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import tempfile
import numpy as np
import pytest
from scipy.sparse import csr_matrix
from datasets import Dataset
from anndata import AnnData
from scgpt.tokenizer import GeneVocab
from scgpt.scbank import DataBank, DataTable, MetaInfo, Setting
tmp_dir = tempfile.gettempdir()
save_path = Path(tmp_dir) / "test_scGPT"
save_path.mkdir(parents=True, exist_ok=True)
def clear_files(directory: Path):
"""helper function to clear files in a dir"""
for f in directory.iterdir():
f.unlink()
def test_empty_databank():
db = DataBank()
assert db.data_tables == {}
assert db.settings == Setting()
assert db.gene_vocab == None
db = DataBank(meta_info=MetaInfo())
assert db.data_tables == {}
assert db.gene_vocab == None
db = DataBank(
meta_info=MetaInfo(on_disk_path=save_path),
settings=Setting(immediate_save=True),
)
assert (save_path / "studytable.json").is_file()
assert (save_path / "manifest.json").is_file()
clear_files(save_path)
def test_empty_datatable():
dt = DataTable(name="test")
assert dt.name == "test"
assert dt.data is None
assert not dt.is_loaded
def test_empty_metainfo():
mi = MetaInfo()
assert mi.study_ids is None
assert mi.cell_ids is None
def test_save_load_metainfo():
mi = MetaInfo(save_path)
mi.save()
assert (save_path / "studytable.json").is_file()
assert (save_path / "manifest.json").is_file()
assert MetaInfo.from_path(save_path) == mi
clear_files(save_path)
def test_datatable_save():
dt = DataTable(name="test")
file_path = save_path / "test.json"
# make sure the path does not exist originally
assert not file_path.exists()
file_path.parent.mkdir(parents=True, exist_ok=True)
# catch the exception if the data is not loaded
with pytest.raises(ValueError):
dt.save(file_path)
# actually load some example data and test saving
dt.data = Dataset.from_dict({"a": [1]})
dt.save(file_path)
assert file_path.is_file()
# delete the file
file_path.unlink()
assert not file_path.exists()
def test_meta_info_on_disk_path():
mi = MetaInfo(on_disk_path=tmp_dir)
assert mi.on_disk_path == Path(tmp_dir)
assert mi.on_disk_format == "json"
def test_add_gene_vocab():
db = DataBank()
db.gene_vocab = GeneVocab.from_dict({"a": 0, "b": 1, "c": 2})
assert len(db.gene_vocab) == 3
assert db.gene_vocab["a"] == 0
assert "c" in db.gene_vocab
with pytest.raises(ValueError):
db.gene_vocab = ["a", "b", "c"]
def test_databank_tokenize():
indptr = np.array([0, 2, 3, 6])
indices = np.array([0, 2, 2, 0, 1, 2])
data = np.array([1, 2, 3, 4, 5, 6])
data = csr_matrix((data, indices, indptr), shape=(3, 3))
# data is like:
# [[1, 0, 2],
# [0, 0, 3],
# [4, 5, 6]]
ind2ind = {0: 4, 2: 6}
tokenized = DataBank()._tokenize(data, ind2ind)
# tokenized is like:
# {'id': [0, 1, 2],
# 'genes': [[4, 6], [6], [4, 6]],
# 'expressions': [[1, 2], [3], [4, 6]]}
assert tokenized["id"] == [0, 1, 2]
assert [d.tolist() for d in tokenized["genes"]] == [[4, 6], [6], [4, 6]]
assert [d.tolist() for d in tokenized["expressions"]] == [[1, 2], [3], [4, 6]]
# tokenize numpy array
data = data.toarray()
tokenized = DataBank()._tokenize(data, ind2ind)
assert tokenized["id"] == [0, 1, 2]
assert [d.tolist() for d in tokenized["genes"]] == [[4, 6], [6], [4, 6]]
assert [d.tolist() for d in tokenized["expressions"]] == [[1, 2], [3], [4, 6]]
