Particle_tracking_using_KNN

1. An introduction to the particle tracking algorithm using K=1 Nearest Neighbor approach. The Kalman filter directs the 'greedy' KNN to find the most probable postion of the particle based on its previous position and its velocity vector.

2. The software requirements are coded in >> clean_environment.yml
3. Notebooks ae provided for more control
4. To run the script on VS code:
   • clone the repo into the desired directory
   • ensure Anaconda or Miniconda or Mambaforge is installed locally.
   • Create the environment >> conda env create -f clean_environment.yml
   • Run the code >> python -m main