## climetlab-power-production
[](https://badge.fury.io/py/climetlab-maelstrom-power-production)
[](https://github.com/psf/black)
A dataset plugin for climetlab for the dataset climetlab-plugin-a6/maelstrom-production-forecasts.
Features
--------
In this README is a description of how to get the CliMetLab Plugin for A6.
## Installation
Via `pip`
```commandline
pip install climetlab-maelstrom-power-production
```
or via [`poetry`](https://python-poetry.org/)
```bash
git clone git@github.com:4castRenewables/climetlab-plugin-a6.git
cd climetlab-plugin-a6
poetry install --no-dev
```
## Datasets description
There are five datasets:
- `maelstrom-constants-a-b`
- `maelstrom-power-production`
- `maelstrom-weather-model-level`
- `maelstrom-weather-pressure-level`
- `maelstrom-weather-surface-level`
A detailed description of each dataset (variables, meta data etc.) is available [here](https://www.maelstrom-eurohpc.eu/content/docs/uploads/doc6.pdf) (see Section 3.6).
### `maelstrom-constants-a-b`
Constants used for calculation of pressure at intermediate model levels.
#### Usage
```Python
import climetlab as cml
production_data = cml.load_dataset("maelstrom-constants-a-b")
```
#### References
IFS Documentation – Cy47r1, Operational implementation 30 June 2020, Part III: Dynamics and Numerical Procedures, ECMWF, 2020, p. 6, Eq. 2.11
### `maelstrom-power-production`
Power production data of wind turbines located in various regions of Germany.
The data were provided by [NOTUS energy GmbH & Co. KG](https://www.notus.de).
For a detailed description see the link above.
#### Usage
```Python
import climetlab as cml
production_data = cml.load_dataset("maelstrom-power-production", wind_turbine_id=1)
```
The `wind_turbine_id` is a number `1` to `N`, where `N` is the maximum number of currently available wind turbines.
Currently available: 4 wind turbines.
### `maelstrom-weather-model-level`
[ECMWF](https://www.ecmwf.int) IFS HRES model level data for whole Europe.
For a detailed description see the link above.
#### Usage
```Python
import climetlab as cml
weather_ml = cml.load_dataset("maelstrom-weather-model-level", date="2019-01-01")
```
Currently available dates:
- `2017-01-01` until `2020-12-31`
### `maelstrom-weather-pressure-level`
[ECMWF](https://www.ecmwf.int) IF HRES pressure level data for whole Europe.
For a detailed description see the link above.
#### Usage
```Python
import climetlab as cml
weather_pl = cml.load_dataset("maelstrom-weather-pressure-level", date="2019-01-01")
```
Currently available dates:
- `2017-01-01` until `2020-12-31`
### `maelstrom-weather-surface-level`
[ECMWF](https://www.ecmwf.int) IFS HRES surface level data for whole Europe.
For a detailed description see the link above.
#### Usage
```Python
import climetlab as cml
weather_pl = cml.load_dataset("maelstrom-weather-surface-level", date="2019-01-01")
```
Currently available dates:
- `2017-01-01` until `2020-12-31`
## Using climetlab to access the data (supports grib, netcdf and zarr)
See the demo notebooks [here](https://github.com/faemmi/climetlab-plugin-a6/tree/main/notebooks).
