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authorDavid Runge <dave@sleepmap.de>2015-03-13 22:22:22 +0100
committerDavid Runge <dave@sleepmap.de>2015-03-13 22:22:22 +0100
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README.md: Restructuring the README.md for better oversight and install section.
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-thesoundofpeople
-================
-
-Supercollider classes for interacting with data sets from http://opensnp.org (parsing, sonification, etc.).
-
+# thesoundofpeople
+## About
+Supercollider classes for interacting with data sets from http://opensnp.org (parsing, sonification, etc.).
This is a prototype! It's quite slow and eats a lot of RAM. I've only tested it on Linux with JACK. So be aware of that, when using it!
-
-So far, what you can do with it:
+## Features
- Parse 23andme files, write them to a different format.
- Loading both of those files into a dictionary.//this will eat so much RAM, cookie monster is a 'lil puppet compared (get ready for 5,7Gb (parsing) and 4,7Gb (reading)
- Playing positions (or rather: bases/base pairs on a chromosome at a position) of up to 25 chromosomes from those dictionaries on 2,4,6 & 8 channels (there's no real limit to that though, but your hardware)
+## Installation
+
+Clone this repository to somewhere sclang will pick it up on recompiling its class library:
+
+ cd ~/.local/share/SuperCollider/Extensions/local
+ git clone git://sleepmap.de/thesoundofpeople.git
+
+If the local folder doesn't exist, create it, otherwise you can also directly clone into the Extensions folder (the folder and all of its children will get picked up on recompile).
+Symlinking is fine, too!
+
+ cd ~/some/other/directory
+ git clone git://sleepmap.de/thesoundofpeople.git
+ cd ./local/share/SuperCollider/Extensios/local/
+ ln -s ~/some/other/directory/thesoundofpeople .
-There are some upcoming TODOs, and I'll list some of them:
+## TODO
- flesh out SNP.sc (a million instances eat too much RAM on filling a SNPDict), that comes with a lot of looking around what has to be changed in the other classes to make this work.
- fine grain the synthesizers used, so each chromosome will become more distinct (currently only X, Y and MT are very recognizable)
- use a set of filters based upon the form of each chromosome and its "interesting areas"