forked from mirrors/nixpkgs
56 lines
1.8 KiB
Nix
56 lines
1.8 KiB
Nix
{ stdenv
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, fetchurl
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, buildPythonApplication
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, pyqt4
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, pythonPackages
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}:
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let
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version = "2.3.2";
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in buildPythonApplication rec {
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name = "mnemosyne-${version}";
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src = fetchurl {
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url = "http://sourceforge.net/projects/mnemosyne-proj/files/mnemosyne/${name}/Mnemosyne-${version}.tar.gz";
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sha256 = "0jkrw45i4v24p6xyq94z7rz5948h7f5dspgs5mcdaslnlp2accfp";
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};
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propagatedBuildInputs = with pythonPackages; [
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pyqt4
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matplotlib
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cherrypy
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sqlite3
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webob
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];
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preConfigure = ''
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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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meta = {
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homepage = http://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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