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from __future__ import annotations
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 get_multiindex_columns_df
M, N = (100, 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(N)],
)
var = pd.DataFrame(index=[f"gene{i:03d}" for i in range(N)])
return AnnData(X, obs=obs, var=var)
def test_assignment_dict(adata: AnnData):
d_obsm = dict(
a=pd.DataFrame(
dict(a1=np.ones(M), a2=[f"a{i}" for i in range(M)]),
index=adata.obs_names,
),
b=np.zeros((M, 2)),
)
d_varm = dict(
a=pd.DataFrame(
dict(a1=np.ones(N), a2=[f"a{i}" for i in range(N)]),
index=adata.var_names,
),
b=np.zeros((N, 2)),
)
adata.obsm = d_obsm
for k, v in d_obsm.items():
assert np.all(adata.obsm[k] == v)
adata.varm = d_varm
for k, v in d_varm.items():
assert np.all(adata.varm[k] == v)
def test_setting_ndarray(adata: AnnData):
adata.obsm["a"] = np.ones((M, 10))
adata.varm["a"] = np.ones((N, 10))
assert np.all(adata.obsm["a"] == np.ones((M, 10)))
assert np.all(adata.varm["a"] == np.ones((N, 10)))
h = joblib.hash(adata)
with pytest.raises(ValueError, match=r"incorrect shape"):
adata.obsm["b"] = np.ones((int(M / 2), 10))
with pytest.raises(ValueError, match=r"incorrect shape"):
adata.obsm["b"] = np.ones((int(M * 2), 10))
with pytest.raises(ValueError, match=r"incorrect shape"):
adata.varm["b"] = np.ones((int(N / 2), 10))
with pytest.raises(ValueError, match=r"incorrect shape"):
adata.varm["b"] = np.ones((int(N * 2), 10))
assert h == joblib.hash(adata)
def test_setting_dataframe(adata: AnnData):
obsm_df = pd.DataFrame(dict(b_1=np.ones(M), b_2=["a"] * M), index=adata.obs_names)
varm_df = pd.DataFrame(dict(b_1=np.ones(N), b_2=["a"] * N), index=adata.var_names)
adata.obsm["b"] = obsm_df
assert np.all(adata.obsm["b"] == obsm_df)
adata.varm["b"] = varm_df
assert np.all(adata.varm["b"] == varm_df)
bad_obsm_df = obsm_df.copy()
bad_obsm_df.reset_index(inplace=True)
with pytest.raises(ValueError, match=r"index does not match.*obs names"):
adata.obsm["c"] = bad_obsm_df
bad_varm_df = varm_df.copy()
bad_varm_df.reset_index(inplace=True)
with pytest.raises(ValueError, match=r"index does not match.*var names"):
adata.varm["c"] = bad_varm_df
def test_setting_sparse(adata: AnnData):
obsm_sparse = sparse.random(M, 100, format="csr")
adata.obsm["a"] = obsm_sparse
assert not np.any((adata.obsm["a"] != obsm_sparse).data)
varm_sparse = sparse.random(N, 100, format="csr")
adata.varm["a"] = varm_sparse
assert not np.any((adata.varm["a"] != varm_sparse).data)
h = joblib.hash(adata)
bad_obsm_sparse = sparse.random(M * 2, M, format="csr")
with pytest.raises(ValueError, match=r"incorrect shape"):
adata.obsm["b"] = bad_obsm_sparse
bad_varm_sparse = sparse.random(N * 2, N, format="csr")
with pytest.raises(ValueError, match=r"incorrect shape"):
adata.varm["b"] = bad_varm_sparse
assert h == joblib.hash(adata)
def test_setting_daskarray(adata: AnnData):
import dask.array as da
adata.obsm["a"] = da.ones((M, 10))
adata.varm["a"] = da.ones((N, 10))
assert da.all(adata.obsm["a"] == da.ones((M, 10)))
assert da.all(adata.varm["a"] == da.ones((N, 10)))
assert isinstance(adata.obsm["a"], da.Array)
assert isinstance(adata.varm["a"], da.Array)
h = joblib.hash(adata)
with pytest.raises(ValueError, match=r"incorrect shape"):
adata.obsm["b"] = da.ones((int(M / 2), 10))
with pytest.raises(ValueError, match=r"incorrect shape"):
adata.obsm["b"] = da.ones((int(M * 2), 10))
with pytest.raises(ValueError, match=r"incorrect shape"):
adata.varm["b"] = da.ones((int(N / 2), 10))
with pytest.raises(ValueError, match=r"incorrect shape"):
adata.varm["b"] = da.ones((int(N * 2), 10))
assert h == joblib.hash(adata)
def test_shape_error(adata: AnnData):
with pytest.raises(
ValueError,
match=(
r"Value passed for key 'b' is of incorrect shape\. "
r"Values of obsm must match dimensions \('obs',\) of parent\. "
r"Value had shape \(101,\) while it should have had \(100,\)\."
),
):
adata.obsm["b"] = np.zeros((adata.shape[0] + 1, adata.shape[0]))
def test_error_set_multiindex_df(adata: AnnData):
df = get_multiindex_columns_df((adata.shape[0], 20))
with pytest.raises(ValueError, match=r"MultiIndex columns are not supported"):
adata.obsm["df"] = df
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