hermes-client 1.0.1

Python client library for the HERMES web service

HERMES Python Client

This Python client provides a simple interface to the HERMES API, allowing users to easily access and interact with the HERMES data.

Installation

You can install the client using pip:

pip install hermes-client

Generally, the client is compatible with the respective vMajor.minor version of the HERMES webservice. For example, the hermes-client version 0.1.x is compatible with the HERMES webservice version 0.1.x.

Usage

You will mainly interact with two client classes. The HermesClient as well as the ForecastSeriesClient. The former should mainly be used to explore the available Projects and Forecastseries, while the latter can be used to access the data linked to specific ForecastSeries, Forecasts and ModelRuns.

Find the correct ForecastSeries

To find the correct ForecastSeries, you can use the HermesClient to search for ForecastSeries by name or other attributes. For example:

from hermes_client import HermesClient
client = HermesClient(url="https://example.com/api/")

projects = client.list_projects()
projects

This will return a list of available projects.

[{'oid': 'c9d163c4-02ea-4d4d-9ef5-26e82c025c92',
 'name': 'project_induced',
 'starttime': '2022-04-21T00:00:00',
 'endtime': '2022-04-21T23:59:59',
 'description': 'This is a test project',
 'creationinfo': {'creationtime': '2025-05-22T16:51:41'}},
 ...

Likewise, you can list all ForecastSeries within a project:

forecastseries = client.list_forecastseries(project="project_induced")

Which will again return a list of ForecastSeries

[{'oid': 'c9d163c4-02ea-4d4d-9ef5-26e82c025c92',
 'name': 'forecastseries_induced',
 ...
 },
 ...
]

Access ForecastSeries data

Once you have identified the correct ForecastSeries, you can use the ForecastSeriesClient to access the data. For example:

from hermes_client import ForecastSeriesClient
client = ForecastSeriesClient(
    url="https://example.com/api/",
    forecastseries="c9d163c4-02ea-4d4d-9ef5-26e82c025c92"
)
client.forecasts

Which will return a list of ForecastClient objects, each allowing you to access the data for a specific forecast:

[Forecast(COMPLETED, 2022-04-21 15:00:00, 2022-04-21 18:00:00),
 Forecast(COMPLETED, 2022-04-21 16:00:00, 2022-04-21 18:00:00),
 Forecast(COMPLETED, 2022-04-21 17:00:00, 2022-04-21 18:00:00)]

You can then access the data for a specific forecast by using the ForecastClient:

forecast = client.forecasts[0]

injectionplan_name = forecast.metadata.injectionplans[0]
model_name = forecast.metadata.modelconfigs[0]

results = forecast.get_results(injectionplan_name, model_name)

This will return a ForecastGRRateGrid object (or ForecastCatalog, depending on the model result types).

Further Information

For more information on the available methods and attributes, please refer to the API reference.

The documentation will be extended in the future to include more examples and use cases - The API Reference should however give a good overview of the available methods and attributes.