def test_pairwise_concat(axis, array_type):
dim_sizes = [[100, 200, 50], [50, 50, 50]]
if axis:
dim_sizes.reverse()
Ms, Ns = dim_sizes
dim = ("obs", "var")[axis]
alt = ("var", "obs")[axis]
dim_attr = f"{dim}p"
alt_attr = f"{alt}p"
def gen_dim_array(m):
return array_type(sparse.random(m, m, format="csr", density=0.1))
adatas = {
k: AnnData(
**{
"X": sparse.csr_matrix((m, n)),
"obsp": {
"arr": gen_dim_array(m)
},
"varp": {
"arr": gen_dim_array(n)
},
})
for k, m, n in zip("abc", Ms, Ns)
}
w_pairwise = concat(adatas, axis=axis, label="orig", pairwise=True)
wo_pairwise = concat(adatas, axis=axis, label="orig", pairwise=False)
# Check that argument controls whether elements are included
assert getattr(wo_pairwise, dim_attr) == {}
assert getattr(w_pairwise, dim_attr) != {}
# Check values of included elements
full_inds = np.arange(w_pairwise.shape[axis])
groups = getattr(w_pairwise, dim).groupby("orig").indices
for k, inds in groups.items():
orig_arr = getattr(adatas[k], dim_attr)["arr"]
full_arr = getattr(w_pairwise, dim_attr)["arr"]
# Check original values are intact
assert_equal(orig_arr, _subset(full_arr, (inds, inds)))
# Check that entries are filled with zeroes
assert_equal(
sparse.csr_matrix((len(inds), len(full_inds) - len(inds))),
_subset(full_arr, (inds, np.setdiff1d(full_inds, inds))),
)
assert_equal(
sparse.csr_matrix((len(full_inds) - len(inds), len(inds))),
_subset(full_arr, (np.setdiff1d(full_inds, inds), inds)),
)
# Check that argument does not affect alternative axis
assert "arr" in getattr(
concat(adatas, axis=axis, pairwise=False, merge="first"), alt_attr)
def test_concatenate_obsm_outer(obsm_adatas, fill_val):
outer = obsm_adatas[0].concatenate(
obsm_adatas[1:], join="outer", fill_value=fill_val
)
inner = obsm_adatas[0].concatenate(obsm_adatas[1:], join="inner")
for k, inner_v in inner.obsm.items():
assert np.array_equal(
_subset(outer.obsm[k], (slice(None), slice(None, inner_v.shape[1]))),
inner_v,
)
assert set(outer.obsm.keys()) == {"dense", "df", "sparse"}
assert isinstance(outer.obsm["dense"], np.ndarray)
np.testing.assert_equal(
outer.obsm["dense"],
np.array(
[
[0, 1, fill_val],
[2, 3, fill_val],
[4, 5, fill_val],
[0, 1, 2],
[3, 4, 5],
[6, 7, 8],
[9, 10, 11],
[4, 5, fill_val],
[6, 7, fill_val],
]
),
)
assert isinstance(outer.obsm["sparse"], sparse.spmatrix)
np.testing.assert_equal(
outer.obsm["sparse"].toarray(),
np.array(
[
[0, 1, fill_val, fill_val],
[2, 3, fill_val, fill_val],
[4, 5, fill_val, fill_val],
[fill_val, fill_val, fill_val, fill_val],
[fill_val, fill_val, fill_val, fill_val],
[fill_val, fill_val, fill_val, fill_val],
[fill_val, fill_val, fill_val, fill_val],
[0, 1, 2, 3],
[4, 5, 6, 7],
]
),
)
# fmt: off
true_df = (
pd.concat([a.obsm["df"] for a in obsm_adatas], join="outer")
.reset_index(drop=True)
)
# fmt: on
cur_df = outer.obsm["df"].reset_index(drop=True)
pd.testing.assert_frame_equal(true_df, cur_df)
def subset_dim(adata, *, obs=slice(None), var=slice(None)):
# Should probably get used for test_inplace_subset_var and test_inplace_subset_obs
from anndata._core.index import _subset
return _subset(adata, (obs, var))
