readme.md

osu.map-data

This project reads .osu files and formats all mapsets, maps, and objects into an sqlite database. There are also several sql files that analyse the database for curiosity satisfaction purposes.

The code is a bit of a bodge job so some things like the query scripts might not be ready to run out of the box.

Thanks to Mr HeliX for providing a list of loved beatmap IDs.

Videos

This is the most common circle in osu!

Setting up

You should probably have Python installed for this, I used 3.9. Sqlite3 is probably a good idea too.

• Clone this repository and navigate to it using your favourite command line interface with cd osu.map-data
• Create a new database file with sqlite3 mapdata.db and then set up the tables with .read schema.sql. Press Ctrl+D to close the database when you're done.
• Create a new directory named maps and add your .osu files in here. If you need a dump of all ranked and loved .osu files you can get them from https://data.ppy.sh/.
• Run python3 run.py to start reading and adding maps to the database. This will probably take some time (took me around an hour for the entire data dump), and will create a database file around 2GB in size.

When you're done you should have a database with four tables you're free to play around with.

Database schema

Hopefully most of these tables and columns should be self-explanatory.

beatmap_sets

Column	Data type	Settings	Notes
beatmap_set_id	text	primary key	Very old beatmaps didn't store the beatmap set id in the .osu, so for these cases we use a hash of a string combining the metadata of the map.
title	text	not null	Uses the first instance found of the set.
title_unicode	text	not null	Uses the first instance found of the set.
artist	text	not null	Uses the first instance found of the set.
artist_unicode	text	not null	Uses the first instance found of the set.
creator	text	not null	Mapset host. Uses the first instance found of the set.
source	text		Uses the first instance found of the set.
tags	text		Uses the first instance found of the set.

beatmaps

Column	Data type	Settings	Notes
beatmap_id	integer	primary key	Very old beatmaps didn't store the beatmap id in the .osu, so for these cases we use the file name the map was read from.
beatmap_set_id	text	not null, foreign key
mode	integer	not null	Can only be 0. We're only processing standard mode maps for now
difficulty_name	text	not null
hp_drain	real	not null
circle_size	real	not null
overall_difficulty	real	not null
approach_rate	real	not null
slider_multiplier	real	not null	Base slider velocity.
slider_tick_rate	real	not null
stack_leniency	real	not null

objects

Column	Data type	Settings	Notes
object_number	integer, primary key	not null	Goes up in the order that they appear in the .osu file.
beatmap_id	integer, primary key, foreign key	not null
type	text	not null	circle, slider, or spinner
time	integer	not null	Time the object appears in milliseconds.
x	integer	not null	x co-ordinate of the object.
y	integer	not null	y co-ordinate of the object.
new_combo	integer	not null	1 if the object is the start of a new combo, 0 otherwise.
anchors	text		List of all slider anchors on this object. See this for more info.
length	real		Length of the slider in osu! pixels. See this for more info.
curve_type	text		Slider curve type. See this for more info.
slides	integer		Number of slides in the slider. See this for more info.

beatmaps_web

This table only contains two columns for now, as it's just a bodge job to store loved beatmap IDs. Will probably re-use this to store all beatmap website data later.

Column	Data type	Settings	Notes
beatmap_id	integer, primary key, foreign key	not null	Only realised I named this column incorrectly whilst writing this documentation, but I'm too lazy to fix it now.
status	text	not null	loved is the only value this can be for now.

Analysing

Feel free to write whatever queries to get interesting info from the database. I've provided a handful I used in the video in the scripts directory, which you can run by opening the database with sqlite3 mapdata.db and entering .read scripts/objectCoordsByFreq.sql.

There are also two python scripts in app/queries that I used for counting which co-ordinates were the least used. These can be simply run with python3 app/queries/coordsWithFewObjects.py.

There is one more script called app/heatmap.py that uses matplotlib to create the heatmaps of object frequency. This code reads .csv outputs made from the queries objectCoords and firstObjectCoords and formats them to make a heatmap.