forked from mirrors/nixpkgs
ee3226bc65
Fast implementation of Dynamic Time Warping algorithm. Dependency for qiskit-aqua.
66 lines
1.6 KiB
Nix
66 lines
1.6 KiB
Nix
{ lib
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, buildPythonPackage
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, fetchFromGitHub
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, fetchpatch
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, cython
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, numpy
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# Check Inputs
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, pytestCheckHook
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, python
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}:
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buildPythonPackage rec {
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pname = "fastdtw";
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version = "0.3.4";
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src = fetchFromGitHub {
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owner = "slaypni";
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repo = pname;
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rev = "v${version}";
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sha256 = "0irc5x4ahfp7f7q4ic97qa898s2awi0vdjznahxrfjirn8b157dw";
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};
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patches = [
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# Removes outdated cythonized C++ file, which doesn't match CPython. Will be auto-used if left.
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# Remove when PR 40 merged
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(fetchpatch {
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url = "https://patch-diff.githubusercontent.com/raw/slaypni/fastdtw/pull/40.patch";
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sha256 = "0xjma0h84bk1n32wgk99rwfc85scp187a7fykhnylmcc73ppal9q";
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})
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];
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nativeBuildInputs = [
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cython
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];
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propagatedBuildInputs = [
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numpy
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];
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pythonImportsCheck = [ "fastdtw.fastdtw" ];
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checkInputs = [ pytestCheckHook ];
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dontUseSetuptoolsCheck = true; # looks for pytest-runner
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preCheck = ''
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echo "Temporarily moving tests to $OUT to find cython modules"
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export PACKAGEDIR=$out/${python.sitePackages}
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cp -r $TMP/source/tests $PACKAGEDIR
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pushd $PACKAGEDIR
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'';
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postCheck = ''
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rm -rf tests
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popd
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'';
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meta = with lib; {
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description = "Python implementation of FastDTW (Dynamic Time Warping)";
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longDescription = ''
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FastDTW is an approximate Dynamic Time Warping (DTW) algorithm that provides
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optimal or near-optimal alignments with an O(N) time and memory complexity.
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'';
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homepage = "https://github.com/slaypni/fastdtw";
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license = licenses.mit;
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maintainers = with maintainers; [ drewrisinger ];
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};
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}
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