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from __future__ import annotations
from importlib.util import find_spec
from typing import TYPE_CHECKING
import numpy as np
import pandas as pd
import pytest
import zarr
from anndata import AnnData
from anndata.compat import DaskArray
from anndata.experimental import read_elem_lazy, read_lazy
from anndata.io import write_elem
from anndata.tests.helpers import (
GEN_ADATA_NO_XARRAY_ARGS,
AccessTrackingStore,
assert_equal,
gen_adata,
gen_typed_df,
)
from .conftest import ANNDATA_ELEMS
if TYPE_CHECKING:
from collections.abc import Callable
from pathlib import Path
from anndata._types import AnnDataElem
pytestmark = pytest.mark.skipif(not find_spec("xarray"), reason="xarray not installed")
@pytest.mark.parametrize(
("elem_key", "sub_key"),
[
("raw", "X"),
("obs", "cat"),
("obs", "int64"),
*((elem_name, None) for elem_name in ANNDATA_ELEMS),
],
)
def test_access_count_elem_access(
remote_store_tall_skinny: AccessTrackingStore,
adata_remote_tall_skinny: AnnData,
elem_key: AnnDataElem,
sub_key: str,
simple_subset_func: Callable[[AnnData], AnnData],
):
full_path = f"{elem_key}/{sub_key}" if sub_key is not None else elem_key
remote_store_tall_skinny.initialize_key_trackers({full_path, "X"})
# a series of methods that should __not__ read in any data
elem = getattr(simple_subset_func(adata_remote_tall_skinny), elem_key)
if sub_key is not None:
if elem_key in {"obs", "var"}:
elem[sub_key]
else:
getattr(elem, sub_key)
remote_store_tall_skinny.assert_access_count(full_path, 0)
remote_store_tall_skinny.assert_access_count("X", 0)
def test_access_count_subset(
remote_store_tall_skinny: AccessTrackingStore,
adata_remote_tall_skinny: AnnData,
):
non_obs_elem_names = filter(lambda e: e != "obs", ANNDATA_ELEMS)
remote_store_tall_skinny.initialize_key_trackers([
"obs/cat/codes",
*non_obs_elem_names,
])
adata_remote_tall_skinny[adata_remote_tall_skinny.obs["cat"] == "a", :]
# all codes read in for subset (from 4 chunks as set in the fixture)
remote_store_tall_skinny.assert_access_count("obs/cat/codes", 4)
for elem_name in non_obs_elem_names:
remote_store_tall_skinny.assert_access_count(elem_name, 0)
def test_access_count_subset_column_compute(
remote_store_tall_skinny: AccessTrackingStore,
adata_remote_tall_skinny: AnnData,
):
remote_store_tall_skinny.initialize_key_trackers(["obs/int64"])
adata_remote_tall_skinny[adata_remote_tall_skinny.shape[0] // 2, :].obs[
"int64"
].compute()
# two chunks needed for 0:10 subset
remote_store_tall_skinny.assert_access_count("obs/int64", 1)
def test_access_count_index(
remote_store_tall_skinny: AccessTrackingStore,
):
remote_store_tall_skinny.initialize_key_trackers(["obs/_index"])
read_lazy(remote_store_tall_skinny, load_annotation_index=False)
remote_store_tall_skinny.assert_access_count("obs/_index", 0)
read_lazy(remote_store_tall_skinny)
# 4 is number of chunks
remote_store_tall_skinny.assert_access_count("obs/_index", 4)
def test_access_count_dtype(
remote_store_tall_skinny: AccessTrackingStore,
adata_remote_tall_skinny: AnnData,
):
remote_store_tall_skinny.initialize_key_trackers(["obs/cat/categories"])
remote_store_tall_skinny.assert_access_count("obs/cat/categories", 0)
# This should only cause categories to be read in once
adata_remote_tall_skinny.obs["cat"].dtype # noqa: B018
adata_remote_tall_skinny.obs["cat"].dtype # noqa: B018
adata_remote_tall_skinny.obs["cat"].dtype # noqa: B018
remote_store_tall_skinny.assert_access_count("obs/cat/categories", 1)
def test_uns_uses_dask(adata_remote: AnnData):
