def test_generate_transition(self):
g = qt.generate_random_graph(20)
mat = qt.generate_transition_matrix(g)
ans = np.sum(mat, axis=1)
self.assertTrue(np.allclose(ans, 1))
mat = qt.generate_transition_matrix(g, seed=10)
ans = np.sum(mat, axis=1)
self.assertTrue(np.allclose(ans, 1))
def test_generate_transition(self):
g = qt.generate_random_graph(20)
mat = qt.generate_transition_matrix(g)
ans = np.sum(mat, axis=1)
self.assertTrue(np.allclose(ans, 1))
mat = qt.generate_transition_matrix(g, seed=10)
ans = np.sum(mat, axis=1)
self.assertTrue(np.allclose(ans, 1))
def test_ResourceQueue_network_data_collection(self):
g = qt.generate_random_graph(100)
q_cls = {1: qt.ResourceQueue, 2: qt.ResourceQueue}
q_arg = {1: {'num_servers': 500},
2: {'num_servers': 500,
'AgentFactory': qt.Agent}}
qn = qt.QueueNetwork(g, q_classes=q_cls, q_args=q_arg)
qn.max_agents = 40000
qn.initialize(queues=range(qn.g.number_of_edges()))
qn.start_collecting_data()
qn.simulate(n=5000)
data = qn.get_queue_data()
self.assertTrue(len(data) > 0)
def creat_network():
g = qt.generate_random_graph(10, seed=3)
q = qt.QueueNetwork(g, seed=3)
q.max_agents = 20
q.initialize(100)
q.simulate(10000)
pos = nx.nx_agraph.graphviz_layout(g.to_undirected(), prog='neato')
scatter_kwargs = {'s': 30}
q.draw(pos=pos,
scatter_kwargs=scatter_kwargs,
bgcolor=[0, 0, 0, 0],
figsize=(6, 6),
fname='fig.png',
bbox_inches='tight')
plt.show()
def test_ResourceQueue_network_data_collection(self):
g = qt.generate_random_graph(100)
q_cls = {1: qt.ResourceQueue, 2: qt.ResourceQueue}
q_arg = {
1: {
'num_servers': 500
},
2: {
'num_servers': 500,
'AgentFactory': qt.Agent
}
}
qn = qt.QueueNetwork(g, q_classes=q_cls, q_args=q_arg)
qn.max_agents = 40000
qn.initialize(queues=range(qn.g.number_of_edges()))
qn.start_collecting_data()
qn.simulate(n=5000)
data = qn.get_queue_data()
self.assertTrue(len(data) > 0)
import queueing_tool as qt
import networkx as nx
g = qt.generate_random_graph(200, seed=3)
q = qt.QueueNetwork(g, seed=3)
q.max_agents = 2000
q.initialize(100)
q.simulate(10000)
pos = nx.nx_agraph.graphviz_layout(g.to_undirected(), prog='fdp')
scatter_kwargs = {'s': 30}
q.draw(pos=pos,
scatter_kwargs=scatter_kwargs,
bgcolor=[0, 0, 0, 0],
figsize=(10, 16),
fname='fig.png',
bbox_inches='tight')
import queueing_tool as qt import networkx as nx import numpy as np import matplotlib.pyplot as plt g = qt.generate_random_graph(5, seed=10) # 5 nodes network net = qt.QueueNetwork(g) net.transitions(False) print(net.transitions(True)) # shown as a matrix print(net.transitions(False)) # shown as a dictionary """ the probability will be changed if the seed is changed """
import queueing_tool as qt
"""
set the routing probability by yourself for any router
it wont be changed if even if the seed changed
"""
g = qt.generate_random_graph(5, seed=10)
net = qt.QueueNetwork(g)
net.transitions(False)
net.set_transitions({1: {2: 0.75, 3: 0.25}})
net.transitions(False)
print(net.transitions(False))
"""
seed = 10
{0: {2: 1.0}, 1: {2: 0.75, 3: 0.25}, 2: {0: 0.3333333333333333, 1: 0.3333333333333333, 4: 0.3333333333333333}, 3: {1: 1.0}, 4: {2: 0.5, 4: 0.5}}
seed = 15
{0: {3: 1.0}, 1: {2: 0.75, 3: 0.25, 4: 0.0}, 2: {1: 0.3333333333333333, 3: 0.3333333333333333, 4: 0.3333333333333333}, 3: {0: 0.25, 1: 0.25, 2: 0.25, 4: 0.25}, 4: {1: 0.25, 2: 0.25, 3: 0.25, 4: 0.25}}
"""
import queueing_tool as qt
import networkx as nx
import matplotlib.pyplot as plt
import numpy as np
pTypes = {1: 0.5, 2: 0.25, 3: 0.25}
g = qt.generate_random_graph(10, proportions=pTypes, seed=17)
non_loops = [e for e in g.edges() if e[0] != e[1]]
p1 = np.sum([g.ep(e, 'edge_type') == 1 for e in non_loops])
float(p1) / len(non_loops)
p2 = np.sum([g.ep(e, 'edge_type') == 2 for e in non_loops])
float(p2) / len(non_loops)
p3 = np.sum([g.ep(e, 'edge_type') == 3 for e in non_loops])
float(p3) / len(non_loops)
nx.draw_networkx(g)
plt.axis('off')
plt.show()
"""
undirected graph (parameters: is_directed=False)
"""
# p = np.random.rand(4)
# p = p / sum(p)
# p = {k + 1: p[k] for k in range(4)}
# g = qt.generate_random_graph(num_vertices=10, is_directed=False, proportions=p)
# nx.draw_networkx(g)
# plt.axis('off')
# plt.show()