def labelling(resources, graph, data, employee, extensionLimit):
'''SPPRC labelling algorithm. See thesis for algorithmic details'''
# Initialize start label
initial_label = Label.initialize(node=graph.nodes[0],
resources=resources,
data=data,
employee=employee)
# Increment label name
label_name = initial_label.name + 1
# Initialize unprocessed_labels and processed_labels
unprocessed_labels = [initial_label]
processed_labels = []
# Assume extension is not limited by extensionLimit
extension_limited = False
# While there are unprocessed labels
while unprocessed_labels:
# Get an unprocessed label
cur_label = unprocessed_labels.pop(0)
# If a limit on the number of labels extended is set, limit extension
if extensionLimit != None:
# Set reference lenght of unprocessed labels and cur_label
referenceLength = 1 + len(unprocessed_labels)
# Find all labels in unprocessed_labels that are in the node
unprocessed_labels, labels_in_node = labelsInNode(
labels=unprocessed_labels,
label=cur_label,
size=extensionLimit)
# Check if no labels are discarded by extensionLimit
if (not extension_limited
and len(unprocessed_labels) + len(labels_in_node) <
referenceLength):
extension_limited = True
# Extend all labels_in_node
extended_labels = []
for label in labels_in_node:
labels, label_name = label.extend(label_name, resources, graph,
data, employee)
extended_labels += labels
else:
# Otherwise, extend the label as if there was no limit
extended_labels, label_name = cur_label.extend(
label_name, resources, graph, data, employee)
# Check dominance among all labels
for extended_label in extended_labels:
# The initial hypothesis is that the extended label is not dominated
extended_label_dominated = False
# Iterate over processed labels
for processed_label in processed_labels:
# Check if extended_label is dominated by processed_label
if processed_label.dominance(extended_label, resources, data,
employee):
extended_label_dominated = True
# Check if processed_label is dominated by extended_label
elif extended_label.dominance(processed_label, resources, data,
employee):
# If so, remove the dominated processed_label
processed_labels.remove(processed_label)
# Iterate over unprocessed labels
for unprocessed_label in unprocessed_labels:
# Check if extended_label is dominated by unprocessed_label
if unprocessed_label.dominance(extended_label, resources, data,
employee):
extended_label_dominated = True
# Check if unprocessed_label is dominated by extended_label
elif extended_label.dominance(unprocessed_label, resources,
data, employee):
unprocessed_labels.remove(unprocessed_label)
# If extended_label is not dominated, add it to unprocessed_labels
if not extended_label_dominated:
unprocessed_labels.append(extended_label)
# Add unprocessed_label to the set of processed_labels
if extensionLimit == None:
processed_labels.append(cur_label)
else:
processed_labels += labels_in_node
return processed_labels, extension_limited
def initialize(self): Label.initialize(self) self.__text = '' self.__url = '' self.set_style(0)
