Python pymodelica.compile_fmu函数代码示例

 def test_ExtFuncShared(self):
     """ 
     Test compiling a model with external functions in a shared library. Real, Integer, and Boolean arrays.
     """
     fmu_name = compile_fmu(self.cpath, self.fpath, compiler_options={'variability_propagation':True})
     model = load_fmu(fmu_name)
     s_ceval = model.get('s')
     res = model.simulate()
     s_sim1 = res.final('s')
     
     fmu_name = compile_fmu(self.cpath, self.fpath, compiler_options={'variability_propagation':False})
     model = load_fmu(fmu_name)
     res = model.simulate()
     s_sim2 = res.final('s')
     nose.tools.assert_equals(s_sim1, s_sim2)

def run_demo(with_plots=True):
    """
    Distillation2 model
    """

    curr_dir = os.path.dirname(os.path.abspath(__file__));

    fmu_name = compile_fmu("JMExamples.Distillation.Distillation2", 
    curr_dir+"/files/JMExamples.mo")
    dist2 = load_fmu(fmu_name)
    
    res = dist2.simulate(final_time=7200)

    # Extract variable profiles
    x16	= res['x[16]']
    x32	= res['x[32]']
    t	= res['time']
    
    print "t = ", repr(N.array(t))
    print "x16 = ", repr(N.array(x16))
    print "x32 = ", repr(N.array(x32))

    if with_plots:
        # Plot
        plt.figure(1)
        plt.plot(t,x16,t,x32)
        plt.grid()
        plt.ylabel('x')
        
        plt.xlabel('time')
        plt.show()

def run_demo(with_plots=True):
    """
    An example on how to simulate a model using the DAE simulator. The result 
    can be compared with that of sim_rlc.py which has solved the same problem 
    using dymola. Also writes information to a file.
    """

    curr_dir = os.path.dirname(os.path.abspath(__file__));

    class_name = 'RLC_Circuit'
    mofile = curr_dir+'/files/RLC_Circuit.mo'
    
    fmu_name = compile_fmu(class_name, mofile)
    rlc = load_fmu(fmu_name)
    
    res = rlc.simulate(final_time=30)
    
    sine_y = res['sine.y']
    resistor_v = res['resistor.v']
    inductor1_i = res['inductor1.i']
    t = res['time']

    assert N.abs(res.final('resistor.v') - 0.159255008028) < 1e-3
    
    if with_plots:
        fig = p.figure()
        p.plot(t, sine_y, t, resistor_v, t, inductor1_i)
        p.legend(('sine.y','resistor.v','inductor1.i'))
        p.show()

def run_demo(with_plots=True):
    """
    An example on how to simulate a model using the ODE simulator.
    """
    curr_dir = os.path.dirname(os.path.abspath(__file__));
    file_name = os.path.join(curr_dir,'files','VDP.mop')
    
    fmu_name = compile_fmu("JMExamples.VDP.VDP", 
    curr_dir+"/files/JMExamples.mo")

    model = load_fmu(fmu_name)
    
    opts = model.simulate_options()
    opts["CVode_options"]["rtol"] = 1e-6
    
    res = model.simulate(final_time=10, options=opts)

    assert N.abs(res.final('x1') - 7.34186386e-01) < 1e-3
    assert N.abs(res.final('x2') + 1.58202722)    < 1e-3
    
    x1 = res['x1']
    x2 = res['x2']
    
    if with_plots:
        plt.figure()
        plt.plot(x2, x1)
        plt.legend(('x1(x2)'))
        plt.show()

def run_demo(with_plots=True):
    
    curr_dir = os.path.dirname(os.path.abspath(__file__));
    class_name = 'ExtFunctions.addTwo'
    mofile = os.path.join(curr_dir, 'files', 'ExtFunctions.mo')
    
