## kalman filter parameters
#the actual, hidden state
actual = numpy.array([[0, 5]])
#the sensor
sensor = Mvn(vectors=[[1, 0], [0, 1]], var=[1, numpy.inf])
#the system noise
noise = Mvn(vectors=[[1, 0], [0, 1]], var=numpy.array([0.5, 1])**2)
#the shear transform to move the system forward
transform = Matrix([[1, 0], [0.5, 1]])
filtered = sensor.measure(actual)
## initial plot
ax = newAx(fig)
#plot the initial actual position
ax.plot(actual[:, 0], actual[:, 1], **actualParams)
ax.set_title('Kalman Filtering: Start')
pylab.xlabel('Position')
pylab.ylabel('Velocity')
P.publish(fig)
#measure the actual position, and plot the measurment
filtered.plot(facecolor=colors['Filter Result'], **otherParams)
ax.set_title('Initialize to first measurement')
## kalman filter parameters
#the actual, hidden state
actual = numpy.array([[0, 5]])
#the sensor
sensor = Mvn(vectors = [[1, 0], [0, 1]],var = [1, numpy.inf])
#the system noise
noise = Mvn(vectors = [[1, 0], [0, 1]], var = numpy.array([0.5, 1])**2)
#the shear transform to move the system forward
transform = Matrix([[1, 0], [0.5, 1]])
filtered = sensor.measure(actual)
## initial plot
ax = newAx(fig)
#plot the initial actual position
ax.plot(actual[:, 0], actual[:, 1], **actualParams)
ax.set_title('Kalman Filtering: Start')
pylab.xlabel('Position')
pylab.ylabel('Velocity')
P.publish(fig)
