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
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
|
from __future__ import annotations
import warnings
import numpy as np
import pandas as pd
import pytest
from anndata import AnnData, ImplicitModificationWarning, read_h5ad
from anndata.tests.helpers import gen_typed_df_t2_size
X_ = np.arange(12).reshape((3, 4))
L = np.arange(12).reshape((3, 4)) + 12
@pytest.fixture(params=[X_, None])
def X(request):
return request.param
def test_creation(X: np.ndarray | None):
adata = AnnData(X=X, layers=dict(L=L.copy()))
assert list(adata.layers.keys()) == ["L"]
assert "L" in adata.layers
assert "X" not in adata.layers
assert "some_other_thing" not in adata.layers
assert (adata.layers["L"] == L).all()
assert adata.shape == L.shape
def test_views():
adata = AnnData(X=X_, layers=dict(L=L.copy()))
adata_view = adata[1:, 1:]
assert adata_view.layers.is_view
assert adata_view.layers.parent_mapping == adata.layers
assert adata_view.layers.keys() == adata.layers.keys()
assert (adata_view.layers["L"] == adata.layers["L"][1:, 1:]).all()
adata.layers["S"] = X_
assert adata_view.layers.keys() == adata.layers.keys()
assert (adata_view.layers["S"] == adata.layers["S"][1:, 1:]).all()
with pytest.warns(ImplicitModificationWarning):
adata_view.layers["T"] = X_[1:, 1:]
assert not adata_view.layers.is_view
assert not adata_view.is_view
@pytest.mark.parametrize(
("df", "homogenous", "dtype"),
[
(lambda: gen_typed_df_t2_size(*X_.shape), True, np.object_),
(lambda: pd.DataFrame(X_**2), False, np.int_),
],
)
def test_set_dataframe(homogenous, df, dtype):
adata = AnnData(X_)
if homogenous:
with pytest.warns(UserWarning, match=r"Layer 'df'.*dtype object"):
adata.layers["df"] = df()
else:
with warnings.catch_warnings():
warnings.simplefilter("error")
adata.layers["df"] = df()
assert isinstance(adata.layers["df"], np.ndarray)
assert np.issubdtype(adata.layers["df"].dtype, dtype)
def test_readwrite(X: np.ndarray | None, backing_h5ad):
adata = AnnData(X=X, layers=dict(L=L.copy()))
adata.write(backing_h5ad)
adata_read = read_h5ad(backing_h5ad)
assert adata.layers.keys() == adata_read.layers.keys()
assert (adata.layers["L"] == adata_read.layers["L"]).all()
def test_backed():
# backed mode for layers isn’t implemented, layers stay in memory
pass
def test_copy():
adata = AnnData(X=X_, layers=dict(L=L.copy()))
bdata = adata.copy()
# check that we don’t create too many references
assert bdata._layers is bdata.layers._data
# check that we have a copy
adata.layers["L"] += 10
assert np.all(adata.layers["L"] != bdata.layers["L"]) # 201
def test_shape_error():
adata = AnnData(X=X_)
with pytest.raises(
ValueError,
match=(
r"Value passed for key 'L' is of incorrect shape\. "
r"Values of layers must match dimensions \('obs', 'var'\) of parent\. "
r"Value had shape \(4, 4\) while it should have had \(3, 4\)\."
),
):
adata.layers["L"] = np.zeros((X_.shape[0] + 1, X_.shape[1]))
|
