mirror of
https://github.com/NixOS/nixpkgs.git
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81 lines
2.3 KiB
Nix
81 lines
2.3 KiB
Nix
{ fetchurl
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, python
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, anki
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}:
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python.pkgs.buildPythonApplication rec {
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pname = "mnemosyne";
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version = "2.7.2";
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src = fetchurl {
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url = "mirror://sourceforge/project/mnemosyne-proj/mnemosyne/mnemosyne-${version}/Mnemosyne-${version}.tar.gz";
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sha256 = "09yp9zc00xrc9dmjbsscnkb3hsv3yj46sxikc0r6s9cbghn3nypy";
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};
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nativeBuildInputs = with python.pkgs; [ pyqtwebengine.wrapQtAppsHook ];
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buildInputs = [ anki ];
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propagatedBuildInputs = with python.pkgs; [
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cheroot
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cherrypy
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googletrans
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gtts
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matplotlib
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pyopengl
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pyqt5
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pyqtwebengine
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webob
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];
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prePatch = ''
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substituteInPlace setup.py --replace /usr $out
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find . -type f -exec grep -H sys.exec_prefix {} ';' | cut -d: -f1 | xargs sed -i s,sys.exec_prefix,\"$out\",
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'';
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# No tests/ directrory in tarball
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doCheck = false;
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postInstall = ''
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mkdir -p $out/share/applications
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mv $out/${python.sitePackages}/$out/share/locale $out/share
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mv mnemosyne.desktop $out/share/applications
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rm -r $out/${python.sitePackages}/nix
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'';
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dontWrapQtApps = true;
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makeWrapperArgs = [
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"\${qtWrapperArgs[@]}"
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];
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meta = {
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homepage = "https://mnemosyne-proj.org/";
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description = "Spaced-repetition software";
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longDescription = ''
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The Mnemosyne Project has two aspects:
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* It's a free flash-card tool which optimizes your learning process.
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* It's a research project into the nature of long-term memory.
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We strive to provide a clear, uncluttered piece of software, easy to use
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and to understand for newbies, but still infinitely customisable through
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plugins and scripts for power users.
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## Efficient learning
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Mnemosyne uses a sophisticated algorithm to schedule the best time for
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a card to come up for review. Difficult cards that you tend to forget
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quickly will be scheduled more often, while Mnemosyne won't waste your
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time on things you remember well.
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## Memory research
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If you want, anonymous statistics on your learning process can be
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uploaded to a central server for analysis. This data will be valuable to
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study the behaviour of our memory over a very long time period. The
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results will be used to improve the scheduling algorithms behind the
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software even further.
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'';
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};
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}
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