i try to build an Kalman Filter for my UM7 Sensor. First to read out a accurate Value from the pitch when i move it and later i want to build it on a Robot. I found an Open Source File and Tried to moderate it for my case but i get a few errors. My first Error to solve is :AttributeError "filter" has no Attribute A... hope anyone can help The Commands i commented out are some Commands i used for my IMU but now for the Kalman Filter i cant use IMU anymore i think

!/usr/bin/env python3

import rospy # Library to use Python import roslib # Library to use rostools from tf.msg import tfMessage # Transformer Library Algebra Bib from std_msgs.msg import Float32, Header # from um7py import UM7Serial # from dataclasses import dataclass # from nav_msgs.msg import Odometry # from geometry_msgs.msg import Point, Pose, Quaternion, Vector3 from sensor_msgs.msg import Imu import numpy as np from numpy.linalg import inv import matplotlib.pyplot as plt

######################################## Funktion to read

def angles_read_pitch(): um7_serial = UM7Serial(port_name='/dev/ttyUSB0')

print ( "  ")
print ( "Port Name: ttyUSB0")
print ( "  ")

a = [0,1,2,3,4,5,6,7,8,9,10]
i = 1

while i < 2:
    for packet in um7_serial.recv_broadcast(num_packets = 10):

        a[i-1] = packet.pitch
        print( "at the Pos = ",i , " is pitch = ", a[i-1] )
        i = i+1
        rospy.sleep(0.1)

print ( "  ")
Diff = a[10] - a[1] 

print ( "The Diff between prev Pos pitch and current Pos pitch is : ", Diff )

##################################### Kalman Filter

##################################### https://thekalmanfilter.com/kalman-fi....

#

def Kalman_Filter_2(z, updateNumber): # Z = position measurment # updateNumber = dt = 0.1 # delta Time 0,1 sec

    # Initialize State

if updateNumber == 1:
    filter.x = np.array[[0],    # x = two element state vect. pos & veloc.
                        [20]]
    filter.P = np.array[[5, 0], # P = 2x2 state Covariance matrix, unc. in x 
                             [0, 5]]
    filter.A = np.array[[1, dt],    # A = state trans. matrix transposed -> .. 
                         [0, 1]]        # .. -> matrix constant linear motion
    filter.H = np.array[1, 0]   # state to measurement transition matrix
    filter.HT = np.array[[1],   # is the H matrix transposed
                          [0]]
    filter.R = 10           # R = position variance
    filter.Q = np.array[[1, 0], # Q = 2x2 szstem noise covariance matrix
                         [0, 3]]        # accounts for inaccuracy in system model

# Predict State Forward x_p = filter.A.dot(filter.x)
# Predict Covariance Forward P_p = filter.A.dot(filter.P).dot(filter.A.T) + filter.Q # Compute Kalman Gain S = filter.H.dot(P_p).dot(filter.HT) + filter.R
print("S:",S) K = P_p.dot(filter.HT)(1/S) print("K:",K) # Estimate State residual = z - filter.H.dot(x_p) filter.x = x_p + Kresidual print("x:",x) # Estimate Covariance filter.P = P_p - K.dot(filter.H).dot(P_p) print("P:",P) return [filter.x[0], filter.x[1], filter.P];

def getMeasurement_2(updateNumber): if updateNumber == 1:

    #Fabio_Imu.currentPosition = 0      ## truth Data
    #Fabio_Imu.currentVelocity = 60 # m/s   ## truth Data

    currentPosition = 0 
    currentVelocity = 60

dt = 0.1
w = 8 * np.random.randn(1)          ## added to velocity for smll acceler
v = 8 ...