3x3_metasurface

This is the codebase for building the custom dataset used for the study presented in the publication, Time-series neural networks to predict electromagnetic wave propagation.

Running from a local Docker container

A library of 3x3 pillar radii between 75 nm and 250 nm is generated using generate_radii.py. This generates a file called neighbors_library_allrandom.pkl. This file stores a numpy array with len(5000). Each index contains a list of 9 radii.

Running main.py allows the user to either generate data or reduce the data into volumes, as presented in the paper linked above.

Generate data. To run a single simulation:
- Set the config file parameter, task to 0.
- Create an instance of the Docker image, v3_lightning.
- From /develop/code/3x3_metasurface, run:
```
mpirun --allow-run-as-root -np {num_cpu_cores} python3 main.py -config configs/config.yaml -idx {neighbors library index}
```
Reduce data.
- Set the config file parameter, task to 0.
- Running the same Docker container in (1), from /develop/code/3x3_metasurface, run:
```
python3 reduce_data.py
```

Using Kubernetes

Generate data. To launch many simulations:

Set the config file parameters.
- task : 0
- deployment_mode : 1
- kube.datagen_job.num_sims : 1500
- kube.datagen_job.start_group_id : 0
- kube.datagen_job.num_parallel_ops : {your preference}
Running an instance of the Docker image, kubernetes launcher, navigate to kubernetes/gen_data/ and run
```
python3 launch_jobs.py
```

Reduce data.

Set the config file parameters.
- task : 1
- deployment_mode : 1
From kubernetes/reduce_data, run:
```
python3 launch_jobs.py
```

mingoosam/3x3_metasurface

3x3_metasurface

Running from a local Docker container

Using Kubernetes