def main(n_proc=multiprocessing.cpu_count() - 1):
# Initialize the FMU model empty
m = Model()
# Assign an existing FMU to the model, depending on the platform identified
dir_path = os.path.dirname(__file__)
# Define the path of the FMU file
if platform.architecture()[0] == "32bit":
print("32-bit architecture")
filePath = os.path.join(dir_path, "..", "..", "modelica",
"FmuExamples", "Resources", "FMUs",
"FirstOrder.fmu")
else:
print("64-bit architecture")
filePath = os.path.join(dir_path, "..", "..", "modelica",
"FmuExamples", "Resources", "FMUs",
"FirstOrder_64bit.fmu")
# Assign an existing FMU to the model
m.re_init(filePath)
# Set the CSV file associated to the input
inputPath = os.path.join(dir_path, "..", "..", "modelica", "FmuExamples",
"Resources", "data",
"SimulationData_FirstOrder.csv")
input_u = m.get_input_by_name("u")
input_u.get_csv_reader().open_csv(inputPath)
input_u.get_csv_reader().set_selected_column("system.u")
# Select the states to be modified
m.add_variable(m.get_variable_object("x"))
# Initialize the simulator
m.initialize_simulator()
# Instantiate the pool
pool = FmuPool(m, processes=n_proc)
# define the vector of initial conditions for which the simulations
# have to be performed.
# values has to be a list of state vectors
# values = [ [x0_0], [x0_1], ... [x0_n]]
vector_values = numpy.linspace(1.0, 5.0, 10)
values = []
for v in vector_values:
temp = {"state": numpy.array([v]), "parameters": []}
values.append(temp)
# Run simulations in parallel
pool_results = pool.run(values)
# plot all the results
show_results(pool_results)
def main(n_proc = multiprocessing.cpu_count()-1):
# Initialize the FMU model empty
m = Model()
# Assign an existing FMU to the model, depending on the platform identified
dir_path = os.path.dirname(__file__)
# Define the path of the FMU file
if platform.architecture()[0]=="32bit":
print "32-bit architecture"
filePath = os.path.join(dir_path, "..", "..", "modelica", "FmuExamples", "Resources", "FMUs", "FirstOrder.fmu")
else:
print "64-bit architecture"
filePath = os.path.join(dir_path, "..", "..", "modelica", "FmuExamples", "Resources", "FMUs", "FirstOrder_64bit.fmu")
# Assign an existing FMU to the model
m.re_init(filePath)
# Set the CSV file associated to the input
inputPath = os.path.join(dir_path, "..", "..", "modelica", "FmuExamples", "Resources", "data", "SimulationData_FirstOrder.csv")
input_u = m.get_input_by_name("u")
input_u.get_csv_reader().open_csv(inputPath)
input_u.get_csv_reader().set_selected_column("system.u")
# Select the states to be modified
m.add_variable(m.get_variable_object("x"))
# Initialize the simulator
m.initialize_simulator()
# Instantiate the pool
pool = FmuPool(m, processes = n_proc)
# define the vector of initial conditions for which the simulations
# have to be performed.
# values has to be a list of state vectors
# values = [ [x0_0], [x0_1], ... [x0_n]]
vector_values = numpy.linspace(1.0, 5.0, 10)
values = []
for v in vector_values:
temp = {"state":numpy.array([v]), "parameters":[]}
values.append(temp)
# Run simulations in parallel
pool_results = pool.run(values)
# plot all the results
show_results(pool_results)
def main():
"""
This example demonstrates how state and parameters can be simultaneously
estimated to identify faults in a valve.
"""
# Assign an existing FMU to the model, depending on the platform identified
dir_path = os.path.dirname(__file__)
# Define the path of the FMU file
filePath = os.path.join(dir_path, "..", "..", "modelica", "FmuExamples", "Resources", "FMUs", "ValveStuck.fmu")
# Initialize the FMU model empty
m = Model(filePath, atol=1e-5, rtol=1e-6)
# Path of the csv file containing the data series
csvPath = os.path.join(dir_path, "..", "..", "modelica", "FmuExamples", "Resources", "data", "NoisyData_ValveStuck.csv")
# Set the CSV file associated to the input, and its covariance
input = m.get_input_by_name("dp")
input.get_csv_reader().open_csv(csvPath)
input.get_csv_reader().set_selected_column("valveStuck.dp")
# Set the CSV file associated to the input, and its covariance
input = m.get_input_by_name("cmd")
input.get_csv_reader().open_csv(csvPath)
input.get_csv_reader().set_selected_column("valveStuck.cmd")
# Set the CSV file associated to the input, and its covariance
input = m.get_input_by_name("T_in")
input.get_csv_reader().open_csv(csvPath)
input.get_csv_reader().set_selected_column("valveStuck.T_in")
# Set the CSV file associated to the output, and its covariance
output = m.get_output_by_name("m_flow")
output.get_csv_reader().open_csv(csvPath)
output.get_csv_reader().set_selected_column("valveStuck.m_flow")
output.set_measured_output()
output.set_covariance(0.05)
#################################################################
# Select the variable to be estimated
m.add_variable(m.get_variable_object("command.y"))
# Set initial value of parameter, and its covariance and the limits (if any)
var = m.get_variables()[0]
var.set_initial_value(1.0)
var.set_covariance(0.05)
var.set_min_value(0.0)
var.set_constraint_low(True)
var.set_max_value(1.00)
var.set_constraint_high(True)
#################################################################
# Select the parameter to be estimated
m.add_parameter(m.get_variable_object("lambda"))
# Set initial value of parameter, and its covariance and the limits (if any)
var = m.get_parameters()[0]
var.set_initial_value(0.00)
var.set_covariance(0.0007)
var.set_min_value(-0.005)
var.set_constraint_low(True)
var.set_max_value(0.025)
var.set_constraint_high(True)
# Initialize the model for the simulation
m.initialize_simulator()
# Set models parameters
use_cmd = m.get_variable_object("use_cmd")
m.set_real(use_cmd, 0.0)
Lambda = m.get_variable_object("lambda")
m.set_real(Lambda, 0.0)
#################################################################
# Instantiate the UKF for the FMU model
ukf_FMU = UkfFmu(m)
# Start filter
t0 = pd.to_datetime(0.0, unit = "s", utc = True)
t1 = pd.to_datetime(360.0, unit = "s", utc = True)
time, x, sqrtP, y, Sy, y_full, Xsmooth, Ssmooth, Yfull_smooth = \
ukf_FMU.filter_and_smooth(start = t0, stop = t1)
# Plot the results
showResults(time, x, sqrtP, y, Sy, y_full, Xsmooth, Ssmooth, Yfull_smooth, m)
def main():
