automatic import of python-ruffus

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+%global _empty_manifest_terminate_build 0
+Name:		python-ruffus
+Version:	2.8.4
+Release:	1
+Summary:	Light-weight Python Computational Pipeline Management
+License:	MIT
+URL:		http://www.ruffus.org.uk
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/3b/d1/154a08615b33bb66c37fa998490a811870355331b696140f125983890efa/ruffus-2.8.4.tar.gz
+BuildArch:	noarch
+
+
+%description
+
+***************************************
+Overview
+***************************************
+
+
+    The Ruffus module is a lightweight way to add support
+    for running computational pipelines.
+
+    Computational pipelines are often conceptually quite simple, especially
+    if we breakdown the process into simple stages, or separate **tasks**.
+
+    Each stage or **task** in a computational pipeline is represented by a python function
+    Each python function can be called in parallel to run multiple **jobs**.
+
+    Ruffus was originally designed for use in bioinformatics to analyse multiple genome
+    data sets.
+
+***************************************
+Documentation
+***************************************
+
+    Ruffus documentation can be found `here <http://www.ruffus.org.uk>`__ ,
+    with `download notes <http://www.ruffus.org.uk/installation.html>`__ ,
+    a `tutorial <http://www.ruffus.org.uk/tutorials/new_tutorial/introduction.html>`__ and
+    an `in-depth manual <http://www.ruffus.org.uk/tutorials/new_tutorial/manual_contents.html>`__ .
+
+
+***************************************
+Background
+***************************************
+
+    The purpose of a pipeline is to determine automatically which parts of a multi-stage
+    process needs to be run and in what order in order to reach an objective ("targets")
+
+    Computational pipelines, especially for analysing large scientific datasets are
+    in widespread use.
+    However, even a conceptually simple series of steps can be difficult to set up and
+    maintain.
+
+***************************************
+Design
+***************************************
+    The ruffus module has the following design goals:
+
+        * Lightweight
+        * Scalable / Flexible / Powerful
+        * Standard Python
+        * Unintrusive
+        * As simple as possible
+
+***************************************
+Features
+***************************************
+
+    Automatic support for
+
+        * Managing dependencies
+        * Parallel jobs, including dispatching work to computational clusters
+        * Re-starting from arbitrary points, especially after errors (checkpointing)
+        * Display of the pipeline as a flowchart
+        * Managing complex pipeline topologies
+
+
+***************************************
+A Simple example
+***************************************
+
+        Use the **@follows(...)** python decorator before the function definitions::
+
+            from ruffus import *
+            import sys
+
+            def first_task():
+                print "First task"
+
+            @follows(first_task)
+            def second_task():
+                print "Second task"
+
+            @follows(second_task)
+            def final_task():
+                print "Final task"
+
+
+
+
+        the ``@follows`` decorator indicate that the ``first_task`` function precedes ``second_task`` in
+        the pipeline.
+
+        The canonical Ruffus decorator is ``@transform`` which **transforms** data flowing down a
+        computational pipeline from one stage to teh next.
+
+********
+Usage
+********
+
+    Each stage or **task** in a computational pipeline is represented by a python function
+    Each python function can be called in parallel to run multiple **jobs**.
+
+    1. Import module::
+
+            import ruffus
+
+
+    1. Annotate functions with python decorators
+
+    2. Print dependency graph if you necessary
+
+        - For a graphical flowchart in ``jpg``, ``svg``, ``dot``, ``png``, ``ps``, ``gif`` formats::
+
+            pipeline_printout_graph ("flowchart.svg")
+
+        This requires ``dot`` to be installed
+
+        - For a text printout of all jobs ::
+
+            pipeline_printout(sys.stdout)
+
+
+    3. Run the pipeline::
+
+        pipeline_run()
+
+
+
+
+%package -n python3-ruffus
+Summary:	Light-weight Python Computational Pipeline Management
+Provides:	python-ruffus
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-ruffus
+
+***************************************
+Overview
+***************************************
+
+
+    The Ruffus module is a lightweight way to add support
+    for running computational pipelines.
+
+    Computational pipelines are often conceptually quite simple, especially
+    if we breakdown the process into simple stages, or separate **tasks**.
+
+    Each stage or **task** in a computational pipeline is represented by a python function
+    Each python function can be called in parallel to run multiple **jobs**.
+
+    Ruffus was originally designed for use in bioinformatics to analyse multiple genome
+    data sets.
+
+***************************************
+Documentation
+***************************************
+
+    Ruffus documentation can be found `here <http://www.ruffus.org.uk>`__ ,
+    with `download notes <http://www.ruffus.org.uk/installation.html>`__ ,
+    a `tutorial <http://www.ruffus.org.uk/tutorials/new_tutorial/introduction.html>`__ and
+    an `in-depth manual <http://www.ruffus.org.uk/tutorials/new_tutorial/manual_contents.html>`__ .
+
+
+***************************************
+Background
+***************************************
+
+    The purpose of a pipeline is to determine automatically which parts of a multi-stage
+    process needs to be run and in what order in order to reach an objective ("targets")
+
+    Computational pipelines, especially for analysing large scientific datasets are
+    in widespread use.
+    However, even a conceptually simple series of steps can be difficult to set up and
+    maintain.