# test array with rows and cols of only zeros
data[:, 2] = 0
tokenized = DataBank()._tokenize(data, ind2ind)
assert tokenized["id"] == [0, 1]
assert [d.tolist() for d in tokenized["genes"]] == [[4], [4]]
assert [d.tolist() for d in tokenized["expressions"]] == [[1], [4]]
# test array w/ rare non-zero values (_tokenize will auto convert it to sparse)
data = np.zeros((3, 3))
data[0, 0] = 1.0
tokenized = DataBank()._tokenize(data, ind2ind)
assert tokenized["id"] == [0]
assert [d.tolist() for d in tokenized["genes"]] == [[4]]
assert [d.tolist() for d in tokenized["expressions"]] == [[1.0]]
# add the test for recursive batch calls
def test_databank_load_anndata():
adata = AnnData(
X=np.array([[1.0, 2.0, 3.0], [4.0, 0.0, 6.0]]),
obs={"cell": ["cell1", "cell2"], "study": ["study1", "study2"]},
var={"gene": ["gene_a", "gene_b", "gene_c"]},
)
gene_vocab = {"gene_a": 1, "gene_b": 0, "gene_c": 2}
# test factory initialization from data
db = DataBank.from_anndata(
adata,
gene_vocab,
to=save_path,
main_table_key="X",
token_col="gene",
)
assert db.main_table_key == "X"
converted_dataset = db.data_tables["X"].data
assert converted_dataset["id"] == [0, 1]
assert converted_dataset["genes"] == [[1, 0, 2], [1, 2]]
assert converted_dataset["expressions"] == [[1.0, 2.0, 3.0], [4.0, 6.0]]
assert (save_path / "X.datatable.json").is_file()
assert (save_path / "studytable.json").is_file()
assert (save_path / "manifest.json").is_file()
assert (save_path / "gene_vocab.json").is_file()
# test factory initialization from path
db = DataBank.from_path(save_path)
assert db.main_table_key == "X"
main_dataset = db.data_tables["X"].data
assert main_dataset["id"] == [0, 1]
assert main_dataset["genes"] == [[1, 0, 2], [1, 2]]
assert main_dataset["expressions"] == [[1.0, 2.0, 3.0], [4.0, 6.0]]
clear_files(save_path)
# test loading from anndata
db = DataBank(
meta_info=MetaInfo(on_disk_format=save_path),
gene_vocab=GeneVocab.from_dict(gene_vocab),
)
data_tables = db.load_anndata(adata, data_keys=["X"], token_col="gene")
assert len(data_tables) == 1
converted_dataset = data_tables[0].data
assert converted_dataset["id"] == [0, 1]
assert converted_dataset["genes"] == [[1, 0, 2], [1, 2]]
assert converted_dataset["expressions"] == [[1.0, 2.0, 3.0], [4.0, 6.0]]
def test_databank_load_multiple_anndata_layers():
adata = AnnData(
X=np.array([[1.0, 2.0, 3.0], [4.0, 0.0, 6.0]]),
obs={"cell": ["cell1", "cell2"], "study": ["study1", "study2"]},
var={"gene": ["gene_a", "gene_b", "gene_c"]},
layers={"layer1": np.array([[1.0, 2.0, 3.0], [4.0, 0.0, 6.0]])},
)
gene_vocab = {"gene_a": 1, "gene_b": 0, "gene_c": 2}
db = DataBank(meta_info=MetaInfo(), gene_vocab=GeneVocab.from_dict(gene_vocab))
data_tables = db.load_anndata(adata, token_col="gene")
assert len(data_tables) == 2
assert data_tables[0].name == "X"
assert data_tables[1].name == "layer1"
converted_dataset = data_tables[0].data
assert converted_dataset["id"] == [0, 1]
assert converted_dataset["genes"] == [[1, 0, 2], [1, 2]]
assert converted_dataset["expressions"] == [[1.0, 2.0, 3.0], [4.0, 6.0]]
converted_dataset = data_tables[1].data
assert converted_dataset["id"] == [0, 1]
assert converted_dataset["genes"] == [[1, 0, 2], [1, 2]]
assert converted_dataset["expressions"] == [[1.0, 2.0, 3.0], [4.0, 6.0]]
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