# Assign an existing FMU to the model, depending on the platform identified
dir_path = os.path.dirname(__file__)
# Define the path of the FMU file
if platform.architecture()[0]=="32bit":
print "32-bit architecture"
filePath = os.path.join(dir_path, "..", "..", "modelica", "FmuExamples", "Resources", "FMUs", "FirstOrder.fmu")
else:
print "64-bit architecture"
filePath = os.path.join(dir_path, "..", "..", "modelica", "FmuExamples", "Resources", "FMUs", "FirstOrder_64bit.fmu")
# Initialize the FMU model empty
m = Model(filePath)
# Path of the csv file containing the data series
csvPath = os.path.join(dir_path, "..", "..", "modelica", "FmuExamples", "Resources", "data", "NoisySimulationData_FirstOrder.csv")
# Set the CSV file associated to the input, and its covariance
input_u = m.get_input_by_name("u")
input_u.get_csv_reader().open_csv(csvPath)
input_u.get_csv_reader().set_selected_column("system.u")
input_u.set_covariance(2.0)
# Set the CSV file associated to the output, and its covariance
output = m.get_output_by_name("y")
output.get_csv_reader().open_csv(csvPath)
output.get_csv_reader().set_selected_column("system.y")
output.set_measured_output()
output.set_covariance(2.0)
# Select the states to be identified, and add it to the list
m.add_variable(m.get_variable_object("x"))
# Set initial value of state, and its covariance and the limits (if any)
var = m.get_variables()[0]
var.set_initial_value(1.5)
var.set_covariance(0.5)
var.set_min_value(0.0)
var.set_constraint_low(True)
# show the info about the variable to be estimated
print var.info()
# Set parameters been identified
par_a = m.get_variable_object("a")
m.set_real(par_a, -0.90717055)
par_b = m.get_variable_object("b")
m.set_real(par_b, 2.28096907)
par_c = m.get_variable_object("c")
m.set_real(par_c, 3.01419707)
par_d = m.get_variable_object("d")
m.set_real(par_d, 0.06112703)
# Initialize the model for the simulation
m.initialize_simulator()
# instantiate the UKF for the FMU
ukf_FMU = UkfFmu(m)
# Start the filter
t0 = pd.to_datetime(0.0, unit = "s", utc = True)
t1 = pd.to_datetime(30.0, unit = "s", utc = True)
time, x, sqrtP, y, Sy, y_full = ukf_FMU.filter(start = t0, stop = t1)
# Path of the csv file containing the True data series
csvTrue = os.path.join(dir_path, "..", "..", "modelica", "FmuExamples", "Resources", "data", "SimulationData_FirstOrder.csv")
# Get the measured outputs
show_results(time, x, sqrtP, y, Sy, y_full, csvTrue, csvPath, m)
class Test(unittest.TestCase):
"""
This class contains tests to verify the correctness
of functionalities provided by object of type
:class:`estimationpy.ukf.ukf_fmu.UkfFmu`.
"""
def setUp(self):
pass
def tearDown(self):
pass
def set_first_order_model(self):
"""
This method is used in different tests and it
loads the model of the first order system.
"""
# Define the path of the FMU file
if platform.architecture()[0] == "32bit":
filePath = os.path.join(dir_path, "..", "modelica", "FmuExamples",
"Resources", "FMUs", "FirstOrder.fmu")
else:
filePath = os.path.join(dir_path, "..", "modelica", "FmuExamples",
"Resources", "FMUs",
"FirstOrder_64bit.fmu")
# Initialize the FMU model empty
self.m = Model(filePath)
return
def set_valve_model(self):
"""
This method is used in different tests and it
loads the model of the faulty valve.
"""
# Define the path of the FMU file
if platform.architecture()[0] == "32bit":
filePath = os.path.join(dir_path, "..", "modelica", "FmuExamples",
"Resources", "FMUs", "ValveStuck.fmu")
else:
filePath = os.path.join(dir_path, "..", "modelica", "FmuExamples",
"Resources", "FMUs", "ValveStuck.fmu")
# Initialize the FMU model empty
self.m = Model(filePath)
return
def set_first_order_model_input_outputs(self, noisy=True):
"""
This methos associates the input and the measured output of
the first order model from a CSV file.
If the boolean flag ``noisy`` is equal to True then the data that is
loaded contains noise, otherwise it is clean.
:param bool noisy: flag that indicates which type of data to load.
"""
# Path of the csv file containing the data series
if noisy:
csvPath = os.path.join(dir_path, "..", "modelica", "FmuExamples",
"Resources", "data",
"NoisySimulationData_FirstOrder.csv")
else:
csvPath = os.path.join(dir_path, "..", "modelica", "FmuExamples",
"Resources", "data",
"SimulationData_FirstOrder.csv")
# Set the CSV file associated to the input, and its covariance
input_u = self.m.get_input_by_name("u")
input_u.get_csv_reader().open_csv(csvPath)
input_u.get_csv_reader().set_selected_column("system.u")
input_u.set_covariance(2.0)
# Set the CSV file associated to the output, and its covariance
output = self.m.get_output_by_name("y")
output.get_csv_reader().open_csv(csvPath)
output.get_csv_reader().set_selected_column("system.y")
output.set_measured_output()
output.set_covariance(2.0)
return
def set_valve_model_input_outputs(self):
"""
This method associates the input and measured outputs of the
valve model from a CSV file.
"""
# Path of the csv file containing the data series
csvPath = os.path.join(dir_path, "..", "modelica", "FmuExamples",
"Resources", "data", "NoisyData_ValveStuck.csv")
# Set the CSV file associated to the input, and its covariance
input = self.m.get_input_by_name("dp")
input.get_csv_reader().open_csv(csvPath)
input.get_csv_reader().set_selected_column("valveStuck.dp")
# Set the CSV file associated to the input, and its covariance
input = self.m.get_input_by_name("cmd")
input.get_csv_reader().open_csv(csvPath)
input.get_csv_reader().set_selected_column("valveStuck.cmd")
# Set the CSV file associated to the input, and its covariance
input = self.m.get_input_by_name("T_in")
input.get_csv_reader().open_csv(csvPath)
input.get_csv_reader().set_selected_column("valveStuck.T_in")
# Set the CSV file associated to the output, and its covariance
output = self.m.get_output_by_name("m_flow")
output.get_csv_reader().open_csv(csvPath)
output.get_csv_reader().set_selected_column("valveStuck.m_flow")
output.set_measured_output()
output.set_covariance(0.05)
return
def set_state_to_estimate_first_order(self):
"""
This method sets the state variable that needs to be estimated
by the UKF. The state variable has also a constraints,
it needs to be higher than zero.
