# ailist version: 0.1.7
from ailist import AIList
# ncls version: 0.0.53
from ncls import NCLS
# numpy version: 1.18.4
import numpy as np
# pandas version: 1.0.3
import pandas as pd
# quicksect version: 0.2.2
import quicksect
# Set seed
np.random.seed(100)
# First values
starts1 = np.random.randint(0, 100000, 100000)
ends1 = starts1 + np.random.randint(1, 10000, 100000)
ids1 = np.arange(len(starts1))
values1 = np.ones(len(starts1))
# Second values
starts2 = np.random.randint(0, 100000, 100000)
ends2 = starts2 + np.random.randint(1, 10000, 100000)
ids2 = np.arange(len(starts2))
values2 = np.ones(len(starts2))
###### Test AIList ######
i = AIList()
%time i.from_array(starts1, ends1, ids1, values1)
# CPU times: user 2.12 ms, sys: 1.55 ms, total: 3.67 ms
# Wall time: 9.6 ms
%time i.construct()
# CPU times: user 11.9 ms, sys: 1.35 ms, total: 13.3 ms
# Wall time: 18.7 ms
%time ai_res = i.intersect_from_array(starts2, ends2, ids2)
# CPU times: user 11.9 s, sys: 5.96 s, total: 17.9 s
# Wall time: 17.9 s
### Resulting memory usage: ~5GB
### Max memory usage: ~9GB
%timeit i.intersect(starts2[50], ends2[50])
# 102 µs ± 1.05 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)
###### Test NCLS ######
%time n = NCLS(starts1, ends1, ids1)
# CPU times: user 36.5 ms, sys: 1.65 ms, total: 38.1 ms
# Wall time: 38.2 ms
%time n_res = n.all_overlaps_both(starts2, ends2, ids2)
# CPU times: user 53.6 s, sys: 1min 18s, total: 2min 12s
# Wall time: 2min 31s
### Resulting memory usage: >30GB
### Max memory usage: >50GB
%timeit list(n.find_overlap(starts2[50], ends2[50]))
# 1.17 ms ± 22.2 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
###### Test pandas IntervalIndex ######
%time p = pd.IntervalIndex.from_tuples(list(zip(starts1, ends1)))
# CPU times: user 873 ms, sys: 10.2 ms, total: 883 ms
# Wall time: 884 ms
%timeit p.overlaps(pd.Interval(starts2[50], ends2[50]))
# 241 µs ± 6.05 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
###### Test quicksect ######
b = quicksect.IntervalTree()
for i in range(len(starts1)):
b.add(starts1[i], ends1[i])
%timeit b.search(starts2[50], ends2[50])
# 1.05 ms ± 79.8 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)