path: root/python-njet.spec

%global _empty_manifest_terminate_build 0
Name:		python-njet
Version:	0.5.2
Release:	1
Summary:	Lightweight automatic differentiation package for higher-order differentiation.
License:	GPL-3.0-or-later
URL:		https://pypi.org/project/njet/
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/82/66/142e17db802d4d66d4ec67461d96a41c4bfdfc81721505d51acd32ea2f73/njet-0.5.2.tar.gz
BuildArch:	noarch

Requires:	python3-numpy
Requires:	python3-sympy
Requires:	python3-more-itertools
Requires:	python3-tqdm
Requires:	python3-ipykernel

%description
[![Documentation Status](https://readthedocs.org/projects/njet/badge/?version=latest)](https://njet.readthedocs.io/en/latest/?badge=latest)

# njet: Lightweight automatic differentiation

A lightweight AD package, using forward-mode automatic differentiation, in order to determine the
higher-order derivatives of a given function in several variables.

## Features

- Higher-order (forward-mode) automatic differentiation in several variables.
- Support for NumPy, SymPy and mpmath.
- Differentiation of expressions containing nested higher-order derivatives.
- Complex differentiation (Wirtinger calculus) possible.
- Faa di Bruno's formula for vector-valued functions implemented.
- Lightweight and easy to use.

## Installation

Install this module with pip

```sh
pip install njet
```

## Quickstart

An example function we want to differentiate
```python
from njet.functions import exp
f = lambda x, y, z: exp(-0.23*x**2 - 0.33*x*y - 0.11*z**2)
```

Generate a class to handle the derivatives of the given function (in this example up to order 3)
```python
from njet import derive
df = derive(f, order=3)
```

Evaluate the derivatives at a specific point
```python
df(0.4, 2.1, 1.73)

{(0, 0, 0): 0.5255977986928584,
 (0, 0, 1): -0.2000425221825019,
 (1, 0, 0): -0.46094926945363685,
 (0, 1, 0): -0.06937890942745731,
 (0, 0, 2): -0.03949533176976862,
 (0, 2, 0): 0.009158016044424365,
 (1, 0, 1): 0.1754372919540542,
 (0, 1, 1): 0.026405612928090252,
 (2, 0, 0): 0.1624775219121247,
 (1, 1, 0): -0.11260197000076322,
 (2, 1, 0): 0.2827794849469999,
 (1, 1, 1): 0.04285630978229049,
 (0, 1, 2): 0.005213383793609458,
 (0, 2, 1): -0.0034855409065079135,
 (0, 3, 0): -0.0012088581178640162,
 (3, 0, 0): 0.2815805411804125,
 (2, 0, 1): -0.061838944839754675,
 (0, 0, 3): 0.10305063303187477,
 (1, 2, 0): 0.03775850015116166,
 (1, 0, 2): 0.034637405962087094}
```
The indices here correspond to the powers of the variables x, y, z
in the multivariate Taylor expansion. They can be translated to
the tensor indices of the corresponding multilinear map using a
built-in routine. Example:

Obtain the gradient and the Hessian
```python
df.grad()

{(2,): -0.2000425221825019,
 (0,): -0.46094926945363685,
 (1,): -0.06937890942745731}
```

```python
df.hess()

{(2, 2): -0.03949533176976862,
 (1, 1): 0.009158016044424365,
 (0, 2): 0.1754372919540542,
 (1, 2): 0.026405612928090252,
 (0, 0): 0.1624775219121247,
 (0, 1): -0.11260197000076322}
```

## Further reading

https://njet.readthedocs.io/en/latest/index.html

## License

njet: Automatic Differentiation Library

Copyright (C) 2021, 2022, 2023 by Malte Titze

njet is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.

njet is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.

You should have received a copy of the GNU General Public License
along with njet. If not, see <https://www.gnu.org/licenses/>.


%package -n python3-njet
Summary:	Lightweight automatic differentiation package for higher-order differentiation.
Provides:	python-njet
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-njet
[![Documentation Status](https://readthedocs.org/projects/njet/badge/?version=latest)](https://njet.readthedocs.io/en/latest/?badge=latest)

# njet: Lightweight automatic differentiation

A lightweight AD package, using forward-mode automatic differentiation, in order to determine the
higher-order derivatives of a given function in several variables.

## Features

- Higher-order (forward-mode) automatic differentiation in several variables.
- Support for NumPy, SymPy and mpmath.
- Differentiation of expressions containing nested higher-order derivatives.
- Complex differentiation (Wirtinger calculus) possible.
- Faa di Bruno's formula for vector-valued functions implemented.
- Lightweight and easy to use.

