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# aequilibrae.paths.TransitAssignment

class aequilibrae.paths.TransitAssignment(*args, project=None, **kwargs)
  __init__(*args, project=None, **kwargs)

  Methods

  `__init__`(*args[, project]) |
  `add_class`(transport_class) | Adds a Transport class to the assignment
  `algorithms_available`() | Returns all algorithms available for use
  `execute`([log_specification]) | Processes assignment
  `info`() | Returns information for the transit assignment procedure
  `log_specification`() |
  `report`() | Returns the assignment convergence report
  `results`() | Prepares the assignment results as a Pandas DataFrame
  `save_results`(table_name[, keep_zero_flows, ...]) | Saves the assignment results to results_database.sqlite
  `set_algorithm`(algorithm) | Chooses the assignment algorithm.
  `set_classes`(classes) | Sets Transport classes to be assigned
  `set_cores`(cores) | Allows one to set the number of cores to be used AFTER transit classes have been added
  `set_frequency_field`(frequency_field) | Sets the graph field that contains the frequency -> e.g. 'freq'.
  `set_time_field`(time_field) | Sets the graph field that contains free flow travel time -> e.g. 'trav_time'.

  Attributes

  `all_algorithms` |

  add_class(transport_class: TransportClassBase) → None

    Adds a Transport class to the assignment

    Arguments:

      transport_class (`TransportClassBase`): Transport class

  algorithms_available() → list

    Returns all algorithms available for use

    Returns:

      `list`: List of string values to be used with set_algorithm

  execute(log_specification=True) → None

    Processes assignment

  info() → dict

    Returns information for the transit assignment procedure

    Dictionary contains keys ‘Algorithm’, ‘Classes’, ‘Computer name’, ‘Procedure ID’.

    The classes key is also a dictionary with all the user classes per transit class and their respective matrix totals

    Returns:

      info (`dict`): Dictionary with summary information

  log_specification()
  report() → DataFrame

    Returns the assignment convergence report

    Returns:

      DataFrame (`pd.DataFrame`): Convergence report

  results() → DataFrame

    Prepares the assignment results as a Pandas DataFrame

    Returns:

      DataFrame (`pd.DataFrame`): Pandas DataFrame with all the assignment results indexed on link_id

  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

  set_algorithm(algorithm: str)

    Chooses the assignment algorithm. Currently only ‘optimal-strategies’ is available.

    ‘os’ is also accepted as an alternative to ‘optimal-strategies’

    Arguments:

      algorithm (`str`): Algorithm to be used

  set_classes(classes: List[TransportClassBase]) → None

    Sets Transport classes to be assigned

    Arguments:

      classes (`List[TransportClassBase]`): List of TransportClass’s for assignment

  set_cores(cores: int) → None

    Allows one to set the number of cores to be used AFTER transit classes have been added

    Inherited from `AssignmentResultsBase`

    Arguments:

      cores (`int`): Number of CPU cores to use

  set_frequency_field(frequency_field: str) → None

    Sets the graph field that contains the frequency -> e.g. ‘freq’

    Arguments:

      frequency_field (`str`): Field name

  set_time_field(time_field: str) → None

    Sets the graph field that contains free flow travel time -> e.g. ‘trav_time’

    Arguments:

      time_field (`str`): Field name

  all_algorithms = ['optimal-strategies', 'os']