# $Id$

PortSystem          1.0
PortGroup           python26 1.0

name                py26-pymc
version             2.0
maintainers         mnick

description         Bayesian statistical models and fitting algorithms for python
long_description    PyMC is a python module that implements Bayesian statistical models \
                    and fitting algorithms, including Markov chain Monte Carlo. \
                    Its flexibility makes it applicable to a large suite of problems as well \
                    as easily extensible. Along with core sampling functionality, \
                    PyMC includes methods for summarizing output, plotting, goodness-of-fit and \
                    convergence diagnostics.

platforms           darwin

homepage            http://code.google.com/p/pymc/
master_sites        googlecode:pymc
distname            pymc-${version}
use_zip             yes

checksums           md5     fc24deb12a72903832c450a913264603 \
                    sha1    228843d16be02dc6f26f2bdfffc8846801f1d8ee \
                    rmd160  6e7775cad5aa185b190e89b0d6a4fed48adf335a

patchfiles-append   patch-pymc-gibbsit.f.diff
depends_lib-append  port:py26-numpy

variant gcc42 description {create Fortran wrappers using gcc42} conflicts gcc43 g95 {
    depends_lib-append    port:gcc42
    set fc                ${prefix}/bin/gfortran-mp-4.2
    build.env-append      F77=${fc} F90=${fc}
}

variant gcc43 description {create Fortran wrappers using gcc43} conflicts gcc42 g95 {
    depends_lib-append    port:gcc43
    set fc                ${prefix}/bin/gfortran-mp-4.3
    build.env-append      F77=${fc} F90=${fc}
}

variant g95 description {create Fortran wrappers using f95} conflicts gcc42 gcc43 {
    depends_lib-append    port:g95
    set fc                ${prefix}/bin/g95
    build.env-append      F77=${fc} F90=${fc}
}

if {![variant_isset gcc42] && ![variant_isset g95]} {
	   default_variants +gcc43
}