arm-test-data

Name	arm-test-data JSON
Version	0.1.2 JSON
	download
home_page	None
Summary	Provides utility functions for accessing data repository for ARM data examples/notebooks
upload_time	2025-10-07 15:26:48
maintainer	None
docs_url	None
author	Atmospheric Data Community Toolkit Dev Team
requires_python	>=3.8
license	MIT License Copyright (c) 2023 ARM User Facility 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	arm-test-data atmosphere meteorology
VCS
bugtrack_url
requirements	pooch
Travis-CI	No Travis.
coveralls test coverage	No coveralls.

            # arm-test-data
[![CI](https://github.com/ARM-DOE/arm-test-data/actions/workflows/ci.yaml/badge.svg)](https://github.com/ARM-DOE/arm-test-data/actions/workflows/ci.yaml)
[![PyPI Version](https://img.shields.io/pypi/v/arm-test-data.svg)](https://pypi.python.org/pypi/arm-test-data)
[![Conda Version](https://img.shields.io/conda/vn/conda-forge/arm-test-data.svg)](https://anaconda.org/conda-forge/arm-test-data)

A place to share atmospheric data with the community, shared throughout the Atmospheric Radiation Measurement user facility and beyond!

## Sample data sets

These files are used as sample data in openradar examples/notebooks and are downloaded by `arm-test-data` package:

- `201509021500.bi`
- `AAFNAV_COR_20181104_R0.ict`
- `AMF_US-CU1_BASE_HH_1-5.csv`
- `AMF_US-CU1_BIF_20250318.xlsx`
- `NEON.D18.BARR.DP1.00002.001.000.010.001.SAAT_1min.2022-10.expanded.20221107T205629Z.csv`
- `NEON.D18.BARR.DP1.00002.001.sensor_positions.20221107T205629Z.csv`
- `NEON.D18.BARR.DP1.00002.001.variables.20221201T110553Z.csv`
- `anltwr_mar19met.data`
- `ayp22199.21m`
- `ayp22200.00m`
- `brw21001.dat`
- `brw_12_2020_hour.dat`
- `brw_CCl4_Day.dat`
- `co2_brw_surface-insitu_1_ccgg_MonthlyData.txt`
- `ctd21125.15w`
- `ctd22187.00t.txt`
- `enametC1.b1.20221109.000000.cdf`
- `gucmetM1.b1.20230301.000000.cdf`
- `list_of_files.txt`
- `maraosmetM1.a1.20180201.000000.nc`
- `marirtsstM1.b1.20190320.000000.nc`
- `marnavM1.a1.20180201.000000.nc`
- `met_brw_insitu_1_obop_hour_2020.txt`
- `met_lcl.nc`
- `mosaossp2M1.00.20191216.000601.raw.20191216000000.ini`
- `mosaossp2M1.00.20191216.130601.raw.20191216x193.sp2b`
- `mosaossp2auxM1.00.20191217.010801.raw.20191216000000.hk`
- `nsacloudphaseC1.c1.20180601.000000.nc`
- `nsasurfspecalb1mlawerC1.c1.20160609.080000.nc`
- `sgp30ebbrE13.b1.20190601.000000.nc`
- `sgp30ebbrE32.b1.20191125.000000.nc`
- `sgp30ebbrE32.b1.20191130.000000.nc`
- `sgp30ecorE14.b1.20190601.000000.cdf`
- `sgpaerich1C1.b1.20190501.000342.nc`
- `sgpaosacsmE13.b2.20230420.000109.nc`
- `sgpaosccn2colaE13.b1.20170903.000000.nc`
- `sgpbrsC1.b1.20190705.000000.cdf`
- `sgpceilC1.b1.20190101.000000.nc`
- `sgpco2flx4mC1.b1.20201007.001500.nc`
- `sgpdlppiC1.b1.20191015.120023.cdf`
- `sgpdlppiC1.b1.20191015.121506.cdf`
- `sgpirt25m20sC1.a0.20190601.000000.cdf`
- `sgpmetE13.b1.20190101.000000.cdf`
- `sgpmetE13.b1.20190102.000000.cdf`
- `sgpmetE13.b1.20190103.000000.cdf`
- `sgpmetE13.b1.20190104.000000.cdf`
- `sgpmetE13.b1.20190105.000000.cdf`
- `sgpmetE13.b1.20190106.000000.cdf`
- `sgpmetE13.b1.20190107.000000.cdf`
- `sgpmetE13.b1.20190508.000000.cdf`
- `sgpmetE13.b1.20210401.000000.csv`
- `sgpmetE13.b1.yaml`
- `sgpmetE15.b1.20190508.000000.cdf`
- `sgpmetE31.b1.20190508.000000.cdf`
- `sgpmetE32.b1.20190508.000000.cdf`
- `sgpmetE33.b1.20190508.000000.cdf`
- `sgpmetE34.b1.20190508.000000.cdf`
- `sgpmetE35.b1.20190508.000000.cdf`
- `sgpmetE36.b1.20190508.000000.cdf`
- `sgpmetE37.b1.20190508.000000.cdf`
- `sgpmetE38.b1.20190508.000000.cdf`
- `sgpmetE39.b1.20190508.000000.cdf`
- `sgpmetE40.b1.20190508.000000.cdf`
- `sgpmetE9.b1.20190508.000000.cdf`
- `sgpmet_no_time.nc`
- `sgpmet_test_time.nc`
- `sgpmfrsr7nchE11.b1.20210329.070000.nc`
- `sgpmmcrC1.b1.1.cdf`
- `sgpmmcrC1.b1.2.cdf`
- `sgpmplpolfsC1.b1.20190502.000000.cdf`
- `sgprlC1.a0.20160131.000000.nc`
- `sgpsebsE14.b1.20190601.000000.cdf`
- `sgpsirsE13.b1.20190101.000000.cdf`
- `sgpsondewnpnC1.b1.20190101.053200.cdf`
- `sgpstampE13.b1.20200101.000000.nc`
- `sgpstampE31.b1.20200101.000000.nc`
- `sgpstampE32.b1.20200101.000000.nc`
- `sgpstampE33.b1.20200101.000000.nc`
- `sgpstampE34.b1.20200101.000000.nc`
- `sgpstampE9.b1.20200101.000000.nc`
- `sodar.20230404.mnd`
- `twpsondewnpnC3.b1.20060119.050300.custom.cdf`
- `twpsondewnpnC3.b1.20060119.112000.custom.cdf`
- `twpsondewnpnC3.b1.20060119.163300.custom.cdf`
- `twpsondewnpnC3.b1.20060119.231600.custom.cdf`
- `twpsondewnpnC3.b1.20060120.043800.custom.cdf`
- `twpsondewnpnC3.b1.20060120.111900.custom.cdf`
- `twpsondewnpnC3.b1.20060120.170800.custom.cdf`
- `twpsondewnpnC3.b1.20060120.231500.custom.cdf`
- `twpsondewnpnC3.b1.20060121.051500.custom.cdf`
- `twpsondewnpnC3.b1.20060121.111600.custom.cdf`
- `twpsondewnpnC3.b1.20060121.171600.custom.cdf`
- `twpsondewnpnC3.b1.20060121.231600.custom.cdf`
- `twpsondewnpnC3.b1.20060122.052600.custom.cdf`
- `twpsondewnpnC3.b1.20060122.111500.custom.cdf`
- `twpsondewnpnC3.b1.20060122.171800.custom.cdf`
- `twpsondewnpnC3.b1.20060122.232600.custom.cdf`
- `twpsondewnpnC3.b1.20060123.052500.custom.cdf`
- `twpsondewnpnC3.b1.20060123.111700.custom.cdf`
- `twpsondewnpnC3.b1.20060123.171600.custom.cdf`
- `twpsondewnpnC3.b1.20060123.231500.custom.cdf`
- `twpsondewnpnC3.b1.20060124.051500.custom.cdf`
- `twpsondewnpnC3.b1.20060124.111800.custom.cdf`
- `twpsondewnpnC3.b1.20060124.171700.custom.cdf`
- `twpsondewnpnC3.b1.20060124.231500.custom.cdf`
- `twpvisstgridirtemp.c1.20050705.002500.nc`
- `vdis.b1`

## Adding new datasets

To add a new dataset file, please follow these steps:

1. Add the dataset file to the `data/` directory
2. From the command line, run `python make_registry.py` script to update the registry file residing in `arm-test-data/registry.txt`
3. Commit and push your changes to GitHub

