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
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
|
# BSD 3-Clause License; see https://github.com/scikit-hep/awkward/blob/main/LICENSE
from __future__ import annotations
import sys
import numpy as np
import awkward as ak
def test_all_empty():
result = ak.concatenate([ak.Array([None]) for _ in range(5000)], axis=0)
expected = ak.Array([None] * 5000)
assert ak.array_equal(result, expected)
result = ak.concatenate([ak.Array([{"x": None}]) for _ in range(5000)], axis=0)
expected = ak.Array([{"x": None}] * 5000)
assert ak.array_equal(result, expected)
result = ak.concatenate(
[ak.Array([{"x": i, "y": None}]) for i in range(5000)], axis=0
)
expected = ak.Array([{"x": i, "y": None} for i in range(5000)])
assert ak.array_equal(result, expected)
def test_empty_and_nonempty():
N = sys.getrecursionlimit()
rng = np.random.default_rng(42)
choices1 = np.concatenate(
(np.array([True, True]), rng.choice([True, False], size=N))
)
choices2 = np.concatenate(
(np.array([False, False]), rng.choice([True, False], size=N))
)
choices3 = np.concatenate(
(np.array([True, False]), rng.choice([True, False], size=N))
)
choices4 = np.concatenate(
(np.array([False, True]), rng.choice([True, False], size=N))
)
all_choices = [choices1, choices2, choices3, choices4]
for choices in all_choices:
rows = []
for i in range(N):
if choices[i]:
rows.append(None)
else:
rows.append(i)
result = ak.concatenate([ak.Array([row]) for row in rows], axis=0)
expected = ak.Array(rows)
assert ak.array_equal(result, expected)
rows = []
for i in range(N):
if choices[i]:
rows.append({"x": None})
else:
rows.append({"x": i})
result = ak.concatenate([ak.Array([row]) for row in rows], axis=0)
expected = ak.Array(rows)
assert ak.array_equal(result, expected)
rows = []
for i in range(N):
if choices[i]:
rows.append({"x": i, "y": None})
else:
rows.append({"x": i, "y": i})
result = ak.concatenate([ak.Array([row]) for row in rows], axis=0)
expected = ak.Array(rows)
assert ak.array_equal(result, expected)
|
