import tsnet # Open an example network and create a transient model inp_file = 'networks/simple_pump.inp' tm = tsnet.network.TransientModel(inp_file) # Set wavespeed tm.set_wavespeed(1200.) # m/s # Set time options dt = 0.1 # time step [s], if not given, use the maximum allowed dt tf = 60 # simulation period [s] tm.set_time(tf) # Set pump shut off tc = 1 # pump closure period ts = 0 # pump closure start time se = 0 # end open percentage m = 1 # closure constant pump_op = [tc,ts,se,m] tm.pump_shut_off('pump', pump_op) # Initialize steady state simulation t0 = 0. # initialize the simulation at 0 [s] engine = 'DD' # demand driven simulator tm = tsnet.simulation.Initializer(tm, t0, engine) # Transient simulation results_obj = 'simple_pump' # name of the object for saving simulation results tm = tsnet.simulation.MOCSimulator(tm, results_obj) # report results import matplotlib.pyplot as plt node = '2' node = tm.get_node(node) fig1 = plt.figure(figsize=(10,4), dpi=80, facecolor='w', edgecolor='k') plt.plot(tm.simulation_timestamps,node.head) plt.xlim([tm.simulation_timestamps[0],tm.simulation_timestamps[-1]]) plt.title('Pressure Head at Node %s '%node) plt.xlabel("Time [s]") plt.ylabel("Pressure Head [m]") plt.legend(loc='best') plt.grid(True) plt.show() # fig1.savefig('./docs/figures/tnet1_node.png', format='png',dpi=100) pipe = 'p2' pipe = tm.get_link(pipe) fig = plt.figure(figsize=(10,4), dpi=80, facecolor='w', edgecolor='k') plt.plot(tm.simulation_timestamps,pipe.start_node_flowrate,label='Start Node') plt.plot(tm.simulation_timestamps,pipe.end_node_flowrate,label='End Node') plt.xlim([tm.simulation_timestamps[0],tm.simulation_timestamps[-1]]) plt.title('Velocity of Pipe %s '%pipe) plt.xlabel("Time [s]") plt.ylabel("Velocity [m/s]") plt.legend(loc='best') plt.grid(True) plt.show()