ae_wrapper#

collections#

class AssignmentResultCollection(ids: Union[numpy.__array_like._SupportsArray[numpy.dtype], numpy.__nested_sequence._NestedSequence[numpy.__array_like._SupportsArray[numpy.dtype]], bool, int, float, complex, str, bytes, numpy.__nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]], NoneType] = None, passenger_flow: Union[numpy.__array_like._SupportsArray[numpy.dtype], numpy.__nested_sequence._NestedSequence[numpy.__array_like._SupportsArray[numpy.dtype]], bool, int, float, complex, str, bytes, numpy.__nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]], NoneType] = None, cargo_flow: Union[numpy.__array_like._SupportsArray[numpy.dtype], numpy.__nested_sequence._NestedSequence[numpy.__array_like._SupportsArray[numpy.dtype]], bool, int, float, complex, str, bytes, numpy.__nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]], NoneType] = None, congested_time: Union[numpy.__array_like._SupportsArray[numpy.dtype], numpy.__nested_sequence._NestedSequence[numpy.__array_like._SupportsArray[numpy.dtype]], bool, int, float, complex, str, bytes, numpy.__nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]], NoneType] = None, delay_factor: Union[numpy.__array_like._SupportsArray[numpy.dtype], numpy.__nested_sequence._NestedSequence[numpy.__array_like._SupportsArray[numpy.dtype]], bool, int, float, complex, str, bytes, numpy.__nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]], NoneType] = None, volume_to_capacity: Union[numpy.__array_like._SupportsArray[numpy.dtype], numpy.__nested_sequence._NestedSequence[numpy.__array_like._SupportsArray[numpy.dtype]], bool, int, float, complex, str, bytes, numpy.__nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]], NoneType] = None, passenger_car_unit: Union[numpy.__array_like._SupportsArray[numpy.dtype], numpy.__nested_sequence._NestedSequence[numpy.__array_like._SupportsArray[numpy.dtype]], bool, int, float, complex, str, bytes, numpy.__nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]], NoneType] = None)#

Bases: object

cargo_flow: ndarray#

congested_time: ndarray#

delay_factor: ndarray#

ids: ndarray#

passenger_car_unit: ndarray#

passenger_flow: ndarray#

volume_to_capacity: ndarray#

class GraphPath(nodes: numpy.ndarray, links: numpy.ndarray, path_results: aequilibrae.paths.results.path_results.PathResults)#

Bases: object

links: ndarray#

nodes: ndarray#

path_results: PathResults#

class LinkCollection(ids: Union[numpy.__array_like._SupportsArray[numpy.dtype], numpy.__nested_sequence._NestedSequence[numpy.__array_like._SupportsArray[numpy.dtype]], bool, int, float, complex, str, bytes, numpy.__nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]], NoneType] = None, from_nodes: Union[numpy.__array_like._SupportsArray[numpy.dtype], numpy.__nested_sequence._NestedSequence[numpy.__array_like._SupportsArray[numpy.dtype]], bool, int, float, complex, str, bytes, numpy.__nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]], NoneType] = None, to_nodes: Union[numpy.__array_like._SupportsArray[numpy.dtype], numpy.__nested_sequence._NestedSequence[numpy.__array_like._SupportsArray[numpy.dtype]], bool, int, float, complex, str, bytes, numpy.__nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]], NoneType] = None, directions: Union[numpy.__array_like._SupportsArray[numpy.dtype], numpy.__nested_sequence._NestedSequence[numpy.__array_like._SupportsArray[numpy.dtype]], bool, int, float, complex, str, bytes, numpy.__nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]], NoneType] = None, max_speeds: Union[numpy.__array_like._SupportsArray[numpy.dtype], numpy.__nested_sequence._NestedSequence[numpy.__array_like._SupportsArray[numpy.dtype]], bool, int, float, complex, str, bytes, numpy.__nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]], NoneType] = None, capacities: Union[numpy.__array_like._SupportsArray[numpy.dtype], numpy.__nested_sequence._NestedSequence[numpy.__array_like._SupportsArray[numpy.dtype]], bool, int, float, complex, str, bytes, numpy.__nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]], NoneType] = None, geometries: Optional[movici_simulation_core.core.arrays.TrackedCSRArray] = None)#

