def process_sample():
s_wait_1 = 0; s_s_wait_1 = 0
s_wait_2 = 0; s_s_wait_2 = 0
s_server_1 = 0; s_server_2 = 0
for client in self.clients:
s_wait_1 += client.wait(1)
s_s_wait_1 += client.wait(1)**2
s_server_1 += client.server[1]
s_wait_2 += client.wait(2)
s_s_wait_2 += client.wait(2)**2
s_server_2 += client.server[2]
self.results['m_s_W1'] += est.mean(s_wait_1, len(self.clients))
self.results['m_s_s_W1'] += est.mean(s_wait_1, len(self.clients))**2
self.results['v_s_W1'] += est.variance(s_wait_1, s_s_wait_1, len(self.clients))
self.results['v_s_s_W1'] += est.variance(s_wait_1, s_s_wait_1, len(self.clients))**2
self.results['m_s_N1'] += est.mean(self.N_samples['N_1'], self.t)
self.results['m_s_s_N1'] += est.mean(self.N_samples['N_1'], self.t)**2
self.results['m_s_Nq1'] += est.mean(self.N_samples['Nq_1'], self.t)
self.results['m_s_s_Nq1'] += est.mean(self.N_samples['Nq_1'], self.t)**2
self.results['m_s_T1'] += est.mean(s_wait_1, len(self.clients)) + est.mean(s_server_1, len(self.clients))
self.results['m_s_s_T1'] += (est.mean(s_wait_1, len(self.clients)) + est.mean(s_server_1, len(self.clients)))**2
self.results['m_s_W2'] += est.mean(s_wait_2, len(self.clients))
self.results['m_s_s_W2'] += est.mean(s_wait_2, len(self.clients))**2
self.results['v_s_W2'] += est.variance(s_wait_2, s_s_wait_2, len(self.clients))
self.results['v_s_s_W2'] += est.variance(s_wait_2, s_s_wait_2, len(self.clients))**2
self.results['m_s_N2'] += est.mean(self.N_samples['N_2'], self.t)
self.results['m_s_s_N2'] += est.mean(self.N_samples['N_2'], self.t)**2
self.results['m_s_Nq2'] += est.mean(self.N_samples['Nq_2'], self.t)
self.results['m_s_s_Nq2'] += est.mean(self.N_samples['Nq_2'], self.t)**2
self.results['m_s_T2'] += est.mean(s_wait_2, len(self.clients)) + est.mean(s_server_2, len(self.clients))
self.results['m_s_s_T2'] += (est.mean(s_wait_2, len(self.clients)) + est.mean(s_server_2, len(self.clients)))**2
self.init_sample()
def process_sample(self):
s_wait_1 = 0; s_s_wait_1 = 0
s_wait_2 = 0; s_s_wait_2 = 0
s_server_1 = 0; s_server_2 = 0
# Loop que faz a soma e a soma dos quadrados dos tempos de espera e a soma dos tempos em servidor
# Dos clientes na fila 1 e na fila 2.
for client in self.clients:
s_wait_1 += client.wait(1)
s_s_wait_1 += client.wait(1)**2
s_server_1 += client.server[1]
s_wait_2 += client.wait(2)
s_s_wait_2 += client.wait(2)**2
s_server_2 += client.server[2]
# Adiciona à soma e à soma dos quadrados dos estimadores os valores estimados na rodada.
self.sums['m_s_W1'] += est.mean(s_wait_1, len(self.clients))
self.sums['m_s_s_W1'] += est.mean(s_wait_1, len(self.clients))**2
self.sums['v_s_W1'] += est.variance(s_wait_1, s_s_wait_1, len(self.clients))
self.sums['v_s_s_W1'] += est.variance(s_wait_1, s_s_wait_1, len(self.clients))**2
self.sums['m_s_N1'] += est.mean(self.N_samples['N_1'], self.t)
self.sums['m_s_s_N1'] += est.mean(self.N_samples['N_1'], self.t)**2
self.sums['m_s_Nq1'] += est.mean(self.N_samples['Nq_1'], self.t)
self.sums['m_s_s_Nq1'] += est.mean(self.N_samples['Nq_1'], self.t)**2
self.sums['m_s_T1'] += est.mean(s_wait_1, len(self.clients)) + est.mean(s_server_1, len(self.clients))
self.sums['m_s_s_T1'] += (est.mean(s_wait_1, len(self.clients)) + est.mean(s_server_1, len(self.clients)))**2
self.sums['m_s_W2'] += est.mean(s_wait_2, len(self.clients))
self.sums['m_s_s_W2'] += est.mean(s_wait_2, len(self.clients))**2
self.sums['v_s_W2'] += est.variance(s_wait_2, s_s_wait_2, len(self.clients))
self.sums['v_s_s_W2'] += est.variance(s_wait_2, s_s_wait_2, len(self.clients))**2
self.sums['m_s_N2'] += est.mean(self.N_samples['N_2'], self.t)
self.sums['m_s_s_N2'] += est.mean(self.N_samples['N_2'], self.t)**2
self.sums['m_s_Nq2'] += est.mean(self.N_samples['Nq_2'], self.t)
self.sums['m_s_s_Nq2'] += est.mean(self.N_samples['Nq_2'], self.t)**2
self.sums['m_s_T2'] += est.mean(s_wait_2, len(self.clients)) + est.mean(s_server_2, len(self.clients))
self.sums['m_s_s_T2'] += (est.mean(s_wait_2, len(self.clients)) + est.mean(s_server_2, len(self.clients)))**2
# Inicializa as estruturas de dados para a próxima rodada.
self.init_sample()