orca residual cleanup

activitysim/abm/models/tour_mode_choice.py

@@ -6,7 +6,6 @@
 
 import numpy as np
 import pandas as pd
-from orca import orca
 
 from activitysim.abm.models.util import annotate, school_escort_tours_trips, trip
 from activitysim.abm.models.util.mode import run_tour_mode_choice_simulate

activitysim/abm/models/tour_od_choice.py

@@ -33,17 +33,15 @@ def tour_od_choice(
 
     Parameters
     ----------
-    tours : orca.DataFrameWrapper
+    tours : pd.DataFrame
         lazy-loaded tours table
-    persons : orca.DataFrameWrapper
+    persons : pd.DataFrame
         lazy-loaded persons table
-    households : orca.DataFrameWrapper
+    households : pd.DataFrame
         lazy-loaded households table
-    land_use : orca.DataFrameWrapper
+    land_use : pd.DataFrame
         lazy-loaded land use data table
-    stop_frequency_alts : orca.DataFrameWrapper
-        lazy-loaded table of stop frequency alternatives, e.g. "1out2in"
-    network_los : orca._InjectableFuncWrapper
+    network_los : los.Network_LOS
         lazy-loaded activitysim.los.Network_LOS object
     chunk_size
         simulation chunk size, set in main settings.yaml

activitysim/abm/models/util/tour_od.py

@@ -7,7 +7,6 @@
 
 import numpy as np
 import pandas as pd
-from orca import orca
 
 from activitysim.abm.models.util import logsums as logsum
 from activitysim.abm.models.util import trip

activitysim/abm/models/vehicle_allocation.py

@@ -105,13 +105,12 @@ def vehicle_allocation(
     Parameters
     ----------
     state : workflow.State
-    persons : orca.DataFrameWrapper
-    households : orca.DataFrameWrapper
-    vehicles : orca.DataFrameWrapper
-    vehicles_merged : orca.DataFrameWrapper
-    tours : orca.DataFrameWrapper
-    tours_merged : orca.DataFrameWrapper
-    chunk_size : orca.injectable
+    persons : pd.DataFrame
+    households : pd.DataFrame
+    vehicles : pd.DataFrame
+    tours : pd.DataFrame
+    tours_merged : pd.DataFrame
+    network_los : los.Network_LOS
     """
     trace_label = "vehicle_allocation"
     model_settings_file_name = "vehicle_allocation.yaml"

activitysim/abm/models/vehicle_type_choice.py

@@ -316,7 +316,7 @@ def iterate_vehicle_type_choice(
     locals_dict : dict
         additional variables available when writing expressions
     estimator : Estimator object
-    chunk_size : orca.injectable
+    chunk_size : int
     trace_label : str
 
     Returns
@@ -516,9 +516,9 @@ def vehicle_type_choice(
 
     Parameters
     ----------
-    persons : orca.DataFrameWrapper
-    households : orca.DataFrameWrapper
-    vehicles : orca.DataFrameWrapper
+    persons : pd.DataFrame
+    households : pd.DataFrame
+    vehicles : pd.DataFrame
     vehicles_merged : DataFrame
     """
     trace_label = "vehicle_type_choice"

activitysim/benchmarking/componentwise.py

@@ -181,7 +181,7 @@ def run_component(state, component_name):
 def teardown_component(state, component_name):
     logger.info("teardown_component: %s", component_name)
 
-    # use the pipeline module to clear out all the orca tables, so
+    # use the pipeline module to clear out all the tables, so
     # the next benchmark run has a clean slate.
     # anything needed should be reloaded from the pipeline checkpoint file
     pipeline_tables = state.registered_tables()

activitysim/cli/benchmark.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import json
 import os
 import shutil
@@ -43,7 +45,6 @@
         "pyyaml": [],
         "pytables": [],
         "toolz": [],
-        "orca": [],
         "psutil": [],
         "requests": [],
         "numba": ["0.54"],

activitysim/core/test/utils_testing.py

@@ -1,6 +1,3 @@
-# Orca
-# Copyright (C) 2016 UrbanSim Inc.
-# See full license in LICENSE.
 from __future__ import annotations
 
 import numpy as np

activitysim/core/workflow/checkpoint.py

@@ -817,7 +817,7 @@ def load(self, checkpoint_name: str, store=None):
                 table_name, checkpoint_name=last_checkpoint[table_name], store=store
             )
             logger.info("load_checkpoint table %s %s" % (table_name, df.shape))
-            # register it as an orca table
+            # register it as an workflow table
             self._obj.add_table(table_name, df)
             loaded_tables[table_name] = df
             if table_name == "land_use" and "_original_zone_id" in df.columns:
@@ -1144,7 +1144,7 @@ def load_dataframe(self, table_name, checkpoint_name=None):
         Return pandas dataframe corresponding to table_name
 
         if checkpoint_name is None, return the current (most recent) version of the table.
-        The table can be a checkpointed table or any registered orca table (e.g. function table)
+        The table can be a checkpointed table or any registered table (e.g. function table)
 
         if checkpoint_name is specified, return table as it was at that checkpoint
         (the most recently checkpointed version of the table at or before checkpoint_name)

