import json from os import path, listdir import unittest import decode_fbs import tempfile import shutil import numpy as np import pandas as pd from server.app.scanpy_engine.scanpy_engine import ScanpyEngine from server.app.util.fbs.matrix import encode_matrix_fbs from server.app.util.data_locator import DataLocator class WritableAnnotationTest(unittest.TestCase): def setUp(self): self.tmpDir = tempfile.mkdtemp() self.label_file = path.join(self.tmpDir, "labels.csv") args = { "layout": ["umap"], "max_category_items": 100, "obs_names": None, "var_names": None, "diffexp_lfc_cutoff": 0.01, "label_file": self.label_file } self.data = ScanpyEngine(DataLocator("example-dataset/pbmc3k.h5ad"), args) def tearDown(self): shutil.rmtree(self.tmpDir) def make_fbs(self, data): df = pd.DataFrame(data) return encode_matrix_fbs(matrix=df, row_idx=None, col_idx=df.columns) def test_error_checks(self): # verify that the expected errors are generated n_rows = self.data.data.obs.shape[0] fbs_bad = self.make_fbs({ 'louvain': pd.Series(['undefined' for l in range(0, n_rows)], dtype='category') }) # ensure attempt to change VAR annotation with self.assertRaises(ValueError): self.data.annotation_put_fbs("var", fbs_bad) # ensure we catch attempt to overwrite non-writable data with self.assertRaises(KeyError): self.data.annotation_put_fbs("obs", fbs_bad) def test_write_to_file(self): # verify the file is written as expected n_rows = self.data.data.obs.shape[0] fbs = self.make_fbs({ 'cat_A': pd.Series(['label_A' for l in range(0, n_rows)], dtype='category'), 'cat_B': pd.Series(['label_B' for l in range(0, n_rows)], dtype='category') }) res = self.data.annotation_put_fbs("obs", fbs) self.assertEqual(res, json.dumps({"status": "OK"})) self.assertTrue(path.exists(self.label_file)) df = pd.read_csv(self.label_file, index_col=0, header=0, comment='#') self.assertEqual(df.shape, (n_rows, 2)) self.assertEqual(set(df.columns), set(['cat_A', 'cat_B'])) self.assertTrue(self.data.original_obs_index.equals(df.index)) self.assertTrue(np.all(df['cat_A'] == ['label_A' for l in range(0, n_rows)])) self.assertTrue(np.all(df['cat_B'] == ['label_B' for l in range(0, n_rows)])) # verify complete overwrite on second attempt, AND rotation occurs fbs = self.make_fbs({ 'cat_A': pd.Series(['label_A1' for l in range(0, n_rows)], dtype='category'), 'cat_C': pd.Series(['label_C' for l in range(0, n_rows)], dtype='category') }) res = self.data.annotation_put_fbs("obs", fbs) self.assertEqual(res, json.dumps({"status": "OK"})) self.assertTrue(path.exists(self.label_file)) df = pd.read_csv(self.label_file, index_col=0, header=0, comment='#') self.assertEqual(set(df.columns), set(['cat_A', 'cat_C'])) self.assertTrue(np.all(df['cat_A'] == ['label_A1' for l in range(0, n_rows)])) self.assertTrue(np.all(df['cat_C'] == ['label_C' for l in range(0, n_rows)])) # rotation name, ext = path.splitext(self.label_file) self.assertTrue(path.exists(f"{name}-1{ext}")) def test_file_rotation_to_max_9(self): # verify we stop rotation at 9 n_rows = self.data.data.obs.shape[0] fbs = self.make_fbs({ 'cat_A': pd.Series(['label_A' for l in range(0, n_rows)], dtype='category'), 'cat_B': pd.Series(['label_B' for l in range(0, n_rows)], dtype='category') }) for i in range(0, 11): res = self.data.annotation_put_fbs("obs", fbs) self.assertEqual(res, json.dumps({"status": "OK"})) name, ext = path.splitext(self.label_file) expected_files = [self.label_file] + [f"{name}-{i}{ext}" for i in range(1, 10)] found_files = [path.join(self.tmpDir, p) for p in listdir(self.tmpDir)] self.assertEqual(set(expected_files), set(found_files)) 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.data.obs.shape[0] fbs = self.make_fbs({ 'cat_A': pd.Series(['label_A' for l in range(0, n_rows)], dtype='category'), 'cat_B': pd.Series(['label_B' for l in range(0, n_rows)], dtype='category') }) # put res = self.data.annotation_put_fbs("obs", fbs) self.assertEqual(res, json.dumps({"status": "OK"})) # get fbsAll = self.data.annotation_to_fbs_matrix("obs") schema = self.data.get_schema() 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' for l in range(0, n_rows) ]) self.assertEqual(annotations["columns"][col_idx.index('cat_B')], [ 'label_B' for l in range(0, 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 })