    # Compile and load model
    fmu_name = compile_fmu(class_name, mofile)
    model = load_fmu(fmu_name)

    # Simulate
    res = model.simulate()
    
    # Load result data
    sim_a = res['a']
    sim_b = res['b']
    sim_c = res['c']
    t     = res['time']

    assert N.abs(res.final('a') - 1) < 1e-6
    assert N.abs(res.final('b') - 2) < 1e-6
    assert N.abs(res.final('c') - 3) < 1e-6

    if with_plots:
        fig = p.figure()
        p.clf()
        p.subplot(3,1,1)
        p.plot(t, sim_a)
        p.subplot(3,1,2) 
        p.plot(t, sim_b) 
        p.subplot(3,1,3)
        p.plot(t, sim_c)
        p.show()

def run_demo(with_plots=True, version="2.0"):	

	# Compile model
	fmu_name = compile_fmu("IBPSA.Fluid.FixedResistances.Examples.PlugFlowPipe","C:\My_Libs\modelica-ibpsa\IBPSA")
	fmu_name=("IBPSA_Fluid_FixedResistances_Examples_PlugFlowPipe.fmu")
	#print("FMU compiled",fmu_name)
	print(fmu_name)

	# Load model
	pipe = load_fmu(fmu_name)

	print("FMU loaded", pipe)

	res = pipe.simulate(final_time=100)
		
	x1 = res['Tin.offset']
	x2 = res['sou.m_flow']
	t = res['time']
		
	plt.figure(1)
	plt.plot(t, x1, t, x2)
	plt.legend(('Tin (K)','mdot (kg/s)'))
	plt.title('Pipe')
	plt.ylabel('y axis')
	plt.xlabel('Time (s)')
	plt.show()

def run_demo(with_plots=True):
    """
    An example on how to simulate a model using a DAE simulator with Assimulo. 
    The model used is made by Maja Djačić.
    """

    curr_dir = os.path.dirname(os.path.abspath(__file__));

    m_name = 'SolAngles'
    mofile = curr_dir+'/files/SolAngles.mo'
    
    fmu_name = compile_fmu(m_name, mofile)
    model = load_fmu(fmu_name)
    
    res = model.simulate(final_time=86400.0, options={'ncp':86400})

    theta = res['theta']
    azim = res['azim']
    N_day = res['N_day']
    time = res['time']
    
    assert N.abs(res.final('theta') - 90.28737353) < 1e-3
    
    # Plot results
    if with_plots:
        p.figure(1)
        p.plot(time, theta)
        p.xlabel('time [s]')
        p.ylabel('theta [deg]')
        p.title('Angle of Incidence on Surface')
        p.grid()
        p.show()

 def test_ExtFuncStatic(self):
     """ 
     Test compiling a model with external functions in a static library.
     """
     cpath = "ExtFunctionTests.ExtFunctionTest1"
     fmu_name = compile_fmu(cpath, TestExternalStatic.fpath)
     model = load_fmu(fmu_name)

    def simulate(self):
        ''' TODO: LOG all command omc '''
#         tic= timeit.default_timer()
        # Simulation process with JModelica
        fullMoFile= self.moPath+ '/'+ self.moFile
        fullMoLib= self.libPath+ '/'+ self.libFile
        '''build the fmu block from the modelica model '''
#         fmu_name= compile_fmu(self.moModel, absolutePath,
#                                compiler_options = {'extra_lib_dirs':self.libPath})
        fmu_name= compile_fmu(self.moModel, [fullMoFile, fullMoLib])
        ''' Load the model '''
        model_fmu= load_fmu(fmu_name)
    
        ''' Load the list of options for the JModelica compiler '''
        opts = model_fmu.simulate_options()
        opts['solver']= self.config.getSolver()
        opts['ncp']= self.config.getNcp()
    #     for key,value in simOpt.getOptions().items():
    #         print key,value
    #         opts[key] = value
        print opts
        result = model_fmu.simulate(start_time= self.config.getStartTime(), 
                                    final_time= self.config.getStopTime(), 
                                    options=opts)
    
