black

tests/test_union_city.py

@@ -68,7 +68,7 @@ def test_highway():
             os.path.join(_EXAMPLES_DIR, r"scenario_config.toml"),
             os.path.join(_EXAMPLES_DIR, r"model_config.toml"),
         ],
-        run_dir=union_city_root
+        run_dir=union_city_root,
     )
     controller.run()
 

tm2py/acceptance/acceptance.py

@@ -165,7 +165,7 @@ def _make_roadway_network_comparisons(self):
         s_bridge_df = self.s.simulated_bridge_details_df.copy()
 
         o_df["time_period"] = o_df.time_period.str.lower()
-        #o_df = o_df.drop(columns = ["standard_link_id"]) 
+        # o_df = o_df.drop(columns = ["standard_link_id"])
         s_trim_df = s_df[
             s_df["ft"] <= self.MAX_FACILITY_TYPE_FOR_ROADWAY_COMPARISONS
         ].copy()
@@ -347,7 +347,7 @@ def _make_transit_network_comparisons(self):
             right_on=["standard_line_name", "daily_line_name", "time_period"],
         )
 
-        boards_df = pd.concat([rail_df, non_df], axis = "rows",ignore_index=True)
+        boards_df = pd.concat([rail_df, non_df], axis="rows", ignore_index=True)
 
         boards_df["operator"] = np.where(
             boards_df["operator"].isnull(),
@@ -378,7 +378,9 @@ def _make_transit_network_comparisons(self):
             ].copy()
         )
         daily_shape_df = pd.merge(c_df, b_df, how="left", on="LINE_ID")
-        daily_shape_df = daily_shape_df.rename(columns={"INODE":"emme_a_node_id","JNODE":"emme_b_node_id"})
+        daily_shape_df = daily_shape_df.rename(
+            columns={"INODE": "emme_a_node_id", "JNODE": "emme_b_node_id"}
+        )
 
         # step 4 -- join the shapes to the boardings
         # for daily, join boardings to shape, as I care about the boardings more than the daily shapes

tm2py/acceptance/canonical.py

@@ -240,4 +240,3 @@ def _read_standard_to_emme_node_crosswalk(self) -> pd.DataFrame:
         self.standard_to_emme_node_crosswalk_df = df
 
         return
-

tm2py/acceptance/observed.py

@@ -869,10 +869,7 @@ def _reduce_pems_counts(self, read_file_from_disk=True):
             )
 
             out_df = pd.merge(
-                self.c.pems_to_link_crosswalk_df,
-                out_df,
-                how="left",
-                on="station_id"
+                self.c.pems_to_link_crosswalk_df, out_df, how="left", on="station_id"
             )
 
             out_df = self._join_tm2_node_ids(out_df)
@@ -893,16 +890,23 @@ def _reduce_pems_counts(self, read_file_from_disk=True):
                 .reset_index()
             )
             join_df = out_df[
-                ["emme_a_node_id","emme_b_node_id", "time_period", "station_id", "type", "vehicle_class"]
+                [
+                    "emme_a_node_id",
+                    "emme_b_node_id",
+                    "time_period",
+                    "station_id",
+                    "type",
+                    "vehicle_class",
+                ]
             ].copy()
             return_df = pd.merge(
                 median_df,
                 join_df,
                 how="left",
-                on=["emme_a_node_id","emme_b_node_id", "time_period", "vehicle_class"]
+                on=["emme_a_node_id", "emme_b_node_id", "time_period", "vehicle_class"],
             ).reset_index(drop=True)
 
-            #return_df = return_df.rename(columns = {"model_link_id" : "standard_link_id"})
+            # return_df = return_df.rename(columns = {"model_link_id" : "standard_link_id"})
             return_df = self._join_ohio_standards(return_df)
             return_df = self._identify_key_arterials_and_bridges(return_df)
 
@@ -955,7 +959,7 @@ def _reduce_caltrans_counts(self):
         out_df = out_df[out_df["observed_flow"].notna()]
 
         # convert to one-way flow
-        out_df["observed_flow"] = out_df["observed_flow"]/2.0
+        out_df["observed_flow"] = out_df["observed_flow"] / 2.0
 
         return_df = self._join_tm2_node_ids(out_df)
         return_df["time_period"] = self.c.ALL_DAY_WORD