aequilibrae.paths.HyperpathGenerating

class aequilibrae.paths.HyperpathGenerating(edges, tail='tail', head='head', trav_time='trav_time', freq='freq', check_edges=False, skim_cols=None, *, o_vert_ids=array([], dtype=int64), d_vert_ids=array([], dtype=int64), nodes_to_indices)

A class for hyperpath generation.

Arguments:

edges (pandas.DataFrame): The edges of the graph.

tail (str): The column name for the tail of the edge (Optional, default is “tail”).

head (str): The column name for the head of the edge (Optional, default is “head”).

trav_time (str): The column name for the travel time of the edge (Optional, default is “trav_time”).

freq (str): The column name for the frequency of the edge (Optional, default is “freq”).

check_edges (bool): If True, check the validity of the edges (Optional, default is False).

__init__(edges, tail='tail', head='head', trav_time='trav_time', freq='freq', check_edges=False, skim_cols=None, *, o_vert_ids=array([], dtype=int64), d_vert_ids=array([], dtype=int64), nodes_to_indices)

Methods

__init__(edges[, tail, head, trav_time, ...])
assign(origin_column, destination_column, ...)	Assigns demand to the edges of the graph.
check_skim_cols(skim_cols)
compute_skim_cols(skim_cols, edges, trav_time)
info()
run(origin, destination, volume)
save_results(table_name[, keep_zero_flows, ...])	Saves the assignment results to results_database.sqlite

assign(origin_column, destination_column, demand_column, check_demand=False, threads=None)

Assigns demand to the edges of the graph.

Assumes the *_column arguments are provided as numpy arrays that form a COO sprase matrix.

Arguments:

origin_column (np.ndarray): The column for the origin vertices (Optional, default is

“orig_vert_idx”).

destination_column (np.ndarray): The column or the destination vertices (Optional, default is

“dest_vert_idx”).

demand_column (np.ndarray): The column for the demand values (Optional, default is “demand”).

check_demand (bool): If True, check the validity of the demand data (Optional, default is

False).

threads (int):The number of threads to use for computation (Optional, default is 0, using all

available threads).

check_skim_cols(skim_cols: Union(list[str], tuple[str], set(str)))

compute_skim_cols(skim_cols, edges: DataFrame, trav_time: str)

info() → dict

run(origin, destination, volume)

save_results(table_name: str, keep_zero_flows=True, project=None) → None

Saves the assignment results to results_database.sqlite

Method fails if table exists

Arguments:

table_name (str): Name of the table to hold this assignment result

keep_zero_flows (bool): Whether we should keep records for zero flows. Defaults to True

project (Project, Optional): Project we want to save the results to. Defaults to the active

project