Neu_sde

Code to paper: Discovering mesoscopic descriptions of collective animal movement with neural stochastic modelling

How to run:

1. Install the dependencies mentioned in the requirements(requirements.txt) file

2. To train :

   • Enter path of the training data in train.py
   • Enter path of where to save model weights in train.py
   • Change hyperparameters for training in train.py (as per need).
   • Run python train.py

3. To visualize the field plots from trained Neural model:

   • Enter path of model weights in plot_field.py
   • Change parameters for visualization in plot_field.py (as per need)
   • Run python plot_field.py

4. Extra utilites:

   • Data augmentation: Use augment.py to augment the data as per discussed in the paper (You will need to train on this new data)
   • Sample path: Use sample_path.py to sample a path from learnt neural model.
   • analysis(directory): This folder contains notebooks for:
     • Goodness-of-fit analysis (Wasserstein metric and relative timescale discrepancy)
     • Generation of drift and diffusion plots for theoretically derived mesoscale SDEs (Appendix A)
     • Analysis of autocorrelation of mx and my components of the polarization.
     • This requires the following packages to be installed: sdeint, pydaddy (can use pip to install)

Data:

analysis(directory)

ACKNOWLEGDEMENT

We used: Dietrich et al. as a reference for our work