automatic import of python-pycpdopeneuler20.03

3 files changed, 262 insertions, 0 deletions

diff --git a/.gitignore b/.gitignore
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+/pycpd-2.0.0.tar.gz
diff --git a/python-pycpd.spec b/python-pycpd.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-pycpd
+Version:	2.0.0
+Release:	1
+Summary:	Pure Numpy Implementation of the Coherent Point Drift Algorithm
+License:	MIT
+URL:		https://github.com/siavashk/pycpd
+Source0:	https://mirrors.aliyun.com/pypi/web/packages/c7/e3/e867299fbc745cbec071c13d45b74e255e86752da9ebd8962bf55fc54aa4/pycpd-2.0.0.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-numpy
+Requires:	python3-future
+
+%description
+#############
+Python-CPD
+#############
+.. image:: https://travis-ci.com/siavashk/pycpd.svg?branch=master
+    :target: https://travis-ci.com/siavashk/pycpd
+
+Pure Numpy Implementation of the Coherent Point Drift Algorithm.
+
+MIT License.
+
+*************
+Introduction
+*************
+
+This is a pure numpy implementation of the coherent point drift `CPD <https://arxiv.org/abs/0905.2635/>`_ algorithm by Myronenko and Song. It provides three registration methods for point clouds: 1) Scale and rigid registration; 2) Affine registration; and 3) Gaussian regularized non-rigid registration.
+
+The CPD algorithm is a registration method for aligning two point clouds. In this method, the moving point cloud is modelled as a Gaussian Mixture Model (GMM) and the fixed point cloud are treated as observations from the GMM. The optimal transformation parameters maximze the Maximum A Posteriori (MAP) estimation that the observed point cloud is drawn from the GMM.
+
+The registration methods work for 2D and 3D point clouds. For more information, please refer to my `blog <http://siavashk.github.io/2017/05/14/coherent-point-drift/>`_.
+
+*************
+Pip Install
+*************
+.. code-block:: bash
+
+  pip install pycpd
+
+************************
+Installation From Source
+************************
+
+Clone the repository to a location, referred to as the ``root`` folder. For example:
+
+.. code-block:: bash
+
+  git clone https://github.com/siavashk/pycpd.git $HOME/pycpd
+
+Install the package:
+
+.. code-block:: bash
+
+  pip install .
+
+For running sample registration examples under ``examples``, you will need ``matplotlib`` to visualize the registration. This can be downloaded by running:
+
+.. code-block:: bash
+
+ pip install matplotlib
+
+*****
+Usage
+*****
+
+Each registration method is contained within a single class inside the ``pycpd`` subfolder. To try out the registration, you can simply run:
+
+.. code-block:: bash
+
+ python examples/fish_{Transform}_{Dimension}.py
+
+where ``Transform`` is either ``rigid``, ``affine`` or ``deformable`` and ``Dimension`` is either ``2D`` or ``3D``. Note that examples are meant to be run from the ``root`` folder.
+
+
+
+
+%package -n python3-pycpd
+Summary:	Pure Numpy Implementation of the Coherent Point Drift Algorithm
+Provides:	python-pycpd
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-pycpd
+#############
+Python-CPD
+#############
+.. image:: https://travis-ci.com/siavashk/pycpd.svg?branch=master
+    :target: https://travis-ci.com/siavashk/pycpd
+
+Pure Numpy Implementation of the Coherent Point Drift Algorithm.
+
+MIT License.
+
+*************
+Introduction
+*************
+
+This is a pure numpy implementation of the coherent point drift `CPD <https://arxiv.org/abs/0905.2635/>`_ algorithm by Myronenko and Song. It provides three registration methods for point clouds: 1) Scale and rigid registration; 2) Affine registration; and 3) Gaussian regularized non-rigid registration.
+
+The CPD algorithm is a registration method for aligning two point clouds. In this method, the moving point cloud is modelled as a Gaussian Mixture Model (GMM) and the fixed point cloud are treated as observations from the GMM. The optimal transformation parameters maximze the Maximum A Posteriori (MAP) estimation that the observed point cloud is drawn from the GMM.
