<jupyter_start><jupyter_text>Clean code<jupyter_code>from filterpy.kalman import KalmanFilter
from filterpy.common import Q_discrete_white_noise
dim_z, X, P, A, Q, dt, R, H, B, U = preprocess(num_measured=1, measured_var=(10,4.5), covar=(500,49), 
                process_model=((1, 1), (0, 1)), white_noise_var=.35, dt=1, 
                sensor_covar=5, measurement_function=(1,0))
#Given data

track, zedd
X
def create_kf_and_assign_predict_update(dim_z, X, P, A, Q, dt, R, H, B, U):
    kf = KalmanFilter(dim_x=X.shape[0], dim_z=dim_z)
    kf.x = X
    kf.P = P
    kf.F = A
    kf.Q = Q
    kf.B = B
    kf.U = U
    kf.R = R
    kf.H = H
    return kf
kf = create_kf_and_assign_predict_update(dim_z, X, P, A, Q, dt, R, H, B, U)
kf
dim_z, X, P, A, Q, dt, R, H, B, U = preprocess(num_measured=1, measured_var=(10,4.5), covar=(500,49), 
                process_model=((1, 1), (0, 1)), white_noise_var=.35, dt=1, 
                sensor_covar=5, measurement_function=(1,0))
#data = zedd
def run_kf(data, dim_of_measurements, measured_var, covar, process_model, white_noise_var, dt, sensor_covar, measurement_function):
    xs, cv = [], []
    dim_z, X, P, A, Q, dt, R, H, B, U = preprocess(num_measured=dim_of_measurements, measured_var=measured_var, covar=covar, 
                process_model=process_model, white_noise_var=white_noise_var, dt=dt, 
                sensor_covar=sensor_covar, measurement_function=measurement_function)
    kf = create_kf_and_assign_predict_update(dim_z, X, P, A, Q, dt, R, H, B, U)
    
    for i in data:
        kf.predict()
        kf.update(i)
        
        xs.append(kf.x)
        cv.append(kf.P)
        
    xs, cv = np.array(xs), np.array(cv)
    return xs,cv
xs,cv = run_kf(data=zedd, dim_of_measurements=1, measured_var=(10,4.5), covar=(500,49), 
                process_model=((1, 1), (0, 1)), white_noise_var=.35, dt=1, 
                sensor_covar=5, measurement_function=(1,0))
plt.plot(range(1, len(zedd)+1), xs[:, 0], c ='r')
#plt.plot(range(1, len(zedd)+1), zedd, 'o')
plt.plot(range(1, len(zedd)+1), track, c='orange')<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.15567157095396297, 'fraction_numerical': 0.0322733744660655, 'mean_word_length': 3.319672131147541, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '23727', 'license': 'Apache-2.0', 'n_tokens_mistral': 935, 'n_tokens_neox': 869, 'n_words': 184, 'path': 'tests/scripts/main/Kalman.ipynb', 'repo_name': 'Siaan/COMS4995', 'size': '2107'}