This repository contains the Python scripts allowing to generate the figures of the **multi-Scale Optimized Neuronal Cavitation (SONIC) model** paper .
- `figure_**.ipynb`: notebooks used to generate the paper figures that result from model simulations (i.e. all except the schematic figures 1 and 3).
- `LICENSE`: license file.
- `utils.py`: module containing utilities functions used in the notebooks.
- `notebook_runner`: module defining functionalities to execute notebooks from the command line.
- `requirements.txt`: text file containing a list of python dependencies.
- `root.py`: module specifying the path to the data root directory.
- `run_notebooks.py`: script used to run the notebooks required to generate the figures, from the command line.
- Python 3.6+
- NEURON 7.x (https://neuron.yale.edu/neuron/download/)
- PySONIC package (https://github.com/tjjlemaire/PySONIC)
- MorphoSONIC package (https://github.com/tjjlemaire/MorphoSONIC)
- nbconvert and nbformat python packages (utilities for jupyter notebooks)
- Install a Python distribution
- Install a NEURON distribution
- Download the PySONIC and MorphoSONIC code bases from their repositories, and follow the README instructions to install them as packages.
- Install the required python dependencies to run the notebooks: `pip install -r requirements.txt`
## Create a data directory
First, you must create a directory on your machine to hold the generated data. Once this is done, open the `root.py` and specify the full path to your data directory (replacing `None`).
## Generating the data
Given the cumbersome model simulations required to create the figures, it is advised to run the `run_notebooks.py` script in order to generate the required dataset before opening and running the notebooks. By default, that script generates the data for all the figures, but you can specify a subset of your choice using the `-f` option.
For instance, to generate data uniquely for figure 4:
python run_notebooks.py -f 4
To generate data for figures 4, 5 & 6:
python run_notebooks.py -f 4 5 6
To generate data for all figures:
python run_notebooks.py -f all
Be aware that the **cumulated computation time required to run all simulations can easily exceed 1 week**, and that the **total size of entire dataset size is about 112 GB**. Therefore, it is highly advised that you run that script on a **high-performance, multi-core machine with enough disk space**.
The generated dataset should be split between 5 sub-folders in the indicated output directory:
- `comparisons`: comparisons between the full NICE model and the SONIC model (figures 5 & 6)
- `maps`: cell-type-specific activation maps (figure 7)
- `STN`: sub-thalamic nucleus neuron modulation by low-intensity US (figure 9)
- `coverage`: effects of partial sonophore membrane coverage on neural responses (figure 10).
- `figs`: output folder containing PDFs of the generated figures
## Generating the figures
To generate a figure:
- start a *jupyter notebook* / *jupyter lab* session:
`jupyter lab` / `jupyter notebook`
- open the figure notebook
- select all the cells (`Ctrl` + `A`) and run them (`Ctrl` + `Enter`)
- wait for the complete notebook execution
Upon completion, the figures panels should appear in the notebook. Additionally, they will be saved as PDFs in the *figs* sub-folder.
Code written and maintained by Theo Lemaire (firstname.lastname@example.org).
This project is licensed under the MIT License - see the LICENSE file for details.
 Lemaire, T., Neufeld, E., Kuster, N., and Micera, S. (2019). Understanding ultrasound neuromodulation using a computationally efficient and interpretable model of intramembrane cavitation. J. Neural Eng.