+
+The registration methods work for 2D and 3D point clouds. For more information, please refer to my `blog <http://siavashk.github.io/2017/05/14/coherent-point-drift/>`_.
+
+*************
+Pip Install
+*************
+.. code-block:: bash
+
+  pip install pycpd
+
+************************
+Installation From Source
+************************
+
+Clone the repository to a location, referred to as the ``root`` folder. For example:
+
+.. code-block:: bash
+
+  git clone https://github.com/siavashk/pycpd.git $HOME/pycpd
+
+Install the package:
+
+.. code-block:: bash
+
+  pip install .
+
+For running sample registration examples under ``examples``, you will need ``matplotlib`` to visualize the registration. This can be downloaded by running:
+
+.. code-block:: bash
+
+ pip install matplotlib
+
+*****
+Usage
+*****
+
+Each registration method is contained within a single class inside the ``pycpd`` subfolder. To try out the registration, you can simply run:
+
+.. code-block:: bash
+
+ python examples/fish_{Transform}_{Dimension}.py
+
+where ``Transform`` is either ``rigid``, ``affine`` or ``deformable`` and ``Dimension`` is either ``2D`` or ``3D``. Note that examples are meant to be run from the ``root`` folder.
+
+
+
+
+%package help
+Summary:	Development documents and examples for pycpd
+Provides:	python3-pycpd-doc
+%description help
+#############
+Python-CPD
+#############
+.. image:: https://travis-ci.com/siavashk/pycpd.svg?branch=master
+    :target: https://travis-ci.com/siavashk/pycpd
+
+Pure Numpy Implementation of the Coherent Point Drift Algorithm.
+
+MIT License.
+
+*************
+Introduction
+*************
+
+This is a pure numpy implementation of the coherent point drift `CPD <https://arxiv.org/abs/0905.2635/>`_ algorithm by Myronenko and Song. It provides three registration methods for point clouds: 1) Scale and rigid registration; 2) Affine registration; and 3) Gaussian regularized non-rigid registration.
+
+The CPD algorithm is a registration method for aligning two point clouds. In this method, the moving point cloud is modelled as a Gaussian Mixture Model (GMM) and the fixed point cloud are treated as observations from the GMM. The optimal transformation parameters maximze the Maximum A Posteriori (MAP) estimation that the observed point cloud is drawn from the GMM.
+
+The registration methods work for 2D and 3D point clouds. For more information, please refer to my `blog <http://siavashk.github.io/2017/05/14/coherent-point-drift/>`_.
+
+*************
+Pip Install
+*************
+.. code-block:: bash
+
+  pip install pycpd
+
+************************
+Installation From Source
+************************
+
+Clone the repository to a location, referred to as the ``root`` folder. For example:
+
+.. code-block:: bash
+
+  git clone https://github.com/siavashk/pycpd.git $HOME/pycpd
+
+Install the package:
+
+.. code-block:: bash
+
+  pip install .
+
+For running sample registration examples under ``examples``, you will need ``matplotlib`` to visualize the registration. This can be downloaded by running:
+
+.. code-block:: bash
+
+ pip install matplotlib
+
+*****
+Usage
+*****
+
+Each registration method is contained within a single class inside the ``pycpd`` subfolder. To try out the registration, you can simply run:
+
+.. code-block:: bash
+
+ python examples/fish_{Transform}_{Dimension}.py
+
+where ``Transform`` is either ``rigid``, ``affine`` or ``deformable`` and ``Dimension`` is either ``2D`` or ``3D``. Note that examples are meant to be run from the ``root`` folder.
+
+
+
+
+%prep
+%autosetup -n pycpd-2.0.0
+
+%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-pycpd -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Tue Jun 20 2023 Python_Bot <Python_Bot@openeuler.org> - 2.0.0-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..70fc4e3
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+6bb6a81ef6b164506069a0023e2d5068  pycpd-2.0.0.tar.gz