common#

ae_util#

calculate_capacities(capacities: ndarray, layouts: ndarray) → ndarray#
fill_project(project: ProjectWrapper, demand_nodes: PointEntity, demand_links: VirtualLinkEntity, transport_nodes: PointEntity, transport_segments: TransportSegmentEntity)#
get_capacities_from_attribute(capacity_attribute: UniformAttribute, layout_attribute: UniformAttribute | None = None) → ndarray#
get_cargo_allowed_from_attribute(cargo_allowed_attribute: UniformAttribute) → ndarray#
get_demand_nodes(demand_nodes: PointEntity, point_generator: PointGenerator) → NodeCollection#
get_max_speeds_from_attribute(max_speed_attribute: UniformAttribute) → ndarray#
get_nodes(nodes: PointEntity, point_generator: PointGenerator) → NodeCollection#
get_transport_directions(segments: TransportSegmentEntity) → ndarray#

attributes#

csv_tape#

class BaseTapefile(timeline_info: TimelineInfo | None = None)#

Bases: object

get_data(key: str)#
get_next_timestamp() → Moment | None#
has_update()#
proceed_to(moment: Moment)#
set_timeline(seconds: ndarray)#
class CsvTape(timeline_info: TimelineInfo | None = None)#

Bases: BaseTapefile

assert_parameter(parameter_name)#
get_data(key: str) → float#
initialize(csv: DataFrame, time_column: str = 'seconds')#

entity_groups#

class GeometryEntity(name: str | None = None)#

Bases: EntityGroup

attributes: t.Dict[str, attribute.AttributeField] = {'reference': <movici_simulation_core.core.attribute.AttributeField object>}#
dimensions()#
ensure_ready() → None#
get_geometry(slice=None) → Geometry#
get_single_geometry(index: int) → BaseGeometry#
is_ready()#
reference#
class GridCellEntity(name: str | None = None)#

Bases: GeometryEntity

attributes: t.Dict[str, attribute.AttributeField] = {'grid_points': <movici_simulation_core.core.attribute.AttributeField object>}#
get_geometry(slice=None) → ClosedPolygonGeometry#
get_single_geometry(index: int) → Polygon#
grid_points#
is_ready()#
points: PointEntity | None = None#
set_points(points: PointEntity)#
class LineEntity(name: str | None = None)#

Bases: GeometryEntity

attributes: t.Dict[str, attribute.AttributeField] = {'_linestring2d': <movici_simulation_core.core.attribute.AttributeField object>, '_linestring3d': <movici_simulation_core.core.attribute.AttributeField object>}#
dimensions()#
get_geometry(slice=None) → LinestringGeometry#
get_single_geometry(index: int) → LineString#
is_ready() → bool#
property linestring: CSRAttribute#
class LinkEntity(name: str | None = None)#

Bases: LineEntity

attributes: t.Dict[str, attribute.AttributeField] = {'from_node_id': <movici_simulation_core.core.attribute.AttributeField object>, 'to_node_id': <movici_simulation_core.core.attribute.AttributeField object>}#
from_node_id#
to_node_id#
class PointEntity(name: str | None = None)#

Bases: GeometryEntity

attributes: t.Dict[str, attribute.AttributeField] = {'x': <movici_simulation_core.core.attribute.AttributeField object>, 'y': <movici_simulation_core.core.attribute.AttributeField object>, 'z': <movici_simulation_core.core.attribute.AttributeField object>}#
dimensions()#
get_geometry(slice=None) → PointGeometry#
get_single_geometry(index: int) → Point#
is_ready()#
x#
y#
z#
class PolygonEntity(name: str | None = None)#

Bases: GeometryEntity

attributes: t.Dict[str, attribute.AttributeField] = {'_polygon2d': <movici_simulation_core.core.attribute.AttributeField object>, '_polygon3d': <movici_simulation_core.core.attribute.AttributeField object>, '_polygon_legacy': <movici_simulation_core.core.attribute.AttributeField object>}#
dimensions()#
get_geometry(slice=None) → ClosedPolygonGeometry#
get_single_geometry(index: int) → Polygon#
is_ready() → bool#
property polygon: CSRAttribute#
class TransportLinkEntity(name: str | None = None)#

