automatic import of python-hyperjetopeneuler20.03

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+/hyperjet-1.4.3.tar.gz
diff --git a/python-hyperjet.spec b/python-hyperjet.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-hyperjet
+Version:	1.4.3
+Release:	1
+Summary:	Automatic differentiation with dual numbers
+License:	MIT License
+URL:		https://github.com/oberbichler/HyperJet
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/22/d7/e13be7083835633362eeac427215e416d7268c5178206488cdec1090d813/hyperjet-1.4.3.tar.gz
+
+Requires:	python3-numpy
+Requires:	python3-msvc-runtime
+Requires:	python3-pytest
+
+%description
+A header-only library for algorithmic differentiation with hyper-dual numbers. Written in C++17 with an extensive Python interface.
+[![PyPI](https://img.shields.io/pypi/v/hyperjet)](https://pypi.org/project/hyperjet) [![DOI](https://zenodo.org/badge/165487832.svg)](https://zenodo.org/badge/latestdoi/165487832) [![Build Status](https://github.com/oberbichler/HyperJet/workflows/Python%20package/badge.svg?branch=master)](https://github.com/oberbichler/HyperJet/actions) ![PyPI - License](https://img.shields.io/pypi/l/hyperjet) ![PyPI - Python Version](https://img.shields.io/pypi/pyversions/hyperjet) ![PyPI - Format](https://img.shields.io/pypi/format/hyperjet)
+## Installation
+```
+pip install hyperjet
+```
+## Quickstart
+Import the module:
+```python
+import hyperjet as hj
+```
+Create a set of variables e.g. `x=3` and `y=6`:
+```python
+x, y = hj.variables([3, 6])
+```
+`x` and `y` are hyper-dual numbers. This is indicated by the postfix `hj`:
+```python
+x
+>>> 3hj
+```
+Get the value as a simple `float`:
+```python
+x.f
+>>> 3
+```
+The hyper-dual number stores the derivatives as a numpy array.
+Get the first order derivatives (Gradient) of a hyper-dual number:
+```python
+x.g  # = [dx/dx, dx/dy]
+>>> array([1., 0.])
+```
+Get the second order derivatives (Hessian matrix):
+```python
+x.hm()  # = [[d^2 x/ dx**2 , d^2 x/(dx*dy)],
+        #    [d^2 x/(dx*dy), d^2 x/ dy**2 ]]
+>>> array([[0., 0.],
+           [0., 0.]])
+```
+For a simple variable these derivatives are trivial.
+Now do some computations:
+```python
+f = (x * y) / (x - y)
+f
+>>> -6hj
+```
+The result is again a hyper-dual number.
+Get the first order derivatives of `f` with respect to `x` and `y`:
+```python
+f.g  # = [df/dx, df/dy]
+>>> array([-4.,  1.])
+```
+Get the second order derivatives of `f`:
+```python
+f.hm()  # = [[d^2 f/ dx**2 , d^2 f/(dx*dy)],
+        #    [d^2 f/(dx*dy), d^2 f/ dy**2 ]]
+>>> array([[-2.66666667,  1.33333333],
+           [ 1.33333333, -0.66666667]])
+```
+You can use numpy to perform vector and matrix operations.
+Compute the nomalized cross product of two vectors `u = [1, 2, 2]` and `v = [4, 1, -1]` with hyper-dual numbers:
+```python
+import numpy as np
+variables = hj.DDScalar.variables([1, 2,  2,
+                                   4, 1, -1])
+u = np.array(variables[:3])  # = [1hj, 2hj,  2hj]
+v = np.array(variables[3:])  # = [4hj, 1hj, -1hj]
+normal = np.cross(u, v)
+normal /= np.linalg.norm(normal)
+normal
+>>> array([-0.331042hj, 0.744845hj, -0.579324hj], dtype=object)
+```
+The result is a three-dimensional numpy array containing hyper-dual numbers.
+Get the value and derivatives of the x-component:
+```python
+normal[0].f
+>>> -0.3310423554409472
+normal[0].g
+>>> array([ 0.00453483, -0.01020336,  0.00793595,  0.07255723, -0.16325376, 0.12697515])
+normal[0].hm()
+>>> array([[ 0.00434846, -0.01091775,  0.00647611, -0.0029818 , -0.01143025, -0.02335746],
+           [-0.01091775,  0.02711578, -0.01655522,  0.00444165,  0.03081974,  0.04858632],
+           [ 0.00647611, -0.01655522,  0.0093492 , -0.00295074, -0.02510461, -0.03690759],
+           [-0.0029818 ,  0.00444165, -0.00295074, -0.02956956,  0.03025289, -0.01546811],
+           [-0.01143025,  0.03081974, -0.02510461,  0.03025289,  0.01355789, -0.02868433],
+           [-0.02335746,  0.04858632, -0.03690759, -0.01546811, -0.02868433,  0.03641839]])
+```
+## Reference
+If you use HyperJet, please refer to the official GitHub repository:
+```bibtex
+@misc{HyperJet,
+  author = "Thomas Oberbichler",
+  title = "HyperJet",
+  howpublished = "\url{http://github.com/oberbichler/HyperJet}",
+}
+```
+
+%package -n python3-hyperjet
+Summary:	Automatic differentiation with dual numbers
+Provides:	python-hyperjet
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+BuildRequires:	python3-cffi
+BuildRequires:	gcc
+BuildRequires:	gdb
+%description -n python3-hyperjet
+A header-only library for algorithmic differentiation with hyper-dual numbers. Written in C++17 with an extensive Python interface.
