah MesoWest group now manages a [HRRR archive in Zarr format](http://hrrr.chpc.utah.edu/). Maybe someday, Herbie will be able to take advantage of that archive.
## How to Cite and Acknowledge
If Herbie played an important role in your work, please [tell me about it](https://github.com/blaylockbk/Herbie/discussions/categories/show-and-tell)! Also, consider including a citation or acknowledgement in your article or product.
**_Suggested Citation_**
> Blaylock, B. K. (2022). Herbie: Retrieve Numerical Weather Prediction Model Data (Version 2022.9.0) [Computer software]. https://doi.org/10.5281/zenodo.4567540
**_Suggested Acknowledgment_**
> A portion of this work used code generously provided by Brian Blaylock's Herbie python package (https://doi.org/10.5281/zenodo.4567540)
<hr>
**Thanks for using Herbie, and happy racing!**
🏁 Brian
| | |
| :-: | --------------------------------------------------------------------------------------------------- |
| 👨🏻💻 | [Contributing Guide/Disclaimer](https://herbie.readthedocs.io/en/stable/user_guide/disclaimer.html) |
| 💬 | [GitHub Discussions](https://github.com/blaylockbk/Herbie/discussions) |
| 🚑 | [GitHub Issues](https://github.com/blaylockbk/Herbie/issues) |
| 🌐 | [Personal Webpage](http://home.chpc.utah.edu/~u0553130/Brian_Blaylock/home.html) |
| 🌐 | [University of Utah HRRR archive](http://hrrr.chpc.utah.edu/) |
<br>
P.S. If you like Herbie, check out my other repos:
- [🌎 GOES-2-go](https://github.com/blaylockbk/goes2go): A python package to download GOES-East/West data and make RGB composites.
- [🌡 SynopticPy](https://github.com/blaylockbk/SynopticPy): A python package to download mesonet data from the Synoptic API.
- [🔨 Carpenter Workshop](https://github.com/blaylockbk/Carpenter_Workshop): A python package with various tools I made that are useful (like easy funxtions to build Cartopy maps).
- [💬 Bubble Print](https://github.com/blaylockbk/BubblePrint): A silly little python package that gives your print statement's personality.
- [📜 MET Syntax](https://github.com/blaylockbk/vscode-met-syntax): An extension for Visual Studio Code that gives syntax highlighting for Model Evaluation Tools (MET) configuration files.
> **Note**: Alternative Download Tools
> As an alternative to Herbie, you can use [rclone](https://rclone.org/) to download files from AWS or GCP. I love rclone. Here is a short [rclone tutorial](https://github.com/blaylockbk/pyBKB_v3/blob/master/rclone_howto.md)
| [Visualize Structure](https://mango-dune-07a8b7110.1.azurestaticapps.net/?repo=blaylockbk%2FHerbie) |
Raw data
{
"_id": null,
"home_page": null,
"name": "herbie-data",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.9",
"maintainer_email": "\"Brian K. Blaylock\" <blaylockbk@gmail.com>",
"keywords": "weather, meteorology, forecast, atmosphere, GRIB2, xarray, HRRR",
"author": null,
"author_email": "\"Brian K. Blaylock\" <blaylockbk@gmail.com>",
"download_url": "https://files.pythonhosted.org/packages/b7/31/f90b0b51d92813d816a1bc3ae84ee6bfc008185ac50e39282369a41afe9d/herbie-data-2024.3.1.tar.gz",
"platform": null,
"description": "<div\n align=\"center\"\n>\n\n\n\n# Herbie: Retrieve NWP Model Data \ud83c\udfc1\n\n<!-- Badges -->\n\n[](https://pypi.python.org/pypi/herbie-data/)\n[\n](https://anaconda.org/conda-forge/herbie-data)\n[](https://zenodo.org/badge/latestdoi/275214142)\n\n[\n](https://pypi.org/project/herbie-data/)\n\n[](https://github.com/astral-sh/ruff)\n\n[](https://github.com/blaylockbk/Herbie/actions/workflows/tests-conda.yml)\n[](https://github.com/blaylockbk/Herbie/actions/workflows/tests-python.yml)\n[](https://herbie.readthedocs.io/?badge=latest)\n\n[](https://anaconda.org/conda-forge/herbie-data)\n\n\n\n\n<!-- (Badges) -->\n\n</div>\n\n**Herbie** is a python package that downloads recent and archived numerical weather prediction (NWP) model output from different cloud archive sources. **Its most popular capability is to download HRRR model data.