| Name | herbie-data JSON |
| Version |
2024.8.0
JSON |
| download |
| home_page | None |
| Summary | Download numerical weather prediction GRIB2 model data. |
| upload_time | 2024-08-16 06:27:39 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | MIT License Copyright (c) 2019-2024 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.org/project/herbie-data/) [](https://pypi.python.org/pypi/herbie-data/)
[
](https://anaconda.org/conda-forge/herbie-data)
[](https://zenodo.org/badge/latestdoi/275214142)
[](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)
[](https://github.com/astral-sh/ruff)
## [📘 Documentation](https://herbie.readthedocs.io/) | [💬 Discussions](https://github.com/blaylockbk/Herbie/discussions) | [❔ Ask For Help](https://github.com/blaylockbk/Herbie/discussions/categories/q-a)
<!-- (Badges) -->
</div>
**Herbie** is a python package that downloads recent and archived numerical weather prediction (NWP) model output from different cloud archive sources. NWP data is distributed in GRIB2 format which Herbie reads using xarray+cfgrib. Herbie also provides some extra features to help visualize and extract data.
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)
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.
## 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 3.9+
- [cURL](https://anaconda.org/conda-forge/curl)
- [eccodes](https://anaconda.org/conda-forge/eccodes), a requirement for [cfgrib](https://github.com/ecmwf/cfgrib).
- _Optional:_ [wgrib2](https://anaconda.org/conda-forge/wgrib2)
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
# Dependecies for extra features
pip install herbie-data[extra]
```
## 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.
- Extra features (herbie xarray accessors)
- Extract data at a point
- Get Cartopy coordinate references system
- 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
## Community
Having trouble using Herbie or have a question? [❔ GitHub Discussions/Ask For Help](https://github.com/blaylockbk/Herbie/discussions/categories/q-a)
Just want to talk about Herbie or have an idea? [💬 GitHub Discussions](https://github.com/blaylockbk/Herbie/discussions/categories)
See something that might be wrong? [🚑 GitHub Issues](https://github.com/blaylockbk/Herbie/issues)
Want to contribute? Great! I'd love your help.
- "Watch" this repo's discussions and issues.
- Participate in [💬 GitHub Discussions](https://github.com/blaylockbk/Herbie/discussions/categories) and answer questions.
- Share how you use Herbie in [🙌 GitHub Discussions/Show and tell](https://github.com/blaylockbk/Herbie/discussions/categories/show-and-tell)
- Open an issue or file a pull request to make Herbie even better!
- Contribute to documentation.
- Test latest releases and report issues.
- Read the [👨🏻💻 Disclaimer & Contributing Guide](https://herbie.readthedocs.io/en/stable/user_guide/housekeeping/disclaimer.html)
## 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. (YEAR). Herbie: Retrieve Numerical Weather Prediction Model Data (Version 20xx.x.x) [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 (Version 20xx.x.x) (https://doi.org/10.5281/zenodo.4567540)
<hr>
## 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>
Herbie was then developed to access HRRR data from that archive and was first used on the Open Science Grid.
<blockquote><cite>
<p style="padding-left: 22px ; text-indent: -22px ;"> Blaylock, B. K., J. D. Horel, and C. Galli, 2018: High-Resolution Rapid Refresh Model Data Analytics Derived on the Open Science Grid to Assist Wildland Fire Weather Assessment. J. Atmos. Oceanic Technol., 35, 2213–2227, <a href="https://doi.org/10.1175/JTECH-D-18-0073.1">https://doi.org/10.1175/JTECH-D-18-0073.1</a>.</p>
</cite></blockquote>
In the later half of 2020, the HRRR dataset from 2014 to present was made available through the [NODD Open Data Dissemination Program](https://www.noaa.gov/information-technology/open-data-dissemination) (formerly NOAA's Big Data Program). The latest version of 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, and it will continues to evolve.
I originally released this package under the name “HRRR-B” because it only worked with the HRRR dataset; the “B” was for Brian. Since then, I have added the ability to download many more models including RAP, GFS, ECMWF, GEFS, and RRFS with the potential to add more models in the future. Thus, this package was renamed **_Herbie_**, named after one of my favorite childhood movies.
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.
