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nixpkgs/pkgs/games/mnemosyne/default.nix
R. RyanTM b5cd99c4ab mnemosyne: 2.6 -> 2.6.1 (#45184)
Semi-automatic update generated by
https://github.com/ryantm/nixpkgs-update tools. This update was made
based on information from
https://repology.org/metapackage/mnemosyne/versions.
2018-08-19 22:51:46 +02:00

67 lines
2 KiB
Nix

{ fetchurl
, python
}:
python.pkgs.buildPythonApplication rec {
pname = "mnemosyne";
version = "2.6.1";
src = fetchurl {
url = "mirror://sourceforge/project/mnemosyne-proj/mnemosyne/mnemosyne-${version}/Mnemosyne-${version}.tar.gz";
sha256 = "0xcwikq51abrlqfn5bv7kcw1ccd3ip0w6cjd5vnnzwnaqwdj8cb3";
};
propagatedBuildInputs = with python.pkgs; [
pyqt5
matplotlib
cherrypy
cheroot
webob
pillow
];
# No tests/ directrory in tarball
doCheck = false;
prePatch = ''
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\",
'';
postInstall = ''
mkdir -p $out/share
mv $out/${python.sitePackages}/$out/share/locale $out/share
rm -r $out/${python.sitePackages}/nix
'';
meta = {
homepage = https://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.
'';
};
}