#         toc= timeit.default_timer()
#         print 'Simulation time ', toc- tic
        
        return result

def run_demo(with_plots=True):
    
    curr_dir = os.path.dirname(os.path.abspath(__file__));
    class_name = 'ExtFunctions.transposeSquareMatrix'
    mofile = os.path.join(curr_dir, 'files', 'ExtFunctions.mo')
    
    # Compile and load model
    fmu_name = compile_fmu(class_name, mofile)
    model = load_fmu(fmu_name)

    # Simulate
    res = model.simulate()
    
    # Get result data
    b1_1 = res['b_out[1,1]']
    b1_2 = res['b_out[1,2]']
    b2_1 = res['b_out[2,1]']
    b2_2 = res['b_out[2,2]']
    t = res['time']

    assert N.abs(res.final('b_out[1,1]') - 1) < 1e-6
    assert N.abs(res.final('b_out[1,2]') - 3) < 1e-6
    assert N.abs(res.final('b_out[2,1]') - 2) < 1e-6
    assert N.abs(res.final('b_out[2,2]') - 4) < 1e-6 
           
    if with_plots:
        fig = p.figure()
        p.clf()
        p.plot(t, b1_1, label='b1_1')
        p.plot(t, b1_2, label='b1_2')
        p.plot(t, b2_1, label='b2_1')
        p.plot(t, b2_2, label='b2_2')
        p.legend()
        p.grid()
        p.show()

def run_demo(with_plots=True):
    """
    This example shows how to simulate a system that contains switches. The 
    example model is simple in the sense that no reinitialization of the 
    variables is needed at the event points.
    """

    curr_dir = os.path.dirname(os.path.abspath(__file__));

    class_name = 'IfExpExamples.IfExpExample2'
    mofile = curr_dir+'/files/IfExpExamples.mo'

    fmu_name = compile_fmu(class_name, mofile)

    # Load the dynamic library and XML data
    model = load_fmu(fmu_name)

    # Simulate
    res = model.simulate(final_time=5.0)
    
    # Get results
    x = res['x']
    u = res['u']
    t = res['time']
    
    assert N.abs(res.final('x') - 3.5297217)    < 1e-3
    assert N.abs(res.final('u') - (-0.2836621)) < 1e-3

    if with_plots:
        fig = p.figure()
        p.plot(t, x, t, u)
        p.legend(('x','u'))
        p.show()

def run_demo(with_plots=True):
    """
    Demonstrates how to use an JMODELICA generated FMU for sensitivity
    calculations.
    """
    curr_dir = os.path.dirname(os.path.abspath(__file__));

    fmu_name = compile_fmu("Robertson", curr_dir+"/files/Robertson.mo")
        
    model = load_fmu(fmu_name)
        
    # Get and set the options
    opts = model.simulate_options()
    opts['sensitivities'] = ["p1","p2","p3"]
    opts['ncp'] = 400

    #Simulate
    res = model.simulate(final_time=4, options=opts)

    dy1dp1 = res['dy1/dp1']
    dy2dp1 = res['dy2/dp1']
    time = res['time']
        
    nose.tools.assert_almost_equal(dy1dp1[40], -0.35590, 3)
    nose.tools.assert_almost_equal(dy2dp1[40],  3.9026e-04, 6)
    
    if with_plots:
        plt.plot(time, dy1dp1, time, dy2dp1)
        plt.legend(('dy1/dp1', 'dy2/dp1'))
        plt.show()

def run_demo(with_plots=True):
    """ 
    Example demonstrating how to use index reduction.
    """

    curr_dir = os.path.dirname(os.path.abspath(__file__));

    # Compile model
    fmu_name = compile_fmu("Pendulum_pack.PlanarPendulum", 
        curr_dir+"/files/Pendulum_pack.mop",compiler='optimica')

