OpenReservoirComputing 0.2.0
GPU accelerated implementations of common RC architectures
Requires Python: <3.14,>=3.10
Keywords: reservoir computing

<div align="center">
  <img src="imgs/ORC_logo_cropped.png" alt="ORC Logo" width="200px" />
</div>

# ORC: Open Reservoir Computing
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ORC is the one-stop-shop for performant reservoir computing in jax. Key high-level features include
- Modular design for mixing and matching layers and reservoir drivers (or creating your own!)
- Continuous, discrete, serial, and parallel implementations

## Installation

To install ORC, first clone the repository onto your local machine
```bash
git clone https://github.com/Jan-Williams/OpenReservoirComputing.git
```

Then navigate to the cloned directory and use `pip` to install:
```bash
pip install .
```

If you would like to use ORC on GPU(s), install the optional GPU dependencies:
```bash
pip install .[gpu]
```

To run the example notebooks, install the optional notebook dependencies:
```bash
pip install .[notebooks]
```

## Quick start example
Below is a minimal quick-start example to train your first RC with ORC. It leverages the built-in data library to integrate the Lorenz63 ODE before training and forecasting with ORC.

```python
import orc

# integrate the Lorenz system 
U,t = orc.data.lorenz63(tN=100, dt=0.01)

# train-test split
test_perc = 0.2
split_idx = int((1 - test_perc) * U.shape[0])
U_train = U[:split_idx, :]
t_train = t[:split_idx]
U_test = U[split_idx:, :]
t_test = t[split_idx:]

# Initialize and train the ESN
esn = orc.forecaster.ESNForecaster(data_dim=3, res_dim=400)
esn, R = orc.forecaster.train_RCForecaster(esn, U_train)

# Forecast! 
U_pred = esn.forecast(fcast_len=U_test.shape[0], res_state=R[-1]) # feed in the last reservoir state seen in training
```

To visualize the forecast and compare it to the test data, we can use `orc.utils.visualization`:
```python 
orc.utils.visualization.plot_time_series(
    [U_test, U_pred],
    (t_test - t_test[0]), # start time at 0
    state_var_names=["$u_1$", "$u_2$", "$u_3$"],
    time_series_labels=["True", "Predicted"],
    line_formats=["-", "r--"],
    x_label= r"$t$",
)
```

<div align="center">
  <img src="imgs/readme_example_forecast.png" alt="ORC Logo"/>
</div>


## Contribution guidelines
First off, thanks for helping out! We appreciate your willingness to contribute! To get started, clone the repo and install the developer dependencies of ORC.

```bash
git clone https://github.com/Jan-Williams/OpenReservoirComputing.git
```

From the root directory of the repository, create an editable install for your given hardware.

CPU:
```bash
pip install -e .[dev]
```

GPU:
```bash
pip install -e .[dev, gpu]
```

The main branch is protected from direct changes. If you would like to make a change please create a new branch and work on your new feature. After you are satisfied with your changes, please run our testing suite to ensure all is working well. We also expect new tests to be written for all changes if additions are made. The tests can be simply run from the root directory of the repository with
```bash
pytest
```
Followed by a formatting check 
```bash
ruff check
```

Finally, submit your changes as a pull request! When you submit the PR, please request reviews from both @dtretiak and @Jan-Williams, we will try to get back to you as soon as possible. When you submit the PR, the above tests will automatically be run on your proposed changes through Github Actions, so it is best to get everything tested first before submitting!