Bases: object

capacities: ndarray#

directions: ndarray#

from_nodes: ndarray#

geometries: Optional[TrackedCSRArray]#

ids: ndarray#

max_speeds: ndarray#

to_nodes: ndarray#

class NodeCollection(ids: Union[numpy.__array_like._SupportsArray[numpy.dtype], numpy.__nested_sequence._NestedSequence[numpy.__array_like._SupportsArray[numpy.dtype]], bool, int, float, complex, str, bytes, numpy.__nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]], NoneType] = None, is_centroids: Union[numpy.__array_like._SupportsArray[numpy.dtype], numpy.__nested_sequence._NestedSequence[numpy.__array_like._SupportsArray[numpy.dtype]], bool, int, float, complex, str, bytes, numpy.__nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]], NoneType] = None, geometries: Union[List[List[float]], List[numpy.ndarray], numpy.ndarray, NoneType] = None)#

Bases: object

geometries: Optional[Union[List[List[float]], List[ndarray], ndarray]]#

ids: ndarray#

is_centroids: ndarray#

id_generator#

class IdGenerator#

Bases: object

Since aequilibrae has a fixed structure for ids we have to be able to convert between our ids and aequilibrae ids

get_new_ids(original_ids: Union[_SupportsArray[dtype], _NestedSequence[_SupportsArray[dtype]], bool, int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]) → ndarray#

query_new_ids(original_ids: Union[_SupportsArray[dtype], _NestedSequence[_SupportsArray[dtype]], bool, int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]) → ndarray#

query_original_ids(new_ids: Union[_SupportsArray[dtype], _NestedSequence[_SupportsArray[dtype]], bool, int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]) → ndarray#

patches#

class AequilibraeMatrix#

Bases: AequilibraeMatrix

close()#: Removes matrix from memory and flushes all data to disk, or closes the OMX file if that is the case

get_matrix(core: str, copy=True)#

Returns the data for a matrix core

Args:

core (str): name of the matrix core to be returned

copy (bool, optional): return a copy of the data. Defaults to False

Returns:: object (np.ndarray): NumPy array

point_generator#

class PointGenerator(increment: float = 0.001)#

Bases: object

Since aequilibrae can’t work with overlapping points we have to be able to generate unique points

add_point(point: Union[Tuple[float, float], List[float], ndarray]) → None#

add_points(points: Union[List[Union[Tuple[float, float], List[float], ndarray]], ndarray]) → None#

generate_and_add(reference_point: Union[Tuple[float, float], List[float], ndarray]) → ndarray#

property point_count: int#

project#

class AssignmentParameters(volume_delay_function: str = 'BPR', vdf_alpha: float = 0.64, vdf_beta: float = 4.0, cargo_pcu: float = 1.9, algorithm: str = 'bfw', max_iter: int = 1000, rgap_target: float = 0.001)#

Bases: object

algorithm: str = 'bfw'#

cargo_pcu: float = 1.9#

max_iter: int = 1000#

rgap_target: float = 0.001#

vdf_alpha: float = 0.64#

vdf_beta: float = 4.0#

volume_delay_function: str = 'BPR'#

class ProjectWrapper(project_path: Optional[Union[str, Path]] = None, project_name: Optional[str] = None, delete_on_close: bool = True)#

Bases: object

This class wraps Aequilibrae methods with sensible methods and bugfixes

add_column(column_name: str, values: Optional[Sequence] = None) → None#

add_links(links: LinkCollection, raise_on_geometry_mismatch: bool = True) → None#

add_nodes(nodes: NodeCollection) → None#

assign_traffic(od_matrix_passenger: ndarray, od_matrix_cargo: ndarray, parameters: Optional[AssignmentParameters] = None) → AssignmentResultCollection#

build_graph(cost_field: str, block_centroid_flows: bool = True) → Graph#

calculate_free_flow_times() → ndarray#: Aequilibrae calculates distances automatically but does not compute free flow time, so we have to calculate them manually