** NWP data in GRIB2 format can be read with xarray+cfgrib. Much of this data is made available through the [NOAA Open Data Dissemination](https://www.noaa.gov/information-technology/open-data-dissemination) (NODD) Program (formerly the Big Data Program) which has made weather data more accessible than ever before.\n\nHerbie helps you discover, download, and read data from:\n\n- [High Resolution Rapid Refresh (HRRR)](https://herbie.readthedocs.io/en/latest/user_guide/_model_notebooks/hrrr.html) | [HRRR-Alaska](https://herbie.readthedocs.io/en/latest/user_guide/_model_notebooks/hrrrak.html)\n- [Rapid Refresh (RAP)](https://herbie.readthedocs.io/en/latest/user_guide/_model_notebooks/rap.html)\n- [Global Forecast System (GFS)](https://herbie.readthedocs.io/en/latest/user_guide/_model_notebooks/gfs.html)\n- [Global Ensemble Forecast System (GEFS)](https://herbie.readthedocs.io/en/latest/user_guide/_model_notebooks/gefs.html)\n- [ECMWF Open Data Forecasts](https://herbie.readthedocs.io/en/latest/user_guide/_model_notebooks/ecmwf.html) (IFS and AIFS)\n- [Navy Global Environmental Model (NAVGEM)](https://herbie.readthedocs.io/en/latest/user_guide/_model_notebooks/navgem.html)\n- [North American Mesoscale Model (NAM)](https://github.com/blaylockbk/Herbie/blob/main/docs/user_guide/_model_notebooks/nam.ipynb)\n- [National Blend of Models (NBM)](https://herbie.readthedocs.io/en/latest/user_guide/_model_notebooks/nbm.html)\n- [Rapid Refresh Forecast System (RRFS)](https://herbie.readthedocs.io/en/latest/user_guide/_model_notebooks/rrfs.html) _prototype_\n- [Real-Time/Un-Restricted Mesoscale Analysis (RTMA/URMA)](https://herbie.readthedocs.io/en/latest/user_guide/_model_notebooks/rtma.html)\n- [Hurricane Analysis And Forecast System (HAFS)](https://herbie.readthedocs.io/en/latest/user_guide/_model_notebooks/hafs.html)\n- [High Resolution Deterministic Prediction System (HRDPS)](https://herbie.readthedocs.io/en/latest/user_guide/_model_notebooks/hrdps.html)\n\n# \ud83d\udcd3 [Herbie Documentation](https://herbie.readthedocs.io/)\n\n## Installation\n\nThe easiest way to instal Herbie and its dependencies is with [Conda](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html) from conda-forge.\n\n```bash\nconda install -c conda-forge herbie-data\n```\n\nYou may also create the provided Conda environment, **[`environment.yml`](https://github.com/blaylockbk/Herbie/blob/main/environment.yml)**.\n\n```bash\n# Download environment file\nwget https://github.com/blaylockbk/Herbie/raw/main/environment.yml\n\n# Modify that file if you wish.\n\n# Create the environment\nconda env create -f environment.yml\n\n# Activate the environment\nconda activate herbie\n```\n\nAlternatively, Herbie is published on PyPI and you can install it with pip, _but_ it requires some dependencies that you will have to install yourself:\n\n- Python 3.8 to 3.11\n- cURL\n- [Cartopy](https://scitools.org.uk/cartopy/docs/latest/installing.html), which requires GEOS and Proj.\n- [cfgrib](https://github.com/ecmwf/cfgrib), which requires eccodes.\n- _Optional:_ wgrib2\n- _Optional:_ [Carpenter Workshop](https://github.com/blaylockbk/Carpenter_Workshop)\n\nWhen those are installed within your environment, _then_ you can install Herbie with pip.\n\n```bash\n# Latest published version\npip install herbie-data\n\n# ~~ or ~~\n\n# Most recent changes\npip install git+https://github.com/blaylockbk/Herbie.git\n```\n\n## Capabilities\n\n- Search for model output from different data sources.\n- Download full GRIB2 files.\n- Download subset GRIB2 files (by grib field).\n- Read data with xarray.\n- Read index file with Pandas.\n- Plot data with Cartopy (very early development).\n\n```mermaid\n graph TD;\n d1[(HRRR)] -..