%global _empty_manifest_terminate_build 0
Name:		python-numpy-ext
Version:	0.9.8
Release:	1
Summary:	numpy extension
License:	MIT License
URL:		https://github.com/3jane/numpy_ext
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/69/8c/7fee8ed19be051a37a4646e8dac78eecf07da0118ddf610eb890b99d0744/numpy_ext-0.9.8.tar.gz
BuildArch:	noarch

Requires:	python3-numpy
Requires:	python3-joblib
Requires:	python3-flake8
Requires:	python3-pytest
Requires:	python3-pytest-cov
Requires:	python3-pandas
Requires:	python3-numpydoc
Requires:	python3-sphinx
Requires:	python3-Jinja2

%description
## Documentation
* [API Reference](http://3jane.github.io/numpy_ext/)
## Installation
Regular installation:
```bash
pip install numpy_ext
```
For development:
```bash
git clone https://github.com/3jane/numpy_ext.git
cd numpy_ext
pip install -e .[dev]  # note: make sure you are using pip>=20
```
## Examples
Here are few common examples of how the library is used. The rest is available in the [documentation](http://3jane.github.io/numpy_ext/).
1) Apply a function to a rolling window over the provided array
```python
import numpy as np
import numpy_ext as npext
a = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
window = 3
npext.rolling_apply(np.sum, window, a)
> array([nan, nan,  3.,  6.,  9., 12., 15., 18., 21., 24.])
```
2) Same as the above, but with a custom function, two input arrays and parallel computation using `joblib`:
```python
def func(array_first, array_second, param):
    return (np.min(array_first) + np.sum(array_second)) * param
a = np.array([0, 1, 2, 3])
b = np.array([3, 2, 1, 0])
npext.rolling_apply(func, 2, a, b, n_jobs=2, param=-1)
> array([nan, -5., -4., -3.])
```
3) Same as the first example, but using **rolling** function:
```python
a = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
window = 3
rolls = npext.rolling(a, window, as_array=True)
np.sum(rolls, axis=1)
> array([nan, nan,  3.,  6.,  9., 12., 15., 18., 21., 24.])
```
4) Apply a function with multiple output to a rolling window over the provided array, with no nans prepend
```python
res = npext.rolling_apply(
        lambda x: (max(x), min(x)),
        3,
        np.array([1, 2, 5, 1, 6, 4, 0]),
        prepend_nans=False,
    )
> array([[5, 1],
       [5, 1],
       [6, 1],
       [6, 1],
       [6, 0]])
```
## License
[![MIT Licence](https://badges.frapsoft.com/os/mit/mit.svg?v=103)](https://tldrlegal.com/license/mit-license)
The software is distributed under MIT license. 

%package -n python3-numpy-ext
Summary:	numpy extension
Provides:	python-numpy-ext
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-numpy-ext
## Documentation
* [API Reference](http://3jane.github.io/numpy_ext/)
## Installation
Regular installation:
```bash
pip install numpy_ext
```
For development:
```bash
git clone https://github.com/3jane/numpy_ext.git
cd numpy_ext
pip install -e .[dev]  # note: make sure you are using pip>=20
```
## Examples
Here are few common examples of how the library is used. The rest is available in the [documentation](http://3jane.github.io/numpy_ext/).
1) Apply a function to a rolling window over the provided array
```python
import numpy as np
import numpy_ext as npext
a = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
window = 3
npext.rolling_apply(np.sum, window, a)
> array([nan, nan,  3.,  6.,  9., 12., 15., 18., 21., 24.])
```
2) Same as the above, but with a custom function, two input arrays and parallel computation using `joblib`:
```python
def func(array_first, array_second, param):
    return (np.min(array_first) + np.sum(array_second)) * param
a = np.array([0, 1, 2, 3])
b = np.array([3, 2, 1, 0])
npext.rolling_apply(func, 2, a, b, n_jobs=2, param=-1)
> array([nan, -5., -4., -3.])
```
3) Same as the first example, but using **rolling** function:
```python
a = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
window = 3
rolls = npext.rolling(a, window, as_array=True)
np.sum(rolls, axis=1)
> array([nan, nan,  3.,  6.,  9., 12., 15., 18., 21., 24.])
```
4) Apply a function with multiple output to a rolling window over the provided array, with no nans prepend
```python
res = npext.rolling_apply(
        lambda x: (max(x), min(x)),
        3,
        np.array([1, 2, 5, 1, 6, 4, 0]),
        prepend_nans=False,
    )
> array([[5, 1],
       [5, 1],
       [6, 1],
       [6, 1],
       [6, 0]])
```
## License
[![MIT Licence](https://badges.frapsoft.com/os/mit/mit.svg?v=103)](https://tldrlegal.com/license/mit-license)
The software is distributed under MIT license. 

%package help
Summary:	Development documents and examples for numpy-ext
Provides:	python3-numpy-ext-doc
%description help
## Documentation
* [API Reference](http://3jane.github.io/numpy_ext/)
## Installation
Regular installation:
```bash
pip install numpy_ext
```
For development:
```bash
git clone https://github.com/3jane/numpy_ext.git
cd numpy_ext
pip install -e .[dev]  # note: make sure you are using pip>=20
```
## Examples
Here are few common examples of how the library is used. The rest is available in the [documentation](http://3jane.github.io/numpy_ext/).
1) Apply a function to a rolling window over the provided array
```python
import numpy as np
import numpy_ext as npext
a = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
window = 3
npext.rolling_apply(np.sum, window, a)
> array([nan, nan,  3.,  6.,  9., 12., 15., 18., 21., 24.])
```
2) Same as the above, but with a custom function, two input arrays and parallel computation using `joblib`:
```python
def func(array_first, array_second, param):
    return (np.min(array_first) + np.sum(array_second)) * param
a = np.array([0, 1, 2, 3])
b = np.array([3, 2, 1, 0])
npext.rolling_apply(func, 2, a, b, n_jobs=2, param=-1)
> array([nan, -5., -4., -3.])
```
3) Same as the first example, but using **rolling** function:
```python
a = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
window = 3
rolls = npext.rolling(a, window, as_array=True)
np.sum(rolls, axis=1)
> array([nan, nan,  3.,  6.,  9., 12., 15., 18., 21., 24.])
```
4) Apply a function with multiple output to a rolling window over the provided array, with no nans prepend
```python
res = npext.rolling_apply(
        lambda x: (max(x), min(x)),
        3,
        np.array([1, 2, 5, 1, 6, 4, 0]),
        prepend_nans=False,
    )
> array([[5, 1],
       [5, 1],
       [6, 1],
       [6, 1],
       [6, 0]])
```
## License
[![MIT Licence](https://badges.frapsoft.com/os/mit/mit.svg?v=103)](https://tldrlegal.com/license/mit-license)
The software is distributed under MIT license. 

%prep
%autosetup -n numpy-ext-0.9.8

%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-numpy-ext -f filelist.lst
%dir %{python3_sitelib}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Wed Apr 12 2023 Python_Bot <Python_Bot@openeuler.org> - 0.9.8-1
- Package Spec generated