Files
cellxgene/server/test/test_writable_annotation.py
bmccandless f69d141336 refactor config to support different config options for datasets in different dataroots. (#1596)
This will give us the ability to specify different config options for
different dataroots.

the key of the dataroot dictionary is no longer the same as the dataroot_url.
Previously key==dataroot_url, and now those are separated.

Added an "is_multi_dataset" function to simplify logic where it branched on single vs multi.

Simplified the rest.py interface by no longer passing in the user annotations object, since
that can be retrieved from the dataset.
2020-07-10 16:21:40 -07:00

157 lines
6.4 KiB
Python

import json
from os import path, listdir
import unittest
import server.test.decode_fbs as decode_fbs
import shutil
import numpy as np
import pandas as pd
from server.common.rest import schema_get_helper, annotations_put_fbs_helper
from server.test import data_with_tmp_annotations, make_fbs
from server.data_common.matrix_loader import MatrixDataType
class WritableAnnotationTest(unittest.TestCase):
def setUp(self):
self.data, self.tmp_dir, self.annotations = data_with_tmp_annotations(MatrixDataType.H5AD)
self.data.dataset_config.user_annotations = self.annotations
def tearDown(self):
shutil.rmtree(self.tmp_dir)
def annotation_put_fbs(self, fbs):
annotations_put_fbs_helper(self.data, fbs)
res = json.dumps({"status": "OK"})
return res
def test_error_checks(self):
# verify that the expected errors are generated
n_rows = self.data.get_shape()[0]
fbs_bad = make_fbs({"louvain": pd.Series(["undefined"] * n_rows, dtype="category")})
# ensure we catch attempt to overwrite non-writable data
with self.assertRaises(KeyError):
self.annotation_put_fbs(fbs_bad)
def test_write_to_file(self):
# verify the file is written as expected
n_rows = self.data.get_shape()[0]
fbs = make_fbs(
{
"cat_A": pd.Series(["label_A"] * n_rows, dtype="category"),
"cat_B": pd.Series(["label_B"] * n_rows, dtype="category"),
}
)
res = self.annotation_put_fbs(fbs)
self.assertEqual(res, json.dumps({"status": "OK"}))
self.assertTrue(path.exists(self.annotations.output_file))
df = pd.read_csv(self.annotations.output_file, index_col=0, header=0, comment="#")
self.assertEqual(df.shape, (n_rows, 2))
self.assertEqual(set(df.columns), {"cat_A", "cat_B"})
self.assertTrue(self.data.original_obs_index.equals(df.index))
self.assertTrue(np.all(df["cat_A"] == ["label_A"] * n_rows))
self.assertTrue(np.all(df["cat_B"] == ["label_B"] * n_rows))
# verify complete overwrite on second attempt, AND rotation occurs
fbs = make_fbs(
{
"cat_A": pd.Series(["label_A1"] * n_rows, dtype="category"),
"cat_C": pd.Series(["label_C"] * n_rows, dtype="category"),
}
)
res = self.annotation_put_fbs(fbs)
self.assertEqual(res, json.dumps({"status": "OK"}))
self.assertTrue(path.exists(self.annotations.output_file))
df = pd.read_csv(self.annotations.output_file, index_col=0, header=0, comment="#")
self.assertEqual(set(df.columns), {"cat_A", "cat_C"})
self.assertTrue(np.all(df["cat_A"] == ["label_A1"] * n_rows))
self.assertTrue(np.all(df["cat_C"] == ["label_C"] * n_rows))
# rotation
name, ext = path.splitext(self.annotations.output_file)
backup_dir = f"{name}-backups"
self.assertTrue(path.isdir(backup_dir))
found_files = listdir(backup_dir)
self.assertEqual(len(found_files), 1)
def test_file_rotation_to_max_9(self):
# verify we stop rotation at 9
n_rows = self.data.get_shape()[0]
fbs = make_fbs(
{
"cat_A": pd.Series(["label_A"] * n_rows, dtype="category"),
"cat_B": pd.Series(["label_B"] * n_rows, dtype="category"),
}
)
for i in range(0, 11):
res = self.annotation_put_fbs(fbs)
self.assertEqual(res, json.dumps({"status": "OK"}))
name, ext = path.splitext(self.annotations.output_file)
backup_dir = f"{name}-backups"
self.assertTrue(path.isdir(backup_dir))
found_files = listdir(backup_dir)
self.assertTrue(len(found_files) <= 9)
def test_put_get_roundtrip(self):
# verify that OBS PUTs (annotation_put_fbs) are accessible via
# GET (annotation_to_fbs_matrix)
n_rows = self.data.get_shape()[0]
fbs = make_fbs(
{
"cat_A": pd.Series(["label_A"] * n_rows, dtype="category"),
"cat_B": pd.Series(["label_B"] * n_rows, dtype="category"),
}
)
# put
res = self.annotation_put_fbs(fbs)
self.assertEqual(res, json.dumps({"status": "OK"}))
# get
labels = self.annotations.read_labels(None)
fbsAll = self.data.annotation_to_fbs_matrix("obs", None, labels)
schema = schema_get_helper(self.data)
annotations = decode_fbs.decode_matrix_FBS(fbsAll)
obs_index_col_name = schema["annotations"]["obs"]["index"]
self.assertEqual(annotations["n_rows"], n_rows)
self.assertEqual(annotations["n_cols"], 7)
self.assertIsNone(annotations["row_idx"])
self.assertEqual(
annotations["col_idx"],
[obs_index_col_name, "n_genes", "percent_mito", "n_counts", "louvain", "cat_A", "cat_B"],
)
col_idx = annotations["col_idx"]
self.assertEqual(annotations["columns"][col_idx.index("cat_A")], ["label_A"] * n_rows)
self.assertEqual(annotations["columns"][col_idx.index("cat_B")], ["label_B"] * n_rows)
# verify the schema was updated
all_col_schema = {c["name"]: c for c in schema["annotations"]["obs"]["columns"]}
self.assertEqual(
all_col_schema["cat_A"],
{"name": "cat_A", "type": "categorical", "categories": ["label_A"], "writable": True},
)
self.assertEqual(
all_col_schema["cat_B"],
{"name": "cat_B", "type": "categorical", "categories": ["label_B"], "writable": True},
)
def test_config(self):
features = self.data.get_features(self.annotations)
# test each for singular presence and accuracy of available flag
def check_feature(method, path, available):
feature = list(
filter(lambda f: f.method == method and f.path == path and f.available == available, features)
)
self.assertIsNotNone(feature)
self.assertEqual(len(feature), 1)
check_feature("POST", "/cluster/", False)
check_feature("POST", "/diffexp/", self.data.dataset_config.diffexp__enable)
check_feature("GET", "/layout/obs", True)
check_feature("PUT", "/layout/obs", self.data.dataset_config.embeddings__enable_reembedding)
check_feature("PUT", "/annotations/obs", True)