Development is ongoing
This Python script generates a data stream of floating point numbers simulating instantaneous gas flow supply through the Easington-Langeled entry point. It includes functionality to insert anomalies into the data stream and then separately detect them. Additionally, it provides a real-time visualiser to display this information.
This code was developed as part of Cobblestone Energy's application process
Python 3.x
matplotlib 3.9.2
numpy 2.1.2
pandas 2.2.3
scikit_learn 1.5.2
scipy 1.14.1
seaborn 0.13.2
The project specification is available to read in docs/specification.txt.
A brief report on anomaly detection algorithms and the
choice of selection is available to read in docs/Algorithm_Selection_Report.pdf.
Further detail on Easington Langeled and my decision making
on the simulation is available to read in docs/about.MD.
Clone the repository into your local machine and install all the requirements.
Running the script main.py will simulate and visualise 1000
days of gas flow data with randomly injected anomalies. An
implementation of an Isolation Forest algorithm will attempt to
detect the anomalies.
Various properties of the simulation, including the baseline data can be
altered inside the config found at src/simulator/config.json
Tests are stored in tests/ and can be run from the terminal with the
command python -m unittest discover -s tests.