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{ lib, stdenv, fetchurl, python3, runtimeShell }:
let
# FIXME: remove conditional on future release
version = if stdenv.isx86_64 then "2.31.0" else "2.30.1";
# includes stanc binaries needed to build cmdstand
srcs = rec {
aarch64-linux = fetchurl {
url = "https://github.com/stan-dev/cmdstan/releases/download/v${version}/cmdstan-${version}-linux-arm64.tar.gz";
sha256 = "sha256-oj/7JHT4LZcRAHiA2KbM6pZbOe6C98Ff//cNsG9DIm8=";
};
x86_64-darwin = fetchurl {
url = "https://github.com/stan-dev/cmdstan/releases/download/v${version}/cmdstan-${version}.tar.gz";
sha256 = "sha256-BMqRRWIC/Z7It2qkESJd9L3ycyxvA6NHiWbAvzVMzIQ=";
};
x86_64-linux = x86_64-darwin;
};
src = srcs.${stdenv.hostPlatform.system} or (throw "Unsupported system: ${stdenv.hostPlatform.system}");
in
stdenv.mkDerivation rec {
pname = "cmdstan";
inherit version src;
buildFlags = [ "build" ];
enableParallelBuilding = true;
doCheck = true;
checkInputs = [ python3 ];
CXXFLAGS = lib.optionalString stdenv.isDarwin "-D_BOOST_LGAMMA";
postPatch = ''
substituteInPlace stan/lib/stan_math/make/libraries \
--replace "/usr/bin/env bash" "bash"
patchShebangs .
'' + lib.optionalString stdenv.isAarch64 ''
sed -z -i "s/TEST(CommandStansummary, check_console_output).*TEST(CommandStansummary, check_csv_output)/TEST(CommandStansummary, check_csv_output)/" \
src/test/interface/stansummary_test.cpp
'';
checkPhase = ''
./runCmdStanTests.py -j$NIX_BUILD_CORES src/test/interface
'';
installPhase = ''
mkdir -p $out/opt $out/bin
cp -r . $out/opt/cmdstan
ln -s $out/opt/cmdstan/bin/stanc $out/bin/stanc
ln -s $out/opt/cmdstan/bin/stansummary $out/bin/stansummary
cat > $out/bin/stan <<EOF
#!${runtimeShell}
make -C $out/opt/cmdstan "\$(realpath "\$1")"
EOF
chmod a+x $out/bin/stan
'';
# Hack to ensure that patchelf --shrink-rpath get rids of a $TMPDIR reference.
preFixup = "rm -rf stan";
meta = {
description = "Command-line interface to Stan";
longDescription = ''
Stan is a probabilistic programming language implementing full Bayesian
statistical inference with MCMC sampling (NUTS, HMC), approximate Bayesian
inference with Variational inference (ADVI) and penalized maximum
likelihood estimation with Optimization (L-BFGS).
'';
homepage = "https://mc-stan.org/interfaces/cmdstan.html";
license = lib.licenses.bsd3;
platforms = [ "aarch64-linux" "x86_64-darwin" "x86_64-linux" ];
};
}
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