-> H\n d2[(RAP)] -.-> H\n d3[(GFS)] -..-> H\n d33[(GEFS)] -.-> H\n d4[(IFS)] -..-> H\n d44[(AIFS)] -..-> H\n d5[(NBM)] -.-> H\n d6[(RRFS)] -..-> H\n d7[(RTMA)] -.-> H\n d8[(URMA)] -..-> H\n H((Herbie))\n H --- .inventory\n H --- .download\n H --- .xarray\n\n style H fill:#d8c89d,stroke:#0c3576,stroke-width:4px,color:#000000\n```\n\n```python\nfrom herbie import Herbie\n\n# Herbie object for the HRRR model 6-hr surface forecast product\nH = Herbie(\n '2021-01-01 12:00',\n model='hrrr',\n product='sfc',\n fxx=6\n)\n\n# Look at file contents\nH.inventory()\n\n# Download the full GRIB2 file\nH.download()\n\n# Download a subset, like all fields at 500 mb\nH.download(\":500 mb\")\n\n# Read subset with xarray, like 2-m temperature.\nH.xarray(\"TMP:2 m\")\n```\n\n## Data Sources\n\nHerbie downloads model data from the following sources, but can be extended to include others:\n\n- [NOMADS](https://nomads.ncep.noaa.gov/)\n- [NOAA Open Data Dissemination Program (NODD)](https://www.noaa.gov/information-technology/open-data-dissemination) partners (i.e., AWS, Google, Azure).\n- [ECMWF Open Data Forecasts](https://www.ecmwf.int/en/forecasts/datasets/open-data)\n- University of Utah CHPC Pando archive\n- Local file system\n\n## History\n\nDuring my PhD at the University of Utah, I created, at the time, the [only publicly-accessible archive of HRRR data](http://hrrr.chpc.utah.edu/). Over 1,000 research scientists and professionals used that archive.\n\n<blockquote><cite>\n<p style=\"padding-left: 22px ; text-indent: -22px ;\"> Blaylock B., J. Horel and S. Liston, 2017: Cloud Archiving and Data Mining of High Resolution Rapid Refresh Model Output. Computers and Geosciences. 109, 43-50. <a href=\"https://doi.org/10.1016/j.cageo.2017.08.005\">https://doi.org/10.1016/j.cageo.2017.08.005</a></p>\n</cite></blockquote>\n\nIn the later half of 2020, the HRRR dataset from 2014 to present was made available through the [NODD Program](https://www.noaa.gov/information-technology/open-data-dissemination) (formerly NOAA's Big Data Program). Herbie organizes and expands my original download scripts into a more coherent package with the extended ability to download data for other models from many different archive sources.\n\nI originally released this package under the name \u201cHRRR-B\u201d because it only worked with the HRRR dataset; the \u201cB\u201d was for my first-name initial. Since then, I have added the ability to download RAP, GFS, ECMWF, GEFS, RRFS, and others with potentially more models in the future. Thus, this package was renamed **_Herbie_**, named after one of my favorite childhood movie characters.\n\nThe University of Utah MesoWest group now manages a [HRRR archive in Zarr format](http://hrrr.chpc.utah.edu/). Maybe someday, Herbie will be able to take advantage of that archive.\n\n## How to Cite and Acknowledge\n\nIf Herbie played an important role in your work, please [tell me about it](https://github.com/blaylockbk/Herbie/discussions/categories/show-and-tell)! Also, consider including a citation or acknowledgement in your article or product.\n\n**_Suggested Citation_**\n\n> Blaylock, B. K. (2022). Herbie: Retrieve Numerical Weather Prediction Model Data (Version 2022.9.0) [Computer software]. https://doi.org/10.5281/zenodo.4567540\n\n**_Suggested Acknowledgment_**\n\n> A portion of this work used code generously provided by Brian Blaylock's Herbie python package (https://doi.org/10.5281/zenodo.4567540)\n\n<hr>\n\n**Thanks for using Herbie, and happy racing!