## Using datasets in notebooks and/or scripts

- Ensure the `arm-test-data` package is installed in your environment

  ```bash
  python -m pip install arm-test-data

  # or

  python -m pip install git+https://github.com/ARM-DOE/arm-test-data

  # or

  conda install -c conda-forge arm-test-data
  ```

- Import `DATASETS` and inspect the registry to find out which datasets are available

  ```python
  In [1]: from arm_test_data import DATASETS

  In [2]: DATASETS.registry_files
  Out[2]: ['sample_file.nc`]
  ```

- To fetch a data file of interest, use the `.fetch` method and provide the filename of the data file. This will

  - download and cache the file if it doesn't exist already.
  - retrieve and return the local path

  ```python
  In [4]: filepath = DATASETS.fetch('sample_data.nc')

  In [5]: filepath
  Out[5]: '/Users/mgrover/Library/Caches/arm-test-data/sample_sgp_data.nc'
  ```

- Once you have access to the local filepath, you can then use it to load your dataset into pandas or xarray or your package of choice:

  ```python
  In [6]: radar = pyart.io.read(filepath)
  ```

## Changing the default data cache location

The default cache location (where the data are saved on your local system) is dependent on the operating system. You can use the `locate()` method to identify it:

```python
from arm_test_data import locate
locate()
```

The location can be overwritten by the `ACT_TEST_DATA_DIR` environment
variable to the desired destination.

## References

### Ameriflux data

AmeriFlux BASE: https://doi.org/10.17190/AMF/2531143
Citation: Bhupendra Raut, Sujan Pal, Paytsar Muradyan, Joseph R. O'Brien, Max Berkelhammer, Matthew Tuftedal, Max Grover, Scott Collis, Robert C. Jackson (2025), AmeriFlux BASE US-CU1 UIC Plant Research Laboratory Chicago, Ver. 1-5, AmeriFlux AMP, (Dataset). https://doi.org/10.17190/AMF/2531143

            