"""
# Select the states to be identified, and add it to the list
self.m.add_variable(self.m.get_variable_object("x"))
# Set initial value of state, and its covariance and the limits (if any)
var = self.m.get_variables()[0]
var.set_initial_value(1.5)
var.set_covariance(0.5)
var.set_min_value(0.0)
var.set_constraint_low(True)
return
def set_state_and_param_to_estimate_valve(self):
"""
This method sets the state variable that needs to be estimated
by the UKF and the smoother in the valve example.
The state variable and the parameter have both constraints.
The valve position needs to be between [0,1] while the thermal drift
coefficient has to be between [-0.005, and 0.025].
"""
# Select the variable to be estimated
self.m.add_variable(self.m.get_variable_object("command.y"))
# Set initial value of parameter, and its covariance and the limits (if any)
var = self.m.get_variables()[0]
var.set_initial_value(1.0)
var.set_covariance(0.05)
var.set_min_value(0.0)
var.set_constraint_low(True)
var.set_max_value(1.00)
var.set_constraint_high(True)
#################################################################
# Select the parameter to be estimated
self.m.add_parameter(self.m.get_variable_object("lambda"))
# Set initial value of parameter, and its covariance and the limits (if any)
var = self.m.get_parameters()[0]
var.set_initial_value(0.00)
var.set_covariance(0.0007)
var.set_min_value(-0.005)
var.set_constraint_low(True)
var.set_max_value(0.025)
var.set_constraint_high(True)
return
def test_instantiate_UKF(self):
"""
This method verifies the baility to correctly instantiate an
object of type :class:`estimationpy.ukf.ukf_fmu.UkfFmu`.
"""
# Initialize the first order model
self.set_first_order_model()
# Instantiate the UKF for the FMU without state/parameters to estimate,
# verify that raises an exception
self.assertRaises(
ValueError, UkfFmu, self.m,
"The object initialized with a bad configured model should raise an exception"
)
# Associate inputs and outputs
self.set_first_order_model_input_outputs()
# Define the variables to estimate
self.set_state_to_estimate_first_order()
# Retry to instantiate, now with a proper model
ukf_FMU = UkfFmu(self.m)
# Get the parameters set by the initialization
(alpha, beta, k, lambd, sqrt_C, N) = ukf_FMU.get_ukf_params()
# Verify their default values
self.assertEqual(1, N,
"The number os states to estimate is not correct")
self.assertEqual(alpha, 1.0 / np.sqrt(3),
"The base value for alpha is wrong")
self.assertEqual(beta, 2, "The base value for beta is wrong")
self.assertEqual(k, 3 - N, "The base value for k is wrong")
# Get the parameters set by default function
ukf_FMU.set_default_ukf_params()
(alpha, beta, k, lambd, sqrt_C, N) = ukf_FMU.get_ukf_params()
# Verify their default values
self.assertEqual(alpha, 0.01, "The default value for alpha is wrong")
self.assertEqual(beta, 2, "The default value for beta is wrong")
self.assertEqual(k, 1, "The default value for k is wrong")
# Compute and get the weights
ukf_FMU.compute_weights()
w_m, w_c = ukf_FMU.get_weights()
# Verify the length
self.assertEqual(len(w_c), 3, "Length of vector w_c is wrong")
self.assertEqual(len(w_m), 3, "Length of vector w_m is wrong")
# Verify that the first element of w_c is different from the first element of w_m
self.assertTrue(w_c[0] != w_m[0],
"The first elements of w_c and w_m must be different")
# The remaining elements must be equal
for i in range(1, N):
self.assertEqual(
w_c[i], w_m[i],
"Weights w_m[{0}] and w_c[{0}] are different".format(i))
# The sum of w_m is equal to 1
self.assertEqual(
np.sum(w_m), 1.0,
"The weigts of w_m must sum up to 1, instead is {0}".format(
np.sum(w_m)))
return
def test_vector_and_matrix_operations(self):
"""
This method contains several checks for the methods provided by the class
to operate with matrices and vectors.
"""
# Initialize the first order model
self.set_first_order_model()
# Associate inputs and outputs
self.set_first_order_model_input_outputs()
# Define the variables to estimate
self.set_state_to_estimate_first_order()
# Retry to instantiate, now with a proper model
ukf_FMU = UkfFmu(self.m)
# Define square root matrix
S = np.random.uniform(size=(6, 6))
S2 = np.dot(S, S.T)
# Compute square matrix S
s = ukf_FMU.square_root(S2)
s2 = np.dot(s, s.T)
np.testing.assert_almost_equal(
s2, S2, 7,
"The product of the square root matrix is not equal to the original S2"
)
# Verify ability to apply constraints
x = np.array([1.1])
x_constr = ukf_FMU.constrained_state(x)
self.assertEqual(
x_constr, x, "This state vector doesn't require to be constrained")
x = np.array([-1.1])
x_constr = ukf_FMU.constrained_state(x)
x_expected = np.zeros(1)
self.assertEqual(
x_constr, x_expected,
"This state vector does require to be constrained and it's not")
return
def test_chol_update(self):
"""
This method tests the Cholesky update method that is used to compute
the squared root covariance matrix by the filter.
