Antares Craft
What is it ?
Antares Craft is a python library to read and edit antares-simulator studies, stored either on you local filesystem or on an antares-web server. It also allows you to trigger simulations and retrieve the corresponding result.
Main features
- Read and edit antares-simulator studies programmatically
- Work seamlessly on filesystem or antares-web studies
- Support for variant studies on antares-web
- Launch simulations, be it on you computer or on antares-web server
- Retrieve and inspect simulation outputs
- Generate availability timeseries, be it on you computer or on antares-web server
Installation
Antares Craft can simply be installed from PyPI repository, typically using pip:
pip install antares_craft
Documentation
- Antares Craft documentation: antares-craft.readthedocs.io
- Antares general user documentation: antares-doc.readthedocs.io
Example
Below as an example, a code snippet where we create a small study with only one area where 100 MW of load are fed with a cluster of 5 power plants of 30 MW each. We then run the simulation and print some results.
For more information and examples please refer to the documentation.
conf = APIconf(api_host="https://antares-web.mydomain", token="my-token", verify=True)
# create a study named "my-study" on the antares-web server
study = create_study_api(study_name="my-study", version="8.8", api_config=conf)
# create an area with 100 MW of load for every hour of the year, and 3000 euros/h for unsupplied energy cost
area = study.create_area(area_name="my-country", properties=AreaProperties(energy_cost_unsupplied=3000))
area.set_load(pd.DataFrame(data=100 * np.ones((8760, 1))))
# create a cluster with 5 nuclear units of 30 MW each, and a generation cost of 30 MW/h
cluster = area.create_thermal_cluster("nuclear",
ThermalClusterProperties(unit_count=5,
nominal_capacity=30,
marginal_cost=10,
market_bid_cost=10,
group=ThermalClusterGroup.NUCLEAR))
cluster.set_series(pd.DataFrame(data=150 * np.ones((8760, 1))))
# launch a simulation on the server and wait for the result
job = study.run_antares_simulation()
study.wait_job_completion(job)
output = study.get_output(job.output_id)
# read some output data as a pandas dataframe:
res = output.aggregate_mc_all_areas(data_type=MCAllAreasDataType.DETAILS, frequency=Frequency.HOURLY)
print(res)
should print the following output, which shows that at every hour the created cluster has generated 100 MW as expected to feed the load, and had to start 4 units (NODU column).
area cluster timeId production NP Cost NODU Profit - Euro
0 my-country nuclear 1 100.0 0.0 4.0 0.0
1 my-country nuclear 2 100.0 0.0 4.0 0.0
2 my-country nuclear 3 100.0 0.0 4.0 0.0
3 my-country nuclear 4 100.0 0.0 4.0 0.0
4 my-country nuclear 5 100.0 0.0 4.0 0.0
... ... ... ... ... ... ... ...
8731 my-country nuclear 8732 100.0 0.0 4.0 0.0
8732 my-country nuclear 8733 100.0 0.0 4.0 0.0
8733 my-country nuclear 8734 100.0 0.0 4.0 0.0
8734 my-country nuclear 8735 100.0 0.0 4.0 0.0
8735 my-country nuclear 8736 100.0 0.0 4.0 0.0
[8736 rows x 7 columns]
3 maintainers
Metadata
3 maintainers
Release files for antares-craft 0.17.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| antares_craft-0.17.1.tar.gz | 169.6 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| antares_craft-0.17.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 415.1 kB
Release files / antares_craft-0.17.1.tar.gz
| Download URL | antares_craft-0.17.1.tar.gz |
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| Size | 169.6 kB |
| Tags | Source |
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