## Installation

Install this module with pip

```sh
pip install njet
```

## Quickstart

An example function we want to differentiate
```python
from njet.functions import exp
f = lambda x, y, z: exp(-0.23*x**2 - 0.33*x*y - 0.11*z**2)
```

Generate a class to handle the derivatives of the given function (in this example up to order 3)
```python
from njet import derive
df = derive(f, order=3)
```

Evaluate the derivatives at a specific point
```python
df(0.4, 2.1, 1.73)

{(0, 0, 0): 0.5255977986928584,
 (0, 0, 1): -0.2000425221825019,
 (1, 0, 0): -0.46094926945363685,
 (0, 1, 0): -0.06937890942745731,
 (0, 0, 2): -0.03949533176976862,
 (0, 2, 0): 0.009158016044424365,
 (1, 0, 1): 0.1754372919540542,
 (0, 1, 1): 0.026405612928090252,
 (2, 0, 0): 0.1624775219121247,
 (1, 1, 0): -0.11260197000076322,
 (2, 1, 0): 0.2827794849469999,
 (1, 1, 1): 0.04285630978229049,
 (0, 1, 2): 0.005213383793609458,
 (0, 2, 1): -0.0034855409065079135,
 (0, 3, 0): -0.0012088581178640162,
 (3, 0, 0): 0.2815805411804125,
 (2, 0, 1): -0.061838944839754675,
 (0, 0, 3): 0.10305063303187477,
 (1, 2, 0): 0.03775850015116166,
 (1, 0, 2): 0.034637405962087094}
```
The indices here correspond to the powers of the variables x, y, z
in the multivariate Taylor expansion. They can be translated to
the tensor indices of the corresponding multilinear map using a
built-in routine. Example:

Obtain the gradient and the Hessian
```python
df.grad()

{(2,): -0.2000425221825019,
 (0,): -0.46094926945363685,
 (1,): -0.06937890942745731}
```

```python
df.hess()

{(2, 2): -0.03949533176976862,
 (1, 1): 0.009158016044424365,
 (0, 2): 0.1754372919540542,
 (1, 2): 0.026405612928090252,
 (0, 0): 0.1624775219121247,
 (0, 1): -0.11260197000076322}
```

## Further reading

https://njet.readthedocs.io/en/latest/index.html

## License

njet: Automatic Differentiation Library

Copyright (C) 2021, 2022, 2023 by Malte Titze

njet is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.

njet is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.

You should have received a copy of the GNU General Public License
along with njet. If not, see <https://www.gnu.org/licenses/>.


%package help
Summary:	Development documents and examples for njet
Provides:	python3-njet-doc
%description help
[![Documentation Status](https://readthedocs.org/projects/njet/badge/?version=latest)](https://njet.readthedocs.io/en/latest/?badge=latest)

# njet: Lightweight automatic differentiation

A lightweight AD package, using forward-mode automatic differentiation, in order to determine the
higher-order derivatives of a given function in several variables.

## Features

- Higher-order (forward-mode) automatic differentiation in several variables.
- Support for NumPy, SymPy and mpmath.
- Differentiation of expressions containing nested higher-order derivatives.
- Complex differentiation (Wirtinger calculus) possible.
- Faa di Bruno's formula for vector-valued functions implemented.
- Lightweight and easy to use.

## Installation

Install this module with pip

```sh
pip install njet
```

## Quickstart

An example function we want to differentiate
```python
from njet.functions import exp
f = lambda x, y, z: exp(-0.23*x**2 - 0.33*x*y - 0.11*z**2)
```

Generate a class to handle the derivatives of the given function (in this example up to order 3)
```python
from njet import derive
df = derive(f, order=3)
```

Evaluate the derivatives at a specific point
```python
df(0.4, 2.1, 1.73)

{(0, 0, 0): 0.5255977986928584,
 (0, 0, 1): -0.2000425221825019,
 (1, 0, 0): -0.46094926945363685,
 (0, 1, 0): -0.06937890942745731,
 (0, 0, 2): -0.03949533176976862,
 (0, 2, 0): 0.009158016044424365,
 (1, 0, 1): 0.1754372919540542,
 (0, 1, 1): 0.026405612928090252,
 (2, 0, 0): 0.1624775219121247,
 (1, 1, 0): -0.11260197000076322,
 (2, 1, 0): 0.2827794849469999,
 (1, 1, 1): 0.04285630978229049,
 (0, 1, 2): 0.005213383793609458,
 (0, 2, 1): -0.0034855409065079135,
 (0, 3, 0): -0.0012088581178640162,
 (3, 0, 0): 0.2815805411804125,
 (2, 0, 1): -0.061838944839754675,
 (0, 0, 3): 0.10305063303187477,
 (1, 2, 0): 0.03775850015116166,
 (1, 0, 2): 0.034637405962087094}
```
The indices here correspond to the powers of the variables x, y, z
in the multivariate Taylor expansion. They can be translated to
the tensor indices of the corresponding multilinear map using a
built-in routine. Example:

Obtain the gradient and the Hessian
```python
df.grad()

{(2,): -0.2000425221825019,
 (0,): -0.46094926945363685,
 (1,): -0.06937890942745731}
```

```python
df.hess()

{(2, 2): -0.03949533176976862,
 (1, 1): 0.009158016044424365,
 (0, 2): 0.1754372919540542,
 (1, 2): 0.026405612928090252,
 (0, 0): 0.1624775219121247,
 (0, 1): -0.11260197000076322}
```

## Further reading

https://njet.readthedocs.io/en/latest/index.html

## License

njet: Automatic Differentiation Library

Copyright (C) 2021, 2022, 2023 by Malte Titze

njet is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.

njet is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.

You should have received a copy of the GNU General Public License
along with njet. If not, see <https://www.gnu.org/licenses/>.


%prep
%autosetup -n njet-0.5.2

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

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

%changelog
* Mon May 29 2023 Python_Bot <Python_Bot@openeuler.org> - 0.5.2-1
- Package Spec generated