"""
# Initialize the first order model
self.set_first_order_model()
# Associate inputs and outputs
self.set_first_order_model_input_outputs()
# Define the variables to estimate
self.set_state_to_estimate_first_order()
# Retry to instantiate, now with a proper model
ukf_FMU = UkfFmu(self.m)
# Number of points for computing the covariance matrix
n = 100
# Number of variables
N = 300
# True mean vector
Xtrue = np.random.uniform(-8.0, 27.5, (1, N))
# Generate the sample for computing the covariance matrix
notUsed, N = Xtrue.shape
Xpoints = np.zeros((n, N))
for i in range(n):
noise = np.random.uniform(-2.0, 2.0, (1, N))
Xpoints[i, :] = Xtrue + noise
# default covariance to be added
Q = 2.0 * np.eye(N)
# definition of the weights
Weights = np.zeros(n)
for i in range(n):
if i == 0:
Weights[i] = 0.5
else:
Weights[i] = (1.0 - Weights[0]) / np.float(n - 1)
#---------------------------------------------------
# Standard method based on Cholesky
i = 0
P = Q
for x in Xpoints:
error = x - Xtrue
P = P + Weights[i] * np.dot(error.T, error)
i += 1
S = ukf_FMU.square_root(P)
np.testing.assert_almost_equal(P, np.dot(S, S.T), 8, \
"Square root computed with basic Cholesky decomposition is not correct")
#----------------------------------------------------
# Test the Cholesky update
sqrtQ = np.linalg.cholesky(Q)
L = ukf_FMU.compute_S(Xpoints, Xtrue, sqrtQ, w=Weights)
np.testing.assert_almost_equal(P, np.dot(L.T, L), 8, \
"Square root computed with basic Cholesky update is not correct")
return
def test_create_sigma_points(self):
"""
This method tests the Cholesky update method that is used to compute
the squared root covariance matrix by the filter.
"""
# Initialize the first order model
self.set_first_order_model()
# Associate inputs and outputs
self.set_first_order_model_input_outputs()
# Define the variables to estimate
self.set_state_to_estimate_first_order()
# Instantiate with a proper model
ukf_FMU = UkfFmu(self.m)
# Verify that the method raises an exception if the
# inputs are wrong
self.assertRaises(ValueError, ukf_FMU.compute_sigma_points,
np.zeros(3), np.zeros(3), np.zeros((3, 3)))
self.assertRaises(ValueError, ukf_FMU.compute_sigma_points,
np.zeros(2), np.zeros(3), np.zeros((3, 3)))
self.assertRaises(ValueError, ukf_FMU.compute_sigma_points,
np.zeros(1), np.array([]), np.zeros((3, 3)))
# Create the sigma points
x0 = np.array([2.5])
sigma_points = ukf_FMU.compute_sigma_points(x0, np.array([]),
np.diag(np.ones(1)))
# Check the size
self.assertTrue(sigma_points.shape == (3, 1),
"The size of the sigma points is not correct")
# Verify that the first sigma point is equal to the center
self.assertEqual(x0, sigma_points[0, :],
"First sigma point is not [0]")
# Verify that the second and the last sigma points are symmetric
self.assertEqual(
0.5 * (sigma_points[1, :] + sigma_points[2, :]), x0,
"The sigma points 1,2 are not symmetric with respect to 0")
return
def test_project_sigma_points(self):
"""
This method tests the function that projects the sigma points
by running a simulation.
"""
# Initialize the first order model
self.set_first_order_model()
# Associate inputs and outputs
self.set_first_order_model_input_outputs(noisy=False)
# Define the variables to estimate
self.set_state_to_estimate_first_order()
# Initialize the simulator
self.m.initialize_simulator()
# Instantiate with a proper model
ukf_FMU = UkfFmu(self.m)
# Define the sigma points
x0 = np.array([2.5])
sigma_points = ukf_FMU.compute_sigma_points(x0, np.array([]),
np.diag(np.ones(1)))
# Propagate the points by simulating from 0 to 14.5 seconds
t0 = pd.to_datetime(0.0, unit="s", utc=True)
t1 = pd.to_datetime(14.5, unit="s", utc=True)
X_proj, Z_proj, Xfull_proj, Zfull_proj = ukf_FMU.sigma_point_proj(
sigma_points, t0, t1)
# Verify that they started from different initial coditions and that they converged
# at the same value after 12 seconds
np.testing.assert_almost_equal(
X_proj, 2.5 * np.ones((3, 1)), 3,
"Verify that the solutions all converge to 2.5")
# Compute their average using the method provided by the object and verify its value
x_avg = ukf_FMU.average_proj(X_proj)
np.testing.assert_almost_equal(
x_avg, np.array([[2.5]]), 4,
"Average of the propagated points is not correct")
return
def test_ukf_filter_first_order(self):
"""
This method tests the ability of the filter to estimate the state
of the first order system.
"""
# Initialize the first order model
self.set_first_order_model()
# Associate inputs and outputs
self.set_first_order_model_input_outputs()
# Define the variables to estimate
self.set_state_to_estimate_first_order()
# Initialize the simulator
self.m.initialize_simulator()
# Retry to instantiate, now with a proper model
ukf_FMU = UkfFmu(self.m)
# Start the filter
t0 = pd.to_datetime(0.0, unit="s", utc=True)
t1 = pd.to_datetime(30.0, unit="s", utc=True)
time, x, sqrtP, y, Sy, y_full = ukf_FMU.filter(start=t0, stop=t1)
# Convert the results to numpy array
time = time - time[0]
time = np.array(map(lambda x: x.total_seconds(), time))
x = np.array(x)
y = np.array(y)
sqrtP = np.array(sqrtP)
Sy = np.array(Sy)
y_full = np.squeeze(np.array(y_full))
# Path of the csv file containing the True data series
path_csv_simulation = os.path.join(dir_path, "..", "modelica",
"FmuExamples", "Resources", "data",
"SimulationData_FirstOrder.csv")
# Compare the estimated states with the ones used to generate the data
df_sim = pd.read_csv(path_csv_simulation, index_col=0)
time_sim = df_sim.index.values
# Difference between state estimated and real state
x_sim = np.interp(time, time_sim, df_sim["system.x"])
err_state = np.abs(x_sim - x[:, 0])
# Identify maximum error and the time when it occurs
max_error = np.max(err_state)
t_max_error = np.where(err_state == max_error)
# Make sure that the maximum error is less or equal than 0.5, and it happens at
# the first time instant t = 0
self.assertTrue(
max_error <= 0.5,
"The maximum error in the estimation has to be less than 0.5")
self.assertTrue(t_max_error[0][0] == 0.0 and len(t_max_error[0]) == 1,\
"The maximum error is one and it is at t = 0")
# Compute the mean absolute error
avg_error = np.mean(err_state)
self.assertTrue(avg_error < 0.06,
"The average error should be less than 0.06")
# Compute that the estimation +/- covariance contains the real state
x_plus_sigma = x[:, 0] + sqrtP[:, 0, 0]
x_minus_sigma = x[:, 0] - sqrtP[:, 0, 0]
self.assertTrue(len(np.where(x_sim < x_minus_sigma)[0]) == 0,\
"The state estimation must contain the real state in its boundaries")
self.assertTrue(len(np.where(x_sim > x_plus_sigma)[0]) == 0,\
"The state estimation must contain the real state in its boundaries")
return
def test_ukf_smoother_valve(self):
"""
This method tests the state and parameter estimation on the valve example performed
with the UKF + Smoother.
