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from __future__ import annotations
import warnings
from importlib.util import find_spec
import numpy as np
import pandas as pd
import pytest
from numba.core.errors import NumbaDeprecationWarning
from anndata import AnnData, ImplicitModificationWarning, read_h5ad
from anndata.io import read_loom
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()
@pytest.mark.skipif(find_spec("loompy") is None, reason="loompy not installed")
def test_readwrite_loom(tmp_path):
loom_path = tmp_path / "test.loom"
adata = AnnData(X=X_, layers=dict(L=L.copy()))
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=NumbaDeprecationWarning)
# loompy uses “is” for ints
warnings.filterwarnings("ignore", category=SyntaxWarning)
warnings.filterwarnings(
"ignore",
message=r"datetime.datetime.utcnow\(\) is deprecated",
category=DeprecationWarning,
)
adata.write_loom(loom_path)
adata_read = read_loom(loom_path, X_name="")
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]))
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