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csaps-1.1.0
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مشاهده بیشتر
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مشاهده بیشترتوضیحات
Cubic spline approximation (smoothing)
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | csaps-1.1.0 |
| نام | csaps |
| نسخه کتابخانه | 1.1.0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Eugene Prilepin |
| ایمیل نویسنده | esp.home@gmail.com |
| آدرس صفحه اصلی | https://github.com/espdev/csaps |
| آدرس اینترنتی | https://pypi.org/project/csaps/ |
| مجوز | MIT |
<p align="center">
<a href="https://github.com/espdev/csaps"><img src="https://user-images.githubusercontent.com/1299189/76571441-8d97e400-64c8-11ea-8c05-58850f8311a1.png" alt="csaps" width="400" /></a><br>
</p>
<p align="center">
<a href="https://pypi.python.org/pypi/csaps"><img src="https://img.shields.io/pypi/v/csaps.svg" alt="PyPI version" /></a>
<a href="https://pypi.python.org/pypi/csaps"><img src="https://img.shields.io/pypi/pyversions/csaps.svg" alt="Supported Python versions" /></a>
<a href="https://github.com/espdev/csaps"><img src="https://github.com/espdev/csaps/workflows/main/badge.svg" alt="GitHub Actions (Tests)" /></a>
<a href="https://csaps.readthedocs.io/en/latest/?badge=latest"><img src="https://readthedocs.org/projects/csaps/badge/?version=latest" alt="Documentation Status" /></a>
<a href="https://coveralls.io/github/espdev/csaps?branch=master"><img src="https://coveralls.io/repos/github/espdev/csaps/badge.svg?branch=master" alt="Coverage Status" /></a>
<a href="https://choosealicense.com/licenses/mit/"><img src="https://img.shields.io/pypi/l/csaps.svg" alt="License" /></a>
</p>
**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
```
The module depends only on NumPy and SciPy. Python 3.6 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()
```
<p align="center">
<img src="https://user-images.githubusercontent.com/1299189/72231304-cd774380-35cb-11ea-821d-d5662cc1eedf.png" alt="univariate" />
<p/>
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()
```
<p align="center">
<img src="https://user-images.githubusercontent.com/1299189/72231252-7a9d8c00-35cb-11ea-8890-487b8a7dbd1d.png" alt="surface" />
<p/>
## Documentation
More examples of usage and the full documentation can be found at https://csaps.readthedocs.io.
## Testing
We use pytest for testing.
```
cd /path/to/csaps/project/directory
pip install -e .[tests]
pytest
```
## 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/)
# Changelog
## v1.1.0
* Introduced optional `normalizedsmooth` argument to reduce dependence on xdata and weights [#47](https://github.com/espdev/csaps/pull/47)
* Update numpy and scipy dependency ranges
## v1.0.4 (04.05.2021)
* Bump numpy dependency version
## v1.0.3 (01.01.2021)
* Bump scipy dependency version
* Bump sphinx dependency version and use m2r2 sphinx extension instead of m2r
* Add Python 3.9 to classifiers list and to Travis CI
* Set development status classifier to "5 - Production/Stable"
* Happy New Year!
## v1.0.2 (19.07.2020)
* Fix using 'nu' argument when n-d grid spline evaluating [#32](https://github.com/espdev/csaps/pull/32)
## v1.0.1 (19.07.2020)
* Fix n-d grid spline evaluating performance regression [#31](https://github.com/espdev/csaps/pull/31)
## v1.0.0 (11.07.2020)
* Use `PPoly` and `NdPPoly` base classes from SciPy interpolate module for `SplinePPForm` and `NdGridSplinePPForm` respectively.
* Remove deprecated classes `UnivariateCubicSmoothingSpline` and `MultivariateCubicSmoothingSpline`
* Update the documentation
**Notes**
In this release the spline representation (the array of spline coefficients) has been changed
according to `PPoly`/`NdPPoly`.
See SciPy [PPoly](https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.PPoly.html)
and [NdPPoly](https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.NdPPoly.html) documentation for details.
## v0.11.0 (28.03.2020)
* Internal re-design `SplinePPForm` and `NdGridSplinePPForm` classes [#17](https://github.com/espdev/csaps/issues/17):
- Remove `shape` and `axis` properties and reshaping data in these classes
- `NdGridSplinePPForm` coefficients array for 1D grid now is 1-d instead of 2-d
* Refactoring the code and decrease memory consumption
* Add `overload` type-hints for `csaps` function signatures
## v0.10.1 (19.03.2020)
* Fix call of `numpy.pad` function for numpy <1.17 [#15](https://github.com/espdev/csaps/issues/15)
## v0.10.0 (18.02.2020)
* Significant performance improvements for make/evaluate splines and memory consumption optimization
* Change format for storing spline coefficients (reshape coeffs array) to improve performance
* Add shape property to `SplinePPForm`/`NdGridSplinePPForm` and axis property to `SplinePPForm`
* Fix issues with the smoothing factor in nd-grid case: inverted ordering and unnable to use 0.0 value
* Update documentation
## v0.9.0 (21.01.2020)
* Drop support of Python 3.5
* `weights`, `smooth` and `axis` arguments in `csaps` function are keyword-only now
* `UnivariateCubicSmoothingSpline` and `MultivariateCubicSmoothingSpline` classes are deprecated
and will be removed in 1.0.0 version. Use `CubicSmoothingSpline` instead.
## v0.8.0 (13.01.2020)
* Add `csaps` function that can be used as the main API
* Refactor the internal structure of the package
* Add the [documentation](https://csaps.readthedocs.io)
**Attention**
This is the last version that supports Python 3.5.
The next versions will support Python 3.6 or above.
## v0.7.0 (19.09.2019)
* Add Generic-based type-hints and mypy-compatibility
## v0.6.1 (13.09.2019)
* A slight refactoring and extra data copies removing
## v0.6.0 (12.09.2019)
* Add "axis" parameter for univariate/multivariate cases
## v0.5.0 (10.06.2019)
* Reorganize the project to package-based structure
* Add the interface class for all smoothing spline classes
## v0.4.2 (07.09.2019)
* FIX: "smooth" value is 0.0 was not used
## v0.4.1 (30.05.2019)
* First PyPI release
نیازمندی
| مقدار | نام |
|---|---|
| <2,>=1.12.0 | numpy |
| <2,>=1.0.0 | scipy |
| <5,>=3.0.0 | sphinx |
| >=3.1 | matplotlib |
| - | numpydoc |
| - | m2r2 |
| - | pytest |
| <7 | coverage |
| - | pytest-cov |
| - | coveralls |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.6, <4 | Python |
نحوه نصب
نصب پکیج whl csaps-1.1.0:
pip install csaps-1.1.0.whl
نصب پکیج tar.gz csaps-1.1.0:
pip install csaps-1.1.0.tar.gz