Bases: LinkEntity

attributes: t.Dict[str, attribute.AttributeField] = {'layout': <movici_simulation_core.core.attribute.AttributeField object>}#
layout#
class TransportSegmentEntity(name: str | None = None)#

Bases: LinkEntity

attributes: t.Dict[str, attribute.AttributeField] = {'_max_speed': <movici_simulation_core.core.attribute.AttributeField object>, 'capacity': <movici_simulation_core.core.attribute.AttributeField object>, 'layout': <movici_simulation_core.core.attribute.AttributeField object>}#
capacity#
layout#
property max_speed#
class VirtualLinkEntity(name: str | None = None)#

Bases: LinkEntity

attributes: t.Dict[str, attribute.AttributeField] = {'capacity': <movici_simulation_core.core.attribute.AttributeField object>, 'max_speed': <movici_simulation_core.core.attribute.AttributeField object>}#
capacity#
max_speed#
delayed_raise(err: Exception)#

model_util#

find_y_in_x(x: ndarray, y: ndarray)#

find position of y in x, adapted from https://stackoverflow.com/a/8251757

get_transport_info(model_config: Dict[str, List[str] | None]) → Tuple[str, str]#
safe_divide(numerator, denominator, fill_value=None)#
try_get_geometry_type(geometry_type)#

network#

class Graph(indptr, indices, cost_factor_indices)#

Bases: object

get_cost(source_idx, target_idx)#
get_neighbours(source_index)#
update_cost_factor(cost_factor)#
class Network(transport_nodes: PointEntity, transport_links: TransportSegmentEntity, virtual_nodes: PointEntity | None, virtual_links: LinkEntity | None, cost_factor: ndarray | None = None)#

Bases: object

Representation of a transport network containing transport nodes and -links and virtual nodes and -links. Virtual nodes can be used as source and target nodes for the the network

MAX_COST_FACTOR = inf#
MIN_COST_FACTOR = 1e-12#
all_shortest_paths(virtual_node_ids: _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes] | None = None)#

Compute the shortest path distance between all virtual nodes

all_shortest_paths_sum(values, virtual_node_ids=None, no_path_found=-1)#

Calculate the sum of a quantity that is defined for every link on the shortest path from all source (virtual) nodes to all target (virtual) nodes. When the source node and the target node are the same, the summed value equals 0

Parameters:

  • values – an array (or array-like) with the quantity values on transport links that are to be summed

  • no_path_found – A fill value for when no valid shortest path can be found between source and target.

all_shortest_paths_weighted_average(values, virtual_node_ids=None, weights=None, no_path_found=-1)#

Calculate the weighted average of a quantity that is defined for every link on the shortest path from all source (virtual) nodes to all target (virtual) nodes. The average is weighted by the cost factor of every link on the shortest path

Parameters:

  • values – an array (or array-like) with the quantity values on transport links that are to be averaged

  • virtual_node_ids – an array (or array-like) for which virtual nodes to calculate the weighted averages

  • weights – (optional) an array with alternative weights to calculate the weighted average. By default the network cost factor is used for both calculating the shortest path and as weights for the weighted average. Supplying this attributes makes it possible to calculate the shortest path by cost factor (eg. travel time) and the weighted average by a different attribute (eg. length). This array should have the same length as the cost_factor array

  • no_path_found – A fill value for when no valid shortest path can be found between source and target. This is also used for when the source and target node are the same

property cost_factor#
get_shortest_path(source_node_id: int)#

see https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.csgraph.shortest_path.html for more information.

:returns a (dist, prev) tuple

graph: Graph | None = None#
initialize_cost_factor()#
classmethod register_required_attributes(state: TrackedState, dataset_name: str, transport_segments_name: str, entities: Dict[str, EntityGroup | Type[EntityGroup]] | None = None) → NetworkEntities#
set_source_node(source_node_id: int)#

Set the current source node for a shortest path calculation, this must be a virtual node id

set_virtual_node_outgoing_cost_factor(vn_index, value)#
shortest_path_sum(source_node_id, values, no_path_found=-1, values_are_mapped=False)#

Calculate the sum of a quantity that is defined for every link on the shortest path from a source node to all target (virtual) nodes. When the source node and the target node are the same, the summed value equals 0

Parameters:

  • source_node_id – The entity id of the source (virtual) node

  • values – See Network.all_shortest_paths_weighted_average

  • no_path_found – See Network.all_shortest_paths_weighted_average

  • values_are_mapped – A boolean indicating whether the value array is in their original order or already mapped to the corresponding link index in the graph (default: False). This is usually False

shortest_path_weighted_average(source_node_id, values, weights=None, no_path_found=-1, values_are_mapped=False)#

Calculate the weighted average of a quantity that is defined for every link on the shortest path from a source node to all target (virtual) nodes. The average is weighted by the cost factor of every link on the shortest path

Parameters:

  • source_node_id – The entity id of the source (virtual) node

  • values – See Network.all_shortest_paths_weighted_average

  • weights – See Network.all_shortest_paths_weighted_average

  • no_path_found – See Network.all_shortest_paths_weighted_average

  • values_are_mapped – A boolean indicating whether the value array is in their original order or already mapped to the corresponding link index in the graph (default: False). This is usually False

source_node_idx = None#
tl_count = 0#
tl_from_node_id: ndarray | None = None#
tl_mapping: ndarray | None = None#
tl_to_node_id: ndarray | None = None#
update_cost_factor(cost_factor=None)#
vl_cost_factor: ndarray | None = None#
vl_count = 0#
vl_directionality: ndarray | None = None#
vl_from_node_id: ndarray | None = None#
vl_to_node_id: ndarray | None = None#
vn_ids: ndarray | None = None#
class NetworkEntities#

Bases: TypedDict

transport_nodes: PointEntity#
virtual_nodes: PointEntity#
build_graph(node_idx: Index, from_node_id, to_node_id)#
Parameters:

  • node_idx – the Index with all node ids

  • from_node_id – an array with source node id for every edge

  • to_node_id – an array with target node id for every edge

Returns:

a tuple of indptr, indices and edge_indices: where indptr and indices form the graph and edge_indices are the positions of the graph data (cost factor) in the original edge data such that cost_factor = original_cost_factor[edge_indices]

return the indices of all links in a csr sparse network going from from_idx to to_idx

time_series#

class TimeSeries(iterable: Iterable[Tuple[int, T]] = ())#

Bases: list, List[Tuple[int, T]]

property next_time#
pop_until(timestamp: int) → Iterable[Tuple[int, T]]#
sort()#

Sort the list in ascending order and return None.

The sort is in-place (i.e. the list itself is modified) and stable (i.e. the order of two equal elements is maintained).

If a key function is given, apply it once to each list item and sort them, ascending or descending, according to their function values.

The reverse flag can be set to sort in descending order.

Module contents#

class CsvTape(timeline_info: TimelineInfo | None = None)#

Bases: BaseTapefile

assert_parameter(parameter_name)#
get_data(key: str) → float#
initialize(csv: DataFrame, time_column: str = 'seconds')#
class Graph(indptr, indices, cost_factor_indices)#

Bases: object

get_cost(source_idx, target_idx)#
get_neighbours(source_index)#
update_cost_factor(cost_factor)#
class Network(transport_nodes: PointEntity, transport_links: TransportSegmentEntity, virtual_nodes: PointEntity | None, virtual_links: LinkEntity | None, cost_factor: ndarray | None = None)#

Bases: object

Representation of a transport network containing transport nodes and -links and virtual nodes and -links. Virtual nodes can be used as source and target nodes for the the network

MAX_COST_FACTOR = inf#
MIN_COST_FACTOR = 1e-12#
all_shortest_paths(virtual_node_ids: _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes] | None = None)#

Compute the shortest path distance between all virtual nodes

all_shortest_paths_sum(values, virtual_node_ids=None, no_path_found=-1)#

Calculate the sum of a quantity that is defined for every link on the shortest path from all source (virtual) nodes to all target (virtual) nodes. When the source node and the target node are the same, the summed value equals 0

Parameters:

  • values – an array (or array-like) with the quantity values on transport links that are to be summed

  • no_path_found – A fill value for when no valid shortest path can be found between source and target.

all_shortest_paths_weighted_average(values, virtual_node_ids=None, weights=None, no_path_found=-1)#

Calculate the weighted average of a quantity that is defined for every link on the shortest path from all source (virtual) nodes to all target (virtual) nodes. The average is weighted by the cost factor of every link on the shortest path

