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()