path: root/python-csaps.spec

%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
<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




%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
<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




%package help
Summary:	Development documents and examples for csaps
Provides:	python3-csaps-doc
%description help
<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




%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 <Python_Bot@openeuler.org> - 1.1.0-1
- Package Spec generated