Implementations of halving and aliquoting distance algorithms (https://drops.dagstuhl.de/opus/volltexte/2021/14371/) in Python 3. The minimum dependencies required are numpy, pandas, matplotlib, and seaborn.

• generate_data.ipynb: Jupyter notebook that generates simulated profiles and evaluates the halving and aliquoting algorithms
• analysis.ipynb: Jupyter notebook that generates figures used in our paper
• outputs.pkl: pickled pandas dataframe containing our results of experiments as shown in our paper (generated with generate_data.ipynb)
• aliquoting.py: implementation of halving and aliquoting algorithms
• distance.py: implementation of copy number distance
• CopyNumberDistanceFunctions.py: other copy number distance-related functions, not used in our implementation or analysis; these functions require Gurobi and other dependencies

To find the halving and aliquoting distances of a profile T:

from aliquoting import cnd_halving, cnd_aliquoting_I

T = [1,2,3,4,5]
p = 3
halving_distance, halving_predup_profile = cnd_halving(T)[:2]
aliquoting_distance, aliquoting_predup_profile = cnd_aliquoting_I(T, p)[:2]