The osu_analysis package has almost everything you need for analyzing osu! beatmaps and replays. Features include
- Beatmap reading and writing
- Replay reading
- Analyzing beatmap data as a pandas data object
- Analyzing replay data as a pandas data object
- Score processing and analysis
- Support for osu and mania gamemodes (I'll add taiko and catch someday)
pip install git+https://github.com/abraker-osu/osu_analysis.git#egg=osu_analysis
from replay_reader import ReplayIO
if __name__ == '__main__':
replay = ReplayIO.open_replay('LeaF - I (Maddy) [Terror] replay_0.osr')
print(replay.get_name())Output:
> py run.py
firebat9 - 21552280 (x1080, 99.68%) | 830/4/0/0
from beatmap_reader import BeatmapIO
if __name__ == '__main__':
beatmap = BeatmapIO.open_beatmap('Mutsuhiko Izumi - Red Goose (nold_1702) [ERT Basic].osu')
print(beatmap.metadata.name)Output:
> py run.py
Mutsuhiko Izumi - Red Goose (nold_1702) [ERT Basic]
from replay_reader import ReplayIO
from osu_analysis import StdReplayData
if __name__ == '__main__':
replay = ReplayIO.open_replay('LeaF - I (Maddy) [Terror] replay_0.osr')
replay_data = StdReplayData.get_replay_data(replay)
print(replay_data)Output:
> py run.py
time x y m1 m2 k1 k2 smoke
0 -1217.0 120.5313 168.5313 1.0 0.0 0.0 0.0 0.0
1 -1213.0 119.1250 168.5313 2.0 0.0 0.0 0.0 0.0
2 -1196.0 117.2500 169.0000 2.0 0.0 0.0 0.0 0.0
3 -1179.0 116.3125 169.0000 2.0 0.0 0.0 0.0 0.0
4 -1179.0 116.3125 169.0000 2.0 0.0 0.0 1.0 0.0
... ... ... ... ... ... ... ... ...
11435 153455.0 246.6250 185.4063 0.0 0.0 0.0 0.0 0.0
11436 153469.0 243.8125 185.4063 0.0 0.0 0.0 0.0 0.0
11437 153488.0 240.0625 191.5000 0.0 0.0 0.0 0.0 0.0
11438 153505.0 237.2500 193.8438 0.0 0.0 0.0 0.0 0.0
11439 153506.0 237.2500 193.8438 0.0 0.0 0.0 0.0 0.0
[11440 rows x 8 columns]
from beatmap_reader import BeatmapIO
from osu_analysis import StdMapData
if __name__ == '__main__':
beatmap = BeatmapIO.open_beatmap('Mutsuhiko Izumi - Red Goose (nold_1702) [ERT Basic].osu')
map_data = StdMapData.get_map_data(beatmap)
print(map_data)Output:
> py run.py
time x y type object
hitobject aimpoint
0 0 799.0 320.000000 328.000000 1.0 2.0
1 1099.0 398.419295 314.615346 2.0 2.0
2 1399.0 460.620298 266.281664 2.0 2.0
3 1699.0 483.120574 190.759944 2.0 2.0
4 1999.0 459.844094 115.487250 2.0 2.0
... ... ... ... ... ...
99 3 113299.0 312.011455 172.984543 2.0 2.0
4 113563.0 323.774576 242.074198 3.0 2.0
100 0 114049.0 316.000000 372.000000 1.0 2.0
1 114349.0 236.000000 372.000000 2.0 2.0
2 114463.0 205.600000 372.000000 3.0 2.0
[348 rows x 5 columns]
from replay_reader import ReplayIO
from beatmap_reader import BeatmapIO
from osu_analysis import StdMapData, StdReplayData, StdScoreData
if __name__ == '__main__':
replay = ReplayIO.open_replay('LeaF - I (Maddy) [Terror] replay_0.osr')
replay_data = StdReplayData.get_replay_data(replay)
beatmap = BeatmapIO.open_beatmap('Mutsuhiko Izumi - Red Goose (nold_1702) [ERT Basic].osu')
map_data = StdMapData.get_map_data(beatmap)
score_data = StdScoreData.get_score_data(replay_data, map_data)
print(score_data)Output:
> py run.py
replay_t map_t replay_x replay_y map_x map_y type action
0 1005.0 799.0 346.93750 265.5625 320.0 328.0 3.0 1.0
1 3405.0 3199.0 116.78130 109.9375 236.0 192.0 3.0 1.0
2 5805.0 5599.0 358.18750 108.0625 318.0 55.0 3.0 1.0
3 8205.0 7999.0 154.75000 194.7813 236.0 192.0 3.0 1.0
4 11805.0 11599.0 61.93751 223.8438 300.0 124.0 3.0 1.0
.. ... ... ... ... ... ... ... ...
103 107805.0 107599.0 138.34380 147.4375 88.0 268.0 3.0 1.0
104 109605.0 109399.0 112.56250 263.2188 256.0 308.0 3.0 1.0
105 110205.0 109999.0 310.84380 112.2813 403.0 245.0 3.0 1.0
106 112605.0 112399.0 98.50002 214.4688 188.0 256.0 3.0 1.0
107 114255.0 114049.0 166.46880 326.5000 316.0 372.0 3.0 1.0
[108 rows x 8 columns]
Note: The map and replay used in this example are from different maps, which is why all types are 3.0 - representing misses.