To run the API, create a virtualenv with Python 3.6+.

Inside the virtualenv, run pip install -r requirements.txt from the root of the weightsapi directory.

IMPORTANT: Inside ./weightsapi/settings.py, add some random value to the SECRET_KEY const.

Then run ./manage.py migrate to create the SQLite database with necessary tables.

Finally, run ./manage.py runserver 0:8000 to launch the Python webserver.

The site will be accessible via http://localhost:8000/animals/

To run tests: ./manage.py test

A time limit of 3 hours was imposed on this task.

Goal: Build an API endpoint that records animal weights at a given point in time. Build a second endpoint that lets users query estimated weights for a given date (e.g. user has records for Jan 1 and Jan 5, but wants the estimated weight for Jan 3).

Uses Linear Interpolation / Extrapolation to calculate the estimated weight of an anmial for a given datetime timestamp.

• Used scipy for calculating the Linear Interpolation/Extrapolation due to time constraints and its well-tested implementation.

• Data is stored in SQLite.

• The estimate_weight endpoint stores the calculated weight in the DB for future, repeated lookups. This acts a sort of cache for the estimated values.

  • "Cache" busting was not implemented to remove or invalidate the stored estimated weights if new data BEFORE the estimated date was added. E.g if we have a stored estimated weight for Jan 6 and a recorded weight for Jan 5 is added, the Jan 6 estimated weight will still exist even though it could be affected by this new value.

• Project set up is simplified due to 3-hour constraint. Typical installation would use separate settings files for different environments.