+[![PyPI](https://img.shields.io/pypi/v/hyperjet)](https://pypi.org/project/hyperjet) [![DOI](https://zenodo.org/badge/165487832.svg)](https://zenodo.org/badge/latestdoi/165487832) [![Build Status](https://github.com/oberbichler/HyperJet/workflows/Python%20package/badge.svg?branch=master)](https://github.com/oberbichler/HyperJet/actions) ![PyPI - License](https://img.shields.io/pypi/l/hyperjet) ![PyPI - Python Version](https://img.shields.io/pypi/pyversions/hyperjet) ![PyPI - Format](https://img.shields.io/pypi/format/hyperjet)
+## Installation
+```
+pip install hyperjet
+```
+## Quickstart
+Import the module:
+```python
+import hyperjet as hj
+```
+Create a set of variables e.g. `x=3` and `y=6`:
+```python
+x, y = hj.variables([3, 6])
+```
+`x` and `y` are hyper-dual numbers. This is indicated by the postfix `hj`:
+```python
+x
+>>> 3hj
+```
+Get the value as a simple `float`:
+```python
+x.f
+>>> 3
+```
+The hyper-dual number stores the derivatives as a numpy array.
+Get the first order derivatives (Gradient) of a hyper-dual number:
+```python
+x.g  # = [dx/dx, dx/dy]
+>>> array([1., 0.])
+```
+Get the second order derivatives (Hessian matrix):
+```python
+x.hm()  # = [[d^2 x/ dx**2 , d^2 x/(dx*dy)],
+        #    [d^2 x/(dx*dy), d^2 x/ dy**2 ]]
+>>> array([[0., 0.],
+           [0., 0.]])
+```
+For a simple variable these derivatives are trivial.
+Now do some computations:
+```python
+f = (x * y) / (x - y)
+f
+>>> -6hj
+```
+The result is again a hyper-dual number.
+Get the first order derivatives of `f` with respect to `x` and `y`:
+```python
+f.g  # = [df/dx, df/dy]
+>>> array([-4.,  1.])
+```
+Get the second order derivatives of `f`:
+```python
+f.hm()  # = [[d^2 f/ dx**2 , d^2 f/(dx*dy)],
+        #    [d^2 f/(dx*dy), d^2 f/ dy**2 ]]
+>>> array([[-2.66666667,  1.33333333],
+           [ 1.33333333, -0.66666667]])
+```
+You can use numpy to perform vector and matrix operations.
+Compute the nomalized cross product of two vectors `u = [1, 2, 2]` and `v = [4, 1, -1]` with hyper-dual numbers:
+```python
+import numpy as np
+variables = hj.DDScalar.variables([1, 2,  2,
+                                   4, 1, -1])
+u = np.array(variables[:3])  # = [1hj, 2hj,  2hj]
+v = np.array(variables[3:])  # = [4hj, 1hj, -1hj]
+normal = np.cross(u, v)
+normal /= np.linalg.norm(normal)
+normal
+>>> array([-0.331042hj, 0.744845hj, -0.579324hj], dtype=object)
+```
+The result is a three-dimensional numpy array containing hyper-dual numbers.
+Get the value and derivatives of the x-component:
+```python
+normal[0].f
+>>> -0.3310423554409472
+normal[0].g
+>>> array([ 0.00453483, -0.01020336,  0.00793595,  0.07255723, -0.16325376, 0.12697515])
+normal[0].hm()
+>>> array([[ 0.00434846, -0.01091775,  0.00647611, -0.0029818 , -0.01143025, -0.02335746],
+           [-0.01091775,  0.02711578, -0.01655522,  0.00444165,  0.03081974,  0.04858632],
+           [ 0.00647611, -0.01655522,  0.0093492 , -0.00295074, -0.02510461, -0.03690759],
+           [-0.0029818 ,  0.00444165, -0.00295074, -0.02956956,  0.03025289, -0.01546811],
+           [-0.01143025,  0.03081974, -0.02510461,  0.03025289,  0.01355789, -0.02868433],
+           [-0.02335746,  0.04858632, -0.03690759, -0.01546811, -0.02868433,  0.03641839]])
+```
+## Reference
+If you use HyperJet, please refer to the official GitHub repository:
+```bibtex
+@misc{HyperJet,
+  author = "Thomas Oberbichler",
+  title = "HyperJet",
+  howpublished = "\url{http://github.com/oberbichler/HyperJet}",
+}
+```
+
+%package help
+Summary:	Development documents and examples for hyperjet
+Provides:	python3-hyperjet-doc
+%description help
+A header-only library for algorithmic differentiation with hyper-dual numbers. Written in C++17 with an extensive Python interface.
