rtedata 1.0.5

Python wrapper for RTE API requests.

1. Usage

pip install rtedata

1.1. Get RTE API credentials

You need to follow these first steps in order to setup your wrapper :

• create an account on the RTE platform
• create an application associated to your account (the name and description of the app is not relevant)
• collect your app IDs (ID Client and ID Secret) available in your application dashboard
• subscribe to the relevant APIs regarding the "data_type" you request (please refer to the table in the last section to get the associated links)

1.2. Generate a data retrieval

To retrieve data using the wrapper, follow this pipeline :

from rtedata import Client
client = Client(client_id="XXX", client_secret="XXX")
dfs = client.retrieve_data(start_date="2024-01-01 00:00:00", end_date="2024-01-02 23:59:00", data_type="actual_generations_per_unit", output_dir="./output")

where :

• start_date is the first date of the data retrieval (format YYYY-MM-DD HH:MM:SS)
• end_date is the last date of the data retrieval (format YYYY-MM-DD HH:MM:SS)
• data_type is the desired data to collect (a keyword list is given in the next section). It can be a single keyword "XXX" or a list of keyword separated by a comma "XXX,YYY,ZZZ"
• output_dir (optionnal): the output directory to store the results

The generic output format is a pandas dataframe / .csv file containing the data for all dates between start_date and end_date. It will generate one file per desired data_type and will store all of them in a ./results folder with the generic name "<data_type><start_date><end_date>.csv".

2. Available data_type options

It is possible to see the full options catalog using the client attribute catalog :

from rtedata import Client
client = Client(client_id="XXX", client_secret="XXX")
client.catalog

The following table is an exhaustive list of all possible (currently handled) options for the data_type argument for the retrieval, and the description of the associated data :

Generation Data

Market Data

Consumption Data