path: root/python-ruffus.spec

%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
* Fri Apr 21 2023 Python_Bot <Python_Bot@openeuler.org> - 2.8.4-1
- Package Spec generated