Remove Three Zone Code and Examples (#1056)

* removing tvpb from models

* remove core 3-zone code

* removing tests and examples

* blacken

* remove pathbuilder import calls

* remove references to initialize_los

* removing sandag 3 zone and doc references

* fix skim name conflict test

* fixing failed choice test

Author: dhensle

.github/workflows/core_tests.yml

@@ -143,9 +143,7 @@ jobs:
         region:
           - prototype_mtc
           - placeholder_psrc
-          - prototype_marin
           - prototype_mtc_extended
-          - prototype_sandag_xborder
           - production_semcog
           - prototype_mwcog
           - placeholder_multiple_zone

activitysim/abm/models/__init__.py

@@ -13,7 +13,6 @@
     disaggregate_accessibility,
     free_parking,
     initialize,
-    initialize_los,
     initialize_tours,
     input_checker,
     joint_tour_composition,

activitysim/abm/models/accessibility.py

@@ -154,9 +154,6 @@ def compute_accessibilities_for_zones(
     locals_d["skim_od"] = skim_dict.wrap("orig", "dest").set_df(od_df)
     locals_d["skim_do"] = skim_dict.wrap("dest", "orig").set_df(od_df)
 
-    if network_los.zone_system == los.THREE_ZONE:
-        locals_d["tvpb"] = network_los.tvpb
-
     logger.info(f"{trace_label}: assign.assign_variables")
     results, trace_results, trace_assigned_locals = assign.assign_variables(
         state,

activitysim/abm/models/atwork_subtour_mode_choice.py

@@ -97,38 +97,6 @@ def atwork_subtour_mode_choice(
         "in_time_col_name": in_time_col_name,
     }
 
-    if network_los.zone_system == los.THREE_ZONE:
-        # fixme - is this a lightweight object?
-        tvpb = network_los.tvpb
-
-        tvpb_logsum_odt = tvpb.wrap_logsum(
-            orig_key=orig_col_name,
-            dest_key=dest_col_name,
-            tod_key="out_period",
-            segment_key="demographic_segment",
-            cache_choices=True,
-            trace_label=trace_label,
-            tag="tvpb_logsum_odt",
-        )
-        tvpb_logsum_dot = tvpb.wrap_logsum(
-            orig_key=dest_col_name,
-            dest_key=orig_col_name,
-            tod_key="in_period",
-            segment_key="demographic_segment",
-            cache_choices=True,
-            trace_label=trace_label,
-            tag="tvpb_logsum_dot",
-        )
-
-        skims.update(
-            {"tvpb_logsum_odt": tvpb_logsum_odt, "tvpb_logsum_dot": tvpb_logsum_dot}
-        )
-
-        # TVPB constants can appear in expressions
-        constants.update(
-            network_los.setting("TVPB_SETTINGS.tour_mode_choice.CONSTANTS")
-        )
-
     estimator = estimation.manager.begin_estimation(state, "atwork_subtour_mode_choice")
     if estimator:
         estimator.write_coefficients(model_settings=model_settings)
@@ -152,32 +120,6 @@ def atwork_subtour_mode_choice(
         trace_choice_name="tour_mode_choice",
     )
 
-    # add cached tvpb_logsum tap choices for modes specified in tvpb_mode_path_types
-    if network_los.zone_system == los.THREE_ZONE:
-        tvpb_mode_path_types = model_settings.tvpb_mode_path_types
-        for mode, path_types in tvpb_mode_path_types.items():
-            for direction, skim in zip(
-                ["od", "do"], [tvpb_logsum_odt, tvpb_logsum_dot]
-            ):
-                path_type = path_types[direction]
-                skim_cache = skim.cache[path_type]
-
-                print(f"mode {mode} direction {direction} path_type {path_type}")
-
-                for c in skim_cache:
-                    dest_col = f"{direction}_{c}"
-
-                    if dest_col not in choices_df:
-                        choices_df[dest_col] = (
-                            np.nan
-                            if pd.api.types.is_numeric_dtype(skim_cache[c])
-                            else ""
-                        )
-
-                    choices_df[dest_col].where(
-                        choices_df.tour_mode != mode, skim_cache[c], inplace=True
-                    )
-
     if estimator:
         estimator.write_choices(choices_df[mode_column_name])
         choices_df[mode_column_name] = estimator.get_survey_values(