"""
# Initialize the first order model
self.set_valve_model()
# Associate inputs and outputs
self.set_valve_model_input_outputs()
# Define the variables to estimate
self.set_state_and_param_to_estimate_valve()
# Initialize the simulator
self.m.initialize_simulator()
# Set models parameters
use_cmd = self.m.get_variable_object("use_cmd")
self.m.set_real(use_cmd, 0.0)
lambd = self.m.get_variable_object("lambda")
self.m.set_real(lambd, 0.0)
# Retry to instantiate, now with a proper model
ukf_FMU = UkfFmu(self.m)
# Start filter and smoother
t0 = pd.to_datetime(0.0, unit="s", utc=True)
t1 = pd.to_datetime(360.0, unit="s", utc=True)
time, x, sqrtP, y, Sy, y_full, Xsmooth, Ssmooth, Yfull_smooth = \
ukf_FMU.filter_and_smooth(start = t0, stop = t1)
# Convert the results to numpy array
time = time - time[0]
time = np.array(map(lambda x: x.total_seconds(), time))
x = np.array(x)
y = np.array(y)
sqrtP = np.array(sqrtP)
Sy = np.array(Sy)
y_full = np.squeeze(np.array(y_full))
xs = np.array(Xsmooth)
Ss = np.array(Ssmooth)
Ys = np.array(Yfull_smooth)
# Path of the csv file containing the True data series generated by a simulation model
path_csv_simulation = os.path.join(dir_path, "..", "modelica",
"FmuExamples", "Resources", "data",
"SimulationData_ValveStuck.csv")
# Compare the estimated states with the ones used to generate the data
df_sim = pd.read_csv(path_csv_simulation, index_col=0)
time_sim = df_sim.index.values
# Difference between state estimated and real state/parameters
opening_sim = np.interp(time, time_sim,
df_sim["valveStuck.valve.opening"])
lambda_sim = np.interp(time, time_sim, df_sim["valveStuck.lambda"])
err_opening = np.abs(opening_sim - x[:, 0])
err_lambda = np.abs(lambda_sim - x[:, 1])
err_opening_s = np.abs(opening_sim - xs[:, 0])
err_lambda_s = np.abs(lambda_sim - xs[:, 1])
# Compute the maximum errors for both filter and smoother
max_opening_error = np.max(err_opening)
max_opening_error_s = np.max(err_opening_s)
max_lambda_error = np.max(err_lambda)
max_lambda_error_s = np.max(err_lambda_s)
# Compute average error for both filter and smoother
avg_opening_error = np.mean(err_opening)
avg_opening_error_s = np.mean(err_opening_s)
avg_lambda_error = np.mean(err_lambda)
avg_lambda_error_s = np.mean(err_lambda_s)
# Compare performances of UKF and Smoother, verify that the smoother improves
# the estimation
self.assertTrue(max_opening_error >= max_opening_error_s,\
"The max error in the estimation of the opening by the smoother is larger than the filter")
self.assertTrue(max_lambda_error >= max_lambda_error_s,\
"The maxerror in the estimation of the drift coeff. by the smoother is larger than the filter")
self.assertTrue(avg_opening_error >= avg_opening_error_s,\
"The avg error in the estimation of the opening by the smoother is larger than the filter")
self.assertTrue(avg_lambda_error >= avg_lambda_error_s,\
"The avg error in the estimation of the drift coeff. by the smoother is larger than the filter")
# Verify that some absolute perfomances are guaranteed
self.assertTrue(
0.08 > max_opening_error,
"The maximum error of the UKF on the opening is too big")
self.assertTrue(
0.065 > max_opening_error_s,
"The maximum error of the Smoother on the opening is too big")
self.assertTrue(
0.0117 > avg_opening_error,
"The average error of the UKF on the opening is too big")
self.assertTrue(
0.0088 > avg_opening_error_s,
"The average error of the Smoother on the opening is too big")
self.assertTrue(
0.0101 > max_lambda_error,
"The maximum error of the UKF on the drift coef. is too big")
self.assertTrue(
0.0096 > max_lambda_error_s,
"The maximum error of the Smoother on the drift coef. is too big")
self.assertTrue(
0.0028 > avg_lambda_error,
"The average error of the UKF on the drift coef. is too big")
self.assertTrue(
0.00144 > avg_lambda_error_s,
"The average error of the Smoother on the drift coef. is too big")
return
class Test(unittest.TestCase):
"""
This class contains tests to verify the correctness
of functionalities provided by object of type
:class:`estimationpy.ukf.ukf_fmu.UkfFmu`.
"""
def setUp(self):
pass
def tearDown(self):
pass
def set_first_order_model(self):
"""
This method is used in different tests and it
loads the model of the first order system.
"""
# Define the path of the FMU file
if platform.architecture()[0]=="32bit":
filePath = os.path.join(dir_path, "..", "modelica", "FmuExamples", "Resources", "FMUs", "FirstOrder.fmu")
else:
filePath = os.path.join(dir_path, "..", "modelica", "FmuExamples", "Resources", "FMUs", "FirstOrder_64bit.fmu")
# Initialize the FMU model empty
self.m = Model(filePath)
return
def set_valve_model(self):
"""
This method is used in different tests and it
loads the model of the faulty valve.
"""
# Define the path of the FMU file
if platform.architecture()[0]=="32bit":
filePath = os.path.join(dir_path, "..", "modelica", "FmuExamples", "Resources", "FMUs", "ValveStuck.fmu")
else:
filePath = os.path.join(dir_path, "..", "modelica", "FmuExamples", "Resources", "FMUs", "ValveStuck.fmu")
# Initialize the FMU model empty
self.m = Model(filePath)
return
def set_first_order_model_input_outputs(self, noisy = True):
"""
This methos associates the input and the measured output of
the first order model from a CSV file.
If the boolean flag ``noisy`` is equal to True then the data that is
loaded contains noise, otherwise it is clean.
