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# aequilibrae.paths.HyperpathGenerating

class aequilibrae.paths.HyperpathGenerating(edges, tail='tail', head='head', trav_time='trav_time', freq='freq', check_edges=False)

  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)

  Methods

  `__init__`(edges[, tail, head, trav_time, ...]) |
  `assign`(origin_column, destination_column, ...) | Assigns demand to the edges of the graph.
  `info`() |
  `run`(origin, destination, volume[, return_inf]) |
  `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).

  info() → dict
  run(origin, destination, volume, return_inf=False)
  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