nixpkgs/pkgs/games/mnemosyne/default.nix

56 lines
1.8 KiB
Nix

{ stdenv
, fetchurl
, buildPythonApplication
, pyqt4
, pythonPackages
}:
let
version = "2.3.2";
in buildPythonApplication rec {
name = "mnemosyne-${version}";
src = fetchurl {
url = "http://sourceforge.net/projects/mnemosyne-proj/files/mnemosyne/${name}/Mnemosyne-${version}.tar.gz";
sha256 = "0jkrw45i4v24p6xyq94z7rz5948h7f5dspgs5mcdaslnlp2accfp";
};
propagatedBuildInputs = with pythonPackages; [
pyqt4
matplotlib
cherrypy
sqlite3
webob
];
preConfigure = ''
substituteInPlace setup.py --replace /usr $out
find . -type f -exec grep -H sys.exec_prefix {} ';' | cut -d: -f1 | xargs sed -i s,sys.exec_prefix,\"$out\",
'';
meta = {
homepage = http://mnemosyne-proj.org/;
description = "Spaced-repetition software";
longDescription = ''
The Mnemosyne Project has two aspects:
* It's a free flash-card tool which optimizes your learning process.
* It's a research project into the nature of long-term memory.
We strive to provide a clear, uncluttered piece of software, easy to use
and to understand for newbies, but still infinitely customisable through
plugins and scripts for power users.
## Efficient learning
Mnemosyne uses a sophisticated algorithm to schedule the best time for
a card to come up for review. Difficult cards that you tend to forget
quickly will be scheduled more often, while Mnemosyne won't waste your
time on things you remember well.
## Memory research
If you want, anonymous statistics on your learning process can be
uploaded to a central server for analysis. This data will be valuable to
study the behaviour of our memory over a very long time period. The
results will be used to improve the scheduling algorithms behind the
software even further.
'';
};
}