https://github.com/brsynth/reservoir
https://github.com/brsynth/reservoir
Last synced: 5 months ago
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- Host: GitHub
- URL: https://github.com/brsynth/reservoir
- Owner: brsynth
- Created: 2024-08-23T14:51:46.000Z (almost 2 years ago)
- Default Branch: master
- Last Pushed: 2025-09-19T11:51:34.000Z (9 months ago)
- Last Synced: 2025-12-14T12:59:57.623Z (6 months ago)
- Language: Jupyter Notebook
- Size: 5.08 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Supporting content for the Reservoir Computing paper with Bacteria paper
[](version)
[](LICENSE.md)
This repository contains code to support the Computing paper with Bacteria publication. See citation for details.
## Table of Contents
- [1. Repository structure](#1-repository-structure)
- [2. Installation](#2-installation)
- [3. Citation](#3-citation)
## 1. Repository structure
```text
.
├── Dataset_input < placeholder for data files >
│ └── ..
├── Reservoir < trained reservoir model>
│ └── ..
├── Result
│ └── ..
├── Library < supporting code for notebook >
│ └── ..
├── 1.Dataset-species.ipynb
├── 2.Fixed-prior.ipynb
├── 3.ML-covid.ipynb
├── 4.Reservoir-covid.ipynb
├── 5.Reservoir-species.ipynb
├── README.md
└── requirements.yaml
```
## 2. Installation
The following steps will set up a `reservoir` conda environment.
0. **Install Conda**
The conda package manager is required. If you do not have it installed, you
can download it from [here](https://docs.conda.io/en/latest/miniconda.html).
Follow the instructions on the page to install Conda. For example, on
Windows, you would download the installer and run it. On macOS and Linux,
you might use a command like:
```bash
bash ~/Downloads/Miniconda3-latest-Linux-x86_64.sh
```
Follow the prompts on the installer to complete the installation.
1. **Install dependencies**
1.1. **Windows & Linux**
```bash
conda env create -f requirements.yml
conda activate reservoir
```
1.2. **macOS (Intel processors)**
```bash
conda env create -f requirements.yml
conda activate reservoir
pip install tensorflow-macos tensorflow-metal
```
1.3. **macOS (Apple Silicon Mx processors)**
```bash
conda env create --platform osx-64 -f requirements.yml
conda activate reservoir
conda env config vars set CONDA_SUBDIR=osx-64
conda deactivate
conda activate reservoir
pip install tensorflow-macos tensorflow-metal
```
2. **Download data**
Trained reservoir models and most important datasets are available as a Zenodo archive: . Extract the files and place them in the `Dataset-input`, `Reservoir`, `Result` directory.
## 3. Citation
If you use this software, please cite it as below.
> *Living Bacterial Reservoir Computers for Information Processing and Sensing*. Paul Ahavi; Thi-Ngoc-An Hoang; Philippe Meyer; Sylvie Berthier; Federica Fiorini; Florence Castelli; Olivier Epaulard; Audrey Le Gouellec; Jean-Loup Faulon. Preprint: .