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# TODO: These tests should share code with test_layers, and test_obsmvarm
from __future__ import annotations
import warnings
import joblib
import numpy as np
import pandas as pd
import pytest
from scipy import sparse
from anndata import AnnData
from anndata.tests.helpers import gen_typed_df_t2_size
from anndata.utils import asarray
M, N = (200, 100)
@pytest.fixture
def adata():
X = np.zeros((M, N))
obs = pd.DataFrame(
dict(batch=np.array(["a", "b"])[np.random.randint(0, 2, M)]),
index=[f"cell{i:03d}" for i in range(M)],
)
var = pd.DataFrame(index=[f"gene{i:03d}" for i in range(N)])
return AnnData(X, obs=obs, var=var)
def test_assigmnent_dict(adata: AnnData):
d_obsp = dict(
a=pd.DataFrame(np.ones((M, M)), columns=adata.obs_names, index=adata.obs_names),
b=np.zeros((M, M)),
c=sparse.random(M, M, format="csr"),
)
d_varp = dict(
a=pd.DataFrame(np.ones((N, N)), columns=adata.var_names, index=adata.var_names),
b=np.zeros((N, N)),
c=sparse.random(N, N, format="csr"),
)
adata.obsp = d_obsp
for k, v in d_obsp.items():
assert np.all(asarray(adata.obsp[k]) == asarray(v))
adata.varp = d_varp
for k, v in d_varp.items():
assert np.all(asarray(adata.varp[k]) == asarray(v))
def test_setting_ndarray(adata: AnnData):
adata.obsp["a"] = np.ones((M, M))
adata.varp["a"] = np.ones((N, N))
assert np.all(adata.obsp["a"] == np.ones((M, M)))
assert np.all(adata.varp["a"] == np.ones((N, N)))
h = joblib.hash(adata)
with pytest.raises(ValueError, match=r"incorrect shape"):
adata.obsp["b"] = np.ones((int(M / 2), M))
with pytest.raises(ValueError, match=r"incorrect shape"):
adata.obsp["b"] = np.ones((M, int(M * 2)))
with pytest.raises(ValueError, match=r"incorrect shape"):
adata.varp["b"] = np.ones((int(N / 2), 10))
with pytest.raises(ValueError, match=r"incorrect shape"):
adata.varp["b"] = np.ones((N, int(N * 2)))
assert h == joblib.hash(adata)
def test_setting_sparse(adata: AnnData):
obsp_sparse = sparse.random(M, M, format="csr")
adata.obsp["a"] = obsp_sparse
assert not np.any((adata.obsp["a"] != obsp_sparse).data)
varp_sparse = sparse.random(N, N, format="csr")
adata.varp["a"] = varp_sparse
assert not np.any((adata.varp["a"] != varp_sparse).data)
h = joblib.hash(adata)
bad_obsp_sparse = sparse.random(M * 2, M, format="csr")
with pytest.raises(ValueError, match=r"incorrect shape"):
adata.obsp["b"] = bad_obsp_sparse
bad_varp_sparse = sparse.random(N * 2, N, format="csr")
with pytest.raises(ValueError, match=r"incorrect shape"):
adata.varp["b"] = bad_varp_sparse
assert h == joblib.hash(adata)
@pytest.mark.parametrize(("field", "dim"), [("obsp", M), ("varp", N)])
@pytest.mark.parametrize(
("df", "homogenous", "dtype"),
[
(lambda dim: gen_typed_df_t2_size(dim, dim), True, np.object_),
(lambda dim: pd.DataFrame(np.random.randn(dim, dim)), False, np.floating),
],
ids=["heterogeneous", "homogeneous"],
)
def test_setting_dataframe(adata: AnnData, field, dim, homogenous, df, dtype):
if homogenous:
with pytest.warns(UserWarning, match=rf"{field.title()} 'df'.*dtype object"):
getattr(adata, field)["df"] = df(dim)
else:
with warnings.catch_warnings():
warnings.simplefilter("error")
getattr(adata, field)["df"] = df(dim)
assert isinstance(getattr(adata, field)["df"], np.ndarray)
assert np.issubdtype(getattr(adata, field)["df"].dtype, dtype)
def test_setting_daskarray(adata: AnnData):
import dask.array as da
adata.obsp["a"] = da.ones((M, M))
adata.varp["a"] = da.ones((N, N))
assert da.all(adata.obsp["a"] == da.ones((M, M)))
assert da.all(adata.varp["a"] == da.ones((N, N)))
assert isinstance(adata.obsp["a"], da.Array)
assert isinstance(adata.varp["a"], da.Array)
h = joblib.hash(adata)
with pytest.raises(ValueError, match=r"incorrect shape"):
adata.obsp["b"] = da.ones((int(M / 2), M))
with pytest.raises(ValueError, match=r"incorrect shape"):
adata.obsp["b"] = da.ones((M, int(M * 2)))
with pytest.raises(ValueError, match=r"incorrect shape"):
adata.varp["b"] = da.ones((int(N / 2), 10))
with pytest.raises(ValueError, match=r"incorrect shape"):
adata.varp["b"] = da.ones((N, int(N * 2)))
assert h == joblib.hash(adata)
def test_shape_error(adata: AnnData):
with pytest.raises(
ValueError,
match=(
r"Value passed for key 'a' is of incorrect shape\. "
r"Values of obsp must match dimensions \('obs', 'obs'\) of parent\. "
r"Value had shape \(201, 200\) while it should have had \(200, 200\)\."
),
):
adata.obsp["a"] = np.zeros((adata.shape[0] + 1, adata.shape[0]))
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