+
+***************************************
+Design
+***************************************
+    The ruffus module has the following design goals:
+
+        * Lightweight
+        * Scalable / Flexible / Powerful
+        * Standard Python
+        * Unintrusive
+        * As simple as possible
+
+***************************************
+Features
+***************************************
+
+    Automatic support for
+
+        * Managing dependencies
+        * Parallel jobs, including dispatching work to computational clusters
+        * Re-starting from arbitrary points, especially after errors (checkpointing)
+        * Display of the pipeline as a flowchart
+        * Managing complex pipeline topologies
+
+
+***************************************
+A Simple example
+***************************************
+
+        Use the **@follows(...)** python decorator before the function definitions::
+
+            from ruffus import *
+            import sys
+
+            def first_task():
+                print "First task"
+
+            @follows(first_task)
+            def second_task():
+                print "Second task"
+
+            @follows(second_task)
+            def final_task():
+                print "Final task"
+
+
+
+
+        the ``@follows`` decorator indicate that the ``first_task`` function precedes ``second_task`` in
+        the pipeline.
+
+        The canonical Ruffus decorator is ``@transform`` which **transforms** data flowing down a
+        computational pipeline from one stage to teh next.
+
+********
+Usage
+********
+
+    Each stage or **task** in a computational pipeline is represented by a python function
+    Each python function can be called in parallel to run multiple **jobs**.
+
+    1. Import module::
+
+            import ruffus
+
+
+    1. Annotate functions with python decorators
+
+    2. Print dependency graph if you necessary
+
+        - For a graphical flowchart in ``jpg``, ``svg``, ``dot``, ``png``, ``ps``, ``gif`` formats::
+
+            pipeline_printout_graph ("flowchart.svg")
+
+        This requires ``dot`` to be installed
+
+        - For a text printout of all jobs ::
+
+            pipeline_printout(sys.stdout)
+
+
+    3. Run the pipeline::
+
+        pipeline_run()
+
+
+
+
+%package help
+Summary:	Development documents and examples for ruffus
+Provides:	python3-ruffus-doc
+%description help
+
+***************************************
+Overview
+***************************************
+
+
+    The Ruffus module is a lightweight way to add support
+    for running computational pipelines.
+
+    Computational pipelines are often conceptually quite simple, especially
+    if we breakdown the process into simple stages, or separate **tasks**.
+
+    Each stage or **task** in a computational pipeline is represented by a python function
+    Each python function can be called in parallel to run multiple **jobs**.
+
+    Ruffus was originally designed for use in bioinformatics to analyse multiple genome
+    data sets.
+
+***************************************
+Documentation
+***************************************
+
+    Ruffus documentation can be found `here <http://www.ruffus.org.uk>`__ ,
+    with `download notes <http://www.ruffus.org.uk/installation.html>`__ ,
+    a `tutorial <http://www.ruffus.org.uk/tutorials/new_tutorial/introduction.html>`__ and
+    an `in-depth manual <http://www.ruffus.org.uk/tutorials/new_tutorial/manual_contents.html>`__ .
+
+
+***************************************
+Background
+***************************************
+
+    The purpose of a pipeline is to determine automatically which parts of a multi-stage
+    process needs to be run and in what order in order to reach an objective ("targets")
+
+    Computational pipelines, especially for analysing large scientific datasets are
+    in widespread use.
+    However, even a conceptually simple series of steps can be difficult to set up and
+    maintain.
+
+***************************************
+Design
+***************************************
+    The ruffus module has the following design goals:
+
+        * Lightweight
+        * Scalable / Flexible / Powerful
+        * Standard Python
+        * Unintrusive
+        * As simple as possible
+
+***************************************
+Features
+***************************************
+
+    Automatic support for
+
+        * Managing dependencies
+        * Parallel jobs, including dispatching work to computational clusters
+        * Re-starting from arbitrary points, especially after errors (checkpointing)
+        * Display of the pipeline as a flowchart
+        * Managing complex pipeline topologies
+
+
+***************************************
+A Simple example
+***************************************
+
+        Use the **@follows(...)** python decorator before the function definitions::
+
+            from ruffus import *
+            import sys
+
+            def first_task():
+                print "First task"
+
+            @follows(first_task)
+            def second_task():
+                print "Second task"
+
+            @follows(second_task)
+            def final_task():
+                print "Final task"
+
+
+
+
+        the ``@follows`` decorator indicate that the ``first_task`` function precedes ``second_task`` in
+        the pipeline.
+
+        The canonical Ruffus decorator is ``@transform`` which **transforms** data flowing down a
+        computational pipeline from one stage to teh next.
+
+********
+Usage
+********
+
+    Each stage or **task** in a computational pipeline is represented by a python function
+    Each python function can be called in parallel to run multiple **jobs**.
+
+    1. Import module::
+
+            import ruffus
+
+
+    1. Annotate functions with python decorators
+
+    2. Print dependency graph if you necessary
+
+        - For a graphical flowchart in ``jpg``, ``svg``, ``dot``, ``png``, ``ps``, ``gif`` formats::
+
+            pipeline_printout_graph ("flowchart.svg")
+
+        This requires ``dot`` to be installed
+
+        - For a text printout of all jobs ::
+
+            pipeline_printout(sys.stdout)
+
+
+    3. Run the pipeline::
+
+        pipeline_run()
+
+
+
+
+%prep
+%autosetup -n ruffus-2.8.4
+
+%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-ruffus -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Thu Mar 09 2023 Python_Bot <Python_Bot@openeuler.org> - 2.8.4-1
+- Package Spec generated