Parameters:

  • values – an array (or array-like) with the quantity values on transport links that are to be averaged

  • virtual_node_ids – an array (or array-like) for which virtual nodes to calculate the weighted averages

  • weights – (optional) an array with alternative weights to calculate the weighted average. By default the network cost factor is used for both calculating the shortest path and as weights for the weighted average. Supplying this attributes makes it possible to calculate the shortest path by cost factor (eg. travel time) and the weighted average by a different attribute (eg. length). This array should have the same length as the cost_factor array

  • no_path_found – A fill value for when no valid shortest path can be found between source and target. This is also used for when the source and target node are the same

property cost_factor#
get_shortest_path(source_node_id: int)#

see https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.csgraph.shortest_path.html for more information.

:returns a (dist, prev) tuple

graph: Graph | None = None#
initialize_cost_factor()#
classmethod register_required_attributes(state: TrackedState, dataset_name: str, transport_segments_name: str, entities: Dict[str, EntityGroup | Type[EntityGroup]] | None = None) → NetworkEntities#
set_source_node(source_node_id: int)#

Set the current source node for a shortest path calculation, this must be a virtual node id

set_virtual_node_outgoing_cost_factor(vn_index, value)#
shortest_path_sum(source_node_id, values, no_path_found=-1, values_are_mapped=False)#

Calculate the sum of a quantity that is defined for every link on the shortest path from a source node to all target (virtual) nodes. When the source node and the target node are the same, the summed value equals 0

Parameters:

  • source_node_id – The entity id of the source (virtual) node

  • values – See Network.all_shortest_paths_weighted_average

  • no_path_found – See Network.all_shortest_paths_weighted_average

  • values_are_mapped – A boolean indicating whether the value array is in their original order or already mapped to the corresponding link index in the graph (default: False). This is usually False

shortest_path_weighted_average(source_node_id, values, weights=None, no_path_found=-1, values_are_mapped=False)#

Calculate the weighted average of a quantity that is defined for every link on the shortest path from a source node to all target (virtual) nodes. The average is weighted by the cost factor of every link on the shortest path

Parameters:

  • source_node_id – The entity id of the source (virtual) node

  • values – See Network.all_shortest_paths_weighted_average

  • weights – See Network.all_shortest_paths_weighted_average

  • no_path_found – See Network.all_shortest_paths_weighted_average

  • values_are_mapped – A boolean indicating whether the value array is in their original order or already mapped to the corresponding link index in the graph (default: False). This is usually False

source_node_idx = None#
tl_count = 0#
tl_from_node_id: ndarray | None = None#
tl_mapping: ndarray | None = None#
tl_to_node_id: ndarray | None = None#
update_cost_factor(cost_factor=None)#
vl_cost_factor: ndarray | None = None#
vl_count = 0#
vl_directionality: ndarray | None = None#
vl_from_node_id: ndarray | None = None#
vl_to_node_id: ndarray | None = None#
vn_ids: ndarray | None = None#
class TimeSeries(iterable: Iterable[Tuple[int, T]] = ())#

Bases: list, List[Tuple[int, T]]

property next_time#
pop_until(timestamp: int) → Iterable[Tuple[int, T]]#
sort()#

Sort the list in ascending order and return None.

The sort is in-place (i.e. the list itself is modified) and stable (i.e. the order of two equal elements is maintained).

If a key function is given, apply it once to each list item and sort them, ascending or descending, according to their function values.

The reverse flag can be set to sort in descending order.

build_graph(node_idx: Index, from_node_id, to_node_id)#
Parameters:

  • node_idx – the Index with all node ids

  • from_node_id – an array with source node id for every edge

  • to_node_id – an array with target node id for every edge

Returns:

a tuple of indptr, indices and edge_indices: where indptr and indices form the graph and edge_indices are the positions of the graph data (cost factor) in the original edge data such that cost_factor = original_cost_factor[edge_indices]

find_y_in_x(x: ndarray, y: ndarray)#

find position of y in x, adapted from https://stackoverflow.com/a/8251757

get_transport_info(model_config: Dict[str, List[str] | None]) → Tuple[str, str]#
safe_divide(numerator, denominator, fill_value=None)#
try_get_geometry_type(geometry_type)#