mirror of
https://github.com/chanzuckerberg/cellxgene.git
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b9e132a00c
* Updates due dependency version changes. h5py recently changes and now values once returned as str are now returned as bytes. This would have caused a much larger change, so instead the version is restricted to <3.0.0. This caused the bulk of the testing failues. A few other changes were needed to make a few other tests pass. #1959
273 lines
11 KiB
Python
273 lines
11 KiB
Python
import json
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import shutil
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import unittest
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from os import path, listdir
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from unittest.mock import MagicMock, patch
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import numpy as np
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import pandas as pd
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import tiledb
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from flask import Flask
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import server.test.unit.decode_fbs as decode_fbs
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from server.common.errors import AnnotationCategoryNameError
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from server.common.rest import schema_get_helper, annotations_put_fbs_helper
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from server.data_common.matrix_loader import MatrixDataType
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from server.db.cellxgene_orm import CellxGeneDataset, Annotation
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from server.test import data_with_tmp_annotations, make_fbs, data_with_tmp_tiledb_annotations
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class auth(object):
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def get_user_id():
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return "1234"
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def get_user_name():
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return "person name"
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class WritableTileDBStoredAnnotationTest(unittest.TestCase):
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def setUp(self):
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self.user_id = "1234"
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self.data, self.tmp_dir, self.annotations = data_with_tmp_tiledb_annotations(MatrixDataType.H5AD)
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self.data.dataset_config.user_annotations = self.annotations
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self.db = self.annotations.db
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self.n_rows = self.data.get_shape()[0]
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self.test_dict = {
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"cat_A": pd.Series(["label_A"] * self.n_rows, dtype="category"),
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"cat_B": pd.Series(["label_B"] * self.n_rows, dtype="category"),
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}
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self.fbs = make_fbs(self.test_dict)
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self.df = pd.DataFrame(self.test_dict)
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self.app = Flask("fake_app")
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self.app.__setattr__("auth", auth)
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def tearDown(self):
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shutil.rmtree(self.tmp_dir)
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def annotation_put_fbs(self, fbs):
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annotations_put_fbs_helper(self.data, fbs)
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res = json.dumps({"status": "OK"})
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return res
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def test_category_name_throws_errors_for_categories_that_cant_be_converted_to_filenames(self):
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with self.app.test_request_context():
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bad_category_names = make_fbs(
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{
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"cat_A": pd.Series(["label_A"] * self.n_rows, dtype="category"),
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"cat/B": pd.Series(["label_B"] * self.n_rows, dtype="category"),
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}
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)
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with self.assertRaises(AnnotationCategoryNameError):
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self.annotation_put_fbs(bad_category_names)
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def test_convert_to_pandas__converts_tiledb_to_pandas_df(self):
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with self.app.test_request_context():
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self.annotations.write_labels(self.df, self.data)
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dataset_id = self.db.query([CellxGeneDataset], [CellxGeneDataset.name == self.data.get_location()])[0].id
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annotation = self.db.query_for_most_recent(
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Annotation, [Annotation.user_id == self.user_id, Annotation.dataset_id == str(dataset_id)]
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)
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# retrieve tiledb array
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df = tiledb.open(annotation.tiledb_uri)
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self.assertEqual(type(df), tiledb.array.SparseArray)
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# convert to pandas df
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pandas_df = self.annotations.convert_to_pandas_df(df, annotation.schema_hints)
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self.assertEqual(type(pandas_df), pd.DataFrame)
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def test_write_labels_creates_a_dataset_if_it_doesnt_exist(self):
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with self.app.test_request_context():
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new_name = "new_dataset/location"
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self.data.get_location = MagicMock(return_value=new_name)
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num_datasets = len(self.db.query([CellxGeneDataset]))
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self.annotation_put_fbs(self.fbs)
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more_datasets = len(self.db.query([CellxGeneDataset]))
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self.assertGreater(more_datasets, num_datasets)
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self.assertGreater(len(self.db.query([CellxGeneDataset], [CellxGeneDataset.name == new_name])), 0)
