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from . import haversine_baseline as baseline
import haversine as current
import numpy as np
import pytest
from timeit import timeit
def assert_performance(func, number):
# Interleave measurements and compare fastest current to median baseline.
# All in an attempt to avoid spurious errors caused by fluctuating load on
# the runner.
t_baseline, t_current = [], []
for repeat in range(5):
t_baseline.append(timeit(lambda: func(baseline), number=number))
t_current.append(timeit(lambda: func(current), number=number))
perf_ratio = np.min(t_current) / np.median(t_baseline)
assert perf_ratio <= 1.1
@pytest.mark.parametrize(
"check", [False, True]
)
@pytest.mark.parametrize(
"normalize", [False, True]
)
def test_haversine(check, normalize):
if check == normalize == True:
pytest.skip()
assert_performance(lambda m: m.haversine((0,1), (2,3), check=check, normalize=normalize),
number=100000)
@pytest.mark.parametrize(
"check", [False, True]
)
@pytest.mark.parametrize(
"normalize", [False, True]
)
def test_haversine_vector(check, normalize):
if check == normalize == True:
pytest.skip()
arr = np.random.uniform(size=(1000, 2))
assert_performance(lambda m: m.haversine_vector(arr, arr, check=check, normalize=normalize),
number=1000)
def test_inverse_haversine():
assert_performance(lambda m: m.inverse_haversine((0,1), 2, 3),
number=100000)
def test_inverse_haversine_vector():
arr = np.random.uniform(size=(1000, 2))
assert_performance(lambda m: m.inverse_haversine_vector(arr, *arr.T),
number=1000)
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