:param bool noisy: flag that indicates which type of data to load.
"""
# Path of the csv file containing the data series
if noisy:
csvPath = os.path.join(dir_path, "..", "modelica", "FmuExamples", "Resources", "data", "NoisySimulationData_FirstOrder.csv")
else:
csvPath = os.path.join(dir_path, "..", "modelica", "FmuExamples", "Resources", "data", "SimulationData_FirstOrder.csv")
# Set the CSV file associated to the input, and its covariance
input_u = self.m.get_input_by_name("u")
input_u.get_csv_reader().open_csv(csvPath)
input_u.get_csv_reader().set_selected_column("system.u")
input_u.set_covariance(2.0)
# Set the CSV file associated to the output, and its covariance
output = self.m.get_output_by_name("y")
output.get_csv_reader().open_csv(csvPath)
output.get_csv_reader().set_selected_column("system.y")
output.set_measured_output()
output.set_covariance(2.0)
return
def set_valve_model_input_outputs(self):
"""
This method associates the input and measured outputs of the
valve model from a CSV file.
"""
# Path of the csv file containing the data series
csvPath = os.path.join(dir_path, "..", "modelica", "FmuExamples", "Resources", "data", "NoisyData_ValveStuck.csv")
# Set the CSV file associated to the input, and its covariance
input = self.m.get_input_by_name("dp")
input.get_csv_reader().open_csv(csvPath)
input.get_csv_reader().set_selected_column("valveStuck.dp")
# Set the CSV file associated to the input, and its covariance
input = self.m.get_input_by_name("cmd")
input.get_csv_reader().open_csv(csvPath)
input.get_csv_reader().set_selected_column("valveStuck.cmd")
# Set the CSV file associated to the input, and its covariance
input = self.m.get_input_by_name("T_in")
input.get_csv_reader().open_csv(csvPath)
input.get_csv_reader().set_selected_column("valveStuck.T_in")
# Set the CSV file associated to the output, and its covariance
output = self.m.get_output_by_name("m_flow")
output.get_csv_reader().open_csv(csvPath)
output.get_csv_reader().set_selected_column("valveStuck.m_flow")
output.set_measured_output()
output.set_covariance(0.05)
return
def set_state_to_estimate_first_order(self):
"""
This method sets the state variable that needs to be estimated
by the UKF. The state variable has also a constraints,
it needs to be higher than zero.
"""
# Select the states to be identified, and add it to the list
self.m.add_variable(self.m.get_variable_object("x"))
# Set initial value of state, and its covariance and the limits (if any)
var = self.m.get_variables()[0]
var.set_initial_value(1.5)
var.set_covariance(0.5)
var.set_min_value(0.0)
var.set_constraint_low(True)
return
def set_state_and_param_to_estimate_valve(self):
"""
This method sets the state variable that needs to be estimated
by the UKF and the smoother in the valve example.
The state variable and the parameter have both constraints.
The valve position needs to be between [0,1] while the thermal drift
coefficient has to be between [-0.005, and 0.025].
"""
# Select the variable to be estimated
self.m.add_variable(self.m.get_variable_object("command.y"))
# Set initial value of parameter, and its covariance and the limits (if any)
var = self.m.get_variables()[0]
var.set_initial_value(1.0)
var.set_covariance(0.05)
var.set_min_value(0.0)
var.set_constraint_low(True)
var.set_max_value(1.00)
var.set_constraint_high(True)
#################################################################
# Select the parameter to be estimated
self.m.add_parameter(self.m.get_variable_object("lambda"))
# Set initial value of parameter, and its covariance and the limits (if any)
var = self.m.get_parameters()[0]
var.set_initial_value(0.00)
var.set_covariance(0.0007)
var.set_min_value(-0.005)
var.set_constraint_low(True)
var.set_max_value(0.025)
var.set_constraint_high(True)
return
def test_instantiate_UKF(self):
"""
This method verifies the baility to correctly instantiate an
object of type :class:`estimationpy.ukf.ukf_fmu.UkfFmu`.
"""
# Initialize the first order model
self.set_first_order_model()
# Instantiate the UKF for the FMU without state/parameters to estimate,
# verify that raises an exception
self.assertRaises(ValueError, UkfFmu, self.m, "The object initialized with a bad configured model should raise an exception")
# Associate inputs and outputs
self.set_first_order_model_input_outputs()
# Define the variables to estimate
self.set_state_to_estimate_first_order()
# Retry to instantiate, now with a proper model
ukf_FMU = UkfFmu(self.m)
# Get the parameters set by the initialization
(alpha, beta, k, lambd, sqrt_C, N) = ukf_FMU.get_ukf_params()
# Verify their default values
self.assertEqual(1, N, "The number os states to estimate is not correct")
self.assertEqual(alpha, 1.0/np.sqrt(3), "The base value for alpha is wrong")
self.assertEqual(beta, 2, "The base value for beta is wrong")
self.assertEqual(k, 3 - N, "The base value for k is wrong")
# Get the parameters set by default function
ukf_FMU.set_default_ukf_params()
(alpha, beta, k, lambd, sqrt_C, N) = ukf_FMU.get_ukf_params()
# Verify their default values
self.assertEqual(alpha, 0.01, "The default value for alpha is wrong")
self.assertEqual(beta, 2, "The default value for beta is wrong")
self.assertEqual(k, 1, "The default value for k is wrong")
# Compute and get the weights
ukf_FMU.compute_weights()
w_m, w_c = ukf_FMU.get_weights()
# Verify the length
self.assertEqual(len(w_c), 3, "Length of vector w_c is wrong")
self.assertEqual(len(w_m), 3, "Length of vector w_m is wrong")
# Verify that the first element of w_c is different from the first element of w_m
self.assertTrue(w_c[0] != w_m[0], "The first elements of w_c and w_m must be different")
# The remaining elements must be equal
for i in range(1,N):
self.assertEqual(w_c[i], w_m[i], "Weights w_m[{0}] and w_c[{0}] are different".format(i))
# The sum of w_m is equal to 1
self.assertEqual(np.sum(w_m), 1.0, "The weigts of w_m must sum up to 1, instead is {0}".format(np.sum(w_m)))
return
def test_vector_and_matrix_operations(self):
"""
This method contains several checks for the methods provided by the class
to operate with matrices and vectors.
