aequilibrae.paths.results.assignment_results

Classes

AssignmentResults()	Assignment result holder for a single TrafficClass with multiple user classes
AssignmentResultsBase()	Assignment results base class for traffic and transit assignments.
TransitAssignmentResults()	Assignment result holder for a single Transit

class aequilibrae.paths.results.assignment_results.AssignmentResults

Assignment result holder for a single TrafficClass with multiple user classes

get_graph_to_network_mapping()

get_load_results() → DataFrame

Translates the assignment results from the graph format into the network format

Returns:

dataset (pd.DataFrame): Pandas DataFrame data with the traffic class assignment results

get_sl_results() → DataFrame

prepare(graph: Graph, matrix: AequilibraeMatrix) → None

Prepares the object with dimensions corresponding to the assignment matrix and graph objects

Arguments:

graph (Graph): Needs to have been set with number of centroids and list of skims (if any)

matrix (AequilibraeMatrix): Matrix properly set for computation with matrix.computational_view(:obj:`list`)

reset() → None

Resets object to prepared and pre-computation state

set_cores(cores: int) → None

Sets number of cores (threads) to be used in computation

Value of zero sets number of threads to all available in the system, while negative values indicate the number of threads to be left out of the computational effort.

Resulting number of cores will be adjusted to a minimum of zero or the maximum available in the system if the inputs result in values outside those limits

Arguments:

cores (int): Number of cores to be used in computation

total_flows() → None

Totals all link flows for this class into a single link load

Results are placed into total_link_loads class member

class aequilibrae.paths.results.assignment_results.AssignmentResultsBase

Assignment results base class for traffic and transit assignments.

abstract prepare(graph: GraphBase, matrix: AequilibraeMatrix) → None

abstract reset() → None

set_cores(cores: int) → None

Sets number of cores (threads) to be used in computation

Value of zero sets number of threads to all available in the system, while negative values indicate the number of threads to be left out of the computational effort.

Resulting number of cores will be adjusted to a minimum of zero or the maximum available in the system if the inputs result in values outside those limits

Arguments:

cores (int): Number of cores to be used in computation

class aequilibrae.paths.results.assignment_results.TransitAssignmentResults

Assignment result holder for a single Transit

get_load_results() → DataFrame

Translates the assignment results from the graph format into the network format

Returns:

dataset (pd.DataFrame): DataFrame data with the transit class assignment results

prepare(graph: TransitGraph, matrix: AequilibraeMatrix) → None

Prepares the object with dimensions corresponding to the assignment matrix and graph objects

Arguments:

graph (TransitGraph): Needs to have been set with number of centroids

matrix (AequilibraeMatrix): Matrix properly set for computation with matrix.computational_view(:obj:`list`)

reset() → None

Resets object to prepared and pre-computation state

set_cores(cores: int) → None

Sets number of cores (threads) to be used in computation

Value of zero sets number of threads to all available in the system, while negative values indicate the number of threads to be left out of the computational effort.

Resulting number of cores will be adjusted to a minimum of zero or the maximum available in the system if the inputs result in values outside those limits

Arguments:

cores (int): Number of cores to be used in computation