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import numpy as np
from inputs import Input
from MMAE.mmae import MMAE
from System.system_simulator import SystemSimulator
from testbench_tools import simulation_configuration_setup
from testbench_tools import mmae_simulator_plots
class MMAESimulatorRealData:
def __init__(self, λs, true_λ, dt, H, Q, R, x0, true_system_noisy, estimator_noisy, max_time, max_steps, amplitude):
# Synthetic system simulator initialization
self.TrueSystem = SystemSimulator(true_λ, dt, H, Q, R, x0, true_system_noisy)
# Input initialization
self.input_signal = Input(self.TrueSystem.model, max_time).step_function(max_steps, amplitude)
# MMAE initialization
self.MMAE = MMAE(λs, dt, H, Q, R, x0, estimator_noisy)
def update(self, t: int, z: np.ndarray, dt: float) -> float:
u = self.input_signal[t, :].reshape(-1, 1)
λ_hat, cumulative_posteriors, pdvs = self.MMAE.update(u, z, dt)
return λ_hat, cumulative_posteriors, pdvs
def real_data(dataset):
data = np.loadtxt(dataset, delimiter=",", usecols=5, skiprows=1) # skiprows=1 if there is a header
dts = np.loadtxt(dataset, delimiter=",", usecols=2, skiprows=1) # skiprows=1 if there is a header
# Init Initial state
time_track = 0.0
times.append(time_track)
zs.append(np.array([[data[0]]], dtype='float64'))
for step_counter in range(1, max_time):
curr_dt = dts[step_counter]
time_track += curr_dt
times.append(time_track)
z = np.array([[data[step_counter]]], dtype='float64')
zs.append(z)
pass
if __name__ == "__main__":
# Load configuration from JSON file
λs, m, k, b, dt, H, Q, R, x0, max_time, max_steps, amplitude = simulation_configuration_setup()
true_λ = [m, k, b]
# MMAE simulator initialization
MMAESimulator = MMAESimulatorRealData(λs, true_λ, dt, H, Q, R, x0, True, False, max_time, max_steps, amplitude)
### Run simulation ###
times = []
zs = []
lambda_hats = []
cumulative_posteriors_summary = []
pdvs_summary = []
data = np.loadtxt("./Data/Dataset_3.csv", delimiter=",", usecols=5, skiprows=1) # skiprows=1 if there is a header
dts = np.loadtxt("./Data/Dataset_3.csv", delimiter=",", usecols=2, skiprows=1) # skiprows=1 if there is a header
# Init Initial state
time_track = 0.0
times.append(time_track)
z = np.array([[data[0]]], dtype='float64')
zs.append(z)
MMAESimulator.update(0.0, z, dt)
# Main simulation loop
for step_counter in range(1, max_time):
dt = dts[step_counter]
time_track += dt
times.append(time_track)
z = np.array([[data[step_counter]]], dtype='float64')
zs.append(z)
λ_hat, cumulative_posteriors, pdvs = MMAESimulator.update(step_counter, z, dt)
lambda_hats.append(λ_hat)
cumulative_posteriors_summary.append(cumulative_posteriors)
pdvs_summary.append(pdvs)
mmae_simulator_plots(times, true_λ, λs, lambda_hats, cumulative_posteriors_summary, pdvs_summary)