"""
# Initialize the first order model
self.set_first_order_model()
# Associate inputs and outputs
self.set_first_order_model_input_outputs()
# Define the variables to estimate
self.set_state_to_estimate_first_order()
# Retry to instantiate, now with a proper model
ukf_FMU = UkfFmu(self.m)
# Define square root matrix
S = np.random.uniform(size=(6,6))
S2 = np.dot(S, S.T)
# Compute square matrix S
s = ukf_FMU.square_root(S2)
s2 = np.dot(s, s.T)
np.testing.assert_almost_equal(s2, S2, 7, "The product of the square root matrix is not equal to the original S2")
# Verify ability to apply constraints
x = np.array([1.1])
x_constr = ukf_FMU.constrained_state(x)
self.assertEqual(x_constr, x, "This state vector doesn't require to be constrained")
x = np.array([-1.1])
x_constr = ukf_FMU.constrained_state(x)
x_expected = np.zeros(1)
self.assertEqual(x_constr, x_expected, "This state vector does require to be constrained and it's not")
return
def test_chol_update(self):
"""
This method tests the Cholesky update method that is used to compute
the squared root covariance matrix by the filter.
"""
# Initialize the first order model
self.set_first_order_model()
# Associate inputs and outputs
self.set_first_order_model_input_outputs()
# Define the variables to estimate
self.set_state_to_estimate_first_order()
# Retry to instantiate, now with a proper model
ukf_FMU = UkfFmu(self.m)
# Number of points for computing the covariance matrix
n = 100
# Number of variables
N = 300
# True mean vector
Xtrue = np.random.uniform(-8.0, 27.5, (1, N))
# Generate the sample for computing the covariance matrix
notUsed, N = Xtrue.shape
Xpoints = np.zeros((n,N))
for i in range(n):
noise = np.random.uniform(-2.0,2.0,(1,N))
Xpoints[i,:] = Xtrue + noise
# default covariance to be added
Q = 2.0*np.eye(N)
# definition of the weights
Weights = np.zeros(n)
for i in range(n):
if i==0:
Weights[i] = 0.5
else:
Weights[i] = (1.0 - Weights[0])/np.float(n-1)
#---------------------------------------------------
# Standard method based on Cholesky
i = 0
P = Q
for x in Xpoints:
error = x - Xtrue
P = P + Weights[i]*np.dot(error.T,error)
i += 1
S = ukf_FMU.square_root(P)
np.testing.assert_almost_equal(P, np.dot(S, S.T), 8, \
"Square root computed with basic Cholesky decomposition is not correct")
#----------------------------------------------------
# Test the Cholesky update
sqrtQ = np.linalg.cholesky(Q)
L = ukf_FMU.compute_S(Xpoints, Xtrue, sqrtQ, w = Weights)
np.testing.assert_almost_equal(P, np.dot(L.T, L), 8, \
"Square root computed with basic Cholesky update is not correct")
return
def test_create_sigma_points(self):
"""
This method tests the Cholesky update method that is used to compute
the squared root covariance matrix by the filter.
"""
# Initialize the first order model
self.set_first_order_model()
# Associate inputs and outputs
self.set_first_order_model_input_outputs()
# Define the variables to estimate
self.set_state_to_estimate_first_order()
# Instantiate with a proper model
ukf_FMU = UkfFmu(self.m)
# Verify that the method raises an exception if the
# inputs are wrong
self.assertRaises(ValueError, ukf_FMU.compute_sigma_points, np.zeros(3), np.zeros(3), np.zeros((3,3)))
self.assertRaises(ValueError, ukf_FMU.compute_sigma_points, np.zeros(2), np.zeros(3), np.zeros((3,3)))
self.assertRaises(ValueError, ukf_FMU.compute_sigma_points, np.zeros(1), np.array([]), np.zeros((3,3)))
# Create the sigma points
x0 = np.array([2.5])
sigma_points = ukf_FMU.compute_sigma_points(x0, np.array([]), np.diag(np.ones(1)))
# Check the size
self.assertTrue(sigma_points.shape == (3,1), "The size of the sigma points is not correct")
# Verify that the first sigma point is equal to the center
self.assertEqual(x0, sigma_points[0,:], "First sigma point is not [0]")
# Verify that the second and the last sigma points are symmetric
self.assertEqual(0.5*(sigma_points[1,:] + sigma_points[2,:]), x0, "The sigma points 1,2 are not symmetric with respect to 0")
return
def test_project_sigma_points(self):
"""
This method tests the function that projects the sigma points
by running a simulation.
"""
# Initialize the first order model
self.set_first_order_model()
# Associate inputs and outputs
self.set_first_order_model_input_outputs(noisy = False)
# Define the variables to estimate
self.set_state_to_estimate_first_order()
# Initialize the simulator
self.m.initialize_simulator()
# Instantiate with a proper model
ukf_FMU = UkfFmu(self.m)
# Define the sigma points
x0 = np.array([2.5])
sigma_points = ukf_FMU.compute_sigma_points(x0, np.array([]), np.diag(np.ones(1)))
# Propagate the points by simulating from 0 to 14.5 seconds
t0 = pd.to_datetime(0.0, unit = "s", utc = True)
t1 = pd.to_datetime(14.5, unit = "s", utc = True)
X_proj, Z_proj, Xfull_proj, Zfull_proj = ukf_FMU.sigma_point_proj(sigma_points, t0, t1)
# Verify that they started from different initial coditions and that they converged
# at the same value after 12 seconds
np.testing.assert_almost_equal(X_proj, 2.5*np.ones((3,1)), 3, "Verify that the solutions all converge to 2.5")
# Compute their average using the method provided by the object and verify its value
x_avg = ukf_FMU.average_proj(X_proj)
np.testing.assert_almost_equal(x_avg, np.array([[2.5]]), 4, "Average of the propagated points is not correct")
return
def test_ukf_filter_first_order(self):
"""
This method tests the ability of the filter to estimate the state
of the first order system.
