1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
|
from __future__ import annotations
import numpy as np
import boost_histogram as bh
# This is not a reasonable approximation of Pandas, but rather a test for an
# arbitrary convertible object (but if this works, so does Pandas)
class Seriesish:
def __init__(self, array):
self.array = np.asarray(array)
def __array__(self, dtype=None, *, copy=None):
# Copy is new in NumPy 2.0
kw = {}
if copy is not None:
kw["copy"] = copy
return np.asarray(self.array, dtype=dtype, **kw)
def test_setting_weighted_profile_convertable():
h = bh.Histogram(bh.axis.Regular(10, 0, 10), storage=bh.storage.WeightedMean())
data = Seriesish([0.3, 0.3, 0.4, 1.2, 1.6])
samples = Seriesish([1, 2, 3, 4, 4])
weights = Seriesish([1, 1, 1, 1, 2])
h.fill(data, sample=samples, weight=weights)
assert h[0] == bh.accumulators.WeightedMean(
sum_of_weights=3, sum_of_weights_squared=3, value=2, variance=1
)
assert h[1] == bh.accumulators.WeightedMean(
sum_of_weights=3, sum_of_weights_squared=5, value=4, variance=0
)
|
