automatic import of python-dispy

3 files changed, 191 insertions, 0 deletions

diff --git a/.gitignore b/.gitignore
index e69de29..2ce234c 100644
--- a/.gitignore
+++ b/.gitignore
@@ -0,0 +1 @@
+/dispy-4.15.2.tar.gz
diff --git a/python-dispy.spec b/python-dispy.spec
new file mode 100644
index 0000000..6b81a77
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+++ b/python-dispy.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-dispy
+Version:	4.15.2
+Release:	1
+Summary:	Distributed and Parallel Computing with/for Python.
+License:	Apache 2.0
+URL:		https://dispy.org
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/59/88/b2bd984a81db9ba0d73a47645ded9da8e0bcfddc231644f9017668aeabcc/dispy-4.15.2.tar.gz
+BuildArch:	noarch
+
+
+%description
+* dispy is implemented with `pycos <https://pycos.org>`_,
+  an independent framework for asynchronous, concurrent, distributed, network
+  programming with tasks (without threads). pycos uses non-blocking sockets with
+  I/O notification mechanisms epoll, kqueue and poll, and Windows I/O Completion
+  Ports (IOCP) for high performance and scalability, so dispy works efficiently
+  with a single node or large cluster(s) of nodes. pycos itself has support for
+  distributed/parallel computing, including transferring computations, files
+  etc., and message passing (for communicating with client and other computation
+  tasks).  While dispy can be used to schedule jobs of a computation to get the
+  results, pycos can be used to create `distributed communicating processes
+  <https://pycos.org/dispycos.html>`_, for broad range of use cases.
+* Computations (Python functions or standalone programs) and their
+  dependencies (files, Python functions, classes, modules) are
+  distributed automatically.
+* Computation nodes can be anywhere on the network (local or
+  remote). For security, either simple hash based authentication or
+  SSL encryption can be used.
+* After each execution is finished, the results of execution, output,
+  errors and exception trace are made available for further
+  processing.
+* Nodes may become available dynamically: dispy will schedule jobs
+  whenever a node is available and computations can use that node.
+* If callback function is provided, dispy executes that function
+  when a job is finished; this can be used for processing job
+  results as they become available.
+* Client-side and server-side fault recovery are supported:
+  If user program (client) terminates unexpectedly (e.g., due to
+  uncaught exception), the nodes continue to execute scheduled
+  jobs. If client-side fault recover option is used when creating a
+  cluster, the results of the scheduled (but unfinished at the time of
+  crash) jobs for that cluster can be retrieved later.
+  If a computation is marked reentrant when a cluster is created and a
+  node (server) executing jobs for that computation fails, dispy
+  automatically resubmits those jobs to other available nodes.
+* dispy can be used in a single process to use all the nodes
+  exclusively (with ``JobCluster`` - simpler to use) or in multiple
+  processes simultaneously sharing the nodes (with
+  ``SharedJobCluster`` and *dispyscheduler* program).
+* Cluster can be `monitored and managed
+  <https:/dispy.org/httpd.html>`_ with web browser.
+
+%package -n python3-dispy
+Summary:	Distributed and Parallel Computing with/for Python.
+Provides:	python-dispy
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-dispy
+* dispy is implemented with `pycos <https://pycos.org>`_,
+  an independent framework for asynchronous, concurrent, distributed, network
+  programming with tasks (without threads). pycos uses non-blocking sockets with
+  I/O notification mechanisms epoll, kqueue and poll, and Windows I/O Completion
+  Ports (IOCP) for high performance and scalability, so dispy works efficiently
+  with a single node or large cluster(s) of nodes. pycos itself has support for
+  distributed/parallel computing, including transferring computations, files
+  etc., and message passing (for communicating with client and other computation
+  tasks).  While dispy can be used to schedule jobs of a computation to get the
+  results, pycos can be used to create `distributed communicating processes
+  <https://pycos.org/dispycos.html>`_, for broad range of use cases.
+* Computations (Python functions or standalone programs) and their
+  dependencies (files, Python functions, classes, modules) are
+  distributed automatically.
