Installation

Neural Field Manifold requires Python 3.10 or later and depends on NumPy, SciPy, and PyTorch.

The PyPI release is forthcoming. Install the current package from GitHub.

Install from GitHub

python -m pip install "neuralfieldmanifold @ git+https://github.com/erfanzabeh/NeuralFieldManifold.git@main"

Optional Dependencies

Use plot for Matplotlib and Plotly support, or tutorials for the complete notebook environment:

python -m pip install "neuralfieldmanifold[tutorials] @ git+https://github.com/erfanzabeh/NeuralFieldManifold.git@main"

For an editable installation:

git clone https://github.com/erfanzabeh/NeuralFieldManifold.git
cd NeuralFieldManifold
python -m pip install -e ".[tutorials]"

Verify the Installation

import neuralfieldmanifold as nfm

recording = nfm.datasets.load("monkey_lfp")
print(recording.signal.shape, recording.sampling_rate_hz)

Continue with Getting Started or open a notebook in docs/tutorials.

Recorded-Data Tutorials

The quick start uses the small one-dimensional recordings bundled with the package. The complete recorded-data tutorials also contain multichannel analyses that require access to the original recordings. Set their paths before starting Jupyter:

export NFM_MONKEY_DATA_DIR=/path/to/monkey/arrays
export NFM_MOUSE_DATA_FILE=/path/to/eke003_spont_250818_175844.npy

The full DeepLagField benchmark reads its saved synthetic training dataset from NFM_TVAR_DATA_FILE (or train_data.npz in the working directory). The data-generation section also shows how to construct a pilot dataset.

The website displays saved notebook results. Documentation builds do not execute notebook code or retrain models.