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def test_write_labels_links_to_existing_dataset(self):
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with self.app.test_request_context():
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# add dataset to to db
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self.annotation_put_fbs(self.fbs)
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num_datasets = len(self.db.query([CellxGeneDataset]))
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# create another annotation with the same dataset
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self.annotation_put_fbs(self.fbs)
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same_num_datasets = len(self.db.query([CellxGeneDataset]))
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self.assertEqual(num_datasets, same_num_datasets)
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def test_read_labels_returns_pandas_df(self):
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with self.app.test_request_context():
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self.annotation_put_fbs(self.fbs)
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pandas_df = self.annotations.read_labels(self.data)
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self.assertEqual(type(pandas_df), pd.DataFrame)
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def test_read_labels_returns_df_matching_original(self):
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with self.app.test_request_context():
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self.annotation_put_fbs(self.fbs)
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pandas_df = self.annotations.read_labels(self.data)
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self.assertEqual(pandas_df.shape, (self.n_rows, 2))
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self.assertEqual(set(pandas_df.columns), {"cat_A", "cat_B"})
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self.assertTrue(self.data.original_obs_index.equals(pandas_df.index))
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self.assertTrue(np.all(pandas_df["cat_A"] == ["label_A"] * self.n_rows))
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self.assertTrue(np.all(pandas_df["cat_B"] == ["label_B"] * self.n_rows))
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def test_error_checks(self):
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# verify that the expected errors are generated
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with self.app.test_request_context():
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n_rows = self.data.get_shape()[0]
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fbs_bad = make_fbs({"louvain": pd.Series(["undefined"] * n_rows, dtype="category")})
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# ensure we catch attempt to overwrite non-writable data
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with self.assertRaises(KeyError):
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self.annotation_put_fbs(fbs_bad)
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@patch("server.common.annotations.hosted_tiledb.AnnotationsHostedTileDB.get_user_id")
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@patch("server.common.annotations.hosted_tiledb.AnnotationsHostedTileDB.get_user_name")
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def test_write_labels_stores_df_as_tiledb_array(self, mock_user_name, mock_user_id):
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mock_user_id.return_value = "1234"
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mock_user_name.return_value = "user1234"
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self.annotations.write_labels(self.df, self.data)
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# get uri
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dataset_id = self.db.query([CellxGeneDataset], [CellxGeneDataset.name == self.data.get_location()])[0].id
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annotation = self.db.query_for_most_recent(
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Annotation, [Annotation.user_id == "1234", Annotation.dataset_id == str(dataset_id)]
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)
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df = tiledb.open(annotation.tiledb_uri)
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self.assertEqual(type(df), tiledb.array.SparseArray)
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class WritableAnnotationTest(unittest.TestCase):
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def setUp(self):
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self.data, self.tmp_dir, self.annotations = data_with_tmp_annotations(MatrixDataType.H5AD)
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self.data.dataset_config.user_annotations = self.annotations
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def tearDown(self):
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shutil.rmtree(self.tmp_dir)
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def annotation_put_fbs(self, fbs):
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annotations_put_fbs_helper(self.data, fbs)
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res = json.dumps({"status": "OK"})
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return res
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def test_error_checks(self):
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# verify that the expected errors are generated
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n_rows = self.data.get_shape()[0]
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fbs_bad = make_fbs({"louvain": pd.Series(["undefined"] * n_rows, dtype="category")})
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# ensure we catch attempt to overwrite non-writable data
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with self.assertRaises(KeyError):
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self.annotation_put_fbs(fbs_bad)
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def test_write_to_file(self):
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# verify the file is written as expected
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n_rows = self.data.get_shape()[0]
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fbs = make_fbs(
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{
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"cat_A": pd.Series(["label_A"] * n_rows, dtype="category"),
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"cat_B": pd.Series(["label_B"] * n_rows, dtype="category"),
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}
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)
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res = self.annotation_put_fbs(fbs)
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self.assertEqual(res, json.dumps({"status": "OK"}))
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self.assertTrue(path.exists(self.annotations.output_file))
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df = pd.read_csv(self.annotations.output_file, index_col=0, header=0, comment="#")
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self.assertEqual(df.shape, (n_rows, 2))
