A python package for fitting splines. Developed by the EPFL Center for Imaging as part of a collaboration with the Uhlmann Group at EMBL-EBI in Feb 2024.
- Flexible spline fitting for various applications.
- Support for many spline types in any dimesnionality.
- High-perfomance implementation in Python
- Extensive documentation with examples
You can install splinebox via pip:
pip install splinebox
Here is a minimal example of a cubic B-spline in 2D with 3 knots.
import splinebox
import numpy as np
import matplotlib.pyplot as plt
n_knots = 4
spline = splinebox.spline_curves.Spline(M=n_knots, basis_function=splinebox.basis_functions.B3(), closed=True)
spline.knots = np.array([[1, 2], [3, 2], [4, 3], [1, 1]])
t = np.linspace(0, n_knots, 100)
vals = spline.eval(t, derivative=0)
plt.scatter(spline.knots[:, 0], spline.knots[:, 1])
plt.plot(vals[:, 0], vals[:, 1])
plt.show()If you encounter any problems, please file and issue describing the issue and include minimal example to reproduce the issue.
We welcome contributions! Before you submit a pull request, please ensure that the tests are passing. You can run the tests with pytest. If you are unsure how to implement something, feel free to open an issue to discuss.
If you use splinebox in the context of scientific publication, please cite it as follows. Note, that you will have to fill in the version yourself. If you are unsure what version you are running, you can find out by running
import splinebox
print(splinbox.__version__)`BibTeX:
@misc{splinebox,
author = {Aymanns, Florian and Andò, Edward and Uhlmann, Virginie},
title = {{S}pline{B}ox},
url = {https://pypi.org/project/splinebox/},
doi = {10.5281/zenodo.13358354},
note = {{V}ersion V.V.Vb1},
year = 2024,
}
This is an open source project licensed under the BSD-3-Clause License.