pyrox.dev
Merge pull request #168773 from Leixb/megam-add-opt
+25
-20
pkgs/applications/science/misc/megam/default.nix
+25
-20
pkgs/applications/science/misc/megam/default.nix
···
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{ fetchurl, lib, stdenv, ocaml, makeWrapper, ncurses }:
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let version = "0.92"; in
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stdenv.mkDerivation {
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pname = "megam";
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inherit version;
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src = fetchurl {
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url = "http://hal3.name/megam/megam_src.tgz";
···
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'';
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strictDeps = true;
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nativeBuildInputs = [ makeWrapper ocaml ];
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buildInputs = [ ncurses ];
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makeFlags = [
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"CAML_INCLUDES=${ocaml}/lib/ocaml/caml"
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("WITHBIGARRAY=" + lib.optionalString (lib.versionOlder ocaml.version "4.08.0") "bigarray.cma")
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];
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# see https://bugzilla.redhat.com/show_bug.cgi?id=435559
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dontStrip = true;
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installPhase = ''
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mkdir -pv $out/bin
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cp -Rv megam $out/bin
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'';
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-
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meta = {
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description = "MEGA Model Optimization Package";
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-
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longDescription =
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'' The software here is an implementation of maximum likelihood
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and maximum a posterior optimization of the parameters of
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these models. The algorithms used are much more efficient
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than the iterative scaling techniques used in almost every
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other maxent package out there. '';
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-
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homepage = "http://www.umiacs.umd.edu/~hal/megam";
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-
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license = "non-commercial";
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-
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maintainers = [ ];
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platforms = lib.platforms.unix;
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};
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}
···
1
+
{ lib
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, stdenv
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, fetchurl
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, ocaml
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, ncurses
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}:
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stdenv.mkDerivation {
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pname = "megam";
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version = "0.92";
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src = fetchurl {
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url = "http://hal3.name/megam/megam_src.tgz";
···
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'';
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strictDeps = true;
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+
nativeBuildInputs = [ ocaml ];
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buildInputs = [ ncurses ];
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makeFlags = [
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"CAML_INCLUDES=${ocaml}/lib/ocaml/caml"
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("WITHBIGARRAY=" + lib.optionalString (lib.versionOlder ocaml.version "4.08.0") "bigarray.cma")
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+
"all"
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"opt"
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];
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# see https://bugzilla.redhat.com/show_bug.cgi?id=435559
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dontStrip = true;
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installPhase = ''
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runHook preInstall
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+
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install -Dm755 megam $out/bin/megam
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install -Dm755 megam.opt $out/bin/megam.opt
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+
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runHook postInstall
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'';
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+
meta = with lib; {
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description = "MEGA Model Optimization Package";
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+
longDescription = ''
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The software here is an implementation of maximum likelihood and maximum a
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+
posterior optimization of the parameters of these models. The algorithms
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+
used are much more efficient than the iterative scaling techniques used in
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+
almost every other maxent package out there.
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+
'';
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homepage = "http://www.umiacs.umd.edu/~hal/megam";
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license = "non-commercial";
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+
maintainers = with maintainers; [ leixb ];
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platforms = platforms.unix;
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};
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}