def prepare(self, graph: Graph) -> None:
"""
Prepares the object with dimensions corresponding to the graph object
Args:
*graph* (:obj:`Graph`): Needs to have been set with number of centroids and list of skims (if any)
"""
if not graph.cost_field:
raise Exception(
'Cost field needs to be set for cost computation. use graph.set_graph("your_cost_field")'
)
self.__integer_type = graph.default_types("int")
self.__float_type = graph.default_types("float")
self.nodes = graph.num_nodes + 1
self.zones = graph.centroids + 1
self.links = graph.num_links + 1
self.num_skims = graph.skims.shape[1]
self.predecessors = np.zeros(self.nodes, dtype=self.__integer_type)
self.connectors = np.zeros(self.nodes, dtype=self.__integer_type)
self.reached_first = np.zeros(self.nodes, dtype=self.__integer_type)
self.skims = np.zeros((self.nodes, self.num_skims), self.__float_type)
self.__graph_id__ = graph.__id__
self.graph = graph
def prepare(self, graph: Graph, matrix: AequilibraeMatrix) -> None:
"""
Prepares the object with dimensions corresponding to the assignment matrix and graph objects
Args:
*graph* (:obj:`Graph`): Needs to have been set with number of centroids and list of skims (if any)
*matrix* (:obj:`AequilibraeMatrix`): Matrix properly set for computation with
matrix.computational_view(:obj:`list`)
"""
self.__float_type = graph.default_types("float")
self.__integer_type = graph.default_types("int")
if matrix.view_names is None:
raise ("Please set the matrix_procedures computational view")
else:
self.classes["number"] = 1
if len(matrix.matrix_view.shape) > 2:
self.classes["number"] = matrix.matrix_view.shape[2]
self.classes["names"] = matrix.view_names
if graph is None:
raise ("Please provide a graph")
else:
self.compact_nodes = graph.compact_num_nodes
self.compact_links = graph.compact_num_links
self.nodes = graph.num_nodes
self.zones = graph.num_zones
self.centroids = graph.centroids
self.links = graph.num_links
self.num_skims = len(graph.skim_fields)
self.skim_names = [x for x in graph.skim_fields]
self.lids = graph.graph.link_id.values
self.direcs = graph.graph.direction.values
self.crosswalk = np.zeros(graph.graph.shape[0],
self.__integer_type)
self.crosswalk[graph.graph.__supernet_id__.
values] = graph.graph.__compressed_id__.values
self.__graph_ids = graph.graph.__supernet_id__.values
self.__redim()
self.__graph_id__ = graph.__id__
def __init__(self):
self.skims = AequilibraeMatrix()
self.cores = mp.cpu_count()
self.links = -1
self.nodes = -1
self.zones = -1
self.num_skims = -1
self.__graph_id__ = None
self.graph = Graph()
def prepare(self, graph: Graph, matrix: AequilibraeMatrix) -> None:
"""
Prepares the object with dimensions corresponding to the assignment matrix and graph objects
Args:
*graph* (:obj:`Graph`): Needs to have been set with number of centroids and list of skims (if any)
*matrix* (:obj:`AequilibraeMatrix`): Matrix properly set for computation with
matrix.computational_view(:obj:`list`)
"""
self.__float_type = graph.default_types("float")
self.__integer_type = graph.default_types("int")
if matrix.view_names is None:
raise ("Please set the matrix_procedures computational view")
else:
self.classes["number"] = 1
if len(matrix.matrix_view.shape) > 2:
self.classes["number"] = matrix.matrix_view.shape[2]
self.classes["names"] = matrix.view_names
if graph is None:
raise ("Please provide a graph")
else:
self.nodes = graph.num_nodes
self.zones = graph.num_zones
self.centroids = graph.centroids
self.links = graph.num_links
self.num_skims = len(graph.skim_fields)
self.skim_names = [x for x in graph.skim_fields]
self.lids = graph.graph["link_id"]
self.direcs = graph.graph["direction"]
self.__redim()
self.__graph_id__ = graph.__id__
# TODO: Enable these methods when the work for select link analysis and saving path files is completed
self.__setSavePathFile(False)
self.__setCriticalLinks(False)
def prepare(self, graph: Graph) -> None:
"""
Prepares the object with dimensions corresponding to the graph object
Args:
*graph* (:obj:`Graph`): Needs to have been set with number of centroids and list of skims (if any)
"""
if not graph.cost_field:
raise Exception(
'Cost field needs to be set for cost computation. use graph.set_graph("your_cost_field")'
)
self.__integer_type = graph.default_types("int")
self.__float_type = graph.default_types("float")
self.nodes = graph.num_nodes + 1
self.zones = graph.centroids + 1
self.links = graph.num_links + 1
self.num_skims = len(graph.skim_fields)
self.predecessors = np.zeros(self.nodes, dtype=self.__integer_type)
self.connectors = np.zeros(self.nodes, dtype=self.__integer_type)
self.reached_first = np.zeros(self.nodes, dtype=self.__integer_type)
if self.num_skims:
self.skims = np.empty((graph.all_nodes[-1] + 1, self.num_skims),
self.__float_type)
self.skims.fill(np.inf)
self._skimming_array = np.zeros((self.nodes, self.num_skims),
self.__float_type)
else:
self._skimming_array = np.zeros((1, 2), self.__float_type)
self.__graph_id__ = graph.__id__
# We can imagine somebody creating a worst-case scenario network (imagining that turn penalties are considered)
# where one needs to traverse all links (or almost all) in both directions.
self.__graph_sum = 2 * graph.cost.sum()
self.graph = graph
def __init__(self, name: str, graph: Graph,
matrix: AequilibraeMatrix) -> None:
"""
Instantiates the class
Args:
name (:obj:`str`): UNIQUE class name.
graph (:obj:`Graph`): Class/mode-specific graph
matrix (:obj:`AequilibraeMatrix`): Class/mode-specific matrix. Supports multiple user classes
"""
if not np.array_equal(matrix.index, graph.centroids):
raise ValueError(
"Matrix and graph do not have compatible sets of centroids.")
if matrix.matrix_view.dtype != graph.default_types('float'):
raise TypeError(
"Matrix's computational view need to be of type np.float64")
self.graph = graph
self.matrix = matrix
self.pce = 1.0
self.vot = 1.0
self.mode = graph.mode
self.class_flow: np.array
self.results = AssignmentResults()
self.results.prepare(self.graph, self.matrix)
self.fixed_cost = np.zeros(graph.graph.shape[0],
graph.default_types('float'))
self.fixed_cost_field = ''
self.fc_multiplier = 1.0
self.results.reset()
self._aon_results = AssignmentResults()
self._aon_results.prepare(self.graph, self.matrix)
self.__id__ = name