Library to calculate difficulty and performance attributes for all osu! modes.
This is a python binding to the Rust library rosu-pp which was bootstrapped through PyO3. As such, its performance is much faster than a native python library.
The library exposes multiple classes:
Beatmap
: Parsed.osu
fileGameMode
- Calculators
Difficulty
: Class to calculate difficulty attributes, strains, or create gradual calculatorsPerformance
: Performance attributes calculatorGradualDifficulty
: Calculator to calculate difficulty attributes after each hitobjectGradualPerformance
: Calculator to calculator performance attributes after each hitresultBeatmapAttributesBuilder
: Beatmap attributes calculator
- Results
DifficultyAttributes
Strains
: Strain values of a difficulty calculation, suitable to plot difficulty over timePerformanceAttributes
BeatmapAttributes
HitResultPriority
: Passed toPerformance
, decides whether specified accuracy should be realized through good or bad hitresultsScoreState
: Hitresults and max combo of a score, found inPerformanceAttributes
and passed to gradual calculators
import rosu_pp_py as rosu
# either `path`, `bytes`, or `content` must be specified when parsing a map
map = rosu.Beatmap(path = "/path/to/file.osu")
# Optionally convert to a specific mode
map.convert(rosu.GameMode.Mania)
perf = rosu.Performance(
# various kwargs available
accuracy = 98.76,
misses = 2,
combo = 700,
hitresult_priority = rosu.HitResultPriority.WorstCase, # favors bad hitresults
)
# Each kwarg can also be specified afterwards through setters
perf.set_accuracy(99.11) # override previously specified accuracy
perf.set_mods(8 + 64) # HDDT
perf.set_clock_rate(1.4)
# Second argument of map attributes specifies whether mods still need to be accounted for
# `True`: mods already considered; `False`: value should still be adjusted
perf.set_ar(10.5, True)
perf.set_od(5, False)
# Calculate for the map
attrs = perf.calculate(map)
# Note that calculating via map will have to calculate difficulty attributes
# internally which is fairly expensive. To speed it up, you can also pass in
# previously calculated attributes, but be sure they were calculated for the
# same difficulty settings like mods, clock rate, custom map attributes, ...
perf.set_accuracy(100)
perf.set_misses(None)
perf.set_combo(None)
# Calculate a new set of attributes by re-using previous attributes instead of the map
max_attrs = perf.calculate(attrs)
print(f'PP: {attrs.pp}/{max_attrs.pp} | Stars: {max_attrs.difficulty.stars}')
import rosu_pp_py as rosu
# Parsing the map, this time through the `content` kwarg
with open("/path/to/file.osu") as file:
map = rosu.Beatmap(content = file.read())
# Specifying some difficulty parameters
diff = rosu.Difficulty(
mods = 16 + 1024, # HRFL
clock_rate = 1.1,
ar = 10.2,
ar_with_mods = True,
)
# Gradually calculating *difficulty* attributes
gradual_diff = diff.gradual_difficulty(map)
for i, attrs in enumerate(gradual_diff, 1):
print(f'Stars after {i} hitobjects: {attrs.stars}')
# Gradually calculating *performance* attributes
gradual_perf = diff.gradual_performance(map)
i = 1
while True:
state = rosu.ScoreState(
max_combo = i,
n300 = i,
n100 = 0,
# ...
)
attrs = gradual_perf.next(state)
if attrs is None:
# All hitobjects have been processed
break
print(f'PP: {attrs.pp}')
i += 1
Installing rosu-pp-py requires a supported version of Python and Rust.
Once Python and Rust are ready to go, you can install the project with pip:
$ pip install rosu-pp-py
or
$ pip install git+https://github.com/MaxOhn/rosu-pp-py