+[![PyPI](https://img.shields.io/pypi/v/hyperjet)](https://pypi.org/project/hyperjet) [![DOI](https://zenodo.org/badge/165487832.svg)](https://zenodo.org/badge/latestdoi/165487832) [![Build Status](https://github.com/oberbichler/HyperJet/workflows/Python%20package/badge.svg?branch=master)](https://github.com/oberbichler/HyperJet/actions) ![PyPI - License](https://img.shields.io/pypi/l/hyperjet) ![PyPI - Python Version](https://img.shields.io/pypi/pyversions/hyperjet) ![PyPI - Format](https://img.shields.io/pypi/format/hyperjet)
+## Installation
+```
+pip install hyperjet
+```
+## Quickstart
+Import the module:
+```python
+import hyperjet as hj
+```
+Create a set of variables e.g. `x=3` and `y=6`:
+```python
+x, y = hj.variables([3, 6])
+```
+`x` and `y` are hyper-dual numbers. This is indicated by the postfix `hj`:
+```python
+x
+>>> 3hj
+```
+Get the value as a simple `float`:
+```python
+x.f
+>>> 3
+```
+The hyper-dual number stores the derivatives as a numpy array.
+Get the first order derivatives (Gradient) of a hyper-dual number:
+```python
+x.g  # = [dx/dx, dx/dy]
+>>> array([1., 0.])
+```
+Get the second order derivatives (Hessian matrix):
+```python
+x.hm()  # = [[d^2 x/ dx**2 , d^2 x/(dx*dy)],
+        #    [d^2 x/(dx*dy), d^2 x/ dy**2 ]]
+>>> array([[0., 0.],
+           [0., 0.]])
+```
+For a simple variable these derivatives are trivial.
+Now do some computations:
+```python
+f = (x * y) / (x - y)
+f
+>>> -6hj
+```
+The result is again a hyper-dual number.
+Get the first order derivatives of `f` with respect to `x` and `y`:
+```python
+f.g  # = [df/dx, df/dy]
+>>> array([-4.,  1.])
+```
+Get the second order derivatives of `f`:
+```python
+f.hm()  # = [[d^2 f/ dx**2 , d^2 f/(dx*dy)],
+        #    [d^2 f/(dx*dy), d^2 f/ dy**2 ]]
+>>> array([[-2.66666667,  1.33333333],
+           [ 1.33333333, -0.66666667]])
+```
+You can use numpy to perform vector and matrix operations.
+Compute the nomalized cross product of two vectors `u = [1, 2, 2]` and `v = [4, 1, -1]` with hyper-dual numbers:
+```python
+import numpy as np
+variables = hj.DDScalar.variables([1, 2,  2,
+                                   4, 1, -1])
+u = np.array(variables[:3])  # = [1hj, 2hj,  2hj]
+v = np.array(variables[3:])  # = [4hj, 1hj, -1hj]
+normal = np.cross(u, v)
+normal /= np.linalg.norm(normal)
+normal
+>>> array([-0.331042hj, 0.744845hj, -0.579324hj], dtype=object)
+```
+The result is a three-dimensional numpy array containing hyper-dual numbers.
+Get the value and derivatives of the x-component:
+```python
+normal[0].f
+>>> -0.3310423554409472
+normal[0].g
+>>> array([ 0.00453483, -0.01020336,  0.00793595,  0.07255723, -0.16325376, 0.12697515])
+normal[0].hm()
+>>> array([[ 0.00434846, -0.01091775,  0.00647611, -0.0029818 , -0.01143025, -0.02335746],
+           [-0.01091775,  0.02711578, -0.01655522,  0.00444165,  0.03081974,  0.04858632],
+           [ 0.00647611, -0.01655522,  0.0093492 , -0.00295074, -0.02510461, -0.03690759],
+           [-0.0029818 ,  0.00444165, -0.00295074, -0.02956956,  0.03025289, -0.01546811],
+           [-0.01143025,  0.03081974, -0.02510461,  0.03025289,  0.01355789, -0.02868433],
+           [-0.02335746,  0.04858632, -0.03690759, -0.01546811, -0.02868433,  0.03641839]])
+```
+## Reference
+If you use HyperJet, please refer to the official GitHub repository:
+```bibtex
+@misc{HyperJet,
+  author = "Thomas Oberbichler",
+  title = "HyperJet",
+  howpublished = "\url{http://github.com/oberbichler/HyperJet}",
+}
+```
+
+%prep
+%autosetup -n hyperjet-1.4.3
+
+%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-hyperjet -f filelist.lst
+%dir %{python3_sitearch}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 1.4.3-1
+- Package Spec generated
diff --git a/sources b/sources
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+b599bac17e5eb6592824a30cb0e310e3  hyperjet-1.4.3.tar.gz