update to tests and lints in light of upcoming ru-sql

cities/__init__.py

@@ -2,4 +2,5 @@
 
 Project short description.
 """
+
 __version__ = "0.0.1"

cities/queries/causal_insight_slim.py

@@ -500,10 +500,10 @@ def get_fips_predictions(
         difference = (
             self.predictions_original["observed"] - self.observed_outcomes_original
         )
-        self.predictions_original[
-            ["observed", "mean", "low", "high"]
-        ] = self.predictions_original[["observed", "mean", "low", "high"]].sub(
-            difference, axis=0
+        self.predictions_original[["observed", "mean", "low", "high"]] = (
+            self.predictions_original[["observed", "mean", "low", "high"]].sub(
+                difference, axis=0
+            )
         )
 
     def plot_predictions(

cities/queries/fips_query.py

@@ -467,9 +467,9 @@ def find_euclidean_kins(self):
                 if col.endswith(feature)
             ]
             if _selected:
-                atemporal_aggregated_dict[
-                    feature
-                ] = atemporal_featurewise_contributions_df[_selected].sum(axis=1)
+                atemporal_aggregated_dict[feature] = (
+                    atemporal_featurewise_contributions_df[_selected].sum(axis=1)
+                )
 
         aggregated_atemporal_featurewise_contributions_df = pd.DataFrame(
             atemporal_aggregated_dict
@@ -489,9 +489,9 @@ def find_euclidean_kins(self):
             axis=1,
         )
         columns_to_normalize = self.aggregated_featurewise_contributions.iloc[:, 3:]
-        self.aggregated_featurewise_contributions.iloc[
-            :, 3:
-        ] = columns_to_normalize.div(columns_to_normalize.sum(axis=1), axis=0)
+        self.aggregated_featurewise_contributions.iloc[:, 3:] = (
+            columns_to_normalize.div(columns_to_normalize.sum(axis=1), axis=0)
+        )
 
         # some sanity checks
         count = sum([1 for distance in distances if distance == 0])

docs/guides/counterfactual-explained.ipynb

@@ -21,10 +21,14 @@
     "import seaborn as sns\n",
     "\n",
     "from cities.queries.causal_insight_slim import CausalInsightSlim\n",
-    "from cities.utils.data_grabber import (DataGrabber, find_repo_root,\n",
-    "                                       list_available_features,\n",
-    "                                       list_interventions, list_outcomes,\n",
-    "                                       list_tensed_features)\n",
+    "from cities.utils.data_grabber import (\n",
+    "    DataGrabber,\n",
+    "    find_repo_root,\n",
+    "    list_available_features,\n",
+    "    list_interventions,\n",
+    "    list_outcomes,\n",
+    "    list_tensed_features,\n",
+    ")\n",
     "\n",
     "root = find_repo_root()"
    ]

docs/guides/similarity_demo.ipynb

@@ -11,10 +11,15 @@
     "import os\n",
     "\n",
     "from cities.queries.fips_query import FipsQuery\n",
+    "\n",
     "# proper imports\n",
-    "from cities.utils.data_grabber import (DataGrabber, list_available_features,\n",
-    "                                       list_interventions, list_outcomes,\n",
-    "                                       list_tensed_features)\n",
+    "from cities.utils.data_grabber import (\n",
+    "    DataGrabber,\n",
+    "    list_available_features,\n",
+    "    list_interventions,\n",
+    "    list_outcomes,\n",
+    "    list_tensed_features,\n",
+    ")\n",
     "\n",
     "smoke_test = \"CI\" in os.environ\n",
     "smoke_test = True\n",

tests/test_inference.py

@@ -34,7 +34,7 @@
 # @pytest.mark.parametrize("intervention", interventions)
 # @pytest.mark.parametrize("outcome", outcomes)
 @pytest.mark.parametrize("shift", shifts)
-def test_smoke_InteractionsModel(shift): #(intervention, outcome, shift):
+def test_smoke_InteractionsModel(shift):  # (intervention, outcome, shift):
     model = InteractionsModel(
         outcome_dataset="unemployment_rate",
         intervention_dataset="spending_commerce",