+* Computation nodes can be anywhere on the network (local or
+  remote). For security, either simple hash based authentication or
+  SSL encryption can be used.
+* After each execution is finished, the results of execution, output,
+  errors and exception trace are made available for further
+  processing.
+* Nodes may become available dynamically: dispy will schedule jobs
+  whenever a node is available and computations can use that node.
+* If callback function is provided, dispy executes that function
+  when a job is finished; this can be used for processing job
+  results as they become available.
+* Client-side and server-side fault recovery are supported:
+  If user program (client) terminates unexpectedly (e.g., due to
+  uncaught exception), the nodes continue to execute scheduled
+  jobs. If client-side fault recover option is used when creating a
+  cluster, the results of the scheduled (but unfinished at the time of
+  crash) jobs for that cluster can be retrieved later.
+  If a computation is marked reentrant when a cluster is created and a
+  node (server) executing jobs for that computation fails, dispy
+  automatically resubmits those jobs to other available nodes.
+* dispy can be used in a single process to use all the nodes
+  exclusively (with ``JobCluster`` - simpler to use) or in multiple
+  processes simultaneously sharing the nodes (with
+  ``SharedJobCluster`` and *dispyscheduler* program).
+* Cluster can be `monitored and managed
+  <https:/dispy.org/httpd.html>`_ with web browser.
+
+%package help
+Summary:	Development documents and examples for dispy
+Provides:	python3-dispy-doc
+%description help
+* dispy is implemented with `pycos <https://pycos.org>`_,
+  an independent framework for asynchronous, concurrent, distributed, network
+  programming with tasks (without threads). pycos uses non-blocking sockets with
+  I/O notification mechanisms epoll, kqueue and poll, and Windows I/O Completion
+  Ports (IOCP) for high performance and scalability, so dispy works efficiently
+  with a single node or large cluster(s) of nodes. pycos itself has support for
+  distributed/parallel computing, including transferring computations, files
+  etc., and message passing (for communicating with client and other computation
+  tasks).  While dispy can be used to schedule jobs of a computation to get the
+  results, pycos can be used to create `distributed communicating processes
+  <https://pycos.org/dispycos.html>`_, for broad range of use cases.
+* Computations (Python functions or standalone programs) and their
+  dependencies (files, Python functions, classes, modules) are
+  distributed automatically.
+* Computation nodes can be anywhere on the network (local or
+  remote). For security, either simple hash based authentication or
+  SSL encryption can be used.
+* After each execution is finished, the results of execution, output,
+  errors and exception trace are made available for further
+  processing.
+* Nodes may become available dynamically: dispy will schedule jobs
+  whenever a node is available and computations can use that node.
+* If callback function is provided, dispy executes that function
+  when a job is finished; this can be used for processing job
+  results as they become available.
+* Client-side and server-side fault recovery are supported:
+  If user program (client) terminates unexpectedly (e.g., due to
+  uncaught exception), the nodes continue to execute scheduled
+  jobs. If client-side fault recover option is used when creating a
+  cluster, the results of the scheduled (but unfinished at the time of
+  crash) jobs for that cluster can be retrieved later.
+  If a computation is marked reentrant when a cluster is created and a
+  node (server) executing jobs for that computation fails, dispy
+  automatically resubmits those jobs to other available nodes.
+* dispy can be used in a single process to use all the nodes
+  exclusively (with ``JobCluster`` - simpler to use) or in multiple
+  processes simultaneously sharing the nodes (with
+  ``SharedJobCluster`` and *dispyscheduler* program).
+* Cluster can be `monitored and managed
+  <https:/dispy.org/httpd.html>`_ with web browser.
+
+%prep
+%autosetup -n dispy-4.15.2
+
+%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-dispy -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Thu May 18 2023 Python_Bot <Python_Bot@openeuler.org> - 4.15.2-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..23fe243
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+e5b0af9f122a047f68d2f20c12408120  dispy-4.15.2.tar.gz