SQUATCH WATCH

The truth is in here.

Introduction

The point of this bare-bones application is to display basic data related to cryptozoological findings in a single place that is easy to view and query by researchers. Sighting data is added to the system in the form of "reports" – CSV files containing rows for individual sightings. The functionality of the app can be broken into two main components:

• An HTTP endpoint that handles requests with a CSV file payload and creates objects for the application from the file
• A front-end table that displays all sightings created from CSV data and can be filtered by monster, researcher, or specific report

Additionally, in order to submit data to the application, a separate HTTP client (http_client.py) is provided to look for files in a directory and send them to the HTTP endpoint.

Data and schema

Each CSV file contains a row per monster sighting being reported by a researcher. The CSV format is as follows:

#"timestamp","researcher","monster","latitude","longitude"
"2018-01-01 23:59:59","Jonathan Barker","Dracula","46.8999964","24.1499994"

When a file is submitted, it creates objects that correspond with the application's four models:

• MonsterReport: refers to a single CSV report that is comprised of many sightings
• Sighting: an instance of a researcher spotting a monster at a specific time and location
• Researcher: the human credited with finding the monster (unique across all sightings)
• Monster: the monster spotted (unique across all sightings)

To test the app's functionality, sample data is provided in the sample folder.

DQ Schema

Installation

Note the python version for the project is 3.8, and this is saved in the .python-version file in the project root directory.

1. Install pipenv: pip install pipenv
2. Activate the virtual environment: pipenv shell
3. Install project dependencies: pipenv install
4. Migrate the data: python manage.py migrate
5. Run the development server: python manage.py runserver
6. Open a brower at http://127.0.0.1:8080