Raw data

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    "description": "# arm-test-data\n[![CI](https://github.com/ARM-DOE/arm-test-data/actions/workflows/ci.yaml/badge.svg)](https://github.com/ARM-DOE/arm-test-data/actions/workflows/ci.yaml)\n[![PyPI Version](https://img.shields.io/pypi/v/arm-test-data.svg)](https://pypi.python.org/pypi/arm-test-data)\n[![Conda Version](https://img.shields.io/conda/vn/conda-forge/arm-test-data.svg)](https://anaconda.org/conda-forge/arm-test-data)\n\nA place to share atmospheric data with the community, shared throughout the Atmospheric Radiation Measurement user facility and beyond!\n\n## Sample data sets\n\nThese files are used as sample data in openradar examples/notebooks and are downloaded by `arm-test-data` package:\n\n- `201509021500.bi`\n- `AAFNAV_COR_20181104_R0.ict`\n- `AMF_US-CU1_BASE_HH_1-5.csv`\n- `AMF_US-CU1_BIF_20250318.xlsx`\n- `NEON.D18.BARR.DP1.00002.001.000.010.001.SAAT_1min.2022-10.expanded.20221107T205629Z.csv`\n- `NEON.D18.BARR.DP1.00002.001.sensor_positions.20221107T205629Z.csv`\n- `NEON.D18.BARR.DP1.00002.001.variables.20221201T110553Z.csv`\n- `anltwr_mar19met.data`\n- `ayp22199.21m`\n- `ayp22200.00m`\n- `brw21001.dat`\n- `brw_12_2020_hour.dat`\n- `brw_CCl4_Day.dat`\n- `co2_brw_surface-insitu_1_ccgg_MonthlyData.txt`\n- `ctd21125.15w`\n- `ctd22187.00t.txt`\n- `enametC1.b1.20221109.000000.cdf`\n- `gucmetM1.b1.20230301.000000.cdf`\n- `list_of_files.txt`\n- `maraosmetM1.a1.20180201.000000.nc`\n- `marirtsstM1.b1.20190320.000000.nc`\n- `marnavM1.a1.20180201.000000.nc`\n- `met_brw_insitu_1_obop_hour_2020.txt`\n- `met_lcl.nc`\n- `mosaossp2M1.00.20191216.000601.raw.20191216000000.ini`\n- `mosaossp2M1.00.20191216.130601.raw.20191216x193.sp2b`\n- `mosaossp2auxM1.00.20191217.010801.raw.20191216000000.hk`\n- `nsacloudphaseC1.c1.20180601.000000.nc`\n- `nsasurfspecalb1mlawerC1.c1.20160609.080000.nc`\n- `sgp30ebbrE13.b1.20190601.000000.nc`\n- `sgp30ebbrE32.b1.20191125.000000.nc`\n- `sgp30ebbrE32.b1.20191130.000000.nc`\n- `sgp30ecorE14.b1.20190601.000000.cdf`\n- `sgpaerich1C1.b1.20190501.000342.nc`\n- `sgpaosacsmE13.b2.20230420.000109.nc`\n- `sgpaosccn2colaE13.b1.20170903.000000.nc`\n- `sgpbrsC1.b1.20190705.000000.cdf`\n- `sgpceilC1.b1.20190101.000000.nc`\n- `sgpco2flx4mC1.b1.20201007.001500.nc`\n- `sgpdlppiC1.b1.20191015.120023.cdf`\n- `sgpdlppiC1.b1.20191015.121506.cdf`\n- `sgpirt25m20sC1.a0.20190601.000000.cdf`\n- `sgpmetE13.b1.20190101.000000.cdf`\n- `sgpmetE13.b1.20190102.000000.cdf`\n- `sgpmetE13.b1.20190103.000000.cdf`\n- `sgpmetE13.b1.20190104.000000.cdf`\n- `sgpmetE13.b1.20190105.000000.cdf`\n- `sgpmetE13.b1.20190106.000000.cdf`\n- `sgpmetE13.b1.20190107.000000.cdf`\n- `sgpmetE13.b1.20190508.000000.cdf`\n- `sgpmetE13.b1.20210401.000000.csv`\n- `sgpmetE13.b1.yaml`\n- `sgpmetE15.b1.20190508.000000.cdf`\n- `sgpmetE31.b1.20190508.000000.cdf`\n- `sgpmetE32.b1.20190508.000000.cdf`\n- `sgpmetE33.b1.20190508.000000.cdf`\n- `sgpmetE34.b1.20190508.000000.cdf`\n- `sgpmetE35.b1.20190508.000000.cdf`\n- `sgpmetE36.b1.20190508.000000.cdf`\n- `sgpmetE37.b1.20190508.000000.cdf`\n- `sgpmetE38.b1.20190508.000000.cdf`\n- `sgpmetE39.b1.20190508.000000.cdf`\n- `sgpmetE40.b1.20190508.000000.cdf`\n- `sgpmetE9.b1.20190508.000000.cdf`\n- `sgpmet_no_time.nc`\n- `sgpmet_test_time.nc`\n- `sgpmfrsr7nchE11.b1.20210329.070000.nc`\n- `sgpmmcrC1.b1.1.cdf`\n- `sgpmmcrC1.b1.2.cdf`\n- `sgpmplpolfsC1.b1.20190502.000000.cdf`\n- `sgprlC1.a0.20160131.000000.nc`\n- `sgpsebsE14.b1.20190601.000000.cdf`\n- `sgpsirsE13.b1.20190101.000000.cdf`\n- `sgpsondewnpnC1.b1.20190101.053200.cdf`\n- `sgpstampE13.b1.20200101.000000.nc`\n- `sgpstampE31.b1.20200101.000000.nc`\n- `sgpstampE32.b1.20200101.000000.nc`\n- `sgpstampE33.b1.20200101.000000.nc`\n- `sgpstampE34.b1.20200101.000000.nc`\n- `sgpstampE9.b1.20200101.000000.nc`\n- `sodar.20230404.mnd`\n- `twpsondewnpnC3.b1.20060119.050300.custom.cdf`\n- `twpsondewnpnC3.b1.20060119.112000.custom.cdf`\n- `twpsondewnpnC3.b1.20060119.163300.custom.cdf`\n- `twpsondewnpnC3.b1.20060119.231600.custom.cdf`\n- `twpsondewnpnC3.b1.20060120.043800.custom.cdf`\n- `twpsondewnpnC3.b1.20060120.111900.custom.cdf`\n- `twpsondewnpnC3.b1.20060120.170800.custom.cdf`\n- `twpsondewnpnC3.b1.20060120.231500.custom.cdf`\n- `twpsondewnpnC3.b1.20060121.051500.custom.cdf`\n- `twpsondewnpnC3.b1.20060121.111600.custom.cdf`\n- `twpsondewnpnC3.b1.20060121.171600.custom.cdf`\n- `twpsondewnpnC3.b1.20060121.231600.custom.cdf`\n- `twpsondewnpnC3.b1.20060122.052600.custom.cdf`\n- `twpsondewnpnC3.b1.20060122.111500.custom.cdf`\n- `twpsondewnpnC3.b1.20060122.171800.custom.cdf`\n- `twpsondewnpnC3.b1.20060122.232600.custom.cdf`\n- `twpsondewnpnC3.b1.20060123.052500.custom.cdf`\n- `twpsondewnpnC3.b1.20060123.111700.custom.cdf`\n- `twpsondewnpnC3.b1.20060123.171600.custom.cdf`\n- `twpsondewnpnC3.b1.20060123.231500.custom.cdf`\n- `twpsondewnpnC3.b1.20060124.051500.custom.cdf`\n- `twpsondewnpnC3.b1.20060124.111800.custom.cdf`\n- `twpsondewnpnC3.b1.20060124.171700.custom.cdf`\n- `twpsondewnpnC3.b1.20060124.231500.custom.cdf`\n- `twpvisstgridirtemp.c1.20050705.002500.nc`\n- `vdis.b1`\n\n## Adding new datasets\n\nTo add a new dataset file, please follow these steps:\n\n1. Add the dataset file to the `data/` directory\n2. From the command line, run `python make_registry.py` script to update the registry file residing in `arm-test-data/registry.txt`\n3. Commit and push your changes to GitHub\n\n## Using datasets in notebooks and/or scripts\n\n- Ensure the `arm-test-data` package is installed in your environment\n\n  ```bash\n  python -m pip install arm-test-data\n\n  # or\n\n  python -m pip install git+https://github.com/ARM-DOE/arm-test-data\n\n  # or\n\n  conda install -c conda-forge arm-test-data\n  ```\n\n- Import `DATASETS` and inspect the registry to find out which datasets are available\n\n  ```python\n  In [1]: from arm_test_data import DATASETS\n\n  In [2]: DATASETS.registry_files\n  Out[2]: ['sample_file.nc`]\n  ```\n\n- To fetch a data file of interest, use the `.fetch` method and provide the filename of the data file. This will\n\n  - download and cache the file if it doesn't exist already.\n  - retrieve and return the local path\n\n  ```python\n  In [4]: filepath = DATASETS.fetch('sample_data.nc')\n\n  In [5]: filepath\n  Out[5]: '/Users/mgrover/Library/Caches/arm-test-data/sample_sgp_data.nc'\n  ```\n\n- Once you have access to the local filepath, you can then use it to load your dataset into pandas or xarray or your package of choice:\n\n  ```python\n  In [6]: radar = pyart.io.read(filepath)\n  ```\n\n## Changing the default data cache location\n\nThe default cache location (where the data are saved on your local system) is dependent on the operating system. You can use the `locate()` method to identify it:\n\n```python\nfrom arm_test_data import locate\nlocate()\n```\n\nThe location can be overwritten by the `ACT_TEST_DATA_DIR` environment\nvariable to the desired destination.\n\n## References\n\n### Ameriflux data\n\nAmeriFlux BASE: https://doi.org/10.17190/AMF/2531143\nCitation: Bhupendra Raut, Sujan Pal, Paytsar Muradyan, Joseph R. O'Brien, Max Berkelhammer, Matthew Tuftedal, Max Grover, Scott Collis, Robert C. Jackson (2025), AmeriFlux BASE US-CU1 UIC Plant Research Laboratory Chicago, Ver. 1-5, AmeriFlux AMP, (Dataset). https://doi.org/10.17190/AMF/2531143\n",
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    "license": "MIT License\n        \n        Copyright (c) 2023 ARM User Facility\n        \n        Permission is hereby granted, free of charge, to any person obtaining a copy\n        of this software and associated documentation files (the \"Software\"), to deal\n        in the Software without restriction, including without limitation the rights\n        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n        copies of the Software, and to permit persons to whom the Software is\n        furnished to do so, subject to the following conditions:\n        \n        The above copyright notice and this permission notice shall be included in all\n        copies or substantial portions of the Software.\n        \n        THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n        AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n        LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n        SOFTWARE.\n        ",
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