casa-mp-base

# -*- coding: utf-8; mode: tcl; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*- vim:fenc=utf-8:ft=tcl:et:sw=4:ts=4:sts=4

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PortSystem          1.0

PortGroup           python 1.0

PortGroup           select 1.0

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set realname        astroML

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name                py-${realname}

version             0.3

revision            1

categories-append   science

license             BSD

platforms           darwin

supported_archs     noarch

maintainers         {aronnax @lpsinger} openmaintainer

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python.versions     27 34 35 36

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description         tools for machine learning and data mining in astronomy

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long_description    AstroML is a Python module for machine learning and data \

                    mining built on numpy, scipy, scikit-learn, and \

                    matplotlib, and distributed under the 3-Clause BSD \

                    license. It contains a growing library of statistical and \

                    machine learning routines for analyzing astronomical data \

                    in python, loaders for several open astronomical datasets, \

                    and a large suite of examples of analyzing and visualizing \

                    astronomical datasets.

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homepage            https://www.astroml.org

master_sites        pypi:[string index ${realname} 0]/${realname}

distname            ${realname}-${version}

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checksums           rmd160  2a029523b0fcc768acb4e2a3efe1050e24a32cc8 \

                    sha256  ea6d0119593aed0e0dadc79c613ac0bddad95e6f12151237562a4fd67552b2b8 \

                    size    242925

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if {${name} ne ${subport}} {

    depends_lib-append \

                    port:py${python.version}-numpy \

                    port:py${python.version}-scipy \

                    port:py${python.version}-matplotlib \

                    port:py${python.version}-scikit-learn \

                    port:py${python.version}-astropy

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    variant addons description "Install optional package py${python.version}-${realname}_addons for faster C implementations of some algorithms" {

        depends_run-append \

                    port:py${python.version}-${realname}_addons

    }

    default_variants +addons

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    depends_test-append \

                    port:py${python.version}-nose

    test.run        yes

    test.cmd        nosetests-${python.branch}

    test.target

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    livecheck.type  none

}