Source: ghmm
Standards-Version: 4.7.4
Maintainer: Debian Med Packaging Team <debian-med-packaging@lists.alioth.debian.org>
Uploaders:
 Steffen Moeller <moeller@debian.org>,
 Étienne Mollier <emollier@debian.org>,
Section: science
Build-Depends:
 debhelper-compat (= 14),
 dh-sequence-python3,
 d-shlibs (>= 0.106~),
 dpkg-dev (>= 1.22.5),
 libgsl-dev,
 liblapacke-dev,
 libxml2-dev,
 pkgconf,
 python3-dev,
 python3-setuptools,
 swig,
 zlib1g-dev,
Vcs-Browser: https://salsa.debian.org/med-team/ghmm
Vcs-Git: https://salsa.debian.org/med-team/ghmm.git
Homepage: https://sourceforge.net/p/ghmm/wiki/Home/

Package: ghmm
Architecture: any
Depends:
 libghmm1t64,
 python3,
 ${misc:Depends},
 ${python3:Depends},
 ${shlibs:Depends},
Recommends:
 libopenblas0 | libblis4,
Description: General Hidden-Markov-Model library - tools
 The General Hidden Markov Model Library (GHMM) is a C library with
 additional Python3 bindings implementing a wide range of types of
 Hidden Markov Models and algorithms: discrete, continuous emissions,
 basic training, HMM clustering, HMM mixtures.
 .
 This package contains some tools using the library.

Package: libghmm-dev
Architecture: any
Section: libdevel
Depends:
 libghmm1t64 (<< ${source:Upstream-Version}+1),
 libghmm1t64 (>= ${source:Upstream-Version}),
 ${misc:Depends},
 ${shlibs:Depends},
Description: General Hidden-Markov-Model library - header files
 The General Hidden Markov Model Library (GHMM) is a C library with
 additional Python3 bindings implementing a wide range of types of
 Hidden Markov Models and algorithms: discrete, continuous emissions,
 basic training, HMM clustering, HMM mixtures.
 .
 Header files and static library to compile against the library.

Package: libghmm1t64
Architecture: any
Section: libs
Depends:
 python3,
 ${misc:Depends},
 ${shlibs:Depends},
Conflicts:
 libghmm1 (<< 0.9~rc3-9),
Provides:
 ${t64:Provides},
Replaces:
 libghmm1,
Description: General Hidden-Markov-Model library
 The General Hidden Markov Model Library (GHMM) is a C library with
 additional Python3 bindings implementing a wide range of types of
 Hidden Markov Models and algorithms: discrete, continuous emissions,
 basic training, HMM clustering, HMM mixtures.
 .
 The dynamic library.