Code used for the manuscript Physics-Informed Deep Learning Characterizes Morphodynamics of Asian Soybean Rust Disease

Please note:

• All figures can be reproduced by running the scripts within morphodynamics/scripts/
• Plots generated will be saved to morphodynamics/outputs/ (where the expected outputs can also be found in folders named via paper sections, as below).
• In the following, replace [compound] with the relevant compound out of [DMSO, compound_A, compound_B, compound_C_0_041, compound_C_10, compound_X].
• Neural networks were trained with a Quadro RTX 6000 GPU. The scripts below will run on a CPU, mostly within < 30s, however for some a GPU is required for reasonable completion times (training networks from scratch & tip model inference).
• Example image data (75 images for each time point and compound) is in morphodynamics/scripts/images/
• To run on the full datasets (available from r.endres@imperial.ac.uk), simply change the load path in morphodynamics/morphospace/dataset.py

Setup

• Tested on: Mac OS Catalina 10.15.3 & CentOS Linux release 7.7.1908

• Typical install time: <10s

• To run the setup so imports of internal modules work:
  python setup.py develop

• Requirements (can be installed via e.g. python -m pip install torch==1.6.0 once the desired python environment has been loaded):
  python 3.7.3
  torch 1.6.0
  torchvision 0.2.1
  tensorflow 2.3.1
  numpy 1.20.2
  opencv-python 4.4.0.46
  pyabc 0.10.3
  scipy 1.6.2
  matplotlib 3.4.1
  mayavi 4.7.2

Section 1: Morphospace

• Fig. 2b&c:
  python run_morphospace.py load
  Note: to train autoencoder from scratch, replace 'load' with 'train' and new weights will be saved to morphodynamics/outputs/. Please note however that only a subset of images are in the /data/images/ folder.

Section 2: Landscape Model

Fig. 3

• Fig. 3d:
  python run_landscape_visualizations.py [compound] landscape
  Note: can be viewed in interactive mode by uncommenting line 191, 'mlab.show()'; please note a window will appear for ~15s as the high resolution output is rendered, though this can be adjusted at the mlab.savefig line.

• To run inference to get the landscape in array form and do eq. 1 simulations over this from scratch:
  python run_landscape_model.py [compound] load 0 0

• To train the PINN from scratch:
  python run_landscape_model.py [compound] train [number of hours to train for] [number of times to save weights during training]
  Note: new weights will be saved to morphodynamics/outputs/.

Fig. S1

• Fig. S1a:
  python run_convergence.py DMSO ablation

• Fig. S1b:
  python run_convergence.py DMSO losses_repeats

• Fig. S1c:
  python run_convergence.py DMSO spore_integrals

• Fig. S1e:
  python run_landscape_visualizations.py landscape
  Note: change 'original' in the script to the desired repeat out of (original, repeat_a, repeat_b)

• Fig. S1f:
  python run_convergence.py [compound] plot_field_uncertainties

Fig. S2

• Fig. S2:
  python run_landscape_visualizations.py [compound] morphospace_connection

Fig. S3

• Fig. S3a-f:
  python run_landscape_visualizations.py [compound] errors

Fig. S4

• Fig. S4b&c:
  python MSDs.py
  Note: please note: fewer trajectories are used in this code than for the paper figure for memory considerations, though the results are near-identical.

• Fig. S4d:
  python run_landscape_visualizations.py [compound] entropy

Section 3: Tip Growth Model

Note: options for model index (idx_model) are [0, 1, 2]; in the manuscript these are called models 1, 2 & 3 respectively.

• Fig. 4b&d and Fig. S6a (comparison of MAP simulations with data for lengthening model & probability distributions associated with the MAP values):
  python L_ABC.py [compound] MAP_simulations
  Note: to run the full inference process: python L_ABC.py [compound] full_inference

• Fig. 4c, Fig. S5d and Fig. S6b (comparison of MAP simulations with data for bending models):
  python theta_ABC.py MAP_vis [compound] [idx_model]
  Note: inference was run with all three models for compound_A, and model 2 only for all other compounds. To run full inference: python theta_ABC.py full_inference [compound] 2. This prints parameters and weights which can then be swapped in to morphodynamics/tip_model/theta/accepted_params_kappa.py for plotting.

• Fig. 4e (Posterior distribution for the two-parameter optimal bending model):
  python theta_ABC.py M2_posterior [compound] 2

• Fig. S5b (comparison of global theta dynamics for all three models, using MAP values):
  python theta_plot_compare_models.py [idx_model]

• Fig. S5c (model probabilities):
  python theta_ABC.py model_probabilities compound_A -1
  Note: the -1 means all models are being used. To run full model selection: python theta_ABC.py full_inference compound_A -1