Small project about developing APIs for Turbit

Task descritpion (refer to DESCRIPTION.md for more details):

Time Series Data Handling and API Development

Turbine 1: .csv file with ; as a separator and two rows of header Turbine 2: .csv file with ; as a separator and two rows of header

Load time series data from the CSV files above into a MongoDB collection and make the data accessible through the FastAPI.

1. Data Preparation and Loading into MongoDB Develop a python script to read the data from the CSV files and load it into a single MongoDB collection.

2. Add FastAPI Endpoint Add an FastAPI endpoint that allows to retrieve the data based on turbine id and time ranges

Usage

It is assumed that poetry and docker-compose are installed on the system. Once that is complete, after cloning the repository, change the relevant paths in docker-compose.yml and simply run, from the project folder:

docker-compose up -d

Then install the python dependencies, found in poetry.lock file, by running

poetry install

and from the directory timeseries/ import the data through

poetry run python import_data.py

Run the API by executing, from within the api/ folder:

poetry run uvicorn main:app --reload

Now the database is up and running. Open your browser at

http://127.0.0.1:8000/

and try to see what happens if looking the endpoint:

/1?startt=2016-01-01T00:00&endt=2016-01-01T18:50

where 1 is the turbine id and startt and endt are the beginning and end of the time range.