drop unrelated changes

activitysim/abm/models/cdap.py

@@ -29,15 +29,7 @@ class CdapSettings(PydanticReadable, extra="forbid"):
     INTERACTION_COEFFICIENTS: str = "cdap_interaction_coefficients.csv"
     FIXED_RELATIVE_PROPORTIONS_SPEC: str = "cdap_fixed_relative_proportions.csv"
     ADD_JOINT_TOUR_UTILITY: bool = False
-    """
-    If True, add joint tour utility to CDAP model.
-    """
-
     JOINT_TOUR_COEFFICIENTS: str = "cdap_joint_tour_coefficients.csv"
-    """
-    If ADD_JOINT_TOUR_UTILITY is True, this is the name of the coefficients file
-    for the joint tour utility spec.
-    """
     annotate_persons: PreprocessorSettings | None = None
     annotate_households: PreprocessorSettings | None = None
     COEFFICIENTS: Path

activitysim/abm/models/disaggregate_accessibility.py

@@ -24,10 +24,6 @@
 
 class DisaggregateAccessibilitySuffixes(PydanticReadable):
     SUFFIX: str = "proto_"
-    """
-    Suffix to append to the proto-population tables.
-    """
-
     ROOTS: list[str] = [
         "persons",
         "households",
@@ -37,9 +33,6 @@ class DisaggregateAccessibilitySuffixes(PydanticReadable):
         "household_id",
         "tour_id",
     ]
-    """
-    The roots of the proto-population tables.
-    """
 
 
 class DisaggregateAccessibilityTableSettings(PydanticReadable, extra="forbid"):
@@ -498,7 +491,7 @@ def read_table_settings(self):
 
         return params
 
-    def generate_replicates(self, table_name: str):
+    def generate_replicates(self, table_name):
         """
         Generates replicates finding the cartesian product of the non-mapped field variables.
         The mapped fields are then annotated after replication
@@ -595,10 +588,7 @@ def expand_template_zones(self, tables):
 
         return [x for x in proto_tables.values()]
 
-    def create_proto_pop(self) -> None:
-        """
-        Creates the proto-population tables.
-        """
+    def create_proto_pop(self):
         # Separate out the mapped data from the varying data and create base replicate tables
         klist = ["proto_households", "proto_persons", "proto_tours"]
 
@@ -668,14 +658,7 @@ def create_proto_pop(self) -> None:
             if len(colnames) > 0:
                 df.rename(columns=colnames, inplace=True)
 
-    def inject_tables(self, state: workflow.State) -> None:
-        """
-        Injects the proto-population tables into the pipeline.
-
-        Parameters
-        ----------
-        state : workflow.State
-        """
+    def inject_tables(self, state: workflow.State):
         # Update canonical tables lists
         state.tracing.traceable_tables = state.tracing.traceable_tables + list(
             self.proto_pop.keys()
@@ -685,14 +668,7 @@ def inject_tables(self, state: workflow.State) -> None:
             self.state.get_rn_generator().add_channel(tablename, df)
             state.tracing.register_traceable_table(tablename, df)
 
-    def annotate_tables(self, state: workflow.State) -> None:
-        """
-        Annotates the proto-population tables with additional fields.
-
-        Parameters
-        ----------
-        state : workflow.State
-        """
+    def annotate_tables(self, state: workflow.State):
         # Extract annotations
         for annot in self.model_settings.annotate_proto_tables:
             tablename = annot.tablename
@@ -710,10 +686,7 @@ def annotate_tables(self, state: workflow.State) -> None:
             )
             self.state.add_table(tablename, df)
 
-    def merge_persons(self) -> None:
-        """
-        Merges the proto-population households into the persons.
-        """
+    def merge_persons(self):
         persons = self.state.get_dataframe("proto_persons")
         households = self.state.get_dataframe("proto_households")
 
@@ -740,21 +713,6 @@ def merge_persons(self) -> None:
 def get_disaggregate_logsums(
     state: workflow.State, network_los: los.Network_LOS, chunk_size: int, trace_hh_id
 ):
-    """
-    Get disaggregate logsums for workplace, school, and non-mandatory tour destinations.
-
-    Parameters
-    ----------
-    state : workflow.State
-    network_los : los.Network_LOS
-    chunk_size : int
-    trace_hh_id : int, optional
-
-    Returns
-    -------
-    logsums : dict
-        Dictionary of logsums for each of the three destination types.
-    """
     logsums = {}
     persons_merged = state.get_dataframe("proto_persons_merged").sort_index(
         inplace=False