"""
# Initialize the first order model
self.set_first_order_model()
# Associate inputs and outputs
self.set_first_order_model_input_outputs()
# Define the variables to estimate
self.set_state_to_estimate_first_order()
# Initialize the simulator
self.m.initialize_simulator()
# Retry to instantiate, now with a proper model
ukf_FMU = UkfFmu(self.m)
# Start the filter
t0 = pd.to_datetime(0.0, unit = "s", utc = True)
t1 = pd.to_datetime(30.0, unit = "s", utc = True)
time, x, sqrtP, y, Sy, y_full = ukf_FMU.filter(start = t0, stop = t1)
# Convert the results to numpy array
time = time - time[0]
time = np.array(map(lambda x: x.total_seconds(), time))
x = np.array(x)
y = np.array(y)
sqrtP = np.array(sqrtP)
Sy = np.array(Sy)
y_full = np.squeeze(np.array(y_full))
# Path of the csv file containing the True data series
path_csv_simulation = os.path.join(dir_path, "..", "modelica", "FmuExamples", "Resources", "data", "SimulationData_FirstOrder.csv")
# Compare the estimated states with the ones used to generate the data
df_sim = pd.read_csv(path_csv_simulation, index_col = 0)
time_sim = df_sim.index.values
# Difference between state estimated and real state
x_sim = np.interp(time, time_sim, df_sim["system.x"])
err_state = np.abs(x_sim - x[:,0])
# Identify maximum error and the time when it occurs
max_error = np.max(err_state)
t_max_error = np.where(err_state == max_error)
# Make sure that the maximum error is less or equal than 0.5, and it happens at
# the first time instant t = 0
self.assertTrue(max_error <= 0.5, "The maximum error in the estimation has to be less than 0.5")
self.assertTrue(t_max_error[0][0] == 0.0 and len(t_max_error[0]) == 1,\
"The maximum error is one and it is at t = 0")
# Compute the mean absolute error
avg_error = np.mean(err_state)
self.assertTrue(avg_error < 0.06, "The average error should be less than 0.06")
# Compute that the estimation +/- covariance contains the real state
x_plus_sigma = x[:,0] + sqrtP[:,0,0]
x_minus_sigma = x[:,0] - sqrtP[:,0,0]
self.assertTrue(len(np.where(x_sim < x_minus_sigma)[0]) == 0,\
"The state estimation must contain the real state in its boundaries")
self.assertTrue(len(np.where(x_sim > x_plus_sigma)[0]) == 0,\
"The state estimation must contain the real state in its boundaries")
return
def test_ukf_smoother_valve(self):
"""
This method tests the state and parameter estimation on the valve example performed
with the UKF + Smoother.
"""
# Initialize the first order model
self.set_valve_model()
# Associate inputs and outputs
self.set_valve_model_input_outputs()
# Define the variables to estimate
self.set_state_and_param_to_estimate_valve()
# Initialize the simulator
self.m.initialize_simulator()
# Set models parameters
use_cmd = self.m.get_variable_object("use_cmd")
self.m.set_real(use_cmd, 0.0)
lambd = self.m.get_variable_object("lambda")
self.m.set_real(lambd, 0.0)
# Retry to instantiate, now with a proper model
ukf_FMU = UkfFmu(self.m)
# Start filter and smoother
t0 = pd.to_datetime(0.0, unit = "s", utc = True)
t1 = pd.to_datetime(360.0, unit = "s", utc = True)
time, x, sqrtP, y, Sy, y_full, Xsmooth, Ssmooth, Yfull_smooth = \
ukf_FMU.filter_and_smooth(start = t0, stop = t1)
# Convert the results to numpy array
time = time - time[0]
time = np.array(map(lambda x: x.total_seconds(), time))
x = np.array(x)
y = np.array(y)
sqrtP = np.array(sqrtP)
Sy = np.array(Sy)
y_full = np.squeeze(np.array(y_full))
xs = np.array(Xsmooth)
Ss = np.array(Ssmooth)
Ys = np.array(Yfull_smooth)
# Path of the csv file containing the True data series generated by a simulation model
path_csv_simulation = os.path.join(dir_path, "..", "modelica", "FmuExamples", "Resources", "data", "SimulationData_ValveStuck.csv")
# Compare the estimated states with the ones used to generate the data
df_sim = pd.read_csv(path_csv_simulation, index_col = 0)
time_sim = df_sim.index.values
# Difference between state estimated and real state/parameters
opening_sim = np.interp(time, time_sim, df_sim["valveStuck.valve.opening"])
lambda_sim = np.interp(time, time_sim, df_sim["valveStuck.lambda"])
err_opening = np.abs(opening_sim - x[:,0])
err_lambda = np.abs(lambda_sim - x[:,1])
err_opening_s = np.abs(opening_sim - xs[:,0])
err_lambda_s = np.abs(lambda_sim - xs[:,1])
# Compute the maximum errors for both filter and smoother
max_opening_error = np.max(err_opening)
max_opening_error_s = np.max(err_opening_s)
max_lambda_error = np.max(err_lambda)
max_lambda_error_s = np.max(err_lambda_s)
# Compute average error for both filter and smoother
avg_opening_error = np.mean(err_opening)
avg_opening_error_s = np.mean(err_opening_s)
avg_lambda_error = np.mean(err_lambda)
avg_lambda_error_s = np.mean(err_lambda_s)
# Compare performances of UKF and Smoother, verify that the smoother improves
# the estimation
self.assertTrue(max_opening_error >= max_opening_error_s,\
"The max error in the estimation of the opening by the smoother is larger than the filter")
self.assertTrue(max_lambda_error >= max_lambda_error_s,\
"The maxerror in the estimation of the drift coeff. by the smoother is larger than the filter")
self.assertTrue(avg_opening_error >= avg_opening_error_s,\
"The avg error in the estimation of the opening by the smoother is larger than the filter")
self.assertTrue(avg_lambda_error >= avg_lambda_error_s,\
"The avg error in the estimation of the drift coeff. by the smoother is larger than the filter")
# Verify that some absolute perfomances are guaranteed
self.assertTrue(0.08 > max_opening_error, "The maximum error of the UKF on the opening is too big")
self.assertTrue(0.065 > max_opening_error_s, "The maximum error of the Smoother on the opening is too big")
self.assertTrue(0.0117 > avg_opening_error, "The average error of the UKF on the opening is too big")
self.assertTrue(0.0088 > avg_opening_error_s, "The average error of the Smoother on the opening is too big")
self.assertTrue(0.0101 > max_lambda_error, "The maximum error of the UKF on the drift coef. is too big")
self.assertTrue(0.0096 > max_lambda_error_s, "The maximum error of the Smoother on the drift coef. is too big")
self.assertTrue(0.0028 > avg_lambda_error, "The average error of the UKF on the drift coef. is too big")
self.assertTrue(0.00144 > avg_lambda_error_s, "The average error of the Smoother on the drift coef. is too big")
return