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"""\
This file contains tests for conversion made during io.
"""
import h5py
import numpy as np
import pytest
from scipy import sparse
import anndata as ad
from anndata.tests.helpers import gen_adata, assert_equal
@pytest.fixture(
params=[sparse.csr_matrix, sparse.csc_matrix, np.array],
ids=["scipy-csr", "scipy-csc", "np-array"],
)
def mtx_format(request):
return request.param
@pytest.fixture(
params=[sparse.csr_matrix, sparse.csc_matrix],
ids=["scipy-csr", "scipy-csc"],
)
def spmtx_format(request):
return request.param
@pytest.fixture(params=[("raw/X",), ("X",), ("X", "raw/X")])
def to_convert(request):
return request.param
def test_sparse_to_dense_disk(tmp_path, mtx_format, to_convert):
mem_pth = tmp_path / "orig.h5ad"
dense_from_mem_pth = tmp_path / "dense_mem.h5ad"
dense_from_disk_pth = tmp_path / "dense_disk.h5ad"
mem = gen_adata((50, 50), mtx_format)
mem.raw = mem
mem.write_h5ad(mem_pth)
disk = ad.read_h5ad(mem_pth, backed="r")
mem.write_h5ad(dense_from_mem_pth, as_dense=to_convert)
disk.write_h5ad(dense_from_disk_pth, as_dense=to_convert)
with h5py.File(dense_from_mem_pth, "r") as f:
for k in to_convert:
assert isinstance(f[k], h5py.Dataset)
with h5py.File(dense_from_disk_pth, "r") as f:
for k in to_convert:
assert isinstance(f[k], h5py.Dataset)
for backed in [None, "r"]:
from_mem = ad.read_h5ad(dense_from_mem_pth, backed=backed)
from_disk = ad.read_h5ad(dense_from_disk_pth, backed=backed)
assert_equal(mem, from_mem)
assert_equal(mem, from_disk)
assert_equal(disk, from_mem)
assert_equal(disk, from_disk)
def test_sparse_to_dense_inplace(tmp_path, spmtx_format):
pth = tmp_path / "adata.h5ad"
orig = gen_adata((50, 50), spmtx_format)
orig.raw = orig
orig.write(pth)
backed = ad.read_h5ad(pth, backed="r+")
backed.write(as_dense=("X", "raw/X"))
new = ad.read_h5ad(pth)
assert_equal(orig, new)
assert_equal(backed, new)
assert isinstance(new.X, np.ndarray)
assert isinstance(new.raw.X, np.ndarray)
assert isinstance(orig.X, spmtx_format)
assert isinstance(orig.raw.X, spmtx_format)
assert isinstance(backed.X, h5py.Dataset)
assert isinstance(backed.raw.X, h5py.Dataset)
def test_sparse_to_dense_errors(tmp_path):
adata = ad.AnnData(X=sparse.random(50, 50, format="csr"))
adata.layers["like_X"] = adata.X.copy()
with pytest.raises(ValueError):
adata.write_h5ad(tmp_path / "failure.h5ad", as_dense=("raw/X"))
adata.write_h5ad(tmp_path / "failure.h5ad", as_dense=("raw", "X"))
with pytest.raises(NotImplementedError):
adata.write_h5ad(tmp_path / "failure.h5ad", as_dense=("layers/like_X"))
def test_dense_to_sparse_memory(tmp_path, spmtx_format, to_convert):
dense_path = tmp_path / "dense.h5ad"
orig = gen_adata((50, 50), np.array)
orig.raw = orig
orig.write_h5ad(dense_path)
assert not isinstance(orig.X, sparse.spmatrix)
assert not isinstance(orig.raw.X, sparse.spmatrix)
curr = ad.read_h5ad(dense_path, as_sparse=to_convert, as_sparse_fmt=spmtx_format)
if "X" in to_convert:
assert isinstance(curr.X, spmtx_format)
if "raw/X" in to_convert:
assert isinstance(curr.raw.X, spmtx_format)
assert_equal(orig, curr)
def test_dense_to_sparse_errors(tmp_path):
dense_pth = tmp_path / "dense.h5ad"
adata = ad.AnnData(X=np.ones((50, 50)))
adata.layers["like_X"] = adata.X.copy()
adata.write(dense_pth)
with pytest.raises(NotImplementedError):
ad.read_h5ad(dense_pth, as_sparse=("X",), as_sparse_fmt=sparse.coo_matrix)
with pytest.raises(NotImplementedError):
ad.read_h5ad(dense_pth, as_sparse=("layers/like_X",))
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