assert isinstance(adata_remote.uns["nested"]["nested_further"]["array"], DaskArray)
def test_to_memory(adata_remote: AnnData, adata_orig: AnnData):
remote_to_memory = adata_remote.to_memory()
assert_equal(remote_to_memory, adata_orig)
def test_access_counts_obsm_df(tmp_path: Path):
adata = AnnData(
X=np.array(np.random.rand(100, 20)),
)
adata.obsm["df"] = pd.DataFrame(
{"col1": np.random.rand(100), "col2": np.random.rand(100)},
index=adata.obs_names,
)
adata.write_zarr(tmp_path)
store = AccessTrackingStore(tmp_path)
store.initialize_key_trackers(["obsm/df"])
read_lazy(store, load_annotation_index=False)
store.assert_access_count("obsm/df", 0)
def test_view_to_memory(adata_remote: AnnData, adata_orig: AnnData):
obs_cats = adata_orig.obs["obs_cat"].cat.categories
subset_obs = adata_orig.obs["obs_cat"] == obs_cats[0]
assert_equal(adata_orig[subset_obs, :], adata_remote[subset_obs, :].to_memory())
var_cats = adata_orig.var["var_cat"].cat.categories
subset_var = adata_orig.var["var_cat"] == var_cats[0]
assert_equal(adata_orig[:, subset_var], adata_remote[:, subset_var].to_memory())
def test_view_of_view_to_memory(adata_remote: AnnData, adata_orig: AnnData):
cats_obs = adata_orig.obs["obs_cat"].cat.categories
subset_obs = (adata_orig.obs["obs_cat"] == cats_obs[0]) | (
adata_orig.obs["obs_cat"] == cats_obs[1]
)
subsetted_adata = adata_orig[subset_obs, :]
subset_subset_obs = subsetted_adata.obs["obs_cat"] == cats_obs[1]
subsetted_subsetted_adata = subsetted_adata[subset_subset_obs, :]
assert_equal(
subsetted_subsetted_adata,
adata_remote[subset_obs, :][subset_subset_obs, :].to_memory(),
)
cats_var = adata_orig.var["var_cat"].cat.categories
subset_var = (adata_orig.var["var_cat"] == cats_var[0]) | (
adata_orig.var["var_cat"] == cats_var[1]
)
subsetted_adata = adata_orig[:, subset_var]
subset_subset_var = subsetted_adata.var["var_cat"] == cats_var[1]
subsetted_subsetted_adata = subsetted_adata[:, subset_subset_var]
assert_equal(
subsetted_subsetted_adata,
adata_remote[:, subset_var][:, subset_subset_var].to_memory(),
)
@pytest.mark.zarr_io
def test_unconsolidated(tmp_path: Path, mtx_format):
adata = gen_adata((10, 10), mtx_format, **GEN_ADATA_NO_XARRAY_ARGS)
orig_pth = tmp_path / "orig.zarr"
adata.write_zarr(orig_pth)
(orig_pth / ".zmetadata").unlink()
store = AccessTrackingStore(orig_pth)
store.initialize_key_trackers(["obs/.zgroup", ".zgroup"])
with pytest.warns(UserWarning, match=r"Did not read zarr as consolidated"):
remote = read_lazy(store)
remote_to_memory = remote.to_memory()
assert_equal(remote_to_memory, adata)
store.assert_access_count("obs/.zgroup", 1)
def test_h5_file_obj(tmp_path: Path):
adata = gen_adata((10, 10), **GEN_ADATA_NO_XARRAY_ARGS)
orig_pth = tmp_path / "adata.h5ad"
adata.write_h5ad(orig_pth)
remote = read_lazy(orig_pth)
assert remote.file.is_open
assert remote.filename == orig_pth
assert_equal(remote.to_memory(), adata)
@pytest.fixture(scope="session")
def df_group(tmp_path_factory) -> zarr.Group:
df = gen_typed_df(120)
path = tmp_path_factory.mktemp("foo.zarr")
g = zarr.open_group(path, mode="w", zarr_format=2)
write_elem(g, "foo", df, dataset_kwargs={"chunks": 25})
return zarr.open(path, mode="r")["foo"]
@pytest.mark.parametrize(
("chunks", "expected_chunks"),
[((1,), (1,)), ((-1,), (120,)), (None, (25,))],
ids=["small", "minus_one_uses_full", "none_uses_ondisk_chunking"],
)
def test_chunks_df(
tmp_path: Path,
chunks: tuple[int] | None,
expected_chunks: tuple[int],
df_group: zarr.Group,
):
ds = read_elem_lazy(df_group, chunks=chunks)
for k in ds:
if isinstance(arr := ds[k].data, DaskArray):
assert arr.chunksize == expected_chunks
|