The climetlab python package allows easy access to the data with a few lines of code such as:
```Python
!pip install climetlab climetlab-maelstrom-power-production
import climetlab as cml
data = cml.load_dataset("maelstrom-weather-surface-level", date="2019-01-01")
data.to_xarray()
```
### Executing the notebooks
Before executing the notebooks, make sure to install the project and the
notebook dependencies correctly
```commandline
poetry install --extras notebooks
```
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"description": "## climetlab-power-production\n[](https://badge.fury.io/py/climetlab-maelstrom-power-production)\n\n[](https://github.com/psf/black)\n\nA dataset plugin for climetlab for the dataset climetlab-plugin-a6/maelstrom-production-forecasts.\n\n\nFeatures\n--------\n\nIn this README is a description of how to get the CliMetLab Plugin for A6.\n\n## Installation\n\nVia `pip`\n\n```commandline\npip install climetlab-maelstrom-power-production\n```\n\nor via [`poetry`](https://python-poetry.org/)\n\n```bash\ngit clone git@github.com:4castRenewables/climetlab-plugin-a6.git\ncd climetlab-plugin-a6\npoetry install --no-dev\n```\n\n## Datasets description\n\nThere are five datasets:\n\n- `maelstrom-constants-a-b`\n- `maelstrom-power-production`\n- `maelstrom-weather-model-level`\n- `maelstrom-weather-pressure-level`\n- `maelstrom-weather-surface-level`\n\nA detailed description of each dataset (variables, meta data etc.) is available [here](https://www.maelstrom-eurohpc.eu/content/docs/uploads/doc6.pdf) (see Section 3.6).\n\n### `maelstrom-constants-a-b`\nConstants used for calculation of pressure at intermediate model levels.\n\n#### Usage\n\n```Python\nimport climetlab as cml\n\nproduction_data = cml.load_dataset(\"maelstrom-constants-a-b\")\n```\n\n#### References\nIFS Documentation \u2013 Cy47r1, Operational implementation 30 June 2020, Part III: Dynamics and Numerical Procedures, ECMWF, 2020, p. 6, Eq. 2.11\n\n### `maelstrom-power-production`\nPower production data of wind turbines located in various regions of Germany.\n\nThe data were provided by [NOTUS energy GmbH & Co. KG](https://www.notus.de).\nFor a detailed description see the link above.\n\n#### Usage\n\n```Python\nimport climetlab as cml\n\nproduction_data = cml.load_dataset(\"maelstrom-power-production\", wind_turbine_id=1)\n```\n\nThe `wind_turbine_id` is a number `1` to `N`, where `N` is the maximum number of currently available wind turbines.\n\nCurrently available: 4 wind turbines.\n\n### `maelstrom-weather-model-level`\n[ECMWF](https://www.ecmwf.int) IFS HRES model level data for whole Europe.\n\nFor a detailed description see the link above.\n\n#### Usage\n\n```Python\nimport climetlab as cml\n\nweather_ml = cml.load_dataset(\"maelstrom-weather-model-level\", date=\"2019-01-01\")\n```\n\nCurrently available dates:\n- `2017-01-01` until `2020-12-31`\n\n### `maelstrom-weather-pressure-level`\n[ECMWF](https://www.ecmwf.int) IF HRES pressure level data for whole Europe.\n\nFor a detailed description see the link above.\n\n#### Usage\n\n```Python\nimport climetlab as cml\n\nweather_pl = cml.load_dataset(\"maelstrom-weather-pressure-level\", date=\"2019-01-01\")\n```\n\nCurrently available dates:\n- `2017-01-01` until `2020-12-31`\n\n### `maelstrom-weather-surface-level`\n[ECMWF](https://www.ecmwf.int) IFS HRES surface level data for whole Europe.\n\nFor a detailed description see the link above.\n\n#### Usage\n\n```Python\nimport climetlab as cml\n\nweather_pl = cml.load_dataset(\"maelstrom-weather-surface-level\", date=\"2019-01-01\")\n```\n\nCurrently available dates:\n- `2017-01-01` until `2020-12-31`\n\n## Using climetlab to access the data (supports grib, netcdf and zarr)\n\nSee the demo notebooks [here](https://github.com/faemmi/climetlab-plugin-a6/tree/main/notebooks).\n\nThe climetlab python package allows easy access to the data with a few lines of code such as:\n```Python\n!pip install climetlab climetlab-maelstrom-power-production\nimport climetlab as cml\n\ndata = cml.load_dataset(\"maelstrom-weather-surface-level\", date=\"2019-01-01\")\ndata.to_xarray()\n```\n\n\n### Executing the notebooks\n\nBefore executing the notebooks, make sure to install the project and the\nnotebook dependencies correctly\n```commandline\npoetry install --extras notebooks\n```\n",
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