activitysim/core/workflow/runner.py

@@ -314,7 +314,7 @@ def by_name(self, model_name, **kwargs):
         Parameters
         ----------
         model_name : str
-            model_name is assumed to be the name of a registered orca step
+            model_name is assumed to be the name of a registered workflow step
         """
         self.t0 = time.time()
         try:

activitysim/core/workflow/state.py

@@ -986,7 +986,7 @@ def get_table(self, table_name, checkpoint_name=None):
         Return pandas dataframe corresponding to table_name
 
         if checkpoint_name is None, return the current (most recent) version of the table.
-        The table can be a checkpointed table or any registered orca table (e.g. function table)
+        The table can be a checkpointed table or any registered table (e.g. function table)
 
         if checkpoint_name is specified, return table as it was at that checkpoint
         (the most recently checkpointed version of the table at or before checkpoint_name)
@@ -1058,7 +1058,7 @@ def extend_table(self, table_name, df, axis=0):
         Parameters
         ----------
         table_name : str
-            orca/inject table name
+            potentially existing table name
         df : pandas DataFrame
         """
         assert axis in [0, 1]
@@ -1095,7 +1095,7 @@ def extend_table(self, table_name, df, axis=0):
 
     def drop_table(self, table_name):
         if self.is_table(table_name):
-            logger.debug("drop_table dropping orca table '%s'" % table_name)
+            logger.debug("drop_table dropping table '%s'" % table_name)
             self._context.pop(table_name, None)
             self.existing_table_status.pop(table_name)
 

activitysim/examples/production_semcog/extensions/parking_location_choice_at_university.py

@@ -69,7 +69,7 @@ def parking_location_choice_at_university(
     the tour mode is auto.  Parking locations are sampled weighted by the number of parking spots.
 
     The main interface to this model is the parking_location_choice_at_university() function.
-    This function is registered as an orca step in the example Pipeline.
+    This function is registered as a step in the example Pipeline.
     """
 
     trace_label = "parking_location_choice_at_university"

activitysim/examples/production_semcog/extensions/stop_frequency_university_parking.py

@@ -24,7 +24,7 @@ def stop_frequency_university_parking(
     and after groups of trips that are on campus zones.
 
     The main interface to this model is the stop_frequency_university_parking() function.
-    This function is registered as an orca step in the example Pipeline.
+    This function is registered as a step in the example Pipeline.
     """
 
     trace_label = "stop_frequency_university_parking"

activitysim/examples/production_semcog/extensions/university_location_zone_override.py

@@ -108,7 +108,7 @@ def university_location_zone_override(
     done to replicate the fact that university students can have classes all over campus.
 
     The main interface to this model is the university_location_zone_override() function.
-    This function is registered as an orca step in the example Pipeline.
+    This function is registered as a step in the example Pipeline.
     """
 
     trace_label = "university_location_zone_override"
@@ -171,7 +171,7 @@ def trip_destination_univ_zone_override(
     already handled in university_location_zone_override.
 
     The main interface to this model is the trip_destination_univ_zone_override() function.
-    This function is registered as an orca step in the example Pipeline.
+    This function is registered as a step in the example Pipeline.
     """
 
     trace_label = "trip_destination_univ_zone_override"

test/cdap/test_cdap.py

@@ -11,7 +11,7 @@
 from numpy import dot
 from numpy.linalg import norm
 
-# import models is necessary to initalize the model steps with orca
+# import models is necessary to initalize the model steps
 from activitysim.abm import models  # noqa: F401
 from activitysim.core import config, tracing, workflow
 from activitysim.core.util import read_csv, to_csv

test/joint_tours/test_joint_tours.py

@@ -9,7 +9,7 @@
 from numpy import dot
 from numpy.linalg import norm
 
-# import models is necessary to initalize the model steps with orca
+# import models is necessary to initalize the model steps
 from activitysim.abm import models  # noqa: F401
 from activitysim.core import workflow
 from activitysim.core.util import read_csv, to_csv

test/non_mandatory_tour_frequency/test_non_mandatory_tour_frequency.py

@@ -11,7 +11,7 @@
 from numpy import dot
 from numpy.linalg import norm
 
-# import models is necessary to initalize the model steps with orca
+# import models is necessary to initalize the model steps
 from activitysim.abm import models  # noqa: F401
 from activitysim.core import config, tracing, workflow
 from activitysim.core.util import read_csv, to_csv

test/parking_location/test_parking_location.py

@@ -6,16 +6,14 @@
 from pathlib import Path
 
 import numpy as np
-import orca
 import pandas as pd
 import pytest
 import yaml
 from numpy import dot
 from numpy.linalg import norm
 
-# import models is necessary to initalize the model steps with orca
+# import models is necessary to initalize the model steps
 from activitysim.abm import models
-from activitysim.abm.models.util import estimation
 from activitysim.core import config, simulate, tracing, workflow
 from activitysim.core.util import read_csv, to_csv
 

test/summarize/test_summarize.py

@@ -1,11 +1,13 @@
+from __future__ import annotations
+
 import logging
 import os
 from pathlib import Path
 
 import pandas as pd
 import pytest
 
-# import models is necessary to initalize the model steps with orca
+# import models is necessary to initalize the model steps
 from activitysim.abm import models
 from activitysim.core import los, workflow