https://github.com/cbueth/delaynet
Analyze delay propagation in transportation networks.
https://github.com/cbueth/delaynet
Last synced: 8 months ago
JSON representation
Analyze delay propagation in transportation networks.
- Host: GitHub
- URL: https://github.com/cbueth/delaynet
- Owner: cbueth
- License: bsd-3-clause
- Created: 2023-12-04T18:01:41.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2025-08-14T17:59:18.000Z (10 months ago)
- Last Synced: 2025-09-24T08:50:29.718Z (9 months ago)
- Language: Python
- Size: 784 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Citation: CITATION.cff
Awesome Lists containing this project
README
[](https://delaynet.readthedocs.io/)
[](https://pypi.org/project/delaynet/)
[](https://pypi.org/project/delaynet/)
[](https://anaconda.org/conda-forge/delaynet)
[](https://doi.org/10.5281/zenodo.16875272)
[](https://github.com/astral-sh/ruff)
[](CODE_OF_CONDUCT.md)
[](https://gitlab.ifisc.uib-csic.es/carlson/delaynet/-/commits/main)
[](https://gitlab.ifisc.uib-csic.es/carlson/delaynet/-/jobs)
Python package to reconstruct and analyse delay functional networks from time series.
It provides tools for data preparation and detrending, multiple connectivity measures
(e.g. Granger causality, transfer entropy, correlations), optimal-lag network
reconstruction, and network analysis.
### Features
- Connectivity measures with hypothesis testing and optimal-lag reconstruction
- Network analysis: betweenness, eigenvector centrality, link density, transitivity,
reciprocity, isolated nodes, global efficiency
- Null-model normalisation for metrics: report z-scores vs directed G(n,m) random
graphs (igraph-based; binary-only; on-the-fly generation)
- Comprehensive documentation and examples
- Tested across multiple Python versions with high coverage
---
For details on how to use this package, see the
[Guide](https://delaynet.readthedocs.io/en/latest/guide/) or
the [Documentation](https://delaynet.readthedocs.io/en/latest/).
## Setup
This package can be installed from PyPI using pip:
```bash
pip install delaynet # when public on PyPI
```
This will automatically install all the necessary dependencies as specified in the
`pyproject.toml` file. It is recommended to use a virtual environment, e.g., using
`conda`, `mamba` or `micromamba` (they can be used interchangeably).
```bash
micromamba create -n delay_net -c conda-forge python
micromamba activate delay_net
pip install delaynet # or `micromamba install delaynet` when on conda-forge
```
### Quickstart
```python
import numpy as np
import delaynet as dn
# Generate toy data: 5 nodes, 300 time points
rng = np.random.default_rng(1520)
data = rng.standard_normal((300, 5))
# Compute a connectivity p-value and lag for one pair
pval, lag = dn.connectivity(data[:, 0], data[:, 1], metric="gc", lag_steps=10)
print(f"GC p-value={pval:.3g}, best lag={lag}")
# Reconstruct a delay network (p-value matrix and lag matrix)
weights, lags = dn.reconstruct_network(data, connectivity_measure="gc", lag_steps=5)
print(weights.shape, lags.shape)
```
## Development Setup
For development, we recommend using [`uv`](https://docs.astral.sh/uv/) or `micromamba`
to create a virtual environment.
After cloning the repository, navigate to the root folder and
create the environment.
When using `uv`, the environment can be created with the following command:
```bash
uv sync
```
Or, if you prefer to use `micromamba`,
with the desired Python version and the dependencies.
```bash
micromamba create -n delay_net -c conda-forge -f requirements.txt
micromamba activate delay_net
```
Either way, using `pip` to install the package in editable mode will also install the
development dependencies.
```bash
pip install -e ".[all]"
```
Or, to let `micromamba` handle the dependencies, use the `requirements.txt` file
```bash
micromamba install --file requirements.txt
pip install --no-build-isolation --no-deps -e .
```
Now, the package can be imported and used in the python environment, from anywhere on
the system if the environment is activated.
## Set up Jupyter kernel
If you want to use `delaynet` with its environment `delay_net` in Jupyter, run:
```bash
pip install --user ipykernel
python -m ipykernel install --user --name=delay_net
```
This allows you to run Jupyter with the kernel `delay_net` (Kernel > Change Kernel >
im_env)
## Acknowledgments
This project has received funding from the European Research Council (ERC) under the
European Union's Horizon 2020 research and innovation programme (grant agreement No
851255).
This work was partially supported by the María de Maeztu project CEX2021-001164-M funded
by the MICIU/AEI/10.13039/501100011033 and FEDER, EU.