%global _empty_manifest_terminate_build 0
Name: python-csaps
Version: 1.1.0
Release: 1
Summary: Cubic spline approximation (smoothing)
License: MIT
URL: https://github.com/espdev/csaps
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/f3/de/ef40ae9b0485b36c0128d0d0065e065d7dc8fa3c2334a886548dc70e8ad7/csaps-1.1.0.tar.gz
BuildArch: noarch
Requires: python3-numpy
Requires: python3-scipy
Requires: python3-sphinx
Requires: python3-matplotlib
Requires: python3-numpydoc
Requires: python3-m2r2
Requires: python3-pytest
Requires: python3-coverage
Requires: python3-pytest-cov
Requires: python3-coveralls
%description
**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()
```
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.
## 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
%package -n python3-csaps
Summary: Cubic spline approximation (smoothing)
Provides: python-csaps
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-csaps
**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()
```
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.
## 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
%package help
Summary: Development documents and examples for csaps
Provides: python3-csaps-doc
%description help
**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()
```
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.
## 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
%prep
%autosetup -n csaps-1.1.0
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-csaps -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Sun Apr 23 2023 Python_Bot - 1.1.0-1
- Package Spec generated