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self.assertEqual(set(df.columns), {"cat_A", "cat_B"})
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self.assertTrue(self.data.original_obs_index.equals(df.index))
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self.assertTrue(np.all(df["cat_A"] == ["label_A"] * n_rows))
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self.assertTrue(np.all(df["cat_B"] == ["label_B"] * n_rows))
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# verify complete overwrite on second attempt, AND rotation occurs
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fbs = make_fbs(
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{
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"cat_A": pd.Series(["label_A1"] * n_rows, dtype="category"),
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"cat_C": pd.Series(["label_C"] * n_rows, dtype="category"),
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}
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)
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res = self.annotation_put_fbs(fbs)
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self.assertEqual(res, json.dumps({"status": "OK"}))
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self.assertTrue(path.exists(self.annotations.output_file))
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df = pd.read_csv(self.annotations.output_file, index_col=0, header=0, comment="#")
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self.assertEqual(set(df.columns), {"cat_A", "cat_C"})
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self.assertTrue(np.all(df["cat_A"] == ["label_A1"] * n_rows))
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self.assertTrue(np.all(df["cat_C"] == ["label_C"] * n_rows))
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# rotation
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name, ext = path.splitext(self.annotations.output_file)
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backup_dir = f"{name}-backups"
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self.assertTrue(path.isdir(backup_dir))
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found_files = listdir(backup_dir)
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self.assertEqual(len(found_files), 1)
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def test_file_rotation_to_max_9(self):
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# verify we stop rotation at 9
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n_rows = self.data.get_shape()[0]
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fbs = make_fbs(
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{
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"cat_A": pd.Series(["label_A"] * n_rows, dtype="category"),
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"cat_B": pd.Series(["label_B"] * n_rows, dtype="category"),
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}
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)
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for i in range(0, 11):
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res = self.annotation_put_fbs(fbs)
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self.assertEqual(res, json.dumps({"status": "OK"}))
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name, ext = path.splitext(self.annotations.output_file)
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backup_dir = f"{name}-backups"
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self.assertTrue(path.isdir(backup_dir))
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found_files = listdir(backup_dir)
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self.assertTrue(len(found_files) <= 9)
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def test_put_get_roundtrip(self):
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# verify that OBS PUTs (annotation_put_fbs) are accessible via
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# GET (annotation_to_fbs_matrix)
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n_rows = self.data.get_shape()[0]
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fbs = make_fbs(
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{
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"cat_A": pd.Series(["label_A"] * n_rows, dtype="category"),
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"cat_B": pd.Series(["label_B"] * n_rows, dtype="category"),
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}
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)
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# put
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res = self.annotation_put_fbs(fbs)
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self.assertEqual(res, json.dumps({"status": "OK"}))
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# get
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labels = self.annotations.read_labels(None)
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fbsAll = self.data.annotation_to_fbs_matrix("obs", None, labels)
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schema = schema_get_helper(self.data)
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annotations = decode_fbs.decode_matrix_FBS(fbsAll)
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obs_index_col_name = schema["annotations"]["obs"]["index"]
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self.assertEqual(annotations["n_rows"], n_rows)
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self.assertEqual(annotations["n_cols"], 7)
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self.assertIsNone(annotations["row_idx"])
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self.assertEqual(
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annotations["col_idx"],
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[obs_index_col_name, "n_genes", "percent_mito", "n_counts", "louvain", "cat_A", "cat_B"],
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)
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col_idx = annotations["col_idx"]
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self.assertEqual(annotations["columns"][col_idx.index("cat_A")], ["label_A"] * n_rows)
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self.assertEqual(annotations["columns"][col_idx.index("cat_B")], ["label_B"] * n_rows)
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# verify the schema was updated
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all_col_schema = {c["name"]: c for c in schema["annotations"]["obs"]["columns"]}
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self.assertEqual(
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all_col_schema["cat_A"],
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{"name": "cat_A", "type": "categorical", "categories": ["label_A"], "writable": True},
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)
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self.assertEqual(
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all_col_schema["cat_B"],
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{"name": "cat_B", "type": "categorical", "categories": ["label_B"], "writable": True},
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)
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