**Thanks for using Herbie, and happy racing!**
🏁 Brian
- [🌐 Personal Webpage](http://home.chpc.utah.edu/~u0553130/Brian_Blaylock/home.html)
<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) | [Star History](https://star-history.com/#blaylockbk/Herbie&Date) | [PyPI Download Statistics](https://pypistats.org/packages/herbie-data)
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/48/59/136b443a12073546ad7987db6d78dd43d8550689134145be4bbfac0d0a57/herbie_data-2024.8.0.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[\n](https://pypi.org/project/herbie-data/) [](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[](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[](https://anaconda.org/conda-forge/herbie-data)\n[](https://github.com/astral-sh/ruff)\n\n\n\n\n\n## [\ud83d\udcd8 Documentation](https://herbie.readthedocs.io/) | [\ud83d\udcac Discussions](https://github.com/blaylockbk/Herbie/discussions) | [\u2754 Ask For Help](https://github.com/blaylockbk/Herbie/discussions/categories/q-a)\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. NWP data is distributed in GRIB2 format which Herbie reads using xarray+cfgrib. Herbie also provides some extra features to help visualize and extract data.\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\nMuch 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\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 3.9+\n- [cURL](https://anaconda.org/conda-forge/curl)\n- [eccodes](https://anaconda.org/conda-forge/eccodes), a requirement for [cfgrib](https://github.com/ecmwf/cfgrib).\n- _Optional:_ [wgrib2](https://anaconda.org/conda-forge/wgrib2)\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# Dependecies for extra features\npip install herbie-data[extra]\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- Extra features (herbie xarray accessors)\n - Extract data at a point\n - Get Cartopy coordinate references system\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## Community\n\nHaving trouble using Herbie or have a question? [\u2754 GitHub Discussions/Ask For Help](https://github.com/blaylockbk/Herbie/discussions/categories/q-a)\n\nJust want to talk about Herbie or have an idea? [\ud83d\udcac GitHub Discussions](https://github.com/blaylockbk/Herbie/discussions/categories)\n\nSee something that might be wrong? [\ud83d\ude91 GitHub Issues](https://github.com/blaylockbk/Herbie/issues)\n\nWant to contribute? Great! I'd love your help.\n\n- \"Watch\" this repo's discussions and issues.\n- Participate in [\ud83d\udcac GitHub Discussions](https://github.com/blaylockbk/Herbie/discussions/categories) and answer questions.\n- Share how you use Herbie in [\ud83d\ude4c GitHub Discussions/Show and tell](https://github.com/blaylockbk/Herbie/discussions/categories/show-and-tell)\n- Open an issue or file a pull request to make Herbie even better!\n- Contribute to documentation.\n- Test latest releases and report issues.\n- Read the [\ud83d\udc68\ud83c\udffb\u200d\ud83d\udcbb Disclaimer & Contributing Guide](https://herbie.readthedocs.io/en/stable/user_guide/housekeeping/disclaimer.html)\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. (YEAR). Herbie: Retrieve Numerical Weather Prediction Model Data (Version 20xx.x.x) [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 (Version 20xx.x.x) (https://doi.org/10.5281/zenodo.4567540)\n\n<hr>\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\nHerbie was then developed to access HRRR data from that archive and was first used on the Open Science Grid.\n\n<blockquote><cite>\n<p style=\"padding-left: 22px ; text-indent: -22px ;\"> Blaylock, B. K., J. D. Horel, and C. Galli, 2018: High-Resolution Rapid Refresh Model Data Analytics Derived on the Open Science Grid to Assist Wildland Fire Weather Assessment. J. Atmos. Oceanic Technol., 35, 2213\u20132227, <a href=\"https://doi.org/10.1175/JTECH-D-18-0073.1\">https://doi.org/10.1175/JTECH-D-18-0073.1</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 Open Data Dissemination Program](https://www.noaa.gov/information-technology/open-data-dissemination) (formerly NOAA's Big Data Program). The latest version of 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, and it will continues to evolve.\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 Brian. Since then, I have added the ability to download many more models including RAP, GFS, ECMWF, GEFS, and RRFS with the potential to add more models in the future. Thus, this package was renamed **_Herbie_**, named after one of my favorite childhood movies.\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**Thanks for using Herbie, and happy racing!**\n\n\ud83c\udfc1 Brian\n\n- [\ud83c\udf10 Personal Webpage](http://home.chpc.utah.edu/~u0553130/Brian_Blaylock/home.html)\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) | [Star History](https://star-history.com/#blaylockbk/Herbie&Date) | [PyPI Download Statistics](https://pypistats.org/packages/herbie-data)\n",
"bugtrack_url": null,
"license": "MIT License Copyright (c) 2019-2024 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": "2024.8.0",
"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": "dc0a9bd4412d9fe1c30e26b58f4dbec64d39276110f84bf6316d12242b2f1296",
"md5": "594df449e6d84368e19fea6db02e0edc",
"sha256": "196ecc028dca71c99ffb7452d8a443a64b57c27605f70ac08f5810f3d606088b"
},
"downloads": -1,
"filename": "herbie_data-2024.8.0-py3-none-any.whl",
"has_sig": false,
"md5_digest": "594df449e6d84368e19fea6db02e0edc",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.9",
"size": 100959,
"upload_time": "2024-08-16T06:27:38",
"upload_time_iso_8601": "2024-08-16T06:27:38.099077Z",
"url": "https://files.pythonhosted.org/packages/dc/0a/9bd4412d9fe1c30e26b58f4dbec64d39276110f84bf6316d12242b2f1296/herbie_data-2024.8.0-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "4859136b443a12073546ad7987db6d78dd43d8550689134145be4bbfac0d0a57",
"md5": "f763da32e6a8e915df267fea67c068e7",
"sha256": "83831205ea415b6f245d829cc13a162263074035e5e365c38f239ee04e3d6c14"
},
"downloads": -1,
"filename": "herbie_data-2024.8.0.tar.gz",
"has_sig": false,
"md5_digest": "f763da32e6a8e915df267fea67c068e7",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.9",
"size": 102912,
"upload_time": "2024-08-16T06:27:39",
"upload_time_iso_8601": "2024-08-16T06:27:39.626128Z",
"url": "https://files.pythonhosted.org/packages/48/59/136b443a12073546ad7987db6d78dd43d8550689134145be4bbfac0d0a57/herbie_data-2024.8.0.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-08-16 06:27:39",
"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"
}
