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import pandas as pd
from scipy import sparse
from itertools import repeat
import pytest
import anndata as ad
from anndata.utils import make_index_unique
from anndata.tests.helpers import gen_typed_df
def test_make_index_unique():
index = pd.Index(["val", "val", "val-1", "val-1"])
with pytest.warns(UserWarning):
result = make_index_unique(index)
expected = pd.Index(["val", "val-2", "val-1", "val-1-1"])
assert list(expected) == list(result)
assert result.is_unique
def test_adata_unique_indices():
m, n = (10, 20)
obs_index = pd.Index(repeat("a", m), name="obs")
var_index = pd.Index(repeat("b", n), name="var")
adata = ad.AnnData(
X=sparse.random(m, n, format="csr"),
obs=gen_typed_df(m, index=obs_index),
var=gen_typed_df(n, index=var_index),
obsm={"df": gen_typed_df(m, index=obs_index)},
varm={"df": gen_typed_df(n, index=var_index)},
)
pd.testing.assert_index_equal(adata.obsm["df"].index, adata.obs_names)
pd.testing.assert_index_equal(adata.varm["df"].index, adata.var_names)
adata.var_names_make_unique()
adata.obs_names_make_unique()
assert adata.obs_names.name == "obs"
assert adata.var_names.name == "var"
assert len(pd.unique(adata.obs_names)) == m
assert len(pd.unique(adata.var_names)) == n
pd.testing.assert_index_equal(adata.obsm["df"].index, adata.obs_names)
pd.testing.assert_index_equal(adata.varm["df"].index, adata.var_names)
v = adata[:5, :5]
assert v.obs_names.name == "obs"
assert v.var_names.name == "var"
pd.testing.assert_index_equal(v.obsm["df"].index, v.obs_names)
pd.testing.assert_index_equal(v.varm["df"].index, v.var_names)
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