Multithreading in Python not working correctly with Raspberry Pi

I'm using a Raspberry Pi 4B and a development kit which contains sensors and sends the data via BLE.

The project consists of a Raspberry Pi working as an Edge device where it reads sensor data via BLE, filters the data and processes it. For the processing part, I want to process the same data with two Machine Learning models and with programmable logic (doing math calculations with the data). The output I want to achieve is to know the position of my development kit (it goes on top of an elevator roof, so I can know the vertical position and also the position of the doors).

When I try to run the processing code separately (one project with the programmable logic and another with the two ML models) they work fine, but when I join them the thread in charge of the programmable logic processing doesn't work well.

from bluepy import btleimport threadingimport numpy as npimport tensorflow as tfimport timeimport variables as varimport functions as funcclass MyDelegate(btle.DefaultDelegate):    def __init__(self):        btle.DefaultDelegate.__init__(self)    def handleNotification(self, cHandle, data):        var.acc_z[0] = int.from_bytes(data[6:8],"little",signed=True)        var.acc_y[0] = int.from_bytes(data[4:6],"little",signed=True)        var.acc_x[0] = int.from_bytes(data[2:4],"little",signed=True)        var.mag_x[0] = int.from_bytes(data[14:16],"little",signed=True)/1000#Convert mGAuss to Gauss        var.mag_y[0] = int.from_bytes(data[16:18],"little",signed=True)/1000#Convert mGAuss to Gauss        var.mag_z[0] = int.from_bytes(data[18:20],"little",signed=True)/1000#Convert mGAuss to Gauss        func.Filter_Data()                    if var.calculate_average == 1:            func.Calculate_Average()        else:            var.ready_read = True            if var.count_buffer_DOORS < var.WINDOW_STEP_DOORS - 1:                var.count_buffer_DOORS += 1                var.window_buffer_DOORS = var.window_buffer_DOORS[1:]+[var.f_acc_x[0]-var.average_acc_x]            else:                 var.count_buffer_DOORS = 0                var.window_buffer_DOORS = var.window_buffer_DOORS[1:]+[var.f_acc_x[0]-var.average_acc_x]                var.ready_model_DOORS = 1            if var.count_buffer_CAB < var.WINDOW_STEP_CAB - 1:                 var.count_buffer_CAB += 1                var.window_buffer_CAB = var.window_buffer_CAB[1:]+[var.f_acc_y[0]-var.average_acc_y]            else:                 var.count_buffer_CAB = 0                var.window_buffer_CAB = var.window_buffer_CAB[1:]+[var.f_acc_y[0]-var.average_acc_y]                var.ready_model_CAB = 1#Thread where programmed logic calculations run def programmedLogic_THREAD():    while var.stop_thread == False:         if (var.ready_read == True):                        func.Calculate_Desv_Est_Mag()            func.Calculate_Position_Cab()            if var.flag_ready == 1 and var.learn_mag == 1:                 func.Learn_Mag()                            func.Calculate_Position_Doors()            if var.update_average == 1:                func.Update_Average()            else:                 var.count_average = 0                var.sum_acc_z = 0                var.sum_acc_y = 0                var.sum_acc_x = 0            if var.state == 1: var.DB_LP_cab = "Up"            elif var.state == -1: var.DB_LP_cab = "Down"            elif var.state == 0: var.DB_LP_cab = "Still"            if var.move_doors == 1: var.DB_LP_doors = "Opening"            elif var.move_doors == -1: var.DB_LP_doors = "Closing"            elif var.move_doors == 0: var.DB_LP_doors = "Still"            var.ready_read = False#Thread where ML Doors Model runs and predictsdef doorsML_THREAD():    while var.stop_thread == False:         if (var.ready_model_DOORS == 1) and (var.ML_flag == True):             var.ready_model_DOORS = 0            x = np.expand_dims(var.window_buffer_DOORS, axis = 1)            var.result_DOORS = custom_model_puertas.predict(x.T.tolist(), verbose = 0)            var.doors_State = np.argmax(var.result_DOORS)                      var.array_doors_State = var.array_doors_State[1:]+[var.doors_State]            if all(element == var.array_doors_State[0] for element in var.array_doors_State):                var.print_doors_State = var.array_doors_State[0]                if var.print_doors_State == 0:                     var.DB_ML_doors = "Opening"                elif var.print_doors_State == 1:                     var.DB_ML_doors = "Closing"                elif var.print_doors_State == 2:                     var.DB_ML_doors = "Still"#Thread where ML Cab Model runs and predictsdef cabML_THREAD():    while var.stop_thread == False:         if (var.ready_model_CAB == 1) and (var.ML_flag == True):             var.ready_model_CAB = 0            x = np.expand_dims(var.window_buffer_CAB, axis = 1)            var.result_CAB = custom_model_cabina.predict(x.T.tolist(), verbose = 0)            var.cab_State = np.argmax(var.result_CAB)            var.array_cab_State = var.array_cab_State[1:]+[var.cab_State]            if all(element == var.array_cab_State[0] for element in var.array_cab_State):                var.print_cab_State = var.array_cab_State[0]                if var.print_cab_State == 0:                     var.DB_ML_cab = "Down"                elif var.print_cab_State == 1:                     var.DB_ML_cab = "Still"                elif var.print_cab_State == 2:                    var.DB_ML_cab="Up"#Thread that prints results in terminaldef print_THREAD():    count = 0    while (var.stop_thread == False):        count+=1        if count>=20:             count=0            var.programmedLogic_flag = not var.programmedLogic_flag            var.ML_flag = not var.ML_flag        if var.programmedLogic_flag == True:                     print(f"\n\tLP Data:\nCab state:\tDoors state:\n{var.DB_LP_cab}\t\t{var.DB_LP_doors}")         elif var.ML_flag == True:            print(f"\n\tML Data:\nCab state:\tDoors state:\n{var.DB_ML_cab}\t\t{var.DB_ML_doors}")        time.sleep(0.5)#Create threadsthread1 = threading.Thread(target=programmedLogic_THREAD)thread2 = threading.Thread(target=doorsML_THREAD)thread3 = threading.Thread(target=cabML_THREAD)thread4 = threading.Thread(target=print_THREAD)#Upload ML Modelsprint("Uploading ML Models...")custom_model_puertas = tf.keras.models.load_model('/home/aag/Desktop/Edge_Impulse_Model/doors_MODEL.h5')custom_model_cabina = tf.keras.models.load_model('/home/aag/Desktop/Edge_Impulse_Model/cab_MODEL.h5')print("Successfully uploaded")#Connect to BLE deviceprint("Connecting ...")p = btle.Peripheral(var.MAC_ADDR,addrType=btle.ADDR_TYPE_RANDOM)p.setDelegate( MyDelegate() )print("Connected to DA:BB:C5:28:12:A7")# Setup to turn notifications onsvc = p.getServiceByUUID("00000000-0001-11e1-9ab4-0002a5d5c51b")ch = svc.getCharacteristics("00e00000-0001-11e1-ac36-0002a5d5c51b")[0]p.writeCharacteristic(ch.valHandle+1, b"\x01\x00")#Initialize threadsthread1.start()thread2.start()thread3.start()thread4.start()while True:    try:        if p.waitForNotifications(1.0):            continue    except KeyboardInterrupt:        p.disconnect()        print("\nDevice disconnected")        var.stop_thread = True        thread1.join()        thread2.join()        thread3.join()                thread4.join()        break         

In the project I have added I use two files (variables and functions) where I have all my variables and functions defined. I have used 4 threads so that I can have several tasks runnning at the same time. Also, I read the BLE data via notifications, so I have an interruption function.

When I inicialize all of the threads, the thread in charge of the programmable logic doesn't work (it does the calculations wrong) but when I don't inicialize threads 2 and 3 (in charge of the ML predictions), the programmable logic thread works perfectly fine (so I can rule out that the functions I am using to calculate the position are wrong).

I attach below a screenshot of the command htop:

screenshot of htop command

Am I making a mistake or missing something?