https://github.com/espdev/csaps
Cubic spline approximation (smoothing)
https://github.com/espdev/csaps
approximation csaps cubic-splines python smooth smoothing smoothing-splines splines
Last synced: 4 months ago
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Cubic spline approximation (smoothing)
- Host: GitHub
- URL: https://github.com/espdev/csaps
- Owner: espdev
- License: mit
- Created: 2017-06-22T23:51:06.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2025-09-07T21:02:38.000Z (5 months ago)
- Last Synced: 2025-10-18T22:59:16.790Z (4 months ago)
- Topics: approximation, csaps, cubic-splines, python, smooth, smoothing, smoothing-splines, splines
- Language: Python
- Homepage: https://csaps.readthedocs.io
- Size: 2.59 MB
- Stars: 173
- Watchers: 12
- Forks: 31
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
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README
**csaps** is a Python package for univariate, multivariate and n-dimensional grid data approximation using cubic smoothing splines.
The package can be useful in practical engineering tasks for data approximation and smoothing.
## Installing
Use pip for installing:
```
pip install -U csaps
```
or Poetry:
```
poetry add csaps
```
The module depends only on NumPy and SciPy. Python 3.10 or above is supported.
## Simple Examples
Here is a couple of examples of smoothing data.
An univariate data smoothing:
```python
import numpy as np
import matplotlib.pyplot as plt
from csaps import csaps
np.random.seed(1234)
x = np.linspace(-5., 5., 25)
y = np.exp(-(x/2.5)**2) + (np.random.rand(25) - 0.2) * 0.3
xs = np.linspace(x[0], x[-1], 150)
ys = csaps(x, y, xs, smooth=0.85)
plt.plot(x, y, 'o', xs, ys, '-')
plt.show()
```
A surface data smoothing:
```python
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from csaps import csaps
np.random.seed(1234)
xdata = [np.linspace(-3, 3, 41), np.linspace(-3.5, 3.5, 31)]
i, j = np.meshgrid(*xdata, indexing='ij')
ydata = (3 * (1 - j)**2. * np.exp(-(j**2) - (i + 1)**2)
- 10 * (j / 5 - j**3 - i**5) * np.exp(-j**2 - i**2)
- 1 / 3 * np.exp(-(j + 1)**2 - i**2))
ydata = ydata + (np.random.randn(*ydata.shape) * 0.75)
ydata_s = csaps(xdata, ydata, xdata, smooth=0.988)
fig = plt.figure(figsize=(7, 4.5))
ax = fig.add_subplot(111, projection='3d')
ax.set_facecolor('none')
c = [s['color'] for s in plt.rcParams['axes.prop_cycle']]
ax.plot_wireframe(j, i, ydata, linewidths=0.5, color=c[0], alpha=0.5)
ax.scatter(j, i, ydata, s=10, c=c[0], alpha=0.5)
ax.plot_surface(j, i, ydata_s, color=c[1], linewidth=0, alpha=1.0)
ax.view_init(elev=9., azim=290)
plt.show()
```
## Documentation
More examples of usage and the full documentation can be found at https://csaps.readthedocs.io.
## Development
We use Poetry to manage the project:
```
git clone https://github.com/espdev/csaps.git
cd csaps
poetry install -E docs
```
Also, install pre-commit hooks:
```
poetry run pre-commit install
```
## Testing and Linting
We use pytest for testing and ruff/mypy for linting.
Use `poethepoet` to run tests and linters:
```
poetry run poe test
poetry run poe check
```
## Algorithm and Implementation
**csaps** Python package is inspired by MATLAB [CSAPS](https://www.mathworks.com/help/curvefit/csaps.html) function that is an implementation of
Fortran routine SMOOTH from [PGS](http://pages.cs.wisc.edu/~deboor/pgs/) (originally written by Carl de Boor).
Also, the algothithm implementation in other languages:
* [csaps-rs](https://github.com/espdev/csaps-rs) Rust ndarray/sprs based implementation
* [csaps-cpp](https://github.com/espdev/csaps-cpp) C++11 Eigen based implementation (incomplete)
## References
C. de Boor, A Practical Guide to Splines, Springer-Verlag, 1978.
## License
[MIT](https://choosealicense.com/licenses/mit/)