close() → None#

convert_od_matrix(od_matrix: ndarray, matrix_name: str) → AequilibraeMatrix#

get_links() → LinkCollection#

get_nodes() → NodeCollection#

get_shortest_path(from_node: int, to_node: int, path_results: Optional[PathResults] = None) → Optional[GraphPath]#

get_shortest_paths(from_node: int, to_nodes: List[int]) → List[Optional[GraphPath]]#

transformer = <Unknown Transformer: unknown> Description: unavailable until proj_trans is called Area of Use: - undefined#

update_column(column_name: str, values: Sequence) → None#

class TransportMode#

Bases: object

CAR = 'c'#

Module contents#

Bases: object

algorithm: str = 'bfw'#

cargo_pcu: float = 1.9#

max_iter: int = 1000#

rgap_target: float = 0.001#

vdf_alpha: float = 0.64#

vdf_beta: float = 4.0#

volume_delay_function: str = 'BPR'#

Bases: object

cargo_flow: ndarray#

congested_time: ndarray#

delay_factor: ndarray#

ids: ndarray#

passenger_car_unit: ndarray#

passenger_flow: ndarray#

volume_to_capacity: ndarray#

class GraphPath(nodes: numpy.ndarray, links: numpy.ndarray, path_results: aequilibrae.paths.results.path_results.PathResults)#

Bases: object

links: ndarray#

nodes: ndarray#

path_results: PathResults#

class IdGenerator#

Bases: object

Since aequilibrae has a fixed structure for ids we have to be able to convert between our ids and aequilibrae ids

get_new_ids(original_ids: Union[_SupportsArray[dtype], _NestedSequence[_SupportsArray[dtype]], bool, int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]) → ndarray#

query_new_ids(original_ids: Union[_SupportsArray[dtype], _NestedSequence[_SupportsArray[dtype]], bool, int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]) → ndarray#

query_original_ids(new_ids: Union[_SupportsArray[dtype], _NestedSequence[_SupportsArray[dtype]], bool, int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]) → ndarray#

Bases: object

capacities: ndarray#

directions: ndarray#

from_nodes: ndarray#

geometries: Optional[TrackedCSRArray]#

ids: ndarray#

max_speeds: ndarray#

to_nodes: ndarray#

Bases: object

geometries: Optional[Union[List[List[float]], List[ndarray], ndarray]]#

ids: ndarray#

is_centroids: ndarray#

class PointGenerator(increment: float = 0.001)#

Bases: object

Since aequilibrae can’t work with overlapping points we have to be able to generate unique points

add_point(point: Union[Tuple[float, float], List[float], ndarray]) → None#

add_points(points: Union[List[Union[Tuple[float, float], List[float], ndarray]], ndarray]) → None#

generate_and_add(reference_point: Union[Tuple[float, float], List[float], ndarray]) → ndarray#

property point_count: int#

class ProjectWrapper(project_path: Optional[Union[str, Path]] = None, project_name: Optional[str] = None, delete_on_close: bool = True)#

Bases: object

This class wraps Aequilibrae methods with sensible methods and bugfixes

add_column(column_name: str, values: Optional[Sequence] = None) → None#

add_links(links: LinkCollection, raise_on_geometry_mismatch: bool = True) → None#

add_nodes(nodes: NodeCollection) → None#

assign_traffic(od_matrix_passenger: ndarray, od_matrix_cargo: ndarray, parameters: Optional[AssignmentParameters] = None) → AssignmentResultCollection#

build_graph(cost_field: str, block_centroid_flows: bool = True) → Graph#

calculate_free_flow_times() → ndarray#: Aequilibrae calculates distances automatically but does not compute free flow time, so we have to calculate them manually

close() → None#

convert_od_matrix(od_matrix: ndarray, matrix_name: str) → AequilibraeMatrix#

get_links() → LinkCollection#

get_nodes() → NodeCollection#

get_shortest_path(from_node: int, to_node: int, path_results: Optional[PathResults] = None) → Optional[GraphPath]#

get_shortest_paths(from_node: int, to_nodes: List[int]) → List[Optional[GraphPath]]#

transformer = <Unknown Transformer: unknown> Description: unavailable until proj_trans is called Area of Use: - undefined#

update_column(column_name: str, values: Sequence) → None#

class TransportMode#

Bases: object

CAR = 'c'#