**\n\n\ud83c\udfc1 Brian\n\n| | |\n| :-: | --------------------------------------------------------------------------------------------------- |\n| \ud83d\udc68\ud83c\udffb\u200d\ud83d\udcbb | [Contributing Guide/Disclaimer](https://herbie.readthedocs.io/en/stable/user_guide/disclaimer.html) |\n| \ud83d\udcac | [GitHub Discussions](https://github.com/blaylockbk/Herbie/discussions) |\n| \ud83d\ude91 | [GitHub Issues](https://github.com/blaylockbk/Herbie/issues) |\n| \ud83c\udf10 | [Personal Webpage](http://home.chpc.utah.edu/~u0553130/Brian_Blaylock/home.html) |\n| \ud83c\udf10 | [University of Utah HRRR archive](http://hrrr.chpc.utah.edu/) |\n\n<br>\n\nP.S. If you like Herbie, check out my other repos:\n\n- [\ud83c\udf0e GOES-2-go](https://github.com/blaylockbk/goes2go): A python package to download GOES-East/West data and make RGB composites.\n- [\ud83c\udf21 SynopticPy](https://github.com/blaylockbk/SynopticPy): A python package to download mesonet data from the Synoptic API.\n- [\ud83d\udd28 Carpenter Workshop](https://github.com/blaylockbk/Carpenter_Workshop): A python package with various tools I made that are useful (like easy funxtions to build Cartopy maps).\n- [\ud83d\udcac Bubble Print](https://github.com/blaylockbk/BubblePrint): A silly little python package that gives your print statement's personality.\n- [\ud83d\udcdc MET Syntax](https://github.com/blaylockbk/vscode-met-syntax): An extension for Visual Studio Code that gives syntax highlighting for Model Evaluation Tools (MET) configuration files.\n\n> **Note**: Alternative Download Tools \n> As an alternative to Herbie, you can use [rclone](https://rclone.org/) to download files from AWS or GCP. I love rclone. Here is a short [rclone tutorial](https://github.com/blaylockbk/pyBKB_v3/blob/master/rclone_howto.md)\n\n| [Visualize Structure](https://mango-dune-07a8b7110.1.azurestaticapps.net/?repo=blaylockbk%2FHerbie) |\n",
"bugtrack_url": null,
"license": "MIT License Copyright (c) 2019-2022 Brian K. Blaylock Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ",
"summary": "Download numerical weather prediction GRIB2 model data.",
"version": "2024.3.1",
"project_urls": {
"Bug Tracker": "https://github.com/blaylockbk/Herbie/issues",
"Changelog": "https://github.com/blaylockbk/Herbie/releases",
"Documentation": "https://herbie.readthedocs.io/",
"Homepage": "https://github.com/blaylockbk/Herbie",
"Repository": "https://github.com/blaylockbk/Herbie"
},
"split_keywords": [
"weather",
" meteorology",
" forecast",
" atmosphere",
" grib2",
" xarray",
" hrrr"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "b5949df5e6a726ee5744cd8b939caa57673854f77bd122903fca311ebdb35cf4",
"md5": "eb46313f04a80c6743c9a68123b0c50a",
"sha256": "3f014bf412ad56d2fcad3bf59ea711b931780f8b3ae5dbcbc64931eacf83bf60"
},
"downloads": -1,
"filename": "herbie_data-2024.3.1-py3-none-any.whl",
"has_sig": false,
"md5_digest": "eb46313f04a80c6743c9a68123b0c50a",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.9",
"size": 62245,
"upload_time": "2024-03-26T22:49:24",
"upload_time_iso_8601": "2024-03-26T22:49:24.183180Z",
"url": "https://files.pythonhosted.org/packages/b5/94/9df5e6a726ee5744cd8b939caa57673854f77bd122903fca311ebdb35cf4/herbie_data-2024.3.1-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "b731f90b0b51d92813d816a1bc3ae84ee6bfc008185ac50e39282369a41afe9d",
"md5": "4d2e88d51924d2ff14a116c870f45ef9",
"sha256": "fc8ae10b45efc6c02045a01d57af446ed9c60b78050bff72bb331d18c773a1b9"
},
"downloads": -1,
"filename": "herbie-data-2024.3.1.tar.gz",
"has_sig": false,
"md5_digest": "4d2e88d51924d2ff14a116c870f45ef9",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.9",
"size": 67684,
"upload_time": "2024-03-26T22:49:27",
"upload_time_iso_8601": "2024-03-26T22:49:27.698237Z",
"url": "https://files.pythonhosted.org/packages/b7/31/f90b0b51d92813d816a1bc3ae84ee6bfc008185ac50e39282369a41afe9d/herbie-data-2024.3.1.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-03-26 22:49:27",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "blaylockbk",
"github_project": "Herbie",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"requirements": [],
"lcname": "herbie-data"
}
