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Diffstat (limited to 'tests/incite/__init__.py')
| -rw-r--r-- | tests/incite/__init__.py | 137 |
1 files changed, 137 insertions, 0 deletions
diff --git a/tests/incite/__init__.py b/tests/incite/__init__.py new file mode 100644 index 0000000..2f736e8 --- /dev/null +++ b/tests/incite/__init__.py @@ -0,0 +1,137 @@ +# class TestParquetBehaviors(CleanTempDirectoryTestCls): +# wall_coll = WallDFCollection( +# start=GLOBAL_VARS["wall"].start, +# offset="49h", +# archive_path=f"{settings.incite_mount_dir}/raw/df-collections/{DFCollectionType.WALL.value}", +# ) +# +# def test_filters(self): +# # Using REAL data here +# start = datetime(year=2024, month=1, day=15, hour=12, tzinfo=timezone.utc) +# end = datetime(year=2024, month=1, day=15, hour=20, tzinfo=timezone.utc) +# end_max = datetime( +# year=2024, month=1, day=15, hour=20, tzinfo=timezone.utc +# ) + timedelta(hours=2) +# +# ir = pd.Interval(left=pd.Timestamp(start), right=pd.Timestamp(end)) +# wall_items = [w for w in self.wall_coll.items if w.interval.overlaps(ir)] +# ddf = self.wall_coll.ddf( +# items=wall_items, +# include_partial=True, +# force_rr_latest=False, +# columns=["started", "finished"], +# filters=[ +# ("started", ">=", start), +# ("started", "<", end), +# ], +# ) +# +# df = ddf.compute() +# self.assertIsInstance(df, pd.DataFrame) +# +# # No started=None, and they're all between the started and the end +# self.assertFalse(df.started.isna().any()) +# self.assertFalse((df.started < start).any()) +# self.assertFalse((df.started > end).any()) +# +# # Has finished=None and finished=time, so +# # the finished is all between the started and +# # the end_max +# self.assertTrue(df.finished.isna().any()) +# self.assertTrue((df.finished.dt.year == 2024).any()) +# +# self.assertFalse((df.finished > end_max).any()) +# self.assertFalse((df.finished < start).any()) +# +# # def test_user_id_list(self): +# # # Calling compute turns it into a np.ndarray +# # user_ids = self.instance.ddf( +# # columns=["user_id"] +# # ).user_id.unique().values.compute() +# # self.assertIsInstance(user_ids, np.ndarray) +# # +# # # If ddf filters work with ndarray +# # user_product_merge = <todo: assign> +# # +# # with self.assertRaises(TypeError) as cm: +# # user_product_merge.ddf( +# # filters=[("id", "in", user_ids)]) +# # self.assertIn("Value of 'in' filter must be a list, set or tuple.", str(cm.exception)) +# # +# # # No compute == dask array +# # user_ids = self.instance.ddf( +# # columns=["user_id"] +# # ).user_id.unique().values +# # self.assertIsInstance(user_ids, da.Array) +# # +# # with self.assertRaises(TypeError) as cm: +# # user_product_merge.ddf( +# # filters=[("id", "in", user_ids)]) +# # self.assertIn("Value of 'in' filter must be a list, set or tuple.", str(cm.exception)) +# # +# # # pick a product_id (most active one) +# # self.product_id = instance.df.product_id.value_counts().index[0] +# # self.expected_columns: int = len(instance._schema.columns) +# # self.instance = instance +# +# # def test_basic(self): +# # # now try to load up the data! +# # self.instance.grouped_key = self.product_id +# # +# # # Confirm any of the items are archived +# # self.assertTrue(self.instance.progress.has_archive.eq(True).any()) +# # +# # # Confirm it returns a df +# # df = self.instance.dd().compute() +# # +# # self.assertFalse(df.empty) +# # self.assertEqual(df.shape[1], self.expected_columns) +# # self.assertGreater(df.shape[0], 1) +# # +# # # Confirm that DF only contains this product_id +# # self.assertEqual(df[df.product_id == self.product_id].shape, df.shape) +# +# # def test_god_vs_product_id(self): +# # self.instance.grouped_key = self.product_id +# # df_product_origin = self.instance.dd(columns=None, filters=None).compute() +# # +# # self.instance.grouped_key = None +# # df_god_origin = self.instance.dd(columns=None, +# # filters=[("product_id", "==", self.product_id)]).compute() +# # +# # self.assertTrue(df_god_origin.equals(df_product_origin)) +# +# # +# # instance = POPSessionMerge( +# # start=START, +# # archive_path=self.PATH, +# # group_by="product_id" +# # ) +# # instance.build(U=GLOBAL_VARS["user"], S=GLOBAL_VARS["session"], W=GLOBAL_VARS["wall"]) +# # instance.save(god_only=False) +# # +# # # pick a product_id (most active one) +# # self.product_id = instance.df.product_id.value_counts().index[0] +# # self.expected_columns: int = len(instance._schema.columns) +# # self.instance = instance +# +# +# class TestValidItem(CleanTempDirectoryTestCls): +# +# def test_interval(self): +# for k in GLOBAL_VARS.keys(): +# coll = GLOBAL_VARS[k] +# item = coll.items[0] +# ir = item.interval +# +# self.assertIsInstance(ir, pd.Interval) +# self.assertLess(a=ir.left, b=ir.right) +# +# def test_str(self): +# for k in GLOBAL_VARS.keys(): +# coll = GLOBAL_VARS[k] +# item = coll.items[0] +# +# offset = coll.offset or "–" +# +# self.assertIn(offset, str(item)) |