    # Load model
    model = load_fmu(fmu_name)
    
    # Options
    opts = model.simulate_options()
    opts["CVode_options"]["rtol"] = 1e-6
    
    # Load result file
    res = model.simulate(final_time=10., options=opts)

    x = res['x']
    st = res['st']
    ct = res['ct']
    err = res['err']
    y = res['y']
    vx = res['vx']
    vy = res['vy']
    t = res['time']
    maxerr = N.max(err)

    if maxerr > 1e-6:
        print "Maximum error: ", maxerr 
        assert maxerr < 1e-4
    
    assert N.abs(res.final('x') - 0.38735171)       < 1e-3
    assert N.abs(res.final('st') - 0.38733358)      < 1e-3
    assert N.abs(res.final('ct') + 0.92193964)      < 1e-3
    assert N.abs(res.final('err') - 1.96716163e-05) < 1e-3
    assert N.abs(res.final('y') + 0.92193202)       < 1e-3
    assert N.abs(res.final('vx') - 6.04839823e-01)  < 1e-3
    assert N.abs(res.final('vy') - 2.54124747e-01)  < 1e-3

    if with_plots:
        plt.figure(1)
        plt.subplot(3,1,1)
        plt.plot(t,x,t,y)
        plt.grid(True)
        plt.legend(['x','y'])
        plt.subplot(3,1,2)
        plt.plot(t,vx,t,vy)
        plt.grid(True)
        plt.legend(['vx','vy'])
        plt.subplot(3,1,3)
        plt.plot(t,err)
        plt.grid(True)
        plt.legend(['err'])
        plt.xlabel('time [s]')
        plt.show()

def run_demo(with_plots=True):
    """
    Simulation of a model that predicts the blood glucose levels of a type-I 
    diabetic. The objective is to predict the relationship between insulin 
    injection and blood glucose levels.
    
    Reference:
     S. M. Lynch and B. W. Bequette, Estimation based Model Predictive Control of Blood Glucose in 
     Type I Diabetes: A Simulation Study, Proc. 27th IEEE Northeast Bioengineering Conference, IEEE, 2001.
     
     S. M. Lynch and B. W. Bequette, Model Predictive Control of Blood Glucose in type I Diabetics 
     using Subcutaneous Glucose Measurements, Proc. ACC, Anchorage, AK, 2002. 
    """

    curr_dir = os.path.dirname(os.path.abspath(__file__))

    fmu_name1 = compile_fmu("JMExamples.BloodGlucose.BloodGlucose1", os.path.join(curr_dir, "files", "JMExamples.mo"))
    bg = load_fmu(fmu_name1)

    opts = bg.simulate_options()
    opts["CVode_options"]["rtol"] = 1e-6

    res = bg.simulate(final_time=400, options=opts)

    # Extract variable profiles
    G = res["G"]
    X = res["X"]
    I = res["I"]
    t = res["time"]

    assert N.abs(res.final("G") - 19.77650) < 1e-4
    assert N.abs(res.final("X") - 14.97815) < 1e-4
    assert N.abs(res.final("I") - 2.7) < 1e-4

    if with_plots:
        plt.figure(1)

        plt.subplot(2, 2, 1)
        plt.plot(t, G)
        plt.title("Plasma Glucose Conc")
        plt.grid(True)
        plt.ylabel("Plasma Glucose Conc. (mmol/L)")
        plt.xlabel("time")

        plt.subplot(2, 2, 2)
        plt.plot(t, X)
        plt.title("Plasma Insulin Conc.")
        plt.grid(True)
        plt.ylabel("Plasma Insulin Conc. (mu/L)")
        plt.xlabel("time")

        plt.subplot(2, 2, 3)
        plt.plot(t, I)
        plt.title("Plasma Insulin Conc.")
        plt.grid(True)
        plt.ylabel("Plasma Insulin Conc. (mu/L)")
        plt.xlabel("time")

        plt.show()

 def test_ExtFuncSharedCeval(self):
     """ 
     Test compiling a model with external functions in a shared library. Constant evaluation during compilation.
     """
     cpath = "ExtFunctionTests.ExtFunctionTest1"
     fmu_name = compile_fmu(cpath, TestExternalShared.fpath, compiler_options={'variability_propagation':True})
     model = load_fmu(fmu_name)
     nose.tools.assert_equals(model.get('c'), 3)