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from __future__ import annotations
import pytest
from pytest import approx
import boost_histogram as bh
hypothesis = pytest.importorskip("hypothesis")
st = pytest.importorskip("hypothesis.strategies")
def test_weighted_sum():
a = bh.accumulators.WeightedSum(1.5, 2.5)
assert repr(a) == "WeightedSum(value=1.5, variance=2.5)"
assert a == bh.accumulators.WeightedSum(1.5, 2.5)
assert a.value == 1.5
assert a.variance == 2.5
a += 1.5
assert a.value == 3.0
assert a.variance == 4.75
vals = [1, 2, 3]
vari = [4, 5, 6]
a = bh.accumulators.WeightedSum()
for val, var in zip(vals, vari, strict=False):
a += bh.accumulators.WeightedSum(val, variance=var)
assert a.value == 6
assert a.variance == 15
a2 = bh.accumulators.WeightedSum().fill(vals, variance=vari)
assert a == a2
assert a == bh.accumulators.WeightedSum(6, 15)
def test_sum():
vals = [1, 2, 3]
a = bh.accumulators.Sum()
for val in vals:
a += val
assert a.value == 6
a2 = bh.accumulators.Sum().fill(vals)
assert a == a2
assert a == bh.accumulators.Sum(6)
assert repr(a) == "Sum(6 + 0)"
def test_weighted_mean():
vals = [4, 1]
weights = [1, 2]
a = bh.accumulators.WeightedMean()
for v, w in zip(vals, weights, strict=False):
a(v, weight=w)
assert a.sum_of_weights == 3.0
assert a.variance == 4.5
assert a.value == 2.0
a2 = bh.accumulators.WeightedMean().fill(vals, weight=weights)
assert a == a2
assert a == bh.accumulators.WeightedMean(3, 5, 2, 4.5)
assert (
repr(a)
== "WeightedMean(sum_of_weights=3, sum_of_weights_squared=5, value=2, variance=4.5)"
)
def test_mean():
vals = [1, 2, 3]
a = bh.accumulators.Mean()
for val in vals:
a(val)
assert a.count == 3
assert a.value == 2
assert a.variance == 1
a2 = bh.accumulators.Mean().fill([1, 2, 3])
assert a == a2
assert a == bh.accumulators.Mean(3, 2, 1)
assert repr(a) == "Mean(count=3, value=2, variance=1)"
float_st = st.floats(
allow_nan=False, allow_infinity=False, min_value=-1e4, max_value=1e4
)
simple_list_st = st.lists(float_st, min_size=1, max_size=10)
@hypothesis.given(
simple_list_st,
simple_list_st,
)
def test_sum_mean(list1, list2):
a = bh.accumulators.Mean()
a.fill(list1)
b = bh.accumulators.Mean()
b.fill(list2)
c = bh.accumulators.Mean()
c.fill(list1 + list2)
ab = a + b
assert ab.value == approx(c.value)
assert ab.variance == approx(c.variance, nan_ok=True, abs=1e-7, rel=1e-3)
assert ab.count == approx(c.count)
a += b
assert a.value == approx(c.value)
assert a.variance == approx(c.variance, nan_ok=True, abs=1e-7, rel=1e-3)
assert a.count == approx(c.count)
dual_lists_st = st.integers(min_value=1, max_value=10).flatmap(
lambda n: st.tuples(
st.lists(float_st, min_size=n, max_size=n),
st.lists(
st.floats(
allow_nan=False, allow_infinity=False, min_value=1e-2, max_value=1e2
),
min_size=n,
max_size=n,
),
)
)
@hypothesis.given(dual_lists_st, dual_lists_st)
def test_sum_weighed_mean(pair1, pair2):
a = bh.accumulators.WeightedMean()
a.fill(pair1[0], weight=pair1[1])
b = bh.accumulators.WeightedMean()
b.fill(pair2[0], weight=pair2[1])
c = bh.accumulators.WeightedMean()
c.fill(pair1[0] + pair2[0], weight=pair1[1] + pair2[1])
ab = a + b
assert ab.value == approx(c.value)
assert ab.variance == approx(c.variance, nan_ok=True, abs=1e-3, rel=1e-2)
assert ab.sum_of_weights == approx(c.sum_of_weights)
assert ab.sum_of_weights_squared == approx(c.sum_of_weights_squared)
a += b
assert a.value == approx(c.value)
assert a.variance == approx(c.variance, nan_ok=True, abs=1e-3, rel=1e-2)
assert a.sum_of_weights == approx(c.sum_of_weights)
assert a.sum_of_weights_squared == approx(c.sum_of_weights_squared)
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