def test_str():
adjacency_dict = {2: {'weight': 3, 'status': True},
3: {'weight': 1, 'status': True},
4: {'weight': 4, 'status': True},
5: {'weight': 2, 'status': True}}
node_1 = Node(1, 'A', adjacency_dict)
string = node_1.__str__()
for key in node_1.get_adjacents():
assert 'Node #' + str(key) in string
def test_is_adjacent():
"""
arrange phase: setting up the instances (creating adjacency)
act phase: performing an action
assert phase:
:return:
"""
node1 = Node(1, 'A', {2: {'weight': 3, 'status': True}})
assert node1.is_adjacent(2)
assert not node1.is_adjacent(3)
assert not node1.is_adjacent(node1.node_id)
def test_get_adjacents():
adjacency_dict = {2: {'weight': 3, 'status': True},
3: {'weight': 1, 'status': True},
4: {'weight': 4, 'status': True},
5: {'weight': 2, 'status': True}}
node_1 = Node(1, 'A', adjacency_dict)
adjacents = node_1.get_adjacents()
assert len(adjacents) is len(adjacency_dict)
for key in adjacency_dict:
assert key in adjacents
def graph_builder(n: int,
max_weight: int = None,
seed: int = None) -> Network:
"""
Returns randomly generated network with n nodes.
:param int n: number of nodes generated graph will contain
:param int max_weight: optional param that sets a maximum weight for edges
:param int seed: optional parameter to control pseudorandom generator
:return: randomly generated network with N nodes
:rtype: Network
"""
network_dict = {}
if not max_weight:
max_weight = 50
for x in range(1, n + 1):
adjacency_dict = GraphBuilder.generate_random_adjacency_dict(
x, n, max_weight)
node = Node(x, 'Node #' + str(x), adjacency_dict)
network_dict[x] = node
network = Network(network_dict)
return network
def import_nodes(worksheet):
"""
Imports node from each row and adds it to the network. These nodes
only contain the following fields: node_id, hospital_name, region,
city, and state. (adjacency_dict is handled in generate_distance_vector()
and status is assumed to be True)
:param Worksheet worksheet: worksheet to read data from
:return: Network
"""
network = Network()
for x in range(2, worksheet.max_row + 1):
network.add_node(
node=Node(node_id=int(worksheet[f'{column_indices["unique id"]}{x}'].value),
hospital_name=worksheet[f'{column_indices["hospital name"]}{x}'].value,
region=int(worksheet[f'{column_indices["region"]}{x}'].value),
city=worksheet[f'{column_indices["city"]}{x}'].value,
state=worksheet[f'{column_indices["state"]}{x}'].value),
feedback=False)
generate_distance_vector(network=network)
return network
ANSI_CYAN = "\033[36m"
ANSI_YELLOW = '\033[33;1m'
ANSI_RED = '\033[31;1m'
ANSI_RESET = "\033[0m"
# builds hospitals (represented as nodes)
hospital_a = Node(
1, 'A', {
2: {
'weight': 3,
'status': True
},
3: {
'weight': 1,
'status': True
},
5: {
'weight': 4,
'status': True
},
9: {
'weight': 2,
'status': True
}
})
hospital_b = Node(
2, 'B', {
1: {
'weight': 3,
'status': True
},
Everything below is just to briefly demonstrate/test
the basic functions of our graph/network
"""
# creates 10 individual nodes (hard coded adjacency lists to match)
node_1 = Node(
1, 'A', {
2: {
'weight': 3,
'status': True
},
3: {
'weight': 1,
'status': True
},
5: {
'weight': 4,
'status': True
},
9: {
'weight': 2,
'status': True
}
})
node_2 = Node(
2, 'B', {
1: {
'weight': 3,
'status': True
},
def build_network(nodes, data):
network = Network()
for node in nodes:
network.add_node(node=Node(node), feedback=False)
return add_edges(network=network, data=data)