Finding the number of Failure for Surface Code by using Pymatching (without STIM): ValueError: The shape of the syndrome vector z is not valid

Question

I run the surface code for distance-3 with 3 round. I am now trying to decode and find the number of failures. My error probability of 0.08. Here are my syndrome results:

x_syndrome_1stround = [0, 0, 0, 1, 1, 1]
x_syndrome_2ndround = [1, 1, 0, 1, 1, 1]
x_syndrome_3rdround = [1, 1, 1, 0, 1, 0]
z_syndrome_1stround = [1, 1, 0, 0, 1, 0]
z_syndrome_2ndround = [1, 1, 1, 0, 1, 0]
z_syndrome_3rdround = [1, 1, 1, 1, 0, 0]

Here are the detector events:

first_detector_xzstab = [1,1,0,0,0,0,0,0,1,0,0,0] # first 6 elements of the list corresponds detection event for the x stabilizers, the last 6 elements of the list corresponds detection event for the z stabilizers
second_detector_xzstab = [0,0,1,1,0,1,0,0,0,1,1,0] 

The following matrices are the parity check matrix for the X and Z stabilizers:

H_d3x = csr_matrix(np.array([
[1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1]
    ]))
H_d3z= csr_matrix(np.array([
[1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1]
]))

The result of measuring data qubits after the stabilizers is here:

 data_q_meas = np.array([0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0])

This is my function for calculating the error probabilities:

import numpy as np
from pymatching import matching

    H_d3x = np.array([
        [1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
        [0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
        [0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0],
        [0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0],
        [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0],
        [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1]
    ])
x_syndrome_1stround = [0, 0, 0, 1, 1, 1]
x_syndrome_2ndround = [1, 1, 0, 1, 1, 1]
x_syndrome_3rdround = [1, 1, 1, 0, 1, 0]
z_syndrome_1stround = [1, 1, 0, 0, 1, 0]
z_syndrome_2ndround = [1, 1, 1, 0, 1, 0]
z_syndrome_3rdround = [1, 1, 1, 1, 0, 0]

actual_observables = np.array([0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0]) #here I am guessing that actual observables are the measurement results of the data qubits in the simulator

observables = csc_matrix([[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])

def surface_code_failures(error_rate, x_syndrome_rounds, z_syndrome_rounds):
    # Parity check matrix
    num_failures = 0
    matching=Matching(H_d3x,weights=np.log((1-p)/p)) 
    print(matching)
    for x_syndrome, z_syndrome in zip(x_syndrome_rounds, z_syndrome_rounds):
        # Calculate the total syndrome (X and Z syndromes combined)
        syndrome = np.concatenate((x_syndrome, z_syndrome))
        #for i in range(syndrome.shape[0]):
        print("syndrome = ",syndrome, syndrome.shape[0])  
        predicted = matching.decode(syndrome)
        print("predicted = ",predicted)
        predicted_observables = observables@predicted % 2
           #if not matching.decode(syndrome): #syndrome
        num_failures += not np.array_equal(predicted_observables, actual_observables)

    return num_failures

# Error rate
p = 0.08

# Combine syndromes for each round
x_syndrome_rounds = [x_syndrome_1stround, x_syndrome_2ndround, x_syndrome_3rdround]
z_syndrome_rounds = [z_syndrome_1stround, z_syndrome_2ndround, z_syndrome_3rdround]

# Calculate the number of failures
num_failures = surface_code_failures(p, x_syndrome_rounds, z_syndrome_rounds)
print("Number of failures:", num_failures)

And this is how I use the function:

p = 0.08

# Combine syndromes for each round
x_syndrome_rounds = [x_syndrome_1stround, x_syndrome_2ndround, x_syndrome_3rdround]
z_syndrome_rounds = [z_syndrome_1stround, z_syndrome_2ndround, z_syndrome_3rdround]

# Calculate the number of failures
num_failures = surface_code_failures(p, x_syndrome_rounds, z_syndrome_rounds)
print("Number of failures:", num_failures)

I am having error in this line if not matching.decode(syndrome): #syndrome The error message is that: ValueError: The shape ((12,)) of the syndrome vector z is not valid. It(the program) probably did not like the dimensions, but I do not know what to give anymore. I thought I needed to give syndrome measurement results to decode the errors. Maybe I am doing wrong with syndromes? Does anyone can help me with the function?